Stocks_Relattion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>AIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>AMAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>APTV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ADM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Symbol\n",
       "0     MMM\n",
       "1     ABT\n",
       "2    ABBV\n",
       "3    ABMD\n",
       "4     ACN\n",
       "45    AIV\n",
       "46   AAPL\n",
       "47   AMAT\n",
       "48   APTV\n",
       "49    ADM"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=pd.read_csv('AllSymbols.csv')\n",
    "data1=data1[0:50]\n",
    "data1.head().append(data1.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_clean(data):\n",
    "    \n",
    "    print('Nan Values')\n",
    "    print(data[data.isnull().any(axis=1)].index)\n",
    "    \n",
    "    print('Duplicated Values')\n",
    "    data_dup=data.duplicated()\n",
    "    for i in range(len(data_dup)) :\n",
    "        if data_dup[i] == True:\n",
    "            print(str(i)+'True')\n",
    "            \n",
    "    print('Negative/Zero Values')\n",
    "    N_N=[x for x in range(len(data))  if data['Open'].iloc[x] <=0 or data['High'].iloc[x]<=0 or data['Low'].iloc[x]<=0 \n",
    "         or data['Close'].iloc[x]<=0 or data['Adj Close'].iloc[x]<=0 or data['Volume'].iloc[x]<0]\n",
    "    print(N_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "path='/Users/buckycheng/Jupyter/Huge_Stock/Stock_Data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/50 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MMM START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  2%|▏         | 1/50 [00:00<00:14,  3.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "MMM FINISH\n",
      "ABT START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3/50 [00:00<00:11,  4.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ABT FINISH\n",
      "ABBV START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n",
      "[]\n",
      "ABBV FINISH\n",
      "ABMD START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  8%|▊         | 4/50 [00:00<00:11,  4.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ABMD FINISH\n",
      "ACN START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 10%|█         | 5/50 [00:01<00:11,  4.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ACN FINISH\n",
      "ATVI START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 12%|█▏        | 6/50 [00:01<00:11,  3.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ATVI FINISH\n",
      "ADBE START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 14%|█▍        | 7/50 [00:01<00:10,  3.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ADBE FINISH\n",
      "AMD START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 16%|█▌        | 8/50 [00:01<00:10,  3.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AMD FINISH\n",
      "AAP START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|█▊        | 9/50 [00:02<00:10,  3.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AAP FINISH\n",
      "AES START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|██        | 10/50 [00:02<00:10,  3.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AES FINISH\n",
      "AFL START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 22%|██▏       | 11/50 [00:02<00:09,  3.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AFL FINISH\n",
      "A START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 24%|██▍       | 12/50 [00:02<00:09,  3.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "A FINISH\n",
      "APD START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 26%|██▌       | 13/50 [00:03<00:09,  3.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "APD FINISH\n",
      "AKAM START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 28%|██▊       | 14/50 [00:03<00:09,  3.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AKAM FINISH\n",
      "ALK START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 30%|███       | 15/50 [00:03<00:08,  4.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ALK FINISH\n",
      "ALB START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 32%|███▏      | 16/50 [00:03<00:08,  4.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ALB FINISH\n",
      "ARE START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 34%|███▍      | 17/50 [00:04<00:08,  4.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ARE FINISH\n",
      "ALXN START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36%|███▌      | 18/50 [00:04<00:07,  4.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ALXN FINISH\n",
      "ALGN START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 20/50 [00:04<00:06,  4.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ALGN FINISH\n",
      "ALLE START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n",
      "[]\n",
      "ALLE FINISH\n",
      "LNT START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 42%|████▏     | 21/50 [00:05<00:06,  4.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "LNT FINISH\n",
      "ALL START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 44%|████▍     | 22/50 [00:05<00:06,  4.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ALL FINISH\n",
      "GOOGL START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 46%|████▌     | 23/50 [00:05<00:06,  4.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "GOOGL FINISH\n",
      "GOOG START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 48%|████▊     | 24/50 [00:05<00:06,  4.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "GOOG FINISH\n",
      "MO START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 25/50 [00:06<00:06,  4.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "MO FINISH\n",
      "AMZN START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 54%|█████▍    | 27/50 [00:06<00:05,  4.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AMZN FINISH\n",
      "AMCR START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n",
      "[]\n",
      "AMCR FINISH\n",
      "AEE START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 56%|█████▌    | 28/50 [00:06<00:04,  4.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AEE FINISH\n",
      "AAL START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 58%|█████▊    | 29/50 [00:06<00:04,  4.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AAL FINISH\n",
      "AEP START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|██████    | 30/50 [00:07<00:04,  4.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AEP FINISH\n",
      "AXP START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 62%|██████▏   | 31/50 [00:07<00:04,  4.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AXP FINISH\n",
      "AIG START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 64%|██████▍   | 32/50 [00:07<00:04,  4.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AIG FINISH\n",
      "AMT START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 66%|██████▌   | 33/50 [00:07<00:04,  4.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "AMT FINISH\n",
      "AWK START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 68%|██████▊   | 34/50 [00:08<00:03,  4.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AWK FINISH\n",
      "AMP START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|███████   | 35/50 [00:08<00:03,  4.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AMP FINISH\n",
      "ABC START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 72%|███████▏  | 36/50 [00:08<00:03,  4.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ABC FINISH\n",
      "AME START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 74%|███████▍  | 37/50 [00:08<00:03,  4.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AME FINISH\n",
      "AMGN START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 76%|███████▌  | 38/50 [00:09<00:02,  4.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AMGN FINISH\n",
      "APH START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 78%|███████▊  | 39/50 [00:09<00:02,  4.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "APH FINISH\n",
      "ADI START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|████████  | 40/50 [00:09<00:02,  4.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ADI FINISH\n",
      "ANSS START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 82%|████████▏ | 41/50 [00:09<00:02,  4.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ANSS FINISH\n",
      "ANTM START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 84%|████████▍ | 42/50 [00:10<00:01,  4.05it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "ANTM FINISH\n",
      "AON START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 86%|████████▌ | 43/50 [00:10<00:01,  4.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AON FINISH\n",
      "AOS START\n",
      "Nan Values\n",
      "Int64Index([1218], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 88%|████████▊ | 44/50 [00:10<00:01,  4.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AOS FINISH\n",
      "APA START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|█████████ | 45/50 [00:10<00:01,  3.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "APA FINISH\n",
      "AIV START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 92%|█████████▏| 46/50 [00:11<00:01,  3.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AIV FINISH\n",
      "AAPL START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 94%|█████████▍| 47/50 [00:11<00:00,  3.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AAPL FINISH\n",
      "AMAT START\n",
      "Nan Values\n",
      "Int64Index([2301], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 49/50 [00:11<00:00,  4.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "AMAT FINISH\n",
      "APTV START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n",
      "Negative/Zero Values\n",
      "[]\n",
      "APTV FINISH\n",
      "ADM START\n",
      "Nan Values\n",
      "Int64Index([], dtype='int64')\n",
      "Duplicated Values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:12<00:00,  4.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative/Zero Values\n",
      "[]\n",
      "ADM FINISH\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Stock_R = pd.DataFrame()\n",
    "for symbol in tqdm(data1['Symbol']):\n",
    "    print(str(symbol)+' START')\n",
    "    stock_path=path+'/'+str(symbol)+'_20080101-20200801.csv'\n",
    "    data=pd.read_csv(stock_path)\n",
    "    data_clean(data)\n",
    "    print(str(symbol)+' FINISH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         2017-02-20\n",
       "Open                NaN\n",
       "High                NaN\n",
       "Low                 NaN\n",
       "Close               NaN\n",
       "Adj Close           NaN\n",
       "Volume              NaN\n",
       "Name: 2301, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_path=path+'/AMAT_20080101-20200801.csv'\n",
    "temp_data=pd.read_csv(stock_path)\n",
    "temp_data.iloc[2301]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_data=temp_data.drop([2301])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_data.to_csv(stock_path,encoding='utf-8',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using last 100 days "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3069</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>54.279999</td>\n",
       "      <td>54.990002</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>52.490002</td>\n",
       "      <td>52.108677</td>\n",
       "      <td>12974200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>48.779999</td>\n",
       "      <td>50.360001</td>\n",
       "      <td>45.099998</td>\n",
       "      <td>45.110001</td>\n",
       "      <td>44.782291</td>\n",
       "      <td>18043700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>48.669998</td>\n",
       "      <td>51.230000</td>\n",
       "      <td>45.349998</td>\n",
       "      <td>50.889999</td>\n",
       "      <td>50.520298</td>\n",
       "      <td>15539300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>44.049999</td>\n",
       "      <td>46.700001</td>\n",
       "      <td>40.410000</td>\n",
       "      <td>40.529999</td>\n",
       "      <td>40.235558</td>\n",
       "      <td>13763000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>41.759998</td>\n",
       "      <td>46.070000</td>\n",
       "      <td>39.770000</td>\n",
       "      <td>44.860001</td>\n",
       "      <td>44.534107</td>\n",
       "      <td>16361200.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3164</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>61.630001</td>\n",
       "      <td>63.070000</td>\n",
       "      <td>61.110001</td>\n",
       "      <td>63.009998</td>\n",
       "      <td>62.801323</td>\n",
       "      <td>7194700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>2020-07-28</td>\n",
       "      <td>62.529999</td>\n",
       "      <td>62.950001</td>\n",
       "      <td>61.900002</td>\n",
       "      <td>62.299999</td>\n",
       "      <td>62.093678</td>\n",
       "      <td>4565700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>2020-07-29</td>\n",
       "      <td>62.669998</td>\n",
       "      <td>63.660000</td>\n",
       "      <td>62.169998</td>\n",
       "      <td>63.250000</td>\n",
       "      <td>63.040531</td>\n",
       "      <td>4621400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3167</th>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>63.230000</td>\n",
       "      <td>64.430000</td>\n",
       "      <td>62.990002</td>\n",
       "      <td>64.419998</td>\n",
       "      <td>64.206657</td>\n",
       "      <td>5525000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>64.790001</td>\n",
       "      <td>64.790001</td>\n",
       "      <td>63.119999</td>\n",
       "      <td>64.330002</td>\n",
       "      <td>64.116959</td>\n",
       "      <td>5901200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date       Open       High        Low      Close  Adj Close  \\\n",
       "3069  2020-03-11  54.279999  54.990002  51.410000  52.490002  52.108677   \n",
       "3070  2020-03-12  48.779999  50.360001  45.099998  45.110001  44.782291   \n",
       "3071  2020-03-13  48.669998  51.230000  45.349998  50.889999  50.520298   \n",
       "3072  2020-03-16  44.049999  46.700001  40.410000  40.529999  40.235558   \n",
       "3073  2020-03-17  41.759998  46.070000  39.770000  44.860001  44.534107   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "3164  2020-07-27  61.630001  63.070000  61.110001  63.009998  62.801323   \n",
       "3165  2020-07-28  62.529999  62.950001  61.900002  62.299999  62.093678   \n",
       "3166  2020-07-29  62.669998  63.660000  62.169998  63.250000  63.040531   \n",
       "3167  2020-07-30  63.230000  64.430000  62.990002  64.419998  64.206657   \n",
       "3168  2020-07-31  64.790001  64.790001  63.119999  64.330002  64.116959   \n",
       "\n",
       "          Volume  \n",
       "3069  12974200.0  \n",
       "3070  18043700.0  \n",
       "3071  15539300.0  \n",
       "3072  13763000.0  \n",
       "3073  16361200.0  \n",
       "...          ...  \n",
       "3164   7194700.0  \n",
       "3165   4565700.0  \n",
       "3166   4621400.0  \n",
       "3167   5525000.0  \n",
       "3168   5901200.0  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_path=path+'/AMAT_20080101-20200801.csv'\n",
    "temp_data=pd.read_csv(stock_path)\n",
    "temp_data.iloc[-100:]\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:00<00:00, 131.82it/s]\n"
     ]
    },
    {
     "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>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>...</th>\n",
       "      <th>ANSS</th>\n",
       "      <th>ANTM</th>\n",
       "      <th>AON</th>\n",
       "      <th>AOS</th>\n",
       "      <th>APA</th>\n",
       "      <th>AIV</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMAT</th>\n",
       "      <th>APTV</th>\n",
       "      <th>ADM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>145.740005</td>\n",
       "      <td>93.989998</td>\n",
       "      <td>84.220001</td>\n",
       "      <td>188.330002</td>\n",
       "      <td>187.110001</td>\n",
       "      <td>73.099998</td>\n",
       "      <td>366.779999</td>\n",
       "      <td>51.950001</td>\n",
       "      <td>119.610001</td>\n",
       "      <td>12.12</td>\n",
       "      <td>...</td>\n",
       "      <td>260.760010</td>\n",
       "      <td>265.730011</td>\n",
       "      <td>184.830002</td>\n",
       "      <td>43.310001</td>\n",
       "      <td>11.88</td>\n",
       "      <td>37.139999</td>\n",
       "      <td>75.934998</td>\n",
       "      <td>51.250000</td>\n",
       "      <td>65.230003</td>\n",
       "      <td>34.759998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>148.509995</td>\n",
       "      <td>93.699997</td>\n",
       "      <td>83.959999</td>\n",
       "      <td>187.070007</td>\n",
       "      <td>188.559998</td>\n",
       "      <td>73.139999</td>\n",
       "      <td>367.510010</td>\n",
       "      <td>53.189999</td>\n",
       "      <td>124.849998</td>\n",
       "      <td>12.87</td>\n",
       "      <td>...</td>\n",
       "      <td>263.019989</td>\n",
       "      <td>274.329987</td>\n",
       "      <td>189.759995</td>\n",
       "      <td>44.790001</td>\n",
       "      <td>12.45</td>\n",
       "      <td>37.840000</td>\n",
       "      <td>77.532501</td>\n",
       "      <td>53.810001</td>\n",
       "      <td>68.150002</td>\n",
       "      <td>36.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>145.729996</td>\n",
       "      <td>96.070000</td>\n",
       "      <td>87.900002</td>\n",
       "      <td>194.610001</td>\n",
       "      <td>187.750000</td>\n",
       "      <td>75.459999</td>\n",
       "      <td>371.420013</td>\n",
       "      <td>55.740002</td>\n",
       "      <td>125.940002</td>\n",
       "      <td>12.40</td>\n",
       "      <td>...</td>\n",
       "      <td>265.850006</td>\n",
       "      <td>272.369995</td>\n",
       "      <td>192.009995</td>\n",
       "      <td>43.939999</td>\n",
       "      <td>12.03</td>\n",
       "      <td>36.930000</td>\n",
       "      <td>78.752502</td>\n",
       "      <td>54.009998</td>\n",
       "      <td>66.709999</td>\n",
       "      <td>35.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141.520004</td>\n",
       "      <td>93.800003</td>\n",
       "      <td>90.459999</td>\n",
       "      <td>191.289993</td>\n",
       "      <td>185.720001</td>\n",
       "      <td>72.250000</td>\n",
       "      <td>365.100006</td>\n",
       "      <td>53.759998</td>\n",
       "      <td>124.500000</td>\n",
       "      <td>12.47</td>\n",
       "      <td>...</td>\n",
       "      <td>259.790009</td>\n",
       "      <td>272.959991</td>\n",
       "      <td>193.220001</td>\n",
       "      <td>42.080002</td>\n",
       "      <td>11.79</td>\n",
       "      <td>35.400002</td>\n",
       "      <td>77.852501</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>34.740002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>136.119995</td>\n",
       "      <td>92.160004</td>\n",
       "      <td>88.870003</td>\n",
       "      <td>185.600006</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>73.669998</td>\n",
       "      <td>358.559998</td>\n",
       "      <td>52.180000</td>\n",
       "      <td>119.239998</td>\n",
       "      <td>11.61</td>\n",
       "      <td>...</td>\n",
       "      <td>252.110001</td>\n",
       "      <td>267.799988</td>\n",
       "      <td>190.229996</td>\n",
       "      <td>39.980000</td>\n",
       "      <td>10.73</td>\n",
       "      <td>34.279999</td>\n",
       "      <td>76.912498</td>\n",
       "      <td>51.490002</td>\n",
       "      <td>60.790001</td>\n",
       "      <td>33.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>163.240005</td>\n",
       "      <td>99.989998</td>\n",
       "      <td>97.160004</td>\n",
       "      <td>298.609985</td>\n",
       "      <td>223.320007</td>\n",
       "      <td>81.629997</td>\n",
       "      <td>437.100006</td>\n",
       "      <td>68.970001</td>\n",
       "      <td>146.830002</td>\n",
       "      <td>15.25</td>\n",
       "      <td>...</td>\n",
       "      <td>300.170013</td>\n",
       "      <td>263.989990</td>\n",
       "      <td>207.020004</td>\n",
       "      <td>52.840000</td>\n",
       "      <td>13.69</td>\n",
       "      <td>35.759998</td>\n",
       "      <td>94.809998</td>\n",
       "      <td>63.009998</td>\n",
       "      <td>83.550003</td>\n",
       "      <td>42.130001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>155.330002</td>\n",
       "      <td>99.860001</td>\n",
       "      <td>96.709999</td>\n",
       "      <td>294.890015</td>\n",
       "      <td>222.179993</td>\n",
       "      <td>80.720001</td>\n",
       "      <td>432.260010</td>\n",
       "      <td>67.610001</td>\n",
       "      <td>146.190002</td>\n",
       "      <td>15.20</td>\n",
       "      <td>...</td>\n",
       "      <td>298.220001</td>\n",
       "      <td>265.230011</td>\n",
       "      <td>205.740005</td>\n",
       "      <td>52.240002</td>\n",
       "      <td>13.32</td>\n",
       "      <td>36.990002</td>\n",
       "      <td>93.252502</td>\n",
       "      <td>62.299999</td>\n",
       "      <td>82.169998</td>\n",
       "      <td>42.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>156.250000</td>\n",
       "      <td>102.519997</td>\n",
       "      <td>97.010002</td>\n",
       "      <td>299.100006</td>\n",
       "      <td>224.380005</td>\n",
       "      <td>81.199997</td>\n",
       "      <td>436.299988</td>\n",
       "      <td>76.089996</td>\n",
       "      <td>148.210007</td>\n",
       "      <td>15.56</td>\n",
       "      <td>...</td>\n",
       "      <td>306.049988</td>\n",
       "      <td>281.559998</td>\n",
       "      <td>205.800003</td>\n",
       "      <td>53.299999</td>\n",
       "      <td>13.65</td>\n",
       "      <td>37.770000</td>\n",
       "      <td>95.040001</td>\n",
       "      <td>63.250000</td>\n",
       "      <td>83.669998</td>\n",
       "      <td>42.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>152.119995</td>\n",
       "      <td>101.529999</td>\n",
       "      <td>96.040001</td>\n",
       "      <td>298.910004</td>\n",
       "      <td>223.979996</td>\n",
       "      <td>81.699997</td>\n",
       "      <td>438.880005</td>\n",
       "      <td>78.199997</td>\n",
       "      <td>152.869995</td>\n",
       "      <td>15.42</td>\n",
       "      <td>...</td>\n",
       "      <td>309.670013</td>\n",
       "      <td>277.190002</td>\n",
       "      <td>207.429993</td>\n",
       "      <td>48.459999</td>\n",
       "      <td>16.01</td>\n",
       "      <td>38.299999</td>\n",
       "      <td>96.190002</td>\n",
       "      <td>64.419998</td>\n",
       "      <td>80.459999</td>\n",
       "      <td>43.360001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>150.470001</td>\n",
       "      <td>100.639999</td>\n",
       "      <td>94.910004</td>\n",
       "      <td>299.940002</td>\n",
       "      <td>224.779999</td>\n",
       "      <td>82.629997</td>\n",
       "      <td>444.320007</td>\n",
       "      <td>77.430000</td>\n",
       "      <td>150.139999</td>\n",
       "      <td>15.23</td>\n",
       "      <td>...</td>\n",
       "      <td>310.600006</td>\n",
       "      <td>273.799988</td>\n",
       "      <td>205.220001</td>\n",
       "      <td>48.139999</td>\n",
       "      <td>15.35</td>\n",
       "      <td>38.820000</td>\n",
       "      <td>106.260002</td>\n",
       "      <td>64.330002</td>\n",
       "      <td>77.750000</td>\n",
       "      <td>42.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           MMM         ABT       ABBV        ABMD         ACN       ATVI  \\\n",
       "0   145.740005   93.989998  84.220001  188.330002  187.110001  73.099998   \n",
       "1   148.509995   93.699997  83.959999  187.070007  188.559998  73.139999   \n",
       "2   145.729996   96.070000  87.900002  194.610001  187.750000  75.459999   \n",
       "3   141.520004   93.800003  90.459999  191.289993  185.720001  72.250000   \n",
       "4   136.119995   92.160004  88.870003  185.600006  181.000000  73.669998   \n",
       "55  163.240005   99.989998  97.160004  298.609985  223.320007  81.629997   \n",
       "56  155.330002   99.860001  96.709999  294.890015  222.179993  80.720001   \n",
       "57  156.250000  102.519997  97.010002  299.100006  224.380005  81.199997   \n",
       "58  152.119995  101.529999  96.040001  298.910004  223.979996  81.699997   \n",
       "59  150.470001  100.639999  94.910004  299.940002  224.779999  82.629997   \n",
       "\n",
       "          ADBE        AMD         AAP    AES  ...        ANSS        ANTM  \\\n",
       "0   366.779999  51.950001  119.610001  12.12  ...  260.760010  265.730011   \n",
       "1   367.510010  53.189999  124.849998  12.87  ...  263.019989  274.329987   \n",
       "2   371.420013  55.740002  125.940002  12.40  ...  265.850006  272.369995   \n",
       "3   365.100006  53.759998  124.500000  12.47  ...  259.790009  272.959991   \n",
       "4   358.559998  52.180000  119.239998  11.61  ...  252.110001  267.799988   \n",
       "55  437.100006  68.970001  146.830002  15.25  ...  300.170013  263.989990   \n",
       "56  432.260010  67.610001  146.190002  15.20  ...  298.220001  265.230011   \n",
       "57  436.299988  76.089996  148.210007  15.56  ...  306.049988  281.559998   \n",
       "58  438.880005  78.199997  152.869995  15.42  ...  309.670013  277.190002   \n",
       "59  444.320007  77.430000  150.139999  15.23  ...  310.600006  273.799988   \n",
       "\n",
       "           AON        AOS    APA        AIV        AAPL       AMAT       APTV  \\\n",
       "0   184.830002  43.310001  11.88  37.139999   75.934998  51.250000  65.230003   \n",
       "1   189.759995  44.790001  12.45  37.840000   77.532501  53.810001  68.150002   \n",
       "2   192.009995  43.939999  12.03  36.930000   78.752502  54.009998  66.709999   \n",
       "3   193.220001  42.080002  11.79  35.400002   77.852501  52.330002  64.500000   \n",
       "4   190.229996  39.980000  10.73  34.279999   76.912498  51.490002  60.790001   \n",
       "55  207.020004  52.840000  13.69  35.759998   94.809998  63.009998  83.550003   \n",
       "56  205.740005  52.240002  13.32  36.990002   93.252502  62.299999  82.169998   \n",
       "57  205.800003  53.299999  13.65  37.770000   95.040001  63.250000  83.669998   \n",
       "58  207.429993  48.459999  16.01  38.299999   96.190002  64.419998  80.459999   \n",
       "59  205.220001  48.139999  15.35  38.820000  106.260002  64.330002  77.750000   \n",
       "\n",
       "          ADM  \n",
       "0   34.759998  \n",
       "1   36.389999  \n",
       "2   35.430000  \n",
       "3   34.740002  \n",
       "4   33.840000  \n",
       "55  42.130001  \n",
       "56  42.590000  \n",
       "57  42.919998  \n",
       "58  43.360001  \n",
       "59  42.830002  \n",
       "\n",
       "[10 rows x 50 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_data=pd.DataFrame()\n",
    "for symbol in tqdm(data1['Symbol']):\n",
    "    stock_path=path+'/'+str(symbol)+'_20080101-20200801.csv'\n",
    "    data=pd.read_csv(stock_path)\n",
    "    data=data[-60:]\n",
    "    r_data[symbol]=data['Close'].values\n",
    "\n",
    "\n",
    "r_data.head().append(r_data.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 50)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_data=r_data.pct_change()[1:]\n",
    "r_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>...</th>\n",
       "      <th>ANSS</th>\n",
       "      <th>ANTM</th>\n",
       "      <th>AON</th>\n",
       "      <th>AOS</th>\n",
       "      <th>APA</th>\n",
       "      <th>AIV</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMAT</th>\n",
       "      <th>APTV</th>\n",
       "      <th>ADM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019006</td>\n",
       "      <td>-0.003085</td>\n",
       "      <td>-0.003087</td>\n",
       "      <td>-0.006690</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.001990</td>\n",
       "      <td>0.023869</td>\n",
       "      <td>0.043809</td>\n",
       "      <td>0.061881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008667</td>\n",
       "      <td>0.032364</td>\n",
       "      <td>0.026673</td>\n",
       "      <td>0.034172</td>\n",
       "      <td>0.047980</td>\n",
       "      <td>0.018848</td>\n",
       "      <td>0.021038</td>\n",
       "      <td>0.049951</td>\n",
       "      <td>0.044765</td>\n",
       "      <td>0.046893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.018719</td>\n",
       "      <td>0.025294</td>\n",
       "      <td>0.046927</td>\n",
       "      <td>0.040306</td>\n",
       "      <td>-0.004296</td>\n",
       "      <td>0.031720</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>0.047941</td>\n",
       "      <td>0.008731</td>\n",
       "      <td>-0.036519</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010760</td>\n",
       "      <td>-0.007145</td>\n",
       "      <td>0.011857</td>\n",
       "      <td>-0.018978</td>\n",
       "      <td>-0.033735</td>\n",
       "      <td>-0.024049</td>\n",
       "      <td>0.015735</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>-0.021130</td>\n",
       "      <td>-0.026381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.028889</td>\n",
       "      <td>-0.023629</td>\n",
       "      <td>0.029124</td>\n",
       "      <td>-0.017060</td>\n",
       "      <td>-0.010812</td>\n",
       "      <td>-0.042539</td>\n",
       "      <td>-0.017016</td>\n",
       "      <td>-0.035522</td>\n",
       "      <td>-0.011434</td>\n",
       "      <td>0.005645</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022795</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.006302</td>\n",
       "      <td>-0.042330</td>\n",
       "      <td>-0.019950</td>\n",
       "      <td>-0.041430</td>\n",
       "      <td>-0.011428</td>\n",
       "      <td>-0.031105</td>\n",
       "      <td>-0.033128</td>\n",
       "      <td>-0.019475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.038157</td>\n",
       "      <td>-0.017484</td>\n",
       "      <td>-0.017577</td>\n",
       "      <td>-0.029745</td>\n",
       "      <td>-0.025415</td>\n",
       "      <td>0.019654</td>\n",
       "      <td>-0.017913</td>\n",
       "      <td>-0.029390</td>\n",
       "      <td>-0.042249</td>\n",
       "      <td>-0.068966</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029562</td>\n",
       "      <td>-0.018904</td>\n",
       "      <td>-0.015475</td>\n",
       "      <td>-0.049905</td>\n",
       "      <td>-0.089907</td>\n",
       "      <td>-0.031638</td>\n",
       "      <td>-0.012074</td>\n",
       "      <td>-0.016052</td>\n",
       "      <td>-0.057519</td>\n",
       "      <td>-0.025907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.000955</td>\n",
       "      <td>-0.004123</td>\n",
       "      <td>0.012040</td>\n",
       "      <td>-0.007166</td>\n",
       "      <td>-0.005580</td>\n",
       "      <td>-0.009638</td>\n",
       "      <td>-0.008841</td>\n",
       "      <td>0.044653</td>\n",
       "      <td>0.015850</td>\n",
       "      <td>0.018088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.020463</td>\n",
       "      <td>0.019450</td>\n",
       "      <td>0.007504</td>\n",
       "      <td>-0.034483</td>\n",
       "      <td>-0.021295</td>\n",
       "      <td>0.006143</td>\n",
       "      <td>0.057098</td>\n",
       "      <td>0.038822</td>\n",
       "      <td>0.009456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.021271</td>\n",
       "      <td>0.010510</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.019355</td>\n",
       "      <td>0.008490</td>\n",
       "      <td>0.020503</td>\n",
       "      <td>0.015779</td>\n",
       "      <td>-0.006196</td>\n",
       "      <td>-0.002988</td>\n",
       "      <td>-0.009740</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022691</td>\n",
       "      <td>-0.024030</td>\n",
       "      <td>-0.000724</td>\n",
       "      <td>0.029819</td>\n",
       "      <td>-0.035916</td>\n",
       "      <td>0.003085</td>\n",
       "      <td>0.023700</td>\n",
       "      <td>0.039426</td>\n",
       "      <td>0.015806</td>\n",
       "      <td>0.000950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>-0.048456</td>\n",
       "      <td>-0.001300</td>\n",
       "      <td>-0.004632</td>\n",
       "      <td>-0.012458</td>\n",
       "      <td>-0.005105</td>\n",
       "      <td>-0.011148</td>\n",
       "      <td>-0.011073</td>\n",
       "      <td>-0.019719</td>\n",
       "      <td>-0.004359</td>\n",
       "      <td>-0.003279</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006496</td>\n",
       "      <td>0.004697</td>\n",
       "      <td>-0.006183</td>\n",
       "      <td>-0.011355</td>\n",
       "      <td>-0.027027</td>\n",
       "      <td>0.034396</td>\n",
       "      <td>-0.016428</td>\n",
       "      <td>-0.011268</td>\n",
       "      <td>-0.016517</td>\n",
       "      <td>0.010919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.005923</td>\n",
       "      <td>0.026637</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.014276</td>\n",
       "      <td>0.009902</td>\n",
       "      <td>0.005946</td>\n",
       "      <td>0.009346</td>\n",
       "      <td>0.125425</td>\n",
       "      <td>0.013818</td>\n",
       "      <td>0.023684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026256</td>\n",
       "      <td>0.061569</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.020291</td>\n",
       "      <td>0.024775</td>\n",
       "      <td>0.021087</td>\n",
       "      <td>0.019168</td>\n",
       "      <td>0.015249</td>\n",
       "      <td>0.018255</td>\n",
       "      <td>0.007748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>-0.026432</td>\n",
       "      <td>-0.009657</td>\n",
       "      <td>-0.009999</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>-0.001783</td>\n",
       "      <td>0.006158</td>\n",
       "      <td>0.005913</td>\n",
       "      <td>0.027730</td>\n",
       "      <td>0.031442</td>\n",
       "      <td>-0.008997</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011828</td>\n",
       "      <td>-0.015521</td>\n",
       "      <td>0.007920</td>\n",
       "      <td>-0.090807</td>\n",
       "      <td>0.172894</td>\n",
       "      <td>0.014032</td>\n",
       "      <td>0.012100</td>\n",
       "      <td>0.018498</td>\n",
       "      <td>-0.038365</td>\n",
       "      <td>0.010252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-0.010847</td>\n",
       "      <td>-0.008766</td>\n",
       "      <td>-0.011766</td>\n",
       "      <td>0.003446</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.011383</td>\n",
       "      <td>0.012395</td>\n",
       "      <td>-0.009847</td>\n",
       "      <td>-0.017858</td>\n",
       "      <td>-0.012322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>-0.012230</td>\n",
       "      <td>-0.010654</td>\n",
       "      <td>-0.006603</td>\n",
       "      <td>-0.041224</td>\n",
       "      <td>0.013577</td>\n",
       "      <td>0.104689</td>\n",
       "      <td>-0.001397</td>\n",
       "      <td>-0.033681</td>\n",
       "      <td>-0.012223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         MMM       ABT      ABBV      ABMD       ACN      ATVI      ADBE  \\\n",
       "1   0.019006 -0.003085 -0.003087 -0.006690  0.007749  0.000547  0.001990   \n",
       "2  -0.018719  0.025294  0.046927  0.040306 -0.004296  0.031720  0.010639   \n",
       "3  -0.028889 -0.023629  0.029124 -0.017060 -0.010812 -0.042539 -0.017016   \n",
       "4  -0.038157 -0.017484 -0.017577 -0.029745 -0.025415  0.019654 -0.017913   \n",
       "5  -0.000955 -0.004123  0.012040 -0.007166 -0.005580 -0.009638 -0.008841   \n",
       "55  0.021271  0.010510  0.000515  0.019355  0.008490  0.020503  0.015779   \n",
       "56 -0.048456 -0.001300 -0.004632 -0.012458 -0.005105 -0.011148 -0.011073   \n",
       "57  0.005923  0.026637  0.003102  0.014276  0.009902  0.005946  0.009346   \n",
       "58 -0.026432 -0.009657 -0.009999 -0.000635 -0.001783  0.006158  0.005913   \n",
       "59 -0.010847 -0.008766 -0.011766  0.003446  0.003572  0.011383  0.012395   \n",
       "\n",
       "         AMD       AAP       AES  ...      ANSS      ANTM       AON       AOS  \\\n",
       "1   0.023869  0.043809  0.061881  ...  0.008667  0.032364  0.026673  0.034172   \n",
       "2   0.047941  0.008731 -0.036519  ...  0.010760 -0.007145  0.011857 -0.018978   \n",
       "3  -0.035522 -0.011434  0.005645  ... -0.022795  0.002166  0.006302 -0.042330   \n",
       "4  -0.029390 -0.042249 -0.068966  ... -0.029562 -0.018904 -0.015475 -0.049905   \n",
       "5   0.044653  0.015850  0.018088  ...  0.000159  0.020463  0.019450  0.007504   \n",
       "55 -0.006196 -0.002988 -0.009740  ...  0.022691 -0.024030 -0.000724  0.029819   \n",
       "56 -0.019719 -0.004359 -0.003279  ... -0.006496  0.004697 -0.006183 -0.011355   \n",
       "57  0.125425  0.013818  0.023684  ...  0.026256  0.061569  0.000292  0.020291   \n",
       "58  0.027730  0.031442 -0.008997  ...  0.011828 -0.015521  0.007920 -0.090807   \n",
       "59 -0.009847 -0.017858 -0.012322  ...  0.003003 -0.012230 -0.010654 -0.006603   \n",
       "\n",
       "         APA       AIV      AAPL      AMAT      APTV       ADM  \n",
       "1   0.047980  0.018848  0.021038  0.049951  0.044765  0.046893  \n",
       "2  -0.033735 -0.024049  0.015735  0.003717 -0.021130 -0.026381  \n",
       "3  -0.019950 -0.041430 -0.011428 -0.031105 -0.033128 -0.019475  \n",
       "4  -0.089907 -0.031638 -0.012074 -0.016052 -0.057519 -0.025907  \n",
       "5  -0.034483 -0.021295  0.006143  0.057098  0.038822  0.009456  \n",
       "55 -0.035916  0.003085  0.023700  0.039426  0.015806  0.000950  \n",
       "56 -0.027027  0.034396 -0.016428 -0.011268 -0.016517  0.010919  \n",
       "57  0.024775  0.021087  0.019168  0.015249  0.018255  0.007748  \n",
       "58  0.172894  0.014032  0.012100  0.018498 -0.038365  0.010252  \n",
       "59 -0.041224  0.013577  0.104689 -0.001397 -0.033681 -0.012223  \n",
       "\n",
       "[10 rows x 50 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_data.head().append(r_data.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABMD</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AMD</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>...</th>\n",
       "      <th>ANSS</th>\n",
       "      <th>ANTM</th>\n",
       "      <th>AON</th>\n",
       "      <th>AOS</th>\n",
       "      <th>APA</th>\n",
       "      <th>AIV</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMAT</th>\n",
       "      <th>APTV</th>\n",
       "      <th>ADM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307415</td>\n",
       "      <td>0.244064</td>\n",
       "      <td>0.438366</td>\n",
       "      <td>0.646519</td>\n",
       "      <td>-0.040410</td>\n",
       "      <td>0.166383</td>\n",
       "      <td>0.147148</td>\n",
       "      <td>0.616921</td>\n",
       "      <td>0.654866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.430540</td>\n",
       "      <td>0.519742</td>\n",
       "      <td>0.575887</td>\n",
       "      <td>0.786918</td>\n",
       "      <td>0.534323</td>\n",
       "      <td>0.620554</td>\n",
       "      <td>0.306333</td>\n",
       "      <td>0.537973</td>\n",
       "      <td>0.665629</td>\n",
       "      <td>0.780672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.307415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.455999</td>\n",
       "      <td>0.610176</td>\n",
       "      <td>0.582031</td>\n",
       "      <td>0.385491</td>\n",
       "      <td>0.434311</td>\n",
       "      <td>0.312379</td>\n",
       "      <td>0.240838</td>\n",
       "      <td>0.200987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.597637</td>\n",
       "      <td>0.544804</td>\n",
       "      <td>0.400141</td>\n",
       "      <td>0.276135</td>\n",
       "      <td>0.166581</td>\n",
       "      <td>0.274443</td>\n",
       "      <td>0.290045</td>\n",
       "      <td>0.386744</td>\n",
       "      <td>0.457973</td>\n",
       "      <td>0.285280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.244064</td>\n",
       "      <td>0.455999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.327751</td>\n",
       "      <td>0.308941</td>\n",
       "      <td>0.117186</td>\n",
       "      <td>0.259462</td>\n",
       "      <td>0.203153</td>\n",
       "      <td>0.283974</td>\n",
       "      <td>0.167228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.301593</td>\n",
       "      <td>0.398955</td>\n",
       "      <td>0.337540</td>\n",
       "      <td>0.113628</td>\n",
       "      <td>0.083678</td>\n",
       "      <td>0.137387</td>\n",
       "      <td>0.206675</td>\n",
       "      <td>0.235862</td>\n",
       "      <td>0.245207</td>\n",
       "      <td>0.190627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABMD</th>\n",
       "      <td>0.438366</td>\n",
       "      <td>0.610176</td>\n",
       "      <td>0.327751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.493319</td>\n",
       "      <td>0.229821</td>\n",
       "      <td>0.476104</td>\n",
       "      <td>0.168924</td>\n",
       "      <td>0.329581</td>\n",
       "      <td>0.360801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.602167</td>\n",
       "      <td>0.432004</td>\n",
       "      <td>0.362320</td>\n",
       "      <td>0.332602</td>\n",
       "      <td>0.362293</td>\n",
       "      <td>0.446033</td>\n",
       "      <td>0.286626</td>\n",
       "      <td>0.355599</td>\n",
       "      <td>0.476857</td>\n",
       "      <td>0.307062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>0.646519</td>\n",
       "      <td>0.582031</td>\n",
       "      <td>0.308941</td>\n",
       "      <td>0.493319</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195719</td>\n",
       "      <td>0.563787</td>\n",
       "      <td>0.185020</td>\n",
       "      <td>0.454333</td>\n",
       "      <td>0.516070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.627744</td>\n",
       "      <td>0.544716</td>\n",
       "      <td>0.662131</td>\n",
       "      <td>0.434573</td>\n",
       "      <td>0.519765</td>\n",
       "      <td>0.595766</td>\n",
       "      <td>0.463380</td>\n",
       "      <td>0.446088</td>\n",
       "      <td>0.633544</td>\n",
       "      <td>0.569989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIV</th>\n",
       "      <td>0.620554</td>\n",
       "      <td>0.274443</td>\n",
       "      <td>0.137387</td>\n",
       "      <td>0.446033</td>\n",
       "      <td>0.595766</td>\n",
       "      <td>-0.183540</td>\n",
       "      <td>0.172044</td>\n",
       "      <td>0.034836</td>\n",
       "      <td>0.467376</td>\n",
       "      <td>0.679244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.451127</td>\n",
       "      <td>0.484305</td>\n",
       "      <td>0.416507</td>\n",
       "      <td>0.538539</td>\n",
       "      <td>0.659112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318654</td>\n",
       "      <td>0.374232</td>\n",
       "      <td>0.514839</td>\n",
       "      <td>0.696019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.306333</td>\n",
       "      <td>0.290045</td>\n",
       "      <td>0.206675</td>\n",
       "      <td>0.286626</td>\n",
       "      <td>0.463380</td>\n",
       "      <td>0.351324</td>\n",
       "      <td>0.523156</td>\n",
       "      <td>0.319191</td>\n",
       "      <td>0.221023</td>\n",
       "      <td>0.187859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.525893</td>\n",
       "      <td>0.185311</td>\n",
       "      <td>0.295975</td>\n",
       "      <td>0.187141</td>\n",
       "      <td>0.235593</td>\n",
       "      <td>0.318654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.446089</td>\n",
       "      <td>0.217944</td>\n",
       "      <td>0.157716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMAT</th>\n",
       "      <td>0.537973</td>\n",
       "      <td>0.386744</td>\n",
       "      <td>0.235862</td>\n",
       "      <td>0.355599</td>\n",
       "      <td>0.446088</td>\n",
       "      <td>0.190214</td>\n",
       "      <td>0.396344</td>\n",
       "      <td>0.270483</td>\n",
       "      <td>0.504119</td>\n",
       "      <td>0.432168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.587887</td>\n",
       "      <td>0.313872</td>\n",
       "      <td>0.599224</td>\n",
       "      <td>0.452025</td>\n",
       "      <td>0.364829</td>\n",
       "      <td>0.374232</td>\n",
       "      <td>0.446089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.558214</td>\n",
       "      <td>0.506465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APTV</th>\n",
       "      <td>0.665629</td>\n",
       "      <td>0.457973</td>\n",
       "      <td>0.245207</td>\n",
       "      <td>0.476857</td>\n",
       "      <td>0.633544</td>\n",
       "      <td>-0.033114</td>\n",
       "      <td>0.331539</td>\n",
       "      <td>0.141986</td>\n",
       "      <td>0.520359</td>\n",
       "      <td>0.615402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.452663</td>\n",
       "      <td>0.543698</td>\n",
       "      <td>0.567715</td>\n",
       "      <td>0.663780</td>\n",
       "      <td>0.441115</td>\n",
       "      <td>0.514839</td>\n",
       "      <td>0.217944</td>\n",
       "      <td>0.558214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.608912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADM</th>\n",
       "      <td>0.780672</td>\n",
       "      <td>0.285280</td>\n",
       "      <td>0.190627</td>\n",
       "      <td>0.307062</td>\n",
       "      <td>0.569989</td>\n",
       "      <td>-0.222583</td>\n",
       "      <td>0.067463</td>\n",
       "      <td>0.015855</td>\n",
       "      <td>0.595269</td>\n",
       "      <td>0.724745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327223</td>\n",
       "      <td>0.600576</td>\n",
       "      <td>0.612829</td>\n",
       "      <td>0.662550</td>\n",
       "      <td>0.610816</td>\n",
       "      <td>0.696019</td>\n",
       "      <td>0.157716</td>\n",
       "      <td>0.506465</td>\n",
       "      <td>0.608912</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           MMM       ABT      ABBV      ABMD       ACN      ATVI      ADBE  \\\n",
       "MMM   1.000000  0.307415  0.244064  0.438366  0.646519 -0.040410  0.166383   \n",
       "ABT   0.307415  1.000000  0.455999  0.610176  0.582031  0.385491  0.434311   \n",
       "ABBV  0.244064  0.455999  1.000000  0.327751  0.308941  0.117186  0.259462   \n",
       "ABMD  0.438366  0.610176  0.327751  1.000000  0.493319  0.229821  0.476104   \n",
       "ACN   0.646519  0.582031  0.308941  0.493319  1.000000  0.195719  0.563787   \n",
       "AIV   0.620554  0.274443  0.137387  0.446033  0.595766 -0.183540  0.172044   \n",
       "AAPL  0.306333  0.290045  0.206675  0.286626  0.463380  0.351324  0.523156   \n",
       "AMAT  0.537973  0.386744  0.235862  0.355599  0.446088  0.190214  0.396344   \n",
       "APTV  0.665629  0.457973  0.245207  0.476857  0.633544 -0.033114  0.331539   \n",
       "ADM   0.780672  0.285280  0.190627  0.307062  0.569989 -0.222583  0.067463   \n",
       "\n",
       "           AMD       AAP       AES  ...      ANSS      ANTM       AON  \\\n",
       "MMM   0.147148  0.616921  0.654866  ...  0.430540  0.519742  0.575887   \n",
       "ABT   0.312379  0.240838  0.200987  ...  0.597637  0.544804  0.400141   \n",
       "ABBV  0.203153  0.283974  0.167228  ...  0.301593  0.398955  0.337540   \n",
       "ABMD  0.168924  0.329581  0.360801  ...  0.602167  0.432004  0.362320   \n",
       "ACN   0.185020  0.454333  0.516070  ...  0.627744  0.544716  0.662131   \n",
       "AIV   0.034836  0.467376  0.679244  ...  0.451127  0.484305  0.416507   \n",
       "AAPL  0.319191  0.221023  0.187859  ...  0.525893  0.185311  0.295975   \n",
       "AMAT  0.270483  0.504119  0.432168  ...  0.587887  0.313872  0.599224   \n",
       "APTV  0.141986  0.520359  0.615402  ...  0.452663  0.543698  0.567715   \n",
       "ADM   0.015855  0.595269  0.724745  ...  0.327223  0.600576  0.612829   \n",
       "\n",
       "           AOS       APA       AIV      AAPL      AMAT      APTV       ADM  \n",
       "MMM   0.786918  0.534323  0.620554  0.306333  0.537973  0.665629  0.780672  \n",
       "ABT   0.276135  0.166581  0.274443  0.290045  0.386744  0.457973  0.285280  \n",
       "ABBV  0.113628  0.083678  0.137387  0.206675  0.235862  0.245207  0.190627  \n",
       "ABMD  0.332602  0.362293  0.446033  0.286626  0.355599  0.476857  0.307062  \n",
       "ACN   0.434573  0.519765  0.595766  0.463380  0.446088  0.633544  0.569989  \n",
       "AIV   0.538539  0.659112  1.000000  0.318654  0.374232  0.514839  0.696019  \n",
       "AAPL  0.187141  0.235593  0.318654  1.000000  0.446089  0.217944  0.157716  \n",
       "AMAT  0.452025  0.364829  0.374232  0.446089  1.000000  0.558214  0.506465  \n",
       "APTV  0.663780  0.441115  0.514839  0.217944  0.558214  1.000000  0.608912  \n",
       "ADM   0.662550  0.610816  0.696019  0.157716  0.506465  0.608912  1.000000  \n",
       "\n",
       "[10 rows x 50 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Corr_Data = r_data.corr()\n",
    "Corr_Data.head().append(Corr_Data.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## correlation heat-map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "ax = plt.subplot()\n",
    "cmap = cm.get_cmap('jet', 30)\n",
    "plt.title('Stock Hot Map')\n",
    "ax.set_xticks(np.arange(len(data1)))\n",
    "ax.set_yticks(np.arange(len(data1)))\n",
    "ax.set_xticklabels(data1['Symbol'].values,fontsize=10,rotation=90)\n",
    "ax.set_yticklabels(data1['Symbol'].values,fontsize=10)\n",
    "#ax.imshow(Corr_Data, interpolation=\"nearest\", cmap=cmap)\n",
    "plt.colorbar(ax.imshow(Corr_Data, interpolation=\"nearest\", cmap=cmap))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cov_Data=r_data.cov()\n",
    "mean_cov=Cov_Data.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## qualified couples\n",
    "### covariance > mean covariance\n",
    "### correlation > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "qual_combi=np.zeros(Cov_Data.shape)\n",
    "for i in range(len(data1['Symbol'].values)):\n",
    "    for j in range(i):\n",
    "        if np.array(Cov_Data)[i,j]<mean_cov[i] and np.array(Cov_Data)[i,j]<mean_cov[j] and np.array(Corr_Data)[i,j]<0.5:\n",
    "            qual_combi[i,j]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_candidates(couple, other_can):\n",
    "    for other in other_can:\n",
    "        count=0\n",
    "        for cou in couple:\n",
    "            if qual_combi[cou, other]==1 or qual_combi[other, cou]==1:\n",
    "                count+=1\n",
    "        if float(count)/len(couple)>=0.6:\n",
    "            couple.append(other)\n",
    "    return couple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## all portfolio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 1, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 42, 46],\n",
       " [0, 2, 6, 11, 12, 13, 16, 22, 23, 24, 25, 27, 29, 33, 35, 41, 42, 46, 49],\n",
       " [0, 3, 7, 12, 17, 20, 22, 29, 35, 42, 43, 46],\n",
       " [3, 4, 7, 8, 11, 12, 17, 20, 22, 24, 26, 27, 29, 35, 42, 46],\n",
       " [0, 5, 29, 36, 37, 38, 46],\n",
       " [0, 3, 4, 7, 20, 24, 46],\n",
       " [1, 8, 12, 13, 16, 17, 22, 23, 24, 26, 27, 29, 33, 35, 42, 46],\n",
       " [1, 9, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 42, 46],\n",
       " [5, 10],\n",
       " [2, 6, 8, 11, 12, 13, 16, 22, 23, 24, 25, 27, 29, 33, 35, 41, 42, 46, 49],\n",
       " [1, 8, 11, 12, 16, 17, 20, 22, 23, 24, 25, 26, 27, 29, 33, 35, 42, 46],\n",
       " [0, 2, 6, 12, 13, 16, 22, 24, 25, 27, 28, 29, 33, 35, 42, 46],\n",
       " [5, 14],\n",
       " [5, 15],\n",
       " [1, 12, 13, 16, 22, 23, 24, 27, 28, 29, 33, 35, 42, 46],\n",
       " [0, 7, 9, 17, 22, 23, 24, 27, 29, 35, 38, 42, 46],\n",
       " [18, 25],\n",
       " [2, 13, 16, 19, 24, 25, 27, 33, 42, 46],\n",
       " [3, 12, 13, 21, 41, 42, 43, 46],\n",
       " [0, 1, 2, 12, 13, 16, 22, 23, 24, 25, 27, 28, 29, 33, 42, 43, 46],\n",
       " [1, 8, 11, 12, 13, 16, 17, 22, 23, 24, 25, 26, 27, 29, 33, 35, 42, 46, 49],\n",
       " [1, 8, 12, 13, 16, 17, 22, 23, 24, 25, 26, 27, 29, 33, 35, 42, 46],\n",
       " [1, 8, 11, 12, 13, 16, 17, 22, 23, 24, 25, 27, 29, 33, 35, 42, 46, 49],\n",
       " [1, 12, 13, 16, 22, 23, 24, 25, 27, 28, 29, 33, 35, 42, 46, 49],\n",
       " [0,\n",
       "  1,\n",
       "  2,\n",
       "  6,\n",
       "  8,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  16,\n",
       "  17,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  29,\n",
       "  33,\n",
       "  35,\n",
       "  42,\n",
       "  46,\n",
       "  49],\n",
       " [1, 25, 26, 29, 30, 33, 36, 37, 42, 46],\n",
       " [5, 31],\n",
       " [2, 12, 13, 16, 22, 24, 27, 32, 33, 35, 41, 42, 46, 47],\n",
       " [1, 8, 11, 12, 13, 16, 17, 22, 23, 24, 25, 27, 28, 29, 33, 35, 42, 46, 49],\n",
       " [5, 34],\n",
       " [6, 11, 12, 16, 17, 20, 22, 23, 24, 25, 26, 27, 29, 35, 42, 46],\n",
       " [1, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 36, 42, 46],\n",
       " [0, 7, 9, 17, 20, 22, 26, 29, 37, 42, 43, 46],\n",
       " [1, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 38, 42, 46],\n",
       " [2, 16, 17, 20, 22, 23, 24, 27, 33, 35, 39, 42, 46],\n",
       " [0, 2, 6, 20, 22, 24, 29, 35, 40, 42, 43, 46],\n",
       " [3, 12, 13, 16, 21, 22, 41, 42, 43, 46],\n",
       " [1, 16, 17, 20, 22, 23, 24, 25, 26, 27, 29, 33, 35, 42, 46],\n",
       " [1, 16, 17, 22, 23, 24, 27, 29, 33, 35, 42, 43, 46],\n",
       " [2, 13, 16, 24, 25, 27, 33, 42, 44, 46],\n",
       " [1, 22, 23, 24, 25, 26, 27, 28, 29, 33, 35, 42, 45, 46],\n",
       " [0, 1, 2, 12, 13, 16, 22, 23, 24, 25, 27, 28, 29, 33, 35, 42, 46],\n",
       " [3, 4, 7, 12, 16, 20, 24, 33, 35, 42, 46, 47],\n",
       " [5, 48],\n",
       " [1, 12, 13, 16, 17, 22, 23, 24, 27, 28, 29, 33, 35, 42, 46, 49]]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_couples=[]\n",
    "for c in range(len(data1['Symbol'].values)):\n",
    "    couple=[]\n",
    "    other_can=[]\n",
    "    couple.append(c)\n",
    "    for i in range(len(data1['Symbol'].values)):\n",
    "        if i not in couple:\n",
    "            other_can.append(i)\n",
    "    couple=add_candidates(couple, other_can)\n",
    "    couple=sorted(couple)\n",
    "    all_couples.append(couple)\n",
    "\n",
    "couples=[]\n",
    "for i in all_couples:\n",
    "    if i not in couples:\n",
    "        couples.append(i)\n",
    "couples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sharpe Ratio\n",
    "### (Rp-Rf)/SIGMAp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 45/45 [00:02<00:00, 17.10it/s]\n"
     ]
    },
    {
     "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>portfolio</th>\n",
       "      <th>weights</th>\n",
       "      <th>sharp_ratio</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['MMM', 'ABMD', 'ACN', 'AMD', 'LNT', 'MO', 'AA...</td>\n",
       "      <td>[0.024305555555555556, 0.2708333333333333, 0.1...</td>\n",
       "      <td>2.724150</td>\n",
       "      <td>0.355197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['ABMD', 'ACN', 'AMD', 'APD', 'ARE', 'LNT', 'M...</td>\n",
       "      <td>[0.2053872053872054, 0.02356902356902357, 0.09...</td>\n",
       "      <td>2.596345</td>\n",
       "      <td>0.285990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['ABMD', 'APD', 'AKAM', 'ALL', 'ANTM', 'AON', ...</td>\n",
       "      <td>[0.18991097922848665, 0.29376854599406527, 0.1...</td>\n",
       "      <td>2.486416</td>\n",
       "      <td>0.286638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['MMM', 'ABMD', 'AMD', 'APD', 'ALXN', 'LNT', '...</td>\n",
       "      <td>[0.022535211267605635, 0.27605633802816903, 0....</td>\n",
       "      <td>2.439113</td>\n",
       "      <td>0.297412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['ABMD', 'ACN', 'AMD', 'AAP', 'A', 'APD', 'ALX...</td>\n",
       "      <td>[0.16847826086956522, 0.014492753623188406, 0....</td>\n",
       "      <td>2.433213</td>\n",
       "      <td>0.283018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>['ABBV', 'AKAM', 'ARE', 'ALLE', 'MO', 'AMZN', ...</td>\n",
       "      <td>[0.21220930232558138, 0.10755813953488372, 0.1...</td>\n",
       "      <td>2.355326</td>\n",
       "      <td>0.205136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>['ABBV', 'AKAM', 'ARE', 'MO', 'AMZN', 'AEE', '...</td>\n",
       "      <td>[0.056485355648535567, 0.12133891213389121, 0....</td>\n",
       "      <td>2.336731</td>\n",
       "      <td>0.217663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>['ABT', 'AMZN', 'AMCR', 'AEP', 'AXP', 'AWK', '...</td>\n",
       "      <td>[0.011235955056179775, 0.23314606741573032, 0....</td>\n",
       "      <td>2.300739</td>\n",
       "      <td>0.230979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>['ABMD', 'APD', 'AKAM', 'ARE', 'ALL', 'GOOGL',...</td>\n",
       "      <td>[0.189873417721519, 0.10379746835443038, 0.225...</td>\n",
       "      <td>2.170290</td>\n",
       "      <td>0.241729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'ARE', 'ALXN', 'LNT...</td>\n",
       "      <td>[0.020161290322580645, 0.10752688172043011, 0....</td>\n",
       "      <td>2.154333</td>\n",
       "      <td>0.209840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...</td>\n",
       "      <td>[0.007575757575757576, 0.06944444444444445, 0....</td>\n",
       "      <td>2.149021</td>\n",
       "      <td>0.208724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>['ABBV', 'ADBE', 'AAP', 'A', 'APD', 'AKAM', 'A...</td>\n",
       "      <td>[0.07247796278158668, 0.06758080313418217, 0.0...</td>\n",
       "      <td>2.130755</td>\n",
       "      <td>0.201031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...</td>\n",
       "      <td>[0.005545286506469501, 0.06469500924214418, 0....</td>\n",
       "      <td>2.084901</td>\n",
       "      <td>0.220189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...</td>\n",
       "      <td>[0.0062266500622665, 0.0759651307596513, 0.113...</td>\n",
       "      <td>2.078548</td>\n",
       "      <td>0.199579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>['MMM', 'ABBV', 'ADBE', 'APD', 'AKAM', 'ARE', ...</td>\n",
       "      <td>[0.014598540145985401, 0.0948905109489051, 0.0...</td>\n",
       "      <td>2.074706</td>\n",
       "      <td>0.194269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...</td>\n",
       "      <td>[0.014012738853503185, 0.10191082802547771, 0....</td>\n",
       "      <td>2.050905</td>\n",
       "      <td>0.198263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>['MMM', 'ABBV', 'ADBE', 'A', 'APD', 'AKAM', 'A...</td>\n",
       "      <td>[0.014476614699331848, 0.08240534521158129, 0....</td>\n",
       "      <td>2.041625</td>\n",
       "      <td>0.194787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...</td>\n",
       "      <td>[0.021560574948665298, 0.09240246406570841, 0....</td>\n",
       "      <td>2.033051</td>\n",
       "      <td>0.207091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>['ADBE', 'A', 'APD', 'ARE', 'ALXN', 'LNT', 'GO...</td>\n",
       "      <td>[0.0873440285204991, 0.03208556149732621, 0.02...</td>\n",
       "      <td>2.024985</td>\n",
       "      <td>0.208825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>['ALGN', 'AMZN']</td>\n",
       "      <td>[0.24528301886792453, 0.7547169811320755]</td>\n",
       "      <td>2.017471</td>\n",
       "      <td>0.318114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>['ABBV', 'APD', 'AKAM', 'ARE', 'GOOGL', 'MO', ...</td>\n",
       "      <td>[0.13428120063191154, 0.12164296998420221, 0.0...</td>\n",
       "      <td>2.004413</td>\n",
       "      <td>0.196222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.03939393939393939, 0.1, 0.05454545454545454...</td>\n",
       "      <td>1.998006</td>\n",
       "      <td>0.206060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>['MMM', 'AMD', 'AES', 'ALXN', 'LNT', 'GOOGL', ...</td>\n",
       "      <td>[0.012145748987854251, 0.17611336032388664, 0....</td>\n",
       "      <td>1.996861</td>\n",
       "      <td>0.246431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...</td>\n",
       "      <td>[0.014771048744460856, 0.008862629246676515, 0...</td>\n",
       "      <td>1.996539</td>\n",
       "      <td>0.199136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>['MMM', 'ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...</td>\n",
       "      <td>[0.017408123791102514, 0.0367504835589942, 0.0...</td>\n",
       "      <td>1.992542</td>\n",
       "      <td>0.199174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>['ABT', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.02717391304347826, 0.08876811594202899, 0.0...</td>\n",
       "      <td>1.992109</td>\n",
       "      <td>0.202377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>['ABT', 'AES', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...</td>\n",
       "      <td>[0.013289036544850499, 0.016611295681063124, 0...</td>\n",
       "      <td>1.980479</td>\n",
       "      <td>0.209244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>['MMM', 'ABT', 'ABBV', 'ADBE', 'AAP', 'A', 'AP...</td>\n",
       "      <td>[0.007194244604316547, 0.041109969167523124, 0...</td>\n",
       "      <td>1.975656</td>\n",
       "      <td>0.193472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.00333889816360601, 0.11018363939899833, 0.0...</td>\n",
       "      <td>1.969905</td>\n",
       "      <td>0.195324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...</td>\n",
       "      <td>[0.028764805414551606, 0.06937394247038917, 0....</td>\n",
       "      <td>1.957159</td>\n",
       "      <td>0.200016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...</td>\n",
       "      <td>[0.0064034151547491995, 0.06510138740661686, 0...</td>\n",
       "      <td>1.925996</td>\n",
       "      <td>0.185426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>['ABBV', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG'...</td>\n",
       "      <td>[0.15765765765765766, 0.05405405405405406, 0.0...</td>\n",
       "      <td>1.907091</td>\n",
       "      <td>0.187892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>['MMM', 'ATVI', 'AEP', 'AME', 'AMGN', 'APH', '...</td>\n",
       "      <td>[0.01702127659574468, 0.2723404255319149, 0.17...</td>\n",
       "      <td>1.890134</td>\n",
       "      <td>0.202181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...</td>\n",
       "      <td>[0.02823529411764706, 0.11529411764705882, 0.0...</td>\n",
       "      <td>1.873219</td>\n",
       "      <td>0.188682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>['MMM', 'AMD', 'AES', 'ALXN', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.016728624535315983, 0.1654275092936803, 0.0...</td>\n",
       "      <td>1.862995</td>\n",
       "      <td>0.214649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...</td>\n",
       "      <td>[0.005805515239477504, 0.1262699564586357, 0.0...</td>\n",
       "      <td>1.851713</td>\n",
       "      <td>0.197720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>['ABT', 'APD', 'AKAM', 'ARE', 'ALXN', 'GOOGL',...</td>\n",
       "      <td>[0.05365853658536585, 0.14471544715447154, 0.0...</td>\n",
       "      <td>1.821050</td>\n",
       "      <td>0.193634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>['MMM', 'ABBV', 'ADBE', 'LNT', 'GOOGL', 'MO', ...</td>\n",
       "      <td>[0.005836575875486381, 0.15369649805447472, 0....</td>\n",
       "      <td>1.776741</td>\n",
       "      <td>0.182428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>['ABT', 'ARE', 'ALXN', 'GOOGL', 'GOOG', 'MO', ...</td>\n",
       "      <td>[0.0072992700729927005, 0.041970802919708027, ...</td>\n",
       "      <td>1.721864</td>\n",
       "      <td>0.179269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>['ATVI', 'ALB']</td>\n",
       "      <td>[0.5181818181818182, 0.4818181818181818]</td>\n",
       "      <td>1.526555</td>\n",
       "      <td>0.239549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>['ATVI', 'AMP']</td>\n",
       "      <td>[0.5742574257425742, 0.42574257425742573]</td>\n",
       "      <td>1.359109</td>\n",
       "      <td>0.186542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>['ATVI', 'AIG']</td>\n",
       "      <td>[0.6867469879518072, 0.3132530120481928]</td>\n",
       "      <td>1.256244</td>\n",
       "      <td>0.178206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>['ATVI', 'ALK']</td>\n",
       "      <td>[0.7848101265822784, 0.21518987341772153]</td>\n",
       "      <td>1.138615</td>\n",
       "      <td>0.165618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>['ATVI', 'APTV']</td>\n",
       "      <td>[0.7108433734939759, 0.2891566265060241]</td>\n",
       "      <td>1.073595</td>\n",
       "      <td>0.157280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>['ATVI', 'AFL']</td>\n",
       "      <td>[0.9888888888888889, 0.011111111111111112]</td>\n",
       "      <td>0.806947</td>\n",
       "      <td>0.130921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            portfolio  \\\n",
       "0   ['MMM', 'ABMD', 'ACN', 'AMD', 'LNT', 'MO', 'AA...   \n",
       "1   ['ABMD', 'ACN', 'AMD', 'APD', 'ARE', 'LNT', 'M...   \n",
       "2   ['ABMD', 'APD', 'AKAM', 'ALL', 'ANTM', 'AON', ...   \n",
       "3   ['MMM', 'ABMD', 'AMD', 'APD', 'ALXN', 'LNT', '...   \n",
       "4   ['ABMD', 'ACN', 'AMD', 'AAP', 'A', 'APD', 'ALX...   \n",
       "5   ['ABBV', 'AKAM', 'ARE', 'ALLE', 'MO', 'AMZN', ...   \n",
       "6   ['ABBV', 'AKAM', 'ARE', 'MO', 'AMZN', 'AEE', '...   \n",
       "7   ['ABT', 'AMZN', 'AMCR', 'AEP', 'AXP', 'AWK', '...   \n",
       "8   ['ABMD', 'APD', 'AKAM', 'ARE', 'ALL', 'GOOGL',...   \n",
       "9   ['ABT', 'AAP', 'A', 'APD', 'ARE', 'ALXN', 'LNT...   \n",
       "10  ['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...   \n",
       "11  ['ABBV', 'ADBE', 'AAP', 'A', 'APD', 'AKAM', 'A...   \n",
       "12  ['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...   \n",
       "13  ['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...   \n",
       "14  ['MMM', 'ABBV', 'ADBE', 'APD', 'AKAM', 'ARE', ...   \n",
       "15  ['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...   \n",
       "16  ['MMM', 'ABBV', 'ADBE', 'A', 'APD', 'AKAM', 'A...   \n",
       "17  ['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...   \n",
       "18  ['ADBE', 'A', 'APD', 'ARE', 'ALXN', 'LNT', 'GO...   \n",
       "19                                   ['ALGN', 'AMZN']   \n",
       "20  ['ABBV', 'APD', 'AKAM', 'ARE', 'GOOGL', 'MO', ...   \n",
       "21  ['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...   \n",
       "22  ['MMM', 'AMD', 'AES', 'ALXN', 'LNT', 'GOOGL', ...   \n",
       "23  ['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...   \n",
       "24  ['MMM', 'ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...   \n",
       "25  ['ABT', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG',...   \n",
       "26  ['ABT', 'AES', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...   \n",
       "27  ['MMM', 'ABT', 'ABBV', 'ADBE', 'AAP', 'A', 'AP...   \n",
       "28  ['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...   \n",
       "29  ['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...   \n",
       "30  ['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...   \n",
       "31  ['ABBV', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG'...   \n",
       "32  ['MMM', 'ATVI', 'AEP', 'AME', 'AMGN', 'APH', '...   \n",
       "33  ['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...   \n",
       "34  ['MMM', 'AMD', 'AES', 'ALXN', 'GOOGL', 'GOOG',...   \n",
       "35  ['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...   \n",
       "36  ['ABT', 'APD', 'AKAM', 'ARE', 'ALXN', 'GOOGL',...   \n",
       "37  ['MMM', 'ABBV', 'ADBE', 'LNT', 'GOOGL', 'MO', ...   \n",
       "38  ['ABT', 'ARE', 'ALXN', 'GOOGL', 'GOOG', 'MO', ...   \n",
       "39                                    ['ATVI', 'ALB']   \n",
       "40                                    ['ATVI', 'AMP']   \n",
       "41                                    ['ATVI', 'AIG']   \n",
       "42                                    ['ATVI', 'ALK']   \n",
       "43                                   ['ATVI', 'APTV']   \n",
       "44                                    ['ATVI', 'AFL']   \n",
       "\n",
       "                                              weights  sharp_ratio    return  \n",
       "0   [0.024305555555555556, 0.2708333333333333, 0.1...     2.724150  0.355197  \n",
       "1   [0.2053872053872054, 0.02356902356902357, 0.09...     2.596345  0.285990  \n",
       "2   [0.18991097922848665, 0.29376854599406527, 0.1...     2.486416  0.286638  \n",
       "3   [0.022535211267605635, 0.27605633802816903, 0....     2.439113  0.297412  \n",
       "4   [0.16847826086956522, 0.014492753623188406, 0....     2.433213  0.283018  \n",
       "5   [0.21220930232558138, 0.10755813953488372, 0.1...     2.355326  0.205136  \n",
       "6   [0.056485355648535567, 0.12133891213389121, 0....     2.336731  0.217663  \n",
       "7   [0.011235955056179775, 0.23314606741573032, 0....     2.300739  0.230979  \n",
       "8   [0.189873417721519, 0.10379746835443038, 0.225...     2.170290  0.241729  \n",
       "9   [0.020161290322580645, 0.10752688172043011, 0....     2.154333  0.209840  \n",
       "10  [0.007575757575757576, 0.06944444444444445, 0....     2.149021  0.208724  \n",
       "11  [0.07247796278158668, 0.06758080313418217, 0.0...     2.130755  0.201031  \n",
       "12  [0.005545286506469501, 0.06469500924214418, 0....     2.084901  0.220189  \n",
       "13  [0.0062266500622665, 0.0759651307596513, 0.113...     2.078548  0.199579  \n",
       "14  [0.014598540145985401, 0.0948905109489051, 0.0...     2.074706  0.194269  \n",
       "15  [0.014012738853503185, 0.10191082802547771, 0....     2.050905  0.198263  \n",
       "16  [0.014476614699331848, 0.08240534521158129, 0....     2.041625  0.194787  \n",
       "17  [0.021560574948665298, 0.09240246406570841, 0....     2.033051  0.207091  \n",
       "18  [0.0873440285204991, 0.03208556149732621, 0.02...     2.024985  0.208825  \n",
       "19          [0.24528301886792453, 0.7547169811320755]     2.017471  0.318114  \n",
       "20  [0.13428120063191154, 0.12164296998420221, 0.0...     2.004413  0.196222  \n",
       "21  [0.03939393939393939, 0.1, 0.05454545454545454...     1.998006  0.206060  \n",
       "22  [0.012145748987854251, 0.17611336032388664, 0....     1.996861  0.246431  \n",
       "23  [0.014771048744460856, 0.008862629246676515, 0...     1.996539  0.199136  \n",
       "24  [0.017408123791102514, 0.0367504835589942, 0.0...     1.992542  0.199174  \n",
       "25  [0.02717391304347826, 0.08876811594202899, 0.0...     1.992109  0.202377  \n",
       "26  [0.013289036544850499, 0.016611295681063124, 0...     1.980479  0.209244  \n",
       "27  [0.007194244604316547, 0.041109969167523124, 0...     1.975656  0.193472  \n",
       "28  [0.00333889816360601, 0.11018363939899833, 0.0...     1.969905  0.195324  \n",
       "29  [0.028764805414551606, 0.06937394247038917, 0....     1.957159  0.200016  \n",
       "30  [0.0064034151547491995, 0.06510138740661686, 0...     1.925996  0.185426  \n",
       "31  [0.15765765765765766, 0.05405405405405406, 0.0...     1.907091  0.187892  \n",
       "32  [0.01702127659574468, 0.2723404255319149, 0.17...     1.890134  0.202181  \n",
       "33  [0.02823529411764706, 0.11529411764705882, 0.0...     1.873219  0.188682  \n",
       "34  [0.016728624535315983, 0.1654275092936803, 0.0...     1.862995  0.214649  \n",
       "35  [0.005805515239477504, 0.1262699564586357, 0.0...     1.851713  0.197720  \n",
       "36  [0.05365853658536585, 0.14471544715447154, 0.0...     1.821050  0.193634  \n",
       "37  [0.005836575875486381, 0.15369649805447472, 0....     1.776741  0.182428  \n",
       "38  [0.0072992700729927005, 0.041970802919708027, ...     1.721864  0.179269  \n",
       "39           [0.5181818181818182, 0.4818181818181818]     1.526555  0.239549  \n",
       "40          [0.5742574257425742, 0.42574257425742573]     1.359109  0.186542  \n",
       "41           [0.6867469879518072, 0.3132530120481928]     1.256244  0.178206  \n",
       "42          [0.7848101265822784, 0.21518987341772153]     1.138615  0.165618  \n",
       "43           [0.7108433734939759, 0.2891566265060241]     1.073595  0.157280  \n",
       "44         [0.9888888888888889, 0.011111111111111112]     0.806947  0.130921  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rf=0.02\n",
    "final_couples=[]\n",
    "final_weights=[]\n",
    "final_ratio=[]\n",
    "final_rp=[]\n",
    "for i in tqdm(couples):\n",
    "    symbols=[]\n",
    "    best_shar=0\n",
    "    best_weight=np.zeros(len(i))\n",
    "    best_rp=0\n",
    "    for num in i:\n",
    "        symbols.append(data1['Symbol'].values[num])\n",
    "    mean_price=np.array(r_data[symbols].mean())*len(r_data)\n",
    "    cov = np.array(r_data[symbols].cov()) \n",
    "    for count in range(1000):\n",
    "        weights = [np.random.randint(1,100) for a in i]\n",
    "        weights = np.array(weights, dtype=float)\n",
    "        weights /= weights.sum()\n",
    "        Rp=weights.dot(mean_price.T)\n",
    "        SIGMAp=weights.dot(cov.dot(weights.T)) * len(r_data) \n",
    "        sha_r=(Rp-Rf)/np.sqrt(SIGMAp)\n",
    "        if sha_r>best_shar:\n",
    "            best_shar=sha_r\n",
    "            best_weight=weights\n",
    "            bset_rp=Rp\n",
    "    final_couples.append(str(symbols))\n",
    "    final_weights.append(best_weight)\n",
    "    final_ratio.append(best_shar)\n",
    "    final_rp.append(bset_rp)\n",
    "    \n",
    "result=pd.DataFrame()\n",
    "result['portfolio']=final_couples\n",
    "result['weights']=final_weights\n",
    "result['sharp_ratio']=final_ratio\n",
    "result['return']=final_rp\n",
    "result=result.sort_values(by=['sharp_ratio'],ascending=False)\n",
    "result=result.reset_index(drop=True)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharp Ratio > 2, portfolio set 1\n",
    "## Sharp Ratio < 2, portfolio set 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_1=result[0:25]\n",
    "\n",
    "portfolio_2=result[25:]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## portfolio set 1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAASACAYAAADvZ15mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXxU5dn/8c+VPRmyEjZJCAEDCiigGOtCFZeK4q5ttdpif61oxUpbH2trlfbhsdWqj09bi9atiq3V1q0qWvcFcQuIIAIqSwgEUAyQQBKy378/TgZCJJBlJmcy+b5fr/M6M2fuc58raRGuue7FnHOIiIiIiIiISOjE+B2AiIiIiIiISLRRsi0iIiIiIiISYkq2RUREREREREJMybaIiIiIiIhIiCnZFhEREREREQkxJdsiIiIiIiIiIRbndwChEhMT45KTk/0OQ0RERERERMKgurraOed6TME4apLt5ORkqqqq/A5DREREREREwsDMdvodQ0f0mG8FRERERERERHoKJdsiIiIiIiIiIRbWZNvMJpvZp2a2ysx+sZfPLzezpWa22Mzmm9moFp8dambvmtmy5jZJ4YxVREREREREJFTMOReejs1igc+Ak4FSYAFwoXNueYs2ac657c2vzwSucM5NNrM4YBHwXefcEjPrC5Q75xrbel4gEHCt52zX19dTWlpKTU1NqH+8HispKYmcnBzi4+P9DkVEREREJOIoh/BfWzmLmVU75wI+hdVh4VwgrRBY5ZxbA2BmjwJnAbuS7WCi3SwABDP/bwAfOeeWNLfb0pkASktLSU1NZejQoZhZZ7qIKs45tmzZQmlpKfn5+X6HIyIiIiIScZRD+CuacpZwDiMfDKxv8b60+doezGy6ma0GbgGuar48AnBm9qKZLTKzn3cmgJqaGvr27as/JM3MjL59++pbOhERERGRNiiH8Fc05Sy+L5DmnJvtnBsOXAtc33w5DjgWuKj5fI6Zndj6XjObZmYLzWxhQ0PDXvvXH5I96fchIiIiIrJv+jezv6Ll9x/OZHsDkNvifU7ztbY8Cpzd/LoUmOecK3POVQPPA4e1vsE5d49zboJzbkJcXGRuGR4bG8u4ceMYM2YMZ5xxBuXl5fts/+9//5vly5fvs42IiIiIiIhEtnAm2wuAAjPLN7ME4ALgmZYNzKygxdspwMrm1y8Ch5hZSvNiacfRYq53uBQVwfTpcNpp3rmoqOt9Jicns3jxYj7++GOysrKYPXv2Ptt3Jtluq6ovIiIiIiLhVVRaxPTnpnPaw6cx/bnpFJWGIIloZejQoZSVlYW836568MEH2bhx4673P/zhD1U4bCFsybZzrgG4Ei9xXgH8yzm3zMxmNa88DnBl89Zei4GfAVOb790G3I6XsC8GFjnnngtXrOAl1jNnQlkZDB7snWfODE3CHXTUUUexYYNX3F+9ejWTJ0/m8MMPZ+LEiXzyySe88847PPPMM1xzzTWMGzeO1atXc/zxx7Nw4UIAysrKGDp0KOD9H/vMM8/khBNO4MQTT+TBBx/k3HPPZfLkyRQUFPDzn3dqmruIiIiIiLRTUWkRM9+YSVl1GYNTB1NWXcbMN2aGJeHurK4W5hob29wQ6ivJ9n333ceoUaPabN/bhHXstXPuebwh4C2vzWzxesY+7v078PdQxnPZZW1/tnkzpKdDRgZs2gQrV0JtLXz/+3DssXu2vfvujj+7sbGRV199lR/84AcATJs2jb/85S8UFBTw/vvvc8UVV/Daa69x5plncvrpp3P++efvt89Fixbx0UcfkZWVxYMPPsjixYv58MMPSUxMZOTIkfz4xz8mNzd3v/2IiIiIiMjeXfZs20nE5qrNpCemk5GUwabKTazcupLahlq+//T3OXbInknE3We0L4moqqriW9/6FqWlpTQ2NnLDDTcAcMcdd/Dss89SX1/PY489xkEHHURRUREzZsygpqaG5ORkHnjgAUaOHMmDDz7Ik08+SWVlJY2Njfz3f/83M2fOJDU1lVWrVjFp0iTuvPNOYmL2Xnvt06cPl112Ga+88gqzZ8/mtdde49lnn2Xnzp0cffTR3H333TzxxBMsXLiQiy66iOTkZN59911OPfVUbrvtNiZMmMAjjzzC7373O5xzTJkyhd///ve7+p4xYwZz584lOTmZp59+mgEDBrTrd9PT+L5AWqTYtAnS0va8lpAAO3Z0rd+dO3cybtw4Bg4cyBdffMHJJ59MZWUl77zzDt/85jcZN24cl112GZs2bepw3yeffDJZWVm73p944omkp6eTlJTEqFGjKCkp6VrwIiIiIiLSpk2Vm0hL3DOJSIhNYEdd55OIF154gQMOOIAlS5bw8ccfM3nyZACys7NZtGgRP/rRj7jtttsAOOigg3jrrbf48MMPmTVrFtddd92ufhYtWsTjjz/Om2++CUBRURF33HEHy5cvZ/Xq1Tz55JNtxlBVVcWRRx7JkiVLOPbYY7nyyitZsGABH3/8MTt37mTu3Lmcf/75TJgwgYcffpjFixeTnJy86/6NGzdy7bXX8tprr7F48WIWLFjAv//97119f+1rX2PJkiV8/etf59577+307yrSReaqYmGyr4r09One0PGMDBg0yDvKyyE7G/YzzXqfgnO2q6urOeWUU5g9ezaXXHIJGRkZLF68eL/3x8XF0dTUBPCV5e8DgT33c09MTNz1OjY2VnO5RURERES6aF8V6enPTaesuoyMpAwG9RnEoD6DKK8pJzslm9lTOpdEHHLIIVx99dVce+21nH766UycOBGAc889F4DDDz98V6JcUVHB1KlTWblyJWZGfX39rn5aF+YKCwsZNmwYABdeeCHz589vczRtbGws55133q73r7/+OrfccgvV1dVs3bqV0aNHc8YZZ7T5MyxYsIDjjz+efv36AXDRRRcxb948zj77bBISEjj99NN3/Swvv/xyh39HPYUq282mToWKCi/BbmryzhUV3vVQSElJ4U9/+hP/+7//S0pKCvn5+Tz22GOAt3H7kiVLAEhNTWVHi3L60KFD+eCDDwB4/PHHQxOMiIiIiIh02dSxU6moraC8ppwm10R5TTkVtRVMHdv5JGLEiBEsWrSIQw45hOuvv55Zs2YBuwtrLYtqN9xwA5MmTeLjjz/m2Wef3aM417ow13o7rX1tr5WUlERsbCzgFfyuuOIKHn/8cZYuXcqll17apT2w4+Pjdz072guESrabFRbCrFleJXvDBu88a5Z3PVTGjx/PoYceyiOPPMLDDz/M/fffz9ixYxk9ejRPP/00ABdccAG33nor48ePZ/Xq1fzXf/0Xd911F+PHj4/IFQhFRERERHqrwpxCZh0/i+yUbDbs2EB2Sjazjp9FYU7nk4iNGzeSkpLCxRdfzDXXXMOiRYvabFtRUcHgwYMBb7GyfSkqKqK4uJimpib++c9/cmzrhanaEEyss7Ozqays3KMA2LpQGFRYWMibb75JWVkZjY2NPPLIIxx33HHtel406VXDyPensDC0yTVAZWUlb64q4v6FcyjZXkzeRfkMmTCS/Px8Xnjhha+0P+aYY76yXP5HH3206/WNN94IwCWXXMIll1yy63rr93Pnzg3tDyIiIiIiIl9RmFPYpeS6taVLl3LNNdcQExNDfHw8d911V5vDvX/+858zdepUbrzxRqZMmbLPfo844giuvPLKXQuknXPOOe2KJyMjg0svvZQxY8YwcOBAjjjiiF2fXXLJJVx++eW7FkgLGjRoEDfffDOTJk3atUDaWWed1a7nRRNzzvkdQ0gEAgFXVVW1x7UVK1Zw8MEH+xSR581VRVz/6kzSEtJJTUxjR+12ttdVcOOJszjuwBBn9u0UCb8XEREREZFIFI3/Vn7jjTe47bbbelRBbm//O5hZtXMu0MYtEUfDyMPs/oVzSEtIJzkuhdIdJaTEp5CWkM79C+f4HZqIiIiIiIiEiYaRh1nJ9mIG9RnMym2fsq32SwByUvMo2V7sc2QiIiIiItIbHH/88Rx//PFfuX7kkUdSW1u7x7W//e1vHHLIId0UWXSL+mTbObfPlfbCLS8tny07yxiUksO22i/5YudGAnGp5KXl+xJPtEwbEBERERGRrnn//ff9DiGqRfUw8qSkJLZs2eJrgvmDCVPZXldBo2skLS6TmoZq1leu4QcTQrSnWAc459iyZQtJSUnd/mwRERERkZ5CBSp/RcvvP6or2zk5OZSWlvLll1/6FkN/Urms4Ps8seZpXGMDsU0x5KQMpM/OWFasWNHt8SQlJZGTk9PtzxURERER6QmCBbu+ffv6OkK2t4qmAmFUr0YeSWprYcYMWBr4A6O+voIzDzqdM0ae4XdYIiIiIiLSQn19PaWlpbv2l5buFywQxsfH73G9p61GHtWV7UiSmAgDB0L55ilUVa/gteLX+Mbwb5AYl+h3aCIiIiIi0iw+Pp78fH/WV5LoomS7Gw0ZAuXlBQzJOIWzCscq0RYREREREYlSGkbejWpqvAq3pn6IiIiIiIh0TE8bRh7Vq5FHmqSkrybaVXWR/QWBiIiIiIiIdJySbR80NEB1XQ2zi2bz6zd+TV1jnd8hiYiIiIiI9Ghmlmtmr5vZcjNbZmYz9tLmGjNb3Hx8bGaNZpYVjniUbHezu++Gq66CzRsT2V67nR21O3ir5C2/wxIREREREenpGoCrnXOjgK8B081sVMsGzrlbnXPjnHPjgF8CbzrntoYjGCXb3SwuDhobYd06Y8qIKQC8uPpF6hvrfY5MRERERESk53LObXLOLWp+vQNYAQzexy0XAo+EKx4l290sL887r1sHh/Q/hNz0XCpqKnh7/dv+BiYiIiIiIhIlzGwoMB54v43PU4DJwBPhikHJdjcbMsQ7l5SAmTGlwKtuv7DqBRqaGnyMTEREREREJKLFmdnCFse0vTUysz54SfRPnHPb2+jrDODtcA0hB+2z3e2CyfaGDd5CaeMGjuOA1APYuGMj765/l4l5E/0NUEREREREJDI1OOcm7KuBmcXjJdoPO+ee3EfTCwjjEHJQZbvbJSXBgAHevO2NG5ur2yOmEBcTx/batr50ERERERERkX0xMwPuB1Y4527fR7t04Djg6XDGo8q2D4YMgS++8IaSDxkChw06jAOzDiQjKcPv0ERERERERHqqY4DvAkvNbHHzteuAIQDOub80XzsHeMk5VxXOYMw5F87+u00gEHBVVWH9XYXMZ59BRQUUFECG8msREREREZH9MrNq51zA7zjaS5VtH4wYsffrTa6JDzZ+QHJ8MmP6j+neoERERERERCRklGxHkA82fsB9i+5jYJ+BjOo3ihjTlHoREREREZGeSNmcTz78EB5+GD7/fPe1wwYdRt+Uvnxe+TmLNi3yLzgRERERERHpEiXbPvngA5g3D1at2n0tNiaWyQdOBuD5lc8TLfPpRUREREREehsl2z4J7rddUrLn9aNzjyYzOZMN2zew+PPFX71RREREREREIp6SbZ/k5Xnndev2vB4XE8cpw08B4LmVz6m6LSIiIiIi0gMp2fZJbq53Li2FxsY9Pzt2yLGkJaaxvmI9y75c1v3BiYiIiIiISJdoNXKfpKRAv37w5ZewcePu5BsgPjaecw4+h9qGWkb2HelfkCIiIiIiItIpSrZ9lJfnJdvr1u2ZbIM3d1tERERERER6JiXbPjrwQCgvh+TkfberbaglITYBM+uewERERERERKRLLFoW4AoEAq6qqsrvMELulTWvMPezuVxxxBWM6DvC73BERERERER8YWbVzrmA33G0lxZIi3C1DbXsrN/J3M/m+h2KiIiIiIiItJOSbZ81NnorkldW7v3zSfmTSIpL4tOyT1m1dVX3BiciIiIiIiKdomTbZw88AP/zP/DRR3v/PCU+hROHnQjAc589142RiYiIiIiISGcp2fZZcBXykpK225yYfyKJcYks/3I5a8vXdktcIiIiIiIi0nlhTbbNbLKZfWpmq8zsF3v5/HIzW2pmi81svpmNavX5EDOrNLP/CmecfhoyxDuvW9d2m0BCgElDJwGqbouIiIiIiPQEYUu2zSwWmA2cCowCLmydTAP/cM4d4pwbB9wC3N7q89uB/4QrxkgQTLbXr4emprbbnTTsJBJiE6isq6Susa57ghMREREREZFOCec+24XAKufcGgAzexQ4C1gebOCc296ifQDYtQ+ZmZ0NFAPRt59XC4EAZGdDWRls2gSDB++9XWpiKjccdwP9Uvppv20REREREZEIF85h5IOB9S3elzZf24OZTTez1XiV7auar/UBrgX+O4zxRYxgdXtf87YB+gf6K9EWERERERHpAXxfIM05N9s5Nxwvub6++fJvgP9zzrWxIZbHzKaZ2UIzW9jQ0BDmSMMnL887l5a2r/3nlZ/zxto3whaPiIiIiIiIdE04h5FvAHJbvM9pvtaWR4G7ml8fCZxvZrcAGUCTmdU45/7c8gbn3D3APQCBQMDRQ33ta3DooTBw4P7bVtdXc+O8G2loamBk35EMSh0U/gBFRERERESkQ8JZ2V4AFJhZvpklABcAz7RsYGYFLd5OAVYCOOcmOueGOueGAn8Aftc60Y4mGRlwwAEQ047/NVLiUzgq5yicc/xnVVSvHSciIiIiItJjhS3Zds41AFcCLwIrgH8555aZ2SwzO7O52ZVmtszMFgM/A6aGK55oMvnAycRYDEUbithctdnvcERERERERKQVc67Hjr7eQyAQcFVVPXfh8nfegfnz4YQTYMKE/bd/aMlDvL3ubY7OPZqp4/QdhYiIiIiIRDczq3bOBfyOo718XyBNPBUVsHq1d7THqQeeSozF8F7pe5RVl4U3OBEREREREekQJdsRor3bfwX1C/SjcHAhTa6JF1a9EL7AREREREREpMPCuRq5dEBw+6/166GpqX2LpZ1WcBoxFsPJw04Ob3AiIiIiIiLSIapsR4g+fSArC+rq4Isv2nfPgD4DmDpuKgP6DAhvcCIiIiIiItIhSrYjSLC63d6h5C0556hvrA9tQCIiIiIiItIpSrYjSHDe9rp1HbuveFsxN82/iac+eSr0QYmIiIiIiEiHac52BDnoIG9V8lGjOnZfXEwcJeUlbNyxkVMPPJXUxNTwBCgiIiIiIiLtosp2BBk2DC68EMaM6dh9uem5jB04lvrGel5e83J4ghMREREREZF2U7IdJaYUTAHgjbVvUFlX6XM0IiIiIiIivZuS7QizZQu8917HF0nLy8hjdP/R1DbU8uqaV8MTnIiIiIiIiLSLku0I89578MADUFTU8XtPH3E6AK8Vv0Z1fXWIIxMREREREZH2UrIdYbqy/dewzGEclH0QMRbDhu0bQhuYiIiIiIiItJtWI48wwe2/1q8H58CsY/d/b+z3CCQESIpLCn1wIiIiIiIi0i6qbEeYtDTIyICaGti8ueP3903pq0RbRERERETEZ0q2I1BXhpIH1TXW8Xrx69Q21IYmKBEREREREWk3JdsRKDiUvCvJ9r0f3MujHz/KmyVvhiYoERERERERaTcl2xEoLw/i4qCurvN9HDf0OABeXv0ydY1d6EhEREREREQ6TMl2BBo1Cv70J7joos73MbrfaPIy8theu523St4KXXAiIiIiIiKyX0q2I1BsrHd0hZkxpWAKAC+tfon6xvoQRCYiIiIiIiLtoWQ7gjnXtaHkhw44lJy0HMprynln/TuhC0xERERERET2Scl2hHrnHfjJT+CZZzrfh5kxZYRX3f7Pqv/Q0NQQouhERERERERkX5RsR6g+fby9truyIjnA+IHjOfyAw/nmqG8Sa10cmy4iIiIiIhKhzCzXzF43s+VmtszMZrTR7ngzW9zcJmzbN8WFq2PpmuD2X+vWecPJzTrXj5kx7fBpoQtMREREREQkMjUAVzvnFplZKvCBmb3snFsebGBmGcCdwGTn3Doz6x+uYFTZjlAZGZCW5lW3v/wydP02NjWGrjMREREREZEI4Zzb5Jxb1Px6B7ACGNyq2XeAJ51z65rbbQ5XPKpsR7C8PFi61Ktu9+/i9y0763dyy9u3MHflXLJTshmWMYypY6dSmFMYmmBFREREREQihJkNBcYD77f6aAQQb2ZvAKnAH51zD4UjBlW2I1hennfu6rxtgCVfLOGhJQ+xpXoLibGJlFWXMfONmRSVFnW9cxERERERkfCLM7OFLY69zpc1sz7AE8BPnHPbW/cBHA5MAU4BbjCzEWEJNhydSmi0nLfdVX9b8jeGZgxlY+VG1les5/ADDgdgzpI5qm6LiIiIiEhP0OCcm7CvBmYWj5doP+yce3IvTUqBLc65KqDKzOYBY4HPQh2sKtsRbPhwuOgiOP/8rvdVXF7M0MyhJMclU91QzaYdm0hLTKO4vLjrnYuIiIiIiPjMzAy4H1jhnLu9jWZPA8eaWZyZpQBH4s3tDjlVtiNYnz7w9a+Hpq/8jHzKqssYljmMZV8uY23FWhJiE8jPyA/NA0RERERERPx1DPBdYKmZLW6+dh0wBMA59xfn3AozewH4CGgC7nPOfRyOYMw5F45+u10gEHBVVVV+hxGxikqLmPnGTNIS01hXsY7NVZtJTUzl3tPv1TByERERERGJeGZW7ZwL+B1He2kYeYTbsAGeeALmz+9aP4U5hcw6fhb9UvqREp9CSnwKZ444kyMGHxGaQEVERERERGQXDSOPcGVl8NJLcNBBcOyxXeurMKdwVxW7rLqM7JTsEEQoIiIiIiIiramyHeFarkgeyhH/SrRFRERERETCR8l2hMvIgNRUqK6GLVtC3//KLSu5b9F9NLmm0HcuIiIiIiLSSynZjnBmkJfnvS4pCW3fTa6JBxc/yIINC5hXMi+0nYuIiIiIiPRiSrZ7gOBQ8lAn2zEWw3mjzgPgmU+fobq+OrQPEBERERER6aWUbPcALedth9r4geMp6FtAVV0Vz332XOgfICIiIiIi0gsp2e4Bhg6F3FzIyQl932bGt0Z/CzPj9bWvs7lqc+gfIiIiIiIi0suYC+US1z4KBAKuqqrK7zB6rIeWPMTb695m7MCxXHHEFX6HIyIiIiIisgczq3bOBfyOo71U2RYAzhp5FolxiazcspKKmgq/wxEREREREenRwlrZNrPJwB+BWOA+59zNrT6/HJgONAKVwDTn3HIzOxm4GUgA6oBrnHOv7etZ0V7Zds7b+quuDg44IDzPWP7lcvLS8wgk9Jgvi0REREREpJfoaZXtsCXbZhYLfAacDJQCC4ALnXPLW7RJc85tb359JnCFc26ymY0HvnDObTSzMcCLzrnB+3petCfbixfDXXfBqFEwY4bf0YiIiIiIiHSvnpZsh3MYeSGwyjm3xjlXBzwKnNWyQTDRbhYAXPP1D51zG5uvLwOSzSwxjLFGvNxc71xS4lW5w6mhqYFX17xKTUNNeB8kIiIiIiISpeLC2PdgYH2L96XAka0bmdl04Gd4Q8ZP2Es/5wGLnHO14Qiyp8jKgkAAqqpg61bo2zd8z3pw8YMs2LCAitoKzj343PA9SEREREREJEr5vkCac262c244cC1wfcvPzGw08Hvgsr3da2bTzGyhmS1saGgIf7A+MoO8PO91OPbbbumkYScB8OqaVymrLgvvw0RERERERKJQOJPtDUBui/c5zdfa8ihwdvCNmeUATwHfc86t3tsNzrl7nHMTnHMT4uLCWaSPDMFku6QkvM8ZmjGUr+V8jYamBp5Y/kR4HyYiIiIiIhKFwplsLwAKzCzfzBKAC4BnWjYws4IWb6cAK5uvZwDPAb9wzr0dxhh7lCFDvHO4K9sA5xx8DgmxCSzatIjPtnwW/geKiIiIRKmiIpg+HU47zTsXFfkdkYh0h7Al2865BuBK4EVgBfAv59wyM5vVvPI4wJVmtszMFuPN254avA4cCMw0s8XNR/9wxdpTBCvb69fvu10oZCRlMPnAyQD8a9m/aHJN4X+oiIiISJQpKoKZM2HjRm/71rIy770SbpHoF9Z9trtTtG/9Bd4q5GvXwuDBkJAQ/ufVNdYx8/WZbNu5jcsnXM74QePD/1ARERGRKDJ9Onz2GZSWeoWTvDwoL4fsbJg92+/oRHqWnrb1V/RPdI4iZpCf333PS4hN4MIxF1LbWMu4geO678EiIiIiUaK4GL780ntdUuIl22lp3nURiW5KtmWfxg4c63cIIiIiIj1Wfv7uxW1TUrzz9u3dW0AREX/4vvWXdMzGjfDHP8Jf/9r9z/6y6ku27dzW/Q8WERER6aGmToX6eqitheHDvSHkFRXedRGJbkq2e5iEBFi+3Du6c7r9wo0L+c0bv+Hx5Y9330NFREREerjCQu9IToZt27y52rNmeddEJLppGHkP07evNwRpxw7vm9HMzO557vDM4ZgZCzcu5IT8ExieNbx7HiwiIiLSg9XXQyAAxx4LN9wA/ft3z0K3IuI/VbZ7GLPu3W87KDM5k1OGnwLAP5f9k2hZxV5EREQknGJjvSQb4H/+p3u2cBWRyKBkuwcK7rcdXGyju3xj+DfISMqgpLyE9ze8370PFxEREemBYmIgJwfGN++guk3L34j0Gkq2e6BgZbu7k+3EuETOOfgcAJ5a8RS1DbXdG4CIiIhID5WV5Z23bvU3DhHpPkq2e6BgZbs7h5EHHTn4SPIy8iivKefF1S92fwAiIiIiPcjbb8Pf/747yVZlW6T3ULLdA2Vnw8SJcOqp0NTUvc82M749+tscOuBQjhx8ZPc+XERERKSHWb4c3noLGhu990q2RXoPrUbeA5nBxRf79/zhWcOZXjjdvwBEREREeogtW7xzQQF89JGGkYv0JqpsS5c456iqq/I7DBEREZGIVFbmnQsKvLMq2yK9hyrbPVRNDXzyCdTWwpE+jebeUr2FBxY/QENTA9cecy1m5k8gIiIiIhGorg527PC2/xoyBK66avdCaSIS/VTZ7qG2b4e77oInnvAvhkBCgC8qv6B4WzELNi7wLxARERGRCBQcQt63r5dwjx4Ngwb5G5OIdB8l2z1Uv36QlAQVFd7hh6S4JM4+6GwAnlzxJHWNdf4EIiIiIhKBWibbItL7KNnuocz822+7paNyjyI3PZdtO7fx8uqX/QtEREREJMLEx8NBB0F+vvd+0SKYM8ebCigi0U/Jdg/m51NE5SoAACAASURBVH7bQTEWw7dGfwuAF1a9QHlNuX/BiIiIiESQkSPhpz+Fs87y3q9ZA++8A8XF/sYlIt1DyXYPFky2/axsA4zoO4LDBh1GXWMdT614yt9gRERERCJUZqZ31orkIr2Dku0eLDiM3M/KdtB5o84jkBBgQJ8BOOf8DkdERETEd2Vl3s4xQcGVyJVsi/QO2vqrB+vfH1JSvIXSamq8s1+yU7K5+aSbSYhN8C8IERERkQhy883e1l+33ALp6apsi/Q2SrZ7MDO49VaIi5D/FVsm2s457bstIiIivVZtrZdox8VBWpp3LVjZ3rrVv7hEpPtoGHkPFymJdlCTa2JeyTxunn8z9Y31focjIiIi4ouW234F6w+pqd5+21VVUKcdU0WinpLtKLFzp98R7Pbm2jdZW76WV9a84ncoIiIiIr7Y2x7bZt4K5aNH7zmXW0Sik5LtHq68HK65Bn7zG78j8cRYDN8c/U0A/rPqP1TUVPgckYiIiEj3Kyvzzi2TbYAZM+Cqq7wqt4hENyXbPVx6uvfNaHk5bN/udzSeg7IPYuzAsdQ21PL0p0/7HY6IiIhIt9tbZVtEehcl2z2cWWRtARZ0/qjziY2J5Z3177CuIoICExEREekGwWQ7O3vP685BdbW3eJqIRDcl21EgEpPt/oH+TBo6Ceccjy17THtvi4iISK9y7rlw5ZUwYsSe1996C376U3hag/9EQs7Mcs3sdTNbbmbLzGzGXtocb2YVZra4+ZgZrngibC1r6Yy8PO9cUuJvHK1NGTGFd0vfpbKuksq6SlITNTlJREREeod+/byjtfR076ztv0TCogG42jm3yMxSgQ/M7GXn3PJW7d5yzp0e7mCUbEeBYGU70pLtlPgUrjn6Ggb0GUCMaRCFiIiISHCv7W3b/I1DJBo55zYBm5pf7zCzFcBgoHWy3S2UAUWBAQMgMdH7j3akzf8ZlDpIibaIiIj0KmVl8NBD3pDx1jIzvbOSbZHwMrOhwHjg/b18fJSZLTGz/5jZ6HDFoMp2FIiJge9+1xuWlJzsdzR7V15TztzP5nLmyDNJS0zzOxwRERGRsNm4Ed5+29stZuLEPT8LBCA+HnbuhJoaSEryJ0aRHirOzBa2eH+Pc+6e1o3MrA/wBPAT51zrPZsWAXnOuUozOw34N1AQlmDD0al0vyOO8DuCffvnx/9k0aZFAFx86MU+RyMiIiISPvva9svMq25v3uxVtwcN6t7YRHq4BufchH01MLN4vET7Yefck60/b5l8O+eeN7M7zSzbOVcW6mA1vle6xVkHnUWMxTB/3XxKt5f6HY6IiIhI2JQ1/5O99bZfQcF521okTSS0zMyA+4EVzrnb22gzsLkdZlaIlxNvCUc8SrajRF0dzJ3rzQ+KRAP7DGRSvrcV2L+W/UtbgYmIiEjU2ldlG+D002HGDBg6tNtCEuktjgG+C5zQYmuv08zscjO7vLnN+cDHZrYE+BNwgQtTcqJh5FEiLg5eeglqa719Hfv08Tuir5pSMIV317/LW2vf4v3S99lRt4P8jHymjp1KYU6h3+GJiIiIhMT+KtsFYZkdKiLOufmA7afNn4E/d0c8qmxHiZgYyMnxXkfaFmBBgYQABVkFLNy0kKWblzIodRBl1WXMfGMmRaVFfocnIiIiEhL7q2yLSO+gZDuK5OV553Xr/I1jX5ZuXrprNfIdtTvISMogPTGdOUvm+ByZiIiISNc1NHj/JjvggLZHGm7bBs88441KFJHopWHkUWTIEO8cqZVtgJKKEsb0H0NcTBx9Ery/gdIS0yguL/Y5MhEREZGui4uDn/xk32127oTnnoMBA+Ab3+ieuESk+6myHUV6QmU7PyMfYFeiDVBcXkxWcpZfIYmIiIh0q8xM77xtG2jNWJHopWQ7igwcCPHx3jyhqiq/o9m7qWOnUlFbQXlNOU2uiU2Vm/hsy2dU11Xz8uqXtUq5iIiI9GhVVVBTs+82ycmQlOTtJrNzZ/fEJSLdT8l2FImJgXHjYMKE/f9H3i+FOYXMOn4W2SnZbNixgcGpg/nRhB/RL9CPx5c/zl0L76K6vtrvMEVEREQ65bnnvG29Xn113+2C1W3ttS0SvcI6Z9vMJgN/BGKB+5xzN7f6/HJgOtAIVALTnHPLmz/7JfCD5s+ucs69GM5Yo8UPf+h3BPtXmFP4la2+lny+hAcXP8iSz5dw47wbmXb4NIZmDPUnQBEREZFOCm77lZGx73aZmbBpkzeUPLijjIhEl7BVts0sFpgNnAqMAi40s1Gtmv3DOXeIc24ccAtwe/O9o4ALgNHAZODO5v4kSo0dOJZfff1X5GXksaV6C7e8fQsLNizwOywRERGRDglu+9XWHttBLedti0h0Cucw8kJglXNujXOuDngUOKtlA+fc9hZvA0Bwwu5ZwKPOuVrnXDGwqrk/aYcdO+DTT/2OouOyU7L5+TE/Z1L+JOJj4hmSPsTvkEREREQ6pL17bA8c6G0PFqe9gUSiVjj/eA8G1rd4Xwoc2bqRmU0HfgYkACe0uPe9VvcO3su904BpAAkJCSEJuqdraoJf/hLq6+H22yEQ8DuijomLieOCMRcw+cDJZCR546+cc2zZuYXslP18RSwiIiLio+pqb8GzxMT9/xvsG9/Qtl8i0c73BdKcc7Odc8OBa4HrO3jvPc65Cc65CXH6WhDwFknLzfVeR/IWYPsTTLQB5pXM49ev/5p5JfO0WrmIiIhErJZVbTN/YxER/4Uz2d4A5LZ4n9N8rS2PAmd38l5pYUjz6OuenGy39Hnl5zQ0NfDwRw9z/4f3U9MQoUuti4iISK8WXBxtf/O1g5yL3B1kRKTrwplsLwAKzCzfzBLwFjx7pmUDMyto8XYKsLL59TPABWaWaGb5QAFQFMZYo0penncuKfE3jlD59phv84PDfkBiXCILNizgd2/9jg3b9d2LiIiIRJaCAvjxj9s3PLy+3tsi7OqrvaRbRKJP2MZeO+cazOxK4EW8rb/+6pxbZmazgIXOuWeAK83sJKAe2AZMbb53mZn9C1gONADTnXON4Yo12gQr29GSbAMUDi5kSPoQ7l54Nxt3bOSm+TfxnUO+w9G5R3eqv6IimDMHioshPx+mToVCLcEnIiIiXdCnD4wZ07628fEQGwu1tVBZCamp4Y1NRLqfRcsc2EAg4KqqqvwOIyI0NcFVV3nfmP7f/0FKit8RhU5dYx2PLH2Ed9a/Q256Lr849hfExXTsO6OiIpg5c8+/1HbsgFmzlHCLiIhI95k1CzZsgOuu2z0yUUTaZmbVzrkeswS0VhWLQjExkJPjVW03bPCGNEWLhNgEpo6byoi+IxieNbzDiTZ4Fe3ERFi71vsmuX9/b/uNOXOUbIuIiEjnPf20V60+4YT2FTsyM71/q5WXK9kWiUZKtqPU97/vDWXqaVt/tddRuUfteu2c428f/Y2RfUdyZM5Xdpf7iiVLvL/UGpsnJgwe7P2eiovDFa2IiIhEO+fg1Ve9YeGTJrXvnqws77x1a/jiEhH/KNmOUgMG+B1B91lRtoK3173N2+veZuXWlXx79LeJj43/SrumJpg7F7Zt8/bAHDwYRoyAuDgv+c7P9yF4ERERiQrV1V6inZTU/il8mZneedu28MUlIv7xfZ9tka46OPtgLj70YuJi4nir5C1unn8zm6s279GmsRH+/Gd47jkvwc7KgkGDvCH35eVQUQGHHQb/+IdWBBUREZGOa7ntV3v32FZlWyS6KdmOYn/9K/zqV943rdHMzJiYN5FfHPsL+gf6U7q9lN/O+y0fbPxgV5vYWG9ediAAv/sd3Hkn9OvnzZPKzoZrr4XFi+HNN+Gxx5Rwi4iISMds2eKd+/Zt/z0HHgjf+x6cdFJ4YhIRf2kYeRT74gvvW9b162HkSL+jCb/c9Fyum3gdDy15iEWbFnHPB/dw8c5LmTh8AgDnnQcnn7x7yFbrxdD694fZs735VomJcNZZ3fwDiIiISI/VmWQ7O9s7RCQ6qbIdxaJxv+39SY5PZtrh0/jmwRdQvjafufePJbgjXGzs7kR7b0aPhksv9YaWP/88vPBC98QsIiIiPV9nkm0RiW5KtqNYcAuJdev8jaO7lZcbCx6bRPrHP6eyIp61a6GmoYaPN3+833vHj4dLLvHmWj31FLz2WtjDFRERkSgQCHhT1vr379h977/vbRkWLA6ISPTQMPIoFky2e1Nl+5NP4N57vf2zs/vGcPnlkJvruHfRQ3yw8QNOGnYS5x58LrExsW32ceSRUFcHf/+7t3r5kUdG7xZqIiIiEhpnnOEdHfXKK15h5NBDtTOKSLRRsh3FBg3ytrXavNnb6io52e+Iwsc5eOklrxrtHIwaBT/8oZckOwfDMofx4aYPeWXNK6zZtoZLD7+UrOSsNvubONE7DxumRFtERETCJzPTS7a3bVOyLRJtlGxHsbg4by/pkhJvkbQRI/yOKHxWr4Ynn/Ren3aa981yTPMkCTPjpGEnMSxzGPd8cA9rtq1h+nPTcTi2124nPyOfqWOnUpiz54ppwYQ7qKIC0tO74YcRERGRHqWx0RsV15nChrb/EolemrMd5SZOhLPP3v0f8mh14IFegn3FFd4q4jF7+X/2sMxh3PD1G+iT0If56+ZTtKGI+sZ6vqz+kplvzKSotKjN/ufP97ZRW7YsjD+EiIiI9Ejr18NPfgK33trxe4OLt27bFtqYRMR/qmxHudbV2WiycKG3XcbQod7700/f/z2BhAA7G3aSn5lPWXUZja6RzKRMDOO2d29jSPoQ0hLTSE9M985J3rlodTq19eO56644rroqukcJiIiISMcEVyLv06fj9wYLIkq2RaKPkm3pcZqa4IknvAVFMjPh17/u2LCtteVrGdl3JDlpOQTivQnZaYlpfLT5IzKTMtlRu4MNbNjjHpcFxxwzm3ffhj//GVK/cTsuuYy0xLRdyXkwMc9LzyMvI69dsRSVFjFnyRyKy4vbHM4uIiIika0r235lZHhnJdsi0UfJdi/w2WdQXAzHHQdJSX5H0zXbt3urjX/2mTdU/JRTOv4z5Wd4Ve2MpIzd/dZu5/BBh3PTiTexvXY7FbUV3rmmgoraCmobavnulDga66GoCN57ewsFY7fQJ7DlK/2fOOzEXcn2mm1ruOP9O3Yn5c0JeXpiOqXbS3l02aNkJWUxOHUwZdVlzHxjJrOOn6WEW0REpAfpSrKdleUVDRITQxuTiPhPyXYv8Nhj3iqXw4ZBQYHf0XTe6tVwzz1QXg5paXDZZd5c7Y6aOnYqM9+YCXgV7WByffVRV5Oe5FWoc8nd673f/z7U10PjhzNJWLiD715aQVLansn5gVm7g6qoqaC6vprq+mo+r/x8j77mr5vPiL4jdiX9wfOcJXOUbIuIiPQgZWXeOTu74/dmZcEf/hDaeEQkMijZ7gXy8rxku6Sk5ybbb70F//iHN4S8oAAuvbTzK4MX5hQy6/hZewzfvvqoq9uV4MbEeFuK1d2ZSElJIjkp2eQOarv9uIHjuP2U26moraCipmKPqvk7pe+QleJN1KprrGPl1pUMzRhKcXlx534wERER8UVXKttmoY1FRCKHku1eYMgQ77xunb9xdEVqqpdon3ginHcexMZ2rb/CnMJOV4/j4uBHP/LmVvXvv++2ZkYgIUAgIcABqQfs8dnrxa/vGs5eUlHClp1bKK8p59jcYzsVl4iIiHQ/53ZXtjuTbLfsB5R8i0QTbf3VC+Q1r9VVUuJvHB1VV7f79bhxcMMN8K1vdT3RDoX4+D0T7YULvX24O2Lq2KlU1FZQXlPO0IyhxBBDRU0FGUkZuODfuCIiIhLxrrgCvve9zu2zDfDPf8JVV3n/nhCR6KFkuxcYPNhLUL/4Ampq/I6mfT76CK67Dtas2X0tJ8e/ePZl4UJv0bY//AEqK9t/X3A4e3ZKNp9Xfk7h4EKOzjmabTXbeGHVC+ELWERERELGDEaNgmOO6VofdXVakVwk2mgYeS8QFwcHHADr13tHJM/bbmqCuXPhuee89+++6y3sFslGjoRBg2DjRvjjH+GnP4WUlPbd23o4+0dffMSdC+7k6U+fJjc9lzH9x4QpahEREYkUwb22t271Nw4RCS1VtnuJvDxvH8fqar8jaVtVlbeH9XPPed/wnnMOfOc7fke1f6mpXoLdr583L/6OO6C2tnN9HTrgUM4ceSbOOe5bdB/bduorbhERkUi2dCk8+yysXdv5PjIzvbMq2yLRRZXtXuI734mMuc5tWbcO/vIXbzXPQMBbbfzgg/2Oqv3S072E+9ZbvaHvs2fDj3/sze3uqFMPPJX1FevJTc/dYy9wERERiTwffQTz5kGfPjB0aOf6ULItEp1U2e4lIjnRrq/3qsFbtngV+Ouv71mJdlDfvvCzn3l7gH/6Kfztb53rx8yYdvg0Tis4DdOSpCIiIhGtK9t+BSnZFolOqmz3Mjt3QkKCv8l3URHMmQPFxZCfD1Onwne/630z/O1vd64aHCn69/cq3PfeC5Mnd76flkn2tp3bWP7lco4Z0oWVV0RERCQsgtt+ZWd3vo/0dIiJge3boaHBW29HRHo+/VHuRe68E5YsgWuugQMP9CeGoiKYOdPbGiMx0fsLauZMmDULLr7Yn5hC7YADvJ8pFEXpmoYabpp/ExU1FfRJ6MPYgWO73qmIiIiEhHOhqWzHxMB553kLrGr3T5HooWS7F/niC5g/Hz78EI44wqsoFxbu/769KS/3FjSrrfWOmprdr/v2hTFjdrd7/PHdn/3rX7Bjh5eINjXB2LHet7lz5nQ+lkjUMtGeNw9KS+HCCzuegCfFJXFC/gk8teIp/vrhX/nlxF8ysM/A0AYrIiIinRKsRPfp4xURuuKkk0ITk4iElpkNAI5oflvknNvc3nuVbPcSRUXwn/94w8hTU+GTT+DKK729rM8+22tTXAyvvLJn4twymb75Zq8iDfDAA14fe3PYYbuT7cZGWLBg92cbN3rf2pp521wkJ3tD2ouLw/ez+6m83PuCob7eGx5//vkdT7hPGX4K6yrW8cHGD7hzwZ388thfkhyfHJ6ARUREpN1CUdUWkchlZt8CbgXeAAy4w8yucc493p77lWz3EnPmePOJd+yAigrvqK315hYHk+0dO2Dhwrb7qK3dnWz36+d9m5uU5H2T2/IYMmT3Pamp8P/+3+521dXes7OyvLnjZl5Cmp8fvp/dTxkZcPnl3hD+V17xfgdnntmxPsyMqWOn8nnl52zYvoH7P7yf6UdM1+JpIiIiPmtogEGDvKOrNm2ClSu9vgoKut6fiITEr4AjgtVsM+sHvAK0K9k2FyUTQwKBgKuqqvI7jIh12mneXOJ167wqdUyMd9TXw1tveW3Ky73/yCcm7j2JTk7u+jzk4Jzt9HRv1e7t273ke9as6BpG3tqHH8I993hD5889F045peN9lFWX8dt5v6W6vprTCk7jrIPOCn2gIiIi4ovXXoN//hOOO87bslVEvsrMqp1zgW583lLn3CEt3scAS1pe2xdt/dVL5Od7lev8fG9brZEjver0oYfubpOR4c3lPvRQGDHC24Zr4EBvO4rg0O+uKiz0EuvsbNiwwTtHe6INMH48XHKJ9zt88kl4/fWO95Gdks20w6cRGxOrYeQiIiJRRtt/iXSdmeWa2etmttzMlpnZjH20PcLMGszs/H10+YKZvWhml5jZJcBzwPPtjUfDyHuJqVO9ijLsWVG++uruj6WwMPqT67058kioq4O//x2ef957n5LSsT4O7ncwvz3ht2QmZ4YnSBEREWm32trd0+K6KivLO2/d2vW+RHqxBuBq59wiM0sFPjCzl51zy1s2MrNY4PfAS/vqzDl3jZmdBwT34L3HOfdUe4NRst1LBCvKLfe3vvrq3pn0+mniRO9cUNDxRDuoZaK9pXoLyfHJpMR3sjMRERHptBtv9AoY11/vjRjsClW2RbrOObcJ2NT8eoeZrQAGA8tbNf0x8AS7VxnfV59PNLftMCXbvUhvrShHmmDCDd4c9rvv9hZFyc/v2HZsq7euZvaC2QzNGMqVhVcSY5oVIiIi0l2c86rQDQ3eqMGuSk2FuDhva9W6Oq9iLiKdZ2ZDgfHA+62uDwbOASbRRrJtZvOdc8ea2Q6g5SJnBjjnXLv+1Otf5yI+KSrytl97800IBKCszBvqX1TUvvszkjIwjGWbl/H0J0+HN1gRERHZQ0WFl2inpnZ9j23whqJnZHivVd0WaVOcmS1scUzbWyMz64NXjf6Jc257q4//AFzrnGtq6yHOuWObz6nOubQWR2p7E21Qsi3imzlzvD3G4+Nh+XLvL+r0dO96e/RN6cu0w6cRYzG8sOoFFm7cx75tIiIiElLh2GM7K8ubZqYNdkTa1OCcm9DiuKd1AzOLx0u0H3bOPbmXPiYAj5rZWuB84E4zO3tvDzOzv7XnWluUbIv4pLgYRo/2/mJtavK2XktL866318jskXxz9DcBmLN4DqXbS8MUrYiIiLRUVuads7ND1+eMGfB//wfDhoWuT5HexMwMuB9Y4Zy7fW9tnHP5zrmhzrmhePtlX+Gc+3cbXY5u1X8ccHh741GyLeKT4HZswSFjVVXeIiv5+R3rZ9LQSRyVexR1jXXcueBOqur0dbiIiEi4haOyHafVlES66hjgu8AJZra4+TjNzC43s8vb24mZ/bJ5vvahZra9+dgBfAG0e/6m/kiL+CS4HZuZt8hKWZk3pLyj27GZGRcdchEbd2xkfcV6PtvyGeMHjQ9P0CIiIgLsrmyHMtkWka5xzs3HW8Ssve0vaeP6TcBNZnaTc+6XnY3HnHP7b9UDBAIBV6UJLtLDFBXBPffASy95Fe577/X23+6MbTu3sblqMyOzR4Y2SBEREfmKdetg/Xo48EAYMCA0fa5ZAw8+CAccAJe3uwYn0nuYWbVzLtDNz8wECoCk4DXn3Lz23BvWyraZTQb+CMQC9znnbm71+c+AH+JtPv4l8P+ccyXNn90CTMEb6v4yMMNFyzcDIs0KC+GII7xq9s6dcPDBne8rMzlzjz246xvriY+ND0GUIiIi0tqQId4RSvHx8MUX3gKqIuI/M/shMAPIARYDXwPeBU5oz/1hm7NtZrHAbOBUYBRwoZmNatXsQ2CCc+5QvMnptzTfezTeePtDgTF4+58dF65YRfxkBtddB3fcEZp9OgGWbV7G9a9dz7qKdaHpUERERMIuK8s7b93qbxwisssMvFy0xDk3CW/f7vL23tzuZNvMBpvZ0Wb29eCxn1sKgVXOuTXOuTrgUeCslg2cc68756qb376H940BeBuHJwEJQCIQjzcZXSQqZWeHdlGUDz//kPKacu5acBc7aneErmMRERFhxw54+mlYsCC0/aakeNXtmhrvEBHf1TjnagDMLNE59wnQ7jmb7Uq2zez3wNvA9cA1zcd/7ee2wcD6Fu9Lm6+15QfAfwCcc+8CrwObmo8XnXMr9hLXtOCG5g0NDe35UUR6hQvGXEB+Zj5bd27l3kX30tjU6HdIIiIiUePzz+H55+HVV0Pbr5mq2yIRptTMMoB/Ay+b2dNASXtvbm9l+2xgpHPuNOfcGc3HmZ0Idq/M7GK8zcVvbX5/IHAwXqV7MN7S7RNb3+ecuye4oXmc9kqQHmzrVrjtNu8IhbiYOC6fcDlpiWl8WvYpT6x4IjQdi4iIyK5tv0K5x3ZQZvPyK9u2hb5vEekY59w5zrly59xvgBvw9vA+a9937dbeZHsN3lDujtgA5LZ4n9N8bQ9mdhLwK+BM51xt8+VzgPecc5XOuUq8ivdRHXy+SI8RCMCqVbB6NYRqkEZGUgaXT7ic2JhYXl3zKu+VvheajkVERHq5cOyxHRRMtlXZFokszrk3gRrg+fbe095kuxpYbGZ3m9mfgsd+7lkAFJhZvpklABcAz7RsYGbjgbvxEu3NLT5aBxxnZnFmFo+3ONpXhpGLRIvERO/b8aYmbxXSUBmeNZwLx1wIwCNLH6G6vno/d4iIiMj+hDPZHj8ezjwT8vJC37eItI+ZnWBmn5lZpZn93cwOMbOFwE3AXe3tp71jr5+hVaK8P865BjO7EngRb+uvvzrnlpnZLGChc+4ZvGHjfYDHzAxgXfPw9MfxllNfirdY2gvOuWc78nyRnmbwYPjyS9iwwXsdKhPzJrJl5xbG9B9DSnxK6DoWERHppcrKvHM4ku2xY71DRHz1v8A0vG2+Tm0+/8I59+eOdLLfZLt5C69Lmpc67xDn3PO0KrM752a2eH1SG/c1Apd19HkiPdngwbB4sZdsh9rZB50d+k5FRER6qXDO2RaRiOCcc280v/63mW3oaKIN7Ui2nXONZtZkZunOuYqOPkBE2idYzS4tDe9zlny+hNXbVnPuweeG90EiIiJRyDlIToakpN0rh4dSfT188om39dcRR4S+fxFplwwza/mP5biW751zT7ank/YOI68ElprZy0BVi4dc1c77RWQ/cpp3mQ9HZTtoe+127l10L/WN9QwIDOCYIceE72EiIiJRyAyuv95Lur1ZkKHV0AB//jMkJMCECeF5hojs15vAGS3ez2vx3gEhTbafbG+HItI5/frB8cd7Fe5w/QWelpjGdw75DnMWz+EfS//BoNRBDMscFvoHiYiIRLlwJcHBqnlNDVRXezuWiEj3cs59PxT9tCvZds7NCcXDRKRtMTFw4YXhf87RuUdTUl7CG2vf4C8L/8KvJv6K9KT08D9YREQkCjQ2en9nh7PinJkJmzZ5e20r2Rbpudq19ZeZFZvZmtbH/2fvvuPrLOv/j7+u7NGsjnQkHele6aINBQqWoYDlWxAVB2B+LlBQUVG/gl+rj+JExQWKgEIdoBZBUNktBStCWuhI90pp06ZJk2Y142Rdvz+unCQtaZu2Oec+4/18PM7jPuM+5/4Qmpz7c1/X9fkEOjgRCYzrpl3HhEETqG2u5f6199PW0U/NvUVERCLcs8/CF74AL70UuGP4O9h/NwAAIABJREFU14Kr17ZIeOtrn+25wLzO24XAL4A/BiookWhVXw9r1sCGDYE9TmxMLDefczNZyVnsqd7D8s3LA3tAERGRCFFVBS0tkJgYuGNkZbltTU3gjiEigdenZNtaW9XjdsBa+zNgUYBjE4k6paXw0EPw/POBP1ZaYhqfnftZhqQOITkumVv/dSvv/dN7ufVft1JUWhT4AERERMKQv8d2INt++ZNtjWyLeMsYk2KM+aYx5sHOxxOMMVf19f19nUY+p8dtrjHmM/S9uJqI9JG//dfBg65IWqCNzhzNVROv4v4376eysZKctBwqGytZsmqJEm4REZFe+HtsDxoUuGP4p5EfPRq4Y4hInzwM+IDzOh8fAL7T1zf3NWH+SY/7bUAJcF1fDyIifZOW5m719a4oSiD6dx7vDxv+QEZiBplJmRw6eohaXy3xMfE8tO4hCnILAh+AiIhImOjocN/PxgT2O3ruXDjnnMBOVReRPhlnrf2QMeYjANbaRmP6Xh6xr8n2J621xxREM8bknUaQItIHxrjR7W3b3JTyYCTbJTUlXSPaO47sAMBay/aq7fzktZ+QPzSfGUNnMDR1KKfxt0VERCTiVFe7hDszE+ICOMczISFwny0ip6XFGJOM662NMWYcbqS7T/paIO3xPj4nImfJP5X8wIHgHC8vM486Xx2DUwYzb8Q8xmWNIykuifTEdHZU7eBvW/7Gt17+FkUHNK1cRESim3+9diCnkItISPkW8Bww0hjzJ2AF8LW+vvmk1+SMMZOBaUCGMebaHi+lA0mnH6uInEqwk+3CmYUsWbUEgPTEdFLjUxk2YBh3LriTlIQUisuL2VSxicmDJ3e95+/b/k750XLyh+aTn51PWmJacIIVERHx0NChUFgISUE4C77/fncu8LWvuSVmIhJ81toXjTFvAfMBA9xmra3s6/tPNQFmEnAVkAn8T4/n64FPn2asItIHOTkQGwvt7cE5XkFuAUsXLmXZhmWU1JSQl5nH7efd3rVee+6IuVhru6aQW2spOlBEVWMVb5W9hTGGMZljmDF0BvnZ+eSm52q6uYiIRKTMTDj//OAc6/BhqKhwFcmVbIt46l3AAtxU8njgyb6+0dg+lDw2xpxnrf3vGYcXBKmpqbahocHrMETOWkeHuwVyLdjZqmqsoriimOLyYrZVbqOto63rtUUTF7F40mIPoxMREQl/994LxcXw2c/CrFleRyMSGowxjdba1CAe71fAeOCxzqc+BOy21t7al/f39XS+yhizAhhqrZ1ujJkBLLbW9rnsuYj0TUyMu4WyQSmDWDhmIQvHLMTX5mNb5TaKK4rZWL6RSYMmde332v7XWFe2rmu6eVZylodRi4iInJ1//9tt58yB1ACf7vt7bVdXB/Y4InJSlwBTbOcItTFmGbC5r2/ua7L9IPBV4DcA1tqNxphHOY0eYyJyeqx1U8lDeYQbIDEukZnDZjJz2EyOnynzVtlbFJe7JBxgZMZI8rNddfMxmWM03VxERMLKv/7lkt/JkwOfbPs7khw5EtjjiMhJ7QJGAW93Ph7Z+Vyf9PU0PsVaW3TciXHbiXYWkbPzn//A44/DhRfCtdeeev9QcXzyfOOMG9lUsYmN5RvZWrmV/bX72V+7n2d2PsM5I87hpnNuOmb/otKiY9aOF84sVK9vEREJCW1tUFPj2nRmBWGilka2RUJCGrDVGFOEW7NdAKw1xjwNYK096drJvibblZ09xfzD5x8Ays44ZBE5qeRkaGwMXkXyQMlIyuCCURdwwagLaG1vZUfVjq7p5hMGTujab/eR3dxbdC+r961mRNqIrr7fS1YtYenCpUq4RUTEczU1btZZVlZwZp35k22NbIt4asnZvLmvfypuBR4AJhtjDgAlwPVnc2ARObFgt/8KhvjYeKZlT2Na9jQ+NO1DWLqnnG8o38ALu1+gqa2Jo61HOXT0EGOzxpKRmMGyDcuUbIuIiOf8PbYHDw7O8YYOdTPccnODczwROZYxJhb4trX24jP9jD6VYbLW7rHWXgYMASbTXf5cRAJgyBCIj3dTxxobvY6m/xljiDHdf34uH3c5GUkZ5KblEhcTR11LHevL11NWX8aOqh0eRioiIuJUVbntoEHBOV5mJtxwAyxcGJzjicixrLXtQIcxJuNMP+OkybYxJt0Yc4cx5l5jzLuBRqAQtyj8ujM9qIicXEwMjBjh7kfS6PaJpCakMnvYbIanDefcnHMZlT6KGGIorS89ZgRcRETEK8Ee2RaRkHAUKDbG/NYY8wv/ra9vPtXI9h+ASUAx8GngZeCDwPustVefacQicmqROJX8ZApnFlLrq6XeV8+ojFFMGDiBlPgUvnHhN7r2qfPVvaPiuYiISDBY62qqBGtkG6C8HDZuhLq64B1TRI7xBPBN4FXgzR63PjEnO3E1xhRba/M778fiiqKNstY2n03EgZCammobGhq8DkOk37z0EixfDhddBNdHSYWEk1Uj77Ad3PXKXSTHJ/OhaR9idOZoj6MVEZFoZK2rSB4Mv/oVbNgAN9/senuLRDtjTKO1NsCN9/rPqQqktfrvWGvbjTGloZhoi0SiadMgIQHGjvU6kuApyC04YTG08qPl1LfUc7D+IN9f/X3Oyz2P9015H+mJ6UGOUkREolmwEm1Qr20RrxljJgDfB6YCSf7nrbV9OkM/1ch2O+AfLjZAMm7dtnHHsCFzlquRbZHI19TaxDM7n2FFyQraO9pJjEvkvRPey6V5lxIfG+91eCIiEqH8p8vBTLQBnn8enngCLrsMPvjB4B5bJBQFe2TbGLMa+BbwU+B/gI8DMdbaPrUEO+mabWttrLU2vfOWZq2N63E/ZBJtEYkOyfHJvH/q+/n2wm8zc9hMfG0+ntz6JPf89x6t5RYRkYA5fBg+/3n46U+De1yNbIt4LtlauwI3SP22tfbbwKK+vrmvfbZFxAPbt8PWrTBrFowZ43U0oSM7NZtb5t3C1sNb+cvmv3D+yPMxwR5uEBGRqFFVBa2t0NER3ONmZbltdXVwjysiXXzGmBhgpzHmc8ABYEBf36xkWySEbdzoCqUlJirZ7s2UIVNY8q5jZ/E8s/MZqpuqWTxpMWmJaR5FJiIikSTYPbb9lGyLeO42IAX4AnAXcAmuFXafKNkWCWH+9l+lpd7GEcpiTPdqGF+bj+d3PU9zWzNrDq7hqolXsXDMQuJi9KdORETOnL/HdrCT7cxMt068thba2yE2NrjHF4l21to1nXeP4tZrnxadgYqEsGjrtX22EuMS+fqCr7N8y3I2V2xm+eblvPr2q1w37TqmZ0/3OjwREQlT/pHtwYODe9zYWFi6FDIylGiLeMEYMxH4KjCaHrmztfaSvrxfybZICBs+3F3RLi+HtjaI02/sKQ1PG87nCz7PpopNLN+ynPKj5fzyjV8yLXsaN51zE0lxSaf+EBERkR68mkYOkJ0d/GOKSJflwP3Ag0D76b5Zp+4iISwhAYYMgYoKOHQIcnO9jig8GGPIH5rPlCFTWLV3Ff/c8U9a21tJjE30OjQREQlDXk0jFxHPtVlrf32mb1ayLRLicnJcsl1aqmT7dMXFxHHZ2Ms4N+dcfO2+rorlZfVlbK/azkWjLzpmzbeIiEhvrr3Wtf/yFywLpqIiWL0azj0XLrgg+McXiUbGmM7Ge/zDGHML8CTg879ure1TQz4l2yIhbvRo9wWvtVpnLi0xjTS6K5P713S/svcVrpt2HVOGTPEwOhERCXXz53t37Lo61wp0xAjvYhCJQm8CFvD3lv1qj9csMLYvH6JkWyTEXXmlu0n/sNZy4agLKT9azsH6g/zs9Z8xc9hMPjD1A2SnamGciIiEFrX/Egk+a21ef3yOkm0RiSrGGGYPn8307OmsKFnBMzufYcOhDWyq2MSleZeyaOIiNh7ayLINyyipKSEvM4/CmYUU5BZ4HbqIiHhgzx7Yvx/Gj+/uEhJMSrZF+s4YMxL4PTAUNwL9gLX258ftczWuZ3YH0AZ80Vq7+rh95gH7rbWHOh9/DHg/8Dbw7b5OI9diRZEwYK37km1t9TqSyBEfG88V46/grovv4vyR59NhO1hZspJX977KklVLqGysJCcth8rGSpasWkJRaZHXIYuIiAc2bIBHH4X16705/sDOlaNH+nRqLxL12oDbrbVTgfnArcaYqcftswKYaa2dBXwCeKiXz/kN0AJgjLkI+AEuia8FHuhrMBrZFgkDP/2pW6/1la/AhAleRxNZMpIyKJxVyMIxCymtK+XR4kfJSMwgIymD5rZmMpMyAVi2YZlGt0VEopCXbb8A0tMhJgbq69UGVORUrLVlQFnn/XpjzFYgB9jSY5+jPd6SihsBP15sj9HrD+FGyP8G/M0Y0+dLbxrZFgkDgwe7bWmpt3FEstGZo7lg1AWU1JSQlphGRUMFmyo2YbGkJ6ZTUlPidYgiIuIBr9t+xcRAprvuq6nkIqfBGDMGmA280ctr7zPGbAP+hRvdPl6sMcZ/aetSYGWP1/p8yUvXxkTCgH+N2IED3sYRDfIy8zjceJgD9QdoamvicONhEmISyMvslzoZIiISZvwj2/4L316YOxeam9WZRASIM8as7fH4AWvtO6Z1G2MGAH/DrceuO/51a+2TwJOdU8TvAi47bpfHgFeMMZVAE/Dvzs8dj5tK3icBHdk2xlxhjNlujNlljPl6L69/2RizxRiz0Rizwhgzusdro4wxLxhjtnbuMyaQsYqEMiXbwVM4s5A6Xx3pCelYa9lZtZMaXw2FMwu9Dk1ERIKstdW13oqNhYwM7+J4//vh+uu712+LRLE2a+3cHrfeEu14XKL9J2vtEyf7MGvtq8BYY8zg457/LnA78AiwwFrrn2oeA3y+r8EGLNk2xsQC9wFXAlOBj/SyOH0dMNdaOwN4HLi7x2u/B35krZ0CFAAVgYpVJNT1TLZtb6tKpN8U5BawdOFSJg6aSGt7K9Zabsy/Ueu1RUSikH9Ue+BAN51bREKbMcYAvwW2WmvvOcE+4zv3wxgzB0gEqo7fz1r7urX2SWttQ4/ndlhr3+prPIGcRl4A7LLW7gEwxvwZuJpjF6e/3GP/14EbOvedCsRZa1/s3K/nInaRqJOW5gqk1NW5L34vp7JFg4LcAgpyC1hZspK/bPoL++r2Ya2l8++yiIhEiYYGSE31br22n88HFRUu4fei/ZhIGLkAuBEo7lHI7E5gFIC19n5cC6+PGWNacVPEP9Rj5LpfBTLZzgH293hcCpx7kv0/CTzbeX8iUGOMeQLIA14Cvm6tbQ9EoCLhICfHJdsHDijZDpYFoxbwzM5neLvmbbZWbmXqkOMn54iISCQbNw7uucdVAffSli1w//2Qnw+f+5y3sYiEss5+2ScdHbHW/hD4YTDiCYkJMcaYG4C5wI86n4oDLgS+AswDxgL/r5f33WSMWWuMWdvm9V9BkQBbvBi+9jWYPNnrSKJHQmwCl4119TLWla3zOBoREfGK1+22/Gu1VY1cJLwE8k/HAWBkj8e5nc8dwxhzGfAN4F3WWl/n06XA+h5T0P+Oa0r+257v7VwQ/wBAamqqVrJKRBs71usIotPCMQsZlTGKKYOneB2KiIhEqawst1WyLRJeAjmyvQaYYIzJM8YkAB8Gnu65gzFmNvAbYLG1tuK492YaY4Z0Pr6EHmu9RUSCJSkuialDpmq9tohIFPrxj2HJEjh40Ns40tLc6HpDA7S0eBuLiPRdwJJta20b8DngeWAr8Fdr7WZjzFJjzOLO3X4EDACWG2PWG2Oe7nxvO24K+QpjTDFu3v2DgYpVJBxYC089Bffd5/3asWhVfrScigY1RhARiRaHDkF5OaSkeBuHMZCZ6e5rdFskfAR0BYq19hngmeOeW9Lj/vHNw3vu9yIwI3DRiYQXY+DNN92XflkZjBx56vdI/3m99HUeWf8Is4fN5ua5N3sdjoiIBJjPB/X1bkTZyx7bfgMHQmUlHDkCQ4d6HY2I9EVIFEgTkb7p2W9bgmvy4MnEmljWHVpHWX2Z1+GIiEiAHTnitgMHugveXtO6bZHwo2RbJIwo2fZOZlIm5488H2stz+16zutwREQkwKqq3NbrHtt+ixfD0qUwb57XkYhIXynZFgkjSra9dfn4y4kxMRQdKKKysdLrcEREJIAqO//MDx7sbRx+gwe76ePx8V5HIiJ9pWRbJIwo2fbW4JTBFOQU0GE7eH7X816HIyIiARRqI9siEn6UbIuEkSFDICEBampc+w8JvivGX4Exhtf2v0ZNc43X4YiISIDMmAFXXw2TJ3sdiVNXB7/7HSxb5nUkItJXAa1GLiL9yxiYPdvdb2mB1FRv44lGw9OGc87wc4iLicNa63U4IiISIBMmuFuoiI2FN96ApCQoLPQ6GhHpCyXbImHmE5/wOgL51JxPYUKhNK2IiESNlBS3Xru52d2SkryOSERORdPIRUROkxJtEZHI1tICq1bBli1eR9LNGNeGDLrbkolIaFOyLRJmrHVfsrt2eR1JdLPWsrF8Iz9//ec0tzV7HY6IiPSjykp47DH485+9juRY6rUtEl6UbIuEmYYGuOMO+MUvXOIt3jDG8MLuF9hyeAuv7H3F63BERKQfhVrbLz+NbIuEFyXbImFmwADIyACfr7stiXjjvRPeC8CLe16ktb3V42hERKS/hGrbL41si4QXJdsiYUj9tkPDlMFTGJ05mnpfPav3rfY6HBER6SehOrI9ciTk50N2tteRiEhfKNkWCUNKtkODMYYrx18JwPO7n6eto83jiEREpD+E6sj27Nnwuc/B/PleRyIifaFkWyQM+ZPt0lJv4xCYNWwWI9JGUN1UzRulb3gdjoiI9INQTbZFJLwo2RYJQxrZDh3GGK4YfwUAr779qsfRiIhIf/D53DbUppEDHD0K+/apSKpIOIjzOgAROX3Dh7t+mxUV0NYGcfpN9tS8nHk0tjYyP1fz+kREIsHSpS7hTkjwOpJ3+sY3oLkZ7rkHUlO9jkZETkan6CJhKD7etf8aOlSJdiiIMTFcnHex12GIiEg/Skz0OoLeZWVBWZlr/6VkWyS0aRq5SJgaPRqSkryOQo7X2t5KZWOl12GIiEiE8vfaVvsvkdCnZFtEpJ+8XfM2d664kwfffBCrxXQiImFp1So3VXvFCq8j6Z16bYuEDyXbImGqrAzuvRceftjrSMRv2IBhdNgO9tbsZVvlNq/DERGRM1BR4fpst7d7HUnvlGyLhA8l2yJhKj4eiothyxavIxG/xLhELht7GQDP7HzG42hERORMhHrbL/808iNHvI1DRE5NybZImBo0yBVvqatzbUAkNLxrzLtIiktiR9UOdh/Z7XU4IiJymio7y26EarKtkW2R8KFkWyRMGaN+26EoJT6lqzL5s7ue9TgaERE5Xf6R7VDssQ2uQOqXvgSFhV5HIiKnomRbJIwp2Q5Nl+ZdSkJsAsXlxeyv3e91OCIi0keNjdDU5GaOhWpbrZQUmDw5dC8GiEg3JdsiYcyfbJeWehuHHCstMY0LR1/IiLQR+Np9XocjIiJ91HO9tjHexiIi4S/O6wBE5MxpZDt0XTP5Gj449YMYna2JiISNAQPgmmsgKcnrSE7u5ZehpATe+14YNszraMJTUREsW+Z+jnl5blp+QYHXUUmk0ci2SBjLyYFzz4X5872ORI6XEJugRFtEJMxkZcGVV8LFF3sdyclt2QJvvOHagMrpKyqCJUtcMbycHLddssQ9L9KflGyLhLHUVPjEJ0L/pCCaVTVW8VjxY1Q1VnkdioiIRIjMTLdVRfIzs2wZZGS4n2NMjNtmZLjnRfqTkm0RkQB6evvTrNq7ihd2v+B1KCIicgobN7pbY6PXkZycv9e2ku0zU1Litlu2dP8M09O7nxfpL0q2RcLc0aNQXAy71dI5JF0x/goAVu9bTW1zrcfRiIjIyTz1FNx3Hxw+7HUkJ6de22cnLw/efttNH6+pcc/V1bnnRfqTkm2RMLdpE9x7L7z0kteRSG+Gpw1n9vDZtHW08dIe/U8SEQlV1rrkC0K/rZZ/ZPvIEW/jCFc33gjl5eDzQX09rF0LtbXqXS79T8m2SJhTRfLQd+X4KwF45e1XaGhp8DgaERHpTWMjNDe7SuQpKV5Hc3Ia2T47mZkwa5b7OZaVuf/vt9+uauTS/5Rsi4S5YcNccY+KCmht9Toa6c3ozNFMy56Gr83HypKVXocjIiK9CKce21lZkJsLo0e7EXk5PWvXQna2q0B+xx2wYAHEx3sdlUQiJdsiYS4+HoYOdV+2agESuvyj2ytLVtLarqsiIiKhpmeyHeri4uCb34TPfCb0LwyEmvZ2eOstd3/ePMjPd/eLi72LSSJXnNcBiMjZy8lxiXZpKYwa5XU00psJgyawaOIiZg+bTXysLp+LiIQa/3rtcEi25cxt2wYNDTB8OIwYAQMGuOe3b3czBDXCLf1JI9siEUDrtsPD4kmLGZkx0uswRESkF/X1bhvqxdH8rHUxh3qbslCTkwPXXgvvfrd7nJ7upuO3trqEW6Q/aWRbJAL4k23/iYKEvqMtRxmQMOCk+xQVwbJlru9nXp6rkqriLSIigXHttXDVVeGzBnr5clixAj74QbjsMq+jCR+ZmXD55cc+l5/vWoEVF8P06d7EJZFJybZIBJg6FX7+c1dBVUJbva+eB958gMONh/nOJd8hLqb3P8NFRa5wS0aGu5hSWekeL12qhFtEJFASEryOoO8yMtxW7b/O3qxZrvXX7NleRyKRRtPIRSJAfLwS7XAxIGEA9S31VDdV80bpGyfcb9ky13qmocEVc8nMdCdWy5YFMVgREQlZ/l7bav/Vd3//O/zjH1BXd+zzI0fCDTfA5MnexCWRS8m2SIQJl+lv0coY01WZ/Lldz9FhO3rdb/Nm2LULdu+GHTvcc+npbkq5iIj0r6NH4c474Ve/8jqSvvP32tbIdt+0tMDKlfDPf7r7IsEQ0GTbGHOFMWa7MWaXMebrvbz+ZWPMFmPMRmPMCmPM6ONeTzfGlBpj7g1knCKR4OWX4etfd18kEtrm5cxjcMpgKhoqeKvsrXe8vmqVmzZ+9Kh7fOQINDW5K/F5ecGNVUQkGlRVdd/ChT/ZrqnxNo5wsWkT+HwwZkzvRfB8Pnj9dfjXv4IemvQjY8xIY8zLnTnmZmPMbb3sc31n/llsjHnNGDMzUPEELNk2xsQC9wFXAlOBjxhjph632zpgrrV2BvA4cPdxr98FvBqoGEUiTXW1KpKHgxgTw+XjXXWWZ3c+iz1uOoLPBxMmuJHsAQOgo8ONctfWuiJpIiLSv8Kpx7ZfZqbrsV1b65YbycmtWeO28+b1/np7u1uq9c9/ugvcErbagNuttVOB+cCtveSgJcC7rLX5uHzzgUAFE8iR7QJgl7V2j7W2BfgzcHXPHay1L1tr/Q0LXgdy/a8ZY84BhgIvBDBGkYiR2/nbo2Q7PJw/8nwykzIprSuluKL4mBOl97wH7r4bHngApkxxbV2SklQcTUQkUPw9tsOl7RdAbKyr5WGtRrdPpbnZVRoHOOec3vdJSYFx49wF7q1bgxeb9C9rbZm19q3O+/XAViDnuH1es9b6qx0ck4P2t0BWI88B9vd4XAqce5L9Pwk8C2CMiQF+AtwAqJmBSB/4238dPOi+eI3xNh45ubiYON497t08vf1p1m6q4S8r4ctfdqMqxriRbYDf/96dRGVmehuviEgkC8eRbYAbb3RFUtPTvY4ktG3c6PpojxvXPf2+N9Onw86dLjGfMyd48UlgGGPGALOBE1ek7ZGDBkJIFEgzxtwAzAV+1PnULcAz1trSU7zvJmPMWmPM2ra2tkCHKRLSUlJcQtbSAocPex2N9MWCkRdxfuP3eeOvF1FZCatX976fEm0RkcAKx5FtcMnhpEku4ZYTW7vWbU80hdwvP99tN21SwdkQFufP/zpvN/W2kzFmAPA34IvW2roT7HMxLtn+34AFG6gPBg4AI3s8zu187hjGmMuAb+Dmzfs6nz4PuNAYcwswAEgwxhy11h5TZM1a+wCdc+xTU1P1KyFRLyfHjYIeOADZ2V5HIydTXw+//W0CW7cmYAxcey28+90n3t9a2L4dkpNh9OgT7yciIqcvXEe2pW8uusgtxzrVaPWIEW7ku7oa9u3T922IarPWzj3ZDsaYeFyi/Sdr7RMn2GcG8BBwpbU2YKURAzmyvQaYYIzJM8YkAB8Gnu65gzFmNvAbYLG1tsL/vLX2emvtKGvtGOArwO+PT7RF5J38U8m1bju07d0L3/2uWxOWlgafv62NlMmr2Vf79gnf8+9/w09/Ck8/fcJdRETkDF14oUvIwm1ku7QUnnzSVdGWE5s+HT7xCbfG/WSMOXZ0W8KPMcYAvwW2WmvvOcE+o4AngButtTsCGU/ARrattW3GmM8BzwOxwO+stZuNMUuBtdbap3HTxgcAy93PhX3W2sWBikkk0s2a5ZK3KVO8jkROpL4efvITN91/7Fi46Sb4b+ULPLXhKWYMncGtBbf2+r5zzoG//tV9+ZeXw9ChQQ5cRCSCXXqp1xGcmYoKeO45mDkT5s/3OprIMGOGG7TQLIewdQFwI1BsjFnf+dydwCgAa+39wBJgEPCrzhz0lKPlZ8oc33ImXKWmptqGhgavwxAROaWXXnLr6j/4QYiLg3pfPXesuIPW9la++a5vkpvee1HMP/zBreteuBA+8pHgxiwiIqFnzx744Q9h1Cj4xje8jib0NDTAo4+6Th4zA9ZJWYLJGNNorU31Oo6+CokCaSIikayyEnb0mKR06aUuWY7rnFuUlpjGRaMvAlzf7RO55BK3/e9/1QNURKS/lJfD+vXdRdLCycCBbltdffL9otVbb7niaK+84nUkEq2UbItEmJ074dln3dQy8d6mTW599q9/3X0i11tbtnePfTexMbG8WfYm5UfLe/2snBxXddbng//8J4BBi4hEkfXr3d/ol1/2OpLTl54OMTGpj7uIAAAgAElEQVRuiVJrq9fRhB5/FfK5pzlB2FpXIO2tt/o/JokuSrZFIswrr8Df/w67dnkdSXSzFv75T7j3XmhshPHjXXu2E8lKzuL8kedjreW5Xc+dcD//usKXX4aOjn4OWkQkCvkvhIbjGt2YmO72kDU13sYSaurqXBePuDhX0+Z0VFa6C+V/+IO+a+XsKNkWiTD+iuSlJ+1SL4HU2OiS7H/8wz2++mq45ZaTJ9sA7xn3HowxvF76OkeajvS6T36+K6w2fz60t/dz4CIiUSjc235lZbmtppIf68033YXvqVNP/f17vCFDXCHSxkbYvTsw8Ul0CGSfbRHxgNp/eau0tHvKeGoqfOpT7ou+L7JTs7li/BVkp2aTnpje6z4xMfC1r/U+FV1ERE6fP9kOt7Zffjk5rsNFhNQ87jdnOoXcb/p0t55/0yaYMKH/4pLoomRbJMIo2faWzwdHjsDo0XDzzac/UnLN5GtOuY8SbRGR/mFt+I9sX3+91xGEnupqt5wuPv7Mq5Dn58OKFVBcDO97X//GJ9FDybZIhBk4EJKSXLGUujpXPEUCy9ruBHjcOLjtNreNjz+7z23raCMupvc/083Nrg1YczNcddXZHUdEJFr5C4ulprrvTokM8fFwzTWuc8eZ/n+dMAESE93gRXV193R9kdOhNdsiEcYYjW4HU3W163G6YUP3c5Mnn12i3WE7WL55OXe8dAcNLQ297lNTA8uXw3PPuT6iIiJy+vzrnMN1Crmfte7iqzgDBsCVV8K11575Z8TFue9zcFPJRc6ERrZFIlBODtTWujVcEjjbtsGDD8LRo/D00zBjRv9M8Y4xMRyoP8Cuql1ct/w6LJa8zDwKZxZSkFsAwLBhMG0abN7sRrgvv/zsjysiEm1Gj+7uGhGuDhyA733PfS9885teRxNZ8vPddHSfz+tIJFwp2RaJQB/9qNb1BpK18PzzrsWatS7p/eQn+/dnnpuWy9qytSTHJ7Ng1AIqGytZsmoJSxcu7Uq4L7nEJdsvvwzvfrcrniYiIqcnPh4yMryO4sylpUFbm6sXIvD66+5nMX++W1p3Ns47Dy64QN+vcub0T0ckAinRDpymJvjNb+DJJ12ivWgRfO5zbr1ff1q5dyVZyVnExsSy4dAGqpqqsNbyyIZHuvaZNg2ys900yPXr+/f4IiISHtLS3JTnxkaNwAK89BI89RTs23f2nxUXp0Rbzo7++YhEsMZGtQLpb/ffD+vWQXIy3HorLF4cmC/ivTV7mTJkCnExcTS2NXKg/gDlDeXsrdnbtc/OIztYsNAt0lu5sv9jEBGJdA8/DD/+Mezf73UkZ84YyMx096O913Z5uft/mZTkLkj3l+Zm1cGRM6Np5CIR6oc/hD174K673Oin9I9rroE//9n1zx4yJHDHycvMo7Kxkvk586lvqaemuYbG1kbyMvMAqPPV8ZPXfoLtiGFz5hj27p/EnOJJLJg6joTYhMAFJiISQUpKXIIWG+t1JGdn4ECorHTJ9rBhXkfjHX9v7Vmzzr4jiN/Bg/Cd77jWcHfd1T+fKdFDI9siESolxW1LS72NI9y1t8PGjd2P8/Lg618PbKINUDizkFpfLXW+OtIS0shIzCAlPoXCmYUA1PvqGZs1lthYGDRuDwNmPcuf9/6MLz33JX782o8pP1oe2ABFRMJcJPTY9vO3pYr2ddv+ZHvevP77zGHDXAuwigp3EzkdGtkWiVA5Oa5VxYEDMGeO19GEl6IiWLYMduxwlcaHDYMlS2D2bPd6MNbEF+QWsHThUpZtWEZJTQl5mXncft7tXcXRctJz+N8F/0tzWzO7juxie+V2tlVuY3/dfnYd2UV6YneD9Wd3PovFMmnQJMZkjiE2JsyHcERE+kFdnSssNmCAS6bCmb8QWDRPIz940N1SU2HKlP773JgYNyV9zRooLoZLL+2/z5bIp2RbJEKp1/aZKSpyiXVMjJtaePSo2+7d251sB0tBbkFXcn0iSXFJTM+ezvTs6QDUNzdy8Oh+kuOTAbDWsqJkBfW+egAS4xIZP3A8kwZNYtLgSYzKGEWM0SQnEYk+lZVuG+6j2uAuqg8bBmPGeB2Jd/yj2rNn9/+ygPx8JdtyZpRsi0QoJdtnZtkyN3V83z43xXDoUBg+3FU3fd/7vI7uxHw+eOwx2LkzhaVLJ3U9b7F8NP+jXSPfh44eYnPFZjZXbAbg2inXcvl416S7tb2VuJg4jMrZi0gU8E8hHzzY2zj6w6hR7hbNJk5007zPPbf/P3vaNDerbedO930b7jMhJHiUbItEqGHD3Ojs4cP6Yjgdb70FNTXufk4OjB3rku6SEm/jOpWEBDf6Xlnp/hv869ViTAxzhs9hznC3lqC2uZYdVTvYXrWd7ZXbmTBoQtdnvLjnRV7c/SKTBk/qGvneX7uf32/4fddU9sKZhaccbRcRCQeRsl5bnMmT3S0QBgxwNVv27IFt22DmzMAcRyKPkm2RCBUX5xLugwehrCy6p5b1lc/XfZs0CUaOdFeya2vdl2woMwYuuQT+9CfXBuxExWEykjKYlzOPeTluB9ujN9zB+oM0tjayrmwd68rWUXG0gvXl6xmUMojRGaOpbKxkyaolfHvht5k1bBaJsYkaBReRsDV6NCxc6EZEw11HB7zyiluHfvXVXkcTmfLzXbK9f7+Sbek7YyOkCW9qaqptaGjwOgyRkLJxo1u3NG6c6zkpp/bcc27Ndl4epKe7E5faWli6FApCfEDX53OV0hsb4Y47Tv8Ci7WWqqYqtlVuY3vldu4tupdaXy2JcYkMSx3GxEETqWmuITk+mcTYROJi4khLTCM9MZ0BCQNIS3D3F45ZyKAUN1RU01xDe0c76YnpxMeeeR+WotKiY4rFaYRdRKSbtfCFL0BLC/zsZ5Cc7HVEwWOta8k5ZQrMmOFm9QVCfb1bZubvaS7eMMY0WmtTvY6jrzSyLRLBZszwOoLwsHdvd2J6xRWuquuyZW7qeF4e3H576Cfa4JYKLFgAL7zgRrc/8YnTe78xhsEpg1kwagELRi3g8a2PMy17GnW+OuJjXKKcnpjOziM7yc/Op6W9heqmaqqbji1/Oz93ftf9p7c/zX/2/cfFF5dIWkIaaYlppCWkMTpzNFdNvApwif62ym2kJaZ1Je7+qulFpUUsWbWEjMQMctJyukbYly5cqoRbRAQ3uykryxX0rK6OrmS7pARWrYL16wM74pyWFrjPlsilZFtEopa18Mwz8PTT8OEPw8UXu+cLCsIjue7NwoXw4ouuKuv73w8ZGWf+WWMzx1LZWMnwAcO7nqvz1TFtyDR++d5f4mvzcbTlKPUt9dT76ru2A5MHdu2fFJdEVnIW9b56fG0+fG0+KhtdCeDWjtau/ZrbmvnZ6z875vgp8SmkJabx0p6XyEzKJDPJDSekJ6VjsSzbsEzJtoicEWth3TpXHC1SCov5k+0jR2DECK+jCR5/FfJzzglOa05robk5ui5oyJlTsi0SwdraXOJ1+DDceGNwvoTChbXw1FPw7LPu5xIp0+wHDYJZs9xJ5NatMH/+qd9zIoUzC1myagngRrTrfHXU+mq5/bzbATdSnRiX2DVlvDfXTbuO66Zdh7WW5rbmYxLzpLjuH3prRyuTB0+mzlfXlcA3tjbS2NpIaV0pOWk5XfseqDtAaV0p29nOurJ1TBky5ZjPEhE5ldpa+M1v3Gjlj3/sdTT9Ixp7bVsLb77p7p+oVkl/Ki2FX//aXcj+2tcCfzwJf0q2RSJYbKxbg9zcDNdc49Ygi/tyXr4cVqxwa7s++UmYO9frqPrP+97nbkOHnt3nFOQWsHTh0mPWSt9+3u1nNJpsjCE5Ppnk+GSyU7Pf8Xp6YjpfOu9LXY+ttTS0NlDvq+doy1Ga25q7Xqtvcc8lxyVz/9r7iY2JZcLACeQPzWfG0Bm9fr6ISE+RWIk8K8ttoynZ3rXLdRAZNCg4hWAHD3Y/36oqaGiA1LBZOSxeUbItEsGMce2rdu92/baVbLtE+7HHXNXW2Fj49Kdh9myvo+pfZ5tk91SQW+DJVG1jDAMSBjAgYQC3nXsbS1YtIa45jvTEdIYPGE6sieWaSdfQ0tHCnuo9bKvcxrbKbZRUl/Dpcz4NQHtHOx2246wKs4lIZKp0q1kiose2XzQm2/4p5HPnBmf2XlISTJjg2n9t2RKc0XQJb0q2RSJcz2R7yhSvo/Hek0+6RDsuDj77WZg+3euIAsdaePtt194mnJcQ9DbC/pXzvtJ1EaChpYHNhzdTXF7MrGGzut63+fBmHnzzQaYMmUJ+dj7Ts6eTlZzl1X+GiISQSBzZHjjQ9YOOi5Kz+46O4E4h98vPd8l2cbGSbTm1KPl1FIleOZ1LXQ8c8DaOULFggVvPfP31MHmy19EE1i9/CZs3w1e/CuPHex3N2TnZCHtqQioFOQUU5Bz7+v7a/bS0t7Dh0AY2HNoAQG56LvlD88nPzmfcwHEBj1tEQlMkjmxPmwY/+YnXUQRPa6srbLp3L+TmBu+4+fluKdqmTS7hD1SrMYkMSrZFIpySbTfC6x/Zzc6Gb3/bTSGPdKNHu2R75crwT7bPxKKJi7hg1AUUlxdTXFHMtsptlNaVUlpXyoZDG/jWwm917dvY2khKfIqH0YpIMEXiyHa0SUyERYuCf9zsbHeRprLSJfpjxwY/BgkfSrZFIpw/2T54MDqvwLa2wgMPwKRJcNll7rloSLQB3vUuVyBv3Tq3hi8rCmdQZyZlcuHoC7lw9IW0dbSxs2onxRXFx7Qnq2ys5P9W/h95mXldo9656bmYcJ57LyIndeSI20bSyLaftW6rP2GBYYwb3X75ZTe6rWRbTsZY/29kmEtNTbUNDQ1ehyESkn70I1cxs7AwuipntrTAr37lWmClpsJdd0XXfz/AQw/BmjVwxRWuQrm807qydTz41oO0d7R3PZeZlMn07OnkD3VrveNi3LXpotKiY9aOF84sVK9vkTDU1uYuQg4cGFkXYH/xC9i+Hb7+dRg50utoAmfXLti5EwoKvJmdUFbmqqBPmBA9a+RDhTGm0VobNmdzSrZFJCI1N8N998GOHa6P6pe/DCNGeB1V8O3ZAz/8obvI8IMfQEKC1xGFpua2ZrZVbqO4vJhNFZuoaa4BID42np9e/lPiY+MpKi3izpV3Mih50DF9x5cuXKqEW0RCwi9+4ZYP3XorzJjhdTSB8/DD8PrrsHixN1PJxTvhlmzrWoyIRJymJnfCsWcPZGbCl74Ew4Z5HZU38vJc79G9e6GoyBWIk3dKikti1rBZzBo2C2stpXWlFFcU09zW3NU67OH1D7OvZh9Hmo6QnZrNyAw3bLRswzIl2yISEvzLhfzT5CNRayusX+/uz53rbSwip6JkWyRKNDZCbS0MH+51JIHV0AA//7lreTVwoBvRHjLE66i8Ywxccgn85S9u2qScmjGGkRkju5Jpvx1VO0iIS6CprYm3a9/mSNMRJg6aSElNiUeRisiZWLcOXnzRTUFeuNDraPpXNPTa3rLFzV4bORKGDvUujrIyeOIJSEmBj3/cuzgktCnZFokCZWWuAveQIfCd73gdTWA1N0NdnSt68+Uvq9IsuCv/c+ZAfLzXkYS3yYMnMyjF/YPaeWQn9S31vFH6BjOGRvBcTZEIdOAA7N4NEyd6HUn/G9hZ+zGSk+01a9zW61HthATYuBGSktzFbK3dlt5EWV1ikeg0ZIgrAFNZCT6f19EE1qBBLsn+6leVaPvFxirR7g+FMwup89VhMMwaOovU+FSa25rp6Ojgia1PeB2eiPRRJLf9ivRp5C0tLsEF75PtQYNcLZjmZlewTaQ3SrZFokBcnFuzbK0b5Y401dXw6qvdj7Oz3VptOZbPB6tWdZ9oyukpyC1g6cKlDE4ZTHlDOQU5BfzfRf/HyMyR5Gfnex2eiPRRZaXbRmLbr0gf2S4udt9lY8aExv+//M4//cXF3sYhoUsTHkSiRE6OmzpXWuq+pCJFZSXcc49LIBMSYP58ryMKXX/9K6xe7X5mH/iA19GEp4LcgncUQ2toaSA1obsw6u4juxmbNVZ9ukVCVCSPbA8cCNdcExqJaCBkZcE554TOEoDp0+H5512/7Q9+0OtoJBRpZFskSuTmuu2BA97G0Z/Ky+HHP3YnTnl5kd3mpD9cdJHbrl4d+csJgqlnor25YjN3/+dufr321zS0qB2lSKjp6Oge9fWPAkeS+Hi48kqYN8/rSAJj7Fi46abQKWw3bhwkJ8OhQ90zJkR6UrItEiVyctw2UpLtsjKXaFdXw/jx8MUvuoqgcmKjR7sTg6Ym159U+l9bRxvJ8clsOLSBu169i51VO70OSUR6qK52CXdmpgpaydmLjYWpU919TSWX3ijZFokS/mS7tNSt3Q5n+/e7RLuuDiZPhi98wVUDlVO75BK3Xbky/P8dhKKZw2byzYu+ydissVQ3VfOT//6Ef+74Jx22w+vQRASXHF18sWv7Fan27IGXX4aDB72OpH+99JIrjhZqbSwvvBCuuw5mzvQ6EglFxgbwbMsYcwXwcyAWeMha+4PjXv8y8CmgDTgMfMJa+7YxZhbwayAdaAe+a639y8mOlZqaahsaNGVP5ESsha1bXeXMjAzXfzkcWQvf/a5LuKdPh898RpW2T0d7O9x5J9TUwG23dV+Rl/7V3tHOP3b8g+d2PYe1lomDJvKpOZ8iIymjXz6/qAiWLYOSEreEorAwspMHEem7Rx+FV16BD32o+wJruGtshK98xc1KuPtuSE/3OiLxijGm0Vqbeuo9Q0PARraNMbHAfcCVwFTgI8aY40/r1gFzrbUzgMeBuzufbwQ+Zq2dBlwB/MwYo9rCImfBGJdYZWaGb6INLvabb3brjz/7WSXapys2tnut24oVnoYS0WJjYrlm8jXcdu5tpCemU9FQQWxMbL98dlERLFni1gfm5LjtkiXueRERf/uvSKpIvn69u1g8aZISbQkvgVytUgDsstbuATDG/Bm4Gtji38Fa+3KP/V8Hbuh8fkePfQ4aYyqAIUBNAOMVkRBWVdVdOXbIELj+em/jCWcXXgibN6tyezBMGTKFJe9awpGmIwxIGAC4UW+LJS7mzL6Cly1zJ5v+9nb+7bJlGt0WOZW9e912+HBITPQ0lIDxF36LpF7ba9e6rde9tU+kstK1IE1JgSuu8DoaCSWBXLOdA+zv8bi087kT+STw7PFPGmMKgARgd79GJxKF9u+Hhx6Cp5/2OpLTs2ULfOtbrr2GnL0BA9x0vEitVhtq0hLTGJ05uuvxU9uf4gerf0D50fIz+rySElevYPt2N9IDLvkuKemPaEUi2+OPw/e/H9m/L5E2sn30qFsGFxMDs2d7HU3vjh515yivvKJ6KHKskCiQZoy5AZgL/Oi454cDfwA+bu07q8sYY24yxqw1xqxtC7VqCSIhqKUF1qwJr4qZGzfCffdBaytUVOhLTMJbS3sL68rWsb92P9/993d5vfT0y8IPHAg7d7rWd/X17rm6Ord2W0ROLpJ7bPv5R7YjJdlet86t1Z4yxV0sDkWjR0NamptNUFbmdTQSSgKZbB8ARvZ4nNv53DGMMZcB3wAWW2t9PZ5PB/4FfMNa2+vZiLX2AWvtXGvt3Dj1bxA5pREj3PbgQffFFereegt+/WtXeXThQrjhhvBebx5qysvhj390U98kOBJiE/jGRd9gXs48fG0+Hl73MA+vexhfW98anzc1uXZFLS3uhDo93Z3Yvf22K5ImIifW3u4SUGO6R38jkX9pSU1NeHzXn8qaNW4bqlPIwf2bmj7d3Q+nAY1IZIwZaYx52RizxRiz2RhzWy/7TDbG/NcY4zPGfCWQ8QQy2V4DTDDG5BljEoAPA8dMXjXGzAZ+g0u0K3o8nwA8CfzeWvt4AGMUiSrJye5qflubGyUOZUVF8OCD7kThPe+BD39YiXZ/KyuDf/8bXnghMk7IwkVSXBKfnP1JPjbzY8THxvN66et859XvsL92/ynf+9hjbp3pokUwY4Zbf7pnjyuU5r+YJiK9q652s6Mivcd2XJy7EJeaCuHeqMdaVxQtNxdmzfI6mpPLz3fbTZu8jUNoA2631k4F5gO39lKk+wjwBeDHgQ4mYH9qrLVtxpjPAc/jWn/9zlq72RizFFhrrX0aN218ALDcuLPofdbaxcB1wEXAIGPM/+v8yP9nrV0fqHhFokVOjptGd+AADBvmdTTH8rczWr/eXZGfOBE+/nH4n/9Roh0IM2a4iy+HD7uTgxkzvI4oehhjuGDUBYzNGsuDbz3IgboD/GPHP7hl3i0nfM8bb7hbQgL83//B0KHu+T/9yc1OeOQRuOMOV3FeRN4pGqaQ+/3gB5Hxt8AYd3Fx0SKvIzm1KVPcuvJdu1yrspQUryOKTtbaMqCs8369MWYrrm5YzyLdFUCFMSbg/7ICumbbWvuMtXaitXactfa7nc8t6Uy0sdZeZq0daq2d1Xlb3Pn8H6218T2en6VEW6R/5HSWKSwt9TaO4/VsZzR2rJvut2+fuyCgRDswYmLg4ovd/ZUrvY0lWg1PG84dC+7g8vGXc+OMG0+4X2Wl650LbpaHP9EGeP/7XfKwfz8891yAAxYJY5WVbhsNyXYkJNrhJiUFxo93M8W2bvU6GgEwxowBZgNveBVDSBRIE5Hg8SfbB95RQcFbjzzS3c4oJQUuuMB9aS1b5nVkke2CC9xI6datbi2/BF98bDzXTrmWtMQ0ADpsBw+8+QA7qrq6YJKW5qrHz5kD559/7PuTkuBjH3P3//Wv0LuQJhIq/CPbgwd7G4f0TU2Nq/Dt//8WDs4917XXHDLE60giWpy/QHbn7abedjLGDAD+BnzRWlsX3BB7xGEjpLRvamqqbQj3hSkiQVBeDn/7G0yeDJdc4nU0jrWuncfQoTBmTPfzHR3uosAzz3gWWlTwT0O+6CL1Lw8Fr779Kn/a+CeMMSyasIhFExcRY9y18Y4ONyOhN48+6trOjByp6eQivWlvdwlcfLy7uBvJiorg7393RcWuvdbraM7MSy/B8uXuIuPNN3sdjYQKY0yjtTb1FPvEA/8EnrfW3nOS/b4NHLXWBmzttka2RaLM0KFwyy2hk2i3tbne362tsHs3NDd3v6Z2RsHh/7fw+uvH/vzFGwtGLeC9E94LwF/W/5O7/30P1U2uh8+JEm3onk5eUaHRbZHexMa635FIT7TBLb+qqnI1OcKVvwr5vHnexiHhxbhCYL8Ftp4s0Q6WCK7FKCKhrrkZ7r/fTWGePt1NY25udtOa6+qgthZuv93rKCPf8OFw3XWuuEtSktfRSIyJ4erJVzMyZRKffeB3bNmyk7eP3MVn5hcyc9jME74vMRFuusn1odU0WZHo5m9tduSIt3GcqcpK120hMbG7pVa4aGhwRUcTEtysPQm6C4AbgWJjjL/m153AKABr7f3GmGHAWiAd6DDGfBGYGojp5kq2RaJQU5Mb+RowwCVaXqivh1/+0vUHTk931ZXLytwa7ZISN6J9++1QUOBNfNHm0ku9jkB6shaKnpnM1MpvUjXyEdpjNvGrNb/ii/O/yJQhU074vp7LMESkW1sb3HOPuxD18Y9HfuFNf7JdXe1tHGdq7Vq3nTnTJa3hZPdu+N3v3N9jJdvBZ61dDZz0N9xaewjIDUY8SrZFotCrr8ITT7hK1B/+cPCPX1UFP/uZm+46eDB88YuumMjIkUquQ0Fzs0a4vfaf/8C6dZCelMbdN32ODfUr2F65ncmDJwNQVFrEsg3LKKkpIS8zj8KZhRTkdv/yWOt+z30+16deJNodOeKSoOrqyE+0wRUbNcbNEmtrC7++4v4p5HPnehvHmZg82f289+51P/9oWLYgJ6Y12yJRyOuK5HFxrtDTyJHwv/+rqp2horwcvv99uO8+ryOJbocOwV/+4u5/9KMwZIjhsrGXccu8WzDGUFRaxB0r72Bb1TZGpI2gsrGSJauWUFRa1PUZ+/a5gmlPPqn12yIQfZXIY2MhI8NdeKut9Tqa01Ne7v5uJSfDtGleR3P6EhJg0iR3f/Nmb2MR7ynZFolCPZNtLxoSZGTAl77kponrim/oyMhwid6OHa5nswSfv2BgS4trIXPuud2vmc7huEc2PEJlQyUH6w+y7tA6SutKqW6u5lurvsUre19hb81eRo+GhQvdRa1HHnFVmEWimT/ZjoYe237hum67vd1Nv543L/xG5P3y8922uNjbOMR7YfpPWETOhr+XdUODu+KdmRn4Y27Y4NZnL17sHkfL6EI4SUpyPZxXroSXX+7u3SzBs369u9AxeLAb1e5NSU0JYzLHsLt6N42tjTS2NmKtpay+jEeLH+WSvEsYkzmGa6+F1Zv2srz2Prb/LpP5MzPJSMogMymTjES3nThoIolxicH9jxTxQGWl20ZTsn3hhS5pHTjQ60hOz4gR8JnPeB3F2cnPhz//GbZscRcP1IoxeinZFolCxrjR7Z073eh2oJPt//wH/vAHN4o+YYKrei2h6eKLXaL9xhvwvvdBWprXEUUX//rEgQNPvG5+bOZYKhsrmZ87n6bWJlraW6hqqiI5LpkLR1/IhEETAFfF993/U83KJ+p4c1cdHWn7GHBcZ9LvX/b9rmT70eJHebvmbTKTMl1CnpTRlZQPSR1Cdmr2O2I51dpxkVARjSPbF1zgdQTRa/BgGDbMzRbbvRsmTvQ6IvGKkm2RKNUz2Q7Umihr4bnn4O9/d4+vusoVDpHQlZ3t2qwUF8Pq1XDllV5HFH1OVRCocGYhS1YtASA9MZ22jjYSYhP41ru+9Y5Ed/G5M6na/UNW/KeGzB21vP/6Gupba6hpdrf0xO51HKV1peyt2dvrMecMn8PNc28GoKa5ht+s/Q3VTdU8u/tZMhJdUr6jagdffuHLfO+S73HRmIsAsNZ2TX8X8VK0rdkOV+vWQXy8uygf7qPBM2ZAaqo3y/UkdCjZFolS/nXbhw8H5vOtheXLYcUKN5L+4QE73x0AACAASURBVA+7NaQS+i65xCXbq1a5StbhfsIT6vy/K+ed54oGnkpBbgFLFy49ZkT59vNu73VEOcbEcMMHMtmzJZPkGJgz6MQzWT4959NUN1dT01xDbXNtV0Je01zDqIxRXftVN1Wzp3oPq/etpqnNjawfbnR/SHxtPh5868GuZPuxTY/x2v7XSI5LJjk+mZT4FFLiU0iOS2Z42nCumnhV1+euObCGpLgk93rnvslxySTEJpw0YdfouvTFtGlupk72OydoRKyGBleDIzbWJX6hzlrXKaWiwtV1CfeL89deGx2V7+XklGyLRKm5c13/ykAUKGtrc/2yi4pccZNPfALOOaf/jyOBMWWK679eXQ0HD/YtAZQz98or7qLUmjXwve+5UZ1TKcgt6HNCmZjoTlyzsk5+4SQrOYus5KxTft7wtOF85fyvsKliE2OSxtDa0UprRyvtHe20trdSdrSsa9/mtmZa21tpbW+lzld3zOeMGziuK9lubW/lobce6vV4MSaGwlmFzM+dD8DG8o1dCfyBugM8vvVx0hPTyUzK5NDRQyxZtYSlC5cq4ZZjLFrkdQTBV1EB998Po0aFR7K9b5+LOT09MqZdK9EWULItErVSUgL32T6fK4aWmAi33BL+V6ejjTFw000nXzcs/ePgQTeqDfCRj/Qt0T4TPafO+qc0numJYFJcEhMGTWDWsFlUNlaSmdQ9VF7TXMPglO6DfXzWx7lhxg00tjbS1NpEU1tT1/2kuO5/XO22nXk5896xX2NrI63trSTEJnTte7D+IOvK1gF0ja43tTVR3lBOjIlhyqApLNuwTMm2RD1/NfLqam/j6Ku1a912zhyIiZB+Sda6opfWwujRXkcjXlCyLSL9LjUVbrvNTWEbNerU+0voGTHC6wgiX2srPPigmwmyYIE7wQy0+nrXf3v48O7OAGfq+LXjdb46an213H7e7V37GGNIiE0gITbhmKT8eElxSXxqzqd6fa2tow1D95WBOcPnkJ2aTVNrE+sOrSMnLYcOOmhqbcIYQ1ZyFiU1JVhreXbXs8wZPodhA4ad3X+shLXaWrdmOzsbBgzwOprgSU93SWt9vft7E6iLef3BWnjzTXf/VHUrwskbb8DDD7uZBbfe6nU04gUl2yJR7IUX4LXX3En32Z7oV1W5aeNXXOFGzAYNiq6qr5GqpQVKSmDSJK8jiTx/+5sb2R46FK67LjjHPHTIFSBavx5mzTq7i2Gns3b8bMTFHHuqkp2a3VUZfd6IeceMrlsstc215GXmsfPITp7a9hRPbXuKcQPHcf7I85k7Yu4xI+oSHTZuhD/+0bU2LCz0OprgiYlxo9tVVW50O5TXq5eUuDgzM2H8eK+j6T/+mX1bt4b+BQ8JjAiZpCEiZ6KxEcrK3BSns3HwINx9t6s6vnp1/8Qm3vP54M474ec/h7q6U+8vfbdxo2uxFhsLn/qUW3IRDBMmuPZuHR3wyCNuVP1sFOQWcN+i+3jm+me4b9F9QZ+6XTizkFqfK+bWYTuoba6l1ldL4cxC0hLSuOD/s3fm4XVW1f7/rDRtpiZNZ+hAByjz0JbSAgUsMgnIJMp8BYGfw8UZlati0aJcFbyAwFVRgaIyVNCLCBcZLshQocy0QBlKZ6Bt2qZThibN+v2x3pOckyZpgZz97iTr8zznOecdTvY373n3fvfae+21dppKUWERC9Ys4A8v/4FvP/htbn7xZt5c/SbqIYJ7DD0x7VeGruJKnnEhnzSpe611rqy0uCcNDRaszul5uLHtOD2YTETy5cs//N9YsACuvBKqq60j353cv3o6RUVmDP7znxah/KKLzHvB+ehs3mzX95RTwi+1OPlkGDzY6v1994Utu7PJzK4PKh3E8g3LGVQ6qDk42o7lO/LZ/T7LlUddyXnjz2PXgbuyectmnl72NL974Xcobmz3FKqq7L0npv3qKsb2wIE2894d+xD77GPvc+emq8NJB3cjd5wezIgR9r5s2Yf7/iuvwI032ojt+PE2Q+cuUt2HOXNsmUFtrRndy5bBpZfC5ZfDlClpq+vaTJoEO+/cfhqufFJUZK60v/gFPPCA1d2uHLhnW5HZiwqLOGjkQRw08iBWbVrF7KWzKetTRoHYfMPGzRu56cWbOGjEQYzfYTy9e3kj1t3oyTPbAwbY+8aN6erYFkccYYO63ZF99oH77zdj+/TTu9fMvbNt3Nh2nB7MkCGWmmv1aqir+2CRp2fPhj/8wdxRDz0Uzjqr+0QPdYyZMy2QVl2dddTefNNcy//jP8wFGmy25O67LehQefnW74MH2z3mGLW1UFJin/tvO8tW3hg3zjq2jzxi7uTf/37P+J0Glw3mpN1Pytk3Z/kcXl35Kq+ufJWS3iUcMOwApu40lVH9RnWY39vpGsyZA7ffDmvW2LKJL3wBJvegQPXHHWdxWbpC/e6u1W30aAscW1UFK1bADh6vsUfRBaqe4zj5olcva/SXLbN112PHbt/3Ghutk97U1PIg764PyZ7MwoW21GDsWFi0yFyfCwpsyUCGNWssP3R7fP/7LW7Sf/87zJ+fa5BnPg8a1PH9N2eOGf8LF8KYMTYz29U6zEuW2GzyKafAtGlpqzF38nnzLK96T16+fOCIAymQAmYvnc3i6sU8vvhxHl/8OMPKhzF1p6kcMeYIN7q7KHPmwA9+YDEnysosKvf06TBjRtdrPz4ssadvbGqCxx6DCRPSHYDMJwUFsPfe1t5WVbmx3dNwY9txejjDh5uxvXz59hvbhYXwla/Aq6/C1Kn51eekx5gx1jGorDRXYzBDO9sVc8gQOP9868Ru3GivDRtatsvLW85dtgzeeqvtssaNg299yz7X18N3vtNijK9eDf/4h6WxGTAAVq7seh3m+nr43e/MS+D999NWY/TpY8sC+vTZ9rndmdLepUwbPY1po6exfP1yZi+dzTPLn+HdDe8yb+U8jhx7JACqSpM20augV8qKnfZQtcCfVVU2mDxzpi2bKCoyo7N/fxsYnjmz67Qd3Z033oA77zSDe8aMtNXkj9NPh/POcw/Anogb247Tw5k40YynbQVp2rLF8kUedJB1Vior3dDu7px7rhm1YIbu+vWWr/biljTKlJdv//rtT3/aXJezjfGMcb7jji3nbdxoRmldnXWan3zS3K8bG20mfeedoV+/rtVhnjXL3AeHDYNTT01bTQvZhnZdnQ2kdQV303wxvGI4n9nrM5yyxynMWzmP0t6lzccWrF3Ar5/7NQeOOJCDRx7MsHJPRp8Gqi2eVM89B2+/bQNymVddnR0bP948YYYNs0G6TOT9igrb31Oor4df/tKuyw9+kLaarcl4RnXHwGjZlJWlrcBJix78SHUcB6xDkpm1bI/6eguENm+edWZOOCGMNiddJk+2mYZs9+2LL/7wBu6gQdsXDXjAALj66hZD/NVXzcBubDRjP2OYL1xo5/Tt++H0hOKFF2zAoLAw3iCCb70FN91kAycnn5y2mvQpLChk/A65DeO8lfPYUL+BhxY8xEMLHmJ05Wim7jSVScMmUdq7lDnL5uTkHD93v3ODp0LrDjQ0mPdHtgFdVdXy+dJLW9qRl17aehlLcbEdHzgw1zsnk15v/Xrb31Po08eWATU22rM8VJrB7aGxEV580T53d2M7Q2YgOY3gmE46uLHtOE6HbNoE111nhk3fvi0pLJyeweTJ4WePRaC01F5DhsB++7V0mEeOtHOqqy2a/mWXwa67wqc+ZcHYYmPtWgskCDazn0m3FxsFBab1H/+wtZNdOTp5vjhpt5PYb+h+PLX0KZ5d/iyLqhexqHoRs16dxbC+w3h8yeP0K+rH8PLhVNVUMf2x6c1pyHoi7cVZqKvb2ogeNqzFU2r5cvjP/2z/765e3WJsT55swacGDmx5lZa2zHyPHr1t75zuTsYTrarK6nhM64Xnzze3/2HD7NXdeeYZqxMHHgif/WzaapxQuLHtOA7vvAOLF8MBB+TOEq5dC9deC++9Z52Yr30Nhg5NT6fTM2nPnf2UU2zG+IUX4OWX4fDD4fjjrbMdC3/8o3Um99knjqBo7bHzzpZ65+GHe1Z08g+CiDCm/xjG9B/DaXudxovvvcjspbOZXzWffy75J5VFlVQWV1LbWEtDUwO9C3pz04s39Uhje84cq7P9+tkAU1WVeXXstZfV4dbsu2+LsT1okHmvZGanBw7M/Zztjrvvvh3r6GzvnK7KgAFxGtsZr4QDDkhXRyiGDbMlefPm5S6HcLo3/ih1HIe//tXSOg0ZYp0hMAP72mvt4Tx8OHz1q+725KRDRx3mY46Be+6Bp582Q3H2bPjkJ+FjH4vDWDztNHs/99z4O1YnnQSvvGKZCf7+d3cn74g+vfowZcQUpoyYwuqa1Zxx9xlUFJkVubZ2LW+vfRtV5eUVL3PJQ5cwpGwIQ/sOZUjZEI4ae1S3j25+yy3mslxebl4TlZXm3v3KKzYolj0TPWhQi8cK2IDvD3/YeVrS8M6JjUyU7zVr0tWRYc4cW7bywAM2eHLKKWkrCsOIEVYXqqstYGj2fe90XyLoijiOkzbDh5uxvWxZi7E9a5YZ2rvsAhddFNdsodPzaK/D3L+/RXg94gj4858tsu2sWbYG+YtfDC5zK4YOtcj9XYE+fexaXnmldYLHjzc3XKdjBpYOZNcBu1JVU0VlcSVFhUUMKh3E2tq1VBRVUF1XTXVdNW+ufpPyonKO3vno5u9e/a+rKSwobDbEh5YNZWjfofQv7t9lDfItW8zjZPNme+21lw007b67rcW+7rr4B566GwMG2PvatenqgBavh5IS63ts2ADXXGMau/ugiIh5OT3xBMyd68Z2T8GNbcdxWLfOOkezZ8Pf/mazcOefD/fdZ5GTYwzo5DjZjBwJ3/iGdWDuuivXZbupKWy6lbo6c2ufPLnrGRWt3cmnT/dUNdvDufudy/THbK1D/+L+9JJelPUu44fTfsi4AeNYsWkFKzauYItuaf5OY1Mjb6x+A1Vl3sp5OX+vd6/enL7X6Rw66lDAZsvX1K5haN+hlPUui9YQr6+H3/zGDO4tW8xtNiO1vt5S/EUqvVsT08z2zJm2vKCy0rzpVK0P0pWyS3wU9t67xdg+7ri01TghcGPbcXo4c+bAn/5kqZX69IFVq1pyGJ9xRtrqHGf7EbE1nHvvnWsgzpxpHbqTT26Z4cknt91mgXDee69rumKffDIsWQJHHeWG9vYyecRkZkybkRON/OKDLm5erz2wdCB7Dt4z5zu9pBeXfeyyZkN8xaYVrNy0khUbV7C+fj1lfVoWJz//3vP8+dU/A5YXfGjfoQwtG9rsnr7/jvs3G+BpRUVfvx6uv97if4wfb55SBQU22NUTA5PFxKhR5r4/blzaSmy98pAhLdsiPSsd2x572BKnrpJNw/nouLHtOD2cmTPtwbdqlW1nIr32lFFmp/uRbSCuXw/PP2/phF54AY48Ej7xCVs/mg+eecZeffpYTvquSO/ebhR9GCaPmPyBjFoRYcfyHdmxfMetjtU21NKroFfzdklhCaMqR7Fi4wpqGmpYuHYhC9eadVJZXMmkYZY3ac6yOZz/t/Pp26cvA4oH8Nbqt/jOw9/hx4f/mENGHfIR/8P2WbnSYnxUVdnz4/LLLd1UTw9MFgujR6e/JGTLFosFUVVlywkOPtjaSehZ6diKimzQ4/XXLTjttoL8OV0fUdW0NXQKZWVlumnTprRlOE6X47jjbN3Uiy9amq+xY831b/lyuP/+tNU5zkdn9WoLApiJfFteDieeCIcc0rkzt1VVZmTU1cG//Zv9/e7AkiUWHdqXk6SPqrJh8wZWbExmwTetoJf04qTdTwLgS3//Eg8ueJA+hX2av1PfWE9JYQnHjTuOT+/5aaaMmAKYa/raurUMLh1M3z59P5Jr+l13wUMP2Qzql7/cdsRxp+eyahX8/vc28LJypQ3EjBtn7uQZr4cZM3rOYMy771ocHA86++EQkRpVLdv2mXHgM9uO08MZM8aMhL33ttm/vn0tUmZPGWV2uj8DB1raoUwQtQULbOnEY4/Bd7/bOUbkli3wu9+ZoT1xYksao67Oo4/CnXfC0UdbLnMnXUSEiqIKKooqGDdwa5/gRdWLmLjjRGoba6lrrKO2oZbahlqqaqtYX7+ePr1ajPBn332Wu1+7G4DiwmIGlw1mcOlgBpcNZmjZUKbutP038ac+ZQGvjjzSZu6c+Fi61J71e+3VMqOcb1QtU8Ttt9ua/f79zcOhurpnez30hJziTgtubDtOD6d1DuPqal9b53RPxoyBb3/bvDjuvtvcKjtrtvbvf7eOY//+cM453ScI1KhR9v7ggzBhgg/Cxc7Y/mOpqqliePnw5n3VddUcWHoglx9+ec468JLCEkb2G8mqTauoa6xj6bqlLF23FIABJQNyjO2rZl9FUa+iHIP8vQWDOXCfQfQr701BgeW4zyatteNO2/z2t7BiBVx2WThj7+67zeMBYP/9rW3MZDbpScZ1e6jaQG0MaSqd/OE/r+P0cDrKYew43Q0Rm3ned19LS5Rh3jxb033iiR/cta+x0SLLilgU/7Iu49y2bcaOtUBpDz5o0ckvvdTdyWMmOyp6RVEF6+vXs65+HRcfdDEDSwfmnHvoqEM5dNShqCqbGjaxatMqVtWsYtWmVTnrxRu2NPDW6reatxWbJV20CPo9Cj8962ymjTkMgPc2vMfyDctZvn451z97PQOKBzC8fDhVNVVMf2w6M6bNcIM7JQYMMGN7zZpwxvaECZbp5LTTLIZFdxmE7AxefNGWXxxwQNcMpOlsP75m23Ecx+nRqMKPf2zRk/v0gWOOMQPzg7jDNjTA/PmWQ7W70dBga9FXrLBr4+7kcdPZM8pN2sT7G99n1aZVrNi4ir89spIX3lhFXcEqRuy6mstP/CL77bAfAA+8/QB/ff2vPLnkSWobaynrU0ZJYQnFhcU0NTWx3w77ccPxNzT/3QLxcPehmDnT0nuecw4cemh+ymhqgtdes2VpGWprbYmBk8trr1lQwREj4Ac/SFtN18LXbDuO4zhOF0IEPv95C6L24otw772WB/Wkk+DAA9sPopYZqxax2d7uaGiD/W/nnQc//7m7k3cFPmhU9G1RIAUMKx/G4OJh/Ot/oOFFmFAIF1wA4yc0kT1pM6RsCBN2nMCjix6lpLCExqZGNmzewIbNGyjqVcTC6pb8Tpc8dAkFUsDA0oEMKh2U8xpWPoy+fTwnUmeSSXuYr1zbq1bBTTdZhO2vfKXF4HZDu2123dUGd5cts+V7Hiyt++LGtuM4jtPjGToUvvhFeOstC6K2eLHNBD3yiBniQ4du/Z3Zsy19y9lnd/8OZbY7+axZ8J3vuEtoT2LTJrjhBgsuWFoK//7vmZzNBZB1H0zccSITd5zI3BVzWVWzirI+ZdQ11lHXWMeG+g2MqbRRmoYtDWzcvJEmbaK6rpoFaxbklHfG3mdw+JjDAXij6g3mLJ/TbIhnjPPyPuUfKYJ6T6N/f3tfu7Zz/66qpTu87baWIGgeJG/bFBZazu2XX7ZlSPnyNnDSx41tx3Ecx0kYN84ilM+ZYzPd69dbepoMc+aYET5/vqWw2WUXGD8eJk1KT3MoTjzR1rkfe6wb2j2Nxx83Q7t/f/jqV7e95jezdlyw6OlNTU0UFhRy7n7nAtC7V29uOP4G1taupaqmqvm1unY1VTVVObnHF6xdwJNLntyqjD69+jC071C+f+j3m43uN6reoLR3KYNKB1HS20bAPFCbkQ9ju6bGjOxMWsWJE81NvTvFrcgn++xjxva8eW5sd2fc2HYcx3GcLERgyhTrOL7/PhQX2/4nnzT3yNGjLWL/pk02E97UlKrcYPTuDWeembYKJw2OOcbW3h5+eIvR1hGTR0xmxrQZOUbuxQddnGPkZlzIB5YOZDd2a/dv7Td0P0p7l7K6ZnWOYV7TUEN9Y33O7PZvnv8NmzZb/J7S3qXUNtTyxJIn6Ffcj5EVI5sDtf1o2o+a8433FDJu5J1lbC9bZt4Oa9bYTPYZZ3gQtA9KxtX+9dct0KZHJe+e5DVAmoh8ArgW6AX8TlV/2ur4N4ELgUZgFXC+qi5Ojp0LXJqc+mNVndlRWR4gzXEcx8knxx9vnaLiYnOdLC6GnXc2F/MbbkhbXViamuCpp2xNu0cn7568/bbd2+XlaStpm9qGWjZu3sjgssEAbGnawq+e+5XNkNesZvOWzc2B2ooKi9hlwC4M6zuM6rpqGpsaGdlvJANKBjCwZCADSgbY51L7PKZyTLdzUd+yxQztysrOMeo2bbJMJv36wYUXwpAhH/1v9kRmzIDly+HrXze3cmfbdLUAaXkztkWkF/AmcBSwDHgWOFNVX8s653DgGVWtEZEvAdNU9XQRGQA8B0zCskw8D+yvqu2Ox7mx7TiO4+STj38c6uqswyoC++0HfftaR+n++9NWF5ZbboF//QuOPhpOPTVtNU5n89xzcPPNFin54ostkFNXQlXZsHkDJ95+Iv2L+1O/pZ4BJQMo611Gkzbx0vsvMXHHiW1+t3ev3lx37HXNxvYfX/kjW5q25BjjmVdhQc+aily92ozrjLG+YgUMGgS9enX8Pad95s616zlunM9sby9dzdjO5886GXhbVd8BEJE7gJOAZmNbVR/NOv9p4Jzk8zHAQ6q6JvnuQ8AngNvzqNdxHMdx2mWPPaCqCkaNMmO7vNyiyPbEyNzTpsHTT8Mf/2hr29essetw7rkwuecth+1WPPKIBcEDC4zXFT0XRGyt+F6D96KqpoohZS3Truvr13PA8AP4+VE/Z3XNatbUrmFN7RpW19rnzPczPP/u89Q01LRZxom7nchx444DYOWmlby26rWWWfKSgc3rxqFrrx3PBEG7/XY47LCWAba2Akc6H4zumsXCaSGfxvZwYGnW9jKgowUyFwD/28F3h3eqOsdxHMf5AJx7LkyfbjM7FRVmaK9bZzN/PY3Ro824vu8+uxYHHmgDEdOnm1ukG9xdD1X4y18s4jxYPvWjj+7aa3AzgdoAKooqWF+/nnX167j4oIvp26cvffv0ZVTlqHa/r6pcOPHCrQzy1TWrqa6rzklP9vaat7l9bu6cUEnvEgaUDGBD/QZeXvEy/Yv7M7x8OCtrVjL9senMmDYjqMH9j39YQK4TT4Tdd9++79TWwp/+1BIEbc0au1e68n0RG5nAmwsXpjtoGYuO7kYUDgsicg7mMv6xD/i9zwOfB+jT1XycHMdxnC7F5MlmSGZ3Ri6+uOd2RhYuNEO7qclcIXfc0dzqZ87sudekq9LYaL/bnDmWV/7cc20ApauzPYHaOkJE2GvIXm0ea9ImmrQlOuLg0sEcOurQZmN8de1qahtqWd6wnNlLZ7PH4D2oLLZkyourF7Nx80YuefgSLj3sUkZXjmanfjvRu1d+3QhWrbKo8u+/v33G9oIF8Pvfm/t4URGcfjocfLAb2p3JnDnwzW9aZPfhw8MPWmZWEz/zDFx2mQ0mp6GjO5NPY3s5MDJre0SyLwcRORL4PvAxVa3P+u60Vt99rPV3VfVG4EawNdudIdpxHMdx2mPyZO94ZFiyxFwgX30VNmyw18SJZoT3RLryrNDTT5v+oiLLN7/nnmkr6jwmj5icl9njAimgQAqat8cNHMe4geOat1WVTQ2bWF2zmnP+cg79iiyHoKLUNtbSpE0sWLuAWa/Oav57w8qHcey4Y5k0LD+5BLc3/VdTk3mt3HefGWOjRnkQtHwxc6bVu9Wr4Z13bLCrvt6u98c/DiedZFkAwGIp3Hqrfc4Yydmht666qiV7xtVXw5tvbn2OKhxwgP19sBSW06dbto3aWovT0L+/BdLL6Osq7Vis5NPYfhYYJyJjMOP5DOCs7BNEZALwG+ATqroy69A/gCtEJJNg4mjgu3nU6jiO4zjOB2DMGJv9mDLFZsw2brQZ0swa9t/9zjrpBx4Yb0TrzmLOnJYlBl1xVmjqVHj3XfstR7XvVe18AESk2VV936H7UlVTRWVxJYJw0IiDeG/DexQWFDJ1p6ksrl7M8g3LWbZ+GVuatjT/jWeXP8ujix5lTOUYRleOZnTlaAaVDvrQkdK319gWscE0gE98Ak44wYN35YuFC2HkSGszNm+2gY6CAlumVFtrbWqGpiYzxLeHpqbtT0spYoOlrfOjV1T03MHTziTfqb+OA67BUn/dpKo/EZEZwHOq+jcReRjYB3gv+coSVT0x+e75wPeS/T9R1Zs7KsujkTuO4zhOOLINzIoKWL/e1rDPmGEu5T/+sZ1XUGCR26dOhb32su3uxr//u7ncNjRYxPrycvs/R42CX/86bXVts2KFzYL165e2ku7PnGVzmP7YdPoV9ctZO569Zru+sZ6l65eyQ98dmteC3zb3Nv656J85f6usTxmj+o1i14G7cuy4Yz+QjvnzbcZz3Dj41re2Pl5fb7OsYMbXe+/Brrt+8P/X2X4uusgM7YoKS88G1o4OHAjXXGMBCjNBCrdssTYGcl35RexVWNiyP/O3Wp/X1udsHZkZbTCDf9Cg+FJbdrVo5Hk1tkPixrbjOI7jhKU91+ktW2wd91NPwbx5LTMslZVw0EEWeKu0NF3tH5U1a2DAAPt87LG2DrauruW4qs1MfeUrcMQRMGFCOjrb4p134Prrbabz299ucT118seHiUa+afMmFlUvynmtr18PwM4DduY7U78D2Prx37/we0b2G8noytGM6jcqJxJ6hhUrbIBs0CD4yU9a9tfWwm23mYfKt7/tqbxC0tGgZUivmFh0bA9ubKeEG9uO4ziOEx/V1RZ858knbX1gSQlceWXLbE3GbTJ2GhvhrbdsEGHePDNcfvYzG0C46CJbu15cbIMIGzaY8d3UBIccAv/2b/YONgP++uuw2242QBHaPfeVV+DGG22GbO+94fOfb5nNdOJGVamuq2Zh9UIKCwrZd+i+ACxfv5wZ/5yRc+7QvkMZXTmaMZVj2H/YeR3kcwAAIABJREFU/lQUVfDUojl8/oaZbOi9kE9OHcN5+53LwPrJzUHQ+vSxGW9fShCWWOI9xKJjW7ixnRJubDuO4zhOvKjC229bpz4T6bq+3iLg7rOPuZlncpjHQn29dUDnzjUX3Oz1kqWl8IUvWFTn9maFvvtd2zdmTIu79qxZlssabMBhl13MVXe33SylWj5nFZ980nKjq9r1PuecrjHQ4XTMps2bmLtybvPs99J1S2lsalnsO/1j01m+fjlfu2c6899sYkt9ERX9GykurWWPlT9ix82HMXqUcMEFnjvbiZ9tGdsiMhK4FRgKKHCjql7b6hwBrgWOA2qA81T1hbzodWPbcRzHcZw0ePll+O//btkeNsxmgKdMsTRioWlqMlfajMFRV2dpeTLrH0eMsIGBvfeGsWNzDdXtnRWaPx9eegneeMOCkmUzerQZ6GAGsWrnGMOqFln63ntt+/jjLehVTAMbTufR2NTI8vXLWVS9iCXrlnD2vmdz2i1f4ckXqmgoXURjr/XU10Oj1lPap4Spw6cxfvdKpow8gJN3PxmAusY6Xn7/ZfqX9KdfUT8qiyspKnQXCCd9tsPY3hHYUVVfEJFy4HngZFV9Leuc44CvYMb2FOBaVZ2SF71ubDuO4ziOkxbvvmtru59+2iKag83ujh8Pn/tci7t5vtiwwVzA586F114zA/Sqq1qM3HvvNVfxvfduiebcmWW/+aYZ3m++aSm3TjvNjlVV2XrJXXaxWe/ddoOddvpwxvcrr1iQIxE46yw47LDO/T+c+Bn1vePoVTOcLcXvs7FhAzX19TQV1FNYvoZzDjwGgGmjp3HmPmcClgv8iieuyPkbxYXF9CvuR//i/py1z1kM7WujUsvWL6O+sZ7K4kr6FfejsKDjtREfZv2642T4oG7kInIPcL2qPpS17zfAY6p6e7L9BjBNVd9r5898aDyQv+M4juM4qTFsGHzmM3DKKWYUPvWUGb9VVbmGdnV1bqTcj8LatVbO3LmweHFuHtqhQ62sTPCzE07onDLborwc9t/fXpCbqmfhQnNbf/VVe4GtCR83zgzvQw/d/sBm++xjQdp2280iwzs9kLVjoLyK0qZhlPaCTU3Qq6SapjWD+OWx/8W6+nU5RnLvXr2ZNGwS6+rXUV1XTXVdNXWNddRtrGPFxhX0KmhZ7/DA2w/w7PJnm7fL+pRRWVxJZXEluwzYhePGHQdYILe/vfE3rptzHQNLBjK8fDhVNVVMf2x6TmT2UMRi9MeiozsiIqOBCcAzrQ4NB5ZmbS9L9nW6se0z247jOI7jRMXatbbuOROo6d13bZZ3t91srfGECR9sxrumxv7eDjvY9tKlLanJCgvt72bcwwcP7tz/5aNQXZ07871ype0vLGxJCwTw4osWYXrECHj2WXNnf+sty9/7hS/EGeTICcunvzaHJ/tMp7SgH320gs2ynpqmdRyyeQZ3XbvtG0RVqW2spbqumnV16xg3cFyzcX7P/Ht4ver15mNN2jJqNH6H8XzpgC8BsK5uHQf//mBqG2spLiymsKAQEaG+sZ6y3mXM+swsdh6wMwCPLXqMZ5Y9Q6+CXvSSXjnv/Yr6Nc/AA9z12l00NjVudV4v6cVug3ZjbP+xAFTVVPFG1Rv0KujF66te57cv/JbyPuX07dOXjZs3gsDl0y5n8ojJLK5eTE1DDWA50wVpfi8vKmeHvtaYNGxpYOn6pTnHAQrEXFAGlw2muNBGxdbXr2fT5k05f++F917g50/9nIo+FQwuG9xmWjgnFxHZDMzN2nWjqt7Yxnl9gX9iKaT/0urY34GfquqTyfYjwCWq+lxn6/WZbcdxHMdxoqJ//1yX7aVLzcCcP99epaVmQB5yiOUCbr1W+oADzECfN89mrxcssGPfsUxJjBhh6cfGjbMAZ336pPN/bovKSvs/M8by2rVmdFdX50Zzv+UWW1++bp15BwwZYoMLy5fDkiWW5skN7p7Nd86ezLKfzaBqxEw2lSykqHYMo5ddzHcu2b4bQ0Qo7V1Kae9ShpUPyzl20u4ncRInAWaUb9i8gXV1NiOenYKsfks9DU0N9O3dlwZtoKGpofnYqppVOUHdVm1axTtr32lTy5CyITnbTyx+grrGujbPPXXPU5uN7YVrF3Lry7cC8OSSJ6ltrGVt4drmc/catBczX57J5BGTueu1u3hz9Ztt/s1Jwybx//b/fwCsqV3Dz578WZvnAXzjoG+w+6DdAXhwwYM8tOChnOMZHf2K+zG071Aqi81956L/vYjjxx1PSWFJ83XPvPYesnfzoMTGzRtZU7uG0t6llBSWUNK7pNnQ/6B0oRn2RlWd1NEJItIbuBv4U2tDO2E5MDJre0Syr9NxY9txHMdxnKiZMsVmnp991ty/Fy+Gxx6zyN6vvWau0cOH2/4LL7T13iVZaYYLCsw4zaQZE4FTT03t3/nQ9O9v1yKb2lqb6X/jDYs2XldnacnAgsyVl9tghBvbPZvJk+GXl0xm5szJLHwlGZi6pPPvCxGhoqiCiqIKRvYbmXNsSNkQjhp7FFU1VfQr7kdjUyNN2kR1bTUDSgYwpv+Y5nM/PubjTNxxIlt0C1uatuS89+mVOzr26T0/zeYtm9miW2jSpubzmrSJMZUtf3Ng6UAOHnkwTdrE7KWzGV4+HMU8fFWViuIKFlYvBGBU5ahmo1VRVLX53MysNpi7/ejK0W2ep6rNs9oA5X1sRjxzHtgAREVRBaWFpS3nFZWzYuMK3tvQtkdz3z59m43tV1e+yk0v3pRzvLiwmLI+ZZQUlnDJIZc0X6/HFj3Gxs0bcwz3jDH/1pq3+OmTP6VfUb/U3fs/Kkmk8d8Dr6vqf7Vz2t+AL4vIHViAtHX5WK8N7kbuOI7jOE4XY9kyM7qvuMLWLY8fb/tXrrQI5yUlcNxx5ha+zz6wxx65xnd3RBWOOspm/devtwGFTMT05cvh/vvTVug4Nns6/bHp9CvqR0VRRWpu0xfddxFVNVXNM8kA1XXVDCodxA3H35CqjrV1a6koqmDG4TOoaahpftU21FLTUMNeQ/ZqNvBffO9F7nvrvpxzMogIvzr+V0iSduAnj/+EJeuWtKnj7bVvM6hkUOrXY3vYjmjkhwBPYK7mmTUN3wN2AlDVXycG+fXAJ7DUX5/Lhws5uLHtOI7jOE4X5dhjLaBZUZKRqKHB3Mfr6+H//q/npba66CILLJcdSK662tZz3xBXf9npwcTgrhyL0d/ZOpq0ibrGOmoaaqhrrGNExYjmY08teYqqmipqG2tzjPiahhr+/ubfmbDDhBwX9CZtYvmG5dx/dlwjdR80GnnauLHtOI7jOE6XxI3LXObMgenToV8/qKiwGe516yy4nLuRO04uMRj9seiIZaZ/e3BjOyXc2HYcx3GcnoUbl1szZ87WAeN66rVwHGf7iGWmf3twYzsl3Nh2HMdxnJ6HG5eO4zgfnRhm2LcHN7ZTwo1tx3Ecx3Ecx3Gc7ktXM7Y/XCI2x3Ecx3Ecx3Ecx3HaxY1tx3Ecx3Ecx3Ecx+lk3Nh2HMdxHMdxHMdxnE7GjW3HcRzHcRzHcRzH6WTc2HYcx3Ecx3Ecx3GcTsaNbcdxHMdxHMdxHMfpZNzYdhzHcRzHcRzHcZxOxo1tx3Ecx3Ecx3Ecx+lk3Nh2HMdxHMdxHMdxnE7GjW3HcRzHcRzHcRzH6WTc2HYcx3Ecx3Ecx3GcTsaNbcdxHMdxHMdxHMfpZNzYdhzHcRzHcRzHcZxOxo1tx3Ecx3Ecx3Ecx+lk3Nh2HMdxHMdxHMdxnE5GVDVtDZ2CiDQBtWnr2AaFQGPaInAdsWkA19Ea15FLDDpi0ACuozWuIy4N4Dpa4zpyiUFHDBrAdbTGdWwfJaraZSaMu42x3RUQkedUdZLriEdHDBpch+voCjpi0OA6XEfsGlyH6+gKOmLQ4DpcR0+hy4wKOI7jOI7jOI7jOE5XwY1tx3Ecx3Ecx3Ecx+lk3NgOy41pC0hwHS3EoAFcR2tcRy4x6IhBA7iO1riOFmLQAK6jNa4jlxh0xKABXEdrXEc3xNdsO47jOI7jOI7jOE4n4zPbjuM4juM4juM4jtPJuLHtOI7jOI7jOI7jOJ1MYdoCuisiMmA7TmtS1eq8i4kAEfnmdpy2SVV/k3cxKSMiE7fjtAZVnZt3MU4zIvKp7TitTlXvz7uYlIml/YpIh98bkSEiv9yO09ar6qV5FxMBsTxXYqkrMeiIQUOiw+uK0y6xtB3dGV+znSdEpA54F5AOTuulqjvlWcfftuO0Nap6Xp51vAf8io6vx9mqumseNbyyHaetUtUj8qUh0bEBeJaOr8UYVR2dZx2xXI9YdKwG7qHj3+UwVd05jxrWb+sU4L181pNERyztVyw6Ur83Eh2xtOep11kRWQxM38Zp/6Gqe+RLQ6Ijljoby3MllrqSuo4YNCQ6YqkrsbRfsdTZWHRE0XZ0Z3xmO3+8rqoTOjpBRF4MoGMP4MKOZAA3BNDxB1Wd0dEJIlKWZw29gOM6kgBsz8Pgo/Ksqn68oxNE5P8C6IjlesSi439V9fyOThCRP+ZZw4JI2o1Y2q9YdMRwb0A87XkMdfZqVZ3Z0Qki0j/PGiCeOhvLcyWWuhKDjhg0QDx1JZb2K5Y6G4uOWNqObovPbOcJESlW1bqPek4n6DhNVWd91HO6AyJyiKo++VHP6S7Ecj1i0REDIjJWVd/5qOd0go5Y2q8odMRCLO2519kWYqmzjhM7EbVfUdTZWHQ4+ceNbScIIvLZ5GOtqv45VTEpIyIZl9ctqro8VTFOMyJyWPJxs6o+naoYJyr83ogPEcm4xW5U1f9KVUwExPJciaWuxKAjBg2JjssAxeuK0waxtB3dGXcjzxMishBr3Fap6pQUddyc6Finqt9ISwcwJnnfkJYAEXkUuxZrVPXTaekAMu5cq4HUdMRyPWLRAXwuea8G0uqcxdJuuI5cUr83IJ72PJI6uzh5r02pfCCqezSK5wqR1JVIdMSgAWBR8p52XYml/Yqizsaig3jajm6Lz2yngIgMU9V3A5X1seTjZlX9V4gyPygi8nVVvSZAOaOSj1tUdVm+y/swiMgUVX0mUFlRXI9YdHSEiJyqqnenrSMGQrZfTi6xtOex11kRuUpVv5W2DidOelp7LiJXqOr3ItARRfvlbD8h+6TdGTe2U0BEluQ7em5WWbfkO7LjRyXk9ehAw1OqOjVNDYmO1K9FoiOW6xGLjtR/lxg0hNaxrZQkqvpCIB0dpi6MwTVTRO5U1dPT1hEDPbSudJhmSlX/EkhHV6grQX6XrOVzbaKqt+ZbQ6LjBVXdnvROPZ6e2HZ0BR1dHXcjT4eOwut3NvsGLOvDEvJ6tEcsjUkM1wLiuR6x6Ijhd4lBA4TV8RwwD6hqo2wFOoyg2olcBbwE/C9QTzy/RTYHhSooSRWTPVIvybYAqqoVobS0Qyy/T0gdd2H36EttlK1AEGObrlFXQmk6oJ39JwLDgSDGNtAriTbe5v+tqmtCiOggZWCm3YihvxrL/eo6uhFubKdDSHeCUhGZQPuNbJCZoW0Qg3tFDBrAdbTGdbQQgwYIq+Ob2BqyWuAO4K+qujFg+RkmAGcCxwPPA7cDj2jPdQ17BNgBM+DuUNUloQWIyID2DhFPBzHk/fEp4AxsgP0e4HZVfTtg+Rm6Ql0JokVVv5L5LCICnA1cgq3f/kkIDQm7Y79FW/VCgbGBdDQl5d0G3EvKa8jbIZb71HV0I9zYzhMich1t36QCVAaUMhz4Be03skFmhrJmQjI6MtdGgJJAGtpzswumIdFxL+3fGwMD6ojlesSiYy7t/y5DA2lozwVTgL4hNCQ6omi/klgO14jIWMyQeEREFgNXqOpLHX+7U3W8DLwM/IeIHIwZE9eJyCWqGiIHPNChW70AvUPpUNWTRaQfZuD9VkSKgTsxwzvILBlmPGQ/U7LZHEhDNHVWVf8H+B8RKQNOAn4hIgOB76vqPwPqiKWupN6eJzoKgfOAb2FG9qdV9Y1Q5Se8tq18ziFQ1fEisjt2T9wGvJa8P6iqjaF0xFJnI9IRRZ+0O+PGdv547kMe62ze3lay+hCoannaGoATOjj292AqzM3uwxzrbGK5HrHo+GTAstqjo3pybTAV8bRfAKjqOyJyDzb48m/ArrS4ywZDRAZjM3f7AMuAlYEl/KKDY/ODqQBUdR1ws4jMxAZCfgkUA0HW5KrqmG2fFYRY6myGOmAdsB4Yhf0mwYmgrqTenovIRcDXME+QT6jqonQVbY2IDFXVFaHKU9X5wGXAZSJyOuZK/zPgylAaiKfOxqIjlj5pt8UDpOWR5GEzCjN4q1PS8GJ7I5oicoCqPhtIRzHwRWAX4BXgppAjmbEhIuOxa/Gqqr6eth7HEJGTsd9lrqr+I0Udg1S1attn5lVDDO1XZkb7JGAp5kp+n6oGdT8UkfOB0zDD5S5glqqGNh6iImvW8lDgSeBOVX0iYPlDgO/R8kz5qaquD1V+G3pSrbMi8nGsrkwGHsa8DIIPjMVUV9Juz0WkCRtkWEUbMQ5CrVEWkfNU9Zas7UrgVOAsYA9VHRZCR1L2cOw+PQVYC8wipeVBadfZmHR4nzS/uLGdJ0TkQuAKYAGWY/rzIV2osnQcraoPZm3viXWQzgSqVXVSIB13Ag3AE8CxwGJV/VqIsrM0TAFuBHYG5gLnp9GoiMh04BzMDXIK8J+q+tsUdMRyPWLR8d/AXsBs4AjgXlW9PLCGTwI3Y3WlCThNVWeH1JDoiKX9asIMqXuwmbqcB1aoyMaJjnm05HZurePEQDrGYTNAu2B15VuqujxE2a10LMJyB98B/B+QM3AaIhaIiDyAtaGPY7OY5Wlk3oiozmbqypPY/dn6Hv1qQB0x1JUY2vNRHR1X1cUdHe9kLSXYoOVZmMdBOXAy8LiqNgXS8M+k3FnA3Vhe52YCBmqLpc7GoiOKPml3xo3tPCEi84DDVXVVMjvzJ1UNFi22lZbRtBjYDdhs1aSQLk0iMldV90k+FwJzQqeiEJHngO9inbMTgQtV9ZiQGhIdrwIHqGpNsqbuAVVtL2ppPnXEcj1i0TEP2E9Vt4hIKfCEqu4fWMMr2AN3fjII8XNV/di2vpcHHVG0XyLyQzoI0KKqPwqko8PfINSaWBF5AnO7zNSVg1S1w5RPedLxGO3/Lhpi6ZKIvKyq+2Vtp5LeKKI6e25Hx1V1ZiAdsdSVGNrzY9qbUReRz6jqnwPpuA3zQHmQlgGyt0MvxUgG6TLtRnbcHrB2I0igtojqbCw6ouiTdmd8zXb+2Kyqq6B5vWFRGiJE5F9ABdbAnqqqb4nIwhTWDjVkPqhqowXmDE6Bqj6UfP6ziHw3DRFAvarWAKjqahEpSElHLNcjFh2bVXULQPLQSeMmbUzWtKGqz4hIWrEOomi/VPWHaZTbmmwDIXGvJ3N9AlOeNeNwpYikkk1CVaelUW5rJDedUU56o1CzZERSZ0MZ09sioroSQ3t+v4g8DpzThgfKd4EgxjawJ+ay/TrwejIAEXymTVVHhy6zHaKosxHpiKVP2m1xYzt/jBCRX7a3HcqlC1iBRSQfCgwG3iKdUP77iUhmPZ0AJcl2yLyslZIb+TpnW1VD5SEdKyIZl1wBds7aDuZmRzzXIxYdu0tLHtDM7/IKYdfXDZHcCKU526Hcpomn/UJEDge+jKWvAeswXq+qjwXUIFhQny8DBcmuRuA6VZ0RSgdQLLmpHEuyt0O4b2dIZkDOIvd3uS2gkduPrdMZZf7/kOmMYqmzmdntrwG7JbteB36pqqHyOcdUV2Joz1/Bom0/LSLfUNW7so4FM/41Nwr4wyJSBZRL4OBo0OzZeCwt7cZrwD80bAyfWOpsLDpi6ZN2W9yNPE/E4tKVaMmkaDkTGIel7jlGVeeE0hADInJzB4dVVc8PpCMWN7tYrkcsOlJfXycil3UsIUxnNZb2S0SOB64HZmCGlAATgUuBL6vq/YF0fBPrIH5eVRcm+8YCv8Jc7q4OpOPRDg4Hcd9OdOyBuaL+A3gR+10mAEcBH8/M1vQEIquzX8dy02fXlSuBa1T1D4F0xFJXYmjPX1DViSKyK/AnbC37RclMeyrLHhJd+2MDZZ8BlqnqwYHKHY61G++R227sgC1bejeQjljqbCw6ouiTdmfc2A6MWFTuE0Kt1Wmj/KFYpNAzgJ1UdWQaOhItZVhEyjNV9fi0dCRago/wtqFhJHCGqoZMgdGeltSvRyw6ROQQ7B69KGUdwbIHdKAhaPuVrA3+mlru3uz9+2IzZUHWt4nIi8BR2ipibOIm+6BGkMNWRHqrasO2z+yUsjJRpme12n8qcJaqnhpCRxu6dsaMiDNUda80NLTSEzLjx9PY/72o1f7RWGTyAwPpiLquhGzPsw3qZEb3x1if57PAr9IytrP0CXCoqj4eqLxbgJdU9ZpW+78K7K+qHQ7yhiCG52wsOmLqk3Z13I08ACLSCzgGm1k+GovInYqxnRgu1wHXbWvkNx+ISB/geKxDdAwWkfLXoXUkWnLSXwDB0l9kaRiMjS6fmZT/19AasrSkfj1i0ZG45mZG/hcCoVzZW+vIyR4ABMke0EpDmu3XDq0NbQBVfSUZOAxF79bGQ6JjlYj0Dqgjh6Sz/HHsXv0ktlwoBPuo6qdb71TVu0XkikAaABCRYcDp2DXYB/hPbDA5FVKssxWtDW0AVV0kIiGWaWWIrq6k2J43u4onbtL/IRZF/3ZsWV8YEblLgtoiiLENHKhtZAxQ1V+KyBuBNGxFDM/ZWHTE1CftTrixnUcS14yzgOOAOcBUYEwmEEEgDdtK1xMqDcfRtHTWH8Ui6h6gqp8LUX6WjnbTXwTUUI659Z8F7Io9+Meo6ohQGrK0pH49YtGRuPplHnRVwJ2Y98/hoTQkOkaTcvaAREfq7Rew6UMe62w2f8hjeUFEDsR+m5OBAcBFwLcCSkj9dxGRz2N1ZDiWSugC4B4NFKG+lZbRpF9nO8o9HzIvfRR1JZL2fKt7UVUfS9y4vxBQxxcxF/ZZwLsEXC/eio7uw5DPlVjqbBQ6YuqTdlfcjTxPiMgyYAm2Tul/VHWDWBTw0KkWVgFLsZHUZ2jVyAZcH9yEzYidl7WO6x0NlOohKS+W9Be1mPFyKfCkqmroa5HoiOV6xKIjc49eoKpvJ/tC36PZ2QPu0JbsAaGvRSztVzVtD7gIcIiq9g+kYwttG5ECFKtqkBm7ZNb4M9hvczs26/BcSvdHW8F7BPh6iOVJIrIZ+Bdwsao+l+xLox2Npc7WAG+3dQgYq6plgXTEUldiaM8HdHRcw+WVHoi1G6cDjdjAw12qWh2i/Cwd79D2oKBgaa92DqQjljobi44o+qTdGZ/Zzh93YbMOpwNbROQe0okCvgMWtOZMbNTqPuB2VX01sI6JmGvfw0mDewfQK7CGKNJfYCk/zgD+G7hdRO5MQQPEcz1i0fEp7Hd5NHH1u4PwMwCxZA+Ipf06qYNjV4USoaqh26r2uBB4ExsEuVdV61OqK7/FvE/a4neBNOyIGRC/EJEdsFm7NNyUY6mze6RQ5lZEVFdiaM+rgGWYgUur8oNFzFfV1dhyvV+LyAjsurwmIpeECpyX8E/ghHaOhfSmi6XOxqIjlj5pt8VntvNIsp5uGmboHoelKrkAuF9VN6agpyjRciXwI1W9PrSGRMfBiY5TgZeBv6rqjYHKzqS/OB17EO4G7J1GEC6xCK1n0BIl/jLsWrwZUEMU1yMWHYmWMszIOxNbD3sr9rs8GKj8KLIHxNZ+ZRND4BZJIcBjsn4+M3h6BLYk50hgpIZNndMuaQT2SQyI07HrUobV1+8FLD+KOtsWIQOCdaAhtWCoabbnInINcDjwFOaJ8qSm2OkWkYnYdTgKS5v3C1V9LS092UjgYKix1NlYdCRaUu+Tdlfc2A5EEhgkE2ToGFUdFLDsIiwo2ZnAaOBvwE2qujyUhnZ0FWAdxjNU9YIUyk8l/UU7WvZOtJymqrukpCGK6xGLjkRL/0TH6ap6RArlD8GMiFSzB6TZfmVp2Cpwi6qGXKfcXoDHv6jqvSF1JFqKsKBoZ2LLMB5R1bNC60i05AT2UdVUAgwlWsZhz5TLUyo/9TrbVkAwVb0usIZo6kqWpuDteatBy8nYcqlfZZbTBdIwA/stXsdm+B+IYXBOWgVDVdW0grKmXmdj0pFoSb1P2p1wYzsFROQwDZdq4VZgb+B+bE3IvBDlbi+RjLpn1n8+kZaGREcxcKK2SqeTgo5YrkcsOkZi9+jPU9ZxoKo+naaGREfI9qutwC2nhw7cIlsHeLwTSz02OqSO9kiu08khXUJjCOzThqYyEvfh0DOo7egJVmfbCQj2LVUNmnWkC9SVVNrzxLA8A7gc+J6q/jZg2U3YoEsmCFmm4y9YPud9A2ppNxiqqjaF0tEeET1nU9cRS5+0O+DGdiDSGvlPGtlMsJLsHzvTyIZMCZLR1HrU/e40XNoTY645bY6qhkwllNGQnVbpKMzNbKuUOoG0pH49YtERwyxqoiOWkf+02q8oArdIBAEe20NSyC0dS2CfREtUM6hp1dkYAoK10hFNXUmrPc9yYT8dW5P7Fyw//ZJ8l91KR4cDLqq6OJCOKIKhtqErluds6jpi6pN2JzxAWh6JYeRfVQtCldUR7Yy6B0+rlGhJO20O0nZapbEaNq1SRkvq1yMGHe3MogZPf9HRyH9gHaNJf+YylsAtMQR4bEbSzy2demCfNmZQU0knmWiJoc7GEBAMIqkrkbTnK7G6cQctdWSSiEwCUNUg+b7bM6YznoXYszYEsQRDjaXOxqQjmj5pd8RntvNETCP/rUkpsE/qo+4SV9qcGNIqxXI9YtGR+ixqLCP/sbVfMQVukXQDPLbOLT0Lyy2dxoxyqoF9YplBjaXOZulJNcBjKy1p1pUY2vNbaH8QSlX1/FBaMqS9nl8iCIYaS52NSEcUfdLuTBSznt2UFdgIVWbkH9IJ6Q+Yq52InCLNI3TNAAAgAElEQVQifwbewwKT/TqghE8l5T4qIr8VkSMIP+p+Ifa7/Ar4g1o6jLTSKmVmpk5IOkdp6IjlesSi47tAETaL+t3EPTc0W438k15KkmjaL1V9R1WvUNV9gEnYQMD9KWmZrapfAUYAVwMHBiz+euy5fZaqXqqqr5DS76Kq61T1ZlU9GpgC/AC4WkSWBpIwEcuz/bCIPCQiF5COt0EsdRYAVd2kqrep6gnYPfoicElKWtKsK6m356p6nqp+rq1Xoi8IIrKriFwmIvOB6zDDSlT18JCGNoCqzlfVy1R1d+BrwEzgWRGZHVBGLHU2Fh2x9Em7LT6znUfSHvlPNEQVrCTNUXeJKG1Osi55GimmVYrlesSiI0tPqrOoMYz8JzpSb79iQUR2Sj5u0RSzOIjIQFrWnmZyS5+nKUatbY2IjAq1BjSrzNRmUJPyo6izMRBLXcmQdnveSkuPXs/fHkl/6FANFHgzKTOKOhuRjtT7pN0ZN7YDIRbS/zTsRg4W0j8WV7u2kBTTKklcaXNiSKsUxfWIRUeWnr0TLadrCukvxNKgnYm1HWmmY0ur/VqIjbCvUtUpIcpsR8ejycfVGkmwGEkxt7SI3Iz9LutU9RshytwexNJJHokFiwvuoptoSKXOel3ZNmm05x2tydVA0bdF5GRswGEqkFnP/7sU3JUvw+7Rjar6XyHL7oiInrOx6Ei9T9rdcGM7BUKO/IvIeKyR/QyQCVYyXQOnA4kZEanA0ubcGoGWElWtTVlDFNcjFh0xkMbIfwdags9cOh0jFoDyDFWdEai8jyUfN6vqv0KU2YaGqGZQWxNTnXXSIZY1uVl6Ul3PLyLnJh9rNcJ0UrHU2Vh0JFpS75N2B9zYzhMxjvynHKwk9VF3Efls8rFWVf+choZEx6PYtViT5sh/RNcjFh0x3KNRjPzH2H45ICKHJR83awS5YNMklhnUWOqsk0sk7flLWIyFW7FAk8vcszB9YqmzEemIok/anfHUX/njluR9c5oislHV2cBsEfkaiasdEMTYTmsktxUZDRtSVQHnJe9b0hRBPNcjCh2R3KOLkve0R5JvSd6jab8cADJpraqBHm1sawppI9thUfKedp11soihPVfV8Vlrch8WkSqgXESGpr2eX1XXYv2/YLENImJR8p52nV2UvKet47zkPe0+abfFZ7a7ObG72jmO4ziO43R3evp6fsfpqbix3c2JxdXOcRynuyEiO2Kud/Vpa3GcmPG60kJMa3Idx8k/bmw7juM4XQoReRhoAG5Q1b+nrGNn4G5V/VZaOmJBRK4A1mGRjlenrccBEXk9+XiDql6foo4eV1diWZMbOyLy78Bq7N4Inu7TcfKNr9l2HMdxuhqfBXYEDkxThKoemcxS7ZmmjoiYgxlUV2O/UXB8BjUXVd0jyc/udSU8i5L3tNfkxo4AhwBnAyemrMVxOh2f2Q6Mj/znEsOou4icBLyvqs+kUX6WjplADXYt5qWoI5brEYuO1GdRYxn59/YrTkRkEvCuqr6btpa0iWUGNZY66+QSQ3vuxEksdTYiHVH0SbsDBWkL6IHMARqxkf8ej6rugY1oLkxRxhTgUhH53xQ1AFwPPAz8W8o6Yrkesej4LHApkGZu+szI/19S1ACB2y8ReVRE/k9E7gpRXgc6ForIOyKS6sBPB3wFuE9E7gxRmIhcJiLTReSbIcr7IKjqkcBY4OaUpQSts7Hco7Ho6IAY2vMeiYh8Nnl9Jm0t7RDLczYWHbH0Sbs8PrPdQ3FXO8dxYkdEMh3iLaq6LFUx7SAiw2KZURaRclXNewo9ETk3+VirqrPyXZ7TPYiprjjhPQuTNewAG3wNu9OTcGM7T8QeGCO0q10MqSdE5LDk42ZVTS1HrYjcjF2Ldar6jRR1xHI9YtHxKPa7rEkrcr+IZNa51qrqn9PQkOiIuv0CEJGnVHVqBDqWqOpO2z6zU8qKPpWjiFzVU54piY4o6mxHhLxHY9ERQ3veFcis51fV+1LW8XVVvSZQWVHU2Yh0RNEn7c54gLT8sSh5jzIwRuhgJao6JkQ52+BzyXs1kJpRB9ySvG9OUQPEcz1i0XFe8r4lRQ2ZepL32cltsCh5j7L9SkjdeEiQgGXNTN5XA7EaEKcBeTe2I3mmQEud3Ziqio4JeY92REgd5yXvabbnbRLLmlyAJPZGqoZ2wjeBIMY28TxnY2k7bkne0+6Tdlt8ZjsAItIXQFXTrlCpISKlQIOqNiTbuwHHAYtVNe11KU6kiMhQVV2Rtg4nPnribF1XQESWqurIAOV0eM1VdUm+NXQVYrlHY9GRNiJyEbA7MEpV8x59OxYvkI4I1W7EhIgcoKrPpq3DyT8+s51HRORLwHeBMtuUDcDPVPW/A2qIpZF9ALgAeEtEdgH+BfwJ+GTS4Hw33wJE5ATgFVVdnGxPB04FFgNfU9UgQdqy3NvaQlX1iEA6OkzNo6q3htDRGhGpxH6Xs4A9gGGBys3UleZdWduqqjsH0PD/gMdU9a3E8+Qm7FosAs5T1RfyrSHRkXErawtV1QsC6fhUe4eAkhAaEh3X0fb1EKAyoI7DOjquqo8H0jGgvUOEm728D/tNsstTYDAwBOgVQkTyHGkPVdXLA+loL1idAH1DaEh0xFJXUm/P20NVbwhcXixeIB0RbOYvlucscGMyGXcHcLuqvhao3Bxi6ZN2Z9zYzhMicilwMDBNVd9J9o0FrhWRAar64xA6OmpkAwcr6a+qbyWfz8Ualq+ISB/geWxQIt/8hCTXqIh8EjgHOBOYAPwaOCaABmjbxfJA4DvAykAaAA5oZ/+JwHAgmLEtIiXASZiBPQEoB04GghgPCZNabRfQ4hL7YiANX6PFpetMYF/M1WwCcC1waCAdbaXEGQl8g0BGTMIJHRwLmbbnuQ95rLP5dhv7FLtPRhLut3merQ3dDEFcEVV1n+xtERkNXAIcCVwRQkPCpjb2lQIXAgOBIMY21ma2x7WBNEA8dSX19jyWNbkdEXgd/QZy242MgRd08JRInrOqOiHx8jwDuEtEGoDbgTtUdVEIDQmx9Em7Le5GnidE5A1gP1Wta7W/BHhZVXdNR1mOlpCN7Cuqum/y+SngSlX9n2T7ZVXdL4CG5nJE5CbgDVX9WbL9gqpOzLeGNjR9DPgBUAz8RFVTSXOVjO6ejXVWX0u0vBKo7Nuwh9uD2Ajv/wFvpzUaLyIFWKqLbwMvAVeEGnEWkZdUdXzy+TbgGVW9NtlO6x4dC3wPOAxL+fV7Ve1xa7tEZDCWLuhtVa1OWw+AiEzF0hj1x+rsvSlLCo6IjAO+j6UK/AUwM7NcKQUt5VhH/gJgFvALVQ3aWRWRQapaFbLMNjREU1dSbs8z0bc3quovQpT5Qemh7tvRPWeTsvfDDO/TgPfTCAAaS5+0u+Ez2/lDWxvayc5aEWlKQ1AbhAxW8oqIXAUsB3bBDKuMy3AoJHHZqQGOALLd+YsD6kBEjsE6yfVYg/ZoyPKzdBRigWS+hQUn+7SqvhFYxp7AWuB14HVV3SIiwUcBRaQ3cD42e/skcLKqvh1YRpNYWr612D36k6xjIUf+EZHdsXt0AnAl8MXQwXxEZApwI5Y5YS5wvqq+3vG38qLjQmy2dAEwRkQ+r6p/C60jS88RWIdIMePhocDlD8EGYHYBXgF+qqrrA2vYGzOy9wJ+DlygqqkEw0rc6r+JDVjOBCaq6trAGj6J5RZvSPoYp6nq7JAaEh1R1JUY2nNV/VHI8j4kId23i4Ev0tJu3JRSgLhonrMZkkGhIcBQbOlp6EG6KPqk3RU3tvPHchE5QlUfyd4pIh8H3ktJU2tCGjT/DxvxHw0crao1yf49gasCabgGG9lejxl1zwGIyAQC/iYi8iy2rvBKbO06ItI8khpwXe5F2G/yCPCJwG5Lzajq+MSwOxN4WESqgPIUgqMtBBqx+2QJsK+I7JulM0Qgv+mYq2Uv4G+q+io0jza/E6B8kvL+DOyPzRR+A4voW2EOEKCqawJJuQEbCHocW95wDeGWe2TzdWAvVV2VzPT/CUjDgDgeMzDXAZeq6pOhNSTcirmSXwd8EvglLdGfQ/EysBRbuz0ZmJy5PwFU9ashRIjIlcCnsEGhfVIMhHoFcKiqzk8GqX4OfCwFHVHUFSJoz309/1bMBBqAJ7AAuXthfZDQRPGcTco8FOv7nIwNKN8BfENV1wXUEEWftDvjbuR5QkT2Au7BRlSfT3ZPAqYCJ2UqdwAdHQUrOVdVK0LoyNJTjI1qgrmYbTX7n+fyh2Ojhy9pcvMnI5y9Q0WvFZHH6DgYxccD6WjCRk9X0UYgmYzbf2hEZH/s4XMasExVDw5U7i10/LucH0hHIVCePTMmImVYex2kIy8ii8gKJkSrIFSqOjaQjhyXvhRd6WPR0QQswwzNre5VDRDZONGRs/QnjeshIud2dFxVZ3Z0vBN1NGGzQY203Y4GecZGdI/GouMWUm7PReTiNnY3r+dX1SCGbpY7e5uEmoEXkbmZWAvJc25Oii7bMTxnl2IBeu8AZoVecpKl4zEi6JN2Z9zYziOJYXkWNnoHthb2TyENzIg6JIXYyPvnsFFmwQL63Ax8P9QaO7GAbGfT8pu8CtymqvUhyo8JERnV0XFNorYH0NGmS2qyjvxQDRRhORaS63ERuffofwee5Y8CEXmH3OAtV2VvB/I2QERWYh2iDGdkbwecRe1wplJV/xlIx8vANFoGYR7N3g7o+ZDR4+k1RZYB/5W165vZ26r6X1t9KT86oqgrseHr+eMZiEnKTv05KyKjQvWznHRxY7sHEEOwEhG5GouW+g1V3ZDsq8A6z7WqmndXIhHZE3Nne4oWb4P9MW+DE0MFTUm0tNXQ35DGyKaIjMnS8Zom0fMDlv8A9ns8jrmklqvqeSE1ZGnZGwukk/27XKWqcwOVPxW4DYuUmn2PngucrapPhdCRaEl9YEosBVl7hPQ2iGLQMkMEHkKLgCbajvsR0vMhO70mwEYCp9fM0nI4LXVlnqo+Frj8jmYvVVVnBNIRTV1Juz1PNLRez39tmuv5sXqb1nr+LbRE789EIK8hvBdIFM9ZEbmXDpZzhvJUSrRE0yftjrixnSekJcXBVocI26jkBCsB0gpW8hawq7a64USkFzBfVccF0PAINmv6UKv9R2Kz64fnW0NSXiwNfQXwO2x5w0vJ7vGJpgs0UMCjGFxSk3JPwgZ//pOWFDWTsM78t1T1ngAanga+pKovtto/HviNqk7Jt4akvI4GpoItg+mIFNb0t6WhGDhBA6X1yfIQOh9zP0zFQygGpCW95pe1VXpNLLpwkPSaydKkvwB15NaVEuAUVV0eQkdHiMgBqvpsyhpC15UY2vPs9fw3pOV5ISKvYAZ283p+VU1jPX8URPScjcVTKYo+abdGVf2VhxfwP1h05+8Ao1LUMQ8YnHweC/wrJR1vfphjnaxhfgfHXg94LZ4GJrSxfzzWSQyl4xbgh0BB1j7BgofcGlDHy1jqogHJK2c7sI7RbewfjaXrC6HhtQ9zLA86HgGOamP/kcCjoXS0UX4l5ob5CPBuShp6YcF9/gCsAO4KWPbV2ABZeda+Cqwzf21av0uiY2csQvqrgcp7AyhuY39JqGdKUt5fgfPa2P9Z4J4Uf489sRzfbwPPpaQhzboSQ3veBNQCG7DArJnXBmB9wGvxQkfbab4wr5RzgPsClhnFc7YDDSOBbwcsL4o+aXd+eTTyPKGqJ4tIP5JRzWRU904sWX3I9WybVXVVoukdESkKWHY2r4nIZ1X11uydInIOMD+QhgIRKdJWbrDJbxOyLlRoqxFVAFV9KVnXFYqp2spdW62FnZF4IoSiHzaamu2Smol+qdggUQgKtY2I7Kq6SCyNTAhERPprKzfDxBWxIJAGgOHaRjopVX04CboYDBEpAU7C4l9MwJajnIwtOwip42OJhuOAOdgs/xhtyawQgk/SykNILb7Bl7B2NGhkXxEZBpyOXZd9sFnEMwIVrxpHes09VfWUNnTcKiLfD6gDERmNBZc8E3MZHgVMaqtdy7OOGOpK6u25qoZssztiiORGJM/Z1kDr+TMkS5SOx+6RY4C7gV+HlRDFcza77MHAZ7C6OwwbxAtFLH3Sbosb23lELXT/zSIyE+uA/BLL5xyyYRshIr9sb1vDBSu5CPiLyP9n783jrh3r9f/38RifZCrRZE5JMmQMTRRbFLLF81QSzfqWolm7kQYVGRo0obapotIgidqJjJkV4mHbbZkiIYrj98fnXO51L+tej37b+pyn+76O12u91rWu636ch7Wu6zzPz3R8tDuT1dlnEw6JDBwFfE/Sni6iFGVzcjDhec9CcxP9EKT1YLe9UtZY88E/Ja3gAVX6IiSX1Qv0QOAUSfsw4XBYD/g00cImC004piQdDTwPOIVoM3UaUaP8yywOhccNhLDjl4gU1DslXZtsPEAYmMNUyFN700t6I7EpfAoh9rQHEcXN7CvcSnvNoXO2om/uAlkkJJ1FZDkcC+xo+6pyj87L4lB4tPKstDCfT0IpOejdE39yXo/prxJOymGfM+eNLYl5Y0tCVPEoYAPbr8viUNDEOlsM2VcQToenE+UoK9t+ahaHCSrN70kf1eiM7TFC0ibExPI8ogXYDrZ/nUzj3QOfzx/6V2OGo25to7IR6gkw/GRwozRmDp+Q9Dbg15IeU07fRQimZEbrmpjogTMVfUA/3r+Bl/QhSq/FWpC0KrEA7WL7WfP7+0cIHyb6fO/PZIfQ+4D3ZhCwfbikPxHpn88iNkKXA5+wfVIGh4JWHFNrAH8BriBKPVKNyj58l4im7wzcL+kHJG5S+9BChhDAocQcMdf2eYVD9vfxduAHkoa210zk8SNJXwX2sn0X0GshdCDwk0QefyacH8sRPXOvos492sqzUn0+l/R+oq1oT5zuLOAOYCFCLO2TGTxGOcEkbZDBoeBkosf2ZravLeN/IXF8oKl19iYi82Nf4AzblvSQLJkEtLInnbboBNLGBIVa6+2El/k0BjyprtwkPlusZAoONQyq3tiLA3hCGT1VQEahDvoeJk/0B2RO9EUg7evAc5gQSFuXmGxf72Tl+ilSUk9wrnLs2sDeTG7X91nbF2VxGIYaz2txTL2H6AsLdRxTSFqdcFruDNwCPANY08niaJJEtLeaQ6THLklEdH/ivL6sPTGuexiSIWT7hiQej2ci5fGJRHR7N9vLZ4zfx6OF9poLEXPVboRoHcAKhDH1Adv3JXLpla7NAVYjNA62sn1OFofCo/qzUnhUnc8lXUC0r+w5YX5ne12FMOyvbG+WwWMIrzWYKDe43fb6SeOuQ2R57gRcQ+yP/8P2yDakWcheZyXtRXwfiwHHEKWmP3dSN4cBLtX3pNMZnbE9Jmhyk3gzOS3XrtAkvkzwWzGRxvNr2/+ezKG6QdXHpcqCM4JPFQdIcXqsUT5ebvuPkhZyXu/zwZTU44mU1JUzxp8fJC1POIQOSB63+vNaeFR1TA1wWY+YO3YCbrC9SSUeCzHx22xle5nk8fszhC7PzBAawuWpxJw+h9g0nmj7A5W4LAbsAMyxvU3y2LOZaMf2R9t3q6JivqKVz87EZn6FbEdIH4+qz8oQPmnzuR7aV3o320eU4/NtrzduDn1jr0QD9fx9fHqZnzsSYnYn2j68Ao/q66yii8IuTDjJPkx8H1dm8hjCq3pQbrqgM7ZnADRcrGSVzBqqVgyqBhec6hN9HxcBmxP3yra2l0sa9z4ivW7vvpTUa2p4d/s4PUSsxPY+SWNXf16HcGrNMSUiFTG7LGcYl+fbThVrGxi/WobQEC6rFR4fTxxzmNjSCbUiMpKWIgyIucAzbT+5Bo9+SNrY9m8b4FHlWak1n0u6EnjWoONaIVR7qRNanpbx+uv5j+2r56/u0C7aBlsQ88YeieM2t84WXmtSMrlsP21+fz+G8ZvZk04ndDXbY4Kk55fD+2oucmpHrKR6jZ8aEZApXFpQa+1x2bhw2Z5ot7UnkGJYFjyJ2Ah9TlIvJTVL/ftBqAGxkoae1+YcU4XTJIcQUZ9ag8ck5wORyp05fk0V8Kk4LQZsXF4Z47UittSLaldXzB/gNMnoJ4zMGjyqPCstzOdE/fpXJL2tN4eX5+TQci0LrdTzPwS2H5B0D9GnPgUtrbODsH0p8MHySkNLe9LpiE5lbnx4XXntXJnHd4lFdmfgZWWirzHJPomoSfmcpD9I+jj5BtWfiU1Qb8GBCt9Fmeg/SYjmrWF7R+Ce7ElN0v6KFl/7ARcTm8SbbR/pAVXKccL2rba/bPsFhIf7duDPkq5QiNtk4SZgd+AThId7byCt3rKgiee1OKZ+TDhkdyzpjndWzADZWNFF4TrgB4QRs3oyh5UkvV/SxYRI3FuAF2dG+SW9UdLpwC+BxxN1sP9r+6OVSnEWlrSDpO8QCuCbk9fC52SiLeBmtl9dItmZLb8AUCjmXwm8hFDMXwn4i+1f2k7lI2m2pF0k/RC4BPgcIQKVqm7cwrNCG/P5hwqP6yWdL+l8YB6xF9k3i4Tt7Qmn3PnARyRdCywtacMsDoOQtK6kAxT6Rh8nRDCz0Mo6e62kaySdnT32AI8m9qTTGV0a+QxAiQa9kMpiJX18qtX4qQEBGUkHEVGPS4GjCePhkuy0aUk3EZvEg4CTbN9bO327H5KeTqSWfWy+f/zIjNeEWEkLz6uk7xPCeT8EjrZ9Zo17ozhbdiKiEMcQvUfPq1B+0kQaZislF0OiyscBhzixjZ8aEVuSdCERuDiKuDduqPSb9LfJ6wmzXj2Dn5Um5vPCpb+e/2pHL/iNbFcxslSpnr+s6b0sh1uI32Sf7Ge2cKm+zraCVvak0xmdsT3DoPbESlINqoGxlwVeSXwXqQIyLUz0pTbnJYXDFsSm+cXA8s7r/9lMyUUPakispObz2ohjqgmHUEPOh1ZUwB8gWvjs5okWPtUcdaostqQGFPMbMvqbeFb6+DQznw/wut72CjU5FB5p9fx988Yetq8u56o7+FvbF9dAC3vS6YzO2J7BUKJYSWsG1SAkrejST7jC2NUneoVgy7ZM9IX/he25SWN/sxzebvudGWM+XKiyWMkAl9m276k0dhXHVCsOocKluvNhgE/NDKEmospDeM0i7o9dbO9eicN6xG/ySpIV81sw+guPpp6VPl4tzef/ne0k6xu7ioifpO2JeWNTogzkWOBr2VkPo1BznW0FLexJpxs6Y3uGQfX6K1Y3qAoHA3e0ZtT10MJEr+i/vb3to2rymIko9bgGbnOjCqC1HFM1HUJDuFTLipmCT80MoSpRZUm9qOD9tv9n3OP9qyiRoudlObSHjF/N6B/g0dSz0gqyI9saIeJXQVtgscJlDqHzcBQxb5ySNH7z62wraGFPOh3QGdvJkLQ+8Cfbf0occyUaUxWuAYXaIkR0/ayKPJqY6CXtWg7vcddHkSIaY0IkbqNKHHqRwftt31CDQ+HRtGOqJYdQpvOh5Qyh7KhymUcBbq08j36YeFb+ZvvztXhMhdpG/wCXzGelhfn8JIYLbwnY3PZiSTyaqOcfBklLExkyO9veImnMJtbZVtDKnnQ6ozO2kyHpSGAt4ErbY1cqb0WspMMEWpnoyyYRQmW6uU1ih3poyDHVhEOoFedDCxlChUfTUeVMSHptObzH9vEVeTRh9LfyrLSAvnl0KGz/KolHE/X8HR4eJJ1KBMYOs/2jhPGa2JNOZ3R9tpNh+7UAih6QGWi2v+JMRa3a8EHY/mhtDh3aRNYm8GGg5xS8syoLOKK8Z7cOmgRX6CE9BY4s77cCMzoSYvvI+f9VCuaV99opn0eU96rPSguYah6VtDxRu5wyz9pep6+e/1RJtwCLS1ouWcSverbBowS7Eu1yN84YrJU96XRGF9keE1ry/LcqVtKhwyjUKLno0KFDhw4dHmlIegITnQSeTNQo71OJSxP1/B06zBR0xvaY0Eo92SBaEyvpDKoOUyG75KJDhw4dOnR4pFAyGF9BiJI9HTiBqE1+alViBS3V889EdLXSMwedsT0mSHpuzVrHh4Oa7a76OFQ3qCTtD9xBtKC4tQaHDlND0uK2a6cRV0V5Tu4margurc2nQ4dWIelJxOb13tpcOsxsSLoHOAfYFzjDtiv1Pm+inr91ZK+zXa30zMGs2gSmMQ6rTQBCrETSNyQdOHittqFdOLzW9rrA6yvSOAf4J/CQ7ygTko6U9KXSC7Qmj+0kpdVTSVqhvJ4y7HptQ1vSqZJ+KmnbijQOBU4FXlORA5L2l/ReSY+vyaPDZEhaX1JKr9xHAb4F/F7SZ2uSkPRWSTtL6rRxGkLyfP5+YBHgi8D7Ja2aMOYwzAOuAzqDbjRS11nb15VXk79LK3vS6YAusj0mSPpdMSJr82hFVbiZGvZWIWkDYAVgQ9vvrchjf+DZwIK2t04Yr8mSix6KEfMkYGPbTTjRakHS9sCqwNq2d53f34+Rx3bAjbbPrsWh8GgiK6aFDKHCo4mockmPXcP2ZRU57AmsDqxo++UVebyVELL7nu1/VuTRyrOSPp9LWoUQROvp5nyYqNm+MmP8Dm2iTzDuwVN9n227lnMmyDSyJ50O6IztMUHS7cCUdTA1F98aaMGg6lKpOrSOrm3Ow0O2Q2gEjyacD318qpZclJY1qxKGXRXxpw6T0ZDR39SzUgslSjiHqN1+Wm0+LUDSFeXwMNuHJozXxDo7JENsFqGptA9wge0d81l1GAc6Y3tMkHQVI1KjG2qtM2PQUD/UVib655fD+2z/tgEeQ5El3tKCWElDmSidY6pBPBoyhLKiyq20EWqlF3yHyWhhPu/w8FEMz41t/zhhrCbW2R4kzSJS198NXAjsb/vyxPGb2JNOZ3S1ROPD3zqDegItGFQN9UM9orzX7kPa69l7O1DN2CYWmEGYSItdHlggicdu5f3+pPEegobmjHnlvWrP3oYcQq04H5rvb+3w4I89fdv2yvP/qxQ00Qu+FaO/od+tbvUAACAASURBVGdlt/JebT5vxSH0aEApLRi7oV3GamKdlbQQsDvwTuAMYHvbV1egckR5r70nnbbojO3x4draBBpDdYOqz3s3DLa9x7g5FFwOPGHQcylpDWJRvjmJx1627xh2QdL6ts/LIGH7ZQNjb0qot94I/L8MDoVHdcHAvmjMMNj2FklUjgMWH7wXS6/YO23/PYlHKw6heeW9qvPB9otqjt9DC0aEpK2Ie/S7A+f/nYjQ/DyDh+2PZozzMNAz+v9WlUUjzwqwELCc7d/0nyzry422/zhuAg05hIYiu56/hXmj8Ghlnb2WEOc9CLgeWEvSWn1ETkji0cqedNqiSyMfE0qaypRfbu2+hrXFSvoMqqWB/WyflDDmsPqX5Qmv4gJO6n0p6Vjgi4P3gKTnAW+xPTeJx7nAlrb/MnB+S+DrTu7BLmkL4EPEc7N/1ma5b/zqYiWS1htyemPgPcBNtjcYN4fC43Dg5MHFXtIOxD3zlgweHSZD0itGXU/cnFWHpN8QkaBBh9AywEm2n5vE4wDgattfGTj/JmBl2+/L4NFhMiT9CHi/7UsGzj+bWF9eNvxfzhy0Us+fjYbW2SMYbfTvnsSjiT3pdEZnbI8JkoYZjw9Gcm1npcYORS2xktoGVR+PVYAPAM8nWn593XZKCo2k82yvP8W1S22ntFmQ9AZgT+AlvQ2rpLnAfsB2ti9O4rEN8EHC+bOf7TMyxh3CoymxkuKw+xCwKPG9/DRx7PNtD9uQIOky289K4jFybrJ9VBKPJrJiCo8eXgb0rzOZm7MVRl23fX0Ch1Hz6MW21xp2bQw8zgfW98BmqtRhXpw4n//HiMu2/fEkHq08K+dOZTRJusT2sxM4XGD7Of/Xv+kwPtRcZ1tBK3vS6YwujXxMaCU1dirY/n7meAMG1b4VDarVid9hXeAA4M0Z6VMDWHzEtYWySNj+qqS/A6eVaPbOwJuBF9mel8WDMBhuINLZ3iPpPQM8UzzuvQyPIWIl2ySLlWxF3KP3Eov/6fP5J+PAY0Zcm5XGAqaKMLwceAqQYmwDPxpy7sGsmCQO2O6l1aNoL/m6UX8/RvyYMKjUd87AE4BlyflOlpC04OD8XeogZyeM38Mig4Y2gO0HilhcFu4acu4xhFDr44EUY5tGnhVgqRHXsu6PZ0oa5bQWsOS4STRUz38nw+eNBYGFbafZJI2ssz11+ncDPQf2ZcBnBzMyxowm9qTTGZ2xPWbUjuQ2JFZS3aCS9B1gPeBzxMJ/P7Fh63G4bdwcCq6W9FLbPxngtzVwTRIHAGx/qxjcvyNqhjazfUsmB6CVOtTqYiUltf8JhCPorHLuwaiH7QuSqNwkaUPb5wzw2wBIq9+y/aBjshgurwLeS9Rv75fI43t9PPqzYj4FfD2LxyCtSuMyGBWUtBLxu7wY2D+JxgnAVyW9zfZdhcdjgS+Ua1m4R9Jqtq/qPylpNRLrlm1/rm/sxYF3EPPZscSal8WjlWflPElvsP3V/pOSXg+cn8Rh9YfxNxkCbk3U89ueZNSV53VP4E3AiVk8WllnJW0HfBb4JBPP6PrACZL2sf2DDB40tCedrujSyMeEhlJjW2l39YJR152gDilpHn01uAx4V22vMm4OhcdqRGToTCYW/fWB5wLb2r4yicclTHwPKxJG1F3ls7PSMPv4LAr0+o5e7TwRrt74NzBZrGQSMuphJf2S0SmYm4+bQ+GxIXA8oVLaf4/uCuxi++wMHoXLgoSy8D6Ekf1J23/IGr+Px2BWzLcrZMX086meflrmsg8CGxGbxSNt/yNp7AWBTxCR2+uIeWt5wqD7UCKPrYFDCpf+Z+X9hAjlT6b6t2Pg8jjgXYRT6kjgC4OaHEk8qj8rkpYjDLj7mPy7LAzsYPvGTD4dJiBpKWAvYj05GjgwUzuooXX2IqJkb97A+ZWAH9heO4lHE3vS6YzO2B4TJD1ARHIvYshDPZPEKPpR26BqBZIWAeYCvVqYy4CjM78PSSuOuu4kde6yad6fiML0b5q/CXwwcdN8BA2IlbSCsll9K5Pv0UNt35TIYU8iQvcL4NPJ5Q39PPqzYo5nIBqVlRVTtEB6DrLnAZMEbbLWlZL6+EEi9fEzwDG2q7RYkjSbyWtKugp2Xypo/7NyQGYqaBFqewVwOHCY7SpRzFaelT4+L6Lvd7F9Wub4LaChev5lgL2JkrVvAId4io4oMwGj9E8kXW57jUQu1fek0xmdsT0mtBDJLTxaEStpxaBamPD699fHHG373ozxp+D0GGAN4DontliQtBfhybygcoTuQKJm6J227yznliDSq+6x/Y5a3GpA0rJEal3/PXpYppE7DGWjdOuw+tQxjvkAcBORdfEQpfhEEax5tJEV08q6cj/w30Q05CFGtu23J/F4AfAX2xdLeiWRrvxHQlm32pxeA+VZuZfIzhn2rCyRxGMeDTwrhcuCwP22LWl5IgPjatsXZnFoAZL2HnL6wXp+249N4nEXMZd/kyF96TNLHVtYZ0tk+2UeEJQsgZCTsrML+8avsiedzuiM7QqQdJztnZPGaqXdVXWDStEz8IfAb5hIlVkP2JRI5bls3BwKj5cDBwO3Eal2hwF/BlYC3mv7yCQenwU2IerKLiG+lzOBMzOjD5KuAp4+aMRJWgD4ve3Vkng8FVipV/Ih6V1AbxNydEb9tkJI8Wgmp2+vB7wWeJUHesaOkcfGRI3lbYSw0reAZQhxtF1tn5zEo4nsi5YgaR0iknuZ7SsqcXjtqOsZc5ikw4juHosCfyCe1ZOJ+XyW7VeNm0Ph0Uulvw34PPBVIuvgj8DrbZ+bwaPDZCi6bXyaqFP+OJF5cAGR2v4N25+uSK8a+ur59yAyDz6XZWBK+gijW+Km9KxvaJ3dnsgM2p/J6dvvI/aCKULGrexJpzM6Y7sCJF1ve2TrlDGNW7PdVXWDStIvgE95QKRO0ouJ6HqKUFfxZu5EqJCeDqxl+5riaf2FE1qSDPBZmJjgNyFqdJ4L3J6VwiTpSttP/1evjYHHMcB/2v5R+fwHIiXzMcDqGZt3Sb8l+lr+buD8OsBXbG80bg5lvPOIuWJJ4jvY2vZvSy3mMbbXzeDRx2dlJiIQl9tOF21pISumpIO+mtiYbUTUr3919L8aO6fHAmSnLffSLEtp0v8Ay9q+X5KIllsp86ikMwhV/CUIJ/ZehCDo84BPZD2zfXxexMQ9eqntX2aOXzi08KxcBmxGOPmvIHpJ31KidudOlb47XdFKPX8LaGWdLWOuTaTWP7i+EWrkFyVyaGpPOh2R2cKlQyVIWl3St4kNwBnAGra/lGVoF3hY+mmp88vy+Dxl0NAuHE4FnpjEAeAB21eWiMe1PcOheJdrpHPPJjaKS5bXn4A0ASzgcg3ppyzp1cDvE3k8o2doF9xt+3Olni3LObbE4AYAoKQ9jmrP8UhjQdunONrE3Gj7t4VH5u+BpCUkHU/UbO9eXqdK+k7JjMnisQaxCXohIZ53fTm+TFLmpn1nYB3bc4i2aG9MHHsSJL1F0vVEWdB1kq6T9NZECn8HKDWF15W1hLLOpJQlFTzW9uG2e1la37H997LWLJJFQtJTJJ0NfARYpbw+KukcSU9J5NHKs3Kf7b+UFN2rXbps2L6bEE2bMSj1/OcSqdvPtv2RWoa2pK0l/ZekW8rrV5JemkyjlXUW2xfZ3tX2euX1GuA2Se9OpNHannTaoWv9NSaor43A4CUS+9apnXZXl0va1fakvrjJBtUsSYsMetdLZCTzWZglaWnC2fVAOe7VtaU5wCQdTnhT7ySM6zOBz1dYhPckWl3szuRUqtmE4E8WFh34vEXf8TJJHCRp6cHfoEQlMp2jD/QdDwpOZaZDHUxs3Hex/QDEF0S0UzyUULPNwCFEJGRYVsyh5LWvu7cYC9i+VdETPh2S9iUyYV7Y25iVzKkvSHqc7U8k0Fi2lHqo75jy+QkJ4/fQ/6z8dcS1ceNQ4Eu2j+g/WRyZXwS2S+LRyrMyW9K6xLy5cDlWeQ3O9dMdexP1/PsCH9RE+/fsev43EG2+3gOcV06vD3xK0lNtH57Bg3bW2f6xn0BEl+cATyaxFRqN7EmnM7o08jFB0umjriemLM+jAbGS4lk/gdi4P8Sgsn1DAod9gY2BPXu1nooWCwcD59n+2Lg5lDHnEZswDbtue+Vh58fA42TCiLyUMLTPItIOq0wKkjZncqrwL5LHPxt4jQfaXJTU6aNsb5jA4Y3AG4g2V71en+sRtYfftP3lcXMoPO5nog3cbODu3iVgUdspDkNJV01VYjLq2hh4/N720J65kq6w/cwkHrczoUD+EEVy56mR/wFY2wNKtQpl8IsySj8kfXjUdefVf94NXE38HquWY8rnVWwvlsTjD7af8a9eGwOPVp6VJvZgHSYg6XJgs8Egj6THA2ck3hutrLOLEwGFucDTiT3yzk7SUurjMY8G9qTTGZ2x3SEVDRhUbyO8qo8pp+4i6mMOyeTRCkqU8FlElGoTou3DbcBZtkduZsfMa1ViAdolq7ZO0r8Rjpf9mLwAfwB4h+2fJvHYlrhHn0U4yC4n2gidlDF+S5iPsX217acNuzYGHlcS6ZfDsmIuSTT6W1EjH2VQTXktC5I2cJIwmRoR8ZvqWSnZD1fOtGdlFCQt5KQOKC1Blev5RzlbMh0xZbzq66yke4BziIyDM2xb0jVZQbAOeeiM7TFB0vPL4X29eseKXKqLlQxDDYOqb+zFATyhjJ62OZuCT7Xvooz/VELFdxNgW6IdyFLJHJ5M1KTOBZ4NfBI4wbm9atdkYgGGeFY+Y/vSLA7DUDaqLys11LU4LAbsAMyxvU3SmEcSqs4f78+4kPQhQnDxNUk8msiKmQqKtka72D4gabxfAPsPOkuLM/VDNaKGpVZ4Tnndbnv9bA4DfDYjnpU9k8Y7kFBk38v2XeXcYoQY6t+d146tyWelOJY3J9aXbW0vV4NHDfRlFv6dyerbs4EdbP9PEo+zgTd6QPxLIRL21YzssVHIXmcV7Vd3ARYDjgGOA37egrFde0863dAZ22OCor81xKL/zoo8mmh31cenukHVx6Xq5qz2dyHp7UxEtP9BaftVXpf0amQTeLyR+A2eQrQiOR74wUxPXVIo9W9FfDdbAr+2/e/JHBYGtiHu0a2A7xH3aIr3XyGC9nXgOUCvN+66RObB623fnsGjcGkqK2ZYjZ/tfZLGfhbwA0Jws78sKLuN4kpMzOH/AFYE1rc9L2P8IXzWJZ6VnYBriWcl5f6QtBCxhuxGiNZBCDseCXzAiYKoLT0rijaGc4HtgccRGiE/HKzXnc6QdCKxph4xcH5XYEfbKfX8xQH1n0Sf7f5547XAq13abmaikXV2FcLongOsBnyYmM+vHPkPH3kezezPpxs6Y3uaQ+20u2rCoGphc9bQd/F5Sm9t2/87cC0zDfM+ol58b9vnlXPpqVSSfjjqemI97AuIxe6lRIrZpkTt590j/+Ejy2FLJjYfpxMe90Nsr5TFYYDPqkCvFd3ltv9YKxW0ZlZMKzV+hcuihUd/y5r/HKzjHuP4ZxFdFI4FjrV9laRrK8yjT2diTbmFeFb2sT0yvXyMfGYTfdgB/mj7bknL2f5zBS41n5X9CafH9UTU8EQisj7jnLit1POX8ZYjHB7988Zhtm/M4lB4VF9np+C1JjGX7JxY+tHEnnQ6ozO2pzkaEiupblA1tDmr/l1MwatKpL+Io/QidE8kJvrdbC+fMX4fj5uB/yY2ZmczIBaSUQ8r6QZic/gl4Pu276x0jz4A/Jr4Ha4t51q4R5tIBa34rDRb45ddZiDp+0TGww+JsqgzKznpes/KHravLueq/yaSlgJ2JJ6VZ9p+ciUetZ6Vm4ArgYOAk2zf28LvUgOt1PNPhQolF02ss62g1T3pdELX+mv6o5V2V08iDKrPSeoZVGkt0Ar+THjuliNaw1xFbhujHlr4LoA2Iv22bwW+DHy51I7vDPxZ0hVEKtUHkqg8EXgJ8V3MBX4MHJNcavFdIt1xZ+B+ST+gzj36HCKt7VRJ1xAOqgUq8ACmTAVNSZnu47AS9VOW30/8Ll8EjpF0XOLYD8EUZQYpSr62t5e0JBHp/4ik1YClJG1o+5wMDgWvIH6T0xUdHo6F4aq+40aJam9H/B7rEj2Dt6dPsT6Jx0rUf1aexMR8fpBCnXy2pAVtz7TewT+S9FWG1/P/pAahUnIxB3glpeQicfgm1llJ15Zxb7a9Ufb4fWhmTzpd0UW2pznUoFhJn0E1hxCGSDOo+jZnvdqYpYCtkjdn/XxqfhdNRPqnQknP3KXSPboI8ZscAHzU9qGJYwt4YRn/pcCSwB7AT2z/LYtHH59NCpcdgYuIezSlH2orqaCtPSu1a/xaKzMonJYlNu5zgBUqZMYsRhi6c4jsi6OI3+SUpPGPJlrBnULcp6cBV8/0Z6VwWoQQ/pxDfEe/sD23Fp9stFLP31LJRWvrbCuouSedzuiM7WRIWh/4k+0/JY7ZjFjJICobVFU3Z0P4pH4XDaVhtqTcvwgRqZsDrER8N99wklrrED4LMSHespXtZWrwKFxmAS8m7tHdk8ZsIhW0lWdlGCrV+DVZZtCDpBWd1HJrivGXJpxEO9veImnMC4FZhJF/rO0bumfloVCILm5v+6jaXLJRu56/4ZKLZtbZllBzfz7d0BnbyVC0slmLqJPZOXnsmmIlzRhUw5C5OWvpu2gh0q92lPuPIvqM/4TYrFZt9zUISbNt35M01grl8P5ajobCYwEmUkG3IKKoLwaWz04FbeFZaQWS1iEi6zsBvTKD/8iMUJV5w8AdNeeNliBpdYrjhYgcPgNYM8uY6uNR/VlRKG0D3OOKLRNbRK16fknbE/PGpkCv5OJrrWTTQe462wpa2pNOV3TGdiVIWrxn9CaPW0uspLpB1crmrIXvYhhai/Rno3jd7yof+ydGAba9RAKH08vYtzm5/cgQHgC31uTRj5ZSQWs9Kw3V+D2IWmUGCjVhiA3iWeMebwSP5n4TAEnrMVETe4PtTSrxqPWsfLgc3mn78xljtoxR9fxOavPZx6V2yUUT62wraHVPOp3QGdtjQiuRocJlJeqLlVRHK5uzRwNqp2HOVEjqRQbvt31DVTINo6VU0O5ZCdQoM+gwf5Ta1OfZThVJm4JL96xUQCv1/MNQqeSiW2c7pKIztseEViJDLYqVdGgDrUT6W4aS2xl1mIxWUkG7Z2UyWnImdwiUSK6Bv9WM5HbPSntopZ6/w8ODpFOJwNhhtn9Um0+H/zu61l9jgu0X1eZQ0Eq7qw7t4YjynqJE+miBKrYz6vAQ9JyC6SU3AziivHfPSuDI8n4rMOPTMBvBvPJeu970iPLePSuNwPY6ffX8p0q6BVg8UxwN2i25aBC7Eu24Nq5NpMMjgy6yPUZIejyxYV+9nLqC6Nt7azKP6mIlHTr8q8hW7m+xnVGHDh06dOjwSKKVev4OHWYKOmN7TJD0TKIu5mfA7wiRpXUJZd3Nbf++Eq+mRLBqtEJrFd13MRnZyv2ttzNqAZKeRIjK3FubS4cOw6DozX4HoXKc6tju0D4kbQfcaPvs2lxqo6V6/pmI1oXauj3pI4fO2B4TJH0XON728QPndwTm2t6xDrNJXKqLldRshdbHoYnNWQvfRYvIUu5voZ3RVCj3xt1EDVe1lmSllmxV4Hu296nFo8NkdDV+EyjthVYF1ra96/z+fow8riiHh9k+tBaPDpNR1vtnAwva3ro2nwy0Us/fOrLX2daF2ro96SOHztgeEyT9wfYz/tVrY+DxqBArqdUKrYzdxOasj0+176IGWhRbqtXOaASfDYAVgA1tv7cWj8JFwBq2L6vJo8MEJD2ZUuNn+7DafDoESinZxrZ/XJtLh5kLSa8th/cMBoA6TCB7nZX0NGA5278ZOL8pkX3xx3FzeDiYaXvScaAztscESRfYfs6/em0MPJpod9WiQVULrX8X2ZH+VpT7h6FrZ9QmWkkFbSUrphVklhl00bqHB0lvJYTsvmf7nxV5ZK8rzy+H99n+7bjH69DhX4WkHwHvt33JwPlnA/vbflkSj6b3pNMBnRr5+LCspHcNOS9CFTwFtn+VNdZ8UF29tqHNWfXvYj44h4j0H0ioYo4bW9keqlwraeVe/fS4MWzBsf0A0Zv0lCQOTWSiPApUYzcCni2pdipo6rPSeo0f8C1gVUkZZQbzyntV9e1HwbMiYDPgVcDLK/LIXldeV95vBzpju2Fkl1y0ss4SUe1LBk/avkTSSok8Wt+TPurRRbbHhGLYTQnbH83i0iHQpVK1CUk/AbYfNLglrQ38IEsNvIUIeyuZKKMgaWnbf6nNYyai9Ro/yCszkLS27YumuPYW218a5/gPB5IWnsqR2KFDhwlklly0ss5Kusr2alNcu9r205J4TDlPZQY8pjM6Y7tDCiS9YtR12yckcFgUWNz2zQPnnwDcafvv4+ZQxlsNOAB4GnAJsE+N1J1WIv2SPgE8F3iZ7bvLuRcSUbLdbf+8FreZCklfs/36IeeXB35qe80kHk2kgjb0rDwqavwyIOkaYCfb5w+c/ygxl2SVav2H7Y8NOb8k4Sx8YRKPXrT4HtvfyRhzCh6tPCsbAYcT0fRLgD1sX16LT4cOg5B0DHCa7a8OnH898JIsUbJWAh7TGV0a+Zgg6eBR122/PYtLI/gucGF5QaS29WBg7MY2cDBw8pCxNiN6K78lgQPAN4CjgP8i0voOIfqgZ2Neea+ahml7X0n7Aj+TtDXxWxwE7GD7vJrcsiHp4qkuAba9VhKVhSR9G9i1pNL32hn+GHiIYTFGtJIKOq+8V31WiOfi/UPO/7Vcy6rxayF1eifgO5JeZfusElH/EvAM4IWJPDaTtJ/tD/ZOSFqOaPuZsa71sHJ5/1vimMMwr7zXflYOA/ZhYp09ENiqKqPGkF3P38i80V+OMwy2vUUSlb2AEyW9Cug5DdcHFgZ2SOIAcAHwU0lDAx6JPKYtusj2mCDpPuBS4HjgT0w2LrF95LB/l4UKYiXbE22Vngb8ADjG9tXjHneAw/m215vi2mW2n5XE40Lb6/R9ThPMe7gotbCpYjpF4+BNxLPy0uz7owVIupDYBBwNnMTAhtVJrfqK4fIVYGniud0IOA54i2d4a6makHSu7Q2muHaJ7Wdnc6oJSWsBJwJ7Am8op+dmCLT1cViUcCZfaftdJXPpp8BnbX85i0craCiDbNK62uI6WxuS9gRWB1a0XbOePxWShu0DNwbeA9w01Rw7Rj4vAnrZYpfZPi1z/MJhX8IZ1R/weMVMC3iMC52xPSaU+pOdgJ2BfxIb1e/avr0qsQJVanclaTFgO+J7eTzwwSwRN0lX2H7mv3ptDDx+T7SV6jlg/hOY2/ts+4IkHmfY3qwcf8v2a/quZSrmn0QYmAI2Ba4Gbuxdn0mbAABJqxP3x8uAywnD+5QaSsIlQ2ddYEXgldmp3MUBc4ftrw+c34PY0B+UxOOHo65n3aMN1fhtRXz/3x04/+/E7zX20g9JjyuHawDfB04F3gY8AGD7tnFz6OOyELHG3wtsAuxl+8Ss8QuHJrLpJB0OnDxYGiZpB2BL2ykZZKXMoF+k77P9nzNK1zpMhqK11jK2fzpw/qXAnwdLQpI4vQD4ELAosN8gt4TxFyQ0OFzKtDYCrrZ94Xz+6Ti4zPiAx7jQGdsJkPRUIjr0LuC9tr9VmVI1SFoA+Dfi+3g28X38LGnsXwHvtn3OwPkNgM/Zfv7wf/mI8/glo1OYNk/i8Tvb65bjwSjAg9cSeLxg1PUsZ8xUUGI7oyFj70ykQ37a9gGJ4x7ChANkLpFm1lOMzdy4n0+I5vxj4PzCwHlZafWSbgb+GzgGOJuHZiplOQxbqfH7DVHjNxi9XAY4yfZzEzj0UlIHS5J6JRerjJtD4dHrOrIQERn7NZG6HISS6pZbyaZrKIPsCEavszMmPbahev7TgNcNZmgphB+/mbX3KWNuBexLOMj2s336fP7JODi8Afg0UfrxceDdxFq7LvAN259O4tEFPMaMrmZ7zJD0HCJK9RIitSzVc9eQWMnmhIG9IRGB+EKF9JR3A8eXRbi/PmbXwi0FWYI5DwOjPG2ZXrilgTNt35Q45r+CzHZGSHoKcT/uAPwFeCeRKpuJ86Y4zsaCg4Y2gO37Sqp7Fp5IzOFzCOfDj4lSmLEqbg9BKzV+iwwa2gC2bynZS2OH7ZXn/1cpWLzv+OAh57LwJNrIpnvMiGuzskjY3i1rrEcBWqnnX3xYKZTt64qjLgWSziVa8B4AnFXOPRhsyMouJObzVYn54goinf8WSY8BziUM8Qx8dorjDo8QOmN7TJD0MWAb4gE6lmhcn54GSjtiJacCFwNnAIsAu/Z5W1MiZbbPKQqlbwV2K6cvAzbKNPQkvcf2Z8rxTv2eZkn72/5AEpWlSmrfrHLcE2kTsGQSB4BXA4dJuhs4E/gNYXxfmshhSth+cTHs1hj3WCX7YnEiOvU6QrwGYGFJj0tMjb0IuMj1U59mSVrO9p/7TxYBqjTYvp8QVzxZ0iKE0f1LSR91Ql/YPh5/BjYZqPH7cYUavyWG6TqUdOrZGQQkfZ+J+eJc12uxdbAbaIXn0F75MvDlvmy6yyVlZ9PdJGnDKTLIHuKgGRckHWR7r3L8Dttf6Lt2xEwyxt1Oq9mlR1wb5aR5pHEX4Xj4d2BHHpodkxVhv6/MHX8pZUC3ANi+u2SqZKH1gMejHl0a+Zgg6QHgWuDucqr3RaeqCjckVvLaUdezUtwGUbypt2YaFf0p2zVFXOaTZoft1011bRyQtBJR77gJ0QpsBWIT/dJMHjUhaR4Tv0n/b5OdGnsesAoRPe0ZNGfZvjNj/D4euwJvB/Ym0usA1iMiEodmzhvFyN6GMLRXAn5IpPqltu1rocZP0qeA5YC32b6rnHss8AXgFtvvTeCwLRPzxdqEY7vfWffnEf/8keRxE3BLb1zgN7avzBh7Cj79Zf32dgAAIABJREFU2XTnEyVSaS2vJG1IOAuPYEgGme2zk3g0sc62gIbq+b9MOJD37e25iiP7o8ATbb8xg0cr0IR+zyzg20xo9wj4tvN0hL5L7LmaDHhMB3TG9phQalCmxLBUmjHxaEWsJDNiOxWHjYFPAbcR9THfApYhJrpdbZ+cxKO/VnpSbXRmrXSLUIiDbUpsoDcmlEFflDR29bYkklbMmhvmh5LKtiETBs0GRB3Xb2y/NZHH1sD7mIjkXgp8yolCNpKOKuP/BDi21iakoRq/BYFPAK8HriM2h8sDXwc+NCz1f8x8FiC+gxcCbwZWtr1A4vhPZ+I52YRIUf0t8ax8JonDYDbdyZWy6XqZJ2+lT2GZcI5lZpCNWmdnmrHdSj3/YsDXiHWl5xxch0iZfr3tlDR3SUsAy9m+qnzeiYmMnJ8lOupG1oln7X166AIe40NnbCdD0mbAHNt7Jo3XilhJ9cWtROs+QKRIHw5sbfu3xcA7JsvIbcXj3kqanaQPEBP7E4A/EJvU3wIXl/TdGYMWnpNBlA3SxoQTZFdgVlaEvRWUTKW7ysdhGQdLJPG4DNiMKWr8subzPj6ziXaOENH11HKlkpnU2xxuTCgKX0hkYdTKlloVeCnwDuAptrPS6pvIphuGShlkFxHOl1nAaeW4Z2SebnvtLC61oca640haBejNVZfZviZ5/MOJyO0R5fPVhKbSbOCftt+cyWcYJC2U7bQs41YLeExndDXbCZC0LpEeshOxGGa2nGhCrARYQNLSDHhUe0iqRV3Q9ikQUQCXNka2f69UrSXWlvRX4ruYXY4pnxdN5NGvvv5aIgW0h8yN2a6EIXMSkcJ0tu07EscHQNKrbX+7HG9q+zd9196WVJubeiNOBUlzicV2HUKt9VxChXsz2zeO+rePMI9liB7KtwHfBD5D3Ld/BPZ2XmuSRWpsfIaglRo/FF0E/mL7YkmvBN4k6Y/AF52g3C/pKuAO4HvAz4BPZEXGBnj0R4KWB64hnIWvZqL0IQNNCMaNyiCTlJZBRjjVz2diTs38LZqC26nn72V9/tX2SeVeeYWkPzq3Vd4GRIurHu60/f8KvzMSeUxCSanfnLAXtiVKdTLGHRbwOBR440wLeIwLnbE9JpS0sjnldQvhSVQFD1ETYiXA6kxe+Pphoj503Hig73gwApPmcc9Mb5wPNMVxKmyvruiZuwkRfXhfqf+8iPA+fzOJyruIuimAQ4D+CPPuxOIzbjxlVH1dVm0d8BVi0f0y8F8Va1CPJtTQnw6cQ9SBHgw8j0hHfGESj7OZfD/UwuzivJ1FiOaty0SNX5qjTtJhhENuUUl/AB5LCMhtCnwDeFUCjW8QkZcdiTaSa0o6C/hd8gbxDMKQOxA40fbd8/n7ceGrtresNHY/DmUig+w0BjLIiPtk7LC9UsY4jyaofnecDxHitJZ0LPBi4JfANpJe0Mu0S8CCA1kWr+k7XiqJw4MoToe5wPbA4wgH89g7n/ShiYDHdEaXRj4mlJSuXwN79KIvkq7JTr9sSKykei2ypPuJCUVEulBvUyRgUdsLVeS2GNG6Z47tbZLGbC7NrtSCrkdEL99EYu1lC7X0kq4D/mOq64m1dQsQolO9qN0zgP8l2qSc5ST1a0kX2V67ePyvs71C37ULba+TxKP6/FV4NFHjJ+ly22soBDj/B1jW9v3ld7rY9rMzePTx6dVMP5dIs7/F9guSxn4iE8/JhkQQ4wImnpWUFNmG7tEHn0tJV7hP5Kk2x5LiP5fY+6SWXNREK/X8ki4nsqUeA1xPiKLdXdb9C22vOfI/8MjxuAjYajBLS9F286dZJReS9icyXq8nHFEnAue5QlvDvoBHL328RsBj2qKLbI8PryBSdU6XdDIxwaVHD91Iu6sW0FBEGQBJCxML4FxgKyId8suJFJpIs5P0cmKC35So47qM8K7uXd6z4CmOh30eF26tVWvajxIZvKC8Di2CRzsRfUE/BmQ9S/cXPpZ0y8C1B4b8/bjwBEnvmuqi7c9nkBhlTCvabmXh7wC2/y7pul4kufxO2eJoqxBG7kbEJnFZolwrBWXDfkJ59YQFdycUllcm71lZUhPtGx8CD4ikjhFNZJD1IOnJRK3yXCID4pPE3mwmYV/imVi7vPYvpXPZ9fx/d7Tou6+kjt9NEPhnchnMAcBJkvYGflfOPYfoMX1AIo/XA1cCXwJOsn2vpCoR0FLK+aNir/QHPHYnSrg6/B/QGdtjgu3vA98vEcvtiE3qspK+RKSanZLI5c/Ah3ufe2IlWeMXfGH+f5KPShHlLYlUri2B04GjgA2c3GqroTS73YhWE+8Bzndfv1zl9lNeXdLFxAZk1XJM+ZyVkVKrV/AkSFqLyerKCxOOj0OI3yoLq0j6IeU3KMeUz5ne/wUIT38TNfU91KrxI9aydxHfx7J9jggRdX9jh6QTCQP7r8S9eSbR8/qKjPH7eCxJRNR7z8q6wFVESmbms7IkcQ9MVaqVZWw3oUki6Y3EOvsUIrtvD+AHbqfndCaaqOcHlioOIQFL9DmHRNy/KbD97eK8/QQTQm2XAv/hxC4XwJOIlP45wEElc2m2pAUzMw8aCnhMW3Rp5IkoAmE7ATvb3iJpzFbaXX2Tqb3atr1HBo/CZVhE+QTbJyWN3ysx2M32teVceonBMLSQZidpKaIOcy7wTNtPThq3ers+SSuMum77+nFzKDwuIGpRzyLaF13fdy1NJbWIcE0J279K4tGUSvwUNX4/LOJpGeN/eNT1DIOmbBDP7InE9Z1fnpi/UiJUkm6mpIwTxvW5TlZlLzyaukdro0RKzyKEFM8r55pYZ7Mh6ZQW6vnLPnBKZAccWoKkRQhn2RxCk+QXtucmjX0Cpbc2QwIeTmqFNp3RGdvTHGqn3dWOQ04vD7wTWMD2UxM4DEaUjwMOyY7wSlqHSGPbiVCuPZbwqI409sbIZ1ia3Qm2L0nkMJvIAJlLRIYWJwyJ/7KdmS5cFZIuIZxS/dEpE9HCZWuVQgxGUG1nZhwM45NtULVSD9tMjd9UkLSB7XOTx3wC8b30Ipkn2M4UGBrkk+60bOUeHYZKGWS9dldzgCcS0e3dbC+fMX5LaPne6CHTqNMIEVJIFSIdCkUf8O1tH1Vp/CoBj+mMztgeEyRdS2ySb7a9UUUezYmVlBq7DxA1IQcCX+/3pI1x3OYiyoq2MXOIie0iosTg8KSxB9PsjifS7FI37pKOJjy5pxCOh9OInr3ZPO5kSA/l3ruTeikPcFoJeC+h2nqw7UOSx68aQR3g0m9QPZl4VlIMKklL1/h/HsLjJqLG7yAmavyqR+skrcFE943bba+fMObihDbKXEKt/gQia2zsjtsp+FR1Wkp6lu3LMsZ6OKidQTbA5anEbzMHWIyYOz6QzaMWJF3DCHXrxHr+SaiYxXYfkTZ+PPAnBkovsnRTJO1aDu+x/Z2MMUdw6QIeY0RXsz0mNBRpaEaspETT9yUe5AOANycrYj6HiCifWhafY8kTrxkK22cCZ0p6B2FQ7UJkIGTgUCLNbm5fml0N79sawF8IpdQrHKrGNXj8goiAnAAcm5WyPQySVgM+SNSkfg54e1bqdhl/MIL6USKCmireNoVBtXIFg+r6vnuytzEzsYYubDtrLW2ixg8edAT1DOx/ACsC69uel0ThJqId3L7AGUWcbYeksR9EQ7XBPxqYN3vOQghn4aoZJFrRJOmH7RuIefRzCuX6mSaQ1ko9/0ijLosDMY/uRDhg/klkOX7X9u2JHGCilv7O5HEnYSDgcQgTAY9f1uQ1ndBFtqc51Ei7K0nfIRQOP0dsSCb1QS1KiGmoHFHu1eXeb/t/MsacgkczaXbFETOHWPxuIVpNrZldK1TEjnqdBBYlFuFjs+5PSWsSRvazgM8QpR6ZPYN7PJqIoEq6h4caVC1Ech9LRPnfRMwde1fgULPG7yxgCcJheaztqyRdm+lklrQX8ZwuRjiEjgN+XuEebaI2uMzn/ZgFvJKIaF5ge1gp1zh4NJFBJun55fA+27/NHLs1tFLP30oW2wCnpxLzyLuA99r+Vi0utSDpQmK+OIqYz29oYZ2dTuiM7Q4pkDSPPi9773Tvc62HWtIsSkTZ9u5JY/Z65d5q+98zxpwfWkqzk7Re4fFK4Abbm1TgMItYgA8G9ndSa6fiHPtv4McMOKQgr5ZM0We7F0HdgohQvRhYPjOC2opB1cdnKaKzxK7A0cCBtrM7OzwE2TV+kr5PZAr9EDja9pm1NmelLGkX4l5djei8caLtK5PGb8ZpWfjMAl4DvBu4kJi/Lk8cvwlNkj4xrtttvzNz7NbQSs12a0adpOcQz+1LiDaon8t8VlpCKwGP6YrO2J6BqCFW0gJaiSi3jl6ane2PVeYh4Hm209LL+jIenkeocR9n+9eJ47921PXsNG6oG0Ht41DboFqGaIOyM/ANQljxjoyxB3i0VOPXywLp/SZLAVvZPqcipzULn51tP63C+NWcloo+67sToqNnAJ+yfXXG2CM4Vcsg6zCBlur5WzDqJH2M0BO4gnAGnZxdhtMyWgh4TDd0xvYMQUtiJX2cUhVbW4wo10QraXaKNkIG/pYVQZ6CxzzgdibS2yYtvrYvqEALAEmLAi9rwMCqqpJaOKQbVJLuAm4GvsmQ+rrEzIdey607az4rg5C0LLExmwOsUCuq2xKynZaSbiDmrIMIrYVJqCWCBXUyyDpMQBOCvQ+eokI9/yBqGXWl1OFaJsoq+7MtbXutDB6to0bAY7qiM7aTIemKcniY7UMTxmui3VUfn+ptpjoEWkmz64vm3mP7+Io8fsnkUodJ7bdsb57MZwHCMdZ7fn+d5SRqKYLaAiR9hKkFJV07C6QlSFrROT3pW+n40YrT8ghG36NZZVJdBlljaKWefypkG3WSRpY0ZMxfoyBpO+BG22cnjddEwGM6ozO2K6BMfBvb/nHCWK2IlTTRZqpDh/8/kLSQk9TAJb2AcEa9lBAH2xRYxfbdI//hI8uhiQhqKwbVKKhCX+naKI46A3d09bBtOC1HQbk9jLsMskbRQD1/00adpM2I8so9K/PYnwhGLWh764Txmgh4TGd0xvY0R0NiJU0otrYMSU8CbrN9b20uHR70tm9OGL7b2l4uYcwbiBTQLwHft31ntspzh/lDFfpKt4TiEIKI5p5VlUyHoVClHsatQ9L6wJ9s/6k2lyy0Us/folEnaV3iGdmJSC0/wfYhdVl1mG7ojO0xocWITE2xktYUW1uEpFOBVYHv2d6nNp+ZCkkbE4vv9sDjiBZPP7T9l4SxDyrjXkqoXf8AuKRzStWH6veV7tBhJDSih7HtB2pyawWSjgTWAq60vXNtPhlouZ6/BoqeQm8uv4Uor9ynQhCqiRKUDuNHZ2zPQNQWK2mpzVTh00xEuURT12hFOXQmoaRu7URsRo4BTgTOy44ql3vghcTz8VJgSWAP4Ce2/5bJpUNADfSVHoXsGr8O7UEN9jBuGZIWt/0QscPpiFbq+VtBX3nlHr0If6XyyuZLUDo8MuiM7WmO1sVKWmgz1UWUJ6OVNDtJbwVuJX6XsbflkHQTcCXh/T/J9r21Sx1K+l9PJG0r28vU4jKToYb6Sg9Ddo1fh/agxnoY10bre59WkFnP3wokbU+UV24KnEw4p75WwbG+pKdoISlp/V7JZYdHPzpje0yQdCdDFI2BBYGFbS+YxKMJsZLW02UyI8otlhj0o5U0O0l7AqsDK9p+ecJ4CwAvIQzbLQj1/hcDy2cY+/ODpNm276nMYcZGUNVgX+kOD0Vxnv6D6Pjxo4o80p2WaqCH8VTIziBrZe/TIrp6/oCkxYiyizmEPstRRKblKUnjnwtsOViiVroIfb12mWV2wGM6ozO2kyDpsUTt55uIh3nvypRS0aXLPPowk9LsBiFpEWBbYhF+HvAL23MTxj2dcMTc1uIGsZUIam2DSpX6Sj8KnJb7A3cQUaJbK3F4MvAkouPHYTU4FB5VnZaq1MN4BJ8ug6wiWq7nb8Gok7Q0UUa2s+0tksZ8A2EXvMT2zeXcXGA/YDvbF2fwGMEvNeAxndEZ22NG8SDuBexKCB4dWGsTMtNRIsoAN9WMKEt6te1vl+NNbf+m79rbnNB/vYzVRJqdGu/pLGkJYHvbRyWM1RNoud/2DeMe79GKVgyqwmVFJ/Vlbd1pWdIzVwXWtr3r/P5+JqC207JkbaX1MH4YXLIyyN5j+zPleKf+tUXS/jV1YrLRej3/TDbqJL0GeA+wJZGR8mbg3zrhzemFztgeEyQtA+xNPDzfAA6Zqjajw8yCpAtsP2fweNjnMfNoIs1O7fR0btroz0TrEdRsqJG+0l2N3wRayQJpyGnZdA/jbLSyzraArp5/Mlor5ZO0E3AIIc76Utu3JI/f7X3GjJS64RmK64CbgW8CdwN7hFM30C2G+Sipnx8AngZcAnzS9l9rUJnieNjnscH2i7LGGgXbH63NoaDn5Z+RqfMDeF15vx2oZmy3YlABR5T3+ypyADhV0pQ1fkBWOnsLht1u5f3+SuP3cGR5vxWoeY/OK++1dR1aMWSaWGdbgO11+ur5T5V0C7B4tjhaK0ZdQxH9S5jQdnoM8HjgtJIBYttrJVHp9j5jRmdsjw8HMNFqYfGaRIYhW6ykERwFnE94ELcFDmZiw5YJT3E87PPY0Be9HIqstENJx9t+ZTn+tO339l07xfaWGTwaMvqrw/br5v9XKditvFc1qGz/qub4fTgcOF3SsBq/bRJ5zCvv1Qw729eVtPWnSbrE9s8q8WjFaXnk/P9q/BhlyEhaetBRNE4qUxwP+zztYfv3wIeBD/fV858rKbOevzPqJmPb2gRgYu9TMnI7jAFdGvkMRStiJZmKrZIusr123+cqqWSS7gauJryZq5ZjyudVbC+WxOOkIadNiPosb3uBJB6/s71uOR5M93vwWoc8SNqIMOxWJbJA9rB9eQUerx1mRChaoh1le042p9poocZP0tq2L5ri2ltsfymBwxeBZwFnEt0DTrL98XGPO4THaoRzvZcxtU/NdPLakPQ1268fcn554Ke210zicT9wF7GuziYyDCmfF7W9UAaPlpFdzy9pedv/PcW1bWuIXdaEpL2I+euCmmrfkrYlsnD/ATwAvNL2mbX4TEd0ke0xQdIBhADFVwbOvwlY2fb76jAL2H5xT6ykJg/g/wFrSUpRbC2Kk70UsgX6P9u+bdzjFzwzaZyRsP2y/s+SNgX2BW4kfpc0Kv8/r80YFGXjuwn17UsThjwM2Af4L+DlwIFEv+9svEPSIrYP751QtGs5ERi6aZvusP0tSX8HfkfU+G2WXeMHnFhEp87vPynpo8DLgLEb28DzCSG2+yU9Bvg1kG5sE5osRzHxrBxCtIibqVhI0reBXXsq15KeCfwY+FgWiSxn8aMBU5V9OKJtmcJ5P5f0EMegpN2BDwIzytgGngocBKxeUsp/QxjfZybuRwH2J5wuvy+O9s8AL0gcf9qji2yPCZLOB9b3wBcsaRZwcZZ399GCDMVWSfMIr92wei1niYVIWr2kdFEMiXv7rm2cLUglaQvgQ8RivL/tnyeP/3sipW0W8G2iNYnK69u2m3BO1ISkDYAVgA370+zHOF4TgkKSHgecTNwHB0t6AvATohVbVYdlDQzU+K1I6IL0ondpNX4lDfU7wKtsn1Uct18i+jpvl6GF0dA9eqHtdWrzaAXlXvgKsDSwC7ARcBzwlszIpaTNbZ9Wjle2fW3ftVfYPiGLS21Iem05vMf28RV5vJQwLrexfVU5935izd/aM7QDh6SFgfWBTYDnltfttlOCYa3MpdMZnbE9Jki6dCqDWtJltp+VxKMJsZJWFFtbQCsqqZK2IbzJdwD72T4jY9whPH7JiAh27ZpISdsBN9o+uyaPTEi6hohs9/DZ/s+ZG1VF67WfEpHL7YAv2/5C1vijoOS+0ppoDTcUTmpBVrisRWQY7Am8oZyem6UD0leOA5NLcrIdDz1nYc+J+59MOAyxfUEGj6mgSj2MJR1M9HNekUhLzXYiN7HOdpiM4tz/CtHj+/XAhoTxnVXLPyUkXVEOD3NSC9Yy7pKEgb1peV8KuCRLO0XSDUC/0OW7+j+7E3T+P6MztscESecSG4+rBs6vBhxje/06zCZxSRMrUQNtptROf+v+GuVJNcmZNcqSHgBuAC5iiLHrGdbvcioUg+rZwIK2tx7zWK20mDqCqR0gtr17Eo9eOu7ixOL/C6JPbI9I1eiUkvtKN1Tj97hyuAbwfeBU4G1E5lBKSU4rjof5OAtte/MMHlNByT2MJR3CRPbFXOACoGfEYPvt4+ZQeDSxznZ4KCQ9j3DUnUk4Yv5emdKDkPR4YGPbP04Y63BCd+JO4Gyi88dvsx0Pmmi/OhTuxGP/z+iM7TFB0tZE7dYnCAVsiDSR9wN72f5JEo8mxEpGQdJGGVHDVjzdDfEYWZPjJAVmSe+x/ZlyvJP7WoJI2t/2BzJ4tIC+3+Q+22dVJdMAivNhKqQZ/a1A0meJVMPVCTGuKjV+fRlT/SU5vc8pJTmtleN0CPSlLA+Fk1TTW1lnO0xA0p1MzBOLEIJc9zMxbyxRkV46JJ0MLANcSszjZwGXDpafdnj0ozO2xwhJawLvBnoG7WXAAbYvSeRwJLAAU4iV2D4ii8tUkHS97RXm/5f/53Ga8HRLuomIzolQE+5F6kR4eZdL4vEM23+Y4tqkyP+YeTSxKdJEK7T7WtyoSzrOCSKCZayDbO9Vjt/Rn7Yt6Qjbu2XwaAVqo690P5+qNX4toKF5owlnoRrpYSxpHeCi2gaDpNsJ8S8Bz2NCCEyEqODStbh1qIviLAS4qWZ5ZeEiIrq9SXmtCdwGnGV7ZMT5EeTQRPvV6YxOjXyMcKgGT/LySlpe0rttH5BEYzeiPuY4SdXESuaDYYJl40ArfTff3Xd83sC1wc/jxBWSvgXsaftvA9cOAbI8/5rieNjncaJXH3U7kc7VGp6bOFZ/D/bXAv010im1sACSbiXS63oR3LNt3z36X40F88p7tb7SA5gNLAEsWV5/IiLdKZD0feL3+A1wru37ssbupzHF8bDP48QuhHovROZav6H7b0BWZk4rPYy/BqyiEInt3SNnecwCqEOwXd/xZweuDX6ekciu55d0EHE//MYJ7V6ngkf0gs9GcUpdWpxDd5TXtkQte4qxDazWd/wSoF+E9QlJHKY1OmM7AQoF3Z0IEZUnE7UqKSgP8huLWMkvCbGSnRqL3GUZuqtLupjYiK1ajimfU5TIYeo0OkmLEm1zsnAZUbN9gaRdB+6JzM1qK06QvWzfMeyCpPVtZzpCamOUIZOJlYGNCY//+4H1SlSit2HLUtY9Dljc9s39J8vcfmdWzeGQGr8zgc9XEBf6GvGb7AesXYSFeobVmbb/nMChlXmjCaO/lbpK2+srWrFtSNwjbwe+JelG4pl9axKPoWVQpYRuFyClTKpxCNgMeBXRtm7cuBrYATggArpRAkPMGxf1si/HjbLXejPwNMJJ+fUaGhiS3s5ERPsfTHwf3yDReUrXfnXs6IztMUHS4kSvzbnA04ETgJVtPzWZR79YyRqEWMlcSXMhVazkJIY/tAIen8GBRvpb90PSAkT/4jnAloTiclYK4D9sf1DSz4D/LCUHnygLXuYEu7akvxL3wuxyTPm8aCKPUyVtOWi4SNoS+Dqw/LgJSJoqm0DAQuMevw+zFD3oZ/UdP9ifPouEo4XUKeWFosf264C9CEGuLGP7YKIF2aAg22bEc/uWJB4rELWOVwH/QzjLbk8a+0GUrKgfwYNz2LrAC4EDCAdJxj3y1OJEVt8x5fNTEsbvoQmjX9IBwNW2vzJw/k3E3iOtVV7JPvmlQij2bEJleVci0p+OmgGPlmH7sOTxDgUOBZD0ZCYMzb2AZYlsnQwcSRi3vwa2JvbG70gaux8rEfu9d9r+3/4Linaf5ybxeIykdYn1fnY57rVfnZ3EYVqjq9keEyTdA5wD7AucYduSrskQjhng0YpYSRNiXMMgaTNgju09E8d8AeGIeSlxn2wKrJKZIjtQ87gU0Sd3BcLLfcJME5CR9AaijdFLehHM4pTaj+gdfPGof/8IcTh91HUntUHT6J70aWl4AxuyDcrp84k0/7Ocpzh9vu31priW1sqxjFe9xq/wWKaPw8aEY+zCwmPs60pDa9v9TPQ6nw305nABi9pOcZKVtO31B2ulJc0CLnaSGGqZMzcB1gHuJQyGs4n74sYMDoXHsIDHztkBjxbQSj1/4SKiu8cmxL5nDeBm4v5Iyc6QdIntZ5fjBYFzWtjvSFqDcAjNITQ4UroWtbLvmM7oItvjw/uJVKUvAsdIOq4Sj4toQKxklDFdvptUY7t47uYS3u5reWjEapxj3wBcTxi3+9i+U9K1FWpRHzSkbN8OzCkb2DOo7M0sEcwdCCfINhlj2v6qpL8Dp5Vo9s5EqtmLbM9L4tDEomZ7pdocCm4gsnEOBN5XqTYY4DEjrs1KY0EbNX6Srirjfg/4GZERM6j7MFa0Uo5jOy3TYz5YZNg6b/uBYuBk4SvAH4AvA/9l+8rEsftxEw8NeOxQiUttNFHPL+nnRPT6QsJhur/tK0b/q7HgH70D2//MfTwmQ9JKTBjY/yBKPdfP2nPA6H2HpMyMummLztgeE2wfBBwkaRXC6P4+8GRJ7wVOTFyAWhErGYUU4SdJT2diUruFqMNUBQPnu8D2hDF3v6QfUKcu5quDJ2wfKenXwHuyyRSF5W0IJ8hWxEb+y5kcbH+rGNy/Ixwim9m+JWt8SUsAy9m+qnzeiQnHx8+SamGn4rYq8dvskhjJ3ZSYH3YA3lUi7meV13nua/c0ZtwkaUPb5/SfLKl+N0/xbx5xNFTj9w0imr0jEaVaU9JZwO9s35/IA6hejjOMT7qzELhH0mq9uaOPy2rkCvstBaxN3KMfkfQM4H8pz63t05J4tBLwqI5W6vmBawiBzdUIYbbxLHYfAAAgAElEQVRbJN2cucYW9ErXYHL5WmoLsjJnLkF0pNnR9lUl8DIvY/wRvARsTqz32wIpHXKmM7o08kQoWoHNIVKZnpY4br9YSS8dM1WsZBSU1/rrAWITtoftq/8/9s493v6xTP/vS1Tf5BBFziFCjuUUqknTWZHG4SuhMtWkiaKUmo4zOqAUUo35OTQJk3MHqVQSkZwPDQZJklKknIrr98f9LHvtZa31Zea77uex9+d6vb6v9Tns7bns9Tk8z33f13WXY+ml/WVcERrHuUQp+WLAm4FvZWeIaqNkkXuT5B8QQZBDsrOrki5nyt9gJWIR1SsPte2Ju3AXA6xzXVrySboO+Dax4P6b7bdNmsMAn2WJoNBOxKLqE4TEIHNh18/nGUTWck9gedspmn5JGxP68KOIMnaI1lu7EMGH85N4fIYpE7KHafxsZ2n8+sddnakWZFsAv7c9VjY0H8euLsfp4zIsWHiS7dOTxn8F0UXiX5l+jb6fMH/8VgaPIbyWJirI9iK046mVAH0Jj7nEIu/D5CY8qqMlPX8Zd1GmjC83JRyvr7A9Vhoy06Do6vAc4DTgWNvn1pqTFj6bEs+vbYAlCFndaYM+Nh0ePbrF9ixCibZvypRZyQJZN/U8jJ++YXuZBA7bEC/dzQmzo+OAI7L0p2N4LcRUVuZltp+aNO6fBg/1bWdGd3tBkN1s31CO1fA3WGnc+Qx9sKSLgef0ykE1vTf8Oba3mDSHMtZbiOtxOWKReQJwao17RdIaTAUKNycyZz8lgoVpLXzKouHthEYaws3/UNu3ZXEYwqmKxq9v/FWY+l42I8ynzre9VcLY/XKcU/rkOKnXaCvBwsJlbaK1ZP81ekBmcEzSukzdr5sBjyeqL84j7tlqXR1qJTxqoxU9f9+4TyCSPr3nxqZEz+t1ksbfsldhIWnl3ryj7G9rO1NWuBjhLdALBi1OzAMvGPuL85fD/kRA7Cbga4SB4IW158YzCd1ie0JQtKcx8Dvbm1Tk0YpZSTMGDCXosDXxcNsSOIaIdJ+ZxWEUJM2xnVLyV6KqTyf06sfZvilj3CE81ieCINsRZWbHAR+yPXbxOwEeexGTwotcoQ1I4XB5/4RD0tq2ryjbV2RNiiTdT0yO9+5NjisFQH5P9JA+j6mM7nWZHEZBYRB2+zCd7ITHfQaVNX6STgY2Af5EX/ueTP2lomfvNsAVwLHAqcDlFa7RJoKFrUDSRYTvR29xfVPfuYVs/3XkL3eYCMa9O5Ro8Cjps8R8dDVCqtV7rp/n8I1Jgaabwz60PWw/E5KWArYnnu0r2p54B5Qy7m3ANcDBwOm275vNz7BJoFtsz3BIuos2zEpGouYLWNHSaDsi0v3ipDF/QARi/mD7HzLGHMOlF1XdkXATPp5YeP+hEp/NiBfN6whzv5Ntfzlp7AOJicAahP71J5SFRNbfQ9KlRFT71oHjywHfzihlL+MtyVSrnKcTme3dsl7+fTwW80Dv8xp62FJe90nC9fvjwFeApxLmaLvYPiOJR7/G77g+jV92Nvc1xH3x+4HjKxBl9Qck8agux2koWLga8AHiGv0M4cnxfOB/gN1rSAwKr2n6T9sp+s9WEh4tQNGCbScP1/N/LasiRuE58RPgEvd5O6iYGjrJKX2gYuyh7WH7tSBppYxqujLW44CXEM/RFxMVOn8PrFAr8TDT0C22ZzjKTdQzK9kMqGVWMsirygu4BfSVKz9g++aqZApKOdmORD/h/W1/pgE+f09M3N+UPPbjCa1jT4f6PKJEd62EsXcm9Mh7E5F/CE3XgcDnbX9l0hyGcFqe0G3PBRYmAiD7JXOorYe9ENiPWMx9GXiF7Z+WEvevZU3OWtP4FU79PYyXI76XfSrwqCLHGeBQM1h4DlGltSjwLkIjfTqx4P7X7AVnp/9sBy3q+TXE1DAr+dBKZlvSkURA6E7b78oYc14oJf5bEd/L84Hv296pLqvHPrrF9ixDA2Yl1V7AXaT74eibHD6fKP073vaPkzn0zPEesP3rzLGHoWT7n8eUE/biRHnqG5PGfzmxsOuV9l0BfNL2tzPGHweFGdaOtj+WNF4TelhJl9hev2xfbXvNvnOpmZBGNH5N9zDOlOOMGD89WDhwjV7Xr0nuP5fAo9N/NogW9PyFR3VTQ0XbxLMBEXOfs3uniA4kT0ni0TOSvN/2eRljPhoojOy2sX1MbS6PdXSL7RmOVsxKuhdwe1C0UbqDKHs8C5hWLmT7oiQePT3/7TXL6hVO4M8mepGeT5hw/XQ2ZmIkvaBs3m/7pxV5NKGHbSUTMoRXLY3fPTy8h3Hq99KKHKeVYGEr12in/+wwCmrH1HBstwTbP8ri0gIk7VI278kq5Z9t6Ppsz3wcRWQsvw18cNCsJJHH7sQL+HCmXsBdpKcubiQmqy8jMofT3MiJMv+Jw/l9zkdhReAJwLXAr4GbiWBEKkrJ3/uYymxfCXwqudSvl8W/gwg61MJzCHnD9yT19LCp1TgFvb6s/T1ZKfsp7ceGweGEfihwqObhpj+f0UIP493KZ3pf7wEcXT5vB2p6cKwh6TLimly1bFP2Mxe6yzCl/zy4BEXmSFqw03/WQUN6/q8TVY07AA9IOpWYa6Ri1GK65zkBzKrFNtALdtxVlcUMRpfZToak7xEOsofZ/kaF8WuZlXQGDI8h1DStq4lyfzybqUqQtYkJynm2P5ww/j8CbwXeC/SqTjYkzLmOyNJ/toiaethW0JrGT10P42Ywr2BLltlSPzr9ZxtoSc/fgqnhAJ9+z4lliedXuudEh5mNbrGdDEnLEpHfTW0fljhuM2Yl3Qt4OCQdDdxNBGKuqDD+rDWtG0QxBev1AN0KWNL24gnjXkVoxv4wcHxJolx3zeG/OXtQ0zxvgEcNV/RmNX6apT2MW4ekLYhrdI/KPJrQf9ZOeNRAK3r+IbyqmBq27jnRYeahW2zPcLSulW7lBdwCJG1ElDJvbHvfxHGbCcTUhKItSS+j/Vem+gefSxikPZjA4epRC+px52YqWtHD9lDbFb1Dh0cCSRsQ1+h2wA3ENXpI0thN6z9rJTxqohU9/zhkmhq24DkxDmXefidRzXZ7bT4d/u/oFtsTQkPmLU2YlbT8ApZ0ddk8zPahVckk4jEQiFmGuH/uSxrvM5Te2rZ/M3Buowxdm6TzgbfYvnTg+HrAv9veeNIcWkJD5nlNuKK3AnWdHZqDolPA3PLv98Q1uo/z+3335DZ3uXILyQ4BSXcD11H0/GWbsr+K7YWTeLQyL96LkL8sTMx9jge+29Biexvie1rP9i7z+vkO7aNbbE8IaqSXcita6dZfwKVMd1Pb30wYqwndZSuBmFEo5X6rAifW0FBJWoupyesdtjdMGHML4KvAkUzvh7orsLPtcybNYRwkbQjcYvuWmjyy0Yoreod5o7Ycp49HdrCwd42+2fZ15disvUZbWdi1gFb0/K3Mi3voPCfGQ9LWwK22z6/N5bGObrE9YUhanLiJAa6xfWdFLp1WugG0ortsJRAzDkVHvpbtK5PGewZTC+y/AisBG9q+MWP8wuHpwNuZciO/ilg43JrFYRTKQmZd4lm2Q20+WZC0PjEp2w7ouaJ/KDtr2GHeqCXHGcIjNVhYsmE7El4TZxDX6BGtVCplo7WFXYtoRc/fAmp4TpQklIE/N5qE2h9YB1jQ9itq83kso1tsTwhlYfslQgd7A1GusxJRqvs22/dXpDcrtdKPhdJHSZvb/kmFcWd9IEbSeYRb63HAcbavVYUeoIXLE4HeC/862/dmcxgHSYvYnpVtQlp0Re80fm0iO1hYxlwY2Jq4RrckXKhPtn1mFoeWUQwW59r+am0uNVBTz99hOiTtWjbvsX1CVTIdJopusT0hSPo40dvybb1JaXFAPAz4pe1/SeLRrFZ6tqJklLcHlgPOsH2FpK2A/YA5tjeozC81ENNKEETSKURf59OAY22fm12GKWlBYH+iz/VNRJBuBaKs/ANOasfWmjFZi2jFFb1wmXUav1bkOC1D0lOIRdUOtl9cm08myntsD+I9exrwXeAdwN7Apba3rkgvFa3o+VtBK3OOwuVpRCLuOtt3VOLwgrJ5v+2f1uAw09EtticESVcQZWx3Dxx/MvBT22sn8WhaK90KJN1ke8V5/+R8GesoYgF1AbAJcAuhy32f7VMyOBQeXSBmAJIWI1qC9DRcixMtSS5IGv+zwCLAu/qCdIsCBxLf055JPJowJhuFCnrYLvjQGBqS4zQzcW8Z2fpPSacCfwTOI2RSSxHByz1tX5LBoRV0ev42IWl3Irj+P8DKhDnqaRV4HFk27+gCl5NBt9ieECRdZnvdEecut71ONqcW0YoBg6Rf2V4haawrgHVtP1jKhW8FVs0u/2wlENO3kBkK2zdlcemHpKWICoS5wIoZ14eka4HVPfBgLtUQv7C92vDfnF2ooIdtIvjwGND4NdHDWNLxs8lTANpf9GfrP/vnWeX5+RviOd6UJCcDrev5WzE1zEaZC77I9u+KWdtXbT+vNq9+SHqd7RNr83isY8HaBGYwXEq4NOTcxPv1PoawCbCOpNoGDJlRp/tdejbbvrdEmNN1lrY/mj3mCHyT+Pv33ysGnkZkIx5Xg5Tt24BDgUPn5eY6f4d9eATU9gOS0q5RSe+1/emyvV1/5YOk/W3vl8VlGGz/fU8PmzTeizLGeQS4sXym9KP9X2AXSg/jyjzSJqySdrb9n2V7mueGpHc4qZ1kKwunUajwzHhIclOenzfPxoU2QKmYO6VPz78XsJSkw2lDz38oYWr4BqCaqWEF3G/7dwC2ry/eOa3hs0C32P4/ostsTwiSbiQW1cMW2+7Kd/Ih6d2jThF62CWSePR6XvbG7vW9FHFtDK2ImC0ojuD7EnrYz2eZt7SgAS268ZMG9fKSdga2t/2aJB4X2X7O4Paw/Q65aEHj1zqSZUHdvdKHVvSfkh4A/tLbBeYQ2dPee3bRWtxawGzW87cCRfvV4/oO7di/b/ud6aQGkFn1OZPRZbYnBNvPqM2hJTTyAl5kzLnPpbGANRPHesxA0mrAB4hqh4OAd2YZghUcVT5rdgrYAzhJ0puY3md7DvDaRB4asT1sf3IkojQW4LYWS2OzMajxk1RL41e9h7GkUYtYAQtlUhmxPWx/NuCN5fMOoNpi23aViqjHCmz/Efhy+ZeCFgLajeE9A/s/H/pTddFlZOcDusX2hDBmIgCA7YuyuAxDBa109RdwQ2XTCwFLe6DFl6TNCf32rIKiv+UHiL7SnyZMXB7I5mH7R9ljDuHwa2ATSVsy1Wf7W7a/n01lxPaw/cmRaLw0tgL2Ap7dr/EjnJazsVv5TL9P+3DQmHO/SGPRyL3SCmy/cd4/NXko+q0/1fa3B46/ggjetbiwmQga0vMfVT6rtr4dhWzPCdtHj+GStj6TdDnDn1UCls7iMZPRlZFPCMX98QqizQIM6FFtb5nPagrZZiUtQNKzCSOy08r+Z4HFyulDswIgkr4BvN/25QPH1wH2t/3qDB6jUME19gHgV4R2+2GT9xZKqWpB0nJMadZvsf23pHF7JZj95ZeU/SfaTskcFpO6/Yie45cDn7D9p4yxHwkquKI3U6ZcTJeeCVxu+zs1OIyCpIWyqmL6ZEH9kiDK/iq2F87g0QokbUJkS1cl7tk3276qAo+zgDfa/uXA8ZWAI2vPwWYrJK1PPDeutH11bT79kLQsxXPC9mEJ451je4uy/RXbb+g7l/Zsn5cnzeA91OHRo1tsTwiS9gL+AbiT0GCcbPvPdVnVwxi9NAAZzrqSTicm6+eW/auAfwGeBLzO9jaT5lDG/ZntjUacq+5UX8E1djfGZIDGRX9nGiS9H1jI9sfK/i+JZ8jjgaNtf6Imv2xIOoMorTsb2ApYxPZuVUn1oYIrehMaP0lfICovziXaKp1u++MZY4/hJGBLYCdgK9spGZnWJ6qSeguawzLM2iRdCLyfuGdfA+xu+2WTHncIj3Hv2ZHdYjpMDpI+BOxMPNM3IeZj/16XVT1Iutj2BmV7MJD60LlakLQFMNf2HjV5zAR0ZeQTgu2DgYNLqd+OwPfLxHl/J/Z4bEQrDdEn+BLg28B91NGyLdNbaBf8yaWlgaS3JvJYfMy5OWksRqCCa+wJHuhH34Ok2VZGvB3w/L79P9jeoLSu+RGQstiWtKXts8r2yrZv6Du3re2TMngQ9+wHyvZ3JFWV3wzCya7otKPxewGwXnF5fhLRw7fKYlvSpsQCextgCcL3YOKBjz7Msf2LwuUJ/VUOhVvVxbbtNSUtSZ5D/AK2v1u2/6sEEGvgKWPOPSmNRYd+7ACsb/vuck2eAaQvtlvwnCgYl+2skgmVtAHxPN0OuAHIetfPaHSL7Qmj2PmfSiyi3gCsTiw6s1BdK12wAdGv+FXEBPFrwPedW1oxzSDNdv/kY6lEHhdK+sfBiG4xP0qbPDcUiLmzZNM/6tISrQ8nAlXdfAu3O4m+pBNv0Wb7L327nyvHHpCUGYg5kKm/++B38EESX8Ca3kLxcf37tv+QxWMUyjPsyqSxmtD4Ec+MBwDKxDk9eFruy+2Am4j3yUeBCytUwhzL1P1xHtPvlS9Q+fkFUJ5b30wabnFJ247aTwzUfU/SvwEf7M0zynX6UeCsJA4dpuO+XmDd9u2SFqjEY7fyWdNzAuLeeC2wANPvEzElcZw4JK1OzM/nEtLX44nK51ZaXj7m0ZWRTwh9Ge2tCT3qccA3bbfaHzUNkjYjbuq/B/bNctMt0cz3DWqRS/bhk7b/LonH0sDJhElIv+P044HX2k4xSSvOoAB31HQGlfTfwM+IctydBrKoLZRSbUNwW8/2LhMe6xrCAOuvA8efAFxhe7VJjt83Xn9527TvIPM7USMtFFsxGGpI41e9fWEpqb8GOJgoY79P0vVZ10Qfj1bulVau0aMYnZWz7Tcl8VgYOALYmKkEx3rAhURp+6yV9dWCpDsIeQHEs+L5ffs4qbXlKJTF/1zbX00a78hx551kNlg8pn5M+CtcV46lP0tnMrrM9uRwHXAZcCrwJ2BF4J96CYAMjXIPCrfn9zDlbnwlcJDty7I49HF5GpHlXge4Gbgtcfh9gePLZKBXjvpcYFeivCkFtn8LbCbpRcDa5fA3e2W7iTyacI0F/mJ7Z0Uv6bMlfcBTfaarRwNtn5I43NeBL0l6Ry8DUCaNh5ZzWWjCYdmNtFD0GFd0SU9xtNHJQL/Z1rMHzmVml1toX7gM8BIicHtwCabOkbSgk4wEC1q5V8Zdo4+3neIA3YqnQqkQmlsSHw/NfWxfX5FWU8jW8xPJp34cmDDmwyBpUUJushzRzeG7wDuAvYFLiS4PE8e4OVhJymRhWyI5+IPik3IcdaSeMxbdYnty+BhTL9on1yKhcJY+kNB69lqlbAicKGkf26cm8XgTsD3wRGLRsL3tzIU2ti8oWew9mCojupJwnvxtFg9JS5TNS8u/acczS2NbCsTY/k9J5wBfkfRKIFNHj6QPE/fsnzODYQP4F+DfgJuKxwNEoO4/yrksrCLpNIqjctmm7Kfp6CXtbPs/y/bm7muXVwISGRNEJB1he/chx1cgfCjWfvhvTQStaPyq65RLGfsZwBml8mMrQq71a0nft73TpDkULC/p88S90dum7C+XxAFJH3IxVhw4vhgR9P+7JB4H296rbO9p+3N9547KWoxrevvVX5fPxXvHXbn9aguooOe/2CO6SUhaMYkDwFeAPxKyj92JjhcCtsn0VBqEpMWB1xGa6TWBZTPGLQmFU0pgf2uixeRSkg4nzJ3PzOAxk9GVkc9wSLoU2Nr2jQPHnwGcanu9JB69Vmi9Sdi0Cy+zfEjSE4nWEwDX2b43a+wy/oNEVr+XfRlsC5dVGtsfiLmwHN6QcJLNDMQMll4uQCwsdyUm9csk8di1bN5j+4SMMcdwmcP0azRVfiLphePOO6kneX9p9GCZdHLZ9NFEC7Zder4CktYkdLAfs31UEo/riezLAsABTBmBCfi07VWTeDTxvYzgtggxaf5K0ni7jjufpSGXdCbwM08ZCvayY98BThq2EJ8QjyauDTXefnU2YuDa+L7tFw87l8Djoa4vCvPR3wArZs8Fy/hziAXuTkTV5yKE2ePZfriHTSavpxCeGDv0f08d/nfoMtsTQlnQ7UBEz04nsocvAP4H+Ljt34/59fmJBQcX2gC2b5SU0iu3oLrRgsJAaH/CNO4m4uW7QtHNfGBQJztBfJ74e/yEMPY5x3WiXh8DXjJwfVym6E96avmXgWnGPeUF81FJ3wE+ksQB20cXmcNKkha3fUfW2D2oEdO6UYvpksndkXBGz4BGbA/bnyR2A75EyFB2JNrWHA/8k+1vJPL4EdFOqbf96r5zZz/8xyeGVr6X6QNLqxKT1h2J7NXEMWoxXeYArx52bkJ4DfB1SZ+x/W5JqxFVFwfa/mIij3HXRibeTbRfvYdZ3n61FT0/06+HJcacmzQemus5zEdvrrTQPpbQrZ8JHEIY911n+4fZXAZh+4+Svs347jkdHiG6xfbkcAxxQy9MZCKuIHSXWwBHESVvGfibpBVt39R/UNEbNE3X1sjk/QAiariK7bvK+IsS2d0DgT0TOGB7L4V4/+8Ih/pDSlbicPeZgyWgiUCM7Q+OOP5T4OVZPBRu8PsTAbGVJb3FSeZ9fWile8BDKAGI7Qht7LKEuV8WWtHDGnhLKRH+IbASsF12QKQhjV8T3wuApGWJwPZOhBfIJ4h3SjpKluxlxL3yUsJ06L8yxrZ9r8LZ+HhJXwM2A/aynXm/AixQsmIL9G0/1E0gi4Qbab/aAlrR89POc2M9Sb1ydhFeD39iyuBx0SQeaxEJuauBq8vCv2q5ceX3/YxFt9ieHNayvXbJpt5su1eWeUYp7c7Ch4kWGPsz3fn6fYRhWDoq3sxbAav3Z5Ft/0nSPwG/IGmxXcY1YUZxMTER+DhwLbk9J5sIxEgapQ9PczYu2ItwAv9dmaB9lTBPScO4xVQmSinutsQCZnWi1dfKtpdPprJGuT4ErNp3rQhIc0qVdAgxGez11L4I2EnSTgC235nFZYBXFY0fDeiUJb2FeIcsB5wAvJmQRn00Y/wBLi8kvoNXAhcAmxP3y92JHN5dNs8H3kss9FfuHU/0oViMmGv0FthVtdGu3361OlrR8xM64HcT18ZSfdesgKclccB2WtBnHGyvL2kN4jn2PUm/BxaRtHSyj1Ar7/sZi26xPTncD2D7b5JuGTiX1tvP9imlhGhv4J/L4asIg7K0RX8jN7OHlWtnRxP7TCh2IF4wJwHPHVz0JqCVQMyDxELmWEJyUas93v22fwcPTdCeUIlHC7iNWDR8kCJzKFmzbLTgeg1TngaD2+kYp/FLpPGevu3Bv0fW3+dQwuBoJ9sXAtTICkm6mZAlHU54Xdwl6YbMhXbBIn3bnx9yLAVupIOAhrdf3T/b/6IRbCHp30bp+RN5/DtT12T/NkSbthRI2gh4qu1vDxx/BXCb7Z8P/835D4fR5IeBD0t6LrHw/lkpbd8siUYr7/sZi84gbUJQ9ADt2efvULYp+9vbziz5qw5J9/Dwmzm1j5+kUwijmGMGju9MfCcpJm2S/kJksY8rn4NmcWkvP0nrEYGYnhv5VYTGL7P6gr7o7qsLh2OBM53Ywqfvnu1hx/79WtnLGpC0F/H/vzDhK3A88N3M+3UcJG1B9EPdI2m89YFLK3kr9PPo1/gdx5TGL80hfhx6OmXbEy+dVrgo9yqknk5kt3ezvcKkxx7gcTAR7LiCeG6dClyefa8otwXdo0K/lt72YMu6SY35INPbrw6+Z2t1nEhHuS+/DlxTWc8/EpI2sv2zpLHOAt5o+5cDx1cCjnRl87wiM3y+7ZQAauvv+5mAbrE9Iagdh9KxZbCJC8zqN7Ok5Ygo7j1Mz+TOAV5r+9ejfnc+8ziK0fok235TBo9WIWkH4DDgU7YPSBy3iXu2JfRlh+YCqxER+JNtX1OBywbEhH074AYicHZI0tgXEmXrPwfOJcwNz+t5P2RB0iWEFvYY4DjbN2cHLYdwephO2fY/JHNYnghqzyXeMSfb3i9x/J4Hx1yilHwxoqz9W1mmXCVY+Hvi2jwX+EmN+7SPzzAt/Um2L08a/yOMf8+muLO3guLDcjxwH/X0/IOc1iLumbnAHbY3TBr3Z7Y3GnHusizpmtpoN9rPp5n3/UxDt9hORmbkv4z3O6KE6muElmua46OTWvj08al+M0vakr5Mru3vZ409L2RqdVoJxBQuyxHXxWsJw5ATSHaPlbSAR7TaUCV38r7xNwRusT0oScnksDZx3+5g+5nz+vn5NObqTE3Gfk9MFvexvVLG+ANcngRsTExUNwM2Am4lFjVvT+TRqwLZgfibPAtYO1PjV3gM0ymvUqF8epDX6kQGtcpiqixqesGHl9l+auLYqzN1fW5GyJR+Slyjn07iMKilP4HQ0jdRfQG5WdQW0KeNXogpPf9DWdPMhZ6i7Wzvmf5XwmxyQw8xa50gh+tGvcPGnZsAj2bajQ6ixvt+JqNbbCegZuS/jP2SMva6RJulr9m+MmP8cah1M0t6EVOL7Stt/yBr7BF8ppkc2U4xOWolECPpR4R26wTgROD2AR5/SOJxEdHK6fyB47sD+1XOHh5N3L/X2N6hFo9slFLQHwNvtn1dOVY7k7swsCmxuNwFWKAWnz6N3/aEEWeKxm9Ap3xKn045bUGlRtrkjYOkObU0wqV0+5WE8edytuckjXs/oaXfu09LX/WeLRyqZFFbQMmgjoSTTAUlnQcsSkhgjrN9bfZzo/D4IjHP+GBPGlSqUz4KPN32WzL5dJj56BbbE0Rrkf9i+DSXaIH1UduHJo49zg3zNE+5tU+SQ6+M/F6mysifS3IZeeEy0uRoVHZ1AhyaCMRIupGpcr/+B1LPjTxlklZ0wIcR9+q+RMT9C8DNwLts35zBYxwkLZJRuqxG+rJK2oaoeNgcOIfWuMwAACAASURBVIOYpB1RYXK2E5EpXJ8ow/wZEaA6z/atmVyGoYLGr7pOWdKRZfMO2+/KGncIjx8Q98ofssvnB3j0stnPA1YAriey2j8FLnJSe6dWtPSFyzOonEVtAa3o+YtvznOILh/H2j63RiCmBEyPICqVes706xHmjrtnVtS1gFbe9zMZ3WJ7Qmgh8t/H5QnAq4gXzjOIB93/S15cngn8zA93wzyDKBeeeMmfpJOJcrajBo7vArzO9taT5lDGa87kqGYgpiUoWvV9BNgD+DORUT0zcfwVy+YDmfdn69CUg/9cYEtCs3xy1ncj6S7gv4EvEgGxKhq2ljR+LeiUW0AxVYK4Z6sF5EoVyEXAZ4l7o2o5P9TV0reSRW0BLen5S4JlW6akhIsTcosLKnBZhelVjtdnc+gwO9AttieEFiL/hccxwNrAt4gXzhWZ4/fxqO6GKem/bT/r0Z6bAI9mTI5aCMSM4JXuXlvG3RH4N0Ib/BLCzfY9iaXsPUnD7TWzZC1D0lOIzNkOtl+cNObjiMxHL3v4LOA3RLnsebbPSuLRpMavpk65Q0DS05m6PjcmWrtexNQ1WnUhka2lbyWL2gpa0PMP4bQUIYGZC6yYVf0g6Tnjztuu2hu+w8xDt9ieIFqI/Jdo91/K7rAS3UUzeBQuVd0wJV1re7UhxxcgggCZuvHqJketBGL6+NR2r/0eITH4Z9s3lOtiD+BdwCdtfzmDxxh+mwzqyTvUQanK2Q7YC1jZ9uMqU2oGNXXKHaagMPR7E8nXaEta+payqC2hlp5/HCSt5IFWXBMc60EiEfb73qG+03b91l9vJzTlJzqx/WmHyaFbbCdhtkf+1YAbpqTPAk8mFvl/KccWJsru7nWlPsoVTY6aCMSoEfdaSa8dFvwpGaMDbe+cyWcIj5tsrzjvn5w5aEVLJmldpmeFHk+UY55HZIYurMWtBlrRKXeYQllYPo+pa3QD4FqmrtGvJ/FoQks/iFpZ1BbQkJ7/SOK5cWdln4W9gH8A7iRkBqmdT+YFSXsAawArObErTIfJoVtsV8BsjPzPww3TSZrthYhs6W7AL4lF5QrA0YTbdMoLZxSyTY5aQavutT1IWoEof0zr+T2Cx69m0wSxJSic6s9hauFyU9+5hWz/tRq5CmhFpzwMaqBNXg0oukucV/79hPBImVXzjEeKzCxqC2hFz68wDYaoejivBod+aKoV7dbEnHB/25eM/60OHR49usX2hNBK5L+VzNA4KLnnpcIJvFcy/j/ZL56WTI5aQEvutX2cntbHaVligrJPLT6FU/XMdim1/ytwmO1v1ORSEyUwtiUhedjK9tKVKXUoUCNt8gqPu4l7pZZXShXvixbQSha1BbSu568JSc8mFtxvAN6b6YVRzHkhPDj+K2vcR4rufT//0C22J4SWI/8tQBV6XkpaFFja9rVlfzui7RfAd7L00q2YHLUYiKnsXrsIoe/bCVidaBO3g+3lM8YvHE5nekn/Q6eALW0vnMVlGIqufhlgU9uH1eRSA5I2Ja6PbYAlCE3/aa7cVqfT+D0cSmqTN2b8jYAVgY1t75s4blXvi1bQWha1JdTS87eCgYz2r4hS8m9mV4L0VXze1WLiZba/7+cnusV2hzSocs9LSV8GznVp/SXpOsIRfQ7wN9tvy+DR4ZGhgnvtPUSP7Q8C59h2djl73wRxKGz/KItLhylI2p+odLgJ+BpwMnBhtq/AKMxGjZ+6NnnT0Ir3RYf20IqevxWUsvrLiC5Bf2IgwN3CwlfSXrYPrs2jw/xBt9jukAI10PNS0sXAc1wuekkX296gbJ9je4ssLh2m0Ip7bTFN2ZHIqH+NcM7/bkPa8eOzymJbkcG0AkWf2muAg4HTbd/Xkq/AbIQaaZPXSrnyY8D7YlZq6VtAp+efDkkfYXgFGSR5CM0LmbKx7n0/eSxYm0CHWYPfEhH3pYn+jtcy+mE3KSzo6dGlN/RtL57MpcMU3lg+7yDcUaugRJEP7isxOwVYVtK+RDn7NbW4FTwvcazdyucDiWM+Yki6umweZvvQhCGXIfquzyWukR8AcyQtmFm2/RjQ+KXplG2/aJL//UeBo8pnVYNN4hrdDjioaHRPILp/tIJ/BtaVVFVLPxth+2mDx1rS85fKoTuBI2zfPunxbH9kDJeNJj3+I4Tm/SPzDbuVzybf9zMBC9Qm0GF2wPY2hH7s58BHil74KZI2TqTxYJmE9DhdASBpOeDBRB4d+mD7jeVfEyY2tq+3vb/tdYANiYqMb1WmlQrbvyz/pvlNSFpA0utr8erB9prAFsANSeM9YPsM27sCqxKBmJ8Av5Z0bAaHgpXLv1Zd6Q8Fvsf0QOZEIOm9fdvbDZzbf9Lj92D7R+Xfw3TBkjZP5HG77S/afiHwYiJ4+VtJV2f+PUbB9q6lkmz32lxmKyQtK+ldkn4GXEmsAXasTAtCvvU3wi09HZLWkvTxIi08vAaHIchMRs3pve8lPaH/RPEp6fB/RFdGnowWHEoLj+zM0OD46T0vJe0M7AnsDVxcDj8HOBD4vO2vTJrDOHQmRx0kPWfUKeAbtpdJ4rEoYf61HHAa8F3gHcS9c6ntrTN4tI7yd9rG9jENcJlVGj9JF9l+zuD2sP0J83gc8S5bDjjD9hWStgL2IyaxG2TwGMMv2/uiaS19dha1BXR6/oejtodQ4XAXow1Rn5RlXNfKs3Qmo1tsJ6OWQ+kILksSLoPfrMxjJSf1vJT0cmIS9GziIXcl8Enb384YfxxaMTmqHYiphRbc2ft0qEORVTor6VTgj4TG78XAUsQEYE8n9iFt4TspPJou34Z0jV91nfKA58bF/Yvawf0J8ziKqDS4ANgEuIWoiHmf7VMyOBQerXhfNKGlHwVJ2xDVKevZ3mVePz8T0IqeX420PW3BQ6gltPIsncnoNNvJcPST/hlwYgNcbgdSFtrjJmdZC+0y1hnAGVnjPRq4kdYKttfsBWJqc8lECy/acYtpSZn6y1VKGT2SjgB+Q1Sg3JvIoYnvpKDHo1orqUeATI3fUeWzpk7ZI7aH7U8SGwLr2n5Q0hOBW4FVK2RNW/G+GPcM28T2+Zl8BpEZAGkIrej5byyftc3ZWvAQGgpJCwOvBebaflXSsK08S2csusz2hNBC5L/waCUzVL3nZUNR1eazZC2gc68FSQK2JIxstrK9dNK4TZSSSdrS9llle2XbN/Sd29b2SdmcWkVyZntR238acW5F2zclcHgA+AsRZJhDyLMo+0+0nbKYaOVeeSwg+Rpt4n3fGiQtT/Rhn0t03jjZ9n51WYGkA23vkzjeYsC2xN9hNcIk92W2L8ji0Mfl8cCriPf8y4hk3Em2T08a/zYiyy/i2jiudwrYPmveMZPRLbYnhBYWl4XHQrb/Wmv8liBp17J5j+0TKvL4cNn8s+2DKvJoIhAzCsXfYF1g1rnXFlOSnYBtgCUI/fRptv+YNH5vIQPTFzMiWqMsmsSj05L1oVGN3/dtv3jYudkASXcD1/V2iRLl65i6V9atxa01SPpVhj9LGauJ933LyNbzz4NLWiBmyNjpHkJl3JeWMV8K/IBoN3qI7WdkjN/HY9dx520fncVlpqJbbM9wzLaJT4eZB0mL2G65fHe+oZj3bAfcRPT6Phm4sKFy6lR0WrI20cL30krVg6SVxp3PlEm1jpoLqtmMVvT845AZiJkHj0wPoQeBHwO79Z5fNbT046Dk9pYzFZ1me0KQdNmoU+RGuzN1fB0eISS9Ang/sFY5dCXwKdtpLaYamqyOda+dLQvtgt2Ba4j2I6fbvk9SekS0GDk+ddA4sFy3t9n+eRKVTks2D8xijd+BRDcJiLLL/qDyB4EsicFCwNK2f9J/UNH269YkDs1A0umMrr5YMpFHT8o3DLb95iwuDaAJPb+kJUadInGu2oqHEPHM2hH4nqTrifLtlOqkfkg6x/YWZfsrtvtbN17A9Gdrh/8FusX25PAgcTMfC5xOPUOIp0l696iTnZYpH5L+EXgr8F7gwnJ4Q+CTkpa3/eUkKq1MVnslSrcDLbrXfo9oDXKY7W9MeLhlgJcQpWUHF2ffORWiy59iaoLWj6uAIwkdeQZWkXQaMRHrbVP2q2f7JW0N3Jpt+jRC4/fFRApLlfeK+rYp+09L4qAR28P2J4mDicDpIP5Uzr06kcvDUMH74sD/5bn5jWHP6hWAd1FhQVMTtoc9y2vg58S8eNj9mSl3PKp81jR4xNHZ4xLgfZI2I977C0n6NqGlz5oLLty3/eyBc13Cbj6gW2xPCLbXl7QGcfMcS0xSjwXOTJ40Pw54Mo3eMJqFPS+Jl/0Wtv/Qd+yskjU8B8h6wDYxWR3nXtsIdiEWwRN3Z7f9AOGWf4akJwBbEXrpXxdt7E6T5lCwyLAIv+1fSnpqEgeA/n7egxP1zIn7KGwCrFOCIa+Y9GBDNH7HABtVmEz/O7DIkG2AI5I4tJBdh8hqXz540Pblil6+tfHPwLqSUrwvbP9o2HFJKxBZvKHnJ8DjoY4vklYhWn6+APgk8B8ZHDpMRytyqFHXaE3YPhc4V9KeRLvNHcmbC457XnYVZPMB3WJ7grD9C+DDwIcl7UBMjD4FHJBI4zctmF+MwQWEocxniUVNOiS9nciqnpgUCNHAQhuIVmxhPp2GViarPYOSPZiKql4JfMH2bzN5DEPJCN1CROUzx72PyFieKGlRwiwtC08Zc+5JaSzg4nGu14k8hqKCi+8ZhMZviz6N3+eSOWD7o9ljDkErVQ+Ljzk3J43FCNjeFcL7IntsSU8jPCjmAssSHhSZ469BVGltQMy73tbpT9uCpFWJCp0dbQ9mVWcdHC0E7wEy22wuLum1wAJle9tyXMBiiTxmLLrF9gQhaTkiOvVa4I9ERjP1ZUOjGe0e3EbPSwFbAK8HXpMw3p8krWf70mkkpPXI7ePbxGS1aBuPJUq7jimHnwucL+n1g1rICfL4ARFk+IPtKuXsGtEWriw4jxn+WxPB9yT9G/BBFxdNRSToo8BZiTx+SJE3DLpeA6eQpCVryGCoFY3fCba3L9ufsr1v37kzbb80gUYrVQ8XSvpH2//ef1DS7iQG6FrxviiL+m2JBdTqhBxpZdvLZ4zfx+O/iPfIQcTc6wFg0V5Ae1jAu0MOJC1LtJjaCVgH+ATxXJu1kLQB8ffYDriBCLRn4UdMzX1/xHTpy9mJPGYsOjfyCUHSj4jSuhOIm2ZaiXTWg17SU7LaBc2DR9fzskDSFsBXCe1rbzK2IbArsLPtc5J4vHDc+axSK0k/Bf7J9sUDx9cHvuSktmR9rsIP2L45Y8whHHpt4e6qeZ8U060jgI0JTRnAeoTHwO62/5zEo7rrdRnryLJ5x6ChTi30afxeB1xKosZv4HsZbMlW1SW+V65sO6WCTNLSRBD9fqY/zx8PvNZ2iklaCRYC3F4rWFh43ENUrH0QOMe2azgsS7qRqQqt3mcv+eCWHJ9rIVvPL+ktxDNrOWJufAJwaivl5dlQtF6bW/79nmj9tY/tsR0OMiFp6RYqDB/r6BbbE8KIBz1MuZGnPOg1vS/rQy8aoqrh8bZTqhvUQM/LUVnDSlyeDrydqbLpqwgDribcayUdn6HvK2NdZXutR3tuQly2AZ4JXG77O1njtoqidXyotN/29cnjd3225wFJC1A0flkOy619L8PKlW3vk8zhRcDaZfdKl04Psw2S9iKylAsT7QuPB77bLW7bg6SjgXWBFD2/pPuB84C9bV9YjjXT6irbQ0hTrb/ebPu6cqz630PS4kQQdydgTdvL1uQzE9CVkU8ITm5KPwq2p+m0JD2Z0Ma+lcSSdttHz/unJo5e9LR6K6myqP5QbR5j8LzEsTSsAkPRJmSBRBJfIBaW5wIfl7Sx7Y9njd8SJPUvlnolqYv3jtu+KIlKC67XSNqEMKtZFbicmBxdlTX+OFTS+D2plD0uQLjlb8BU+54UnXJD5cq9dkaXln/TjidWsa1G6JKfSVyj+wwrJ580bB9MdFJYhVh0nwIsK2lfIghyTQYPSZ8APmP7d0POTZM+zFZU0PMvQwTFDioJhxOI1nmtINtDaFviHvmBpDMIWVAV6aekOYQ0ZyfC42ARwiemKyOfD+gy2xPCwGT1YUicrAIPRar2Ih4gxwKfzYjcDXDYFdgTeFY5dDXwedspWlRJ77B9aMZY8+DRSg/2kZB0k+0UA6pSWvaPwD5A7754LmEm+P9sfymJxxXAerYfkPQk4Me2n5sxdmsoEfcriNI2mD4BsO2U1l99ZfVDkWXUJelCor3T2YS2bXfbL8sYewynh2n8sp5vfSXLQ+GEDgMNlSs/CNwM9Iy3Bu+VrCq2HxO+Dr1r9Hm2tx3/WzmQtDZxrW5v+5lJY95J9JV+u+1vDpybVVUx89Lz14Ck5Qnd9lyiCuJk5xtONoEi29qa+FtsSdzHJ9s+M2n8Y4HnA2cSC/6zgOtma3n/JNBltieHCxkzWSWpT62iTc/exEPt/wEb2L4zY+wBHrsSi/13EwsqEWY/B0iy7a8k0HgTUH2xTSM92McEhERitNn2lyXdAnyc6W7k/2r79CwehPnVA4XT3VKuNXxjeDfR8/we4uV7cpZOux/jFtOSNkqksoDt75bt/5I0rK/yxDFC46eMxW0/xo0nKevZ8X4iK/QF4GuSjk8adxCfB14E/IQomz7HdbIYi/SZtB0gKTWgPw62r5D0Mab8HzJwPfAG4FhFW829HR0eoFL2sCJ6lYW3E8/16ii+KAcRWe7VSDRIa81DyPZfiPngsZKeQgRQ9yUWvxlYizBxvhq4uiQcukzsfESX2Z4Qim7pHwj9R7XJqqS/AL8jzLgeVj6d9aApJlg72r5x4PgzgONsT7yHcUvRbE31YH81lXqwt5CdagmS7gau6+0S5WTX0UDFgaStgVttn588bq8UdGvgl8D+tjMnzIN81mJqsXmH7Q2Txr2eqLzo4cD+fdsnJfFoVeMnIoC8E7CV7aUTx+5do3OB1Yh2m2nlyoWDgL8rHDYmJsmHu7RnS+LwizJ+byH5VeL7EORX0xVOjwNeVni9hAhEpCz2eu97SU8EPk25Pm1fpsomftmQ9Dzb59XmMQwlq7stMT98VdKY1T2EWkPfnHQHIpD7LGDtzhxt/qBbbE8YtSerkj7C6J7JdlIP7hZMsCT9Dbh72Cnib7HopDkMg6IH+2HAp5zkoDsvSNoka2En6fPjztt+ZxKPsQ6gtn+ZwWMYinHLOsCCtl+RPPaziWfYG4D3Zk9OSkCut8D+K7ASsOFg4G7CHI5i/HP0TUk8tiG+i82JntvHEWY+Vcr9JG1KLOi2AZYg/EBOG/RfSOSzNmXCmFWuPDD+4sT383FgPw+0A5vw2D9k/DWaUk1XuLyQuC5eSZT6bw6sYnvY+3dSHAaN+14BfJGoRNh5li22m0k0AEh6PPAq4hp5GdGx56TkSrahkHSgk8wVJd1A3LO/c1LXlUcCSc8lnqPbAzfb3qwypcc8usV2AmpPVkdB0l7FzCRjrJ+P0r+OOzefOTQTzdbDe7CfQKXqh2FI1mzfT0guTgBuYaDEz0nmepLWsP2Lsv2EvpJDJG3quv2VUzEQJPwVsaj7pu1UyYOk84BFy/jH2b5W0g2zXUvWgMZvf6LU8SaidPpk4MLZ+L30fRc7EKZ9JwEn2L6pKrFKkHQzcV0cDpxi+64a9+yw972kpQg53Stsp/enr4VW5j6SXko8s14K/ICQwRziRgyFIXfu0zpKxc7zbXcmaf9HdIvtCaGVyeo4JC+o+kt0p50iIt4LJ3Bo5YXTRA/2cZD0K9srJI21JDFx34EwGToe+LrtOzLG7+NRvZ2RpBeUzftrLu5LufJlwKnAnxjImCXKT04hvB1OA461fW4lE6yDbe9Vtve0/bm+c0fZ3i2TzwC3nsZvB9svThrzNuAa4GDgdNv3ZX8vrWSFilTrWuIdfy0Pv1eyJAbvtf3psr2d+9pbStrfSeZTkg4mKh2uIORRpxKtFLPv2eWLLnjYudkWPL2DMa7Stl+TxKMng9mtJ7FoQQbTj8y5TytoTcM+E9EttieEViar45C8oKpeoitpP9v7T3qcR8DjRhrowT4OtaK7CofSHQmDrn2dY5zXG/uhYMxgYCYrUCPpyLJ5h+13TXq8MTw+QgPyk8JlMULT19PkLg68zPYFiRyqB2JaQtHivoT4Tl5MZKn+HljBib4TLaAhiUEz1+iAhv2VwGLAm4FvZVVvSRrrxJ4VBGkBkq4Fdh913vaPknisT7zftyMM7I4DPmR77PxwAjyWGHUKuNTJ7QNro9OwTx6dG/nk8DGmXsBPrklkDNIiLbZ/WfSGzySi3N/JGrsPC0ka1dvaTuqp3ErJlKTTGX4NCFgymQ4Kd/Sekc63gZ8nU/CI7WH7kyFgvzFjnHnB9kdGnVOuCziO7glHAkeWMtDtgc9KWjExA6ER26loJZvrcO0/AzhD0hOArYj+2r+W9H3bO9Xilo1xVQ2S0oziGH+Npl6zjizOD4j+wQsxZZL2BeCpSTS+Trif9zxyBjvCzJrFNpGxTFlQj4PDr+gS4H2SNiOuiYUkfZuQwXw5icrPiWtg2H1xfxKHZpAl1ZvN6DLbMxyS7mL0gmqO7ZSAi6QvEG2dziUyIadnLW77OOw95PCTiIjvkrZTgiJqpOdlMbEZicRo98cIs5SriUj3GTWyY6U09jji3tihbFP2t3eCw3Kr5cqq5AI+DpJWyqiIKWNdSmTqFiB6kP4dUxO1H9heL4NH65C0KLCN7WNqc6mFYpL2OsL8aU3byyaN20xmexQkzcmS0mnKTPCZRIXh11wc/GcbJJ3kRnquD0LSAkRVzI5ZVSAdOmSjW2xPCGrEYbkVSLoCWM/Rv+9JwI+dYIo2hs8iwJ5EadsJwEG2b0sau9dy63YntUF5NJC0AvHiS3FGL5KLG5hyiu89lFJbbvWVUg1FRvS3pQmz2nABP5K4Hu6sXFZ/I/AgIzKEnmXGYJJ2KZv39GuDZyskzSH8WXYCNiA8ObYBzrb9YBKHB4C/UALpTD1PBTzRdkr/8/J+M/CHFt5vAwZ2SwIfaCHLm4lWSuobSjQ8jkg2/bnsbwo8vpy+2PbD2uRmQtLVZfMw24fW5NJh/qArI58c3sYYh+VZiPtL6SG27y6arnQUrc67gdcDRwPPcXKbGjfYv1rS0wgd1VxgWcJdOAtNLFRGLaYVfVpfnUSjlXLlfhfw13nKBfzGZCpHlc+qpX2tSD8aQu+erTopbQGSjgWeT/TWPoSofLjO9g8zebgdd+3dyucDNUn04V7gTsI7ZyXgiXXpVEErJfW9d+ztQM1AzKeA24j+6xAdFa4gro2LgH0r8QLA9prFOHbTmjw6zD90i+3JYRnacFhuQuMHrCHpsh4tYNWyn5a9lHQAYbT0ZWCdLKOWVlGy+9sS2ZjViRfuytnmIFmlwI8GJfLd0xm+lHBQzcjgLVDcpRfo2+5NjDIn078FlgOWJtoZPcxlOQMtZ6AkrUrcOzvafnZtPpmw/dHaHEZB0veISozDbH8jYci1iPaNVwNXl+qtJkoGS1b3tcBc26/KGLOV57mkLYky8o2B7wGfs31hXVbVsC3xt1iXiiX1DSUaXgz0e4/cYfvVJQn040qcpsH27cA3a3KQ9HYiMHLibDO+nN/oysgTUNNhuRU04kb+IHAfEfwY5gK+6KQ5tARJ9wAXAB8EzrHtGm04GgoI9XTsOxEOuhcAmxOt6e4e+4vzb/wbaaRcuQUX8NYgaVkigLoTsA7wCeAk25dXJdbhIZTvaBlgU9uHJY25BnGf7AD8HngWsLbt32aMP8Dl8YQHxk5EwPBE4ho9PZtLTfR1hDmHeL8MdoSZVVI+6Erqe5B0ab/PhqSX2j6zbF9ie/0kHs3MfYZB0h7AGsBKTmoPN1PRLbYnjAGH5Z8T2uCr6rJqB5K2IKLue9TmMtsgaS8iCLQwUUZ1PPDd7MV2K5B0M3ATcDhwiu27Sul0E2XuNdHnAj4XyHQBbwKS3kL8vy9HSINOAE5t5droNH7tQNJziWtle+Bm25sljftSpipxfkA8zw+ZrRKIFjw4WkOp2Ho58d5fh0j+1OgMUxXlebnxoDa7BJjPt71GHWYdZiq6xfaE0IrDcouQtAERdd+OMMY6yfYhlTlV6Ss9wGEZwlTmvuRxVyFevr0M5oeJNhzXZPKoDUkHE6ZGVwDHEuV2l7cQfJC0OvAe2//YAJc0F/BWIOl+4Dxg714pao0qkHHoafxsVy09zEJrRlyDKCWpz7d9dtJ4DxIlsLvZvqEca+oabQE9D47ZZOw3pKT+uFlcUo+kdxMO6G+zfVM5thIRaD/L9oFJPLa0fVbZXrl335b9bRON6zrDywmjW2xPCK04LLeCsljoORv/noi672N7bHl5FiT9qna2rmgNVyX0MftU4rA2pRzS9jNrcKiJMkH+O+Jv8EpgMcKx/lsZGn9J6wIHEiZ1pwCHAYcCmxBVMZ+dNIfCowkX8FGQtD9henRE0bZNerwlmTIQfDqR2d6t9jOjNUjaGrjV9vkJY/XeHQ/YvnnS443h8WHiXvmz7c9U5LE+saDaDrieCPJ/qKF37NHEfOgw21ckj/0wD44WAzSTQusl9TUSDZLeBuxHVPaJMHv8pO3DEzk00X2kPMMA7qr5DJvJ6BbbE0ILGuWW0Bd1f3PPmKOlqHsLme3CQ8Batq+szWW2Q9JCTE3QXmb7qQljnk9E188jyv32IxxcP2T73kmP38ej14P9ftvnZY37SKHoobsq0U5wl3n9/Hwee3lC8ziXmKidbHu/pLFb1/jtT5SnLmj7FbX5ZKCvXPke2ydUJVMgaTPi+nwdcClxjX65MqeNgBWJ8t0Ut+faHhytoPWS+pqJhmIUy2BJedLYF9veYHB72H4tSFrY9l9q83iso1tsz1Jka/zK5HhH4mV3BhF1PyLZ8Ondo04RRiFLZHFpAa1P3FuCpDm270kY3fuHdAAAIABJREFUZ5o5S0sBqQ4PR6nY2dH2x2pzmW3oe349dKhv37ZXzWfVFiQtQJTL7mj7TbX5ZKLz4Jg3Wiqpz040lCq+9wC9ThJXAgdmml22ktku4y1HGEteZvv+4tOyF1HBtWwWj5mKrvXXhND6QsbJffxsnwKc0ueGuRewlKTDiaj7mQk0Fhlz7nMJ4wPtXButTzqU3MJnnAY0Y6Fd8MTiadBzI7+vf9/2RUk8mkBDJbovKJv32/5p73jxNUhbaDek8Rv690jGhgP7CxCmZPsAF+fTqQtJvcqsB2z/GsD2g0T/74z3a49HKxKUrxMeHDsAD0g6lQrtC1vDsJJ6ctpajoUj85e10N6akGt9AjioHN4QOEnSPrZPzeABrCLpNOL93tum7GcmovYCPgBcBzxB0heIXuTHAM/N4jGT0WW2O1SDoofwdoQ++MW1+bQAScvavqU2jxag5BY+LWhAy4J/FGx7yzQyDaCVEt2ygIDox1ptAdFKJqSVv0fhsgDwBiJLdQmwv2dhx4++Z8ftNfXILUlQantwtIQWSupbSTRIuhTY2vaNA8efQXSZWG/Ir02CxwvHnXdSWzZJVwFb2P5DCdpdA2xu++cZ488GdIvtGY5WHm4tQNKHymbVLNk4tKId79AeJC1k+6+1ebQCSSu6OMnOFjwWNH5ZKJ4KbwLeRRg/fbLnB9KhHiQtavtPI85Vu2dreHC0gq6kfjokXWn72SPOXWV7rSQeTdwrQwK3l2YFHGYLujLyCeGRZBkyMhGtPEwbWfT3TOmySoL/N9C8f2RmYVz59mxHycxsSWQktgKWrswn1QW8jPk8or/12bZvUzi2vw94PjDb3MA9YnvY/sSgqVYxQ2H7mAQaNwB/Aw4mFhLrlmujxyGlpH4UJL0duJ0wfZpNbT9/CPSqL74/ULV2Su9cNkqg8hvANyTNqcGhIpooqZe0s+3/LNub2/5J37l3ZPgHFfxt2GK2VLdl3qs/pI17ZXlJn+/bX6Z/35Xd6mcCusz2hCDpHuDacT8CLDbpLGYrGr8OjwyzMbPdQvl2a5C0KbHA3gZYAtgDOM32HyvzSnUBl3QAEWS4BHgm8B1gd0Jr9yUnOrS3AEl3AGcT74/nl23K/ha2n5LE45ARp14DLGd74oF8SUcxesHg2oZgkvYA1gBWsv2amlwy0Ur1RRfEnY4WSuobksFsA3wa2B/olUpvSARx9y0eQxk8WrlXmnarnwnoFtsTgubR+qtg4ouLVh5uHaZQJqrDbjwBu9peNJnSdBL5xmRr2P5F2X6C+3ptStq0ogFTOkrmeDsiU/c14GTgwlYqVLJRtGTPsX1v8Xj4FbD2oNZutqAVjV8/yiT+9cC+wFXAv9m+LJtHhzbQypyjC+KORq2S+lYWl2W89YC9mXIjv4pwI780kUMr98oTgUVs/27g+NOI3tuzKqg9CXRl5BOC2+mjrRHbw/Y75ODC/+W5LOxCMSZLGu9YpsqlzmN66dQXqFR22IOko4G7ieDDFRMebnfCnORw4HTb90mqUe7XhAs4cG/vRW/7j5KubWmhLWlD4JZEU8OLx2n8kjj0xlsQ2I1w//4p8A+2/ztx/POIlo1nDTk3WJI5SR69Co973EALpUFIWobI7t43zx+eP1hK0WZTfduU/aclcWhpDtYcKpbUNyGDASiL6l0kPbns1zDMa+JeAT5PtOQdrHTdgnCs/6dELjMS3WJ75qOZh1uHQOslOWXhcAtT5VWTRusBoUOBFQnH430nPNYywEuIjMPBpRRyjqQFk3WfN5bP2v4G/e1QAFbu32+gPPefCa3wNbZ3SBjvhzSg8Ssl0nsC3wdeXikAsiJwqKRvAe8fMA9cIpFHr+qkVXfrrwCrSjrR9j4J4/07U202+7cBjkgYv8MAxpXUO6+tJcAaki4j3uurlm3K/iqJPHqeCu8DFi77fwY+ZfsLiTRauVeea/stgwdtnyzpXxN5zFh0ZeQzHK1o/FpGtpGNpC2IlhvHlP2vMzU5/NdhmZoJ8WhC09ZKKVVrkPQEQq88l7h3v297p7qsctFi2fQwSFrE9l0J4zRRhinpQeA24HdMD9qK0EuvO/QX5y+Hi4jMy+eJIMPcXmZ9Nj83hqGU+q9lO6WPcYe20EpJ/bzklVnVCJI+CGwGvMP29eXYKsDngPNtV19gStrI9s+Sxrra9pqP9lyHR44usz3zsXXf9oED5wb30yHp6rJ5WKIT5cNoEJO21xMGP5PGR4mMWA/PIsoxFwb2A1IW22VMgAeSxhuFnhOmmO6KKcKFOgWK3sEG7nTl3sEApezzROBESYsSZmkp6PtbjKDmNydRuQp4mgf6Jktai1jopaCvRPsB278ePJ+x0O4NNWJ72P4k0YSHgKNH8O6StgW+K2l/218ksSJGUy0lh8G2P57FZRwJIGWhLekE29uX7U/Z3rfv3Jm2X5rBo8MUGiqpnzPOn4WpjjGTxhsIk8+HtMi2r5e0PXApUGWxXd5rc8u/OwjTtgzcJmlj2xcM8NmIxPfsTEa32J7hGJf5kbR5JpdhsL2mpCXJ0wcP43BY8pCLDiwerrX9cwBJn0jkMfbFl2hM9p6+7UHNeqaG/ajyeX/imNMwSv9ZdLoZ7ZR6GGaMtwLR0/hxiTwOIXT7g1gS+CDh2J6BnvTjdqCms3ErGr81bH9n2AlJ25E3aQaizZek84GjJL0SeHLi8H8ZcuxJhP/CkkDKYltttNcEWK1v+yVMl95kXqNDkezB0WE6WvFn8TDTL9v3lKqdNEh6BlML7L8CKwEbJktz3gOcoOjw0O/OvguwYyKPGYtusT3DIelxwPZEhvAM21dI2orIoM4B0twfR8HRr/ebkx6nISObxft3bG/bt5vZR7mJF984DXum6dO8AlPu6wk6QfQyhlmZ0qGwfWJvu5TX7Qe8APgk8B+JVJ5p++zBg7Z/LOnwLBK2X5Q11jzQisbvW5LOBnYekul/P5DxfL2tf6fweImk9xCmPimwfVBvW9IihJb9TcBxwEGjfm8CPJqoNmB8hUULusVMD44O09GKP8uvJb3Y9venEZC2BH6TRaKYPC5KPCteZ/taSTdke2DYvkDSJsDbmap4vBLYxPZtI3+xwyNGt9ie+fgPIiN1AfB5SbdQ+gk6qZcgNBN1b8XI5heSXmV7WoChBEHS3Hxp58WHpOcRAaGzbd8maV3CvOT5xPWbwaF6YMr2Ryc9xiOFpDWI7PEGwAHA25JN2mD6YnIQC2WRkPRe258u29v1B+tK6fJ+GTzGXR+l5C8LlxHBup9Kepftr/dTySBg++Ujjh9AXK9pkLQE8G5CinQ00a7uj8kcdrb9n2V7WnBQ0jsSZVpPkrQBsABh7rgBcU2IeI5WRdHB/oyQ6HTIRSsymHcCp0o6h+mZ3M2ZLr2cNH5LzDeWJqo+rqVSQMr2b4EP1xh7NqAzSJvhkHQFsK7tBxW99G4FVi3Z5A4VIOmZRCb/XOCicvi5hGHHVravSeLRhDGZpAMII7BLgGcC3yFKMD8BfGlYudeEeBzFVGBqE8KRPT0w1QIk/RdxTR4EnMCArt/2H5J4fJMo9/zWwPFXAO+0/YokHk3cK0N4TdP42U7R+PX+nyWtDnwVuALYw/bdWX+PPm+HobD9zklzKDwOALYFvkxcq1WCua1co8V4cySyqkRa8+BoEdkl9ZJuI7K4AnYo25T97W2nVfaV+fBOTO+z/dWs+UYfj8WI58dcQoKxONH7/IKxvzh/OVw26hRJhpczHd1ie4ajpYlhGX8dYI2ye3WmZqolI5viNP16ph70VwLHZj7oW3nxSbqKyATdK+kpwK+AtbNLqbrA1BQk3chUhL332ctY2nZKmxZJqzEVmOrPQDyP3MBUEy7gZbxnUFnjN7CwW5AwFHotofE7PGmxfT+xyD+BCIxNy6iPk6fMZx4PAvcBf2O4M/uiSTyauUZHQdJCnt6ibZJj9ToZ3G/7vIwxH2so1TArAhv3G9lNcLxdx53PumdbhaSliOq6ucCKtrOq+i4hnl3HAqcz0PLT7RjsPWbRlZHPfKyh6b0Me70NUyNWJXp3KvFgv7SMv46km4CtiwHUpNGEkQ2E03TJAPT0MFdlR1Rpx5js3t7/u+0/Sro2e6FdcL/tBwuPeyVdPxsX2gC2n1GbA0DRsK1DZCDWLod/BLw1+X5povyxFY0ffQvbIi14n6QzgK+RZ4K1DLAdESj8G3A88HXbdySND4DtBTLHG4MmrtFBSBKwJXEPb0WSL0nPg0PSEyX1nh3XVXjPNovskvpRi+kS3H51Bocy3puBJYrkBEk3E89VAe9xdDRIR9FHHwocqnm0SZvP465fZGNziQX3VeXzzArSsRmJLrM9wzGvGzYrYlVK/u4H3ttb0EhagDBcmmP7n8f9/gT49Ixs3kxkRg7KMoJQtHE6gijTvYR4wK9PZO7enBR4GAtJK9q+KWmsXi/4Hl7Qv287ox0bku4GruvtAquW/eqlVJK2Bm61fX7SeJ8APmP7YW0/NNDOZzZA0gNEsK6nO727dwp4ou0U/bikUwjjwtOISphzS1AopdKgj8c2w6QVpTLlrbY/mcxnecI1993Avra/kjj2W4Gj3NfNoe/cnrY/l8Sj9/zqf3ZR9lexvXAGjz4+mxIL7G2AJYA9gNOytOyl4mJ/wqzul8TfYQXgSOADWRn2FtBiSX3xSHkZscB7KfBj2ymdHiT9DHh5L5jeq/woi/7v2H7h+P/CfOPR3PcCIGkH4DDgU72ARIf/G7rF9iyFpC2Aubb3SBrvKqJE928DxxcELre9ZhKPQSObz2W9/Ps4HAXcCHysL/Ag4F8I9+VdRv/2fOcy0pgssYRp7IvNY1zC5zOPJgJTwyBpf2AdYMEMnbKkO4k+n2/3w438MvWfdzE8K5daotsSGtH4LTHuvJM0/YXLc4i/xUuIgOVBHujLPuHx7wGuB15v+5KBc5n3ShPPr/Ks2g64iah0OBm40Mlu6ZI+Sxgsvsv2XeXYosCBREeSPTP51ERLJfWFy07AKwl/lM2JYNDdY39x/nK40H3+FpL2s71/2b7A9sZJPFr6XpYjApavBf5IJKFOdiUPipmGbrE9i6BwBd2JeBHeAJxk+5CksS+xvf6jPTefObRiZHOt7dUe7bkJ8GjCmGwUJK0A7JgVWVUY1y3tgRZfin70t9r+nwweLUDSxURrnGOJKoO9e5m7TP1nyeQ+HTgJOL5WwEPSlrbPKtsr276h79y29v9n78zjbx3r9f++UJHQLLMyJGPGVOoIhUNRMpaSOk0aaBKlDpUIpUHOr5wMlakiZEikog4ls00oYx0RmbJF2/X743Mv3/Vde621ddrr89zWeq7Xa7/Wep77u173tdd6nvu5P9P18UkN8Wqqxu9R4DYifRum10s7I9IuaT9gC+AaIq3+rCZSHsu9sifxXPma7YO7xxLvlZVsX1veP6U70i5pfdsXJvG4A7gOOBQ4rZRMNZF9cT2wons2uSWiem3Wc7YGSFp4UMZcchbbbYQT5nDgh7bvL2Uw2Y6YG2wv3+f8PESpQeq12jQk/ZxwTJ1IlBRMK5/LdJ6OK1pje8yhUIvtiOn8hahr+4jttHqQwuPawqFfi6nvZES2KxKyGWZs930IjIhHFcJkPZyeQziDdgQWJzyrH0ma+0fAXrav7Dm/GrC/7ZHXlEl6ZXn7cNbmeACPjtr0/MAXKDWXtq/INCAKl04kdwdgfmINOz45elqF0vMwSFomMXp5KPAq4JdE9PKCXqMmgcOjhNO4ExHrFvJLK/vouleeTZQHLUT0H//f5Mh2FddoMWZfTazhGwPnAZsAS2U6QyRdZ3vFf3ZsHNFzbZxre+N+Ywk8DiXKCq4iHLmnEJmN2Y6YrwN32/5kz/nPAs+2/e5MPk1D/QVRYWotnSjnwyjQCqSNP64FzieUe28AkNREbcjtwBeHjI0crkfI5lcKZfTPdG9QJe0DZKYSVSFMpqiffwORdbEiEcV8vu0lk6ks2mtoA9i+UqEAnYG3ldd7gMaM7Q7K9fEBRaut0zSHdksj4nAvcKSiTc0OwFcIo3vQejIKVNGTfliNX2bU3/bupfRlQyID4quSziaUyG8c+uG5h9Ro2Jxg+y/A1pLeDVzUwHO2imvU9izgLOAsRdeNLQmdgz8WQ2+nJCozJL3F9jHdJyW9mdgXTRK6f//eEpDMa2P3cl9sSDhjvgAsImk74IzEbMOPAkdIuoEQ7AVYgxCGfUcSh2rgSgRRxxmtsT3+6ESEzlOoxXZaPaXC9obZc/ZC0roO9c1+Yzs7T1Tn/cB/AzcoWi4ArEn03H57EgeAF0g6tev4+d3HThImIxTZfw18khIhk/T6pLm78fQhYwtkELD9tjn/VQp62yidqWgT8y0gVShO0suIjdkrgAuA19s+P5MD9Sg9H1VeH06csy+Ko/C8kka9A9HN4Xrgm0nz93UuqOiREIJcGei9V/5L0s+I/uOr9P3EaFDLNTo1aaSy/wD4QamX3jpx+t2AkyTtyvS2gQsQdamThGqujc66QawdT2JKJO3rwLOTOPwN2FHSC+jqsz1JpWJzgqTliADIDrYz17GxRJtGPiGQtCCwFbGobQQcQ6Tonp00/xuGjWfUPCpanv2SSBW+p5xblVjk77aduRHoLGYrl8P0hV71CJPtTmzWFyRSUk8AftJAatlxwE9tf7Pn/DuAV9vePoHDh4aN206J5kpa0vZtA8Yy6z9vIqL8xwM/ZapGGADblyTx6CjmizD6O2r5Ajaw/YwMHrWg63myPdHq6yTgxKzazz58mtQjWc99xOmKIbGN7eOTeNzBlDN9+/Kecryd7ZSWW5I6Ap8zbX8vY85hkLQR0w2qc5vk0wRKrfQXiWthD6ayggTs7iSth2GQtIDtmXP+y7ky19Ll7Szbf8yY85+BQmTwXuAIJ7YflbQ4sXbsRAiyfp5YS2fL+Gvxz6E1ticQpT53W2D77tqdEc/5KCHE1Ynk9grq7JrAYT4ifeidRBRmNUIR88O2fzTq+Xu4PJlQRO9sAq4mWvnM1j4mG0oWJuua9wWE0d1RWf404RC6Lmn+RQnl3IeZHgl5MhFNHXm5Q9d9ciahL9AbNdt31BwKj8adY4XHz5heS9a7bmyUxKMKx1QtkPQ3Iop9fHmdtpFIcp7WokcytN410SH01jnw6NvjeAQ8Pl3e3p/lHBzAoxrF/KbR9Zv0ReJz5TxirbjbSW2+hvAAuKtJHoMgaWuifd8aTuhOI+mdxDq6BCGSdiJwipOF68YZrbHdIgVl8diBUL0+BTiuU0PeAJePAgcCfwLWs/2n5PlXJvrk/pIpo25togXG65zYtqaLU2PCZAP4rFq4bO8kwbiuuV8FrFoOr3ZRoU6aew3i/70ZcW0cB5zbkPhUo86xOUHSk9xwr9ymHFNNQ9G+cNA1meU8fZTQI3l7lx5JE6rXjxKCT3/pnOoaTnMIDUIROXxtDVHmTKgCxfwW06Gp9nSzBmVOtciHpIcJvaAP2764nEtfS8cZrbE95pB0I7EputP2Syrg051++CzgE4npyssBhxHfx+7A5sD7gM/ZPjKDQ+FxLnCA7Z/0nN+E+D5elcSjnzDZ9g0Ik1WB2iIhXbXKmwB72j51Dh+Zm3NX4xzrRhHl2oi4ZrfMSo3t4VCVY6o2SFrU9p8T5ulcoy8nBLmOJ9Ius9sI7Q68kUj7PJ4KetMqFME7tbCvAc6vMYI3SqgCxfxaIOlE29uV9wfa3rNr7Gzbr2mO3eSiZBwYeKDhLJBnMfVMex4R2d6lhvKCcUFrbLdIRdkEbEZsklYjjIgfJ819A/Bx29/vOrc4Ub+0lO2XJ/G41vZKA8aucUIbtDLXTGYXJmsiMlSFQ6imSEgx6LYjHoCPAPtk1Un38GjMOdbDY33CwN6aUNPdDTjV9l+T5q/aMdVUjV/X/E8HtiG+nxfZXjxx7kb1SLp4dMpgtgJuJtoFXjb8U3Odw78Rv8G/E2v7y4EX2H5w6AfHFMU5tyFxbawHZCvmVwF1tWvU7G3hUls5tphCV+nHTNsnNkqmQNKSxPN+R0JH52TbezfL6omPVo28RQqKSMkOxAPvHODLnXSVRLy4N+JQUsh3ULQDycI8kp7SW59d0v0y78m9iN/k68Bxkk5InPsxVFQX9BWa7x28K2Fkzw98nxA2uiOTQw8eIoy4+4BlCq80FCNyW+AW4jfZF7g4q/60C7Uo5g/Cr4kavy8BI6/xgxA0IgzLnYhuCgsRzpBfDPvc3IZDWfhY4NguPZI9CcMqk8cfJJ1CqF3vTDhl0oztIoJ1C3A4Ubt+v6QbJ9XQhuYV8yvCsOdYG3FrCMOeY5KWdgOCkyW9/xDgkKKLsUM2h3FEG9lukYISNbyCaN1jZhfU+UATvDqQdIvtpef8l3Nlrk8C6wO7ubSvUfRx/gphSOyXwaOLT6PCZDWh6UhIV/1np61R732S0o6tj3Ps+AacYx2F5euAQ4HTbP+9oeyLKhTza4GkYwlV9rOZUoq/oSLHWRp6Itq3Et/H6U5SVu7icSjh7LiKcD6cAlxZyzUqaSvgdtsXJc1XlWJ+k5B0LfFMmwf4DuEgU/n3naxsutohaTFCvC1NqFbSSwlhsl/YvkPS6sDHgVdkpXFLemV5+3ATGXSTgNbYbpGCWpRSB0HSrZn1KZLeB3wMeCrxwHsAONhJ7WqG8GpMmKwmlLTYTiRkb/e0AxvhvFWoXtfiHCtlJ68mrsmNif6smxBlH/8Y9tkR8WlaMb+WGr/LiI37MYQj5rZsJ0hl5SdXEMbtfcx+r6T9Tj3Own8HFgHeDpxRQR35/kTp2Hy2N0+Yr3HF/FrQpb7dF1k6MYMg6WjgQeAw21c1yOMcIkPoBxk6HJIOArYkMmCWB34MvINoufX/bD80ag6FR0ez6B7be2TMOWloje0WLciNbPfMuxCA7fuz524xHTVHQrJVr2t0jkl6CrEx2ZGIqp5re6dsHl180h1TNdX4SVqJ8v8nlLhfCKyaIY5WEyT9J8OV2VMzlTpQ9PnuiKRtavvZTfBoCjUo5j8RoDq6OqwLLE10h9lzTn8/Yi4CVrZ9dcJcM4C1bD9USmBuJdbQm0Y9d4tctMb2hELSNeXtYba/ljDfswlRo78C3wIOIjbMvyfaDYxc6VjSV+n/8BXwVtsLj5pD4bEksKztC8rxh4CnleFjM76LMm8VkaFaUFskpEbVa1XSRkjSwsDWto9pkkdNaKrGr8y9NnGdbgfcZvtlTfCoDZLWtf2bCngskJXW3qak1o9iUDba1aFFK1Y3SWiN7QlGkftf3/bpCXOdDVxMiOhsDBwJnEYY3G+yvWEChyqidZKOA75r+0fl+HfAN4iU8pVsvymDR+0oKV2PEA6hHyXMdxTN9w6uTvVaDbYRktQR+5rZpIFfk2Oqhhq/IdxUeKSKpNUESSsT98qORFrmOknznkdco3dn3Z8DeFSRklrL2lET1HxXhyOJa/Tehq+NKtZzSfcwXVDyld3HWRotLUaP1thukQJJl9teo2zGbu5O2ZZ0me0XN8gtNVo3zJsp6Xzbr8jgUTsUbdkWIxxChzXMJat3cBXt2AqXxtsIlRplgPubrFGuBRXV+FVRO14LisBlx8B+hFDuXyczHVTSMuXtrKIoPNFo144paPauDicTYqzZPek7miQP2/6fzLl7eCzjIk7bJGrRaGkxerStv8YctXjwgFkQ4UFJf+kZezSbTL9oHZDl/e5tn7Rx1/uJqqsbBkdbtj8Bv21ifvX0DiZSuUeNKtqxqZI2Qrb3zZzvCYAtgDUrqPHrzJequF0jJP0PsDBRfrKN7evLvXJTJo8ajAeYFlHui6zSj3btmIZ3EF0dDmeqq0N6pK0i4/FkYK05/tXoMQN4ju0Z3SdLdsydzVCaxmMd4E9lL9biX0BrbI85sj2XQ/ACSacS9dGd95TjNI4DonXPTzYi7pe0YkfB2PbdhdtKwMQJpdWS/li4NNo72PahwKFdqtc/BBaXtCe57di+T/y/twdmKfoHt2lQzeOhTvTa9l8lXd+EmE4TAnmPF9l6JMCfibT+RQlhxdn0HiYM6w44/zrie2p1FvKxGFNdHQ4tz9wFJM3nxK4OXc/6frDtjQeMzXUqSfPMCV8lHOu9eBaR3daYAGjB+4HVJV1ne/uGuTyh0aaRTwgkrQasVA6vcXJ7hRrSZXqidT/sitZlp1JtRvTU/hxwSTm9NrA38EHbZ2byaRq1pD+q0t7BTahel3mrbSM0qWhr/B4fMvVIynyLEFoLnZZwTycUwH+dMX+tKGvIm4A9iSje52xf0SyryUaTXR2KkGIv1ifaoN5he5CjZm7zuIN4xveF81pbXjxI00HSVbZXzeAxJ0hayG3HnH8JrbE95iibgFOItgqXEx691Qijcyvb9zVIL7WlkaRDiWjdVcCxxPdyZUP1sKsSD5hVyqmrgIOynSD9kC1MVgtUR+/gT7lPq6ByH59qe6jTalTQhLcRqgU1OC1bDIek5xKq7DsCSzctWtcEJM0H7AJ8BLgQ+Lzt3zVKqsVsaLKrQ1nL9iHK6j6XGWSQdDPwqUHjiWK5v7P9wn92bAQ8OhpKs2z/MWPOSUNrbI85JH0FeBj4mO1Hy7l5gAOABWy/vwFOjbU0aqN1c0a2MJmk+5meWqZyLCK1LKUlW+HSaO/gotr/G9uf6Dq3KHAWcZ800rO3G0psIzSEw1bA7bYvaphHtmL+cxhS42e78Tq/LFSkRzIQNQgxSToaeJC4RkfuzJW0G/BB4FzgwCbKHIahlrUjEzUps0valEiR/jthZJ/XAIdpIrVNQdLpxH15Rs/5zYEP2N48iUfnN7ir6VK+cUVrbI85JM0AVu+tyyme5yttvyiJR40tjRqJ1tXS/qIWSPoh8DzimjjeDfUK7oUa6B1clPG/D1xn+0OSVgDOBA62/V+jnr+LRzV19P1Q1HVXA+bL2pAM4JHtmDoe+Lp7Wms8PlOVAAAgAElEQVRJegXwnqx00EGQ9F7gLuAHmbWgTaL29VzSukRm23q290yY71HgDkLgaTYnqu3VR81hGGpZOzJRizK7pN8QugYHAbOpkdu+ZLYPjYbHhbbXz5hrDjxWAE4HfsWUEOw6wEuJ/udZGi0tRozW2B5zaEhbrWFjI+BRTUujfsiM1lXU/qIag6qr5nEHIq3sBMLwvrtJXvBYNkRa7+DiBDqB8Py/DNjd9skZc3dxqKKOvsV01F7jV6KaKwHLZNWPS3oVU+U4V2dHympZz2tB19rRF01H+ls0B0k/Y8oB08le68C2N0risfSw8UyHf6mh3wnorN1XA8c6qY1j4fDKYeNZe59xRmtsjzkkXUtE53rVFwV8JzGyvTthSC1I9Hk8AfhJcj1sNcZlDajRoColDjsQAnL7Z3rhVUHvYEkfKm+fRNT0n890AayJ6hfbtQl42PaFDfKoYu2opcavBkhagsiGeYipqNDawALA6yet9rCWCLukTW3/eMDYtllpzLWsHS3qg6Qr6WPsE1H359qetxFiDUHSaX1OG1gdWGrSvo9RoG39Nf64HRi0Qb89i4TraGm0S3mdlTBX9agpwiDpZUypo15AbJbPT6ZxU3ltsh55oa73X+k5N4me0beV13sIoaWmsEt5bXrtuEHSvw+o8ftDFolKakC/Bhxu+6juk4Xb14kWfpOEo8rrw02SAM6Q9AvgzX0cHnsBWddLLWtHiwJJxwH72b6mz9i3be+cwcP2aj1zL0so5m8C7J/Boczbq1fz2BCJejW2X9vD6+VEFurtRPuvFv8i2sh2i8aghloatQjUIkwm6SZiQ9RptzWt1jOrjqt2SFrX9m+a5tGiOdRS49dVA/qA7UMy5uzDoY3yVwhJlxLOjk8Be9j+fveY7TUbI9eiUUi6kxDr+0KvxkUT10ZZTz8BvAQ4BDja9iOJ83fr1ZzQdABE0saEQryJzMKfNMlnnNAa22OOspgcBCwPXAl8ZNLS61r0Ry3CZLXUcdWIojK9Y/l3z6B63XFFVwS1L5zUsqZL+fqxU13Htr1cBo/CpfEavxog6XrbK/Q5Pw8hMDhRDtyuUod+sO2Nk3hcYnstSSsC3yXaWu5m+8FMFWhJSwLL2r6gHH8IeFoZPtb2DRk8aka2MntxxGwOHE10cdjF9l/KWOa1sSphZK8CfAE4znYjWUs16NVI2oL4Pu4lFOIvyJp7UtAa22MOSecTfYN/AbwOeKntNzTAo/o2LU2jqKTeCxxh+66kORtf6IdB0pMyPc01oKS0dQzsR4BlgHVcQQudBtoIfXXA0OuAJWynlEJJelbPqXkIlfqPAJfY3iaDR00oqet7ASuXU1cTrZ7OGPypuTr/lwjjaXfbfyvnFgS+BDxk+wMZPIbwS13PS/eEXqxPaD/cYXvdUXMoPB4zmkrXk88CrwfeQqT9ZxlUxwHfdWnJJ+l3wDeApwIr2X5TBo+aka3M3nNtfAj4APBu22dlRrYlzQJuJbKEZjOym1g7GtareRS4DbicPg67LLHLcUZrbI85ehXHM72HLf45SNoaWA5Yw/bQiN4I5m5soe/DRcBGRPRuS9uLNsUlG5L+B1iYSKk/3vb1km60/fyGqQGktxHqmVvAm4jauhmEB/6KZA7zADsDHwUuI+6VGcM/NVfnr6LGT9J/AO8iDLmLy+l1gAMI4/IbCRyeBHyeqKe/mfgOliKiZnvbbrR2ueH1/N+IdND5ifvkzMS5ZzOaJG0IfIvoEb9Q3w/OfR7T9jrdvCSdb/sVGTxaTKHPb7IG8B3gZ8BGtlcZ9Nm5zOOtQ4adlTFVuPTq1ZyQrVfT1VGhL2z/PIvLuKIVSBt/zC9pTaZScxfoPm7rYfOjdYNg+4fZc1YiTNbhsj5hYG8NPBPYjYgcNgrl9g7+M7AEsCihjHo9FQmjlZrx3wA/yJqzRMd2Ia6FC4E32v5d1vyFw5OAXYE9iPtk64bSUM+ljhq/PYANejJgflqi3RcQEcSRomS8fETSPkSZFMDvbT846rkfDxpazzclhI3+ThjZqW3QCvbtPWH7ZyXy/q5EHvP3HHen0T87kUfjqEiZfVpXHNuXFwfuIUBKZ5wy79GDxiQdnMWjR6/mnRS9GklrQer+/PZBz9QiltbiX0Qb2R5zlDquQUirh308EfWmou7Z0TpV0GKq8LiJCoTJSirbtsAtRFu4k4GLK4rmpvYO7krt3xFYAXg6sKntX4967i4OtbQR2g34IGFkHthUKr2k24j741DiOp0G2yclcmm89EPSNR7QNnLY2FzmMLQcKus3qWg9/w3hoDsImK3fd+J6/sxh41nXqaSLgJ3dIxooaSXgGNvrZfCoAWU9h9D9aHI9n8f2owPGFrP9v9mc+vC4xfbQPtxzca6fUYFeTUkj/zahrfBAz1ibDTsX0BrbE4zMelhJM4ko3cA/ARbJWuSaRFcK00zbJzbI42fUsdDfAVxHGDKn2f67pD84sQd7rZD0XKI2eEdgadtLJc3bSSt72PZsG/cslE3AHcCd9BEos716Eo+jGJxhYNu7ZvDoRsM1fhcB77R9ec/5NYBvZhgy5dq4rPyD2devlN+kXc9n49Gp/+w4b3t5pKzrkjYj7o3PAR1Hw9rA3sAHM1PrWwRKnfZANOms6kDSrVnP2TnwyNyfXwmcSgQ93tKd/ZBZSz/OaI3tCUNT9bCSlnkcfzbL9m0j5FBLtG5/23s3Nf/jQfJCPy/wasKg3Bg4j+h3uVRC2nY3jxp6Bw+EpGWy04Ylzc9Uiu4NTla8ntO60WAadWOopMZvA0Jp+kimtyB7K9FfeeRqtqUmegfi+jyFUBRuVGFa0tMAeqNDkwRJhwKvAn5JZCpd4IY2mgrV6Y8RqtMQIn5faLJcrCmU7+KjTP8uDsnUvehykJ1JlDr0ppXPVoIwIh6Dsi8EXG57yQwes03e3P6800HglcSafjTwWduPtpHtuYPW2J4QDKiHPdX2XxslloiKonVVLl41CJMp2hptyZQxca7tnZLmrqF3cC0OofmA/Yk65W7xqSOBTyQ6Yja1/eMBY9tmOUWKcN0nbP+0z9i5zmurdBMVlH4ULs8D3svU5n0GoXtxexaHwmNBYCtge+BZxO+UKugj6T2EMvuCxL1yP1H28PVEDscB+9m+ps/Yt23vnMhFwIbEOr4ecDahRH5jFocePhPtBFG0+DqYEBTsFjTci2gHe0oSjzWIa2Izwkl3HPGMTzVENNUdR32G07Ivuvg0uj/XdJX4pwOHE6WVbwJOqnG/+kRDa2yPOWqvh20Kkp4DYPvOBua+nNiI9Fvo02rauvg0vdAvZfvWPucXIoSovp3BowZU5BD6ErAQsIft+8u5hYkN20zbH0ziMYtoW/hm23/sGcvsy/pHoo3TGcBe3c6GzDS7WlKFa0LJjNmMiHKvBuw5yEEzovk/CbwMeJ/tP5RzLwC+DFxk+7NJPO4khD6/YPuwnrFGUkHLxn0H4DOEQvw3k+d/L/BxwgkC8ADJTpAaUPYcW/VqXijaTJ5ie40GOHUydDYh7tlTszk0jVr25/3Wh1Ie8zlgAdu9rS9b/JNo1cjHH+8g6mEPZ6oedmI9LCV6+X6iT64k/QP4qu39EmmsRHh1+3pVgayatt6Ffl9ioR+o1Dki/ETSZr0bAeCNhLJuirEt6VNDhm37M6PmkB2RG4ItgRW7Iw627ysRvGsJ0bIMXAEcC1woaQ/b3+8a6+usGhH+DGxA1IBeJGlHT6m3pq2ntjccNKZQTE+BpEGpp2m19JI2Igy59YBzgC/bvnj4p0aCnYn2Xo+VWNj+g6TtiL61KcY2USe9OXC0QhV+F9t/6VBK4tCbafAcQjl/bduzCQuOmEfHCbJhrxNE0jOznCCVYL4+z1ds35S5bnRQgh1rEs6x2whdjsz5O9pAs3qduMmoZX8+mxPM9tGSzifKMFr8i2iN7fHHYkzVwx6qUCdfQNJ8mfWwNaCIc2wArNtJZysP38PLRv5LSVRmVCI4UctC/yHgbElb2L4eQNJeRLR9aP/HuYy/9Tn3VOJ7ehYRnZkUuF9qn+1ZydeIbX9T0s+B70raglBMfZDklmhlzncoVLB/UrQX/otco38aeks/iJZxGXiU+P6PBU4DZibN241zCGfMBcBTgLd06S5g+wNJPNxPy8D2zFKjmgWXFP5Ny7PuYknvtn0WudfoHYQY6vFMtS5cR9I6hWSWcn8tTpAa8A9JS/c6PIomRqYuyq6E4Of8wPeB7WynGtoFnYDCXYRTvylUsT8flOlRnFTvzuIxzmiN7TGH7VnAWcBZXfWwCwB/LLWGKfWwlWBn4NVd3v7Ow/fNRE1ZlrFdC2pZ6M+Q9HfgzCJ69A4iWvXKTE2B7jrtksL+QaJm+XiiD+gkYYakt9g+pvtkuVeuzSZj+zpJLyU2yJd2G1UNcDlJocZ9lKR/B56WzWFA6UdaT3rbL1a0UNqRMLhnlNezE9eOXamjB/0fJW1s+9zukyXy3kgrI9tflHQu8J3ioHpy4vTfI36XF5Z/06gRke4M1OIEqQGfBs4p2WzdgoYfB0be7rQLRwBXETogmwKvCX9hwAmtNcs8r8qYZ06oZX8u6b7eU9NpeuEMHuOMtmZ7QlHqL7fu3UyPMyRdZXvVf3ZsBDx2sX1UxlyPF2pQmKyLwyuImqVfER7vVOXrwuGZRKT9TYT3+8uZBn8tkLQEsSmeyfTN2QLA67NS7wbUkm0IfAt4ju2FknicZXuzPuc/CnzG9vxJPKqo8evDa3vgMKIe9qAmuWRD0iqEGvoFTL9XXk7UyV6dxKPfvTI/4Sh8j+15MngMg6RFbf85aa5ziZZ4/Zwg+9RicGVBIU72YaYLGh7snvZ9I+YwNFOtojKqRpG9P5f0Q+B5xDP/+OySj0lAa2yPOVR5O6NMDBNUShZb6ihO94Ntvz2JRxXCZJLuZ0rs6SnAI8Aspuo/U7yqkg4C3gB8g1BVrka5thhZ9wJH2L4rcd6N6Nqc9W5cE+bf2vYP+5x/BvAu2wdk8mkaqqgnfXHI7AC8HvgrcCJwctZ9o0qU+wuX+YlMg25D5ruZDkNJ89juG7GVtJjtRqLsCpG0bYjv50W2F0+atwonSIs5Q9JSwA4T6KirZn8uaRFi/7MDkeZ/AmF4pwr2jitaY3vMoal2Rvfb/mKjZBqGQtn4b0ylyHQufgHz204RCpG0TZ/TSwF7APM6qcejpGuB2YTJJL0N+KTt5TJ41IKSWvh3ooate2FMNfr7oaTXL0fUII48hVqD+5ACeYr5FfH4yhx4pNQHq56e9D8n1OpPBH5A1D4+hozfRZUo99eCUqc9EJnPf0kLECJpOxFCWAsRJQ+/GOQQGBGPxp0gNUDSUKXvrPTtbhSRtG2JtWxxwlGXVgpTA2rcn0uahzC4v0JkhlTB64mO1thu0aJBFIG2vYFXEjXj/2374aS5/52IkPUTJtvc9m0ZPAZwW5CImO1oe4umeEwqiuPhNqbEc3pruLIU82vh8TBRa3gi8KceHjhfwb/R0g9Fv+/uFmSPDdFAn9om0ZWZM9sQuZk5jwKXAWcSTsPea3TfJB7HEtfj2Uz1g7+hqVIHSc9nenbOH5rg0SQUbeFuJUpPLmL2ayMlfbtkzb2B2GOsSKQtb58VYJgTJC0G3G37701zyYamWrG9gsgGOcH2+c2yGh+0xnaLiUHxcr8bWJ5Qsf1WZjSoh8tKRFurNYGDgO80wUXSxsD/I6IOHWGyLZqoU5b0ZGAL4kG8KRExO8n2aUnzvws4qt+DVtIHbX85gcOniY37A016lCUdCrwK+CWxQbvADTwsKuLxLCIKsz1h+J8AfN/2Pdlc+mESNThqQU+94wm2b26IxxrEZnkzIm36OMIBk3q/SLqMaK15DJGGelsTpQ7lnjgCWJtwQgh4MfHdvN12ryjU2KInI2Z14HTguOxUekkzgV8Te58LbLupMph+kHQOkUH2g0mKshfn6T1MOcem7UVtX9IArbFCa2y3mBhIOoGoBz6f6Ed6s+2sfsHdPL5HbAAOISJls7rHs2tkmhYmk/QaYhPwGiIt9gSi9/myyTxmAn8A3mT7sp6xlJp+SW8tb2faPnHU882Bi4ANid9mPSJSdbhL27xJ49HFZ0kize5DwJ5Z2gZl7ipq/FRPn9oqUFu9Y1eUahPiGh2aRjyC+TtK9dsDfyFUyVfNEkcrHI4CbgL266Sul7VkH2D5jHKcGlEyYnYknPz72v5a4ty7E/fIgoQz6ATgJ7UY2/DYNbLyJNX0S/oZ0zOVejPINkonNWZoje0WEwNJV9perbyfD/h1lihaD4+bmD0F87E68sTU2FqEyR4lHCC7eKr/eRORkEuJNijfAL5m++DuMdfRGz0dReBoB6LP+N62vzmpPCStRWxUX01EyA6xPSNx/ipq/BRtAgHust1kn9qqUEO9Y6mF3Y7IxHiEUN6+MJtHF5+1iXtmO+A22y9Lmvd62yv8s2PjimJkb0H8FssCpxLZfenOslI+t0PhsgLRmuxk29dlc2kxHJKeZPuRpnk80dEa2xMKSVsBt9u+qGkuWeiNTmYqkD9eSFpi0iJFkl5MPHi3JSLLxwOfsr1MMo9LbK8l6dlE+uFCwJtt/2/mtVKi2x9kqkftNcBXMlOES838VkRk6jlEiuyJTm4JUhGP/YiN6jXE9XlWUyUoLYZDDSj311DvKGlXwqCdH/g+cZ/ckclhGErE8BW2f5E03zBj+wbby2fwqAGSjgFWBc4gMi6uapjSY5C0KiULIus3kXQjEWi40/ZLMub8Z9D0/rzcqxsRJX1b2l60CR7jhNbYnlCUDclqwHy2N2+aTwY0pUYOEb1dAHiQ5EjuMEi6xfbSc/7Lkc3fqDBZ16Z1G+Bywtv9jaS5e50x7ybE6/YAPpGYRr47kaJ8CXFtrkWk/B2albIs6W/A9YRheT09AlC2T5owHo8CNxLrBUzPSLHt1TN4tJgzlK/cfxMV1DuWa/QqoFMz3nuvpChOV6Q7cTTwe+Az3XXrkvYBVrS9c1PcslGujc7ep6pOGy1mR1P7c0nrEwb21sAzgd2AU5vQ8Bk3tMZ2ixYVQdKttpdKnrNRYbIBnOYh6g13sL1r0pyzpYqX2sPvEvWGT0ngcCHxf76p5/yyRERi/VFzKPMdxfBe8Fm/SS08hmZZNCWK1aJ51FLvqKlWaH3hPMXpKnQnikDafxPOyo4Gx5qEE/Pttu9titukopaIcpfmRF9kZ041jWLcbwvcQtTSnwxc7IY6CIwjWmN7zCHpleXtw03WbdWKpiO5ffikRbYrEiarQmxJ0nq2f93n/JOAbWwfn8Bhhu2V/9mxSYSkRTMFlwZw2IBYO3ZrksckopYIau2QtBThwDuoaS5NQNJyQGfdnGH7903yadE8JF1JH8cYUar0XNvzJvGoYn8u6Q7gOqIV7Gm2/16TSvw4YL6mCbQYOd5WXu8BWmObgZHc/0qc/6sM7sv69CwewFmEMNkGXcJkI29v1QedHsV3AU2KLf2jCGD1Q5Zwy8z/49hcRS2q170oImnbEPfui4DFG+CwZpl/WyK1PCWVfRiarvHr4pHZp/am8pp2X/RDLRvmbhSRtG0JZ+riRKRqoqBod7VAMa5/X1Jkl5C0BHCp7fubZZiHWiLKtaAjlNtByRzbk8im2z+RSi3788WYag13aBHAXEDSfK02ydxBa2yPOWy/bc5/NRnoE8k9Bli3ge/o4v/j2NzGWoQw2TmSOsJkKR7dbth+VfacA3AxUfP4l3Lc6/XOSAd9kaQr+pwXkOll7qSPNb4hlbQAIZK2E5EGuhBRU5YitFQ4rEisHTsS18cJRGZYLdfuS4DVyuaoSQ2ObwPLSRp5n1rbRw8ak3TwqOfvQhUbZkkLEe3HdgJWJJxAz7e9ZFOcGsaBwB3AF8rxscDVhIDcJYRxNRFo04H7Q9IKwCeI9fMQ4AOZytu17M9tzyKCL2cV1fotCU2jP0o61/ZOjRIcA7Rp5BMASS8E3gmsVE5dA3zT9u+aY5WPWlpM1YgmhclqgaIH6BsJJePjie/ggWQObW1wFyQdSyg8n82UANUN2ZvHrrXj7bZvKOfataMPipJto31qmxaabAKSZgK/Bj4JXGDbk3yNKlo5rtuJzHU0Ocr1eb7tDZpl2KIpFAX0TwCrEM6Y44rBmc1jqIBjZgeSfii6B1s3zWMc0BrbYw5JLyU83N9gSt14TeA/gDfUkvaWgYpaTB1JRErvtb1H5txzQhPCZLVBUz1AtyKUffe3fdnwT83V+bcGlgeutP3jrHlrhKTLgHmILJTjbd/WhAFRfpMdgJcTEYDjidZS2UZ/dSnLNaIJocmmUZyFOwALEiJHJwA/qcXYlvReolToBxmpqZIut71G1/FrbJ9d3l9m+8Wj5tCiTpTONLcCpwOzGdm2P5DE46sDhl4HLGE7Jfu41rKxcUJrbI85JJ0JHGj7Zz3n/w34eMMph42h4RZTHdXYh23/T8acA3hUIUxWIyStQmxcdwY+lqWqK+nrhLf9V8DGhFjJZzLmrhVFEX5Hotf2X4j+46s2IY6mqb7fOxJlBccQa8fZSfMfWd7e06SjroYaUEnPHDQEXD6p6dNdzsIdgRWATxPXaJbuxCBeuxHZdcs4oQ2ZpGuA9XprsyUtAlxke6X+n2yRDUnnAI8Ah9n+UcJ8uzC4y8XQEpVRoWRcvIkob5gBfM52v5KyUcz96fL2/lZscjRoje0xh6TrbK84YOx3tl+YzakmTHIkt4hgANxlu0lhsr5IFlvqjWjfSkQvT7edKUx2FdEfeJakpxLpjmtnzV87JK1NGBHbAbfZflmDXJ5BZMlsb3vjpnhMKroMfvUZdi0R3SZR0mV3JK7R5ZvmkwlJHyKe7e/utHIqZTqHAz+1fXCT/FpMQdLihEjX+rYPS5hvHtuPDhh7uu17Rs2ha775gF2AjxCaD5+ftBLPSUBrbI85JP120GZd0iW2B6kvjx3aSO4TC8XbvRyRdjhysaNSl3sFcApwHz2e7wyPb+89Wds9WpHqtYBX2E4TSasBtdT4dSk9P1CO1weeXIYnSul5ECStA/zJ9p+a5pKJmlJSJb0b2JtIrQd4ADjA9uHNsaoHJfoPEVH+WqNkEiHpEuA9vc8xSe8A9s5y1JVsjw8C5xIZqDdlzNsiH62xPeZQ9M/r1x9YwHa2F02m1Bhqj+S2mB2ZYkuS/pPBqWW2vV8ChweBGzqHhLPhhvLetlcfNYdhkLQ/sBqQonqtSnop15A2XXjUUuN3MHCH7S+U4xsJJf/5gUtsj1zpuXaDX9LRwOrAdba3T5ivlmu0k5L6gO1DmuLRjaLUTtPXRI2Q9Cwionx6wlznEdfo3U3uwSRtABxGCAruCSwDfB24DdjD9m1JPB4lFPPvZPreo4rnfYu5h9bYHnNIeuuw8SZqU1q0eKJB0rq2f5MwT6tG3oWu9WtmVt38EwUN1/g1rvRcg8H/eCBpodbIy0dNEfYWga7n26wsg3YIl/mA/wR2IzIe3p6lvdHFoX3eTwjaPttjjtaYfuKgRA3vJVSO72qaTxbKJhli49xYRKYXklZmqrfyPcA6o55z0MO1eOJ3JDYGI0ctqtft+jU7+tT4vbGBGr95ehSl94QIxUh6WhKHjYF1u47vsf3ajsGfxGGO5UmTaGhL2hzYC1i5nLqaSJM9I5FGp1PAxH3/vagl66HzfJP0fElbltMzbP+hATpvJJ6phwOvBraXdLHtuxM5rOQBHUckbUt0Q2kMtZSNjQNaY3vMIenUYeMZqqAtHjd+TaQNfwkYWps5SmQLkzm5fdIwSFqWKQP7ESK9bJ0maqkkrQnsRIhw3Ui08MvC28rrPYRB16IC9NT4bdZgjd+TuyO2nmqptAgRWc5ADQY/QMchdBexgZ9oSPoP4F3Ax4CLy+l1gAMkLemkrh+2982Y54mAWp6xir7NRxDXQ6ed5osl/ZaILN+XxOMc4CFgE9s3Svok4cj+jaQDs65R4AxJvwDe3MdRtxfQdEbGS4DVJKWUjY0z2jTyMYekOwll5eOAi+hRbrX98yZ4tagXDQiTzQC+CxzXkIe7w+N/gIUJjYPjbV8v6cbMjYqkFZky9v9C9Mr9iJN7wbeoE7XU+NWg9Ny2dqoTZT3foDdCWGqDL7D9omaYtegHSbfYXnrOfzlX5joKuAnYr6MGXjJR9gGWt50SZJD0etsn9zn/POBg229O4nEpUSv+KaJW/PvdY7bXzODRYvRoI9vjj+cRKTI7ElGy0wmjZuSCU08UZEdyaxF9GgTbm3SEyZKm3JFoufUTSXcRjqETGlDx/TOwBLAo8Bzgeob04hwRriVSYLe0fQOApPR+ypKWBJa1fUE5/hDQiRYe2+HWIh1VRKhsf7GI+V2g6D0uIl03U+n5m8AJkvoZ/EckcehE6xa1fX053hZYoAz/2A30g28Y6peKa/uueKy0qAyZP8rLbe/SfcIR8dtP0vVZJPoZ2uX87ZKelMUjpvQ3Jf0c+K6kLYDdbD9I4t6jlrKxccY8TRNoMVrYnmX7LNtvBdYnlI1/Jul9DVOrCd8Gri2COxm4iajFaVQgZBgcSHHI2L7c9l62lwM+ACwNXCjpvJKSmALbWxNK278F/rPUuT1D0npZHIA3AP8LnCfpm5I2Jncz1MFBwNO7jt8F/I3YADSeninpvZK2L/XLTfK4pvzLWk9Xsn1zv39A5nWK7f8qEbFlgWVsL5NoaHda8Z1KGPx3Sbob+AVwWkZkvQsHAy/vOv48UUv+Suq4V86RdGZXjeyocZ+kNfrwWIO2frpG1JLeWosn5qXZE9q+rsz7Z+BSSdl19W8r/0beNWFS0aaRTwAkPQXYgoggLktsUL7VT8xlUtGJ5E5SxL9WYTIASRsStesr235KQxyeC2xH3DdL214qce4Fga3K3BsBxwAnZ6mlavZ+34+ltEk63/YrMngMQqldXokw8hrVnVBu65xZhM4cJb8AACAASURBVEE5W41f72+WwGVV4KPAKuXU1UQK5pVZHLq4NNbaqaSCrlUidL33ygVOUGafA7/FgcWIa/SwhPk2IMqCjiQclxA1um8lrtsLRs1hGCZR9KlkJvUdAj5h+5lJPI4Gfg98xl3Gh6R9gBVt75zBYxiS0+pnSxUve59vAc+xvVAGjxajR2tsjzkkHQOsCpxB1KFe1TCliYekIxnez/ntmXxqgaR1CeNyG0IQ7Hjge65AmV3SMm6oDYekZxAiadvb3jhpzhm2V+46fmYnNVTSNW3dZTOopcavGCwHE1HcbhGsvQiNgVOSeDRu8Eu60vZq3Zw6z1lJV9leNYtLLSi1r+9l6neZARxm+/bmWAUUXT9WAyZG9ElTvc/7wklicqXk4r+BtegSSAMuBd5h+54kHoOckgJ+ZHuxJB5b2/5hn/PPAN5l+4AMHmXOfmvpIU5qJznuaI3tMUcR1PlbOewnqLNwPqtmoEraX0japs/ppYA9gHltL5nEoxZhsv2J9KW7CQP7BDfQg7PLCXKv7fQ66Zog6SJg55Le1n1+JeAY2ykpy6qkV25Fa8clttcqQnrfJfpK72b7wczItqTLga3co4auUPM/xfZsacQj4FCLwX85sGmvISlpCeBM54nWnUdco3fbnnhV9Bb1QtJyTGnCzLD9++T5zxs2bvtVSTyGZhT00z4YEY8q1tJxRiuQNuaw3dblF7iS9he2f9B5L+kFwN5Efd8BhNc3C7UIkz1EtDFKE0gZgKPK68NNEajFqAM+DfxI0ueAS8q5tYlr9YOJPDr37AOJc86GWtaODmxfJ+mlwGeJGr/sVoHz9RrahddNiQJD+wGv7uFxhaSfAqeUfxk4CDhN0oeJCB1E5O7gMpaFXcrrrMQ5Z4OkQZGwbMX8VvSpQNIqwHK2Ty3HXwIWKcNfs33JwA/PXR7dzsBOGcwinfNZPIYZ08kCaX8htHs6LQy769YNvCCJRy1r6diijWxPIEo96OuBHW1v0TSfTBRBpc2Jek+Aa4CzPL1fawaPlYBPAmsSG7LvZHPo4bM+EV3ehqipOtb2N5PmXoCojb4b+BHRn/UVTNV2/SWDR4vpKGllH2N6WtkXJrkURdKr6Po+bA+NkIxg/ipq/Eo097UuKuBd55chBMpGblBJutr2KgPGppVBJHDZjHBEdfhcRSizn5nFoYvL87t4zMjOWpJ0GWEoHAucBszsHs8qxymZSgD3tJlKOg34vO1fleMZRLutpwLbOMRBM3g8StwbnWf6NOPS9kYZPHpRNHs2Ijr2bGl70aR5DwVeBfySCHZc4AaMsprW0nFFa2xPCCQ9mRBJ2wnYFPgBcJLt0xolloiS1vdTQu35UmKhX5Noj/aqrIiupO8RUcJDgBPpiURkpQ71QxPCZJJOBB4BFgSeQTyMTwM2AF5sO0tFt0UfSHoagO1GosuSNifS2ToP/KuBA22fkchhCeAkIgujI/q0NtHi6fW9YmUj5FFFjZ+krYEvAPszXQTr48Ce/TiOgEPjBn9NKPWwRxC/Q3c97G+Bt9u+L5HLSkTm1GuJeu1jgbObdCh3Q9I23Rlm4w5JF9tep+v4Qtvrl/dpIn6SdgfeCNxLlIyd3NRzpfBZn9gTbw08E9gNONX2XxM5CNiQuF/WA84GDrd947DPzWUO7Vo6YrTG9phD0muIm/g1wHnACcBXbS/bJK8mIOko4DLbh/ac/wCwtqM9WgaPm5iqn++8djy8tp2VOtTh06gwWUdEqGQd3Gb7eV1jl2fUf7aYHZLeSxhPC5ZTDxBG7tcTOfwH0XbsY0yvJTsAOML2N5J4nEzUIh/Vc/4tRGRoqyQeVdT4FS5rAB9mugjWwbYvT5q/cYO/i8vmZd7uLJBsh9BRRFvJ/Ww/Ws6JiGAubzu71KDDa3vgMOL7yEyrHwglKk7XAEm/s/3CAWPX2V4xmc8LiBK2rYg2qPvbvmz4p+bq/PsToqO3EBHlk4GLmywXkvR04jv5DLB3VmZhmbuatXRc0RrbY46StnM+sEvHUybpD9kGXQ2QdK3tlQaMDXwYjSsqEiZ7TNipV+QpU/SpxRQkfRJ4GfC+Thpq2SB9GbjI9meTeMwANug1IhXtti5wkir6HDaraWtHWc8H1vg1sa43mfnQtMFfONTiELre9gr/7NiIuCxBGA6vB/5KZHA1GsXshqRbndjKsWkUQbCPu6fdWYnsHmB7wwY4rUJcIzsDH7N9YuLcdwDXAYcSkdu/N7Ev1lSLz+2B5xDZUyf2RpiTuDS+lo4zWoG08cdaxIJ2jqQ/EEbVvM1Sagwzh4w9mEViUApbSfXf0/ZnkqjUIky2pKSvEIZD5z3leInmaBUS4ZS4l9g4N96GLAk7A2vYfqhzwvYfJG0HXE4Ic2VA/aK1tu+KoF0a+gpNSpqH3PX0K1RQ4wezZz5ISs98KBvBtzRc6rAHszuEflqi3RcAKcb2HJB2s0j6ObAQYWC/DeismU9WVwvBhjFpUaY9gRNK9kO34OVbCUMvBT0R7VuJ/ej+toftzUaBxYBXExl9hxZnxAKS5ksudbgDuJ74Hq4nrst1JK0DYPukLCKdtTRrvklDG9meIEh6GVPpwpcTnuYaNgIpKM6Gj/QbIoSflkvi8WOiTrs7arg5USt9lu3dk3hUIUwmaWj6vu2jM3gMQkmxWo4wPptKxbymvD3M9tcS5huWBTJwbAQ8LgLe2etdL174bzqvBdmXgKcBu9v+Wzm3IHHPPmT7Axk8yrw11PjVkvlQQ6nDwL7zw8ZGwONoptZud53fB1jR9s5JPG5i9jIp4DE18pTooaQr6W9Ui/g+UjRJaoGkRYma5O5Sh8Ns/zmRw6PAFYS69X30/D62v5jFpYvTU4AtifX0FcC5tndKmvsoBjt+bHvXJB6nDhu3/boMHuOM1tieQJRozCbADlk3cw3QlDppX9h+WyKXHYno4LHAqsBziX65mXVLrTDZEwgldXp926cnzHUuEXE4t+f8RsA+zutDugHRT/pIpteSvRV4s+0Lkng8iehBugtRYyhgKeBoor4uvV1cwzV+v6Mn86GcXwC4PKMGtCKDvxaH0MJE68i1mC6QdinwDtv3ZPCoBUXcaSCcpIpeEyTNDyxfDm/ovX8T5v9PhmQV2N43j83sKPfQ1raPaZJH4bJoliNE0p1EpsFxwEX0ZMLY/nkGj3FGa2yPOSR1REBmOUkxt8WcIWleYF9gd+AeYCPb1yVzqEKYTNKzCY/7X4kWRgcxFWH/sO0bknh8mtgIPNCEh70mlHq6U4g02G4j9+XAVravTuTyPOC9TK8lO8z27VkcurgswNRm9fe208pPyvxV1PjVkPlQg8Ff5qvCIdTFZzmmlPtn2P598vztnqMylGf8/kRa/y1MOQuPBD5h+5EG6aVDIWwJMNP29xol04XiQN2GUEh/ke3Fk+adl6m0+tWB04HjMp/z4462Znv80UnBvYtouTCxkPShYeNZBlbZnB0G/Ip44P0bcJqkE4DP2f57Bg/gYQDb/5DU2/ZsVp+/HxWOJYSFVgB+TWwAvkwY3EcQKbMZuKm8ZtePPQZJNxIG/522X9IUD9tXK/ps78SUkfsLor1UajSkGNWfypyzF5Le0Of0Cp268cTaulpq/P4oaeMBmQ//m8TB/a5F2zNLumoWiQskvYRwCO1STs8gslDSHEKSuoUkO0buIp3zti+Z/VMjQRV7DklvB57pooAu6Y9ELbmAj9r+r6a4NYCDiP/7C2zfD49FcQ8u/z6YQUJSZx1v2qHdUR2/v0EOwGPOwa2IZ+2axO+0NfG8TYHtWcBZwFklrX5H4GeS9s0oW5sEtJHtFhODsgG7DDgT+Duzp8qkpDBJuhh4r+1fd517KvBpImqYVQ97B7FpFxEpO74zBGxne9EkHpfbXqPUot7srpYski6z/eIkHvvb3jtjricKJD2frohyJ103cf4rBg0RxlZK/8+utaOTnturAp5VW3cUddT4NZ75UEupQ9e8TafoPkqUAnW0Nnqv0Y0y+TQNSb8hBEDvKseX2l6z/E4/tv1vzTLMg6TriTp195yfF7jWSUr1XfosM52oPl4rJB1LBBXOJvZfPyXWjvQWZMXI3oIwtJcFTgW+1WanzB20ke0Wk4Q1iYVkC2KDeBwhhpHtcVrPpQ9qByUddU9JpyTy+GjX+4t7xnqPR4lZELtBSb2ibGkRKmAzoApjW9JqQMfpco3tq5LnX5jIKlibMDAFvFjSb4G3274vicqjhHF5LKEn0FTWwRuI+ujVCSPzuKzyhm7Y3mXQWBFAyuJRQ+bDB4BTJPU1+JM4DEzRLRohmSm6HyIiyTOJjXs1rbYagjy9e8T3AGw/VKKJkwT32+fYniUpbf/jhsVOK8TKRPncNcRzPvX36EDSMYR20BnAvtn7jUlAG9luMZHQlDL7JkS7raFqjFmQdEt3ZHcSIOkeYqMuwsvbSZ8S0VLnGUk8LidS1vu2yXFCyxpJixDG3NJExwABqxGb+K2yjNwSQb0J2K/jGCqZB/sAyztRlV3SSsS9+loiPfdY4GzntmjpcOmumX4WYUw1Jh7TVI1fLShRym6Dfwbw3cyoskKpfiFgjz4pujNtp6TodvHpbq90MxH9TxPerAWSbrC9fJ/z8xDRw/Se9E1B0g+Bk3qFvyS9mchia9WmG0LX8217IivlhcCqWeJohcOjwN/KYb8OAgtncRlXtMZ2i4mDpOcQLa+2JdS497F9YbOsApJutb1U0ly1CJMNTefLMmYk/Z2odexnbDtjc6boMf4w8LEuI3ce4ABgAdvvHzWHMuf1g1ILh42NGpK2J/QODuzUYibPPy+RAbED4QTZ0/aPkzkMrPHrzZgZIYfeetjbgIWZwHrYWlJ0e+ZehbhGdybWkolL2ZX0deBu25/sOf9Z4Nm2390Ms3xIWoIQU5zJ9CyQBYDXt6nCdUDS2oThvR0hWvuyhim1mEtoje0JhaTFiAdRlhhX45C0K7GIzQ98n1DxvaNZVtORGdmWdDaRLr4QsDEhTHYaYXC/yfaGGTwGQdJSRHu6FKOqU9OXMdcQDjOA1XujtiVV9Urn9ewdZmz3jRiNkMsShOHwesIxdCLJKbKlDngHoq/1OcDxtjNLLTo8qqjxq6EethaDX9J1HqB8PmxsBDy6I9q3EtfH6bYbE3zsRvaeo2ShHAGsS2QJQbRC+w3RCm3iUuzLOtatwXHusL+fNEjaCrjd9kUN8xDwCttpImktRou2Znty8W1gOUk/sP2Rpskk4QhCQOZmYFPgNR01YYCsVCpJp9Ff5EhEamoWFrW9d5cwWceovVbSbok8HkPJOtiW8O4uDpzcBI8G8XC/9GiHYnymY+xXRTn2M90RO0n7AP+TRULSzwln0IlETWynBvPJkp6ZkdpfcA5wBSEI9hTgLZpqH4PtDyTxqKLGjzrqYd9NZBl0cKftJTsGP5AVXZ8h6S0DUnSvTeIAcANxjZ4C3EeUorxHU4r5TbczTN1z2P4bsGNxQnQbmKmt0CqDmdp7ZOqhDIWk9xJr+w+aKA/qwkuA1STNZ3vzUU+mAe1GyzM3zdBWJV1QxhmtsT2hsL1JMbJWnuMfjw9S1WmH4OD/49jcRhXCZJIWIgSodgJWJNLdnm97ySwOBV9Onq8f5pe0JrOnsosw8rLwfuC/gRskdeo91wQuAd6eyGMZYhPwLuCdXedVzmfVXe7KYBXwNNh+cVeN3znlvl1I0qKZNX7A03t47Q+PlTw8O4lDDQY/RCnOSSVzarYU3UQe+zF1jT4tcd7HhSb2HJKeTLTW7Bjbz5Z02yRl9MG0NPKHmLpGt5V0IHWkkQvYAHgT0Fj9uPO7kdxUXhvNPsnOjJpEtGnkLSYe2enKtfCoSJhsJtFf+5PABcX4/0O2gE2Jqr+XBmvYJf2MIUad89sZLcfU5njSo0LVoakavxrqYWsTwGpTdOuCpJWJ9kW/ZMrAXJtQq3+d7RlNccuGpJOBU2wf1XP+LcA2ttPU+2uApFeWtw/XotfTYrzRGttjjjY9pD/6pSs3kU7fJI+KhMl2J2oNFyTasZ0A/KSBzXLVNeyZKOJOC3TqGiWtDzy5DF/aUV1O4NHRL5jVZPRF0cbJwL2292iKxyBk1/gNqIddg7h/UuphazD4B/BaApi3HP4pKy22lH1AT0pqNmrZcyj6sB9g+yc95zchugjUkuk2ckj6ne0X/rNjI+DRKb2Zaft7GXMO4HFkeXtPjet5i/FDa2y3mBgMSFfePjtduRYeg9BUpL9L4GdHYAXg04Tz4bqk+S+3vUZXDfvSXWOX2X5xAoc3DBu3fdKoORQeBwN32P5COf4DcDUhLniJ7T2TeJxX3t5l+40Zcw7g0XFMPWw7rWa9D4++NX4N8mmsHrYGg7/w2At4ku39yvHNwL2Ec+po259P4vHW8namJ1B9vBeSrrW90oCxa7LEJmvAIMHLkgVyXZbgZVm/AO6vYf1q0SILrbE95uiKDPWF7VuyuDSNitKVq+DRw6mKSH8Xn1ULl+0TNwKX2F6r932/4xFyeBS4rPyD6bXbtr3rqDkUHpcC63aicppSmxZwvu0NMni0mI5aDKpaMg4Kl0YFsCRdQmQV/K0cd+6VeYGfT9q9UlFWzHXAar312UVA78p+xue4QtEL/mnA7l3X6YLAl4CHEgUeq4Ck1wJX2L65HH8K2IYQz/2g7Rub5Ndi/NAa22MOSVcSkZBpm3bgOcBzbc/b94NjiIrSlWvhUXWEPRs11LBL2pq4NpYnVIWPy6gV78PjcttrdB2/xvbZ5X1KlL9Fvagh46AWg7+PY26XTm2spN/aXrspbk2gT1bMjUQXkOysmE8C6wO7dRlVywJfAS7uZCJMAiQ9Cfg8sAthUApYCjga2Nv2w0k8VgGWs31qOf4SsEgZ/prtS5J4XAGsb/tBSVsCXySc+2sC29reNIPHEH5VqLNLuqa8Pcz215riMQ5oje0JQ3nY7AlsAnzF9lcbJdQAmk5XroVHLRH2imr8qqhhL1wWJPrlbk+0g/tE8vzXAOv1RqEkLQJcNCg9s0WLLNRg8Bce1wGr2H6k5/xTgKsmKYIKdWXFSHof8DHgqYSB+QBw8CTuewAUKv2dTLHf234wef7TgM/b/lU5ngHsQ/w+29jeOonHY85kSd8Cfmf7wHKcksU2B367ASsByzipJe0QLs8iHBOnN8njiY7W2J4QSFoB+ATRR/AQopbskeGfGn80ka5cC49aIuy1o4ka9pKKuRnx+6wG7Gn7x4nzf4hwyL27U2oiaRngcOCntjNb1LVoUS0k7Q88D3hfx3gpzrKvAbfb3qtJftmoMSumZHGRlcJeGyQtDCxq+/pyvC3Rmg7gx05qGyjpYtvrdB1faHv98v6CLEdMiWy/DHgQuJEw9C8uYzNsT1JL3BYJaI3tMUcx4j5B1LR9gUhLndUsqxY1oekIe41oqoZd0T5oB2A94Bzg+M4mIBuS3g3sTThjIKJCB9g+vAk+3ZC0GKFE3Wi/3GJo3Qsc4ek9n1tMCIpj7HPAO4gUXYCliT71n2wyDRTyU1Jryoop+5+PMlXTfzUR2b4yi0MNkPQN4Fdd5Q03AGcSBvc/nKTcr+Gq6NfZXjGJx67Es+0+ouRhs3J+TeL62DiJRy3q7FVkF44zWmN7zCFpFnArcDowm5E9ScIYtSwotfDoh6aFyf6Vv5kLPBqvYS8CaVcAFxDXyLQFuon7tcaokKRzgOUIA6JJIb+tC481bL9lTn8/Qh5V1PhNMnpSdG+wPbNJPh1kp6TWkhUjaSvgYKJWueOwXAfYC/iI7VMyeNSAktq/lsuGv5PaX95nRpTPAz5u+6Ke8+sTjtwNM3iUOZcAngtcbvvRcm4xorNAinCwptTZH7B9SMacLZpBa2yPOSTtQs+GvRu2j85j06LF7Ci149cP+xNgEXe14hohj0Zr2DWlNt0XWfdrLR73YSg1oCvbvrppLk2jlhq/WjIOMiHpleXtw7YvbJRMJejJihFwP8lZMZIuB7ayfVPP+WWBU7pT3ccdkq60vVrX8aq2ryrvr7K9ahKP9YhytaOAjhja2sBbCcf2r5N4vNn2d8r7l9v+ZdfY+yZdDKw4IjriyX9qHbj/Olpje8yhaHOxkO07e84/h+h1+FAzzFo0jVoi7CXqMSfMsn3biHm0NewFavuhToMq629dK2rIOMg2+CUdWd7eY3uPjDkH8KjOQdZkVoykq22vMmBsoupyi+NhU9u395xfAjjT9uqJXBYFdmN6av9hWXXjhUPjbT675tucyLboXI9XAwfaPiORw15ERH+/cnwLcA/Rsu9o25/P4jKumK9pAi1Gjq8AZxEpsd3YAHgN8J50Ri2qgO3nN80BwKUtS9OwfShwaFcN+w+BxSXtSVINu6RnExuRvwLfAg4i2pD9Hviwk9qA2d43Y545oRaHEHBTeW00NbhGg6obtjfpZBw0SOPbwHKSUgx+228b9RyPE531vPFyj95aaUlN1Er/Q9LSvSnBxbk7aZG6g4DTJH0YuLScW4tIs08T/gSw/eeiddFdcpEd9NGA9/2OR0dC+g/gXYRifnepwwGSlrT9jSQq2xL7jA7ucnQQmBf4OVGK0eJfQBvZHnNoSJ/PYZ7fFi1a5NewSzqbeOguBGwMHAmcRjwI35RZ09aiPrQ1fo8PbYlBc6ilVrroKXwB2B/4bRePjxPdHX6YwaMWSNqMSO1fhXBgXk2k9p+ZyGE+4vd4G3ALPNbv+0iivWVKh5xaItuK1mcb2L675/yziFK2FyXx6P0OdukS0xtoQ7R4/GiN7TGHpGsG3bDDxlqMP2oRJmsxBZW2OcVYuLm7Tl0Ntc1pEsWzvoDtB8rx+kRqG8ClWempkjYAXmD7mHL8feCZZfiztn+awaMWVJRx0KJA0irAcrZPLcdfAhYpw1+zfcnAD89dHtXUSktaA/gwUynLM4gI++VZHFpMoVyTCwF7dNZuRVuyg4lsnQ8m8XgQuIEw9pcr7ynHL7C94KDPzmUeVezPJV0HrNLr7JD0FOAq2ytk8BhntGnk4487JK3XKzwhaV3gzgGfmSiUOsNHiLqhH00Qjxcp+k0OpMTUZm3sUYkBMQugiLP9pWfs0Qb4NI0DgTuICBVELf1VwPyEwM6eSTz2Bd7fdfxCYBeivn9vIM3YrqHGb1gJiqTFbf9p1BwquV9rwgFMT/fcFNgHeCrwKWDrJB7z9RraALZvkvSkJA6dOS8HGusSUAsq0pzYEljRXVE+2/dJeg9wLZBibAO1BJnuk7RGr/OnOIkyy0G+D/y/Ig73YOGwIPC1MtbiX0RrbI8/PgqcKOkopqdSvYWoS20R38ViwPoTxuPx9DudmJ7sldSwv0DSqRQPe3lPOW6cX0kRvd09rVtGiI2BdbuO77H92hL5Pz+JA8DCtmd0HV9v+7cAktLq2Sqq8RuGC4k+0yNFJffrQEhah1DyHbnjoWAx27/qOr7P9g8Kl3clcYBKaqXbbJRpuKm8Nt2Ozt2GdtfJWZLS0mwH6cSUa2ZHQjclAx8GTi0ii93787cCb07iAOGU+xxwi6SbmUrv/+8y1uJfRJtGPgGQ9Fxi8ei0d7iaSCu7ozlWLVq06IWkfxs2bvvnWVz6oQjbrEZErzZPmO/y7rRTSa+xfXZ5n5ZWL+n6Qal0km7IqOcvc1VR4zcMkm61vVTCPFW375F0NLA6cJ3t7RPm+53tFw4Yu872iqPmUOaqolZa0rnA+ztOMklX0pWNYnuzDB4tpiDph/D/2Tv3uMvG8v+/P+M4ZIro4Hz6ImQcIj+UklJRRA6jwjcdRAdKlKhvkhIiTAep6DCiGIyzpIOEfB0yzmfJV0o5hWR8fn/c955nPXv2fobMc617nn2/X6/n9ey11n5e92f2rLX2uu77uj4Xp3cmQBr73wvs4BbaFkpaB9iZZBJ2V9Z3bOD4rwD2ZHipw2R3OccHaRnPcOO6tidnxgw12B5QOjN4tqNm8FpH0iWkVKq/2373oOuoDKfkGnZJywA72Q51jm0bSTcBG3TXZkt6MXCF7eeSnTEndEwDvm37nK79WwEfsb1lkI4iavxGQtK9Ta+BURynCJOj2SFpkQhvgfy98pnurJPsc/DVSHPFEmqlJf3B9vqN7dNtb5tf/872xlFaKgmlVmOnk1bYmxMx44F32f5zkI5VSCvYk4C/kdp87mv7ubQhHZM0FuW6W7LVRbk5QE0jHyDyDN4kYAfyDF67isLZLf9uOzV6t/y7bR2V4RRVwy5pCdJs+yRgSWBq4Nivzy+ftn151Lg9+C5wiqQ9OmmpOR31W8AJgTr2Ac6R9G5SrTjAesBGpDrEKIqo8ZN0LGnCcJZDwEuiZPR53Wt79ERInYmFGb2ChSgTP5J/wSm5ZKx5ju4KjPrKepNOrbSkF+XtxyPHzww7DzuBdublwVoqQL4+XitpM4aCunNtXxws5WZSGdJWzu00Je0TrIERnjdESrkP6X8uaWNgCnAi0Mk6WA+4UtJ7mllDlf+MGmyPcfrM4Mn2G1sV1gKdOh1JL8kGcZBS/B4ZRB2VWWi9hl3SIsC2pLS2VUgTYivYXno0x+1Bp3fww6Qa3Faw/fXsHHtpNmwRKaj8qu1vBeq4XdJawHsYekj8DbCHY3vEllLjd9V/eGxO4j6ve22PJifl3w8BrWUq2b4yr2LvxdCE7g3Ahrb/EqlF0p6ktPGF8/bjJBO/bwbKuFnSln2yUW4J1FGZFTN0jbZh/LktybPoEknnAz8lcIKuwbOkz2EKqcVnW2nbRwLb2L6mse8sSVOB7wDVgPIFUtPIxziSniXN4O3emMG70/aK7SqLJ7cx+A7JlfUu0s11OdKK4R62nx4kHZXykPQkcCVwIKkG16Vdr5K26xgvBY+7CISuFPbSsAKN1Fjbd7agoZgavzZRIe17RkLSawPNBDtjLsjwusvIySAkHUjK+Pho5/qQtCLwDVLpxyFBOlYGzgEuwjN3GAAAIABJREFUo0c2iu1bI3SUTJ4UeQg4zfaom9c10sifYmiycD2C08gbehYGtiYtRm1GWtWd2vEFCdKwWh7/HaR7+RTgwoj/j4aGG22v/nyPVZ47Ndge42Szkp2AjYHODN4JpTu5jgaSvgSsSApoOz0eFwEmk3oah7gulqKjUh6S9iZdrwuT2lydAlxUWLAdUpPbGG9NUleFZi3ZEbavD9QwgZS2vh5wLSmgW5v0wLi77UejtJRACU7PuZygL/0chyOJvFYkzUsyJftv4F6GHIV/AHzOXT10R1HHLcDE7iA/my9dF2XUlsdcgOHZKDcAU6InIEpF0l6kjK7lIszJ8krpmbZP7Nq/C7Cd7a1HW0M/JC1KKtva0fabWtKwI+k58LBIf5bsjbKR7X907V8MuCzKG2UsU4PtAaGEGby2kTSdZLb0RNf+FwGX216z91+OTR2VcskrQTuRrtf/Ar5Aul5bX42JcpvOY20NHEHqH9xsdfVZkqHNmUE6TiS1zznY9rN5n0htUVa2HdLLt6Aav2KdniWNI5l//qQtDQ0tkdfKUcAiwD6NSdwJpOvnSdshPYwl3dzv4XykY5Wxj0Z2zO97bCyTV/t3At4F/AM4lfRdH+ZzIOlDwAeBfRmeBXIY8H3b34nSMlapwfYAUsIMXhtI+mO/h1FJ19t+9SDpqMwd5JXdSaTrNaTF1Gz0RK7WXQdsbfvurv3Lk1ZIJvb4s9HQMVLrr77HRkHHtYxQ4xe1mluC03MOJPcClgLOAi4CPkqqa7+uzVWyDsHXym3AKu56qJM0D3Bz4Dl6MXBot+lVNsU6KMovRtJdpGvlr7YHuuY0rxxDmnT5WYs6et4r8wTZrVHfb6WcG5J+TZogOxU4jZTSPxN3tXgcZS1bAfsxPAvkcNvTojSMZapB2gCSU0WOzz+DhPNEQy8jjEiTjlJ0VOYCbE8HPpd/Qsgrlf3cpiOdfOftDrQBbN8tab5AHSMRZqxje+1Gjd8UWqrxowyn5x+RVoJ+D3wAOID0f7GN7WuDNKDUFq7ftfLSKB2kzIZZdNieISlyVeXjwJmSLmW4id/GpOy6EAaxVG4EOp9Fa34XmbMlfRfY2/Y/YWbW5VHAuVEiCjo3liPdOz4MfKixX3l/WPmY7bOBs6PGGzTqyvYYp5QZvBKQdDcpmO31cOyouthSdFTKo5TrtZR62Lyy/Q7ntl+N/csB0wLTpk8C7gC+1AxoJB1EWk18X4SOHrraqvFrve94Mwsor97+H7BsC4Zgm4503Pavg3ScAZzeqaNv7H8vsENETW5jzAVJHRWaJn4/qbXSg02eIP0KqeTkHoZ8BU4ilZ9Uc9iWkPQ2UgeB5sr2YbbDJkHGMjXYrlQqlUqRZIPHr5GMn5qrZJ8B9rd9RpCOCcD3gHVJBmkA65Dq23Z3YNu+Qmr8Wnd6lnS17XX7bQ8aDafnJxl+rbTi9FwpB0mn2t4hvz7M9v6NYxfafkuwnvEMOebf0e1fMyhI6pSYzGjz+pT0QdLq+n4M90b5KslQedCyYOc4NdiuDAySRnwQs331SMfnoI6XkVIeVwb+SOoZPFBuxpWykbQ7sFhntVTSn0m1ZQI+bfvbgVomkupwm6tkR9i+LkpDQ8tKQKcNyo227wgev6Qav1adniXNAP7Z2SQFlU8wZBY3IUjH1sDStifn7SuAJfLh/Wz/PEJHQ89mDG9Pd/FI7x+F8bvvHfcBE2jh3lFJSLrG9jr5dfck1cxjATomAC+3fVve3p503QJc4OB+8G0j6ZL88iHb725Rx43AJt3fH5JeSmpB+qp2lI0das12ZZC4CpgO/C1vN9O4TXJpj+CHpJWHY4GtgGNIaVWVAee5rM4FreDtATQdpR+0vVROD70ACHtgzkH1Ltmtn8gV3CaS5gdez1Ags7ik+2z/K1BGSTV+/8oPiw/mXTdGpgnbnidqrNmwHynToMMCwPokZ/YfAKHBNuk86KyitOEB0n3v+Kvtpdu4d1RmMtKqWuSK2xGkbJjb8vZXgPNIAfdGpHNnYIgyC3wOqNdEre2HUtONygulBtuVQeKTwLtJaXY/JTj1ssErbXfMri6QFLKiXpkreNUI7Z0gBVUvDtAh281V058B2H4qpwCGIWlPUtr4wnn7cVIt2TcDNaxOcrz+HUMpum8APifpnc4tsEYb28tHjDM71KfvuKRW+45ns6V3kVp/jXrdeGZ+239qbF+ar52Hsp4QGmnkTzF0jm4v6TBi08iLuXf0QtIvgH8Dk7Mp1CCwkKR1gHHA+PxaDGWERLE+aaKww2O2PwaQDfVaRanfNKRz47hWxcTyqKSJ3dliOausbVO9MUENtisDg+2jgaM11MP4Ykn3kNqUhLnXwsz2a50pw3ma25GpoJXieC49aGeMuopZ3aYPhZktWhYPGJ883oGkFY832L4z71sR+IakxWwfEiTlWJLx10Vd+jYnGZRFtTMqosaPlI1zI7CTZ+07fhwQ0nc8jzs/sCXJkGsLUnp95Orpos0N2x9tbC5BHMcB37J9YnNnbvv0TeKcwIu4d4zALsArgQ3bFhLI/wFfz68faLzubEcxb5djftNY8iXdb47G9qty6vQgnRuQyrTOkvQDhvs97Aq8tzVVY4hasz2gDPAMHgCS1iAF3O8j1dWdGjj23VQ38krBSPom8HfbB3btPwRY3HZIup+kW4CJ3enJeYXsOturBOm42XbPiRBJN0XVtBVU49d633FJbyG1QHsLcAlwCnBs9Oq/pJ8Av7L93a79HyZNEk0K0nGL7VWf77FR0FHEvaNSHrm7xBa2H+javxRwXlR3icqsSHoFsCfDvVEmd/9fVf4z6sr2gDKIM3iNFe2tgT+RUskPtf1kpI5SUkErlRH4NHCCpNuBTmrZ2sAfSD2No3CvOmDbT0qKrEcdJ2mB7vrsXIca9j1aUI3fSEQV+Z0P/JZk7HMXgKRvBI3dZB/gDEk7M9yZfQFgm0Ad43rtzCvKkfXtve4dE0meKWH3jjwxZVLg39rEVMlIejNpseHNQUMeDkyT9CngmrxvXVItd2TrwiJabPZD0itJ522YH0gOqj8fNd6gUVe2KwNDfjj/I3Am8ChdxiC2v97r7yLILsc7k9Iy15jd+yuVCPIEVdPZONp9+2LShNjFXfs3Aw6KCj5zOvuGwF7OPcYlLU9Kp77K9sEROkpBBfQdl7Q2afJ0e+BO0uTp522P2CN+FPU0XcBvsP3L4PGPAl4E7G37n3nfwsBRwFO2Px6sp+17R+c8mGH7vsixSyOfm98GlgTOAA4jmfcJ+LLt0wO1vJXUjWUN0jPYDaSOLOdFaSid7CuwEnCa7X0Dxhupo8L+tn822hrGOjXYHuOUPoMXiaT/YQTnTdtfjFMDkpYEdiQF2a8mOXOebvv6SB2VSi9yLWyv1k5hs+253ONM4FKG15JtDGxt+4ZALR8lOU8vRHpIfZzUguzYKA2loIL6jmc9G5FSyrcjraZOdXBvWElvZHiwfclI7x+F8ecjfYfsBtxDOkeXAU4CDrD9dJCOInwF1EL/6FKRdA0pA+P3wNuAHwOfGcQSwiaSXs2QT8pNtqe3qadJ9sBYPeI7TtLvSAs9f8rb1wJvIndUsP2m0dYw1qnBdqUSjKQPkR4MlyL1yz0VONP2Cq0Kq1Qyfdy31yMFuWHu21nLgqQJqWYt2U8i20x16VkEwPbAu7Sq5b7jPfSMAzYnPTi+P2jMXi7g65FcniNdwDt6xgMr5807bD8RPH4pvgJh/aNLR7P21g6r4e/S8QXSgsfjLWcSvpg0ibssaXJOpAWPe0mTuK10U2gLSX+wvX5j+7iO0aOky20PTLnpaFGD7QGh5Bm8KPJD+47AP4BppNqy1zOUDvm3Ef58Tup4mjTD/CnbV+V9d1ZjtEop5PTtr/Zx3/7cXFI7PMeQtDSwvO1L8/YnSSm7kFb7b29NHO3U+LWd+VDQCupU0mTpiV37dwG2sx3iAp6zDV5u+7a8vT1DbZ0usP2XCB2lIOlOoG8KbmTqdNv0+CwOJz3/AHGfhaRd88snI01pe+g4BniaVK/e6aYwDvgqMN65HVmAjiIyTyXdbnvlPsfusL1StKaxRg22xzh1Bm8ISaeS+msuTGrXMp0UdG8CrG17qyAdLyXVGU4CXkFa2d7N9jIR41cqs6Mg9+3dgcVsH5637wMmkO5jn7Yd0uJJ0smk1fSz8/YtwPGklPLVbL8nQscI+qJr/FrPfChoBbUUF/Djgcs6QX82KDuPFHA/M2gu4JIeIj379Ov6EZL5UAJKLZ36MVCfBYCkG4G1bD/TtX9e4Pqo77dSKKWjwlimBttjnFJm8EpA0nTba+Yb6n22X9E4dp3tiS1oWpq02j6JNAkw1fYB0ToqlSaSbgVe3cd9+3oHtHbK4/0BeKvth/L2NbbXyTousL1pkI7uNMyZKaqSfmv7dRE6RiK4xq9mPmTUp9VZ/p69td+K0SjouAZYt2NY13WOXmp7kwgdpdB9zVZ6I+nlA5j1cK3ttZ/vsVHQMQ/pOfzxvL0hMH8+fE1UqZKkl5GM8/5Fj44Kg3Z+jAa19dfYZ3PSDN7MNjm2n5V0ADBoRlxPA9h+RtL9XcdmtKCH7JJ6JHCkpFVI7rqVStv8EDhNUi/37R8F6lAn0M78DMD2U7k2NYoFu7abhjGLB+roSw6yogzjluoOtLOGX0gaNMO4syV9l94u4OcG6pi3E2hnmo7wLwnUUQpRLejmOiS9hGQmuDPwKpJL+SCxoKR1mPUcESnAjOIw4EHga3n7ZFLG5YKkoHf/CBG2HwQ20vCOCuc4uKPCWKYG22Ofp7tTZWBmwBlW31cIS+eVfjVek7eXihIh6fX55dO2L+/st30rMFAthCplYvuQ7L79W0ltum8PCxJsHwozVw0jg9zHJK2Sr1Fs/z3rWA0IM0orpcaPQvqOF8J+JBfweyTN4gIeqONZSa9w6pdLx5clG7hF9qTvSQu+Au8NGmeuIE9Obk0KsNcBFiH1gf9Nm7pa4gGgn0HbA4E63gSs39h+2PY7cpbSbwN1AJCD6xpgjwI1jXyMI+lmUopyrxm8Hw9SbUrDnKMntk8K0tGpn3rY9j4RY1Yq/yltum9L+ibpAf3Arv2HAItH1aEq9YY9Bvgyw9PsDgA+4QHrEavad3wWCnABfy/wCeBTwDV597rAEcAxtiMzUmahBV+BzsTUzF2NbQ+S6ZOkKcDrgAtJ/eh/CdzuQjqgSNoTeIh0bsyyODRW6S5flPQW2xfm15Hp7KVM4o5ZarA9xpH0K0buLT0wtXX9yKsx77D9s7a1VCqlIGlNkmNt0236CAf2gc/puCeQZv+vy7snAlcBH+jUugVpWZO0itn5PKYDhzuws0MpNX557CL7jrfkzP4yYC+GXyuTc3pmGHlS6ICso1NW8NVSJoOCfQVe2rVrHLADyZX7atvbjbaGUlDqmzyOVB70U9v3qaAOKJL2InXLWc72OwPG23ak445zZ78J2KD7vp2Nja/oZ1JamfuowXZlIMkPrVuQVv3fAvy2TUfbSqUkJG1NWhH7CimwBXgN8FlgX9tnButZkUafbbfcz7ktJB0BPGj7a3n7Lho1frZDavy6NBXVd7yFFdSNgSnAiQx3Zt8VeI/t3422hkp/csnJ+0gTh9cCh0a45ZdGLnmZRDJk/RuwKrDmIJpfSXqWdC5c29nVOBzmzq7UQnJzYA/b9+Z9ywHfAn5p+4gIHZXRpwbbY5xSZvBKQdKmpJqltwNXklrVrBid8leplIyk60itAe/u2r88qadwiHO/yuml/APSSuEjbZZ+ZMfp9Tuplg13dpEmDEMcp1V+3/HIFdTLgY/YvqZr/9rAd6LSMiV9gXSOPm67Xz1qhI4iUlIlzQe8H9gHuJS0wt/qeVkKktYjBd47kDqzbBQ07i755ZNtZhJK2oZkRrsyqT3cyW2dG5L2IGWjLEwK+h8jnavfakNPZXSowfYYp5QZvBJQ6tF7L2nW8Azbj0m6q5S6pUqlFCTdYHuNPsdutL16kI5Seil3Wow9bfv3Leoopcav6L7jkYx0PQRfKx1Pkidtnxox5vNF0pK2uzuBjNZY9wHPAEeTvveHMWgLDb3Ik1Kvsx1ikpYnhAAea3NCqEMuU9qatNr/UlLbwl+3pKWoDKHKnGXQXEMHkW1JM3hr0fIMXgH8nOS+uSMwQ9KZjFDPHo2k1wD3Rz2MVCoj8IykZTupbR1yiluYgU0pnhJtPYD1YH5Ji3QeyBqB9ouZtT3ZaLJqJ9DOPGH7yKwlxEW3lBXUJEWL2v5H187FSHWyIUQZfL5ALgeWne275gy/IJ0fE/NPEwMDE2z3y3pwWm0LcyO3/cV+xyTtbfvoKC2Zp4BHgEeB5Yi9hwKzeqNICvdGqYw+dWV7QChpBq9N8kzuG0gpVG8HXgzsDpwbabbUC0knkSZFbrW9Y5taKoNNTrP7GnAoQ3WorwE+A+xv+4y2tA0ypdT4da/YSlrMQ+3QbvJgdbn4EPBBsvFW3r0eqYfu921/py1tpSHpT7aXKUDHdrZPa1tHFHNJ1sO9tkMmYpT6Se8EbECalPmp7atG/qtR0VGUN0o32cANktnjcW1qmdupwfaAkA3B3kq6wbya9MB8Qbuq2iXXdHVM0rawHdm3ty/NlatKpS0kTSS1EZppTEaacb+u/19VRpsSavwkXQG8z7nveGP/asAPbW8QoGHdkY7bvnqk43NYy1YMd6q/geRUPy1Kw9xAZEA1N+ioDBE5EZPLK/9IquU3XRmOtj8epKMIb5SRyK7+G9o+p20tczM12B7jlDKDVzqSxtt+MmisIkyfKpXK3EubNX4qoO94fmCeTnJWhln9SDYbbQ2VWZF0LL3LswTsantCsKRZhRSywl4ZInhle9eRjkeVZJTijVIZfWqwPcYpZQavBLLZkkk9WNs0WyrC9KlS6YekTUgu/T/M2z8HFsuHD7H9y9bEJT3hvZT76DiUVPN3gu2HgsZsvf95Q0drfccl7Q28m/T5/xSY2kYpkKQ1gJVsn5W3jyKVJwEcF7nC3gtJewIPkVqhjbrfQimBzEjUle12kPQY/Sdixttu1UdK0oLAO6Kc0vPK9jv6eKNMs71WkI5S/C/GLDXYHuPMDV98UeQbGKQV5ftaFdMHSfPZ/nfbOiqDjaSLgY8596OVdD2wGyl1+QDbb21RXngv5RF0bJN1TLS9y+zePwfGK7rGrw2UerDvRPIkuYfUR/nakf9qjo4/DfiK7cvy9o3AQSRn9u1sbxOlpY++vYDVgOVsv7NNLZHke1a/wG4V2wsES6oUSC6x7JQTvoXUQjFkEaR6owwONdiuVFomm7ZtRur/vZXtl7csqTLgSPqD7fUb26fb3ja//p3tjdtTN1NTWC/lUiilxk+F9B1v6FmDFHC/D9gv0gRK0lW2X9PYvtz2hvn1pQ7qfV4KpWTFNCbXe2L7nggdJROd9dBj/KWAefLm/ZEalNo57kwyyr0S2Jh03j4RpSHrKNobpWaBzBlq668xjqTFgb2AfwDfBw4HXgfcAXxqgNuAtY6kDUk3+21IDyN7kRxtK5W2eUlzoxNoZ4qYDMpta0IC7X6tc1pg3u5AG8D23dnwMYoT8++nA8ccRteK9p9IqeSHRnlvNFikudEJtDMvixIhqZNZ8WRUGmwfvgh8rLG9Ko2sGCAk2K7B9HNCwCbAe4BRz3qQ9FlgPtsH512/J5WBzAecRMrYGXWUerDfS+risK/txyTdFR1oA+SgehdJL8rbrXbF6YFm/5bK7AjrAVlpjSnAAsB/kWbv7iTVuZ0NnNCiroFF0qGSbiOZC/0RWIdUK3OSu3q1ViotcbOkLbt3ZtflW6JESLpL0p3Z/bpN7ialKLddfvJMw2BxJorvf/7r/PP7qDF7cDuwA3A+6aF9WeAjkj6ZW6RFcb+kWeoc82Tq/YE6Vsg/bRt/TeiUn2Rus/2/tn9D18TEaFLQvaNYbE+2/bHA8oLtgSMb2w/ZfjVpVXeW75tR5OfAkqRWuO9Qao3bSpqvpD0l3Uv6frlH0j0546AUavrzHKCmkY9xJF1ne2JOubynmQ4i6Vrba7cobyCR9CBwK3A0yQTjX5LutL1iy9IqFQAkrQycA1zGcLfpjUilDrf2+9soJC1pOzKY6afjiKi68VrjN4Sk/6H/g6Abq2ejrWMD4BTSan/zWtkV2NH2lRE6RkLS3raPDhrrNtv/1efY7bZXjtBRGaKUrAdJV9tet7G9m+0T8+v/tb1eoBYBbyDVar+dZGq4O3Bu1OqypANJ36kftX1n3rci8A3gCtuHBOnoNzkp4HO2F+tzvPIcqcH2GKd5c+txoxu2PahIOgl4Apgc4aSbDTneTLrJvwm4BNgcWKaNuqlKpReSFiClFzZdr6fYfqo9VUOUUksWraP0Gr8SkLS+7T8EjvdyUhlQ81qZbPsvURpGIvIczYZx33ZXX96cFfMR25ErmBVmlsFAKoM5csQ3j66OW4E1uk1g83fN9H6TNAG65mPIJG0L24sHjXsLyVzzqa7944HrbK8SpOMLIx23/cUIHWOZGmyPcSQ9DPyGNEP1uvyavL2J7UXb0lYKktYnpSBuYHv/4LEXALYi3eRfB1xse+dIDZXK3IgK6ZXblo6Ca/xaQdLqpPvoJODhpmnZoBN5js4NWTGVdlBqlfgK0kruE3nfwsBxwAO2P9umvqxnfJTvg6Sbba/2fI9V5j5qsD3GyY6LfbH96ygtlZGRNAHYpuPiWqm0heaCvpvBq3X90uhEWoFYOkJH1rInKW184bzrceAw29+M0tAPBfcdzy7snQD738BywGt6mciNooZLSNfK36NaBj1fWsi+KDorZtCQdMxIx21/PEjHPCSvmg+QapRF8hj4HnBgVGZfKdesUovNQ21f3LV/M+Ag228M0rEGsJLts/L2UaS0eoDjbF/d948rz4kabA8okpYBdrJ9eNtaoiilXU0p9VOVSulIOpb+vXJ3tT0hSEdn8qGXM6uj/BZKqfEbQV9Y33FJvwcmkFzIf2r7tuwovMJojttDR6fF1AzbrRnoSXqM/tfKeNuh3WckrUCj1KFzvlbikfQ0MB04lWTaN+w+ZvukYD3jgU7t/u3RHQQKumbXAM4ELmW4B8fGpBaPUd02pgFfsX1Z3r4ROAhYCNjO9jYROsYytfXXACFpCZIb5CSSE+PUdhWFc2L+3Vq7mkznYfCxVlVUKuVz1X94bI4SHcCNwPvoqvGzfaekHYDrgFaD7WCDtr8AS5Fa0S0B3EYLzrmltJiyHeb0PRI5Q+sEUur4taTAbm1J/wvsbvvRlvX9gpQFMdn22W1qCeSVpGe/HUldC04Bfm774Wghkl5Gl7+BpMm2H4zSUNA1e4OkNUktYDufx2+ADwdngbyyE2hnHrV9GoCkDwfqGLPUle0xjqRFgG1JF/MqwOkkh9SwtMdKpVKZm8npj+M79dG5rdP8+fA1tkMmzkqp8VMhfcclvZj0/TaJ1N7yJSSDo9YdwNtG0lLAPHnz/sAU3RNJrfIOtv1s3ifSStnKo53xMDskLUkKPje0PblNLW0gaWlSf/pPkjoY/Chw7I1J7WhPZGglt+Pc/x7bv4vSUhlC0i22V+1z7NYoo7axTF3ZHvs8SOqvfSBwqW1LelfLmlpB0h/7HSKlgq4VqadSqYyMpE2AFTs+BpJ+DnTqpw+x/csgKYeR7qVfy9snk9IyFySZQEUZK/5Z0pv61Pj9X5AGSMEUQGj6Zze2HwF+APwgr5jtABwladkSzPMikfRZYD4PtTz7Pal+fj7gJOArQVI2tr1bc4fTqs7Bkm4L0tAXp3aB9zMU7A0MktYlTUy9GTiP+M/gSJIvzTWNfWdJmgp8ByjSH2S0kLQ7sFinnFPSfaTSGAGftv3tICn3S3qt7WE96fOkcuvtNccCdWV7jCNpb9Is5sKkB8RTgIuiagxLQtK1pNWYKcA0uh4US0ktqlQqiWwg8zHbN+bt64HdSPezA2y/NUjHNcD6ndVBSdfYXiev2P3W9iZBOoqo8SsBSW+2fVGfY6fa3iFaU5tIuhp4ne1/5u3OOToP8OvAc7SIPtulmGCVgKSDgS2Bm0geB+dHZTp06bjR9urP99hYRdIfgLd2zCQb1+yCwAW2RzQ4noM6NiDFBicyvIPArqRM2IHPFHqh1JXtMY7to4Gjs4nOTsAZwJKS9gemDlIbDttrS1qNNLM7hdSfdgpwYRtfPJXK3EYL9Y4TOoF25jbb/5u1RK3UAYzrukfsD2nFrtOCK4JSavwaZpO9sO3dA2RMlrSPG/2cJY0jORuHGOeNhKSTgCdI18r0iDE7gXbmG3nfjGxIFcVlkj4PfMmN1RxJB5FW26PYLf+eEThmqRwI3AVMzD+HpnnC8Kw+SVrU9j+6di4GjAvS0JcWrll5eNeGnwHYfirymrV9ZV7F3ouh6+YGUqnFX6J0jGXqyvYAkh/WJpFmrEJmmUtE0o7AZFLbnNZd2SVtTeo1ecVs31yptEB0vWNBq2Q3ARt012bnmuEromqlS0HSdj12LwPsA8wT4QmS3a7PAz5re2peDfo58CjJqf7fo61hNvrWB5YlnTejXmYg6VZgje5/d27DNb3fdTQKOiaQJjzWJRmkAaxDWjHbPaf+VwJpuG/3JCqrT9KHgA8C+zJ8BfUw4Pu2vxOhox8tXLM9v8PypOHt0Rmo+R7adImvrfrmEDXYrgwU2TRmJ+BdwD9IrTCmdoyP2kSpR+2rgXltv61tPZVK2+SWJN9url7m/VsBH7G9ZZCOTwKbA3vYvjfvWw74FvBL20cE6Silxq+paUXgAOD1wFHA92yHdHzIZk8XAMcC7wX+4BbbOrZJ/v54Bakt3BN538LAcaRJ3M8G61kJ6KQF32j7juDxO+36Zu5qbNv2SpF6SiR7YkyyvVfgmFsB+zG8B/vhtqdFaSgFSd8klTkc2LX/EGBx23sE6ZgXOBT4b+BemNk471nmAAAgAElEQVT//AfA59qeuBwL1GB7jNP4wvmr7YEyn+hG0q+BRUgB9mlAM30H239vQ1elUhql1DtKWhk4B7iM4SshGwFbRZbBSNqDFFQuTHoYeQz4qu1vBWooosYvj70aKT11HeBw4MeR5TjZ7AlSG8uTgIsYMrDD9tW9/m4UdHRS6h9pM9DPtdlfBj4A3MPQA/P3gAOD/2/mB97D8IBqiu1/BWp4adeucSQDvX2Bq233ys4Y80hah1SGsj0ptfx028e2qyqWgq7ZhUlt8tYntW6ElOZ/FfCBqEUgSUeRno336WRv5QyVI4AnbX8iQsdYpgbblYFB0t00Zrabh0gz3SEpO5Jen18+bfvyiDErledDI+1whu37WtayAL0f3FtJcVNqp0h3SnnQ2FfZfk1j+wDbh+bXV9reIEjHz0iTHkeSJi+H1cVGTFzmCaF+2PZmo60h6+hMcDxtO7ImuSe51rOZChrqGC9pdeAs4HcMb++0MfDOLg+GCD3jSP3pP01Kaz80WkPbSFqFVDo4CfgbyQxrX9sjppePkpa3AZ9h+P38MNvnBmoo7ZpdkaHPo40skNuAVdwVEOYJvJujSlDGMjXYrlSCybOqAA8PaspjpWwkXWj7LW3r6JDrc5sPI3e2oGFN0gN78yHxCNvXB2ooosavz8SlOtvRtYbdSJpvEFMfldqf7cXwc3Sy7QcDNVxMyvi4qGv/5qSU1DcG6ZgPeD/JR+DSrOn2iLFLQ9KzwG9JNfO35313tlAT/EHgw6Q08qvy7tcAXwVOsH18pJ62kbRsfjnD9p9b1NG3l/ZIxyrPnRpsj3EkXW173Rf6nrFAI+2wJ1Fph5VK6XTSkwvQMYGUZrceaVVKwNqkFbPdbT8apGNrUkrdVxj+kPhZ0grRmUE6iqjxKxEle+XNSCmyW9l+edC4nZKLXtj2m4J0bEzqrnEiw1eUdwXeY/t3QTpu7mcYKOkm268K0nEf8AxwNKkOdRi2T4/QUQKStiF51WwMnE9q/3WC7RWCddwIbNKd+ZJT/i8NPDf+2O8Qge7sjeych1ou1zqDVE7ww6797wV2sP3OdpSNHWqwPcaR9CRw20hvAV5se9kR3jMmyLO700lpVDC0EgOxaYevBY4HVgKuJwUNA5XWVikbSXeSaht7EvWgKulE4G7gYNvP5n0CDgJWtr1LkI7rSH2s7+7avzxwpu2JQTpKqfHbzvZpPfbPD+xv+0sROvKYG5IC7G2AxUgru2e5q73QKI6/Xo/dG5JW7x60vX6QjstJpoHXdO1fG/hOlGdLdkV/dXd9dvYVuD4qJTXfO0aaBHl/hI6SyPePrUnp5JsBPyQZxF4YNH7fyZbgiZhrSefGFGAaMKzUwkHu7KWQjYNPJ30OnYm61wDjgXe1ueo+VqjB9hhndi0fMq3XZUYgaW/g3cAjpJndVlzIJV1FWhH7DfBO0kPyFtE6KpV+SHoIOJPhE1Idwh5UNXLrr77HRkHHDbbX6HPsRtur9zo2inrarvG7gFSn/dFOSn+uxTwKON/23gEaDiWZPN0LnAxMBa6KXq3r0rQpaSJoQeDLts8LHLvveRh5jko6kDTZsFcnaMmTUseQ/n8OjtBRGRlJi5Kunx0Dsy+uAD5k+7qu/ROB70Z5TuQxVyNNOrwDuJEUeF8YaSRYGpI2Y/j3ysVt6hlL1GC7MnDkB9WdSDO895AMU64d+a/m6PjD0vYHJY2/MvdQyjk5m2A7ss/2dcA7nNt+NfYvB0wLTDssosYva5kEHEJ6SF0TeBkpwAq5l0p6ELiVlCY8zfa/2qhDzVq2IDmz/4sUZI9k3jZaGm4CNupe0Ze0GHBZv9TuUdLyUdLK/kKkCbvHSf4GYa7Xko4FDnCXkWEOso6zvXmUlraRdILtD/TYvwxpcqznROIo6NgE+AmppVRzBXVX4L22L43Q0UPXjsBkklHb4W1oKAFJb6Th99DGfWysMm/bAiqVaGzfKelMUorM+4BVSPWgUbxE0rb9tgeplqxSLL1WtNvgMkmfB77UdEqVdBAQ6SL7BeAXeTW1+ZD4GWD/QB0n5d8PkbJ02uRU0oPZPsDDwGYObMUGvBJ4M2l16uhc/zhe0ryRq1NK7diWILU/+33eN3OiKtAL5CjgQkn7MrxN3mH5WBi2jwOOU4vO/cADwLWSDrI9RdJCwP8A7yJNBAwS80r6MbBLoxxndVIKdVimge1LcxndnsBuefeNwIa2H4jSATNTp3cinQ//IN3HpkZqKIVGGvlTDH2/bS/pMGoa+RyhrmxXBoauFe0/kVLJz3F8a5QfjHB4IGvJKmUhaQ3bNxSgYwKpT/C6DE2IrUMKJna3/UiglonAp2ik2ZFW667r/1djk7xCNZnU//wAYFNSUHcKaWU3rJ9y1rMAsBUp8H4dcLHtnYPG/hXDndlb8QLJWrYiBZJNN/LDbU8L1LA0sHxnlVLSJ4EX5cNTHOgInr/zjyX1EF6SNEF0iO0nojSUQPa5+A6wKOkZ6LWka/Ujts9uU1sbSPo16Zw4FTiNNHk5k24Dt2gkvZJkhBlyH5U0leQ9cmLX/l2A7WxvHaFjLFOD7crAkA3S/kiqRX2ULvMU219vQ1cTSS+3/Ze2dVQGG0l30aMXfX5t2ysF61kJ6NSchtcod2l5EUAbfg+lkH0n9rR9ZWPfQqQMgK0jU5Z7aJsAbNPtrFuJQdLJwE86QZykW0iGoAsBq9l+T6CW5UmTQouSMiE+b/tHUeOXhqRjSJOVy5Fcpi8PHn9rYGnbk/P2FaSsEEjGij8L0nE3s7YuhCE38rZbF/6CZKB7mu2+RqVzcLxbbK/6fI9Vnjs1jbwySBzM0I31RSO9MRJJLwG2Iznqvoo0A1+ptMlrurbHATuQHMqvmfXto0d2uH49Q6t1i0u6r4XV0z1JaeML5+3HSTV+34zUUQgbdNJRO+TVwv1zic6ok1ddAJ5sPqQ7tYMLC7QlfQr4urtWLpTaGX3N9u6BWt5GOkebK9uH2T43SgOwatdq6RO2j8z6fhslIpea7Erq7X1KTpX9hqQPkFZ0B6YDSK5f72RdrE7KDNpZ0s4Atj8eJGU/0sp6hwVInRUWJtVxhwTbtpePGOc/xfbmORshynhzXK+dksYB8wRpGNPUYLsyMNj+n7Y1dJA0npTOvjNppnkRUuua37Spq1IBsP0QzPyyfR/waVIa95aRD6m5rvAs4HcM1ZK9AficpHdGackOyxsBb2i4b69IenhfzPYhETpKoTvQ7uKnQEQryY7reBv1wE1WBa6RtJdzL+s8MbM/gbXSkj4IfJgU0DR7wX9V0tK2jw+SsmDXdtPpevEgDZ2x1unUi+e603fnCYnTSBPbg8JVfV5HM7/tPzW2L83fNQ8ptSULoemp0ItAn4WRNJg0WRbB2ZK+C+xt+58ws03cUUDkRN2YpaaRVwaGnELVl6jZXUlTSHWFF5IeTH8J3N5my5pKpYmk+YD3k0xjLgW+Gllr2dBxcR77oq79m5NWrN4YpOMWYKLtp7r2jweus71KhI5+RNf4zUbLn2wv07aOSCRtREpXng6sBtwGfMr2/wVquBHYpLveNK+wX+q4HsZXAO/rNsvLLuA/dGB7p35I+qztr7Sto22Uep+/IzB9u28HCUl3RJUn5ZLC6cDfOrsah8N8FhrlWn+1/dqIMfvomA/4Csm07h7S57EMyZDzANtPt6VtrFBXtiuDxB6kG+ypwP2057i8Osn98ibgJtszJNVZr0pJ3AU8Q2qrdC+wlqSZ7a0CHfOX6g608/i/yKmRUbg70M47n8wPbm3zI2AlSSE1frNhEO9l04ErgbeSUjJDA+2Mehk72X4oZaSG8QXSStmXGe6KfgDwiUghI/ARUnAxcEiaB9iCZCb4FuC3BKVvA1dI+qDt73Zp+jDp+onik6RuDk+SFjymtuHBMdICi6Qlbd8fpOPfwL659KIzGXLHoBkJjiY12K4MEq8Etgd2JAUSpwA/t/1wpAjba+dZ/kmkdkJ/Axap5miVgvgFKWiamH+amNQmJIJxkhboXrHNKzKR319/lvQm2xd36dgMiA6qZiG6xk/SNHoH1QJeGqGhFCS9D/giye15JdL1MlnSrcC+th8MkvKopInd7vjZRT8s1d72+UqtLPcDOtli04FtbU+P0jEbSmltGIakTUlla28nBbYbAysEB1T7AGfkWvHmRMwCpDK6EGwfTWoX2OlQc7Gke4BDbUe2gR2Jy4kpx+mYSr7c9m3A9ZK2B9bJk3QX1OfSF05NI68MJLk9yU6kGc7923QolbQe6Utwe+A+2xu1paVSmR2StrN9WtBYBwIbAnvZvifvWx44BrjKdkiPWElrkLoYXMrwPtsbk9y3W2+TFkl+cO+L7V9HaWmbbAj38c75mfeJlEn16ShnY6V2bD8hGU01z9Fdgfd2WnFVQNK9tkMCmRKQdB8pQ+lbwBm2H5N0V1ula3mScqaJn+1ftqEja1mD9Cz4PmA/26e2paVJZDmOpOOBy5xbf0m6HTgPGA88Y3uPCB1jmRpsVwaObI4xCXgz6aHkyBKcSfMD2ia2wxxbK5XnS/SDqqSPklbJFiKtSD1O6m8dmUbeWU3fmeF9tn/SK718FDUUUePXD0nLADvZPrxFDVsDD9i+oi0NDS07RD68S3oFsCfDz9HJth8I1PAD0jn6iO19osbtoeMx+mdfLGR7YFyWJR1NWjmeDkwhTRxe33aLq7ZorGhvDfyJlEp+ju0nWxXWIPJ7VtI1wLqdjgqSrrG9Tn59qe1NInSMZWqwXRkYJB0MbEmqlf4pcL7tZ9pVNTPI3oz0IL+V7Ze3LKlS6UtbBliSFgHouAtXhhNZ49c17hKkrJxJpLaFU9usG5d0KPBqYF7bb2tLR9YyUCuoMCzr4Wnbv29VTGUm+TnjDaTr9O3Ai4HdgXOj6pVLmSzMPht/JE06PErXpIztrwfp6LRkm+UQsKvtCUE6rrf96sb2mp2SD0nTba8ZoWMsU4PtysCQb7B3AZ0apc7JL5IB0lo9/3D09GxICrC3ARYD9gLOsv2PSB2VyvMheMZ9aWD5TgqspE8CL8qHp0Q5pEvaHViss2Kb0zInkO4dn7b97QgdIxH8/7IIsC3p/rUKqYZ/R9tLR4w/txCcCro1sLTtyXn7CmCJfHj/KMfpksntjN4FTLK9Zdt62iK7T3dM0rawHdmSrXUk/Q8jGDna/mKQjl1HOm77pCAd15HOgwe69i8FnBf9bDwWqcF2ZWCQtNxIx5s1d6Os41DSStC9wMnAVFL9aW39VSkCSdfTf8Z9FdsLBOk4mZSqfXbevgU4npRSvprt9wTp+APwVg/1H7/G9jo5tfwC2yPWMEcQHNg9STJZOpDUVsqS7oxMS5X0+vzyaduXR437fAieAPkdKYX/T3n7WlKP64WBH9h+00h/P1aRND8po21nUoB5GnC67WmtCisESeNLSp+uxCPpvaROAZ8Crsm71wWOAI5p09NorFDdyCsDQ1Qw/Rz4AHAryaxkmu1/1dZflcLYqm0BmVU7gXbmCdtHAkiK9DZQJ9DO/AzA9lNKvbZLIPIe8llSzeM3gZMlnRI4dof/zr8fJjn3tsJsJqYiS4Lm7wTamUvzOftQXtEdKCS9haH2VpcAPwTWt/3fI/7hGETSJaRz9O+23908NoiBtqRjRjpu++MjHZ+DOjYBVrT9w7z9c1KWI8AhUcZxtn+cu+IcQvJ7MHAD8Hnb50VoGOvUle3KwFBQvdA8JHO2SaSVh0uAzYFlSqghr1RKQdKNtldvbC/m3EtY0k22XxWk43bbK/fYPw64PdBxuogav4aejtHQJOC/SD2Wp9q+NVJHmxSUMdXzHM3H7rC9UoSOUshlY78FdrN9V94Xmn1RCo1zdIbt+1oVUwCSniaZxZ0K3E9XK7jA9O2LgY91DHrzxN1upGyUA2y/NUJHZfSpK9uVgaGUNG3bM4DzgfMlLUBaRRxP6uV7se2dWxVYGXhKmZgCHpO0Sid4awTaqxHYOxi4UNIhtg/s2n8wcGGgjqv+w2Ojgu07gUOBQyWtSQq6zwV6Bn1zGkmrAh8CVsu7bgK+a/uWiPGhfzCdV60mkbw4IrhC0gdtf7dLx4dJKf+tksunHgFO6MoSGS3WJU0E/ULSnSRT1IFxIG9SUFZfKbySVMq3I/AMcArwc9sPB+uY0NUJ5zbb/wsg6StRIiR9gfR9/3iUOdygUVe2K5VCkDQB2KaTUlSpDDqS3krqqf1l4Oq8ez3gAOATUSluOQ33BGB94Lq8eyIpwP1AlJtvZQhJ/49kzHY86dwQsA7wQWDbNuq4Ja1Dqg3enmTGebqDWtRJehlwBvAvhl8rC5C+V/4SoaMfkrYBVgIm2t4leOyNSBMf25Gu36m2j4/UUOmPpJvyy8m2jwsee2nSpMwnSUaCYfXJkm6z/V99jvXNVBkFHR2jtiddSJ/xsUYNtisDg6Srba/7Qt8zB3R0HjSerA6xlcrI5BXT/RjqHTwdOLzTmiRYy4oNHTfaviN4/CJq/ErIfJB0HnCY7V917d8U+IyD2n5JWoUUyE0C/kZaJdvX9ojp5aOoZzOGztEbos6JuYFc9rE5yUju/W3rqQwh6aXAhrbPCRxzXdJ1+2bgf4Eju1aaR3v8acC3u//NkrYCPjLIjvljjRpsVwaG7KB720hvAV482u6xOWUH4LGaslOplI+kzj1hhu0/t6ij1vhlJN1qe5U+x26xvWqQjk5t8O6dVnSDWhsM5aSklnLNVspD0sEkh/qbSOUF57fhlyNpZeAc4DKGZ6NsBGw1SN4XY51as10ZJFab/VuYMdoiono4VipzO5J+QHpwf8T2Pi1K6RjmPAS8e6Q3jjJF1PgVwkg1+/8MU5H6je8EXCLpfNLDu0b+kzlPCdkGmbvz77Zdrku5ZotF0knAE6T07VHPFCroHD2QVOYxMf8cKgnSdWsH9ZW2fbuktYD3MJSN8htgD9tPRWioxFBXtiuVSqVSJDklGFIv5d+3KqYASqnxKwFJD5IC21kOATvYjmy71anr35qUlroZqdXUVNuRBnqVynNG0vrAssAGtvdvW08UpXQQ6CBpBYaXJ90ZOX5l9KnBdqVSqVSeE5J+AfybtBJy9uzeX5mz1Bq/IRqmPj2Jat/TC0mLkt2Obb+pLR1t0MhG6YVt7x6pp1IOkjbreAhIWqHTki1vb2v79PbUxZNNcU8gpY5fS5ooXJtUP7677UdblIekPUmZIafVtrQvjBpsVyqVSuU5IWlJUtuUDW1PblvPoFFr/IYjaQlgOVKv8+i2PZUeSNqux+5lgH2AeWwvHSxp4CmlHKdpQNttRhthTtsYq4h0dkknksouDrb9bN4n4CBg5WjX/m4k7UUqv1zO9jvb1DK3U4PtSqUQJG0NPGD7ira1VCqVMpG0AMNr/G4AppRQ4xeZ+SDpA6Qe33cAKwAfsn3WaI7ZR0cRD+4lkt37DwBeDxwFfM/20+2qGjxKKceRdI3tdbpf99oeBGZTFtT3WGXuoxqkVSrl8Frg1ZLmjWpbU6n0QtIlpADi77aruVAfJL2S9Bn9K2pM2//K/z8P5l03lhBoZ3YhZz4EjLU3sIbtv+ag7idAeLBte4XoMUtH0mokE6p1gMNJhk9FpKG2cc22je1fd17nbBBs/7UNKX1e99oedMJMFms72tGnBtuVSiHYPqBtDZVKZrf8e9Td+f8TJB0KPAKcYPuhFqX8CFhJ0mm29x3twfrV+EkqosbP9v3A/aSaw9Hm6U7AYPvOvOJf6ULSTfnlZNvHBYz3M9L5eSQpdXwGMCG7PWP776OtYTaEXrOlkFuyfQwYlzb1DHCs7YMDZawo6SzSfavzmrwdNmn1XFLWg9LaL5P0eeBLbqQZSzoIiMxA6Hz2I3V4qLwAahp5pRKMpNfnl0/bvrxVMZXKXIikbYCVgIkF1LUJWN32DQFjnUgBNX4lZD70cCPfqblt++PhogpF0ktJPgvnzPbNL3ysuxlapez87qzSuYT+45HXbAlI+iTwNlKpxV1534rAt0g9po8K0rHpSMebK/CjrONJ4LaR3gK82PayI7xnTuiYAHwPWJc0eQopG+Rq0uTpI6M5fkPHRyMm4gaZGmxXKsFksxKAh1vuHVyp9KRRhzpzV2PbtleKV1Uppcav0Tpnhu37IsbsoaFYN/JKpSQkXQO82fbfuvYvAVxYQq20pI1t/y5orBFbf2XC7m2SVgJWz5s32r4jYtzG+GHmdINKTSOvVIKx/d9ta6hUZsNrurbHATsA+wLXRInIqY8GHrf99ahxe+iYG0ywwmr8gPG2b4Zk2Nasf5W0YUTGTr9gWtKCwDtGe/zSKOUclbSd7dN67J8f2N/2l4J0FPF5FMJ83YE2pLptSfNFiZA0D+l7ZCnSivr03LbwAGA8aVV31Inuoz0S+bp4PUOGl4tLum+QPAUGgXFtC6hUBhFJq0o6UtI5+ecISau2ratSAbD9UK6F/gewFXAJ8P+ALW33au0zWtwN3AO0snrawfYKtlcs4KH9MkmfV6cANtNCjd+Uxuvucb8ZqANID/GS3i7pR6TzZcdoDT003ZR/PhoxXkHn6IcknZvTlAGQ9Dbgj8BLo0QU9HmUwEgO8JHu8N8DPkA6D46R9GPgCOBrJayuRyNpdeBG4A3AvfnnDcAN+VgUa0l6tMfPY5Ja9QEZK9SV7UolGEn/DzgdOD7/iDSje4mkbWsdd6Vt8mrH+0kGR5cC29i+PVpHKanAkt5r+8f59bB0x+B6t4+RHlhvlzRLjV+QBhi+it69oh7porspsDPwduBKYGNgBdtPRGnoh+1XdWqlI8aTtJntX+bXK3Rqc/P2trZPj9BhewtJk4CLJE0B1gReBuxk+9qR/3rOUdA1WwITc9A0s3Y+/xawYKCO1wBr2X42Z6A8AKzUssllmxwLfMT2Rc2dkjYHJgNvDNJx/SBOdkRSa7YrlWAknQccZvtXXfs3BT5T235V2kbSfcAzwNGk2fZhRD24Z3+Dfl9Sth0SYDZr2rrr29qodyupxq+tzyOfo/eSTJ7OsP2YpLsGtRVXCf8njfHmAb5Ias/2MLCZ7Vujxs8aivk8Kon6/zAcSTfbXq3PsZtsvypIx8D1OI+mrmxXKvGs1B1oQ3LilHR8C3oqlW5+QQpyJ+afJiZlZkRwdo99y5BW3OcJ0gCFrORCMTV+S0s6hvRv77wmby8VpOHnwDaklPEZks6khV69BdUGF3GOStqEtCp3Gela3RSYJukU4MuB52kRn0cJ5FXkPYCVSen833c7fc9Xk/THjixSC7Y/5te2vVYLmtpkXLfnBcz8/4qMz2pv7VGmBtuVSjwj9TL8Z5iKSqUPtndrWwNA02gp14AeQAo0v0pKpw6T0ud1r+1RI9fxnQX8jqFe1m8APifpnbZvDJLy6cbrq7qOdW+PCrb3lrQP6d8/Cfga8GJJOwDn2n48SEcpK+lFnKOkbJgP2r4yb58h6ULgC8B1QM+VvFGglM+jBE4C/g38llRysQbwiRZ0hKzUzkX8EDhN0l4d0zZJywPHkPrBR3FU7u7wD2AasB/wOuAOUg/wWcz1Ks+PmkZeqQSjWfvDzjwE7GD75cGSKpVhSDoWOMD2Y137VwOOs715oJbVgANJtcmHAz+OXpWR9ARwO3k1Jr8mb69oe+EgHRcDX+1T4/c521E1fn2RtKztWUoPAsadD9iCFHhvYXvxoHGLqJWW9DDwG9I5+br8mry9ie1Fg3SMc+4B3+PYRrYvC9JRxDVbApKut/3q/Hpe4MqS0rdzNsQk23u1rSWabKC4H7AQ6dx8HDjC9rGBGk4lTcYsDCwKTCcF3ZsAa9veKkrLWKUG25VKMKr9YSuFI+lzJIO0g2xPkbQQ8D/Au4D9bE8N0vEzYD3gSOBUYEbzuO2/B+kYsS9rVCuZUmr88nj/j5Qy/hvbD0paC/gM8Drby0Tp6KNtvO0ng8YqojY4e370xfavI3SMhKR7bS8bNFYR12wJlFgrLWkdkrnh9sBdwOmRAWZpSFoEoHuCO2js6bbXzBMx99l+RePYdba7S8kqz5OaRl6pBFOD6Urp2P6ypJOBYyXtASxJCnYnBjs9r09K+dwX+FTe13TUXbHXH81p+j2YSxpHWkmNenAvosZP0uGklnDXAvtLuoDU0ucrpEmaCA2XkM6Bv9t+d/NYVKDdkdLnda/tUWOkYFrSxlE6ZkPk51HKNVsCHTdySP8H4xvu5LY9IUKEpFVIn/0k4G/AKaRFv9YzctpA0tLA8rYvzQaPn5T0onx4SmAHkKcBbD8j6f6uYzN6vL/yPKnBdqUSjKSzRjpu+51RWiqVEeikgs5LMiO7Kbqlku3lI8frh6QJwF6kldyzgIuAj5ImAK4DfhIkpZQavy2BdWw/JWlR4E/AmrbvDtSwW/7d9sNgEbXB2QF8B9I5er7t6ZK2IvkcjCeVYbRN5OdRyjXbOrYjzSRH4mZS3fhWnUAy+y4MKocz/Dz8MKkd7EIkN//3BOkowfByTFPTyCuVYCT9lfRwejJwBV2z/SWk+1UGG0kHAbuS6oBPkbQU8A1gCVJf0BAjLknbNU3SGvvnB/a3/aUgHWeSzGN+D7yJ1DdYwCcc2Ds4aymhxq87LXVgW8cUVCt9Isn9+0rgtcD9pL7Gn7F9RoSGrGMavYNqkVqARfkbFHPNloikhUllQZNsbxk05jbATsDGwPkk75oTCjIZDGWk+6ik39p+XZCOWto4ytRgu1IJJq9AvJmUSrUWcA5wsu0bWhVWqWQkfQM4sIdB2tuArwf2/7yAtHL5Udt3NjQcRVq92ztIR9NgaB7g/4BlbT8VMX4fTW3W+HUCzA6vb24PUnZOKbXSkqYDa9l+NpcVPEBqM/lQxPgNHaV8HsVds22TJym3JNVKbwGcRqqVnhasY2Fga9Iz0GakjJ2pti+M1NE2km60vXpje7GOD0m0B0cv8n3kHbZra7AXSE0jr6P4aTAAACAASURBVFSCsT2DNKt7vqQFSF84v5L0RdvHtauuUgHbPdvC2D5P0tqBOraQNAm4SNIUYE3SCtVOwatT/25omqHU1zr8ob2gGr+tu7aPDBq3OAqqlX664wKe0/vvjA6089g9Pw9Jy5BWNaMyt4q4ZktA0ltIzxlvAS4hBbfr2/7vNvTY/icwBZiSy1C2B/YHBirYBh6TtIrtW2HI8DN34AifRM1jz8NQV4e3kNL+a7D9Aqkr25VKC+Qge0vSDW15Uk3Z923/uU1dlcrsiHQUzuPNQ6pf2xt4mJSKemvU+FnDDOCfnU1SDewTxBsMnQz8xPbZefsWhmr8VrMdVePXT98ypImQw9vUEcnsaqWj0usbra5geLurzjm6VoSOLk1LkAKpSSSTxam29w0au4hrtgQkPUsKmnZzbk2XJ2NCDCYrvZH0VpLfxpeBq/Pu9Uj3jk/YPi9Qy6akjIe3k0pRNia1yAv1aRmr1JXtSiUYST8krdCdC3zR9vSWJVUqz4cwR2Gl/quTgctI9aibAtMknQJ8uduVe7QoyGBo1U6gnXnC9pGQavzaENQroGpDR0PPSaSganLQvfV7DNVKH5PdfMNrpYFWU0475PKGbUkP7qsApwMr2F46UkdB12wJrEvKKviFpDtJtdLhn4+ku0j1/H+1/dro8UvD9vmStiV5cHw8754ObBv5XCjpPuBe4FvAvjlr6q4aaM856sp2pRJMnmXuzLg3L8CBm3GvzH0E98q9CtjT9pWNfQsBXwC2dp+e00Ha2jAYKqLGr09AtWN0QNULSesDywIb2N4/YLwiaqX7kSesJtneK2i8J0kTDwcCl9p2KauobVyzpSFpI9LE2HYkV/apto9vV1WlTSQdDWxDCvSnAGcC15dwzY4VarBdqVQqlWFIeoz+jsILRa0aSRrXqUPtcWwj25dF6GiM2arBkKQrgPd1p9HnGr8f2t4gSEexAVU0PRyFh223pGkd0jm6PXAX6RwNcauXtDdpFXVhUseNU4CL2jo32r5mS0Wp3/jmpLKP97etZxCR9APS9+wjtlttgSZJwBtIEzFvB14M7A6ca/vxFqWNCWqwXakUQJ1xr1SeH8Er7N0GQ6cAxzq4D3gpNX4lBFSlPKiWUistaRXSOToJ+Bvp/2Rf28tFjN9Dz4qkc2QS8F+kbJSpUX4LpVyzJSCpc5+cUX1hyqHh3P+07d+3KqaBpPkYMknbwvbiLUua66nBdqXSEnXGvTI3UdqEkKQ/2V4maKxiDIYkrUmq8Vsj75oOHN6G90ObAVUpD6qSRgxmbd8TpKNzju7ecaUvJdsgn7OTSKUGKweNWcw12zaSLskvH7L97lbFVOYqJI23/WTbOuZ2arBdqQRTZ9wrcwslTwgFr2yvTQoqtwc6BkOfb2vVsESiAypJE2w/2ufYsrbvHW0NI9FCrfQ2pHN0Y1JryZ8CJ9heIWL80qjXbKXy3MiTMQb+XidjRocabFcqwdQZ90rplDIhJGka/WvHN7O9cKQeaNdgqJTU6RJo1kZLutj2m3odC9bUWq10Q8PCpD7ok4DNSD2Vp9oO6WFcouN0NQUrG0k35ZeTbR/XqpgBpJGdM8P2fa2KGaPUYLtSCabOuFdKp5QJoUaqcE9s/zpKSzdtGAwVlDrdekAl6RrnHtbN1722R1lHUbXSTSQtSvqe2bE5GTGoVFOwcpH0UmBD2+e0raVSmdPUYLtSaZE6414pkdInhCQtQ3pgPjxovGowVBhdK9utOYKXXCs9yNRrtjK3IulQ4BFSGUgRLQQrL4wabFcqBVBn3CulUsqEkKQlSMH/JGDJrGPfoLGrwVBhSLoP+DqppGCf/Jq8vXegeV4RtdIlZBuURL1mZ4+kV5LqdP8VNF49R58D+Z6yEjDR9i5t66m8cGqwXakEU2fcK3MjLaVNLwJsS6qD/f/s3Xm03VWZ7vvvQ1NAxRBQAtIkoZEuIm1oBEKrpKQNbRqPgEg5xpUqCmzKwvKIyrmpe6RQpBGL4grIMYQgEAQpBDTSlFgISBMBCSYBA16aQBAkEgjP/WP+Nnvtlb1XQsmecybr/YzByFrrt+N83Fk7WfM35/vOrYBrSVtiN8kxfqiXpDM7Xbf9tVxZoHytdAjvlKTbSJO6a3LduAyhG8VkO4TM4o57qF0tN4QkLQLuAb4M3GXbsUU31C5qpcOKQpKA0bZ/k2Gs93a6bvvFwc5Qk+aGoYFXbX9zWV+fm6TLgddIjeuyHy25MonJdgghhD5quSEk6TTSFt0hwJWk5lO3xmS7r26s8ZO0JjABeAm4AfgCsA/wO+As2y8UjBfaNKuob5A+uN9YOk/Ir+lvMB94s+ellsvutr/XJZ3QPFxke3rRMP2QtCswEtjN9hdL51mRxWQ7hBBC1SRtTpp0TwK2BM4kbdF9vGiwStRS45dzQiVpejPWEGBdYBZp0r03sKPtQwdz/JYcUYe6HCRtBGxI6jh9Yek83aR5jwI8V/I9KulcYH/gP0k3T+9yF09CJE2x/aXSOcLgi8l2CCGEFYak7UiT7gm2P1A4S9YGQ7XLOaGSNMv2dpJWA+bbfn/LtQdt7zCY44cVU/zMltVsW9+P9Hf4bsAtwEU9R0x2k5ynJiwjx6WkG4Yv2z69dJ6VUUy2QwghhP+G3A2Gaq/xy6mWo79qJ+nR5uGFti/IMN5M0nv0xRp7knRjUzBJjwA/AK60Pad0HgBJ65B2K50FfMn2vxeOlJ2kB0k3HtTf9Vw17JL2bR4utn13jjG7zWqlA4QQkrjjHkJftW/Rtf2RngZDmYac1/y6KNN4/apkQrWJpPNIH1R7HtM837hQpurY3lbS+4A9Mg15YvPrkkzjvSMFfmZrMIk0sb1V0gKa/he2n8kZoqVj/wRgOOl0iV1sP5UzR0W2Ae6j/8m2gSw17LZvzzFON4uV7RAq0Y133MOKJW4IlVVLjZ+kUc3DJbbnF8pwQqfrti/PlSX0krSN7ceax2u0/l0haQ/bvyyXLkjagzTZPZrUTHBqrlVlSX8CZpPOop9NmlC+zfa1OXLUQtKvbe9UQY6HBrpEaly3fc48K6OYbIdQkZzHcITwTnXrDaFaVthje3QYSI3v0ZLb+2v5ftRK0n7At0ifN9bINOZltE2wW9j2STly1KKiyfYDpD+XqaQmk312Ttl+skSulUlsIw+hIk1nzphohyp16RZMbG9WOkNj1eYM59I1fq/Q90Ozmuc9KyFrZ8pxAvAPwNbNS48C59n+fo7xO8ldK13Re1QDPO7v+aCp6PtRjeYop0mkVe25wL8BV+ca3/aJA12TtEGuHBX5dukAALZ3lLQN6b0xFXik+fUW2292/M1hucRkO4TM4o57WJF14w0hSR1X42zfnylKFTV+wE+B95NqLqeVqLlsJtqnAZ8F7id9T3YGzpZk21fkztQqd620pPcuI0+WGzH0vQnTvoqZdStl06n+Y6SfG0g3Y27utgmEpCmkreMvkrZw71Wq/KNV0yTtaGAysC2wUdlE2f24aXr5EvA94GxgLGl7/+dsP5ErSFP6cSZwpqQJwPeB/91kCn+h2EYeQgihj7gh1Jekt0jnOL/Q81LLZds+IFOOKrYdAkgaBhxFary0JnAVaeKda3X9l8BE2/PaXt+0yZGrIVgVmvfofKBnItn+Hs1yI0bSc6QJnUgTvGkteY6znWUFU9LGwM+APwC/bsbfiXSTaP/czcFKkvQVUify2RVkWYvUJG0y6c9jKDAeuMP2WyWz5SbpFuBe0vfgQOBS0jbuscDHbe+XMcvGpL/LjyRN/qcD19l+NVeGlVlMtkPITNLITte7uDNnCMulqR1/g7RF98YM450GHAO8TJo8FPkQUtNku4ekVUgf0s4DpuQ6kkzSI7b7LWfodG0QclRxY0rSucD+wH+Suk3f5QIf8GppXNfUBz9g+9y2108ldcDumHNl0kxwjyOtbN8I/CO9K6hn2X6hw29/N3NMbca9hfT36M+AJ7p1y7+kB23v0JRmPWl7ZMu1B2zvmCnH7aQJ/3TgGmBB6/WMu2JWWjHZDiEzSQ/TW9/Yw6SjMNa3vWqRYCE0JP0P2/+nebyX7f9sufZ3OWpQO5G0EbAhsIftCzOOuzlpYnkE8CRpcvlAxvFPtH1ZrvE6kbQnqcZvLHAX6SihOzOOf5/tXd7ptZVZ86F9P9Kfy26kSc1FtueWzNVD0shcN5MlPWZ7mwGu/db21v1dWxlJmk66OTkEWJe0S+cGYG9gR9uHZsrxALAKaYvyNNvzJc3JteuiNhU1E5xHb4nHUr04uvXP590Uk+0QCmu2PX4R+Aipuc/5RQOFrlfLh4AaSfogacL9CeAfbU/POPZw4DMUrvFrPpwtpHd1qk8NbI4adkmvAf39/xWwue0hg52hyVFLrfTbmlrYicBZwJdyHe3UMv6HSWed32H7OUnbA/8EjLU9IlOGAXeB1LhDZDBJmmV7u6aGfb7t97dce9D2Dhmz9DTimkAqy9ka2M72s7ky1ELSQuAO0t9ZY5vHNM/3tr1uqWzh3RUN0kIoRNKWwD8DuwPnAKfafqNsqhCASjoKS5pJutP+ou1jco3bT47WFe3fkyaZU2wv6vgb330/INX4bQncQ6rx+zbpg9olpFXNHOaR/lzGAQex9C6dHDXs22YYY3m8QIdaaTI1rZM0hPT+nEDaJXUtabt01rIkSWcDhwIPAF+U9BPgZOBfgJxHOw2TdFR/EYEs3fIrshjA9puS2mvVl+QM0taIaxfSxPtXkubb3jNnlgoc0fL4X9uutT8fNBU1AF1pxcp2CJlJ2o40yf4g8A1S45Ks/+CF0EktK9uSRjUPl5Tsnts0n3oIuB74I21dlTPWKVdR49eJpNVL3jSUtDcwyfYpmcarpVb6T8Bs0o2g2Sz9Hr02U45HgJ1t/7k5pu73pJXLeTnGb8lxaafrtj+ZK0tpFTWt28H2g/28LuB/2v56jhy1kzSC1PwxSyfwWhqArsxiZTuE/B4kfQD5Mamubrf0b01i+9RCuULosY2kh0j/6G7RPKZ5nrN+a61mJQRJa9h+veeCpD1s/zJTjq/TO3l5T9u1nBOrJZA+/Uhqb2pUrJNv82H5AFKH4UOBrGfmStqpGftY0vnBWSaWALZPa6mV/gRwftNlOHet9NWk9+LW9J47/nZM8n1P/mz7zwC2X5I0O/dEuxm7aybTy+ELLY/vbbvW/nwwXSfpWNv3tb1+JnA46e/ZrtSUCB1LWunfCLgu4/CfJTUAXUTBBqArs1jZDiEzSSfS4QN6rm6tIQykZUW5X7afzJSjihX2TiTtavtXmcaqqsZP0h6kSe544L3AKcCPbL+UYeytSB9MJ5FWZK4CPm+743t3kDMVrZUeiKQNctXEtrxHe+zT+tz24Zly7A5cDGwBPAycZPvRHGOH/jXbxq8mHWt1d3OT6iLSzaEjbP+xaMDMJA0lHZ84GdiKdENsgu1NCuUp2gB0ZRaT7RAyk7QmMNT2822vDwde6VkVCKE2zTFPk2z/INN4bzcyam9qVLLJkaTR9E70Ftoek2ncfTtdt317phxTSKswT5G2Tl8H3JvzCJ9m6+OdwKd6GsOV6Gw8QK309Ny10v3kWgc4mvRBflvbG2Uat5b36L3AGaSJ/uHAybbH5Ri7NpLWI90IK9pYscmyPenvi1OAv21enty6a6lbSFpE6r3xZZryk9Ld2Us2AF2ZxTbyEPI7D7iZpbf17U1qNvR/ZU8UQgtJa5M+DG0M/Ai4Ffg74HOkMogsk2367gBpvzOc9U5xc2pAzwT7DWAUMCbnFtmBJio9NX5AlokMqeHV46RVqRtsvy4p9537o0j/n2dKupnemtTcnmPpWukxksZAvlppoOc85SNIE+ydSGfnjqfvSvOgqug9uortW5vHV0s6I9O4NZpKBY0Vm87984ETgBnAbaR/V4ZIGlKic39hZ5B+Jr4DXCnpqhIhKmoAutKKle0QMlPn82F/Y/uDuTOF0ErS9aRVkLuBA4H1SROZf8i5rayixj53kzoYTyOdDztb0tycK7n9ZFqqxs/25zONvSrw0WbsA4GZpKMLR9h+s9PvHYQsPSvLk0h1498nfS9uyTT+ZQx848e2s3TgljSVNHm6hd4j2Z7o4vfoHKB1rH9tfZ7zJkhptTRWlDSX3p+Vnhtjbh675IpuSS2T3UmkGyJnkn5WHs80fhUNQFdmsbIdQn5/3eHaKtlShDCwzW1/CEDSJcAfgJEFShxqaezzLGmVfwPSVuGluj3nMECN32a5a/ya0xNuBm6WtAapKdpawNOSfmp7csYsfyKt3E1tul8fC3yRNOnMMf6JA12TlLNR3GjSDbJHgUdtLymw26Ca9yhpBf2wAZ7nbBhXgyoaK5a88VMz23OAKcCU5rSaScBNwAcyRejUADS8C2JlO4TMJN0OfMH2PW2v7wqcY3ufMslCSGptRtZK0sicdbGShpEmET2rD+sA49p/jgc5QxU1fpJG2P59P68PBcbbviJnnpqUqpVuxt6G9P6cQGoatzXp2K0szdGaDFW8RzvJ2TCuBrU1VmzLtgXpZ2Vi7OoLK6uYbIeQmaTdgOnAZUDPERhjgONJ/+D8V6FoIQAgaQnwp56npFXL1+jd7rd2xiwfJq0q32H7uabBzj8BY22PyJWjLdP6wHGkic3IXDkknUbabjiE1JjsKuDWApPtx4C/aa9Xl/RJ4Mu2t8iQoWdL6vO2dx/s8ZaRZcBaadtFjmRrOj9PIr1P59veM9O4VbxH+8lV7CZIabU0reshaSPSDaHJwIeAfwGutf1wzhyl1fJ3mKTzOl13HEf7F4vJdggFNB/WTwG2a176DXCB7efKpQqhLpLOJm1RfoC0pe4npOZc/wL8W65t7ZI+2tJsqf3adNvH5cjRMmbpGr+DgXOBQ2zPbl47g/Th+WO25+fIUYNaaqUl7WD7wX5eF/A/bWc9w7j0e7TJUN1NkJr0NK2zfXam8T5Nej9sTFpwmA5cH9vLy5K0GJhF+vN4hrZGk47jaP9iMdkOoRKS9iYdq3RK6SwhtGsaUR1Jeo8ekmnMR4Cdbf+5qcf9PWlb7Lwc47fkeBw43faPW15bBfh/gQ1t/03OPG3Zemr8JtjOVeOHpAOBfyNNXk4GdiNNvgf9jO2aSHqA1Gvj+6TmefMLbe2fAxxr+762178KHF6yDKTEe7SWmyC1Kdy0bjGp6ebnbN/bvFZViUE3kvQ+0ntiAvAmaTfKD20vLBpsJRLNmEIoSNJOkr4haR5wFvBY4UghvE3SX0k6UtLVpCZpBwLfzRjhzz2r180kbnbuiXZjHHCOpCMBJK1JOhJtDfo2YcrO9izb/5xzot2M+1Pgk8DPgc2BA7ptog3QdHI+jrRqepuku4ChmZujQfqwfHVTdoGS7wL7kulop4EUeo8u1TCOAk0NayBpqKQTJP2EVE+/Balp3Ra5JtqNDUmlBedI+q2ks4DVM45fFUn3vxtf85eyvcD2d23vT/o7fR3gEUmfGOyxu0WsbIeQmaSt6D2v9wXSXcTP2x5VNFgIDUkHkd6fB5GOdboKON/2pplz9DT26bFP63Pbh2fMsglpG/v5wP8AfmX79FzjNxlqqfF7hd4je9YgnTu+hAI1/bUpVSvdjL09cB2pROlvm5cn2349Y4Yq3qNNluIN42pQY9O65u/TCaQ/nyGkFfYvlcpTQvPnMrvTlwDDWo9qG+Q8O5P+PD5K6id0ju1Hcoy9sovJdgiZNWca3gl8yvYTzWuxlSpUo+U9eqLtuc1rJbbGVtHYp/kQAmnb5eXArcA3WnIM+upDqFMttdKS3ts8HA3MAG4D/o7maCfbL+bIUauSN0FKq7VpXY9mAWJi7r4CpUlangWWJYPdA0PS14FDSLtApgE3235zMMfsNjHZDiEzSeNJ//DtRTqrdhpwSbfXkoV6SNqR9B49FphDeo9+pZbdFwUa+8zscNm2D8iRo2YlavoHyPFo8/BC2xdkGK+KWumWFWXobXDUs/vAtUysSmtugoy1fccyv3glU7ppnaSeY00X2/5ljjHDsjU31+eSThyBvn+P2Pb2RYKtRGKyHUIhzYfTI0j/8B1AarBzne1bigYLoYWkPUnv0aOBB0nv0YsL5CjW2KcTSavbfiPTWMs873x5vuZdzPNXpBWRyaS69mtIR/jckGP8DrneB+zR2tBuEMfaBbga+Ljtu5vJ3EWkLctH2P7jYGcIS5N0JmnS8Krtb5bOU5tCTesubR4uzF2CEwa2rBV220/myrKyisl2CBVoOi0fS/qH78DSeUJo13Tf/ghpRfmkTGMOBY4iTea2Aq4l/YxskmP8DrlEukE2GTjUdpZmWLXU+NVS01+LGmql+yNpC9J7dKLtD5bMkpukE5qHi2xPLxomhNDVYrIdQgihD0k9k7Ultp8umKOqxj6S9iBNXsYD7yVNrn6Uqwt3RTV+xWv6a2nEVVuttKSNSI2nJgMfIp1Jf63th3PmCPWo5Wcl1CneH4MvJtshZBZ/sYXatdQoL7B9TMEcVTT2kTSFtPPkqSbHdcC93dpnofaa/pxqqZWW9GnSboONgenNf9fX8h6VdBupa/2Ftm8snSeEEHKJyXYIIYSqVdDY5zngceBc4Abbr8cJAkmpmn5JPwamAjNs/2mwx6udpMXA3cDnbN/bvFbNe7RZcd+QVEt/Yek8IYSQyyqlA4QQQgid2J5je4rtDwFjgLWBmzJG2BD4X8BhwO8kXQGsJWm1jBmqZPsXtv8e2AT4FrBHpqEvBg4F5kmaLunIpmFbcZK2kPQ/Jf0m47AbknZdnCPpt5LOAlbPOH5Htp+xfV9MtPOTtMyjCZfnawaLpDHNzZhQQO3vj5VBrGyHEEIIy0nSGqRJ3iRgLPBT25PLpsqrlpr+Jstfk26CTAQ+DPwHMNX2rZlzVFMrLWmTJsskUgnGdba/lGnsmaRt9C+WLEEZiKTPAAuAa7rlLOFaGisOOLh0ObA98LjtCSUydLPa3x8rg5hshxBCqFLt/Q0krQ2Mt/390llyqqWmv13TFfxyYHvbq2Yas/Za6a1I3ci/nmm8nrr9QW/U998h6RRgG2CU7cNL58mhlsaKyyJpqO1XSmboRivK+2NFFpPtEEIIy0XShqQVq6JHGuUm6fjm4SLbVxcNE/qQtAFwHGlle0PSZPdK2w9mGr+KWmlJ+zQPF9v+Zc6xQ1geNe2ICSGnrq83C6EWkh5tHl5o+4KiYULo3xXAFpKusf350mEy6lmljFWXSkj6W9KK8tbANcAXbP+iQJQNSV3Zz5H0ftJkv0St9CebXxcCxSbbkl6htzs7NF3Ze361vXamHHGDrD6XN78uAKrZERPCYIuV7RAqIul9pG6tPy6dJYT+SBIw2nbO5k8h9CHpe6SGYD+1/VbpPFC2VroWkmYA7weuBabZfqpQjjObh6/Y/maJDCGEADHZDiGEEMIKprnpsw+prOFhScc1z38HfKd0qUPuWumaSBoGHEXa2r8mcBVp4v1i0WANSbva/lXpHN2oWVCYTKqbB3iUVPaxoFyqEAZXTLZDyKz2pk8hxHs0/HfkrOmXdCGpg/GawG+B9wA3A3sBq9j++GBnaHJErfQAJK1CmnCfB0wpucIsaTRpx8EkYKHtMaWydCtJ2wI/A34C/JpUWrAT8FHgANuPFYwXwqCJyXYIIYQViqTbgDdI/Q1uLJ0nJM2fyxakY5UGtaZf0iO2R0taE3gaWN/2kmbF+6HmTPZBJ+nS5uFC26fnGLN2kvak92i8u4CrbN9ZIMem9E6w3wBGAWNsz8udJYCkHwLTbU9ve/1oYLLto8skC2FwxWQ7hEIkfYiWrVS2Z5XME0IrSasBH6Pvdr+bazibtjnTeENSf4MLC+Y4Avj/bP9XqQy1yVXTL+l+2zu3P+7veTeTNAZ4xvYzmcabR2rSNo20itnn7wvb92fKcTewdpNjmu3ZkubWciRbN5L0W9tbv9NrIazooht5CJk19WzXAyOBB0lbqT4k6SngCNt/LJkvBEkbkz4o/4He7X6Hkjou75/rg/tAmvGfAe4rmQPYnfSzu5rtjxXOUgWnO/g5muetL+mzpPdmz2Oa58MzjL+i+Htge0mP256QYbx5pBKUccBBpD+PHgYOyJAB4FnS2ecbkN4Ps+nbJT3k96f/5rUQVmixsh1CZpLOAxYD/9jTRbepbft/gLVs/33JfCFIugx4wPa5ba+fCuxi+4RMOWaSPiC/aDuOiqlEDTX9Ld2m+2X7a7myrAgkDbVd9Og6SavbfiPjeD2N2iYBWwLrAONs35MrQ+glaT7QX92+gNNsj8gcKYQsYrIdQmaSHgG2b9+O22zbfdj2tmWShZBIesz2NgNcy7bdT9Ko5uES2/NzjDlAjmiCFaomaWTzcIntp4uGadGUFRxA6kB9qO0NCuVYHziONPEeGRO7/OIGWehWsY08hPwW91f3avtNSUWPqwmhsajDtddyhbD9ZK6xluGTza8LgZhsU76mv2mMNgF4CbgB+AK9R3+dZfuFHDkGkrtWGri8+XUBUHwXiKQ9SBPs8cB7gVOAQW2a14nt54ALgAtabuKFjHom05LWK/3zGUJOsbIdQmaSHiPdXVf7JeD/xMp2KE3SHPr/YCzgG7a3yJTjFfrWWap5LlJ58No5coS+Bqjp3wl4P5Clpl/SdFKH6SHAusAs0qR7b2BH24cOdoZOJF1OOposV610FSRNAY4FngKuBK4D7s3dmKzpEm/g5egSXwdJhwKXkn5u3wKOs/2LsqlCGHwx2Q4hM0k/p0OjFtv750sTwtJajjPql+1Pdrr+LuaYQZrAXUvqKPxUjnEHyLI18Gn6ruT+u+3flspUSg01/ZJm2d6uWWGfb/v9LdcetL3DYGdYHjlrpSW9j7Sa3PoevdL2ghzjNxmeAx4HzgVusP26pDm2N8+Vocmxb/Nwse27c44d+ifpIdIE+zFJu5Nu3O67rN8XwoouJtsh9XE6kwAAIABJREFUhBCq1dLkaCKwJnAVaeL9YsYMHyZN+C8G7qd3JfdvgaO6rY67hpr+Wo7+qqVWWtK2pN0GP6HvboOPAgfYfixTjlWbMScBBwIzgY8AI2o4NjCUE0f0hW4VNdshZCbpqE7XbV+bK0sI/WlWHS4GtgAeBk6y/WiJLLZfBi5ttuVOBM4jTbr762o7WL4CTLL985bXZkj6GXAmqXa5m9RQ079Jc7KDWh7TPN84Uwaop1b6LOAfbE9vfVHS0cD/DRydI4TtJcDNwM2S1iAdGbgW8LSkn9qenCNHqFLrEX1LPbed8+/0ELKJle0QMpP0FvBA8x+0nUNq+6T8qULoJele4AzgDuBw4GTb4wpl2ZO0SjYWuAu4yvadmTM8bnurAa5l685eixpq+iV13Kpu+/JO11c2nd6HmU8QGGH79/28PhQYb/uKHDlCfZbRjdy2v54tTAgZxcp2CPn1bIndHrieVFP3RNlIIfSxiu1bm8dXSzqjRAhJ80gdwKeR6qXfbF7fGcD2/ZmidKq5/VOmDDW5HThsgGt35Agw0GS66VI+ULZBUUOtNJ3fhznfo7dK+hvb89pePwb4MhCT7S7V6WgvSbvmzBJCTrGyHUIhkoYAR5COr3kf8M+2by+bKoR+Vy7/tfV5rlKHtmaCPV3IW2L4gEw5niNN+Je6RGr4U+Ts4JA0dcLjSDsgDgLutJ1lS3dFtdLz6b+0QsBpuc6VlnQwqTnaIbZnN6+dQboZ8THb83Pk6JBvCvAycEnmmyGhjaTRpJ/ZScBC22MKRwphUMTKdgjl/Jn0j/4fgVGkOtQQatC+ctn63KRmYYPO9n4DXZO0eo4MjS90uHZvthSVqKWmv+k4PRk4GLgH2AvYzHa2s+CppFYa+Hdg6ADXLsmUAds3SXod+A9J44GTgd2AfWy/lCtHB/eQ3rffAo4vnKXrSNqU3gn2G6TPPmP62QkRwkojVrZDyEzSAaRt5LsBt5E6K3fdB/awYpK0ge1nC40t4ADSBOvQnCvKkoaTPhg+YXthrnFrVENNf7OS+xRwETDD9iuS5hY4z7mKWumWMdez/ULOMQfIMZZ0xvYvSLs//lw4UihM0t3A2qRdQtNszy7xMxtCbquUDhBCF7qNNNG+C1gDOF7SeT3/lY0WwtIkrSPpU5J+Stoqm3v8PZqfjSdJfQ7uoLc+Nsf4JwO/Ac4HHpN0eK6xK7WK7Vttv277amB4gQw/BDYileEc1pTllFg9qKJWWtKhkp4HHpI0v2ksmJ2kVyT9EbiJNLE6EHiu5fVcOc6U9JW27tehrGdJuy82oPfvjFjxCyu9WNkOIbPoohtWBJLWIvUUmEyqQR0KjAfusP1WpgxTgGNJK5hXklbK7i2wejkL2N/285I2B35g+8M5M9Skopp+AfuRtqQeDAwDPgXcZPvVTBlqqZV+iLSC/Fizzf8btvfNMXaNWv6dXdS+xT+UI2kYqUnsJGBLYB1gnO17igYLYRDFZDuEEEIfkqaSjtq6hbTl72ek7dO5J7nPAY+TGi7dYPt1SXNsb545x/22dx7oebeRdGmHy0WOL2xq+HuapI2zvV6mcTsdZ9SxA/O7nKPa92iz6+BI0ln1h1SQZzXbb5bO0e0krQ8cR/qZHZnrxlQIucVkO4TMJK0HnAK8BHwPOJs0sfkd8Lk4BiyUJukBUpnR90m1dfMLTXJXJXV1nkTajjoT+AgwIueH5X66kU9sfW771FxZaleypr8lw1q2F2Ues2itdD8r7J9tfW67v9X3wczzV8AhpJ0x44BrgGtt35Bp/Lts7908vsL2J1quVXMjIiSSRtl+snSOEAZDdCMPIb+ppA7GW5I6o14KfJs04b6EtC0yhGJs7yhpG9Ik9zZJLwBDc0+kbC8BbgZulrQGcCiwFvC0pJ/anpwpSns38vsyjbtCkLQOqev2ZGBbUi31YI85k1Tv+WL7MV85J9qSDiX9Hf6GpLdIW7l/kWv8Fu3dyFufZ1tVkXQQvUewzSTdsNvV9idzZWgMaXn8wbZrImTX7Igx8LLt01uvxUQ7rMxiZTuEzCQ9aHuHpt7wSdsjW649YHvHgvFCWIqkXUgfoI8D5tvO0nxJ0gjbv+/n9aHAeNtX5MgxEElrAoc1TcK6SumafkmjmodLSp7dvCLUSkva1favMo31FnAncKLtuc1rRUs/at5i302ao/oAFtu+u2iYEDKKle0Q8lsCqbCxWTFslaXxVAjvhO37gPskfYG0AyOXWyX9TT9nsB4DfBnIPtlutrb31AYfRJpYdNVku62m/3x6a/p/nitDRSthb9p+DMD2fzU3goqTNJre84wXAmMyDb0zqczitqaR3jRg1Uxjt1pH0pGkcph1JB3VvC5SI72Qme3bS2cIoYRY2Q4hM0kLSUcXifSB9Y6eS8DettctlS0EeLvpk4FXc9d6tuU4mNQc7RDbs5vXziCtpn4s54pmsyozmdT1+h5gL2Bz26/lylCLWmr6a1BTrbSkTemdYL9BOhd+TD83q3Ll2bPJcjTwIHCd7Yszjd2piR8FtrWHELpUTLZDyKxlK1W/4u5vKK2mY3MkHQj8G2mL8smkM+oPsf1SxgzzScePXQTMsP2KpLm5u7PXpKWmfwLwArA1sF3p5mi5LaMbuW1/PVOOu0nnWk8j3QCZXct7VNIqpMaGE0t0qg8hhJJish1CJSSNIH0YObt0lhBqImks6YztX5DqY/+cefxzSZP9WaQGh9cDD3fjSm5/StX01y5zrfQM0hbuHwFTbf+iUK10Tw+SJbafzjl2P1n2BV6y/ZCk44B9SKd+fMf26yWzhRC6R0y2QyhI0nDgWNIH1Y1I2+w+XzZVCHWQ9AppO7uANUhbY5c0z2177YxZRDopYBJpK/kw4FPATbZfzZWjZs33aKztO5b5xYOX4XLgNeBC27MKjN+nVtp2rlppJA0DjmrG3hJYh3Tm+D0ZM8xsHi5o7xKfk6QLge2BNYHfAu8hnWywF7CK7Y+Xyhb6kjQFeBm4xPaC0nlCeLfFZDuEzJoGOkeR6j+3Aq4FJtjepGiwEMJykbQ6vU3Sxtler3CkrGqp6e+PpF2BkcButr+YacxNqahWusm0PmmnwSRgpO0RpbKUIOkR26ObEwOeBta3vaS5IfSQ7Q8VjhgaksYDWwA72D6+dJ4Q3m0x2Q4hM0mLSA2Wvgzc1XQl78rmQiG8U5KGAEcCk2wfUkGetXKe61yDmmr6S6u5VrqHpFEVdW/PIo7+CiHUIo7+CiG/M0hHo3wHuFLSVYXzhLBcJH0GWABcY/vNjOP+FXAIaTfIOOAa4LsZx59JWsl9sX1rbLdNtAFsX146Q9Nt2sDLtk8vGOVZYGNgA2A4MLvJlVWn70e3TbQb60v6LKnkpOcxzfPh5WJ1r5p3xIQwmGJlO4RCJG1OmnT31NedSarZfrxosBAGIOkUYBtglO3DM4x3EL3nWc8ErgLOt73pYI/dlmNU83BJzuPGwsBaTnVYbPvuwllqqJWu5vtRg2V0icf213JlCUnsiAndKibbIVRA0nY0x+jY/kDpPCHUQNJbwJ3AibbnNq9FyUWoVrfXSg9E0oaknSHRBbxLSZpi+0ulc4SQW0y2Qwgh9CGpp0nNIttXF8yxI2n3x7HAHFJd7Fdsj+r4G8NKT9JDA10idarfPmeefoN0Ya30QCTdRmqCdU2uEzck7Q/8HWk3DsCjwAW2f55j/NBX1MqHbhU12yFkJmkuqW7pedu7l84TQj96mju9UjKE7QeAB4B/krQnabVwdUn/QSq5uLhkvtBX5pr+t0h/j04FbgCK1M5HrfTysf2RphP46BzjSToEuAD4evOfSOeQf0/S39m+KUeO0MeqktYl/VksxfaLmfOEkEWsbIcQQlhukna1/auC468CfASYaPukUjnC0grU9G9DugFzGPAIaeJ9S+bmfVErXSFJPwf+wfaDba9vT+r7sG+/vzEMGkmvk45h62+y7SgPCiurmGyHEELoSNJoes8RXmh7TKZxRzYPl9h+OseY74Sky4HXgAttzyqdp5tJmgBcCPxv22eXzlMLSVOAl4FLbC/IMF4VO7ckPWZ7m3d6LQweSb+2vVPpHCHkFtvIQ8hseeqWorYplCZpU3on2G8Ao4AxtudljNFzxNQC4JhOX1jIBcBI4BPAFwtnyaaimv6NSTX9RwIvAacD15XKU6l7SLXS3wKOX8bX/sUqOl/8T//NayGE8K6Kle0QMpO0iHQW64BfAgyzPbLD14QwaCTdDaxNakg2zfZsSXMr+iAdCmo5VumVUuflSrodGApMJ5273mfVNuo/y5G0GvAx+jYmuznz9v6FwB39XQL2tr1uriwhkXSi7ctK5wght5hsh5BZy5m9ncR5vqEYSTNIzYR+BEy1/YtuPnKrUxOs0Feumn5J80h/JrT8Cr3dyLvqvdrcADHwaqkbIE2OjYGfAX8Afk3689gJeD+wv+1nMuXoWJNt+/YcOUIvScOBz5B2oXwPOBsYC/wO+JztJwrGC2HQxGQ7hBDCUiQNA44ibSPfElgHGGf7nqLBCogmWJ2VqumvWYFa6ROah4tsTx/s8TrkuAx4wPa5ba+fCuxi+4R+f2MmkkaQmitGXX9mkm4B7iXtSDkQuJR0ksBY4OO29yuXLoTBE5PtEEIIHUlaHziONJkaaXtE4UhV6OYP7qVr+iV17Glh+/4cOQYiaTypVnoH24NeK90hx5rAYblq65fRmOy3trfOkaNt3OHAsaT36kakYwOznPUdekl60PYOzRFwT7aWykl6wPaOBeOFMGiiQVoIIYSObD9HagZ2wXKWQQwqSRsCL9p+vcDYS31wz52htLaa/qNbavrnZYxxLzALeKEnVss1AwdkzLIU2zNKjS1pVWAc6T16EHAnkKuRXafzzl/LlAFJQ0k7cyYDWwHXApvZ3iRXhrCUJZBqPCS90HbtrQJ5QsgiJtshhBD66FSjbPvJMqn6uALYQtI1OVao4oP7Up4FNgY2AIaTGj7m3ib3WVKH+kWkSf91tl/NnKGaWukmy76k9+jBpC7ke5Hep9kmucAwSUf1F490gyaX50jfgy8DdzUTvCMzjh+WtrmkH5HeCz2PaZ5H882w0opt5CGEEPpYEWqUm62Io23/JsNYi1j6g3vXNoyDemr6JW1OOv7rCOBJYIrtBzKOX0ut9HzgKeAiYIbtV0qcINDcqBuQ7U9mynEa6X0xBLgSuAq4tZt/ZkuLpnWhW8VkO4QQQuggPrh3VrqmX9IHSX8+nwD+MfektyktGAU8YXthzrFbMpwLjCdtrZ8KXA883O3v0ZabMT03hc4k7YJ4vGiw8LZu7n0RukNMtkMIIVRJ0lzSFt3nbe9eQZ744L4MkkblKDVoW9H+PWkr+Y9td6oZHowcJwNTSMcXbQZ82vaPOv+uQcsiYD/S+/NgYBjwKeCmXFvsJe0OXExqDvcwcJLtR3OMvSyStiN9bybY/kDpPN0smtaFbhKT7RBCCOEd6uYP7jWcOy7pLeAh0gruH2mrGc9VPy1pFun86OebGwA/sP3hHGMvI9fq9DZJG2d7vUzj3gucAdwBHA6cbHtcjrFD3QbofTGhi3tfhC4Rk+0QQgjVkrQa8DGg5zihR4Gbbb9ZLlV3q6GmX9JX6dCUzfbXMuW43/bOAz2vgaS1cq341/L9qG1XTIjeF6F7xWQ7hBDCcpE0BXgZuMT2ggzjbQz8DPgD8GtS19qdgPeTVhOfGewMTY744B76Jek50hb2HhNbn9s+NVOOmaT36Iu2j8kx5gA55gCt24H/tfW57WuzhwpViN4XoVvFZDuEEMJykTSeVIu5g+3jM4x3GfCA7XPbXj8V2MX2Cf3+xrDSk3Rep+sZJ7kd34O2L8+UY1TzcInt+TnGHCBHp27ktn1StjChStH7InSbmGyHEEKokqTHbG8zwLXf2t46d6ZQB0mLSZ23pwPPkHY9vC3XJHcgktYEDrN9dckcNZG0ge1nM421zO3rNW757zbd3PsidI+YbIcQQuhD0pmkLamv5mo0NUCOX9ve6Z1eCys/Se8jdTOeALxJ2pL6w1JHbzWZVqW3KdlBwJ0lt3TXQNI6wNGkpljb2t4o07iLgNmdvgQYZntkjjwhhO61WukAIYQQqjOv+TXrMUr9GCbpqH5eF7B27jChs5w1/c3//neB70rahLQt9RFJX7R9xWCO3a5pGDeZdNzWPcBewGa2X8uZoxaS1iIdyTaZ1GNhKOkM8Dsyxuh3R0ybJYOeIrwtel+EbhUr2yGEEJbSnIM6Cnii1GrhMuo/sf3JXFnCsuWu6W/G3Jm0kvxR4D7gHNuP5Bi7GX8+8BRwETDD9iuS5treLFeGmkiaCowFbiE1ivsZ6e+Qrvx+hBBCrGyHEELoQ9LJwBTgd8Bmkj5t+0e5c9Q+mZZ0G/AGcKHtG0vnKc32jFxjSfo6cAjpKLhpwBmFjoP7IWnVdgKwRNL1dDiSLDdJlwOvkd6jszIMORp4ifTn8qjtJZKq+X6EEEJusbIdQgihD0mzSEdrPd90jv2B7Q8XyLE7cDFptfRh4CTbj+bOMRBJGwEbAnvYvrB0nlxqqOmX9BYwlzSRhN4Jrkhdr7fPmEXAfqQV9oOBYcCngJtsv5orR38k7QqMBHaz/cVMY25D0/QKeAHYGtguV3O0UKdoWhe6VUy2Qwgh9NH+gafUByBJ9wJnkGo9DwdOtj0ud47QV8txV4tsTy+UYVSn67afzJWllaTV6W2SNs72eiVy1ELSLqTvxXHAfNt7Fo4UCommdaFbxWQ7hBBCH5KeI23N7TGx9XnGM4xrmfTPJK2cvtjt3aV71FDTXztJ+9jO0hSs6W9g4GXbp+cY851oVv/H5vp+hPos6wZZo+g58SEMhqjZDiGE0O4Lbc/vK5IC1mnrRt7nue1rM+U4sfk1uhdTR01/rZ2NJY0mreROAhYCYzINfVnz6+JM4/VroBIDp5WdmGh3sVK7TUIoLVa2QwghLBdJawKH2b4603idupHb9kk5cjRZxgMfAB62/ZNc49aolpr+WkjalN4J9hukFf8xtudlzLC27T8OcG2k7acy5SheYhBCCDWJyXYIIYQBSVqV3hrUg4A7a9hKLWmDXA2XJH0H+CDwC+BA4AbbZ+UYu0a1bO+vgaS7SWe+TwOm2Z5d4uiv1j8DST+1fWB/10IIIeQV28hDCCEsRdK+wGRSd+V7gL2AzWy/1vE3Dm6mdYCjm1zbAhtlGnof0tnRSyT9NXAn0LWTbWATSecN9DxHTX9FnY2fBTYGNgCGkxpAlVjFUMvj93a4FkIIIaOYbIcQQuhD0nzgKeAi4PO2X2lW67JPtCWtBRxBmmDvBAwlnWucs/5zse0lALZfa5o9dbMaavq3lfRQh+siHcE1qGyPlzQMOAr4qqQtSb0FdrN9z2CP3xplgMf9PQ8hhJBJTLZDCCG0+yFpQjsBWCLpegp8YJc0FRgL3AKcD/yM1P3655mjbNMysROwRfM8+5nONbB9eX+v99T0Z4qxzXJ8TZaGdrZfBi4FLpW0PumYq281tdIjcmQA1pf0WdJ7sucxzfPhmTKEEEJoEzXbIYQQltKs3u5HqtU+mLRK+CngJtuvZsrwALAK8H1SPex8SXNsb55j/JYcVZ7pXINaa/prIGlUrvdG0wV8QLa/liPHQCR9BlgAXGP7zZJZQgghp5hshxBC6EjS6vROqMbZXi/j2Ns0404AXgC2BrbL1RytE0l7A5Nsn1I6S24D1PRvXrKmv4Taz7euhaRTSLsRRtk+vHSeEELIJSbbIYQQlpukfWwXOS9X0i6kifdxwHzbexbIsBNpknksMBe41vb5uXOU1FbTP6Olpj9rB+4aNDcdINX1310wx5qkG1IvATeQ6ur3IZ2FfpbtF0plCyGEbhaT7RBCCB1JGk3vOcILbY8pnEfA2FyTfklb0fv//wXgKlLjuI7by1dWks4l1fTPAqYC15POH8+6vT/0kjSddMb3EGBd0p/NDcDewI62D82U4/jm4SLbV+cYM4QQahaT7RBCCEuRtCm9E8w3gFHAGNvzMmY4k7RF91Xb38w1bj853iId9/Up2080r2WvHa9JDTX9oZekWba3k7QaadfH+1uuPWh7h0w5emrHXyn5MxtCCLWIbuQhhBD6kHQ3sDYwDTja9uxmm/C8zFF6xluUedx2RwETgZmSbiZ9X7r6+C+nO/UzSd+T1pr+7wDZavrD2xYD2H5T0jNt17J0ZW/GH7ARm6Rdbf8qV5YQQqhBTLZDCCG0exbYGNiAdGzQbAoc/TXQEVO52Z4BzJA0hHTm92mk45UuAq6zfUvRgIXZfgO4EbhR0j6l83SpTSSdR7oJ1POY5vnGpUK1l6AARUtQQgght9hGHkIIYSmShpFWdCcBWwLrkDqR31M0WCUkrUtqkjbB9oGl85RUW01/DSRNAV4GLrG9IMN4J3S6nvPGVQ0lKCGEUIuYbIcQQuhI0vqkDuCTgJG2RxSOVAVJI0hHf32jdJbcYkLVmaTxwBbADraPX9bXryzaSlCmtZSgdF2n+hBCAFildIAQQgh1s/2c7Qts70Xqbty1JA2X9BlJdwI/B9YvHCm7ZkL1Y1Ip2tG2dyE1xJpXNFhFbM+wfU7OibakEyTdL+lPzX/3tnQHz+VZYCi9JShQoAQlhBBqETXbIYQQ+pB0KekD8su2T2+9ZvvJMql6SfoMsAC4xvabGcYbStpSPxnYCrgW2Mz2JoM9dqWqqOmvQUUd808g9RL4LHA/qVZ7Z+BsSbZ9RY4ctse3lKB8VdKWwDqSdosSlBBCN4pt5CGEEPqQtG/zcLHtu4uG6YekU4BtgFG2D88w3iLgHuDLwF22HUd/RU0/9KmVXmR7esEcvwQmtu8uaLb7T7O9R4FYUYISQuh6MdkOIYQQOpB0GunoryHAlcBVwK3dPNlu1c0TKklTbH+pghyP2B79Tq/lJGlUDTtjQgghp5hshxBCqFJLveki21cXDQNI2pw06e5ZzT2TdPTX40WDVaTbJlSS7re9cwU57mtq59/RtUHIMWAJSgghdKOo2Q4hhFCrng7GrxRN0bA9B5gCTJG0HWnSfRPwgaLBMqu9pj+zVZtj4NTfRdsvZsqxraSH+nldQM4dGJc1vy7OOGYIIVQrVrZDCCGscCTtavtXpXN0o9pr+nOS9DrwNP1Ptp2r1EDSqE7Xu/AmSAghVCEm2yGEEJaLpCnAy8AlthcUGH80vWc7L7Q9JtO4c0kruc/b3j3HmGHFIOnXtncqnWMgkvYmnQV/SuksIYTQjWIbeQghhOV1D7AF8C0gy/m9TTflngn2G8AoYEzOM51tb7bsrwqhDpJ2Ih1Tdywwl3RUXQghhAJish1CCGG52J6RczxJdwNrA9OAo23PljQ350Q7hGX4dukAAJK2ovem1AukjvmyvX/RYCGE0OVish1CCKEPSWeStk2/avubBaM8C2wMbAAMB2Y3uUKoxY+bn5eXgO8BZwNjgd8Bn7P9RKYcjwF3Aof2jCmpmm7gpUtQQgihlFVKBwghhFCdecCTwPySIWyPBz4E3Ad8tamdXlfSbiVzhf5JmiLpi5LeVzpLRj8A1iAdBXcPMAc4BrgRuCRjjqOAPwAzJf27pAMZoEN6IfcAb5JKUEIIoWtEg7QQQgh9SJpi+0ulc7STtD5wHGmr7EjbIwpHCi0kjSfV9O9gO0tNf2mSHrS9gyQBT9oe2XLtAds7Zs4zBDiC9DNyAPB90lnwt+TMEUIIIYnJdgghhD4k3W9759I5OpE0qvRxRpJuIzVtu9D2jSWzhDJaf1baf25K/xw1538fC0ywfWCmMWspQQkhhCpEzXYIIYR2qzYf1Pvdhmr7xRwhJF1K+uD+su0+9aelJ9qN44ENgT1KB8kpJlR9bC7pR6SflZ7HNM+LdrG3/RJwcfNfLvOaXxdlHDOEEKoVK9shhBD6kPQ68DT9T7Zte/NMOfZtHi62fXeOMcOySTqhebjI9vSiYQpreY/2y/btmXJUcxa8pOGkI/qesL2wZJYQQigtJtshhBD6kPRr2zuVzlELSTNJE5kXbR9TOk9ptdb010TSCGCi7bNLZ8lJ0snAFFI39s2AT9v+UeffFUIIK6/YRh5CCCF0dmLz65KSISryN0BMtts0K7rHkpqTbQRcVzZREacBH7T9vKTNSd3aY7IdQuhaMdkOIYTQ7tulA1RmLduPAUhaw/brPRck7WH7l+WiFVFFTX8NJA0lHbs1GdgKuBbYzPYmRYOVs9j28wC250hao3SgEEIoKbaRhxBC6KNZofsM8BLwPeBsYCxpa+jnbD9RMF52NXecLqGWmv4aSFpEOkP6y8Bdti1pTjd9D1pJeg6Y1vLSxNbntk/NHiqEEAqKle0QQgjtfgDcC2xJmkhcSlrtHgtcAuxXLBmpZhh4GbjE9oIcQw7wuL/n3eCRqOl/2xmkCeV3gCslXVU4T2lfaHt+X5EUIYRQiVjZDiGE0IekB23vIEnAk7ZHtlx7wPaOBeMhaTywBbCD7eMzjBcr2y2igd7SmvrkiaR67S2BM4HrbD9eONejzcMLbV9QMMeawGG2ry6VIYQQSoiV7RBCCO2WQNoPLOmFtmtvFcjTh+0ZmYfcRNJ5pFXsnsc0zzfOnKUGUdPfxvYcUhfuKZK2I026bwI+UDjXtpLeR4Gz4CWtCowjfS8OAu4EYrIdQugqsbIdQgihD0kLgTtIk8mxzWOa53vbXjdTjjNJR269avubOcYcIMcJna7bvjxXlhpETX/opDl7fDJwMKkMZS9gc9uvFQ0WQggFxGQ7hBBCH82H5QHZvj1Tjp5J7iLb03OMGZZN0i2kmv6hwIGkmv4bSBPuj9ver1y6vCTNJd0Qet727pFD84GngIuAGbZfkTTX9malMoUQQkkx2Q4hhLBcJI0AJto+O+OYw4FRwBO2F+Yaty3DesApxEouUH9NfyhH0rnAeGAWMBW4HnhOwbK7AAAVYklEQVS4W7uzhxDCKqUDhBBCqJek4ZI+I+lO4OfABhnHPhn4DXA+8Jikw3ON3WYqsAa93dnnAMcAN5K6s3ebt2v6gepq+ruRpB9L+rikISVz2D4N2Aw4h3RqwW+B4ZKOk/SektlCCKGEWNkOIYTQh6ShwFGkusutgGuBCbY3yZxjFrC/7eebbs8/sP3hnBmaHLGS26KWmv4aLE83+hwd6yUdQeqG/hFgJnAl8GPbiwdz3OXItTq9TdLG2V6vZJ4QQsgtJtshhBD6kLSItIL7ZeCupiv5nNxbQWs5ZiuO/uqrlpr+GjQ/K7M7fQkwrPUGzSDn+WvgMNLE+8PAfwBTbd+aY/xOJO1j+45lf2UIIaw8YrIdQgihD0mnkT6sDyGtkF0F3Fpgsv0cMK3lpYmtz22fmilHrOQuhxI1/aVJGrUcX7bE9vxBD9NG0vbA5cD2tlfNPX6TYTRpVXsSsND2mBI5QgihlJhshxBC6FezdXsi6YPylsCZwHW2H880fhVHbsVK7sCaBnbHkt4jG5HeH58vm6p7SdoAOI70c7shMB240vaDGTNsSu8E+w1Sg8MxtuflyhBCCLWIyXYIIYRlkrQd6cPzBNsfKJxlTeAw21cXGHs4gO3nc49di1pq+kMvSX9L+vncGrgGmGb7FwVy3A2sTdqBMs327Dj6K4TQzaIbeQghhGWyPcv2P5eaaEtaVdLBkq4AngQmZBxbks6U9AKpu/Ljkp6X9JVcGSrzHHAS8L+AzW1/DijaiCvwYeBfgBG2Ty0x0W48Szp/fQNgePNarOqEELpWTLZDCCH0IWmupDmS/quCLPtK+jdgHvAp4KPAZraPyRjjdGBvYFfb721qtHcH9pJ0esYctTiDdBTad4AzJG1ROE9IPxuLgQ8CNEdtXSDpdElr5AphezzwIeA+4KuS5gLrStotV4YQQqhJbCMPIYRQJUnzgaeAi4AZtl8psSVV0q+Bj9p+oe314cAttnfKmacWpWv6Qy9JFwLbA2uSdl+8B7gZ2AtYxfbHC+Van1RDPgkYaXtEiRwhhFBKTLZDCCFUSdK5wHhgFjAVuB54uEBX9Fm2t3un17pJTTX93UjSI7ZHN/0MngbWt72kORv+IdsfKhwRSaNsP1k6Rwgh5BTbyEMIIfQh6f5342v+UrZPAzYDzgH2I63YDW+2yL5nsMdv0akeOWqVKV/TH/gzgO0/A0/aXtI8N6kjeBaSLpX0PUnfar8WE+0QQjdarXSAEEII1dlW0kMdrgsYliNIM1mYCcyUtDowjrSC+h1gvRwZgB0k/bGf10XatttVmjpcA8/b3r10ngDA+pI+S3pP9jymeT584N/2rrus+TVuQoUQArGNPIQQQhtJo5bjy5bYnj/oYQYgaR/bd5QaP4SaSDqz03XbX8uVJYQQQq+YbIcQQlghSBpNWtWeBCy0PaZgliHAkcAk24eUyhFCCCGEesU28hBCCNWStCm9E+w3gFHAGNvzCmT5K+AQYDJpO/s1wHdz5yhN0v22d/5Lvya8e5rGaBOAl4AbgC8A+wC/A85q76QfQgghj1jZDiGEUCVJdwNrA9OAabZnFzr66yDSZP8gUv34VcD5tjfNmaMWkhYBszt9CTDM9shMkbqepOmkm1FDgHVJHfxvIJ0Pv6PtQwvGCyGErhUr2yGEEGr1LLAxsAGpydNsUmOu3G4G7gT2tj0XQNK3C+SoxTbL8TVLBj1FaDXa9naSVgPm2963ef1mSQ+WDAYgaQrwMnCJ7QWl84QQQi4x2Q4hhFAl2+MlDQOOAr4qaUtgHUm72b4nY5SdgYnAbZLmkFbaV804flXiCKcqLQaw/aakZ9qu1XDj4x5gC+BbwPGFs4QQQjaxjTyEEMIKQdL6wHGkLd0jbY8okGHPZvyjgQeB62xfnDtHCK0kPUe6CSRS7fa0nkvAcbY3KJUthBC6WUy2QwghrHAkjSq5wippFeAjwETbJ5XKEQKApBM6Xbd9eaYcZ5JKPV61/c0cY4YQQs1ish1CCKFKki4lfXB/2fbpBXP0NPpaYvvpUjlCeKeaLuWH2b4603g9k/5FtqfnGDOEEGoWNdshhBBqdVnz6+KSIYCeVcEFwDElg4SwLJJWJR1N19NB/04gy2Qb2Nr2lzKNFUII1YuV7RBCCOH/b+/eg+0q6zOOfx8vVQREFO2ICDECKkUSEEWFSFS8lHZUvEXEa60ztk6tIi3WG4gMVmO9TVUcKYKIyFVRVEq1CKFVueRmvIGKWMQxSkSNRIHk1z/WOmTnsM8lcLLXOuT7mTlz1trrXWs9e+/MJL+873pfaZZLcjDNGvCH0kxIdiAwt6puHmEG11eXpAH2bEuSJM1iSa4HfgZ8HDiqqn7frkk/skK7dc8kO9JMzHYHVbVmxHkkqVMW25IkSbPbOcDzaGYiX5/kfLpZk/7RwFUML7YLmDvaOJLULYeRS5IkzXJJAiykeVb7UGAH4DXAV6pq7YgyLKuqfUdxL0maDSy2JUmzSpITgN8CJ1XVjR3meCiwpqr+1FUGaZgk92bjJGnPqqqdRnRfi21JGnCPrgNIkrSZLgduAz7YcY7TgB8keX/HOaRNVNWtVXVBVR0BPHyEt/7wCO8lSb1nz7YkSXdSO3R3r6r6btdZtPVKcjHNM9Frqqqz5emSPBj4e+A3wMnAYmAB8GPgzVX1o66ySVIXLLYlSb2U5BiaAmJtVX2g6zxSXyXZrd1cX1XXd5jjIuBKYHvg6cCngC/RFNxHVNXCrrJJUhcstiVJvZTkle3muqo6q8Mc17abq6vqgK5ySH2XZEVVzWtHfFxXVbsOHFteVfM7jCdJI+fSX5KkvnpUVb216xBV9YiuM0izxHqAqqokvx53bEMHeSSpU/ZsS5J6KcnSqtqvBzm+B5wOnFFVP+k6j9RXSW4CLqVZZ3tBu027f1BV7dhVNknqgsW2JKmXkqygWTc4w45X1ZoR5ZgHvAR4MXAjcAZwZlXdMIr7S7NFkoMnO15Vl4wqiyT1gcW2JKmXkvwJ+DnDi+2qqrkjjkSSJwKLgBfQzLD82ar65KhzSNOR5FTgZuCjVbWqwxwPB15SVYu7yiBJXbDYliT1UpJlVbVv1zmGSbKQZp3vvarqPh3HkYZK8nhgV+AJVXX0iO/9YOBFwOHAzsDnq+qoUWaQpK5ZbEuSeqlvxXZbuBxO06t9LfA54OyqurHTYFJPJNkeeD7wUmBP4DxgUVXt0mkwSeqIs5FLkvrqw10HAEhyAs3Q8TU0BfaBXa5lLI2X5FM0a9L/tqre1GGU1cDlwNuBy9pZyQ/rMI8kdcpiW5LUV19OcgzwG+BkYDHNDMc/Bt5cVT8aUY4/As+uqmtGdD9pc53S/r6lyxDAv9BMJvgx4IwkZ3acR5I65TBySVIvJbkIuBLYHng68CngSzQF9xFVtXBEObahmYl8DXAB8M9sLPrfXVXj1xOWtmpJ5tIU3YcDewDH0DyzfXWnwSRpxCy2JUm9lGRFVc1LEuC6qtp14Njyqpo/ohxnAbcC2wI7Aqtoiv6DgPlV9dejyCFNJMnKiQ7RzNy/zyjzbBIg2Zum6F5UVbt3lUOSuuAwcklSX62HplJIMr73eMMIc+xVVXsnuRdwfVWNrSV8YbsWuNS1DTTPbH+W5j+C1nUbZ6N2ybG3tT+StFWx2JYk9dXcJF+k6Z0b26bdf8QIc9wCUFW3Jblh3LH1I8whDVVV85M8mqYH+bPA99rfF1XVbaPKkeRamqL/V1V1wKjuK0l95TBySVIvJTl4suNVdcmIcqymmYU8NLOSf27sEPDiqvrzUeSQpivJIuCjwHuranHXeSRpa2WxLUmaVZI8HHjJqIqIJK+c7HhVnTqKHNJkkjyMZlKyw2hm8D+LZlKytZ0Gk6StmMW2JKn3kjwYeBHNMNmdaYqIo7pNJfVDkktoZu0/CzgXuHHweFWtGVGOpVW1311tI0l3FxbbkqReSrI98HzgpcCewHk0MxrvMuIcOwGvp/v1vqWhkvyU5llpBn7DxtnI544oxzpgsvXoA+wwuLKAJN2dWWxLknqp/Yf75cDbgcvaWcl/MqrCYSBHL9b7lvouyW7TaLa+qq7f4mEkqQcstiVJvZTkjTTPoG4LnAGcCfxXB8V2L9b7liaSZNJh2VW1dFRZJEkbufSXJKmXqupDwIeSzKUpur8A7JzkaJpntq8eUZS+rPctTeRKYBUw9uczA8cKeNrIE0mS7NmWJM0eSfammSRtUVXtPqJ73gRcSlPALGi3afcPqqodR5FDmkg7CuSFwG9plqZzFnJJ6gGLbUmSJtGX9b6lqQyMAnkucB1wQlUt7zaVJG29HEYuSeqlJNfSDIH9VVUd0FWOiYrpsfW+AYtt9UJV/STJ+cA2wMtpZvG32JakjlhsS5J6qaoe0XWG8Yat991tIukOPdr/RzOU/ISqWtdpMEnayjmMXJKkSfRlvW9pIkk2ACuB84Hfsela21TVB7rIJUlbO3u2JUm9lGRpVU26pNF02syA1dxxve/DtvA9pc1xHBsL7O26DCJJ2siebUlSLyVZB1wzWRNgh8F1r7dQjl6s9y1JkmYXi21JUi8l2W0azdZX1fVbPAybPBd7OLAHcAyjXe9bGirJRyY7XlVvGFUWSdJGFtuSJG2mLtb7liaS5BZgFXAWcAPNqI/bVdWpXeSSpK2dxbYkSdIsluRBNLPkLwJuo3nU4ZyquqnTYJK0lbPYliRpEn1Z71uajiS70DzucCRwdFWd1nEkSdpqORu5JEmT6ON639IwSfajebzhGcBXgau6TSRJWzd7tiVJkmaxJMcBfwV8H/gccGFV3dZtKkmSxbYkSZPo0Xrf0lBJNgDXAje3L4394y5AVdU+nQSTpK2cw8glSZrcY5KsnOR4gB1GFUYawkcdJKmH7NmWJGkSfVvvW5IkzQ4W25IkSbOYM+ZLUj9ZbEuSJEmSNMPu0XUASZIkSZLubiy2JUmSZrEkS2eijSRpZjmMXJIkaRZLsg64ZrImwA5VteuIIkmScOkvSZKk2e7R02izfounkCRtwp5tSZIkSZJmmM9sS5IkSZI0wyy2JUmSJEmaYRbbkqSRSrI+yfIkq5KcneR+m3Hu/CSHDuzfJ8nX2ustmuS8byTZv93+SpIH3LV3MWXO1yV5Rbv9qiQ7Dxz7aZKdpjj/fklOT/Kd9nO6LMl2SeYkWbUls0+lzf+dJCuTXJJktynaz0ny0oH9/ZN8ZMsnnTDPkUm+1+b/+lT5JUm6syy2JUmjtq6q5lfV3sAtwOumc1KSewHzgUMHXt4XoL3emdO5TlUdWlU3bWbmzVJVJ1bVp9vdVwE7T9J8mH8EfllVj20/p9cAt97VXO1nOBOeWlX7AN8A3j5F2znA7cV2VV1ZVW+Y7o2SbJvk3ncm5ASWAfu3+c8B3jeD15Yk6XYW25KkLi0Bdk/ywCRfaHsbv5VkH4AkxyY5Lcn/AKcBxwGLBnqyPwM8vt1/ZJKnJ1nW9ryenOQ+42842LPc9nKuan/e2L62bZIvJ1nRvr5o3PkPSXJVuz0vSSXZtd3/cdsrfWySo5K8ENgfOL3NuE17mX9IsrTNOWwm6YcCPx/bqaofVtWf2t17Jvlkku8muWjsmklem+SKNve5YyMGkpyS5MQk3wbeN/CZfjPJNUleO/De/qm9xsok75rG9/dN4GHtuXOSLGnf19IkT27b/CuwoH3/b0qyMMkF7TlDv/dx9gSuTvL+JI+ZRqZJVdXFVXVzu/stYJe7ek1Jkoax2JYkdaLtZf1L4DvAu4BlbW/jW4FPDzTdCzikqg4H3gmcOdCT/bfAkqqaT1OcngIsqqrH0ixv+XeT3P9xwKuBA4AnAq9Nsi/wbOCGqprX9ipfOHheVa0G7pvk/sAC4EqaYnI3YPVAIUdVndMeP6LNvK499Ouq2g/4OHDUkHgnA0e3BfHxSfYYOLYH8NGq+gvgJuAF7evnVdXjq2oe8H2a3vAxuwBPrqoj2/19gKcBTwLemWTnJM9sr/0EmhEEj0vylIk+v9azgS+026uBZ7TvaxEwNlT8LbTfUVV9cNz5k33vAFTVsjbvD4CT2iH1r06y7VibtshfPuTnkCnyvwb46hRtJEm6U1xnW5I0atskWd5uLwH+A/g2bdFYVf+d5EFtMQvwxYEidTKPAq6tqqvb/VOB1wMfmqD9QcDnq+oPAEnOoymeLwT+Lcl7gQuqasmQc/8XOBB4CnACTdGZ9v1Mx3nt76uA548/WFXLk8wFngkcAlyR5EnAuvY9Lh84f067vXeS44EHANsB/zlwybOranCd5fPbz3RdkotpCuyD2vsta9tsR1N8Xzok/8VJHgisBd7RvnZv4N+TzKdZ03nPKT+F5p53+N6r6nfjPo/fAyfRFNuPofkz82Hg/u3xBdO41yaSvIxm1MHBm3uuJEnTYbEtSRq1dW1P9O2STNb+D1s2zqaq6uok+9E8G358kq9X1XHjml1KU5jvBpwPHA0U8OVp3mZsSPh6Jvi7uKrW0hTl5yXZ0OY5d+DcsfPHhqafAjyvqlYkeRWwcKDd+M+whuwHeE9VfWIa+Z9K06t+Ok3v9JHAm4BfAvNoRs79cRrXmbYkc4BXAocDK4BjB44tAbYfctpRVfW1Idc6BHgbcPDA8HxJkmaUw8glSX2wBDgCIMlCmmHWvxvS7vcML6oAfgjMSbJ7u/9y4JIp7vm89hnrbYHDgCVpZg6/uao+AywG9pvg3JcB11TVBmANTTF82WZmHirJgUl2bLf/jGYo/XVTnLY98Is0k4kdMUXb5ya5b5IH0RTlV9D0hP9Nku3a+z4syUMmukBV3Qa8EXhF28u9A/CL9vN4OXDPtulk73/K7719FvxrNMPVbwIOrKpFVXXRQJYF7TD18T/DCu19gU8Az2kfCZAkaYuwZ1uS1AfHAicnWQncTNODOczFwFvaYejvoelJBaCq/pjk1cDZ7fPgVwAnTnTDqlqa5BTg8valk6pqWZJnAYvb3uRbGfLcd1X9NE13/NgQ68uAXarqN0NudQpwYpJ1NM9IT8cjgY+397gHTY/5uTQ96RN5B81w/F+1vycr8FfSfJY7Ae+uqhuAG9oh2t9sRxqspfkPhQkL0qr6RZIzaIbrfww4N82SZxeysTd9JbA+yQqaz2LZwCWOZervfT3w1qq6fMixO2MxzRD5s9v3+bOqes4MXVuSpNulavxIMkmSdHeV5FhgbVW9v+sskiTdnTmMXJIkSZKkGWbPtiRJkiRJM8yebUmSJEmSZpjFtiRJkiRJM8xiW5IkSZKkGWaxLUmSJEnSDLPYliRJkiRphllsS5IkSZI0w/4fVemQYWXZbbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>portfolio</th>\n",
       "      <th>weights</th>\n",
       "      <th>sharp_ratio</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['MMM', 'ABMD', 'ACN', 'AMD', 'LNT', 'MO', 'AA...</td>\n",
       "      <td>[0.024305555555555556, 0.2708333333333333, 0.1...</td>\n",
       "      <td>2.724150</td>\n",
       "      <td>0.355197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['ABMD', 'ACN', 'AMD', 'APD', 'ARE', 'LNT', 'M...</td>\n",
       "      <td>[0.2053872053872054, 0.02356902356902357, 0.09...</td>\n",
       "      <td>2.596345</td>\n",
       "      <td>0.285990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['ABMD', 'APD', 'AKAM', 'ALL', 'ANTM', 'AON', ...</td>\n",
       "      <td>[0.18991097922848665, 0.29376854599406527, 0.1...</td>\n",
       "      <td>2.486416</td>\n",
       "      <td>0.286638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['MMM', 'ABMD', 'AMD', 'APD', 'ALXN', 'LNT', '...</td>\n",
       "      <td>[0.022535211267605635, 0.27605633802816903, 0....</td>\n",
       "      <td>2.439113</td>\n",
       "      <td>0.297412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['ABMD', 'ACN', 'AMD', 'AAP', 'A', 'APD', 'ALX...</td>\n",
       "      <td>[0.16847826086956522, 0.014492753623188406, 0....</td>\n",
       "      <td>2.433213</td>\n",
       "      <td>0.283018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>['ABBV', 'AKAM', 'ARE', 'ALLE', 'MO', 'AMZN', ...</td>\n",
       "      <td>[0.21220930232558138, 0.10755813953488372, 0.1...</td>\n",
       "      <td>2.355326</td>\n",
       "      <td>0.205136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>['ABBV', 'AKAM', 'ARE', 'MO', 'AMZN', 'AEE', '...</td>\n",
       "      <td>[0.056485355648535567, 0.12133891213389121, 0....</td>\n",
       "      <td>2.336731</td>\n",
       "      <td>0.217663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>['ABT', 'AMZN', 'AMCR', 'AEP', 'AXP', 'AWK', '...</td>\n",
       "      <td>[0.011235955056179775, 0.23314606741573032, 0....</td>\n",
       "      <td>2.300739</td>\n",
       "      <td>0.230979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>['ABMD', 'APD', 'AKAM', 'ARE', 'ALL', 'GOOGL',...</td>\n",
       "      <td>[0.189873417721519, 0.10379746835443038, 0.225...</td>\n",
       "      <td>2.170290</td>\n",
       "      <td>0.241729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'ARE', 'ALXN', 'LNT...</td>\n",
       "      <td>[0.020161290322580645, 0.10752688172043011, 0....</td>\n",
       "      <td>2.154333</td>\n",
       "      <td>0.209840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...</td>\n",
       "      <td>[0.007575757575757576, 0.06944444444444445, 0....</td>\n",
       "      <td>2.149021</td>\n",
       "      <td>0.208724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>['ABBV', 'ADBE', 'AAP', 'A', 'APD', 'AKAM', 'A...</td>\n",
       "      <td>[0.07247796278158668, 0.06758080313418217, 0.0...</td>\n",
       "      <td>2.130755</td>\n",
       "      <td>0.201031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...</td>\n",
       "      <td>[0.005545286506469501, 0.06469500924214418, 0....</td>\n",
       "      <td>2.084901</td>\n",
       "      <td>0.220189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...</td>\n",
       "      <td>[0.0062266500622665, 0.0759651307596513, 0.113...</td>\n",
       "      <td>2.078548</td>\n",
       "      <td>0.199579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>['MMM', 'ABBV', 'ADBE', 'APD', 'AKAM', 'ARE', ...</td>\n",
       "      <td>[0.014598540145985401, 0.0948905109489051, 0.0...</td>\n",
       "      <td>2.074706</td>\n",
       "      <td>0.194269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...</td>\n",
       "      <td>[0.014012738853503185, 0.10191082802547771, 0....</td>\n",
       "      <td>2.050905</td>\n",
       "      <td>0.198263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>['MMM', 'ABBV', 'ADBE', 'A', 'APD', 'AKAM', 'A...</td>\n",
       "      <td>[0.014476614699331848, 0.08240534521158129, 0....</td>\n",
       "      <td>2.041625</td>\n",
       "      <td>0.194787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...</td>\n",
       "      <td>[0.021560574948665298, 0.09240246406570841, 0....</td>\n",
       "      <td>2.033051</td>\n",
       "      <td>0.207091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>['ADBE', 'A', 'APD', 'ARE', 'ALXN', 'LNT', 'GO...</td>\n",
       "      <td>[0.0873440285204991, 0.03208556149732621, 0.02...</td>\n",
       "      <td>2.024985</td>\n",
       "      <td>0.208825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>['ALGN', 'AMZN']</td>\n",
       "      <td>[0.24528301886792453, 0.7547169811320755]</td>\n",
       "      <td>2.017471</td>\n",
       "      <td>0.318114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>['ABBV', 'APD', 'AKAM', 'ARE', 'GOOGL', 'MO', ...</td>\n",
       "      <td>[0.13428120063191154, 0.12164296998420221, 0.0...</td>\n",
       "      <td>2.004413</td>\n",
       "      <td>0.196222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.03939393939393939, 0.1, 0.05454545454545454...</td>\n",
       "      <td>1.998006</td>\n",
       "      <td>0.206060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>['MMM', 'AMD', 'AES', 'ALXN', 'LNT', 'GOOGL', ...</td>\n",
       "      <td>[0.012145748987854251, 0.17611336032388664, 0....</td>\n",
       "      <td>1.996861</td>\n",
       "      <td>0.246431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...</td>\n",
       "      <td>[0.014771048744460856, 0.008862629246676515, 0...</td>\n",
       "      <td>1.996539</td>\n",
       "      <td>0.199136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>['MMM', 'ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...</td>\n",
       "      <td>[0.017408123791102514, 0.0367504835589942, 0.0...</td>\n",
       "      <td>1.992542</td>\n",
       "      <td>0.199174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            portfolio  \\\n",
       "0   ['MMM', 'ABMD', 'ACN', 'AMD', 'LNT', 'MO', 'AA...   \n",
       "1   ['ABMD', 'ACN', 'AMD', 'APD', 'ARE', 'LNT', 'M...   \n",
       "2   ['ABMD', 'APD', 'AKAM', 'ALL', 'ANTM', 'AON', ...   \n",
       "3   ['MMM', 'ABMD', 'AMD', 'APD', 'ALXN', 'LNT', '...   \n",
       "4   ['ABMD', 'ACN', 'AMD', 'AAP', 'A', 'APD', 'ALX...   \n",
       "5   ['ABBV', 'AKAM', 'ARE', 'ALLE', 'MO', 'AMZN', ...   \n",
       "6   ['ABBV', 'AKAM', 'ARE', 'MO', 'AMZN', 'AEE', '...   \n",
       "7   ['ABT', 'AMZN', 'AMCR', 'AEP', 'AXP', 'AWK', '...   \n",
       "8   ['ABMD', 'APD', 'AKAM', 'ARE', 'ALL', 'GOOGL',...   \n",
       "9   ['ABT', 'AAP', 'A', 'APD', 'ARE', 'ALXN', 'LNT...   \n",
       "10  ['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...   \n",
       "11  ['ABBV', 'ADBE', 'AAP', 'A', 'APD', 'AKAM', 'A...   \n",
       "12  ['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...   \n",
       "13  ['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...   \n",
       "14  ['MMM', 'ABBV', 'ADBE', 'APD', 'AKAM', 'ARE', ...   \n",
       "15  ['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...   \n",
       "16  ['MMM', 'ABBV', 'ADBE', 'A', 'APD', 'AKAM', 'A...   \n",
       "17  ['ABT', 'AAP', 'A', 'APD', 'AKAM', 'ARE', 'ALX...   \n",
       "18  ['ADBE', 'A', 'APD', 'ARE', 'ALXN', 'LNT', 'GO...   \n",
       "19                                   ['ALGN', 'AMZN']   \n",
       "20  ['ABBV', 'APD', 'AKAM', 'ARE', 'GOOGL', 'MO', ...   \n",
       "21  ['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...   \n",
       "22  ['MMM', 'AMD', 'AES', 'ALXN', 'LNT', 'GOOGL', ...   \n",
       "23  ['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...   \n",
       "24  ['MMM', 'ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...   \n",
       "\n",
       "                                              weights  sharp_ratio    return  \n",
       "0   [0.024305555555555556, 0.2708333333333333, 0.1...     2.724150  0.355197  \n",
       "1   [0.2053872053872054, 0.02356902356902357, 0.09...     2.596345  0.285990  \n",
       "2   [0.18991097922848665, 0.29376854599406527, 0.1...     2.486416  0.286638  \n",
       "3   [0.022535211267605635, 0.27605633802816903, 0....     2.439113  0.297412  \n",
       "4   [0.16847826086956522, 0.014492753623188406, 0....     2.433213  0.283018  \n",
       "5   [0.21220930232558138, 0.10755813953488372, 0.1...     2.355326  0.205136  \n",
       "6   [0.056485355648535567, 0.12133891213389121, 0....     2.336731  0.217663  \n",
       "7   [0.011235955056179775, 0.23314606741573032, 0....     2.300739  0.230979  \n",
       "8   [0.189873417721519, 0.10379746835443038, 0.225...     2.170290  0.241729  \n",
       "9   [0.020161290322580645, 0.10752688172043011, 0....     2.154333  0.209840  \n",
       "10  [0.007575757575757576, 0.06944444444444445, 0....     2.149021  0.208724  \n",
       "11  [0.07247796278158668, 0.06758080313418217, 0.0...     2.130755  0.201031  \n",
       "12  [0.005545286506469501, 0.06469500924214418, 0....     2.084901  0.220189  \n",
       "13  [0.0062266500622665, 0.0759651307596513, 0.113...     2.078548  0.199579  \n",
       "14  [0.014598540145985401, 0.0948905109489051, 0.0...     2.074706  0.194269  \n",
       "15  [0.014012738853503185, 0.10191082802547771, 0....     2.050905  0.198263  \n",
       "16  [0.014476614699331848, 0.08240534521158129, 0....     2.041625  0.194787  \n",
       "17  [0.021560574948665298, 0.09240246406570841, 0....     2.033051  0.207091  \n",
       "18  [0.0873440285204991, 0.03208556149732621, 0.02...     2.024985  0.208825  \n",
       "19          [0.24528301886792453, 0.7547169811320755]     2.017471  0.318114  \n",
       "20  [0.13428120063191154, 0.12164296998420221, 0.0...     2.004413  0.196222  \n",
       "21  [0.03939393939393939, 0.1, 0.05454545454545454...     1.998006  0.206060  \n",
       "22  [0.012145748987854251, 0.17611336032388664, 0....     1.996861  0.246431  \n",
       "23  [0.014771048744460856, 0.008862629246676515, 0...     1.996539  0.199136  \n",
       "24  [0.017408123791102514, 0.0367504835589942, 0.0...     1.992542  0.199174  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "fig,ax1 = plt.subplots(figsize=(16,8))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(portfolio_1['portfolio'], portfolio_1['sharp_ratio'], 'g-', alpha=0.6, marker='o', \n",
    "         linestyle='dashed', linewidth=2, markersize=6)\n",
    "ax1.plot(portfolio_1['portfolio'], portfolio_1['return'], 'b-', alpha=0.6, marker='o', \n",
    "         linestyle='dashed', linewidth=2, markersize=6)\n",
    "\n",
    "\n",
    "ax1.set_xlabel('Portfolios with Sharpe Ratio >= 2')\n",
    "ax1.set_ylabel('Return')\n",
    "ax2.set_ylabel('Sharpe Ratio')\n",
    "ax1.xaxis.set_tick_params(rotation=90)\n",
    "ax1.legend(['Return'],loc='upper left')\n",
    "ax2.legend(['sharp_ration'],loc='upper right')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "portfolio_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## portfolio set 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAATnCAYAAAD6lmGEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeXzU1b3/8dfJJJnsO0tIgCQQFtkVgrjiQkVp0Vqrttpib1tvf9p7r16uy7XKbam97a3epV7tYm0rtl602lZbtVoXcNewiIgbMQRICCQkkHWSyTLn98chQ8ImkJnMJLyfj8c8Zub7/c73+wlKmM/3nPP5GGstIiIiIiIiIhI6MZEOQERERERERGSoUbItIiIiIiIiEmJKtkVERERERERCTMm2iIiIiIiISIgp2RYREREREREJMSXbIiIiIiIiIiEWG+kAQiUmJsYmJiZGOgwREREREREJA5/PZ621g2bAeMgk24mJibS2tkY6DBEREREREQkDY0xbpGM4FoPmroCIiIiIiIjIYKFkW0RERERERCTElGyLiIiIiIiIhNiQWbN9KJ2dnVRVVdHe3h7pUKJGQkIC+fn5xMXFRToUEREREZGooxwi8oZKzmKstZGOISSSk5PtgQXSKioqSE1NJTs7G2NMhCKLHtZa6uvraW5uprCwMNLhiIiIiIhEHeUQkXWknMUY47PWJkcotGM2pKeRt7e36y9JL8YYsrOzdZdOREREROQwlENE1lDKWYZ0sg3oL8kB9OchIiIiInJk+s4cWUPlz3/IJ9uR5vF4mDlzJlOnTuVzn/scDQ0NRzz+iSee4IMPPhig6ERERERERCQclGyHWWJiIhs2bGDTpk1kZWVx3333HfH440m2u7q6+hOiiIiIiIhEsYKCAurq6iIdxkEefPBBqqurg++/8Y1vaOCwFyXbvZSWwvXXw0UXuefS0tCef968eezYsQOA8vJyFi5cyCmnnMKZZ57JRx99xBtvvMGf//xnbrrpJmbOnEl5eTnz589n7dq1ANTV1VFQUAC4/7EXL17Mueeey3nnnceDDz7IpZdeysKFCykuLubmm28ObfAiIiIiInKQ0qpSrn/6ei56+CKuf/p6SqtCnET0U38H5rq7uw+778Bk+4EHHuCkk07q1/WGEiXb+5SWwrJlUFcHeXnuedmy0CXc3d3dvPjiiyxevBiAa6+9lv/93/9l3bp13H333Vx33XWcdtppLF68mLvuuosNGzYwbty4I55z/fr1PP7447z88ssAbNiwgUcffZT33nuPRx99lMrKytAELyIiIiIiBymtKmXZ6mXU+erIS82jzlfHstXL+pVwt7a2smjRImbMmMHUqVN59NFHAfjf//1fTj75ZKZNm8ZHH33krl9ayrx585g1axannXYaH3/8MXDwwNzq1as566yzWLRoERMnTuRb3/oWgUDgsDGkpKSwdOlSZsyYwZtvvsny5cuZM2cOU6dO5dprr8Vay+OPP87atWu56qqrmDlzJm1tbX0GCleuXMm0adOYOnUqt9xyS59zf+c732HGjBmceuqp1NTUHPefVbQb0n22D/T3f3/4fbW1kJ4OGRmwcyeUlYHfD1/7GpxxRt9jf/GLo79mW1sbM2fOZMeOHUyePJkFCxbQ0tLCG2+8wRe/+MXgcX6//xh/GliwYAFZWVnB9+eddx7p6ekAnHTSSWzbto3Ro0cf83lFRERERMT5+78cPomoba0l3ZtORkIGO1t2UranDH+Xn689+TXOGNM3ifjF544uiXj22WcZNWoUTz/9NACNjY3ccsst5OTksH79en76059y991388ADDzBp0iReffVVYmNjeeGFF7jtttv4wx/+ALiBuY0bN5KVlcXq1aspLS3lgw8+YOzYsSxcuJA//vGPXHbZZYeMobW1lblz5/Kf//mfgMstli1bBsBXvvIVnnrqKS677DLuvfde7r77bmbPnt3n89XV1dxyyy2sW7eOzMxMPvOZz/DEE09wySWX0NrayqmnnsoPfvADbr75Zn75y19y++23H9WfzacxxowGHgJGABa431r7kwOOMcBPgIsAH3CNtXZ9SAI4QFhHto0xC40xHxtjPjHG3HqI/f9sjPnAGLPRGPOiMWbsAfvTjDFVxph7wxknuAQ7La3vtvh4aG7u33l71mxv27YNay333XcfgUCAjIwMNmzYEHx8+OGHh/x8bGxs8K7TgeXvk5P7tpjzer3B1x6PR2u5RURERETCaGfLTtK8fZOIeE88zR3Hn0RMmzaN559/nltuuYVXX301OJh26aWXAnDKKaewdetWwCXiX/ziF5k6dSo33ngj77//fvA8Bw7MlZSUUFRUhMfj4Utf+hKvvfbaYWPweDx84QtfCL5ftWoVc+fOZdq0abz00kt9rnMoa9asYf78+QwbNozY2FiuuuoqXnnlFQDi4+P57Gc/e9DPEiJdwFJr7UnAqcD1xpgD57VfCBTve1wL/CyUAfQWtpFtY4wHuA9YAFQBa4wxf7bW9l4x/w4w21rrM8b8P+DHwBW99n8feCVUMR1pRPr6693U8YwMyM11j4YGyMmBT6lpdlSSkpK45557uOSSS7juuusoLCzkscce44tf/CLWWjZu3MiMGTNITU2luVeGX1BQwLp16ygpKeHxxx/vfyAiIiIiInLUjjQiff3T11PnqyMjIYPclFxyU3JpaG8gJymH+xYdXxIxYcIE1q9fzzPPPMPtt9/OeeedB+wfWOs9qHbHHXdwzjnn8Kc//YmtW7cyf/784HkOHJg7sJ3WkdprJSQk4PF4ADfgd91117F27VpGjx7Nd7/73X71wI6LiwteO9QDhNbancDOfa+bjTEfAnlA7xz0YuAha60F3jLGZBhjcvd9NqTCObJdAnxird1ire0AHsH9YEHW2lXWWt++t28B+T37jDGn4Ib//xbGGIOWLIHGRpdgBwLuubHRbQ+VWbNmMX36dFauXMnDDz/Mr371K2bMmMGUKVN48sknAbjyyiu56667mDVrFuXl5fzLv/wLP/vZz5g1a1ZUViAUERERETlRLZmxhEZ/Iw3tDQRsgIb2Bhr9jSyZcfxJRHV1NUlJSVx99dXcdNNNrF9/+BnOjY2N5OXlAW6d9pGUlpZSUVFBIBDg0Ucf5YwD18oeRk9inZOTQ0tLS58BwAMHCnuUlJTw8ssvU1dXR3d3NytXruTss88+quuFijGmAJgFvH3Arjygd3Grqn3bQi6ca7YP9UPMPcLxXwf+CmCMiQH+E7gaOD9cAfZWUgLLl8OKFVBRAYWFsHSp294fLS0tlFaVsuLdFVQ0VFD4rUImzphIYX4hzz777EHHn3766QeVy9+4cWPw9Z133gnANddcwzXXXBPcfuD7p556qn+Bi4iIiESh0tK+39eWLOn/9zWR41WSX8Ly+cv3f9fPKGTpvKWU5B///5TvvfceN910EzExMcTFxfGzn/3ssGurb775ZpYsWcKdd97JokWLjnjeOXPm8O1vf5tPPvmEc845h89//vNHFU9GRgbf/OY3mTp1KiNHjmTOnDnBfddccw3f+ta3SExM5M033wxuz83N5Uc/+hHnnHMO1loWLVrExRdffKjTH6tYY8zaXu/vt9bef+BBxpgU4A/ADdbaplBc+HgYN3oehhMbcxmw0Fr7jX3vvwLMtdZ++xDHXg18GzjbWus3xnwbSLLW/tgYcw1uqvmhPnctbp498fHxpxxYZOzDDz9k8uTJIf7Jjk1PhcJ0bzqp3lSa/c00+htZPn95v/4S9kc0/LmIiIiIHKue7jHp6a7WTlOTm4m4fLkSbgmdofhdefXq1dx9992DakDuUP8djDE+a23yYT7Sc0wc8BTwnLX2vw6x/xfAamvtyn3vPwbmh2MaeThHtncAvUth5+/b1ocx5nzgO+xLtPdtngecaYy5DkgB4o0xLdbaPkXW9t3FuB8gOTk5PHcN+mnFuyuCFQrfqHoDgEAgwA3P3cA1M68hzZtGmjeNKcOmMDbD1Yfr7O4EIM4TF7G4RURERKLNihX7u8fA/ucVK5Rsi0iw0vivgA8PlWjv82fg28aYR3AzrxvDkWhDeJPtNUCxMaYQl2RfCXy59wHGmFnAL3Aj4LU92621V/U65hrcyPZB1cwHg4qGCvJS8wgQIBAIECCAtZaqpirWVa8LHpcclxxMttdUr2HFhhUkxiUGk/Hej4XjFxJj3HL7Zn8zCbEJSsxFRERkyKuogLwDVlampbntInJ48+fP71M8rcfcuXMPakH829/+lmnTpg1QZCF3OvAV4D1jzIZ9224DxgBYa38OPINr+/UJrvXX18IVTNiSbWtt177p4M8BHuDX1tr3jTHLgbXW2j8Dd+FGrh/bV5Fuu7V2cYjjOGKlvXArzCgMVig8fczpdAW6qPPVkRKfwjdO/gZN/iaa/E0UZBQEP+Pv8hNjYmjrbKOts42alv2N3uM8cVw4/sLg+/9687+obq7uk5inxqe60fLhU5g+YjoAHd0dNPubSY1PPWK8fdaXZxSyZMaSiE13FxEREemtsBC2boWaGoiLg7FjwVq3XUSO3dtvH1g7bHCz1r4GHDH521eF/PqBiCecI9tYa5/B3TnovW1Zr9efWvzMWvsg8ODxXD8hIYH6+nqys7MjlnAvmbGEZavdj5zmTaO1o5WO7g5umHsDc/LmHPIz5xSew/yC+fg6fcFkvOfRGejs87PEmJjDJuZJcUnBZPuTPZ/wP2/9D/52PzGBGEbvGh1MzNMT0vnchM/xfu37LFu9jARPApmJmVQ3V3PbS7fx3bO/y+ljTo/oTYsD6aaAiIjIiWfJEteudc8eiI+Hzk7XpnXp0khHJkNNpAfsTnThqis20MJWIG2gJScn29bW1j7bOjs7qaqq6lcfuFDYWL+RJ7c+SVVrFfnJ+VxccDHTs6eH7PzWWtq622jtbKWls4WWrhZaOlvIS85jTMoYADY3bOYv2/5Cu20nMSmRmJi+Xd/+64L/4ubnb6bOV0dlUyV72/cCbpQ9MTaRM8acQWxMLLNHzeZrs9xMi4b2Bn6+9ufEe+KJi4kj3hPf53F2wdkMTx4OwNaGrexq2dXn2DiPe06MTSQ7KbvPz3OkX269i86ledNo8jdFvOiciIiIDIzbb4c//QmamyE7G37xC63XltCqqKggNTU1ogN2JzJrLfX19TQ3N1N4wLSVoymQFk3COrIdaXFxcQf9B4qEyUzmijOuiHgMF8+7GGvtIUfMk+KSguvL42LiSIpNott2E2tiafK7avldgb4N59s626jYe/hFUjNHzgwm26U7Snlxy4uHPG5EygiWn7M8+P6GZ2/AYg+ZmJ9beG6w6JwnxsOWvVvwxHho62zjzlfv5KbTbsIb68Xr8TJtxLTg2vYmfxMGgzfWS1xM3ID+4tQovIiISOjExkLv9sDFxZGLRYam/Px8qqqq2L17d6RDOWElJCSQn58f6TD6bUgn23IwYwzJ8ckkxyeTm5rbZ1/P+vJJOZOC2xraG8hJyuHei+6lK9CFZf9MiOykbG454xY6ujuCj87uzuDrYcnDgseOTR/Lqfmn9jm255GTlBM8zlqLv9vvnrv6FmsAKMkrCd4U2O3bTXVLdfBz2xq38buNvwsee9+i+4LJ9s/X/pzyPeXBP4N4TzxejxdvrJfZo2ZzyaRLANjbtpcnPnqCeE88CbEJ7rh9ybs31suUYVNI9aYG/2w6ujv2H+fxHpTE9x6Fz0vNo85Xx7LVyzQKLyIichw6OmDXLoiJgfHjYfNmKC+H2bMjHZkMJdEyYCeDn5JtCTpwfXnP9Oyl85ZijDmo4nm8J56izKKjOvfc/LnMzZ/7qccZY/jpop8eMnnvSeB7bgqkxqcyLnMcARugqb2JpPQkzhhzBv5uPx3dHXiMJ3jehNgEkuOT8Xf56Qp04e/yu2TeD60d+5cfNPmbeKvqrcPGd9uZtwWT7ac2P8Wr217tsz/OE0dCbAJj08fyD3P/gRXvriDNm0Z1SzU1rTXEe+Lp6O7gx2/8mB+d/6NgWzhVkxcREfl0VVWuINqoUTBxoku2KyqUbItIdFKyLUEl+SUsn7+8z5TnpfOWDvgIbIyJISE2gYTYhEPu77kpkO5NJzcllyZ/EwEbOOJo8T/O/cfg64ANuGS72yXc3lhvcF9WYhbXzLwmuK/3c0d3B2netOCxKfEp5CTl0NHdETyus7uTzu5O2rraANf6LTcll/d87wU/Z61ly94t3PX6XQBcPf1qzhx7JgCbajdRuqOUjIQM0r3ppCek93nuHauIiMiJZvt29zxmDBTtu9+/ZUvk4hERORIl29JHSX5J1E9v7u9NgRgTQ2JcIolxiQftS/WmMm/0vKM6zyWTLglOPweXRPeMwAdsAHBT83f7djNl2BQ3Sh/oYG/bXkakjKAos4iG9gYyEzOD59jWsI23qw7dgsEb6+WeC+8Jvv/DB38ACCbiGQkZSspFRGRIGzUKzjwTJk1y7b5iYiAQcKPdqmMlItFmSFcjF4m0Y62cvrN5JxUNFTS2N9Lob6ShvSH4OiE2gWVnBzvnccOzN9DW2XbI61486WIuKr4IgMrGSt7e8fZBCXlGQsZBSbmKuYmIyGDi94NX95dFThiDrRq5km2RMAtVAtu7JZq1lreq3jooIe95fcXUKzhr7FkAvL79dR5696FDntMb6+WuBXfhjfVSWlXKt//6bRJjE11xOwstnS0q5iYiIiIiUUHJdoQo2RZxrLVYbLASe1VTFe/Xvu8S8QOSc4PhngvvwRjD9U9fz+ptq4NT4A0GT4yH3JRc/uP8/6A4u5iU+JRI/mgiInIC27vXrc8uKHD9tXtYC62tkKJ/okSGPCXbEaJkW+TY9LRZ6ylEd9HDFxFjYmjvaqe5o5kWfwvdthtfp48Lx1/IRcUXcfGkiwFXwb0z0ElGQkYkfwQRETmBvP46PPQQzJkD3/iG27Z3L/zgB6739o9+FNn4RCT8BluyrQJpIicoY0yfiu89LdXyUvMA6Lbd7GjagTGGiTkTmZgzMXjsW1Vv8fv3f8/w5OEUZxczIXsCxVnFZCVmHdRrXEREJBR6VyLvkZEBXV3Q3AwNDe69iEi0ULItIsDBfdab/c1YLN87+3sHrdn2d7uWabWttdS21vL69tcByEzMZObImVw59coBj19ERIa2QyXbxriq5B984KaYn3xyZGITETkUJdsiAhxbS7WLii9i4fiFVDZWUranjM31mymrL2Nv217qffXB4zq6O3hww4MUZxVTnF1MXmqeRr5FROSYBQJQWelejx7dd19RkUu2y8uVbItIdFGyLSJBx9JnPcbEMDZjLGMzxnJ+0flYa6lurg4WWAPYsncL66rXsa56HQBJcUmMzxrvpp1nFzMmfUywkJuIiMjh7NoFnZ2uMFryAas1i4rc85YtAx+XiMiRKNkWkZAwxpCXltdn26jUUVw9/erg6Pfetr1srNnIxpqNAPzw/B+SlZgFQJ2vjoyEDGJj9GtJRET6OtQU8h6FhfuP6epyxdJERKKBfh2JSNikedM4c+yZnDn2TADqffXBxLvOVxdMtAHuLb2XOl8dhRmFwZHvoswi4j3xkQpfRESiRGMjxMQcOtlOSoLcXNi5000170m+RUQiTa2/RCTiOrs7+eFrP2RH044+2z0xHsamj2XRhEVMHT61z77SqtI+68uXzFhy1FPgRURk8OnshO5uSEg4eN/777vtY8dqZFtkKBtsrb+UbItI1GjtaKVsTxll9WWU7Slje+N2rLVcX3I900dMB+Cdne/w17K/8nTZ04xMGUlWYhZN/iYa/Y0sn79cCbeIiIjIEKVkO0KUbIsMPW2dbZTvLWd81vhgT/AH1j/Af7/537R1teGN9eL1eEmMTcRiGZU6ij9e8ccIRy0iIqEUCLgWX2pmISKDLdnWRBsRiVqJcYkHTR8/p+Acfr7256TGp9Lc0Yy/24+/24+1luaO5uBxrR2t/Oi1HzE8eTgjUka452T3nJWYpRZkIiKDxPr18H//B2efDRdffPjjnn8ePvwQvvpVyMgYuPhERA5HybaIDCrjssYxN28udb460hPS8Xf5aetqY3frboYnDw8eV9NaQ21rLbWttWyq3dTnHHGeOP553j9TlOn6xVQ2VuLv9jMieQQp8SlKxEVEokhlJbS2fvrI9vvvu2S7ogJmzRqY2EREjkTJtogMOktmLGHZ6mWAq3je3tWON9bL0nlLg8eMSR/Dd+d/l5rWGmpaXOLd87rJ30RmQmbw2OfKn2PNjjWAG03vGQEfkTKCgoyCg0bXRURk4Byp7VdvRUUu2d6yRcm2iEQHJdsiMuiU5JewfP7yPtXIl85b2qc4WmxMLLmpueSm5h70+faudrweb/D98OThFGQUUNNaQ1tnG1sbtrK1YSsAs3JnBZPtZn8zv1j3iz5T0kekjGBY0jDiPHGHjFVV00VEjp+1x5Zsg0u2RUSigZJtERmUSvJLjjtp7Sm21mPxxMUsnrgYay0tHS3BUfDa1lpyU/Yn6zWtNa5Sen1Zn88bY8hKzOK6OdeRn5YPQG1rLRt2buA/3/pPMrwZ5KXmUeerY9nqZaqaLiJylBoaoKUFkpMhM/PIx/b01962Dbq61AJMRCJPv4ZERPYxxpDqTSXVm8q4rHEH7c9Py+eGU28IJuI909PrfHXU++pJjU8NHvvER0/wk7d+gr/LT3piOomxiSTEJtAd6Oae0nv4Xf7vBvJHExEZlHqPan/amu3kZBgxAmpqoKoKCgrCHp6IyBEp2RYROUoJsQlMHjaZycMm99neHeimzldHmjctuC0pLon2rnbiPHH4On34On0AWGvZ7dsdPK6hvYH7193PsKRh5CTlMCzZPeck5ZDuTVexNhE5oR3tFPIeRUUu2a6oULItIpGnZFtEpJ88MR5GpIzos+3q6VfzZuWb1LTWkBCbgK/TR3tXO3va9jAqdVTwuN2tuynfU075nvKDzhsbE8stZ9zCmHT3LbOsvgxfpy+YjHtjvQd9RkRkKJk9G1JSjj5xnjrV9eUeMeLTjxURCTcl2yIiYdJTNd1jPGQnZtPkbyJgA9x6+q3BY0anj+af5/0zdb466nx17PbtDr5u9jeTkbC/WewLW15gw64Nwfep3tTgiPiE7AmcOfZMwI2eWywxJmbgflgRkTDIzXWPozV7tnuIiEQDY62NdAwhkZycbFtbWyMdhohIH/2pRt5TNb1nKvlznzzH5vrNwWS8K9AVPHZO3hy+cfI3AKj31XPHqjvITszuMy29JzEfmTLykNXTVTldREREopkxxmetTY50HEdLybaIyCBkraXR38juVjcSnpGQEVxLXlZfxt1v3H3Yz/7rmf9KQUYBAG9Wvsmull3Uttby242/JScxh5zkHJr9zTT6G1U5XUQiZts22LABTjoJiouP/nMtLVBeDsOGwahRn368iAwegy3Z1jRyEZFByBhDRkIGGQkZFGf3/RZanF3MPRfeQ31bfXAUvM5XF0zMc5Jygseu37mejTUbeW37a7R1tVHfVs/mPZvxxnpJ8CSw4t0VlOSXELABtjduZ1jSMJLiklS4TUTC7oMP4JlnwO8/tmT7xRfd5y64AC69NHzxiYh8GiXbIiJDkDfWy6jUUX2KsR3KWWPPYmzGWN6sepNhScPwd/vp6O6gvaudtPg0KhoqADc1/Yev/hBwVdl7T0/PScrh5NyT+1RjFxHpr2OtRN6jqMg9b9kS2nhERI6Vkm0RkRPYtBHTmDZiGn8t+2twOnrABvB3+2lsbyQ/LR8Af7ef/LR8dvt2097VTmVjJZWNlcHzFGUWBZPtv3z8Fz6u/zi4Rjy4Zjx5GKnxqRoVF5Gj0t9ke+tW6O4GjyekYYmIHDUl2yIiEqycDpDmTcPf5cff7WfJjCUA5Kflc8fZd2CtpbWztc+09DpfHcOShgXPtbVhK2X1ZZTVlx10nUk5k7hx3o0AdAW6eGXbK30S8nhP/AD8tCIS7Xw+qKuDuDgYOfLYPpucDMOHQ20tVFXB2LHhiVFE5NMo2RYREUryS1g+f3mfauRL5y09qDiaMYaU+BRS4lOCRdYOdNX0q6hpqQm2Mqv31QdbmvVuZbanbQ+Pbnq0z2fTvGnBKeoXjr+Q3FTX88ff5SfeEx8cFVfldJGhrWdUOy8PYo6ji2FRkUu2t2xRsi0ikaNkW0REAJdwhyJhzUrMIisx65D7ugPdwdce4+HsgrP7FHFr8jfR5G+ifE85C4oWBI/9w4d/4PXtr5OdlE1rRysvbHmBjIQMMhMz2dq4lWWrl6lyusgQUrlvlcqxTiHvUVQEb70FFRVwzjmhi0tE5Fgo2RYRkQHjidm/eDI7KZsvT/ty8H3ABmhsbwyOgg9PHh7c19rRSlegi5qWmmDl9I5AB7W+WjITMhmdNpoV765g6oip3PrCrcHR95T4FFLjU4OvTxl1SvC8vk4f1lpVVxeJQvHxkJsLBQXH9/meddstLSELSUTkmKnPtoiIDAr+Lj/1bfVc/tjlpHvTg5XTk+OTyU/NZ0fzDh685EHueOmOw57jH+b+A1OHTwXgzx//mac3P91nanzPY1jSML5w0heCn9tcv5mE2IRg8h7niTuqmDXdXSQyAgFob4ekpEhHIiKhpD7bIiIiYdDTzmzGiBnU+erIT8gP7mtob6Awo5BhScP4n4X/Q0tHC80dzbR0tPR5jEzpW2kpKS4JX6ePZn8zzf7m4PaRKSP7JNv3lt6Lv8sffB/viSfV60bMFxQtYE7eHAB2teyirL6MlPgUyveUc9/a+8hKyGJU6ijqfHWa7i4yQGJilGiLSOQp2RYRkUHlwMrpTf4mGv2NLJ23FGMMiXGJJMYlMix52BHPs3jiYhZPXEx3oJvWzlaXoPtdgh5j9ldkCtgAhRmFfZL2ju4O6n311Pvqae9qDx67uX4zD298GCA43X1X7C4MhriYOCbkTGDFuysoyS9hVcUq18/cm0aqN9U9x7vnox05FxmKWluhsxPS0yEUKzz8fvB6+38eEZFjpWRbREQGlaOtnH60PDEe0rxprk946sH7Y0xMsF0ZgLUWf7c/mHj3LgY3PHk4p485nZaOFl7e9jLp3nS6bBddgS4CNkCGN4OKhk5g8ZkAACAASURBVAoAVm1dRU1LzSFjOq/oPC6fcjkAta21PF/+/CGT8jRvGgmxCVpzLkNKaSk88ogrbHbllcd/nm3b4Kc/dW3Ali4NXXwiIkdLybaIiAw6oaqcfjyMMSTEJpAQm0BOUk6ffZNyJjEpZxIA79e+H2x3ZrF0Bbpo8jdRmFEIwLmF51Lvq6e5o5kmfxPN/n3PHc0kxiYGz1nTUsMr2145bDzfP/f7waJvz33yHLtadh2UlKd6U8lMyCQ5/vDL3LS+XKJFT9uvY+2vfaCsLGhocD27A4HjayEmItIfSrZFRETC4MDp7q0drcHp7gDzC+Yf8nPWWgI2EHyfm5rLl6Z9KdgWrScp70nM07xpwWM/2P0BH9V9dMjzzhg5g+vmXAdAs7+ZB9Y/EEzKq5uqWblpJRkJGWQnZbPbt1vryyViepLt42371SM1FYYNg927YccOGD26/7GJiBwLJdsiIiJhcLzT3Y0xeMz+Fmk5STmHTcwPtGjCIubkzTlopLzJ39SnlVpDe0OfpLxnfXlTRxPbm7YzZdgU0r3prHh3BZOHTSYxLpHYGH1lkPDr6oLqardWOy+v/+crKnLJdnm5km0RGXj6l1NERCRMBnq6+4TsCUzInvCpxw1LHsY/nfpPwYR8TfUacpJy6Ap04evykeZNw2M8VDRU8NgHj7Gueh2FmYUUZxUzPms847LGkRCbMAA/kZxoduxwU75zc0NT1KyoCN5+G7Zsgfnz+38+EZFjoWRbRETkBJMQm8BJw04Kvn/ioyeC68t79LRTa+looSvQRVl9GWX1ZYAbfR+TPoYzxpzBWWPPGvD4ZegK1RTyHkVF7rmiIjTnExE5Fkq2RURETnBHaqdWkl9Ca0crn+z5hE/2fMLm+s1sb9zOtoZtTB8xPXiOysZKXt72cnD0OzspO1I/jgxioU628/MhPh5qa6G52a3jFhEZKMZaG+kYQiI5Odm2trZGOgwREZFB6Viqkfu7/FQ0VJCdmB3sZ/7cJ8/xxw//GDwmMzGTCdkTGJ81nuKsYkamjFSLMvlU7e1QWQnZ2a6aeCi8/TZkZrpR7lgNM4kMasYYn7X28K01ooySbREREem3nc072VizMTgC7uv0BfelelO5a8FdwWS7urmakSkjiTHqxSQiIkdvsCXbur8nIiIi/Zabmktuai4XcAHWWnY07+CTPZ9QVl9GcnxyMNFu72pn+cvLiffEU5RZRHFWMcXZxRRmFBLniYvwTyEiIhI6GtkWERGRAVPdXM3P1vyM2tbaPts9MR4KMgq4atpV5KWFoOeTDDrvvANvvAGnngqnnBK681oLzz4L27bBtddCjCZUiAxaGtkWEREROYxRqaP4/rnfp7G9kbI9ZcHR7x3NOyjfU06qd38Fq6c3P01zR3Nw9DvNmwYc2/pyGTw2b4aNG/dXEA8VY+C116CuzrUWU79tERkoSrZFRERkwKUnpDN71Gxmj5oNgK/Tx/bG7cGEGuDNqjfZ3bqbVRWrABiePJwYE8Nz5c+Rm5JLXmoedb46lq1exvL5y5VwD3KhrkTeW1GRS7YrKpRsi8jA0UQaERERibikuCQm5UwKvrfW8tUZX2XxxMVMHjYZb6yX2tZaHv/gcWpba/F1+ogxMWQkZJDmTWPFuysiGL30l7WuCjmEL9kGKC8P/blFRA5HI9siIiISdYwxTMiewITsCQB0B7qpbKrk8scuJyMhg8zEzOCxvk4ffyv/G8+UPcOcUXOC7chk8KitBb/ftegKRy/snmS7oiL05xYRORwl2yIiIhL1egqozRk1hzpfHene9OC+6qZq4j3xPPnRkzz50ZPuuLw5zB41m4yEjAhGLUcrnFPIAfLzIS4OamqgtRWSB015JREZzDSNXERERAaNJTOW0OhvpKG9gYAN0NDewLCUYXznzO8wN38u3lgvWxu28tj7j3HrC7fyhw/+EOmQ5SiEO9n2eGDsWPd6y5bwXENE5EAa2RYREZFBoyS/hOXzl/epRr503tJgcbSO7g7eq3mPNdVreK/mPUamjAx+dkfTDiqbKpk5ciYJsQmR+hHkEAoKYM4cKC4O3zWmT4f0dEhKCt81RCSyjDG/Bj4L1Fprpx5ifzrwO2AMLhe+21r7m7DFoz7bIiIiMhS1dbbhifEQ74kHYOV7K1m9dTVxnjimDZ/GnLw5TBs+jThPXIQjFRGRo/FpfbaNMWcBLcBDh0m2bwPSrbW3GGOGAR8DI621HeGIVyPbIiIiMiQlxiX2eV+UWcSO5h2U1Zexfud61u9cjzfWy8yRMzl99OlMzJkYoUhFRCQUrLWvGGMKjnQIkGqMMUAKsAfoClc8SrZFRETkhDA3fy5z8+eyt20v63auo3RHKdsatvF21dukxKcEk+2uQBcxJoYYo9I2A2HXLtcDe+zY8FQi783nc2u28/MhQ7XzRE5E9wJ/BqqBVOAKa20gXBdTsi0iIiInlMzETM4vOp/zi86ntrWWtdVrmTZ8WnD/G5Vv8NTmp5g9ajZzRs2hIKMANwgi4fD22/DMM7BwIXz+8+G91v/9H6xZA1dfDWeeGd5riUhYxBpj1vZ6f7+19v5j+PwFwAbgXGAc8Lwx5lVrbVMog+yhZFtEREROWMOTh3NR8UV9tm2u30xjeyMvbnmRF7e8SE5SDnPy5jBn1Bzy0vIiFOnQFe5K5L0VFblke8sWJdsig1SXtXZ2Pz7/NeBH1hUu+8QYUwFMAkpDEt0BND9KREREpJevz/o6t55xK+cVnUd6Qjp1vjr+WvZXlr+8nIfefSjS4Q05A51sg9p/iZzAtgPnARhjRgATgbD9RgjryLYxZiHwE8ADPGCt/dEB+/8Z+AZuUfpu4O+stduMMTOBnwFpQDfwA2vto+GMVURERATAGENhZiGFmYVcdtJlfLLnE0p3lLJ+53oKMwqDx1U1VfFR3UecknsKmYmZEYx48GpshKYmSEyEnJzwXy8/H+Li3Drx1lZIPmxNYxEZjIwxK4H5QI4xpgr4NyAOwFr7c+D7wIPGmPcAA9xira0LWzzhav1ljPEAm4EFQBWwBviStfaDXsecA7xtrfUZY/4fMN9ae4UxZgJgrbVlxphRwDpgsrW24XDXU+svERERCafuQDcBGwi2Cnt006O8VPESxhjGZ42nJK+Ek3NPJiU+JcKRDh7vvQf33gsTJsDSpQNzzR//GMrL4R/+AaYe1BhIRKLZp7X+ijbhHNkuAT6x1m4BMMY8AlwMBJNta+2qXse/BVy9b/vmXsdUG2NqgWHAYZNtERERkXDyxHjw4Am+nzJ8Cg3tDWys2UhZfRll9WWsfG8lk4dN5swxZzIrdxalVaWseHcFFQ0VFGYUsmTGEkrySyL4U0SXgZxC3mPcOJdsb9miZFtEwiucyXYeUNnrfRUw9wjHfx3464EbjTElQDxQHtLoRERERPph6vCpTB0+lfaudjbs2sCaHWv4YPcHvF/7PsOTh9PZ3cmy1ctIjU8lNyWXOl8dy1YvY/n85Uq499m71z0PZLJduG8lwJ49A3dNETkxhXMa+WXAQmvtN/a9/wow11r77UMcezXwbeBsa62/1/ZcYDWwxFr71iE+dy1wLUB8fPwpfr//wENEREREBkxLRwvrd66nKLOIH776Q+p8dbR3tbO1YSsjU0aSFJfEqNRR3LfovkiHGjV8PvB4wOsdmOt1dLhHimb7iww6mka+3w5gdK/3+fu29WGMOR/4Dgcn2mnA08B3DpVoA+zrqXY/uDXboQtdRERE5NilxKdw1tizAKhoqCAvNY/dvt10BDrY3rQdLHyy5xPK95RTlFmk/t1AUtLAXi8+3j1ERMItnK2/1gDFxphCY0w8cCXw594HGGNmAb8AFltra3ttjwf+BDxkrX08jDGKiIiIhEVhRiFN/ibGZ41n5oiZDEsaRkd3BwEb4Mev/5h/f/Xf2VizMdJhRkyYJlcek+7uSEcgIkNZ2JJta20Xbmr4c8CHwO+tte8bY5YbYxbvO+wuIAV4zBizwRjTk4xfDpwFXLNv+4Z97cBEREREBoUlM5bQ6G+ksb2RlPgUclNyKcgsYMnMJSTHJ7O9cTuN7Y3B48O1tC9aPf883HYbvPrqwF970yZ37YcfHvhri8iJI6x9tq21zwDPHLBtWa/X5x/mc78DfhfO2ERERETCqSS/hOXzl/epRr503lJK8kvo7O5kbfVaTs49OXj8nz76E/W+es4tPPeEmGK+fTvU10NMOOdZHkZysrv2li0Df20ROXGENdkWEREROZGV5JccsvJ4nCeOeaPnBd93B7p5ffvrtHS0sLZ6LWPSx3Bu4bnMHjU72Nd7qIlE268eo0dDbCzs3OkKtA30unEROTFE4F6iiIiIiPTmifFw+1m3c2HxhcEp5g9ueJBbX7iVJz96kiZ/U6RDDKn2dqipcQlvbu7AXz82dn+SX1Ex8NcXkRODkm0RERGRKJCZmMklky7hP87/D5bMXMLo9NG0dLTwTNkz1PvqIx1eSFVWuue8PJf4RkJRkXvWVHIRCRdNIxcRERGJInGeOE4bfRrz8udRvrec92reozCzMLj/kU2PMDZ97KCeYt4zhXz06CMfF05KtkUk3JRsi4iIiEQhYwzjs8YzPmt8cNvO5p2sqlgFwOMfPM5ZY8/i7IKzyUjIiFSYx6VnZDsS67V79CTbFRWuDdkQr0cnIhFghkqbieTkZNva2hrpMERERETCprO7kzXVa3ip4iUqG13GGmNiODn35EFVxby8HMrKYNYsGDEicnH87W9udH3ixMhURReRY2OM8VlrkyMdx9FSsi0iIiIyyFhrKd9bzksVL/HOzncI2ABxnjh+vODHJMWptLaIDE2DLdnWNHIRERGRQab3FPO9bXt5edvLBGwgmGh3B7p5rvw5Tht92qCbYi4iMlRoZFtERERkiFmzYw0PrH8gKqeYf/ihK5A2daqrRh5Jfj+8/DLs3QtXXBHZWETk02lkW0REREQiamTKSGaPms36netZW72WtdVrGZM+hnMLz414FfN16+DVV13Lr0gn2x4PPPkkdHfD4sWQmBjZeERkaFEpCBEREZEhZnT6aL55yjf59/P+nQuLLyQlPoXtjdt5cMOD/Pdb/x3R2HrafkWyEnmP2FgXh7WwdWukoxGRoUYj2yIiIiJDVGZiJpdMuoRFxYuCVcznjJoT3N/Q3kC9r546Xx0PvfsQFQ0VFGYUsmTGEkryS0IeT1cX7NjhXkeyx3ZvhYWu13Z5OUyeHOloRGQoUbItIiISJqWlsGKF6+NbWAhLlkBJ6PMXkU8V54njtNGnMS9/Hpb99XpWVazioQ0PsaluE2PSx1CQXkCdr45lq5exfP7ykCfcO3e6hHv4cEhICOmpj1tREbz4oku4RURCSdPIRUREwqC0FJYtg5oaiI93z8uWue0ikWKMIcbs//qXHJ9MRWMF3YFuqpurWVu9ljhPHOnedFa8uyLk1690rcGjYgp5j3Hj3HNFhZtOLiISKkq2RUREwmDFCkhPd1WOy8vd2tD0dLddJFp8ZtxnGJkykmnDp5ESl0JHoIOP6z4m1ZtKRUNFyK8XTeu1e2RkuIfP526KiYiEipJtERGRMKiocJWNq6vdtNnYWEhLc9tFosm4zHEkxCYwY+QMEmITaOls4eP6jynMKAz5tRISICsrupJtY2D6dJg5EwKBSEcjIkOJ1myLiIiEQWEhbNzoXmdlQXIyNDRAQUFEwxI5yJIZS1i2ehkA4zPH887Od2jtaGXJjCUhv9Yll7hHtE3XvuqqSEcgIkORRrZFRETC4KqroKoK/H4YNWr/dPLExOhLNOTEVpJfwvL5y8lJyqG1s5WzC87m54t+HpZq5D2MCdupRUSihka2RUREwiAQcNNSq6uhpcW1OZo40b1+8UU4//xIRyiyX0l+ySGT64AN9Cmo1h/NzeDxQFJSSE4Xcu3tbplHURF4vZGORkSGAo1si4iIhJi18MILrr3RPffAX/8K998PN9/s9v/pTy4JF4lmG3Zt4I6X7qC2tTYk53v2WbjxRnezKRr9z/+4h1qAiUioKNkWEREJsT173PTxzEyYPXv/9hkz4PTTXcG0X//aPYtEq3d2vkOdr46H3n0IG4K1Dz2VyIcP7/epwqJwXz04JdsiEipKtkVEREIsOxvuvNON4nk8ffddfjnk5Lh+w089FZn4RI7G5VMuJ82bRll9GS9ve7lf57J2f4/t0aNDEFwYFBW5ZyXbIhIqSrZFRETCwOOBESMO3p6QANdc4wpEPfusK5omEo2S45P58rQvA/DHD/9Iva/+uM9VXw9tba79XXp6qCIMrZ5ku6JCRQxFJDSUbIuIiITQ+vWuENSRFBfDZz7jvtxHa+IhAjArdxanjDoFf5ef32787XFPJ++ZQj5mTPRWIs/KcjcDWluhNjTL1EXkBKdkW0REJET27IFf/hJuv91VNj6Siy+Gf/kXN6VcJJpdOfVKkuOT+XD3h7xR+cZxnaN3sh2tjIFx49xrTSUXkVBQsi0iIhIiL77oWn7NmOGmix+JxwMx+/4VttZNsxWJRmneNK6YcgXZSdnkJB3f3aHBkGzD/qnkO3dGNg4RGRpMKKpLRoPk5GTb2toa6TBEROQE5fPBrbe6KuS33370RaDa2txoeGUlLFsGqanhjVPkeFhr6Qp0EeeJO67PNzW5hLugAFJSQhtbKLW0QHe3lneIRCtjjM9amxzpOI6WRrZFRERC4LXXXKI9adKxVVtOSIDOTpeMPPywCjNJdDLG9Em0fZ2+Y/p8WhpMnRrdiTa4+JRoi0ioKNkWERHpp64uN4UcYMGCY/usMa46eUICvPMOvPVWyMMTCZmuQBePbHqEO166gyZ/U6TDCSvd+BKR/lKyLSIi0k9r10JDA+TmwpQpx/757Gy48kr3+pFHtH5bopfHeNjVsouWjhYe2fTIUX3mlVfgV7+CzZvDHFyIvP023HGHa80nItIfSrZFRET6afhwl2QvWHD8bY1OPRVmzXJVzH/zG1doTSTaGGP4yvSv4I31sq56Het3rv/Uz2zaBKWl0Ng4AAGGgDGu9ZcqkotIfynZlj5KS+H66+Gii9xzaWmkIxIRiX5FRfCP/winnXb85zAGrr7arW0tK9N0cole2UnZXDr5UgBWvreS1o4jF6gdLJXIe/RUJN+yRVPJRaR/lGxLUGmpq4RbVwd5ee552TIl3CIiR+t4R7V7pKTAV7/qbniWlIQmJpFwOHvs2RRnF9Pkb+L37//+sMc1N8PeveD1uhkgg0F2tusK0NICu3dHOhoRGcyUbEvQihUQG+t6S9bXQ0aGq8i5YkWkIxMRiU7V1fDzn4d2uum0aXDxxe73sUi0Msbw1RlfJc4Tx1tVb7GpdtMhj6usdM+jR/f/ZtRAMabv6LaIyPFSsi2Aaznz2mvwySfQ2rq/z2taGlRURDY2EZFo9fzzroJ4uGYAtbRodpFEr+HJw1k8cTFz8uYwNn3sIY8ZbFPIe4wb556VbItIf+i++QmuuxtWrYK//MW1runqctOn4uPd/qYmKCyMbIwiItGoocFVLTYGzjsv9Of3++H733dFpTIzobg49NcQ6a8FRQswRxiyHqzJtka2RSQUNLJ9AvvwQ1i+HB57zFW/vfBC949hZqbbv3cvVFW59YMiItLX6tXuhuWsWTBsWOjP7/XCvHmuQNNvfuN+T4tEm96Jdnegm9rW2j77i4th+nQoKBjgwPpp7FhXO+Hzn490JCIymGlk+wS2aRPs2uUKllx+uVsnWFrq1mhXVLgvdsOHg88X6UhFRKKL3w8vv+xeL1gQvut89rPud3VlJfz+97r5KdFrb9te7i29l/audpadvQxvrBeAc85xj8EmPt7VThAR6Q+NbJ9A/H7YsWP/+89+Fr74Rfi3f3OJNrjqt/fdB888A3ff7ZLtxx+HbdsiE7OISDR6/XV3I3LcuP3TTcMhNha+/nX3/Prr8O674buWSH+kedMwxlDnq+OJj56IdDgiIlFByfYJwFpYs8a18brvPujsdNsTE+H88w9f8fbkk2H+fLeO+5e/1BRGERFwv1NXr3avP/OZ8F8vNxcudS2N+e1vXS0NkWjjifGwZMYSYkwMq7auonxPOZWVrme83x/p6I5PczO8+CL87W+RjkREBisl20NcZaUboX7gAVfMJyXl2L6oXXaZa9exe7f7kmdt+GIVERkMjIEbb3RTTKdPH5hrnnsuTJrkvvxvOnSHJZGIG50+moXjF2KtZcW7K/jr3zq5+25XSHAwam93yzf+9jd9/xGR46M120NUczM8+aRr52Wta+X1+c/DaacdW5/LuDi49lq4805YuxYmToSzzgpf3CIig0FmpiueNFCMgWuugZoal3SLRKtFExbxzq532Nm8k48r/0IGlw66SuQ9cnLc96fmZqirC08hRBEZ2jSyPQRZCz/5Cbz6qvuCdv75rur46acfW6LdY/hw+MpX3Os1a3R3V0ROXK2tkfsdmJmpRFuiX2xMLF+d8VUCAcMm3/O0xe4kLy/SUR0fY/a3P1ULMBE5HhrZHkKsdf8wGONGXF591VUZz83t/7nnzAGPx02ZPJ6EXURkKFixwnVx+Lu/i2wro82b4ZVXXBwxum0uUaYos4iSjEXUtGUwfsRI4uIiHdHxKyqCjRtdsj13bqSjEZHBRsn2EFBf7yqGZ2W56uLg+r7OmhXaxPjkk/e/7hnZUeItIieKmhr3pdvjcb9vI6WzE371K1eHY/RouOCCyMUicjjF9nPkdrh+1YPZuHHuuaIisnGIyOCk++GDWEcH/OUvrnXX+vWuLUxbm9vXM8IdDq2t8NOfwhtvhOf8IiLR6IUX3I3GefMgLS1yccTF7e+3/eSTrhCmSLTZvt09jx4Nu1t3s6tlV2QDOk5jx7rZI5WV7nuXiMixULI9CFnrkut/+zd46ik3yjFnjnufmBj+63/wgRvdWbkSqqvDfz0RkUhrboY333Svzz8/srEATJniWjN2d8Ovf72/paNItKirc8+d6R/xvZe/x6/f+TUBG4hsUMfB63XFYadMcYMNIiLHQtPIB5mODrj3Xvj4Y/c+Px+uvBKKiwcuhjlz4P333RfP+++Hf/1X94+RiMhQ9fLLLqGdPh1Gjox0NM6ll7qbn9XVboT7sssiHZHIfjfe6JY6xCUWsHpvKtsatvF8+fNcMH7wrXu44YZIRyAig5VGtgeZ+Hg3ep2cDFddBd/5zsAm2j2+9CX3hXPnTnj00YG/vojIQOnshFWr3OsFCyIbS29e7/4CaS+84IqmiUQLY1wF/ZSEBK6efjUAf/74z4N2OrmIyPFQsh3lAgFXcXbbtv3bvvQl+P73Xb/rSFWh9Xpd/+24OLdW/O23IxOHiEi41dRAbKxbuxmJm5tHUljouk+kpEBXV6SjEXEObI83ZfgUTht9Gl2BLh5696FBOZ28owPKytT+VESOjbFD5LdGcnKybR1ii2nKytyocWWl+0J1yy3RV/37tdfgt791yfftt7ue3CIiQ01XFzQ2QnZ2pCM5WHc3+HyQmhrpSEScRx5xy92+8AWYOtVt83X6+O7q79LY3sgVU6/g3MJzIxvkMbDWfQdrbIQf/AByciIdkciJyxjjs9YmRzqOo6WR7Si0dy888ADcfbdLtLOy4DOfiXRUh3b66VBSAmeeGdlWOCIi4RQbG52JNrhWZL0T7fb2yMUiArB1q6slENurMlBSXBJXTbsKgL98/Bf8Xf7IBHccjNnfwmzLlsjGIiJHZoz5tTGm1hiz6QjHzDfGbDDGvG+MeTmc8ahAWhTp7HTr7p55xk1Xiotz/VMvuMCt1Y5Gxrg1g9E24i4iEgpvvAEzZrg6GdEuEICnn3ZLj26/HdLTIx2RnIgCAaiqcq/HjOm7b8bIGSyeuJhZubPwxg6uyqqFha4Ty5YtbpBBRKLWg8C9wEOH2mmMyQB+Ciy01m43xoR1Xq5GtqNIWxs8+6xLtE8+Gb73Pfjc56I30e7RO9FubXXT30VEBruKClixApYvdwlEtDMGysuhqQkeekhrSyUydu1ygwc5OZCUdPD+RRMWMSp11MAH1k9FRe5ZI9si0c1a+wqw5wiHfBn4o7V2+77ja8MZj5LtCKup2f8lLi3NFT+78Ub4+7+P3imLh9PYCHfe6VqT7d4d6WhERPrn+efd89y5kStGeSyMgWuucQnOpk3w6quRjkhORNu3u+fRo498nLWWNTvWsLdtb/iDCoGCAvd3rLJSfe1FBrkJQKYxZrUxZp0x5qvhvNgg+PowNLW1wWOPwXe/6/q39jj1VJg0KWJh9UtamvvHqL3d9d9WZVwRGazq6mD9epdknzt46jiRkQFf/rJ7/dhjUBvW+/UiB+tJtg+cQn6gv5X/jQfWP8DvNv6OwVCsNyEB8vLcAEnvDjEiMuBijTFrez2uPdbPA6cAi4ALgDuMMRNCHuU+SrYHQGkpXH+9a89y3XXwy1/CHXe49dnWwp4jTXQYRIyBr3zFTR3bvh3+8IdIRyQicnxefNH9fi4pcQnsYDJnjnt0dMBvfjM4psDL0HG0yfap+aeSFJfEptpNvL1jcPQP7ZlKXlkZ2ThETnBd1trZvR73H+Pnq4DnrLWt1to64BVgRujDdJRsh1lpKSxb5kZJUlPdtL7ly926unHj4LbbXGuMoSIpCb75TVcd96WX4J13Ih2RiByt3jcGr7/evT8R+Xzw+uvu9YIFkY3leH35y+4mwZYt+6fDiwyECy5wv0N6qncfTnpCOpdPuRyARzc9SmN74wBE1z8LF8IPfwjz50c6EhHphyeBM4wxscaYJGAu8GG4LqZkO8xWrHAVYY1xVSy7ulxVW48Hbrrp0+/8DkYFBftvIDz0ENTXRzQcETkKvW8M5uW552XLTsyE+5VXwO+HyZMhPz/S0RyfpCS3fnvKFLfmXGSgTJsGF198dH3fT80/lSnDp+Dr9LFy08qon06ene3atSemxgAAIABJREFUnKoDi0j0MsasBN4EJhpjqowxXzfGfMsY8y0Aa+2HwLPARqAUeMBae9g2Yf2l1l9hVlHhvrga49Y0Z2S497t2De1f1ueeCx9/DO++624ynHNOpCMSkSNZsQJSUiAx0a1T7pk6vWLFidfmpqAAJk4cvKPaPSZPdjVAhvK/NTK4GWO4evrVfG/193hn5zus37meU0adEumwRGQQs9Z+6SiOuQu4awDCUbIdboWFboQoI8P1ajUGGhrc9qHMGFiyxE1hnDYt0tGIyKfZssW1jGppcaOhqanuBmFFRaQjG3iTJg3eQpUH6km0AwHYvHno/FwSndavd79Hpk07+o4qWYlZfOGkL/DwxodZtXUVJ+eejIniO0SrVrnZL4sWwezZkY5GRKKdppGH2ZIlriVWQ4MrttPQ4N4vWRLpyMIvOblvoh3ls8NETng7d7qkLDbW/X3duvXT111K9LMWfvIT+O//ho8+inQ0MpS98gqsXAlVVcf2uTPHnMlV06/in+b+U1Qn2uBqOlRXq9+2iBwdJdthVlLiCqLl5MCOHe55+fITb1rmli3w7/8Oe/8/e3ceHmV5/X/8fYeEhASSsIOsYRVBEYGIIoq74IK11LZuUXFXrNqvXazVFotarV2suFvFrdat6s/ivqKAERRQZCfsayALJCQkzP374yQmIMuQzGSWfF7XNVcyk5nnOUAS5jz3uc+JjXGaIo1Kbq6NtamosD3Kyckwb54lZu3aNZ4LZfPn27SIeOs07Bz0qRpq8tRTliyIhJr3wXci351zjmO7HUtSk6TQBxZi1R3Jly6NbBwiEhuUbDeA7GyYNAmmTLGPjS3RBnj7bftP+PHHNYZGJJosX26NDNu1g9/+1t5IrlljH4880vpLvP12pKNsGO+8AzNnwnffRTqS0Bs1yrYvFRTACy9EOhqJRwUFUFJiVW31GZdXWlHKS/NeorQiOq8KZWXZBaxVq+wCpYjIvoQ12XbOneacW+icW+Kc+80evn6Tc+4759xc59wHzrlutb6W45xbXHVrBEXX8e3CC+0/3yVL4I03Ih2NiIBtaXnoIXvDOGIEXH11zYXBZ56BW2+1N5WvvRb/XclXrbKV7eRk+7uINwkJcMklkJQEX3xhFxVEQqm6IqRr1/o15Xt6ztO8v+x9Xv7u5dAEFmIpKdCxI+zcWbOSLyKyN2FLtp1zTYBJwCjgEODnzrlDdnva18AQ7/1hwMvAPVWvbQXcjs09ywZud861DFesEn4tWsBll9l/wG+/HZ8rR/FAc5YblwULLOHu3Rt+9rMfvkE+/HD4yU/s88mTrcFWvKqeRX3MMTY2Kx61b1/z7/ncc9ZDRCRU6lpCvruzDz6bxIREPl/5OfM3hW30bb307GkftW9bRPYnnCvb2cAS7/0y7/0O4AVgTO0neO8/8t5X1wnNAKonmp4KvOe93+K9LwDeA04LY6zSAHr3hrPOsn1d//qXvcmX6KE5y43PkUfC+PFw5ZXWFG1PTjzRRvlVVtoq+Pr1DRtjQygogC+/tIsNJ54Y6WjC69hjrdt8aSl8+GGko5F4Eqpku0PzDpzZ90wAnpn7DGWVZfWMLPSq920r2RaR/Qlnst0JqN1mZnXVY3szDnirjq+VGHHaaTZ6ZutWeOKJxtN4KdoFAnDffVZCm5lZM2c5I8NWNCW+lJfXfF495mtffvITG13YqpWVUMabDz+0n4HBg4MfVxSrnIOLLoKxY+HssyMdjcSTlBSrCqlvsg1wSs9T6JbZjc2lm/nv/P/W/4Ah1qcPnHqqXbwSEdmXqJiz7Zy7ABgCHHeAr7sCuAKgadOmYYhMQi0hAcaNg7/+1VaQonzCR9zbsgU+/xw++wymT7fqg2rFxZaENcY5y/Hs22+tI/UVV9R0qN6fhATbBhIIxF+yXVlpPwMAJ58c2VgaSmZm4/mzSsMZNy50F9ATXAIXDbyIiZ9O5OPlHzPkoCH0bt17/y9sIG3awDnnRDoKEYkF4Uy21wBdat3vXPXYLpxzJwG/A47z3pfXeu3I3V778e6v9d4/CjwKkJaWpjXSGJGebuXJCeqFHxGBAHzzjc1DnTev5s1R7RFPa9ZYeVzLlrZvV+LDunU22qqszPZfB5tsA9S+num9bS8YOjT2f44TE+GWW2DOHOjePdLRNLzCQtuv/uMfx/6/pUReKC+gd07vzOjeo3lz0ZvMz58fVcm2iEiwwplsfwn0ds5lYcnzz4Dzaj/BOTcIeAQ4zXu/sdaX3gHurNUU7RTgt2GMVRpY7Td1eXlWupmeHrl4GpNXXoH337fPExNh0CArhSsogNtvtzffqalWarxyJVx+eWTjldAoLYUHH7RE+4gj4PTT636sF16Ajz+2sWE//WmoIoycNm3if6/2nngPDzxgXaSbNYMzzoh0RBKriovteygpxGOyR/UeRd82fenT+gCuDDaQwkKYPdv+3EceGeloRCRahe06tve+ErgOS5znAy967+c55yY4586qetq9QHPgJefcbOfcG1Wv3QLcgSXsXwITqh6TODNzJtxzjzVM0/7t0AsEbMWudhfp7GzrSjx2LPz5z1Ye3KePvVmYMMESj23b4NBDYcgQ+zdatWrv55DoFwjAo4/Cxo3QpQtcfHH9VqAGD4YmTWyv8wcfhCzMBrd1a+P+veOc/R4A+N//7OKJSF08/zxcf739fxNKiQmJUZloA6xdC//+t114FBHZG+fj5J1GWlqaLykpiXQYcoAKC+GOOyy5O/tsGDUq0hHFh82bbR/q55/b33GfPvDLX9rXqn/k95dseW+zlj//3Bpj3XLL/htpSXR68UVLilu0sH/HVq3qf8wvvrCLZM5ZN/NBg+p/zIbkPdx5p+3ZvuoquwDVWL30klW7tG9vs9XVAkUO1C232P87t98OBx0UnnMsK1jGu0vf5bIjLiMxIfIth7ZvhxtvtAuP//jH3ic6iEhoOedKvfdpkY4jWNqhJRGVmQmXXmqfv/46LF4c2XhiWfUq9j//Cb/7na1UFRbaG+jDDts1yQ5mVdM5+PnPISvLGqk99hjs3BneP4OE3rp18NFH9obwqqtCk2iDVUKMGWPfV088EXuN9BYvtm0SW7eG7u8kVp19NnTsaL0czjgDRo+Ga6/V2D8JTmmpJdpJSdChQ3jOEfABnp7zNF+v+5r/LfpfeE5ygJo1s5+bysqasWciIrtTsi0R17+/jdDwHh5/3Fa55cB99pntyf32W0ushg611ew//tE6D9elbDgpyRK09HSbr7x5c+jjlvDq2NHKOy+6CHr1Cu2xR42C4cOhogImTbLZ7LHi3Xft48iRod9nGmuSkmxrwKxZsGQJpKXZv+Vttynhlv2rTjQ7dw5fk70El8CFh12Ic463l7zNqqLo2Nukedsisj9KtiUqjBkDPXvaSuyTTzbufZTBqF7Fnj695rEhQ2w/7u57sevbHTYzE8aPtzLBdu3qdyxpOLV/hvr1g2HDQn8O5+D88+34rVvbnPZYsG6dreImJVmyLfD229aNPSXFymMzMyEjAyZPjnRkEu2qk+1QzNfel56tejKy+0gCPsDkOZPZGYh8qZWSbRHZH+0wkajQpIklh3fcYf9pbdzYuPdQ7s2WLbaCXb0Xu0ULW8FOTLQO4rfeGp7z7v4mqqws/uYtx5PycqtyOO00S4TDqbo8PSEhdvb6VnfjP+ooaN48srFEi7w8uzjXpUvN30mLFvDJJzB1qnWwT4uZHXLSkBoq2Qb40cE/Yu6GucxaM4sxL4wh4ANkZWaRMzCH7M7Z4Q9gN0q2RWR/tLItUaNVK7j6aksYlWjXqL0X+5Zbdt2Lfeqp9vWG4j288Qb84Q826kWij/e2GrlggY3oaojvj5SUmkTbe2ueFq3VKcXFMGOGrcqfdFKko4keWVm2f732xYc1a+zf8dln4eabbatAbq5dzBGp1pDJdnJiMoe3P5yZ62ayYNMCWqa0JL80n9s+vo3c1Q2/56FDB6vq6dgRduxo8NOLSAzQyrZElT67Tfjwvv5l0LEuL89WKcFWsI84AkaMgN69G/7vZudOS+IKCuCRR6wTqzqwRpcpU2zvbUpKzYpzQ3r6aZg2zUq1zz67Yc8djPXrrbFRz566qFdbTo7t0Qbr0VBcbD/vN94IJSX2cz93rt2aNrWmixdcYH+X0rhdd50l3OHqQr67T1Z8Qqf0TpRUlLAjsINWKdbhcPKcyQ2+uu0cTJyo9ykisnca/SVRyXsr9Vy+3MrLG8t/ZIGA7SVduRLOPNMe8x4eesguRAwbFvmy1+Jie3NRWAjHHQfnnRfZeKTG11/Dww/bz8t118GAAQ0fw7x58MAD9r18wQV2YSjaVFRYB+WMjEhHEl1yc60qIi/PVrpzciC7KnfZutUu4uTmwtKltppXO8lYscJK0Bv64o40PqOfG037tPbgoGmCldQEfIA1W9cw5fwpEY5ORMIt1kZ/aU1KolJRkZVLb99uK1AnnBDpiMJr82bbiz1tmiWxztl+0jZt7PNrrol0hDXS063c/957bT9nly7RmVA1NqtXW3NBgHPOiUyiDTZd4LzzrPT4+edte0j//pGJZW+SkpRo70l2dk1yvbsWLayZ3MiR9vtq8+aaRLuoCO66y54zZIj1kcjKajwXSaVhZWVmkV+aT2ZK5vePFZQVkJWZFbGYKithwwbo1CliIYhIlNI1aIlKmZk2qgjg5Zdt1STeBAIwe3bNXOwpU2r2Yo8daw3PolX37taFGuDf/7aVLomcQMDK+svLbf71ySdHNp4RI2wsWHVcq1dHNh6wWD7+2C7gSf20br3rlp/Nm6FtW6t6+fBDm4Zw663w2mu271vi15QpdmFt7dqGO2fOwByKyosoLCtkp9/JgvwFzFw7kzF9xzRcELWUl8MvfgF33mlJt4hIbUq2JWodcYStouzcCY8+GrtvknNz4dprYfRo+1g9t7akxP5c1XOxs7Nr5mKfdFJ0J9sARx8Nxx9v/z4vvhi9DbEag4QEuzh16KFw4YXRsaI4ZoytcJaX2wWlgoLIxjNnjl0Y+stf9L0aaj16wIQJ1sDxpJPsYml+Prz1FvzpT/a7TuLTrFnWrb6srOHOmd05mwkjJ9AmtQ1rttrVnEHtB5G7NpeKnRUNF0iV5GS72FRZCauiY/y3iEQRlZFLVBs71lZNV62CZ56Byy+PjkQiWLm51nSoRQtrJPTll7BkiY04y862N6bp6dGxF7sufvITa5B2yimx9e8Sj3r3tlu0cA4uvtiqNSDyY8Hee88+HnOMvlfDwTno1s1uP/6x/Z7LzbUOzdUjwwIB6z9x8MFWbq5S/thWUWEr2s41fPl0dufs75uhlVaUMvHTiawqWsWL817k/MPOb9hgsAtO69bZCLCsyFWzi0gUUoM0iXobN9rqSHl59DZcqq2szK72r18P//iHrfBAzWpa167Qq5eN0RGpj6lTLWE57LBIR7J3paWWaEeya/3SpXDPPVYtcvfdthIlDW/hQvjrX+1z56BvX6t+OOKI6K/kkR9avtz26nfsaOMgI2lV0Sru/uxuKgOVXDLoEoZ1Htag55861crphwyxRQERCZ9Ya5CmMnKJeu3aWWlsVhYcckiko7GkefNm67r8wQfw3HPw+us1X6+stPFH775rb0aq5xynpVmzt44drdtvvAkE4NVXbV+shN+CBdaA7MEHbUUlWqWm1iTagUBkZnBXr2ofd5wS7UjKyrJEZOBA2zqzYIFVLP3f/9nFR10vjy3VJdMNMV97f7pkdOHnh/4cgOfmPsfarQ24iRz7vx1sZVtEpDaVkUtMGDoUBg9u2LEyFRW2+lKdKHz8sXUM37DBSiNra9/e9qiClYOPGGFdmLdssRX59u1rYi8sjM8ys4UL4Z137M/ZqVN0lTTHm02bbL9/IACnnmoXcGLBY4/BV1/Zz8WoUQ1zzk2brBFhYqL1GJDIadrUVv6GDLGKh6+/tq01CxZY4lZ7dXvpUitJj2RFhOzbypX2MRqSbYDhXYazZMsSpq+azrNzn+Xmo2/GNdCekY4dISXFfrcVFlrfAhERULItMaQ6WfXeVpX79w/N3suSEiv5Xr/eVgirP8/Ptw6j/frZ87Ztq7mSn54OHTrYrWPHHyY7F1xgH9u0sT3bxcX2muJiG5Pzy1/WP+5o06+fdcF+7z3rQP2730HLlpGOKv6UldWsAh52GJx9dqQjCl52tiVYr71mHa33NmYqlN5/335nZGdrj3A0SU2F4cPtVlxsF0Wqf58XF9towWbNrMR86FDrfq4Z3tEl2pJt5xznHXoeAGf1PavBEm07t11Enz/fKtoOP7zBTi0iUU57tiXmPP64rYZcdJG9UQuG93bFef16K/MeONAeLy2FG2/c82ucs3McfbTdz8+3N4EdOhzY/sLcXJg82UrHs7IgJ6dhkoxICATg/vvtDUe3bnDzzTbTWEKjusHU3Ll2gec3v7HVlFjywQfWvT4x0S5m1R4hFQ7ffANvv22j6g46KLznktBYscJ+Z9YeG5aRUTPDu3t3+z+gsfxejVavvWYVCFdfrT33YCMOk5PtIruaMIqET6zt2VayLTFn+nRrdrRkiY3b6Nfvh2+08vJs9bt6lXr9eisLB9sDfscdNc+99VZ7o1C9Ul19a9dOJYx1UVJi80bz863L+sUX641HqLz+us21TU21MUtt20Y6orr5z39sHnNqKvz61/bzJrK7tWstqc7NrWk0mZAA555rs7wzMnatGJowQQm31PDe8+7Sd+nTug9ZLeNw75ZII6VkO0KUbDceubkwbpwldZmZtrJXUGAJ9Nix9pwpU3ZtWgY1pd+dOsFPf1qTAHqvZDDUVq+2N8M7dsRGB/lYkZdnJfoXX2zjk2JVIAAPP2yzr1u3thX69PRIRyXRynsrzf3yS/u9P3OmJd8ZGbB4sTWn2rrVVhQ15UGqTV0xlWfnPkvLZi35/bG/J61pzLw3F5F9ULIdIUq2G49rr7UmZXl5VgYO1oRswAB44w27v2yZNUWqvVKtMreGNWuWjUO5/PKaObtSfxUV8VGav2MH3Hef7cu96qrQl8NPn26Nt047LXYayElwRo+2i6bFxbalokULm1SxcaNdaJXwW7bMfg917Bi9FWCVgUr+Mu0v5BXkMaDdAK7Lvi7s+7inTLEmkBdeaFupRCT0IpFsO+faA0Or7uZ67zcG+1q1G5GYk5dnjbf697c9mN27WzOS7dtrntOjB5xzju237tFDiXYkDB5se3KVaNdPYaFdOKoWD4k2WGfq66+H8eNDn2h7b53xZ8yoaeIk8SMryxLtlBS7bd1qF/c6dYp0ZI3HK6/An/4EixZFOpK9S0xI5IrBV5DWNI1vN37LW0veCvs5N260RqpLl4b9VCLSQJxz5wK5wE+Ac4EvnHNjg329km2JOdVvtJo1g169rBNqUlL4Gy3JgateRAgEbO747iPTZN8qKqwh2kMP2UptvElLs3nLYI0Lc3NDc9xvv7XJAi1bWlMtiS85ObZHu6wMDj3U9nEXFtrvmcLCSEcX/7yvmczRpUtkY9mfVs1aMW7QOJxzvLHwDRbkLwjr+Xr0sI+aty0SV34HDPXe53jvLwKygd8H+2Il2xJzqt9o1X5zVVRkj0t0evZZWwl5+ml7oyb75739vS1fbvuaBwyIdETh471dUHjiCetWXl/vvmsfTzihJpmX+JGdbc3Q2rSxvdtHH22l5YmJNjKsupmahMfGjbZ1q2VLK+GPdv3b9Wd079F473n8q8cpLAvfFRkl2yJxKWG3svHNHEAOrWRbYk7tN1pr1thHdaGNbiefbCNRvvzS5nDL/r33npVBJyfDNdfExpvaunIOjjzSPn/ppV3L5g/UihVW2pqSosZ88Sw725qhTZkCjz0Gf/2rbSnKz4fnn490dPEt2uZrB+OMPmdwcJuDaZvalnD2KjroIPudvXmzVeCJSFx42zn3jnPuYufcxcD/gKA7hERpWwuRfcvOVnIdSzp2hEsvtdXLV1+1vZX9+0c6quj17bf29wRwySXQuXNk42kI2dmWKL3+Ojz+OPzf/1nydKCqL+aMGGFbTaRxSEuDG2+0sXI/+lGko4lvsZhsJ7gErhxyJU2bNCUxIXxvfRMSbKvbggW2b3vQoLCdSkQaiPf+Zufcj4HhVQ896r3/b7Cv18q2iDSIww+HM86wkuHHH7dSRPmhdetspc57OPPMxvVmbdQoGD7c9qo/8MCBlwOXlFgn4IQEKyGXxiUlxbYTVY+R814l5eEQi8k2QGpS6veJtveeDds2hOU81aXkeXlhObyIRID3/hXv/U1Vt6ATbVCyLSIN6IwzYOBAG9n20EO27092tXOnrcgOHgynnx7paBqWc3D++TbGaetWuP9+S6CDlZYGf/iDjd1p1SpsYUoM8B5eftm2GC1eHOlo4suGqhw11pLtajt27mDSl5O467O7yC8N/dWYAQPgpJPiu8+GSGPgnPus6uNW51xxrdtW51zQG0U0Z1tEGlRZGdx1lzX9uuwyjWXbk+Ji2/eXnBzpSCKjrAzuucf6MYwb13j/HqTuAgH417+sT0RSElx9tbauhEogYJVJ7dvXTJyIJd57Hp75MLPXz6ZLRhd+PfzXJDWJk5mKIo1AJOZs14eSbRFpcMXF0Ly5lfuKWbECunWLdBTRY9s2uxAT7PdIYSFkZMTmm38Jj0DAOvp//rl1Kr/8ctvOIlJaUcrETyeSX5rPiG4juOCwCyIdkogEqaGTbefcM977C/f32N7ora6INLj09JokqrISVq+ObDyR9vnncOed1hxMTO2LMRUVMHPm3p9bWWnVEhMnqgOw1EhIsC0FJ5xg3yOPPGIr3SKpSalcNeQqEhMSmbpiKjNWzwjp8TdvhqlTrVGaiMS8XeqinHOJwOBgX6xkW0QipqwM/vY3m427ITy9aqLekiXw3HP2eZs2kY0lGnlve7cfe8zevO7JrFm2sl1ZGd8j0uTAOQfnnmvN9wIBm+X+1VeRjip2PfII/PnPsGpVpCOpvy4ZXfj5oT8H4Lm5z7F269qQHXvevJqqChGJTc653zrntgKH1d6vDWwAgl4eUbItIhGTnGyr3GVl8OCD9rEx2bIFHn7YmqKdeKJ14pZdOVcz5u/55+1NbG3ew7vv2ucnn6wycvkh5+Dss+3WoQP06RPpiGKT99Zsbtmy+BmrN7zLcIZ1HsaOnTt4d+m7ITtuVpZ9XLYsZIcUkQbmvb/Le98CuNd7n151a+G9b+29/22wx1GyLSIR4xxcfDEcdBCsX28NjeKkjcR+lZfbBYatW6FfPxg7NtIRRa8RI2pWJh95ZNdtBwsW2P30dDjyyMjFKNFv1Ci45RbbogCN53dNqBQV2e+rZs2swWU8cM5x3qHncU6/c7jwsKC2XwalUye7mJyfb39nIhK7vPe/dc61dM5lO+eOrb4F+3ol2yISUcnJcM011gxrzhx4881IRxR+3sNTT1kpZrt2cMUVaha3P2PGwNChdpHin/+EggJ7/L337OPxx1sTLJF9adrUPnoPL7wAr72mpDtY1fO1u3SJrwqS5MRkTu11Kk0SmoTsmAkJ0L27fa7VbZHY5py7DPgUeAf4Y9XHPwT7er29E5GIa9vWxoA5Z8n27NmRjii8tm61N64pKTUXGmTfqqsgeve2/dm/+Q1ccAH85S8wbZrN2BYJ1urV8Omn8NZb8NJLSriDUZ1sx+p87WCU7CjhwS8fJK8gr97H6tHDPirZFol5vwCGAiu898cDg4DCYF8cdLLtnOvknDu6LsvnIiL7078/nHOOfR7vDYzS0+G3v4Xrr4eOHSMdTexITLR5yZWV1nhowwYb99W8uXUjz82NdIQSK7p0gSuvtO+pDz6wZlaBQKSjim7VTdHiOdn+MO9D5qyfwyOzHqFkR/3GySrZFokbZd77MgDnXLL3fgHQN9gXB1V055z7M/BT4DtgZ9XDHltSFxEJiZNPtr2ARxwR6UjCo7S0ZhW7efOavaMSvLQ0K9E85BBo2dJmkwcCUFICkyfXNFMT2Z/DD7fKkocegs8+gx07rHqiSeiqieNKY1jZHtV7FPM2zSOvII8nvn6C8dnjcXWsmc/Ksm1SKSkhDlJEGtpq51wm8BrwnnOuAFgR7IudD6J2yjm3EDjMe19e5zDDLC0tzZeU1O8qpIhEl8pKS6xieT9zbq4lgYsX2+zVH//YSqBj+c8UaaNHWwOi2n+HgQCsWQNTpkQuLolNixbBAw9YP4CBA62Hgvb/78p7mDHDVrfHjo3v319btm/hT5/+iZIdJYw5eAyje4+u87ECgfj+uxKJBOdcqfc+IpvHnHPHARnAW977imBeE+yvgGVAUl0DExE5UIWFth/3jTciHUnd5ebCbbfBxo3WybegwBqjzZgR6chiW1YWFBfv+lhxcc24HZED0acP3HijVZ1s3mwr3LIr5+Coo2xmebwnj62atWLcoHE453hj4RssyF9Q52PF+9+VSGPjvf8EKAOCvrQf7K+BUmC2c+4R59z91be6BCkiEoyNG2HFCmtgNHNmpKM5cAsXwh//aH+ORYssGUxPhwED4LnnIh1dbMvJsYsXhYW2clRYaPdzciIdmcSqrCz45S/hhhvUsFCgf7v+jO49Gu89j3/1OEVlRXU+1s6ddhFHRGKLc+4E59wi59w259yzzrlDnXMzgbuAh4I9TrDJ9hvAHcA0YFatm4hIWPTpUzN7evLkXWcrR5MdO2DpUmuyVFlZ8/hbb8E339g+7R07bO/eIYfYnvS8+je6bdSys2HCBGjTxkrH27Sx+9qvLfXRuTO0aGGfew/vvGO9AMQ6/k+f3rj+Ps7ocwb92vbjqM5H0bxp3RpsbNgAv/gF/O1vIQ5ORBrCfcAVQGvgZWA68JT3frD3/tVgD7LfXUnOuSbAxVWtzkVEGswJJ1hTnhkzrInRLbdEdsRTIABr18Ly5TW3NWtquhj37FkzW3Wn4tSxAAAgAElEQVTIEOjXz7520EGWbDtnq7Aqd66/7Gwl1xI+b70Fr79uW0FuuKEmCW+s3nrLqnR+//vGM2YvwSUwPnt8veZvt21rv/c3bbKRj439+0gkxnjv/cdVn7/mnFvjvX/gQA+y35Vt7/1OIOCcyzjQg4uI1IdzNku5WzfIz4fHHmu48Tze2zlX1Oo3mZ8Pd9wBzzwDU6dasyDvbUXsmGOgadOa5x5zjD23SRMoK7PnqdxZJDYcdRS0b28VNX/5i/3sNlZlZZZoJyY2vlGFtRPtbTu2sWTLkgN6fXWDz88+g1Gj4NprNaJQJIZkOufOqb4BibvdD0qw3chfxwZ4vwd8X0Tkvb++DoGHhbqRi8SvggKYONFWBi65BIYNC/05tm61xHr5civzXr4ctm2zMTe/+509x3u4805o185Wp7t3t3m9ycl7P251N/K8PHtNTo5WZEViQXEx/P3vNVsVbrzRPjY2ixfbBYdu3ay6qDEqLCvk7s/upqyyjFuPvZU2qcF9I+Tm2jz3oiLo1cvGFRYVaduLSH00VDdy59yT+/iy995fGtRxgky297gO472fHMxJGoKSbZH4tnix7Y0+9VRb8a6P8qohhtVJ8ptvwv/7fz98XvPmVhp+9dX1P6eIxJ6SErj/frv4lpkJN91kK96NyQcfwIsvwogRVmnUGHnveXjmw8xeP5suGV349fBfk9Rk/0N6rr0Wliyx7VDJydC/v/X2aNMGJk1qgMBF4lAkR3/VRVCTJKMpqRaRxql3b7sdqOp91tWr1cuX2/0LLoDhw+057dtbCXi3bnarXrVu3VpJtkhjlpZmK9r//KclTS++COPHRzqqhrVypX3s2jWycUSSc46cw3NYM3UNq4pW8Z95/+GCw/Z/5SEvDzp1gi1brFJq9mw44gg1yRQJJ+fcv4AzgI3e+wH7eN5QrOnZz7z3L4crnqCSbedcHvCDJXDvfY+QRyQish9btsCf/2wf16zZe3n2/ffb2K2Kil0fT0iw0vRqgwbB4MGaiSoiP5SSAtdfD6++CmedFeloGp6SbZOalMqVg6/k7s/uZuqKqfRq1Ythnfe9pykry3p9DBxoCbZzNp1CTTJFwuop4AHg6b09oaoB+J+Bd8MdTFDJNjCk1ucpwE+AVqEPR0Rk/+66C154wVaee/WCuXMt2R46FJ580pqSgb2pqaiwjrDdu9stK8v2WdduZpYY7G9CEWmUkpPh5z+vue89rF8f/w3DvIdmzezP36lTpKOJvC4ZXfj5oT/nmTnP8OzcZ+ma0ZWDWhy01+fn5MBtt9nnPXrYfu2iIpvpvnSpfX7EEQ0UvEgj4b3/1DnXfT9PGw+8AgwNdzxB7dne4wudm+W9HxzieOpMe7ZFGo/LLrNu4LVXosvL7U3hO+9Yd3CwGafNmzeeUTUiEn7ew/PP29zpa66BQw6JdEThFwio8qea957JcyYzfdV0zu1/Lif2OHGfz99Tk8yBA+GPf4TNm207009/uu9GmyJSI5g921XJ9pt7KiN3znUCngeOB/5V9by9lpE751KBXwJdvfeXO+d6A329928GE2+wZeS1r7slYCvdWgsSkYhYu9ZKvxcvthXqFi0sod66ddfVl8bWyEhEws972LnTqmYmTYIrrrDkKZ4p0a7hnOO8Q88ju1M2h7Td/5WW7OwfbnHyHk45BV5+GT7/3LY7jRun8nKRICU652bWuv+o9/7RA3j934Ffe+8DLrjGPE8Cs4Cjqu6vAV4Cgkq2g+1G/lGtu5VAHnCf935hMCdpCFrZFmk8rr3W9sFlZtY8VlioDq8i0jC8h//8Bz76yBLRSy+1bSzxZutWqw5So8h92xnYuctM7mCtXQtPPGHz3BMS4Mwz4bTTdHFDZF9CsLKdB1T/VmsDlAJXeO9f28uxZnrvhzjnvvbeD6p6bI73PqjLrMH+OI/z3h9fdTvZe38FsCPI14qIhFROju11Kyy08sbCQrufs8chhSIioeWclf6edpr9DnriCVuhjDf33w+/+AWsWhXpSKLX0i1Lue2j21hWsOyAX3vQQfDb38LJJ9v30euvwyOPhCFIEfme9z7Le9/de98deBm4Zm+JdpUdzrlmVDULd871BMqDPV+wyfae6tjD1iJdRGRfsrNhwgRbyV6zxj5OmPDDUj0RkXBxDn70Izj7bFvpfvppmDlz/6+LFZWV9vu1vNyaTMqezVo3i/zSfB6d9SglOw68wjIxEcaOhRtusGqtYftucC4i++Gc+zc20quvc261c26cc+4q59xVdTzk7cDbQBfn3HPAB8Cvgo5nX2XkzrmDgf7APcDNtb6UDtzsve9fl4jDQWXkIiIiEgkffAAzZthM7tTUSEcTGqtWwZ/+ZL0vJkyIdDTRqzJQyV+m/YW8gjz6t+vP+OzxBLkP9AfKy3dtlDZnDvTpY80/RcQEU0YehnO2BoZh5eczvPf5wb52fyvbfbGh4JnAmbVuRwCX1ylaERERkThy4onw61/XJNre2y2WVc/X7tIlsnFEu8SERK4YfAVpTdOYt3Eeby15q87Hqp1or1gBDz8Md9xhzUBFJKKOA07EOpiPOJAX7jPZ9t6/7r2/BDjDe39Jrdv13vtpdY9XREREJH4kVs1o8R6eeQZeeim2E+7qZLtr18jGEQtaNWvFuEHjcM7xxsI3mL9pfr2P2ayZXejYvBnuu8/2c+/cGYJgReSAOOceBK4CvgG+Ba50zgXdjjfYPdubnXMfOOe+rTrpYc65Ww84WhEREZE4tnq1lZR/8AE895w1vopFSrYPTP92/Tm99+l473ni6ycorwy6f9IetWtn1RKjRtn9KVPgnntg48YQBCsiB+IE4FTv/ZPe+yeB0VWPBSXYZPsx4LdABYD3fi7wswMMVERERCSudeli4wmTkmDqVLjtNrjmGhg92h7PzY10hPsXCNR0IFeyHbzT+5zOkIOGcNHAi0hOTN7/C/ajSRNrwHfTTdCyJSxfbvvov/mm/rGKSNCWALV/E3apeiwowc7Z/tJ7P3S3+WKzvfeHH2i04aIGaSIiIhItFi2CW2+F6dNtlfLww2HbNhtTGO3TE7y3Ffp166I7zsaktNQqJebNg9//Hlq3jnREIpHR0A3SnHOfAEOBXGz8VzYwEygC8N6fta/XJwZ5nvyqmWLV88XGAuvqGLOIiIhIXOvTB5o2tb23paXwxRc1ievkydGdxDpnK/RqjlY/S7YsoayyjAHtBtT7WKmpcNllsGVLTaLtvZX7d+tW78OLyN7dVp8XB7uy3QN4FDgaKADygPO99yvqc/JQ0sq2iIiIRJPRoyEjA5YssdLsIUPs45o11sG8ZUs45BA4+GCbsSzxY0XhCu7+7G4KtheQmpTK+pL1ZGVmkTMwh+zOobnS8v778PLLcPLJMGZMTZM+kXjWkCvbzrkmwPve++Preoygfiy998uAk5xzadg+71Jsz3bUJNsiIiIi0SQrC/Lz4YgjajpJFxdDx45WZg624g1w0EHQr5/d+vTZdQxUQ3vpJft48sm6CFBXXTO6kp6czpuL3iSzWSbZB2WTX5rPbR/fxoSRE0KScFdWWhXCu+/C/Pkwbpx9b4lIaHjvdzrnAs65DO99UV2Osc8Gac65dOfcb51zDzjnTsaS7BxsU/i5dTmhiIiISGOQk2N7tAsLLSkqLLT7V1wBt98O554LAwZYYr12rXUwf+ABWwmvtm1bw3Y09x4++8xWTZ1ruPPGG+cchWWFtEhuQWWgksVbFpOenE5GcgaT50wOyTlOOw1uvhnatLGGdhMnwqefxvbIOZEotA34xjn3hHPu/upbsC/eZxm5c+51rGx8OjbIux3ggF9472fXL+7QUhm5iIiIRJvcXNujnZdnK905OT/cr11ZCcuW2erkwoVwww223xvg4Yftsb59a1a+27YNXyK8aZM1dktPh3vvDc85GovRz40mIzmDbzZ+w06/kxZNW3Bwm4PJL81nyvlTQnaesjL4z39g2jS7f9hhcOml1i9AJN5EoEFazp4e994HddVsf2XkPbz3h1ad6HGsKVpX733ZAUUpIiIi0ghlZ++/GVpiopWO9+mz6+PeW0Os0lL4+mu7gTXIOuQQOPJI6N07tPFqvnboZGVmkV+az+EdDmfepnls3bGVr9d9zYhuI0J6npQUu4jTv791LN+2LbLbEETiSbBJ9d7sb852Ra0T7QRWH0ii7Zw7zTm30Dm3xDn3mz18/Vjn3FfOucqqDue1v3aPc26ec25+1XK9iplERESk0XAObrnFyoMvuMD2fqemwubNNsM7L6/mufn5tgJeWVm/cyrZDp2cgTkUlRdRsbOCge0H0qxJM1o2a8nFAy8Oy/mGDLG57pddBglV7/BLSqCiYt+vE5G9c871ds697Jz7zjm3rPoW7Ov3t7I90DlXXH0uoFnVfQd47336PgJrAkwCTgZWA186597w3n9X62krgYuB/9vttUcDw4HDqh76DDgO+DiIP5OIiIhI3GjTBkaMsFsgYAnx/PkwcGDNc6ZPhzfftPLz3r1t5btfP2u8diDLFUq2Qye7czYTRk5g8pzJ5BXmcVSXo3bpRj5n/Rz6t+tPYkLo2oi3bFnzuffw5JN2cWbcOOjcOWSnEWlMngRuB/4GHA9cwv4XrL+3z59u732TegSWDSyp6mSOc+4FYAzwfbLtvV9e9bXdW394IAVoiiX2ScCGesQiIiIiEvMSEqB7d7vV1qIFdOpkY8XmzbMb2N7rI4+EsWN3P9IPVc9tBiXboZLdOXuPncdnr5/NQ18+RFbLLK4achWZKaFv+75tG2zcCBs2wF13wTnnwAknqPGdyAFq5r3/wDnnqsZe/8E5N4sg528HnZXXQSdgVa37q6se2y/v/XTgI2yP+DrgHe/9/JBHKCIiIhIHRo60EuJ777XmWMOG2Yzv4mIrJa62bZs105o7F8rLax7PzYWrr7Yu5N98s2tHdAm9Vs1a0apZK/IK8pj46USWblka8nO0aAG/+x0ce6xtL3jxRfjHP6wrvogErdw5lwAsds5d55z7EdA82Bfvsxt5fVTtwT7Ne39Z1f0LgSO999ft4blPAW9671+uut8L+Afw06qnvAf8yns/dbfXXQFcAdC0adPB5bX/1xARERFpxLyH9ettJbNDB3ts5kx47DH7PCEBeva0j6+9Zs+pTtCLimDChP03d5O621q+lUdnPcqizYtoktCEnw34Gcd2OzYs55ozB55+2i62pKXBRRfB4YeH5VQiYRWBbuRDgflAJnAHkAHc472fEczrw7myvQboUut+56rHgvEjYIb3fpv3fhvwFnDU7k/y3j/qvR/ivR+SmBi6/S4iIiIisc456NixJtEG27c7erSNIfMeFi+GJ56A1athwQJ7LDPTku7JoRkHLXvRIrkFNwy7gRN7nMjOwE6em/scz859lspAPbvc7cHAgVb5cMghVumwLOj2TiKNm/f+y6qcdLX3/hLv/TnBJtqw/wZp9fEl0Ns5l4Ul2T8DzgvytSuBy51zd2F7to8D/h6WKEVEREQaiQ4dYMwYu5WWWgfzL76wxmpJSdCkqltPevqu3c4lPJokNOHc/ufSJb0Lz859lm82fMOYvmNokdwi5OfKyIDrr7d/7yFDah6vqLB/exH5IedcH+BmoBu1cmfv/QlBvT5cZeQAzrnRWJLcBPiX936ic24CMNN7/0bVsvx/gZZAGbDee9+/qpP5g8CxWLO0t733N+3rXGlpab6k9qYkEREREdmva6+10WEtWtQk24WF1gV90qTIxtaYLC9cjsPRLbNbg52zpMRGyx17rFU0PPOMXWTJyrLZ3dpGINEmAmXkc4CHgVnAzurHvfezgnp9OJPthqRkW0REROTA5eZaiXFGhq1oa892dHhj4Ru0btaa4V2Hh+0c06fDU09Z1/LvvrMy8zZt9D0g0SsCyfYs7/3gOr9eybaIiIhI45aba3u0taoZHVYWrWTipxMBGNl9JOf2P5cmCfWZyLt3334LF1xgCXZamiXcmZmqbpDo1FDJtnOuVdWn1wMbsWrs77txe++3BHUcJdsiIiIiItHl85Wf8/w3z1MZqKR3695cOfjKsOzlBjjlFCsp37LFthIceig0b25z26dMCcspReqkAZPtPGw7854m03vvfY+gjqNkW0REREQk+iwrWMYjMx+hsKyQls1acvWQq8Oyp/vaa2HTJisn37ABEhOhd2/rZq+VbYkmDV1GXl/hHP0lIiIiIiJ11KNlD24ZcQs9W/WkYHsB93x+D/M2zgv5eXJyrIy8XTto3RpatbJu9Tk5IT+VSExwzg11znWodf8i59zrzrn7a5WY75eSbRERERGRKJWRksFNR93Esd2OJSMlIywr29nZ1gytbVvrSj9ggJqjSaP3CLADwDl3LHA38DRQBDwa7EFURi4iIiIiEgNKdpSQ1tQqaAM+wPaK7d/fD4eNG+Gll+Dii615mkikNeCe7Tne+4FVn08CNnnv/1B1f7b3/vBgjqOVbRERERGRGFA7sX7lu1eYOHUiq4pWhe18zz0Hc+fC/fdDWVnYTiMSjZo45xKrPj8R+LDW1xL38Pw9UrItIiIiIhJDKnZWsLRgKZtLN/Pnz//Ml2u+DMt5LrnE9nAvXw4PPQQVFWE5jUg0+jfwiXPudWA7MBXAOdcLKyUPisrIRURERERiTMXOCv797b/5fOXnAJzc82TO6XcOCS60a2kbN8K991oDtcMPhyuvhAQt10mENGQ3cufcMKAj8K73vqTqsT5Ac+/9V0EdQ8m2iIiIiEjs8d7zyYpP+M+3/yHgA/Rr24/Lj7g85Pu4V6+G++6zDuVHHWVdyt2epg+LhJlGf4mIiIiISNg55xjZfSQ3HXUTLZJbMH/TfF6d/2rIz9O5M4wfD02bwvTpMH9+yE8hEpe0si0iIiIiEuMKthfw8ncvc/5h55OalBqWc8yfDxs2wMiRYTm8yH7F2sq2km0RERERkThTGahk2qppHNP1mJDv465WXg7JyWE5tMgexVqyrTJyEREREZE48/J3L/Pc3Od4IPcBSitKQ378devg9tthxoyQH1okbijZFhERERGJM4M6DKJ50+bM2ziPO6feydqta0N6/IULoaAAJk+GOXNCemiRuKEychERERGROLS5dDMPzXyIVUWrSE5M5pLDL2FQx0EhO/7rr8OUKZCYCNdfD337huzQInsUa2XkSrZFREREROLUjp07eGbOM+SuyQVgdO/RnNX3LFwIZnd5Dy+8AB9/bHu3b7oJunev92FF9irWkm2VkYuIiIiIxKmmTZpy6aBLGXvIWJxzrNm6JmTHdg5+9jM48khrlnb//baXW0RMYqQDEBERERGR8HHOcXLPk+mW2Y0u6V2+X9X23td7hds5yMmB7dth7lxYvBg6dgxF1CKxT2XkIiIiIiKNTGWgkkm5kxjZfSQDOwys9/EqKuC772Bg/Q8lslexVkaulW0RERERkUZm+qrpfLfpO77b9B1n9j2TtqlteXrO0+QV5pGVmUXOwByyO2cHfbykpF0T7fx8aNYM0mImLRIJPa1si4iIiIg0Mt573l36Lv9d8F82bN3Awi0LOaTtIbRMaUlxeTFF5UVMGDnhgBLuamvXwt/+Bm3awA03WPM0kVCItZVtNUgTEREREWlknHOc2utUxmePZ1nhMsoqy8gryKN8ZzmZKZlkJGcwec7kOh27WTNb6V62DB56CCorQxy8SIxQsi0iIiIi0kj1b9efNmltaJnSktLKUuasn0NZZRnpyenkFebV6ZgtW9qKdosWMH8+PPEEBAIhDlwkBijZFhERERFpxA5ufTBZLbNomdKSjJQMkhOTKS4vJiszq87HbNcOfvELW+X+6it49lmbyy3SmCjZFhERERFpxHIG5rBtxzY6pXeid+veFJUVUVRexIUDL6zXcbt0gfHjraT888/hlVdCFLBIjFCyLSIiIiLSiGV3zmbCyAm0S23Huq3raJPahltH3MqnKz5l2qpp9Tp2z55w9dWQmAitW4coYJEYoW7kIiIiIiKyixmrZ/Dk108C8ONDfswpPU+p1/EKCmwvt0h9qBu5iIiIiIjEtGGdh/HTAT8F4JXvXuGV716hPot0tRPt9ettH7dIvFOyLSIiIiIiP3BC1glcOuhSElwC7y59l6fnPE3A16+teHEx3HsvPPYYzJ0bokBFopSSbRERERER2aMjOx/JtdnXktQkiWmrpvHwzIepDNR9cHZ6OhxzjI0Ce/RRWLQohMGKRBkl2yIiIiIislcD2g3gxmE3kpqUSmpSKk1ck3od7+yz4dhjoaICJk2CFStCFKhIlFGDNBERERER2a9NJZtondqaBFf/9bpAAJ54AmbOhObN4eaboUOHEAQpcU0N0kREREREJO60TWv7faK9vWI7j8x8hPzS/DodKyEBLrkEBgyAbdvg73+H8vJQRisSeUq2RURERETkgLy24DW+WvcV93x+D2uK19TpGImJcOWV0KcPnHkmJCeHOEiRCFMZuYiIiIiIHJCyyjIm5U5i0eZFpCalcl32dfRs1bNOx/IenAtxgBKXVEYuIiIiIiJxLSUxheuPvJ5BHQdRWlHK32b8jW83flunY9VOtFevhkcegR07QhSoSAQp2RYRERERkQOW1CSJKwZfwfCuw6nYWcGk3El8sfqLOh/Pe3jySfjqK3j4Yais+4QxkaigZFtEREREROokwSVw4WEXcmqvUwn4AEsLltb5WM7BFVdYd/J58yzxDgRCGKzEPefcv5xzG51zeyyzcM6d75yb65z7xjk3zTk3MKzxaM+2iIiIiIjU15z1czi0/aH1Hg22ciXcdx+UlcGIEXD++drTLWZ/e7adc8cC24CnvfcD9vD1o4H53vsC59wo4A/e+yPDFa9WtkVEREREpN4Gdhj4faJdsqOEKYunEPAHvjTdtStcdx0kJcHUqfDf/4Y6UolX3vtPgS37+Po0731B1d0ZQOdwxqNkW0REREREQsZ7z8MzH+b1Ba/z2KzHqAwc+Obr3r3hqqtsHvc779hqt0iIjQPeCucJVEYuIiIiIiIhtXjzYh7IfYCyyjIObnMwVw+9mpTElAM+zpdf2r7tI8NW6CuxxDm3A/im1kOPeu8f3e053YE391RGXus5xwMPAsd47zeHIVQ7j5JtEREREREJtVVFq7j/i/spLi+mW2Y3xmePp0Vyi3ods6LCysulcQpmzvb+km3n3GHAf4FR3vtFIQ+y9rmUbIuIiIiISDhsKtnE32f8nfzSfNo3b88Nw26gVbNWdTrWihXwu9/ZSLDiYsjKgpwcyM4OcdASteqbbDvnugIfAhd576eFJchatGdbRERERETCom1aW341/Fd0Su/Ehm0bmLaq7vnNiy/CJ5/AzJk2Hiw/H267DXJzQxiwxDTn3L+B6UBf59xq59w459xVzrmrqp5yG9AaeNA5N9s5NzOs8WhlW0REREREwqm0opRPV3zKqT1PxdVxjtc118CsWbaqnZgIffrYxzZtYNKkEAcsUSmYle1oopVtEREREREJq9SkVE7rddr3ifbW8q0szF94QMdYvhwOPRTatbNS8u++gw0bYOnSMAQsEgJKtkVEREREpMGUV5Zz/xf3848v/sHMtcFX8WZlwdat0Lcv9OplY8FWroTCQoiTYl2JM0q2RURERESkwTRt0pQ+rfuwM7CTx796nE+WfxLU63JyoKjIbh06QM+elnBfcw3UsTJdJKy0Z1tERERERBqU9553lr7Df+f/F4Az+57J6b1P3+9+7txcmDwZ8vJspfvCC2HYsJqvz5xpj7duHc7oJVJibc+2km0REREREYmIqSum8tw3z+G95/is4/lp/5/WuYHaypVw993QtCmcfz4MHRriYCXiYi3ZVhm5iIiIiIhExIhuI7hy8JUkJiTyUd5HzFo3q87HatUKBgyA7dvh8cfhX/+yz0UiRSvbIiIiIiISUQvyF/DNhm8Ye8jYOq9sgzVK++wzm8m9Y4eVk48bZ/u7JfbF2sq2km0REREREYkqhWWFJCUkkda0bnnVhg3wxBOwYoU1T7voIjj66BAHKQ0u1pJtlZGLiIiIiEjUKNlRwt9n/J2/TPsLhWWFdTpG+/bwq1/BqFHQrJmNCxNpaEq2RUREREQkalQEKgBYu3Ut93x+Dxu2bajTcRIT4eyz4U9/qulO7j18+63mckvDULItIiIiIiJRIzMlk5uPvpkeLXuwuXQz9067lxWFK+p8vLRaRccffQT//Cc89hhoB6qEm5JtERERERGJKmlN07hh2A30b9efreVb+ev0v7Iwf2H9j5sGKSkwaxbccQcsrP8hRfZKDdJERERERCQqVQYqeWr2U3y55ksSExL5/XG/p0PzDvU6Zn6+NU9btsyap51yCpx1lpWdS3SLtQZpSrZFRERERCRqee954dsXaJLQhG4Z3Xh6ztPkFeaRlZlFzsAcsjtnH/AxAwH43//s5j107QrXXQcZGWH4A0jIKNmOECXbIiIiIiLxyXtP7ppcbv/4djKSM0hNSqWkooTi8mImjJxQp4QbYOlSW+Vu0cK6lzdpEuLAJaSUbEeIkm0RERERkfh17f+uJb80n7SmacxZPwfnHK1SWtGrdS8ePP3BOh+3rAxKS6FVK7tfUmIr3y1ahChwCZlYS7bD2iDNOXeac26hc26Jc+43e/j6sc65r5xzlc65sbt9ratz7l3n3Hzn3HfOue7hjFVERERERKJXXmEe6cnplOwoIeADlFSUsLJ4Je8ve5/Z62dT10XElJSaRNt7mDwZJkyAefNCGLw0SmFLtp1zTYBJwCjgEODnzrlDdnvaSuBi4Pk9HOJp4F7vfT8gG9gYrlhFRERERCS6ZWVmUVxeTGZKJkM6DaF3q94AJCUk8dCXD3Hn1DuZu2FunZNugPJyW+UuLob774cXX4SKilD9CaSxCefKdjawxHu/zHu/A3gBGFP7Cd775QG/Cs8AACAASURBVN77uUCg9uNVSXmi9/69qudt896XhjFWERERERGJYjkDcygqL6KwrBA8NEtsRreMblw79FrSk9NZWbSSV+e/iqfuyXZKCtx0E/zoR5CQAB98AHfdBWvWhPAPIo1GOBvcdwJW1bq/GjgyyNf2AQqdc68CWcD7wG+89ztDG6KIiIiIiMSC7M7ZTBg5gclzJn/fjfyXR/2S7M7ZVOys4JMVn9AurR0JztYTC7YXsH7beg5uczDOuaDPk5AAp50G/frB449bon3nnTB2LBx/fLj+dBKPonWaXCIwAhiElZr/Bys3f6L2k5xzVwBXADRt2rRhIxQRERERkQaV3Tl7j53Hk5okcVKPk3Z5bMriKXy64lN6t+7NWX3Pok/rPgd0rm7d4NZb4aWXYOpU2Ly5XqFLIxTOZHsN0KXW/c5VjwVjNTDbe78MwDn3GjCM3ZJt7/2jwKNg3cjrG7CIiIiIiMSHdmntSGuaxuLNi7lv2n30bdOXs/qeRa9WvYI+RnIyXHABDBoEffvWPF5aCqmpYQha4krYRn855xKBRcCJWJL9JXCe9/4Hff2cc08Bb3rvX6663wT4CjjJe7/JOfckMNN7P2lv59PoLxERERERqa2ssowP8z7kvaXvUVphLaD6te3Huf3P5aAWB9XpmKWl1q380EPhJz8BFdg2HI3+quK9rwSuA94B5gMveu/nOecmOOfOAnDODXXOrQZ+AjzinJtX9dqdwP8BHzjnvgEc8Fi4YhURERERkfiTkpjC6N6jmXjiRM7ocwYpiSnM3zSfnYG6t4JauhS2boVPP4U//QlWrgxhwBJXwray3dC0si0iIiIiIvtSsqOEbzd+y5Gda/o2/7+F/4/DOxxOl4wu+3jlrlavtuZp69ZBkyYwZgyccgocQB82qYNYW9lWsi0iIiIiIo3Sos2LuG/afQAM6jiIM/qcQef0zkG9tqICXnkFPvrI7vftC5dcAi1bhitaUbIdIUq2RURERETkQGwt38o7S9/h4+UfU7GzAoDBBw3mjD5nBL2n+9tv4amnrLR8/HgYMCCMATdySrYjRMm2iIiIiIjURVFZEe8sfYdPln9CZaAS5xzDuwznwoEXBvX64mKYMwdGjKh5bMYMeOYZyMuDrCzIyYHsH04tkwMQa8l22BqkiYiIiIiIxIKMlAzO7X8uE0+cyMjuI0lwCaQ1DT6nS0/fNdF+9VUbGbZ8OXTqBPn5cNttkJsb+tglemllW0REREREpJYt27eQ3CT5+4R7+qrpLNq8iNP7nE6b1Db7ff1xx8GqVZCSAj16WMJdWAht2sCkvQ4zlv2JtZXtxEgHICIiIiIiEk1aNWv1/efee6YsnsLGko3MWD2D4V2HM6rXKFqntt7r61NTLcleswaWLYPWrW31Oy+vIaKXaKEychERERERkb1wzjH+yPEc1eUoPJ6pK6by+49+z/PfPE/B9oI9vqZHD0uw27cH72HtWtvXnZXVwMFLRKmMXEREREREJAgbtm3gf4v/R+6aXLz3JCYkctNRN9GzVc9dnpeba3u0k5Jg8WIIBKBbN5g4UU3S6iPWysi1si0iIiIiIhKE9s3bc+mgS7n9uNsZctAQWjZrSffM7t9/vTJQCVhCPWECdO0KzlnSPWaMEu3GRnu2RUREREREDkDHFh25fPDllFeW0yShCWAzu//4yR85qvNRnNLzFDhoPoyeTJtD82iyNYsuw3IAZduNicrIRURERERE6mnaqmlMnj0ZgILtBSzespgeLXvQqlkrisuLKSovYsLICWR3VsJdVyojFxERERERaWSO7nI0t4y4hUPbH8q8TfMoKi9i0eZFrC9ZT2ZKJhnJGUyeMznSYUoDUrItIiIiIiISAt0yu3Fd9nW0TWtLh7QOVPpKlm5ZyvbK7QS2p/Ph7DyWLo10lNJQlGyLiIiIiIiEUP+2/emS0YUOzTuQ1CSJssoy1hUUk1CYxfvvRzo6aShKtkVEREREREIoZ2AOReVFtEppxZCOQ3A4UjKKOLg8h6+/hvz8SEcoDUHJtoiIiIiISAhld85mwsgJdGjegXXb1tEmtQ13njSBM4/Ixnv48MNIRygNQd3IRUREREREwqi0opSPl39MVpNj+Puf00lOhrvvhtTUSEcWW9SNXERERERERL7372/+zesLXue7svc4+GAoL4fPPot0VBJuSrZFRERERETC6KQeJwHwyYpPGH68VeN+9hnESZGx7IWSbRERERERkTDqltmN/u36U15ZzvqUD/nZz+BXvwLnIh2ZhJOSbRERERERkTAb1WsUAB8t/5CjRpTRvHmEA5KwU7ItIiIiIiISZv+fvTsPm6us7z/+/iRhiWFHQMJOBCHssokiiuwIQl2AIAqKtVZsKwXrhlWgRSvYWhBt0VbFXwui4oLKIhgKKKvIFhBkJ+yyhrBk+/7+uM8kkycz8zxgZu77fvJ5XVeuzJyZwOe6z8yZ8z3nXjZadSNeu8preX728/zfvf8HwNy5MGNG5mDWNy62zczMzMzMBmDfjfYF4Fd3/4pbb5/NZz8L//M/mUNZ34zLHcDMzMzMzGxJMHm1ybx5vTez9Wu2Zq1lxjFjBtxwAzz+OKy2Wu50trj5zraZmZmZmdkASOKwLQ9j89U3Z8UVxQ47pBnJL7kkdzLrBxfbZmZmZmZmGeyy62wAfvtbeP75zGFssXOxbWZmZmZmNkAzZ83kW9d/i+/c9U+8bpN5vPQSXH557lS2uLnYNjMzMzMzG6DxS43nnqfu4ZHnHmHitr8D4Ne/TrOT2+jhYtvMzMzMzGyAxmgMe792bwBun3c+r1kzWHppeOKJzMFssXKxbWZmZmZmNmA7rbMTKy27Eg/NeJA93nszJ5wAq6+eO5UtTi62zczMzMzMBmzcmHHsOWlPAK549JdA5A1ki52LbTMzMzMzswx2Xndnllt6Oe556h5uf+J2nn4arrwydypbXFxsm5mZmZmZZbDMuGXYfcPdkcSdj9/HP/4jfPe78NhjuZPZ4uBi28zMzMzMLJNdN9iVE3Y9gf023YvttoMIuOSS3KnqJOm/JT0m6ZYur0vSqZLulHSTpNf3M4+LbTMzMzMzs0yWHbcsq09IM6Ptvnva9tvfwsyZGUPV6zvA3j1e3wfYqPnzYeAb/QzjYtvMzMzMzCyziODJcbew1iYPM2sWXH557kT1iYjLgCd7vOUA4MxIrgJWkrRmv/K42DYzMzMzM8vsorsu4rSrT2Pea88DYOpUmDMnc6jRZy3ggbbn05ttfeFi28zMzMzMLLMd196RcWPG8bCuZ/mJj/D00/C73+VOVZxxkq5r+/Ph3IF6GZc7gJmZmZmZ2ZJupWVXYqd1duLy+y5nmc0uYNLqR/Ca1+ROVZw5EbHdn/HvHwTWaXu+drOtL3xn28zMzMzMrAB7v3ZvxmgMTy57NQcd/gTrrZc70ajzM+D9zazkbwCeiYiH+/U/c7FtZmZmZmZWgFe/6tVsv9b2zIt5XHjXhbnjVEfSWcCVwOskTZd0pKSPSPpI85ZfAncDdwLfBD7a1zwR0c///sBMmDAhZnp+fDMzMzMzq9jDMx7mC5d+gXFjxvHB9U7it1NX5KCDYI01cifLT9LzETEhd46R8phtMzMzMzOzQqy5/Jq8eb03s8r4Vbjht8twyy2w6qpw6KG5k9nL5TvbZmZmZmZmBXroITj+eFhqKfiXf4EJ1dzT7Y/a7mx7zLaZmZmZmVmBJk6EyZOD2bPhsstyp7GXy8W2mZmZmZlZYSKCi+66iDsmHs8cXuTXv4Y5c3KnspfDxbaZmZmZmVlhJHHTozcxe9mHmTXxUp59Fq67LncqezlcbJuZmZmZmRVo3432RcDc9S5mLrOYOjV3Ins5XGybmZmZmZkVaNNXb8p6K63HhFVmMGnXKzjqqNyJ7OVwsW1mZmZmZlYgSey70b6METzz6ot41XIetF0TF9tmZmZmZmaF2mqNrZi4/ESeeuEprpp+FfPmwQsv5E5lI+Fi28zMzMzMrFCS2GejfQC48MYbOe44OPfczKFsRMblDmBmZmZmZmbdbTdxO5YdtyyvnrsFx/8crrwSDjgAllsudzLrxXe2zczMzMzMCjZGY9hyjS2ZOFFsvjnMng2XXZY7lQ3HxbaZmZmZmVkltt/lSZ4b8wBTp8Icz5dWNBfbZmZmZmZmFbjt8dv4zgOf5bE1/x/PPBtcc03uRNaLi20zMzMzM7MKTFplEsstPYEJa93L0+P+wMUXQ0TuVNaNi20zMzMzM7MKLD12aXbbYDdWWw0eX/GXvPgiPPNM7lTWjYttMzMzMzOzSrx1/bcyYenxrLP1HXzg7+9ipZVyJ7JuXGybmZmZmZlVYvxS49l1/V0ZPx4uuOuXueNYDy62zczMzMzMKrLbhrux9NilueWxW7j1wQe48cbciawTF9tmZmZmZmYVWW7p5Xjr+m9l05W34eQvLcUZZ8CMGblT2VCKUTJ93YQJE2LmzJm5Y5iZmZmZmfVdRCCJ00+Hm26C/feH/fbLnaq/JD0fERNy5xgp39k2MzMzMzOrjCQAdt89Pb/0Upg9O18eW1Rfi21Je0u6XdKdkj7V4fVdJF0vaY6kd3d4fQVJ0yV9rZ85zczMzMzMqrTqHTyw1ld5fOafuOaa3GGsXd+KbUljgdOBfYDJwBRJk4e87X7gCOB/u/xnTgQu61dGMzMzMzOzmv32gd+w9Fq38cAyF3LxxTBKRgmPCv28s70DcGdE3B0Rs4CzgQPa3xAR90bETcC8of9Y0rbAGsBFfcxoZmZmZmZWrX022ofVVxNPLfdb7nn4aW67LXcia+lnsb0W8EDb8+nNtmFJGgN8BTi2D7nMzMzMzMxGhdcs9xq2nbgNa641h7Eb/YoJ1UwfNvqVOkHaR4FfRsT0Xm+S9GFJ10m6bs6cOQOKZmZmZmZmVo59N9qXtdeGV216Gauu+VzuONboZ7H9ILBO2/O1m20jsRPwMUn3AqcA75f0paFviogzImK7iNhu3Lhxf25eMzMzMzOz6qyz4jpssfrmzJo7i0vuviR3HGv0s9i+FthI0gaSlgYOAX42kn8YEe+NiHUjYn1SV/IzI2KR2czNzMzMzMws3d0G+MXNv+Ub/zGPGTMyB7L+FdsRMQf4GHAhcBtwTkRMk3SCpHcASNpe0nTgPcB/SprWrzxmZmZmZmaj1aRVJnHYloexyWOf44bfj+HSS3MnMsUomRt+woQJMXPmzNwxzMzMzMzMsrnjDvjKV2D55eGLX4SllsqdaPGR9HxEVDMFXKkTpJmZmZmZmdnLtNFGsNY6c3h05sNcfXXuNEs2F9tmZmZmZmajxJMvPMFd636Omyd8lYsunsMo6chcJRfbZmZmZmZmo8Qq41dh/bWXhWWf5oYnruTWW3MnWnK52DYzMzMzMxslJLHf6/Zl4lrwwDIX8KuL5+WOtMRysW1mZmZmZjaKbDtxW7actDqrr/cnNt/r2txxllguts3MzMzMzEaRMRrD/pvszfrrwxWPnM9oWYGqNi62zczMzMzMRpkd196RlcevzMMzHub6h25g9uzciZY8LrbNzMzMzMxGmXFjxrHnpD2J51fjP/9jDOefnzvRksfFtpmZmZmZ2Si0y3q7cMw2J6DHtuLSS/Hd7QFzsW1mZmZmZjYKjRszjo03GsN668HMmXDVVbkTLVlcbJuZmZmZmY1SEuz01me5e9kfcc7Fd+G50gbHxbaZmZmZmdko9szK/8fjK1zE9TN+wbRpudMsOVxsm5mZmZmZjWJ7THob66+9DE+Om8b3L7wvd5wlhottMzMzMzOzUWzC0hM4aIe3MHYsXPPk+Tz/fO5ESwYX22ZmZmZmZqPc2zfdnddvPY71dvw9T899KHecJYKLbTMzMzMzs1FuxWVXZO/Jb0KCC+68IHecJYKLbTMzMzMzsyXAXpP2YozG8Jt7r+HaW57KHWfUc7FtZmZmZma2BFj1Vauyx1rvZtbUT3DWf6/MrFm5E41uLrbNzMzMzMyWEH+x9W5MXnMSM2fCVVflTjO6udg2MzMzMzNbQkiwxx7p8S8ufpaIvHlGMxfbZmZmZmZmS5Att57DPaudzi9e+CxXXT8jd5xRy8W2mZmZmZnZEmTpcePYeONgrmZxxsWX5I4zarnYNjMzMzMzW8J8dI99GTsWrvvTVO645/nccUYlF9tmZmZmZmZLmMlrbsjr130dy6/yIlc+fGnuOKOSi20zMzMzM7Ml0KfetS9bbA43zriYl+a8lDvOqDMudwAzMzMzMzMbvE1Xex0brLwB5998Na+79D3MfGEOayyzAcfsdjgf2HOH3PGq5zvbZmZmZmZmSyBJzHpsHa6673f8aeZTrDxuLZ6Z/Sc+8ct/5NsXXZM7XvVcbJuZmZmZmS2hzrn6NyzzxHYs9dRkXnpxDMuNW4nxY1bkK5d8N3e0V0TS3pJul3SnpE91eH1dSVMl/V7STZL27VcWF9tmZmZmZmZLqEdfupdVX7UqK60Erxqftk0YuwKPvnRP3mCvgKSxwOnAPsBkYIqkyUPedhxwTkRsAxwCfL1feVxsm5mZmZmZLaHWWGYD5ox9lmXHA0rbZs59ljWW2SBrrldoB+DOiLg7ImYBZwMHDHlPACs0j1cEHupXGBfbZmZmZmZmS6hjdjucF+Y9w3NzniZiHs/NeZoX5j3DMbsdnjvaK7EW8EDb8+nNtnZfAA6TNB34JfA3/QrjYtvMzMzMzGwJ9YE9d+DkfU9gxaVezROzH2TFpV7NyfueUOps5OMkXdf258Ov4L8xBfhORKwN7At8T1Jf6mJFRD/+uwM3YcKEmDlzZu4YZmZmZmZm1geSno+ICT1e3wn4QkTs1Tz/NEBEfLHtPdOAvSPigeb53cAbIuKxxZ3Xd7bNzMzMzMxsNLgW2EjSBpKWJk2A9rMh77kf2A1A0qbAssDj/QjjYtvMzMzMzMyqFxFzgI8BFwK3kWYdnybpBEnvaN52DPCXkm4EzgKOiD5193Y3cjMzMzMzMyvecN3IS+M722ZmZmZmZmaLmYttMzMzMzMzs8XMxbaZmZmZmZnZYuZi28zMzMzMzGwxc7FtZmZmZmZmtpi52DYzMzMzMzNbzFxsm5mZmZmZmS1mLrbNzMzMzMzMFjMX22ZmZmZmZmaLmYttMzMzMzMzs8XMxbaZmZmZmZnZYuZi28zMzMzMzGwxU0TkzrBYSJoHvJA7xzDGAXNyhxihWrLWkhOctV9qyVpLTnDWfqglJzhrv9SStZac4Kz9UkvWWnKCsy5O4yOimhvGo6bYroGk6yJiu9w5RqKWrLXkBGftl1qy1pITnLUfaskJztovtWStJSc4a7/UkrWWnOCsS7JqrgqYmZmZmZmZ1cLFtpmZmZmZmdli5mJ7sM7IHeBlqCVrLTnBWfullqy15ARn7YdacoKz9kstWWvJCc7aL7VkrSUnOOsSy2O2zczMzMzMzBYz39k2MzMzMzMzW8xcbJuZmZmZmZktZuNyBxitJJ06grc9GxHH9T2MDVxN+1/SKiN427yIeLrvYYZRS7tKeucI3vZiRPyy72GGUdn+ryarLX61fP+hnmOApNeP4G2zI+LmvocZRWpp11pyQnXf/78fwdtmRsR/9j2MZecx230i6T7gH4d526ciYtNB5OlF0s9G8LYnI+KIfmfpRdKzw70FeDgiNh5Enp5B6tr/LwIPkdqvm7ERse6AInVVS7tKegL4Kb3bdJeImDSgSF1Vtv+ryCrpphG87fGI2K3vYYZRWdYqvv9QzzFA0gzgWnrn3CAi1h9Mou4qOweool1ryQnVff8fBr5B73Z9byGf1Wp+A2rlO9v9828R8d1eb5C08qDCDGNT4EM9Xhdw+oCy9HJXRGzT6w2Sfj+oMMOoaf/f5nZd7M6PiA/2eoOk/zeoMMOoaf/XknUssG+P1wWM5CLnINSUtZbvP9RzDLg2It7W6w2Sfj2oMMOo6RyglnatJSfU9f3/XkSc0OsNkiYMKswwavoNqJLvbBuSDoqIc/7c9/SbpA0j4u4/9z22MEnLRsSLf+57rE417f9askraOSKu+HPfMwg1ZbUlm88BzBY//wb0n4vtPpHU6uryXET8a9YwNnDe//1RS7tK2qV5OCsirsoaxmyUqOX7D/UcAyS1hlzMjYgHs4YZRWpp11pyQnXf//c3D1+IiB9kDWPZuRt5/9zX/P1C1hQjIOnbQADPRMTRufN0I+keUs7HI2LH3HmGUdP+d7sufh9o/n4aKPZEG+ra/7VklTSVlPPJiHh37jy91JSVer7/UM8xoNUt9wmg6P1fy/e/UUu71pIT6vr+b9D8/VzWFCNQ2W9AlXxnOwNJp0TEsblztEh6S/NwVkRcmTXMEqC0/d+LpIkR8VDuHCNRS7tKeldE/Ch3jpGobP8Xk1XSes3DuRExPWuYYdSUdbSo5RggaceIuDp3DjMASVtFxI1dXvvriPjGoDN1I2n7iLg2d46R8G9A/7nYzkDS/blnyx0pSd+PiINz54Dhl/2JiCcHleXPUdn+d9bFrJac4Kz9IOk3EfGm3DlGorSsw62cERHvGFSWP0dFn9Wic0p6FTAZuC8iHs+dp52kjYCTgdcCNwPHlthNW9IKwBoR8cfm+XuA8c3LF0bEo9nCDSHpbuA9EfG7IduPB/aPiJEsYzYQzUR9ywFnA2dFxK2ZI70ipf0G1MrdyPPotRRAaXbKHaDNn4DpwJzmeXs7BrDhwBO9MjXtf2dd/GrJCc7aD8UWLx2UlnUn4AHgLOBq6tnnQ9WSu6ickt4BnAo8CRxHWiXlUWB9SZ8cbqbqAftv4EzgMuAdwGnASNZeH7RTgN8Cf2yefxE4n1RwvxH4SKZcnbwH+IGk90bElZJEWl7rdcBbsyYbIiK2kfQ64BDgh5Jmk45bZ0fEvVnDvTyl/QZUycV2n/S4CysK+wGryKnArsBvSAetK6LQrhmjaP8X1b6jpF2LatNhOOviV0tOKC/ra4A9gCnAocAvSHeNpmVN9fKV1q7dlJbzRGBPYEVgKrBlRNwtaXXgEhaMPy7B8hHxzebxyZKuz5qmu+2Bv2p7PiMi/gZAUlGzT0fE7yQdCPxY0lHAXzYv7R0RL2WM1lFE3A4cDxwvaStS4X2JpEcqultc2jGgSi62++d3pA9ppwJg1oCz9CSpW9cbAUsNMksvEfHx5krmW4H3AadJugj4RkTckzXcomra/6fR+YAqYKUBxxlOFe0q6Wa6t+kaA47TU037v5askrrdwRILumgWoaasETEXuAC4QNIypKL7UknHR8TX8qZbWC3HAEnn0T3nqgOOM5x5EXEHpMnSWkt8RcRjkub0/qcDt6ykbVjwWzW+/XlElFJ8jxty0+J9bY+LOabC/Ivt04HDgZ8AFwMfAyZImlDqUEJJY4DVSd/7CcBjeRMtrKbfgFq52O6TiNhg+HcV4ys9XvvDwFKMQPOjMLUZD3MI6Ur3H4Fv9vyHA1bZ/r/uFb42cBW16365A7wM1ex/6sm6f4/Xfj6wFCNTU1aaIvvtpEJ7fVKPpx/nzNRFLceAU17hazmMkbQyMAaY1zxuFbNj8sXq6BHgX7s8D+BtA0/U2TxJr4mIRwAi4hYASWsB87ImW1TrYjvADGBH4BrSZ6C4oYSS3kw6Th1IGrd/NnB0RDyTNdiiqvoNqJEnSOuTplvTZ0iTY9wEfCkins2bqm6SJgAHAAcDqwHnAudExP1Zg3VQ2/6XtBqwHnBnRDydO083NbVr093ttcDNEXFh7jy91LL/oa6stnhJOhPYHPglaezjLZkj9VTLMUDS1qSc0yLittx5upF0L6kA7NSzKSKiqGKrBpIOA/4OOAb4fbP59aQLLadGxPdyZXs5JK1V0gR0kh4gLVV2Nuk8tai72TZYLrb7RNIFpKtwl5GucC8fEUdkDdVFRbNmziTdxT67+XuhD29EnJsjVyeV7f8PAScBd5HWhvxwRPSc9TeXWtpV0teBzUgTz+wGnBcRJ+ZN1Vll+7+KrJJ2BM4AJpGOqR8stYipLOs8YGbbptZvgEjF1gqDT9VZLccASf8IHEY6ru4IfLFtrHE1Ciy2ek6GVtj5yt6ki9ibNZtuIV3IPj9fqpentJnzJZ0VEVNy5xgJSd9pnUdJOrywiQZHBRfbfSLpxojYqu359SUtS9BO0uUsPGvmThFR3KyZkr5D98kaIiI+OMA4PVW2/28Bdo2IxyVtCPxPRJQ0C/18tbRr06ZbRcTcZnmayyNi29y5Oqls/1eRVdJ1wKdZcEz9UETslTdVZzVlrUktxwBJ04DtI+J5SasCF0TE9rlzvVwFFlvzgBuaPzBk9ZSSzldGA0kPRMQ6uXO0lHpu0omk30fENs3janLXxGO2+2jIeKKx7c8Lm8ihilkzS7yD2UtF+39Wa43SZmbXZXIH6qWSdp3VTOZEcxJb8kzpNe3/WrKOiYhfNY9/IOnTWdP0VlNWACRtAWzSPL210NnIazkGvBQRzwNExBPNZE41Kq1930maV2ZL4KekWfPvzBtpUT0mnQQgIv52gHH+HKXdORw75FxlIQWdq0B5bTfquNjunxVJ3bLav2itIra0iRxqmTWTZt3CD7PgROs24IzWDKUFqWn/ry3p1G7PC/uxraVdN5F0U/NYwKTmeau765b5oi2ipv1fS9aVhnQjXeh5SV1IqSirpBVJhcu6wI2k79MWku4HDihs/oZajgEbSmoNxWjlnD80IyLekSfWy1ZUwRARPwF+0jbXzFeangOfjYj/y5tuISVNLNlTjwsDRa1G0diERc9VWko6V4EFv6Ni0d/Ykn5Xq+Vu5IakqT1ejogoYtZMSTuRJkU7g1RgCdiGtNbiOyPiqozxqiXp8F6ve/zOyydpvV6vR8R9g8oynJr2fy1ZJX27x8tFdSGtLOuppCX+/iEi5jXbxgBfAsZHsz5wCWo5Bkh6S6/XSyoMhym2Di9pzH6LpLHA3qS7MousCQAAIABJREFU3FsAnyx5srwWScsC+0fED3Jnaanl+A8Ld80uXU3tWisX2wMkaRJwKHBIRGw23PtLIGmpiJidOweApPOBf4mIS4dsfwvwqYjYJ0uwEapt/5f4Y9tJTe0qaWdgSkQclTvLcGrZ/1Bd1jUi4tHcOUaitKySbgW2jIg5Q7aPI834vWmeZCNXyzFA0jqkY+rJubO01FQUSHobqcDegbQe9NkRUfRd5ObCwF6k5ar2JM0z8O68qYZX4vG/pmK7mxLbtVa1js2phqSJko6WdC0wjdTmh2SO1ZOS3ST9FzA9d542k4YW2jD/yntJXXLmq23/SxoraV9J3yMtW3Fw7kyd1NSukraRdLLSsjUnUtja9e1q2f9QXdaVJB0p6RIWLK9TpMKzzhpaaAM0217KkGdEajkGSFpN0keVJk29FFgjc6SFRMR3O/0Bvg88nzvfEBeTCu0rgGWA90s6tfUnb7SFSXqLpP8E7gWOBPYANii50K7g+P/vzfdpO0mldXHvqoJ2rZLHbPeJpA+Trg6uBZxDOoD9NCKOzxqsB0lvIN0hPBBYBTgKODZrqIXN6PHazB6vDVxt+7/pHXAosC9wDfAm0o9tUScwtbSrpI1JOacAfyKdDCoids0arIta9j/Uk1XSeNJYzUNJw12WJx1bL8uZq5OKsg6dX6RFpIKmGLUcAyQtT5rM61BgY9JQrQ0iYu2swYbR6S4sUNIduA/kDjASkqYD9wPfIC37OkPSPaUdT1tqOf6T6qtpNMtUSipymcqWitq1Su5G3ieSZgFXAse0ug5JujsiirsDK+kk4D2kA+5ZwI+B6yJig6zBhpD0GGmN7UVeAg6KiGKuwle2/9t/bH/S9mNb1P6HetpVadmXy4EjWzPQlpgTqtv/VWSV9L/Am4GLSMesXwN3lpYTqsvaa34RSipkazkGSHqBdHJ9HHBFRESJOVu6FAUb1lIUlNY1V9JXSRfWbgH+lzQB4c0l7v9ajv9ANctUQl3tWivf2e6fNUkF7FckvYZ0F26pvJG6+hBwB+mLdl5EvCSpxKswn+jxWmljoWra/z8k/dgeDMyV9FMKm9m1TS3t2lr2ZaqkC0hFTGlL07TUtP9ryToZeIq0WsJtkdZaLjEnVJS1VzEtqbTjQC3HgE+Tcn4dOEvS9zPn6aq2u7AtJd+Fj4iPSzoaeCsp35eBFSUdBPwyIp7LmW+IWo7/UM8ylVBXu1bJd7YHQNLapA/xFGAC8OOI+EzeVAs0PwR7kPLtBkwFdgfW6TQ+rjSlXSkeqvT9D2mcPgt+bPclLbF1JOX92M5XSbu2ln2ZArwNOJOU86KswYaoaf/XklXSJqSMB5O6Eb8O2LykCcdaasrarvksvI10p3O/kno3tVR0DNiQVHRPATYCPk/KWcyymjXdhYU678I3F61aFwb2iohXZ460kIqO/0N7Yh7S/jwKW06rlnatlYvtAZO0EWmGzxNzZ+mkufq2H+kL92bgkog4NG+qRXW6UlzyZB4tpe9/KP/HtpNK2nVl0l35gyNit9x5uqlp/9eSVdK2pJPu9wDTI+KNmSN1VUPWLvOL/CwinsoabBgVHQM2J7XvQRHx2tx52tVSFIyGrrmSxkfEC7lzdFPy8V8VzZw/VMntWisX2wPUXOF+J6koeHvuPMNpJk45MCK+lztLS41Xiltq2/8AknaJiNImSVpITe2qtJzOlIj4cu4sI1HD/m+pIWtTKOwcEZfnzjKcErPWMr9IL7UcA5oeY++IiHNyZ+mm5KKglrvwzTwIATxZww2Lbkq/MNBSek/MoWpp19J56a8+k7S0pL+Q9APgYVI3sv/IHGtYSmsXfxz4VO4sLc2V4i+SltKYHBHvAl4oudCucf9LmizpREl3Av+aO08nNbWrFl1OZ/XMkXqqYf+31JJVyW7At0jj44pVeNYPAY+S7hZ+LyKeoIKxhbUcA7Twsj/3AgdljtRTRMyOiJ9HxHuBdXLnaRcRHwc2AL5CuhN/O7CapIMkLZcz2xBHkGZO/3jmHMOSNFXSryUtclwquSBU4ctp1dquNfGd7T6RtCcLujhPJS37cVpErJ8zVy+SJpIOAocCW5AK23Mj4uaswRq1XCmG+va/pPVZsEzNbGA9YLuIuDdfqkXV0q7qvJzOwVHocjq17H+oLms13Z1ryKqK5hep6RhQS4+xmu/ClnwXvhaS1msezo2I6VnDjEBF36uq2rVGLrb7RAuW/TgiIu5pthW5nIYWXbv4HNLaxcV1zatovFZN+/9KYAXS5B1nR8QfSx1bVku7qqLldCrb/1Vkram7c01Z26nw+UVqOQbUNLZ4tBQF7po7+tX0vbL+czfy/nk9aT3giyX9StKRwNjMmbr5GumzcGhEHBcRN1Fo17xIpkbEh0ldtKaQZnq9N2uwRdW0/x8FlgfWAFZrthW5/6mnXT8NLENaTufTzbCMUtW0/2vJWlN355qyzhcRL0XEj5o7nBsBF+TONEQtx4AfAq1ebfs3c2AUuf8j4r7mT/GFtrvmLvGq+V5Z//nO9gBIeiOpKHwXcCNpOY0z8qZaQNKqpDsbU4DW2sVHRERRY6B6KflKcen7H0DSiqQuj61lX1YidXW7JmuwHipp1+KX04G69n8NWSvr7lxT1vc3D1+oaIKh4o8BtfQYq8louQtvr5y/V9biYnuAJI0hncQcEhEfzJ2nExW8dnHN47Wgjv0PIGl10sQ4U4B1S7/oUlG7bk6zlnEUtpxOu5r2fw1ZS+/u3K70rJI+3zycERHFTojXTQ3HAI8tNgBJ3wWeB06PiFty56mdv1dLNhfbfSJp3ebh3Ih4MGuYV0jSxqQC5oTcWaCuK8WjYf9DavOIuC93jpbR0q61KG3/91JDVkkrkJZTPDN3luHUlNX6o+QeY9ZfkrYH1gV2iIhP5s7TS20XBmr5XtXWriVzsd0nzV1YgCdKvwsraZfm4ayIuCprmFGisv3/bVKPgWci4ujceXqppV0l3UNq08cjYsfceXqpbP9XkbWm7s41Za1JLceA2nuMgYuCJV2JFwZGyfequHatlYtta53AAjxd8gms9UezPAWkiy1XZg1jA1fT/q8la03dnWvKaotfTT3GunFR8MrVcgGzNqPhe2WLj4ttMzMzM7M+K+0ufC0XMMEXBvrF7dp/LrbNzMysWpIOAB6JiKtzZ7H+GQ1Fge/Cv3I1XRioidu1/1xsW/VKu1JsZmaDI+kkYAtgXETskzuP9YeLgsVP0s10Xv9ZQETElgOOZDbquNgeMElrkiZMeCl3ltGipivF3v/94XY1M7NS1HIXvm1scUclrfDgCwP94XbtPxfbAybpYmAS8KOIODZ3nl4kbQc8FBEP5c4yWlS2/08CngG+FRFP5M7TSy3t2uScTeqF8fPceXqpbP9XkbWm7s4lZh0NK2fUcgwoscdYTUVBLXfhJZ0O/G9E/CZ3luHUdGGgm0K/V9W3a+lcbGcgScDkiJiWO0svzUFhS+COiDi4gDxVXCkeTkX7/0BSAbtVRLx/uPfnVkO7SpoIrAm8ISJOz52nl5r2fy1Za+ruXGLW0bByRi3HgBJ7jLkoWPwk/R1wCOkzeQ5wVkT8Pm+qzmq6MNBNod+r6tu1dC62bViSlo+IGQXkqOJKsZmZDZakd0XEj3LnsP6pqSio6S48zL+QcUjzZzxwFqnwviNrsDY1XRioidu1/1xs94mke0gH2scjYsfceXqRtG7zcG5EPJg1zCsk6fsl3H1vqWz/f56U9bnS19mtpV0lTSXlfDIi3p07Ty+V7f8qstbU3bmmrL1Iuj8i1h3+nYNRyzGgph5jNRUFNd+Fl7QN8N/AlhExNneeoSq5MFDN96qlhnatlYtta50UADxR8klBL6WdaNVE0uHNwxci4pysYUaJthOtuRExPWuYYdS0/2vJWlN355qy9iLpgYhYJ3eOllqOATX2GKuhKKjpLjyApHHAPqQ23Q24lNSmP82ZazilXhio8XvVrtR2rZWL7T5pu1vcUUTcP6gsS4LSim3v//5wu5pZJ6X9BgwlaVVgF+D+iPhd7jzdSFoK2Bx4MCIey51nOKUWBbXchZe0BzAF2Be4Bjgb+GlEzMwarIdaLwyUzu3aPy62+6RtvI7aNgewGrB6YT8Ku/R6PSIuG1SWXiS9vttLwM8jYs1B5umlsv3f6u7USUTEkYPM00st7drW3X3+prbnERGTBp+qM0nn0X3/ExHvGGCcnmr5rErqOUlbRJw5qCzDkbQ/cFOrW6ukfwTeBdwH/F1E3JMzX7thxsFuHBHLDDhSV5J+DnwqIm5plia8HriONJHfGRHx1awBG5L+AzgtIqZJWhG4EpgLrAIcGxFnZQ3YQU1FQel34SVdQsr0o4h4KneeXmq6MFDTmP2a2rVWLrYHRNL6wCeB3YFTI+K0rIHaNCfbQwVpJvJ1CipgpvZ6PSJ2HVSWl6vw/f+uDpvXAY4GxkbE2gOONGKltmtzF6vdGOAg4Fjg+ojo1OZZtHV3E/BN4EPtr0fE/w08VBe1fFYldfscvgNYKyLGDTJPL5JuIs2M/byk/YB/JZ14bQO8JyL2yhqwTU3jYCVNi4jNmsefATaJiPdLWh74TSkn20Nyfhx4a0QcKOk1wPkRsU3ehAvUXhSUeBde0u9L2se9VHZhoKZjVTXtWqtifvBHK0kbAZ8FdgS+AvxtRMzOm2phEbF/+3NJbwKOAx4B/iZLqA5KLqa7qWT/z5/BV9KGwGdI3R2/BPxXrly9lN6u0az1LGkM8D7gE8ANwNsj4tac2YZqL6YlPVdScT1ULZ/ViJh/3GyWpHsv6aLQVcA/58rVRUTE883jdwL/1XRz/p2kj2bMtYiIuK9Z5u21wM0RcWHuTD20H492I13IIiJmSJqXJ1JHs9oe7wH8ACAiHkkf3aJ8ilQUHFNLUdDlLvwXMkYaqqY7bqtExLdyhxihf6CeMfs1tWuVXGz3iaTNScXAZsCXgSMjYm7eVL1J2g34HOnge1JE/CpzpIU0BdbJNCdapC5uRc6eXtv+l7QJ6QLLNqQ2/khEzMmbalG1tGsz7vGDpDuuVwAHRsSdeVONSPEnXhV9VscBR5B6M1wFvDsibs8aqjNJWg54nlQMfL3ttWXzROpM0tdJ3/3fAidK2iEiTswcq5sHJP0NMB14PXABgKTxwFI5gw3xdNOj4UHgTcCRMP/zOz5nsA6qKQq63IX/cIF34VeX9PfdXoyyVn0o/vepzR3AKc0QkmLH7DdqatcquRt5n0iaCzwA/II0/mkhEfG3Aw/VhaS3kwqYZ4B/jogrMkfqSNLlwJnAZaTumDtFxDvzpuqssv3/A2Bb0h3icxiSNyKezJGrk1raVdJ0YA7wVWCRSdsi4tyBh+pC0iptT6cCb6VtTHxh+7+Kz6qko4C/Ay4B/iUi7s2bqDtJHyT1EHgWeCwi9m62bwOcEhG75czXTtItwFYRMVfSq4DLI2Lb3Lk6kbQ6cAJpgqzTI+KiZvuuwLYRcUrOfC2SNgZOJeX8t4j4TrN9L2DPiDgmY7yFSLo+IrrN3VKUWrrmSnoY+AYLz4MyX0QcP9hE3TW/q12L/8IuDADlj9mHOtu1Ni62+0QLlqjpKCK+O6gsw2m6tE0HbqTDFa5SJkiSdENEbN32vNgf3sr2/720Td7FkMnHImLDgYfqopZ2lfQdek/k9cEBxumpbTK3Tidbpe3/e6ngs9ocUx8DHqfDRHmljNdtkbQWsDpwQzQnBc0dmaWioBn+hx7zS/4NaNf0HCAinsudpWY1FQW1jIWu5TsEdV0Y6KTEMftQf7vWwN3I+6T9pL+CH9paxkIv2xysWgeE8e3PI+L6bMmGqGn/R8T6uTOMVC3tGhFH5M4wUhGxQe4MI1XRZ7WaNm08DuwJvLcZpzuNNN7wpaypFrVJM6EbpOP+pOZ5qRcx/hr4NDAhPdUMUk+Hr/f+l4MlaR/SeOjNmk3TSDl/mS9VR2OB5ehSFBSmljtZHdtS0jrAIRFx8oDz9PJwRJyQO8TLUcGYfaiwXWvjYruPhvzQIuk5CvyhbU2IJGlZ0nhogDsj4sV8qTp6mIWvaj/S9jyAtw08UQ+17H8ASUuTJnFqP9kq8WS7mnZtxpd/goXb9JSIuDlfqkU13V0/Q/ru3wR8KSKezZuquxo+q7FgGa0NWJDz1oi4O1+qziRNBn4G/AZorf/8VuCzkt5R2IR+m+YOMFKSjgPeSJrd++5m24bAv0taJSL+KWvAhqS/BP6KNKHTdc3m7YAvSVo7Is7IFm5RNRUFtYyFnj9MRNJqwHtIY80nAj/OFaqLai4MVDRmHypq11q5G3mftP3QfmzoDy1wdSk/tDD/yttJpAmd7iN98dYBvg18tqRZnruRtFRJOSvb/51OtrclTZZzQERMy5VtqFraVdIBwCnAF1n4BPbTpIn9ilkPVtIFpP1+GbAfsHypd+Zr+axKWgH4Fmmf39Bs3pqU+ciSLmY0Y0u/NHRCTEm7k47/xfd8krQzMCUijsqdpUXS7aTx5S8O2T4euDEiNs6TbGGSbgV2HjrfgdLyhVdERDEXOLp1zS6xKKila67SUnTvBA4FNgbOBQ6OQpZRbNdcpHqyebzIhYGIODZnvna1jNmHutq1Vi62+6SWH1oASf8GLA8cHREzmm0rkIqFFyLi73Lm60apv+PbSD8S+0XEGpkjzVfZ/q/mZLuWdpV0I6n4u3fI9vVJ68JulSFWR5JubM9T8hi+Wj6rzZj9e4ETImJes02k1R5eGxHvz5duYZL+EBGbdHnttpKKrXbNEKJDSSeG9wDnRkS39c0Hbph27fraoPXax6Xt/5qKgpKPo+0kvUC683oc6eJKSLq7lPkv2lV2YaCKMftQV7vWakzuAKNYdOqGHREvACWtsQnpbtZftgptgObOy1+TusAURdIbJJ1Kugv/U9IduSJOXNrUtP/XGlq8AETExcBrMuTppZZ2HTe00AZotpW07A8AklaWtIrSzORjhzwvSS2f1TdFxBdahTakD27TBXanjLk6GSNpmaEbm2FFRQ01k7SxpM9L+gNwGmmmf0XEriUV2o0HlZbTXIikt5GGRJXiWUmLXPxrts3o8P6cZks6XNKFpAJxErBBREwqqdBudO2aK+kTgw7Tw6eBZUhL/n1a0qTMeXp5jNQD85+ADSPNlD+r9z/JpqY7mTW1a5WK+iEdZR6UtFtEXNK+scAfWmjOAztsnCupmAOGpJNIV7LvJ3XPOR64LgqZgXqImvb/GEnLDB3zWuLJNvW06xxJ68aQmZyVlgEpbU3oFUndm9tPDluTDQZQ0h2Omj6r3ZQ2udOZwI8kHdU21nx90nJQ38uYq5M/AJeTejLdCSDp6LyRuvpb4KeSrmDBkIftaIY8ZEu1qGOAn0n6NgvnPBw4LFuqzh5j0buwf5E5UzdVjIWOiK8CX22GYx0C/ASYKOmTpN4CxSxRRbowcAjpwsBZkr6fOU8vtYzZh7ratUruRt4nkjYj3XXt+ENbythCAEk/IXXBO3PI9sOAg6Kcpb8eA+4grV18XkS8VHB3p5r2/3HAG4BOJ9vXlTQhTS3tKulA4MukuRDac34K+GRE/CRXtprV8lmV9F3gLuDE9guZkj4HbBwR78sWrgNJHyNNkPWqZtNM0mR+Rd0tbr5Xh5C+7xeQJh36VhQ6o35zEehQ2ibJA/6nU++cnCStARzFwjlPj4hH8qValKSPk/b/BNIF9+8Dvyr0HKDarrlKk3tOIeV97XDvH7S2CwNTgI2Az1PYhYFaxuy3q6Fda+Viu48q+qFdi/RD8AILFwbjgXdGxPRc2dpJGgu0ZnjcDZgK7A6sExGl3S2sZv9DPSfbUE+7Nt0wj2HhnKdExI35Uo1M05XwUNKkQ5sN9/5BquGz2sx58V/A61kwQdo2pB4DH4qIp3Nl66UpEGibu2P7iLg2b6pFSZpAujs8hTRvx5mkk8KLsgYbRpP7L0iTub09d55eVOCkcy01FAU1jYWuWakXBmoZs99Nqe1aKxfbA1T6D23TFbd9mZpLer0/p2aM4X6kg8GbgUsi4tC8qXorff9DPSfb7Wpo1xYVOGtui6SJwMGkInsL0kzq50ZhS5W11PBZbS5aTG6e3hoRd6mwlROGUprxfUrz5+mI2C5zpJ4krUzqontwRCwyRjo3paXq3k76Xu0F/Ij0vTova7AOmknnpgAHUeCkc52UWhTUchde0j2k4UKPR8SOufOMFt0mSCv5HMD6x8V2n9X0Q9uu5DtbQzUn3QdGRGnjC6vc/zWcbNfUrhXMmvthUra1gHOaPz8ttWtuuxo+q1D2ygkwvyt+qx1nA+sB23Wa5K9EzQnslIj4cu4sLZL2JLXnnqReWN8HTouI9XPmGkrSxizY938i5Tw2ItbLGmyUqOEufC1qujCgumbOr6Zda+Viu09q+aFtV9udLSj3okBt+7+Wk+1a2rWm8XqSZgFXAsdExHXNtmK7O9byWYW0cgLpM3AgsAppXOzPoqB1VyVdCaxAGv98dkT8UdI9pV9sqeAEdh5pMrcjIuKeZltx36u2nEe2TTpXXE6ovygo9S68LX41nQNY/3npr/65gDSL784RcVhzx62kpYnmk/RhSVOBS4FVgSOBhyPi+BILbUkTJR0t6VpgGulzfEjmWEPVtP+vBH5Bms35XRGxLTCjxOKFetq1pqU01iR1c/yKpNslnUiBy5NBPZ9VSSdJ+iPwz8BNpPHaj0fEd0sqtBuPAssDawCrNduKvAovaXnVs/TT60kXsS6W9CtJRwJjM2fq5J2klRymSvqm0nJlpc2YD0BEbBARG9ZYaANExC0R8dmSCm1J1y+O99giajoHsD5zsd0/tfzQAnyN9Fk4NCKOi4ibKPBkq7KLAjXt/2pOtqmnXatZuzQinoiI/4iIt5AmHnwaeFTSbUrL7ZWkls/qh0hZvwF8LyKeoMycRMSBpJ5MvwO+0Nw9XFnSDnmTdVTNCWxE3BARn4qISaSuw1sDS0k6vxm6UYSI+ElEHAJsQuot9HHSskXfaHoS2Ssg6R5Jd0u6OneWYWwq6aYef24GXp07JFR3YaCac4DK2rVK7kY+AJLeSOo69C7gRlJ3tzPyplpA0qos6I73GtKYzSMiYp2swYaorbtrS+n7H0DSiqQ7HK1xZSsBe0XENVmD9VBJu1Y7Xq8Zy3lIFLKcVksNn1VVtnJCO0mrkybImgKsW9LvQC2TTnUjaQzpc3BIRHwwd55uSp10TiOY4Xkk77EFJI1kbP7cKGBVGqUZ3v/Y6y3AihGx7oAiDauGc4Aa27U2LrYHqIYfWklrk8ZtTyGd0Pw4Ij6TN1VSy0WBbmrY/1D2yXYnFbVrkeP1JO3SPJwVEVdlDfMy1fBZVYUrJ7RIWi+a9cxLUskJbOvEdG5EPJg1zCjhomDJVtOFgU4KPgeoul1r4GK7T0bDD22pd7ag7IsCMDr2P5R3sj1a2rUkkr7dPHw6Io7OGubPUNpntROl9bcPjIgzc2dpafZ/AM/UuP8LPoGd2jx8IiLenTVMDzVNOlZTUeC78GbW4mK7T2r5oYW672xBmRcFKtv/1Zxs19KuNZ3A1qSWz6qk9zcPX4iIH2QNMwxJb2kezoqIK7OGMRslfBd+yeZzAGvnYtuqubNV+0WBUvlk22pRy2dV0uebhzMi4l+zhhlFfAJrtajpLryZ9ZeLbatGLRcFzMzMzMzMXGybmZmZmZktBh6zb+1cbJuZ2SIkbQc8FBEP5c5iNlpIWhN4MiJeyp3FzPrDY/atnYvtAfMP7ZLN+78/3K6Ln6TvAlsCd0TEwbnz2GBJOgl4BvhWRDyRO89oIeliYBLwo4g4NneebiTd1jw8PSK+ljWMWWU8Zt/audgesFp+aMF3tvqhsv1fzcl2Le3a5JxNOoH9ee48IyFp+YiYkTtHL7V8ViUdADwSEVfnzjIcSQeSvlNbRcT7h3u/jZwkAZMjYlruLL1IWhV4Q0T8IncWM7NaudjOoKIf2irubNV2UaCi/V/VyXYN7SppIrAm6QT29Nx5YHSsXV7LZ7W5KLAFMC4i9smdZ7So8SKWmZktGVxs27BKv7NVy0UBsxLVsna59UezTFkAz9W6TFmhF7GqWKaslpxmZrVysd0nNf2AjYY7W1DWRYHK9n81J9u1tGtTwAZpHLkL2MWkls+qpF2ah7Mi4qqsYYYh6fDm4QsRcU7WMGZmZqPMuNwBRquI2CB3hpfhu83fTwDFFgbDXRQopdCG6vb/vc3fL+QMMRIVtesRzd9zc4YYibbCsKOIuGxQWUbg3ubv0j+rH2j+fhooutiOiO8O/64y1HQRq+33qqOIuH9QWczMLB/f2e4TSXsBy0fED4dsfzfwTET8Kk+yetXU3XW07H9J4yJiTu4cLaOlXUsi6bwOm4M0NGOdiBg74EhdSVqWtP8fH7J9NWBGRLyYJ1m9JH2btL87iYg4cpB5emmb4bf4WXwl3UxqV7VtDmA1YPVSvleSZtA55zhg6YjwTRkzsz+Di+0+kfQb4MAOJ4WvBs6LiJ3yJFuUpMNIn4XvDdn+PtJJzf/mSVavyvb/FRGxc/P4exHxvrbXro+I1+dLt7Ba2rXtBBYWnMS2TmgjIlbIEmwEJL0JOA5YGfjniOhUjGch6Qzggog4d8j2vwD2jIi/zpNsYZL+vtfrJXWBl/SuDpvXAY4GxkbE2gOONCpJWh/4JLA7cGpEnJY1UBeSlgOOAv4K+HFEHJM5kplZ1XzFsn+WGVoQAETEnyRNyBGoh78Bduuw/VzgMqCIYruy7q417f/2PJsNeU2UpYp2jYjlc2d4uSTtBnyOdFHgpEJ7CWwbER8eujEifizpn3IE6qJ9//8V8J+5ggwnIn7UeixpQ+AzwC7Al4D/ypWrk7Y5G+ZvanseETFp8Kl6k7QR8FlgR+ArwN9GxOy8qRYlaSXg48D7Sb/525e8jJ6ZWS1cbPfPCp264EpaChgMKOgpAAAgAElEQVSfKVM3S0XEc0M3RsTMJm8pPtFh2/zurkAR3fIaNe3/Xt1bSuv6UlO7ImkLYJPm6a0lLksm6e2kYuAZ4LiIuCJzpF5e1eO1MQNLMYyIOL71WNKB7c9LJGkTUm+GbYCTgY+UNHykzXZDno8BDgKOBX4/+DjdSdqc9L3aDPgycGREFDeHQ9Mr6BjgYOC/gW0i4pm8qczMRg8X2/1zLvBNSR+LiJkwv3vWvzevlWS8pAmtnC2SlgeWzpRpERGxf/vztu6uj5Duzpekpv2/UtMNd0zz+J3NdgEr5ovVURXtKmlF4KfAusCNpLbcQtL9wAER8WzOfEOcB0wnTZD4D5L+of3FiHhHllSdPSZph4i4pn2jpO2BRXo8FKK0C1YLkfQDYFvSXdejSZP6rZCWrYeIeDJfuoW17rRKGgO8j3QB9gbg7RFxa85sHdwIPAD8AtgB2KHVpgAR8beZcg11H+m7823geeDIITmLGfJgZlYjj9nuE0njgH8CPkT6MRPp7ut/AZ8rqRuZpGNJ3cg/EhH3NdvWB04HLo2Ik/OlW1QN3V0r2//f7vV6RHyg1+uDVEu7SjoVmAX8Q0TMa7aNIXXNHR8RxVwckvSWXq9HxP8NKstwJO0AnAN8B/hds3k7UtfXQyLi6kzRuipt3oOhJN1LW1dshkyUFREbDjxUF00Plg+SLgpcAXwpIu7Mm6qztiXVOiplFnhJX6DHBaHSe2WYmZXOxXafSRoPvLZ5emdEFLlkjaSPAJ8GliOdbM0gnch8I2uwNkO6u/5z4d1dgXr2f21Kb1dJtwJbdujuPg64OSI2zZOsfpLWAD4KbN5smgZ8LSIey5dqYUNmop4EtArC1gR5W+bKVjNJ04E5wFeBRZbOGjpxXima3jd0Gq5lZmajm4vtPmruGD0VETdJOog06cxdwNcj4qW86Tpruo4XtWZ1i6R5pO6uN9LhSnxh3V2r2f81zZwMdbSrpBsiYuuX+1oOkg4A1o6I05vnV5OWJ4J0Z/6HXf9xAZoxp09EQT9mbUtUddTqQVQKSUsD72XBBInTgP8t5fvUIuk79F6m7IMDjDMsSX9NuojdmrzxOeBfIuLr+VItStI+pJyTm03TSDl/mS+Vmdno4GK7TySdTpq4a1ngdtId4wuANwFjIuK9GeMtpFm79mDgSeDnpHFwrQLmxIj4U8Z481XW3bWm/T+PNO7xfOAlhsxAXlI3wlraVdIfgCksOpu7gP9X0p3tZjm1QyLigeb5DaRhJROAb0dEp5UKspD0BlJX/CeBE4HvAa8mzTfw/oi4IGO8hUg6kNT74uaIuDB3nm4kTQZ+BvyGBV3ztyV9pw4ocVK/Gkg6Dngj8LGIuLvZtiFpfomrI6KI2fMl/SVpxvx/AK5rNm9H+p59KyLOyJXNzGw0cLHdJ5JujYjJTSH7ILB6RMxVmnnkpojYInPE+SSdA8wmnVyvDNxCmjRpZ2DriNgvY7xFNG3a3oX4xZx5Oqls/29FKgz3Jp1snwVcUtKdwpZa2lXS1F6vR8Sug8oyHEnXRsT2bc+/FhEfax5fFRFvyJduYZKuIy1NtSJwBrBPRFzVzKZ9VkRskzVgQ9LXSXeJf0u6cHFeRJyYN1Vnki4hDRn61ZDtuwOfLemzCvNn+f4EC9+FPyUibs6XalGSbge2Gvr71AyBuTEiNs6TbGHNkJedh06EJ2lV4IqSLgyamdXIs5H3z4sAEfGipPtaS35EREgqYhKnNpMjYvNmPOn0iGjdQb5A0o05g7Vr8p1EmiBn/uRYzQRfny1lcqxGNfs/Im4kdc3/lKQ3kgrv0yR9MiJ+ljfdIqpo114FispaTg/SBbb5WoV2YzXKMi4iLgKQdEJEXAUQEX9on0G5ALuQCq25kl4FXE66E1+itTpNMhkRF0s6LUegbpohD6cAXyTNng7pLuy5ko6NiJ9mC7eo6HQhOCJeaHoTlUJDC21IM78X9p0yM6uSi+3+Wb0ZC6u2xzTPSzuBnQUQEXMkPTTktZLWBT0ZWB7YoDWmXNIKpJOvU4C/y5htqJr2PwCSViOts7sFaWx8MRNOtamuXQGaO+9vAw4F9gPWyJtoIVdL+suI+Gb7Rkl/BVzT5d/k0l6kDJ0Ur6SeGLPaLgQ9r7KrljGSlhk6PrvpPVLaOcIJwB4RcW/btpsk/Zq01F5JxfaDknaLiEvaN0p6G/BwpkydPCtpq+ai63xNj6fi5m4xM6uNu5H3iaTP93q9sHGwjwFnkwqWg5vHNM8PiogiCgNJfwQ2Htq9WdJY4A8RsVGeZIuqbP9/EDiINA76h8A5Jc3s3K6mdoX5Y4wPBQ4EVgGOAn4WEU9lDdZG0urAT0jj9a9vNm8LLAMcGBGP5so2lKS5wEzSsWk8aV1gmufLRkQRvQYkPc/CM5C3ZiQvbjbyZmzxG4CjYuGlH08FrouIE/KlW5ikaRGxWZfXbo2IyZ1ey0HSZqTi/woWXqauqLHwknYG/oe0znZ7zsOBw6KCVT/MzErmYjsDSdtHxLW5c7SonvVA7+g2zq3Xa6UpcP/PI43Tb82QvNBBIQqb5b2bktpV0knAe0jLE50F/JhUuGyQNVgPzR23+eNgI+LXOfPUrMLZyD9GmiDrVc2mmaRx0KV1I78R2D8i7h+yfT3SuPhiLmLA/N4Bh7Lge3Ur8D+lzTOitJzeUSyc8/SIeCRfKjOz0cHF9oA0M75Oaf48HRHbZY40rOZEYf+I+EHuLACSfgKcGxFnDtl+GOkOfLFFYcn7v6ZZ3ocqtV2b3iJ3kNYDPi8iXpJ0d0RsmDnaIiSt0uv1TuM5SyJpAvAXwJSIeHvuPL00dxGnRMRRubN0oiFLP5Z0AQvmz/D+ZdLcHe13YT8FfDIifpIr20j4s2pmtuQpbTzWqNJ0xWsVArOB9YDthow3K0rTJXsvUuY9SRP7FFFsk668n9t0e24/0RoPvDNbqi5q2f/dimlJ6wCHAEUV25W065rAHqSMX21mJx8vaVxEzMkbbRF/Io3Rb+VqH18cQIkXCJYG3k66a7gX8CPgP7KG6kLSNqSc7wHuAc7Nm6i7iJghabKk+RewSMfYIkTETyTdAxwD/E2z+VbSxdZiJvNsV+FndQppWFHRn1Uzs1r4znafSLoSWIE0/vnsiPijpHtK7Uba3N08FNiXNCnSm4ANI+L5nv8wgyHdXW8dOgFNCWrb/y3NJGnvIZ1wTQR+HBHH5k21QI3tKmkZ0qRoU4A3k5ZVOzRvqgUkfRXYlbTO8lmk5X6K/GGQtCcLLgROBb4PnBYR6+fMNZSkjVlwQehPpJzHRkTP7uW5VHIBq6vWhcGIODl3lhZ/Vs3MDGBM7gCj2KOkmbPXYMEsyaWewE4nLaVyBWkZsHcBL5RYaANExK8j4rRmPOG9kj4nqYjJZtrUtP+Xl3S4pAtJF1omkWZ8n1RSod2opl1bIuKliPhRRLwb2Ai4IHemdhHxcWBrUg+W9wG/l/RlSSVewLiAdKd954g4LCLOY+EZykvxB9Ls8/tFxM7NsaqklR3may5g/YLU0+1dEbEtMKP0QlvSapI+Kuly4FLKmuEf/Fk1MzNcbPdNRBxIWkLpd8AXmq5vK0vaIW+yjn5Iuot5MLB/M66s2AJG0kRJR0u6FphG+hwf8v/Zu/Noyar6/P/vBwVtCaOiyCigBpFBJiEMohDBgQii0IOKIsavBqOgxCl+w09jME6IA2ocAQ0yKIiiEkQQVFpkEBTFLyCTgAQBwUZaG5vn98fe1bdu3aq6t5Gqvfftz2utu/pUHZr1rH2q6579OXsoHGuSxq7/HaS9y99LGs3wFvJ2cLVppV0lHZx/Dux+3/YfetccqIGT80mLZH0aOAT4+7Kp+toOWAicK+m7kg4FHlE4Uz8HkLZ3Ol/SZyXtxeTh+TVppoDVWGEwPqshhBBiGPm45O11DiIN1drI9oaFI02S94F9NinfC4A1gEOBb9u+r2C0ZSS9lpRvfeDU/HNmzUOIO2q+/pIOJxUrViUNIz4F+G6Ni3n1qrVdu7YoW2T7mKJhppGLa/uRim3rkOZpntq74nNtJO1Cuu4vAa4kTXn4TNlUk3W17XzS08MTSTnPKRqsh6Q1SJ2u+aTRF2sC+9iuap91SYtJnex3kac71LrwYLf4rIYQwoorOtsFSNq4tq1fuklamYlF0vax/bjCkQCQtIT0pOAtti/N71V/o9Wr1usvaVNSp7tzw30U6WbrmqLBZqjWdq2dpD8C15LmwV/L1K3fql4kSdJKpKfw82y/unSeQSStRVoPYa7tvUrnGaTWAha0XRiE+KyGEMKKKDrbIyLpi6Sb1nttH1E6z0MlaY7txaVzAEh6LBOLd61LerL9qppuBjtav/6StiS181zbTy6dp6P1dq2RpOMZPGzYNXUKJG2UD5favrVomBVArQWsFgqD8VkNIYQA0dkema69i5fYXlg0zDTytkQG7s6LOFVP0gakYa/zSU85zrD9zrKpJrR0/VsS7Tpekp5g+39L5+jI31UAd9X8XZXXEjDwO9s7lc4zTOsFrIoLg/FZDSGEEJ3tkJ5e5MOltm8pGuYhyFuXzLP9ntJZWhQ3W6GbpDVJc0sXAE+zvV7hSGGEooAVQgghjE50tkMzJD0rHy6x/eOiYUJ4CCTtB9xu++LSWbpJmkNaHGkBsC1pder9gQtt17hdUVgBRWEwhBBCax5ZOkAIy+GQ/Oc9QHS2Q4t2AraS9Ejbzy8dBkDSScDuwDnAx4HzgOtsf79krhB6tbDzRAghhNAtnmyHEMIKTNIVpL3qTwROtn1Li6v8hxBCCCHUJp5sj5mko4F7gc/Zvqt0njBecf1Ho7Z2bWnKg+1nSNqctMjUuZLuBFarbXG0YSQ9kbTA459LZwlhmPishhDCimWl0gFWQD8B/gJ8pHSQ6Ug6QdKn8mqv4eHRzPVvTG3tekj+mVs6yEzY/pXto2xvDrwJOAG4RNJFhaPN1JeAX0n6UOkgw0i6Ov+8oXSW6Ug6WtLb8paL4eETn9UQQliBxDDyMJCkHYGNgGfaflvpPGG8JJ0LPAAcZ/us0nnCeEkSsLvtC0tnmYmcdwvbvyidZZjced3Z9rdKZxlG0v7AZsA2tg8unWc2ic9qCCGsOKKzPSKSjiKtmnqf7WNK55nNJO0A3Gb7ttJZOmbD9Ze0HvBE0s3WcaXzQDvtKunNpH2LP9/z/qHAaraPLZNsqlbaNIRBojAYQgihVjFne3RuzH8uLhliJiR9kXSzfa/tI0rneQj+Gdha0jW2axm2e2P+s/rrP0guXtwGXFY6S5cb85+1t+vLgJ37vP8l4FKgms427bRpM1s/tZITZk2x5WByYbB0kI5WPgOt5AwhhFbFk+0RkbSN7SsHnHu97U+NO9MgkvbIh0tsLywa5q8gaTXbi0rngOau//mkm627bb+0dJ5hWmlXSVfa3mbAuZ/b3mrcmWY7SWvZ/n3pHK2R9Mp8uNj2qUXDhBBCCLNMLJA2OmdI2r73TUnvBv6xQJ5hVrF9Qb+OtqT3lwjUj6SN8s/6/c7X0tHOWrr+ryIt5nV44Rwz0Uq7riTpCb1v9nsvzJykzw14f0PgB2OOM5CkHSVN2Udd0gv6fX4Lu8L2Cf062pJeXyLQIJLOl3SepK+WzjIdSRtLWqPr9XMkfVTSmyWtUjJbt8Y+qyGE0JzobI/OgcBpkv4O0oIokj4NPAt4dslgfRwn6YXdb0haSdLxQN+nc4WckH8+WjrIDLR0/VcGNrB9S/ebknaVtFmhTIO00q4fBL4laQ9Jq+WfZwNnAVWvQly5lSV9WdKy312SngZcQF3t+n7gl33e/wXps1GTVgpY0FZh8FRgVQBJzwBOA24m/U79ZMFcvVr6rIYQQnNiGPkISdoaOAM4jImblgW17a8paRPgO8A7bJ8h6dHAV0n7Fr/K9gNFAzaqoet/Funa/7zn/a2Ao23/Q5lk/TXUrs8H3g50ts67CvhP298pl6pteRXn/wLWAuYBOwGnAK+vaWEsSZfY3nHAuZ/Z3nrcmQbJHe3TgJfZXpjb+FPA3wL72f5D0YBdJD0ZeILtH/W8vytwu+1fl0k2Vfd1ztt8PWj7rblQdEUtn4GWPqshhNCieLI9IpLWBm4BXgl8mbRS6v8BVs3nqmH7BuDvgfdKeh1wLnCt7ZfV1NGW9HJJr+jz/iskLSiRaZCWrj/p5vXnvW/m9540/jiDtdSutr9jew/bj80/e7TU0Zb0T5LmSqpmIU0nrwV+C3wfOBk4sKaOdrbWkHOPGVuKGbB9GbA/8GVJzyMVWtcBnldTRzs7FuiX6Q/UtegggLqO9wS+B2D7wTJxBmrmsxpCCC2KJ9sj0rXCZ/cv3M5r2960SLA+JG2XD9cjDdP+LvCBznnbl5fI1UvSxcBetu/reX9V4ELb1cwva+z6X2v7KQPOXWf7yePONEgr7SrpcaQn73cDXyT9e3oW8GvgLbavKxhvRiQdBmwObGz7RaXzAEj6OBPXewFwOXB157ztNxaKNkme2nAX8C7nX7L5ifG7gXVzwaAKXUWqLYCvk4qtbwAeBLB9d6FoU0zzFLaqhQclfZS0QvpvgRcBT7X9gKQnAt+0vUPRgFlLn9UQQmhRdLZDZzXqQWx7z7GFGULS5ba3G3Auhrs9RJK+Apxn+7M9778GeG5F26k1Q9I5pC2+VgP2Ao4HvgHsThqu++xi4RqmiZWz+7J9wriyDJMLgJ8Dnglckd9+BnAJ8JregmFJrRSwoLnCoIC5pA73qbZvze9vCzze9v+UzNfR0mc1hBBaFJ3tEZH0deAi4EfAJbaXFI7UPElXAzvY/mPP+6uR2njzMsmmaun6K62QfQawhIk9tXcAVgFebPv2Utl6tdKuylt/5Rvum2xv1HXuCtvPKBhvEkkH58PFtk8rGmYaeaGpK93ILy5JmwJPzy9/Yfv6knla13JhUNJjSaNbbs5D96sSn9UQQhiN6GyPiKR9gV3yzzakoY6dTsJFtv+3YLxJJF1JytXJdkPhSH1JOpL0lPB1tm/K7z0JOA74vu1qVk5t6fp3SHoOE4t5/cL2eSXz9NNKu3aPwugdkTFshEYJko7Kh4tsH1M0zDQkXQpsSioKda77wsq2/QPS1k/AH23fKWlnYDfg17bPKBxtklYKWNBcYfAs4O22r8pDxy8njXbZDPiM7WrmmLfyWQ0hhBZFZ3sMJD0C2Ja0NdHrgE1sP6JoqC6StmSiA7MLabuShUx0YC4uGG+SvIDbO4C/IQ1zXERa4flTRYMNUfv1B8iLYC21baU9i3cCrrN9xTR/tZia21XSPcCFpM/o7vmY/Ho328MWJQpDSHoMachr5/tqR+B24Ee2/6lktg5J/5e0TZVJi7j9PWlBt51IT+ar2bqqlQJWt0YKg7+w/fR8/E5gc9sH55FYP6pl2lNLn9UQQmhRdLZHKC+S1LmJ2Rl4NGlO1MJa5hb2k3PPI+1lWk0Hplu+YaHGJ1odrVx/Sf9I2mv1PuDfgX8hPYXZFviC7fcXjDdFC+0qaY9h521fMK4s05F0qu2D8vH7bb+t69w5tvcul26wPNd0Z2BX4GBgpVrmF0v6JWne62NIeyuva/v+XNS6wvaWQ/8HhdRcwOpopTDYPV1E0veAz9o+ufdcaa1+VkMIoRXVbOky20i6lrRP9deA/wHeW+tCI103WLuQblw3A24lLZqysGC0SZT2/55LWuH5LOBfJHVWeP5323eWzNetpetPKqpsRlrM62rS6tN35ieIl5A64lVopV0HdaZz52AeUE1nG+hecOq5wNu6Xq8z5ixD5S3+diF1Dv5M+nxeTBotUM0QYuBPeTj2Ekm/tn0/gO2/SKpumPaAAta5VPT9D5MLg5ImFQYl1VYY/I2kfyZtVbgdcDaApDnAyiWD9WjqsxpCCK2JzvbofIF00/ISYCtgS0kLgZ/aXlo02VSLgF+S5j6/vdY528CJpH2VVwWOBK4CPkGaX3Y8sG+xZFO1dP2X2P498Pu8ou+dAPnpRm03Wy21KwCS1gEOBOaTtterbR7ksOFNtQ19+i/g/wGfJm33d03hPIOsKekA0rSB1fMx+fUa5WJN1UoBK2umMAgcCryHNCx7ru178vs7k7YDrEUzn9UQQmhRDCMfA0lPJT0x+DtSx/BO20OHmY6TpPmkbNsDS0k3LQtJw3JvLZmtm6SrbG+Zh7fdYnvdrnNX2t6mYLyBGrj+vyJ1BFcCvkzav1j558u2n1Yw3kA1t2ue5nAAqS2fCpxOuuHeoGiwPlq6/nkUzjZMPIX9W9I+xp3vqyrm7koa2pmyfci4skxH0jtIHcD1gWvIbUmFBSxJP7W9bT6e9J3ffS7MXEuf1RBCaFF0tkcsb6fRGZ69C+nJ1sW2a3oKu0zP4kOHAKvY3rhsqqSlFZ47Wrj+Gr7POrafM64sM1V7u0paDPwEeBfwwzy/9Ppa5hR3k/R9hjzBrvH6d+TVqQ+k4vUlekl6Qo2LjkHdBSxorjD0RQb/u7LtQ8eZ56Go+bMaQgitiM72iEg6g7Rwyx9IK7teRFqB9OqiwQbIiw3txEQHZkfgN6TMbyiZrUPSHaTVUkWau31y5xRwkO0nlMrWq7XrP4iklW0/UDpHRyvtKulw0tzsVYGvAKcA362xs90SSVszeeeEVUifgYWkz8GlBeMNJGlN0tSHBcDTbK9XONIUtRewoK3CoKSX9Hl7Q+AI4BE1jnKBNj6rIYTQkuhsj4ikF5G2Tbmz5/0NgXmua0/on5JuAi5logPz49rm7Ul65bDztaxEDW1d/16SBOxJutnat7IiRlPtmjsw80hP454CHAWcUdNc4645mn3ZPn1cWaYj6XLgh0x0rm/uOldbYWgOsB/p39G2pHnG+5Pmmj9YMlu3VgpY06nt+nfL3wPvBJ4FfAT4vCvaz7yVz2oIIbQoOttj0LNA0vrA6baPLJtqQn5a9HP3fBgk7QbMt31YmWQzk1cp/wfbp5XO0k/t179D0s6km639gbWBw4Bv5MXTqtNKu3Yo7Wc/nzR3+8ml83RIepC0dVpn6yR1nbbtV48/1czUWhiSdBJpf/VzSCNwziNtT7VJ0WB9tFbA6lbr9e+QtDlpKsm2wAdJQ93/UjbVZC19VkMIoUWxGvmIDFggaZMah47Z/lnnWNK2pMwHAjeQclcnL5S0D6nzsjfwA6CaznZL11/S0aTrfTNpyPO7gUtrGinQ0VK79rJ9FfCv+acmB5Cevm8NnAl8xfZ1ZSMNN6AwVFOhZQvg96QVs6+2vVRSlZVt29/oHPcrYJXKNUwD1x9Jp5EWHf0waej4UtJq3wDYvrtcukma+ayGEEKL4sn2iDS2QNJTSTdX84E7SfNLj6xlYbRukvYg3WS9gNS+uwKbdvYGrUVj1/8O0irExwLftP3nirM20a6SbiAtjvQ72zuVzjMTed2G/UjrITwW+FcP2C+8lD6FoTNIhaHqnsLlp5rzSe15J2nl9C1rW3CqsZXzW7r+NzKxQFrnz86oEdf0ndXKZzWEEFoUne0RaWmBpDyM9AfAoZ0nWpV2YG4h3WR9Cvi67UWSbqj0Rqul6/8I4Lmkm629gPNJe8NuWOGQx2batTX5c/A8UvtuBbzN9v+UTTVZS4WhbpK2J/37Ooi0beEuhSMt00oBC9q9/i2p+bMaQggtis72iDWyQNL+pIy7AmeT5m19rrZOrKRjScMGrwJOIg15/XnNN1otXP9ukh4F7EvKuzvwPdsLyqaaqrV2rZmkPUlt+UzgXODkilf1bqYw1E+eY7y77QtLZ+loqYA1C67/ZqQRBPNsP710nmFq/KyGEEKLorM9RrUukNTRNYx0PmnRmRNJHZhzigbrkm8Ank3K+AJgDeBQ4Nu1rZ7eq/br30vS6sD+tk8snWWY1tq1Nnlky89Iq3ybnr2Bbb+xRK7p1FwYknQUqR3vs31M6Twz0VoBq+br303SeqTh2QtII0beR1rM8edFg2UtflZDCKEl0dkOfUlaizQ3bq7tvUrn6UfSykwskraP7ccVjtQkSQfnw8W1rugeRqelLfUGqa0w1NWmi22fWjTMQ9BaAau26w8g6bVMLDR3av45s8IRY01/VkMIoXbR2R6RFhdIapmkObYXl87R0dL1z082ABbV/mSjpXZtXY1b6kVhaMXW0vWXtIS0H/xbOtMyYn55CCGseGLrrxGprXo9TCsdGEnnk3Lebful3edq6mhDW9ff9rtLZ5ipltq1H0nnAg8Ax9k+q3SeXrVvqQd0rv+ioilmkVa+/7OWrv8TSaPDPixpXdKT7ZXLRgohhDBu8WQ7NENSZyuypbZvKRomhIcgz998IrCz7eNK5+loZUu9EFokaQPSvO35pIXozrD9zrKpQgghjEN0tkMIYQXW0pZ6IbRO0lNJq5G/p3SWEEIIoxed7RBCeJgNm/JQmxa31GuVpH8C7gK+1sJWVeGhk/SsfLjE9o+LhnkI4rMaQggPj+hshxD6krQfcLvti0tnaU1rUx5a3lKvJZIOAzYHNrb9otJ5wuhI+mI+vMf2EUXDPATxWQ0hhIdHdLbHrPYFksJotXT9JR1N2hf2kbafXzrPMLW1q6RX9tsyK29Xd6Lt+QVizUiLW+pFYWjFFtc/hBBCrWI18vE7mLxAUukg05F0dT48zvYnioYZQtIJwP2knFeVzjONZq5/Ywv41Naub5L0KNuf6bwhaVXgDOA35WJNz/YDwFnAWZLmlM4zQzsBW0mqojDU0hZVg9RWwJpGVde/JbPhsxpCCDWLJ9thKEmPJa2c/K3SWQaRtCOwEfBM228rnac1rc8trJGktYGzgS/b/pikdYBvA9+z/fay6SZraX55K1rau36QWlfODw+v2fBZDSGEmkVne0TiBnbF1tL1b2luYWPtujrwHdJe1fsBn7b90bKppmppfnlrhSFJj7N9Z+kcs0Vr178Fkja03Xe0jaR9GxjVEEIIVYvO9og0dgN7Qz68w+g9jz8AACAASURBVPZORcMMkTuFBu5toFPY0vVfw/a9A87tYPvScWcapJV2lXRAPlwNOAb4HnBy57zt00vkal0rhSFJ+wJfJA3DfhA4yPZFZVP111gBq4nrP4ykHYDbbN9WOguApF8Bz7N9Y8/7rwb+1fZmRYKFEMIsEZ3tEZK0P/Bk0jY6/1M6T+sk7ZEPl9heWDTMDLRy/SVdAuxt+/c97+8NfN72hmWS9ddCu3Z1Cvqx7VePLUwYO0k/I3WwfyVpJ+ADtveY7u+V0EoBC9oqDA6S1xjZGrjG9twK8rwAOBZ4oe1r83vvABYAz6/9MxFCCLWLzvaISPok8HTgImAv4Ju2/71sqv4kPRp4HbkDQ+pgVbevpqTn2v7ugHPvr2m+dmPX/x+Bw4Dn2v5dfm8B8B/AfrZ/VjJft5baNTz8csf1M8BmpO+qQ23/smyqqSRdbnu7Qa9r00IBC9orDA4jaTXbi0rnAJC0F/BfwP7Aa4Bnkjrfvx/6F0MIIUwrOtsjIukqYBvbSyU9BviB7e1L5+pH0imk4Y4/AJ4P3GT7TWVTTSXpGuCI7sXaJK0EfAFY1/bzioXr0dL1B5D0CuCtwN7AXFLxZcrQwtJaaVdJdwEXAz8iFQYutn1/2VTtk3Qp8A7gQuBFwGts71M21VSSbiFNH+h4c/frmhaiaqmA1VhhcKN8uNT2rUXDzICk3Um7JVxEGpXxp8KRQghhVoitv0Znie2lALbvl6TSgYbYwvZWAJI+D/ykcJ5B9gG+I2kV22fkJ/JfBe4F/qFstClauv7Y/pKkPwE/BW4Gdqt0YadW2nUT0jZku5A6h9vntRF+BPzI9qklw81EpVvqrdQ1uuW0PNy1Rp8lzdcf9Lomz6KngAVU2dm2/dn8PXVefprdKQw+p7bCIHBC/vMuoNq58JIWkebsC3gUqeByR/5ute3VS+YLIYTWxZPtEZF0P3Bd5yVp2ON1+di2ty6VrVdLQx4lbQD8D/Bx4OXAJTUulNPY9f85EzdbGwO/A/5InVmbaddueY/tQ4DDgU1sP6JwpGnVuKWepOuBI7ve+lD361h4bvm19P3fIelA0u+Am4EXVFoYDCGEEKKzPSpdi870ZfumcWWZjqSlpM4VpE7LHNITraoq25I6N4DrkZ4afBf4QOe87ctL5OqnsesfWR9meY/iXfLPjvnty4AfAwtrydkaSceTCkP9VLPwnKRTbR+UjyetJyHpHNt7l0s3WUsFrMYKgy8n3WN9qef9V5CGlp9UJtlkko5lYsRNFSukhxDCbBKd7RGRtLntX+XjR9n+c9e5nR17hC63vEXNILa959jCTKOl6y/pcNI8vctrXBivWyvtKulB4HLgI8BptpcUjjRQS1vqtULST21vm497nxwvO1eDVgpY0FzWi4G9bN/X8/6qwIW1rDUh6Q1MFAYh/S64iNQBv9L2g6WyhRDCbLBS6QCzWHfVunebqk+OM8h0JO3ZdbxJz7kDpv6NMmw/Z8hPNR3trJnrD2xA2vrlDkkXSDpa0r6S1i4drI9W2nVXUtYXAwslfU3SkZJ2lfSowtl6HU8aKdLCPPJju47f1HPu+LEHGmxYFbu2Cvcc2zfljurtneP8+omlw/V4MfAE4NbunF15a7Jyb0cbwPYfgZUL5OnL9idsL7D9JFKH+3RgU+A04J6S2UIIYTaIzvboaMBxv9elfajr+Gs95941ziDDSLpS0iclvay3KFChZq6/7SNt7wKsS1rM627S/OKrJNW2rVIT7Wp7oe1jbL80P8F6C/BnUqe27z7Bpdi+gJRpfUlPK51nGs/qOn5lz7lqhhADj5G0raTtgTn5eLvO69LherRSwIK2CoNz8lPsSSStBqxSIM9ASrYmrfC/H7AHaSrBh4sGCyGEWSBWIx8dDzju97q0JjowwMtIlffnAkflG5mF5O2VbF9cMlyPlq5/xxxgdWCN/HMbaS/jmjTTrpI2Z2J45q7AmqQ5258umauXpH8jLTZ4GfABSe+z/dnCsQYZ9l1Vk98ysdXX7UzeBuz28ccZqpXvf2wfCSBpFWAH0r+tQ4DPSLrH9hYl8/X4PPBVSa/rPHWX9CTguHyuCpK+S/rev4L0/XS07avLpgohhNkjOtujs4Gkj5FuVjrH5Nfrl4vVVxMdmLz90FXAZwAkPQ6YR1rh+UNATSs8N3P9JX2GtM/uItLe0BcBx9j+fdFg/TXRrpLuJBUrFpL2hP5P29cN/1vFzAWekbdSeyxwNmmrqhqtJGkt0qisznGnQ1jNv3/bzxl0TlI1Q4izJr7/e1RfGLT9IUn3ARdK+hvS53QR6bvgU2XTTXI9aVTIU0jblN0p6XexwnsIITw8YoG0EZHUO8RxEtsnDDs/TpLuIXUIBOyej8mvd7O9Vqls3SQ9AtiWiSeFmwG3kjo0C/Nw2Co0dv3PBh5HKmRcRGrPq1zhl0Mr7SppDdv39ry3KmnO6XzbLyyTbKo+C3hdVsviTb0k3Qg8yIAnrrarnF6S9yzeE1gA7Gv7CYUjLSPpDuBkUpvOzcfk1wdVlrW3MPhj4MeVFgaXyUPHsb2odJZBJK0O7Ez6/bozsA7p98DQ79wQQgjDRWd7zCQ9GvgH26eVztIhaY9h52vpxOYtan5JGob3fds3FI603Gq8/rCsM/B0JoY9b0mau73Q9lEls81Exe26CvBCUidrH9KaCKfb/mbRYF26im0wteCG7ReVyDUbSNqZdO33B9YGDgO+UVPnsJUCFjRXGHw0qXhxN3AW8C+kNQd+Dfx7bU+O88KNO5IK2Z0O9x22tyoaLIQQGhed7THIT2T3AeYDewM/sP3SsqmmJ2lDYJ7tD5bOAiBpPvB3wPbAUuASJp5q31oy2zAtXX9JGzBxs7Uv8Fjba5ZN1V/N7SppbyZynQ+cAnw8r/hblVaKbYNI2ozUoZ1n++ml8wBIOho4ELgZ+ApwBnBprU/e+6m4gNVEYVDSqcADwKrAWqQCwTeB3UjTNvYtGG8ZSR8hteNTgJ8ysQ7KQtuxGnkIIfyVorM9QvkmdgHwAuAnpE7MprbvLxpsCEnrkG4S5wPrAWd0FqWpiaTHAM9kYoGcVWwP3YN13Fq5/pLeyMSN6wNM7LN6EfDz2vZZbaFd8z7bPwBe1RmBIel625uWTTaVpNVt/2HAuY1s3zzuTNORtB7pqeECYCvgfaQRA1XM281Ds68hrZz9Tdt/rvX6d6u5gNWr9sKgpKtsbynpkcAtttftOnel7W0Kxlsmf///CLjC9tKu96sstoQQQmtigbQRkXQL6anGp4AjbS+SdENNHYKOPJ/sANKN61NJ+2xuYnuDosH6yPNed2Ji3vaOwG9INwvVaOn6A08i7al6hO3fdp+QtCNpBEEVGmrX7UiL950r6XrSPNhqFvDq8X1SXiR9z/ZeXee+3jlXA0mvJXUE1yftC34ocKbtdxcNNtUTSbsmzAeOlXQ+aSuoR9r+S9loUw0oYG1S27+rIYXBL1DZAmnAEgDbf5F0W8+5pX3++yJsdxaZ7FtsIf1uCCGE8BBFZ3t0vkqapzcXWCrpTOpd2fUO0g3Wu4Af2rakFxfONIWknwIbApeSbrA+TFoc576iwfpr5vrbfnP3a0lbkG625gP3kLbYqUUT7Wr7CtJWOm+XtAupLVeW9B3SaJHPFA04WfdiY737FVe19RPwCdIw1wW2LwWQVOP1X0pa1f3sPBd2X9IK2rfmgsaCogG7NFTAgoYKgzSycwK0U2wJIYQWxTDyEcpzy55NutF+AWmLkkOBb9fUQZR0OOkp3Kqk+YWnAN+tbcijpK1Jw5rd8/5upBWeDyuTrL9Wrj8s2/+108F+ANgY2MH2jeVS9ddSu3aTtBLw96S5xa8unaejezXyPiuTT3pdWt6arDPNZV3S0+1X2d6waLAZyis+72/7xNJZOiQdSypgXQWcBJxJ+p6t6vu/n97CoO1qCoOtLDzXU2z5elexpZn1BUIIoWbR2R6TvLdqZ3jWPrYfVzjSFJI2JXW655MWSzmK9BTumqLB+pC0LakSfyBwA2m+5sfLphqs5usvaSFpz9qTgZNtX9vKzVat7Sppo3y4tObF+2DZzfYxpCduR+Rj8uvDa+3I5jm7c0nXflXSd9U7y6ZKJB2cDxe3MOe1pQJWS4XBfmqbC91ysSWEEFoQne0CJM2xvbh0jmEkbUm6mZlr+8ml8wBIeioTN1l3kp7AH1nbwmjTqe36S+rMy/0GcJLti1pYzKlXTe2a5+gC3FXrAlMdkoau4FzhfOgp8nfDPNvvKZ0FJrXpItvHDP2PK1NrAQvaLQzWvvBcS8WWEEJoTXS2RyTfbBu4u6Zfqi3rWuH5UNvX5feq7BS2dv0lrUFaJK8zqmFN0o32T4oG69Fau7ZO0o62q5kHK+lZ+XCJ7R8XDbMCqKmABe0VBlvYOaFXzcWWEEJoUXS2R0RS52nrUtu3FA0zDUk3kDowv7O9U+k8g0janzTMfVfS4kMnA5+r8alGS9e/l6THAweRbrY2qmkYccvt2orK58F+MR/eY/uIomFmidYKWA0VBpufC11bsSWEEFoUne3QnLz9136km609gRNJ8zXPKRpsFpK0se2bSucIo9X6PNjw0LVcwKq8MNjEXOjWii0hhNCa6GyHpklai7RI2tye/YHDDOWnhQbujaeFK55W58GG0K3GwmALc6FbLraEEEILorMdwgouzyuENA92YdEws5ykJ5KeIP25dJaO1ubBtkzSfsDtti8unaVFLRcGYy50CCGsmKKzHZrRytzyEAaRdC6wGfA120eWztPRyjzY1kk6GtgKeKTt55fO05rZUhiMudAhhLDiiM72mEk6AbgfOM72VaXzhPGK6z8aLbVrHlq6he1flM7ST83zYAeRtANwm+3bSmcJAWIudAghhCQ622MmaUdgI+CZtt9WOs8w+SncA6QOzFml88wGLV3/lkS7jkaN82D7ycWWrYFrbM+tIE/zW5S1VMCqUcyFDiGEANHZDkNIWg94IrCz7eNK5wmhFS1NeWh5HmwvSavZXlRBjua3KIsC1ootii0hhPDwiM72iMymG9iw/GbD9c/zS+8l7WV+V+k8MDvatTYtzYOVtFE+XGr71qJhQpjFotgSQggPj+hsj0hjN7Axt+xh1tL1H0TS/qTFvLaxfXDpPNBOu0p6ue0v5+Ndbf+o69wbbH+iXLp25e8qgLtq/q6SdKztw/Pxm2x/tOvc8bZfVSxcj9lQwKqxMBhCCCFAdLZHRtLqtv8w4NxGtm8ed6ZBWp9bJunqfHhcC50YSafUMK90tqmpXSVdbnu73uN+r8Ps09L1b6WANUyNhcFWzIZiSwgh1OyRpQPMYt8n7V2LpO/Z3qvrXGdf2yoMWwBJ0o9s7zrOPMvL9tMkPRbYuXSWGfq70gG6STqKdLN1n+1jSuf5K9TUrhpw3O91mCFJb7X9gXx8oO3Tus4dbfud5dJNMuz6V8X2BYPO1VTAGsb210tnmKkK50Ifn/9cUjJECCHMViuVDjCLdd9grT3kXO02mv4/Kc/2Xba/VTpHo24EbgKaG9VQMQ847vc6zNy8ruN39Jx73jiDTGMlSWvlImDneG1JawOPKB1uOdRUwELSUZL+TdKbS2f5K3wCOBd4RekgsKzYci+wvqSnlc4TQgizTTzZHp3ZcrNdTda8wjPAHQ2s8Dxo5IKAlceZZTq2T5C0DrCxpDVt31M60yANtevmkn5GyrVZPia/3rRcrJmrdB5sKyMG1gAuYyLT5V3nqvlObdCN+c/FJUP8NWxfAlwCfK10FgBJ/wa8nPR5/YCk99n+bOFYIYQwa0Rne3Qen6vv6jomv16nXKypJB0w6BQwZ5xZhrG9SekMy+HDQ879amwpZkDSa4CjgV8Dm0h6re1vFI41SCvtOhueEP2ENA/2I0At82CbKGLaflLpDDPVUAGrtcJgK3Oh5wLPsH1/HolxNhCd7RBCeJjEAmkjkufBDmT73ePKMp2uPWH7sn3IuLIMI+nRwOuAJwM/Bz5v+y9lUy0/SSvbfqB0jg5JVwHPsf07SZsC/227quGjM1FTu0ra3Pav8vGjbP+569zOtn9cLl27JC0F/shEIfD+zing0bar6By2tBp91wrvfdl+zriyTKe3MAhUWxhsZeG5Pgv4XWZ7+5KZQghhNonOdhhK0hNs/2/pHJAW6wEeAH4APB+4yfabyqaaGUkC9gQWAPvafkLhSMvUvlryMLW2a2OrUc+WBfKq0dL1H6amAha0VxiU9AxScfgXtq+e7r8vQdI9wIWdl8DuXa+x/aISuUIIYbaIYeQjIulU2wfl4/fbflvXuXNs710u3XCS1gReQurAPA1Yr2yiZbawvRWApM+ThrlWTdLOpHbcn7RQ3mHAkUVDTbWBpI8Nem37jQUyDdVAu7YytxgamgcraU/b5+XjTWzf0HXuANunl0s3SUvXf5LeAhZQRQErW2L7dwC2r5f0qNKBBmloLvR+Pa8/VCRFCCHMUtHZHp2ndB0/F3hb1+uq5mwDSJpD+qW7ANgWWI3Ukblw2N8bs2VPWGz/Jd0T1ikvLnUgcDPwFeDdwKW2TygarL9/6Xl9WZEUM9BQuzYxtxjSPNjSGZbDh5jYNvFrTN5C8V1ALZ3tZq5/RwMFLGirMNjKXOif2v5DvxOSmtiNJIQQahad7dEZdkNV1c2WpJNIQ8fOAT4OnAdcZ/v7JXP1sY2kzk2BgDn5tQDbXr1ctCleA1wDfAr4pu0/S6rquncM62xJqu07opV27XQCxOQOgYD1y8WaStI3GfKdVNkw0laeGDezGn1DBSxoqDAI/Nn2/ZC2ppRU61ar3ycXrSR9z/ZeXee+zuSCVgghhOVU2430bPIYSduS9jKfk49FZSt8Z1sAvweuBq62vbTGDoztlvanfSJpRMN84Ni8CNEcSY+sbVE3ST+0vVs+/pLt7v1ff0JdN1uttGt3p+DSnnO9r0tradhoK0+MW1qNvpUCVmuFwU0ldRZv6xRdli3mVlERq7tItfaQcyGEEB6C2n45zSa/BTqLDd3eddx5XQ3bz5C0OakDc66kO4HValocDZqar4ntpaRhg2fneYX7koost+anBwuKBpxs1a7jp/ecq+pmq5V2HdQpyCvq/8OY40znmcDJtn9TOsgMdDowYmpnppqtAW3f1O99SbuRvmcPG2+ioVopYLVWGGxlLnQrBawQQmhSdLZHZNh2KZKq2J6mW96m6CjgKEnbk+buXSLpFtu7lE23TCvzNSfJ2z59DfiapM5c+Jo0M+WhWwPtCoCkRwD7kDoze5NW0z+taKjJ1gMuknQjaRjxaZ1FqCrU3YHp7bxU2ZnJo5oWkIZq30Bl31OtFLCyZgqDtDMX+vGS3kxqv84x+XV168uEEEJrorM9JpWv8DqJ7cuAyyQdCexWOk+XVuZr9iVpM9L1nwd8qXCcbmtKejFpysOakg7I7wtYo1ysmam1XfM+uwuAF5Ceuu0KbNKZx1kL20fkG+xnkdrw/0q6ktTxPt32oqIBu9i+oN/7kjYkZe97ftwkPZVUXJkP3AmcQtpqs5o9q/tpoIDVUmHw+7QxF/qzpAVRe48BPjf+OCGEMLtEZ3vEGlnhdZKKCwPNDXeTtB5pVdoFwFbA+0idgppcALyo67h7mHNNq9EvU3u7SrqFtODUp4AjbS+SdENtHe0O2yZd+wskvQH4e+A/SfkfUzLbIJLWIT0tnk96On9G2UST/Io0gmFf29cBSDqibKSZq7WARVuFwSbmQtt+96BzknYcZ5YQQpiNorM9Io2t8Ao0URhoYr4mgKTXkjoB6wOnAocCZw67sSnF9iGDzkmqpdACNNWuXyX9O5oLLJV0JpUWhLpJ2orUwZpLeiL7jrKJJstPWw8gfU89lTQkexPbGxQNNtUBpHY8X9LZwMlU1MHqp/YCVtZSYbC54jCApC2YGJVxD7BD2UQhhNA2pQca4eEm6Q7SCq/HMrHC6/W2q9r2BfoWBs4gFQZq68DuMez8oCGmJUhaAiwE3mL70vxelde/l6Q1gZeQbrqfZnu9wpGWaald8wiRZ5NuWl9AevJ2KPBt2/cVjDaJpKeQOlXzgKWkjuHJtq8vGqwPSYtJQ/LfBfzQtmu9/gCSViXNM59PGi10InCG7XOKBuvSp4B1KqmAVdX3/3QqXNDzFtLCqAKOYGKRVAGH296wVLZekp7ERAf7AWBjYAfbN5ZLFUIIs0N0tkckL4rUWeF1L+B80tDMDStc4bWZwkA/nfmatj9YOkuHpMcyMcR1XdIN7KtqusHqJmkOqVOwANiWNG9vf+BC2w+WzNattXbtyIsidhZJ28f24wpHWkbSr0lFtpNtX1U6zzCSDicVBVYlZT4F+G4L31WS1iJ9duf2zN8tqqUCVq/KC4NHDTtfy2gcSQuB1Zkosl2bp7w0VWwJIYRaRWd7DLpWeJ0P7A5UtcJrS4WBjn7zNW3XNOR9GUkbkIZnzid1Es6w/c6yqSZIOon0uTyHdMN1HnBd7TdbtbfrIJLm2F5cOkeHpO/afm7pHMtD0qakTvd84CmknRTOsH1N0WANaq2A1UphcBhJO9q+pHQOAEmdxdq+AZxk+6JWii0hhNCC6GyPmaTVgf1tn1g6Sz81FwYGzNecW+F8zYHyKsXzbL+ndJYOSVeQFhw6kfRk45bWbrZqa9e8V7GBu22/tHSeYSRdbruWlZGXm6QtSd9Xc20/uXQeAEk3kK7/72zvVDrPTNVewGq1MAhT50LbrmYutKQ1SL9bO8WrNUkjcH5SNFgIIcwC0dkeEUkH58PFtmvaU3fGaisMtDRfU9Kz8uES2z8uGmYGJG1O7rCQFsb6W2DLmuZAQjvtKmnjfLjU9i1Fw0xD0vUMWQjRdlX7QofxqK2ABe0VBlucCy3p8cBBpMwb1TrCIYQQWhGd7RHpmq+1yPYxQ//jwlopDLQ0X1PSF/PhPbab2fIHQNL2pButg4BbbO9SONIyLbdrrSTdBZxJ/9WybfvVY440UKtPjGvWSgGro6HCYPNzoSVtbPum0jlCCKFl0dkOTRUGIOZrjkteTXt327VtqRMeRq0PIw9/nZYLWJUXBpuYC52vv4F7W7v+IYTQguhsh6bVOF+zNbnYYuC+Foot4eEl6ae2t+3z/qOBf6h5tEsIUG9hsIW50F1bai6xvbBomBBCmIWisx3CCk7SK/PhYtunFg0Txk7Slp0tv/LOBJ0tyvYGflD7Am9hxdFyYTDmQocQwoopOtuhGTFfM7RO0gnA/cBxNe1pnZ9uLQBeQFqEcFdgU9v3Fw02y0i6Oh8eZ/sTRcM0aLYUBmMudAghrDiisz1mkvYDbrd9ceksYfwk7QDcZvu20llmk1baVdKOwEbAM22/rXQeAEm3ADcDnwK+bntRaws5tSTva72z7W+VzhJGJ+ZChxBCgOhsj52ko4GtgEfafn7pPMNEYeDhl59sbg1cY3tu6TyzRbTrQyfpWGB/4CrgJNLK5D+vbSGnYSSdS9pa6TjbZ5XOMxu0UsCqVcyFDiGEANHZDkO0VBhojaTVbC8qnWO2qaVdW3uqlReYejZpPukLgDWAQ4Fv276vYLQZkbQe8ETSE+PjKsjT/JSXKGCt2PLv/3uBz9m+q3SeEEJoVXS2R6S1vUvDw0vSRvlwqe1bi4Z5iCT9E3AX8DXbfymdB9pp15afaklamYlF0vax/bjCkUJBtRSwwnhJ2h/YDNjG9sGl84QQQquisz0iLe1dGoWBh5+k8/PhXa2u5izpMGBzYGPbLyqdB9pr17x9VmdLuuts/6lknuUlaY7txaVzdOTrb+DuRq7/c4Cn55e/sH3+sP++hFYKWMPUWBgMIYQQIDrbgbYKA/3EfM1QG0mPBI4GXg3cBAjYEPgi8K+2HygYb5KWOrCSNs6HS23fUjTMEJLWB04H/gRclt/eHpgDvLimTm1rBax+aiwMtqLl7dRCCKEF0dkeobxn7Vq278yvVwFeBRxh+2kls80mtc3XBJD0VtsfyMcH2j6t69zRtt9ZLt1kkjpDBBd356xRK+0q6SPAaqR/64vye6sDHyK185tK5uvWSgd2GEkrAfNt/3fpLACSzgDOtH18z/sHAy+xvV+RYKG42uZCz5bt1EIIoVYrlQ4wW0maB9wN/EzSBZL2Bq4Hng+8rGi4Hnk14s7xm3rOHT/2QMvJ9m22L6ulo53N6zp+R8+5540zyAxskn82LB1kBlpp132Bf+ye62r7D8DrSQuQVcP2Tfmn+o62pNUlvUPSJyTtreSfSd+tB5XO12WL3o42gO0TSU9gqyHprV3HB/acO3r8iQaTdHD+OXD6/7paPwH+AnykdBAA2yfknykd7TxCJ4QQwl8hvkhH513A9ravk7QdsBB4qe1vFs7Vz7O6jl8JfLTr9dZjzjJQS8NdScOG+x33e12U7XcPOifpcNvHDjpfQCvtavcZNmR7qaQYTvTQfQn4Pen79DXAO0nXfX/bV5QM1qNvITs/gX/EmLNMZx7wgXz8DqB7dMvzSG1ci87e780u2Gb766UzdJP0Q9u75eMv2X5F1+mfANuVSRZCCLNDdLZHZ4nt6wBsXy7p2ko72jC8A1OTV+U/l5YMMUMecNzvdc3eDNTU2W6lXX8p6eD8JHMZSS8HflUo02ywqe2tACR9DvgtsFGFC8+dJemzwOG2/wggaVXS08xvF002VSsFrKYKgw3NhV616/jpPeequv4hhNCi6GyPzuMlvbnr9Zrdryv75buSpLVIT2M6x51fsjU9hZlj+1cAkh5l+8+dE5J2rmwl9W0k/YHUjnPyMfn1o8vFWm613Wy10q6HAadLejUTC2TtQF4gq1iq9i1bWC6PErilwo42wFuB9wE3SepeIO8E6npSDO0UsKZTW2HwxvxnNav5DzDsGrd0/UMIoUqxQNqI5Kr2QMMq9OMm6UbgQfp3rGx70/Em6k/S5ba36z3u9zo8PCTdbHuj6f/L0I+k0OJWOQAAIABJREFUPZl4WvRL298rmWd5SDoBuJ+0yv9VpfMASFoK/LHzklS8uD8f2/bqpbL1I2kOE1u//dr2/SXz9NPVpt3tSX79aNsrl8q2PCT9xnaV605I+hsA2/eVztJL0vXAW0jF9g8CR3ZOAR+wvVmpbCGEMBtEZzs0Q9JPbW/be9zvdWmS9rR9Xj7exPYNXecOsH16uXSTSVpE/ycYAh5ju5rRDa20q6S1h523ffe4sjxUknYENgKeafttpfO0RNIBw87X8jmdTWosDEp6PWke/Kqk79NFwPttf7JosC55EdSBN4K2DxlfmhBCmH2isz0ikj4IXGf7v3re/z/AJrbfXibZVJJebvvL+XhX2z/qOvcG258ol25CS0+2W8raklbaVdKDwC2kVYdh8qiRakaLtCYXAB5n+zs97z8fuMP2Zf3/5njl639F/oGp1//V40/VXysFLGiuMPguYBfgDbavz+9tSlqA9GLb7y2ZL4QQwnjE1l+jsyfwmT7vf5a0LVBNuueWf7znXDU3hcAGkj4m6eNdx53X65cO16OZRYf6kbSqpJdL+lbpLD1aadePkVbNPpu0wv+mtjfJP1V1tCV9UdIX8t7gtXs/8Ms+7/+SNAS2FgcA15B2c7gB+A/bh+Sfmr5TIe393vG1nnPvGmeQ6dhezfbqfX5Wq6mjnb0COKDT0QbIxwcBBxdL1aP1rT9DCKF2sUDa6DxqwNY/D0qqqVMA7XRg/qXr+NKec72vS2tu0SFJqwAvBBYA+5BuvD9dNNRUTbSr7cPzv/Nnk266Py7pHOBT3U8OK3F8/nNJyRAztJrtm3rftH2TpMeVCNRP3t7p63kF8v2AD0t6LPCvti8om26KVr7/+8pt/GJgvu0Xls7Txf0W77O9OI98qEUTW3+GEEKrorM9OoslPcX2td1vSnoK9a1O2koH5oRB5yTV9lneVNI3SDernWPy600G/7Xxk7Q3MB/YGzgfOBHYsdK5es20ay62nS/pp6S9jP8duJY0uqUaFXb+hllryLnHjC3FzP0JuBf4A7Axda2Y39HE93+3RgqDt0raq3dRxLxo4m8LZeqnla0/QwihSTFne0TyHMKPA+9l8tY/7yDtvVrNXquS7geuI/2i3Swfk19vanvVQX93nCT90PZu+fhLtl/Rda6a+boAkvYYdr6mDk5+yvID4FWdp66Srq9tuDO0065dTzTnAusApwOn2r65aLA+JP1s0ClSzaCap1uSPg3cBbyrM3IojyB4N7Cu7deWzNeRO1TzgGcC5wIn265t9A0Aku4BLiRd793zMfn1braHFTjGqk9h8BTg47afVDJXP5KeDpwJ/JDJ9wC7AvvZ/kWpbN0kXUkagbMScF4+7nS6z7e9TZlkIYQwO0Rne4QkbUka+rxlfusXwAdt/7xcqqkkbTzsfL9hmyX0rEbeuzhWVauRDyJpQ2Ce7Wrml0p6BqljcCBwPXAy8G+2h34ualJbu0r6I+kp9sn5z0lftJUtOnUFKd9JwDfpGXlTy79/WFbE+BypE9tZfGwb0jSS19SytVIuYP2M1NEyU6//G0vk6qeVAha0VRgEkPRo0tP3Zdv/Af/db3h5KdNs/YntqkYMhRBCa2obejur5L1pX1k6x3QG3UxL2o30FOGw8SYaaFhlqNqqkaR1SB3Z+cB6wBllE01mu7Nq8tsl7ULKubKk7wBn2O630F9xlbfraaTP5N/mn24mPemugu1nSNqc1I4nkToEJwHn2P7L0L88Zrb/CMzPqzp3OjC/6F6EqhKvpuLvpG6DOtOdAhZQTWcb2I6U6dy8P/TJQG0Loy2TO9VfKJ1jmBpHBYQQwmwST7ZHpGsuaV+2XzSuLMtD0rakSvyBpFV0T7fdu0J5Efnm6i2k4W4fBI7snAI+YHuzUtl6SVqNtCLxAuCppM7VXNsbFA02Q5JWAvYiPS0+tHSejtbbtQWS5gLHkfYDrmKkQIekoVNFbF8+riyzUb8Clu0jh/+tMroKgy8BrqSywuA025TZ9upjjjRjkjYjfcfOs/306f77EEIIg0Vne0Qk/Q74DfAV4GJ6hmhVNjTvqaSblvnAnaR5cEfWNoxY0heHna9pQS9Ji4GfkLbO+aFt1zzcsZ/OyAbbtYxsaKZdJXW29lls+7SiYWZA0vqkJ4YvJm1Zdiqp81LFsOyOPIz4KtL3FEzdv3rP8aeaKn9XGbjX9hGl8wzTegGr4sLg14F1Se15Sk3TMfqRtB5pjYkFwFbA+0jF9qqmvYUQQmtiGPnorAs8l9SBXQB8C/hKLYui9PgVaR7cvravA5BU3Q3isM60pCeMM8sMvIPUefkk8BVJpxTOMyN9Rjb07rtbWivt2pnnuKhoihmQdAGwGqmDfQhpATKAVSStbfvuYuGmejPwUtK88pOpsCCQHZ//bGE7tTuYWsB6ceFMM5a301xMWvm9Grb3l7QGqZDxmTx/+xTSYnnV/JuS9FrSfcr6pO+AQ4Ezbb+7aLAQQpgl4sn2GEh6FOmX2QeBd9v+ROFIk0jan9SB2RU4m3QT+7naF0aRtCZpCOEC4Gm21yscaYo8t3Qe6fo/BTiK1EG4pmiwLq2MbOjWQru2Ii+Q1PlF0P0LoTPctapRAzDp+u8H3AQcndceCMtJ0uGktlyVNBLrFOC7NV73bv0Kg7X9bu3IT9/nAR8jfVaPKRxpGUlLgIXAWzor5tc4WiiEEFoVne0Ryp3sF5I6BE8CvgF8wfatJXMN0rVd0XxgT9J+y2fYPqdosC6S5pAyLgC2JT2R2x+40PaDJbNNJ69OP580RPPJpfN0dK3we2jXyIZmbrZqbdcwWnlrpXnAK4C32j61cKSmtVDAaq0w2DWvfHfSyvSn2P5B2VSTSXosE/P01yU93X6V7Q2LBgshhFkiOtsjIulE0pZf3yYNG7uqcKTlImkt0i/gubb3Kp0HQNJJpJuWc0hP388Drqv9CXztWh3ZEB4eLS061vNE+zekz+q3bC8e+hfDcqm1gNVSYTCPGLmHid9Vk1b2r+nfVYekDUjztueTRjqcYfudZVOFEELborM9Ivmm4I/5Zb+hmdWuRFqrvB/wSqQn7ifbvqXiG60bSNf9d7Z3Kp1nJhoZ2dBcu9aulUXHYNL+1WcCf2Dq/tXVDM8ND7+WCoOSvs/k6RnV/rvqJ48imGf7PaWzhBBCy6KzHZrqwHTtBzyX1Dn4W2BL2/9bNNgsU+PIhtlA0n7A7bYvLp2lI8/ZfSlwL3UvOoak/4/B+1e79o6BpKNJ7fw523dN99+PQ0vf/x0tFAZbIelZ+XCJ7R8XDRNCCLNQdLbHKN8gvJi0ndILS+dpnaTtSTdbBwG32N6lcKQQhsqdra2AR9p+fuk83VpfdEzSjrYvKZ1jmPxkdjNgG9sHT/ffh+nVWhhspRPbtaXmPbVvUxdCCC2KzvaISVqFtEjaAmAf0lZKp9v+ZtFgs4gkAbvbvrB0lha1+GQrjEZri45J2oKJBbPusb1D4UghANGJDSGEkERne0Qk7U26AdwbOJ+0aurHbT+pZK6WSTqK1Cm8L+Zmhpq18lQL2lt0TNKTmOhgPwBsDOxg+8ZyqSaL76rRiMJgCCGE1jyydIBZ7GzSqqm72b4BQNJHy0Zq3o35zyo7ASF0OST/eQ9QdWcbuI7Ji45tBLw+DRipa9ExSQuB1UkFgZfYvlbSDTV1tLMb85/xXfUwqnEhtBBCCGGY6GyPznakp0XnSrqedHP4iLKR2mb7hNIZ/lqSziU9jTvO9lml88wWtbWr7UOm/6+q8R4mFh37m5JBZuB/gfWBJwDrANcyeMG0YmbDd1UIIYQQ/noxjHwMJO1CGvL4EuBK0qqpnymbanqSrs6Hx9n+RNEws4Sk9YAnAjvbPq50ntmitnbN+9U+yfYP8+s3M9GRPamzR3BYfpLWAA4gfac+BVgT2Mf2T4oG6yJpN2BT2yfm118F1s6n32v7vGLhZqi2AlYYL0k7ALfZvq10lhBCaFl0tsdI0krA35P2rnx16TwzIemxpA7Mt0pnCaEVkr4C/HenkyLp/wGfAR4DbG77ZSXzdZP0sWHnbb9xXFmWl6THk3YjmA9sZHvDwpEAkPQ94J9t/zK//jnwKmBV4J22n1cw3ozUVsCaLVrpxEo6AdgauMb23NJ5QgihVdHZHhFJG+XDpbZvLRomjJ2k80nDW++2/dLSeR6KGkc2tNKuki63vV3X65/a3jYf/8D27uXSTSZpCXAVcCpwG6Du860MiZa0se2bSucAkHSJ7R27Xp9u+4B8/CPbu5ZLF0pqrRMraTXbi0rnCCGEVkVne0RypwDgrpo7BdD+Cq+S/gm4C/ia7b+UzgPpxj8fLrV9S9Ewf4XaRja00q6Sfml7i67Xa9u+Ox9fbftp5dJNlq/xgcBc4C+knRO+avueosH6yNspGbi35u2UJF1r+ykDzl1n+8njzjRIKwWsYWosDE6nlk5sPBgIIYTRis52aJ6kw4DNgY1tv6h0nm6SNgGenl/+0vb1JfOE8ZB0MfAK29f0vL85cKLtZ5ZJNlyeaz4PeDPwNttfKhxpEkl75MMlthcWDTOEpG8Cn+4tUknaF3i97ReWSTZVKwWs6VRYGGyiE9vSg4EQQmhRdLbDQJLWBA6z/R+ls7RG0urA54AdgCvy288ALgMOtf2HUtl6tTSyoSvrsre6Xtv2ZuNPNZWk5wEfA/4DuDy/vT3wTuBNtr9TKtsgkrYjzX1+Lulz+uHOnOOwfCQ9GfgWcBGTr/8uwL69RZgw+0QnNoQQAkRnOwCSNgT+L7Ae8HXgK6TtgF4BfMX2mwrGW0bSwflwse3TioaZhqTjSXvtvsf2g/k9kdr5ybYPHvy3wyD56VW3lUgLZB0JXG77JeNP1Z+kLYG3MjGy4RfAB2xfVS7VVJLeA7wQuJq0ReHZtUzHaJmkRwEvY/L1P8n2n8qlmqqVAha0VRgcRtJOti8unaMjLzR4GJM/q5+0/b/lUoUQwuwQne3QqcBfACwEnpd/rgCOsH17yWzdJB2VDxfZPqZomGlMM2dz4LkSJO3Z2YpI0ia2b+g6d4Dt08ul6y+v7P8K4F9In9Wja30KK+lvAP5/9s47StKqWt/PS0aSgglJCohIznAJioCKVwREkShguqiggpcgAnrlAoLgBZFgwMuAP6KSRRQJKgOCIAMKQxqiwFUUJEkU398f59RMdXVVT88MVed85X7WmtXfOV/3rHd1V1d/e5+93237mdJauiHpn8B9wLN5q/VHQaRga5UiwoaAJrSRNCmBNSxIetD2ktP/zP4jaQPgDGACqaIFUhXGrsBOtq8pJC0IgmAoiGB7wEhalGRE80JpLS0k3WJ71bb1Q6QxOv8sKGuGkDSf7b+X1tGiYQZJU52zu7hoj1iXRtKcwMeBvYGJwBG1zqzOxn1fIo17AngGONL2ieVUjaatZ7crtTh8N4m2NpI1SckgUWkbSYsmJLCamBjshqQ/VjSm7jqSj8Ckjv3VgO82uYIgCIKgBuYoLeBfkB8Cy0g61/Y+pcW0kPQapo38eQxYKJc903JRrgFJi5Fmv/7e9ou5/G0v0gzbN5XU1sG1kr4C/LfbMlqSDiZVENSEelx3W5fmPpJj9rHAg8AqkqaevNbysC3pIFJ/7sat00xJSwPfys7khxYV2MYwBNOSDgeeBE62/VhpPaR+/cnA9l3aSI4Hqmkj6ZLA2rrWBBZwNNBK/p3bdg1wEFDF7/84qOmUY8HOQBvA9s2SFighKAiCYJiIYHvA2N4sP3StMN1PHhwLkU5c2gOrlqmPgaUHrqgLkvYCDgSmAHNLOhE4EjiNdIJUE58DfgBMkdRukDYJ+GQxVd1xj+tu69JcTtK0av7XjqnnYfujwKrt/bm275X0EeAWoJpge0j6YH8LLAMcQx2B7Aa2d2vfyEm3QyTdXUZSTxqRwMo0JjGYHem7vX8K6CzdL4kkvcb23zo2Fya1FARBEASzQATbBcgPXbeV1tHC9ptLaxgn/wG8zfbjeazKXaSH2t9N5+sGTi4T3VbSMkxLrEy2fU9BWb1YWtJFpIfA1jV5/ZZyskbTGcBUjLsZYdl+LvdIV4Ptqn7GM4PtC0prmAGqCgppTgILmpUYPHom7w2aY4DLJO3DSOf8I/O9IAiCYBaInu0+0aTTIknvBRaw/eOO/Q8BT9n+RRllI+nSTzyi17wm8hilnti+aaz7g6RtdnFXbP9qUFqmh6TfAAe2+jY77l1he9MCskYh6QpSz+sVHfubAAfbflcZZc0mmyQaeKZmk0RJpwL30L2NZDnbHy0mrsFIegL4NSlhsVG+Jq83tP2aUtqaTJ7/3jk54SjbF5dTFQRBMBxEsB0g6RpSn95fOvZfC1xs+9/KKBuJpEdJo4labN++tv35gYvqQT69vBX4a2ur7bZtbzJ4Vd2RtGAvwyZJS9p+cNCaeiHpYVJv7k+BA2y/1HZvku3Vi4lrQ9KKwIWkHthW5cVawAbAVrarqWwZjwleLUZ5knbNl8/ZPqeomDHIBmk/IPUUt9pIViedHH7C9pOltHXSlAQWNC4xuBWwuO0T8vp64HX59n6dye0gCIJgOIlgu0/kMueeVBbA3Gh7rR73fl/L6J+2B+2u2D51UFqmR+4v/zApMDwLOL/i0U/tbuQjHq5rCbJaSLoJ2JBkQLUGsIPtO1v3KtM6D7AjbaOfgNMrnLP8HDBWH7GAhWoZVdSi9pFqAE1oI2lKAgsalxi8hmSQ98e8vhnYlDSd4JRakhiSjhvrfk1J7CAIgiYSPdv94xJSueOIE01SZvv1wOwlRPVgQUlz2P5H+2Z2qZ23kKZunE0qd+88gX8d8HQZSd2xfSxwbHag3h64QtIDpNLim8f+6oHT/hpdeIx7VWD7WeCTkrYBfiHpcNvfoTKttp9XmmH/aN6aXFugnVl+HJ/zct9VjBNJnwEOIAUtkvQ0dY5Umwt4B9OSLa+V9FBNYx8zf2ZaAut6SVMTWNTXB/1LsgN5l1P3CxjpTl6auVqBdmZidsp/TNJ8vb6oAJ8mVWGdAzxCZe+jQRAETSeC7T5he+X2taQ3A/sDmwGHF5A0FucB35e0p/Os6nxq9C3qMsc5DvgZozVtCLwH+MzAFU2H7EB9ISlp8VFgOaaVldZCk0yHpmL7vFyaOUHSvwPzl9bUotecZUnVzVlu0uivpoxUk7QCcBFwDdPaCDYGDpS0ZW3zq5uSwKJZicER/eO292xbvo56WBTYFtiO5Ep/NvBj208UVRUEQTAkRBl5n5H0VtK4qnWBbwKntpfp1YCkOUijiD4JPEB6aFmC1HN4cC16Jf3OdtcRX5Jus71it3slaDvR3gr4I6mU/BLbzxUV1gVJDwH/Q/q5752vyeu9bC9RSlsnkn5me/Mu+/uSzKjmKSBrFJImAPcDh3SZs7ys7RrGUzUOSXfSMVIt788L3GJ7uTLKRpIN8o7oNJeUtBmpP7oag7wuxpOLAROA54C3235rKW2ddLS8dOqurY3kdOCXtr/fsb87KVm0QxllvZG0OOnv1heB/W3/sLCkIAiCxhPBdp+QtBIpyF4R+AZwpu1qSjG7kR9Yl83LKbUFhpJut/32Gb1XgmyQ9nuSSdZTdJwQ1+SknB2ee2L7a4PSMixIurtXkDLWvWBsJN1hu2vZ+1j3Bs10dNb2XtWIBBY0LjH4elJp+wuMHKk1N8mQ9M+ltHUjT9DYAXg3qRrjm7VVYARBEDSRKCPvH7eQTjQvAdYB1kkHW4maTEdy6WAnb23ptV1LKfmjktax/dv2TUlrA3/p8TWlOIRpAXY15c3dGCuYzt/bahgSM5/ayl2bxMOSNu0xUu3/CmnqxmyS5u7sz86meVX93e0WaOf9o4CjBixnenwfWKDLNaS2jWqw/Siwfn5ttqquLunm+l4SSYcA7wduJ1VgHdDp3xIEQRDMPHGy3Sck7cYY/a6VOWf/k9RX2uol7hxT9fHBqxqNpHVIJi4TGDlOaReS6+v1haQNFbnfdIf874leTvUlkPQiY5j51PJ7pZiz3BeaMlIt95avB+zR6onPvh3HATfaPqScupEMSQILSWvbvqG0jk4kvYu2+dW2ryqpp5P89/8+4Nm81Xq/EunvfxXTSIIgCJpKBNt9QtKrsulMt3tvsX3foDX1QtLWpD6tZUkPsmfanlJWVXckvQH4LLBS3roNOD6fIlSDpK/ky2dqKhnvRQ4EWgH2S8BSwFq27y+najSSFqEBZj5q0JzlptGgkWp7AvsBryIFLs8AR9v+dlFhHTQlgdWNyhODi5HMPJ9nWmJoTZJZ5gdtP1xKWzuSlhrrfpMMFIMgCGokgu0+Ieklkuv411oGSW33qjJyaZHHkWxFCmQWIRn5/KqsqmaiaTPBn7N9TlEx00HSb4AFSSWEZ9m+W9J9tt9SWNqYNMHMpwlzloP+ImkBANtVjSds0ZQEVosGJQbPBy60PaFjfxfgQ7a3KiIsCIIgGChV9Y4NGfcCywDXSNqx4yS71p7N54EnSYZeSwHVGOMASPp9r1tUVu5W82lQF/4MLAa8gTSS5m4qHvkFo8x8LmXayVEVSJodmDcH1/dIWg9YLJ92Tao18KqdPFO722uz9R6w4IAldSUngt5se6LtpyV9MY9TBDijpsqhPPv5O8B32hJYkyVVl8DqSAx+qC0xeH9ZZV1ZwfYHOzdtnybpwBKCuiHpPtLv1F9sr1taTxAEwbARwXb/+LvtnSXtDPxa0oG2T8v3qgpksoHL9iQjt8uBb9m+sayqrvyT9L07A7iYNJommEVsby1pIWAb4L/yuLpXdzOjK02DzHyOBB4lTSKA9Jq9jZTAugnYv5CupnMF8EZSee7ZFZe4HgWc3rbeHfgeqaT8a8BOJUSNRe0JrEyTEoOzdduUNBsw+4C19KT2CqYgCIKmE2XkfaJjHuibgR8CD5Meuq6qqYy8bUzVRNKDS+eYqmoMciQtT3og/ACpV/MM4LJKA65GkkfWfIT0fV6ysnE6jTDzkTQJWLv1upQ0yfbqedb21bY3LKuwubQlhrYnJS/OJrU/PF5UWBtdZkBPsr16vr7a9kbl1I2kSwLrZzW/n7b9/HcA3gq8GnhvhYnBY0iTKPay/fe8Nx9wDPB8TX9XgyAIgv4RwXafaH+4yuvZgIOBXUnlpYsWE9dBW39xV2otiZa0HXACcGQeUxO8wkhaqqbTw6aY+Ui6xfaqbev32L4sX99se7Vy6oaD/J66Pcnh+/CajAglTba9Qtt64VYyoMI5241IYHWj8sTgnMDXgd2AB0jfzyWAU4Ev236xnLogCIJgUESw3SckHWr7oC776wH/1Wu2aU1k198P2P5RaS0tcs/r9sAHgb+RHHTPt/1MUWHjRNJngceAc2s5PZJ0CukB+0nbe5fWMzNI2hDYwfYepbVACqiAdTp7s/Op3PW2ly+jrPlIWp8UXG1EqsY52/bVZVWNRNL1wEdt39Wxvzxwmu11yigbTVMSWNOjtsRgC0nzkiZ9ANzTa0pJEARBMJxEsB2MIBs7vZf0MPseUsnrh8uqSkj6FbAAKcA+lxS0TqWmMtJeSNoDWB5YyvaWpfUASHpnvnzR9m+KipkBJK1OGgG1Lelk7rxaxipJ+iKwGfBp2w/mvaWAk4ArbR9dUl9TkXQ/8ASp3PlKknv2VGzfVEDWKCRtTjpxP4zUow9p7NOXgS/YvrSUtvFSWwILmpcYzCfve9A2Zxs4obZRlUEQBEH/iGC7TzTJORumBlw7Av8O/BbYAFi6pix8ftBuvWDbX7it7+nSAxcVDBRJyzFt7M9fSf26+9ge83SuBJI+TQqu5stbzwBH2D6pnKpmI+mXjHwPaJ/sYNubDFxUDyStRJqz3Qq0bgWOsn1rOVVjU3MCC5qVGJS0AclTZAIj52zvCuxk+5pC0saFpMtJo9VOsP2T0nqCIAiaSgTbfULSzYzhnF1TuZukh4AHSaduF+RRNdXPWa6ZPEsV0pztasrwm07uL70a+ERrfJKke2tOtNQ+Zzn416ZJCawmIek64DO2J3XsrwZ8t/YxW5LeBCwKrGf7hNJ6giAImkqM/uoTtldrc84+g7qds38MbA1sB7ws6ULqHacyAknLkE5itre94vQ+f4C0EhURYL2ytFyor5L0M1I5cXVz6yPZ0h8kvSNfvmj7uqJixqBh5c53kBJYW7QlsGrX3AQW7Ay0AWzf3ErA1YztR4BHqHMEXBAEQWOIk+0BUbtzdh5JtDEpOfDvwELAJ4Cf1mY+ljPu25GC7JVJjq/n2f5DUWHBwMgjdLYivV43AU4jGeVdVlRYRtJX8+XTNblkN50cxAI8UXMQ27By561JCawNgFYC6+SobJo1skni+rb/1rG/MHBtLSaJkq4iJYYer8WfJQiCYJiIYLuPNNU5O48saZmkvdf2awtLAkDSf5A0LUb6Xp4DXFjjQ6GkFYFlbF+U18eQEhgAx9di5DQWkg4HniQ9eD82vc8vhaTXkHpMt7O9aWk9QdBEak9gNY389+pTwD6MNMk7Evhf298tpa2dNjf6l20/VFRMEATBEBLBdp8YBudsSGNLbD83/c/sP5JeBH4D/KftG/Nelf26ki4Gvm772ryeTJqz/irgQ7a3LqlvPOQTr2WAVW3vMr3PD4JgOGhaAqvWxKCkLRhpkncbySTv4nKqgiAIgkESwXafaJJzdlPKyCQtQnoA3AF4IymRsZvtJYoK64KkG22v1ba+zvZ6+Xqi7Q3LqWsuku4jvVb/UrvBUBAEgyESgzOPpE8AC7fa2yQ9TDooELCv7e+U1BcEQdB0ItgOGllGJmlxUt/2DqTRSufb/nJZVdOQdKftt/W4d5ft5QatqRe5v9jAM9FfHAT/WkQCqz80pZVI0g3A5q2KAEmTbK8uaR7g57bfOfb/EARBEIzFbKUF/CshaRlJB0u6rbSWdmw/kP81ItAGsP2Q7W/m0+OtgOdLa+rgEUmjHlwlrUdyeK3N5hVMAAAgAElEQVSJ+4EHgMb8/JuKpK26vS6CWUPSWtk4sWokHS5p/1ylUwW232J76SYE2pK+Kukrkr5YWss4OII0Sq3Fe4FLgKuArxRR1B11lN7/CMD288C8ZSQFQRAMD3Gy3WfCOfuVoyljfwAkrUOaVzuBkeY4u5L6IH9bSNoMIWlJ2w+W1jEs5N7SlYE5bL+vtJ5hQdKpwCrAXba3K62nF1HuPGtI2jVfPmf7nKJipkNTWokkTbG9bJf92YApNbW8BUEQNJEItvtEk5yzm0JTxv60kPQGYA9GmuOcYPvP5VR1R9K/kV6rv7b9qKRVgC8BG9XYEx8E3ZC0gO2Ybf8vgKT5AWqd7tGUViJJJ5L8Wg7q2D8UeK3tT5dRFgRBMBxEsN0nmuScHfSP3PfWOjWYkkvzqkLSUcAWwM0krT8HPkmqwvhujZprp0lVGE1C0pL58mXbDxcVMwbhg9A/JH0GOIDk1SHgaeBI2ycWFdZBNh79ku3rO/bXA46wvXERYR3kkW8nA2sDt+Tt1YAbgE/WmswIgiBoCnOUFjDELEpyzv6mpJZz9pxlJc0YuTTzWdJp7K2l9TQJSXMAhwMfAx4kPRQukU/nD7T9Ukl9HbwfWN3283nkzx+BlWzfX1bW+JF0e748wfbxRcUkPpY/PgFEsP3KcWr++BhQ7eQEkg8CQBVjE4cFSQcB6wMb27437y0NfEvSwrYPLSpwJPsDZ0uaQJdWolKiOrH9d2CH/H1sVWFNtn1PQVlBEARDQ5xsD4DanbN7IWltYElgHdv7l9bTJLLz7ALA3q2yVkkLAkeT+g2/UFJfO5Jusr1G23qS7dVLapoZsunUerYvKa1lLCR9yPa5pXUEZZB0tO19SuuYHhUmsJB0J6nf/fmO/XmBW2opzW7RlFYiSXMBOzFS5xm2XyinKgiCYDiIYHvASFoO2N72IaW1BP1D0t3Acu74BZM0O3CH7beWUTYaSU8Av27bekf72vaWAxc1xEh60PaS0//MoJOcsHqD7bvzelumOSb/vLYgphtN+vnXlsCSdIft5Wf0XtAbSSsAFwHXAL/L22sCGwBb2p5cSlsQBMEwEGXkfaJXz6btu4CqAu1c2mzgySYYj3UiaS3gEds1jdRyZ6CdN1+WVFuGa6uO9TeLqBgHQzITWKUFNJijgWuBu/P668ClpIB7faAJZk6N+fnnkVBVBNqZhyVtavuK9k1JmwD/V0hTV3LPtknmYzW3PHwb+IztX7RvStoMOAF4VxFVQRAEQ0IE2/2jST2bE/LHF0uKmAU+B6ySHV5r6YWbLGkX26e1b0raGbijkKZeTAZe13mCkU88/lJGUneGxM2/tmRLk1gb2L1t/bTtz0Eap1RG0mgkLdzrFpUF2w1LYH0euDD/rFunsGuRTmE7k4al2S1/fLmkiHGwWGegDWD7cknfLiEoCIJgmIgy8mBoqGnsj6TFgPNIBkntD4XzAh+syUlZ0lnAibZ/3bG/EenEY8cyynoj6V209Rfavqqknk4k/YHuQbVI7QVzD1jSUCDpD7ZXbluv1DJvlHSr7ZXKqZtGWwDbLbB2TKWYefKEhx1pM/MCTo+pCTOHpLuAlTv7s/P3+Q81tTwFQRA0kQi2g/Zyt27Y9qaD1NOLpoz9aSeXN7Y7vF4x1ueXQNKNttfqca+aAAZGJDGeZ2R/YVVJDElLjXXf9gOD0jJMSLoFeK/tP3XsLwZcanuVMsqaT+0JrKA/ZIf39YA9Wu9Lkt4MHAfcGP4yQRAEs0aUkQcA3Zxx1wP2Ax4dsJaxaMrYn3bMtETGP0sKGYMFxrhX27i644GTbE9o35S0C3AilZSSRjDdN44CLpb0n8CkvLcGqZf7qGKqOshGiPO2ZhTn2cpz5duTaqnAgZ4JrG0lHUlFCSwASU/Tu2LEthccsKTGY/tQSXsCV0t6Fel7+QxwtO0oIw+CIJhF4mQ7GIGkdwIHA/MAh9m+tLCkRtKUE1gASZeQxtH8tGP/fcDnbb+vjLLRSLrT9ttm9N6gkfQJYGHbR+X1w6SkhoB9bX+npL4mI2lz4MtMO4W9FTiipvcqSUcDj9r+Rl7fR9I5D3BTTaMUJZ0PXNgjgfUh21UksAAkXQC8kfTeenYktV5ZJC0AUFMyKAiCoOlEsD1gKnXORtJ7gYOAF0hBdnUlhG0O713p7DkuScMeYN9Kchy+lpH95f8GbJEd9KtA0t3degglzQbcZXvZArJGIekGYPPs5jx1dnnug/y57XeWVRj0E0mTgLVt/6O1zj9/AVfb3rCswmk0JYHVQtJCwDbA9qTkxdnAWbYfLypsnEg6FXiWlOC8tbQeSN4HwL6MnLN9tO0/lFMVBEEwHEQZ+eCpzjk7BwavI5Vh/ibvrdG6b/umQtI62bfLnoFVgCWA2QcrZ0xWsP3Bzk3bp0k6sISgXti+W9LKJNOhVn/2r4DdKzQd+omk7wN72f47gKT5gGOAn475lYNFrUA78yMA289LmrfH1wTjIFdcfImRgcGRnZUZhZmtFWhn9odU5yxp/kKaejFbt82cwKrpPRUA208Cp+SgdXtSb/E8wP8UFTZ+jgeWBD5Kfl2URNJWpDaMrzNt7ONawHmS9rF9YTFxQRAEQ0CcbBeiMufsXzKtD67TQde2Nxm4qHEgaQPSafxrSKfxFxeWNJWmnMA2DUlzkh4KdwMeIL1WlyD183/ZdhXj6yRN6fYzzj//KeFGPXNI+hRp9Nd+wI15ey3gCOBk298rpa0dSbcD63S+x+dT2ettL19G2WgkHQPMT/cE1vO2P19SXyeS1gd2ADYCJpLKya8uq6q5ZNPBrWzf37H/ZlJ11qoFZAVBEAwNEWz3iSY6ZzcFSZuS+soNHN5tRmhpmvQA20TToXw63Apm77H9bEk9nUg6EXjc9kEd+4cCr7X96TLKmo2kycCGnSXDkhYBJtp+exllI5H0RWAz4NO2H8x7SwEnAVfaPrqkvnaaksACkHQ/8ARwFnAl0F49UFMVFpJOIb2vPml779J6eiHpNtsr9rg32fYKg9YUBEEwTESw3SfyOC2Ax2xX7Zwt6UzgENu3d7n3Q9sfLSBrFJLeDxwIPEk6yZ5YWFJPGvYA2xjTIUnbjHXf9nmD0jIWObFyMrA2cEveXg24Afhky6U6mDEk3d4roB7rXgkkfZpk5DYf6ff/aZKR20lFhfWg9gQWNKsKK5uNArxo+zdFxYxBPtn+QCsp1La/FHBxjNMLgiCYNSLYDpD0F5Jhyzdsn9Bxb5Lt1csoG4mkfwIPkYKXUS9c21sOXNR0aMIDLDTHdCi/Bm7O/2D0w/bHB6+qN5KWZuSc9XtK6mk6kq4H/sP2LR37qwLft71OGWW9qd3huSkJrKYhaYLt3UrrmB6Stga+ARzOSIPMLwH7276glLYgCIJhIILtPiLp9cAejDTyOdH2n8upGk12zn0f6dT1JWA323/N926yvcZYXz8o2k4KumL7V4PSMj0kLQi8wfbdeb0taewXJDfqql4DLXJPcct06HDbVZkO5QfD7UkJjAuBM21PKauqO5LmAnZi5O//GbZfKKeq2UjaEDgdOIWRgcGuwM41Vbs0xeG5SQmstokUL9q+rqiY6VDT387pkZNV/0lbYpD0Wr2l91cFQRAE4yGC7T6RzbvOACYwcs7yrsBOtq8pJG0U7Q8Fudfw86Rew59VdrL9Ntt39ri3QWXf0+8B17ZGf0maAlxKCrj/UVvPbtNMh3KZ9lbAdsAiwIGVJVtWAC4CrmHk7/8GwJa2J5fS1nQkvRH4LCMDgxNs/6mcqpF0ODy3G7kdAFTl8NywBNYp+fKJmvugASTdQXpPVbf7NfWXB0EQBP0jgu0+Iek64DO2J3XsrwZ81/a6ZZSNpjMDn7Pc/w/4JbBJL/OUQZNPYH4I7NHZ81rbKUKuFljD+ResPWkhaaLrmrN7Pw0xHWohaXZgc1KQsDKp3PHnZVVNQ9IVpP7cX3Tsb0ZKDLyrjLLhQGleeas9Y4orG1HXRIfn2hNYTSMbT95A92C7mv7yXC2ytO3T8vrHwML59qG2rywmLgiCYAiIOdv9Y8HOQBvA9s2tHr6KGPEwYPsWSWuTZm5WYzhEKsN8CLhJ0i4dZYRdTw8KModHZrLaTeZePWgx0+F+Ug/8e4H30FFGClTxUAggaRNSgL0OcDnwLds3jv1VRVism0u+7cslfbuEoGFA0hyk3tKPAQ+SjQfzieeBtl8qqa+NOToDbQDb92fzxBp5nmQ++RSwFMm7IZh5ptQSUE+HrwGfa1u/jWTsOR/J4C+C7SAIglkggu3+IUmvsf23js2FgdkKaerFmp0b+aRojzyqqBZesn2gpJ8Dp0s6lZR5/yfdR1eV5J+S3tgqbbV9K4CkxYB/FlXWge2Ne92rMDC4HPg9qdR9bmAXSbu0blY0Um02SXN39mfnE9l43515jgIWIJ3EPQ1T/RGOzv++UFBbO/+QtGQPh+d/9PiaIjQogRX0hwU72lrutv07AElfL6QpCIJgaIiHvv5xDHCZpH2AVhnumsCR+V5N7CWNeTBclUmW7V9LWpM0s/ZqSTuV1tSFo4CLJf0n0KpwWIMUEBxVTNU4UHoxbALsCGwBvKGsohF8nPoSK904DThX0h6tUWq5hPg4UitEMHNsASzXXjVi+ylJnwHuoJ5g+6vA5ZK6OjwXU9WdpiSwmsZ+pQWMkxGVVrbb3elreu8PgiBoJNGz3UckbUH6g9vuRnuU7YvLqRpNmxvtpcALjC4r/1oJXZ10M2uTtCtwGDCv7UXKKOuOpM1JZXgrkgLE20h9vJcWFdYDSeuRAuytST17ewAXdVZnBOND0p6k3/9XkX6nniE5/EYZ+Uwi6S7by83ovRI0xeFZ0m6MkcCyferg1MwcktYCHrH9SGktLSRdRe/vq21vOkg9vZB0MfAd25d07G9B8p15fxllQRAEw0EE20HroXAHkuHU74AzgStc2YtD0mdtn9hlf2lgv9ocvptCPn3bltQDeyZwPnCj7bcUFdaF3Jtr4Mna3Yhb1D5nuUlIugA4r2Xm1La/M/AR21uWUdYbSfMDdJo6Bq8cuaVoFeAu29uV1gOQq686WY+UgHvU9toDltQVScsClwDXMrIKb31gC9t3ldIWBEEwDESw3SckHTfW/VpL89pGQG1Gcni+qLCkRiLpq6Sg8JnaZlV3IulR4C7gWOBi2y9Iutf20oWljaJt1vqLtn9TVMx0aMqc5SaRPQ/OA55jZHn2vMAHbT9cSlsnkj5LKhufL289AxzZLWFYkiYmsHohaYEak1r5fetgkuncYbVVN0maG9iJke9VZ9Tm8h8EQdBEome7f3wauBU4B3iE+tyyRyHpdcDqpFFKDwGPllU0EklPdW61Xdv2goPUMx3uzx+fKylinCwKvJuUZDk2lz/OK2kO21WZOTVlFFHHnOVv5u21gPMkVTVnuUnkYHrdbOrVCgx+avuKgrJGIekg0sngxrbvzXtLA9+StLDtmownJ+SPL5YUMR4kLZkvX+6WWKkt0Jb0XuAgUnvWYbavKiypK9nI8X9L6wiCIBhG4mS7T0hahFSaux3JffZs4Me2nygqrAuSPg58hJR1/zFwju2qAm2YWkL6RtLJ1lmdTr/BrJNPOLYgBd4bkdoJdiyrqnk0cc5yE8kn3bPn5SO1JIck3Qms2nkyKGle4JaaesubRE4EAjxm+8NFxUwHSTcAryMZYo6qwrF906gvKoCk+0iVDX+xvW5pPUEQBMNGBNsDQNLipNEqXySVZlflRpwN0m4FHshbI14UNfVBSloI2Ib0/ZyHlMQ4y/bjRYUNIXmk0tad/bHB9JF0m+0Ve9ybbHuFQWsaBiQdAMxp+5C8foA0G3ou4FTbVYwqknSH7eVn9F4wPEj6JdP+lprRlVhNmMEdBEEQzCJRRt5nJK1BOiV8N8nt+3djf0UR3lVawHix/SRwSjbE2Z40SmkeKhtP1iTaxvw8Z/tHrX3bT5FGWAUzTmPmLDeMbUkVFy0et726pNmBX5HK9mvgYUmbdpa35/L3/yukqfHkBOAbbN+d19uS+vUBfm77z8XEdWB741738uSHIAiC4F+ACLb7hKRDgPcDtwNnAQfUUuLYSa8+WElLkALaavpk2wzcNiLNhf2g7avLqmo8LdfxqvodZ4TsqP4kcLLtx0rroVlzlhuF7b+3Lb+V917OJdq18HngQkkTGfnz3wDYqpiq5nM0yTX77rz+OimJPS+pR74pEynOAZac7mcFQRAEjSfKyPtELs2+D3g2b7W+0SKVkK1SRNh0yCZp25IC2jcB59vep6yqhKT7gSdIyYsr6TghrKUHbiyyQ/FjwLm1Jl+aiKStgWVIfbK7TO/zB0FT5iw3CUl3ASvafqljf27gVttvLaNsNJLmIc2tb//5n94Uh+cKE1hImgSs0RpLKWmS7dXz9UTbGxYVOE4k/dH2EqV1BEEQBP0ngu0+kctFe2L7gbHuD5I8B3gb0oPhciQDsu1sL15UWAfD0AMnaQ9geWCpmnrhg6AJ5ADwjcCetp/Ne/MBxwN/sn1ASX3DRKUJrD/YXrltvZLtW/P1rbZXKqdu/Eh60HacbAdBEPwLEMF2gKTngN+SRpRMtO1a5yz3QtKcnaddwfDRlPnlkjYElm6Zy0n6MbBwvn2o7SuLiWswuTf7MOCTTDN0XBL4AXBQLdUikj4BLGz7qLx+CFiQlCDc1/Z3SuprKtnl/722/9SxvxhwaU0VY5IupsNstHUL2MT2fF3uVYOky4GXgBNs/6S0niAIgqYSwXafaNI4DUl7kXqz5wPOJDl8/6L2YFuSgE1IJ/Jb2H5DYUlT6WU6FswaknbNl8/ZPqeomDGQdAXwOduT8/oPwG6k37Ev2968oLzGk/uzl83LKbarmmefxz5t3iq/bpU759Lyn9t+Z1mF02hKAgtA0s7AF0jtGZPy9hqkXu7japr0IWnMn3Evr5RakPQmYFFgPdsnlNYTBEHQVMIgrU/Yfsv0P6sObB8LHCtpaVLQfQHwJkn7k3q27yoqsIPs5LojsDXptHAPoIq+8jaGwXRsK1Jp7vWltbSwfWppDeNkwVagnbnb9u8AJNXimN04JL0jX75o+7qiYsZGHX3OPwKw/XxlRm4A9+ePVSUsumH7/0n6K3Ao03rhbwW+YvvScspGU3swPT1sPwI8Qp0TVIIgCBpDnGwHXZG0EskkbTvby07v8wdB7tfcFniQdAJ/PnBjkxIbkCoJcoKjavL3e2VgDtvvK60HmlOeLenuXmZdkqbU8jvVNCSdki+fsL13UTFj0OtnLGk20kl81VVDwawj6SpSxcDjtj9cWk8vmqIzCIKgqUSwHTQGSY8CdwHHAhfbfqFpveUQ5jizQlPKs3O/5ndsX9KxvwXwGdvvL6MsGASSTiQFLwd17B8KvNZ2NSOqmpLAaiHpfaQReq2T7duAI23/tJyq0bSZpL5s+6GiYsagKTqDIAiaSpSRB03qL18UeDfpxP3YnJGfV9IctRgjjRNN/1MGR4NKc6E55dl7A5dI+jDQGkm3JmkW8BbFVAWDYl/gZElTgNaot1WBG0nmbjXxNeBzbeu30ZbAIo1ZrAJJnwJ2B/YjfS8hzS8/QtLitr9XTFwHNU0cGYuxdEq6xvYGg9QTBEEwbMTJdtBI8lzdLUiB90bAFbZ3LKtqfNR2st2U0lxoVnl2fo3uxMgTuDOaMmc5mHWyD8bUOdu27ymppxuSbrC9dtv6PNvb5Ouqgi1Jk4ENbT/esb8IaZLG28soG05iHngQBMGsEyfbAybGabwy2H4BOBc4V9KCJLO0apD0NL3HvlRlkGT7Y6U1zAB3SHp/j/LsOwtp6kp+jf5vaR3B4JHUSqa9YPviomKmz6vbF61AO1PNhIeMOgNtANuPpeEUwStMnMYEQRDMIhFsD55dyOM0SgtpGr3Gadl+CjitjKru2F6gtIbx0vZ97Uqrl7MSGlGe3aDWjKFA0lrAI9lBuQZarvmPAbWbTjUmgQU8JWlV27e0b0palQZPfiiJpG163aKyxHAQBEETiTLyoDHkebAAT9c+D7YTSYsBs+flIzX1mEv6do9bWwKL2a4qKRfl2UEnkk4FVgHusr1daT1NQtKywCXAtXRJYNU0+jGbuZ0OnMK0kVRrAbsCO9ueWErbeMmv1WdJ1W23VqDnlLHuN6zyKQiCoDoi2O4TMU7jXxtJBwBz2j4krx8EngTmBE61XZOZ11SUajF3AvYHJgOH2f59WVWjkfQWRvbC3ltST1AHkhawHSecM0iTEliS3gh8lrbff1Lg+qdyqsaPpLWBJYF1bO9fWk8QBEHQXyLY7hPDME4j+stnHkk3ARvZ/nteT7K9uqTZgV/Z3rCswpFImoPkQLwPcB3wddu1lZCS+/NPJp283UwqdVyNdMr1idxSEAwpbb3QL9t+uKiYIaNJCSxJ8wAtM8QpNSYFmoKkxYE3t6oCJH0RmD/fPsP2lGLigiAIhoCqykOHiaaM/ZgO0V8+C7QC7cy38t7Lkqrqg5O0B/AF4Apgc9v3l1U0JseRTrK2t/1PmHoafzBwPOk1GwwvTeqFbgS9EliSqktg5aTg4cDHgAdJWpfIpdAH2n6ppL52siYDT1Y+5eEoUml+i92B7wGvIo2F26mEqCAIgmEhTrb7hKRPAAvbPiqvHwYWID0c7Gv7OyX1Bf1F0l3Aip0Pf7lc89Ze46tKIOmfwKPAXxjpPivAtlcpIqwL0xn91fNeMPxIWtf29aV1jIWkRUmtRS+U1tJC0gTgfuCQLgmsZW1Xk8CSdAzp7+jerXaBnCw4mmSc+YWS+tqR9M58+aLt3xQVMwaSbrK9Rtt6ku3V8/XVtjcqpy4IgqD5RLDdJyTdQDolfCyvW2XE8wA/t/3Osf+HwdH0/nJJWwF/qulBW9LhwBuBPW0/m/fmI52+/sn2ASX1tdPW8tCVmqo0mjRnuxvRmtE/aptf3438818GONf2PqX1QLMSWJLuBpZzx4NLbs+5ozKtC/aqCpC0pO0HB62pG5Im216hbb1wa7yapNtjdnkQBMGsEWXk/UOtQDvzIwDbz9dWRkzq1QV4uaSIWWBdYGVJc9h+X2kxmYOBw4AHJT1ALncEfpDv1cTytn/e7YakbYFqgm3gWklfAf67/YFb0sFAtadHbURrRv+oftCy7c3yqfEK0/3kOqjte+rOQDtvviyptpODXwJrAEi6wvambfcuaN2rgKclLddynW8LtJcnxqkFQRDMMnGy3Sd6nbJJmo1k6LJ0AVlj0iSDnKaQEyvtRj7PldTTDUkvA78mjc55uOPeiBLD0uSS0R+QHlRvzturk0YWfcL2k6W0BWVpwsl2jeRRVPfQPYG1nO2PFhPXgaQLgPNsn9axvzPwEdtbllE2mo5y7KnX3dYlkbQ5yQvjMEaOfvsy8AXbl5bSFgRBMAzEyXb/uEzSobYP6tg/BLishKBetBnkrMW0AKY6gxxJ78iXL9q+rqiYcSDp9cAetI3TkXSC7UcLyurG74EzgOsk7W37x233qjrZyq/FbSUtw7TTwf1s31NQ1iia3ppRK5IuZqSvwNRbwCIDltMTSfeRdP7F9rql9UyHz5ESWFMkjUpgFVPVnT2A8yR9nJFztucFPlhMVXfc47rbuhi2fyZpG2A/4PN5+zZgmxrmgAdBEDSdONnuE7k/92RgbeCWvL0acAPwSdvPlNLWSVMMcrK7K8ATlbu7ImkDUgA7gWkPhWsCuwI72b6mkLRRtE6vJS1HcqW9FdjD9rO1nWwDSJqL7jOBazKdavzovxppM53qiu1fDUrLsNGRwJpcWwKrHUmbMLIK64qSeroh6SHgf0iJoL3zNXm9l+0lSmnrhaT5AWp6PgmCIGg6EWz3GUlLM/KhoLoHmCYZ5DQFSdcBn7E9qWN/NeC7NZ12tQfUebTOoaRTol2Ak2oKtiWtAFwEXMPIJMYGwJa2J5fS1o6ky2y/p7SOoAxt88C7Uos5VosmJLC6IWkxYPa8fMT2P0rqaUfSV8e6b/trg9IyPSR9FvgSMF/eegY40vaJ5VQFQRAMB1FG3kfyA8w7mfYA81pJD9X+ANNBVWXEklYC9mXkQ+E3bf++nKquLNgZaAPYvlnSAiUEjcHUn3F+WP2SpJ8BZwKvK6aqO98mJTF+0b4paTPgBOBdRVSNprbv21CQJw8sbvuEvL6ead/r/TpaIEpyCalUuP390yStr2dagFicHgmsjYEDJVWTwAKQdAAwp+1D8ta1wJPAXKQZ7F8vpa2TmoLpsZB0ELA+sHHLpyUfEnwrO5MfWlRgEARBw4mT7T7RlBM4aI5BTn7QPpr0QHVj3l4LOADYx/aFpbR1Iul2YH3bf+vYXxi41vbyZZSNRtLWti/osv8aYHfbRxSQ1RVJd/T63tU0pkbSvUDP0U62zxugnKFB0jXA9rb/mNc3A5uSTuRO6XB8rgZJbwb2BzYDjrP97aKC2pB0BXBEjwTWgbZrSWAh6SZgI9t/z+vWSM3ZgV/Z3rCswmnkMZ/bAX8DLiYlid/BtL+1fy0obyqS7gRWtf18x/68wC22lyujLAiCYDiIk+3+0ZQTOOhukLMaMAn4ZDFVozkEeLft+9v2fi/pSuDC/K8WjiGZ5O3DSIfXI/O9mvh1TgJ043sDVTJ9ZpM0d2d1SH6wren9bCFgC7pXhhiIYHvmmKsVaGcm5hGLj2WfjKqQ9FbgQNJ4wm8Cn7f9UllVo1is8+8UgO3LJVWTFGjRCrQz38p7L1c4UvM04CVSIug/SV4YxwMbkrw8tiimbCTuDLTz5nOS/llCUBAEwTBR08PpsNGYB5geDs819pfP0RFoA2D7fklzFtDTE9vfk/QI8N+MLHk/1PbF5ZR15a/AQ0Cr37Gz9LWmMXWnAedK2sP2AzD11PA44IcFdXXygO2PlxYxhLymfWF7z7ZlNaX7ud3lQNLv/jdIUx1eLquqJ01JYAHML2nOVsLC9gonSLoAACAASURBVAQASXMDC5YU1oUVbK+UfTAest0y9/uZpFvG+sIB87CkTTtN5rIJ3f8V0hQEQTA01PaHdJhozAOMpHYDrNac5YVa+7ZvGv1VRfiHpCU7zYWy83M1xjgtbP8E+ElpHePgOFKlxTWkPu2J7e0ENWH7UEl7AldLehUpMfAMcHRNpblU5nUwRFwv6VO2v9++KWl34LeFNHXjFuCPpN7tdYB10oCHhO3P9/i6EjQlgQXwY+C7kva0/SxMnfxxfL5XEy9C8sHIidd2akq8fB64UNJERo5T2wDYqpiqIAiCISF6tvtENh1ZjzRCqfMB5sY2g5fi5FKxW0knnNBxsml7k8GrGo2krUmnRIcz8qHgS8D+3fqOSyLpfSRt7SfbR9r+aTlV3cmj3jYGdiAFB5eRnMjvK6lrLFpGc7afLq2lE0kr2r6ttI5hQ2l2/QXAC4xsz5gb2Nr2n0tpa0fSbowxS9n2qYNTM31yAms/oOYEFrk3+zBSe9MDeXtJUhvUQZW5kT8KnEX6fm6Xr8nrj9h+QyltneRDgB1pm5wCnN6tvDwIgiCYMSLY7iMNeoDZC/gwydX1LOD8WudsSlqV1P/W/lBwtO2ayvKQ9Clgd9LPv93M7QjgZNu19UIDIOnVwPak8vcvd54glkbS4sCbbU/M6y8C8+fbZ9ieUkxcG5LuY2Swpba1bS8zeFXDQ8ec5dtsX1lSTyeSZrPdtd9V0qttPzFoTeOh5gRWO7k/e9m8nGL7uZJ6uiFp17HuV5hweQsjx5TeW1JPEATBsBDB9gBo0APM0qRAayvSqcHhtm8e+6uCbkiaDGxo+/GO/UVIZdpVuGbD1DLMrUinL68jmXedU9ssYABJZ5JOXH6S13eSTNxeBSxve6eS+lrkn3M7swEfITmU32T7Q4NXNTxIehcjg+2rSurpJLtmf8b29R37nyQlsarxQWhKAgtA0jvy5Yu2rysqZkiQtCBwMqlC5GZSYnA1UvXYJ7KnSxAEQTCTVNU7PGx0zoSWdBvpFPYPRYX1wPa9ki4E5gU+CixH+uNbBZIuGuu+7S0HpWUcqDPQBrD9WHvvZiU8CtxNqmq4m3QCu5aktaC6MVVvawXamWdtfxNA0tWFNI0iO2QjaTbS79K+pN+l99c09q9pSFqMlAx6nmmtJNtKOhL4oO2He37xYPk88D1JvyWN/FoKOJFkRPiOsb6wAEcBp7etd2daAutrQBUJrMzH8scngOqD7Xy6/QXgbXnrdtLot9PKqRrFcaQKse1b1Ri5rehgUi/8LgW1BUEQNJ4ItvtEx0zob+bttYDzJNU2E7r9RPuPpKDr8ApL8/6NpO9M4HrqNqF6StKqneXtuQy+tgqHH5EC7Lcx7aGwRW1jqubpWLfPVX7tIIWMRXbH/ziwNzCR1E9czQlhgzme5CUwoX1T0i6kYLYKQyfbEyWtCfwXaa7yM6RTwsuKCutOIxJYALY/Nv3PqoMcaO8FfJHkLyBgDeAoSbZdi/ncBrZ3a9/IBpmHSLq7jKQgCILhIcrI+0Qe7bFV56iqbJJ2oe1VC8jqSjZI+z1pTvVTdBj72P6fEro6yeY47yaZeK1Ccvo9s0YjKkkbkk6LTmGkmduuwM6tks3akfSGWkynACRdD3zU9l0d+8sDp9lep4yykUhqjVI7FhhVjl9ZtUBjkHSn7c6E0HTvlUDS9iQzr7NJ71u/B/btVvFSEkmTba/Qtl64pVHS7TW1vDQJSdeRTovv79h/M3CW7fUKyBqFpLttv7XHvSm2l+12LwiCIBgfcbLdPxozExo4hGkB9vxjfWJJ8pzan5HmlM5NCrp/Kelrto8vq24k+WRrXeCzwG55ezKwnu0/FRM2DrJJ2odI7rRvB95UVtEIvgr8RNJhjHSj/jKpXLMWLif9Tq2a/7VTW7VAk5it22Yu1599wFp6IulyUqn7Zrbvy9Mp9gBukHRkZQaJT0tarpXAagu0l6e+KpwmseAYzwA1zQS/VtJXgP9uH/ko6WDgN+VkBUEQDAdxst0n8sn2B3rMhL7Y9ipllDWbHGS/nxRovxm4CPjfino1G0l2992KFGCvDiwAbA38upercimyF8J+TDPIuhU4yvat5VSNH0kfsn1uaR1NRNIxpITgXrb/nvfmA44Bnq9lfrWkD9o+v8v+G0m+HTsXkNUVSZuT+na7JrBsX1pKW5OR9Dvba87ovUGTA/8fkErcWx4tq5NeC5+w/WQpbUEQBMNABNt9okkzoXNWG+CZWkrGuyHpNGAl4KekMrxqg6vcs7+47RPy+nqS0zekn/+PionrQNIZwEak2dpnAVeSxum8paiwIUXSg7aXLK2jieSqoK+TqkUeIPXBLgGcSnL5frGcuvEh6Wzb25XW0c4QJLDWAh6x/UhpLS0kPQt082kQsLTt+QYsaUwkLQO02gkm276npJ4gCIJhIYLtPtKgmdCteaDP2T6nqJgxyL3lf8/LUTOMbVdTmifpGlK/3h/z+maSmdd8wCm2Nx3r6wdJ1jYbcBopifGQpHtrGk/UQtIppJ/9k7b3Lq1nZpD0R9tLlNbRZDrmLN9j+9mSemaESLa88kg6leTjcVctiYxcxdYT2w8MSstYZC+UeW0/k9frAXPl25NqH1kaBEFQO9Gz3UdyUF392Azbp5bWMB5sd+3XrJS5WoF2ZmIeB/VYLnutBtur5f7MHYDLJf0VWKA2c7TMhPyx+hPMMYgM5ywg6fWk/uepc7YlnWD70YKyGsswJLBs7wogaYHSWlr0CqazeeYOpNdwDRxJGv/4jbw+A7iNNPnhJtLouiAIgmAmiZPtPpH/oC7dmqcp6cfAwvn2obavLCYu6DtjubhKusf2MoPWNF7yyKIdgI8AD9lev7CkxiHpD3QPqgUsZ3vuAUsaCiRtQAoGJjCtPWdNksv/TravKSRtBJLW6HUL+IntRQepZywkvTNfvmi7akMsSa2KgJeb5NMhaXWSH8a2wH3Aeba/XVZVQtIkYG3b/2itba+eZ21fbXvDsgqDIAiaTQTbfULSFcDnbE/O6z+Q+gznI/UWbl5QXiORdB8pgPmL7XVL6xkLSacDv7T9/Y793YGNbe9QRtn4yQ9bG9n+dWktTaMpJaRNI49T+oztSR37qwHfreV9QdJVY923/a5BaRkm2r6vj9n+cFEx00HScqSk5Q7AX0kj4PaxPeZ7w6CRdEv7KFJJ73GeBy/pZturlVMXBEHQfCLY7hOSbrC9dtv6PNvb5OtrbG9QTl3Qb3Kp6wXAC4x0+J0b2Lqm8mxJXyUlMao2yAuCzpnQ471XE5LmtP1SaR3DhqR1bV9fWkeL7DFyNcnRe0req84LQ9LtwDqdvdmSFgKut718GWVBEATDQZN6YJvGq9sXrUA784YBa5kpJH1W0naSord/BrH9aC6//m/g/vzvENv/VlOgnbmf5Oz8UGEdQ4Ok+yTdm13og1cOSXpNl82FqfjvmRKbSvoB8XvWL6qZ8JDZBvg/4CpJ35e0KamNoDa+D5zdVqLfqsw5Ezi5mKogCIIhIYKo/nGHpPfbvqR9U9IWwJ2FNM0oAjYEdgK2LKylkeTe/Kr785tikDcWkg4HngROzkZ0RYmxaX3jGOAySfswsmLkyHyvKrKz846kmfULk0yx9ikqanipKpDN4z0vyIaYWwF7Aa+XdBJwfqtUuzS2/yePKZvYZt75DHCE7ZMKSguCIBgKooy8T0haFrgEuJaRD4XrA1vYvquUtqD/NKm/fBjIc+2XAVa1Xf0EgGDmyQnL9pnQt5FmQl9cTtVIcvJnW+BB0gnh+cCNTUrC1JbAmh5NGKmWqzK2Bbarafxji5abe4z7CoIgeOWIYLuPSJqbdCrc/lB4hu3ny6kajaRWcPKc7dpK8YIgCBqFpEeBu4BjgYttv1Bjv+5Y1JjAknQxvV3+N7Fd1VjFJhB//4MgCPpLlJH3EdsvAP9bWsc4aJ22NDKbnQ1eAE6wfXxRMUFfCTO3f20krQgsY/uivD4GWCjfPt72TT2/eLAsCryb5ER9bHbRnlfSHK0RS7WTy6Br4+iZvDdwGlTd1Oi//0EQBLUTJ9t9okF/aIcCSYsA63X2yAczj6TPAo8B59YSIEjaNV8+Z/ucomKCgZNPNr9u+9q8ngwcDLwK+JDtrUvq60aucNqCFHhvBFxhe8eyqqYRCawgCIIg6B8RbAdNOi0KBoikPYDlgaVsV2+QJ+lo21WbT0m6HHiJVIXxk9J6moakG22v1ba+zvZ6+Xqi7Q3LqZs+khYkjf47rbSWFk1KYEnaCljc9gl5fT3wunx7P9s/LiYuCIIgCLoQwXbQmNOiqBYIxqIhBklvIpUYr9cKGILxI+lO22/rce8u28sNWlM3hqUPtrYElqRrgO1t/zGvbwY2BeYDTqnRdCwIgiD41yZ6tgOARVuBduYp2+cCSNq9kKZRNMnJtxc19pcPS2BAZaN/umH7EeAR4HeltTSURySta3vE/PI8YuuRQpq6MSx9sB+hrlFlc7UC7czE7JT+WNvYqiAIgiCohgi2A4AF2hetsszM6wesZdxIWgyYPS8fqaWveCxsv73VX15aSxutwOCZoirGgaSFe92iomA7G2IZeNz2h0vrGSL2B86WNIGRIxV3BbYrJaoT218rreEVoprfqcxr2he292xbvo7gFSOX7P+pM7EVBEEQzBgRbAfQkNMiSQcAc9o+JG/9BngCmAs4Ffh6KW0zQj6JqcbIrWGBwe9IQWy3IODFAWsZi93yx5dLihg2bP82vy/twbTv8W2ksvw/FxPWYJqSwMpcL+lTtr/fvpkrsH5bSNMMUWN1Uw/WBVbODvrvKy0mCIKgqUTP9oCp0SBJ0jrA2cAEupwW2a7iIUbSTcBGtv+e15Nsry5pduBXNZkjNa2/XNL7gAOAFfLWbcCRtn9aTlUQBP2m7b2qW2DtmmaDS3o9cAHwAiP/Vs1NMp5rRMIlpmcEQRD86xAn24NnF7JBUmkhLZp0WtQKtDPfynsvS5q3kKSuNKm/XNKngN2B/YAb8/ZawBGSFrf9vWLiOsiJlXltP5PX65EqGwAm2a6iR7YtgJm61ba27WUGr6r5RHn+K0+T3qtsPwqsL2kTYMW8fYntKwvKmmFqqm6S9I58+aLt64qKCYIgGELiZDuYiqR5gGXzcort50vq6UTSXcCKtl/q2J8buNX2W8soG42kTVoPgJLeYvu+tnvb2D6vnLqRZPf5DW0/3rG/CMmA6O1llI1G0tHAo7a/kdf3AbcC8wA32d6/pL4W+XvXzmxMM5u6yfaHBq+q+UhaKl++bPuhomJmghr7YJuSwGpH0ruYFmzfZvuqknq60ZTqJkmn5MsnbO9dVEwQBMEQEsF2n2jSCYykOYDDgY8BD5JO4ZYATgEO7AxuSyHpcOCNwJ62n8178wHHkx5gDyiprx1JN9leo/O627o0km7vFVCPda8EkiYBa7fM8NpaCQRcXVMrAYCk2YCPAvsCNwOH255cVlVQivwetjJQTR9sUxJYMNUU8zzgeaY5+q8JzAt80PbDpbQFQRAEQTeijLx/7JY/NsEg6SiSI/nSrVMMSQsCR+d/XyiorZ2DgcOAByU9wLSkwA/yvZpQj+tu69I8JWlV27e0b0palfpGF83W4Tq/P6S6bEnzF9I0CklzAh8H9gYmkvpJp5RVFZTG9pdLa+jCpsDabesnbH+glcAqpKkXxwMn2Z7QvpnHF54IbFVCVDckXQKcAVzQ0f5UFW2jH7ti+7RBaQmCIBhG4mS7AJKusb1BaR0tJN0NLOeOF0MuL7yjpvJsgNyf3V7u/lxJPd1o2Mn2hsDppEqG1mnRWiSDvJ1tTyylrZPs5LtOZ2mrpIWA620vX0bZSCQ9BPwDOJZULTKCmtoIgleeJvXBSrrF9qpt6/fYvixf32x7tXLqRiLpTttvm9F7JcgtA9sDmwFXAWeS+strmpqApG/3uLUlsJjtOJQJgiCYBeJNtAxLlhbQgTsD7bz5sqSqsjHZjXYP2vr1JJ2QjXNqYmlJF5FOsVvX5HVVhkS2J0paF/gs0yoyJpMM8v5UTFh3vk+as/xp2w/C1D7ek4CTiyobyeWkNpJV8792TCqFDYaXj+WPTwBVB9vAXJIWaCWw2gLthUil5DUxW7fN3K4x+4C1jIntC4ELJb0K+ADJHPUkSZcCZ9j+RVGBGdufa13naoadSBVD15EqyYIgCIJZIE62CyDpQdvVBNySLgDO6ywXk7Qz8BHbW5ZRNhJJG5DK8iYwsl9vV2An29cUkjYKSe8c677tXw1Ky7Ah6dPAl4H5SMmLp4EjbJ9UVFhQDEmnAs+SRireWlpPk5D0RdLpa7cE1pW2jy6prx1JxwDzA/+fvTuPt32s+z/+ekdJMhdlDBnDMfNDJSpuEZnOOSRKt7rTnUQpEak00GRootBgLEPuJpGKMs/HIQcHnYQQERmO9++P61r22uustfc+h7Wu67t9no+Hx1nf73efx+Ntnz2sa/p8PtrWAnI+4OvAf2x/pGS+0UhaEzgZWNN2NZMDuW7LnqQCjpcBX7T9l6KhQghhnIjBdp9I2qHXI+A7tl89yDwjaSs68wTDtxFXVXRG0mXA/9i+tuP+WsB3a6742k7SJpVNDNzQ6xFp18Oag8wzVpLmB6i0WvIxwEFdtruvAhxr+61lko1PktYn7RjaoIaCXpK2BW6wfVe+/gywI3AXsG97d4IaNGUCK9dC+CJpYNhet+Nk0vdbVVu0ASQtTupEMInU9vMM4NTOGhmlSNqHVJflQuDLtu8smyiEEMaXGGz3SVs7ja5sv3ek5yV09C6davvCknk6SZpqe7XZfVZCPu++C7Ak8GvbUyRtQ3pDO6/ttYsGbCPpOtLW5lOA80iTLs9pDRhqIWl1UnXv544SAEfZvrFcquEkfZpUIO0Q26fkraSHAe8CPmH77JL5Qn/lCayNbD+ev++/BkwG1gZ2tr1l0YA91DyB1a6jbsftre4UNZH036R/85WBnwGn2f5z2VSzkvQscD/wD9LvgeceUfFkawghNEWc2e6TGgfTY2CGftk+WzJID5K0sO1/dtxchB5n+Qr6PmnF5QrgaEn3kHYLfNL2OUWTdbC9Vl5xnUwacE/Nf57fUfm7uFx06CjS6tZX8+31gLMkHZDPSRZn+wuSTgWOyauGS5BWtCbUODBoijyJaeCRynsCu+3feQfg+7avBq6W9KGCubrqnMCSVN0EVkuD6nb8P9LPqQtt1/j7tKWqGiIhhDDexMp2n0haCnhdq5JzPhfXak10Sk1tgJrSu1TS3sB/k86VXZNvrwt8GfiB7e+WytZJ0hTSubxnJb0cuBdYwfaDhaONStJE4DjSlsIjS+dpJ+l6YLvOrY6SXgec215VubSc6ThgYdL20c/Y/lHJTE3XVgvhKduXFg0zgryyvTHpHPl0YEfbV+Vnte3CaZ/AuirfXg/4FFDNBBY0rm6HgDcBD9m+UdIu+fp24Fu2nywaMJO0TOusfpdnb7RdW/u3EEJolBhs90le1fqJ7f/L138Bvge8AljF9m4l87WTdDZpoHJSx/33kN4k1tS7dBvgEwzfQnyk7fPKpZpV7e2+OuUJl0mkbc7/JK3Cnm37saLBOki6yfYbejyrZhAj6RDSAODTtk/Pn99vAq8m1R2YWjRg6CtJ7yMdGfkXcL/trfL9tUkrxluUzNeuYRNYjanbIek4YE1SRfe/kCbbfw1sAryklvcAku4AvgN81fbMfG9x0s6hVWyvVzJfCCE0XQy2+6TLYOva1jldSRfbfmO5dMM1qXdpU0h6HGjtXhCwQr6u7hycpD8A85MG2D8Dhq2+236oRK5u8sBg286VmFw9+bxaPq+Svgkc3KVA2n8BX7O9aplkzSbpIoafK23nygaxSwKLAde3thFLei3w0l4riSU0ZQILGle3Y6rt1fLOpr8Bizm10xSpeN4ahSMCIGlh4EuknRj7AmsAHwO+Any78i3wIYRQvTiz3T+d/Unb3wS+apBBxqAxvUvzYOWTDF/Z/rLtX5ZL1VWTBlPLkgYwHwD2bruvfH/5EqF6OBS4QNIRDK+c/0lSb9gq2N63x/1f5VW4MGcO6HJvI9Jul2rO7Ep6t+0fA3/LW5//BGD775I+DBxbNOBwz3TbSpwnsKqq2UCz6nb8B8D2fyTd1Vo1tm1JT5eNNiR/Lj8gaV/gAuAeUnG/GWWThRDC+BCD7f55VNJKtm+FodXBXIiqtkqv/yfpeLr3Lq1mEJuru36A9Ma6/WzhlyQtZft7xcJ16FXBW9KmpEJk+ww2UW+2X1c6w1jZPkfSdGB/4H/z7amkfvBVtNIZg/8hnY8NsykXGQOeO799CGli84O2f1Us2Kw+Bvw4vz4GaD9C8j7qGmw3YgIr+zpwvqRudTu+XixVd4vlWi1qe02+rqn150Kkz9+GwFbA1sCvJO1r+3dFw4UQwjgQ28j7RNJWwNHAFxj+puAgUp/Vat4YqiG9SyVNBTbt3NYsaVHgklq35uZzmrsCO5OKJZ1l+5iyqYZIWia/nFlLMbyxkPRKgNrOlY9G0l9tL106R1NJ2hI4GHgS+ILtiwpHmkXHsaHnXne7roGkCaQJrOdaP5LOllc3gdWguh2HjvTc9mcHlWUk+cz2t4BvtLpP5N033wLusj25ZL4QQmi6GGz3UW6n0vmm4Cu2p5RL1Zsq710q6eZeA+qRnpUgaSXSCvZk4AHgdFJl32WLBusin4MFeND2TkXDjEFunfRJYL586zHSUYJvlUs1dpLutr3M6B8ZOkm6krQqeCQwSzVy29fM8pcKaK/Z0aRiiU2dwApzLu8K67plXNL7bZ8w6EwhhDCexGB7AGp/AyNpAWBx29Py9c6ktl8Av7F9X7FwbSRdDuzdudqSV2WOt71BmWSzkvQscDGwl3ObN0l32K7p/HPjSDqYVMjnw7bvyPeWJ1X6vtz250vma5H0KN0LeQl4he2qaiE0haTfM/R5Nenz2WLbmw88VBdtBRLbiyOSr5e3PV+vv1tCUyawJL2B1ELx5/n668CC+fGxtUy2AOTCaBNJ3R3OI/Uxb7X++pztBwrGG5OYGAwhhOcvBtt91KA3MN8D/txq/SXpNuBXpAH3M7Y/WDDec/J5558AJzL8bOEewLude5rXQNL2pFZam5DavZwGnGB7uaLBGi630Jtg+z8d9+clVX1eqUyyEIbk4mI99arpUEJTJrAAJJ0HfNH2n/P1VNK5/VeQ2lRuXzJfO0lnAE+Tfv8vDEwhDbo3BdayvU3BeGMSR15CCOH5i8F2nzTsDcy1wDrOXwwd5w0vsb1p0YBtJL0G+BDDzxYeZ/vecql6y4XmtiNtJ98c+CGpf/X5RYM1lKRbbK8yu89qkL8W3gVMtv2O0nmaSNL+pNZp7ri/KOmIzl5lko1Nq0Ci7WoKJDZpAkvSVW7r+yzpMtsb5de1/a6aYnt1SXMDM2y/pu3Z9a6of3kvsbIdQgjPX1Qj75/d6XgDY/sOSbsA1wPVDLaBuTvevO7e9nqhQYcZSR5Uf6Z0jrHK1d1PAU7J/Ux3JlX4jcH2nPmbpC1sX9h+U9LmwN8LZepJ0suAd5AK5G1J6mP+naKhmm1l4FpJ+9j+Ezy3g+hA6qtGDXQvkFg20SzcOdDON5/Ix2FqMn/7RWugnS024CyjeQrA9jOS7ul4NrNAnq7aqqTP8gh45SCzhBDCeBSD7f5p0huYZyW9prU63CrgJmlJoJqskrYDlrJ9XL6+nKEWKgfaPrNYuDHI/Uy/l/+rnqTXAg/ZfrJ0ljYfAc6VdAnDjxJsQtpBUAVJbyftZng7cBFpR8P6tt9bNFjD2d5b0sbAcZKmAKsA00h9gauZbOlRIFG231I0WHdNmsC6R9KGti9vvylpI1J/6JosJelo0qC19Zp8vWS5WLOYf4Rn3xxYihBCGKdisN0/TXoDcyRwXt6ieW2+tw5wVH5Wi0+QzkG3zAOsTzoTdyJQzWA794I28A/bG5bOM4d+BKwg6We2DygdBsD2TbnK/64MHSX4I/CBbpNbBf2aVCBvU9vTASTFG9cXxhTgClJP4JcA+9c00M5uIf37b9NWIHG/spF6asQEVnYgcLqkkxjeUnMPUjGymny87fVVHc86r4sZqQWZpPUHmSWEEMajGGz3T2PewNj+saQHSFvb30AaJN4EfMYV9QMHXmb7r23Xl9h+EHgwn4etxngohGb7rZIErFY6S7s8qP5B6RyjWIc0MXRB7mN7GhAVyJ8nSbsDnwW+S6ryPYG0yn0rqbXe/SXztdmB9O9/kaRWgUSN/FfKaNAEFravyKvY+wB75ts3kXY2VNE1o8X2yd3u5yrl2w44zphJWo2hXRkPk963hBBCmENRIK2P8i/V9jcwU4Gf1PYGpikk3Wb79T2e3W57hUFnCoMlaS9gEdtH5usZwAKkgczHbVd3Hjpve54M7Eiq13C27UYcJaiNpHOBj7RX884TQh8k/ftX1VovCiQGAElzkWo2tI6WXGx7p7Kphkh6HUMD7KeBZYH1bN9ZLlUIIYwPMdjuM0nL0TbYblUmr4mkQ0mr2Y/Z/lrpPL1I+gnwe9vHd9z/ALCZ7cllkjVbk7a8S7oS2CrvaHiucn6e2PqN7TeXTdibpJcAbwUm2X5f6TzjjaRdbJ9ROkcvbQUSJ9reonSeliZNYEm6iPSz6qGaBqu9SHozacJ9a9LRh01IfdYfLxqsjaRLSf/epwGn2Z4mafp42J0VQgg1iG3kfSJpAeAE0nmy60hvXNaSdDWwl+1/lczX4c785xMlQ4zBfsA5knZl+Hm9eYBq+qs2TcPeVKk10M7OhLS1PLcqqoKkVrucmbb/BmD7WVIV+ljV7I+jgGoH2xUXSPwg6fx7yz9sL9WawKKu6vl75j+rqebdS560uBv4NumIw6N5EFvNQDu7j1SwbXFSwdFppAmNEEIIL4AYbPfP0aRt45Pym+zWdsdDgGOB9xTMPl4XTQAAIABJREFUNkyvs2W1yecxN85F5lq7BX5h+3cFYzVe3uI4r+3H8vVGwMvy42ttP1os3KyGtaKzfQQ8t2r8qiKJumt9Tz0IVL8CN05Ucya6SbtFaMgEFkD78YEG+ClpEngiMDMfgahuEGt7e0kLkuoMHCZpRWAhSRvYvqJwvBBCaLzYRt4nkqbZXnF2n4XxTdLN+eVxto8tGiaTdBRwv+2v5OvppIrPLweusX1gyXztJH2LtIX04I77nwdeZfuDZZKF0iTdbXuZ0T8ytOtVCyNPYN1W2zn4JskT7JuRzkJvDSwI7AX8sjW5WRtJiwG7kDIvY3vpwpFCCKHRYrDdJ6MMtnsW+gq9NWy1qCdJi5Kq5/6idBZI555JPaCfaV3nc9AiFfLZtGzCIbng1Amklm/X59sTSK103l/rG9jwwpB0I91XBwWsZHueAUdqvJjAGgxJL2WoSNqWtmvaidOVpGUbtpsghBCqE4PtPpF0MnA78Dm3fZIlHUJ6U7h7sXAhtJF0ve0Jbddvb1VLlnSd7bXKpetO0vIMLzx4e8k8YTAkLTvS8xgYzL6YwBo8SfParqJGiqQTSRNYj9iutRd8CCE0Vgy2+yQXSPs+qd/udfn22qTCXnvZfqRUtrGS9CHSmdOftVY9w9g0aRU+b23foPNsdj7Hd7ntVcokm1W3wmMhSNoUmGx7n9JZmqrJE1h5cvtx0vGcKaXzQHMqp+eK6QBP2b60aJgQQhiHYrDdZ5JWAFbLl017A7MPsAqwrO13ls4T+kPSx0gtqT5o++58b1lSFd3f2T6qZL52+Q0swIM1v4HtRdJrSW++nyydpekkrU1qq7QzMB04y/YxZVM1z3iYwJK0PrAMadKwihoTbbswZtqeUTRMCCGEYmKw3ScNq/AcXmCSFhnpue2HBpVlLCR9EDgImI90/vVR4Eu2v1002Dgj6QJgBdJukQNK52kaSSuRzrxOBh4ATie1VRpxe3ktKi2Q2OgJrBBCCKFmMdjuky4Vnu8AbqLOCs+tNmRP2D6zaJhxQtKzwAygtf2+vS2Ra63wK2l+gJgM6p9ceG412zeVztI0+fvqYtJRnNvyvTtq/X7qprYCiU0S54tDCCE0TfTZ7p8tSAVnWh6xvW2rwnOhTL0sl/9s5ACrxtUiUp/1twB/Ak4FLnHFM1uSVgc+Tj6zKekm4CjbNxYNNg7lr4MYaM+ZHYBJwEWSfg2cRkX9tcci97SOgfacOSn/+VTJECGEEMJYxcp2nzSxwnM3kj5q+xulc4ymxtWijh6rGwDnA9+2Pb1krk6StgOOAr5IqkAMsB7wKdIW3XNLZWuqJhXIa6JcQXs70vfW5sAPgbNbP2NLi3//0HSSjgAeAU7IE0QhhBDmQAy2+6RJFZ5HIulu28uM/pGhF0kLkVbjPgccZPv4wpGGkXQ9sJ3tOzvuvw44t33SqFZReOzFS9LCpCJpE21vUTpP6J+2Ct/duAn//jVWTu9G0vak+hITbL9ntI8PIYTQXQy2+6RJFZ5HIumvtpcunQOatVrUtvI2EXg1cBZwRutroSaSbrL9hh7PptperduzmtRWeEzSu23/OL/exPaf2p59uKLjDqGPJK1B6ugAcHPNg6tONU5gSVq3y+2NgE+QaqSs3+V5VWqsnB5CCKF/YrDdRx0VngEeo2EVnmNle85I+jcwjXSmdBodqzG2zyqRq5u8sr1t50RAnhw6z/aaZZLNnpoKj0m6xvY6na+7XYexa8qEW97BdC5pUHU96Vz5GsDdpF0k/yoYb0xqm8DqlPtDH0IqOvoF278qHKmRJB1K+p56zPbXSucJIYTxJgqk9ZHt7wDfqb3Cs6RH6b41T8C8A44zRyqcFDiT9DldOf/XzqSV7locClyQz+hdne+tB3wSaMzKS2WFx9TjdbfrMEa2lxv9o6rwOVL9g81tPwsg6SXAl4AvAP9bMNuY2H5rawKrdJZ2krYEDgaeJA2yLxrlrxTRoMrpd+Y/nygZIoQQxqtY2e6TaKc1WDVtdx+NpMVt31c6RztJE4D9ydXIgamkauTXl0s1qwatbMbK9ouYpKnAmraf6bg/N3Cj7VXLJGs2SVeSjuUcCVza+dz2NQMP1UNeeQd4yvYsWZtA0tydX8MhhBBmT6xs909j22lJWhKYK1/e05BftlXPGuUiaTsCuwKrAkuUTTRcHlS/R9Ir8/VjhSN11aCVzVUk3UBaxV4hvyZfN6YndJhjT3X7uWn7GUnVnIGG5kxgZf8mHcfaifTztH2XiEmV6atg+w+lM4yFpEtsb5pf/8j27m2PrwBiYjCEEJ6HGGz3ie3Pls4wVpI+BbzU9uH51qWklh8vBU4mtYQqLhed6/oIeOUgs4yFpHlJRdJ2BdYG5ge2B/5YMlc3kj5E2jY+X75+DPiy7W8VDdahQYXHYuXyxe3lktam+xGCeQrk6alBE1jY3qx0hrFqm2Cb5RHp1EsttTDma3vdWSgzjryEEMLzFIPtAKltzhvbrh+0vbakuYA/UMlgmzRY7eWbA0sxBpJOIX1OzweOAX4H3Gb79yVzdSPpYGBjYDPbd+R7ywPflLSI7c8XDTjcx4Af59fHMHzV5X1AFYNt23d1u5/P7U4Guj4P48a9QK9iU/cOMshoGjSBhaT9ga+54/ybpEWBr9jeq0yyrp4lrbafApxHvWeiR9oVVvWOsRBCaIIYbAcAbP+77fKb+d7MvDpbhSbtFiAVFfoncDOp5c9MSbW+cdmd1Ev1P60btu+QtAupknJNg+1GFB6TtACwD7Ak8HPgt8CHSefirwd+Ui7d+CPp5vzyuBoGh01agaUhE1jZysC1kvZpTQrkXTkHAl8vmqyD7bUkrUKaXDuFVAfjFOD8yo5mLSTpXcBL8usd8n0BC5aLFUII40MMtgPAKyW91PbTALZPApA0D7BAyWDtJL0BWMH2z/P11xl6M3BsTcVxOt5oXSDpAWD+GoujkbY0/qfLzSckPVsi0Ajc43W365J+RJpsuRR4P6kFoIDtbV9XMth4ZHvVvLq5UeksAG0Dlq5qav1HQyawAGzvLWlj4DhJU0g9zKcBG9n+e9l0s7J9C6nbw6GSJgI/BL5MKvBWiz8A72x7vW3bs+qOPIUQQtNENfIBk7QdcK/ty0tnacktn14DfNj24/nefKQVjXttf6pkvhZJ5wFftP3nfD2V1Gf1FcCOtrcvmW8kktYlDbx3AWbY3rhwpOdIuhA4wvaFHfc3Bw6x/ZYyyWYl6XHgNnLhsfyafL287fl6/d1BknSj7TXy67mAvwPLdJvUCONPnqS6Lv8HHYW8bL9v8Km6a1rl/Lxr5EhgK9Jq7G62qxwU5mKjk4B3kSbfzgDOrrUAZQghhBderGwP3obAGrmlxn+VDpMdQur9ereku0hvDJcGvp+f1eK1rYF29i/bPwOQ9IFCmbqSNKG9bZbtq4GrJX2cuj6nAB8BzpV0CcP7bG9CKvBWk6YUHnu69SIfIZgRA+3nr0GVs3cgDbLWBM4FTrV928h/pZjGVM6XtDvwWeC7pMm2CaRV7luBA2zfXzJfO0l/INUZOQN4L/BgfvSyXAvjoWLhOuQJwYVtP5CvXwbsCewXbepCCOH5iZXt8Jx8Pvv1+fI221UVdJH0F9sr93h2q+2VBp2pF0l3ADvnQXb7/cOAd1a4WvRyUtX09j7bP2nKALFVeMx2FWehJc0ktSmCNGiZF3icoUrE1RzPCP2TdwhtB0wEFgU+XVtLKEnLjvS8V7G/EiSdC3ykPZMkAR8EPm67mskBSXcydLSl/Y1W62dAFVklTSJNXvybtCX/C8APgCuBz9V0PCuEEJooVrb7RNKb8sunbF9WNMwYSFqMVNCpNdi6SdJxNa0UAPdI2rBzC76kjYB7CmXqZWfgTEm72b40vyH8NqnAz2ZFk3WRB9U/KJ1jNE0pPGZ7rtE/KswuSZvb/l1+vZzt6W3PdqjsLDTAf0htFP8FLAu8vGycWTWpcr7tWXba5Mrk35b0YJe/Uozt15XOMEYHA+vavk3SOqQ6EzvZPq9wrhBCGBdiZbtPJJ2YXz5se7+iYUYhaRNSldSTGNpGvC6wB+k83J96/NWBkrQBcDopZ2u2vZVzou0rCkXrStKawNmkweF/59u72n6yXKpZSdoLWMT2kfl6BqkwnkirRd8pma9dXtlqFR7bAliMlHPf2guP5VXOd5FW4N9ROk8TNeV8ca53MAnYALgAOM32VWVTdTfaBFa3AW6NJN1te5nSOVrywLWnWlaMu3wfTbG9eslMIYQwnsRgu08qXWXpStJlwP/Yvrbj/lrAd2s6GylpcTpW4Entfqqq8C1pkfxyNeAc0hvuD5N6r1LZeb0rga1sP5ivr8191l8O/Mb2m8smHNK0wmP57OM7SFv0twR+BpwVq0ZzpvW12fm623VJuUDaDcAlpC3Ew37R2v5IiVzdNHkCq52kv9peunSOlvw1MAV4oHWr7bFtbz74VLPKk6vtPeE/1n5tu1e/+BBCCGMQ28j752CgEYNtYIHOgTaA7eskzV8iUC+278vV09vPltc40LqaoTfYj5IK411BPq9HXYWH1BpoZ2dC2lpeU5/1rBGFxyS9nbQF9+3ARaSWP+vbfm/RYM3XlNZvTfp3Xr5tAusEKp/AGkFN//6QBq07AU8Ap1FvFfLjSYXcul3X9jkNIYTGicF2gFRjZmHb/+y4uQiptUoVJM0NHEF6I3s3uWp63rL/aec+4TWwvVzpDLNhofYL20fAc2c2X1UkUW8TJP0rvxYwb76urfDYr4GLgU1b54olfbNspHFheUk/J1fKzq/J19V8z9k+udv9vFtk227PCmrEBBaknS10HwAKWHzAcUZk+xvANyQtTzpScGHu9nFETTsGbH+21zNJ6w8ySwghjEcx2O6fVjuVTq1BwZqDDjSCrwPnSzqA4Wehv5yf1eJI0oz78rYfhefOGx6V/9u3YLZRSVqBtJ14ku03jPbxA3S+pM/bPrjj/uHA+SUC9dKgwmPrkN5gX5Ar058GNCV7zdrPDx/V8azzugr5uMOWDO10uJi8e6QSTZnAAtimdIDZZfuOvFV/XmB3YCWG+q9XR9JqpK/VycDDpDaQIYQQ5lCc2e4TSTcBW/d6XlM7FQBJ2wCfYPhZ6CNrOlsqaRqwkju+aPOb2Vtsr1gmWW+SliC1/dkVWAP4IunM7o1Fg7XJhbtOANYnVfSG1L/2KuD9lW59fE7thcckbUx647oj6fN7tu3vlU01/kjapJZijgCS3kz6vt+adIRkE9JE4eNFg41DkjYlff/vUzpLS9uK9nbAX0kTbr+oraUmgKTXMTTAfppUOX8923eWSxVCCONDDLb7pKZiPePFSL20K+yzvTfpjcuSwBn5v3Nr3l6e3xw+12fb9u0l84ykiYXH8rb8t5J2NryvdJ4myhNru5C+r35te0qeKDwImLeWn7m56NTdpHZ/59h+VNL0mr//29U+gQUgaW3S9//OwHTS9/8xZVMNaSuSdy6p9VtnkbwqCo9JupTUfeI0UtX8aU36Wg0hhNrFNvL+qWaFZSwk/RfwSYavbH/Z9i/LpZrFVEnvsf3D9puS3g3cUihTL8eSqvvu2mr5I6nKmS1JrXY5T9Y8WIXmFB5r+5zOtP03ANvPkrblV7U1v2G+DyxNWik+WtI9pG2un7R9TtFkw/0U2J60q2Vm3kZc5fd/S48JrGra/gFIWomhFdgHSK0gZfstRYN1dzhD/+avLBlkFPeRJq8WB14NTKPyr9UQQmiSWNnuE0l7MMIvrM4BY0mS/hv4AGkbeasX7HrAl4ATatnyKmlJUoX3JxjqB74e6Szcu1qDmhpIWpS04jIZeA1pZXvPmlrTtEi6KL980PZORcOMIq8WXUz6XLYKj91hu6bq7o36nDaJpCnAmrafzcXG7gVW6KimXwVJAjYj/QzYGlgQ2Av4ZU1HM7pMYJ0OHGP7dSVzddP2/b+X7dvyveq+/5tG0oLADqSvgxVJRTO3tH1F0WAhhDAOxGC7TyT12s72TmBJ29XsKpA0lVQ1+aGO+4sCl9hetUyy7iRtzvDtzheWzDMaSUuRVrgmA/ORzuweVDZVM+Xe75NIExmtwmOfsb1s0WBhICRdY3udXte1kvRShoqkbWm7mir/TZnAApC0Pen7fxNSxf/TSBPC1W15lnT0SM9r6rXeTtJipKMak0kt4KqbIA4hhCaJwfYA5BWO3YADganAF2x3q1RehKSbew2oR3pWiqS30Lbd3fZFI318TfI2yEm2Dy+dpemi8NiLj6THgdtal8AK+brGLg9dSZq3piJZTZzAymfKtyN9/29OOk5ytu1qjmhIegqYQtrVdA/pa/Q5vdrD1UTSsrUVcw0hhKaJwXYf5b7QewIHAJcBX7T9l6KhupB0ObC37es77k8Ajre9QZlkw7VtI/8PQ9vI16XObeRvyi+fsn1Z0TDjXBQee/GQNOIAsJaBQT5GYOChJh0jaOIElqSFSRMFE21vUTpPS9tRoonAM6Tt+T+1/XDRYB0knUj6Wn3E9n6l84QQwngTg+0+kbQPqe/zhaRCY3eWTdRbbpvyE+BEhp+F3gN4t+1LSmVrJ+lsUkXvkzruvwfY0fZ2Xf9iAfkNDMDD8QbmhdOt8FgItbV+apsUmGl7RtEwcyAmsF5Y+SjRJOBjwIG2f1Q40nNyizpIE8OXFg0TQgjjUAy2+ySfg7sf+AfDC6VVud1R0muAD9F2Fho4zva95VINJ+kvtlee3Wdhzkh6LWll7snSWVqaXnisxs9pU9Xe+qkpmjSBJWk66ffpP2xvWDrPWEhah7Rb4G2kyeyv2p5aNlUIIYRBicF2nzRlu2OTSJpme8Uu918C3Gr79QVijVuSLiCdif2Z7QNK5xkP4nP6/PRo/XRAzeeLa9f0CaxaSTqc1ErtZtI5+F/bfqZsqhBCCIMWg+0CJP3J9ialc7RI2g5YyvZx+fpyUr9NSFveziwWro2kr5P6lX7U9r/zvfmArwP/qbW6a5Pl4n6r2b6pdJbxIj6ncy5aP4WmyF+r04HH863Wm60qd7eFEELoj2raT73ILDP6hwzUJ0jnyVrmAdYntak6EahisE3K+UXgLkl3kd60LA2cDEQrrT5wmo2LQeELKD6nz8sOpJ9VF0lqtX7SyH8lhCKqa0cWQghh8GJluwBJd9uuZsAt6Urb67ddH2v7w/n1ZbY3KpduVpLmBVpbxm+3/fhIH18TSesB99i+p3SWliaeg6xd/pwC3B+f0xdeE1o/dSPpZNJK53G2p5TOE0Ivko4AHiH1MX+wdJ4QQmiqGGz3iaQdej0CvmP71T2eD5yk23qdd5Z0u+0VBp2pG0kLAIvbnpavdya1/QL4je37ioUbo/xme03SGfOJpfOMRtISNU0M9BKFx168am391I2k9Uk7mzawfWDpPKF/mj6JKWl7Un2JCbbfUzpPCCE0VQy2+6St9VNXtt87qCyjkfQT4Pe2j++4/wFgM9uTyyQbTtL3gD+3Wn9Jug34FWnA/YztDxaMN1skzW/70dI5RlPbLoxeais8JmkqqZ3eqbbvKJ0nhNnVpAksSTfnl8fZPrZomBBCCKFNDLYDkhYDzgGeBK7Jt9clnd3evpYVY0nXAuvkM69Iutb22vn1JbY3LRqwTZPa6YxE0l9tL106x1jUVHhM0gTS2eJdgAeBU4HTm7BLoGZNWS3Mk60GHrG9X+k8c6K2CazRSFoU2Mj2L0pnaRJJh5K+Vh+z/bXSeUIIYbyJwXafSGptu3qilmreo5G0OUN9tm+y/buSeTpJutH2Gm3Xq7fOPUqaYnv1cumGGy/tdJqysl0zSRsBE4EdgduBUzp3kYTxRdKb88unbF9aNMzzUNMEVtNIusb2Os/3Y/pN0h755RO2zyiZJYQQxqMYbPdJni0GeDRmi18Ykq4HtrR9b8f9JYFfRSuVOSPpGIba0gx7BOxhe4EBR+qpKSub3UjajNSmbjXb8xSOE0LjNOn7X9ITwLSRPgRYsPRkpqQjbEc3jxBC6JMYbIfGvIGR9G5gX2B/4Np8ex3gKOBo2z8qla2bvK1xV2CVfOtm0hneqiq7tq1sdGX75EFlGW9yQazJpFXt6aRWVWfW9jUQXliSbuj1iMp6LDfl53/TSFp2DB820/aMvocZQQ2r6yGEMJ7FYDs0iqStSD2130B6g3gT8CXbvyoarIOkVYHfAb8hTQwIWBt4G7C57VsKxmssSSO+KbR9zUjPByW3zZkIPEQaYJ9e+k11GBxJ15F+Pp0CnAc80f7c9l0lcjWdpM1bx5skLWd7etuzHWyfVS5dM+UdY5vRo1+97YcGGiiEEMaZGGyH0AeSfgqc0XkGTtKOwK62dyyTbFaSNgWWt/3DfP1TYJH8+PM1nd2X9CwwBXigdavtsW1vPvhUs5L0GdIuhpG2kYZxTNIqpF0N2wJTSQPv820/UzRYh6ZMYMHwVdjOFdlYoZ0zkp4E/kb3wbZtLz/gSCGEMK7EYDs0RpOqpkr6i+2VZ/dZCZIuBP7X9tR8fSOwJzAfcJDtrQrGG0bSR4GdgEdIK8Zn236sbKpZSZqXVIn8IeD/gE8AbyQVSPuc7QdG+OthNtXe+knSROA44Mu2jyydp11TJrBglg4Uz73udh3GJj5vIYTQX3OXDvBiI2k74F7bl5fO0kB35j+fGOmDKvHvOXxWwgKtgXY2zfbVAJK+WChTV7a/AXxD0vKk1loXSroLOML2dWXTDXMy8DRpwuIA0mDmWGBT4CRgm2LJxiHbq7ZaP5XO0pILN04C3gX8E9gPOLtoqO4+RprAeoKKJ7Ay93jd7TqEEEIoLla2Byyf5VwDmNv2f5XOE/pD0gyg2+q7gI/W1Lta0jTbK/Z4dpvt1w8601hIegNpMLM78Ima2ta0WtFJmhuYYfs1bc+utz2hYLzQZ5L+AMwPnAH8jNRr/Tk1noNtm8DaDqhxAgtJDwN/JP0cfWN+Tb7e1PbCpbI1laQ9bZ9UOkcIIYxXsbI9YE1qsVH71szKHU96s93NCYMMMga3SHqH7V+035S0DfCXQpm66hgQ/JW0EneE7dp2OzwFYPsZSfd0PJtZIM+40KDK2cuScn4A2LvtvvL96s7B2r5D0rnAvKQJrJWAqgbbpO/7lqM6nnVeh7H5RT6i9U/gB8CRDB152d/2bSXDhRBC08XKdp9IelN++ZTty4qGeR5aWzM7B2JhbCS9qvbzuZJeD/wC+DPQKoa0LrAxsI3tW0tl65TPl94AnAv8i46to7Wc5Zd0P2kiQKSq5Ke1HgG72F68VLYQ2vWYwPpFhRNYI5K0ie0/lc7RNJLOB64iTQ5vAZxIqqD/RmA325uVSxdCCM0Xg+0+kXRifvmw7f2KhgkDl1eFTySd232WNMD6c9lUvUmaB9iN1FINUku1U2z/p1yqWUk6jN5nM2378AHG6Sl6l/eHpEVGel7L9uyGVfhuxAQWgKS5SIUHlwR+bXtK/ll7EDBvFPqafa1jLZIE3GV7mbZn19leq2C8EEJovBhshyZtzexK0odIZyJ/VktbHUk3kAbYt0jaEPiK7TeXzjUSScsxNNieavuOknlml6T1bV9ZOkfonzwwnAG0vs87K2dXsT27YRW+D6MBE1gAkk4ClgauADYE7gHWAz5p+5yC0Ror2qmFEEJ/xZntPpH0npGet3oa18D2cqUzPE8iVXneDXhn4Swtz9i+BcD25ZJ6nd8uTtICpHPk65LOaApYS9LVwF62/1Uy30gkrUbqZTwZeJj0xrs4Sa8C9iHOQb7QjgbeAvwJOBW4xHXOGDemwrftw3o9k7T+AKOMxXrAmraflfRy4F5gBdsPjvL3Qm/LS/o56ed+6zX5uunvDUIIobhY2e4TScf0ePROYEnb1Ux0SHoF8LTtp/P1ysDWpC1lZxUN11BdqpF/rP26sq2ZJ5Haqh1u+9l8T8AhwOttjzhxNGiSXsfQAPtpUjGq9WzfWS7VcHEOsn/y1+ZmpH//DYDzgW/bnl4yVzdNqPDdqXMCy3YVE1gQK6/9IGnEHVe2/zCoLCGEMB7FYHsA8pvD3YADganAF2zfUDbVEEl/JK1gTsvFsq4AfgKsBlxh+1NFA2ZtuwWesH1m0TCjyNVde6lta+ZIrb96PitB0qXAAqTVwtPy1+z02nZnxDnI/pO0EGkg+zngINvHF47UVc0t6lqaMIEFIOlxoLUrRMAK+Vqkn6trlso23khaGphk+8jSWUIIocmqWV0dj3KP3T2BA4DLgJ1sV9VKKVvY9rT8eg/gVNv/K+llwNVAFYNthra0Vbkds53tz/Z6VuHWzJFo9A8ZqPtIxZEWB14NTKP3edOSZkJ69y+psxr9swXyjAuS5iOtEk8k/fufBaxr++6iwTo0qEVd5wTWjm0TWHeWTdbVqqUDjGeSXg3sTJp0WQI4u2yiEEJovhhs94mkfYB9gQuBrSp949LSPljZnHS+FNtP5UI/VRhpAFu7Ws8WZ3+W9Bngc+3nXyUdAlxaLtasbG8vaUFgB+AwSSsCC0nawPYVheO1i3OQ/XE/aYLlNIYmWtaTtB5ARcdebmN4he9lgP9JGx3qOkZCcyawsH1Xt/uSNiX9bN1nsImaL9cT2QHYldRb/SxgOdtLFQ0WQgjjRGwj75M8SL0f+AfD37hUt91N0o9JhWb+BnyS9Iv28bxN8w+2JxQNmOUBYS+2/bmBhRmDBm3NXAD4PrAOqUAawNqkntt72X6kVLbRSFqM1ApoMrCM7aULRwLiHGS/5PoCI1XOft8A4/Q0SoXv6iYO2yawJgMrAgsBW1Y2gTWMpLVJA8SdgenAWbZ71UoJPUh6gnR07GBywUFJd9RS2T+EEJouBtt9ImnZkZ73mqEvQdK8pFX41wI/sH19vr8xqdLrj0rma5G0f5fbrwDeDyxq+5UDjtRTU84Wt5O0AumcPqTWX7eXzNONpLfZ/m2PZ2fY3mXQmWZHnIPsH0ns0DtdAAAgAElEQVSL276vdI6mq3UCC0DSSgxNYD4AnA4cYHvE37ehN0kfJR15mI9U4f904Lcx2A4hhBdGDLb7RNIyvc4RSnqj7YsHnakXSQv0au800v9HSXnr277AXsAZwFdt31821RBJ55BWin8OnGL7zzWvFuTz+bsx1Gf7JlLuJ8ulmpWkW4H9bP+i7d5LSCvzr7W9VbFwPXQ7B2n7gLKpxoe8+2ZH0grnqraXKBwJAElHj/Tc9kcGlWU0TZrAyjvGLibtuLkt36v252qTtNUZaO1uOJT0s+rWosFCCKHhXlI6wDj2e0mfkDRX64akxfOW7a8XzNXN71svJF3Y8eycwUYZmaRFJH2edB5ybmAd2wfWNNCGdLYYWINUYO4wSdOBhSVtUDbZrPJ58qmkdkp35/82A27Kz2qyJfBVSe8CyL12fw7MA2xbMlg7SfNL2kPSb0hbNFcgHc9YIQbaz4+keSVNyufgbwS+SqpIXtMZ0w8CmwL3kFrAXd3xX02Ok/SO9huSXiLpRNLunJrsAPwduEjS8ZK2oL4ijo1k+w7bR9heg1RTZAHgl4VjhRBC48XKdp9IWhj4ErAxaQV2DVKv5a+QesJWU3hM0rW21+583e26JElHkt5sfQ84znb1VclbKt+aeSHwpc7VLUlvBT5t+y1lknUnaSngN8AxwLuBK23vVzbVcHEOsj8knULqVX4+6YjG74DbajueIWlR0m6GicAzpK25P7X9cNFgXUhaDvgV8CnbZ+cJrJ+SCrvtYfvpogG7aKtKP5lU1POHpFXY84sGCyGEEDrEYLvPJO1LWsm+B9jI9ozCkWYh6Rrb63S+7nZdUt5C+CTpzWu3onO1rcJ0JWnZys7s32J7lR7PbrZdTbsdSa2vxSWAk4HfkiawALB9TYlcneIcZH9Iuo60I+uHpFoIM2qfxMiTQ5NIk60H1lIDo10TJrB6yRPbOwMTbW9ROk/T5F1XBv5he8PSeUIIYbyJwXaf5LOEXwY2BD4BbA1sAexr+3cls3WSNAP4GmnQul9+Tb7+aE2rsE2Rt2AaeKT2N635HPQaneez8wrXjbZXLJNsVpIuGuGxbW8+sDBjEOcgX3iSViF9PieSimStDKxeY3G0PDk0GXgbafv4V21PLZtquKZMYIUQQghNFIPtPpF0B/At4Bu2n8n31sr37rI9uWS+dpIOHel5LW1qJK1v+8oez3avacWorfXTU7ar6lXdSdLBwEbAPq0V99y27GjgKtuHl0s3dpJeWuOW1xZJq5MHibZfXzrPeCBpXdLndBdghu2NC0cCQNLhwDuAm0nb3X/d+j1QmyZNYMUqbAghhKaJwXafSFqq15ZxSe+3fcKgM82JkQa4gybpBuBPpLOFD+d7q5MmMB7KRcnCHJD0YdIOjFeQdjQ8BhxVe99aSSKd2dwV2Mb24oUjhT6SNKHVmrDjvoBDapkYykdepgOP51utX7StIy9rFgk2m2qfwArP31iOitV0nCyEEJomBtsFSLrb9jKlc/SSK1C3epk+bHu9wpEAkDQ38HFgb1L14TVI2/P3t/1/JbONF7mlGrYfLZ1lJJI2Ig2wtwcWAfYBfm77n0WDZbEC1x95x9DOtq/uuH8Y8M5aBgSSRuz7XFPNhk4xgfXikos5ThvpQ4AFa37PEkIINYvBdgGS/lrbOei8bbg1wH4aWBZYz/ad5VJ1J+njpPPw9wAb2L6ncKRGy8WRXmf7knz9MeCV+fEprX62NZB0BKkY0t2kwmNnk7a6V1WNOvRH3jZ+JrCb7UvzwPDbpHPb29n+V9GADVb7BFboj9EmhrKZNRZ3DSGEJog+22VUNcMh6VLgF6S+1TvaXhd4tLaBtqQVJP2atOqyKnAU8EdJ7y2brPGOBBZqu/4A8G/S12kV5/XbvB+4jzTA+pHtB6ns+yn0T17R3h74saStSC2qXg1sVdNAW9J0SXdIurx0ltFIOkLSNOALwA3A2qQdGSfHQHv8s33XGP6LgXYIIcyhuUsHGK/y6mDXRwytGtbiPmBJYHHSG9dp1DmA+Q3wSds/zdd/kXQG8LV8Dn6TgtnGJK/MPgKckAeKNVi5Yxv+47a/CiDp4kKZenktqbLzZOAbubjTvJLmrqkAVZyD7A9JiwAzgD2Ac4ALgA8D80maz/ZDJfO1NGynxfuBW0kTWOfZflJSjT//QwghhMaJbeR90pQK3y2SFgR2YKhF0ULAlravKBqsjaRX2n6sx7N32/7xoDPNLknbAysAE2y/p3QeAElTba/Wdr1Ia9BSW5/tdpLmAbYhfc2+EbjQ9q5lUyVxDrI/2s7CQ/ockq9bhceq7bddK0lzMTSBtQVwEfBWYOmaJrBGIunm/PI428cWDRNCCCG0icF2ATVV+O5G0mKkVjqTgWVqO1/eTe1F52qWt7ru3tn7Ofcz/qHtDcokGztJCwDb2/5h6SwQ5yBf7Jq6s6HmCazRSFoU2Mj2L0pnCSGEEFpisD0gtVb4Ho2kZWuunNtSW9G5vLPBwGO2v1Y6z0jy2dejSWc2r8m31wUOAva1/atS2TpJau0GeML2mUXDhCpIWoFU2GuS7TeUzgPjY2dDbRNYIYQQQhPFme0+akqFb0knkgaGj9jer/1ZEwbaWW2zRnfmP58oGWIsbP9a0g6kPtsfybenADvYnlIuWVets7BVtyYL/SVpCWAiaZC9BvBFYFLRUMOtMoaPmdn3FGPQawIrF5yraqAdLfVCCCE0Taxs90mu8L0AcBpwmu1pkqbXWDhH0pvzy6dsX1o0zAgkHUP3QbWAPWwvMOBIPUk6wvZBpXOEMJ5I2ps0ebkkcEb+79waf642RVt9kUdr34UTQgghNE2sbPdPUyp8Y/sPpTOM0VVz+KyErUjbsKs30s6GECpzLHApsKvtqwCicvbzU1uxzpHkavQ91VKNPoQQQmiJwXaf2N6+rcL3YZJWBBaStEFNFb6bxPbJ3e5Lejmw7YDjjGYuSQszVDF5mMreFJ6U/3yqZIgQxuC1wM7AVyW9hrSy/dKykcIAPUBq/daqkt7+89VAVKMPIYRQldhGPiBNrPBds9yuZkvS5/PtwMW2dyqbaoikJ4G/0X2wHS2KQnieJC1FOrc9GZgPODuOboxvkr4BvAX4E3AqcInjTUwIIYSKxWC7gKZU+K5RPl++K7A1cAWwCbC87ceLBusg6Vrba5fO8WIhaTvgXtuXl84SBk/SSqRq5IeXzhL6S5KAzUiTLBsA5wPftj29ZK4QQgihmxhs98l4OAcr6QjgEeAE2w9WkGcGcDfwbeAc249WXHQuBtsDlL9W1wDmtv1fpfOE/pD0pvzyKduXFQ0zztU+gSVpIVIF+s8BB9k+vnCkEEIIYRZxZrt/Tsp/Nvkc7BXACsDXgfeM8rGD8FNge9LW0ZmSzqXSonPAN0sHeDGJ7cMvGu/Nfz4MxGC7vzYE1pBUzQSWpPmA7Ui/A14NnAWsa/vuosFCCCGEHmJlOzRKxxbCrYEFgb2AX9p+rGC0YSS9GvgQ8E/gB8CRwBuB24H9bd9WMN6Y1LazAWJlM4QXM0n/JnX2OI0uHT5sn1UiVwghhNBLDLZDq8+qgcea1GdV0ksZKpK2pe1XFY70HEnnk9qRzQ9sAZwInEcacO9me7Ny6cZG0vaknQ0TbNews6F1PAPg4aYezwihJk2awJJ0Er13M9n2+wYYJ4QQQhhVDLYDkvbIL5+wfUbRMHNI0ry2nyido0XS9bYn5JX4u2wv0/bsOttrFYwXQgjA+JnAkrS47ftK5wghhBDaxZntALByE868SrqItKrxUGebr5oG2tlMSEstkh7oePZsgTw9NW1ng6SVgb2BVfKtm4Hjbf+lXKoQmsn2e0f/qDrlImk7kjpUrAosUTZRCCGEMFwMtgesxnOwwFZA9YNtYM/858ySIcZoeUk/J/XZbr0mX9dWPf3O/GdtExazkPT/SEWRvpf/E7A2cJGkHWrfBhv6Q9J6wD227ymdpYmaNIElaV5SkbRdSd/785MKZ/6xZK4QQgihm9hGPmCVnoO9nlR0TN2e235ooIHGgdwPvCfbfxhUltFIOqIJOxsAJP0K+LLt33fcfzPwyVqqJofBknQysCZwq+2JpfM0SccE1jUMTWD9N1DVBJakU0h1L84nFUn7HXBbje0fQwghBIjBdgAkPQn8je6DbdtefsCRxi1JSwOTbB9ZOkuLpGtsr1M6x1hIutX2Sj2e/cX2yoPOFOohaX7bj5bO0SRNmsCSdB3wEuCHwGm2Z0i6I35HhRBCqFVsI++Thp2DnWp77dIhxqvcBmxnUtX0JYCzyyaaxVySFqYZOxtGGkj9e2ApQhGSWoUGZ9r+W+fzGGjPkRU6B9qQdt9I+l6BPD3ZXkvSKqSfpRfkehjzR3G0EEIItYrBdv/cmf+s/hxseOFJmh/YgXSucCXSNs3lbC9VNFh3qwBX02NnA1DTqtHSko7ucl/AkoMOEwbu5Pzng8BOI31gGLNGTWDZvgU4FDhU0rqkgfeVkmbY3rhsuhBCCGG42EZegKS5bT9TOkeLpD1tn1Q6x5zK5zUfB46zPaV0HgBJTwBXAAcDl+Sq5FVud5R0bVN2NrS1qevK9skjPQ8hDCfpftL551keAbvYXnzAkXqSNMH29V3uCzjE9uEFYoUQQgg9xWC7TyRdYnvT/PpHtndve1bVGdm8zflDwD+BHwBHkorQ3A7sb/u2gvFGJWl9YBlgA9sHls4DIOmjwCRgPuBU4HTgtzHYfv7y1+uypMJID5fOEwZL0qKkHSPtlbNPrai7Q6M0aQJL0h3Azrav7rh/GPDOmn6vhhBCCBCD7b5pH8B0Dq5rG9xIOh+4itRCZQvgROA80oB7N9ublUvXbJKWJw26JwMrkrY/nm371qLB2jRpZ4Ok9wNHkCaClgP2tv3zkf9WGC8krUqqQP0b4FqGKme/Ddg8bzEOs6kpE1h52/iZpN9Ll+YV7W8DKwPb2f5X0YAhhBBChxhs90n7ALvLYLu2le3rbU/Ib1zusr1M27PrbK9VMN5zJJ1IOkP8iO39SueZXZJWJw26J9p+fek8LU3a2SBpCvAW2//IExk/sf3/SucKgyHpp8AZts/ouL8jsKvtHcska66mTWBJWpNUZHIfUnsySP/2T5ZLFUIIIXQXBdL6ZyFJ7yK1KVlI0g75voAFy8XqaiakHl+5umu7Zwvk6eWk/OdTJUPMqXye/NP5v5r8hLSzYUXSOfMTgW+SBtwnkHqw1+Ip2/8AsH2HpHlKBwoDtYbtWQqj2f6ZpCNKBBoHPgq8oX0CC6hysC1pEWAGsAdwDnAB8GFgPknzVdY5IYQQQoiV7X7Jq7A92X7voLKMRtLDwB9JEwFvzK/J15vaXrhUtnaSFui1TVDSMrbvHnSmXiRNJ63C/8P2hqXzjKQpOxugazGnSe3Xtj8y8FBhYEbaFVTbjqGmqH3nVbu2n6sw1D3B+bVrrIkRQgjhxS1WtvukpsH0GGzX9vqojmed1yX9Hmhtzb/Q9hZtz85pPauB7eVKZ5gNTdnZAPDxjuuru35UGK8Wk/SxLvcFvHrQYcaJpTra6Q27rmkCq2E/V0MIIYQYbPeTpLmAhW0/kK9fBuwJ7Gd71ZLZ2tn+Q7f7kpYmrRx2fV5Aex/oRUZ4FmbP8pJ+Tvoctl6Tr6t6c9urMrKklwPbDjhOGLzjSYUcuzlhkEHGkUZPYElagVSdfpLtN5TOE0IIIbSLwXafSJoEfBf4t6RpwBdIxaeuBHYrmW0kuVjWzqRCXkuQCtHUwj1ed7suaixbMSvartmUnQ3D5MmsLUlfq28HLiZVKg7jlO3PAkh6VWsSMzw/TZzAkrQEMJE0yF4D+CJpYjiEEEKoSgy2++dgYF3bt0laB7gU2Mn2eYVzzULS/MAOpDcuKwFnAcvZXqposFm1tpCK4dtJa9xCuqqkG0Z4Xk2hvAbtbABA0ptJX6tbkwq6bUL6en28aLDQd5K2IRXwe1rSs8Autv9cONa4UfsElqS9SdmWBM4A9gLObU3ChBBCCLWJAml90qXozBTbq5fM1IukJ0iDloOBS/LZ3TtqKzYj6dCRntf0hkvSsmP4sJm2Z/Q9zGzotrPB9gFlUw2RNAO4m9Rb9xzbj0qaHmc5XxzyBNYutm+RtCHwFdtvLp2r6XpMYC1f2wSWpKdIE9f7274q36vud1UIIYTQEivb/dNZyGeh9mvbXyuQqZdPkVYwvwWcKun0wnm6GmkwLWm+QWYZje27SmcYqwbtbAD4KbA9aQvpTEnnUtkRgtBXz9i+BcD25flrNzwPHRNYB7RNYFU10M5eS5oM/Kqk15BWt19aNlIIIYTQW6xs98koq7C2ffjAwoxR7rE6ibSquSJwKGll89aiwdpIWpL0husG209JWozUJ3ZP20uUTddMTdnZ0JJblG1G+jrdmrQdfy/gl7YfKxgt9FkeGLZPVH6s/bqyScxGkPQN0gTWFOAU4Fzgxlq//1skLUWadJsMzEf6XXVQ2VQhhBDCcDHYLkDS+ravLJ1jJJJWJ72JmWj79aXzAEj6KPBp4DZgHtJK/JeBH5K2k/69YLzGyp/XSaQ3rKcCpwO/rf3NNoCklzJ0xnRL268qHCn0URMnMZug6RNYklYiVSOPf/8QQghVicH2gEhajfRGZjLwsO31CkdqHElTgU1tPyRpGeBWYBPbjWpVU6sm7GwYiaR5bT9ROkcoowmTmE1Q8wSWpDfll0/ZvqxomBBCCGEMYrDdR5Jex9AA+2lgWWA923eWSzUrSdNJ517/YXvD0nl66VJ07nrbE0pmGq9q3NkAIOki0tfqQ7Z3Kp0nlBWTmP1V2wSWpBPzy4dt71c0TAghhDAGMdjuE0mXAgsApwGn2Z4WVZOfH0n3kz6fLZPar21/ZOChwkC1VXmvrpJ7GIymTGI2RUxghRBCCP0T1cj75z5SL9DFST2gpxFVk5+vj3dcx/bxF0BTdjZAs6q8hxdexyTmjm2TmHeWTdZoe+Y/Z5YMEUIIIYxHMdjuE9vbS1qQ1FLpMEkrktp/bWD7isLxhuncnj2nH9Nvtk/udl/Sy4FtBxxn3IjdFqFBYhLzBRYTWCGEEEL/xDbyAcktqnYhbX1cxvbShSM9J7d+mjbShwAL2l5mQJFGJWkuhor4vB24OLZAhjD+tU1itgr5LUQq5FXVJGYIIYQQQgy2C5C0bE2rCW3nYEdSxRlZSW8GdiW1p7kC2AT4/+3de5hldX3n+/cHMdIgghHFgHIzCChyk4sXEDAqEyJCBGnAqGGMeXL0jCPGhJiTUaI5JEpijJHBGR2BmCACIsRoPBwNCB6JN64dUZRriBmVq2C3abv5nj/WKqguqqq7Ye/9q1X9fj1PPXutvdbe9anajdb3d92pqpY3DTZgQxnZIM20kBsxNTlJ9gV+UFU/aJ1FkqTpLLbHpF81tYD7XDV1NJLcAdwOnAFcVFX3u+jcYzfEkQ0zJTkbWA6cXlXLWufR5C20RkxNTv/f/x7AjVW1tHUeSZKmOGd7fM7qH1e2DLHIXAAcBSwFVie5GOdrjsKu63DPQl886cPAdsDrgJMbZ9GYzNeIaaE9WkNqwKqqNwAk2bx1FkmSprNnW4OSJMAhdMNGDwe2AN4IfL6qHmgYTdKY9dNIAFZW1ZVNwyxySfaja8Dav6oWRANWkqnRNaur6t+ahpEkaR1YbGuwkjyehxdJO6yqtmocSWPm9Axpw9XvCQ5wlwtiSpKGwGJbi0KSJVW1onUOjZc9m9Jo2YAlSdL4OGdbg9H3ahRw98xeDQvtDcb+wLlV9a+tg0iLxFn94yDWF0nyFLodKabWmrgB+GRV3dUulSRJs9uodYANTZJTk5zc/8Gg9fObwInA2xrnUDvbAF9NckWSNyd5autA0sDtD9w8hJEiSXYDlgHPB26k20VhP+D6JOuy0KMkSRPlMPIJS3IU8Cxgz6p6fes80tD0i+S9BDiObnX6a4FPAhdW1f0ts6mNJKcC9wEfs4dz/ST5S+AY4Fa6/47Or6ofNw01hyQXAOdV1Xkznj8aOKGqjm6TTJKk2VlsSxqsJI8DXgb8GbBLVW3aOJIasBHzsRlKA1aS71bVLut7TZKkViy2xyTJu+nmFz9QVR9onUdabJI8j644WArcSTdv86/appKGbSE3YCW5qqr2Wd9rkiS14gJp43Nr/+jCXdKIJNmZbqu3pcBq4FzgFVV1c9NgmggbMcdrlgasd7ZN9AhPS/L2WZ4P4PoNkqQFx2J7fHapqj9sHWJDkORsYDlwelUta51HY/UFuuGtS/2sN0i39o82Yo7IwBqwPgpsPse1j00yiCRJ68Jh5GPikLbJSbIfsB2wf1Wd3DqPxivJxsDqqqokzwQOAG6qqqsbR9ME9CvQbw98v6rubZ1n6JLcRNeAde5QGrCSbFVVd7bOIUnS2lhsj0mSa4FD6Ia3PUJV3T3RQNIikOS3gPcDDwDvBX4PuArYG/h4Vb2vYTyNWf/5nwrcBOwI/HZV/X3bVMM3lAasJK8EzgR+DjwIHFtVX22bSpKkuVlsj0mS/wD+jdmL7aqqnSYcafCSnEk3X/O+qjqpdR5NXpJ/AQ6kG0p6A7B9Vd2ZZFPgG1X13KYBNVZJlgGHVtWPk+wE/F1VvbB1riEbUgNWkuvoCuzvJDkAeH9VHdw6lyRJc3HO9vh8u6r2bh1ikTmrf1zZMoSaWllV9wD3JPn+1FDSqlqexH8Xi9/KqT2gq+rmJE9oHWgROIlu27RZG7CABVNsA6uq6jsAVfW1JHPN35YkaUGw2NZgVNWXW2dQc0uS7A1sBPxCf5z+a5OmyTQJz0jyobnOq+qtDTIN3ZAasGauRr7GuSvUS5IWGovt8XG/3xHrhxDOeoluaP4ek8yjJv4dmPqD+n9PO5461+L2ezPOv9UkxeIypAasmauRTz93TpwkacFxzvaY9Cvmvhm4B/g4cBpwEN3CPr9bVd9vGG+QklxD9wfVOcBnmbH9T1Xd1iKXpLaSbAIcUVXnt84yNEkune96VR06qSyPRZL9quobrXNIkjSdxfaYJLkE+CZdq/uv0K2g+lm6gvu1VXVIu3TDlWRXuj1hjwC+TVd4X1JVq5oG08Qk2R74aT+v9AV0C6Z9v6ouahxNE5TkccBhdP978Argiqo6pm0qTVKS59B9/scD91bVvo0jSZK0BovtMUlybVXtmSTAbVW13bRr11TVXg3jLQpJlgKnA++rqtNa59H4JXkX8Aa6EQ7nAi8DLqPbqujaqnpbu3SahCQHAycAhwNfB14M7FRVy5sGG7AhNWAl2YGHC+yf0+25vm9V3doulSRJs3PO9vishm4icZI7Z1x7sEGeRSHJtsBxwK/TDdE/CfhM01CapOOA3YBNgduBp/cLOW0MXNM0mcYuyR10n/sZwDuq6v4kt1hoP3rTG7CSTG/A+rUkhyykBqwkVwJPomtoO7qqvtd//re2TSZJ0uwstsdnpyR/T7fIzNQx/fmO7WINV5Iv0w3LPw84Ebirv/QLSX6xqu5uFk6T8rOqWgmsTHLTVJFVVasW4MrJGr0LgKOApcDqJBfjwliP1ZAasH4IbAtsDTwV+B5+/pKkBcxh5GPSD3Wck9tYrb8kt/LwH1bT/+FOrUa+08RDaaKS3Ay8g+4zfz8Pr04d4P1V9axW2TQZ/dScQ+iGER8ObAG8Efh8VT3QMNogJbmqqvbpj6+uqr1nu7ZQJNkCeDXd578zsCVwWFV9vWkwSZJmYbE9YUmeCRznHGNp/SU5c77rVXXipLKovSSP5+FF0g6rqq0aRxqcITdgJXkacCzd579dVT2zcSRJktZgsT0B/TZgr6H7g2Ab4DNV9Y62qRaHJM+iWyzpuKp6bus8aifJ1lX1w9Y51EaSl1TV5a1zDM1iacBKsr3bP0qSFhqL7TFJsjndULcTgGcDFwJLq+oZTYMtAkm2oZuzeQLwPOBPgQur6vqmwTRxSbYEjqb7t7BbVW3TOJImyK2fxmuhNWD1DQMF3FdVJ7XOI0nS2lhsj0mSFXTb0vwR8JV+VfKbnVf86CX5bbo/qrelWyTtPODiqnLBuQ1IkiXAkXQF9t50i+YdBVxeVa70v8i59dN4LeQGrGlroaysqiubhpEkaR1YbI9JkrfRrfK6GfBJ4FPA/2ux/ej1q01fCfxuVX2zf84GjA1IknOAg4BL6Lb/+Se6/YBtcNkAzNj66dxpWz/5+T8GNmBJkjQeG7UOsFhV1Qer6gV0f8AAXARsk+TkJM9uGG3Ifomu4eIvknw3yXuBxzfOpMl6Dt3+6jcAN1TVatz6Z0PyQ7pCcGrrJ/Dzf0z6BqwbgZcDfw3sANxTVZdZaEuS9NhYbI9ZVd1cVadW1fOAfel6ZT7fONYgVdVdVfWRqjoY+BXgXuCHSW5IcmrjeJqAqtqLbvXhzYEvJvkKsHmSrdsm0yRU1VF06zR8CzglyS3Ak5Ps3zbZoNmAJUnSmDiMXIORZIuqum+W53emW438vQ1iqaEkz6ebu3sscEdVvahxJE2QWz+NRpJd6X6HS4E7gV2A3RfS4miSJA2RxfaY9D0uBfy4qg5onWcxSPIN4BVVdc+M518B/C//0N5wJQlwkFs/bbjc+mk0htiA1Y9sug/4WFXd1TqPJElTLLY1GEneBLwFeHlV/bh/7gTg/waOrKrrWubT+CV5N10j1gNV9YHWeTRZbv00OUNqwEpyFPAsYM+qen3rPJIkTdm4dQBpXVXVR5P8DPinvjd7KfA7wKFu+7PBuLV/XNEyhJo5q39c2TLEYjJXA1Z1LfELvtAGqKqLWmeQJGk2FttjkuSqqtrnsd6jNVXVJ/qC+2rgduDAqrqzcSxNSFWd3TqD2qmqL7fOsAjd2j8u+AYsR7ZIkobGYnt8dksy37DmAFtMKsxikOR6uj+0AmwKPIWulzt0HTF7tMwnSUMzsAasW/vHBd8wIEkSWGyP067rcM/qsadYXF7ZOoAkqUgZSfQAACAASURBVJldquoPW4eQJGlduUCaBi/JgcDxVfWW1lkkSePh1CtJ0tDYs61BSrI3cALwGuAW4MK2idRSkjcDdwGfrqpVrfNostz6aYPxuCRPpptK9AhVdfeE80iSNC+LbQ1GkmfT7f96PHAn8Cm60RmHNg2mhSDAgcBrgVc1zqLJ+zrd1k9/Cbj10wgs0AasXYFvMXuxXcBOk40jSdL8HEauwUjyIHAF8Maq+n7/3M1V5R9YkjRCSd5CV9xuX1ULogErydVVtXfrHJIkrSt7tjUkrwaOAy5N8gXgXOYYTqjFKclUr+WKqjq/aRhNnFs/TU5Vnd46gyRJQ2exrcGoqouAi5JsBhwJvA14WpIzgM9U1SVNA2oSduwf72+aQq3c2j+69dOIDKwB669aB5AkaX1YbGtwquqnwDnAOf1iOa8BTgYsthe/u6rqw61DqBm3fhq9ITVgfa4f3XAP8HHgNOAg4Cbgd6emF0mStFA4Z1vSYLj1z4bNz3/0kvyfQ2nASnIJ8E1gc+BXgDOBz9IV3K+tqkPapZMk6ZEstjUYSW6hm6/546o6oHUeTZ7F1oYtybXAIbj108gM6b+pJNdW1Z5JAtxWVdtNu3ZNVe3VMJ4kSY/gMHINRlXtuPa7tMjtkeQnszwfoKrqSZMOpIly66cN22ro/kNPcueMaw82yCNJ0rzs2ZY0GG79s2Hz8x+9JKuA5bNdYoE1YCW5F7icLttB/TH9+YFV9eRW2SRJmo0925IkbbiuH1ADxpHTjv98xrWZ55IkNWexLWlIFvrWRBovt37agFXVl2d7PskzgeOAWa9LktTKRq0DSNJ6+Mskb0jyqnROTvIPSf4qyVatw2nsPpfk3UnemuSJSc5IsizJxUl+uXW4gRpkA1aSpyZ5c5IrgMuArRtHkiTpEZyzrcFLckN/ePpQtrDRo5PkPODnwGbAk4FldFv/HAjsVVWvbBhPY+bWT6OXZAlwLN3e1Z8Ffp+H965+b1XNXIismSSbA68GTgCeDVwILK2qZzQNJknSHCy2tSgkeQrwgqr6XOssGp8ky6pq9yQbA3dU1dOnXbu2qvZsGE9j5tZPozekBqwkK4CvA38EfKVflfzmqnIVeknSguScbS0KVXUXYKG9+K0EqKpVSX4w49rqBnk0WW79NHrPmdGAdXD//Bf6fc0XknfSzc3+78Ank3yqcR5JkuZlsa3BSHIL3V66P66qA1rnURPPSPIhuq1+po7pz7dtF0sTslOSv6f7vKeO6c93bBdr0AbTgFVVHwQ+mGQnuqL7ImCbJCcDn6mqG5sGlCRpBoeRSxqMJG+Y73pVnT2pLJq8JAfPd32u1ao1tyQ/As6la7BY2h/Tnx9bVQt64bEkuwPH083ddpE8SdKCYrGtwUnyPGDX/vSGqlrWMo/aS7IJcERVDXJlZT02U1s/VdVprbMMjQ1YkiSNj8PINRhJtgAuBrYDrqXreXlektuBI6vqJy3zabKSPA44jK5X6xXAFQx0GyOtvyRPBV5D9/lvA3ymbaJhmquYnmrAmnCceTmVSJI0NPZsazD6+bkrgd+vqgf75zYC/gxYUlX/pWU+TUY/lPgE4HC6lYlfDOxUVcubBtPYufXTeM3WgFVVx7RNJUnScFlsazCSfBvYo6pWzXh+Y+D6qtqtTTJNSpI7gNuBM4CLqur+JLdUlYtjbQDc+mk8bMCSJGk8NmodQFoPK2cW2tCtogv8R4M8mrwL6IYMLwWOSLIZ3bBSbRjeCTyBbuundyZ5VuM8g9c3YP0p8BW6bcCOBlYsxEI7yVWjuEeSpEmxZ1uDkeQ7dMMbM/MS8Lf2bG8YkgQ4hO7fwuHAFsAbgc9X1QMNo2lCpm39dDywM/Bu3PrpUUnyQeAoYBlwDt26GNcvxNEC/ciG7813C7BFVW03oUiSJM3LYluDkeQy5unFrKpDJ5dGC0GSx/PwHNPDqmqrxpE0YW799NgNpQEryfbrcNvqqrpj7GEkSVoHFtuSFoUkS6pqResc0pDZgCVJ0uhYbGswkuwMnAb8MnA98I6q+re2qTRJSS6lG91wt6skb3jc+mmybMCSJOmxsdjWYCS5Avgb4HLgVcALq+rVbVNpkqYNI3WoqDQCNmBJkjQ+FtsajCTXVNVe086vqqp9WmaSpCGzAUuSpPHZuHUAaT1skmRvHl6NfMn086pyyxdpEVuXBjYb4dZPVd3WOoMkSYuVPdsajH6441yqql46sTCSJs6tnyRJ0pBYbGtRSPL4qvp56xySxsetnyRJ0pBYbGuw+r1hXwqcALyyqrZuHEmNJDkbWA6cXlXLWueRJEmSNmodQFpfSV6Q5EPAbcDFdKuT79o2lRr7MPBF4HWtg0iLQZKzk5yRZPfWWSRJGip7tjUYSU4FXgPcDnwS+AzwzarasWkwSVpkkuwHbAfsX1Unt84jSdIQWWxrMJL8CLgR+CDw2ar6jyQ3V9VOjaNpQpKcSbcn8H1VdVLrPJIkSdJc3PpLQ/JLwMuB44EP9quTL0mycVWtahtNE3JW/7iyZQhpsbABS5Kk8bFnW4OU5AnAK+kK74OAL1XVCW1TSdKwJDm4P1xZVVc2DSNJ0iJjsa3BS/Ik4Kiq+pvWWTReSa6b6xLdXut7TDKPJEmSNBeLbQ1Gktf3hyuq6vymYdREkmvohryeA3wWWDH9elXd1iKXNFQ2YEmSND7O2daQTK06fn/TFGqmqvZKsivd9IFzgG/3j5c4b196VB5kngYsSZL06NmzLWmwkiwFTgfeV1Wntc4jDdG0BqwjsAFLkqSRsdiWNChJtgWOA34duAc4D/hMVT3QNJi0CNiAJUnS6FhsSxqMJF8GNqcrsD8N3DX9elXd3SKXNGQ2YEmSNB4W25IGI8mtdPNLmfYIDy/mtNPEQ0kDZgOWJEnjY7GtwUtyJPC/q+prrbNI0pDYgCVJ0vi4GrkWgwOA5yXZuKp+tXUYjU+Sfea7XlVXTSqLtBhU1Q6tM0iStFjZsy1pMJI8CCwD7px6atrlqqqXTj6VNFw2YEmSND4W2xqMJC/pD1dW1T83DaMmkrwNOAa4DzgXF3GSHhMbsCRJGh+LbQ1GkjP7w3ur6qSmYdRUkp3oVk8+ErgNOLWqrmmbShoeG7AkSRofi20tCkm2rqofts6hyUnyXLqC+3XA71fVeY0jSYNlA5YkSaPnAmkarCRbAkcDJwC7Adu0TaRxm1EQ/CtdT9ypVbWiaTBp4Krq5iQXA0voGrCeDVhsS5L0GNizrUFJsoSu0DoB2Jtuf9ijgMur6sGW2TR+/fzS64CLgZ+w5lZFVNUHWuSShmqOBqzP2YAlSdJjZ7GtwUhyDnAQcAndH4T/BHy/qnZsGkwTk+QUZhTY01XVH08ujTR8NmBJkjQ+DiPXkDwHuAe4AbihqlYnsbVoA1JVp7TOIC0y7+HhAvuJLYNIkrTY2LOtQUmyK3A8sJRuq5pdgN1dHG3DkORD812vqrdOKoskSZI0H4ttDVaS59MV3scCd1TVixpH0pglWUm3J/B5wA9Yc09gqursFrmkobIBS5Kk8bHY1uAlCXBQVV3eOovGK8lTgNfQjWxYBXwKuKCq7m0aTBooG7AkSRofi20NRpJ3080tfMBFe5TkGXSrKL8dOLmqPtE4kjQ4NmBJkjQ+G7UOIK2HW4HbgDsa51BjSfYB/ivwG8A/At9qm0gapqq6q6o+UlWHAicCWwLfTvK6xtEkSRo8e7YlDUaS9wC/Rrci/bnAF6pqVdtU0vD1DVjHAy+na7z6i6r6dttUkiQNm8W2pMHo9wS+BVjePzX1P2ABqqr2aBJMGigbsCRJGh+LbUmDkWT7+a5X1W2TyiItBjZgSZI0Phu3DiBJ68piWhq5HVsHkCRpsbJnW4OX5M3AXcCnHf64uCW5ha7n7cdVdUDrPJIkSdJcLLY1eEneAuwKbF9Vr2qdR5KGwgYsSZLGx2JbkiRJkqQRc59tDUaS1/dfr2mdRW0kuWoU90iSJEnj5gJpGpKphXzub5pCLe2W5Lp5rgfYYlJhpKFLclVV7fNY75EkSY/kMHINTpKtqurO1jk0eWvb+qu3uqruGHsYaRFIsgL43ny3AFtU1XYTiiRJ0qJhsa3BSPJK4Ezg58CDwLFV9dW2qSRpuGzAkiRpfCy2NRj98OFjq+o7SQ4A3l9VB7fOJUmSJEkzuUCahmRVVX0HoKq+BmzeOI8kSZIkzcoF0jQkT0vy9rnOq+oDDTJJkiRJ0iNYbGtIPsqavdnTz50PIUmSJGnBcM62FoUk+1XVN1rnkCRJkiSwZ1sDluQ5wPH9173Avm0TSZIkSVLHYluDkmQHHi6wfw5sD+xbVbe2SyVJkiRJa3I1cg1GkiuBz9E1Eh1dVc8H7rfQliRJkrTQWGxrSH5ItyDa1sBT++dcdECSJEnSguMCaRqUJFsAr6YbRr4zsCVwWFV9vWkwSZIkSZrGYluDleRpwFLgOGC7qnpm40iSJEmSBFhsa5FI8oKq+ufWOSRJkiQJXI1cA5ZkS+Bo4ARgN2CbtokkSZIkqWOxrUFJsgQ4kq7A3ptuwbSjgMtb5pIkSZKk6VyNXIOR5BzgRuDlwF8DOwD3VNVlVfVgy2ySJEmSNJ3FtobkOcA9wA3ADVW1Grf+kiRJkrQAWWxrMKpqL+BYuqHjX0zyFWDzJFu3TSZJkiRJa3I1cg1WkufT7bd9LHBHVb2ocSRJkiRJAiy2tQgkCXBQVblImiRJkqQFwWJbg5Hk3XRztB+oqg+0ziNJkiRJc3HrLw3Jrf3jipYhJEmSJGlt7NmWJEmSJGnEXI1ckiRJkqQRs9iWJEmSJGnELLYlSZIkSRoxi20NXpI3J1maxAX/JEmSJC0IFttaDAIcCFzYOogkSZIkgauRS5IkSZI0cg671WAkeX1/uKKqzm8aRpIkSZLmYbGtIdmxf7y/aQpJkiRJWguHkUuSJEmSNGIukKbBSHLetOP3zbh2yeQTSZIkSdLsLLY1JDtPO375jGtPnWQQSZIkSZqPxbaGZL45D86HkCRJkrRguECahmTTJHvTNRIt6Y/Tfy1pmkySJEmSpnGBNA1Gkkvnu15Vh04qiyRJkiTNx2JbkiRJkqQRcxi5BiXJ9sBPq+rOJC8ADgS+X1UXNY4mSZIkSQ+xZ1uDkeRdwBvoFkM7F3gZcBlwAHBtVb2tXTpJkiRJepg92xqS44DdgE2B24GnV9XyJBsD1zRNJkmSJEnTWGxrSH5WVSuBlUluqqrlAFW1KsnKxtkkSZIk6SEW2xqSLZO8mm6rryf1x/TnW7SLJUmSJElrcs62BiPJmfNdr6oTJ5VFkiRJkuZjsS1JkiRJ0og5jFyDkmQX4LeBXfunbgD+Z1Xd2C6VJEmSJK1po9YBpHWV5IV0W309APxP4KPAT4HL+j23JUmSJGlBcBi5BiPJPwLvq6rLZjx/MPAHVfWrTYJJkiRJ0gwW2xqMJDdW1bPnuPbdqtpl0pkkSZIkaTYOI9eQ3D/PtZ9OLIUkSZIkrYULpGlInpnkQ7M8H2DbSYeRJEmSpLlYbGtIfm+ea9+cWApJkiRJWgvnbGvwkmwCHFFV57fOIkmSJEngnG0NVJLHJTk8ySeA24ClrTNJkiRJ0hSHkWtQ+m2+TgAOB74OvBjYsaqWNw0mSZIkSdM4jFyDkeQO4HbgDOCiqro/yS1VtWPjaJIkSZK0BoeRa0guALahGzJ+RJLNAFuLJEmSJC049mxrUJIEOAQ4nm4o+RbAG4HPV9UDDaNJkiRJ0kMstjVYSR4PHEZXeB9WVVs1jiRJkiRJgMW2FokkS6pqResckiRJkgQW2xqQJJfSzdG+u6qOaZ1HkiRJkuZisa3BSLJ9f7i6qu5oGkaSJEmS5mGxLUmSJEnSiLn1lyRJkiRJI2axLUmSJEnSiFlsS5IkSZI0YhbbGrwkZyc5I8nurbNIkiRJErhAmhaBJPsB2wH7V9XJrfNIkiRJksW2JEmSJEkjtnHrANK6SnImUMB9VXVS6zySJEmSNBeLbQ3JWf3jypYhJEmSJGltXCBNQ7I/cHNVXdk6iCRJkiTNx2JbQ7IN8NUkVyR5c5Kntg4kSZIkSbNxgTQNSpIALwGOA44CrgU+CVxYVfe3zCZJkiRJUyy2NVhJHge8DPgzYJeq2rRxJEmSJEkCXCBNA5XkeXS920uBO4F3tk0kSZIkSQ+z2NZgJNkZOJ6uwF4NnAu8oqpubhpMkiRJkmZwGLkGI8lNdPOzz62qZa3zSJIkSdJcLLY1KEk2BlZXVSV5JnAAcFNVXd04miRJkiQ9xK2/NBhJfgv4EXBbkjcBXwKOAc5NcnLTcJIkSZI0jT3bGowk/wIcCGwO3ABsX1V3JtkU+EZVPbdpQEmSJEnquUCahmRlVd0D3JPk+1V1J0BVLU+ysnE2SZIkSXqIxbaGZEmSvemmP/xCf5z+a5OmySRJkiRpGoeRazCSXDrf9ao6dFJZJEmSJGk+FtuSJEmSJI2Yw8g1KEm2B37aL4z2AroF075fVRc1jiZJkiRJD7FnW4OR5F3AG4ACzgVeBlxGt9f2tVX1tnbpJEmSJOlh9mxrSI4DdgM2BW4Hnt6vRL4xcE3TZJIkSZI0jcW2huRnVbUSWJnkpqpaDlBVq9z6S5IkSdJCYrGtIdkyyavptvp6Un9Mf75Fu1iSJEmStCbnbGswkpw53/WqOnFSWSRJkiRpPhbbWhSSbF1VP2ydQ5IkSZIANmodQHq0kmyZ5I1JvgRc3TqPJEmSJE1xzrYGJckS4EjgBGBvYHPgKODylrkkSZIkaTp7tjUYSc4BbgReDvw1sANwT1VdVlUPtswmSZIkSdNZbGtIngPcA9wA3FBVqwEXHZAkSZK04FhsazCqai/gWLqh419M8hVg8yRbt00mSZIkSWtyNXINVpLnA8fTFeB3VNWLGkeSJEmSJMBiW4tAkgAHVZWLpEmSJElaECy2NRhJ3k03R/uBqvpA6zySJEmSNBe3/tKQ3No/rmgZQpIkSZLWxp5tSZIkSZJGzNXIJUmSJEkaMYttSZIkSZJGzGJbkiRJkqQRs9jW4CV5c5KlSVzwT5IkSdKCYLGtxSDAgcCFrYNIkiRJErgauSRJkiRJI+ewWw1Gktf3hyuq6vymYSRJkiRpHhbbGpId+8f7m6aQJEmSpLWw2NaQ3FVVH24dQpIkSZLWxgXSNCT/uXUASZIkSVoXFtuSJEmSJI2Yq5FrMJKsApbPdgmoqnrShCNJkiRJ0qycs60hub6q9m4dQpIkSZLWxmHkkiRJkiSNmMW2hsS9tSVJkiQNgnO2NRhJlgDHAvcAnwV+HzgIuAl4b1Xd2TCeJEmSJD3EYluDkeQ84OfAZsCTgWV0RfeBwF5V9cqG8SRJkiTpIRbbGowky6pq9yQbA3dU1dOnXbu2qvZsGE+SJEmSHuKcbQ3JSoCqWgX8YMa11ZOPI0mSJEmzc+svDckzknyIbl/tqWP6823bxZIkSZKkNTmMXIOR5A3zXa+qsyeVRZIkSZLmY7GtwUuyCXBEVbk1mCRJkqQFwTnbGqQkj0tyeJJPALcBS1tnkiRJkqQpztnWoCQ5GDgBOBz4OvBiYMeqWt40mCRJkiRN4zByDUaSO4DbgTOAi6rq/iS3VNWOjaNJkiRJ0hocRq4huQDYhm7I+BFJNgNsLZIkSZK04NizrUFJEuAQ4Hi6oeRbAG8EPl9VDzSMJkmSJEkPsdjWYCV5PHAYXeF9WFVt1TiSJEmSJAEW21okkiypqhWtc0iSJEkSWGxrQJJcSjdH++6qOqZ1HkmSJEmai8W2BiPJ9v3h6qq6o2kYSZIkSZqHxbYkSZIkSSPm1l+SJEmSJI2YxbYkSZIkSSNmsS1JkiRJ0ohZbGvwkpyd5Iwku7fOIkmSJEngAmlaBJLsB2wH7F9VJ7fOI0mSJEkW25IkSZIkjdjGrQNI6yrJmUAB91XVSa3zSJIkSdJcLLY1JGf1jytbhpAkSZKktXEYuSRJkiRJI2bPtgYjyXVzXQKqqvaYZB5JkiRJmovFtobkQbo52+cAnwVWtI0jSZIkSbNzGLkGJcmuwPHAEcC36QrvS6pqVdNgkiRJkjSNxbYGK8lS4HTgfVV1Wus8kiRJkjTFYluDkmRb4Djg14F7gPOAz1TVA02DSZIkSdI0FtsajCRfBjanK7A/Ddw1/XpV3d0ilyRJkiTNZLGtwUhyK90CaUx7hIdXI99p4qEkSZIkaRYW25IkSZIkjZhbf2kwkuwz3/WqumpSWSRJkiRpPvZsazCSPAgsA+6cemra5aqql04+lSRJkiQ9kj3bGpK3A8cAK4BzcRVySZIkSQuUPdsanCQ70W3/dSRwG3BqVV3TNpUkSZIkPWyj1gGk9VVVNwMXA5cA+wPPbptIkiRJktZkz7YGY0aP9r/SDSX/XFWtaBpMkiRJkmaw2NZg9AukXUfXq/0T1txrm6r6QItckiRJkjSTC6RpSN7DwwX2E1sGkSRJkqT52LMtSZIkSdKI2bOtwUjyofmuV9VbJ5VFkiRJkuZjsa0h+R1gGXAe8AMgbeNIkiRJ0uwcRq7BSPIU4DXAUmAV8Cnggqq6t2kwSZIkSZrBfbY1GFV1V1V9pKoOBU4EtgS+neR1jaNJkiRJ0hocRq7BSbIPcDzwcuAfgW+1TSRJkiRJa3IYuQYjyXuAXwNuAM4FvlBVq9qmkiRJkqRHstjWYCR5ELgFWN4/NfWPN0BV1R5NgkmSJEnSDA4j15Ds2DqAJEmSJK0Le7YlSZIkSRoxe7Y1GEluoRs6/uOqOqB1HkmSJEmaiz3bkiRJkiSNmPtsS5IkSZI0YhbbGowkV43iHkmSJEkaN4eRazCSrAC+N98twBZVtd2EIkmSJEnSrFwgTUOy6zrcs3rsKSRJkiRpLezZliRJkiRpxJyzLUmSJEnSiFlsS5IkSZI0YhbbkqSJSrI6yTVJliU5P8mm6/HavZIcPu38CUm+2L/f0nled1mSffvjzyfZ8rH9FGvN+TtJXt8f/2aSbaZduzXJVmt5/aZJ/i7J9f3v6StJnphkhyTLxpl9bfr81ye5LsmXk2y/lvt3SHLCtPN9k3xo/EnnzPP2JN/u839pbfklSXq0LLYlSZO2oqr2qqrdgZXA76zLi5JsDOwFHD7t6b0B+vf71Lq8T1UdXlX3rmfm9VJVH6mqv+lPfxPYZp7bZ/NfgR9W1fP639MbgZ8/1lz973AUDq2qPYDLgD9ay707AA8V21X1zap667p+oySbJXn8owk5h6uBffv8FwDvH+F7S5L0EIttSVJLVwC/nOQXk1zU9zb+c5I9AJKckuQTSf4/4BPAe4Cl03qy/xbYrz9/VpJfSXJ13/P68SRPmPkNp/cs972cy/qvt/XPbZbkc0mu7Z9fOuP1T0vyrf54zySVZLv+/Ka+V/qUJO9IcgywL/B3fcYl/dv8lyRX9Tln22nhl4B/mzqpqu9W1X/0p49L8tEk/5Lkkqn3TPKmJN/oc396asRAkrOSfCTJ14D3T/udXpnke0neNO1n+73+Pa5L8sfr8PldCWzbv3aHJFf0P9dVSV7U3/NnwEH9z39SkkOS/EP/mlk/9xmeDdyY5M+T7LYOmeZVVZdW1fL+9J+BZzzW95QkaTYW25KkJvpe1l8Frgf+GLi67238Q+Bvpt36HOBlVXU88C7gU9N6sn8LuKKq9qIrTs8CllbV8+i2t/w/5vn+zwdOBA4AXgC8KcnewH8CflBVe/a9yl+Y/rqq+hGwSZInAQcB36QrJrcHfjStkKOqLuivv7bPvKK/dGdV7QOcAbxjlngfB07uC+I/SbLztGs7A6dX1XOBe4Gj++cvrKr9qmpP4Aa63vApzwBeVFVv78/3AF4KvBB4V5Jtkryif+/96UYQPD/JS+b6/fX+E3BRf/wj4OX9z7UUmBoq/gf0n1FV/eWM18/3uQNQVVf3eb8DfKwfUn9iks2m7umL/Gtm+XrZWvK/EfjHtdwjSdKj4j7bkqRJW5Lkmv74CuB/AV+jLxqr6p+SPKUvZgH+flqROp9dgFuq6sb+/GzgLcAH57j/QOAzVfVTgCQX0hXPXwD+Isn7gH+oqitmee1XgRcDLwFOpSs60/886+LC/vFbwKtnXqyqa5LsBLwCeBnwjSQvBFb0P+M1016/Q3+8e5I/AbYEngj8P9Pe8vyqWj3t/OL+d7oiyaV0BfaB/fe7ur/niXTF9+Wz5L80yS8CDwD/rX/u8cCHk+wFrKbrkV6bA5nlc6+qn8z4fdwPfIyu2N6N7t/MXwFP6q8ftA7faw1JfoNu1MHB6/taSZLWhcW2JGnSVvQ90Q9JMt/9Px1vnDVV1Y1J9qGbG/4nSb5UVe+ZcdvldIX59sDFwMlAAZ9bx28zNSR8NXP8f3FVPUBXlF+Y5ME+z6envXbq9VND088Cjqqqa5P8JnDItPtm/g5rlvMAf1pV/2Md8h9K16v+d3S9028HTgJ+COxJN3LuZ+vwPussyQ7AG4DjgWuBU6ZduwLYfJaXvaOqvjjLe70M+L+Ag6cNz5ckaaQcRi5JWgiuAF4LkOQQumHWP5nlvvuZvagC+C6wQ5Jf7s9fB3x5Ld/zqH6O9WbArwNXpFs5fHlV/S1wGrDPHK/9DeB7VfUgcDddMfyV9cw8qyQvTvLk/vgX6IbS37aWl20O/Hu6xcReu5Z7j0yySZKn0BXl36DrCf/PSZ7Yf99tkzxtrjeoqlXA24DX973cWwD/3v8+Xgc8rr91vp9/rZ97Pxf8i3TD1e8FXlxVS6vqkmlZDuqHqc/8mq3Q3hv4H8Cr+ikBkiSNhT3bkqSF4BTg40muA5bT9WDO5lLgD/ph6H9K15MKQFX9LMmJwPn9fPBvAB+Z6xtW1VVJzgK+3j/1saq6OslhwGl9b/LPmWXed1Xdmq47fmqI9VeAZ1TVPbN8q7OAjyRZQTdHel08Czij/x4b0fWYf5quJ30u/41uOP6P+8f5Cvzr9A0D/QAAAMBJREFU6H6XWwHvraofAD/oh2hf2Y80eICuQWHOgrSq/j3JJ+mG6/934NPptjz7Ag/3pl8HrE5yLd3v4uppb3EKa//cVwN/WFVfn+Xao3Ea3RD58/uf8/aqetWI3luSpIekauZIMkmStFglOQV4oKr+vHUWSZIWM4eRS5IkSZI0YvZsS5IkSZI0YvZsS5IkSZI0YhbbkiRJkiSNmMW2JEmSJEkjZrEtSZIkSdKIWWxLkiRJkjRiFtuSJEmSJI3Y/w8EsbPBaWrqKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>portfolio</th>\n",
       "      <th>weights</th>\n",
       "      <th>sharp_ratio</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>['ABT', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.02717391304347826, 0.08876811594202899, 0.0...</td>\n",
       "      <td>1.992109</td>\n",
       "      <td>0.202377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>['ABT', 'AES', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...</td>\n",
       "      <td>[0.013289036544850499, 0.016611295681063124, 0...</td>\n",
       "      <td>1.980479</td>\n",
       "      <td>0.209244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>['MMM', 'ABT', 'ABBV', 'ADBE', 'AAP', 'A', 'AP...</td>\n",
       "      <td>[0.007194244604316547, 0.041109969167523124, 0...</td>\n",
       "      <td>1.975656</td>\n",
       "      <td>0.193472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.00333889816360601, 0.11018363939899833, 0.0...</td>\n",
       "      <td>1.969905</td>\n",
       "      <td>0.195324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...</td>\n",
       "      <td>[0.028764805414551606, 0.06937394247038917, 0....</td>\n",
       "      <td>1.957159</td>\n",
       "      <td>0.200016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...</td>\n",
       "      <td>[0.0064034151547491995, 0.06510138740661686, 0...</td>\n",
       "      <td>1.925996</td>\n",
       "      <td>0.185426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>['ABBV', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG'...</td>\n",
       "      <td>[0.15765765765765766, 0.05405405405405406, 0.0...</td>\n",
       "      <td>1.907091</td>\n",
       "      <td>0.187892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>['MMM', 'ATVI', 'AEP', 'AME', 'AMGN', 'APH', '...</td>\n",
       "      <td>[0.01702127659574468, 0.2723404255319149, 0.17...</td>\n",
       "      <td>1.890134</td>\n",
       "      <td>0.202181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...</td>\n",
       "      <td>[0.02823529411764706, 0.11529411764705882, 0.0...</td>\n",
       "      <td>1.873219</td>\n",
       "      <td>0.188682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>['MMM', 'AMD', 'AES', 'ALXN', 'GOOGL', 'GOOG',...</td>\n",
       "      <td>[0.016728624535315983, 0.1654275092936803, 0.0...</td>\n",
       "      <td>1.862995</td>\n",
       "      <td>0.214649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...</td>\n",
       "      <td>[0.005805515239477504, 0.1262699564586357, 0.0...</td>\n",
       "      <td>1.851713</td>\n",
       "      <td>0.197720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>['ABT', 'APD', 'AKAM', 'ARE', 'ALXN', 'GOOGL',...</td>\n",
       "      <td>[0.05365853658536585, 0.14471544715447154, 0.0...</td>\n",
       "      <td>1.821050</td>\n",
       "      <td>0.193634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>['MMM', 'ABBV', 'ADBE', 'LNT', 'GOOGL', 'MO', ...</td>\n",
       "      <td>[0.005836575875486381, 0.15369649805447472, 0....</td>\n",
       "      <td>1.776741</td>\n",
       "      <td>0.182428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>['ABT', 'ARE', 'ALXN', 'GOOGL', 'GOOG', 'MO', ...</td>\n",
       "      <td>[0.0072992700729927005, 0.041970802919708027, ...</td>\n",
       "      <td>1.721864</td>\n",
       "      <td>0.179269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>['ATVI', 'ALB']</td>\n",
       "      <td>[0.5181818181818182, 0.4818181818181818]</td>\n",
       "      <td>1.526555</td>\n",
       "      <td>0.239549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>['ATVI', 'AMP']</td>\n",
       "      <td>[0.5742574257425742, 0.42574257425742573]</td>\n",
       "      <td>1.359109</td>\n",
       "      <td>0.186542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>['ATVI', 'AIG']</td>\n",
       "      <td>[0.6867469879518072, 0.3132530120481928]</td>\n",
       "      <td>1.256244</td>\n",
       "      <td>0.178206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>['ATVI', 'ALK']</td>\n",
       "      <td>[0.7848101265822784, 0.21518987341772153]</td>\n",
       "      <td>1.138615</td>\n",
       "      <td>0.165618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>['ATVI', 'APTV']</td>\n",
       "      <td>[0.7108433734939759, 0.2891566265060241]</td>\n",
       "      <td>1.073595</td>\n",
       "      <td>0.157280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>['ATVI', 'AFL']</td>\n",
       "      <td>[0.9888888888888889, 0.011111111111111112]</td>\n",
       "      <td>0.806947</td>\n",
       "      <td>0.130921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            portfolio  \\\n",
       "25  ['ABT', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG',...   \n",
       "26  ['ABT', 'AES', 'GOOGL', 'GOOG', 'MO', 'AMZN', ...   \n",
       "27  ['MMM', 'ABT', 'ABBV', 'ADBE', 'AAP', 'A', 'AP...   \n",
       "28  ['ABT', 'APD', 'AKAM', 'ARE', 'GOOGL', 'GOOG',...   \n",
       "29  ['ABT', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR',...   \n",
       "30  ['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...   \n",
       "31  ['ABBV', 'ARE', 'ALXN', 'LNT', 'GOOGL', 'GOOG'...   \n",
       "32  ['MMM', 'ATVI', 'AEP', 'AME', 'AMGN', 'APH', '...   \n",
       "33  ['ABT', 'AAP', 'APD', 'AKAM', 'ARE', 'ALXN', '...   \n",
       "34  ['MMM', 'AMD', 'AES', 'ALXN', 'GOOGL', 'GOOG',...   \n",
       "35  ['MMM', 'ABT', 'ABBV', 'APD', 'AKAM', 'ARE', '...   \n",
       "36  ['ABT', 'APD', 'AKAM', 'ARE', 'ALXN', 'GOOGL',...   \n",
       "37  ['MMM', 'ABBV', 'ADBE', 'LNT', 'GOOGL', 'MO', ...   \n",
       "38  ['ABT', 'ARE', 'ALXN', 'GOOGL', 'GOOG', 'MO', ...   \n",
       "39                                    ['ATVI', 'ALB']   \n",
       "40                                    ['ATVI', 'AMP']   \n",
       "41                                    ['ATVI', 'AIG']   \n",
       "42                                    ['ATVI', 'ALK']   \n",
       "43                                   ['ATVI', 'APTV']   \n",
       "44                                    ['ATVI', 'AFL']   \n",
       "\n",
       "                                              weights  sharp_ratio    return  \n",
       "25  [0.02717391304347826, 0.08876811594202899, 0.0...     1.992109  0.202377  \n",
       "26  [0.013289036544850499, 0.016611295681063124, 0...     1.980479  0.209244  \n",
       "27  [0.007194244604316547, 0.041109969167523124, 0...     1.975656  0.193472  \n",
       "28  [0.00333889816360601, 0.11018363939899833, 0.0...     1.969905  0.195324  \n",
       "29  [0.028764805414551606, 0.06937394247038917, 0....     1.957159  0.200016  \n",
       "30  [0.0064034151547491995, 0.06510138740661686, 0...     1.925996  0.185426  \n",
       "31  [0.15765765765765766, 0.05405405405405406, 0.0...     1.907091  0.187892  \n",
       "32  [0.01702127659574468, 0.2723404255319149, 0.17...     1.890134  0.202181  \n",
       "33  [0.02823529411764706, 0.11529411764705882, 0.0...     1.873219  0.188682  \n",
       "34  [0.016728624535315983, 0.1654275092936803, 0.0...     1.862995  0.214649  \n",
       "35  [0.005805515239477504, 0.1262699564586357, 0.0...     1.851713  0.197720  \n",
       "36  [0.05365853658536585, 0.14471544715447154, 0.0...     1.821050  0.193634  \n",
       "37  [0.005836575875486381, 0.15369649805447472, 0....     1.776741  0.182428  \n",
       "38  [0.0072992700729927005, 0.041970802919708027, ...     1.721864  0.179269  \n",
       "39           [0.5181818181818182, 0.4818181818181818]     1.526555  0.239549  \n",
       "40          [0.5742574257425742, 0.42574257425742573]     1.359109  0.186542  \n",
       "41           [0.6867469879518072, 0.3132530120481928]     1.256244  0.178206  \n",
       "42          [0.7848101265822784, 0.21518987341772153]     1.138615  0.165618  \n",
       "43           [0.7108433734939759, 0.2891566265060241]     1.073595  0.157280  \n",
       "44         [0.9888888888888889, 0.011111111111111112]     0.806947  0.130921  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "fig,ax1 = plt.subplots(figsize=(16,8))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(portfolio_2['portfolio'], portfolio_2['sharp_ratio'], 'g-', alpha=0.6, marker='o', \n",
    "         linestyle='dashed', linewidth=2, markersize=6)\n",
    "ax1.plot(portfolio_2['portfolio'], portfolio_2['return'], 'b-', alpha=0.6, marker='o', \n",
    "         linestyle='dashed', linewidth=2, markersize=6)\n",
    "\n",
    "\n",
    "ax1.set_xlabel('Portfolios with Sharpe Ratio >= 2')\n",
    "ax1.set_ylabel('Return')\n",
    "ax2.set_ylabel('Sharpe Ratio')\n",
    "ax1.xaxis.set_tick_params(rotation=90)\n",
    "ax1.legend(['Return'],loc='upper left')\n",
    "ax2.legend(['sharp_ration'],loc='upper right')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "portfolio_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}