ml-binary-classification-predict-bad-debt-fix.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.7"},"colab":{"name":"[Temp] Classification.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"IJO-n2byJ_lY"},"source":["## CAPSTONE PROJECT - CLASSIFICATION"]},{"cell_type":"markdown","metadata":{"id":"hsQEJWXgbl7O"},"source":["**Nama Peserta: RAHMAT ZAKI MUHAROM**"]},{"cell_type":"markdown","metadata":{"id":"rZR9IwjIJ_lb"},"source":["#### IMPORT PACKAGES"]},{"cell_type":"code","metadata":{"id":"7EZ9sGkuJ_lc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630385202473,"user_tz":-480,"elapsed":704,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"19fa636b-0534-4b56-e7ae-d39b9a04d425"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":99,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"]}]},{"cell_type":"code","metadata":{"id":"URINzQJ4IfBE","executionInfo":{"status":"ok","timestamp":1630385203874,"user_tz":-480,"elapsed":795,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# Import Package\n","import os\n","import pandas as pd\n","import numpy as np\n","\n","# Package untuk visualisasi\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Package untuk cleaning, modelling, dan evaluation model\n","import statsmodels.api as sm\n","from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler\n","from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, KFold, GridSearchCV\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC"],"execution_count":100,"outputs":[]},{"cell_type":"code","metadata":{"id":"l9EdnL1pIlu2","executionInfo":{"status":"ok","timestamp":1630385203876,"user_tz":-480,"elapsed":101,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# menentukan path data yang akan digunakan\n","path = '/content/gdrive/MyDrive/RMDS_Narasio_Data/Intermediate/Meet 5/2. Classification - NPL Credit Scoring/'"],"execution_count":101,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SdHIdktbJtF_","executionInfo":{"status":"ok","timestamp":1630385203879,"user_tz":-480,"elapsed":102,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"45deb6f0-27f7-4509-def5-972b4a22f884"},"source":["# mengecek isi path\n","os.listdir(path)"],"execution_count":102,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['npl_credit_scoring_train.csv',\n"," 'npl_credit_scoring_test.csv',\n"," 'Predict_New_Data_Kartu_Kredit_Macet_Machine_Learning_Classification.csv']"]},"metadata":{},"execution_count":102}]},{"cell_type":"code","metadata":{"id":"q9kZ6MoSJw4r","executionInfo":{"status":"ok","timestamp":1630385203881,"user_tz":-480,"elapsed":95,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat variabel yang berisikan data train\n","dtrain = pd.read_csv(path + 'npl_credit_scoring_train.csv')"],"execution_count":103,"outputs":[]},{"cell_type":"code","metadata":{"id":"jrzmxxGfJ_pl","executionInfo":{"status":"ok","timestamp":1630385203883,"user_tz":-480,"elapsed":96,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat variabel yang berisikan data test\n","dtes = pd.read_csv(path + 'npl_credit_scoring_test.csv')"],"execution_count":104,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f4DCJr9kJ_lc"},"source":["#### READ DATASET"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"4utbBgSUKkfZ","executionInfo":{"status":"ok","timestamp":1630385203884,"user_tz":-480,"elapsed":96,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"7ea01582-e1dd-4e3a-bfde-99e6ec7df017"},"source":["# load data dtrain sebagai data utama untuk membuat model machine learning\n","dtrain"],"execution_count":105,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>92</td>\n","      <td>5</td>\n","      <td>5972272</td>\n","      <td>6000000.0</td>\n","      <td>5068230.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>5206434.0</td>\n","      <td>NaN</td>\n","      <td>12.19</td>\n","      <td>0.00</td>\n","      <td>7.84</td>\n","      <td>43.92</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9.916667</td>\n","      <td>0.0</td>\n","      <td>1041286.8</td>\n","      <td>0.867739</td>\n","      <td>0.000000</td>\n","      <td>0.059401</td>\n","      <td>0.041536</td>\n","      <td>0.919164</td>\n","      <td>0.919635</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>316</td>\n","      <td>2</td>\n","      <td>136453</td>\n","      <td>65000000.0</td>\n","      <td>128750.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>100.00</td>\n","      <td>0.00</td>\n","      <td>74.92</td>\n","      <td>100.36</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3.500000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.023722</td>\n","      <td>0.013271</td>\n","      <td>0.022847</td>\n","      <td>0.007724</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>624</td>\n","      <td>1</td>\n","      <td>1354080</td>\n","      <td>3000000.0</td>\n","      <td>1369203.0</td>\n","      <td>0.0</td>\n","      <td>15000.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>100.00</td>\n","      <td>0.00</td>\n","      <td>82.01</td>\n","      <td>0.00</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>8.500000</td>\n","      <td>15000.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.005000</td>\n","      <td>0.145833</td>\n","      <td>0.000000</td>\n","      <td>0.627426</td>\n","      <td>0.899309</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>628</td>\n","      <td>2</td>\n","      <td>1076660</td>\n","      <td>49000000.0</td>\n","      <td>752108.0</td>\n","      <td>0.0</td>\n","      <td>775440.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>99.07</td>\n","      <td>0.00</td>\n","      <td>29.72</td>\n","      <td>57.32</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>4.333333</td>\n","      <td>775440.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.015825</td>\n","      <td>0.017617</td>\n","      <td>0.177094</td>\n","      <td>0.013699</td>\n","      <td>0.255363</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>947</td>\n","      <td>3</td>\n","      <td>42281368</td>\n","      <td>34000000.0</td>\n","      <td>43130581.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>41144841.0</td>\n","      <td>NaN</td>\n","      <td>0.00</td>\n","      <td>22.63</td>\n","      <td>37.49</td>\n","      <td>43.58</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>7.000000</td>\n","      <td>0.0</td>\n","      <td>13714947.0</td>\n","      <td>1.210142</td>\n","      <td>0.000000</td>\n","      <td>0.412361</td>\n","      <td>0.014556</td>\n","      <td>1.205087</td>\n","      <td>1.116092</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>15338</th>\n","      <td>4443</td>\n","      <td>2</td>\n","      <td>11570320</td>\n","      <td>15000000.0</td>\n","      <td>2756602.0</td>\n","      <td>0.0</td>\n","      <td>8696313.0</td>\n","      <td>4036479.0</td>\n","      <td>A</td>\n","      <td>84.50</td>\n","      <td>0.00</td>\n","      <td>81.30</td>\n","      <td>61.90</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5.330000</td>\n","      <td>8696313.0</td>\n","      <td>2018240.0</td>\n","      <td>0.269000</td>\n","      <td>0.580000</td>\n","      <td>0.290000</td>\n","      <td>0.269000</td>\n","      <td>0.336000</td>\n","      <td>0.330000</td>\n","    </tr>\n","    <tr>\n","      <th>15339</th>\n","      <td>6572</td>\n","      <td>3</td>\n","      <td>12931913</td>\n","      <td>11000000.0</td>\n","      <td>11797694.0</td>\n","      <td>0.0</td>\n","      <td>9500.0</td>\n","      <td>12266233.0</td>\n","      <td>F</td>\n","      <td>0.00</td>\n","      <td>18.10</td>\n","      <td>21.90</td>\n","      <td>33.10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.670000</td>\n","      <td>9500.0</td>\n","      <td>4088744.0</td>\n","      <td>1.120000</td>\n","      <td>0.000864</td>\n","      <td>0.141000</td>\n","      <td>0.089200</td>\n","      <td>1.080000</td>\n","      <td>0.795000</td>\n","    </tr>\n","    <tr>\n","      <th>15340</th>\n","      <td>8813</td>\n","      <td>2</td>\n","      <td>4743642</td>\n","      <td>7000000.0</td>\n","      <td>5669470.0</td>\n","      <td>0.0</td>\n","      <td>279498.0</td>\n","      <td>4546997.0</td>\n","      <td>A</td>\n","      <td>8.00</td>\n","      <td>0.00</td>\n","      <td>9.56</td>\n","      <td>16.80</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9.250000</td>\n","      <td>279498.0</td>\n","      <td>2273499.0</td>\n","      <td>0.650000</td>\n","      <td>0.039900</td>\n","      <td>0.045400</td>\n","      <td>0.044300</td>\n","      <td>0.723000</td>\n","      <td>0.637000</td>\n","    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   <td>66.80</td>\n","      <td>73.10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2.830000</td>\n","      <td>2621904.0</td>\n","      <td>5274872.0</td>\n","      <td>1.050000</td>\n","      <td>0.131000</td>\n","      <td>0.484000</td>\n","      <td>0.012200</td>\n","      <td>0.893000</td>\n","      <td>0.012800</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15343 rows × 24 columns</p>\n","</div>"],"text/plain":["           X  jumlah_kartu  ...  utilisasi_3bulan  utilisasi_6bulan\n","0         92             5  ...          0.919164          0.919635\n","1        316             2  ...          0.022847          0.007724\n","2        624             1  ...          0.627426          0.899309\n","3        628             2  ...          0.013699          0.255363\n","4        947             3  ...          1.205087          1.116092\n","...      ...           ...  ...               ...               ...\n","15338   4443             2  ...          0.336000          0.330000\n","15339   6572             3  ...          1.080000          0.795000\n","15340   8813             2  ...          0.723000          0.637000\n","15341    446             2  ...          0.909339          0.965757\n","15342  14086             4  ...          0.893000          0.012800\n","\n","[15343 rows x 24 columns]"]},"metadata":{},"execution_count":105}]},{"cell_type":"markdown","metadata":{"id":"cqfJOHfWNruu"},"source":[">Diberikan data dari sebuah bank yang berisikan data terkait kredit dari nasabah debitur.\n","Sebagai seorang data scientist / analyst, kita diminta untuk menilai mana nasabah debitur yang memiliki nilai kredit layak / bagus dan mana nasabah yang memiliki nilai kredit macet berdasarkan riwayat nasabah. Karena tiap harinya data yang terkumpul terus bertambah maka bank ini ingin membuat sistem penilaian kelayakan kredit secara otomatis berdasarkan data yang mereka miliki. Data yang diberikan yakni sebagai berikut.\n","- X : ID pelanggan\n","- jumlah_kartu : Jumlah kartu aktif yang dimiliki pelanggan\n","- outstanding : Total saldo pemakaian kartu kredit\n","- limit_kredit : Jumlah maks. limit kredit yang dapat digunakan\n","- tagihan : Jumlah tagihan pada bulan terakhir\n","- total_pemakaian_tunai : Total pemakaian transaksi tunai pada bulan terakhir\n","- total_pemakaian_retail : Total pemakaian transaksi retail pada bulan terakhir \n","- sisa_tagihan_tidak_terbayar : Jumlah tagihan tidak terbayar pada bulan terakhir\n","- kode_cabang : kode unik tiap cabang dari bank\n","- rasio_pembayaran : Rasio perbandingan jumlah yang dibayar dengan tagihan pada bulan terakhir \n","- persentasi_overlimit :Persentase overlimit\n","- rasio_pembayaran_3bulan : Rata-rata rasio pembayaran 3 bulan terakhir\n","- rasio_pembayaran_6bulan : Rata-rata rasio pembayaran 3 bulan sebelum 3 bulan terakhir\n","- skor_delikuensi : Nilai kelayakan debitur saat mengajukan pinjaman / kredit\n","- flag_kredit_macet : Flag yang menandakan apakah kartu kredit macet atau tidak\n","- jumlah_tahun_sejak_pembukaan_kredit : Jumlah kredit sejak pembuatan kartu kredit pertama kali\n","- total_pemakaian : Total pemakaian kartu kredit (tunai & retail) pada bulan terakhir\n","- sisa_tagihan_per_jumlah_kartu : Jumlah tagihan yang tak terbayar pada bulan terakhir dibagi jumlah kartu aktif\n","- sisa_tagihan_per_limit : Jumlah tagihan yang tak terbayar pada bulan terakhir dibagi total limit\n","- total_pemakaian_per_limit : Total pemakaian kartu kredit (tunai & retail) pada bulan terakhir dibagi total limit\n","- pemakaian_3bln_per_limit : Rata-rata pemakaian selama 3 bulan terakhir dibagi total limit\n","- pemakaian_6bln_per_limit : Rata-rata pemakaian selama 3 bulan sebelum 3 bulan terakhir dibagi total limit\n","- utilisasi_3bulan : Utilisasi kartu kredit selama 3 bulan terakhir\n","- utilisasi_6bulan : Utilisasi kartu kredit selama 3 bulan sebelum 3 bulan terakhir\n",">\n",">\n","> Dengan variabel dependen / y adalah variabel flag_kredit_macet & untuk variabel independen / X adalah seluruh variabel selain variabel dependen / y."]},{"cell_type":"code","metadata":{"id":"No_gwhdIOhMU","executionInfo":{"status":"ok","timestamp":1630385203886,"user_tz":-480,"elapsed":93,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":105,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fd42oBYAJ_ld"},"source":["#### PROBLEM STATEMENT"]},{"cell_type":"markdown","metadata":{"id":"1FrqQ9N6oiRw"},"source":["1. Apakah nasabah dengan status kredit macet lebih banyak dibandingkan dengan yang tidak? Atau sebaliknya?\n","2. Bagaimana pengaruh jumlah kartu terhadap total pemakaian tunai nasabah?\n","3. Seberapa besar proporsi nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail?\n","4. Apakah status kredit macet dipengaruhi oleh persentase overlimit nasabah?\n","5. Apakah skor delikuensi mempengaruhi status kredit macet seorang nasabah?\n","6. Bagaimanakah pengaruh distribusi sisa tagihan tidak terbayar pada flag kredit macet nasabah?\n","7. Apakah flag kredit macet dipengaruhi oleh distribusi sisa tagihan per jumlah kartu dari nasabah?\n","8. Bagaimanakah pengaruh distribusi sisa tagihan per limit pada flag kredit macet nasabah?"]},{"cell_type":"code","metadata":{"id":"uoDmac3UjPIr","executionInfo":{"status":"ok","timestamp":1630385203888,"user_tz":-480,"elapsed":94,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":105,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1LTjPkJzJ_lh"},"source":["#### EDA"]},{"cell_type":"code","metadata":{"id":"fcrxS0OHJ_ld","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630385203889,"user_tz":-480,"elapsed":94,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"47285af2-fb40-43c0-9785-c0e01cbaa93d"},"source":["# mengecek info dari data\n","dtrain.info()"],"execution_count":106,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 15343 entries, 0 to 15342\n","Data columns (total 24 columns):\n"," #   Column                               Non-Null Count  Dtype  \n","---  ------                               --------------  -----  \n"," 0   X                                    15343 non-null  int64  \n"," 1   jumlah_kartu                         15343 non-null  int64  \n"," 2   outstanding                          15343 non-null  int64  \n"," 3   limit_kredit                         15343 non-null  float64\n"," 4   tagihan                              15343 non-null  float64\n"," 5   total_pemakaian_tunai                15343 non-null  float64\n"," 6   total_pemakaian_retail               15343 non-null  float64\n"," 7   sisa_tagihan_tidak_terbayar          15343 non-null  float64\n"," 8   kode_cabang                          15243 non-null  object \n"," 9   rasio_pembayaran                     15343 non-null  float64\n"," 10  persentasi_overlimit                 15343 non-null  float64\n"," 11  rasio_pembayaran_3bulan              15343 non-null  float64\n"," 12  rasio_pembayaran_6bulan              15343 non-null  float64\n"," 13  skor_delikuensi                      15343 non-null  int64  \n"," 14  flag_kredit_macet                    15343 non-null  int64  \n"," 15  jumlah_tahun_sejak_pembukaan_kredit  15343 non-null  float64\n"," 16  total_pemakaian                      15343 non-null  float64\n"," 17  sisa_tagihan_per_jumlah_kartu        15343 non-null  float64\n"," 18  sisa_tagihan_per_limit               15343 non-null  float64\n"," 19  total_pemakaian_per_limit            15343 non-null  float64\n"," 20  pemakaian_3bln_per_limit             15343 non-null  float64\n"," 21  pemakaian_6bln_per_limit             15343 non-null  float64\n"," 22  utilisasi_3bulan                     15343 non-null  float64\n"," 23  utilisasi_6bulan                     15343 non-null  float64\n","dtypes: float64(18), int64(5), object(1)\n","memory usage: 2.8+ MB\n"]}]},{"cell_type":"markdown","metadata":{"id":"CH-ggILw92z0"},"source":["**1. Pertama-tama akan dicari tahu, apakah nasabah / debitur dengan status kredit macet lebih banyak dibandingkan dengan yang tidak? Atau sebaliknya?**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":248},"id":"ZKbWbHrPikP1","executionInfo":{"status":"ok","timestamp":1630385203890,"user_tz":-480,"elapsed":82,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"e12a3c16-ea66-405b-922b-ca39e0c02999"},"source":["# Mencari perbandingan banyak/tidaknya nasabah dengan status kredit macet\n","dtrain['flag_kredit_macet'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True)\n","plt.show()"],"execution_count":107,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"F15P6up8iUZN"},"source":[">Berdasarkan hasil temuan di atas dapat diketahui bahwa ternyata debitur dengan status kredit macet lebih sedikit dibandingkan dengan yang tidak macet."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":316},"id":"CaroK7jCKkwo","executionInfo":{"status":"ok","timestamp":1630385203892,"user_tz":-480,"elapsed":81,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"1ab91b85-d027-4dd7-cf9a-76cacd9882ed"},"source":["# melihat summary data\n","dtrain.describe()"],"execution_count":108,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>1.534300e+04</td>\n","      <td>1.534300e+04</td>\n","      <td>1.534300e+04</td>\n","      <td>1.534300e+04</td>\n","      <td>1.534300e+04</td>\n","      <td>1.534300e+04</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>1.534300e+04</td>\n","      <td>1.534300e+04</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","      <td>15343.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7750.138956</td>\n","      <td>2.507332</td>\n","      <td>1.160634e+07</td>\n","      <td>2.081037e+07</td>\n","      <td>8.087595e+06</td>\n","      <td>7.468477e+04</td>\n","      <td>2.032315e+06</td>\n","      <td>8.138304e+06</td>\n","      <td>64.482234</td>\n","      <td>3.347169</td>\n","      <td>50.539467</td>\n","      <td>81.291786</td>\n","      <td>0.004758</td>\n","      <td>0.087597</td>\n","      <td>6.636067</td>\n","      <td>2.107030e+06</td>\n","      <td>2.964641e+06</td>\n","      <td>0.468332</td>\n","      <td>0.109579</td>\n","      <td>0.167506</td>\n","      <td>0.202896</td>\n","      <td>0.571362</td>\n","      <td>0.534084</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4472.966163</td>\n","      <td>1.014525</td>\n","      <td>2.415412e+07</td>\n","      <td>2.960443e+07</td>\n","      <td>1.766835e+07</td>\n","      <td>6.205923e+05</td>\n","      <td>7.959575e+06</td>\n","      <td>1.925786e+07</td>\n","      <td>797.324307</td>\n","      <td>9.257389</td>\n","      <td>1333.723881</td>\n","      <td>1472.735798</td>\n","      <td>0.114362</td>\n","      <td>0.282717</td>\n","      <td>4.682317</td>\n","      <td>8.125462e+06</td>\n","      <td>6.262742e+06</td>\n","      <td>0.473221</td>\n","      <td>0.200651</td>\n","      <td>0.192300</td>\n","      <td>0.275018</td>\n","      <td>0.412303</td>\n","      <td>0.432826</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>3.000000e+06</td>\n","      <td>2.004300e+04</td>\n","      <td>0.000000e+00</td>\n","      <td>-1.566720e+07</td>\n","      <td>0.000000e+00</td>\n","      <td>-18138.000000</td>\n","      <td>0.000000</td>\n","      <td>-57792.340000</td>\n","      <td>-77056.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","      <td>-1.566720e+07</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>-0.632000</td>\n","      <td>-0.126000</td>\n","      <td>-0.569000</td>\n","      <td>0.000288</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3875.500000</td>\n","      <td>2.000000</td>\n","      <td>1.997592e+06</td>\n","      <td>5.000000e+06</td>\n","      <td>8.150830e+05</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>20.200000</td>\n","      <td>21.600000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.920000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.035000</td>\n","      <td>0.032900</td>\n","      <td>0.189000</td>\n","      <td>0.155000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7752.000000</td>\n","      <td>2.000000</td>\n","      <td>4.724856e+06</td>\n","      <td>9.000000e+06</td>\n","      <td>3.141692e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>2.010420e+05</td>\n","      <td>2.750772e+06</td>\n","      <td>26.900000</td>\n","      <td>0.000000</td>\n","      <td>50.000000</td>\n","      <td>65.600000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>5.666667</td>\n","      <td>2.480000e+05</td>\n","      <td>1.209318e+06</td>\n","      <td>0.335929</td>\n","      <td>0.022100</td>\n","      <td>0.108000</td>\n","      <td>0.117000</td>\n","      <td>0.576000</td>\n","      <td>0.496000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>11624.500000</td>\n","      <td>3.000000</td>\n","      <td>1.061836e+07</td>\n","      <td>2.200000e+07</td>\n","      <td>7.420343e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.403758e+06</td>\n","      <td>7.139904e+06</td>\n","      <td>100.000000</td>\n","      <td>1.070000</td>\n","      <td>91.335000</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>9.330000</td>\n","      <td>1.513168e+06</td>\n","      <td>3.083416e+06</td>\n","      <td>0.930000</td>\n","      <td>0.128000</td>\n","      <td>0.239424</td>\n","      <td>0.285000</td>\n","      <td>0.917000</td>\n","      <td>0.864000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>15493.000000</td>\n","      <td>16.000000</td>\n","      <td>7.980586e+08</td>\n","      <td>1.000000e+09</td>\n","      <td>6.280000e+08</td>\n","      <td>2.884057e+07</td>\n","      <td>2.850000e+08</td>\n","      <td>4.440000e+08</td>\n","      <td>68983.000000</td>\n","      <td>190.000000</td>\n","      <td>75575.000000</td>\n","      <td>54899.000000</td>\n","      <td>5.000000</td>\n","      <td>1.000000</td>\n","      <td>34.416667</td>\n","      <td>3.140000e+08</td>\n","      <td>1.480000e+08</td>\n","      <td>7.240000</td>\n","      <td>4.600000</td>\n","      <td>3.490000</td>\n","      <td>8.110000</td>\n","      <td>8.125671</td>\n","      <td>9.730000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  X  jumlah_kartu  ...  utilisasi_3bulan  utilisasi_6bulan\n","count  15343.000000  15343.000000  ...      15343.000000      15343.000000\n","mean    7750.138956      2.507332  ...          0.571362          0.534084\n","std     4472.966163      1.014525  ...          0.412303          0.432826\n","min        1.000000      1.000000  ...          0.000288          0.000000\n","25%     3875.500000      2.000000  ...          0.189000          0.155000\n","50%     7752.000000      2.000000  ...          0.576000          0.496000\n","75%    11624.500000      3.000000  ...          0.917000          0.864000\n","max    15493.000000     16.000000  ...          8.125671          9.730000\n","\n","[8 rows x 23 columns]"]},"metadata":{},"execution_count":108}]},{"cell_type":"markdown","metadata":{"id":"dKDTBRkaAEMY"},"source":["**2. Dapat dilihat bahwa pada total_pemakaian_tunai didapat minimum adalah 0 dan 0 juga di Q1, Q2, & Q3. Akan dilihat apakah jumlah kartu yang dimiliki nasabah berpengaruh terhadap total pemakaian tunai?**"]},{"cell_type":"code","metadata":{"id":"cjf1VxaJjdZv","executionInfo":{"status":"ok","timestamp":1630385203893,"user_tz":-480,"elapsed":79,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# Buat Crosstab antara jumlah kartu dan total pemakaian tunai == 0; melihat proporsi nasabah yang tidak memiliki total_pemakaian_tunai berdasarkan banyaknya kartu yang dimiliki\n","banding1 = pd.crosstab(dtrain['jumlah_kartu'], (dtrain['total_pemakaian_tunai']==0))"],"execution_count":109,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":543},"id":"5xbpcSTCktX1","executionInfo":{"status":"ok","timestamp":1630385203894,"user_tz":-480,"elapsed":79,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"dce653b1-8918-4041-a413-60714f09f694"},"source":["banding1"],"execution_count":110,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>total_pemakaian_tunai</th>\n","      <th>False</th>\n","      <th>True</th>\n","    </tr>\n","    <tr>\n","      <th>jumlah_kartu</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>76</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>499</td>\n","      <td>10127</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>133</td>\n","      <td>2636</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>52</td>\n","      <td>1073</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>21</td>\n","      <td>435</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>12</td>\n","      <td>138</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>2</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>26</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>4</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>1</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["total_pemakaian_tunai  False  True \n","jumlah_kartu                       \n","1                          0     76\n","2                        499  10127\n","3                        133   2636\n","4                         52   1073\n","5                         21    435\n","6                         12    138\n","7                          2     73\n","8                          1     26\n","9                          4     12\n","10                         1      6\n","11                         0      7\n","12                         0      2\n","13                         0      4\n","15                         0      1\n","16                         0      2"]},"metadata":{},"execution_count":110}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qdI3I2fQI9it","executionInfo":{"status":"ok","timestamp":1630385203895,"user_tz":-480,"elapsed":78,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"056080c6-cabf-46d5-f8c3-089c180ee1ec"},"source":["# melihat jumlah nasabah yang tidak memiliki total pemakaian tunai berdasarkan jumlah kartu kredit yang dimiliki\n","banding1.sum()"],"execution_count":111,"outputs":[{"output_type":"execute_result","data":{"text/plain":["total_pemakaian_tunai\n","False      725\n","True     14618\n","dtype: int64"]},"metadata":{},"execution_count":111}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":715},"id":"LO3KLNovyMZh","executionInfo":{"status":"ok","timestamp":1630385204761,"user_tz":-480,"elapsed":930,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"66276620-3ff9-45ef-b842-ac5ff71dd1c0"},"source":["# membuat barplot untuk melihat proporsi nasabah yang tidak melakukan pemakaian tunai berdasarkan jumlah kartu kredit yang dimiliki\n","plt.figure(figsize=(20,12))\n","banding1_stacked = banding1.stack().reset_index().rename(columns={0: 'value'})\n","sns.barplot(x=banding1_stacked.jumlah_kartu, y=banding1_stacked.value, hue=banding1_stacked.total_pemakaian_tunai)\n","plt.show()"],"execution_count":112,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x864 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"O0mja6jn_wmx"},"source":["> Ternyata jumlah nasabah yang tidak memiliki total pemakaian tunai lebih banyak dibandingkan yang memiliki berdasarkan jumlah kartu kredit yang dimiliki nasabah. Dapat dilihat juga bahwa semakin sedikit jumlah kartu yang dimiliki nasabah, maka semakin banyak pula nasabah yang tidak memiliki total pemakaian tunai dan begitu sebaliknya.\n","\n","**3. Seberapa besar proporsi nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail?**"]},{"cell_type":"code","metadata":{"id":"zG6C8BpNoauo","executionInfo":{"status":"ok","timestamp":1630385204764,"user_tz":-480,"elapsed":93,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat variabel yang membandingkan & melihat proporsi nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail\n","banding_pemakaian = pd.crosstab((dtrain['total_pemakaian_tunai']==0), (dtrain['total_pemakaian_retail']==0))"],"execution_count":113,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"_g_HwTY6oa60","executionInfo":{"status":"ok","timestamp":1630385204765,"user_tz":-480,"elapsed":92,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"cde1db17-6e6b-45f8-f965-3b286b5f720e"},"source":["banding_pemakaian"],"execution_count":114,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>total_pemakaian_retail</th>\n","      <th>False</th>\n","      <th>True</th>\n","    </tr>\n","    <tr>\n","      <th>total_pemakaian_tunai</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>False</th>\n","      <td>519</td>\n","      <td>206</td>\n","    </tr>\n","    <tr>\n","      <th>True</th>\n","      <td>9681</td>\n","      <td>4937</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["total_pemakaian_retail  False  True \n","total_pemakaian_tunai               \n","False                     519    206\n","True                     9681   4937"]},"metadata":{},"execution_count":114}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NcWdgDnPobEr","executionInfo":{"status":"ok","timestamp":1630385204766,"user_tz":-480,"elapsed":89,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"16f9ace8-c1b2-468e-cc1f-a0ba0dfa15a1"},"source":["# melihat jumlah nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail\n","banding_pemakaian.sum()"],"execution_count":115,"outputs":[{"output_type":"execute_result","data":{"text/plain":["total_pemakaian_retail\n","False    10200\n","True      5143\n","dtype: int64"]},"metadata":{},"execution_count":115}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"rPLgfzAjobPW","executionInfo":{"status":"ok","timestamp":1630385204769,"user_tz":-480,"elapsed":78,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"9ba719a9-9413-468a-b1bc-a1b983dd31ef"},"source":["# membuat barplot untuk melihat proporsi nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail\n","banding_pemakaian_stacked = banding_pemakaian.stack().reset_index().rename(columns={0: 'value'})\n","sns.barplot(x=banding_pemakaian_stacked.total_pemakaian_tunai, y=banding_pemakaian_stacked.value, hue=banding_pemakaian_stacked.total_pemakaian_retail)\n","plt.show()"],"execution_count":116,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"KuUoNFt2D5fG"},"source":["> Berdasarkan temuan di atas dapat diketahui bahwa terdapat nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail yakni sejumlah 5143 nasabah. Meskipun begitu proporsi nasabah yang memiliki total pemakaian tunai & total pemakaian retail lebih besar dibandingkan yang tidak, yakni sejumlah 10200 nasabah.\n","\n","**4. Lalu, akan dicari tahu, apakah flag kredit macet dipengaruhi oleh persentase overlimit debitur?**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8mo3Rqn4UjB6","executionInfo":{"status":"ok","timestamp":1630385204770,"user_tz":-480,"elapsed":76,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"fb61981a-15e2-4a58-ffee-a1dc0e06fdbf"},"source":["# melihat summary dari persentase overlimit\n","dtrain['persentasi_overlimit'].describe()"],"execution_count":117,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    15343.000000\n","mean         3.347169\n","std          9.257389\n","min          0.000000\n","25%          0.000000\n","50%          0.000000\n","75%          1.070000\n","max        190.000000\n","Name: persentasi_overlimit, dtype: float64"]},"metadata":{},"execution_count":117}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YeYYkhWPU0ns","executionInfo":{"status":"ok","timestamp":1630385204772,"user_tz":-480,"elapsed":67,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"2a31b6fe-2e9e-40a8-ed95-551be48819b6"},"source":["# membuat & membagi bin untuk tiap nilai rendah, sedang, & tinggi dari persentase overlimit\n","bin = np.linspace(dtrain['persentasi_overlimit'].min(), dtrain['persentasi_overlimit'].max(), 4)\n","label = ['rendah', 'sedang', 'tinggi']\n","print(bin)"],"execution_count":118,"outputs":[{"output_type":"stream","name":"stdout","text":["[  0.          63.33333333 126.66666667 190.        ]\n"]}]},{"cell_type":"code","metadata":{"id":"1sQ9iUtOXt1U","executionInfo":{"status":"ok","timestamp":1630385204773,"user_tz":-480,"elapsed":58,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat kolom baru yang berisikan bin dari cut / pembagian nilai persentase overlimit\n","dtrain['bin'] = pd.cut(dtrain['persentasi_overlimit'], bins=bin, labels=label, include_lowest=True)"],"execution_count":119,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":418},"id":"ydTfEInEZxbm","executionInfo":{"status":"ok","timestamp":1630385204774,"user_tz":-480,"elapsed":58,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"efa2d92b-2821-4f16-879f-24ba15a24831"},"source":["# memanggil bin & persentase overlimit\n","dtrain[['bin', 'persentasi_overlimit']]"],"execution_count":120,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>bin</th>\n","      <th>persentasi_overlimit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>rendah</td>\n","      <td>22.63</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>15338</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>15339</th>\n","      <td>rendah</td>\n","      <td>18.10</td>\n","    </tr>\n","    <tr>\n","      <th>15340</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>15341</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>15342</th>\n","      <td>rendah</td>\n","      <td>3.21</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15343 rows × 2 columns</p>\n","</div>"],"text/plain":["          bin  persentasi_overlimit\n","0      rendah                  0.00\n","1      rendah                  0.00\n","2      rendah                  0.00\n","3      rendah                  0.00\n","4      rendah                 22.63\n","...       ...                   ...\n","15338  rendah                  0.00\n","15339  rendah                 18.10\n","15340  rendah                  0.00\n","15341  rendah                  0.00\n","15342  rendah                  3.21\n","\n","[15343 rows x 2 columns]"]},"metadata":{},"execution_count":120}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"GJiGf5anaJU0","executionInfo":{"status":"ok","timestamp":1630385205576,"user_tz":-480,"elapsed":858,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"9c1298df-eaf9-46c5-f83f-850bf1ed635c"},"source":["# membuat crosstab untuk membandingkan flag kredit macet dengan tiap bin\n","banding2 = pd.crosstab(dtrain['flag_kredit_macet'], dtrain['bin'])\n","banding2"],"execution_count":121,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>bin</th>\n","      <th>rendah</th>\n","      <th>sedang</th>\n","      <th>tinggi</th>\n","    </tr>\n","    <tr>\n","      <th>flag_kredit_macet</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13959</td>\n","      <td>32</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1323</td>\n","      <td>18</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["bin                rendah  sedang  tinggi\n","flag_kredit_macet                        \n","0                   13959      32       8\n","1                    1323      18       3"]},"metadata":{},"execution_count":121}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"HJObBhYVcNS_","executionInfo":{"status":"ok","timestamp":1630385205578,"user_tz":-480,"elapsed":201,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"7954f9b9-ec06-488c-fc34-7d20c0b4117e"},"source":["# Buat hasil crosstab menjadi dataframe\n","banding2_stacked = banding2.stack().reset_index().rename(columns={0: 'value', 'bin': 'level_overlimit'})\n","banding2_stacked"],"execution_count":122,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>flag_kredit_macet</th>\n","      <th>level_overlimit</th>\n","      <th>value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>rendah</td>\n","      <td>13959</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>sedang</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>tinggi</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>rendah</td>\n","      <td>1323</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>sedang</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1</td>\n","      <td>tinggi</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   flag_kredit_macet level_overlimit  value\n","0                  0          rendah  13959\n","1                  0          sedang     32\n","2                  0          tinggi      8\n","3                  1          rendah   1323\n","4                  1          sedang     18\n","5                  1          tinggi      3"]},"metadata":{},"execution_count":122}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"BAieF9AtcU--","executionInfo":{"status":"ok","timestamp":1630385205580,"user_tz":-480,"elapsed":192,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"d498dd0d-4bec-4274-a9de-dc048a7e7c95"},"source":["# membuat barplot untuk melihat proporsi flag kredit macet berdasarkan level overlimit\n","sns.barplot(x=banding2_stacked.flag_kredit_macet, y=banding2_stacked.value, hue=banding2_stacked.level_overlimit)\n","plt.show()"],"execution_count":123,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"iwf-DmtdVup3"},"source":[">Ternyata berdasarkan hasil temuan di atas, didapatkan bahwa lebih banyak persentase overlimit yang rendah cenderung tidak memiliki status kredit macet / flag kredit macet bernilai nol (0). Sedangkan, untuk flag kredit macet bernilai satu (1) juga lebih banyak didapatkan persentase overlimit yang rendah dibandingkan dengan persentase overlimit yang tinggi.\n","Sehingga dapat disimpulkan bahwa persentase overlimit tidak begitu mempengaruhi status kredit macet dari seorang debitur.\n","\n","**5. Lalu, bagaimana dengan pegaruh skor delikuensi terhadap flag kredit macet?**"]},{"cell_type":"markdown","metadata":{"id":"8JS5FdzOJEZq"},"source":["Perlu diketahui terlebih dahulu, bahwa pada skor delikuensi semakin tinggi nilainya, maka semakin beresiko untuk dipinjamkan / memiliki kredit."]},{"cell_type":"code","metadata":{"id":"LtHqAfoUc1-k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630385205581,"user_tz":-480,"elapsed":189,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"30aa5fe9-2057-4365-9a46-382310a8ca8a"},"source":["# melihat summary skor delikuensi\n","dtrain['skor_delikuensi'].describe()"],"execution_count":124,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    15343.000000\n","mean         0.004758\n","std          0.114362\n","min          0.000000\n","25%          0.000000\n","50%          0.000000\n","75%          0.000000\n","max          5.000000\n","Name: skor_delikuensi, dtype: float64"]},"metadata":{},"execution_count":124}]},{"cell_type":"code","metadata":{"id":"C_a4vXrMI4uS","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630385205582,"user_tz":-480,"elapsed":167,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"65f55509-f694-4b9f-f64a-667333249a65"},"source":["# membuat & membagi bin untuk tiap nilai rendah, sedang, & tinggi dari skor delikuensi\n","bin2 = np.linspace(dtrain['skor_delikuensi'].min(), dtrain['skor_delikuensi'].max(), 4)\n","print(bin2)"],"execution_count":125,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.         1.66666667 3.33333333 5.        ]\n"]}]},{"cell_type":"code","metadata":{"id":"pErx_jQYlftw","executionInfo":{"status":"ok","timestamp":1630385205584,"user_tz":-480,"elapsed":158,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat kolom baru yang berisikan bin dari cut / pembagian nilai skor delikuensi\n","dtrain['bin_delikuensi'] = pd.cut(dtrain['skor_delikuensi'], bins=bin2, labels=label, include_lowest=True)"],"execution_count":126,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":418},"id":"vv2yKnskYbu8","executionInfo":{"status":"ok","timestamp":1630385205586,"user_tz":-480,"elapsed":158,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"77d00064-35b1-43e7-d5f2-ea871943f072"},"source":["# memanggil bin & nilai skor delikuensi\n","dtrain[['bin_delikuensi', 'skor_delikuensi']]"],"execution_count":127,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>bin_delikuensi</th>\n","      <th>skor_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>sedang</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>15338</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15339</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15340</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15341</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15342</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15343 rows × 2 columns</p>\n","</div>"],"text/plain":["      bin_delikuensi  skor_delikuensi\n","0             rendah                0\n","1             rendah                0\n","2             sedang                3\n","3             rendah                0\n","4             rendah                0\n","...              ...              ...\n","15338         rendah                0\n","15339         rendah                0\n","15340         rendah                0\n","15341         rendah                0\n","15342         rendah                0\n","\n","[15343 rows x 2 columns]"]},"metadata":{},"execution_count":127}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"ZPS6-jdFY6qh","executionInfo":{"status":"ok","timestamp":1630385205588,"user_tz":-480,"elapsed":157,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"3723ada8-1d1b-41a3-eb7b-2aea0bca2162"},"source":["# membuat crosstab untuk membandingkan flag kredit macet dengan tiap bin skor delikuensi\n","banding3 = pd.crosstab(dtrain['flag_kredit_macet'], dtrain['bin_delikuensi'])\n","banding3"],"execution_count":128,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>bin_delikuensi</th>\n","      <th>rendah</th>\n","      <th>sedang</th>\n","      <th>tinggi</th>\n","    </tr>\n","    <tr>\n","      <th>flag_kredit_macet</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13987</td>\n","      <td>5</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1343</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["bin_delikuensi     rendah  sedang  tinggi\n","flag_kredit_macet                        \n","0                   13987       5       7\n","1                    1343       1       0"]},"metadata":{},"execution_count":128}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"n8YYqcOvaZ0l","executionInfo":{"status":"ok","timestamp":1630385205589,"user_tz":-480,"elapsed":155,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"e1dfbf5d-a1ef-4a7c-f385-7644b7f1550d"},"source":["# Buat hasil crosstab menjadi dataframe\n","banding3_stacked = banding3.stack().reset_index().rename(columns={0: 'value', 'bin_delikuensi': 'level_delikuensi'})\n","banding3_stacked"],"execution_count":129,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>flag_kredit_macet</th>\n","      <th>level_delikuensi</th>\n","      <th>value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>rendah</td>\n","      <td>13987</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>sedang</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>tinggi</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>rendah</td>\n","      <td>1343</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>sedang</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1</td>\n","      <td>tinggi</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   flag_kredit_macet level_delikuensi  value\n","0                  0           rendah  13987\n","1                  0           sedang      5\n","2                  0           tinggi      7\n","3                  1           rendah   1343\n","4                  1           sedang      1\n","5                  1           tinggi      0"]},"metadata":{},"execution_count":129}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"pcoMPcumaZ-s","executionInfo":{"status":"ok","timestamp":1630385205591,"user_tz":-480,"elapsed":154,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"14088276-61bd-4acf-aaf0-91beec01b918"},"source":["# membuat barplot untuk melihat proporsi flag kredit macet berdasarkan skor delikuensi\n","sns.barplot(x=banding3_stacked.flag_kredit_macet, y=banding3_stacked.value, hue=banding3_stacked.level_delikuensi)\n","plt.show()"],"execution_count":130,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7Uj5oRcFb_5o"},"source":["> Dapat diketahui dari hasil di atas bahwa lebih banyak skor delikuensi yang rendah dengan status flag kredit macet bernilai nol (0). Sedangkan pada status flag kredit macet bernilai satu (1), skor delikuensi yang sedang hanya dijumpai 1 & skor delikuensi yang tinggi tidak dijumpai sama sekali. Sehingga, skor delikuensi juga tidak begitu mempengaruhi ilai flag kredit macet."]},{"cell_type":"markdown","metadata":{"id":"Yta8ff7k2_3K"},"source":["**6. Bagaimanakah pengaruh distribusi sisa tagihan tidak terbayar pada flag kredit macet?**"]},{"cell_type":"code","metadata":{"id":"YcXPCxx_Y7BG","colab":{"base_uri":"https://localhost:8080/","height":308},"executionInfo":{"status":"ok","timestamp":1630385206455,"user_tz":-480,"elapsed":1013,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"1786f164-ac14-458c-f0b7-eecd7d791e65"},"source":["# membuat boxplot untuk melihat pengaruh distribusi sisa_tagihan_tidak_terbayar pada flag_kredit_macet\n","sns.boxplot(x=\"flag_kredit_macet\", y=\"sisa_tagihan_tidak_terbayar\", data=dtrain)"],"execution_count":131,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f25f0866e50>"]},"metadata":{},"execution_count":131},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"mKcJyd3x3aZX"},"source":["> Ternyata berdasarkan hasil boxplot di atas, dapat diketahui bahwa distribusi sisa tagihan tidak terbayar di kedua nilai flag kredit macet berdistribusi kurang lebih sama. Sehingga, distribusi sisa tagihan tidak terbayar tidak mempengaruhi nilai flag kredit macet."]},{"cell_type":"markdown","metadata":{"id":"5AFkUQNs3CNc"},"source":["**7. Selanjutnya, bagaimana dengan pengaruh distribusi sisa tagihan per jumlah kartu pada flag kredit macet?**"]},{"cell_type":"code","metadata":{"id":"7Zeeaddxlf5u","colab":{"base_uri":"https://localhost:8080/","height":308},"executionInfo":{"status":"ok","timestamp":1630385206457,"user_tz":-480,"elapsed":111,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"11822553-1117-4568-d453-59fb20a35b8a"},"source":["# membuat boxplot untuk melihat pengaruh distribusi sisa_tagihan_per_jumlah_kartu pada flag_kredit_macet\n","sns.boxplot(x=\"flag_kredit_macet\", y=\"sisa_tagihan_per_jumlah_kartu\", data=dtrain)"],"execution_count":132,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f25f07ca250>"]},"metadata":{},"execution_count":132},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYMAAAESCAYAAAAfXrn0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3xdZZ3v8c83CddyKaYdLim1aAtOB1SceBsdKZLWgFLGCwgz2iBI57wcKOI4RznTQUSOysw4LwEZNHgYUg8jA+McqVoCDRbqKCgtOC3IoLEWaUQoWy6FIjTJ7/yxVspOyG21e+2VZH/fr9d6ZT/PXnutXyDNL89lPY8iAjMzq211RQdgZmbFczIwMzMnAzMzczIwMzOcDMzMDCcDMzNjEicDSddKelzS/eM4d7akNZLuk7RB0knViNHMbLKYtMkAuA5oHee5y4EbI+JY4HTgn/MKysxsMpq0ySAi1gK/K6+T9GpJnZLWS/qBpNcMnA4ckL4+EPhNFUM1M5vwGooOoMLagf8REb+Q9GaSFsA7gYuB2ySdB0wDWooL0cxs4pkyyUDSfsCfADdJGqjeK/16BnBdRHxJ0luBb0g6OiL6CwjVzGzCmTLJgKTL66mIeP0w751NOr4QEXdJ2huYATxexfjMzCasSTtmMFREPAP8StKpAEq8Ln3718AJaf0fAnsDWwsJ1MxsAtJkXbVU0jeBBSR/4T8GfAb4PnA1cCiwB3BDRFwiaT5wDbAfyWDy/4yI24qI28xsIpq0ycDMzCpnynQTmZnZrpuUA8gzZsyIOXPmFB2Gmdmksn79+iciYuZw703KZDBnzhzWrVtXdBhmZpOKpIdHes/dRGZm5mRgZmZOBmZmhpOBmZnhZFDzSqUSy5Yto1QqFR2K2SDd3d28+93vpru7u+hQaoKTQY3r6Ohg48aNrFixouhQzAa59NJLee6557j00kuLDqUmOBnUsFKpRGdnJxFBZ2enWwc2YXR3d7N582YANm/e7NZBFTgZ1LCOjg76+5NVvPv6+tw6sAljaGvArYP8ORnUsK6uLnp7ewHo7e1l9erVBUdklhhoFYxUtspzMqhhLS0tNDQkD6E3NDSwcOHCgiMySwxdbsbLz+TPyaCGtbW1UVeX/AjU19ezZMmSgiMySyxfvnzUslWek0ENa2xspLW1FUm0trbS2NhYdEhmAMydO3dna2DOnDnMnTu32IBqgJNBjWtra+OYY45xq8AmnOXLlzNt2jS3CqpkUm5u09zcHF611MwsG0nrI6J5uPfcMjAzMycDMzPLORlIulbS45LuH+O8N0rqlfSBPOMxM7Ph5d0yuA5oHe0ESfXAZcBtOcdiZmYjyDUZRMRa4HdjnHYe8C3g8TxjMTOzkRU6ZiCpCXgvcPU4zl0qaZ2kdVu3bs0/ODOzGlL0APKXgU9FRP9YJ0ZEe0Q0R0TzzJkzqxCamVntaCj4/s3ADZIAZgAnSeqNiG8XG5aZWW0pNBlExBEDryVdB3zXicDMrPpyTQaSvgksAGZI2gJ8BtgDICK+mue9zcxs/HJNBhFxRoZzz8wxFDMzG0XRA8hmZjYBOBmYmZmTQa0rlUosW7aMUqlUdChmViAngxrX0dHBxo0bWbFiRdGhmFmBnAxqWKlUorOzk4igs7PTrQOzGuZkUMM6Ojro708e/u7r63PrwKyGORnUsK6uLnp7ewHo7e1l9erVBUdkZkVxMqhhLS0tNDQkj5o0NDSwcOHCgiMys6KM+6EzSf8CvGzD5Ig4q6IRWdW0tbXR2dkJQH19PUuWLCk4IjMrSpaWwXeB76XH7cABwLN5BGXV0djYSGtrK5JobW2lsbGx6JDMrCDjbhlExLfKy+m6Q/9Z8Yisqtra2ti8ebNbBWY1bnfWJpoH/EGlArFiNDY2csUVVxQdhpkVLMuYwTYGjxn8FvhUxSMyM7Oqy9JNtH+egZiZWXHGPYAs6fbx1JmZ2eQzZstA0t7AviQb1BwEKH3rAKApx9jMzKxKxtNN9JfAx4HDgPW8lAyeAb6SU1xmZlZFY3YTRcTlwFzg0oh4VUQckR6vi4hRk4GkayU9Lun+Ed7/C0kbJG2U9CNJr9u1b8PMzHbHuMYMIqIPeN8uXP86oHWU938FHBcRxwCfA9p34R5mZrabsjyBfLuk90vS2KcmImIt8LtR3v9RRDyZFu8GZmWIx8zMKiRLMvhL4CbgBUnPSNom6ZkKxnI2cEsFr2dmZuM0IZ4zkHQ8STJ4+yjnLAWWAsyePTuvUMzMalKm5SjSqaXzgL0H6tKuoF0m6bXA14ETI2LErbYiop10TKG5ufllq6eamdmuy7IcxUeB80n69X8KvAW4C3jnrt5c0mzgP4APR8TPd/U6Zma2e7K0DM4H3gjcHRHHS3oN8PnRPpCubLqA5IG1LcBngD0AIuKrwEVAI/DP6bh0b0Q0Z/0mzMxs92RJBr+PiN9LQtJeEfHfko4a7QMRccYY738U+GiGGMzMLAdZksEWSdOBbwOrJT0JPJxPWGZmVk1ZZhO9N315saQ1wIF4KqiZ2ZSQZdXSSwZeR8SdJNtfXpdDTGZmVmVZHjo7XNKFAJL2IpkF9ItcojKzmlcqlVi2bBml0ogzzq2CsiSDs4Bj0oTwHeCOiLg4l6jMrOZ1dHSwceNGVqxYUXQoNWHMZCDpDZLeABwLXA58kKRFcGdab2ZWUaVSic7OTiKCzs5Otw6qYDwDyF8aUn4SmJ/WB7vx0JmZ2XA6Ojro7+8HoK+vjxUrVnDBBRcUHNXUNmYyiIjjx3MhSW0R0bH7IZlZrevq6qK3txeA3t5eVq9e7WSQsyxjBmM5v4LXMrMa1tLSQkND8rdqQ0MDCxcuLDiiqa+SyWDc+xyYmY2mra2Nurrk11N9fT1LliwpOKKpr5LJwCuJmllFNDY20traiiRaW1tpbGwsOqQpL9MS1mNwy8DMKqatrY3Nmze7VVAllUwGP6zgtcysxjU2NnLFFVcUHUbNyLKfwV7A+4E55Z+LiEvSr+dWOjgzM6uOLC2Dm4GngfXAC/mEY2ZmRciSDGZFRGtukZiZWWGyzCb6kaRjcovEzMwKM2bLQNJGkmmjDcBHJG0i6SYSEBHx2nxDNDOzvI2nm+g9u3pxSdemn388Io4e5n2RLH53ErAdODMi7t3V+5mZ2a4Zs5soIh4uP4DnSVoKA8dorgNGG2c4EZiXHkuBq8cTtJmZVVaWnc4WS/oF8CvgTmAzY2x7GRFrgd+NcsopwIpI3A1Ml3ToeGMyM7PKyDKA/DngLcDPI+II4ATg7t28fxPwSFl5S1r3MpKWSlonad3WrVt387ZmZlYuSzLYEREloE5SXUSsAZpziutlIqI9IpojonnmzJnVuq2ZWU3I8pzBU5L2A9YC10t6HHhuN+/fAxxeVp6V1pmZWRVlaRmcQjJ4fAHQCfwSOHk3778SWKLEW4CnI+LR3bymmZllNO6WQUSUtwLGtaOZpG8CC4AZkrYAnwH2SK/3VWAVybTSbpKppR8ZbzxmZlY543nobBuDp5AqLQ88dHbASJ+NiDNGu3ZEBPBX4wvVzMzyMp7nDPaPiAPKjv3Lv1YjSMtPqVRi2bJllEqlokMxswJl2ulM0kGSXivpDQNHXoFZdXR0dLBx40ZWrFhRdChmVqAs+xl8DjgT2AT0p9UBvLPyYVk1lEolOjs7iQg6OztZsmSJtxc0q1FZWganAa+OiOMi4vj0cCKYxDo6OujvT/J6X1+fWwdmNSxLMrgfmJ5XIFZ9XV1d9Pb2AtDb28vq1asLjsjMipIlGXwBuE/SrZJWDhx5BWb5a2lpob6+HoD6+noWLlxYcERmVpQsTyB3AJcBG3lpzMAmsba2Nr773e8CEBEsWbKk4IjMrChZksH2iLgit0jMzKwwWbqJfiDpC5Le6qmlU0NHRwd1dcmPQF1dnQeQzWpYlpbBsenXt5TVeWrpJDbcAPIFF1xQcFRmVoQsaxMdn2cgVn0tLS2sWrWK3t5eGhoaPIBsVsOyPHR20XD1EXFJ5cKxampra6OzsxNIZhN5ANmsdmUZM3iu7Ogj2b94Tg4xWZU0NjbS2tqKJFpbW/30sVkNy9JN9KXysqR/BG6teERWVW1tbWzevNmtArMal2UAeah9SXYms0mssbGRK67wjGGzWjfubiJJGyVtSI8HgIeAL+cXmpnVMi+vXl1ZxgzeQ7LN5cnAIuCwiPhKLlGZWc3z8urVNWYykDSwgc22suN54IB0f4P6HOOznPmvL5uISqUSt9xyCxHBLbfc4p/PKhhPy+Bf06/rgXXp14HjXuC3kj4/0ocltUp6SFK3pE8P8/5sSWsk3Zd2QZ2U/duwXdXe3s6GDRtob28vOhSznTo6OnY+ELljxw63DqpgPNtevif9ekREvCr9uvMADgHeK2n+0M+mrYarSKahzgfOGOa85cCNEXEscDrwz7v3Ldl4lUqlnctWr1692n992YSxevVqki3Sk0UUb7vttoIjmvoybXs5nIjoi4g/BP7vMG+/CeiOiE0R8SJwA3DK0EsAA11RBwK/2d2YbHza29t3bm7T39/v1oFNGAcffPCoZau83U4GZTRMXRPwSFl5S1pX7mLgQ5K2AKuA84a9uLRU0jpJ67Zu3VqBcO32228ftWxWlEcffXTUslVeJZNB7OLnzgCui4hZwEnANyS9LK6IaI+I5ohonjlz5u7EaamBZvhIZbOi7LHHHqOWrfIqmQyG0wMcXlaeldaVOxu4ESAi7gL2BmbkHJcBJ5xwwqByS0tLQZGYDfbss8+OWrbKG1cyUOLwMU57cZi6e4B5ko6QtCfJAPHQrTJ/DZyQ3ucPSZKB+4Gq4LTTThtUPvXUUwuKxGywOXPmjFq2yhtXMoik/2DVGOe8ZZi6XuBckjWMHiSZNfSApEskLU5P+2vgHEn/BXwTODPcX1EVN95446DyTTfdVFAkZoMtX7581LJVXpZuonslvTHrDSJiVUQcGRGvjoj/ndZdFBEr09c/i4i3RcTrIuL1EeE5ZFUydMC4q6uroEjMBps7d+7O1sCcOXOYO3dusQHVgCzJ4M3AXZJ+mT4ctlHShrwCM7Patnz5cqZNm+ZWQZVkWbX0XblFYYU49NBD2bJly87yYYcdVmA0ZoPNnTuX733ve0WHUTPG3TKIiIdJZga9M329PcvnbeIZ+sTxE088UVAkZla0LEtYfwb4FHBhWrUHwz91bJPE0D2PFy1aVFAkZla0LH/ZvxdYTLLtJRHxG2D/PIKy6mhra9v5MM+ee+7p3c7MaliWZPBiOuUzACRNyyckq5bGxkZOPPFEJHHiiSd6D2SzGpYlGdwo6WvAdEnnAF3ANfmEZdWyePFi9t13X04++eSiQzGzAmUZQP5H4N+BbwFHAhdFxJV5BWbVsXLlSrZv3853vvOdokMxswJlnQ20EfgBsDZ9bZNYqVSis7OTiKCzs9P7GZjVsCyziT4K/AR4H/AB4G5JZ+UVmOWvo6ODvr4+AHp7e72blFkNy9Iy+Bvg2Ig4MyLagD8mmWpqk1RXV9fOZNDX17dz1zMzqz1ZkkEJ2FZW3pbW2ST19re/fVD5T//0TwuKxMyKlmU5im7gx5JuJpleegqwQdInACLin3KIz3IkDbc5nZnVoiwtg18C3+alHc1uBn5F8uCZHz6bhNauXTtq2axIpVKJZcuWeWJDlYy7ZRARnx3tfUlXRsSw+xfbxHTwwQezefPmQWWziaKjo4ONGzeyYsUKLrjggqLDmfIqudDc2yp4LasCbzpuE5WnPVefVx2tYd503Caqjo4O+vv7gWSmm6c958/JoIZ503GbqLq6uujt7QWSZ2A87Tl/lUwGw05NkdQq6SFJ3ZI+PcI5p0n6maQHJP1rBWOyURx++OGjls2K0tLSQkNDMqTZ0NDwsuXWrfIqmQwuH1ohqR64CjgRmA+cIWn+kHPmkeyR8LaI+CPg4xWMyUbR1NQ0qDxr1qyCIjEbrK2tjbq65NdTfX29l1evgizLURwp6RpJt0n6/sAx8H5EXDfMx94EdEfEpoh4EbiB5PmEcucAV0XEk+l1Hs/8XdguueeeewaVf/KTnxQUidlgjY2NLFiwAIAFCxZ4efUqyPLQ2U3AV0mWre4b52eagEfKyluANw8550gAST8E6oGLI6Jz6IUkLQWWAsyePTtD2GY2GfmhyOrK0k3UGxFXR8RPImL9wFGBGBqAecAC4AzgGknTh54UEe0R0RwRzTNnzqzAbe2www4btWxWlFKpxJo1awBYs2aNp5ZWQZZk8B1JH5N0qKRXDBxjfKYHKB+VnJXWldsCrIyIHRHxK+DnJMnBcvbEE0+MWjYrSkdHBzt27ABgx44dnlpaBVmSQRvJyqU/Atanx7oxPnMPME/SEZL2BE4HVg4559skrQIkzSDpNtqUIS7bRe94xztGLZsVZfXq1SS77EJEcNtttxUc0dSXZTmKI7JePCJ6JZ0L3EoyHnBtRDwg6RJgXUSsTN9bJOlnJGMRfxMRbhNWwcA/NrOJxkulVF+WAWQkHU0yRXTvgbqIGLX9FhGrgFVD6i4qex3AJ9LDqugHP/jBoPLatWu58MILC4rG7CWPPfbYqGWrvCxTSz8DXJkexwN/DyzOKS6rgqHT9WbMmFFQJGaDLVy4cOdsIkksWrSo4IimvixjBh8ATgB+GxEfAV4HHJhLVFYVQxem+81vflNQJGaDtbW1UV9fDyRPIPuhs/xlSQbPR0Q/0CvpAOBxBs8UsklmYMvLkcpmRWlsbOSQQw4B4JBDDvFDZ1WQJRmsS+f/X0Myk+he4K5cojKzmlYqlXa2VHt6evycQRWMOxlExMci4qmI+CqwEGhLu4tskhpoho9UNitKe3v7ziWs+/v7aW9vLziiqS/TQnWSmiT9CTAbmC7JE9MnsZaWllHLZkXp6uoatWyVN+6ppZIuAz4IDDwPAMl+yN44d5KaP38+t956687y0UcfXWA0Zi/xeFb1ZXnO4M+AoyLihbyCseq68sorB5Uvv/xyTj755IKiMbMiZekm2gR4X8QpZGAnqZHKZlY7xmwZSLqSpDtoO/BTSbcDO1sHEbEsv/DMrBYdeuihg56D8Yq6+RtPN9HAYnTrefkic2ZmFXfUUUcNSgZHHnlkgdHUhjGTQUR0VCMQM7MBP/7xj0ctW+WNp5voxog4TdJGku6iQSLitblEZmY1a/r06Tz//PODypav8XQTnZ9+fU+egVj1SRq0jLW3GbSJYui6WUPLVnljziaKiEfTrw8Pd+QfouVl6EqQXhnSrHZleehsGy/vJnqaZID5ryPCu5NNMkOXrPYGIma1K8tzBl8m2fayiWQv408C/wrcAFxb+dAsb9dff/2gsveZtYniuOOOG1ResGBBMYHUkCzJYHFEfC0itkXEMxHRDrwrIv4NOGikD0lqlfSQpG5Jnx7lvPdLCknNGWIysylo2bLBjy+dd955BUVSO7Ikg+2STpNUlx6nAb9P3xt2M11J9cBVwIkk22WeIWn+MOftTzJQ7fljZkZjY+PObswZM2Z4P4MqyJIM/gL4MMmmNo+lrz8kaR/g3BE+8yagOyI2RcSLJF1Kpwxz3ueAy3gpuZhZDSuVSjzxxBM7X3s/g/xl2c9gU0ScHBEzImJm+ro7Ip6PiP8c4WNNwCNl5S1p3U6S3gAcHhHfyxy9mU1J5YsoRsTLFlW0yssym2hv4Gzgj4C9B+oj4qxdvbmkOuCfgDPHce5SYCnA7Nmzd/WWZjYJ3HnnnaOWrfKydBN9AzgEeBdwJ8mMom1jfKaHwfskz0rrBuwPHA3cIWkz8BZg5XCDyBHRHhHNEdE8c+bMDGGb2WRT/jDkcGWrvCzJYG5E/B3wXLpe0buBN4/xmXuAeZKOkLQncDpli91FxNNpt9OciJgD3E0ya2nd8Jczs1qwzz77jFq2ysuSDHakX5+SdDRwIPAHo30gInpJBpdvBR4EboyIByRdImnxrgRsZlPfiy++OGrZKi/LTmftkg4ClpP8db8f8HdjfSgiVgGrhtRdNMK5CzLEY2ZmFZIlGdweEU+S7Hn8KgBJR+QSlZmZVVWWbqJvDVP375UKxMxsQF9f36hlq7zx7GfwGpLppAdKel/ZWwdQNsXUzMwmr/F0Ex1FspfBdODksvptwDl5BGVmta2+vn5Qa6C+vr7AaGrDeLa9vBm4WdJbI+Kukc6TdGFEfKGi0ZlZTaqrqxuUDOrqsvRo267IshzFiIkgdepuxmJmBrz8l7+TQf4q+V/YeyaaWUW88MILo5at8iqZDPy8uJnZJOWWgZmZVTQZ3FTBa5mZWRVVbAnriPh8xaMzM7OqyHsJazOzzPbYY49Ry1Z5eS9hbWaW2Y4dO0YtW+XluoS1mZlNDru7hPWwS1GbmdnkMu5kEBFfT1/uXMLazMymhnF3E0k6X9IBSnxd0r2SFuUZnJmZVUeWMYOzIuIZYBHQCHwY+GIuUZmZWVVlSQYDTxifBKyIiAcYx1PHklolPSSpW9Knh3n/E5J+JmmDpNslvTJDTGZmVgFZksF6SbeRJINbJe0P9I/2AUn1wFXAicB84AxJ84ecdh/QHBGvJdk57e8zxGRmZhWQJRmcDXwaeGNEbAf2BD4yxmfeBHRHxKaIeBG4ATil/ISIWJNeD+BukofZzMysisZMBum2lwCvT7++StIbgFcy9mykJuCRsvKWtG4kZwO3jBDHUknrJK3bunXrWGGbmVkG45la+glgKfAlkmWqxeDlqt9ZiUAkfQhoBo4b7v2IaAfaAZqbm71ctplZBY3ZMoiIpenLq4E/i4jjgTXA08Anx/h4D3B4WXlWWjeIpBbgb4HFEeFdLMzMqizLmMHyiHhG0ttJWgNfJ0kQo7kHmCfpCEl7AqeTPL28k6Rjga+RJILHM8RjZmYVkiUZDOxO/W7gmoj4Hskg8ogiohc4F7gVeBC4MSIekHSJpMXpaf9AsrTFTZJ+KmnlCJczM7OcZFmbqEfS14CFwGWS9mJ83UyrgFVD6i4qe92SIQYzM8tBlpbBaSR/4b8rIp4CXgH8TS5RmZlZVWVZqG478B9l5UeBR/MIysyKdeWVV9Ld3V10GIOcf/75hd177ty5nHfeeYXdvxoquQeymZlNUlnGDMysRhT9V/A111zD9ddfv7O8ZMkSzjrrrAIjmvrcMjCzCeecc84ZVHYiyJ+TgZlNSDNmzACSVoHlz91EZjYhNTU10dTU5FZBlbhlYGZmTgZmZuZkYGZmeMygMBPxoR4o7sGeWniox2wic8vAzMxQxOTbJ6a5uTnWrVtXdBiT3oIFC15Wd8cdd1Q9DnvJRG0xFmHgv8PcuXMLjmRiqETrWdL6iGge7j13E5lNIN3d3fzigfuYvV/f2CdPcXvuSDouXnjYf/j9+tn63O/hZFDD7rjjjkGtA7cKJobZ+/Xxv97wTNFh2ATy+XsPyP0eHjMwM7PabBm4X/Yl06ZNA5L+yCKXCJ4oPKvJalVNJoPu7m5+ev+D9O37iqJDKVzdi8kEgvWbHis4kuLVb/9d0SHQ09PDc9vqq9ItYJPHw9vqmdbTk+s9ck8GklqBy4F64OsR8cUh7+8FrAD+GCgBH4yIzXnH1bfvK3j+NSflfRubRPb571Vjn2Q2ReWaDCTVA1eR7Ju8BbhH0sqI+FnZaWcDT0bEXEmnA5cBH8wzrp6eHuq3P+1//DZI/fYSPT29hcbQ1NTEC72PegDZBvn8vQewV1NTrvfIu2XwJqA7IjYBSLoBOAUoTwanABenr/8d+IokRd4PQPT1Ur+9lOstRtXfB5PwGY/cSFCX//S5UfUVmwgG/PrZ4ruJHttex+/7VGgME8ne9cHB+/YXdv9fP1vPvJzvkXcyaAIeKStvAd480jkR0SvpaaAReKL8JElLgaUAs2fP3q2gjjvuuMIHkHt6enj++ecLjWEi2WeffWjK+S+f8Sj6Aaei7z+gvqeHOv987lS/zz65/2U+mnnk/7MxaQaQI6IdaIfkCeTduZZni9hE5Z9NK0rezxn0AIeXlWeldcOeI6kBOJBkINnMzKok72RwDzBP0hGS9gROB1YOOWcl0Ja+/gDw/dzHC8zMbJBcu4nSMYBzgVtJppZeGxEPSLoEWBcRK4H/A3xDUjfwO5KEYWZmVZT7mEFErAJWDam7qOz174FT847DzMxG5rWJzMzMycDMzJwMzMwMJwMzM2OSbnspaSvwcNFxTCEzGPLEt9kE4Z/NynplRMwc7o1JmQyssiStG2lfVLMi+WezetxNZGZmTgZmZuZkYIn2ogMwG4F/NqvEYwZmZuaWgZmZORmYmRlOBjVNUqukhyR1S/p00fGYDZB0raTHJd1fdCy1wsmgRkmqB64CTgTmA2dIml9sVGY7XQe0Fh1ELXEyqF1vArojYlNEvAjcAJxScExmAETEWpL9TaxKnAxqVxPwSFl5S1pnZjXIycDMzJwMalgPcHhZeVZaZ2Y1yMmgdt0DzJN0hKQ9SfaeXllwTGZWECeDGhURvcC5wK3Ag8CNEfFAsVGZJSR9E7gLOErSFklnFx3TVOflKMzMzC0DMzNzMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDGwSkLRM0oOSeiR9pYLXnVPJJZIl3SGpOX29StL09PhYpe5RCZI+LmnfouOwicXJwCaDjwELgb+t9o0lNezK5yLipIh4CphOEv9E8nHAycAGcTKwCU3SV4FXAbcAB5XVnyzpx5Luk9Ql6eC0fqak1ZIekPR1SQ9LmjGO+7wqvdYbJZ0paaWk7wO3S5qWbrbyk/ScU9LP7CPphrTV8v+Afcqutzm97xeBV0v6qaR/GOHeCyTdKelmSZskfVHSX6T32yjp1WN8z/tJ+pf03A2S3p/WL5J0l6R7Jd2UnrcMOAxYI2nNLvwvsakqInz4mNAHsBmYAZwJfCWtO4iXnqD/KPCl9PVXgAvT161AADNGuO4c4H7gKOA+4HVp/ZkkS3q/Ii1/HvhQ+no68HNgGvAJ4Nq0/rVAL9A8JOY5wP1jfH8LgKeAQ4G9SBYM/Gz63vnAl8f4ni8bOKfsvBnAWmBaWvcp4KLy2Ir+/+pjYh271AQ2mwBmAf8m6VBgT+BXaf3bgfcCRESnpCfHuM5M4GbgfRKNbA0AAAG8SURBVBHxs7L61RExsLnKImCxpE+m5b2B2cA7gCvSe22QtGE3vp97IuJRAEm/BG5L6zcCx6evR/qeW0gWGiSN5UlJ7yHZwe6HkkjPv2s34rMpzsnAJqsrgX+KiJWSFgAX7+J1ngZ+TZJEypPBc2WvBbw/Ih4q/2D6S7ZSXih73V9W7uelf6dZvmeRJLQzKhmkTV0eM7DJ6kBe2n+hraz+h8BpkPSZUzbOMIIXSVoSSyT9+Qjn3Aqcp/S3v6Rj0/q1wJ+ndUeTdBUNtQ3Yf4wYxmuk73k18FcDBUkHAXcDb5M0N62bJunIHGKyKcLJwCari4GbJK0Hniir/yywKJ0yeirwW5JffiOKiOeA9wAXSFo8zCmfA/YANkh6IC0DXA3sJ+lB4BJg/TDXLpF01dw/0gByBhcz/Pd8KXBQeo//Ao6PiK0kYx/fTLuv7gJek57fDnR6ANnKeQlrm1Ik7QX0RUSvpLcCV0fE64uOy2yi85iBTTWzgRsl1ZF0AZ1TcDxmk4JbBjblSWoEbh/mrRPSbpxqxXEM8I0h1S9ExJurFYPZSJwMzMzMA8hmZuZkYGZmOBmYmRlOBmZmBvx/nz+tEXgEIvkAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"GlFgJsBP3dVB"},"source":["> Pada hasil di atas juga didapatkan bahwa distribusi sisa tagihan per jumlah kartu di kedua nilai flag kredit macet juga berdistribusi kurang lebih sama. Sehingga sisa tagihan per jumlah kartu tidak banyak berpengaruh juga terhadap nilai flag kredit macet."]},{"cell_type":"markdown","metadata":{"id":"ucPqf9563Duw"},"source":["**8. Lalu, bagaimanakah pengaruh distribusi sisa tagihan per limit pada flag kredit macet?**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":298},"id":"TfgbAbxeI9dt","executionInfo":{"status":"ok","timestamp":1630385206459,"user_tz":-480,"elapsed":91,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"04e7bab9-f883-4923-d1c2-a1b39150a526"},"source":["# membuat boxplot untuk melihat pengaruh distribusi sisa_tagihan_per_limit pada flag_kredit_macet\n","sns.boxplot(x=\"flag_kredit_macet\", y=\"sisa_tagihan_per_limit\", data=dtrain)"],"execution_count":133,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f25f070dfd0>"]},"metadata":{},"execution_count":133},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"DyScWV-p3iS-"},"source":["> Pada hasil boxplot distribusi sisa tagihan per limit pun juga demikian. Tidak menunjukkan banyaknya pengaruh terhadap nilai flag kredit macet."]},{"cell_type":"code","metadata":{"id":"PlWEAk9vwIU2","executionInfo":{"status":"ok","timestamp":1630385206460,"user_tz":-480,"elapsed":79,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":133,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YCOLd_9KJ_lg"},"source":["#### DATA PRE-PROCESSING"]},{"cell_type":"markdown","metadata":{"id":"WFQ9BabHnm_l"},"source":["**CEK DUPLIKASI DATA**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":69},"id":"xUsEb50_nl1i","executionInfo":{"status":"ok","timestamp":1630385206462,"user_tz":-480,"elapsed":79,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"e3468eba-95ff-40bb-d54e-35c5bb739190"},"source":["# mengecek duplikasi data\n","dtrain[dtrain.duplicated(keep=False)]"],"execution_count":134,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [X, jumlah_kartu, outstanding, limit_kredit, tagihan, total_pemakaian_tunai, total_pemakaian_retail, sisa_tagihan_tidak_terbayar, kode_cabang, rasio_pembayaran, persentasi_overlimit, rasio_pembayaran_3bulan, rasio_pembayaran_6bulan, skor_delikuensi, flag_kredit_macet, jumlah_tahun_sejak_pembukaan_kredit, total_pemakaian, sisa_tagihan_per_jumlah_kartu, sisa_tagihan_per_limit, total_pemakaian_per_limit, pemakaian_3bln_per_limit, pemakaian_6bln_per_limit, utilisasi_3bulan, utilisasi_6bulan, bin, bin_delikuensi]\n","Index: []"]},"metadata":{},"execution_count":134}]},{"cell_type":"markdown","metadata":{"id":"09T7cVNSnhQX"},"source":["**CEK MISSING VALUE**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3xerASEznVKE","executionInfo":{"status":"ok","timestamp":1630385206464,"user_tz":-480,"elapsed":77,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"40133b18-f316-4d36-f6e6-8efc7259197f"},"source":["# mengecek missing value pada tiap-tiap feature\n","dtrain.isnull().sum()"],"execution_count":135,"outputs":[{"output_type":"execute_result","data":{"text/plain":["X                                        0\n","jumlah_kartu                             0\n","outstanding                              0\n","limit_kredit                             0\n","tagihan                                  0\n","total_pemakaian_tunai                    0\n","total_pemakaian_retail                   0\n","sisa_tagihan_tidak_terbayar              0\n","kode_cabang                            100\n","rasio_pembayaran                         0\n","persentasi_overlimit                     0\n","rasio_pembayaran_3bulan                  0\n","rasio_pembayaran_6bulan                  0\n","skor_delikuensi                          0\n","flag_kredit_macet                        0\n","jumlah_tahun_sejak_pembukaan_kredit      0\n","total_pemakaian                          0\n","sisa_tagihan_per_jumlah_kartu            0\n","sisa_tagihan_per_limit                   0\n","total_pemakaian_per_limit                0\n","pemakaian_3bln_per_limit                 0\n","pemakaian_6bln_per_limit                 0\n","utilisasi_3bulan                         0\n","utilisasi_6bulan                         0\n","bin                                      0\n","bin_delikuensi                           0\n","dtype: int64"]},"metadata":{},"execution_count":135}]},{"cell_type":"code","metadata":{"id":"7CareuoIAmkR","executionInfo":{"status":"ok","timestamp":1630385206465,"user_tz":-480,"elapsed":67,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# mendrop nilai NaN / nul yang ada di data\n","dtrain = dtrain.dropna()"],"execution_count":136,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xKs2oi-ve98O","executionInfo":{"status":"ok","timestamp":1630385206467,"user_tz":-480,"elapsed":66,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"26a7e67f-e0a4-42fc-845b-ae2176b06eea"},"source":["# mengecek kembali keberadaan nilai NaN / null\n","dtrain.isnull().sum()"],"execution_count":137,"outputs":[{"output_type":"execute_result","data":{"text/plain":["X                                      0\n","jumlah_kartu                           0\n","outstanding                            0\n","limit_kredit                           0\n","tagihan                                0\n","total_pemakaian_tunai                  0\n","total_pemakaian_retail                 0\n","sisa_tagihan_tidak_terbayar            0\n","kode_cabang                            0\n","rasio_pembayaran                       0\n","persentasi_overlimit                   0\n","rasio_pembayaran_3bulan                0\n","rasio_pembayaran_6bulan                0\n","skor_delikuensi                        0\n","flag_kredit_macet                      0\n","jumlah_tahun_sejak_pembukaan_kredit    0\n","total_pemakaian                        0\n","sisa_tagihan_per_jumlah_kartu          0\n","sisa_tagihan_per_limit                 0\n","total_pemakaian_per_limit              0\n","pemakaian_3bln_per_limit               0\n","pemakaian_6bln_per_limit               0\n","utilisasi_3bulan                       0\n","utilisasi_6bulan                       0\n","bin                                    0\n","bin_delikuensi                         0\n","dtype: int64"]},"metadata":{},"execution_count":137}]},{"cell_type":"markdown","metadata":{"id":"rIFNOmqBGFIf"},"source":["**LABEL ENCODER**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":330},"id":"7_57jtNoGHmC","executionInfo":{"status":"ok","timestamp":1630385207121,"user_tz":-480,"elapsed":708,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"1c120ec9-193d-47b3-f36e-a51ff4427aaa"},"source":["# Mengubah data kategorikal kedalam bentuk numerik\n","cat = ['kode_cabang', 'bin', 'bin_delikuensi']\n","\n","encoded_data = LabelEncoder()\n","\n","#dtrain[['kode_cabang']] = encoded_data.fit_transform(dtrain['kode_cabang'])\n","\n","for feature in cat:\n","        if feature in dtrain.columns.values:\n","            dtrain[feature] = encoded_data.fit_transform(dtrain[feature])\n","\n","dtrain.head()"],"execution_count":138,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  # Remove the CWD from sys.path while we load stuff.\n"]},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>100</th>\n","      <td>13625</td>\n","      <td>2</td>\n","      <td>4306728</td>\n","      <td>4000000.0</td>\n","      <td>4316980.0</td>\n","      <td>0.0</td>\n","      <td>161999.0</td>\n","      <td>4290920.0</td>\n","      <td>0</td>\n","      <td>7.98</td>\n","      <td>6.87</td>\n","      <td>4.39</td>\n","      <td>9.96</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6.83</td>\n","      <td>161999.0</td>\n","      <td>2145460.0</td>\n","      <td>1.0700</td>\n","      <td>0.04050</td>\n","      <td>0.06300</td>\n","      <td>0.11900</td>\n","      <td>0.95200</td>\n","      <td>0.83300</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>3563</td>\n","      <td>2</td>\n","      <td>921499</td>\n","      <td>59000000.0</td>\n","      <td>563269.0</td>\n","      <td>0.0</td>\n","      <td>382810.0</td>\n","      <td>893878.0</td>\n","      <td>2</td>\n","      <td>100.00</td>\n","      <td>0.00</td>\n","      <td>78.10</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9.58</td>\n","      <td>382810.0</td>\n","      <td>446939.0</td>\n","      <td>0.0152</td>\n","      <td>0.00649</td>\n","      <td>0.00747</td>\n","      <td>0.02140</td>\n","      <td>0.00906</td>\n","      <td>0.02080</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>12481</td>\n","      <td>2</td>\n","      <td>2986142</td>\n","      <td>10000000.0</td>\n","      <td>54771.0</td>\n","      <td>0.0</td>\n","      <td>2783345.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>38.70</td>\n","      <td>64.90</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5.92</td>\n","      <td>2783345.0</td>\n","      <td>0.0</td>\n","      <td>0.0000</td>\n","      <td>0.27800</td>\n","      <td>0.07620</td>\n","      <td>0.00737</td>\n","      <td>0.07810</td>\n","      <td>0.00853</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>4254</td>\n","      <td>2</td>\n","      <td>6437645</td>\n","      <td>6000000.0</td>\n","      <td>5970411.0</td>\n","      <td>0.0</td>\n","      <td>866750.0</td>\n","      <td>6424640.0</td>\n","      <td>0</td>\n","      <td>34.10</td>\n","      <td>6.79</td>\n","      <td>24.90</td>\n","      <td>41.70</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.83</td>\n","      <td>866750.0</td>\n","      <td>3212320.0</td>\n","      <td>1.0700</td>\n","      <td>0.14400</td>\n","      <td>0.15300</td>\n","      <td>0.39300</td>\n","      <td>1.13000</td>\n","      <td>0.98300</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>12541</td>\n","      <td>3</td>\n","      <td>1370140</td>\n","      <td>7000000.0</td>\n","      <td>278500.0</td>\n","      <td>0.0</td>\n","      <td>390074.0</td>\n","      <td>821072.0</td>\n","      <td>5</td>\n","      <td>105.00</td>\n","      <td>0.00</td>\n","      <td>99.80</td>\n","      <td>99.50</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>11.80</td>\n","      <td>390074.0</td>\n","      <td>273691.0</td>\n","      <td>0.1170</td>\n","      <td>0.05570</td>\n","      <td>0.27700</td>\n","      <td>0.37700</td>\n","      <td>0.27100</td>\n","      <td>0.44600</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         X  jumlah_kartu  outstanding  ...  utilisasi_6bulan  bin  bin_delikuensi\n","100  13625             2      4306728  ...           0.83300    0               0\n","101   3563             2       921499  ...           0.02080    0               0\n","102  12481             2      2986142  ...           0.00853    0               0\n","103   4254             2      6437645  ...           0.98300    0               0\n","104  12541             3      1370140  ...           0.44600    0               0\n","\n","[5 rows x 26 columns]"]},"metadata":{},"execution_count":138}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gbSn4hcbhK3T","executionInfo":{"status":"ok","timestamp":1630385207123,"user_tz":-480,"elapsed":71,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"f0421790-972b-4018-da08-ad7d8147cac2"},"source":["# mengecek tipe data\n","dtrain.info()"],"execution_count":139,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 15243 entries, 100 to 15342\n","Data columns (total 26 columns):\n"," #   Column                               Non-Null Count  Dtype  \n","---  ------                               --------------  -----  \n"," 0   X                                    15243 non-null  int64  \n"," 1   jumlah_kartu                         15243 non-null  int64  \n"," 2   outstanding                          15243 non-null  int64  \n"," 3   limit_kredit                         15243 non-null  float64\n"," 4   tagihan                              15243 non-null  float64\n"," 5   total_pemakaian_tunai                15243 non-null  float64\n"," 6   total_pemakaian_retail               15243 non-null  float64\n"," 7   sisa_tagihan_tidak_terbayar          15243 non-null  float64\n"," 8   kode_cabang                          15243 non-null  int64  \n"," 9   rasio_pembayaran                     15243 non-null  float64\n"," 10  persentasi_overlimit                 15243 non-null  float64\n"," 11  rasio_pembayaran_3bulan              15243 non-null  float64\n"," 12  rasio_pembayaran_6bulan              15243 non-null  float64\n"," 13  skor_delikuensi                      15243 non-null  int64  \n"," 14  flag_kredit_macet                    15243 non-null  int64  \n"," 15  jumlah_tahun_sejak_pembukaan_kredit  15243 non-null  float64\n"," 16  total_pemakaian                      15243 non-null  float64\n"," 17  sisa_tagihan_per_jumlah_kartu        15243 non-null  float64\n"," 18  sisa_tagihan_per_limit               15243 non-null  float64\n"," 19  total_pemakaian_per_limit            15243 non-null  float64\n"," 20  pemakaian_3bln_per_limit             15243 non-null  float64\n"," 21  pemakaian_6bln_per_limit             15243 non-null  float64\n"," 22  utilisasi_3bulan                     15243 non-null  float64\n"," 23  utilisasi_6bulan                     15243 non-null  float64\n"," 24  bin                                  15243 non-null  int64  \n"," 25  bin_delikuensi                       15243 non-null  int64  \n","dtypes: float64(18), int64(8)\n","memory usage: 3.1 MB\n"]}]},{"cell_type":"markdown","metadata":{"id":"OTZguvSvaquB"},"source":["**DATA WRANGLING**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":316},"id":"Y8enWirK49b0","executionInfo":{"status":"ok","timestamp":1630385207124,"user_tz":-480,"elapsed":65,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"aa6759ab-47b7-41d5-cb2e-8423bb9cc8d8"},"source":["dtrain.describe()"],"execution_count":140,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7746.216952</td>\n","      <td>2.507446</td>\n","      <td>1.160716e+07</td>\n","      <td>2.077908e+07</td>\n","      <td>8.097279e+06</td>\n","      <td>7.449573e+04</td>\n","      <td>2.027562e+06</td>\n","      <td>8.146400e+06</td>\n","      <td>2.132520</td>\n","      <td>64.507861</td>\n","      <td>3.355427</td>\n","      <td>50.484064</td>\n","      <td>81.310183</td>\n","      <td>0.004592</td>\n","      <td>0.087844</td>\n","      <td>6.641929</td>\n","      <td>2.102089e+06</td>\n","      <td>2.965263e+06</td>\n","      <td>0.468735</td>\n","      <td>0.109667</td>\n","      <td>0.167551</td>\n","      <td>0.203137</td>\n","      <td>0.571827</td>\n","      <td>0.534449</td>\n","      <td>0.004723</td>\n","      <td>0.001246</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4474.179993</td>\n","      <td>1.013875</td>\n","      <td>2.419846e+07</td>\n","      <td>2.960812e+07</td>\n","      <td>1.770636e+07</td>\n","      <td>6.207148e+05</td>\n","      <td>7.966212e+06</td>\n","      <td>1.929042e+07</td>\n","      <td>3.004418</td>\n","      <td>799.924593</td>\n","      <td>9.275493</td>\n","      <td>1338.088142</td>\n","      <td>1477.508529</td>\n","      <td>0.112141</td>\n","      <td>0.283077</td>\n","      <td>4.690106</td>\n","      <td>8.132887e+06</td>\n","      <td>6.267090e+06</td>\n","      <td>0.473290</td>\n","      <td>0.200845</td>\n","      <td>0.192419</td>\n","      <td>0.275335</td>\n","      <td>0.412530</td>\n","      <td>0.432970</td>\n","      <td>0.078389</td>\n","      <td>0.046514</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>3.000000e+06</td>\n","      <td>2.004300e+04</td>\n","      <td>0.000000e+00</td>\n","      <td>-1.566720e+07</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>-18138.000000</td>\n","      <td>0.000000</td>\n","      <td>-57792.340000</td>\n","      <td>-77056.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","      <td>-1.566720e+07</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>-0.632000</td>\n","      <td>-0.126000</td>\n","      <td>-0.569000</td>\n","      <td>0.000288</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3867.500000</td>\n","      <td>2.000000</td>\n","      <td>2.000551e+06</td>\n","      <td>5.000000e+06</td>\n","      <td>8.148450e+05</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>20.200000</td>\n","      <td>21.600000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.920000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.035150</td>\n","      <td>0.032908</td>\n","      <td>0.189000</td>\n","      <td>0.155000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7746.000000</td>\n","      <td>2.000000</td>\n","      <td>4.725195e+06</td>\n","      <td>9.000000e+06</td>\n","      <td>3.145469e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>2.020000e+05</td>\n","      <td>2.753848e+06</td>\n","      <td>0.000000</td>\n","      <td>26.800000</td>\n","      <td>0.000000</td>\n","      <td>50.000000</td>\n","      <td>65.600000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>5.666667</td>\n","      <td>2.490000e+05</td>\n","      <td>1.213084e+06</td>\n","      <td>0.337000</td>\n","      <td>0.022200</td>\n","      <td>0.108000</td>\n","      <td>0.117000</td>\n","      <td>0.577000</td>\n","      <td>0.498000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>11623.500000</td>\n","      <td>3.000000</td>\n","      <td>1.057580e+07</td>\n","      <td>2.200000e+07</td>\n","      <td>7.420343e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.397323e+06</td>\n","      <td>7.142066e+06</td>\n","      <td>5.000000</td>\n","      <td>100.000000</td>\n","      <td>1.090000</td>\n","      <td>91.200000</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>9.330000</td>\n","      <td>1.504334e+06</td>\n","      <td>3.082346e+06</td>\n","      <td>0.930000</td>\n","      <td>0.128000</td>\n","      <td>0.239299</td>\n","      <td>0.285000</td>\n","      <td>0.918000</td>\n","      <td>0.864000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>15493.000000</td>\n","      <td>16.000000</td>\n","      <td>7.980586e+08</td>\n","      <td>1.000000e+09</td>\n","      <td>6.280000e+08</td>\n","      <td>2.884057e+07</td>\n","      <td>2.850000e+08</td>\n","      <td>4.440000e+08</td>\n","      <td>10.000000</td>\n","      <td>68983.000000</td>\n","      <td>190.000000</td>\n","      <td>75575.000000</td>\n","      <td>54899.000000</td>\n","      <td>5.000000</td>\n","      <td>1.000000</td>\n","      <td>34.416667</td>\n","      <td>3.140000e+08</td>\n","      <td>1.480000e+08</td>\n","      <td>7.240000</td>\n","      <td>4.600000</td>\n","      <td>3.490000</td>\n","      <td>8.110000</td>\n","      <td>8.125671</td>\n","      <td>9.730000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  X  jumlah_kartu  ...           bin  bin_delikuensi\n","count  15243.000000  15243.000000  ...  15243.000000    15243.000000\n","mean    7746.216952      2.507446  ...      0.004723        0.001246\n","std     4474.179993      1.013875  ...      0.078389        0.046514\n","min        1.000000      1.000000  ...      0.000000        0.000000\n","25%     3867.500000      2.000000  ...      0.000000        0.000000\n","50%     7746.000000      2.000000  ...      0.000000        0.000000\n","75%    11623.500000      3.000000  ...      0.000000        0.000000\n","max    15493.000000     16.000000  ...      2.000000        2.000000\n","\n","[8 rows x 26 columns]"]},"metadata":{},"execution_count":140}]},{"cell_type":"markdown","metadata":{"id":"36ZCct1Mk9G8"},"source":[">Berdasarkan hasil temuan dari summary (describe) keseluruhan data di atas, ditemukan anomali pada beberapa variabel feature yang memiliki nilai minimum bernilai minus / negatif. Beberapat variabel feature tersebut yakni 'total_pemakaian_retail', 'rasio_pembayaran', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'total_pemakaian', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', dan 'pemakaian_6bln_per_limit'. \n",">\n",">Berdasarkan hasil temuan ini **akan dilakukan imputasi data yang bernilai minus** berupa masing-masing nilai mean pada tiap-tiap feature yang telah disebutkan."]},{"cell_type":"code","metadata":{"id":"Eto9-jFqz4a7","executionInfo":{"status":"ok","timestamp":1630385207125,"user_tz":-480,"elapsed":64,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat kategori yang berisikan feature yang memiliki nilai minus\n","kategori = ['total_pemakaian_retail', 'rasio_pembayaran', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'total_pemakaian', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit']"],"execution_count":141,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UUUaPitUz5th","executionInfo":{"status":"ok","timestamp":1630385207127,"user_tz":-480,"elapsed":65,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"09b9a2c9-fa5d-440e-b325-db1afabde5c3"},"source":["# membuat looping yang mengisi nilai minus dalam variabel kategori dengan masing-masing mean pada tiap feature\n","for feature in kategori:\n","            dtrain[feature] = dtrain[feature].apply(lambda x: dtrain[feature].mean() if x < 0 else x)"],"execution_count":142,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  This is separate from the ipykernel package so we can avoid doing imports until\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"68oItuHuDl1d","executionInfo":{"status":"ok","timestamp":1630385207129,"user_tz":-480,"elapsed":58,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"807fac04-07d0-452d-fb09-b4662f87ff37"},"source":["# mengecek perubahan nilai minus pada data\n","dtrain"],"execution_count":143,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>100</th>\n","      <td>13625</td>\n","      <td>2</td>\n","      <td>4306728</td>\n","      <td>4000000.0</td>\n","      <td>4316980.0</td>\n","      <td>0.0</td>\n","      <td>161999.0</td>\n","      <td>4290920.0</td>\n","      <td>0</td>\n","      <td>7.98</td>\n","      <td>6.87</td>\n","      <td>4.39</td>\n","      <td>9.96</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6.830000</td>\n","      <td>161999.0</td>\n","      <td>2145460.0</td>\n","      <td>1.070000</td>\n","      <td>0.040500</td>\n","      <td>0.063000</td>\n","      <td>0.119000</td>\n","      <td>0.952000</td>\n","      <td>0.833000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>3563</td>\n","      <td>2</td>\n","      <td>921499</td>\n","      <td>59000000.0</td>\n","      <td>563269.0</td>\n","      <td>0.0</td>\n","      <td>382810.0</td>\n","      <td>893878.0</td>\n","      <td>2</td>\n","      <td>100.00</td>\n","      <td>0.00</td>\n","      <td>78.10</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9.580000</td>\n","      <td>382810.0</td>\n","      <td>446939.0</td>\n","      <td>0.015200</td>\n","      <td>0.006490</td>\n","      <td>0.007470</td>\n","      <td>0.021400</td>\n","      <td>0.009060</td>\n","      <td>0.020800</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>12481</td>\n","      <td>2</td>\n","      <td>2986142</td>\n","      <td>10000000.0</td>\n","      <td>54771.0</td>\n","      <td>0.0</td>\n","      <td>2783345.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>38.70</td>\n","      <td>64.90</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5.920000</td>\n","      <td>2783345.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.278000</td>\n","      <td>0.076200</td>\n","      <td>0.007370</td>\n","      <td>0.078100</td>\n","      <td>0.008530</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>4254</td>\n","      <td>2</td>\n","      <td>6437645</td>\n","      <td>6000000.0</td>\n","      <td>5970411.0</td>\n","      <td>0.0</td>\n","      <td>866750.0</td>\n","      <td>6424640.0</td>\n","      <td>0</td>\n","      <td>34.10</td>\n","      <td>6.79</td>\n","      <td>24.90</td>\n","      <td>41.70</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.830000</td>\n","      <td>866750.0</td>\n","      <td>3212320.0</td>\n","      <td>1.070000</td>\n","      <td>0.144000</td>\n","      <td>0.153000</td>\n","      <td>0.393000</td>\n","      <td>1.130000</td>\n","      <td>0.983000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>12541</td>\n","      <td>3</td>\n","      <td>1370140</td>\n","      <td>7000000.0</td>\n","      <td>278500.0</td>\n","      <td>0.0</td>\n","      <td>390074.0</td>\n","      <td>821072.0</td>\n","      <td>5</td>\n","      <td>105.00</td>\n","      <td>0.00</td>\n","      <td>99.80</td>\n","      <td>99.50</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>11.800000</td>\n","      <td>390074.0</td>\n","      <td>273691.0</td>\n","      <td>0.117000</td>\n","      <td>0.055700</td>\n","      <td>0.277000</td>\n","      <td>0.377000</td>\n","      <td>0.271000</td>\n","      <td>0.446000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>15338</th>\n","      <td>4443</td>\n","      <td>2</td>\n","      <td>11570320</td>\n","      <td>15000000.0</td>\n","      <td>2756602.0</td>\n","      <td>0.0</td>\n","      <td>8696313.0</td>\n","      <td>4036479.0</td>\n","      <td>0</td>\n","      <td>84.50</td>\n","      <td>0.00</td>\n","      <td>81.30</td>\n","      <td>61.90</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5.330000</td>\n","      <td>8696313.0</td>\n","      <td>2018240.0</td>\n","      <td>0.269000</td>\n","      <td>0.580000</td>\n","      <td>0.290000</td>\n","      <td>0.269000</td>\n","      <td>0.336000</td>\n","      <td>0.330000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15339</th>\n","      <td>6572</td>\n","      <td>3</td>\n","      <td>12931913</td>\n","      <td>11000000.0</td>\n","      <td>11797694.0</td>\n","      <td>0.0</td>\n","      <td>9500.0</td>\n","      <td>12266233.0</td>\n","      <td>5</td>\n","      <td>0.00</td>\n","      <td>18.10</td>\n","      <td>21.90</td>\n","      <td>33.10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.670000</td>\n","      <td>9500.0</td>\n","      <td>4088744.0</td>\n","      <td>1.120000</td>\n","      <td>0.000864</td>\n","      <td>0.141000</td>\n","      <td>0.089200</td>\n","      <td>1.080000</td>\n","      <td>0.795000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15340</th>\n","      <td>8813</td>\n","      <td>2</td>\n","      <td>4743642</td>\n","      <td>7000000.0</td>\n","      <td>5669470.0</td>\n","      <td>0.0</td>\n","      <td>279498.0</td>\n","      <td>4546997.0</td>\n","      <td>0</td>\n","      <td>8.00</td>\n","      <td>0.00</td>\n","      <td>9.56</td>\n","      <td>16.80</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9.250000</td>\n","      <td>279498.0</td>\n","      <td>2273499.0</td>\n","      <td>0.650000</td>\n","      <td>0.039900</td>\n","      <td>0.045400</td>\n","      <td>0.044300</td>\n","      <td>0.723000</td>\n","      <td>0.637000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15341</th>\n","      <td>446</td>\n","      <td>2</td>\n","      <td>2545407</td>\n","      <td>3000000.0</td>\n","      <td>2475793.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2497853.0</td>\n","      <td>1</td>\n","      <td>23.04</td>\n","      <td>0.00</td>\n","      <td>7.60</td>\n","      <td>9.47</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1.666667</td>\n","      <td>0.0</td>\n","      <td>1248926.5</td>\n","      <td>0.832618</td>\n","      <td>0.000000</td>\n","      <td>0.020833</td>\n","      <td>0.061111</td>\n","      <td>0.909339</td>\n","      <td>0.965757</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15342</th>\n","      <td>14086</td>\n","      <td>4</td>\n","      <td>20328979</td>\n","      <td>20000000.0</td>\n","      <td>19715539.0</td>\n","      <td>0.0</td>\n","      <td>2621904.0</td>\n","      <td>21099488.0</td>\n","      <td>5</td>\n","      <td>22.70</td>\n","      <td>3.21</td>\n","      <td>66.80</td>\n","      <td>73.10</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2.830000</td>\n","      <td>2621904.0</td>\n","      <td>5274872.0</td>\n","      <td>1.050000</td>\n","      <td>0.131000</td>\n","      <td>0.484000</td>\n","      <td>0.012200</td>\n","      <td>0.893000</td>\n","      <td>0.012800</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15243 rows × 26 columns</p>\n","</div>"],"text/plain":["           X  jumlah_kartu  outstanding  ...  utilisasi_6bulan  bin  bin_delikuensi\n","100    13625             2      4306728  ...          0.833000    0               0\n","101     3563             2       921499  ...          0.020800    0               0\n","102    12481             2      2986142  ...          0.008530    0               0\n","103     4254             2      6437645  ...          0.983000    0               0\n","104    12541             3      1370140  ...          0.446000    0               0\n","...      ...           ...          ...  ...               ...  ...             ...\n","15338   4443             2     11570320  ...          0.330000    0               0\n","15339   6572             3     12931913  ...          0.795000    0               0\n","15340   8813             2      4743642  ...          0.637000    0               0\n","15341    446             2      2545407  ...          0.965757    0               0\n","15342  14086             4     20328979  ...          0.012800    0               0\n","\n","[15243 rows x 26 columns]"]},"metadata":{},"execution_count":143}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":316},"id":"-Vdfx38RvalA","executionInfo":{"status":"ok","timestamp":1630385207131,"user_tz":-480,"elapsed":58,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"4e4d3180-1f0a-4220-8763-f0a6b1de479b"},"source":["# mengecek kembali summary pada data\n","dtrain.describe()"],"execution_count":144,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7746.216952</td>\n","      <td>2.507446</td>\n","      <td>1.160716e+07</td>\n","      <td>2.077908e+07</td>\n","      <td>8.097279e+06</td>\n","      <td>7.449573e+04</td>\n","      <td>2.037738e+06</td>\n","      <td>8.146400e+06</td>\n","      <td>2.132520</td>\n","      <td>67.547946</td>\n","      <td>3.355427</td>\n","      <td>80.862504</td>\n","      <td>100.918574</td>\n","      <td>0.004592</td>\n","      <td>0.087844</td>\n","      <td>6.641929</td>\n","      <td>2.112311e+06</td>\n","      <td>2.965263e+06</td>\n","      <td>0.468735</td>\n","      <td>0.110225</td>\n","      <td>0.167668</td>\n","      <td>0.203438</td>\n","      <td>0.571827</td>\n","      <td>0.534449</td>\n","      <td>0.004723</td>\n","      <td>0.001246</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4474.179993</td>\n","      <td>1.013875</td>\n","      <td>2.419846e+07</td>\n","      <td>2.960812e+07</td>\n","      <td>1.770636e+07</td>\n","      <td>6.207148e+05</td>\n","      <td>7.961983e+06</td>\n","      <td>1.929042e+07</td>\n","      <td>3.004418</td>\n","      <td>782.900621</td>\n","      <td>9.275493</td>\n","      <td>802.092762</td>\n","      <td>991.163091</td>\n","      <td>0.112141</td>\n","      <td>0.283077</td>\n","      <td>4.690106</td>\n","      <td>8.128677e+06</td>\n","      <td>6.267090e+06</td>\n","      <td>0.473290</td>\n","      <td>0.200455</td>\n","      <td>0.192356</td>\n","      <td>0.275151</td>\n","      <td>0.412530</td>\n","      <td>0.432970</td>\n","      <td>0.078389</td>\n","      <td>0.046514</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>3.000000e+06</td>\n","      <td>2.004300e+04</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000288</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3867.500000</td>\n","      <td>2.000000</td>\n","      <td>2.000551e+06</td>\n","      <td>5.000000e+06</td>\n","      <td>8.148450e+05</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>20.800000</td>\n","      <td>22.400000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.920000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.035300</td>\n","      <td>0.033219</td>\n","      <td>0.189000</td>\n","      <td>0.155000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7746.000000</td>\n","      <td>2.000000</td>\n","      <td>4.725195e+06</td>\n","      <td>9.000000e+06</td>\n","      <td>3.145469e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>2.080000e+05</td>\n","      <td>2.753848e+06</td>\n","      <td>0.000000</td>\n","      <td>27.000000</td>\n","      <td>0.000000</td>\n","      <td>50.100000</td>\n","      <td>66.300000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>5.666667</td>\n","      <td>2.550000e+05</td>\n","      <td>1.213084e+06</td>\n","      <td>0.337000</td>\n","      <td>0.022900</td>\n","      <td>0.108000</td>\n","      <td>0.118000</td>\n","      <td>0.577000</td>\n","      <td>0.498000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>11623.500000</td>\n","      <td>3.000000</td>\n","      <td>1.057580e+07</td>\n","      <td>2.200000e+07</td>\n","      <td>7.420343e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.432000e+06</td>\n","      <td>7.142066e+06</td>\n","      <td>5.000000</td>\n","      <td>100.000000</td>\n","      <td>1.090000</td>\n","      <td>91.200000</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>9.330000</td>\n","      <td>1.533900e+06</td>\n","      <td>3.082346e+06</td>\n","      <td>0.930000</td>\n","      <td>0.128000</td>\n","      <td>0.239299</td>\n","      <td>0.285000</td>\n","      <td>0.918000</td>\n","      <td>0.864000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>15493.000000</td>\n","      <td>16.000000</td>\n","      <td>7.980586e+08</td>\n","      <td>1.000000e+09</td>\n","      <td>6.280000e+08</td>\n","      <td>2.884057e+07</td>\n","      <td>2.850000e+08</td>\n","      <td>4.440000e+08</td>\n","      <td>10.000000</td>\n","      <td>68983.000000</td>\n","      <td>190.000000</td>\n","      <td>75575.000000</td>\n","      <td>54899.000000</td>\n","      <td>5.000000</td>\n","      <td>1.000000</td>\n","      <td>34.416667</td>\n","      <td>3.140000e+08</td>\n","      <td>1.480000e+08</td>\n","      <td>7.240000</td>\n","      <td>4.600000</td>\n","      <td>3.490000</td>\n","      <td>8.110000</td>\n","      <td>8.125671</td>\n","      <td>9.730000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  X  jumlah_kartu  ...           bin  bin_delikuensi\n","count  15243.000000  15243.000000  ...  15243.000000    15243.000000\n","mean    7746.216952      2.507446  ...      0.004723        0.001246\n","std     4474.179993      1.013875  ...      0.078389        0.046514\n","min        1.000000      1.000000  ...      0.000000        0.000000\n","25%     3867.500000      2.000000  ...      0.000000        0.000000\n","50%     7746.000000      2.000000  ...      0.000000        0.000000\n","75%    11623.500000      3.000000  ...      0.000000        0.000000\n","max    15493.000000     16.000000  ...      2.000000        2.000000\n","\n","[8 rows x 26 columns]"]},"metadata":{},"execution_count":144}]},{"cell_type":"markdown","metadata":{"id":"9gfkbhMmnXct"},"source":["**CEK KORELASI**"]},{"cell_type":"code","metadata":{"id":"npD283TpJ_lh","colab":{"base_uri":"https://localhost:8080/","height":826},"executionInfo":{"status":"ok","timestamp":1630385210909,"user_tz":-480,"elapsed":3834,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"6fdfe8e8-c682-4f94-ea0a-7ef54ffd688a"},"source":["# Melakukan perhitungan nilai korelasi dengan mengasumsikan data adalah data interval.\n","sns.heatmap(dtrain.corr(),linewidths=.5,annot=True,cmap='RdYlGn', vmin=-1, vmax=1)\n","fig=plt.gcf()\n","fig.set_size_inches(20,12)\n","plt.show()"],"execution_count":145,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABJMAAAMpCAYAAACwhXIZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd1gUV9uH71mKgEoRZUHEjh3sETUWQNQYjdhSPlteW5oauxGxRzSxpJjEbtTYCwIKKioqRtFYg2AvYKNYQKRI2d3vj4WFZQEbKyQ593V5JcycmfPb5zznOTOnjaRSqRAIBAKBQCAQCAQCgUAgEAheBllJCxAIBAKBQCAQCAQCgUAgEPxzEJ1JAoFAIBAIBAKBQCAQCASCl0Z0JgkEAoFAIBAIBAKBQCAQCF4a0ZkkEAgEAoFAIBAIBAKBQCB4aURnkkAgEAgEAoFAIBAIBAKB4KURnUkCgUAgEAgEAoFAIBAIBIKXRnQmCQQCgUAgEAgEAoFAIBCUYiRJWiNJUrwkSRGFnJckSfpZkqQbkiSFS5LULM+5wZIkXc/+N7g49IjOJIFAIBAIBAKBQCAQCASC0s1aoGsR598DHLP/jQCWAkiSVAGYAbQC3gFmSJJk9aZiRGeSQCAQCAQCgUAgEAgEAkEpRqVShQJPikjSE1ivUnMSsJQkyQ7oAhxQqVRPVCpVAnCAojulXgrRmSQQCAQCgUAgEAgEAoFA8M/GHrib5+972ccKO/5GGL7pDQSCbFTSFy4lrQHV0pOgOlzSMkByRak6VNIqkEnuAKgoeZtIlB6bZCkPlLQMDGUeAKXGJqXFR1CWvD2Qla56U1p0lJbYCpQeLaXFX0uLDig1ZSNiay4S6nojbJKLiGkFIJWe9qa06ABKTdmUGh0glbQMfSJ94aIqUQHLTn2GenlaDitUKtWKkpLzIkRnkkAgEAgEAoFAIBAIBAJBCZLdcfQmnUf3AYc8f1fJPnYf6Jjv+JE3yAcQy9wEAoFAIBAIBAKBQCAQCP7pBACDsr/q5gI8ValUMcB+oLMkSVbZG293zj72RoiZSQKBQCAQCAQCgUAgEAj+00iy0r2KT5KkzahnGFWUJOke6i+0GQGoVKplQBDQDbgBpAL/yz73RJKkOcDp7FvNVqlURW3k/VKIziSBQCAQCAQCgUAgEAgEglKMSqX65AXnVcBXhZxbA6wpTj2iM0kgEAgEAoFAIBAIBALBf5rSPjOptCH2TBIIBAKBQCAQCAQCgUAgELw0ojNJIBAIBAKBQCAQCAQCgUDw0ohlboISZ/XAqXR3akv8swSc5vQvlnuqVCrmzt3G0dAITEyMmT9vMA0bVtVJFxERzZQp63ienkmH9o2YOvVDJEkiMTGFseNWcv/+Y+ztrfnxh+FYWJTVXBd+MYqPP/6exYuG0rVrc+7ff8zIUctQKlVkZSkYMCCWjz624dixSHzmbkepVNG3bxuGj+iilX9GRiaTJ6/jUuRdLC3LsnjxUOyrWAOwYvk+du4MQyaTmDr1Q95t1wCAtWsPsWPHCSQJ6jja4zNvIGXKGLFxwxHWrz/MnTsPORH2PVZW5TT5HAuNZO7cbSiVSvr2a8uIEV11dUxaS2TkHbWOH4ZRpUpFAJYv38fOHceRyWRM9f6Qdu0aAuA1ZT1HjlzE2ro8u/dM19zr++92cvhwOEZGhlStWhGfeYMxNzfL1aIHm6xfH8L27cdRqaBfv7YMHuymud+GPw6zaVMoMgMZHTo0ZPIkdx0/OHbsEvN9dqBQKunTtw3Dh3fW0TRl8h9EXlLbZ9HiIdjbW5OYkMyYMauJiIjG09MF72kf5rkmi7nfbuP0X9eRyWSMHtOdzp2bFpB38drj9q04xo1brbn+7t1HjBrdncGD3bhy5R4zZ2wmNTUde/sKLFj4P8qVM83Vogc/AVAolPTtMw8buSXLl6uXcYeFXWHB9ztRKlWYmZVh3vzBVK+mYx51XfbZztHQSExMjJjvM6jguhx5hylT1mfX5YZM9eqHJEns3XeOX34J5OatWLZvm4RTI3Umx49fZtFiPzIzFRgZGTBxYm9au9TVLpu3aA+VSsWPP/qzb985DGQyPv6kPYMGuelNy61bsYwbu0rLT0aP7sHgT90ZO2Ylt2/HAZD0LBXz8mb4+7vmloeeYuupU1fxmbedrCwFVpbl2LBhPABubl6ULWuCJJN4+NCLcuXKYWKiKLa8X/SbkpPT6Pb+LDq5N2b6dO2tCj7/cil37zykdet6r+2jal2r8+gahoWFGU+fpuI19Q/u3H1ImTJG+Hw7kDp1Kqtt4u5N2bImyAxkGBj8iu8O7e0R9FVvEhKSGT1mJRERd+jl6cL0aR/p3FNfPrJqdTC7d/8FqOvQzZsxhJ1YiKVlWY2PqO0hw9fXVSe/t932aOVdzPU3PT2TAf0XkpGRhUKhpHOXZowe3UNj//yxZPAgXXu8iU0SEpIZ87XaDzw9XZg2PdcPfvzBH3//UyQlpXH23A8F5vu2bZLDt99uxXfnCc6d/0lzTF/+eurUVb78aqlGs4dHU0Z+9T63bsUydlze2Due0aNH8+nganrTcvNWLF5T1hF56S5jx3zA0KHqZ5z09Ez6D8i1WZcuFxk12kmv5QSQlJSKt/cfXL/2AEmSmOsziKZNa5aoj2zYcJj160K4c+chYWELsapQTiuvt+0nAOvWH8qOLyr69YvWq48cPHSBn37ajUwmYWAgw8vrQ1o0rw1A/QZfUKeOPQB2dltZtmyZTn7/JsQyt1dDzEwSaCFJkoMkSbclSaqQ/bdV9t/V9ZXn2rBAui4ZW6z3DA2NICo6nuD9s5kzuz8zZ20qMN3MWZuYM2cAwftnExUdT+ixSABWrNxHa5d6BO+fQ2uXeqxYmfvlRIVCycKFu2jbtr7mWKVKFmzdMgl/P2+2bZ3MypUriXnwhDmzt7Ji5Uh275lGYOAZbtyI0cp/x44TWJibsT94FoMGu7Fw0S4AbtyIISjoLLv3eLNy1Uhmz96CQqEkLi6RDX8cYceOyezePQ2lUklQ4BkAmjarxZo1o6lcuYJWHgqFgtmzN7Ny1Uj2BM4gcM9pbtx4oK1j+3HMzc0IPjCHwZ+6s2hhjo4HBAWeZk/gdFatGsXsWZtRKJQA9OrdmpWrRunYtE3b+uzeM52A3dOoXl3OiuX7tGxX3Da5du0B27cfZ9u2yfj5eXHkyEWio+MBOHXyKodCwvHz92LPnmkMGeKho1ehUDJ3zjaWrfiSgN3eBAWe1dG0c0cY5ham7Ns/k0GDXFm80B8A4zJGjBrdnYkTe+ncd8Xy/VSoUJ6gfTMI2DOVli0dC8hbUez2qFFTzi4/L3b5ebFj5zeYmhrTqVNjAKZ5b2Dc+J4E7Pamk0cTVq8+qGUHffgJqF+4atay1brXzJmbWLBwCH7+3nTv3pKlS4N07AMQGhqprsv7ZjJnVn9mzt5SYLqZszYzZ3Z/gvfNzK7LlwCo42jHkiUjaNmitlZ6K6tyLF36BbsDvJk/bzCTJq/VKZu3aQ9f3zBiYxLYu3cmQXtn8v77LfJoKf6yqVnTFj9/b/z8vdnp66X2E48mAPzw43DNuc6dm+HhkdsJqq/YmpSUyqzZm1n625cE7pnBTz8N17rfuvXjGD/Ok4YNGxIcHFyseb/oN/34UwAtW+jW3+DgYMqalSE1Nf2NfHTFyv20bl2X4P2zaN26rkbXshX7qF+/Crv9vflu/mDmztuubZN1Y/Df5YWvr69OXvqqN2XKGPH16B5MKiDm5eatHx8ZNrQz/n7e+Pt5M26sJy1b1sHSMneQZ936cfj7eeO700snr7fd9uTPu7jrr7GxIWvXjcU/YBq7/Lz581gkFy7cAoqOJcVlkzJljBj9dQ8mTtL1g46uzmzdNrnAPEvKJgAXL0aT9DRVR4s+nxdbNHfU+GxOB0HNmraaY747vTA1NcXDw0OvWiwtzJjq/RFDh3TSuo+xsSHr1o4lwH8afru8OXbsmJbN9PVcMHfuNtq1a8jefbPw8/emVr72UF95F+UjzZrVYs3vX1PZvoKOFn2WDRTsJ9eu3Wf79uNs3/YN/n7eHDlyhOjoeL3paO1SjwB/tQYfn0F4e/+huZeJibFG37+9I0nw6ojOJIEWKpXqLrAUmJ99aD6wQqVSRekrz2M3LvAkJalY73noUDiePV2QJIkmTWqSlJRGfPxTrTTx8U9JTn5OkyY1kSQJz54uHDr4d+71nq0B8PRszcHs4wB/bDhMl85Nsa5QXnPM2NgQY2MjQD0jRalUcvnyPapWrYSDQ0WMjQ3p1q05IYf+Ji8hh8Lp6ekCQJcuTTkZdhWVSkXIob/p1q05xsZGVKlSkapVKxEeHgWoX3KfP88kK0tBWloGNjYWADRo4KAZRc1LeHg4VavZ4OBQSa3j/ZYcOhSuba+QcDx7tc7W0YywsCuoVCoOHQqn2/st1TocKlK1mo1GR8uWjlhYmOXPjnffbYChoQEAjZvUIDY2IY+WqGK3ya1bsTg7V8fU1BhDQwNatnTkwIELAGzZcozhw7toysbaujz5uRgehUPVink0NeNwiLZ9QkLC6dmzFQCduzTl5Em1JjOzMjRvXgvjMkY6993lG8bwEerRP5lMpjVTTKts9OAjOZwMu4KDQ0Xs7dV+ERUVr+nUatOmHgeCz2uXjR78JDY2gaNHLtKvb1ute0lIJCc/B+BZ8nNsbCx17KPJs2er7Lpcg6Sk1CLqco3sutyKQ9l2rFXLjpo15Dr3bdDAAXl2no6OdqSnZ5KRkZnHHvqpN4XZY8vmo3z51fvIZOpm2draPI8W/ZRNDmH5/CQHlUrFvr1neb977suovmLr7j1/4eHRVNMZnvf3a+Xt6VnseRf1myIionn8+JnW4AFASspzfv/9d774/D2SU9LfyEfVPq6u4549XTiYffzmjRhcWqlny9Wqacv9+4959Ojl2kp91RszszK0aF6bMgXEPE3eemx/cwgMPE33QjpJCuJttz06eRdz/ZUkibJlTQDIylKQlaVAktSj6UXFkuKyibrtq00ZY10/aNKkhua5pMjyeIs2USiULPh+JxMm9tbR8jb8tTDUsdcBe3t7vWqxtjbH2am65tksB12bZWlsBvopp2fP0jhz+jp9s9tAY2NDrdnr+sy7KB9p0KCqZnZQQbxtP7mpE19aEnzgvN50lC1rorFFWmqGlh/815BkUon++6chOpMEBfED4CJJ0hjgXWBhCet5ZeLiErG1s9L8bWtrSVxcom4a24LTPH6cpHkYqlTJnMePk7KvSeDggQt88kl7nTxjYp7Q44M5dHSdwvDhw8nMzNLSILe1Ii5OO+DHxSdil53G0NCA8uVNSUxMIS7uab5rLYmPS0Qut+R/Qzrh7uZN+3ZTKF/elLbvNniBLeKwy/s75ZbExSVopYmPK0BHQgpxcQkvvLYodu48Qfv2jbTyKW6bODracfbMTRISkklLyyD0aCSxMWqNUVHxnD1zg48+/J6BAxZz8WKUrn3in2r9RrlcV1N8nrzzaiqMpCT16OeSn/fQt/d8xo5ZXeBLYFxcXLHbIy9BQWe1RqVr17bTvCzu33eemJjcsnyZsn4dP/Hx2caEib11Gshv5w5gxIhf6ND+GwL8TzIi3/KKXF3566kVcfH56nJ8Irby3M4oW7mVTn0viv3B52lQ30HT6ajOVz/1pjB73Ln7iL1BZ+jT24fhw5YQFRWXR4t+yiaHoMAzvN+9pY5dzpy5gbV1eapXz+1U0FdsjYqKJykplYEDF9G7tw9+fidzbyhJDB36E4FBp7l06VKx513Yb1IqlXz33Q4mT+qjY5uffg5gyJAhmJgak5WleCMfffz4WT5dzwCoV68KwdmdE+HhUTx48ITYnN8rSQwduoTefeaxdetWHX1vo94Uhr58JIe0tAyO/RlJ587Ncg9m+0jv3j5s3XpMR9Pbbnu0f6t+6q9CocSz57e0bTORNm3q07hxDaDoWFJcNnlT3rZNNm44jJu7c4GdXPr01wsXbvFBzzkMG76E69e1Z9UABAadoXv37m9FS2EoFEp6en5Lm7YTadOmjcZm6ryKv5zu3XtEhQrlmDJlHb085+I99Q9SU9N1dL1tH3kRb9tP6jhW5uyZG7nxJTSU2JgEveo4cOA8Xd+bwWef/4LP3EGa4+npmfTu48OHH33HwYO5M9oFAhCdSYICUKlUmcBE1J1KY7L/1kGSpBGSJJ2RJOnMihUr3qrGt4kkSZoe+rk+25kwoZdmxC8vdnYV2B0wjeD9c9i1axdJz9KKXcvTp6mEHArnwMHZHA2dR1paOgEBp4o9n+Jg2dIgDA1k9PjgHb3mU6uWHcOGezBs6BKGD/+FevWrIDNQl0+WQsHTpyls2TqRiZN6M3bMalQqlV71gPphJTY2kSZNa7LD9xsaN6nOwu936T3fvGRkZBESEk6XrrkvXHN9BrJ5Uyh9es8jJeU5Rkb63Tbv8OFwrCuUp1Ej3c2Q1q09xIoVIzkaOp/evdswf94OvWopjOvXH7BwkR+zZ/2f3vMqyh6ZGVkYlzFip68X/T58l6lefxRwh+JH7Sd/07Vrc51zgXtOF9jJVFzkja2KLAWRkXdYvnwkq1aP5relgZp9mzZvmsAu36k4O1cnODiY06dPF2vehbFp01Had2ik9VAOcPnyXe7ceaRZmlKcqHWp/3/E8M48e5ZKz14+/LHhCPXrV8EguxNy88bx7PKdwsoVI9m4cSOnT18vdi2lgYLK6fDhcJo1raW1xC3HR1auHMnGTUeKxUdeRFFtz9vAwECGn783R47OIzw8imvX7gMlF0tKAwXZJC4ukX37zjFgQMH7RhUnef21YcOqhITMJcB/GgMHdOSrkUu10ubG3q4F3apYtRSFgYEMfz9vjh6ZR3h4uMaP9EVWlpJLl+7yyScd2OU3FVNTY1au2P/iC4uJwurN2+Rl/EQdX7owdOjPDBv+M/Xq1Sv2+JLfRzw8mrJv7yx+/eULfvo5QHP8cMhcfHd6sWjhEHx8fKhbt26tYhUi+EcjNuAWFMZ7QAzQCDhQUAKVSrUCyOlFUn32xZq3JK1gLO49p2fPnkAyTk7VtEYIY2PVs3ryIpdbai3BypvG2tqc+Pin2NhYEB//lArZS9oiIqIZl71xYkJiCkdDIzE0NKBTpyZa93V0dOTxoyQtDXGxCcjl2qNichtLYmISsLW1IitLwbNnaVhalkUut8h3bSI2ckvCwq5gX8Vao6eTRxPOn7/FBx+0KtQucrmcmLy/My4RuVz75chGXoAOq7LI5VYvvLYgfH1PcPjIRdauHavVUNnILYvdJgB9+7bVTJn+YbE/clv1cVu5FR4eTZAkCWfn6shkEgkJCZhb5s3PQus3xsXparLJzju/psKwtCyLqakxHh7qvYq6dGmG744wnXRyuVwv9gD1xqoNGjhQsWLuEoeaNW1ZvWY0ALdvx3H0aEQeLS8u61f1k5CQvwkJCedoaAQZ6VkkJ6cxccIapnj148qVe5oRwfe6tWD4sJ8112/ceJRtO44D4NSoWr56mqBZnpbXRrF5RuZi4xJ06ntBxMYmMHLUCr6bP5iqVStp31MP9aYweyxYOAS53JLO2XsTeXg0wWvKujxair9scjgWGkGDhlW1/ATUSwAOHDjPTl8vNm48wvZtP6LP2Gpra4WlZTnMzMqol1K1cOTK1XucOHGZbdv/BMDJqRrlylUlPDycli1qFlve8nxxKeea8xducfbsDTZvOkpKajqZmQrMyppQuXIFTp++RqNGjVCpFCgUKqZP30RQ4PTs61/NR62tyxeoq1w5U+b5qEeHVSoV7p2m4eBQUaM551oPDw/CL0Zx7dqDt1JvCkJdZ9V12MnJWi8+kkNg0Gnef79lvvtZaa716NSE8PBwmreorjn/ttue/Nr02Qabm5vRqlVdjh2LpE4d+yJjSf48X9cmb8rbtEmtWnbcufOQzp2nAeqZba1dJiCXV0GfMS3vxy06dHBi1qzNPElIpkL2kvfQYxE0bFCV/fv3s23bNr1qeRnUNmul8SN1XsVfTra2lshtLTXtf5euzQrsTHrb9aYgNm7cqPeyKcpP+vVtS7++bdm48Qi//nYAQ0MF7ds31LuPtGzpyN27jzQ6cmzn4FCJd955h127djUFbhZotH8B/+Ulfq+DmJkk0EGSpCaAB+ACjJUkya6EJb0UT6uY4O/vj7+fN53cm+DnfxKVSsWFC7coX95EZ3qzjY0F5cqZcOHCLVQqFX7+J3F3dwbAzc0ZPz/1y7+fX5jmeMihuYSE+BAS4kOXzk2ZMf1jOnVqQmxsAs+fZ6h1PE3h3LlzdOjQiOjoeO7de0RGRhZBQWdxdXPW0uDq5ox/9nKO/fvP4+JSF0mScHVzJijoLBkZmdy794jo6HicnatjZ2fF339HkZaWgUql4mTYVWrV1N24MC9OTk5ER8Vz7262jsDTuOXT4ebmjN+usGwd5zQ63NycCQo8rdZx9xHRUWodRXEsNJLVq4JZuvRLTE2N82mpVuw2ATTLQh48eMKBAxfonj2bwr2TM6f+ugaoO08yM7OwstJ+oGjkVI070Q/zaDqHq2s+Ta5O+PurZ4AF7z9PK5c6RTY2kiTRsWMj/vpLPVvg5Mmr1KqtW42cnJz0Yg+AwMAzOi9cOXZSKpUsW7aXjz5ul0dLtWL3k/Hje3E0dD4hIT4sWjyUVi71WLBwCObmZjx7lqaZeXLi+GVq1sq1T//+HfDf5YX/Li86uTvj538quy7fpnx50yLq8u3sunwK93za85OUlMqIz39j/LieNG+mO8imj3pTmD0AOnVqwqlTVwH4669rWkvL9FE2ORTkJwBhJ65Qo6YttrZW9O/fUe+x1d29MWfP3dDsBRceHkWtmrb06tWajRsm4O/nTbt3GxIWFkbt2rWLNW83N+cCf9OihUM5cngeISE+TJ7UB8+erZgwvhf/90kHzp75kYiICPbvm4WdrSWV7Su8to/m5A+o9WYfT0pKJSMjC4Dt24/TokVtypUzJTU1neQU9X5jqanpHD9+HEfHym+l3hRG//4d1D7i7683HwF49iyN06ev4+7eWHMsNTVds/+a2h6XcXTU3jD9bbc9OnkXc/198uSZZjn18+cZnDhxmZrZzwJFxZLissmb8jZt0rGjE38e/17z7GZqakzYyYV6j2kPHz7VzIQOD7+NUqXCKk9HXE7s7d+/v961FIauzU5o/Ehf5VSpkgV2thW4dSsWUO8bVatWQc9Hb7feFMTbKJui/CRnCZqrqzPm5ubs2T1dbzqio+M1OiIj75CRkYmVZVmePk3R7Cf5JCGZc+fOAVxCIMhGehtLPgT/HCT1U8IJYLpKpTogSdIowEWlUvV/waUq6QuX18pz05DZdKzTjIrlLIlLesKMPStZc2L3a91LtfQkqA6jUqmYPWcLx45FYmpijI/PYJyc1EtLenp+i7+fN6D+uscUr3U8f55B+3YNmTbtYyRJUn/2duxKYmKeULmy+tOZ+UfjvvlmLR07OtG1a3OOH7/E/O92IkmgUsGAAZ/R78OKHD0awTyfHSiVSnr3ac3nn7/Hzz/vplGjari5OZOerv6s6eXL97CwMGPR4qGakedly/biuzMMAwMZU7z60b69+pOqS37ew969ZzEwlFG/vgPfftsfY2Mj/lh/mNWrD/DoURIVKpSnfYeG+Mz9HYAjR3/Gx2c7SoWSPn3a8PkX3fj5pwC1DvfGpKdnMmni71y+fBcLCzMW/zAMBwf1TI1lS4PYufMEBgYGeHn1o30H9R5I48at4vRf10hISMba2pxRo3rQt19bOntMIyMjS2Ovxo1rMGt2fyRcUaoO6cUmA/ovIjExBUNDAyZ/04fWresB6mnk3lP/4PKVexgZGTJpUm/atP6SLKX2ZLvQo5HMn7cDpVJFr94ufPZ5V5b8vIeGjapqNH0zeX22fcqycNH/NJo83KeTnPKczMwszMubsWLVV9SubceD+0/4ZvI6nj1Lw6pCOb6dO0DrS3uGMvUSmcNHfip2e6SmpuPm6s2Bg7MpXz531Gv9+hA2bQxV6+7chHHjeiJJEjLJHRWHOXr0YrH7SQ6nTl1lzZqDLF+u/oT5gQPn+fnn3cgkCXMLM3x8BlHV4UNQHtKu0yoVs+ds5difl7Lr8kDNZ8p79vLBf5f6y00XI6I1nzhv364h07zVn8E9cOACc+Zu48mTZMzNTalfrwqrV43it6V7WbFyP9Wq2WjyWrNqlHqTdpm73upNYfZISkpl4oQ1PIh5gplZGWbN6k+9elWQcNVb2aSmpuPq6sXBg99q+Qmo41uTxjX5OHt/OAlXvcfWVauD8fU9gUwmo2/ftnw62J27dx/y1Uj112OyshSULWtDQkICpiaKYsu7qN+Ug6/vCSIiopk+/ZPcg5Ir9+5u47PPfuWdd+q8to8mJCQzZtxqYh48oXLlCvz4wzAsLcty/vwtvpmyHiRwrG3H3G8HYmFhxt27j/hq1HIAFFlKuvf4iC8+q/NW6g2Am7t3dsxTUL68KWtWjaJ2bTtNvVEpQ/TmI76+Jzj25yV+WDxM81vz+ohCoaR795Z88cUClCrtWPI2254c9BVbr165xzffrEOhUKJSqejatTlfjVR/BaqgWFK/3kCAYrWJu5s3KXn8YNVqtR8sWOBL4J4zmtkPffu2YeSo7iVqk7w0a/o1587/pPeYtmHDYTZvCcXAQIaJiTHfTO5Ls+yBC63Ya94tu9LqT8vDh0/p03ceycnPkckkzMzKEBQ4g3v3H+ezWR++Gtkwn48Uf9tz+fJdvKf+QWamAgeHivjMG4SFRe4ztr7avaJ8ZP36EFavCtY8Q3fo0Ii5c9fovWyK8pP/67+QxMRkDA0NmDJlLq1dnutNx4qV+/H3P4mhoQEmZYyYOKkPLZrX5ty5m8yYsRFJJqFSqhg0+Ev69ev3r566YzyuXYl2jmQsPvaPsq/oTBJoIUnSCMBdpVJ9lP23AXAaGKtSqY4WcelrdyYVJzmdSSWO5Krz0FYSyKTsh3tK3iY5nUkljUxy1+lMKglyOpNKi01Ki4/k70wqEWSlq96UFh2lJbYCpUdLafHX0qIDSk3ZiNiai4S63gib5CJiWgFIpae9KS06gMpM6sAAACAASURBVFJTNqVGB/yjOjtelTIT2pdo50j6wtB/lH3FnkkCLfLtg4RKpVIAzQq/QiAQCAQCgUAgEAgEAsF/CdGZJBAIBAKBQCAQCAQCgeA/jST7R00MKnHEBtwCgUAgEAgEAoFAIBAIBIKXRnQmCQQCgUAgEAgEAoFAIBAIXhqxzE0gEAgEAoFAIBAIBALBfxqxzO3VEDOTBAKBQCAQCAQCgUAgEAgEL42YmSQQCAQCgUAgEAgEAoHgP42YmfRqSCqVqqQ1CP4dCEcSCAQCgUAgEAgEgn8v/+reFjMv1xJ9p031OfyPsq9Y5iYQCAQCgUAgEAgEAoFAIHhpxDI3QfGhOlzSCkByRfrCpaRVoFp6Et+bY0paBr1r/QjAqsgvS1gJDGv4G3uixpe0DLpXX0RKpn9Jy6CsUU8AriQsKGElUM9qIkfuTy1pGXS0nwtpJV82mKrLRkXJxzQJV54rAktaBiYG76NUHSppGcgkd4DSo0VZ8jqQuZOp3F/SKjCSdQFgy7WRJawEPq7zS6mJrfFp60taBjamgwBKjZ+UlthaWnRA6YlppaW9yVIeKGkZGMo8AEjN2l3CSsDMsEepec/6tyOWub0aYmaSQCAQCAQCgUAgEAgEAoHgpREzkwQCgUAgEAgEAoFAIBD8pxEzk14NMTNJIBAIBAKBQCAQCAQCgUDw0ojOJIFAIBAIBAKBQCAQCAQCwUsjlrkJBAKBQCAQCAQCgUAg+E8jlrm9GmJmkkAgEAgEAoFAIBAIBAKB4KURM5MExYpKpWLu3G0cDY3AxMSY+fMG07BhVZ10ERHRTJmyjufpmXRo34ipUz9EkiQSE1MYO24l9+8/xt7emh9/GI6FRVnNdeEXo/j44+9ZvGgoXbs25/79x4wctQylUkVWloIBA2JfW/vqgVPp7tSW+GcJOM3p/9r3eRmunnnInuWXUSpVtOxShY4f1tI6fyrwDmF7opEZSBibGNJrdEPkVctz/vB9ju28rUkXe/sZI39uS+Va5q+t5fa5xxxacw2VUoVzp8q06l29YM1h8QQsuMjA71tiWzs3v6SHz1nz9UnafFiDdzyrvbaOK6fj8VsWiVKhotV7VXH/qLbW+RN7ojm+OwqZTMLY1IB+XztjW608T2JT+W74EWyqlAOgWj1L+n7t/Ep5H//zKgvn+6NQqOjV5x3+N0z706cZGVlMm7KFy5fuY2lpxvyF/alsX4EH95/Q54OFVKteCQAn56pMndEHgP17L7B6RQhKpYp2Herz9bhur2yTc2F3WfnDSZRKFR4f1KXvoMZa5/03XSQ44CoGBhIWVqaMmtoOG7vyAMwcs49rEfHUbyxn2qIur5x3YUT8Fcu2Xy6gVKp4t1sNuv5fPa3zRwNucsT/JjKZRBlTQwaMa07l6q/vn3lRqVTM/T6Ao39ewcTEiPmzP6Rh/Sq6Gi/dY8r0ber48m49pk76AElSjzT9sfk4G7eewEAmo0O7ekwa+36h+R0LjWTu3G0olUr69mvLiBFdtc5nZGQyedJaIiPvYGlZlsU/DKNKlYoALF++j507jiOTyZjq/SHt2jUEwGvKeo4cuYi1dXl275muudeVK/eYMWMjqanp2Ntbs3DhEMqVMy1Q1/Fjl/lunh9KhZJefV0YOtw9n64spn6zicuRd7GwLMv3iwdhb19Bcz7mQQK9enzHF191YfAQV6JuxzNpXO7ny+/de8yXo7oyYFCHQm0DcOxYJD5zt6NUqujbtw3DR2j7WUZGJpMnr+NS5F21fRYPxb6KNQkJyYz5eiUREXfw9HRh2vSPNNf8+IM//v6nSEpK4+y5H4o9b4AVy/exc2cYMpnE1Kkf8m67BgCsXx/C9u3HUamgX7+2DB7sBsCC7305fPgiRkYGOFSthI/PQMzNzQrUpVKpmOuznaOhkWof9RlUcBsYeYcpU9Znt4ENmerVD0mS2LvvHL/8EsjNW7Fs3zYJp0bqmJqQkMzoMWqb9fJ0Yfq0j3TumZc/j11ivo8vCqWSPn1bM2y4h459pkzewKVLavssXPwp9vbWnDh+hR8XB5CZqcDIyIDxEz1p5VJH69qRX67g3t3H+O2eUqSGF3H97CP2rryKSqmimYc97frVKDDdpeNxbJ0fzojF72DvaPFGeeZQWmLrqeM3+en7YJRKFd17NWHAkDZa5y+cvcPPC4K5dT2eGfN74epRX3Nu/JebuRR+H6emDny/pGh/KIzX9ZOL4dHMnLEFUPv8l1+9RycPtQ29p24k9EgkFSqUL9JHiju+3roVy7ixqzTX3737iNGjezD4U3fGjlnJ7dtxACQ9S8W8vBn+/q560QHg5uZF2bImGMhkGBjI2OnrBcDly3eZOWMT6emZGBjImDHzExo7F/ypdX3E1+HDfuHhw6dkKZS0aF6LadM/xsCg6HkFxd3eACQlpTFr+lZuXI9FkmDWtx/TuEn1InVo2+YS8312ZPttG4YP76xjmymT/yDykrrMFi0ekh3fLvPD4gAyM7MwMjJk/ERPXFzqvnS+uTa5woL5/igVSjz7tGLIcDcdm0ybspnLkfewsDTju0UDqZzPJn0+WMDnX3Vm0P86EnU7nsnjN2jO37/3mC9GdqH/oPY6eevzPevUqav4zNtOVpYCK8tybNgwHsj1Z5mBDAODJfj6+r6yzf5J5DwvCl4OMTNJUKyEhkYQFR1P8P7ZzJndn5mzNhWYbuasTcyZM4Dg/bOJio4n9FgkACtW7qO1Sz2C98+htUs9Vqzcr7lGoVCycOEu2rbNfZiqVMmCrVsm4e/nzbatk1m5ciUG6crX0r42LJCuS8a+1rWvglKhIuC3SP43uwVjl7Xj76MxxN15ppWmsasdY5a2Y/Qv79K+bw0CV14BoKmrPaN/eZfRv7zLh+MbYyU3faOOJKVCxYGVV+nr3YQhP7lw+Vgcj+4m66TLSMviXOBd7Bx18zr8+zVqNLV+bQ05Onx/jWD4t+8waWVHzh++T2y0tk2auVZm4vIOjF/aHtd+tQhYfklzrqJdWcYvbc/4pe1fuSNJoVDy3be7WLJ0KDsDxrMv6AK3bsZppfHz/Qtzc1MC9k6m/8B2/LQ4SHOuioM1W3aOZcvOsZqOpMTEFH5aFMTy1SPY4T+ex4+ecerk9VfWtXzhCWb80IVfNvfhWPBN7txO0EpTo641i9d68vPGPrRxrc7aX/7SnOvV34kxM4ruDHhVlAoVm386z6j57zLz9y6cDrnLg6gkrTTvuFdlxurOTFvpQZeP6rJ96d/Fln/on1eIuvOI4IBJzJnWh5lzdxWYbubcXcyZ3ofggElE3XlE6PGrAJw8fYNDRyIJ2DaWQN/xDB1cuH0UCgWzZ29m5aqR7AmcQeCe09y48UArzY7txzE3NyP4wBwGf+rOooVqPTduPCAo8DR7AqezatUoZs/ajEKhjku9erdm5apROvl5T/2D8eN7sXv3dDw6NWH1qgOF6FLi860vvy0fwa7dk9kXdI6bN7Q70XftPIW5uSl79k9lwOAO/Lhoj9b5hd/782673DhavYYN23ZNYNuuCWzeMQ4TE2Pc3J0KtU2Ojjmzt7Ji5Uh275lGYOAZbtyI0bbPjhNYmJuxP3gWgwa7sXCR2j5lyhgx+useTJzUS+e+HV2d2bpt8gvyVrx23jduxBAUdJbde7xZuWoks2dvQaFQcu3aA7ZvP862bZPx8/PiyJGLREfHA9CmTT0CdnvjH+BN9eo2rFixX0dTDqGhkeo2cN9M5szqz8zZWwpMN3PWZubM7k/wvpnZbaA6ntVxtGPJkhG0bKHdmV6mjBFfj+7BpIm6NtO1j5Jv52xn6YrPCdjtRVDgWW7ms4/vjpOYW5ixd/90Bg7qyOKFAQBYWZXll6WfsStgCnPnDWDK5D+0rjsQ/DdmZmVeqOFFKBUqApddYcDMpnz1axsuhsYSf0e37UlPzeLk7jtUqVs8nUhQemKrQqFk8bx9LPz1Y/7w/YyD+yK5ffOhVhq5rTles3vQ6b1GOtd/MtgF77kfvFH+r+sntR3t2Lp9Ajt3TWb5ii+YPXMrWVkKADw9W7FsxRcvzLu442vNmrb4+Xvj5+/NTl8vTE2N6eTRBIAffhyuOde5czM8PJrqTUcO69eN02jJYcECX7766n38/L0Z/XUPFiwo+KVcX/H1hx+H4uc/ld27vXnyJJl9+869sJyKu70B+H7eLtq+Ww//wG/Y7juBGjXlRerIr2nunG0sW/ElAbu9CQo8q2ObnTvCMLcwZd/+mQwa5Mrihf4AWFmV49eln+EXMBWfeQOZMnl9QVm8MP/5c3fxy7Jh7AyYyL6g8zo28dt5ivLmpgTsm0L/Qe35aXGg1vlF3wfQtl3uQFz1GjZs9R3HVt9xbNo+BhMTY1w76dZ50N97VlJSKrNmb2bpb18SuGcGP/00XOt+69aPw9/P+1/fkSR4dURnkqBYOXQoHM+eLkiSRJMmNUlKSiM+/qlWmvj4pyQnP6dJk5pIkoRnTxcOHfw793rP1gB4erbm4MHcF9E/NhymS+emWFcorzlmbGyIsbERoB4JUCpfryMJ4NiNCzxJSXpxwjfk7rVErCuXpYKdGYZGMhq3t+NyWLxWGhMzI83/ZzxXUFAf+d9HH+DcofIbaYm5kYSVnSmWtqYYGMmo966cG3890kn356ZbvONZDUNj7ZBx/dRDLOSmVHQoq3PNq3Dnqtom1nZlMTSS0bSjPZFh2h06JmXz2aSYBg4iLt6lStWKVHGwxsjIkC7vNeZISKRWmiMhl+jeswUA7p2dOH3qBiqVqtB73r/7BIdqFbGqoJ4t9Y5LbUIORLySruuXHmJbxRxbe3OMjAxo51GTv0KjtdI4N69MGRP1BNO6jWx4HJ+iOde4pT2mefyoOLh95Qk29uWoVLkchkYyWrg58PcJ7Qdv0zzllP48q9jKCeDQkUt4dm+mji/O1Uh6lkb8Q+06G/8wieSU5zRxrqaOL92bceiwujw3bzvJiP+5Ymystpl1dvkURHh4OFWr2eDgUAljY0O6vd+SQ4fCtfWEhOPZSx2vunRpRljYFVQqFYcOhdPt/ZYYGxtRxaEiVavZEB4eBUDLlo5YWOjOaomKiqNlS0cA2rStT3BwwQ/5ERfv4JDjr8aGdH2vKUdCtH3rcEgEH3i2BMCjszN/nbyu8deQgxext69ArdoFP7yfOnkdh6rWWqOoBdsniqpVK+HgUFFtn27NCTmk3XEYciicnp4u2fZpysmwq6hUKszMytC8eW3KGOv6Z5MmNbCxKbrjIDw8/LXzDjn0N926NVeXTZWKVK1aifDwKG7disXZuTqmpsYYGhrQsqUjBw5cAKDtuw0wNDQAoHHjGsTFJhaq7VBIOJ49W2W3gTVISkotog2skd0GtuJQtv5ateyoWUO3bMzMytCieW3KlHlxnb4YHq2xj5GxIe91a0ZIyEVt+4RcpGfPdwDo3KUJp05eQ6VSUb+Bg8b+tR3teJ6eSUZGJgCpKemsX3eYzz7XngXwOty//pQKdmZUsFW3g43a23Ll1EOddCEbb/Jun+oYGhXf42ppia2XIx5g71CBylWsMDIywL1LA/48ck0rjZ29JbXryAscKW/RqsYbdey9iZ/k1BOA9Iws8gb6Fi1rY2FZ8My9HMLDo/QSX3MIC7uCg0NF7O21B7pUKhX79p7l/e4t3oqO/EiSRHLKcwCePXuOjY1l4fbRQ3zNme2alaUkM/PF7bM+2ptnz9I4e+YWvfq0AsDI2BBz84Jn4RbExfAoHKpWzGObZhwO0S6zkJBwevZU379zl6acPHk1T3xT2zx/fHtZIi7ewcHBWmOTLt2acORw/mfGSHpkPzN2ymeTw4cisK9SeBv818nrVHGwpnLlgttgfb1n7d7zFx4eTTX5WlsXz4xywb8f0ZkkKFbi4hKxtbPS/G1ra0lcXKJuGtuC0zx+nKR5kK1UyZzHj5Oyr0ng4IELfPKJ7pTPmJgn9PhgDh1dpzB8+HAUZUq3Wyc9fo5FRRPN3+YVTXj6+LlOurDd0SwYcoR9a67S4/MGOufDQ2No3MHujbQkP35OeetcLeWty5D8JF0rTdzNJJIeP6dWi4paxzPSsji1K4o2Hxa8NOFVePo4DctKuTosKprw9FGaTro/A6Lw+TSEPasu4/llQ83xJ7GpLPoylF8nnODWxcevlPfD+KfY2ua+vNrILYiPTyo0jaGhAeXKmZCYmArA/ftP+KTvjwz7dCnnzqqXIDpUtSY66iEP7j8hK0vBkZBIYot4AS2Ixw9TqWiT20lnbVOWxw9TC01/YPc1mrd2eKU8XpXER2lY2eQ+9FlVNCXxoW45Hfa7wdT+e/FdcZGPRjYptvzj4p9ia5v78G0rtyQu30NUXPxTbOUWBaaJin7ImXO36TdgCQOGLiU84m7hecXFYZc3TsktiYvTnr0QH5eIXXa8MzQ0oHx5UxITUoiLS3jhtfmp7VhZ06Gwb985YmIKTh8fp20DG1tdG+RNY2hoQLnyJiQmppCaks7vq0P4/MvCl+bsCzpP125Ni9SqzkM71sttrYiLy18WBdgnMYU3JS4u7rXzjot7mu9aS+LjEnF0tOPsmZskJCSTlpZB6NFIYgsoA9+dJ2jXXjce52rL375ZERefrw2MT8RWntePrXTayTchPj5Ry0fkckvi4wrwETtdH8nLgeALNKhfRTNgs+TnQAZ/6oqJqfEba0x6nI5FxdyOEAvrMjx7rN32PLiRxNOHz6nTstIb55eX0hJbH8Y/w8Y2d3CsktycR/HPiriieHlTPwn/O4qe3X3o1XMe02d8qOlcehleJka+SXwNCjzD+91b6uR75swNrK3LU726XK86JCSGDv2J3r192Lr1mCaNl1c/Fny/k44dpvD9dzsYN86zQPvoM74OG7qEd9tOomxZE7p0aVZkWn20N/fvPcGqQlmmT93Ch70XMXPaVlJTtet+UcTFP9Wyu1yua5v4PHG+MNsEB1+gQX0HTXx7WeLjniK30643D/PnH5/fJqYkJqZm2+Qwn31ReIf8/r0X6Nqt8Ocmfb1nRUXFk5SUysCBi+jd2wc/v5O5N5Ty+vPWQrX9W5BkUon++6dRut+6Bf9pJEnSjMbN9dnOhAm9kMl0XdbOrgK7A6YRvH8Ou3btwiDj9WcnlSZa96jGxDUd6fq/uoRsual17s6VRIzKGGBbvXwhVxcPKqWKw2uv4/qpo86541tv06JHVYxN397Wa+9+UB2vtW50H1qfg5tuAGBeoQzeG9wZ/1t7PvisARvmn+d5yquNNL0uFSuZE3TAi807xjBuYg+mTtpEcvJzzC3MmDKtF99M2MjQwUupbG/1wn0J3oQje69z4/JDeg14tSV++sLVszZzN75H7xFOBG24UtJyNCgUSp4mpbLtj5FMGvM+YyZtKHKG2dvEZ+4gNm06Su/ePqSkPMfIuPjr1dJf9zNgUAfMyhY8myEzI4ujhyPp3KX4OgD/KdSqZcew4R4MG7qE4cN/oV79Ksjy1dlly/ZiYGhAjx7vlJDKt8eN6zEsXhTA9Fnq/VauXL7H3buPNPvi6BulUsX+1dfoMrTOixPrkdIWW0sTzo2r47/Hiy3bJrBq5QHS099Ou/siMjKyCAn5m65dm+ucC9xzusBOpuJm0+YJ+O6aysqVI9m08QinT6uXuW/eHMo3U/px5Og8pkzph/fUP15wp+Jn1epRhB6bT0ZGFidPXtVbPoW1NwqFkiuX7tPvozZs8x2Pqakxa1aF6E1HQdy4HsMPi/yZMevjt5rvst+CGTCo3QvbYI8ubyfO5n3PUmQpiIy8w/LlI1m1ejS/LQ3U7DO2edMEdvmq/Xnjxo3UrVtXd2Rf8J9FbMAteGPq1q37Vb169YBknJyqaY3mxsYmIpdrT+OVyy2JjS04jbW1OfHxT7GxsSA+/ikVspe0RUREM26cemPFhMQUjoZGYmhoQKdOTbTu6+joiOm1WyTbvPnIqb4wtzbh6aPcmUhJj55jkWd2UH6cO9jh96v2FNrw0Bgad3yzJW4A5axNeJZnVtSzx+mUq5DbyGWkKXh0J4Ut09RLblISM/Cd9ze9pzQm5vpTroXFc3T9DdJTspBkYGgso1m3Vx/BtbA2JfFhro6nj55jUbHwac9NOlZm5xL1dHxDYwMMjdUjog6OllSsbMbD+yk41Cl4+nh+KtlYEBubO6oUH/cUGxvzAtPIbS3JylKQnPwcS0szJEnSLJlq0LAKVRysuRP1kAaNHOjQsQEdOqpnMOzcfrLAjtCisK5kxqM8Sysex6dgXUl36cCFv+6zfe0F5i7tjpHxy48Mvw6WFU1JiM+diZTwKA3LSoWXUwtXBzb+eA54/Yf3jVtOsM33FABODR20ZnjFxiUiz7ckSm5jQWyeUcK8aeRyCzzcGyFJEs5OVZHJJBISUqhQwHI3uVxOTN44FZeIXG6llcZGbklMTAK2tlZkZSl49iwNS6uyyOVWL7w2PzVr2bJmzdcA3L4dx9EjFwtMZyO30LJBfKyuDXLSaPz12XMsLctyMTyag8F/8+Oi3Tx7lqb23zKGfNK/HQB/HrtCvQb2WFd8cSe1jdxSK9bHxSYgl+cviwLsY/lmS2JBXTavm7dcbpHv2kRsstufvn3b0rdvWwB+WOyPPM+I/C7fMI4cjuD3tV/rLDnauPEo23YcB8CpUbV87VsC8nxLWeQ2lsTG5fXjBJ128k2wsbHU8pG4uERs5AX4SEyixj45PpKj+etRq/CZP5CqVdWzgi5cuE1kxB06u89EoVDw+Ekynw76mbXrR7+WRnPrMjx9lDsb4enjdMpb5217soiPTmat1xkAkhMy2PztBT7xbvLGm3CXlthayaY88bG5M5EexiVR0Ua/A0R5eVM/yaFWLVvMzMpw/XoMjRrpbgRcEC8TI183vh4LjaBBw6pUrKjdjmdlKThw4LzWHkb60pHzX2trczp5NCE8/DYtWzrityuMqVM/BKDre83x9t5AQeg7vpYpY4SbuzMhh8K19iHV1VH87Y1H58bI5RY4N1Z/XMCjc2PWrDr0UrrVv9tCy+5xcbq2scmO8wXZJjY2gdGjVmjFt1fBRm5BXIx2vamUP3+b/DZJw9LSjIjwOxwMDufHRYE8e5aGLPsZ8uP+7wLw559XqNegik4bvHXTcfx2rkKf71m2tlZYWpbDzKyMell1C0euXL1HjRpyLX/28PDg6tWr7wChr2y8fwj/xNlBJYmYmSR4Y65evfqrv78//n7edHJvgp//SVQqFRcu3KJ8eROd/S9sbCwoV86ECxduoVKp8PM/ibu7etTPzc0ZP78wAPz8wjTHQw7NJSTEh5AQH7p0bsqM6R/TqVMTYmMTeP48A4CnT1M4d+4cGWb6faF+U6rUseDRgxSexKaSlank79AY6rvYaKV5dD/3Qffq6XgqVs590FUqVVw8FkPj9m+2xA3ArnZ5EmJSSYxLQ5Gp5MqfcdRumbucrUxZQ0aua89ny9vy2fK2VK5jTu8pjbGtbc7/zW2hOd68uwOteld/rY4kAIe6Fjy6n8LjbJucP3Kfhi7a68kf3s/dnPXyX/FUtFc/GCQnpqNUqGeXPI5J4eH9FKxti96vIS8NG1Xh7p1H3L/3hMzMLPbv/ZsOrtrLWDq4NmCPv/ql5lDwRVq2qo0kSSQ8SdZsuHnv7mPu3HmEvYN6j4Ynj9V6k56msn1LGL36vNpsBsf6lYi5m0Tcg2dkZio4duAW77TT/lrerauPWPrdn0xd0BnLCi+/58DrUr2eFfH3k3kUk0JWppIzIXdp3FrbD+Pu5b4cXTwZg439m70c9f+4Df7bxuK/bSydXBvit+ecOr6ER1O+nCk2lbRfGGwqmVOurAkXwqPV8WXPOdyzO/U6uTbk1Gn1LL/b0Q/JzFRgZVXww7eTkxPRUfHcu/uIjIwsggJP4+amPTvBzc0Zv13qeLV//zlcXOoiSRJubs4EBZ4mIyOTe3cfER0Vj7Nz9SJ/Z85Uc6VSybKlQXz8ccEDfw0bOXAn+iH37j0mMyOLfXvP08FVe6POjq4NCfA7DcCB4HDeyfbXtRtGsffgNPYenEb/ge0ZNqKTpiMJYG/QOd7rVvSyh1z7VCM6Op5797LtE3QW13z2cXVzxj97qvz+/ec19nlTnJycXjtvVzdngoLOqsvm3iOio3PL5vFjte8+ePCEAwcu0D17BsOxY5GsXn2A35Z+jmkBS7z69++A/y4v/Hd50cndGT//U9lt4G3Klzctog28nd0GnsLdrfhmvjRyqqrlI3uDzuHqqr2huqtrI/z91ZtKB++/QCsXRyRJIikplS8/X86YcR/QrFlNTfqPP2nH4dBvCT40k/Ubx1C9ms1rdyQBVHY058mDVBJi08jKVBIRGku9d3Jf7EzKGjF5U0fGrm7H2NXtqFLXolg6kqD0xNZ6DStz784THtxPJDNTwaH9l3i3w9ubifUmfnLv3mPNhtsP7j/h9q04rS94vQgnp2p6i6+BgWd4/33dAYywE1eoUdNWa/mPPnSkpqaTnKweHEtNTef48cvUcbQH1B14f/2l3hfr5MmrVKuu/eynpauY42tKynPN3jpZWQqOHo2g5gs2vtZHe1OxkjlyW0uibqv3Cj118ho1a738BtyNnKpla8qxzTlcXfPZxtUJf3/1IFTw/vO0cqmjiW9ffL6MseN60qxZrYJu/0IaNnLgzp1H3M+2yf6gC3R0baiVpoNrQ3ZnPzMeDA7XPDOu+eMrgg5MJejAVPoPbMfQEe6ajiSAfUEFL3H76P/aou/3LHf3xpw9d4OsLAVpaRmEh0dRq6ZtAf58HODVNgEV/KsRM5MExUqHDo04GhqBR+dpmJoY4+MzWHOup+e3+Pt5AzBj+v8xxWsdz59n0L5dQ9q3VzdOI4Z3YczYlezYeZzKldWfrCyKmzdjmP/dTiQJVCoYMuQzgo8U/jnpotg0ZDYd6zSjYjlL7voEMGPPQK1OpwAAIABJREFUStac2P1a9yoKAwMZH3zRgDXep1EpVbToXAV5tfIc+OMa9o4WNHCRE7Y7mhsXHmNgKGFazoh+43MbyqiIJ1hUNKGC3ct3mBSGzEBGp2F12TH7PEolOLnbUbFqOf7cfBPbWubUfqd496ooDAMDGb2/asgKr1OolCre6eyAbfXy7Ft3lSp1LGjU2pbjAVFcO/cIA0MZpuWM+GSCusG9dfEJ+9ZfxcBQhiSDvqOdMTN/+ZlphoYGTPbqyVefrUKpUPJBr5bUqm3L0l/206BhFTq4NsSzd0umTdnCB+99h4WFGfMW/B8A587eZukvwRgaypDJJLym99Zsrrxgvj/Xrqq/MDLi805Uq/5qtjQwlDFiQhtmfr0XpVKFe/c6VK1pxcYVZ6ldryKt2lfj9yV/kZaayfdT1aN6FeXl8F6oXos/5bPd3It+yvO0TIb02MTIqe1p5lLllTToaDKQ8fGoJvw0+RhKhYq271Wncg0LAn6PpFodKxq3rcwRv5tcPhuPgaGEWXlj/je5xRvlmZcO7epx9M8rePT4Th1fZvXTnOv54Q/4b1N/jXGGlydTpm/jeXom7dvWo/276q+m9PFsideM7XTvswgjIwPmz/mo0IdvQ0NDpk3/iKHDfkapUNKnTxscHSvz808BNGpUDTf3xvTt25ZJE3+ns8c0LCzMWPzDMAAcHSvz3nvNeb/bLAwMDJie5/PL48at4vRf10hISKZD+28YNaoHffu1JXDPaTZuOgpAZ4+m9O7TphBdBkyZ2psvhq9AqVTi2esdajva8uuSvTRs6EBHt0b06tOKqZM30b3LXMwtzfh+4aAX2jY1NZ2TJ64xbWa/F6bN0eE97SOGDf0FpVJJ7z6t1fb5ebfaPm7O9O3bhsmT1tKl8wwsLMxYtHio5np3N29SUp6rX6IP/c2q1aOoXduOBQt8CdxzhrS0DDp28KJv3zaMHNVdp2xeN29Hx8p0fa8Z3d+fg4GBTOvT2F+PXkFiYgqGhgZMm/4R5ubquvztnG1kZGQydMgSABo3rs7MWf9XoF3UbWAkHl1mZLeBAzXnevby4f/ZO++wKI43jn/2jt6RXu2KYq9YwIItViwxRVOM0TSTmJhoECtGjS0aayzxp7ElduwNC9iNRlHs2FE4miICAnf7++MQOA8DIuZMMp/n4YHbeXfny3uz75SdmQ3boJ0VMXrUmwQH/6oto/6+BARoOyO7d59m3PjVJCen8dHHc6nm48kvuW//ax04grRcn+0JP8PiRVqfFfbdDB/Ri48+nItao6F7Dz8qVXZj9syt+NbwplXrmvTo1YTgYct4rX0otrYWTJn2PgCrVkRy+1YiP8/bwc/zdgCwYNGnODiU7owZpVJBx4+rsmz0KTQambpt3HEua8Xe5Vdxr2yDT+PCO9mlkvcrEluNjBR89V17hnyyCo1GQ6dutSlfyYlFcw/gU92N5i2rcOHcXUK+XsvD1EwOR1xh8bwIlq3/CIDP+v3KzRtJZKRn0aPdTIaN6UTjpsXvIL9IOTl1MoZfFu7ByFiJQpIYMao39vbaGZ7fDlnCieNXuX8/jcCWI/l0UEd69mqil/fLiK/p6Y85dPgCY0P76P2/W7edoPNTg0wvQ0dSUiqDPvsZ0C7p6ty5If659/e4cX0ZP2E16hw1pqbGhBai84mu0o6vdnaWfPbpPO3LamSZxo2q8Mab/oXmX1DHy6hvvgvpQfDQ5WRnq/H0dCB0fPGXmxkZKQkZ0ZuBH85Bo5Hzyu2smVvwreFN69a16NmrKd8N+5UO7cdga2vJ1Gn9AFi5IoLbtxKYN2878+ZtB2DhokHPFd+MjJQMC+nOpwMXotHIdMttM86dtYPqvl60bO1LUM9GjPhuFV07TMTG1oIfpvYt8roZ6Y85dvgyI3LfCvwsXlY/q2JFN/z9fenabRwKhYJevZpRpYoHt28n8NmgguX5DX777bcdxXaY4F+P9KrsFyH4xyMj7zO0BpBaIX3iZ2gVyPOOsj5msKFl0KPiDAAWRX9qYCXwoe9cttwYYmgZdC43jUfZYYaWgaVxNwAupkwxsBLwsf+W/bEhhpZBS4/xkGH47wZz7XcjY/iYJtGKTPXWog1fMmbKTmjk4i9FeFkopECAV0eLxvA6UASSrdlpaBUYK7Qb7f52eZCBlcCbVWa/MrFVlfH8rx8vbZzNtR38V6WcvCqx9VXRAa9OTHtV6psczW5Dy8BI0RaA9JzSf7D8vFgYdeFV6WdBoS+Z/tdgP7GDQQdHUoJ3/KP8K5a5CQQCgUAgEAgEAoFAIBAIio0YTBIIBAKBQCAQCAQCgUAgEBQbsWeSQCAQCAQCgUAgEAgEgv804m1uz4eYmSQQCAQCgUAgEAgEAoFAICg2YmaSQCAQCAQCgUAgEAgEgv80YmbS8yFmJgkEAoFAIBAIBAKBQCAQCIqNGEwSCAQCgUAgEAgEAoFAIBAUG0mWZUNrEPw7EAVJIBAIBAKBQCAQCP69/KvXgTlO62TQPm3ikK3/KP+KmUkCgUAgEAgEAoFAIBAIBIJiIzbgFpQaGjnc0BJQSIGsjxlsaBn0qDgD6RM/Q8tAnncUAP/VbxtYCUT2Xon9xA6GlkFK8A5OJ04wtAzqOA4HIPjwQAMrgYlNF+A2N8jQMrj36UZ4uMbQMsD6de1veZ9hdQBIrYhKmmhoFdRyCH5l/AGQlLnKwELAwewtUO82tAxQtiXl8e+GVoG96RsAXL4/1cBKoIrdNww9ZPjYOrnZglem/gVemViSmLnc0DJwNOvLnbQFhpaBp5W2nL4qWvbHhhhaBi09xnMxZYqhZeBj/y0AMQ9mGFgJVLQdDBrD97NQBBpawUtHbMD9fIiZSQKBQCAQCAQCgUAgEAgEgmIjBpMEAoFAIBAIBAKBQCAQCATFRixzEwgEAoFAIBAIBAKBQPCfRixzez7EzCSBQCAQCAQCgUAgEAgEAkGxETOTBAKBQCAQCAQCgUAgEPynkSQxM+l5EDOTBAKBQCAQCAQCgUAgEAgExUbMTBKUGpGR0UwYvwaNRqZXr6YMGNheJz0rK5thw5ZyPvo2dnaW/Phjfzw8HQBYMH8H69YdQaGQCAnpTXP/6gAsWRLO2rWHkSSoUtmDCRPfwdTUmBXL9/Prr/u4dSuBw0cmY29vVSyNl/5IYMv8C2g0Mg3be9Kyd0Wd9GNbb3Fky00USgkTMyO6f+GLi7c1f+6LJXLd9Ty7uOsPGTSzGe4VbV7EZc/kl3dC6FyzGaqHKdQc1+el5PGERq61+LLOuygkBVuu72PFxc066Z/X6UtdJ+33YWZkip2pDR03DgBgqv8wqjtU4mziJYYdfLHXQQdWqM/ENp+gVChYdnoHM46u1kn3tHFibudvsDW1RKlQMnb/YnbHnOB131Z83rhXnp2vc3laLB7EOdW1Euk4fTSWJTOOo9HItO5SmaB3auqkb/ktmr2br6BUKrCxM+Xj4c1wcs0vf+mPshjSJ4yG/l58MMSvRBqeEHc2maiVV5E1MuUC3KjayVsn/ebBOM7+fg1zexMAKgR6UL6FW156dkYOu0NO4F7XkTrvVC6xjlZedQlt/iFKhYKV53cz+8/1OukeVo78FPglNiaWKBUKxh9Zxt5bJ7E3tWZhh6HUca7E7xf3EhK58LnzlmWZ8VO3cuDQZczMjPlhTE98fdz17M5diCV4zHoyH2fTolkVQr7phCRJXLh0j9ETw3iclYNSqWDMsK7UquHJpu2nWbg0EmSwtDRhzHdd8aniVnj+41dzIOIcZmYm/DDxPXx9vfXzP3eT4OCl2vwDahAS0htJkrh//xFffb2Q2NgkPDwcmDF9ALa2lsRci2N48FKiz9/mq8Fd6d+/HQCPH2fTp+9UsrJyUKs1tG9/lpZ/EQL+PHqH/804jkYtE9ilMt3fraWTvnlVNOGbL+eWVzM+Hd4MJzcrEu6lMSV4LxpZRp0j81ovH9p19yne9/ES/LHol11s3nwcALVaQ0zMPY4cnkpGxmOGDltCUlIqkiTRu/ct3nvvPb38jh66woxJO1BrNHTpXo93+/vrpGdl5TAuZAMXL9zF1taCcZN74eZhT3Z2DpNCt3Dx/F0UConBQztQr2F5Hj16zKf9Fuedr4pPpX2nWgwe+lrR/pmwlgMR0ZiZm/DDhHfwre6l75/oWwQPX0ZmZjYtAnwJGd4LSZLYvuMUs+dsI+ZaPGt+/4aaNcoCkJ2tZsSoFZw/f5sctYagro346Kk69lkcOXiF6ZO2odHIdO1Rj3f7B+j5ZmzIei6dv4uNrTnfT+mNe65vfgjdzMXoWCSFxFfDOlK/Yfli5VkcTh65zcIfj6DRyLTtWpXX36ujk75xZRS7wi6hNNKW3S9HBODsZl0qecefTebsyqsgy3j7u1Hlqdh662Ac0auvYVYgtpYN0MaHsP4HsPG0BMDCwYzGX9QosY5Xpf6Fvz+WFIejh64yY9JONBqZLt3r8k7/Zjrpp0/e5KfJu4i5Es/YST1o1bZ6qeQLcPzwdeZM3YdGLdMxqAZv9Wuskx516g5zpu7j2tUERkzoTIs2VfLS2jb8kfKVHAFwdrXm++nd/zVannDueByrZ59Go5Fp3rE8Hd7W/c4PbIphf1gMCoWEqbkRfb+uj3u50mkznzpym4XTj+bFjl7v1tZJD1t5ll2bLqFUStjam/N5iH9e7BgzeAeXz6moVtuFkdOKF0P/ij+O3GL+tINoNDLtu1Wj93v1dNLPnrrLgumHuH41ie++b0vzwPx+x+JZRzhx6CYAb/ZvQIu2lZ4rb219s0Zb35gZ88OEdwuvj6NvERz8a2597EvI8Nfz65vZW4m5Fsea1UPz6ptDhy4w7ceNZGerMTZW8u23PWjiV/V5XSP4jyAGk/5mJEl6H9gly/Ldv7AJAi7Lsny+lPJcAmyRZXmtJEmLgB9L69pPUKvVjAv9nV8Wf4GLix29X59Eq9a1qFQpv3O2du1hbG0s2LlrLFu3/sHUaRuYPv1Drl69x7ZtJ9m8ZQQq1QM+6DeT7TvGkJiYyvJl+9mydSRmZiZ8NXgR27b+QfceTahbryItW9bk3XenF1ujRi2zaW40/cc3wsbRjDmDD1PNzxkX7/zGae1WbjTObVSePxrP1oUX+WBcQ+q28qBuKw9AO5C0bNzJlzaQBLDkyFZm71/Lr++Peml5ACgkia/r9eOrAxNJyEhiYZvvOXT3FDdSY/NsZp1envd3z0rtqGxfLu/zqktbMFWa0q1i6xfUoWBKu8/o/ttw7qYmsvf9mWy/cpRLSbfybIY0fYuNFyJY/OdWqjp4s7r3OGrPe4810ftYE70PgOpO5Vjec1SJB5I0ag2Lpx0lZEY7HJwtCP5wKw2ae+FZ3i7PplzlMkz8pTOmZkbs2nCRFXNOMnhci7z01QtPU62OSwk9kY+skTmz7ArNv6mFeRlT9oWewq2OAzYeljp2no2cnjlQdH79DRyr2BWaVlwUkoIJAR/xxubR3EtLYnuvKey6cZzLKXfybAbX782mq4f4NXoHVew9Wd5pFI2WDyRTncXkYyvxcfCmahn9Bk5xiDh0mRu3k9i14SvOnLvDmImbWLP0Yz27MRM3MW5EELVreDLgy1+JOHyFFs2qMGXmDj4b0JoWzapw4OAlpszcwbIFH+LpXoblCz7E1sacA4cuM3J8WKHXjYg4x42bKnbtDOXMmeuMGbuSNau/089/7ErGjetL7drlGTBwNhGR0bQIqMGChTto4ufDwIEdWLBgBwsW7uTbb3pgZ2tByIg3CN9zWuc6JiZGLF3yFZaWZmRnq3m7z3zca3tSpYazXp5qtYZfph5j5E/tKONsQXD/LTTw98arQHktX6UMkxZ3wdTMiJ3rL7Js7h98Pa4ldo7mjF/QCWMTJRnp2Qzpu5EGzb0p42Tx19/HS/LHh/3b8WHugNrevVEsWRqOnZ0lWVk5fDesF76+3qSlZdKz1wyaNWuGvaeuH6ZO2MZP89/B2cWG/m8vxL9lVcpXzPfZ5g2nsLYxY82WL9m9/SxzZ+xh3JTX2bTuFADL131KclIaQz5bwS8rB2BpacrS1Z/knd/vzfm0CKz2l77R+uc8N24msGvHaM5E3WDM2N9Y8/u3+v4J/Z1xoW9Tu1Y5Bnw0j4jI87QI8KVKZXdmzRzA6DGrdOx37DxFVlYOm8NCyMjIolOX7+nUqQGeHg5/qUfrmy3MXPAezi429HtrPv4tfXR8s2n9KWxszFi7dTC7t59lzozdjJ/Sm7B1JwFYsX4QyUlpfPXpMv636iMUihef1K5Wa/h5yiHGzeqIg7MlX7+/kcb+ZfGuYJ9nU6GKIz8urY6ZmRHb1p3nf7OPM2x84AvnLWtkopZfoekQbWw9EHoK10Jiq0cjJ2r11Y+tShMFrcY2eGEdr0r9C4aJJcXRNG3CDmbM74Oziw0fvr2I5i2rUL6iU56Ni6stIeO6smrpkRfKq7C8Z/4QzuS5vXBysebTd1bQpEUlylXIv9+cXa0ZOrYDa5b9oXe+iakRC1a9+6/T8gSNWmbVT38yeIo/9k4WTPwknFpN3XUGixoFetOiq3bg5Myhu6yZd4YvJ/k/65LFRq3WMH/qYcbOfA0HZ0u+6RdGI39vvMvnx47yVR34cUkQpmZGbF93niWzjzM0N3Z071OTx5k57Nx4sVS0zJ0cyfjZXXB0tmTwe+vw8y+Hd4UyeTbOrlZ8Pao165br1vXHD97k6qVEZi/vTXa2mmEfh9GwiTcWVibFzj8iIlpbH+8Yw5kzNxgT+htrfh+qZzdm7CrGhfahdu1yDPhoToH6xo1ZswYyevRKHXt7eyvmzfsEF2c7Ll++S/8Bs4g8MPE5vfPPRWzA/XyIZW5/P+8D+o/VdQkCSu/xSgFkWf6wtAeSAKKiovD2dsLLyxETEyM6dqzP3vAzOjZ7w6PoFqSdqdG+fV2OHrmELMvsDT9Dx471MTExxtPTEW9vJ6KibgDaQarMzGxyctRkZGTh7GwLQPXqXnmzmorL7cv3cXC3pIybBUbGCmoHuHHhiErHxszCOO/vrEw1hYWTMwfuUqtFUV/hixF59TTJj1Jfah4A1cpUIjYtnnuPVORo1ITfOkJz9/rPtA/0bsqeW4fzPp9URZOek/HCOuq7V+Vayj1u3o8jW5PD+gsH6FiliZ6dtam2cWpjZklcWpJees/qLVl//kCJdVy9kIiLpw0uHtYYGStpGlieE5G3dWxq1HfD1Ew7Dl/Z14mkhEd5adcuJnE/OYNaDV+8fCRfS8XS2RxLZ3MURgo8Gzlz70/9//lZpNx4yOPULFxq2Bdt/BfUda7MjQf3uJUaT7Ymh7CrB2lfXvepqIyMtYk5ANYmlsSlJwOQkfOY43EXyMzJLnH+4QcuENSxDpIkUaemF6kPM1ElPtSxUSU+JO3RY+rU9EKSJII61iF8vzbMSZLEo0ePAXiYlomzk7axW6+2N7Y2Ws11anoRp3pQeP7hUQR189PmX6cCqakZqJ6yVakekJaWSZ06FbT5d/MjfM+Z/PODtGU5KKgJe3KPOzjYUKtmOYyMlDrXkiQJS0szAHJy1OTk5Dxz/f7V84m4elrj4mGNsbGSZm3K80fkLR2bguW1iq8Tyap0AIyNlRibaPPOyVajkQvN4m/zR0G2bj1B507azrqzs23ek1YrKzMqVKhAfHy8jv35c7F4epXBw7MMxsZGtOlQg8j9l3RsIvdd4rWu2pkvrdpW54/j15BlmevXEqjfSDvbpoyDFVbWZlyM1n3Wc+tGIinJj6hTr2zR/tkbRVC3Rlr/1C5P6sMMVAlP+Sch1z+1y+f6pxHh4VEAVKzoSoXy+oPRkiSRkZFFTo6azMdZGBsrscotJ3/F+XN38PTO903bDjWJ2KfbiYrcf4GOBX1zLNc3MQk0KOAba2szLkQ/8znYc3HlfAJunja4ethgbKwkoG1FjkXc1LGp1cAds9yyW7WGM0mqR4Vd6rlJeSq2ejR2Ju508WNrafGq1L9gmFhSFBfO3cXTyx4PT3uMjZUEdvDVu6/dPOyoVMWl1Dt/F6Pj8PCyw93TDmNjJa3aVeXw/qs6Nq7utlSs7PTS91d5lbQ84frFZJw9rHByt8LIWEGD1l6cOawbG8wt89vTjzNzKC1pV84n4Fogdvi3rcDxp2NHffe8svp07Kjd0APzAm39F+FytAp3T1vcnsSxdpU4EnFDx8bF3YbylR1QPFVGb11PpkZdN5RGCszMjSlfyYE/jujec0WhrW8a59bH5UlNTf+L+vhJfdOY8Nz+WcWKboXWN9Wre+HirB1IrlzZjcePs8nKKnk7TvDvRgwmlQKSJH0tSdK53J/BkiSVkyTpXIH0byRJGiNJUi+gAbBCkqTTkiSZS5L0gyRJ5yVJipIkaaokSU2BrsCUXJuKkiQNkCTphCRJZyRJWidJkkXudZdIkjRTkqTDkiRdy70+kpbZkiRdkiRpD+BcQMt+SZIa5P6dJknS+NzrHpUkySX3eMXcz2clSfpekqS0onwQHx+Pq1t+p9XF1Z74eN2AFq+6j1uujZGREmtrc+7ff0R8/IOnzrVDFX8fFxc7+n3QhsDWIwjwD8ba2pxmzUs+xpaalImtY37j28bRjAdJmXp2RzbfZMoH+9mx+BJdPtbPLyriHrVb6C+H+SfiZG6PKj2/EZ2QkYyjeZlCbV0sHHG3dOKUKrrUdbhZORCbmpD3+e7DRNysdQcLf4hcTm/f1pz7bBmrXw9l6O65etfpXi2Adef3l1hHckI6Ds75T6cdnC1ISXh2B2bf5ivU8dPOWNNoZJbNPsE7g178iTVAZkoW5mVM8z6blzElI+Wxnl3syUT2jPyDo3OiSc8tz7JG5uxvMdR4o6Ke/fPialmG2LTEvM/30pJwtdQtI1NP/EbPKi05+e4ilncayYgSLGd7FvEJD3F1tc3X42JDvEp3oDVelYqri00BG1viE7QDTsOHdGTyTzto0Wkyk37awdeD2urlsTbsJAFNq+gdB4iPv68Tn1xd7YiPv69v41q4TVJSat4guJOTDUlJRQ8Sq9UaugV9T9Nm39K0aVMq+zoVapeckI6DS355LeNkSVJC+jOvG77lCnVzyytAYvwjhrwTxsdBawjqW6NYMwletj8yMrKIPBhNu3a6SwUA7txJ5MKFC9SurbukIUGViotr/vfv5GxDQnzqM22MjJRYWpnx4H46laq4cPDAJXJy1Ny9k8KlC3eJf+rcPTvOEdjet1gdtXjVU/+7yzP842Kna6PStXma9u3qYm5uQvMWIbQKHMUH/QKxs7P8y3MAEuIf4uySf/84u9iQ8NT9kxD/EJdcGyMjJVZWpjy4n07lqq5E7s/3zcUL94iPK3zQ9XlJUj3C0SV/ebCDs6XOwPzT7N50ifpNPJ+Z/jxk3n8qttqbkllIbL17MpF9o/7g+JxoMpLz2wqabA37x54k4vtT3DuVqHdecXlV6l8wTCwpigRVKs4F7mtnZxsS4h/+xRmlR6IqDSeX/FnrTi7WJCYU2RTOIysrh0/6LmfQeys5uO/Kv0bLE+4nZmDvbJ732d7RnPsJ+gOb+zZeJaTPdtYvOMsbg+ropZeEpIR0HHXaaX9dVndvvkz9JvpLjUtHyyMcC9w3jkXEsYJUqOzIySO3yczM5sH9DKJOxpKoKv73CoXVtfZ6dUm86un6xl6vTvordu76k+rVvDAxKZ0BuH8CkkIy6M8/DbHM7QWRJKk+0A9oDEjAMaDQqRG5y8wGAd/IsvyHJEkOQHfAR5ZlWZIkO1mW70uStIncZWm5edyXZXlh7t/fA/2BWbmXdQOaAz7AJmBt7jWrop3d5AKcB/I3f8jHEjgqy3KIJEmTgQHA98BPwE+yLK+SJEl/3Uf+/z4QGAgwZMgQTEyfZVkyHjxIZ294FLv3hGJtbcFXgxeyadMxunZtXPTJL0CTLmVp0qUsp/fdZe9vMfQekr9vwK2L9zE2VeJarnT2bfgnEejdhP13jqORS+mx43PSs3pLVp7dzZzj62noUY2fu3xL04UfI6PVU9+9KhnZj7mQeLOIK5UOkTtjiLmYxJg5HQDYtf4idZp46gxGvWxc6zjg2dgZpbGCa/vucnLRJfyH1eba3ru41iqDRZlSvimfQffK/vx+cS/zz4RR36UqswIH0/K3L/K+G0Oyau1xgr/uSPtAX7btPkvIuA0smftBXvrRP66xNuwkKxcNeOlaJEkq1oCEUqkgbOMIUlPT+WzQb1SLccO74ovNMIvYEcO1i4mMnZO/54+jiyXTlnUjOSGdyd/txa9VOezKmD/7IqVMYf7Yty+KenUr6g2UPHqUyRdfLGD48OFYWVnxWP85QInoHFSXm9cT6f/2Alzc7KhZ20vvCfKenecYNb5H6WRYQqLO3kChUBC5fzypqem8/c50mjbxwcvL8aXl2TmoLjeuJdDvrfm45vpGqfz7n0Hu236FqxcSmfhz578tT9c6DnjkxtYb++9yatElmg3VDmK2neKHub0pj1QZHJpyBhtPSyydX+59Y+j6tyCvYix51Vi5ZQBOztbcvXOfbz5eQ4VKTrh7vdiS83+illZBlWgVVInj4bfYtvwi/b5r+Lfk+4T9269w9UICE+b9fbGjuNTz8+LyeRXf9N+Ajb05PjVd9eoeQ3Plyl2mTtvI4kWfG1qK4BVGDCa9OM2BDbIsPwKQJGk9UNxFwQ+ATOAXSZK2AFueYVcjdxDJDrACdhZI2yjLsgY4/2RmERAArJJlWQ3clSRp7zOum1Ugz5PAk0f2TdAutQNYCRS6s6MsywuABQB//vmnPGvW2Ly0+LiUvCedT3BxtuPevRRcXe3JyVHz8GEGdnaWuLjYEncvpcC593F2sePIkYt4eDpQpox24KZN2zr8+ee1Eg8m2TiY8SAxvweSmpiJrcOzlwnUauHGxjm6TwGjIu5Ru+XLXeL2d5KQkYKzRf4MICfzMiRmJBdqG+jVhOnCk4hTAAAgAElEQVSn/vdSdNxLS8LDJn/2hbu1I/ce6i476Fu7Pa//HgLAidgLmClNcLCwITFd+6S8R7UWLzQrCaCMk4XOdOgkVTr2TvqDQ1En7rJ+6VnGzGmfN73/8rkELkap2L3+IpkZOeRkazCzMObtT569bOGvMLM3ISM5/2l5RvJjzO11B4dMrfKfFJVv4ca5Ndq9opJjUkm8/IBre++S81iNJkfGyExJjdcrPLeOuEfJeFjld1jdrByIe6RbRt6q1oa3t4QCcDL+EqZKY8qY25CUUbJZDCtWH2X1Ru3eDzWrexBXYDZEXHwqLs66+5W5ONsQV2A2SVz8A1yctHFjw5Y/CfmmEwCvtanBiO835tldvBLHiHEbWDjzPezt8p+kr1ixgtWrVwNp1KxZVic+xcVpZ03q5O9iR1xc4TYODjaoVA9wdrZFpXqQF8+Kg42NBY0bN+b0sSOFDiaVcbIgKT6/vCYnPMKhkBkB2vIaxdg5HfLK69PX8a5gx4XT8TRpXU4vfce6C4zc2o2/wx9bt52gUyfdDkd2tpovvlhAly6NaNeunZ4+J2cb4uPyv/8EVSpOLjaF2ji72JKTo+ZRWia2dhZIksSX33bIsxv47iK8y+bHxCuX4lDnaPCp/uy4v2LlAVav0S4/qlmzrO7/Hv8M/xR4MhwXfz9vScGz2LL1D/z9q2NsrMTBwZp6dStw9tytIgeTnFysURWYJayKT8XpqfvHycWa+PgHOLtqfZOW9jjPNwU3HB/wzkId37wIDs6WJMbnP4VPUj3CoZBYe/p4LKuXnGbivM6Flt2SYGb3VGxNeYzZU7HVpEBsLRvgRvSa/H34nsRhS2dzHH3seHArrUSDSa9K/Qt/Xyx5HpycbVAVuK9VqlSdGTovE0dnK51ZUAnxD3F0Kt6LXgCcnLU63T3tqF3fiyuXVCUewHmVtDzBztGcFFX+TKSUxAzsnJ59DzRo5cWKGaeAFx9McnCyIFGnnVZ4WT19PJY1S04zvhRjh74WSxIL3DeJz4hjz+LND+rz5gfaNuKkEbvx8C76e1mx4gCr1x4CoGaNp+qbuBS9usTF+en6JkWvTiqMuLgUBn2+gEk/vIe3d+GzowUCEMvcXhZ26Pq20BELWZZzgEZoZxN1BnY843pLgEGyLNcExj51vYJzs593SDtblvMec6l5gcHFmjVrcvOmijt3EsnKymHbtpO0aq37JpBWrWsRtvEoADt3/omfX1UkSaJV61ps23aSrKxs7txJ5OZNFbVqlcPNzZ4zZ26QkZGFLMscPXKJihVcSyoRzyq2JN59RHJcOjnZGs5E3KOan+6mtomx+ZXCpRMqHN3zKyiNRuZs5D1qB/w7lrgBXEyOwdPKFTdLJ4wUSgK9m3Dw7kk9O29rd6xNLDmXVDpTpJ/m1N1LVLR3x9vWBWOFET2qtWD7laM6NrGpKgLK1QWgioMXpkYmeQNJEhJB1QJYd6Hk+yUBVPRxJO5OKqq7D8nJVnM4/DoNmusurbh+OYlFk48wdFJrbO3zG09fjAlg7vpezF7Xi76fNSCgQ4USDyQB2Je3IU2VwaOEDDQ5Gu4cV+FWV7cjl3E///a/+2cS1m7a8trwo2q8Ns2PDlP9qPlGRbybupRoIAngtOoK5W3d8LJ2xlhhRLdKzdl5/biOTezDBJp7aO/3yvaemBqZlHggCaBPbz/CVg4ibOUg2rSszsZtp5FlmdNnb2NtZYqzo26HwtnRGitLU06fvY0sy2zcdprAFtrNkp2dbDh+UvsmxqMnrlHOS+vDu3H3+fzblUwOfZ3yZXU75H369CEsLIywjSNoE1iHjWFHtfmfvoa1tVneMq28/J1tsbIy4/Rp7V4zG8OOEhio9Ufr1rXYuFG7OezGjUfyjj+L5OSHpKZqp+xnZmZx+PBhPMraFmpbqZoj9+6kEn/3IdnZag7tuU6D5rrT+a9fSmLBpCMMmxyIbYGZAkmqRzx+nANAWupjLkapcH9GPh16Vvtb/PHwYQYnTlwhMDB/GZssy4SM+JUKFV3p169Nofqq+bpz51YSd++kkJ2dw54d52jeQvfNM/4tq7J9k3YD1H27z1O/kXb/iMyMLDLSswA4fiQGpVKhszn17u1nafua7hsdn6bP2y0I2xBM2IZg2gTWYmPYca1/zlzH2tocZ6en/OOU658z13P9c5zA1n9dLtzcynDsqHa/mPT0x5w5c4MKFYre6L+arwe3bybn+Wb3jrP4t9R965J/Sx+2FfBNg0J8c+zIVT3fvAiVqzlx93YqcXdTyc5WE7E7hkYBupv0x1xKZM4PkYyc0q5UZ7nYlbfhUXx+bI09psK1jm5szSwQW+8ViK1Zj7JRZ2sAePwwm+QrqVi5lWxJ16tS/8LfF0ueBx9fd+7celJ21YTviKZ5i8KXI5c2PtVdib19n3uxD8jOVrNv1yWatijesvGHqZlkZWn98SAlnegzsZStUPJB2FdJyxPK+dijik0j8d4jcrI1/LH3NrWb6LaN4+/kD4CdPXoPZ4/SGQisXM2Je7fzy2rk7ms08tfdz+7apUTmTTpISCnHjqepUt2Zu7fvExebG8d2XcXPv1yxzlWrNaTe1z7gvn4liRtXk6jXuOjleH36tCBsw3DCNgzPrW+O5dbHufXNM+vjJ/XNsSLrm9TUdAZ+PJchX3ejfr0X3y7hn4ZY5vZ8iJlJL04ksESSpB/QDuZ0R7vs7YvcZWxp6A4UPQSsASRJsgIsZFneJknSIeDa0za5WAP3JEkyBvoAsfw1EcBHkiQtRbtfUiu0M4yKy1GgJ/A78GZxTjAyMmLEyDf4sP9sNBoNPXo2oXJld2bO3EyNGmVp3boWvXo1ZdjQJbRvNxpbWwum/dgfgMqV3enwWj06dxqHUqlg5Kg3USoV1K5dnvbt6tKzx0SURgqqVfOi9xvNAVj26z5++WU3iYmpdOs6noAWvnz/fd+/1KhUKuj6SXUWjziBrJFp0M4Tl7LW7F52GY/KtlT3c+HI5ptcPZ2E0kjC3MqY1wsscbtxLhlbRzPKlLDh+Dys/CCUllXq4Whlx+0Jmxi9ZSGLD28u+sTnRC1rmH5qCdMCvkMhKdh6fT83UmPp79uLiynXOHRX+6ajQO8mhN/Sf1vK7FajKGvtjrmRGes6z2LSiYUcj48qkY6hu+ey7s3xKCUFK6J2cTHxJsH+73D63hW2Xz3KiPCF/NTxSz5t2B0Zmc+2Tss7v6l3TWJTE7h5P67kzgCURgo++KoxE77eg0atoWXnynhVsGf1wj+p4ONAA39vls85SWZGDtNH7Ae00/uHTn7xNww9jUIpUadPJQ5NO4uskSnr74qNhyXnN1zHrpw17nUdidkdy73TSSiUEsaWRjT4sHRex1wQtaxheORCVnUZjVJS8tvFPVxOuc23Dd/iTMJVdt04wdjD/2NKy88YWLsLMjB478y884/3XYCViTkmSiM6lG/MW5vH6LwJrihaNKvCgUOXaRv0I+ZmJkwYnb/cqNvbswlbOQiA0d91JXjMOjIfZxPQtAoBzbSdjnEjujFh6jZy1BpMTYwIDekGwJyF+7j/IJ2xkzYB2viwftmn+vm3qMGBiHO0bTdSm/+E/NfSdwv6nrCNI7T5j3qb4OFLyczMIsDfl4AA7evCBw5oz+CvFrJ23SHc3R2YMV27nC4h4QE9e00kLS0ThUJi6a972bZ1NKqEB3z33VLUag2yLNOhQ0/qNyt8nxClkYL+X/sx/qvdaNQyrTpXwquCPb8t/JOKPg409Pdm2Zw/yMzIZtoI7RsPHV2s+G5yIHduPODXWSeQJJBl6PKWL2WLsZTuZfkDYPfuP2nWrDoWFvmzRE6eiiEs7BhVqnjQLeh7YAZff/01NQpMUDUyUvJ1cEe++mQZao1M56C6VKjkzMI5e/Hxdce/pQ+du9clNGQDr3f+CRsbc0In9wIgJfkRX32yHEkh4eRsrbecbe+uaKbO6VOkX/L8E+DLgYho2nYYi7mZMRPG59dL3bpPJGxDsNY/I3sTPHy5trz6VycgQLs/3+49Zxg3fg3JyWl89MnPVPPx4JeFg+jzVgDBIcvp1OV7ZBl6dPfDp6pHoRoKYmSk5Jvhnfjyk1/RqDV0DqpHhUrOLJgTjk91DwJa+dClez3GDl9Pr04zsLE1Z9zk1wFITn7E4I9/zfWNDaMn9Cy2H4pCaaTg42+aMvqL7Wg0Mm26VKVshTIsn/8Hlas50TigLP+bdYzM9Bx+GL4HACdXK0ZOffFXeSuUErX6VuLIj9rY6t1cG1sv5MZWt7qOXNsTS9zpJCSFhImVEXX7a2Nr2r10Ti+9knffVO7opfcWuOLyqtS/YJhYUhRGRgq+Cu7A15+szL2va+fe1/vx8XXDv2VVLpy7S/BXq3mYmsmhA1dYNPcAKzZ8UvTFi0BppODzoa0ZNmgdGrWG17rVoFxFR/437xBVq7vQtEUlLkbHMfqbMNJSMzkSGcPS+YdZvOZ9bl1PYvr4PUgKCVkj8+b7jXTevPZP1pKnSangzc/r8NOwSDRqmWavlcO9vC2b/hdN2Sr21G7mzv6NMVw4qUJpJGFhbUK/YaWzn6TSSMHAb5oy5ktt7AjsXAXvCvasWHCSSj6OubHjOBnp2UwOCQe0ZXXEVO2s1uCPNnPn5gMyM7L5oMtKBoUEUM+vZPuxKY0UfPKtPyO+2IJGI9Ouiw9lK5Zh2fzjVK7mhF9AeS6fVzFu6A7SUh9zLPIGyxec4Off30Sdo+Hbj7QzpS0sjfkmtA1Ko+eb46Gtj6Np2350bn38Tl5at+4TCNswHIDRo94kOPjX3PrGl4AAXwB27z7NuPGrtfXNx3Op5uPJL4s+Z/mKA9y6lcCceduZM287AIsXfY6Dw39viw9B0UjyK7D++p+OJElfA0824Vgky/IMSZK+AL5EO/BzDbghy/IYSZJ6AhOADOA1IAztTCMJmCrL8lJJkpoBC9HOOuoFtAOGAglo92SylmX5fUmSlqC7t1KaLMtWknYTillol63dArKBxbl7Nu0nf8+mNFmWrXLP7QV0zr1uZWA5YI52EKyPLMtFtVpljRxecieWEgopkPUxgw0tgx4VZyB94mdoGcjztLN7/Fe/bWAlENl7JfYTOxRt+JJJCd7B6cQJhpZBHUdtJR98eKCBlcDEpgtwmxtUtOFL5t6nG+HhGkPLAGttZxp5n2F1AEitiEoy/Ct5azkEvzL+AEjKXGVgIeBg9haodxtaBijbkvL4d0OrwN70DQAu3y90ZfzfShW7bxh6yPCxdXKzBa9M/Qu8MrEkMXO5oWXgaNaXO2kLDC0DTyttOX1VtOyPDTG0DFp6jOdiyhRDy8DH/lsAYh7MMLASqGg7GDSG72ehCITnXwnzj8JzQXeDDo7cGbjhH+VfMTOpFJBl+Ufgx6eOzQRmFmK7DlhX4FCjQmwOod08+wnzcn+etnv/qc9Wub9lYNAztLZ82j7377Vol9uBdgDML3dT8DfRbuYtEAgEAoFAIBAIBAKBQCAGkwSFUh+YnTvD6T75s64EAoFAIBAIBAKBQCAQ/McRg0kCPWRZjgRqF2koEAgEAoFAIBAIBALBvwCl9I9aZWZwxNvcBAKBQCAQCAQCgUAgEAgExUYMJgkEAoFAIBAIBAKBQCAQCIqNWOYmEAgEAoFAIBAIBAKB4D+NUiGWuT0PYmaSQCAQCAQCgUAgEAgEAoGg2Ejat8gLBC+MKEgCgUAgEAgEAoFA8O/lXz11p/LiXgbt0175YO0/yr9iZpJAIBAIBAKBQCAQCAQCgaDYiD2TBKWGzD5DS0CiFYuiPzW0DD70nYv/6rcNLYPI3isBkD7xM7ASkOcd5d1dHxhaBr+2W4wq41dDy8DZ/F0All38zMBK4B2fOYw/8ZGhZRDScD6yaoGhZSA5DwRAI4cbWAkopMBXJra+KjoAbj6ca2AlUNb6U5AN7xOkVjxWbze0CkyVrwGw5cYQAyuBzuWmvTKxdekFw7dJ3qumvV/kZMPXfVKZdyFnp6FlgFF7SFtnaBVg1VP7OyPMsDoAzLshJy0xtAokh/eRExcbWgaSo7bNmppl+HJiY9ITNIZvk6AINLQCwSuGGEwSCAQCgUAgEAgEAoFA8J9GKdZtPRfCXQKBQCAQCAQCgUAgEAgErziSJHWQJOmSJElXJUn6rpD06ZIknc79uSxJ0v0CaeoCaZteVIuYmSQQCAQCgUAgEAgEAoHgP41SerX3v5YkSQnMAdoCd4ATkiRtkmX5/BMbWZa/KmD/OVC3wCUyZFmuU1p6xMwkgUAgEAgEAoFAIBAIBIJXm0bAVVmWr8mynAX8BnT7C/u3gFUvS4wYTBIIBAKBQCAQCAQCgUAgMCCSJA2UJOmPAj8DnzLxAG4X+Hwn91hh1yoLlAf2Fjhslnvdo5IkBb2oXrHMTSAQCAQCgUAgEAgEAsF/GkMvc5NleQFQWq82fhNYK8uyusCxsrIsx0qSVAHYK0nSWVmWY0qagRhMEpQakRHRjB+/Go1GQ6/XmzFwYAed9KysbIYNXUJ09C3s7Cz5cfqHeHo6AjB//g7WrT2EQqEgZERv/P19ARge/Cv795/FwcGazVtG5V1r8qR17NsXhbGxEd7ejkyY+B42NhZFarx+KonwxZeRNTK12rjTuEe5Qu0uHVGxacpZ3pncENdKNnnHUxMyWfzlUZr2Lk+joLLP66I8GrnW4ss676KQFGy5vo8VFzfrpH9epy91naoDYGZkip2pDR03DgBgqv8wqjtU4mziJYYdnFpiDcXhl3dC6FyzGaqHKdQc1+el5lXToQZ9fd5GIUkcuBPJlhvb9GwauTSke8VuyMjcfnibeWcX4G3txfvV3sHMyByNrGHztS0ciz9RYh3HDsXw0+RdaDQynbvXoe8HTXXST5+8xcwpu7h2RcXoH7rTqm21vLQhn67ifFQsNet6MXnWGyXW8ISYU4nsXKgtr3XaetCsV7lC7S4cjmfdpLN8MLUR7pVtSE/NYt2ks9y9mkrt1m50+MjnhXTEnknixLKryBqZSi3dqNlVt+xfjbjHyVXXsLA3AcCnrQeVW7kDsGfSGRJiUnGuYkvgN7VeSIcsy4z/aR8RR69jZmrExOEd8K3qomc3fcFBwnZGk/rwMad2faGXvnP/Zb4cuZk1C/tQ08e12PlHRkYzYfwaNBqZXr2aMmBge530rKxshg1byvno29oY92N/PDwdSElJY/CXCzl37hZBQX6MHKUtGxkZWQwevJDbtxJRKBW0alWTIUMKf0j0d8bXJyxevJvJk9Zx5MhUypR5eToA1GoNvXpOxNnFjvnzta90l2WZGTPC2LHjFEqFgjffCuC9d1sV+T2dOHyDeVMPoNHIdAjy5c33G+qkR52K5edpB7h2NZHh418joE3lvDRVXCo/jgsnIf4hkiTx/U/dcHW3eToLHWRZZvz41RyIOIeZmQk/THwPX19vPbtz524SHLyUzMfZtAioQUhIbyRJ4v79R3z19UJiY5Pw8HBgxvQB2NpaAnDs2CUmTFxDTo4aezsrli8fwr17yQwdtoSkpFQkSaJ371u82ddZJ6+DkReYNHE9GrVMj15+9B/QRic9KyuHkO+Wcz76DrZ2Fkz58T08PBw4G3WT0NG/a/8v4JPPOhDYRnvfjgpZyYED5ylTxooNm/T2+ywWF0+o2PhzNBq1TOPXvAl8o5JO+uEtNzm0+QYKhYSJuZLXv6yFa1lrkuPSmTRgP86eVgCU9bGj15cljyevSmyNOZXE7kVaHbXbutO0Z+E6Lh5WsX7yWfpNbYhbJRuun05i368xqHM0KI0UtH6/EuVqlXkhLbIsM376LiIOx2BmZszEkZ3xreqmZzf9532EbT9L6sNMTu0dmnf8xJ+3mDhjF5diVEwL7U6H1tX0zi22jonrOBBxHjNzE34Y3wff6l56dueibxEcsoLMzGxaBFQnJLgnkiQxY+ZWwvedRSFJODhYMXF8X1ycbYuf95QtHDh0SXsvj+mJbzX9B/7nLsQSPHqt9l5uVpWQbzsjSRKDv1vF9ZuJADx8mIG1tTlhqz7PO+/uvft0en0GgwYG0v9d/+fzyeRNHDh4ETMzY34I7Y1vNU99XefvEDxqtVZXcx9ChnZFyu0YL1t1iBW/H0apUNDC34ehX3Uqdv46OqbvJuJIbhkZ0Rnfqvp16PSfDxC2I7eMhH+Td/x/q46zdvNplEoFZewsGD+8Ex5uxftuCtUyIzxfS0jHwrXMjyBsxzmtlj1f5x3/bcOfrFivrVssLIwJHdqBSuUdi5X34YOXmTZpCxq1hm49GvL+hy100rOychg9fA0Xz8dia2fBhClv4e5hz/Ytp1m2JDLP7urlOJat/oyqPu58/vH/SEp4SI5aQ9165Rga0hVlEa8Vk2WZ8RPWcCAiWlsuJrxbeN0TfYvg4F9z6x5fQoa/jiRJbN9xitmztxJzLY41q4dSs4a2bZeSksYXg7Vtlu5Bfowa+eLtWUGpEgsUDIqeuccK403gs4IHZFmOzf19TZKk/Wj3UyrxYJJY5iYoFdRqNaGhq1i4aBBbto5m65YTXL16V8dm7ZpD2NhYsGv3ON57P5BpUzcAcPXqXbZtPcGWraNYtOhzQseuQq3WANC9RxMWLvpcL7+mzaqxecsoNm0eSblyLiyYv6NIjRq1zO6Fl+g1og4f/OTHhch4Em+n6dllZeRwautt3CrrdyD2/e8y5es6FMsnz0IhSXxdrx/fRE7mnZ3f0sa7KeVsdBsrs04v54Pdw/lg93DWXdlJRGz+4MiqS1v4/ti8F9JQXJYc2UqHWV8VbfiCSEi8W60vU09N57tDI/Bza4y7pbuOjYuFM13Kd2Tc8QkMPzyS5Ze0y3+z1FnMP7eI4YdHMvXUdPr4vIWFkXmJdKjVGn6cuIOpc95k2fqP2LMjmusxCbo6XG0YHtqFNq/V0Dv/rff8GDG+a4nyfhqNWmb7/Eu8NboOH89uQnRkHAm39Mvr4/Qcjm++jUeV/PJqZKKkRZ+KtHm/sp79c+vQyBxbeoXAobXoOrkRN46quB/7SM+unJ8TXSY0pMuEhnkDSQC+nbxp/nHJOhVPE3H0OjfvpLBz1QeEDm3L2Gl7CrVr1awCq+cXPviZlp7FsrWnqF1dv5P0V6jVGsaF/s6ChYPYvGUkW7f+wdWr93Rs1q49jK2NBTt3jeXd91ozdZo2xpmaGvPFl134dmh3vet+0K8N27aPZv36YP48FUNERHShef+d8RXg3r1kDh26gLt7fgf1ZekA+PXXvVSoqNsRWL/+CHH3Uti+fQzbto+hU6cGhWp92lezJ+1n/MwgFq55h/07L3PzWpKOjbOrNd+MaUvr9lX1zp88ahevv1OPX9a+y6ylb2BXpuhYEhFxjhs3VezaGcq40D6MGbuyULsxY1cyblxfdu0M5cZNFRGR2u96wcIdNPHzYdfOcTTx82HBwp0ApKamMzZ0FfPmfsrWLaP56SftAwWlUsl3w3qxbesYfv9tGCtXriTmapyODyZ8v5Z58z9i4+bv2L7tlE46wPp1R7GxsWDrzhG8815LZkzTPtCoVNmNVWuGsGbDUOYt+IjQMavJydE+0OzavTHzFnxUpD+ehUYts37OOQZ834ihC1vy575Y4m4+1LGp18qdb+e3YMi8AFq9XpFN8/P2E8XRzZIh8wIYMi/ghQaSXpnYqpbZOf8Sb4yqw8BZfpyPjCehkDbJ44wcTmy5jXsBHeY2Jrw+ojYDZvrR+cvqbJpxXu+85yXiSAw3byezc80nhH7XkbGTC29XtWpehdW/9NM77uZqw8SRXejcVr9ufC4dkee5cTOBXdtHMm7MG4wJXV2o3ZjQ1Ywb+ya7to/kxs0EIg5eAODDD1qzecN3hK0fRssWNZgzr+j2YV7ehy5z43YSuzYOYdyIIMZMDCs874lhjBvZnV0bh3DjdhIRhy8DMOOHtwhb9Tlhqz6nXesatG1VXee8H6Zvxb9plWLrydN18CI3biWya9NQxo3syZjxGwrXNX4D40b1ZNemody4lUjEoUsAHD1xlfD90Wxa/RVb1w+h/3stCj2/SB1HYrR18OqPCR32GmOnPKuMVGL1ovf1jler4sLaxf3YtOxD2rfyYercfSXSodVyjZt3ktn5+0BCh7Zn7NRdhWtpVpHVC9/VO965XXU2L+vPxqX9+PDtxvwwa28hZ+ujVmuYPH4TP819n9Vhg9m1/QzXYuJ1bMLW/4GNjTkbtn3D2+80Y9Z0rZ9e61yHlWs/Z+Xazwmd8DruHvZU9dG2lyZOfYuV677g9w1fkpLyiPBdZ4v2QUS0tu7ZMYZxY/swJvS3Qu3GjF3FuNA+7NoxJrfu0caLKpXdmDVrIA0b6A7qm5oa8+UXXRj6rX6b5b+AUiEZ9KcYnAAqS5JUXpIkE7QDRnpvZZMkyQewB44UOGYvSZJp7t+OQDPghSoQMZgkKBWioqLwLuuMl5cTJiZGdOzUkPDwKB2b8L1RBHVvAkD79vU4cuQisiwTHh5Fx04NMTExxtPLEe+yzkRF3QCgYcPK2Nrqzzhq3rw6RkZKAGrXKU9cXEqRGu9dTcXezRw7V3OUxgp8mrtw9Xiint3BlddoFFQWIxPd2+PKsQRsXcxx9LIslk+eRbUylYhNi+feIxU5GjXht47Q3L3+M+0DvZuy59bhvM8nVdGk52S8kIbiEnn1NMmPUl96PhVtK6BKV5GQkYBaVnM07hj1nHVfNNDSowV7bu8lPScdgIdZ2k5IXHo88ekqAO4/vk9q1kOsTaxLpOPCubt4eJXB3dMeY2Mlge2rc3D/ZR0bNw87KlVxyXvaV5AGjctjYWFaoryf5u6VB5RxNcfe1QKlsQJffxcuH0/QszuwMoamPcuhLFBeTcyUeFe30yvDJSEpJhVrF3Osnc1RGiko5+fM7ZP6982zcKthj7GZ8oV1AIQfjKFbh+pIkkQdX3dS0x6jStTvfNXxdS8tFLEAACAASURBVMfZ0arQa8xcdIgP326EicnzaYqKuoG3txNeXo7aGNexPnvDz+jY7A2PoluQHwDt29fl6JFLyLKMhYUp9etXwtTEWMfe3NyExn7aAQ0TEyOqV/cqNJZFRd34W+MrwMSJa/j22x5QoJi/LB1xcSkc2H+W13s107nWb6sO8OlnnVAotOXYweGvZwgBXIqOx93LFjdPW4yNlbRoV4XDB67p2Li621ChshPSU422m9eSUKs11PfTPp01tzDBzEz3OyuM8PAogrr5actlnQqkpmagUj3QsVGpHpCWlkmdOhWQJImgbn6E7zmTf36Q1mdBQU3Yk3t885bjtG1bN29A78n/7+xsm/f02crKjAoVKujkd+7sTby9HfH0csTYxIgOr9Vl317djsn+vWfpGqSdsdW2XW2OHb2CLMuYm5vk1a2PH+dQMMw1aFDxmeWlONy6dB8Hd0sc3CwxMlZQt6UH0Ud0O2Fmlvn+zspU8zJWG7wqsfXuFW2bxD63TVK9uQtXjunH1ogV12jSoyxGxvl5ulawxrqMtq5x8rYkJ0tNTrZG79znITziMt1eq6UtxzU8SE3LRJX4UM+uTg0PnB3161hPNzuqVnLRu6+eW8feswR1baTVUbs8qQ8zUCU8dT8lPCDtUSZ1apfX3k9dG+XFIiur/AHgjIzHz1WGwg+cJ6hTXW3eNb21PkjQbQOpElK193JNb23eneoSvl+3LybLMtv3nKVzh9p5x/bsO4+HexkqV9SdRVgsXfvPE9S5nlZXrbK5PilE16NM6tQqq9XVuR7h+7QD1qtWH2Vgv1aYmGgXpDiUKbx+LFJH5BW6dahRoIw8ow6u4VFoHexXvyzmuTG1tq87caqSty/DDz6l5eHzabGyzG+rpWdmF7ucRJ+9g5e3A55eZTA2NqLta7U4sO+Cjk3Evgt06loPgNZta3DiWAyyLOvY7Nx+hnav5Q+KW1mZAaDO0ZCdraY4gsL3RhHUrXFu3VOe1NT0v6h7cu+Vbo0Jz227VKzoRoXy+rO7LSxMaVC/EqamRdd/gr8fWZZzgEHATuACsFqW5WhJkkIlSSr4RPtN4DdZt/BVA/6QJOkMsA/4oeBb4EqCGEwSlArx8fG4udrnfXZ1sSM+XrdTpIq/j5ub1sbISIm1tTn3Ux4RH59S5Ll/xbp1hwkIKPpJWFpSJtYOZnmfrR1MSUt+rPt/xKSSmpRJxQa6U12zMnI4tuEGTXuXL7auZ+Fkbo8qPf9JeUJGMo7mhU9Pd7FwxN3SiVMq/dkK/ybszexIykzO+5ycmYK9qb2OjaulC64WroxoGMyoRiHUdND/zivYlMdIUqJK1+8YFIcE1UOcXfMbyU4uNiSq9BvTfwcPkx5j41iwvJr9n737Dovi6AM4/t07kN6kHCAqNuzYIzZAUUyMvSXRVFveJGpMTDSWqImxa4q9RGOLMXbsvffesBdUQO7oHYG7ff84AhygAmqOJPN5Hh+Fnb39OTc7Ozs7M0titGF5fXw3gYSoNKo0LNzQ7OJIiX2CVemcRpdlaTNSYp/kS/fwdBSbR5zh4C9XSY5OeyWxqCOTcHPJ+X5cnW1QF9B4fJrgm2oeaxLxb1qxyMfWqONwdcspkypXB9Rqw0abWlNAHReXfxRXQRISUjhw4ApNmuSfNlOYOvJl1q/79l5E5WJPtWqG0yheVRwTJ67hq6+75rsJffgoih3bz9Kt60T695tFSIhhx0NBojRJOKtyncMu1kRrCldGQh/GYW1jxndfb+WTXqtY+MsRg9FTT6POUzZcXe1Rq+Pyp3EtOE10dAIuWdNwnJ1tiY7W32CFhGhISEjhvfdm0LXrRDZtOpk/5tAorl+/Tm3vnKmnanU8KtfcZdU+3w1G7jQmJkqsbcyzy+rlSyF06TCZbp2m8O3YntmdSy8qPjoVe+ecOs3OyZz4qPwPRo5uDmHih/vZ+ut1On+aMxUyJiKFGZ8eZs5Xx7l3JTrffoVVUurWxJi0PHGYkZinTRKRFUflZ8Rx44QG14o2Bp1NxaGOTMRNldNh6+psizry77/+qTXxuLra58Shss9f16rjcVXlSuNqjzpXGf/pl634BYxhy9ZzfD6wXRGOnYCrKmfalauLLeo8nTbqyDxpVLao83SKnL0QgmNpazzL6b+35JQnLFp2iIEDWhU6FsO4CsiTvOe0Jj5PXDlpQh5Ecvb8fXq8O4t3+87j8tVHFEf+MmJT7DKybuslfH0qFWtffSxJuLnkisWl6LH8vv48bXosYPrcg4wa0vr5OwCRmnhUrjn5rFLZEanO07GXK42JiRJra3Pi41IM0uzZecWgMwlg0Me/Eeg3ASvLUgQUYoRf/uuKA2pNnmuPJs7wXFE55Ls+Cf88sixvl2XZS5blSrIsT8j63RhZljfnSjNOluVv8ux3XJbl2rIs18n6e/GLxiI6k4R/tPnztmOiVNCh42sv/FmyTubA0tu0LGD4+rE/79OwQzlKWfy9y4wFlGvCwdDT6PI80fgvUkpKVJYqJp2dytwrC+hT80OD6Wx2pez4uHZ/FgUvQebfn1+yTmbPklu0/qjoQ+ZfNo96TnT92YeOkxrhXqs0xxbcMHZI+eh0MpNnH2T4Z8Ub3v8qZWZq+WroEt59ryVly766m9fCSE1NZ8GCnQz+/OVM13yeAwcu41jahlq18q9Bl5GeSSkzU9ZvGEmPns0ZNXLFK41Fm6njyoVwBnzegtnL3yYiNJ7dW158+lBRSJKUPepRm6klOPghCxYM5NfFg5k7bxv37+d0qCUnpzF48EJGjhyZ/VT7ZfCu48nGLd/wx5ovWbxoL0+eZLy0zy6M5h09Gbm0Fe37VmfvqjsA2JY2Y/TKAIbO9aXjxzVYOfkCacmvJq6SUrfKOpm9S24T8NHTp9RFPkziwLK7vPHJi63d9G/zxeftObTvezq0b8DKVUeev8NLtnXnJdq3zekomL1gHx/0aobVSxq5XFRarY74hBTWrBjIsCFvMmTYynwjZf5Om3deJfhGBH17NzZaDAC9u9Vnz9qPGfqJP/OWnnj+Di/J1cuPMDc3pXIVw6ndsxZ8xI4DI0jP0HL2VLGXsBFekFIy7p9/GrEAt/BSqFQqHueanhGhjkOlMhxZ4qKy5/HjWFxdHcjM1JKYmIq9gxUqlcNz9y3Ihg3HOXDwCkuXflHglKO8rB3NScw1YiIx+gnWuUZcpKdqiXqYzOpvzwOQHJfOhkmX6DqiDo9vx3PrhIZDy+/wJDkTSQEmpRTUb5d/UcjniUyNxcUyZ90lZ4vSRKXGFJg2oGwTfjr/W5GP8U8TmxaHo3nO6KzS5g7EPjEc8RCTFsPd+PtoZS1RqVFEJEegslRxPyEEc6U5Q+sPYd2d9dyNv5f34wvN2cUGTUTOk61IdQJOLsWbMveibBzNSIjKXV7TsHHMKa9PUrVEPkhmxehzACTFprNmwkV6jqqLewHrfRWXpYMZybmelqfEPMHSwbBBbG6TMxS6cks3zq1+eY2g3zdcYO0W/RSd2tVceZxrpFhEZCKqp0xnyys5JZ3b96N4f7B+/Y2omGQ+/WYTcyd3LtQi3C4qeyIe55RJdUQsKpXhwqEqlwLqOPvnT4sdO2YV5cu78MEHBT+1Lkwd+bLq14cPIwkNjaZTp/FZ/884unadwNq1Pq8kjv37L7F//2UOHb5K+pNMkpJS+fqrJUyb3geVyp7ANvUAaNOmLiNHLHteVuLkYk2kOtc5rEnC0aVwZcRZZU2lqs64eei/16b+lbh+NaLAtJvXXGLv5k5AErVrlzcoGxERcahyPQkGUKnsDaYw5k7j6GiLRhOPi4sdGk08pUvr6xxXVwfs7a2xtDTTTztoWIUbN0OpUEFFRoaWwYMX0qHDawQGBvJEuyPXsexQR+Quq3HZI5/ypnF1tSczU0tSYlq+slqxkisWlmbcuf2YmrXyL+paVHaOFsRF5tRp8VFp2Dk9fU2quv7urJ+lP/dNSikxyZqaWraKPU7ulkSGJVPWy/6p+z9NSalbbUqb54njSfbUtew4Hibz+2h9myQpLp21Ey7RY1Qd3CrbkhCVxvrJl+kwpAYObsWbfvj7urOs3XwBgNrV3Xmca4RFRGQCKue/5/r3+6rDrFmnv5mvXascERE5Iyf0dUX+8huRa3RFRERcgYtsd3izIQM+WcDgZ4xO+n3NCdZsPKs/do0yROQaBRWhSUDlbPidq5xtDdOoE1DlGiGTmallz4FgNqwcmP27S1cfsWvfVabP3ElCYhoKhYSZmQnvvtXk6XGtPs6aDaf0cdUsmz9P8p7TLnZ54spJo1LZ0SZAPyXMu3Y5FAqJ2NhkShdiutvv68+xdvNFfRzV3PKUkcQil5HjZ+4zf9lxVszpnT3trrB+X3+etZv1U7RqV3flca4RYRGaosfylzdbV+e76buA5y9K7uxihzoiJ5/V6nicVYZlxCUrjcrVTl+/JqVhZ59zju7ecZm27epQEDMzU/xaVufQges0bpq/I/n33w+xZt0xAGrXKp/nuhKLyiXPtcfF3vBcUcfmuz4JwosQI5OEl6J27do8CNEQ+iiK9PRMtm87Q6tWhsM3W7XyZtNGfWNh167z+PhURZIkWrXyZvu2M6SnZxD6KIoHIRq8vT2febwjh4NZ/Otu5s37FAuLUoWK0a2yDbGPU4hTp6LN0HHjqJrKjXJGAZhZmTBwmS8fL2jGxwua4e5lS9cRdXCtbEuvCQ2zf9+gfVkad/UsVkcSwI2Yu3hYu+Jm5YyJQklAuSYcDT+XL105G3dsSllxNfp2sY7zT3Iv4T4qSxVOFk4oJSU+ro25oLlokOac5gLVS+vXl7E2tcbVypXI1EiUkpLP6w7kWPhxzqjz52NRVKvpTujDGMLD4sjI0LJv1zWa+xnn6bR7FVtiHqcSm1Veg4+o8XrNOXu7uZUJQ1f6MWhRcwYtak6ZqrYv/WYHwLGiDYkRqSRqUtFm6gg5qaFsfcPRM7mnvYWei8LOvfjrquTVu2s9Nv32Ppt+e5+AFpUJ2nkNWZa5GByOjbXZU9dGysvG2oyTWz9j/9r+7F/bnzo13ArdkQRQu3Z5HjzQEBqaVcdtP0fLPHVcy1beBGVNRdq160J2HfcsP/+8mcTEVEaM7P7sY/9N9WvVqmU4fmIa+/dPZP/+iahc7dmwYRTOzs6vJI6hQ7tw6PBk9u+fyIwf+9LYpxrTpvcBoHXrupw6pV9A9vTpW3h65l/bIV/8NVSEPYrjcVg8GRlaDu2+RRPfwk1r9KqhIjnxCXGx+ukIF88+onyFgqcgd+xZh6CgIII2jaZ1QF02BZ3Ul8uL97CxMc/XeePiYoe1tTkXL95DlmU2BZ0kIMA7J8826fNs06YT2b8PCKjDufN3yMzUkpqazuXLIVSq6Iosy4wavZyKlVz56KP8UzNq1irHgwdRhIZGk5Geyc4dF/BvaThlwr9lLTZv0r/YYc/uS7zWuAqSJBEaGp294HZ4WAwh99S4l3mxt4T9pWxVO6LCkomOSCEzQ8eFg2HU9DH8TiPDcqYkXj+twamMvoMrKe4JOq1+JEX042Qiw5JxdC1ePVNS6lb3KoZtkmtH1VR5LaduNbcy4YsVvny2qBmfLWpGGS/b7I6ktKQM1vxwCf/3KlO2evFvDHt3b8im5f3ZtLw/Ab5eBO24rC/HV8OwsTIrcG2kV6F3L1+CNgwnaMNwWgd4s2nzaX0cl+5jY22Oi3Oe88nZDmsrcy5euq8/nzafJqBVbQBCHmiy0+07cIWKFZ69RlHvnk2yF81u7V+DTdsu6I995WHWsfN0FDjb6s/lKw/1x952gQC/nIW2j5++S0VPZ4MpZ6sWf8z+rcPYv3UYH/Rqyscf+T+zIwmg99tNCVrzBUFrvqB1y5ps2npeH9flB9hYWxQcl5U5Fy8/0Me19TwB/vq4Wresyakz+oc89x9EkpGhxcGhcOt/9u7WgE3L+rJpWV99Gdl5NU8ZKfz6S9duRjB2yk7mTu2OY+mirz/au1t9Ni37iE3LPsofSxHaAwAhj3Ie4h48fpfyHoWr52rUKsPDB1GEhcaQkZHJnh2X8fU3fMlIC/9qbNus7wTev+cqjV6rmN0W0Ol07N19hTav51w7U1KeEJU1nTIzU8vRwzfxrOBMQXr39iNo40iCNo7UnytBp7KuPfexsbF4xrUn61wJOkVAnuu2YOgfsAB3iSJGJgkvhYmJCd+OeYu+/Wai0+ro1q0pVaq4M/OXzdSqVZ5WAXXo3r0Zw77+jcA232JnZ8mPP/UDoEoVd954owFvtvsOpVLJmDFvZ78O88svf+XM6VvExibh5/sNgwZ1oHuPZowfv5r09Ez6fPQLAHXqVOC775/96nqFUkHrflVZ9/0FdDqoHeCGUzlrjv5xF9dKtlR+reCK+2XTyjp+Or+UGb7foJAUbLt/kJCEMPrW7M6N2HscC9dfgALKNWHfw/zDbme3HEN5G3csTMxZ334WU84s4rT6cr50L8OqPt/j71UfJ2t7Hk3czNiti1hyfMtLP45O1rH8xkqG1f8SSVJwOOwoYcnhdK3UmfsJIVyIvMiV6KvUdqzJpKY/oJN1rL61hqSMZJq6+VDVwQtrU2uau+sX8F0UvJiHiUVfE8DERMEX37Rl6Cd/oNPpeLNTHSpUdubXuYeoVsON5v5eXL8azqgv15GYkMbxw7dZMu8wKzbo33D02UfLeRASTWpKOl0DZzJ83Js0blq8NQEUSgWvD6jKH+MuoNPJ1A1wx7mcNQd/v4t7ZVu8Gj+7vM7qf5QnKZloM2Vunoqk17h6OJcr+qKbCqWC1z6owt6pl5F1MpX93LD3sOLiuvs4VrChbAMnbuwO49H5KBRKiVJWpjTL9brsnd9fIP5xCplpWtYNOk6T/tUoU8xXWPs1qcDhk/cIfHsx5uamTBzRNntb54+Ws+k3/Rtbps09xNa9N0hNy8Cv6wK6t6/NoD5Ni3XMv5iYKBn97Vv06zsbnU5H125N9HXczC36Oq6VN927N2X4sKW0DRyLnZ0lM37sm71/QKvRJCen6Tsp913i18WDsLY2Z8H8nVSsqKJb18kA9OrtR48ezfId+++sX5+VB68ijqfpP6AtX3+1hKXL9mFpacYPE9577vekNFEw8Gt/Rg7ahE4r07ZjDTwrObJs/gm8qqto4leRm8ERfPf1NhIT0jh55D4rFp5k0Zr3UCoV9P+8OcM/2YAsQ5XqLrzR5fnrVvj51eLQ4au0CfwWC/NSTJz4Qfa2Tp1/IGjTaADGjunFiJHLSEtLx7dFzey1/gb0b8uQLxaxbv0x3N0d+fkn/VvbKlVyo0WLmnTsNB6FQkH37s3w8irD2XN3CAo6hZdXGTp1/gH4mUGf+9Ii64bWxETJyFHd+KT/fLQ6HZ27NKZyFTfmzNpOjZrlaNmqFl26+TBy+ErebPsDdvaWTJ2uP3cunL/HkkX7MDFRICkUjPq2Ow4O+npj2FfLOHv6LnFxSbRuOZZPB75B124+z82f7O9GqaDrZzVZOPIUsk7mtcCyuHrasHPZTTy87KjVxJVjm0O4dT4KpYkCC2tT3vlK/yKGe1di2Ln8JkoTBZICug/2xtK2cA+S8ipJdWtg/6qs/u4COi3Uae2GczlrDq26i1tlW4MOrrzObg8l9nEKR/+8z9E/7wPwzrh6WNkXL08A/JpW5vDxuwT2mIu5mSkTR7fP3tb5/UVsWq4vl9Nm72Pr7mB9/dpxJt071mVQP1+uXAtn4DfrSEhM48DR28z+9TBbVxX97X9+vjU4dDiYNm98rz+ffshp23XqOoWgDcMBGPttT0aM+p20J+n4Nq+Bbwt9+Z/x4xbuh2iQFBJl3Bz4bmzhX2vu17wqh47dpE2nGViYmzJxXLecY78zi6A/9G+/HPtNR0aMW0daWia+zbzwbZbz0Gn7rsu82bbgUSfF5deiGoeO3qBNhyn6PPmuR05cPX8iaI3+rbtjR3ZmxJg1pD3JwLdZNXyb66/F3To3YuTYtbTvNgNTUyWTx79VqBH9+eJoWonDJ+4S2GO+/ho8KmckT+cPFrNpmf6aN23OfrbuvqYvI51m071DHQb1a8G0OQdISU1nyGj92+jcVLbMm9qjwGM9N5YmFfWx9FyIubkJE0fmjD7r/MFvbFr2UVYsB9i6JyuWznP0sfRtzu/rz3PiTAgmJkpsbcyZPLpwa2uZmCgZNrIjg//3G1qtTMcuDahUWcX82XuoXtMDv5bV6dS1IWNHrKVLu+nY2lkyYerb2ftfOBeCytUOj7I57aDUlHS+HLSCjHQtOllHw0YV6drz+ct36K89wbRpOzbr2pNzjezUZSJBG0cCMHbM24wYsVxfLlrUxNdXvxbdnj0XGT9hDTExSXz8v7lUr+bB4qw3vLYKGE1SVptl775LLPl1EJUrF+0tuMJ/g2TMObPCv4osU/xXfL4sEi35NfhTY4dBv5pzabGml7HD4EhP/SuqpU8K3+B/VeR5J3l/dx9jh8HywCVoUpcbOwxcLPQ3bytufGbkSOC9anOYcKb4r/x+WUY1WoCsWWjsMJBcBgCgk/cZORJQSAGUlLq1pMQB8CBxrpEjgfI2n4Js/DxBamkwzc1YzJRvALA1ZKiRI4H2njNKTN267Lrx2yQfVNefL3KM8a99Uun3IXOXscMAk7aQtN7YUYB1VodVapBx4wCw6IQcvdTYUSA5fogctcTYYSA56dusCenGLye2pbqBzvhtEhQBYPCe13+fxn+8bdTOkVPvrP5H5a8YmSQIgiAIgiAIgiAIwn+ashij9v7LxJpJgiAIgiAIgiAIgiAIQqGJziRBEARBEARBEARBEASh0MQ0N0EQBEEQBEEQBEEQ/tP+iW9UMyYxMkkQBEEQBEEQBEEQBEEoNDEySRAEQRAEQRAEQRCE/zSlGJhUJGJkkiAIgiAIgiAIgiAIglBokizLxo5B+HcQBUkQBEEQBEEQBOHf6189dsd/bS+j3tMe7LHqH5W/YpqbIAiCIAiCIAiCIAj/aWIB7qIRnUnCS6OT9xk7BBRSAFtDhho7DNp7zsBh0uvGDoPYETsBeH93HyNHAssDlyB94mPsMJDnneRG7DRjh0E1h68B2B7ytZEjgXae03D5pYOxw0Dz+RZIXGvsMMCmh/5vnfHrNBQBnFZ/b+woeE01psTkB0BI4mwjBwKeNgNBu8fYYYCyDVFpK40dBU7m7wJwL2GmkSOBiraDS0xboN7Kt4wdBhfe/ROA/aEjjRwJtPKYSKbO+OeNiaINoUkLjR0GHtYDAEpMLLsfDjd2GASWm8Ll6EnGDgNvxxEAJafNWIKuwYLwF9GZJAiCIAiCIAiCIAjCf5pSEiOTikIswC0IgiAIgiAIgiAIgiAUmuhMEgRBEARBEARBEARBEApNTHMTBEEQBEEQBEEQBOE/TUxzKxoxMkkQBEEQBEEQBEEQBEEoNDEySRAEQRAEQRAEQRCE/zSlGGpTJKIzSXhpjhwJZuKEteh0Mt27N6X/gLYG29PTMxg+fBnXgh9hb2/Fjz/2pYyHIwALF+xk/foTKBQSo0b1pHmLGgAsX76ftWuPIcvQo0czPvigVfbnrVxxgFWrDqNQKvDzq8nXX3d9bow3zmjYND8YnVam8RvlCHirssH241sfcGxLCAqFRCkLJT0+98a1vA0xESlM6X8QFw9rAMpXs6f7597FzquAig2Y1PoTlAoFKy7u5OeTawy2e9g6M7f9V9iZWaFUKPnu4BL23D1Dj5otGdS4e3a6mi4V8FsykKuae8WOpbZjLd6t1guFJHEo9AhbQ7bnS/OaqhFdKnVCRuZR4iPmXVlIOZuyfFj9PcxNLNDJOrbc28op9Zlix/Esi98bRfvazdAkxlJ7fO9XcoyCnD/xiEU/nUSnk2nTsSrd369jsD1o1RV2b76JUilh52DBoFEtcHGzeWnHv35Gw8b5V5C1Mo3fKE/rt6oYbD+2NYRjW+4jKSTMLEzo+XkdXMvnHD9Wk8Lk/gd4/d2qtOxROe/HF1rL8vWZ4NcfpaRgZfAeZp1dZ7C9jI0zs9oMySqvCsYfW8a+kHPUU1VhRsBAACQkpp1axfa7J4t0bFmWmTB9G4eO3cLc3JTJ47pRs5p7vnRXr4cxYtwG0p5k4NfMi1FfvYkkSVy/+Zixk4J4kp6JUqlg3PCOeNfyYO/B6/wyfy8KhYRSqWDk0HY0rOuZ/9gT13LocLD+2BPfp2bNcvmPHfyQESOW64/tW5NRI3sgSRJxccl88eViwsKiKVPGkZ9/6oednSXx8SmMHLWCh48iMTMzZeIP7+Hlpf8/LctT53340bNfw3v5VDgrZp5Fp5Pxf7MyHd6tabB9x5/XObj1DkqlAht7M/p/44OTq74ei1Ins3jKSWI0KSDBV1Nb4uxmXbTv5gXyZ8fO88yevY279yJYu2YYtWuVN9gvPDyGNzuMZ+Bn7ejbp02h4zpz/AHzpx9Gq5N5o3MN3vqwocH2K+fDmD/jCPfuRDFywuu0aK0/Ny6eDWXBj0ey0z0KiWXkxLY09a9UxDxZp88Ti1JMnvgeNWuULThPRq4gLe2vPOmekydztnP3npq1f35lkCc3boYxdtwfJCWloVBIrFszDDMz0+fGdPLYHX6esgudTqZDl3q817eZwfaL5x7wy9Td3L2t5rspXWnZpkb2ti8/WUXwlVC865Zj2uy3C50PT3P2+APmzziKTqfj9U416PlhA4PtV86Hs+DHI9y/E803EwJpEZBTby2eeZzTR0OQZajX2IP/DW2BVMzpCCWlLdDUrQ5fN/oQhaRg0539/BYcZLB9aIP3aaTSn9PmJqUobW6H75o+2dutTC1Y334GB0LPMOXMb8WOAyD4dARr5lxC1sk0a1eBtu9UNdh+eMs9DgXdRZF1ven9RX3cPG1Jin/Cou9O8eBmDD5ty/P24HpFPvaRI9eYPHEdWp2Obt2b0r9/oMH29PQMRgxfQfC1nHFUSgAAIABJREFUh9jbWzHjxz6UKeNIXGwSQ4Ys5urVB3Tu7MPob3vm2ieTCT+s4czp2ygUCgYPaU9gYOFjO338PnOmH0CnlWnXuRbvfNTYYPvl86HMmX6Ae3ciGT2xPX6tvQy2Jyc9oU+PpTTzr8zg4S/2OvWSEsu1M2rWz72CTifT5I3yBL5teJyjW+5zePN9FAowszDh7S/q4lbelhvnNGxeHExmhoyJqUSn/rWoWs+52HFcOBnKbz+fRqeVCehQhS7vG56DW/4IZt+WWyiVCmztzfl0ZDOc3ayJfJzEtBH70cky2kyZN7pXI7BLtWLHAS/WThw3ZCe3rmqoXkfFtzPaFvTxz/SqrsGxsUkMHrKIq1cf0qWzD2O+fasYOSP8V4jOpBJIkiR7oJcsy3OLuf/3wGFZlvdKkhQCNJRlOeplxpiXVqtl/Pd/snjJYFQqe3r2mELLVt5UruyWnWbduuPY2Vqya/d3bNt2lukzNvLTT/24c+cx27efY8vW0Wg08fT5aCY7do7j7t0I1q49xpo1wzE1VdK//2z8/WtRvrwLp07eZN/+y2wKGkmpUqZERyc+N0adVmbDnKt8PKkxdk4W/DzoCDV9VAY33/VbutO0vb4yvXoigs0LrjFgov6i7eRmxdB5vi+cVwpJwbTAz+iyeiThCVHs/3AmO26f5Gb0w+w0Q5u+w6brh1lyYRtVHcuxpud46sz7gLXBB1gbfACAGs6erOw25oU6kiQk3q/+LlPPzSAmLYbvfMZwPvIi4cnh2WlUli50qNCO8acnkpKZgk0pfX6la9NZcPVX1Cka7M3s+d5nDFeir5KSmVrseJ5m6YltzD64juUfjnnpn/00Wq2OBdOP893MN3B0seKrj4J4rUU5ylVwyE5ToaojPy7tjJm5CTvWX2Pp7NMMm/Bijca/6LQy6+dc5n+TmmDvZMFPgw5Ty8fVoLw2aFmGZu09AX15DVpwlY8nNsnevmlBMNUbubxQHApJwRT//9Fj47eEJ0Wz++0f2XXvFLdiHmWn+aJRTzbfPsrSKzvwKl2WVZ3G0vC3ftyIfkibP75AK+twsXTgQO+Z7Lp3Gq2sK/TxDx+7RcijaHZv/IJLV0MZN2kza5f9L1+6cZM2M350Z+rU8qD/58s5fPw2fs28mDZzJ5/1b4VfMy8OHb3JtJk7WbGwH01eq0iA30AkSeLG7QiGfLOaneuHGB77cDAhDzTs3jmOS5dCGPf9atb+OSz/sb/7g/Hf96ZOHU/6fzyHw0eu4edbk4WLdtGkSVUG9G/LwkW7WLhoF19/1YX5C3dSvboHc2Z/zN17EXw//k+W/fY5t26Fs3btMdZm1Xn9+s+mZasHYF5w3ui0Opb9dIbhP7aitLMlYwbspH5zD8p42mWnKV/Fge8XvYGZuQl7N91i9bwLDPyuBQALJhyn43u1qN3IjbSUDCRF0W7MXzR/vKq4MWvWAMaOXVXg50+esp4WLWoUuO1ptFodc6YcZNKczjiprBn0/p/4+FakfMXS2WmcXW0YOq4161acN9i3bkMP5q16B4CE+DQ+6rKc+j75G+bPcvjwNUIeRLJ751guXQ5h3HerWfvn1/nSjfv+T8Z/34s63p70/3herjxxZ9bM/owd94dB+sxMLV8PX8a0ye9TrZoHsXFJmJgoC5UfMybu5OcFvXFR2dKv16809/eiQqWcGzmVqx2jxnfkj2Un8u3f68MmpKVmELTufL5tRaXV6pgz9TATZ3fESWXN5x+spbFvBYPvxsXVmqFjA1i/8qLBvtcuPebapcfM/UPfofVV/w1cOR+Od4MyRY6j5LQFJL55rQ+f7JuAOiWa39+YxKHQs9yLD8tOM+Pc8ux/v131dao6eBp8xqd1enJec/2FY9FpZVbPvMjgqc1xcLZk8qf78W7ihpunbXaaRq3K4tuhIgCXjoezbv5lBk1ujmkpJR0+qkF4SALh9+OLfGytVseE8WtYtHggKpU9b/WcRsuWtQ3aj+vXncDWzoKdu8axfdtZfpwexIyf+lDKzJRBg9tz53Y4t28/NvjchQt2Ubq0Ddt3jkWn0xEfn1KkmGZO3sfUud1xVtnw6Xu/08SvMp4VHbPTuLjaMOy711m74myBn/HbvGN41/MoYm6U3Fh0Wpm1sy7x2ZRm2DtZMG3gQWo3ccWtfE4ZadDKg+YdKgBw5fhjNs6/yqeTmmJlV4qPv/fBzsmC8PsJzB1xnB9Wv16sOLRaHYunn+LbXwIp7WLJiL5badiiHGUr2GenqeBVmilLOmBmbsKuDTdYMfcsX473x97JggkL38S0lJLUlAyGvruJhs3LUdrZstixvEg7sUvv2jxJy2TXphvFOv6rugabmZny+eAO3C7gvBKEvMRArpLJHvi0uDvLsjxGluW9LzGe57p8+TLlyjlTtqwTpUqZ0K5dA/bvu2SQZv++y3Tq7ANA27b1OHniJrIss3/fJdq1a0CpUqZ4eDhRrpwzly+HcO9eBN7enlhYlMLEREmjRlXYs0ffwFy9+gj9+7elVCn9U1lHx+ePBnl4Mw5Hdysc3awwMVVQz78MwSfUBmnMrXKe8qanaXkVa7A1cK/KvdjHPIiLIEOXyYbrh2jn1SRfOhsz/cXN1tyKiKTofNu71fBnw7VDLxRLJbuKaFI0RKZGopW1nIw4RX2XugZp/Mv4sffRflIy9Q2xxHR9x11Eihp1igaAuCdxJKQnZnc0vWxH7lwkJjnhlXz209y+Fomrhy2uZWwxNVXSok1FTh9+YJDGu4E7Zub6PvmqtVyI1iS/tOM/vBmLk7sVTrnK69UTEQZpDMtrJrkL7JXjj3F0tTS4QSqO+qoq3I9/zIMENRm6TDbeOszrFRvnS2dTKqu8lrJEnRQDQGrmk+yOI3OTUoBc5OPvO3Sdzu3qIkkSdWuXJSExDU2UYeexJiqRpOQn1K1dFkmS6NyuLvsOXgNAkiSSk58AkJiUhouzvuFrZWmWPaohNTW9wBEO+/ZfpnOnxvpj161AQkIKGo3hzZJGE09SUhp161bQH7tTY/Zl1X36/fV1XudOPuzN+v3dO4/xaax/6l+poithYdFERSVwt4A6b/fu3U/Nm7vXo1GVscHF3QYTUyU+AeU5d/SRQZoa9V2zy2jlGk7EROrP47CQeHRamdqN9Dds5pam2ekK60Xzp1IlNypWUBX42Xv3XqSMhyNVct1QFsbNYDXuZe1x87DD1FSJf6AXJw4Zdri7uttSsYoTimd0nh3dd4dGTctjbv78kT+56fPkNX2e1KlAQmIqmsg8eRKZlSd1/sqT19i37zIAlSq5Fpgnx47doKpXGapV098QOthboyzEWPzrV8PxKOtAGQ8HTE2VBLxekyMHbxqkcStjT2UvVYGdiQ0bV8DSqlSh///PcitYg3tZu+zvxq9NFU4eum+QRuVuS4UqTvnOR0mSSE/XkpmhIyNDizZTh31pi2LFUVLaArUcK/MoUU1YkoZMnZZdIcfx92j01PSvezZlZ8ix7J+rl66Ao7k9Jx5ffuFYQm7E4FzGCmd3a0xMFTRs6cGl4+EGaSzy5MlfzCxMqFzbCVPT4t1SXLkcQtlyTrnaj/U5sN/w/7R//2U6ddJfdwLb1uPkSX370dLSjAYNKlGqgBF6GzecoP8A/QgnhUKBg0PhR13eCI6gTFl73D3sMTVV0jKwKscP3jFI4+puR6UqzgVeO25dVxMbk0IDn/L5thVVSYnlwc1YnNyts9skDfw9uHLcsE2Su4w8SdNCVjhlK9tj56Q/X908bchI15KRrqU47lyLwtXDBlUZG0xNlTRrXYGzRx4apKnVwC37euZV01k/+hYwNVViWkrfCZ+ZoUVX9CaJgRdtJ9ZpVAYLy6JdY3J7VddgS0szGjaoXKiRr/9GSkky6p9/GtGZVDJNBipJknRRkqSfJEnaJ0nSeUmSrkiS1OmvRJIkfStJ0k1Jko5KkvSHJElfZf1+qSRJ3XN93qBc+1fLSvOaJEknJEm6IEnScUmSqmb9/kNJkjZIkrRTkqTbkiRNLUzAarUaV7ecnniVqwNqtWGFptbE4ZaVxsREiY2NBXFxyajV8Xn2tUejjqNKFTfOnb1LbGwSqanpHD4UTMTjWABCQjScO3uHt3pO5b13f+TKlZDnxhgfnYq9c84jfjsnc+Kj8o+iObo5hIkf7mfrr9fp/GnOlJGYiBRmfHqYOV8d596V/J07heVm7UhYQmT2z+GJUbjZOBqkmXxkJT1rtuLqZytY0+N7hu3JP0itS3Vf1l87WOw4ABzM7YlOi8n+OSYtFgczB4M0rlYqXC1dGd1oBGNeG0Vtx1r5PqeibQVMJCWalMh82/6poiNTcHKxyv7Z0cWK6MinP9ncs+UWDZrkn85SXHHRadg759wsPb283ueHD/ey5ddrdP1U/908Sc1k35o7tH23ar70ReVq7UhYYs7AxsdJ0bhZG5bXqSdX0a2aPxf7/MaqTuMYcWhB9rb6Ki8OvzuHQ71n8fX+uUUalQSgjkzE1TVnpI2ryha1xrBjUa1JwFVlmyuNHepIfYfTyKHtmPrLTvzenMqUX3by5cCc6VJ7Dlzj9W4/8/GQFUwc0yX/sdVxuLrmnA+urg6oNXF5jh2HqyrniairygG1Wp8mOjoRFxd97M7OttkjKKtV82B3Vsf45cshhIfHEKGOw6uKG+fO5arzDgcTEWHYWM8tNiqV0i45T1RLO1sSG/n0kYGHtt3Fu7F+Ot3jRwlYWpvyy6jDjO67nT/mnkenLeJ384L58zTJyWks+nUPAz9tV6R4AKI1yTircm4anVysidIkFflzDu6+jX9br+cnzEOtyZMnKvt8/1+1Om+e2OfLt7zuP9AgSdC3/2y6dJvMosV7ChVPpCYBF9ecc8PFxZZI9fNH8r4KUZFJht+NyproyMJ1wFf3dsW7QRl6v/EbvV9fSn2fcpSrUPr5OxagpLQFXCxLo07J2V+dEo2zpUOBad2snHC3duGM+iqgH1X8ZYP3+PH8imIfP7e4qFQcco3OcHC2IK6APDm46S7fvruTjQuv8NbAOvm2F4daE49brnNGpcrfftTkaifmbj8+TUKC/lo9a+ZWunedzBdDFhMVVfgHUlGaJJxVOQ9inFU2REUWrh7R6WTm/3SQ/w3xK/Tx/gmx6MtITpvE3sm8wDJyOOge372/m6Bfg+n+af4poBePhONR2T67U6eoYiJTcFTltM1KOz+7bbZv623q+eSMYIxSJzP0vSD+13ktnd+tVexRSWD8duKrugYLQlGIzqSS6RvgrizLdYGvgS6yLNcHWgIzJL1GQDegDvAG0PCpnwZRWfvPA77K+t0NoIUsy/WAMcDEXOnrAm8BtYG3JEl6eTVfEVSq5Ea//m3o13cW/fvPplp1DxRZT2IztVri45NZ/efXfD2sK18MWYwsv+AjhizNO3oycmkr2vetzt5V+qc/tqXNGL0ygKFzfen4cQ1WTr5AWnLGSzleQbrV8GfVlT3UmvMePdeOYX6Hr5HI6a1u4F6V1IwnXI968IxPeTmUkhKVpYpJZ6cy98oC+tT8EEuTXJ0cpez4uHZ/FgUvQS7GyJN/g4M7bnPneiRd3i3+2hnF1bxjBUYvbU37vjXYveoWADtX3MSvS0XMLP6emcxdq/ry57V91F3yEb2CxjEn8Mvs8npefQvflZ8RuPpLBjfsgZny733S9ce604z4sh2Htg1jxJftGDV+Y/a2Ni1rsHP9EOZM78Uv81/tYE5JkrJHNwzoH0hiYgqdukxkxcqDVK/ugVIh6eu8fm3o228W/frPplo1DxSKl3OZPrb7PvdvRvPmO/ppYzqtzM3LkbzzWT2+W/A6mvAkDu8o/pTZl2n2nG188EErrKyeMr/vFYuOSibkThQNmxRtiturpM3Ucu78PaZN/ZBVK79k795LnDhx8/k7/kuEP4rjUUgsK7Z9wMrtH3DpbChXL4Q/f8cXUBLaAn9pW74p+x6cQpfVzunpFcjRsItoUmKes+fL5d+5EuNXvk7n/rXYvrJ4U3P+DlqtjoiIOOrWq8i6Dd9Qp64n06dufP6OL8HmtRd5rVkFgw4gYzFGLL6dKjJ2eSCd+tVg1yrDOupxSAKbfw3m7SF1n7L3y3V4513u3YiiY++ch6BOKitmrOjErDXdOLj9LnExL39phoIYs50oFI1SIRn1zz+NWDOp5JOAiZIk+QI6oAygApoBQbIspwFpkiRtecZnbMj6+xzw1yrVdsAySZKqoJ97kvsOb58sy/EAkiRdA8oDhnMn9NsGAAMAxo4dmz1qCEAdEYtKZWeQXuViz+PHsbi6OpCZqSUxMRV7eytUKrs8+8bhktWL3r17M7p31y8W+tOPQahc9b93VTnQpo1+6ou3tycKhURsbBJOhgMmDNg5WhAXmZb9c3xUWvaw24LU9Xdn/awrAJiUUmKS9RSlbBV7nNwtiQxLpqyX/VP3f5rHSdGUsc1Zr8LdxonHiYZPN9+t05Yef44C4EzYdcyVpXC0tCUqRf+0rmt1vxcelQQQmxaHo3nO093S5g7EPok1SBOTFsPd+PtoZS1RqVFEJEegslRxPyEEc6U5Q+sPYd2d9dyNLxk3oi+Lo7MlUbmGI0drknEs4AnWxdNhrF16kQnz2hf7SVtB7B3Nics1yuR55bWefxnWzdJPDXhwI5ZLR8PZsvgaqUkZKCQJk1JKWnSqUOQ4IpKiKWPjlP2zm7Ujj/NMu+xVM5C3N40F4GzETcxNSuFoYUtUas7T5duxoSRnpFLNsTyXNIZD9PP6fc1J1mzSr/1Qu0YZIiJyPidCnYDKxdYgvcrFlgh1Qq408aic9Y3njVsvMOqrNwF4o3UtRv+wKd/xGtWvwKOwDcTEJbNj9xXWbF6pP3YtRyIics6HiIhYVC6G57zKxZ6IXE/5ItSxqLLqL0dHGzSaeFxc7NBo4ildWh+TtbUFkya+D+gX0Axo/S1ly+rzuEf3ZvTIqvN+/CkIlasncLfAfHJwssgevg/6J7a5nxz/5erZx2xefpWRs9pkl9HSzpaUq+yAi7s+pgYtPLgT/Pyl9X7//RBr1h3Lyp/yL5Q/T3Ppcgi7dl1g+vSNJCSm6hf8NTPl3d7+z43P0cWKSHXOU/soTRJOLoWf3gJweM9tmrasVKg1iQB+X3WINWuPA1C7dp48Ucfl+/+qVHnzJC5fvuXl6mpPo4aVKJ01VcfXtybB1x7RpMmzRx86u9iiicg5NzSaBKPd5Do5Wxt+N+okHJ2tnrFHjuMH71GtlgoLS/2Uu4ZNynP9SgS16uVfjP95SkpbQJMSg8oyp9GisnQkMiW2wLRtPZsy+fSS7J+9nb2o51KNnl5tsDAxx1RhQmpGGjMv/lHg/s9j72RBbK4RFbGRqdg/I08atizLH79cKNax8lK52PE41zmjVudvP7pktRPzth+fxt7eCguLUrRpox891bZtfTasy78m2NM4uVgbjOCLVCfi5Fy4euTa5XCuXAhj89pLpKakk5mpw8LClP6Di7fOVkmJRV9GctokcVFpzywj9f09+POXnOUuYiNTWTTuFO8Na4Cze+HO+4KUdrYkWp3TNouJLLhtdvlMOBuWXea7Oa8X2DYr7WxJuYr2XL+opkkrz2LFYox24t9xDRaEohAjk0q+3oAz0CBrpJKapy7H+lRPsv7WktOBOB44IMtyLaBDns98kuvfufcxIMvyQlmWG8qy3HD06NE8eKAhNDSK9PRMtm8/R8tWhr3vLVt5E7RJ/yanXbsu4ONTFUmSaNnKm+3bz5GenkFoaBQPHmjw9vYEyJ4WEh4ew549F2nfXr+eQEBrb06d1o/CuH9fTUZG5nPnw5etakdUWDLRESlkZui4cDCMmj6Gc4Ujw3IautdPa3Aqo7/gJcU9QafVPxGMfpxMZFgyjq7FGxp7PvwmlRzcKWenwlRhQtfqfuy4bfiGq7AEDb6e+reOeDmWxcykVHZHkoRE5+q+rL/+YuslAdxLuI/KUoWThRNKSYmPa2MuaAwXPj2nuUD10vobFmtTa1ytXIlMjUQpKfm87kCOhR/njPrcC8dS0lSp7szjRwmowxPJyNByZM89XmthuObAvZtRzJtylFHTAou9fsfTlK1qT2RYMtERyYUqr9dOq7PL6+AfmzNmeRvGLG+DX5eKtH67SrE6kgAuqG9T0d6dcrb68trFy5dd904bpAlLjKRFWX2jvYqDB2ZKU6JS4ylnq0Ip6S8zHjbOVHHw4FGC5rnH7N3Th6BVAwlaNZDW/jXYtP0isixz8cojbKzNcHEyvBl2cbLB2sqMi1ceIcsym7ZfJMCvun6bsy2nz+nXZTl55h6eZfU3bw8eRWePZgy+EU56eiYOdpb6YwcFERQUROsAbzYFndIf++J9bGwssqetZR/bxQ5ra3MuXryvP3bQKQKy6r5WrbzZFKQ/tzcFncz+fUJCCunpmQCsXXuMhg0rY22tLz+567zdey7SoUOHp+ZTxWqORIQmoglPIjNDy8l9D6jfzHCR1ZBbMfw2/TRfTPLDzsE8176lSUlKJyFOf1N97bzaYOHup343vf0I2jiSoI0jXzh/nmbVyqHs3/cD+/f9wAfvt+TjAW0L1ZEEULWGirBHcUSExZORoeXg7lv4+Bat7B/cdatIU9x69/IjaOMIgjaOyMqT0/o8uZSVJ8558sQ5K08u/ZUnp5+bJ82b1eDWrXBSU9PJzNRy5swdKld2fW5s1Wq6E/owhvDQWDIytOzbGUxzv6JP33sZvGq4EP4wnoiwBDIytBzacxsfX89C7eussuHK+XC0mToyM7VcOR9GWc+Cp4Q9T0lpCwRH36WcjSvuVs6YKJS09WzKwdD8Cyh72rpjW8qKS1G3sn836tgs2m38jDc3DeKn8yvZev9wsTuSAMpXc0ATlkTUY/315uyBULybGnbUaUJzOjSunnyMS5middI+Ta3a5Xn4IDJX+/E8LVvmaT+2rE1Q0CkAdu+6QGMfr2e+yU+SJPz9a3H69G0ATp68SaUirL9WrYYrYY/ieJxVjxzYfZOmfoV7q+PICW/yx/YBrNran4+H+NHmzRrF7kgqSbGUq2pPZK4ycu5gKLWbGNZBmtCc8yb4VATOWWUkJSmd+aNP0LFvTSrWesZT30KoXN2Jx6E5bbNje+/TsLnhBIr7N6NZOOUEw6cGYJerbRatSebJE/21NynhCTcua3Av//zr3tMYo534d1yDBaEoxMikkikR+OtuyQ7QyLKcIUlSS/SjhACOAQskSZqE/ntsDywswjHsgL9eGfLhiwZsYmLC6G/fol/f2eh0Orp2a0KVKu7MnLmFWrXK06qVN927N2X4sKW0DRyLnZ0lM37sC0CVKu68/kZ92r85HqVSwbdj3s5eWPTzwQuJi0vGxETJt2PewtZW32jr2rUpo0etoEOH8ZiamjBp8gfPfUWwUqmg62c1WTjyFLJO5rXAsrh62rBz2U08vOyo1cSVY5tDuHU+CqWJAgtrU975Sj8U996VGHYuv4nSRIGkgO6DvbG0Ld6ipFpZx7A9c1n/9gSUkoLfL+/mRtQDRrR4j4uPb7PjzklG71vEL+0+59NGXZCR+WzbjOz9m5arTVhCJA/inr6WSmHpZB3Lb6xkWP0vkSQFh8OOEpYcTtdKnbmfEMKFyItcib5KbceaTGr6AzpZx+pba0jKSKapmw9VHbywNrWmubt+JMWi4MU8TMw3iO2FrerzPf5e9XGytufRxM2M3bqIJcefNRjvxSlNFAz4qinjPt+BTicT0N6LchUd+H3hOSpXc6Kxb3l+m3Wa1JQMpo7aB+jXABk9PfA5n1zI4ysVdPusNgtG6l852ziwHG6etuxYdoOyXvbUauLKkc33s8qrhKW1Kb2+KvormZ9HK+v45uB8/uz8HUpJwapre7kZ85DhPr25qL7NrvunGXtkMT8GDOR/9TohIzN4zy8ANHavwaCG3cnUZaKTZYYfmE9MWtEWUvdr5sWhY7do0/lHLMxLMXFs1+xtnXrNJmjVQADGftOREePWk/YkA9+mXvg2098wjx/diYnTt5Op1WFWyoTvR+mXndu1L5ig7RcxMVFgbmbKT5PeyleH+PnV4tDhYNq0Has/9sT3co7dZSJBG0fqjz3m7ezX7vq2qImvr359lQH9Ahny5WLWrTuOu3tpfv6pHwB370bwzYjlIEGVym5M+CHncwd9nlPnjf32LWxtbeEpo/CVJgreH9KQaV/tR6eT8W1XCY8K9qxffIkKVR2p39yD1fMukJaayayxRwFwdLHky8n+KJQK3vm0PpOH7EOWZTyrOtKyQ+WCD/S07+YF82fPnouMn7CGmJgkPv7fXKpX82Dxr4OKFENBefLZ136MHLQZnVZHYMcaeFZyZNn8k3hVd6GJX0VuBqv5/uttJCY84eSREJYvPMWiNb0BiAhPIFKdhHf9or8lDMDPt6Y+T17/DgtzUyZOeDdXnkwiaOMIfZ5825MRI1dm5UkNfH310w/37L3E+Alr9XnyyXyqVyvD4kUDsbOz5MMPWtG951QkScLXtyb+fvnXr8vLxETBFyNe58tPVqHVybTvXIeKlV1YNOcg1Wq60cK/KtevhjPiizUkJqRx7NBtfp17iN83fgLAJx8u5WFINCkp6XRu8zMjxnWgcbPC3cjmpTRR8MmwFowevBmtViawY3XKV3Jk+fxTeFV3wcevAjeD1YwftoOkhCecOnqflQtOs2BNL5oHVOLS2VA+eWc1SNCwSbkidxJmx1GC2gJTzixhbsBIFJKCoLsHuRcfyifePbgWc49DofqHNG09m7Ir5HixjlFYSqWCtwfVZdbwo+h0Mk3f8MTd05YtvwVTrqoDdZq6c3DTXW6c16A0UWBpXYoPhucsFj6q1w7SUjLQZui4dOwxg6c0N3gT3LOYmCgZNbonA/rNQaeT6dLVh8pV3Jg1cys1a5WjVStvunVvyjfDl/N623HY2VkxfcYgOiT7AAAgAElEQVRH2fu3CRhDUnIaGRmZ7N93mYW/fkblym58ObQz3wxfxpRJ63Eobc0Puc7F5+aHiYJBw1oxfOB6dFodb3SqhWclJ36bd4yqNVQ09avMjeAIxn4VRFJCGieO3GXZguMsWfthoY/xT4tFqVTQY6A3c0ccR9bJ+LQtj5unLduWXqeclz21m7pxOOgeNy9EolRKWNqU4r1h9QE4HHSfqPBkdq68wc6s6ZGfTW6GjYNZ0eMwUdD3Sx8mfLEHnVamZfvKlK3owOpFF6hUzZFGLcqxYs5Z0lIzmDFa//ZjJ5U130wNIDQknuWzziBJIMvQ4Z2alK9UvE7pv2J5kXbiiI+3EPognrTUDPp0WMXAUb7U9yn8W/de5TW4VcDorPNKy959l1jy6yCDNyz+m/0TF8E2JullrTMjvFySJK0CvIEzQDXAGjgL+ABvyLIcIknSOKAX+tFKGmCnLMuLJElaCmyVZXmdJEkhQENZlqMkSWoITJdl2V+SpCbAMiAZ2Aa8K8uypyRJH2alH5gVx9asfQ4+J2RZJ+97eRlQTAopgK0hQ40dBu09Z+AwqXivPX2ZYkfsBOD93X2MHAksD1yC9ImPscNAnneSG7HTjB0G1Rz0rwvfHpL/teF/t3ae03D55ekjYP4ums+3QOJaY4cBNj30f+uMX6ehCOC0+ntjR8FrqjElJj8AQhJnGzkQ8LQZCNrCLYT9SinbEJW20thR4GSuv1m/lzDTyJFARdvBJaYtUG/lW8YOgwvv/gnA/tCRRo4EWnlMJFNn/PPGRNGG0KSiPIN9NTysBwCUmFh2Pxxu7DAILDeFy9GTjB0G3o76zv8S02YsOdfgf3VvS49tHxq1c2Ttm0v/UfkrRiaVULIs9ypEsumyLI+TJMkSOIx+TSRkWf4w1+d45vr3WcA/698ngNxj3Udn/X4psDTXPu2L9z8QBEEQBEEQBEEQhH8GpVgEqEhEZ9I/20JJkmqgX+9omSzL540dkCAIgiAIgiAIgiAI/26iM+kfrJCjlwRBEARBEARBEARBEF4a0ZkkCIIgCIIgCIIgCMJ/mliAu2jErEBBEARBEARBEARBEASh0ERnkiAIgiAIgiAIgiAIglBoYpqbIAiCIAiCIAiCIAj/aUqFmOZWFJIsy8aOQfh3EAVJEARBEARBEATh3+tf3dvy/u4+Rr2nXR645B+Vv2JkkiAIgiAIgiAIgiAI/2liAe6iEZ1JwkuTqdtj7BAwUbQhOSPI2GFgZdqJi1ETjR0GdZ1GAqBJXW7kSMDF4n1uxE4zdhhUc/ga6RMfY4eBPO8kAJejJxk5EvB2HMG9hJnGDoOKtoNBa/x6BGUb/d/yAePGASC15Il2h7GjwEz5RonJD6DkxFJC4tDJ+4wdBQopAAAZ4+eJREsOho0ydhj4l5lAePKvxg4Dd6t+ACWmfRSatNDYYeBhPYA07TZjh4G58k0AMnS7jBwJmCrakpC+3thhYFuqGymZW4wdBpYmHYCSU6eVlOuNIOQmFuAWBEEQBEEQBEEQBEEQCk2MTBIEQRAEQRAEQRAE4T9NKYbaFInILkEQBEEQBEEQBEEQBKHQxMgkQRAEQRAEQRAEQRD+08QC3EUjRiYJgiAIgiAIgiAIgiAIhSY6kwRBEARBEARBEARBEIRCE9PchFfiyJFrTJ64Dq1OR7fuTenfP9Bge3p6BiOGryD42kPs7a2Y8WMfypRxJC42iSFDFnP16gM6d/Zh9Lc9c+2TyYQf1nDm9G0UCgWDh7QnMLDec2M5dvQm0ycHodXKdOn2Gh/1M3ytZXp6Jt+OWM31a2HY21syeXpv3MuUJjwshm4dp1Pe0xmA2t7lGDW2GwC7dlxk8cL96HQyLfyq8/mX7YqUPxdPhrH059PodDKtOlSh83u1DbZvXR3M/i23USoV2Nqb8b+RzXB2tc7enpKcztDeQTRqUZY+Q1/sNfenjt3ll6m70elk2nepy7t9mhrGeu4hM6ft5t5tDWMnd6Flm+rZ24Z++gfXLodRu15Zps5664XiyO38iUcs+ukkOp1Mm45V6f5+HYPtQauusPv/7J13WBTX14DfoQmodFik2XsjsWEXFbuCNTHWxKgxicauYMEGNtQUjbHGqNHEjh0URLBrLBQrKgIqLB1BpOzu98fiwgICFiRffvM+D4/uzpk5Z8/ce+6dc8scvoempoChsR4T57THokrlD6a/OLaMmEOfxm2Rvkii8eJhZarrxqVofv/xCnKZgi59a9N/ZBO140d2h+F35H5uOdHlW7e2mFepRNzzNFa6+iNXKJDlKOg5qB7d+td7ZzuuXXjCb6vOIZfL6eHcgCGjm6kdD7n+jA2rg3gcnsBsj26071ILgFvXotm4+pxKLupJMrM9utGmU41S61YoFHh47uNsYBi6ejos8xxBwwa2heRCwyJxddvBq1fZdOzQkDlugxAEgeUrD3ImIBRtbU3sbM1Y6jEcAwN9kpLTmDR5C6EhT+jf34H5c4fk6VuyhLNnz6KrK2PZ0lE0bGhXWF/oE1xd/+BVZjYdOzRizpwhCIJAcnI6U6Zu4unTBKytTflxzVgMDSsqr+uxh7OBoejq6qhdt36DCdSpYw1AlSom/Lb+WwCmTd9CaOgyNLVe0bixHfMWfIa2tibngu6wfOkB5DIFAwY5MGZsVzXbsrJymDN7J7fDojE00mfl6lFYW5sSEvyERe5/K38nMOG7HnTpmlemZDI5QwevwkJiyNr149TuwZIlSzh79mQh28vCJ5cu3WPpsr2q6z56FMOa1V/Ttas9Fy9eZMWKFchkKSQmpqGtpUmlynofzKaHj2Jwc/2DsNtRTJncjzFjuqlsmDI17xXvUVHxTJo0hdGjqhZ7b9/HltcEh0Tw+ecrWL1qDD16KOveSq8DnD0bCsC332bQo2cFNV1BQWF4euxFLlcwaFAbxo7rXqCMZDNr1h/cDovCyKgiq1ePwdrGlKSkNCb/sInQ0EhcXByYNz8vro8csYa4uBR0dXUA2LxlIqamRcfdoMAwPDz2IJfLGTS4LePG9Sisf+Y2wsKUfYDVa77GxsYMgA0bTrJ/33k0NDSYM3cI7ds3VJ0nk8kZNHApFhIjNmz4DoA5btsJDX2CQgHVqluwdOkoKlWk1IReiWHP2pvI5Qra9apOjy/UY+XZww8J8H6IhoZABT0thk9thlU1g9IrKIYr5x+z1ssPmUxB7/5N+OLLVmrHb/0TxbpV/jx8EMf8pX3p2LWu2vH0tExGD9pKu061+WG2ehwoDe/aPwK4f+85Hov2k56WiYaGwI6/JiKXK5g1dSfR0QloaGjQoVN9Jk15u/7RlQuPWed1BrlMQS+XRgwt4JPg69Gs8zrDo/A45nr2oWPXOqpjTi1WU72WshxZWFZmyZr+b+ePoDssX3oIuUxO/0EOjBnbpZA/5szexZ2wKAyNKrJi9Uisc/0B8PxZEv37LmfCd90Z9VWeL5WxdU1ubP26VLacC7rNMs8Duf3o1nw91qmALdm4ztrJ7dvKOuy1ejTW1qZcOH+XH1cfJjtbhra2JtNmuNDKQemjE8evs3GDL3KZnI6dGjJ1unOJdlw4d59Vy48il8lxHtCC0V93LOQTd7e93L39FEMjfTxXDsXK2pgTR2+yY1uQSi78fgw79nxH3XpWqu+mTtzO0+hE/j44uUQ7zgfdZeUyb+QyOS4DW/HV2M6F7Jjnups7ue3e8lUjVGUVlPdmYL+VfPNdN0Z+2QmAXTuCOLDvEgoFDBjUimEjO7xRf1nENDfX7QQEhGBqWpkjR+cX0rl16ylWLN/PxYtemOT+lLJqb0773eSnn46goSGgqamBm9sQmjerxZ07USxYsIu09FdoaGgwYUIGvXq9XZ3+/4amuMrtrRBnJol8cGQyOR6L9/Dbxm85fGQux4/9Q3j4czWZ/fsuYmCox0mfBYwc6chqL28AdCpoM3FSH2bMKNwB2LjBBxOTyhw/6c7ho3No0aJ2qWxZvuQgv6wfw/7D0zh5/CaPHsaqyRw6cAUDAz0On5jFsBHt+Wn1cdUxG1tT/to/hb/2T1ElkpKT0/lp1XE2bBnHPu9pJMS/4PKlB6X2j1wmZ+uqS7iu6srqP505f/ox0Y+T1WSq1TZh6ZY+rNzej1aOVflz3T9qx/dsukl9e0mpdb4JmUzO6qUn8Vr3OTsOjOf0yTAeP4xTk5FYGuC2qC9dezYqdP7QUQ7M9ej33nYUtGmD1wXc13Rn7e6BBPk+JPJxkppM9bqmrN7mws9/DqSNYzW2rb3yQW0ojm0Xj9Hjlyllrkcmk7PF6zJzVjmxZpcL508/JqpAOalex4TlW/uyaoczDo5V2fHrNQCMzPTw2Ngbrz+c8dzUm0M7QkiMe/nOdqxbEcjin/qwYc8XBPg+4MmjRDUZC8tKTHPvgmP3OmrfN21uw7pdn7Nu1+csW+9CBV0tPnUonAgqjsDA20Q8icP3pDuLFw5lwcK/ipRbsOhvFi/6At+T7kQ8iSMw6DYAbdvU46i3G0cOuVGtmgUbNvkCUEFHmx8m9mFmgVgTGHibiIgIfH19WbxoGAsW7ipa38JdLF48HF+fRUQ8kRIYFAbAxk0nae1QD1+fxbR2qMfGTT651w0l4okUX59Fha6rq6uD96G5eB+aq0okAfTr25KTJ09ywHsWrzKzObD/IjKZHM8l+1i/YTyHjszmxPHrPAyPUbPtwP5LGBjoc8xnLiNGdeLHVUcAqFW7Crv3TmPvwZms3zieRQv2kJMjU533546zVK9ZOK6cC7yj9EkRtpeFTxwc6qr88ce2Kejp6dC2bQOljgUL8PLyYtpUFwwM9GnZsvYHtcnIUJ85cz9jzFfqD+Y1aliqbDqw3w09PR2cnJxKvLfvYwso65+X10Hats1L4gcEhHD7diSHDs5hz9+z2LJlC2lpGWrnLF70Nxs3fc+Ro/M4duxaoTZ4374LGBro4+O7kJGjOuO16iAAFSpoM+mHvsyYWfRD+MqVX3LwkBsHD7m9MZEkk8lZtGg3mzZ/z9Fj7hw7epXw8Gfq+veex8BAH99Tixk1ugurvJT6w8OfcfzYVY4em8/mzRNZtHA3Mplcdd727f7UqGmpdi1Xt8F4H57H4SPzqFLFhD//DCjSrqKQyxTs/ukGE5e1Y8Hv3bnqH8WziFQ1mZZd7HDf0o15m5zo/lld9q6/VerrF4dMJuen5adY9ssgtu3/Cr+Td4h4FK8mI6liwKwFPenSo36R19i6/hxNPn27mJpf/7v2j3JyZMydvZs58wawz3saG38fj5aWJgAjvuzAgSMz2L3vB27eiOB80N23sunnZX4s/XkAW/eNxt/nHhGPEtRkLCwrM3NhjyJ9olNBi427R7Jx98i3TiQpY+sBft0wjoNHZnGyiNh6cP9lDAz0OOozh+GjOvLjqqNqx71WeNOufWG7/twRSI2aFm9ly5LFe1m/8RsOH3Hj+LF/eFigDh/YdwkDQ31O+MxnxMhOrPY6DICxcUXWrh/PwcOueCwdjuusHQAkJ6WzysubLb9/h/dRN+LjX3Dp4r0S7VjhcZiffh3NHu/J+J64VaiMeB+4hoGBHgePT+eLEW35Zc1JAHr2sWfXvons2jeRRZ6DsbI2Vksk+Z8ORV9Pp9T+WOZxkLW/fc3+wzM4efxGoXtzaP9lKhvocfikK8NGduCn1cfUjq9acZi27fMSxeEPnnNg3yV2/PUDfx+YSuDZO0Q+Ua9/+fWXRUzrP6A1mzZPLFLn8+eJnD9/BysrE7Xvy6q9ae1Qj8PeyjbO03Mkc+cqy42urg7Ll4/m2FF3Nm+aiKenJ3Xr1jUqUqnI/yRiMknkgxMSHIGtnRm2tmbo6GjRq9ennPEPVpPx9w/G2Vk52tSt+ydcunQPhUKBvn4FmjWriU4F7ULXPXjgImPHKUeJNTQ0MDauVEimIKEhUdjYmWFja4q2thbdezYlwD9MTSbA/zZ9nJsD0KVbY65eDkehULzxmk+jErGtaoaxiVJ/S4da+J8KLdGW14TfiUdiY4DEujJa2pq06VKdq0FRajKNmlWhgq5y4mDthuYkxKWrjj26m0ByYgZNWljxvtwJfYa1rQlWNsZoa2vSpXsDzgXcV5OpYm1ErToShCI2pGveqjr6+hUKff8+PLgdh6WNAZbWBmhra9LeqQZXAp+oyTRpZqXyT91GFiRI04u6VJkQFH6TxPTUkgXfk/Db8VjaVEZiXRltbU3adq3OtaBINZn85aROQ3MSpcqEkba2Jto6yo59TrYM+ZuLc4ncD5NiZWtIFRtDtLU16ehUm0tnH6vJSKwMqF7brMgy8pogv4c0b22Hrm7hul0cfv7BuDi3RBAE7JtWJ/VFBtK4FDUZaVwKaWmvsG9aHUEQcHFuiZ+fMua0a1tf9ZBj37Q6MTHKhJy+fgWaN6tJhQKxxs8/GBcXF6U++xqkpmYglRbQJ83VZ18jV58DfqeVD5h+fsG4uLQGwMWlNafzf+/sUOx1C9KxY2MEQUAQBBo3rkpsTAqhIU+wszPDxtYMbR0tevT8hDP+IWrnBfiH0M+lBQBO3Zpy+dIDFAoFeno6Kl9kZuaQ/3bFxCQTePY2AwYWnul4xj+k3Hzi43Od9u0bopfvoSMtLQ0/v2Dq1rFGIjH+oDaZmhrQpHE1lZ+K4uLFu9jammFtbV3q3/EutgDs2HmG7t0+wdQkL3ET/vA5zZvXRktLE339CtStW5eg3OQpQHBwBHZ25vna4Gb4+6knQPz9gnF2Ud7r7t0/4dLF/G1wLSrovF09zU9wcAR2VS2wtTVX6u/dQlUfX+PnH4xL/9a5+j/l4sW7KBQK/PyC6dW7BTo62tjYmmFX1YLg4AgAYmKSOBsQwuBBbdWuVamSHqAcsc98lY1A6YeVH99NxMK6EuZWldDS1qB5Z1tuXVB/SNSrmOeLzFfq9eZ9uBv6HCsbY6xsjNDW1qRz93qcDwhXk7G0MqRmHQs0NAorvXc7hqSEl7RwqPZO+t+nf3Tpwn1q16lCndzkgJFRRTQ1NdDT06FFS+XMVG1tLerXtyY2tvg4l5+7YTFY2xqpfOLYrS4XivJJbfNi25t3ITQkEtvX/siNrQH+6n27M/6h+WJrE67kxlYA/9MhWFubULOWekI+NiaZoLN36F9EbH0TIcFPVHVYW0eLnr0+xb9AnPf3D8HZuSUA3brbc/nSfRQKBfUb2GJhYQgoBxBeZWaTlZVNVHQ8VauaY5IbSxxa1+GUb/GJ0bCQaGztTLGxNUFbWwunnk04e+aOmkzgmTv07vcpAJ2dGnH18sNCfWifE7fo1jNvFuzLl5ns2n6er8arz4R7E6EhkdjamqruTfde9gScKVhWw+ibW1a7Frg3Z/xCsbZRvzePH0lp1KSqql1s1rwG/qfVffyasoppLVrUxtBQv0idS5fuZcaMARQMZ2XV3lSsqKuqUxkvs1T/r15dQrVqSr9JJEaYKKdImRdp9H8EDUEo17//b4jJJJEPTqw0hSqWxqrPEolxoc6ENDYFyypKGS0tTSpX1iM5+c0JgdRU5UPyLz8fZdCAZUyZvIX4+JIf6OOkKVhaGqo+W0gMkUpT3yijpaVJpUq6JCcr9T19msjQQT/y9ej1XP9H+QBta2fKk4g4nj1NJCdHRoB/mOoBtTQkxr3E1CJvDr6phT5JcW/+7WeOPMDeQfnQIpcr2LH2KiO+b15qfcURJ32BhWXeQ4q5xIB46YsPcu13JSHuJWZq/qlIQjGzak4duU+z1u82MvtvJjHuJaaSPD+YmBfvB7+jD/gkt5wAxMemM22EN9+47MVleCNMzIvusJREfFwa5pK8xK2ZpJJacrO0BJ56QKcCM5dKQ6w0Gct88cRSYkRsrHp9i41NxlJipC4jLVwn9x+4SIf2DUqhL2/mg6XlG/TltymfTEJCqqojb25uQEJCat45VYo+JzMzmwEDPRny2XJOn75ZyKbsbBlHDl+jbbt6xMamIMkfXy2NCnUk88toaWlSqbKuKr4G34qgf99lDHRezjz3IaqkyYplB5k6vV+RD61SacpH98lrjh2/Rp/eLVSfPTw8GDduHAcPXeTmrUeMy12+9aFsKg0FbSrN73g3/yRx+tRNhg5VX3pRr64NQUFhZGRkkZiUxuXLl4l5njd7U1rAHoll4TY4VppMlbdog1/j5raD/i6e/Prr8TcOusTGJqn1AZR1Vn12qTS2CP1J6cWe6+m5h+kzBiAUUUZdXf+gXduZPHoUw/ARpXtABUiOz8DYQk/12dhMj+S4jEJyZw6FM2fYCQ5sDOGz7+1Lff3iiI9LU29/LSoTL00r1blyuYL1awKYMKXTO+t/n/7RkyfxCILAt+M288XgH9m2NaDQ9V+kZhB49g4tW9UqtU3x0jTMJfn7JJWJjyudT0C51GnC8J18P2oX586UfsY45PZLLfPaEQtLI2ILPqTnk8kfW1+mZ/L7Fn+++VZ9OSnAimWHmDK9T5Gx9Y22SJPVbJFIjJAW2Y8ubEt+TvnepEF9G3R0tLGzMyficSxPnyaQkyPD3y+EmBj1elmQOGkKknxlRCIxJC5WvYxI88m8LiMpyep9lVMnQ9SSSb/9cppho9qplsyWhDQ2BUkVdX/EFfSHtOC90SM5+WXuvTnD+Anq223UrGXJjX8ekZycTkZGFueC7r6xL19WMe1N+J2+icTCiHr1bIqwpWzaG4BTp27Qo6c7479Zi6fHyEK6g4Mfk52dDfCw2B8g8j+FmEwS+X+BTCYnJiYZ+09qsO/AbJraV8NrxcEy1WlmbsDxU27s3jeZqTP6MmfmLtLSXmFgqI/rvP7Mnv4nY0atx8raGE3NsqlKQT4PeXg3gX5fKJeY+R64i31rG7Vk1P8yASceEH4njv7Dm5Qs/B8m8ORDHt2Np9+wvKWIZpKKrNrhzC97BhJw/CHJiYUfkD4WifHpPA5PKNek3/rfTqKpqUG/vi1KFv5AvJ5VVBJn/D04sN+NVV5f4em5h8hI9aWmHov30qx5DZo1r/neNjVpWo2DR2aze89Utmw6TWZmNmcDwjAxqUSDhmV/f0rrE1A+HNy//5R27fL2zNm2bRsbN26kTev6dOzYiKXL9n1Um7KycvD3v6Xau+hDk98WD8+9TJ/eHw0N9falXbsGdOzYiM+HrmDatM3Y29ujUUZtUH5Wen3J4SNz2blzKv9cC8fb+3KZ63zNmTPBmJpUplGjqkUeX7p0FIFBy6lZ05Ljx699cP2OLrXw+LMnA8Y15vjO0i/bKiu899ygVdvqaomXj4ksR87NG4/xWD6ULdu/5YxfqNpy/5wcGa4zd/H5sLbY2Jp+NLt2HR3L+p3DcfPoxa+rAngWVfqBvvdh/Tofho/siH5F9ZnaHzO2FiT8wXNWrzrM/IXKfc8MDfWZ5z6E6VO3MWr4T1hbm5RZ3zU/ocFR6OpqU6u2ckDi3t1nREcn4NilYQlnfhh++9WX4SPbF7o3NWpKGD3GkW/HbuS78ZuoW88KTY3yfyzOyMhiw4aTTPrhw24hURQF2z4np084eWIh69ZO4KefD6vJSqUpzJi5jaVLl3Lv3j15wWuJ/O8ibsAt8sGRWBjyPN9oR2xsEhKJoZqMhcSQmOdJWFoak5Mj48WLDIyM3pwgMTKqmLtHhXIj5u7dP+XAvosl2mJuYUhMTN7ohTQ2BQsLgyJlJJZG5OTISEt7hZGRPoIgoKOjrCINGtpgY2tKZEQcDRrZ0rFTAzp2Us5w2L/3UqHOfnGYmOurLctKkL7E2Lzwbw+++owDf4SwYF131ZKl+6Fx3A2WcurAXV5l5JCTLUdXX5svJrzbg425RWWkMXkzkeJiUzGzKJ/O6WtMzfWJV/NPOqZFzKq5eeUpe7fdxGN9H5V//kuYmOuTEJvnh8S4ov2gLCfBLFzXo0g/mJjrY1fDiDs3Y2ndudpb22FmXom42LyR4fjYNEyLKK/FEXgqnDadahS7dCg/f+46y569FwBo3Liq2uhpTGwyEon6cn2JxIiYfCNzMbHJSCzyZA4cvETA2VC2bZ1UZNLg3v1nOPdfmk9f3l4MMTFv0JffpnwypqYGSKUpWFgYIpWmqJYUSCRGarNH8p8jkShHEG1tzWnZsg63b0diZ6ecRb527VqSEtOY//NXubKGxOaPrzHJqlHGPPuUMpavY9qLV4Xia42alujpVyD8wXNuXn9EwJlQzgXeJjMzh/T0VwwdsoqcbOV+Sg0b2+X6pOJH8wnAiZPXcOpqj7a2stxs3HSSgIAAoqKiaNzYlJo1qrD7r7Mf1KaSCAwKpWEDO3x8rrNn71YgTVlmivkd72pLaOgTpuZu+p2UnM7ZwDC0tDTp2tWeCd/0YsI3yk1Qp00/SrVqefuxWBTwa2xM4TZYYmHE87dog1//BoCKlXTp06cFIcHKF2UUljNW6wMo66yxmoyFpAj9xhXfeK6//y38/YM5GxhKVmYOaWkZzJi+lZVeX6lkNTU16NW7BZs3+zJoYLE/RYWRmR5J0rxEe1J8Bkbmem+Ub+5oy58/XgfePyltZl5Jvf2VvsDMouTl+wBhIc8IuRGN996bZGRkk5MtQ09fm3GTOpZ8ci7v0z+SSAz5tFkNjI2VZaZd+3rcvf2UVg7KvSyXLNiPnZ0Zw0a0L7U9AGYWlYiLzd8neYGZeel8orRXWXesbIxo2syWB/ekWNmWbnsXC4mh2swUaUwyEosi+q4xyXn+yI2tIcFPOO17ix9XHeHFiwxl/7GCFtLYFALOhHEu8I4qtrrO3MnSFcOLt8XCSM2W2NhkLIrsRyer6lD+OB8Tk8QPEzfjuWyEqi0B6OTYmE6Oyhe+7N1zvsS+q7mFIbH5ykhsbArmEvUyYpErI7E0VJURQ6O8vorviWC698p7iUrIrUjuhD2lX/cVyHLkJCamM/7LTWz4feyb/SExJPa5uj/MC/rDouC9ycDISJ/Q4EhO+wbz46pjvHiRgUZu3937zIwAACAASURBVP7zYe3oP7AV/Qcqt9z45cfjheLka8oipr2JyMg4oqMTcHZerPytMcl07z4PC4vf0NJ6VWbtTX5atKhNVFQ8iUlpmBhXIi0tg/HfrGXK5H7Y23+YmZn/ZsQNuN+O8k/BivznaNS4KpFP4oiOjicrK4fjx6/j6Kg+c8TRsbFqVNPX5watHOoUOzIsCAKdOjXiyhXlyNelS/eoWatKibY0bGRDVGQ8T6MTyc7OwefELTo6qi9z6ejYgKPeypFMP98QWrSqhSAIJCWmqTbJi45KIDIyHuvcEbbEBOXDdWrKS/b+dZH+A1uWxjUA1KxnRkx0KtJnL8jJlnHB7zHN26lPZX18P4HNKy4yc3lnDI3zOraTFnTg1wODWLt/EMO/a06HHjXeOZEEUK+hFdGRiTx7mkx2tgw/n9u06/j2S5E+JLXrm/M8KpXYZy/IzpYRdOoRLdurj0Y/uhfP+uXnmLOyG0Ymb+74/3+mVn0znkfn+eH86cc0b6c+uvn4XgIbl19k1oouGObzQ4I0nczMHADSUjO5GyzFqmrRnaSSqNPAgmeRKcQ8TSU7W8bZUw9w6FDtra4R4PuATt1L3jD/NcO+6Ij3QVe8D7rStUsTDnlfQaFQcPPWYypX1sPCvEAn0tyQSpV0uXnrMQqFgkPeV+jSWRlzAoNus3nLadavG6+2705+6taxUtd36JBS381HVK6sWyhZY2GRq+/mo1x9l+jSRamvc+cmHDqkTHQfOnRR/XvvS4Wum5KSTlZWNgCJSWlcv/GQWrmxbe/ec5w7d47lXiNVnf6Gjex48iSe6OgEsrNyOHniBp0c1TfH7+TYiMOHrgJwyvcWLVvVRhAEoqMTVBtuP3uaSMSjWKysTfhhal9On1nIydPurFg1kpatarN7j3Kj7r0HZ9K5S+OP6pPXHDt2jd75lpN99aUTFStW5Mcff6RrF3v+3hNEjeqWH9Smknht07BhnfD29sb70Fy6drEv9ne8qy3+fh74+3vi7+9J926f4D7/c7p2tUcmk5OUpGyD7t6L5t69e2obdDduXJUnT6T52uB/cOxcoA3u3ATvQ5cA8PG5gYND3WLb4JwcmUpndraMgIBQatcpug1u3LgqTyKkREfl6j92lc4F9Hfu3IRDBy/m6r+u0t+5cxOOH7tKVlY20VHxPImQ0qRJNaZN68/ZwGX4+3uyavUYWjnUY6XXVygUCp48kQLKPZP8/W9Ro0bpX05RrZ4x0qdpxD9PJydbzjX/KJq2Vv9dsdF5yY2QS8+xsP4wAy71GlbhaVQSz3PbX3+fu7TpWLolYXM9+vD38W/469h4JkzuRLfeDd8qkQTv1z9q3bYO4Q+ek5GRRU6OjH+uPaJG7ub9634+SVraK6bP7vtW9gDUa2DJ06hknj9NITtbxhnfe7TpWLoZmS9SX5GVpWz3UpJeEnbrKVVrlH5WVMNGtrl917zY2rFQbG2YL7YG0zLXH9t2TuTE6XmcOD2PYSM68PW4rgwd1p4fpvbh1Bl3Tpyex/JVI2jRqnaJiSSARo3t1Gw5cfw6jo7qb/11dGyEt7fy5SO+Pjdp5aCM86mpL/n2mw1MntqPTz9Vf3NqQoKyLKekvOSv3ecYOKh1sXY0aGRN5JO8MnLqRDAdOqlvMN6+Uz2OHb4OgP+pUFq0rKGKJXK5nNO+ITj1yKv/gz5z4IS/K4d9ZrJp+3jsqpkWm0iC3HsTGc/TXH/4HL9JJ0f1mU0dHRtyJLesnvYNVpXVrTu+4/ipORw/NYdhI9ozZlwXPh/WDoDEXH88f5aE/+kQevb+tEj9ZRHT3kTdutZcuLhSFfsllkb4+Czm2LFjZdrePHkiVS1dDguLJCsrG2OjimRl5fDd97/h7OxQZjNyRf5/I85MEvngaGlpMmfuEMZ9vQ65XEH/AQ7Uql2FX34+SsNGdnTu3ISBg9owe9Z2enRfgKFhRbxWfak636nLfNLSX5GdnYO/XzAbN39HrVpVmDrNhdmz/mD50v0Ym1RiiUfJDbKWliaz3Jz5bvxm5DI5/fq3oGYtS9av9aFBQxs6OjbEZUAL5rn+Rb+eyzE01Gfpyi8AuP7PY9av9UVLSwMNDQG3+QNUG+WtXObN/XvKN2uM+6YrVauVfi86TS0NvprSCs+pp5HL5HTqUxvbGsbs2XSDGvVMad7ejp3r/uFVRg5r5gYAyiVLM1d0Kf7C74CWlgZTZndn2oTdyOVyejs3pXotczb/epZ6DarQrlMd7oQ+Y87UfbxIfcWFwAdsXR/IjgPjAfjuy+08iUgg42UWA7r9zKwFvWnV5v2W4mhqaTBuehsW/HACuVxBlz51sKthzJ8b/6FWPTNadajK779cIeNlNivm+AHKfXzmenUr4cofhl1fLaJTnU8xq2RElOdh3I9uYuuFIx9cj6aWBmOmOuAx5RRymQLHPrWwrWHMX5tuULOeKS3a27Fj3TVeZWSzau4ZQOmH2Su6EB2RwvZfriIIoFBA36ENqVrzzSNhJdkxYWZ75k46jEymoFu/+lStacr23y5Tp74FDh2rcy8slsUzT5CWmsnlc4/ZueEKG/Yo61Hss1TiY9No/Kl1CZqKpmOHhpwNDMOpx0L0dLXxzFfvnfsvxfugKwDu84bg6raTV5nZdGjfgA4dlA9Fi5fsISs7hy/HrAWgadNqLFowFIDOXeeTlqaMNaf9gtm66TulvqBUnJyc0NOV4ek5Kk+fyxK8D81V6pv/Ba5uf/DqVRYd2jekQwflQ8e4sd2ZPGUT+/afx8pK+dpdgI4dG3E2MBSnbvPQ09VRXffhwxjc3f9E0BBQyBWMHduDWrWUm9q6L9iFlZU1I4b+CEAXpyZ8820P3OYMZMLY35DJ5bj0b0Wt2lVY98txGjS0w7FzI/oPdMBt1k56d1+CoZE+K7yUex/cuP6IrZv80NLSQNDQYM68QaV6kUH7Dg24EJReyPay8glAdHQ8z58n0rJlXhJSS0uTJUuWMGnSJCCNlJR0QsOeMG/ezg9mU1xcCgMHLSUt7RUaGgJ/bPfn+DF3KlXS4+XLTC6cv8OihcPU/FPc73gfW95ETo6MYcO9AOXm0ytXrkFLK2/TaC0tTebO+4yvx6xFLpczYGBrate24uefj9CoUVU6d27CoEFtmDVzG927uWNoqM+q1WNU53fpPJf09FfKAQa/W2zeMhErKxO+HvMLOTkyZHIFbVrXZfDgdkXap6Wlybz5nzHm65+Ry+QMHNhGqf+nw0r9XZoyaFBbZs74nW5O8zA01Gf1GuXr0mvXtqJnz2b07rUQTU1N5s//vNilOAqFgtmztpGW/goUygexBQu/KNZ/+dHU1ODzifb8NCsIuUxB257VsKpuyOHfw6hax5imba0IOPSQO/9I0dQS0K+sw5ezPsyehZpaGkya1ZWZ3+1DLpfTs19jqtc0Y+v6c9RtYEnbjrW4G/acedMOkZaaycXAh/z+23m27fuq5IuXgvfpHxkY6jNsZAdGfP4LggBt29ejfcf6xMYks2WjP9WqW/DF4J8A+GxoG/oPalVqn0yc2ZlZ3+9HLpPT07kR1Wqa8fv689RtIKFNx1rcDYvBfbo3aamvuBj0kD82XGDr3tFEPk5gjcdpVSz9fHRLqr1FMklLSxPXOQOYMHYjcrkcl/4tqVXbknW/nKBhQ1s6dW5E/4GtmDNrF326e2CQL7Z+aLS0NHGbO4jxX/+KTC5X9aPX/nyMho3scOzcmAGDWuM6awc9uy/C0FCflatGA7D7zyCiIuP5bf1JfluvfLPaxs3fYmpamWWe+7l37ykA30zoQbXqxb9hTktLk5lu/Zj0ze/IZAr69W9GzVoSflt7ivoNbejoWB/nAc1xd91L/15eGBjq47Hic9X5N/6JQGJpiI2tSTFaSuePWXP68+24TcjlCpxzy+qvv5ykQUNbOnVuiMvAlsydvZt+PZZiYKjPMq+SnxGmT95OcnI6WlqazJ47gMoGRQ9OllVMmzp1M1ev3CcpKY2OHWYzcWJfBg1uW6QNrymr9sbH9wbe3pfQ0tJEt4I2a9aMRRAETpz8h2vXHpCcnM7BgxeBH7l79679vXv3Cm/w+B/hLbY3EwGE4t5aJSLyFihy5KfK2wa0NJxIz/YubzOoqO3MzXjP8jYDezM3AKQZ28vZErDQG8ndpJXlbQb1jGcgTCj9W1XKCsV65ayA4ISl5WwJNDF15VHqz+VtBjUMJoGs/OMImspXvqM4U752AAiOZMpOlLcVVNDs+a/xB/DvseVfYodc4VfeVqAhKAc8FJS/TwQcCXg6p7zNoJO1B8/SN5e3GVhVVD7Y/lv6R9FpG8vbDGwqjeOV7FjJgmWMrmZvALLlPuVsCWhrdCc1a395m4GBzkBe5nz4Qbq3RV9LObvu3xLT/i3tDYXeMfffYkrg2HJNjqzpsOn/lX/FZW4iIiIiIiIiIiIiIiIiIiIiIqVGXOYmIiIiIiIiIiIiIiIiIiLyP424AffbIc5MEhEREREREREREREREREREREpNeLMJBERERERERERERERERERkf9pNMQduN8KcWaSiIiIiIiIiIiIiIiIiIiIiEipEZNJIiIiIiIiIiIiIiIiIiIiIiKlRlzmJiIiIiIiIiIiIiIiIiIi8j+NuAH32yHOTBIRERERERERERERERERERERKTWCQqEobxtE/huIBUlEREREREREREREROS/y3967o7rhXHl+ky7tM3G/1f+FZe5iXww5Aq/8jYBDaELd5NWlrcZ1DOegeuFceVtBkvbbARgx93vytkSGFFvHccjZpS3GfSqtpLghKXlbQZNTF0BECY4lLMloFh/6V9Tb8g8Vt5mQIXewL8npmXLfcrbDLQ1uv9r/AGQKTtRzpZABc2eIC9/n6DRBV4dKW8rQLcvANKM7eVsCFjojfzXxNbotI3lbQY2lXL7Ixne5WsIgJ4za25+U95WMMX+Nx6m/FjeZlDTcDIAqVn7y9kSMNAZiIIz5W0GAo7/qr7Av6UNRlH+9wbBsbwtKHPEl7m9HeIyNxERERERERERERERERERERGRUiPOTBIREREREREREREREREREfmfRtyA++0QZyaJiIiIiIiIiIiIiIiIiIiIiJQaMZkkIiIiIiIiIiIiIiIiIiIiIlJqxGVuIiIiIiIiIiIiIiIiIiIi/9NoCOI6t7dBnJkkIiIiIiIiIiIiIiIiIiIiIlJqxJlJIh+MoKAwPD32IpcrGDSoDWPHdVc7npWVzaxZf3A7LAojo4qsXj0GaxtTADZuOMn+/RfR0BCYM2cI7do34PGjWKZO3aI6PyoqnomT+jBqVGfu3o1mgftuXr7MxNrahJVeX1Kpkl6JNl6/GMWmNZeQyxU49avLoJFN1Y577wrB9/A9NDUFDI31mDinPRZVKgOwYPJJ7odKqd9UwrxV3Yu6fKmJCUkkeFc4CrmCah2qULe3ndrxJ+diCPn7EXrGOgDU6GJN9Y5VVMezM3I4NecqVp+YYT+i9nvZ8vB6PD6b7qOQK7B3sqbtoGpFyt25EMv+5SF85dUSq9oGvEzNYv/yEJ6Fp9K0cxV6jK/3XnbcuSrl4G8hKGQKWvWsStfP1H/X+aMRnD/yGEFDoIKeFkN+aIpl1cqq40nSlywbe4Yew+viOLjWO9tx41I0v/94BblMQZe+tek/sona8SO7w/A7ch9NTQ0MjHT51q0t5lUqEfc8jZWu/sgVCmQ5CnoOqke3/u/nk+LYMmIOfRq3RfoiicaLh5WZHijfeqNQKPBYfpCzQXfQ1dVh2eKhNGxgU0gu9HYUrnN38yozm47t6zNnVn+EfKNLW/8IYPmqw1w8uwgT40oAXL4ajueKQ+TkyDA2qsjO378v1pZ3jXFJSWlM/mEToaGRuLg4MG/+Z4Wu/e2E9URFx3PkyLwSfXIu6DbLPA8gk8sZOKg1X491KmSH66yd3L6ttMNr9WisrU25cP4uP64+THa2DG1tTabNcKGVQx0AThy/zsYNvshlcjp2asjU6c4l2vGh/ZGRkcXkyZuIioxHQ1MDR8fGTJvmUqIdSp/cYfnSA8hlCgYMcmDM2K4FbMlhzuyd3A6LxtBIn5WrR2FtbUpI8BMWuf8NgAKY8F0PunRtQszzJOa4/klC/AsEQWDgkNYMH9GxSN0KhQIPz72cDQxDV1ebZZ4jadjQrpBcaFgkrq7blWW0Q0PmuA1GEASSk9OZMnULT58mYG1tyo9rvsbQUJ+UlJe4zdlBZFQcFSpo47lkBHXqWKmuJ5PJGTh4GRLJHjb81KewTcu9OXvudb35jIb1i6o30bjO+0tpU7v6zJnlXLjerD7KxYCFmBhXJCX1JW7z9xAZnUAFHS08Fw6hTu0qha77Ji6ff8hPK3yRyxX06W/P8K/aqB2/+U8kP6/05dEDKe7L+uPoVF91bNq3u7kd/JTGn9iy4pfCdehD8TFj65ULj1nndQa5TEEvl0YM/bKV2vHg69Gs8zrDo/A45nr2oWPXOqpjTi1WU72WGQAWlpVZsqb/W+tXKBR4rDjM2XN3lWV30ZA3l5P5e3LLST3mzOyHIAj8st6XPQeuYGJcEYCpE3vQsX19srNlzF24j9t3n5Ijk+PS51PGj+lcKpsibyZwftsDFHKo37kKn7hULVLu0WUpvqvDGODZDIuaBtwPiuHWkSjV8YTINAYta45ZtcpFnl8S1y5GsmHVOeRyBd2d6zNk1Kdqx0OuP2PjmvM8Dk9g9hIn2nWpqTq25eeLXD3/BIVCwSctbRk/ra1avSoNF87dZ9Xyo8hlcpwHtGD01+rxJysrB3e3vdy9/RRDI308Vw7FytqYE0dvsmNbkEou/H4MO/Z8R916ebFj6sTtPI1O5O+Dk4vUHRQYhofHHuRyOYMGt2XcuB4FdGcza+Y2wsIilbF9zdfY2CjL4oYNJ9m/7zwaGhrMmTuE9u0bAtC5sxsVK+qiqaGBpqYG+w+4AXDnThQL3HeRmZmNpqYG7guG0rRJ4dfPl1Vf4PLVcL79YSs21iYAOHVpzPffFN9XKYs2ODsrB48l+7h65QEaGgKTJvfBqZt9sXYoFAo8PPZwNjBU6ZOlo4pue0Kf4Or6R27b04g5c4bka3s25Wt7xmJoqKzLly/fw3Pp3tz+USV27pxWrC3/JcQNuN8OcWaSyAdBJpOxeNHfbNz0PUeOzuPYsWuEhz9Xk9m37wKGBvr4+C5k5KjOeK06CEB4+HOOH/+HI0fnsmnz9yxa9BcymZzqNSQcPOTGwUNu7Ns/Gz09Hbp2VT7Ezpu7k6nTnDl8ZC5dnezZsuV0KWyUs8HrAu5rurN290CCfB8S+ThJTaZ6XVNWb3Ph5z8H0saxGtvWXlEd6z+sMZPdi36YeBsUcgW3djyg7ZTGOHm0IPqylNSn6YXkbFqa02VRc7osaq6WSAK4fSACszpG722LXKbgxIZ7DHW355u1rQkLiiEuMq2QXObLHK4cicK6joHqOy0dTToOq0nX0e+XzHptx/51wYxb4sCsTZ25ceYpMU9eqMk0c7Rm5gZHZqzvROfBtfDeEKp2/NCGMOq3sHgvO2QyOVu8LjNnlRNrdrlw/vRjoh4nq8lUr2PC8q19WbXDGQfHquz49RoARmZ6eGzsjdcfznhu6s2hHSEkxr18L3uKY9vFY/T4ZUqZXf815V1vAs/dIeJJPL5H3Vg8fzALluwrUm7Bkn0sdh+C71E3Ip7EE3jururY85gkzl+8h1UVY9V3qakZLPTYz/qfx3Ds4Cx+8hpVrB0ymfydY1yFCtpM+qEvM2YW/bDn63sDff0KpfKHTCZnyeK9rN/4DYePuHH82D88LGDHgX2XMDDU54TPfEaM7MRqr8MAGBtXZO368Rw87IrH0uG4ztoBQHJSOqu8vNny+3d4H3UjPv4Fly7eKxd/fPVlV46fcOfAAVduXH9IYGBYqXziuWQf6zeM59CR2Zw4fp2H4THqPtl/CQMDfY75zGXEqE78uOoIALVqV2H33mnsPTiT9RvHs2jBHnJyZGhqaTBtpjOHjrqy86/J/L3rXKFrviYwMIyIJ1J8Ty5g8cJhLFj0V5FyCxbuZvGiYfieXEDEEymBQbcB2LjJh9at6+Lrs5DWreuycZMPAL9tPEn9+jYc8Z7L8mWj8Fi6V+1623ecoWYNy6JtOneXiMg4fI/MZvH8QSxYsr9om5bsZ7H7YHyPzCYiMo7A8/nrTTLnL97HqkpeW/PbZj/q17PiyL5pLPcYiscK7yKvWxQymZzVS0/ite5zdhwYz+mTYTx+GKcmI7E0wG1RX7r2bFTo/KGjHJjr0a/U+t6Vjxlbf17mx9KfB7B132j8fe4R8ShBTcbCsjIzF/agS4/6hc7XqaDFxt0j2bh75DslkuB1OYnH9/BMFs8byAKPg0XKLfA4yOL5A/E9PJOIyHgCz+fFh9HD2+O9Zwree6bQsb3SzpOngsnKzuHIvqkc2DWJv/ddJvppYon2yOUKzm29T2/Xpny2uiXh52NJjC7cP8rKyCHkeDQWtfL6JHXaWzJ4RQsGr2hB5+/rY2Ch+86JJJlMzq8rglj0Ux9++/tzzvqEE/lI3X4Ly0pMnd+ZTt3U+0C3g2O4HRzDul1D+HX3Z9y/LSXk+rO31r/C4zA//TqaPd6T8T1xi0cPY9VkvA9cw8BAj4PHp/PFiLb8suYkAD372LNr30R27ZvIIs/BWFkbqyWS/E+Hoq+nU6zuRYt2s2nz9xw95s6xo1cJD1e3f9/e8xgY6ON7ajGjRndhldfr/vwzjh+7ytFj89m8eSKLFu5GJpOrztv+x1QOec9VJZIAVq48wHff9eaQ91wm/dCXlSsPFGlXWfUFAJp/WgPvvdPx3ju9xERSWbTBABs2+GJiUoljJ+fhfdSN5i1KHhQNDAxVtj0+i1i8aBgLFu4q2icLd7F48XB8fRbltj3KdnXjppO0dqiHr89iWjvUU7U9qakvWbhoN+t//ZZjR9356aexJdoi8nERBKGHIAj3BEEIFwRhdhHHRwuCECcIws3cv6/zHRslCMKD3L/iO7+l4KMkkwRBMBIE4dsSZKoJgvBFKa5VTRCE0JLkPhaCIHQSBOHoW8j3K+qmlzWluQf5ZC/k/ltqXwcHB2NnZ46trRk6Olr06tUMf79bajL+fsE4uzgA0L37J1y6eA+FQoG/3y169WqGjo42NjZm2NmZExwcoXbupYt3sbU1w9paOZMpIkJKixbKBrxNm3qc8r1Roo0PbsdhaWOApbUB2tqatHeqwZXAJ2oyTZpZUUFXOWGvbiMLEqR5nZimLazR09cujTuKJfFRKhUt9KhooYeGlgY2LS14fiOh5BNzSYp4QWZqFpJGxiULl8CzBymYWOphbKmPprYGDdtLuH8lrpDc2V0PaTOwGpo6eSFDR1cTuwZGaOm8fxiJvJeEmVVFzKpUREtbg086WRN6Uf3BTbdinu+zXuVAvpGmkAvPMbXUV5up9C6E347H0qYyEuvKaGtr0rZrda4FRarJNGpWRVVG6jQ0J1GqTBhpa2uiraMJQE62DLnivUwpkaDwmySmp5atEsq/3vidCcWlb3MEQcC+aTVSX2QgjVP/3dK4VNLSMrFvWg1BEHDp2xy/MyGq40tXeDNjSp/8RYYjx6/j1KWxqlNpalp82QkOjnjnGKevX4FmzWpRQaewH9LTX/HHNn++mdCzVP4ICX6iskNbR4uevT7F3z9ETcbfPwRn55YAdOtuz+VL91EoFNRvYIuFhSGgTKK8yswmKyubqOh4qlY1x8RE6QOH1nU45av+2z6GP/T0dGjlUBcAHR0tGjSwJSZGPXFZFKEhT7CzM8Mm1yc9en7CmQI+CfAPoZ9LCwCcujXl8qUHKBQK9PR00NJS1tvMzBxVGTE3N6RBA1sAKlbUpXoNCVJpSpH6/fyDcXFupSyj9tVJTX1ZSFYqTSEt7RX29tWVZdS5FX65/lKer/STi7MDp3O/fxj+HIdWSn/UrGHJ06cJxMcry35MTBIBZ0MZNKht0TadCcurN02qkvriVdH1Jv0V9k2q5tUb/7zk3dKVr+tNXsV5+CgWh5bKh5ya1S14+iyJ+AT1xP+buBP6DGtbE6xsjNHW1qRL9wacC7ivJlPF2ohadSRFzuJo3qp6qZOu78PHiq13w2KwtjXCysYIbW1NHLvV5UJAuJqMpZUhNWubv/WsltLiF3Ablz6f5isnb4iv+ctJn0/xO1N8klcQlDMNc3JkvMrMRltbk0qVdEu0RxqeioFEDwOJHppaGtRsIyHianwhuat/P8be2U6tT5Kf8POx1GwjKVHfm7gfJsXKxpAque1eh261uBgYoSYjsTKgem1TNDTU742AcpZJTrac7GwZOTlyjEz030p/WEg0tnam2NiaoK2thVPPJpw9c0dNJvDMHXr3U86W6uzUiKuXH6JQqHc8fE7colvPvNnVL19msmv7eb4aX3jmz2uCgyOwq2qBra25Mrb3boGfX7CajJ9/MC79WwPQvfunXLx4F4VCgZ9fML16t1D2523NsKtqUag/XxBBEEhLfwXAixevsLAoeqC0rPoCb0tZtMEABw9c4utxyhlOGhoaGOfOoC4OPz9l26Fse2qQmppRTNtTI7ftccDv9K28812U99HFpTWnc78/cvQKTk6fYGWlnK1lamqAyL8HQRA0gXVAT6ABMFQQhAZFiP6tUCjsc/82555rArgDrYCWgLsgCO/1QPmxZiYZASUlMqoBJSaT/r+jUCgOKxSKZWVxbUHJm+5pae4BAAqFok3JUurExsZimS/TL7E0JjZWPaDFSpOpkiujpaVJ5cp6JCenExubUuBcI6Sx6rNBjh//h969m6s+16pVRdUR9zl5g+fPS37gSIh7iZlFRdVnU4uKJBQzc+TUkfs0a21b4nXflldJWeiZ5HWI9UwqkJGUWUju6T/xnJ53jUvrwniZoGxoFXIFIX89pNFnNQvJvwsvEjIxhuPiJwAAIABJREFUMMvr4FU21eVFgrotzx+mkhr/itrNzT6IzqJITniFkXneMkVDM11S4jMKyZ07/Jglo09zZPNtBnyrHLnOzMjBb0843YfXfW87EuNeYirJKyMm5sWXEb+jD/jEwVr1OT42nWkjvPnGZS8uwxthYv52Hch/I+Vdb2KlqVha5nUuLSVGxEoLxpYULCWGBWSUnczTZ0KxsDCkXl1rtXMinkhJTc1gxFfrGPDZag4dvlqsHdLY5HeOccXx889HGf1lF/R03zxSrGaHNFnNHxKJEdICdkhjU7DMnU2ipaVJpcq6hew45XuTBvVt0NHRxs7OnIjHsTx9mkBOjgx/v5ASkzhl5Y/XpKa+5MyZEFq3LnmpaGxsChLLAm1IwTKST6agT4JvRdC/7zIGOi9nnvsQVXLpNU+fJnD3TjSNmxS93CY2NhnLfPotLY2Jlaq3YbHSZCwl+cuxMbG57VxCwgvVA4a5uQEJucmZevVs8D11U2ljcATPniUSk3uO59J9zJjev9CDbJ6+lAL6DN9Qb4qWyas3Vmrn1Ktjha+f8sEpOCSSZ8+TiIktOslWkDjpCyws85K25hID4qWlS0T9F4mXpmEuye+PysTHFZ4Z/CaysnKYMHwn34/axbkzD97JhlhpyjvG1zyZP/+6QN/Bq3F130NKqrJt6N61CXp6OrRzWoJjD0++GtkBI8OS28P0xEwqmeb1SSqZViC9QP8o7tEL0hIyqfrpm/skDy9Kqd3m3WcqJ8SlY5avL2BmUZGEuNLFrvpNLGnSzJrhvf5geM/tNHOwxa762z2rxUlTkFjm+VwiMSQutkDiJJ+MlpYyWZeSrN42nzoZopZM+u2X0wwb1Q7dYtqb2NgkquSPZxIjYmPV2wNpbBGxPSm92HMFBMaM+YkBAzz5+++8ZXhuboNZuWI/nTq6smL5PqZOLXppc1n1BQBu3oqg36CVfD1hIw/eMANV9dvLoA1Oza03a38+xuABK5g6eatq4KA4Ygu0w5aWRqp2RU3GsmiZhITUAm2PUmdEhJTU1JeMGLGKAQM8OXToUom2/JfQEMr3rxS0BMIVCsUjhUKRBfwFlLw3gZLuwCmFQpGoUCiSgFNAjxLOKZaPlUxaBtTMnWa1MvcvVBCEEEEQPssn0z5XZkrurJggQRCu5/6VKsGRO63LWxCEgNzpW+75jg0XBOFKro4NuZk9BEFIy7UpTBCE04IgtMw9/5EgCP1yZUq0RxCEFoIg3BAEoaYgCH0FQbic+/m0IAiSfPatzf3/m2QWCIKwNZ8Nk4r5vdVyp7ltB0IBW0EQZgiCcFUQhGBBEBa+4R5UEgTBL/e3hAiC4JzvmqXvzXwEsrJy8PcPpnuPvPXqHp4j2L0rkIEDlpKe/gpt7Q+7/VfAiQeE34mj//AmJQuXAZb2pvRY2Yqui5tj0cCYfzYrp5Q/8n+GZRMT9E3KfnQWlMmrU1vv0/XLOiULfwTa9avO3G1d6TOmAb67lCPaJ3fco2P/GlTQ+7hbwAWefMiju/H0G5a3HMNMUpFVO5z5Zc9AAo4/JDmxcELsv0x515uCZGRksWHTaX74rnA7KZPJCbsdxYa1X7P5t3H8uvEUjyOkH9W+O3eiiIqMw8mp+H0RPjThD56zetVh5i9UNr+GhvrMcx/C9KnbGDX8J6ytTdDULL9V8Dk5MqZP28rwEY7Y2pZdEvs1TZpW4+CR2ezeM5Utm06TmZmtOvYyPZOpP/zOTNf+pZpZ8b4IgqAaNR83thsvXrzEub8nO3YGUL++DZoaAmfOhGBiUolGReyN8SHIyMhiw2Y/fvi28HKPcV915kVqBs5DVrNj9znq17NCs5S9X5EPy66jY1m/czhuHr34dVUAz6KSSz7pAzN0SGtOHZ2F99+TsTAzYNkq5UT94NAoNDQEgnzn4nfcla07AomKLv0M7DehkCu4sCOc1iPePKAW+yAFLR1NTOxKntlRFjyLSiEqIontR0ey49hIbl17SuiNt1vm9iEIDY5CV1ebWrWVS2Hv3X1GdHQCjl0afnRbAHbtns6Bg3PYtOl7dv0ZwNWrygTo7t2BzHYdTMDZpbi6DmbunB0lXOntKa4v0LC+Df4+8zi8bwYjvmjHd5O3fnD9BSnYBstkcmJjkrH/pDp7D8ykqX01vFYcKnM78qNse5SxXJYjIywskg0bvmfzlkn8uv4Yjx/HlnAFkY+INRCV73N07ncFGZibC9gnCMLrUd7SnltqPtbT12ygkUKhsBcEYSDwDdAUMAOuCoIQmCszXaFQ9AEQBEEfcFIoFK8EQagN7AaaF335QrQEGgEvc69/DEgHPgPaKhSKbEEQfgWGAduBioC/QqGYIQjCQWAJ4IRy6tgfwGFAWpw9ucmlXwBnhUIRKQhCIuCgUCgUgnKd4kyg4O5l54qRqQc4ApWBe4IgrFcoFNkUTW1glEKhuCQIQrfczy1RzrY9LAhCB/Ldg1x7tYD+CoUiVRAEM+CSIAiHFQXnyBaDIAjjgHEA7u7uxOSbHRQbk4Qk3+gAgMTCiOfPk7C0NCYnR8aLFxkYGVVEIjEscG4yFvlGS4OCwmjQwBYzs7xpljVqWLJlqzLH9vhxLGfPlrwaz9Rcn/h8y28SpOmYFjFz5OaVp+zddhOP9X1Uy5Y+JLrGOmQk5o20ZSRmomesnhyqUClv+Uf1jlUI3fsIgMSHqcTfT+GR/zNyMmXIcxRo6WrSaHCNd7KlsmkF/o+98w6L6vga8Ht3QQHpvQiiAqJgb1iwgDX2lhhN8suXGE3TJCbRIPaC3SRqYktMolEjVjT2WLB3xd5FAaWLgPTd+/1xEVhABdRgknmfx0fYO/fO2cPcMzNnzpxJjs/I+z0lIQMTq3xZMtM1xN15xPLRpwBIfZBF8JSzvB5YD0f3Fxf2am5lQFJcvuPlYXwGZtZPTqhev40Ta+cpYdd3rjwg7OA9Nv98ifTUbFSShF4FNb49qpZaDksbIxJi8ttIYlzxbeTciXus/+0cE37oVGwbsbQxwqWaOZfPxtDMz7XUcrxKlMd7s+KPgwSvU1bCans5Ex2dP0mKjknCzrawbTHTiY5QyphyNyKeyKhEevSblfv5Q3q/MYc1Kz/H3s4cc7NKGBlVxMioIo0aVuPKtXtUdS1+NdvWzrzMNu5JnD17mwsX7uLvNxqNRktiYgrvvP0ty5Y/OV+Lra25jj5iYpKwLSSHrZ0Z0feT8uRITcnIkyM6+gGfDf2JoGlv4+Jik3dPm7a1adO2NgBrgpXkqU/jZejjMePGrqRKFVv+97+SJey1szMjJrpQH1K4jeSWsbc3L6KTx1Srbo+hUUVuXL+Pl7cL2dkahn++lC5dG9KuvW7S+T9WHmD92oVAKrW9q+hEckVHP8Cu0FYNO1vzvKgigOiYB9jl9nNWVibExj7E1taM2NiHedsNjY0NmRr0DqAkWvVvNwZnZ2u2bjvFnr3n2b//IplZOaSmZvFVQDL161YheP0xIPe90anv4RPem6Jl7kYmKO/N63PyPu/d/1vWrBiGjbUpUyf1z5fptSCccw/SeBY2tibERudHIsXFJGNt+3xbk//JWNsaExdTUB8pWNuU3AFik6s7x8rm1G3ozPWrsTg6PzuX4oo/Duu2kzLZV6WMdYHtwf16N+HDYb8A8Oe2M/i2qIG+vhorS2Ma1HPl/MXIZ7aVSpYVSU3IH5OkJmRSqcD4KCtDw4OIR2yaqETspSdlsX3meTp9XRvb6sqY5MbhWNxaPF/+RCubSsQXGAvExz7CyubZtgvg8L5b1PC2y9ve3ai5C5fPx+Bd3/EZd+ZjY2tGTHS+zmNiHmJjpzvmss0tY2dvpti01AzMzPP75p3bztHxtXy7dT7sLpcvRtG94ww0OVoSEx8x5P+WsOgX3Xw4dnYW3C9oz2KSsLPTjayytSvGtltUeuq9j/+3sjKlXft6nDt3m8aN3dm44QiBga8D0KlzQ0aP/j3v/r9jLGBTYG7R2rcWE6asI/FBat5BHYV5GX2wuXklJS9sbj/ToWN91q8tPhpoxYp9BK85qOikdhWdfjg6OimvX8nTiZ15of4pqUDfY1ps32Nvb4G5uXH++KiRO1euRlK1atm3jv6TUL+kbcUlpeD8OpfFsiwvLuVjNgOrZFnOlCRpCIo/o2SDqlJSHkuPLVG+nEaW5RggFGhcTDl9YIkkSeeBNSiOnZKyS5blBFmW04H1uXX6Aw1RnEtnc39/PAPPArbn/nweCM113JxH2X73LHlqAouBbrIsP06wUhnYkVv+a6C4pYCnldkiy3KmLMvxKI6sp73Bd2RZfmx1OuT+OwOcRnFKFZchWQKCJEk6B/yF4pUslZWQZXmxLMuNZFluNHr0aO7ciSUyMp6srBy2bj1FWz/d6IS2fnUIyQ2V3LHjDD4+NZAkibZ+ddi69RRZWdlERsZz504sdeq45itiy0m6dNFtIo+3AWi1WhYu3MYb/X2fKa97TRvuRyQTcy+F7GwNB3bdoomv7raFW1fjWTD9IIEzO2Bu+ezT4cqCRVVTUmPTeRSXjjZHS+TxWBzq6w6w0pPynU33ziRg4qAMEBoPqUnn2T50muVD7Teq49LcrsyOJABHd1MS76fzICYdTbaWiwdi8GiSP8E0qKTHl7+3ZuiSlgxd0hKnGqYv3JEE4FzDnLioRyREPyInW8uZfVF4+eg2x7io/IC5S8djsHZSOuZhc1oydll7xi5rT+te1WjX371MjiQAt5rW3I/MbyOH/rpNo5a6W7ZuX01g8fQjjJzhj1mBNpIQ+4jMzBwAUpMzuXIuFscqugOMfyLl8d4M7N8yLxlmO7/abNx8ElmWORsWjomJAbY2hQbVNqYYG1fkbFg4siyzcfNJ/Nt6U8PDkSOhE9mzfQx7to/B3s6M9auHY2Ntin9bb06duU1Ojob09CzOnbtL9acMlGrXrlJmG/ck3nyzFfsPTGX3nsmsWPElVVxtn+pIAvCu7cLdO3FERiaQnZXDtq2naZvrBMqTo603ISFKEvSdO87S1McdSZJITk7j4w8X8fnw7jRooGs3HtvUhw/T+GPVQfr0bfZUOV6GPgC++24TKSnpBIzq+9RyBfHyduHOnfg8nWzfdoY2bXUTOLdp682mjcpWxl07w2jSVNFJZKSytQ/gXlQi4bdicHSyRJZlxo1ZRdVqdrzzbtH8Iv0H+BISEkLIhlG086/DxpBjShs9exsTE8MizixbWzOMjQ04e/a20kZDjuGfqy8/vzpsDFH0tDHkaN7nyclpZGUpNmXNmkM0auSGsbEhXw7vyf59QezZPZk5s9/Dx8eHWVMHMLB/C0KChxMSPJx2bb3y35tzdzAxfsJ7U8mAs+fuFHhvvKjh7sCRfRPYsy2QPdsClffmjy+wsTYlOTmdrOxcmdYfo1GDaiWO2PL0ciTybiL3opLIztawe8clWrZ+NaJeywPPWvZERSRxP+oh2dka9u68SvPWJdvCnpKckdc2Hj5I42JYFFWqlcypN7B/87yE2e3aerHxz9MF2onhs9vJn6fxb6MMgQvmrPlrzwXc3ZQoGAcHc44dvwlAWnoWYefvUq3qsx08ttVNeBidTnJsOpocLTcPx+BaYIt9RSM93v2pJW/Nb8Zb85th626q40iStTI3j8Ti9hz5kgA8atlyLyKJ6KhksrM17N95Ax9f1xLda2NvwoXT99DkaMnJ0XD+9L1Sb3Or5e3E3TvxREUmkp2dw65t52jVRjcJu28bT7ZsOg3Anl0XaNykWp591Wq1/LXzPO075dvkvm/4sG1PAJt2jGDJsiG4uFoVcSRBrm0PjyUyIte2bzmBXyHb7udXh40bjgCwY8fpPNvu51eHrVtOKOP5iHjuhCvj+bS0TFJTFSdhWlomhw5dxsNdCYawtTXn+HEl0vzo0atUKbCg83eMBeLik/NyTZ07fwetVsbiKYseL6MPliSJ1m28OXFcyZl27Og1qrsVf7jCwIFtCNk4mpCNo2nnX4+NIUdz+55bik6e2Pfcyu17juLvX6Dv2aj8HTduPJL3ub9/XU6dvlFgfBT+xMMeBC+egvPr3H+FHUlRQMHJSeXczwo+I0GW5ceTyZ9QfCAlure0/L37QkrHF0AMSgSTCsh4enEdCkfXyCjOk99kWQ4opnx2gYgcLZAJIMuyNjeC51ny3AcMgPrA41jWecAcWZY3SZLUBhhfTL1PK1Nwk7iGp/+tCm7ElYCpsiwvKlhAkiTXQvcMBGyAhrmRWuG536FM6OnpMXrMGwx6fz5arZbefZrh7u7I3Lmb8faugp9fHfr2bc7IEb/SscM4zMyMmD3nfQDc3R3p1LkBXbtMQq1WMWZs/7wtFmlpmRw+dIUJE3TTaW3ZcoKVK/YD0L5DPXr3fvrEB0Ctp2LwV80Z/9k2tFoZ/64euFSzYMXiU7h5WtO0VRV+mXec9LRsZgTuBsDazpjRszoAEDBkM5F3HpKRns173VbyaWArGvgUPZb0WajUEvUGunFo9nlkrUwVX3tMnSpxacNtzF1NcKxvzc1dUdw/m4BKLaFfSY9Gg17OEfMqtYpOg2uwavwZtFqZev6O2LgYs2/FTRzdTPFoavPU++d9cJDMtBw0OTJXj8UxYHx9bMoQWq5Wq+jzSW0WjVKOn2/awQUHV1O2/XYFZw9zvJvZc2DTba6djketJ2FkrM+Ar+qX9Ws/WQ49Fe8P92HKF7vQamTadnXDuZoFfyw5Q3VPKxr7urD8h5NkpGcze/ReQGkj38zwJzL8IcvmnUCSQJah25teVKn+/EnSn8TK9ybSxqMB1sbmRARtYtyfS1h6ePMLr6e835vWvjUJPXCZ9l2CMDTQJ2jSm3nXevSbRciarwAYF9g37zjgVi09adWy6OlHBalezQ7fFjXo3ncWKkmib++mTz3iXE9PXWYbB+DvN5pHjzKUSfTuMH76eShubiU/Ur2gHKNG92XIoB/RaLX06u2Dm7sD8+duwcvbhbZ+tendtxkBI5fTueNEzMyMmDn7XQBWrThAxN14Fi7YzsIFyvrJ4p8+xsrKhGlB67h6VRlPfPhRJ1yfMfF7GfowNjZg0cLtVKtmR5/eSmrBAQNb069f8UmmdXQS2IePPliIRqulZ6+muLk78MO8rdTycqGtnze9+vgwauTvdOk4GTNzI2bMUiJ+zpy+xdIlu9HTUyGpVASO6YuFhTGnT93iz00ncfdwoF+vGQAM+7wrvq2Lrmu1bu1N6P6LtO84DkODCgQFvZ13rUevIEI2KCcXjRvbn4CAZUob9fWiVStl/WjwoA58Pvxn1q49jKOjJd99qxy6cvNmNN8ELAMJ3N0cmDL57SJ1P4nWvjUJPXiF9l2nKe/NxDfyrvV4fQ4hwcMVmQJ7EzDmDzIyc2jVogatWj69r7l5O4ZvRv8BkoR7dTumTHi9xDLp6an44puOfPnRKrRaLV161KWqmw0//RiKZy0HWrbx4PKFewQOX0tKcgaH919n6YL9LF8/BIBP/m8Zd8ITSE/LoneHuYwc34WmzV9M/sCC/J22degIP0Z+ug6tRkvnHt64VrfmlwWHqFHLjuat3bhyMZpxX4WQmpzBkQM3+W3RYZaueZe7txP4dspfSCoJWSvT/90muJbQmVSQ1r6eSjvpNl1puxP65V3r8fq3hAQrzu1xo3oSMDZYabstPPPayczvtnLl6j2QwMnRgomj+wAw8I3mBIwNpkvv2cjI9O7eCE+PZ9s7lVpFy/c82BIUhqyVqdHGAUvnSpwIvoVNNVMdx1Jx3LuchLGVAaZ2z7eoodZT8dHXvowe9idarUyHbp5UqW7J8kXHca9pg0+rqly7FMukEdtJTc7k2IFwfl98goWr+9PSrxrnTkbx8YDVIEk09HGmaQkdUY/R01MzYlR3hn34CxqNTPdeDanuZsfC+buo6VWZ1m1r0qN3I8YFrKHXa7MwNTNiyoz+efefORWOnb0ZlZ0tS/3d9fTUjBn7Bu8PmotWo6VPn+aKbf9+k2Lb/evSt28LRnz9Cx3aj8HMzIg5uTbL3d2Rzp0b0uW1CajVasbmjucTEpL59JOFgLKlq2vXxvjm2r9Jk95iSlAwmhwNFSvqM3HiwGLlelljgR27wlgVfBi1WoVBRX3mzHj7qYseL6sPHv5ldwJGLmfa1PVYWhozecqz0wgrfc8F2ncYk9v35B/M1aPnZEI2jlZ0MnYAAaN+IyMjK7fvURZbBn/Qkc+/WMLadYdwdLTiu28V52L16g74+nrRvcckVCoVffu2wMPjuXZCCV4sJwB3SZKqojiC+lMo77QkSQ6yLD8+ZrA78DiD/w6UYJLHE5QOQHG+kRIjlWJXU9krkSQr4LQsy1UkSeoNDAFeAyyBkygZxZ1QHCutc+/5FoiUZXm2JEn/ByxVdoNJrsCfsiwXPTdWue9dIAhlm1s6cAx4D2XLWwjKNrdYSclmbiLL8h1JklJlWTbOvX88kCrL8qzc31NlWTZ+ijxtgK+A91GSWA2TZXmfJElngEGyLJ+SJOkXoKosy21y5Wsky/KnTylTWIYLQFdZlsOL+b46+sjd5jYJ8JdlOVWSJCcgG8UhdVqW5Sq55T4D3GRZHipJUltgT2794QW+81N1XQhZK+8uQbGXi0ry58qDmeUtBp4WXxNwePCzC75kpjZXnNnLr3xSzpLA254/sDX86/IWg9dcZ3IuYWp5i0EdK8V2Sx/5lLMkIC84+sq8N2RuKW8xoGIXAF4Vm5at3VHeYqCv6vjK6AMgU7OtnCWBiurOoC1/naDyh4wX7+woNQbdAIhNX1bOgoCt4TuvjG2NTC3t7oQXT2Xj3PFIekj5CgJg2INvz35Y3lLwRb2F3Hz4XXmLQXWzzwFIzlpXzpKAaYU+yOwtbzGQaPtKjQVelT4Yufz/NkhtQQlc+Ncy49SHL9858hRGNFz4TP1KkvQa8B2gRvFJTJEkaSJwMjdIZSqKEykHSAQ+kmX5Su697wGjch81RZblX55H3r8lMkmW5QRJkg7lOkW2AeeAMJSIoRGyLEdLkpQAaCRJCgN+BX4E1kmS9A7KFrSSHaOgcBxYhxK69bssyycBJEkaDeyUlBPPsoFPgDtPfIouT5VHluUYSZK6Atty/0jjgTWSJD0g11FTzDNLUqZUyLK8U5KkmsCRXM96KvCWLMs3C/0NpgObc7fYnQSuPG/dAoFAIBAIBAKBQCAQCF4OsixvBbYW+mxsgZ8DeELEkSzLS4EXlmn+b9vmJsty4Xi9rwtdz6ZoYqiCm3RH5pYLR4k6ehqRsiwXOVtSluXVwOpiPjcu8PP44q7Jsnz9CfLsA/bl/nwX3bxHRZaAZFn+FcVZhizLIU8oU1iGJ37f4vQhy/L3wPfFlC38Nyh2b1iB71zk2QKBQCAQCAQCgUAgEPzbUP+r465ePOV39q9AIBAIBAKBQCAQCAQCgeAfx6ucgPupSJLUEWWrVkFuy7Lci9zIn38TuXmnikvO4C/LcsLfLY9AIBAIBAKBQCAQCASC/yb/WGeSLMs7UDKS/yfIdRjVK285BAKBQCAQCAQCgUAg+LehEvu2SoVQl0AgEAgEAoFAIBAIBAKBoMQIZ5JAIBAIBAKBQCAQCAQCgaDE/GO3uQkEAoFAIBAIBAKBQCAQvAjUkjjOrTRIsiyXtwyCfweiIQkEAoFAIBAIBALBv5d/tbdlbthH5TqnHVZ3wT9KvyIySfDCkNlb3iIg0ZZ9UYHlLQZtnKbg8GPP8haD+x9vBGDKiSHlLAkENl6E7ffdylsMYj/bzK3kueUtBtVMhwFw5cHMcpYEPC2+RvrIp7zFQF5wFNI2lLcYYNRL+V+zq3zlAFC35+D9seUtBS0dJr4y+gCITV9WzoKAreE7oC3ukNW/GZU/99OWlrcUOBi9B/DK2NdLiYUP/P37qWU5EuOx/uUtBqkTlXb608WPy1kSGOT1I+S8Aufn6HUkJbv8+xsTfaW/iUn7tXwFAeyM3mXT7eHlLQbdq855dfo94ELCtHKWBLytvnll+pt/O6p/lCun/BE5kwQCgUAgEAgEAoFAIBAIBCVGOJMEAoFAIBAIBAKBQCAQCAQlRmxzEwgEAoFAIBAIBAKBQPCfRi22uZUKEZkkEAgEAoFAIBAIBAKBQCAoMSIySSAQCAQCgUAgEAgEAsF/GpGAu3SIyCSBQCAQCAQCgUAgEAgEAkGJEZFJghfGgf0XmTIlGK1WS99+LRg8uJPO9aysbEaO+JWLF+9ibl6JOd8OonJlawAWLdrOurWHUKlUBI5+HV9fr7z7NBotfftMxdbOnEWLPgHgyJErzJyxDq1WxsioIlOn/Y8qVWxLJe+F49EEzz+LVivT8rWqdBrgqXM9dNNN9oXcRKWSqGiox1vDG+LoaloW1RShrXN9JrYchFqlYuWlXcw/s17nupOxNd/7f4ZphUqoVSqmHFnOnrunsKhowpJOI6hn68bqK3sIPLDkuWWJCkvgxPIbyFoZtzYO1O5eRef6jf33ObXqFkYWFQDwbO+Ee1tHAP6aHkbczWRsPczw/6rOc8nRtkoDprT+ALWk4veLu5h3cq3OdScTG+a1/xyziopOJh36jd3hp6hv585s/08BkJCYeWwlW28eLbMcJw/fYeHsg2i1Wjr1qMXr7zbUuX7+9D0WzTnA7RsJfDOlA77+bgCEnYxk8ZyDeeUi7iTxzZQONG9TrcyynD4SwZJvj6LVyrTvXoO+79TVuR6y8jw7N11FrZYwszBkaKAvtg4mAIz/fDvXLsRSs64dY2Z3LLMMz+LntwPpWrsFsSkPqD1p4EurB0CWZabM2EzooasYGOgzbUI/vGo6FSl34VIkAePWkJGZQ+sWNQgc0Q1Jkvh85Epuh8cBkJKSjomJISGrPytd/UFrCd1/EQPDCkwLehuvWs5F6794l4BRy8nIyKZ1Ky8CR/VFkiSmz9zA3n0X0NdX4+JszdQpb2FqasS5c+GMGbdKqQMY+slrtG9BUvCIAAAgAElEQVRXt8hzi+P8sfusmn8GWSPj26Uarw2sqXN9R/BVDmy5hVotYWxekf8b0QRr+0oArFkUxrkj9wDo9o4XTfxcSqyLF6GPbdtPM/+Hrdy8FcOa1V9R21uxPVlZOYwbv4oLF+8iqVQEBvShaROPEst17NBNvp+xE61Wpmuverz1XnOd62dP3WXuzJ3cuh7LuGm9aNte0dn1K9HMDtrOo9RMVGoV7wxqgX/HWmXQyRpFJwb6TAt6By+vonq9cPEuAQHLyMh8rJN++TqZv4Wbt6JZEzwiTyeRUQm81mUiVavaAVC3risTxw8ooT5uMX/mbjRaLV161mXgez4618NORTB/1m5uXo9l7NTutGmv9IXR9x4y5ssNaLUymhwNvfo3pEe/+qXSR2HKal8Bfp57mOMHw5FlqN+0Mh9+6YsklW0J+fSRSH7+7ihajUy77h70KWxbV13gr03XUKslTM0N+DTQF1sHY25fS2DhzMOkP8pGpZLo+25dWrYru41v59aYGa99glpS8dvprcw58IfOdWczWxb0+hprI3MepCfz/rqp3EuOB2DD21NpXLkWR+5eoN+KwDLL8JjbpxPYvfQaslamTjtHmvZ2Lbbc1SOxbJp5nrdnNMbeLX9MlByXwdLPjtL89ao06Vml2HufhCzLTJm6jtD9lxRbMmXgk21J4IpcW1KLwIA+im2dtTHXtuoptnXyAExNjcjO1jB67CouXY4gR6OlZ/fGDPmgwxPlOHzwKrOmbUarkenZpzHvDmqjcz0rK4dxAcFcvhSFmbkRU2e9iaOTJQDXr94naOIGHqVmIKkklv3xKRUr6ufd+8WnvxEVmUjwxi9KpRtQbNrcmX+h1Wrp0rMeb73XTOf62VN3mTfrL8WmTe2Z9w5fvxrDnCnbefQoC5Va4u33m5faphXkyslYNi24hFYr06STM35vuOlcP7LlDoc330FSSVQ0UNP3s9rYVVHGJPduJbNu7nky03KQVBLD5rZAv4K6THI8V7+3MIxzR+8ha2VqNbLnzaH1y2xHAM4cjWTpd8fQamT8u3nQ+x3dsfCmVRfYvfkaKrUKM3MDPh7VElsHY2LvpzIjYDeyDDk5Wl7rW5OOvTyfUEs+z9vHJCU94ovhPxMVlYCTkxXffTsIMzMjHj5MY1Tgcu5GxFGxoj5Bk9/Gw0MZ6/+2bA9r1hxClqHf6xG8++67ZdaX4N+HiEwSvBA0Gg0TJ65iyU+f8ueWcWz58wQ3btzTKbN2zSFMTY3YuWsS/3vXn9mzNgBw48Y9tm45wZ9bxvLTT0OZOGEVGo02775ly/ZQrbq9zrPGj1/JzFnvsTFkNF27NmbBgq2lklerkVn1/RmGTmvJ+F86cmJPBPfCk3XKNPF3YdzPHRizpD0d36jBmgVhparjSagkFUGthjBwy0RarxpKT3dfPCwq65T5vOHrbLpxiA5rhvPRzllMazUEgAxNFjOOrWTi4V9fiCxarcyx367jP6IO3Wc0IfxoLElRj4qUc/WxoVtQY7oFNc5zJAF4dXGh5Yc1i5QvLSpJxfQ2H/LmxvG0XP4JvT1a4WGpO5D8ovHrbLp+EP9VnzN420ymt/0IgCsJd2m/6gv8Vn7GGxvHMdNPGZiXBY1Gyw8z9jPp+64sCh7Avp3XuXMrUaeMrb0xX47zp21H3clt3UaV+WFlf35Y2Z9pC3pS0UCPBj5FB8OlkWXRrMOM+7Yj81f14cDOm9y9/UCnTNUaVsz5tSdzV/SheVtXfp1/PO9ar4G1+Xxc6zLXX1J+PbKFTvNKPzguC/sPXiX8bjw7Q75i0ujejA/aWGy58UEbmTSmDztDviL8bjz7D10D4LvpAwhZ/Rkhqz+jg7837f28ir3/ifXvv0T4nTh2bh/HpAlvMn7CH8WWGz9xNZMmDmDn9nGE34lj/4FLALRo7smfIaPYvHEUrq62LFqyEwB3d0fWrRlByIYAflr8MWPHryInR/NMebQaLSu+P8UX01sx6bdOHNtzh3vhD3XKVHE3Z8yi9kxY2olGrZ1Zu0ixY2FH7nH32gPG/9SR0Qvas2P1FdIfZf+t+vBwd2Te3A9o3Ki6Tvk1aw8BsDkkkF9++pTpMzag1WqLPLc4NBotc6ZuZ9YP/Vm+fgh/bb/I7ZtxOmXs7E0ZNbEb7Tp763xe0VCfwEndWb5+CLN/6M/cmTtJSc4oUb2P2b//IuF3Ytm5fTyTJgxk/MQn6GTCKiZNHMjO7eMJvxNbQCcOzJs3mMaN3Irc4+JsTciGUYRsGFViR5JGo+X7abuYPr8fv60bxJ7tlwi/Ga9TxtbBlG8mvEa7TrqTTCsbY3747S1+Xv1//Lj8HVb+cpT42JQS1fskWcpqXy+F3edS2H1+XNWfBX/059qlWM6f1h1jlEaOxbOPMGZOB+au6s3BXbeIKGRbq3lYMeuX7nz3ey+a+7my7IcTAFQw0OOzsa2Yu7I3Y7/tyNLvjvEoJbNMcqgkFXO6DqP38gAazX+PfrX98LTRdcIEdfyQlWd34fPjB0zbt5wJ7QblXfv+UDAfrJ9WproLo9XI7Fpylb6j6/He9z5cPhBDfERqkXJZ6Tmc3hKBg3vRhbW9v1yjan2rMtW//0CuLdk2hknj32D8xOBiy42fGMykCf3ZuW2MYksOXgagRbMa/LkxgM0bvsG1ig2LluwCYPuOM2Rl57B5YwDrg79mdfBhIqMSin22RqNl+uQQ5i74P9Zs+oIdW89y62aMTpmQ9ScwMTVk47avGfB2S+bN2Q5ATo6GMd+sJmBMT4JDhrPol8Ho6eU7SvbsuoCRUYUy6Uaj0fLttJ3MnP86y9YNZncx77CdgymjJnSlXSfdPs3AQI9Rk7qxbN0HzJr/BvNm/UVKSuls2mO0GpkNP1zk/clN+Gpxa87uu0fMHV17UL+NI18ubMXwH31p0686mxZfzvsOq2acpc+w2ny1uDUfzvBBrS7bOO15+r0bF+K5cSGOCT93ZOIvnbh9JZGrZ+OKq6ZEaDRalsw6SuDsDny3shcH/7pFxO0knTJVPayYsbQ73y7viU9bV5b/qNgSC2tDpi7uyuzfejBtSVc2LD9PYlzaM+t83j5m8ZIdNGtWg507JtCsWQ0WL9kBwMLF26lZszKbQ0Yzfdr/mDJ1DQDXrt1jzZpDrAkeScjGUezbt48aNWoU7aD+RaglqVz//dMQziTBC+HcuXO4VLHF2dmGChX0eK1LY3bvPqdTZveec/TspaymdOzYgCNHriDLMrt3n+O1Lo2pUEGfys7WuFSx5dy5cACiox8Quu88/fq20HmWhERqqtIhpqRmYGtrXip5b19JxNbJGBtHY/T0VTTycybssO7A1LBS/opSZkYOL+r9rm/rTvjD+9xNjiFbm0PIjYN0rNpUp4yMjEkFQwBMKlQiOk0ZcKfnZHI8+jIZOaWb9D2JhJvJmNgZYmJriFpPhauPLRGn4p99Yy4O3hboG5RtZakgDezcuf3wPndydbLh2n46VWtapJxJBSMATCsYEZOarxONrEw0DfQqoMR2lI1rF2NxdDbDobIZ+vpqWrd352jobZ0ydo6mVHW3fupK1oHdN2nUzAUDA/0nlnkW1y/FYV/ZFHsnU/T11fi2r8bx/Xd0ytRp6EhFAyXAtIa3LQmx+Y7Auo2dMDQqe/0l5cCNsyQ+Sn52wRfA7tBL9OzaAEmSqFfHheSUdGLjdOuOjUsm9VEm9eq4IEkSPbs2YPe+izplZFlm267zdO1Ur3T17zlHzx5NlPrrVs2tX3cQGxv3kNTUDOrVrarU36NJni1s2aJm3gSjXt2qREcrg05Dwwp5n2dmZpd4lfTWlURsnUxy7ZiaJn4unDkUpVPGs75dXhupVsuKB7mD1ft3kvGoa4NaT0VFQz0qVzfnwvH7f6s+qle3p1pupE1BbtyMpqlPDQCsrEwwMTHkwoW7JZLp8oV7ODlb4ljZAn19Nf4da3Fw3zWdMg5O5rh52BXRs0sVK5yrKFEG1rYmWFhWIunBswf3BVF00lTRSb2qJCenERtbSCexuTqp91gnTdm9W5nsVK/uUKxOysqVC/dxcjbHsbI5+vpq/DrW5NC+6zplHBzNqO5hi1QoUYS+vpoKFZS2k52lQZbLblvh+eyrJElkZWnIydaSna1Bk6PF3NKwTHJcvxSPQwHb2rJdNY7v121ftRs65L03Hl75ttXJxQxHZzMALG2MMLMw4GFS2SbnjSp7cisxivAH98nW5LD2/F66eOpG0XnaViH01hkAQm+f1bm+79YZUjNL1z6fxP0byVg4GGJub4haX4VnSztuHC86Fji48hZNelZBr4Lu9OH6sTjM7Ayxdq5Upvp37zlPz+4lsCWPCtiS7k+yra5Exyi2VZIk0tMyycnRkJGZjb6+GuNKBsXKcPF8BM4uVlR2tkJfX48OnesSuueSTpnQPZfo2qMBAP4dvDl+7AayLHP08HXcPezx8FQW28zNK+U5S9LSMlmx7ADvD/Erk24Um2ZRwKbVLGrTHM2LfYedC9s0i0okJZatzdy9moS1gxFWDkbo6auo19qRi0d0nW0GBcbNWQXGzddOxeNQ1QTHaooTspJpBVRlPDLrefo9JMjO0pKToyU7W4smR4upZfHtoSTcuBSPfWUT7J1M8mzJiQNPsyU2JMQqsujrq/Mis3KyS25fn7ePUe5XolN79vDhr9zPb964j09Tpd+tXs2eqKgE4uOTuXkrmjp1XPPGKY0bNwboXXptCf6tCGeS4IUQExODg71F3u/2dubExOiu9MXGJOHgoJTR01NjYmJI0oNHxMQ8eOK9QUHBfPV17yId5OQpbzF48Hxat/qGTSFHGTy4dNt4kuLTsbDNH4haWBuSFJdepNzejTcIHLiN9YvP88anpZt4Pgn7SpZEpeYP0u6nJmBfyVKnzKwTf9DHow2n3vmJ37uMYfQL2M5WHGkPMqlkWTHvdyPLiqQ9KLrKevd4PJsCTrDv+ws8SijbwPlp2BtbEZWiqxMHY90VzhlHV9LHsw1n3/uFlT3GExC6KO9aAzsP9r/1A6ED5/H1nh/znEulJT4uFRs747zfre2MSYgrGqn1LPbvuk6bjiXfllMcCXFpWNvmD8ytbCuR8JRVq12br9GwWdkjof4JxMQmY2+f7zi2tzMjJja5aBlbs6eWOXn6NlaWxrhWsS5l/UnYF7FVuquQMTFJ2NuZ65aJ1S0DsG79EVr55keChIWF06XbZLr3CGLCuP46q9pPIikuHUubAnbMxqhYO/aYg1tu4d3EASDPeZSZkUNKUiZXzsSSGFu6ScaL1EdBPGs4sWfPeXJyNERExnPxUgT3ox889Z7HxMWmYGtvkve7jZ1pmaJpLp2PIidbg5OzxbMLFyAmppBO7C2KfN+Y2MI6sSiit+KIjEqgZ+8g3np7DidP3iiRPHGxKdjY5UeR2NiZEBdXNOLkScRGJ/Pe60t5vfOPvPmuD9a2Js++6Qk8j32tWceeOg2dGNj5FwZ2+pUGPi64VLV89o3FkBj3qFS29a/N12jQrHKRz69djCM7W4u9U9m2vzuaWBP5MD8qIio5DkdTXZt0PvomPWr5AtC9ZktMDSphafhittsXJDUhAxOr/Em1iVVFUhN1xwIxN5NJTsigeiNdGbPSczi2IZzmr1ctc/0xsQ8L2XZzYmJ0J8gxMQ913xt7c2IKTaIB1q0/mmdbO3aoh6FRRVq2GU3bduN4710/zM2Ld3jFxiZjZ5/fd9jamRFbqO9Qyigy6OmpMTY24GFSGnfvxIMk8engnxnYby6/LQ3Nu2fBvJ289T/fMi8uxcemYlvkHS6DTbtwj+yc0tu0xyQnZGBeoL8xszbgYTHjwUObwpn6f3vZ8vMVenykRErFRz1CkiSWjDrGd58cYO+am2WSAZ6v33PzsqZGPVuG997El3024d3EHscqZX+fEuPSsLbLb0+WNkZPtWm7/7xGA5/8rfnxMal88fZGBvcMpudbtbG0MXpmnc/bxyQkpGCbO0aysTElIUFpS56eldm56ywA586Fc+9eItExSXi4O3Dq1E0ePEglPT2L/fv3A/yrB5sqqXz//dMQziTBK8veveewsjTB27vo3vvfft3N4sWfErp/Gr17N2fa1LXFPOH5advTjSkrOtN7cG22/n7lpdRRHL3cfVl9ZQ8Nlw3irS2TmOf/ORLlY2Eq17em93c+dJ/aGEdvSw4t+vv0UJDeNVqx+tJu6i39PwaEjOeHDsPzdHI65hqtfv+EDn8MZ1ijflRUv/yInCeRGP+I2zcS/lbHzr5t17lxOY5ebz1f3qr/Cn9uD6Nrp5LlJHoZLFi4HbVaRfdujfM+q1vXlS2bR7M2eASLluwkM/PFRB8+5sjOcMKvJtKpv5KTwbuxPbWbOjL1k90snnSE6l5WqF6RUUyf3s2wtzenT78ZBE1dR/16VVGr/r7hSnxcCpNHbyJgQrdXRie2Nqbs3T2ZjetH8c03ffny66Wkpj55AvXC6rU3ZWnwe6wIGcyOzRdITCi9c/1FcC8iiYjwByzf8j9+3/o/wk5GcuFM2ba5lYZ9229w80o8PQfW1vk8MT6N7yeGMnS070ttI6N2LKKlax0OfbSQlq51iXoYh0Z+9hbYF42sldn763Xavute5Nqh1bdp1M2FCobln4Z1waIdqPXUdO/aCIBz5++gUkkc2DuZ3TvGsfS3vURElDz6uqRocrSEnQln8vT+/LzsQ/btvsjxoze4euUekRGJtG3n/eyHvETi41KZMnozAeO7vHSb1qK7KwG/tKXL+57sXqVEQWo0Wm5fTGTAyPp8PLs5Fw5Fc/3Mi/87FKZwvxcTmcL9u8nMWtONWWu6cfl0LNfOlX2bW2kI3X6Tm1cS6FHAlljbGfPt8p78ENyXfVtvkJT48m16QSRJyoseG/xBB1JS0ujRK4jlv++jZs3KqFUS1as7MGhQe94fNI9BH8zH09MT4O83QoJXlvK3/IJ/BXZ2djorx9ExSdjZ6a5+2NqZc//+A+ztLcjJ0ZCSko65RSXs7CyKvXfPnjD27DlH6P4LZGXmkJqaztdfLSVgVD+uXImkbl1lFazza434YNDcUslrbm3Ig9h8o/0gPl1nxaUwjdo6s+K700DjJ5YpKdGPEnEyzl/ZczC2IvqRbt6IN2u2Y8CfEwE4FXOVimp9LA1NSUgvugr3PBhZVORRgdXHtMRMjCwq6pQxMMl3zLi1deDUH2VfUXoS0akJOJno6uR+qm5egwFeHei/cRwAJ6OvYqBXAStDU+IL6OT6g0geZafjaVWFsNiSrdwXxNrGmLiY/FX7+JhUrGxKF7a/f9cNmrepVqLIkqdhZWNEfIFtawmxj7AqZtXq7PEo1vx6likLupY5meWrzIrVRwher+SCqu1VOW9rGEB0zEPsbHVXFe1sTYkusFpduExOjoZdey6yfuXQktW/MpTgNYeV+mtXIbqIrdLdYmtnZ563xSKvTIFtuOs3HGVf6AV+XTqs2O1s1avbY2RUkWvX7+UlX34S5jaGJBZYkX0Ql1asHbt0Mpotv19ixPd+Om2k69u16Pq2soK/eNIR7JyfHXXyovVRHHp6akZ90yfv9/4DZuPqWrIDFmxsTYiNzl+1j4tJLlU0zaPUTEYMXc0Hn7bBq07R5O7FsWJFKMG5eZ5qexfSSfSDIt/XzrawTh4U0VthKlTQp0IFxRZ7e7ng4mzD7fDYZ7YRG1sT4mLyoyviYlKwsTF+yh3FY21rQlU3a86djshL7lvqZzyHfT287xae3nYY5uadadSsCpfPR+Nd3/EZdxbF0qZSiWxr2PEo1v4axuQfX9N5b9IeZTHly10MHNKQGt6lO/ijIPdS4qlsZpP3u5OpTV5y7cdEpyQw4I/xAFSqYECPWr48zHjxDj1jKwNSCkSZpCRkYlwgajkrXUP83Uf8MeY0AI+Sslg/NYzeAXW5f/0h147EErrsBpmPcpBUoFdBRYPXnr6gsmLlfoLXHgGgtrdLIduehJ2dmU55Ozsz3fcmOgm7AlGo6zccY1/oRX79+dM82/rnlpP4tqyJvr4aKysTGtSvyvmLd3F2LhqVamtrSkx0ft8RG/MQ20L9i1ImCTt7M3JyNKSmZmBmboStnRn1G1bF3EJpzy18a3DlUhRGRhW5fDGSbh2modFoSUx4xOB3F7H41yFP1U1BrG2NiS3yDpfOpo0cFswHn7QusU0rDlMrA50IoIfxGZhZPXmLWN3WjqyfdwFQxtzValtSySz3IJfGtkTdeIh7/dJFB8Pz9XtnDkZRvZYVBrkpAGo3deDmxXg86tgUub8kWNoYER+T/z4mxqUVa9PCTtxj3W9hTPqhc7HjNEsbI1yqWXD5bAzN/FyLXN+27jKBW3oAqc/dx1hZmRAb+xBbWzNiYx9iaam0JWNjQ6YGvQMoqQD8243Je0/69W2Rl25kzndhALr7LAX/aURkkuCFULt2be6ExxIZEU9WVg5bt5zAz083SsLPrw4bNygDhx07TuPjUwNJkvDzq8PWLSfIysomMiKeO+Gx1Knjypdf9iJ0/zT27Ali9pz3aerjycxZ72FqakRKSjq3byt7tQ8fuky16g6lktfV04LYqFTi7z8iJ1vLyT0R1G2m+4yYyPwJyfmj97F1Knt4f0HOxl6nqpkDzia26Kv06OHWkh23j+uUiUqJo6WToj93i8pU1Kvwwh1JAFbVTEiJTiclNh1Njpbwo7E4N9Dt3Atue4s8FY+Z47PDcEvLmZjrVDN3xMXUDn2VHr08WrHjVlGd+Dor0STuFpWpqNYnPv0hLqZ2eQm3K5vY4G5RmYjk2DLJ4VHLlnt3HxIdlUx2tobQXdfxaeVaqmfs23mdNh2Lrt6WFveaNtyPSCbmXgrZ2RoO7LpFE1/dieOtq/EsmH6QwJkdypw/5FVn4BvN8pJmt2vrxcY/TyPLMmfP3cXE2ABbm0KDfRtTjCtV5Oy5u8iyzMY/T+PfOn872eFjN6jmaoN9oYnKE+sf0JqQDQGEbAignX8dNoYcV+oPu42JiSG2NrrPsbUxw9jYgLNht5X6Q47jn2sL9x+4xE8//8WCH4ZgaJifjDUiMj4v4XZUVCK3bkXj5PTsRLZVa1gSE5lC3P1UcrI1HN9zl3rNdScLd64/YNmckwwN8sXUIn/gr9VoSX2ovNsRN5OIuJmEVyPdgw5etj6eRHp6FmlpimyHDl9GrVbh5lYyG+/p5Ujk3UTuRSWRna1h945LtGxdsi2n2dkaRg1fS6eudfJOeCsJAwe2zkuMrejkmKKTs7k6sS2kE9tcnZx9rJNjz9RJYmJK3sEUERHxhN+JxbnysydiNbwciLz7gPu5+tiz4zLN25Qsd2psTDKZGUqEXEpyBufPROLiWrYEy/B89tXGzoTzp++hydGSk6Ph/OkonF3Ltl3HvaY19yMe5tnWg3/dorGv7mlIt64msGDGYUbNbKdjW7OzNUwbuZs2nd1o7lf2bV0Ap6KuUN3SiSrm9uir9ehbuy1brxzWKWNlZJrnGPnKdwDLz2x/rjqfhIObCQ/up5EUk44mW8uVgzG4Nc5vXxUr6fHpb60YsqgFQxa1wNHDlN4BdbF3M2XAlEZ5nzfs6kzT3q7PdCQBDBzQipD1IwlZP1J5bzYVsCXGBsXbkkoFbMmm4/j7KVEe+w9c4qelf7Fg/gc6ttXBwYJjx5TomLS0TMLCwp+Yk6yWd2Ui7iYQFZlIdnYOO7eF0aqtblL6Vm1r8WeI4lDbvfMCjZtWR5IkmrVw58b1aDLSs8jJ0XD65G2qVbejb38ftu8NZPPOb/hp2Ye4uFqXypEEj23agwI27TIt2pRsjJGdrSHwy3V07OpdZifwY5xrmBF/7xGJ0WnkZGs5G3qPWj66uowrcIDLleOxWDspjhWPhjZE304hK0ODRqPl1vkE7FxK79SG5+v3LG2NuHo2LteOaLkaFovDc2xzc6tpzf3IZB1b0qilbtu/dTWBRdMP880Mf8wK2JKE2EdkZuYAkJqcyeVzMU/ccte5T01CQkJeSB/j51eHjSHKiccbQ47mfZ6cnEZWliLPmjWHaNTIDWNjRd7HW+Hu3Utk586dACvLrLR/AGqpfP/90xCRSYIXgp6eHmPGvsH7g+ai1Wjp06c57u6OzP1+E97eVfDzr0vfvi0Y8fUvdGg/BjMzI+Z8q5xK4u7uSOfODeny2gTUajVjx/Z/6ikPenpqJk1+i2HDFqGSJEzNjAjK9aaXFLVaRf+h9fh+5AG0GpkWnV1xrGrGpl8uUsXDgrotHNm38SaXT8Wi1pMwMqnA/41s9Fw6eoxG1jLqwBJWdRuHWlLzx5W/uPYggq8bv0lY3A12hp9gwuFfmNnmEwbX7YYMfL4nP/Lq+FuLMa5gSAW1Hp2qNuXNzeO59iCyTLKo1Cqa/M+dv2acQ9bKuLV2wLxyJc6uvY1VVROcG1pzZWcUEafjUaklKlTSp8WQ/AHJ9olneHg/jZwMDWuHHqbZB5441Sl9HguNrOWbfQtZ3XMCaknFykt/cTXxLiN9BnI25jo7bh9n3IGfmeP/KR/W74GMzLBd3wPQ1LEWQxv1JUebg1aWGbl3IYkZZUsIrdZT8dEIX0YP24RGI9Ohe02qVLdi2cJjeNS0xad1Va5ejGHSiG2kJmdy7OBtfl90nEXByslKMfeSiY9JpXaDsq/+FZRl8FfNGf/ZNrRaGf+uHrhUs2DF4lO4eVrTtFUVfpl3nPS0bGYE7gaUkOnRs5RjjwOGbCbyzkMy0rN5r9tKPg1sRQOfonk/npeV702kjUcDrI3NiQjaxLg/l7D08OYXXg9A65Y1CD14hfbdZ2JooE/Q+H5513q88T0hqz8DYFxATwLGrSEjM5tWLWrQqmWNvHJbd4TRpYxb3Fq38iJ0/0Xad5qg1D/lrfz6e00lZEOAUv+Y1wkY9btSv28tWrVSJiSTJgeTlZ3D/70/H3h8vPubnDp9iyVLdkpHeC8AACAASURBVKKnp0alkhg/5g0sLZ490FbrqRj4WQO+/ToUrVamZedqOFU1Y+PS87jWsKReCyfWLAgjMz2HBeOUSaqlnRHDgnzR5MhMG7YHAEMjPT4I9EGtV7r1pefVx66/wpg0ZQ2JiakM+WghNT2d+HnJpyQkpvD+Bz+gUknY2ZozY9r/SiyTnp6KL77pyJcfrVKO0e5Rl6puNvz0YyietRxo2caDyxfuETh8LSnJGRzef52lC/azfP0Q9uy8RNjpuyQnpbFtk5KUdNTEbrh7PtvJlqeT1t6KTjqOw9CgAkFBbxfQSRAhG0YpOhnbP+/Y5la+XrRqpeQW2bXrLJOmBCs6+fBHanpW5uefhnLi5A3mzv0TPX01Kkliwvg3n5j7pbA+PhvZnq8/DkarlencozZVq9uw9McD1KhlT4s27ly5eJ/Rw9eTmpzJkf03+HXhQX5dN4i7txP4cc5eJJRjDd54pwnV3Mu2gg/PZ19b+lcn7GQkH735B0jQqJkLPq3K5sxR66n44MtmTPh8R65tdcelmgUrF5/GraY1TXxd+G3+cTLSspkZuBcAG7tKjJrZnkO7b3PpbDQpyZns2ao4KYaN9qWqR+mdbBqtli+3zGPjO9NRq1QsP72Ny3F3GO33LqejrrL16hF8Xesxvv37yDIcunOO4X/mjwV2vv8dHtbOVKpgyNUv/+DjkFnsvnGyTDpRqVW0G1SDtRPPoNVCbX8HrF2MObjqJvbVTXFrUva/e0lo3aqW8t50nqi8N5MH5l3r0Xs6IetHArm2JHAFGZlZtGpZKy830qQpaxXbOuhHINe2jnuDgW+2ImD0Crp0D0KWZXr38sGzRvH9s56emq9HdWfokKVoNFq692pEdTc7Fs7fSU2vyrRuW4sevRsxNiCYnp1nYmpmSNDMNwEwNTNi4Du+vNN/PkgSLXxr0LL18zlv8uVS8fnI9nz18R9otTKv9ahD1eo2/PzjfmrUcqBlG3cuX7zH6OHr823awgMsW/cBe3deJux0BMlJ6WzfdB6AgIldca9R+iT/arWKnh97syTwOFqtTJMOlbF3NWHHsqtUdjfHq5kdhzeFc/1MPCo9FUbGerzxpdLXGpno49u7KnOHHQRJiUyq2bRsBw08T7/XqHVlrpyJYdx720GS8G5iX8QRVVpZBg33YdIXO9FqZPxybcmqJadx87Smsa8Ly344QUZ6NrNH7wPA2q4SATPaERmexK/zTiBJIMvQ/U1vqlR/9vj5efuYwYM68Pnwn1m79jCOjpZ8lzsXu3kzmm8CloEE7m4OTJmc/9yhny0mKekRenpqxo2bQrNmzZ6d5E/wn0F63tM5BIJcZJm95S0DEm3ZFxVY3mLQxmkKDj/2LG8xuP+xcnT6lBOlWwl7GQQ2XoTt993KWwxiP9vMreTSbYt8GVQzHQbAlQczy1kS8LT4Gukjn/IWA3nBUUjbUN5igFEv5X/NrvKVA0DdnoP3x5a3FLR0mPjK6AMgNn1ZOQsCtobvgHZ3eYsBKn/upy0tbylwMHoP4JWxr5cSp5e3GNSyHInxWP/yFoPUiUo7/enix+UsCQzy+hFydpS3GKDXkZTs8u9vTPSV/iYm7dfyFQSwM3qXTbeHl7cYdK8659Xp94ALCdPKWRLwtvrmlelvoJySuP5NLL/ySbk6R972/OEfpV+xzU0gEAgEAoFAIBAIBAKBQFBihDNJIBAIBAKBQCAQCAQCgUBQYkTOJIFAIBAIBAKBQCAQCAT/af6JSbDLExGZJBAIBAKBQCAQCAQCgUAgKDEiMkkgEAgEAoFAIBAIBALBfxqVJEKTSoOITBIIBAKBQCAQCAQCgUAgEJQY4UwSCAQCgUAgEAgEAoFAIBCUGLHNTSAQCAQCgUAgEAgEAsF/GrHNrXRIsiyXtwyCfweiIQkEAoFAIBAIBALBv5d/tbcl+PrQcp3Tvu4+7x+lXxGZJHhxaHeXtwSg8of0kPKWAgx7QMqa8pYCTPoBIMcuLmdBQLId/OroRLOrvKUAdXvl/8wt5SsHQMUukLahvKUAo15IH/mUtxTIC47m/rC3fAUBkNqSoy3/9qqnao9M+etDoi3AKyOLVi7/fk8l+b8ybRWA7G3lKweAfmdhWwti1AvglbEl2dod5S0G+qqOpOVsLm8xMNLrBvDKyJKpKf/3t6K68ysjB/DK2NdXpr8RCAognEkCgUAgEAgEAoFAIBAI/tOIbW6lQyTgFggEAoFAIBAIBAKBQCAQlBgRmSQQCAQCgUAgEAgEAoHgP41KErE2pUFoSyAQCAQCgUAgEAgEAoFAUGKEM0kgEAgEAoFAIBAIBAKBQFBixDY3gUAgEAgEAoFAIBAIBP9pRALu0iGcSYKXhizLTAlaQ+j+ixgY6DMt6B28vFyKlLtw8S4BAcvIyMymdSsvAkf1Q5Iktm0/zfz5W7h5K5o1wSOo7V0FgEOHLjN7zkayszXo66v5+uveNPOpUXKZZmwi9OAVRaaJr+NVs3JRmS5FEjA2WJGppSeBI7oj5RqX5asOsWL1YdQqFa19PRnxRZeS1z1rC6GHril1j++Dl6dj0bovRxEwfr1SdwsPAr/qgiRJXL56n3FTQ8jMykGtVjF+ZHfqeFdm07azLPntAMhQqVIFxn/THU8PhxLJlCfX93vZf/Q2BhX1mDqqE1417IqU+3bxQUJ2XCQ5JZPTO4cVub5j3zU+G7OZNUsGUtvTvlx18te+y3y/8C9UKgm1WsWoL1+jUT3Xp8sRtFZpq4YVmBb0Nl61nIvKcfEuAaOWk5HxuK32RZIkps/cwN59F9DXV+PibM3UKW9hamrEg6RUhn3+MxfO36FXLx/Gjn69ZDqZvoHQA5cxMKjAtElv4lWruDYaQcDoVYpOfGsSOLJXXhsFWPrbPqbP3sSR0IlYWhgDcOzEDYJmbCQnR4OFeSV+/+XTZ8qTJ9OMzYQeuqr8nSb0w6umUzEyRRIwbg0ZmTm0blGDwBHdkCSJz0eu5HZ4HAApKemYmBgSsvqzEtVdGn5+O5CutVsQm/KA2pMGlu0hsszkyZMJDQ3FwEDDtKn/K95uXbhDQMBvuXbLm8DA15EkiaSkR3wxfAlRUQk4OVnx3bcfYGZWSdHhlGBC919Q/q65z42KSuDToQvRamVycjS89VZb3uzfSqeuTz5eSGREAiGbAwE4cOAS04LWotFq6dO3OR980EGnfFZWNgEjl3Px0l3MzSsxe857ODlZkfQglc8//5kLF+7Qs6cPo8fkt8dtW0+xeNEONBotrdt48+VXPYtVz4H9F5kyJRitVkvffi0YPLhTkbpHjviVixeVuv+fvfOOi+L6+vAzNAUVUKRYsHdFjbEbCyBqbGBLTEw0saXYYg+IiqKosaUbS0wxagQpC6KIgoI9UaOILbFQLBQFQZC27Lx/zLqwsBQjqPm98+TjJzpzZ+53zzn3zMyde+9s2DiZ+vVrA7B5cwi+e0+gp6fHIve36N27Lffvp7Bwwc88fJiOIAi89dYbjJ+g/enh7dsP8cUaX06dWketWpWn5Sn5+SpGj1qFlbU5mzdPA2CR269ER8ciitCosRWrVk2gejUd9jl2Ga+VPqhUIqNH92TK1IHFNS38hSuX4yVNGyZRr74FqakZfDZrK9HRcbi4dGfxkrc1x0yZ/C3JyWko81V0fr0pi5eMRV+/+MDykmKsKM8au4FBZ9i6NRREkWrVquLh8S6tWtXn1q0EZs/ZpjlvfPxcZs6cyQfjtK8foiiycpWfOqcZsnrluyXk13hc3XdJ+bV3axa5jkQQBL78Zj9h4ZfQ0xOwqFWDVSvfxdrKTHNc1KU4xr73JRvWjmfQgI7FnVJYh5xbtfi3ueTkiats3BBIXp4SQ0MD5s53oXv3lmRl5TLnsx+Jj3+Anp5AP3s75sx1LlPH8WNXWO3lp9bRg8lTnHTo+I0rV6R2s27DB2od1/hyQ6DmfnDufBe6dW8BwAfjv+ZBcjpVqhoCsGXbp1hY1ChVx4lj11i7WoEqX4XLqG5MnOJQRIeSxa67uXr5DmbmJqxZ/z5169Xi3t0URg77goaNrACw69AA96WjtY6dNW07d+88ZK9ifpn2qCwt06ZuJTk5nfx8Fa+93hhX95E6c0lhjh+7yppVfqjyRUaO7s6kKf2L6Vj0+W9cUetYu2EC9epZcCkqluVL9wAgAp9MG4Rj//YA/LYjAl+fUyDCyDHdeX98vzLtUdE6cnLy+HD8N+TmKslXqug/oAPTZrxZYv2VlV/PnLnOp9M2aa5NTk6vMX3aEJ35dcbMN5kwoSAOKuN68+VGBQrFGdLTszh3fmOZfpGRkae5yVQakZGXiYlNIjTEA89l4/BY/rvOch7LduO5fByhIR7ExCYReewKAC2a1+Gbb6bSpXMzrfI1a1Zn06ZPCAp0Z/WqCSxY+HP5NR2/RkzcA0IDF+C5eBQeK/11a1rpj+eSUYQGLiAm7gGRJ64DcPrPG4QdvUyg92yC/eYyaULf8td94m9i4h8S6j8bz0UueKwK1F33qkA83V0I9Z9NTPxDIk/+A8Dar0OYNsUBxa7pzPrIkbVfhwBQv24tftsymaA9M/hkkj2LVyrKrQkg8vRtYu+kcnD3RJYvcGLZ+sM6y9n3aoL3Zt0P6BlPctmx9zwd2pS/EwsqzyY9ujYhcPd0FLum47VkJO6eAaXriLxCTGwyoSFL8Vz2Dh7LSojV5XvwXP4uoSFLiYlN1sRqr56t2KdwIyjAjUaNrNi8NRSAKkaGzJoxlAXzR5TfJsevEhP7gNB9bnguGYPHir26tazYi+fStwjd50ZM7AMij1/T7LufkMqJU9epW6emZlt6ehbLVvqy6etJBPsv5Kt1E55B03Wp3Sjm4ek+Eg8v3fb08ArAc/EoQhXz1O3mbwC+XPMuij2zUOyZxQDHdjg5tNV5/PPy86lgBn0z+7nOUe2hkpiYGEJDQ/FcPg6PZbt0lvNYtgtPz/cIPbhcnbcuA7Blawg9urci9KAnPbq3YsvWgwBERkZL+fDgcq3zWlqasef3BSgC3PHes5CtW0JITHykqSc0NBQTkyqaf+fnq1jp6c0PWz4lMMid/cHnuHHjvpY2372nMDUzJuSgB+PH27NhnZQTjKoYMmPmUOYXicdHqRmsWxfAjz/NIHCfOw8epHP61PVivzk/X8Xy5bvZum06+4KXErzvT27cuKdVZq/PCUxNTQg95MmEDxxZv07KsTdu3GN/8J/sC17Ctm0zWL5sN/n5KvT19Vn4+WiC93vw+56F7NwVoXXO+/dTOHHiKnXr1qp0LU/59ddwmjTV7hB3dRuDInAxgUGLqVOnFjt3HtVpH8/le9iydTpB+xYTHHy2mG/27j2JmakJB0OXMX6CA+vWS5qqVDFk5qxhzF9QPFds/HISAYpFBAW5k5KSQUjI+WJloOQYK8qzxm79erX5bcccgoKW8Mmng1m85DcAmjSxQRHgjiLAHT9fN4yNjXFycipWX+Sxq8TEJRO6fxGeHm/j4emjW5enD54ebxO6fxExcclEHr8KwOQPHQjyX4jCdwH9+rbhu00HtWy+bmMQvXqW/UJJzq3aPE8uqVmzOt9t+oiAwEV4rXof14W/ao75YKIj+/YvZq/f5/z11y2ORV4uU8cKTx82bfmYwCA39gef42YRHX57T2NqZsKBg0t4f3w/NqwLVOuoxrebPsI/0JWVq97DdeEOreNWrx2Pr/9CfP0XltmRlJ+vYvVKf779YTK+gfMJ2f8XN28kaJUJ8D1DDVNjAkNcGTe+D19tCNbsq29rwR6/Oezxm1OsIyns0CWtPF4WlaVlzYb38fafy17FPFJTMjl08GKZOrxW7GXT5o8ICPqcA/vPF9Ph53saU1MTgg+68/6Efny5PgiAZs3rsNtnLj7+C9i05SOWe3ijVObzzz/38fU5xa49c/Dxn0/k0SvExSa/cB1GRgZs2z6Nvf4L8Pabz4njV7l4MaZEDZWVXwE6v95ck0unT5NeUOvKr/37d9CySWVcb/rZt2eP98IS7fD/AT1BeKl//mvInUnlRBCERoIgRL+o4/5FPT8LgjC67JIvjrDwKFycuyEIAh07NiY9/QlJSWlaZZKS0sjIyKZjx8YIgoCLczfCwqSLW9OmdWjSuPgImTZtbLG2MgegefM65OTkkZubVz5NR6/gMrSTpKl9Q9IfZ5GUnK6tKTmdjMxsOrZvKGka2omwI9LFYLf3aaZ+aI+RkTSoz6JW9fLbI+IqLoM7SnXb2ZL+OJukB4+1637wmIzMHDra2Up1D+5I2FGpw0IQBDIzcwB4nJGNlaUpAJ06NMDM1BiAjna2JBSxcZm6jt/EeVAbSVfbuqRn5JD0IKNYuY5t62JVW/fv/XrbCSa/2xUjI/1nq7uSbFLNpIrmTXJWVq7WW2WdOsKjcHHuKuno0FgdF0ViNVkdqx2exmpXwsKiAHijV2sMDKTf3rFDYxISpM4AE5MqdH69KVWqGJbfJkeicRnWWa2lUckxmpFDxw6NJC3DOhN25JJm/6ovFMyfPZTCPzto/3mcHO00D0Fl3VhraYoo3G4alNJucujYvkFBuzmq/RAhiiIHDl1i6KCSRw88D8duXCAlM73sgqVQ7UEuLi4u6rzVhPT0rFLyVhN1LHQn7LCUt8LConBx6QGAi0sPDhfe7ty92HmNjAwwMpLiIzdXiUoUNfVkZmbz008/8dHHBSNuLkXFYNugNra2tTEyMmDw4E4cCY/S0hceHoWzczcABgx8jdOnryOKIiYmVXj99aYYFYnH+DsPadjQklq1pJjo0aMVoaEXitkmKiqGBg2tsLW1lOoe0kXTBp4SFh6Fywjp9w8c2IlTp64hiiJhYVEMHtIFIyND6tvWpkFDK6KiYrCyMtO83a1evSpNm9hodaatWuXD/PkjoUgTrgwtAAkJqUQcvcSY0b20zlW9upRjRVEkJzsPoaigp5oaWBbyzeuEh2k/rIWHReHs0l2t6TVOnyrsm2ZUMSqeK57WrVSqyMtTUlI6KynGCvNvYrdTp6aYmUnDsKT8llqs7lOnrmFra0u9esVH1YQduYTL8C5FcpqO/JqZXZDThnchLPyS+vdX1ZST8nnBcTt2RTLQqX25rsVybtXmeXJJ6za2WKnvw5o1r0O2+j7M2NiIbt2kkUFGRga0aWOruR6WrCNW024MjQx4c3AnwsMvaZUJD7+Es3NXtY6OnDn9dyEdZsV0/BuiL8Vha2tBfVsLDI0MGDi4I0ePaNv5aPhlhjl3BqD/gPb8cfofxEI5WxdPMnP47ZcIJn/kWGq5F6HlaVtSKlUo85Rl3htFX4qlQYPa1Ff7ZtCbr3GkiG+Ohl9iuEsXAJwGdOCMWoexsZHmvignpyBv3b6ZSPv2DTX7O3dpyuHD2nH3InQIgoBJtSpqe+SjVKp0ZPUCKiu/loeC/Gqh2VZZ15uOHRtr2pSMTHmQO5NkKo3ExEfY2BS8vbOxqUlikvZNRWLSI2yszQvKWNfUepAoi4Ohf9Gmta3mYaxMTUlp2NgUrs+cxCIXg8SkNGyszXSWiYlN5uz524x57xvem7SJqOj4cmtNTH6MjU3h85qSmKR905iYlI6NtWmhMmYkJkudK25zB/PFVyH0HfIFa74KYc704m+A9yrO0adni3JrknRlUMeq4ObXxrIGiTo6k0ri8vVE7ic9pl/PJs9Ur1R35dnk0JErDBr1JR99tgOvJaWPDEpMKhKr1ubF4jAxsWismheLZwBfv1P06d2m1PpK15L+L2NUstvhI9FYWZnRqqX2Q11MbBLp6Vm8P/E7Rr69gYDAP59Dk5luP1mZlVrm7PnbWNSqTqOGtctd94vGIEfExqZgVIqNTQmxoJXbCso8fJiuuRGztDTl4cP0gmPq6D7m/v0Uhg33pJ+9K1MmD8RaHWdffR3IxIkTMTY2Kqg7KY06heq2tq5JYmKRG9rENE1dBgb61KhhzKNHmSX+5gYNLIm5ncTduw9RKvMJC7uos8MgMTFVq26pnWiXS0p8RJ2idadmluvYO3cecPVqPB06NAYg7PAFrK3MadWq+FSkytLi5eXNvPkjEfSKP1a4uv7CG70WcOtWAu+9b19sf1IRH1vbFPdNYpIOTaX45imTJ33DG70WUK1aVQYO7KSzTGkxplXmGWO3MHv3nqBPn3bFtgfvP8vQoUNL0JWmI7+mFS9TNL8WKrPxq2D6OnoQFHyOWdMHa37L4bBLvPO2dsdfSci5tWjdFZNLQkMv6LwPS09/wtEjl+jeo/RRY0lJj7RsYG1tTpJOHeYaHdVrVC2m41DoBdq0rq+lY7HbTkaNWMMP34eU2dGSlJiGdR1tHclFdRS6h5R0GPPo0RMA7t5NYeyoDUya8D3nz93SHPP9NyG8/0FfrTxeFpWlBeDTKVtw7OOBSbWq9B/QvlQdiYlpWBeOERvzYh0ohcsU9U3UxRhGDFvNKOc1LF76FgYG+jRrbsP5c7d49CiTrKxcjkVeIfF+6ff9laEDpNE9Y0Z8Qb833OnRswXtOzQqRUPl5dcLF24x3NmTyVO+4Z9/tEfYgu78WpnXGxmZZ0HuTPoXCILQRBCEvwRB6CIIwmlBEKIEQfAXBKGmev/rgiBcFAThIjCt0HH6giCsFQThT/UxH5VRz0JBEC6pz7VavW2K+viLgiD4CoJgUuiQ/oIgnBUE4W9BEIaqyzcSBOGYIAjn1X96qrf3EwThqCAIewVBuCYIwk5B/YpCEITB6m3nBEH4WhCEfRVrwYrhn3/usW59AMuXvfvC6szPV5GW/gTvHdNZ8NkQPlvwW5k3KBXF7r1/4DpnMBHBC3CdM5hFntpT9E6fvcVexTnmzRhYwhkqHpVKZPW3R1k4rfzT/SqS0mziZN+GEN/P+G7du3z1g+6pexXNph9C0NfXY/iwLi+kvqJkZeWyeethZk0bVGxffr6Ky1fi2fztZLb9MJXvtxzidkzSC9W3L+QiQwd1KLvg/wiCIJT55hegTp1aBAUuJvSgJ/4Bp3jwIJ2rV+OJi3ugc9pQRWNmZsLipW8zd852xr+3kXr1LMpcR6OiyczMZubMLbi6vUX16sZSLG8OYeas4S9Mw5EjUVjUqkE79fp8RVm1agKRx9bQtKkN+/effWG6ALb9OIPIY6vJzVVy+nTxKYgVja7YPX36Ont9TzJvrnbnfG6ukvDwiwwaVDzvVBSzZw0hIsyDYUNe57ddxwBYucafebOHoadX+bEq51bd3PjnPhvXK1i6bKzWdqUyn/nzfmbce/2wta38lwc3/rnPhvWBLFlWsPbLmrXj8Q905dffZnHu3E0CFeXv5HtWaluacuCwO7/7zmHuguG4LdhJRkY216/eJT7+IQ797Sqt7vJqecr3W6dy6OgScnOV/HnmRqVqad+hEf5Bn7Pbew4/bj1MTk4eTZra8OFkRz6avIlPpv5Ay1b10NOv3Kk9unQA6Ovr4eO/gENHPIi+FMc//9wv40wVQ+H82rZtA8LDVxKoWMz77/Vj2vRNWmVfRH6V0Uae5vZsyAtwPyOCILQEfgc+AHYAM0RRjBAEYTmwFPgM+AmYLopipCAIawsdPglIE0WxiyAIVYATgiCEiqJ4W0c9bwLOQDdRFJ8IgvB0wQg/URS3qsusUJ/zG/W+RkBXoClwRBCEZkAS4CSKYrYgCM2B3UBndfnXgLbAPeAE0EsQhLPAZqCPKIq3BUHYXYotpgJTATZv3szUyU3ZuTMC770nALBr11Dr7XZCQqpmetpTrK3MSSjUs5+QmKp5K18aCQmpTJ+xhTWrJ9CggWWpZXf+fhJvvzOSprbaQ64TEh9pLeQpaTIjoVDvfuEy1tZmODm2QxAE2ts1QE9PIDU1k1olDLHf6X0a7wDpocOuTT0SEgqfNx1rK1Ot8tZWpiQkphcqk4a1pTRqyH/fXyyaJ82lfrN/O9xXFKytcO2fBNw9/dn69QRqmptQFjv9/sInSBoibNfKhvtJBVPLEpIfY13CdLaiZD7J5Z/bDxg/0xuABymZfPp5AN+vdilxEe4XZZOndOnUmPi7fqQ8yqSWecGKuTt3ReDtc1LSYVckVhMfFYtDa+uisfpIK579/E9zNCKan7fPLFcHQmF2/n4cb9/TkpZ/HaOmxMU/4M7dFJzHrFNvT2Pk2xvw2fUZNtbmmJtVw8Skinr6XROu/X2PxupFOotp2nMKb78/1JrqF9GUpttPSWklllEq8zkUfhm/XTOeyTYvArM72ZjdywUgp4Y+CQkFazEkJJQQC1q5raCMhYUpSUlpWFmZkZSUppk6Zm1tTsJ93ccUPm/z5vU4e/YfUlIziI6OxcHBAaUyk4cpj/lg/Jd8Nns49wvVnZiYirW1dnxYWZuRcD8VG5uaKJX5PH6chbm5jtWiC2Fvb4e9vfTA4+19XOcDurV1Ta26pXZSU6uMlbU594vWXbNaqcfm5eUzc+YWhg3ryoABrwEQF5fMnTsPcXb2lH5nwiNGjlyJj093LC0tK0VLePhFwsOjiIiMJjdHSUZGFvPnbWftuomasvr6egwe0oVt20IZPYpi9RX2cWJCcd9YW+nQVIZvnlKliiEOju0JD4uiV6/WAOzcGcFen6+BDCmPlSPGnjV2Aa5dv4P74h1s3TKDmjW1rw+Rx6Jp26YBtWsXdBrs3H0M772nALBr10BHfi1iF2uz4vm1SBmAYUM7M/WTzcyc/ibRl+OZM/8XAFJTM4k4dhUDfT36OxaMtpBza8lYW5k9Vy5JSEhl5owteK1+v9h9mMfS3TRsaMn4CcVH8BXFyspcywaJiY+w0qnjkUZHxuNsLR2zZmwrpuNpXFerVpUhQzsTfSkWZ5euJeuwNtMaIZOY+AjLojqszKQ2Y2Ou1pGFubkJgiBolj9o07Y+9W0tiI1J5nJ0PFcu32Gw00ry81WkPMxg8gffs+3nT0u3SSVoaduuYNH7KlUM6efQlqPh0XQvZTS7tbUZiYVjo21HigAAIABJREFUJOFRsSlQT8vYaHRkF8tpTZraYGxShRv/3KdtuwaMHNWdkaOk6VdfbdyHtU3p9/2VpeMppqYmdOnajBPHrtK8ecHanzt37sTb25vKzK9PpzED9O1rx7Jlu0lJzdAs7l84v6oKvbuu7OuNjEx5kUcmPRuWgAIYB8QA5qIoRqj3/QL0EQTBXL09Ur298GqAA4DxgiBcAM4AFkDzEurqD/wkiuITAFEUU9Tb26lHGl1S6yi82qK3KIoqURT/AW4BrQBDYKu6vA9QeP7NH6Io3hFFUQVcQOqMagXcKtTBVWJnkiiKW0RR7CyKYuepU6cCMG5cXxT+bij83ejv2J4AxRlEUeTChdvUqGFcLPlbWZlRvXpVLly4jSiKBCjO4OhQ+rDb9PQnTP34e+bOceb1Tk1LLQswbmxPFN6zUXjPpr99WwL2nZc0RcVSo7qxZp0djSZLU6pXq8qFqFhJ077zOPaTzNbfvi1n/rwJwO3YZPLy8qlZs+TEPO6t7ih2SQtB9+/XhoD9F6S6L8VTo3oVrGprr61gVbsG1atV4cKleKnu/Rdw7Ntao+uPc5JbTv95i0a20tzpewmPmDF/F18sH0Pjcg5xHzfyNQJ+Gk/AT+Nx7N0MRcgVSdfle2pd5etMqlG9Cqf3TSPcZwrhPlPo0KZOqR1JL8omsfEPNSPGLl+7R26ukppm2p1s497ti8LfFYW/qzpW/5B0XFTHqmWRWLVUx+rFp7H6hyZWI49dYduPh9n03UfPNJRdo2XsGyh85qHwmUd/BzsCgs6qtcRQo0ZV3TFavQoXLsZIWoLO4mjfjpYt6nIqYjnhIYsJD1mMjbUZfnvmYFnbFEf7dpz76zZKZT5ZWblERcXRVMeaZBpNb/fQLOyq3W7iqFG9BE3VqnAhKq6g3fQtSDcnz9ygSSNLrSkkrwpp9asS19WUuK6mZFgaERAQoM5btyT7l5i3bqlj4TSO6gdYB4f2BARID9EBAae0tytOFztvQkIq2dlSR1ZaWibnz92gcWMb3n2nL8ePrSE8PJwdO2fTqKEVP//6Ge3sGhIXm8ydOw/IzVWyf/957O21c6a9vR0KhdSBHnrwL7p1b1FmB+fDh4/VGp7w++5jjB7do1gZO7uGxMYkcSdeXXfwnzgUydcODu0J8Jd+/8GD5+nevSWCIODg0J79wX+Sm5vHnfgHxMYk0b59I0RRxH3RrzRtYsOHHxZ8nadly3qcPLWW8HAvwsO9sLYxx89vEZaWlpWmZe7cEUREriY83Iv1GybRrXsr1q6biCiKxMZKI01EUSQ8/CJNmhRvO3Z2DYmNTSrkm3PYF9Fk79AeRcBptaa/NJpKIjMzWzOdQ6nMJyIiWqvuceP6olAoUAS409+xo84YK8y/id1791KYMWMzX6z5kMY6ckZw8FmGDNEejTnund4ofBeg8F0g5bTAPwtyWvUS8mu1qgU5LfBPHNWdmzGFFucNC7+kWUsx/OASwkOXEh66lIEDOrDUfbRWRxLIubU0nieXpKc/4ZOPf2D2HGc6FbkP++rLIB4/zuJz1yK9rSXqaKDW8ZC8XCUH9p/XdGwX6GiHQvGHWscFunVvrtHx6ceb+WzOcDp1Kphqr1Tmk5oqTdfPy8sn4mg0zZqX/oGQtu1siYt7wF21joP7L9DPXntB8772bQlSSC/DDodG0aVbMwRBICUlQ7OI/534h8TFPqB+fQveGtuTQ0eXsP/QIn7aMY2GjWqX2ZFUWVqeZOaQrF6PS6nM53jkVRo11t3hWaCjAbGxDzS+CTnwF/3stae59rNvR2CANOrrUOhFunaTfHPnjjRtGuDe3RRibiVSt570Xvzp9eb+vVTCDkcxeIjuqbuVqSMlJYP0dGlaYHZ2LqdO/k3jInl93LhxlZ5fk5PTNPerUVG3UYkiNQt1+OjKr1A51xsZCb2X/N9/DXlk0rORBsQBbwB7/sXxAtJIpoNlliyZnwEXURQvCoLwAdCv0L6i861EYDaQCHRA6jzMLrQ/p9Df86ngeOjbtx0RkZdxGrgU46pGeHm9r9nnPMILhb8bAEuXjMXV9Veyc/Lo07stffpIF8xDhy7gudKblJQMPvr4e1q3qs+P22bw284I4uKS+W7TAb7bdACA7dtmlGvRy769WxFx/BpOw9ZImpaNKdD01kYU3tKXoJa6ueC6xFvS1KsVfd5oBcAoly64LfVh6Kj1GBrqs9rz7XIn5r69WhBx4m+cXDZIdS8dWVD3u9+i2CV9Snjp58Nx9fCV6u7Zgj69pLdGnu7OeK3bjzJfRRUjA5Yvkj63+93WIzxKe8KyNdLXTfT19fDbUfbNikZXj8ZEnr7FgLE/UrWqIV6uBdPkXD78lYCfxgOw9vsI9h2+RlZ2Hn1Hbmb0UDtmTOxZ7npepE0Ohl1Gsf8CBgZ6VK1iyMZVpfupb5+2UqwOWoZxVUO8Vr5XoGPEKhT+rpKOxW/h6vabOlbb0KePdEPvucKb3DwlH076FoAOHRqx3OMdABz6LyEjI5u8PCWHw6LYvnUazZqVfFPbt3drIo5dxWmIl6TF850CLWPWofCZJ2lZNFrz+eo+b7SizxutS7V10ybW9O7VkuGj16EnCIwe2Y0WZdxcazS90VJqN8PXSpo8CrWbt7/SfIp6qauL+vPVefTp1ZI+bxSslbH/4EWGVPI0jF0Tl9OvRSdqVzcn3iuQpfu2sv1k0DOdI9PCANs6tjg5OWFcNR8vr4IvMzm7rEAR4A7A0iXv4ur2C9nZueq8Jd3YTp0ykM9mb2Wv7wnq1pU+/wtP82E0TgMWq/OhdN6bN++zeo0vggCiCBMnOtGyZfFFjJ9iYKDPIve3mDr5O1QqkREju9OseR2++Xofbds1wMGhPaNG9+Tzhb8yaKAHZmbVWLf+Q83xTo5LyMiU4jE8LIot26R4XOW1l+vX7wLwySeDaKTjYdjAQJ/FS95m0uSvUeWrGDWqJ82b1+XrrwJp164hDo4dGD26Fwvm/8QAp8WYmZmwYeNkAJo3r8ubb77OkMHL0NfXZ4n68/bnzt5AoThDixb1cHFeAcDsOc707Vv6tJDK0FISoijy+cKfycjMBlHq6PLQMb3awEAf98VvM3nSt6hUKkaO6iFp+jpI0uTQntGje7Jwwc8MHLAUMzMT1m+YpDne0cGdzMxs8vKkdau2/TgDc/NqTPt0k2Zx9m5dW/D22N46dZYUY/B8sfvd98E8epTJsuXSuyV9fT38fKXr95MnOZw8cZXly3R/6ROgb582Uk57cwXGxkbaOW3UFyh8F0i63Efj6r6L7Ow8+vRuTZ/eUk5bvzGI2zFJCIJAvbq1WLZkjM56ykLOrdo8Ty7ZtTOS+LhkNm06wCb1fdjWbdPJy1OyZfNBmjSxZvSoNQC8+25fRo8p+V7BwEAfN/fRfDT5e/JVKo2Ob78Opm27Btg72DFydA9cF+7gzYHLMTMzYe36DwDYvfMY8XEP+GFTCD9skr7mumXbpxgbG/HR5O/JU6pQ5avo3rNlqRqe6li4aASfTt2KSiXiPKILTZvZ8P03IbRpa0s/h7a4jOqK++e7GT5oFaZmJqxeJ90rnD97i03fHsTAQB89PYFFS0ZhVo5R4i9Sy8MHj/ls2nby8vJRqVR07tqM0W8Xf2lQzDeLRvHJlB/IV6lwGdGNZs3r8N03+2nTtgH2Du0YMao7bgt/Y8jAFZiZm/DFOume8a/zt9i+NQwDAz0EPT0WLR6tGdU4Z9ZPpD3KxMBQ8r2paem2qgwdf1+/h7vrTvJVKlQqkYGDOtK3X8lfQ6ys/Hrw4Hl2/x6Jvr4eVasasWH9ZM39amn5tTKuN82a1WHtWj+C950lKyuXfn3dGD26J9Nn6F4PT0YGQHhR67381xEEoRGwD+gGHAS+BxYiTWc7JgiCB2AmiuJsQRCigE9FUTwuCMIaYIgoiu3U08IGA2NEUcwTBKEFcFcUxWKroQmCMAhYAvR/Os1NFMUUQRAeII0uSgX2q4//QBCEnwErYCjQGIgAmgGrgDuiKK4XBOFDYLsoioIgCP2AeaIoPl1b6VvgLFIn2d9Ab1EUYwRB2Kn+XWVlEhFV2LOateLRc4QsxctWAcbO8Fj3549fKDWkG1MxactLFgKC1dRXxyb5h162CtBXr4WTE1x6uRdBlSHwxL/scpWNyQiET7q/bBWIm06r/3Lk5QoBEOxRql5+vBroOSHy8u0hIE2beVW0qMSXf93TExxfmVgFIO/Ay9UBYPimnFsLYyKtc/Wq5JI81fO8U60YDPUG8kT5bC8bKgMTg2EAr4yWnPyX336r6L/5yugAXpn8+spcb4p9X/V/i/0x819q58jgRmv/U/aVRyY9I6IoZqoXtz4E+AJr1Ytg3wKevv79ENguCIIIhBY6fBvSVLLz6sWukwGXEuoJEQShI3BWEIRcpI4jN2Ax0hS5ZPX/Cw/HiQP+AEyBj9XrJH0P+AqCMB4IAUpdxl8UxSxBED4FQgRByAQqb8VCGRkZGRkZGRkZGRkZGZlXgP/iItgvE7kzqZyIohgDtFP//RHwdALrch1lzyFNK3vKAvV2FVKHkFs561wNrC6ybROwSUfZD0o4xz9A4Um0C9XbjwJHC5WbXqjMEVEUW6k7vL5DGrEkIyMjIyMjIyMjIyMjIyMjI3cmyehkiiAIEwAj4C+kr7vJyMjIyMjIyMjIyMjIyPxPIo9MejbkzqSXjCAIdmh/8Q0gRxTFbi9DD4AoihuBjS+rfhkZGRkZGRkZGRkZGRkZmVcXuTPpJSOK4iWg48vWISMjIyMjIyMjIyMjIyMjI1Me5M4kGRkZGRkZGRkZGRkZGRmZ/9foCXovW8J/CtlaMjIyMjIyMjIyMjIyMjIyMjLlRh6ZJCMjIyMjIyMjIyMjIyMj8/8aeQHuZ0MQRfFla5D530AOJBkZGRkZGRkZGRkZmf9d/qd7W8LvuL3UZ1qH+l7/KfvKI5NkKgyRIy9bAgL2r4wOxJevA8EeAJUY9pKFgJ7gCKqXrwM9R9k3RdATHCH/0MuWAfpOr5RvhE+6v2QhIG46/cr45o/E5S9bBV2tlwDwKMfnJSsB8ypjXp14fUViBF6de4FXJrcqD75sGWAwUPp/dtDL1QFQddgr45vs/OCXLYOq+kMAUKpefhs20HN6deL1VdEBr06ef1V0yMgUQu5MkpGRkZGRkZGRkZGRkZGR+X+NPM3t2ZAX4JaRkZGRkZGRkZGRkZGRkZGRKTdyZ5KMjIyMjIyMjIyMjIyMjIyMTLmRp7nJyMjIyMjIyMjIyMjIyMj8v0ae5vZsyCOTZGRkZGRkZGRkZGRkZGRkZGTKjTwySUZGRkZGRkZGRkZGRkZG5v81eoI81uZZkK0lIyMjIyMjIyMjIyMjIyMjI1Nu5JFJMhXGscjLrFzpjUqlYvSYXkydOkhrf25uHgsX/Mzly3GYm1djw8bJ1K9fG4DNm0Pw3XsCPT09Frm/Re/ebTXH5eerGD1qFVbW5mzePA0AURT58ksFISHn0dfTY+w7fRg/3qFStbi5/srRo5ewsKhB0L4lmnNdu3aHpUt38uRJDvXqWbBu3URqVEejc+VKbyIio6la1YjVqybQtm2DYraLjo7F1fUXsnPy6NunHYsWvYUgCDx6lMnsOVu5e/ch9epZ8OXGKZiZVePmrQTcXH/h8pV4Zn82nEmTBgCQk5PHuPfWkZurJD9fxcCBl5g5c2ZxXx27jNdKH1QqkdGjezJl6sDi9ln4C1cux0v22TCJevUtSE3N4LNZW4mOjsPFpTuLl7wNQFZWLp99tpX4uAfo6ethb2/H3LkuOuNEFEVWevkQEXmZqlUNWe01XrdNLsfh6vqr2iZtWeQ2ppBNfixkk8mYmZmQlvYEt0U7iItPpkoVQ7xWvE+LFnUB+OXXcHx8TqBSiZjX/JHk5LgK9Udpfm7d5hNatKgHQJ06tfhh06cAzJ07l+joaAwMc2hv1wiPZe9iaKhf4b4pzKefbCL+zgOCghbr9E1xP+2V/GRsxGqv92nbxla3n9x2kJ391E+jEQSBNWv9OXI0GkNDfRrY1mbVyvcwNTUhKiqGxUt3S3UAM6YNxql/B6m+FSuIiAh5Ib65e/ch02f8gEololTm89579rwzto9WXXWjMjDMyie2m1mZ9irMj+8vYqhdL5Iep2LnOe6Zji0Pz+ubAyHn+fa7/dy8lYjPnnnYtWsIQG6ukqUeu4m+HIegp8ci11F069qiXJqiztxjx9dnUalE+g1pxrD32mrtP7DnKkf33UBfX48a5lWY8nl3attIifJBYiY/rjlNStITEGDeF/ZY1qn+TDY5dfxvNqzZj0qlYvjI15kwqa/W/txcJcsW7eXalXuYmZmwYu3b1K1XE2VePis9/Ll+9T75+SreHNaRDyYXHJufr+KDdzZhaWXKhm/f11l3ZeX5wKAzbN0aCqJItWpV8fB4l1at6gPg4OBGtWpV0dPXQ1//G/x8PinRNpXVlkujoq/Bt24lMGf2Ns3x8fEPmDlzGBM+cNRs2779EF+s8eXUqXXUqqVDUyXk1imTvyU5OQ1lvorOrzdl8ZKx6OuX/p5WFEVWrvIlIvKK5I+V40r2x6Kdan+0YZHrKMkf6wLU/jCQ/LHiXUxNTbhz9yGDh3nRuJEVAB06NGL50uLXgWJa1iiIOH5Vil3Pt2nbun5xLVfu4Lr4dyl232jNooXOCILAN5sO4u17hlq1pPY6Z8ab9O3dmhOn/mb9V8Hk5eVjaKjP/NlD6dGteYk6KsM349/fSHJyGlWrGgGw7ccZWFjUKNUeRTlx7CprVgWgylcxYnR3Jk1x1Nqfm6tk0ee7uHo5HjPzanyxYTz16hUE3/17qYwYtoZPpg1kwkT7Z6r72LErrPbaS75KxajRPZkyZUAxm7gu3MHlK1IbWr9hIvXqWfAoNYPPPvuR6OhYXFy64774Lc0xwcFn2br5IIIgYGllxpovJlCzZum59nnj9cuvgwk7cgk9QcDCojqrVr6HtZUZN28l4ua+U7qPnTWUSR866qi94nQ8b7uprDz/lKhLMYwd+wUb1k9i0KDXuXo1Hg+PXWRkZqOnp8cnn2Qx+E3jF/5cAZCe/gR39x38/c89BOELbt261eP69eunSnWYzP8b5JFJMhVCfn4+y5fvZuu26ewLXkrwvj+5ceOeVpm9PicwNTUh9JAnEz5wZP06fwBu3LjH/uA/2Re8hG3bZrB82W7y81Wa4379NZwmTW20zuXnd4qE+6kcOODB/gMeDBnSuZAWVaVoGTGyB1u3zSj2290X7WDu3BEEBS3BqX9Hftx2SLMvMjKamNgkQg8ux3P5ODyW7dJpP49lu/D0fI/Qg8uJiU0i8thlALZsDaFH91aEHvSkR/dWbNl6EABzMxMWub/NpIn9tc5jZGTALz/PJlCxmAB/d44dO8aFCxeK+EqF5/I9bNk6naB9iwkOPsuNG/e17bP3JGamJhwMXcb4CQ6sWy/Zp0oVQ2bOGsb8BSOK/YaJH/Zn/4Gl+Pm58tf5m0RGXtb5WyMjL0s2CfHAc9k4PJb/XoJNduO5fByhIR5qm1xR2+QgPXq0JPTgMnr0aKmxyQ9bQmjduj5BCnfWrJ7AylU+APz99z18fE7g472QeXNduHnzJlu3TK9Qf5Tm56pVjVAEuKMIcNd0JAEMHz6ckJAQAgPdyc7OY+/eE5XmG4DQ0L8wMamic58uIiOvEBObTGjIUjyXvYPHshL8tHwPnsvfJTRkKTGxyRo/9erZin0KN4IC3GjUyIrNW0MBaN68Lr4+C1D4u7Jty6cs8diNUpkv1RcTU+FtpSTfWFqasef3BSgC3PHes5CtW0JITHxUyF6hqPTLbS4tfj4VzKBvZv+7g8vB8/qmRfO6fPP1FLp0bqpV3mfvCQCCFIv4adt01nzhj0qlKnbeoqjyVfyy8U/mr7Vnza9DORUWw92YNK0yDZvXZPnWN/H6eQhd+jXg901/afZtXnmSwe+0Yc1vw1i2eRCmNas+kz3y81Ws9Qriy03j+T1gJqEHLnHrZpJWmUC/c9QwNcY3eA5j3+/Jd19K8REWGk1uXj67/Gbwy++fELD3T+7dTdUct2fnKRo1tiy1/srK8/Xr1ea3HXMIClrCJ58OZvGS37TO98uvc1AEuOPn51eGvsppyyVRGdfgJk1sCFC4E6Bwx9fPDWNjI/o7ddSc7/79FE6cuErdujp6kai8697GLycRoFhEUJA7KSkZhIScL9U2AJHH1P44sBhPj7fxWO6ts5zHcm88l40l9MBiyR/HrwLQq0dL9gW4EuT/OY0aWrJ5a8E9RwPb2ij8FqLwW1hmRxJA5PFrxMQlExr0OZ5LRuOxwle3lhW+eC4dQ2jQ58TEJRN54ppm3wfv90HhPQeF9xz69m4NQE3zamz6eiJBvvNY7TmWBYt2l6ihMq97a9d+iH+AG/4Bbs/ckZSfr8JrhR/fb56Kf9BCQvaf5+aNBK0y/r5nMDU1Zt/BRbw3oS9frt+ntX/dFwreUNvkWete6enND1s+JTDInf3B54rZxHfvKUzNjAk56MH48fZsWKcAwKiKITNmDmX+fG2bKJX5rPbay0+/zMJf4UaLFvXYtTOiTC3PG6+TJzoQ5P85Cr+F9Ovbju82hQDq+1jXUWV2IlWUjudtN5WV50Hy97p1/vTqVRArVasasWbNBwTvW8q2rTPw8vIiPf3JC3+uAFi50pvevdsScmAZCoUC4KrOSv9H0EN4qX/+a8idSTIVQlRUFA0aWmFra4mRkQGDh3QhLCxKq0xYeBQuI3oAMHBgJ06duoYoioSFRTF4SBeMjAypb1ubBg2tiIqKASAhIZWIo5cYM7qX1rl+3x3Bp9OGoKcnhbCFhWkhLTGVoqVLl+aYmRV/GxsTk0iXLtIbt569WhMaWnAzGRYWhYtzdwRBoGPHJqSnZ5GUpP2QlZSURkZGNh07NkEQBFycuxN2+GLB8S6STheXHhxWb7ewMKW9XSMMDLSfdgVBoFo16UFMqcxHqVQiFPkqQVRUDA0aWGJrW1uyz+DXCQ+7qFUmPCwKZ5fuavu8xulT1xFFEROTKrz+ejOqGBlqlTc2NqJb95aA1KHVpo0tCQmp6CIsPAoX525qmzQmPf1JKTZprLZJN8LUGqXjJW0uzt05rN5+88Z9uneTNDRtYsPduw958CCdm7cSaN++EcbGRhyNiKZbt24cOnyhQv1RHj8XpW/fvgiCgCAI2LVvSGJCaqX4BiAzM5tffg7n40/eLFVTYSQ7d5V+U4fGpD/OIim5iK2S1bbq8NRPXTVt7Y1erTXx2bFDYxISpI4aY2MjzfacnDxNfIaFR+Hi4lLhbaUk3xgZGWCktlVurhKVKBayVyY//fQTKY2My22vwhy7cYGUzPR/dWx5eF7fNG1qQ5PG1sXOe+NmgqYdW1jUoEYNY6Kj48rUc/PqQ6zr1cCqbg0MDPXp7tiQc8fjtcq06WRDlarSYOhmbWqTkvwEgLsxaajyRey61AGgqomhplx5uRJ9h/oNLKhXvxaGhgY4DbIj8oj2vW7k0asMGf4aAA5ObfnzzC1EUQQBsp/kolTmk5OjxMBQn2rVpU7XxIQ0TkRex3nk66XWX1l5vlOnppo311Ib0p1Ty6Ky2nJJVNY1+CmnTl3D1rY29epZaLatWuXD/PkjKek+vLJya/XqUo5QKlXk5Skpz0eAwsIv4TK8HP7ILOSP4SX5oxEJiaX7o1QtRy7jMqyzpKV9Q9IfZ5OUrJ27kpLTJS3tG0pahnUmLFz3y6KntGldD2sraURn82Y25OTkkZur1Fm2snzzvERfisO2QW3q21pgaGTAoDdf42h4tFaZI+HRDHfpAoDTgPb8cfofKa8A4YcvUa9eLZo2K55ry+JSVAy2DWoXskknjoRrt6Hw8CicnbsBMGDga5w+XdgmTTGqom0TUZT+ZD3JRRRFMjOzsLQqe9Tt88br0zYCkJWVo2kjFhY1aG/XEAOD8j2Kvux2U1l5HmDHb0cYOOA1LGoVdHg2bmxNo0ZS7Fhbm1OrVi1SUh6/8OeKx4+z+PPsP4xWP4cZGRlx/fr1f590ZP7nkDuTZCqExMRE6tjU1PzbxtqcxETtG9+kxEfUqSOVMTDQp0YNYx6lZpKYmFrisV5e3sybPxJBT/sOLS7+AQf2n2XUSC+mTP6GmJjEQlpKPt/zaCmJZs3rajo6QkLOc/9+QfnExEfY1Cl0PhtzrdEPmjI2uss8fJiOlfpib2lpysOHZT+g5uercHZZQc9e8+nZsycdOnQo9tsLa7K2qUliovaFKDFJh30eZZZZN0jDYY8cuUSPHq107i/+e2uSmFTEJkmPsLE2LyhjXbOQTR4XscljAFq1qk/oIWkUVlRUDPfupZCQ+IgWzetw7txNUlMzuHcvhZs3b5Kg9lFF+aM0P+fk5DFylBdvvb2Gw4e1R4kB5OXlExj4B2/0bltpvvn663188KEjxurh/uUhMamIHaxLsJWWn8yL+RLA1+8UfXq30fz74sUYhgxbwXBnL5YtHYuBgb66voIRiC/CN/fvpzBsuCf97F2ZMnkg1urf8tVXXzFx4kRUr+gVsiJ9U5hWLesRHn4JpTKf+DsPuHwlnvvl6MBIfZBFLauCjvZaliakJmeVWD4i+Cbtu0lTUO/Hp2NS3ZCvFkXiPmk/u78/jyq/7NFQhUlKTMfauuChyMralOQk7VyZnJiOlbqMgYE+1atXIe3RExyd2lHVxIghjmsYPmAt4ya8oXlpsPGL/UyfM7DY9acoLyLP7917gj592hVsEAQmTfqKkSO92LNnT+n6KrEt66yvkq/B+4PPMmRoF82/ww5fwNrKXDMFUBeVed2bPOkb3ui1gGrVqjJwYKcyyycmpWFjU8TWRbUkpmn7w8acRB0vKHz9Tmv5487dh7iMWsN7E77i7Lmb5dOi5XezYvWUVWbn7ycYNno9rkv2kJb+pFi+3YiIAAAgAElEQVQdBw9H0aZ1fYyMdHcSV6Zv3Nx2MMLFi++/36/p5CkvSYnafrLS4YPCZQwM9KleoyqPHmXyJDOHn34M5+NPtafrlZfEpDStdmBtXdwmSYlpGruVxyaGhvosXvo2Ls5e9OuziJs3Ehg1qme5tDxvvG78ah99HZcQtO8cs6YPLrPOytLxlH/TbiorzycmpnL40AXeeUd7mn1hoqJuk5eXR4MGli/8ueLOnQfUqlUdV9dfcBmxkkWLFtGyZctqpR70H0dPEF7qn/8ar+itsowMHDkShUWtGrRTr+dRmLxcJUZVDPH1c2PMW2+wyG3HS1Ao4bVyPLt2RTBypBeZmdkYlnDD9Lw8HcVSFvr6eigC3Ik4uoqoqCj+/vvvStGjC6Uyn3lzt/Pe+/bY2tau9Pokm0h/nzplAI8fP8F5hBc7fjtK69b10dcTaNq0DpMnOzFp8jdEXYqhYcOG6JWxpkX56y7bH0fCV+Ln68b6dRPx8vImLi5Za//y5b/TuXMzOndu9tyadHH1ajzxcck4FZoS8iLZ9EMI+vp6DB9W8PDXoUMjgoPc2eu9gM1bQ8nJyavQOsvrmzp1ahEUuJjQg574B5ziwYN0rl6NJy4uDicnpwrV9F9g1Mge2NiYM2rMF3it8uW1jo3R16vY24QTobe5ff0hQ96RbuRV+SLXo5J5Z9prLNs8iKR7GUQeuFWhdZbG5eg76OsJBB9eiP+Buez65QR376RwPOIatWpVo3Wbei9MC+iO3dOnr7PX9yTz5hZMW9m9ax7+fovYunU6O3fu5M+zNypdm662/KLJzVUSHn6RQYOk0WJZWbls3hzCzFnDX5qmbT/OIPLYanJzlZw+ff2F1btp80H0DfQZPlSa5m9lacqRw8sI8F3I5wtGMHfBL2RklNypWxG881ZPDu1zReE9GytLU1avC9La/8+NBNZ9uZ/li0dVqg5drF33IYFB7vz22xzOnb2BQnHmhdW96buDvDe+LybVyj+1vLLJy8tnz+/H2Ou3kKORK2nRsh5bt5Q+ZbWimD1rKBFhyxk29HV+23XshdRZEq9Cuymc51d6+TBv3gjNTIuiJCWlMX/Bz6xatarEMhWhoySUShVXrsTzzjt9CfBfhLGxMcDnFSpE5j+NvAC3TIVgbW2t9QY7IfER1tY1tcpYWZtz/34qNjY1USrzefw4C/Oa1bC2rqnz2PDwi4SHRxERGU1ujpKMjCzmz9vO2nUTsbY2Z4CTNGXByakjbq6/FNKi+3zPo6U0mjS1Yfv2WQDcvp2In+9JnJ2dgQzs7BpqRsEAJCQ80ox+KNBrrjV9oXAZCwtTkpLSsLIyIykpjVq1yj/n39TUhG7dunHs2DGaNW+k9dsLa0pMSNV6qw9gbaXDPuZlv4hYumQXDRtaMWGCg9b2nTsj2OvzNZCBXbuGRX5vKtZWRWxiZa41BDkhMbWQTWrotEn16sas8hoPSAslOvZfjK1tbXbujMBbvRbMAKfXSEmFRuqFFivKH9ZFbFr4mKfxY2trSdeuLbhyJY4GDaQ1WL799ltSUx6z7JupQOX45sKF20RHx+Ho4E5+voqUlMeMf38jv+4ovqbPzl0RePucBJBit1hb0GErLT890vKln/9pjkZE8/P2mTpvWE6fuc7NmwkMd/GiS5fmJCQkANWK2VCrvgr0TeHzNm9ej7Nn/yElNYPo6GgcHBywffAYg1yR+ucfc6fTs623UdFUtG90YWCgj9vnBQ99Y99dr2krpVGztrG0eLaalOQn1LQsPkUw+ux9An+Nxu0bJwyNpKH0tSxNaNCsJlZ1Jfu+3rs+Ny4/KLPOwlhZm2q9oU5KTMfSylSrjKW1KUmJaVjbmKFU5pORkYOZuQkH90fRvVdzDAz1qWVRnfavNeDq5btcv3afyKPXOHn8b3JylGRm5rDU1Ydlq8YA4PP7afb5/UZl5/lr1+/gvngHW7fM0Foo92lesbAwxcnJiaioGLoU6pB+0W1Z+1yVdw0+FhlNm7YNqF1b8m9cXDJ37jzE2dkTgMSER4wcuRIfn+5Y1Naur7KueyCt3ePg2J7wsCitdU+esnNXJN57pfVq7do10JoqKP3GIlqszbT9kfBIM20MwM//DEcjLvPzj9M1/jAyMtRM3W3XtgENbGtzOyYZu3bai/Pu/P0E3n5Sx4pdW9sifk/Tqkeyi1mJZWoXWodozMhufDzjR63fNX32z6xZMZYGpbxcqizfPI3xatWrMnRoFy5FSQtSlxcrazMtPyUV8UHhMtY25lJeeZyNuXk1LkXFcjj0Il+uD+Lx4ywEQcCoigHvjOtdrrqtrcy02kFiYnGbWFmbkfAMNrl27Q6A5v5j0KBObCth/bOKjtenDBvSmamfbGZmOUcnvex2s3PnTry9vanMPB8dHcucOdLHBVIfZRIReRkDA3369+9IRkYWH338LZ1fb8rSpUsrVUdJ2NiYY2NtTocOjQEYNGgQO3bsKHsIpsz/G+SRSTIVgp2dHbExSdyJf0BurpL9wX/i4NBeq4yDQ3sC/KWLwsGD5+nevSWCIODg0J79wX+Sm5vHnfgHxMYk0b59I+bOHUFE5GrCw71Yv2ES3bq3Yu26iQD079+RM2ekN4B//PG3Zl6xpKVhhWspjadDRFUqFT9s2s+0aUNQKBQoAtzp79iRAMVpRFHkwoVb1KhRVTO89ClWVmZUr16VCxekNTwCFKdxdGxfoDNA0hkQcEqzvSRSUh6Trh5mnp2dy8mTJ2nSpIlWGTu7hsTGJnHnjto++89hX8Q+9g7tUQScVtvnL419SuPLLwN5/DgLV7fRxfaNG9dXsom/G/0d2xOgOKO2yW1q1DAuxSa31TY5g6NDIZsoJG0BitOa7enpTzRrMvj4nKBz52ZUr27MuHF92b5tBgp/N157rQknT55k6JAuFeqPp5qK+jktLZPcXGnUTUpqBuf/ukmzZnXUGn04fvw469ZP1LxtqgzfvPNOHyKPrSIsfAU7d86lYSMrnR1JAOPe7YvC3xWFv6vaT39Iv+mi2k+WRWxlqbbVxad++kPjj8hjV9j242E2ffcRxsYF0+vi7zxAqcwHoF9fO6pVq8LuXXOl+gICKrytlOSbhIRUsrNzAUhLy+T8uRs0bmzDu+/05fjx44SHhxPfqQa5JnovvSMJKtY3JZGVlcuTJzkAnDh5FX19PU28lkaTVhYk3HlM0r0MlHn5nA6LpVMv7SlHMX+n8NO6P5i9qi9mhRbYbtKqFk8yckl/lA3AlfOJ1Gv0bF/Pa922HvGxD7l3J4W8PCWHQi7Rp5/2NNve/VoRHCgt+h1+6DKdu0prSdjUMePsH9JIqKwnuURHxdOwsSXTZg1g3+EFBITMY8UXb9G5axNNRxLAmLHdKz3P37uXwowZm/lizYc0LrTG1ZMnOWRkZGv+fuLECZo3r6tV34toyyVRmdfg4OCzDBlSMDKqZct6nDy1lvBwL8LDvbC2McfPbxGWltqLpldGbs3MzNasVaJU5hMREU2TJrrXxxn3bh/NAr/9HdsTEFjIH9Wr6vZHtUL+CPwDRwc7QO2P7YfZ9O0ULX+kpDzWfDAkPv4BMbHJ2Na3oCjjxvbSLJjd374tAUFnJS1RsWot2h2xVpamkpaoWElL0Fkc7aWvNRZeX+lweDTN1fkiPT2LqdN/ZO6sIbz+WuMSbQiV4xulMp/U1AxAGpFz9Gg0zVuUncsK07adLXGxUmdlXq6SkAN/0de+nVaZfvZtCQz4E4BDoVF07dYMQRD4+bcZHDi8mAOHFzPu/T5Mntq/3B1JAO3sGqrrfmqT89jbF7GJvZ1mtFXowb/o1r1FqTaxtjbj5o0EUlKkpQFOnrxW7OM2T6nIeI2JLfgYQtiRSzRpXPYLisrQ8W/azbhx4yo9z4eHrdTkr4EDXmPpkrH079+R3Fwl06b/gLNzd1auHP/SnissLc2wqVOLW7ekxedPnToFcKVs7/130RP0Xuqf/xrCs84hlpEpAfFoxNd4efmgylcxalRPPv5kMF9/FUi7dg1xcOxATk4eC+b/xNWr8ZiZmbBh42RsbaUbvh827cfX9yT6+vq4uY2hT1/tC/aZM9fZvv0wmzdPA6SOg/nztnPvfgomJlVYtmwcrVrVR8AekSNERFyqcC1z5mzjzz/+JjU1AwsLU2bMGMboMb349Zcwdu6SvogxwOk15sx1QU9wAPEIoiiy3PN3jh27jHFVI7y8JmBnJ03bc3ZZgSLAHYBLl2JxdfuF7Oxc+vRuy+LFYxEEQfrs7eyt3L+fQt260ic8zc2rkZycxqjRq8j4P/bOOz6K4m3g37lLQhJIIZ0UOoJ0kRJESijSO+pPaSqiImJBBZJQQoBQBFFRkaYIUkMLnUBCk95DQlFKqKm0EEpI7ub9444jl0IChDdR58snH253nt159plnZ2afnZlNvY9GI7C1Lcb6daO4fOUaw4b9jk6nR0pJmzbd+eSTT9DLCDN7bt8ezfiQZej1erp1b8hHH7Xlhx/WGOzTvCZpaYbPNp88eRkHB1umfNvPNG2tRfPh3Llzn/R0HXZ2NsyeM4gSJazxaxZI+fLupjc8b/dsyuuvP1o4XSNagD7CaJMl7PzzhNEmvU2fJu/cNYSwlQEGm0RfwN9/HvfT0g02Gf7GI5sMnkPc1et4ejrx3dT3cXQszpEj5xjmPw8EVKpYinFje5vWPnm71xRu3ryDVqvB27sKf/8dVaDlkVs5Hz58llGjFiA0AqmX9OnbwrSYfNVqA/H09KR4cUMd3LJVbQYObFfgZZM5GHDl8jU+GvAza9aMMPMHjWgBus1m+6SUBI9dys4/T2JjbUnIuF6Zymk8YSv9H5VTwB/GcqrKiMDXEULQqnUQD9IzcDQuIFyrVlmCg95i1er9zJoVjoWFFo1GMHBAW1q2rGXIb9wedu4M/38pm127TjBh4nKEMCxK2qtnM95809jZF4ZPOFu+Uw+vqFQuNHiy4MbC94Jp9kIdXEo4kpBynVFrZ/Hr7jV5H5gDcvreAi+bzVuOMWZcKNevp2Jvb8OLVbyYM+sTLl+5Rr/+P6HRCNzdHBk3puejz1xrW7E/IThXPY/uucKCaYfQ6yVN2lWgc5/qLJ9zjHKVnanzqjcTvojg0rmbODobRiw5u9kyeEIzg54H4lj002GklJSt7Ey/r+tjYZnzp/Tqu48E4GZaqNn+XTtPM3XSevQ6PR27vMy7HzRjxk9beLGqF038XiQtLZ2ggGX8dSoOewcbxk56Ey9vJ+7eTWPMiBWcP5eElJIOnevQ+13zh75DB86x4PddfPtjb7P9jsVef671fODw+YSHHzF9oUyr1bBieQCXLiUx8JNfAMMaeR06vMmAD3KfJvu87uVsaA1TQ59XG3z3bhp+fgFs2TIWO7ucF8dv3jyA5csCcHLq+NzbPUfH4gz46GfTAv4N6r/AMP8eZgvYakQLyNhkpoehPELZueukwV/G9jSNHurcbSJhK4Yay8P4ifO0BzR5tSojAnsYyqNNcPbyGPUmm8KP8sOP601166CBbWnuZ3iQxsK4ds/9Ndl1Gb+SnbtOG3wj+E1qVDN8br3zG98StnSwQZeYS/iPWMz9tAyaNKrMCP+uCCH4OmAhp05fBSHw8ixJ8IgeuLna8/PMLcycE0GZMo+Cer9O72/4opr18y8bT08nevf6lowMHTq95JWGlRk6rAfaTFPcNaIF93XrcvSjh+zcfoJJE8LQ6/V06Vqf/h+14qdpG6hWzYdmzauTlpZO4NCFnDp5GXtHWyZN7oO3j3kAb/qPG7G1LUbf9/xyzMNa2x6ADL15Pb9jewwTxi9Dr5d07ebLhx+1YdoPa6lWvbTJJsOGzjPeQ8WZPOVdk01atRhJ6p37pKdnYG9ny8zZA6lYsRRLFu/kj/nbsLDQUsrTiZCQXjhmGvFooWlV4P466LM5nI9NRGgEXqVKMnrUm7i7O5KUlEL3N78x9mM12NpasX51gGHBbovWReu+eY71fGaGDZtLs2Y1aNPmZcJW7yMg4HcqVnz4oqAEE8Z3p0oV7//X54oSJWw4efISgcPnk56uw8enChEREU6nT59+ui9C/APYnxBcqMGR+u4j81ynQQjRBvge0AKzpZQTsqQPBt4HMoAk4D0p5QVjmg44bhS9KKV8pnniKpikKCikZGth62AKJhU2Aj+Qha/Hw4firB23wuBhMKnQ0bRQZZOFnIJJhYK2VZEqGzEg/1Minhc5BZMKhTyCSf9f5BZMKgweBpMKHeFXZHwEKDJtcJGpW7M8FBcKuQSTCoUcgkmFQX6CSf8f5BZMKgxyCiYVjiLZg0mFpgcUnXq+qOiR63cz/x0cTBxTqMGRum4jHmtfIYQW+AtoBVwGDgBvSSlPZJLxA/ZJKe8KIQYAzaSUbxrTUqWUJXI49VPxzxtLpVAoFAqFQqFQKBQKhULx36I+cEZKeU5K+QBYDHTOLCCl3CqlfLiY5V4g98+dPiMqmKRQKBQKhUKhUCgUCoVCUYgIIT4QQhzM9PdBFhEv4FKm7cvGfbnRD9iQadvaeN69Qoguz6qv+pqbQqFQKBQKhUKhUCgUiv80mjw+OPS8kVLOBGYWxLmEEL2AukDTTLvLSCmvCCHKA5FCiONSyrNPm4camaRQKBQKhUKhUCgUCoVCUbS5Avhk2vY27jNDCNESCAQ6SSnTHu6XUl4x/n8O2Aa89CzKqJFJCoVCoVAoFAqFQqFQKP7TaESRH2tzAKgkhCiHIYj0P+DtzAJCiJeAGUAbKWVipv0lgbtSyjQhhAvQCJj0LMqoYJJCoVAoFAqFQqFQKBQKRRFGSpkhhPgE2ARogV+llDFCiGDgoJRyNfANUAIIFYZpexellJ2AF4EZQgg9hhlqEzJ/Be5pUMEkhUKhUCgUCoVCoVAoFIoijpRyPbA+y76RmX63zOW43UCNgtRFSCkL8nyK/y7KkRQKhUKhUCgUCoXi30vhrlD9nIm6Nr5Qn2lrOvv/o+yrRiYpCgzJ1sJWAYEf93XrClsNrLXtibo2vrDVoKazP1B0ymZ/QnBhq0F995Gk6TbkLficKaZtC0C6flMhawKWmtb8GTcyb8HnzKulgsnQby5sNbDQtDL80BW+LmhbIQb4FrYWyOl7QRZ+PYLwAyDlwfJCVgTsrbqDPqKw1QBNCy6nFsiHX54J7xLGrxcXkfumqNStO64OL2w1aOI5FoBxBz4sZE0gsN6MItNPO5ocUthqUNslAKDI9I/G7C98HxlRfwYLTw8sbDV4u/JPALRd2buQNYENXecXmfZGociMCiYpFAqFQqFQKBQKhUKh+E+j+XcPvCpwivxy5QqFQqFQKBQKhUKhUCgUiqKDCiYpFAqFQqFQKBQKhUKhUCjyjZrmplAoFAqFQqFQKBQKheI/jUaoaW5PghqZpFAoFAqFQqFQKBQKhUKhyDcqmKRQKBQKhUKhUCgUCoVCocg3apqbQqFQKBQKhUKhUCgUiv80GqHG2jwJKpikKDB27ohh3Lil6PV6erzeiA8+aGOW/uBBOkOHzCUm5iKOjsX5dur7eHu7ADBjxkaWL9uFRqMhcPgbNG5cjXPn4hn8xWzT8ZcuJfPppx3p+04Lvvh8FufPJwCQcvsu9na2rAobnqNeu3aeZOL4Veh1err28KVf/xZZ9MogcNhCTsZcwsGxOJO+7YOXl5MpPe7qDbp2nMiAga3p+54fsecTGTJ4nin98uVrfDyoDb36NM23rY7svcxv3+1Hr5O06FiJrn1qmqWvWRRDxJq/0Go12Dta83FAI1xLlSApLpVv/CPRS4kuQ9K2RxVe61olz/wKumwAAvznsW3bcZyd7VizdmS2PH/9dTOTJi5nz57JlHQqkaeOUfuuMv+Hg+j1kmbtK9KxVzWz9A1LTrJt7Rm0Wg12jsXoP8wXFw/DeZMT7jBn4l6uJ94FAV9N8sO1VN55PuTPnSeZOH4Fep2kWw9f+vVvmcU+GQQO+4MTMZdxcLTlm2/74uXlzPGoCwSPWgKABAYMbEOLlo/KUqfT89brU3Bzd+DH6R/kU5cTTAhZgU6vp3uPhrzfv1UWXdLxH/oHJ05cwtGxOJO/fQcvL2d27zrFd9+uJj1dh6Wlli+/7kID3xcMtlt/mJkzwtHr9DRtVo3BX3XOt20Aju+LY9GPR5A6SeP25WnX80Wz9E1LT7Nz3Tm0WkEJx2K8O6Q+Lh7FAQidcYyoPVcB6NinGvWbl36ivHfuPMGEkGVGe7xC//6v5WCP+cScMPjulG/fw8vLmZs3Uvn88zlER1+gSxdfho94w3TMhvWHmDljEzqdnqbNqvPlV13ypYuUknEhy9i+IwZrGysmhPSmWlWfbHLRMRfxD5jP/fvpNG1SjcCAHggh2LDxMD/+tJ6z5xIIXfIVNaqXMV5DBqOCFhEdcxGh0RDo350G9V94IjvlxJzegXSo0YjE2zeoMabnM58vK1JKxo1byvYd0VhbWzFhfF+qVctevtHRF/D3/537aek0bVKdwMA3EEJw8+Ydvhg8iytXruHl5cx3U/vj4FCcs+fiCfD/nZgTl/ji80706/daDrmbs/vPv5gycS16nZ7O3erxzvvm9fGDBxmMCgjl1IkrODjaEvLNW3h6lWTD2qPMn7vTJHfmr3jmLx1I5Sqepn2DB83jyuXrLFn5ef5sEhJq8BFrSyaE9MnZJjEX8fefZ7RJNQIDXn/kIz+u4+y5eEKXDjH5SFRULCNGLTTlMWhge1q1qp2nPgD7d5/np8lb0esk7bpU5613G5ilRx2+zE+Tt3LuTBLDQzrQtKW5791JTeO91+fSqFlFPh1q3n7mh2e9byZ+s5Kt26KxtNRS2seF8eN6YW9va7TJIkMewKCB7WjVslauejxt3Xo86gJBoxabruXjgW1p2cqQz/DABezYFoOTkx2r1vg/sW2y2WB/PIt/PILeWNe2fdu8fd+2+izbVp1BaATWNhb0/rIunmXtnznfrFw5do0D888g9ZKKzUpRo1MZs/QzO+I4tOgctiWtAKjSyotKfp45nSpfFMV+2tG9V5j73X70eknzjpXo0ruGWfraxTFErvnb2E8rxkcBjXD1eNTvuHvnAV/2DKNeYx/e+9L3SU1iRmH2jzJzNcrcL6p3NPeLszviOLz4kV+80MqLSs0MfhEx6RjJZ1Nwe8EBvy9rZjv3s3DmUDIbZ/+FXiep85oXr/Yom6Pcid0JhE44Tv8p9fGsVDD3zctuNfioZm80QsPGC9sI/WttNpnGXvXpVaUbEsm5WxeZdHA6AO9V+x/1PWohhOBIYgy/RM3PM79nbWMM7e6cTO3u+zg42HL79j2+HvIbV+NuoMvQ8957LenerSFXrlzjk0Ez0UtJRrqOXr0Teeutt57dcIp/DSr0pigQdDodwcGLmDX7E9auG8W6tQc4c+aqmcyy0F3Y29sSvnkMfd9pwZTJKwE4c+Yq69cdYO26kcyePYjg0YvQ6fSUL+/BqrDhrAobzvIVAdjYWNHS2Hme+l1/U9prr9WhVauXctFLT8jYFfw84wNWrhnKxvWHOXsm3kxm5fJ92NvbsHZTIL36NuW7KeYNweRJYbza+NHDc9lybixd+RVLV37FomWDsba2onkL807G422lZ87kfQROacXUhV3YteU8l87fNJMp94ITE3/tyJT5nfH1K8P8nw8C4Ohiw7iZ7Zn8e2dCZrVn1fzjXE+6m0d+BV82AF27NWTW7EE55hkXd51du07i6emUY3pW9Do9v089wNff+DFxXgf2RMRyJfaWmUyZSiUJntWWkLntqdesNIunHzGlzRi3m3ZvVWXiHx0ZPaMN9iWt85WvwT56QsYuY/qMD1m1ZhgbcvCRFcv3Ym9vy7pNw+ndtxnfTVkDQMVKpVgU+iWhK4cwfeaHBActJSNDZzpuwfztlKvg/kS6jB0TyvSZH7F6TQDr1x3i7Jk4c12W7cXewZYNm0bSu08zvp28GoCSJYvz4/QPWbnan3Hje+E/1NApuXnjDlMmhzHnt4GErQ0gOfk2e/eczrdOep2eBd8f4ouJTRjzexv2RV7garaycWTEjFaM/rUNdZv6sGzGMQCO7bnKxb9uEDS7NcOnt2LTklPcu5P+RPYYN2Ypv8z8mNVrhrN+3SHOZLHH8mV7sHewYeOmIPr08ePbyWEAWBWzZNCnHfj6665m8jdvpDJ58irm/DaI1WuHk5yckm977NhxgtgLSYRvHMWY0W8RNHpxjnJBwUsYE/w24RtHEXshiR07TwDwQiVPpv3Qn3p1K5jJhy7bBcCasEB+m/0JEyetRK/X50unxzF3zzraTPvimc+TGzt2RBN7IZHwTcGMCe5J0OiFOcoFjV7ImDG9CN8UTOyFRHbsjAFg5qyNNPStQvimMTT0rcLMWZsAcHSwJXD4m/R7r2WO58uKTqdn0rjVfP/zOywN+5zwDcc4dzbBTCZsxUHs7W1Yuf4r3u7diGlTNwLQtkNtFi4bxMJlgwgOeR1Pr5JmgaTILdHY2lg9gU1iDDbZGMSY0T0JCs7FR0YvYkxwT8I3Bhlt8tBHSjFt2gfUq1vRTL5SJU+Whw4lbGUAs2d+wsighWZ1zeNs88OECMb/0I1fl71D5KbTxJ67Zibj5mHHkNFtaNHmxRzP8dv0XdR8yTs/l58jz3rfNHqlCmvDAlizKoCyZd2YMSsceGiTIYSt9Gf2zI8ZGbQoV5s8S91asVIploR+xfKVQ5kxcwDBQUtM+XTp0oBfZg54attkRq+TLPz+MJ9NaEzw3Dbsj7jI1dgUM5kGLUoT9GtrRs1+jdb/q8LSn48WSN5meugl+37/mxZDatJpUn1i9yZy88qdbHJlfV3pGFKPjiH1nimQVBT7aXqdnl+n7MV/Sku+XdCZXVvOczlLP61sJSfGz+nAN/M60cCvDAt+OmSWvnTWUV6snf/2/3G6FFb/yEwPvWT/73/T/OuadJxYn9g9OftFmQautHoyitUAACAASURBVB9Xj/bj6pkCSQBV25em0Yc51zHPgl4nWT/jND1H1WbgTw2J3hFP0sXUbHJpdzPYt/oSXi8UXPBVg2Bgrb6M2P0NH24ZSjPvhpS2M78XPIu78+YLHflyRzAfRfgzI2oBAC86VaKqcyU+jghgwBZ/XihZjhoueb8cftY2ZuasTTRsWJnwTaNp2LCyqd1dsHA7FSqUYvWqQObP+5yJk5bz4EEGrq4OLFn8FWErA1i65GtmzZpF5cqVn/6G/wegEaJQ//5pqGBSHgghPhdC2D7lsR8JIfoUtE75yDdICPGV8XewECJ/PXKDvKcQYpnxd20hRLv8HBcVFUXpMm74+LhiZWVBu/b1iIiIMpOJiIyiS9eGALRuXYc9e04hpSQiIop27ethZWWJt48Lpcu4ERUVa3bsnj2n8PFxwcvL2Wy/lJKNGw7RvkPdHPWKPn4Rn9IuePs4Y2llQZu2L7EtMtpMZmtkNJ261AOg1Ws12b/3b6SUAERuOY6XlxMVKubcIdi39298Sjvj6ZW/oAnAmRPJeHjb4e5lh6WllkYty3Fw50Uzmeovl6KYtWHg4AvVXA1vlABLSy2WVloAMtJ16GXe+T2vsqlXrxIODjnfGuPHh/L1190gn3Xi2ZPXcPeyw83TDgtLLb4tynDoz0tmMlXreJhsUrGqiymIdiX2FnqdpEa9UgBY21qa5PJD9PELlC7tgrePi8lHtkYeN5PZFnk8k4/UYp/RR2xsrLCwMJRHWloGmduA+Pib7Nh+gm7d8/9G8njUBUqXdsXHqEvbdnWIzKJLZORxOneuD8BrrWuzb+9fSCl5saoPbm4OBvtUKsX9tHQePEjn0uVkypRxxcnJDgDfhi+wOfxYvnU6d+o6bl52uHqWwMJSS/3mpTmy64qZTJWX3E02L1/VmRvGsom7kMILtVzRWmgoZmOBdwVHovfHZcsjd3vE4lPaBR8fF4PvtqvD1khz342MjKJz5wZGe7zE3r2nkVJia1uMl1+ugFUxSzP5S5evmdmjYcMqhIfn74EsIjKKLp3rI4Sgdq1ypNy+R2KSeac+MekWqan3qV2rHEIIunSub7rfKlTwoHy57HXJmbPxNPCtDICzsx12djZER1/MJvek7DxzlOt3UvIWfEoiIqLo0tnXYI/a5UlJuUdiYhZ7JBrtUbu80R6+RGw59uj4LoZ6p0uXhmwx7nd2tqdmjbKmeysvYo5fxqe0M94+TlhaWtCqbU22bz1pJrNj60nad6oDQPNW1Tmw76ypnn/Ipg3HeK3tozfmd++msXDeLt770C//NomMokvnBkablCMl5e5jbPLQRxoQEWG49goVSuXoI2Z1zYN0RD47nKdi4vHyccTT2xFLSy1+r1Vm97YzZjIeng5UqOSa4zn/OpnAjet3edm3TLa0/PKs982rjV40XXvtWuWIjzc81JvXv4+3ybPUrea2zyBzRV+3XkUcHJ+qi5iN86eu4+pZwljXaqjX3IejWepam+KP6rO0+xn59oMn4drZFOzcbbBzs0FroaGsrxuXDiUXeD4PKZL9tJPJuHvb4+5l6JO80qIcB3aa90ky99MqVXPlWtKjwMq5U9e4ef0eNes9+zN3YfaPMpOTX1x+Ar8oVa0kFjb5q9OfhCt/38KplA0lPWzRWmqo1tidU/uSssltXXCWRt3LYmFVcI++LzhV4OqdBOLvJpEhdWy/vBffUi+bybQp68eac1tITTeUya0HhjZZIrHSWGKhscBSa4lWaLmZlnd7/axtjOF4Q7+0S2dfthj3CwF37txHSsmdu2k4OBTHwkKDlZUFVlaGeufBg4wCedGl+HfxrwwmCSEKcvre58BT9RSklL9IKeflLVlwZL12KeVIKeWW/B4vpbwqpexh3KwN5CuYlJCQQCmPkqZtD3dHEhJumMkkJtykVCmDjIWFFjs7G27euENCwo08j12/7iDtO9TLlu/Bg2dwdrajbNmcOxGJCbfw8HA0bbt5OJKQtdLNJGNhoaWEnTU3b97h7p00fpsTyUcft871ujeuP0KbdjmPisqN60l3cXYvbtp2ci3OtceMLopY+zcv+XqZtpMT7vBl7zA+6hJKl17VcXJ9vHs+77LJpu+Wo7i7OVKlSv7fZN9IvoeT26PrcHK15UbSvVzlt687S80Ghk5a3KUUbEtY8n3gDob3W8+inw+j1+W/sUtIuIV7pmt093DM1jBnlsnsIwBRx2Lp2nEC3TtPZMSoN0wPHZMmrGTwV53QaPLf2U9MvGnmr+7ujiQm5OCvpbL7a2Y2hx+l6oveWFlZUrq0K7HnE7hy5RoZGToiI44TH//4MszMzaR7OLnamLZLutpy8zFl8+e6c1Svb+i4Pgwepd3P4PbNNE4dSTQFRvNDQuItM/9zdy9JQo72yOK7N7O/LX2IwR6JJntERBzLtz0SEm/ike1+MH9bnZBwEw93R3OZRHOZrFSp7EVk5HEyMnRcupxMzIlLxD1BGRUWCQk3TbYH8PDIxR4eOctcu5ZiCoC6utpz7drTBb6SEm/h7uFg2nZ3dyApwfxciZlkLCy0lChhza2b5r64eeNxs2DSL9O20LPvq1hb539kUvbrLZmt/BMSs/pIyWx2y4ljx87TvsMYOnUex+hRb+Ur2JacmIqru51p29XdjuSk7G/tc0Kvl/wydRsffZ7/qUE5UZD3zfIVe2jSuKpp+9ixWNp3HEunziGMHvW/XG3yrHVr1LFYOncIoWvn8YzMVM8XJDeztIMlXW25mZy9rt268gwBPdezfEYU/xuUv6mOT8LdG2kUdypm2rZ1KsbdG2nZ5C7uT2a1/wG2fR/NnWv3nzq/IttPc3vUT3N2s+VGUu7tytY1f1Pb2E/T6yXzfzxA709yfsn5pBRm/ygzd2+kYZsfvziQzNqAA+z44dn8Ir/cvpaGvcuj0Vb2LtbcvmauV9zZFFKS7/NCPZcCzdvFuiRJ966btpPvXcfZuqSZjFcJD7xKlGJykxFMbTqKl90MI+ROXT9DVPJJFrSdxoK20ziceJxLt81nDeTEs7Yx167dztLu3gagZ89mnD0XT+Mm/nTqPI5A/x5oNIYwQVzcdTp2Hkuz5oH079+f06dP562o4j9DkQ0mCSHKCiFOCSEWCCFOCiGWCSFshRAvCyG2CyEOCSE2CSFKGeW3CSG+E0IcBD4TQrwuhIgWQhwTQuwwymiFEN8IIQ4IIaKEEB8a9zczHr8sU55CCPEp4AlsFUJsNcpOF0IcFELECCFGZ9J3ghDihPG8k437TCOEcrnG2kKIvcZjVgohSgohqggh9mexw3Hj73xde5Y85gohehh/xwohxgshjhqvoY7xPGeFEB9lyi9aCGEFBANvGuXfzEH/D4znObhlS77jVU/MgwcZREYeo02bl7OlrVt7IMcgU0Ew/adN9OrTFNvixXJMT3+QwfatMbzWuuA7cw/ZsfEs504l06lnddM+F/fiTJnfmWlLu7Nt/VluXs+9U/H/zb17D5gxYyOfftbpueWxK/w8509fo/1bhgcKvU5yOiqJtwa+xOgZbUi8msqODeeeW/5ZqVmrLCvXDGPR0sHMmbWFtLR0tm+LwcmpBFWrZV8X5Hlz5u84vp2ympGjDbesg4MtI0a9wVeD59K31/d4eTmh1T6fqn9PeCyxp6/T5n+GodrV63lQo4En4wdGMHPMHipUc36i4NrzwGCPN/ly8K/06TUVLy/n52aP/NK9W0M8PBzp/vokQsYv56Xa5dBqimzz/FwQQjyXURb5JTrqEtbWllSs5AHA6VNXuXz5Gn4tquVx5P8ftWqVY93aESxbOoQZszaRlpb/KaNPw+rQo9RvVM4sGFWYTP9lI1qthk4dH7X5tWqVZd2a4UabhD83m9SsVZawtQEsXvoVs2dtfu62fxx+XSsSsqAd3T+oybr5J/M+4Dng/ZIL3b7zpdP4enhWd2LXjFOFokdR6Kft3HSWs6eu0eltQz8tfMUpajf0NgtG/X9R2P0j75dc6DrVlw4h9fCo7sTuQvKLzEi9ZNOcv3jtvWdfh/Bp0Go0eJVwZ+jOECYc+JnPXupHcUtbShV3w8fOk94bP6PXhk+p5VqVas7/vzoa2l3D7z//PMGLVXzYuWM8q1b4Ezx2KamphueLUqWcWBM2nPBNo1m5ciWVK1d+9vmbRRghNIX690+jqC/AXRnoJ6XcJYT4FRgIdAU6SymTjAGOccB7RnkrKWVdAGMAprWU8ooQ4mF4th9wS0pZTwhRDNglhAg3pr0EVAOuAruARlLKH4QQgwE/KeXDsZyBUsrrQggtECGEqAlcMepVRUopM+WXF/OAQVLK7UKIYGCUlPJzIYSVEKKclPI88CawRAhhCUzL57UHPSbPi1LK2kKIqcBcoBFgDUQDvzwUklI+EEKMBOpKKT/J6URSypnATIAjR47IaT8Gm9LiE27i7m4enXdzdyQu7gYeHiXJyNBx+/Y9HEsWx929pNlb+KzH7twRTdVqpXFxMZ/nnJGhY/PmIyxfEZDrxbq5O5iGxAMkxt/E3c0hRxl3D0cyMnSk3r6Po2NxjkddYEv4Mb6bsobbt+8hhMCqmAVv9WwMwJ87T1GlqhfOLk/W0XZyteVawqM3XNeT7uCcw+iiqANXWfF7FKN/amOa2pb1PKXLO3LyaAINm5fNNT93d/fH2tdgg6crm6xcvJjE5cvX6Nx5DAAJ8Tfp1m0cS0OH4eaa62GUdLExG7FyPekuJTONhnlI9ME4Vs+LJmBaK5NNnFxtKV2xJG6ehnJ4ubE3Z2LyP/Ta3d2BhEzXmBB/0/TWJquMRxYfyUz5Ch7Y2BbjzN9xHD18jm1bo/lzxwnS0jK4c+c+/kPmM35S78fq4ubmaOavCQk3cXPPwV/jbprKKrMu8fE3+GzQbEIm9KZ06UcGb+ZXg2Z+hrdhoUt3md425QdHVxuuZ3oLeiPpLo45lM2Jg/Gs++MEQ75vbuavHXpXpUNvQ8d25pg9uPvk/35xd3Mw87+EhBu452iPLL7r+PhOvJ9fDfyM9li69M/H2mPBwu0sDd0NQI0aZcxGMRnuB/Pq3t3dkfhMoy7iE27i7vb4JsHCQkvAsO6m7f+9PYWyZd0ee0xhsWDBNpaG/gkY7RGXyR7xudgjPmcZZ2d7EhNv4ebmQGLiLdPUwyfF1c2BhPhHIxkSEm7h6m7eXrgZZdw9HAz3Tep9s+lJ4RuiaN3u0eLNx49d5GTMFTq1noQuQ8/163f48N1ZzPitf7b8FyzYzlLjulc1qmfxkfgb2crf3S2rj9zIZrfHUaFCKWxti/HX31dNC3TnhotbCZISbpu2kxJu4+Kav8V3T0Rd5fiRK6wOPca9uw/IyNBjY2NJ/0+b5HlsQd83K1buZdv2aOb++mmOQccKFTwea5NnrVuz5vP333FUr/5kHxPIC8cs7eCNpLs4umSvax9Sr7kPC747lGv602Jbshh3rj8a2XH3ehq2Jc2DNdZ2j6bbVfQrxaHFZ586vyLbT0t81E+7lniXkq7Z2xVDP+04QT+1NrV7f0UncSoqkc0rTnH/XgYZ6XqsbS15e0D2F6L5oTD7R5mxLVmMu3n4RbHMftGsFEeewS/yi51zMVKSH42ASkm+j53zI73S7ulIvHCHuYGGeyX1xgMWjTvKW4G1n3kR7uT7N3C1eTR90sXGiWv3zUcVJ9+7zunrZ9FJHQl3k7iSGo9XcXdquL7IqetnuK8z2PRgfBQvOlUi5tpf2fK5tSeBznM7A6nP3MY4O9vl2O6uWLGHD/q3RghBmTJueHs7c+5cAjVrljU7b6VKlTh+/HhjYNlTGU3xr6Ooh78uSSl3GX//AbQGqgObhRBHgeFA5rk0SzL93gXMFUL0Bx4+2bwG9DEeuw9wBioZ0/ZLKS9LKfXAUaBsLjq9IYQ4DBzBEHyqCtwC7gNzhBDdgDzncQghHABHKeV2467fgYc9tKUYgkgY/1+CIbCW32t/HKuN/x8H9kkpb0spk4C0JwiCZaNGjRpciE3k8qVkHjzIYP26AzRvbv61hubNa7Jq5R4ANm06jK9vZYQQNG9ek/XrDvDgQTqXLyVzITbRrPJat+4g7dtnH320Z/cpypX3MBvumZVq1X24eMEQ4Eh/kMHGDUdo6lfdTKaZXzVWrzoAwObwKOo3qIgQgrl/DGLDlhFs2DKCnr2b8P4HLU0dFDB8IattuzpPbKuKL7oQdzmFhKu3SU/XsWvLeeq+aj6C5fzpa8ycuIehk1rg4PSo03At8Q5paRkApKakcSoqEc8y5p2urDzPsslK5cpe7N7zDZGRIURGhuDu4ciKFYG4uj5ex/JVnIm/fJvEq6lkpOvYG3GBOo3Mp8nF/nWd3ybv54vxTXHItIBk+SpO3E19QMpNQ2fixOEEvMo+Pr/MVKtemgsXks18pFk2H6meyUeOUb9BJYQQXL58zbQQ69Ur14k9l4CnlxOfDe7Ilq2j2bhlFJOm9KF+g0p5BpIAqtcobeavG9YfNgU9HuLnV52wMMPgxfBNR2nga9AlJeUuH380g88Hd6JOnfJmxzwcxnzr1l0WL/qT7j0a5ts+5So7kXD5NklxhrLZH3mR2q94mclc+PsG8749yKCQxmaLe+p1elJvGTpKl87e5NLZm1Sr65HvvKvXKGO0h9F31x/Gz8/cd/38ahAWts9ojyM08H0hzxEu5vbYSY/H2KPn200JW+lP2Ep/Wraoyaqw/UgpOXrsPHZ2Nrhl8W03VwdKlLDm6LHzSClZFbafFs0f/+Wae/cecPeuwU67dp9Eq9VQsWKpxx5TWPTs2YywVcMJWzWcli1qsypsr8EeR89hZ2edLRDr5ma0x9FzRnvspUULgz2aN6/JqlWGemfVqj2m/U9K1epeXLyQzJXL10lPz2DzhiiaNDNf6LVxsyqsW30YgMjN0dSrX97kJ3q9ni3hx2nV5lH+Pd70ZUOkP6s3DWHWvA8pXdY5x0CSwSZNCVsZQNjKAKOP7DPaxOgjudrkoY/sy9NHLl1ONtU1V65c49y5hGxrCOZElaoeXLl0k7grt0hP17E1/DSvNK2Q53EAAePas2j9Byxc258PP29Kq/ZV8xVIgoK9b3bsPMHsOVuY/tOH2GRaDN3cJtc5dy4+V5s8S92atZ4/fy7B7EtiBUXZKiVJvJJKUtwdMtL1HIi8RK1XzNfcSbj8KDB4fG8cbl4FP2rMubwdt+PvcTvxHroMPbF7E/GpYz49KPP0psuHknHwfPp1o4piP61CFRfiL6eQePU2Gek6dkecp+6r5n2S839dY/akPQyZ2ByHko/6aZ8GNeHnFT34cXkPeg2sS5M25Z86kASF2z/KzEO/SM3kF95Z/eJmJr84/Gx+kV+8Ktlz7eo9bsTfQ5euJ2ZnApUbPHqZZl3cgiELmvL57Ff5fPareFe2L5BAEsBfN87hWcIDd1tXLISWpt6+7I07bCaz5+oharoa2iN7qxJ4lfAg7m4SSXevUcOlChqhQSu01HCpkus0N4eG7oSFhRVIG9O8eU1Whe0FMLTHxv2lSjmxZ69hJFlycgrnzyfg7eNCfPwN7t9/ABj6TIcPHwbI/1dc/oFoCvnfP42iPjIp6/LCt4EYKWVuPX/TawQp5UdCiAZAe+CQEOJlDMsBD5JSbsp8kBCiGZB5gq2OHGwjhCgHfAXUk1LeEELMBayllBlCiPpAC6AH8AnQPN9XmZ0lQKgQYoXhUuTfQoga5PPa8+Dhdeoxv2Y9z+APFhYWjBj5Jv3e/wG9Tk/37q9QqZInP3y/murVy9C8RS169GjEkK9/47VWI3BwsOXbqe8Dhi+ytG37Mu3bjUar1TJy5P9M007u3k1j1+6TjA7O/lnrdesP0CGHIJO5Xlr8A7sxoP9M9Ho9XbrWp2IlD36atoFq1Xxo1rw6Xbs3IHDoQjq0Hoe9oy2TJue9Zvrdu2ns3f0XI4Jef2JbaS009Bvsy7gvNqPXSfw6VMSnfEkWzzpChSrO1Gtcmvk/HeT+vXSmDN8KgIt7CYZNasHl2FvMm3YAIUBK6PhWNcpUyD2YZrDB8ymbwYNnc2D/X9y4kUrTJsMYNKgjPV5v9MT2eGiTPp/X5ZuvItHrJU3aVcC7nCPL5xyjXGVn6rzqzeLpR7h/L4NpowyjIpzdbBk8oRkarYa3Pq7DhM8jkFJStrIzfh0r5pFjZvtoCQjszoD+v6DT6+nStQEVK5Xip2nrqVqtNH7Nq9O1uy8BQ/+gfeuxOGTykSOHz/HrrAgsLDSGT7qP6EHJkk/3yV2TLsN78OH7P6PT6+nazZeKlUrx4w/rqFa9NH7Na9CtR0P8h86nbetgHBxs+WbKOwAsWrCTSxeT+WX6Rn6ZbvhS1czZH+PsbMeEkOWcPm1YyPWjAW0oWy7/o160Fhp6flaHqV9vR6+XvNq2PF7lHFj163HKVnaidiMvQqcfI+1eBtNHGUYiOLnb8mlIY3QZkgmfRgJgY2tB/0BftBb5bygtLLQEDn+DD97/Cb1emuwx7Ye1VKtemubNa9K9xysMGzqPNq2DcHAozuQp75qOb9ViJKl37pOenkFkRBQzZw+kYsVSjA9ZZrLHgAFtKJvDgsc50bRJNbbviKFVm9HYWFsSMq6XKa1z1/GErTR8GnzUiDfwD/iD+2npNGlclSZNDCOzNm85xphxoVy/nsqHA37hxSpezJn1Cdeu36Zf/5/QaATubo5MmtA33zZ6HAvfC6bZC3VwKeHIpZDVjFo7i193rymQcwM0bVqd7TuiafXaCGysrQgJeaR35y5jCVs1HIBRI9/GP+B37t9/QJPG1WjSxPCg+EH/1nz+xSyWLd+Fp6cz3001BGuSkm7Rvcd4UlPvo9EIfp8Xyfp1oyhRIudRGhYWWoYEdOLTj35Dp5N06voyFSq688uPm3mxmjdN/V6kc7e6jPIPpWu7ydg72DJu0v9Mxx85FIu7hwPePs8eIDDYJIZWrUcZbfIoiNy5awhhKwOMNvmf6bPNBpsYptNt3nyUMeOWGnzko595sYo3c2YP4tChs8yaFY6FpRaNEASNfBOnfNQ1WgsNg4Y0Z+gny9Hr9LTtXJ2yFVz4bfouKld155WmFTkVE8+or8JITbnPnp1n+X3Gbn4NfeeZbWGyyTPeN2PGLuVBegbv9vsRMExtCw56i0OHzxlsYqFFoxEEjcjdJs9Stx4+dJY5s7aYbD985Bumev7rL+dyYP8Zbt5MpUWzEXz8SbsnCtZnRqvV8PanL/HdkB1IvaRR23J4lXMg7NdoylR2onYjT7auPMOJQ4loLQTF7ax4d1jBT/PXaDXU71uJLZOiDJ+Ab1oKR+/iHF12Hudydvi87MKp8CtcOpyMRiuwKm5Jow/z/gpVbhTVftp7XzQgZPAW9Do9zTpUwqd8SZbOOkL5Ks7UbVyaP346xP17GUwdvg0wLEEwZFKLJ84rP7oUVv8oMxqthnp9KhHxjcEvKjQx+MWx5edxKmeHTx0XTm+6wuUjyQiNoFgJSxp+8MgvNo05QkrcXTLu61jx6W5836+CZ81nr3M1Wg3tPqzMH0FHkHpJ7ZaeuJUuwdYFZ/GsaG8WWCpo9FLP9GPzGNvoa7RoCL+wg4u3r9D7xW78deM8++KPcCjxOHXcazCjxQR0Us+c6MXcfpDKn1f2U8u1KtNbhICEg4lR7Is/kmeez9rGfPD+a3w+eA7Llu3G09OJ74z9/Y8/bou//zw6dhqLlJKvvuyCU8kS7Np1kgmTliOEQErJe+99xJtvvnk8u2aK/yoi69dMigpCiLLAeeAVKeUeIcRs4G+gP9DbuM8SeEFKGSOE2AZ8JaU8aDy+gpTyrPH3AeNx9TEsKP26lDJdCPEChilq9YzHdjDK/wgclFLONU6X6ySlPC+EqIVhatpLgCsQBQzFMNTPVkqZaBxxdE5K6WycbpYqpZycyzUeAz6RUu40yjpIKb/IpPMp4LiUcpJxDaMT+bx2U77GgNdaKeUyIUQshmlryUKId8g0he1hGlDCKF9dCNHdeO35ebKRkq35EHu+CPy4r1tX2GpgrW1P1LXxha0GNZ0NnfSiUjb7E4LzFnzO1HcfSZpuQ2GrQTFtWwDS9ZvykHz+WGpa82fcyMJWg1dLBZOh31zYamChaWX4oSt8XdC2QgzI/xcBnxdy+l6QhV+PIAxfVkt5sLyQFQF7q+6gjyhsNUDTgsupMwtbC7xLfGD4UUTum6JSt+64Oryw1aCJ51gAxh34sJA1gcB6M4pMP+1ockhhq0FtF8PDf1HpH43ZX/g+MqL+DBaeHljYavB25Z8AaLsy75Hlz5sNXecXmfaGfH+r+Z/J2VvfFWpwpILD5/8o+xb1kUmngYHG9ZJOYFgzaBPwgzFoYwF8B8TkcOw3QohKGBw+AjiGIfhTFjgsDOPbk4AueegwE9gohLgqpfQTQhzBEOS5hGEqHYAdECaEsDbmNzif19cX+EUIYQucA97NlLYE+AYoB6Y1jHrk89oLiq3AMOO0uvFSyvxOpVMoFAqFQqFQKBQKheIfwz9xEezCpKgHkzKklL2y7DvKo7WFTEgpm2XZ7pbD+SQQYPzLzDbj38NjP8n0exqGINbD7Xdy0bV+DjoF5SL7MP0okOMrZ+Nopsk5yOfn2oMy/X4n0++ymX7PxbAAd9a0ZAxrMyGlvI5h1JZCoVAoFAqFQqFQKBQKBVD0g0kKhUKhUCgUCoVCoVAoFM8VjRqZ9EQU2WCSlDIW4wiZfzpCiJ+ArCsTfy+l/K0w9FEoFAqFQqFQKBQKhUKheFqKbDDp34SUsvBXkVMoFAqFQqFQKBQKhUKhKABUMEmhUCgUCoVCoVAoFArFfxqBmub2JChrKRQKhUKhUCgUCoVCoVAo8o2QUha2Dop/B8qRFAqFQqFQKBQKheLfiyhsBZ4nl1J/KdRnWp8SH/2j7KumuSkKDrm1sDUA4YdeRhS2FmhEiyJjDwBJ4esi8AN94ZcNmqJVmerIdwAAIABJREFUNkXGX3WbC1sN0LYqOr4K7E8ILmRNoL77yCLjr2KAb2FrgZy+F4AdV4cXsibQxHNskSmbonL/QtGp04qKHkWpbEhbV7h6ABRrz31d4ethrW3PnfSwwlaD4padAUjTbShkTaCYtm3R8deiogcUnXq+qOihUGRCTXNTKBQKhUKhUCgUCoVCoVDkGzUySaFQKBQKhUKhUCgUCsV/GrUA95OhrKVQKBQKhUKhUCgUCoVCocg3KpikUCgUCoVCoVAoFAqFQqHIN2qam0KhUCgUCoVCoVAoFIr/NBqhxto8CcpaCoVCoVAoFAqFQqFQKBSKfKNGJikUCoVCoVAoFAqFQqH4TyPUyKQnQgWTFAWGlJJx45ayfUc01tZWTBjfl2rVSmeTi46+gL//79xPS6dpk+oEBr6BEIKbN+/wxeBZXLlyDS8vZ76b2h8Hh+IA7Nt3mpDxoWRk6CjpWII//vgSgObNAyhe3BqNVoNWq2HFCr9s+e3cGUPIuFD0ekmPHq/Q/4PWZukPHqQzdOjvnIi5hKNjcb79th9e3s7cuJHK55/NIjr6Il26+DJi5JumY76bGkZY2D5SUu5x6PDU/3ebzJ4Tzpo1+wHQ6fScPRvHnt2TuXcvjSFD53LtWgpCCN544yJ9+/Zl544Yxo1bil6vp8frjfjggzbZbTBkLjExFw02mPo+3t4uAMyYsZHly3ah0WgIHP4GjRtXMx2n0+np0X08bu6OzJgx0HTN330XxsaNh9FqNPzvrSb06dM8d/uEhLJ9RwzW1pZMCOmTs31iLuLvP89on2oEBryOEIINGw/z44/rOHsuntClQ6hRvYzZcVevXqd9xzF8MrAd/d5rZchv7Fi2b99YoOWRWznv3Xua8RNCTec9dy6eqd++T8uWtdmzZw+TJk1Cr0/B1rYYIeN7U6aMW4H76717D/j881lcupiMRqvBz68GX37ZJcfyyF42ywxlY2PFhJDeVKvqk3PZBMzn/v2HZdPjUdn8tJ6z5xIIXfKVqWwePMhgVNAiomMuIjQaAv2706D+C2bnLGh/jYu7ztAhme+LV+nTt4XZOX/9dTOTJi5nz57JODnlaR6i9l1l/g8H0eslzdpXpGOvambpG5acZNvaM2i1Guwci9F/mC8uHiUASE64w5yJe7meeBcEfDXJD9dSJfLO1MjzqlfOnosnwP93Yk5c4ovPO9Gv32v51ikv5vQOpEONRiTevkGNMT0L7Lw5Eb0/nsU/HkGvkzRuX562b1cxSw9f+hd/rj+HRqvBzqEY7wypi7OHoa1ZNiOK43vjAOjQuyr1mmf3+aw8r/LYEnGU779fg0Yj0Go1BAS8Qd2XKwLQ7/0fOHbsPC/XqciMmdnbvUK9f5+yDgOYOWMjy5fvQaMRBAa+wauNqwIwb14koaG7kBJef70Rffsa2pRvJq1g69bjWFpq8SntSkhIb+ztbXMsp39L3Trxm5Vs3RaNpaWW0j4ujB/XC3t7W3btPsmUb1eTnp6BpaUFX3/VhYa+lfPWZeJKtu88afDdMW9Rrap3dl1OXMJ/+CKD7zZ+kcChXRFCmNJ//X0bE6esZs/2YJxKGuqyfQfOEDJplbHfVpw/fvskVz127TzJxPGr0Ov0dO3hS7/+5vXzgwcZBA5byMmYSzg4FmfSt33w8npUUcddvUHXjhMZMLA1fd97dD/odHreen0qbu4O/Dj9/cfaAmDXn6eZPCEMnU7StXt93n3f/N568CCDEf6LOXniCo6OtkyY3BNPLyeuXrlO906TKVPWFYAaNUsTOKo7AP3f+YXk5BSKFbME4OeZ/XFyzru+/3PnSSaOX4FeJ+nWw5d+/VvmYJM/OBFzGQdHW775ti9eXs4cj7pA8KglAEhgwMA2tGhZE4A/5m9neegekNDtdV9692mWpx5FxV+fV50GcOr0FUYFLSI19T4ajWDZ0iFYWVkY+4vbsbbWPff+IsA3k1ewfXs0AB8PaEe7dnUBeLvnZO7c+Q5IJTk5BWtrKzQaUaDtzeo1+5g1KxykpHhxa4KC3qZKFUNd4B8wj23bjuPsbMfatdnbG8V/GxV6UxQYO3ZEE3shkfBNwYwJ7knQ6IU5ygWNXsiYMb0I3xRM7IVEduyMAWDmrI009K1C+KYxNPStwsxZmwBISbnL6OBFTP/5Y9atHcX33/c3O9/v8wYTtmo4K5YHZMtLp9MzJngJM2d9wpq1I1i37iBnzsSZySxbthsHe1s2hY+mT9/mTJ6yEoBixSz59LOOfD2ka7bzNvOryZKlQwvNJu/3e42wVcMJWzWcwV90oV69F3B0LI5Wq2XY0B6sXxfEksVDWbhwIadPnyY4eBGzZn/C2nWjWLf2AGfOXDW3Qegu7O1tCd88hr7vtGDKZIMNzpy5yvp1B1i7biSzZw8iePQidDq96bh58yIpX8HD7FwrVuwhPu4GGzYEsX5DEO3b132MfWIM9tkYxJjRPQkKXpyLfRYxJrgn4RuDjPY5AcALlUoxbdoH1KtbMcfjJkxcTmPjw4gpv9jYAi+P3MrZ17eyqZx+n/sFNjZWNGpk0CcoKIjJkyezclUA7TvU5ZfpG5+bv773bkvWbxjFihX+HDl8lh07YnIukEzs2HGC2AtJhG8cxZjRbxE0OpeyCV7CmOC3Cd84itgLSZnKxpNpP/SnXt0KZvKhy3YBsCYskN9mf8LESSvR6x/5lE6nL3B/1Wq1DB3Wg3Xrg1i8ZCgLFm43O2dc3HV27TqJp2c+okiAXqfn96kH+PobPybO68CeiFiuxN4ykylTqSTBs9oSMrc99ZqVZvH0I6a0GeN20+6tqkz8oyOjZ7TBvqR1vvJ9yPOqVxwdbAkc/ib93muZ4/mehbl71tFm2hcFft6s6HWShd8f5rMJjQme24b9ERe5GptiJlO6kiOBv7QkaM5rvNzUm2UzogCI2hPHxb9vMHJ2KwJ+bsGmpae5dyc9zzyfV3k09K3C6jBD/RES0ofhw+ebzvV+v9eYNPHdx+hUWPev7qnrsDNn4li//hBr1g5n1uxPCA5ejE6n56+/rhIauoulS4eyalUA27Yd58KFRABeeaUKq9cMJ2z1cMqWdWPmzE05Xue/qW5t9EoV1oYFsGZVAGXLujFjVjgAJR1LMP3nD1kTFsiE8b0ZMmxe3rr8eZLYC8mErw1gzMjXCRq7LGddxi5jzKg3CF8bQOyFZHb8ecqUFhd/g117TuNZqqRpX0rKPUaPW870H/qxbuVQvp/cN1cddDo9IWNX8POMD1i5Zigb1x/m7Jl4M5mVy/dhb2/D2k2B9OrblO+mrDVLnzwpjFcbv5jt3Avm76B8Bbc87fBQj4ljVzJtej+Wr/6SjeuPcu5sgpnMqhX7sbe3YfWGofTs3Zjvv11vSvP2cWbx8i9YvPwLUyDpIeMmvGVKy08gyWCTZUyf8SGr1gxjQw42WbF8L/b2tqzbNJzefZvx3ZQ1AFSsVIpFoV8SunII02d+SHDQUjIydPz9dxzLQ/ewcMlgQld+zY5tJ7h4ISlPXYqKvz6vOi0jQ8fXQ39n9Kj/sW7NcOb9/hkWFlpDfrGxhIeH/7/0F7dtO86JExdZtTKQpUuGMufXzaSm3gNg4YKvCAsLI2zVcHy8XbC1LVbg7Y23lwt/zB/MmjUjGfBxO0aM/MN0rm5dGzJ71qDHlo/iv4sKJikKjIiIKLp09kUIQe3a5UlJuUdiovkDVmLiLVJT71O7dnmEEHTp7EvElmOPju/SEIAuXRqyxbh/zdr9tGr1kulBz9nZPt86RUXFUrq0Kz4+LlhZWdCu3ctERhwzk4mMiKJzF18AWrd+ib17TiOlxNa2GC+/XJFiVpbZzlu7djnc3BwKzSaZWbfuAB2MARs3NwfTG4oSJawpX748e/fupXQZN3x8XA02aF+PiIgocz0jo+jStaHRBnXYs+cUUkoiIqJo174eVlaWePu4ULqMG1FRsQDEx99g+7bjvN6jkdm5Fi/azscD26PRGKqXx5VXRGQUXTo3MNqnHCkpdx9jn3JG+zQgwliGFSqUonw59xzPvWXLUby8nalUsZR5fl26FHh55KecN206TOPG1bCxsTLtS01NNfx/+x5ubg7PxV9tbKxoYHzbZ2VlQdWqPsTH38jRZpkxlE19wzXVKkfK7XskJmWxVZLRVrUelk19k29VqOCRY9mcORtv0sfZ2Q47Oxuioy+a0qOiYgvcX7PeFxXKe5CQcNN0vvHjQ/n6624gyBdnT17D3csON087LCy1+LYow6E/L5nJVK3jQTFrw+DfilVduJ50F4ArsbfQ6yQ16hn80trW0iSXX55XveLsbE/NGmWxsNA+kT75YeeZo1y/k5K34DNy/tR1XD1L4OpZAgtLDfWa+3B01xUzmSovuZlsXr6qEzeSDB32uAspvFDTFa1WQzEbC7zLOxC9Pz5bHll5XuVRvLi1afTHvbsPzEaCNGxYheLFi+WuU6Hdv1FPXYdFRhyjXbuXDfevtwulS7sSFRXLuXPx1KxZFhsbKywstNSrV4nNm48C0OjVqiZ/rVWrHAnxN8mJf1Pd+mqjF03XXLtWOeKN11y1qg/ubo4AVKpYirT76Tx48PhgaMTWaLp0rGvUpaxRF/P7NDEphdTUNGrXKmvQpWNdIrYeN6WPnxTG1190IJN7smb9YVq1qGEKMDk72+WqQ/Txi/iUdsHbxxlLKwvatH2JbZHRZjJbI6Pp1KUeAK1eq8n+vX8jpQQgcstxvLycqFDR3F8T4m+yc/tJunb3fawNHulxCe+Helha0LptLbZFmgcHt0WeoENnQ3+rxWs1OLDvjEmPgiT6+IX/Y++8w6I6vgb8DqACIiAIi4q9K3aNJlZQ7MaeZstnS0w0xZaIGjuWqEmMxm6iMSY/O9hRUazELmLBCooKWECKStm93x93WVlYEBIQksybxyfs3XPvnD1zZu7cc2fOULZsCVzLlDDY5KD/RSOZQ/4X09ikLn/qbZLaTgASE1MM9XL7ZiR16pQzfN+ocSX27ze+t5qioPhrXvVpx45dpVrV0oZZOMXtbTA3N3vt48UbNx/QqFEVLCzMsbYuQrVqpQ0BoFTi458TfOkOAwd45LpeDRpUMqwGUevpZV/WuHEV7OxMz/j8N2KWz//90/jnaSwpsERGxuCS5s2Ui4u90QObQcbFtMzjx7GGAI2Tky2PH6sDmtDQKGJjn9G//3x69vRm27bAlxcUgsGDf6BnT2/+978jGXSKSqeTxqU4kZHGnW5kVAwl9TIWFuYUK2ZFTEzCXzFBBvLKJqk8f57EkaOXaNeuQYayw8MfceXKFezs7CiZ9voaeyIjjQe8UZEmbBCdQGRkdKbnentvYMzYnggz4yfwO3cfsXvXaXr19GbokB8JDTV+s5f1by9OZFQ6+0TF4KKxT6ND8Qw2TE9CwgtWrNzHiE86mSjv5Uyq3KqP7NTzzl2n6dK5seHzzJkzGTZsGK1beeHre5Khw9rlub/Gxj7j4MGLvPlm9VfKRkals4MmE1sZ1Y19hvpLT/VqpfH3v0hKipa74Y+4dPkuD9IMWrLyuVT+ir+moraLu9StWwGAA/vPo3G2Nwwks0P0o+c4OL8cWDk4WRsCEqYI2HmTOk1KAfDgbizWNoX4YcJhJg7exe8/nUWXZrZfdsjrfuWfTEy6uinuZE3Mo8zr5uiu27g1UfsE10pq8CjxRQpxTxMJOf+QaH0QMCvysj727TtHh46T+ejjRXjPHPBKXQzl5Vv7jfzLfVhk5NN059oTFRlDlSolOXP6JtHR8Tx/nsThgEtEPMgYtNmy+TgtWtbMcBzyfiyQX33r5i0naNki42/e63eemjXLUNjEyzBjXWJxcUlfTnq7PMVFY5dORvXR/QeDcXa2o3q10kbnhIZFERv7nP6DFtPz3QVs8z2VqQ5RkU+NdHB2yahDWhkLC3NsilkSE5PAs4REfl7lz8efGC9ZBJg7extfjumCmVn23hI8jHqKi8vL3+mssSMqKjZTGQsLc2xsLImJ0b8ouPeE93t/z5APl3D2zG2j86ZM2sh7vb5jxdL92Qo+RUY+ReOSri2kr5c0MmltAhB0IZQeXWfTq9scJk1+BwsLcypXceHsmVvExCSoY8fDl4l8kHV7h4Ljr3nVp90Oi0IIGDx0ET16zWbFqn1pynt948Xq1Vw5cuQSz58n8SQ6nj//vJahn9u//wL2dtaUTxMUy4v7/6ZNx2jZ0i1Lu0kkqcicSZICiRDC8BZWm6Ll0qU7/PLzF7xITOa99+ZQt24FKlTQ8Pv6MWg0xXn8OJb/G/QDFSt1pGHmq6r+0aS1SSoHDwbRoH4l7O2LGh1PSHjBZ58tx8vLy2gJQm5x8GAQjg7FcHMrx59/hhh9l5yUQuEihdi8xQs/v3NM8PqV39aPyXUdsmLR4p0MHOhB0aI5Wz6UE0zVR2ZERT3l2rV7NG/+Mq/OL7/8wvLly6ld5xGrVu1j9uzNtGhu+kEoN0hJ0TJm9Gr69XenTJkSeVbOq+jV801u3oqkV5+5lCrlQP16FTA3ez3vNVLbxXivd7CxseL58ySWLdvDqtWf51mZx/xuczvkMRMWegLqMqyQoIfMWNURR+eiLJpylMO7b9G6i+mlmnlNTvz430bgvjBCQ6IZ+31rAGo1diE0JJrZI/wpZl+EijUds/0gmlukrw9Pz/p4etbn1Knr/LDQl19+/uK16pOe/Gi/lSqVZMhQT4YM/hEr6yJUr+GKmblxmUuX7sbcwpyuXd/IU11MkV9965KlezA3N+Ptro2Njl+//oB5C3xYveLTPC3/+fMklq3Yz+plH2X4TqvVcenyXX5ZMVwdt/VfSN065ahQPntLzrLLksV76TegFdbpZukFHLqEg4MNNWuV4dTJG7lapilKONmya58X9vZFuXwpnNGfrWGjz2hsbCyZOed9nDV2JCS8YOwXv7LT9yxdujXMU33q1C3P1u1fc+tmBBO91tO8RQ0qVnLh/4a04aMhS7CyKky16qUxM399/Vt++2tmaFO0nDl7i00bxmJlWZgPBy3ErWbGHER/h+zcZ5s3r8nF4FDee38uDg421KtXIUM/t2PnKZyd7TO5Qu7oFRgYwqbNx1n/2+sdtxckZALunCGDSZK/RbVq1T4FhlavXp3atR2NougRETFoNMadnkZjbzR1Mq2Mo6MtUVFPcXa2IyrqKQ4O6rRoF5fi2NvbYG1dBGvrIjRqVIWrIeFUqKBBoyluONezbT2CgoJo2Ki84frOGnsjnSIjotFojJenaZztefAgGheX4qSkaImLe54hOJMTfvstgE0bFwLx1K5dLk9sksrOXafo3Nn4xpycrOWzz5bTtesbtGvXjnPnzhm9OY6IjDHYLRVnjQkbFC+KRlPc5Ln+/hfw9w8i4HAwSYkpxMc/Z+yY1Xw7bxAajT3tPOsD4OlZD6/xazLYZ4M+70Ztt3Lpfnu0YdqzwT7O9kSkeesSERmdwYbpuRAUyt6955g3byuPHseRnJzCmrUHadmiFhEREUDRDLY2lPcX6kOTzs/SX3f3ntN4tq1HoULqVO8nT+K4evUqdevWRaccoGPHhgwbuohevd7KM3+d/M16ypVzNiSuNcVv6wPYsPE4gOq7GerehK2M6iYmQ/2lx8LCHK+vX+aTeO+D+ZRP84CRmc+lJaf+Cunbheqfd+48JDz8Md26TQfUZRE9e85k48amODk5ZfobipewUpNn63ny8BnFnawyyAWffoDv2mC8fvSkUGG17h2crClbuTjOpVTfadjClRuXHmVpM4DffjvEho1HAfK8X/knY5+ubqIfPsO+RMa6uXwmkp3rrjD2+9aGugHo3K8GnfupuVdWTA9E42raNge33mC+XzdeRz8P6jKDu3cf8SQ63pDgOD0Fo/1q/nIfptHYpTs3Bme9zr17N6O3fkn1dwt80KSZybJ1ywkOHQzm518+z/SBLS/HAvnRt27ZGsihgGB+Wf2Z0W+OiIhmxGfLmTOrP2XLmu7DfvvjKBs2qzO8a9cqY1h29LKc9HaxIyLNLC5VxpY7dx8Rfu8J3frM0x9/Ss93F7Bx/Re4aOyxtyv6ctzWsCJXr903GUxy1tgZ6RAVkVGHVBmNiz0pKVri415gb1+Ui0Fh7Pe7wPfztxMX9xwhBIWLWBAV+ZRDBy9x9PAVEhNTSEh4wfhx65g1t59JmwA4OdsREfHyd0ZFPsXZ2dakjEGP+BfY21ur5RZWH6lq1nLFtYwjd0IfUtOtDM56Pyta1JIOnesTHHz3lcEkjcaOyIh0bSF9vehlXNLZJC0VK7lgZV2EG9cfUMutLD17NaWnftnfD9/tMGpHaSko/vo6+jQXF3saN6qEQ3EbflsfQPi9J4z9eg2tW7npx4upuub9eHH4x50Y/rE6o3706FWG9vLbb4f4/Y/53Lx5k+7dm+TZ/eZqSDgTJ/3KiuUjKZ7JfUYiSY8MvUn+FiEhIYtDQkLq+fj40LZNPbb5BKIoCufP36JYMcsMNz9nZztsbCw5f/4WiqKwzSeQNm3UXSY8POqwbdsJALZtO2E43qZNXc6cvUFKipbnz5MICgqlUkUXnj1LJD7+BQDPniVy7NgVqlSpYlRe7drlCAuLIjz8EUlJKezadQZ3jzpGMu4edfDRL53bu/ccTZtW+1tv6vv2bWVIlJdXNgGIi3vOqVPXadOmruGYoihMmLhWfQP1f231NqhNWGgU4Xf1Nth5Co90NvDwqMO2rSf0NjhrsIGHRx127TxFUlIy4XcfERYaRZ065Rk9ugcBh2fj7+/N/AWDadK0Ot/OGwRA27b1DLOVTp68RvnyxmvU+/Zthc9WL3y2etG2TR22+fypt89tihWzysI+t/X2+ZM26fRPz/p1o/E/MAP/AzMYOsSTMaO7c/iQt1retm25Xh8eHnWyrOedO08bBf1sba2Ji4vj9m11Kvzx41eoWNElz/z1++99iYt7zniv3lnK9f2gFT5bx+Ozdby+bk6qv+mCvm6c0tnKSW+rC6l1c/KVdfP8eRLPniUCcOz4FczNzaicJq9V7drlct1fFUVh4oS1VKr4sl0AVKtWmuMnvsXf3xt/f280LvZs2TIhy0ASQMXqjkSExxF1P56UZC2BB8Jo0Mx4mVzotSf8PO8kX85qhV2aBNsVqzvwLD6J2Bi177p8NpLS5V+df61v39aGZO552a/80ylfvThR9+J5+CCBlGQdp/zvUvetUkYyd65Hs27BGUbMbGaU/FynVYh/qvpm+M0Ywm89pWZj0znZ3HtUzvN+PiwsyrAk5tKlOyQlJVM8i+BGwWi/tf9yH+buUYddu86o7Tf8EWFhavsFePw4DlB36Ny37zxduqj96ZEjl1i1ah8/LfnYKB9dev5NfevhI5dZuWo/SxZ/ZPSbY2OfMWz4UkaP6kbDBsZJho10ea85PhvH4LNxDG09arNt+2m9LqGq7zoZB1CcnWyxsSnC+Quhqi7bT9PG3Y1qVUtxImAa/nsm4b9nEi4aO7b8bxROJWxp4+7GmXO304zb7lApk/yGtdzKcCdMDewnJ6WwZ/c5WrkbL7Fp7V4L323qUrl9fkG80aQyQgh+WTeS3fsnsXv/JPr2b8mQYW15v28LPh/VhX0HJ7N7/yTmzO9P4yZVsgwkqXq4cvfOI+6FPyE5OYW9uy/Qyt14tnAr95rs8DkNwAG/izTW6xH9JN6wOUn43cfcufOI0mUcSUnREh2tLj1LTtZyJOAKlSubtoOxLmUJC3tkZJPWGWzilsYmF3ijSRWEEISHPyYlRQvA/XtPCL0VSSn9znep7ejB/WgO7A+iU+eMKRKg4Pjr6+jTmjerybVr93n+PIl332lOlcolmTHtg9c+XtRqdURHq3k0r4aEE3LtnmHDlr59W9O3b1/e7voG7ds1yJP7zf37Txg5chlz5/wfFTJpqxKJKUReJI6T/CdRFJ0/06b/wZEjl7CyLIy390Bq11a33uzWfQY+2yYCcPFiGOO91vDiRRItW9Ri0qT31JtxdDxffLmCBw+eUKqUul1l6luWlav82LLlOGZmZvTu3YwPB7bh7t2HfDpiKaBOqe7SpTHDh3+LTjlgpFhAQDCzvDeh0+no2etNPv64IwsXbsfNrRweHnVITFS3Gb9yJRw7O2vmLxhsmKrexmMiCQkvSE7WUqyYFStXjaRy5ZJ8++0Wdu44bYju9+79FiNGdjGUaSbagHIQRVHyzCZbthznyNHLfLfg5Xa3p8/coG/feVStWlq/PMOGUaNGoXABb++N6LQ6evV6i4+Hd2LhD76qDdrUJTExmXFjf+bKlbvY2Vmz4LshlCmjPlAvXbKLzZuPY25ujpdXH1q2Mh7Q/PlnCKtX72fZMnWKcmzsM8aOWc39B0+wti7C1Kl9qV7dFYE76IzrRrXP/zhy9LLePv0N27V26+GNz1Z1h76LwWGMH7+WF4nJqn0mqluc7tt3nukzN/DkSTy2tlbUqO7KqpXGO078uGgH1tZFGDzIUy1vxjGOHPHL1frIqp7Dwx/x/gffEnBoliEpOcC+/SksXLgQIRKwtbVmpnd/ypQpkev+amNjiXvrCVSsqDHkI/igbyv69HmZON1MtAHtvox1M2MDR45ewcqyEN4z+6Wpm1n4bB3/sm681unrpiaTJvRR62b/BabP3JimbkqzasUIwu89ZvDQxZiZCTTO9syc3vflFs/mnigcJCDgYq7665nT6dsFfDmqG61a1Tb6zR4eXmze5IWDQ1cATkZOIzPOn7jHbz+eQadTaNmpEt0GuLF51QUqVHOkQXNXZn95gLu3YrB3VGfFODpbM2p2a9Vmpx7w++KzKIpC+WqODB77BhaFTCe9fkPzDSgHM9ZNHvQrDx8+pVfvWYbtka2ti7Br52RsbKxAuCOGZy+RrSnWD5pG66oNKGFjT2TsEybvWMHq49tzfB1lifqwf/j+xExlLgY+4I/F51F0Cs06VqBzvxqb693ZAAAgAElEQVT4rA6mXDUH6jUrxYLRAYTffoqdgxpIctRYM2Jmc5KTtEwfprYDS+tC9BvVkLKVM3+r3bLUjDzt55ev2IuPTyAWFuZYFinE2HG9aNRQXQ75Qd953LoVwbNnidjbOzBzei+jZbL51X4BDh764S/3YUuX7mbL5hOYm5sx3qsPLVuqS4P79Z1PTEwCFhbmfPV1L0NuovbtJpOUlIy9vfoWvW7d8kyZ+gFmok2ejwXyq2/1bD+FpOQU7PXJcuvWLc+0Ke/z09I9LF/hR7k0MzxWrxyhJr/W1w2JOzPq4r2FI8euqrpMf5/atdTt1rv1mYfPRnWpy8VLdxk/8XdVl+bVmTS+Z4ZAm0eH6Wz6/UvDzLmVP/uzxecUZkLQu2cTPuzfShUs0pkXWmM9jgRcZu5sH3Q6Hd17vMHQjz1Z/ONuatUqQ2sPNxITk5nw1XquXgnH1t6aufMG4FrG0egaSxbtwdq6CAMHGW9dfurkDdb8fIhFS4YYHbc070xCso/RsaOHrzBvznZ0Wh1v92jMkI/asGTRXmrWcqWVey0SE5OZNP4Prl65j52dNbO+/QDXMo4c2HeRJYv8sLAww8xM8NGn7WjVuibPnyUx+MMlpCRr0ekUmjStzKhxXTFPs3ypaKFuatVod5uwyVa0Oh3dezRh2MftWPzjLmrWKou73iZeX63j6pV72BlsUoLtvqdYveIAFhZmCDMzPh7eDo+2asBgYL+FPI1JwKKQOWPGdafpm1WNyixi3rHg+OtrGpMA+PieZPkKP4QQtGxZi3FjuqvlzTzBkSNHsLLU5vl4MTExmR49ZwJgY2PF1CkfUKNGGcPv7z9gNUOHNqFF85p5cr+ZMPFX/PzOGTY7Mjc3M+ySPWrUSk6eukZ0dDyOjk5ERkYOCQkJWcW/lEcv1uVrcKSEZb9/VO4BGUyS5BZK+geefEG4ZxhA5gepwaR8R6iDKoX818VUMClfMCtYdVNg/DXdwC1f0AeT8huBWjdZBZNeF6aCSfnC3wwm5RbZCSa9LlKDSfmOcC8w7RcKTp9WUPQoSHWTPpiUL5gIJuUHpoJJ+UFmwaT8wFQwKV8wEUzKNz2g4PTzBUWPbO99+89EBpNyhsyZJJFIJBKJRCKRSCQSieQ/jcD0THGJaWTOJIlEIpFIJBKJRCKRSCQSSbaRwSSJRCKRSCQSiUQikUgkEkm2kcvcJBKJRCKRSCQSiUQikfynMRNyrk1OkNaSSCQSiUQikUgkEolEIpFkGzkzSSKRSCQSiUQikUgkEsl/GiHn2uQIaS2JRCKRSCQSiUQikUgkEkm2EYqi5LcOkn8H0pEkEolEIpFIJBKJ5N+LyG8F8pKYxI35+kxrX6TPP8q+cpmbRCKRSCQSiUQikUgkkv80MgF3zpDBJEnuoRzMbw1AuKNTDuS3FpiJNjx+8Xt+q4Gj5fsAhMX9lM+aQLlinxAatyi/1aB8sREFxlcBErW781kRKGLekajna/NbDZytBqCQ/3UjUOsmJnFjPmsC9kX6EJu0Ob/VwLZwLw7fn5jfatCy1AwAxPCm+awJKEsCC0xfUpDaTUG5BxeUvrUgjQXQ5X/dYNaGFN2+/NYCCzPPAqMHQLJubz5rAoXM2hcYHykwekDBacMF5H4jkaRFBpMkEolEIpFIJBKJRCKR/KcRcmZSjpDWkkgkEolEIpFIJBKJRCKRZBsZTJJIJBKJRCKRSCQSiUQikWQbucxNIpFIJBKJRCKRSCQSyX8aMznXJkdIa0kkEolEIpFIJBKJRCKRSLKNDCZJJBKJRCKRSCQSiUQikUiyjVzmJvlbVKtWTQA/lC1bFktLLbNnDaRWrbIZ5IKDwxg/fg0vEpNp1dKNCRPeQQhBTEwCX45awb17jyld2pHvvxuKnV1RFEVh5swNBBwOxtKycIbrxsc/p1PnqbRtU5dvvnnfqKxPhi/hakg4RQoXQqdT6N37LYYOa28kk5SUzFdfreHypbvY2xdlwYLBlHZ1BGD5sj1s3nwCMzPBhAnv0LxFTQDWrvVn48ZjKAr06dOMgQM9APh27hYOHrxIoULmlCnrhLd3f+ztMtoq8Nh1vp+zB61OR9ceDRgwuEU6nVKYPmErV6/cx87Omulze1OydHGSk1OYM20HVy/fx8xM8MW4DjRoXIGEhEQ++b/VhvOjImNp37kOX4zrmP0KBE4dD2XJvAB0OoUO3Wvx3oeNjb4POnuPpfMDuHXjEV4zO9KybZWXZUbEsmD6AR5GxiGEYMYP3XApZZuj8l/qEcbSeYfR6hQ6dq/Jux82Mvr+4tl7LJ1/RK9HB1q0rQzA+dPhLFtwxCB3NzQaL+/2vNW6UpblvcrHUsmp7968FYHX+DVcunyXL794m8GD2wFw61YEX45a+VLPu6P57LPPeL+/xqi8o0euMGfWFnRahZ69mzJ4aFuj75OSUpjw9TouXwrHzt6abxcMpHRpRy4GhTFt8v/U3wYM/7QDbdrWIeJBNBPG/8bjR2od9XrnTfr1b/WK2jDmz2M3+WGuHzqdQpce9eg36C2j78+fucPCb/24dT2KybN74O5ZA4DrVyOY772HhPhEzMzNGDCkGW3a13xleUcOX2LmzA3odDp692nGsGEd0tkgma/G/cKlS3fU9vvdEFxdSwCwbNkeNm86hpmZGRMmvkOLFrUM52m1Onr3moWzxp5lyz4FYILXWoKDw1AUKF/BmVmzBmJT1LReJ45eY8GcXeh0Ot7u2ZCBg43tmJSUwtQJm7h6WW3DM759l1Kli5OSrGXmlK2EXHmAVqujY9d6fDjk5blarY4P31+Ck7MtCxb1f6V9jh+9xvw5O9BpdXTr2djoWql6TPbayNXL97Czt8b72/cpVbo4u3ec59dfXraVG9ci+HXDp1SrXspwbNTItdwLf8L/tn7xSj3SEnwygj8WnUOnVWjRuSIdP6hu9L3fhmsc3XULM3MzitkV4cNxjXB0UQ29aVkQFwMfANClf00ae5TJUdk5YVX/CXSp3YyouGhqT++bK9fMq77Ed/ufrFjhB4pC0aKWTJnyAdWruwIQG/uMiRN/5fyF2zx5koCDQ1H69mudK20ls/bX94N5JCS8AODx4zjq1CnP4p+Gs91XrycLKFq0KF26VuXXtQfz7f5ra2ttsp5yu28F+GbCegICLuPgYMNW369f4Smv5lXjhHNnQvlh7h5uXo9k6pzeeHjWyuRK2UNRFGZ6byTg8CUsLQsx23uAad+9dIfx49fqfbcWE7z6IIRg956zLFq0k5u3Iti4YRy13coBEH7vMZ06T6NCBfX+VrdueaZN+SBTPY4cucxs701odTp69X6LoUPbGX2flJTM+K9+5dJl1Y/nLxhE6dKOxETH88UXqwgODqN796ZMnPROmnNSmDljA6dOXsfMzIzPvuhCu3b1s7RHbuuRkPCC/v2+M5wfGRFDl66NGe/VO0s9AI4eucxs7y16Xd5kyFBPE7qs4/JltR3NW/AhpUs7cvzYVb5f4EtyspZChcwZPbY7TZpWJSHhBQP6/ZBOl0Z87dUrSz3yykeio+P57IsVBAffoUf3pnwz6d1cL1vtW1el6VuHYGdnTVzcc8aO+5n7D6LRpugYNKgtvXq+CcDcb7cQEHAJnaKjWbMTTJgwIUN5uT2eB/h08M88fhhPEUv10fy7Jf1xcLQxbZPXOHZNRavV0av3LDSaP1i2bFmG8v5NyN3ccoa0luTv0hGo4ufnx/RpfZkydb1JoSlT1zN9ej/89k4jNCyKw0cuAbB8xR7ebFodv73TebNpdZav2AvA4cPBhIZF4bd3msnrfv+DL40bVclQjp+fH1ZWhXn4MJblK0awfcckdu48zY0bD4zkNm06jp2tNXv9pjJgoAfz5m8F4MaNB+zadYbtOyayYuUIpk37A61Wx7Vr99m48RgbNnzFtm1eHDp0kbCwKADeeqs6vtsn4uM7kfLlnVm+fG8GvbRaHfO8dzH/p76s3/op+/cEc/tmlJHM9q1nKWZrycYdn/Nuv6b89P1+AHw3nwVg3eZP+H5pf36c74dOp6No0SKs2TDc8M+lpD2t2tTIsrJM6bVoziFmLuzOio39ObT3GmG3HhvJOLsUY8wUTzzaV8tw/txv/OjTvwGrNg3gxzXvYu9glaPy0+qxeM4hZix8mxUb+3Jw7zXCbj0xknFyKcboKW1xb1/V6Hi9Rq4sWf8+S9a/z5wlPShiaUGDphlvrOl5lY+lklPftbezZsLEdxk8yPhBpWJFF3y2TcRn20S2bPbCysoKT0/jwaFWq8N7xiaWLPuIbdu/Zveus9y8EWEks2VzILa21uzcO5H+A1vz/fztAFSuUpLfN45m49ZxLFn+EdOmbCAlRYu5hRmjx3Vj247xrPvjC/63/miGa2aFVqtjwaw9zFv8Hr9u+Yj9ey5x++ZDIxmNiy1e07rStqOb0fEiVoWYMP1tft3yEfMXv8fCb/2Ii33xyvKmTfudFStHsGPnZHbuOMWNG/eNZDZtPIatrTV++6Yz8MM2zJ+X2n7vs2vnKXbs/IaVK0cybervaLU6w3lr1/pTsZKL0bXGe/XBx3cSvtsnUbKkA7/9dihTvb713s73Swbwx7bP8Nt9kVvp2rDvljMUs7Vi885RvNf/LRZ/r/rEAb9gkpK1rN8ykjV/DGfbplPcvxdtOO9/v52gfAWnLO2SVo+5M3354acP2eDzBX67L3DrZqSRjM+W09jaWrF11xg+6N+MH7/bA0DHLvVYv2kk6zeNZJp3H0qVLm4USPLfH4y1VeFs6ZEWnVZh/Q9n+Xx2C6b90oGTB+5wPzTWSKZsFXsmLG3LlFXtaNjKlU3LggAIOvGAO9ej+WalJ14/tWHvhhCeJyTnWIfs8suJnXT48ctcvWZe9SWupUuw7tdRbN/+DcM/6cSkb9YZrjVz5gaaNatB4cIW+Pj4sGWrV660laza32/rx7DNZyLbfCZSr35FPPUP5qVdS/DrulFs376djz76iDmzNxeo+y/kTd8K8HaPJixZ/lGmvpETsjNOcHGxY+L07nh2rJ0rZR4+fEn13T1TmD61L1Om/WFSbsrU35k+rS9+e6boffcyAFWrlOTHH4fRuFHlDOeULVMCn61e+Gz1yjKQpNXqmDl9A0uXf4Lv9ons2nkmg89s3nQCWzsr9uydwoAB7iyY5wNA4SKFGPlZF8aO7ZHhusuX7cXBoRi79kzGd8cEGjfOOGbMaz2KFrVky9bxhn+lSjng6VkvSz1SdZkxfSNLln+M73Yvdu08w810umzZFIitnTW7935D/wGtWTDPF4DixYuyaMlHbPUdz8xZ/Rj/1a8GXTZv/crwr1Sp4rT1rPtKXfLKR4oUKcTnn3VlnIm6y62yl6/Yy5tvVsNv71TefLOaoW/9bX0AlSqVxHfbBH5d+wVz5m4mKSmFs+ducvbcLXx9JrDDdxIXL17k5MmTRmXlxXg+lcmzehrG9KYCSapNXu/YNZW1a/2pVNHF5HeS/zZ5GkwSQnwshBiQl2XkBCHEISFEo1dLFjyEECOFEFeFEJeEEHP1xz4UQizK4XV+EUK8+pVI9ukGrBVCUK9eRWJjnxMV9dRIICrqKfHxL6hXryJCCLp3a8qB/RcAOHAgiO7d1bcB3bu/yf60x7s1xdR1g4PDePw4jmbNjAMnCQkv+Pnnn3F3r6O+pSxTgsKFLejUqSH+By4YyfofCKJb96YAtG9fn8ATISiKgv+BC3Tq1JDChQvh6lqCsmWdCAoK5datCOrUKY+VVWEsLMxp3LgK+/adB6BZ85pYWJgDULduBSIjYjIY6XLwPVzLOFDa1YFChSxo28GNI4dCjGSOHAyh49vqIMPdsyanT95CURRu33pIwzfUNxcOjjbYFLPk6iXjB4Y7oY+IfpJAvQblXlVfRoRciqRUGTtKutpRqJA5rdpV5XjALSMZl1K2VKzihDATRsfDbj1Gq9XRsKlappV1YSwtC+WofGM97A16tG5XlRMm9SiBWTo90nL0wA0av1UuW3pk5WOp/BXfdXS0pU7t8gafMMWJE1cpU6YMpUuXNjoefDGMsmVL4FqmBIUKW9ChY30O+l80kjnkf5G3u6uzxzzb1eXPwOsoimLwTYDExBSE3kxOTnbUrKnO9Cha1JIKFTUZfmdWXAm+T+kyDpRyLU6hQua0aV+To4euGcmULG1P5aoahDCum7LlHClTzgGAEs7FKO5QlJjoZ1mWFxQUStlyzpQp46S2386NOXAgyEjmgH8Q3Xuotm/fvgEnTlxFURQOHAiiU+fGavstU4Ky5ZwJCgoFICIimoBDF+nTu5nRtWxs1ACooigkvkhGYNq/LgeH41rW0dCGPTvU5vDBK0Yyhw9dofPb6gO2h2ctTv2ptmEEvHiWREqKlsTEFCwKmVPUpggAkRFPOXY4hG49G2Zpl1QuXQynTFlHXMvo9ehYh4D0ehy8Que3G+j1cOPUnzdVPdKwd/cF2nWsY/j87Fki69ceY9BH7tnSIy23rz7BqZQNTqVssChkRmOPMpw/ds9Ipnp9Z8Mb14o1HYh++ByAB2GxVK3jhLm5GUWsLHCtaEfwyewHO3PKkRvneZIQ+2rBHJBXfUmDBpWws1Nnb9WrW4GICDUAGRf3nFOnr1OlSmnKlXWmUqVKODra5kpbyU77i49/zp+BIbRtWzeDnhYWah0XpPsv5E3fCtCoUSXs7EzPhMop2RknlCxdnMpVXbK8D+aEA/5BdO/WRO+7FYiNfZaF71bQ+24TDujrtFKlklSsoDF16WxzMSiUMmVLpPGZBhz0N/Y5f/8gunVrAkC79vUJDFR9xtq6CA0bVqJwkYz3/K1bTjB0mDq7wszMjOLFTT+c57UeqYTejuTJkzgaNsp61rSqSxhlyzpRRu+vHTs1wD+dv/r7X6Rbtzf0utTjz8BrKIpCjZplcHZWp8dXrlKSF4nJJCUZB+hDb0fx+El8tnTJKx+xti5Co4aVKZKFzf5u2er5al/TvVtT9uuPC6E+MyiKQsKzROzsimJhYYZAkJSYTHJyCklJKSQnJ1OiRAmj8vJ6PP8q8mPsGhERzaGAi/Tu0yzDd/9GzIRZvv77p5EjjYVKts9RFGWpoihrc67WPx8hRK4tIRRCuKMGbeoqilILmJdb184FSgN3Uz+4uNgTGWk8mIuMjMHFpTimZB4/jjXc9JycbHn8OPblOSUznqPT6ZgzZxNfjcs4LfeHhb4MGjSIuLhnFErTGWpcihMZadzRRkbFUFJ/fQsLc4oVsyImJoHIyKdG5Wpc7ImKjKFKlZKcOX2T6Oh4nj9P4nDAJSIeRJOeLZuP06JlxmU8D6Ni0bi8XP7l5GzLw8jYTGUsLMwpamPJ05hnVK6q4WhACCkpWu6HRxNy5T6R6c7dvyeYNu1rZXiYfxWPouJx0hRLo5cNj6Pis3Vu+J0YbIoVYerYHQz/YD3LfzhiNAskJzyOSsBJ83KgV8LZhkfZ1CMth/yu0zrdzKXMyMzHMsjk0Hezw85dp+nSpYsJnZ6icUnnf+kGCWllLCzMsSlmSUxMAgBBF0Lp0XU2vbrNYdLkdzIMCu7de8zVK+HUrpP9oOPDqDicXdL4iMaWR1Fx2T4/lcsX75GSrKV0meJZykVGRlMyrc019kRGGre1qEgT7Tc6Ictzvb03MGZszwxBUYDx49fQvNk4bt2KoF9/08GUqMhYNJqX61edNbY8jErXhiNjcdbLWFiYY2NThKcxz2jj6YaldWE6t5nD2+2+pe/A5oYH0O/m7mLEqPYm9TLFw6inaFxe6qHR2GXoS6LSyKh6qH1JWvbtuWgUTFr64376DmyOpWXOZybFPHqOg/PLB+riTtbEPHqeqfzRXbdxa6K+4XStpAaPEl+kEPc0kZDzD4l+mHXAsaDxOvqSTZuO0bKlOvMvPPwRDg42fPfdNi4GhzFhwgSePUvMlbaSnfa3f/8Fmr5ZzRCITcv27dspVcrB8Lkg3H8h7/vW3CA744TcJqNfFicyKp3vRsXgorF/KaMpnsG/TRF+7zHde3rTr/8CTp++kbkOUU+NfE6jyegzUWl8I63PZEZsrNqH/LhwB717zubLL1bx6FHWtswLPdKya9dZOnRskK1xWlRUDC4uL22u0dgTZVIXe4Muaf01lX1+56lZw5XChY0DNrt3ncm2LnnpI3ld9uPHcen6VnXs0rdva27eiqBFy/G83W0mE8b3xszMjPr1K9KkSVWatxxP85Zf06JFCypVMg645eV4fuY3Pgx8Zwk/LwvI8ALIyCaveezq7b2BsWN6YpbDZwzJf4NXBoaEEOWFECFCiLVAMLBKCHFaP0Nmahq52UKIy0KIICHEPP2xKUKIMfq/6wkhAvXfbxVCZPpEoZ9B9IMQ4rwQIlgI8Yb+eFEhxGohxEkhxDkhRDf98Q+FENuEEPuEEKFCiBFCiFF6mUAhhEOay/c3cd03hBAn9PLHhRDV9McPCyHqpdHrqBCibhbyHwohfIUQ/sABIYSNEOKAEOKsEOJiGn3LCyGuCCFW6O3oJ4TIan3QcGC2oiiJAIqipJ1PWUZvr+tCiMlprh+cRu8xQogpJuz8jRDilN4Wy4X+rqK/3hy9na8JIVqkP1cvN+zZs2fNw8PDVy5fvjwL9bOHEOKVN7b16wNo2crNqJMEuHLlLnfuPMqwbCi3qFSpJEOGejJk8I8MHbqI6jVcMTM3bj5Ll+7G3MKcrl3fyNWyu3Svj7PGlsEfLOf7b/dQu26ZDG8l9+8NzrVp79lFm6Lj4rn7DPu8BYvWvkdE+FP8tl9+rTqk5fGjBEJvPKLRm69e4pbbZMd3U0lKSsHf/wIdOnR4tXAOqVO3PFu3f83vG0axasV+EhNfvo18lpDIqM9/Ztz4HtjYWOZ62Vnx6GEcMyb6Mn5q11x7o54TDh4MwtGhGG5upoNos2YN5PCROVSq5MKuXadzvfxLweGYmwl27v+KrbtHs37NMe6FP+FowFUcHIpSo2bpV18kFwkOuoulZSEqV1EDOiFX7xMe/hj3Nn8v/0p2CNwXRmhINO3fVZfN1mrsQu2mJZk9wp8V0wOpWNMxX3ykoGCqLwkMDGHT5uOMGa0uB0lJ0XH58l2aN6+Jp2c9rKysWJHJ8q68YOeOU3Tu3DjD8cDAQE6cOEHt2jmbIfsq8vP+m0pWfaskI85Othw8MINtW7z4+uvejB67mvj4zAPMuY1WqyMiIoZ69SuyacvX1K1Xnnlzt7628k2xe/cZOnV+fYsjblx/wIL5vnwzNWMuot27z9Kpc4PXpktBQO1b1b+PHr1MjeplOHJ4Ftu2jGfajA3Exz8nLCyKmzcjCDg4k8OHvAkMDOT06dwbE2Q1np/i3Yt1mz/hp58Hcf5sGHt2XHjF1f4+2Rm7HjwYhINj5uMniSS7s2eqAAMVRQkUQjgoivJECGGOGjCpA9wDegDVFUVRhBD2Jq6xFhipKEqAEGIaMBnIKsOntaIo9YQQLYHVgBswAfBXFGWQvoyTQoj9enk3oD5gCdwAvlIUpb4Q4jtgAPB9Fte9CrRQFCVFCNEW8AZ6AauAD4EvhBBVAUtFUS4IIWwzkQdoANTR28gC6KEoSqwQogQQKITwTWPT9xVFGSqE2KA//2VCBGOqAi2EEDOBF8AYRVFO6b97Q/8bngGnhBA7gUdZ2DUtixRFmQYghPgV6AJs139noSjKG0KITqh1ZVhEW61atU+BoVWrVgXYaG1t7T9s2LD1KAeJiIhBozGufo3G3jA9HzCScXS0JSrqKc7OdkRFPcXBodjLcx5kPOfc+VucOXOD39cHkPAskeRkLdZFLSlVyoHg4DA8PDx49uwpcXHPGdD/O9b++iWREdFGMwoANM72PHgQjYtLcVJStMTFPcfevigajZ1RuZERMTjrde3duxm99UtkvlvggybNW6OtW05w6GAwP//yucmO2cnZlsiIl9H/h1GxOGlsTco4a+xISdGSEP8CO3trhBB8PvZl4GHYgJWULedo+Hw9JAJtio7qNUuRU0o42/Aw8uUsk4dR8Tg6Zz0V3KCvxoZK1Zwo6ara9q3WlbgS/NeWpzg6F+Vh5MuZSI+i4imRTT1SObzvOm+5V8ryjbHvhiAO+HYD4qldu5xJH0vLX/HdV+p5JJhaNctmmDqtlmdHZEQ6/3O2Mynj4mJPSoqW+LgX2NsbZ4yuWMkFK+si3Lj+gFpuZUlO1jLqi9V07tIwWzkS0uLkXIyoiDQ+EhlLCefs/VaAhPhExo38H0NHtKZWnVcHTTSa4jxIa/PIGDQa4+Cxs8ZE+y1eNNNz/f0v4O8fRMDhYJISU4iPf87YMav5dt4gg6y5uRmdOjdm5Uo/epvIR+qssTV6Sx0VGYuTc7o2rLElKlKdFZSSoiU+PhE7e2v27gqiabMqWBQyx8HRhjr1y3Ll0j1Crj7g8KGrHD96jcTEFBISEpk8fiNTZ/XJ1D5OznZERrzUIzLyaYa+xFkv81IPtS9JxW93EO07vfSDixfucOXSPd5uPxdtio4nTxL46P9WsOznoZnqkRb7ElY8iXo5myj64TPsS2R8P3L5TCQ7111h7PetKVT4ZTvt3K8Gnfupy5ZXTA9E45p9/8ov7MJf0K1b3vclV0PCmTjpV1YsH2lYpuPiYo+Lxp433qjGyZPXGf5JB5Yvn4WlVeG/3VaALNtf9JN4gi6Gsmjxx0blhFwNZ+LENXz99dds3rzCcLwg3H9V++dN35qbZGeckBv89lsAGzYdA6C2W7l0fhmNxjmd7zrbE5Fm5kNEZHQG/05P4cKFDLNh3GqVpWwZJ26HRhmSLxtf387I5yIjM/qMs9430vtMZtjbF8XKqjCe+vtd+/YN2LLpRJY654UeqVy9Go42RWsyUbIpnJ3tiUizZDMyMsYw69VYlxiDLmn9NSIims9HrsR7dn/KljXOx3f16j20KbosdXkdPpJ12QuB+L9dtqNjMZN965YtJxg2tAQF3P4AACAASURBVD1CCMqVc8bV1ZFbtyI5eeo6detWoGhR9YVbixYtOHfuHBXcXo5H82o8n3qNokWL0K5TbS5fvEfHrup8hs1/nGTn1j/Ij7Hr2bM38fcP4nBAMIlJKcTHJ1GtWrV1ISEh/bI88R+MkCmlc0R2rRWmKEqg/u93hBBngXNALaAm8BQ1yLFKCNETNbBhQAhhB9grihKgP7QGaPmKMn8HUBTlMGCrDx61A74WQpwHDqEGjlJ7w4OKosQpivJQr09qUOQiUP4V17UDNupn83yn/10AG4EuQohCwCDgF/3xzOQB9imKkpo5WADeQoggYD/qkrDUhcO3FUU5r//7TDod02MBOABNgbHABvFyxLRPUZTHiqI8B7YAzbO4TnrchRB/CiEuAh7pfseWzHQLCQlZHBISUi8kJKQesA0YoCgK58/folgxywwDNGdnO2xsLDl/Xl0zvM0nkDZt1OUVHh512LZNvcFv23bC+LhPIOmvO3/eYA4dnIW/vzdfjetF925NGDO6Bx+834qjR+bg7+/PH3+MwdzcHO9Z/UlKSmHXrjO4e9Qx0sndow4+21SX3rv3HE2bVkMIgbtHHXbtOkNSUjLh4Y8IC4uiTh3156dOj71//wn79p2nSxf1zeyRI5dYtWofPy35GKtMktfWqFWK8DuPuR8eTXJyCvv3BNO8lXFC6xatq7HbV3WJg/su0/ANdf33i+dJPH+WBMDJEzcxNzejQiVnw3n7dl/8y7OSqtXUcO9uDA/uPSU5WUuA3zXebFkxW+dWrakhIS7RkAPn/Om7lKvg8IqzstYjQq/HIb9rNG1ZIUfXOLT32iuXuL39Th18fHzw2TaRtm3qmfSxtPwV330VO3eeNvlWH6CWW1nCwh4RHv6Y5KQU9uw+R2t346TWrd3d8N2mxpL3+V3gjSZVEEIQHv7YkBT2/r0nhN6KpFRpBxRFYfKk36lQUcOAD3OeD6d6rVKE33nC/XsxJCdrObD3Ms1bZW8pYXKyFq9Rm+jQpY5hh7dXUbt2OcJCowi/+0htvztP4ZGu/Xp41GHbVtX2e/eeNbRfD4867Np5Sm2/dx8RFqq239GjexBweDb+/t7MXzCYJk2r8+28QSiKYkjkqygK/v4XqFjRdP6PGrVKczfsMffDn5CcnMK+PRdp2dp4x7IWrauz0/ccAP77LtHoDTVfgUtJO06fVHOAPX+WRHDQXcpVcOLTz9uxY/84tu0Zw4y579DojYpZBpIAarqV5k7YI+6l6rE7iJatjW2r6nFWr0cwjfV6AOh0Ovb7XcSzw0ub9n63Kbv9x+O7dxwr1n5E2fKO2Q4kAZSvXpyoe/E8fJBASrKOU/53qfuWcXD7zvVo1i04w4iZzbAt/nJmnE6rEP80EYDwmzGE33pKzcZ/LwfL6+Cpq2We9yX37z9h5MhlzJ3zf4ZdsUDNg+ZS0gEbmyKEhkWxd+9eypfX5EpbeVX727v3LK1b1zbKc2LQc+5c2rdvT1hYFOHhjwrM/Rfypm/NbbIzTsgN+vZtZUiM3bZNHbb5/Kn33dsUK2aVhe/e1vvun7TxyPp+9+RJnGHZ+927jwgNi6KMa8YXKAButctxJ+xhGp85i7t7Op9xr42Pz58A+O09R5OmVbOcUSGEoHVrN06evA6os/sqVS6Zpc55oUcqu3bmbFaSW+2yel1Uf9296yzu7sbjPHd3N3x8Tup1OU+Tpqq/xsY+45OPl/HFqLdp0CDjeG73zjN07Jx1jr7X4SNZlu3jkytlpz5LAGqfqz9esqQDJwKvAvDoUSy3b0fiWqYEpUoW59Sp66SkaElO1nLq1KkMy9zyYjyfkqIlJlpdopiSrOXY4WtUrPxynN/rvTfybew6enQPDgeo46cF8wfTtGlT/s2BJEnOye7MpAQAIUQFYAzQWFGUaCHEL6izdVL0S8baAL2BEajBib9D+sWiCmpwppeiKEaZzoQQTYDENId0aT7rMP6dpq47HTUY1UMIUR41UIWiKM+EEPtQ8xW9A6T2vibl9aRdsNwXcAIaKoqSLIQIRQ2AkU5fLZDVMrdwYIuiLqA9KYTQAal3ZVO/JwXjQGGGNS1CCEvgJ6CRoih39cvg0sql6qclaz/ZBXTy9PTEylKLt/dAwxfdus/AZ9tEACZ/8wHjvdbw4kUSLVvUMuR+GDa0PV98uYJNm49RqpS6RSVAq1ZuBBwOxrPdJKwsCxtd91WYm5vj5GzLkMGL0Ol09Oz1JlWqlGLhwu24uZXDw6MOvXu/xVfjfqF9u8nY2Vkzf8FgAKpUKUWHjg3o0nk65uZmTPrmPcz10+k//2w5MTEJWFiYM+mbdw3bD8+YvoGkpGQGD/oR0G+BO62NkU4WFuaMGt+JL4f/ilan0KV7fSpWdmbFYn+q1ypFi9bV6dKjPtMmbKVPlx+wtbVi2lw1T3r0kwS+HL4OYSZwci7GNzN7Gl3b3+8S8xb/tS2uzS3MGDG2NV4jt6HTKrR/uyblKzmyZukJqtbQ8GarioRcimDq2J3Exb4g8Mhtfl0eyIoN/TE3N2Po5835avgWFAWq1HCmYw+3VxeaiR6fjm2F10hfdFod7Qx6BFK1hrNej0imjd1JXGwigUdCWbv8T1ZsUH93xP1YHkbGU6dB9pcLZeVjf8d3Hz58Sq/es4iPf4GZmWDNWn927ZyMjY0Vz54lcvzYFaZNNV1fFhbmeE3oxfChS9HqdHTv0YTKVUqy+Mdd1KxVFncPN3r0aorXV+vo3H4GdvbWzJ2n7nFw7uwtVq84oCaRNDNjwqTeFC9uw9kzt9jhe5oqVUvSp8dcAD77ogstWpnOLZJRJzO+/Lo9o4f/jk6no3O3ulSo7MTKnwKoXrMkzVtX5UrwfSaM2kRc7AuOH77O6iWH+XXLR/j7XebC2TvExjxjt686ZdtrWleqVM98R5DU9jV4yEJ0Wh29er2ltt8ffNX226YuvXs3Y9zYn2nnOQk7O2sWfDcEUNtvx44N6dxpKubm5nyTpv2aQlEUvv7qF+ITXoAC1aqVZspU07sOWViYM8arC58NX4NOq6Nr94ZUrKxh2eL91KhZmpbuNXi7R0OmeG2iV+cF2NpZMWOuuryg93tNmD5pC+/1WIiiKHTp1oAqVf/arigWFuaM83qbzz7+Ga1W4e0eDalUWcPSRfuoUcuVVu416NazEZPHb6RHp3nY2lkzc+57hvPPnQlF42KHa5ncexg2Nzfjg8/q8/24wyg6hWYdK1C6gh0+q4MpV82Bes1KsWlpEC+ep7B0ijqIddRYM2Jmc3V3us8PAmBpXYjBE5pkWWd/l/WDptG6agNK2Nhz19uXyTtWsPr49lefmAV51Zcs/mknMTEJTJ32O6DaectmLwAmTXyXr75eg06nsHHjRhwcrOnTp1mutBVT7S+VnbtOMWyo8RLdnxbr9ZyqZj4wMzPL1/uvqTacF30rwLgxazh98iYxMfG0dZ/MJyM60rNX05y6kEHHV40TLgffY/yXfxAX+4KjAddY9dMhftv66V8qD1J99xKe7Sfrfbe/4btuPbzx2ar62+Rv3jNsva76rvrucd++80yfuYEnT+L56OOfqFHdlVUrR3Lq9A0WLtyBRSFzzIRg6pT3M53BY2FhzoSJ7zBsyGJ0OoUePZtSuUpJfly4g1puZfHwqEOv3m/x9Vdr6dB+CnZ2RZk3//8M53u2+Yb4hBckJ6fgfyCI5Ss/pXLlkowa3Z2vv1rDnFmbKe5gw4yZWT//5pUeAHv3nGXJsuHZrhcLC3O8JvbmoyE/odXpDLosWriTWm5lcfeoTc/ebzL+q1/p2H4adnbWfDv/QwB+/+0Id+88YumSPSxdou7kuXzlJzg6FtPrco6fln2cWdEZyCsfAfBoM1FvMy37D1xg9cqRBpvlRtnDhrTji1Gr2LTpOKVKOfC9vv/75JOOjB+/lq5vz0BRFMaM7o5DcRvat29A4J/X6NptBkIIWrTogIeHB49f/G5UN7k9nk9O0vLl8HWkpGjRaRUaNa3I271MB/zyY+z6X+OfmAQ7PxGZJfgyCKjBkh2KorgJIeqiLlerjxokCQK+AjahLh+L0s9CuqUoiqM+QBGvKMo8IcQFYISiKEf0x+0URTG5P68Q4hBwVVGUj4UQzYEliqLUFkJ4A7aoy+UUIUR9RVHOCSE+RA2KjNCfH6r//Cjtd1lcdyuwTlGUzXrdPlQUpbz+Wg1RZzkdURTlXf0xk/Im9PgcqKwoykihJtH2B1KnW+xQFMVNLzcGsFEUZUom9vgYKKUoyjf65XYHUGdkDURdYucGPAf+RJ1BdQF4AFQD4oEAYI+iKFP0AcAdqDOlQlBnHZkDgcAmvcwh1KV0p/XL806n2iMLFJSDrxB5DQh3dMqB/NYCM9HG6OaTXzhavg9AWNxP+awJlCv2CaFxOdp8ME8oX2wEBcVXARK1u/NZEShi3pGo5/m/V4Kz1QAU8r9uBGrdxCRuzGdNwL5IH2KTNue3GtgW7sXh+xPzWw1alpoBgBj+1x7YcxNlSWCB6UsKUrspKPfggtK3FqSxALr8rxvM2pCi25ffWmBh5llg9ABI1r2+vGeZUcisfYHxkQKjBxScNlxA7jeQyba3/xKSdXuzDo7kMYXM2v+j7JujHcf0+YLOoeYYugsc039VDPDRz3YRwCgTpw8ElgohrIFbwP+ZkEnLC31ZqUvMQJ0R9D0QJNRd5W6j5vnJCaauOxdYI4SYCOxMK6woyhkhRCzwc5rDmcqn4zdgu34Z2WlUu/0VVgOr9cvqklDzVyn6qbUngc2AK2qA6zSAPi/VSdR8VhnKVRQlRgixAjWpegRwKr2MRCKRSCQSiUQikUgkEkl6XhlMUhQlFHXmS+rnDzMRzbCFRtqZNvr8QDl5lbhOURSjBN36vEAfmSjnF17mMyLtLJq03ymK0tpUQYqinEBNcp2K4fWrEKIU6pIxv1fJm9DjEfCmqTIxtum8TGRSv08CMszPTV9euu8WAgtNHP8wzd8TSfNb0xxvnebvR2Sdz0kikUgkEolEIpFIJJJ/NDIBd86Q1soCIcQA1KVjExRF0eW3PhKJRCKRSCQSiUQikUgk+U2OlrnlNkKIxUCzdId/yGwG0etGUZS1qDmiXgtZ2ONnU/ISiUQikUgkEolEIpFI/j4yAXfOyNdgkqIof30Lin8h0h4SiUQikUgkEolEIpFITCGE6AD8gLqJ1kpFUWan+74I6oSYhsBj4F196iKEEOOBwag7tn+mKMrfyv4vQ28SiUQikUgkEolEIpFIJAUYIYQ5sBjoCNQE3hdC1EwnNhiIVhSlMvAdMEd/bk3gPaAW0AH4SX+9v4wMJkkkEolEIpFIJBKJRCL5TyOEWb7+ywZvADcURbml36TrD6BbOpluwBr935uANkLdBr4b8IeiKImKotwGbmBiE7Uc2UtRlL9zvkSSinQkiUQikUgkEolEIvn3IvJbgbxE4WC+PtMK3LO0rxCiN9BBUZQh+s/9gSaKooxIIxOslwnXf74JNAGmAIGKoqzTH18F7FYUZdNf1TdfcyZJJBKJRCKRSCQSiUQikeQ3Ip+nRwgzMQwYlubQckVRlueXPq9CBpMkuYfuQH5rAGZtCo4e2n35rQWYe6r/Vw7mrx4Awr3g2KSg2AMKjr8WED10Sv7rYSbaqH8UFD8pIHVTYOwBBUYXMbxpfmuBsiQQhfy3h0CtmwKjS0FpNwXlvgeQ8rfyrOYOFu0Ljk0Kij2g4OiSuDO/tYAinSF5d35rAYU6qv8vIPebohM98lsLEmb457cK/3r0gaOsgkf3gDJpPrvqj5mSCRdCWAB2qIm4s3NujpA5kyQSiUQikUgkEolEIpFICjangCpCiApCiMKoCbV908n4AgP1f/cG/BU1t5Hv/7N35mFRVW8c/1wQZBNwYVHANfc1l7TcRdNccak0UivTNrWycgFUFMHdyixTtDK3ZBFGRAEFFVxzR3ApFxBUBhQUUGQZ7u+POwwMA4ImYL/u53l8hDvn3vPlPee8571nG2CsIAjVBUFoBDQF/vwnYuSVSTIyMjIyMjIyMjIyMjIyMv9txPyqzb+ME6lEUcwTBGEqEAroA7+IohgrCMJC4JQoiruAjcBmQRCuAqlIA06o0/kAF4E84DNRFFX/RK48mCQjIyMjIyMjIyMjIyMjIyPzgiOK4h5gT7Fr84r8/Bh4s5R7PQHP56VFHkySkZGRkZGRkZGRkZGRkZH5b1PVK5P+ZchnJsnIyMjIyMjIyMjIyMjIyMjIlBt5MElGRkZGRkZGRkZGRkZGRkZGptzI29xknhuiKOLp5cuhyFiMjAxY4jWB1q3r66SLib3JnDm/8zg7l969WuPq8iaCIHD//kO+nLGRW7fuYWdXm+++/RALCxMePHiEi+tmbiakUL26AV6LxtOsWT0A+jm6YWpqhJ6+Hvr6euzc6fjcde0NOcOaNcFcu56Er89M2rZpAEBaWibTv/AmJuYmI526MW/u2+Wwj5+kw9iQJV7jad3KQSddTOxN5rhs5vHjAh1jCnX8uIdr15X47vhaoyM3V4XbvK1cvJhAniofp+Gv8NGUgbp5e/pwKDIGIyNDliyeWLINYuKZM2eT2gZtcHV9q0jZeBcpm8lYWJgCcOLEFbwW+5KXp6KmpRlbtnzFnTupzJz1G/fupSMIAm+91YOJE/tWmk0ALl+5xXz37WRmPkZPT8DPZybVqxtUik0Aoi/EMXbsMlatnMSgQZ0AWL5iJ4cOxQDw6adZDB48uIgdKrftAKhU+Yx+cwk2Nj6sW/tWCWXz/NtN4q17DB6ykEaNbABo374hC93f0XluAVFRsXh5+pKfLzJmzGtMLla3c3JymTVrExdjE7C0NGXVqknY2dcmLS2TLz6X2qeTUzfmzitsn5M/XENKygPyVPl07tSEufPGoq+vO7dSUXVkV9AJvL3DQBQxNTXC3f0dWrSwB6BfP5diPq20dvP8yyY6Oo6587dp8pj22RAGDOhQKTbZH36O778PQk9PQF9fDxeXt+jc6SUAJn24mvPnb9Cp40usW9+3ysomPf0Rbm6b+evv2wiCgJeXpU5+5WXjeFeGtu1OckYabT2cn/k5RYmKjMXT04f8/HzGvNmdKVMGaX2ek5PLrJm/ERt7U2or336IvX0dANatC8Hf7wh6enq4ur1Fz56tyc7O5V3nFeTk5KFS5fP6wI5Mnz4MgGPHLrN8mT/5+SImJtVZvGQiDRtUjI7r15OY8eUGzf0JCXeZPn0YE99z5MsvvLlxQwlAesYjzGuYEKhw06StCt+anv4It7lb1fVkBV6LnHi5Q+Nimp5/v7cr6CQbf9mvuf/KX7cJ8JtFy5b2pdYZURTxXOzPociLkhZP59K1uG5Va2mF65zRCILA0hWBHDgYg4FBNeo71GHxoncwNzeR/PwwLxo1tAbUfn5+6TFSVcZHlWGPAm7fTmXIcC+mfvYGk97XjVdfRC2iKOK5NIBDUZckX+sxjtatdOtUzMUE5rhtl9pQz5a4zhqJIAh8t2Yv4Qdi0NMTqF3LjMUe47CxtuBB+iNc5v3BzYR7VK9eDa8FY2nWtG4Z9tip1mHAEs93SrFHAnPctkn26NkS1zmjJB0/7CE84oJaRw0We76DjbUFGRlZfDN7C7fvpKFS5fPBe30ZPbJryflXaTzwA7Qq1TwMaNqFZYOnoq+nx6bTe1gZuV3rcwdLG34e+Q11TC1Izcpgkq8Xt9Pv0s62Cd8N/4Ia1U3JF1UsO7gV/5iDpWf0X0De5vZUyCuTZJ4bkZGxxMUnExbijscCZ9wX/lFiOvcF2/FY6ExYiDtx8clERl0EYL13KK++2pyw0AW8+mpz1nuHAvDz+hBatrQnSOHG0iUT8Vzsq/W8TZu+QBHgwk6/2RWiq1nTuvzwwxS6dH5JK3316gZ8Pn0YM78ZWU77XCQuPoWwkPl4LBiH+4JSdCzcgcfCdwgLmU9cfEoRHfX4YfVkunRuopU+JPQMOTl5BClc2ek7ix0+R0i8da9Y3jGSDUIX4rHQGfcF20qxwTY8PN4lLHSh2gaxAKz3DuHVbi0IC/Xg1W4tNGWTnv6IBQu3s/anTwnePZ/vv58MgL6+PrNnjWFPsDs7/pjFtq2HuHr1aqXZJC9PxTezNrFg/liCg9z4fdPnVKumXyk2AWmQZsWKALp3b6m5dvDgBS5evElggCs+O2axceNGMjMz1Vqqpu38vvkATRrblphXRbUbgPoOdVAEuKAIcHniQJJKlY/Hwh2s955K0O65BAef4urVO1pp/PyOYmFuQmjYAiZM7MeKlQGA1D6nfz6Mb2bqts9vv5tEoMKVoCA3UlMzCQk5U4oNKqaO2NvVYcvmGQQFzeOTTwczd94Wredt+n0GikA3dvq7lKKrYsqmadN6+PvOQhHgwob1U5nnvo28PO0v+agom7zarQW7FG4oAt3w8pqAm9tmzbM+nPQ6y5a+Xyk6nlQ2np4+9OzZmpC9C1AEutGkSZMS8ywPvx0LZtAPXz7z/cVRqfJZuHA73humsjt4PsG7T3L16m2tNH6+RzA3NyFsnwcT33Nk5QqprVy9eps9wSfZHTyPDRumsXDBdlSqfAwNq/Hbpi9R7JpLQKAbh6NiOXfuOgDu7ttYvuIDAhVuDB3ahbVr96h1qJ67jsaNbQlUuBGocMN/pwvGxob0Vw9yfvvdZM1nr7/ekQEDXtbKqyp8q6eXLz17tCJkz3wUCoWOj62ofm/4sC4oAuagCJjDsqUTsLev/cSBJIDIKLWWvXPxcH8b94U+pWjxwWPBWML2zpW0HL4EQPdXm7M7cA5BAbNp2MCKdd77NPfUd6iDYucsFDtnPXEgqSJtUp74qLLsAbBkWQA9ez5hROAF1BJ5+BJx8XcJ2+2Cx7w3cV/kV7KORX54zH+LsN0uxMXfJfLwZQA+fK8vQf7foPD9mj69WvHjujAAfvbeT8vmdgT5f8NSz3fwXBpYhj0uEXczhbA9rpI9PHxLTOfu4YuH+9uE7XEl7mahPT58vx9BAbNQ+M+kT+9W/LhWastbtx+mSRMbdu2cyeZfp7J0uYKc3Dzd/Ks6Hti5s1Tb6Al6rBr2OSN/n02n1e/zZtt+tLBqoJXGa9DHbDsXRtc1k1lyYDMLX5fi9Ue52Uz2X0KXHz5gxKbZLBvyGRZGpiVlIyNTIvJgksxzIzwiGqcRXREEgQ4dGpGe/ojk5AdaaZKTH5CZ+ZgOHRohCAJOI7oSHn6+yP3dAHAa0Y396uvXrt6hW9fmADRpbMutW/e4eze90nQ1aVKXxupVFEUxMalO504vaa12KVvHK5KO9o1Iz8giOaWYjhS1jvYFOl4hPDxarcO2RB2CIJCVlUNenorH2TkYGOhjZmqknXe4ZFvJBo1JT896gg0aq/PuRvj+84X3O70KgJPTq+xXXw/a/ScDBrxMvXq1AKhd2xwAa2sLzYyNmZkRjZvYolQqK80mR45cpnkzO83sTk1LM52VJxVlE4DNWw4w8PWXqV2rhuba1Wt36Ny5KdWq6WNiUp3mzZsTGRlZxA6V23aSktI4eCiGMWO669jveWgqrd08DdHRcdSvb4WDQx0MDasxeHAnIsLPa6WJCI9mhJP0tw8c+DLHj11BFKXVEp06vUR1Q932aWZmDEBeXj65uXkIpXwNa0XVkY4dm2hWsXVo34ikpLSnsktFlY2xsaFm0DU7JxehBMNUlE1MTY00+WU9ytHK+9VXW2BqWr1SdJRWNhkZWZw89bemvRgaVsPc3FzHPuUl6uo5Uh+Wvx8ri+joOOo3sMbBwUpqK0O6aPxkAeER0TiNlP7mgQM7cuzYZURRJDw8msFDumBoaIC9Qx3qN7AmOjoOQRAwVfcleXkq8vJUmnIREMjMfAxARuZjrK0t1Tqin7uOohw7dhkHhzrY2dXWui6KIiF7TzNkaGfdvCrRt0r15CpjxrwGgKGhodZqkMJnPv9+ryjBwacZ8kbHJ6aRtFzAaXg5tDwsomV4oZYe3VtqfEaH9g1JUt4vM8+SdVRdfFRZ9tgfHo2dfW2avlTyBM6LqiX8QAxOwzqrdTRU69D2Xckp6WRmZtOhfUNJx7DOhB+4AEgxYAFZWTkI6u89v3ZdSbdXmgLQpJENt26ncvdexhN0XMBpeJdiOkqzh1rH8C6ER5SiQ93FCAI8fJiNKIo8fJSNhYUJ1UpYqfyixgMAne1bcP3eLeLS7pCrysPvQgRDW76mlaaFVQMOXj8LwKHrZxnSQvr86r1Ert27BUBSxj1SMu9Tx/TZV93K/PeQB5NknhtK5X1sbWtqfre1rYkyWTuwUCbfx9am0EnZ2tREqe7g7t3LwNraAgArK3PuqTuVFi3sCdt3DpAC5tu3Uws7RUFg0qQfGDV6MTt8DleIrueFMrmYDhtLnTyUyuI6LHW0Fmfg6y9jbGxIj96u9HWcxwfvO2JpqT2roFTex7ZuURuUkrdtyWnu3UsvVjZSIBEXl0x6+iPGj1/JqFFeBAYe19GXmHiXS5cSaN++faXZ5EZ8MoIAkyavYeToJXhv3KeTpqJsolSmsX/fOcaN66X1rBbN7YmKiiUrK4fUtExOnDhBUlJSKflUfNvxWuzHN1+PRE+v5JGUimw3ibfu4TTKi3fHr+LUKd0VawUkFysjG9uaKJXawZsy+T511WmqVdOnRg1j7t9/WKaGDyf9QI/uMzE1NWLgwJJfuiqqjhTFz+8IvXq1KbwgCEya9D2jRnmxY0dU6boqqGzOn7/BkKEeDB/hyYL543RW9FWkTfbtO8ugN+bz0cdr8PKc8ESdlV02iYl3qVXLjDlzNuE00hNXt808evToiRorE6Uyjbo6vlT7pSRZWUJbSXv4xHtVqnycRiyi+2vf8NprLWnfvhEAizzfZcqUNfTuNZtdiuNMUW8dUiqVFaKjgD3BpxgytIvO33/q1FVq165Bw4baAwqV7Vs19cRlM06jvHB1deXRo2zdNbFRyQAAIABJREFU/Cqg3yvKnpAzDBnSucx0yuQH2NoWy6e4j1U+0NZia4my2Es0gP/O4/QqstIl8dY9nEYv5d2J33Pq9LUydFRdfKSto2Ls8fBhNt4b9zP1kzeeqPdF1KJMTtfVkVy8H36ArY1FsTSFPvXb1XvoPWAhQcFn+Pwzadtri2b1CFMPfEVfiOf2nbQnDkYqlQ9KqCNl2KNYmm+/D6a3oztBwaf5fKp0zIDzOz25dl1Jz77zGT5yKa6zR6Knp/t6XPXxwI5SbVPPvA6JD5I1v99Kv0tdcyutNDFJ1xjRqicAw1v1xNzIlFrG2hMinexaYKBfjeup2qtJ/3OI+VX771+GPJgk80IiCIJm1mDK5NfJyHjEiJFebN5ykJYt7dFXvwBv3/oVATvn4L1+Klu3HeLkyZNVqLpqiL4Qh56eHlEHPQkPW8Avv0WQkHC3wvKTykayvypPRWzsTdatm8qGjdP5aW2w5vwKgIcPHzN9+npc5ryFmZlZhWkqjipPxekz11m+7D22bZnB/v3nOXbsSoXlV9Qmnl6+fP21bjDSo0crevduw9hxy/jqqw106NChxIDl+WiRfi6t7Rw4cIFatcxoU8J+/4rG2sqcA+GLCNzpwuzZY/jqm1/IzMyqdB0bNk4jMmoJOTl5HD9ecXWjgKJ1pIDjx6/g53+Ur78q3Iq3fdvXBOx0xdt7Klu3Hax0n9a+fSOCd8/Fz2cm67xDyc7OrbC8ittkwICXCdm7gB/XfML3q3dVWL5l6QDdssnLy+fixQTGjetNYIArxsaGrF+/vtI0VhX6+noEKtw4eGgx0dFx/PWXNIO96bdw1q+fyqHIJYwa9RpLFpe89eV5kpOTR0TEec0ZdEUJ3n2yxEGm50l5fGueSl1PxvYkcKcLxsbGrN+gO5lRkZw/H4exkQHNmtYrO/FzYu26UPSr6TNcvTLM2sqcA/sXEOg/i9kzR/LVzE1V4ucrOz4qoLg91vy0l4kT+uissKwMXgQtX04fzKF98xg2pCNbtksTv1MmOZKRkcWIN1ewefthWrawQ78CYiItHZ8P4VC4O8OGdGLLNmmy5vCRy7RsYUfUgQUE+n/DQi9/zarLiuKZ4oGtWzFMyS7+qHIzJ+RnejRsz9FP19GzYTtuPUhBJRZuY7c1q8WGMXP4eOcyRFF85nxk/nvIB3DL/COaN2/+GTC5RYsWtG1TW2t5ZlJSGjbW2kslbawttWYekpRp2KhnEWrXrkFy8gOsrS1ITn5ALfUWITMzYxZ7SbPUoiji2H8uDg7SYZ1F7x3Qvz3R0dF06dSQrVsP4eN3BIC2bRr8I13/hK3bDuHje1TS0baYDuV9nTxsbIrruK+jtTi7g0/Rs2crDAz0qV27Bh1fbsyFmJtERsXi47dGnXdtku4UtUEpeSeVnKZ2bfMSy8bWtiaWlmaYmFSXtv11bsrlK4k0amRDbq6K6dPXM2zYK7z+euE5FpVhE1tbS7p0bkKtmtIAVq9erYm9mMD1G0lqm2RKeVeATWJi4pkxQzosNu3+Qw5FxlKtmj79+3fgk48H88nH0mzY6DFrOHfuHAEB/7yOPm3b2bP3NBEHLhAZGUt2Th6ZmTl8PfMhL7dvXOHtxtDQAEP11rM2retT38GKG3HJWoenF2BtY6lVRsqkNGyKzH4WaLhzJw1b25rk5anIyMh64sxzUapXN6CfYzsiwqM151tt3XoIP9/VQMW1G4DLVxJxm7sZ7/XTqFnTrMjzamruHdC/Q5X5tCZN6mJiUp2//r5N9Pk4fPxWU5HtpihdujQlIeEuqWmZmjYMcOvWPUaMGCHZoJLLxtbWElsbS83KnEEDO7Le+xRoL9yqMmxsanJHx5fW1EpjbVNCW6lpWq57zc1N6Nq1OVFRsdSpY87ly4kaW7wxuDOTP1yt1mFTYTqiImNo1bo+depoz6bn5anYt+8s/julM8a2bj2Ir893QGal+9asrBztejJoEOvXeVVKv1dA8N7TDBlc+qqkrdsi8fE7JmlpU5+kpGL5FPexNhbaWpLuY2NdmGZnwAkOHorlt41TNS/Iun6+DjfiUmjbpnACoyrjo4IYsrLscT46jtCwc6xYuYv0jCz0BIHqhga866y9gvlF0bL1j8P4+Esrzdu2dtDVYV28H7YgqcgKICmN7jbgYUM6MeVTb6Z/NggzMyMWe4wD1G3ojUU42GtvX926PaqYPYrXkTLsUUIagGFDOzPlk3VMn/oGOwP+ZMqHjgiCQIP6Vtjb1eb6DSXt2jZg69at+Pj4UNF9X7nigQEDOBH5OzlWuoOAt9PvYm9hrfndzrwOd9JTtNIkZdzjne3zATA1NGJE6148eCyt4q5R3QT/CYtZsH8jJxMv6Tz/P0f+v291UFUir0yS+UdcuXLlxytXrnRQKBT0d2xHoOIEoihy7twNatQw1izpLMDa2gIzMyPOnbuBKIoEKk7g2K8dAP36tSNQIXVegYrjmuvp6Y/IyZEOw/P1PULnzi9hZmbMo0fZZD6UZg8ePcrmyJFLNG0q7b92du6tOeD3n+r6Jzi/01tzIKak409Jx3m1DqtiOqzUOs4X6PizTB1169bihHplxaNH2Zw/H0fjxjZS3goFUtl0IFBxXG2D69SoYfQEG1xX530cR8ciZRModeiBgcc01x0d23P6zFXy8lRkZeUQHR1Hk8a2iKKIq9vvNG5iy/vv9690m/To3oq//rqtOSvh5MmrvPSSbaFNAt0qzCYR4Z5ERHgREeHFwNdfZv68sfTv3wGVKp+0NOnA7ctXEsnOziY0NPS51NGnbTtfzXAi8qAXEeGLWLXyA7p168aKZe9XSrtJTc1ApZI66oSEu8TFJ+NgX6fEtG3bNiA+PpnExLvk5OSxZ89p+hZ7ft9+7VCot1eGhp6lW7fmJZ71U8DDh4815xzk5ak4dCiGxo0Lt8Y4O1d8u7l9O5Vp09axbOn7mm+1A6n9FsyIVoVPS0i8qzlw+9ate1y/rsTOrnahTSqw3cTHJ2tmQ2Njb5KTk0vNYoOCdna1q6xsrKwssK1bi+vXpa2px45d/kcHcD9v2rZtQHxcMokJ6rYSfJJ+xcq7X792BAZIf3No6BlNW+nXrx17gk+Sk5NLYsJd4uOSadeuIampGaSnS1v5Hj/O4ejRSzRubIu5uQkZGVmaVahHj1yicZO6ah1tn7uOAoKDTzFkiO7qo2NHL9Oosa1me4mzcx+pnlSBb5XqSU2uq21z7NgxmjSxrZR+DyA/P5+9IWcYMlh39VYBzu/00hyM3d+xHYG7imgxMypZi2kRLbv+xLFfW0A6JHrDL/tZu2YyxsaGmnt0/XyKzkBBVcZHlW2PbZu/IGKfOxH73Jk4vjcfTRmgM5D0ImlxHtsDhe/XKHy/pn+/tgQGnVLriJN8rZV5MR3mmJlV59z5OElH0Ckc+0rbteLiCwc1wg/E0LiRNOiRnp6lOeja1/84nTs20TrXCMB5XE8U/jNR+M+UdOw6WajDrJQ6YmpUqGPXSRz7ttXVEXFBc8ZW3bqWHDv+FwB372ZwIy4Ze3VddXZ2rvC+r/zxwBFyLUo+o/X0rcs0qW1Hg5q2GOhXY0zbfgRfPqaVpraJuSY2+rrXO/x+Zi8ABvrV+OOdhWw7G0ZgbGSJz5eReRKCvJRN5jkhiqr9LPTYQdThixgbGeLlNV6z2mDESC8UAdKs4YWYeM1X8Pbq2Zq5btLXZqalZfLFjI3cuZ1KvXq1+O7bD7G0NOXs2evMnvM7CND0pbp4LhqPhYUJCQl3+WzaOgBUefkMHdqZTz5dAfnhxYSJ/0jXvn3n8PD0ITU1E3NzY1q2sGfjhmkA9HN0I/PhY3JzVdSoYcwvG6bx0kt1Qc8RVPt0dSzyIerwJYyNDPDyfLeIjsUoAuYU6nDZotbRirmu0lcU79t/Hg9P3yI67NjoPZWHD7OZ47qFa9fuIIowamQ3PpykHsDRHyDlnR/BQo8/iIqKVdtgIm3bqvN2WoQiUPoq5QsX4pnjsonHj3MkG8wdW1g2X3pz504q9epJX2lasPpjw8Ywdu48ip6eHmPGdOe9iY6cOn0VZ+cVNGtmpzmTZ8aMefTukVMpNgFQ7PqT9d5hCIJAr16tmfm1U6FNxAPqelExNilg9uzf6NOnLYMGdSI7O5eRozwBaVZ7wYJvadmyJeSH/+M6+rRtpygn/vyLX349x7q1b1VKuwkNO8vq1bupZqCPniAwbdoQ+vVVvxDoOZIvarffQ4diWOzlR35+PqNGv8rHH7/B6tVBtGnTgH792pGdLX3N+KVLiVhYmLBy1STNrLNjPzceFmmfGzZOw9LSlE8+/omcnDzyRZGurzRj9pwxWmcD6QmOFdpuXN02ExZ2VnNwvb6+Hjv9XUhISOGzqT8D0lk1Q4d24ZNPlleaTwtUSF9RXFA2n336Bv37d9CUTUW2m/XeoSgUx6lWTR+j6gZ8M3M0nTtJ3zb3jvMKrl9P4tGjbCwta+Hp6UmP7rmVWjYAly4l4Oq2mdxcFQ4OdVi82BvL2aV/zfiT2PbBQvo060gdM0uU6anM3+3NL0eDnulZ4trjiBzg0KELeHn5kq/KZ/To1/j4k8Gs/n6X1FYc25OdncvMb37l0qUELCxMWPXthzg4SOdq/Lx2D/7+R9HX18fF5U169W7DlcuJzJ69CZUqH1EUGTSoE59NHQJI51utXh2EniBgbmGCl9cE6jtIPuTgodXPVQdIL1N9+7qwf/8iatQw1vr7Z8/+jQ7tGzO2yDl1An2rzLdeupSA69yt5Obm4eDQksWLBmr53Yrs9078+RcrV+3C54+vtSuJOhYgL1TrsqTFl6gjlyT7LHLWrB4aMWopip2z1FrUXz+fnUOvHq2Y6zoGQRAYMGghObl5WKoPEG7fviEL579NaNg5Vq/ZQ7Vq+ujpCUz77A36qV/qqTbwxYmPKskeRfnhxz2YmFRn0vuOhfaoxLIpU0t2sK4Or51EHbkslY3HONq2dpB0vLkCha9U1y7EJjDHbbtUNj1aMHfOKARBYNqXv3IjLgVBT8Cubk0WzB2DjY0lZ8/HMdtN+vr6pi/Z4rngbSwKDquvPgRy9+rq8PSX6oixoaSjwB6jl6Hwn1loD7dtPH6cS6+eLZnrMlrS8cUv3IhLRhAE7OrVYsG8N7FRn/80x3UbKXfTEUWRyZP6M2KYemWfgfpcqQrs+8ofD7zN17dK/gY7gIHNurJ08Kfo6+nz++m9LD+0FTfH9zhz6y/2XD6KU+teLBjwISIiR+Ki+TJoNTmqXMa278/Po2ZyKTlO86yP/JcSnVTyOWcPF0UAlD5j9/9AdnDVDo5UH/Kvsq88mCTzvBCLv/BUCXqOOi9eVaZDVbnnJJRIQQApHqhaHQBC3xfHJi+KPeDFqa8viI7ig0lVIkM9mPTC1JMXpGxeGHvAC6NF+KRbVavQDCZVNQJS2bwwWl6UdvOi9HugM2BRJZQwmFQllDCYVCWUMphUJZQwmFQllDCYVCUUGUyqcoS+mLr1q2oV/43BpMdBVTs4YjTsX2VfeZubjIyMjIyMjIyMjIyMjIyMjEy5kQ/glpGRkZGRkZGRkZGRkZGR+W8jygdwPw3yyiQZGRkZGRkZGRkZGRkZGRkZmXIjDybJyMjIyMjIyMjIyMjIyMjIyJQbeZubjIyMjIyMjIyMjIyMjIzMfxt5m9tTIa9MkpGRkZGRkZGRkZGRkZGRkZEpN/LKJBkZGRkZGRkZGRkZGRkZmf82+fLKpKdBEEWxqjXI/H8gVyQZGRkZGRkZGRkZGZn/X4SqFlChZPpX7Tut2eh/lX3lbW4yMjIyMjIyMjIyMjIyMjIyMuVG3uYm8/zID69qBaDnSG5+aFWrwEBvIGnZO6paBjWrvw1AtmpvFSuB6vpvcPfxlqqWQR2jd8kXq76u6gmO0g+Pg6pWCIDRMO48+qWqVVDX5AMQD1S1DBD6Sv+r9lWtDgD9ASRmrq9qFdibTXlh7AEgUvX1RKDvC6ND+KRbVctAXHtc+v8FsQnZwVUtA6oPQfnot6pWgY3JewDcfrihaoUA9Uw/REz9vaplINSaAI8CqloGmIwEIC+/6v1rNb0BL0ycdu/x9qqWQW2jcQA8yqv6OM2k2rAXKz76f0Y+gPupkFcmycjIyMjIyMjIyMjIyMjIyMiUG3llkoyMjIyMjIyMjIyMjIyMzH8beWXSUyGvTJKRkZGRkZGRkZGRkZGRkZGRKTfyYJKMjIyMjIyMjIyMjIyMjIyMTLmRt7nJyMjIyMjIyMjIyMjIyMj8t5G3uT0V8sokGRkZGRkZGRkZGRkZGRkZGZlyI69MkqkwRFHE08uXQ5GxGBkZsMRrAq1b19dJFxN7kzlzfudxdi69e7XG1eVNBEFgb8gZ1qwJ5tr1JHx9ZtK2TQMA0tIymf6FNzExNxnp1I15c99+oo7DURdZ4rUTVX4+o8e8yoeTB2h9npOTy5xZW7h4MQFLS1NWrHoPO7vaHD1yme9W7SI3V4WBgT5ffeNE127NtO6d+ul6EhPuERg056lsc+zw33y7dA/5+SLDR3VkwqRexTTlscB1J1cu3sbcwphFy9+inl1NcnPzWLIwiMuxtxD0BL6cNZhOXRo9Vd6STS6xdPFO8lUio8Z0Y9Lk/jr5u87ewsXYRCwsTVi+aiJ2drW5EB3Pwvk7ABCBTz4bhGP/dgDMc93GoUMXqVXLjIBds59a0/EjV/luaSj5+SLDRr7M+EndtT4/dzqe75eFce1vJQuWjqLvgFaaz2Z8so3YC4m061Cf5WvGPnXeUVGxeHn6kp8vMmbMa0yeMlDr85ycXGbN2sTFWKmOrFo1CTv72qSlZfLF51JddHLqxtx5hXVxwvhvSUl5gJGRIQAbNk6jdu0aZWoRRRHPpQoOHb6EkZEhSzzepnVLe510MRcTmTP3D6nd9GiJ66wRCIKg+fyXTQdZumo3xw4uoFZNUx6kP8Jlng83E+9R3bAaXgveolnTuuWyz4kj11mzPBxVfj5DnNrj/IH215CfP53AmhXhXPs7mXmLh9NnQAsAkm4/YO5XAeTni6jyVIwc24kRb75cPht4+nAoMkayweKJJfuOmHjmzNmk9h1tcHV9C0EQuH//IV/O8ObWrXvY2dXmu28nY2Fhyq6gE3h7h4EoYmpqhLv7O7RoYc/160l8OaPwK7MTEr5i+vTpvDfe7skavfwk/2ZsyBKv8bRu5aCrMfYmc1w28/hxgX8bgyAILF0ewIGDMRgY6FPfoQ6LPd/F3NykTNsU58+jN/hxxQHyVSKDndow7v2uWp9Hn0nkxxUHuH41BTevofTur+3DHmZm88Gbv9G9z0tMn+X4VHn/UxvsDTnDmh/3cO26Et8dX2t8fE5OHvPdtxMTexNBTw/XOaPp+kozIqMu4rl4Bfn5+Yx5syNTpgzSyicnJ5dZM38jNvam1E6//RB7+zoArFsXgr/fEfT09HB1e4uePVsDEBUZi6enj/qZ3TXPdH5nBQ8fPgbg3r0M2rVryI8/fULQLqkOiYiYma5jvvsQUpIflPiMZ9WVnZ3Lu84ryMnJQ6XK5/WBHZk+fRgAx45dZvkyf/LzRUxMqrN4yUQaNniqYtNh43hXhrbtTnJGGm09nJ/5OdnZ2Tg7O5OTk6qju4Bbt+7h6vI7qamZUt+y/ANsbWuWO48C3xB5SPINi5cU+obbt1Nxc9tM0p00BGE569eMw96ulva9SwM4FFXgW8fRulVJvjWBOW7bJb/SsyWus0bq+taVuzh2aCG1apqx4dcIgvacAUCVl8+1G0qOHVqIpYVpmX/PiSPXWL18P/n5+Qxx6sC7H7yq9fm50zf5YcV+rv+dzPzFThrf+vcVJas8Q3j4MAc9fYHxk17DcWCrkrIoN38eucGaFeGoVCJDRrbjnWK+5PzpBH5cGcG1v1OYt3gYvfs31/r8YWY27435hR59mvL5bO2Y4mkQRRHPb8OIPHoNIyMDFs8dSuvmun3Vtz8fQLH3AukZjzkTMVNz/dftJ/DbdQ59fT1qWZrg6ToUu7oW5c97WRCHjlyR4tYFb9K6pW4/EHMxkTnzfXmcnUfv7s1xnTlMU0c2bz/CVp/j6OsJ9O7ZgplfDAbg8l93mL8ogMyHj9HTE/DbMpXq1Q1K1RIVdZElXn7q2PU1Jk9+XetzKXbdTOxFya+sXPUBdna1uZ+WyRdfbCQmJh4np264zX1Lc09w8Cm814UiCAJW1hYsXTaRmjXNymWb4vyTmO3Z8vub75aGoMrPZ9jIjkyY1FPr85ycPDxcA7h86TYWFiZ4LBtDXXXcvHThbi5fvI2ensAXMwfRUR035+bmsXLxHs6ejEPQE/homiN9+5et80jUZZYvUZCvysdpdFc+mNxPR8vcOdu5pI6hl64cTz27Wty+lcqoYcto0NAagLbt6+M2fwxZWTnMnPE7iQn30NPTo1efVnw+Y0iJeVdUfLQ//Bzffx+Enp6Avr4eLi5v0bnTS9y6dY+p034mP18kL0/Fu+8mMW7cuHKVmcx/A3llkkyFERkZS1x8MmEh7ngscMZ94R8lpnNfsB2Phc6EhbgTF59MZNRFAJo1rcsPP0yhS+eXtNJXr27A59OHMfObkWVqUKnyWeThy9r1H7MryIU9wae5dvWOVpqdfscxtzBhb+g8xk/ow6oVuwCoWdOUNWs/ImDXHDwXv8ucWZu17tsXdh4Tk+rltkdRTSu8dvPt2vFsD5xK2N4L3LiWrJVm184zmJsb4Rf8BePGv8aP3+0DQOF/GoCtO6eyet1EVq8IIT//6ZZjqlT5eC3yY+26jwgMms3ePWe4djVJK81O/+OYm5sQHOrG+Il9+G5lEAAvNa3Ldt+v8A2Yydr1H7HQ3Ye8PBUAw0d2Ze36j57aHgWaVnqFsPKnd9ga8An7Q2K4cS1FK42NrQWuHsMZ8EYbnfvfee9V5i5yeua8PRbuYL33VIJ2zyU4+BRXi9URP7+jWJibEBq2gAkT+7FiZQAg1cXpnw/jm5kl18Xly98nINCFgECXcg0kAUQevkzczRTCgmbjMW8M7ov8S0znvsgfj/lvEhY0m7ibKUQeuaz57E7SfY4c+4t6dS01137eEE7LFvUI8vuKpZ7j8FymKJcelSqf75fsY+maN9nk/yERIReJu3ZXK411XXNmLxhM/0HaQVhtKzN+3PQuG3e8z0+bJ7Dt1+PcTc4o2waRMZLvCF2Ix0Jn3BdsK9kGC7bh4fEuYaEL1b4jFoD13iG82q0FYaEevNqtBeu9QwGwt6vDls0zCAqaxyefDmbuvC0ANG5siyLQDUWgGzv9XTA2NmbAgAEl5lmo8SJx8SmEhczHY8E43BeU4t8W7sBj4TuEhcwnLj5F49+6v9aC3QoXggJdaNjQmnXeYWXapTgqVT6rl4SzePUofvF7j4jQK8Rdv6eVxtq2BjMXDMJxUMsSn/Hr2iO0e1n3hbo8/FMbNGtajx9WT6ZL5yZa6X39jgAQpHDl1w1TWbosgNzcPBYu8mHDhg0EBwcTvPskV6/e1rrPz/cI5uYmhO3zYOJ7jqxcIbXTq1dvsyf4JLuD57FhwzQWLtiOSpWPSpXPwoXb8d4wld3B87WeuXXb1wQq3AhUuNHh5cYMeF0aBLWzr8PmLeo69MknzHXbXOoznlWXoWE1ftv0JYpdcwkIdONwVCznzl2XbOm+jeUrPiBQ4cbQoV1Yu3bPM5VdUX47FsygH778x88xNDRk06ZNJeouYNlSf0Y4dWNX0Fw++3QIq1YGPlUekZExxMclExq2kIUezixwL/QNs2b9yqRJA9iz1x1fX19q19J+SY48fIm4+LuE7XbBY96buC/yKzEP90V+eMx/i7DdLsTF3yXycFHfmsaRY1eoV7dwAOzD9/uh8P0ahe/XzPh8CF06NSnXQJJKlc+3S8JYvuYtfvefQngJvtWmrjkuC4bSf1BrretGRtVw8RjG7/6TWbHmbX5YsZ+MjMdl5vkkLd8v3ceSH8bwm/8HhIdcIu66rpZZ7m+U6kt+WXuYdh11B5Oflshj14hPSCXU9xMWzh7MgmUhJabr26MZPhvf17nespkNfr9+wK4tkxnYrwUrfgwvf96HrxB38y5hiq/xcBuFu1fJ9dPdKxCPuaMJU3xN3M27RB75C4DjJ68RfvASu3Z8TrD/DCZNkCYK8/JUfOO2gwWuTgT7z+B37ylUq6Zfqg6VKh9PDx9+Xv8pu4Lc2BN8Wicu8fc7hrmFMSGh7kyY0JdVK6Q+3bC6AdOmD+WbYjFyXp6KJV5+/LrpcwIULjRrZse2rYfKbZvi+v5JzPYs+a3w2sPKn5zZFvCZOj/tuDko4Aw1zI3w3f05b7/bjZ++2w/ALn9poHeL/6d89/N4flgZpombN3lHUbOWKTuCprMt4DNe7lT2yLxKlc8SzwDW/Pwh/ru+IWTPWZ0YOtD/BDXMjdkVMgfnCb34flWw5jN7h9rs2DmDHTtn4DZ/jOb6hPf6ELB7Fn/4fcn5s3EcjrpUYv4VFR+92q0FuxRSHOTlNQE3N+mdx8rKgh1/zEQR6IbPjll4e3vTvHnzemUa6l+MKKqq9N+/jf+rwSRBEKYLgnBJEIRbgiCseY7PbSgIQsxzfN5BQRA6q3/eIwiCpfrfp88rj+eBIAhfCILw9FPlasIjonEa0RVBEOjQoRHp6Y9ITn6glSY5+QGZmY/p0KERgiDgNKIr4eHnAWjSpC6NG9noPNfEpDqdO730xBmdAi5Ex1O/vhUODnUwMKzGG4M7EhFxQStNRMQFRox4BYDXB3bgxPG/EEWRlq0csLaWZrNealqXx9m55OTkAvDoYTa/bzrARx9rzxSVh4sxidjXr4WdfS0MDKoxYFBbIg9c1koTdfASg4d3AKDvgFacOnEdURS5cS2Fzq9IMyq1aptRo4YRl2Jv6+TxJGIuxFO/fh3s1TYZ9MbLHChmk4MRFxju1AWAAa+358TxvxFFEWObitxVAAAgAElEQVRjQ00AlJ2dR5HJWjp3boKFxbNVl0sxt7F3qImdfU0MDPRxHNSaqINXtNLUtbPkpWY2CHqCzv2duzbCxNTwmfKOjo7T1BFDw2oMHtyJCHUdLCAiPJoRTtJqnIEDX+b4sSuIorQyoFOnl6huWHZdLC/hB2JxGtZZajftGpCe8ZjklHStNMkp6WQ+fEyHdg2kdjOsM+ERsZrPFy9X8M2XQ7Vm069dV9LtFWlgtkkja27dTuPuvbIHdi7H3MHOwZJ69pYYGOjTb2BLjhz8WytN3XoWNGlmrVM2Bgb6GBpKC2Bzc1SIolg+G4RH4zSim9p3NCY9PesJvqOx2nd0I3z/+cL7naQZfienV9mvvt6xYxMs1C95Hdo3IikpTSfvY8cu4+DggJ1d6auSoMC/vSJpbN+I9IwsklOKaUxRa2xf4N9eITw8GoAe3Vtq2pKk5X65bFOUy7FJWmXT9/XmHD14VSuNbT0LmjS10qoLBfx1SUla6iM6dXu2pS3/1AZNmtiW6OOvXkuiazdp1UPt2jWoUcOYQMWfNKhfBwcHBwwNDRk8pIvmOVp6RkrlPnBgR44du4woioSHRzN4SBcMDQ2wd6hD/QbWREfHSW2/gTUODlZS2y/hmZmZWZw4foX+/dsDxepQhw4kJt4t8xlPq0sQBExNjQDpBTAvT6UpPwGBzExp0CAj8zHW1pb8U6KuniP1YXrZCctA0i3ZprjuAq5du0M3ddl27dZc098DbNwQxpjRixk+zIPVq4NKzCNc7YuL+4arV2+jysune3dpQNvU1BRjY+0+IfxATKFvbd9QXV9L8K2Z2XRo37DQtx4o7B8XLyvwrSXbIHjvGYa+UfbqS5D6PTuHmtQr6PcGtuTwwb+00tStZ1mib3VoUBuHBtKqqzrWNahZ05T7qY/KlW9JXI65Qz37mkX8fAuOlORLmlmjV0IffOViEmn3HtGlW8Nn1lBAeORfjHijnVRObexIz3xM8l3dvqpDGzus6+hO0nTr1BBjI6lPbt/ajqRyTGBo8j50EaehHdX9b/3S68jDbDq0qy/VkaEdCT8o9b/bfY8z5f3emn6vYEDzyLG/ad7Ulhbqd/Calqbo65f+CnYhOg6H+nWKxCUdORCh7VciIqIZMUJaPfb6wJc5frxoXNIEw2IxsihK/7Ie5SCKIg8fZmFlXb4VW8X5pzHb03Ix5hb2DoVxc/9BbXTyizpwhTeKxs1/quPm6yl0KhI3m9Uw4rI6bt4deJYJH0grnPT09LCsWfYgcMyFmzg41MbeoTYGhtUYOLgDBw/EaqU5GBHLsBGdAej/ejv+VMfQpWFsbEiXrlJ8ZmBYjRat7EhOelBi2oqKj0xNjTT+OutRjuZnQ8NqGKpj3JycvKeewJb5/+f/ajAJ+BQYALhWdsaCIDzTlkFRFAeLongfsETS/yLxBfDMg0lK5X2t5eu2tjVRJmu/MCmT72NrUxgM29rURKl8+peq0khOvo+tbeHzbWwsSVYWc7rKB9iqV3BUq6aPWQ0j7t9/qJVmX9g5WrW01zjUH1YHM/G9vhgZP/0ARooyA2ubwg7c2saclOR0nTQ26jTVquljZladB/cf0bS5LVEHr5CXp+J2YhqXL91BWUqHUxpK5QNsipSLja2lTkdUNE1xm0Sfj2PksCWMHrGUufPfeuLsWnlJSU7H2tZc87u1tTkpyvIHgP+EZOV9bOsWtUdNlMXqiDL5PnXrFtqjRg1jnTpSEi4umxnp5MVPP+0p90CKMvlBsTZhgbJ4+Twhzf4DMVhbW2iC1gJaNKtHWLj0UhR94Sa376SRpCy77qQkZ2BlU1g2VjY1SEnJLNffApCclM4Hb/3CW2/8xLj3ulHHuuwVWspiZWJra6njF3T9S2Gae/fSNQPBVlbm3Lun+6Ls53eEXr10Z0yD95xi6NChZWtMLpa/TSkatcrJUscHAvjvPEavnk+/BeBuciZWNoX2tLKpwd1ylk1+vsjP3x7k4y96P3W+BTxPGxSlRXM7IiIukJenIiHxLrEXE7h2PamEvLQHA5OVJbTTtIcolWnULeHe0q4XZf/+83R7tTlmZsY6Ov38/Gje3L7MZzytLpBmv51GLKL7a9/w2mstad9eehla5PkuU6asoXev2exSHGdKsS25VY1KpSpRdwHNW9izL+wsAPv2nePhw8ekpWVy+PBF4uKT8fWbTaDCldjYm5w8+bfO85XK+9o2U7f7uLhkapibMG3qz4x08mTp0qWoVNovPcrkdK14QKqLJflWi2JpJP9R6FtLHmjOysoh6shlXh/Qrjym4m5yJtY6vvXp+72LMbfJzVNh51D+7YI6WlIysbYt4kusa3A3ufy+ZO23B/nkyz7PnH9RlCkZ1C1iF1src5TPYBcAv6Bz9Hq1SdkJC/LWqSMWmvLXSmNtUWKauPi7nDobx5vjf+TdSeuIjk0A4MbNuwiCwKRPNzJy3Gq8f3vyiiBl8gOtem5joxuXSLFr+eMSAwN95s5/G6cRXvTp5cq1q0mMHv3aE3WURmXHbCnJ6dgUyc/K2pwUZXqpaapV08fUzIgH9x/xUjMbDh8qjJuvXLqNUplORnoWAOt/jOC9t3/G9WsfUu+VXeeTlQ+wqav9XpFSvGySH2jqkRRDG3P/vjTYe+tWKmNHr2LSxJ84c1p75SZARnoWkQcv8kq3piXmX5Hx0b59Zxn0xnw++ngNXp4TNNfv3Ell2HAP+vSdw+TJk7ly5crTzWL/28jPr9p//zL+bwaTBEH4GWgM7AVqFrk+TBCEE4IgnBUEYb8gCDbq61aCIOwTBCFWEIQNgiDEC4JQpxz5NFY/q4sgCO8JgrBLEIQIIFwQBFNBEH4RBOFPdZoR6nuMBUH4Q71qKgAwLvK8OHW+S4AmgiCcEwRheSl59xEE4ZAgCApBEK4LgrBEEARndX4XBEFoUsbfbCYIwq/qtNGCIIxWX39dEIRjgiCcEQTBV51uOlAPOCAIwoFnKJL/G67+fYdVK3cxb4F0Hs7lS4kkJNyl/4D2la5lqNPLWNuY8/64dXy7bC9t2zs8cXarImjXviEBQbPZ7jODjd77yc7OrdT8/y0sX/E+u4Lc2LJlBqdPXUWhOFHheWZl5bBuQziff6r7gjnlg35kpGcx4q1VbN5+mJYt6qH/HGYMy8La1pxffD5gq2IKoUExpN4rexDueSIIgs7qiOPHr+Dnf5Svv9LeBpCTk0dExHkGDdI+86YiWftzCPr6egwf1qXS8gTY5XuOV7o30hqMelEYPepVbG0tGf3mMrwW+/Nyh0bolbYUpIIJ3n2SIUN0y+b48Sv4+fkxbPgrFZKvvr4egQo3Dh5aTHR0HH/9dQuATb+Fs379VA5FLmHUqNdYsrjkrVpVhb6+fom6C5g5czQnT/7NSCdPTv75FzY2lujr63HkyEWOHLnISCdPRo304sb1JOLjkkvJRZe8PBWnT/3NzFmj8fWbTWJiIjsVfz63vysrK4d13vv5/LPSfcOBQ7F07NCoXFvcnhd3UzLxdAtijvuQElcMVQYKn7N0fQF9ya6QC8RevsMk525lJ35OqFT5PHjwCJ/fP2Xml4P5YuY2RFFEpcrn9Nk4lnuOZdsvH7M/IpZjJ66W/cDnSG6uih1/ROG3cxYHIz1p1twO7/VPv73630ZB3DzpnfV8tzyEtu0d0NMTUKnySVam07aDA7/t+Jg27ez5YWXF2qOOlTl797vxh/8Mvpo5HJeZWzUrTUHyY7O/2cI45x7YO9SuUC2gGx8NGPAyIXsX8OOaT/h+9S7N9bp1axG0ay5hoR4EBATQvHlz3SXFMv9Z/m8O4BZF8WNBEAYBfYGi08qHgW6iKIqCIHwIzAS+AuYDEaIoLlbfN6msPARBaA78AbwniuJ5QRBaAx2BdqIopgqC4KV+5geCIFgCfwqCsB/4CHgkimJLQRDaAWdKePxsoI0oih3KkNEeaAmkAteBDaIoviIIwufANKTVRKX9zXOBB6IotlX/PTXVA1luQH9RFB8KgjALmCGK4kJBEGYAfUVRvFtchPr+KcAUgHXr1jHlwyZs3XoIH/V5F23bNNDaRpKUlIZNsSX5NtaWJBUZUU9SpmFj88+X7RdgbW2ptX1EqbyvtSoIwNrGgqQ70ih+Xp6KzIzHWFqaajR/Pm0DXkvGU7++FQDnzt0gNuYmrzu6o1KpuJeayXsTVvPb79PLpcnKpobW6qhkZTpW1uY6aZTKB1jbWkiaMrOxsDRBEAS+mPmGJt3k8d7Ub/B0HY6NjQXKIuWiTLqvmaUonsbW1lLHJgU0bmKLsUl1rv59h9ZtdA//exqsrM1JTiqcHUlOTq+0wNTaxpKkO0XtkaZZFVaAjbUld+6kaepIRkaWjj2KU1CPTc2MGDq0CxeipcMwS2LrH0fw2SkNNrVt7VCsTTzApnj5WFuUmOZm4j0Sb6Uy4q1Vmuujxn6L79bpWNUxZ7GHdDi5KIo4DvbCwb7sumNlXUNrBjBFmYGV1dMf2FnHugaNXqpD9JkEzSGyRQnYcYbQQAWQSdu2DbTKJCnpvo5fsLGxLOZfCtPUrm1OcvIDrK0tSE5+QK1ahXXp8pVE3OZuxnv9NJ2DRyOjYmjdqj516pQ8r7B12yF8fI8CSBqL5q8sRaNWOd3X8oE7A45z8FAMv/0yvcRtaGVRx9pMazY4RZlBnXKWzcXo21w4e4tdvufJepRDXl4+xsYGTJ7e64n3PW8blES1avq4zB6t+X3sOytp2dKewCIDslJe2isxrG1KaKc1TbGxqckdHZ3SvaVdB0hLzST6QhxrfvxYK58rlxOZ67YZb+8tpN2PYM+e089dVwHm5iZ07dqcqKhY6tQx5/LlRM1qnzcGd2byh6ufaMuqoqjuZs0KV/LY2FjywxrJng8fPiYs7Czm5iaIIkyZMoixY7Xr39atB/H1OQzAuvVTsbGx1LaZut2rVCpatHTAwUHqpx0dHTl/Zhdb/ziMj/9xQO1bk4rXxZJ864Niacy5mXBX8q1vrlBff8Cot1fhu+0LrOpI/XdwyFmGlHOLG0jtN1nHt5a/33uYmc2s6T5M/qw3rds9eVtumVqszEhOKuJLkjOoY10+XxJ74TYXziai8D1HVlYuebkqjE0MmDK9/Kset/qdwneXtGKtbct63Clil6SUdGyewi4AR/+8wc+/HWHzT+M1W85KzXvHMXx2SgOPbVvbF6sjD7ApFp/ZWJuTlPygxDQ2NhYMcGyDIAi0ayMNWqSlPcTW2oIuHRtRS72NqleP5sRevsWrXbXPBC3Mw0KrniuVunGJFLuWPy65fDkRQBPLDhrUkQ3PcFYfVH7MZmVtjrJIfinJ6VorpoumsbaR4uaHmY81cfPn3xQOAk+ZsIH6DWpjYWmCkZEBfRylM8D6vd6a3QFny9RibWOB8o72e4VV8bKxtpB8kyaGzsJSraWgPrZqbY+9Q23i41Jo3UY6a2yRux/1G1jhPEHbD+7YdoRA/w1UdHxUQJcuTUlIuEtqWia1isRJNjaWNG3alAsXLvQEXqyZDJkq4/9mZdITsAdCBUG4AHwDFJxi2ANpYAhRFEMA3cMztLECFICzKIpFD1TZJ4piqvrn14HZgiCcAw4CRkB9oBewRZ1XNKC98fnpOCmK4h1RFLOBa0BBT3ABaKj+ubS/uT/wY8GDRFFMA7oBrYAjat0TgXIdoCGK4npRFDuLoth5ypQpADg790YR4IIiwIX+ju0IVJxAFEXOnbtBjRrGOoMW1tYWmJkZce7cDURRJFBxAsd+5VsiXh7atK3PzfgUEhPvkZuTx949Z+jbt61Wmr5926BQz2CGhZ6ja7emCIJAevojPv14HV/MGE7Hjo016ceO68mByEWEhbvz+9YvaNjAutwDSQAtW9uREJ/K7cQ0cnPz2BdygZ59tF+ue/ZpwZ5d5wA4sO8inV+Rzht5nJVD1qMcAE4cu4q+vh6Nmlg/lU1at6lPfPxdjU1C9p6lT1/t7T59+rZhV+BJQDpo/JWukk0SE+9pDty+fSuVuOtK6hX5tpxnpUXreiTeLLCJivCQWHr0blb2jc+Btm0bEB+fTGLiXXJy8tiz5zR9i9XBvv3aoQiUXkhCQ8/SrVvzJ7785+WpSEuTlkvn5qo4eDCGps1K/+Y057HdUfjMQOEzg/59WxMYdEpqN9Hx1DAzwtpKO2iytjLHzNSIc9HxUrsJOoVj39Y0b1qXYwcXELHXlYi9rtjaWLDzjy+xqmNOenoWObl5APjuPEHnjo0xMzMq0z7NW9cl8WYad27dJzdXRUToJV7rU3IAXJxkZTrZj6WVaxnpj7lwNpH6DUsewBr5dkcUCgWKQDf6O3YgUHFc7TuuU6OG0RN8x3W17ziOo6NUbv36tSMw8BgAgYHHNNdv305l2rR1LFv6Po1KOKsnOPhUiatQCnB+pzeKgDkoAuao/dufksbzav9mVUyjlVrj+QL/9qfGv0VGXWTDxv2s/fEjnbNdykuLVrbcSrjPnVsPyM1VcSDsCq/1Lt+WDhfPIWzfM4Vtuyfz0Re9GTCkVZkDSfB8bVAaWVk5PHqUDcCRo5fQ19fjjUEdiYtPISEhgZycHPYEn6Rfsef069eOwACp3ENDz2jaab9+7dgTfJKcnFwSE+4SH5dMu3YNpbYfl0xigrrtF3tmaOgZ+vRpq3U+X0EdWrrsfRo1alTmM55FV2pqBunp0paIx49zOHr0Eo0b22JubkJGRhY3bigBOHrkEo2blO8bGSuD1NRU0tOlF76iuouSlpqpOXNj/foQzTabHj1asdP/qOYb9JTKNO7dS8fZuY/mIHQbG0v6qX1xcd/Qtm1DMtIfkZoqDYicOHGCl5rY4Dy2h+Zw7P792hb61vNx0r0l+Vaz6pw7H1fEt7ahebN6HDu0kIiQuUSEzJV8644ZmoGkjIwsTp66hmPf8h82LPV7adxW+9bw0Et071Py1pbi5OaqcP3Kn4FD/8feeYdVcXRx+F0uKIICinS7YgNbEmOlidgLKpZEE40tn9HEWKIiqAiKvcTYW+wmYgG7GFRQ7BpFsBfARhELNkTu3e+PReAKKtgwybzPwwPcmd353bOzZ2ZnZ87Y5zg4n1cq21lx83pWP3+e+k658/Pe41vx5/b/8ce27+n3szNNWtrlaSAJoKvHFwSu6EPgij64OlYkaEeEcp0ib1LEsGCOsZFexdkLcYyZvJ25UzphWuzNs8S6dq5H0J8DCfpzoNL+bj2Z3v7Gvqb9LcipiFiljmw9iauTsky5sXNVjhy7AsC1mESeP1dTtKghDevbcvFyHE+fppKWpubYiWtUKPfqyR321Uqn911f9EtO4uLyUr/EpVrGjOfgXX9Tp27F1/ZLLCyMuXI5LuMeOXjwPOXKW74y/+v42H22KnbW3IhNyug3/7UzkoZO2rsJOjhXYkeWfvPnOfSbjx66ktFvliSJBk4VOXksGoDjR65SprzZG7XY2ZckNvYON9P70Lu2n8LZRTtAvpOLHVuCjgPwV3AEtetUQJIk7t59lLH89sb1JGJj7lAi/aXenF938PBhCr+MaJOtzM5fN/jg/aOYmISMcAxRUbGkpj6nqIkhcXH3SElR7PfgwWNOnjwJoB2w6t+GrMnfn38Y/5qZSa/hN2C6LMubJUlyBnze8jwPgFiUQaizWT7PumZDAjrIsqx1k73NG+fX8CzL35os/2vIvJ55+c4SyoDYe9/n0cnJntCwKNyajqGQfgH8/b/JSGvbzp+gTSMBGDO6C56eK0h59hxHBzscHRWnvHv3KfzGr+Pu3Ud8/7+5VKlcgiWLfwSgkas3jx6n8Py5mr9CTrN08Y9UqJC9U62rq2Kktwff956LWqOhXfu6VLC1YvasbdjZl8KlUTXae9TDc/hKmjf1xdjYgCnTegCwdvV+rsfeYf68ncyfp+wmsnDxD7neletV6OqqGDqyJQP7rUCj1tDK/TPKVTBn4ZwQKle1wdGlMq3bfcbYkRvxaDkTI+NC+E3uCMDdu4/5+X8rkHQkzMyNGOPf4Q2l5Vz+SK8O9OszH7VGg3u7OlSwtWLOb9upalcKl0b2tOtQl5HDV9Gy6TiMTQyYPFVZO/33yassXRSCrq6OsmX3KI+M2R3Dhi7n+NEr3L//iMYuY/hhQHPad8jd9HJdXR0GeTZjcL81qDUyrdxrUK6COYvm7KOynRUOzpU4F3kLz0HreJicQnjoJRbPDWX1pn4A9OuxjNjoJJ48ScXdbSaePq2p0yB3D9W6uiq8R3Wmd6/ZaDQa2neoh62tNbNmbcHevjSNGlXHw6M+w4cto2mTMRgbGzBteuZERtdG3jxOr4shIadZvORHrK2L0bvXb6SlqVFrZOrXq0THjg1zpcfJoQqhB87j1moihfT18PftnJHWttN0gtYNBmCMV3s8R/1ByrM0HBtUwrHh6x8orlyLZ4T3HyBJ2Ja3YPzYTq/Nn2kfHQYOd+OXH9ah0cg0b1uNsuXNWDp3P5WqWtLA2ZbzUbfxHryRR8nPOBR2mWXzD7BsQ29iryUxd/peJEAGOn/7JeVs39xZU3xHJG5NRqX7ju6ZNnAfR1Cgt2KD0V/jOXI5KSmp6b5DeZDr26cpPw9axPoN4VhbK1vfAsyZu4379x8z1nctoCwl2rhB8UNPnjzjYPg5fMfmbmt0J0c7xb81G6tcp/HdMjW2m0DQJk9F46hOeI5cle7fquLoqDx0+I1bR+rzNL7rpewVUaNGGXx98uaGVbo6/DisEcMHbECj1tC8rT1lyhfn93nhVKpqQX2nCpyPimPM0CAeJadwaP8Vli84yNKAHnkq50PZYPdfp/EbH6D4+H7zqVLZhiWLBpB09yG9+sxBR0fCwtyEyRO7o6urYrRXJ3r37o1araZDh8+V+/TXzcp96loDD48GDPvld5q4jcLY2IDpM3oDYGtrTfPmn9OyxVhUKhWjR3fJWB48anRnevWehUatoUOH+tjaZsYa27b9GH37aC9rmjtHqUO+Y9cCW1CpnuR4jnfRlZjwgBEjlqNWa5BlmWbNPs94kPQb142fflqAjiRhZGyAv/+3vCtrevriXPEzihc24br/ZsZsXcTSgzkHwH4dCQkJjBgxArU6WUt3VlscOXqBGdMDQZKo/YUto8cosyUbNqzK1Su36dJlMqBstDFlSk9MXxp7dnKyJyw0kiZuo9AvlOkbVCodhg3vQI/uM5GRsberQ8cO9bSPdahC6P5zuLX0V+qrX+b91rbjVIIChgIwxssDT++1Sn1tWBnHhjnvXpaV3XvO0KB+pTzt8Kqrq8PPw90Y+sMfaDQyLdpWp2x5M5bMDaNSVSsaOttyLuoW3oM38jA5hYNhl1g6fz8rNvRhb/A5Tp+8TvL9p+zcrMTC8/Rthe1brj5R6erw0/DGDOu/Ho1GQ/M21ShbvjhL5x1Q/LxTBc5H3WbUkMB0P3+F3+eHs2x9z7cq73U41a9A2MErNOk4F/2Cevh7Zy42cP92EYErFH8+ZXYIW4OjeJryHKc2s/BoU5MfezsyZXYIT54852cvZSdUKwtj5k3JXXvn1LCS0v62maLUEZ+OGWltO/9K0J8DARjj6Y7nmACljjSohGNDZWCjg/sXjPRZTyuPGejpqZjo2xFJkjA2MqBHNwc8us1GkiQcG1bC2eHVbbaurgov70707T0HjUbO6Lv+NmsrdvalaNSoOh086jNi+AqaNfXB2NiQqdMyd7Zzcx2d3kdOY09IBAsX96dCBSt+6N+c7t/MRFdXhZV1Mfz9u71Sw+t41z5b3stTMdizBYP6rUwvr1Z6eXuobGeNg3NlWrWrha/XJjq2+hUjo0L4TlZ2Srt39zGD+q1K7zcXYfT49hnn/eFnN3y9NvLrlJ2YFDXEy7dtrrQM92rHD30XodHItG1Xm/IVLJn7206q2pXEuZEd7h2+xHvEWto0m4CRsQETpyp2Pnn8KvNm70JXV4WOjoTX6A4YmxgQH3efxQtDKFvOnK88ZgLKAFJ7jzrZyv9Q/aNdwX8TFHQYXV0V+gX1mDGjD5IkceXKbSZO2oAkKQHce/b8ns6dO5/JJkzwn0XKbVDYfwKSJEUDX6Asc/tCluUBkiT9DfSWZfmEJEm/A2VlWXaWJGkOECvL8iRJkpoAuwCznJZ0SZJUBtgK1EnPN1eW5TWSJPV4UU56Pn/ACPgxfYlZLVmW/05fLlZVluXekiTZA6dQlqEdz6JZBk7KsvzKWUHpA0NDZVlulf7/vvT/j2dNe813ngjoy7L8c/rxRVEGoE4AjWRZvixJkiFgI8vyxfSZTW1kWb6WC/PLaHK//eoHQ8eV55pd+a0CPZ2m3Hv2Z37LoGhBZSDimXpHPiuBgqrm3ElZld8yKK7fDY2c/3VVR3JV/kjJ+0Pbe0e/NbefLM1vFVgZ9AT5EwjRJrkov9W781cHgMqNG48W5rcKShTu+8nYA0Am/+uJhMsno0Pq9/HiwrwKeZ4ye/NTsQnPtr0544emYEvinyzLbxVYGPQA4NbjxfkrBLA27I18d0V+y0Aq9i082ZTfMsBAid2Xpsl//6qr4/bJ9NOSUtbmtwxM9ZUB5ydp+d9PM9Bt/Sn1j/InQNtHQr6zNF8HR6TiPf9R9v0vLHPzAQIkSToBZB0oGgs0kSQpEugIxAGv3YpAluXHKANVgyRJyj4PEfwAPSBCkqSo9P8B5gGFJUk6B/iiDN68fO4klKVmka8KwJ0HfMj5O48DiqaXcRolHlIi0ANYK0lSBHAIePG6ZCGw878egFsgEAgEAoFAIBAIBAJBJv+qZW6yLJdJ/3NZ+g+yLAehxDp6mQdAU1mW0yRJqgfUTo9DlNN5owH79L/vA1mDaizLku8pSrDtl49/CnR5g2ZkWf46pzxZ0vehxGJ68b9zTmmv+s6yLD9CiYn08ud70P5OLz7/DWXJnEAgEAgEAoFAIBAIBAIB8C8bTMojpYB1kiTpAKlAn3zWIxAIBEaf9wYAACAASURBVAKBQCAQCAQCgSA/+AcGwc5P/rODSbIsXwK09nCVJMkUyCmYimv6MrSPgiRJ1YCVL338TJbl7JHYBAKBQCAQCAQCgUAgEAg+Iv/ZwaScSB8wqvkJ6DjzKegQCAQCgUAgEAgEAoHgP4GYmZQn/gsBuAUCgUAgEAgEAoFAIBAIBO8JMZgkEAgEAoFAIBAIBAKBQCDINZIsy/mtQfDvQFQkgUAgEAgEAoFAIPj3IuW3gA+JHDc/X59pJcv//aPsK2YmCQQCgUAgEAgEAoFAIBAIco0IwC14f8h781sBSC78cXFAfqugS8XZXLw/Nb9lUNFkKABbo4fksxJoVWYaV5Nn5bcMyhn9hEz+11UJFwASnq7IZyVgXujbT+ba8HxHfssAveYAn049Ue/ObxmgckMj57TZ6cdFR3IF+GS0fCp15FPRASD1q5vPSkCed5g7KavyWwbF9btx+8nS/JaBlUFP5Y/kP/NXCIBR50/Hz38q9gB4GJC/OgCKdES+m/99EqnYt5+MDoBn6vyvrwVVzT8pP/+vRgTgzhNiZpJAIBAIBAKBQCAQCAQCgSDXiMEkgUAgEAgEAoFAIBAIBAJBrhHL3AQCgUAgEAgEAoFAIBD8txHL3PKEmJkkEAgEAoFAIBAIBAKBQCDINWJmkkAgEAgEAoFAIBAIBIL/NmJmUp4QM5MEAoFAIBAIBAKBQCAQCAS5RsxMErw3ZFlm/Ph1hIZFoq9fgIkTumNnVypbvsjIGDw9l5Py7DlOjvZ4eXVCkiTu33/MoMGLuHkzCRsbU2bO6IOxsSGLlwSzZctRANRqDVeu3ObQwamYmBjSqNFIDA310VHpoFLpsHFj7resvHTiDjsWXUDWyHzmZoNDx7I55jsbHs+fEyPoO/1LbGyN3844r+HEoessmn4IjUbGrU0lOnavqZUeuCaC4KALqHR1MDLRZ6C3I+ZWRd5b+eePJRA4PwqNWqZO81K4dq6glX5wawzhW6LR0ZEoUEhFx4HVsSxdhLtxT5jUZx/mJQoDULqyCR4Dq7+1juMHY5g/7QAajYZmbavSqcfnWulnTt5iwfT9XLucxIjxTXBwzdS5ZNZBjh6IRpahVp0S/G+IA5Ikvba8/WFRjB+/Do1Gg0fHBvTt20wrPTX1OcOHLSMqKhYTE0Omz+hNiRLFAViwYCcb1oejo6ODl3cnHBzsMo5TqzV4dJiAuYUJCxb0B8Br5AoiI2OQZShT1pwJE7pT2PDNNjkSfoVfJwej0ci0aleTbj3ra6WfOhHLrCnBXL2UwJiJ7XBxq5KRNuSHtZyNuEm1WiWZ/FvnNxf2Gj72tcmKLMuMn7CR0P3n0NfXY+L4r7GrWjJbvsio63h6ryEl5TlODlXw8myPJEnM/G07IXvOoKMjYVqsCBPGf42FeeZ9HHEmli7dZjJ9yrc0a6J9773vOnL1ahyDBy3OOP769Tv89FNruvdwzfhs6dLdTJ60gUOHplK0WOFX28R/PaFhUegXKsBE/29eYZNYPEeuVGziaIfXSA8kSWLSlE3s3ReJnp6KUiWLM2F8N4yMDIiIiGbUmLVKGcCP/Vvg1riGtk32R+E/PgCNRsbDoz59+jbNbpPhyzkbdV2xyfRe2JQwBWDhgp1s2HAIHR0JL69ONHSoCsCKFXsICAhHlqFjxwZ0794IgCmTN7J37xn09FSULGWGv/83mBh/GjqMjAzypZ4M+nkR167FA5D88AmFDQtRsOAcUlPvolZraNL0M376qbVW+TdvJuE1cgV37z7C2MSAKVN6YmlZNFt9eRUv2vawUKVtnzAxs22/desu3t4ribt9D0mawsKFC3N93pdZ8o0Xrao1IOHhPar5dX3r8+SGw+GXmTlpFxqNTOt2tfimVwOt9FMnYvh1cjBXLsUzdlJ7XNyqZqQN7reGqDM3qF6zFFNmd3knHUfCrzJ7SghqjYaW7jXo2rOuVvrpE9eZPTWEK5cSGD2hDc5ulQGIu/WAUUM2odHIqNPUtOvyOW071spz+bIsM37adkLDLyn+dUw77CpbZ8sXee4WnmM3kvIsDacGtngNaYEkSZy/GMeYiZt58iQVGysTpvp5ULiwPhFRNxg1frNSBjI/9nHBzaVqtvNq6fgAfv7hw6f8MmIVt27fQ63W0LOHCx3a1flg9jh34TZjJm7h2bM0VLo6+AxvRXW7EjxIfspIv0Bib9ylYAFd/Ee5U7GCRW4uUaauqdsIDb+o6PLp8ApdN/H02aj0rxtUxGtoy0xdE4J4lpqGSqWDz/A2VLcvkevytXTMCCbs4BX09fWYMKoVdpWssuWbMX8vQTvOkPwwhZN7hmV8/vvaI6zffAqVSodiJgaM92qFjVXe+9XvquPY37FMmBnMhSsJTPNtR7NGVbId+zoO7D/HpAkb0ahl2nvUpVefxlrpqalpeI1YxdmoG4rPnd4dGxtTzkTE4DvmT+U7AP36N8O1cXWePXvOd9/+RmpqGuo0DY2b1KD/j81zLPtD9F1Heq5g374zmJoWYcvW0VrnW7lyL2tW70Ol0sHJyZ5hw3L/nCX4byBmJgneG2FhkUTHJBC8yxc/3674jF2TYz6fsWvw8+tG8C5fomMSCNsfBcDCRTupV7cywbv8qFe3MgsX7QKgd68mBAV6ExTozeBB7tSuXRETk8wn8eUrBhMU6M3GDSNzrVWjltk2/zzdfGrRf059zoTFkRD7KFu+Z0/SOLwllhKV3v8gEigDD/OnhOMzsxlz/vAgLPgKsVfvaeUpV7E405e347fVHWjQqCy/zz763srXqGU2zomkz7gvGbbImb/33iQu5qFWns9crPllgRND5jni0rE8mxeczUgrbmXIkHmODJnn+E4DSWq1hjmTw/D7tRUL1n3NvuBLxFy9q5XH3LIwQ8a44tK0otbnZ0/f5uzp28xd24V5f3Th4tkEzpy89cbyfH3XsmjxALZuG8O2rce4fFn7mPUB4RgZGRC824/uPVyZNnUTAJcv32L7tmNs3TaaxYt/xHfsWtTqzCmxK1bsoVx5S61zeY7sSNDmUWzeMgorq2KsXr0vVzaZPmEnU+d0YeXG7/lrZxTXriRq5bGwNGKkb2saN7fPdvxX3eviPb7NG8vJjY6PeW1eJmz/OaJjEwne7oWfT2d8/AJyzOfjF4CfT2eCt3sRHZtI2IFzAPT+rhFbNg0naMMwnJ2qMmfeLq3vNnXGFhrUr5TD91a/9zpSrpwlgUHeBAZ5s2HjSAoVKkBjt8wBrNu37xIefg5r62Kvt0nYWaJjEgneOQa/sV/hM/aPnG3i+yd+vl8TvHMM0TGJhO1X7t0G9SuzNWgkWwJHUqaMOQsWBQNga2vNhoBhBG3yZPHCHxjts5a0NLWWvfx8/2ThogFs2TqKbduOc/nybW2brD+IsZEBu4LH8m33Rkyd9sImt9m+/QRbtnqzaPEAfH3/QK3WcPHiLQICwlm3bjiBgSPZt+8MMTEJANSvX5nNW7wJ2uxNmTLmLFy4K+PafAo6XtjkY9aTGTP7ZKQ1afIZTZrUYvny5QRtHsWmQG8O7I/i1KmrWuVPnrSBtu512bxlFP1/aMn0aYGvrlw5EBYWSUx0AruCffH168pYn8y2ffjw3+nVy43tO3wICAjA1NQ0T+fOyrJD22j226C3Pj63qNUapvnvZNrcr1m9qR9/7YzMwbca4+XXBrccfOvXPeoxapz7e9Hx68TdTJrdkeUberNn51mir9zRymNuZcSIsS1o3Ex7IMbUrDBzlndjyZ/fMXflt6z5/TB3ErTb7twQdvAS0bFJBG8ciN/INvhM3JJjPp+JW/DzakvwxoFExyYRdvASAF7jAhnS340tfwygsUtVFq8MB8C2vDkbVnxP0JofWDzrW0ZP2KLlS7Lp+EB+fvXaA5Qvb8HmjcNY+fsAJk0JIvV52gezx5Tfgunf25mgNT8w8PtGTJml+Nb5v4dRpaIlW9b2Z9LY9oyftv2VGnLUFX6R6OtJBG8ahJ+XOz4TNuesa8Jm/LzdCd40iOjrWXTN2kn/Po0IWjOAgd+7MmXWzjyVn6Hj0BVirt9lV0A/fEe0YOzknM/j0rAi65Z8l+3zKhUtWP97Tzav6kPTRpWZOickX3RYWRoxYVRrWrllv7/fhFqtwX/ceuYt+J7ALSPYsf0kVy7HaeXZuOEwRkYGbNvlzTfdnZk5TalHFWytWBswhIBNw5i38Ht8fdaRlqamQAFdFi/tz/pNw1i38RfCD5zj9OnoHMv+EH3Xdu3rsWjxj9nKO3z4AntCThO02Zut28bQs5dbnu31j0Sjyd+ffxhiMEnw3ggJicC9bV0kSaJmzXIkJz8lIeGBVp6EhAc8epRCzZrlkCQJ97Z1CfnrdObx7vUAcHevx1/pn2dl27ZjtGr5xTtrvXnpAcWsDChmaYCung72jpacP5KYLd+e1Vdo2KEMunof5la5dDYRqxJGWNoYoaenwtGtPEfCYrTyVP/CGn19ZRJhJXtzkhIev7fyYy/cx9TaEFMrQ3T1dKjlbEPUoXitPPqGehl/p6aoycOkklxzMSoB65LGWJUwRk9PhZObLYdDr2nlsbA2oqxt8WyzWiRJIjVVTdpzDc+fq1GnaTApVui15UVERFOqtDklS5pRoIAuLVrWJiQkQitPyJ4I3Nsp9bFp0884dOg8siwTEhJBi5a1KVBAjxIli1OqtDkREdEAxMXdI3TfGTp6aL/lLlxY0SPLMs9SniPxZiOei7yFTcliWJcoip6eCtemVTmw76JWHisbEypUtMhxps8XdcpiYFDwjeW8iY99bV4mZO8Z3NvUVvxKjTIkP3xKQuJLfiXxAY8ep1CzRhnFr7SpTcieMwAULqyfke/p01St+rtyTRhN3apjmsMMoIiIiA9SR15w6NB5SpYsjo1N5sP3hAkB/PJLe95UPUL2RODe9st0m5R9tU0epVCzRtl0X/tlhv6GDaqgq6sCoGaNssTF3QegUKECGZ8/e/Y82/WMiIimVCkzSpYsrtikxefsCdH203tCImjrXjfdJrU4fOgCsiyzJ+Q0LVp8rtikRHFKlTIjIiKaq1fjqF69TEbZtWvbsnv3KQAaNKyaoadGjbLEp+uMiIj4JHRk2OQj1xNQfMnOHSdo1bo2hobKy5W0NDVpaeps1+3KldvUrasMmNapW4mQLLZasjgYjw4TaNPaj1mzcn5wDkm35ctt++XLt1CnaWjQQBnoMDQ0pFChvN3fWdl/+RR3Hye/9fG55VzkLUqULIrNC9/azI79+y5o5cnwrTqv8K2GBd5Zx/nI29iUNMG6hAl6eioaNa1C+L5L2jqsjSlf0TybDj09FQUKKP2C56lqZFl+Kw0hoedxb1lTubbVSpL8MIWEO9qDUgl3HvLo8TNqViup+JKWNQkJPQ9AdGwStT8rA0CDL8sTvFcZsC6kn9WXpL2x3/Ch/LwkwePHz5BlmcdPnmFsbICu6tX9uXe1x4vyAB4+SsHcTJlFfuVaAnW/UGa/ly9jxs3b97mTlP0F5qt1ncO9RR51tahJyL6z6bqkl3QZ5bpsLR1hF2nbvLqiw96G5EfZdQDUtLfBvHj2GfR1Py9DIX2lT1nDzoa4txgAfR86SliZUKlCzvf3m4g8E0OpUsUpUbI4egV0ada8FnvT6+EL9u05Qxv32gC4NanBkcOXkGX5pTY2LUs9lTAwVPprih/X5NgN+FDtTe3athgbG7xcHH+sDaVP36YUKKBcM1PTt6s3gn83YjBJ8N6Ij7+PpVXm1HlLSxPi4+9nz2OZc56kpGTM05efmJkZkZSk3al8+jSV/QeiaNLks8wPJYlevX6lfXt//vxzf661Jic9w7h45oO2sWlBHiY908pz63IyDxJTqFjbLNfnzStJCY8pbpH5IGtqbkhS4qsHi3ZvvsDn9fI+NflVPEh6iolZZifMuLg+D+48zZbvwOZo/HvsYevic7j/kLmk627cE6b9EMacoQe5eibprXXcSXyEWRY7FLco/Fo7ZKVKdUuqf25D1+a/07XZMj6rW4pSZV8/syM+/h5WWeuhhQnx8dozwhLi72OVXp91dVUUKVKI+/cev/ZYf/91DP2lfY4dFE/P5TRsMIyrV+Po9s2bpwknJjzE3DKzE2RmYfRWb57flY99bV4mPv6Bts+wMCE+/kH2PBYmr8wz49dtOLn6sGXbCQYOaJF+zH3+CjnDV521B/4yzxn/QerIC7ZvO07LVrUz/g/56xQW5iZUrvzm+zs+4X4ONsnB175skwTtPAAbNh7C0SFz1sPp09G0bD2ONm39GTumS0bH98X3zerjLSyLZr8WCTnY5P5j5RppHWtCQvx9bG2tOHH8CvfuPeLp01TCQqOIu61tK4CNGw7i4Fg1/bvFfxI6FC0fxpe84OV68oLjxy9jalqEMmUsUKvVuLcdR4P6v1C/fhVq1NBesl2pcgl2B/8NwO7dp3j8OIV79x5x4MBZomMSCFg/gsAgL6KiYjl27FK2suLj72vrTG+3o6MTKGJkwI8D5tPOfTyTJk1CrX717JNPhcSEZMwtMx+KzM2NSIz/+L41MeEhZhaZOswsipCYmPsBhoS4ZHp2Wkqn5nP5qkddipvnffl7fGIylhaZM68tzY2IT9Due8UnJGNpbqSdJ1HJY1vOPGMgZWdIJLez3IenI6/TstNvtPlqDmNHtNbyJdl0fCA/3/VrB65cjcfBZQxt2k3Ca0Q7dHRe/ejzrvYYObgFk2cF49RyKpN+3cXg/spMjsq2lgTvVWZRRUTd4FbcA+IScj9wGp/4EEvLLLosXqErS32ytDAmPlGp1yOHtGDyrztxajmZSb/uZPCAt5thEp/4EKusZZgZZZSRV9ZvOYVjvfL5riPPZcc/wMLypTYkIXtdfZFHV1dF4SL63L+v9JsiTkfTrvVEOrSdxKgxnTLuC7VaQ8d2k3Fu6E29+hWpXqNMDmV/2PbmZaKjEzh+/DKdOk6kW7dpnHnpZYdAAGIwSfCJIklStrere/dG8Fmt8lpL3NauGcqmjV4sWjSA1Wv2cezYsfdSvkYjs2vJRZr2qvjmzB+JvTsucfncHdp3q/HmzO+Zhm3KMHJZI1r1qsJfay4DYFSsIN6rXBky15E231dl1cS/SXn8/KNru3X9Ptej77FyW3dWbe/O6eM3iPw7b0up3gd790ZgWqwI9valc0yfMKE7YfsnUb68Jdu3H//I6vKHT+XaDBrYktAQH1q3/JxVa5RB5/GTNjF0UOvXPlh8KFJT09iz5zTNmimxp54+TWXBgp38NPDdlyXmhXnzd6JS6dCmdeZgRY0aZdi2xZv164axYFEwz5592Hu6fHkrevdxo3ev3+jTZzaVq5RA56WZA/Pn70Clq6J16y//9Tqy8nI9ycq2rccyBplUKhWBQd7sC51AREQ0Fy/e1Mo7bFgHjh27RDv38Rw7ehELCxNUKh3Cw88SHn6Wdu7jad/On2tX44iJTsi1vrQ0NSeOX2LY8A4ErB/BjRs32Lhx47t9aUGuMbc0Yum6nqwO6suuLZHcTXp/s5Zzy/jR7qxZf5T238zj8ZNUCuhlDhjVsC/JtnU/sn759yxYtv+D+5Kc/PyB8PNUqWzD/r1jCdzwC77+G3j0KOWDaVi74Sieg5sRum0onoOa4+WnLCnt292Bhw9TaPv1XFb+eYQqFS1RvcWsmLfWtf4onoNbELptGJ6DW+Dlt+mjlZ0Tm3eeIer8bXp1rfvmzP8yqtcow6YtI1i7bjBLFv2VcV+oVDoEbBrG7r0+RJ6J5dKl228404dHrdbw4MFj/lw3nGHD2vPzz4veehbkPwpZk78//zBEAG7BO1GpUqX+QJ/KlStTrZqp1pvcuLj7WGR5iwRgYWFCXFzOeUxNjUhIeIC5uTEJCQ8oVkz7Ldu27cdo2bL2S+crmnGsW+OaREREYOjwZt1GpgV5cCdzJtKDpGcUMc2cqZT6NI2EmEcsG6k89D+6l8racaf4yrvmew3CbWpuyJ34zDeRSQmPMTXLHpn51NGbrFt2ignzWqFX4NVv9/KKsWkh7idmdqwe3EnBuPirlynUdLZmw2/KdF7dAip007WUtDWhuLUBiTcfU7KiySuPfxXFzQqTmMUOd+If5WiHnDi47yqV7S0oZKAsO/iiXmnOnYnDvlb24JQvsLAoyu2s9TD+fkZdeoG5hQm3b9/D0rIoaWlqHj58iklRw1ceu2fPafbsiSA0LJLUZ2k8evSUX4YuZcrUnhl5VSodWrSszeLFwXh0eP33MjMvQkJc5pu2xPjkt3rz/K587GsDsHrtftatPwRANftS2j4j/j4WFtr3oIWFMXFZZubklAegdasv6NtvAT8NaE5k1HUG/7IcgHv3HhO6/xy6Kh0au1ZPP6fFe68jL9gfFklVu1IUL668WY2NTeTGjSTatvUDID7uPu3bj2ddwAjM0ydGrl4TyrqAg4pNqpXOwSY5+NqXbWKemWfjpsPsC41k2dKfclwmWb68JQYGBbl46RbV0gdIzS1MtHx8fNy97NfCPAebmBgq10jr2PuYp2v28GiAR/rS0BnTg7CwzNS5aeMh9u2N5PdlAzN0WlhYfBI6FC3v35e84OV68oK0NDW7d//Nho3acQKNjAyoU6cS+/dHUbGiTRaNJvw2+38APH6cQnDw3xgZGSDL0LdvM7p0cdQ6z+rV+whYdwCABQsHYGFhoq0zvd1Wq9VUrlKSkiWVSurq6srp09mXp39qmJkbkRCXOasjISEZM4uP71vNzIuQGJ+pIzH+IWZmOQfdfx3FzYtQtkJxIk5ezwjQ/TpWrzvCusATAFSrakNcltk9cQnJWJhr1zcLcyOtWTRxCclYpC+TKl/GjKWzuwNwLeYO+w5oL8UGKF/WDAODAly8kkC1qpn18mP4+Y2bjtK3tyuSJFG6lBklbEy5ei2e6tUyX/q8T3ts2noKryHKrKjmje3wHh8EKMvwJoxpByhLVF3bzqCkzeuD4K9ed5h1gcczdcVl0RX/Cl1Z6lNc/AMs0pfZbdr6N15DW6brssd7XO7jpq1ef5yAzcrMxmpVrLmdtYzE5IwycsvBo9eYvyyclXO/yViqmR863hYLC2Pi415qQ8yz19X4uHtYWpqQlqbm0cMUrRfhAOXKW1LIoCCXL93Gzj5zsyIjIwNqf1mB8P3nsLW1eum8H669yfm7muDmVgtJkqhevSw6OhL37t2jWLG8zTAX/LsRM5ME78SFCxfmXLhwoWZQUBCNXWsSGHQYWZY5deoqRYroZ3Ow5ubGFC6sz6lTV5FlmcCgw7imP7w1alSdwEClYxEYeCjjc4CHD59y7NglXF0zZ+U8efIs4w3TkyfPCA8/h62tba50W9sacffWE+7FPSXtuYbIsDgqf5m5nE3fUI/ha5wZtMSBQUscKFHJ+L0PJAHYVjHj1vVk4m4l8/y5mrDdV/jSUXsHvCsX7jBn4n5GTWmS53gzb6JkJWPu3HxMUtwT0p5r+HvfTezqau8wkngzcyDh3NEEitsoDeKj+8/QqJU3FEm3H5N48zGmltnXXOeGilXNuRX7gLibih1Cd1+irmOZXB1rZlGEMyeV2B1paWrOnLxJyTKvbyCrVStNTHQCN67fITU1je3bjtGokXYA8UaNqhO4SamPu3adpG7dSkiSRKNG1dm+7Ripqc+5cf0OMdEJVK9ehiFD2hEaNpE9e/yZNr0XdepWZsrUnsiynBHIV5Zl9uw5Tblyb97FpbKdNTdi73Lr5n2eP1cTsussDZ0+/ky5j31tALp+5UDQhmEEbRhG40bVCNx8TPErp6MpUrgQ5mYv+RUzYwob6nPqdLTiVzYfw9WlGgDRMZmx0EL2nKFcWcX2e3aNZk/wGPYEj6FpkxqM8fbIGEgCqFat2nuvIy/Ytu241sB4pUo2HDw0hT17/Nmzxx8LSxM2bvTCLMv37Pq1E0GbPAna5Elj1+oEBh1Nt8k1ihR5hU0K63Pq9LV0X3sU13T9YfvPsnjJX8yb8z2FCmXGfrl+405GkNybN+9y9WqcVqyeatVKExOTwI0b6TbZfgKXl2zi0qg6QYGH023yd4ZNXBpVZ/v2E4pNbtwhJibTJklJyqDprVt32b37FK3SZ9zs3x/FkiW7mTvvf1o6q1Wr9knoyLDJR6onLzh08Dxly1liaVmUu3cfkpysPFSlpKRy8OA5ypXT3gDg3t1HaNKDei5cuJMOHZRdIRs2rMrGDQd5/FhpR+Pj75GUlEzXrs4ZQb4tLExolG7Ll9v2atXK8DD5CXfvKnY7cuQIFSpo7wb6KZLhW2/cU3zrzqh88a2V7Ky4EXuP2+k+fs+uc9R3zp39EuKTeZaizGh4mJzCmb9vUKpM7oKfd+1Uh6A1PxC05gcaO1cmcNsp5dqeuU6RwvrZYsyYFy9CYcOCnDpzXfEl207h6qQMWiXdVfoHGo2GeUtD6dJBqa/Xb97L9CW373M1+g421toD3h/Dz1tZmXDosDLAdefOQ65FJ1CihLad3qc9zM2KcPRkNACHj12lTEnloTv54dOMwN8BgSf4olZprThPOV+nugStGUDQmgE0dq5K4Pasugq+Wdf2U7g6VUnXZcTRE9ey6Mp9oPyuHl8QuKIPgSv64OpYkaAdEYqOyJsUMcyu43WcvRDHmMnbmTulE6bFcvdS6kPoeBfs7EsRE3OHGzeSeJ6axs4df+Psoh3I29nFns2BykqJ3cGn+bKOLZIkceNGUsZ9cevmXaKvxmNtU4y7dx+RnPwEUPz4oYMXKZtDP/FDtjc50bhxTY4eUeLJXbsWz/PnaooWzf1OoP9YxMykPCFmJgneG05O9oSGReLWZBSF9Avg7989I62t+ziCAr0BGDP6azxHLiclJRVHBzscHRUn3LdPU34etIj1G8KxtjZl5ow+Gcfv3v03DRpU1QoonJSUTP8B8wFlKmarVrVxdHTkj4vr3qhVpdKhxf8qsXLMSTQamVqN6sg11AAAIABJREFUrTEvXZg9qy5jbWtE5Trm78Umb9Shq8P/htZnzE870GhkGreuROlyxVi14Di2Vcyo41ia3387QsqTNCaO/AsAM8vCjJra9A1nzmX5Kh3a97dj4cgjyBqZL5uUxLJMEXYuv0CJisbY17MkfHM0F0/eQaWrQ6HCenw1VNlV6OqZu+xccQGVrg6SDnj8VB0Do7cLSqrS1aHfMAe8f9qMWi3TpE0VSpc3ZcX8I1SsYk5dp7JciIrHb9gOHiU/48iBa6xacJQF676moWt5Th+/Qb+v/gAJvqhXirqOZV9bnq6uilGjO9Or9yw0ag0dOtTH1taaWb9uxt6+NI1ca+Dh0YBhv/xOE7dRGBsbMH1Gb0DZ8ap5889p2WIsKpWK0aO7oHpNQE9ZlhkxfBmPHqeArAwc+Iz9+o020dXVYdCIpgzptxaNRkPLtjUoW8GMxXNDqVzViobOFTkXeQuvwet5mJzCwbBLLJ0XxsqN3wPQ/7sVxEQn8fRJKu2bzGK4T0vq1M97fIKPfW1exsmxKqH7z+HWfByFChXA3++rjLS2HSYTtEHZ7neMt0fGltGODlVwdFA60dNmbOFadAKSJGFjXYyxozvmqlxdXd0PUkeePHlG+MFzjPV9+63PnRztCA2Lwq3ZWArp6+E/vlumTdpNIGiTp2KTUZ3wHLmKlGfPcXSoimN6rB+/cetIfZ7Gd71mA8rSNl+frzhx8iqLFgWjq6tCR0fCZ1RnihXNnCmhq6vCe1RneveajUajoX2HeopNZm1RbNKoOh4e9Rk+bBlNm4zB2NiAadN7ZdikWfPPaNXSD5VKh1FZbDLwp4Xcv/844740MlIGpcf5rSM19Tm9ev6WqdPXFV1d3XzX8eIe/lC+5HX1ZNv2Y7RKH2RKTHjAiBHfolYnI8syzZp9jotLda3yjxy9wIzpgSBJ1P7CltFjlO3sGzasytUrt+nSZTIABgYFmTKlJy9vyObkZE9YaCRN3EahXyizbVepdBg2vAM9us9ERsberg4dO3ak24HfclGLs7Ompy/OFT+jeGETrvtvZszWRSw9mHNQ8HdBV1eHQZ7NGNxvDWqNTCv3GpSrYM6iOfuobGeFg3MlzkXewnPQOh4mpxAeeonFc0NZvakfAP16LCM2OoknT1Jxd5uJp09r6jTIu2/V1dVh4HA3fvlhHRqNTPO21Shb3oylc/dTqaolDZxtOR91G+/BG3mU/IxDYZdZNv8Ayzb0JvZaEnOn70VC2WK887dfUs427/EdnRpUJDT8Em7tZiq+ZHS7jLS2X88laM0PAIwZ3grPsZsUX1LfFsf6you7rbvOsGa9ssOsm3MVOrSuBcCJ0zEsWrY/05cMb0Uxk1cPHnwoP//D/5ri6bWG1u0mIcsyQwe11vJp79sefl5t8Z+2nTS1hoIFdPEd2RaAK9cSGTFWWVpmW86c8aPythugousibu7Tlf71mPZZdM0maM0ARdeINnj6bEjXVRHHBsogqZ93W/ynZtHl1TZP5WfoqF+BsINXaNJxLvoF9fD3bpWR5v7tIgJXKP32KbND2BocxdOU5zi1mYVHm5r82NuRKbNDePLkOT97bQDAysKYeVM6fXQdZ87eYsCI9SQ/TGHvgUvMXhzG1jXf56psXV0VI7060K/PfNQaDe7t6lDB1oo5v22nql0pXBrZ065DXUYOX0XLpuMwNjFg8tRvAfj75FWWLgpBV1cHSUcHr1EeFC1amIsXbuHtuRq1RoNGI9O0WU2cnO1yLPtDtDeDBy/m2NGL3Lv3CCfHEfz4Y2s8OjagfYf6eI1cQetWvujpqZg4sXuOM5kF/22k/8TaR8HHQEbem98aQHLhj4sD8lsFXSrO5uL9qfktg4omQwHYGj0kn5VAqzLTuJo8K79lUM7oJ2Tyv65KKEG4E56uyGclYF7o20/m2vB8R37LAL3mAJ9OPVHvzm8ZoHJDI7/dNs7vEx3JFeCT0fKp1JFPRQeA1C//46DI8w5zJ2VVfsuguH43bj9Zmt8ysDJIX26d/Gf+CgEw6vzp+PlPxR4ADwPyVwdAkY7Id/O/TyIV+/aT0QHwTJ3/9bWgqvmn5Of/1SNKcszUfB0ckUoP/UfZV8xMEggEAoFAIBAIBAKBQPDfRvPPW2qWn4iYSQKBQCAQCAQCgUAgEAgEglwjZiYJBAKBQCAQCAQCgUAg+G+jESGA8oKYmSQQCAQCgUAgEAgEAoFAIMg1YjBJIBAIBAKBQCAQCAQCgUCQa8QyN4FAIBAIBAKBQCAQCAT/bUQA7jwhZiYJBAKBQCAQCAQCgUAgEAhyjSTLIsiU4L0gKpJAIBAIBAKBQCAQ/HuR8lvAh0S+MD5fn2mlSl7/KPuKmUkCgUAgEAgEAoFAIBAIBIJcI2ImCd4bGjkkvyWgI7ly/t6U/JZB5aK/MCy8b37LYHKDhQCsPN8/n5XAN5XnsDV6SH7LoFWZaey76ZXfMnC2GQ+A1K9uPisBed5hzt6dlN8yqFpsODzblt8yoGBL4NPxac81u/JbBno6TT8ZewA8U+/IZyVQUNUcNPlvE3RcP6n75k7KqnwWAsX1u30yvvX6o/n5LYOShf+n/JGyJX+FAOi3Zsap/+W3CgbVnM+VBzPzWwbljX8GIDl1Qz4rAaMCHT4dP/8J+bRPpQ1G3pvfMkByyW8Fgk8MMZgkEAgEAoFAIBAIBAKB4L+NRkRuyQtimZtAIBAIBAKBQCAQCAQCwT8USZKKSZK0W5KkS+m/i+aQp6YkSYckSYqSJClCkqTOWdKWSZJ0TZKkU+k/Nd9UphhMEggEAoFAIBAIBAKBQPDfRqPJ3593YwQQIsuyLRCS/v/LPAG+lWXZDmgGzJQkySRL+i+yLNdM/zn1pgLFYJJAIBAIBAKBQCAQCAQCwT+XtsDy9L+XA+4vZ5Bl+aIsy5fS/74FJABmb1ugGEwSCAQCgUAgEAgEAoFAIMhHJEnqK0nS8Sw/ednRyUKW5dvpf8cBFm8o60ugAHAly8fj05e/zZAkqeCbChQBuAUCgUAgEAgEAoFAIBD8t3n3pWbvhCzLC4GFr0qXJOkvwDKHJK2tqmVZliVJemU0cUmSrICVQHdZll98aU+UQagC6RqGA76v0ysGkwQfjP37o/AfH4BGI+PhUZ8+fZtqpaemPmf48OWcjbqOiYkh06f3wqaEKQALF+xkw4ZD6OhIeHl1oqFDVQBWrNhDQEA4sgwdOzage/dGedJ08tB1Fs04jEYj49amEh7f1tBKD1pzhuDNF1CpJIyLFuJHLwfMrYoA4PPzTi5GJlClhgWjpjXN6fS5Jv7MXc6suQyyTCkHKyq2LKWVHnsgjqh1V9EvWgCAcq42lHa0UjT2CsWohCEABqb61PnJ/p20XDl5h12LLiJrZGq62dDAo0yO+c4djGfDpDP0nPol1rZGPElOZcOkM9y6nEyNRlY0+77yO+k4fyyBwPlRaNQydZqXwrVzBa30g1tjCN8SjY6ORIFCKjoOrI5l6SLcjXvCpD77MC9RGIDSlU3wGFj9nbRkJfJoHOtmn0KjkWnYoizNvtb+nqGbr7Av6Ao6OhIFC+nSbfDnWJcxem/lv4ol33jRqloDEh7eo5pf1w9a1slDN1gy8zAatUzjNhXp8PJ9szaSvzZfRKWSMDLRZ4CXA+ZWhbl2MYn5Uw7y9PFzdHQkPHrUoGHjcnkqW5Zlxk/aROj+c+jrF2Ci31fYVS2RLV/k2et4eq8l5dlznByq4DW8HZIkZaQvXb6PSdM2cyjUl2JFlbpy5Nhl/CcHkpampqiJIat+H/BaLW/r0+7de8TPAxcRGRmLu3tdRo3OiHVIn96zSUx8QJpawxefl2fU6C6oVK+fNHxg/1km+m9ErdHQwaMevfu4ZdPhOXwVZ88qOqZO74GNjSlnImLwGfNHhl1/6N+cxm7KtfT2Wk3YviiKFStC4BbP15b/oezx9GkqP/+8iOuxd9BR6eDiUo0hQ7LN0H6FTc4xacJGNGqZ9h516dWn8Uta0vAasYqzUTcwNjFgyvTuGTbxHfOnYhOgX/9muDZW/MdorzWEhp6lWLHCbNqcU9gBMmw53j+A0LAo9PX1mOj/LXZ2pbLli4yKxdNzhVJHHe3wGtkRSZK4f/8xgwYv4ebNJGxsTJk5ozfGxgY8ePCEkV4rib2eSMGCeviP+4aKFa0BSE5+gveo1Vy8dAtJmor/2FbUqlFGW9MHuG8W/76HLdtPAqBO03DlWjyHQn0xMTbM1XU6HH6ZmZN2odHItG5Xi296NdBKP3Uihl8nB3PlUjxjJ7XHxa1qRtrgfmuIOnOD6jVLMWV2l1yV9zZ8TN969GA0c6fuQ6PW0Nzdnq+++1IrPeLkDeZODeXq5US8/Vvg2LhiRlqT2jMpW6E4AOaWRfCb0TbP5Sv1JIjQAy/qSWfsquRUT27gOeoPpZ40rILX8LZIksRv83axbsMRihVTfOrgH5vj5FCF8EMXmfbrNp4/V6Onp+KXQa2oV8c2V5piTyURvuwSsgaqNLKilnvpHPNdPZJA8PQo2vt/jnl5Iy7uj+P0lusZ6Umxj/CY+AXFyxTJs10Ajh+KZcG0A2g0Mk3bVqFT98+00s+cvMXCGeFcu5zEiHFuNHQtn5G2ZNYhjoXHIMsytb4syfdDGmjdV7nh4IGLTJu0FY1aQ9v2tenR20krPTU1jTEjAzh/9ibGJgb4T/kKa5ui7Nh6ipXL9mfku3wxjpXr+lOpsjXff7eIO3ceUrCgHgCzF3xHMdPCr9XxIdq9mTOCCAo6QnLyU06cnJEnu7zgQ/YN3sTbtsEHw88zc/rmjPtiyC/u1Kmr3NPPU9MYP249x45eQkdH4qefW+HWJHvsY1mWGT9+HaFhkcr3ntA95/YmMgZPz+Xp7Y09Xl6dsrQ3i7K0N30wTvffR45cwH9CQHqfqDCrVg0BwHPkCvbtO4OpaRG2bnXJlY0EHw5Zlhu/Kk2SpHhJkqxkWb6dPliU8Ip8RsA2wEuW5cNZzv1iVtMzSZJ+B4a+SY8YTBJ8ENRqDX6+f7Jk6U9YWJjQqeMkXBpVp0IFq4w869cfxNjIgF3BY9m27ThTp21ixozeXL58m+3bT7BlqzcJCQ/o+d0sduz04cqVOAICwlm3bjh6eir69JmNs7M9pUub51rTgqkHGTurOabmhgz9LogvHUpRqmxmoPuylUyZvsydgvq67NhwlmWzjzJsvCsA7bpW41lKGrsCz7+TbWSNTMSqS9QfUp1CxQoS6nsSy5qmGNlod8ZtvjSjerfsnS9VAR1cxn7xThpeoFHL7Fhwga5ja2Fkqs+SoUep+GVxzEppN6jPnqRxdMt1bCpmDpLoFlDh1LU8iTGPSIx99M46Ns6J5PsJdTAuXoiZP+7Hrq4FlqUzO4GfuVhTv5XSqYw8FMfmBWfp618HgOJWhgyZ5/hOGl6la+2vf/PzFAeKmhkwoV8I1etbaw0WfelaCqc2SifydPgtAuadZuAkh/eu5WWWHdrG7H3rWdFj9ActR63WsHDaIXx+bYqpuSHDem7mS4dSlMxy35SraMrU39tQUF+XnRvPsWLOMYaOc6GAvi4DRztiXdKYu4lPGPpdELXq2GBY5I2zZjMIO3CO6Jg7BG8dyemIGHzGrSdgzc/Z8vmMW4/fmE7UqF6aPj8sIuzAeZwcqgBwO+4e4YcuYG2VqTk5+Sljx29g8by+WFsVJSnp4Rvt8LY+rWBBPX4a2JpLl25x6eJtrfPOmNmLwoULIcsyA39axM6dJ2nZ8tX3t1qtYZxfAIuW9MfSwoTOnabi4mJP+Sw6Nq4/jJGxATt2jWb7thNMn7qZaTO+o4KtFX8GDEVXV0ViwgM6tJuEs4s9uroq3N3r8PXXjowcser1F+QD26Pnd42pU7cSqalp9PzuV8LConB0tHujFv9x61m4uB8WFiZ81Xk6zi72lK+Q+eJu44bDGBkZsG2XNzu2n2TmtC1Mmd6DCrZWrA0Yotgk8QEe7abg5GyHrq6KNu3q0KWrA14jVr+2/LCwKKJjEgje6cPp09H4+P5BwJ/DsuXzGbsWP9+u1KhRhj7fzyFs/1mcHO1YuGgX9epVom+fpixctIuFi3bxy9B2zF+4kypVSjBn9vdcuRqHr9+fLP99IADj/QNwaFiVWb/2ITXNgZQHm7U1faD7pvd3jej9nfISZ8++KJatDM31QJJarWGa/05mLuiKuYURvb9eTEPnipQtnxmqwcLSGC+/Nqxdfijb8V/3qEfK0+cErT+Zq/Lelo/pW3+buIdJc9tjZlGE/t+sob5TeUqXM83IY25ZhGFjm7Bu5YlsxxcoqMuCtd3eSUPYgfNExyYSvGUEp8/E4jNuAwGrB2bL5zNuA35jOlKjWin69F9MWPh5nBoq9aTHN4706u6slb+oiSHzZvXEwtyYi5du06vfIvb/9WZ7ajQyB5ZepJVXTQxNC7LR8zilvyhOsRLadSz1aRpntt/AvEJmW1zRwZKKDso9nxT7iF1Tz7z1QJJarWHu5P2Mn92a4uaG/Nx9A3UdylCqXLGMPOaWhRk8uhEbVmnHpz0bEcfZiDjmrOkEwC99Ajlz8hbVP7fJU/mTx29m9sKeWFga0b3LXBxdKlOufOaKlaCNxzEyKsSm7UMJ3nGa32bsZMLUr2jeqibNWymDEJcvxjF04CoqVbbOOM5vYieq2mUfdHmVjg/h551dqvN1V2eaN/PJtU1e5kP5uDfxLm1w0aKGzJ73Pebmxly6eIvv+8xjT6gfAAsWBFOsWGG27RyFRqPhwYMnOX/vsEilvdnly+nT1/AZu4aAddlfdviMXYOfXzdq1ChLn76zCdsfhZOjPQsX7aRe3cr07duMhQt3prc37UlOfsJY37UsXvQT1tbFSEpKzjhX+3b16NbVmeEjluXaTv9oNK+czPNPYDPQHZiY/jvo5QySJBUANgErZFle/1Lai4EoCSXeUuSbCvzXx0ySJMlEkqQf3pCnjCRJX+fiXGUkSXqjUT8WkiQ5S5K0NQ/520iS9OrXq++RiIhoSpUyo2TJ4hQooEuLFp+zJ+S0Vp49IRG0da8LQNOmtTh86AKyLLMn5DQtWnxOgQJ6lChRnFKlzIiIiObq1TiqVy9DoUIF0NVVUbu2Lbt3vzHIfAaXziZiWcIISxsj9PRUOLiV42hYjFae6p9bU1BfGWOtZG9OUsLjjLQatW0oZKD3tibJ4N7VZAzNC2FoXggdXR1s6pgTdyrpnc/7Nty69IBiloUoammASk8HOwcLLh5NzJYvdM0V6ncog6pApssooK+iVFUTdAu8uxuJvXAfU2tDTK0M0dXToZazDVGH4rXy6Btm2j41RU0eX/K9FdfO38XcpjBm1oXR1dPhi0YlOX3wllaeQll0PUtJ+yi6APZfPsXdx8lvzviOXDp7B6ss903DxuU4Gharlafa51YZ901Fu8z7xqaUMdYljQEoZmaAcVF9HtxPyVP5IXsjcW/9BZIkUbNGGZIfPiUhUft7JyQm8+j/7J1nWFRHF4Dfu/TedwEFUYq9xBK72LuiRo1R09U0NYmaGEvU2GOJSewxMRq7xoK9odi7YsGOogKyS+9l2b3fj0VgKVLUYPLd93l83N177s7h7MycuWdmziRnUK+uB4Ig0KtHQwKOXs+5PmuOP9983V3vt9m19zId2tXOGUQ6ODz/oeNF+jRzcxMaNPDCxLhg/2FpaQZAVpYWtbr4+nP92qMcPYyMDenStT5HjlzXkzly5Dp+froVDh071ePc2buIopjTdwJkZGaRt7CGjbywsTV/fuGv2B5mZsY0blIVAGNjQ2rUcCMyMq5YXW5cf4S7uyMVs23SucsbHM1nk8Aj1+nZqxEAHTrW5dzZewVtkqFv/4YNPbGxKd4mAUeu0cuvsa6O1qtMYmIqKlWCnoxKlUBycjr16lXW1VG/xgRk20t3v85OvfyacDj785D7T2nSWGcPzyrOhIfHEB2dSFJSGhcu3qdv32bZtjLG2tpMX6dX1G7ysmffZbp3eaNY+zzj1o0IKrrZUaGiHUZGBrTrXJMTgXf0ZFwq2OLlo0CQFSy0YePKmFsYl7i8svJP9a13giNxdbPFtaItRkYGtO5YlVOBIXoyzq42VPF2QvaKHEvA0eDcelKnEolJ6YXXk5R06tWplFtPjgQ/93trVK+AQq7r+729nMnIUJOZmVWsPqr7iVgrzLBWmGFgKMOzmYLQC9EF5C5sekg9P3e9MUle7p9S4tnsualCnsvdYBWuFW1wyfZ7rTp6ceZ4qJ6MwtWayt4OyPLVVQHdKpMstRa1WkNWlhZb+5L3rQDB18Nwc3egops9RkaGdOhSh2NHb+nJHD96i249daul2naoxYVzIYii/kPwgX1X6dil7Cu1X5Xfq1evMvLs+lFW/ok+rjBexAdXr+GW83d7ebuQnqEmM1MNwPZtZxkyTLfCSSaTYVfEKqmAAJ2/0PmbKiQmpj3H31TJ9jdNCDh8Nff+Xk0B6NWrKYezP9+1+zwdOryBq6suYOrgkBuobdTIu0S+UOK1YDbQQRCEe0D77PcIgtBQEITfs2X6A62ADwRBCMr+92wZ3DpBEK4D1wFHYHpxBf7ng0mALfDcYBLgARQbTPq3I4riTlEUZ/8TZamU8TjnifQrnO1QKvU7O6UqHpdsGUNDA6yszIiPT0GpTMh3ry0qZTze3i5cuhhCXFwyaWmZHD8WTOTT4h80nhETlYqjPHd2y0FuQUxU4ZF/gEO77tKgqVuJv7+kpMdnYmafuzLDzM6E9LiMAnIRl6I5Ouki5xcHkxab+wCuVWsJ/OESx6df5unlgoOs0pAUk4G1o2nOeysHU5Ji9HV5GpJIYnQ63g0dX6is55EQk4atU64eNo6mJESnFZA7uTOUmR8cYffvt+j1ee5qhdjIVOZ/fpzFY07z4PrLC8zFR6dhJ899SLNzNCM+qqBeR3fcZ8KgfWz77TpvDy+4LPnfTGxUSqnazeFdd6nftOCs593gKNRqLc4VSrcFUKlKxNk598RSZ4UtSlX+viQBZ4VNPhndoPLw0RvI5TZUq6o/Kxz6SEViYhrvfrSYPm//xI6dF56rx4v0acUx5OOFtGj+LRYWpnTqVP+5sipVvJ49FApbVPn0UCkTcHaxzdHD0so0R49rV0Px6z6T3n6zmDS5f04gpbS8SnuAbhvX0aPXadq0+O2zSmUCCud8PiN/HckjU5hNeveYzVt+P/J9GWyiVMbjnKd8Z2c7lKp4fRlVPM6KvPXYDqVSJxMTk5TzgOHkZJ2zSq5atYoczJ4wuXYtlIiIWCKV8YSFRWNvb8m48Wvo1WcmEyZMIDU1I195r6bdPCMtLZMTp27TsUPJH1SjVInInXPbv1xuTZTy+SsC/8tEq5KRK3KD2E4KS2KiSr7KNzMzi88Hr2P4+xs4dfR+mXTQ1YG89cSmiHpStMy6jafo0Xc+4yZtIiGxoG84cPgaNapXxNi4+M0QKbEZWDrkjgUsHUxIyTc+inqQRHJMBpXqFz0mCTmjwrtZyVatF0ZMVAqOily/5yi3ICaqZH1X9TrO1GlQgcFdVzO4y180aOKmtwK+JESpElA457ZNhcKGKGW+QEkeGUNDAywtTUmI17f/of3XCwSTpk7cysC+C/l92ZECwaf8vOp+/kV41X1cUbyoD37GoYNB2e3CiMTsdrPo1z306zOHUV+tJDq68IC2Mt9v4uxsm+NL9GScC5eJiUnM52905YSGqkhMTOXdd+fTp89Mduw4i8S/D1EUY0RRbCeKorcoiu1FUYzN/vyiKIpDsl+vFUXRSBTFenn+BWVfayuKYm1RFGuJojhYFMVindL/QzBpNuCZHXWbm/3vhiAI1wVBeDuPTMtsma+zVyCdEAThcva/ZiUpSBCEDwRB8BcEIVAQhHuCIEzOc22wIAjns8tYLgiCQfbnydk6BQuCcFgQhDez738gCELPbJli9REEoZEgCFcEQfAUBKGHIAjnst8fFgRBkUe/Rdmvi5KZIgjCyjw6jHwB279UPD1dGDK0A0M+XsjQoYuoVr0ismJyi5SVwH33uH8rit6DX17undLgXM+BDnMa02ZqQ+Q17bj8e+7sbYe5TWg9uQENhlXn+ob7pKgKBjdeFqJW5NDKu7T/0Kd44X+AFj09GL+qLd0/rs7h9brBs7W9CRPXtmP0klb0/KQGa2dfIT1F/Y/q1aaXFzPWdaHPsNrsXfti2yD/zQTuv0/I7Wh6Daqt93lsdCq/TD3GiIktC8zivkrS0jJZvuIwX37RucA1jUZL8M0nLF80hN+XDWPJb4d4GFro1vJXzu9/jOD4idlkZmZx9uyd4m94AerU9cB/93g2bh7D7ysOkZHxz7aVkpCVpWHM6JUMfrcNbm6vLoj9jDp1Pdi+6zs2bB7FHysOl6tNBEHImSUfNrQjSUmp+PWeyZq1gVSvXhEDmUCWRsvNm094Z0BLdmwbj5mZGb+tPPLSdHheu3nG0WPB1K9XucRb3CRePut3D2HJ2kGMn9GFJfOPEfEkvvibXjLv9G/God3j8N/8NXIna2bP26V3/d79SOb9vJep37/1UsoTtSKn19yn6bueRcoo7yVgaGyAvXvJ8t+8bCKeJPAkNI6/dr/Hmj3vcfViODeuRBR/40vmxrUnmJoa4eWdu9132uz+bNz+JStWDyPocih7d135x/V6HShJH/cquX/vKT/N38mkH3SPoRqNFmVkPPXeqMyWbd9St54H8+bseOV66PyNzuFosjQEBz9m+fLh/P7HSJYs3cPDh8pivuE/ilZbvv/+Zfw/BJO+A0JEUawHnAXqAXXRLf2am52c6jvgRHZkbgG6ZFUdRFGsD7wN/FqK8t4E3gLqAP2yl5VVz/6e5tl6aIBnWR0tgCOiKNYEktAtJ+sA9CY3e/pz9ckOLi0D/ERRDAFOAk1EUXwD2AgUTN7wfJlqQKfsv2WyIAiF7u3Ke3Thb7/pJ53QcJMEAAAgAElEQVSXK2z1Vg0pI+NQKPSXtCrktjzNlsnK0pCUlIatrQUKhU2+e+ORZ8+K9e3bnK3bxrF27ShsrM3x8Cj5zJODkznRebatxahScHAquGwz6Hw4W1YFMWFuR4yMyzZr/zxMbY1Ji82daUuLy8DUTj+HjLGlEQZGuuZZqZUL8Y9yZ2/NsmUt5GY4VrMl4QXyFVk5mJAYnbvqKSkmHSuHXF0y0jREPUphzcRLLBx6kvA7iWyeEUTEvZe7BcDGwYz4qFw9EqLTsXE0K1K+XmtXbpyOBHS5myysdVsf3LxtcXQ1Jyr85cyK2TqaEZcnWBcXnYatU9F6NWzjRtCp8JdS9uuCvZNFidrN1fPh/L3qKuPmtNdrN6kpmcwYfYhBnzSgaq2Stdd1G0/i128efv3m4eRoRWRk7kNSpDI+Z/vEMxRyGyLzzAzqZKx5/CSasPBY/PrNo23naUQqE+jz9k9ERSfirLClRbNqmJubYG9nScMGVbh9t+gB/4v0aSXBxMSItu3qcCTg2nPl5HJbPXsolfHI8+khV9gQ+TQ+R4/kpPQCenh6OmNubsK9e/q5LErKq7TH5EnrqVRJXuIDFhQKG5SR+XxG/jqSR6Yom1TxdMbM3IT7JbDJxvUn8PPzw6/3TJycbPS240VGxqGQ2+rJK+S2RCrz1uM4FNl+zcHBKmcllUqVgL29brWKpaUZs2a+h//28cz58X3iYpNxc3PEWWGLs8KWunUrA9C5c2du3gr7R9rNM/bsv0K3UmxxA3CSW6OKzP0OlSoRJ0XZctr8F3CUW6LKszIrSpmMg1PJAyCOcp2sa0Vb6jaoyP07JQuGr9t4Cr/+P+HX/yecnKzz1cuEIupJ4TKODlYYGMiQyWT069OY6zce55GLZ/jXq/hx+gDcSxgUtrA3ITkmdyyQHJOBRZ7xUWa6hrgnKeycGsTa4WdQ3Utk/9zrqEJy69X90yq8mpd9VRKAg5MF0cpcvxetSsHBqWR9+enAB1StpcDM3AgzcyMaNnPn1vXSPZQ7yW1QRua2TaUyASeF/qpeeR6ZrCwNycnpeluVD+67Rqeu+odlPPMVFhYmdOpal+DrYc/V41X7vdLyT/ZxRfGiPjgyMo4vR/zOzNnv4u6uyxdna2uBmZlxzoEYHTu9wa2bub/NhnXHdf6m13Sc5NZ6v0lkZHyOL8n5uxW2+XxSfB5/Y12ov3F2tqNF8xq5Y6KG3ty+8/z6ISEB/x/BpLy0ADaIoqgRRVEJHAMaFSJnBKzI3jO4BahRiExRHMpeYpYGbMsusx3QALggCEJQ9vtnRxplAvuzX18HjomiqM5+7VECfaqjO7qvhyiKz7x4ReBAtvw3QGHZS58ns0cUxQxRFKPRBbIK3XguiuJvoig2FEWx4bBhw/Su1a5diUePVISFRZOZmcXevZdo01Z/lU+btnXwz15GeeDAFZo0qYogCLRpW4e9ey+RmakmLCyaR49U1KmjM8Wz5f8REbEcOhRE9+6F/XyF413diadPElFGJKFWazhx6AFvttQ/JeTBnWiW/niSCXM7YmtfdNDgRbCtbE2KMo2UqDS0WVrCz6lwruegJ5MenxtsenolBisX3QAhM0WNRq2LWmckqYm9l4ilS9n3Mbt6WxP7NI04ZRoatZbgE0p83sxNhmpqYcjotb6MWNGCEStaUKGqNf0n1MPV++WeVuZW1Ybo8BRiIlPJUmu5EhhOzSb61S4qPDdoduu8CsfshOXJ8RloNbql2jFPU4gKT8HB+eXs7faoZocqPJnopylkqbVcPPKEuk1d9GSUYbkPA9fPPkVe4b/1cORd3ZGnTxJy2s3Jww9o1FL/5JAHd2JYOuc04+e212s3arWG2WMDaN3Fi2ZtK5e4zEEDWuC/ZQz+W8bQvm1tduy6iCiKBF0NxcrKFLlTvkG1kzWWliYEXQ1FFEV27LpIuza1qOrjypljUzmy/3uO7P8eZ4UN2zaNwsnRmnZtanHpykOysjSkpWVy7dpjPCsXnWPjRfq0okhJSc8Z1GVlaTh27AZVqjw/z0et2u48fhRFWFgM6sws9u29TJs2+ivB2rSphb//eQAOHgiicRNvBEEgLCyGrCwNABHhsTx8oKRCBfsCZZSEV2EPgJ9/3klSUhrjxvctsS41a7nz6FF0jk3277tC6zb6p1y2blOLnTt0WxkPHbzKm40Lt0noAyWuJbDJgIEt8ff3x3/7eNq3q8MO/3O6Ohr0ECsrswLBLLncBktLU4KCHurqqP852mXbq23bOuzw19lph//ZnM8TE1Nz8sxs2XKKhg29sLQ0w8nJBmcXOx5kzxqfOXMGzyqKf6TdANk5m0Jo16Z0J4lWq+lK2ONYIsLiUKs1BOwPpoXv67HqtTyoWsOZ8CdxPA1PQK3WEHjwDs18S3baZVJiek7dSIhLI/hqhF7i7ucxaEBz/DePwn/zKNq3qZlbT649wsqyiHpiYUrQtUd56oluyJg3R83hIzfwzk5CnJiYxrDhfzD6y240eKPkfb/c04qEyDQSVWm60wJPK/HIs8XexNyQD35vweBFTRm8qClyb2s6f1MbuadOZ1ErEnJGhdcL5EsC8KkhJ+JJPJHhiajVGo4fvE+Tlh4lutfJ2YoblyPQZGnJytJw/XJEqbe51ahVgcePogkPi0WtzuLQvmu0al1dT6Zl62rs2alLRn/k0A0avVklp3/VarUcPnidDp1z++SsLA3xcboAWZZaw8njt/H0fr6dXlU/X1b+qT7uebyID05MTOXzT5fz1aie1K+f29YFQcC3dS0unNetuD939q7eARLvDGql8zc7JtK+XT12+J/N9jcPdH93kf7mQba/OUu7dnn8zQ7dAQc7dpzJ+bxdu7pcunw/z5goFM8qhZ0+LyGhj3SaW+F8DSjRrWCSAaXJGJt/A7KILh/falEUCztvWS3mblrWAhkAoihqBUF49vs8T5+ngCnwBvBsWn0h8JMoijsFQWgNTCmk3OfJ5N2grqEM9cTQ0ICJ37/NkI8XodVq6fNWU7y9Xfn1113UqlWJtm3r0LdvM8Z+u4pOHSdjY2PO/J8+BsDb25XOXerTvds0DAxkekdlfznyN+LjUzA0NOD7SW9jbV3yoIGBoYxhY5ox5ct9aLUi7br74F7FjnW/XcKrmiONW1Xiz4XnSUtVM2dCAACOCksmzusIwLhPdhH2KIH0NDUf9VjP8AmtqN+kZCdi5EVmIFBnsBdnfrqOqBVxb+GMdQULbm1/iK2HFS5vOPLgcDiRQTEIMgFjS0Pe+FiXNyT5aSpBq+8hCCCK4N3VrcApcKXTRUbnYVXZMOUKWq1IvXauOLlbErguBFcva3waOz33/oVDT5KRmoUmS+TOuSgGTnmjwElwJcHAQEafL2ry2/hziFqRNzu64exhxf7Vd6joY0Otps6c2hnK3cvRGBjKMLM04p0xutxED67Hsv+vOxgYyhBk0HdkHcytX06SVgMDGQNG1OOXsSfQakSad/HAtbINO/8MppKPHXWbuxK4I4Rbl1QYGAqYWxnz4diXc9Jecaz/aCqtferjaGnLk5k7mbx7BStP7yr+xlJiYChj6Oim/PDVgex24417FTvW/3YZr+qOvNnSndWLzpOeqmbuhKMAOCksGD+3A6cCHnIzKJKkxAyO7L0HwMiJLansU7KHHgDfltU5duIWHbrNxMzUiJnT3sm55tdvHv5bdKeWTp7QN+f431YtqtGqRfWivhIAzyoKWjavSs++85AJAn37NMbH26VI+Rfp0wDatZ1ISkq67iE64Cq//zECW1sLvvh8KZmZWWhFkcZv+vD2gOefBGhoaMD4iX35ZMgSNFotvfs0wcvbhUW/7qFmLXfatK1Nn75NGTd2DV06TcXGxpy58z8A4PKlEP5YcRhDIwNkgsDESf1zknx+M3oVF87fJz4+mXatv+fz4V15q2/Tf9QelpamLF+2nypVFLzVR5feb+AgX/r1a16UGrk2mfAWnw1dhkarpVfvxnh5u7B44V5q1HSnTdta9H6rCePHrqVbp+nY2JozZ957AFy5/ICVKwIwNJQhyGRM+L5vjk2+HbOai+dDiI9Ppn2byXw+vAt93mpSoHxf31ocOx5Mh06TMTM1ZubMd3Ou+fWeif/28QBMnjSAceP+0tXRljVzTqkbNqQjX436g7//Po2rqz0/LxgCQEhIJN+N+wsE8PZyYcb03O/9fkJ/xnzzJ2p1Fm5u1Zn1g/5Jwa+q3QAcOnKd5s2qYm5e8lMZAQwNZXw9rjOjPluPRivSvVddqnjJWbE4kGo1XWjZuiq3bkQw7uvNJCWmc+rYPX5fcox12z8D4LMPVvE4NIbU1Ex6dfiZcVN60Lh50dudyso/2beO+LYt3w3fhlYj0tmvJh6ejqxaehqfGgqa+XpyOziSKWN2kZyYzpkTD1i9/Ax/bHmfxw9jWTDjMDKZgFYrMuCDRiUOJuXFt2V1jp28TYfus3X1ZGru8e1+/X/Cf/MoACZP6MO47zeSnpFFq+ZVadVCNyaZu2A3t+9EgCBQwdWOqd/rgsBrN57i8eNoFv92iMW/HQJg5dKhxR50IDOQ0eIjH/bMvIqoFana2gV7NwsubH6AUxVrvcBSYUTcisfSwRRrxYtNBhoYyvjsm5ZMHLkbrVakY49qVPK0Z83y83hXd6JJq8rcvali2rf7SU7M4NyJUNb+doFlmwbQom0Vrl0M5/OBm0AQaNDEjcYlDEQ9w9DQgG/H92Tkp3+i0Yj07N0ATy8FyxYdonrNivi2qY5fn4ZMHreF3l3nYW1jzow5A3Luv3IpFIWzDRXdcgPj6kwNIz75k6wsLRqtljebeNLrredPyL6Kft7Ly4W5c7exZ/dF0tIyae07nr59mzF8RPdS2ehV9nHF2aSsPnjDuhM8eRzNsqX7WbZUt47gt98/x8HBilGjezJu7Bpmz9qGvb0l02cUnspX529u0KHj99n+5v3cv7vXdPx3TNT93ZMGMm78atLTM7P9jS74P2xoJ776egV/bz2Fq6sDPy8YCuhSibRsWZOeftOQyWT07dscHx9dPqlRo37n/IW7xMUl06pVK5RK5cd37tz544UM+Trz7z7N7R9HKC752r8dQRAcgMuiKFYSBKEP8AnQFbAHLgKNgQroAiu+2fcsAMJEUZwvCMKHwEpRFAVBEDyA3aIoFjodJwjCB8BMoBaQBpwDPgJS0R3N11wURZUgCPaAlSiKjwRBSBZF0TL7/ilAsiiK87LfJ4uiaPkcfVoDY4CPgUPASFEUAwVBuAIMEUXxkiAIfwKVRVFsna1fQ1EUhz9HJr8ON4DuoiiGFmNqUSsGFPt7vGpkQjtux80tbzWoZvcN354aVrzgK2ZOc932wzW3vyhnTeDdaovZHTq6vNWgu8d8AsMnlLcatK4wAwDhs4IPp/804tKz3Iz9sbzVoIb9WMjYU95qgEk3AF6XPk2tPVDeamAk6/Ta2AMgQ7OvnDUBE4MuoC1/myBr91q1m+j0teWsCDiaDn5t+tYnycvKWw3cLD/VvUh/+UGxUmPagwVBn5a3FnxdbxkhCT+Xtxp42uiOs0/M3FrOmoC18VuvTz//GvVpr4sPRjxa3mqA0AZ0iyT+s4iXJpZrcERoMP1fZd///MokURRjBEE4lR0U2QdcA66iWzH0rSiKkYIgxAAaQRCuAquAJcBWQRDeQ7cFrTQJWM4DW9FtI1sriuJFAEEQJgIHBUGQAWrgC+BRkd+iz3P1EUVRKQhCd2CfIAgfoVtltEUQhDjgCFDYGuOSyEhISEhISEhISEhISEhI/Pf5FybBLk/+88EkAFEU868V/CbfdTWQP9Nn3k3BY7PlQtGtOnoeYaIo9ipEh03ApkI+t8zzekph10RRvFeEPoFAYPbrx+jnPfIvpKxV6IJliKLoX4RMfh1KlxRBQkJCQkJCQkJCQkJCQkLiP83/WwJuCQkJCQkJCQkJCQkJCQkJCYkX4P9iZdLLRhCETkD+BCMPRVHsTfbKHwkJCQkJCQkJCQkJCQkJiX8J0ja3UiEFk8qAKIoHgPLPxiYhISEhISEhISEhISEhISHxDyMFkyQkJCQkJCQkJCQkJCQkJP6vKe+T7v9VR7kh5UySkJCQkJCQkJCQkJCQkJCQkCgFQnlH3yT+M0gVSUJCQkJCQkJCQkJC4r/Lv23xTKnQnh1brs+0siY//qvsK21zk5CQkJCQkJCQkJCQkJCQ+P9GSsBdKqRgksRLQ+RoeauAQBtUaX+VtxrIzd6j5eaB5a0GJ/qvB2D1rc/LWRN4v/oS3lj7dnmrwZXBm4hI+b281cDVYggAYcm/lbMmUNFyGJaT2pW3GiRPDYDU7eWtBpj31v2f9Rqcs2DYieMRE8tbC1q5TgfNofJWAww6ABCTvqGcFQEH03deG5soU1eVtxYozD8A4GnqyvJVBHAx/4gnycvKWw3cLD9F+KxJeauBuPQsAPMvf1rOmsDo+stAva+81QCjLiSpy9/fWBnp/M3r0oY33xtR3mrQ33shR8LGl7catK04E4AbMbPLWROo5fAdaAPKWw2Qlf9YUeL1QgomSUhISEhISEhISEhISEhI/H8jrUwqFVICbgkJCQkJCQkJCQkJCQkJCQmJEiMFkyQkJCQkJCQkJCQkJCQkJCQkSoy0zU1CQkJCQkJCQkJCQkJCQuL/G610QHlpkFYmSUhISEhISEhISEhISEhISEiUGGllkoSEhISEhISEhISEhISExP83UgLuUiEFkyReGieOBzNjxma0Wi19+zVn2LDOetczM9WM/XYVwcGPsbW14KcFQ6hY0RGA5cv3s/XvU8hkMiZM7E/LljXJyFAzeNA8MjOz0Gi0dOxUn5EjewAgiiI//+zP/v2XMZDJGPBOK957r22xOp47FcIvcw6i1Yp0712PwR8107sedOkxv849yIN7KibP7k2bDtVzro3+fAM3r4VT+w035ix8sSPu33Suw5f13kMmyNj98Cjrbu/Suz6i3mDecKoBgKmhCbYm1nTdMRSAeS3HUsPBi+vRdxh7ct4L6QEQcjmGQ7/fRdSK1O3gSrO3PAqVu31axbY51/lwXiNcvKx5GBTD0b9C0GRpMTCU0fYDLzzq2JdZj2Yudfmm0QfIBBk77h/hz2B/veujG7xHI0VNAEwNjbE3taHV5o9yrlsYmbG1+3yOhl3gxwt/llmP86cesmheABqNSLfedRj4YWO961cvPWHx/COE3Iti0qwe+Lavqnc9JTmDD/qupEVrb778rn2Z9QA4f/ohi+cdRasR6dqrFu/k0+Xa5TAWzzvKg/tRTJzZHd/2PjnXOjT6icpeuvYld7Zi+oLeZdajvVcj5nT9AgNBxurLe/npxEa96242cpb2/gZHc1vi0hL5eOssIhKjAdj+7iwaVazBmcc36LduQpl1AF27nzFnF8dO3cHU1IjZP/SjZvUKBeRu3Axj3OQtpGdk4du8KhO+7YEgCHw1dj0PQ6MASEpKw8rKDP9NX5au/FlbOXb8JqZmxsyeMYiaNdwKlh/8mHET1pGersa3VQ0mjHsLQRD4cd4OjgbewMjIEHc3R2ZNH4i1tTlh4TF07TGTyh5yAOrW9WDq5NL3MTfOR7Jx0RW0GpGW3arQZWA1veuBO0MI3HEfQSZgambIu6Mb4uphXepyniGKIjNm/s2x48E6e8x8t2h7jF+TbY+aTBjfV2ePuduz7WGgs8eMwVhbm3Pq9C3m/7QTtToLIyNDvhnTi6ZNqhaiQfGcPXWPn3/cj0arpUfv+rz3cUu961cuhfLLnP2E3FPyw499aduhZpnKecaL2mTf/sssWryXkAdKtmwaQ+1alQDYuesCf6w8nHP/nbsRbP97LNWrVyxWp3OnQvh17mG0Wi3detVj8EdN9a4HXXrMwnmHdX5vVi9ad9DVm3t3lPw0Yz8pKZnIDATe/bgZ7TrVeBHzcO7UAxbNDUCj1dKtV10GfdRE7/rVS09YNC+AkHsqJs3qmaNLZEQC34/ejlYrosnS0HtAA/z6vVFmPc6fDmXJvEC0Gi1detXinQ/f1Lt+7XIYS+Ydy+5bu9IqT9/asdHPen3rtAV+ZdajOP54dwLdazdHlRRH7WmDXlk5AE+CYjj91z1ELVRr40I9v0qFyj04p+Lwz8H0nt4AJ09rtFlajv12h+jQJESNiHdLZ97oVfi9RaHrW7dx7MQtXd8+Y2AR7eYJ4yau17WbltWZMK4PgiDw88K9BBy5jkwm4GBvxawZA1HIbTh85Dq/LNyLTCZgYGDA+O9607B+lSL1OH3yDvNm70KrEen1ViM+GNJa73pmZhaTx23m1s1wbGzNmTXvHVwr6MY99+48ZebU7aQkpyPIBP7aOBwTEyPU6izmzNjJpQsPEGQCn4/sSLsOtUtln9elDd+7FM2e324jakUadKxIq36V9a6f3/uEc3ueIJMJGJsZ4De8BnJ3S7LUWnYuvkn4vUQEAboNq0blFxgvBp+PZPPiq4hakeZdK9PpHX3/cHzXA475hyCTCZiYGTLo6/q4eFiTnJDBih/O8ehOLE06VWLAyLL3Ic+4cjaMlT+fQ6sRadfDhz7v1dG7vnPDDQJ23UVmIMPG1pTPx7dA7mLJw7sx/Db3DKmpamQygb7v16F5+6LrZmHo/M0Wnb8xNWL2zPeoWdO9gNyN4MeMG/cX6RnP/E2/XH+zaA8hDyLZsvnbHH8TF5fMyK9WcOPGY3r3asKk71/smUfiv40UTJJ4KWg0GqZO3cDKP79EobCjX99ZtG1bBy8v1xyZv7ecwtranIOHprFnzwXmz9vOgp+Hcv9+BHv3XGD3nkmolAl8+OHP7D8wFWNjQ1at/hoLC1PUag2DBs6lVaua1KtXhW3bzhD5NI59+6Ygk8mIiUksgY5afpq1nwXLBuKksGbooJU09/WmsqdTjozC2ZrxU3uw8a9zBe5/5/0mZKSr8f/7ygvZSiYIjKr/IV8fm0VUWgwr2k/nVMRlQhPDc2QWBq3Nef2WV0e87Txy3m+4sxsTAxP8PIsPnhWHViNyYPkd3vnhDawdTPjzmwt4v+mIk5ulnlxGWhYXdj/B1Sf3wdPM2ph+E+tiZW+C6lEyG38IYuTKFmXSQyYIfPfmR3wWMANlagzrusziWNhFHiTk2mT+pb9yXg+o2pmqeWwC8Hnd/lxW3SpT+c/QaLT88uMh5i7pj5PCik8Hr6GZryceVRxzZBQu1oyd0oVNay4U+h0rl56kTv2Cg+Cy6PLr7ADmLOmLk8KKz99dR1NfLzyqOOTIyJ2t+PaHzmxZc7HA/cYmhvy24b0X1kMmyPip+0h6rv6W8MQojn+yhL23z3A76lGOzMxOn7I+6BDrgw7iW7keP7QfwtBtswH45dRmzIxM+ahh9xfW5fjJO4Q+juag/xiuXn/ClJk72LLmiwJyU2buYNr3b1G3thtDh//J8VN38W1RlZ9/HJgjM3v+biwtTUtX/ombhD6K4uC+77l6LZQpUzezZePoguVP3cy0HwZQt44HQz9dxvGTt/BtWYPmTasy+qseGBoaMHe+P8tXHOKb0boHUXc3R/y3jS2lRXLRakTW/3KZr+e2ws7JnBmfHqZuM1e9YFHjdu607ukJQNCpCDYvCeKrOa3KXObx49n22D9ZZ48fNrJl0zcF5KZM3cS0qQN19vhkKcdP3MS3VU2aN6vG6K97ZttjB8tXHOSb0b2ws7Vk6ZJPUMhtuXsvgo+HLuZE4IxS66fRaJk3cy+/LH8XucKajweuoGXrqlT2lOfIODvbMHFaL9avPl1mO+TlRW3i4+3Kwl+HMnnKBj35nj0a0bNHIwDu3A3nixErShRI0mi0LJh9kJ+WDsBJYc2wQato4euNh6d+nzb+h+4F/J6pqSHjp/XArZI90aokhgz6kzebVcHKqnTtJq8uv8w+xLylb+v610Grae7rpaeL3MWa737oyqa/zuvd6+BkyeLVgzE2NiQ1NZMP+/5Bc18vHOVWZdJj4ewj/LikD04KK754dz3NfD2pVKBv7cjmNZcK3G9sYsjyDYNLXW5ZWHVmD4sC/+avDya90nK0WpGTf96l2/h6WDiYsH3CRSo1cMSuooWeXGZaFjf2hyH3yu1XHpyLQpOlpd+cN8nK0LB5zHm8msuxcjIrcfnHT9wi9HEUB/dO4Oq1R0yZtoUtG0YVkJsybQvTprxN3TqVGPrZ8py+dciHbflqRFcA/lp7jMVLDzB1cn+aNvGhXZtaCILA7TsRfDVmFft3jS9UB41Gy4/T/Vm84mMUzja89/YiWrWpThVPRY6M/7YLWFmbsWPfNxzYe5WFP+1n1vyBZGVp+P67TUyd1R+faq7Ex6dgaGgAwMrlR7Gzt2DbnjFotVoSE9JKbJdner0ObVirEdm19BYfTG+AtYMpy74+S7XGTsjdc8eLdVq78GZX3fjn1jkV+36/w/tTG3DpQBgAIxY3Izk+gzWTL/PJgibIZEKZ9Nj4axAj57TAzsmc2Z8foU5TF1zy+LpGbd1o1UMXmLl6OoK/l11jxOwWGBkb0OPDGkSEJhLxMKHUZedHo9GyYt5ZJv3SCQe5OWM/3kWjlu64VbbNkans48CclT0xMTVk/7bbrFlygdHT2mBiasiISS1xdbMhNiqVbz7aSb3GFbCwMilx+cePBxP6SMXB/VO4ejWUKVM3smXTtwXkpvywgWlTB1G3rgdDP1mcx9+4sHDhMCZPXq8nb2JixJcje3DvXgT37j0tu4Ek/i+QciZJvBSuXbuGeyU5bm5OGBsb0rVbIwICrunJBBy5Rq/eutmUTp3qc+bMbURRJCDgGl27NcLY2IiKbo64V5Jz7VoogiBgYaFzeFlZGrKyNAiCzvFs3HCMz7/ohkymq8IODsXPrt+6EUEFN3tcK9phZGRAu041OBl4V0/GpYItXj6KnHLy0rBxZczNS97JF0V1ey/Ck5U8TVGRpdUQ8PgMLVwbFCnfzr0Zh4Z6tfkAACAASURBVB/nPuhcUgWTmlW6wUhRRNxLxM7FDDtnMwyMZNRooeDeuegCcsfXPaBpn0oYGuV2Gc5VrLCy19nDyd2CrEwNWeqyLQ2t5eDFkyQl4ck6mxwIPU3rio2KlO/s0Yz9oady3le3r4yDqS1nnl4r8p6ScPvGU1wr2uFa0RYjIwPadqrGqcD7ejLOrjZ4+sgLHQTduRlJXEwqjZp4vJAeALeDI6ngZpujS5uOVTldmC7eToXW15dFw4rVeBAbTmjcU9SaLP6+fpRu1fRX9FWTV+LYA12Q9djDIL3rgQ+ukJyR+lJ0CTh2k17d6yMIAvXquJOYlIYqSj+QrIpKJDklg3p13BEEgV7d6xMQGKwnI4oi+w5dp3vneqUr/8h1evV8U1d+3crZ5esPSFVRCSSnpFOvbmVd+T3fzOkLWzSvnvOAUa+uB5HK+NKaoEge3o7FydUSJ1dLDI1kNGrrRtCpcD0ZMwujnNcZ6VkvXG8Cjlyjl18J7JGcxx5+RdmjMpGROnvUqOGGQq4bjHt7uZCRriYzU11q/W7eCKeimz0VKtpjZGRI+861OBF4R0/GpYIdXj7OZXqoKYwXtYmnpzNVKisK++oc9uy5RLcu9Uukj87v2eXxe9UL+j1XWzx95Aj5bOBWyQG3SrrVA45yK+zsLIiPLXtbvn3jqV6f1rZTdU4F3suni02huhgZGWBsrJv/VGdqEMWyJ0i9ExyJax49WnesyqnAED0ZZ1cbqng7IXuFfWtJOHE/iNiU4ifLXpSo+4nYOJthrTDDwFCGZ1MFoRcLjgUubn5IvR7uGBjpPz5kZWjQarRkZWoxMBQwMivdXHXA0ev06tkou914FNO3emT3rY0IOHIdQG9iIC0tk2c/m4W5SU4/l5aWwfN+zeDrT3Bzd6CimwNGRoZ07FKXY0du6skcO3KT7n66tteuYy3On7uPKIqcPX0Pbx9nfKrpJlBtbS0wMNDZaOf2i3w4pA0AMpkMWzv9AF1xvC5tOOxuAg4u5tg7m2NoJKN2K2dunVXpyZia5/7u6nRNzu+gepJCleyVSJa2JphaGBFxr2z1OvR2LE4VLHJ8XcM2Fbl6OkJPJq+vy0zX5Lw2MTPEq7YjRkYv5/H3/s1onCta4VzBCiMjA1q0r8KFE4/1ZGo3cMHEVGcXn5pOxKh09nd1t8HVzQYAeydzbOxMSYhPL1X5On/TWNdu6lUmMTEVlSpfu1Fl+5t6z/xNYwICrgLg6elSqL8xNzehYQMvTEyMClz7v0CrLd9//zKkYJLES0GpVOLibJfz3llhi1IZpyejUsbj4qKTMTQ0wMrKjPi4FJTKuCLv1Wi09PKbTvNm39CsWXXq1tUtqX38JJp9ey/yVp+ZDB2ykNBQZbE6RqmSkDvnzmI6KayJViWV/Y8uI05mdqhSY3L1SovF0azw5b4Kc0dcLZy4rAou9PqLkhSbjrVj7iDMysGEpNgMPZnIkEQSo9PxauiY//Ycbp9R4VzFSi/YVBrk5vYo89hEmRqDk7ldobIuFo64Wsq5oLwBgIDAqAbv8tPlNWUqOy/RUcn6dURuRbQquUT3arUiSxcE8tnXrV9YD4BoVTJOirz11YroqJLpArrl+J8NXsvw99dz8ui94m8oAlcrR8ISonLehydG4WqtXxeuR4bgV0O3fahn9RZYm1pgb1b27VNFoVQl4uycO+PnrLBBqUosKCO3ea7MxcsPcbC3xKNS0XW68PIT8pVvi1KpP3BTKhNwVuSRcbZFqSo4A7p121latczdbhAWHkOvt35k8Pu/cPFSSAH54oiPTsNebp7z3s7JnPjogkHno9vvM37QXrYuv8aAEaULpuVHqYrHuUDfrR8gUyrj9e2hsEWpKhhE27rtjJ49nnHgYBA1arhhbFz6QW2UKhGFc249dJJbE6V8tQ/mL9MmRbF3/2W6dWtYItloVTJyRR4bKKyIiiq937t5IwJ1loYKboX3yyUhSpWEUwFdSt6nqSIT+aj/Svp3WcI7HzQp06okeGaTvH2rJTGl7Fs/H7yO4e9v4NTR+8Xf8C8gJS4DC4fcsYCFgwkpcfpjgeiHSSTHZuBeX7/frNLYCUMTA9Z+dpr1I05Tp7s7ppala69KZUIh7aaYvjWfzIJf9uDbbgq79lziy+Fdcz4/dPganXvM5JPPVzBz2jtF6qBSJaJwzvUdcoUNqny+Qyej08HQ0ABLS1MS4lN5/CgaBIHhw/5gUL9fWb3yGABJibo+eOmigwzq9ytjR60jJrp07e91acOJMenYOOXWERtHU5JiMgrIndv9mJ+GnODAn3fpNky33c65shW3z0Wh0WiJi0wlIiSRhOjSBU6eER+dhp1TXl9nVqivC9wRwveD97P9t+u8PbxumcoqjtioVBwVucFBeydzYqJSipQP2H2X+k0Kbs2/dzOKLLUW5wqlGzcplfn8jbNdAV+iVOX3N3YFfJKExIsgBZMkXmsMDGTs8J9I4LFZXLsWyt27upl2dWYWxiZGbN02nn79WzBh/IsHEl5H2rk3JTDsPNoXmIV9EUStyOGV92j3oXeRMlGPkzm6OoQun1UrUuZl0qlSMwIencuxSX+fjpwMD0KVGvuPlF8U/puv0Lh5Zb0AUHmyfvdQlq4dzPgZXVkyP5CIJ69u8DD+wHJaeNTh1GfLaOFRl/CEKDSipvgby4nd+6/SvfOrGVyWhKXLD2BgaEDP7rqAgNzJmqOHf2DH1rF8921vRn+7muTkl7P6MD9tensxc11X3hpWhz1rXmxb6Mti6bL9GBjIcrZxPePevafM+8mfqVMGlJNmrx9Xr4ZiZmqEj7dr8cIvieioZGZM3MW4Kd1e2gqusiB3tmbl5o9Y5z+MA7tuEBtT9EPbq2T97iEsWTuI8TO6sGT+sVfat74uiFqRM2vu03SwZ4FrqpBEZDKBwUua8c4vTbm25zGJylfTfz2Pr7/sxrGAKfTo1oC160/kfN6hfR327xrP4l8/5pdF+15J2ZosLVevhDL9xwH88denBAYEc/7sfTQaLUplAnXqVWLdlpHUruvOz/P2vhIdnsc/2YYbd3dn1O8t6fiBD4GbHgBQv4Mr1o4mLPvqHHtX3MGtmm2BVVQvm9a9PJm2tjO9htZi79rbr7SsknBsfwght2PwG6SfLysuOpVfpx5n+IQW5dq/SkiUFSlnksRLQaFQ8DQydyVSpDIehUJ/9kOusOXp0zicne3IytKQlJSGrZ0FCoVdsfdaW5vTuHFVTpwIxsenAgqFLR076BLndehQj/HjVhero5PcClVk7mxOlDKxzDObL0JUWhxy89zcDE5m9kSnFR4IaefWlAWXy55Mujis7E1JzDM7lBSTkbN1DSAjTUPU4xTWTbwMQHJ8JltmXKXfhLq4eFmTGJ3O1tnX6PFVDexczAt8f0lRpcaiyGMThbkDUalxhcp28mjG7PMrc97XcfLhDXk1+vt0wMzQFCOZIWnqdH4N2lDo/c/D0clSv46oknCUWz7njlyCr0dw/UoY/luCSEtTk6XWYGZuxLCRvqXWA8BRbkmUMm99TcLRqWS6gK6+A7hWtKVuAzfu3VHh6mZbzF0FiUiKpqJNbl6xCtZOOcm1nxGZFMPAjVMAsDA2xa9GSxLSX86D3rpNZ9i8TZc/pXbNijlboQAilQko5PozeQq5NZF5VgLll8nK0nDoSDDb1o8oWfnrj7P57zO68mu55ys/HoXCRk9eobDR274WGRmPIs9KqW3bzxF4LJhVfwzP2X5hbGyUs/KmVk133N0ceRgaRe1aBRNpFoWtoxmxqtztC3FRqdg6Fp2zpFFbN9b9XDAfTHGsW3+MzVt0225r165EZIG+W7+OKRS2+vZQxudsYQPYtv0sgcdusGrlSL1td5GRcQwf+Rs/znoXd3cnyoKT3BplZO7KgihVot7KmJfFy7bJ89iz7xLdupZsVRLo+hFVntVYUcoknJxK7vdSkjMYO3IzQ7/wpWadgjPqpcFJbqW3MkynS8n7tGc4yq2o7OXItctPchINl+5+S1R6fWsyDqXQ45lP0PWtFblfxr71dcLCzoSUmNyxQEpMBhZ2uWMBdbqG2Ccp7JoaBEBaQiYH5l2n05ja3D+lomJde2SGMsxsjFH42BD1IAlrxfNzJq3bcCJf35q/3RTTtxYiA9Cje0OGfbackcO76H3eqKEnT8JiiI1Lxt6u4O8tl1ujjMz1HSplAvJ8/kUnE4/C2YasLA3JyenY2JojV9jwRoPKOVvYmresyu2b4TRq7ImpmRFt2+sS+7fvWJud2wrPt1gUr0sbtnYwJSEqt44kRKdj5VB06ofarZzZtUQ3YWFgIKPr0Ny2+tuYczhWKNuY0dbRjLiovL4u7bm+rmEbNzb88mK5TovC3smcaGXuWCc2KhUHp4LbGK9eiGDr6qtMW9wFI2ODnM9TUzKZMeYQA4c1wKeWvMB9hbFu3TE2/61L81C7Vj5/ExlXwJco5Pn9TVwBnySRD235TOD/W5FWJkm8FGrXrs2jUBVhT6LJzMxi754LtG2rf6JB27Z12LFdN3A4cOAyTZpURRAE2ratw949F8jMVBP2JJpHoSrq1PEgNjaJxESdw0hPz+T06VtUqeIMQPv29Th3Tpf74vz5u3h4PD/HBEC1mq6EPY4lIjwetVpDwIGbtPD1Kfa+l83t2BAqWjrjYuGEocyAdu5NORlR8KHO3coVK2MLbsSUfYtScbh6WxH3NJV4ZRoatZabJ5V4v5m7hN3UwpCv17TiixXN+WJFcyr4WOcEktKT1WyefpXW73rhVv3FHFNwTAjuVs64Ztukk0czAsMKJpX2sHbF2tiCq9G5+QImnFpI1+1f0G3HCBZcXsvuh8fLFEgCqFbThfAncTzNriNHDtymma9Xie6dOKM7m/Z+ysY9n/DZV63p2K1mmQNJANVqOBP+JJ6n4Qmo1RqOHrxDM9+Cs8KFkZSYTmZmFgAJcakEXw3XSy5bGi6F38bTvgKVbJ0xMjCkb+027L2tn6zYwdw6JxAwpuVA1lzZX6ayCmPQ203x3/Ql/pu+pH2bmuzYfRlRFAm69hgrS1PkTvkG+07WWFqYEHTtMaIosmP3Zdr55m6fOn3uPlU8nHAu5CGk0PIHtsJ/21j8t42lfbs67Nh5Xlf+1YfZ5et/j9zJBksLU4KuPtSVv/M87drqZiKPn7jJ7ysPs3TRUMzMjHPuiY1NQqPR7ZN/8iSa0EdRuFUs3e/lUc0OVXgyUU9TyFJruXDkCXWb6a9eUYblPkBfP/sUeYXSB9MHDfTFf/s4/LeP09nDP489rMwKt4dlHnv4n6ddtm84fuImv/9xmKWLP9GzR2JiKsM+W8boUX40qF+yOl8Y1Wu6EvY4hoiwONTqLA7vv0EL37KdCvc8XqZNnodWq2Xf/st061p0jr386PxeXB6/d4vmrYteaZoXtVrDhNFb6dS9VpmCNvmpWtOFsMd5+9dbNGtdsv5VpUwkI12XNyspMZ3rV8Jw9yhbn1a1hnN2P6/rWwMP3qGZb8lOUdLvW9MIvhpR5r71dcLJ04qEyDQSVWlosrSEnFFSqUHuWMDY3JD3V7Rg4MKmDFzYFLmXNZ3G1MbJ0xpLRxMignUPtOp0Dar7idi6Fh8oGPROS/y3fov/1m9p37Y2O3ZeyG43oVhZFtFuLEwJuhqa3bdeoF0bXd8a+ih3K3bAkes5eWAePY7Kya8VfPMJmZlZ2NkWnrOoRq2KPHkcQ3hYLGp1Fgf3XaVVG/2tt63a1GC3v25yLeDgDRo19kQQBJo29+b+vUjS0zLJytJw+eJDqnjq8m+29K3OpQu6FToXzt2nsmfx49W8vC5tuIKPNTERqcRFppKl1nL9eCTVGusHQGLCcwMrdy9E4ZBdDzLTNWSm69rN/SsxyAwEvcTdpaFStq+LzvZ1F4+GUSefr1Pl8XU3zj5FXqFsZRWHV3VHnoYlooxIQq3WcPLwAxq20D+A5cGdGJb/eJrv5rTDxj436KVWa5jz3RFad/GiaVuPEpc5aJAv/tvH4799fLa/OadrN0HZ/kaer93Is/1N0DN/c65E/kZCoqRIK5MkXgqGhoZ8P+ltPh7yK1qNlrfeaoa3tyu//rKTWrUq0bZdXfr2bc633/xJxw7fY2Njzk8LhgDg7e1Kly4N6Nb1BwwMDJg0aQAGBjKiVAl8991qNBotoijSuXMD2rTRdYBDh3XimzErWbU6AHNzE6bPeLcEOsr4+rtOjP5sg+54Vb+6VPZy4vclx6hWw4UWrX24dSOCCaP+JikxndPH77Fy6XHWbPsEgC8+/ItHoTGkpWbSp+OvjJ3SjcbNSv+goxG1LLi8ivmtvkMmyNjzMJDQxHA+rtmX23EPOBWhG6i0c29KwOMzBe5f1GYSlaxcMTM0ZWv3hfx4YQXnlWVLPC0zkNFxaFU2/nAFrQbqtnfByd2SY+tDcPGyxufNolcEXNwbRtzTVE5uesjJTQ8BeGfKG1jYGhd5T1FoRC0/XljJknbjkQky/EMCeZAQxmd1+nEz9gHHwnTBtk4ezTgQ+nJOXSoMA0MZI8e259sv/kar1dKlZ20qezqyculJqtZwprmvF7eDn/L96B0kJ2Zw5ngIfy47xaq/P3oluoz4ti1jh2/VHV/tVwsPT0f+XHqKqjUUNPP14nZwJJPH+JOcmM6ZEyGsXn6alVs+4PHDGBbMOIwgExC1IgM+eFPvFLjSoNFqGb1nITve+xEDmYw1l/dxK+oRE9t+wOXwO+y9c4aWHvWY0uFjRBFOPbrGqN2/5tx/8OOf8XF0w8LYjDujN/K5/zwC7hcMFJYE3xZVOXbyNh16zsXM1IiZU/rlXPN7+xf8N30JwORxvRg3eQvpGWpaNa9Kqxa5AYS9B67SrYxb3Hxb1eDY8WA6dJmKmakxM6fnHtXt1+fHnNPYJn/fn3ET1pGekUmrFjVycgFNm/E3meosPhyyBIC6dT2YOvltLlwM4ddFezE0NEAmE/hhUn9si3jgKQoDAxkDR77Bz98e1x2X3KUyFSrb4L/yBpWq2lOvuStHt9/n5iUVBoYCFlbGfPhd0UnuS2aPmjp7dP5B93vMyD3hyq/3LPy3j8u1x/i1ut+jZQ1atcq2x/TNOnt8vCjXHlPeYe364zx+HMXiJftYvES3LWXl78NxcChd8MvQ0IBR47ry9Wdr0GhFuvd6gypeclYsPkK1mq60bF2NmzfCGff1RpIS0zl57C5/LAlk3faCJwT+UzY5dPgq02ZsITY2mU8+W0b1ahX4Y8VwAC5cvI+Lsx1ubiXP9WVoKOOrsR0Y8/lGtFqRrn51qOzpxB9LjlO1hgstWntzKziCiaO25fq9ZSf4a+tQjh68xdXLT0iMT2P/Tl2i43FTu+NdtXQPw3l1+XJsB775fDNarUgXv9pU9nRi5ZITuv61tTe3g58ycdS27P71PquWnWTV1iE8fhjDkp+OIgAi8PZ7b1LFu2wr1p71rd8N34ZWI9LZryYeno6sWnoanxoKmvl6cjs4kiljdmX3rQ9YvfwMf2x5n8cPY1kw4zAymYBWKzLgg0avNJi0/qOptPapj6OlLU9m7mTy7hWsPL3rpZcjM5DR/AMf9s26ilYrUrW1C/ZuFlzc8gDHytZ4PCdnYs2OFQhcdpstY84hAlV9XXCoVLqHd99WNTh24hYdukzHzMxYL7eR31tz8N+qO6Fq8sS+jJu4nvR0Na1aVqdVy+oAzF+wi4ehKgRBoIKrPT9M0vmGA4eu4r/zIoaGMkxNjVgw7/0iDx4wNDTgm/E9GfHJSjQaLT17N8TTS8GyRQepXrMivm1q4NenIZPGbaZXl7lY25gxc65OT2sbcwa915L3BiwCQaB5y6q08NUFb0aO6sKkcZuYP3s3dvYWTJ7er9Dyi+J1acMGBjK6f1qN1ZMuo9WK1O9QAUUlSwLW3sfV25rqjeWc3f2EkKsxGBjIMLM0pM/XtQBISchk9aRLCIKAtYMJfUfXLqa05+sxYEQ9Fo49iVYr0qyLB64e1uz6Mxj3qnbUbeZK4I4Qbl9WYWAow9zSmPfH5vq6CQP3kZ6qRqPWcvXUU0b+2ELvJLhS6WIoY8ioJkz7+iBajUjb7t64V7Fjw4rLeFVzpFFLd/5afIH0NDXzJwYC4KiwYNyc9pwOCOVmUCRJiRkc3avLvTZ8Qgsq+5S8P/H1raXzN50m68YkM3Ofhfx6z8R/u+7kwsmTBjBu3F/Z/qYmrVrpVsodOhTEtBmbdf7m0yVUr1aRP37Xrdpu+z/2zjsqquPvw88svaOUBREb2GvsHdGoMbFgiUnsSdQ00xMN9l5iS2KMsSXGqFGs2DWCir0rirFgBRUWsCBI3b3vH7sCKyiLJWve3zzncNi9852Zz06/c2fmthpOSmo6WVladoSd4rcFn+Lv7/1U6fSf4z94CLY5Ec/yRgyJJA+Kwk5za0AQiCZtceGGLxhPuz40C+lRuOELZk93/es+//jnYzMrgb6Vf+GVJW+ZWwYneq3gZuoCc8ughIN+MjU2ZZ6ZlUBJx4E4jmxlbhmkjA2DB2vNLQPsO+v/Z28zrw4Ay7ZE3BxubhU0LzEetH+bWwZYtAYgKf3pVh8+T9xs33lp0iT+wSJzq0Bt3w+AWw9+e7Lhv4C3/XvEpPxqbhn4On6I+KihuWWgzDkIwPTjH5pZCXxd+1fIejFnFxUJq3bczzJ/f+Nkpe9vXpY6HHLRtC3gL5Lu5WcRHjvU3DJoWXIiAGeSJptZCVRz+w50YeaWAapWwBNfjPifR7f9E7NOjqjazP5Ppa/c5iaRSCQSiUQikUgkEolEIjEZuc1NIpFIJBKJRCKRSCQSyf82cptbkZArkyQSiUQikUgkEolEIpFIJCYjVyZJJBKJRCKRSCQSiUQi+d9GJ8+TLgpyZZJEIpFIJBKJRCKRSCQSicRk5GSSRCKRSCQSiUQikUgkEonEZOQ2N4lEIpFIJBKJRCKRSCT/28gDuIuEXJkkkUgkEolEIpFIJBKJRCIxGaEo8pApyXNBFiSJRCKRSCQSiUQi+f+LMLeAF4lufX+z3tOqOi74T6Wv3OYmeW7olDBzS0AlWpGl22ZuGVip2hKZNMncMqjhFgyAcnuxmZWAKN6H8Nih5pZBy5ITSc0KNbcMHKw66T+kmV8Ldp1YEPWxuVXQv+ovZOv+NrcMLFWt9R/SN5hXCIBtByYc+cDcKhhWby5kbDK3DLB5Q/9fZ/7+BlUryDZ/f4NlW26mLjC3Cko49Nd/SF5hXiEAzm+9NPV3+vEPza2Cr2v/CoD4qKGZlYAy5+BLU29iUn41twp8HfXl4+ztKWZWAlWKD6H0/C7mlsG1AWt4e0s/c8tgebtFAFy8N8O8QoDyLl+9PP2eRJIHuc1NIpFIJBKJRCKRSCQSiURiMnJlkkQikUgkEolEIpFIJJL/bXTy5JaiIFcmSSQSiUQikUgkEolEIpFITEauTJJIJBKJRCKRSCQSiUTyv41OZ24F/ynkyiSJRCKRSCQSiUQikUgkEonJyMkkiUQikUgkEolEIpFIJBKJychtbpIXwp49UUycsBKdTqFbt8YMGNjWyD0zM4shQ/7gbFQMrq4OzJjxPj4l3bhzJ4UvPp/PmTPXCQpqyIiRb+X4+WFmKKGhh0hOTuPY8Zkma9m75yyTJ65Bq9PRtVsj+g9onU9L8JAlnD2r1zJtRj98fNw4HXmN0aOWA6AoCh9/0o5XW9cEYPiwpUTsiqJ4cSfWbQgucvqcOBjL7z8cRqdVaNWhPJ371DBy3/BXFGEbLmBhocLZ1ZaPhzbBw9uRhFspTA0OR6coaLMV2nWrRJvOlYocf14URWHCzO1E7L+Era0Vk0a0p2pF73x2M3/dSeiW0yTfT+d4+OCc60dOXGfSD9s5f0nD9LGdea1l5afSEXU4jpDZp1B0Ck1eL0vbdyoauUdsuMzu0EuoVAIbO0t6flkb7zLOpNzLYP6YQ1w7f5uGbUvz9mevFDnufXvPM21yKFqtQueu9Xm3f6CRe2ZmNiOCl/PP2Ru4utozeVpPSvgUB+DC+VtMGLua1JQMVCrBn8s/RadTGPLVEmJjk1CpVDRvUZnPvnzdJC2KojDh+/Xs3nsOW1srJo/tTtXKJfPZnTkbS/DIENIzsghoWolhgzsihGDWnO2ErDlM8WIOAHz16WsENKtMVpaW4WNWcfbcDbK1OoLa1+aD91uapOnK8STCfruAolOo8WoJGnQpU6Dd+QMa1k89Te/v6+Hl75xzPTkhnd8+P0jj7mWpH1TapDgfsmfPWSZPXGWov40ZMKCNkbu+/v5J1NnruLo6MH3Ge/j4uLF/3z/MnLGerKxsrKws+frbIBo2rEhaWiZffbGQmJhEVCpBi8DqfPV1J5O0KIrChCmh7N77D7a21kwe99bj82bEckPeVGbYkE6GvNlGyOpDFC/uCMBXn7YjoFll9h24wPQfN5GVpcXKyoJvv2xPowbli5ROADdOJXHkz2gUnYJ/C2+qdzRO6+iIWxz76zL2xawBqNTah/KBJYocz0P06bGW3Xsepsc7VK1SUHrEEDz8L316NKvMsCGdEULkuP/2xy6mTF/Pgd1jKV5MnzaHjkQz8ft1ZGdrKebqwJLfBxWuZeJKdkdE6evNxD5UrVoqv5ao6wQHL9ZraV6VYUPfRAjBlq3H+fnnTVy6HMfKkMFUr6ZPu9gbSbz+xljKllUDULNmGcaO7vFkHZNWszviLLZ21kye0JOqVXwL1jFsKenpWQQ0r8Kw4K4IIZgybR07d53BysqSUr7uTBrfA2dne72ODhMpW8YzV8eot/KFWxCH913h52lhaLUKb3SuQY93Gxi5nzoWw+zp4Vy6mMDISR0IeNW47U1NyaBft99o2qI8n3/3qklx5kuT6ZvZve+iPm9GdaZqpfzl7sw/Nwkes4b0jGwCmpRn2NevI4Tg3IU4Rk1ez4MHmfh4uzJtXDccHW2JjIplpzJLSgAAIABJREFUxIT1+jhQ+HRAIK0DqzxZx0tWf2NOJrF/8UUUHVQK9KZWp4Lbx8uHNOz4IYrO4+vg4eeMLlvH7nnnSbx6H0WrUL6ZF68UsW0tCgt7D6N99SZo7t+h+riezz38F1VvMjOzGTVmBWeiriOEYFhwVxrUNy1vDu+/yi/TdqHT6mgXVI133q1v5B55PJZfpu3mcnQCwye+TvNXK+S4tan3A2X93QHw9HJi3EzT+pjHcfxALAt/OIhOq/Bqxwp07VPTyD30rzPsWH8BCwuBs6stg4Y1w9NbX07HfrGN81EJVK6hZvj01gUFbzIBJV9hVKP3sBAqlp/fwZxTa43cRzR8l0YlqgFgZ2mDm60LNRb3BiC4fm9alqqDChV7bpxi9IGFT62jpnt1+lbugUqoCI+NYP3lTflsGnrVo1v5IBQFrt+/zqxTc3G3dePr2p8ihAoLYcG2azvYEbPzqXUAHDtwnXnT96PTKbTpVIk3+xqPQc8cv8n8mQe4Ep3E4PGv0rRVuRy332cd5Mi+6wC8/X5tmrf2L1LcL6rfe8jNm7d5o8M4Bn3yOu+/92xl57+EopUHcBcFOZkkee5otTrGjV3Bwt8+Q612pfubUwhsWQN//9wJilWr9uPibM+27WPYtOko06avZebM/tjYWPHZ5x24ePEmFy/cMgq3RWANevRsQbvXRhdJy/hxK5m/8BO81K681X0agYHV8MujZc2qgzi72LNl20g2bzrGjGnrmT7zXfzLe7Ni5TdYWlqQoLlH185TaBFYDUtLC4KCGtCjR3OGfrfkqdJn4bRDjPixDcU97Ql+fyN1m5XCt6xrjk3ZCsWZ8lsHbGwt2bbmHH/+cpSvxrXA1d2OCfPewMragrQHWXzdax11m5aiuId9kXU8JOLAJa7F3Gbbyo84FXWTMd9vJWThu/nsAptWoGe3urzWfY7RdW8vZyaN6MBvSw89tQadVmH5Tyf57PumFPOwZ/LH4dRo5I13mdwJiXotfWneQd8Jn9p/k1W/RvLp5KZYWVvQ4d0q3LyazM0r94oct1arY8r4tfwyfwBqLxd6vTWLgMAqlPNT59isW3MYZ2c71m8ZwrbNJ/lxxmamTO9FdraW4d/9xfhJb1OhUgnu3k3F0tKCzMxser/bnHr1/cnKyuaD9+exb885mjQrfOIvYu85rl5PZPv6wZw6fZ3RE9aycsmn+exGT1jLuJFdqVm9FAMG/UbEvvMENNWH369XM97vG2Bkv/XvSDKzstmw6ivS0jJ5o8t03nitFiUNk2KPQ6dV+Hv+ebqPegUnNxv+HHwEv3ruuPs6GtllpmVzfFMM3uWd84Wx8/cLlH3FrdDf/iharY4J40KYv3AQarUrb3WfSmBgdaO2ZPWqAzi72LF122g2bzrKjGmhTJ/5HsWKOTJ7zgd4erpy8cJNBg6Yzc7dE/Tp814rGjSoQGZmNu+/N4s9EVE0a161UD36vElg+4bv9HkzfjUrl36ez270+NWMG/WmPm8+WUDEvnMENNVPsvbr3Zz3+7Ywsi/m6sCcn95D7enChYu3eP+j+ezZMbJIaaXTKRz64yKtv6uJfXEbNo88hm8dd1x9HIzsyjT0oEHfCo8JpWhE7P2Hq9cS2b5xKKcirzF6/CpWLvsin93o8asYN6o7NWuUZsDH84nYe46AZvr0uBV3h30HzlPCu1iOfXJyGmMmrGbBnIGU8C5GUtL9wrVERHH1mobtW0dz6tRVRo9dzsoVg/PZjR7zF+PG9qRmzTIM+GA2EXvOEtC8KhXKezNr1kBGjVqWz08pX3dC1w41LU32nOXqtQS2bxnBqcirjB4bwsrlX+fXMTaEcWPepmaNMgz48Fci9v5DQLMqNGlUka+/6IClpQVTp4cyd/7ffGuY7Czl607omiEm6XiIVqvjxyl/M/WX7nionfiw1580DvCjTDn3HBu1tzNDRrdjxZ9HCgzjtzl7qVE7/429qUTsv8jV60lsX/M5p87EMnryBlYu+iCf3ejJGxg3rBM1q5VkwOd/ErH/IgFNKjBs/DqGfN6W+nXKsmr9cRb8uY8vPmpFeT9PVi/+AEtLCzSJ9+nU4xcCm1XE0tKiYB0vWf3V6RT2/n6BN4bWwsHNhrXDjlK6jjvFShrX2cy0bM5sjcUzzwT95UMJaLN1vPl9fbIztIR8cxj/Jp44edgVGu/TsOjAJn7etYrF/YrWLpnKi6o3K1ftB2DDumCSku4z4MM5rFrxDSrVkzdmaLU6Zk0OZ8ovXfBQO/FJ72U0DvCjdLncfszTy4nBY9oQ8uexfP6tbSyZ+1evZ0yVXC3zph9g9I9tcfN0YPB766nfrBS+ZXPbzHIV3Jj2e0dsbC3ZuuYfFs8+wjfj9Q/FgnpWJyM9m23rzj+TDpVQMa7JAHpuHkNcahLrg75nx7UjXLwbm2Mz7uDvOZ/7VX2dqm5lAajjWZG66sq0Xf0VAKs7TKChd1UO3ooqsg6B4L2qvZlweCpJ6beZ2HgUxzQnuJFyM8fGy15NJ7/2jDowgdTsBzhbOwFwJ+MuIw6OJ1uXjY2FDdOaTuCY5gR3Mu4+VZpotTrmfL+P8T+/gZunA1/2XUODZmUoVS43bzy8nPhiZAvWLDll5PfI3mtcOp/IrCXdyMrSEvzhBuo2KoW9o7XJ8b/Ifg9g8pTVNGv2+Al6iQRekm1uQogFQojnUlqFEEHPEpYQooQQYpXhcz8hxM/PQ9e/iRDiqhDC3fB5fxH9fiiE6GP43E8IUeTH1pGRVylVygNfX3esrS15/fU6hIcZN6LhYZF0CmoIQNu2r3DwwHkURcHe3oY6dfyxsbbKF26tWmXx9HQpkpbTkddytFhZW9Lu9dqEh5821hJ+mk6d9E+c2rStxaGDF1AUBTs765xBaUZmNuR5kl63nj8urk83gRN9NhGvkk6ofZywsrKgyatlObrnupFNtTre2Njq53orVPXgtuYBAFZWFlhZ6zVlZ2mfy9srwyIu0KldDYQQ1KrmQ3JKOprE/Ddvtar54OnulO96SW9XKvqrESqRz81Urp67jYePAx4lHLG0UlE3sCSn9t80srFzyC0TmenanM82dpb4V3fHyurpmrMzp2MoWcqdkr5uWFlZ0rZdTXaFGw9wdoWfpX2nugC0alOdI4eiURSFg/svUL6CNxUMT9ldXR2wsFBhZ2dNvfr6J0xWVpZUruxDfLxpE11hu84S1L62Pj9qlCb5fhqahGQjG01CMimp6dSqURohBEHtaxO288mDMiEgLS2T7Gwt6RlZWFlZ4OhoW6ieW9HJFPO2w9XLDgsrFZWaqok+nJjPbu+yy9QPKo2ltXE+XDyUgIvaDndfh3x+CuN05FV8S7nnaUtqszM80sgmPDySTp30Ky3atH2Fgwf1bUnlKr54euonaP3Le5OekUVmZhZ2dtY0aKCfTLG2tqRKFV/i4kwbSIbtjCKoQ908eZNeeN50qEtY+JPzpkplH9SGtq28vxcZGVlkZmabpOkhSZeScVLb4eRph4WlijINPYk5lj+fnidhO8/kpkfNMo8vqykZ1KpZJjc9dua2wZO+D+XbL9vnbV7ZsPk4rVtVz5lgcnPL3+7k0xIeSVCnBnottcqSnPwAjca4zmk090hJSadWrbJ6LZ0aEGbom/z8vClXVl1Q0EUiLPw0QR3rG9KkrCFNHtGRcE9fRmoadHSsT1iYvlw3bVI5p9+pVbMMcfFPd5PzkHNnblGiZDFKlHTFysqClm0rsW9XtJGNVwkX/Cp4oiqgDT9/No47SQ+o17DMU2sI232OoDdq6dOkuq++3jzSx2gS75OSmkGt6r76NHmjFmG7zwFw9XoS9Wrr429S34/tO88CYGebp4/OyDYqQwXqeMnqb0J0Mi5edjir9XXWr5Gaq0fz19mjIVeo1aEUFo/0cdkZWnRaHdmZOiwsBVZ2L+758J7ok9xOTS7c8Cl5UfUm+lIcDQyrxNzcnHBysufMmZhC9ZyPiqOEr2tOvWnRpiL7dl0ysvEq4UK58h6oCit4z8jFs4l4l3TGy8cZKysLmr5ajsMRxmPG6kZjRk+SNKk5bjXqlTAaPz0ttTz8uZp8i5j78WTpstlwaS+tS9d/rH1Hv6aEXtoL6FcO2lhYYaWyxFpliaXKgsS0p2vb/F3LEZcajyYtAa2iZf+tQ9T1NF4N1NI3gO3XwkjN1o+dkzP17Y1W0ZKt09dNK5Wl0QrZp+FClMYob5q38edgxFUjG3UJJ8qWd8vXvl6/coeqr3hjYanC1s6KMv7FOXag8LKZlxfZ7+3YcRKfkm6U98+/U+H/PTrFvH//MV6KySRFUforinL2OQUXBDz1ZJKiKDcVRen2nLS8MISeQvNPUZTGRQlXUZRfFUVZbPjaDyjyZJIm/i5eeZ4yq72K5buRjtfcxdtgY2lpgZOTHXfvpvK80Wju4uWVu+JHrXZF84gWTfw9vLxdc7Q4OtnmaIk8dZVO7SfSudMkRo7q/tgnnkXhdsID3NS5N9bFPRxISnjwWPuwjRd5paFPzvfE+FS+7h3Kh0ErCepV7ZlWJQHEJ9zHW537xNPLw5n4hMJXAjxP7iamUSzP7yjmYcfdxLR8drvWXWJEr62snXeatwbVzOf+NCRo7uHllTtJ6al2QaNJfqyNpaV+Eubu3Qdcu5aIEIKPBy6gx5s/sOi3XfnCv5+cRsTuf6jfwLTly/Gae0Zl1kvtSrzm0fpzDy+1y2Ntli7fT4c3ZxA8KoR7yfqy1fbVGtjZWdO09XgCX5vIe32a4+pSeNlJSUrHyS130snJzYaU2xnGei4lk5yUjl9dd6PrmWnZHFp7lcbdy5rwy/MTr7mHt1eetkSdvy3R198ntyXbt5+kSmVfrB+ZpE5OfsCunadp2Mh4W8+T9Hip8+aNy2Py5vE2S5fvo0O36QSPXJGTN3nZtiOSKpVLYm1dtBvDB3cycChuk/PdvrgND+5k5LO7fjiR9cFH2PXjGVKT0osUx6PEa5Kfsqzq69eOnWfw9HShUkUfIz9Xr2lITk6j93uz6fLWDNatL3jFjFE88XfxylNWvLyKEa8xvlmJ19x9JG+KEW/CZE3sjSSCukykV+8ZHD0a/UTbAuvvo/1f/CNlxCt/ugGsXnOQ5nmeCsfeSCKo6xR69f2Ro8cu5bMviMSEFDy9cifjPDydSNSkmORXp1OYM3MXH33ZwiT7xxGfkGxcBjydc8pAjo0mGS9PZ2Mbw0RP+XKeORNLW8POcCtPep46E8Mb3WfR8Z3ZjPmuwxP76Jet/qbeycAhT9vq4GZD6iN1NvHKfVJuZ1CqtnHbWq6BB5Y2Fiz5aD/LPt1PjfalsHV89gkDc/Gi6k2lij6E7zxDdraWmNgkos7GcCvuTqF6EjUpeKrz1Bu1I0kJptUb0G+N/7jXUgb1/Yt9O5/cZhTG7YRU3D1zx4xunk8eM+7YcIHajfJv33xWvBzcuJWSlPP9VmoSXg4Fr2z2cfTA10nN/pv6BwfHNRc4cOsMR3ou5EivhUTEniT67o2n0lHcthhJ6bdzvt9Ov0Nx22JGNt4OXng7eDGm4TDGNRpBTffqOW5utsWZ0mQcswNnsP7y5qdelQSQlPAAD3XuKm13TweSEky7lylb3o3jB2JIT8/i3t00Io/dJMHEtvkhL6rfS01NZ/6Cvxn0sWnHM0j+t/nXJ5OEEA5CiE1CiFNCiDNCiLeEELuEEHWFEBZCiEWG66eFEF8a/AwQQhwx+FkthCjwDkgI0RjoCEwVQpwUQvg9zq/B7aAhnvFCiBTD9TJCiDN5gi0hhNgqhLgohPg+T1xzhBBHhRBRQogxea5fFUKMEUIcN4T92H0tQojRQog/hRAHDOEPyOP2rUF35MPwDdrOCyEWA2eAQted5/ldLYQQu4UQoUKIy0KIyUKInkKIwwadfnk0fSOE6AbUBZYa0vLFrJ1+yalRswyhG4eyPOQbFsz/m4yMrH81/oitl7h8LpGOPavlXHNXOzD9z07MCunKrs2XuHs7/6TL/1daBPkxbslrBA2oxuYl58wtB222jpMnrjBhyjssXPwxO8POcOjgxRz37GwtwYOX8XbPJpT0Lfo2r6fhne6N+HvjEEJXfIGnuzOTp28EIPJMDCqVYM/24YRtDua3PyOIiU0qJLTCUXQKOxddJLBf/nMo9q24Qt0OpbB+gU/MCyP64i1mTg9l1Ji3ja5nZ2v59ptF9OzVAl9f98f4fr68070xf28MJjTkSzw9nJk8bYOR+8XoOKb9sJmxI7q+kPhLvuJOlx8a0nFSPUpUK86+uearQ2lpmcydv4PPP3ktn5tWqyPqbAxzf+7Pgl8H8su8v7lyVWMGleDp4czOsPGsWzOU777rxtff/kZKyotvc+fM3YaFpQUd29fN1bFjDOtWD+G7wZ35evAfL1xHaMgJGjQpi4e68JVhL5IJI4NYtuowXXrPIfVBJtZWuRNGNav5sinkU1b98QFzF+15oX30v11/FZ3CgT+jadTLL5+b5lIyKpWg1y+NeefHRkRuuk5y/P/OWOBxPFpvunZpiJfala7dpzFx8mpeqVUWC4sXf+uzbGN/flnSk6ET2vHL9N3cjHm2FYamsmtrNJfOJRLUs3rhxi+QDn5N2XzlADpF/4r10s5e+LuWpOGyATRYOoDGJapTz+vpztc0BQuhwstezdhDk/np5BwGVuuHvaX+1jEp/TZD9o3gi91DaO7TBBfr/Fvz/w1qN/SlbuNSfPt+KFOHh1GpuhqLZ1jh/zz5efYm+vZtiYND4avXJRJzjPBfA24qivIGgBDCBfjI4FYL8FEUpZrB7eFU6hpFUeYbro0H3gdmPRqwoij7hRDrgY2Kojzcqnb3MX5/BH5UFOUvIcSHT9BbC3gFyADOCyFmKYoSAwxTFOW2EMICCBNC1FAU5eH+i0RFUWoLIT4GvgH6PyH8GkBDwAE4IYTYBFQDygP1AQGsF0I0B64brvdVFOXgE8J8HDWBysBt4DKwQFGU+kKIz4FPgZzDLhRFWSWEGAR8oyjK0YICE0IMBAYCzJ07l/4D9AMeT7Urcbdyn/zEx91BrTbenqb2dOXWrTt4eRUjO1vL/ftpuLoWfRtMYXh6uhptYYmPv4vnI1o81S7E3bqboyXlfno+LX5+Xtjb23Dx4i2qVct/uF1RKO5hT1J87pOL2wmpuBWwuijyyE3W/BHJmNmv5WxtezScUuVc+edkPI1alimShqWrjrJy/QkAqlcuwa343KfEcQnJqD3+3ZsHV3c77uR50nYnIQ1X98fPX9YN9OWvH088l7g9PF2Ii8t9uqmJv4enp3OBNmovV30ZSUnH1dUetdqF2nXKUcxw2HXTZpU4d/YGDRrqJ1bGj15NqVLu9Ozd7Ikali7fT8ga/ZlT1asab7uKi7+bs4XiIWpPF+LyPLXNa+OeZ0vQm13q8+Fn+jMMNm45QbMmFbGyssCtuCO1a5XhdFQsviWfPMnl6GbL/TwrWO4nZeCYZwVMZpqWxOupLB9xHIDUu5msmXSKLsE1uXXxHhcOaNi9OJqM1GyECiytVdR+3bTzV9SeLkZPkePj87cl+vpbcFsSF3eHzz6dx8TJvSlVysPI3+hRf1G6tAd9+hoftv4oS5fvM86b+Lx5c+8xeVOwjXHeNODDTxfmsbvLoC8XMWX825R6iskt+2I2pOZZMfbgdgb2xWyMbGydclcu+Ad6c2y5aatb8rJ0+V5CVuu7n6cvq85cj0kk9sZtOr05zXD9Hl3emsHKZV/gpXbF1cUBe3sb7O1tqFunHOcu3Mw5fDpHy9LdhKzap9dSrTRxecpKXNwd1J6uRvZqT9dH8uYOarWxzaNYW1vlrGirVrUUpXw9uHJVY3RQ6dJlEYSsOmDQUSp/mjza/6kfKSNxxum2Zu0hdu2OYtHCQTnbMPLrcOfK1QSqF9IfuXs4oonLXWmaoLmPu6fjE3zkEnX6JqdPxBK68iRpaVlkZ2mxs7di4GcBhfpdGnKIkHX6s2SqV/ExLgOaZNSPtLFqT2fi8qxWitMko/bQ2/iV8eC3n/sCcOVaIrv2XsgXn19ZD+ztrblwSUP1Krkr3V7m+utQzMZodWBqUgYOeepsVrqW2zGpbBh7EoC0e5lsm3aatt9UJ3qfhpI1i6OyVGHnYo26ggsJl+/jrP7vPPf7N+qNpaUFQ7/rkmPzds8ZlClt3BcUhLunI5r4PPUmPgU3D9PqzUP/ACVKulKzTkmiz2so4fvktuZxFPdwIDHPtrUkTcFjxlOHb7Bq0SnG//J6gWPGZyUuNQlvx9zxgreDG3Gptwu07ViuCSP2zc/5/lqZBpzQXOBBtr6874w5Tm3PihyJ+6fIOm6n38HNNndFVHHbYtxON15tlpR+h+i7l9AqWhLSErmVGo+Xg5rL967k2NzJuEtMSiyVilfgUFyBtzmF4uZhT0J87mqiRE0qbh6m38u89V5t3nqvNgBTh4dRolThZeTf6PdORV5l27YTTJu2luT7afoX39hY0atnC1N/2n8beQB3kTDHNrfTQGshxBQhRDNFUfKuUb0MlBNCzBJCvAY8HFlUE0LsEUKcBnoChZ+Umsvj/DYCVho+F3zymJ4wRVHuKYqSDpwFHo4guwshjgMnDGHm3Vq3xvD/GFCmEH2hiqKkKYqSCOxEP4HUxvB3AjgOVEI/iQRw7SknkgCOKIpyS1GUDOASsN1w/bQJOvOhKMo8RVHqKopSd+DAgTnXq1cvzbVrGmJjE8nMzGbz5mMEtjR+W1lgyxqErtP/jG3bTtCwYcVn3rtcENWql+L6tQRiY5PIysxmy+bjBAYaP7EJDKxGaOhhALZvO0mDhuURQhAbm0R2tv5snps3bnPlcjw+hRxWbAr+ld25FZtM/M37ZGVp2bfjCnWbGt9cXzmfxLwpBxjyfStciucODpM0qWRk6Pd7pyRncC5SQ4nSRTtHCqBnt7qsWzyAdYsH0Kp5BUK3RKIoCifP3MDJwabAs5FeJKUrFUNzI4XEW6lkZ+k4ujOWGo2Nd1hqYnMHdmcO3sLTx/SB3ZOoWq0kMdcTuRF7m6ysbLZtOUXAI28ECgiswsZQ/WAjbPtp6jXwRwhBoyYViL54K+csomNHL+cc3D37p62kpKTzzXcdCtXQ8+3GhIZ8SWjIl7waWJV1G4/r8yPyGk6Odnh6GN94eXo44+hgy8nIayiKwrqNx2nVQq857xkgO8LPUN7fCwBvb1cOHdZPHjxIy+TU6euUK2t8c14Q3v5O3Ln1gLvxaWizdJzbG49/vdybJRsHSwb90ZwP5jbhg7lNKFHBmS7BNfHyd6bHhLo51+u096VBlzImTyQBVKte2lB/H7YlxwkMfKQtCaxOaKj+ZnH7thM0aFgBIQTJyQ/46MNf+fKrTtSubfxk/8cfNnD/fhrfBRe+gqDn200IDfmK0JCv9Hmz4WievLEtPG82HKVVoL7byZ83+rMIkpPTGDhoIV9//gZ1Xnm6LYFu5Zy4H5fGfU0a2mwdVw9q8H1ka0zebW+xxxJxKVH0LbI9325K6MpvCF35Da+2rJ6bHqeu4uT0mPRwtOHkqat50qMaFSuU4MDusYRvHUH41hF4qV1Ys+IrPNydaRVYjWMnrpCdrSUtLZPIyOv4FXCuQ8+eAYSuHUro2qG82qoG60IP6bWcvIKTk12+M/Y8PV1wdLTl5Mkrei2hh2j1SN/0KLdv30er1T9dj4lJ5Oo1Db4ljdO1Z4/mhK4ZQuiaIXod6w8b0uSKoYw8osPDRV9GThl0rD9Mq5b6filiz1kW/LaDOT8PwM4u9yDW/DoSCp0IBqhU1ZsbMXe4deMuWVlawredo3GAaVtuh09oz4rNH7J80wd89EUL2rxR1aSJJICe3RsQuuxjQpd9zKstKrFu00l9mpyO0afJI32Mp7sTjg42nDwdo0+TTSdpFaBf2J10W3+zptPpmPPbbt7uWk+fDjfu5PTRN27d5fLVRHxKGN8kvcz118PPiXtxaSQb6uylA/GUrpNbtqztLek7vyk9ZjWix6xGePo70/ab6nj4OePobsPNKP1NZFa6Fk10Mq5PUZ/Nyb9Rb9LSMnnwQN/u7dt/DgsLC6MXODyOilW8DPXmHllZWnZtP0/jgHKF+gO4n5yec2bWvTtpRJ26aXRwd1EpX9mdWzH3csaMe3dcpl4z40nky+eTmPP9foZOfRXX4i9mQvFUQjRlnb3xdfLESmVJB7+m/H09/xZkPxcfnG0cOabJPfD7RkoiDbyrYCFUWAoLGnpXJTrPwd1F4dK9K3g5qPGwc8dCWNDYuwHHNMYPF4/GH6dKcX374WTliLeDGs0DDcVti2Gl0k/KO1jaU6lYBW6mxD2VDoAKVTy5GXOPuBvJZGVpidgeTYNmpr1VUavVkXxXP7l25WISV6KTqN2g8O2J/0a/t2zJ14SHjSc8bDx9+wTywcC2/zsTSZIi86+vTFIU5YIQojbwOjBeCBGWx+2OEKIm0Bb4EOgOvAcsAoIURTklhOgHtChClM/iF/Qrkh6iBSyFEGXRrziqZ9C8CLAtwI+WwtP40elPBf1qpEmKoszN6yCEKAM8y8FCeX+LLs93Hc+xLFhaWjB8xFv0f/9ndDodXbo2onz5Evz00waqVStNy5Y16NatMUMGL6Jtm1G4uNgzfcb7Of5btRxOamo6WVlawsJOsWDhp/j7ezN16ho2bTxKWlomLQKG0q1bYwZ92r5QLUOHd+OD/r+g1eno3KUh/uW9+fmnTVStVorAltXp0q0RwUP+pF3bsbi42DN1ej8Ajh+7xML5O7C0skAlBMNHdqeY4bXV3369iCOHo7l7N4VWLUbw8aDX6dqtkUnpY2Gp4v2vGjLhy7/RaRUC2/vjW64Yy+efwK+SG/WaleLP2UdJT8ti+nD9K0vd1Y58930rYq/eY/GsIwgBigId3qlKab9ihcT4ZAIa+xOx/xJt3vwFWxsrJg7PTdOgPvNZt1i/+3LD9LdPAAAgAElEQVTqz2Fs3B5FWnoWAR1/olvHWnzavzmnz95k0HerSL6fzs69F/l5QQQbl+V/U88T08RCxduf1mLWkL3odAqN25WhRBlnNvweRamKxajZuAS71l3i3HENFpYq7B2t6TukXo7/YT22kP4gC22WjlP7bvHZlKZGb4J7EpaWFgwZ2olPPliATqujY+d6+Pl7MefnbVSpWpKAwKoEdanHiODldGw3BRcXeyZN1b8a3NnFnp59mtP77VkIAU2aVaJZQGXi4+6ycF44Zcp60uPNHwF4653GdO7W4ElSAAhoVonde8/RusMU7GytmTjmzRy3Tt1nEhryJQCjhgYRPDKE9IwsmjepRHPDm9ym/rCZc+dvggCfEsUYO1w/YdLzrcYEjwzhjS7TUVDo0rEulSoUPqhWWah4tX9FVo09gU4H1Vt5417Kkb1/XcLLzxn/+oU/5X1aLC0tGDa8OwP7z0anU3Lq76yfNlK1WilatqxB126N+W7IYl5rOxoXFwemTde/iXDZ0ghiricwZ84W5szZAsD8BYPIyspm3txtlCunplvXKQD06BFAtzcLP14uoFllfd60n4ydrRUTx+a+mr1T9xmEhujfVDNqWBfDq8Wzad6kYm7ezNxoyBuhz5sR+uP5lizfx/Xricye9zez5/0NwG9zBph08PRDVBYq6vctz47vI1F0Cv4B3riWdODkqiu4lXXCt44757bfIOZ4IioLgbWDFU0+KPztgoWmx55/aP3GRH16jHsnNz3enEboym8M6dGN4OF/6ctq00o0b/rk7Q1+5dQ0a1KRjt2moRKCbl0aUKH8k8tqQEA1dkdE0brtKH29mdg7V0vniTlvYxs18u2cVyQ3b1aV5oa3+P3990nGTQjh9u0UPvjwFypXKsnCBZ9y5Gg0P/20MacfGDP6nSeuog1oXkWvo91YvY7xua9R79RlSs7b2EaN6K5/xXlGJs2bVsk542XchFVkZmXzbv9fAKhZswxjR73FkaOX+OnnzVhaWqBSCcaM7G7Sal4LSxWfDXmVwZ+sQqfT0a5jdcr6ufPbnL1UrOJFkwB/zkXdYsTX60hJzuBAxCV+/3Ufi1a9V2jYphLQpAK7912kdecf9OVkZOfcNOnxC6HLPtanyZD2BI9Zq8+bxuVp3lj/DG3jttMsW6V/4NO6RWW6dtAftnvs1DXmL9qTkyajh7Sn+JPy5iWrvyoLFU36VWDLpFPodAoVW3hT3NeBoysv417WmTJ1H7/CqWobH3b9eo6V3xxCASoGeONW+vk8YCmIZe+NpUWF2rg7uhIzcT2jNs7nt/0bCvdoIi+q3iTdvs/7A+egUgnUni58P7l3/sgLwMJSxaeDW/LdoDXotAqvdapKGT93Fs3ZT4UqahoH+HEuKo7R32wgJTmdA3su88fcAyxc2ZfrV24zc8IOVCqBTqfwdr96zzSZZGGpYsDXjRjzxTZ0OoVW7ctTqlwxls07jn9ld+o3K8UfPx8m/UEWU4fpx4weageGTtW/yn3oh5u4ce0e6Q+y6N9xOZ8MbcorDYt+ppJW0TFy/wIWtxuJhVARcj6Mi3di+KrO20QmXGKHYWKpg19TNhgO3n7I5isHaFyiOtu7/oCCwu7YE4Rdf7rVQDpFx+9nlzC03jeohIqdsXuITbnJm+U7c/neFY5pTnIq8TQ13KsyrdkEdIqOJedDSMlKpbpzGXrVeZuHt1obr2whJuXpJrVAnzcfftuUkZ9tRqdTaN2hIqX9irNk7hHKV/agQfMyXDirYcLg7aQkZ3B4zzWWzTvKLyu6o83WMeSDUADsHaz5ZmxLLCyLtsbjRfV7EklREIry7y7lEvq3g91WFCVdCNEe/RYwV/STM1eBTEVRkoUQ1YAliqLUEkIkol/5cwfYDNxQFKXfY8KfBRxXFOV3w/cC/Rq2ky1WFGWFYbvWDEVRHA0TNhsVRalmmHyqqyjKIENYG4FphrAWo9/+5gFEAkMURVkkhLhq8JMohKgLTFMUpcVjtI5Gf2B4zjY3w+dqwDiglaIoKUIIHyALsH+orZA0zqshxfC7WqDfstbeYLPL8P1oXjeDphRFUaYJITYY0mXnk+IzoOiUsMKtXjAq0Yos3TZzy8BK1ZbIpEnmlkENt2AAlNuLC7F88YjifQiPNe312i+SliUnkpoVam4ZOFjpX/dNmvm1YNeJBVEfm1sF/av+Qrbub3PLwFKlH4ST/vxulp4a2w5MOFK0ydkXwbB6cyFjk7llgM0b+v868/c3qFpBtvn7GyzbcjN1gblVUMLBsKM/eYV5hQA4v/XS1N/px590ksK/w9e1fwVAfNTQzEpAmXPwpak3MSm/mlsFvo768nH29hQzK4EqxYdQen6Xwg1fMNcGrOHtLf3MLYPl7RYBcPHeDPMKAcq7fPXy9Hv6RQ//b8le0tus+9wse/35n0pfc5yZVB39Adk69BMkH6GfoAHwAX4XuW8pCzb8HwEcAhIM/5/0uGc5MF8I8RnQ7Ql+vwCWCCGGAVsB097bDRhWOZ0AzgExwD5T/RZAJPrtbe7AOEVRbgI3hRCVgQOGrV8pQC/0K53+TRYBvwoh0oBGiqLI0x0lEolEIpFIJBKJRCL5H8cc29y2AY8+kmiR53PtAvzMAeaYGP4+jM8vepzfG0BDRVEUIcTbQEWD/6voVwahKMoi9BMqD8Nun+dzv8fEXybP56MUvq0uUlGUPgWE8yP6Q8If5YmrkgrQ4Gj4vwvYled6izyfc9wURRmd5/pqYHVh8UkkEolEIpFIJBKJRPKfRh7AXSTM975m81MH+Fnol/7cRX82k0QikUgkEolEIpFIJBKJ5An8ZyeTDNvT3nzk8kpFUSaY4l9RlD1AzecurACEEO8Cnz9yeZ+iKJ88Y7iHAJtHLvdWFOX0s4QrkUgkEolEIpFIJBKJRPI4/rOTSYZJI5MmjsyN4TDw319AuIW/GkoikUgkEolEIpFIJBLJk9HqzK3gP0XR3kEokUgkEolEIpFIJBKJRCL5n+Y/uzJJIpFIJBKJRCKRSCQSieR5oOjkAdxFQa5MkkgkEolEIpFIJBKJRCKRmIxQFDn7JnkuyIIkkUgkEolEIpFIJP9/EeYW8CLJWvCOWe9prfr/9Z9KX7nNTfLcUNhpbgkIAl8aHYnpS8wtA3fbXvoP2dvMKwTAsi3Zur/NrQJLVWtiU+aZWwYlHQcCMPPkh2ZWAl/W+vWlKSNZOvPrsFK1BUCnhJlZCahEK9K1m8wtA1uLN14aHcBL05agNb8OLFqj3F5sbhWI4n30H7K2mFcIgFW7l6dtfUnSA3hp2nnxUUNzq0CZc5CfIz8ytwwG1ZgDwMnEiWZWArXch3I/a625ZeBk1fmlGgu8LPcV6Mw/JkHVytwKXjxauT6iKMhtbhKJRCKRSCQSiUQikUgkEpORK5MkEolEIpFIJBKJRCKR/G8jD+AuEnJlkkQikUgkEolEIpFIJBKJxGTkZJJEIpFIJBKJRCKRSCQSicRk5DY3iUQikUgkEolEIpFIJP/TKPIA7iIhVyZJJBKJRCKRSCQSiUQikUhMRq5Mkjw39kREMWFCCDqdjm5vNmHgwNeM3DMzsxgyeBFRUddxdXVgxsz+lCzpDsDcuVtZvWofKpWKYcO706xZVTIysujVcxqZmdlotTratK3NZ591MApz/PgVrFm9n+MnfnyhWi5fjuOrLxfk+I+JSeSzzzrQt18rvvxiPleuxAOQfP8Bzk72hIYGmpxuB/dF88OUbeh0Ch06v0Lv95sYuZ88do0fv9/OpYvxjJnShcDWVUwO2xQURWHCpNXsjjiLrZ01kyf0pGoV33x2Z6KuEzxsKenpWQQ0r8Kw4K4IIfjhp02E7TyNSgjc3ByZNKEXak8Xk+Les+cskyeuQqvT0bVbYwYMaGPknpmZRfCQP4k6q8+n6TPew8fHjbt3Uvjii4WcOXONoKCGDB/RPY+fbCaMD+HI4YuoVCo++6I9bdq8YnJ6HN5/hdnTdqLTKrweVI133m1g5B55PJbZ03ZyOTqB4RPbE/BqhRy31vVmUNZfX448vZwYP7OzyfEWxPWTSexbdBFFB5VbevNKUOkC7S4f0rB9RhRdJtbB08+ZC3viOLUhJsc96XoK3SbXxb2Mk8lxP2u5mDJtHTt3ncHKypJSvu5MGt8DZ2d7srK0DB/5F2f/iSFbqyOoYz0+eCTf87J3z1kmT1xjKCON6D+gtZG7vows4ezZGFxdHZg2ox8+Pm7s33eOH2asJytLi5WVBV9/G0SDhvq86tfnJxITkrGxtQJg3oKPcXMrPG327Ili4oSV6HQK3bo1ZsDAtvm0DBnyB2ej9FpmzHgfn5Ju3LmTwhefz+fMmesEBTVkxMi3cvz06T2ThIR72NpaA7Bg4aeFatm35x+mTFqHTqujc7eGvD/A+FW9mZnZDPtuGf9ExeDi6sD3M/rg41M8x/3WzTt07jCFjz5pS9/3Arl6RcPgr3JfLR8bm8THn75Grz4B/6qOh2i1Ot55cyaeahd+ntP/iRoe8rK0JYqiMGHiKnZHROnrzcTej683Q/801JuqDBvaDSEEW7Ye5+fZm7l0OZ6VK76hejV9ndfXm6WcPfuw3tTng0fK3xM1zdxOxP5L2NpaMWlEe6pW9M5nN/PXnYRuOU3y/XSOhw/Ouf77X4dYtf4kFhYqirvaM2FYe3y8TWvjc+KftIbde/7B1taKyRN6PCZNYggevkyfJs0qMyy4i76PmbWZsPDTqFQCt+JOTJrQA7WnC/fvp/Htd0u4eesOWq2O9/oF0rVzgwIU5Mf8bevzT48d4af5cdZmVCqBhYUFQ7/rTN3a5UzQ8vzb+czMbEaNWcGZqOsIIRgW3JUG9cubnEaPY2HvYbSv3gTN/TtUH9fzmcN7EtdOJBHx+0UUnUKVVt7U7VymQLvogxq2TD9D98l1Ufs5A5B4LYWdc8+RmaZFCOg+uS6W1hZPreXkwRss+uEwOp1Cyw7lCepd3ch94/IowjdcxMJChbOrDR8ObYKHl2OO+4PUTL7uGUq9Zr6893XDp9axf+95pk3egE6rENS1Hv36tzByz8zMZlRwCP+cvYGLqz2Tpr1DCZ/ibNl4gj9/j8ixu3ghjiUrP6VipRImx/0ixgJbNh9n3tzt6LQ6AlpU5atvOhUY9/O+pwBo2XIoDg62WKhUWFioWL1mKECB9xTrQocXqEvf36zU9ze2Vkye2IeqVUvlszsTdZ3g4MWkZzzsb97M7W9+3sSly3GsDBmc09/s2/cP02esy0mzb7/tQqOGFU3Kp/8X6HTmVvCfQq5MkjwXtFotY8f+xfwFg9i4aRSbNh4hOvqmkc2qlftwdrZn+9/j6NuvFdOnrQUgOvommzcdYeOmkSxY8Cljx/yFVqvD2tqSRX98Sej6EaxdN5y9e6I4efJyTninT18j+d6DArTonruWcuW8WBc6nHWhw1m9Zih2dta82roWADN/GJDj1qZNbVq3Nn3iQqvVMX3iVqb/0oOlaz9ix9YzXLmUYGSj9nJh2LiOtG5XzeRwi0LEnrNcvZbA9i0jGDf6LUaPDSnQbvTYEMaNeZvtW0Zw9VoCEXv/AaD/ey3ZsPY7QtcMoUVANWbP2WpSvFqtjgnjQvh13ses3zCczZuOER19y8hm9aoDOLvYsXXbaPr0CWTGtFAArG2s+PSz9nz7bf7Jmnlzt1G8uBObt45i/cZh1Ktn+gBWq9Xx0+QwJv3Uhd9W9SN823muXk4ysvH0cmLwmNdo9VrlfP6tbSyZ91cf5v3V55knknQ6hb2/XeCN4Jq8NaM+0fviuR2bms8uMy2b05tj8fR3zrlWoZkXb35fjze/r0fLQZVx9rQt0s0OPHu5aNKoIhvXBbNh7XeUKe3B3Pl/A7B12wkys7LZsC6YNSHfsiJkP7E3kgoMW6vVMX7cSubM+5D1G4ayedMxLj1SRtasOoiziz1bto2kd58WzJi2HoBixRz4ec4HrF0fzIRJvQge8qeRv8lT+7B67RBWrx1i0kSSVqtj3NgVzJs/iA0bR7Bp09F85XXVqv24ONuzbfsY+vRtybTp+nbFxsaKzz7vwLeDCy4TU6e+y9p1Q1m7bmihWrRaHRPHr+GXuQNZu2EIWzcf51J0nJHN2tWHcHa2Y+O2YfTqG8AP0zcauU/7PpSmzXLLb5mynoSs/YaQtd/w16qvsLW1pmUr45uVf0PHQ5b+GUE5P88nxv+olpelLYmIMNSbraMYN+YdRo9ZXqDd6LErGDe2B9u3jtLXmz1nAahQvgSzfhpAvbp+RvZbtx0nMzObDaHDWLNyCCtC9j223uTTdOAS12Jus23lR4z97nXGfF9wGx3YtAIhC9/Nd71yBTWrfn+P9UsG0LZlJabNDjMp3pz49/zD1esJbN88TN+WjFtZoN3ocSsZN/ottm8extXrefqYd1uyYe0QQlcPpkVAFWbP2QbA0r/24uenZv2awfz5+yCmTA0lMyu7UD3mb1tfTHo0aliB9WsGE7p6MBPHvcPwUQWXPWMtL6adX7lqPwAb1gXz+4JPmDJ1LbrncIO26MAmXpv15TOHUxg6rcKuhefpOKwmPWc24MI+DbdjCi4jpzbHoC7vnMevju0/RdFiYEV6zmxA5zG1UVk8/e2WTqvjt+kHCZ7+KjOWdmLfjivEXrlrZFOmfHEmLWzP1MUdaRBYmqWzjxm5h8w/SeVa6qfWAPp2dsr4UH6a8y4r13/Jts0nuXwp3sgmdM0RnJztWLflW3r0bsqsGfq2pl37V1i2+nOWrf6csZPeooRPsSJNJL2IscDdO6lMnxbKwt8/IXTjUBIT73PwwPkC4n7+9zcPWfzHVzn3FQ8pyj1FREQUV69p2L51NOPG9GT02Mf0N2P+YtzYnmzfOpqr1zR5+htvZs0aSL26/kb2xYo5MmfOR2xYP5zJk/oyeMiix2qQvFwIIYoLIf4WQlw0/C/2GDutEOKk4W99nutlhRCHhBDRQogVQgjrwuKUk0mS50JkZCSlSnvi6+uBtbUlr79Rj7CwSCObsPBIgjo3AqBt29ocOHAORVEIC4vk9TfqYW1tRUlfd0qV9iQy8ipCCBwcbAHIztaSna1FCAHoO5ap36/mm2+7FKDl6nPXkpcDB87h6+uOj4+b0XVFUdi65RhvtK9rcrr9c+YmJX2L4VOyGFZWFrR6rSp7dhl3Zt4+rvhXUCNUwuRwi0JY+GmCOtZHCEGtmmVJvp+GJuGekY0m4R4pqenUqlkWIQRBHevnpKmjo12OXVpaBsJEmacjr+Jbyh1fX3d9Pr1em53hxvkUHh5Jp076J81t2r7CwYPnURQFe3sb6tTxw9rGKl+4a9ccYMBA/aoElUpFsWKO+Wwex7moOHx8XSlR0hUrKwsC21Rk/65oIxuvEi74lffIKYsvCk10Ms5qO5zVdlhYqvBrrObqkcR8dkdWXKFWp1JYWBfcnEfvi8evcdEHkc9aLpo2qYylpf5JbK2aZYiL1w9+hRCkPcggO1tLekYWVlYWOBrq+aOcjrxGqVIe+Pq6Y2VtSbvXaxMeftrIJjz8NJ061QegTdtaHDp4AUVRqFzFF0/DCjn/8t6kZ2SRmZlV5HR4SGTk1Rwt+vJah/CwU8ZawiLpFKR/6tu27SscPJC3vPpjY52/vBaVM6ev41vKnZK+blhZW/Jau1fYFX7GyGZn+Bk6BtUDoHWbGhw+eBFF0Z8BEL7jND4+xfHzL7hMHDp4Ed9SbpTIs4Lo39QRH3eXPbv/oXNX05+ev0xtSVh4JEGdTKg3KXnqTafceuPn50W5svnzRghBWlqmod5kPrHe5NMUcYFO7WroNVXzITklHU3i/Xx2tar54Omef2KkYZ0y2BlW8dWs6kOcJr/fJ8a/8zRBHesZ0qRMIW1JGUNbUo8wQ113dMz9nWlpmTl9jBCQmpqBoiikPsjAxcUeSxNu2s3etr6g9HCwt8npl9LSMjClh3pR7Xz0pTgaNNBPvrq5OeHkZM+ZMzE8K3uiT3I7NfmZwymM+OhkXL3scVHbYWGlokITTy4fTchnd3D5ZWp3Ko2lVW4ZuX7qNu6lHfEwTDLaOVmhsnj68UL0P4moSzqj9nHC0sqCxq3KcmSPcVpWq+ONja1+s0n5qh4kJeROfF0+l8Td22nUqGf65E1BRJ2OwbeUm77Nt7KkTbua7A4/a2SzO/ws7TvVBqBVm2ocPhSd0+Y/ZNvmk7RpV7NIcb+IsUBMbCKlS3tQvLg+nxo2qsDf2437dHgx9zemYMo9hb6/aaCvv7XKkpz8AI3mkfqrMfQ3tR72Nw0IM4xd/Py8C+xvqlTxRe3pCkD58t5kPOP4SfKv8h0QpihKeSDM8L0g0hRFqWX465jn+hRgpqIo/sAd4P3CIpSTSZLnQnx8PN5euZOfXmpX4uPvGNlo4u/i7a23sbS0wMnJjrt3UomPv/NYv1qtjqBO42nS+FsaN65MzZplAVi6ZCctW9XI6SCMtTw+vGfR8pDNm47yRvt6+eI9ejQaNzcnypQxfXCZoEnG0yv3iZanpzMJ8UUbqD8r8Zp7eHm55nzX/2bjzig+/h5e6jw2Xq7E5+mwZv64kYBWI9mw8RifD3rd5HjzprVaXSxfvJr4e3g9mk938z8dfEhysn6l2qyfNtKty2S+/GIhiYmmDzwTNSl4qHNvpjzUTiQmpJjsPzMzm496LWFQ32Xs3XnRZH8FkXo7A0e33JsGRzcbUu9kGNkkXL5PSlIGpWu7PzacSwc0lG9s+iqPhzyPcvGQ1WsO0ryZfntm2za1sLO3oWmL4QS+Oor3+rXE1dWhQA0azV0jDWq1K5oCy4jextLSAkcn23xl5O/tJ6lSuSTWeSZzRgxdStfOU/j1l635BrsFaom/m1MWAf6PvfMOi+r4GvA7FKUJSFss2LFQ7MYOgl2JPSaxJCZRU9QUk6jYezcmxhJLEk0ssYOKBSsaewexx4YFFkGaSNu93x93XVg6lmB+332fhweWe+bO2Wln5twzc1XOOdtrlDqXcSWf9vqc0aP/pHu36SxevLNAXdRRhvXilEuZZ5XJWibJT1P5/dcDfPZF3tujdu88T4dOBUdXvi49Zs8M4Jvv/DAqgvP8TRpLotRxOOewIYZRBFFRcYb9RmVLlNpQJjvt29XD3LwELbzH4NN6PB9/1DrPfpNDp+hEyqgy7YyzozVR0S9mZzZtv4BX06oFC2bNPyo+lzIpYCzJJjP/pyC8W09ke1CmjenbpyX/3Iqipc8EunSfxZhR3TEyKnhaW+xj62sqD4C9+0Lp8PZ0Pv1iOdOnvF+wLq9pnK9ZoxwHDl4iI0NDxP0Ywi9H8CjySY40bypyGymp/2xlV5KkGMM2ota1kcoNDNtI3KNnAAROvcBfI05xNvDuS+kSG52MvVNmX7d3suBJdN5j18HtN6jbpBwgR+H9ufA0/YcW/iFnXqjVCaicM+fcTiob1OqEXGSyjPlWZsTHGe4gCN4dSvtORXMmvY65QIUKjty5HcWDBzFkZGg4sD+MyFza6Ota3wgEn3zyEz16TGf9+iM58i3MmiIqKpu9cS6dw5ZEqbPbm9I5bFJ+7Ak+j1stF4P50/88Gql4f16OrsAq3d+rgG6FTSjkpxG+wKaipFecSQpvNMbGRgQEjuVQyAxCQ+9w/foDoqLi2L37HP36Ff5coldFWloGBw5cpEOHBjmuBe04nauT6f8D33zlR8j+ybzt14DVa3MaxX8LjUZLZGQcdetVYdOWUdSpW4m5s7f+a/mv3TGIJav7MXpaJxbPO8TDiMIb7KIiaSWO/XmTpv3zXthF3YjHpIQxdhUKH531qlmydA/GJsZ00T1dCw27i5GR4MjBqezfM4HfVh0kIiJnVMCr4uaNR/wwbxvjJ2WeUzRrzgds3ebPH6u/4uzZf9gWePq15V8Qc+Z+xLbtY1m9ejhnz9wkMPDka8tryaI99PvAGwvLkrleT0/LIORgOO3a131tOuSnR8ihcOzsrHBzz3lmy79NcY8l2QkNu4ORkRFHDk1jf/Akflt54LX2m9zYtjuM8KuP+KTvi5+58qJ881VnQvZP5O3OmTbm76NXqVWzHEcOTiJg8/dMnr6ZpKSUl87rvzC25lYeAG3b1Gb39tEsWvAJPy3c9a/pk32c79mjCc4qW3r2nsv0mZupV7cyxi+x1etNQ9JK/L3qBi0+qJbjmlYj8ehqPO2+dKPnlAbcOhlNRFjsv6LXkT3/8M/VGLr0kY9FCN5ylbpNyxs4o4qTS6H3MDM3pZqr87+ed/a5gI2NBeMm9Oa74Sv5sN9PlCtn96+20bXrvmPL1jEsXz6UtWsOcfq04QPIN2FNcePGQ+bOC2DypD7FqodCkVBJkvR8D2gkkJc30kwIcUYIcUII8dxhZA/ESZL0fL/4faBcQRkqB3ArvBJUKpXBU6fIqDhUKsNtmk4qWx49eoKzc2kyMjQkJj7DtrQlKlXpAtNaW1vQuHENjhwJp2rVMty7F027duMAOcy7XdtxBO+dotOl4Pu9qC5HDl/Czb0CDg7WBvfLyNCwd+95g33PhcHRyRp1ZOaTHbU6wSAy5nWxZu1hNmw6DoCnRwUiIzOdHvJ3Noz4Uqls9OHrAJGRcbkesv1254YM/nwpXxYiOknlZGNQ1lFRT3Lk66SyITJ7PeXzNN7W1hJz8xK0bSs/9Wrfvj5bdN+zMDg4WRlEhkVHJeLgWPjFgqOTXHdly9tSp4ELN66pKetiW0Cq3LG0K0lSTObCKCkmFcvSmYvvtBQNTyKesm3yBQCexaWxe04YHb73xEl3COjNY2qqNS/8k/NX3S62bD3JoZBwVv46VL/9YkfQGVq2qIWpqTH29qWoX68yYeH3cHHJGQHg5GRroENUVBxOubaROH0bSUpM0beRyMgnfDVsBdNn9qdCBccsest1YmlpRme/hlwKu0vXbm/lWzZOKlsiH2Vpr5E526vKKZdxpe+tmikAACAASURBVIDoEb0uVmb4+TUiLFQ+CDpvPWwMykSdS198LqNytjUok7DQu+wLvsiP87aTmPgMIQQlSprwft+WAPx95Co13cphn8tWp39DD3VUPIcOhvP34Sukpmbw9GkK/iNWM2N2v/zLsJjHkjVrQ9iwUT4jxtOzosHTbbnfGI4BKpWtYb+JitNvKciLHUFnaNnSLUu/qULYpdz7DcCaTWfYuO28rFOtsjyKyrQzkdEJqByLZmeOnbrNLyuP8ufi/pQoUfDUcc26I9nGkuxlUsBYkosMwNt+z21MR7ZsPcXgga0RQlCxgiPly9lz63YUtT1zP0z7OcUytv4L5ZGVRg2rEnE/htgnSdhl2575b4zzJibGjB6VeQzBe31/oFLFzDH4TccyWyRSUrZIpbRnGmIinrJlotzHkuPSCJoVSueRtbGyL0lZN1vMreWjRirWtyf6ViIunvlvHc4LO0cLYtSZETYx6mRKO+Ycu0JPP2TLqjAmLmqPqe6w7+uXorkaqmbvlqukPMsgI12LmYUpfT7P+UC0IJycrImKzIwGUkfF4+RknYtMHCpnG3nMT0rBxtZCf33Prou071j0hxWvay7QyseTVj7y+YAbNxzNNbLxda1vnv+2t7emTdu6hIbe1p/Ll9+aYs2aEDZsOgqAp0c2exP5JIctUTlltzdPctik3IiMfMLQYcuYNfNDgzL7/4CkfenooJdCCDEYGJzlX8skSVqW5fo+IDeP7JisHyRJkoQQeX2ZipIkPRBCVAEOCCHCgJwhp4Xgf+cxgUKx4unpyd07au5HPCYtLYOdQafx9a1tIOPrW5uArfIEZs+eczRpUgMhBL6+tdkZdJq0tHTuRzzm7h01tWtXIjY2Ub/VICUljWPHrlClijOtWnny99HZHDgwnQMHpmNuXkLvSJJ1qfjKdXlOUNAZOnfO+aTg+LGrVK7ibBBuWhhqupfl/r1YHt5/Qnq6hv27w2nhXb3ghC9J3z5eBG4ZSeCWkbRpXZuAbaeQJIkLF29TysoMJ8dsRtrRBitLMy5cvI0kSQRsO0VrX9kA37mr1svtPxhGlcqFm2B7eFbk3t1o7t/X1dPOc/j4GNaTj4+nPlIjeM95Gjepnu9ZRUIIWrXy4NQp+QnPiRPXqFot51uL8qKmmzMPIuJ49CCe9HQNB4Ov0cy7cFs6EhNSSEuTnfnxT5IJv/iAilXsC0iVN05VSxEf+YwE9TM0GVr+ORZFpYaZC8eSFiYMWNGCfgub0m9hU5xcrQ0WO5JW4p/jaqoV4UyPV9kuDh+5zIrf9rFk4SDMzTPP7ytTpjQnT8r1k5ycysWLd3Ldsw/g4VlB10ZiSE/LYNfOc/j4GB4M7ePjQWDgKQCC91ygcRNXhBAkJCTzxWdL+Xp4F+pneaNRRoaGJ0/krYvp6RpCDl2immvBbcTTsyJ376qztNez+GQbV3x8axMYcAKAPXvO68eVvMiuy6FDl3Ctnr8u7h4uBmWye9d5vH0MD+dv5ePOtgA52mpvcChvNa6GEIKVq4exa984du0bR9/+Xgwc3EbvSAL5zTYdO9UvsCxelx5fDfdj78EJ7No3jlnz+tOosWuBjiQo/rGkbx9vArf6E7jVX+43gVn6TSnz3PuNVZZ+E3iK1tnaUnbKlLHj5An5PD19v6mSd9/u26shAX8MIuCPQbT2qk7grlBZp0sPKGVZMtezkfLi8rVIJszeyeI5vbG3K1yEQ9/3WxK4WT4Muo2vJwHbTuvK5A6lrPIoE0szLly8oxtLTtPa57mNyTyrZv+BMP14UaaMLcdPXAfg8eNEbt9RU758wWNusYyt/0J53L0Xrd8mG345grS0DErn4jD9N8b5Z8/SSE6WnTFHj13F2NiYakWwxcWNqlop4h4lEx/1DE26lutH1VTO2kYsTRj0W0sGLG7GgMXNcHa1pvPI2qiqWlOhjh0x95JIT9Wg1Wh5cDmO0uVfPDKoak0HIu8noH6YSEa6hmP7b9OwRXkDmdvXY1gx+zgjZvliUzrzHMsvJ3qxeEsvFm7uRb8hDfHqUOWFHEkAbh7libgXw4P7saSnZxC86yJePoZvF/bycWNH4DkA9gdfolHjqvpxVqvVsm9PGO065j/W5cbrmAsAxMTIDw/j45P5a93f9OzVNEfer2N9k5ycqo+iTE5O5ejRK1R3zQwAyW9N0bevN4FbRxO4dbTO3pyU++8Fnb3J/lDHSWdvLjy3NycLtDcJCckM/mwx3w7vSoP6RdvWrPDySJK0TJKkhll+lmW73kaSJI9cfgKBKCFEGQDdb3UeeTzQ/b4FHALqATGArRDi+ROj8sCDgvRVIpMUXgkmJiaMG/8unwxcgFajpWfPZri6lmXBT9vw8KiIb+s69OrVnBHf/067tuOwsbHgh/nyK59dXcvSsWMDOneahLGxMePHv4exsRHR6nhGjVqFRqNFkiQ6dGiQY4GQuy7Gr1wX0A34x64waXLO19EG7TyNXy5OpoJ1NeIb/w4M/3wtGq2EX7c6VKnmxPJFh6jpXoaWrWpw5dJD/L/ZQGJCCkdDbrBicQhrtn5e5LzywtvLjZDD4bTtOBlzsxJMn5r5/br2mEXglpEATBjXW341cGoaXi3c9GcjzPthO7fvqBFGgnJlSjNpwru55pPzuxszZmxvBg9chFYr0b1HE6q5luHnBTtw96iAr29tevZqxqiRf9Ch/URsbCyZOy/zTUNtW48n6WkK6ekZHNgfyrIVQ6hWrQzDv+3GqJGrmDVjM6XtrJg6reDF6HOMTYwYNsKXkUM3o9Vo6djVg0pVHfh9yVFquKlo5l2Nq+GRTPgukKSEFI4f+YdVS4/x28YB3Lsdw/xp+xBGAkkr8d6At6j0Es4kI2MjWnxcnaDpF5G0EjValcHOxZLTG27hWMXaYPGTGw+vxGFlb4a1yjxfubx42XYxZdom0tIz+GjgYgDq1KnE5Anv0vd9L/zHrqFzl+lIkkSP7k2oWSP3KFoTE2NGj+3FpwMXo9Fq9W1k4YIg3D0q4OPrSY9eTfEf+Scd20/GxsaCOfMGALBuzREi7j3mlyW7+UX3hsFlK77A3LwEnw5cTHqGFq1GS5NmNej1TrMCy8PExJix495l4CcL0Wq19OjZVB5XFmyXxxXf2vTq1YyRI1bSvt0EbGwsmPdD5rmFrX3H8vRpiuw03n+RFb8Oo2xZOwZ+8jMZGRo0WolmTWvwzjstCtTDf0wPPh+0DK1WS7fub1HN1ZlFP+/C3d2FVr4edO/ZmDEj1+LXfhrWthbMnvtBgd8vOTmVE8euM27iOwXKvk49XoQ3aSzx9nKX+02HSZibmTI9S5qu3WcQuNUf0PWb0atJSU3Hq6UbXl5yv9m77yJTpm0kNjaJTz//hVo1y/Hr8qFyvxmzms5vT0WSyLff5NCpWTUOH/uHdu8sxqykKdPH+umvdftgOQF/DAJgzsL97AgO51lKOt5dFtCrS12GDfRizsL9JCen8/WYzQCUUdmwZE7vQuUtl4kbIUeu0LbjVMzNSxic5dO152wCN4+Qy2RsL/zHriUlJR2vlrXw0r3pb958nY0RgnJl7Zg0Xm6jX3zWHv8xa3m7+ywkSeK7b97OEYWTG2/E2PoaymPP3osEbjuDiYkRZmamzJ/7YYEvinhd43xMbCKfDF6CkZFA5WTD7Jn9X6issrP248m0ql4fBytbIqZvY8KO5fx2bPsruXdWjIyN8P6kOtumXUCrlXDzKYu9ixUn/rqFU9VSVGmUd6SGmZUpdf0qsGHUGRBQqZ59jnOVioKxiREff9OY6cP3odVoaeXnikuV0mxYfp4qNe1p2LICqxedJeVZBvPHHgLAQWXJiNmtXzjP3DAxMeb70V0Y9ulvaDRaunRvSNVqKn5ZGEwt9/J4+7jRtUdDxvtvoFvHOVjbmDN9TmbbPnfmNipnG8q7FH1e9DrmAvb2pZg5fTPXrslr5c8+70ClXB6Gvo71TUxMAkOH/ALIW6r9/BrR0stdn2dh1xTe3h5y/20/Qe6/0zP7Wdfu0wncKkc2TRj/Hv7+f+jsjTteurz27r3AlGkbZHvz2WJq1SzPryuGsXpNCPfuRbNoyS4WLZG3y/62Ylih3nyrUOxsAz4EZup+B2YX0L3hLVmSpFQhhAPQHJiti2Q6CPQC/sorfY77FebgUQWFQiBJHCxuHRD48Kbo8ThldXGrgYOZbiGTsad4FQEwaU+Gdm9xa4GJUVvuJy0rWPA1U95KjmCdf+GzYtYEvqn7yxvTRtK1xa+HqZF8MLRWKtor0F8HRqI1KZqg4lYDM+POb4wewBszlqApfj0wbosU+0dxa4Gw0zkL0/+9s3ryxLTjmzO2viHlAbwx47z4/N8/dys70pITLAx9dQ/mXpShtZcAcOHx9GLWBOo6jCYxvfjOhntOKdPub9Rc4E1ZV6At/jkJRq2BQr0s8j9L6uzuxeocKTli6wuXrxDCHtgAVADuAr0lSYoVQjQEPpMkaaAQohmwFNAi71L7UZKkX3XpqyA7kuyA80A/SZJSc8lKjxKZpKCgoKCgoKCgoKCgoKCgoPAfRZKkGCBHWKIkSWeAgbq/jwGe2WV0124B+R8img3lzCQFBQUFBQUFBQUFBQUFBQUFhUKjRCYpKCgoKCgoKCgoKCgoKCj8/0ajHAFUFJTIJAUFBQUFBQUFBQUFBQUFBQWFQqNEJikoKCgoKCgoKCgoKCgoKPy/RtIqkUlFQYlMUlBQUFBQUFBQUFBQUFBQUFAoNIozSUFBQUFBQUFBQUFBQUFBQUGh0Cjb3BQUFBQUFBQUFBQUFBQUFP5/o9EWtwb/KYQkKfsCFV4JSkNSUFBQUFBQUFBQUFD430UUtwKvk5TJbxfrmtZs/Pb/VPkqkUkKrw7pYHFrAMIHieLXQ+DD/aRlxa0G5a0Gy38kbS5eRQCser4xZZKiCSpuNTAz7gzAP/E/FrMmUNXmaxLTtxa3GpQy7U5yxvbiVgMLk7cB3ph2cuHx9OJWg7oOo3maHljcamBp2hWADO3eYtYETIzaQsae4lYDTNpDcvH3Xyy6y78T1hevHgDW7ypjaxZKmcp1E5H0SzFrAi5Wn7Ew9PPiVoOhtZcgPm9S3GogLTkBQN/dHxWzJrCmw+9suDGsuNWgt+vPnIueWtxqUN9xLAAJacU/h7Yu0RM0xW/3MG5b3Bq8dpQDuIuGcmaSgoKCgoKCgoKCgoKCgoKCgkKhUZxJCgoKCgoKCgoKCgoKCgoKCgqFRtnmpqCgoKCgoKCgoKCgoKCg8P8bjbLNrSgokUkKCgoKCgoKCgoKCgoKCgoKCoVGiUxSUFBQUFBQUFBQUFBQUFD4/41yAHeRUCKTFBQUFBQUFBQUFBQUFBQUFBQKjeJMUlBQUFBQUFBQUFBQUFBQUFAoNMo2N4VXhiRJTJu2gZDDlzAzK8HMGR/i7l4hh9ylS3fx919FSmo63l4ejBnTGyEEcXFP+Wb4ch48iKFcOXt+nD8IGxtLTp68xhdDllC+vAMAbdvWY+iQzty6Fck3w1fo7xsR8Zgvv/yGDwdU5MjhcKZN24BWq6XXO80ZPLiDgQ5paemMHLGS8PB72Npa8sP8gfr7L126m82bjmJkZMSYsb1p2dIdAF/f0VhammFsZISxsRGbt4wG4MqVCCZOWEtqajrGxkZMmPg+dWr75FtWp47dZtHcg2g1Ep26efD+R40Nroeeu8+iuQe5dTOasdP98G5TXX+tbaMfqFxN1tXJuRRT53cvVP1kRZIkps3ZQcjRa3JdTeyJe61yOeQuXXmA/4RNcl01r8GY7/0QQvD1qHXcvvsYgMTEZ5QqZU7gumH6dA8fxdH5nR8ZOrg1n3zQslA6vUyZADxNSuXjd1bSvFU1vhzZukjlcfTIFWbNCECr0dK9VxM+GWSYPi0tgzGj1nIlPAIbW0tm//AB5crZ6a8/eviE7m/P4vMh7fnw48y612i0vP/OfJxUNixcMrBIOgGcOX6PpfP+RquVaN+1Fr0/rG9wPezcQ5bNP8rtmzGMmtqWFq2r6q/9uuA4p4/eRZIk6r3lwqffNkcIUei8j/19jbkzt6PVSHTr2YgBA1sZXE9Ly2CC/wauXH6Aja0FM+a+T1ldmdy49ojpk7fyNCkFYST446+hlCxpqk/7zdBVPLgfy4aAbwrU4+iRq8yZGYhWo6Vbz8Z8PMg3hx7j/NdxJfw+NrYWzJrXn7Ll7Hj4IJYeb8+mYiUnADzrVGDshF4Gab8a8hsP7sewKfD7QpdLpl6vp80UlQsnHrDyx1NotRK+b7vSrb+nwfUdf4VzYPsNjI2NsLYtyWejm+PobKW/nvw0jW/7BtKopQsff9ukSHkf/fsac2cGotFIdO/5Fh8NNPwect38xZXLD7C1tWDm3L76uunZZS4VKzkC4Fm7AmMm9ARg0IBfePw4Qd9eFi8bhJ29FQVx5MhlZk7fhEarpWevZgwa1C6bLun4j/yT8MvymD/vh48pV86euCdJfP31r1y6dJdu3ZowdlxvAJ4+TaF/v/n69FGRcfi93Qj/0YZtKDuSJDFtxmZCDl/GzLwEM6f1xd3NJYfcpfB7+I9ZQ0pKOt5ebozx74kQgllzAzh46BKmpiZUcHFgxtQ+WFtb6NM9fBhL5y7TGTqkI598lPc4J0kS02Zv143xpsyc9E7uY/zl+/hP2EhKaoY8xo94Wz9O/LnuKGs2nMDYSODdsiYjvu4EwNXrj5gwdStJT1MwMhJsWm3Yv3PVZd5OQo7ekHWZ0B33mmVz6nLlIf6Ttuh0cWXMt50QQnDl2iMmzNxOamoGxiZGTBzpR2338sQnPGP0lADu3Y+lZAkTpo/rRvVqqrwrJwv/a2NrenoGs6dt4+zpWwgjwRdftqN1W89ccs+bU8fusHjuIbQaLR27efD+R28ZXA89d5/Fc0N0NrgTXllscLtGPxrMS6bM71qkvLNy93wMh3+/gaSVcGtdhobdK+Uqd/OEml3zLtF7ZkNUVa0BeHw3iYNLr5L2TIMQ0HtmQ0xKGL+wLvnxa/8x+Hk2R534BM8pfV9LHs+p7eBB/1p9MMKIQ/cPs/32zhwyjZ0b0bNaVyQJ7iVGsCh0KQ5m9nxdbxhGQmAsjAm+t4/9EYdeWI8bZx8TtOwqklaiQbvyeL1T2eD6qZ0RnAyKwMhIUMLcmK5D3XCqYIUmQ0vAgnAe/pOIViNR17cM3r2rvLAeF0484I+fzqDVSvj4VaNrfw+D60F/XebgjpsYGQusbc341L+p3u718VpNhSq2ANirLPl+VtHt77G/rzNv1g60Gi1dezRiwEBvg+tpaRlMGL2Rq7o+PH3O+5QtV5pdOy7w58ojermb1yP5c8MQKlZ0YNS367gfEYORsREtvWsy7JsO2bPNgSRJTJu+iZDD4bK9md4/b3sz+k+dvXFnzOheCCHYtfscCxft5J9bUWxc/x2eHhUB2Lb9NL/+tk+f/tr1h2zdNJJatcoXuaz+i0jKAdxF4o10JgkhkiRJKnj2KMuWBRZIkpT/DK9o+f8KNAQEcB0YIElSkhBiJbBDkqRN2eRbAd9JkuT3qnR4GYQQE4EkSZLmCiEmA4clSdpXQLLnafXlKYSoC5SVJCmn1cqFw4cvceeumuA9k7l48TYTJ61l44ZROeQmTlrLlCn9qFOnMoMGL+TwkXC8vTxYtnw3TZvUZPDgDixbtptly/fw/Xc9AGjYwJWlS4cY3KdKFWcCA8YC8qLdy3sUbdu2RaO5yuTJ6/jt969QqUrzTq8Z+PrWplq1zMnrpo1Hsba2IHjvFIKCTjNv7lbm/ziImzcfsjPoNDuCxqOOiuejj35k957JGBvLQXx/rBpOaTvDpjlnzhaGDOmMl7cHISFhzJmzhdV/fpxnOWk0WhbM3M/sxb1wVJXii/5raOpdjUpV7PUyTs6lGDGpAxv/PJMjfYmSJixb90FB1ZEvh49e505EDMEB33LxUgQTZwSy8Y8vcshNnBHIlHHdqePhwqAvV3H42HW8m9fgx5nv62Vm/rATK6uSBulmzg+iZbPq2W+XJy9bJgC/LzlK7XpFN3QajZbpU7ewdMVnqFQ29Hl3Pq183KlazVkvs3XzSaytzdmxZwy7dp7nx3k7mPNDZh3MnR1Ii5a1ctx7zZ+HqVLViaSk1BfSa/HsI0xb+DYOTpZ8/eFmmrSsRIUqmQ4JJ2crho/3ZfPqCwZpL4dGcjk0kkVr5YXx94MCCDv3kNoNci4m88p71tRAFi3/BJWzDR+8uxAvn1pUqZq5UAvccppS1uYE7PqePTsv8vMPu5kxrw8ZGRrGjVrP5Bm9qV6zLHFxTzExyZzMH9h7CQuLEoXWY+a0rSxZPhiVyoa+7/6Et4+bQd0EbD5JKWtztu32Z/fO8/z0QxCz5vUHoLyLPeu3DM/13vv3hmFhUTLXa4XR63W1maKg1Wj5bd4JxvzYDnsnC/wHBtGwhQvlK9vqZSq52jHjVz9KmpkQvPUqaxad5espmRPfDcsvUKtu4RbgWZHbyFYWLx+EytmGfu/+jLePm0EbCdhyCmtrc7btGsmenRf46YedzJrXD5Dr5q/NuTsTp818HzePnBPi/HSZNmUDy38dikply7u95+Dj40m1amX0Mps3HcfaxpzdeyayM+gMP8wNZN78jylR0pRhX/px88ZDbtx4pJe3tDRjy1Z//ed3es6ibdu6Bepy+Mhl7tyNJnjXOC6G3mHi5A1s/OvbHHITJ29gyqT3qFO7EoM++4XDf1/Bu6UbzZvW4Nuv38bExJg58wJZunwv33+buTCfOXsrLVu6FazH39e4c+8xwYHfcTEsgonTA9j455AcchOnBzBlXE/qeLowaOjvHD56He8WNThx+h/2H7rCtvVfUaKECTGxSQBkZGj4fux65kzpTc0aZXmSrX/nqsuxG9y5F0Pwlq+4eOk+E2duZ+PKT3PqMnM7U8Z0pY5HeQZ99SeHj93Au3l15vwczJCBrfBuXp2Qo9eZsyCYP5d+zC+/H6ZWdWcWzXmff+5EM3nWDlYt+ajAsvlfHFt/W3qQ0naWbAn6Dq1WS0L8s0Lpk1Wvn2ceYNbiHjiqSjGk/1qaeVelYg4b3I4Nf57Nkb5ESROWrutXpDxzQ6uROPTrNbqNq4eVXUnW+5+hSkNH7FwsDeTSnmVwcWcEKlfrLGm1BC8Ip+0wNxwrleJZYjpGxq9vA8bK40EsPLSJPwaMf215AAgEA9z6M+P0XGJTYpnSdDzn1Bd48PShXkZloaJLlc5MPDGd5IxkrEuUAuBJahwTT0wlQ8qgpHFJZrWYyln1BeJS44qsh1YjsX3JFQZMbYC1vRm/fHOCmo0dcaqQOSeu3aoMb3WSx+4rJ9XsWnGNDyc34NLfUWSkSwxb1Iy0FA0/f3GU2t5lKK0yfwE9tPz+wylGz2+DvZMFYwbuokGL8oZ2r7od01Z0oqSZCXu3XmPt4nN8NdkLgBIljZm58sWXaxqNltnTtrFw2ceonK358L3FePnUzNaHz2Btbc7Wnd8RvOsiP8/fzYy579PRry4d/WQ7cvN6JN99tZoaNcuS8iyNfgNa0PCtqqSnZ/DFwF85euQazVvWyFeXw4d19mb3BNneTPqLjetzPhybOHk9Uyb3ke3Np0s4fOQy3l7uVHcty88LBjFh4joD+S5vN6LL240AuHb9AUOGLf9/40hSKDr/+W1ukiQ9fJWOJB3fSJJUR5Kk2sA9YOgrvv8rQQhRoDNQkqTxhXUk6eSzlmddoFNh0+7fH0q3rk0QQlC3bhUSEp6hVscbyKjV8SQlpVC3bhWEEHTr2oT9+y5mpu/WFIBu3ZqyT/f/wnD8+FVcXBwoV64coaF3qFDRCRcXR0qUMKFT50bs3x9qqOuBULp1l/Nq374+x49fRZIk9u8PpVPnRpQoYUp5FwcqVHQiNPROvnkLIUh6mgJAYmIKTk62+cpfDY+knIstZcvbYmpqjE+7Ghw7dNNAxrmsDVVdHYv0tLMo7A+5TLfO9eS68qxAQlIK6ugEAxl1dIJcV54V5LrqXI/9hy4byEiSxK59Yfh1qKP/376DlylX1g7Xqk6F1udly+T6lSiexCbToEnFQuf5nEth93Cp4EB5F3tMS5jQoWM9Dh24ZCBz8MAlunSTDWvbdrU5deIGkiQ/uTiwL4xy5eyomu2JeFRkHEdCrtC9Z9GiPfTfKVxN2fI2lClnjampMV7tqnH88B0DGVVZayq72mNkZFgmAkhPyyAjXUt6uoaMDC22dhYUlvCwCFwq2MtlYmpCu451CDlgWPchBy7j11V+mt+6nQenTt5EkiROHLuBa3VnqusiD2xtLfXO2OTkVNb8cYRPPjWMLsqLS2H3cHGx19dN+051OXQw3EDm0IFw3u7aEIA22eomL5KfprJ6VQgDPy1aBJuBXq+hzRSVm1ceoypvjapcKUxMjWnWujKnj0QYyHg0KENJM9lUuLo7EhP9VH/t1tUY4mKfUbtRziiRgrgUFkH552VgakL7jnU4dCB73VzGT1c3rdt5clrXRl41YaF3cKnggIuLgzzmd6rPwQOGY/6BA6F07SpHO7ZrX48TJ64hSRIWFiVp0KAqJfKJrLlzO4rY2EQaNKyap8xz9h8Io1uXt+SxtU5lEhKfoY7OZgej40l6mkLdOpXlsbXLW3ob1aJ5Lb2DoG6dSkRGZS769u0PpVx5e1yzOC3z1CPkMt386st61K6g0yOXMf5pKnVr68Z4v/rsPyTX4bqNJxj8kTclSshtx173EOXo8RvUcHWmZg25zZTO0r/z1uUq3TrX1dkbFxISU1A/TjTU5XGirIuni87e1GV/yFUAhICnT2WHfGJSCk6O8kL5n9tqmjSUIyOqVnLkwaM4HsckFVg2/4tj67atZ/SRgUZGRtiWNnS+FMS18EjKZrHBrdrV4OihfwxknMvaUMXVEaPXNC8BiLqZgK2zBTYqc4xNjaje3IlbZ6JzyJ346xb1x29q5gAAIABJREFUu1bExDSz7d27GItDRSscK8ntw7yUKUbGr0/XIzcvEPs0oWDBl6SqbRWiktVEP4tGI2k4EXmKBqp6BjK+5b3Ye+8AyRnJACSkyf1LI2nIkDIAMDUyQfDi5XH/ejz2ZSywc7bAxNQITy9nrpxQG8iYWWQuS9JT5OgwkPtwekoGGo2WjDQNxiZGlLR4sXiGm1dicC5fSm/3mrapyJm/De2ee31nvd2r5u5IbHTyC+WVG+Fh93V92A5TUxPadqxNyMErBjKHD16hcxe5D/u29eD0yX9y2L49uy7SrmNtAMzMS9DwLdm+mJqaUKNWWdRRBbet/QdC6da1EPYmKYu96Zppb6pWdaZK5fznIUFBZ+ncsX6+Mv9zaKXi/fmPUShnkhCikhDiqhBijRDiihBikxDCQgjRQAgRIoQ4K4TYI4Qoo5M/JISYL4Q4o5NvJITYIoS4IYSYmuW+Abq04UKIwbnk6yCEOC6E6KzT4YgQ4pzup1kW3S5l+Ts3mVY6nTZl+R55jqiSJCXo0gnAHMhas2103+u6ECKHa1sIMVEI8Zsuv1tCiC+LWq66a/mV7Y9CiDPAV3nXmj6PlUKIXrq/7wghZgghLui+Q33dvf8RQnyWtTyFECWAycC7Ovl3C8orKioO5zKl9Z+dnW2JiorLKeOcu0xMTAJOTjYAODpaExOTOZBeuHCLLl2nMHDQz9y48ZDsBO08g1/nRro8nlAmax4qW6KinhjIq6PiKKPT1cTEmFKlzIl78jTftALBJ5/8RI8e01m/PjNMdfTod5gzezOtvP2ZPWsTw4d3y7ecHquTcFSV0n92VJXicXTBE+DnpKVl8Hm/1Qz9cC1/H7xR6HRZiVIn4Kyy0X92drImKttCIyo6m4zKmii1ocyZ83ewt7OiUgU5vP1pcirLV4UwdHDhnAXPeZky0Wolfpl/iM++9i5YOBfUUfE4O2c6AJ2cbYnK7gTNImNiYoxVKTPi4p6S/DSV3389wGdftM9x39kzA/jmO78ci5HCEhP9FAdV5oLAwcnSwBGQH7VqO1O7QTn6dVpFv45/0KCJCxUqly44oQ61OgGVc2bdO6lsUGere1kmS5lYmREfl8y9u49BCIYO/pW+7yxg1W8h+jRLfg6m34ctMTPLe+FukEdUPKoymXWjUtkSHZXTQW1YN+bExckTxwcPYnmv5w988uFizp29pU+z+Ofd9B/gjbl54SKkctPrdbSZohIbnYy9U2YbsXey4Ek+beTg9hvUbSJHUGi1En8uPE3/oQ1fKO9odTzOBbSRrDLP20jWunm/148MHLCEc2dvG6SbOG4j7/Wcz/Jf9hXK+RSljjcYt1Wq0kRlbydR8Xr7pB/z4wrXn3buPEeHjvUL5dyPUhu2DdmGGOoSFRWPsyqLTC7tB2DzlhN46aKQnj5NZfmv+xj6ecdC6RylTsimh02O8TtKnYCzk02uMnfuPubM+Tu8038R/T5ZSmi4vFi7fe8xQgg++eJXur+/gOUrQyiIHLbEKactkXWxNpTR2aTRwzsxe0Ew3p3nMuunPQwf0haAmq7OBOsWcaHh93kYGU+kuuAF2P/a2JqYIEchLVkYTN93FjBy+BpisjnrCuKxOgknAxtsRUwR5yVf9FvD0A/XcfTgzYIT5MHT2FSs7DMjRq3sSpIUYxjZq76VSFJMKpUbOBj8P+6RXA6BUy/w14hTnA28+8J6vEnYlSxNzLNY/efYlFhKlzRsc86WzpSxUDGh8WgmNRlLbYfMbV92ZnbMaD6ZBa3mseP2zheKSgJIiEnBxtFM/9nGwYzEmJxR1yd33OOHgUfY8/t1Og+uCYB7cxWmZibM7h/C3I8O07xHJSxKFW4ekJ0n2e2eoyVPovOOxDu04yZ1Gmc+MElP0zD6kyDGDd7F6cP3ipx/tDreoA+rVDZER2Xvw5kyWftwVvbuDtM7k7KSmPCMI4eu0qhxwQ8votTZ1lSqPNZdqmw2SV34NrBz9zk6d36xeYLC/w+K4hauAXwiSdJRIcRvwBCgO9BVkqRonbNhGvB8f0+aJEkNhRBfAYFAAyAW+EcIMV+SpBjgY0mSYoUQ5sBpIcRm3f8RQqiAbcBYSZL26pwsbSVJShFCuALrkLeiZUWdj0w9wB14CBwFmgN/5/VlhRC/I0flXAayxqhXAt4CqgIHhRDVckleE/ABSgHXhBBLJElKzyOr7OX6hRDiJ+Bn8i7bEpIkvWjPvidJUl0hxHxgJXI5mAGXgF+eC0mSlCaEGA80lCQp18gsnQNwMMDSpUtfUJ3cEULoJ+7u7hU4cGAalpZmhISEMWToEoL3TNHLpqVlcODARb4twInzsqxd9x0qVWliYhL4+KOfqFLFmUaNXFm37jCj/N+hffv67Np5hrFj/mTlylcdLJdFjx2DcHQqxcP7cXz32UaqVHOkrEv+0VCvix27L+LXPtMYLly6nw/7NMfyBbcPvQjbNl7greaVDZxR/xZLFu2h3wfeWFgaft+QQ+HY2Vnh5u7C6VMvPrF+UR5GxBNx5wl/7JC3VY0Zup1L5x/iUa/oEShFRZOh5eL5O/zx11DMzEz5fOAKarmVw8bWgvsRsXw78m0ePogt+EYviYOjNbv2jcXW1pLL4fcZ/uXvbAr8ngcRMURExPDdqK7/ih7ZyavNvG6O7PmHf67GMHGRfA5D8Jar1G1a3mBS/m/h4GjNzr2j9XXz7Zer2Bj4LVZWZkyb9T5OKhuePk3h+6//JGjbOfy6NvjXdczKrl1nmTnr5bYWF5UlS/dgbGJMFz/Z1C9cvIsPP2iF5b/UbjQaLfHxyWz44wvCwu/z9Yi17N8xAo1Gy9nzd9i0eijmZqYM+HQFHrXK0bRxblOhV8O6zafwH96B9r7u7Nx7iTFTAli5eACDP2zJtHm76NpnMdWrqahV3RnjF3TeF5Y3cWytXqMMUVHx1K5bkeEj/Fi96gg/zt3JlJkFPgN8ZazdMRAHJyse3o/j+882U7maw2uZl0haib9X3aDNkJxbhLUaiUdX4+VzkkoaEzDpPE5VSuHiaZfLnf63MBZGqCxVTD01Czuz0ox7y59RR8eSnPGM2JRY/I+Ox7akLcPrDeNk5BkS0l5fRFVjvwo09qvAxUOPOLT+Fj2He3L/ejxGRjDiD2+eJaWzYuRpqta1w8658FF9L8KRPbe4dTWG8Qszz9D7eVMP7BwtiHqQyNSv9lKhamlU5f7d+eOl0AjMzEyp5moYZZqRoWHMiPW827cZ5V2Kv91evHgHczNTqru+/vFN4b9LUZxJEZIkHdX9vRoYDXgAe3WLfmPgURb5bbrfYUC4JEmPAIQQtwAXIAb4Ugjx/PRgF8BV939TYD8wRJKk54+9TIGFQj7HRwPkdiBLfjKnJEm6r9PhArJTKE9nkiRJHwkhjJGdOu8Cv+subZAkSQvc0H2XmrkkD5IkKRVIFUKoARVwP4+sspfrl8Bu8i/b9XnpXQiy1ouVJEmJQKIQIlUIUSTLL0nSsho1apgCg4KCgvD0tCfyUWYEUGRkHCqV4S1VKlsiI3OXsbe3Rq2Ox8nJBrU6Hjs7eXC3ssrcU+3t7cmkSeuIfZKEXWk57P7wkUu4u1XAwcFal0dpHmXNIyoOlcrwKY6TypZHj57g7FyajAwNiYnPsC1tmW/a57/t7a1p07YuoaG3adTIlYCtxxkzRj4/oUPHBowduzrfcnNwsiI6KvOpYXRUIg6OhToiDABHJ7lcypa3pU4DF25cUxdq0rZmw3E2bJXPG/J0K0dklqflkeoEVI7WBvIqR2tDmagEVFmeHGdkaNh7MJwtqzN9jRcvRbBn/yXmLthNQqJ8OGvJkib0e7dpvrq9TJlcDn1I2PkHbNt4kWfJaWRkaDE3N2XQl16FSu+ksiEyMvMpjToyDlWWp/VZZVTOtmRkaEhKTMHW1pKw0LvsC77Ij/O2k5j4DCEEJUqaoI6K59DBcP4+fIXU1AyePk3Bf8RqZswu/JkS9o6WPI7KfFr+WP0Ue8fCLfyPHbpFDQ8V5hbyk7+GzSpwJSyq0AseJydroiIz614dFY+Tk3UuMnGonG3kMklKwcbWAieVDfUaVNZvs2jesgZXLz/AwqIkV8Lv83a7mWg0WmJjnjJ4wFKW5XJ2ij4PlQ1RjzLrJioqDkdVtrpxyl43z7C1tZDrQrdFx829POVd7Ll7J5rwSxFcDr9Pp7bTdHokMXDAYlaszHlmWH56veo2837fwh1UnxU7Rwti1JltJEadTOlc2kjo6YdsWRXGxEXtMdUdRnv9UjRXQ9Xs3XKVlGfyth0zC1P6fF44x42jkw2RBbSR5zL6MkhKybNu7t2Jxs3DBSdd/VpamtGhcz0uXYoo0JmkcrIxGLejop6gyt5OVDZEZh/zbQvuT1ev3keTocn1ZRLPWbP2MBs2HQfA06OCQduQbYihLiqVjcH2tchs7WfL1pMcCgln5a9D9Q9VLobeYU/wBebO20ZC4jOMhKBkCVP69c0c59asP86GLadkPdzLZ9Mj3mD8BlA5WROpjs9VRqWyoW1rD4QQ1PZwwchI8OTJU5ydbGhUvzJ2uv7t1aIG4Vcf5HAmrdlwkg0B8tk6udqbXHVJMJTR2aStOy4w5lt5x33HNu6MnRYIgJWVGTMmyFNISZJo3XU+LuUKjhL6XxtbGzWuipm5Kb5t5JeFtGnnybYtpwulz3McnKxQG9jgJOyLMC9xcJJl5XlJeW4Wcl6SHctskUhJ2SKV0p5piIl4ypaJ5wFIjksjaFYonUfWxsq+JGXdbDG3liNOK9a3J/pW4n/emRSb+gR788zvYGdmx5NUw2j72JQn3Iy/hUbSEP3sMY+SI3G2cOZWQmbUZ1xqHBFJD6hZujqnonI/ezI/rO3NiI9O0X+Of5xCKfu8ndueXs5sX6yLHAyJxLWBA8YmRljZlqRiLVse3Eh4IWdS6ex2L/oppR1znr0UdvoRAX+EMX5hO73dA9luAqjKlcKtnoo712OL5ExydLIx6MNRUfE4qrL3YVkmex9+TvCuUNp3qkN2pk8KoEJFe/r0b55n/mvWhrBh4zEAPD0rGq6povJYd0Vls0kFHMfxnKBdZ+nc6f9hVJJGW9wa/KcoyplJ2WPNE5GdRHV1P56SJGV9fcpza6DN8vfzzyZCPrS6DdBUkqQ6wHnkCBmADOAskHUfwDdAFFAHOdoot/0J+clk1UFDIRxpkiRpgL+Anln/nV0sl6RFySu3+wnyL9vCxWPnTr71UtSbXbt2bdG1a9fqBgYG0qZ1XQICTyBJEhcu3KJUKTP9trXnODnZYGVlxoULt5AkiYDAE7RuLUe2+PrWJiBAnpAHBBzX/z86Ol6/1SE09DZaSaJ0lkVAUNAZOuu2uIE8uN69o+Z+xGPS0jLYGXQaX1/DUFJf39oEbJXz2rPnHE2a1EAIga9vbXYGnSYtLZ37EY+5e0dN7dqVSE5OJSlJNqLJyakcPXqF6q7ldN/JllOnrgNw4sQ1/duj8qKmmzMPIuJ49CCe9HQNB4Ov0cy74HBWgMSEFNLS5P3v8U+SCb/4wOCAzPzo27spgeuGEbhuGG1auREQdF6uq7B7lLIywymbM8nJ0Vquq7B7cl0Fnae1d+bBr8dO/UOVSo4G2xfW/vopB3aM4MCOEXzYpxmfftSqQEcSvFyZjJ7WmXU7B7N2xyA+/dqbtp3dCu1IAnD3cOHe3Wju348hPS2D3bvO4+1j+GaQVj7ubAuQJ+d7g0N5q3E1hBCsXD2MXfvGsWvfOPr292Lg4Da837clXw33Y+/BCezaN45Z8/rTqLFrkRxJANXdnHgYEUfkgwTS0zUcDr5Jk5aVCpXW0bkUl849RJOhJSNDQ9i5h0XaiuHmUZ6IezE8uB9LenoGwbsu4uVjeOivl48bOwLPAbA/+BKNGldFCEHT5q7cvBFJyrM0MjI0nDtzmypVVfR6rwm7D45he/AoVvzxGRUqOeTrSAJd3dx7zANd3ezZeYFWPu4GMt4+7mwPlCfG+4JDaaSrm9jYJDS6CcH9iBju3X1M+fL29H6vGXsPjWfn3jH8/ucQKlZyKJIjSa/XK24zL0LVmg5E3k9A/TCRjHQNx/bfpmELw0Myb1+PYcXs44yY5YtN6cwJ95cTvVi8pRcLN/ei35CGeHWoUmhHklwG5Ym491jfRvbsuoh3tjbi7ePGDl3d7A8O09fNk+x1c+8x5VzsycjQ8OSJbN7S0zUcCblCtUKcK+XhWVFXH7oxf+c5fHwMx3wfH08CA08CELznPI2bVC/UtrWdQWfpVECIf98+XgRuGUnglpG0aV2bgG2n5LH14m3d2JrNDjraYGVpxoWLt+WxddspWvvKb946fOQyK37bx5KFgwy2Ya7982sO7J3Igb0T+bC/N58ObmvgSALo+25TAtd/ReD6r2jj407AjnOyHqH5jPGWJbkQqhvjd5zTj/FtWrlx8rR8Zs7tu9Gkp2soXdqSFs1cuX4zkme6/n367G2qVclZR317NyZw7RcErv2CNq1qEhB0QWdvImRdHAwXb04OpWRdwiJ09uYCrb1r6vQsxalzdwA4cfoWlXRP7BMSn5GWLtvDjQFnaVivIlZWZhTE/9rYKoSgpXctzp6Wt/KePnmTylWLdh5bDTdnHkQ80dvgQ8HXaOZduLdtGc5LnhF+8WGh5yXZUVUrRdyjZOKjnqFJ13L9qJrKDTO3s5W0NGHQby0ZsLgZAxY3w9nVms4ja6Oqak2FOnbE3EsiPVWDVqPlweU4Spf/9yMvXzW34m/jbOGEo7kDxsKYJs5vcVZ93kDmjPoctezk/mJlakUZC2fUz9TYlSyNqZHs+LQwsaBGaVcePY18IT3KVbcm5mEyTyKTyUjXEnY4kpqNDee7MQ8ylyfXT0djX1Z2oNg4mnErVI4ETkvJIOJaPI4vWDdVa9oTGZGot3vH992lQXPDFzbcvh7Lijkn+G6mj4HdS0pIJT1NA0BCXArXw6IpV8lwfC4IN49y3Lubafv27grFq5VhpFzLVjUJ2ib34QN7L9HorSp6e6PVatkXHEbbDoY2asmCYJKSUhg+snO++fft403gVn8Ct/rL9iYwi70pZZ67vbHKYm8CT9HaN+f2uuxotVp27T5H507FGxms8OZTFOdBBSFEU0mSjgN9gBPAoOf/E0KYAtUlSQrP/zZ6bIAnkiQlCyFqAllPqZWQt3RtFEKMlCRplk7+viRJWiHEh8jROrndsyCZfNGdk1RVkqSbur+7AFeziLwjhFgFVAaqANey6V5Uspfr37p7Or5E2b4qEpG36hUKb28PQg5fom27cZiblWD69A/117p2m6p/89qE8X3wH72KlJQ0vFq64+UlL8IGD2rP198sZ9Pmo5Qta8+P8wcBsrNn3V+HMTY2wsysBD/MG6gflJOTUzl29AqTJ2W+ltXExJhx49/lk4EL0Gq09OzZDFfXsiz4aRseHhXxbV2HXr2aM+L732nXdhw2Nhb8MF9+bbura1k6dmxA506TMDY2Zvz49zA2NiImJoGhQ+RdgBqNFj+/RrT0khe1U6b0Y9r0DWgyNJQsacrkyfm/ItbYxIhhI3wZOXSz/Arerh5UqurA70uOUsNNRTPvalwNj2TCd4EkJaRw/Mg/rFp6jN82DuDe7RjmT9uHMBJIWon3Brxl8MazQtdVixqEHL1G267zMDczZfrETH9p1/d/JnDdMLmuRnXBf+ImUlIy8GpeHa/mmcF+O/eE0rl9zicrL8LLlMnLYmJijP+YHnw+aBlarZZu3d+imqszi37ehbu7C618PejeszFjRq7Fr/00rG0tmD339W95MTYx4vPvWzL2yx1otRLt3q5Jxap2/Ln0FK61HGniVZnrl9VMGbGbpIRUTh65w+plp/ll/Xu08K1C6JkHfNFnPQhBgyYuNC7kYgnkMvl+dBeGffobGo2WLt0bUrWail8WBlPLvTzePm507dGQ8f4b6NZxDtY25kyfI7/hz9rGgr4ftOSD9xaCEDRvWYMW3rkFcBZOj5FjuvPF4OVotRJduzeiajVnFv+8Gzd3F1r5utOt51uMHbWOLh1mYG1jwcy5stPu3JlbLFm4BxMTY4yMBGPG9zR4MvgyvCltxtjEiI+/acz04fvQarS08nPFpUppNiw/T5Wa9jRsWYHVi86S8iyD+WMPAeCgsmTE7Bc7eDwrJibGjBzdlSGfrkCr0dJFVzdLFu7Bzb083j7udOvRiHH+f9Gl4yxsbCyYMacPAOfO3mbJwmBMTIwwMhKMHt8DGxsLniWnMeTTFWSka9BqJRo3qUb3Xo0LpcuYsb0ZPHARWq1E9x5NqOZahp8X7MDdowK+vrXp2asZo0b+QYf2E7GxsWTuvMy3frVtPZ6kpymkp2dwYH8oy1YM0b8Jbs/ucyxZ+nmhy8Xby42Qw+G07ThZtoNTM+1B1x6zCNwyEoAJ43rjP2YNKalpeLVw05+NNGXaJtLSM/ho4GIA6tSpxOQJRd+u5N2iBiF/X6Vtlzm6Mf6dTD3e/YnA9fKRixP8u+E/YSMpqel4Na+BVwv57UE9uzVk9MRN+PWaj6mpMTMnv4MQAhtrCwb0a0mvfgsRQuDVogatWubfv+W3sN2gbfcfZV3Gd9df69pnMYFrZWfuhJF++E/aKuvSzBWvZq5ymYzpyvR5O8nQaClZwoTJo+W32/1zO5pRk7YC4FrFiWnjCrfV/X9xbP1yeEfG+69n3swdlLazZMLUd/JTI9cyGTbCl1FDt6DVSHTo6k6lqg6sXHKM6m4qmnlX5Wp4JBO/266zwbdYtfQ4v278kHu3Y5k/bR9GRgKtVuK9AY1e2JlkZGyE9yfV2TbtAlqthJtPWexdrDjx1y2cqpaiSiPHPNOaWZlS168CG0adAQGV6tnnOFfpVbL248m0ql4fBytbIqZvY8KO5fx2bPsrz0craVl5eQ0jG36LkTAi5P4RHiQ9pGe1btyOv8O56AuEPr6Ep4MHs1tMRStJrL22nqT0p3jYV6RvzfeQJAkhBEG3dxORlNdGifwxNjbC77OarBp/Dq1Won7bcqgqWrF/9U3KulpTq7ETJ3ZE8M/FGIyNjTC3MqHHN/L8vnFnF7b+GM6CL46CBPXblMW58ottLTM2MWLA8LeYMXw/Wq1Eq87VcKliy8YVF6hc056GLVxYq7N7P407DIC9ypLvZ/nw8G48K+acRAiBJEl06edu8Ba4wmBiYsyI0V348rPf0WgkunRvoOvDe3V9uBZdezRkgv9Guneai7WNBdNmv6dPf/7sHVTONgbb2KIi4/lt+SEqVXakX+9FAPR+vwndejbKkX9WvL3cZXvTYZI8tk7LfGjZtfsMAnVvJJ0wrjf+o1fLY2tLN7y8ZHuzd99FpkzbSGxsEp9+/gu1apbj1+XyboPTZ25Sxrk0Li6vrw+9qUj/wUOwixNRmMMthRCVkLdenUE+++gy0B95G9kCZCeOCfCjJEnLhRCHgO8kSTqji0D6TpIkP929DgHfIW+zCkDebnYNsAUmSpJ0SAiRJEmSlRCiJPK2rEBgL7AZ2dG0G3kLnJVOtx2SJHnozknKTSa7DguBM5IkrczluxoBRwBr5Aihi8DnkiQlCCFWAinIUU/WwHBJknZkvb8QYiKQJEnSXN39LgF+kiTdKWy56hxsdQsq23zqS6+DTucd/8feeYdFdbR9+N5C73VBig0riF2xIhpjVyzRJKYYY4mpxuTVWGLvNcVujFETNXaMXbFg12gEsWIvyC6IVKXtnu+PxYUFlMUSki9zX5eXsPOcMz+eM/NMOTOzkiStl8lkN9GfgZQgk8n6kO88pCdpgG0+fzoDu9BvH5wiSdKzttdJSPufkfw3IQtBovR1yAjhbtri0paBt23uufZpG0pXCIBt93+MTzK020pbBpYK/duna8nflbISqOgwmNTsTaUtAzuzrjzKefmd8ZJirewE8I8pJ2cTJpe2DGq5jiA9O6y0ZWBjpp9IyNHtKWUloJS3hpxdpS0DlG3gUenXX6xzJ4lSXmQn/kvCvpeIrfmwM9M/mztpC4uxfPX42H7E3CjTJ2hfFZ8GLkA26EXeBb8cpAXHAei984NiLF89v7VdxtqYz0pbBj0r/ciZ+InFG75i6rjpX4KnZJV+H9revDtoS7/dQ9EaeIGvBfwXkP5l61KdTbKZs+df5d+SrEzKkSSp4D6Ns0Ch/SSSJLXI9/MB4EBRaUCRX0siSZJt7v+ZGG91y78ub1iuzU305wshSVLMU2wKaijyQOncNB36Q6mLSuvzlM8N95ckaWyBtIDCVxhRlF+RJKlY3z6N/Brya5YkqVy+n39BfwB3wbQE8vyZCDx7WlwgEAgEAoFAIBAIBALBf4oSn5EjEAgEAoFAIBAIBAKBQPD/Cq3Y5lYSTJpMyr/65/8TMplsE/qzj/IzTJKkl7puXSaTuaD/drqCtDJh5dKz7jsSKLgxfp0kSZOe954CgUAgEAgEAoFAIBAIBM/iP70ySZKkrsVbvZR8HgC1XsF9JwFi4kggEAgEAoFAIBAIBALB38Z/ejJJIBAIBAKBQCAQCAQCgUB8m1vJkJe2AIFAIBAIBAKBQCAQCAQCwb8HsTJJIBAIBAKBQCAQCAQCwX8aSRzAXSJkkiQcJngpiIIkEAgEAoFAIBAIBP9/kZW2gFdJ6qCWpTqmtVuw71/lX7EySfDykPaXtgKQhaCTivrivL8XuawVd9MWl7YMvG0H6H94HFa6QgCsuvxjfJKte6lf2PhcmMnbAJCStaGUlYC9eXfUj34pbRmorPvwKOeP0paBtbITADm6PaWsBJTy1pxUjy9tGTRQjSZTu6O0ZWChaAfwz6nDOaWvA2Wbf0xZBSB1XekKAbB7Q8TWfKis+wBwIXFa6QoBqjsP42zC5NKWQS3XEfTe+UFpy+C3tssAkA0KKmUlIC04zs8XPi5tGfStPp9F0YNKWwYDAxYAcP/Rz6WsBDyt+4K29OOxZr1DAAAgAElEQVQ8italrUDwD0NMJgkEAoFAIBAIBAKBQCD4TyMO4C4Z4gBugUAgEAgEAoFAIBAIBAKByYiVSQKBQCAQCAQCgUAgEAj+0+jEAdwlQqxMEggEAoFAIBAIBAKBQCAQmIyYTBIIBAKBQCAQCAQCgUAgEJiM2OYmEAgEAoFAIBAIBAKB4D+NOIC7ZIiVSQKBQCAQCAQCgUAgEAgEApMRK5MELw1Jkpg0aS0HI6KxtDRn6pT38ff3LWQXHX2L4cOXk5GZTXDzAEaO7IlMJiMpKZ0vhyzh3r0HeHm58N2c/jg42HDtehwjhi/n/IU7fDm4Mx9++DoAmZnZ9H5nJllZOWi1Otq8XofPvwgplN+hQ+eZPGkdOp1Ejx6N6T+gjVF6VlY2w4Yt58L5Ozg62jB79od4ebvw8GEag79YQnT0bUJDg/h2dC/DNf37zSU+PpkcrY56dSvy7eg3UShMn5s9efQG82buR6eVaB8awFsfNDRKjzpzl3kz93P9ajyjJnck+LXKhrTW9WdT3s8VAHcPOybO6WpyvkUhSRKTpm/h4OFLWFqaMXV8T/yreReyi75wl+Gj1+qfW9OqjBzaGZlMBsDK1Uf47fejKORygptVZeiXHUqs40V8ApCelknfN36hSQs/Ph/WqkR5Hz50gamTN6LV6ejeoxH9+rc2Ss/Kymb4sF+5cEFfRmbO7oOXlwtHj1ziu9lbyM7WYmam4Kv/hdIwSK9rx/YzLF60G51WR3ALf4Z83cUkLUcPX2HWtK3otDq6dKtPn37BBbTkMGbEOi5duIeDozWTZ7xFGS8ndmw9y8pfDhnsrl6JY+XaT6hStYzhsyGfreDe3UR+3zS4RP45ceQaP8zYi06no0NoLd7p28go/ezp2/w4cy/XYzSMmRJKi9ZVAYi5rGb2pJ2kp2chV8h498PGtGpTvUR5Hzl0iRlTw9BpdYR2b0jf/i0L+ePb4au5eP4uDo7WTJv1LmW8nIm9l0i3TtMpW84dgBo1fRk1pgcAnwxYQnx8Clqtjtp1yzN8VDeT6u+hQxeYOnl9bjlpTP/+rxfQks3wYSs5f+E2jo42zJrdFy8vF5IepjF48FKio28RGhrEqG97Gq7Ztu1PlizahUwmw83dgWnT38fJydZk/0SdiGXlD3+i00m06OBHp3f8jdJ3/H6RA1uvolDIsXO0oP83Qbh66O+foE5n6bTjJGoegQy+nh6Cm6fpeR8+dJFpUzai00p06xHEh/1fK+CPHEZ+8ysXcp/NjNnv4+XlwrmoW4wf8zsAEjDok7a0ei0QgF9XHmTDumMgQbc3gnj3vRYmanm5dTg9PYP33vnecL06LomOnerxzYjuz9QhSRKTpmzgYMQFLK3MmTqpN/7VfQrZRZ+/zfCRv5GRkU1w8+qMHN4dmUzGtJmb2X8gGjMzJb4+rkyZ+Db29taG62JjE+nQeTKfftKODz94epz7J5ZVg39mbuPgkSv69mZsd/zzxSiDfy7eY/jYjfr2pkllRn7dAZlMxsXL9xkzJYzMrBwUCjljh3UmMKBwe1UUryK2DvxgCQkJqVhYmAEwd9EHOLuY7pPSjK0FOXPsLku/O45OK/Fa58p0f6+mUXrY6mj2brmCQiHD3tGST0c2wz03XowfvIvL5+OpFqhi1KzWRd3eZM4ev8cv351Ep5No2akSoe/WMErfuuY8+/6IQaGQY+9owUcjmuDmkefzR+lZfNU7jPrNfOj7VdBz6wh0DeDdam8jR86BuxH8cWN7IZuGHvXp7tcFSYLbqXeYF7UIV0sXBtf+DLlMhkKmYPftvYTfOfDcOopj6bsj6VijCZrUh9SY0PuV5QNw/cwDwpdeQaeTqPlaGYK6lyvS7vIxDZunn+O9GfXx9LMn9koyuxZcAvQxv2mv8lQOcn8hLTf+esCBn2PQ6SRqtPKkQbeitVw5pmHrzGjenlYPDz97w+cp8RksH3yCRj3LU69L4bGKqZw4cp25M8LR6nR0CK1J777GZS7y9B3mzgznWoyG0VM6G+pwXGwy3361CZ1OQpujpeubdenyRu0S5S1JEpMmr+dgxHl9ezP53ae3NyNW5rY3/owc0QOZTMaOnWeYO287166rWff719QIKAvAlj9OsfTnvYbrL1+JZdP6YVQrYmzw/xFJpyttCf8qxGSS4KURERHNzVsadu8aT2TkDcaOW8W6td8Ushs7bhUTJrxDzZrl6T9gLhGHzhPcPIDFS3bSKKgqAwa0ZfHinSxesov/fd0NRwdrRo7qRfjes0b3MTdXsvyXL7GxsSQ7W8vbvWfQPPgsgfn6P1qtjgnjf2fpz5+jUjnS841phLQMxM/P02Czfv1RHOyt2bV7HNu2/cnMWZuYM6cfFhZmfP5FJ2JiYom5ct8o7znffYitrRWSJPHF50vYufMMHTrUM8lPWq2OH6aGM31+D9xUdnz87m80CvajXAUXg427hx1Dx7Vl3co/C11vbqFk8er3TMrLFCIOX+Lm7QR2bxlK5LnbjJ20iXW/flbIbuykTUwY3Z2aNXzp/+nPRBy5THDTqhw/dZXwA+fZsvZLzM2VPEhMK7GGF/UJwLIFRwisXfKGTqvVMXHCOpYs/QQPlSO9es4kJCSAivnKyMb1x7F3sGbHrtFs33aa2TO3MGvOBzg52TB3wUDc3R2IuRLLwP4L2HdwAkkP05k1M4y167/G2dmOEd/8yvFjlwlqVKVYLdMnbWHu4r6oPOx5/835NA+pSoWKKoNN2MY/sbe3YtP2r9m9I5If5+xkysy3aNexFu061gL0g52vv/jVaCJp395orK3Mn8s/c6buZvaCN3FT2TOg9y80Da5EuYquBhuVpz0jxnVkzYoTRtdaWioZMaETPmWdSdCk0q/3Mho0roCdnaXJeU+dtIkFSwagUjnQu9f3BIdUp6Kfh8Fm84YT2NlbsWXncHZu/4vvZ29j2qx3AfD2ceH3jUMK3Xfa7HextbVEkiS+HryCPbsiadv+2Z04rVbHpAlrWbL0U1QqR3r1nEFISA2jWLJh/THsHazYuWss27f9yeyZYcya0xdzCzM++7wjV2NiiYnJiyU5OVqmTl7Plq2jcHKyZeaMzaz67SCffGraZKxOq2P5nFMMm90SZzdrRg/YSZ2m3niVczDYlK3kxPgl7bCwVLJ38xXWLPiLT8c1A2DRpKN0fjeAGvU9yXiUjUwuMynfJ/6YPHE9i38ahErlyFu9ZtMiJMDo2WzccBx7e2u27RrFju1n+G7WH8yY3Qe/Sp6sXvcVSqWC+PhkenSdQXALf27c0LBh3TFW/T4EMzMFgwYsIjjYH9+ybsVqedl12MbGkg2bhhmu79l9Oq+1rllU9kZEHLrAzVvx7N7xLZFRNxk7fi3r1nxVyG7s+LVMGPcmNQPL0f+jhUQcvkhws+o0aVSFrwZ3QqlUMGNWGIuW7OF/X+VNRE+dvolmzZ49afBPLKsG/xy5ws07D9i96Usio+8ydsoW1i3/qLB/pmxhwqhQagZ40/+LFUQcjSG4SWVm/LCTT/q3JLhJZQ4evsyMH3aycnG/YvN9lbF1wtSeVPd/vrantGJrUVoWzzrG2O/b4OJuw9C+W2jQzBef8k4GmwqVXZi5rDMWlkp2brzIinmn+Hqi/gVeaO8aZGbksGvz5efK/wk6rY6fZx1n5Hev4+JuzfB+26jX1Afv8o4Gm3KVnJmytCMWlkp2b7rEb/NOM3hC3sTg2iVnqVZLVdTtTUaGjD7V32XKqZkkZiQyodFozmjOci891mCjslbRuUIHxh6fzKOcR9ib2wHwMDOJsccnkiPlYKGwYFrTiZzWnCUpM+mFND2NX45tY+6B9azoM/qV3P8JOq3EnsWX6TW2NnYuFiwfegq/Bq64+hhPnmY+zuHPrXfwrJw3ceNW1pb3Z9ZHrpCTlpjJsi9P4FffFXkJXsIW1LJvyWW6j9Zr+W3Yn1Ss74aLj42RXdbjHP7adgePSvaF7nHwlxjK1XZ+rvyfoNXq+H7qHmYu6IWbyo6Pei+nSbCfUR1297Tnm3Ht+X3FSaNrXdxsmbf8HczNlTx6lMUHPZbSJNgPV3c7k/OPiMhtb3aO0bc349aw7vf/FbIbO/53Jox/W9/eDFxAxKELBDf3p3KlMvz4Q3/GjF1tZN+5U306d6oPwOUr9/jksyX/mYkkQckR29wEL43w8ChCuwQhk8moVasCKSmP0WiSjWw0mmTS0jKoVasCMpmM0C5BhO+NzLs+VP9WLjS0EXtzP3dxsSewRjmUSoXRvWQyGTY2+o5TTo6WnBytYaXME6KibuLr64aPjyvm5krat6/LvvBII5t94VF0CdW/SWjTpjbHj11GkiSsrS2oW9cPC3OzQn+rra1Vbr46srNzkJk+BuPS+Ti8fBwp4+2ImZmCkNercPTAVSMbjzIOVKzkVujveRWEH7hAaMc6+ucWWJaU1Mdo4lOMbDTxKaSlZ1ArsKz+uXWsQ/j+8wCsXnucAR+EYG6un5t2cS7Zm2p4cZ9cuajmYeIj6gaVLXHe56JuGcqImbmSdu3rsG/fOSObffvO0aVLAwBeb1OLE8evIEkS1ar74O6uH7j7VfIkIzObrKxs7txNoGxZN5yd9Z2CoEaV2bPbuNwVxflzd/HxdcHbxxkzMyWt2wVycP9FI5uI/Rfp0LkOAC1bB3DqxDUkyXh/964dkbzeLtDw+6NHmaxacYS+Awuv3CuOi9GxePk4UcbbCTMzBa3aVOPwgStGNp5lHKlY2b3QZIRPWRd8yuo7a67udjg52ZCU+MjkvKPP3cbHxwVvHxfMzJW0aV+LA7nl7gkH9p2nUxf9RO5rrwdy8nhMIX8UxNb2SdzQkZOdY1I9Oxd1Ex9f13yxpA7790UZ2ezbF0WXLvoVda+3qc3x4/ljSUXMLYxjiSTp/z1+lIUkSaSnP8bN3QFTuXbxASovO9zL2KE0UxDUqiynD98xsqlexwMLS33d9KvuSmK83v/3biaj00rUqK+fYLC0NjPYmUL0uVv4+rrinVtv2rarzf4C9ebAvnN0DtV3SFu/XpMTuc/GysrcEM8zM/Pi541ragIDyxrS69WvyN69xj4uildRh/Nz84aGB4lp1K1XsVgt4fvOEdq5gT6e1iyfG08LtIPxyfp4WrO8Pp52bkB4uP7vbNqkmsE3tWqWI06dNwDdGx6Fl7cLlfJN2BXtj39eWTX45+BFQtvX0vunhg8pqRloElKN/ZOQSlp6JrVq+Oj9074W4QcuAPp2Pz09E4DUtAzc3QoPEoviVcXWF6E0Y2tBYi4k4Oltj4eXPWZmCpq+VoGTEbeNbGrU9TTEiMr+7jzQpBvSAuuXwcqmcF+ppFy9mIDK2x6Vlz6mNW5VnlOHjGNaQD4dlfzdeBCfp+P6pQckJT4msH7h1W4loaJjBdSPNMQ/jkcraTked5K6KuMXDi29m7Pn9j4e5ej9npKlL8daSUuOlAOAmVyJjFfbjzt09SyJ6SnFG74g92NScPS0wtHDCoWZnGpNVcScTCisZ9V1grqWRWmWN8Q0s1AYJo5ysnW8qEvirqbg6GFt0FK1qTvXTsUXsjuy+jr1u5ZFaW483L16Ih57d6tCk08l5VL0faO+a8s21ThyIMbIxrOMQ5F12MxMYeg3Z2dpi+23FEX4vihCu5jQ3qTla2+65LU3FSt6UKH8sydet207TYd2dUqsTfDfQUwmCV4aanUSHp55b7E8PBxRq5MK23gUbfPgQYqhU+/mZs+DB8U3jlqtji6hE2nc5H80blyNmjWN3xxrCmhSeTihVhsHWrUmCc9cG6VSgZ2dFUlJ6RRHvw9/pGmTodjYWNKmjemBNkGThpsq782Dm8qOhHjTV/NkZeUw6J1f+fT9VRzeH1P8BcWg1iTj4ZH31s9D5YhaU9BHyXioHIq0uXkrnj/P3OCNd37knQ8XEBVt3PEzhRfxiU4nsXDOAT4aHFy8cRFoNElGf79K5YimQBnRqJPx8NTbKJUKbO0sC5WRPbvPUr2aN+bmZvj6unHzhpp79x6Qk6NlX/g54uIeFqslXpOMyiPPzyqVA/HqAhN7+WyUSgW2tpYkJxkPIvbsPGc04Fn44156v98US8uSr0xK0KThrsr3hlFlR3x86jOuKJoL0bFk52jx8nEq3jgXjToZlafxs4kv+GzylV/9s7EiKdcf9+4l8mb32Xz4/nzOnL5udN3H/RfTqvlYrG0see314geHak0ynvlil0pVOJboy4npscTMTMG3Y3oR2mUyLZqP5NrVOLp3b1yslic8THiMs3veFihnN2sexj9+qv3BbdcIbKgfZN2/k4K1rRnfj4xg1IfbWT3/DDqt6Uu71epkVPn94eFY6OVBfpuC9SYq8iZdO02le5dpfDumJ0qlAr9KHpw5fZ2kpHQeP87iUMQF1PeLf5v/KupwfnZsP03bdnVMmnQsMp4WbHPUyXio8tl4FI65ABs2Hqd57iqk9PRMlizdy6eD2pmk4Z9WVg3a4lPx8Mjfltij1hjHOLUmBY98McdD5YA6N+aM+Ko907/fSXCH6Uz7fidDPjVtS9Wriq0A40dt4O0eP/LTwn0lGhCWZmwtSGJ8Oq7ueQNrF3cbHsQ/fXJq7x9XqNPo5a9USIx/hIuRDmsexj+9XO7/I4ZaQV6Avi+wcu4p3v3UtFXiz8LZwokHjxPzdGUk4mRh7F8PGw88rVWMaTiCcUGjCHQNyLve0pkpTcbzQ4tZbL2x/ZWtSvo7SU3MwN41b+WbnYsFaQ8yjWzirqWQmpBBxXquBS8n9koyP31+nJ8Hn6DNR1Wfe1USQFpiJnauFobfbZ0tSC2gRX09ldSETCrUNdaS9TiHU5tv0ahnuefO/wnxmlTcCtVh0/vzmrgU+vb8mZ7t5vNWn6ASrUoC/fjFaEylesq4S1Wwj296edxegp0X/1+QtFKp/vu3ISaTBP9IZDKZSR13hUJO2OZRHDwwhaiom1y5cqXYa14WPy39jIhDU8nKyuH48Rdb2l0SVm3tz4Jf32HEpPbMn3WA2Dul20nRanUkpzxi7cpPGTq4A4OH/vpcb1iely3rztKgSXmjyai/m6sx95k9awujx+nP1XJwsObbMT35esgvvP/O93h5OZfoTK0XITrqDpaWZvhV0q9euHwplrt3HxDSyr+YK18dCfFpTBr1B8PHdkBegq1UL4Krmz079o5izYYhfDW0MyOG/kZaWoYhff6SAew5MJqsrBxOnbj6jDu9OrKztfy+5hDrNw7jQMQkKlfxYsni3a8kryO7b3Dj8gM6vKWfnNBpJS5HxfPWJ7UZt6gtmtg0InZcL+YuL4/AmuXY9Mc3rF47hKVL9pKZmU2Fih580K8VA/stYNCAhVSp6oVc8feUl4J1OD87dpyhfYe/983sgkW7UCgVdO6o78TPnb+D999rgY2NRTFXvhr+zrL6LFavP8nwIe05uG0ow4e0Z+SETX9b3gVjK+i3uK3Z9AVLlg/g7JmbbP/jr79ND5RObD2w8yrXLiUQ2rtG8cavkEO7rnHt0gM6v62fxNm98RK1GnkbTUa9ShQyOSobFRNPTmNu5EL6+X+AtVK/aj0xI5HhR0YzJOIbmpVpgr25aSvo/s1IOol9y2Jo+UGlItPLVHag3w9BvDe9Psc33CInS/tKtRz8JYbgPn6F0o6tvUGdjj6YW5X+SS/uHvb8vLYvv4UNYNcf0SQ+KP5F9t9JZORNrCzNqFzpxVb6Cf5/U/o1SfCvpkqVKp8A/atWrUqNGi7E3c9bfREXl4Qq32w46N8Y51+hkd/GxcUejSYZd3cHNJpkwxYhU7C3t6ZhwyocOnQIv0rlDJ+7qxyNNKnjHqJSGS/NV7k7cv/+Qzw8nMjJ0ZKa+hhHR9M6IxYWZrRsFci+8CiaNKlm0jWu7rbEq/PePsarU3F1M31rmFvum4sy3o7UrOtDzGUNZXwci7nKmN/WHGXtRv0ZDDX8fYiLy5uQilMnoXIv6CMH4vK92c5vo1I50LpVADKZjMAavsjlMh4+TMe5BNvdXsQnF6JiOffXPbasi+TxoyxycnRYWZnR//PmJl3v7u5o9Per1Um4Fygj7ioH4u4nGcpIWmqGoYzExT3ki89+YvLUd/H1zTvbpUVIDVqE6Dvb69YeQS4vfjLJzd0BdVyen9XqZKO3Xnq9ehuVh4NeS1oGDo55q1N274iiTfu8FXrnIm9z8fw9OreZjjZHR2JiOgM/WMKiZf1NcQ+u7rZo8r3Bj1en4uZmet1MT8tk2Odr6f9JMP6BXiZfB3q/51+ZolYn4Vbw2bg76OOIh2Pus3mMo6M1MpnMsIS8ur833j4u3LoZj39A3uGUFhZmtGjpz4F90QQ1Nj7QvSAqdwfu54tdanXhWKIvJ6bHkkuX7gIYyk3btnX4aYnpA3QnVyv94dm5JMY/wsnNqpBd9J/32bIimhE/tsbMXL+FytnNGl8/J9zL6J9l3WbeXD1feMvC01CpHFDn90dckmFlaUEbD8OzySjkjwoVPbCytuBqzH38A3zp1j2Ibt31246/n7MVlUfxse1V1WGAS5fuoc3RFfllEk/4bVUEa9cfA6BGgG/heFqwzVE5GG1fi4szjrkbN53gwMHz/LL0U8NLlciom+zafZaZs7aQkvoYuUyGhbkZ7/QuHOf+aWX1t7XHWbtZf9ZdjepexMXlb0tSULkbxziVuz1x+WJOnDoZVW7M2bT1L0Z+rT+nqd1rAYyauNkkDa8itgKGcmZjY0Gb9jU5f+6uYatccZRmbC2Is5sNCfm2rT3QpOPiZl3ILvLkPdb/EsnE+e0NseRl4uxmbbR97oHmEU5uhctl1KlYNi4/x9h5bQw6rkTHcylKw56Nl8h4nENOtg5LazPeHlS3xDoSMx/iYpV3no6zpTMPM41XFydmPORq8nW0kpb4xwncfxSHh7UH11NuGGySMpO4k3aPqk6VOaku+rzHfwt2zpakJOS9kEl9kImtS97kdtZjLQm301k16gwA6UlZbJwcSbcRNfHMd/C1q48NZpYK4m+nG31eEmydLUhNyFuJlJaYiV0RWtaN/sugJWxqFF2+CSQuJoWYY/EcWnmNzPQckIPCTE7t9iVfaefmbme0wlFfh0t+1IOrux3l/VyJOnPHcED30/ht1UHWrjsKQI0aZY3HVOqnjLvUBfv4po0Ztu04TYf2/61VSaCfjBSYjliZJHghLl++PO/y5cu1wsLCeK1VLTaHHUeSJM6evY6dnWWhwYW7uwO2tpacPXsdSZLYHHacVq30S8Zbtgxk82Z9h3zz5mOGz59GYmIqKSn6gVRGRhZHj16kQoUKRjY1apTl1i0Nd+8mkJWVw/btpwlpaXzfkJaBhG0+DsCuXX8RFFTlmaui0tMzDNs5cnK0HDwYTYUKph/2WLW6B/fuJHH/XjLZ2Vr2775M4+Diz+IASE3JICtLvxc/+eEjzkfeo2y+Q6pNpfebjQlb+yVha7/ktRB/Nm89o39uUbews7UqdA6Fu5s9tjaWnI26pX9uW8/QqoV+hcNrIf6cOHUNgBu34snO1uLkVLI3gy/ikxGTOrB6+wBWbe3PwMHBtO5Q3eSJJICAGr7cvhXP3bsPyM7KYcf2M4SEGL9xDQkJICxMf3ji7l1naRhUCZlMRkrKIz7+aBGDh3SmTh3jsvfggX5yLDn5EWtWH6Z7D+Nv6SmK6gFe3L6VwL27iWRn57BnRxTNWxhPUjZrUZVtW/SdtX17oqnfoIKhvOp0OvbuPkfrtnllvEevIHbsG86WXUNZsmIgvuVcTJ5IAqjqX4a7tx8Sey+J7Gwt4bsu0qRF0W8eC5KdrWXkVxto0zGg2A5SUfgH+HD7dgL3cp/Nru1naRFivMIqOMSfP8L0nfS9u6Oo39APmUxGYmIa2txtW3fvPOD2rQS8vV14lJ5JfO6ZYDk5Wg5HXKRc+eK/VSagRtnccvIklpwhJKRALAmpQViYfpJ2966/aBhU+ZmxRKVy4NrVOBIT9WXl6NFLVKj47PNw8lOhqgtxd1PRxKaRk63lePgt6jQx7hDfvJLIspkn+XJKMA5OlvmudeZRWhYpSfrBwYUzaqODu4vDP8CXW7cSDPVm546/aBESYGTTIiSALZtPAbBndyQNGurrzd27+u2fALH3Erl5XU0ZL/3A7Um9uR/7kPC9USatCHpVdRhgx7bTtOvw7MFo77ebE7ZxGGEbh/Faq0A2bzmpj6eRN7CztcTdrUA76Oagj6eRN/TxdMtJWrXU6404dIGfft7Lgrn9scp3YP6qlYPZt2cs+/aM5f13gxk4oHWRE0l6f/yzymrvnkGErfqUsFWf8lqL6mzeflbvn3N3sLO1wN3VeALF3dUOWxsLzp67o/fP9rO0Cq6W6zt7Tp7WD9aPn7pOOR/T2r9XEVtzcrQkPdRPfORkazkccYmKlUrQFyjF2FqQStVcuX8nGXVsKtnZWg7vvU79ZsYTqNcvP2DB9KOMmPEajs6FJ61fBhWruhJ3NwVNbCo52VqOht+gXlPjmHbjygN+mn6ModNa4uCUp+Pzsc2Zv7EHczf04J1P6tG8bYXnmkgCuJ58Aw9rd9ysXFHIFAR5NOC0xnjV2Z+aM1Rz1vve1swWT2sPNI81OFs4YSbXb5W1VlpTxakS99PjnkvHPwnPSnY8vP+IJPVjtNk6Lh5W41c/bwuZhY2Sz1c0Z9DiJgxa3IQyle0NE0lJ6seGbdTJmsck3kvHwf35DosH8PCzI+n+I5JztVw6rKFCPWMtH//SjH4LG9NvYWM8K9vT5ZtAPPzs6TWxruHz2h29adit3HNNJAFU8ffk7u2H3M+tw/t2XaRxi8KroYpCo04hM0N/Pl9qSgbn/rqLb7ni41nvt4MJ2zScsE3D9e1NWL72xs6q6PbGNl97E3aSVi2L39qv0+nYsfMMHdo/Xx0S/HcQK5MEL43g4AAORkTT+vVvsbI0Z/Lk9w1pXUInErZ5FDyAAVEAACAASURBVABjRr/N8BHLycjIonkzf5o31w9ABvRvw+Avl7B+wxHKlHHhuzn6AW98fDLde0whLS0DuVzG8hX72L5tDJr4ZL75ZjlarQ5Jkmjbti4hISHopHBDvkqlglHf9qLfh3PR6XR0696ISpXK8MMPfxAQUJaWLQPp0aMxw4b+QpvXx+DgYM2s2R8arm/VchTp6Rn6jl54JD8t/QxHRxs++XgBWVk56CSJhg0q0+vNZib7SaGU89nQlgz7dAM6rY52XQIoV9GVZQuOUKW6isbBflw6H8eYr8NIS8ng2KFrLF90lJ/X9eH2jQfMmbQXmVyGpJN4s08Do288e67n1qwqBw9fonWnafrnNu6NvOfWcw5ha7/UP7cRoQwfvZaMzGyaN6lK86b6TlT30PqMGLOOjt1nYWamYOqEXiU+OPxFfPKiKJUKRozqwcB+89HqdHTtFoRfJU/m/rAN/wBfQlrWoFuPRgwftpJ2bcbj4GDNjFn6fFf/dog7txNYuGAnCxfsBGDxTx/j4mLH1MkbuHz5HgAfDWpr0oSFUqlg6IjOfP7RMrRaic5d61LRT8XCuXuo5u9NcEg1unSrx5jh6+jafib2DtZMmv6m4fq/Tt9E5eGAt8+LfUOJsSY5g4e15uuP16DTSbTvEkj5im4snR9BleqeNG1RiYvnYxk1ZCOpKRkcjYjh54WHWLGhP/t3XyTyzB1Skh6zc4v+QOTh4ztSqYppAy6lUsGwkV35eMASdDqJLl3rU9HPg/k/7qS6vw8tWvoT2r0Bo75ZTee2U7B3sGbqzHcAOPPndRbM3YVSqUAulzFydHccHK15kJDK4E9+Jjtbi06no14DP3r0Kn6iT6lUMHJUTwb0m4dOJxnKyY8/bMU/wJeWLQPp3qMx3wxbQds2Y3FwsGHmrA8M17duNZq09Ayys3PYFx7F4p8+wc/Pk48/acf7736HUqnAs4wzkye/Y/KzUSjlvDe4HjO+3odOJ9G8fUW8yzuyYWkk5au4UKepN2sW/EXG4xx+HHMY0J9BMmRqC+QKOW99XIepg8ORJIlyVVwI6WRaJ/iJP0aM7M6g/gvR6nSEdm2IXyVP5v24ner+voS0DKBr9yBGDPuVDm0m4uBozfSZ+m+h/OvMdX5eEo5SKUcmlzPy2x6Gr5gf8sUykpPSUZrp66W9feHVEUVqeQV1GGDXzr+Yv6jwt409jeDm1TkYcZ7W7cbr4+nEvK/q7tJtGmEb9d8QN+bbngwf+RsZmVk0b1rdcDbShEnrycrO4YN+8wGoWbMc48cU3npXnD/+aWXV4J8mlTl45AqtQ2fr/TOmW55/3p5L2KpP9f75pjPDx27QtzeNK9O8iX7l4IRRXZg8czs5Wh0W5krGj+xSZD5F+eRlx9bsLC2fDVxGTo4OrU5Hg6CKhHavb7IvSjO2FkShlNP/q0aMG7wLnU6iVcdK+FZwYtXiM/hVc6VBM1+Wzz1JxqNsZozcD4CbyoYRM/RnVo34aBv3biWT8Sibfp3X8MmIptQOKvkAXaGU0/fLhkweshedVkeLjpXwqeDE2iV/UaGqC/Wa+fLrvNNkPM5hzqgDALiqbBg6vdVz/d1PQyfp+OXCbwyr9xVymZyDdw9xLy2W7n6h3Ei+yZn4s0QlRFPDNYDpTSeikyRWXf6dtOx0AlzK0rvqm0iShEwmY9uNndxJu/tS9eVnVd/xtKhcB1dbR+5M3sKYrUv4+egfLz0fuUJO6/5VWDvuLyQd1GjliZuvLYdWXcPDz55KDZ7+rZt3LyZxfOMtFAoZMrmM1gOrYm1f8jMc82sJ6VeZDRPOIukkAlqWwdXXliOrr+PhZ0fF+s/+BtCXhVIp54thrfnfx2vR6STadalB+Ypu/Dz/EFWqe9CkRSUunb/PqCEbSUvJ5FjEVX5ZeJhfNvTj9o0HzJ+9HxkgAb3ea0CFSiXTHdzcX9/etB2HlaUZkyflxeQuXacQtmk4kNvejPhVH0+bVad5c317s2dvJBMmrSMxMY2BgxZSraoXS5foY/CpP6/i6eGEj0/h868EgvzI/s6zTQT/r5GQ9pe2BpAZTyaVFnJZK+6mLS5tGXjbDtD/8DisdIUAWHX5x/gkW7ertGVgJm8DQErWhlJWAvbm3VE/+qW0ZaCy7sOjnJffCS4p1spOAOTo9pSyElDKW3NSPb60ZdBANZpM7Y7SloGFQn8A9T+mDueUvg6Ubf4xZRWA1HWlKwTA7g0RW/Ohsu4DwIXEaaUrBKjuPIyzCZNLWwa1XEfQe+cHxRu+Yn5ruwwA2aCgUlYC0oLj/Hzh49KWQd/q81kUPai0ZTAwYAEA9x/9XMpKwNO6L2hLP86jaA0v/H18/2zi32xcqpMjbmuO/qv8K7a5CQQCgUAgEAgEAoFAIBAITEZMJgkEAoFAIBAIBAKBQCAQCExGnJkkEAgEAoFAIBAIBAKB4D+N+Da3kiFWJgkEAoFAIBAIBAKBQCAQCExGrEwSCAQCgUAgEAgEAoFA8J9GJ1YmlQixMkkgEAgEAoFAIBAIBAKBQGAyYjJJIBAIBAKBQCAQCAQCgUBgMjJJEku5BC8FUZAEAoFAIBAIBAKB4P8vstIW8Cq537VhqY5pPTed+Ff5V5yZJHhpSOwvbQnICCFDu620ZWCp6MCBeyNLWwYtvCYBID34pXSFADKXPuy+Pay0ZfC67zRSsjaUtgzszbsD/5x6s+XGkNKWQefys8nU7ihtGVgo2ul/yNlVukIAlG2YcHJgaavg2waLQLuntGWAorX+f1146eoAkLeCzNJvb7DoQELGr6WtAlfLdwCQEleUshKQOb+HTir9MiKXtWJtzGelLYOelX4EoOySbqWsBG7130hq9qbSloGdWdd/1LP5+cLHpawE+lafj2xQUGnLQFpwHNdZHUpbBglf6eP7laSZpawEKjt+/Y/pkwgE+RGTSQKBQCAQCAQCgUAgEAj+00jiAO4SIc5MEggEAoFAIBAIBAKBQCAQmIyYTBIIBAKBQCAQCAQCgUAgEJiM2OYmEAgEAoFAIBAIBAKB4D+N2OZWMsTKJIFAIBAIBAKBQCAQCAQCgcmIlUkCgUAgEAgEAoFAIBAI/tNIWrEyqSSIlUkCgUAgEAgEAoFAIBAIBAKTESuTBK+MQxHnmTRpLTqdjh5vNGHAgLZG6VlZ2Qwb+gvnz9/G0dGG2XP64e3tCsCiRTvZsP4IcrmckaN60qyZPwApKY8YNWolMVdikclkTJr8HrVrV3imjiOHLjJtymZ0Wh1dewTxYf9WBXTkMPKbVVw8fwcHRxumz34PLy9nQ/r92Id07TSNQZ+04f2+Ibk6HjNu9O9cjYlDJoNxE9+kZq1yz+2r6JNxrJ17Fp1Oomn78rR9u6pR+sEt1zgQdg25XIaFlZJ3htSlTDn7584vP5IkMWnOHiKOXcPS0owpozriX8WjkN2chQcJ23mOlNQMzoR/bfh82eqTrP/jLAqFHGdHayaN6ICXp0OJdVw4pWbD/HPodBKN2pXl9TcrG6Uf/uMGEVtuIJeDhZWSN7+shWdZey6d1rBl6XlysiWUZjK69A+gSm23EuV99PAVZk3bik6ro0u3+vTpF2yUnpWVw5gR67h04R4OjtZMnvEWZbyc2LH1LCt/OWSwu3oljpVrP6FK1TKGz4Z8toJ7dxP5fdPgIvN+FfWkZcsR2NhYopDLUSjkbNg4AoCLF+8wdswqMjOzUSjkjBn7FoGB5Yv1z6U/NWxZcAGdTqJBWx9a9vIzSj+27RZH/7iFTC7DwlJBjy9qoCprB0Ds9RQ2/HCOzEc5yOQyPv+hCWbmimLzfMLhQxeZNmUjOq1Etx5BfNj/tQL+yWHkN79y4fxdHBytmTH7fby8XDgXdYvxY34HQAIGfdKWVq8FAvDryoNsWHcMJOj2RhDvvtfCJC2SJDFpygYORlzA0sqcqZN641/dp5Bd9PnbDB/5GxkZ2QQ3r87I4d2RyWR898M2wvefQy6T4eJiy5RJ76Byd+DadTUjRv3G+Qt3+PKLjnz4Qasici+a2KgHnFp5FUkn4dfCk4BOZY3Sr0Xc58ya61g7mQNQubUXlVroy2f49EgSrqXgXtmBkK8CTc7TyB+T13Mw4rzeH5Pffbo/RqzM9Yc/I0f0QCaTMW3GJvYfiMbMTIGvjytTJr2Dvb01R45eZNbsLWRn52BmpuR/X4fSKKiKCVrW6bVYmjF18nv4+/sWrWX4CjIyn2h5A5lMxo6dZ5g7dxvXrsexbu1QagTo/fjwYRqfD15CdPRtuoYGMfrbXsXrmLaJg4cuYmlpztQJb+Ff3buwjgt3GD5qtV5Hs2qMHNZVX0bm7iB8fzRyuQwXZ1umTHgLlbsDySmPGDF6DbfvPMDCQsnkcW9SuZLnM7U8jeNHrvLdtF3odBKdutbm3Q+bGKWfPX2L76fv5lqMmnHTuhHSuvpz5VMU+vZmNxFHc9ubbzviX6Xw3zFn4X7CduS2N/uGGj5ftvoE67fka29GdjS5vTl06DyTJ61Dp5Po0aMx/Qe0MUrPyspm2LDlXDh/Rx9rZ3+Il7cLDx+mMfgLfRkIDQ3i29F5ZeC7OWGEhZ0gJeUxp8/MKbE/Yk4nsG3xJSSdRN3XvWn+hnE8Prn9Die23UEul2FupaDLp9Vx97UlJ1vHlnkXuBeTgkwGHQZUpXyg81NyMY1g79qMadQXhUzOmst7WRC5ySj926APaFQmAAArpQUulg4ErngXgOEN3qWlb13kyDl0L5Kxx5Y+t46jhy8zc+of6LQSod3r06dfC6P0rKwcxgxfy8Xc9njKzLco4+XMjq1/sXJZhMEu5kocv677zKg9LinP+3y0OTo2/3Ce2Gup6LQStVp6Etzz2X3VZ3H9zAPCl15Bp5Oo+VoZgrqXK9Lu8jENm6ef470Z9fH0syf2SjK7FlwC9O1g017lqRzk/tw6imPpuyPpWKMJmtSH1JjQ+5Xl07JcXSaHDEAuk/Nr9G5+OLnOKN3Lzo15bYdgb2mDQiZnwqFf2HvjT3zs3TnaZyFXH94D4PT9S3y9d95L03X62B2WzD6GTifRunMV3ni/llH65lVR7A67jEIpx97Rki9GNcfd0+6583vRPsm0mZtz22Clvg2e+Db29tbcvfeA9p0mU76cvqzUrFmO8WOe3fYJ/ruIySTBK0Gr1TF+/Gp+XvYFKpUTb/SYQsuWgfj55TXq69cdwd7emt17JrBt2ylmzdzEnO/6c/VqLNu3nWLrttFo1Ml88MF37Nw1HoVCzqRJa2nWzJ8ffhhIVlYOGRlZxeqYPHEji376CJXKgbd7zaFFiD8V/fImSzZtOIG9vRVbd41kx/a/+G7WVmbMfs+QPnN6GE2bVTO67/Qpm2jStCqzvutDdlYOjzOyn9tXOq3E6u//YvCMZji5WTNlUDiBjcsYTRY1aOVLcOeKAEQeiWXdgki+mNbsufPMT8Sxa9y6+5Bdaz8i8nws42bsZO1PfQrZhTT1o3ePurTttdDo82qVVaz/+QOsLM1YvfEMM+fvZ86E0BJp0Gkl1v0YySfTmuDoasWMTw9Qo5EHnmXzfFC3pTdNO+k7cueO3mfTwmg+ntIYGwdzBo4PwsHVitgbKcwffpSJa9o+LatCaLU6pk/awtzFfVF52PP+m/NpHlKVChVVBpuwjX9ib2/Fpu1fs3tHJD/O2cmUmW/RrmMt2nXUdxauXonj6y9+Neq47tsbjbWV+TPy1r6SegKwYvkQnJxtjfKbMWMjn3zSgebBARw8eI4ZMzaycuVXz/SPTiuxad55BkxuiIOrJT98fhj/IJVhsgigdosyNOqgH3yfP6Zmy+KL9J/UAK1Wx+rpZ3lraC3KVLAnPSXLoM8U9PV3PYt/GoRK5chbvWbTIiTAqP5u3HAce3trtu0axY7tZ/hu1h/MmN0Hv0qerF73FUqlgvj4ZHp0nUFwC39u3NCwYd0xVv0+BDMzBYMGLCI42B/fssVPQEYcusDNW/Hs3vEtkVE3GTt+LevWFPbf2PFrmTDuTWoGlqP/RwuJOHyR4GbV6de3JYM/7wDAil8PMm/BTsaP6YWjgzUjh3cnfN85k30DoNNJnFweQ6thNbF2tmDH6NN413HF0cvGyK5sQzcavF+50PXVO/iizdQSsz+2RPk+ISIi1x87x+j9MW4N637/XyG7seN/Z8L4t/X+GLiAiEMXCG7uT5PGVfnqy84olQpmzNrMoiW7+d9XoTg52rJg/kBU7o5ciYnlw/7zOHRgUjFaznPzlobdO8cSGXmTsePXsO73oYXsxo5bzYTxvalZsxz9B84zaKlcyZMffxzAmDGrjOwtLMz44vNOxMTEEhNzv3ifHL7IzVsJ7N46gsioW4yduJ51qwpPJI+duJ4JY3pSM7As/T9eQsThSwQ3q0a/PiEM/rQdACt+i2Deot2M//YNFi7ZS7UqXsz7ri/XbqgZP2kjy38aVKyegmi1OmZN3sl3i3rjrrKn39s/0bRFZcpXzCv/Kg8HRk7ozOrlx0p8/+KIOHaNW3cS2bVukL69mb6TtUs/KGQX0rQyvXvUo23PBUafV6usYv2yvrntzWlmzgtnzsRuxear1eqYMP53lv78OSqVIz3fmEZIy0D8/PImstavP4qDvTW7do9j27Y/mTlrE3Pm9MPCwozPv8gtA1eMy0CLkEDe7t2Cdm3HltgXOq3EHwsu0mdiXexdLFn45XGqNnTD3Tcvbge28KRBe/3g8OIJDTt+usz74+tyetddAD6b15i0pExWjjnDwDlByOWyEusAkMvkTGjSn97bxxGX/oAtodPZe+sUMUl3DTYTji8z/NzHvz3+Lvr2uK57FeqpqtFmwxAANnSaRJCnP8fvny+xDq1Wx7SJYcxb8iEqDwfe6zWX5iHVCrTHp7Czt2Lzjv+xa3skP87eyZRZb9OuY23adawN6Nvjrz5f8UITSS/yfKIPq8nJlvhsXmOyMrT8+PERAoM9cVJZPZeOPYsv02tsbexcLFg+9BR+DVxx9TFu3zMf5/Dn1jt4Vs7rN7mVteX9mfWRK+SkJWay7MsT+NV3RV6Cdrgk/HJsG3MPrGdFn9Gv5P6gL6vTWg2ix/pRxKYmsKf3HHZePc6VxDsGm6+C3iTsyiGWRW6nsrMPa7qNo85PfQG4mXyfkJWfvXRdWq2OhTOOMOHH9ri42zCkz2YaNiuLbwUng02Fyq7MXl4dS0sl2zdcYNnckwybZPrLo4K8aJ+kSaMqfDW4U24bHMaiJXv431ddAPD1cSVs47Dn1vZvRtLpSlvCvwqxza0AMpmsj0wmK5Pv959kMln13J9vymQy19yfj77EPD+SyWTvPSN9gkwmi5LJZGdlMtnuJ/pkMtlYmUz29dOue8q9Dshksnovqrk4oqJu4lvWHR8fN8zNlbTvUJ/w8Cgjm/B9UYR2bQRAmzZ1OHbsEpIkER4eRfsO9TE3N8PbxxXfsu5ERd0kNfUxf56KoUcP/VtUc3Ml9vbWz9QRfe42Pr6uePu4YGaupG272hzYF21ks39fNJ1D6wPQ+vVATh6PQZL0+2X37T2Hl5czFf3yOjKpqY85/ed1unZvCICZuRJ7+5J3EJ5w41Ii7l62uJWxRWkmp15LHyKPGg/urGzMDD9nZuQge77+YpGEH4qhS9sAZDIZtQK8SEnLRJOQVsiuVoAX7q62hT4PqlsWK0u9vpr+ZYjTpJRYw63LD3EtY4urpw1KMzl1W3hz7mickY2xD7SQ6wMfP0ccXPX+9yxnR3aWluwsrcl5nz93Fx9fF7x9nDEzU9K6XSAH9180sonYf5EOnesA0LJ1AKdOXDOUkSfs2hHJ6+3yVnc8epTJqhVH6Dsw5Kl5R0VFvfR68ixkMhlp6RkApKZm4O7u+GznALcvJ+HqaY2LpzVKMzm1gstw/pjayMYy37PJylc+r5xOwLO8HWUq6Du3NvbmyBWmF97oc7fw9XXF28fVUH/3F5hwObDvXL76W5MTufXXysocpVK/AiozM0/TjWtqAgPLGtLr1a/I3r3GPn8a4fvOEdq5gb6u1CxPSupjNPHJRjaa+GTS0jOoVbM8MpmM0M4NDM/U1jYvTjx+nGnQ5OJiR2CNsiiVJWuSH1xLwU5lhZ27FQqlnHJB7tw9nWDy9Z7+TiitTF8lVpDwfVGEdjHBH2n5/NElzx9Nm1QzPKNaNcsTF5cEQPXqPqhyy2YlP08yM7LJynr2hL1eS0O9llrlSUl5hEZTQIsmV0utJ1oaEh4eCUDFip5UKK8qdF9rawvq1fXDwsKsUFqROvZHE9qpXq5PyuX6xDgmauJTSEvLpFbNcnodneoRvl9frm1tLQ12jx9nIcsNdNeuqwlqUEmvtbyKe7GJJDxINUlTfi5Gx+Lt44SXtxNmZgpatfXn0IHLRjaeXo74VVYhe86JiWcRHnGFLu0C87U3GWgSCv8d+vam8Bv7oLrl8rU3XsRpTPNBVNRNfH3d8PFx1cfa9nXZl/vsn7AvPIouoUEAtGlTm+PHLiNJEtbWFtSt64eFeeEyUKtWedzdS74SF+DulWRcPK1x9tDH1hrNPbh4XGNkY2md9843O0NriBmaO+lUyF2JZOtogaWNGbExJW97n1DLzY+bKfe5k6omW5fDH9cO07psg6fad67YlLBrhwGQkLBQmGEmV2IuV6KUK0h4nPRcOs6fu5PbHrtgZqbk9XY1ObjvgpHNwX0X6NhF3x63ej2AkyeuFm6Pt5/l9XY1n0vDE17k+chkkJ2Rg1arIydLi0Ipx8L6+d7f349JwdHTCkcPKxRmcqo1VRFzsnCcP7TqOkFdy6I0y2tHzCwUhomjnGydod/0qjh09SyJ6c9fDk2hjkdlbiTFcis5jmxdDpsuR9DOL8jIRpIkbM314wN7Cxvi0hNfqSaAmAvxeHrb4+Flj5mZguatK3Ii4paRTWC9Mlha6stBlQB3HmjSXyjPF+2TGLfB5YhTP1+9Ffy3ESuTCtMHiAZiASRJ6leUkSRJjV9WhpIkLSzGZIYkSd8CyGSyz4HRwEcvK/9XgVr9EE+PvNl4D5UjkVE3jGw06iQ8PfU2SqUCOzsrkh6mo1Y/pFbNCkbXqtUPsbQ0w9nZluHDl3P50j38/X0ZMbIn1tYWT9WhUSfj4ZE3YHb3cORc1K2n2iiVCmztLElKSsfC3IxlS/ex6KePWL5sv8H+3t1EnJxtGD1yDZcvxVLd35uhw0OfqeNZJCU8xsk9b5Dp5GrFjYuFG779m6+yd10M2hwdX85q/lx5FYU6PhVPVd6bLA83O9TxqUVOHBXH+q2RNA+qWOLrkhIe4+SW5wNHV0tuXnpYyC4i7Dr7N1wlJ0fis+lNCqWfPRSLt59jibZRxWuSUXnkDQZUKgeio+4Y2Wjy2SiVCmxtLUlOeoSjU94KkD07zzHzh3cMvy/8cS+932+KpeXTVyap1eqXXk8AZMj48MPvQSajV69m9OqlX8U2YsQb9PvwB6ZP24BOp2P1msIrNwqS8iADx3zPxsHVktuXC3c4jmy5ScSmG2izdQycpu/YJdxLRyaTsWTECdKTs6jZogwhb5hePtTqZFT5/KMqov7mt8lff52cbImKvMmYUWuIjU1k8rR3UCoV+FXy4Mfvt+nruIUZhyIuFLkdqkg9GuN4ovd5Mu5ueeVHrU7GQ5XPxsMRdb5JjTnfb2XzlpPY2VqxYtmnJvuiKB49zMTaOS/uWDtbkHCtcEf+9qkENJeTsfewom5vP2xcLAvZPA9qTRIeBcqvWp1UwB9Jxv5QOaLWFC4/GzYeo13bOoU+37X7LNWr+2BexEDeSIu6gBYPJ9SaJKOBvlpTUIsT6pfceVZrUgqXEU0y7m72+WyS8VA5FLDJe25zftjO5j/+xM7WkhVLPwagauUy7A6Pol7dCkSdu0Xs/YfEqZNwdSnZFol4TQruHnla3N3tOX/uXon/zuelcHtjn9velHyrx/o/ztK8kWnxRKNOwsMzfyxxIiryprE2TRGxNjeWvApSHmTg4JZXFx1cLbl7ObmQ3Ymttzmy+RbaHB19J+nfBXqUt+PSiXhqBHuQEp9B7LUUkhMy8K7yfBNbHjYu3E97YPj9fvoDartXKtLWy9YNHzsVR2P1E6BnNFc4dj+aU72XIpPBivM7uJr0fGVKo0kxao/dVQ5EnyvYHqegyt9nK6I93r0zilk/PvUdrUm8yPPxb6Li4vF4pr97kOxMLe36V8XazrQJ6YKkJmZg75qnw87FgvtXjON83LUUUhMyqFjPlRObjdvI2CvJbJ97kZT4DDp+Uf2VrUr6u/D8P/bOOyyK63vc7+wCAtLb0m1gA9TYGyAYey8xJkZTLIkpphkVsRuNPYmJscZe8rEC9oIFe4m9N0BRepEidXd+f8wKLEXB8iX5Zd7n4WF35szcs3dum3PPudfEmsdpBca0x2kJNHLQDYOedXIdm/r8yNC3umGsb0ifTYH551zN7Tk4cD7p2U+ZfnwNpx6V34OuJBLjMrBRFbQV1naVuX0trlT5/SG3aNSieAh0eXgdY5JnbNl6ik6dCvrgqEeJ9OwzExMTQ74Z0ZXGjco/tv+3Ii/AXT7+3S3KKyAIQlVBEK4W+j5S+70xsE7rBWRUmiePIAjp2v8OgiCEaeWvCoLgrT2+UBCEc4IgXBMEYXKh62YIgnBd62k0R3vsuR5GoigW7jUqI4U+P6O+IAgnBUG4IwjCUO392giCsKNQmr8LgvBRCb+hNB0jBEGYLAjCeUEQrgiCULvotVq5Ydrrzy1ZsqQ09V8beXkarl9/yHvv+bItKBAjIwOWLtn7xtJbuGAvHwzyxbiyrpFIrdZw8/oj3nm3JRu3fo+RkQHLlx18Y3o8w6+nG9PWdaL3MC92rb35xtMrLyF7rnLtZgyDBzR7Y2n49KjOxNXt6TGkLnvX686mR0ekErLsGv2/aVDK1W+Oq5cfYmioj5u7FH516+ZjoqIS8Wvr8X+uC8D6DSPZusmpPAAAIABJREFUui2QpUu/ZP26w5w9eweADRvCGBPwDoeP/ERAwDuMC1zz2tJs1b0qASv86DK4NqEbpPTUag3h15J4f/RbfD63JVePx3DnQtk9Z16VevWrsm37GDZs/I4/lx4gOzuX6jXs+XhIWz4dspDhwxZRq7ZTubylXpVvv+7KkdApdOvaiLXrj774glfE+S0bev3cnK7Tm2DvacWJxf+8tmPhoj0olQq6d2uic/zOnWjmzAtmyqT+FaRZxfDtiM4c2T+Bbl0asnaD5P0xbHBb0tIy6fHOHNZsOEad2k4oFf/ZIRwhe65w7WY0gwc0f7Hwv5xmXV35bpk37T+qyeH/3QegYTtHzGwqseib0+xaeguX2hZvxJOsJLrVaM2u8JNoRCkUpIqZPW4WzjRfP5Rm64bS0tGLJvZ1XnCXN8fVyw8wNCroj980JT2fqNtPUChg1GpfvvvTm+PbIkiKefpG0hc1IgdX3MH/45KNf441zRkyvzmDZjXh1JZI8srhuf1vpXdtX/66doB6Sz6k/9aJ/NH5ewQEYjOSaLDkI/zXjGD84WUs7vIDJgYvH1nwshzafYe7NxLo/cGrec+9LhYu3otST0n3rtLrrp2tGYcOTCZoy2jGjOrF96NWkZ6eWcFayvxTkT2TdNkMtAFGiqJ4DqSwkBfwPrBXFMVpgiAogWdxV4GiKCZpj4UKglAPeAT0AmqLoigKgvDiGBMtgiBMAwYBT4DCcTP1gOZIRqYLgiDsLOs9S9JRFMVn8R4Joig2FAThc2AkUMxDSxTFJcCS/K8UeO+oVJZExxR4lsTEpqBSWepcb6eyIDo6GXt7S/Ly1KSlZWJhWbnUa+3tLVDZW1C/vhSn36Fjwxcak+xU5vmhEwBxMSmoirikP5NR2VuQl6cmPS0LC4vKXLkcyYF9l/hl7nbS0jIRBAGDSnq0a18flcqcevWlNWLata/P8mWhz9XjeVjYGJEcV9BIJydk6niCFKWxnwvrfjkPNClV5kWs2/I3m0IuAuBV24Ho2AJ7ZUx8Girb8s0SnzgbzqJVJ1izYAAGBuVvVixsjEiOL8iDlIQsLGxKz4OGbZz5368FIQrJ8ZksnXSagaMaYetYudTrSsLWzpzYmIJZmtjYJ9iqdBc3t9PKqOzNpTKSnoW5RUGI5b7dl+nQuWBQcOXSA25ce0T3DrNQ52lISsrg04+XsnjFUJ37qlSq115PpPtK/62tzXi7XQMuXw6nSRN3gradJDCwHwAdOzVi3Li1L8wfM2tDUgo9mycJWZg/x7Olvq8jW3+T7PQWNkZU97KisrnknVW7iR2P7j7B/S2bF6Yr/Q5zYgv9xtiYlGIhJc9k7IvU38JUr2GPkXEl7t6JxsPTld59mtO7j/QS+uvPO/JnuUti3fowNm6W1o7x8nTVaU+kPC+uT2FX8ZgS2hyAbl0aM2z4YkZ82flF2VAqxpaVeJqUnf/9aVI2xpa6xu9KhWbD3do4cOGvey+dHsC69UfYuEmK8vbyqkJMsTKom5cqlYVufsSm5IewAWzddorDR66ycvkInf42JiaZL0csYeZPA3F1LXk9q3XrjrBx83FJF88iusQk66QDoLIrqktyMX1fhnV/HWPjllOSHh4uxctI0TJrZ05M7JMiMsU3VOjWpRHDPl/KiC86YmJiyE9T3wOkEI62nX7Exdm63Lra2pkRF1PQ3sfFpWKrevkFYMvCus3n2BRyAQCvOo5F+pvU8vc3Z8JZtPI4a/4YWOb+xk5lQUx04bYkuXjdtSuhrbUoX39SHsysDXkSn5X//UlCFqbWpXs4e/nYs/0PKQRbqVTQeWjBPN+SkaexcXp+2P/ziMlIxMGkoDw5VLYuNTSoe/VWjD++NP97x6rNuBB3m6d50m859PA8De1qcTbmRonXPw87OzOd/jgu9gl2dkX7YzNiY1JK7Y/37r5Eh06vPqn0Ks/n8pEY3BvZoNRTYGJRiSp1LHh0JxUr+/I/I1MrQ1ITCvRIS8zGpJAeOZlqEh5ksH7ceQAyUnLYOv0SvcfWx8GtIO9sXCqjb6gk/kGGzvF/G9HpiTiaFowhHE1tdLzqAAZ4tqffVmndpnPRN6mkNMDayIyEzCfkqKXQ2Etxd4lIicbN0omLsXdfWS9ru8okxBYsEZEYl4G1bfH24+KZR2xceZGfFnYtlxf9M173mGTrttMcPnKNlX9+md8HGxjo53sDe3q44upiQ3hEPF6eZfPilvlv8d+d1np9nAU+FgRhEuAliuKzAP5+giCcBy4AHkBdJENQFvCnIAi9gTJPU4iiGCiKoguwDigcGxEsimKmKIoJwCGg9CD34pSk4zO2av//DVQtxz0B6SUjMiKOqIcJ5OTksWvnWfz9dXcL8vevR9A2qUHcu/c8zZvXQhAE/P3rsWvnWXJycol6mEBkRBz16lXF1tYcB3sr7t+X1tI5efImNWo8fzcbD08XHkTGExWVSG5OHnt2X8DXz1NHpo2fByFBZwHYv+8yTZu5IQgCK9d+xe4D49l9YDwDBvowZNjbvDfAGxtbM1T2FkSES+6rp0/d1lkcsrxUrW1J3KN0EqIzyMvVcO7gQ+q30P1dsVEF60JcORWNndOrDf4H9GlE0KrBBK0aTFufmgTvuYooily8+gjTypXKFeJ2/VYME2fu4Y9ZfbG2ermBt2stC+IL5cHfh6PwaqE7qxgXVdBJXzsdg62TpOPT9BwWjTtJ98EeVPcs/8tVXU8nHkQm8CgqidzcPPbvvoxPG91ZVe82tdkZIg3UDu6/SpOm1fM7XY1Gw4F9V2jXsaB89323ObsPBhCydxRLV3+Ka1XrYoYkAC8vr9deT54+zSY9XRp4Pn2azfHjN6jp7gSAnZ0FZ87cBuDUqVtUqfriXV1capmT8DiDpJin5OVquHjkMXWb65b3+EcFcf83z8Rho10AumYjW2LC08jJUqNWa7h/JRGVa9nLloenK5GRCTr1t02x+utZqP5eomkzdwRBICoqkbw8aQb28aMkIu7H4qjdpTFRu9ZM9ONkQg9cpnOX4uFVzxjwvg/BW0cTvHU0b7etR1DIGamuXArH1MRQx50cwM7WHJPKhly8FI4oigSFnKGtvxcAEZEFLu+hh65Qvdqr7apjXd2UtJhM0uMyUedpiDgVh3NDXUPd05QCY1PU+QTMHV/+hRNgwPu+BG8LIHhbgJQfwYXyw9So5PwwKZQfwWdoqy3jYUevs+zPAyxc8ClGhRaqT019yrDhi/j+ux40ali6a/2AAb4EbxtL8LaxWl1OS7pc1OpSdOLATqvLxWe6nM7X5ZXypH9rgjeNJHjTSN729yJo+zltnkRgamqoE+Im5YkZJiaVuHgpQtJj+znaast1RGR8vlzooav5ZSQ1NZOc3DwANm05ReOGNXTWVyortT0ciXqQxOOoZHJz1YTuuUZr3+KLs79OBvRtTNDqoQStHir1N7svF+lvyt6fXb8Vw8RZu/hjdr9y9TdeXlWIjIwjKkrb1u76G78iz97Pvx7BQZJRcO/eC/lt7ZvCqaYZiY+fkqxtW6+ExVC7mW6bkFiobb19Nh5rbf3NyVKTkyWVh7sXElEoBZ2FocvLpfi7VDNzwMXUDn2FHt1qtGb/g7PF5GqYO2FWyYS/4wo8gx+lJ9DMoS5KQYGeoKS5gwd3Cy3cXR7qejrz8EFifn+8b/clfPx0dxP08avLjmCpPw7dd5UmzWro9sd7r+isX/iyvMrzMbc15P5lyRiXk5XHw1tPsHV+ufGRg7spydFPSYnNRJ2r4caxWNyaFLTzlSrrMWK1D8OXtGL4klY41jTLNySlxGaiUUseZE/iMkl6lIG53esJc64oLsTcprqFE65mKvQVevSq5cOee6d1ZKLS4vFxlQyK7lYuGOrpk5D5BGsjMxSC9Opbxdye6haORDyJKZbGy+Bex5bHD1OJeZxKbq6asP33aOqja3y5dyuBBTOOMn52eyysXs4j6nWOScKOXmfZ8gMs/H2oTh+clJSGWltuHj5MICIy/qUmL/6tiBqxQv/+bfyXPZPy0DWmvVTrKopimCAIPkAXYKUgCPOAo0jePE1EUUwWBGElYCiKYp4gCE2BtkBfJKOQfzmTXAfsAiY+U6GoSpThtwmCUK0kHQuJPHsDUfMS5URPT8n4Ce8yeMh8NGoNffq0xN3dkfm/huDpWQX/tvXp27cVo35YQft24zE3N2bez5Lzk7u7I506NaJL58kolUomTOifvwPUuPHv8sPI5eTmqnFxsWH6T8+PidfTUxIQ2JvhQ5eg0Wjo2aspbu72LPhtNx4eLrTx96RXn2YEjl5P1w7TMLMwZtacF8fZjwnsTcCoteTmqnF2tmbKtJcPw1AqFfT/qgG/jj6KRi3SqlNVHKuZE7LiGlVqWlK/lSOHg+5x4+84lHoCxqYGfDz69a2h7tuyBmEn79H+nUUYGuozPbBL/rmeH/5J0KrBAMxecJAd+66TmZWLb4/f6dutPl8N8Wb2gkM8zczhm3HSNsIOKjMWznqn3Hnwzpf1+CPgBKJGpHmHKjhUNWPnyhu41rTAq6UDYcH3uXUhHqVSyoOBoyQDQFhwOAmPM9iz9iZ7tOF/X8xohall2daw0tNTMmpsd0Z8tgK1WqR7r0bUcFOx6Pf91PFwxtevDj16N2ZiwCZ6dZ6Dmbkx02YVPO8Lf0egsjfH2aX82zLr6em99nqSmJjKl19Iy7Cp1Rq6dm2Ct48Ubjd16gdMm74RdZ6aSpX0mTLlxVv3KpUKen7uydLAM2g0Ik3bO2Nf1ZS9q2/h7G6BRwsVJ0IiuHMhAYWeAmMTPd79XvLSMjbVx7t3NeaPOAaC5JlUp1nZDa96ekrGBvZh+NBFqDUaevZqhpu7Awt+20VdD1f8/D3p1ac5Y0evpUuHHzEvVH8vnL/P8qWh6OkpEBQKAsf3zV/75LuvV/AkJQM9fSVjx/V94UL+z/D1qcuRsGu06zQFI0MDpv9YkH89es/M3/lk4vh+0ja82Tn4tK6Lj7f0MjR33nbCI+IQFAJODpZM1m61Gx+fSp93Z5OenoVCoWDVmsPsChmrs2B3SSiUCpoMcid09mVEjUgNHwcsnCtzaUs4VtVMcWlow629j4i6kICgEKhkok+LYQXeDHunXiA1+il5WWq2jjhB8yG1cSzH9uK+Ph5SfnScjJGhPtOnFawZ1qPXTwRvCyjIj7FrycrOxce7Lj4+Un5M/XEjObl5fDz4d0C7/fCk91i7PowHD+JZ8MduFvyxG4Dly77E+jnrA/n6ekq6dJgoPZvpAwvpMp3gbWMlXSb0JyBgtVYXD3y0dWP//otMnbaRpKR0Pv3sD+rUdubPZdKOP/5tx5GekUVurpoDoZdYvuwrnV3AdPTwrsORozdo12W6lCdabyKAHu/MIXiTFNE+MbAvAeM2SHq0ro1Pa8mAPfeXHYRHxBeUkfF9AbgXHsuYcRsAcHezZ9rkl9umWU9PwbcBHflu+HrUGpGuPetT3c2OpQsOU9vDAe82tbhx9TEB324kLTWL40fusOyPI6zbVv6d40rCt6UbYSfu0f6dPzCspM/0cV3zz/UctJSg1ZLRffbvoezYd03qb7rPp2/3Bnw1xIfZv4fy9Gku3wRuAcBBZc7C2f3K8LuVjBv/LkMG/45Go6F3nxZSWzt/u9TW+tejb9+WjB61kg7tJ2JubszceYPzr2/rP44MbRkIDb3Esj+lMjB79lZ27jhHZmYObXzH0rdvS778qutzNClAqVTQ9bParJpwHo1GpGE7J1RVTAhdexdHdzPqNLPj1I6H3LuUiFKpwMhEj97fSkbHjCc5rJrwN4IgYGZdib7fe5X5GZSEWtQw4cQyVneagFJQsPFWKHeSH/Jdo/5cjr/HAa1hqVuN1mzXLrz9jF3hJ2np6MW+Pr8gInIk6gKhD869lB56ekp+GNudrz5djlqtoXuvxtr+eJ+2P65Lj96NmRCwkZ6dZmNmbsT02QV17Py5cG1//Oovvq/yfJp1cWHbL9eY//lxEKHh247YV3u5SUCFUkG7obXYOPkCoga82jpg62rC0fX3sHczw71p6TuRRt1I4dTWSJRKAUEh0O7T2hiblb6O46uy/pMptKnZEBsTCx5OD2HijqUsP7H9taahFjWMObiQTX2molAoWH91P7cSHzCm5QdcjL3DnnunmXB4GT+3H8FnDXsgAl/u+RmAFs6ejGn5AbkaNaKoYeSBBaRkFd9w5mVQ6in4bGRLJo7YjUYj8na3WlSpbsXaxedwr2NLM58qrPjtNFlP85gx9gAAtvYmjJ/T4aXTfNUxydRpm6U+eMgfgLYPnvguZ8/dY/7vu9DTU6JQCEye0O+NemnKvD4EQbAC/ofkCBIB9BNFMbmIjB/wc6FDtYH+oigGae0BvkgOMAAfiaJ48blpFt0B4b+CIAj6QDRQC0gHjgB7gEbAPFEUD2nlDqMNexMEIQJoLIpigiAI6aIomgiCUAWIEkVRLQjCl4AbsAJYDbwF2AKXgdFIYXTGoijGCYJgDtwXRdFa69WULorinFJ0dRdF8Y7281eAryiKfbXX9aRQmJv2sxLJoFULMNIenyyK4spnvwfILUlHrUzh39kYmCOKYpsXZKlOmFtFIeBHlro8kX5vBkNlFw4/Cnyx4BumjZO0nbaYuLJiFQEE64/Y96Ditxlt7zqT1JwtFa0GZgZ9APin1JuQ8O8qWg26V5tHtnp3RatBJaW0NTt5b25NtjKj14GpZz6taC0Y33QxqPdXtBqgbCf917x8aPFrQ9EWsiu+v6FSFxKyXhyy+qaxMZSMiWLS6grWBASrQWjEii8jCqEtG++8/m3Iy0s/998AqLK0dwVrApFDt5KWu62i1cBUv9c/6tksv/55BWsCn9T9A2F4xa9DJi48hc3cLi8WfMMkfC+177dTSnw9+z+lpsXIf8yYhDe+L2DFEu5Xv0KNI9UOXXrp/BUEYRaQJIriDEEQxgCWoiiW+vKlNT7dBZxFUXyqNSbtEEVxc1nT/M96JomimCsIwhTgDNJaRs9WJV0JLBIEIRNoUYZbtQF+EAQhF8koNUgUxXBBEC5o7/kQOK6VNQWCBUEwRKqIZX17myEIQi1AA0Siu5PbZaTwNhtgqiiKjwEEQdiItCtdOJIxSQdRFC+VoqOMjIyMjIyMjIyMjIyMjMy/hx5ItgmAVcBhJIeW0ugL7BZF8aV3CPjPGpMARFGcD8wv4VRht4U2heSrFvpsov2/CulhFb33R6UkW2xNI1EUJ71Azz6lHC/1OlEURwHF9v0u7GFUmo5Ffuc5CuWBjIyMjIyMjIyMjIyMjIzMPwqVKIrR2s8xwIvWlugPzCtybJogCBOAUGCMKIrZxS8r4D9tTJKRkZGRkZGRkZGRkZGRkZER1RW7BJAgCMOAYYUOLdHuoP7s/AHAvtiFoLO+inbn+FJ/jCAIDoAXUDh+MgDJCGWAtGP7aGDK8/SVjUn/IARBWAC0KnL4V1EUV1SEPjIyMjIyMjIyMjIyMjIyMm8ereFoyXPOv13aOUEQYgVBcBBFMVprLIorTRboB2wTRTG30L2feTVlC4KwAmmd5eciG5P+QYii+EVF6yAjIyMjIyMjIyMjIyMjI/OvIgT4EJih/R/8HNn3kDyR8ilkiBKQNvm6+qIEZWOSjIyMjIyMjIyMjIyMjIzMfxpR86/e6X4GsFEQhMFIm3b1A9Duzv6ZKIpDtN+rAi5Iu9kXZp0gCLZIG4VdRHfTrxKRjUkyMjIyMjIyMjIyMjIyMjIy/1JEUUwE2pZw/BwwpND3CMCpBDn/8qYpiOK/2vom889BLkgyMjIyMjIyMjIyMjL//yJUtAJvkrstPCv0ndbt5NV/Vf4qKloBGRkZGRkZGRkZGRkZGRkZGZl/D3KYm8xrQ+RQRauAgB95mv0VrQZ6inbcTJ5d0WpQ2/IHAMSE5RWsCQg2n3A58aeKVoN61gE8zdte0WpgrNdN+pC9s2IVAajUhWPREypaC1o7TCFbvbui1aCSspP0IW/v8wX/L9DrwPpbFb83w/u1FoC64ttWlO2k/5rQitUDQNEWciu+vKLficSsDRWtBdaG7wEgJq2uYE1AsBr0j2lbD0aNrWgt8HeeDkD/3R9VrCLAX51Wkqup+LZVX9GB8/E/VrQaNLQdB8Diq8MrWBP41HMhNnO7VLQaJHy/E2F484pWA3HhKQDup86vYE2gutmIf8yYREamMLIxSUZGRkZGRkZGRkZGRkZG5j/Nv3wB7v9z5DA3GRkZGRkZGRkZGRkZGRkZGZkyI3smycjIyMjIyMjIyMjIyMjI/KeRPZPKh+yZJCMjIyMjIyMjIyMjIyMjIyNTZmRjkoyMjIyMjIyMjIyMjIyMjIxMmZHD3GRkZGRkZGRkZGRkZGRkZP7TiGo5zK08yMYkmdfG0bBrTJu2EY1GQ993WjFsWEed8zk5uYwetZJr1x5gYVGZeT8PwdnZBoDFi/ewZfNxFAoFgeP64e3tQXZ2Lh8MmENOTh5qtYb2HRoyYoS0nfratYdYveogDx7Ec/LkHCytTMqm49HrzJi+GbVGQ5++LRk6tH0xHQNGr+HadUnHufM+wcnJmhPHb/DzvBByc/PQ19fj+x960rx5rZfOq/MnH7L051NoNCLtutei76D6OueD119hX8gtlEoBc0sjvgr0xs7BFIBJ3+zh9tU46tRXMX7uq2/RKYoi034JJezkPQwN9fkpsDMeteyLyf28OIzgPVdJTcvi/IHv8o//te0C67aeR6lQYGysz5RRHXGrZlNuPS6cimLFL2fQqEXadnOn16B6Oue3b7hG6PbbKJUKzCwM+XxsK2wdTIiPTmd2wEE0oog6T6RT39q071W7XGkfP3qT2TOC0ag19OzTjE+G+uucz8nJY3zABm5ci8LcwpiZcwfi6GSVfz76cTJ9us/msy/aM+jjNgCsX3OUrZtPIYrQu28zBgzyKZMuoigybeY2jhy9gaGhATOmvodHXediclevPyRg3AaysnPx9a5D4OheCIKQf375qsPMnBvCySNTsLI04fTZu3z+9XKctXq3a+vFl5+VrfxcOR3Nht8vIKpFvLtUp/OAOjrn9268xdGd91EqBUwsKvHxqKbY2FcGYNOiS1w+9RhRI1K3sT3vffWWjp4v4tjRG8z8aSsatUjvvs0ZPPRtnfM5OXkEjlnLde2zmT3vQ5ycrLlyOZIpE/8n5Skw/IuOtH27HtnZuXw86DepXcnT8Hb7+nzxVacy6SKKItN+2sKRsOsYGhkwY9oAPOq6FJO7eu0BAYHryMrKxdenLoEBfRAEgZlzgjh0+Cr6+nq4utjw04/vY2ZmTNSjRDp3m061qnYA1K9flSkT3y1zHj3j7t8J7Fl2G41apGF7J1r3rVqi3PUTsWyacYWhc5vi6G5W7nSeIYoi06Zv5kjYNSk/pg8sPT/GrtHmhweBY/siCAK795zn9wW7uHc/lk3/G4mXZ5X8a27eesTESRtIT89CoRDYvHEUlSrpl5D+Jil9Q31mTB+Eh4dryekHrJbqio8HgWPfQRAEUlIy+Pa7P3n0KBEnJ2t++XkI5ubGpKVl8sOoFTyOTkadp+GTT96mT+8WAMyavZUjR66hETW0anWSwFENdcqzVEa2auuvPjOmvV9KnjwkYNx6KU+86xAY0BtBEPjlt12EHryCQiFgbWXKT9PeR2VnLuk0Zq2kk1rDJx/50adXs1Kfzanjd/hl5h7UGg3dejVk0GBvnfM5OXlMDdzGzRuPMTc3Zuqsvjg4WZKbm8fMKTu4ef0xCoXAN6M60rBJNQByc/OY+9MuLpyNQFAIfPpVW/zerluqDiUhiiLTft5H2AltfzO+Kx61HIrJ/bzoEMG7r0j9zcFR+cfPXnjAT7/s49a9OOZO6UVH/zrFri2zHm+gnS0v187EsHHBJUSNSKvO1ejwnu64Imz7fY4E30OhEKhkpMeAbxviUNWM9CfZLJ18mshbSTTvUIX+I94qfyYUob6NFx/WeR+FoOBgVBgh93cWk2lu34S+7j0RRXiQ9oDfLi3GxtCa7xt+hSAoUApK9kYe4MDDQ+VK+9jR68yYvlU7NmvBkKHtdM5LY7O1XL/+EAuLysyZ95F2bHaTX+aFkJurRl9fyfc/9KRZ85oA7N51niWL96FRa/Bt48F3I3uUS6eLpx6x+tdzaDQifl3d6DHQU+f8zr+uc2jHXRRKATMLQz4NaIGtvVQG3vdZi2t1CwCsVZX5YaZfudIuSviFRA4vv4NGI+LV1oGmvauWKHf7ZBw75lzl/ZmNsXcraNtT47NY9c1pWvSrRuMexdvIsuJftRHT/YahEBSsvbqP+Wc26Zx3MrVlQcfvMDOsjFJQMPXoSg6En8PFzI4THy3ibvIjAP6OvsnIAwteWo8X8efAQLp6tSIuLRmvqQPeWDoA505EsmjuMTQaDR171KXfR410zl85/5jF844SfjeRMdPa493WDYBL56JYMu9YvtzDyBTGTGtPyzbVy5z2P31MIvPfQA5zk3ktqNVqpkzZwNJlX7Jj50R27jjL3buPdWQ2bzqOmZkx+/ZP5cOP2jJ3zjYA7t59zK6dZ9mxcwLLln3FlMkbUKs1GBjosXLVtwSHjGdb0DiOHb3GxYv3AWjYsAbLV3yt80L/Yh01TJu6kUVLPidk+zh27fybu3ejdWS2bD6JmbkRe/ZOYtAgP+bNCQbA0tKEBQs/JSgkkOk/DSRg9OpXyCsNi+ecYOLPHfh9Qx+O7rvHg/BkHZlqtayZt7In89f1oaVfVVb+fib/XK8BXnwz0fel0y9K2Mn7REYlsfd/w5gyqgOT5+wrUc6vVQ02Lh1U7HjX9nXZvmYwQas+Zsj7zZjx28Fy66BWa/hzzmkC57bj5/U9OX4gnIfhKToy1WpaMXN5N+au6UFzvyqs+eMcABY2Rkxb0oU5q3owfWkXgtZcISn+abnSnjFtG78vGsINET4aAAAgAElEQVSWkB/Ys+sC9+7G6MgEbTmNqZkRIXsCGDDIh1/n6Q6y584KoZV3gQHr7p1otm4+xZq/vuZ/W78j7MgNHkQmlEmfsGM3iIhMYN+OsUyd8A6TftxcotykHzczdWI/9u0YS0RkAmHHbuafi45J5vjJWzg6WOpc07hhdYI3jSR408gyG5I0ag3rfv2bb2f6MHVVR04fjORxxBMdmSruFoxf3I7JyzvS2NeFzYsvSflwNYG7V+OZ/GcHpqzoSPjNJG5djC9TuiA9m+k/bmbh4k8J2j6G3bvOF3s2W7ecwszMmJ17xzHwwzb8Mnc7AG7uDmzY9D2bto1i4ZJPmTJpI3l5agwM9Fi2/As2bxvFxq0/cPzYDS5diiiTPmFHrxMRGc++3eOZOuldJk3ZWKLcpCkbmTq5P/t2jyciMp6wYzcAaNWiFjuCAti+bQxVq9iyeOn+/GtcXWwI3jqa4K2jX2rQplGL7Fp8iwETG/DFghZcDYsh/kF6Mbnsp3mcDnmIU82XNyI9IyxMmx97JjJ18ntMmvxXiXKTpvyPqVPeZ9+eiVJ+HL0OQE13R36bP5QmjWvoyOflqflh9ComT+zPzu3jWL3qa/T0lCWkf42IyDj27ZnE1MkDmDSllPQnb2DqlAHs2zOJiMi4/PSXLN1Lixa12Ld3Mi1a1GLJ0r0ArFt/hBo1HAgJCmTN6m+YOWsLOTl5nL9wj/MX7hMSHMiOkPFcuXKFM2fv6up09AYRD+LZtytQKiNTNxXTB2DS1E1MnfQu+3YFEvGgoIwM+dif7dtGE7xlFG1867JgoVanDceoUUNFyNZRrFnxJTNnB5OTm1fivdVqDXOm72LuHwNYv+0LDuy5Svi9OB2Z7dvOY2pmyKYdX/PuB83545cDAIRsOQ/A2i2f88uigfw2dx8ajQaAVUuPYmlVmf9tH8H6bV/wVqMqlJewk/eIfJjE3k3DmTKmM5Nn7SlRzq91TTb++XGx4w72Zvw0vhtd23mWcFU59HiD7WxZ0ahF/pp/kS9/asWE5e05e/Ah0RGpOjJN/F0Yv6wdgUvept27Ndm86DIA+gZKun1cl96f1Svp1uVGQOATj4HMODeP74+OpZVDM5xMHHVk7I1V9KjRlYknp/HDsUBW3VgPQHJ2CuNP/ciY4xMYd3IKPap3wbKSRZnTVqs1/Dh1EwuXfEbI9rHs2vk394qMzbZuPoWZuTG7905g4KA2zJsTAoClZWV+X/gp20ICmPbTBwSMXgNASnIGc+cE8+eKLwjeMZaEhDROnbxVZp00ag0r5p1h9Bx/5qztxokDEUQVGZNUrWnFtGWdmbWqG83auLL+j/P55wwqKZmxsiszVnZ9ZUOSRi1ycOktegXW56NfmnHzWByJDzOKyeVk5nFh50PsS5ggOLLyDlXfKvt4uSQUgoKZbYfz7taJtFo5nN61fKhppWu4+L55f4JvH8V/zQiG7pjJrLaf55+LeBKN35qv8Fvz1Rs1JAGsPLmTjr99+0bTAKnsLpgVxtRfu7J44/sc3neHyPtJOjJ29iZ8P7Etfh1q6hyv39iZBev7s2B9f2Ys7EklQz0aNi9uCHoe/+Qxyb8ZjUas0L9/G7IxSea1cPnyZVyr2OHiYouBgR6duzQhNPSyjkzowcv07CXN7nbo0JCTJ28iiiKhoZfp3KUJBgb6OLvY4FrFjsuXIxAEgcqVDQHp5SIvT50/I1i3rmu+V1NZuXI5AhdXG1xcbCQdOzfk0EFdHQ8evEyPHtJsb/sOb3Hq1C1EUaROXRfs7KTBkZu7A1nZueTk5JY/o4A71+OxdzbD3skMfX0l3u2qcyYsUkemXiNHKhlKjoO1PO1IjCsYONRv4oSRse4M/asQeuwOPTp6IggCDTydSE3LJi6h+EtoA08n7GyKz7yaVK6U//lpVi7lcDrJ5+71BOydTVE5maKvr6TV29U4d/SBjoxnI4f8PKnpYUtSnGQw0tdXom8gvWjm5aopbzt89coDXFyscXaxRt9Ajw6dG3D40DUdmcMHr9GtR2MA3m5fjzOn7iCKUkKHQq/i5GxFDTdVvnz4/Tg861XByMgAPT0ljRpX5+CBK2XSJ/TQVXp2ayw9j/pVSU3LJC5e9wUjLj6V9PRsGtSviiAI9OzWmNBDBff/aVYwP3zb9aWeRVHu30zCzskUW0cT9PSVNPV35cLxRzoytd9S5T+b6nWtSX5mzBMgN0dDXp6G3FwN6jwNZlaGZU776pVIXF1tcHaxQd9Aj46d3uLQQd18PHzwCt17NgGgXfv6nNY+m2d5D5CdnZefF4IgYKwts1K7oqGs2RR68Ao9uzfVPptq2meja1iLi39CekYWDepXk55N96b5bWHrVnXydWpQvyoxsSnF0nhZHt15gpWDEZb2xij1FXh4q7h5urjh7tC6e7TqUxU9g1fv/kMPXqZnjzLkR3qh/OhRkB81athTvZqq2H2PH79JrZpO1K4teYpYWpigVBbXV0q/mZR+g2qkpj4lLq5I+nHa9Bs8S78ZoaGXCl3fHICePZpzQHtcECAjIwtRFMl4mo25eWX09BQICORk55Kbm0dOTh65ubnYWJvq6nToCj27NylSf0srI9r6270JodpybWJSUD8yM3MKlVvIyMgupJMxeiXkCcD1q49wdrHCydkKfX093u7oydHDui/SRw/dolP3BgD4tavLuTP3EUWR8PvxNGoqeSJZWZtgYmrIzWvSxNCOoAsM+kTycFIoFFhYVi4x/ecRGnabHp3qFfQ36VnEJaQVk5P6G9Nix50dLKjlpkJQvFrj9k9oZyNuJmHrVFnbtipo7OfMpRO6k3BGlQv6+pwsdf7nSkZ6uHnZoK//eobxbhbVicmIJS4zHrWo5kT0aRrb6Xo7+bv4si8ylIw8qX1PzZGem1pUk6eRDJv6Cr1yeZ4CXLkciaurLS7adr5T54YcLNLOHzx4hR49mgLQvkMDTp+6XWhsZi79hkJjs4dRCVSpYouVlVSGmreoyf59l8qs090bifljEj19JS3ersK5Yw91ZDwa2uf3e24etuWaxCoPMXdTsbA3xsLeCKW+gtqt7bh3tnjbfnzDfZr0qlKsbb97Oh4zOyOsXcpfXwvT0L4m4SmPiXwSQ64mj223wujk1lxHRhRFTAyMATCrVJmYjKSSbvXGOXr3IkkZqS8WfEVuX4vD0cUcB2dz9PWV+LZz59SRcB0ZlaMZ1dxtnlsvjobeo3ELVwwNyze2/yePSWT+O8jGJJnXQmxsLA72BbNz9ioLYmN1vW3iYlNw0M7g6ekpMTU1IiU5g9jY5FKvVas19OzxI61a/kDLlnWoX7/ay+sY90QnHZXKktjYIo1u7BPsi+qYojsDtG/fRerWccHA4OUMOonxT7GxK+jUre0qk/icQcj+7bdp1KJ8sxXlITY+HQe7gpkseztTYuOLD+6fx7ot52n3zmLm/HGYwG/efvEFRUiKf4q1qiBPrGyfnyehO+7wVnOn/O8JsRl8PzCYz3puoucHnljZGpc57bjYJ6gcCmZRVSoL4ouWi7gn2NtLMnp6SkxMjUhJecrTjGxW/HmIT4frhkvWcLPnwt/3SUnJIDMzh2NHbxITU7ZOOjYuNT8t0NaHIi/IsXFPsFeZF5GRBk4HDl3Fzs6c2rWcKMrFSxF07zubIcOXcKeIh09ppMRnYmVrlP/d0taYlPjMUuWP7byPZ1MpbMXNw4ZaDez4rncI3/cJwbOpPY5Vyu4RExv7BFXhOmtvUcxYUFhGejaG+XX28qUIenWbQZ8eMxk/sV/+oEmt1vBOr1m0aT2OFi1rUq9+1bLpU6gcwLO2qrg+9qpCMvbFnx/Alq2n8PEuCBGKepRIzz4z+eDDXzn3970y6VOYtMRszGwKDBFmNoakJWbryETfSyU1IYuaTcofhloSsXEp2Bdru3XLeWxsim5+qCyIjXt+XQiPjEMQYPDQ3+nVZwZL/9xfolxsbJH07S2L3Ts2rmj6lvk6Jiam5b+I2tqakZgotXsDBrTh3v0YvH0C6N5jGoEBfVEoFLz1VnWaNatJa58AWvuMwdvbmxo1dEOCY2OflJAnLygjRWR+/nUnvm0nsX3n33z9ZWdJp/e9uXc/Fm+/iXTvNZPAMb1QKEoewsXHpaKyL6hntnZmxMemliqjp6eksokhT1Ke4lZTxbEjt8jLU/M4KplbNx4TG5tKWqpU55csOMhH7y4icORGkhKLTzq8iNj4NBxUhfobW7Ny9zevgzfZzpaVlIRMLAv1VZa2RqQkFG9bDwfdY/wHe9i25Arvflm/2PnXgZWhJYlZBS/+SVnJWBnqelw5VLbHobI9k5sHMrXFeOrbeOWfsza0YmarqSzwm0fI/V0kZ5f9pTQuLkXnWahUFsSVODYr3AcbFhub7d93kbp1nDEw0MfV1ZaI8FgePUokL0/NwdArxMTojkmfR3L8U6wLj9NsK5P8nH7v8I671G9W4MmVm6Nm7OCdjB+2m7NhD0q9riykJ2VjalMwaWdiValY2x57P420hGyqN9Jt23My8zgbFEmLflVfSQcABxNrHqcVeFg/TkvAwcRaR2bWyXW8U8ePy8NW8VfvyQSELso/52puz8GB8wnpN4PmTh6vrM8/gYT4dGxVBZOsNioTEuOLe429iLD9d2hTxHOpLPyTxyQy/x1kY5LMPxqlUkFQ8DgOH/mJy5cjuH370YsveoPcvRPNz3ODmTi5//9Jeod33+HujXh6ffB6XNnfFAP6NGT/pk/5fngbFq48+UbTCttzj/s3E+g+oCDMwUZVmblrevDbxj4c3nWPlKTSB32vk0V/7OODQd75ni7PqF5DxUeD/fh86BK++HQptWo7oizlxe91kpmZw+KlB/j6i47FznnUcebg3vGEbP6Bge+35otvlr/29E/uiyDiVhId+0shf7FRaUQ/SGXOpm7M2dSNG+fjuH257GFur0q9+lXZtn0MGzZ+x59LD5CdLXkTKpUKNm0bxf5Dk7h65QF37kS/4E6vl4WL96LUU9K9q+TtZmdrxqEDkwnaMpoxo3rx/ahVpKe/3jIsakT2/nmb9p+Uf8D6f406T83f5+8ze9ZHrF/7HQcOXOJkOUJUXgZBEPI9TI4du06d2i4cDfuJoK0BTPlxI+npmURGxnHvXgxHDk0j7PB0Tp069UYG2d9+3YUjoZPo1qURa9cflXQ6fpM6tZ04emgyQVt+YMr0LaSnZ732tLv2fAs7lRmD31/CL7P34FXfBYVCQK3WEBebilcDF1b+7zM86znz29ySQ6L/f+d57eyboE3PGkxd25GeQz3Ztfbmiy94QygFBfbGKqacnsH8iwsZ5vkRxnqSMSwxK4nRx8fzzZHR+Di1wtzg1cNoy8PdO9HMmxvChMlSOI65uTHjJ/Zj5Hcr+fCDX3FysirRu/F1cHTvfe7fTKTb+wUGkt8292b6n134cmJrVs8/R+yjN2cwFTUiR1bewfcjt2LnTm4Mp2FXFwyM/m+WyO1d25e/rh2g3pIP6b91In90/h4BgdiMJBos+Qj/NSMYf3gZi7v8gImB0Ytv+B8gKSGD8LuJb3TS+EVUxJjkn4xGU7F//zbkBbhlXgsqlYroQrM+MbEpqFS6s1p2Kguio5Oxt7ckL09NWlomFpaVUaksX3itmZkxzZrV4ujRa9Ss+XIzgSo7c510YmOTURWadZR0NCemqI4W0uxUTEwyI75awvQZA3F1tX0pHQCsbY1JKBS2lhiXgXUJnjQXzzxi08qLTFvYNT+M63Wxbst5NoVILt9edeyJjiuYtY6JS0NlWzy8oCx0ebsOk+fsBbqU6zorW2MSYwvyJCm+5Dy5fPYxW1ddZvKCjiXmiZWtMa7VLbhxMZYW/lXLlLadypzY6IJZ1NjYFGyLlgs7c2JiUlDZW5CXpyY9LRMLC2OuXn7AgX2X+WXuTtLSMlEIAgYGevQf0JpefZrRq48UMvnbL7uKlbXCrPvrGBu3nALAy8NFx4spJjYFlZ3utSo7c2IKzT5JMmY8eJhA1KMkerwzR3v8Cb3fncem9d9ga1MwuPf1rsvkaVtISk5/4aKxFrZGJBWakU2Of4qFbfFB4PVzMexce51Rv/rnP5sLxx5Ro641htqwTK9mDty7lkDNemWrPyqVObGF62xMSr4nSVEZ+/xnk5VfZ59RvYY9RsaVuHsnGg/PgsVHzcyMadLUjeNHb+DuXnwRYIB168PYuFkykHp5uhZ/Nqri+hR2FY+J0X1+W7ed5vCRa6z888t8t3cDA/18T0dPD1dcXWwIj4jHq5CuL8LUuhKpCQXGhdSELEytC4yc2Zlq4iIzWBn4NwDpyTlsmHaR9wIblGsR7nXrj7Bx0wkAvLyq6Mz2S/mhu1aKSmWhmx+xKajsnr+eir29BU0a18gvmz4+Hly7/pAWLWqxbt06Nm7cCKTj5Vkk/ZjkYvdW2RVNPzlfR2trU+LinmBnZ05c3JP8kJitW08ybGgHBEGgShU7nJ2tuX8/ljNn71C/frX88Gtvb28uXIrg1u3HRcpI0Tx5QRkpQQagW9fGDBu+mBFfdmLrtjMMG9JW0snVFmcna+6Hx1LPq/i6RbZ2ZsTGFLTp8XGp2KrMSpSxU5mTl6cmIz0LcwtjBEHg6x8KjCTDBi3DtYo15hbGGBrq06attOC1f3sPdmy7UCztkli3+RybQiRZrzqORBfykoqJT33p/qa8/F+3sy/CwsaoICQYSI7PxMKm9Bfsxn4ubPi1bHleXpKykrE2LFhTx8rQkqQsXU+exKxk7qbcQy2qic9MIDojFvvKKu4/KQjrSc5O4WF6FLWtanI65lyZ0razs9B5FrGxKdiVODZLyR+bFW7nY2KS+fqrZcXGZm38vGjjJ3lPbdp4vFRPvpKwtDXWWV4gMT4DyxL6vStnowlafYUJv7fXGZM8845WOZlS9y0VEbeTUDm9XDk3sapEWkKBJ1J6UrZO256TqSbhQQabJkhlIyMlh+AZl+kxph4xd1K5czKeo2vukZ2RBwpQ6it4q3PxxeZfRHR6Io6mBZ5PjqY2RKcn6sgM8GxPv60TADgXfZNKSgOsjcxIyHxCjloyqF2Ku0tESjRulk5cjNVdc+7fho2tCfGxBR6aCbHpWNuWL5wwbP9dWrapXuK6gCXxbxmTyPx3kD2TZF4LXl5eREbEEfUwgZycPHbtPIu/v643jb9/PYK2SQ3g3r3nad68FoIg4O9fj107z5KTk0vUwwQiI+KoV68qSUlppKZKA62srBxOnLhB9erFdxkrK55eVXgQGU9UlFbHXefx89PV0c/Pi+Dg0wDs23uBZs1rIggCqalPGf7ZIr79rgcNG9Yo6fZlxr2OLdEPU4l9nEZurpqj++/T1Fv3heD+rQQWzjxG4Oz2WFi9/tmbAX0aErTqY4JWfUxbn5oE77mKKIpcvPoIU5NKJa6NVBoRDwtc4w+fuEcV5/Iv8uhWx4boqII8OX4gnMatdWdpwm8lsmTmSUbPaot5oTxJjMsgO1taryE9NZubl+NwrFK64aYoHp4uPHiQwKOoRHJz8ti76yJt/HRdsH39PNgeLA2MD+y7TJNmbgiCwPI1X7BrfyC79gcyYKA3g4e1pf+A1gAkaUNmoh8nc/DAFTp1aViqDgP6t85fGPttfy+Ctp+TnselCExNDbGz1X1BsbM1w8SkEhcvRSCKIkHbz9HWz5NaNR05eWQKB/eM5+Ce8dirzNn6v++wtTEjPiE1f52ny1ci0WhELC1ePOipVsuK2Kg04qPTyctVc+bgAxq01DXoRt5JZvW8c3w13Rszy4JQKys7Y25djEedJ62bdOtSHA7lCHPz8HQlMjKBKO2z2bP7Am38dBfebePnSUjQWQD277tE02buCIJAVJQU3gDw+FESEfdjcXSyIikpXaddOXniNtWqF1+35xkD3vfJX4Ty7bb1CAo5o3024ZiaGGJnW+Slx9Yck8qGXLwULj2bkDO09ZdeaMKOXmfZ8gMs/H0oRkYG+dckJaWhVkvTUQ8fJhARGY+Ls274wItwcjcj8XEmyTGZqHM1XDsaS61mBS9WhpX1GLXOl2+WteabZa1xrmVWbkOSlB++BG8LIHhbgJQfwYXyw9So5PwwKZQfwWdo6/98T8vWrepy+/ZjMjNzyMtTc/bsXdzcpLZ/wIABBAcHE7xtrDb901L6F7Xp2xU3BJuYGHLx4rP0T+en7+9fj6BgybgQFHwq/7iDgxUnT0keIAkJqYSHx+LsYoOjgyVnz94hL09Nbq6as2fPUqO6igHveRO8ZRTBW0ZJ9TfkbEH9NSklTyobFtTfkLO01b70RkQWeO6FHrySv6aUg4MFJ0/d1uqURnhEHM6llJE6Ho5EPUjkcVQyubl5HNhzlda+uruEebepxe6QiwAc2n+dRk2l9TSyMnPIfJoDwJmT91AqFVSrYYcgCLTyrcn5sxEAnDt9n6o1ymYUHtC3MUGrhxK0eqjU3+y+XNDfVK5U4tpIb4L/i3a2PFSpbUnco3QSojPIy9Vw7lAU9VrqLnodF1Xg0XL1VDR2TuXfMa4s3HsSjn1lFbZGNigFJS0dmvF3nK7h6lzseepaSV6npvomOFRWEfc0DitDS/QV0otnZT1jalvW5HF62cKoATy9XLVjM6md373rPH5+Xjoyfn6eBAdLG5Hs23uRZs3d88dmn3+2mG++607Dhrq7YD0LW33y5Cl/bThGn74tyqxTjdrWxDxMI+5xGnm5ak4eiKRRqyJjkttJLJt9ipEz/DC3LBiTpKdmk5sj9T2pKVncvhKPU9Wyj0mKYu9mSkr0U57ESm37zWNxVG9cYNSpVFmPz1d6M2RRS4YsaolDTTN6jKmHvZsZ7/7YKP/4W12dada76ksZkgAuxNymuoUTrmYq9BV69Krlw557p3VkotLi8XGV1mJzt3LBUE+fhMwnWBuZoRCkV84q5vZUt3Ak4knZy8g/lZp17Xj84Akxj1LJzVVzZP8dmvtULdc9Du+7Q5sO7mWW/7eMSf7NyJ5J5UP2TJJ5Lejp6TF+wrsMHjIfjVpDnz4tcXd3ZP6vIXh6VsG/bX369m3FqB9W0L7deMzNjZn38xAA3N0d6dSpEV06T0apVDJhQn+USgXxcU8YM2YVarUGURTp2LFRvvFn9eqD/LlsHwkJqXTvPhVfX09+nDbwBToqCRzXj2FDFqDRiPTq3Rw3dwd+m78DD09X/P3r0advS8aMXk3HDpMwN6/MnLnSbjLr14Xx8EE8CxfuZuHC3QAsXfYl1tblHwQr9RQMG9mSSV/vRqMRadu1Jq7VLVm35G/catvQzKcKK347Q+bTXGYFhgJSHPa4OdK6PAGfbicq8glZmbl80m09Xwb60LD5yw0OAHxbVCfs5D3a91uCoaEe08d2zj/X88MVBK2S8mD2gkPs2H+dzKxcfHsuoG+3+nw1uDXrtpzn5NkI9PSUmJkaMmNc59KSem6eDP6uOdO+3Y9GLW3D61Ldkr+WXqBGbWuaeLuyZsE5sjJzmTvuUH6ejJnVlqiIJ6z+7SyCAKII3d7zoEqNsu+uo6enZHRgLz4fthSNRqRHrybUcLPnj9/2UNfDhTb+HvTs05RxYzbQveNPmJkbM2POBy+878hvVpOSkoGenpIx43pjalY2o6Cvdx2OHL1Buy7TMTLUZ/rU9/LP9XhnDsGbRgIwMbBv/pbVPq1r49P6+Vtk791/iQ0bT6BUKjCspM+8WQPLtFCqUk/BgK8b8vMPR9BoRFp3qo5TNXOCll+hai0rGrRyYtPCS2Rn5rFwouSxYqUyZsR0bxr7OnPzQiwTP9kDgoBnU/tihqjnoaenZGxgH4YPXYRao6Fnr2a4uTuw4Ldd1PVwxc/fk159mjN29Fq6dPgRcwtjZs2Rdhy8cP4+y5eGSgsnKxQEju+LpaUJt289ZlzAOtQaDRqNSIeODfBtU7b1G3x96nIk7BrtOk3ByNCA6T8WbDnco/dMgreOBmDi+H7SNrzZOfi0rpu/DsHUaZvJyc3j4yF/AAXb7Z49d4/5v+9CT0+JQiEweUK/Yt5VL0KhVND501qsnXQBUSPS4G1H7FxNOLTuHo5uZjqGpdeFr4+HlB8dJ0tldVpBvejR6yeCtwUA2vwYu1Yqq9518fGR8mP/gUtMnbaJpKR0Ph2+iDq1nfhz6ZeYmxvz0Yf+9O03C0EQ8PHxoI1v8d27fH09pfQ7TJSex/SBhdKfTvC2sVL6E/oTELBam74HPj7S8x42pD3ffPcnmzefwNHRil+0fdLnn3ciIGA13br/iCiKjPy+J1aWJnTo0JBTp2/TrcePCIKAt3dH/Nvo6uXrU1eqv51+xMjIQLf+9plF8BZpm/uJ4/oSMG49WVm5+HjXwcdbqr9zf95OeEQcgiDg5GjF5AnvSDp91oGAwPV06zVT0unbbqV6FerpKfkuoDPfDl+DWiPStedbVHezY+mCg9T2cMS7TW269nqLKYHbeKfrr5iZGTFlVl8AkpMy+Hb4WgSFgK2dKROm9c6/7+fftGNK4FZ+nb0HC8vKBE4p31brAL4t3Qg7cY/27/yBYSV9po/rmn+u56ClBK0eCsDs30PZse+a1N90n0/f7g34aogPV64/5ssxm0lNy+LQsTv8viyMHes/Lb8eb6idLQ9KpYL+XzXgt9HH0GhEWnaqimNVM7avuIZrLUvqt3TkcNA9bp6PQ6mnwNjEgA9HN8m/PvD93WQ9zUWdq+HS8WhGzGyNQ9WXCy/TiBpWXF/L2CYjUQgKDkUdJSr9Me+49+L+k3D+jrvIpYQr1LPxYI73NDSihrW3NpKem4GXWVU+aNQfEAGBHeG7eZge9f/YO++wqI4uDr93F5AmHRYUUFQUFdRYIsbYUOxdbFGjsSVGTYyxoaiJBbspmlgwJrF+sYKKvZfYjWLFXpHeRERgd78/FheWohjFJcm8z+Pjsnfund/OnDtz7syZuYXO28BAzvgAP61ixwcAACAASURBVD4d+DNKlUrrmy38MZSqnq408fGis189/MeupFWLKVhamjJnXj8A1q4+woP7sSxetJPFizRvBly67HNsbUsyM3Aj4eGarRE+G9KSsm4OhdYkN5DRb+T7zBi5D5VKTeM2FXApZ8X6Zedx87Cl9ocurPnpLGnPMvlh4mEAbBVmjJ7VhIh7SSybcxJJklCr1bTvXRVnt8K/3S43MrmMJgMrsnHqedQqNZ4+pbBzNefY2ts4VihJ+Tpvv23PD6Vaxbj9i1jfZSoymYw1l/YQHnefcR/05nzUDXbeOsmkg8v4rvkXfFazA2pg2M7vAKjn7Mm4D3qToVKiVqsYtfcnEtNef8+1wrKm/xQaV6yJnbkVDwK3MHlbEMv/3PrW85EbyBgypgEBX2xBqVTTvH1lypS3ZcXik1Ss7IB3IzfCL0cxdcwOUpKfc/LoHVYtOcWSdR8BEBWRTGxUCl41/96Ki+Lskwj+O0gvZqoFgjdEreaAvjUg0YRMVf6btb5LDGS+XEuYo28ZeFiPBkAd+/b3x3ldJLv+hMXN0LcMqtn6k5r59p2K18XUoJ3mw/NQ/QoBKNGGo48n6VsFHzpN4blyh75lUELeSvMhc5d+hQAYtGBN+FB9q+CjSj+BUv9tK3Jfzf+qffrVASBrChn6t1cMWxGXtlbfKrA11gzIqONX6FkJSDYfF5u2df/D8fpWgY9zIAA9dvTTrxDgf61+I0Ol/7bVUNaCczHT9C2DmvYBACy5NETPSuBTz0XYzXu9bQqKgtivQ5GGeL86YRGjXqSJXL2d/KOelUA5iy+KjU8ChX4J7j+Si54eeh0c8bp07R9VviIySSAQCAQCgUAgEAgEAsF/mn/iUjN9IvZMEggEAoFAIBAIBAKBQCAQFBoRmSQQCAQCgUAgEAgEAoHgP41K7AD0WojIJIFAIBAIBAKBQCAQCAQCQaERg0kCgUAgEAgEAoFAIBAIBIJCI5a5CQQCgUAgEAgEAoFAIPhPIzbgfj1EZJJAIBAIBAKBQCAQCAQCgaDQSGq12GVK8FYQhiQQCAQCgUAgEAgE/14kfQsoSs5W8tDrM22t8Gv/qPIVy9wEAoFAIBAIBAKBQCAQ/KcRy9xeDzGYJHh7qA/oWwFITUjN3KpvFZgatONW0vf6lkF5yxEAJKdv1LMSsDDqwrWEOfqWgYf1aNTo31YlmgCQodqlZyVgKGvBpbiZ+paBp+24YtOOAMVGS6vNffStgh2dVhab8gCIS1urZyFga9yz2JRJcen3AJ4rd+hZCZSQtxJtaw48bccBcCNpvp6VgLvlyGLTBxcX3wjgcepyPSsBJ9P+XE+cq28ZVLQaxe3kH/Utg3IWXwAgDfHWsxJQLzpRbPobgSAnYjBJIBAIBAKBQCAQCAQCwX8aEZn0eogNuAUCgUAgEAgEAoFAIBAIBIVGDCYJBAKBQCAQCAQCgUAgEAgKjVjmJhAIBAKBQCAQCAQCgeA/jVjm9nqIyCSBQCAQCAQCgUAgEAgEAkGhEZFJAoFAIBAIBAKBQCAQCP7TiMik10MMJgneGmq1munT13Ho8CWMjY2YOaMvVau65kl36dI9/P1/J+15Bo0aejJhQjckSSIx8SlfjQzi0aM4Spe25fvvBmFpacbJk+F8PnQRzs52APj6vsewoW0A+H3FPtavP4ZaraZr1w/p1y/vKyuPHbnGnJkhqJQqOnapS/9BPjrH09Mzmei/lquXH2JpZcqseX0oVdpGe/xxRAJd2s/hs6HN+fiTxty9E83Yr1dpjz96GMeQYS3o9XHDQpfVmeP3WTLvKCqVmhYdKtOtb02d4xfPRbD0u2PcuRnHuGm+fNi0vPbY8gXHOX3sHgA9BtSmkW+FQuf7gj+PXmferG2olCo6dK5Dv4GNdI6np2cyefx6rl15hKWVKYFzelKqtDU7tp1n5W9HtOluXo9k5bqhVPIoxfDPfiUu5gmZShXv1SzLmAntkcsLH/x47vgDgr47gUqlxrd9Jfw+rq5zPGTNRXZvCUcul7C0NmH4hAY4OJUE4JsRO7l+KZrK1RVMnNeiUPkdOXyZ6dPXoVKp8Otan8GDW+YqgwzGjvmNy5fvY2VlxvzvBmptcMmSnWzccAyZTMaEgG40aFAVgPH+Kzh48CK2tiXZum1SnjyXL9/D7FkbOX58LjY2eQ4DcPTIFWYGbkKpUtHFrx4DB/nm0eU/dhVXrjzAysqMufP7Ubq0LX8eu8b387eQkaHE0FDO16M7Ute7IgAZ6ZlMn7aB06duIJNJfDGiLb7NaxSqnAD+OvGQ5d+fRKVU07RdRTp/XE3n+Ja1l9i39ToyuQxLK2M+H/8hDk7mRD9OYbb/PtRqyMxU0dqvMi06ebwyv3fdlty+HclXI5dpr/vgwdd88cUX9Otbpsi0vCDs4l169JjN/HkDaNmyFlevPuCbb9aQ8jQNmUzGkCHPXlpWtRy8+KxaH2SSjJ33DrL++rY8aRqUfp/eHp1Ro+Z20n1mn1kEQP+qPXjfsTqSJPFX9GUWh60ssD6mTZvGoUM732oZvKxs58zdxKFDlwD4fEhrWreuDcBHH33E06dPUaoTSIh/SmXP0sz6vicnjt3g+1k7UapUtOtUk48HNNDRlp6eydQJm7l2NQJLS1OmzvbDqbQ1GRmZzJqyjWtXIpDJJEaMaUnNOm4ADB3wK3ExKZQw1rhK3y3qg42teb7lUxQ2smXrSYKCdoNajZmZMd988xEeHs4A+PiMx8zMGJlchly+gFXrPtHJ6+/2exGP4uncbjZlyjoA4FXdlYDJfjx7ls6YkSt4+CAOmUxGw8ZV+HJkm3ztJTdHj1xl1oxNqJRqOvt5M2BQszxaJoxbxZUsLXPm96V0aVsuht1jyuQ/NGUMDBnakqbNqvH8eQaffLyA9PRMlJkqmjWvztDhrQqh49/Ttt65HsfSOcdJTc1AJpPw61uN+s3KFTrf/Dh7/D5L5/2JSqWmeQcPuvZ9T+f4pXMRBH13nDs34xgzrRkfNs3O79cFJzh97D4APQbUpGEh/JKi6INf3BdymQy5XMbGTeMB+GpEEHfuRAGQ/CQVi5KmhITk9Rfftm9Upowd475eq7lv5DIaNPJg+Fctc2f7Sk4eu83COftQqlS06VidXv11X1V/4ewDFs7dx60b0Uya0Z7Gvpp+NjIiiYlfb0alUqPMVNKpRy06dH0vvyxem7PHHxA0/7jWZ+vaV9fug9eEsTskHLmBDAsrY74MaKj12d6UM3/eY/G8o6hUKlp2qEK3frV0jl88F8GS+Uc0PvT05jRoqrHHC2cesnT+UW26B/cSGTe9OR80frN7pyB+6TOBtl71iX6SgNfUXm/lmkXV39y6Hcl4/9+5fOUBX41oz4ABzbXXSk5OJSBgJddvRCBJs7l9+3a98PDw42/lBwn+8YjBpP8YkiSVBbap1WrPXN8vA+ar1eorf/fahw9f4u69aHbvmsKFC3f45ts1rF83Lk+6b75dw9Spvale3Y1Bgxdy+MhlGjX0ZGnQTup5ezB4cEuWLt3J0qBdjB7VGYDatdxZsmSoznWuX3/E+vXHWL9uHIaGcgYOWkCTJvewL52dRqlUMXP6ZhYFDUahsKRX9x9o1KQK5Ss4atMEbzxJSQsTtuz0Z+f2v/hhfiiz5vXRHp83ewv1G2Q/AJd1c+CPTSO112/RZCpNmukU50tRKlX8PPsI0xe2w87BjBF9N+LdoCyu5bJHFxwczRk5yYeNq87rnHvq6D1uhseycFU3MjKUjP0shDr1XDE1N3qt/GdP38LCpf1ROFrQt8fPNGziQbnyCm2akE1nsLAwYfP2UezecYEF3+1kxtyetGpbg1ZtNQ7DzeuRjPpyFZU8SgEwY25PzM2NUavVjB25hn27L9K8VfV8NeSnacncP/n2x1bYOpgx6pMQ3m/giqubtTaNWyVb5v/WkRLGBuzYeIXfFp5izPSmAHTq5cXztEx2BV8rdH5Tpqxl+a9folBY09VvBj4+1ahQoZQ2zYb1x7CwMGX3nqmEhp5m3tzNfPf9IG7ejGB76Gm2hU4iOiqJTz75np27piCXy+jUuR69ejdm3Njf8uT5+HE8x45dpVSpAkaRsnRNm7qeoF+G4qiwonu3uTRp4kn5Ck7aNJs2nMDC0pQduyaxPfQs8+duYd53n2BtbcbCRZ/i4GDJjesRfDpoEfsPTQVgyZLd2NiYE7pzIiqViqSk1EKV0wtNQXNPMOmHFtg6mDJ2wFbqNHDFxc1Km8atoi2zl7enhLEBOzddY+XPp/l6ahOs7UyYsbQthkZynqVm8FXvYOp86IqNvelL83zXbUm5co6EBAdof2/DRpPw9fUFrhepFqVSxdy5m6lfv7L2OsbGRsya1Y+yZRVERSXSxS8Q089dkJvk7a5lSAyt3pfxx2YR+yyeH5pM4eTjc9x/EqFNU8pMQfeK7fj68BRSMlKxNLIAoLKNO1Vs3fl8n+Zha26jiXjZeXAxNu899Cw8ibt37771MiiobA8evMiVK/cJ3jyB9PRM+nw8n4YNq2JubsKaNWsAiEtby/iRf9CgSSVNOQZu54clfXBQWDDgoyAaNK6EW3kHrbatm89R0sKY9du+ZM+Oi/z8/V6mzunKlo3nAFi18XPi41L4euhqflkzCJlMMxA+eUZnKlctnee35qSobMS5tB2rVo7E0tKMQ4cvMXHSKp3r/r5iJDbW5iA1ITVzq/b7N+33nF1stX1cTj7u15g6dSuQkZ7JpwOWcPTIVT5sUDlPupwolSoCp21g6bIhKBRW9Ow+n8ZNPHW0bNp4AgsLU0J3BbBj+zm+n7eVOfP7UcHdibXrv8bAQE5MTBJ+nebQqHFVjIwMWLZ8KKZmJcjIUNK39w982LAy1auXfamOf1PbWsLYgOGTGlDKxZL4mFRG999CjbqlMStZotD559ayaPYxpi1sg62DGV/13UTdBmVxLZfdB9s7lmTEpMZsWnVB59zTR+9xKzyWBav8yMhQ4v/ZVmq/wi8pqj4YYMXvI7G20R30/e77QdrPM2duoKS5Sb6a3rZvlPYsnd79PqT2++XJyMjk84G/cOxIOPUbVCpMtWh1/TBzD3MXdcdeUZLPev1O/UYVKFveTpvGwcmCcd+25o8Vp3TOtbU356ffe2NkZEBqajqf+P1C/UYVsHN4s0EdpVLF4jnHmLqgNbYOZozsF0zdBmV07KVcRTvm/14FY2MDtm+8wq8LTzE2y2d707x/mn2YwIXtsVOY82Xf9dRt6EaZXD7015Ob5vGhq9d25qc1PQB4kpRG/86rqOnt8saaCuK346EsPLiBFf3yTi7+XYqqv7GyNGVCQHf27T2f51rTp6+jQYOq/Pjjp6Rn1MfLy+vqW/tBgn88Ys8kAQBqtXrgmwwkAezbF0bHDt5IkkSNGuVITn5GdHSSTpro6CRSUtKoUaMckiTRsYM3+/ZeyD6/Yz0AOnasx969F/LkkZNbtyOpVq0sJiZGGBjIqVPHnd27d+ukuXTxPi4utji72GJoZECL1jU4eOCyTpqD+y/TroNm1rtZ82qcOnEDtVoNwIF9lyjtbEP5Cgry49SJGzi72L50gCA31y9HU8rZEqfSFhgaymnYvALHD9/VSaMoZYGbuy0ymaTz/f078Xi+54TcQIaxiSFuFWw5c/x+ofMGuHzxIS6utji72GBoaIBvq2ocOqDbLxw+cJU27TXRUj6+npw+eUtbJi/YteMCzVtlz6KamxsDoMxUkZGhBElX+8u4cSUGR2cLHLPKpIFvOU4dvqeTplqtUtoIgUqeDsRFP9Ueq16nNCamhoXOLyzsLq5lHHBxscfIyIDWbeqwb1+YTpp9+8Po2Eljjy1a1OT48Wuo1Wr27QujdZs6GBkZ4uxih2sZB8LC7gJQp447lpb5D5TMmLGe0aM7w0uK5WLYPVxd7XFxscPQyIBWrWuyf/9FnTT791+kQ4f3AWjeogYnT1xHrVZTuYoLDg6WAFRwdyLteQbp6RkAbN50goGDNbPwMpkMa+u80RUFcfNKLI7OJXEsXRJDQzkfNivH6SO6NudVy0lbNxWr2hMXrXmgMjSUY2gkByAzQ5nHhgriXbclOTl+/BouLi6ULl26yLWsXHWAFs3fw9Ym27F3c1NQtqymvVEorLCxsUH1NDNfrRVtyhPxNIrI1Bgy1UoOPTyBt5PuDG3Lsk3YensvKRmaOklKTwZAjRojmSEGMgMM5YbIJTmJz5PzzefplQQ6duz41sugoLK9eesxtWu7Y2Agx9S0BJUqlebwEd12+2lKGmdP3aFhEw+uXHqEs4sNpZ01bVqzlp4cORiuk/7IgXBatdc87DXxrcKZU7dRq9XcuR1Drfc1kUg2tuaYlzTm2uUIXoeispGaNctro9hqVHcjMjKhUHretN/LDxMTI+rU1czuGxoZ4FGlNNGRSQWmz9ZyD1dXO5yz2rSWrd7jQK427eD+i7TvWAcA3+bVOZml5UXfDvD8eaa2S5EkCVMzzaBJZqaSzEzVy5pV4N/XtpZytaSUi0aTjb0pltbGJCWmFTrv3Fy/HI1Tjj64YfMKnMjjl5QswC9JoGoOv6RsBRvOHn/w0vyKqg9+FWq1mp07ztKmbe08x4rCNzI2MaL2+5rIckNDAypVLkV0VP7tbEFcu/SY0i5WlHK2wtBQjk+Lyhw7eEMnjVMpS8pXdEDKVTeGhnKMjDT2k5Fe+D74Vdy4EqNrL77lOZnbZ6tdCuMCfLY34frlaEq5WOLkbImhoZxGvu6cOHRHJ43Gh7ZDeokfemTfLWrXc8XYuPC+4+ty5OZ54p++Xn2/iqLqb2xtLajmVVbb5r7gyZNnnD5zAz+/+gAYGRkRHh6e+FZ/VDFDpdLvv38aYjDpv4mBJEmrJUm6KknSBkmSTCVJOihJUm0ASZJSJEmaLknSBUmSTkiSlP9ISi6iohJxdMqelXB0tCIqKjFvGsf808TFJWsdNnt7C+Lishvg8+dv077DVAYOWsCNGxpHv6J7Kc6euUlCQgrPnqVz+NAlIiMjdfKLjkpC4ZQ9y6dQWBETlbfRdXTUpDEwkGNe0oTExFRSnz7n118O8OmQ5hTErh3nadm68CHtAHExT7FTZC9zsXMwIy6mcJ1sOXc7zh5/QFpaBkmJzwg7+4jY6JTXyj8mOgmFo6X2b4XCkphczk10jjQGBnLMzY1JStSdcd2z86LOYBLA8E9/pXmj6ZiZGtHUt/DRWnExqdg5ZJeJrYMZcTEFz/Du2XqdWvX+/mxSVFQCTjntUGFFVJTug1p0VCJOWfZsYCCnZEkTEhOeFurc3Ozbex6Fg5V2iUpBREcnam0RNPYandteo5JwdMppr8YkJuraz57d56lS2RkjI0OSkzXluPDHULp2ns3IEcuJjS28cxMfk6pjrzb2pi+1133brlPTOzuSIzYqha/6BDO44zo69vZ6ZVQSvPu2JCeh28/Qtm3bItcSFZXA3j3n6dmz4OWxYWF3yMjIwMAm/2gDO2NrYp7Fa/+OfRaPrbG1TprS5o6UNndibsOJfNdoMrUcvAC4Fn+TsNirrG61gNWtFnAu+iIPnuQ/iKJMTsfRMTuS5O2VQf5l61HJmSNHLvPsWTrxCSmcPHmdyMe699jhA9eoVdcNM3NjYqKTUThaaI/ZO1jkadNypjEwkGOW1aZVqKjg6KFwMjOVRDxMIPxqBFE5zp0+KYS+3Rbx65JDBT6IFaW9vmDDhmM0bJijTZUkBgz4gc6dA/njjz900r5Jvwfw6FE8PbrMZ0Dfnzl39nYeLU+Sn3H44BXe93bPtzx0f3cSihy/W+FolefBJ2ea3G1a2IW7dGo3ky4dZjFxcjftg45SqaJrp9k0/jCAeh9UpNpLopI0v/ff17a+4MaVGDIzVDiWtsjnrMIRF5OKvSJ7IOx1/BI3d1vO6fglEcS8wi8pqj5YIud9cYTcnDlzE1vbktoB+5wUpW8EmvvmyMFr1KlbPs+xlxET/QR7RY72TVGSmJjC+33Rkcn077acbq1+pmc/7zeOSgKIi36KXQ57sX2FvezZEk6tei/3fwpLbEyKrq0qzAttqzk5vOcGjVtUfCua3iXvor/JycOHsdjYmOPv/zsdO01nwoQJVKpUyeylJwn+U4jBpP8mlYCf1Wp1ZSAZ+DzXcTPghFqtrg4cBgaRD5IkDZYk6YwkSWeWLl36VgVKkqSdUaha1ZX9+6ezJWQifXo3ZugwzX4f5cs7MXBQCwYM+JGBg37Eo7KLdmnC22Dxz7vp/XED7QxobjLSMzl04DK+LQq3lOttUNPbhTofuDJqwGZmBezFw8sxzyzhu+BS2AOMjQ2p4O6o8/2CJZ+w44A/6RlKzpy8VSR5H9xxg5tXY+jUO6+zVhx59iydJUt28sWX7d9JfjdvPGb+vC1M+rY7oHnoiopMpMZ7bqzfNIbqNcoyd3ZwkeR9aOctbl2Lo0MvL+13dgpzvlvZkZ/W+XFw+00S41++B9DbpjBtyQvS0zPZv/8CLVu+/p4Wr6tleuB6Ro3qVGCbFR2dxOgxvzFjxow8s82vg1wmo7S5grFHApl5+me+fG8AZoamOJk54FKyFH12fknvHV9Q3b4KVW2L3rHOWQYF8eGHVWjUyJMePWfz9dfLqFHDDVmu/df27LiEbyuvAq5QeNp2fC9radxSvp+zE6/qLto29ZvALqza+Dk//9qf8+fusXNb4SPc/i75lc+JE+Fs2Pgno77upP1u7ZpRbN40gaCgYaxevZqzZ95Oe2tnb8GOvQH8b+NIvh7TnvFjVpOSkh3xkpmpZNzoVfTs9SHOLrZvJc+XUa16WTZvHcfadSP5JWgvz59rIoLkchnrN49hz4FvuHTxPjduPC5yLcWtbQVIiE3lxymHGTbhQ734AqDxS2p/4MroASHMCdiHh5cCuZ60rFk7ik2bNffFmtUHOX1aN4IndNtp2rStU2T5F+QbZWYqmTDmD7r3+gBnl8JHsr8NHBwtWL6uP6tDBrNr6yXi495OhFBhObDjBjevxtK597vzlV9FfOxT7tyMe6NJyX8DhemPMzNVXLnygJ49GxG8eQImJiYAedfV/YsQkUmvhxhM+m/yQK1WH8v6vAr4MNfxdODFLq5ngbL5XUStVi+tWLHirxUrVjQIDQ3F3sFCZ/Y4MjIRhcJK5xyFwkonVD9nGltbC+2MZXR0EjZZSz/MzU0wM9MsoWrUyIvMDCXxCZpZma5+9dm0aTyrV43C0sKUsmV1pTooLIl6nD1iHxWViL3CUjeNgyWRkZo0mZlKUp48w8rKlEth9/l+XiitfaezeuURflm6j/+tzt647+jRa3hUccbW7vVmeWztzYiNyu7MY6OfYmtf+EH+Hv1rsXB1NwIXtkOtVlPa1erVJ+XA3sGSqBzLE6KiknRmvUBTJi/SZGYqSUlJw9IqO6Jk944wWrTO3zEoUcKQRk0q5wkPfxm29qbE5giBjot+im0+ESznTz1i/W/nmTCnuXb51N9BobDmcU47jEpEodCN6HBQWPE4y54zM5U8efIMK2uzQp2bk/v3Y3j4MI4OHabi4zOeqMhEOneeTkxMTJ60Dg5WWlsEjb065LZXhSWRj3PaaxpWVhr7iYxM4Mvhywic2QdXV3sArKzMMDExopmvpr6at3iPq1cevrqQsrCxN9Wx1/iY1Hzt9cLpCDb+fgH/WU3zrRsbe1Ncy1lz9XxUvvns2HiVDh060KHjNL20JQCHj1yiahVXdu3aVeRaLl26x8iRy/DxGc+u3X/x7ZT/sTdrr4KUlGd8+tlCvhrRnho1Co58jE1LwN4k+8HEzsSGuDTd2f3YZ/GceHwOpVpJVGoMj1IiKW2m4INStbkWf5M05XPSlM85ExlGZZvsSJOk41E8/OESD3+4hLykkU7U59sqA4XCqsCyHfJZa0KCA/h1+QhQg1vZ7P2P4uPjuXLpER800Oi1d7AgKjJ7djUmOjlPm5YzTWamkqdZbZqBgZwvR7fk93VDmP1DT548ScO1jGaQ5MU1zMxK0Ly1F1cuPtJeb+P/ThW5jQBcC39IwMSV/PzTEJ0lVC/aHFtbC3x9fbl8MXtZ0Zv0e0ZGBtr2pEpVZ5xdbLl3N7utmvbNBlzL2Bf6hRMKhSVROX53VGSidlY8vzS527QXlCvviIlpCW7mGjSysDClzvsVOHbk5f3Nv7FtTX2azvRRe/hocC0qejrkOe91sLU3JSYquy18Xb+ke/+aLFjtx7SFbUENpV7hlxRVH5zzvmjmW4OwsOzlT5mZSvbs+Uu7mX9uitI3Cvw2GNcytnzUp/5LSiV/7B1K6kRIxUQ9wd6+8MspX2DnUBK3CnaEnXv5EsTCYOtgRmwOe4krwF7On3rEut/OEzD3zXy2nNjZm+vaalTKa9kqwOE9N/mgcbk8S7qKK5YP095Jf5Mfjo5WOCqsqF5dsxw8a7Kt5ktPEvynEINJ/01yx+rn/jtDnR3Pr+QlG7WHh4f/FB4eXiMkJIRmTWsQHHICtVrN+fO3KVnSOI/T6OBgibm5MefPa/arCA45QdOmmggTH59qBAdrXg4QHHxc+31MTJJ2eUFY2B1UajXWWQ7ei/DMiIh4du/5i3bt2unkV9XThfv3Y3n0MI6M9Ex2bT9P4yZVddI0alKVrSFnANi7O4w6dSsgSRLLVw5l+54JbN8zgV59GjBgcFN69Moed9u5/fWXuAFUrOJAxINEIh8lk5Gh5PDum3g3KFuoc5VKFclZ+yLcuRHH3Ztx1Kz7ejMrVTxLc/9eLI8expORkcmeHWE0bKy7iWqDxh6EbtFsTLt/zyXqvF9OO3uhUqnYu/sivi2zI4NSU58TG5P9oHb0cDhl3ewLrcm9sj2PHyQTFfGEjAwlR/bc5v0GZXTS3A6PZdGso0yY0xwrm7ybZ74OXl5luHc3mocPYklPz2R76Gl8fHQjnXx8qhG8WWOPu3adw9u7EpIk4eNTje2hp0lPz+Dh+xTOjQAAIABJREFUg1ju3Y2mWrWyBeZVqVJp/jw+h/37A9m/PxCFoxWbNk3A3j5v+Xh6uXL/nmbwKSM9kx3bz9Gkie5MdJMmnoSEaDbZ3L3rPHW93ZEkieTkVD7/bAkjRranZs3sN5NIkkSjxp6cPnUTgJMnrutsfvsqKlS24/HD7Lo5uvc2tT/Utbnb4XEsmfUn42Y3xTJH3cRFP+X5c81+PynJz7kaFkWpMvkvxWjVpTIhISGEBAfopS0BCA09Q5s2dejVq1eRa9m/b7rWJlo0f4/Jk3rQrFkN0tMzGTpsMR06eNOype7+R7m5nnCbUuaOKEztMZDkNHL25sTjczppjkecpZq95v62MDKntLkjj1NjiEmNw8vOA5kkQy7J8bLz0FnmZllPgfOXnjh/6YlZVWuCg4Pfehn4+FTLt2yVShUJWYN818IfEn79EfXrV9HmtWvXLuo3rEiJEpq9LipXLcXD+3FEPEwgIyOTvTsv8WEj3Q1uGzSuxI4tmsG6A3uuUOt9NyRJIu1ZOs9S0wE4dfwWcrkMt/IOZGYqSUzQPOhnZig5dvg65SpkP7B36fF+kdtIREQ8w4cvYfasT3Bzy16Wk5r6XBstlJr6nGPHjunc02/S78XHp6BUaqZGHz6I4/69WJydNYNrP/2wgydP0hg9rvBRllU9Xbl3L1bbpu3c8ReNm+gugW7cxJMtwacB2LP7Au/X1bRpDx/GkZmp1JTFo3ju3o6iVGkb4uNTtEvM0tLSOf7nddzKvXw1/r+tbc3IUDJ73H4at6pAPZ+yhc6zIDR+SZKOX1I3Vx9cELn9kjs346hZ9+VLmoqiD857X1ylonv2ssDjf17DrZyjzvKfnBSFbwSw6MfdpKSkMXJs4d5+mJtKVZ14eD+Bx48SychQsn/XVT5oXLi3+EZHJfM8TRPN9yQ5jYt/PcS17JtHFLpXtifiQTKREVn2sucW7zfUfaPYrfBYfpp5hIlvwWfLScUqDkTcz7bVQ3tu4N2w7Gtd4+DuGzRu8eplusWFJGfjIu9vCsLe3hJHJxtu39ZMKB0/fhzgjfbYFfy7EG9z+2/iKklSPbVafRz4CDgKtHvFOa+kUSNPDh2+hG/ziZgYGxEY2Fd7rEPHadq3JU2e9BH+438nLS2dhg2qaveBGDyoBSO+CmLDxmOUKqV5XSVonIi1/zuMXC7D2NiI+fMGajvv4V8sJTExBQMDOZMn9cTCwoLUHHvVGhjIGTuhE58PDkKlUtOhUx3KV3Dk5wU7qVLVhcY+VenY5X0Cxq2lfcsZWFiaMnNu71f+1mepzzn553UCJnd57XKSG8gYMroBAV9s07yCt50HZcrbsHLJKdwr2+Pd0I3rV6KZOmYnKcnPOXnkLquWnmbxHz1QZqoY/akmjN7UzJBRU5ohN3i9MWEDAzljxrfni89+RalU075TLcpXULB44R4qV3WmUZPKdOhcm8n+6+nUei4WlqZMn91De/5fZ++icLTUCdV+lprOyOEryUhXolKrqF2nHJ27vf9aZTJ41Ad88+UOVCo1TdtWxLWcNauXnqWChx11G5bh1wWneJaawewJ+wDN8qmAuZr9rPw/3crDe0mkPcugf7s1DJvQkJreBTuzBgZyJk7qzoCBP6JSqujS5QPc3Uvx4w9b8PQsg0/T6vj51WfM6F9p7jsRS0tT5n83EAB391K0alWLNq2/RS6XM2lSD+1bZEaOXMbpU9dJSEihUcNxDB/eDr+uhZ+JNDCQMz7Aj08H/oxSpaJTZ28quDux8MdQqnq60sTHi85+9fAfu5JWLaZgaWnKnHn9AFi7+ggP7seyeNFOFi/aCcDSZZ9ja1uSkV+3x3/sSmbO2ISNjTnTpn/0WnUzcKQ3U7/ajUqpxqetO67lrFkbdI4KHnbUaeDKip9Ok/Ysg3kBB7Pqxgz/2c14eDeR3xacRpJArYb2PT0pU/7VIf76aEtSU5/z57GrTPm21zvRUhA7dp7lzJkbJCY+ZfPm48D3PG8up0SpvDOvKrWKRRdWMK3+aOTI2H3vMPefPKJP5c5cT7jDyci/OBt9kZoKL5Y0nYlSreKXS//jSXoKRx+dorp9FRY1DQQ1nIkO42TkX/lqMqlkiUu6y1svg4LKNjNTSa/ecwFNNNmc2Z/ozCBv376dHv2yByQMDOSM9G/NV0NWolSpadvxPcpVcCDop/14VC1Fg8YetO30HlMmbKZr2x+wsDBhymw/ABLin/LVkFVIMgl7h5JMmq55015GupKvhqwiM1OJSqmmtnc52nfJf3CvqGzkp59DSUx8yrdT1gKapV2bNo4nLi6ZocMWA5oH+bZtu1O/QXZ79yb93rkzt1m0cBcGBnJkMokJk7pgaWVKVGQiy5buw62cAz39vgeg+0f16exXN98yyall/IQuDBm0GKVKRcdOdang7sRPC7ZTpaorTXw86dTFm/FjV9GmxTQsrUyZPfdjAP46d5vlQfswMJAhyWRMmOiHtbU518MjCPBfjVKlQqVS06JlDRo1rvpqHf+itvXPfXe5cj6SJ8nPObBdM5g1bMKHuFX8ewMFcgMZn43+kElfbNe86r1dJcqUt2HVktO4V7anbsOyXL8SzfQxu0lJfs6pI/dYs/QMP//RDWWmirGfhgBgambEqCk+r/RLiqIPjotLZtjQnPdFHRo0zLaL0O2nadum4CVuReEbRUUmsTzoIGXd7Ond7ScAuvX0pmOXwi+1MzCQ8eVYX0Z/vg6VSk2rDl64lbdn+c9HqFTFkfqN3bl2+TEBIzeRkvyc44dv8tvio/y2cSD378Tx8/wDSGhmjbt//D7l3As/0VcQcgMZn436gMlfaHy2Zu0qUaacDauWnMmylzL8uuAkaamZzBy/FwB7R3Mmzm3xVvIeMqYBAV9sQalU07x9ZcqUt2XF4pNUrOyAdyM3wi9HMXXMDo0PffQOq5acYsk6zb0ZFZFMbFQKXjVf/pbOt8Ga/lNoXLEmduZWPAjcwuRtQSz/c+urT3wJRdXfxMQk0cVvBikpachkEr+v2M/20MmYm5swMaA7o0YvJyNDiYuLB0DgG/2IYs4/camZPpHe1s7+gn8GkiSVBXYCZ4BaaEaX+wDbgVFqtfqMJEkparXaPCu9H9BWrVb3e8Wl1agPFJXswpPrFcn6wtSgHbeSvte3DMpbjgAgOX2jnpWAhVEXriXM0bcMPKxHo0b/tirRBIAM1S49KwFDWQsuxc3Utww8bcdRXNoRoNhoabW5j75VsKPTymJTHgBxaWv1LARsjXsWmzIpLv0ewHPlDj0rgRLyVqJtzYGnrWaLkxtJ8/WsBNwtRxabPri4+EYAj1OX61kJOJn253riXH3LoKLVKG4n/6hvGZSz+AIAaYi3npWAetGJYtPf8NL3Ev/zOeDkodfBkSaPr/2jyldEJv3HUKvVdwGPfA41zpHGPMfnDcCGIhcmEAgEAoFAIBAIBAKBnhCBNq+H2DNJIBAIBAKBQCAQCAQCgUBQaMRgkkAgEAgEAoFAIBAIBAKBoNCIZW4CgUAgEAgEAoFAIBAI/tOIDbhfDxGZJBAIBAKBQCAQCAQCgUAgKDRiMEkgEAgEAoFAIBAIBAKBQFBoxDI3gUAgEAgEAoFAIBAIBP9pxDK310MSr78TvCWEIQkEAoFAIBAIBALBvxdJ3wKKkt12lfT6TNs8NvwfVb4iMkkgEAgEAoFAIBAIBALBfxoRmfR6iMEkwdtDfUDfCkBqUnx0qPbpWwXImmr+Ly5aiouO4mIjUHy0FJO6Uan1r0MmZd03om6yEfdNXqQmmAX46FsFT6ftLzblAaBG/1okhC+gg/AF8teh3KNvFSD31fxfXLRk7tK3CjBoUXx0QLFpS6Qh3vpWgXrRCX1LEBQzxAbcAoFAIBAIBAKBQCAQCASCQiMikwQCgUAgEAgEAoFAIBD8pxHL3F4PEZkkEAgEAoFAIBAIBAKBQCAoNCIySSAQCAQCgUAgEAgEAsF/GhGZ9HqIyCSBQCAQCAQCgUAgEAgEAkGhEYNJAoFAIBAIBAKBQCAQCASCQiOWuQneGmq1munT13Ho8CWMjY2YOaMvVau65kl36dI9/P1/J+15Bo0aejJhQjckSSIx8SlfjQzi0aM4Spe25fvvBmFpacbefef54YetyGQScrmM8eO7UbtWBQAqVxlCxYqlAXBysmHx4iZFqgXg5MlwAmesJzNTibWVOatWfQ2Aj894zMyMkcllyOUL2LRhaN7yCVzPocOXMTY2ZGbgx/lrunwff/8VWZqqMmF8VyRJYsfOcyxcGMqt25GsXzcGL88yABw7dpV584PJyFBiaChn9OjO1POu9Oq6KgItCQkpfDEiiEuX7tOpozeTJnYvtjretb1evfqAb75ZQ8rTNGQyGUOGPKN169bvTBcUbLtvqz40ef+SI++BWFqakpSUyvgJK7n/IIYSJQwJnNaHihVLAfD7iv2sX38MtRq6dnvAx31ddPI6cuQygdPXo1Kp8fP7gEGDW+gcT0/PYOzY37ly+QFWVmbMnz+A0s62JCSkMOJLjQ107OjNxEnZNvD9dyGEhJwkOfkZZ899l+f3FXVd3LodyXj/37l85QFfjWjPgAHNtddKTk4lIGAl129EIEkSgYFWvFe96OrmyZNnjB7zKxGPE1BmqujfvxldOtfj0aM4hg1fikqtJjNDSe8+0fTs4VhkZbJl60mCgnaDWo2ZmTHffPMRHh7OAPiPX8HBgxextS3Jtm1N9Fo/AEqlii5+M1Ao/gf2BZoPvu51mN16GHKZjN/Pbmfe4bU6x12sFCzuNBo7M0vinz1hwPpAIpJjqeZYnu/bj6BkCTNUaiWzD65m46WDBWdUxOVRUJumsZHFqFRqMjOV9O4dSc+ePTly+DLTp69DpVLh17U+gwe31Mk/PT2DsWN+4/Ll+5p79ruBODvbAbBkyU42bjiGTCZjQkA3GjSoCsB4/2wb2Lptks71Vq48wJrVB5HLZTRq5MnoMV2KvEyg4Pa0IHvV0VQM+t+i1PKCiIh42rSbyrChrRnQ3/ed63hd/0ijY4NGh4kRMwP7ULWKS550ly7fx3/8StLSXujwy9bx03Zu3Y5i/R+jtDq2bD3NL8v3as8Pvx7B5g1jqVzZudhrUavVTJ+xkUOHr2h0TO9VsI4Jq7N0VGGCfxckSWLW3GAOHLyEoaEBri52zJj2ERYWpjx8FEfrdoG4lXUAoHr1skyZXLDN6luH/v2B2RhbZ5Jm+fce3X/pM4G2XvWJfpKA19Ref+sa/xXEMrfXQ0QmCd4ahw9f4u69aHbvmsLUKb345ts1+ab75ts1TJ3am927pnD3XjSHj1wGYGnQTup5e7B711TqeXuwNGgXAPW8PdgSEkBIcACBgR8TELBSey1jYyNCgjXHFi/6vMi1JCen8u2UtSz6+XNCt03mhx8G6Vzv9xUjCQkOYNOmTfmUz2WNpp3fMPXbXnwz5X8FaFrL1Cm92L3zmyxNVwCo6O7EggWDqVO7gk56a2tzFi0awtYtAcyc0ZcxY3/L97rvQkuJEoZ8+UU7xozu9EoN+tbxru3V2NiIWbP6EbptMsuChhMYGEhycvI70/Uq29Xk/Wb1sTRoF/XqVWL3rm+pV6+SNu/FS3dSubIzW0MCmDWzL9NnrAfg+vUI1q8/xvp1YwkJHs/Bgwe5dy9am49SqWLqlD9YGjSMrdsmEhp6hps3H+to2bDhTywtTNm1+1s+7uvD3HmbAY0NfPFlO0aPyWsDjZtU4491Y/P9bbrlUTR1YWVpyoSA7gzo3yzPtaZPX0eDBlXZueNbQoIDKF++fJaWoqmb1WsOUb68E1uCJ7ByxQhmzd5Ienom9vaW/PG/UYRsHs+6P0YTFBREVFRikZWJc2k7Vq0cydatkxjyeWsmTlqlvVbnTvVYFjS8WNQPwIoV+ylfzjHfYy+QSTLmt/uSTivGUevHT+jq5YOHve6DdmDLz1hzfjd1Fw5i5oGVTGmuuSdTM54zaONM6izoT4ffxzG7zVAsjc1eml9RlkdBbZrGRsYQEhzAuj/GEhQUREREBFOmrCVo2TC2hU4mdNtpbt6M0Ml/w/pjWFiYsnvPVPr2a8q8uZp79ubNCLaHnmZb6CSWLRvOlG/XolRqvPpOnesRtCyvDZw4Ec7+fRcI2RLAttDJ9B+gO2Chj/a0IHvN1lQ8+t+i1PKCmbM20qBBFb3peF3/6PDhK9y9F8PunZOZ+m1Pvvm2AB1T/mDqlI/YvXMyd+/F5NBRigU/DqJO7fI66du3q0PIZn9CNvsze9bHODvbvnQgqThpOXwkS8eOiUz9pjvfTFlXgI51TP22B7t3TNToOHoVgPr1KrEt2J+tm8dRtow9S4L2aM9xdbEjZNNYQjaNfelAUnHQoXd/ICSEdNO//9j+2/FQWi746m+fLxAUhBhMErw19u0Lo2MHbyRJokaNciQnPyM6OkknTXR0EikpadSoUQ5JkujYwZt9ey9kn9+xHgAdO9Zjb9b3ZmbGSJIEwLPUdO1nfWjZuu0Uvr7vUaqUDQC2thaFL5/9YXTsUDdLkxvJyakv0eSWpaku+/Zp8i5f3olyboo8161SxQWFgxUA7u5OPH+eQXp6hl60mJqWoHatCpQoYajXMimMjndtr25uCsqW1WhVKKywsbEhPj7+nekqjO2+aX1ozvfW5N3Bm71Z39+6+RjvuprZ4PLlHHn0KI7Y2GRu3Y6kWrWymJgYYWAgp06dOuzZc16bV1jYXVxd7XFxscPIyIDWrWuxP+uaL9i/L4wOHTV5tmjxHieOh6NWqzE1LUGtWhUoYZTXBmrUcMPBwTLP9++qLmxtLajmVRYDA7nOtZ48ecbpMzfw86sPgJGRARYWFkVaN5IET5+moVareZr6HEtLMwwMZBgZGWCUVXbp6ZmosqbqiqpMatYsr434qFHdjcjIBO316tRxx9LSVO/1AxAZmcDBQxfx61o/z7Gc1Hb24HbcI+4mPCZDmcmGi/tpW/kDnTQe9mU4ePsvAA7d/os2HprjN+MecivukSa/J3HEpCRiZ2b10vyKsjwKatPys5GrV6/iWsYBFxd7zT3bpg779oXp6twfRsdOmnxatKjJ8ePXUKvV7NsXRus2dTAyMsTZxQ7XMg6Ehd0FCraB/609xKDBLbQ6crdr+mhPC9Kq8/uLQf9blFoA9u49T2lnW9wrOOlNx+v6Rxod72t0VHcj+ckzomNy6YjJ0lH9hY73tTZevrxjgeXxgtDQs7RpVbOQZaJ/Lfv2X6Rj+0LoeJpDR/tsHR/Wr6xtS2tUL0tkVOIrf3tx1KF/f8AIleHff2w/cvM88U/zTmAK8qJS6fffPw0xmCR4a0RFJeLoZK3929HRiqhcjXVUVCKOjvmniYtL1j7g2dtbEBeX3ejt2fMXLVtN5tPPFhI4/WPt98+fZ9C5SyDdus9i797sh9Ci0nL3bjTJyan06TOPzp0DCQ4+kX1BSWLAgB/o3DmQP/74I//y0cnPmqjoXJqiE3FUZD80OCqs8+h+Gbt2/0WVyi5ax7og3oWWwqBPHfqw1xeEhd0hIyMDV9e8IdJ6sd0Cr/t69REX9yRX3k8A8PBwZnfWIFFY2F0iIuKJjEqkorsTZ8/eIiEhhWfP0jl8+DCRj7MHEaJzlYXC0ZqoKF3nLSo6EaesNAYGckqWNCEx8Wme3/Z3KEobyY+HD2OxsTHH3/93OnaazoSAlaSmphaQz9upm169GnPrdiQNGvrTvsN0Jvj7IZNpXIPHj+Np12EajX0mMGjQIBQKq3dSJhs2HKNhQ8+XlpU2n3dYPwCBgesYPaozsldMapSysONhUnaU3aPkWJwsdNfEXYq8RYcqDQBoX6UBFsZm2JjoDobUKu2BodyA2/G60T35oY827fHjeNq1n0rjJv4MGjSIjIwMnHJeX2FFVFT2PQ2a+zrPPZvwlKiohFeem5u7d6M5c+Ym3brOpHfveVzMGnwq6jIpTHtaEMWl/y1KLU+fphG0bA/DPs+7lPtd6shJYfyjqOhcOhQF2IuODqs8Wl/G9p3naNOm9ivTFRctUdFJODrmyiN3PxyVpKvD0YqoXAMtABs3naBhjki1h4/i6NhlFr37/sCZs7eKtQ69+wMTJiAp1S89RyDQB2LPJEGxRJIknQgkX9/38PV9j9Onb/DDj1v47dcRABzYPx2FwpoHD2Lo2/c7KlbqgmveJdRvTYsyU8nly/f57dcRpD3PoEePWVSv7oabm4K1a0ahUFgTF5fMJ/1/oVzZ1tSp4/52xbyEGzcimDsvmOX5LAUQFC2FtVfQzFyNHvMbs2b9qHloL0LfoLC2W3R5az4PHtSc6YHr6dApkIrupahc2Rm5TKJ8eScGDvRlwMAFmJiUwMOjNjJ5VJHo0Te5bSQ/MjNVXLnygIkBPahe3Y1p0/9g6dKljPjCqwi0aD4fPXqFyh4urPhtBPfvx/DJgAXUrl0Bc3MTnJxs2BoSQFR0IkOHraVF8z5FoEO3TE6cCGfDxj9Zs3rUW83r72jJzYEDYdjYlsTTswwnT4a/cZ7+Oxczv+0X9H6vBcfuhvEoKQalWqk97mhuwzI/fwZvnIla/W4fIgrbpjk52bB1y0SiohIZOmw13bu/ep+et4lSqSIp6Sl/rBvLxYt3GTEiiL37pvGKqvxb6LM9/aex8KdQ+vb1wczMWN9SgOLjH124cBcTY0MqupfSqw59aFm0ZBdyAznt22oGrxzsLTiw91usrcy4dPk+Q79YRmiIP+bmJv96HX/PHziGzb004soVbfkIBK+LGEwSvBGVKlUaCgzy8PDAy8tWJ6ogMjIRRY4ZAtAs78m5fCFnGltbC6Kjk3BwsCQ6Ogkbm5J58qtTx50HD2KJT0jBxtochUIzA+DiYo+dnQX9+vWjZEkJL68yRaLF0dEaKytzTE1LaELKa7tzLfwhbm4KrRZbWwt8fX0Ju3iX69cjWLfhGABenmVy5ZegDb/WanKw0gm9jYxKyKM7PyIjExg2fCmzZvbF1TX/HWFXrz70TrS8Cn3q0OT9I5BSZDaSk9z2mpLyjE8/W8hXI9pTo0aNHLoOsm79UYB3brt//nmVdeu/15TJG9aHrW3JfPM2NzdhRqAmmkGtVtO02URcXDQb7nb1q0/XrDDu+d9fwEGR/dDsoLDSKYuoyAQUCt3laQoHKx4/TsDR0ZrMTCVPnjzDyurV+8sUxOrVh9iw/keAd9Km5cTR0QpHhRXVq7sBIJNJrFixggMHrIusbjZtOs7gQS2QJIkyZRxwdrbl9u0oqlUrq73O3j0XuH//Pt26zeSD+pWLrEyuhT8kYOJKgpYOx9raPN8yWr16NevWreNd3cM5OXfuFvv3h3H40CWep2eSkpKOtYNEQl2bPGkjkmNxtnTQ/l3awo7HyTE6aSKfxPHR2skAmBkZ06FqQ5LSNFF1JUuYsvHjGXy79xdOP7xaoCazmyl06NDhnZVH7jYt53Xd3d2JjY3lcc7rRyVq+8YXOCjyuWetzVAorF95bm4UCit8fd9DkiSqVXNDJpNYFrSL0NAfirRMXuYL5Edx6X/flZYLYXfZtesv5s7dTPKTZ8hkEiVKGNK7V+N3quPFtV7mH61ec4h16//U6PDKpSOqAHvR0ZGYR2tBhO44S5vWBUcCFRctq9ccZt2G4xodnq5ERubKI3c/rLDU1RGZiCLHUvJNm09y8NBlfvtlWI5lsobaKDHPqq64uthx524MXp7ZEdv61qHP/ia3P9CyZUsWbV/70nMEbweVCAB7LcQyN8EbER4e/lN4eHiNkJAQmjWtQXDICdRqNefP36ZkSeM8+5I4OFhibm7M+fO3UavVBIecoGnTagD4+FQjOFjTaQQHH9d+f+9etHZW9vLl+6SnZ2BtZUZS0lPt2vf4hBQSk56ydOlSQoIDikxL06bVOXvuJpmZSp49Sycs7C7lyzmSmvqclJQ0AFJTn3Ps2DHc3UvRq1cjQjaPJ2TzeJo1rUZwyMksTXcoWdLkJZruZGk6SVOfai+tg+TkVAZ/9jNfj+xArZrlC0z3LrQUBn3q6NWrESEhIUVqIwXZa3p6JkOHLaZDB29atqyVS1dj7Uby79p2e/VqrCmTt1AfPj7VCA7RLPcIDjmh/T45OZX09EwA1q8/po18AbTLrSIi4tm9ezdt29bR5uXlVYZ796J5+DCW9PRMtm8/S5Ncdd/EpxohWUtMdu36C2/vSoXaV60gtDZShG1aQdjbW+LoZMPt25EAlDQ3oXv37kVaN05ONhw/cQ2A2Nhk7tyJwtnFjsjIBNLS0gFo27YO1tbWLFo0tMjKJCIinuHDlzB71icvje7o1atXkd/DBfH11504fGgm+/cHMn/eALy9vfMdSAI4++ga5W1LU8baEUO5AX5ePoReO66TxtbUQmuroxp+xIpzOwAwlBvwv4+msOav3QRfPvxSTU8rmOutTctpI0lJTzl37hyNGjXi3t1oHj7IumdDT+OT65718alG8GZNPrt2ndPesz4+1dgeepr09AwePojl3t1onUHN/GjWrAansqLE7tyJIiNDycBBLYq8TApqTwuiuPS/70rLmlVfs3/fNPbvm0bfj5vw6eAWOgNJ70pHYfyjXh810m5IrdFxSqPjQpYO+1w67LN0XHih41Sh6kalUrFj5znatK5VYJrioqXXRw21G1I3a1qN4C05dJgb56/DLIeOLado6qOJqD185ArLlu9l0cJBmJgYac+Jj3+i3WD/wYNY7t6LwcXZtljp0Gd/k9sfOH78OOlmeffxEwj0jfSuQ6cF/1rUatV+pkz9H0eOXMbE2IjAwL54eWneXtOh4zRCggMAuHjxHv7jfyctLZ2GDaoycWIPJEnSvMr7qyAeP46nVCnNazOtrMxYGrSLkJATGBjIMS5hyOgxXahdqwLnzt1i8uTVSDIJtUrNx32b0rVrAKgPoFari0QLwLJfdrNp05/IZDL8/OrTr29THjyqD7FZAAAgAElEQVSIYeiwxYAm7L5t2+4M+bRirgJSM2XqHxw5eiVLUx/tq1o7dAokZPN4jaZL97Svvm3YoCoTAzSvFd2z5zxTp68jPj4FCwsTKns488uy4fy8aAdLg3ZRpkz2LPjyZcOxtS0JsqaaL1T73okWAJ+mAaQ8TSMjQ0nJkiYsXzacChWcNFqKi44itJGC7DVky0nGj/+dChVehJSbM3PmTCp7ROZTJu/OdgGQmoBq3xvXR0JCCiNG/sLjiHhKlbLh++8GYmVlxl9/3Wac/wqQwL2CE9On9dFuUPtR73kkJj7FwECOv/906nqn6pTHoUOXmBG4AZVKRecu9fjss1b8+ONWPD3L4ONTjefPNa8Zv3r1IZaWpsybP0Ab9dTUJ4CnOWxg2S8aG5gzZxOh285oZwn9/D5g2PC22jxlkqZciqpNi4lJoovfDFJS0pDJJExNS7A9dDLm5iZcvfqACQErychQ4uJix4wZQViWPFNkdRMVnYi//wpiYpJRq9UMGtScDu3rcuzYVWbO3ogkSajVanr3/ozu3RyKzD4nBKxk9+6/tBsay+UyNm3U/KaRI5dx6vR1EhJSsLW1Z/jw4XT1sysyLS+rnxecPBnO8l//YpX9DQqiRcW6zGr9OXKZnBX/b+/e4y6b6/6Pv94zDuM0hkiRhELCOIbSgVJ3OVSOHUgRIofojnSSQ4lKhcohSag7kkKE5HzIHIwZxzspKko/aQxyGPP+/bHWNvs6zYw7s7/rutb7+Xjsx77W2tc13o9l77X3+uzv9/OddClfu+YcPv+2jzD5r//LJXffyHtf92aO2PJjGHPDn6Zy0EUn8Mxzz/L+8W/n5O0O4a6H//T8v7X3+ccy9W+D9/J44ujfFjmn3XDDnXz12PORwIZddtmbnXfemauvOYGvfOU8Zj03i+23fwMf3+fdnPDtC6vX7NvG8/TTz3LIp8/grrv+zJJLLsrx3/wYK65YjRg5+XuXcP75NzJ69Gg++9kdefNb1nr+OTDhls5zYCz7778NO+z4Rp55Ziaf++yPuPvuv7DggqM55JDt2WTTNRCbF/ksMOjzdb+3Pz/6Egq+70FP34M7TjzpYhZddGH22L1eaa+HnwXm+vnouSsG5jj6XK67/i4WGbMgX/nyLl05juGXFxw2O8dnz65zrMkXPrdjleM3t3HUl8/ryrECp5+2HwC/u+V/+cbxF3Lu//Sbwju6Pi5NyTLzskFynMd1N9xV/b85+kPPjx56z3bH8sufH1rneIDDPncOTz39DG/ebE2+8LkdkMSW/3Ukzzw7k3H1Agvjx7+KIw/fmcsun8IJJ13CAguMZtQosf8n3sUWm9dTuhd4Z3NywHw9l8z754E1OOWZif/nJtw/3v1I3rra+iyz+Dj+/tg/Ofzi0/jBjRe94H/H37sZYD5MJG6Ony28etHiyA5P3zOsjm+KSfFiMb6qdIbqorgpOfp9WCpiiA+QRQzyAbJYjqY8R6A5WRry/2aWy+foFJPy/6ZLXjcDaXMW+/wWpVM8X0wqrv5/Y8pn6RSTimvS6xeak6UpOfoVcIoYophUxCDFpCIGKSYVywGNOZdon01Kp0gxqQeGWzEp09wiIiIiIiIiImKepQF3RERERERERLTarFmlEwwvGZkUERERERERERHzLCOTIiIiIiIiIqLVMjLphcnIpIiIiIiIiIiImGcpJkVEREREREREDFOSdpR0h6RZkjacw+/9l6R7JN0r6TNd+1eW9Lt6/08lLTS3/2aKSRERERERERHRarNmlb39h24HtgOuHeoXJI0GvgO8C1gT+ICkNeuHjwW+afvVwKPAHnP7D8r2fxo6AiBPpIiIiIiIiJFLpQPMTz/W6kWvaT/oe/7j4yvpauC/bU8c5LFNgS/Zfme9fVj90FeBfwAvsz2z/+8NJQ2448XyYjzx97J96osRZqTIMekrx2OgHJOBckwGyjHpK8djoByTgXJM+srxGCjHZKAck75yPIaXF6OY85+QtBewV9euU1/k588KwJ+7tv8CbAy8BPiX7Zld+1eY2z+WaW7RJHvN/VdaJ8ekrxyPgXJMBsoxGSjHpK8cj4FyTAbKMekrx2OgHJOBckz6yvGIeWb7VNsbdt36FJIk/UbS7YPc3lMib0YmRUREREREREQ0mO23/4f/xF+BFbu2X1HvewQYJ2mBenRSZ/8cZWRSRERERERERMTINgF4Tb1y20LA+4ELXTXSvgrYof693YBfzu0fSzEpmiTziQfKMekrx2OgHJOBckwGyjHpK8djoByTgXJM+srxGCjHZKAck75yPKInJL1P0l+ATYFfSbqs3r+8pEsA6lFH+wGXAXcB59q+o/4nDgUOlnQvVQ+l0+f638xqbhERERERERERMa8yMikiIiIiIiIiIuZZikkRERERERERETHPUkyKnpP0yjk89qZeZomIiIiIiIiIFybFpCjhakmHSBrd2SFpOUlnA98smCsiYsSQtOO87IuIGIqkhedlX5vk3BoRUUkD7ug5SUsBXwXeABwIrA0cDBwHfM/2rILxipI0A+j/opwOTAQ+Zfu+3qcqT9IKwErAAp19tq8tl6gMSdMY+PwAEGDb6/Q4UmNIuoihXzun2H6q96nKkjTZ9vpz29cmkt4IfInZ55POa2eVkrlKkXSl7bfNbV+b1F90LUff95sHyiUqK+eRgXJMBpK0GvBpBn5W26JYqIIkLQvsCbyKvsdj91KZIuaHBeb+KxEvLtuPAntLOhD4DfAgsIntv5RN1gjfAv4C/JjqIuf9wKrAZOAHwFuLJStE0rHAzsCdwHP1bgOtKyYBW5cO0GD3AcsCP6m3dwZmAKsBpwG7FsrVc5LeBbwbWEHSCV0PjQVmlknVGKcDBwGTmH0+aR1JY4BFgWXqL3hUPzQWWKFYsMIk7Q8cDvwd6HyxZaB1hXpJL6N6LiwiaT36PkcWLRasoJxb5+g84GSq99vWnlu7/BK4juo6J8cjRqwUk6LnJI0DjgU2Bv6L6o35UkkH2v5t0XDlbWt7fNf2qZKm2D5U0meLpSrrvcDqtp8uHaQ02/eXztBgb7C9Udf2RZIm2N5I0h3FUpXxINWIrG2piiYdM6gKKW023falpUM0wN7AJ4Hlqb6s6HgMOKlIomY4kOr95pHSQRrgncBHgFcAx3ftnwG09fNIzq1Dm2n7e6VDNMiitg8tHSJifss0t+g5SfcB3wW+ZXtmvW/det/9tj9QMl9Jkm6i6hv1s3rXDsDBtjepi0rrlktXhqRLgR1tP146S2mSrre92SDTITtTdcYWilacpLuAd3amo9SN/i+z/VpJt9per2zC3pO0QOccGxVJXwVGAz8Hni9Q25485B+NYJL2t31i6RxNIekqYMu8bmaTtL3t80vnaJKcWweS9CXgYeAC+p5b/1kqU0mSjgZutH1J6SwR81OKSdFzkl4x1JQ2SXvaPq3XmZpC0irAt4FNqYoFN1N92/VXYAPb1xeMV4Sk84HxwJX0/YByQLFQ0TiS3k01xP4PVMW1lYF9gauBPW1/q1y63pJ0ru2dhuqx1fLeWlcNstst7uuxGNV7zCtt7yXpNVQjcy4uHK0ISacDqwO/ou/7zfFD/tEIJWkX22dL+hSDn0faeExybh2CpD8OsrvN/ehmAItRnUeeJV/6xQiVYlJENJqk3Qbbb/vMXmdpGkkvBcZ0ttvcJBaeX2FojXrznjY23QaQ9HLbD0laabDHM10yOiT9lGq6zodtryVpUapv01s3ChZA0uGD7bd9RK+zlCZpb9un5JjMlnNrRERfKSZFNEhWf4h5IWlb4BtU/U4eplo95S7brysarDBJb2Dga+dHxQI1hKSx9D0mrZx20CFpK+B19C3EHlkuUTmSJtresHsqqKTb+vXui4hB5Nzal6S1gDXpe25t1XuwpDVs3y1p0JX92jqlOkauNOCOaJas/tBPPe3iGAZ+QGnl0OnaUcAmwG9srydpc2CXwpmKknQW1cqHU+i76l+rPsh2k7Q3cATwFLOnZBho7WtH0slUK1FtDnyfqi/dLUVDlfWMpEWonx+SVqVrelfb1F/oHMLAYmMrp0ECSFoZ2J+BhfptS2UqLefWgeoRbG+l+qx2CfAu4Hra9x58MLAX1Rd+A3pbAq09l8TIlJFJEQ3S1ibbcyLpeqqlmr8JbAN8FBhl+4tFgxXUNZrgNmA927PaPpqgbsC9pvOm9jxJvwc2tf3/SmdpCklTba/Tdb84cKntN5XOVoKkLYHPU10AXg68EfiI7atL5ipF0uXAT4H/Bj4O7Ab8o82rMtXvM6cD04BZnf22rykWqrCcWweq+0iNB261PV7ScsDZtrcsHK2Iuki/L7AZVRHpOuB7bZ1+HyNXRiZFNMvFkt6d1R/6WMT2lZJU9yP4kqRJQGuLScC/6ovga4FzJD0MPFE4U2m3Ay8DHiodpEH+ADxZOkTD/Lu+f1LS8sAjwMsL5inK9hWSJlONdBRwYMsvkF9i+3RJB9bFkmskTSgdqrCnbJ9QOkTD5Nw60L/rL7Zm1tP/HgZWLB2qoDOBx4DOa+eDVKO0diqWKGI+SDEpolkOBD4rKas/zPa0pFHA7yXtR7Wy3eKFM5X2HqqL4oOADwFLUg25b7NlgDsl3ULfVZhaOxUDOAy4UdLvyEqIHRdLGgd8DZhM9Y3x98tGKkfS+4Df2v5VvT1O0ntt/6JwtFKere8fqntrPQgsXTBPE3y7nsJ0OX3PI23u/ZJz60AT63PraVRN/R8Hbiobqai1bK/ZtX2VpDuLpYmYTzLNLSIaTdJGwF3AOKpeQUsCx9m+uWiwgiQd23/axWD72kTSWwbb3/KpGLdQ9azoPz2l9SshwvOr/42xPb10llIGm1rd3Yy7bSRtTTUdZUXgRGAscITtC4sGK0jSMcCuVKNxOucRt7yPVM6tcyDpVcBY21MLRylG0tnASZ3PqpI2Bj5h+8Nlk0W8uFJMimiArP4QL4SkybbX77dvqu11SmWK5mlzUaA/SdvN6XHbP+9VliYZ7LwhaZrttUtlimaRdC9VP7pnSmdpipxbZxvqc2tH2z6/1r2jDCwIrA48UG+vBNzdb7RSxLCXaW4RzfApYE+q1R/6a/XqD5JWAz5N9UbcvZJM646JpH2oGjquIqn7G78lgBvKpGoGSZtQjSR4LbAQMBp4ouVTRC+VtBdwEX2nYrRx+ept5vCYgVYWk6imphwPfKfe/gTVFJVWkXSI7eMknUjfFZiA1k9fup1qZPDDpYM0SM6tsw32ubWjjZ9fty4dIKKXMjIpIhqtXknmZKoLnM6S79hu4wXPksBSwDHAZ7oemtHSD7HPkzQReD9wHrAh8GFgNduHFQ1WkKQ/DrLbtlu7fHX0JWkx4AvA2+tdVwBH225VQ39J29i+SNJugz3e5ulLkq4G1gEmkH50QM6tEREdKSZFNECmYAxN0iTbG5TO0TSSNgNeY/sMScsAS9ge7ANuK0iaaHvD7mk7mYoQ/UkadBVI20f2OkvEcJB+dDEvJA3aC8j2j3qdJSJ6J9PcIpqhMwXjpcAbgN/W25sDN9LCKRiSOivoXCRpX+ACMpwcgHplnQ2p5uOfQTWt62zgjSVzFfakpIWAKZKOAx4CRhXOVISkLWz/dqgidZuL00D3iJsxVFMS7iqUpThJywKHAK+jOh5AO6cRw/PH41BgTXI8gBSNuuXcOkcbdf08Bngb1YqZKSZFjGApJkU0gO2PAki6nKrR5UP19suBHxaMVtIkqvn2qrc/3fWYgTYPJ38fsB7VBzVsPyhpibKRituVqni0H3AQ1WpM2xdNVM5bqArSg/UJanN/IGz36e8h6evAZYXiNME5wE+pimofB3YD/lE0UVmd47EVLT8ekq63vZmkGfTtIyWqKV1t7EeXc+sQbO/fvS1pHPA/heJERI9kmltEg0i6y/Zru7ZHAXd074uQdIvt13dWdav7ntzU9tXc6pFJa1B9qL8nqw/F3EhaCphg+9Wls5TQmUbcb3roBNsbze1vR6Icj4gXh6QFgdttr146S0TMPxmZFNEsV0q6DPhJvb0z8JuCeYrJcPI5OlfSKcA4SXsCuwOnFc5UlKStqBq1/4Hqm/OVJe1t+9KyyXpP0sFzetz28b3K0jRdyzZDteLfskCb+yU9W98/VL+GHgSWnsPvj3Q5HrWuqeaDauNU85xbhybpIvqeW18LnFsuUUT0QopJEQ1ie7+6ePKmetepti8omamgDCcfhCRRTcNYA3iMqm/SF21fUTRYed8ANrd9L4CkVYFfAa0rJgFtn/I4J93LNs8E/m57ZqkwDXB0vUrkp4ATgbFU00TbarDj8cmykYrpP9W8W1unmufcOrSvd/08E7jf9l9KhYmI3sg0t4iIYUbSNNtrl87RJP2notRFt1vaOj1F0mjgANvfLJ2laSStD2xGdUF8ve1bC0eKhpC0cv9VMSVtZHtCqUxNJ+l1tu8onaNJJB1m+5jSOXpN0suA11OdWyfY/lvhSBExn6WYFNEg9aikY6lWdRPtbnQJDDmsfDowyfaUXudpAklnAiflAuf51wzAlsBKVMPqDewIPGB731LZSuv01iqdo0kkfZHqudEZ2fhe4DzbR5dLVY6klYH9gVfRNVrd9ralMpUkaRKwre2/1ttvBr6T4v3QOr37SudokjYeE0kfA75INaJcVKPLj7T9g6LBImK+SjEpokEk3QtsY7u1S1X3J+nHwIbARfWurYGpVBc/59k+rlC0YiTdDbwauJ9qqfNO0bF1DbglnTGnxzsrJbaRpG8CC1JNi3yis9/25GKhCpN0DzDe9lP19iLAlLY2iZV0G3A6MA2Y1dnf1uXgJW0EfJdqevX6wDHA1rb/XDRYg0m61fZ6pXM0SRuPSX1ufYPtR+rtlwA3tvXcGtEW6ZkU0Sx/TyFpgFcA69t+HEDS4VS9cN5M1dOhdcUk4J1zelDSUrYf7VWYktpcLJoH69b33Q2mDWxRIEtTPAiMAZ6qtxcG/louTnFP2T6hdIimsD1B0gHA5VTPkbfb/kfhWE2Xb6UHauMxeQSY0bU9o94XESNYikkRzTJR0k+BXwBPd3a2fOWyl9J1LKhW21nO9r8lPT3E34xotu+fy69cSfWtemtIGgPsAbyOqlgAgO3di4UqzPbmpTM0haQTqS7wpgN3SLqi3t4SuKVktsK+XRfoL6fve06rRq/1W4kKYFGq58rpklo77S/+zwZrWj4idbUiuBf4naRfUr2W3kM1ijwiRrAUkyKaZSzwJPCOrn2tXbmsdg6zP6BANf3gx5IWA+4sF6vRWvNBtstZwN1Uo7aOBD4EtHqUn6TlgK8Ay9t+l6Q1gU1tn144WgkT6/tJQPcKmVf3PkqjrA3sSjVarTPNrY2j174+919pn3ohg1fMZZrfM73K0wTzuLjBeb3K0wCdFe7+UN86fjnI70bECJOeSRHReHUfizfUmzfYnjin32+7ljb/vNX2epKm2l5H0oLAdbY3KZ2tFEmXAmcAn7M9XtICwK1pJlyR9JJOf4+2qvv0rWm7VQWBmHdZPXSgLG4QEVEZVTpARMwmaYykT0j6rqQfdG6lc5VWr1r2E6oRBQ9LemXhSNE8z9b3/5K0FrAk1RTJNlvG9rnUI05szwSeKxupDElflbRM/fOGku6jGvF4v6S3FI5X0u3AuNIhmkLSJpImSHpc0jOSnpP0WOlchU2uv9CJ2W6QdJKkN0lav3MrHaoESS+rP7N+R9JLJH1J0lRJ50p6eel8ETF/ZZpbRLNkqk4/krYFvgEsDzwMvJLqGL2uZK6Ga+M0t1MlLQV8HrgQWJxqmeI2e6JeUcdQXShT9YFpo61sf6b++WvAznWz5dWAzoqRbTQOuFvSBPr2TGprj6CTgPdTTVPaEPgwsFrRROVtDHxIUutXD+2SxQ1m+yHVoiiLAVdRtSbYCngvcDJV76SIGKEyzS2iQTJVZ6B66eotgN/Ux2ZzYBfbexSOVlTdt2E5ur4UsP1A/djStv9ZKls0g6QNgBOAtahGoCwL7GC7dU1RJd0FrG17pqSbu8+pbZ7GM9SoLNvX9DpLE0iaaHvDzntwva91y7x3k7TSYPvnYSGIaIHu14ekB2y/suuxKbbXHfqvI2K4y8ikiGbpP1Xnb2SqzrO2H5E0StIo21dJ+lbpUCVJ2h84HPg7fZvmrgPQxkKSpK8Ax9n+V729FPAp258vm6wc25PqYsHqVKMJ7rH97Fz+bKT6LnCJpK8Cv5b0baqFDbYAphRNVtDcikaSbrK9aa/yNMCTkhYCpkg6DniIlreEsH2/pM2A19g+Q9KyVCM/WyuLG/TR/fr40Rwei4gRKC/yiGbpP1XnTuDYspGK+5ekxYFrgXPqi8DHC2cq7UBgdduvs712fWvzlAOAd3UKSQC2HwXeXTBPcZKmAocAT9m+vcWFJGyfSHXxtzfVtIstgEOBvwK7F4zWdGNKB+ixXYHRwH5UU7pWBLYvmqgwSYdTvVYOq3ctCJxdLlEj/BC4jGr6PcD/Ap8slqasX9af0ej+8kbSq6mOS0SMYBmZFNEsV9YXwdcCqwBIWrlspOJuA54EDqLqIbUkLf9WFPgz7e19M5TRkha2/TSApEWAhQtnKm0bYGfgXEmzgJ8C53amQ7aN7auBq+f0O5J2s31mTwIND63qhdA1devfwBElszTI+4D1gMkAth+UtMSc/2TEW8b2uZIOg2pxA0mtXNzA9qC9CW3fC+zQ2c65NWJkysikiGY5f5B9P+t5imbZ3PYs2zNtn2n7BKDtK8vcB1wt6TBJB3dupUMVdg5wpaQ9JO0BXAG0+oOr7fttH2d7A+CDVNMg/1g4VtMdWDpAlCNpa0m3SvqnpMckzchqbjzjqsFqp5H/YoXzNEEWN3jhcm6NGIEyMimiASStQbU62ZKStut6aCztm2YAgKR9gH2BVevpOh1LADeUSdUYD9S3hepb69k+tm7W/vZ611G2LyuZqQnq5rk717fnqKa9xdDauBLinLTteHwL2A6Y5qxQ03GupFOAcZL2pJoWelrhTKUdTNWKYFVJN1AvblA2UuO17VwS0QpZzS2iASS9h2oZ1W2pPqB0zAD+x/aNRYIVJGlJYCngGOAzXQ/NaGOD6fjPtLCRMJJ+R9Xf5Dzgp7bvKxyp8SRNtr1+6Ry9IulY24cOtU/SWrZvL5Ou9yRdBbzN9qy5/nKLSNoSeEe9ebntK0rmaQJJC5DFDeZZ286tEW2RYlJEg0ja1PZNpXNEs9Wr6RxCNZrt+ZFrtrcoFqrh2ri8t6TVbd9TOsdw0rbnyWAXeJKmtrWhv6SNgKOAa4CnO/ttH18sVANIehnweqppXRNs/61wpKIkjaEaOb0Z1TG5DjjZ9lNFgzVY286tEW2RnkkRzfI+SWMlLSjpSkn/kLRL6VDROOcAdwMrUzWJ/RMwoWSgYaCN35z8TdLxkibWt2/UI/5iaK2YQitpH0nTgNUlTe26/RGYOre/H8G+TLXgwxiqKdWdW2tJ+hhwC9X0vx2AmyW1fQXEH1F9mXMicFL981lFEzVfK86tEW2TkUkRDSJpiu11Jb0P2JpqXv61tscXjhYNImmS7Q26RxBImmC77Y3Jh9TGIfaSzgduZ3Yj8l2B8ba3G/qvRjZJC1Mt9f4quvpG2j6yVKYSMo14cJJut71W6RxNIuke4A22H6m3XwLcaHv1ssnKkXSn7TXntq8NJO1i++yhFgFp+6i+iJEuDbgjmmXB+n4r4Dzb06X0LIwBOr0ZHpK0FfAgsHTBPMNBG19Iq9revmv7CElTiqVphl9Srbo0ia5pTC1k23+S9In+D0hausUFpUskvcP25aWDNMgjVP0bO2bU+9pssqRNbN8MIGljYGLhTKV0Vvdr9Qi+iLbKyKSIBpH0VapG3P+m6k8wDrjY9sZFg0WjSNqaqkfDilTD7McCR9i+cI5/OMLVK5e9xvZvJC0CLGB7Rv1YqxoJQ9V0HPi07evr7TcCX29bI/JuGXlSkXSx7a3raW2mb7HVtlcpFK0oSTOoLo6fqW+iOh5jiwYrSNKPgLWpCrEG3kM1FXIqtHPkiaS7qJpvP1DveiVwDzCT6vnSyp5jEdE+KSZFNEg9BWMxYLrt5yQtBixu+++Fo0U0Wr1k9V7A0rZXlfQaqoaobyscrRhJ46l6e3T6JD0K7Ga7tT1xJJ0KnGh7WuksEcOBpMPn9LjtI3qVpSnqLy7m5DHbj/YkTENIOg44murL0F8D6wAH2T67aLCImK9STIpokCFW1mldr5cYnKRDbB8n6UQGaSht+4ACsRqhnr71euB3nRVjJE2zvXbZZOVJGgtg+7F++3ezfebgfzUySboTeDXwR6ppbp2RJ60aSSBpju8ptif3KkuTqJpX/iFgZdtHSVoReLntWwpHayxJJ9rev3SOJmnj57b0/Ixop/RMimiAetndFYBFJK3H7CkHY4FFiwWLprmrvm9rb4Y5edr2M50eY5IWoJ0ruA3Qv4jU5UBmN+dui3eVDtAQ36jvxwAbArdRve+sQ3V+aetUyO8Cs4AtgKOAx4HvAFncYGhvLB2ggdrYo69zTZmenxEtkmJSRDO8E/gI8Aqgu//ADOCzJQJF89i+qL5vWwFgXlwj6bNUBdktgX2BiwpnarrWfdK3fT+ApJdSFVJayfbmAJJ+DqzfmfYnaS3gSwWjlbax7fUl3Qpg+1FJC5UOFcNOG7/IuFjS3VTT3PaRtCzwVOFMETGfpZgU0QB1ceBMSdvbPr90nmi2+kPaocCadF0Q296iWKjyPgPsAUwD9gYuAb5fNFHzte6CR9K2VKNylgceBlaiGvH3upK5Clq9u3+U7dslvbZkoMKelTSa+rVRn2tnlY0U0Xy2P1P3Ter0/HyCqll7RIxgKSZFNMtakgZc1Ng+skSYaKxzgJ9SDSf/OLAb8I+iiQqzPQs4rb7FvGndyCSqqUubAL+xvZ6kzYFdCmcqaaqk7z2t2tYAABsxSURBVAOdJrkfol6lq6VOAC4AlpP0ZWAH4PNlIzVeG88jc9PWY7I88HZJ3aM+f1QqTETMfykmRTTL410/j6FqYnjXEL8b7fUS26dLOtD2NVRTvCaUDlWCpGnMYYRN2xorv0A3lA5QwLO2H5E0StIo21dJ+lbpUAV9FNiHqn8WwLXA98rFKcv2OZImAZ1VIN9rO+/Bc/bt0gFKqEewLUfXtZTtB+ofW7eKaL3q31upRkxfQtWf7npSTIoY0VJMimgQ29/o3pb0deCyQnGiuZ6t7x+StBXwILB0wTwlbV3ff6K+P6u+34UWTuMCkLSL7bMlHTzY47aPr+/3622yRviXpMWpiibnSHoYeKJwpmJsPyXpZOAS2/eUztMQiwKdqW6LFM5SnKTVgE9TTQntLpxsUd//sEyyciTtDxwO/J3Z0yBN1cAe2/8sFK2kHYDxwK22PyppOWaPeIyIESrFpIhmW5SqKXdEt6MlLQl8CjiRatW/T5aNVEZXQ+Utba/X9dChkiZT9VJqm8Xq+yWKpmim91A1iD2IakrXkkBrpxHXPaS+BiwErCxpXeBI29uWTVaGpC8COwLnU01VOkPSebaPLpusqPOAk6mmED9XOEtTHEjVb+yR0kEa5N+2Z0maKWksVU+6FUuHioj5K8WkiAbpN2VnFPBSqh4fEd3usD0dmA50VmVq+9LVkvRG2zfUG2+geg21ju1T6vsjSmdpknpaysX1SmazgKyKWI2ueD1wNYDtKZJWLpqorA8B420/BSDpq8AUoM3FpJm2Wzv1cQh/pnr/jdkmShpHVXScRNW24aaykSJifksxKaJZtgaWAt4EjKOaejCpbKRooJ9J2tb2XwEkvRn4DrB22VhF7QH8oB6xJeBRYPeykcqqV9Y5mmokzq+ppmAcZLuVUw/qFYZmSVqyLsZG1UNqutSnX3Arp4fWHqTqV9hZ0nxh4K/l4jTCRZL2pWpM/nRnZ0uncnXcB1wt6Vf0PSbHl4tUlu196x9PlvRrYKztNjfzj2iFFJMimuU9wJ7Az5k9xP402yeWjRUN83HgF5K2AdYHjgHeXTZSWXXRdXxdTCLFAgDeYfsQSe8D/gRsR9UrqJXFpNrjwDRJV9DVK8n2AeUiFXWHpA8CoyW9BjgAuLFwppKmUx2TK6iKalsCt0g6AVr7PNmtvv901z4DqxTI0hQP1LeF6lvrSXojMMX2E8BmwPqSvt2Zih4RI5PsNn8BFdEskqYCm9ZvxkhaDLgpK1JFf5I2BU6h+gZ9K9v/KBypqLqIdDjw5nrXNVS9X1pbVJJ0u+216qXff2b715Jusz2+dLZSJO022H7brZzyJmlR4HPAO+pdlwFHd6Z5tc1Qz4+Otj5PIuam/vw6nmoE7A+B7wM72X5LyVwRMX9lZFJEs4i+DS6fq/dFIOki+k5BWZTqm/TTJdHWprm1HwC3AzvV27sCZ1CNxmmriyXdTTXNbR9JyzJ7+k4rpRgwW91D6ld1D6nPlc7TBHN7fkg63/b2vcrTFJLWolryfUxnn+3WLfk+yHtwHy1/D55p25LeA5xk+3RJe5QOFRHzV4pJEc1yBvA7SRfU2+8FTi+YJ5rl66UDNNiq/S7yjpA0pViaBrD9mbpv0vS6X9ATVFNpW6ueynUMAy+MWzdlJz2k/k9a9zyRdDjwVqrXzCXAu4DrgdYVk8h78JzMkHQYsAvwZkmjgAULZ4qI+SzFpIgGsX28pKup5psDfNT2rQUjRYPYvqZ0hgb7t6TNbF8Pz/dv+HfhTE2wPPB2SWO69rXxIrDjDKrpkN+kWgnxo7R01b9aeki9MG3sDbED1fSlW21/VNJytLTvWvd7sKSFgDWonhP32H6mWLBm2Bn4ILCH7b9JeiXwtcKZImI+S8+kiIhhRtImwInAa6maf44GnrA9tmiwgiRtSDWKb8l616PA+bZbu6T3UCMKbO9QMldJkibZ3kDSNNtrd+8rna2E9JB6YSRNtr1+6Ry9JOkW26+XNImqADsDuMv2GoWjFSNpK+Bk4A9UrQhWBva2fWnRYBERPZaRSRERw89JwPuB84ANgQ8DqxVNVN6pVMPrOyvHbAV8EmhtMYmMKBjM0/X0i99L2o9q2ffFC2cq6fZ6JcTnSdq6VJhhoI09DCdKGgecBkyiGs12U9lIxX0D2Nz2vQCSVgV+BbSumCTpetubSZpB35F7AtzmL7ki2iAjkyIihhlJE21vKGlqZ6U/SbfaXq90tlIkrUJVXPsg1YpuuwLbtLkXTEYUDCRpI+AuYBxwFDAW+Jrtm4sGK0TSZODDtm+vtz8AfNL2xmWTNZOkd9i+vHSOUiS9Chhre2rhKEVJmmB7o65tAbd074uIaIOMTIqIGH6erPs1TKkbLD9Eu/u+YPu++kL4F8ADwDttt71nUkYUDPSU7cepjsVHS4dpgB2An0n6IPAmqlGO7ygbqfckTWPwfkid0RXrUP3QykKSpBWAlaivGyS92fa1ZVP1nqTO6qATJV0CnEv1vNkRmFAsWEGSlp7T47b/2assEdF7GZkUETHMSFoJeJhqpZSDqPoEfbcz5L5NBrkIfCkwHXgaoHMR2HYZUVCRdB2wMPBD4Jw2j1zrkLQas4uw72tjEbY+pw7J9v1zenwkk3QsVXPlO4Hn6t22vW25VGVIOmMOD9v27j0L0xCS/kj1HjzYFFC3caXMiDZJMSkiIoatXAQOTdKVtt82t31tI+k1wO7MHk3wA9tXlE3VWynCxrySdA+wju2nS2eJiIhmyTS3iIhhpm6QexSzpx20ttFlm4tFQ5E0BlgUWEbSUsz+xngssEKxYA1h+/eSPg9MBE4A1q17nnzW9s/LpuuZeWqyLWkp24/O7zBNkZUyB3Uf1SjY1heTJC0K7EdViD2RasTW9sDdwJH1FNpWkbSG7bslDbrKoe3Jvc4UEb2TkUkREcOMpHuB7YBpzkk8+pF0INVKdstTrVbWKSY9Bpxm+6RS2UqTtA5Vr6StgCuA021PlrQ8cJPtOY50axtJk20PepE4EkmayCArZdo+rGiwAiSdSFU0WYFqVcgr6Soo2T6gULRiJJ0L/BlYBFidqpn/T4FtgZfZ3rVgvCIknWp7L0lXDfKwbW/R81AR0TMpJkVEDDP1h7a32Z5VOks0l6T9bZ9YOkeTSLoG+D7ws/69gSTtavusMsmaqW2rRGalzNkk7Tanx22f2assTSFpiu3OSMaHgJfbdr19W5unh0oaY/upue2LiJEl09wiIoafQ4BL6gvj7m+Kjy8XKZrG9omS3gC8iq73e9s/KhaqMNtvmcNjKSQN1LZvHLNSZq2NxaJ5VReQLumMDK632/Za6e9GoP8oxsH2RcQIkmJSRMTw82Wqpc3HUPX1iBhA0lnAqsAUulZhAlpbTKqbbx8DrEn1+gEgKw5FbVeq4tF+VCtlrkg1pbh1JJ1re6dBmrUDrW3SPlHS4rYf7165TdKqwIyCuYqR9DKqqZCL9uubNJaqd19EjGCZ5hYRMcxIut32WqVzRLNJugtYM321ZpN0PXA48E1gG6r+SaNsf7FosIZq2xQvSQfa/vbc9rWBpJfbfmioFTOz+EFfktQ510rasi0rRNbTIT8CbEC1qEHHDOCHti8okSsieiPFpIiIYaaefvEb25eXzhLNJek84ADbD5XO0hSSJtneQNI022t37yudrQRJSw+ye4btZzuP2/5nj2MVM1jD8bYV1OI/17bG9QD1CLbzmL3gA1QzAI8sFCkieiDT3CIihp99gP+W9AzwDNWHN7d8+eoYaBngTkm30Le31rblIhX3tKRRwO8l7Ue12t3ihTOVNJlqKtejVOeRccDfJP0d2NP2pJLhekXSB4APAitLurDrobFAa4ppg5E0g4HT3KZTjUL5lO37ep+q8TT3Xxlxftj18xhga6rV7iJiBEsxKSJimLG9ROkMMSx8qXSABjqQqo/HAcBRwBbAHFetGuGuoFrZ7jIASe8AtgfOAL4LbFwwWy/dSNVsexngG137ZwBTiyRqjm8BfwF+TFUkeT9VL7bJwA+AtxZL1lytm/Zhu/t1g6SvA5cVihMRPZJpbhERw0y9DPGHgJVtHyVpRaolim8pHC0apu538hrbv5G0KDDadisbxXaTNJZqNF+rj0X3dL+ufVNtr9NZBr1UtlIkLQdsVG/eYvvhknlKk3Sb7fH99k2xve5gj0U7p7n1J2kpYILtV5fOEhHzTyuXO42IGOa+C2xKNS0DqpXdvlMuTjSRpD2BnwGn1LtWAH5RLlF5kjase3tMBaZJuk1SK/sl1R6SdKiklerbIcDfJY0GZpUO12uSdgRuAXYEdgJ+J2mHsqmKe1LSTpJG1bedgKfqx/KN9OD+VDpAr0maJmlqfbsDuIdqVFtEjGAZmRQRMcx0vvXsbgybb4ijP0lTgNcDv+t6ngwYidImkqYCn7B9Xb29GfDdli5zjqRlqFa326zedQNwBFVPnFfavrdUthIk3QZs2RmNJGlZqsUOWntulbQK8G2qLzAM3AwcRNVvbAPb1xeMV4ykNwCvoqtliO0fFQtUWL9V/2YCf7c9s1SeiOiN9EyKiBh+nq1HDnSWIV6WFo4iiLl62vYz1axIkLQAGUnwXKeQBGD7ekmtveCx/f+A/Yd4uFWFpNqoftPaHqHlo/jrBtvbDPFwWwtJZ1H1jZoCPFfvNtDaYpLt+0tniIjeSzEpImL4OQG4AFhO0peBHYDPl40UDXSNpM8Ci0jaEtgXuKhwpiIkdfqXXCPpFOAnVBd/OwNXl8pVWl2IPgR4HdUKTADY3qJYqLIulXQZ1fMDqufHJQXzFCdpDLAHA58juxcLVd6GwJrO9I6IaLkUkyIihhnb50iaBLyt3vVe21mCN/r7DNVF4DRgb6qL4u8XTVTON/ptH971c5svCM8Bfkq1jPfHqVa2+0fRRGWZqsdYZ9rfqcAm5eI0wlnA3cA7gSOpFn9o+/vN7cDLqFYAjIhorfRMiogYhuqRFptRXfzcYHty4UjRYJKWBl5hu+3LnEcXSZNsb9BZwa3eN8H2RnP725FosFW4uo9NG3V683Wt8rcgcJ3t1hbZJF0FrEvVrP3pzn7b2xYLFRFRQEYmRUQMM5K+SLXa0PmAgDMknWf76LLJokkkXQ1sS/VePwl4WNKNtg8qGqwgSeOADzOwce4BpTIV9mx9/5CkrYAHgaUL5ilC0j5U00BXqZu0dyxB1ZS8zTrPkX9JWgv4G/DSgnma4EulA0RENEFGJkVEDDOS7gHG236q3l4EmGJ79bLJokm6RhR8DFjR9uEZZaEbqVajmkZX03rbZxYLVZCkrYHrgBWBE4GxwBG2LywarMckLQksBRxDNT20Y4btf5ZJ1Qz1+eN8YG3gh8DiwBdsn1IyV0RElJeRSRERw8+DVI1Qn6q3F6Zapjmi2wKSXg7sBHyudJiGGGP74NIhmsL2xfWP04HNS2YpyfZ0qmPwgdJZGuhK248C1wKrAEhauWyksiRtQlV8fS2wEDAaeML22KLBIiJ6LMWkiIjhZzpwh6QrqHombQncIukEaPWUnejrCOAy4HrbEyStAvy+cKbSzpK0J3AxfXudtGr0SedcMZScQ6LL+cD6/fb9DNigQJamOAl4P3Ae1cpuHwZWK5ooIqKAFJMiIoafC+pbx9WFckRDSRpNNbXt+Slttu8Dti+XqhGeAb5GNVKrM8/f1CMuWmQ7qmOwFPBo4SzRQJLWAF4HLClpu66HxlKNjG012/dKGm37Oaq+hbcCh5XOFRHRSykmRUQMM3Pr7yLpfNttLxq0mu3nJH0A+GbpLA3zKeDVtv9f6SCFPQZcAVwKvJWqkX9Et9WBrYFxwDZd+2cAexZJ1BxPSloImCLpOOAhYFThTBERPZcG3BERI0yn8XLpHFGWpG8CCwI/BZ7o7Lc9uViowiRdDrzX9pOls5Qk6QBgH6oRWd391gTYdttGasUQJG1q+6Y5PH6Y7WN6mak0SSsBD1OdXw8ClgS+a/veosEiInosxaSIiBFG0mTb/XtcRMtIumqQ3ba9Rc/DNISkC6im7lxF355JrewRJOl7tvcpnSOGr7zfRES0V6a5RUREjEC2W7s61xz8or4FkEJSvAhaM0VS0rm2d5I0jdk9157X3aMuIqINUkyKiBh5WvPhPoYmaTngK8Dytt8laU1gU9unF45WjO0zJS0CvNL2PaXzRIwAbZricGB9v3XRFBERDZFmcRERI8+hpQNEI/wQuAxYvt7+X+CTxdI0gKRtgCnAr+vtdSVdWDZVxLDWmi8vbD9U398/2K10voiIXsvIpIiIYWKoofXMbpq7DtUPl/c0WDTVMrbPlXQYgO2Zkp4rHaqwLwGvB64GsD1FUppNR/zfnVc6QK9J2gQ4EXgtsBAwGnjC9tiiwSIieizFpIiI4SND6+OFeELSS6gLkPUF0PSykYp71vZ0qc9gilmlwkQ0naQxwB5UjevHdPbb3r2+/0qhaCWdBLyfqpC2IfBhYLWiiSIiCkgxKSJimMgw+niBDgYuBFaRdAOwLLBD2UjF3SHpg8BoSa8BDgBuLJwposnOAu4G3gkcCXwIuKtoogawfa+k0bafA86QdCtwWOlcERG9lJ5JERHDjKRNJE2Q9LikZyQ9J+mx0rmice4ELgAmAH8HTqPqm9Rm+1ONsHga+AnwGC3vIxUxF6+2/QWqaVxnAlsBGxfOVNqTkhYCpkg6TtJB5JoqIlpIdpsWYYiIGP4kTWSQIfa2861oPE/SuVTFknPqXR8ExtnesVyqZpA0lqrP2IzSWSKaTNIttl8v6VpgX+BvwC22W9trTNJKVAX6hYCDgCWB79j+Q9FgERE9lmluERHDUIbYxzxYy/aaXdtXSbqzWJoGkLQR8ANgiXp7OrC77UlFg0U016mSlgI+TzVtdnHgi2UjFbdmPe38KeAIAEkfB1JMiohWSTEpImL46TPEHniIDLGPgSZL2sT2zQCSNgYmFs5U2unAvravA5C0GXAGsE7RVBENZfv79Y/XAq0djdTPFyQ9bfu3AJI+DWwBnFw2VkREb+XiIyJi+NmV6vy9H/AEsCKwXdFE0UQbADdK+pOkPwE3ARtJmiZpatloxTzXKSQB2L4emFkwT0SjSTpQ0lhVvi9psqR3lM5V2LbAVyS9SdKXgU2A9xTOFBHRc+mZFBExzEg60Pa357Yv2q3u6zGkNq4OKOlbwCJUzbcN7Ew1VeVsANuTy6WLaB5Jt9keL+mdwMepprudZXv9wtGKkvRS4DfAJKqpsrmgiojWSTEpImKYkTS5/wd5SbfaXq9UpojhQNJVc3jYtrfoWZiIYUDSVNvrSPo2cLXtC9r6fiNpBlURumMhqpGNpjp/jC0SLCKikPRMiogYJiR9gGpFrpUlXdj10Fjgn2VSRQwftjef0+OSdquXP4+IyiRJlwMrA4dJWgKYVThTEbaXKJ0hIqJJMjIpImKYqKctrQwcA3ym66EZwFTb6f0S8R8YbNRfRJtJGgWsC9xn+1+SXgKsYLutfdcAqFe4ew0wprPP9rXlEkVE9F5GJkVEDBN1j5v7gU0lLQdsVD90VwpJES8KlQ4Q0QSS1rB9N1UhCWAVKS8PAEkfAw4EXgFMoWrAfRPVim4REa2RYlJExDAjaUfg68DVVBe/J0r6tO2fFQ0WMfxluHZE5WBgL+AbVK8L0ff10ebCyYFUX+bcbHtzSWsAXymcKSKi51JMiogYfj4PbGT7YQBJy1KtKpNiUsR/JkMvIgDbe9U/fg/4te3HJH0BWB84qlyyRnjK9lOSkLSw7bslrV46VEREr40qHSAiIl6wUZ1CUu0Rcj6PeDHcUDpARMN8vi4kbUY1Gun7VAWmNvuLpHHAL4ArJP2Sagp6RESrpAF3RMQwI+k4YDzwk3rXzlQNuA8tlyqi+SQtCXwJeFO96xrgSNvTi4WKaDBJt9peT9IxwDTbP+7sK52tCSS9BViSavTWM6XzRET0Ur7JjogYfgycAqxT304tGydi2PgB8BiwU317DDijaKKIZvurpFOovrS4RNLC5PoBSUtJWodqNdW/AGsVjhQR0XMZmRQRMcwMtny5pKm21ymVKWI4kDTF9rpz2xcRFUmLAv9FNSrp95JeDqxt+/LC0YqRdBTwEeA+YFa927bb3JQ8IlooDbgjIoYJSfsA+1It0Ty166ElSK+XiHnxb0mb2b4eQNIbgX8XzhTRWLafBH7etf0Q8FC5RI2wE7BqprVFRNulmBQRMXz8GLgUOAb4TNf+Gbb/WSZSxLCyD3Bm3TtJwD+pRhhERMyr24FxwMNz+8WIiJEs09wiIiKiVSSNBbD9WOksETG8SNoQ+CVVUenpzn7b2xYLFRFRQEYmRURExIgmaRfbZ0s6uN9+AGwfXyRYRAxHZwLHAtOY3TMpIqJ1UkyKiIiIkW6x+n6JoikiYiR40vYJpUNERJSWaW4RERERERHzQNLxVNPbLqTvNLfJxUJFRBSQYlJERES0gqTjgKOpVnD7NbAOcJDts4sGi4hhQ9JVg+y27S16HiYioqAUkyIiIqIVJE2xva6k9wFbAwcD19oeXzhaRERExLAyqnSAiIiIiB7p9IrcCjjP9vSSYSJi+JG0nKTTJV1ab68paY/SuSIiei3FpIiIiGiLiyXdDWwAXClpWeCpwpkiYnj5IXAZsHy9/b/AJ4uliYgoJNPcIiIiojUkLQ1Mt/2cpEWBsbb/VjpXRAwPkibY3kjSrbbXq/dNsb1u6WwREb20wNx/JSIiImLEWB54u6QxXft+VCpMRAw7T0h6CWAASZsAmTIbEa2TYlJERES0gqTDgbcCawKXAO8CrifFpIiYdwcDFwKrSLoBWBbYoWykiIjeSzEpIiIi2mIHYDxwq+2PSloOOLtwpogYXu4ELgCeBGYAv6DqmxQR0SppwB0RERFt8ZTtWcBMSWOBh4EVC2eKiOHlR8AawFeAE4HVgLOKJoqIKCAjkyIiImLEkyRgqqRxwGnAJOBx4KaiwSJiuFnL9ppd21dJurNYmoiIQlJMioiIiBHPtiW93va/gJMl/ZpqJbeppbNFxLAyWdImtm8GkLQxMLFwpoiInpPt0hkiIiIi5jtJZwIn2Z5QOktEDE+S7gJWBx6od70SuAeYSVW3XqdUtoiIXkoxKSIiIlpB0t3Aq4H7gScAkYu/iHgBJK00p8dt39+rLBERJaWYFBEREa0w1EVgLv4iIiIiXpgUkyIiIiIiIiIiYp6NKh0gIiIiIiIiIiKGjxSTIiIiIiIiIiJinqWYFBERERERERER8yzFpIiIiIiIiIiImGcpJkVERERERERExDz7/ySfryT8t+XBAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1440x864 with 2 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DIDjV0ZLe2z-","executionInfo":{"status":"ok","timestamp":1630385210911,"user_tz":-480,"elapsed":46,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"261543fd-ea9e-4eaa-f352-c4d01ee04631"},"source":["# Cek Urutan korelasi terendah ke tertinggi\n","korelasi = dtrain.corr()[\"flag_kredit_macet\"].sort_values()\n","korelasi"],"execution_count":146,"outputs":[{"output_type":"execute_result","data":{"text/plain":["total_pemakaian_per_limit             -0.116349\n","pemakaian_3bln_per_limit              -0.066432\n","total_pemakaian_retail                -0.048911\n","total_pemakaian                       -0.046733\n","rasio_pembayaran                      -0.020330\n","rasio_pembayaran_3bulan               -0.018774\n","X                                     -0.011476\n","bin_delikuensi                        -0.003334\n","kode_cabang                           -0.002734\n","jumlah_tahun_sejak_pembukaan_kredit    0.000004\n","rasio_pembayaran_6bulan                0.000723\n","skor_delikuensi                        0.003825\n","total_pemakaian_tunai                  0.015235\n","pemakaian_6bln_per_limit               0.018276\n","jumlah_kartu                           0.023895\n","limit_kredit                           0.031490\n","bin                                    0.052259\n","outstanding                            0.089548\n","utilisasi_6bulan                       0.095746\n","utilisasi_3bulan                       0.102866\n","tagihan                                0.107476\n","sisa_tagihan_per_jumlah_kartu          0.122446\n","sisa_tagihan_tidak_terbayar            0.129040\n","sisa_tagihan_per_limit                 0.137765\n","persentasi_overlimit                   0.142912\n","flag_kredit_macet                      1.000000\n","Name: flag_kredit_macet, dtype: float64"]},"metadata":{},"execution_count":146}]},{"cell_type":"markdown","metadata":{"id":"obriLN86UjTv"},"source":["> Dari hasil heatmap di atas didapatkan bahwa feature \"total pemakaian retail\" & \"total pemakaian\" memiliki korelasi yang sangat positif & kuat (bernilai 1)."]},{"cell_type":"markdown","metadata":{"id":"31xYfZiLJ_li"},"source":["#### FEATURE ENGINEERING"]},{"cell_type":"markdown","metadata":{"id":"vjVVguuUp4Em"},"source":["**FEATURE ENGINEERING**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HrWztm2_p3M_","executionInfo":{"status":"ok","timestamp":1630385210912,"user_tz":-480,"elapsed":33,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"f3c0c41f-295d-4a48-af3a-a6a8bf571dd8"},"source":["# menambah feature 'rasio_tagihan_sisa_tagihan_tak_terbayar' untuk melihat sisa tagihan tak terbayar pada bulan terakhir per jumlah tagihan bulan akhir dari nasabah\n","dtrain['rasio_tagihan_sisa_tagihan_tak_terbayar'] = dtrain['sisa_tagihan_tidak_terbayar']/dtrain['tagihan']\n","\n","# menambah feature 'utilisasi_3bulan_per_limit' untuk melihat utilisasi 3 bulan terakhir per jumlah limit kredit yang dimiliki nasabah\n","dtrain['utilisasi_3bulan_per_limit'] = dtrain['utilisasi_3bulan']/dtrain['limit_kredit']\n","\n","# menambah feature 'utilisasi_6bulan_per_limit' untuk melihat utilisasi 3 bulan sebelum 3 bulan terakhir per jumlah limit kredit yang dimiliki nasabah\n","dtrain['utilisasi_6bulan_per_limit'] = dtrain['utilisasi_6bulan']/dtrain['limit_kredit']"],"execution_count":147,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \"\"\"\n","/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  \n"]}]},{"cell_type":"code","metadata":{"id":"9mcmEGC-rjCm","executionInfo":{"status":"ok","timestamp":1630385210914,"user_tz":-480,"elapsed":24,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":147,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pLHt_C3CnbuX"},"source":["**SCALING**"]},{"cell_type":"code","metadata":{"id":"X-sP2k5vJ_lj","colab":{"base_uri":"https://localhost:8080/","height":224},"executionInfo":{"status":"ok","timestamp":1630385210922,"user_tz":-480,"elapsed":31,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"222f0c60-20b4-4bec-b76c-9781dcaec82e"},"source":["dtrain.head()"],"execution_count":148,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>100</th>\n","      <td>13625</td>\n","      <td>2</td>\n","      <td>4306728</td>\n","      <td>4000000.0</td>\n","      <td>4316980.0</td>\n","      <td>0.0</td>\n","      <td>161999.0</td>\n","      <td>4290920.0</td>\n","      <td>0</td>\n","      <td>7.98</td>\n","      <td>6.87</td>\n","      <td>4.39</td>\n","      <td>9.96</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6.83</td>\n","      <td>161999.0</td>\n","      <td>2145460.0</td>\n","      <td>1.0700</td>\n","      <td>0.04050</td>\n","      <td>0.06300</td>\n","      <td>0.11900</td>\n","      <td>0.95200</td>\n","      <td>0.83300</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.993963</td>\n","      <td>2.380000e-07</td>\n","      <td>2.082500e-07</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>3563</td>\n","      <td>2</td>\n","      <td>921499</td>\n","      <td>59000000.0</td>\n","      <td>563269.0</td>\n","      <td>0.0</td>\n","      <td>382810.0</td>\n","      <td>893878.0</td>\n","      <td>2</td>\n","      <td>100.00</td>\n","      <td>0.00</td>\n","      <td>78.10</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>9.58</td>\n","      <td>382810.0</td>\n","      <td>446939.0</td>\n","      <td>0.0152</td>\n","      <td>0.00649</td>\n","      <td>0.00747</td>\n","      <td>0.02140</td>\n","      <td>0.00906</td>\n","      <td>0.02080</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1.586947</td>\n","      <td>1.535593e-10</td>\n","      <td>3.525424e-10</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>12481</td>\n","      <td>2</td>\n","      <td>2986142</td>\n","      <td>10000000.0</td>\n","      <td>54771.0</td>\n","      <td>0.0</td>\n","      <td>2783345.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>38.70</td>\n","      <td>64.90</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5.92</td>\n","      <td>2783345.0</td>\n","      <td>0.0</td>\n","      <td>0.0000</td>\n","      <td>0.27800</td>\n","      <td>0.07620</td>\n","      <td>0.00737</td>\n","      <td>0.07810</td>\n","      <td>0.00853</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>7.810000e-09</td>\n","      <td>8.530000e-10</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>4254</td>\n","      <td>2</td>\n","      <td>6437645</td>\n","      <td>6000000.0</td>\n","      <td>5970411.0</td>\n","      <td>0.0</td>\n","      <td>866750.0</td>\n","      <td>6424640.0</td>\n","      <td>0</td>\n","      <td>34.10</td>\n","      <td>6.79</td>\n","      <td>24.90</td>\n","      <td>41.70</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.83</td>\n","      <td>866750.0</td>\n","      <td>3212320.0</td>\n","      <td>1.0700</td>\n","      <td>0.14400</td>\n","      <td>0.15300</td>\n","      <td>0.39300</td>\n","      <td>1.13000</td>\n","      <td>0.98300</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1.076080</td>\n","      <td>1.883333e-07</td>\n","      <td>1.638333e-07</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>12541</td>\n","      <td>3</td>\n","      <td>1370140</td>\n","      <td>7000000.0</td>\n","      <td>278500.0</td>\n","      <td>0.0</td>\n","      <td>390074.0</td>\n","      <td>821072.0</td>\n","      <td>5</td>\n","      <td>105.00</td>\n","      <td>0.00</td>\n","      <td>99.80</td>\n","      <td>99.50</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>11.80</td>\n","      <td>390074.0</td>\n","      <td>273691.0</td>\n","      <td>0.1170</td>\n","      <td>0.05570</td>\n","      <td>0.27700</td>\n","      <td>0.37700</td>\n","      <td>0.27100</td>\n","      <td>0.44600</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2.948194</td>\n","      <td>3.871429e-08</td>\n","      <td>6.371429e-08</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         X  ...  utilisasi_6bulan_per_limit\n","100  13625  ...                2.082500e-07\n","101   3563  ...                3.525424e-10\n","102  12481  ...                8.530000e-10\n","103   4254  ...                1.638333e-07\n","104  12541  ...                6.371429e-08\n","\n","[5 rows x 29 columns]"]},"metadata":{},"execution_count":148}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":316},"id":"DvGyhagw2JRI","executionInfo":{"status":"ok","timestamp":1630385211757,"user_tz":-480,"elapsed":863,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"8377855a-c586-49c6-ac12-65e42fe46502"},"source":["dtrain.describe()"],"execution_count":149,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>15243.000000</td>\n","      <td>1.524300e+04</td>\n","      <td>1.524300e+04</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7746.216952</td>\n","      <td>2.507446</td>\n","      <td>1.160716e+07</td>\n","      <td>2.077908e+07</td>\n","      <td>8.097279e+06</td>\n","      <td>7.449573e+04</td>\n","      <td>2.037738e+06</td>\n","      <td>8.146400e+06</td>\n","      <td>2.132520</td>\n","      <td>67.547946</td>\n","      <td>3.355427</td>\n","      <td>80.862504</td>\n","      <td>100.918574</td>\n","      <td>0.004592</td>\n","      <td>0.087844</td>\n","      <td>6.641929</td>\n","      <td>2.112311e+06</td>\n","      <td>2.965263e+06</td>\n","      <td>0.468735</td>\n","      <td>0.110225</td>\n","      <td>0.167668</td>\n","      <td>0.203438</td>\n","      <td>0.571827</td>\n","      <td>0.534449</td>\n","      <td>0.004723</td>\n","      <td>0.001246</td>\n","      <td>1.266691</td>\n","      <td>8.528490e-08</td>\n","      <td>8.036585e-08</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4474.179993</td>\n","      <td>1.013875</td>\n","      <td>2.419846e+07</td>\n","      <td>2.960812e+07</td>\n","      <td>1.770636e+07</td>\n","      <td>6.207148e+05</td>\n","      <td>7.961983e+06</td>\n","      <td>1.929042e+07</td>\n","      <td>3.004418</td>\n","      <td>782.900621</td>\n","      <td>9.275493</td>\n","      <td>802.092762</td>\n","      <td>991.163091</td>\n","      <td>0.112141</td>\n","      <td>0.283077</td>\n","      <td>4.690106</td>\n","      <td>8.128677e+06</td>\n","      <td>6.267090e+06</td>\n","      <td>0.473290</td>\n","      <td>0.200455</td>\n","      <td>0.192356</td>\n","      <td>0.275151</td>\n","      <td>0.412530</td>\n","      <td>0.432970</td>\n","      <td>0.078389</td>\n","      <td>0.046514</td>\n","      <td>3.910308</td>\n","      <td>1.006076e-07</td>\n","      <td>1.022573e-07</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>3.000000e+06</td>\n","      <td>2.004300e+04</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000288</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.716667e-12</td>\n","      <td>0.000000e+00</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3867.500000</td>\n","      <td>2.000000</td>\n","      <td>2.000551e+06</td>\n","      <td>5.000000e+06</td>\n","      <td>8.148450e+05</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>20.800000</td>\n","      <td>22.400000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>2.920000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.035300</td>\n","      <td>0.033219</td>\n","      <td>0.189000</td>\n","      <td>0.155000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.014492e-08</td>\n","      <td>7.939616e-09</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7746.000000</td>\n","      <td>2.000000</td>\n","      <td>4.725195e+06</td>\n","      <td>9.000000e+06</td>\n","      <td>3.145469e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>2.080000e+05</td>\n","      <td>2.753848e+06</td>\n","      <td>0.000000</td>\n","      <td>27.000000</td>\n","      <td>0.000000</td>\n","      <td>50.100000</td>\n","      <td>66.300000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>5.666667</td>\n","      <td>2.550000e+05</td>\n","      <td>1.213084e+06</td>\n","      <td>0.337000</td>\n","      <td>0.022900</td>\n","      <td>0.108000</td>\n","      <td>0.118000</td>\n","      <td>0.577000</td>\n","      <td>0.498000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>4.620000e-08</td>\n","      <td>4.023778e-08</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>11623.500000</td>\n","      <td>3.000000</td>\n","      <td>1.057580e+07</td>\n","      <td>2.200000e+07</td>\n","      <td>7.420343e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.432000e+06</td>\n","      <td>7.142066e+06</td>\n","      <td>5.000000</td>\n","      <td>100.000000</td>\n","      <td>1.090000</td>\n","      <td>91.200000</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>9.330000</td>\n","      <td>1.533900e+06</td>\n","      <td>3.082346e+06</td>\n","      <td>0.930000</td>\n","      <td>0.128000</td>\n","      <td>0.239299</td>\n","      <td>0.285000</td>\n","      <td>0.918000</td>\n","      <td>0.864000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.146158</td>\n","      <td>1.313756e-07</td>\n","      <td>1.200000e-07</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>15493.000000</td>\n","      <td>16.000000</td>\n","      <td>7.980586e+08</td>\n","      <td>1.000000e+09</td>\n","      <td>6.280000e+08</td>\n","      <td>2.884057e+07</td>\n","      <td>2.850000e+08</td>\n","      <td>4.440000e+08</td>\n","      <td>10.000000</td>\n","      <td>68983.000000</td>\n","      <td>190.000000</td>\n","      <td>75575.000000</td>\n","      <td>54899.000000</td>\n","      <td>5.000000</td>\n","      <td>1.000000</td>\n","      <td>34.416667</td>\n","      <td>3.140000e+08</td>\n","      <td>1.480000e+08</td>\n","      <td>7.240000</td>\n","      <td>4.600000</td>\n","      <td>3.490000</td>\n","      <td>8.110000</td>\n","      <td>8.125671</td>\n","      <td>9.730000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>186.894015</td>\n","      <td>2.480000e-06</td>\n","      <td>2.345000e-06</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  X  ...  utilisasi_6bulan_per_limit\n","count  15243.000000  ...                1.524300e+04\n","mean    7746.216952  ...                8.036585e-08\n","std     4474.179993  ...                1.022573e-07\n","min        1.000000  ...                0.000000e+00\n","25%     3867.500000  ...                7.939616e-09\n","50%     7746.000000  ...                4.023778e-08\n","75%    11623.500000  ...                1.200000e-07\n","max    15493.000000  ...                2.345000e-06\n","\n","[8 rows x 29 columns]"]},"metadata":{},"execution_count":149}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":651},"id":"welZZH0JKC7Z","executionInfo":{"status":"ok","timestamp":1630385211758,"user_tz":-480,"elapsed":168,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"d620ec20-0531-4459-c2e4-79ec0b3f0943"},"source":["# melakukan scaling untuk menghindari perbedaan range nilai yang tinggi diantara beberapa variabel.\n","transformation_svm = MinMaxScaler().fit(dtrain[['X','jumlah_kartu','outstanding','limit_kredit','tagihan', 'total_pemakaian_tunai','total_pemakaian_retail', 'sisa_tagihan_tidak_terbayar', 'kode_cabang', 'rasio_pembayaran', 'persentasi_overlimit', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'skor_delikuensi', 'jumlah_tahun_sejak_pembukaan_kredit', 'total_pemakaian', 'sisa_tagihan_per_jumlah_kartu', 'sisa_tagihan_per_limit', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit', 'utilisasi_3bulan', 'utilisasi_6bulan', 'bin', 'bin_delikuensi', 'rasio_tagihan_sisa_tagihan_tak_terbayar', 'utilisasi_3bulan_per_limit', 'utilisasi_6bulan_per_limit']])\n","dtrain[['X','jumlah_kartu','outstanding','limit_kredit','tagihan', 'total_pemakaian_tunai','total_pemakaian_retail', 'sisa_tagihan_tidak_terbayar', 'kode_cabang', 'rasio_pembayaran', 'persentasi_overlimit', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'skor_delikuensi', 'jumlah_tahun_sejak_pembukaan_kredit', 'total_pemakaian', 'sisa_tagihan_per_jumlah_kartu', 'sisa_tagihan_per_limit', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit', 'utilisasi_3bulan', 'utilisasi_6bulan', 'bin', 'bin_delikuensi', 'rasio_tagihan_sisa_tagihan_tak_terbayar', 'utilisasi_3bulan_per_limit', 'utilisasi_6bulan_per_limit']]=transformation_svm.transform(dtrain[['X','jumlah_kartu','outstanding','limit_kredit','tagihan', 'total_pemakaian_tunai','total_pemakaian_retail', 'sisa_tagihan_tidak_terbayar', 'kode_cabang', 'rasio_pembayaran', 'persentasi_overlimit', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'skor_delikuensi', 'jumlah_tahun_sejak_pembukaan_kredit', 'total_pemakaian', 'sisa_tagihan_per_jumlah_kartu', 'sisa_tagihan_per_limit', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit', 'utilisasi_3bulan', 'utilisasi_6bulan', 'bin', 'bin_delikuensi', 'rasio_tagihan_sisa_tagihan_tak_terbayar', 'utilisasi_3bulan_per_limit', 'utilisasi_6bulan_per_limit']])\n","\n","dtrain"],"execution_count":150,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  This is separate from the ipykernel package so we can avoid doing imports until\n","/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1734: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  isetter(loc, value[:, i].tolist())\n"]},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>100</th>\n","      <td>0.879422</td>\n","      <td>0.066667</td>\n","      <td>0.005397</td>\n","      <td>0.001003</td>\n","      <td>0.006842</td>\n","      <td>0.0</td>\n","      <td>0.000568</td>\n","      <td>0.009664</td>\n","      <td>0.0</td>\n","      <td>0.000116</td>\n","      <td>0.036158</td>\n","      <td>0.000058</td>\n","      <td>0.000181</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.180594</td>\n","      <td>0.000516</td>\n","      <td>0.014496</td>\n","      <td>0.147790</td>\n","      <td>0.008804</td>\n","      <td>0.018052</td>\n","      <td>0.014673</td>\n","      <td>0.117128</td>\n","      <td>0.085612</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005318</td>\n","      <td>0.095967</td>\n","      <td>0.088806</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>0.229925</td>\n","      <td>0.066667</td>\n","      <td>0.001155</td>\n","      <td>0.056169</td>\n","      <td>0.000865</td>\n","      <td>0.0</td>\n","      <td>0.001343</td>\n","      <td>0.002013</td>\n","      <td>0.2</td>\n","      <td>0.001450</td>\n","      <td>0.000000</td>\n","      <td>0.001033</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.262277</td>\n","      <td>0.001219</td>\n","      <td>0.003020</td>\n","      <td>0.002099</td>\n","      <td>0.001411</td>\n","      <td>0.002140</td>\n","      <td>0.002639</td>\n","      <td>0.001080</td>\n","      <td>0.002138</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.008491</td>\n","      <td>0.000061</td>\n","      <td>0.000150</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>0.805577</td>\n","      <td>0.066667</td>\n","      <td>0.003742</td>\n","      <td>0.007021</td>\n","      <td>0.000055</td>\n","      <td>0.0</td>\n","      <td>0.009766</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000512</td>\n","      <td>0.001182</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.153564</td>\n","      <td>0.008864</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.060435</td>\n","      <td>0.021834</td>\n","      <td>0.000909</td>\n","      <td>0.009576</td>\n","      <td>0.000877</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.003148</td>\n","      <td>0.000364</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>0.274529</td>\n","      <td>0.066667</td>\n","      <td>0.008067</td>\n","      <td>0.003009</td>\n","      <td>0.009475</td>\n","      <td>0.0</td>\n","      <td>0.003041</td>\n","      <td>0.014470</td>\n","      <td>0.0</td>\n","      <td>0.000494</td>\n","      <td>0.035737</td>\n","      <td>0.000329</td>\n","      <td>0.000760</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.240000</td>\n","      <td>0.002760</td>\n","      <td>0.021705</td>\n","      <td>0.147790</td>\n","      <td>0.031304</td>\n","      <td>0.043840</td>\n","      <td>0.048459</td>\n","      <td>0.139035</td>\n","      <td>0.101028</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005758</td>\n","      <td>0.075940</td>\n","      <td>0.069865</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>0.809450</td>\n","      <td>0.133333</td>\n","      <td>0.001717</td>\n","      <td>0.004012</td>\n","      <td>0.000412</td>\n","      <td>0.0</td>\n","      <td>0.001369</td>\n","      <td>0.001849</td>\n","      <td>0.5</td>\n","      <td>0.001522</td>\n","      <td>0.000000</td>\n","      <td>0.001321</td>\n","      <td>0.001812</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.328218</td>\n","      <td>0.001242</td>\n","      <td>0.001849</td>\n","      <td>0.016160</td>\n","      <td>0.012109</td>\n","      <td>0.079370</td>\n","      <td>0.046486</td>\n","      <td>0.033317</td>\n","      <td>0.045838</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.015775</td>\n","      <td>0.015610</td>\n","      <td>0.027170</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>15338</th>\n","      <td>0.286729</td>\n","      <td>0.066667</td>\n","      <td>0.014498</td>\n","      <td>0.012036</td>\n","      <td>0.004358</td>\n","      <td>0.0</td>\n","      <td>0.030513</td>\n","      <td>0.009091</td>\n","      <td>0.0</td>\n","      <td>0.001225</td>\n","      <td>0.000000</td>\n","      <td>0.001076</td>\n","      <td>0.001128</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.136040</td>\n","      <td>0.027695</td>\n","      <td>0.013637</td>\n","      <td>0.037155</td>\n","      <td>0.126087</td>\n","      <td>0.083095</td>\n","      <td>0.033169</td>\n","      <td>0.041316</td>\n","      <td>0.033916</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.007835</td>\n","      <td>0.009031</td>\n","      <td>0.009382</td>\n","    </tr>\n","    <tr>\n","      <th>15339</th>\n","      <td>0.424154</td>\n","      <td>0.133333</td>\n","      <td>0.016204</td>\n","      <td>0.008024</td>\n","      <td>0.018755</td>\n","      <td>0.0</td>\n","      <td>0.000033</td>\n","      <td>0.027627</td>\n","      <td>0.5</td>\n","      <td>0.000000</td>\n","      <td>0.095263</td>\n","      <td>0.000290</td>\n","      <td>0.000603</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.235248</td>\n","      <td>0.000030</td>\n","      <td>0.027627</td>\n","      <td>0.154696</td>\n","      <td>0.000188</td>\n","      <td>0.040401</td>\n","      <td>0.010999</td>\n","      <td>0.132881</td>\n","      <td>0.081706</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005563</td>\n","      <td>0.039588</td>\n","      <td>0.030820</td>\n","    </tr>\n","    <tr>\n","      <th>15340</th>\n","      <td>0.568810</td>\n","      <td>0.066667</td>\n","      <td>0.005944</td>\n","      <td>0.004012</td>\n","      <td>0.008996</td>\n","      <td>0.0</td>\n","      <td>0.000981</td>\n","      <td>0.010241</td>\n","      <td>0.0</td>\n","      <td>0.000116</td>\n","      <td>0.000000</td>\n","      <td>0.000126</td>\n","      <td>0.000306</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.252475</td>\n","      <td>0.000890</td>\n","      <td>0.015361</td>\n","      <td>0.089779</td>\n","      <td>0.008674</td>\n","      <td>0.013009</td>\n","      <td>0.005462</td>\n","      <td>0.088945</td>\n","      <td>0.065468</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.004291</td>\n","      <td>0.041646</td>\n","      <td>0.038806</td>\n","    </tr>\n","    <tr>\n","      <th>15341</th>\n","      <td>0.028725</td>\n","      <td>0.066667</td>\n","      <td>0.003189</td>\n","      <td>0.000000</td>\n","      <td>0.003911</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.005626</td>\n","      <td>0.1</td>\n","      <td>0.000334</td>\n","      <td>0.000000</td>\n","      <td>0.000101</td>\n","      <td>0.000172</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.027228</td>\n","      <td>0.000000</td>\n","      <td>0.008439</td>\n","      <td>0.115002</td>\n","      <td>0.000000</td>\n","      <td>0.005969</td>\n","      <td>0.007535</td>\n","      <td>0.111878</td>\n","      <td>0.099256</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005398</td>\n","      <td>0.122222</td>\n","      <td>0.137279</td>\n","    </tr>\n","    <tr>\n","      <th>15342</th>\n","      <td>0.909179</td>\n","      <td>0.200000</td>\n","      <td>0.025473</td>\n","      <td>0.017051</td>\n","      <td>0.031363</td>\n","      <td>0.0</td>\n","      <td>0.009200</td>\n","      <td>0.047521</td>\n","      <td>0.5</td>\n","      <td>0.000329</td>\n","      <td>0.016895</td>\n","      <td>0.000884</td>\n","      <td>0.001332</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.061782</td>\n","      <td>0.008350</td>\n","      <td>0.035641</td>\n","      <td>0.145028</td>\n","      <td>0.028478</td>\n","      <td>0.138682</td>\n","      <td>0.001504</td>\n","      <td>0.109867</td>\n","      <td>0.001316</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005726</td>\n","      <td>0.018003</td>\n","      <td>0.000273</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15243 rows × 29 columns</p>\n","</div>"],"text/plain":["              X  ...  utilisasi_6bulan_per_limit\n","100    0.879422  ...                    0.088806\n","101    0.229925  ...                    0.000150\n","102    0.805577  ...                    0.000364\n","103    0.274529  ...                    0.069865\n","104    0.809450  ...                    0.027170\n","...         ...  ...                         ...\n","15338  0.286729  ...                    0.009382\n","15339  0.424154  ...                    0.030820\n","15340  0.568810  ...                    0.038806\n","15341  0.028725  ...                    0.137279\n","15342  0.909179  ...                    0.000273\n","\n","[15243 rows x 29 columns]"]},"metadata":{},"execution_count":150}]},{"cell_type":"markdown","metadata":{"id":"O9d4448Q-H2P"},"source":["**MEMILIH-MEMILAH FEATURE YANG DIGUNAKAN**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"tETTWySk7x-B","executionInfo":{"status":"ok","timestamp":1630385211772,"user_tz":-480,"elapsed":167,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"39090da2-e24f-4f48-92f9-e4afe5107581"},"source":["# mendrop beberapa feature agar proses pembuatan model machine learning lebih optimal\n","dtrain = dtrain.drop(columns=['X', 'kode_cabang', 'jumlah_tahun_sejak_pembukaan_kredit', 'outstanding', 'utilisasi_3bulan', 'utilisasi_6bulan', 'total_pemakaian_tunai'])\n","\n","dtrain"],"execution_count":151,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>jumlah_kartu</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>flag_kredit_macet</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>100</th>\n","      <td>0.066667</td>\n","      <td>0.001003</td>\n","      <td>0.006842</td>\n","      <td>0.000568</td>\n","      <td>0.009664</td>\n","      <td>0.000116</td>\n","      <td>0.036158</td>\n","      <td>0.000058</td>\n","      <td>0.000181</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.000516</td>\n","      <td>0.014496</td>\n","      <td>0.147790</td>\n","      <td>0.008804</td>\n","      <td>0.018052</td>\n","      <td>0.014673</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005318</td>\n","      <td>0.095967</td>\n","      <td>0.088806</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>0.066667</td>\n","      <td>0.056169</td>\n","      <td>0.000865</td>\n","      <td>0.001343</td>\n","      <td>0.002013</td>\n","      <td>0.001450</td>\n","      <td>0.000000</td>\n","      <td>0.001033</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.001219</td>\n","      <td>0.003020</td>\n","      <td>0.002099</td>\n","      <td>0.001411</td>\n","      <td>0.002140</td>\n","      <td>0.002639</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.008491</td>\n","      <td>0.000061</td>\n","      <td>0.000150</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>0.066667</td>\n","      <td>0.007021</td>\n","      <td>0.000055</td>\n","      <td>0.009766</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000512</td>\n","      <td>0.001182</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.008864</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.060435</td>\n","      <td>0.021834</td>\n","      <td>0.000909</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.003148</td>\n","      <td>0.000364</td>\n","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>0.066667</td>\n","      <td>0.003009</td>\n","      <td>0.009475</td>\n","      <td>0.003041</td>\n","      <td>0.014470</td>\n","      <td>0.000494</td>\n","      <td>0.035737</td>\n","      <td>0.000329</td>\n","      <td>0.000760</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.002760</td>\n","      <td>0.021705</td>\n","      <td>0.147790</td>\n","      <td>0.031304</td>\n","      <td>0.043840</td>\n","      <td>0.048459</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005758</td>\n","      <td>0.075940</td>\n","      <td>0.069865</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>0.133333</td>\n","      <td>0.004012</td>\n","      <td>0.000412</td>\n","      <td>0.001369</td>\n","      <td>0.001849</td>\n","      <td>0.001522</td>\n","      <td>0.000000</td>\n","      <td>0.001321</td>\n","      <td>0.001812</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.001242</td>\n","      <td>0.001849</td>\n","      <td>0.016160</td>\n","      <td>0.012109</td>\n","      <td>0.079370</td>\n","      <td>0.046486</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.015775</td>\n","      <td>0.015610</td>\n","      <td>0.027170</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>15338</th>\n","      <td>0.066667</td>\n","      <td>0.012036</td>\n","      <td>0.004358</td>\n","      <td>0.030513</td>\n","      <td>0.009091</td>\n","      <td>0.001225</td>\n","      <td>0.000000</td>\n","      <td>0.001076</td>\n","      <td>0.001128</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.027695</td>\n","      <td>0.013637</td>\n","      <td>0.037155</td>\n","      <td>0.126087</td>\n","      <td>0.083095</td>\n","      <td>0.033169</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.007835</td>\n","      <td>0.009031</td>\n","      <td>0.009382</td>\n","    </tr>\n","    <tr>\n","      <th>15339</th>\n","      <td>0.133333</td>\n","      <td>0.008024</td>\n","      <td>0.018755</td>\n","      <td>0.000033</td>\n","      <td>0.027627</td>\n","      <td>0.000000</td>\n","      <td>0.095263</td>\n","      <td>0.000290</td>\n","      <td>0.000603</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.000030</td>\n","      <td>0.027627</td>\n","      <td>0.154696</td>\n","      <td>0.000188</td>\n","      <td>0.040401</td>\n","      <td>0.010999</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005563</td>\n","      <td>0.039588</td>\n","      <td>0.030820</td>\n","    </tr>\n","    <tr>\n","      <th>15340</th>\n","      <td>0.066667</td>\n","      <td>0.004012</td>\n","      <td>0.008996</td>\n","      <td>0.000981</td>\n","      <td>0.010241</td>\n","      <td>0.000116</td>\n","      <td>0.000000</td>\n","      <td>0.000126</td>\n","      <td>0.000306</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.000890</td>\n","      <td>0.015361</td>\n","      <td>0.089779</td>\n","      <td>0.008674</td>\n","      <td>0.013009</td>\n","      <td>0.005462</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.004291</td>\n","      <td>0.041646</td>\n","      <td>0.038806</td>\n","    </tr>\n","    <tr>\n","      <th>15341</th>\n","      <td>0.066667</td>\n","      <td>0.000000</td>\n","      <td>0.003911</td>\n","      <td>0.000000</td>\n","      <td>0.005626</td>\n","      <td>0.000334</td>\n","      <td>0.000000</td>\n","      <td>0.000101</td>\n","      <td>0.000172</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>0.008439</td>\n","      <td>0.115002</td>\n","      <td>0.000000</td>\n","      <td>0.005969</td>\n","      <td>0.007535</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005398</td>\n","      <td>0.122222</td>\n","      <td>0.137279</td>\n","    </tr>\n","    <tr>\n","      <th>15342</th>\n","      <td>0.200000</td>\n","      <td>0.017051</td>\n","      <td>0.031363</td>\n","      <td>0.009200</td>\n","      <td>0.047521</td>\n","      <td>0.000329</td>\n","      <td>0.016895</td>\n","      <td>0.000884</td>\n","      <td>0.001332</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.008350</td>\n","      <td>0.035641</td>\n","      <td>0.145028</td>\n","      <td>0.028478</td>\n","      <td>0.138682</td>\n","      <td>0.001504</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.005726</td>\n","      <td>0.018003</td>\n","      <td>0.000273</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15243 rows × 22 columns</p>\n","</div>"],"text/plain":["       jumlah_kartu  ...  utilisasi_6bulan_per_limit\n","100        0.066667  ...                    0.088806\n","101        0.066667  ...                    0.000150\n","102        0.066667  ...                    0.000364\n","103        0.066667  ...                    0.069865\n","104        0.133333  ...                    0.027170\n","...             ...  ...                         ...\n","15338      0.066667  ...                    0.009382\n","15339      0.133333  ...                    0.030820\n","15340      0.066667  ...                    0.038806\n","15341      0.066667  ...                    0.137279\n","15342      0.200000  ...                    0.000273\n","\n","[15243 rows x 22 columns]"]},"metadata":{},"execution_count":151}]},{"cell_type":"code","metadata":{"id":"C8iVYTC5o22f","executionInfo":{"status":"ok","timestamp":1630385211774,"user_tz":-480,"elapsed":166,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":151,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5w3A1_M7J_lj"},"source":["#### MODELING\n","Akan diterapkan & dibandingkan dua metode yang berbeda dalam pembuatan model machine learning classification, yakni metode statsmodel & sci-kit learn yang keduanya sama-sama menggunakan Logistic Regression untuk membangun model machine learning. Serta, akan dicari apakah ada perbedaan hasil dari kedua metode tersebut?"]},{"cell_type":"markdown","metadata":{"id":"Cn2h1m_v11CY"},"source":["**Membagi Train Test Data**"]},{"cell_type":"code","metadata":{"id":"HxVM-E6kJ_lk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630385211775,"user_tz":-480,"elapsed":166,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"c1a42803-d524-44d0-c63d-ba31f2a83c04"},"source":["# membagi data menjadi data train & data test untuk membuat model machine learning\n","X = dtrain.drop('flag_kredit_macet', axis=1)\n","y = dtrain['flag_kredit_macet']\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n","\n","print(\"X Train : \", len(X_train))\n","print(\"y Train : \", len(y_train))\n","print(\"X Test : \", len(X_test))\n","print(\"t Test : \", len(y_test))"],"execution_count":152,"outputs":[{"output_type":"stream","name":"stdout","text":["X Train :  10670\n","y Train :  10670\n","X Test :  4573\n","t Test :  4573\n"]}]},{"cell_type":"markdown","metadata":{"id":"e-R58CTQ17Go"},"source":["**STATS MODEL (LOGISTIC REGRESSION)**"]},{"cell_type":"code","metadata":{"id":"pOXQGUrK13yB","executionInfo":{"status":"ok","timestamp":1630385211776,"user_tz":-480,"elapsed":156,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["Input = sm.add_constant(X_train)"],"execution_count":153,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dRU660XQ9x5O","executionInfo":{"status":"ok","timestamp":1630385211777,"user_tz":-480,"elapsed":156,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"663a0256-67e6-4b6a-995c-d3c2552f1aa3"},"source":["# membuat model dengan data train yang telah di split\n","model = sm.Logit(y_train, X_train, missing='drop')\n","result = model.fit()\n","print(result.summary2())"],"execution_count":154,"outputs":[{"output_type":"stream","name":"stdout","text":["Optimization terminated successfully.\n","         Current function value: 0.265466\n","         Iterations 13\n","                                         Results: Logit\n","================================================================================================\n","Model:                         Logit                      Pseudo R-squared:          0.112      \n","Dependent Variable:            flag_kredit_macet          AIC:                       5707.0528  \n","Date:                          2021-08-31 04:46           BIC:                       5859.8318  \n","No. Observations:              10670                      Log-Likelihood:            -2832.5    \n","Df Model:                      20                         LL-Null:                   -3190.2    \n","Df Residuals:                  10649                      LLR p-value:               1.2338e-138\n","Converged:                     1.0000                     Scale:                     1.0000     \n","No. Iterations:                13.0000                                                          \n","------------------------------------------------------------------------------------------------\n","                                          Coef.    Std.Err.    z     P>|z|    [0.025     0.975] \n","------------------------------------------------------------------------------------------------\n","jumlah_kartu                               -6.6185   0.6907  -9.5827 0.0000    -7.9722   -5.2648\n","limit_kredit                                1.1912   1.9906   0.5984 0.5496    -2.7103    5.0927\n","tagihan                                    -1.8557   2.8587  -0.6491 0.5162    -7.4587    3.7473\n","total_pemakaian_retail                    -55.4571  18.6870  -2.9677 0.0030   -92.0829  -18.8314\n","sisa_tagihan_tidak_terbayar                17.7470   2.7324   6.4951 0.0000    12.3917   23.1023\n","rasio_pembayaran                        -1123.0925  95.4637 -11.7646 0.0000 -1310.1980 -935.9871\n","persentasi_overlimit                       11.1924   0.9779  11.4454 0.0000     9.2758   13.1090\n","rasio_pembayaran_3bulan                 -1130.2033 117.4718  -9.6211 0.0000 -1360.4438 -899.9627\n","rasio_pembayaran_6bulan                    -2.3633   1.7521  -1.3488 0.1774    -5.7974    1.0708\n","skor_delikuensi                             1.0874   2.8696   0.3789 0.7047    -4.5370    6.7118\n","total_pemakaian                            53.8082  18.8405   2.8560 0.0043    16.8815   90.7348\n","sisa_tagihan_per_jumlah_kartu             -15.8965   2.4719  -6.4307 0.0000   -20.7414  -11.0515\n","sisa_tagihan_per_limit                     -4.2168   1.0746  -3.9241 0.0001    -6.3229   -2.1106\n","total_pemakaian_per_limit                 -16.8303   2.3956  -7.0255 0.0000   -21.5257  -12.1350\n","pemakaian_3bln_per_limit                   -1.8220   1.2834  -1.4196 0.1557    -4.3375    0.6935\n","pemakaian_6bln_per_limit                    7.0007   1.4725   4.7544 0.0000     4.1148    9.8867\n","bin                                        -5.4632   1.0520  -5.1933 0.0000    -7.5251   -3.4014\n","bin_delikuensi                             -2.7664   3.0906  -0.8951 0.3707    -8.8238    3.2910\n","rasio_tagihan_sisa_tagihan_tak_terbayar     1.1516   3.2220   0.3574 0.7208    -5.1635    7.4667\n","utilisasi_3bulan_per_limit                 -8.2801   2.4282  -3.4099 0.0006   -13.0393   -3.5208\n","utilisasi_6bulan_per_limit                  0.8920   1.9889   0.4485 0.6538    -3.0061    4.7902\n","================================================================================================\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/discrete/discrete_model.py:1736: RuntimeWarning: overflow encountered in exp\n","  return 1/(1+np.exp(-X))\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TV4wvZ9s93yV","executionInfo":{"status":"ok","timestamp":1630385211778,"user_tz":-480,"elapsed":146,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"3082f6b7-1b68-4859-a7bd-2c40c2eb43de"},"source":["# odds ratio\n","round(np.exp(result.params), 3)"],"execution_count":155,"outputs":[{"output_type":"execute_result","data":{"text/plain":["jumlah_kartu                               1.000000e-03\n","limit_kredit                               3.291000e+00\n","tagihan                                    1.560000e-01\n","total_pemakaian_retail                     0.000000e+00\n","sisa_tagihan_tidak_terbayar                5.098409e+07\n","rasio_pembayaran                           0.000000e+00\n","persentasi_overlimit                       7.257567e+04\n","rasio_pembayaran_3bulan                    0.000000e+00\n","rasio_pembayaran_6bulan                    9.400000e-02\n","skor_delikuensi                            2.967000e+00\n","total_pemakaian                            2.336653e+23\n","sisa_tagihan_per_jumlah_kartu              0.000000e+00\n","sisa_tagihan_per_limit                     1.500000e-02\n","total_pemakaian_per_limit                  0.000000e+00\n","pemakaian_3bln_per_limit                   1.620000e-01\n","pemakaian_6bln_per_limit                   1.097427e+03\n","bin                                        4.000000e-03\n","bin_delikuensi                             6.300000e-02\n","rasio_tagihan_sisa_tagihan_tak_terbayar    3.163000e+00\n","utilisasi_3bulan_per_limit                 0.000000e+00\n","utilisasi_6bulan_per_limit                 2.440000e+00\n","dtype: float64"]},"metadata":{},"execution_count":155}]},{"cell_type":"code","metadata":{"id":"bRMAQ9-z9s0c","executionInfo":{"status":"ok","timestamp":1630385211779,"user_tz":-480,"elapsed":133,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat variabel prediction yang berisikan prediksi terhadap data\n","prediction = result.predict(X_test)"],"execution_count":156,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"egVoSrwk9tMM","executionInfo":{"status":"ok","timestamp":1630385211780,"user_tz":-480,"elapsed":132,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"28963ea5-0872-45a6-8184-19878de704d9"},"source":["# membandingkan hasil prediksi & aktual\n","pred_results = pd.DataFrame(prediction, columns = [\"logreg_pred\"])\n","pred_results[\"prediksi\"] = pred_results['logreg_pred'].apply(lambda x: 1 if x > 0.5 else 0)\n","pred_results['actual'] = y_test\n","pred_results.head()"],"execution_count":157,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>logreg_pred</th>\n","      <th>prediksi</th>\n","      <th>actual</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4193</th>\n","      <td>0.133569</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10865</th>\n","      <td>0.043745</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10475</th>\n","      <td>0.001053</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3874</th>\n","      <td>0.049764</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8879</th>\n","      <td>0.392849</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       logreg_pred  prediksi  actual\n","4193      0.133569         0       0\n","10865     0.043745         0       0\n","10475     0.001053         0       0\n","3874      0.049764         0       0\n","8879      0.392849         0       0"]},"metadata":{},"execution_count":157}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZVbsUGu6AVwY","executionInfo":{"status":"ok","timestamp":1630385211782,"user_tz":-480,"elapsed":131,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"e1658b01-9f59-4e32-e00e-2dcbd0e2c82c"},"source":["print('Test accuracy =  {:.3f}'.format(accuracy_score(pred_results['actual'], pred_results[\"prediksi\"])))\n","print('Test precision =  {:.3f}'.format(precision_score(pred_results['actual'], pred_results[\"prediksi\"], average='binary')))\n","print('Test recall =  {:.3f}'.format(recall_score(pred_results['actual'], pred_results[\"prediksi\"], average='binary')))\n","print('Test f-score =  {:.3f}'.format(f1_score(pred_results['actual'], pred_results[\"prediksi\"], average='binary')))"],"execution_count":158,"outputs":[{"output_type":"stream","name":"stdout","text":["Test accuracy =  0.913\n","Test precision =  0.458\n","Test recall =  0.028\n","Test f-score =  0.053\n"]}]},{"cell_type":"markdown","metadata":{"id":"ne4w7Joo2DAV"},"source":["**SCIKIT LEARN (LOGISTIC REGRESSION)**"]},{"cell_type":"code","metadata":{"id":"epbxp0Ia1_dN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1630385211783,"user_tz":-480,"elapsed":121,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"77f71dbc-4788-473d-c00d-28869e0b2c83"},"source":["# membuat model menggunakan library / tools logistic regression dari sci-kit learn\n","LR = LogisticRegression()\n","LR.fit(X_train, y_train)"],"execution_count":159,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n","                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n","                   multi_class='auto', n_jobs=None, penalty='l2',\n","                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n","                   warm_start=False)"]},"metadata":{},"execution_count":159}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"vs9IAzdeA7oi","executionInfo":{"status":"ok","timestamp":1630385211784,"user_tz":-480,"elapsed":111,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"395babd0-73a7-4bf2-84c6-5df355b1f3b2"},"source":["# menambahkan kolom variabel/feature baru untuk membandingkan secara side-to-side antara flag kredit macet (aktual) dengan prediksi dari model\n","y_pred = LR.predict(X_test)\n","logreg_test = pd.merge(X_test, y_test, left_index=True, right_index=True, how='outer')\n","logreg_test['prediction'] = y_pred\n","logreg_test.head()\n"],"execution_count":160,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>jumlah_kartu</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","      <th>flag_kredit_macet</th>\n","      <th>prediction</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>4193</th>\n","      <td>0.066667</td>\n","      <td>0.003009</td>\n","      <td>0.010806</td>\n","      <td>0.000000</td>\n","      <td>0.01183</td>\n","      <td>0.000183</td>\n","      <td>0.000000</td>\n","      <td>0.000175</td>\n","      <td>0.000217</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.017746</td>\n","      <td>0.120856</td>\n","      <td>0.000000</td>\n","      <td>0.017937</td>\n","      <td>0.014057</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.004129</td>\n","      <td>0.066666</td>\n","      <td>0.060839</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10865</th>\n","      <td>0.066667</td>\n","      <td>0.004012</td>\n","      <td>0.002441</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.001493</td>\n","      <td>0.000000</td>\n","      <td>0.000992</td>\n","      <td>0.001297</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.077364</td>\n","      <td>0.081504</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.040494</td>\n","      <td>0.071276</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>10475</th>\n","      <td>0.066667</td>\n","      <td>0.002006</td>\n","      <td>0.003015</td>\n","      <td>0.005426</td>\n","      <td>0.00000</td>\n","      <td>0.002957</td>\n","      <td>0.042368</td>\n","      <td>0.001654</td>\n","      <td>0.001822</td>\n","      <td>0.0</td>\n","      <td>0.004925</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.067174</td>\n","      <td>0.134097</td>\n","      <td>0.041554</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.075644</td>\n","      <td>0.063028</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3874</th>\n","      <td>0.066667</td>\n","      <td>0.002006</td>\n","      <td>0.000419</td>\n","      <td>0.000000</td>\n","      <td>0.00000</td>\n","      <td>0.001508</td>\n","      <td>0.000000</td>\n","      <td>0.000662</td>\n","      <td>0.000328</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.004585</td>\n","      <td>0.001776</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.011370</td>\n","      <td>0.024648</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8879</th>\n","      <td>0.066667</td>\n","      <td>0.007021</td>\n","      <td>0.000373</td>\n","      <td>0.000004</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000003</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000022</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000170</td>\n","      <td>0.000013</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       jumlah_kartu  limit_kredit  ...  flag_kredit_macet  prediction\n","4193       0.066667      0.003009  ...                  0           0\n","10865      0.066667      0.004012  ...                  0           0\n","10475      0.066667      0.002006  ...                  0           0\n","3874       0.066667      0.002006  ...                  0           0\n","8879       0.066667      0.007021  ...                  0           0\n","\n","[5 rows x 23 columns]"]},"metadata":{},"execution_count":160}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JEXJacWjA78h","executionInfo":{"status":"ok","timestamp":1630385211786,"user_tz":-480,"elapsed":110,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"0f079578-7d8e-4b52-8a07-d988fd7347cf"},"source":["print('Accuracy of logistic regression classifier train data: {:.3f}'.format(LR.score(X_train, y_train)))\n","print('Accuracy of logistic regression classifier test data: {:.3f}'.format(LR.score(X_test, y_test)))"],"execution_count":161,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of logistic regression classifier train data: 0.912\n","Accuracy of logistic regression classifier test data: 0.914\n"]}]},{"cell_type":"markdown","metadata":{"id":"0kDPq8lMqMsd"},"source":[">Ternyata berdasarkan kedua metode di atas, Stats model & Sklearn dengan Logistic Regression, tidak ditemukan adanya perbedaan yang signifikan pada hasil nilai akurasi & keduanya sama-sama menghasilkan nilai akurasi yang kurang lebih sama yakni 91,2% - 91,4%."]},{"cell_type":"markdown","metadata":{"id":"gDbQSokoJ_lk"},"source":["#### EVALUATION"]},{"cell_type":"markdown","metadata":{"id":"P_7CEtHKbRKj"},"source":["**Cross-Validation**\n","\n","\n","Membantu memeriksa performa rata-rata dari model yang telah dibuat berdasarkan data yang ada"]},{"cell_type":"code","metadata":{"id":"T6xfhZ__J_lk","executionInfo":{"status":"ok","timestamp":1630385211787,"user_tz":-480,"elapsed":100,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# Memilih k-fold (dipilih k = 5)\n","kfold = KFold(n_splits=5, shuffle=True, random_state=42)"],"execution_count":162,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ua__StWBBVkE","executionInfo":{"status":"ok","timestamp":1630385211788,"user_tz":-480,"elapsed":100,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"805a606e-b1c9-4fe5-af5c-8ea4b14b3516"},"source":["logreg = LogisticRegression(solver='lbfgs', max_iter=1000)\n","scores = cross_val_score(logreg, X_train, y_train, cv=kfold)\n","print(\"Cross-validation scores: {:.3f}\".format(scores.mean()))"],"execution_count":163,"outputs":[{"output_type":"stream","name":"stdout","text":["Cross-validation scores: 0.912\n"]}]},{"cell_type":"markdown","metadata":{"id":"YA_26j6HbZmI"},"source":["**Grid Search**\n","\n","Membantu mengevaluasi model dengan melakukan tuning hyperparameter pada model machine learning yang telah dibuat"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xYvOgyaCBWJL","executionInfo":{"status":"ok","timestamp":1630385220839,"user_tz":-480,"elapsed":9140,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"803cf015-6be5-4440-8c1a-3ae4e768a8d9"},"source":["param_grid_lr = { 'C': [0.001, 0.01, 0.1, 1, 10, 100],\n","                 'class_weight':['balanced', None]}\n","\n","grid_search_lr = GridSearchCV(logreg, param_grid_lr, cv=kfold)\n","grid_search_lr.fit(X_train, y_train)\n","print(\"Best parameters: {}\".format(grid_search_lr.best_params_))\n","print(\"Best cross-validation score: {:.3}\".format(grid_search_lr.best_score_))"],"execution_count":164,"outputs":[{"output_type":"stream","name":"stdout","text":["Best parameters: {'C': 1, 'class_weight': None}\n","Best cross-validation score: 0.912\n"]}]},{"cell_type":"markdown","metadata":{"id":"Fphoi0KTbfB9"},"source":["**Evaluation Metrics**\n","\n","Membantu mendapatkan nilai accuracy, precision, recall, & f-score untuk dapat mengevaluasi model machine learning berdasarkan 4 metriks (True Positive, False Positive, False Negative, True Negative)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Wmy2KkfqBZLF","executionInfo":{"status":"ok","timestamp":1630385221758,"user_tz":-480,"elapsed":948,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"08941c9c-8d7a-427a-f9b4-bda21c784abd"},"source":["scoring = {'acc': 'accuracy',\n","           'F1': 'f1',\n","           'prec': 'precision',\n","           'recall':'recall'}\n"," \n","acc = []\n","f1 = []\n","prec = []\n","recall = []\n"," \n","logreg = LogisticRegression(C = 1)\n","scores = cross_validate(logreg, X, y, cv=kfold, scoring=scoring)\n"," \n","acc.append(scores['test_acc'].mean())\n","f1.append(scores['test_F1'].mean())\n","prec.append(scores['test_prec'].mean())\n","recall.append(scores['test_recall'].mean())\n"," \n"," \n","print(\"Accuracy scores: \", acc)\n","print(\"f1 scores: \", f1)\n","print(\"Precision scores: \", prec)\n","print(\"Recall scores: \", recall)"],"execution_count":165,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy scores:  [0.9124184685999197]\n","f1 scores:  [0.026169175805612104]\n","Precision scores:  [0.5609523809523809]\n","Recall scores:  [0.013402296248538911]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FnVkFjKVBcX2","executionInfo":{"status":"ok","timestamp":1630385221760,"user_tz":-480,"elapsed":80,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"ad5279c7-5c75-4f34-99dd-f9a67e1cc933"},"source":["# Terapkan pada data test\n","logreg.fit(X_train, y_train)\n","predictions = logreg.predict(X_test)\n"," \n","print(\"Accuracy score: \", accuracy_score(predictions, y_test))\n","print(\"Recall score: \", recall_score(predictions, y_test))\n"],"execution_count":166,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy score:  0.9140607916028866\n","Recall score:  0.625\n"]}]},{"cell_type":"markdown","metadata":{"id":"qLZAG3gUKPHg"},"source":["### LOAD AND PREDICT NEW DATA"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"5JvSl4XlTr0m","executionInfo":{"status":"ok","timestamp":1630385221763,"user_tz":-480,"elapsed":64,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"3f52b324-d711-4ba9-e917-699b815e26ec"},"source":["dtes = pd.read_csv(path + 'npl_credit_scoring_test.csv')\n","dtes"],"execution_count":167,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6188</td>\n","      <td>2</td>\n","      <td>5025000</td>\n","      <td>5000000.0</td>\n","      <td>833333.0</td>\n","      <td>900000.0</td>\n","      <td>538002.0</td>\n","      <td>3290742.0</td>\n","      <td>F</td>\n","      <td>100.0</td>\n","      <td>0.00</td>\n","      <td>100.0</td>\n","      <td>150.0</td>\n","      <td>0</td>\n","      <td>6.75</td>\n","      <td>1438002.0</td>\n","      <td>1645371.0</td>\n","      <td>0.65800</td>\n","      <td>0.2880</td>\n","      <td>0.03220</td>\n","      <td>6.600000e-08</td>\n","      <td>1.02000</td>\n","      <td>1.200000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4783</td>\n","      <td>2</td>\n","      <td>509019</td>\n","      <td>3000000.0</td>\n","      <td>258000.0</td>\n","      <td>0.0</td>\n","      <td>418000.0</td>\n","      <td>0.0</td>\n","      <td>A</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>24.5</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>5.42</td>\n","      <td>418000.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.1390</td>\n","      <td>0.06950</td>\n","      <td>0.000000e+00</td>\n","      <td>0.08970</td>\n","      <td>0.059200</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13414</td>\n","      <td>2</td>\n","      <td>5153766</td>\n","      <td>5000000.0</td>\n","      <td>5007495.0</td>\n","      <td>0.0</td>\n","      <td>5019950.0</td>\n","      <td>5515204.0</td>\n","      <td>F</td>\n","      <td>0.0</td>\n","      <td>9.88</td>\n","      <td>75.2</td>\n","      <td>10.0</td>\n","      <td>0</td>\n","      <td>7.67</td>\n","      <td>5019950.0</td>\n","      <td>2757602.0</td>\n","      <td>1.10000</td>\n","      <td>1.0000</td>\n","      <td>0.43900</td>\n","      <td>3.550000e-01</td>\n","      <td>0.67400</td>\n","      <td>0.363000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>11819</td>\n","      <td>3</td>\n","      <td>29214412</td>\n","      <td>35000000.0</td>\n","      <td>28509908.0</td>\n","      <td>0.0</td>\n","      <td>4148000.0</td>\n","      <td>29214022.0</td>\n","      <td>A</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>15.0</td>\n","      <td>30.2</td>\n","      <td>0</td>\n","      <td>13.70</td>\n","      <td>4148000.0</td>\n","      <td>9738007.0</td>\n","      <td>0.83500</td>\n","      <td>0.1190</td>\n","      <td>0.08330</td>\n","      <td>3.620000e-02</td>\n","      <td>0.79200</td>\n","      <td>0.743000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7416</td>\n","      <td>2</td>\n","      <td>40929</td>\n","      <td>3000000.0</td>\n","      <td>40100.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>B</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>6.42</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.0000</td>\n","      <td>0.00000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.01070</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>6135</td>\n","      <td>2</td>\n","      <td>4308502</td>\n","      <td>15000000.0</td>\n","      <td>1320926.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>A</td>\n","      <td>106.0</td>\n","      <td>0.00</td>\n","      <td>100.0</td>\n","      <td>101.0</td>\n","      <td>0</td>\n","      <td>5.25</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.0000</td>\n","      <td>0.02770</td>\n","      <td>9.020000e-02</td>\n","      <td>0.42000</td>\n","      <td>0.585000</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>14707</td>\n","      <td>2</td>\n","      <td>49859</td>\n","      <td>17000000.0</td>\n","      <td>45100.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>21989.0</td>\n","      <td>A</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>72.7</td>\n","      <td>144.0</td>\n","      <td>0</td>\n","      <td>8.83</td>\n","      <td>0.0</td>\n","      <td>10994.0</td>\n","      <td>0.00129</td>\n","      <td>0.0000</td>\n","      <td>0.00326</td>\n","      <td>4.410000e-03</td>\n","      <td>0.00178</td>\n","      <td>0.000541</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>7313</td>\n","      <td>2</td>\n","      <td>4692329</td>\n","      <td>4000000.0</td>\n","      <td>4691569.0</td>\n","      <td>0.0</td>\n","      <td>113200.0</td>\n","      <td>4699929.0</td>\n","      <td>H</td>\n","      <td>0.0</td>\n","      <td>18.60</td>\n","      <td>42.2</td>\n","      <td>48.7</td>\n","      <td>0</td>\n","      <td>4.25</td>\n","      <td>113200.0</td>\n","      <td>2349965.0</td>\n","      <td>1.17000</td>\n","      <td>0.0283</td>\n","      <td>0.30700</td>\n","      <td>3.230000e-01</td>\n","      <td>0.66400</td>\n","      <td>0.228000</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>13729</td>\n","      <td>2</td>\n","      <td>462814</td>\n","      <td>3000000.0</td>\n","      <td>215238.0</td>\n","      <td>0.0</td>\n","      <td>224840.0</td>\n","      <td>493348.0</td>\n","      <td>H</td>\n","      <td>100.0</td>\n","      <td>0.00</td>\n","      <td>75.0</td>\n","      <td>100.0</td>\n","      <td>0</td>\n","      <td>5.83</td>\n","      <td>224840.0</td>\n","      <td>246674.0</td>\n","      <td>0.16400</td>\n","      <td>0.0749</td>\n","      <td>0.06670</td>\n","      <td>2.330000e-02</td>\n","      <td>0.10900</td>\n","      <td>0.033800</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>10612</td>\n","      <td>2</td>\n","      <td>16271373</td>\n","      <td>15000000.0</td>\n","      <td>6465057.0</td>\n","      <td>0.0</td>\n","      <td>5907118.0</td>\n","      <td>0.0</td>\n","      <td>B</td>\n","      <td>100.0</td>\n","      <td>7.58</td>\n","      <td>50.1</td>\n","      <td>100.0</td>\n","      <td>0</td>\n","      <td>5.58</td>\n","      <td>5907118.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.3940</td>\n","      <td>0.58700</td>\n","      <td>4.440000e-01</td>\n","      <td>0.82100</td>\n","      <td>0.385000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 23 columns</p>\n","</div>"],"text/plain":["         X  jumlah_kartu  ...  utilisasi_3bulan  utilisasi_6bulan\n","0     6188             2  ...           1.02000          1.200000\n","1     4783             2  ...           0.08970          0.059200\n","2    13414             2  ...           0.67400          0.363000\n","3    11819             3  ...           0.79200          0.743000\n","4     7416             2  ...           0.01070          0.000000\n","..     ...           ...  ...               ...               ...\n","145   6135             2  ...           0.42000          0.585000\n","146  14707             2  ...           0.00178          0.000541\n","147   7313             2  ...           0.66400          0.228000\n","148  13729             2  ...           0.10900          0.033800\n","149  10612             2  ...           0.82100          0.385000\n","\n","[150 rows x 23 columns]"]},"metadata":{},"execution_count":167}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":316},"id":"1baXlnD-Q6B3","executionInfo":{"status":"ok","timestamp":1630385221769,"user_tz":-480,"elapsed":65,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"7ffa57c5-a1e8-49a8-8939-3ad7e59e8ee6"},"source":["dtes.describe()"],"execution_count":168,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7425.926667</td>\n","      <td>2.426667</td>\n","      <td>1.077944e+07</td>\n","      <td>1.956667e+07</td>\n","      <td>7.164982e+06</td>\n","      <td>5.900000e+04</td>\n","      <td>1.365373e+06</td>\n","      <td>8.403871e+06</td>\n","      <td>81.512800</td>\n","      <td>2.971667</td>\n","      <td>49.597933</td>\n","      <td>119.967733</td>\n","      <td>0.006667</td>\n","      <td>6.432200</td>\n","      <td>1.424373e+06</td>\n","      <td>3.317756e+06</td>\n","      <td>0.459443</td>\n","      <td>0.087215</td>\n","      <td>0.161383</td>\n","      <td>0.173214</td>\n","      <td>0.544561</td>\n","      <td>0.496405</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4436.819080</td>\n","      <td>0.797199</td>\n","      <td>1.560297e+07</td>\n","      <td>2.380272e+07</td>\n","      <td>1.048165e+07</td>\n","      <td>3.268761e+05</td>\n","      <td>3.262666e+06</td>\n","      <td>1.546054e+07</td>\n","      <td>343.005366</td>\n","      <td>7.508772</td>\n","      <td>81.224174</td>\n","      <td>824.508422</td>\n","      <td>0.081650</td>\n","      <td>3.918161</td>\n","      <td>3.257153e+06</td>\n","      <td>6.216436e+06</td>\n","      <td>0.457270</td>\n","      <td>0.139763</td>\n","      <td>0.195670</td>\n","      <td>0.183953</td>\n","      <td>0.371804</td>\n","      <td>0.380026</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>180.000000</td>\n","      <td>2.000000</td>\n","      <td>2.312000e+04</td>\n","      <td>3.000000e+06</td>\n","      <td>2.300000e+04</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>-821.550000</td>\n","      <td>-1116.350000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.001290</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3708.250000</td>\n","      <td>2.000000</td>\n","      <td>2.257512e+06</td>\n","      <td>5.000000e+06</td>\n","      <td>1.138527e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>24.100000</td>\n","      <td>21.170000</td>\n","      <td>0.000000</td>\n","      <td>3.330000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.044226</td>\n","      <td>0.032300</td>\n","      <td>0.197961</td>\n","      <td>0.169000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7384.000000</td>\n","      <td>2.000000</td>\n","      <td>5.038225e+06</td>\n","      <td>9.000000e+06</td>\n","      <td>3.462150e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.758790e+05</td>\n","      <td>2.899340e+06</td>\n","      <td>40.450000</td>\n","      <td>0.000000</td>\n","      <td>50.000000</td>\n","      <td>63.150000</td>\n","      <td>0.000000</td>\n","      <td>5.500000</td>\n","      <td>2.474190e+05</td>\n","      <td>1.209952e+06</td>\n","      <td>0.291500</td>\n","      <td>0.024900</td>\n","      <td>0.103150</td>\n","      <td>0.109000</td>\n","      <td>0.504500</td>\n","      <td>0.428000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>11177.500000</td>\n","      <td>3.000000</td>\n","      <td>1.267008e+07</td>\n","      <td>2.000000e+07</td>\n","      <td>7.161343e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.080250e+06</td>\n","      <td>8.133821e+06</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>95.477500</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>8.957500</td>\n","      <td>1.416973e+06</td>\n","      <td>3.595408e+06</td>\n","      <td>0.914887</td>\n","      <td>0.121250</td>\n","      <td>0.212014</td>\n","      <td>0.281750</td>\n","      <td>0.916274</td>\n","      <td>0.834750</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>15347.000000</td>\n","      <td>6.000000</td>\n","      <td>9.859690e+07</td>\n","      <td>1.200000e+08</td>\n","      <td>6.276755e+07</td>\n","      <td>3.000000e+06</td>\n","      <td>2.143566e+07</td>\n","      <td>9.213385e+07</td>\n","      <td>4198.850000</td>\n","      <td>52.600000</td>\n","      <td>204.000000</td>\n","      <td>10066.000000</td>\n","      <td>1.000000</td>\n","      <td>19.100000</td>\n","      <td>2.143566e+07</td>\n","      <td>4.606692e+07</td>\n","      <td>1.410000</td>\n","      <td>1.000000</td>\n","      <td>1.700000</td>\n","      <td>0.955000</td>\n","      <td>1.280000</td>\n","      <td>1.440000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  X  jumlah_kartu  ...  utilisasi_3bulan  utilisasi_6bulan\n","count    150.000000    150.000000  ...        150.000000        150.000000\n","mean    7425.926667      2.426667  ...          0.544561          0.496405\n","std     4436.819080      0.797199  ...          0.371804          0.380026\n","min      180.000000      2.000000  ...          0.001290          0.000000\n","25%     3708.250000      2.000000  ...          0.197961          0.169000\n","50%     7384.000000      2.000000  ...          0.504500          0.428000\n","75%    11177.500000      3.000000  ...          0.916274          0.834750\n","max    15347.000000      6.000000  ...          1.280000          1.440000\n","\n","[8 rows x 22 columns]"]},"metadata":{},"execution_count":168}]},{"cell_type":"markdown","metadata":{"id":"un7zbUi2Jx9S"},"source":["**MENYESUAIKAN JUMLAH FEATURE**"]},{"cell_type":"markdown","metadata":{"id":"lvLOja0xNH09"},"source":["- Membuat bin berdasarkan persentase overlimit"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"76h4b4u4JxBG","executionInfo":{"status":"ok","timestamp":1630385221771,"user_tz":-480,"elapsed":61,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"afd48594-b21d-4a74-c532-1c20ec5da762"},"source":["# membuat & membagi bin untuk tiap nilai rendah, sedang, & tinggi dari persentase overlimit\n","bin1_dtes = np.linspace(dtes['persentasi_overlimit'].min(), dtes['persentasi_overlimit'].max(), 4)\n","label2 = ['rendah', 'sedang', 'tinggi']\n","print(bin1_dtes)"],"execution_count":169,"outputs":[{"output_type":"stream","name":"stdout","text":["[ 0.         17.53333333 35.06666667 52.6       ]\n"]}]},{"cell_type":"code","metadata":{"id":"g6DfXs-NLVme","executionInfo":{"status":"ok","timestamp":1630385222524,"user_tz":-480,"elapsed":796,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat kolom baru yang berisikan bin dari cut / pembagian nilai persentase overlimit\n","dtes['bin'] = pd.cut(dtes['persentasi_overlimit'], bins=bin1_dtes, labels=label2, include_lowest=True)\n"],"execution_count":170,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":418},"id":"EHQmt23nLXNp","executionInfo":{"status":"ok","timestamp":1630385222526,"user_tz":-480,"elapsed":237,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"bd5423fa-15a7-4df7-e9d7-ad6bc9f494d6"},"source":["# memanggil bin & persentase overlimit\n","dtes[['bin', 'persentasi_overlimit']]\n"],"execution_count":171,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>bin</th>\n","      <th>persentasi_overlimit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>rendah</td>\n","      <td>9.88</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>sedang</td>\n","      <td>18.60</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>rendah</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>rendah</td>\n","      <td>7.58</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 2 columns</p>\n","</div>"],"text/plain":["        bin  persentasi_overlimit\n","0    rendah                  0.00\n","1    rendah                  0.00\n","2    rendah                  9.88\n","3    rendah                  0.00\n","4    rendah                  0.00\n","..      ...                   ...\n","145  rendah                  0.00\n","146  rendah                  0.00\n","147  sedang                 18.60\n","148  rendah                  0.00\n","149  rendah                  7.58\n","\n","[150 rows x 2 columns]"]},"metadata":{},"execution_count":171}]},{"cell_type":"markdown","metadata":{"id":"CCP2yaX6NQ3A"},"source":["- Membuat bin berdasarkan skor delikuensi"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"C2K9CNi3NQCj","executionInfo":{"status":"ok","timestamp":1630385222528,"user_tz":-480,"elapsed":235,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"67ea6ef6-79bc-43d6-84db-858ecbc2a701"},"source":["# membuat & membagi bin untuk tiap nilai rendah, sedang, & tinggi dari skor delikuensi\n","bin2_dtes = np.linspace(dtes['skor_delikuensi'].min(), dtes['skor_delikuensi'].max(), 4)\n","print(bin2_dtes)\n"],"execution_count":172,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.         0.33333333 0.66666667 1.        ]\n"]}]},{"cell_type":"code","metadata":{"id":"3ymiGCtAOLH3","executionInfo":{"status":"ok","timestamp":1630385222529,"user_tz":-480,"elapsed":219,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat kolom baru yang berisikan bin dari cut / pembagian nilai skor delikuensi\n","dtes['bin_delikuensi'] = pd.cut(dtes['skor_delikuensi'], bins=bin2_dtes, labels=label2, include_lowest=True)\n"],"execution_count":173,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":418},"id":"3NqG6hAROLT5","executionInfo":{"status":"ok","timestamp":1630385222530,"user_tz":-480,"elapsed":219,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"fbe829d4-15a8-4a86-a63c-cd080b341a43"},"source":["# memanggil bin & nilai skor delikuensi\n","dtes[['bin_delikuensi', 'skor_delikuensi']]"],"execution_count":174,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>bin_delikuensi</th>\n","      <th>skor_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>rendah</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 2 columns</p>\n","</div>"],"text/plain":["    bin_delikuensi  skor_delikuensi\n","0           rendah                0\n","1           rendah                0\n","2           rendah                0\n","3           rendah                0\n","4           rendah                0\n","..             ...              ...\n","145         rendah                0\n","146         rendah                0\n","147         rendah                0\n","148         rendah                0\n","149         rendah                0\n","\n","[150 rows x 2 columns]"]},"metadata":{},"execution_count":174}]},{"cell_type":"markdown","metadata":{"id":"rEse2pH_JB29"},"source":["**DATA PRE-PROCESSING**"]},{"cell_type":"markdown","metadata":{"id":"1aIi-PeHPFi3"},"source":["- Cek Duplicate"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":69},"id":"8hFESqbFI_Ce","executionInfo":{"status":"ok","timestamp":1630385222532,"user_tz":-480,"elapsed":216,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"26f773cb-e552-4648-dddc-76e51eb95ed4"},"source":["# mengecek duplikasi data\n","dtes[dtes.duplicated(keep=False)]"],"execution_count":175,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [X, jumlah_kartu, outstanding, limit_kredit, tagihan, total_pemakaian_tunai, total_pemakaian_retail, sisa_tagihan_tidak_terbayar, kode_cabang, rasio_pembayaran, persentasi_overlimit, rasio_pembayaran_3bulan, rasio_pembayaran_6bulan, skor_delikuensi, jumlah_tahun_sejak_pembukaan_kredit, total_pemakaian, sisa_tagihan_per_jumlah_kartu, sisa_tagihan_per_limit, total_pemakaian_per_limit, pemakaian_3bln_per_limit, pemakaian_6bln_per_limit, utilisasi_3bulan, utilisasi_6bulan, bin, bin_delikuensi]\n","Index: []"]},"metadata":{},"execution_count":175}]},{"cell_type":"markdown","metadata":{"id":"cB5EijF1PI9r"},"source":["- Cek Missing Value"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5fHo8y9IJj9B","executionInfo":{"status":"ok","timestamp":1630385222534,"user_tz":-480,"elapsed":210,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"bb5baac2-fe5e-4648-8a6b-6439d691b428"},"source":["# mengecek missing value pada tiap-tiap feature\n","dtes.isnull().sum()"],"execution_count":176,"outputs":[{"output_type":"execute_result","data":{"text/plain":["X                                      0\n","jumlah_kartu                           0\n","outstanding                            0\n","limit_kredit                           0\n","tagihan                                0\n","total_pemakaian_tunai                  0\n","total_pemakaian_retail                 0\n","sisa_tagihan_tidak_terbayar            0\n","kode_cabang                            0\n","rasio_pembayaran                       0\n","persentasi_overlimit                   0\n","rasio_pembayaran_3bulan                0\n","rasio_pembayaran_6bulan                0\n","skor_delikuensi                        0\n","jumlah_tahun_sejak_pembukaan_kredit    0\n","total_pemakaian                        0\n","sisa_tagihan_per_jumlah_kartu          0\n","sisa_tagihan_per_limit                 0\n","total_pemakaian_per_limit              0\n","pemakaian_3bln_per_limit               0\n","pemakaian_6bln_per_limit               0\n","utilisasi_3bulan                       0\n","utilisasi_6bulan                       0\n","bin                                    0\n","bin_delikuensi                         0\n","dtype: int64"]},"metadata":{},"execution_count":176}]},{"cell_type":"markdown","metadata":{"id":"PzkBiEJ6PffV"},"source":["- Label Encoder"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"NZ8JvHsyPTFa","executionInfo":{"status":"ok","timestamp":1630385222535,"user_tz":-480,"elapsed":196,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"884d8859-0269-4a98-ff58-952ce474fde5"},"source":["# Mengubah data kategorikal kedalam bentuk numerik\n","cat2 = ['kode_cabang', 'bin', 'bin_delikuensi']\n","\n","encoded_data = LabelEncoder()\n","\n","for feature in cat2:\n","        if feature in dtes.columns.values:\n","            dtes[feature] = encoded_data.fit_transform(dtes[feature])\n","\n","dtes.head()"],"execution_count":177,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6188</td>\n","      <td>2</td>\n","      <td>5025000</td>\n","      <td>5000000.0</td>\n","      <td>833333.0</td>\n","      <td>900000.0</td>\n","      <td>538002.0</td>\n","      <td>3290742.0</td>\n","      <td>5</td>\n","      <td>100.0</td>\n","      <td>0.00</td>\n","      <td>100.0</td>\n","      <td>150.0</td>\n","      <td>0</td>\n","      <td>6.75</td>\n","      <td>1438002.0</td>\n","      <td>1645371.0</td>\n","      <td>0.658</td>\n","      <td>0.288</td>\n","      <td>0.0322</td>\n","      <td>6.600000e-08</td>\n","      <td>1.0200</td>\n","      <td>1.2000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4783</td>\n","      <td>2</td>\n","      <td>509019</td>\n","      <td>3000000.0</td>\n","      <td>258000.0</td>\n","      <td>0.0</td>\n","      <td>418000.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>24.5</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>5.42</td>\n","      <td>418000.0</td>\n","      <td>0.0</td>\n","      <td>0.000</td>\n","      <td>0.139</td>\n","      <td>0.0695</td>\n","      <td>0.000000e+00</td>\n","      <td>0.0897</td>\n","      <td>0.0592</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13414</td>\n","      <td>2</td>\n","      <td>5153766</td>\n","      <td>5000000.0</td>\n","      <td>5007495.0</td>\n","      <td>0.0</td>\n","      <td>5019950.0</td>\n","      <td>5515204.0</td>\n","      <td>5</td>\n","      <td>0.0</td>\n","      <td>9.88</td>\n","      <td>75.2</td>\n","      <td>10.0</td>\n","      <td>0</td>\n","      <td>7.67</td>\n","      <td>5019950.0</td>\n","      <td>2757602.0</td>\n","      <td>1.100</td>\n","      <td>1.000</td>\n","      <td>0.4390</td>\n","      <td>3.550000e-01</td>\n","      <td>0.6740</td>\n","      <td>0.3630</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>11819</td>\n","      <td>3</td>\n","      <td>29214412</td>\n","      <td>35000000.0</td>\n","      <td>28509908.0</td>\n","      <td>0.0</td>\n","      <td>4148000.0</td>\n","      <td>29214022.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>15.0</td>\n","      <td>30.2</td>\n","      <td>0</td>\n","      <td>13.70</td>\n","      <td>4148000.0</td>\n","      <td>9738007.0</td>\n","      <td>0.835</td>\n","      <td>0.119</td>\n","      <td>0.0833</td>\n","      <td>3.620000e-02</td>\n","      <td>0.7920</td>\n","      <td>0.7430</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7416</td>\n","      <td>2</td>\n","      <td>40929</td>\n","      <td>3000000.0</td>\n","      <td>40100.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>6.42</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.0000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.0107</td>\n","      <td>0.0000</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       X  jumlah_kartu  outstanding  ...  utilisasi_6bulan  bin  bin_delikuensi\n","0   6188             2      5025000  ...            1.2000    0               0\n","1   4783             2       509019  ...            0.0592    0               0\n","2  13414             2      5153766  ...            0.3630    0               0\n","3  11819             3     29214412  ...            0.7430    0               0\n","4   7416             2        40929  ...            0.0000    0               0\n","\n","[5 rows x 25 columns]"]},"metadata":{},"execution_count":177}]},{"cell_type":"markdown","metadata":{"id":"ud26NeOpRGdn"},"source":["- Data wrangling - imputasi"]},{"cell_type":"code","metadata":{"id":"DwMvvHvERD3H","executionInfo":{"status":"ok","timestamp":1630385222537,"user_tz":-480,"elapsed":194,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat kategori yang berisikan feature yang memiliki nilai minus\n","kategori2 = ['total_pemakaian_retail', 'rasio_pembayaran', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'total_pemakaian', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit']\n"],"execution_count":178,"outputs":[]},{"cell_type":"code","metadata":{"id":"wi3MO2EORfnC","executionInfo":{"status":"ok","timestamp":1630385222538,"user_tz":-480,"elapsed":193,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# membuat looping yang mengisi nilai minus dalam variabel kategori dengan masing-masing mean pada tiap feature\n","for feature in kategori2:\n","            dtes[feature] = dtes[feature].apply(lambda x: dtes[feature].mean() if x < 0 else x)"],"execution_count":179,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":316},"id":"-LWS-Bd8RmbY","executionInfo":{"status":"ok","timestamp":1630385222539,"user_tz":-480,"elapsed":192,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"64237b75-4f6d-4418-b3d5-8511aa3710fb"},"source":["dtes.describe()"],"execution_count":180,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>1.500000e+02</td>\n","      <td>1.500000e+02</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","      <td>150.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7425.926667</td>\n","      <td>2.426667</td>\n","      <td>1.077944e+07</td>\n","      <td>1.956667e+07</td>\n","      <td>7.164982e+06</td>\n","      <td>5.900000e+04</td>\n","      <td>1.365373e+06</td>\n","      <td>8.403871e+06</td>\n","      <td>2.093333</td>\n","      <td>81.512800</td>\n","      <td>2.971667</td>\n","      <td>55.405586</td>\n","      <td>129.451636</td>\n","      <td>0.006667</td>\n","      <td>6.432200</td>\n","      <td>1.424373e+06</td>\n","      <td>3.317756e+06</td>\n","      <td>0.459443</td>\n","      <td>0.087215</td>\n","      <td>0.161383</td>\n","      <td>0.173214</td>\n","      <td>0.544561</td>\n","      <td>0.496405</td>\n","      <td>0.073333</td>\n","      <td>0.006667</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>4436.819080</td>\n","      <td>0.797199</td>\n","      <td>1.560297e+07</td>\n","      <td>2.380272e+07</td>\n","      <td>1.048165e+07</td>\n","      <td>3.268761e+05</td>\n","      <td>3.262666e+06</td>\n","      <td>1.546054e+07</td>\n","      <td>3.024168</td>\n","      <td>343.005366</td>\n","      <td>7.508772</td>\n","      <td>38.342291</td>\n","      <td>818.066298</td>\n","      <td>0.081650</td>\n","      <td>3.918161</td>\n","      <td>3.257153e+06</td>\n","      <td>6.216436e+06</td>\n","      <td>0.457270</td>\n","      <td>0.139763</td>\n","      <td>0.195670</td>\n","      <td>0.183953</td>\n","      <td>0.371804</td>\n","      <td>0.380026</td>\n","      <td>0.286067</td>\n","      <td>0.081650</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>180.000000</td>\n","      <td>2.000000</td>\n","      <td>2.312000e+04</td>\n","      <td>3.000000e+06</td>\n","      <td>2.300000e+04</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.750000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.001290</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3708.250000</td>\n","      <td>2.000000</td>\n","      <td>2.257512e+06</td>\n","      <td>5.000000e+06</td>\n","      <td>1.138527e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>24.425000</td>\n","      <td>24.515000</td>\n","      <td>0.000000</td>\n","      <td>3.330000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.044226</td>\n","      <td>0.032300</td>\n","      <td>0.197961</td>\n","      <td>0.169000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>7384.000000</td>\n","      <td>2.000000</td>\n","      <td>5.038225e+06</td>\n","      <td>9.000000e+06</td>\n","      <td>3.462150e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.758790e+05</td>\n","      <td>2.899340e+06</td>\n","      <td>0.000000</td>\n","      <td>40.450000</td>\n","      <td>0.000000</td>\n","      <td>50.000000</td>\n","      <td>65.450000</td>\n","      <td>0.000000</td>\n","      <td>5.500000</td>\n","      <td>2.474190e+05</td>\n","      <td>1.209952e+06</td>\n","      <td>0.291500</td>\n","      <td>0.024900</td>\n","      <td>0.103150</td>\n","      <td>0.109000</td>\n","      <td>0.504500</td>\n","      <td>0.428000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>11177.500000</td>\n","      <td>3.000000</td>\n","      <td>1.267008e+07</td>\n","      <td>2.000000e+07</td>\n","      <td>7.161343e+06</td>\n","      <td>0.000000e+00</td>\n","      <td>1.080250e+06</td>\n","      <td>8.133821e+06</td>\n","      <td>5.000000</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>95.477500</td>\n","      <td>100.000000</td>\n","      <td>0.000000</td>\n","      <td>8.957500</td>\n","      <td>1.416973e+06</td>\n","      <td>3.595408e+06</td>\n","      <td>0.914887</td>\n","      <td>0.121250</td>\n","      <td>0.212014</td>\n","      <td>0.281750</td>\n","      <td>0.916274</td>\n","      <td>0.834750</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>15347.000000</td>\n","      <td>6.000000</td>\n","      <td>9.859690e+07</td>\n","      <td>1.200000e+08</td>\n","      <td>6.276755e+07</td>\n","      <td>3.000000e+06</td>\n","      <td>2.143566e+07</td>\n","      <td>9.213385e+07</td>\n","      <td>10.000000</td>\n","      <td>4198.850000</td>\n","      <td>52.600000</td>\n","      <td>204.000000</td>\n","      <td>10066.000000</td>\n","      <td>1.000000</td>\n","      <td>19.100000</td>\n","      <td>2.143566e+07</td>\n","      <td>4.606692e+07</td>\n","      <td>1.410000</td>\n","      <td>1.000000</td>\n","      <td>1.700000</td>\n","      <td>0.955000</td>\n","      <td>1.280000</td>\n","      <td>1.440000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  X  jumlah_kartu  ...         bin  bin_delikuensi\n","count    150.000000    150.000000  ...  150.000000      150.000000\n","mean    7425.926667      2.426667  ...    0.073333        0.006667\n","std     4436.819080      0.797199  ...    0.286067        0.081650\n","min      180.000000      2.000000  ...    0.000000        0.000000\n","25%     3708.250000      2.000000  ...    0.000000        0.000000\n","50%     7384.000000      2.000000  ...    0.000000        0.000000\n","75%    11177.500000      3.000000  ...    0.000000        0.000000\n","max    15347.000000      6.000000  ...    2.000000        1.000000\n","\n","[8 rows x 25 columns]"]},"metadata":{},"execution_count":180}]},{"cell_type":"markdown","metadata":{"id":"eaO8hcEXJU1R"},"source":["**FEATURE ENGINEERING**"]},{"cell_type":"markdown","metadata":{"id":"Odq0_Wm3SKm_"},"source":["- Feature Engineering"]},{"cell_type":"code","metadata":{"id":"XN-s1uAVJYlk","executionInfo":{"status":"ok","timestamp":1630385222541,"user_tz":-480,"elapsed":190,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":["# menambah feature 'rasio_tagihan_sisa_tagihan_tak_terbayar' untuk melihat sisa tagihan tak terbayar pada bulan terakhir per jumlah tagihan bulan akhir dari nasabah\n","dtes['rasio_tagihan_sisa_tagihan_tak_terbayar'] = dtes['sisa_tagihan_tidak_terbayar']/dtes['tagihan']\n","\n","# menambah feature 'utilisasi_3bulan_per_limit' untuk melihat utilisasi 3 bulan terakhir per jumlah limit kredit yang dimiliki nasabah\n","dtes['utilisasi_3bulan_per_limit'] = dtes['utilisasi_3bulan']/dtes['limit_kredit']\n","\n","# menambah feature 'utilisasi_6bulan_per_limit' untuk melihat utilisasi 3 bulan sebelum 3 bulan terakhir per jumlah limit kredit yang dimiliki nasabah\n","dtes['utilisasi_6bulan_per_limit'] = dtes['utilisasi_6bulan']/dtes['limit_kredit']"],"execution_count":181,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"DtGCWqqlR4Kb","executionInfo":{"status":"ok","timestamp":1630385222542,"user_tz":-480,"elapsed":190,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"76ebd08a-bd54-41b8-f615-12ffe73d2229"},"source":["dtes"],"execution_count":182,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6188</td>\n","      <td>2</td>\n","      <td>5025000</td>\n","      <td>5000000.0</td>\n","      <td>833333.0</td>\n","      <td>900000.0</td>\n","      <td>538002.0</td>\n","      <td>3290742.0</td>\n","      <td>5</td>\n","      <td>100.0</td>\n","      <td>0.00</td>\n","      <td>100.0</td>\n","      <td>150.0</td>\n","      <td>0</td>\n","      <td>6.75</td>\n","      <td>1438002.0</td>\n","      <td>1645371.0</td>\n","      <td>0.65800</td>\n","      <td>0.2880</td>\n","      <td>0.03220</td>\n","      <td>6.600000e-08</td>\n","      <td>1.02000</td>\n","      <td>1.200000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3.948892</td>\n","      <td>2.040000e-07</td>\n","      <td>2.400000e-07</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4783</td>\n","      <td>2</td>\n","      <td>509019</td>\n","      <td>3000000.0</td>\n","      <td>258000.0</td>\n","      <td>0.0</td>\n","      <td>418000.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>24.5</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>5.42</td>\n","      <td>418000.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.1390</td>\n","      <td>0.06950</td>\n","      <td>0.000000e+00</td>\n","      <td>0.08970</td>\n","      <td>0.059200</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2.990000e-08</td>\n","      <td>1.973333e-08</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13414</td>\n","      <td>2</td>\n","      <td>5153766</td>\n","      <td>5000000.0</td>\n","      <td>5007495.0</td>\n","      <td>0.0</td>\n","      <td>5019950.0</td>\n","      <td>5515204.0</td>\n","      <td>5</td>\n","      <td>0.0</td>\n","      <td>9.88</td>\n","      <td>75.2</td>\n","      <td>10.0</td>\n","      <td>0</td>\n","      <td>7.67</td>\n","      <td>5019950.0</td>\n","      <td>2757602.0</td>\n","      <td>1.10000</td>\n","      <td>1.0000</td>\n","      <td>0.43900</td>\n","      <td>3.550000e-01</td>\n","      <td>0.67400</td>\n","      <td>0.363000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1.101390</td>\n","      <td>1.348000e-07</td>\n","      <td>7.260000e-08</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>11819</td>\n","      <td>3</td>\n","      <td>29214412</td>\n","      <td>35000000.0</td>\n","      <td>28509908.0</td>\n","      <td>0.0</td>\n","      <td>4148000.0</td>\n","      <td>29214022.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>15.0</td>\n","      <td>30.2</td>\n","      <td>0</td>\n","      <td>13.70</td>\n","      <td>4148000.0</td>\n","      <td>9738007.0</td>\n","      <td>0.83500</td>\n","      <td>0.1190</td>\n","      <td>0.08330</td>\n","      <td>3.620000e-02</td>\n","      <td>0.79200</td>\n","      <td>0.743000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1.024697</td>\n","      <td>2.262857e-08</td>\n","      <td>2.122857e-08</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7416</td>\n","      <td>2</td>\n","      <td>40929</td>\n","      <td>3000000.0</td>\n","      <td>40100.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>6.42</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.0000</td>\n","      <td>0.00000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.01070</td>\n","      <td>0.000000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>3.566667e-09</td>\n","      <td>0.000000e+00</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>6135</td>\n","      <td>2</td>\n","      <td>4308502</td>\n","      <td>15000000.0</td>\n","      <td>1320926.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>106.0</td>\n","      <td>0.00</td>\n","      <td>100.0</td>\n","      <td>101.0</td>\n","      <td>0</td>\n","      <td>5.25</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.0000</td>\n","      <td>0.02770</td>\n","      <td>9.020000e-02</td>\n","      <td>0.42000</td>\n","      <td>0.585000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>2.800000e-08</td>\n","      <td>3.900000e-08</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>14707</td>\n","      <td>2</td>\n","      <td>49859</td>\n","      <td>17000000.0</td>\n","      <td>45100.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>21989.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>72.7</td>\n","      <td>144.0</td>\n","      <td>0</td>\n","      <td>8.83</td>\n","      <td>0.0</td>\n","      <td>10994.0</td>\n","      <td>0.00129</td>\n","      <td>0.0000</td>\n","      <td>0.00326</td>\n","      <td>4.410000e-03</td>\n","      <td>0.00178</td>\n","      <td>0.000541</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.487561</td>\n","      <td>1.047059e-10</td>\n","      <td>3.182353e-11</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>7313</td>\n","      <td>2</td>\n","      <td>4692329</td>\n","      <td>4000000.0</td>\n","      <td>4691569.0</td>\n","      <td>0.0</td>\n","      <td>113200.0</td>\n","      <td>4699929.0</td>\n","      <td>7</td>\n","      <td>0.0</td>\n","      <td>18.60</td>\n","      <td>42.2</td>\n","      <td>48.7</td>\n","      <td>0</td>\n","      <td>4.25</td>\n","      <td>113200.0</td>\n","      <td>2349965.0</td>\n","      <td>1.17000</td>\n","      <td>0.0283</td>\n","      <td>0.30700</td>\n","      <td>3.230000e-01</td>\n","      <td>0.66400</td>\n","      <td>0.228000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1.001782</td>\n","      <td>1.660000e-07</td>\n","      <td>5.700000e-08</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>13729</td>\n","      <td>2</td>\n","      <td>462814</td>\n","      <td>3000000.0</td>\n","      <td>215238.0</td>\n","      <td>0.0</td>\n","      <td>224840.0</td>\n","      <td>493348.0</td>\n","      <td>7</td>\n","      <td>100.0</td>\n","      <td>0.00</td>\n","      <td>75.0</td>\n","      <td>100.0</td>\n","      <td>0</td>\n","      <td>5.83</td>\n","      <td>224840.0</td>\n","      <td>246674.0</td>\n","      <td>0.16400</td>\n","      <td>0.0749</td>\n","      <td>0.06670</td>\n","      <td>2.330000e-02</td>\n","      <td>0.10900</td>\n","      <td>0.033800</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2.292105</td>\n","      <td>3.633333e-08</td>\n","      <td>1.126667e-08</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>10612</td>\n","      <td>2</td>\n","      <td>16271373</td>\n","      <td>15000000.0</td>\n","      <td>6465057.0</td>\n","      <td>0.0</td>\n","      <td>5907118.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>100.0</td>\n","      <td>7.58</td>\n","      <td>50.1</td>\n","      <td>100.0</td>\n","      <td>0</td>\n","      <td>5.58</td>\n","      <td>5907118.0</td>\n","      <td>0.0</td>\n","      <td>0.00000</td>\n","      <td>0.3940</td>\n","      <td>0.58700</td>\n","      <td>4.440000e-01</td>\n","      <td>0.82100</td>\n","      <td>0.385000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>5.473333e-08</td>\n","      <td>2.566667e-08</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 28 columns</p>\n","</div>"],"text/plain":["         X  ...  utilisasi_6bulan_per_limit\n","0     6188  ...                2.400000e-07\n","1     4783  ...                1.973333e-08\n","2    13414  ...                7.260000e-08\n","3    11819  ...                2.122857e-08\n","4     7416  ...                0.000000e+00\n","..     ...  ...                         ...\n","145   6135  ...                3.900000e-08\n","146  14707  ...                3.182353e-11\n","147   7313  ...                5.700000e-08\n","148  13729  ...                1.126667e-08\n","149  10612  ...                2.566667e-08\n","\n","[150 rows x 28 columns]"]},"metadata":{},"execution_count":182}]},{"cell_type":"markdown","metadata":{"id":"sNU208Z9SNxz"},"source":["- Scaling"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"jlqph8BfSNQg","executionInfo":{"status":"ok","timestamp":1630385222544,"user_tz":-480,"elapsed":189,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"afe5d0c5-02fc-43ed-b7e2-8f04505b5eaa"},"source":["# melakukan scaling untuk menghindari perbedaan range nilai yang tinggi diantara beberapa variabel.\n","transformation_svm = MinMaxScaler().fit(dtes[['X','jumlah_kartu','outstanding','limit_kredit','tagihan', 'total_pemakaian_tunai','total_pemakaian_retail', 'sisa_tagihan_tidak_terbayar', 'kode_cabang', 'rasio_pembayaran', 'persentasi_overlimit', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'skor_delikuensi', 'jumlah_tahun_sejak_pembukaan_kredit', 'total_pemakaian', 'sisa_tagihan_per_jumlah_kartu', 'sisa_tagihan_per_limit', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit', 'utilisasi_3bulan', 'utilisasi_6bulan', 'bin', 'bin_delikuensi', 'rasio_tagihan_sisa_tagihan_tak_terbayar', 'utilisasi_3bulan_per_limit', 'utilisasi_6bulan_per_limit']])\n","dtes[['X','jumlah_kartu','outstanding','limit_kredit','tagihan', 'total_pemakaian_tunai','total_pemakaian_retail', 'sisa_tagihan_tidak_terbayar', 'kode_cabang', 'rasio_pembayaran', 'persentasi_overlimit', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'skor_delikuensi', 'jumlah_tahun_sejak_pembukaan_kredit', 'total_pemakaian', 'sisa_tagihan_per_jumlah_kartu', 'sisa_tagihan_per_limit', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit', 'utilisasi_3bulan', 'utilisasi_6bulan', 'bin', 'bin_delikuensi', 'rasio_tagihan_sisa_tagihan_tak_terbayar', 'utilisasi_3bulan_per_limit', 'utilisasi_6bulan_per_limit']]=transformation_svm.transform(dtes[['X','jumlah_kartu','outstanding','limit_kredit','tagihan', 'total_pemakaian_tunai','total_pemakaian_retail', 'sisa_tagihan_tidak_terbayar', 'kode_cabang', 'rasio_pembayaran', 'persentasi_overlimit', 'rasio_pembayaran_3bulan', 'rasio_pembayaran_6bulan', 'skor_delikuensi', 'jumlah_tahun_sejak_pembukaan_kredit', 'total_pemakaian', 'sisa_tagihan_per_jumlah_kartu', 'sisa_tagihan_per_limit', 'total_pemakaian_per_limit', 'pemakaian_3bln_per_limit', 'pemakaian_6bln_per_limit', 'utilisasi_3bulan', 'utilisasi_6bulan', 'bin', 'bin_delikuensi', 'rasio_tagihan_sisa_tagihan_tak_terbayar', 'utilisasi_3bulan_per_limit', 'utilisasi_6bulan_per_limit']])\n","\n","dtes"],"execution_count":183,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>jumlah_kartu</th>\n","      <th>outstanding</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_tunai</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>kode_cabang</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>jumlah_tahun_sejak_pembukaan_kredit</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>utilisasi_3bulan</th>\n","      <th>utilisasi_6bulan</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.396123</td>\n","      <td>0.00</td>\n","      <td>0.050742</td>\n","      <td>0.017094</td>\n","      <td>0.012915</td>\n","      <td>0.3</td>\n","      <td>0.025098</td>\n","      <td>0.035717</td>\n","      <td>0.5</td>\n","      <td>0.023816</td>\n","      <td>0.000000</td>\n","      <td>0.490196</td>\n","      <td>0.014902</td>\n","      <td>0.0</td>\n","      <td>0.326975</td>\n","      <td>0.067085</td>\n","      <td>0.035717</td>\n","      <td>0.466667</td>\n","      <td>0.2880</td>\n","      <td>0.018941</td>\n","      <td>6.910995e-08</td>\n","      <td>0.796670</td>\n","      <td>0.833333</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.286168</td>\n","      <td>0.551231</td>\n","      <td>0.648649</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.303488</td>\n","      <td>0.00</td>\n","      <td>0.004929</td>\n","      <td>0.000000</td>\n","      <td>0.003745</td>\n","      <td>0.0</td>\n","      <td>0.019500</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.120098</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.254496</td>\n","      <td>0.019500</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.1390</td>\n","      <td>0.040882</td>\n","      <td>0.000000e+00</td>\n","      <td>0.069140</td>\n","      <td>0.041111</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.080564</td>\n","      <td>0.053333</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.872552</td>\n","      <td>0.00</td>\n","      <td>0.052049</td>\n","      <td>0.017094</td>\n","      <td>0.079441</td>\n","      <td>0.0</td>\n","      <td>0.234187</td>\n","      <td>0.059861</td>\n","      <td>0.5</td>\n","      <td>0.000000</td>\n","      <td>0.187833</td>\n","      <td>0.368627</td>\n","      <td>0.000993</td>\n","      <td>0.0</td>\n","      <td>0.377112</td>\n","      <td>0.234187</td>\n","      <td>0.059861</td>\n","      <td>0.780142</td>\n","      <td>1.0000</td>\n","      <td>0.258235</td>\n","      <td>3.717277e-01</td>\n","      <td>0.526085</td>\n","      <td>0.252083</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.079816</td>\n","      <td>0.364154</td>\n","      <td>0.196216</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.767390</td>\n","      <td>0.25</td>\n","      <td>0.296136</td>\n","      <td>0.273504</td>\n","      <td>0.454014</td>\n","      <td>0.0</td>\n","      <td>0.193509</td>\n","      <td>0.317082</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.073529</td>\n","      <td>0.003000</td>\n","      <td>0.0</td>\n","      <td>0.705722</td>\n","      <td>0.193509</td>\n","      <td>0.211388</td>\n","      <td>0.592199</td>\n","      <td>0.1190</td>\n","      <td>0.049000</td>\n","      <td>3.790576e-02</td>\n","      <td>0.618365</td>\n","      <td>0.515972</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.074258</td>\n","      <td>0.060906</td>\n","      <td>0.057375</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.477088</td>\n","      <td>0.00</td>\n","      <td>0.000181</td>\n","      <td>0.000000</td>\n","      <td>0.000273</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.1</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.308992</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0000</td>\n","      <td>0.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.007359</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.009374</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>0.392629</td>\n","      <td>0.00</td>\n","      <td>0.043474</td>\n","      <td>0.102564</td>\n","      <td>0.020686</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.025245</td>\n","      <td>0.000000</td>\n","      <td>0.490196</td>\n","      <td>0.010034</td>\n","      <td>0.0</td>\n","      <td>0.245232</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0000</td>\n","      <td>0.016294</td>\n","      <td>9.445026e-02</td>\n","      <td>0.327447</td>\n","      <td>0.406250</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.075428</td>\n","      <td>0.105405</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>0.957803</td>\n","      <td>0.00</td>\n","      <td>0.000271</td>\n","      <td>0.119658</td>\n","      <td>0.000352</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000239</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.356373</td>\n","      <td>0.014306</td>\n","      <td>0.0</td>\n","      <td>0.440327</td>\n","      <td>0.000000</td>\n","      <td>0.000239</td>\n","      <td>0.000915</td>\n","      <td>0.0000</td>\n","      <td>0.001918</td>\n","      <td>4.617801e-03</td>\n","      <td>0.000383</td>\n","      <td>0.000376</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.035333</td>\n","      <td>0.000015</td>\n","      <td>0.000086</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>0.470297</td>\n","      <td>0.00</td>\n","      <td>0.047368</td>\n","      <td>0.008547</td>\n","      <td>0.074406</td>\n","      <td>0.0</td>\n","      <td>0.005281</td>\n","      <td>0.051012</td>\n","      <td>0.7</td>\n","      <td>0.000000</td>\n","      <td>0.353612</td>\n","      <td>0.206863</td>\n","      <td>0.004838</td>\n","      <td>0.0</td>\n","      <td>0.190736</td>\n","      <td>0.005281</td>\n","      <td>0.051012</td>\n","      <td>0.829787</td>\n","      <td>0.0283</td>\n","      <td>0.180588</td>\n","      <td>3.382199e-01</td>\n","      <td>0.518265</td>\n","      <td>0.158333</td>\n","      <td>0.5</td>\n","      <td>0.0</td>\n","      <td>0.072597</td>\n","      <td>0.448501</td>\n","      <td>0.154054</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>0.893321</td>\n","      <td>0.00</td>\n","      <td>0.004461</td>\n","      <td>0.000000</td>\n","      <td>0.003064</td>\n","      <td>0.0</td>\n","      <td>0.010489</td>\n","      <td>0.005355</td>\n","      <td>0.7</td>\n","      <td>0.023816</td>\n","      <td>0.000000</td>\n","      <td>0.367647</td>\n","      <td>0.009934</td>\n","      <td>0.0</td>\n","      <td>0.276839</td>\n","      <td>0.010489</td>\n","      <td>0.005355</td>\n","      <td>0.116312</td>\n","      <td>0.0749</td>\n","      <td>0.039235</td>\n","      <td>2.439791e-02</td>\n","      <td>0.084233</td>\n","      <td>0.023472</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.166104</td>\n","      <td>0.097956</td>\n","      <td>0.030450</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>0.687809</td>\n","      <td>0.00</td>\n","      <td>0.164833</td>\n","      <td>0.102564</td>\n","      <td>0.102671</td>\n","      <td>0.0</td>\n","      <td>0.275574</td>\n","      <td>0.000000</td>\n","      <td>0.1</td>\n","      <td>0.023816</td>\n","      <td>0.144106</td>\n","      <td>0.245588</td>\n","      <td>0.009934</td>\n","      <td>0.0</td>\n","      <td>0.263215</td>\n","      <td>0.275574</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.3940</td>\n","      <td>0.345294</td>\n","      <td>4.649215e-01</td>\n","      <td>0.641044</td>\n","      <td>0.267361</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.147699</td>\n","      <td>0.069369</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 28 columns</p>\n","</div>"],"text/plain":["            X  ...  utilisasi_6bulan_per_limit\n","0    0.396123  ...                    0.648649\n","1    0.303488  ...                    0.053333\n","2    0.872552  ...                    0.196216\n","3    0.767390  ...                    0.057375\n","4    0.477088  ...                    0.000000\n","..        ...  ...                         ...\n","145  0.392629  ...                    0.105405\n","146  0.957803  ...                    0.000086\n","147  0.470297  ...                    0.154054\n","148  0.893321  ...                    0.030450\n","149  0.687809  ...                    0.069369\n","\n","[150 rows x 28 columns]"]},"metadata":{},"execution_count":183}]},{"cell_type":"markdown","metadata":{"id":"x145OG49S6Bl"},"source":["- Memilih-memilah Feature"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"YOr-349aS9M4","executionInfo":{"status":"ok","timestamp":1630385222546,"user_tz":-480,"elapsed":189,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"fefedd60-609b-42e5-d98f-90110967366c"},"source":["# mendrop beberapa feature agar sesuai model\n","dtes = dtes.drop(columns=['X', 'kode_cabang', 'jumlah_tahun_sejak_pembukaan_kredit', 'outstanding', 'utilisasi_3bulan', 'utilisasi_6bulan', 'total_pemakaian_tunai'])\n","\n","dtes"],"execution_count":184,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>jumlah_kartu</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00</td>\n","      <td>0.017094</td>\n","      <td>0.012915</td>\n","      <td>0.025098</td>\n","      <td>0.035717</td>\n","      <td>0.023816</td>\n","      <td>0.000000</td>\n","      <td>0.490196</td>\n","      <td>0.014902</td>\n","      <td>0.0</td>\n","      <td>0.067085</td>\n","      <td>0.035717</td>\n","      <td>0.466667</td>\n","      <td>0.2880</td>\n","      <td>0.018941</td>\n","      <td>6.910995e-08</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.286168</td>\n","      <td>0.551231</td>\n","      <td>0.648649</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.00</td>\n","      <td>0.000000</td>\n","      <td>0.003745</td>\n","      <td>0.019500</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.120098</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.019500</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.1390</td>\n","      <td>0.040882</td>\n","      <td>0.000000e+00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.080564</td>\n","      <td>0.053333</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.00</td>\n","      <td>0.017094</td>\n","      <td>0.079441</td>\n","      <td>0.234187</td>\n","      <td>0.059861</td>\n","      <td>0.000000</td>\n","      <td>0.187833</td>\n","      <td>0.368627</td>\n","      <td>0.000993</td>\n","      <td>0.0</td>\n","      <td>0.234187</td>\n","      <td>0.059861</td>\n","      <td>0.780142</td>\n","      <td>1.0000</td>\n","      <td>0.258235</td>\n","      <td>3.717277e-01</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.079816</td>\n","      <td>0.364154</td>\n","      <td>0.196216</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.25</td>\n","      <td>0.273504</td>\n","      <td>0.454014</td>\n","      <td>0.193509</td>\n","      <td>0.317082</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.073529</td>\n","      <td>0.003000</td>\n","      <td>0.0</td>\n","      <td>0.193509</td>\n","      <td>0.211388</td>\n","      <td>0.592199</td>\n","      <td>0.1190</td>\n","      <td>0.049000</td>\n","      <td>3.790576e-02</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.074258</td>\n","      <td>0.060906</td>\n","      <td>0.057375</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.00</td>\n","      <td>0.000000</td>\n","      <td>0.000273</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0000</td>\n","      <td>0.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.009374</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>0.00</td>\n","      <td>0.102564</td>\n","      <td>0.020686</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.025245</td>\n","      <td>0.000000</td>\n","      <td>0.490196</td>\n","      <td>0.010034</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0000</td>\n","      <td>0.016294</td>\n","      <td>9.445026e-02</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.075428</td>\n","      <td>0.105405</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>0.00</td>\n","      <td>0.119658</td>\n","      <td>0.000352</td>\n","      <td>0.000000</td>\n","      <td>0.000239</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.356373</td>\n","      <td>0.014306</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000239</td>\n","      <td>0.000915</td>\n","      <td>0.0000</td>\n","      <td>0.001918</td>\n","      <td>4.617801e-03</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.035333</td>\n","      <td>0.000015</td>\n","      <td>0.000086</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>0.00</td>\n","      <td>0.008547</td>\n","      <td>0.074406</td>\n","      <td>0.005281</td>\n","      <td>0.051012</td>\n","      <td>0.000000</td>\n","      <td>0.353612</td>\n","      <td>0.206863</td>\n","      <td>0.004838</td>\n","      <td>0.0</td>\n","      <td>0.005281</td>\n","      <td>0.051012</td>\n","      <td>0.829787</td>\n","      <td>0.0283</td>\n","      <td>0.180588</td>\n","      <td>3.382199e-01</td>\n","      <td>0.5</td>\n","      <td>0.0</td>\n","      <td>0.072597</td>\n","      <td>0.448501</td>\n","      <td>0.154054</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>0.00</td>\n","      <td>0.000000</td>\n","      <td>0.003064</td>\n","      <td>0.010489</td>\n","      <td>0.005355</td>\n","      <td>0.023816</td>\n","      <td>0.000000</td>\n","      <td>0.367647</td>\n","      <td>0.009934</td>\n","      <td>0.0</td>\n","      <td>0.010489</td>\n","      <td>0.005355</td>\n","      <td>0.116312</td>\n","      <td>0.0749</td>\n","      <td>0.039235</td>\n","      <td>2.439791e-02</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.166104</td>\n","      <td>0.097956</td>\n","      <td>0.030450</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>0.00</td>\n","      <td>0.102564</td>\n","      <td>0.102671</td>\n","      <td>0.275574</td>\n","      <td>0.000000</td>\n","      <td>0.023816</td>\n","      <td>0.144106</td>\n","      <td>0.245588</td>\n","      <td>0.009934</td>\n","      <td>0.0</td>\n","      <td>0.275574</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.3940</td>\n","      <td>0.345294</td>\n","      <td>4.649215e-01</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.147699</td>\n","      <td>0.069369</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>150 rows × 21 columns</p>\n","</div>"],"text/plain":["     jumlah_kartu  ...  utilisasi_6bulan_per_limit\n","0            0.00  ...                    0.648649\n","1            0.00  ...                    0.053333\n","2            0.00  ...                    0.196216\n","3            0.25  ...                    0.057375\n","4            0.00  ...                    0.000000\n","..            ...  ...                         ...\n","145          0.00  ...                    0.105405\n","146          0.00  ...                    0.000086\n","147          0.00  ...                    0.154054\n","148          0.00  ...                    0.030450\n","149          0.00  ...                    0.069369\n","\n","[150 rows x 21 columns]"]},"metadata":{},"execution_count":184}]},{"cell_type":"markdown","metadata":{"id":"kaMHFw6gJY5H"},"source":["**PREDICT NEW DATA / VALUE**"]},{"cell_type":"code","metadata":{"id":"lYpKG-VWKPwT","colab":{"base_uri":"https://localhost:8080/","height":224},"executionInfo":{"status":"ok","timestamp":1630385222548,"user_tz":-480,"elapsed":189,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"579ba48a-0e56-4a3b-9519-cbb2ccec71b2"},"source":["# membuat feature untuk memprediksi value dari flag kredit macet\n","dtes_predict = LR.predict(dtes)\n","dtes['flag_kredit_macet_predict'] = dtes_predict\n","dtes.head()"],"execution_count":185,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>jumlah_kartu</th>\n","      <th>limit_kredit</th>\n","      <th>tagihan</th>\n","      <th>total_pemakaian_retail</th>\n","      <th>sisa_tagihan_tidak_terbayar</th>\n","      <th>rasio_pembayaran</th>\n","      <th>persentasi_overlimit</th>\n","      <th>rasio_pembayaran_3bulan</th>\n","      <th>rasio_pembayaran_6bulan</th>\n","      <th>skor_delikuensi</th>\n","      <th>total_pemakaian</th>\n","      <th>sisa_tagihan_per_jumlah_kartu</th>\n","      <th>sisa_tagihan_per_limit</th>\n","      <th>total_pemakaian_per_limit</th>\n","      <th>pemakaian_3bln_per_limit</th>\n","      <th>pemakaian_6bln_per_limit</th>\n","      <th>bin</th>\n","      <th>bin_delikuensi</th>\n","      <th>rasio_tagihan_sisa_tagihan_tak_terbayar</th>\n","      <th>utilisasi_3bulan_per_limit</th>\n","      <th>utilisasi_6bulan_per_limit</th>\n","      <th>flag_kredit_macet_predict</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00</td>\n","      <td>0.017094</td>\n","      <td>0.012915</td>\n","      <td>0.025098</td>\n","      <td>0.035717</td>\n","      <td>0.023816</td>\n","      <td>0.000000</td>\n","      <td>0.490196</td>\n","      <td>0.014902</td>\n","      <td>0.0</td>\n","      <td>0.067085</td>\n","      <td>0.035717</td>\n","      <td>0.466667</td>\n","      <td>0.288</td>\n","      <td>0.018941</td>\n","      <td>6.910995e-08</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.286168</td>\n","      <td>0.551231</td>\n","      <td>0.648649</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.00</td>\n","      <td>0.000000</td>\n","      <td>0.003745</td>\n","      <td>0.019500</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.120098</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.019500</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.139</td>\n","      <td>0.040882</td>\n","      <td>0.000000e+00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.080564</td>\n","      <td>0.053333</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.00</td>\n","      <td>0.017094</td>\n","      <td>0.079441</td>\n","      <td>0.234187</td>\n","      <td>0.059861</td>\n","      <td>0.000000</td>\n","      <td>0.187833</td>\n","      <td>0.368627</td>\n","      <td>0.000993</td>\n","      <td>0.0</td>\n","      <td>0.234187</td>\n","      <td>0.059861</td>\n","      <td>0.780142</td>\n","      <td>1.000</td>\n","      <td>0.258235</td>\n","      <td>3.717277e-01</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.079816</td>\n","      <td>0.364154</td>\n","      <td>0.196216</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.25</td>\n","      <td>0.273504</td>\n","      <td>0.454014</td>\n","      <td>0.193509</td>\n","      <td>0.317082</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.073529</td>\n","      <td>0.003000</td>\n","      <td>0.0</td>\n","      <td>0.193509</td>\n","      <td>0.211388</td>\n","      <td>0.592199</td>\n","      <td>0.119</td>\n","      <td>0.049000</td>\n","      <td>3.790576e-02</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.074258</td>\n","      <td>0.060906</td>\n","      <td>0.057375</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.00</td>\n","      <td>0.000000</td>\n","      <td>0.000273</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000</td>\n","      <td>0.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.009374</td>\n","      <td>0.000000</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   jumlah_kartu  ...  flag_kredit_macet_predict\n","0          0.00  ...                          0\n","1          0.00  ...                          0\n","2          0.00  ...                          0\n","3          0.25  ...                          0\n","4          0.00  ...                          0\n","\n","[5 rows x 22 columns]"]},"metadata":{},"execution_count":185}]},{"cell_type":"code","metadata":{"id":"jmO9jiGBWsj_","colab":{"base_uri":"https://localhost:8080/","height":248},"executionInfo":{"status":"ok","timestamp":1630385223339,"user_tz":-480,"elapsed":58,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}},"outputId":"ad4d6f25-ce9d-475f-ffae-38d1f7c7a5cf"},"source":["# melihat proporsi prediksi flag kredit macet\n","dtes['flag_kredit_macet_predict'].value_counts().plot.pie(autopct='%1.1f%%')\n","plt.show()"],"execution_count":187,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"gMEBNXGL8ZNX"},"source":[">Didapatkan prediksi nilai flag kredit pada data tes berdasarkan model yang telah dibuat & tidak ditemukan error; lancar dalam membuat prediksi baru."]},{"cell_type":"code","metadata":{"id":"R_D_43Qnh8nt","executionInfo":{"status":"ok","timestamp":1630385223340,"user_tz":-480,"elapsed":54,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":187,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qFWul082J_lk"},"source":["### KESIMPULAN/SARAN"]},{"cell_type":"markdown","metadata":{"id":"CPKz6zR2gqrw"},"source":["**KESIMPULAN:**\n","1. **Insight dari data**\n","- Berdasarkan insight yang didapat pada tahapan EDA, didapatkan bahwa jumlah nasabah dengan status kredit macet jauh lebih sedikit (8,8%) dibandingkan dengan yang tidak (91,2%). Lalu, pada summary data yang didapatkan, telah ditemukan adanya anomali pada data yakni berupa nilai minus/negatif pada beberapa variabel/feature.\n","- Berdasarkan insight & summary dari data pula, dapat diasumsikan bahwasannya para nasabah cenderung lebih banyak menggunakan kartu kredit pada pemakaian retail dibandingan dengan pemakaian tunai. \n","- Lalu, didapatkan pula nasabah yang tidak memiliki total pemakaian tunai & total pemakaian retail yakni sejumlah 5143 dari 15343 nasabah. Artinya sejumlah nasabah ini tidak melakukan pemakaian kredit, baik secara tunai / retail, pada bulan terakhir dari riwayat penggunaan kartu kredit nasabah.\n","- Berdasarkan persentase overlimit & skor delikuensi yang dimiliki nasabah, didapatkan lebih banyak nilai yang rendah pada masing-masing persentase overlimit & skor delikuensi di status flag kredit macet bernilai nol (0) atau tidak memiliki flag kredit macet. Sehingga persentase overlimit & skor delikuensi tidak begitu mempengaruhi nilai flag kredit macet.\n","- Lalu, berdasarkan distribusi masing-masing sisa tagihan (tidak terbayar, per jumlah kartu, & per limit) tidak didapatkan adanya pengaruh yang besar pula terhadap nilai flag kredit macet pada nasabah.\n","- Untuk insight yang didapatkan dari heatmap korelasi sendiri didapatkan 3 hubungan antar feature yang tertinggi yakni korelasi antara 'total_pemakaian_retail' dengan 'total_pemakaian', korelasi antara 'outstanding' dengan 'tagihan', & korelasi antara 'sisa_tagihan_tidak_terbayar' dengan 'sisa_tagihan_per_jumlah_kartu'.\n","- Untuk korelasi antara 'total_pemakaian_retail' dengan 'total_pemakaian' mendapatkan nilai 1 atau hubungannya bernilai positif. Maka antar dua variabel feature ini saling terikat & saling mempengaruhi; nilai total pemakaian retail sangat mempengaruhi nilai dari total pemakaian. Sehingga hal ini dapat membenarkan asumsi bahwa lebih banyak nasabah yang menggunakan kredit untuk pemakaian retail daripada tunai.\n","- Sedangkan untuk korelasi antara 'outstanding' dengan 'tagihan' memiliki hubungan positif dengan nilai  0,87. Oleh karena itu, berarti untuk setiap total saldo pemakaian kartu kredit yang digunakan nasabah bertambah, maka bertambah pula jumlah tagihan kredit yang dimiliki nasabah.\n","- Dan untuk korelasi antara 'sisa_tagihan_tidak_terbayar' dengan 'sisa_tagihan_per_jumlah_kartu' memiliki hubungan positif dengan nilai 0,91. Untuk hubungan ini dapat diasumsikan bahwa para nasabah yang memiliki kartu lebih dari satu / banyak, maka semakin banyak pula sisa tagihan yang tidak terbayar yang dimiliki oleh nasabah.\n","\n","2. **Hasil model machine learning**\n","- Berdasarkan dua metode dari logistic linear (statsmodel & sci-kitlearn) didapatkan hasil akurasi dari model machine learning yang kurang lebih sama yakni 91,2% - 91,4%.\n","- Namun, setelah melalui evaluasi model menggunakan K-Fold, Grid Search, & Confussion metrics didapatkan hasil evaluasi model sebagai berikut:\n","> Accuracy: 0.912\n",">\n","> Precision: 0.560\n",">\n","> Recall: 0.013\n",">\n","> F-Score: 0.026 \n","- Berdasarkan hasil evaluasi model tersebut dapat disimpulkan bahwa secara keseluruhan model sudah dapat memprediksi dengan akurasi 91,2%. Namun, untuk memprediksi nilai positif yang benar secara keseluruhan di kelas positif (recall), model machine learning ini masih sangatlah buruk dengan persentase keberhasilan hanya sebesar 1,3%. Sedangkan, untuk memprediksi  banyaknya nilai yang benar diantara prediksi yang positif (precision), model machine learning ini sudah cukup lumayan dengan persentase keberhasilan 56%.\n","- Hasil model machine learning di atas dapat disimpulkan bahwa masih belum cukup baik & belum sepenuhnya dapat diterapkan. Karena masih adanya kecacatan model dalam memprediksi data. Diperlukan adanya analisis & pengerjaan lebih lanjut untuk mendapatkan hasil model machine learning yang lebih baik.\n",">\n","\n","**SARAN:**\n","- Untuk dapat mengantisipasi kecacatan pada data yang akan dianalisa & dibuat model machine learning, bank dapat memperbaiki / membuat sistem penginputan & pengecekkan data yang lebih baik agar dapat menghasilkan data yang lebih baik pula.\n","- Untuk dapat dilakukan analisis lebih lanjut dapat menambahkan beberapa variabel feature lain seperti 'pendapatan nasabah', 'jumlah tanggungan nasabah', dsb.\n","- Untuk mendapatkan persebaran nilai yang merata pada 'total pemakaian retail' & 'total pemakaian tunai', bank bisa menawarkan promo kepada nasabah untuk setiap pemakaian kredit di kedua variabel feature tersebut. Namun, jika bank hanya ingin memfokuskan pendapatan nilai yang tinggi di salah satu variabel feature antara 'total pemakaian retail' & 'total pemakaian tunai', maka penawaran promo hanya dilakukan di setiap pemakaian kartu kredit di salah satu variabel feature tersebut.\n","- Untuk dapat mengatasi sisa tagihan tidak terbayar & sisa tagihan per jumlah kartu dari nasabah, bank bisa mengadakan pembuatan persyaratan / ketentuan terbaru terkait jumlah maksimum kartu kredit yang dapat dimiliki nasabah. "]},{"cell_type":"code","metadata":{"id":"wjzE1KfX7GI9","executionInfo":{"status":"ok","timestamp":1630385223342,"user_tz":-480,"elapsed":53,"user":{"displayName":"Rahmat Zaki M.","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgnvA63CKQ3Yp88919ydJVGcv-48s4I8u5HMBMQsg=s64","userId":"01200825977235645922"}}},"source":[""],"execution_count":187,"outputs":[]}]}