diff --git a/01_materials/notebooks/dataset/LCRweek1_day1.ipynb b/01_materials/notebooks/dataset/LCRweek1_day1.ipynb
new file mode 100644
index 000000000..ed3b91897
--- /dev/null
+++ b/01_materials/notebooks/dataset/LCRweek1_day1.ipynb
@@ -0,0 +1,601 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</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>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x168730430>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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OZBRFRZYkjKqKj5sjb9wwhLhefvXnejg70C/YoQNH2zWJrr2CIAiCYIe8Mh3rjudQVq0nws+Ny/oH4qAR2Q7WiK69giAIgtCKAjycuH10eEcPo1VV6AxsTcinQmcgKsCNYWHeHbK0JIIRQRC6tRqDwqZTuWQWV+Hr7sRl/QJxcxJ/+oSLm6qqfLTxDPM3JVJZY6y/vU+gO2/cOIRh4T7tOh7xGykIQrOUVuvZdCqPsmo9UX5ujIn2Q7ajDkNH+PVQJi/9cpSiSj2SZCrh7eqo4anL+zJvfFS3Sy5Mzq8gu7SaAA8n0bpesOqNNaf4eFNik9sT88q5deEufn54HP1D2i8tQgQjgiDYRVFU3l+fwCebE9EZzlWkDPNx4Y0bL2mUxNcZrD2WzWPLDtTvgKjLjqusMfKv308AcO+E6I4ZXCuLTynkX7+d4EBacf1tg3p48o+r+jO2l3/HDUzolLJLqlmwuWkgAqaKsgZF5e21p/n0rhHtNiaReSMIgl3eWHOK99cnNApEADKKq5jz+W4ONrgQdjRVVfn3qhNWz3ln3WkqawztNKK2sze5kFsX7uJQenGj249nlXLnp7vZcjqvYwYmdForD2VYPW5UVNafzKG4sqadRiSCEUEQ7JBTWs3CLZbfSRkVlTfXnGznUVl2NKOUlIJKqw3MKmuMbDiZ225jaguqqvLCT0cwKmqTHimKamrs9o+fjqDY00BFuGjklelslrhXVSisaL9gRCzTdDUVBXBgKZxeAwYdhI6EkfMgIKajRyZ0Y78eyrR6XFFh+5kCcsuqCfRwbqdRWVZoxzs6ifb9Y9sWjmWWciqn3OJxVYX0oip2JxV2umU0oeMEeTpjtBGgyhLtWsJezIx0JWl74YNLYP2rkLoTMvfDvk/ho9GwZ1FHj07oxgoratDYkexZVNE52qT39LYdEKlADy+Xth9MG0ovqmzV84SLw7VDe1hN3tbIEtMGBuPl0n7F20Qw0lVUl8DXN0BNBagN1uwVI6DCqmcgeVuHDU/o3kK8nDHYqI8oSxDo0TmagfUO9GBIqJfVTqp+bo5Miglov0G1AW9XR7vO87HzPOHiEOjhzGNT+pg9JkvgrJV5Zlrfdh2TCEa6ikPfQnVp40CkIUkDOz5s3zEJF41rLumB1sqVXSNLTB0QhI9b57novXztQLSy3CQgkWo//m/WoC5fPXNEhI/NANDTWcv4PmJHjdDYY5f15qWrBzSZ/Rjc04sVfxlL70CPdh2PyBnpKs5utH5cNcLZDe0zFuGi4+3qyN+u6Fe/JbYhjSTh7CDz1+mdK29peLgP3z4whld/Pd5op09UgBvPX9mfy/oHddzgWolWI/O3K/rx9PeHLJ7zzPQYnB007TgqoSuQJIl7xkdxx5hw9iYVUa7TE+XvTkxw+wYhdUQw0lXULcdYPcfCrIkgtIJ7J0Tj7qTlnXWnyS3T1d8+PMKbf80a3O7vpOwxPNyHnx8ex5nccrJKqvB1c2RAiGe3KnZ2Q2woOoPCv1cdp0JnRJZMCcUuDhqemR7DHNG+XrDCSavpFDNnolFeV7HlTdj4mvVlmvAxMHdV+45LuOgYjAoH0ooprzYQ4edKtKj02SlU1RhZdyKH7JIqAjycmDYgWJS9FzqcaJTX3QybA5vfBGMNZmdIVCOM+Uu7D0u4+Gg1MiMjfTt6GMJ5XBw1XHtJj44ehiBckK6dvXUx8QiCmxaDrDF91JFq/z32Ueh3dceMTRAEQRBaQMyMdCX9roIHt8HuBXBqlWmWpOcIGP0g9Jna0aMTBEEQhAsickYEoTPKPAj7Poesw+DoBv2vgaG3gbNXR4+sU0stqGTF/nQyiqrwdXNg5tCeDOopvmaC0FHsvX5f0DLN/PnziYqKwtnZmdjYWLZu3Wrx3G3btjFu3Dj8/PxwcXGhX79+vPvuuxfytIJwcdj4OiycBAe/hqwDkLIdVv8d/hcLuZ2n/0tnoqqm3jiT3tzIRxvO8PPBDBZvT+bq/23j4a/3ozMYO3qIgiBY0exgZPny5TzxxBM8//zzHDhwgAkTJjBjxgxSU1PNnu/m5sYjjzzCli1bOHHiBC+88AIvvPACCxcubPHgBaHbOfojbP6P6d9KXUdZ1fRRWQhfXQfGzlFyvTNZsiOZjzYmogJGVcWoqBhqe2/8cTSLf/5yrGMHKAiCVc1ephk9ejTDhw/n448/rr+tf//+zJo1i9dff92ux7j++utxc3Pjyy+/tOt8sUwjXDQWTIbsQ5a3cAPcuBgGXd9uQ+rs9EaFMa+tp8BK0ztZgl3/uKxTNPEThItJmyzT1NTUEB8fz7Rp0xrdPm3aNHbs2GHXYxw4cIAdO3YwadIki+fodDpKS0sbfQhCt6crNy3LWAtEZC2c3dRuQ+oKDqeXWA1EwFQEbOPJ3HYakSAIzdWsYCQ/Px+j0UhQUOMyykFBQWRnZ1u9b2hoKE5OTowYMYKHH36Ye++91+K5r7/+Ol5eXvUfYWFhzRmmIHRNqp15Dfaed5Go1tv+ekiSqSiYIAid0wUlsJ5fSllVVZvllbdu3cq+ffv45JNPeO+991i2bJnFc5977jlKSkrqP9LS0i5kmILQtTh5gl9vTG3cLFAMEDa63YbUFfQOdLfanRdAVSEmWCzxCkJn1aw6I/7+/mg0miazILm5uU1mS84XFRUFwODBg8nJyeHll1/mtttuM3uuk5MTTk6doxW5ILQbSYK4h+G3Jy0cl8HJAwbd0OpPraoqe5OLWLYnlTO55Xi7mrbFXj0kpNM3WQvydGbqgCDWn8jFqDRNgZMlCPd1ZUy0qBorCJ1Vs2ZGHB0diY2NZd26dY1uX7duHWPHjrX7cVRVRafT2T5REC42w++GS2qDdOm8SrsaJ7h1manuSCtSFJXnfjzCzQt2svJQJkcySth+Jp9nvj/EjPe3klNa3arP1xZeuXYgAe5OaM6bItHIEk5aDe/fOqxbNccThO6m2RVYn3rqKWbPns2IESOIi4tj4cKFpKam8uCDDwKmJZaMjAyWLl0KwEcffUR4eDj9+vUDTHVH3nrrLR599NFWfBmC0E3IMsz6GGJmwJ6FkHMMtC4w8DoYdR/4RrX6U36+PYlv95qWQutmFuomGFILK3ngy3h+emhsp76Yh3i5sPLRcXy8KZHle9OorDHioJG4ekgPHr60N70DRTM/QejMmh2M3HLLLRQUFPDqq6+SlZXFoEGDWLVqFREREQBkZWU1qjmiKArPPfccSUlJaLVaevXqxX/+8x8eeOCB1nsVgtCdSBIMmGn6aGNGRWXRlrNWjx9MK+ZAWjHDw33afDwtEejhzD+vGcgLVw2grFqPq6MWR61ovyV0L5U1BjafyqOkSk+4nytjovyQbSVNdQGiHLwgXMTO5JYx9Z0tVs/RyBJPXNaHRy/r006jEgThfKqqsmDLWT5Yn0Blg51hoT4u/PeGIYzr7d+Bo7OsTcvBC4LQPRitlDSpI0F9NVNBEDrGhxvO8J8/TjYKRAAyiqu46/M97E0u7KCRtQ4RjAjCRSzS3xVPZ+urtQZFJTaicy/RCEJ3VlRRwwcbEsweU1VQVJX//tG1+1aJYEQQLmJOWg13jomwWKdDI0tE+LoyvpNOAQvCxWDV0SwMRsuzk4oK+1KKSC+qbMdRta5mJ7AKgtC9PHZZHw6mFbMjsQBZOreTRpbAw1nLwjkjukWC3MWmWm/k98NZHEovRiNLTI4JZEJvf/G97IIKymvQyJLN5dKC8hpCfVzbaVStSwQjgnCRc3bQ8MU9o/hpfwZf7U4huaACDycHrh/ek9ljIgj0FM3lupqdiQU8+FU8JVV6tLXBx+LtyfQOdGfJ3JFd9oJ1sQr2crYrbyuoC/+uit00giAI3cjZvHJmvL8VvVHh/OuXRpbo6e3C2icndvrKusI5ZdV6Rv77T6r15jPONZLE2N5+fDmv87WKELtpBEEQLkKfbUvCoKhNAhEw1Y1JLaxk1ZGs9h+YcME8nB34x5X9zR6TJXDQSvx9Rr92HlXrEsGIIAhCN/L74SyzPXrqyBKsPmq9y7rQ+cyJi+TNG4cQ6NG4b9ugnl58/8BYBvbw6qCRtQ6RMyIIgtCNVOmNVo8rKpTrDO00GqE13TQijOuG9WR/ajGltRVY+wZ5dPSwWoUIRgShm1BVlfiUIrYk5KMoKsPCvZkcE9ikeZzQvfUKcOdEdimWsgE1stRtLmAXI61GZlRU9+tALYIRQegGskuquX/pPg5nlKCRpfqqqT28nVk0Z0SXn8IV7DcnLoK//3jE4nGjonL76PB2HJEg2CaCEUHo4qr1Rm5btIvUQlPBo4b5AjklOm5buIs1T04kxMulo4YoNIOqqmw4mcvSnSmczC7FzVHL1UNCuGNMhF1bN2+MDWX1sWw2n85rNDsiSaZqnU9d3lfMjAidjkhgFYQu7vfDWSTlV5hNWjSqKhU6I1/sSOmAkQnNpSgqz644zLwv9rHtTD45pTrO5lfw4cYzTH1nM0fSS2w+hlYjs2jOCP46vR9BDZId+wV78MFtw3hMNDwUOiFRZ0QQuri7Pt/D1oQ8s1s56wR7OrPrH5e136CEC/LlrhRe/Pmo2WOyBL5ujuz4+2U4au17H6koKvkVOhxkGR83x9YcqiDYRdQZEYSLREmV3mogAmL3RFegqiqfbjmLpXRjRYX88hpWH7N/W64sSwR6OItAROj0RDAiCF1cn0B3qztmJAmi/N3acUTChSisqCGlsBJrcaVWltib1LVbxQuCOSIYEYQu7rbR4VaLXKkq3DlG7J7o7CTJvi3Ydp4mCF2KCEYEoYsbHu7D7DERZo/JEozr5cf1w0PbeVRCc/m4OtArwM3iMg2YtmvHRfu125gEob2IYEQQuoFXZw7klWsH0sP73NZPLxcHHr60N5/PHYmDRvyqd3aSJPHAxF4Wl2k0skSIlzNTBwS167gEoT2IOiOC0A1IksRdYyOZPSaC1MJKDIpKuK+r3bsuhM7hphGhHM8qZcmOZDSyVL/8Jkmm4PKLe0aJwFLolkQwIgjdiCxLRIpk1S5LkiT+ec0ArhgUzNe7UjieVYq7k5arhoRw84gwvF3FrhihexLBiCAIQiciSRJjov0YI3JDhIuICEYEQRDakaKoHEgroqhCT6ivC/2CRSFHQRDBiCB0Y6qqkl9eg96oEOjhhFbkG3SolYcyeX3VCbJKqutvG9jDk1dnDiI2wqcDRyYIHUsEI4LQTa08lMn8jWc4mV0GgJ+bI3eNjeSBSdE4aTUdPLquR2cw8seRbP44mkWFzkifIHduHxVOHzubzn2/L41nVxxucvuJrFJuXbiT7x6IY1i4CEiEi5PoTSMI3dCHGxJ4a+3p+k6tdWQJxkT7sWTuKLHTphkyi6u4fdEukgsqkSVTafa63S5PX96XR200n6vWGxn57z8pqzZfll+WYFi4Dz/8ZWxbDF8QOozoTSMIF6nEvHLeWnsaaByIgOkiujOxgOV7UztgZF2ToqjMXbyHtKIq0+e1X9O6bbdvrzvNr4cyrT7GnydyLAYidY8Zn1JEcn5F6wxaELoYEYwIQjezbHeq1V41AF/sTGmn0XR92xPzOZVTbrXk/vyNZ7A2yZxdUo2NbwlAo1wSQbiYiGBEELqZhFzrF04VSBLvwO22+VQeWhuRxInsMgoraiwe93N3tNlZGcDf/cLriJRU6TmTW0Zeme6CH0MQOopIYBWEbsbdSVuf12CJi4NIYLWX3qhYnfWos+ZYNrePNt8j6PIBwbg4HKVKbzR7XJKgX7AHvQPdmz2+lIIK3lxzij+OZtcHoXHRfjwzPUbs0BG6DDEzIgjdzIzBwVYDEY0scfWQkPYbUBc3JNQbox2zGsv3plk85u6k5elpfc0ek2o/nr9ygN2de+sk5Vdw7YfbGwUiALuTCrhlwU62JuQ16/EEoaOIYEQQuplpA4KJDnAzmzciS6CVJe6dENUBI+uarhoSgoPGdpBwKL0EncH8zAfAvPFRvHT1ANydGk9IB3k68+ldIxjfx7/ZY3t55THKdYYmy3KKCkZV5ZnvD1ldsgPTzE9emY6qGstjF4S2JpZpBKGbcdTKvHXjJdyzZC/FVfpGx9yctCycPYLegfbVxhDA2UHDmCg/tp7Jt3mutdUcSZK4Z3wUt48OZ9OpPIoqawj1cWFsL3+bCcfmZBZXsfm05ZkPVYWcUh1bEvK4NCawyfG8Mh0fbkjgu33pVOmNyBJMGxjMY1P6MKCHKKEgtC8RjAhCN3Mmt5y5S/Y02Upqqjmi4usmmq0114zBIVaDEVmCmGAPnO3IxXF20HDFoOAWj2nRlrM2z5ElSMqr4NKYxrfnlFYz66Pt5Jbp6mdOFBXWHc9h48lcvpw3mlFRvi0eoyDYSyzTCJ2WoqhsS8jny53J/Hwgg5Lz3uUL5v11xSHKq41N8kZUFapqFJ5dcajNnltVVQ6mFfPyymM8tuwA//njJIl55W32fO1l5tAeeDprsTR/oahw7/jodhvP4u1JLN6RbPM8RQV356bvOV/99XijQKSOUVHRGxUe//aAzeUdQWhNYmZE6JR2JObz7PeHySiuqr/NSStz34Ronrq8L/IFTGtfDBJyytifWmzxuFFVOZxewrHMEgb28GrV567WG3n82wOsOZaDRpZQVRVJkvhkcyL3jo/i+av6NztBs7Nwc9Ly3xuG8NDX+80eH9/bj+uH92yXsVToDLy55pRd52plian9gxrdll+u44+jWRaTnBXVVO/E0vKOILQFMTMidDoHUouY8/keskqqGt2uMyh8uPEM/1l9soNG1vmdzrFvFuJMbuvPVrz0yzHWHc8BTO+wFfVcldJPtyWxaKvtZYXOyqiovL3uNLKFYGp7YgF7kgrbZSx/nsih0s5k03vGRzVZlkvOr7BZ80QjSSTklF3oEAWh2UQwInQ6b645hVJ7MTPn061nySkVlSrNcXW0r36IPbkNzZFTWs2K+DSrF7mPNyVSY1Ba9Xnby4aTuZzJLcdoIUNVrp0Bag/55TV2VXOdNiCQv13Rr8ntLnb8jCiqKmrRCO1KBCNCp5JXpmNHYoHNd262eoFcrMZE+9kMSFwcNIzr3fxtpNZsPpVn83tWVKnnUHpxqz5ve/nzeI7VHS9GRWXT6Tz0xrYPtoI9ne2q5vq3Gf3Njrl/sCchXs5W7ytJMHVAkNVzBKE1iWBE6FSKKi2X1K6jkSUKrJTevpi5OGp4cFIvq+fcNyGqSa2Llqo2GC0mdzak03fNmZFqg9H6vl1Mhw32VEdrocv6B+JhJim1jizBJaFe9AowX81VliWemGq5y7AswY2xoYR4ubR4rIJgLxGMCJ1KoIeTzSlog6LSw1v8obTkkUt7c8+4SMC09q+Vpfp3yLPHRPD4VPOVQFuif4gnti7DsgR9gppf7rwzsOf19fR2wdmh7f+kOjtoePHqAWaPyZIpWH/pGvPH69wyMpxnp8cgS+cK4dX9jMwYFMKrMwe1+rgFwRqxm0boVLxdHbl8QBB/nsi1uLXQQZa5dkiPdh5Z1yHLEi9dM5A5cZH8eCCD3NJqAj2cuG54KFH+bm3ynCMifOgV4EaSheRIjSwxtX8gQZ7Wlwc6q5tiQ3l77SkUCzMfEnD32Mh22y1084gwnLQyr/9xkuwGnX77Bnnwf7MGERthu0bIw5f25obhofywP520wkq8XR259pIeouCZ0CEk1Z4OUB2stLQULy8vSkpK8PQUvyjdXWJeOTM/3E5VjcFsT5AXrx7AvPGinHlncyyzhJsX7KRarzQKJDWSRKCnEz8/PK5DghFVVVl1JJsvdiZzPLMUR63M9IHBzBsf2axKtD8fyODJ7w4iS1Kj1ycB43r78/ndI3HUtu9ks1FR2Z9aRGFFDT29XRjYw7NTbp+u1hvRGRRTrZYOGF9OaTXpRZV4uTjQK8C9U36Nuit7r98iGBE6pYScMl74+Si7G2yXDPZ04qnLY7h5ZFgHjkywJjm/go83J/LzgQx0BgUPZy23jQrngYnR+Lk7tft4VFXlrysO8318eqNOxhpZQpZg0ZwRTG5GLY09SYV8vOkMm07noaqmpZm54yKZExfZ7oFIV7DrbAHzN55ha0I+KhDg7sScuAjumxjd6ju6zDmTW86/fz/OplN59ctsfYPc+ev0fiJBt52IYEToFlIKKkguqMTdScvQMO8L6uEhtD+jolKtN+LqqOnQd6Hf7UvjrysOmz0mYcq/2PWPy/BycWjW4+qNCgajirODbNfrqzEoFFXW4OakbfXk4c7ql4MZPLH8IDI0muGUJRga5s03941p04AkMa+cWR9tp7LG2GQmC+C9W4cyc2j7FKq7mNl7/b44fiuEtqGvhiPfw/6lUJoB7kEw7E645DZwdG2Vp4jwcyPCr3XyHFRV5VhmKRnFVfi6OTI83EcEN21EI0u4dYKL7mfbkmp78jQ9pmJaPvghPp17mrns56CRsec6WlhRw/82JLB8bxqVNaYdR5P7BfDYlD4MC/dp1nN2JcWVNTz7/WFUFc4vz6aocDCtmEVbzvLoZZZ39bTUa7+faBKIAPUzJM//dJTpA4PbZYZGsK3j/1oIXVN1CSydBZn7QZJBVaA00/T5nkVw9+/g5tfRo6y3N7mQF38+ysnsc1Ulgz2dee7KfuLdUTdVY1A4lW29iqgkwYG04jZ5/oJyHdfN30FGcVX9BVEFtpzKZ+vpfBbdNaLbllv/YX8GesXyNm5FhaU7U3j40t5t0toht6yaDadyre7GLtcZWHMsW/z+dxJikVO4MKv+Clm1DdfUuj86tb/5+adh5cMdMixz4lMKuX3RLk6fV946u7Sax789yHf70jpoZEJbkiVs1j6RMG19rrMvuZAHvtxH/xdX0/f5P7jxkx2sOpLFhaxm/3f1yUaBSB2jqmJUVJ5cfhCdwb6y7l3NqexSNDaWr/LKdZRWt03zy4yiKltlYdDKEqkFlW3y/ELziWBEaL7yPDj6PagW/pCqRji1GoqS23VYlvzfbyfqe6WY86/fjlOt754XhdaWUVzF9/vSWL43tUlw19loNTKjo32t1q0xqioT+piq0X63N42bPtnJnydyqdIbqTEq7E8p4qGv9/PyymPNCkjKqvX8fCDT4vZ0FSiu1LP2WE5zXlKXYW8p+bZK+vVxdbR5jlFR8XazfZ7QPsQyjdB86XtBsXXxViF1N/hEtseILDqbV85BG9PwpdUGNpzM5crBIe0zqC6orFrP3344wh9Hsxq94xwV5ct7twxtdhG6cp2BH+LT+WF/OoUVNUT4unLb6HCuGBiMVtN6F6gHJvVi11nzDew0Evi5O3Hl4BBSCyr5+4+HUaFRAFH3zy92pjC2tz/TBwbb9bzpRVXU2CgNr5W7bzO6ywcE88XOFIvHNZJEXC8/XB3b5hIU6e/GgBBPTmSXWpwhkWWJGYPs+34KbU/MjAjNZ+/uiE6wl79hQShLZMnUMl0wz2BUuOvzPaw+LxABiE8p4sZPdlBsRxn/Otkl1Vz1/lZeXnmMI+klpBdVsfNsAY98c4C5S/a26tLFpTGBvHBVf4D6ZGWp9sPbzZEv543G2UHD13tSrO6KkSVYsj3Z7ue1p2Ghoqq4tNHFuKON7eXH4J5eFhPEFVXloUutty1oqWeviAHV/FKdBMwbH4V/B2w3F8wTwYjQfKGjQLa1FVKC8Lh2GY41/h62/9goKvi7i+laS9Ydz2F/arHZZS6jopJdUs3Xu1PtfryHv9lPenEVKud2NtQ99vYz+by99nSLx9zQvROi+fOpicweE0FshA/jevvx8rUD2fTMZGKCTUXPDqYWW1xSqRvf/tQi4lOK7FquCfd1pU+gu9WcFUWF6QM7vtaFzmDkTG4ZZ/PKrX4NmkOWJT6/eyT9a7++deXmZQkcNBJv33wJY3u1brPG810aE8iHtw+v7+OjkSWk2rHcPynabEdjoeN0z7BcaFtufnDJrXDw6wbJqw1IGuh3JXh3fHGyPoHu9Av24FROmcXpWldHDZeLAkgW/bg/o1HBsPMpqqmex8OX9rb5WEczSohPKbJ4XFHhq10pPDG1T6tO4fcO9ODlawdaPO6olZHAav8ZnUHhho93EOHnyqszBzGpb4DFcyVJ4ompfXn4m/1mj8sSXDEohOjaZnaqaqqk+tvhLMqqDUT5u3FTbCiBbVixVmcw8r/1Z1i6M5nSagMAIV7O3D8xulVK2wd4OLHykfHsSCxgzbFsqvRG+gV7cMPwUHzaKVfjqiEhTB0QyJ/Hc0ktNFVgnT4wqEMK8AnWiWBEuDBX/AfyEyBtlyn4UI3ntvgGDYRr/9fRIwRMF4UXrhrAnM93A+YvNs9Oj2mztevuILes2mbL+oJy+5Zp9iQVWg1sACprjJzIKiM2ov3qcFwaE8i2hHy7zk0tqGTu4j0snjvKakBy1ZAQ8ssH8n+/HUdRVeTai7tBUbmsfxBv33QJYMqfefDLeLadya9f1lBVlXfWnub5q/o3uwaKPfRGhXlL9rEjMb/R9yKrpJpXfj1OYm45/7pucIufR5YlxvfxZ3yftp0FscZJq+GqISIfrLMTyzTChXFyh7t/gxs+g8hx4BttWpaZ9THc+ye4dJ6CTuP7+PPZXSMJ9mr8LtPTWcurMwcyd5zoc2NNTx9Xq8XhJEzvqLuyG2JD8XRxsNkxGqhfXnrFjh02d42NZNc/LuO5Gf25ZWQY906I5rdHx7NozghcavNKHl92gB2JpkDIqKj1O7+Mqsqrvx3nt8OZLXx1Tf10IINtZ/ItBoVf7U4lPsV84q8gtAXxdlC4cBoHGHyj6aOTu7RfINv/NoVdZwtIL67Cx9WRCX38RfVFO9w8IpRVR7KsnnP76HC7Hmt0tK/NWRYXB5n+IfY3sGsNXi4OfDlvFHM+30NJpd7qcg2YKrqeza/gSEYJQ0K9rZ7r7+7EfROjzR47lV3G+pO5Fu8rAR+sT+CqwSGtWlb/q50pVmeoNLLEN7tT7er+KwitQQQjwkVDliXG9u646WJrSir1rDmeTUmlnjBfF6b0C2rzxmsGo8KBtGLKa3MUIv3Nl92f2CeAy/oHsvFkbpOLl0aS6BPkzi12Ni8c2MOLgT08OZZZavGcfiGe9ctm1XojfxzNIiGnHFdHDdMHBtMnqG0ClSGh3mz966X8fCCDb3ancsJG9VYw7QwaEmr+WE5pNcezSnHUyAwP96mfCWlo3fFsNJKE0cIMiwqcziknvaiKMN/WabEAkFRQYTUoNCoqibkVrfZ8gmCLCEYEoQ3pDMb6i1taURU+rg7cEBvK7aPC8XZ1RFVV3v0zgU82JVJjVOrfrfq4OvDv6wa3Se0TVVX5Zk8q7647TX6DXI/RUb78+7pB9A5sfLGXZYn5dwznzdWn+Gp3CtV6U9KyRpa4ZkgIr1w7qFk5NwE2djgdSismp7SaI+klPPXdQUqrDWhlCVVVeWvtaaYPDOLdW4a2SZ6Ph7MDs+Mi6enjwj1L9tk839xrySvT8dIvR1lzLLv+gu/mpGHeuCgen9q30ZJXld5o2gFvYyqmtYvyeThpKatNWjVHlsDDRVwehPYjuvYKQhuprDEw+7M9xKcUNWrWJkmmvjjfPRDH9/HpfLA+wez9JQk+v3tkq/cv+XhTIv9dfbLJ7RpJwtVJw8pHxhNlYZakXGfgQGoRBkVlcE+vZtdpKK3WM/SVtVbflcsS3DkmnK92paKqTa/TsmRKOP3s7pHNeu7myCiq5Ir3tlKmM3/BljBt39307ORGyycllXqu/XAb6WbKwEvAdcN78vZNl9Tf55eDGTz+7UGrY3F2kNn/4uWtGnz9d/VJFm5ObNRN93xv3jiEm0Z0/I44oWuz9/otElgFoY3854+THEg1bWNtGPKrKuSW6Xjwy318vOmM1cf47x8nL6gviiX55TreXnvK7DGjqlJZY7R4HMDdScuEPgFcGhN4QQWjCstrbOaMyJLE+hO5FrfaKiqsP5nL0YySZj+/LWmFldz3xT7Gv7HRaiAC8NI1A5rkcSzekURaUaXZeh0qpm3Sh9LPjfuKQcF4uThYrA+okSVuig1r9Vmgu+IicXd2QGPmeTWyRJSfK9dc0qNVn1MQrBHBiCC0gbJqPcv3plm88BoVlWNZZeitvDVVVTiZXUZiXuut3a88mIliJbgxKip/HM1uswZmPm6ONnesGBWVjOJqq+/aNbLEb4etJ9U2V2ZxFbPmb7fZ7TXYy5kFs2O5rH/T2jTL9qRaDbY0stSoMaOTVsMHtw1DI0lNvi4yEOnnytPT+jbzldgW7OXMt/ePIaS2jH9dUTKAgSGeLLs/TiR3C+1KLAoKwnkURWXn2QLO5lfg6axlckwgXi62Ks42djK7DJ3Bem8SwGbNDYCiZpRaLyjX8c3uVH4+mEFplYHoADfuGBPBVYND0MgSWSVVaCTJZkCSX6bD07l5r9keXi4OTB0QxPoTuS2q9ikB5brWDZje+/M0xZV6i+OSJfjo9uFMGxhscatzbpnO6nMYFZXM4qpGt8VF+zE5JoA/TzTeVaPRSDwwqRfedjR9uxD9QzzZ/OylbDmdx/7UIjSyxIQ+/gwP92nVnTuCYA8RjAhCA7vOFvD0d4fIaHDBcNLK3Dchmqcu74tsTyEKsKteBdgORAB62tmE7kxuObcs2ElR5bmlkIIKHbuTCvn5QAYLZsfi6+ZkcedGHQn7up5eqKcvj2Hr6Xx0qtHs679vYjRf7UqhssZy0qZRVYn0M5/XciGqaoxWu+zWySiuahKIlFTp+Xp3Csv3ptlsW6+RpSbLW8//dMTs9l6DUeVvPxwm2NOZiVaKq7WERpa4tF8gl/Zr3bwkQWgusUwjCLUOpRUz+7PdZJU0fueqMyh8uPEMr/9xwu7HGhDihbuT7VjfzcnyVLhGgvG9/e3qiKsoKvct3Udxlb7RBb7u3xtP5fLRxjNcO7SH1Y0bGklickxAm5brjgn24LsH4uh73hZdNycNz06P4bkZ/bh1ZDgaK+/OtbLE9cMt7Km9APnlOptddsFUF6Sh3NJqrv3fNt5ac4qUgkqb9zcqKtcP61n/eWpBJSvi080GMSqmwPDdda3bq0cQOiMRjAhCrTfXnEKprX5pzmfbkuzqAgzg4qhhTlyE1cTE8b39eK225Pb5p2kkCScHDS9c3d+u59uemE9SfoXFd/aqahq/Vpa4Z2yk2QZusmQa11OXx9j1nC0xONSLPx6fwK+PjOe9W4by6ZwR7Hv+ch6+tDeSJPHolN6E+bo0mYWo+3q+fO1AfFsxYPJ0cbDa1A5Mgd2K+HS+adAU8K8/HCa9uMquGS5Zggl9/Inr5Vd/26qjWVabWysqHEgrbhIgC0J3I4IRQcD0znjbmXyrSZMAvx6yvzT3k5f3rW/AV3dRrbu29g5w5/1bhzFzaE8Wzo4lOqDxksOoKF9+fGgs/YLt28q+N7nI7M6IhsqqDcS9vp7M4mruHBOB03lF1UJ9XPnq3tEMDvWy6zlbSpIkBod6MWtYT6YOCGpUFMzHzZEfHxrHLSPDGo2zX5AHn9wZyx2jI1p1LF4uDkyOCbA6GwOm2Yp//HSEbQn5pBZUsvlUnl25L7IE1w3rycLZIxrlY5RW6W0+J2C1JoggdAciZ0QQgGI7kkRlSSK/wnqCYkMOGplP7ohl8+k8vt2bSkpBJf7uTlw/vCdXDg6p360wbWAwlw8I4nROOUWVNYT6uBDq07xqm7IEthcZTO+0157Iwd1Jyw9/GUtiXjnlOgPR/u6MifbtVImLvm6OvHbdYJ6/sj+ZxVW4Omnp4eXcZmN88vK+pn4tRus1yGQJ5m86w22jwm2WjQeYPSaCR6b0JshMB95IPzf0NoIZrUZq0ldJELobEYwIAhDg7mxzZ4tRUenhZV8yaR3ZzgRBSZKICb7wMueDenjZTJ6sY1RUynUG3v3zNJ/d1XaFw1qLm5O2zUrANzQk1Jsv5o5i3hd7qdJbDu0UFXYkFnCrnSXwx/X2NxuIgKmz78u/HrOYrKuRJa4d0qNNdjYJQmdyQcs08+fPJyoqCmdnZ2JjY9m6davFc3/88Ucuv/xyAgIC8PT0JC4ujjVr1lzwgAWhLXi5OjDdypZNML1DnTm0ZYWg8sp0rDuew7rjOeSX2z/LYktGse3kyYaMisqGk7nkltqXA3OxGNvbnxmDQ+zaDXVJmDdaGydqZYlRUaZmcyVVevYmFxKfUlRf3t3NSctr1w1GoukOLI0s4evmyLNXtH0OjyB0tGbPjCxfvpwnnniC+fPnM27cOBYsWMCMGTM4fvw44eFNO3du2bKFyy+/nNdeew1vb28WL17MNddcw+7duxk2bFirvIiLWlEy7P0MTq8GQw2EjYJR90NY53/H29k8Oz2GbWfyqdQZzW5//dsV/S645kNZtZ5/rjzGLwfPbR/VyKbg5tWZg+zaeWNNbpkOrSxhaEbtDlWFtKJKAi28a+/KagwKfxzN4qcDGRRW1BDh68pto8KJ6+Vnc5lnYA8vftqfYfUcdydtbXXUUJbvM1/cTpbg5hFhOGgknvvxMD/EZ9Tv2PF01nLP+CgendKHWcN64uXiwNtrT3G0toGgVpa4ekgIf5vRj5BmzsYJQlfU7N40o0ePZvjw4Xz88cf1t/Xv359Zs2bx+uuv2/UYAwcO5JZbbuGll16y63zRm8aCM+vh29vAaAC1dppX1oJigMtegglPd+z4uqCEnDJe+Pkou5MK628L9HDi6Wl9uWVk02DbHjqDkZs/2cnRjJImCbKyZFoe+O6BuBZ16f1iRzIv/3rM7qWaOuuenNguSyDtqbCihjsW7eJEdln90ptGljAqKtde0oN3bxlqdQaspFLPqNf+pMagWMwJkTDNlL1901C+25fGtjP59c9R9/8Jffz5323DmP3ZHo5nNv3eS8DMoabx1AVIaYWVlOsM9PBywctVLM0IXZ+91+9mvR2rqakhPj6ev//9741unzZtGjt27LDrMRRFoaysDF9fX4vn6HQ6dLpzU9ilpZbbjV+0KvJh+R1g0NModVGpzbpf/yqEDIXel3XE6LqsPkEeLH8gjuT8CpILKvBw1jI0zMfqxcuWXw9lNepH0pCiwsG0Yn47nNmiuhlXDwnh/347jsHOaEQCogPc6B3obvW8vDIdy/emsje5CFmCsb38uWlEaJtVBW0Njy07wOnccuBcDlDdbNSvhzLpFeDO41P7WLy/l6sD7986lIe+3o+MZHaWTMVUlOyp7w7yxxMTSC+qYsW+dLJKqujh5cKNI0KZ2CeApTuTOZpRYjaoUYGfD2Zy66hwxkSbtvuG+TYvcbkzySqp4qtdKfx+OIvKGiP9gj24c0wElw8I6lSJ0ULn1KxgJD8/H6PRSFBQ454MQUFBZGdn2/UYb7/9NhUVFdx8880Wz3n99dd55ZVXmjO0i8/+pWDQYXEPhaSBXfNFMHKBIv3diLTQuba5vt2bajU5VpZg+d60FgUjfu5OPHZZH96xs0CWimlZytpFYv2JHB76ej96o1I/9k2n8nhv/Wk+v2sko6P9LN63o5zOKWPbmXyLx1Xg8+1JPDg5Giet5YJzVwwK4bO7RvLk8oMUV5kvO6/Wfny1M4VXZg4y21356wY1SczRyBLL96bVByP2SiusZP2JHKr0CjHB7kzqG9iigLmlDqYVc+enu6msMZyr/ltew5aEfG6MDeWNG4bYXb1YuDhd0EL1+X/AVFW1K/JdtmwZL7/8Mr/88guBgZZ3Fzz33HM89dRT9Z+XlpYSFiZaWTeSsh1UK5s5VSMkb2+/8QgWZZVUW92lo6iQ2QpFrR6d0hsXBw0frE9o0nFWkkyF1AyKiouDhleuHcgVg0IsPlZiXjkPfhWPwag2elevYiqdPnfJXjY9M7nT5ZtsTci3uSuqpErP8cxSKnRG/jyRQ41RYUCIJzOH9sCjdtfKgdQiHlm2nwqd5ZL0YJpx2XgqD0tvnTKKq6xu/zUqKikF9jdCrNYbee7HI/x8IAMk03Zzo6IS4uXM+7cOq0+WbU86g5F5S/Y2CkSA+hmlFfHpXBLmzewxrVsbRuhemhWM+Pv7o9FomsyC5ObmNpktOd/y5cuZN28e33//PVOnTrV6rpOTE05OzW9PfnER7zK6iiAPJzKLqyzmc0gSBHm0/KIuSRL3TYxmdlwEOxLzKas2EOHnRqCHI6uOZFNSpSfMx5Urh4TYTJhduiMZRTVfb0NRTRfFZXvSLC536AxGVh/N5vfDWZTrDPQOdOfWkeEM6NG2OV+KnQm8T313kKT8yvrdMEZF5bVVJ3j/1mGM7+3PPUv2UmWlN05D1oqeebs4WO2xI0vg52b/37rHlh3gzxM5pu+Leu6Cn1NazezPdvPzw+PoH9K+eXV/HMmmoMJynR4J+HTrWe4cHS6WawSLmpUx5+joSGxsLOvWrWt0+7p16xg7dqzF+y1btoy7776bb775hquuuurCRio0FjkOJCvfPkkDkePbbzyCRTePCLOaWKqqpnNai6qCt6sjoT6u9A1yp4e3K/dOiObpaTHcPDLMrp07f9roqquopmUcc7JLqpnx/lYe//Ygf57IYUdiAV/vTuXKD7by5pqTNDNnvlmGR3jbLM0uAamFppkog6JiUNT6GZ8Hv4rn481nKKrU21XiXdNg6645N8SGWt0mrKhw3fCelk9o4HB6MWuP55gdl6KaXstHG8/Y9VitaW9yodUtziqQUlBJcWXrdlkWupdmL9M89dRTzJ49mxEjRhAXF8fChQtJTU3lwQcfBExLLBkZGSxduhQwBSJz5szh/fffZ8yYMfWzKi4uLnh5tU/Z6W5p2GzY/AboqzD7/lU1QtzD7T4soalZw3ryxc5kTmeXN0mG1MgSMUHuXNvC+iVg2s76zrrTfLkruX55wcVRwx2jwnlmekx9xVd76O1oGqczc46qqsz7Ym9907jzE0g/2phIpJ8bN7Vi8NXQ8HAf+od4cDqn3GwwJWH6bTF3rO6WH/dn1O+IscWoqNw1NtLi8TlxkSzbk0pRpb7J42lkiQEhnvUtA2z59VCm1e3bRkXlj6PZ6AxGq/kwrc3eyQ4xKSJY0+y9hLfccgvvvfcer776KkOHDmXLli2sWrWKiAjTemBWVhapqeeSthYsWIDBYODhhx8mJCSk/uPxxx9vvVdxMXLzh1u/Aa2jaRakjlwbX059BXpd2jFjExpxdtCw7L4xTB0Q2OgPsiTB5f2DWHZfXLMCBXOMisoDX+1jwZbERnkOVTVGPt+exD1L9mKwI8CoMyzc22pCpEaWGBHh0+T23UmFHMsstXghl4CPNyW22eyIJEnMvyMWH1eHRjMSdf/0cXW0+rqMimrK87BzfC9c1Z+hYd4Wjwd4OPH9g2PpG2TatSRL58YysY8/X84bhYPGvj/DxZV6m+XnjYpqM8+ltY3t5W+1vo0E9A1yx8tFbFUWLGt2nZGOIOqMWFGcCvs+h1N/mIqehY+GUfdBz9iOHlmXpaoqh9JLWL43jfSiSvzcHJk5tCcT+wa0eMdCelEl8SlFAIyI9KWnd+sUtFp9NJsHv4q3es77tw5l5lD7lgR2JOZz+6Ld1p/ziQlNGvn9d/VJFm05a7P42s7npjQp5qWqKvtSijidU4aro4ZJfQMvuDNvQbmOL3el8EN8OsW1uTJ3jAln6+l81hzPbnY9lvNpZYnXrh9s9/KaqqrsTy3mYFoxWllifB9/egVY31Z9vg/WJ/Den6etLh+5OWk49NI0tHYGOK1Bb1SY+MZGcst0FoPQt266hBtjL3y3mNB1tUmdEaET8g6HqS+bPoQWMyoqz/14mO/2pTcqYvXzwUxGRvrw+d0j63dcXIhQH9dmN8Gzx7I9qWgkLHYdliX4Zneq3cHI2F7+PHZZHz5Yn4BGOldro+5r8s9rBpjtKGzP0gaYanQ0dDi9mCeXHyQx79zOEq0sMScugueu7G/37EEdP3cnnpjalyem9m10e16ZjrXHs63OMIT6uFBUUUOFlcRTRVV5/qcjBHs6M7FvgM3xSJJEbIQPsWZmk+x1Y2wo7/5peeu2Rpa4dWR4uwYiYGoI+cU9o7ht4S4KK2rqv7Z1Pyv3jo/iBjvzYoSLV/v+1ApCJzd/4xm+25cOnLuw1v1/f0oRz6443GFjsya1sNJiIAKm3I26PA57PXV5XxbfPZK4Xn44aWVcHDRMjglg2X1jmDsuyux9hoR62ZwV8XV1bNSF9kxuGbcu3EVSfuMtrgZFZfH2ZP7x45FmjduaW2w0t5MkGB3lazUQgXMJo48uO1DfZ6at9fB24enL+5o9ppElgj2deWhyr3YZy/n6Bnmw4enJPH9Vf4aFedM3yINrhoSw4sE4Xrh6gNhFI9gkZkYEoZbOYGTR1rMWjxtVWHM0m7TCyk5XKdPXzZHk/Aqr5csvZMnDno7DDU0bEIy/uyOFFTUW+7XMjotoNNPxwfoz6AyK2fNV4Pv4dB6YFE3vwJaXrQ/xcuGVawfy4i/HmtQjkSUYEeHD3uTC+kRXa1TVVLNk1ZGsFhWsa45HpvQhwMOJ99cnkFlsanKolSWuGhLC81f1x8+940oieLk6cO+EaO6dEN1hYxC6LhGMCEKtoxkllFYbbJ63+XQed3ayAk7XDetZn4tiyQ3NWLOvSyWrqq0nsmx3KhnFVfi4OnBjbChzxkbib+bC56iVWTB7BLM/243OoNTPKtW9Lx4T7cdDl557916tN/L7kSyryzsaWeKnAxk8O72f3eO3ZnZcJD19XJi/MZF9tV8zf3dH7oqLZFxvf67/2L7WFgAOssSJrPZtV3HLyHBuig3jVE4ZVXojUX5u+Fxgbo0gdBYiGBGEWjUGO/IdJPu2vba364f35LOtZ0ktqjK7hTTEy5mbRlgPRlRVZeWhTBZvT+ZwejGyZNoJVLc7QwWqSoz8b8MZvt6dyo8PjSXCr2nJ/NgIH/54fAKfb0ti5aFMKmuMRPq7MScugptiwxo1BCyrNtjMM5EwlRZvTVP6BTGlXxCl1XpqDEr9LpsdVkrJm6NAu26jrSPLUrsXNxOEtiSCEUGo1S/Yw2odBzBNzQ8J9W6/QdnJ1VHLtw/E8cg3++ub2oFpGWJwTy8+umM4nlYSb1VV5cWfj/LV7nN9dBQVys1sE1WBgooabvpkJ7v/cZnZfIAIPzdemTmIV2YOsjpuLxcHnLQyOoPlAE9VabLzprWc/zVp7vKbUVGZel6dkOLKGpbvTWPVEVPDuAE9PLlzTAQjI9u/VLsgdBUiGBGEWj5ujswc2oOfD2Sa7dSqkSV6B7ozPNy7/QdnhyBPZ75/cCzHMkvYdbYQVVUZHeXH4FDbxQXXHMvhq9qmbnZuiCG3TMc/Vx7jVRsBhzWOWpnrh4fy3b40izMkiqpyQ2xPVFVlS0I+S3cmcyyjFGdHDVcOCubOMRH0aKUt0mG+rsRF+7EnqcBqQjCYfh5iI3y4pMHX92R2Kbcv2k1RZU399uGz+RX8cjCT+yZE8Y8r+4tkTkEwQ+ymEYQGXrx6ANEBbk1KeGtkCU9nLR/dPrzTX0wG9vBi3vgo7p0QzeBQL2oMCkczSjiUVkxljfmcmCU7kqyWLbfk272pdvdwseTRKb3xdnFAY+Hr+pfJvejp7cI/Vx7jrs/3sOlUHtml1STnV/DJ5kSmvrOZ+JTCFo2hoZevHYizg8ZiTZm6mwf39GLBnbH1Pw81BoW7P99LSYNABM7txlq0NYmfDmS02jgFoTsRRc8E4TzlOgNf7kzh690pZJVU4+XiwE2xocwdF9VoS2pnZ1RUPt50hs+2JVFU2xfE1VHDbaPCeWZaDC6O53IdBv1ztdklGXssnB3LtIHBLRprWmElz/90hK0J+fW7WHxcHXj40t7MGx/Fj/szePr7Q2bvK0vg4ezAzuem4OrYOpO9CTll/HvVCTafyqsfT6CHE9H+bkT6u3Hl4BDG9/ZHbhCw/HY4k0e+OWDxMSUJ+ga6s+bJSa0yRkHoCkTRM0G4QO5OWv4yuRd/6aCaDa1BVVWe/v6QqdV8A5U1RhZvT+JwejFf3zumPpnUVCjrwoKRcp3tHUi2hPm6snTeaNIKK0nMK8fFQcOwcJ/68X269SyShNnKqUrtFttfD2Vyy8jwFo8FoE+QB0vmjiK3tJqskmp8XB0J97OeT7IjscBqzpGqwqmcckqq9KI0uiCcRyzTCEI3tPNsQZNApI6iwt7kIn7cn15/27QBQRdc6j7Sv+mOmgsV5uvK5JhARkf71QciVTVGTmSXWS3hrpEl9iRZ39p8IQI9nbkkzNtmIALmAyXzJ7ZsTILQHYlgRBC6qMziKg6nF5NdUt3k2Ld70qwGF5IEX+1Kqf/8nvFRSJyrB2IPCegd6M4wK43imsvU6M2A0mB2oat0hR0V5WOzYVy0vxueLmJCWhDOJ34rhHZRY1DYdbaA0mo9kX5uDOzh2aGJoIqisul0Lt/sTiOlsAJfV0euH96TmUN7triDbmsrqdSz4VQO5Toj0f5uaGSJ5344TFKD8u4B7o7889qBXD2kBwBJ+RVW63eoqqmEfJ1+wZ58dMdwHvp6v139ZWTAQSvz3xuG2Pw+VugM/Hook5PZZTg5yEwbEMzwcO9G90stqOTjzWf4cX8GOoOCu5OWW0aG8cCkaAI9nBnc04tjmSUWd/oYFZW4aD+b425LMwaF8K/fTlBUab76rArcOyG60ydAC0JHEAmsQptSVZUvd6XwztrTFFfp62/vF+zB69cPZlj4hTcOu1B6o8LD3+xn7bGc+uZydfkIvQLcWHb/GAI9Oj5R1aiovLX2FJ9tTaLGqNhVovyBidGU6Qz8GJ9OtZXaHQBhPi5s/duU+s9VVWXiGxtJK6qyObbJMQE8My2GQT2tbxteeyybJ5cfpKLG2Gj8Eb6ufHPfaHr6uHIyu5SbPtlJZY2xUSCkkSX83R356aFxxKcU8egy88mhEuDr7sj2v03p8EDyYFoxd366m8oaQ31AUtcw7pYRYfznhsHdKhjJKqniaEYpDhrTNueWNJEUuid7r98iGBHa1CebE/nPHyeb3C5Lpm6fP/xlrM0LWmt7a80pPtp0xuwav0aWGBHhw/IH4tp1TOa8svIYi3ckN/t+5/dcsXTOo1P68GSDxmul1XqGvLzW5v2uGhzC/24fbnMcB1KLuH7+DosBlINGYu0TE/jL1wdIyC03OyOjkSUm9vXntVmDmfbuFsrMJMtKEiy9ZxQT+tjuntseskuq+WpXCr8dzqRKb6RfsCdz4iKY0i+w2wQieWU6nv/pCOtO5NT/Hjk7yMyJi+TZ6THN7rIsdF9iN43Q4Yora3h77SmzxxTV1Eb+v6tP8uW80XY93p6kQhZtOcuWhDwUVWVYuA/3jIti+sAgu//IV+uNfLEj2WKyoVFR2Z1UyIms0g4tt51RXMWSnckXdF9bgYhGlvB2dWB2XOP+Olo7ElhlScLd2b4/Gx9tTMTadI7eqHLjJ7soqLBc6t2oqGw6mcdjyw5QaaE7rozE0p0pLQ5Gskuq+feq4+xMLKBarxDm48L9E6O5+pIezbq4Bns588z0GJ6ZHtOi8XRWJVV6bvx4B+nFVY1+j6r1Cou2nCW1sJKP7+j89XiEzkUEI0Kb+e1wFgYrZSyNqsrWhHxyS6sJ9LS+LPLVrhRe+Plo/ZQ3QHxyIXuSCrl3QhTP21nZ8nhWqdl31w3JkmmbZnsHI+U6A5tO5VJSpedIeoldyzLNUfd4vQLc+OTO2CaN7lwdtQwN8+ZwerHFgMagqEy046KvMxhZfyLH5vitBSJ1VGCvlSaARlXlzxM5ZBZXXXAl1hXxaTz7/eFG4z2RXcaT3x1i8fYkvn0grlk1THLLqvnlQCZZJdV4u2rxcnGkrFqPp4sDVwwMtvnz3pkt3ZFMWlGlxbyY1Uez2ZNUyOgOzuERuhYRjAhtJrdMh8ZGr5e686z9cU7MK+fFX44CNJrKr4tzPt2axLhe/na1urd3UbI9Vy9VVeWjjWf4aGMiVRbe/bcGDxctn84ZychIH4uB24OTonnwq/1mj2lkiWBP5ya9WMzRGZR23cGqqnA4vfiCgpHD6cU88/1hy8czSnnp52O8dfMldoxD5cMNZ3hvfQKqqiIh1bcWqAsGX155jDlxkbxwVf/a+i5dy7d706zOvmlkie/j00UwIjRL1/tNELqMAA8nu3ZmBHg0bUXf0Ne7UpGtzHpoZIklduZW9Av2wMVGkqOi0q5Nzd79M4G31p5u00AEwMvZgVFRvlZnkK4YFMIz00x5JHVbg+vO9nNzZOm8UXYtWbg7avG2o7CXhO0tuba+X3W0cvP/nCmKyuPfWq6aWufHA+kU2jGLs3RnCm+vO41RUVFUGvU4qvuXosIXO5J59bfjzR5vZ5Bb1nQreUNGRSWz2HYStCA0JIIRoc1cPTgErcbylUaWYFxvP4JsTFnvTy2yGtQYFZUDafYVvHJz0nLbqHCLfVg0ssSQnl5c0oq1M6wpKNcxf+OZNn8ejSwx3s6cikem9GHNExO5Y3Q4sRE+jO/jz7+vG8TGZybTK8DdrseQZYk5YyNsnqcC43v7Ww1I5o6LtFmQzVEjX1AA+Z/VJ0nKr7R5nqJCvJWlIjDt0nr/zwS7nlfFtPSYVdL1Ltq+bo5Wj2tkyebvtCCcTwQjQpvxcXPkial9zR6TJdM72b9d0c/m4zhpbf+YNifB8K9XxNRfuBpe42TJ1H/kozts7xRpLauOZts1e9RSqqpy99hIu8+PCfbg1ZmD+OEvY/ly3mjuGB2Bm1PzVnUfmNiLICuzXhKmr/fHdw7nykEhgCmJVpbOzcrcFRfBM9NiuH5YT4sBpCzBraPC8HI9NxNTVq3n821JXPXBVsa+vp5bF+5k5aFMDMZz250zi6tYuOWs3a/H1tJdfEoRhZW2Z08a+v1wVrPO7wxuHhFmtamiUVG5fnjP9huQ0C2InBGhTT00uRdOWpkP1idQWn0ucTQ6wJ3/XD+YIaHeNh/j0n6B7EkutJjvoZElpvZvnMegqip7kgpZezyHKr2RmCAPZg3riZeLA84OGr66dzQrD2by9e4UUgsr8XF15IbYUG4bGd7ootbWCsrty6tpaGwvP+4YHc63e9JIKazE3cnU/M7NScuzK0y5D3UBjkaWUFWVt266hJhgjzZ5DZa4OWlZ++REZn20g6SCikbHZAncHLV8etcI3J0c+OiO4TyQXsxPBzIoKK8hxNuZm2JD6R1oGvOrMweRUVzFjsSC+iTmuv9P6hvAP67sX//YmcVV3LxgJxlFVfVLI9ml1ew6W8j3ffz59K4ROGk1/HIw065t0HWGhntbPV7RzB49siRR0qD2Tp3CihoqawwEeDjhpO1cBfgA7h4byff70skr1zUJpE2znf6M6+XfQaMTuipRZ0RoF9V6IzsS8ymtMhDu58qwMG+7t/4VVtQw6c2NpjLh5/20SpiWBH5/bDz9gj3rz7/3i73sTy2u365qVFSctDJv3nQJ11zSozVfWiOKomJUVbtnapbvTeVvPxyxed6nc0bgoJWJ9ncjzNdyn5Sk/Aq+3JnC1oS8+iWQO8dE0DvQvuWVtmBUVFYeymDJ9mSSC0zB07VDezInLoIQL/sTThVFZXNCHst2pXI0s4SSaj0aSaJPoDt3xkVwzZAeaDUysz7azpGMErMzTrIE902I5rkr+/N/vx1n6c5k9FZ2fNWZNiCIhXNGWD0nKb+CS9/aZPfrAXjjxiHcPCIMgM2n83j/z9PsTy0GwM1Jw60jw3nssj6drrFeWmElT39/iD1JhfW3aWSJ64f15NWZgxp1hBYubqLomdA5qSrkHANdGfhGgYd9recPpBZx9+I9lFaZ3n2qUD+d/7/bhnPFoODah1e5fv4ODpu5GNUlSy67b0yrZ/ofTi/mk82JrD2Wg0FRCfNx4a6xkcyOi7D67rasWs+If/2JzkK1VI0kMaGvP0vmjmrV8XZVZ/PKuXnBTgorzpVcr5vdmNDHnyen9uX6j3dYfQxXRw3xL1zOV7tSeP2PEzZnRoI9nVjz5CS7AoKbF+wkPrmoUeKqtXHsfX4qbk5aVsSn8+z3h5DOm6nRSBLRAW6s+MvYTheQAJzOKeNwegkOGom4Xn6donKx0LmIYETofI6sgA3/gqKk2hskiJkB018zBSY2lFbr+TE+nW1n8jEoKrHhPtwyKqzRH8Adifncvmg3AP2lFOZqVjNFcwAtRvYrfVhqnI7aeypf3NN6F/d1x3N48Kt44NzyiFT7n9FRvnxxzyirAcnSncm89MuxJrdLgLODhp8eHls/63MxU1WVae9u4ayFvjuyBGOi/dh1tsBmgPHjQ2MJ9XZhzOvrrZ4b5e/GH49PsLvM/JncMq6fv4OK80rbm/PWTZdwY2woxZU1jPr3emqM5gNSWTL1tGm4FCUIXYW912+RwCq0jz2L4Id5DQIRABVOr4FFU6AoxeJd63g6O3D3uCg+vWskS+aO4tHL+jR5J7b2WA5aWeJqeSe/OT7P9Zqt+EuleEsVTJQPs8Txv4w6+yFVNa2zjbasWs9jyw6YlmcaXHxUTJNAu5MK+XRrkuUHAObERXLX2MgmHXNVYHiENxG+bhc0tvxyHUczSlp1m6VRUdlyOo8vdiSzIj6dIju2u7aWXWcLLZaNh3M7Xux5eyUBgZ7OPHZZH7PHZcDdyZTT0px+N70DPVj5yHiG2siFCvV25obaJM+fDmSgtxCIgOl1LdudavUcQejqRAKr0PaqimDNP8wfU42gKzHNmNywqMVPVVljIJh83nWYj4RCw53FWsn0x/xh7S+UnVoNg69q8fP9fDCTar3RYoEvVYUl25P5y6ReyBa2IOw+W8CXFkq/70ws4InlB1gw23q+QkNncst4/Y+TbDiZW39hHhHpw1+n92NU1IXXT9mZWMBT3x0kq6S6vrGgViNxz7go/nZFP3QGI5U1RrxdHNqkmFd8SmGjCrzmWFruasjdSVs/0/T4ZX3wdHbgg/UJjRo5Dg335vXrh9i9lRlgX3Ihn25LYltCHhU2gt304mp+iM/gxhGhJOaV20xiLtMZKKyoEVtmhW5LBCNC2zuyAoxNdw3UU4xw7Ee46i1wblnTvD6BHoTL65FRLW4/NCDjfmBRqwQjxzNLbV5I8sp1FFXW4Odufpvr++tNtSnMPYKiwppjOZzMLrVrqeZUdhnXf7ydar3SaIZgf0oRty3axed3j2RS3+b3cDmUVsycz3fXBwJ1j20wqizccpbfD2eRWWzaveLpbKrl8tClvVs1z0GSJLvq4w8I8eRUTpnZoEWSYHZcRH2CpSRJ3DM+ijvHRLA3uZBynYFeAW71u3js9dWuFF78+SiyjWCpoed+PMzlA4Jws7PMvEgKFbozsUwjtL3iVJBt/MFVDFCW0+KnuiE2lFj5NBrJ8jtkLQpS+p4WPxfYVwMFwNHCeSWVenYkWs9x0MiS3fUo/rnyKNU1SpMLoqKCoqr8bcWhC6pr8vbaUyi1VUXNySg+t422tNrAp1uTuH7+doqbWXfDmrG9/GwmhjpoJIb09CTI08mUsFx7e11gOrlvAE+aqX3jqJUZ19uf6QODmx2InMkt48Wfj6JCs762ekXly53JXDEo2Gowq5Ekxvbyw9O58yWwCkJrETMjQttz8QHVjvVuF58WP5WvmyPRgV6oeTbKjEut8y7zsv6BVkvRyxLERvjgYeFCUqazMmPU4DHKqm3XsEgtqGTX2UKLx1UVskt1bE3IY3JM4z4+xZU1rIhPZ/uZ/Npy+D7cMjKcAA8nCitq2JqQ36xeM0ZVJTm/knfXneaVmYOacU/LhoZ5MzTM2+K2XTB1Av4uPh1FNQUvVXojhRU1RPq5cduoMC4fEGyzmqs5aYWVfLU7hW21X4ex0X7cOSaCSH83vtqV2qwZkYa+3ZfGw1N6M763PzsT8zl/l7Gpn43Ko1PM57YIQnchghGh7Q26Hta/avm4JEPEeHBvWQv4OkHDrkRdtwuLc/qyFnpf1irPNa6XP/1DPDidYz6xUlHhL5N6sS+5kE2n8tAbFS4J8+byAUE4aGT83Z1wcdBY7UtjMKpE+VtPYj2VXcYbq0/aHK8kQUpB4/Lnu88WcM8Xe6nUnct92ZqQxwcbzvDxHcOJ8ne7oKZ3RlXlu33p/H1G/1ZZYpAkiY/vHM7183eQVWK5P0rdt2FHYgEvXNWfeydEt+h51xzL5pFv9qMo53rNnMoqY/GOZN69ZSj7kgsvuIpuhc5Q/7oe+no/WxPy0dRWoTUYVZwdNLx50xDieommc0L3JoIRoe35RELs3RC/hKYBgmy6Qk55vvWeb9idSJv/C/pK8zMyihHGPNysh1RVlbwyHTVGhSBP5/qiZrIssWTuKGZ/tpvTOaZEREVV65cHnpkew/vrEziUXlJfgM2gqPi7O7JgdiyxEb7cNCKUr3enmrmgqYyVjzFWc4pbyvdD0iSInNBkyufTrWf51+8nrJboPvc6wNPl3K99bmk1c5fsbZKEq6igNyg8+FU8P/5lrM3EUUuq9EaySqqIbkYiqDUhXi7Ehnvz+9Fsu3bNfLwpkbvGRjarXUBDKQUVPPLNfgxGtdHXx6iqoMITyw7g5nThgVZd0TcPZwe+nDeaI+klrD6WRYXOSJ8gd2YO7Yl7M8vwC0JXJH7KhfZx5VugcYR9n4KimGZDVCO4+cOsjyF8TOs9l6sv3PkDfHVD44BE0gAqXPMBhI20++FWHsrkow0JnMopB8DX1ZHZcRH8ZXIvnB00BHk688fjE9lwMpfVR7Op1psuJDcM78m8L/aRmGcqhd4wL6CwooY7P93Dqscn8Phlfdh4MpfMkur6C36klMUih3foI2egSBrkXRLseAsC+sGt34BfL8BUtfNfv58A7Ctr7qSVmdLvXOn8b/akUq03mr2vWvuYP+zP4KrBIfx+JOuCApLWTrzceqbArkAEoKCihmOZpQy9wMaHX+1KQVEs580qQJnuwreJPzKlV6PPB4d6MTi0ZUncgtAViWBEaB8aLVz5Bkx8Bk7+bqrA6tcb+kwzHWtt4WPg8UNw4EtTLRPFAGGjYcQ99Rdye3y08QxvrjnVaDKisLKG/21IYNfZApbOMxU008gSlw8I4vIB5y70vx/O4nRtAHM+RYUao8Jn287yr1mD+enhcby99hQ/7s/A2VDKcsf/w08qBUBWjeeuhvkJsOQqeGgXuHizcEsiGkmyq+InwAMToxvtcNlwItdqEGNUVNYdz2HZfWPYfDqPcp3B7oBEkqB/iGezSr7bw6A0r96GzsoSmC1bTufb/bVtrgEhHsyobRAoCBc7sZtGaF/ugTBiLox7DPpd2TaBSB03fxj/JNyzGu79E6b/u1mByNm8ct5ccwqgyTtxRYU9SYUs251q8f6rjmTZ7G668mAmAP7uTvx71mBuGxXOLZqNBFCCFjMXXdUIZdlw8GsURWVnYoFdF0uNLPGXyb2adFG2VPWzIb1RIdzPlZ8eGsvY83IXHLWyxURhVYWs4ioGv7yGWR9t54f49EZdcy9Uc0uOt6Qvj9IKgYijpukX6NKYAH59ZLzd/ZkEobsTMyOCYMG3e9Ns5kos3ZnC3ePMl7Ivq9bbXDqpbFAc64MNCSzZkczvjjuwXlBDhSPfw+iH7FquGBDiwRf3jCbAo2mdk+ERPlarmmpkiWG13WqjA9z5ct5o0osqSc6vxN1ZS5S/G498vZ+tZ/LPNSVUVVTVtBOkqNK0W+hwejFPf1/Mb4czWThnxAXncAD08HYmKb/C9omYtvpaqu9ij9HRvhbLz9vrrZsuwdPZgaOZJbg7a5k1tCfero4X/HiC0B2JmRFBsOCMlYs0mMKFpIIKVh7M4KtdKaZtsQ3Ojw5wt7qNVAIi/EwdeMt1BhZsPguAFxW2k1GripFliSGhXlbPlSS4fnio2UAEYPaYCKuv0aio3D22cbAV6uPK+D7+DA3zxsvFgaXzRvHTQ2O5e2wk1w/viZujpnZL6jl1T7HpdB4LNifaeHHWRfu7Nymdb8nUBstmF2JOXGSLZ0eGhHozuV8gj0zpw91jo0QgIghmiGBEuDhVl8C+z2Hti7D1bSg82+QUNyetzaBAVeGxbw/yws9HuePT3Yx/YwPbEvIBuG1UuM131HPiIgHYejqvfnvvGbUHBtXKr6akAX/Tcsu8CdEWZ18kTAmrN8aGWnyo/iGe/POaAQCNAidN7fLBY5f1sbmtVJIkhoX78MLVA7g0JpBynfXy+Iu3J7douWbawCC7thrLEjw0qbfF48cyS3j6u4MMfXUtg19ew+zPdrPhZA4Ne4f2DfLgP9cNvqBxamSJ8b39ibSxLVsQBLFMI1yM9n8Jq54Bg85Uc0RVTHVQht4BV78HWtM71xmDgvn1UGazHjqrpJq7F+9hwexYAj2cmRMXwdKdKU1mCmQJRkT6cuvIUDi7iV77V/B/2iyOqxH8YJzIZM1hy0+iGk2JuMA1Q0LYl1zI0p0pjZaU6mpVfHxnrM134nPHRdEv2JNPt51lx5kCFFVlZKQv94yPbLTzxh4H0orR2iiPX1BRQ1ZJNWG+rs167DrjevkzqIcnJ7LNl3wH07ust2++xOLOlF8PZfL4tweQpHNfsx2JBWxNyOf+idE8N6NffT7HwJ7N390iSxDo4cQbNw6pv81gVPhuXzpLdiRxJrccJ62GKwYFc9+EaAb0EF2ZhYubCEaEi8uJ32DlI+c+VxpUQD20zBScXPsBAJcPCKJPoHuzcgZUFQyqyrwv9tXf1sPbGUVRyS7VAeDl4sDsMRE8MsoDp88uhezD9Ja0RGlUtBipwIm9xr7EyqebzMyoSEgDrzPtQsI0K/HKtQOZ1DeAL3Ykczi9BEetzPSBwdw1NtLu5M1wP1cifN044FhMWbWezOIqkvMrqdYbG3WtVVWV/alF9ZVeR0X5MiLCp/7CLduZkHkhVVDryLLE4rmjmLt4D0czS9HKUqM8lSn9A3n5moEWg52skiqeXH7QNKPUYBak7nu8cMtZRkb61u+MKmpmSXtfVwduGx3OPeOi6vNV9EaFB76MZ+PJXMAUmFbpjaw8lMmvhzJZMDuWy/q3bElJELoySVXbaN9aKyotLcXLy4uSkhI8PcU7COECqSrMj4O8k1hOEJXgyaPgZVrayC2tZt4X+ziS0bhoWXPUdbitCxp6eLvgKCnwyXgoOG0qwtaAUZVQkPjGOJWrNLvwr93iWyR54zPlcRj3OMitV7vjWGYJty7cRWWNsf6CXBcqXBLmzdf3jsbNSUt6USUPfhnP0cxS0zKOZLqA9w/xYOHsEYT5urL9TD53fLrb8tcCCPd1ZdOzk1u8kySzuIqFmxM5nFGCi4OGSTEB3DwizOZM0DvrTvPhhgSLy1saCUZF+bHsflPtm7N55Ux5e7PN8TjIEkvuGcm43k0rCX+69Sz//v2E2Z86CXB20LDn+csstg0QhK7K3uu3mBlpb4VnIX2fqehX5HjwCO7oEV08Cs9C3gnr50gSnPgVxvwFgEBPZ1Y+Mo49SYVsOJWL3qCSXlTJ+hM5TfqIWFL3jv2/q08yMtIHB40Ep9ZYHItGUlFUCW+pnDG6D4mU83B20PD2/bPw6dHy/j0NKYrKg1/GU6kzNtoiXPevw+nFvL32NE9e3odbFuwiu9RUhr2uAinA6Zxybl6wk9VPTGRsLz9igtw5k2d+NkkFHpjUq0WBSI1B4eWVx/h2byqqSn1fmFM5ZcQEe1rtSqyqKhtO5FivraLCgbSi+s+jA9wZHu7NwbRii/dz0sqsfGQcMWY6K6uqyuLtyZbzaIBqvZGfDmTU5xB1BZU1Bn49lMmh9BIcNTKT+gYwsW9Ai2a9hIuXCEbaS1kO/PIQnPnz3G2SBgbfCFe9A06tUy67WYwGyDwA+grwjwHPbl6AqbrE9jmSbCrI1vAmSWJ0tB+jo02JnN/uSWXd8eZ1GFYxbeO98oNt9Al0Z4n3cnpKGlP+hxkOksIMeQ9PSY8QM3A4T03rS69WKqne0JaEPNKKqiweV1T4dm8qwV5OZDbozNuQUVHJLqnm+31p3Dshms/uHsltC3eRVlRVPytUl89yz7gobhsV1qIx/+OnI/ywP71+haUu6CmoqGHekr1892Acw8ObBm1J+RXc+8Xe+oq41mjOC5ZenTmIGz7egd6oNApI6k57/9ZhZgMRgDKdgYxiy19jMAVUh9Pt+PnsJLafyefBr+IpqzbUzxgu2ZFMrwA3lswddcH5QMLFS+ymaQ/VJbD4Ckjc2Ph21QhHVsDXN5kCg/aiqrB7IbzTHz6bCktnmv697DYoTmu/cbQ3n0jbyxuKwWZhtCuHhOCovfBfnTO55RxOykS10cnYUTJw4MUpfHTH8DYJRAAOp5fYfCdbWWNkRXy61XNU4Mf9GYBp6+/aJyfxxg1DGN/bn6Gh3twwvCc/PTSWl64Z0KJZkbN55ayITzdbX0VVTUXK3lt3usmxkko9tyzYSfJ5TQLN0cgSE8+bXRnU04sf/jKWkZG+jW6PCfJg8d0juWKQ5RlOB9n2z4oELfqZak9ncsuYu3gv5TrT3yyDotYvXSYXVHL7ol1Ut6DqrXBxEjMj7SH+CyhKalrGE0wBSeoOOL0a+l/dPuPZ+BpseeP8gUDCGsjYDw9s7p7LR66+0H8mnPilSZ6GiQTOXtDP+vfB09mBf1zZn3+uPHZBw1CB02ool6vxaCUruSs+EXi6tm4p9fNpNRL2pI2VVxtsbqctqTqX6OniqOHmkWHcPLJlsyDn+/VQltVCdIoKWxPyKa6saZQ7snxfKnnlOruKxCmKarbT76CeXix/II60wkoyi6vwc3ekV4C7zeDKxVHDyEgf4lOKLC7zGBSVKTGBtgfXCXy6Nak+Yfh8RkUlraiKP45mcd0wy1vKBeF8XSMU7+oOfGk+EKkjaeDg1+0zluJU2PKm+WOKESryTHU3uqtp/weu/k1nSKTa7sGz5oPWdsXOu8ZG8uaNQwj2vLDqnssNlyJbubyrQMnguVTVtO07zEl9A2xWiQ3ydKJvkEeTpYuGNBJE+jWup6E3KtQYWl7+vaHiqhrbtV+A0qrGM40/H8i0KxCRJfjvDUOIjTi3zKOqKgfTivnf+gTe+/M0Z/MrGBnpS+9AD7tneR6a3NtywqwsEeXnyqX9ukYwsspGw0RZgj+OZLfjiITuQMyMtIfyXOvHVSOUNq+exQU7uOxcx1xLYznwFUx/DTTdMLPfKxTu2wgbXoUjP5zb2hs2Gi59HqIm2P1QN40I4/rhoRxMK6a0Wk9WcRXP/3TUroJcmfjzqmE2LzssxahKaBrMkBhVib1qP+asjURdv4aZQ3vw+GV922QdfmAPL+Ki/diTXGjxAvPAxF6E+riw6XSexccxqnDHmAgA1h7LZuGWs+xLMSWBDgjx5N4JUVw3rGeLd9CE+7ra3M3kqJHx92i8o6a0Wm/h7HM8nLX89uh4IhoEVbml1TzwVTwHUovRyBISplmMcF9XFsyOpX+Ifbv7Lu0XyMvXDOCV344jY9qKLEummZye3i4snTe6yyR+VuutB5iKChW6dlx2FroFEYy0B4/g2uRJS6UyNfVbSdtcSWrtXlMr5+grTeN182+fMbU3r55w3QKY8SaU54CTJ3hcWI0HjSw1ehft5qTl+Z+O1q+nW7PEeAXpagAPaX9huHQGgHzVky8M01hovJoaHMCo8tOBTNafyOWnh8a1STXPj+4YzuzPdnMss7T+Alm3FHL32EjmjotEVWH6wCDWHstp8qMjSXBpTCDTBwbzwfoE3lnXuD7KyexSnvruEAdSi3l15sAWBSSzhvbktVUn0FvYyqSRJWYN64GrY+M/bb0C3MkqrrbYVFAjS4yI9GkUiOgMRm5btKs+z6RhsJZRVMmtC3ex5omJBHvZ17jv7nFRTOkXxLK9qZzKLsPFQcO0gUFcMSgYJ23rbdVua70D3TmRXWpxpkkjS/SzM0gThDqizkh72Dkf1vwDqxHAbcsh5oq2H8u6l2DnR6ZETUtkLfwj067lCqGpqhojq49l8eaaU2QVV9s1U+JJOU4YKMATxczqqSTBqEhflj8Q1/oDxlQd9M8TOaw8lElxpZ4ofzduHRneqIKp3qjw0cYzLN6eTEmVaabB01nL3WMjefSyPpzMKuOaD7dZfZ7P7x7R7Kqu51uyPYmXfz3epKqtRpbwdXNk5SPjCPFqnGuz9lg2938Zb/VxP50zolEvm58OpPPk8kMWz9dIpm3Kf72i34W8jC7r2z2p/P3HI1bP+fOpSS3qlix0H/Zev0Uw0h505bBoChScabo8IskQNRHu/LFVC1lZlH0UPhln+bisgQHXwY2ftf1Y7JRbWs2ZvHJcHDQM7umFtgUdX83ZlpDPkh3JHE4vxkEjc/mAIO4aG0lUC2chdp0t4PZFu2hQkqPFnp0Ww8NTLPdbaQ86g5HE3ApUVHoHute/q//rikP8sD/Dagfg8b39+eKeUS0ewy8HM3hn7WlSCk2zFrIE0wYG8+LVA+jp3TTpV1FUHl12gFVHsprO7ABXDQnhg1uHITeY0rl78R62nM6zmlMT4uXMzucua/Hr6UoMRoX7a6vJnt/iQFHhr1fE8NDkjv0ZFToPEYx0NhUF8NsTpoJadb/CGkcYeidc8Ro4tO2uiUZWzINjP5p6sjQkaUxjun8TBHb8u72skipeXnmMdcfPFakKcHfi0ct6M3tMRIvzD1RV5b+rT/HJ5sQmfV00ksSiu0ZYLaBlj3XHc3h2xSGKK/WmsuWK2uLAZNGcEfWlyluqtFpPbqkOLxcHi5197TXj/S2cyCqzeo6/uyP7Xri8Rc9TR1VVTueUU64zEOHnir+79fEbjAoLtpzl821JFFSYdv74uTlyz/goHpzUq0nOxqyPtnMwrdjqY7o7aTj6SjvMaHYyeqPC4u1JLN6eTFaJqRDekFAv/jKpFzMGd/N6RUKziGCksyrJgMz9pgt/+BjTdtP2ZqiB1X8zbTlWjbUJrQr4RMENn0LoiPYf03lyy6q59n/bySvXmX2n/diU3jw1LaZFz7H6aDYPfmV+6l4CnBxkdv79MnzcWtbyXWcwsnxvGst2p3Iqp8zm7hVrJAkGhnjy22N2JNqW55kK2rkHg0PjvIa0wkreWnuK3w9n1SeEjon245lpfRkReWE/k5Pf3GizjkdPbxe2/32KxePxKUV8vi2JnYkFqKiM7eXPPeMjiY1ovd8TvVEhtbASVVUprzZQVKknwMOJgT08GwW4z3x/kJ8OZFqc6ZEkU3Lu7/Z8L7opRVEpqqxBq5HxcumGCe9Ci4ly8J2VV0/TR0dRFDj9B+QngIu36bbgITB8Dgy87lxJyQ42f2OixUAE4H8bznDTiLAW7TD5bNvZ+qnl86mAzqDwfXwa90+0XgTNltxSHe//mUBxld7sc2lk07fFnhhFVeFoZilZJVVN8iLqnfkTNv0H0veaPndwheF3weS/gYsPqQWVzJq/nZIqfaOv756kAm5ZuItP7xrBpc2seTF/0xmbgYhGlphhpTjYFzuS+efKY41mqVYfy+b3I1m8fM0A7h4X1awxWeKgkUkvquLVX481qsbaJ9Cdf14zkPF9TInbt40KZ0V8hsXHUVW4s3YH0cVKlqX6ZoCC0BKizsjFRDHCj/fBd3MgZQdUFpg+krbAr4+ZZmw6AVOr9TQbtQwkfthvvSqoNabus417jThRw3R5L7dp1jNZPohGNbI3ucjyg9jpX78fp/i8C39DGlniycv7cnOs/TuqKi3VHzm4DL66ETIazPjoK2HPQvhsGlQV8epvx5sEImAKyhRV5dnvD6E32l8fJK2wkjdXn7J6jgRoZcli75WjGSX1ReQajqvu3y//epwjrVQufcPJHOYu3sPZ/MZl4c/kljPn891sqd3CPDzchztGh5t9DFmC0VG+3DC8/Qt75ZXpWHc8hz+P51BQrmv35xeEtiBmRi4mexbC0R9M/26YSKsaoaYSvrkVnjwG2pYtS7RUabXB8sW2jgQZVnqq2EOWoO5Z7tSs41ntcrykSlNjOwnyVC9+rHgcuPBlq7oLh7WlmRqDipujlteuH0xKYSW7kwqtPqaLg4Ye5mZFqorg18cBtWmRPdUIBYlU/vkf1p+caHFbpqpCfnkNG0/mMm2gfVV4l+9NQ5Yki9tm6yyeO5JwP/MzWUt3JlutrApw79K9rHliIpIkUVRRg6+7I57N7HKrKCov/nzMbFJx3ecv/nKUTc+Yugr/38xBRAe4s3BzIjllpgu/h5OW20eHM6VfIFsT8gj2cmZAiGeLc5hsKa3W89Ivx/j10LmlI60scd2wnrx87UDcnMSfc6HrEj+9FwtFMW3ptbQYoBqhIhdO/gqDbmjXoZ3PzUmDVpZsFrfybUEuhyRJjIn2Y0diAbdJa/mXw+IGx0z/96OE+3P+D072g35XXdDzpBVV2pUj8uWuZOZNiGLJ3FHE/mudxWBMI0vcNCIUF0czO68Ofw/Gmqa311GNOB3+Eq06Fr2VX32NJJGUb7uZXJ2k/AoUG4tMKjDCSt7HniTLRdfq5JTqmPjGRspqS9PLEkwfGMzT02Ls3ka6J7nQatM6VYWUgkqeXXGYKH83pg0IYt74KO4eG0lSfjkGReVMTjlvrDnFgi1n6+8XE+TOKzMHMaa2mWJrq9YbuWPRbo5lljT6eTIoKj/sT+dsfgXf3j8Gh1beaSYI7UX85F4sKvKgxEYTPFkLqbvbZzxWOGk1XDk4xGpFSqOicu3QHjYfq7iyhlPZZeSUVjc5dt+EaLSKjr9qvzV73/qnX/uC9XL+DcZ0fj64u53vVpMLKskuqcbFUcN7twxFlkx1LBrSSBDuWM5Tx26CfwXCgklw4Ov6Pjtq/mmMkvXt4Rp9BX5YX+5QVBV3Z/vfp7g5aayWigdw0Eg4nP+CGo7LzuqjpQ165CgqrD2ew8wPt3E8s9Su+2eV2Deb9uP+dN5ee4rL393CvV/spUpvpHegB2dyy3l02QHSChvnxyTklnPnp7vZdbbArsdvrl8OZnAko8RsYKuopsTfVUey2uS5BaE9iGDkYiHZ+a2297w29uiU3jhoJLN9SGQJrh4SwsAeXk0P1kopqOChr+MZ/n/rmP7eFka/tp6bPtnBnsRcKDwLRSlM7O3H/0bm4ylZvkBJqKbzLeTTKIrKd3vTuOK9LfT6xyp6/+MP5i7eU39R8nZxwNPOC3tm7YVy2sBgvn8wjvF9Aqh7+e4OcI/mD36Wnsa7JhMMOpSsQ6i/PMSZD68nq6iM4wVGVMV2roefjw/WLv2yLDVr6/CVg0OszmJpZImrh/SwuoxxaUygzYDGHKOiUq038o+frBfhquPrZl+ypSl/xvTvjSdzefDLeGoMRv75yzFUms4v1uXbvLzymF2NB5vr271pVnPLZQm+29uNO24L3Z5YprlYuPmDf1/TLhpLU+qKwVSAra3lnYLdn8DJ303LCj1iYfQD0Ofy+jWSPkEeLLtvDI9/e5DUwsr6XS+yBDePCOOVmQMtPnxSfgWzPtpOuc5Qf0HRYmBM+uf0WroapNpaGN7hTAsdiQpWL84AVOQ3uUlRVJ787iC/HMysv1AYVZUtCflsPJXHEG8dakkGGtUfsL0l3bdBl9nYCF++uGcU5ToDlWUl+CwchkNNaaMZmrpGe9EFm/jXW89x0nEg30iWgxGDKlMTGscDo2J5bNkBs+dIwOzREQR62FfiHGBinwAuCfXiaGZpk6UW0wyPxAOTmnbBbWh2XARf7EzGeAF9AY0qHEwr5lR2GTHBHlbPHdvLDz83x/o6I/Y+/rYzpsJ41u6nqHAyu4wTWWUM6NG6JQiyiqutTs4pKlaXnwShsxPByMVCkmDsY7DyEQvHNeAdBn2nt+04Tq2G5XcC6rmS9Gc3QuKfMOYhU4O+2iv7sHAfNj0zmZ1nCzidY+rlcWm/QII8rV8oX/31GOXVhvqESg1GPnF4lynyQeQGDekoToPiVNuBCIBn0+3YPx/M4JeDpgaHDS8UpguyytFiB7Y4vUcIhaxWRvKa/nYyaFpETZZMDevM9Z1xd9LifmQl1FhfWpkkHeLzqhlschjCePko2vOCEkU1BS8bAu/m2kt6UFKl5/9+PY7eqKCRJZTalvC3jQrnhav72/EFaTB+WWLJ3FH85et4dp0tbNRQzsvFgY/uGE6/YOsXZ1dHLSMifNh51nryrjWJeeU2gxEHjcxzV/bnme8tl3k3RytLbDxpuVFgQ9mlVXYHI4qisiUhj58OZJBXpqOHtws3xYYyKsq30UxSkKcTOaWWWwvIEjZ/LwShMxPByMVk2J2QewJ2fWTKD1EM1M8JuAXAHSvatiR9RT58P6f2eRv8Wa3b2bNrvql77sBZ9YdkWWJcb3/G9bbetO9oRglf705hf6rpHXJD18g7maoxNxNgx3S6JEPgQAge1OTQku3JFuuUgISEwjLDFJ51+I7p8l5GO51gpu7/GgUkdZebv8+wUvE2c3+D71dTsgS+suk1P6x/nP85fMAUzSEMqoyChANGqnHib8YHiPEYBsDsMRFcO6QHKw9nkl5YiberI1cPCbngui0+bo58e38ch9OLWX8iF51BYUAPT6YPDLLZBK6gXMesj7aRWdI0r6c5zs/PySyuYkdiAYqiMizcmz5BpkDlxthQFEXl36tO1PfYsUVVVZtJunX87FwKqqwxcO8X+9iRWICmdjeSRpZYEZ/OVYNDeO/WofUJqTeNCOOQla3NimqaMRSErkoEIxcTSTKVnh8wE/Z9BjnHwMnDVOzsklvB2XIORqs48CUY9VjtXrxrfqNgxB4fbkjgrbWnz9saqqJBIYJs5mjXYlQlNJJ9F5MGAzIFIzP+i8GoUKU34uaore9fciK71OpOGSMajqqRAGglBW+1nGcdlvOE/tzslJ+7I/+5foj1YEu2/WsaKWUDKhW4cI/+bww0JDNdswc3dJxRe7DSOJYKXLiuwTt2L1cHZrdy0a4hod4MCfVu1n3+tyGBjOKWBSKezlpGR5t265RV6/n7j0dMfWgafH/GRPvy7i1DCfFy4eaRYcwc1oMXfz7Kd/ts16tRgdGRvpzILKW02nxQKAHhfq4MCbXv9+j5n47W5xbVzeLV/fyuOppF6FoXnpthmqW6YXgoS3cmk5hX0WQpTCNJ9Avx4OpLRBl2oesSwcjFKHy06aO9pe22vitFNUL6PuoLfdhhzbFs3lp7GuC8P9Km+5fixlApsfHyjL0kiYwZS3h7txu/LlqN3qji4azltlHhPDipFw4aGb2VJAcJBSfOvfPWSgpXybt5ibncMXkwIyJ8mdQ3wHbjv95TYd/nFg8bVYkEtScNM1+OqZEcM0TWfy5LEOLpzKS+zaus2tb0RoVle1qeePnYZX1w0mo4mlHM3Yv3kl/eNLdjb3IRN368k1WPTcDL1aF+xsaebeQqcOuocAI9nXnh56MWz3vhqgF21RvJLqnml4MZFoNZVYWlO1J4bEof3Jy0uDhq+Pb+OP664hDrT5xrUCdJMG1gEP+5fojNGShB6MxEMCK0OqOisuFkLnuTC5Ew9TyZ2DcAjY1tp4BpJqIZuyoWbrFc0t2Ihny8qcYRV5pfqVJVVT77dQO/GC6vD3TKqg18tjWJVUeyGN/bn/Uncy3Wx1CRmCo33oXjIBkJlgqZPSaSHma6y5rVZzr4RqMUpiDTNPjRSCoL9NcA4KiVMRiVRl8PjSzhqJH56I7hTbbQKorKtjP5HMkowVEjMzkmoH45oz0UV+rRGeyv9mrOPeMimTc+isXbk3jl1+MWzzMqKlklVXy9J6W+q6yLg30X8Kem9qWHtwt3jolAkuC/f5xsNEPi7+7IqzMH2b0LafuZfJv1Z6r0RvanFjGhj2lZz9fNkU/vGklaYSX7UgqRkBgZ5Wu2S7EgdDUiGBFa1ansMu5ZspeM4iq0tRe+BVvOEuHryooRcQSc/N3ynSUNRE+2+7lqDArxKU3LtYdKeVwqH8CZGk6roexU+nOZ5mAzX0nt1Lx6hM+VqY1uN6oqWSXVRPm5oqoqEk0XnjQY8aOUazQ7mzxuj6DgpoGIrgwSN4CuHPz7QOjIc0GZRgt3/oC65BooTa9fcjKoMlpJ4T/6W1mjjATg+Sv7UVih5+vdKeSX1+CklZk5tAf3T+zVpDDY0YwSHvp6P6mFlWhkCVU15VFMjgng/VuHtXrjs2q9qWHg17tTyCyuwtvVkVlDL7xPkwRcPaQHL10zkN1nC6wGInUU1bQFti4YmTYwmC92pli9T0yQO49e1qf+8ztGR3DD8FA2n86joLyGEG9nJvT2tz3D1YDBji3YAAZj04glzNe1RT2ZBKEzEsGI0GoKynXcunBn/TvGhlPf6cVV3Lgjgo3OXsi6UlOX4POpRvCJhO/nmmZIoiebqsE6mv/Dq5y35OOMjv84LOJaeQemCiESGkmhSDXtUmnG6g91d5Axf9EwKio7zhby+vWDeeHno7UFz0DCiIKGAIr5yvF1XKRzywVGVeKg2pu7rhjb4EUosPm/sP19MDTYmukfA7M+htBY0+e+0Wge3cf+Pz6nYO8PuErVnFTC+cY4hSRMF/RrhoQwe0wkcm2vG53BiKNGNrtskFZYya0Ld1FVW+m14ezO1tP5zF28hxUPjq3Pj2mpsmo9ty3axbEMU3EyFSjXVTF/0xmzwZwlEqbvoaLCVUNCeOvmIQB8ui3JZjn5Og2XcMb28mNwTy+OZzXdllzn5WubJi87O2iYbme5fHPsyauRJVp9i7AgdFYiGBFazbI9qZRY6EwbpmZyU/VmMgL7Eabf3ziRVdaYqojKjrD309qIQYKjK+DPl+HOH6DH0CaP6eygISbYg9M5ZaiqyocOH3CpfLC2UNq50lRemEqb11+T6xJCLexOqSNhqk/iRTklNJ5V8KGU26QNXLvtBW7yKiPbIZQ/nGdwpNyLiaUruVLejbN0Ll9EUU2PVzH271zarzZvw6g3bXM+vbrpkxckwJIr4d7153byOLgw/NqHOTD0dhZuOcv6E7kYUIgJ9mDuuChuHB7aKHiwlkPw6dazVOmNZvvJGGubCG4+nXdurBYcTi/mpwMZFJTX0MPbhRtjQ82WZv/37yc4kVlqtliYPeHO5JgABvf0Ir9ch7erI9de0oP+Iecu1DvO5NsViEhAiNe5LbCSJLF47kjmLt7LkYwStLKEiinQ1coSb9w4hLherV/ivX+IJ7ERPhxMKzY7bo0sMX1gkNiuK1w0JLUtygW2stLSUry8vCgpKcHTU7xT6Kwuf2czCbnl592q8nfttzyo/RWDKiPVFsFCNYKLD3hHQECMqYGfosD5MxGSBpw84bH94Nq0t8l3+9L464rDDJMS+Mnpn7YHGTIUoieZHlfjYJqVsEBVTaPJxYeba14iTTXlA0RKWXzv+Cq+lCJLtcs0koykKpSFTsIjMBIOfGm6vySjUQ3otJ4oV7+Py9DrTQ9eXQqLr4QcK5VDJQ30vQJu+8bKGFWrCZOnskv57+qTHM8sRSPLjO/jz3Mz+jGhtseLJRpZ4tpLevDuLUPNHtcZjDy5/CCrjmSjra1TIkmmmYm5YyN58eoB9YFRSaWeEf9eh97MkoMtEuDp4sC6JycSaOXCPOCl1babK9Y+3kvXDGDuuKhGtyuKyo7EAtYez6aqxkhMsAc3DA/FpwX9j2xJK6zkho93kF+uaxTAyxJE+Lmx4sE4/Nzt2yYsCJ2VvdfvC5oZmT9/Pm+++SZZWVkMHDiQ9957jwkTJpg9Nysri6effpr4+HgSEhJ47LHHeO+99y7kaYVOrlzX9OI2V7OaB7W/ApwrxFX3h7e6BAL7m4INVaVJIAKmoEVXDAe/hrGPNjl8U2woB1KKiDmwBL2qwUGycUEa8xBcckvtY6umXI1d88HMYoEkgQYIUEv4xOE9rqp5DYCFDu/gQ1mjrcJS7bKTW9oWFmcFEHHlZqYYd6DRlYJvNE79rgaHBhfTnx6wHojUvfbTq6CqGFy8zZ5iLRD5v9+O89m2pEa3Ld+bZlfZcKOiUlRpudroKyuPs/poNtBgOa72fc3iHckEejrzl8m9ANMWaHsCkR5ezmSWVCNJpu+GokK4rysL54ywGogAjIryZWuC9dkRSYK+gR5m63HIssT4Pv6M72O9no29DEaFjafyahODJSbHBDKoZ+Mtv2G+rqx6fAJf7Ehm+d40iiprCPJw5rbR4cyOi2h2R2JB6MqaHYwsX76cJ554gvnz5zNu3DgWLFjAjBkzOH78OOHh4U3O1+l0BAQE8Pzzz/Puu++2yqAFK6qKYe8iiP8CyrJNswnD7oTRfwH3ptU/W1PfIA9yS3X1U/9aDDys/cXyHVQFDn0L7oHnCp+ZPU+Fk7+ZDUYkSeK16weTXeKEnGrHO+/eDZJRJclU8TViLHx3l8UxaCWFgVIKw6UEnKQa+soZFh9ellSuN6xi5A/XENd3GB/fGYur43m/ZqWZcGqV7bGC6bVXFlgMRiz5cX96k0Ck/iHtuL9GloiwkCSZV6Zj+b40q7tBPtmcyD3jI3HSauzK05GAByf3YmAPL7Yl5GNUVWIjfJjQ29+uvJV546PYdMp6hdRpA4J444ZLcLOzeeGFOphWzINfxpNdWl2/7PPW2tPERfsx/47hjWZb/N2deHpaDE9Pi2nTMQlCZ9fsrmjvvPMO8+bN495776V///689957hIWF8fHHH5s9PzIykvfff585c+bg5dXGRbUuduW5sOhS2PiaqUOvoofyHNj2HnwyHgrNX5xayx2jwxvlIAySkvGX7OimWnP+0o4ZestFsSRJIiSir+1Ga06epqUhgJSd8O0d8FoIrJhrPRgCjKrMLM025mvftzlUL6mSaCmLzafzeeDL+KaN0w58ZfMx6kkaU1+hZnprzalm36cho6Jy66imby4ANp/Os5mfUVKl50BqMeU6A//946TN51OBCX0CiI3w4fGpfXjq8r5M6htgdwLthD4BPDOtL9C4A7AsmT5/+6ZLWDB7BF6ubTvbkJxfwe2LdpFbZvp5NShq/ddqT3Ihsz/fjcHYsq3MgtAdNSsYqampIT4+nmnTpjW6fdq0aezYsaPVBqXT6SgtLW30Idjh96ehKKXpThXVCBV58OP9bfr0U/sHcf1gP26Qt7DM4f/4yMH2hRtJAo8epouuJbIGegyz/jjD7jS/Q6f+eTQwYi7IMsQvgcUzTImj+qraZFpbVG7TbMBLrrDj3HOzD1sT8tmddK7filFRSU46Y9djIMmmarnNrIxbUqVvcWn1e8ZFNUoQbahab183O51B4a8rDnMgtdjmuRoJeni3LFnzkSl9+P7BOGYMCibEy5kwHxfmxEWy7smJ3BAb2qLHtteirWfRGRTzdW8UlaMZpWy0MYMjCBejZs1X5ufnYzQaCQpqXNgnKCiI7OzsVhvU66+/ziuvvNJqj9fqSrOgLBNc/cGndctp26WysLalvQQ9Y01T+KVZpqUMSxdk1Qjpe0wl4IMsd7xtCbm6iLdLn0FyPIIRCQ2q7e20qmIqRb/h/yyfoxhh5DzrT+4bDROehq1vmRmYBrzCYNwTUJAIvz1Bo0Z9dtBIptdiT/Ser3qSqPYwPbUEPx/IYEy0Hz/uT+fNNafQlMaxxXE5Nt/0a53g0n/YPcY69uwqsSTQw4m/TO7F3WMjAVOSZXGlnmAvZwI8TMmUloKUhiQJ3J00/HEky65lIaMKuaW6FtfPGBnpy8hIXwxGhZ1nCygoryG3TEekn1urbVO2ZuXBTKtff40Evx3OtLs4miBcLC5o8fT8pDlbGf3N9dxzz/HUU0/Vf15aWkpYWCdoApVzHNa+CInrqX/vGzoCpr4CkePb/vl1ZbD6OVOehVL7bl7jCMPvgqiJ1mcG6mTEt1kwwi8PI+WaCk9par8+Nn8sXHxh3JOmYGrfp6bZgLrXIWlMQdTUlyF4sO3nn/ICeATDlregvDY4ljUw4Dq44nVT/szWtzGFFPb3qjeoMhoUtHaUlFdV+NwwA0Ptr5aiQmFFDV/uTObFX47VnhXIBmUYk+VDTbrrNnL3H6YCaM3k7eKAi4NMld7+5QAJuHlkGK9dNxiNLLHjTD5vrDnFwbRi03EJpvQL5B9X9md4uDd9g9xJzC3HXF6qRpaYHBPAiayypgetaK1cjp8OpPPv3080qifS09uFV2cO5LL+bRsElNdYD3CNKpTa2ZxPEC4mzVqm8ff3R6PRNJkFyc3NbTJb0hJOTk54eno2+uhw2Ufg06mmdvcN3+tl7IcvroWEdW37/AYdfHkdHPzmXCACYKwxNb2zskW1EU0bbVUsTIJTf9jMvWiiqtA0m3HVW3D9IggeUntAgvA4uP07GP+kfY8lSTDqPnjqONy/Ce5ZA08nwI2fmZJkAdL22BxjXYqHokqoKmSpvjaDqrr7rFTG8onxmvrbNbJEgIcT//r9RKPz/6p/gGQ1GEWVzCeCjphnmsn65RH4/Rk482ft1mfbZFmymO9hcfyY3rHXGBT+PJ7DnZ/t5nB6caPXt+lUHjM/3E5iXjnv3TIMF0dtk/LyEuDhpOXlawagNyp2FRGRgBGRPvi2wjbaFfHpPLn8UJPeNJnFVdy7dB8bT+a2+DnMqdYb+XF/epPOwefTyBKR/m5tMgZB6MqaFYw4OjoSGxvLunWNL7zr1q1j7NixFu7VTax6FgzVTS9kqmL6WPmIaTmhrRz5HtL3mr+QqgrkHLUdaDSz3HqzpOzA/jqa59nyJlQVwZCb4YHN8GIBvFQAc3+HvtOb/3h1OSbhY8DtvIJVWtt1GyQJUpUA9igxPGe4lxtqXsag2v5V+dowhcf1D6M0+LUyKir+7o7UnNd/pRBPrq35F68Y5nBSDSdf9cRAbd5M0GDTVuY//r+98w6Pqtj7+Oec3U1CQhJqSEIg9Bp6lW4DpFpBVIp6FVRU4KpYX73ca0PBhojYsYEFEAtNRYr0EkBAQEpogVACIT27Z94/ZjfJJltTCAnzeZ4l2TlzzpnfJmS+Z+ZXJsP2r2HLJ/DFLfB+Lxkh5QNPD2hOjJ8+GGlZNjYnnGPy9ztkjpQCP06bIcjIsfHCot20iA7jp4e70yyycB2b8xk5jP18C3WrBXusi+hAAI9c4/8KUEGyrQYv/uw6Jbyw/zPlp92FHYqLyap9p+ny0q9M+mY7aS7C2/NjMwS3d/JPKCoUVwJ+R9NMmjSJDz/8kI8//pg9e/YwceJEjhw5wrhx4wC5xTJq1Cinc+Lj44mPjyc1NZXTp08THx/P7t3e60hcNpw9AEfWeXiiFnKSOLCi9Maw5VO5heEOzSS3KNw9imq6nOxDi57C2jPF+ANvWGHX/Lz3Gedg62ewejrs/gGs7vNd+E2Dq70PR8ByoyO35/wfc23XcFarzjKjE4YHJ1srJqZZh1Hw8x91VSxCQJBu5Xnzp+wMvIdDgXewP/AuPrS8xnqjOQOyX6Fj1iz2Rg6Gq5+BpN1yJczh1+LwbTm9Gz6/2SfRazHprHisD01rFc6G6oktCcmcTct2+9O02QvrHT+fwcbDyew64exc7jhv76lUZq08QJNalQutnuRH12DabW3o1aT4Yeer9p0mOd39FogADp1JY+fxC8W+l4O/jl/gns825ZZAcOcu4vgEHujTkKYuBJxCcaXj9ybt8OHDOXv2LFOmTCExMZG4uDh++eUXYmOlI2diYiJHjhxxOqddu7xIiC1btvDVV18RGxvL4cOHizf6S0WyLyGxmo/9isiFY559QoQNdAu0vElO7LpZTmIOv4v6vWDg9NIbX52uRT9XN0HqaTnJLnsONs6SWxK6LtsqVYUhM6D5oOKPs8UQ+H2Kxy6aBqMCVnCzZS27zS3YGnkbeqPn4M8R2LLTMLlIzvaG9VbOkbedWDXYwtjeDbm/ZwO+WrWLVZbx1NRSch16LRh0M+1hsf4kY7Mnslx0otqIWfDLvfb8ay5mNcMGSbvklmDT/l5NtZhNLH60F9OW7+X9lQfwVhzXrGto4LYKcn6OnE3j3RXu68rYDMGmw8nMGNGOZ3/4i4sZ1kKp52tUDuD7B7oRW71kti1Op/pWmTkpxf8Kzu54748DCOH6x5Wf2lUr8WCfRozofBn4vikUlyFF8hh78MEHefDBB10e+/TTTwu1lYOM854JqupDJwFBVUpvDCE17Uv07h69dLnqcevH0Pl+mcviwjEIrSWjVer3kZN7aVGjETS8Bg6u9N9vxLBBWBQsfRo2vE+ujY4VgIzz8M1IGLmg+NtMVetDQAhkuw/R1QCLkUlVMumas57uR9eRecRCgqhGgCmE2iJvq+SMCONN6y18Ybsu99zbO9fhP0PiCDDLz3vYgSewIFcQCvqeaMDMgLcYHbWQqNAA2LfYs+jUzTJqygcxAtJ/5PF+zbi/Z0MGz1jNseQMl0LDpGsMbhNNVJVKXoUIQEa2QcLZdI99TLrG/qRUFj/ak49WH+L7rce4kJFDZFgQd3aNZdRVsYSWYJbRiFDfUqeXVL0XmyFYsuuk1+iltjFVmP9gyRUdVCgqIqpQni9Et4PwGDm5u8McVDT/Bl9pdxcsfsL9cWFA6+FytouMg9rtZRVYW47cZqrdEYJKyBE4O136MmydAxcTITQK2o+CgW/ClzfL+8lBOUfHuMMcIFdWfpqIa7ElAA1+m1J8MWIyQ6d/wZ/v4DL9fMHu9vEEaTnEcgoMjQMikrest3JSVGWraJwbOWPSILSShX/3bZorRMhKI+DoWve7Z/ZVkqnRa8DW2ftnJQyZG8VPwoMtfH5vF26dtY6zBWqhaBo0rBnC84NbIARYTJrH9O2hQWbSsr1HhGhAjs0gKrwSzw5qwbODWvg9bn/o2bgm1YIDOOcmjb2mQYMaIcTVLpn/Bzk2w6cw6vhj5/n75EVVgVeh8EApPipXIHQdrn3Bc5+e/y65yd4VMV2898lOk9Eib8TBz5Ng1wLYvVA6377Rwu5kWkzSz8moop8nQeJ2meE1cbt8P/d2GLkQbpgqQ3FDIyG6PfR5EkxB7n1ern3BHqXkAWHIsOTzRzz384UeE6F6w0KJ1rxNK7om073Hakn0NW9lo2gOugWz/Ym3ZmgQX9/XlRr5i5vt/dnrlQUQc3aNFLThPiTnimjmvY8LYquHsPjRnoy/uhGRYUEEmnXqVQ/mqRuaMf/B7lQJDqBqSAD39Wzg8TppWVYe/jo+1253WA1BqwL1WEqTALPO/w12LXjsdaB5fnDLEktDEGjWfVqN0YBP15Zu9mOForyjVkZ8pfVtkJMm83zkZOQre2+Wk1uvx0v3/ju/QWpHD0/Omz6EFf/Le3LO/5SdnSYjMsZv8m3Cc8dPE+C0I723cP56eq/cahn+OXQpkO21cT+58pEYn9cWUlPmBukwRqaw10zeVwbSz8naLts+h/NHZchu6+HQ8Frft6EqVSV91GK2fvoYHc79QiVNPkn7OkWZNYMbWM+7g19i01kZwdS5fjWub1ELi8l5DAmbl+ItLZ7m+FfToPNY+PX/3DshaDq0G+njSAtTo3Igk/o2ZZKHWiiP9W2KTQg+XH3I5ZO/o8lqCLc+I7oG1UICuK6Iyb2EEMQfPc/3W49x+mIWEaFB3NIhhjYx4R7FxI3taqPrGi/+vJtT+XxD6lYL5j9DW5aIo6wDTdMY2TWWacv3ebYFvNbNUSiudJQY8YcOYyDuVtizSG7ZBFeHFjcWDh8tDc7sxeu2wrmDckJzNaELQ0ZobP4Yrv2/oo3hwnHYvQi3T/rCBnt+lJ9NQcFTu70M2z21S+YkCQqXobcmu89AlVjn/Cku0WDNG3K1J7+D7s5vIbY7dBsvHWErVc0riLfnR7maElwVmg+FyjUxDMG98w6w4eQwAsUQbtA38rzlM8I137c/TJpB+4DDDBxyk/tOthzeOliL/5ksBGtebGtxo/zaZSzsWwpH1jr/HB2OyAOnl2JElETXNZ66oTkjOtfl+mkryfGwFWHfQMv9HqSvSIBJ5/2RHQqJM1/IsRlMmhfPjzsSMekaNkNg0jU+X5/AkDZRTBvWFqtN8MvORBLOphFWyUL/uEhiqsrsrUPaRDOwVRQbDskMrFHhQXSIrVqiiRkd3NuzPu/+8Q+ZXhLMFScrrkJxJaDEiL8EVoa2d1z6+wZU9u5/4U6IOHCIhaKKkWOb8L6ZIeRWUX4xYs2WTpfHt8gVpYbXyIy1+SeHFkPhl8cgx41TpGaSAmb3QvneEe7qcJZN+FO+HJgC5Vhs2XbhYpN5O7o9zMqYcaw7eBaAvvpmXre8X6TA5MpBnpfoMy6c5gdrV0bpi2nDQZeJ04R9Ntda3SYbzIEwcj6sfQc2fpCXSTb2KugxCRpdW4SRFo2Dp1M9ChEHN7evzeaEZI6cSyc4wMSQNrW5r2d9GtT0L6zYwSu/7OGnnYlA3iTu+Prj9kRSM61sOHyOtCwbZl3DEIIXf9nD7Z3qMGVoHBaTjknX6NbQ/wKD/hIcYGZYxzp8vi7B7e+QSdfoXL9aqY9FoSjPKDFSXmgxJG8idoVuBkswZHkpKuih+q1XfH2yzN/v6CaYewekJcnQYwT8+RZEtJDZVavYQx0DK8OA1+GHB6Hg4r9jVSDjHD5jyxe+6RAuhhXWvEF2tdOY9IGEGilMtcwGBCY/H5pt6CRXacHMJX9zICmVkEAz/eMiubZZBGb7asBFQ4qVetoptx+dptkt3fmNXBUBKUh6PSbFR+Z5+d6wyqrMaWeKVMW3KBRM1OaOHo1rMG1Y2xIpC3EhI4fP1x9xu0slgN/3ns5djbHmE0tzNx0F4OWbWxc+sRS5u3t9vlif4HbMNkNwd/f6l3RMCkV5QzmwlheaDYYaTeTKQkE0XYqRut3kV3f4Uv3WE3W6eq6uC/J43avk98mH4fOhkC5XITBy8oTBmb3w2SDnyJB2d8LwL6SdeRe0Z5YtuSX2a87NJdRI4RbTaizYvBesc4GOwcHZI/lw1T8s232KRdtPMPbzLQx6Z01u+fjwKlWI0pKponmu9GugQ9Kewgd0XYqRH8bD1PowoyO81hDm3AjHNvs/aD9pFumbQ3aLKOmkWhLbIOsOnCXb5l0EuYy5ElKQHD/vf7RRcahfI4Spt7ZB03BK8Ob4fnL/ZmplRKHwghIj5QVzAIz6AWraowV0s32lAbl9ced30HOS50q0hk3WbikqobWg1W3uBYlmgrib83waNrwvV2Jc5R0xbFKs7Frg3N58MDy0AR7aCPcul/VqrBkUK8NrAcxY6WfeQlPtKLYi/hfQgD6mHVzLJiBvG2F/Uir3fbYZIQSBZhPXRXjP9qkLwynSKDktmxV7k1ixMZ7kWYMQu39wyrpqHFyJ+Li/zOlSitSrEUKPRjXcZlA16RodYquWaEZRX4SIJzTglx2JJTMYP7i1Qww/ju/B0DbRVA8JoGqwheuaRzDv/q480KfhJR+PQlHeUNs05YmwaBi3Gg6vhv3LZA6R6HbS+dFiT+TU6wlYNTVvawPyvu8+Aer3LN4YBr4uHWWPbczzYXFcv3Z7GPRGXt+d33lOgKbpUowU9MHRNKhpj/bYvzzPWbWk0HQqizQytOIVZrMKnRGm31lidM5tsxmC7ccusDkhmU71qjG2UQqbNjahvf4PJjcVejUNqHsVaVlW/vvTbr7bcix3+8HMy9yir+b/LHMI0eTWk47Mb2F8PxbLv3e5Xi0rIV65pRU3z1zL2bRsJydMk65RpZKFabe1KdH7tYgqXni8rmlczCybqrhxtcOZPrxtmdxboSjvKDFS3tA0mdq9fi/Xx695BqLawNq3pSMpQoqEq8ZLJ9HiEhgKd/8ia8Zs+wJSjkNYbZmUrcXQvOgYkKHQnhAGZHpZOfBUj6eIaMIgMqY+y47GMtpc9GrLZs2grnaqcLuu8dueJDrVq0ZURAQmPQkdA0NQaEvI4cC62mjD2x9vZOuRZKeEZFbMfGv04p/saL4O+B8BmhR3JgSmtERs/6zA1OS6ItvgjZiqwfz0SA8+XH2IuRuPkJJppXKgdNq8v1cDIsO9ZzP96/gFvlifwF8nUgi26PSPi+KWDjGEVyqcfbVRRGW61K/G5oTkIkWgWA1B3RJKL69QKC4dmigHudpTUlIIDw/nwoULhIWpLIY+41jaL8UnZ4+83xtO7nAf4aPpUshUqStzjjQdAE1vcE4el7AOPvEt9bk/GNHtWRB8G632v0sDTmB2s2rhCZvQ2CYacWv2f5zazSaNUV3ryQRcaWdhWlMwcnIdHB3hsJq9BsyvRnsesD6Gh6SnALxheZebTHkRQ4bQ2Nf+GZoNtee4ycmQIjFpt3RmbjZQJp8rQbKtBhaT5rN/yJu/7uPNX/fnhug6zqoWEsDX93elSa3CWzxHz6Vz83trOVdwNUbTMJs0rDZRqM4NyM80ONDE5meup1JAGf3OKxQKJ3ydv5XPSEVGN5WdEAHpn+Ix1NiAC0dlSO7uhbDgfng1ViZHy0qVfep2hcjW3h1n/UQ/sZVb/nmKJtoxNLOMehG6WW4JaZoMDW41HE+OszqC+bbCK1RWm8hLOR5S3SmUWtPk6oimgVVopBHEK9Y7vAoRHRtfW69xbtMEfyfb3+xbJkXPgrGwbiasnAqzeshEd5leIqz8wKRrnEvLJi3L+7bZ4p2JvPnrfiDPp0bYX+czchj10UaXETt1qgXz88M9uLdHfcKC5OJteCUL/+pZn2/HXUWVEEshPxaHNnrpplZKiCgU5RC1MqIoPWxW+Ho4HPjNe1nTgkS1hXuWSl+Y80dl5E3y4bzjDj+VdiNlyOv+pUUfp2aS20vNB8sw2ogW0GaEnOFmdIa00xR0oLUKnSMigoHZL5FB3laFpkFYkIUNT19LkMU+KQrBsrcfpMe57wjW8uqmrLc141nrPfwjYnBOH+aaGJJYEzQh932OMDG9zU9M7hwIH/e1r4QV+Jw1E9TrDqMW+R6a7YL0bCuzVh7ki/UJnEuTNnRvVJ2Hrm7kNp/Hje/+yY5j5z0W3nt7RDuGtIn2eG+rzcgNlwY4lpzOq4v/5pe/8orUtaodzqS+Tbi6aYSflikUitLE1/lb+YwoCpOdLjOA5mTIibl6EaMBTGa4/WtY+xZsmC1zjfhKYjx8fqOMEqpSR9a7+eWxvNo0wga1WkJKIhz8vWjjcyBsYENmqL3lQ9l2fCvMu8vtmPcGtOTu1HFk5hMiJl3DpGm8e0f7PCGSkwHfjqFv8hJy0LEJKQhMmiBeNEKg8aZlBp9Z+9rfu16s1DCoqZ3PG7KAj2z9admoHqyaZBd7LmZ9YYNDq6T/UF0f6hu5ID3byojZ69l5/IKTsFh/4BxrD2zgjWFtubFdbadzMrJtxB89jyfMusaf+894FSPmAllcY6oG884d7flvejYnzmcSGmSmTrVgv2xSKBSXF0qMKPIwDFj9ukxKlp2a116vFwx5C6p5LqDmEnOArNvTfSJcPCFr0Gyfi0+hukfWwbTGENXeObuqg1O75KskEDaZJTYjGS6ego/7OydOy0WDGo1peN9v3L/xOHPWJXDkXDqBZp1BraO4v1dD51DXnybKyCfAUsAvZZz5J+4xLUHHIMMUyDZrY/fDQ+M20ypsQkdDMMd2PZ8EjWZ14ypY5y9ju9GAdBFIff0kMdoZ55N1s4xaciVGstNgxzewbwlYM6Xzc4e7oVpekq5ZKw8WEiJArt/GE9/toE/TmlQJDih0zBPCx37uqBIc4HRPhUJRflFiRJHHksmwcXbh9oQ/4cPrYewqKVJO7pTbGfV6QqUqvl3bZJaOqhcT8StnSHY6JKzxvT8AmhyfNRuv9XzyIww484/cWnIpRAAEnNlH0KFf+VfPgfyrZwMMQ6BpLpJ+XTjuVXhZsKJpcJNpDZ/a+vGPqI0NZ58HEzYaaie4ybSGr2zX8L5tCCmBkXwxpgvfbk7gzcy3OU1Vu+WCnvoOppg/pZ6eL9In62Lhmyf9DXOGyMrLjqy3h1bLSKwB06DTvdgMwRfrEzxuteTYDL7fepx7e+QJmJAAEw1rhnDwdJpb622GoENsVfcXVigUVwzKgVUhOb3PtRABuWqQfk5Gx7zbGb6/V25hTK0P73aFXQulf4g3DEMKmVJHyKf80Fp+Or5qMkeLI2Os224m+Ov73Le67ia6xL4i4vFS9tOCtBy+DniR3vp2tHzTt4agt76duQH/w6JZCQ/Suenqriyf1JvV+8/wzM8HOU2V3P4CjT+NOG7KnsJRw+7LIYzCW205mXIbLO1M7pnyi01u+fw8CQ6u5EJGTq6PiDtMusb+U85iR9M0/tWzgVshomsQGmRmaFvPWzQKheLKQK2MKCTbv5aRN4a7JGUGpBcogy4MOL0Hvh0N1RvByIV5tWZccWKr94m+JOkyVqZZ37XQw0qHHc0ki/ft9yHviLB5z48CUhBpms/Ou9W0i3wc8DoJRgSbhEz61knbS6ye57cypKEZ+jYl6WIm03NL1zsLIRsmUgjmLestvB7wvgyhbnun8812LbCvUrlBM8Hatwka1t2nsef6yORjeMc6bElI5rstx9DtYcwgxYvFpPHBqI4EB6g/QQqFQomRK4+0s7D1M9izSFbIjWoLnf4lJ6bixFWdOySftB/cILdkCrJ3Cfz6QjFuUARWT89XONAermtzUSjQEU1Tvycc8iHFuqZDNR+cemvFeQ5tdkOsnkQsbpx9g6oA8MO2E3gKhLNh4gejO1PEpwT3+59cJcrPP786Z+ktiLDBgd8JNut0b1Sd9QfOufXvsBqCfi0jC7XrusZrt7bmuuYRzFmXwJ7EFIIsJm6Ii2JMt3rUra6cThUKhUSJkfJKSqLMzZF+FsLrQMubnJOFuSJxO3w2RE7Qjkny7AHYMQ9qtaZYakTY4Ow/sG+xDJHNz+rp8Nt/uOS7gk4VjIVrIQJyC2PoTDl+XxAGtB/lvV+9HlK0JB/2nBbfH0KjADh+PgNd1zA8JCjJwczZAR8S3GVI4YNGjnehJAwQBg9d3Yi1Bza47GLSNVrXDqdrA9eF4DRNo39cFP3jojzfS6FQXNEon5HyhmHA0mfgjZaw9GlY8yb8+Ci83gQ2f+z+vJxMmQAr66LzJOSo+XLKQ6ZUX9HN8Pcvzm0nd9qFCPjlTFrq6DIZ2b3LOTt6NZ8fj+DtYw35ztaLNBHo+dRO/4LIOO+30DS49SOZK6Vg8jnNBAGh+F2NOECuJlQLCfD649I0CG9zg+uD0e095x3RdLmyYzLTrWEN3hjWlgCTjoYMyTXbk461rh3OR2M6lUjFXoVCceWiVkbKG7//F9a9S57DoX1GsmbIMNLAMGh1a+Hzdi+0J+8qRYRhr7Cbj80fl3yhu5JAA5GSyLTd4cxa+Ts2Q2DSNazGWL7TevKq5QMnX41caneCAa97v37KCenAmpMpiwceWg07v5G5TILCocMY6PoQ/P4/2DYnr+igN/58E5r0Y2jbBvl8Rgpj0jSubhZBWFDh+i+ATBa34iWwZeM6P4kh08nbubFdbfo0rcn8rcfZd+oilQJM9GsZSZf61fwWIhczc9h3KhWzrtE8KowAs3omUiiudJQYKU+kn4N1M/C4nfLbFGh5M+gF/sAfWu3ZR6CkqFVgxeD4tstPiAAIg/M7lzDj/NW5TVZDMML0Oy+aP8LmatEwrDaMWuB5RSEnU0aibP/aXtHYLjKqN4K7F0PN5mCplHeNoe9Ax7vho+t9EyNZafD5TcQ+uoM7Otfl641HCv026JrcPplwnfu8JYRUh5veh+/GuO+zbqYsgFilLiDzetyTL3zXX1KzrLyyeA/fbj5Glj0NfBV7mvcH+jQqlOJdoVBcOahHkvLE3sX2J1kPnE+QxekKcQmy/mu6fOLOj8V7VdeyIiXD+bNsrB3jRfPH6FrhBGXyhOMyN4c7hIDv78kTIpD39dwh+GywdBQuKGYOrfZDsBlyhWvXfKYMbck9PepjNsnrOa4aXaUSX97Xhbja4XmnZZyHFS/DjE7wUm14JRbWTPd8q5x02PC+j+PyTGaOjRGz1/P1hiO5QgRkjZrXl+3jie+2e3TIVSgUFRu1MlKeyLzg23K+k+OmnbpdIf7L0hmXZpJjGvwWHN8Cmz+C03shMFSuJpQUpgDvYsxHBLDW1typbaRpOQYauifh9tUwaHgNtB8J9Xs7C4vjW+Hvn93c0CZDfVdPhxvfzWs/fwRWvuLX2A10svcsJajdXTw3qAUPXd2I3/9OIj3bSqOalenaoDp6/lWGA7/DVyMKO/CePO/5RsImnZv7vejX+Fzx9cYj/HX8gttP9vutx7m9c1061XPtCKtQKCo2SoyUJ6o18G0pv2q9wm1xt8Ly/5OCpiiOqo4tnsq1oFYrufqSliQn4wZ9IKYT/PZfSD3pfN7pv8l7Zi/mk28JCRGQI5pv9HRq66zvwexqRSQ/Gedg9wL46ztoPlQ6qJrsfhk7v/HsH2PYJ/fWwyG8toziWTBO1q/xByH44++TpG05xi0dYqgWEsCtHWJc9z17AL4cJqNnikJWqvc+PvDl+iMej5t0jXmbjigxolBcoSgxUp5odJ0UA6lJuJzYNRPU75W7x+9EQDCMmAef32Sf/HwRJJq8VrdH5FN9RDNocE2eP0pOBuRkwZzB7p/uixuhU0oYmol4Q/pUBJHFVfpuQnAT+lvoZLvfzZ5F0gn0uufl+/Sz3hOcGTny8wJZhDBpdxFGD1ttDfngu+3UqRZM5/oeJvANs0AU02fnlXoQGAJxt0DnsVJI+cnx8xkepajNEBw+m17kISoUivKN8hkpT5jMMPRduVWjFfjRaSYICIEbXnV/ft0uMH4jdHvIt3LypgC49WPo/C/oNl6KofyOsYYVZnZx46NyGaOZoPVwqoZU4iHTQjYHPsAnAa9RRz/j/VwnhEyhn22fRMPr4NfqT9IeP+8ns5hmY+ZbW290TeP9lQc8n7DnR58zwLrEmgmZyXDhGKydATO7woltfl8mrJLn5x5dg+ohquidQnGlosRIeaPx9TD6R6jTOa9N06HpDXDfCqjZ1PP54THQ93+yim5BQZMfSwj0mAhn9sHFk677fDO68LZMeUDY0Pcv44+AR3nc8g2VNecVEb/m7uzUvMm55c1+rgT5JxKsQseGifE5j5BMGDZDsGJvEoanKnZWL2nw/RmjsMkqv1+PAJt/2z63dojB5EH/GgJubFuC/kUKhaJcobZpLley02H3D9LnIiAEmg2CWi3ksXrd4Z6lsipsxjkIjZahmv7Q50lI3AEHXKQF18yQk5a39aKZoO0ImV/DUkm2Jf0NB34rvp1lRfoZ3CUjd5ST8Tl9hsNH5PCqkhiZ86WFRjoB5GBhqa0jH9tuYJ+ok+842IRAd5c8LaoNHPwDr8LHyTlYc99f2GRE0N5foMVQn+0Y3a0eX288yoWMHGwFxJNJ12geFcp1LWq5OVuhUFR0lBi5HPn7Z1gwVmZL1S3yaXvFi9B0ANz8AQRWlv3Caxdp/x4AcyDcMQ/2/ACbP4FzByCgslyOL/g0LWwQ/xWknpbnaJp0xCwH+CUq8uHzOboFIlvJ7zd/jMeJ3E8MofGR7QZetN7l8rgGNIyojMXkYYWry1g4uML7zW75SPqwXDgqawx5QrfA0Y1+iZGI0CC+GXsVD325hb2nUtE1+SkJAT0a1eCt29t6tkOhUFRolBi53DiyHuaNzFvuzx8FsW8pfDsG7vquZO5lMkunxLhb5PslT8m8Eq4SowkD9i+FI+sgtpuswFsO0DSwCTxuERTj6tB6GARXk8nOkg9THCFiFTpmzcj9usToxFTr7R7PGdOtnueLNukPHe/xXCqg92RoYa9f41j58ojwvMXnhkZBKSy57jQHT6eyMacRGSG16dWkBo0iQv2+lkKhqFgoMXK5sXKq/RtXKbpt8M9y6aMQ3c6361mz4O+f4OxBuaLSfLD0GylITqZcIfGUoVU3w/a5UozobtKMlxEGoAkQaNjQMWEjGwsmbK4TmJUIAkJqwu5FsGh8kTPNnhWhPJrzEDea1hJBMomiOt/ZerFJNMVV7RpHS9+WtRjR2UXklFNnDQZOhzpdZSp5R/SOpZJMbX/t/0GdTnn9w6JlCPm5Q7gVVoYVGl7t+pgrMi/AjxNh9wI0YdAQaIgGTftDu3e9nq5QKCo+SoxcTmSmyARVnp6udTPsWuCbGPn7F1j4AGSel+cJQ65+tB8l/T/M9uiF03thztDCdWUKYlghzR5xUruDFEaXAULT+dXajq+tV1NPP0U17SInRHV+sl3FxsAHsVBy+UkK8eeb+F3srgCvWYexxmjNGqO12z4Na4SQcC4dqyGoXyOE1jFVqFOtEl9tSGBAqyiqV/ZQ3E/ToM1w+bJZ5fuChfvy9+32CPw0wc1xE9RoDPX7+GacNVuGk5+IL+DcK2DfMvh0ANz3u/SLUigUVyxKjFxOHFqF92V+TfqSeOPwGph3Z15oSP6n9m2fy/c3zpSOsnOG2nOXeEE35/motBnud+bQUuHupazNjOX+T+zbRvaFHQ2DOO0we4w6tNYPYC7Vsie+bM249iXZbdThrAhjhOk30kQl/jDakELhifnjuztRt1owH605xNQlezl0Jg2TrmETgv/8uJuHrm7EhOsaey9aZ/Lhv3yHMTKKav3MfEnc7OMPi5Z+QwVrH7lj9w8yK68rhE0K4fivoPN9vl1PoVBUSJQYuVxY8yb8+rz3fsIqIx/m3y/Tj1uCoNlgOYGE5otG+P1/jhNcXMOQE0Cvx+DwnzI6whcMK7S9U35frQF0ug82feDbuaXFsmeofdMip6bB+lqeMM+jjl7KVYr9IaoNJMYXam6uHeWDgDdyHW0zhYUPbAOZbr0VYY+814AqlQKYu+ko//s5LzeJ1R6VYhWCt37bT6BF58E+jYo/Vk2D/i9Dy5vk1l3SHggKk+9bD/NvFSP+C+8lDLZ9rsSIQnGFo8TI5UDCWt+ECIDQZFbN/GnHT+2Cde/AyIUQ0xEunpKOpp7QdLndc3wLvkWAaLKCa3TbvKYbXpUT059v+jb20uD4Zupxgo71qrI1IZlh+u+8YvmwUK6QokbVFBtNh3ajpFj8oE/hw5rz1yAth4dMCwkmk/9aR2HSNHo1qUFIoIlpy/Z6vNWM3/9hTLd6BAeU0H/rOp2d89kUhZQTXnKvCPd5bBQKxRWDiqW7HFhvFxceccykjiibfNsuwpBbN58OkLlDMi94v6emy37WLHzaZug9WRbCy49uguv/AzWbuz7nUvHVMF4Y3JIq5myeN89xKTzKRIiA/NnEdID9y3w+RdfgbtNSYrQz6DpMvL4JmxOSOZPq2fclPdvGqn3FWw3KsRkkpWSSllXMFPIOwmKkn4lbNLn1o1AormjUysjlwJG13iMxLEHyD/u5g+4jXqxZ8H5P6PeyjHbxVBzNsMqCeroZDqzwEEWjQVRruPopWYY+/iv463tZGbhmc+h4t0wZXpacO0jcd71YG3SewOzsshMerrAEy+2NTwf6dZpAY0TwRtrfMYXWMVVYtsu31YOUjKKJiHNp2bzz+37mbTpKerYNDbi6WQSPXNuYtnWqFOmagKxufPB3L31GF/36CoWiQqDEyOWALzkbIlq4dwQsyNKnoeG1cGhFXlG3gpgDZX6RzAuw5g0PFxNQtzuc+UdOqKmnyF1JOXdAJk1zF5lxKUk+TBAUN7Cl5IlqK30ucvwTbJpu4oH2weiRgGFQr4Zvfhp1q7vLK+ues6lZ3DRzLceTM7DZ97cEsHLvaVbtO82HozvSp2mE39cFZCh53avg6IbC2zWaSf5et/GcS0WhUFR81DbN5UDDazxv02g6NPAjr4OmAwIqVXchFOyz9YDXIChcVuUd6EmMABvegzlDIO00Tls6DqHjTvAo4NgG+Oh6uUrlh2jTRQ76ptnwWgOY1oQme2bSqXYQuhuxpWlQt1owXewVfE+cz2DxzkSW/HWSM6me69O8uuRvjp/PEyIObEJgE4IJ8+LJshbxZ2yywJ3fQZs7nH/HNRO0uBHG/OhjojWFQlGR0YQoTknPS0NKSgrh4eFcuHCBsLCwsh5OyXMiHj642o2jnybzgTwSD3PvgMTtvhVjMwfB+M2w/P9keKVjG6ZmM7jmWfnE6uDwGi/bCDq5viqKskHTSa/Zlm6JE7loNTsJB12T9V3m3NOFZpGhPDV/J0t3n8x14jXpGje3q81/hrYs5Nx6MTOHDv/9lWyb55/vOyPaMbhNMX07Uk/LFRIE1O4IYVHFu55Cobjs8XX+Vts0lwPRbWHoTPjhQUDLEw6aSeaFuP0r6eTX7WH47h7frmnNkplWb/sE0s7C+QQIDIPqDQt7c+5e5BydUwglRMocYRB8Op5lXXcwOakvf+w9nbtG1bl+dZ7o35RmkaHc+O6fHDid5hRNZDME3289RsLZNL66ryvmfDVgjiVneBUiZl1j/ykfctt4o3JNaD6o+NdRKBQVDiVGypKkPbD2HRlia82UWybhdfMypja6TjqIOqINWt4sc4usm+HDxYVcSYlqC61vg9rt3XfNTqWkirspShFhELFnDp/8+xlOp2ZzKiWTGpUDiQwPAuDTPw+x/1Sqy5+kIWDj4WSW7T7FgFZ5KxLBAd63jgwhqOQmXDjbapB0MZNAs4maoR6ywCoUCoUHlBgpKw6sgK+GyS0Xx4pEcoJ81e8Fd34rnUzzo2nQ70WIbA0L7vd+j72LZXG9P16SCcpueNW130L1RhRKzHGF4wgPFlxmPrGppyArhZqh4YUm/3mbj3o8Vdfg281HncRI3WrBNIqozIEk1yIGpJDp17KWU1t6tpV3fv+HL9cnkJIpf39bRofx8DWN6B+ntl8UCoV/KAfWsiA7Hb4dLUWI09aIkK/Dq+HPt92f36SvjzcSeVs+mz7Ml5W1AG3vLFIV1pLmctJDucnILvmNvf0cNDC5XoE4lZLlcX3LEHDignNUj6ZpTLiusdvzdA0GtIqiQc3KuW0Z2Tbu+GAD7688kCtEAPYkpjDui618uPqgFxsUCoXCmbKfga5Eds2XIbXuHFGFARtnu49SCQyH4Op+3lTA+nedE6Jlp8m8JbpZrpqAf6KkhAWMQwBcTqLELwLDvCT48gFPzsmaSW7dWYJcHo4IDfQonkwaRIUXPndQ62heGNwCk66ha9JHxGQP27mmWQSv3+ZcwO/jPw+x49h5jAI/J8f7F3/Zw4nzXoouKhQKRT7UNk1ZcCJeTixuE40BaUmyeJ2riANdh4DKkH7Wv/tas2D/cojtDitehB3zwGbP6lm/F1z7POxZBCe2+XY9X6J6isBllbTMH4Tjn/zp9bX8Bz2e6tls+zV7TnLbY3inOkz5cbfb4zYBt3Wo4/LYmO71Gdg6mgXbjnH4bDqhQWYGt44mrnZ4ob6fr08oJEQKjnTupqNMur6J+04KhUKRDyVGyoLsVM9CxIEpwHV74g4ZHVMU/nhZ1gvJycQpSubwGlk0r9djvosRhTPZKfZvHHs8Jpnltt2dsPI1sLpfLXArRHSzXCEzB8CN70FsN7fXGNaxDl9uOMKhM2nYCqgFXYP2davSt4DvR35qhgZyf6+Gbo8DZFltnLzgPYHbgdOpXvsoFAqFA7VNUxak+lA/pEoshLjZitm/rOjbAWf/gZx0CoXrCkO+Vr5atOsq8mEXAsIG1z4HPf8to6KKsq1V9yro/wr8e6/MmOuBkEAz8+7vyrXNIpxWl0y6xo3tavPZPZ2xmIr3X96i65jdZV6zo2kalUuqWJ9CobgiUH8xyoKjXirqAoRGuj9my8kL9ShRyquzxmWKpsO6d2Vtmmuek9tzR9ZioKH78lnrZhnp1HWcz7esXjmQ2aM6ciw5nW1HzqNrGp3qVyUi1LWfib/oukb/uEgW/3Wy0OqLA5shuKGVh99fhUKhKIBaGSkLcnxw7guq4v5YdDvvhfUUZY8w4NgmKR4DgmHUD1zs+yY7jPqcFz7WkCmiA01M1WAGt4lmYOuoEhMiDsb1boimuR6aSddoVTucXo1rlug9FQpFxUaJkUuNEG5DM3PRdKjR2P3xy6EwXQlRbiNn/MI+a5sDOFT3Jm7NfoFVhoxQ8Wi/YZXOxpcZcbXD+XBURyoHyoVVsykv+qZtnSp8encndC9bOQqFQpEftU1zqTm+xaMjIyCfqDvc7f747y+W7JjKkHIbOeMLmklmvjXl/TerGhzAY+ZvGKRvkF3c2G+go1eOgOZDLsVI/aZP0wg2PXMdP+1IZPeJFAItOtc1r0X7ulXQKvQPVaFQlAZKjJQWp/fKXCH7l8toiNhu0Pl++PtH72G94N55NTkBEitetItjhaBCzWPCJusJ5aNOsJW7zcs8+owIAQSF2rPwuomougwIspi4tUMMdCjrkSgUivKOEiOlwe4f4Dv7yoYjcdmu+bDzG6jTVW7DeBMj2elQqWrh9szzJTrUy4UKJUIcYjOyNaydAVs+g5Y3QtytkLCWQLI9n66B1u+/ENXaYz+FQqGoKCifkZLm/BFZWdcwnDOoOhxOj64HI8fzNQLDoHKE62NhMZdF6vbLhqh2ZT2CwoTHyK9Ju+HYRjjwOyx6GN7tDBeO+XYNU8k6nSoUCsXljJrVSprNn9jX2d0sw2sme44QN0sBmgk6jAGTxfXxkOrQbJASJA6yLnjvc6lxJKTLjXiy/y6knPCx4jIQ1abEh6VQKBSXK2pGK2kOr/a8BSNsYLY/9RZMXKaZoGYz6PW453tcPwUsPoaGVnTOlaOibMIGyYcgqq37iCjNJP2LIppd0qEpFApFWaLESInjg/ODJQhG/wj1euS1BYVD90fgniUQFOb5/Gr14ZaPijdMn1G/IiWKbparHpUjXYvRkJoy7btCoVBcQSgH1pKm4dUyfNfd6ohuhgZXQ/2e8pWZItOzB9dwCgH1SpN+ENECkvZQaplTI1rIibNqfVnTRmVoLRkslWDsKlj/Hmz9DNLOQHA1aDcSrnrIvb+QQqFQVFDUY29J02GMFBzuVkgMG3R9IO99UJhM/e6PEAEZctH/ldL1HUk5Lh1m427xnIRNkYenzLkg/UhiOkFIDVm35vF/4PlkeOIgXP8fJUQUCsUViRIjJU1YNAz/Qjqg5l+GdwiUQW9ATMeSuVeD3jByPlQvJaGQeQFWT4MZHS6Nj8qljCAZOL10rutJiGq6XAFrPrhAe0WKa1YoFAr/UWKkNGjSFx7eIhNe1YqDmk2h/Wh4YK2s3uoLp/fJkNCTOz3nDG/QB8ZvgmufL5GhF8Ze3TcxvpSubyfuFrBlle49HFSyb4lEti75laX0c1Cljvw+/7U1k3RcHvE1mL2UA1AoFIorDE2Iy786SEpKCuHh4Vy4cIGwMC/OneWdoxth8WQ4sTWvrUYT6Ps/6SfiDms2TGsKGedKcXA6ueKkRNHkRO0tTX5J0eFuGPwmHP4T5gyx54Mpwf8G5iC44VXYOkdm4g0IkWKry1ioWq/k7qNQKBSXOb7O38qB9XLi6Eb4dGDhirxn9sNXw2HYHGjhplaJOQB6ToJlz5biAEtDiNi5VEIEYOd3ULkWVGsgs9ymnXY+rpvBElL0HCbWTKjbTfoPKRQKhcIrV64YycmEg3/IlYQqdeXkoXtZsk87C+tnyskrspV8wvbX8dSBzSq3YQ6vAjQZtfLrFFluvtBTupB9fv43NB0g72kYkPAnXDgqtx0aXg1t7oB178LFxKKNqcy4xItz2Rdh5avu72sYMvzall10kWSpVOThKRQKxZVGkWbSmTNn8tprr5GYmEjLli1588036dmzp9v+K1euZNKkSezatYvo6GieeOIJxo0bV+RBFwshYNOH8Pt/pYOmgyqxcum+4TWFzzEMmHsH7Fvs3L7kSbj+v3DVg/6NYce38PMkyErxZ+CQlgQHV0iHx58mytTzDkyBMs28KMXVi9LAkZHWcCXCShNP9zIg9RRc8xyc/Qf+mp/nzxJQGbJT3Z+q6VCzeV5KeIVCoVB4xW/vvXnz5jFhwgSeeeYZtm3bRs+ePbnhhhs4cuSIy/6HDh1iwIAB9OzZk23btvH000/zyCOP8P333xd78EVi/Xvwy2POQgTkCsOXt8LhNYXP+fzGwkIE5HbK0qdg08e+33/ndzD/X34KkXz88xt8OQzOH3Vut2WVLyHicO6sHAFGNpdfDhMNDqyAm2bBc0nw/Hl47gw8fRwa9y2csMyBMKDPZBUho1AoFH7gtwNrly5daN++Pe+9l5clsnnz5tx44428/PLLhfpPnjyZRYsWsWfPnty2cePGsX37dtatW+fTPUvMgTXrIrzeRCYZc4Wmy1Td96/Ia0v6G2Z28XzdgMrw1DHvE5DNCm80h9Qkv4btRJVYKZzKk/DIT0gEhEZB5ZrQ+nbY8J5MEnc5UisOHvizcHtmCnwzUm7z6fbFRWHI359+L0lHVYVCoVCUjgNrdnY2W7Zs4cknn3Rq79u3L2vXrnV5zrp16+jbt69TW79+/fjoo4/IycnBYilcEC4rK4usrLwwz5SUIq4iFOTvn90LEZATyomtcOYfqNFItq16zft1s1MhYS3U6+653+HVxRMiluC8ImylSeVacpuiNGh7F1yfLwx5zbTSuU9x0c2yTpArgsJg5EI4tgl2LZCrXNUaQts7IbTWJR2mQqFQVAT82qY5c+YMNpuNWrWc/+DWqlWLkydPujzn5MmTLvtbrVbOnDnj8pyXX36Z8PDw3FedOnX8GaZ7Uk+5X14v2M+Br86gyYd9uG4xhAjIya40sQRDv5c9b0MUl6hWzu8jWuatLlxOGFbPOWE0Dep0hv4vw9B3ZSSTEiIKhUJRJIqU8UkrsB0hhCjU5q2/q3YHTz31FBcuXMh9HT161GU/vwmN9lxR10FYVL7va/t27eqNfLh/pG/XKogluPSX/ytVg8f2S2fcsOjSuUdQFWg20Lmt072FQ5kvB9qPgVgvK10KhUKhKBH8EiM1atTAZDIVWgVJSkoqtPrhIDIy0mV/s9lM9erVXZ4TGBhIWFiY06tEaDZAJqByh6ZDTGeZf8JB7yfd93cQGCafkr1Rr4f0l/AVTZOhvI//Iwuo1WgM0e1LNmuopudlBg2sLNva3O6baPMLXVajLZh9NLYbdHFEVhXT6TMgVL78xZwvDDc8Bm6YKtP2KydUhUKhuCT4NasFBATQoUMHli9f7tS+fPlyunXr5vKcq666qlD/ZcuW0bFjR5f+IqVKQIgMxXWFpsutiX4vOrfXaAhN+nu+7oCpvk1cugkG+OCDAnIslaNg8NvOAqr/y/ZwWD8FSZcHYPTP0PDavMlXN0PLm+G+FVC3a17fag2g60O+DDLfywPVGsKYH6UYdEX/V2DIO95XlzSTFH4BlZ3bdTO0vQMm7IAH1kCr21yMyf556fl+58JjYMDr8NRReHgrPLodHt0pV6C85ZxRKBQKRYnhdzTNvHnzGDlyJLNmzeKqq65i9uzZfPDBB+zatYvY2Fieeuopjh8/zpw5cwAZ2hsXF8fYsWO57777WLduHePGjePrr7/mlltu8emeJZ4OPv4r+PUFZ9+QiBayeFrsVYX7CwHz75NhuflDUM1BUly0H+Xf/f/+GX58tHDmz1w0KRqGvA3hLraJDv8p85Sc/juvLSBEpoQ3cpz7BoRAj4nQ4995E6xhk06XlhCZudUVhgFrpsOfb8ooJFfUaCrTnh9cARtmOycIC4+BZoOg471Qs4kbOwsghPxMbFaZC2bDe84Oxw2ulp9JSAQc+A3Sz0J4HajfSwq9/GRehB3z4O8fIeOCdEhuP1pWzE0+LAVMtQZKdCgUCkUp4uv8XaTaNDNnzmTq1KkkJiYSFxfHG2+8Qa9evQAYM2YMhw8f5o8//sjtv3LlSiZOnJib9Gzy5Ml+JT0rldo0NiscWWfPwBorM6B6W93IToPNH0sRE9Ue4m4q+lK+YcCxjVJYICC6ncy+auTIkNJq9T2fL4QsXnf+CARXh7pXQU6GtOncITnJVq0n/R6Kkw00J0Nmes3JkNWB089C+hkpAqLb5dmfdRES1sl8J5GtSqYGS1aqtMeaCbVaOm+fKRQKheKyp1TFyKXmiiqUp1AoFApFBcHX+VutUSsUCoVCoShTlBhRKBQKhUJRpigxolAoFAqFokxRYkShUCgUCkWZosSIQqFQKBSKMkWJEYVCoVAoFGWKEiMKhUKhUCjKFCVGFAqFQqFQlClKjCgUCoVCoShTzGU9AF9wJIlNSUkp45EoFAqFQqHwFce87S3Ze7kQIxcvykJtderUKeORKBQKhUKh8JeLFy8SHh7u9ni5qE1jGAYnTpwgNDQUzUthupSUFOrUqcPRo0evqDo2yu4ry264cm1Xdiu7rxQqgu1CCC5evEh0dDS6hyrp5WJlRNd1YmJi/DonLCys3P7wioOy+8rjSrVd2X1lcaXaDeXfdk8rIg6UA6tCoVAoFIoyRYkRhUKhUCgUZUqFEyOBgYE8//zzBAYGlvVQLinK7ivLbrhybVd2K7uvFK4k28uFA6tCoVAoFIqKS4VbGVEoFAqFQlG+UGJEoVAoFApFmaLEiEKhUCgUijJFiRGFQqFQKBRlSrkXIy+//DKapjFhwoTcNiEEL7zwAtHR0VSqVIk+ffqwa9eushtkCXL8+HHuuusuqlevTnBwMG3btmXLli25xyui7VarlWeffZb69etTqVIlGjRowJQpUzAMI7dPRbF71apVDB48mOjoaDRNY+HChU7HfbEzKyuLhx9+mBo1ahASEsKQIUM4duzYJbTCfzzZnZOTw+TJk2nVqhUhISFER0czatQoTpw44XSNimZ3QcaOHYumabz55ptO7eXRbvDN9j179jBkyBDCw8MJDQ2la9euHDlyJPd4ebTdm92pqamMHz+emJgYKlWqRPPmzXnvvfec+pRHu71RrsXIpk2bmD17Nq1bt3Zqnzp1KtOnT2fGjBls2rSJyMhIrr/++twaN+WV5ORkunfvjsViYfHixezevZtp06ZRpUqV3D4V0fZXX32VWbNmMWPGDPbs2cPUqVN57bXXeOedd3L7VBS709LSaNOmDTNmzHB53Bc7J0yYwIIFC5g7dy5r1qwhNTWVQYMGYbPZLpUZfuPJ7vT0dLZu3cpzzz3H1q1bmT9/Pvv27WPIkCFO/Sqa3flZuHAhGzZsIDo6utCx8mg3eLf9wIED9OjRg2bNmvHHH3+wfft2nnvuOYKCgnL7lEfbvdk9ceJElixZwhdffMGePXuYOHEiDz/8MD/88ENun/Jot1dEOeXixYuicePGYvny5aJ3797i0UcfFUIIYRiGiIyMFK+88kpu38zMTBEeHi5mzZpVRqMtGSZPnix69Ojh9nhFtX3gwIHinnvucWq7+eabxV133SWEqLh2A2LBggW5732x8/z588JisYi5c+fm9jl+/LjQdV0sWbLkko29OBS02xUbN24UgEhISBBCVGy7jx07JmrXri3++usvERsbK954443cYxXBbiFc2z58+PDc/+OuqAi2u7K7ZcuWYsqUKU5t7du3F88++6wQomLY7YpyuzLy0EMPMXDgQK677jqn9kOHDnHy5En69u2b2xYYGEjv3r1Zu3btpR5mibJo0SI6duzIbbfdRkREBO3ateODDz7IPV5Rbe/Rowe//fYb+/btA2D79u2sWbOGAQMGABXX7oL4YueWLVvIyclx6hMdHU1cXFyF+iwuXLiApmm5q4IV1W7DMBg5ciSPP/44LVu2LHS8Itv9888/06RJE/r160dERARdunRx2tKoqLb36NGDRYsWcfz4cYQQrFixgn379tGvXz+g4tpdLsXI3Llz2bp1Ky+//HKhYydPngSgVq1aTu21atXKPVZeOXjwIO+99x6NGzdm6dKljBs3jkceeYQ5c+YAFdf2yZMnM2LECJo1a4bFYqFdu3ZMmDCBESNGABXX7oL4YufJkycJCAigatWqbvuUdzIzM3nyySe54447couHVVS7X331VcxmM4888ojL4xXV7qSkJFJTU3nllVfo378/y5Yt46abbuLmm29m5cqVQMW1/e2336ZFixbExMQQEBBA//79mTlzJj169AAqrt3lompvfo4ePcqjjz7KsmXLnPYOC6JpmtN7IUShtvKGYRh07NiRl156CYB27dqxa9cu3nvvPUaNGpXbr6LZPm/ePL744gu++uorWrZsSXx8PBMmTCA6OprRo0fn9qtodrujKHZWlM8iJyeH22+/HcMwmDlzptf+5dnuLVu28NZbb7F161a/bSjPdgO5zulDhw5l4sSJALRt25a1a9cya9Ysevfu7fbc8m7722+/zfr161m0aBGxsbGsWrWKBx98kKioqEI7Afkp73aXu5WRLVu2kJSURIcOHTCbzZjNZlauXMnbb7+N2WzOfWosqBCTkpIKPVGWN6KiomjRooVTW/PmzXO9yyMjI4GKZ/vjjz/Ok08+ye23306rVq0YOXIkEydOzF0Zq6h2F8QXOyMjI8nOziY5Odltn/JKTk4Ow4YN49ChQyxfvtyppHpFtHv16tUkJSVRt27d3L91CQkJ/Pvf/6ZevXpAxbQboEaNGpjNZq9/7yqa7RkZGTz99NNMnz6dwYMH07p1a8aPH8/w4cN5/fXXgYppN5RDMXLttdeyc+dO4uPjc18dO3bkzjvvJD4+ngYNGhAZGcny5ctzz8nOzmblypV069atDEdefLp3787evXud2vbt20dsbCwA9evXr5C2p6eno+vOv6omkyn36ami2l0QX+zs0KEDFovFqU9iYiJ//fVXuf4sHEJk//79/Prrr1SvXt3peEW0e+TIkezYscPpb110dDSPP/44S5cuBSqm3QABAQF06tTJ49+7imh7Tk4OOTk5Hv/eVUS7gfIbTZOf/NE0QgjxyiuviPDwcDF//nyxc+dOMWLECBEVFSVSUlLKbpAlwMaNG4XZbBYvvvii2L9/v/jyyy9FcHCw+OKLL3L7VETbR48eLWrXri1++ukncejQITF//nxRo0YN8cQTT+T2qSh2X7x4UWzbtk1s27ZNAGL69Oli27ZtuVEjvtg5btw4ERMTI3799VexdetWcc0114g2bdoIq9VaVmZ5xZPdOTk5YsiQISImJkbEx8eLxMTE3FdWVlbuNSqa3a4oGE0jRPm0Wwjvts+fP19YLBYxe/ZssX//fvHOO+8Ik8kkVq9enXuN8mi7N7t79+4tWrZsKVasWCEOHjwoPvnkExEUFCRmzpyZe43yaLc3KqQYMQxDPP/88yIyMlIEBgaKXr16iZ07d5bdAEuQH3/8UcTFxYnAwEDRrFkzMXv2bKfjFdH2lJQU8eijj4q6deuKoKAg0aBBA/HMM884TUQVxe4VK1YIoNBr9OjRQgjf7MzIyBDjx48X1apVE5UqVRKDBg0SR44cKQNrfMeT3YcOHXJ5DBArVqzIvUZFs9sVrsRIebRbCN9s/+ijj0SjRo1EUFCQaNOmjVi4cKHTNcqj7d7sTkxMFGPGjBHR0dEiKChING3aVEybNk0YhpF7jfJotzc0IYQo3bUXhUKhUCgUCveUO58RhUKhUCgUFQslRhQKhUKhUJQpSowoFAqFQqEoU5QYUSgUCoVCUaYoMaJQKBQKhaJMUWJEoVAoFApFmaLEiEKhUCgUijJFiRGFQqFQKBRlihIjCoVCoVAoyhQlRhQKhUKhUJQpSowoFAqFQqEoU5QYUSgUCoVCUab8P5PXFBuKLhFAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'], \n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20, 'symmetry_mean': 0.22}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x168853e50>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot the new observation\n",
+    "ax.scatter3D(new_observation['perimeter_mean'], new_observation['concavity_mean'], \n",
+    "             new_observation['symmetry_mean'], color='red', edgecolor='black', \n",
+    "             s=100, marker='o', label='New Observation')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 593bceaed..997856f76 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of observations (rows) in the Wine dataset is: 178\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "\n",
+    "from sklearn.datasets import load_wine\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load the Wine dataset\n",
+    "wine_data = load_wine()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)\n",
+    "\n",
+    "# Bind the 'class' (wine target) to the DataFrame\n",
+    "wine_df['class'] = wine_data.target\n",
+    "\n",
+    "# Get the number of observations (rows)\n",
+    "num_rows = wine_df.shape[0]\n",
+    "\n",
+    "print(f\"The number of observations (rows) in the Wine dataset is: {num_rows}\")"
    ]
   },
   {
@@ -109,12 +411,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of variables (columns) in the Wine dataset is: 14\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "\n",
+    "# Get the number of variables (columns)\n",
+    "num_columns = wine_df.shape[1]\n",
+    "\n",
+    "print(f\"The number of variables (columns) in the Wine dataset is: {num_columns}\")"
    ]
   },
   {
@@ -127,12 +441,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The variable type of 'class' is: int64\n",
+      "The unique levels of 'class' are: [0 1 2]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "\n",
+    "# Determine the variable type of the response variable 'class'\n",
+    "class_dtype = wine_df['class'].dtype\n",
+    "\n",
+    "# Get the unique values (levels) of the 'class' variable\n",
+    "class_levels = wine_df['class'].unique()\n",
+    "\n",
+    "print(f\"The variable type of 'class' is: {class_dtype}\")\n",
+    "print(f\"The unique levels of 'class' are: {class_levels}\")"
    ]
   },
   {
@@ -146,12 +477,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The number of predictor variables is: 13\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "\n",
+    "# Calculate the number of predictor variables (all variables excluding 'class')\n",
+    "num_predictor_variables = wine_df.shape[1] - 1\n",
+    "\n",
+    "print(f\"The number of predictor variables is: {num_predictor_variables}\")"
    ]
   },
   {
@@ -175,10 +518,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -204,7 +574,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Standardizing predictor variables is needed for KNN because it makes sure all features are on the same scale. If you don't do this variables with larger ranges could dominate the distance calculations, messing up the model’s accuracy."
    ]
   },
   {
@@ -220,7 +590,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "We didn’t standardize class because it’s just a category, not a number. Standardizing is for continuous data, and class doesn’t need it."
    ]
   },
   {
@@ -236,7 +606,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Setting a seed makes results reproducible so you get the same output every time you run the code. The actual seed value doesn’t matter, as long as you set one."
    ]
   },
   {
@@ -251,15 +621,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Do not touch\n",
     "np.random.seed(123)\n",
+    "\n",
     "# Create a random vector of True and False values to split the data\n",
-    "split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])"
+    "split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])\n",
+    "\n",
+    "# Create training and testing sets for predictors and response variables\n",
+    "X_train = predictors_standardized[split]\n",
+    "X_test = predictors_standardized[~split]\n",
+    "y_train = wine_df['class'][split]\n",
+    "y_test = wine_df['class'][~split]"
    ]
   },
   {
@@ -282,12 +659,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best value for n_neighbors is: 8\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# 1 Initialize the KNN classifier\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "# 2 Define the parameter grid for 'n_neighbors'\n",
+    "param_grid = {'n_neighbors': range(1, 51)}\n",
+    "\n",
+    "# 3 Implement grid search 10-fold cross-validation\n",
+    "grid_search = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=10,  # 10-fold cross-validation\n",
+    "    scoring='accuracy',  # Use accuracy as the scoring metric\n",
+    "    n_jobs=-1  # Use all available cores for computation\n",
+    ")\n",
+    "\n",
+    "# Fit the model on the training data\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "# 4 Identify the best value for 'n_neighbors'\n",
+    "best_n_neighbors = grid_search.best_params_['n_neighbors']\n",
+    "\n",
+    "print(f\"The best value for n_neighbors is: {best_n_neighbors}\")\n"
    ]
   },
   {
@@ -303,12 +709,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The test set accuracy of the best KNN model is: 0.95\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Fit the best KNN model and evaluate on test data\n",
+    "best_knn = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n",
+    "best_knn.fit(X_train, y_train)\n",
+    "y_pred = best_knn.predict(X_test)\n",
+    "test_accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"The test set accuracy of the best KNN model is: {test_accuracy:.2f}\")"
    ]
   },
   {