6. 预测d带中心.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "167530a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35f3fec5",
   "metadata": {
    "scrolled": true
   },
   "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>Fe</th>\n",
       "      <th>Co</th>\n",
       "      <th>Ni</th>\n",
       "      <th>Cu</th>\n",
       "      <th>Ru</th>\n",
       "      <th>Rh</th>\n",
       "      <th>Pd</th>\n",
       "      <th>Ag</th>\n",
       "      <th>Ir</th>\n",
       "      <th>Pt</th>\n",
       "      <th>Au</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fe</th>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Co</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>-1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ni</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cu</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.27</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ru</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rh</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pd</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ag</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-4.30</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ir</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pt</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Au</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-3.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fe    Co    Ni    Cu    Ru    Rh    Pd    Ag    Ir    Pt    Au\n",
       "Fe -0.92  0.05 -0.20 -0.13 -0.29 -0.54 -1.24 -0.83 -0.36 -1.09 -1.42\n",
       "Co  0.01 -1.17 -0.28 -0.16 -0.24 -0.58 -1.37 -0.91 -0.36 -1.19 -1.56\n",
       "Ni  0.09  0.19 -1.29  0.19 -0.14 -0.31 -0.97 -0.53 -0.14 -0.80 -1.13\n",
       "Cu  0.56  0.60  0.27 -2.67  0.58  0.32 -0.64 -0.70  0.58 -0.33 -1.09\n",
       "Ru  0.21  0.26  0.01  0.12 -1.41 -0.17 -0.82 -0.27  0.02 -0.62 -0.84\n",
       "Rh  0.24  0.34  0.16  0.44  0.04 -1.73 -0.54  0.07  0.17 -0.35 -0.49\n",
       "Pd  0.37  0.54  0.50  0.94  0.24  0.36 -1.83  0.59  0.53  0.19  0.17\n",
       "Ag  0.72  0.84  0.67  0.47  0.84  0.86  0.14 -4.30  1.14  0.50 -0.15\n",
       "Ir  0.21  0.27  0.05  0.21  0.09 -0.15 -0.73 -0.13 -2.11 -0.56 -0.74\n",
       "Pt  0.33  0.48  0.40  0.72  0.14  0.23 -0.17  0.44  0.38 -2.25 -0.05\n",
       "Au  0.63  0.77  0.63  0.55  0.70  0.75  0.17  0.21  0.98  0.46 -3.56"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data_impurities.csv', encoding = 'UTF-8') as f:\n",
    "    df = pd.read_csv(f, index_col = 0)\n",
    "    \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "361d0139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Au','Fe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8a6889e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fe', 'Co', 'Ni', 'Cu', 'Ru', 'Rh', 'Pd', 'Ag', 'Ir', 'Pt', 'Au'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7915ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fe', 'Co', 'Ni', 'Cu', 'Ru', 'Rh', 'Pd', 'Ag', 'Ir', 'Pt', 'Au'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2ae81ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fe\n",
      "host Fe, guest Fe, val -0.92\n",
      "Fe\n",
      "host Fe, guest Co, val 0.05\n",
      "Fe\n",
      "host Fe, guest Ni, val -0.2\n",
      "Fe\n",
      "host Fe, guest Cu, val -0.13\n",
      "Fe\n",
      "host Fe, guest Ru, val -0.29\n",
      "Fe\n",
      "host Fe, guest Rh, val -0.54\n",
      "Fe\n",
      "host Fe, guest Pd, val -1.24\n",
      "Fe\n",
      "host Fe, guest Ag, val -0.83\n",
      "Fe\n",
      "host Fe, guest Ir, val -0.36\n",
      "Fe\n",
      "host Fe, guest Pt, val -1.09\n",
      "Fe\n",
      "host Fe, guest Au, val -1.42\n",
      "Co\n",
      "host Co, guest Fe, val 0.01\n",
      "Co\n",
      "host Co, guest Co, val -1.17\n",
      "Co\n",
      "host Co, guest Ni, val -0.28\n",
      "Co\n",
      "host Co, guest Cu, val -0.16\n",
      "Co\n",
      "host Co, guest Ru, val -0.24\n",
      "Co\n",
      "host Co, guest Rh, val -0.58\n",
      "Co\n",
      "host Co, guest Pd, val -1.37\n",
      "Co\n",
      "host Co, guest Ag, val -0.91\n",
      "Co\n",
      "host Co, guest Ir, val -0.36\n",
      "Co\n",
      "host Co, guest Pt, val -1.19\n",
      "Co\n",
      "host Co, guest Au, val -1.56\n",
      "Ni\n",
      "host Ni, guest Fe, val 0.09\n",
      "Ni\n",
      "host Ni, guest Co, val 0.19\n",
      "Ni\n",
      "host Ni, guest Ni, val -1.29\n",
      "Ni\n",
      "host Ni, guest Cu, val 0.19\n",
      "Ni\n",
      "host Ni, guest Ru, val -0.14\n",
      "Ni\n",
      "host Ni, guest Rh, val -0.31\n",
      "Ni\n",
      "host Ni, guest Pd, val -0.97\n",
      "Ni\n",
      "host Ni, guest Ag, val -0.53\n",
      "Ni\n",
      "host Ni, guest Ir, val -0.14\n",
      "Ni\n",
      "host Ni, guest Pt, val -0.8\n",
      "Ni\n",
      "host Ni, guest Au, val -1.13\n",
      "Cu\n",
      "host Cu, guest Fe, val 0.56\n",
      "Cu\n",
      "host Cu, guest Co, val 0.6\n",
      "Cu\n",
      "host Cu, guest Ni, val 0.27\n",
      "Cu\n",
      "host Cu, guest Cu, val -2.67\n",
      "Cu\n",
      "host Cu, guest Ru, val 0.58\n",
      "Cu\n",
      "host Cu, guest Rh, val 0.32\n",
      "Cu\n",
      "host Cu, guest Pd, val -0.64\n",
      "Cu\n",
      "host Cu, guest Ag, val -0.7\n",
      "Cu\n",
      "host Cu, guest Ir, val 0.58\n",
      "Cu\n",
      "host Cu, guest Pt, val -0.33\n",
      "Cu\n",
      "host Cu, guest Au, val -1.09\n",
      "Ru\n",
      "host Ru, guest Fe, val 0.21\n",
      "Ru\n",
      "host Ru, guest Co, val 0.26\n",
      "Ru\n",
      "host Ru, guest Ni, val 0.01\n",
      "Ru\n",
      "host Ru, guest Cu, val 0.12\n",
      "Ru\n",
      "host Ru, guest Ru, val -1.41\n",
      "Ru\n",
      "host Ru, guest Rh, val -0.17\n",
      "Ru\n",
      "host Ru, guest Pd, val -0.82\n",
      "Ru\n",
      "host Ru, guest Ag, val -0.27\n",
      "Ru\n",
      "host Ru, guest Ir, val 0.02\n",
      "Ru\n",
      "host Ru, guest Pt, val -0.62\n",
      "Ru\n",
      "host Ru, guest Au, val -0.84\n",
      "Rh\n",
      "host Rh, guest Fe, val 0.24\n",
      "Rh\n",
      "host Rh, guest Co, val 0.34\n",
      "Rh\n",
      "host Rh, guest Ni, val 0.16\n",
      "Rh\n",
      "host Rh, guest Cu, val 0.44\n",
      "Rh\n",
      "host Rh, guest Ru, val 0.04\n",
      "Rh\n",
      "host Rh, guest Rh, val -1.73\n",
      "Rh\n",
      "host Rh, guest Pd, val -0.54\n",
      "Rh\n",
      "host Rh, guest Ag, val 0.07\n",
      "Rh\n",
      "host Rh, guest Ir, val 0.17\n",
      "Rh\n",
      "host Rh, guest Pt, val -0.35\n",
      "Rh\n",
      "host Rh, guest Au, val -0.49\n",
      "Pd\n",
      "host Pd, guest Fe, val 0.37\n",
      "Pd\n",
      "host Pd, guest Co, val 0.54\n",
      "Pd\n",
      "host Pd, guest Ni, val 0.5\n",
      "Pd\n",
      "host Pd, guest Cu, val 0.94\n",
      "Pd\n",
      "host Pd, guest Ru, val 0.24\n",
      "Pd\n",
      "host Pd, guest Rh, val 0.36\n",
      "Pd\n",
      "host Pd, guest Pd, val -1.83\n",
      "Pd\n",
      "host Pd, guest Ag, val 0.59\n",
      "Pd\n",
      "host Pd, guest Ir, val 0.53\n",
      "Pd\n",
      "host Pd, guest Pt, val 0.19\n",
      "Pd\n",
      "host Pd, guest Au, val 0.17\n",
      "Ag\n",
      "host Ag, guest Fe, val 0.72\n",
      "Ag\n",
      "host Ag, guest Co, val 0.84\n",
      "Ag\n",
      "host Ag, guest Ni, val 0.67\n",
      "Ag\n",
      "host Ag, guest Cu, val 0.47\n",
      "Ag\n",
      "host Ag, guest Ru, val 0.84\n",
      "Ag\n",
      "host Ag, guest Rh, val 0.86\n",
      "Ag\n",
      "host Ag, guest Pd, val 0.14\n",
      "Ag\n",
      "host Ag, guest Ag, val -4.3\n",
      "Ag\n",
      "host Ag, guest Ir, val 1.14\n",
      "Ag\n",
      "host Ag, guest Pt, val 0.5\n",
      "Ag\n",
      "host Ag, guest Au, val -0.15\n",
      "Ir\n",
      "host Ir, guest Fe, val 0.21\n",
      "Ir\n",
      "host Ir, guest Co, val 0.27\n",
      "Ir\n",
      "host Ir, guest Ni, val 0.05\n",
      "Ir\n",
      "host Ir, guest Cu, val 0.21\n",
      "Ir\n",
      "host Ir, guest Ru, val 0.09\n",
      "Ir\n",
      "host Ir, guest Rh, val -0.15\n",
      "Ir\n",
      "host Ir, guest Pd, val -0.73\n",
      "Ir\n",
      "host Ir, guest Ag, val -0.13\n",
      "Ir\n",
      "host Ir, guest Ir, val -2.11\n",
      "Ir\n",
      "host Ir, guest Pt, val -0.56\n",
      "Ir\n",
      "host Ir, guest Au, val -0.74\n",
      "Pt\n",
      "host Pt, guest Fe, val 0.33\n",
      "Pt\n",
      "host Pt, guest Co, val 0.48\n",
      "Pt\n",
      "host Pt, guest Ni, val 0.4\n",
      "Pt\n",
      "host Pt, guest Cu, val 0.72\n",
      "Pt\n",
      "host Pt, guest Ru, val 0.14\n",
      "Pt\n",
      "host Pt, guest Rh, val 0.23\n",
      "Pt\n",
      "host Pt, guest Pd, val -0.17\n",
      "Pt\n",
      "host Pt, guest Ag, val 0.44\n",
      "Pt\n",
      "host Pt, guest Ir, val 0.38\n",
      "Pt\n",
      "host Pt, guest Pt, val -2.25\n",
      "Pt\n",
      "host Pt, guest Au, val -0.05\n",
      "Au\n",
      "host Au, guest Fe, val 0.63\n",
      "Au\n",
      "host Au, guest Co, val 0.77\n",
      "Au\n",
      "host Au, guest Ni, val 0.63\n",
      "Au\n",
      "host Au, guest Cu, val 0.55\n",
      "Au\n",
      "host Au, guest Ru, val 0.7\n",
      "Au\n",
      "host Au, guest Rh, val 0.75\n",
      "Au\n",
      "host Au, guest Pd, val 0.17\n",
      "Au\n",
      "host Au, guest Ag, val 0.21\n",
      "Au\n",
      "host Au, guest Ir, val 0.98\n",
      "Au\n",
      "host Au, guest Pt, val 0.46\n",
      "Au\n",
      "host Au, guest Au, val -3.56\n"
     ]
    }
   ],
   "source": [
    "for i in df.index:\n",
    "    for j in df.columns:\n",
    "        print(i)\n",
    "        print(f'host {i}, guest {j}, val {df.loc[i, j]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fad31f0c",
   "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>Fe</th>\n",
       "      <th>Co</th>\n",
       "      <th>Ni</th>\n",
       "      <th>Cu</th>\n",
       "      <th>Ru</th>\n",
       "      <th>Rh</th>\n",
       "      <th>Pd</th>\n",
       "      <th>Ag</th>\n",
       "      <th>Ir</th>\n",
       "      <th>Pt</th>\n",
       "      <th>Au</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fe</th>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-2.16</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Co</th>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>-2.54</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ni</th>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-1.60</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cu</th>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.07</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-2.35</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>-3.37</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ru</th>\n",
       "      <td>-1.20</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rh</th>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pd</th>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.29</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ag</th>\n",
       "      <td>-3.58</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>-3.83</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-4.16</td>\n",
       "      <td>-4.30</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.80</td>\n",
       "      <td>-4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ir</th>\n",
       "      <td>-1.90</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-2.06</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.26</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.67</td>\n",
       "      <td>-2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pt</th>\n",
       "      <td>-1.92</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>-1.85</td>\n",
       "      <td>-1.53</td>\n",
       "      <td>-2.11</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Au</th>\n",
       "      <td>-2.93</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-3.01</td>\n",
       "      <td>-2.86</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>-3.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fe    Co    Ni    Cu    Ru    Rh    Pd    Ag    Ir    Pt    Au\n",
       "Fe -0.92 -0.87 -1.12 -1.05 -1.21 -1.46 -2.16 -1.75 -1.28 -2.01 -2.34\n",
       "Co -1.16 -1.17 -1.45 -1.33 -1.41 -1.75 -2.54 -2.08 -1.53 -2.36 -2.73\n",
       "Ni -1.20 -1.10 -1.29 -1.10 -1.43 -1.60 -2.26 -1.82 -1.43 -2.09 -2.42\n",
       "Cu -2.11 -2.07 -2.40 -2.67 -2.09 -2.35 -3.31 -3.37 -2.09 -3.00 -3.76\n",
       "Ru -1.20 -1.15 -1.40 -1.29 -1.41 -1.58 -2.23 -1.68 -1.39 -2.03 -2.25\n",
       "Rh -1.49 -1.39 -1.57 -1.29 -1.69 -1.73 -2.27 -1.66 -1.56 -2.08 -2.22\n",
       "Pd -1.46 -1.29 -1.33 -0.89 -1.59 -1.47 -1.83 -1.24 -1.30 -1.64 -1.66\n",
       "Ag -3.58 -3.46 -3.63 -3.83 -3.46 -3.44 -4.16 -4.30 -3.16 -3.80 -4.45\n",
       "Ir -1.90 -1.84 -2.06 -1.90 -2.02 -2.26 -2.84 -2.24 -2.11 -2.67 -2.85\n",
       "Pt -1.92 -1.77 -1.85 -1.53 -2.11 -2.02 -2.42 -1.81 -1.87 -2.25 -2.30\n",
       "Au -2.93 -2.79 -2.93 -3.01 -2.86 -2.81 -3.39 -3.35 -2.58 -3.10 -3.56"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in df.index:\n",
    "    for j in df.columns:\n",
    "        if i != j:\n",
    "            df.loc[i, j] += df.loc[i, i]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8581b566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>name</th>\n",
       "      <th>group</th>\n",
       "      <th>bulk wigner-seitz radius</th>\n",
       "      <th>atomic number</th>\n",
       "      <th>atomic mass</th>\n",
       "      <th>period</th>\n",
       "      <th>electronegativity</th>\n",
       "      <th>Iionization energy(eV)</th>\n",
       "      <th>H_fus</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fe</th>\n",
       "      <td>Iron</td>\n",
       "      <td>8</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Co</th>\n",
       "      <td>Cobalt</td>\n",
       "      <td>9</td>\n",
       "      <td>2.62</td>\n",
       "      <td>27</td>\n",
       "      <td>58.93320</td>\n",
       "      <td>4</td>\n",
       "      <td>1.88</td>\n",
       "      <td>7.8810</td>\n",
       "      <td>272.5</td>\n",
       "      <td>8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ni</th>\n",
       "      <td>Nickel</td>\n",
       "      <td>10</td>\n",
       "      <td>2.60</td>\n",
       "      <td>28</td>\n",
       "      <td>58.69340</td>\n",
       "      <td>4</td>\n",
       "      <td>1.91</td>\n",
       "      <td>7.6398</td>\n",
       "      <td>290.3</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cu</th>\n",
       "      <td>Copper</td>\n",
       "      <td>11</td>\n",
       "      <td>2.67</td>\n",
       "      <td>29</td>\n",
       "      <td>63.54600</td>\n",
       "      <td>4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>7.7264</td>\n",
       "      <td>203.5</td>\n",
       "      <td>8.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ru</th>\n",
       "      <td>Ruthenium</td>\n",
       "      <td>8</td>\n",
       "      <td>2.79</td>\n",
       "      <td>44</td>\n",
       "      <td>101.07000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7.3605</td>\n",
       "      <td>381.8</td>\n",
       "      <td>12.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rh</th>\n",
       "      <td>Rhodium</td>\n",
       "      <td>9</td>\n",
       "      <td>2.81</td>\n",
       "      <td>45</td>\n",
       "      <td>102.90550</td>\n",
       "      <td>5</td>\n",
       "      <td>2.28</td>\n",
       "      <td>7.4589</td>\n",
       "      <td>258.4</td>\n",
       "      <td>12.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pd</th>\n",
       "      <td>Palladium</td>\n",
       "      <td>10</td>\n",
       "      <td>2.87</td>\n",
       "      <td>46</td>\n",
       "      <td>106.42000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.3369</td>\n",
       "      <td>157.3</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ag</th>\n",
       "      <td>Silver</td>\n",
       "      <td>11</td>\n",
       "      <td>3.01</td>\n",
       "      <td>47</td>\n",
       "      <td>107.86820</td>\n",
       "      <td>5</td>\n",
       "      <td>1.93</td>\n",
       "      <td>7.5762</td>\n",
       "      <td>104.6</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ir</th>\n",
       "      <td>Iridium</td>\n",
       "      <td>9</td>\n",
       "      <td>2.84</td>\n",
       "      <td>77</td>\n",
       "      <td>192.21700</td>\n",
       "      <td>6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.9670</td>\n",
       "      <td>213.9</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pt</th>\n",
       "      <td>Platinum</td>\n",
       "      <td>10</td>\n",
       "      <td>2.90</td>\n",
       "      <td>78</td>\n",
       "      <td>195.07800</td>\n",
       "      <td>6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.9588</td>\n",
       "      <td>113.6</td>\n",
       "      <td>21.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Au</th>\n",
       "      <td>Gold</td>\n",
       "      <td>11</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  group  bulk wigner-seitz radius  atomic number  atomic mass  \\\n",
       "Fe       Iron      8                      2.66             26     55.84500   \n",
       "Co     Cobalt      9                      2.62             27     58.93320   \n",
       "Ni     Nickel     10                      2.60             28     58.69340   \n",
       "Cu     Copper     11                      2.67             29     63.54600   \n",
       "Ru  Ruthenium      8                      2.79             44    101.07000   \n",
       "Rh    Rhodium      9                      2.81             45    102.90550   \n",
       "Pd  Palladium     10                      2.87             46    106.42000   \n",
       "Ag     Silver     11                      3.01             47    107.86820   \n",
       "Ir    Iridium      9                      2.84             77    192.21700   \n",
       "Pt   Platinum     10                      2.90             78    195.07800   \n",
       "Au       Gold     11                      3.00             79    196.96655   \n",
       "\n",
       "    period  electronegativity  Iionization energy(eV)  H_fus  density  \n",
       "Fe       4               1.83                  7.9024  247.3     7.87  \n",
       "Co       4               1.88                  7.8810  272.5     8.86  \n",
       "Ni       4               1.91                  7.6398  290.3     8.90  \n",
       "Cu       4               1.90                  7.7264  203.5     8.96  \n",
       "Ru       5               2.20                  7.3605  381.8    12.10  \n",
       "Rh       5               2.28                  7.4589  258.4    12.40  \n",
       "Pd       5               2.20                  8.3369  157.3    12.00  \n",
       "Ag       5               1.93                  7.5762  104.6    10.50  \n",
       "Ir       6               2.20                  8.9670  213.9    22.50  \n",
       "Pt       6               2.20                  8.9588  113.6    21.50  \n",
       "Au       6               2.40                  9.2255   64.6    19.30  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data_impurities_features.csv', encoding = 'UTF-8') as f:\n",
    "    feat = pd.read_csv(f, index_col = 0)\n",
    "feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1ef8c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                         Cobalt\n",
       "group                             9\n",
       "bulk wigner-seitz radius       2.62\n",
       "atomic number                    27\n",
       "atomic mass                 58.9332\n",
       "period                            4\n",
       "electronegativity              1.88\n",
       "Iionization energy(eV)        7.881\n",
       "H_fus                         272.5\n",
       "density                        8.86\n",
       "Name: Co, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat.loc['Co']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d657882e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>bulk wigner-seitz radius</th>\n",
       "      <th>atomic number</th>\n",
       "      <th>atomic mass</th>\n",
       "      <th>period</th>\n",
       "      <th>electronegativity</th>\n",
       "      <th>Iionization energy(eV)</th>\n",
       "      <th>H_fus</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fe</th>\n",
       "      <td>8</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4</td>\n",
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       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Co</th>\n",
       "      <td>9</td>\n",
       "      <td>2.62</td>\n",
       "      <td>27</td>\n",
       "      <td>58.93320</td>\n",
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       "      <td>272.5</td>\n",
       "      <td>8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ni</th>\n",
       "      <td>10</td>\n",
       "      <td>2.60</td>\n",
       "      <td>28</td>\n",
       "      <td>58.69340</td>\n",
       "      <td>4</td>\n",
       "      <td>1.91</td>\n",
       "      <td>7.6398</td>\n",
       "      <td>290.3</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cu</th>\n",
       "      <td>11</td>\n",
       "      <td>2.67</td>\n",
       "      <td>29</td>\n",
       "      <td>63.54600</td>\n",
       "      <td>4</td>\n",
       "      <td>1.90</td>\n",
       "      <td>7.7264</td>\n",
       "      <td>203.5</td>\n",
       "      <td>8.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ru</th>\n",
       "      <td>8</td>\n",
       "      <td>2.79</td>\n",
       "      <td>44</td>\n",
       "      <td>101.07000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7.3605</td>\n",
       "      <td>381.8</td>\n",
       "      <td>12.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rh</th>\n",
       "      <td>9</td>\n",
       "      <td>2.81</td>\n",
       "      <td>45</td>\n",
       "      <td>102.90550</td>\n",
       "      <td>5</td>\n",
       "      <td>2.28</td>\n",
       "      <td>7.4589</td>\n",
       "      <td>258.4</td>\n",
       "      <td>12.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pd</th>\n",
       "      <td>10</td>\n",
       "      <td>2.87</td>\n",
       "      <td>46</td>\n",
       "      <td>106.42000</td>\n",
       "      <td>5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.3369</td>\n",
       "      <td>157.3</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ag</th>\n",
       "      <td>11</td>\n",
       "      <td>3.01</td>\n",
       "      <td>47</td>\n",
       "      <td>107.86820</td>\n",
       "      <td>5</td>\n",
       "      <td>1.93</td>\n",
       "      <td>7.5762</td>\n",
       "      <td>104.6</td>\n",
       "      <td>10.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ir</th>\n",
       "      <td>9</td>\n",
       "      <td>2.84</td>\n",
       "      <td>77</td>\n",
       "      <td>192.21700</td>\n",
       "      <td>6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.9670</td>\n",
       "      <td>213.9</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pt</th>\n",
       "      <td>10</td>\n",
       "      <td>2.90</td>\n",
       "      <td>78</td>\n",
       "      <td>195.07800</td>\n",
       "      <td>6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.9588</td>\n",
       "      <td>113.6</td>\n",
       "      <td>21.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Au</th>\n",
       "      <td>11</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    group  bulk wigner-seitz radius  atomic number  atomic mass  period  \\\n",
       "Fe      8                      2.66             26     55.84500       4   \n",
       "Co      9                      2.62             27     58.93320       4   \n",
       "Ni     10                      2.60             28     58.69340       4   \n",
       "Cu     11                      2.67             29     63.54600       4   \n",
       "Ru      8                      2.79             44    101.07000       5   \n",
       "Rh      9                      2.81             45    102.90550       5   \n",
       "Pd     10                      2.87             46    106.42000       5   \n",
       "Ag     11                      3.01             47    107.86820       5   \n",
       "Ir      9                      2.84             77    192.21700       6   \n",
       "Pt     10                      2.90             78    195.07800       6   \n",
       "Au     11                      3.00             79    196.96655       6   \n",
       "\n",
       "    electronegativity  Iionization energy(eV)  H_fus  density  \n",
       "Fe               1.83                  7.9024  247.3     7.87  \n",
       "Co               1.88                  7.8810  272.5     8.86  \n",
       "Ni               1.91                  7.6398  290.3     8.90  \n",
       "Cu               1.90                  7.7264  203.5     8.96  \n",
       "Ru               2.20                  7.3605  381.8    12.10  \n",
       "Rh               2.28                  7.4589  258.4    12.40  \n",
       "Pd               2.20                  8.3369  157.3    12.00  \n",
       "Ag               1.93                  7.5762  104.6    10.50  \n",
       "Ir               2.20                  8.9670  213.9    22.50  \n",
       "Pt               2.20                  8.9588  113.6    21.50  \n",
       "Au               2.40                  9.2255   64.6    19.30  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat.drop('name', axis = 'columns', inplace = True)\n",
    "# feat = feat.drop('name', axis = 'columns')\n",
    "feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f0c1cc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "host(Fe), guest(Co), input=[  8.       2.66    26.      55.845    4.       1.83     7.9024 247.3\n",
      "   7.87     9.       2.62    27.      58.9332   4.       1.88     7.881\n",
      " 272.5      8.86  ], output=-0.87\n"
     ]
    }
   ],
   "source": [
    "x = list()\n",
    "y = list()\n",
    "\n",
    "for i in df.index:\n",
    "    for j in df.columns:\n",
    "        vec_i = feat.loc[i].to_numpy()\n",
    "        vec_j = feat.loc[j].to_numpy()\n",
    "        x_val = np.concatenate((vec_i, vec_j))\n",
    "        y_val = df.loc[i][j]\n",
    "        x.append(x_val)\n",
    "        y.append(y_val)\n",
    "        if i == 'Fe' and j == 'Co':\n",
    "            print(f'host({i}), guest({j}), input={x_val}, output={y_val}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a5c6bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
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       "      <th>x11</th>\n",
       "      <th>x12</th>\n",
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       "      <th>x14</th>\n",
       "      <th>x15</th>\n",
       "      <th>x16</th>\n",
       "      <th>x17</th>\n",
       "      <th>x18</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "      <td>-0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.62</td>\n",
       "      <td>27.0</td>\n",
       "      <td>58.93320</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>7.8810</td>\n",
       "      <td>272.5</td>\n",
       "      <td>8.86</td>\n",
       "      <td>-0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.69340</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.91</td>\n",
       "      <td>7.6398</td>\n",
       "      <td>290.3</td>\n",
       "      <td>8.90</td>\n",
       "      <td>-1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.67</td>\n",
       "      <td>29.0</td>\n",
       "      <td>63.54600</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>7.7264</td>\n",
       "      <td>203.5</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.66</td>\n",
       "      <td>26.0</td>\n",
       "      <td>55.84500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>7.9024</td>\n",
       "      <td>247.3</td>\n",
       "      <td>7.87</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.79</td>\n",
       "      <td>44.0</td>\n",
       "      <td>101.07000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7.3605</td>\n",
       "      <td>381.8</td>\n",
       "      <td>12.10</td>\n",
       "      <td>-1.21</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.87</td>\n",
       "      <td>46.0</td>\n",
       "      <td>106.42000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.3369</td>\n",
       "      <td>157.3</td>\n",
       "      <td>12.00</td>\n",
       "      <td>-3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>47.0</td>\n",
       "      <td>107.86820</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>7.5762</td>\n",
       "      <td>104.6</td>\n",
       "      <td>10.50</td>\n",
       "      <td>-3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.84</td>\n",
       "      <td>77.0</td>\n",
       "      <td>192.21700</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.9670</td>\n",
       "      <td>213.9</td>\n",
       "      <td>22.50</td>\n",
       "      <td>-2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>78.0</td>\n",
       "      <td>195.07800</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.9588</td>\n",
       "      <td>113.6</td>\n",
       "      <td>21.50</td>\n",
       "      <td>-3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>196.96655</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9.2255</td>\n",
       "      <td>64.6</td>\n",
       "      <td>19.30</td>\n",
       "      <td>-3.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       x1    x2    x3         x4   x5    x6      x7     x8     x9   x10   x11  \\\n",
       "0     8.0  2.66  26.0   55.84500  4.0  1.83  7.9024  247.3   7.87   8.0  2.66   \n",
       "1     8.0  2.66  26.0   55.84500  4.0  1.83  7.9024  247.3   7.87   9.0  2.62   \n",
       "2     8.0  2.66  26.0   55.84500  4.0  1.83  7.9024  247.3   7.87  10.0  2.60   \n",
       "3     8.0  2.66  26.0   55.84500  4.0  1.83  7.9024  247.3   7.87  11.0  2.67   \n",
       "4     8.0  2.66  26.0   55.84500  4.0  1.83  7.9024  247.3   7.87   8.0  2.79   \n",
       "..    ...   ...   ...        ...  ...   ...     ...    ...    ...   ...   ...   \n",
       "116  11.0  3.00  79.0  196.96655  6.0  2.40  9.2255   64.6  19.30  10.0  2.87   \n",
       "117  11.0  3.00  79.0  196.96655  6.0  2.40  9.2255   64.6  19.30  11.0  3.01   \n",
       "118  11.0  3.00  79.0  196.96655  6.0  2.40  9.2255   64.6  19.30   9.0  2.84   \n",
       "119  11.0  3.00  79.0  196.96655  6.0  2.40  9.2255   64.6  19.30  10.0  2.90   \n",
       "120  11.0  3.00  79.0  196.96655  6.0  2.40  9.2255   64.6  19.30  11.0  3.00   \n",
       "\n",
       "      x12        x13  x14   x15     x16    x17    x18     y  \n",
       "0    26.0   55.84500  4.0  1.83  7.9024  247.3   7.87 -0.92  \n",
       "1    27.0   58.93320  4.0  1.88  7.8810  272.5   8.86 -0.87  \n",
       "2    28.0   58.69340  4.0  1.91  7.6398  290.3   8.90 -1.12  \n",
       "3    29.0   63.54600  4.0  1.90  7.7264  203.5   8.96 -1.05  \n",
       "4    44.0  101.07000  5.0  2.20  7.3605  381.8  12.10 -1.21  \n",
       "..    ...        ...  ...   ...     ...    ...    ...   ...  \n",
       "116  46.0  106.42000  5.0  2.20  8.3369  157.3  12.00 -3.39  \n",
       "117  47.0  107.86820  5.0  1.93  7.5762  104.6  10.50 -3.35  \n",
       "118  77.0  192.21700  6.0  2.20  8.9670  213.9  22.50 -2.58  \n",
       "119  78.0  195.07800  6.0  2.20  8.9588  113.6  21.50 -3.10  \n",
       "120  79.0  196.96655  6.0  2.40  9.2255   64.6  19.30 -3.56  \n",
       "\n",
       "[121 rows x 19 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(x, columns = [f'x{i+1}' for i in range(18)])\n",
    "df['y'] = y\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "baf542e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aaaaa, Hello world!\n"
     ]
    }
   ],
   "source": [
    "a = 'Hello'\n",
    "b = 'world!'\n",
    "print('aaaaa, %s %s'%(a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f0eaac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aaaaa, Hello world!\n"
     ]
    }
   ],
   "source": [
    "a = 'Hello'\n",
    "b = 'world!'\n",
    "print(f'aaaaa, {a} {b}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1f8b213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fd85860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = ['x1', 'y']\n",
    "df[idx].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61e51a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x1', ylabel='y'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SS3PUiDdFaI4aX7mlspOIZOmKzS3SnKNgHRoYY+tdP+C3//ZHbL2r8iPRlCjQzOywcsAMomaYmsBDQTPmw6cWSxBpwh2amR1G2bW8hX3M9xwc0j7mIaCZ2eFSi10HVaNAT0lhpFpeuE2enuXl46eYPD0bdCjLXndHKzPJ3D6imeR8RR90VaPI0FNSuKiWF15f+u7zOcvd7Nqyjjt3XB1gROLuJY/Pl2oUWTrb42xae5GSRAiolhdOI8dP5SQJgHufeo2R49qiNiijk9O0xnKf+VtjTRWtfatGIaGlWl74DBx7s2i5VvcNRi1q34HUKMzsbjN70cyGzOwBM7uoyHmvmNnzZjZgZv01DlNCQLW8cOlde9GSyqX6alH7DqpG8RjwRXdPmtldwBeBvUXO/Yi7v1G70ESkmJ7VK9i1ZR33PpXbR6HaRLAacj8Kd3806/Bp4KYg4pDwm5hKqOkpZO7ccTW7PniFNpQKmWruRxGGPorfBf6xyHsOPGpmDnzD3fcX+xAz2w3sBli3bl3Fg5TaOzQwxt6DQ8QiEeZSKfbt3KiNi0KiZ/UKJYhlpGp9FGb2uJkdKfCzI+ucO4Ak8K0iH7PV3a8BPg78gZldW+z73H2/u/e5e98ll1xS0WuR2qvFbFMRKU/VahTufl2p983sM8CNwEe9yKBfdx/P/Pm6mT0AbAaeqHSsEj61mG0q0kiq2UwbSNOTmd1AuvP619397SLntAERdz+VeX09cGcNw5QAacKdSPmq3Uwb1IS7rwErgMcyQ1//BsDMuszscOac1cCTZjYIPAM85O4PBxOu1Jom3ImUp2EXBXT3niLl48C2zOujwKZaxiXhogl3IourRTNtGEY9iRRVzSF/Io2gYWdmi5Srmrt2iTSChWbaeFOEC5qjxJsCmJltZrcB33L3yYp9q0gZNI9CpDwLWznj1dnKuZwaxWXAs2Z2wMxuMNNeY1J9mkchUp5abOW8aKJw9/8ArAe+CfwO8LKZ/Rcze1fFohDJo42LRMpTi38rZfVRZCbE/SLzkwQ6gPvNbF/FIhHJUotdu0QaQSg6s83sD83sOWAf8H+Bq9393wLvB3ZWLBKRPNXetUukEYRlmfFVwCfd/dXsQndPmdmNFYtEJMvCrl2nEslflS3s2qXhsiK5tveu4ap3XFi1FX0XTRTu/qUS771Q0WhEMrSEh0j5Dg2Msef+QaIWYd5T3H3TpoZYwkOkJC3hIVKeiakEnz8wkDPq6XMHBup/CQ+RclS7Oi3SCIbH3yKZW/kmmUqXX/vuymy5oEQhoVXt6rRIYyg2yKNygz/U9CShVIvqtEgj2NC1kmgkdx50NGJs6FpZse9QopBQKlWdFpFclld7yD8+X0oUElLVr06LNIKFoeTZFoaSV4oShYTShq6VxKK51elYtLLVaZFGEIqZ2SJB6GyPc8/Nm3KWTr7n5k0aHiuSp7M9zgcu78gp+8DlHTWfmS0SCO1wJ7K4keOn+OHIRE7ZD0cmGDl+qmJDypUoJNS0w51IaQPH3ixaXqlEEUjTk5n9uZkNmdmAmT1qZl1FzrvBzF4ysxEzu73WcYqIhF3v2ouWVH4uguqjuNvdN7p7L/A94Kz1pMwsCnwd+DhwFfApM7uqplGKiIRcz+oVvGd1W07Ze1a3VXQlg0AShbtnD4Zvo/CYx83AiLsfdfdZ4D5gRy3iExGpFyPHT/HS8dM5ZS8dP83I8VMV+47ARj2Z2V+Y2THg0xSoUQBrgGNZx6OZsmKft9vM+s2s/8SJE5UNVkQkpEr1UVRK1RKFmT1uZkcK/OwAcPc73H0t8C3gtkIfUaCs6Gwrd9/v7n3u3nfJJZVZCEtEJOxq0UdRtVFP7n5dmad+G3gI+NO88lFgbdZxNzBegdBERBpGz+oV7Nqyjnufeu1XZbu2rKv/PgozW591uB14scBpzwLrzexKM2sGbgUerEV8IiL15P2XX0y8KUJLU4R4U4S+yy+u6OcH1Ufx5Uwz1BBwPfBZADPrMrPDAO6eJN0k9QjwAnDA3YcDildEJJQmphLsPThEIpliJpkikUyx5+BQ/W9c5O47i5SPA9uyjg8Dh2sVl4hIvRmdnCYWiTDDmfWeYpFIRfeX11pPIiJ1TIsCiohISZ3tcW7p684pu6Wvu6JL3yhRiIjUsYmpBAf6R3PKDvSPVrSPQolCRKSOLfRRZFvoo6gUJQoRkTqmPgoRESmpsz3Ovp0baYlFWBFvoiUWYd/Ojdq4SEREzqj2Jl9KFCIiDaCam3yp6UlEpAFMTCUYPPZmRUc7LVCNQkSkzh0aGGPvwSFikQhzqRT7dm5ke2/RXRmWTDUKEZE6trDW08xcilOJJDNzlV/rSYlCRKSOjU5O46ncrXo85ZpHISIiaW3NURLzuYkiMe+0NUcr9h1KFCIidWz8ZOGaQ7Hyc6FEISJSx96aTi6p/FwoUYiI1LELWwsPXi1Wfi6UKERE6tiGrpVEI5ZTFo0YG7pWVuw7lChEROpcKm/UU/7x+QokUZjZn5vZkJkNmNmjZtZV5LxXzOz5zHn9tY5TRCTsnvrpBPlpwTPllRJUjeJud9/o7r3A94AvlTj3I+7e6+59tQlNRKR+vFFkYl2x8nMRSKJw97eyDtvgrIQoIiJl+HDPqiWVn4vA+ijM7C/M7BjwaYrXKBx41MyeM7Pdi3zebjPrN7P+EydOVDpcEZFQ6lm9gl1b1uWU7dqyjp7VKyr2HeZenYd5M3scuKzAW3e4+6Gs874ItLj7nxb4jC53HzezS4HHgH/n7k8s9t19fX3e368uDRFZPvp/NsETL7/BtetX0Xdl55J/38yeK9bEX7XVY939ujJP/TbwEHBWonD38cyfr5vZA8BmYNFEISKynBwaGGPP/UNEI8Y3njjK3Tc1wOqxZrY+63A78GKBc9rMbMXCa+B64EhtIhQRqQ8TUwm+8J1BEskUb8/Ok0im+Px3Bhti9dgvm9kRMxsinQA+C+mmJjM7nDlnNfCkmQ0CzwAPufvDwYQrIhJOw+MnmctbFHBu3hkeP1mx7whk4yJ331mkfBzYlnl9FNhUy7hEROqPLbF86TQzW0Skjm3ouvCslGCZ8kpRohARqXNNUSt5fL6UKERE6tjo5DQtTbmbFLU0RbXDnYiIpHV3tDKXSuWUzaVSdHe0Vuw7lChEROpYZ3ucfTs30hKLsCLeREsswr6dG+lsj1fsOwIZ9SQiIpWzvXcNW3tWMTo5TXdHa0WTBKhGISIii1CNQkSkzh0aGGPvwSFikQhzqRT7djbAEh4iIlIZE1MJ9h4cYmYuxalEkpm5FHsODjXEEh4iIlIBo5PTeN7Wp55yDY8VEZG0tuYoiby1nhLzTltztMhvLJ0ShYhIHTs9O08sbyZ2LGqcnp2v2HcoUYiI1LG25mjB1WNVoxARESBdo4jn1SjiqlGIiMiC7o5WLJKbKCxiWsJDRETStISHiIgsqtpLeChRiIg0gM72eMUTxAI1PYmISEmBJgoz+4KZuZmtKvL+DWb2kpmNmNnttY5PREQCTBRmthb4GPBakfejwNeBjwNXAZ8ys6tqF6GIiECwNYq/AvYAXuT9zcCIux9191ngPmBHrYITEZG0QBKFmW0Hxtx9sMRpa4BjWcejmbJin7nbzPrNrP/EiRMVilRERKo26snMHgcuK/DWHcCfANcv9hEFyorVPnD3/cB+gL6+vqLniYjI0lQtUbj7dYXKzexq4Epg0MwAuoEfm9lmd/9F1qmjwNqs425gvErhiohIETWfR+HuzwOXLhyb2StAn7u/kXfqs8B6M7sSGANuBX6rVnGKiEhaqOZRmFmXmR0GcPckcBvwCPACcMDdh4OMT0RkOQp8Zra7X5H1ehzYlnV8GDgcQFgiIpIRqhqFiIicm4mpBIPH3qzoXtkLAq9RiIjI+Tk0MMbeg0PEIhHmUin27dzI9t6iswmWTDUKEZE6NjGVYO/BIWbmUpxKJJmZS7Hn4FBFaxZKFCIidWx0cppYJPc/5bFIhNHJ6Yp9hxKFiEgd6+5oZS6VyimbS6W0w52IiKRphzsREVmUdrgTEZFFaYc7EREpSfMoRESkKM2jEBGRojSPQkREStI8ChERKUnzKEREpCTNoxARkUVpHoWIiCxK8yhERKQkzaMQEZGiNI9CRESKavh5FGb2BTNzM1tV5P1XzOx5Mxsws/5axyciEna1mEcRWNOTma0FPga8tsipH3H3N2oQkohI3Wn0eRR/BewBPMAYRETqWsPOozCz7cCYuw+aWalTHXjUzBz4hrvvL/GZu4HdAOvWratkuCIioVa38yjM7HHgsgJv3QH8CXB9GR+z1d3HzexS4DEze9Hdnyh0YiaJ7Afo6+tTLUVElpVqzqOoWqJw9+sKlZvZ1cCVwEJtohv4sZltdvdf5H3GeObP183sAWAzUDBRiIhIddS8j8Ldn3f3S939Cne/AhgFrslPEmbWZmYrFl6TroEcqXW8IiLLXajmUZhZl5kdzhyuBp40s0HgGeAhd384uOhERJanwGdmZ2oVC6/HgW2Z10eBTQGFJSIiGaGqUYiISPiYe+MNEDKzE8Cr5/jrq4BGmeDXKNfSKNcBupYwapTrgPO7lsvd/ZJCbzRkojgfZtbv7n1Bx1EJjXItjXIdoGsJo0a5DqjetajpSURESlKiEBGRkpQozlZ0mZA61CjX0ijXAbqWMGqU64AqXYv6KEREpCTVKEREpCQlChERKWlZJgoz+2MzGzazI2b2D2bWkve+mdl/NbMRMxsys2uCinUxZVzLb5jZycwugQNm9qWgYl2MmX02cx3DZvZHBd6vp/uy2LWE9r6Y2d+Z2etmdiSr7GIze8zMXs782VHkd28ws5cy9+j22kVdMJbzuY5Q7a5Z5Fpuzvz9SplZ0SGxFbkn7r6sfoA1wM+A1szxAeB38s7ZBvwTYMAHgR8FHfd5XMtvAN8LOtYyruV9pBd9vID00jKPA+vr9L6Ucy2hvS/AtcA1wJGssn3A7ZnXtwN3Ffi9KPBT4J1AMzAIXFVv15F57xVgVdD3YpFreS/wHuB/A31Ffq8i92RZ1ihI/+NtNbMm0v+Yx/Pe3wHc62lPAxeZ2TtqHWSZFruWevFe4Gl3f9vdk8D/AT6Rd0693JdyriW0PL3nyy/zincAf595/ffAvy7wq5uBEXc/6u6zwH2Z3wvEeVxH6BS6Fnd/wd1fWuRXK3JPll2icPcx4C9J79X9c+Ckuz+ad9oa4FjW8WimLFTKvBaALWY2aGb/ZGYbahpk+Y4A15pZp5ldQLr2sDbvnLq4L5R3LVAf92XBanf/OUDmz0sLnFMP96ec64Azu2s+l9k9s15V5J4su0SRaZPcQXrzpC6gzcx+O/+0Ar8aunHEZV7Lj0mv4bIJ+GvguzUNskzu/gJwF/AY8DDpKnIy77S6uC9lXktd3Jclqov7U6at7n4N8HHgD8zs2qADOkcVuSfLLlEA1wE/c/cT7j4H/E/gQ3nnjJL7BNhNOJt0Fr0Wd3/L3acyrw8DMTNbVftQF+fu33T3a9z9WtLV7JfzTqmX+7LotdTTfck4vtDMl/nz9QLn1MP9Kec68KzdNYGF3TXrUUXuyXJMFK8BHzSzC8zMgI8CL+Sd8yCwKzPK5oOkm3R+XutAy7DotZjZZZn3MLPNpO/5RM0jLYOl90bHzNYBnwT+Ie+Uerkvi15LPd2XjAeBz2RefwY4VOCcZ4H1ZnalmTUDt2Z+L0wWvQ5rrN01K3NPgu7ND+IH+DPgRdI3/78DceD3gd/PvG/A10mPFnieIiMKwvBTxrXcBgyTbv54GvhQ0DGXuJYfAj/JxPrRTFm93pfFriW094V0Uvs5MEf6ifT3gE7g+6RrRt8HLs6c2wUczvrdbcA/Z+7RHfV4HaRHCA1mfoaDvo4S1/KJzOsEcBx4pFr3REt4iIhIScux6UlERJZAiUJEREpSohARkZKUKEREpCQlChERKUmJQqSGzOxhM3vTzL4XdCwi5VKiEKmtu4F/E3QQIkuhRCFSBWb2gcyeGS2Zmb7DZvY+d/8+cCro+ESWoinoAEQakbs/a2YPAv8ZaAX+h7vX6zIQsswpUYhUz52k19qZAf4w4FhEzpmankSq52KgHVgBtCxyrkhoKVGIVM9+4D8C3yK9P4VIXVLTk0gVmNkuIOnu3zazKPD/zOxfkV7t99eAdjMbBX7P3R8JMlaRxWj1WBERKUlNTyIiUpIShYiIlKREISIiJSlRiIhISUoUIiJSkhKFiIiUpEQhIiIl/X/d1uAe13t8lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = 'x1', y = 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cbf44b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x1    -0.628485\n",
       "x2    -0.528149\n",
       "x3    -0.291100\n",
       "x4    -0.286463\n",
       "x5    -0.262104\n",
       "x6    -0.024741\n",
       "x7    -0.152404\n",
       "x8     0.564631\n",
       "x9    -0.169864\n",
       "x10   -0.236039\n",
       "x11   -0.347411\n",
       "x12   -0.264994\n",
       "x13   -0.260405\n",
       "x14   -0.274515\n",
       "x15   -0.279717\n",
       "x16   -0.240431\n",
       "x17    0.329626\n",
       "x18   -0.196517\n",
       "y      1.000000\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b55fd4ef",
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "      <th>x10</th>\n",
       "      <th>x11</th>\n",
       "      <th>x12</th>\n",
       "      <th>x13</th>\n",
       "      <th>x14</th>\n",
       "      <th>x15</th>\n",
       "      <th>x16</th>\n",
       "      <th>x17</th>\n",
       "      <th>x18</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>121.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.636364</td>\n",
       "      <td>2.797273</td>\n",
       "      <td>47.818182</td>\n",
       "      <td>112.685714</td>\n",
       "      <td>4.909091</td>\n",
       "      <td>2.084545</td>\n",
       "      <td>8.093945</td>\n",
       "      <td>209.800000</td>\n",
       "      <td>13.171818</td>\n",
       "      <td>9.636364</td>\n",
       "      <td>2.797273</td>\n",
       "      <td>47.818182</td>\n",
       "      <td>112.685714</td>\n",
       "      <td>4.909091</td>\n",
       "      <td>2.084545</td>\n",
       "      <td>8.093945</td>\n",
       "      <td>209.800000</td>\n",
       "      <td>13.171818</td>\n",
       "      <td>-2.115868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.072381</td>\n",
       "      <td>0.138618</td>\n",
       "      <td>20.123369</td>\n",
       "      <td>54.126480</td>\n",
       "      <td>0.795822</td>\n",
       "      <td>0.188122</td>\n",
       "      <td>0.640997</td>\n",
       "      <td>89.804938</td>\n",
       "      <td>5.128033</td>\n",
       "      <td>1.072381</td>\n",
       "      <td>0.138618</td>\n",
       "      <td>20.123369</td>\n",
       "      <td>54.126480</td>\n",
       "      <td>0.795822</td>\n",
       "      <td>0.188122</td>\n",
       "      <td>0.640997</td>\n",
       "      <td>89.804938</td>\n",
       "      <td>5.128033</td>\n",
       "      <td>0.818380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>55.845000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>7.360500</td>\n",
       "      <td>64.600000</td>\n",
       "      <td>7.870000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>55.845000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>7.360500</td>\n",
       "      <td>64.600000</td>\n",
       "      <td>7.870000</td>\n",
       "      <td>-4.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>58.933200</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>7.576200</td>\n",
       "      <td>113.600000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>58.933200</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>7.576200</td>\n",
       "      <td>113.600000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>-2.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.810000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>102.905500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>7.881000</td>\n",
       "      <td>213.900000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.810000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>102.905500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>7.881000</td>\n",
       "      <td>213.900000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>-2.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>192.217000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>8.958800</td>\n",
       "      <td>272.500000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>192.217000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>8.958800</td>\n",
       "      <td>272.500000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>-1.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>196.966550</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>9.225500</td>\n",
       "      <td>381.800000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>196.966550</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>9.225500</td>\n",
       "      <td>381.800000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>-0.870000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x1          x2          x3          x4          x5          x6  \\\n",
       "count  121.000000  121.000000  121.000000  121.000000  121.000000  121.000000   \n",
       "mean     9.636364    2.797273   47.818182  112.685714    4.909091    2.084545   \n",
       "std      1.072381    0.138618   20.123369   54.126480    0.795822    0.188122   \n",
       "min      8.000000    2.600000   26.000000   55.845000    4.000000    1.830000   \n",
       "25%      9.000000    2.660000   28.000000   58.933200    4.000000    1.900000   \n",
       "50%     10.000000    2.810000   45.000000  102.905500    5.000000    2.200000   \n",
       "75%     11.000000    2.900000   77.000000  192.217000    6.000000    2.200000   \n",
       "max     11.000000    3.010000   79.000000  196.966550    6.000000    2.400000   \n",
       "\n",
       "               x7          x8          x9         x10         x11         x12  \\\n",
       "count  121.000000  121.000000  121.000000  121.000000  121.000000  121.000000   \n",
       "mean     8.093945  209.800000   13.171818    9.636364    2.797273   47.818182   \n",
       "std      0.640997   89.804938    5.128033    1.072381    0.138618   20.123369   \n",
       "min      7.360500   64.600000    7.870000    8.000000    2.600000   26.000000   \n",
       "25%      7.576200  113.600000    8.900000    9.000000    2.660000   28.000000   \n",
       "50%      7.881000  213.900000   12.000000   10.000000    2.810000   45.000000   \n",
       "75%      8.958800  272.500000   19.300000   11.000000    2.900000   77.000000   \n",
       "max      9.225500  381.800000   22.500000   11.000000    3.010000   79.000000   \n",
       "\n",
       "              x13         x14         x15         x16         x17         x18  \\\n",
       "count  121.000000  121.000000  121.000000  121.000000  121.000000  121.000000   \n",
       "mean   112.685714    4.909091    2.084545    8.093945  209.800000   13.171818   \n",
       "std     54.126480    0.795822    0.188122    0.640997   89.804938    5.128033   \n",
       "min     55.845000    4.000000    1.830000    7.360500   64.600000    7.870000   \n",
       "25%     58.933200    4.000000    1.900000    7.576200  113.600000    8.900000   \n",
       "50%    102.905500    5.000000    2.200000    7.881000  213.900000   12.000000   \n",
       "75%    192.217000    6.000000    2.200000    8.958800  272.500000   19.300000   \n",
       "max    196.966550    6.000000    2.400000    9.225500  381.800000   22.500000   \n",
       "\n",
       "                y  \n",
       "count  121.000000  \n",
       "mean    -2.115868  \n",
       "std      0.818380  \n",
       "min     -4.450000  \n",
       "25%     -2.580000  \n",
       "50%     -2.020000  \n",
       "75%     -1.460000  \n",
       "max     -0.870000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2e93793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize = (15,1))\n",
    "sns.heatmap(df.corr()['y'].to_frame().T, annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2bd53e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CkfZ+9CiezZqWWd738kvJ+cfR3u1JSWR8+gWR336OmZtL7srV5K5cXanxnqpWdBSHDhaexB0+dIRa0VFlJimGDh3En38tIz298ITukkvasWb1bxyJi2fiw9PYsuXc9Ugoi2fNEPKKtPe8+GT82zQ6zRqnsFhoNv95vOpHkfi/+WSu3XnmdSpR9KUdOLp0U8GFUJMJQzm6dDOx983BI9CXnvOnkbhkU7m6l/vVDaf3bzPIz8hmy8wvSF6x/ZzH3fzhq2l6/5UkLtnE5umfYc+znjexl+ZsY/dvEAWmSZdPH8YrNICD3//Dztd/OmfxtXluHGsnziNzbzw12jSk9cybWXrVdKfX929QE4AePzyB4WZh63Nfk/DXhnMWnzPavTSeyD6tSd9xiI1PfgS4fr1XZuxQ+e29/qieJPy5HoCARtHUHtKJvwc/iWm10XrmzdQd3pUDXy4p1zbbvTQe02Yn7udVbHvx23MWX2kq43fmbNrMRdOuZ9O0TwuG6p1qz7xf6fTBA1y6/nXc/X1YOf4VMM1zVvcAdYZ14tAP/5R7vTOJqhlJ3OHC86YjcQlE1YwskaSoF1OHwcMGMfCyPhxLPsaUh59h74kbegMv68PDkycQFh7KDSPvOOcxipRHRXtSvAvULWuhYRi3ArcCvPH804y74doK7uY8ZJT2YfF8pPc1t5P7zdzSM3Kmncypt4OPH753PIEluj72uH2VEWlJRinBl5FK9WrfCv+hA4kfcx8APt06Yj+WQt7WnXi1a1WJQZah1NhLD967fSv8hw4i/uZ7AUfstuOO2L3bX1SJQZahtNidZAn0J6DvJezsOQZbWia1X3uEoCG9SP3+r3MY4ClKidcs0VBK//8w3N3wbxnDjkfnkRa7i0ZP30S9u4eyd1bh/CERw7qQ8O2ycxx0aUr5HmW0Gc+2rfEdfClJtzmGBxkB/nh360ziVddiT8+gxvQn8RnQl+yFlTjM5hRGaf8Pp+lVMPLqIbz7XuGcG7FrN9LggovJzMxi0MDefP3lPJo271opsRZXWtzlWN1uZ8uA+3AL9KPh3IfxblyXnO3Oj9c+12oP68S+jwuPt4ieLYka0JZGt18GgJuXB761Qp2+G5uTkMKCdveQdzyD4ItiuOS9+/m9x8Rid4PP1ubpn5OTmILF0502z43jwrsGs+2Fb8+L2MtytrFb3N0I7diYvwZOxpadS9cvHyNl/V6OLt185pXPwM3Xi9D2F9LxncLhhRZPj3Jtw3C34B8TxeIrn8YnOoQe303h956TCsb7V4U1974NFoPWM26i9pBO7P/sb5eu98qOvbLbe1iXZtS7tieLhzwFQHi3FgRfFEOvBdMc8Xp7lrtHzao7Xicn/jjuft50fPde6o7oVqEL7dLiO1Vl/c5UtM2cnAckZcNewjqXfkMiotdFpGzaz5Lh0/GrH0nXLx4h6d9Hzkndn1R7aCdW3/VmhdY9HWfPCTw9PcnNzeWyPtcw6PK+PPfqNIZf5hj2ueDnP1jw8x907NSOhx65i2uvvOWcxynirDKTFIZh/FDWIiD0dBs1TXMOMAcgP2nPf6rHkHk8CUtI4TACo0Y49pRjxcq41b8Qn1scY9MM/yDcW1xMjt2Gdd3ywkLZmVh3bMC9RXvyqihJYU08intURGGcEeHYjpbswu7RKIbQyQ+QePcj2FMdP9JerVrg06MTtbpejOHpieHnS+jTD5P8+Mwqid2WcBT3qMJ6d48MKzv2J+4n4c5Hsac6Jlzzat0c3x6d8C0Se9j0SSQ9NqtKYrfGJ+FRs7Drp3tUGPlODtnw69KavIMJ2I45/h/SFy7Hp23TSk1S5B5Jxiu68CfAKzqUvPjjJcp41wojFcddJa+aoeTGHwfTJDcumbQTXZoTf/yXencPLVjPcLMQcdnFrOr3cKXFf5Lt6FHcIou09/DwUodsuDdsQPAjD5J8/8OYaSfae/t2WOPisaekApCzaAmeLVtUepLi9ttuZOzY0QCsXr2O2nWiC5bVql2TuDKGwIWE1KBDhzYMHzGu4LOiPSrmL/iTV1+ZQWhoDZKTj5e2iXMm70gynkXau2dUKPnxx06zRulsaZmk/7OJoJ5tzjpJ0eDmftQf3QuA5aNnk5OQ4tR6njX8qdG6If/e/GKRTw1WjH2JjN1HipVt99J4glrUIychheWjZ5e5TXuelbw8x/9Nyoa9ZO5PwL9hVJljyCsSe05iSsG+9n/2d8HFwvkQe9nOLvbsuGMk/bO1YCLOhD/WEXxRzDm5WDYsFvLTMkvOXWAx6P2rozfFkV9j2Tr7q9PGdyx2F6bVRtaBo6TvPoJ/gyiOr9tT5jplOat6t5sc+v4fGt1x+YkJBau23l0p9vK29/IIbFqHts/fwvJRswqHMxhw4IvFbJ5RfFLw6EHtafKAY+6h2AfeOe3+c078rbZm5nDw2+XUaNPQqSTFqfXuGRJQMr5T91VJvzMVPVZDO1xIzf5tiezTGjcvD9z9fWj/2h2svuuNgjL1R/Zg+6uOy5/MfQlkHjhKQKPoc1L3AEHN6mK4uZGy4ezbCMCNY0cy6gbH8Or1azcRXSuqYFnN6EgSSulZeSQunl9+cHSGn//T7zz/2rQSZVb8s4Z6MXWoERLM8WMp5yRWkfI6XU+KbsB1wKm/PgZwcaVFdJ6z7duOJaIWRlgU5vEkPDr0IHtu8Qv1jEduKHjtffODWNevwLpuOYZ/EKbNCtmZ4OGJe9M25C34ospiz9u8Hfc6tXCPjsKamITfgJ4kPTqjWBm3qAjCn3uS5MkzsR4onIMg5bV3SXntXQC82rUi8IYRVZagAMjdvB33usVjP/roM6fEHk7E80+Q9Pis4rG/Oo+UVx0zcnu3v4jAG0ZUWYICIHvDDjzr18KjdiT5CckEXd6dw/c969S61rij+LRujOHthZmTi1/nVmRvLH1M87mSvnY3vg1q4l03nNwjx4gY2pktt79SrEzSwtXUHjOQhG+XEdiuEbb0LPJOnLDkxiXj27AmWbuPENKtJZk7CuciqNG9JZk748g9Uv6L1vLK37oN99q1cKsZhe1oEj59e3P8yaeLlXGLjCDkmakcf+oZbAcL47QlJOLZvJljTorcXLzatyV/W+V3a3/zrf/x5lv/A+DSQX244/ab+Pzz7+l4cVvSUtPKHOpx1fDL+fmX38nNLexOGxkZTkKCY4hUh/atsVgslZ6gAMhcvxPvmJp41okgP/4YIUO6sucu556K4R4SiGm1YUvLxPD2JLBrK+Lf+ObMK57Bnvd+Y8975R/BWGtwR+J/X1tsVvnERRtoOHZAwazuQS3qkbppf8GcFWfiGRrgOOm3m/jWjcA/JorM/aX/v1Y0du+I4IILiOiB7Unbdui8ib0sZxt7wqINXHjn5bj5eGLPsxLWqSm75vxyTmKzZmSTeeAotQZ35PCPKxzxNatL6pYDTk+6eGTBamoP68yBzxfjGRKAf4Oap63b06lIvfvVjyRznyMJGtW/Lekn5tOo6np3pdjL296d5VMrlEvm3cfqu94gY09h9/2jSzbT6f0H2DVnPrlJaXgE++Hu70Pc/NXEzT/zcEPDzYJHkB95x9Ix3N2o2a8NiYtLPhGqNEXrvaz4TlVZvzMVPVY3z/i8IMkQ1rkpjW6/rFiCAiDrcDIR3VqQvGI7XmGBBDR0HGfWzJyzqvuTag/rzKHvlp+5oJP+9+5n/O9dxxOuevfrzs23XMv338ynbfuLSE/LKDHUA2DhL3/SpXtHPv/4Wzp16cCeXY55furH1GHfXsdcaC0uaoqnh4cSFKeyOzeBqpwbRlndgw3DmA/MNk2zxC1ZwzAWm6bZ3ZkdVFZPioeemMmqtRtISUkjNCSYO8Zez/DBA87pPrIfua1C67m36IDXyNsxDAt5yxaS98unePRwZJDz/y7+iKyCJEXsEiy1YvAZ8xBYLGBYyF/9N3k/fVyhGI6vqdjkLt5dLnY8gtRiIeOHBaS9+wn+wy8HIOPrnwiZfD++fbphO3HH1rTZiL/uzmLbOJmkqOgjSE17xYY/+HS9mJCHbnfE/v1CUud+QsBVjtjTv/qJ0Cn349u3K7YjjpMI02rjyOjisZ9MUlT0EaRZ6Z5nLlQK/57tiXz8VgyLhZSvfiPpjc+pce0gAI5/Oh+3sBo0+O4lLP6+YNqxZ+awe+Bt2DOyCZ8wmsDLumHabORs3sORR1/GzCv/rPTxGX5Olw3t04ZG027EcLMQ9+lf7H/pW6Jv6AdA3AeOE5kLnxlLaO9W2LLz2DrhDdLXO+74+TevR5MXbsPi6U72/kS2TngD64nJ4Jq+fAepa3YWbMNZTRoePXOhUnh16kjQhDvBzULWT/PJ+N/H+A51PCEj67sfCXr4QXx6dscWX9jek8Y6fhcCxt6Ed99eYLORv2MnKc88B/nlf1xh3dUVnwfilZenM6B/T7Kysxk37n7WxDrGqP/4/QfcettDHDlxnP7x25fMfvZ1Fv66qGDdO26/ifHjb8BqtZGTncODDz3FP/+Wb16NfyNKTuTqjKDe7ajz5BiwuJH8+e8cefUrwq9z/H4f/Wgh7uHBNPvlOdz8fTHtJvasbDb1uhuvOhHEvDgB3CwYhsGxn5Zx5KWKJXIPWEufO8crPIjeC5/GPcAH025iy8zht+6OLtydP55I7P1zCu7edvvmcXa8+kOxuQEs3h60mnoDIR0agWGQdfBoicfcgeMOXKvpN+IZGkh+Whapm/az7NqZRF/WgWYTR2C32sBmZ8uzXxP/W6xT38nZ2Lt+9RheoQFgGKRu2s/aie9iy8o9L2JvOHYAF955OV4RweQmpZHwxzpiH3jnrGMHx8R4je8ZgmmaJPyxjk3TPi2xvjM6vHkX4Z2b4hkSQO7RVLY8+zVHl22mzcwxeEUGY/Fw49B3/7DthZLzAbSYfC11hnXGO6oGOfHH2ffJIrY+9zXgeDJFZO+LMG12tr/0PYe+P/tx7U7Ve2IqPb6f4ngagmGQuvkA6ybNw5qRXa31Xt2xV7S9l9Y+LB5uAOz94A/aPn8L0ZddTNYhx98102bnrwGO86laQy6h8d1XYFgs2PNtrHvkvVInWx2w6mU8/H2weLqTn5rJ0pEzyTqURPdvp2DxcMNws5C4eBMbnviwxOSPZ3K6+KridwbO/lg9maT45/rniLmhD+Coe+/IYNq9fBvekcFgGOx49QcOfu0Yfno2dZ9+YmLxASteYtno2aVOmnvSPXkVn2vm6dmP0bNPV3Kys7n/rslsWOfoXfLB52/w0IQnSIg/SmBgAK/OmUWt2lFkZmbx8P3T2Lp5O3fcM4bhI6/Amm8lJyeHp6c8X+5HkB46tqni45fPA3n7Y/9fjg7wrNfWJf/fykxSFBQwjGamaW455bOepmkucmYH5/Nwj4omKVxBRZMUrqCiSQpXUNEkhSsoT5LC1VQ0SeEKziZJUd0qmqRwBWUlKURERKrL2SQpqpuSFOcnV01SWJwo84VhGJMMBx/DMF4FnjnjWiIiIiIiIiIi5eDM0z06ArOA5UAA8DHQpTKDEhEREREREXEJpT2VUSqNMz0p8oFswAfwBvaapv6XREREREREROTcciZJsQpHkqID0BW41jCMsp+TJSIiIiIiIiJSAc4M9xhrmubJKd/jgSGGYVxfiTGJiIiIiIiIyH/QGZMURRIURT/7sHLCEREREREREXEhds12UJWcGe4hIiIiIiIiIlLplKQQEREREREREZegJIWIiIiIiIiIuAQlKURERERERETEJTjzdA8RERERERGR/yTT1MSZVUk9KURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJmpNCREREREREpCx2zUlRldSTQkRERERERERcgpIUIiIiIiIiIuISlKQQEREREREREZegOSlEREREREREymJqToqqpJ4UIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuATNSSEiIiIiIiJSFrutuiP4T6n0JEX2I7dV9i4qjc8zb1V3CBXmseaX6g6h4qzW6o6gwozg8OoOocJidqyv7hAqzLYxp7pDqDCPtedvrrjZpOjqDqHCDkxPqe4QREREign3DKruEERcgoZ7iIiIiIiIiIhLUJJCRERERERERFyCkhQiIiIiIiIi4hLO38HQIiIiIiIiIpXNtFd3BP8p6kkhIiIiIiIiIi5BSQoRERERERERcQlKUoiIiIiIiIiIS9CcFCIiIiIiIiJlsWtOiqqknhQiIiIiIiIi4hKUpBARERERERERl6AkhYiIiIiIiIi4BM1JISIiIiIiIlIWU3NSVCX1pBARERERERERl6AkhYiIiIiIiIi4BCUpRERERERERMQlaE4KERERERERkbLYNSdFVVJPChERERERERFxCUpSiIiIiIiIiIhLUJJCRERERERERFyCkhQiIiIiIiIi4hI0caaIiIiIiIhIGUzTVt0h/KeoJ4WIiIiIiIiIuAQlKURERERERETEJShJISIiIiIiIiIuwWXnpHBr3h7vkbdjWCzkLVlA3oLPSy1nqX8hfo+8TPbbM7DGLgF3D/wmPg/uHuDmhnXNEnJ/+LCKoz+9x2e8wOJlKwmpEcx3H71V3eEUs2zLfmZ/sxi73WRYp2aM6de+2PL3/4jll9XbAbDZ7eyNP85fM8ZxPCObie8vKCh3OCmV2y+9hOt6ta662LceYPZ3Sx2xX9KUMX3aFo/9z7X8EruzMPaEFP6aehNBft4MmvYRfl4eWCwG7hYLn9x/VZXFDbBs4y5mfbIQu2lnWLc2jL2sa7Hl6Vk5PPrOt8Qnp2G127lxQCeGdmtN/LFUHpv7HcmpmRiGwVU92jK6X8eqjX1PArN/34jdDsNa1WVMpwtLlFm1P4ln/9iI1W5Sw8eTd0c7vl9aTj5T569l19F0DAOevLQNrWqFVFnsbk3a4jV0HFjcyP/3V/L//LrUcpY6F+Az4VlyPngW24blAHh0vwL3S/qDaWI/sp/cz14Ga36VxQ7w/PNPMmBAL7Kysrn11gdZt25TiTK///4l/v5+AEREhLF69TquvvpWRo4cyv333wZAZmYW99zzGBs3bq2SuJftT+bZJTuwmyZDm0Uzpl39EmVWHzrOs0t3YLWbBHt78O6V7QB48o8tLN6XRIiPJ1+NuuScx1bnyi5ceNdgAKyZOaybNI/ULQdKlGv7wi0Et2qAYRhk7DnC6nvewpaV6/R+ag3uSNMHhxPQKJq/Bk0mZf1eAHzrhNFv8XOk744D4NiaXaybNO//fexhnZvS6f0HyDyQCEDcL6vY9sK3VRY7QGDTOrR5dhweAT6Ydjt/DZyMPffMx/SAVS9jzcjGtNkxbXb+GvB4seX+F0TT7qXxBLesz5aZX7DzzZ+djhfAs4Y/HedOoEbrhuz/fDHrH30fAHc/b7p/P6WgnE/NUA5+vZQNU5w/56k5oB3NJo3AtDti3zD5Q5JXbi9RLrxrc1pOGQUWA2tmLmsmvEXmvgSn91MZ9e5sew/v0oyWT4zG8HQnZcNeYu+bg2mzn3XsFW3vPtEhtH/1drzCg8E02fvhn+yeu6BYmcpqMwDdvnkc74hgbDl5ACwbOZPcpLRybR+gRusG9Px5KivGv0LcTytLLG8wpj8X3DIQ/5gofmo2nrxj6eXa/rluM87Uu7PHQ1lOV++GhxutZ9xMWOemYDfZPPNz4n5e5fS2nTHx6Xvp0qcTOdk5PDFhOts27ihR5qmXH6Ndp9ZkpGUCMGXCdHZs3knPAV25fdItmHYTm83Gs5NfZt3KDec0vvOe6fzvhpw910xSGBZ8Rt1F5osPYx5Pwu+xV7Gu/wf7kQMlynkPH4d185rCz6z5ZD4/EXJzwM0Nv4kvYt20CtuebVX7HU5j6KX9GDX8Ch6d9lx1h1KMzW7nmS8X8dadQ4kM9mf0c5/To0UDGtYsvGi8qU9bbjpx8f/3xr18tGgdQX7eBPl588Wkawu203/ye/Ru1aBqY/9mCW/dNpjIID9Gv/g1PZrXp2FUkdh7t+Gm3m0csW/ex0d/ryfIz7tg+Tt3XEENf58qi7lo7DM+ms/bD1xHZEggo6bOpWfrxjSsFV5Q5vM/V9EgOpxXJ1zLsbRMhjz2Opd1aombxcKD1/Snab2aZGbnMnLqO1zSrEGxdSs3dpNnft3AWyM7Exngw+j3/6ZHoygahgUWlEnLyeeZX9fz+tWdqBnky7HMwguK2b9vpHODSJ4bdjH5NjvZ+VU4KZFhwevK8WS/NQUzNRmf+57HunklZsLBEuU8L78J2/a1hR8FheDRbTBZs++E/Dy8bpiIe5tuWFf9WWXhDxjQi4YNY2jRogcXX9yGV155mu7dh5Yo17fviILXn376Fj/++CsA+/YdpH//q0lJSaN//568/vozpa5/rtnsJjP/3s6bQ9oQ6e/F6C9W0SMmjIYh/gVl0nPzmfH3Nl6/og01A7w5lpVXsGxwk5pc07I2k3/fUinxZR5IZPGwaeSnZhLZuxVtnhvHokunlCi3YcpHWDOyAWj55HU0HNOfHa/96PR+0rYd5N8xL9Lm2bEllmXsT+DPvo/+p2IHSFqxjX+uP/3fxcqK3XCz0OH1O1l91xukbjmAZw1/7PlWp7e7ZPj0Mi/C8lMy2PD4/6g5sH2py8/ElpvPlllfEdikNoFN6hR8bs3MKVbXvRZO5/Av5bvoSVyyiSMLHedQgU3r0HHOBH7r9mCJcq1njeHfm54nfWccDW7qS5P7hrJmwttO76cy6t2p9m4YtHvldpaOmE7GnniaTryKuld3Z/+ni846dqhYezetdjY++TEpG/fh7udNr1+nk7h4I+k7DheUqaw2c9KqO18vdtFfbhaD5o9fS8Kisi9ik1duJ/63WLp9M7lCuzjXbcaZenf2eCjL6eq9yb1DyU1K5bcuD4Bh4FnDv4ytVEzXPp2o26A2QzpdQ8u2zXl01oPccOmtpZZ9aerr/P7TomKfrViyhkULlwLQqGlDZs2ZxpXdRp3TGEXKwyWTFG4xjbEfjcNMigcgf9XfuLfuTN4pSQrP3kPIX7MEt5jGxTeQm3NiQ+7g5gZmVUTtvPatW3L4iPN3IKrKpv0J1AkPpnZYEAAD2l7Ioo17iiUpipofu4OB7RqV+HzF9kPUDgsiOiSwlLUqx6YDidQJC6J2qGOfA9pcwKJN+4olKYqaH7uTgW1Kxl4dNu05TJ2IGtSOqAHAwI7NWbRue7FEg2EYZOXkYZomWbl5BPn54GaxEB4cQHhwAAB+Pl40qBlGYkpalSUpNh05Tp0aftQOdtypH9CsFot2xhdLUszfcojejaOpGeQLQIifFwAZufnEHkxm2mWOxJGHmwUPt6obgWap2wh70hHMY45j0bp2Ce4tOpJ/SpLCo9vl2DYsx1LnlPZisYCHJ9isGB5emKnHqip0AC6/vB+ffOLo+bFy5VqCggKJioogPj6x1PL+/n706NGZW291nHD9+29hcnflylhq1apZ+UEDmxLSqBPkQ+0gR0JwQKNIFu1JKpakmL8jgT4NI6gZ4Egihvh6FixrV6sGcWnZlRbfsdU7C1+v2YVPGb9/Jy+UAdx8CuNz8/Wi1fQbCWpaB8PNja3PfV1w0ltU+s64cxj1iXjP49idVVmxR/S8iNQtBwruxOcdzzhnMecmpZGblEZU3zYlltUZ3oWG4wZi8XDjeOxu1j48D+zFT1psWbkkr9yOX0xkmfvwi4nCKyyQ5H/Ld0OmaC8Ud19vTLOMEybTxP1EEt89wJec+BSgeuvdmfbuGeKPPS+fjD2O88nEvzfS+J4h7P90UbW195zEFHISUwBHoil952F8omoUu1iuijZzNhqOHUDczyup0brsm1Gpm/aX+nl1tRln6v10x8PZ1nu9kT0LEx6mWe6eJWfSY0BXfvrC0TNkY+xmAgIDCIsIJSkx2an1s7MKf1t9TvdbIFJFTpukMAwjEAg3TXP3KZ9fZJpmpfUBMoLDsB87WvDePH4Ut5gmp5QJxb1NF7Ken1gySWFY8Jv8OpbwaPIW/YBtr+v0onBliSmZRAUXXihEBvuzcX98qWWz8/JZvnU/j1zVo8SyhbE7GFRK8qIyJaZmEnXiQhkgMtiPjftLv1jLzstn+baDPHJlt4LPDANuf/snDAOGd2rOVZ2aVXrMJyWmpBMVElTwPqJGIBv3HC5WZmTvDtzz6mf0vf9FMnNymX3bcCwWo1iZw0kpbDsQT8sGtaskboDE9ByiAgp7n0QG+LAx7nixMvuPZWC12xn78VKy8qyMat+AwS3rciglixq+nkz5eS07EtNoFhXExL4t8fGsmtypERSKmZJU8N5MScJSr/EpZUJwb3kJ2W88jtc1hW3aTD1G/qLv8Jv8LuTnYd2+FtuOdVUS90nR0VEcOlR4Enf4cDzR0ZFlJimuuGIAixYtIz295AndTTeNZOHCRZUVajGJmTlEBhT2YIr092JTQvGuxvtTsrDa7Yz7Zg1Z+TaubVWHwU2qJolSVP1RPUn4c32Zy9u9NJ7IPq1J33GIjU9+BECTCUM5unQzsffNwSPQl57zp5G4ZFO5hiT41Q2n928zyM/IZsvML0he4Xx34/M59pB2jej9xzPkJBxn41Mfk779cKnlKiN2/wZRYJp0+fRhvEIDOPj9P+x8/SfnAjdNun72MKYJez/8g30fOdejKqBRNLWHdOLvwU9iWm20nnkzdYd35cCXS5zbbxF1hnXi0A//lHs9gOhB7Wn+6Ei8wgJZft2zpZaJfeAdOn88EXtOHvkZ2Sy69Amgmuu9iLLae15yOhYPN4JbxZCyfi+1Lu+IT3TIOYkdzv5Y9a0TRnCL+hyL3X3mwpy7NtPupfGYNjtxP69i24slh1WdjndUDaIv7cCS4U/TrnXpd+pPxxXazOnqvbTj4Wzr3SPQcZOm2cQRhHduSsb+RNY/8l6FhtmUJaJmOPFxhX//E44kElEzvNQkxZ0Pj+eW+29m5ZI1vDL9TfLzHENleg3qzt2P3kZIWA3uuc75HiQilaHMqwHDMK4GXgISDcPwAG4yTfNkP8L3gbZlrIphGLcCtwK81LUpNzcp50WTUdqHxTN63tfcTu43c0sfH2TayZx6O/j44XvHE1ii62OP21e+GP6DzFK6nBhGqf8ZLN60l9YxNYsNlwDIt9r4e9Ne7hncuVJiLEtpCd8yQmfx5v20jokqFvv7dw8jIsiPY+lZ3PbWT8REBNOuYXQlRVucM7Ev37ybJnWimPvQDRxMPM745z+i7YX18Pdx9ErIysnjgde/5KFrBxR8VhVKbTOnvLfZTbbGpzJnZGdyrDZu+HAJF9UKwWa3sy0+lYf7taRldAizftvIvH93cmf3plUTfGkN5JT/DK8ht5D70/9K/s74+OHWoiOZT98C2Zl43zgJ93Y9sa5ZVHnxnqK0Y/N0dz6uvnoI77//WYnPu3fvxI03XkOfPsPPaXxnw2Y32ZqYzttD25JjtXHjV6u5KDKIejV8qyyGsC7NqHdtTxYPearMMmvufRssBq1n3ETtIZ3Y/9nfRPRsSdSAtjS6/TIA3Lw88K0V6vTd2JyEFBa0u4e84xkEXxTDJe/dz+89JhbrQfD/MfaUDftY0P4ebFm5RPZpTaf3HuDXzvdXWewWdzdCOzbmr4GTsWXn0vXLx0hZv5ejSzefcd2/Bz9JTkIKXmGBdPn8EdJ3xTnVoyG8WwuCL4qh14Jpjpi9PSt80VJ7aCdW3/VmhdaNm7+auPmrCb2kCc0mjWDp1TNKlLng1kEsHz2b42t30+iOy7noqeuIfeCdaq33k87U3leOf42Lnroei5cHiYs2YFodv+fVfay6+XrRce59bJjyodPrnIs2s+qO18mJP467nzcd372XuiO6lSvJcdG0G9g07dMSvQicVd1t5kz1XtrxcLb1bri74VsrlORV29n45EdcMP5SWj4xmtV3V+yYLXUfTp4TvDr9LZISk/Hw9GDyc5O4+a7rmPPCewD8NX8xf81fTNtLWnHHpFu47ep7z1l8/y/YNSdFVTrdLctHgXamaR4xDONi4EPDMB41TfMbykgjnGSa5hxgDkDaLf3L/StmHk/CElKkq3uNcOwpxbtSu9W/EJ9bHOMADf8g3FtcTI7dhnXd8sJC2ZlYd2zAvUV78pSkOKPIYH/iUwrvsiakZBAe6Fdq2QWxOxnYruQEiUu37KdJ7XBCA6vuYgIcPSfiUzIL3iekZJYd+9pdDGxzQbHPIoIcZUMCfOnVMoZNBxKrLEkRWSOA+GOpBe8Tj6cRcWIIx0nfL13HmEu7YBgGdSNDqBUWzN4jSbRsUIt8q437X/+CSy9pQd92VXSBfzL2AB/i0wv/yCekZxMe4H1KGW+CfSLw8XTHx9OddnVC2Z6YStvaoUQEeNPyxF2tfk2imffvTqqKmZKEERxW8N4IDsNMK/47Y6lzAd7XO+4mGH6BuDVtR67dBm7ujmEimY6TFOvGf3Cr36TSkxTjx9/AzTePBGDNmg3Url3YRmvViuLIkdJ7UYSEBNO+fSuuuab4Xa8WLZrw5puzGDLkRo4dS6m0uIuK8PMmIT2n4H1CRi7hfsUTaxH+XgR7h+Lj4YaPhxtto4PZkZxeaUmKBjf3o/7oXgAsHz0bz5AA2j5/C8tHzTpzV2K7yaHv/6HRHZez/7O/AYMVY18iY/eRYsXavTSeoBb1yElIYfno2WVvLs9KXp5jnykb9pK5PwH/hlFljh///xR7TkIKAAl/rMOYeTOeIQFld4k+x7Fnxx0j6Z+tBftL+GMdwRfFOHXhczLu3KQ0jsxfTUibhs4NuzDgwBeL2Tyj+MTg0YPa0+SBKwFHD4YzzR0Q1KwuhpsbKRucm2OgrHpP/ncbfvUjStS7Z2gAQc3rcXyt467zoe//ocunkwq+RFXWe0Xa+7E1O1k8dCoAET1a4t/wZK+sqm3vRRnublzy7n0c/GYZceWZR+QctJmceEdvR2tmDge/XU6NNg3PmKQoWu8egb5c/PbdAHiFBBDZpzWm1c6RBaud/hLVdayWp96LHg9nW+95x9KxZuUQ94ujjg7/+C/1R/U8Y7xncvXNV3Ll6CsA2LxuK1HREQXLImtGcDQ+qcQ6J3tW5Ofl8/1nP3PD7deWKBP773pq169FcEgQKUXOT0Wq0umSFG6maR4BME1zpWEYvYCfDMOoTSXP8mDbtx1LRC2MsCjM40l4dOhB9tyZxcpkPHJDwWvvmx/Eun4F1nXLMfyDMG1WyM4ED0/cm7Yhb8EXlRnu/xvN60Zy4GgKh5NTiQjyZ2HsDmbcOKBEufTsXNbsOsyM6/uXWLYgdkepyYvK1rxOxInY04gI8mPh2l3MuL5viXLp2bms2R3HjNF9Cj7Lzs3Hbpr4eXuSnZvPPzsOMv6Up5pUauwxtTiQcIxDR48TWSOQBSs288z4YcXKRIUEsWLLXtpeWI/k1Az2xSdTO7wGpmny5Hs/0qBmODcM6FRlMRfEXjOYA8cyOZySSUSADwu3HGbGFe2KlenZqCYzf9uA1W4n32ZnY9xxruvQkDB/b6ICfdiXnE790ABW7DtKg9CAMvZ07tkP7sQSHo0REomZmox7m27kflh80r6s6bcUvPYaOQHrllXYNq3AUvdCx9AQD0/Iz8OtUSvsBys/wfL22x/w9tsfADBwYG9uu+1GvvjiBy6+uA1paellDvW48srLmD//D3JzC7vT1qkTzWefvc3Ysfexa9dZTKBWTs0jAziQmsXhtGwi/LxYuDOBZ/o3L1amZ0w4sxZvP9FmTDYlpHFd67qVFtOe935jz3u/AeBTK5RL5t3H6rveKBjHXhq/+pEFTzeI6t+W9F2Ou4CJizbQcOyAglndg1rUI3XTfsfdfyd4hgY4LrbsJr51I/CPiSKzjKFr/59i9wovHPJWo01DDMMoNUFRWbEnLNrAhXdejpuPJ/Y8K2GdmrJrzi9nXM/N1wvDMLBm5uDm60VEj5Zse+Ebp/Z5dMlmOr3/ALvmzCc3KQ2PYD/c/X0K7uQ6q/awzhz6bvmZC55QtN796heOnQ9uWR+Lh3uJes9PycQjwBf/BlFk7IknontL0ndUT71XpL17hQWSm5SGxdOdC+8azPaXvjsnsZe3vRfV9sVbSd95mF1vn7mNFXW2bcZws+AR5EfesXQMdzdq9mtD4uKST4U6VdF6L6rdy+M58tvaciQoqu9YhTPXe9Hfl6LHw7k4Vo/8upbwzk05umwL4d1akLaj9OFs5fHFe9/wxXuO35uufTsxcsxwFnz3Oy3bNicjPaPUoR5F56noNbA7u7ftAaBO/Voc3OeIqUnLC/Hw8FCCQqqVUVb3YMMwlgPXF52PwjCMAOA7oKtpmk71Ka9ITwoA9xYd8Bp5O4ZhIW/ZQvJ++RSPHo6uYfl/F38UU0GSInYJllox+Ix5yDGpnWEhf/Xf5P30cUVCwOeZynk86ENPzGTV2g2kpKQRGhLMHWOvZ/jgksmAs2FdU74/fCct2byPZ79Zgt1uZ8glzbhlQAe+XLoRgBFdWwLw/YqtLN+6n1k3DSy2bnZePgOnvM9PT9xAwNkMObA6P6N6sdi37OfZ75dht5sMubgJt/Rrx5fLHZn1EZ0dF0Hfr9zG8m0HmXVDv4L1DiWncf88x2RDVrudQW0bcUu/diV34AQjuGITVi7ZsJPZny7EbjcZ2rU1twzuxhd/Of7wXd2rPYnH05k873uSUjIwMRlzaRcu73QRsTsOcPPM92lUOwLLia5+dw/vTbeLyj8niLmj7PHrp419dwLP/r4Ru2ky5KK63NK5MV+udVz0jmgTA8D7K3byw4YDGIbBsFb1uK5DQwC2JaQydf5a8m0mtYJ9mXpZGwK9PcvcV1lsGys274xb03Z4DRkHFgv5K38n//cvce/kaNfWf4o/mqwgSXHiEaSeA67FvXU3TLsN++E95H7+KtjK33bD36xYvQO8+OI0+vfvQVZWNuPHP0hsrONY/fbb97njjokFPSsWLvyM5557k99++7tg3TfemMXQoYM4cOAQAFarja5dB5dr/8mzL6tQ3Ev2JfHckh3YTRjSrCbj2sfw5SZHHCNaOIYH/i92P99vjcNiGAxrFs3oE0mKhxduYs3h46Tk5BPi48ltHRswrFn5ez0tmJ5S6udtn7+F6MsuJuuQY16koo+U7PzxRGLvn0NOYio9vp+Ce4APGAapmw+wbtI8rBnZWLw9aDX1BkI6NALDIOvg0VKfWBE9qD2tpt+IZ2gg+WlZpG7az7JrZxJ9WQeaTRyB3WoDm50tz35N/G+xTn2n8zn2BmP60+DGvtitNuw5eWx44qOCiRGrInZwTIzX+J4hmKZJwh/rHF3az8C3bgSXvHcf4OiGfvCbZWx/+XtibnAkwvd+8Ade4UH0Xvg07gE+jsf7ZebwW3fHsIBaQy6h8d1XYFgs2PNtrHvkPY7H7iqxnwGrXsbD3weLpzv5qZksHTmzYMK/ASteYtno2WTsKv8EjxfeNZi6I7phz7diy8ln09RPCh65WFDvCSlED2pP04lXYdpN8lMzWXPvHLIOJFZbvYOT7T0hhRZTRhHVtw2GxWDP/35n9zuO3/bqau+hFzemxw9PkLrlAOaJ7uObn/kC31qhQOW2maxDSXT/dgoWDzcMNwuJizex4YkPKzx042SS4uQjSIvWe8OxA7jwzsvxiggmNymNhD/WEfvAO9XWZpyp99MdD2d7rPrUDqPDq7fjEeRHbnIaa+59m+zDJZMI0yh9wlFnPPzM/XTudQk52Tk8ee8Mtqx3nBu9+vFzTL1/JkcTknj7q1eoERqMYRhs37ST6ROfJTsrm5vuGs3lIwZhzbeSm5PLi1NfL/cjSNfGLzttT/vzXU7sD/8vZxP1bnuFS/6/nS5J0QrIAjxM09xS5HMPYKRpmk49iLuiSQpXUFlJiqpQ0SSFS6hgksIVVDRJ4QoqmqRwBRVNUriCs0lSVLeKJilcQVlJChERkepyNkmK6vb/Pkmx5rvz9pr2dLzbDXXJ/7cyn/dnmuZ60zR3Al8YhjHJcPABXgDuqLIIRUREREREROQ/ocwkRREdgTrAcmAVEAd0qcygREREREREROS/x5kkRT6QDfgA3sBe0yztuZ8iIiIiIiIiIhXnTJJiFY4kRQegK3CtYRhfVWpUIiIiIiIiIvKfc7pHkJ401jTNk8/XiQeGGIZxfSXGJCIiIiIiIuIa7LbqjuA/5Yw9KYokKIp+5tSTPUREREREREREnOXMcA8RERERERERkUqnJIWIiIiIiIiIuARn5qQQERERERER+W/Swy2rlHpSiIiIiIiIiIhLUJJCRERERERERFyCkhQiIiIiIiIi4hI0J4WIiIiIiIhIWeyak6IqqSeFiIiIiIiIiLgEJSlERERERERExCUoSSEiIiIiIiIiLkFzUoiIiIiIiIiUxdScFFVJPSlERERERERExCUoSSEiIiIiIiIiLkFJChERERERERFxCUpSiIiIiIiIiIhL0MSZIiIiIiIiImWxa+LMqqSeFCIiIiIiIiLiEpSkEBERERERERGXUOnDPY6vOX+7xnis+aW6Q6gw93aXVncI/022/OqOoMKsOZnVHUKFuTdpU90hVFjtDw9XdwgV5j78zuoOoeKmT6/uCERERIq52Du6ukMQcQmak0JERERERESkLJqTokppuIeIiIiIiIiIuAQlKURERERERETEJShJISIiIiIiIiIuQXNSiIiIiIiIiJTBNG3VHcJ/inpSiIiIiIiIiEgJhmEMNAxju2EYuwzDeLiU5UGGYfxoGMZ6wzA2G4Zx89nuU0kKERERERERESnGMAw34HVgENAMuNYwjGanFLsT2GKaZiugJ/C8YRieZ7NfJSlERERERERE5FQXA7tM09xjmmYe8Bkw5JQyJhBgGIYB+APHAOvZ7FRzUoiIiIiIiIiUxW6v7giqSy3gYJH3h4COp5R5DfgBiAMCgGtM0zyrClNPChEREREREZH/GMMwbjUMY3WRf7eeWqSU1cxT3g8A1gHRQGvgNcMwAs8mLvWkEBEREREREfmPMU1zDjDnNEUOAXWKvK+No8dEUTcDM03TNIFdhmHsBZoAKysal3pSiIiIiIiIiMipVgGNDMOIOTEZ5kgcQzuKOgD0ATAMIxJoDOw5m52qJ4WIiIiIiIiIFGOaptUwjLuAhYAbMM80zc2GYdx2YvlbwDTgfcMwNuIYHjLJNM2ks9mvkhQiIiIiIiIiZTm7eSDPa6Zp/gL8cspnbxV5HQf0P5f71HAPEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXILmpBAREREREREpi/2/OydFdVBPChERERERERFxCUpSiIiIiIiIiIhLUJJCRERERERERFyC5qQQERERERERKYupOSmqknpSiIiIiIiIiIhLcNmeFN6dOxDy4B3gZiHj2/mkvf9ZseV+g3oTeNNIAOxZ2Ryb8TL5O/cUFrBYqPnRG1iPJnF0wuNVGTrLtuxn9jeLsdtNhnVqxph+7Ystf/+PWH5ZvR0Am93O3vjj/DVjHMczspn4/oKCcoeTUrn90ku4rlfrqgz/tB6f8QKLl60kpEYw3330VnWHUy6uHPvSFWuY+cpcbHYbwy/rz7jrriq2PDU9g8kzX+Hg4SN4eXoy7eF7aNSgHgAffPE9X//0K4Zh0KhBPZ5+eAJeXp5VFvuyzXuZ/eVf2E2TYZ1bMGZAx2LL3/9tFb+s2gqAzWZnb/wx/pp9O0F+Pnz4xxq+Xb4RA2hUK4ynrh+Il0fV/Swt27CTWZ8swG63M6x7W8Ze3q3Y8vSsHB59+xvij6Vitdm5cVBnhnZrQ3xyKo+98y3JqRkYhsFVPdsxuv8lVRb3SZNnPESPvl3Izsph0j1PsmXDthJlZr36JB06tSUjPQOASXc/ydZNO/AP8Of5N6dRs1YU7u5uvPvGh3z96Y9VEvfSlbHMem0eNpudKy/ry7hRVxZbnpqewZTZr3EwLgEvTw+mTryTRjH12HvgMA9Nfb6g3KEjCdx580iuv2rwOYutzpVduPAux/asmTmsmzSP1C0HSi3b7OGrqTW4I6bNzt7//c7udxc6vZ9agzvS9MHhBDSK5q9Bk0lZvxcA3zph9Fv8HOm74wA4tmYX6ybNc2qbNQe0o9mkEZh2O6bNzobJH5K8cvt5EftJNVo3oOfPU1kx/hXiflpZYnl41+a0nDIKLAbWzFzWTHiLzH0JZx07QGDTOrR5dhweAT6Ydjt/DZyMPTffuQ1bDHovnE52/DH+uf65Yos8gvxo9+Kt+NWPxJabT+x9b5O27ZDTMXvW8Kfj3AnUaN2Q/Z8vZv2j7wPg7udN9++nFJTzqRnKwa+XsmHKh05v29n2Ht6lGS2fGI3h6U7Khr3E3jcH0+b8XcXKqPfz9Vj1iQ6h/au34xUeDKbJ3g//ZPfcBcXK+F8QTbuXxhPcsj5bZn7Bzjd/djpeKLvNAHT75nG8I4Kx5eQBsGzkTHKT0pzedljnpnR6/wEyDyQCEPfLKra98G2Jcg3G9OeCWwbiHxPFT83Gk3csvVzf4Vy3GWfq3dnvVpbT1bvh4UbrGTcT1rkp2E02z/ycuJ9XOb1tZ1zzxM206NWWvOxc3n/wdQ5u3luizJiX7qFey4bYrFb2rd/FR4/OwW614Rvoxw3P3kF43Ujyc/P5YOIbxO04eE7jEykP10xSWCyETLqbxDsmYU04Ss2PXif77+Xk7y3842M9HE/CuPuxp2fg3bkDoY/fR/yNdxcsD7h2GPl7D2D4+1Zp6Da7nWe+XMRbdw4lMtif0c99To8WDWhYM6SgzE192nJTn7YA/L1xLx8tWkeQnzdBft58Menagu30n/wevVs1qNL4z2Topf0YNfwKHp323JkLuxhXjd1ms/H0i2/zzgtTiQoP5ZpbH6BX14tpWL9uQZl3PvySJhfE8Mr0R9mz/xDTX3yLd196moSjyXz81Y98/+HreHt58cATs5j/5xKGDupTNbHb7Tzz+R+8dc9VRAYHMHrWx/S46AIa1gwtKHNTvw7c1K8DAH9v2M1Hf64hyM+HhJR0Pl0UyzeTb8Lb04OH5v7IgtXbGNKpRZXFPuPDX3j7oeuJDAlk1FPv0LNNYxrWiigo8/kfK2lQK5xX7xvFsbRMhjzyKpd1aombm4UHR/anaf1oMrNzGfnk21zSvEGxdStbj75dqNegDn0vHkrrdi2YOvsRrhp4Y6llZz/1Mgt+/KPYZ9eNHcGu7XsYf919hIQGs/Cfb/jhq/nk51srNW6bzcb0l99hzrNPEBUeysjbJtKrcwca1q9TUGbux1/T5IIYXp72MHsOHGLGS+8w94WniKlbi6/mvlCwnT4jbqFP145l7apCMg8ksnjYNPJTM4ns3Yo2z41j0aVTSpSrN7IHPrVC+a3rg2CaeIUFlms/adsO8u+YF2nz7NgSyzL2J/Bn30fLHXvikk0cWbgGcJzEd5wzgd+6PXhexA6AxaD549eSsGhDmUVazxrDvzc9T/rOOBrc1Jcm9w1lzYS3nd5FWbEbbhY6vH4nq+96g9QtB/Cs4Y+9HMfCBbcMIn3nYdwDfEosazxhCCmb9/PvmBfxvyCa1s/cxNIRM5zeti03ny2zviKwSW0CmxQeJ9bMnGJ13WvhdA7/Ur6LHqfau2HQ7pXbWTpiOhl74mk68SrqXt2d/Z8ucno/lVHv5+uxalrtbHzyY1I27sPdz5tev04ncfFG0nccLiiTn5LBhsf/R82B7U+zpbKV1WZOWnXn68Uu+ssracW2Esm4UyWv3E78b7F0+2ZyhfZxrtuMM/UOzn23spyu3pvcO5TcpFR+6/IAGAaeNfwrtI+ytOjZhoiYmkzueTcxbRoxevotzBxasm2u/G4J8+59BYCxr0yg68g+LP7oVwbdeSWHtuzlrfHPEtkwmlFTx/Hi6KnnNEaR8nDJJIVni8ZYD8VhPXwEgMyFi/Dp2aVYkiJ3w5aC13kbt+IWGV7w3i0iDJ9uHUl79xMCTrkjXdk27U+gTngwtcOCABjQ9kIWbdxTLElR1PzYHQxs16jE5yu2H6J2WBDRIeX7Y1rZ2rduyeEjzt+1ciWuGvvGrTupW6smdaKjABjUpxt/Ll1RLEmxe99BbjnRlhvUq83h+ESSjh0HwGqzk5ubh7ubO9k5uYSHlt7WKsOmffEn2nswAAPaNWbR+l3FkhRFzV+9jYHtmxS8t9ns5OZbcXdzIyfPSnjQuf2jfTqb9hymTmQItSMc9TWwYwsWrd1eLNFgGAZZObmYpklWbh5Bfj64WSyEBwcQHhwAgJ+PFw2iw0k8nl6lSYq+A3vw3eeOu2vr1mwiIMif8MgwjiYkObW+aYKfvx8Avn6+pKakYbXaKi3ekzZu20Xd6CLtvXdX/lq2sliSYve+g4wbPRyABnVrczghkaRjKYSFBBeUWRG7kTrRkURHnds6P7Z6Z+HrNbvwKeO3O+bGvqy6/TVHRULBnUg3Xy9aTb+RoKZ1MNzc2Prc1wWJg6LSd8ad07gBbFm5Ba/dfb0xT8R2PsQO0HDsAOJ+XkmN1qdJzpsm7v6ORIB7gC858SnA2cce0fMiUrccKLgTn3c8w+m4fWqGENW3Ndtf+o4Lbru0xPLAC2ux/ZUfAMjYFYdvnXC8wgLJTUqjzvAuNBw3EIuHG8djd7P24XlgL/7/ZsvKJXnldvxiIsuMwS8mCq+wQJL/Ldmb6nScae+eIf7Y8/LJ2BMPQOLfG2l8zxD2f7qoWuv9fD1WcxJTyElMARyJpvSdh/GJqlHsYjk3KY3cpDSi+rYpsf65ajOVLXXT/lI/r64240y9n87Z1nu9kT0Lk8amWe6eJWfSqn8H/v3mbwD2rt2JT4AfgeHBpB1NKVZu06K1Ba/3rd9FjSjH+VrNRrWZ/4aj10jC7jhCa4cTEBZEelLqOY3zvGbXnBRVySXnpHAPD8Man1jw3pZ4FLeI0i96APyHDiJ7WWG30BoP3kHKy+9g2ks/QatMiSmZRAUXXmhFBvuTmFr6D2h2Xj7Lt+6nb6sLSixbGLuDQaUkL+T/n8SkZKIiwgreR4aHkXg0uViZxhfU5/fF/wCwccsOjiQkknA0mcjwUG4aOZS+I8bSa9iNBPj50eXikic1lRZ7SgZRNQIKY68RcPr2vmUffds42nVkcAA39O3AwMffod8jb+Hv40nnZvWrImwAEo+nEVUkCRhRI5CE48W7vI7sczF74pLoe+/zXPX4G0wcNQiLpfjP5uGjx9m2/wgtG9aqkrhPiqwZwZG4wqRbfFwikVHhpZa979E7+HHRZzw67X48PT0A+Gju5zS8MIZlmxby0+LPefqx58q8qD2XHO298Pc8MjyUhKRjxco0blif3xf/CziSeEfij5JwyjEx/8+lDOpTfHjOuVZ/VE8S/lxf6jK/ehHUHnIJvRY+TedPJuIX40i6NJkwlKNLN/PXwMksGf40LaaMws3Xq1z79asbTu/fZtDt28mEdmxcrnWjB7Wn35Ln6PzRQ8TeN+e8id07qgbRl3Zgz/9+P2252AfeofPHExkU+yp1R3Rl+6s/nJPY/RtEgWnS5dOH6f3rdBrdebnT61407Xo2Tfu0zOMndfMBoi919Car0aYhvrXD8IkOJaBRNLWHdOLvwU/yZ99HMe126g7v6vR+i6ozrBOHfvinQuueVFZ7z0tOx+LhRnCrGABqXd4Rn2hHQqA6692Z2ME12/tJvnXCCG5Rn2Oxu50qf67aTLuXxtP79xk0uW9YudcFCGnXiN5/PEPnTyYS0Lh8f/tcoc2crt5L+25nW+8egY5e3c0mjqD3r9O5+J0J5e7RcybBkSEciyv8O5kSn0yNqLJvWlnc3bhkWHc2/+1IWhzauo+2Ax09E+u3uoCQWuEFCQyR6nDanhSGYUQBmKYZbxhGONAN2G6a5uZKjcowSn5WxrmzV/tW+A8dSPyY+wDw6dYR+7EU8rbuxKtdq0oMsnRmKYEapX0fYPGmvbSOqUmQn3exz/OtNv7etJd7BneulBjFtZR2Yntqmxk3+ipmvvIOw8dMoFGDejRp1AA3NzdS0zP4a+kKFn7+DgH+fjwwZRY//voXg/v3qprYS2vvZZRdvGE3rRtEE+TnuAualpXDog27+HnqOAJ8vXjonR/5ecUWLuvYrBIjLlTa9YRxSvTLN+2iSd0o5k66kYOJxxj/7Ie0bVwXfx/HMZuVk8sDr33BQ6MGFnxWVUr7XSmtLT339GscTUjC09ODaS88zq1338Rrz79Dt96d2LppO9cPG0/dmNq8/+UbrP5nLRkZmZUad6n1fspXGTvqSma+9i5Xjbv/RHuPwd2tMDmUn5/PouWrmHDLdZUWZ1iXZtS7tieLhzxV6nI3Lw9sufn8NeBxoi/tQLsXb2Xx0KlE9GxJ1IC2NLr9soJyvrVCnb4bm5OQwoJ295B3PIPgi2K45L37+b3HRKwZ2U6tHzd/NXHzVxN6SROaTRrB0qtLDitwxdgvmnYDm6Z9WuLO5KkuuHUQy0fP5vja3TS643Iueuo6Yh9456xjt7i7EdqxMX8NnIwtO5euXz5Gyvq9HF16+tOdqH5tyE1KI2XDXsdY81Jsf/UHWj19A71/n0Ha1oOkbtqHabUR3q0FwRfF0GvBNEfM3p7lmhugqNpDO7H6rjcrtC6cub2vHP8aFz11PRYvDxIXbcC0Ou4oVle9lyd2V2zv4OhR0HHufWyY8qHT65yLNrPqjtfJiT+Ou583Hd+9l7ojunHgyyVOr5+yYR8L2t+DLSuXyD6t6fTeA/za+X6n16/uNnO6ei/ru51tvRvubvjWCiV51XY2PvkRF4y/lJZPjGb13RU/Zkvso9RzgrLLj5o2jp0rt7JrlaP31YI3v+OaJ27m8V+e5fC2AxzcvBe7rfJ7V4qUpcwkhWEY44GHHS+NWcBNwGbgGcMwZpum+e5p1r0VuBVgRp0mjAorX5bVmngU9yJdeN0iwrGdchcNwKNRDKGTHyDx7kewpzp+LLxatcCnRydqdb0Yw9MTw8+X0KcfJvnxmeWKoaIig/2JTym8k5yQkkF4oF+pZRfE7mRguwtLfL50y36a1A4nNLBq59OQ6hEZHkZ8YmEX/YSjSYSHFc9++/v58vQjEwDHheiAa26hds1Ilq2MpVbNSEKCHcOL+nTvxLpN26osSREZHED88cIuiwnH08scsrFgzXYGdigc6vHvtv3UCg0iJMDRzvu0bsS6PXFVlqSIDAkk/ljhSUbi8TQiivQKAfh+yTrGXNYVwzCoGxlKrfBg9h5JomWD2uRbbdz/2hdc2qklfdtXTcyjx4zgmusdd742rN1CzejCLqVR0REkljLU4+Twj7y8fL7+5AfG3Xk9AMOvvYK3X3kPgAN7D3HoQBwNGtVnw9rKzUFHhocSn1j4e55wNJmI0FLa+yTHHEOmaTLw2tuoVbPwuy5ZsZamFzYoNvzjbDS4uR/1RzuOmeWjZ+MZEkDb529h+ahZZXYlzo47RtzPjh58cb+sot1L408sMVgx9iUydh8pVr7dS+MJalGPnIQUlo+eXWYs9jwreXmOfaZs2Evm/gT8G0aVOX781NhzElIASP53G371I/AMCSjRrdgVY/cI9OXitx3/514hAUT2aY1ptXNkweqC8p6hAQQ1r8fxtY67n4e+/4cun046J7Fnxx0j6Z+tBXWV8Mc6gi+KOeOFT2iHC6nZvy2RfVrj5uWBu78P7V+7g9V3vVFQxpqRzZp7C+fNGLDqZTIPHCW0UxMOfLGYzTM+L7bN6EHtafKAYzLZ2AfeOePcAUHN6mK4uZGywbk5BirS3o+t2cnioY7x6RE9WuLfsOaJJVVb7+fzsVqU4e7GJe/ex8FvlhFXnnlEDM66zeTEnxgqmpnDwW+XU6NNwzMmKcr6nUn4Yx3GzJtL/Z053ZeojmMVzlzvRZMWRb/b2dZ73rF0rFk5xP3i+D07/OO/1B/V84zxnknP6wfQ9dq+gGPoRkh0KCf7hgRHhZKScKzU9S6fcBUBoYG8Nb5w7o2cjGz+91Dh79b0pa+TdDCxtNVFqsTpelLcBTQHfID9wAUnelTUAP4CykxSmKY5B5gDsL9t33L3H87bvB33OrVwj47CmpiE34CeJD1a/G6QW1QE4c89SfLkmVgPFI4nS3ntXVJec4Tm1a4VgTeMqLIEBUDzupEcOJrC4eRUIoL8WRi7gxk3DihRLj07lzW7DjPj+v4lli2I3VFq8kL+f2rRpBEHDsVxKC6eyPBQ5v+xhNlTik92l5aegY+3Fx4eHnz906+0a9Ucfz9fakaGs2HLdrJzcvH28mTFmvU0b1J1w4Sa14viQGIKh5NSiQj2Z+Ga7cy4ueSY7PTsXNbsPMSMmwqX1awRyIZ9R8jOy8fbw50V2w/QvG7VjZ9tHhPNgYRkDh09TmSNABas2MQztw0vViYqNIgVW/bQtnE9klMz2HckmdrhNTBNkyfnfU+DmmHcMLDqejx9PO9LPp73JQA9+3XlurFX89O3C2ndrgXpaRmlzkdRdJ6Kfpf2ZMc2xylM3KF4OnW7mNX/riM0PISYC+pxcL9zY3PPRosmF7D/8BEOHUkgMiyE+X8uZdbj9xUrk5aRiY+Xp6O9//w77S5qhr9fYdJ2/p9LGNS7Yt3iS7Pnvd/Y895vAPjUCuWSefex+q43CsbglyZuwWrCuzZn/6d/E9a5KRl7HCfciYs20HDsgIJZ3YNa1CN10/5iF6mn4xka4LjYspv41o3APyaKzP1lnygWjd2vfuHxE9yyPhYP91IvHFwx9qLavTyeI7+tLZagAMhPycQjwBf/BlFk7IknontL0nfEnZPYExZt4MI7L8fNxxN7npWwTk3ZNeeXM663ecbnBRcuYZ2b0uj2y4olKMCRgLFm52Lm26g/uhdJ/27DmpHN0SWb6fT+A+yaM5/cpDQ8gv1w9/cp6A3jrNrDOnPou+VOl69Iez85h4bF050L7xrM9pe+A6q+3s/nY7Woti/eSvrOw+x6+8xtrKizbTOGmwWPID/yjqVjuLtRs18bEhdvOuN6RevdKzyo4PMabRpiGEa55leormMVzlzvXuFB5B51zMFQ9Ludi2P1yK9rCe/clKPLthDerQVpTs6FcTqLPlzIog8dT6pp0astvW4cyKoflhHTphHZ6Vkl5qMA6HJNb5p1b82Lo6YW633pE+hLXnYetnwrXUf2YeeKreQ42cPnP8PUnBRVyShrDKVhGLGmabY98Xq9aZqtiixba5qmUwPfK5KkAPDucrHjEaQWCxk/LCDt3U/wH+4Yd5bx9U+ETL4f3z7dsJ2YCNG02Yi/7s5i2ziZpKjoI0gjnhlaofWWbN7Hs98swW63M+SSZtwyoANfLt0IwIiuLQH4fsVWlm/dz6ybBhZbNzsvn4FT3uenJ24gwKd8YyOLcm9X8kLxXHjoiZmsWruBlJQ0QkOCuWPs9QwfXDIJ44qqJHabk4+rO8Xif1Yz69W52Ox2hl3al/E3XM3n388H4Johg1i3aRuPTn8RNzcLDerVYerD9xAU4Oix8Nq8T1j45xLc3Nxo0qgBUyfeXTDvQHlYN/1dodiXbNrDs18tcrT3Ti24ZdAlfLnYMTZ4RHfHz8b3/2xi+ZZ9zBpbfOzoGz8t49c123GzWGhSJ4InRvfHswKPIDV8A85cqLTY1+9g9icLsNtNhnZrwy1XdOeLPx13V67u3YHE42lMnvsdSakZmKbJmMu6cnnnVsTu2M/NM96jUe0ILCe6WN59VR+6tSp/crHlFRV/2swTsybRvVdnsrNzePieJ9m03vGo13c+fZnH7p1GYkISH3zzFiGhNTAM2LppB1MemkFWZjYRkWHMevUpwiPDMAx4+5X3+eGr+eXa/+b1H1Qo7sX/rmH26/Mc7X1QH2697iq++MFxonX1FQNYt3k7jz3zChaLhYb1a/PUQ3cWtPfsnFz6XXML8z9+kwD/0nupOeOnttNL/bzt87cQfdnFZB06CoBps/PXAMffkM4fTyT2/jnkJKTgEehLhzfuxKdWKNbMXNZNfJfULQeweHvQauoNhHRoBIZB1sGjpc4UHz2oPa2m34hnaCD5aVmkbtrPsmtnEn1ZB5pNHIHdagObnS3Pfk38b7FOfacL7xpM3RHdsOdbseXks2nqJwWPIHX12Is6maQ4+QjSorFHD2pP04lXYdpN8lMzWXPvHLIOJJ517OCYGK/xPUMwTZOEP9Y5hp+Uw8kkxT/XP0fMDY4nLO394A9C2jWi/au3Y9rspO04ROz975Cf6hhWVWvIJTS++woMiwV7vo11j7zH8dhdJbY9YNXLePj7YPF0Jz81k6UjZxZM+DdgxUssGz2bjF3ln+DR2fbeYsooovq2wbAY7Pnf7+x+x/Hoxuqs9/P1WA29uDE9fniC1C0HME9MxLf5mS/wreUY/7/3gz/wCg+i98KncQ/wwbSb2DJz+K27YyjJ2bSZrENJdP92ChYPNww3C4mLN7HhiQ/POMyqqAZj+tPgxr7YrTbsOXlseOKjgklMi9Z7w7EDuPDOy/GKCCY3KY2EP9YR+8A71dZmnKn30323sz1WfWqH0eHV2/EI8iM3OY01975N9uGSvcQXelfsPBLg2qljad6jNXnZefzvodfZv3EPAHe99wgfTnqL1MTjvLHrM44dPkpOZg4Aaxes4OdXvqJB2wu56fm7MO12juw8xAcT3yQrrXzDP9/e92VZI37/X8he+FrVT3ZYBXwG3OWS/2+nS1KsBjqZpplvGEZt0zQPnfjcG1hRNGlxOhVNUriCiiYpXEFlJSnkDCqYpHAFFU1SuIKKJilcwdkkKapbRZMUrqCsJIWIiEh1OZskRXVTkuL85KpJitM93eNKAMMwmp1MUJwQApR88LqIiIiIiIiIyFkoM0lhmuYB0zTzgS8Mw5hkOPgAjwBTqyxCEREREREREflPcGbwd0dgFrAcCAA+BrpUZlAiIiIiIiIiLsGuiTOr0umGe5yUD2TjeMqHN7DXNDW9qYiIiIiIiIicW84kKVbhSFJ0ALoC1xqG8VWlRiUiIiIiIiIi/znODPcYa5rmyYcAxwNDDMO4vhJjEhEREREREZH/oDMmKYokKIp+9mHlhCMiIiIiIiLiQjQnRZVyZriHiIiIiIiIiEilU5JCRERERERERFyCkhQiIiIiIiIi4hKcmThTRERERERE5L/J1JwUVUk9KURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJmpNCREREREREpCx2zUlRldSTQkRERERERERcgpIUIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuARNnCkiIiIiIiJSFlMTZ1Yl9aQQEREREREREZegJIWIiIiIiIiIuAQlKURERERERETEJWhOChEREREREZGy2DUnRVWq9CSFaTcqexeVx2qt7gjkfHM+/4CdzxMCuSnfWh0MQ53xREREzhUL5/F1k8g5pDNMEREREREREXEJSlKIiIiIiIiIiEtQH2kRERERERGRspzPw6LPQ+pJISIiIiIiIiIuQUkKEREREREREXEJSlKIiIiIiIiIiEvQnBQiIiIiIiIiZbFrToqqpJ4UIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuAQlKURERERERETEJWjiTBEREREREZGyaOLMKqWeFCIiIiIiIiLiEpSkEBERERERERGXoCSFiIiIiIiIiLgEzUkhIiIiIiIiUhbTrO4I/lPUk0JEREREREREXIKSFCIiIiIiIiLiEpSkEBERERERERGXoDkpRERERERERMpit1d3BP8p6kkhIiIiIiIiIi5BSQoRERERERERcQlKUoiIiIiIiIiIS3DZOSl8OrcnZOIdYLGQ8e18Ut/7vNhyv0t7E3TTNQDYs7NJnv4K+Tv2FBawWKj5yevYEpNIvGdyVYbOsq0HmP3dUux2k2GXNGVMn7bFlr//51p+id0JgM1uZ29CCn9NvYkgP28GTfsIPy8PLBYDd4uFT+6/qkpjP5PHZ7zA4mUrCakRzHcfvVXd4ZSLK8e+dEUsM1+bi81mZ/hl/Rg3enix5anpGUye9SoH4+Lx8vRk2sS7aNSgHgAffPkDX//8GwYGjRrU4+lJd+Pl5VllsS/bvI/ZXy3CbrczrEsLxvS/uNjy939bzS+rtgEn2nv8Mf6adRvHM7KY+O4vBeUOJ6dy+2WduK538eOlUmPfsINZH/7iiL1nO8YO7lFseXpWDo+++SXxyalY7XZuvLQLQ7u3K1hus9u5dsqbRNQI5LUHrq+yuE+aPOMhevTtQnZWDpPueZItG7aVKDPr1Sfp0KktGekZAEy6+0m2btqBf4A/z785jZq1onB3d+PdNz7k609/rJK4Xbm917myCxfeNRgAa2YO6ybNI3XLgVLLNnv4amoN7ohps7P3f7+z+92FTu+n1uCONH1wOAGNovlr0GRS1u8FwLdOGP0WP0f67jgAjq3ZxbpJ85zaZs0B7Wg2aQSm3Y5ps7Nh8ockr9x+XsR+Uo3WDej581RWjH+FuJ9Wllge3rU5LaeMAouBNTOXNRPeInNfwlnHDhDYtA5tnh2HR4APpt3OXwMnY8/NP+M2B6x6GWtGNqbNUe9/DXi8Qt+tLJ41/Ok4dwI1Wjdk/+eLWf/o+wC4+3nT/fspBeV8aoZy8OulbJjyodPbdrbNNBjTnwtuGYh/TBQ/NRtP3rF0p/cBlVPv5/Oxeiqf6BDav3o7XuHBYJrs/fBPds9d4PT6zR6+mrojuuEZ7McPDccUfF73mu60nDKK7CPHANgz71f2fbKoQjGe6ozHapdmtHxiNIanOykb9hJ73xxMm/Nj+iujzZyqsuodoNYVjvgxIXXzflbd8Xq54yuvq5+4mea92pCXncsHD77Bwc17S5S5+aW7qdeyITarlX3rd/Pxo3OwW234Bvpx/bO3E1Y3EmtuPh9OfJO4HQcrPebziuakqFKumaSwWAh55G4SbpuENSGJ6I9fI+vvf8jfU/jHx3o4nvixD2BPz8CnSwfCJt/LkevvKVgeOGoY+XsPYPHzrdLQbXY7z3yzhLduG0xkkB+jX/yaHs3r0zAqpKDMTb3bcFPvNgD8vXkfH/29niA/74Ll79xxBTX8fao0bmcNvbQfo4ZfwaPTnqvuUMrNVWO32Ww8/fLbvPPcU0SFh3LNbQ/Rq8vFNKxfp6DMOx99RZMLYnjl6UfYs/8Q019+m3dfmEbC0WQ+/vonvv/fq3h7efHAk7OZ/+cShg7qUzWx2+0888WfvHX3lUQGBzB69if0aNmQhjVDC8rc1K89N/VrD8DfG3fz0Z9rCfLzJsjPmy8eva5gO/0ffYferS6okrhP7nPG/37k7Uk3ExkSyKgpb9GzbVMa1oooKPP57//SoFYErz5wPcfSMhky8SUu69wKD3fHT+fHC/+hQXQ4Gdm5VRb3ST36dqFegzr0vXgordu1YOrsR7hq4I2llp391Mss+PGPYp9dN3YEu7bvYfx19xESGszCf77hh6/mk59vrdS4Xb29Zx5IZPGwaeSnZhLZuxVtnhvHokunlChXb2QPfGqF8lvXB8E08QoLLNd+0rYd5N8xL9Lm2bEllmXsT+DPvo+WO/bEJZs4snAN4DiJ7zhnAr91e/C8iB0Ai0Hzx68lYdGGMou0njWGf296nvSdcTS4qS9N7hvKmglvO72LsmI33Cx0eP1OVt/1BqlbDuBZwx97OY6FJcOnn/7C3YnvVhZbbj5bZn1FYJPaBDYpPE6smTnF6rrXwukc/mVVubbtbJtJXrmd+N9i6fZNxW76VEa9n8/H6qlMq52NT35MysZ9uPt50+vX6SQu3kj6jsNOrX/k11j2zPuV/v+8UGLZoe//LUhsnTNnas+GQbtXbmfpiOlk7Imn6cSrqHt1d/Z/usjpXVTWsVpUZdW7X0wUje8ewt+DnyI/NbPcba4imvdsQ0RMFE/0vIeYNo24dvo4Zg99rES5ld8t5b17XwVgzCsT6DqyN4s/+o2Bdw7j0JZ9vD3+OSIbRjNy6lheHj2t0uMWKYtLJim8WjTGejAO6+F4ADIXLsK3Z2dSiyQpctdvKXy9YStukeEF790iwvDp1pHUuZ8QeH3xO3SVbdOBROqEBVE71PGDNKDNBSzatK9YkqKo+bE7GdimUVWGeFbat27J4SPO37VyJa4a+8ZtO6lbqyZ1oqMAGNS7K38uW1Hsom33/oPcMsrRlhvUq83h+ESSjqUAYLXZyM3Nw93NneycPMLDSm9rlWHTvnjqhAdTOywYgAHtGrNow+5iSYqi5q/ezsD2jUt8vmL7QWqHBxEdWvl/yE/atPsQdSJDqR3hqK+Bl7Rk0ZqtxZIUBgZZObmYpklWTi5Bfj64WRyj5BKOpbJk3XbGXdGDDxcsr7K4T+o7sAffff4zAOvWbCIgyJ/wyDCOJiQ5tb5pgp+/HwC+fr6kpqRhtdoqLd6TXL29H1u9s/D1ml341Cx9+zE39mXV7a85KhLITUoDwM3Xi1bTbySoaR0MNze2Pvd1wUVgUek7485p3AC2rMJkmbuvN+aJ2M6H2AEajh1A3M8rqdG6QdmFTBP3E0l89wBfcuJTgLOPPaLnRaRuOVBwJz7veMbZfZlTlPXd6gzvQsNxA7F4uHE8djdrH54H9uL/b7asXJJXbscvJrLM7fvFROEVFkjyvyV7U52Os20mddP+Uj+vzno/n4/VU+UkppCTmAI4kk/pOw/jE1UDe24+rZ+5Gc/QQGzZucQ+OJeMXSXjOR67q9JjLOpMx6pniD/2vHwy9jjO4xP/3kjje4aw/9NFLnWsVla9x1zXiz3v/Up+aiZQ2OYqU6v+7fn3m8UA7F27E98APwLDg0k7mlKs3OZFawte71u/i+Aox/laVKPaLHzjWwASdscRWjucgLAg0pNSKz12kdK45JwUbhFhWOOPFry3JiThFhFWZnn/YQPJXlp49yDkods5/tI7YFZ9t5zE1Eyigv0K3kcG+5F44kfqVNl5+SzfdpC+FxX+yBsG3P72T1z7wpd89c+WUteT/18Sjx4jKrywfUeGh5J49FixMo0b1uf3Jf8CsHHrDo7EHyXhaBKR4aHcdM1Q+l59C72G30yAvy9dOrSputhTMoiqEVAYe7A/iSmlnzBk5+WzfMs++rYumZRbuHo7g9o1qbQ4S5N4PI2okKCC9xEhgSQcL34iMbLfJeyJO0rfu2dx1aOvMfH6y7CcSFLM/ugX7hs5AIvFqNK4T4qsGcGRuMKkW3xcIpFR4aWWve/RO/hx0Wc8Ou1+PD09APho7uc0vDCGZZsW8tPiz3n6sefKvEA5l86n9l5/VE8S/lxf6jK/ehHUHnIJvRY+TedPJuIX40i6NJkwlKNLN/PXwMksGf40LaaMws3Xq1z79asbTu/fZtDt28mEdiyZ1Dud6EHt6bfkOTp/9BCx9805b2L3jqpB9KUd2PO/309bLvaBd+j88UQGxb5K3RFd2f7qD+ckdv8GUWCadPn0YXr/Op1Gd17u9LqYJl0/e5heC6dT/7reTn+3gEbR1B7Sib8HP8mffR/FtNupO7yr8/stos6wThz64Z8KretMmylLtdZ7EefjsVoW3zphBLeoz7HY3bR5bhzrHvsffw14jI1PfUzrmTeXe3u1LutAnz9n0nHuBHyizz6p68yxmpecjsXDjeBWMY4YLu9YsG9XaTOnOpf17t+gJv4Na9Ljhyfo+fNTRPa66JzEeDrBkSEcjyu8SXE8PpngMm6QAljc3eg4rBtb/l4HwOGt+2k9sCMA9Vo1JKRWODVOs75IZStXTwrDMGaYpnn2/drOvKOSn5Vx8uzdvhX+QwcRf/O9APh064jteAp5W3fi3b7yfxROVVqYpX0dgMWb99M6JqrYUI/37x5GRJAfx9KzuO2tn4iJCKZdw+hKilZcgUnJRnNqmxk3ajgzX53L8LH30qhBPZo0aoCbmxup6Rn8tWwlCz97mwB/Px54YjY//rqIwf17VlHsJRllNPjFG/fQukF0sfYOkG+18ffG3dwzpEslRFi20o/V4rEv37iTJnVrMveRMRxMPMb4me/RtnE91mzbR0igH81iarFq656SG6oCpdVzaUmG555+jaMJSXh6ejDthce59e6beO35d+jWuxNbN23n+mHjqRtTm/e/fIPV/6wlI6P0pOq5cr6097Auzah3bU8WD3mq1OVuXh7YcvP5a8DjRF/agXYv3srioVOJ6NmSqAFtaXT7ZQXlfGuFOn03NichhQXt7iHveAbBF8VwyXv383uPiVgzsp1aP27+auLmryb0kiY0mzSCpVfPOC9iv2jaDWya9mmJXgSnuuDWQSwfPZvja3fT6I7Lueip64h94J2zjt3i7kZox8b8NXAytuxcun75GCnr93J06eYzrvv34CfJSUjBKyyQLp8/QvquuGI9Gsr6buHdWhB8UQy9Fji6VLt5e1b4jmvtoZ1YfdebFVrXmTZTluqs95PO12O11Fh9veg49z42TPkQ024ntP2FdHyncCiz5USS2Vnxv8Zy6Nvl2POsxNzQxzEE46rpFY4PnD9WV45/jYueuh6LlweJizZgWh03Dl2hzZzqXNe74W7BPyaKxVc+jU90CD2+m8LvPSeRn5ZV4RjPvFPnr50Arp02jl0rt7LrxJxhC9/8jhFP3MSjv8wmbtsBDm7ei60cc4iInGtlJikMw3jl1I+A6w3D8AcwTfOekmsVrHsrcCvA9NpNGBVau1xB2RKO4l7kjqB7ZBi2o8klynk0iiH0iftJuPNR7KmOsaBerZvj26MTvl0vxvD0xPDzJWz6JJIem1WuGCoqMtiP+JTCk/yElEzCA/1KLbtg7S4Gtik+Bj8iyFE2JMCXXi1j2HQgUUmK/+ciw0OJP1qY/U44mlyiC7u/ny9PP+w45EzTZMDIW6ldM5Jlq9ZSq2YEIcGOHgF9undi3eZtVZakiAz2J/544TjshJQMwoPKaO9rtjOwfcneEks376NJnQhCyzhOKktkSCDxxwq7MSYeSyMiOKBYme8XxzJmcHcMw6BuZCi1wmuwNy6JdTsOsCh2G0vX7yA330pmdi6PvPklz9w+olJjHj1mBNdcPwyADWu3UDO6sPt3VHQEiaUM9Tg5/CMvL5+vP/mBcXc6Jvgcfu0VvP3KewAc2HuIQwfiaNCoPhvWVvxEzxmu2N4b3NyP+qN7AbB89Gw8QwJo+/wtLB81q8yuxNlxx4j72TFZXNwvq2j30vgTSwxWjH2JjN1HipVv99J4glrUIychheWjZ5cZiz3PSl6eY58pG/aSuT8B/4ZRxSaNO13sOQkpACT/uw2/+hF4hgSUmCvBFWP3CPTl4rfvBsArJIDIPq0xrXaOLFhdUN4zNICg5vU4vnY3AIe+/4cun046J7Fnxx0j6Z+tBXWV8Mc6gi+KcerC52Sd5yalcWT+akLaNCyWpKjRKqbU74YBB75YzOYZxScGjx7UniYPXAk4eo6UVX8nBTWri+HmRsqG05c7qSJtpmxVW+/n87F6Joa7G5e8ex8Hv1lG3C+rcPf3IT8ts+ScFxaD3r86Eg1Hfo1l6+yvytxm0TrZ+9GftHj82grFVt5jFeDYmp0sHjoVgIgeLfFvWPPkN622Y7U0lVHv2XHHOBa7C9NqI+vAUdJ3H8G/QRTH153bmxo9rh9Al2sdczLtX7+bGtFhgGPi2xpRoaQkHC91vcsmXIV/aCBzxhf2nMrJyObDhwoTnU8vfY3kg4nnNN7zXjX00P8vO11PiiuBRcCvOBIUACOBkgPHTmGa5hxgDsC+1v3K3X84d/N23OvWwj06CmtiEn4DenL00WeKlXGLCifi+SdIenwW1gOFE9ykvDqPlFcdsyt7t7+IwBtGVFmCAqB5nQgOHE3hcHIaEUF+LFy7ixnX9y1RLj07lzW745gxunDCt+zcfOymiZ+3J9m5+fyz4yDjT0w4KP9/tWjciAOHjnDoSAKRYSHM/3Mpsx+/v1iZtPQMfLy98PDw4Ouff6Ndq+b4+/lSMyKcDVt2kJ2Ti7eXJytiN9C8ccMqi715vSgOJB7ncFIqEcH+LFyznRk3DSpRLj07lzU7DzHjxpLLFqzZVmryorI1b1CLA/HJHEo8RmRIIAv+3cgzdxRPMkSFBrNi827aNq5PcmoG++KTqB1RgwnX9GfCNf0BWLV1D//7ZVmlJygAPp73JR/P+xKAnv26ct3Yq/np24W0bteC9LSMUuejKDpPRb9Le7Jjm+MCL+5QPJ26Xczqf9cRGh5CzAX1OLjfucnCzoYrtvc97/3Gnvd+A8CnViiXzLuP1Xe9UTCeujRxC1YT3rU5+z/9m7DOTcnY4zjhTly0gYZjBxRMVBfUoh6pm/az5l7nJnf0DA1wXFjYTXzrRuAfE0Xm/rJPFIvG7le/MGkV3LI+Fg/3Ui82XTH2otq9PJ4jv60tcdGTn5KJR4Av/g2iyNgTT0T3lqTviDsnsScs2sCFd16Om48n9jwrYZ2asmvOL2dcz83XC8MwsGbm4ObrRUSPlmx74ZtiZRZefG+p3y3gwlp0ev8Bds2ZT25SGh7Bfrj7+xT0bHBW7WGdOfSd8/PiVKTNlKWq6/18PlbPpO2Lt5K+8zC73nZ8f2tGNpkHjlJrcEcO/7jCEWOzuqRuOeD0ZJ3eEcEFcy5ED2hH+s6K/caX91gF8AoLJDcpDYunOxfeNZjtL30HVN+xWpbKqPcjC1ZTe1hnDny+GM+QAPwb1DyrtlGWvz9cyN8fOp5U06JXG3reOJDVPywjpk0jstOzSsxHAdDlmt407d6Kl0dNLdb70ifQl7zsXGz5NrqM7MPOFVvJOYteQSJnyyhrDLJhGAHANCACeMg0zcOGYewxTfM0M1qVVJEkBYBP14sJeeh2xyNIv19I6txPCLjKMe4s/aufCJ1yP759u2I74jjoTauNI6PvLLaNk0mKij6CNHL6ZRVab8mW/Tz7/TLsdpMhFzfhln7t+HK5I8M7onNzAL5fuY3l2w4y64Z+BesdSk7j/nmORx9Z7XYGtW3ELf3aldyBE9w7XlGh9c7koSdmsmrtBlJS0ggNCeaOsdczfPCAStnXuVYlsedX7CkPi/9dzazX5mGz2xg2qC/jrx/B59872sI1QwaybvM2Hp3xMm4WCw3q12HqxLsICvAH4LX3PmXhn0txc3OjSaMYpj50V8G8A+Vh3fx3hWJfsmkvz369yNHeOzXnloEd+XKJY2zwiG6tAPj+n80s37qPWWOKH1PZefkMfHwuPz01hgCf8o0FLsoILH2izjPGvm47sz92PIJ0aPd23DKkJ1/84bjjdnWfi0k8nsbkOV+TlJKBaZqMGdydy7u0LraNk0mKij6CtOVlMyu0HsATsybRvVdnsrNzePieJ9m0fisA73z6Mo/dO43EhCQ++OYtQkJrYBiwddMOpjw0g6zMbCIiw5j16lOER4ZhGPD2K+/zw1fzy7X/LRs+qlDcrtDef2xT+qzlbZ+/hejLLibrkGNepKKPlOz88URi759DTkIKHoG+dHjjTnxqhWLNzGXdxHdJ3XIAi7cHrabeQEiHRmAYZB08yj/Xl3yiUPSg9rSafiOeoYHkp2WRumk/y66dSfRlHWg2cQR2qw1sdrY8+zXxv8U69Z0uvGswdUd0w55vxZaTz6apnxQ8TtLVYy/q5IXPyccaFo09elB7mk68CtNukp+ayZp755B1IPGsYwfHJJaN7xmCaZok/LHO0aX9DHzrRnDJe/cBjm7oB79ZxvaXvyfmBscNiL0fFH+qzqnfrdaQS2h89xUYFgv2fBvrHnmv1Mn4Bqx6GQ9/Hyye7uSnZrJ05MyCJxAMWPESy0bPLnVivzNxts00HDuAC++8HK+IYHKT0kj4Yx2xD7xTbfUO5/exeqrQixvT44cnSN1yAPPEYw43P/MF6TsP02bmGLwig7F4uHHou3/Y9sK3JdZvMfla6gzrjHdUDXLij7Pvk0Vsfe5rmj96DTUHtMNutZGfksHaSe9VqJ2U5XTHaospo4jq2wbDYrDnf7+z+x3Hb3x1tplTVVa9A7R88joie1+EabOz/aXvOfS9c3PG/OZd8SdsjZw6lmY9WpGXnccHD73BgY2Onht3vvcwH016m9TE47y261OOHT5KTmYOAOsWrOCXV74mpm0jbnr+Lux2O0d2HuKjiW+RlVa+4Z9v7vuieibpqiLZHz1W+RN3VQOf66a75P9bmUmKggKG0RZ4HvgZuMs0zfrl2UFFkxSuoKJJCldQWUkKOYMKJilcQUWTFK6gokkKV3A2SYrqVtEkhSsoK0khIiJSXc4mSVHdlKQ4P7lqksKZiTNzgN7AHcBSAMMwepqmuagS4xIRERERERGpfnbNSVGVnElSfAF8ADwLvGcYxqtAe6BTZQYmIiIiIiIiIv8tFifKdATqAsuBlUAcULXPChQRERERERGR//ecSVLkA9mAD+AN7DVNPYNFRERERERERM4tZ5IUq3AkKToAXYFrDcMo++HAIiIiIiIiIv9fmOb/z38uypk5KcaapnnyAcjxwBDDMCr2rD0RERERERERkTKcsSdFkQRF0c8+rJxwREREREREROS/ypnhHiIiIiIiIiIilc6Z4R4iIiIiIiIi/012PTeiKqknhYiIiIiIiIi4BCUpRERERERERMQlKEkhIiIiIiIiIi5BSQoRERERERERcQmaOFNERERERESkLJo4s0qpJ4WIiIiIiIiIuAQlKURERERERETEJShJISIiIiIiIiIuQXNSiIiIiIiIiJTF1JwUVUk9KURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJmpNCREREREREpAym3azuEP5T1JNCRERERERERFyCkhQiIiIiIiIi4hIqfbhHVrpnZe+i0hjB4dUdQsXZ8qs7goqzn8eP+PHwqu4IKszwr1HdIVSYGb+/ukOoMH837+oOocLM3MzqDkFEROT/DQ+M6g5BxCVoTgoRERERERGRspzPN1HPQxruISIiIiIiIiIuQUkKEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXIImzhQREREREREpi6mJM6uSelKIiIiIiIiIiEtQkkJEREREREREXIKSFCIiIiIiIiLiEjQnhYiIiIiIiEhZ7GZ1R/Cfop4UIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuATNSSEiIiIiIiJSFru9uiP4T1FPChERERERERFxCUpSiIiIiIiIiEgJhmEMNAxju2EYuwzDeLiMMj0Nw1hnGMZmwzD+Ptt9ariHiIiIiIiIiBRjGIYb8DrQDzgErDIM4wfTNLcUKRMMvAEMNE3zgGEYEWe7XyUpRERERERERMry352T4mJgl2maewAMw/gMGAJsKVJmFPCNaZoHAEzTTDzbnWq4h4iIiIiIiIicqhZwsMj7Qyc+K+pCoIZhGIsMw1hjGMYNZ7tT9aQQERERERER+Y8xDONW4NYiH80xTXNO0SKlrGae8t4daAf0AXyAfwzD+Nc0zR0VjUtJChEREREREZH/mBMJiTmnKXIIqFPkfW0grpQySaZpZgKZhmEsBloBFU5SaLiHiIiIiIiIiJxqFdDIMIwYwzA8gZHAD6eU+R7oZhiGu2EYvkBHYOvZ7FQ9KURERERERETKYp46wuG/wTRNq2EYdwELATdgnmmamw3DuO3E8rdM09xqGMYCYANgB+aaprnpbParJIWIiIiIiIiIlGCa5i/AL6d89tYp758Fnj1X+3TZJIVf93ZETb4Vw83C8c9/JfntL4st92xQm+hZ9+Ld/AKOvvAByXO/KVgWcvNQgq/uD5jkbt9P3MQXMfPyqyz2ZRt3MeuThdhNO8O6tWHsZV2LLU/PyuHRd74lPjkNq93OjQM6MbRba+KPpfLY3O9ITs3EMAyu6tGW0f06VlncAEtXrGHmK3Ox2W0Mv6w/4667qtjy1PQMJs98hYOHj+Dl6cm0h++hUYN6AHzwxfd8/dOvGIZBowb1ePrhCXh5eVZh7LHMfG0uNpud4Zf1Y9zo4SVjn/UqB+PiHbFPvKsw9i9/4Ouff8PgROyT7q7S2M/k8RkvsHjZSkJqBPPdR2+deYUqtGzDTmZ9/At2u8mwHm0Ze3n3YsvTs3J49O2viE9OxWqzc+OgLgzt3rZguc1u59on3iKiRiCv3X9d1ca+7SCzf/jHEfvFjRnTu3Wx5e8vWs8vsbtOxGmyNzGFv568jiBf74LYR738HRFBvrw6ZmCVxg4w8el76dKnEznZOTwxYTrbNpYc+vfUy4/RrlNrMtIyAZgyYTo7Nu+k54Cu3D7pFky7ic1m49nJL7Nu5YYqiXvpqvXMeutDbDY7Vw7qybhrrii2PDU9kykvzOHgkQS8PDyY+sCtNKpfOBzSZrMz8u7HiQitwevTHjqnsdW5sgsX3jUYAGtmDusmzSN1y4Eyy7eafiP1Rvbgh4ZjyrWfWoM70vTB4QQ0iuavQZNJWb8XAN86YfRb/Bzpux3DPY+t2cW6SfOc2mbNAe1oNmkEpt2OabOzYfKHJK/cXqJcgzH9ueCWgfjHRPFTs/HkHUuv9thPqtG6AT1/nsqK8a8Q99PKKosdILBpHdo8Ow6PAB9Mu52/Bk7Gnlv+cwef6BDav3o7XuHBYJrs/fBPds9d4PT6zR6+mrojuuEZ7FeiXdW6whE/JqRu3s+qO14vd3xFVXd7h4rXe3lib/bw1dQa3BHTZmfv/35n97sLzzr2irZ3Z9qHs8dyWTxr+NNx7gRqtG7I/s8Xs/7R9wuWdfvmcbwjgrHl5AGwbORMcpPSnN52WOemdHr/ATIPOJ4wGPfLKra98G2JcuFdm9NyyiiwGFgzc1kz4S0y9yU4vZ9z3WYsXh50/24KFk93LO5uHP5pBVuf/brUsmf6HSrL6erd8HCj9YybCevcFOwmm2d+TtzPq5zetjOGP3ETzXq1IS87l48ffJNDm/eWKHPDS3dTp2UDbFYbB9bv4rNH38FutQFwwSXNuHLKjbi5u5F5PJ1XrnnqnMYnUh6umaSwWKj55O3sv/Fx8uOTaPDti6T/8S95uwqffmJLTSd+6tsE9O9UbFX3yFBCbhzM7gG3Y+bmUeuVhwkc3IPUr3+vktBtdjszPprP2w9cR2RIIKOmzqXn/7F33+FRVG8bx7+zm957QgolAekQeofQmwiKIMVCE6wUUVB+CiiCgA1FVEBFBcQuKtJLpNfQO6EECOm9Z3fn/WMhJCSBTUhZXp/PdXlJds7s3DuZmew8c+ZMcG2C/Dzz2vy09QCBvp4snDCUhJR0+v9vEX3bNESr0fDqEz2oW60K6ZnZDHlnKa3rBRaYt1yz6/W8+/Filn70Dj6e7jwxdjKd27ckqHrVvDZLl/9CnZo1+HT2NC5eucbsj7/k6wXvEh0bz8pf/+bP5YuwsbZm8ox5rNu6gwG9u1Zc9k8Ws/SDt43Zn3uNzu1aEpTvxGbpil+N2d99w5j9k8V8/dEsY/bf1vDndwuN2WfOr9DsphjQpzvDBj7CtFkfVHaUAvQGA3O+X8PiKc8Yt/eZiwlpUocgP6+8Nj9t2UegrxcLJz1p3N5f/5S+bRthaWE8/KzcuIdAX0/SMrMrPPt7f+ziy7F98Ha2Z/inq+lUvxpB3q55bUaENGZESGMA/j11hRXbj+cVKAB+2HGCGl4upGfnVGh2gPZd21A10J/+bZ6gYdP6TJv3Kk/3GVtk2wXvLGLzmtACr+3bcYjQDTsBqFU3iHlLZvFYh2HlHRu93sDsRd+y5L038PFwY8jLb9G5dVOCqvnntfnqxz+pE1SVT2ZM4mJEJHMWfctX86blTV+xej01AnxJz8gs83zpETFsf3QWucnpeHdpTJMPxhDaZ3qRbV0a18DSya5Uy0k5c5W9oz6myfujC01LuxLN1m7Tipjr7mJ2nODGhkOA8Ut8qyUT2NTh1ULt4vefJWpTGB1+f6vkwSmf7ABoFOq/OZTo0OKLZeWVXdFqaLHoRQ6+9DnJpyKwcnXAkKsr1TJUnYHjM1eSdPwyFvY2dN44m5jtx0k9d92k+W9sDOPiNxvpseejAq/b1/Ch9sv9+bff2+Qmp2Pt4VSqfPlV9vZ+P+vd1OzVhnTC1s+dTe1fBVUt8Xor6+3dlO3D1H25OPrsXE7N+xWnOv441QkoNP3Ai4sKnPSXVNy+M+x56u7fR4LnjWLviA9JPR9J4Ihu1Jk0gEMTFpu8jLLeZgzZuewY+C76jGwUCy2d/ppB1JajJN68EJHHhONQce623utMHEB2XDKb2k0GRcHK1aHE73839UKC8azhw6yQCVRvUovBs0fz0YA3C7U7uHoH309cCMAzn46n7ZAu7FyxCVsnOwbPGs0Xz8whMTIeB/f7P74IcT/Mskhh2/ghcq5Ekns1CoDkNdtx7Naa+PxFivhk9PHJOHZuUWh+xUKLYmOFqtOhsbVGFx1fYdlPXLxOgJcr/l7GE51ereoTeuRsgUKDoihkZOWgqioZ2Tk429ui1WjwdHHE08URAHtbawKreBCTlFJhRYrjp89T1a8KAb4+APTu2oGtO/cVKFKEX77Kszd7VwRW8+d6VAxxCYkA6PQGsrNzsNBakJmVjae7W4XkBjh+5o7sXdqzdde+AkWK8CtXeXbYwDuyJ93Mrs+XPQdPj4rLbormwQ25fsP0KxAV5cTFawR4u+HvZVxfvVo1JDTsTIEihYJCRlZ2oe0dIDohmR1HzzGmXyeWr99dsdkjYgnwcML/5h/insFBhJ68UqBIkd+6w+H0alIz7+fopDR2nLnKmK7BLN9+vEIy59epZ3vW/Gy8+nY87CSOTo54eLkTF2Pa8S4z3wm+rZ0NagXda3n8bDhVfb0JqGLcRnqHtGbbnkMFihThEdfzelcEVvXlenQscYnJeLg6ExUbz479R3h2aH++/21tkcu4HwkHz9/+96EL2FYp5ligUWg4fRgHnl+Eb5/bf4e0dtY0nv0MznUDULRaTn/wW97JRn6p5+8cGPv+6TNuF/os7vI7TT5xpcjXKzM7QNDonkT+sx/X4MBi25RXdq+QRiSfisi7Ep+TmFaKT2CUFZNEVkwSYLzCn3r+OrY+rhiycwl+byRW7k7oM7MJe/Ur0i4UzlPopOmmGk925uKyjeQmG3tFleTqd3Eqe3u/n/VuavYaz3TjwPOf5d1Pfmu9Vdb2Xtz2kb9Icbd9OWBgO4LG9EJjqSUxLJzDr38DhoL7uj4jm/j9Z7Gv4V2m2UtEVbFwsAXAwtGOrKgkoHK3mVvrVWOpRWOhLXKMgeKOQ/e73qsNCbldaFLVEvcCu5eGPVqw//ftAFw+fB5bR3ucPF1IiU0q0O5U6JG8f185egFnH+N+0+yR9hxdv5/ESON3iLT4+z++/L9jMFR2gv8Uk4sUiqLUAJoAp1RVPVN+kYy9IXJvxOX9rIuKw7ZxbZPm1UXHE//V7zy041sMWTmk7wwjfefh8opaSExSKj5uznk/e7k6cfxiwasnQ7q0YPzCH+n2ysekZ2Uz/7mBaDQFH0F7PS6JMxFRNAz0p6LExMXj4+WR97O3pwfHTxXsXli7ZnU2b99D00b1OH7qHDeiY4iOjad+7ZqMGDKAboNGY2NlRdsWTWjXsknFZY9NwMczf3Z3jp86X6BN7aDqbN6x15j99DluRMUSHRtnzP7EALoNfhYbayvatgimXYuKy/4gi0m8Y3t3c+J4+LUCbYZ0a8X4BSvpNuF90rNymP/CIDQ3ixTzV65j0uCepGdVbC8KgJiUdHxcbl/J8Ha25/jN7qt3yszRsfvsNd54tG3ea+//tZeJfVuSXoru4GXBq4onUZG380bfiMGrimeRRYoXXx/Hs6+MZP+OQ3w6+wtyb97+1rl3R16e9hxuHq6Mf9L0q3T3IyY+AR9P97yfvT3cOHYmvECb2jWqsnnXAZo2qM3xM+HciI4jOi4BD1dn5n+5nEljhpJRDr0o7lR9WAjRW48WOS1oVE9ubAjLO9m4pc6EAcTuPEnYpCVYOtkRsm4WMTtOFDjpuBf7qp502TSH3LRMTs39mfh9pnfz9u3dnPrThmDt4cTuJ0t2a2hlZrfxccW3Twt2DHyXZsFF9wgqz+wOgT6gqrRb9TrW7o5c/XMP5xetKXGOO9kFeODSoDoJYeG0XjaJw1O+If1SFK5NggieO5Kdj882+b0cAqsA0OmvGShaDac/+I3obWV3i1ZlbO9ltd7vlt2+mhf+/Vvj26cF2fEpHP3f96Rfiqr0fRUKbh93Kmpfdqzli3//NvzbbyaqTk/w3JFUHdieiF92lGi5zRaMQ9UbiPznAGc+Lnyrxr24NatFly3vkRWdyPG3V5J6tnAvobDJS2m7cgqGrBxy0zIJ7TMDqORtRqPQZeNsHGr4EL5sI4mHC6734o5D97veb/VAqjdlEJ5t65J2JYajbywrk0LjLc7eriRF3v77nxQVj7OPW6EixS0aCy0tHu3Ib29/C4BXYBW0Flpe/nE6Nva2hC5bx4GbRQ8hKkOxRQpFUVarqjrg5r/7AwuAUOA9RVHeU1X127vMOxYYCzDDowGDnaoW17S4NyhZ+3w0Tg44dmvN+ZBR6FPS8f/sDZz7dyb5z22lfs+SKOrC1Z0fZ/fJcOoE+PDVa09zNSaRcR+uoOlD1XCwtQYgIyuHyYt+4bWhPfNeqwhFXXVT7gg/ZvjjzP10KQNHTaBWYDXq1ApEq9WSnJrGtp372PDTUhwd7Jk8fR5/b9xGvx6dKyY7RWUv+POYYQOZu/ArBo6eWDj7rv1s+HGxMfuM+fy9MZR+PUIqJPuDzJRtZveJC9SpWoWvXh/J1ZgExs3/jqa1q3Ho7BXcnOypV8OXA6dL3+20tEzZV2/ZfuoKwdW982712H7qCq4ONtTz9+RAePlcVb6XO9czFP37WDj7S+Ji4rG0suStD6Yy8qUnWfLRMgC2rdvOtnXbadq6MS9MfZbnBk8s79jFrPeCn2X0E/2Y+8VyHn/+DWrVCKBOzepYaDT8uzcMNxdn6teqwYGjp8o1p0e7elQbGsL2/oXvybXxdsGvXyt2PDar0DSvkIb49GxKref7AqC1tsTOz93kq7FZ0UmsbzaenMQ0XBrVoPWyV9jcaQq6NNOKMpHrDhK57iDuretQb+ogdg6eY9J8lZ290aynOTFrVaErkxWVXWOhxb1Vbbb1egt9Zjbtf/kfSUcvEbvzZKnygPGKcauvJnFs+nJUgwH35g/Raun428u0sizR+ykWGhxq+LD9sXex9XWj0+rpbA6ZSm5KRqkz3lJZ23tZrPe7Zb+VSZ+dy7aeb+LbpwXNPh7L9gHvVPq+mn/7KGqeovZlzw4NcGlUg87rjb8LrY1ViU90D7ywiKyoRCzsbWj19USqDupQoiJH0rHLrG8+Hn1GNt5dg2mzbDIb275SqF3Nsb3ZPXw+iYfDqfXCwzR6+0nCJi+t3G3GoLK12zQsnexovWwSTnX8STlz+8JKcceh+13vioUWOz934g+c5fjMFdQc14eGM4Zz8OUvTH6Pey6jqC8wd+khOXjWaML3n+biAeN1Z41WQ0DDQD4bNgtLGysm/T6Ly4fPE3vpRpllFKIk7taTolq+f08FuqiqeklRFA9gC/BtcTOqqroEWAJwKqhvib9x6KLisKxy+6q4hY8HuSbesmHfLpicq9HoE4wHj9QNu7FtWrfCihTero5EJSTn/RyTmILXzVs4bvlz5xFG9WmHoihU9XbDz8OFSzfiaBjoR65OzyuLfqZP6wZ0a1a3QjLnZff0ICrmdg+W6Ni4Qrc9ONjb8e4bEwDjCVHPJ57Fv4o3u/aH4VfFGzcX41X1rh3bcOTEmQorUnh7uhMVmz97fNHZXx9/O/uQscbsBw7jV8WrYPaTZ6RIYQJvN6eC23tCEdv7jjBG9e1wc3t3x8/TlUuRcRw5F0Ho4bPsPHae7Fwd6ZnZvPHlr7z33ON3LqZ8sjvbE5V0u5todHI6nk72RbZdfyScXk2C8n4+cjmaf09FsPPMKnJy9aRn5zDth23MGVa+2/vgkY/x2HDjbRAnj5zGx/f2bTXeVbyIjYorNM+tnhW5Obn8+eM/PP380EJtwvYexb+6Hy5uziTl+32WB28PN6Jibx/Po+MS8HJ3KdDGwd6Od18dBxj31V7PTMTPx5N1/+5h295D7DhwhOycXNIzMnl93ufMnfrCfWUKHNmd6sONv7vdw+dj5eZI0w+fZfeweUV2JXZpWB2HGt702PsxAFpbK3rs+YiNbV4BFPaNXkBaeMEvds0WjMO5QTWyopPYPXx+sVkMOTpycozLTDp2ifQr0TgE+RR7//id2bOikwCI33sG++peWLk5lqBbceVlt3Syo+XilwGwdnPEu2swqs7AjfUHKyR7ZmQCcXtO562r6C1HcGlUo9RFCsVCS+uvJ3H1911Erj2AhYMtuSnphccvuHllF4xjUZye/+tdMyaEXUDV6cmIiCU1/AYOgT4kHrlYomzmtL2XdL2XNPutZUT+Yxz8MHLtAZotGHdzSsVu7/nduX3cTf59GQUift7OyTk/FWjj27s5dSY/Bhh7MNwtQ1bUzVt007O4+sduXJsE3bNIUdxxJnrLEZS5IwsdZ6zcHXGuXy2vp8K1P/fQbtXUW5++0vfV3JQMYnefxrtz4wJFCtfGNYo8Dt3ves9JSEWXkUXkWuPx7Prfe6k+LMTkvMXp8FQP2gw1jp8WcTQcF9/bvRRdfNxJjk4scr5eEx7Hwd2Jr8d9mPdaUlQC6Ymp5GRmk5OZTfj+0/jVrSZFClFp7lakyF9csFBV9RKAqqpxiqKU6005mcfOYVXdD0t/b3Kj43F+uCPXJ5nWbVUXGYttcG0UG2vUrGzs2zYm83jR93eWh/o1/IiITuBabCLerk6s33eS98Y9WqCNj5sz+05doulD1YhPTuNyVDz+nq6oqsrMZX8TWMWTp3u2KWYJ5adBnVpEXIvkWmQU3p7urNuyg/nTC3YBT0lNw9bGGktLS35bs5FmjevjYG9HFW9Pjp06S2ZWNjbWVuw7dJT6dWpVXPbatYi4doNrN6Lx9nBj3dadzH+zYGW/QPZ/Nt3O7uXJsVPnbmcPO0b92kHFLEnkV3B7d2T9vuO899ygAm183FzYd+oiTWtXN27vN+Lw93JlwuDuTBjcHYADpy/x3bpdFVagAKgf4ElEXArXE1LwcrJnw5HwIosMqZk5HLoYVWDa+D4tGd+npTF7eCTf/3us3AsUAD8v+52flxmfZNS+WxuGjBrI+tWbadi0PmmpaUXe6pF/nIrOvToSfsZ4QhNQ3Y+rl41ddOs0fAhLS8tyL1AANKgdyJXrUVyLisHb3Y11oXuZ9/qLBdqkpKVja22NpaUFv63bRrMGdXCwt2PiqCFMHDUEgANHT/Htr//cd4EC4OKyTVxctgkAWz93Wn8ziYMvfU7axagi20dtPsLaRreX+0j4NzdP2CAm9BhBo3vmjeru3KAaySeucGiiaQPGWbk7Gk+2DCp2Vb1wqOFD+pWib0O6M7t99dv3Qbs0rI7G0qJE9z1XZvb8mn0yjhubDpegQHH/2aNDj/HQiw+jtbXCkKPDo01dLiwp/ZgnTT8eS+r561xYbHwPXVom6RGx+PVrxfW/9xkz1qtK8qkIkwdevLH+IP6PtiXip+1YuTniEFjlruu3OOa0vZd0vZc0O0Dk+oN4tq/PlVX/4tG2LmkXb5RJ9pJu7/nduX3cyb66d96TMPLvy7E7TtLm28lcWLKO7LgULF3ssXCwzet1cS+KVoOlsz05CakoFlqqdG9CzPYT95wv/3q39rx9i6drkyAURSl0nMlNSsfS0Q6HQB/SLkbh1bEhqeeMPSUqa1+1cndEzdWTm5KBxsYSrw4NOLfo7wJtNrScmPfv/Mchx4f87mu9A9zYeBjPtnWJ3XUKzw4NSDFxIN272bF8IzuWbwSgXucmdHymJ2F/7aZ6k1pkpWYUeatHmye6ULdjIz4bNqtA78vjGw/y+Dsj0Wg1aC0tqBZci9Cvy37cpwdaKXv6idK5W5GisaIoKYACWCuK4qOqapSiKFaAtlxT6Q1Evf0FVb+dhaLRkPTrJrLPR+A6tDcAiavWofVwJXD1AjQOdqAacBvRn/Bez5F59Cyp63cR+NcnqHo9WScvkvTjunKNm5+FVsMbT/bm+Y9WYjCoDGgfTE0/L37eZjyIDe7cnLH9OvLWN38y8K0vUVGZOKgrro52hJ2LYM2eY9Ty92LwDOPB+uWBXejQqGJO9i0stEybOI5xr85EbzDwaJ9u1KxRlZ/+NK6/J/r35uKVa0yb/TFarYbAagG8c7NnQqN6teke0o7BYyai1WqpUyuQQf16VkjuvOwTnmXca2+jN+h5tPet7OtvZu/FxYhrTJvzCVqNhsDqAbwz5aWb2R+ie6e2DH72lZvZazDo4YrLborXZszlwOFjJCWl0HXAk7ww+ikGVuD6LY6FVssbT/Xl+fe/x2AwMKBjU2r6e/HzVuOVocFdWjC2fyfeWvoHA//3GaoKEwf3wNWx6B4LFclCq+H1AW15fuk6DAaV/i1rU9PHjV/2GG8jGNSmHgBbT1ymzUN+2Jawa3Z527l5D+27tuGvvT+TlZnFzIm3u/UvXPkB77wyl9joOGZ/PgNXdxcUReHsifPMnmIs+HZ9OISHB/VGl6sjOyubqeOKHtG/rFlotUx7cQTPTZtnPM706ETN6v78vMb4BKbBD3fjYkQk/3v/CzQaDUHV/Hh7UsnHKCituq88hpWrI8FzRwKg6g1s62kcIb3tyimEvbIk7ypiUU5//DuN33martvmgqKQcTW2yFHwfXs3p/HsZ7Byd6Ltiikkn7jCrqFz8Whdh3pTBhkfCac3cHjKN+QmpZuU3e/hllQd1AFDrg59Vi77xy3Mm5Y/e9Donjz04sNYe7nQdetcorccIWzy0krNfjcVkT03OZ3zi9fSef27qKpK9JYjRG0+Uqq87i1rU21QB5JPRdBls3G/PPnezxx4cRFN5o6i9sQBaCy1XFu9p8hHZjZ4aygBj7ZFa2tF77CFXP4hNG/8Ca9Ojei2fT6q3sCJd364rwE+ofK39/tZ76ZmP7fwL1p8/iI1x/ZGl55N2CtLyyR7abf34rYPOz/jVfBL328pdl9OPXedk/N+pt2Pr6NoNBhy9Rx5YxmZ1wr3out54BMsHWzRWFng26sZO4fMJeNaHO1WvY7GUoui1RCz/QSXVmw1aX3f4tevFYHPdMOg02PIymH/c0UfZw6/upRWX09ENajkJqdzaOKSMlnvpd1mbLxcaP7p8yhaDWgUrv+1l6hNh6nxdNe89V6c+13vqeeuc+LdVbRY+DyNZj1NdnyKyUUZU53adpj6nZsw/d9PyMnMYeVrt28lGbfsdVZNXUxKTCKDZ48h8Xosk/54F4Bj6/ez/tPfiA6/zul/j/L6+vcxGFT2/rSVG+euFrc4Icqdcq8R3RVFqa+q6sl8P7sAz6mqOteUBZTmdg9zEfh9+T+Or7xoazav7Ail9yCPnmtZcWOIlDX9hbJ9XndFUmMe3D+kbceWfNAyc7Fv7yeVHaHU1rT+6N6NhBBCiAoUaqOv7Ail9unln0o/qOADIOODMQ/sOe3d2L36lVn+3jQmtPlJUZQpipEtMAvoX865hBBCCCGEEEII8R9jyiNIWwHzgN2AI7ASaFeeoYQQQgghhBBCCLOgPsA9vR9ApvSkyAUyAVvABrikqvJbEkIIIYQQQgghRNkypUhxAGORogXQHhiqKErxz8gSQgghhBBCCCGEKAVTbvcYrarqrefrRAH9FUV5qhwzCSGEEEIIIYQQ4j/onj0p8hUo8r+2vHziCCGEEEIIIYQQ4r/KlJ4UQgghhBBCCCHEf5Ph/+UTSM2WKWNSCCGEEEIIIYQQQpQ7KVIIIYQQQgghhBDCLEiRQgghhBBCCCGEEGZBxqQQQgghhBBCCCGKoRoMlR3hP0V6UgghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALMiaFEEIIIYQQQghRHINa2Qn+U6QnhRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgY1IIIYQQQgghhBDFUQ2VneA/RXpSCCGEEEIIIYQQwixIkUIIIYQQQgghhBBmQYoUQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZKPeBM6PS7Mt7EeWmxrmjlR2h1HRZ6ZUdofQe4IFpFAfXyo5QatqaLSo7QqnlrF9d2RFKrbWNX2VHKDX9sdDKjiCEEEL8v9EuW55pYLYMamUn+E+RnhRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMgtz4JIQQQgghhBBCFMfw4I6Z9yCSnhRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMgoxJIYQQQgghhBBCFMegVnaC/xTpSSGEEEIIIYQQQgizIEUKIYQQQgghhBBCmAUpUgghhBBCCCGEEMIsyJgUQgghhBBCCCFEcVRDZSf4T5GeFEIIIYQQQgghhDALUqQQQgghhBBCCCGEWZAihRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFmTgTCGEEEIIIYQQojgGtbIT/KdITwohhBBCCCGEEEKYBSlSCCGEEEIIIYQQwixIkUIIIYQQQgghhBBmQcakEEIIIYQQQgghiqEaDJUd4T9FelIIIYQQQgghhBDCLEiRQgghhBBCCCGEEGZBihRCCCGEEEIIIYQwC2Y1JoVb58bUenckilbDjZVbuLLwz0Jtas0eiXvXJhgyszk1/nPSjl8CwMLJjjofPYd9nQBQVU5P+oKUg+epv2QidkG+eW10KRkc6DqlXD/HrovRzN98HIMBHm1clVFtHirU5sCVON7fchydQcXV1oqvh7cHICUrl3fWHeZCbCqKAjP7NKGxn1u55i2Q/eQl5v+yDYOq8mjbBozq2arA9G83HWDtgdMA6PUGLkUlsG3+8zjb27J8yyH+2H0cBajl58HbT/XC2rLiNrFdJy8z/9dQDAYDj7ZrwKgeLe/IfpC1B84YsxtuZp/3HIlpGUz5em1eu+vxyTzftw1PdmlacdmPnWfeyrUYDCqPdmrK6Ic7FpiempHFtMW/EhWfjE5v4Jne7RjQ8XY+vcHA0Blf4uXqxGevPFlhuU3x5pyP2L5rP26uLqxe8WVlxylAW7MxVr2eBo0GXdg2cnf+VWC6pnpdbIa8iiEpBgD96QPk/vs7WFhiM3I6aC1RNFp0p/aRG/prhecfPGMk9Ts3ISczm+9f/ZyrJy8VajNywctUaxiEXqfj8tFwVk5bgkGnx8bRlpEfj8fNzx2NVsvmpX+z55fQCsm968xV5v+1x7i9t6zNqC7BBaZ/G3qUtWEXANAbVC7FJLFt5pPYWFow6os15Or06AwGujUM5IWezco0m0NNX5otGIdLw+qcmvsz57/4p8h2nu3r03D6MNAo6NKzOTThS9IvR5u8HL9+raj76kAca/myrfdbJB01/u7sAjzovv0DUsMjAUg4dIEjU78x6T0DHmvHQy/1A0CXnsWRqd+QfCqiyLb1Xh+MX79WqHoDl77bTPjXGx6I7J7t6tFwxnAUKwuSjl0ibNISVL3p9wkXlx3AqW4ATd4fg6WjLarBwLZeb2HIzr3r+zX9eCw+3ZuQHZfClpCpRbbxaFuXRu88hcbSguyEVHY8OsvkvFauDrT6agKuwUFc+Wk7R6d9mzdNsdQSPGckHm3rgkHl5NyfiPzngMnvDaav946rp2PhYAOAtYcziYfD2TvyI5OXU9br/X4/T3Hutr47/P4mNl4u6LNyANg1ZC7ZcSmlynknW183mi98HmtPF1BVLi3fSvhX602ev97rg6k6qANWLvb8FTQq7/WqT3Sk4fRhZN5IAODiNxu5/ENomWR2DQ4k5J932DfuUyLX7C803dy2GVPWca0XHibgsbYAKBZanGr5sab+OHKT0k3KXN77a1GazHqaKl0bo8/MYf/ExSQev1yojX2AJ22+fAkrFwcSj19m38ufY8jVU+2xttR58fb+cuj1ZSTd3F8e3r+A3LQsVL0BVa9nU6+37jvrA8+gVnaC/xTzKVJoFGrPHc3hwe+SHRlP8w3vEbvhIBnnruc1ce/aBLsaPuxtPR6nZrWoPX8Mh3r/D4Ba744kftsRToz5CMVSi9bWGoCTYxfkzV9z5lPoUjLK9WPoDSrvbTzGl0Pa4u1oy/Bv/6VTLR+CPJzy2qRk5fLexqMsGtyGKs52JKRn502bv/k4bQO9+eDRluTqDWTm6ss1b8HsBt77aQtfjn8cbxdHhs9bSadGNQmq4p7XZkT3Fozo3gKAf4+Fs2LrIZztbYlOSmVVaBi/vzUCGytLXvvqb9YfPEP/Ng0qLvvPW/ny5ceM2ef/QKeGQXdkb86I7s2N2Y+Hs2LrYZztbXC2t+HnaU/mvU+PaUvp0rhmheS+tcw5369h8ZRn8HZzYtjMxYQ0qUOQn1dem5+27CPQ14uFk54kISWd/q9/St+2jbC0MO7CKzfuIdDXk7TM7OIWU2kG9OnOsIGPMG3WB5UdpSBFwarPSLKWz0FNicfm2dnozh5Cjb1eoJk+4gzZP7xfcF5dLlnfvQs52aDRYjNqJvoLRzBcu1Bh8euHNMGrhg8zQsZTo0kths4ew/wB/yvUbv/qnSybuBCAUZ9OoP2QLmxfsYmQp3px48I1vhgzDwc3R2Zu/YT9q3egL+djjt5g4L0/dvHl2D54O9sz/NPVdKpfjSBv17w2I0IaMyKkMQD/nrrCiu3HcbazQVVVlo7ri521Jbl6AyMX/UX7Ov40quZdZvlyk9I49uZ3VOnV/K7tgueNYu+ID0k9H0ngiG7UmTSAQxMWm7yclDNX2TvqY5q8P7rQtLQr0WztNq3E2dMjYtj+6Cxyk9Px7tKYJh+MIbTP9ELtqg3phK2fO5vavwqqinW+v09mnV1RaPbp8+wcNJu0i1HUnfI4VQd35Mqq0PvOrmg1tFj0Igdf+pzkUxFYuTpgyNXd8/2u/LSdi99spNnC54ucbulkR/DckewaOo/M6/ElXtf67FxOzfsVpzr+ONUJKDCtzsQBZMcls6ndZOPxzNWhRO8Npm8z2we8k/fvVl9N5MaGQyVaTlmv9+KY+nmKc7f1DXDgxUUFTpbLiqozcHzmSpKOX8bC3obOG2cTs/04qeeu33tm4MbGMC5+s5EeewoXAa79ubfAyXKZ0CjUf3Mo0aHHim1ibtuMKev4/OdrOP/5GgB8ujel5rjeJhcooPz31ztV6dIYx0Af1radjHvTmjSbO5LNfWcUatfozSGcXbKOq3/updm8UdQYGkL491tIi4hl62OzyE3OwKdLY5q/P7rA/Nsef5echLT7zilEaZhNkcKpaU0yLkWRdcV4xTJm9W48e7XgSr6Dh0ev5kT9sh2AlEPnsXCyx8rLBX1GNi5t6nJ6/CIA1Fw9utzCxQivR9pweOA7hV4vSyduJBLgao+/iz0APev5EXo+qkCRYt2pa3Sp7UsVZzsA3OyNBZW07FzCrsYzq28TACy1Giy1FXdHzonLUQR4uuDv4WLM3qw2oUcvFDjRz2/dwTP0al4n72e93kB2rg4LrZasHB2ezvd/ADZVkdmPhd8l+1l6Na9d6PV9Z6/i7+mMr3vJvkjejxMXrxHg7Ya/l7HHTK9WDQkNO1OgSKGgkJGVjaqqZGTn4Gxvi1Zj3DaiE5LZcfQcY/p1Yvn63RWW21TNgxty/YbpV5grisavJoaEKNTEm70kTuzBonZzcmNN+1JIzs2CkFZr/E+t2Ap74x7N2fu78Xh46fB57BztcfJ0ISU2qUC7k6GH8/59+egFXHyM+4SKio39zatcdjakJ6Vh0JX/yNUnImIJ8HDC/+Y+1jM4iNCTVwoUKfJbdzicXk2MRUNFUbCztgRApzegMxhQFKVM82XHpZAdl4JPtyZ3b6iqWDjYAmDhaEdWVBIAWjtrGs9+Bue6AShaLac/+K3IL+ep5yPLNDdAwsHzt/996AK2VYruhVfjmW4ceP6zvG321tVgc89u5eaAISeXtItRAMT8e5za4/tzZVXofWf3CmlE8qmIvKvuOYmmfTGP33sGuwCPYqcHPNaWyH8OkHk9HqDAlfeAge0IGtMLjaWWxLBwDr/+TaErdfqMbOL3n8W+RuFCXLUhIWzq8KrxB1UlJyHVpMz5mbrN3GJhb4Nn+/ocmmgsyFXWei/O3T7P/a7v8pQVk0RWTBJgvKKdev46tj6uGLJzCX5vJFbuTugzswl79SvSLhRel4lhFVcgBwga3ZPIf/bjGhx4z7bmss0Ut46LKwQFPNqGa3/c/k5lDvvrnfx6NePyLzsAiA+7gKWTHTZeLnmf8xbv9vXZ+4LxHOnyz9up/+pAwr/fQny+/SX+0Pl77v9CVKRiixSKolQFYlRVzVKM3wJHAE2BU8BSVVVLX+ougrWPG9mR8Xk/Z0fG49S0VsE2VdzIuh53u82NeKyruKHq9OTGp1D3kxdwqF+N1GMXOffmtxgybl9Vdmldl5zYZDIvRZVl7EJiUrPwcbTN+9nb0ZbjkYkF2lxJSENnMDB65U4ycnQMax5Iv4ZVuZaUgaudFdP/Ocy5mBTq+TgzpVtDbK0qppYUk5SGj6vj7eyujhy/fKPItpk5uew+dZk3nuhibOviyNPdWtDrzaXYWFrQum412tarXhGxgSKyuzhw/HLRv+u87IO7FJq24eBZejerU8Rc5ScmMRUfN+e8n73cnDgefq1AmyHdWjF+wUq6TXif9Kwc5r8wCM3NIsX8leuYNLgn6Vnm14vCnClOrqgpt485ako8Gv/CPWi0/rWweW4uamoiORtXosbe/N0oCjbj5qBx8yF3/0YM18MrKjoALt5uJEbePh4mRsXj4uNWqEhxi8ZCS6tHO/DL298CEPrdep7/agpz9y/G2t6Wr1/6GLUCCi0xKen4uNwuYHo723M8IqbItpk5OnafvcYbj7bNe01vMDB0wR9cjU/hibb1aFjVq8h5y1vY5KW0XTkFQ1YOuWmZhPYxXn2qM2EAsTtPEjZpCZZOdoSsm0XMjhPoM0zfP+2retJl0xxy0zI5Nfdn4vedLXG+6sNCiN56tOj3r+aFf//W+PZpQXZ8Ckf/9z3pl6LMPntOfCoaSy0ujWuQdPQSfg+3wtbX+KX6frM7BPqAqtJu1etYuzty9c89nF+0psTZC79vFRRLLR1+fxMLe1vCv1pPxC87cKzli3//NvzbbyaqTk/w3JFUHdieiJsnHPdi6WS8yFFvyiA829Yl7UoMR99Ydl+3H9xtm7nFt08LYneeQJeWCZjveoeCn+d+1/ctzRaMQ9UbiPznAGc+/qNMct7JLsADlwbVSQgLp/WySRye8g3pl6JwbRJE8NyR7Hx8donez69vCzxa1yHt4g2OTV9OZmTCfeWz8XHFt08Ldgx8l2bBY+/Z3hy3mfzruChaWyu8OzfmyM0eKOa4vwLY+riRke/cKfNGArZVXAsUKazcHMhJTs+7LS7jRgJ2PoUvCgQODSEq3/6vqiohP76OqkL48i1cXLHtvrIKUVJ3O/tdC9y6qX8uEASsBroALYBRRc8GiqKMBcYCTHJsxsO29660UsTVMJU7vzAXccVMVVEstDg0rMG5ad+QEnaBWu+OoNrLA7g076e8Zl6PtiP6j133znGfCmcunFpvUDkdlcySIW3J0ul5evkOGvm5oTcYOBOVzOvdG9LQ1415m47zzd7zvNixbrnnNjX7LduPhRMc6IuzvbEgk5KRReixC/zzzhgc7ax5benf/LPvFH1b1SvHxLcVdWpV3BXW7ccv3sxuU+D1XJ2ef4+HM75/u3JIWLyiTgzvzL77xAXqVK3CV6+P5GpMAuPmf0fT2tU4dPYKbk721Kvhy4HTZd8F9f+3oo4nBX803LhMxoKXIScbba1gbIa8QubCV262Vcn68g2wscPmiVfQefmjxlwr/J7lpajt+y5FhqGzxnBh/2ku3ByXpV7Hxlw7dYUFQ9/Bs5o341e8xYXer5F184tkeSkqYnGdIbafukJwdW+c7W7vq1qNhp9fGUhKZjavfLeJC1EJ1PSp+Ks/Ncf2Zvfw+SQeDqfWCw/T6O0nCZu8FK+Qhvj0bEqt5/sa81pbYufnbnLvg6zoJNY3G09OYhoujWrQetkrbO40Je8Lvik82tWj2tAQtvd/u8jpWmtL9Nm5bOv5Jr59WtDs47FsH/DOA5F9/7jPaPT2U2isLYkJPYZ6s/fP/WbXWGhxb1Wbbb3eQp+ZTftf/kfS0UvE7jxpcvaiKBZaXBvVYMegOWhtrAhZ8zYJh87j2aEBLo1q0Hm9cXwKrY1ViU5YFAstdn7uxB84y/GZK6g5rg8NZwzn4MtflCrnvdb7Lf6PtuHyytsnK+a63u/8PPe7vgEOvLCIrKhELOxtaPX1RKoO6lDiIse9aO2safXVJI5NX45qMODe/CFaLR2fN11jZVmi94vaGMa1P3ZjyNFR4+muxtulSljkuFOjWU9zYtYqk+/PN7dtJv86Lu7Y5NOjKfEHzuXd6mFu++vtNy7itTv+yBb1XfjOv8NebesROCyELf1v9zbf8sjbZEUnYe3uRMhPr5N64Qaxe8/cX94HnYxJUaHuVqTQqKp6656JbkALVVUNwApFUe5aaldVdQmwBGCr92CTfqPZN+Kx9r3dNd/a152cqMRCbWz8PEjGeGXGuoo72VGJoKpkR8aTcrO7W8zfe6n28oC8+RStBq++LTnQ/XVTotwXb0dbolJvH/SiUzPxdLS5o40NLrZe2FpZYGtlQbMAd87GJNPU3x0vRxsa3rwy1L2OL9/sPU9F8XZxJCrxdvez6MTUYm/ZWH/oLL1a3O5xsPfMFfzcnXFzNFaLuwbX4sjFyAorUni7OBTMnpSGp7N9kW3XHzpb4DaVW3aevEydAC/cnYqer7x4uzkRlZCc93NMQgpeLo4F2vy5I4xRfTugKApVvd3x83TlUmQcR85FEHr4LDuPnSc7V0d6ZjZvfPkr7z33eIV+hgeRmpKA4nT7mKM4uaOmFjzmkH17X9afPwJ9R4GdI2Tk66aZlYH+8mm0NRujK+ciRaenetJuaFcArhwNx9XXA24eD1193EmKTixyvr4THsfB3Ykl45bkvdZmUGc2frEagNgr0cRfjcE7yJcrR8u3R4i3sz1RSbe750Ynp+NZzD63/kg4vZoEFTnNydaa5oFV2HXm2n0XKQJHdqf68M4A7B4+n6zopLu2t3J3xLl+NRIPG9fVtT/30G7VrUETFfaNXkBaeMFeaM0WjMO5QTWyopPYPXx+se9tyNGRk2NcP0nHLpF+JRqHIJ9i74O/M7uVmyNNP3yW3cPmFdsNOjMygch/jAPdRa49QLMF4x6Y7AmHzufd6+7VqSEOQVXKJHtmZAJxe07ndcGO3nIEl0Y17vtkOTMynpyEVPQZ2egzsonbexrn+tVAgYift3Nyzk8F2vv2bk6dyY8Bxt46xa27nIRUdBlZRK49CMD1v/dSfViISZlKs97BOCiga3AQe0d+nO/Vyl/vJn2e+1zfAFk3v5Pq0rO4+sduXJsElWmRQrHQ0vrrSVz9fReRaw9g4WBLbkp64TFeNApdNhoLDTc2hnF6fvGDNuf/fV5asZUGbw4tVbb869jSyY6Wi18GwNrNEe+uwag6AzfWHyw0n7ltM3eu4+IE9G/D1Xy3elTm/nqnmiO6E3jzd5Fw9CJ2+c6dbKu4kXnz1sNbsuNTsXK2R9FqUPUG7Kq4kZnvu4Jz3QBafDiGf4fPL7C93Po7mB2fwrV1B3ELDpQihahQdxvw4KqiKLf6w18GAgAURSn6Jv/7lHo4HLvAKthU9USx1OI1oC1xGwoe8OI2HMRnkPGpB07NaqFPzSAnJomc2GSyI+Oxu/llxa1DQ9LP3T5ZcO3YkPTzkWTfuL8ubqaoX8WFiIR0rielk6s3sOHUdTrV9CnQJqRWFQ5fi0dnMJCZq+N4ZCKB7o54ONjg42TL5XjjgXff5VgC3R2LWkz5ZK/mQ0RMEtfjksnV6dlw6CydGhU+QUjNzObQ+Wt0bnS7a3wVVyeOXb5BZk4uqqqy72wEgRV4ddOYPbFg9oaFe/Dczl74c60/dKbI4kV5q1/Dj4joBK7FJpKr07F+33E6NSmYw8fNhX2nLgIQn5zG5Rtx+Hu5MmFwdzYteJV1H77CvOcH0aJuDSlQmMgQGY7G3QfFxRO0WrQN2qA7W/C+WMXh9m04Gr8g4yX/jFRjocLGWJDDwhJtYAPUuLK/T/9O/y7fwJw+U5jTZwpHN+6n9WPG42GNJrXITM0o8laPdk90oW7Hxnzz8oICvXYSI+Oo3a4hAI4ezngH+hJXzG0XZal+gCcRcSlcT0gx7qtHwulUr2qhdqmZORy6GEXn+tXyXktIyyTl5uCwWbk69l24Tg0v50LzltTFZZvY2m0aW7tNu2eBAiA3KR1LRztjt2PAq2NDUs8Zf/8xoccIGt0zr61zA2P+QxMXs7XbtLt++QZjAQSN8cqXXVUvHGr4kH6l+N9L/uyKhZbW30zi4Euf543bUJTI9QfxbF8fMD55Iu3ijQcm+62BJzVWFjz0Uj8ufbe5TLJHhx7DuW5VtLZWKFoNHm3qknru/ouONzYcwr1VbRStBq2tFa5Na5J6/jqxO07i93CrvM9j6WKPrb8HkesO5q2Tew3QeGPjYTzbGntaenZoQIqJgyyWZr2D8WkLUZsPF3iKgjmsd1M+z/2ub0WrwcrN+H1MsdBSpXsTUs5cLVHOe2n68VhSz1/nwmLj08Z0aZmkR8Ti1+/2U9ac61UFg5qX+W4FCgAbL5e8f/v2bEbqeRPHXLpD/nW8oeVENrSYwIYWE7i+Zh9HXl9WZIECzG+buXMdF8XC0RaPNnULjJNRmfvrnS58u4mN3aexsfs0rq87SPVBHQBwb1qT3NTMQuNRAMTsOoX/w8bO8dUHdyRyvfGz2fm50+7riex9+YsC+4vW1hqLm72NtbbW+HRqSPLZCuwpKgSgFHcPsqIoAcD3gBZIBtoDhwFX4FVVVbeYsgBTe1KA8ekdtWY9g6LVELlqG1cW/IHv090BiPx+EwAPvTca9y7GR+2cnvA5qUeNJ24O9atR56Pn0FhZkHklhtMTPkeXbOymVfeTF0g+dD7vPUzVZm71ErW/ZUd4NO9vPo5BVenfqCrPtq3NL4eNB69BTWoA8O2+8/x1LAJFUXi0cTWebGE8aT4Tncw76w6Tq1fxc7Hjnb5NcLKxKnmIqqV7OsWOExd5/+ZjPPu3acCzvVvzy3Zjx5lBHY2j7f+55wS7T11m3uiHC8z7+ZpdbDx0Fq1GQ50AL2YM74FVaR5BqpZu8L4dJy7x/m+hGAwq/dvU59lerfhlx83sHW5lP8nu05eZN6pvgXkzc3Lp9eZXrHl7FI43nwxTGopD0YP/3TP70XPMX7kOg8HAgI5NefaRTvy81VjlH9ylBTGJKby19A/iklNRVRjVtwMPt2tc4D0OnL7Ed+t2lfoRpNqaLUo13728NmMuBw4fIykpBXc3F14Y/RQD+/W894wlkPNZ4adamEJbK9j4CFJFg+5wKLk7VmPRvBsAuoObsWjZA8vm3VENetDlkLNhOYar51G8q2I94HkUjQYUBd3JvcZHk5bCq9+W/mkaQ94ZTb1OjcnJzOH71z4n4rjxePjistdZMXUxyTGJfHZhFQnXY8lKzwLgyPp9rP30N5y9XHn6gxdw9nJFUWDDF3+yf3XJrgp+9GnLezcqwo7TEbx/8xGk/VvW5tmuTfhlzykABrUx9r7688A5dp+9yrwnu+bNdy4ynrd++heDQcWgqvRoHMi47qV7VPC6sWFFvm7t6UyXDe9i4WiLalDRp2exqaPxloW2K6cQ9soSsqKT8O3dnLpTHkc1qOQmp3No4hIyImLQ2FjS+J2ncWtRCxSFjKux7Hmq8JNtfHs3p/HsZ7BydyI3JYPkE1fYNXQuvn1bUG/KIAw6PegNnHr/N6I2FZ31Tk0/fBbfvi3JuBYLgKo3sK3nmwAFsls62dHi8xex9XNHl57NkSlfk3wq4oHI3mD6MHy6NUHRKFz8bjPhS42PELzf7GAcGK/2+P6oqkr0liPGLu330OKLl/BsWxcrN0eyY5M59f5vaCy1AFz63vg1qdYLD1NtSEdUg8rlldvyMvv1b03tlx9B0Wgw5Oo58sayIgdA7HngEywdbNFYWZCbnM7OIXNJPXcdW38PWix8Hktne7LjUzg0cXHeAJ2mMnW9g/ERnOcW/kX0tttPdKis9V6az3M/6zvjWhwd/5iOxlKLotUQs/0Ex2YsL7Pu3+4ta9Pprxkkn4pANRi/A51872dSz1+nydxRWHu7oLHUcm31Hs58VHgsjAZvDSXg0bbY+LiSFZXI5R9COf3Bb9Sf9gRVejbDoNOTm5TG4anLihx4s7SafTKOG5sO5z2C1Jy3meLWsZ2f8drrrf216hMd8e7cmAPPLSwwf0Xtr7klHAy66ZwRVOncCF1mDvsnLSbxZrGkw4rXODB5KVnRSdhX9aTNly9j5WJP0okr7H3pcww5Olp8MAb/vi1Jv2Yc3+rWo0btq3rS/ptJgLEod+WP3Zz+5M97ZnnixsqyHcnazKS99uj/y/s9HN7/wyx/b8UWKfIaKEpDIBDjrSHXgANAR1VVQ01ZQEmKFOamtEUKs1DKIoVZKGWRwhyUtkhhDsqrSFERSlukMAf3U6SobKUtUpiD4ooUQgghRGUpaZHCnEiR4sFkrkUKUy5zrwKWA/MBG+AToDnQphxzCSGEEEIIIYQQle8Bvoj6ILrbmBS3tMI4HsVujL0oIoGKffyBEEIIIYQQQggh/t8zpUiRC2QCthh7Uly6+ZQPIYQQQgghhBBCiDJjSpHiAMYiRQuMg2cOVRTl7sMJCyGEEEIIIYQQQpSQKWNSjFZV9dazhaKA/oqiPFWOmYQQQgghhBBCCPNQRk/zEaa5Z0+KfAWK/K8tL584QgghhBBCCCGE+K8y5XYPIYQQQgghhBBCiHInRQohhBBCCCGEEEKYBVPGpBBCCCGEEEIIIf6TVBmTokJJTwohhBBCCCGEEEKYBSlSCCGEEEIIIYQQwixIkUIIIYQQQgghhBBmQcakEEIIIYQQQgghiiNjUlQo6UkhhBBCCCGEEEIIsyBFCiGEEEIIIYQQQpgFKVIIIYQQQgghhBDCLEiRQgghhBBCCCGEEGZBBs4UQgghhBBCCCGKYzBUdoL/FOlJIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBSlSCCGEEEIIIYQQwizImBRCCCGEEEIIIURxDGplJ/hPkZ4UQgghhBBCCCGEMAvl3pOiTlBseS+i3OiPZ1V2hFKzqNOksiOUnvbB7eCjRl2p7AillrN+dWVHKDWrl2ZXdoRS+/Xj3pUdodQ+9hlU2RHuQ1hlBxBCCCEKuGSlVHYEIcyC9KQQQgghhBBCCCGEWXhwL1kLIYQQQgghhBDlTcakqFDSk0IIIYQQQgghhBBmQYoUQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkDEphBBCCCGEEEKIYqiqjElRkaQnhRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBRk4UwghhBBCCCGEKI5BBs6sSNKTQgghhBBCCCGEEGZBihRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQMSmEEEIIIYQQQojiyJgUFUp6UgghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALMiaFEEIIIYQQQghRDFXGpKhQ0pNCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALUqQQQgghhBBCCCGEWZAxKYQQQgghhBBCiOLImBQVSnpSCCGEEEIIIYQQwiyYbU8K61YtcJ74Emi1ZPz9D2nLVxWYbtujGw5PDgFAzcwk6f0F6C6EA2D/xOPY9esLqOSGXyRp9jzIya2w7No6TbEeMAY0WnL3biR3629FttME1MR2wvtkff8++mO7AbDs+AgWrXuAqmK4cYXsHz8BXcVl33XsPPN+WI/BYODRjk0Z/XCHAtNTM7KYtvh3ohKS0ekNPNO7LQM6NCEqPpn/Lf2D+OQ0FEXh8ZBmDO/RusJyG7OfY97ytcbsIc0Y3a9T4exf/EJUfDI6g4Fn+rRjQMdmedP1BgNDp3+Bl6sTn01+qmKzn7nK/L/2YDCoPNqyNqO6BBeY/m3oUdaGXbiZU+VSTBLbZj6Js51NXvZhn6zGy9mOhaN6VWh2bc3GWPV6GjQadGHbyN35V4Hpmup1sRnyKoakGGPW0wfI/fd3sLDEZuR00FqiaLToTu0jN/TXCs1+L2/O+Yjtu/bj5urC6hVfVnacQubM+x/denQiIyOL8S+8zrGjp4psN+2tiTwyoBd6vYFvv17F0sXLAWjbviWz35uGhaUFCfGJ9O9bMdu9Oe+rAY+146GX+gGgS8/iyNRvSD4VUahd04+exaVxIIqikHbxBgfHf4k+I9vk5fj1a0XdVwfiWMuXbb3fIunoJQDsAjzovv0DUsMjAUg4dIEjU78x6T2r9GxGvamDUA0GVL2BY28tJ37/2ULtmi96EdfGNTDo9CQeDufwa1+j6vSVmt2hpi/NFozDpWF1Ts39mfNf/FNku/LKDuBUN4Am74/B0tEW1WBgW6+3MGTf/e+vra8bzRc+j7WnC6gql5ZvJfyr9QXamLpNFcfK1YFWX03ANTiIKz9t5+i0b/OmKZZagueMxKNtXTConJz7E5H/HDDpfTXWlnRcPR2NlQUaCy3X1+zj9PsFv6+Ya3YwfZtp9sk4PNrUJTclA4BDExaTfPKKycspj+09aEwvqj/ZGUVRuLRiK+FLH4xtBsCjbV3afDuZ9Ajj3/TItQc489Efhdp5tq9Pw+nDQKOgS8/m0IQvSb8cbfJyynpfNWV7N/WzFac817spus18iqDOweRmZvPPq0uIPnG5UJumz3SnxaheuFb35pPg58hMTAOgauu6PLZ0EslXYwE4t/4Auz5dXab5hCgJ8yxSaDQ4vzqB+AmvoY+JxfPrL8nasRvd5dt/VHSRN4h7cSJqahrWrVviMnUycc++gMbDA/tBjxEzbATk5OA6awa23bqQuXZDxWRXNFg/No7ML6ejJsdjO+lDdCf3o0ZfLdTO6uER6M8evv2SsxuWHfqRMf9FyM3B+ukpWDTpgO7A1gqJrjcYmLN8LYtfewpvNyeGvb2UkCa1CfLzymvz05b9BPp5snDSMBJS0un/xkL6tmmIVqvh1SE9qFvdl/TMbIbMXEzr+oEF5i337N/9zeKpI43Zp39JSNO6BbNv3kugnxcLJz9lzD5lAX3bNsbSwrgbrNywh0BfT9IyTT/RKKvs7/2xiy/H9sHb2Z7hn66mU/1qBHm75rUZEdKYESGNAfj31BVWbD+eV6AA+GHHCWp4uZCenVOh2VEUrPqMJGv5HNSUeGyenY3u7CHU2OsFmukjzpD9w/sF59XlkvXdu5CTDRotNqNmor9wBMO1CxWX/x4G9OnOsIGPMG3WB5UdpZBu3TsSGFSdlk160Kx5Y+Z/NJNeXQcXajd0+GP4+lWhTfPeqKqKh4cbAE7Ojsz/cAZPDBzD9Ws38l4vb+a+r6ZHxLD90VnkJqfj3aUxTT4YQ2if6YXaHZu+Al1aJgANZz5J0KgenPvsb5OXk3LmKntHfUyT90cXmpZ2JZqt3aaVOHvMjhPc2HAIMH6Jb7VkAps6vFqo3dXfd3HwxUUAtPjiJaoP78yl7zZXavbcpDSOvfkdVXo1v2u78squaDW0WPQiB1/6nORTEVi5OmDI1d3z/VSdgeMzV5J0/DIW9jZ03jibmO3HST13+xho6jZVHH12Lqfm/YpTHX+c6gQUmFZn4gCy45LZ1G6y8Xjs6mDy+xqyc9kx8F30GdkoFlo6/TWDqC1HSQy7fQw21+xg+jYDcPydH4hcs79E739LWW/vTnX8qf5kZ0J7v4UhR0e7Va8TtfkI6Zei8tqY83oHiNt3hj1P3f3vYvC8Uewd8SGp5yMJHNGNOpMGcGjCYpOXUdb7qinbu6mfrTjlvd7vJrBzY1xr+LC402R8mwTR890RfD9gZqF21w+eI3zLYYb9+L9C064dOMuvoz4s01zi/wdFUXoBnwBa4CtVVecW064FsBd4QlXV+7ryaJZFCst6ddBdi0QfeQOAzM1bsenQjrR8RYrcEyfz/p1z8hRaL4+8nxWtFsXaGlWvQ7GxxhAXX2HZNVVrYYi7gZpgrBbrDu/AokErcu8oUlh2eBj9sd1oAmrd8QYasLQCvQ7F0ho1OaGionPi4nUCvN3w9zKerPRq1YDQw2cLnDwoikJGVjaqqpKRnYOzvS1ajQZPF0c8XRwBsLe1JtDXk5jE1AorUpwIv0aAt/vt7K0bEnrodMHs5MuelZ2XHSA6IZkdR84y5pFOLF+/u0Iy52WPiCXAwwl/dycAegYHEXrySoEiRX7rDofTq0nNvJ+jk9LYceYqY7oGs3z78QrJfIvGryaGhCjUxJu9JE7swaJ2c3LvKFIUK+fmSaZWa/xPNa/7/ZoHN+T6DdOv/FSkXn278tOq1QAcOngUZ2cnvL09iY6OLdBuxOihPDdmMurNdRsXZzymDBzUj3/+3sT1azcKvF7ezH1fTTh4/va/D13AtkrRxZtbBQoAra3V7X/bWdN49jM41w1A0Wo5/cFveYWD/FLPR5ZhaqP8PTks7Gzyfud3it5yJO/fiYfD8z5jZWbPjkshOy4Fn25N7tquvLJ7hTQi+VRE3tXqnJtXF+8lKyaJrJgkwHjFO/X8dWx9XAsUKe62TQUMbEfQmF5oLLUkhoVz+PVvCt33rM/IJn7/WexreBdafrUhIbcLUapKTkKqSbnzvzeAxlKLxqLwMdics5u6zRSnsrZ3x1p+JB66gD7TeFEhbs9pfPs05/yiNXltzHm9m0xVsXCwBcDC0Y6sqCSg8vZVuPf2fjfmvt5rdW/Gid92AhB5OBxrJ3vsvVxIv3l8uiW6BL2IhABQFEULLAK6A9eAA4qi/KWq6qki2s0DyqRnQLFjUiiK8oiiKDbFTS9PWk8P9NExeT/rY2PRenoU297u4T5k7TFWyA1xcaSt+hnvP37C+6/fMKSlk73/YLlnvkVxdkdNisv7WU2KQ3F2v6ONGxYNW5O7u2D3PjU5gdzQ1di/9TX2M79DzUpHf+5IRcQGICYxBR83p7yfvVydiE5MKdBmSNeWXIyMo9vED3n8zc+ZMqw3Gk3Bzeh6bCJnrtygYZBfheSGW9md8372cisie/fWXIyMpdvL83h82mdMeapvXvb5K9YyaUhPNBqlwjLfEpOSjo/L7Yq6t7M9McnpRbbNzNGx++w1ujWsnvfa+3/tZWLflihKxWdXnFxRU24XAdWUeBSnwsUVrX8tbJ6bi/XwqSie/vneQMHmufewe20x+vDjGK6HV0Ts/xeqVPEm8vrtK2+RkVH4+Bb+YlS9RgADHuvDptDf+PHXpQQGVgMgKKg6Li5OrF7zPZv//Y3BQ/pXSO4HaV+tPiyE6K1Hi53ebME4+hz/AseaVQj/2vg3uc6EAcTuPMm2Xm+xY+C7NJg+DK2ddYmWa1/Vky6b5tDhj7dwb1W7RPP69m5O9x0f0HbFa4RNWnLXtoqFlqqPtyd621GzyF4SZZ3dIdAHVJV2q16ny8bZ1Hrx4RJnsgvwwKVBdRLCij+O5d+mHGv54t+/Df/2m8nWbtNQDQaqDmxv8vIsnewAqDdlEF02zqbl0glYezjdY647aBS6bJ5D3xNfEr39OImHH6DsJVD/9cF03TqXhm8/icbKeH2usrb3lDNXcW9dBytXB7S2Vnh3DcbO173Y9ua43t2a1aLLlvdo+8MUHGsX/V0vbPJS2q6cQu+whVQd1J6zC423glbqvmrC9l7UZzOX9X43jj6upEbe/j6WGpWAYzEXu4rj17Qmo9bNZtB3r+FRq+K+wz8wDP9P/7u3lsAFVVUvqqqaA/wIFPWl8WXgNyCmiGkldreeFD8B6YqirANWARtUVTX9xs/7UvjLZ3FXhKyaBmPXrw9xz403zunogE2HtsQ8PhRDahqus2di27MbmRtM7w56X4o6Ubwju3X/Z8le8x2od2wZtvZoG7Qi/d1nITMdm2emYtEsBN2h0PLLW3xMwHhFM7/dJy5Qp6oPX019hqsxCYx7fzlNa1fFwdZYz8rIymbyZz/z2rBeea9VhCKz3/G72H38PHWqVuGrN0YZs89dRtPa1Th05jJuTvbUq+HHgdMXKyjxbUVnL7rt9lNXCK7unXerx/ZTV3B1sKGevycHwsv+6ua9FbW9F/zRcOMyGQtehpxstLWCsRnyCpkLX7nZViXryzfAxg6bJ15B5+WPGnOt/GP/P1BUUaqo46S1lRVZWdl0DxlI337d+WTRHPr1Ho6FhZZGwfUZ+MgIbGxsWLf5Rw4eOMrF8MvlmvtB2Vc92tWj2tAQtvd/u9g2hyYuBo1C8JwR+Pdvw5Uf/8UrpCE+PZtS6/m+AGitLbHzczf5amxWdBLrm40nJzENl0Y1aL3sFTZ3mlKg58bdRK47SOS6g7i3rkO9qYPYOXhOsW2D544kbu8Z4vcZx62o7OwlUdbZNRZa3FvVZluvt9BnZtP+l/+RdPQSsTtP3ntmjFeHW301iWPTlxf7ee/cpjw7NMClUQ06r59lfA8bK7LjUoqctyiKhRY7P3fiD5zl+MwV1BzXh4YzhnPw5S9Mfg8MKlu7TcPSyY7WyybhVMeflDOFj8Fmmd1EJ2f/RFZMEhorC5p8MIaHXurHmY/+qLTtPfV8JOc++5v2P72BLj2L5JNXMBQzroo5rvekY5dZ33w8+oxsvLsG02bZZDa2faVQu5pje7N7+HwSD4dT64WHafT2k4RNXlq5++o9tvfiPps5rPd7L8T0c6eiRJ24zOdtJ5KbkU1g58Y8tnQSS0IK3y4o/pP8gPy3BFwDWuVvoCiKH/Ao0AVoURYLvVuR4szNBT0OTAaWKYryB7BKVdV/7/amiqKMBcYCzAt8iCe9fUsUSh8bi9b7dtdfradnkbdsWAQF4vLGq8S/8jpqivFgYd28GbrIKAxJyQBkhe7AqmGDCitSqElxKC75bj1x8UBNKdiNWhNQE5unjDu+Yu+Etm4zsg160FoYbxNJN34W3fE9aKvXqbAihbebE1EJtw+6MYkpeLk6Fmjz544jjOrbHkVRqOrtjp+nC5duxNEw0J9cnZ5XPvuZPm0a0q15vQrJXDB78u3sCSl4udyRfXsYo/p1zJfdlUuRcRw5F0Fo2Bl2Hj1Hdq6O9Mxs3vjiF957flDFZHe2JyrpdnfF6OR0PJ3si2y7/kg4vZoE5f185HI0/56KYOeZVeTk6knPzmHaD9uYM6xzuecGUFMSUJxuXwFSnNxRUxMLNsq+/YVNf/4I9B0Fdo6Qka+rY1YG+sun0dZsjE6KFMUaNWYYTz1jHHfi8OHj+Pr55E3z9fUh+kbh4nVkZDRr/toIwD9/b+LTRe/dfD2K+PhEMjIyycjIZM/ugzRoWKfcixTmuK8GjuxO9eHGfWb38PlYuTnS9MNn2T1s3r27EhtUrv25h1ovPMyVH/8FFPaNXkBa+I0CzZotGIdzg2pkRSexe/j84t8uR0dOjnGZSccukX4lGocgnwKDxt0te1Z0EgDxe89gX90LKzfHIrsV15n8GNbuTux97eN8r5pH9nspj+yZkQnE7Tmdt66itxzBpVENk058FAstrb+exNXfdxG5tuiB8JzqBhTephSI+Hk7J+f8VKCtb+/m1Jn8GGC8Gl3c+stJSEWXkUXkWmNv0et/76X6sJB75i1KbkoGsbtP4925caEihTllL802c+t2HEOOjis//pt3clzR23t+V1aFcmVVKAD133iCzBuFv+M+COs9essRlLkjCx1nrNwdca5fLa+nwrU/99Bu1VRufYjK2ldvKW57z19gyv/ZzGlfza/p091oPMT4e7lx7CKO+XrkOPq4kXbHrR53k5Pvs1/cdhTtrBHYujrkDawp/v/Kf95+0xJVVfN3xSzq0umdFbAFwFRVVfVl1bP7bkUKVVXVRGApsFRRFB9gMDBXURR/VVUD7jLjEmAJQGTbziW+yTz39Bks/P3QVvFBHxuHbbcuJM58t0AbrbcXbu+9Q+Lb76G/evsAo4+Owap+PeOYFNnZWDdvSu6ZwqOblxfD1fNoPH1R3LxRk+OxaNKB7OUFB+DJmP1s3r+th0xAd+oA+hP70FR9CE212sYxKXJz0NZqjOHq+TsXUW7q1/AlIjqea7GJeLs6sn7fCd57bmCBNj7uzuw7dZGmtasRn5zG5Rvx+Hu6oqoqM7/5k8AqHjzdq22FZc7LHuhHRFQ812IS8HZzYv3e47z3QsETFx93F/adDKdp7erG7FFx+Hu5MuGJHkx4ogcAB05f5Lu1uyqsQAFQP8CTiLgUriek4OVkz4Yj4UUWGVIzczh0MarAtPF9WjK+T0tj9vBIvv/3WIUVKAAMkeFo3H1QXDxRUxPQNmhD9m+fFWijODijphlPSjV+QcZqf0aqsVBh0ENWBlhYog1sQO6uv4pajLjpm69+4JuvfgCge49OjB77JH/89g/NmjcmJSW10HgUAOv+2UyHjq35YcVvtG3fkvCbRYh1/2xh7gfT0Wq1WFlZ0rRZI75c9G25fwZz3FcvLtvExWWbALD1c6f1N5M4+NLnpF2MKnYe++reeSPV+/RoSuoF41XAmNBjBI3umTequ3ODaiSfuGLsdWECK3dH4wmJQcWuqhcONXxIv1J8z8n82e2r377dx6VhdTSWFkUWKKoPC8E7pBE7Bs0u0LWlMrObqryyR4ce46EXH0Zra4UhR4dHm7pcWLLWpHmbfjyW1PPXubC46PbFbVOxO07S5tvJXFiyjuy4FCxd7LFwsM3rDWOKGxsP49m2LrG7TuHZoQEp50wcDwjj70vN1ZObkoHGxhKvDg04t6jg4K/mlr0024yNl0teocK3V/O8k9KK3t7zs/ZwIjsuBVs/d3z7tCD04RkFppvzerf2vH27nmuTIBRFKXScyU1Kx9LRDodAH9IuRuHVsSGp58rmGFnafdWU7d3a05ns2ORCn62y99XihH2/mbDvjRdhg7oE0/SZ7pz+aw++TYLITs0oNB7F3dh7OpN+87NXaRwIGkUKFP8R+c/bi3ENyH/e7w/c2fWpOfDjzQKFB9BHURSdqqqrS5tLKa4rkKIoh1VVLXI0IkVRqqmqatLIK6UpUgBYt2mF84QXQashY8060r5bid0A4+OYMlb/jfPrr2Ib0hF9lPFLoqrXEzf6OQAcR4/Apltn0OvJPXeepPc+gNySP8bTqbXjvRsVQVu3Gdb9x4BGQ+7+zeRu/gWLNsbHQur2FByHIq9IcfMRpFY9h2IR3AHVoMdw/SLZPy0E/b1HLb6TxaChpcq+4+g55v+wHoNBZUCHJjz7SEd+3mq8MjS4SwtiElN466vVxCWnoaoqo/q25+G2jQk7d4WRc5ZRy98Lzc0K2suPd6VD44dKHkJbuvFcdxw5y/yVxscaDujYjGf7h/DzFuNYJYO7tjRmX/IbcUk3s/fryMPtggu8x60Tn9I+1lCNKt2ARDtOR/D+zUeQ9m9Zm2e7NuGXPcbxaAa1MfZK+fPAOXafvcq8J7sW+R63ihSlfQSpIazwwFWm0NYKNj6CVNGgOxxK7o7VWDTvBoDu4GYsWvbAsnl3VIMedDnkbFiO4ep5FO+qWA94HkWjAUVBd3Kv8dGkpWD10uxSzXcvr82Yy4HDx0hKSsHdzYUXRj/FwH49y3QZvkG9Sz3vvA+m07lbBzIzMhn/4jSOHj4BwKpfljDx5TeJjorBydmRL5d+gJ9/FdLTM3ht0gxOnjAWbl8cP5qhwx/DYDCw8vtfWfzFdyVa/tVN7967URHMYV9d+0jRj5Vr+uGz+PZtScY1Y8FH1RvY1vNNANqunELYK0vIikmm05/TsXC0BUUh+WQER6Z+gy4tE42NJY3feRq3FrVAUci4GlvkSPG+vZvTePYzWLk7kZuSQfKJK+waOhffvi2oN2WQsfu33sCp938jalOYSZ/poZf6UXVQBwy5OvRZuZx454e8R5DmZY9OYsC15WRci8u7anjrMXuVmd3a05kuG97FwtEW1aCiT89iU0dj1/mKyA7GgfFqj++PqqpEbznCiVmrCs1/J/eWten01wyST0WgGoy3cJ5872fs/IxXNC99v+Wu25Rf/9bUfvkRFI0GQ66eI28sK/S0AYCeBz7B0sEWjZUFucnp7Bwyl9Rz17H196DFwuexdLYnOz6FQxMXk3ndtMHCneoG0PzT51G0GtAoXP9rL2c++oMaT3c1++xg+jbT/tf/Ye3uaNxXT1zh8JSv0WdkV+r23nH1dKzcHDDk6jk+YwWxO08+MOs9cFQPAp/phkGnx5CVw7EZK/IG+sy/3n17N6fulMdRDSq5yekcmriEjIiYSttXTdne7/bZKmq9n7MudrjAe+o+6xkCOzUiNzOHta8uIeq4sWfHoG9fZd2Ur0iLSaLZiB60eu5hHDydSY9P4eK2o6yb+hVNn+lOkye7our05GblsvXdlVw/VLILpa9fWVHxg6NVoKThXcxrdPcy4rJy611/b4qiWADngK7AdeAAMExV1SK7LymK8i2w5n6f7nG3IkWIqqqhiqLUK2L0zhBVVUNNWUBpixTmoLRFCnNQ2iKFWShlkcIclLZIYQ5KW6QwB+VVpKgI91OkqGylLVKYg+KKFEIIIURluZ8iRWWTIsWD6V5FCgBFUfpgvKVDC3yjqupsRVGeA1BV9cs72n5LGRQpij0bzFeE+FlRlOXAfMDm5v+bA23uZ8FCCCGEEEIIIYQwX6qqrgXW3vHal8W0HVEWyzSlXNcK430ouzF274gE2pXFwoUQQgghhBBCCCFuMaVffS6QCdhi7ElxSVXvfHamEEIIIYQQQgjx/5Dh/+XdHmbLlJ4UBzAWKVoA7YGhiqLc1z0mQgghhBBCCCGEEHcypSfFaFVVbz1fJwroryhK6YZTF0IIIYQQQgghhCjGPXtS5CtQ5H9tefnEEUIIIYQQQgghxH/Vg/usRyGEEEIIIYQQorzJiIwV6sF9GK8QQgghhBBCCCH+X5EihRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgA2cKIYQQQgghhBDFUA1qZUf4T5GeFEIIIYQQQgghhDALUqQQQgghhBBCCCGEWZAihRBCCCGEEEIIIcyCjEkhhBBCCCGEEEIUx1DZAf5bpCeFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWpEghhBBCCCGEEEIIsyBjUgghhBBCCCGEEMVQDWplR/hPkZ4UQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIKMSSGEEEIIIYQQQhTHUNkB/lvKvUhR9eC58l5EubE8/ODWcPyXX6/sCP9JDlqbyo5Qaq1t/Co7Qqn9+nHvyo5QapHh6yo7Qql1Dx5b2RFKbTxelR1BCCGEKGD8C5aVHUEIsyC3ewghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALD+6gC0IIIYQQQgghRDlTZeDMCiU9KYQQQgghhBBCCGEWpEghhBBCCCGEEEIIsyBFCiGEEEIIIYQQQpgFGZNCCCGEEEIIIYQojoxJUaGkJ4UQQgghhBBCCCHMghQphBBCCCGEEEIIYRakSCGEEEIIIYQQQgizIGNSCCGEEEIIIYQQxVBlTIoKJT0phBBCCCGEEEIIYRakSCGEEEIIIYQQQgizIEUKIYQQQgghhBBCmAUZk0IIIYQQQgghhCiOjElRoaQnhRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBRk4UwghhBBCCCGEKIYqA2dWKOlJIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBbO+3ePjj96hd68uZGRmMnr0JA4fOVGoTejW33FwdADAy9OdAwePMPDx0XTq2Ibff/uGS5evArB69Vrenb2gwrJ/+OFMevbsTEZGJmPHvsqRIrJv3vwLDg72xuxeHhw8eITBg8cyZMgAXnnlOQDS0zMYP/5/HD9+usKyvzXnNTp1a0dmRhZTx8/k1LEzhdrMWziTFm2akpaaBsDUl2dy+sQ5HBwd+PCLWVTx88HCQsvXny/nt1V/S3YTTHl3Iu26tiErM4sZE2Zz5vi5Qm3e/uR/NGsTTFpKOgDTJ8zm3MnzhPRsz/NTn0U1qOj1et5/6xOO7D9WYdkHzxhJ/c5NyMnM5vtXP+fqyUuF2oxc8DLVGgah1+m4fDScldOWYNDpsXG0ZeTH43Hzc0ej1bJ56d/s+SW0wrLPmfc/uvXoREZGFuNfeJ1jR08V2W7aWxN5ZEAv9HoD3369iqWLlwPQtn1LZr83DQtLCxLiE+nf96kKy343b875iO279uPm6sLqFV9WdpxCXn7nRVp3aUlWZjZzJ83n/IkLxbYdP+sleg/uSe/a/fJeC27TmJdmPo/WwoLkxGQmPj75vjPZ+rrRfOHzWHu6gKpyaflWwr9aX6BNlZ7NqDd1EKrBgKo3cOyt5cTvP2vyMqxcHWj11QRcg4O48tN2jk77Nm9ah9/fxMbLBX1WDgC7hswlOy6lzLLf4hocSMg/77Bv3KdErtn/QGT3aFuXNt9OJj0iBoDItQc489EfZZJdsdQSPGckHm3rgkHl5NyfiPzngMnvXZJs9V4fjF+/Vqh6A5e+20z41xtMXo5fv1bUfXUgjrV82db7LZKOGo+zAY+1o9YLffPaOderytbu/yP55BWT3zu/Wi88TMBjbQFQLLQ41fJjTf1x5CalmzR/cevawt6Gjn9Oz2tnW8Wdq7/t5Nj05SXO2PTjsfh0b0J2XApbQqYWmm7pbE+zj8diX90bfXYuYZMWk3LmmsnvX17beknzBY7qQc1ne+FQw4c19caRk5BaouUUt80AONUNoMn7Y7B0tEU1GNjW6y0M2bmmvbFGocuG2WRGJbDnqQ8KTDLnfdWhpi/NFozDpWF1Ts39mfNf/FNkO8/29Wk4fRhoFHTp2Rya8CXpl6NNXk65rfe70NRogFXXYaDRoDu6Hd2+tQWnB9TGeuB41KQ4AHTnDqHb/ZdxorUtVr1HovHwB1Ry1n6DITL8vjMJcT/MtkjRu1cXatWsQZ167WnVsimLPnuPtu37FWoX0uWxvH///NMS/vp7Y97PO3fup/+jz1RI3vx69uxMUFANGjToRMuWTfj003fp2HFAoXbdug3K+/eqVV/y983sly9fpUePwSQlpdCjRwiLFr1X5PzloVO3dlQLDKBbywEEN2vAO/Pf4PFeRa/D+W9/wvq/txR47cnRg7hw9iLjnpyEm7sLG/b8zl+/riM3VyfZ76J91zZUDfSnf5snaNi0PtPmvcrTfcYW2XbBO4vYvCa0wGv7dhwidMNOAGrVDWLeklk81mFYeccGoH5IE7xq+DAjZDw1mtRi6OwxzB/wv0Lt9q/eybKJCwEY9ekE2g/pwvYVmwh5qhc3LlzjizHzcHBzZObWT9i/egf6XH25Z+/WvSOBQdVp2aQHzZo3Zv5HM+nVdXChdkOHP4avXxXaNO+Nqqp4eLgB4OTsyPwPZ/DEwDFcv3Yj73VzMKBPd4YNfIRpsz64d+MK1qpLS/xr+DG8/TPUa1qXSe9N4IV+LxfZtnajh3Bwsi/wmoOTPRNnj2fKk28QExmDi7tLmeRSdQaOz1xJ0vHLWNjb0HnjbGK2Hyf13PW8NjE7TnBjwyHA+GWz1ZIJbOrwqsnL0GfncmrerzjV8cepTkCh6QdeXFTgC21ZZgdAo1D/zaFEh5a8iFnZ2eP2nSl0QlQW2etMHEB2XDKb2k0GRcHK1aHE729KtmpDOmHr586m9q+CqmLt4VSiZaScucreUR/T5P3RBV6/+vsurv6+CwCnOgG0+W5yqQsUAOc/X8P5z9cA4NO9KTXH9Ta5QAHFr2tdehZbu03L+7nzhtlcX2v6CWZ+V37azsVvNtJs4fNFTq89oT9JJ6+wd9THONT0Jfi9EewcNOe+P8Mtpd3WS5ovfv9ZojaF0eH3t0q1nOK2GUWrocWiFzn40uckn4rAytUBQwm+69R8tjep569j4Whb5HRz3Vdzk9I49uZ3VOnV/K7tgueNYu+ID0k9H0ngiG7UmTSAQxMWm7yc8lrvxVIUrLo/RfZPH6CmJmDzzHT0F46gxkcWaGa4eo7s3z4pNLtV1+HoL54gZ/XnoNGCpdX9Z/p/SMakqFhme7tHv349Wb7yVwD27Q/D2cUZHx+vYts7ONjTOaQdf/5Z9JWjivTww9354YffANi//zDOzk73zN6pU9u8IsXevYdISkq5OX8Yfn5Vyj/0Td16dWL1T8bK8pFDJ3B0dsDT28Pk+VUV7G/2DrGztyM5KQWdrvxPNuHBzt6pZ3vW/Gzcdo+HncTRyREPL3eT58/MyMz7t62dDaqqlnnG4jTu0Zy9v28H4NLh89g52uPk6VKo3cnQw3n/vnz0Ai4+xs+nomJjbwOAtZ0N6UlpGHQV85egV9+u/LRqNQCHDh7F2dkJb2/PQu1GjB7Kh/MX5a3XuLgEAAYO6sc/f2/i+rUbBV43B82DG+Ls5FjZMYrUrkdbNvy6CYBTYadxcHLAzatwgUej0fDcm2P5cvbSAq93HdCVHet2EhNpvFKXFJ9UJrmyYpJIOn4ZMJ5QpZ6/jq2Pa4E2+ozsvH9b3LGvBQxsR8i6WXTZPIcm80eDRim0DH1GNvH7z6IvgytnJc0OEDS6J5H/7Cc7LrnA6w9C9uLcb/ZqQ0I4u/DmFUVVLfHValPVeKYbZz783fjHBvKuvmvtrGn68Vg6r59Fl01zqNKzWZHzp56PJC38xl2XEfBoW67+sbvMMgc82oZr+d6vrLYT+xo+WHs4Eb+3cG9HU8TvPUNOUlqx050e8iN2x0kA0i5EYhfgmVcUqsxt3ZR8+SWfuELG1bhCr9/vNuMV0ojkUxEkn4oAICcxDQymfW+wreKGT7dgLq/cZlL7/Cp7X82OSyHxyEXUe323U1UsHIwFGAtHO7KikoDKXe93o6kSiJoUg5ocCwY9utP70dZqYtrMVjZoAh5Cf8z4PQ6DHrIz7z6PEBXAbIsUfr4+XLt6uwJ4/doN/Hx9im0/YEBvtm7bRWrq7T9arVs349DBTaz5azn16j1Urnnz8/X14dq1fNmvR+Hr611s+0ce6UloaMHst4wYMYQNG0LLI2aRvKt4cSPydpe2qMgYvH0Kn7QBTJr2An+H/si0Wa9gZWUJwIqvfiLooRrsOrGBNdt/4t3/fVBhJ8wPcnavKp5E3TzhAoi+EYNXlaKzv/j6OH7a+h2T3x6P5c3sAJ17d+T3HT/w6YoPeHuS6VeM7peLtxuJkbe/RCVGxePiU3yPAo2FllaPduDUv0cACP1uPT41/Zi7fzFvbviQX95eVmHrvUoVbyKvR+X9HBkZhU8R+2r1GgEMeKwPm0J/48dflxIYWA2AoKDquLg4sXrN92z+9zcGD+lfIbkfdJ4+HsRGxub9HHsjFk+fwgXFR0f2Z9fGPSTEFCz+BAT64eDswIJfPmTx2s/pMbB7mWe0C/DApUF1EsIKd3n17d2c7js+oO2K1wibtAQAx1q++Pdvw7/9ZrK12zRUg4GqA9uXeLnNFoyjy+Y51Jn0aJlnt/FxxbdPCy5+t7nA6w9CdgC3ZrXosuU92v4wBcfafmWS3dLJDoB6UwbRZeNsWi6dUOIeDsVlu5N9NS/8+7em84Z3afvDFOxrGL/T1JkwgNidJ9nW6y12DHyXBtOHobWzLnEGAL/+rbm2umyKFFpbK7w7N+b6P8ZbgspqO4GbxY+/9pRJzqIkn4zAt08LAFybBGHn74Gtr7vZbOvF5TPV/W4zDoE+oKq0W/U6XTbOptaLD5s8b6NZT3Fi1qq7/p02533VFGGTl9J25RR6hy2k6qD2eYWRylzvd6M4uqKm3P47qaYmoDgULvRq/GpiM/JtrAdNQvHwNc7r4omakYpVn9HYjJiJVa+R0pNCmIW73u6hKEpHIFpV1bOKorQHWgOnVVUt+iauMqQohaurdzsgDhncn6+Xrcr7OezwcQJrtiQ9PYPevbrw2y/fULd+6f6YllRJsw8e3J9vv/2x0OsdO7bhmWeeoGvXgWWa725Mzf7Bu58RGx2HlZUlsz56k7Evj+CzD5fSoUsbTp84y1OPjqNqDX++/eVzDu45TFqa6V1FJXvx2RfO/pK4mHgsrSx564OpjHzpSZZ8tAyAbeu2s23ddpq2bswLU5/lucETyzu2URHZucv2PnTWGC7sP82FA8YraPU6NubaqSssGPoOntW8Gb/iLS70fo2stPKv5Ju63q2trMjKyqZ7yED69uvOJ4vm0K/3cCwstDQKrs/AR0ZgY2PDus0/cvDAUS6GXy737A80E9a7u7c7IX07MXHQK4Xaai201G70EK888RrWNlYs+utTToWd4tql64XalobWzppWX03i2PTl6IrYDiPXHSRy3UHcW9eh3tRB7Bw8B88ODXBpVIPO62cZ38PGqsT3qR94YRFZUYlY2NvQ6uuJVB3UgYhfdpRZ9kaznubErFWFrtw9CNmTjl1mffPx6DOy8e4aTJtlk9nY9pX7zq5YaLHzcyf+wFmOz1xBzXF9aDhjOAdf/sLk9yguW6HPZ22JPjuXbT3fxLdPC5p9PJbtA97BK6QhPj2bUuv5vnnt7PzcST0fWeg97sa1SRD6zOwSjb1wNz49mhJ/4FzerR5lsZ3c4j+gDQdfMn0dl9TZhX/R+N2n6bJ5Dimnr5J84jKqTm8223px+Ux1v9uMxkKLe6vabOv1FvrMbNr/8j+Sjl4idufJu853axyQpGOXjONCFMGc91VT1Rzbm93D55N4OJxaLzxMo7efJGzy0kpb76VT8DhviL5C5hevQm42msBGWD86nqylr6NotGh8qpG9eSWGGxex7DoMy9Z9yd1h+jgiQpSHYosUiqIsAFoCFoqibAC6AuuASYqihKiq+tpd5h0LjAVQtM5oNPbFNS3g+eeeYfTo4QAcPHgE/wDfvGl+/lWIvFH0oDVubq60aNGEgYPG5L2Wv1fCuvVbWfjpHNzdXYmPTzQpS0mNG/c0I0cOAeDQoWP4++fL7ufDjRsxRc7n5uZC8+aNeeKJguMPNGhQhy++mEf//s+QkJBULplvGT5qEE88ZbwacOzwKarku5Ls4+tFTHThroaxN1/Lycnltx/+YsyLxsECBw59hMWfGk+aIy5d41pEJIG1qnPscHkcgB/s7INHPsZjwx8B4OSR0/j43r4lyLuKF7FRhbPHxcQDkJuTy58//sPTzw8t1CZs71H8q/vh4uZMUkJyoellodNTPWk3tCsAV46G4+rrARgHD3T1cScpuuj9rO+Ex3Fwd2LJuCV5r7UZ1JmNX6wGIPZKNPFXY/AO8uXK0fIZtGnUmGE89Yxx3InDh4/j63e7h5avrw/RReyrkZHRrPnLeDvWP39v4tNF7918PYr4+EQyMjLJyMhkz+6DNGhYR4oURRjwzCM8PKwPAGeOnsPT93ZPIc8qnsRFxxdoX6t+Tfyq+7Jy5/cAWNtas3Lndwxv/wyxN+JITkghKzOLrMwsju47TlC9oDIpUigWWlp/PYmrv+8i8h73y8fvPYN9dS+s3BxBgYift3Nyzk8F2vj2bk6dycaxk8ImL73rfexZUcb9RpeexdU/duPaJKhEJz/3yu7auAYtFxvH/rB2c8S7azCqzvBAZM9ftIjecgRl7sgyWe85CanoMrKIXHsQgOt/76X6sJB75g0c2Z3qwzsDsHv4fLKikwplu7MremZkApE3eyVErj1AswXjbn169o1eUKh7eLMF43BuUI2s6CR2D59/z0z+A9pw7Y/S9U4o6vME9G9T8NaRMthOwDiwp6LVknSs9GM63IsuLZNDE2+PIdDzwCekR8Ti3qZOpW3rd67jovKZ7v62mczIBOL2nM7bRqO3HMGlUY17niy7t3iIKj2a4t01GK21JRYOtjT/7AUOvvR5XpsHZV8tjpW7I871q5F42Pgd5Nqfe2i36tbgrJWz3u9FTU1Ecbrdg1VxdENNSyrYKCcr75+Gi8egx1Ng64AhNQE1NRHDjYsA6M8ewLJ1X0RhMiZFxbrb7R7dgXZACPAS0E1V1VlA75v/FUtV1SWqqjZXVbW5qQUKgC++/I7mLXrQvEUP/vprA08NfxyAVi2bkpKcQlRU0Sf6jw98mH/WbiY7+/a9wvnvK2/RPBiNRlNuBQqAxYu/p3XrPrRu3Ye//97IsGHG3g8tWzYhJSW12OyPPdaXdeu2FMgeEODLjz8uZvToSVy4UH5/xG9Z+c0vPNJ5GI90HsbmdaEMeMJ4cApu1oDUlLS8k/r88o/10L1PCOfOGA/mkdeiaNOhJQDunm7UqFmNq1fK5urm/7fsPy/7nSHdRjCk2wi2rd/Ow4N7AdCwaX3SUtPyChL55R+nonOvjoSfMf5RCah+u3txnYYPYWlpWW4FCoB/l29gTp8pzOkzhaMb99P6sY4A1GhSi8zUDFJikwrN0+6JLtTt2JhvXl5Q4Kp5YmQctds1BMDRwxnvQF/iIoreX8rCN1/9QOcOA+jcYQDr1mzmiaEDAGjWvDEpKalERxf+orjun8106NgaMD7NI/xmEWLdP1to3bY5Wq0WW1sbmjZrxLmzMiJ2UVZ/9xdjej7HmJ7PsXP9Lno+brxFo17TuqSnphe6pWPv1n081nQwQ9o8yZA2T5Kdmc3w9saBcHdu2E3Dlg3QajVY21hTL7gOERciyiRn04/Hknr+OhcWry1yun3124VQl4bV0VhakJOQSuyOk/g93Cqv+7Gliz22/h5ErjvI1m7T2Npt2l1PfBStxvhFHuMJe5XuTUg5c7VMs29oOZENLSawocUErq/Zx5HXl3Fj/cEHIru1p3Pev12bBKEoSpmsd4AbGw/jefOqsGeHBqTcOdhoES4u25T3/mq+nin5s90pcv1BPNvXB4xPQEi7aDzRiQk9RtDonnntnBsYbyc7NHExW7tNM6lAgaLg368VV1eXrkiR//NkRSdh4WiLR5u6eYPEAmWyrgH8H21bZrekFMfSyQ7FUgtA9eGdidt7Bl1aZqVu6/nXsT4zp8h8prrfbSY69BjOdauitbVC0WrwaFOX1HP37oFzcs5PrGv6MhtaTGD/cwuJ3XWyQIECzHtfvVeBAiA3KR1LRzvjrRmAV8eGpJ4z9pSorPV+L4Ybl1BcvVCcPUCjxaJuS/QXDhdsZH/71hhNlRrGHo2ZaZCegpqSgOJm/LzaavUwxJWsF5cQ5UEp7jYERVFOqKraQFEUG+AG4KuqaqaiKFrguKqq9UxZgIWVX6lvLv/0k9n07BFCRmYmY8a8wqEw42jkf//5PWOfe40bN3tWbNn0C/PfX8SGjaF5877w/AjGjXsanU5PVmYWr772Nnv2HizR8i21pX/4yccfz6JHj05kZGQybtyrhIUdB+CPP77lhRem5PWs2LDhRz744As2bfo3b97PP5/HgAG9iYgwHrh0Oj3ti3iyyd34O5g+YOSdZsybSsfObcnMzOL18TM5cdT4+NOlqz7hfxNnERMdx/e/f4mbuyuKAqdPnGP6a3PISM/Ey9uDeQvfxtPbA0WBxZ9+y1+/rit1lgctu4PWptTZX3/vFdp2bk1WZhYzJ87h1FHj7RALV37AO6/MJTY6jsW/foqruwuKonD2xHlmT3mfzIxMRrw0nIcH9UaXqyM7K5uP31lU4keQtrYp+j5qUwx5ZzT1OjUmJzOH71/7nIjjxuLJi8teZ8XUxSTHJPLZhVUkXI8lK91YzT+yfh9rP/0NZy9Xnv7gBZy9jL+TDV/8yf7VJes6+2vi0VJnn/fBdDp360BmRibjX5zG0cPGxwWv+mUJE19+k+ioGJycHfly6Qf4+VchPT2D1ybN4OQJY8+RF8ePZujwxzAYDKz8/lcWf/FdiZYfGV4++8drM+Zy4PAxkpJScHdz4YXRTzGwX897z1gC3YOLfgKNKSa8+zItQ1qQnZXNvFfe5+wx4yN3534/m/df+4j4O3pWrDv7d4FHkD7x3GB6D+6JajDwz6p1/Pr17yVa/nhd4cGM3VvWptNfM0g+FYFqMF4yOfnez9j5GYuDl77fwkMv9aPqoA4YcnXos3I58c4PeY8g9evfmtovP4Ki0WDI1XPkjWUkhhV+tGrPA59g6WCLxsqC3OR0dg6ZS8a1ODr+MR2NpRZFqyFm+wmOzVhu8qBqpmTPr9kn47ix6XDeI0jNPXvgqB4EPtMNg06PISuHYzNWkHDw/H1nTz13HVt/D1osfB5LZ3uy41M4NHExmdcLF4mLc7dsbVdOIeyVJWRFJ2HpZEeLz1/E1s8dXXo2R6Z8TfKpCDQ2ljR+52ncWtQCRSHjamyRT0bw7d2cxrOfwcrdidyUDJJPXGHX0LmAsejR4H9DCO07w+Tcd1P1iY54d27MgecWFnj9ftc1QM99C9g1fD5pF0p/ItTii5fwbFsXKzdHsmOTOfX+b2hunvRf+n4Lbs1q0Xzh86h6AynnrhH2ylJyk9Pv+zPc77Z+y93y5d9mgkb35KEXH8bay4XsuBSitxwhbPLSMtlmAga2o/b4/qiqSvSWI8ZbwUrAo21daj3flz1PfUCNp7vmrXtz3letPZ3psuFdLBxtjY9sT89iU8cp6NIyC6x3397NqTvlcVSDSm5yOocmLiEjIqZC1nuvSaUbj0YT2AirrkNB0aA7vgPdnjVYBIcAoDsSikXTrlg06QwGPaoul9ytP2K4blz3ilcAVr1GomgtMCTFkrP2a8jOKHEGu6nLirj/9/+P6M6dKm5U+grkve1fs/y93a1IMQ9oC9gAoUAdYC/GnhUXVFUt+rlPd7ifIkVlu58iRWW7nyKFKL37KVJUtvspUlS2+ylSVLbyKlJUhPspUlS2oooUQgghRGUqbZHCHEiR4sFkrkWKYs/CVVWdqihKG0CjquouRVGCgEeBpUBJbpwTQgghhBBCCCEeTKpZnsv/v3XXR5CqqroHWKwoyhTgIvA50AGouOcbCiGEEEIIIYQQ4j/hrkWKm1oBVYHdwH4gEuOAmkIIIYQQQgghhBBlxpQiRS6QCdhiHJ/ikqrKQ1iEEEIIIYQQQghRtkwpUhzAWKRoAbQHhiqK8mu5phJCCCGEEEIIIcR/jimPrxitquqtZ3dGAf0VRXmqHDMJIYQQQgghhBBmQe4jqFj37EmRr0CR/7Xl5RNHCCGEEEIIIYQQ/1Wm3O4hhBBCCCGEEEIIUe6kSCGEEEIIIYQQQgizYMqYFEIIIYQQQgghxH+SalAqO8J/ivSkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMghQphBBCCCGEEEIIYRZkTAohhBBCCCGEEKIYqqGyE/y3SE8KIYQQQgghhBBCmAUpUgghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkHGpBBCCCGEEEIIIYqhqkplR/hPkZ4UQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWZOBMIYQQQgghhBCiGKqhshP8t0hPCiGEEEIIIYQQQpiFcu9JsderRXkvotzUm+pb2RFKzWLgi5UdodQU5cGtnanZ6ZUdodT0x0IrO0KpfewzqLIjlFr34LGVHaHUNh1ZUtkRSu3vBm9WdgQhhBCigI8/z6nsCKX2v6mVnUD8f/Lgng0KIYQQQgghhBDi/xUZk0IIIYQQQgghhCiGalAqO8J/ivSkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMghQphBBCCCGEEEIIYRZkTAohhBBCCCGEEKIYqlrZCf5bpCeFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWpEghhBBCCCGEEEIIsyBjUgghhBBCCCGEEMVQDUplR/hPkZ4UQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWZOBMIYQQQgghhBCiGDJwZsWSnhRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMgoxJIYQQQgghhBBCFENVKzvBf4v0pBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWZEwKIYQQQgghhBCiGKpBqewI/ynSk0IIIYQQQgghhBBmwWx7UjiFNKHq22NAqyFu1SaiFv1eYLpNkB/VP3oZuwZBXJ+/gujFfwKgWFtS57fZKFaWKFotiWt3E/nhjxWafdeVeN7fcQ6DqjKgni+jmlUv1ObgtUTe33kOnUHFxcaSrx9rBsDMLafYfjkON1srfh3WukJzA+zcH8a8z75BrzfwWN9ujBn2WIHpyalpTJ//GVcjo7G2suSdKS9Sq0Y1LkVc57V3Psxrd+1GNC+OHMJTj/eruOz7wpj72Vfo9QYG9u3OmOEDC2V/a95CrkZGYW1lxawpL1ErsBoA3//yF7/9swkFhVqB1Xh36stYW1tVXPYDR5n35XLjeu8dwpgnHrkjezrTP1rC1RvRWFta8s7ksdSqHpA3Xa83MOTlN/Fyd2XRrNcqLDfArjNXmf/XHgwGlUdb1mZUl+AC078NPcrasAvGnAaVSzFJbJv5JDaWFoz6Yg25Oj06g4FuDQN5oWezis1+7Bzzlq/FYDDwaEgzRvfrVGB6akYW0774haj4ZHQGA8/0aceAjrcz6g0Ghk7/Ai9XJz6b/FSFZgd4+Z0Xad2lJVmZ2cydNJ/zJy4U23b8rJfoPbgnvWvf3ieD2zTmpZnPo7WwIDkxmYmPT66I2Pf05pyP2L5rP26uLqxe8WWFLjvgsXY89JJxHenSszgy9RuST0UU277x7GeoNqQTfwWNKtFy/Pq1ou6rA3Gs5cu23m+RdPQSAHYBHnTf/gGp4ZEAJBy6wJGp35j0nlV6NqPe1EGoBgOq3sCxt5YTv//sA5H9FtfgQEL+eYd94z4lcs3+QtObL3oR18Y1MOj0JB4O5/BrX6Pq9PedHcCpbgBN3h+DpaMtqsHAtl5vYcjOvev7Nf14LD7dm5Adl8KWkKmFppd0e7qTlasDrb6agGtwEFd+2s7Rad/mTVMstQTPGYlH27pgUDk59yci/zlg8nuXJl9ZbzNQuvVekuxNP3oWl8aBKIpC2sUbHBz/JfqM7PvOXtrt/V7bjKWzPc0+Hot9dW/02bmETVpMyplrJue92zbT4fc3sfFyQZ+VA8CuIXPJjksx+b3zu9e+6tmuHg1nDEexsiDp2CXCJi1B1RtMfv+y3mZsfd1ovvB5rD1dQFW5tHwr4V+tL9TOo21dGr3zFBpLC7ITUtnx6CyTM5f3/novPWY+TVDnxuRm5rDm1cVEnbhcqE3zZ7rTYlQv3Kr78FHwODIT0wBoPa4vDfq3M2a10OBR04+PmzxHVnJ6mWYUwlTmWaTQaKj67jjODZtB7o146v7zPkkb95N1/vZBWpeURsT0r3Dt2arArGp2LmcHT8eQkYVioaX2H++RvC2M9LBzFRJdb1CZ++9ZvujfBG8Ha4b/fIBONTwIcnPIa5Oancucf8+w6JEmVHG0ISEjJ29avzpVeKKhP29tPlUheQtk1+uZ/clSlrw/Ax9Pd4Y8N4XObVsQlO9k+KuVv1GnZg0+mfU6FyOuMWfBUr766G1qVPXj168+ynufroOepWv7VsUtqlyyv/vJYpZ+8DY+nu488dxrdG7XskD2pSt+pU7NGnz67htcvHKN2Z8s5uuPZhEdG8/K39bw53cLsbG2ZvLM+azbuoMBvbtWUHYDsxd9y5L33sDHw40hL79F59ZNCarmn9fmqx//pE5QVT6ZMYmLEZHMWfQtX82bljd9xer11AjwJT0js0Iy52U3GHjvj118ObYP3s72DP90NZ3qVyPI2zWvzYiQxowIaQzAv6eusGL7cZztbFBVlaXj+mJnbUmu3sDIRX/Rvo4/jap5V1j2Od/9zeKpI/F2c2LY9C8JaVqXID+vvDY/bd5LoJ8XCyc/RUJKOv2nLKBv28ZYWhgPnSs37CHQ15O0TNO/8JaVVl1a4l/Dj+Htn6Fe07pMem8CL/R7uci2tRs9hIOTfYHXHJzsmTh7PFOefIOYyBhc3F0qILVpBvTpzrCBjzBt1gcVvuz0iBi2PzqL3OR0vLs0pskHYwjtM73Iti6Na2DpZFeq5aScucreUR/T5P3RhaalXYlma7dpRcx1dzE7TnBjwyHA+CW+1ZIJbOrwapFtzS07ABqF+m8OJTr0WLFNrv6+i4MvLgKgxRcvUX14Zy59t9nkRRSXXdFqaLHoRQ6+9DnJpyKwcnXAkKu75/td+Wk7F7/ZSLOFzxc5vSTbU1H02bmcmvcrTnX8caoTUGBanYkDyI5LZlO7yaAoWLk6FPMuxavs7b20670k2Y9NX4Euzfi3seHMJwka1YNzn/1939mhdNv7vbaZ2hP6k3TyCntHfYxDTV+C3xvBzkFzTH7/u20zAAdeXFTghL9U7rWvKgrNPn2enYNmk3YxirpTHqfq4I5cO8GsrwABAABJREFUWRVq8iLKeptRdQaOz1xJ0vHLWNjb0HnjbGK2Hyf13PW8NpZOdgTPHcmuofPIvB6PtYeTyXmh/PfXuwnq3Bi3Gj580Wkyvk1q0uvdkXw7YEahdlcPnuP8lsM8+eObBV7fu/gf9i7+B4BaXZvQckxvKVCISmWWt3vYB9ci+/INciKiUXN1JPy5E5ceBU94dfHJZBy9UOQVFENGFgCKhRbFQluhD7Y9EZ1CgLMt/s62WGo19KzlTejFuAJt1p2LpmuQF1UcbQBws7t9xb6ZnyvONpYVlje/42cuUNW3CgG+PlhaWtK7S3u27SpYHQ+/fJVWTRsBEFjVn+vRMcQlJBVosy/sOAG+3vj6eFFRjp85T1W/gtm37tpXoE34lau0vpW9mj/Xo25n1+n1ZGfnoNPpyczKwdPDreKynw2nqq83AVW8sLS0oHdIa7btOVQwe8R1WgU3MGav6sv16FjiEpMBiIqNZ8f+Iwzs3bnCMt9yIiKWAA8n/N2dsLTQ0jM4iNCTV4ptv+5wOL2a1ARAURTsrI3buk5vQGcwoCgVd7/fifBrBHi74+/lhqWFBb1aNyT00OkCbRQUMrKyUVWVjKxsnO1t0WqMh83ohGR2HDnLo50qtvfHLe16tGXDr5sAOBV2GgcnB9y8Cm+3Go2G594cy5ezlxZ4veuAruxYt5OYyBgAkuKTyj2zqZoHN8TZybFSlp1w8Dy5N7+YJRy6gG2VYo4FGoWG04dxYtaqAi9r7axp+vFYOq+fRZdNc6jyf+zdd3QUVRvH8e/spveekNASiPQaepFeFQERRVBBQBAVEVBAXkWlCdjFhhQL2BU7RZDeS4AQeg+Q3nvZ3Xn/WNhkSSEJJFnk+ZzjMbtzZ+eX4e5m55k7d4oZHZR2JpL0c1G3NXvBs8NW1wqBRbLA7AB1xvQh8u995MSnFNsm5t/Dpp+TDp0z/fvcanafrk1JOR5hOhOfm5QOhpt/d0jYc5Lc5PRil5fUn2oM6UjXtXPovnE+LRaNAU3hzz99Zg4J+06hL+Isca1hXTm1+A/jA1UlNzHtpnnLks9MBfWZ8u73smS/XqAA0Nrnf9+qqv5+sz7jck8AcduPAZB+NhKHGt6mg+Vb7TO3y83eqzYeThhy80g/Hw1A7NajBNzfBqi6PpMdm0zy0YuAceRN2pmr2Pu5m7Wp8WAHIv/eT9bVBACzUSaW8H4tyT29Qgj7ZTsAkYfOYufigJOPW6F2MccukXIlvtDzBTUc2IFjv+++rfn+C1RV+U/+Z6lKLFIoiuKkKMpDiqJMVhRloqIofRVFqfDChk01D3Kj8t9AudEJ2BT3h7MoGg0N179HsyNfkbr9CBmHzlRAyqLFZmTje634AODrZEtchvlZ1kvJmaTm5DF29UGG/7CPP0/e/i975REbn4Cfj6fpsa+3JzHxiWZt6tWpzcZtewA4euIMUdFxxMQlmLVZu2kH/Xp0rvjABcTGJeLn7WV67OvtSWxcEdm3X89++lr2eHy9PRn1yCB6PvwU3YY8ibOTAx1bt6i87AmJ+HkX2O9eHsTEJ5lnD6zJxp3GYYFHT54jKibe9G+z6LOVTB77KJpKPMC/LjY1Az+3/LMBvq6OxBZTec/K1bHr1BV6Nqltek5vMPDwu7/Q/Y2VtAsOoEnNyitsxSal4ufhanrs4+FCTJL5sNdhvdpxPjKOnhMX8tDMj5j2+H1orhUpFq1aw+RhfdAU8UWlMnj7eREXGWd6HBcVh7efV6F2g58cyM5/dpMYa/5+qBEUgJOrE+//9A5L1nxC7yG9Kjzznab28K7EbDpS5LI6o/sQtT6U7Nhks+frTxpE3I5jbO77KtuHzKXxrOFoHWzLtF3Hmt503zCfzr++imfbemVa179fK3ptf5sOq14idPLnd0x2Oz93/Pu35nwpR0UoVlpqPtSJmM1Hbkt2pyA/UFU6fjeD7v/MI/jZ+0u9bmkV7E/Owf5UH9ierQNeZ1PPmagGAzWHdCr1a10f0dBw2lC6/zOPNksnlfmsb0n5blRRfeZ27feSsgOEvD+e/kc/xbluNc4tX39bssOtvVeLk3IsAv/+rQFwb1EHh+pe2Pt73nKfuS7k/fF03zif+pMHlytfad6ruQlpaKy1uDULBCDg/rbY+xu/x1tCn3Go4YVb49okhp674bWrYe3mSOfVr9Bt/TxqDjV+l7XE9+uNnP08SI3M/z6eGp2Is697CWsUzcrOhjpdmnJybeFLeISoTMVe7qEoysPAS8ARoBuwC2gHLFIUZYSqqkdLWHccMA7gZbdmPOhYu4yxCn/pL9NgCIOB430mo3VxpM6yGdjVq0n2qdJfA1rR9AaVE7FpLBnUkmydnpE/H6Cpryu13Ms3jPJ2KWof33jcO2b4gyz4aDkPjZ1CcFAt6gcHYqXNr1vl5eWxZdd+Jj31WAWnNadSOPyN2ccOH8KCxcsYMuaFa9mD0Gq1pKSls3nnPtZ/vwRnJ0emvraIP//ZwoDeXSsne5H73Tz8mEcGsODTlTw04WWCA2tQv25trDQatu4JxcPNlUbBgew/UvmXCJWmz1y37fglmtf2xdUhv4in1Wj4ccoQUrNymPLVBs5GJ1LXr3JGsZRmv+86eob6Naux7OXRXI5NZPyCL2hZrxYHT17Ew8WRhoEB7D9xvlLyFhG20FM3njn39PWk631deGHolEJttVZa6jW9hymPvIStnQ0f//Ehx0OPc+XC1UJt70ZeHRtS69GubBv4RqFldr5uBAxoy/YHC1+r7NO1CX59WhI84T4AtLbWOAR4knYmslTbzY5JZl3I8+QmpePWNJB2X0xhY5dpZmeDSxK59gCRaw/g2a4+DacPZcfD5sPELTV70zlPGM/Sl/IsevMFTxK/5yQJe0/dluwaKy2ebeuxue+r6LNy6PTT/0g+coG4HcdKtf7N3NifvDs3xq1pIN3WGf8dtHY2ZZobQLHS4hDgScL+Uxx9fRV1x/enyWsjODDx09uSr6CK7DO3Y7+XlP26gy8sAY1C8/mjqD6wPZe+31rl79XinFr8B83mPkH3jfNJPXGZlPCLqDr9LfcZgP3PfEx2dBJWjna0Xf4CNYd2JuKn7WV6jdK+V/eN/4imbzyOxtaa2C1hqDrjfBRV3We0Dra0XTaZsFkrC/1bKVZa3JsGsn3ofLR2NnT96w0SD56xuPdrkdso4rtXsaPpShDcsyVXDpyWSz1ElStpTopXgHaqqmYqiuIFfKOqah9FUZoCS4AOxa2oqurnwOcAB6oPKvM7JDcqAZtq+WcEbfw8yYtOLGGNoulTM0jbHY5r1xaVVqTwcbQjJi3b9DgmPQdvR/MKsY+TLW52nthba7G31tLS343TCWlVXqTw9fYkOja/ChsTl4CPp/kBo5OjA3OnG697V1WVvo8+TUC1/DkEtu89RIN7gvDycKuUzNf5ensSHZc/+iYmLqHQJRtOjg7MnfE8YMzeZ9g4qlfzZef+QwRU88HDzXhWvce97Tl87GSlFSl8vTyILjAaJSY+EZ8b5gdwcnRg7ovjTdn7jnyBAD9v1m7dzeY9B9m+/zA5uXlkZGYxY+EnLJj+TOVkd3UkusCw1ZiUDLxvmPvgunWHz9G3RZ0il7nY29IqqBo7T16ptCKFr4cL0Yn5Q1VjE1PxcTO/xOD3baGMHnAviqJQ09eTAG93LkTGc/h0BFtCT7LjyGly8nRkZOXw8qc/8eaEoRWaedDIB7h/eH8ATh45jbe/t2mZdzVv4mPMRzUFN6pLQG1/vtnxNQC29rZ8s+MrRnQaSVxUPCmJqWRnZZOdlc2RvUep07DOXVmkCHqyF7VHGC+X2jViETYezrR85yl2DV9oHEp8A7cmtXEK9KX3nvcA4xDy3rvf5Z/2UwCFvWPeLzRMOeT98bg2rkV2TDK7RiwqNoshV0durnGbyWEXyLgUg1Mdv2KvIb8xe3ZMMmAcUu5Y2wcbD2ezYcWWmt3axYE2S4x/W2w9nPHt0RxVZyBq3YFC69Wf+iC2ni7seem9As/eWvasyETid58w7auYfw/j1jTwthQpXBrUKNyfFIj4cRvH5v9g1ta/XyvqTzVOWB06dWmx+y43MQ1dZjaRa4z75+qfe6g9vGup8lhSfy/rfi9rdjMGlSu/7yb4mfu59P3WW85e1v5eWrr0LGNR5Zo++z8gIyIOz/b1b6nPAGRHG0dp6jKyufzrLtxb1ClVkaI879XEg2fYNmg2AD5dmuBUp9q1JVX3XlWstLRbPpnLq3cSuabwpJVZkQnkJqahz8xBn5lD/J4TuDaqVaXv15KEPNGLFsOM/y6RYedx8c8flevi50H6DSOfSqPRgHYc+0Mu9RBVr6QihQJcLzFmAD4AqqqGKYpye8co3SDjyBnsAqthU8OHvOhEPAZ24vxz75ZqXSsPF1SdHn1qBoqdDS6dmhH9yeqbr3ibNPJ1JiIlk6upWfg42rL+TAxv9m5k1qZroDcLt51CZzCQp1cJj0nlseY1Ky1jcRrXr8ulq1FciYrB18uDtZt2sPCVyWZtUtMzsLe1wdraml/+3khI04Y4OeYXV9Zu2k6/7mUffnirGtcLJuKKefZFr5ifPU5NS8fezvZa9g2ENGuEk6MD1Xy8CTt+mqzsHOxsbdgbGkajekUfTFdM9iAuXY3mSnQsvp4erN2yh4UznjXPnp6Bva0t1tZW/LJ2MyGN6+Pk6MALo4fxwuhhAOw/cpwvf/670goUAI1qeBMRn8rVxFR8XBxZf/gc84cXnhsjLSuXg+ejzZYlpmdhpdXgYm9Ldp6OvWev8uS1CTYrJXtQABHRCVyJTcTXw4V1e47y5jPmRQY/Tzf2HjtHy3q1SUhJ52J0PNV93Jn0SG8mPdIbgP0nzvPVmp0VXqAA+O2rP/jtK+M1re26t2XwkwPZ9PtmGrZsQEZaRqFLOvZs2suDLR82PV576k9GdBoJwI71u5g09zm0Wg1W1tY0bF6fn5f+UuG/gyU6/8UGzn9hnN/DPsCTdismc+C5T0zXU98oeuNh1jTNf589cG7FtQM2iN0SRp0xfUyzurs2rkVK+CWzg46S2Hg6m66xdqjpg1OgHxmXYkuV3bF2fsHYrUltNNZWha57ttTsBYV8MJ6oDYeKLFDUHt4V365N2T50ntlwqFvNHrMljHuevR+tvQ2GXB1e7Rtw9vM1pVq3JMX1p7jtx2j/5VTOfr6WnPhUrN0csXKyN42EKY2ofw7h3aEBcTuP4925MamnS1dgtKT+Xtb9XtbsYHxfZFyMAcCvd0vSzkbeluxl7e+lZe3igC4rBzVPT+0R3YjfcxJdetYt9xlFq8Ha1ZHcxDQUKy3VerUgdlt4qTKV571q6+VCTnwqGhsr7nluAKfe/w2o2vdqy/fGkXbmKmeXFN0+av1Bms0fhaLVoLGxwr1lXc5+vpbUU1eq7P1akoNfb+Dg18Z/l7rdm9NqZG+O/7Eb/xZ1yUnLKnORwtbZnprtGvD7C7dvhMd/iVr6m9OI26CkIsUaYJ2iKFuBfsBPAIqieFDU9Ri3k95AxKtLueeb10CjJeGHjWSfvoz3Y30AiFu1HitvNxqueRutkwOqQcV37ADCu03E2tedwPcmgVaDoigk/rWTlH9L9wFyO1hpNEy/tx7P/H4IgwoDG1ajjqcTP4Ub70wytHF1gjwc6VDTk4e/24tGURjc0J+6nsbr+mesD+fg1SSSs/Po88UOnm4bxOCG/pWTXatl5vNjeXrabPQGA4P79aBuYE1+/MN4/ebDD/Th/KUr/O/ND9FoNNSpXZ03Xso/mM7KzmH3wSPMmvJ0peQ1y26lZeakpxj/0hvoDXoG9+tJ3cCa/PC78fZSjwzsy/mIK8yc/wFajYag2jWYPe05AJo2vIdeXTrw8FNT0Gq11A8OZOj9fSovu1bLzGdH8fTMhcb93rsLdWtX58e/jNd7Pnx/T85HRPK/tz417vdaAbwxeVyl5SuJlVbDjEEdmLB0LQaDysA29ajr58FPu42Xngxt3xCATeEXaX9PAPY2+ZPCxqdm8uoPWzEYVAyqSu9mQdzbsFYlZtfy8hP3M+GtrzAYDAy6N4S61X358V/jdZgP92jDuEFdefXzXxjy8mJUVeWFR/rg7lz0SJHKtmfTXtp2b8M3O74mJzuHhVPeMi1b8PU83nrpXRJuGFlRUMTZCPZtOcDyDUtRDQb+/m4tF05drITkN/fSawvYfyiM5ORUegx6jGfGPM6QAZXznmww5UFs3J1pvuBJAFS9gc19jLOgd/hmGqFTPjeNVijKifdW02z2E/TYvAAUhczLcex+vPBdSvz7taLZvJHYeLrQYdU0UsIvsfPRBXi1q0/DaUMx6PSgN3Bo2grykks37Dbg/jbUHNoZQ54OfXYe+8YvNi2z9OwlKZi9+aIxZF6Jp+tfxqH9kWv2c/LdX285e15KBmeWrKHburmoqkrMv4eJ3nj4ptlaf/oc3h0aYOPhTL/QxRx/6xc01loALnz9b7H9Ke30VY4t/JGO389A0Wgw5Ok5/PIXZBUxoV2f/R9g7WSPxsYK/74h7Bi2gLTTVwmf+x2tF0+g6ZwnyElILfVBXkFV3d/Lu99LnT02hVYfPo2Vsz0oCinHIky3Ca2q/n6zPuMcHECrxRNQ9QZST18hdIpx0uNb7TOZV+Lp+N0MNNZaFK2G2G3hXFi1qVT7urQK9pngZ+7Hr2cLFI3C+a82ErfT+L2gqvqMZ5t61BramZTjEXTfaLwM7tibP+IQYBx9cOHrf0k7E0nM5jB6bF6AalC5+M1m0+1fLeH9WpKzmw5Tp1tzntn2rukWpNc98uVL/D1tKemxybQa1Yf2T9+Pk7crT61fwLnNh/l7+jIA6vVpzfltR8mrgjuWCXEjpaTrlRRF6Q+0APapqrrh2nMK0EdV1cI3Fy5CeS73sBQNp1dOcaAiWA159uaNLFQlzM1aYdScO/caPn3YlqqOUG6KX+UVN263vg/e3i8qlWnD4aInZrwT/Nn4lZs3EkIIISrRCVvLvdvCzfzv0jd3bvhSONuwzx17TFuSusfXW+S/W4lHg6qqrgEeBVoqRvbAh0DhG+8KIYQQQgghhBBC3ILSnLJuC9TAeHeP/UAk0LEiQwkhhBBCCCGEEKJqKYrSV1GUU4qinFUUZUYRy0coihJ27b9diqLc8iRzJc1JcV0exgk07QE74IKqytQhQgghhBBCCCH++wyqRV4VUeEURdECHwO9gCvAfkVR/lBV9XiBZheALqqqJimK0g/jXT7b3sp2SzOSYj/GIkVroBPwqKIoP9/KRoUQQgghhBBCCGHR2gBnVVU9r6pqLvA9MLBgA1VVd6mqmnTt4R6g+q1utDQjKcaoqnr99hjRwEBFUR6/1Q0LIYQQQgghhBDCYgUAlws8vkLJoyTGAGtvdaM3LVIUKFAUfG7lrW5YCCGEEEIIIYQQVUNRlHHAuAJPfa6qasHbtxV1nUuRdzpRFKUbxiJFp1vNVZqRFEIIIYQQQgghxF1J/Y/OSXGtIFHSPeWvYLyJxnXVMd5Iw4yiKE2BZUA/VVUTbjVXaeakEEIIIYQQQgghxN1lPxCsKEqgoig2wDDgj4INFEWpCawGHldV9fTt2KiMpBBCCCGEEEIIIYQZVVV1iqI8B6wHtMAKVVWPKYry9LXlnwGzAE/gE0VRAHSqqra6le1KkUIIIYQQQgghhBCFqKq6Blhzw3OfFfh5LDD2dm5TihRCCCGEEEIIIUQxVMN/c04KSyVzUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIMieFEEIIIYQQQghRDFWt6gR3FxlJIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiJIkUIIIYQQQgghhBAWQSbOFEIIIYQQQgghiqEalKqOcFeRkRRCCCGEEEIIIYSwCFKkEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsgqKqaoVuYLXf8IrdgBBCCFFGA8LnVnWEcst6+emqjlBuSQcNVR2h3O7k65Ez02yqOkK5Rac7VnWEcqtfJ66qI5RbzQOnqzpCue3xaV3VEcrteyv7qo5Qbm9f/O7O/ZAshfCg+/+Tx7SNz/9lkf9uMpJCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIUqQQQgghhBBCCCGERbCq6gBCCCGEEEIIIYSlUlWLnLrhP0tGUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIMieFEEIIIYQQQghRDFWt6gR3FxlJIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiJIkUIIIYQQQgghhBAWQSbOFEIIIYQQQgghimFQlaqOcFeRkRRCCCGEEEIIIYSwCFKkEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsgsxJIYQQQgghhBBCFEOVOSkqlYykEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsghQphBBCCCGEEEIIYRFkTgohhBBCCCGEEKIYqlrVCe4uMpJCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIUqQQQgghhBBCCCGERaiyOSmc6voT8v543JrU5viCHznz6d9Ftrv3t1lYOdkBYOvlStKhc+x58t1SbydgQFsavDgE52B/Nvd7leQjFwBwqOFFr21vk3YuEoDEg2c5PH1FqV6zxoMduee5AQDoMrI5PH0FKccjCrVr+e5TuDULQlEU0s9HceD5z9Bn5twR2QEazniYgAFtUfUGLny1kXPL10v2SsgO0GzeSGoN68IfdUaXOndFZS/te9W7UyOazBoOGgVdRg4HJ31GxsWYKs1+p75X7f09aLV4ArbebqCqXFi5iXPL1pm1qdYnhIbTh6IaDKh6A2GvriRh36lSZ7Zxd6Ltskm4N6/DpR+2cWTml6ZlnVe/gp2PG/rsXAB2DltATnxqqV/7Tu7vt+qV+e+ybec+PNzd+G3VZ5WyzdLSNmqF3bAJKBoNudvXkbvuhyLbaWrfg+PLH5C1ZD660O1gZY3jtHfAyhq0WnQHt5Pzx8pKzW7XoTUeLz4DWg3pv64l9cvvzZY79uuOy6hhABgys0ic/wF5Z87nN9BoqLbqE3Rx8cRNeqUyo2PfoRUe054BjTF7yhfm+92xf3dcRz0CgCEri4R5H5J3+obs336MPjae2OdfrczoON4bgt+r41C0GpJ++IeEJT+ZLbcJqo7/whewa1SXuHe/JmHZatMyjycH4fZwb0Al59QlIqe9h5qbV6F5Pbo1I3jukyhaDVHf/Mulxb8XahM870k8e7TAkJXD8ec/If2o8XPDysWB+u8+jWP9GqCqnJj8KakHztDo8xdwqONvaqNLzWR/j2kV+nvYtm2N6wvPgVZL5p9/k77yO7Pl9r174vSYsb+rWVkkv/U+urPnAHB85CEcBtwHqOSdO0/yvIVQwfv9Ru+9O5t+fbuTmZXFmDGTOXQ4vFCbLZtW4+TsBICPtyf7DxxmyENj6HJve1b/soILFy8D8Ntva5g77/1Kye3StQU13xgLWg3x320g+uPVZsvt6gRQ+92JODSuw9VFq4hZYuxfiq019X+Zh2JjjaLVkrRmF5HvfF/UJirUwNdG0qBbc3KzcvnhxU+5euxioTbD33+W6k2CMOj0RBw5x88zl2HQ6U3LazQNYuKvc1j13AeErd1Xiektn0FVqjrCXaXKihR5yemEvfIV1fq2KrHdtkGzTT+3XfYCUesPlmk7qScvs2f0e7R4a0yhZemXYtjUc2aZXg8gIyKWbYPnkJeSgW/3ZrR4eyxb+s8q1C5s1ip06VkANHn9MeqM7s3pj/68I7LXGtYF+wBPNnR6EVQVWy+XMm1HspcvO4Bbs0CsXRzKvA2omOylfa82XziaPaPeIe1MJEGjelJ/8iAOTlpSpdnv1PeqqjNw9PVvSD56EStHO7r9M4/YbUdJO33V1CZ2e7jp89ClQQ3afj6JDZ1fLPU29Dl5HF/4My71q+NSv0ah5fuf/dh00F9Wd3J/v1WD+vdi+JAHmDnn7UrfdokUDfbDnyPjvRmoSfE4/m8xuiO7MURFFGpnN2QsumMF/tbq8sh4ZxrkZINWi+O099CF70d//mTlZNdo8Jg+kdhnpqOLiaPaqo/J2rqLvAv52XVXo4kZOwVDWjp2HVrj+cpkokdONC13fnQweRciUJzK19duKfvLE4l5ejq6mHj8v/mIzK27yTtvnj16zFQMaenYd2yN16svEPX486blLsON2TWOlZ+92usTuDTyFfKi4wn69T3S/t1D7tnLpib6lDSiZy/BuXd7s1WtfD3xGDmAc30moObkEvDhDFwGdCHll40VmFeh3oIxHHp4LjmRCbRa/yZx6w+QWeBz07NHCxwC/djT7nlcQoKpt2gsB/v9D4DguU+SsPkw4WPfRbHWorW3BeDYuPdN69d9/XF0qZkV9zsAaDS4vjiJhEkvoY+Nw3v5Z2Rv34Xu4iVTE11kFPHPvoCalo5tuza4TZ9K/FPPoPHywnHog8QOHwW5ubjPeQ37nt3JWlP6EzW3ql/f7gTXDaR+w060bdOSjz96kw6dBhRq17X7g6aff/zhc/748x/T4x079jFw8MhKyWui0VBz7nhOD3+NvKgEGvz9Fsn/7CP7zBVTE11yOhGzluHep63ZqmpOHqcenoUhMxvFSku9X98kZXMoGaGnKy1+/a7N8Q70Y0HXydRsUZch88bw4aDCRc3Q33by7QsfAzDiw4m0HdaN3auM70tFo3DfjOGc2nak0nILUZwqK1LkxKeSE5+KX88WpWpv5WiHd6dGHHzBeNCjdbCl2byRuDaogaLVcuLtX4osYKSdibytuQESD5zJ//ngWeyreRTZ7vpBD4DW3ib/5zsge+DInuyf8JFpKtvrZ1Ele8VmR6PQZNZw9k/4GP/+rU1PV2X2Ur9XVRUrJ3sArJwdyI5OBu6M/W5p79Xs2GSyY5ON2TKySTtzFXs/d7MiRcGRHlYOdqgFpp2uMaQjdcb2RWOtJSn0HIdmrACD+bTU+swcEvadwjHQ97Zmhzu7v9+qVs2bcDWq9COIKos2sB6GuEjU+GgA8vZvxap5B3JvKFLYdB9I3sHtaAPrmb9ATva1F7ICrRYqcZZzm8b10F2JRHc1CoCM9Vuw79rRrEiRE3bc9HPu0RNofb1Nj7U+Xth3bkvq8m9xfuyhygsO2Dauh+5yJLqrxv2esX4LDl07kFKgSJFzJD97TljR2VOWfYvL40MqLzhg3+weci9FknfZmD3lr20492xHQsEiRUIK+oQUnLu1LrS+YqVFsbNB1enQ2Nuii0mo0LwuLeuSeSGa7EuxAMT+tgvvvq25VOBz06tvK6J/2gZA6sEzWLk4YuPjhj4zB7f2DTjxvPHgTc3To8srXIzweaA9h4bMLvT87WTdsD66K5HoI439PWvjJuw6dyS9QJEiL/yY6efcY8fR+niZHitaLYqtLapeh2JniyG+Yvf7jQYM6MPKb34GYO++UFzdXPHz8yE6OrbI9k5OjnTr2pExY6dUZsxCHJsHk3MxitwI4+d34u87cOvdluiCRYqEFHQJKbj1KHzSxpBp/IxUrLQoVtpKvxVEo94hHFi9HYCIQ2exc3bA2duNtLhks3Yntxw2/Rxx5Cyufvl/nzuN6kvY2r3UaFanMiILUaJiixSKolgBY4DBgD/GrySRwO/AclVVK3XsmH//1sTtCDcdTNSfNIi4HccInfw51i4OdF07h9jt4WUaou1Y05vuG+aTl57F8QU/krC39EOlr6s9vCsxm4qvOIa8Px7fHs1JO32Fo6+vumOyO9byofrAdvj3b01OQipH/vc1GReiJXsFZ68zug9R60NNB6jXWUr2koROXUqHb6ZhyM4lLz2LLf1fs6jsd+p71aGGF26Na5MYeq7QMv9+rWg0cxi2Xi7seuwtAJyD/ak+sD1bB7yOqtPTfMGT1BzSiYiftpdpuyHvj0fVG4j8ez8n3/u1TOsW9F/t73caxc0LQ2Kc6bGaFIc2sP4NbTyxatGRzHemFS5SKBocX/0Yjbc/uVv+QH+hkkZRAFbeXugKHODoY+OwaVy/2PZOg/qRtTN/mLL7i8+Q/MFSFIdKHomAscigi87f77qYeGyblJB9cF+yduw3PfZ4aQJJ7y9F42hfoTmLYuXrSV5UvOmxLjoe+2b1Slgjny4mgYRlq7ln+5cYsnPJ2BFKxo5DFRUVAFs/D3Ii8w/IcyITcGkZbN6mmgfZV/N/p5yoBGyreaDq9OQlpNLgg2dwalSLtLDznH7lSwwFPnPc2jUgNy6FrAvRFfp7aL290McU6O9xcdg0bFBse4f7+5O929jfDfHxpH/3I76//oCak0POvgPk7DtQoXlvFODvx5XL+QXkq1eiCPD3K7ZIMWhQPzZt3klaWrrpuXbtQjh4YANRkdFMmzGH48crfkSCTTUPcgv099zoBJxaBJewxg00GhqufQfb2n7EfrWWjENnbr7ObeTq60Fygf6fEp2Iq59HoSLFdRorLSGDO/P7G18B4OLrTuM+rfns0TlSpBAWoaSRFCuBZOB14HoZsTowElgFPFLcioqijAPGAYx3bk1vh7q3HLT64PZc/Gaz6bFP1yb49WlJ8IT7ANDaWuMQ4FnqM2vZMcmsC3me3KR03JoG0u6LKWzsMs3sjOrNeHVsSK1Hu7Jt4BvFtjn4whLQKDSfP4rqA9tz6futd0R2ra01+pw8Nvd5Bf/+rQl5bxzbBs2W7BWY3c7XjYABbdn+4JxCyywh+83UHdePXSMWkXToHMHP3E/TNx4jdOpSi8h+p75XtQ62tF02mbBZK4tcJ3LtASLXHsCzXX0aTh/Kjofn4925MW5NA+m2ztiPtHY2ZZpPAmD/Mx+THZ2ElaMdbZe/QM2hnctc5ID/dn+/4xR5Ka35mT67RyaQs3oZqIYimhrImD0B7B1xeOY1NP61MURerIikhSlFhC/mJKVtq2Y4DepL9OjJANh3boshMZncE2ewDWlWgSGLUWT2osPbtWqG06B+RD/5AmDMrk8yZrdr1bQCQxajqOylpHFxwrlnO850HY0+NYPqH72M68BupPy++eYrl1cRedVCHaXofw/FSotTk0BOz1xBauhZgueOotbEQVxYmD9/iM/gjsT8uvM2hy5KEb9HMX3GpmVzHAb0J/5p4+VBirMTdp07EPvQoxjS0nGf9zr2fXqStb4CL7O5gVLUv0MJowqGPTyQ5V/kz7kReugoQXXbkJGRSb++3fnlpxU0aNSpQrKaKyp3GVY3GDjeZzJaF0fqLJuBXb2aZJ8qfi6m262s+/3BOaM5v+8kF/YbC/gDZz3B3wu+RTVU7ggQIYpTUpGipaqqN5bMrwB7FEUpsaSpqurnwOcAq/2Gm3p70JO9qD2iGwC7RiwiOya5VCFt3J1wb16HPU++V+BZhb1j3if9XJRZ25D3x+PauBbZMcnsGrGo2Nc05OrIzTVWbZPDLpBxKQanOn7FXoN9Y3YbD2davvMUu4YvJDcpvch18jemcuX33QQ/cz+Xvt96R2TPikwk8m9jZT5yzX5C3h9/bYlkr6jsbk1q4xToS+89xn6utbeh9+53+af9lCrPfrP3qo2nM66NapF0yHi2/8rvu+n43fRrSy17v5tvrGrfqwUpVlraLZ/M5dU7iVyzv8S2CXtO4ljbBxsPZ1Ag4sdtHJtvPjmff79W1J9qvAY4dOrSEjNkRycBxktNLv+6C/cWdW5apLiT+/vdQE2KR+ORfxmB4u6NITnRrI229j3YP2Wcx0NxcsWqcRuyDXp0h3flN8rKQHc6DKvGrcitpCKFLjYOKz+f/Jw+3ujjCg9htw4OxPPVqcROfBlDirEwZ9usMfZd2hPQqQ2KjQ2KowOec2eQ8MqCSsmuj4nDyi9/v1v5ehWf/bUpxDw7E0NKmjF780Y4dGmPQ4HsXvOmE/+/hZWSXRcdj3W1/MsIrPy8yCvlJRuOHZuTezkGfaLx3yFt/S7sWzao0CJFTlQCtv6epse2/p7kXvssK9jGLsCLFIwHZrbVPMmJTgJVJScygdTQswDE/rmHWhMHmdZTtBp87mvD/l4zKiz/dfq4OLS+Bfq7t3eRl2xY1QnC7eUXSZgyAzX1Wn9vFYIuMhpDcgoA2Vu2Y9OkcYUXKSY8PZIxY0YAcODAYarX8DctC6hejchiLoHz8HCndesWDBk61vRcwREVa9dtYvGH8/H0dCchIamol7htcqMSsCnQ3238PMmLTixhjaLpUzNI2x2Oa9cWFV6k6PB4L9o+2h2Ay0fO41ag/7v6eZAaU/Q+6zVpCE6eznw1fpnpuRpNg3hssbHY5ejuTIOuzdHrDRz7p3JH4lgyVSbOrFQlFSmSFEUZCvyiqsbTKoqiaIChQLk+Kc5/sYHzX2wo83oBA9oSvfEQhpz8K0xit4RRZ0wf04z0ro1rkRJ+yTRnxc3YeDobvzgbVBxq+uAU6EfGpaKHot2Y3T7Ak3YrJnPguU9IP1/8sD/H2r6muxv49W5J2tnIOyZ75LoDeHdqxKXvtuLVoQHp56MkewVnj954mDVNnzE9fuDcimsHbFWbvTTykjOwdnbAKciP9PPR+NzbhLTTd05/t5T3akEt3xtH2pmrnF2y5qaZ3ZrURmNtRW5iGnHbj9H+y6mc/XwtOfGpWLs5YuVkbxp1cTOKVoO1qyO5iWkoVlqq9WpB7LbCM7Pf6E7u73cD/cVTaHwCULz8UJPisW7dhaxl5gfq6S8/YfrZ7skX0R3Zi+7wLhQnV1S9DrIywNoGqwYtyF33Y6Vlzz12CqsaAVj5+6GLjcexT1fiZ843a6P188H77ddJeHUBuoj8OQiSP1pO8kfLAbANaYbLE0MrrUABkHPsFFY1zbPHzXzzhuze+LzzGvGvLDTPvngFyYuNd6Wxa9UUlyeGVlqBAiAr7DQ2tQOwru5LXkwCrvffy9XJb5VqXV1kHPbN66HY2aJm5+DYoRlZR89WaN60Q+dwCKqGXU1vcqIS8RnUgeMTPjRrE7/+ANVH9yXm1524hASjT8sk99rlZjmRCTjUqUbmuSg8Ojch43T+XATu9zYh40wkOVFlP2gtq7wTJ7GqHoC2mh/6uHjse3Yn6fW5Zm20vj54vDmbpDfeRH85P6c+JhabRg2Nc1Lk5GDbqiV5Jyv+UrdPP/uKTz8zXjbQv18Pnpkwih9++J22bVqSmpJa7KUeDw25n7/XbCQnJ/+yGl9fb2JijJdItW7VHI1GU+EFCoCMI2ewC6yGTQ0f8qIT8RjYifPPle5uglYeLqg6PfrUDBQ7G1w6NSP6k9U3X/EW7Vq5gV0rjX93G3RrQceRvTn8xy5qtqhLdlpmkZd6tHmkG/Xubcpnw+eajbSY33mS6edH3n6aE/+GSoFCVCmluKFAiqLUBhYC3ckvSrgBm4EZqqqW6pRUwZEUBdl6u9J9/VysnO1RDSr6jGw23Gscjtvhm2mETvncdPa28+pXOL34D2I2h5nW19hZ02z2E3i0DgZFIfNyHLsfLzybun+/VjSbNxIbTxfyUjNJCb/EzkcX4H9faxpOG2q87Y7ewPG3fiF6Q2hpfiVavvMU/ve1IfOK8UNU1RvY3Md4SzNT9tgUuvw+Cytne1AUUo5FcHj6CnTpWZafPSYZaxcHWn/yLPYBnugycjg8bTkpxyMkewVnL+iBcytMt2Ssyuylfa/692tFg2kPoRpU8lIyOPjC52RGxFr+frfA96pnm3p0+eM1Uo5HoBqMQ++PvfkjDgHGsyQXvv6Xe54bQM2hnTHk6dBn5xE++1vTLUgDBraj3sQHUDQaDHl6Dr/8BUmhhQ8Q+uz/AGsnezQ2VuSlZLBj2AIyr8Rz76+z0FhrUbQaYreFE/baykITb97yfq/i/j4gfG6h17sdXnptAfsPhZGcnIqnhxvPjHmcIQP63NZtZL38dLnWs2rcGtthE1AUDbk715O75jusuxgvqcnban5rYVORInQ7moBA7Ee/BBoNKBryDmwl969vypUh6WARl5KUgl3HNsZbkGo0pP+xjtTl3+I05H4A0n/5C49Xp+DQozP6a2dsVb2e6MeeNXuN60WK8t6CVDWU7yyafac2eLw0wZj99/WkLPsW54eM2dN+/gvPWVNw6NkJfZTxQE7V6YkaYZ79epGivLcgzUyzuXmjIjh1bYXvK+NQNBqSf95A/Cc/4P5oPwCSvluL1sudoN/eR+PkAKoBQ0Y25/o+jSE9C+9JI3C5rzOqXk/2sfNEzfwANVdX5gzR6Y6lbuvZowXBc0aiaDVEfreZS+//iv8TvQCI/Np4MHfPm2Pw7N4MfVYuJyZ9QtoR4+1enRrVov67T6OxsSLrUiwnJn2CLiUDgAYfPEPKwTOm1yit+nXibt6oCLbt2+I66VnQasj8ay3pX32DwyDjHTIyf/sT1xkvYt/1XvTR+f09fozxc8F5zCjsenYDvZ6802dIfvNtyCv7NHI1D5R/HogPP5hHn95dyczKYuzYKRwMNX5///P3rxn39EtEXXuf/rvhJxa99THr/9liWveZCaMYP/4JdDo92VnZvPjSG+zeU7aD5T0+hSdyLQ3X7iHUeH00aLQk/LCRqMU/4/2Y8fM7btV6rLzdaLjmbbRODqgGFUNmFuHdJmJbw4fA9yaBVoOiKCT+tZOo98tXyP3eqvzzzwye/ST1ujQjLyuHH15awpWjxr495otp/DR9KamxSSw8u4qkq/HkZBgvfwxft58NH5oXVK4XKcp6C9K3L373nx5qsD9g8H/yWpjWV3+1yH+3YosUZo0UxfNa2/ibNr5BcUUKIYQQoqpUVJGiMpS3SGEJyluksATlLVJYgvIWKSxBWYoUlqa8RQpLcCtFiqpW3iKFJbiVIkVVkyLFnclSixSlugWpqqpmF8MpitJLVdWyX7chhBBCCCGEEELcQQwyJ0Wl0pRzveW3NYUQQgghhBBCCCHuesWOpFAU5Y/iFgGexSwTQgghhBBCCCGEKJeSLvfoDDwG3HjvOAVoU2GJhBBCCCGEEEIIcVcqqUixB8hUVXXrjQsURan4+xkJIYQQQgghhBBV7D85a6YFK7ZIoapqPwBFURqqqnr8hsWzKjSVEEIIIYQQQggh7jqlmTjzR0VRpitG9oqiLAberOhgQgghhBBCCCGEuLuUpkjRFqgB7AL2A5FAx4oMJYQQQgghhBBCiLtPSXNSXJcHZAH2gB1wQVVVQ4WmEkIIIYQQQgghLIBBVao6wl2lNCMp9mMsUrQGOgGPKoryc4WmEkIIIYQQQgghxF2nNCMpxqiqeuDaz9HAQEVRHq/ATEIIIYQQQgghhLgL3XQkRYECRcHnVlZMHCGEEEIIIYQQQtytSnO5hxBCCCGEEEIIIUSFK83lHkIIIYQQQgghxF1JlYkzK5WMpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZE4KIYQQQgghhBCiGIaqDnCXkZEUQgghhBBCCCGEsAhSpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLILMSSGEEEIIIYQQQhRDRanqCHcVGUkhhBBCCCGEEEIIiyAjKYQQQtx1sl5+uqojlJv9m59VdYRysz64pqojlJ9OV9UJyk1x867qCOUWePpIVUcoN/3R7KqOUG7Wh+7cQ4SG0/2rOkK5Rb4dVdURhLAIMpJCCCGEEEIIIYQQFuHOLZMKIYQQQgghhBAVzKBWdYK7i4ykEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsghQphBBCCCGEEEIIYRGkSCGEEEIIIYQQQgiLIBNnCiGEEEIIIYQQxTCgVHWEu4qMpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZE4KIYQQQgghhBCiGKrMSVGpZCSFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERpEghhBBCCCGEEEIIiyBzUgghhBBCCCGEEMUwVHWAu4yMpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZE4KIYQQQgghhBCiGCpKVUe4q8hICiGEEEIIIYQQQlgEKVIIIYQQQgghhBDCIkiRQgghhBBCCCGEEBZBihRCCCGEEEIIIYSwCDJxphBCCCGEEEIIUQxDVQe4y1RZkcKprj8h74/HrUltji/4kTOf/l1ku5APxuPVvgF5qZkAHJy0hJRjl0q9nYABbWnw4hCcg/3Z3O9Vko9cAMChhhe9tr1N2rlIABIPnuXw9BWles0aD3bknucGAKDLyObw9BWkHI8o1K7lu0/h1iwIRVFIPx/Fgec/Q5+ZU6XZq/UJoeH0oagGA6reQNirK0nYd6rItg1nPEzAgLaoegMXvtrIueXrJXsFZw8a3Zu6T/XFKdCPvxqOJzcxrdS5Kyp7afs73Ln7vdXHz+LeLBCDTk/SoXMcemk5qk5fZdnt/T1otXgCtt5uoKpcWLmJc8vWFdnWvXkQXf+ezd7xHxL5175SZ7Zxd6Ltskm4N6/DpR+2cWTml6ZlnVe/gp2PG/rsXAB2DltATnxqqV+7LO9VgGbzRlJrWBf+qDO61NuAiukzN6Nt1Aq7YRNQNBpyt68jd90PRbbT1L4Hx5c/IGvJfHSh28HKGsdp74CVNWi16A5uJ+ePlbcl0+3yyvx32bZzHx7ubvy26rOqjmNm5/FLLFq9DYNBZXD7hozu1cps+Zf/hrLmgLGP6Q0GLkQnsXn+WJLSs5j2Zf5752p8ChP6t+Oxbs0rL/uJCBb9tsOYvV0DRvdoaZ590yHWhJ7Jzx6TzObZo3B1tKPfnFU42lqj0ShYaTR8O+WhSssNsPPoWRZ+ux6DamBw5xaMua+T2fK0zGxmLv2V6IRUdAYDI/u0Z1Dn5kQnpvC/Zb+RkJKBoig81KUlI3q1rdzs52NYtPEoBgMMblaT0e3vKdRm/6V43vr3KDqDiru9DctHGH+/1Ow8Zq89xNm4NBQFXu/fgmYBHpWWXVu/JbaDxoJGS96ef8jb9EuR7TQ16mI/6S2yv34LfdguAKzvfQCrdr1BVTFEXSLn+w9Al1dp2QHeeed1+vTpRmZmFuPGvcjhw+GF2mzc+BNOTo4A+Ph4ceDAYR5+eBzDhg1iypSnAcjIyOT55//H0aMnKiX3zksJvLX9NAZVZVBDf0aH1C7U5sCVJN7acRqdQcXNzprlD4YA8Pq/x9l2MR4Pext+Ht6uUvLe6InXx9C8Wwi5WTl89uJiLoafL9TmqUXPEtSkDoqiEHUhks+mLiYnM5sG7RoxdenLxF6OBWD/uj38+uGPlf0rCGFSZUWKvOR0wl75imp9W9207dHZ35bpy3dBqScvs2f0e7R4a0yhZemXYtjUc2aZXzMjIpZtg+eQl5KBb/dmtHh7LFv6zyrULmzWKnTpWQA0ef0x6ozuzemP/qzS7LHbw4lafxAAlwY1aPv5JDZ0frFQu1rDumAf4MmGTi+CqmLr5VKm7Uj28mVP2HeK6A2hdF79apm3AVXb3+/k/X559U4OPPsxAK0/fY7aI7px4auNVZZd1Rk4+vo3JB+9iJWjHd3+mUfstqOknb5q3lCj0OiVR4nZElam1wfQ5+RxfOHPuNSvjkv9GoWW73/2Y9NBf1mVdr8DuDULxNrFoVzbqYg+UyJFg/3w58h4bwZqUjyO/1uM7shuDFERhdrZDRmL7tjB/Od0eWS8Mw1yskGrxXHae+jC96M/f/L2ZrwFg/r3YviQB5g55+2qjmJGbzDw5k9b+OzZQfi6OTHi7R/o0jiIOtXyDxpH9WjJqGsH/1uPXmDVlsO4Otrh6mjHj9MfNb1O71e/oHuzoMrNvno7nz09AF9XR0a89wtdGtWmjl+B7N1bMKp7C2P2YxdZtfUIro52puVLn3kAdyf7SstcMPv8VWtZMvUxfD1cGD57GV2b16NOgLepzQ+b9hPk783iSY+SmJrBwP99zH3tm6DVaHjxkd40qFWNjKwchs1eSruGQWbrVmx2lTf/CeOzYR3wdbZnxJdb6RLsR50Cf5dSs/N4858jfPxwe6q5OpCYkX8SadHGo3QI8uXtwW3I0xvIyit90fqWKRpsHxxP1mezUFMSsJ/8Drpj+1BjLhdqZ3P/KPSnDuU/5eqBdecBZC56FvJysX1iGlYtOqPbv6nS4vfp0406dQJp3LgLbdq04MMP53LvvYMKtevZc6jp5++++4w///wHgIsXL9O798MkJ6fSu3dXPv74zSLXv930BpUFW0/x6cAW+DrZMuLH/XQJ9KKOh5OpTVpOHvO3nuTjB1pQzdmOxMxc07IB9avxSJPqvLrxeIVnLUrzbi3xC/RnSpdnqNviHkbPHc+sQdMLtVs1ewVZ145NHnv1SXqP7M+fn64G4OT+E7w9el6l5haiOFVWpMiJTyUnPhW/ni3Ktb7WwZZm80bi2qAGilbLibd/MX0pLijtTOStRi0k8cCZ/J8PnsW+WtHV9esFCgCtvU3+z1WYveBIDisHO1RVLbJd4Mie7J/wEVxbfv0sqmSv2Owp4UWPEroT+vudvN9j/j1s+jnp0DnT71hV2bNjk8mOTQaMo1fSzlzF3s+9UJGizpg+RP69D/fm5gddNYZ0pM7YvmistSSFnuPQjBVgMP/d9Zk5JOw7hWOg723Nfv21rytpv6NRaDJrOPsnfIx//9amp6uyz5REG1gPQ1wkanw0AHn7t2LVvAO5NxQpbLoPJO/gdrSB9cxfICf72gtZgVYLxeyWqtKqeROuRsVUdYxCwi/FUMPbjepergD0aXkPW46eNytSFLQ29DR9Q4ILPb/31BWqe7ni71G2AuqtCI+IpYaXK9U9jdvs06IuW8IvmhUpClobeoa+LQpnrwrh569Sw8ed6j7uAPRt24gth0+ZFRoURSEzOxdVVcnMycXV0R6tRoO3mzPebs4AONrbElTNi9jk1EorUoRHJVHD3ZHqbsYz9X0aBrDlTLRZkWLt8St0r+dPNVdjkdTD0RaA9Jw8Qi8nMOc+4/dTa60Ga23lTeGmqRmMIT4KNdH4XtQd2o5V47bk3VCksO58P/qwXWhq3NBfNBqwtgG9DsXaFjUlsbKiA3D//b349lvjyI99+w7h6uqCn58P0dGxRbZ3cnKkS5cOjBtnLGTv2ZP/Wb9vXygBAdUqPjQQHpNKDVd7qrsaC4J9gn3Zcj7erEix9nQMPer4UM3ZWET0cMj/bh8S4E5kahZVJaRXG7b/shmAs4dO4+DiiJuPO8mxSWbtsgocm9jY2pi+swlhae6IOSkazXiYBlMeJHZ7OMfmfY8hV0f9SYOI23GM0MmfY+3iQNe1c4jdHl6myykca3rTfcN88tKzOL7gRxL2Fj8cuTi1h3clZtORYpeHvD8e3x7NSTt9haOvrwKo8uz+/VrRaOYwbL1c2PXYW0W/fi0fqg9sh3//1uQkpHLkf1+TcSFasldw9uJUdfbrSurv/4X9rlhpqflQJ468+jVgGfvdoYYXbo1rkxh6zux5Oz93/Pu3ZvuQuYQ0H2d63jnYn+oD27N1wOuoOj3NFzxJzSGdiPhpe5m2G/L+eFS9gci/93PyvV/LtC6Ubr/XGd2HqPWhpoLMdZaw34uiuHlhSIwzPVaT4tAG1r+hjSdWLTqS+c60wkUKRYPjqx+j8fYnd8sf6C9YzigKSxabnIGfW/6Bgq+bE0cvRRfZNis3j10nLvHyQ10KLVsfepp+RRQvKlJsSgZ+1w6UAXzdHDl6qeiDtazcPHadvMzLD3Y2PacoMGHJXygKDGnfiIfaN6zwzNfFJqfh5+Fqeuzj7sLR8+aF0mHdW/P84u/pOeU9MrJzWPT0EDQaxazN1fhkTkZE0ySoeqXkBohNy8bPOX/0ia+zPUcjzQ/WLiWmozMYGPPNDjJzdQxvFcSAJjW5kpyJu4MNs/4+xOnYVBr6uTKtZxPsbSrnK7Pi6omaHG96rCbHo6lV74Y2Hlg1aUfWJ69g+0h+n1ZTEsnb8huOry6HvFx0pw6hP324UnJf5+/vx5Ur+QXkq1ej8ff3LbZI8cADfdiyZSdpaemFlo0aNYz167dUVFQzsRnZ+Drnj2DydbIlPMb8MsdLyZnoDAbGrj5IZp6eR5vVYED9yimi3Iy7nyeJkQmmx4nRCbj7ehQqUgCMf+s5mncL4crZy6ya+4Xp+eCW9Xhz7bskxSbxzdwvuXrmcqF172YyJ0XlKtcnrqIon6uqOq6E5eOAcQDjnVvT26FuOePBsXk/kB2bjMbGihZvj+We5wZw8t1f8enaBL8+LQmecB8AWltrHAI8S31mLTsmmXUhz5OblI5b00DafTGFjV2mmY1+uBmvjg2p9WhXtg18o9g2B19YAhqF5vNHUX1gey59v7XKs0euPUDk2gN4tqtPw+lD2fHw/EJttLbW6HPy2NznFfz7tybkvXFsGzRbsldw9uJUdXa4eX//L+z35gueJH7PSdMBbVVn1zrY0nbZZMJmrSy0TtM5TxA+57tCIyS8OzfGrWkg3dbNMb6GnU2Z5pMA2P/Mx2RHJ2HlaEfb5S9Qc2jnMhc5brbf7XzdCBjQlu0Pzim0blXv92IpRT1pvv/tHplAzuploBbxdUY1kDF7Atg74vDMa2j8a2OIvHhrme4CahFDThSlyH8MtoVfoHlgNbPLJQDydHq2hl/g+QEdKiRjcYo6SVlMdLYdu0TzQD+z7F9OHIyPqyOJaZk8/dlfBPq4EVLHv4LSmitN9l3HzlG/hh/LXnqCy7FJjH9nFS3vqYWTvXFUQmZ2LlM//omXHu1jeq4yFNlnbnisN6iciE7h82EdyNbpeWLldpoGeKA3GDgZncKMXk1o4u/Bwg1HWbHnDM/e26BywhfVQW74x7Ad+BQ5f31V+HPG3hFt47ZkzH0KsjKwGzkdq5Cu6A5uqbi8NyjqvVnsaDrg4YcH8uWX3xd6/t572zNy5CP06DHktua7FXqDyonYNJYMakm2Ts/Inw/Q1NeVWu7lu2Txdiryc6WY/b7kpY9QNBpGzR5L+wGd2PrTJi6Gn+f5DuPIycymebeWTF06gyldn63Y0EKUoNgihaIoxc0QpAD9S3pRVVU/Bz4HWO033PQOCXqyF7VHdANg14hFZMck3zTg9TNshlwdl77favrSCgp7x7xP+rkos/Yh74/HtXEtsmOS2TViUbGva8jVkZtrrNomh10g41IMTnX8ir0G+8bsNh7OtHznKXYNX0huUuHqr/nGVK78vpvgZ+7n0vdbqzz79f2esOckjrV9sPFwLjRBY1ZkIpF/G+cBiVyzn5D3x19bItkrMnvxLL+/3+n7vf7UB7H1dGHPS+8VeLZysxekWGlpt3wyl1fvJHLN/kLL3ZsF0mbJRABsPZzx7dEcVWcABSJ+3Max+eYTOvr3a0X9qQ8CEDp1aYkZsqONZ150Gdlc/nUX7i3q3LRIUdb97takNk6BvvTeY9zfWnsbeu9+l3/aT6Eq93tJ1KR4NB4Fhrq7e2NINh9Kra19D/ZPGefCUJxcsWrchmyDHt3hXfmNsjLQnQ7DqnErcqVIcVO+bk5EJ+d/7sQkp+Pt4lhk23WhZ+gbUniCxB3HL1G/ujee5Zz/pLx83RyJTs4wPY5Jzig++6Gz9G1hflLHx9XY1sPZgW5NAgmPiK20IoWvuzPRiSmmx7FJqfhcu4Tjut93HGZ0/44oikJNXw8CvNy4EBVPk6AA8nR6pnz8I/3bNaZnSCUd4F/P7mxPdFp+UTImLQtvZ7sb2tjhZu+DvY0V9jZWhNTw5FRsCi2re+LjbEcTf+PX4F71/Vmx5wyVRU2OR3HzMj1W3LxQU80/ZzQ16mL3uPHyCMXRBW2DEHIMetBaGS8TyTAWpnVHd6OtXb/CixTjxz/Bk08OA+DgwTCqV8/vowEBfkRFFT2KwsPDjVatmvHII+bnPRs3rs+nny5k4MCRJCYmV1jugnwc7YhJyzY9jknPwdvRvLDm42SLm50n9tZa7K21tPR343RCWpUVKXo90Y9uw3oBcD7sLB7+nqZlHn6eJBUxiuI61WBg9587uX/8ILb+tMnsMpDDm0N5cs54nN2dSUsq2wTuQtwuJY2kiAMuYV58Vq899inPxs5/sYHzX2wo0zp2Pm6mQoV/31aknrwCQOyWMOqM6WOakd61cS1Swi8ZRy6Ugo2ns/Fgy6DiUNMHp0A/MooZgnljdvsAT9qtmMyB5z4h/XzRQ04BHGv7knHReE2hX++WpJ2NrPLsjrXzrz93a1IbjbVVkQdskesO4N2pEZe+24pXhwakn4+S7JWQvTh3Qn+/k/d77eFd8e3alO1D55mdeajs7AW1fG8caWeucnbJmiKXr2/zgunnkA/GE7XhEFHrDuB8TwDtv5zK2c/XkhOfirWbI1ZO9qaRDTejaDVYuzqSm5iGYqWlWq8WxG4rPDP7jcq636M3HmZN02dMjx84t+JagaJq93tJ9BdPofEJQPHyQ02Kx7p1F7KWLTBrk/7yE6af7Z58Ed2RvegO70JxckXV6yArA6xtsGrQgtx1MnN6aTSq6UtEXDJXE1LwcXVifehp5o/sU6hdWlYOB89eZf7jvQstWxd6usjiRUVrVMPnWvZUfFwdWX/oLPMf71moXVpWDgfPRTJ/RA/Tc1k5eRhUFUc7G7Jy8th9+jLjb7irSYVmDwwgIiaRK3FJ+Lq7sG7vMd4cP9isjZ+HK3uPX6DlPbVISEnnYnQC1b3dUVWV17/4k6Bq3jzRp32lZTZlr+ZGRGIGV5Mz8HG2Z/3xq8x/IMSsTdfgaizYEIbOYCBPb+BoZBKPta6Dl5Mdfi72XExIo7anM3svxhHk6VzMlm4/w+UzaLz9UTx8UVMSsGrRmZyV5pPZZs57yvSz7bBJ6I7vRx++F03Ne4yXhljbQF4u2uBmGC5XfIFlyZKvWbLEeJlk377defrpkfz44x+0adOC1NS0Yi/1ePDB+1i79l9ycvIv5atRw5/vv1/CmDGTOXv21grLZdHI15mIlEyupmbh42jL+jMxvNm7kVmbroHeLNx26lqfUQmPSeWx5jUrLeONNny9lg1frwWgefcQeo/sz+4/dlC3xT1kpWUWeamHby0/Yq5dLteyZysizxmPq1y93UiJSwagTrNgFI0iBQpRpZTihmApinIG6KGqaqF7DSqKcllV1cLTwReh4EiKgmy9Xem+fi5WzvaoBhV9RjYb7jUOx+3wzTRCp3xOdkwynX7+H7aezqAopIRf4tC05egzc9DYWdNs9hN4tA4GRSHzchy7Hy88I7l/v1Y0mzcSG08X8lIzSQm/xM5HF+B/X2saThuKQacHvYHjb/1C9IbQ0vxKtHznKfzva0PmFeO1yarewOY+rwDkZ49Nocvvs7BytjdmPxbB4ekr0KVnVWn2e54bQM2hnTHk6dBn5xE++1vTrQEL7ndrFwdaf/Is9gGe6DJyODxtOSnHIyR7BWevM6YP9zx7P7Y+buTEpxLz72FCpy61/P5+h+/3QVdWknkl3nQ5QOSa/Zx899cqy+7Zph5d/niNlOMRqAbjcN5jb/6IQ4DxLMmFr/81a3+9SHH9LkgBA9tRb+IDKBoNhjw9h1/+gqTQs4W202f/B1g72aOxsSIvJYMdwxaQeSWee3+dhcZai6LVELstnLDXVha6rOR27PeCHji3wnQL0srY7z0HxBd6vdKwatwa22ETUBQNuTvXk7vmO6y7GEf45W01v5W2qUgRuh1NQCD2o18yTmqnaMg7sJXcv74pVwb7Nyvm9qAvvbaA/YfCSE5OxdPDjWfGPM6QAYWLAbdCd7DootvNbD92kbdWb8dgMDCwXUOe6tOan3YcBWBopyYA/L73BLtOXGLhqL5m62bl5tF31pf89doTON/KJQc6XfmyH7/EW7/vxGBQGdimPk/1CuGnXceM2TsYD4J+33eSXScvs/CJXqb1riSkMmWF8fapOoOBfi2DeapXSOENlILiVr4JK7eHnWHRd+sxGFQGdWrOUwM68+NmY7Hz4W6tiE1K49UVvxOfnI6Kyuj+Hbm/fVNCT0fw5IIvCa7ug+baOPSJQ7rTuWnZ5wRRTxc/71eJ2c/F8NbGoxhUlYFNa/JUh3r8dMh40Du0RSAAX+49wx9hESiKwuBmtXisdR0ATsakMHvtIfL0KgFuDsy+rwUudjbFbqs4+qPlm3dG2yAE24FjQaMhb99G8jb+hFV7Y7/W7Ta/HbWpSHHtFqQ2fR7FqnlnVIMew9Xz5PywGPRl77ven5ZvvwO8994cevfuQmZmFuPHv0hoqPG9+uuvX/LMM9NMIyvWr/+et9/+lA0btprW/eSThQwa1I+ICOPBs06np1OnAWXafsKi+27eqAjbL8bz9vbTGFQY2LAaY1sF8lO4McfQxsY5Vb4KvcTvJyLRKAqDG/oz4lqRYsb6cA5eTSI5Ow8PexuebhvE4IZlH/U09u2omzcqxqg542jWpQU5WTkseXExF44a57Ka9uUrfD7tY1Likpn18zzsnRxQFIWIExdY8b8lZKVn0XtkP3o+1he9Tk9udi6r5n7BmYNlm8vp20u/FnMx23/D376P/idnGb0v5juL/HcrqUjxLLBDVdVCn1KKokxUVXVxaTZQXJFCCCGEqCrlLVJYgooqUlSG8hYpLEI5ixSWoLxFCktQ3iKFJShvkcIS3EqRoqqVt0hhCW6lSFHVpEhxZ7LUIkWxl3uoqvpxCctKVaAQQgghhBBCCCGEKK1y3fhZUZReN28lhBBCCCGEEEIIUXrlvenzcqDqZooRQgghhBBCCCEqgcEiL4r47yrpFqR/FLcI8CxmmRBCCCGEEEIIIUS5lDSSojPwGJB+w/MK0KbCEgkhhBBCCCGEEOKuVFKRYg+Qqarq1hsXKIpStnvSCCGEEEIIIYQQQtxESXf36AegKEpDVVWP37B4VoWmEkIIIYQQQgghxF2nNBNn/qgoykpgEWB37f+tgPYVGUwIIYQQQgghhKhqBmTmzMpUmluQtgVqALuA/UAk0LEiQwkhhBBCCCGEEOLuU5oiRR6QBdhjHElxQVVVQ4WmEkIIIYQQQgghxF2nNEWK/RiLFK2BTsCjiqL8XKGphBBCCCGEEEIIcdcpTZFijKqqs1RVzVNVNVpV1YHA7xUdTAghhBBCCCGEqGrqf/S/0lAUpa+iKKcURTmrKMqMIpYriqJ8eG15mKIoLUv50sW6aZFCVdUDRTy38lY3LIQQQgghhBBCCMukKIoW+BjoBzTEeFVFwxua9QOCr/03Dvj0VrdbmpEUQgghhBBCCCGEuLu0Ac6qqnpeVdVc4Htg4A1tBgJfq0Z7ADdFUardykalSCGEEEIIIYQQQogbBQCXCzy+cu25srYpE6tbWVkIIYQQQgghhPgv+6/e2lJRlHEYL9G47nNVVT8v2KSI1W6czqI0bcpEihRCCCGEEEIIIcRd5lpB4vMSmlwBahR4XB2ILEebMpHLPYQQQgghhBBCCHGj/UCwoiiBiqLYAMOAP25o8wfwxLW7fLQDUlRVjbqVjcpICiGEEEIIIYQQQphRVVWnKMpzwHpAC6xQVfWYoihPX1v+GbAG6A+cBTKBJ291u1KkEEIIIYQQQgghimFQipp24e6gquoajIWIgs99VuBnFXj2dm5TLvcQQgghhBBCCCGERZAihRBCCCGEEEIIISyCFCmEEEIIIYQQQghhEaRIIYQQQgghhBBCCIsgE2cKIYS46yQdNFR1hHKzPrjm5o0slFVI/6qOcHfS51V1gnLTZWdUdYRys6rfoqojlFv1lVerOkK5WQ25rfP3VSqvd2ZXdQRRDLWqA9xlZCSFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERpEghhBBCCCGEEEIIiyBzUgghhBBCCCGEEMW4c2eyujPJSAohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiJIkUIIIYQQQgghhBAWQeakEEIIIYQQQgghimFQqjrB3UVGUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIMieFEEIIIYQQQghRDAMyKUVlkpEUQgghhBBCCCGEsAhSpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZOJMIYQQQgghhBCiGGpVB7jLyEgKIYQQQgghhBBCWAQpUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkHmpBBCCCGEEEIIIYphUKo6wd1FRlIIIYQQQgghhBDCIkiRQgghhBBCCCGEEBahyi73cKrrT8j743FrUpvjC37kzKd/F9kuaHRv6j7VF6dAP/5qOJ7cxLQybSdgQFsavDgE52B/Nvd7leQjF0zLXBrUoMVbY7F2tkc1GNjc91UMOXk3fU2vDg1o/+VUMiJiAYhcs5+T7/5aqF3Ld5/CrVkQiqKQfj6KA89/hj4zp0qzX+fePIiuf89m7/gPifxrX6Hl3p0a0WTWcNAo6DJyODjpMzIuxkj2Cs5uif29xoMduee5AQDoMrI5PH0FKccjCrXz7tiQJq+NQLGxIjnsAqGTP0fVG6o0e2k/Z1p9/CzuzQIx6PQkHTrHoZeWo+r0VZbd3t+DVosnYOvtBqrKhZWbOLdsnVmb0n4OFcfG3Ym2yybh3rwOl37YxpGZX5qWKdZams9/Eq8ODcCgcmzBD0T+vb/Ur33dzfq7pe330rDr0BqPF58BrYb0X9eS+uX3Zssd+3XHZdQwAAyZWSTO/4C8M+fzG2g0VFv1Cbq4eOImvXLLecpi5/FLLFq9DYNBZXD7hozu1cps+Zf/hrLmwCkA9AYDF6KT2Dx/LEnpWUz7Mr//XY1PYUL/djzWrXllxi/RK/PfZdvOfXi4u/Hbqs+qOk6ZWHL2HXsPsuDDZegNeobc15uxjz1ktjwlLZ1XF3zI5atR2NrYMGfG8wQH1QLg6x9/55e//kFRFIKDajF3xiRsbW0qLfvOYxdY9NNmDKrK4A6NGd2nrdnyLzfsZ83+EwDo9QYuRCeyedEEXB3tWfnvQX7ddRQFCA7w4o3H+2JrXXlfmXeGnWHht+swGAwMvrclY+7vbLY8LTObmUtWE52Ygk5vYGS/Dgzq3ILohBT+t/RXElLSURSFh7qGMKJ3u0rLfd2r81+iS8+OZGVmM/351zkedrJQm4WLX6d1+5akp6UDMH3i65wIP42TsxPvfDqHagF+WFlpWf7JSn757s9Kyb1jXygLP1qBXm/gwft6Mnb4g2bLU9LSmbXoIy5HxmBrY83sac8SHFiLCxFXeWn2O6Z2V6JiePbJYTz+0IBKyX3dkNdG0bBbC3KzcvjmxU+5cuxCoTZPvD+RGk2C0Ov0RBw5y/czl2K49ne3bruGPDhrJForLRlJaXz4yBuVml+IgqqsSJGXnE7YK19RrW+rEtsl7DtF9IZQOq9+tVzbST15mT2j36PFW2PMnle0Glp//CwHnvuElOMR2Lg7YcjTlfp14/eeZPfjb5fYJmzWKnTpWQA0ef0x6ozuzemPSv9BW1HZ0Sg0euVRYraEFduk+cLR7Bn1DmlnIgka1ZP6kwdxcNISyV7B2S2xv2dExLJt8BzyUjLw7d6MFm+PZUv/WeaNFIWQDyewY+g80s9H02DaQ9R8+F4ufbelSrOX9nPm8uqdHHj2YwBaf/octUd048JXG6ssu6ozcPT1b0g+ehErRzu6/TOP2G1HSTt91axdaT6HiqPPyeP4wp9xqV8dl/o1zJbVf2EQOfEpbOg4FRQFG3ensm+gFP3d0vb7TWk0eEyfSOwz09HFxFFt1cdkbd1F3oX8op3uajQxY6dgSEvHrkNrPF+ZTPTIiablzo8OJu9CBIqTw63nKQO9wcCbP23hs2cH4evmxIi3f6BL4yDqVPMwtRnVoyWjerQEYOvRC6zachhXRztcHe34cfqjptfp/eoXdG8WVKn5b2ZQ/14MH/IAM+eU7/1QlSw1u16vZ+57S1j67mz8vD15ZNxUunVqQ53aNU1tlq78ifp1A/lw3kzOX7rCvPc+Y/n7c4mJS+Cbn//k95UfY2dry9TXFrJ203YG9etROdkNBt784V8+e/4hfN2cGbHwG7o0rUudap6mNqN6tWZUr9YAbA07x6pNB3F1tCcmOY3vtoSy+tVR2NlY89KyP1l34CQD2zeutOzzV65hyUuP4+vhwvA3ltK1RT3qBPiY2vzw7z6CArxZPHk4iakZDHx5Mfe1b4JWq+HFYb1pUNufjKwchr2+hHaNgszWrWhdenakVlANerYZRPOQxsxe9DIP9R1ZZNtFb3zAuj//NXvusTFDOXvqPOMfm4yHpxvrd6/mj5/Xknc7PsNLoNfrmffBUj5/6zX8vD0Z9vQ0unVoTZ3a+X8fl33zC/XrBvLBnBmcj7jC/PeXsuzdNwisGcDPy941vU6PoU/Ro1Pb4jZVIRp2bY53oB9zuk6idotgHp43hncHFS6EH/htO1+/sBiAkR8+T4dh3dmxagP2Lg48PGcMn46cT1JkAk6eLpWa/05Q+tNu4naosiJFTnwqOfGp+PVsUWK7lPBLRT6vdbCl2byRuDaogaLVcuLtX4haf7BQu7QzkUWu79O1KSnHI0xnhHOT0sv4G9zc9QIFgNY+/+xBVWevM6YPkX/vw715CV8yVRUrJ3sArJwdyI5OluyVkN0S+3vigTP5Px88i32Bg5rrbDycMOTmkX4+GoDYrUep9/xALn23pUqzl/ZzJubfw6afkw6dM/2OVZU9OzaZ7NhkwDh6Je3MVez93AsVKYpTY0hH6ozti8ZaS1LoOQ7NWAEG1ayNPjOHhH2ncAz0LbR+rWFd2dD5ReMDVS3ziB4oXX+3tP1+MzaN66G7EonuahQAGeu3YN+1o1mRIifsuOnn3KMn0Pp6mx5rfbyw79yW1OXf4nzDGemKFn4phhreblT3cgWgT8t72HL0vFmRoqC1oafpGxJc6Pm9p65Q3csVfw/L+gLbqnkTrkaVfsScJbHU7EdPnKFmQDVq+PsB0K9HZzbt2GtWpDh38TJPXevLQbWqczU6lvjEJAB0egM5OblYaa3Iys7B27PovlYRwi9GX+vvbgD0CanHliNnzYoUBa09cJK+reqbHuv1BnLydFhptWTn6vB2LUehtpzCz1+lhq8H1X2M+6tv28ZsOXTKrNCgKAqZ2TmoqkpmTi6ujvZoNRq83ZzxdnMGwNHeliB/b2KT0iq1SNGzbxd++8E4avHwwXCcXZ3w9vUiLia+VOurKjg6OQLg4OhASnIqujKMsCuvoyfPUtO/QH/v3onNO/eZFSnOXbzM2BFDAAiqWZ2rMbHEJybj5eFmarM39Cg1/H3x96u8fQ7QpHdr9q3eBsDFQ2ewd3bExduN1Lhks3bHtxw2/XzpyFlc/Yz9LOSBThxZt4+kyAQA0hNSKyW3EMW5Y+ekqD9pEHE7jrG576tsHzKXxrOGo3WwLfX6TkF+oKp0/G4G3f+ZR/Cz95dp+x4hwXT/9006fDsN53oBxbYLeX88/Y9+inPdapxbvr7Ks9v5uePfvzXnb3K2MnTqUjp8M41+oYupObQTpxb/IdkrIXtxqrq/X1d7eFdiNh0p9HxuQhoaay1uzQIBCLi/Lfb+HhaVvTQUKy01H+pEzGbj72gJ2R1qeOHWuDaJoecKLSvqc8g52J/qA9uzdcDrbOo5E9VgoOaQTqXenrWL8Qx/w2lD6f7PPNosnYStV9kOSMva3y1xvxfFytsLXXSs6bE+Ng6tT9EHPQBOg/qRtTP/Mhf3F58h+YOlqDcUjCpDbHIGfm75B1q+bk7EphRdvMnKzWPXiUv0bFa30LL1oafpV0TxQvz3xMYn4OfjZXrs6+1FbFyCWZt6dWuzcdtuAI4eP01UTCwxcQn4ensyatggeg4dQ7fBI3F2dKRjm5KLxbc1e3I6fu7O+dndnUvu78cv0rOFsV/7ujnzRM/W9H1lKb1e/gwnexs6NKxdGbEBiE1Kxa9AEdDH3YWYJPMDxmE92nA+Mp6eL7zDQ698wrTh/dBozL/SX41L4uSlKJrUKf47akXwreZDVGR+0S06MhZfP+8i206e+Qx/bvmemXOmYGNjDcCqZT9Q555Adoav569tPzD3f2+jqhX/mWns7/mf577ensTEJ5q1qVenNhu37QGMRbyo6DhibnhPrN20g349zC/PqQyuvu4kR+ZnSY5OMBUgiqKx0tJ68L2c2Gr8u+sTVA0HV0cmfj+Ll/58k9YP3lvhmYUoSbEjKRRF0QJjgerAOlVVdxZY9oqqqnMrIV+xfLo2wa9PS4In3AeA1tYahwDPYs+s3UhjpcWzbT02930VfVYOnX76H8lHLhC349hN100Ou8i6Vs+jz8zBt0dz2n8xlX86TCmy7cEXloBGofn8UVQf2J5L32+t0uxN5zxB+JzvCp1VvVHdcf3YNWIRSYfOEfzM/TR94zFCpy6V7BWcvThVmf06r44NqfVoV7YNLPoaxX3jP6LpG4+jsbUmdksYqs5gMdlLq/mCJ4nfc5KEvacsIrvWwZa2yyYTNmul2cgsKP5zyLtzY9yaBtJt3Rzja9jZkBNf+jMiipUWhwBPEvaf4ujrq6g7vj9NXhvBgYmflvo1ytrfLW2/F0sp4v5jxfyKtq2a4TSoL9GjJwNg37kthsRkck+cwTak2a3lKAe1iKBKUb8PsC38As0Dq+HqaGf2fJ5Oz9bwCzw/oEOFZBSWpagDwxv7zNgRD7Hgw6UMGT2J4KBa1A8OQqvVkpKWzuYde1n/w1KcnRyZOmshf/6zmQG9u1VO9qL6ezFtt4Wdo3mQP66OxhGYqZnZbAk7y9+zx+LsYMtLS//k773Hua9twwpMnK+o43HlhvS7ws9Sv6Yfy6aP5HJsIuPfWknLejVxsje+ZzOzc5j60Y+8NLyv6bnKUtTnSlF96e25HxEXE4+NjTVz3n2FcRNH8dE7S+ncvT0nwk/x+ODx1Ayszpc/fcKB3YdIT8+o0NxF7vcbfpUxwx9kwUfLeWjslGv9PRArbX5xKC8vjy279jPpqccqNGtRivw8L6G48/CcMZzbd4Lz+43zhWi0Gmo0CeKj4XOwtrNh8uo5XDx0hrgLURUVWYgSlXS5xxLAAdgHfKgoylZVVa8fiT8IFFukUBRlHDAOYLxza3o7GM/GBD3Zi9ojjH+gdo1YRHZM8i1EV9g75n3Sz5m/eULeH49r41pkxySza8SiYtfOikwkfvcJ0zDmmH8P49Y0sNgvscVlj/n3MMqCJ7HxcC5+SLRB5crvuwl+5n4ufb+1SrNbuzjQZonx+mhbD2d8ezRH1RmIWnfA1N7G0xnXRrVIOmQ8c3vl9910/G76taWSvaKyl6xq+7uNhzMt33mKXcMXFjt0PvHgGbYNmg2AT5cmONWpZhHZS/s5U3/qg9h6urDnpfcKPFu52QtSrLS0Wz6Zy6t3Ermm8KSVBYsWBT+HUCDix20cm/+DWXv/fq2oP9U4CVjo1KVmk0wWlJuYhi4zm8g1xr559c891B7e9aZ5y9vfLW2/l0QXG4dVgSG8Wh9v9DecRQOwDg7E89WpxE58GUOKsUBk26wx9l3aE9CpDYqNDYqjA55zZ5DwyoJbylRavm5ORCfnv3djktPxdnEssu260DP0Dbmn0PM7jl+ifnVvPF0qdz4NUTV8vb2Ijs0foh8TF4+3l/mZWSdHB+a+PAkwHoj2eeQpqlfzZee+UAKq+eLhZry8qMe97TkcfrLSihS+bs5EJ+V/J4tJSiv2ko11B0/Rt3X+pR57Tl4iwNMVD2djP+/RPJjD5yMrrUjh6+FCdGJ+YTk2KRWfAqNCAH7ffpjR93VCURRq+noS4O3Ghah4mgRVJ0+nZ8pHP9K/fRN6tqqczCNGD+WRxwcDEHboONX88y8j9PP3IbaISz2uX/6Rm5vHL9/+wdhnHwdgyKMPsOTDLwCIuHCFKxGRBAXXJuzQ7T8xUZCvtyfRsfmf5zFxCfh4FtHfpxv/tqmqSt9HnyagWv7vun3vIRrcE2R2+UdF6vx4b9o/apznJeLIOdz880eCuPl5khKTVOR6fSc9hJOnC8vH50/2mRydSEZSGrlZOeRm5XBu3wkCGtSSIkUBlT8G8u5WUpGijaqqTQEURfkI+ERRlNXAoxRfkAZAVdXPgc8BVvsNN/2bnv9iA+e/2HDLoQFit4RRZ0wf04z0ro1rkRJ+yThyoRRitoRxz7P3o7W3wZCrw6t9A85+vqbY9gWz23q7mp53b1EHRVGKLFA41vY13VXCr3dL0s5GVnn2gkI+GE/UhkOFDhzykjOwdnbAKciP9PPR+NzbhLTTkr2is5ekKrPbB3jSbsVkDjz3iWnOiaLYermQE5+KxsaKe54bwKn3f6vy7KVVe3hXfLs2ZfvQeWZnHio7e0Et3xtH2pmrnF1SdHtbb1dy4lIA88+huO3HaP/lVM5+vpac+FSs3RyxcrIncu0BIteWrs9F/XMI7w4NiNt5HO/OjUktxVwY5envlrjfS5J77BRWNQKw8vdDFxuPY5+uxM+cb9ZG6+eD99uvk/DqAnQR+fst+aPlJH+0HADbkGa4PDG00goUAI1q+hIRl8zVhBR8XJ1YH3qa+SP7FGqXlpXDwbNXmf9470LL1oWeLrJ4If6bGtcPJuJKJFcio/H19mTtv9tZNOtFszapaenY29libW3NL3/9Q0izRjg5OlDN15uw46fIys7BztaGvQeP0Kh+5V0m1KiWHxGxyVyNT8HHzYn1B08x/8n+hdqlZeVw8MwV5o/KX1bN3YWwi1Fk5eZhZ23F3lMRNKpZeO6eCsse6E9ETAJX4pLwdXdm3d5w3nx6iFkbP09X9h4/T8t6tUhISediVALVvd1RVZXXV/xOUDUvnuhbeSOevlnxE9+s+AmArr068diYh/nr1/U0D2lMWmp6kfNRFJynolf/rpw+aTy5FHklmvad23Bgz2E8vT0IrFuLy5dKNx/TrWhcvy6XrkZxJSoGXy8P1m7awcJXJpu1SU3PwN7Wxtjf/95ISNOGODnmF23XbtpOv+6lv7zyVm1f+Q/bV/4DQMNuLbh3ZB9C/9hF7RbBZKdlFpqPAqD9I91pcG9TPho+x2yEy9F/DvDQ7CfRaDVora2o1TyYLctv/e+mEOWlFHedl6IoJ1VVrX/Dc7OAPoCPqqql+mtTsEhRkK23K93Xz8XK2R7VoKLPyGbDvdPQpWfR4ZtphE75nOyYZOqM6cM9z96PrY8bOfGpxPx7mNCpS9HYWdNs9hN4tA4GRSHzclyRs9z792tFs3kjsfF0IS81k5TwS+x81PjFsMaQjtR7fiCqqhLz72Hj8ORSCBrdm6CRPTHo9Biycwl7bZVpckFT9tgUuvw+Cytne1AUUo5FcHj6CnTpWVWavaDrBw/Xbw1YcL/792tFg2kPoRpU8lIyOPjC52RGxEr2Cs5uif295TtP4X9fGzKvxAGg6g1s7vNKoeyNZw3Hr2cLFI3C+a82cm6p8ZaFVZm9tJ8zg66sJPNKvGl0wvXbeVZVds829ejyx2ukHI9ANRgvmzn25o84BBjPklz4+t8SP4cCBraj3sQHUDQaDHl6Dr/8BUmhZwttp8/+D7B2skdjY0VeSgY7hi0g7fRV7Kt70XrxBKxdHclJSOXgC0vIulp4xEBplNTfq3K/h/jHFnq90rDr2MZ4C1KNhvQ/1pG6/FuchhjnvEj/5S88Xp2CQ4/O6K9NhKjq9UQ/9qzZa1wvUpT3FqQ+bw4q13rbj13krdXbMRgMDGzXkKf6tOanHUcBGNqpCQC/7z3BrhOXWDiqr9m6Wbl59J31JX+99gTO9qWfH+RGViGFDxRvh5deW8D+Q2EkJ6fi6eHGM2MeZ8iAwkUYS1Qp2fXlu/3utt0HWLh4GXqDgcH9ezL+iYf54fe1ADwysB+Hw08yc957aLUagmrVYPaM53F1No5Y+GjFt6zftB2tVkv94CBmT5tomnegLHThW8uVfXv4ed76eYuxv7dvzFP92vHTNuP190PvNV5y9fvucHYdv8jCMebz1nzy107+OXgKrUZD/Ro+vDaiNzbluAWp4uB880ZFZT9ymkXfrsNgUBnUuQVPPXAvP24yjqh7uHtrYpNSeXXZb8SnpKOqKqPv68T9HZoRevoST87/guDqPmiuDf+f+FAPOjcre3GxyQPlv9vMawunc2+3DmRlZTPj+dcJP2K81evS7z7gfy/MITYmnq9Xf4aHpzuKAifCTzPrpflkZmTh4+vFwsVv4O3rhaLAkg+/5I+f15Zp+8eOfF2u3Nv2HGTRxyuM/b1fD8Y99hA//mGcT+7hB/pw+Ngp/vfmh2g0GurUrs4bLz1r6u9Z2Tn0euQp1n7zKc5ORY9SK40XO8wu97pDZ4+mQZdm5Gbl8s1Ln3L5qPH21+O/mMF305eQGpvEe2e/JelqHNkZ2QCErdvHug9/AaD7uAG0G9oVg0Flzw+b2LKibEWKDy/+UOJJ7DvdFwGP/ScHUzx5dZVF/ruVVKRYBaxSVXXdDc+PBT5VVbVUf2mKK1IIIYQQVaW8RQpLUN4ihSWoqCKFuIlyFiksQXmLFJagvEUKS3ArRYqqVt4ihSW4lSJFVZMixZ3JUosUxd7dQ1XVx24sUFx7fllpCxRCCCGEEEIIIYQQpVX2sWuAoii9VFW9PZNLCCGEEEIIIYQQFspgkeMN/ruKHUlxE8tvawohhBBCCCGEEELc9YodSaEoyh/FLQI8i1kmhBBCCCGEEEIIUS4lXe7RGXgMSL/heQVoU2GJhBBCCCGEEEIIcVcqqUixB8hUVbXQtMqKopyquEhCCCGEEEIIIYRlMFR1gLtMsUUKVVX7ASiK0lBV1eM3LJ5VoamEEEIIIYQQQghx1ynNxJk/KooyXTGyVxRlMfBmRQcTQgghhBBCCCHE3aU0RYq2QA1gF7AfiAQ6VmQoIYQQQgghhBBC3H1KmpPiujwgC7AH7IALqqrKZTlCCCGEEEIIIf7z5OC3cpVmJMV+jEWK1kAn4FFFUX6u0FRCCCGEEEIIIYS465RmJMUYVVUPXPs5GhioKMrjFZhJCCGEEEIIIYQQd6GbjqQoUKAo+NzKiokjhBBCCCGEEEKIu1VpRlIIIYQQQgghhBB3JVWp6gR3l9LMSSGEEEIIIYQQQghR4aRIIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiLIxJlCCCGEEEIIIUQxDFUd4C4jIymEEEIIIYQQQghhEaRIIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBJmTQgghhBBCCCGEKIbMSVG5ZCSFEEIIIYQQQgghLIKMpBBCCHHXUQ1KVUcoP52uqhOIO43hDj4HqN7B2bXyNbsqKIqcgxXiTifvYiGEEEIIIYQQQlgEKfEKIYQQQgghhBDFUKs6wF1GRlIIIYQQQgghhBDCIkiRQgghhBBCCCGEEBZBihRCCCGEEEIIIYSwCDInhRBCCCGEEEIIUYw7+aZgdyIZSSGEEEIIIYQQQgiLIEUKIYQQQgghhBBCWAQpUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkEmzhRCCCGEEEIIIYphqOoAdxkZSSGEEEIIIYQQQgiLIEUKIYQQQgghhBBCWAQpUgghhBBCCCGEEMIiyJwUQgghhBBCCCFEMWROisolIymEEEIIIYQQQghhEaRIIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBJmTQgghhBBCCCGEKIZa1QHuMjKSQgghhBBCCCGEEBZBihRCCCGEEEIIIYSwCFKkEEIIIYQQQgghhEWQOSmEEEIIIYQQQohiGJSqTnB3sZgiRcv3xuHXqwU58an823V6mdZtOONhag7tjI2bI3/UGW22LOCBtjR4cQiokHLsEvuf+fiWs/bZ/wG69CxUvQFVb2Bzn1fMljvV9Sfk/fG4NanN8QU/cubTv8v0+jbuTrRdNgn35nW49MM2jsz8EgArRzvu/X2WqZ19NU8u/7KDsFkry/YLaBS6r59HVnQiux9/22yRtasjIe+Nw7G2L/qcPEInLyH15BWLyH6z/X6de/Mguv49m73jPyTyr30Wkf1G9v4etFo8AVtvN1BVLqzcxLll60q9fmX2+dK8N706NKDp7MfRWFuRk5jG9sFzSv36xe13AMVaS/P5T+LVoQEYVI4t+IHIv/eX6nVLs49rPNiRe54bAIAuI5vD01eQcjyiyrODcZ+2/3IqGRGxAESu2c/Jd38tsm3DGQ8TMKAtqt7Aha82cm75+lJvJ2CAsb84B/uzud+rJB+5ABj3TfAz95nauTasyaZe/yPl2KUSX+9m/cWS93lp2Hdohce0Z0CjIf3XtaR88YPZcsf+3XEd9QgAhqwsEuZ9SN7p8/kNNBqqffsx+th4Yp9/9bZmu5mdJyJY9NsODAaVwe0aMLpHS7PlX246xJrQMwDoDQYuxCSzefYoXB3t6DdnFY621mg0ClYaDd9OeahSs9/MK/PfZdvOfXi4u/Hbqs+qOk6ZWHL2HXtDWfDRMvR6A0Pu68XYEUPMlqekpfPqwsVcjozG1saGOdOeIzioFgBf//QHv/y9AQWF4KBazJ0+EVtbm0rLvvPYRRb9vAWDwcDgjo0Z3buN2fIvNxxgzf6TwLX+Hp3I5oVPk5SeybTla0ztriakMOG+9jzW3fz9UqHZw06zcOUaY/auIYwZ0MVseVpmNjM//YnohBR0BgMj+3dk0L0hpuV6g4FHZ32Kj7sLH019vNJyX/fq/Jfo0rMjWZnZTH/+dY6HnSzUZuHi12ndviXpaekATJ/4OifCT+Pk7MQ7n86hWoAfVlZaln+ykl+++7NSct/J/R1gyGujaNitBblZOXzz4qdcOXahUJsn3p9IjSZB6HV6Io6c5fuZSzHo9ADUbdeQB2eNRGulJSMpjQ8feaNS8wtRkMUUKS79sI3zK/4hZPGEMq8b9U8o51f8Q+/d75o97xjoR72JA9k64A3yUjKw9XK5XXHZPmQeuYlpRS7LS04n7JWvqNa3VbleW5+Tx/GFP+NSvzou9WuYntdlZLOp50zT427r53F1Tdm/gNd9qh9pZ65i5WxfaFm9SQNJPnaJPaPfw6muP83fHMWOofMtJntJ+x0AjUKjVx4lZktYmV+7orMXpOoMHH39G5KPXsTK0Y5u/8wjdttR0k5fLdX6ldnnb/betHZxoPmCJ9n56EKyriaUeZvF7XeA+i8MIic+hQ0dp4KiYOPuVOrXLc0+zoiIZdvgOeSlZODbvRkt3h7Llv6zSnjVysl+Xfzek4UKiTeqNawL9gGebOj0Iqhqmfd/6snL7Bn9Hi3eGmP2/OXVO7m8eicALvVr0P6rqTctUMDN+4ul7/MSaTR4vDyRmKeno4uJx/+bj8jcupu88/lFFt3VaKLHTMWQlo59x9Z4vfoCUY8/b1ruMnwweRci0Dg63N5sN6E3GHhz9XY+e3oAvq6OjHjvF7o0qk0dPw9Tm1HdWzCqewsAth67yKqtR3B1tDMtX/rMA7g7Ff67YQkG9e/F8CEPMHNOye8XS2Sp2fV6PXM/WMLSt9/Az9uTR55+iW4d21Cndv77bumqn6lfN5AP577M+UtXmPfBEpa/O4eYuAS++eUvfv9qMXa2tkx9fRFrN21nUL8elZPdYODNHzfx2cQH8XVzZsSib+nSpA51qnma2ozq1YpRvYzf07YePceqTYdwdbTD1dGOH2c+Znqd3jOX0r1Z3UrJfX2b87/6kyXTn8TXw4Xhsz6ja8sG1AnwMbX5YeMeggJ8WDz1cRJTMxg47X3u69AMayvj1/pv1u8myN+b9KycSst9XZeeHakVVIOebQbRPKQxsxe9zEN9RxbZdtEbH7Duz3/NnntszFDOnjrP+Mcm4+Hpxvrdq/nj57Xk5ekqNPed3N8BGnZtjnegH3O6TqJ2i2AenjeGdwcVPpl34LftfP3CYgBGfvg8HYZ1Z8eqDdi7OPDwnDF8OnI+SZEJOHnevmMmIcrDYuakSNhzktzkdLPnHGv50PHb6XRbP497f5uFU13/ItdNCj1LdmxyoecDH+vG+S/+IS8lA4Cc+NTbnrsoOfGpJB0+j3qtMllQjSEd6bp2Dt03zqfFojGgKTx2SJ+ZQ8K+U+hz8ordhmOgH7ZeLiTsKVydLol9NQ/8ejbn4jebi1zuck8AcduPAZB+NhKHGt6mg56qzl4adcb0IfLvfeTEp5g9b2nZs2OTST56ETAWQdLOXMXez90i+3xR782CajzYgci/95N1NaHQNm91v9ca1pVTi/8wPlDVkgtUNyhuHxeUeOCMaV8lHjyLfbX8A7aqzF4WgSN7cvKd1aAab451ff9rHWxp+d44uq2bQ/cN86nWJ6TI9dPORJJ+LqrEbdQY3IHLv+4qVZ6b9Zc7eZ/bNq6H7nIkuqvRoNORsX4LDl07mLXJOXIcw7UzgzlhJ9D6epuWaX28sO/clvTVa29rrtIIj4ilhpcr1T1dsLbS0qdFXbaEXyy2/drQM/RtEVx5AW9Rq+ZNcHVxruoY5WKp2Y+ePEPNgGrU8PfD2tqaft07sWnnXrM25y5dpl3LpgAE1arO1ehY4hOTAdDp9eTk5KLT6cnKzsXby+PGTVSY8IvR1PB2o7qXm7G/h9RjS9i5YtuvPXCKvq3qFXp+76nLVPd2xb8SD9jCz12hhq8n1X08sLayom+7Jmw5eMKsjYJCZnYOqqqSmZ2Dq6M9Wo3xK31MYgrbD59icJeiP/MrWs++XfjtB+MI4sMHw3F2dcLb16vU66sqODo5AuDg6EBKciq6Ir5P3253cn8HaNK7NftWbwPg4qEz2Ds74uLtVqjd8S2HTT9fOnIW12uF6pAHOnFk3T6SIo3f5dITKueYSYjiWEyRoigt3h7L4f99xeY+/+PoG9/QfMGTZVrfKagaTnWq0eWP1+j69xv4dmt6e4KpKp2+n0G39fOo/Vj3Uq/mHOxP9YHt2TrgdTb1nIlqMFBzSKdyRagxuD1X/thd5vWaznmc8DnfoapF3+035VgE/v1bA+Deog4O1b2w9/e0iOw32+92fu7492/N+a82mj1vEdlL4FDDC7fGtUkMPWe5ff4m27R2c6Tz6lfotn4eNYd2Bm59v1u7GM80N5w2lO7/zKPN0knlHhlScB8Xp/bwrsRsOmJR2T1Cgun+75t0+HYazvUCimzjWMuH6gPb0W39XDp8Ow3HQD8A6k8aRNyOY2zu+yrbh8yl8azhaB1sy5wBIGBgO678VroiRVlY4j4vidbHC110nOmxLiYerU/xX76dBvcla0f+qCuPlyaQ9P5SUA23NVdpxKZk4OfmaHrs6+ZI7LVi0Y2ycvPYdfIyPZsGmZ5TFJiw5C8effcnft59vMLziqoXG5eIn3d+//b19iQ2LtGsTb06tdm4fQ8AR0+cJio6jpi4eHy9PRn1yCB6PvwU3YY8ibOTAx1bt6i87Mnp+LnnF3583ZyILaZ4mpWbx67jF+nZvHBRbv2BU/QLqV9hOYsSm5SKn4er6bGPhwsxSeYHjMN6teN8ZBw9Jy7koZkfMe3x+9BcK1IsWrWGycP6oCmiyFsZfKv5EBUZY3ocHRmLr593kW0nz3yGP7d8z8w5U7CxsQZg1bIfqHNPIDvD1/PXth+Y+7+3i/3Oejvdyf0dwNXXneRrBQaA5OgEUwGiKBorLa0H38uJrca/wT5B1XBwdWTi97N46c83af3gvRWeWYiSFHu5h6IoDsBzgAosBoYBDwIngdmqqhZ/quw20DrY4tnqHtouzR8mq7n2AVZaipUGp0A/tj04F3t/D7r8NouNXaeTl5p5S9m2Dnid7JhkbL1c6PjDy6SdjSzVmXXvzo1xaxpIt3XG6/W1djblPtNdfVB7Djz3aZnWuX6deHLYBeM120U4tfgPms19gu4b55N64jIp4RdRdfoqzw433+9N5zxB+JzvwGD+x8wSshdH62BL22WTCZu1EtVgsNg+X/I2tbg3DWT70Plo7Wzo+tcbJB48c8v7XbHS4hDgScL+Uxx9fRV1x/enyWsjODCxbPu/4D7WpWcV2carY0NqPdqVbQON119aQvbksIusa/U8+swcfHs0p/0XU/mnw5TCv5+tNfqcPDb3eQX//q0JeW8c2wbNxqdrE/z6tCR4wn2mdg4BnqSdiSx1BjAWK/VZOWWam6Y0LHGf33wjRXzpL+bLs12rZjgN6kf0ky8AYN+5LfqkZHJPnMGuVcUXD29UVMyifh2Abccu0TzQz+xSjy8nDsbH1ZHEtEye/uwvAn3cCKlT9Egv8d+gUrjT3Nhnxg4fwoLFyxgy5gWCg2pRPzgIrVZLSlo6m3fuY/33S3B2cmTqa4v4858tDOjdtZKyF6YU0+G3HT1P8yB/s/4OkKfTs/XoOZ4f2LECEhav6PeqefZdR89Qv2Y1lr08msuxiYxf8AUt69Xi4MmLeLg40jAwgP0nzhd+oUpQ1H4uqsjw9tyPiIuJx8bGmjnvvsK4iaP46J2ldO7enhPhp3h88HhqBlbny58+4cDuQ6SnF11UvV3u5P5uzFr6v08AD88Zw7l9Jzh/bV4WjVZDjSZBfDR8DtZ2NkxePYeLh84Qd6HkkZZ3k8o/vXB3K2lOii+By4A98DdwAngbGAB8ChQ7E4+iKOOAcQDjnVvT26Hs1/IpGg15qRlmcwEAxkkf/5kHGK/LP7Ho52JfIysykcTQs6g6PZkRcaSdi8IpyI+kw7f2wZ0dkwwYh1VHrT2AR4s6pRv+r0DEj9s4Nt98ojX/fq2oP/VBAEKnLjVNXFcc14Y1UbRaksNKbncjz9b3UK13S3x7NEdra42Vkz2tPnqGA899YmqjS8/i4AtLTI/77P+AjIg4PNvXr9LscPP97t4skDZLJgJg6+GMb4/mqDpDle/34ihWWtotn8zl1TuJXLMfKyd7i+3zJcmKTCA3MQ19Zg76zBzi95zAtVGtW97vuYlp6DKziVxzAICrf+6h9vCuZcp24z4uikuDGrR85yl2DV9IbtK12msVZQ96she1R3QDYNeIRaY+H/PvYZQFT2Lj4VzoEoasyEQi/zZOEBu5Zj8h74+//tuzd8z7hS7lCHl/PK6Na5Edk8yuEYtumqn6oPZc+fX2jh6ypH1eFvqYOKwKnBG08vVCH5dQqJ11cCCer00h5tmZGFKM/162zRvh0KU9Dp3aoNjYoDg64DVvOvH/W3hbMxbH182R6OT8L/kxyRl4uzgW2XbdobP0bWH+d9vH1djWw9mBbk0CCY+IlSLFf5yvtyfRcfGmxzFxCYWGsDs5OjB3hrGwrqoqfYaNo3o1X3buP0RANR883IwjAnrc257Dx05W2kGbr5sT0Un5n5Uxyel4uxbT3w+eom+rwqMldhy7SP0aPngW8z6pKL4eLkQn5l+yGpuYio+b+eVAv28LZfSAe1EUhZq+ngR4u3MhMp7DpyPYEnqSHUdOk5OnIyMrh5c//Yk3Jwyt0MwjRg/lkccHAxB26DjV/H1Ny/z8fYiNiS+0Tty153Jz8/jl2z8Y+6zxsGLIow+w5MMvAIi4cIUrEZEEBdcm7NCxCv0d7sT+3vnx3rR/1DjvRcSRc7j558+54ubnSUpMUpHr9Z30EE6eLiwf/47pueToRDKS0sjNyiE3K4dz+04Q0KCWFClElSnpco97VFWdCjwLNAImqqq6DZgGNCvpRVVV/VxV1VaqqrYqT4ECjAfLGRFxBAxoa3rOtWFNMKhs6jmTTT1nlniwBhC17gDeHRsCYOPhjFNQNTIuxZYrz3VaB1usrlXbtQ62+HRpQurJy6VaN277MQLub2sagmzt5oh9dS8i1x4w/U43O1AGqD64Q7mGXh+b/wNrW05kfetJ7Ht6MXE7j5kVKMA4ZFqx1gJQe0Q34vecRJeeVeXZS7Pf17d5gfWtJ7G+9SSu/rWXwzO+IGrdgSrPXpyW740j7cxVzi4xziJuqX3+ZqLWH8SzbT0UrQatvQ3uLeuSdubqbdnvUf8cwvvaqB/vzo1JLeXEotfduI9vZB/gSbsVkznw3Cekn482PV9V2c9/scH0+mqBEUHuLeqgKEqRcyxErjuAd6dGgPGOIOnnjV8oYreEUWdMH1M718bGGcgPvrCETT1nlqpAgaJQfUBbLv92+4oUlrbPyyLn2CmsagZg5e8HVlY49ulK5lbzfaP188bnndeIf2Uhuoj87ScvXsGVPsO50v9x4mbMI3v/4UorUAA0quFDRFwyVxNSydPpWX/oLF0a1y7ULi0rh4PnIunWOND0XFZOHhnZuaafd5++TN0ShhGL/4bG9YKJuBLFlagY8vLyWLtpB906mN8hIzUtnbw84/wwv/y9gZBmjXBydKCajzdhx0+TdW3ehL2hYQTVql5p2RvV8iMiNomr8SnG/n7wFF2aBBVql5aVw8EzV+jWtE6hZesOniyyeFHRGgUFEBGdwJXYRPJ0OtbtOUqXluY5/Dzd2HvMePliQko6F6Pjqe7jzqRHerPhw2msfe9FFj77MK0bBlV4gQLgmxU/8UC34TzQbTgb125h0CPGEXzNQxqTlppuKkgUVHCeil79u3L6pPH3ibwSTfvOxn7m6e1BYN1aXL50ez/Li3In9vftK/9hUf/pLOo/nbB/9tPm2iUatVsEk52WSWpccqF12j/SnQb3NuWriR+YjXA5+s8BglrXR6PVYG1nQ63mwcScrfj9LkRxbnp3D1VVVUVR1qjXevK1x7f94rDWnz6Hd4cG2Hg40y90Mcff+oX9z35MiwWjqffCIDTWWq78trvIW9U1fvVRagzugNbehn6hi7n47RZOvP0LMZvD8OnSlJ7bFqHqDYTP/jb/rF052Xq50u6LyYDxeq7Lq3cSszmMwCeMlcwLX/+Lrbcr3dfPxcrZHtWgUvepvmy4dxppp69ybOGPdPx+BopGgyFPz+GXvyDrSuEP7z77P8DayR6NjRX+fUPYMWyB6a4E1R9ox87SHGCUUsHszsEBtFo8AVVvIPX0FUKnLAWo8uyl2e/FqersRfFsU49aQzuTcjyC7huNd0859uaPFtnni3pvaq4Vsi58/S9pZyKJ2RxGj80LUA0qF7/ZbLo04Fb3e/j/2bvP8CiqPgzj92w6pIcUEnqRLr333kRQBCk2imAHu6KigiBgFxvYRcUuYkFEpSO9915DEhJIIT27837YEAhJYAkkWV6en5cX2Z0zs8+ezG52/3PmzEuzaTr9Xq6feAfpcYm5RvlcTEF9XCoiKCd7rUduxj3AJ2fujzOXtr0S+8zlZAf7pUGr3NkFW5YVW1oGq++ZnrOs1VdPsP6RmaRFx7N7+lyavnc/1Ub1JCs5Pec1u+ONn6g/4Q46L5wChkHKkRP5XikkvGcT6k+6E/cgX1p9+QQJWw+xfPAUAMq0rEnq8ZOkHHa80HWx/cWZ+/yirDZOTnmH0Pdftl+C9Jf5ZO47hM8tNwCQ9MNv+I+6HYu/L0Hjso+2ZVk5PvT+K5ujEFxdLDx1c1vunfkbNptJ32Y1qRYWyPcr7EcnB7SyF7r+3XKAljXK4+Vx9lSzuNOpPPKJ/fK9WTYbPRtVp3WtCsX/JC7g8eensGbDZuLjE+nc7zbuG3E7/ft0v/iKTsBZs7u6ujBuzN2MfvxFrDYrN/XsQrXKFfj2F/u+cGvfHuw/fJRxk9/CxWKhSqXyTHjiAQCur30dXdu3YuDdj+Di4kLN6pUZcEPxPSdXFwtPDezEve/+ZN/fW9ahWngZvl9qP/9+QFv7sbZ/N+6lZa2KufZ3sM9TsXLnYZ4d3KXYMp/N7sLTd9zAva98js1mo1+7xlQrF8p3/9hHzA3s3IxR/Trw3Mwf6f/0dEzTZOyt3QnwKd4RHwVZtGAZ7bu05p/Vv5CamsZTD72Qs+zD2W/xzNiJxETH8tr7LxEYFIBhwI6tuxn/uP3v9LuvfcjU6S/y2+JvMQx4ZcLbnMqenLIoXc37O8D2hRuo07Eh4xe/RUZqBl89fvZUx9GfPsXsJ2eQGHOKgZNGcurYCR7++SUANv+5mj/f/pHofcfYsXgTT/35Cjabycpv/+X4bscOwooUBaOgyWgMw/gIGHv+3BOGYVQFPjdN06EZzX4KG1L0s92IiIhcgkZhJy7eyEmFTupd0hEKzbX5jSUd4dqUWfyXorxSsrYtLukIhWb4Bl28kZOq13tKSUcotO2bvyzpCIX2aMsXSjpCob198NuSma21mLxc8bb/y++0Tx/60il/bwWOpDBNc2QB9+8zDKNt0UUSERERERERkWtRYS9BWvzj30RERERERETk/1phixQfX9EUIiIiIiIiInLNK/B0D8Mw5ha0CLh6T7ITERERERERcZCN/8spKZzWha7u0Ra4DTj/0gAG0CxvcxERERERERGRwrtQkWIlkGKaZp5plQ3D2FV0kURERERERETkWnShq3v0BDAMo7ZpmtvPWzy+SFOJiIiIiIiIyDXnQiMpzvjOMIxZwDTAM/vfJkDLogwmIiIiIiIiUtJsJR3gGuPI1T2aA+WBFcAaIBJoXZShREREREREROTa40iRIhNIBbywj6Q4YJqmikkiIiIiIiIickU5UqRYg71I0RRoAww2DOOHIk0lIiIiIiIiItccR+akGGGa5trsn6OAvoZh3F6EmURERERERETkGnTRIsU5BYpz75tVNHFEREREREREnIdZ0gGuMY6c7iEiIiIiIiIiUuRUpBARERERERERp6AihYiIiIiIiIg4BUcmzhQRERERERG5JtlKOsA1RiMpRERERERERMQpqEghIiIiIiIiIk5BRQoRERERERERcQqak0JERERERESkADajpBNcWzSSQkREREREREQcZhhGoGEYCwzD2JP9b0A+bcobhrHQMIwdhmFsMwxjjCPbVpFCRERERERERC7FU8A/pmlWB/7Jvn2+LOBR0zRrAS2A+w3DqH2xDatIISIiIiIiIiKXoi/wefbPnwP9zm9gmuZx0zTXZ/+cBOwAIi62Yc1JISIi15yUJPeSjlBohn9wSUcoPGtmSScoPJutpBMUnptHSScoNMM7z+jhq4YZdaikIxSat4tnSUcoNDM9uaQjFFoWZklHkALY9LvJT6hpmsfBXowwDCPkQo0Nw6gENARWXWzDKlKIiIiIiIiIXGMMwxgFjDrnrpmmac48Z/nfQFg+qz5ziY/jDfwIjDVNM/Fi7VWkEBEREREREbnGZBckZl5geZeClhmGEW0YRtnsURRlgZgC2rlhL1B8ZZrmT47k0pwUIiIiIiIiInIp5gJ3Zv98J/DL+Q0MwzCAj4Edpmm+7uiGVaQQERERERERkUsxBehqGMYeoGv2bQzDCDcM44/sNq2B24FOhmFszP6/18U2rNM9RERERERERAqgaTPzMk0zDuicz/2RQK/sn5cBxqVuWyMpRERERERERMQpqEghIiIiIiIiIk5BRQoRERERERERcQqak0JERERERESkALaSDnCN0UgKEREREREREXEKKlKIiIiIiIiIiFNQkUJEREREREREnILmpBAREREREREpgA2zpCNcUzSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECqAZKYqXRlKIiIiIiIiIiFNQkUJEREREREREnIKKFCIiIiIiIiLiFFSkEBERERERERGnoIkzRURERERERApgK+kA1xiNpBARERERERERp6AihYiIiIiIiIg4hRI73cO7WjiN3xyNf71KbJ/yHXve/z3fdo1evxv/+lUwDIPT+4+z9qEPsKakO/w4EX2aU+ux/vhUD2dhz+eI33QgZ5lvrfI0fGUkbj5emDYbC3s8hy0986LbLNu9MbWfHIBps2FabWx+bhZxq3flaRfcpg71xg8Bi0FWcjrrxnxA8sHoEs1e/ubWXPdAHwCyktPY+OQnJGw/nDd769rUe34ohrsr8ZsPsP7hmZhWxwc6lWS/VxnejWp398C7chi/1R5Nxskkh3MXVXZH+/2M+pPupOKg9sytOvyqyd5uznhcvT0B8Cjjx6kN+1g57PUSy27xcKPdnPFY3F2xuLpw7LdV7Hjlx0I9t4K4B3jT/KMxBDSoyqFvl7Bp3Gc5yww3FxpMHkaZVrXAZrJtyrdE/r7G4W3np/p9N1D+5lb27bu64Fs9gt/qjCYzPvmy8rqW9qTdL+Nz2nmVDeLIj8vYPH5WoXJezfv7xZRu15iw50ZhuFg49e1fxM34Ptdy9yrlCJ86Fs861Tjx+hfEffRTzrLAYf3wH9gNMEnfdYjIJ97AzLj8TI5avmUvU7+ej820cVPbhozo3SbX8qSUNMZ9+DNRcYlk2Wzc2b0l/do2IOpkAs98NIe4hGQMw+CW9o0Y2rV5seUGWLZqHVPe/girzUr/3t0YedstuZYnJJ3muSlvc+TYcTzc3Zn41ENUr1IRgC+++4Uff/sLwzCoXqUiLz01Bg8P92LMvp4p73yE1Wqjf++ujBzaP2/2qdM5Ehllz/7EA2ezfz+XH39fgEF29icfLNbsF/Ls5NdZsnw1gQH+zPnyg5KOk8fyzXuY+tUf2GwmN7VvxIgb2uVanpSSxrgZPxAVl0CW1cadPVvTr12jnOVWm43Bz39ASIAv7zxyW/Fm33mEaXP/s2dvVoPhnRrkWv7Zok38sX5vdk6TAzHxLHzhNvxKeeZkH/LWHEL8SjF9eI9izQ7wxEtjad25JWmpaTw/ZhI7t+zO0+bFt56hccsGnE60//0aP2YSu7ftoUP3Ntz75N2YNhOr1corz73FxtWbiyX3sjWbmPrBLKxWGzf37MDIW2/MtTwhKZnxr8/kyPFoPNzcmPDoKKpXKp+z3Gq1MejBZwkJCuDdiY8XS+ZzDXh+GHU6NiQzNZ0vHnuPI9sO5Glz15sPUrFeVaxZWRzctI+vx83ElmWly6g+NO3XFgAXFwth1crxRKMRpCQ49vlC5EorsSJFZvxpNj/7OWV7NLlgu83jvyTrdCoA9V64jarDu7H7nV8dfpzEnUdYOfwNGr4yItf9houFpu/ez9oH3iNh+2HcA7yxZWY5tM2YpVs5Pn8dYP8g3HzmGBa0fSxPuwZTh7PyrtdI2hNJlbu6UPPhfqwbM6NEsycfjmHJTRPJTEgmtFN9Gr46kkW9xuduZBg0fvtelg2YxOn9UdR64hYqDGzHodmLSjS7o/0et3oXUQvW0/an5xzOW9TZHer3bP71K+PmW+qqy76k34Scn5t/NDbnd1VS2W3pmSzt/xLWlHQMVxfaz32eqH82cSr7g92lPLeCWNMz2T71B3xrlsO3Zvlcy2qO7Ud6bAILWj8KhoF7gLfD2y3Invd+Y897vwEQ1rUR1Ub3dLhAcaG8Wclp/NtlXM7tjvMnceyPwhdUrub9/YIsFsq+cC+H7nyWzKhYqvz8Bkn/rCRj75GcJtaEJKImzMCnW8tcq7qGBhF4Zx/2db8XMz2DiLefwrdPexJ+/PvycznAarMx+ct5zHj0NkIDfRky4SM6NKhB1YjgnDbf/ruGKuHBTB8zmJOJyfR95l16t6yHi8XCY7d2o1bFsiSnpjNowoe0qF0l17pFmt1q5aU3ZvDh6xMICw7i1lGP0rFNM6pWqpDT5sNZ31OzWmXenjSO/YeOMumND/j4zZeIPhHHVz/8yi+z3sXTw4NHn5/KvH+X0q9n5+LL/tYMPnz1RXv2ex6nY+tmVD3ni82HX/5gz/7S0/bsb83g49cn2rP/+Bu/fD7dnv2FacWa/WL69erKkP43Mm7iqyUdJQ+rzcbkL35jxhN32vf3F2bQoWFNqkaE5LT59p9VVAkPYfrDt9n396fepner63FztX80/uqv/6gSHszpVMcPjF2p7C//vJwPRvUi1K80Q9+eQ/s6FakaGpDT5q4O9bmrQ30AFm8/xJdLtuQUKAC+XrqVyiH+JKdnFGt2gDadW1KhSjn6tryVeo3qMG7qY9zRa1S+bd+c8C5//7Yo132rlq5j0fxlAFSvVZWpMydyc9shRR0bq9XGpHc/Y+bLTxNWJpBBDz5HxxaNqFqxXE6bj775hZpVK/DW8w+z/3Akk9/9jI+mnv3b+eWcP6lcPpzklNQiz3u+Oh0aElI5jBc6PESlhtUZNGkkr/R7Jk+7NXOW8dnY6QAMe3sMrQd1YumXC/h75q/8PdP+/ape58Z0GtFbBYrz2DBLOsI1pcRO90iPTeTUxv2YWdYLtjtToABw8Tp79MCllAeN3hhFxz8n0mnBZMp2b5zv+kl7Ijm973ie+0M6XE/C9sM5R/cyTp0Gm2M737kjOVxLeWKaBaxnmrh6e9nb+ZQiLSq+xLOfXLuHzOw3nZPr9uJVNjBPG/dAb2wZmZzeHwVAzOItRNzQrMSzO9rvCVsPkXIkNs/9zt7vAFgM6o0fwtaJs6++7NlcS3sS3KYOkfPWlnj2M/uMxc0Fi6sLnLfPXOi5le/fmg7zJtLp78k0nDYCLEa+249bvQtrPkfoKw7qwK7pc+03TPOSR/RcTPmbWnL05xVXLO8ZpSuH4VHGl7iVOwud7Wre3y/Eq/51ZByKJPNIFGRmkfDbEny6tMjVxhqXQNqWPZBPUcRwdcHwdAcXCxYvD7Ki4y47k6O27j9G+ZAAyoUE4ObqQo/mdVi0MfdINMMwSEnLwDRNUtIz8CvthYvFQrC/D7UqlgWgtJcHVcqWISY+sdiyb9mxhwoRZSkfHoabmxs9O7fl32WrcrXZd/AILRrbv7RVqViOY1ExxJ48BUCW1UZ6egZZWVZS09IJDrrwe9gVzb7zvOyd2vDv8vOyHzpCi0bXn5c9Pju79ZzsGQSXKb7sF9OkQT38fH1KOka+tu4/SvnQQMqFBOLm6kqP5vVYtD73e5qBQUpaep79HSD6ZAJLN+3mpvb5v/cUafbDJyhfxpdyQb64ubrQvUFVFm07VGD7eRv20aNhtZzb0fGnWbrzCDc3r1EccfNo370Nv333JwBb1m/Dx9eHMiFBDq+fes4XfK8Lfca+wrbs2keF8FDKlw3Bzc2Vnh1asPC/3Adb9h0+RvMGdQGoUiGcY9EniD2VAEDUiTiWrt5I/54diyXv+a7v1oRVPy0B4OCGPZTyKY1vsH+edtsWbcj5+dCmvQSE5f3dNLmxNWvnLi+yrCKOuKSRFIZh7DZN87qiClOQxm+OJrRzA5J2H2XLC18CUHNMP04s28b6h2fi5luKDvMmErN0q8OngnhXCQPTpPXsp/AI8uHIL/+x593fHM4U3rMJdcYNwqOMLytueyXfNusf/ZBWXz2BLS2DzNOpLOr1vFNkP6PSkA5E/7spz/0ZcUlY3Fzwr1+Z+E0HiLihOV7hgU6R3ZF+L0hJZz+joH4HqDq8O8fnryctJv6qy35GeK+mnFi2NafAWKLZLQad/pqEd+Uw9n36F6c27HPouflUD6dc35Ys7vMCZpaVBlOGUaF/Gw5/v9Shhz0zMqD2EwMIblWL04di2PT0p6THXpkvdi5e7oR2rM/G7NM1Ljfvucrf1JKjc/+7Ijnh6t7fz+caGkTm8bMF0KyoWLzqO/ZFICs6jriPfuK6pZ9hS8sgedl6kpdtuPiKV0hMfBJhgX45t0MCfNmy/1iuNoM6NeWh6d/Q5ZE3SE5LZ9o9/bGcV+w6FhvPzsNR1KtSjuISExtHWEiZnNuhwWXYsj13gaVGtUr8veQ/Gl1fmy3bd3M8OoboE3HUqVGNuwb1o8uAEXi6u9OqaUNaN2tYfNlPnCQs+NzsQWzZvid39qqV+HvpSnv2Hbs5HnWC6BOx9uy39qPLwLvx9HCnVdMGtG5afNmvZjGnztvfA33Zsu9orjaDujTnoTe/osuYV0hOy2DafQOwZBcppn01j4cHdic5rXhHUQDEJCYT5n925F2oX2m2HI7Jt21qRhYrdh3l6Zta5dz3ytyVjO3djOQrcHpbYYSUDSYq8mze6OMxhJQNJjYmb1H2/qdGc/cjw1i9dB1vT3qfzOzT3zr2bMeD4+4hsEwAD92Wd8RsUYiJO0lY8Nkv7KFlAtm8M/dnhhqVK/D38jU0qluDLTv3cTw6lujYk5QJ8GPaB7N4eORgUkpgFAWAf2ggpyLP/n06FRWHf1ggiSfi821vcXWh2U1t+f7Fz3Ld7+bpTu32Dfh2/MdFmFbk4gosUhiGkQQ541rOfEopdeZ+0zR9L7DuKGAUwGifpnQrVa2gpg5ZN3YGWAwaTL6Lcn1bcuibxYR0qEdY90ZUv7c3AC4ebpSKCCJpT6RD27S4uhDUvAYLezyHNTWdNt8/Q/ymA5xYts2h9SPnrSVy3lqCWtSk9pMDWDZwcp421Ub1ZMXQaZzasI/q993A9S/exvpHPyzx7ABlWtem4uAOLOn7Yr7LV49+h+tfvB2LhxsxizZjZtnnoyjp7I70e0FKOjtcuN89Q/2J6NOcpTdPvOqyn6vcTS05+NVC58huM/m3yzjcfEvR4tOH8a1ZjsSdR/M0O/+5Bbeti//1len4p/134eLpfkkFBsPVhVIRQcSt2cWWF76k2uhe1Ht+KGsffN/hbVxIWLdGxK3ZnXOqx+XmPVe5fi1Z+8CVyXk17+/5MvKOTnGUxdcbny4t2NNhONbEZMq98zR+fTuS8MvCi698BeR3MPL8p7Ni2z5qlg/jo8fv4EjMKUa/9iWNrquIt5cHAClpGTz67vc8Prh7zn3FIb8jqcZ54UcOvYUpb39I/+FjqF6lIjWrV8HFxYWEpNMsXLaK+d9+iI93aR4dP5Vf/1pIn27Fc7TTzGd48Pn9PnJIf6ZM/4j+I8bmzb58NfO/mWHP/vw0fv1rEX26dSiW7FczR/aZFVv3UrNCWT56ahhHYk4yetrnNKpRkXW7DhHoW5ralcNZsyPvOf1FzZHX6hlLth+iQaXQnFM9lmw/RIC3J7XLBbNmn2Pvl1fa+f0M+f8+pk/6gNiYONzc3Xju1ScZ9sBtzHz9UwAWzlvCwnlLaNSiPvc9eTf3DBxb1LEL6Pfcz2XErX2Y8v4sbrn3aapXLk/NapVwtVhYvHI9gf5+1KlemTWbthd51vw42u9nDJo4kr2rd7BvTe4RRtd3acz+tbt0qoeUuAuNpPgM8AMeN00zGsAwjAOmaVa+2EZN05wJzAT4KWxIziukyrCuVBpq/2CwYug00qLjHU9qMzn6y39Uv+8GDn2zGDBYNeLNPEN9G785Gr+6FUmLjmfF0GkFbi418iSx/+3IGYId/c9G/K+vXOCH2IKyx63cSelKIbgH+uQazu0e5INfnYo5R26P/vIfrWc/mb20ZLO7B/rQ6LW7WTFkqn0YdD5OrtuTM8dASPt6eFct6xTZL9bvF+bc/e5frxLelUPptvINwH60vNt/r/NXy0ecPvsZ7gHeBDSoysphb5xzb/Fmz09mYgonVuwgtGP9PEUK31rl8z43Aw5/t4Rtk7/N1Ta8ZxNqPnozYB8pde5EjefKOJlEVkoakX/YT3k59utKKg3p4HDec+X3GijftyVHzjnV43LznuFXuwKGiwvxmy/9g/nVvL87KisqFreyZ4+Ku4aVIdPBUzZKt25AxpForCftxaOk+SvwalSr2IoUoQE+RJ1MyLkdcyqREP/cQ/V/WbaR4b1aYxgGFUIDiSjjz4HjsdSrEkFmlpVH3v2OXi3q0qVxrWLJnJM9uAxRMWePEEafiM1z2oN36VK89PQYwP7BvPutd1OubCjLV68nomwogf72o+qd27Vk49adxVakCA0OIurEudnj8s/+1ENnsw8aZc++ZgMRZUNyZ9+2U0UKB4QG+ube30/ms78vXc/w3m2z9/cgIoIDOBAZy8bdh1m0YRfLNu8hPTOL5NR0nv7gB16+55bzH6ZosvuVJir+7HtmdEIywb6l823758Z99GhYNef2xoPRLN5+mGU7Z5ORaSU5PYNxXy9k8pCi3d8HDruZm4faJ5nctnEHYeFn5/4ILRvCiai8p+CeGVmRmZHJL9/8zh33Ds7TZv3KTZSrFIF/oB/x5/w+i0JomUCiTpx9P4+OPUlIkH+uNt6lS/HSY6MB+2u1x51jiQgLZt7i/1i4ch1L12wkPSOT5JRUnpr6HlOevK9IM7e7vTutB9vnqDm0aR8B4WUA+yizgLAgEqJP5bterzG34BPky8zRM/Msa9ynNWvmLiuyzFczzUhRvAosUpim+aBhGI2B2YZhzAHe4TJ/P/s/XcD+Txdc0jqlK4XmXBEjrFsjkvbaK8MxizZTdUT3nNnp/epWJGHrIfuoCwdEL9rMdfffgIuXO7aMLMq0rMXemX84lL10pdCc+/3rVcLi5prni3JmfDJuPqXwrhLG6f1RhLSrR9Luks/uFRFEi08eZu0D7+XMOZEfjzK+pMcmYnF35boH+rDrzTklnt2Rfr8QZ+/3qL838sf1Z/+g3bjvk+wvbM6f/YyIPs2J+ntDrqsoFHf2M9yDfDAzrWQmpmDxdCOkbV12v5t70t2CntuJpdto+dmj7J05j/TYRNz8S+Pq7ZUzkscRx//aQHCrWpxYvp3gtnVJ3H3s4ivl4/z3TVcfL8q0rMWaB967onkByt3UiqNzVly84UVyXm37u6NSN+/GvVIEbuVCyYyOw++Gdhx72LHTzrIiT+DVoAaGpwdmWjqlW9Undcvei694hdSpHMHh6JMcPXGK0ABf/ly1jZdH35SrTVigH6u2H6DRdRWJSzjNwag4ygUHYJomL3z6K1XKBnNH95YFPELRqVuzOoePRnI0MorQ4CDm/bOUaeNzDwFPTDqNl6cHbm5u/PjbXzSuXwfv0qUoGxrM5u27SE1Lx9PDnVXrNlGnZvXiy16jOoePHufo8WhCywQy799lTHv2kYKz/77gbPaQYDZv3302+/rN1KlRtYBHknPl3t99+HPVFl6+Z0CuNmGB/qzavp9GNSrZ9/fjsZQLCWDMwK6MGdgVgDU7DvD5vOXFVqAAqFM+mMOxiRw7mUiIb2nmb9yXb5EhKTWDdfujci17qFczHupln0Nszb5Ivli8ucgLFADfffoT331qv5JRmy4tGTS8P3/O+Zt6jepwOul0vqd6lAkJyrm/Y4927Nu5H4DylSI4ctD+97Jmvetwc3Mr8gIFQN0aVTh0LIqjUTGEBgUyb9FKpj51f642iaeT8fLwwM3NlR/nLaRx3Zp4ly7F2OGDGDt8EABrNm3nsx9+L/ICBcCSWfNZMmu+PX/HhrS/swdr5y6nUsPqpCal5HuqR6tbO1G7XX3eGjIhz0gLTx8vqjevnTOxpkhJMi42IY1hGBbgAWAAUNU0zfBLeYBzR1KcyyPYj07zX8LVx8t+maHkNBa0e4Ks06m0+uoJ1j8yk7SYBNr/Mh5XHy8wDBK2HWbjk5+QdToVi6cb9SfcQWDT6mAYpBw5wX+3551hOrxnE+pPuhP3IF8yE1NI2HqI5YOnAPaJ5mo81BfTNIn+Z2OeydsKct0DfagwoC22zCysaZlsnfB1zqUwc7JHxxPeswm1nrgF02aSmZDMurEzSTkcU6LZG712N+G9m5Fy9AQAptXGwu7P5sled/wQwro0xLAY7P/8b/Z9aJ8E6Wro96ojunPd/TfgEeJPemwi0f9sZP2jH14V/X6uG/d9knNJxqsle9ufnmX39LlELzx7ubCSyu5bqzxN3r4Xw8UCFoNjc1ey8/WfqXyH/ajDgS/+ueBzi+jbghoP3ohhsWDLtLLx6U9zXRnkjO5r3sLN2wuLuyuZCcksGzSFpN3H8CpXhqbT78XNrzTpcYmsGzuD1GOXP1FihVvbEdqxPmvuyf0h4nLzAnRf9SbLh07j9N7LGyZ8NezvNUsX7kOvd4cmhD47CsNiIf6HBcS+9y0Bg3sCcGr2PFzKBFBlzptYvEuBacOWnMa+HvdgO51K8Jih+PZui2m1krZtP8fHvYWZcelXHanyReFmul+6eQ/TZs/HZjPp16YBd/dpy3cL7UWsgR2bEHMqiec++YXY+NOYmAzv1ZobWl7P+t2HGTblM6qXC8GSPaT4wf6daHv9pX/Zd6l24St6FWTJf2uZOv0jrDYbN/Xqwug7BvLtL/MAuLVvTzZu3cm4SW/g4mKhSsXyTHjqIfx87Of1v/PJ18z/dykuLi7UrF6FCU88iLu726WHsDl+Ge5c2VeuZeo7n2C1WbmpZxdG3z6Ab3/5Mzt7DzZu28m4yW/hYrFQpVJ5JjzxwNnsn85m/r/LsrNXZsLjDxQuu9uVPz3n8eensGbDZuLjEwkK9Oe+EbfTv0/3K/441r2Fu9LQ0k27mfbVPGw2G/3aNeLuG9vz3b/2bQ3s1JSYU4k89+HPxCYkYZowvHdbbmhdP9c2zhQpCnsJUjPmyMUb5Zd9x2Feyb4Ead9mNbi7c0O+/89+GsGAlrUB+GXNblbsOsLU2/K/2suZIkVhL0HaatTPhVoP4KmXH6FVxxakpabxwtjJbN9kP6Vg+levMuGRKZyIjmXGD28TEOSPYRjs2rqHSU+8QmpKKnc9MJQbBvQkKzOL9LR03pjw7iVfgnTVyrcKlXvJ6o1M+2CW/X2mW3tGDenHd7/Zr8A08IYubNy+h2deeR+LxULVihG8+PAo/Hxyj3I5U6Qo7CVIx3YoeJTgxdw6YQS129cnIzWDWY+/x+Et9sLPfZ8+xVdPziAh5hTT987m5LETpCWnAbDxz1XMe9t+efYWt7SndvsGfPJg4frvvYPfFf6cyKvAw5UG/V8Opnjj4DdO+Xu7aJEip6FhlAUamqZ5SYejCipSiIiIlJTCFimcQWGLFM6gsEUKp1DIIoVTKIIiRXEpbJHCGRS2SOEMLqdIUdIKW6RwBpdTpChpKlJcnZy1SOHw1T1M0zwOHAcwDKOraZqXdt6GiIiIiIiIyFXmKi5TX5UshVxP16URERERERERkSvqQpcgnVvQIiCogGUiIiIiIiIiIoVyodM92gK3AedfO84AmhVZIhERERERERG5Jl2oSLESSDFNc/H5CwzD2FV0kURERERERETkWlRgkcI0zZ4AhmHUNk1z+3mLxxdpKhEREREREREnYPJ/eXEPp+XIxJnfGYbxpGHnZRjGdODlog4mIiIiIiIiItcWR4oUzYHywApgDRAJtC7KUCIiIiIiIiJy7XGkSJEJpAJegCdwwDRNXSpWRERERERERK4oR4oUa7AXKZoCbYDBhmH8UKSpRERERERERJyA7f/0f2d1oat7nDHCNM212T9HAX0Nw7i9CDOJiIiIiIiIyDXooiMpzilQnHvfrKKJIyIiIiIiIiLXKkdO9xARERERERERKXKOnO4hIiIiIiIick2yYZZ0hGuKRlKIiIiIiIiIiFNQkUJEREREREREnIKKFCIiIiIiIiLiFDQnhYiIiIiIiEgBNCNF8dJIChERERERERFxCipSiIiIiIiIiIhTUJFCRERERERERJyCihQiIiIiIiIi4hQ0caaIiIiIiIhIAWyaOrNYqUghIiLXnKjTpUs6QqFV3r2ppCMUWlZacklHKDzTVtIJCs3wDijpCIXmUq1pSUcotIw/55R0hEJr4RlR0hEKzbp5UUlHEJHLpNM9RERERERERMQpqEghIiIiIiIiIk5Bp3uIiIiIiIiIFODqPeHv6qSRFCIiIiIiIiLiFFSkEBERERERERGnoCKFiIiIiIiIiDgFzUkhIiIiIiIiUgATs6QjXFM0kkJEREREREREnIKKFCIiIiIiIiLiFFSkEBERERERERGnoDkpRERERERERApgK+kA1xiNpBARERERERERp6AihYiIiIiIiIg4BRUpRERERERERMQpaE4KERERERERkQKYmCUd4ZqikRQiIiIiIiIi4hRUpBARERERERERp6AihYiIiIiIiIg4BRUpRERERERERMQpaOJMERERERERkQLYSjrANUYjKURERERERETEKahIISIiIiIiIiJOQUUKEREREREREXEKmpNCREREREREpAA20yzpCNeUEitSeFcLp/Gbo/GvV4ntU75jz/u/59uu0et341+/CoZhcHr/cdY+9AHWlHSHHyeiT3NqPdYfn+rhLOz5HPGbDgBQqnwZui55laR9kQCcXLeXjU9+4tA2y9/cmuse6ANAVnIaG5/8hITth/O0C25dm3rPD8VwdyV+8wHWPzwT0+r4tCslmR2g9lMDiejTHNNq48Dnf7Pv4/nKfg1mv9znkx/3AG+afzSGgAZVOfTtEjaN+yxnWdufnsUzxB9rWgYAywdNIT02sVA5HX2fafzWaMq0rEVmYgoA68bMIGHbIYcf50r3e6M3RhHWtSHpsYn80+HJPMvd/ErT+I1RlK4UijU9k/UPzyBx51GH8xZ1/zu6fzjj+3t+AjvWp/pLwzBcLBz/6h8OTf8lT5vqk4YR1LkhttR0tj/0Hqe32LO4+pai5uv3ULpmeTBNdjz8Polr91Bn5lhKVQ3PaZOVmMKazk8UOqMjlu+PZtrfW7DZ4Kb6FRje8ro8bdYciuWVf7aQZTMJ8HLn46FtAEhMy2TCvA3sPZGEYcALvRpSPyKwSPPmyr7tANO+X4jNNLmpVV2Gd2+ea/lnC9bwx5odAFitNg5EnWThtHvxK+3FrH/W8fOKLRhA9YgyvHh7Dzzciu/jz/JtB5n2wyJsNhs3ta7L8G7Nzsu+lj/W7LRnt2Vnn3oPp06n8MTHf+S0OxaXwL29W3Jbp0bFl33zHqZ+9Qc2m8lN7Rsx4oZ2uZYnpaQxbsYPRMUlkGW1cWfP1vRrdzaf1WZj8PMfEBLgyzuP3FZsuS/m2cmvs2T5agID/Jnz5QclHScPl2r1ce9xB1gsZK1fSOayubmWWyrVwnPQY9jiYwCw7lhD5uKfwNUNz2HjwcUNw+JC1vZVZC76odjzD3x+GHU6NiQjNZ0vHnuPI9sO5Gkz7M0HqVivKtasLA5u2sdX42Ziy7Li6ePFsDceIjAiCIuLC39/+Cv/fb+oWHIv33mEaXP/s+/vzWowvFODXMs/W7SJP9bvBcBqMzkQE8/CF27D082V4e//RmaWlSybjS71qnBf98bFkvlcA7L7PfMC/X7Xef3+9Xn9HnBOv68spn4XyU+JFSky40+z+dnPKdujyQXbbR7/JVmnUwGo98JtVB3ejd3v/Orw4yTuPMLK4W/Q8JUReZadPhTNv13GXVpwIPlwDEtumkhmQjKhnerT8NWRLOo1Pncjw6Dx2/eybMAkTu+PotYTt1BhYDsOzV7k/NmBioPa4xURxII2j4Fp4lHG95IeR9n/f7Lnx9HnUxBreibbp/6Ab81y+NYsn2f5mvvfzfnCeTkcfZ8B2DLhayJ/W12ox7nS/X7o2yXs/+QvGk+/N9/lNcb0JX7bIVYOfwPvauE0ePkulg2Y7PD2i7r/Hd0/nPH9PQ+LQY0pI9gw8CXSI+NoMv9lTsxfS8ruYzlNgjo3pFTlMFa2eAjfxtWpMW0k63o+A0D1l4YRt3AjW0e+juHmgouXBwDbRr2Zs361F24nK7tAVlSsNpOX/9rMB4NaEerjxdDPFtO+ehhVz3mPSUzL5OW/NvHuwJaU9SvFyeSzBaNpf2+hVZVQXr2pGZlWG6mZ1iLNmzu7jZe//YcPHrqFUH8fhk79ivbXV6Nq2aCcNnd1bcpdXZsCsHjzPr78dx1+pb2Ijk9i9qL1/PTcXXi6u/H4R7/y59qd9G1Zt/iyf/cvHzx4sz37tK9pX6/qedmbcFdX+3vU4i37+PLfDfiV9sSvtCffjbstZzvdxn1Ip/rViiX3mcec/MVvzHjiTkIDfRnywgw6NKxJ1YiQnDbf/rOKKuEhTH/4Nk4mJtP3qbfp3ep63FztHy+/+us/qoQHczrV8eJjcejXqytD+t/IuImvlnSUvAwD917DSJs1GTMxDs+7J5G1ax3miWO5mlkP7yT961dyr5uVSdrnL0FGOlhc8Bz+Ata9G7Ed3Vts8et0aEhI5TCe7/AQlRtWZ/CkkUzr90yedqvnLOPTsdMBGP72GNoM6sSSLxfQ4fYeHN97lPdHTsU70IcX/n2L1XOWYi3i9xyrzcbLPy/ng1G9CPUrzdC359C+TkWqhgbktLmrQ33u6lAfgMXbD/Hlki34lfLENE0+HN2bUh5uZFptDHt3Lm1qluP6iqFFmvlcZ/r9hQ4PUalhdQZNGskr+fT7mjnL+Cy734e9PYbWgzqx9MsFtD+v35//9y3WFEO/ixSkxIoU6bGJpMcmEtal4QXbnfkAC+Di5X7251Ie1J90J361ymO4uLDj1R85Pn9dnvWT9kReudDZTq7dc/bndXvxKpv3aJJ7oDe2jExO748CIGbxFmo81JdDsxc5fXaAynd2Yc2970D20KYzR1KV/drLnp8LPZ/y/VtTdWQPLG4unFq/jw1PfQK23EPkrCnpxK3eRenKRfsH3NH3mYKUVL/HrdxJqfJlClzue10Eu962H1k7vTeSUuWD8SjjS3psolP0v6P7uzO+v5/Pt1E1Ug5EkXbIfsQyZs4Kgns05dA5RYoyPZoQ9f0SABLX7cHVtzTuIf5YU9Lxb1mLHQ+9C4CZaSUrM28xIuTGlmzoP6FIn8fW46coH1Cacv6lAeheO4JFe6JyFSnmbT9KpxrhlPUrBUBgaXtB5XR6JuuPxDGxt/115OZiwc2l+Ka02nowivLB/pQr42/P3rgGizbtzfVF/1zz1u6kR5OaObetVhvpmVm4uriQlpFFsJ93ccQGCsi+ed8Fsu+iR5Maee5ftesI5YL9CA+6tML15di6/yjlQwMpF2J//fZoXo9F63fmKlIYGKSkpWOaJinpGfiV9sLFYt83ok8msHTTbkb2ac+sP1cUW25HNGlQj2PHo0s6Rr4sEdWwnYzCPJU9SmLrf7jWaELmeUWKAmVkF4RcXOz/F/MQ9frdmrDyJ/v74YENeyjlUxrfYH8ST8Tnardt0Yacnw9u2ot/mP01YWLiWdoTAI9SniTHn8aWVfQXf9x6+ATly/hSLvs11r1BVRZtO5SrSHGueRv20aOhvWhoGAalPNwAyLLayLLZMAyjyDOf6/puTViV3e8HHez3Q5v2EhB25r2oZPpdpCBXxZwUjd8cTWjnBiTtPsqWF74EoOaYfpxYto31D8/EzbcUHeZNJGbp1ksaKly6QjCdFkwm83Qq26d8R9yqXZecrdKQDkT/uynP/RlxSVjcXPCvX5n4TQeIuKE5XuGBV0V2gNIVQyjXtwXhvZqSHpfIpme+IPlAlLJf49kv9nx8qodTrm9LFvd5ATPLSoMpw6jQvw2Hv196Sdts/OZoTKuNyN/XsPONn69Izoup89RAaj1yMzFLt7Jt0jfYMrKctt8Tth0mvFdT4lbvIqBhVUqVK4NXeBDuAd5O1/8X2t/PPJazvr8DeIQFkh4Zl3M7PTIO30bVc7cpG0jasdizbY7H4VE2EDPLSmZcIrXeug/vOhVJ2ryf3c9+hu2c5+HfohYZJxJIPRBVqHyOiklKI8zHK+d2qI8XWyJP5Wpz6ORpsmw2Rny1jJSMLIY0qUKfehU4Gp9CQCl3xv++gd0xidQO8+OJLvXwci+ejxAx8acJC/A5mz3Ahy0Hj+fbNjUjkxXbD/L0rZ3sbf19uKNLU3o8+yGebq60qFWRVrUrFUdsIJ/s/t5sOZj/7zon+8BOeZbNX7uLno1r5rNW0Yk5lURYoF/O7ZBAX7bsy31a2aAuzXnoza/oMuYVktMymHbfACzZRYppX83j4YHdSU5zrlEUzs7wDcBMPPueYybGYSmXdwSNS7nqeN4zBTPpFBl/fYV5Ivt3Yxh4jp6MJTCMzNV/YTu2r7iiA+AfGsipyLPvh6ei4vAPC8zzZfkMi6sLzW9qy/cvfgbAos//5N6PnmDK6hl4lPbi4wfewCyGQktMYjJh/mcLmKF+pdlyOCbftqkZWazYdZSnb2qVc5/VZmPwmz9zJC6RW1vVpl6FkHzXLSqF6fdm+fT7y9n9/kkx9fvVRL1RvAr8hGEYxvWmaW7O/tkNeBJoBmwFXjJNs8CxqYZhjAJGAYz2aUq3Upc3PHHd2BlgMWgw+S7K9W3JoW8WE9KhHmHdG1H93t4AuHi4USoiyOEja2nR8fzZ+CEyTp3G//rKtPj0Ef5u/0SuI3sXU6Z1bSoO7sCSvi/mu3z16He4/sXbsXi4EbNoM2Z2RfJqyO7i4YY1PZOF3Z8lvFdTGr8xiiX9Jij7NZzdkecT3LYu/tdXpuOfE+05Pd0veT6DNfe9S1rUKVxLe9L847FUGND2kr9kX6ptk74lLSYei7srDV8dyXUP9GHn6z87bb/vmj6X+i/dQae/J5O44wgJWw9iZlmdrv8vtr+D876/58jnaJiZ56NKPkfMTBPD1QXvepXZPe4TEtfvpfpLd1HxwX4cmPptTrOQm1oT/fPyS891ifJmzpvaajPZEZXAzEGtSMuycsespVwfEYjVZmNnVAJPda1HvfBApi7Ywicr93B/u1pFntvR7Gcs2byPBlXC8SttL8gkpqSxaPNefp8wEp9SHjz+4a/8vmo7vZvXLsLEZ+X3obagI6xLtuzPzu6Z6/7MLCuLt+zjob6tiyBhwfL7gnJ+9hVb91KzQlk+emoYR2JOMnra5zSqUZF1uw4R6Fua2pXDWbPj8k/du7bk936S+6bt+EFS3nwQMtJxqd4Az0GPkDr9key2JmkfPA2epfC89RGyQsphxjg+Z9Fly2//vsCX3cETR7J39Q72Zs/LUrtdfY5uP8SbgycQXDGUh758jr09HyftMv9uXkx+EQsaDLFk+yEaVArFr9TZ16qLxcJ3j/QnMTWdRz5fwN6ok1QLK755e/J7X7lQkWFQdr/vO6ffj5zT7w8WU7+LFORCh0E+A87MfjQFCAJeA/oBHwB3FLSiaZozgZkAP4UNyXmFVBnWlUpDOwKwYug00qLjHU9qMzn6y39Uv+8GDn2zGDBYNeJNTu/LfTSl8Zuj8atbkbToeFYMnVbw5jKyyMg4DUD85gMkH4rGu2pYgedhn5/dPdCHRq/dzYohU8k4dTrfdU6u28OSfvYhvCHt6+FdtWz2EufPnhp5ksjf7efnR/6xhsZvjlb2ayh7oZ+PAYe/W8K2yd/mWje8ZxNqPnozAOsf/fCCj5sWZT+6m5WcxpGfVxDQsOolfUkuzPtMWoy9jS0ji0PfLM75cuwM/Z6frNOp9i/32bqveYvkwycIalmzxPq/MPt7jhJ+f7+Q9ONxeISfHZrvER5ERtSpPG08I8qQgH20hkfZINKjToFpkh4ZR2L2RGsxv66k4oP9ctYzXCyE9G7Gmq5PXXKuSxXq40VU0tkPm9FJqQT7eJ7XxhN/rxC83F3xcnelcfkgdsUk0KhcECE+ntTLHg3YtWY4n6zcQ3EJ9fch6lTS2eynkgo8ZePPdbvo0fTsiIOVOw8REeRHoI/9FJbODaqzcX9ksRUpQv29c2ePP02wX+l82/65bleu01TOWLbtIDXLhxDkm/96RSU00Jeokwk5t2NOJhLi75OrzS9L1zO8d1sMw6BCaBARwQEciIxl4+7DLNqwi2Wb95CemUVyajpPf/ADL99zS7E+h6uRmXgSw/fse47hG4SZlPs9h/Szr2Xrno3QeziU8oGUs/saaSlYD+7ApVp9soq4SNH+9u60HtwZgEOb9hEQXgay3w8DwoKIjz6V73q9x9yCd5AvM0fPzLmv5YCO/PX+HABOHIom7kgMoVXDObSpaEeEhPqVJir+7N+q6IRkggt4zf25cR89GlbNd5mvlwdNqpRl+c6jRV6kaHeRfk8ooN97jbkFn3z6fX4J9LtIQS5UpDi3JNcZaGqaZqZhGEuAgsfuXsD+Txew/9MFl7RO6UqhJB+0nzcY1q0RSXvtR9JiFm2m6ojuObPS+9WtSMLWQ7k+uF+Ie5CP/YOzzaRUhRC8K4eRfCj/YV3nZ/eKCKLFJw+z9oH3cuacyM+Zc8Qt7q5c90Afdr0556rJHvnnWoLb1OHQ7MWUaVWL0/uPK/s1lL2wz+fE0m20/OxR9s6cR3psIm7+pXH19iJy3loi56296GMYLhbc/EqTcTIJw9WFsl0bErNka6FzOsozxD+nUBHeo0nOlTKcod/z4+ZbiqzUdMxMK5WGdiR25U6yTqeWaP8XZn93lvf3C0nasI9SVcriWSGY9OMnCenXiu33vp2rTez8tZQb3oPon5fj27g61qQUMrL3p/TIOEpVLUvKvuMEtq1H8u6zXxYC2tUjeU8k6cdPFirbpahT1p/DJ5M5Fp9MiI8X87cfY/KNuWef71C9LFMWbCbLZiPTamNL5Clua1qVMt6ehPl6cTAuiUpBPqw6eIIqQT4FPFIRZK8YxuGYeI7FJhDi7838dbuYPKxXnnZJqems23OUyXedXVY2wJfNB4+TmpGJp5srq3Ydpk6FYpzMrmIYh2NO5c5+V8+Cs9+Zd9mf63bmW7woanUqR3A4+iRHT5wiNMCHP1dt4eV7BuRqExboz6rt+2lUoxJxCac5eDyWciEBjBnYlTEDuwKwZscBPp+3XAUKB9ki92EJCsPwD8ZMOolL3Zak//hOrjaGtx/maXsByRJR1X7IPyXJXqiwWSEtBVzdcKlSl8zlc/N7mCtq8az5LJ5lvxJZ3Y4N6XBnD9bOXU7lhtVJTUrJ95SD1rd2ola7+rw1ZEKuI/6nImOp0boee9fsxKeMH6FVwokt4LSLK6lO+WAOxyZy7GQiIb6lmb9xH5OHdMzTLik1g3X7o3ItO3k6FVcXC75eHqRlZrFq7zGGZU+wWZSWzJrPknP6vX12v1e6QL+3urUTtfPp95ORsdRsXY99xdzvIgUxChoKZBjGfuBRwIL99I5a5yzbZJqmQ6++c0dSnMsj2I9O81/C1ccL02ZiTU5jQTv7cNxWXz3B+kdmkhaTQPtfxuPq4wWGQcK2w2x88hOyTqdi8XSj/oQ7CGxaHQyDlCMn+O/2vLM0h/dsQv1Jd+Ie5EtmYgoJWw+xfPAUwns3pfYTA7BlWcFqY/srPxK1YL0jT4lGr91NeO9mpBw9AYBptbGw+7MAZ7NHx1N3/BDCujTEsBjs//xv9n34J8BVkd3NtxRN37sfr4ggspLT2fjExyRsP6zs12D2S30+EX1bUOPBGzEsFmyZVjY+/Smn1uedWbz7mrdw8/bC4u5KZkIyywZNIeVoLO1+Ho/FzQXDxULMkq1sfn5WnokfHeXQ+0x0PG1+eAaPIB/7+8zWQ2x44mOsKekl1u9N33+A4Fa1cA/0If1EAttf+RGLmwsAB774h8DG1Wky/V5Mq43E3UdZ/8iHZCYkO03/O7S/l/D7u7+Z5fDzCerckOoT78RwsRA5eyGH3vyZ8DvsX8Aiv7AXZq57eQRBnepjTc1gx5j3SNq0HwDvOhWp+fo9WNxdST0Uw44x75GV/buq9dZ9JKzbk7MNR7WcUumS2p+xdF80r/y9BZtp0vf6Ctzdqgbfb7CPLhnQsDIAn63aw9zNhzEMg5vqV+S2pvajhTujE5gwbwOZVpMI/1JM6N0QX0/3Ah+rQBUKd/rn0q37eSX7Mp59W9bl7p4t+H6J/XjJgHb2jyO//LeVFdsPMnXEDbnWfe+35fy1bhcuFgs1y4fw/NBuuBfmEqRm4SaRW7r1AK/8uAibzaRvyzrc3aM53y/Nzt72TPZtrNhxkKnDe+daNzUjkx7PfsRvLw7HJ/vKMIVheOc/+d9Fs2/azbSv5mGz2ejXrhF339ie7/5dA8DATk2JOZXIcx/+TGxCEqYJw3u35YbWuT8enilSFPYSpC7VmhZqvQt5/PkprNmwmfj4RIIC/blvxO3079P9ij9Oxjt5r67gCJfqDeyXIDUsZG1YRObSObg26QJA1tq/cW3WDbcmXTFtVsjKIGP+LGxH9mCEVsCj370YFgsYBlnbVtovTVoIj31W+Ks6DJowgtrt65ORmsEXj7/H4S3298P7P32KL5+cQULMKd7ZO5uTx06QlpwGwMY/V/HH2z/iFxLAHa/eh19IAIYB89//hdVzLu2Uw9ffbnbxRvlYuuMwr2RfgrRvsxrc3bkh3/+3HYABLe2jr35Zs5sVu44w9bbOOevtjozjuW8XY7OZ2EyTbvWrMLpr4S4V/OhDhbvKGMCt5/T7rHP6/b5Pn+Kr7H6fnk+/zzun332z+/2vQvT7ewe/K97ZQovZkIo3/V9OS/H1oZ+d8vd2oSLFp+fd9ZRpmtGGYYQBX5mm2Tm/9c5XUJFCRESkpFxKkcLZFLZI4RQKWaRwCoUsUjiDwhYpnEFRFCmKS2GLFM7gcooUJa2wRQpncDlFipKmIsXVyVmLFAUeSjBNc1gB90dhP/1DREREREREROSKKdSFzg3D6Hqlg4iIiIiIiIjIta1QRQrg4yuaQkRERERERESueQWe7mEYRkHTARvYL0cqIiIiIiIi8n/N5P9ySgqndaHprdsCtwHnX+DeAK7eGWlERERERERExCldqEixEkgxTXPx+QsMw9hVdJFERERERERE5Fp0oat79AQwDKO2aZrbz1s8vkhTiYiIiIiIiMg150IjKc74zjCMWcA0wDP73yZAy6IMJiIiIiIiIlLSbCUd4BrjyNU9mgPlgRXAGiASaF2UoURERERERETk2uNIkSITSAW8sI+kOGCapopJIiIiIiIiInJFOVKkWIO9SNEUaAMMNgzjhyJNJSIiIiIiIiLXHEfmpBhhmuba7J+jgL6GYdxehJlEREREREREnIINs6QjXFMuOpLinALFuffNKpo4IiIiIiIiInKtcuR0DxERERERERGRIqcihYiIiIiIiIg4BUfmpBARERERERG5Jpmak6JYaSSFiIiIiIiIiDgFFSlERERERERExCmoSCEiIiIiIiIiTkFFChERERERERFxCpo4U0RERERERKQAtpIOcI3RSAoRERERERERcQoqUoiIiIiIiIiIU9DpHiIics2pWfVESUcoNOuWtJKOUGiuNRuWdITCc7l6PzKZUYdKOkKhZfw5p6QjFJr7A5NKOkKh/fBGz5KOUGhvhA0o6QiF5snako4g4hSu3r+4IiIiIiIiIkXMNM2SjnBN0ekeIiIiIiIiIuIUVKQQEREREREREaegIoWIiIiIiIiIOAXNSSEiIiIiIiJSABuak6I4aSSFiIiIiIiIiDgFFSlERERERERExGGGYQQahrHAMIw92f8GXKCti2EYGwzD+M2RbatIISIiIiIiIiKX4ingH9M0qwP/ZN8uyBhgh6MbVpFCREREREREpAC2/9P/L1Nf4PPsnz8H+uXXyDCMckBv4CNHN6wihYiIiIiIiIhcilDTNI8DZP8bUkC7N4EnuIS6iK7uISIiIiIiInKNMQxjFDDqnLtmmqY585zlfwNh+az6jIPbvwGIMU1znWEYHRzNpSKFiIiIiIiIyDUmuyAx8wLLuxS0zDCMaMMwypqmedwwjLJATD7NWgM3GobRC/AEfA3D+NI0zdsulEune4iIiIiIiIjIpZgL3Jn9853AL+c3ME3zadM0y5mmWQkYBPx7sQIFaCSFiIiIiIiISIFMzJKO4IymAN8ZhjECOAwMADAMIxz4yDTNXoXdsIoUIiIiIiIiIuIw0zTjgM753B8J5ClQmKa5CFjkyLZ1uoeIiIiIiIiIOAUVKURERERERETEKeh0DxEREREREZEC2DQnRbHSSAoRERERERERcQoqUoiIiIiIiIiIU1CRQkREREREREScguakEBERERERESmAaWpOiuKkkRQiIiIiIiIi4hRUpBARERERERERp1Bip3s0emMUYV0bkh6byD8dnsyzvPzNrbnugT4AZCWnsfHJT0jYftjh7bsHeNP8ozEENKjKoW+XsGncZznL2v70LJ4h/ljTMgBYPmgK6bGJDm3XKzyQJtPvxSPYH0yTA7P+Zd9Hf+Zq410tnMZvjsa/XiW2T/mOPe//7nBuZXecI8/nQmo/NZAKA9ri7l+auVWH59xf4dZ21Bs/hNTjJwHY/8lfHPx6UZHmLNu9MbWfHIBps2FabWx+bhZxq3c5/Bjq9/xVHdmDSrd1xDAMDnz5L/s+zJ2zKN9nDDcXGkweRplWtcBmsm3Kt0T+vsbhbbv5labxG6MoXSkUa3om6x+eQeLOo3naVRnejWp398C7chi/1R5Nxskkhx8DIKJPc2o91h+f6uEs7Pkc8ZsO5CzzrVWehq+MxM3HC9NmY2GP57ClZ15wexd7b3f0eRWkOPf1/Hg0b4rf2AfAxYWUX3/n9KzZuZZ7deuC922DADBTU4l/5U2y9u4DoPStt1CqT2/AJHPffuInTYWMC/fnleRSsxEe/UaCxYXMlX+R+e+P+bazlK+G15hXSPviFaybVwDg1u5GXFt0A9PEdvwQ6d+8BVnFl3355j1M/fpPbDYbN7VrxIgb2uZanpSSxrgZPxF1MoEsq407e7aiX9uGRMUl8MyHPxOXcBrDMLilQ2OGdmtRbLnt2XczddYf9uwdGjOiT/u82d//nqi4BLJsNu7s1Zp+7RrnLLfabAwe/z4hAb688+jtxZt95xGmzf0Pm83kpmY1GN6pQa7lny3axB/r92bnNDkQE8/CF27Dr5RnTvYhb80hxK8U04f3KNbsLtXq497jDrBYyFq/kMxlc3Mtt1Sqheegx7DFx9iz7lhD5uKfwNUNz2HjwcUNw+JC1vZVZC76oVizX8izk19nyfLVBAb4M+fLD0o6Tr4mT32GLt3ak5KSxkP3PcXmTdvzbTfuubHc2K8HVquNzz6ezYczZgHQqk0zJr08Dlc3V07GnaJv7+LZ76/m1yrATc/fSa2ODclITWf2Y+9zbNvBPG2GvvkA5etVwZpl5fCmvXw/7iNsWdac5eWvr8KYn1/iiwfeYvO8VcWYXiS3EitSHPp2Cfs/+YvG0+/Nd3ny4RiW3DSRzIRkQjvVp+GrI1nUa7zD27emZ7J96g/41iyHb83yeZavuf/dXB/GHWVm2djywlfEbzmIa2lPOv41iZglW0jafSynTWb8aTY/+zllezS55O0ru+MceT4Xcvyv9ez/5C+6/fd6nmVHf1mZ68tPUeeMWbqV4/PXAfYvhs1njmFB28ccfgz1e16+NctR6baOLOr5HLaMLFrPfoqovzeSfCAqp01Rvs/UHNuP9NgEFrR+FAwD9wDvS8pfY0xf4rcdYuXwN/CuFk6Dl+9i2YDJedrFrd5F1IL1tP3puUva/hmJO4+wcvgbNHxlRK77DRcLTd+9n7UPvEfC9sO4B3hjy8y66PYu9t7u6PMqSHHu63lYLPg9Noa4MY9jjTlB8McfkLZ0BVkHD+U0yYo8Tuz9YzGTTuPRohn+Tz5K7N33YSlThtIDbiZmyF2QkUHAxOfx6tKJ1D/mF03W8xkWPG4eTeoH4zET4vB6+DWytq3GjD6Sp537DXdh3bXh7F1+gbi17UPKtPshMwOPO57AtWFbstb8WyzRrTYbk2f9wYzHbyc00JchL35Ih4Y1qBoRktPm239WUyUimOkPD+FkYjJ9n55O75b1cHGx8NigbtSqFE5yajqDXphBizpVcq1b5Nk//5UZTw6zZx//AR0a1cqd/e+VVIkIYfqjt9uzP/EmvVvVx83V/hHtq/n/USU8mNOp6cWS+dzsL/+8nA9G9SLUrzRD355D+zoVqRoakNPmrg71uatDfQAWbz/El0u25BQoAL5eupXKIf4kp2cUa3YMA/dew0ibNRkzMQ7PuyeRtWsd5oncf6esh3eS/vUrudfNyiTt85cgIx0sLngOfwHr3o3Yju4tvvwX0K9XV4b0v5FxE18t6Sj56tK1HVWqVqJZw240blKfaa+/QI/OA/O0Gzz0ZsIjytKySU9M06RMmUAAfP18mPba89zafyTHjh7Pub+oXc2vVYBaHRpQpnJZJncYS8WG1bhl0kje6vdsnnbr5yzjq7HvAHDb2w/SYlAnVny5AADDYnDDU0PYtWRTsWa/WthKOsA1psRO94hbuZOM+NMFLj+5dg+ZCcn2n9ftxavs2Tep8v1b02HeRDr9PZmG00aAxcizvjUlnbjVu7Be5KjfpUqLiSd+y0HAfuQ1ac8xvMICcrVJj03k1Mb9mOdUJpX9yivo+ZSuGELrr5+k4/xJtJszHu9q4fmuf2r9XtJi4kss57msKWf/oLmW8sw1OY/6vXB8qkdwat1erKkZmFYbsf/tILxX7uJbUb7PVBzUgV3Ts4/cmeYlj3DwvS6CE0u3AXB6bySlygfjUcY3T7uErYdIORKb536XUh40emMUHf+cSKcFkynbvXGeNgBJeyI5ve94nvtDOlxPwvbDOSNLMk6dBtvFJ4262Hv7hZ6Xs+3r53OrXZOso5FYI49DVhapf/+LZ9vWudpkbt2GmWR//hnbtuMSUiZnmeHiguHhAS4WDE8PbLFxxZbdUqE6ttjjmCejwZpF1oaluNZtnqedW9sbsG5egZmUcN4GLODmDhYLhpsHZsLJYkoOW/cfo3xoIOVCAnFzdaVH87os2pB7pJlhGKSkpWOaJinpGfiV9sLFYiHY34dalezvRaW9PKgSHkzMqUt7LV5W9n1HKR8adDZ7i3osWrcjd3bOyZ6WnpMdIPpkAks37uKm9vm/fos0++ETlC/jS7kgX9xcXejeoCqLth0qsP28Dfvo0bBazu3o+NMs3XmEm5vXKI64uVgiqmE7GYV5KgasVqxb/8O1xiUcfMnI/pvs4mL/34kmzGvSoB5+vj4lHaNAPXp35tvZcwBYt3YTfn6+hIYG52l314jBvDbt3ZzPO7Gx9veU/gP68PuvCzh29Hiu+4va1fxaBajbrQlrf1oCwKENe/HyKYVPsH+edjsWbcz5+fCmffiFnf3c0/auHmyet5qkuCs7AlGkMK6KOSkqDelA9L/2qp5P9XDK9W3J4j4v8G+XcZg2GxX6t7nkbTZ+czSd/p5MzYdvKnSuUuXL4F+3EifX73OovbJfmez5Off5NHx1JBuf+ZyF3Z9hy4tf0WDKsEveXkTvpnT+dwrNPxqDV/iVq+JfqN/Dezah69JXafXl46x/eCagfr8ciTuPENSiJu4B3rh4uRPauQGlwoMKbH8l32fcfEsBUPuJAXT6axLNPhyTb4HhQhK2HSa8V1MAAhpWpVS5MnhdIP/5ao7px4ll21jY4zmW9n+JuuOH4FLKw+H1vauEgWnSevZTdPprEtXvv+GS8hekoOfl7Ps6gEtwGazRMTm3rSdO4BJcpsD2pW7oRdp/qwGwxcZyevZ3hP78LaFzf8R2Opn01WuveMaCGH5BmPFni1lmfCyGX9B5bQJxrdeCzBW5T4syE06SuWgOpZ/7mNIvfI6Zlox198biiA1AzKlEwgLPvn5CAnyJPpX7Q/Sgzs3YHxlLl7Gvccuz7/HEkJ5YLLk/4hw7cYqdh45Tr2pEseSGM9n9cm6HBOaTvWsL9keeoMuDU7ll3Ds8cXvvnOzTvvyDhwd1x5JPwa6oxSQmE+Z/dgRYqF9pYrKLuudLzchixa6jdKlXKee+V+auZGzvZhhG8Wc3fAMwE88WAc3EOAzfgDztXMpVx/OeKXgMfRIjuNw5GzDwvOdlSj0+A+u+LdiOOfZZSaBs2VAij50dsRgZGUVYeGiedpUql6ffzb1YsOhHvvnhQ6pUqQhA1aqV8Pf3Zc5vX/D34h8ZOKhvseS+ml+rAL6hgcRHnt3n46NO5ipAnM/i6kKTm9qyc7H9c49faAD1ujdlxVcLijyriCMKPN3DMIwHgG9M04w1DKMa8AlwPbALGGma5pbiCFimdW0qDu7Akr4vAhDcti7+11em458TAXDxdL/kc47X3PcuaVGncC3tSfOPx1JhQFsOf7/0krbhUsqD5h89zObxs8g6nerQOsp++dnzc+7zMW02gppcR/MPH8pZbnF3u6TtRf21nqM/r8CWkUXlOzrT+O17WXbLpCuaM79+j5y3lsh5awlqUZPaTw5g2cDJ6vfL6PekPZHsfudX2nz7NFnJaSRsO5TrvMtzXen3GcPVhVIRQcSt2cWWF76k2uhe1Ht+KGsffN/hbeyaPpf6L91Bp78nk7jjCAlbD+Y7wqkgIR3qEda9EdXv7W1/Dh5ulIoIImlPpEPrW1xdCGpeg4U9nsOamk6b758hftMBTizb5nCG/BT0vJx5Xz8r74fPgi5J5t6oAaX69CL2HvtrwvDxxrNtK2JuGYwt6TQBk17Aq3sXUuf/fQXzXUB+XxTPy+7R927Sf/sczPMGtXqVxqVuc5JfuhtSk/G880lcG3cga92iostbcEzAfkTzXCu27qVmhTA+evJOjsScZPQrs2hUowLeXvZTD1LS0nn0ne94fEiPnPuKQ77Zz/tdrNiyh5oVyvLR08Pt2ad8SqMaFVm38yCBvqWpXTmCNTv2F1Pis/LPnn/bJdsP0aBSaM6pHku2HyLA25Pa5YJZs8+x95wrK7/9PfdN2/GDpLz5IGSk41K9AZ6DHiF1+iPZbU3SPngaPEvheesjZIWUw4xxfO6ca1l+Ran83ic93N1JS0una4f+9O7TlbfenUyfnkNxdXXh+gZ16H/jXXh6ejLv729Yu2YT+/cdLNLcV/Nr1Z41nzsvMALolonD2b96BwfW7ASg7/g7+W3K15gOjJgUKQ4XmpPiXtM038n++S3gDdM0fzYMowPwAdC6oBUNwxgFjAIY7dOUbqWqFdT0gnxrlafRa3ezYshU+1BjAAMOf7eEbZO/zdU2vGcTaj56MwDrH/3wguckp0WdAuxD1Y/8vIKAhlUv6YOs4epCi48f5shPy4n8w/GJ8JT98rLnG+u85+Pq7UVmYjL/dhmXu6HFoNNf9i+8x/9az45pBU+ClbOvAQe+/Je6zw6+rIz55byQuJU7KV0pBPdAH/X7ZTo0exGHZi8CoM7Tt5J6PO/w+qJ4n8k4mURWShqRf9iPlB/7dSWVhnS4aN4qw7pSaWhHAFYMnca6sTNylnVf8xbJh09cdBtnGawa8WaeUzkavzkav7oVSYuOZ8XQaQWunRp5ktj/duScphL9z0b8r6982UWKrNOp+T6voJY1nXJfP5f1xAlcQs+en+wSHJzvKRuuVavg//RjxD3yFGaivdDi0aQxWZFR2OLtp1GkLVqKe726xVakMONjMfzPOfXEvwxmYu5h1Jby1fC83T4XjlHaF5dajUm3WcHF1X6aSLL9uWRt+Q+XSjWLrUgRGuhL1MmzBauYU4mEBOQe7v7L0o0M790GwzCoEBpERLA/B47HUq9KOTKzrDzyznf0almPLk1qF0vm3NnPnjoTczKREP/zsi9Zz/A+7c7JHsCByFg27j7MovU7WbZpN+mZWSSnpvP0+9/z8r0Diie7X2mizjl1KzohmWDf0vm2/XPjPno0rJpze+PBaBZvP8yynbPJyLSSnJ7BuK8XMnlIxyLPDWAmnsTwPTtSyPANwkw6lbtR+tmDBdY9G6H3cCjlAynnnA6UloL14A5cqtUnS0WKAg0fOYTb77TPO7FhwxbCI8JyloWHhxF9PCbPOpGR0fw29y8Afv91AW+/+3L2/VHExZ0iJSWVlJRU/luxlrr1ahZ5keJqfK22vr0bLQZ3AuDIpn34nzPa0j8skIToU/mu121Mf0oH+fL96LNzg5W/vgq3Tx8DQOkAH2p1aIDNamXrX8U34k/kXBcqUpy7LMQ0zZ8BTNNcZBjGBU+GM01zJjAT4KewIYUqyXlFBNHik4dZ+8B7nN5/dtjYiaXbaPnZo+ydOY/02ETc/Evj6u2VcxT6YgwXC25+pck4mYTh6kLZrg2JWbL1krI1emMUSXuOsXfGH5e0nrJfXvb8nP98sk6nknz4BBF9mnPsV/usxH61K5Cw/XDeL9AF8Azxz5kzIbx7Y5L2ODYh5KXkPF/pSqEkH4wGwL9eJSxurmScTFK/XyaPMr6kxybiFRFEeK+mLLrh+VzLi+p9BuD4XxsIblWLE8u3E9y2LokOTCy6/9MF7P/UPtTSzbcUhpsLZqaVSkM7Ertyp8MjnwBiFm2m6ojuOROR+tWtSMLWQ7kKBBcSvWgz191/Ay5e7tgysijTshZ7Z17a+0Z+3HxLkZWanud5Oeu+fq7MHTtxLReBS9kwrCdi8erSiVMvvJSrjUtoCIEvT+DUiy9jPXL2S401Ogb3OrUxPDww09PxaNKIzJ2OX8HnctmO7MESHI4RGIqZEIdrw7akz8o98V7KpLtzfvYYNIas7Wuwbl2FpcJ1WCrWsM9JkZmBS/X62I7sKbbsdSqHczg6jqMnThEa4MOfq7by8j39c7UJC/Jj1fb9NKpRkbiE0xw8Hke54ABM0+SFT36hStky3NGjVbFlzsleJYLDUXEcjTlJaKAvf67cwsv35f7iEhbkz6pt+2hUo5I9e1Qs5UICGHNrN8bc2g2ANTv28/kfy4utQAFQp3wwh2MTOXYykRDf0szfuC/fIkNSagbr9kflWvZQr2Y81KuZPfu+SL5YvLnYChQAtsh9WILCMPyDMZNO4lK3Jek/vpOrjeHth3na/qXUElHVfig6JcleqLBZIS0FXN1wqVKXzOVz83sYyfbJR1/zyUdfA9C1W3tGjLqNn3/8ncZN6pOYmER0dN4C+7zf/6ZtuxZ8/eWPtGrTjH3ZRYh5v//DlFfH4+Ligru7G40aX88H735W5M/hanytLp/1F8tn2Qs9tTo2pM2d3dkwdwUVG1YjLSmFpBPxedZpfmtHarSrzwdDJuYa4TKp7dmRsINevZft/6xXgeI85vnDsaRIGQUNVTUMYxIQAUwABgEpwE9AZ6C/aZoOnaBcUJGi6fsPENyqFu6BPqSfSGD7Kz9icXMB4MAX/9DotbsJ792MlKP2NzbTamNhd/sstRF9W1DjwRsxLBZsmVY2Pv0pp9bnnXW5+5q3cPP2wuLuSmZCMssGTSHlaCztfh6Pxc0Fw8VCzJKtbH5+lkMTwgEENatB+7nPk7D9MKbNPiR228vfUSoiKCe7R7Afnea/hKuPF6bNxJqcxoJ2T5B1OlXZC5n9Up5P0p5jNJwyHI9QfyxuLhyd8x87X/85z/p1nxtM+Zta4RkWQFrUKQ5+vYgdr/5InXG3UrZ7Y2xZVjLjT7PhyU85vbfww1Ud6ffrHuhDhQFtsWVmYU3LZOuEr3MuQap+L7x2c8bjHuiNLdPKlue/5MSybVS+ozNQtO8zSbuP4VWuDE2n34ubX2nS4xJZN3YGqcccnygxsHF1mky/F9NqI3H3UdY/8mHOJJ+tvnqC9Y/MJC06nqojunPd/TfgEeJPemwi0f9sZP2jH2LxdKP+hDsIbFodDIOUIyf47/a8s8GH92xC/Ul34h7kS2ZiCglbD7F88BTAPpFljYf6Ypom0f9sZOvE2XnWP9/F3tsv9LyKc19vUSXvZKGO8GjZHL8x94OLhZTf5nH6868o1c9+GduUOb/i99RjeHVohzXKXnQ0rVZiR9wDgM+Iu/Ds0hGsVjJ37yH+5Vch89InAPVtUbhJ81xqNcaj70iwWMhc/TeZf3+Pa0v7ZSGz/ss9D0VOkSL7EqTu3Qfj2qAtps2K7dh+0r+dDtaLX+3lfK4DCjdCaumm3Uz7+k9sNpN+bRty943t+O5f+6i0gZ2aEnMqkec+mkNswmlM02R47zbc0Ko+63cfYtjkT6leLgRL9njoB2/pTNv61116CJfCXRBt6cZdTPvKflnDfu0ac3ffDnz3j32ukoGdm9mzz/yR2Pjs7H3acUPrBrm2ceaLT2Eva2hGFTzh5QWz7zjMK9mXIO3brAZ3d27I9//ZLyc5oKV9VMova3azYtcRpt7WOd9tnClSFPYSpLb16wq1nkv1BvZLkBoWsjYsInPpHFybdAEga+3fuDbrhluTrpg2K2RlkDF/FrYjezBCK+DR714MiwUMg6xtK+2XJi0E9wcu/1TR8z3+/BTWbNhMfHwiQYH+3Dfidvr36X7FHye8as9Crzv11fF07NKW1JRUHrp/HJs22AvGs7+fydgHnyU6KgZfPx8++PBVIsqVJTk5hccffp5tW+2fe+5/aASDh96MzWbjqy9+YMb7n1/S4x9Z8NLFG+XDGV6r4wbmf2loR9w8YRg12zcgMzWd2Y9/wNEt9lNP7v70Sb59ciaJMad4Ze9XnDoWS3qy/aDHlj9X89fbuffvM0WKS70E6esHvymZCTmKSbfyPf4vqxR/HfnTKX9vBRYpAAzDuAu4F6gKeABHgDnAVNM0Ewpc8RyFHUkhIiJSVApbpHAGhS1SOIPCFimcQiGLFM6gsEUKZ1DYIoUzKIoiRXG5nCJFSStskcIZXE6RoqSpSHF1ctYixQX/4pqm+RnwWbEkEREREREREZFrWqEOCxiG0dU0TV2jRkRERERERP6v2TQnRbGyXLxJvj6+oilERERERERE5JpX4EgKwzAKmsrYAIIKWCYiIiIiIiIiUigXOt2jLXAbcPq8+w2gWZElEhEREREREZFr0oWKFCuBFNM0F5+/wDCM4ru4u4iIiIiIiEgJudAVMeXKK7BIYZpmTwDDMGqbprn9vMXjizSViIiIiIiIiFxzHJk48zvDMJ407LwMw5gOvFzUwURERERERETk2uJIkaI5UB5YAawBIoHWRRlKRERERERERK49F5qT4oxMIBXwAjyBA6Zp2oo0lYiIiIiIiIgTsKE5KYqTIyMp1mAvUjQF2gCDDcP4oUhTiYiIiIiIiMg1x5GRFCNM01yb/XMU0NcwjNuLMJOIiIiIiIiIXIMuOpLinALFuffNKpo4IiIiIiIiInKtcuR0DxERERERERGRIufI6R4iIiIiIiIi1yRTE2cWK42kEBERERERERGnoCKFiIiIiIiIiDgFFSlERERERERExCloTgoRERERERGRAthMzUlRnDSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECqAZKYqXRlKIiIiIiIiIiFPQSAoREbnmVFi7u6QjFJrbhqv3T3e5WcdKOsI1ydvFs6QjFFoLz4iSjlBoP7zRs6QjFFrkvnklHaHQujYYVdIRCu2HepklHUHEKWgkhYiIiIiIiIg4hav3cIyIiIiIiIhIEbNpVopipZEUIiIiIiIiIuIUVKQQEREREREREaegIoWIiIiIiIiIOAUVKURERERERETEKWjiTBEREREREZECaOLM4qWRFCIiIiIiIiLiFFSkEBERERERERGnoCKFiIiIiIiIiDgFzUkhIiIiIiIiUgDT1JwUxUkjKURERERERETEKahIISIiIiIiIiJOQUUKEREREREREXEKmpNCREREREREpAA2NCdFcdJIChERERERERFxCipSiIiIiIiIiIhTUJFCRERERERERJyC5qQQERERERERKYCpOSmKlUZSiIiIiIiIiIhTUJFCRERERERERJyCihQiIiIiIiIi4hRUpBARERERERERp1BiE2eWv7k11z3QB4Cs5DQ2PvkJCdsP52nX+K3RlGlZi8zEFADWjZlBwrZDDj9ORJ/m1HqsPz7Vw1nY8zniNx3IWeZbqzwNXxmJm48Xps3Gwh7PYUvPdHjbAQ2q0OH3Cawa/TaRv63Os7zK8G5Uu7sH3pXD+K32aDJOJjm87aLKXqZVLVp+9ijJh2MAiPxjDTtf//mqyH7Gxfo9uHVt6j0/FMPdlfjNB1j/8ExMq61Eszva78Ft6lBv/BCwGGQlp7NuzAckH4wu0exnXKzf280Zj6u3JwAeZfw4tWEfK4e9XqLZr/p+txh0mj+J1KiT/Hf7q4V6bgVxD/Cm+UdjCGhQlUPfLmHTuM9ylhluLjSYPIwyrWqBzWTblG+J/H2Nw9s+42p8rTrijdcn0LNHJ1JSUxkx4mE2bNyap82if3/C28cbgJDgINas3Uj/W0bQvl1LfvrxEw4cPALAnDl/8NKkN69ILke89toLdO/ekZSUVEaNeoyN+WT/++/v8fYubc8eUoa1azcycOAoBg3qxyOP3ANAcnIKDz30DFu27Ci27M9Nfpz2XVqTmpLGkw+9wPbNO/O0mTr9BZq2bMTppNMAPPngC+zYuhtvH29ee38iZSPCcHV14eP3ZvHj7F+V3QFPvDSW1p1bkpaaxvNjJrFzy+48bV586xkat2zA6cRkAMaPmcTubXvo0L0N9z55N6bNxGq18spzb7Fx9eZiyz7w+WHU6diQjNR0vnjsPY5sO5CnzbA3H6RivapYs7I4uGkfX42biS3LiqePF8PeeIjAiCAsLi78/eGv/Pf9omLLPnnqM3Tp1p6UlDQeuu8pNm/anm+7cc+N5cZ+PbBabXz28Ww+nDELgFZtmjHp5XG4urlyMu4UfXvfXmzZC/Ls5NdZsnw1gQH+zPnyg5KOk68HJ9xPi07NSEtNZ8rD09izdW+BbR+a+AA9B3anZ40+Ofc1aFmfB164FxdXVxJOJTD2lkeLIzZujZtR+p4HMSwW0v78ndTvv8613L1Fa0rdMQJsNkyrleSZ75C1bQu4ueP3ytsYbm7g4kLGssWkfPlpsWS+2pimJs4sTiVWpEg+HMOSmyaSmZBMaKf6NHx1JIt6jc+37ZYJX+f7AdcRiTuPsHL4GzR8ZUSu+w0XC03fvZ+1D7xHwvbDuAd4Y8vMcnzDFoM6zw4melHBf2zjVu8iasF62v70nFNlj121M88Xnqsl+0X73TBo/Pa9LBswidP7o6j1xC1UGNiOQ7MXlXh2R/q9wdThrLzrNZL2RFLlri7UfLgf68bMKPHsjuzvS/pNyPm5+UdjOT5/nePbL8LsV3O/V7u7J0l7juHq45XvckeeW0Gs6Zlsn/oDvjXL4VuzfK5lNcf2Iz02gQWtHwXDwD3A+9If4Cp+rV5Izx6dqF6tMjVrt6F5s0a8+87LtGrTJ0+7Dp1uzvn5u29nMvfXv3JuL1u2mr433XlF8lyK7t07UrVqZerWbU+zZg15++2XaNeuX552XboMyPl59uwP+DU7+8GDR+jWbSDx8Yl069aBd999Od/1i0L7Lq2pWKU8XZr1o0HjukyY9jS39Mi/D6e9+BZ//vpPrvtuGzGAvbv2M/q2hwkM8mf+fz8x94d5ZF6h/eL/NXubzi2pUKUcfVveSr1GdRg39THu6DUq37ZvTniXv39blOu+VUvXsWj+MgCq16rK1JkTubntkKKODUCdDg0JqRzG8x0eonLD6gyeNJJp/Z7J0271nGV8OnY6AMPfHkObQZ1Y8uUCOtzeg+N7j/L+yKl4B/rwwr9vsXrOUqyZ1iLP3qVrO6pUrUSzht1o3KQ+015/gR6dB+ZpN3jozYRHlKVlk56YpkmZMoEA+Pr5MO2157m1/0iOHT2ec39J69erK0P638i4iYX7u1XUmndqRrnKEQxtcye1G9Xi4ZfHcF+fB/NtW+P66/D2LZ3rPm/f0oyd9BBP3PY0MZEx+Af5F0NqwGLB+/6xJIx7FFvsCfzfmkHGquVYD589qJuxcT0ZK5cD4FKpCj7jXiB+1B2QmUHCUw9DWiq4uOD36ju4rl1F1s78i2IixaXEihQn1+45+/O6vXiVvbQ3UJdSHtSfdCd+tcpjuLiw49Uf8/1SlLQnMt/1QzpcT8L2wzmjNzJOnb6kx686ojuRv68moEGVAtskbM1/xEdJZ3eEs2a/WL+7B3pjy8jk9P4oAGIWb6HGQ305NHtRiWd3iGni6m3/QurqU4q0qHjA+fv9XK6lPQluU4d1Y2c4RXaHOGG/e5UNJKxLA3a9OYdq9/S6pKdTvn9rqo7sgcXNhVPr97HhqU/AlvsIgDUlnbjVuyhdOTTP+hUHdWBB28fsN0zzkkdSwf/va7VPn+7M+uoHAFatXo+fvx9hYSFERcXk297buzQdO7RmxMhHrliGwrrhhq58/fWPAKxevQE/P9+LZm/fvhWjRtn3hZUrz/4OVq9eT0RE2aIPna1Lj/bM+fZ3ADau24qPnzfBoWU4ER3r0PqmCaWzR4eUKl2KhPhEsrKK/ssmXN3Z23dvw2/f/QnAlvXb8PH1oUxIELExcQ6tn5qSmvOzVynPYj0SWb9bE1b+tASAAxv2UMqnNL7B/iSeiM/VbtuiDTk/H9y0F/+wIMB+uUHP0tmjA0t5khx/GluW4yO9LkeP3p35dvYcANat3YSfny+hocFER5/I1e6uEYO5Z+SjOf0aG3sSgP4D+vD7rws4dvR4rvtLWpMG9Th23PFRisWtdbdWzP9hAQDb1+/A29ebwJBATsbk7j+LxcI9z45i4gOTadujTc79nft1Zum8ZcRE2t9T4+PiiyW363W1sEYewxZl/32nL/4X9xZtSD2nSEHa2dei4elFrqtpnlnm6orh6mp/0xEpYU4xJ0WlIR2I/ndTgcvrPDWQzv9Ood6Lt2Fxt9dVao7px4ll21jY4zmW9n+JuuOH4FLKw+HH9K4SBqZJ69lP0emvSVS//waH1/UMCyC8V1P2f/63w+ucqySzAwQ2rk6nf16m1ddP4FMj4qrJ7ki/Z8QlYXFzwb9+ZQAibmiOV3hgiWcHx/p9/aMf0uqrJ+i5fjoVBrRh1/S5JZ79Uvf38F5NObFsK1mnU0s8O1y9/X79xNvZOnH2BT/U5/fcfKqHU65vSxb3eYF/u4zDtNmo0L9Ngds4n5tvKQBqPzGATn9NotmHY/Ao4+vw+nD1v1YvJCI8jKNHzhZHjh09TkR4WIHt+/Xryb8Ll5OUdLZQ0qJFY9atXcBvc2dRu/Z1VyzbxYSHh3H06DnZj0URHp63SHXGjTd2Z9Gi3NnPuOuuQcyfv6goYuYrtGwIxyPPfrmJiowhNCw437YPj7uPXxd9w7iJj+Du7gbAlx99S9XrKrN863x+W/ItLz3zarF9Yb6as4eUDSYq8mwRK/p4DCFl889+/1Oj+fbfz3n0xYdwy84O0LFnO35a+jVvf/kqLz48ucgzn+EfGsipyLOFoFNRcfiHFXxAzOLqQvOb2rJ98UYAFn3+J2HVIpiyegbPzn+N71/8tNj6vWzZUCKPReXcjoyMIiyf12qlyuXpd3MvFiz6kW9++JAqVSoCULVqJfz9fZnz2xf8vfhHBg7qWyy5r3bBYWU4EXm2EHTi+AmCw8rkaXfTsL4s/+u/PMWL8lUi8Pbz5s3vX2PGH+/RrX/XIs8MYClTBtuJs69TW+wJLEF5c7u3aov/zC/wnTCF029MPWcDFvzf+Yig2XPI2LCWrF3FdxqfSEEKHElhGMZPwE/AHNM0i+DQpV2Z1rWpOLgDS/q+mO/ybZO+JS0mHou7Kw1fHcl1D/Rh5+s/E9KhHmHdG1H93t4AuHi4USoiqMAja+ezuLoQ1LwGC3s8hzU1nTbfP0P8pgOcWLbtouteP/EOtk6cnefIpKNKMnv85oP82eQhrCnphHZuQMtPH+WvVo4f4bsa+n316He4/sXbsXi4EbNoM2b2kY+rod+rjerJiqHTOLVhH9Xvu4HrX7yN9Y9+eFX0+xnlbmrJwa8W5txWv1969rCuDUmPTSR+8wH7vBCX8NyC29bF//rKdPxzoj2zpzvpsYkO5QUwXF0oFRFE3JpdbHnhS6qN7kW954ey9sH3Hd7G1fxavRjDMPLcd6EvLoMG9uXjT2fn3F6/YQtVqjUjOTmFnj068eP3n1CrjuNFpMtxqdkHDuzLZ599k+f+du1acuedt9K5c/8rmu9CHM3+6kvvcCI6Fnd3Nya+/iyjHryLd177kLadWrJj6y5uv2k0FSqX47Pv32Ptfxs4fTpZ2a9A9umTPiA2Jg43dzeee/VJhj1wGzNft5/XvnDeEhbOW0KjFvW578m7uWfg2KKObZdP9gsdIR48cSR7V+9g7xr7fCG129Xn6PZDvDl4AsEVQ3noy+fY2/Nx0k6nFriNK8XRfvdwdyctLZ2uHfrTu09X3np3Mn16DsXV1YXrG9Sh/4134enpyby/v2Htmk3s33ewyLNf1Rzo96DQIDr0bs/YAXk/S7i4ulDj+ut45NbH8fB05925b7N9/XaOHjhWZJHt8tnX85GxYikZK5biWvd6St0xnMRx2fNl2GzEPzASo7Q3Ps+9hEvFylgP5Z2/5VpnQyNMitOFTvdoDtiAtw3D+BuYDfxummbGxTZqGMYoYBTAaJ+mdCtVDYAqw7pSaWhHAFYMnYZ7oA+NXrubFUOmFjgcNy0mHgBbRhaHvlmc86EVDFaNeJPT+47nat/4zdH41a1IWnQ8K4ZOKzBjauRJYv/bkTOMOfqfjfhfX7nAD7HnZnfzLUWzGfZz1DwCfQjt3AAzy8bxP9depGfOKLnsK4ZOIy06Pmc9Y8ow3AN9LmE4t/P3+8l1e3LmRwhpXw/vqmeGJDt3v7sH+eBXpyKnNuwD4Ogv/9F69pMlnv1S9nf3AG8CGlRl5bA3zrlX/e5o9jOCml5H2W6NCO3cABcPN1y9vWjyzn2sfeC9nDZZ53xQPve5YcDh75awbfK3ubYZ3rMJNR+1z5Ow/tEPc00yea6Mk0lkpaQR+Yf993vs15VUGtLhgnnh6n6tXsy999zJiBFDAVi7diPlyofnLIsoV5bIAoYvBwYG0LRpQ/oPGJlz37mjEub9+S/T355MUFAAcXGnCpXtYkaPvoNhwwYBsG7dZsqVOyd7RBjHj+d/qkdgoD9NmtTn1ltzzz9Qt25N3n9/Kn373snJk/FFkvmMocMHcOvtNwGwecN2yp5zJDksPISYfE6XOHMKRUZGJj9+PZeR99snC+w/+EZmvG3/0nz4wFGOHo6kSvVKbN5w+YWr/7fsA4fdzM1DbwRg28YdhIWH5CwLLRvCiai82c+c/pGZkckv3/zOHfcOztNm/cpNlKsUgX+gH/EnE4oke/vbu9N6cGcADm3aR0B4GWAXAAFhQcRH5/866z3mFryDfJk5embOfS0HdOSv9+cAcOJQNHFHYgitGs6hTfuKJPvwkUO4/U77vBMbNmwhPOLsCK3w8DCi83mtRkZG89tc+5wxv/+6gLfffTn7/iji4k6RkpJKSkoq/61YS916NVWkyEe/O2/khiH2Uyp3btpNcPjZkULBZYOJjc59alP1OtWIqBTOV8u+AMDDy4Ovln3O0DZ3cuJ4LAknE0lLTSMtNY1Nq7ZQtXbVIi9S2GJPYAk++zq1lAnGFlfw6WRZWzfjUjYCw9cPM/Hsa9FMPk3m5g24N2lGqooUUsIuVKSIMU3zFsMwfIB+wN3ATMMwfgNmm6b5V0ErmqY5E5gJ8FPYkJyy0/5PF7D/U/u5Xl4RQbT45GHWPvBezjnJ+fEM8c8pVIT3aELizqP2cIs2U3VE95wZ6f3qViRh66Gc8+AvJnrRZq67/wZcvNyxZWRRpmUt9s78o8D252Y/V+O3RnN8wYZLKFCUbHaPYL+c+wMaVsUwjEs63/xq6HePMr6kxyZicXflugf6sOvNOSWe3ZF+z4xPxs2nFN5Vwji9P4qQdvVI2h1Z4tnPdbH9PaJPc6L+3pDrKgrqd8ezn7Ft8rc5RYYyrWpR/d7euQoUZ55b+omEPM/txNJttPzsUfbOnEd6bCJu/qVx9fYict5aIuc59j51/K8NBLeqxYnl2wluW5fE3Rf/gHU1v1Yv5v0PPuf9Dz4HoFfPztx37118++0vNG/WiMSExALndLil/w38/sffpKen59x37nnlTZs0wGKxFFmBAmDGjC+YMcP+YbpHj07cc8+dfPfdXJo1a0hiYlKB2W++uTfz5v2TK3v58uF8880MRox4mL17i/4D7FeffM9Xn3wPQIeubbhtxEB++3k+DRrXJSnxdL5zOpw710PXXh3YvdP+hTLyaBQt2zZj7cqNBAUHUrlaRY4cKrovDldz9u8+/YnvPv0JgDZdWjJoeH/+nPM39RrV4XTS6Xznozh3noqOPdqxb+d+AMpXiuDIQXvWmvWuw83NrcgKFACLZ81n8az5ANTt2JAOd/Zg7dzlVG5YndSklDzzUQC0vrUTtdrV560hE3IdNT8VGUuN1vXYu2YnPmX8CK0STuzh/F8vV8InH33NJx/Zr8jQtVt7Roy6jZ9//J3GTeqTmJiUZz4KgHm//03bdi34+ssfadWmGfuyixDzfv+HKa+Ox8XFBXd3Nxo1vp4P3v2syLJfzeZ8Ppc5n9tP8WzRqTk3DevLv78spHajWiQnJec5pWPlv6u4udHZSUzn7fqVoW3sE+Eum7+CMS89gIuLBVc3N2o3qMkPH/5Y5M8ha/dOXMLLYQkNwxYXi0f7TiRNnZirjaVsBLbj9teiS9Xq4OqKmZiA4ecHWVbM5NPg7o57wyaknHdlEJGSYBQ01NMwjPWmaTY6775AYCAw0DTNTo48wLlFinM1eu1uwns3I+Wo/U3XtNpY2P1ZAFp99QTrH5lJWnQ8bX54Bo8gHzAMErYeYsMTH2NNScfi6Ub9CXcQ2LQ6GAYpR07kO8t9eM8m1J90J+5BvmQmppCw9RDLB08B7JPL1XioL6ZpEv3PRvvw5Et05gP4mauPnJu96ojuXHf/DXiE+JMem0j0PxtZ/+iHJZq9yvBuVLmzC7YsK7a0DDY//2XOJKbOnt3Rfq87fghhXRpiWAz2f/43+z60T/p1NfR7eM8m1HriFkybSWZCMuvGziTlcMxV0e8AbX96lt3T5xK98OwVHdTvl9fvZ4oU/93+KpXvsB8hPPDFPxd8bhF9W1DjwRsxLBZsmVY2Pv0pp9bnvYxa9zVv4ebthcXdlcyEZJYNmkLS7mN4lStD0+n34uZXmvS4RNaNnUHqMccmyjufs75WB55cXKjnA/D2W5Po3q0DKampjBz5COvW2/f3X3/5glH3PM7x7JEV/yz4nmmvvMv8vxblrHvfvXcxevQdZGVZSUtN47HHX+S/lY4XuQHcXAo/5/Ubb0ykW7f2pKSkMnr0Y6xfvwWAn3/+jPvueyJnZMX8+d/w6qvvs2DB2X56772p9OvXk8OH7QcLsrKstMnnyiYXUs4773nSjnp+6pO069iK1NQ0nnroBbZusp83/eHst3hm7ERiomP54qcPCAwKwDBgx9bdjH98MinJqYSElmHq9BcJDi2DYcCMtz9j7g/zCp3lasvu7eJZ6OxPvfwIrTq2IC01jRfGTmb7JvvpENO/epUJj0zhRHQsM354m4AgfwzDYNfWPUx64hVSU1K564Gh3DCgJ1mZWaSnpfPGhHcv+RKkLTwvbe6scw2aMILa7euTkZrBF4+/x+Et9uLJ/Z8+xZdPziAh5hTv7J3NyWMnSEtOA2Djn6v44+0f8QsJ4I5X78MvxP47mf/+L6yes/SSHv+HUwXPt3YxU18dT8cubUlNSeWh+8exaYP9csGzv5/J2AefJToqBl8/Hz748FUiypUlOTmFxx9+nm1b7SNH7n9oBIOH3ozNZuOrL35gxvufX9LjR+678q+Px5+fwpoNm4mPTyQo0J/7RtxO/z7dr/jjdG2Q/xVoHDHmpQdp1qEp6WnpTH3kFXZttl9yd8oXk3jl8deJO29kxbxdv+a6BOmt9wyk58DumDYbv8+exw8f/3RJj/9DvcJdKtutaXO8Rz0ILhbS/vqD1G++xLOXfURU2h9z8RowGI/O3SErCzMjg+SP3ydr2xb7lT4eGwcWCxgG6UsXkfr1pe0rZ5SZt9ix806uUo3Ktvm/PN9j/fFlTvl7u1CRYolpmu0u9wEKKlKIiIiUlMspUpS0yylSlLTLKVJI4V1OkaKkXU6RoqRdTpGipBVFkaK4XE6RoqQVtkjhDP7fixQNw1r/X36n3RC13Cl/bwVe3eNKFChERERERERERBxVqEuQGoZRPNfUEREREREREZFrRqGKFMDHVzSFiIiIiIiIiFzzCjyx1TCMuQUtAoKKJo6IiIiIiIiI87DxfzklhdO60OxbbYHbgNPn3W8AzYoskYiIiIiIiIhcky5UpFgJpJimmWcKdMMwdhVdJBERERERERG5FhVYpDBNsyeAYRi1TdPcft7i8UWaSkRERERERESuOY5MnPmdYRhPGnZehmFMB14u6mAiIiIiIiIicm1xpEjRHCgPrADWAJFA66IMJSIiIiIiIuIMzP/T/5yVI0WKTCAV8AI8gQOmadqKNJWIiIiIiIiIXHMcKVKswV6kaAq0AQYbhvFDkaYSERERERERkWvOha7uccYI0zTXZv8cBfQ1DOP2IswkIiIiIiIiItegixYpzilQnHvfrKKJIyIiIiIiIuI8bKbzzt/w/8iR0z1ERERERERERIqcihQiIiIiIiIi4hRUpBARERERERERp+DIxJkiIiIiIiIi1yQTzUlRnDSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECmAzNSdFcdJIChERERERERFxCipSiIiIiIiIiIhTUJFCRERERERERJyC5qQQEZFrzsqQpiUdodBqPxle0hEKzbX//SUdodAM4+o9rmOmJ5d0hEKzbl5U0hEK7Y2wASUdodC6NhhV0hEKbcHGmSUdodAmNnmupCMU2oSSDiD/V1SkEBERERERESmAiSbOLE5X72EBEREREREREfm/oiKFiIiIiIiIiDgFFSlERERERERExCloTgoRERERERGRAthMzUlRnDSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECmCiOSmKk0ZSiIiIiIiIiIhTUJFCRERERERERJyCihQiIiIiIiIi4hQ0J4WIiIiIiIhIAWym5qQoThpJISIiIiIiIiJOQUUKEREREREREXEKKlKIiIiIiIiIiFNQkUJEREREREREHGYYRqBhGAsMw9iT/W9AAe38DcP4wTCMnYZh7DAMo+XFtq0ihYiIiIiIiEgBzP/T/y7TU8A/pmlWB/7Jvp2ft4A/TdOsCdQHdlxswypSiIiIiIiIiMil6At8nv3z50C/8xsYhuELtAM+BjBNM8M0zfiLbVhFChEREREREZFrjGEYowzDWHvO/6MuYfVQ0zSPA2T/G5JPmyrACeBTwzA2GIbxkWEYpS+2YddLCCEiIiIiIiIi/wdM05wJzCxouWEYfwNh+Sx6xsGHcAUaAQ+aprnKMIy3sJ8W8tzFVhIRERERERGRfJimraQjlAjTNLsUtMwwjGjDMMqapnncMIyyQEw+zY4CR03TXJV9+wcKnrsih073EBEREREREZFLMRe4M/vnO4Ffzm9gmmYUcMQwjBrZd3UGtl9swypSiIiIiIiIiMilmAJ0NQxjD9A1+zaGYYQbhvHHOe0eBL4yDGMz0ACYfLENl+zpHhaDTvMnkRp1kv9ufzXXIlcfL5q+ez9eEUFYXF3Y8/7vHPpmscObdg/wpvlHYwhoUJVD3y5h07jP7Nst7Um7X8bntPMqG8SRH5exefwsh7brFR5Ik+n34hHsD6bJgVn/su+jP3O1Kdu9MbWfHIBps2FabWx+bhZxq3dddnYAw82FBpOHUaZVLbCZbJvyLZG/r7li2cu0qkXLzx4l+bB9tE7kH2vY+frPJZ69sM/nQmo/NZAKA9ri7l+auVWH51oWcWNzaj3WH0xI2HaINfe9W+icFg832s0Zj8XdFYurC8d+W8WOV37Mt21Agyp0+H0Cq0a/TeRvqx1+jJLcZ6rfdwPlb25lfyxXF3yrR/BbndFkxieXaHZH+t2Z93fvauE0fnM0/vUqsX3Kd+x5//d82wW3qUO98UPAYpCVnM66MR+QfDDa4ceJ6GPf132qh7Ow52Z2BtIAAEQrSURBVHPEbzqQs8y3VnkavjISNx8vTJuNhT2ew5aeecHtOfq6LNOqFtdPuB2LmyvpJ5NYetNEhzMX5/vM+Xw7NKTCiyPBxULs7AVEvftTruWeVSOo9PqDlKpblWPTviR6hv2gguHhRs0fJ2G4u2G4uHDqjxVEvvbNFcvliOWH4nhl6W5spkm/2uEMb1wpT5u1R0/xyrLdZNlM/D3d+PjmxgC88M92lhyMJdDLnR+GtCjW3ADLVq9n6jufYLXauLl3F0YOuTnX8oSk04yf9g5HIqPxcHdjwhP3U71yRQ4cPsbjE17LaXf0eDT3DxvE7bf0Kb7sq9Yz5Z2PsFpt9O/dlZFD++fJ/tzU6RyJjMLD3Z2JTzxA9SoVAfji+7n8+PsCDAyqV6nIS08+iIeHe/FlX7OJqR/Msvd7zw6MvPXG87InM/71mRw5Ho2HmxsTHh1F9Urlc5ZbrTYGPfgsIUEBvDvx8WLLDbB85xGmzf0Pm83kpmY1GN6pQa7lny3axB/r99pz2kwOxMSz8IXb8HRzZfj7v5GZZSXLZqNLvSrc171x8WbfvJups/7AZrNxU4fGjOjTPtfypJQ0xr3/PVFxCWTZbNzZqzX92p3NaLXZGDz+fUICfHnn0duLNTvAgxPup0WnZqSlpjPl4Wns2bq3wLYPTXyAngO707PG2ddkg5b1eeCFe3FxdSXhVAJjb3m0OGJf0LOTX2fJ8tUEBvgz58sPSjrORfV6/g6qd6xPZmoGPz82g+PbDuZp0//N+4ioVxlrlpVjm/Yxd9wn2LKsxR9WrlqmacZhHxlx/v2RQK9zbm8EmlzKtku0SFHt7p4k7TmGq49XnmVVh3UjcfdR/rvjVdyDfOi27DUO/7gMM9OxF481PZPtU3/At2Y5fGue/YOZlZzGv13G5dzuOH8Sx/5w/AOsmWVjywtfEb/lIK6lPen41yRilmwhafexnDYxS7dyfP46wP4hv/nMMSxo+5jDj1FQdoCaY/uRHpvAgtaPgmHgHuB9RbMDxK7amadoVNLZ8+Po8ynI8b/Ws/+Tv+j23+u57i9dOYwaD/ZlcZ8XyUxIxqOM72XltKVnsrT/S1hT0jFcXWg/93mi/tnEqfXn/dG2GNR5djDRizZf8mOU5D6z573f2PPebwCEdW1EtdE9HS5QFGV2R/vdWff3zPjTbH72c8r2uPB7eoOpw1l512sk7Ymkyl1dqPlwP9aNmeHw4yTuPMLK4W/Q8JURue43XCw0ffd+1j7wHgnbD+Me4I0tM+ui23Nkn3HzLUWDKcNYPngqqcfiLvk1VpzvM7lYLFR4aTS7hzxP5vE4/tfefYZHUbVxGL9PGukJCSGFmkAUkBZ6J6GjohQVpCMoFpoFRCwozYKvCqgIqCiCvQBKL4L03nuRGgiBENLrzvthlpCQtglsCT4/r1xmd2eX/56cnZl95syZ6kumErtyOyknLmQtkhGbwLm3vqR0x8Y5nqqlpnPsibcwJKWgHOy5/493ufH3bhJ3H797+QqQadB4b/0xZj4ahr97Kfr8vIPWwWWo4nOrfeJT05my/iifPRJGoIczMUlpWY91qRZIz1rleXN1oSM07372zEwmT5vD7KnjCfDzpdezY4ho1pAq2b4Mf7ngN6pVDWbaxLGcPneBKZ/M4cuP3iG4Yjl+/fKjrNdp+/jTtG3ROL9/yizZJ02bxZwP3yHAz5eez44monmjHNnnzP+ValWDmT7pNU6fvcDkabP46qOJREVfY8Fvf7Ho2xk4lyrFy29/wLK1G+jaOdf+oJmyG5j82TfMfvc1Asr40Gv4m0Q0qUeVSuWzlvnyx0VUq1KRaeNf5PS5SKZ89g1fvn9rH2v+wuUEVwgiMSnZIpmzshsMvPvHJr545kH8vdzoM30hrR+oRBX/0lnLDAyvw8DwOgCsP3yW+f8cwMvVGU3TmDP0IVxLOZKeaWDQZ4tpUa08tSv5Wyz7lG//ZNarg/D38aT3W18QXq86VcrdmjT/p9VbCSlXlhkv9yMmLpFHx3zCQ83q4Oig79YvWLGFkCA/EpJTLZI5u8ZtGlE+uBx9WgygRr3qvPjuSJ7vMjzPZe+vfR/unjkn+Xf3dGPU5BGM6fsaVyKv4O3rbYHUhev6YHt693iEcROLt79gSaHhdfANDmBa+MuUD6tKl8mDmN11fK7l9i/cxG+jPgfgsekvUL9XODvmr7F03BLDgGbtCP8pVjvdwyXQh4B2dTmz4O88H9c0DUd3vXjh4OZMWmwCWoY+YUmFHs0JXzaRNqunEPbBYLBTuZ6fmZTKte3HyCzgqJ9bcAClynhybetRk3OnXIkl9sAZQC94xJ+4iEtA6RzLZCbd2ig4GDd4N91p9kq9wjk2Y7F+Q9NIi4m/q9kLYs3secnv/bhVKkvz718lYsVkWi18C/eqQXk+//ruk6Rcic11f3DfCE7PXUn6Df2LdurVuDvKCbf6hJ2jPXYO9qDlXtFVGdyRyCXbSb16I8f9JanPVOjWlAt/bLaJ7DdfGwpu93zfi5Wzp16N4/re02iFHdXQNBxuris9XEm5HAuAvWsp6n38DBHLJ9Jm1RQC8zkSGH8ikoRTl3LdXza8NjcOn+PG4XMApF1PAEPh7WdKn6nQvRmRS3aQfPFa1nvNeszG1jPZudUNJfXMJdLORaGlZxCzaCPeHXJ+4c24doOkfSfz/LsZklIAfcSRKmJ/vFMHo+Ko4OVCeS8XHO3t6Bjqz7rTV3Mss+x4FG2rlCXQwxkAH9dbR+zrlyuNl7OjxfJmd+DoSSoGBVIhKABHR0c6t2nB35tyjjQ7deY8jevVBiCkYnkuRl3hakxsjmW27T5AhSB/ggLyukKaeRw4eoKK5XJmX7tpW45lTp09T5Ob2SuV5+LlW9kzMjNJTU0jIyOT5JQ0/Mr4WC77sVNUDPKnQmBZHB0d6BzehL+37MqZ/dxFGtetqWevGMTFqGiuXte3YZejr7Fh+156dI6wWOabDp6LpkIZT8r7euLoYE/HulVYd+hsvssv23OKTmFVAVBK4VpK7+sZmQYyDAaUyr0eMpeDpy5Qwd+X8mV9cHRwoFOTWqzbdSTHMgpFUkoqmqaRlJKKl5sL9nb6Ln1UzA027D1Gt9aWHf1xU/MOzVjx6yoADu8+grunOz5lc/dbOzs7nn3jGb6YPCfH/W27tmXDso1cidRHOMZeizV7ZlM0qFsLL08Pa8cwSbUO9dn7+wYALuw5ibOHK+5+3rmWO7FuX9bvF/edwjPAcusXIQqTb5FCKRWilPpaKTVJKeWulJqjlDqolPpFKVX5Tv/h2hP7cXDiDzm+wGd3+uuVeIQG8eC+z2j39/vsf3MeaBoeoUGUf7Qp67u8zdp249AMBir2aFGsDBW6NeXC4i3Ffg+uFcrgXbMyMbtP5XosqHMD2m/4kGbzR7P7Rf2qLnea3dHTFYAaYx6nzcrJNJozsthH+QvK7lM/lDZr3qXZ92PwuL+czWXPS/b3E/bhEPa+/i1/d3ydA+8soO57g4r0Wu4hgbhXCaT14vGEL3kH/4jadx7QTtFm9RQeOvgFUf8c4PqenO3uHFCaoAcbcvrb1Tnut6V2L6jPANi7OOEfUYeLS7bbTvZC2h1KZn/PbvfLc2i2YAydd8+g4uMtsr6gVxvZleiNh/i705ts6DGJmm/1xt61lMmv6x4SAJpG8x/G0mblZEJfeLjI2fLrM+4hgTh6u9Hy9zeIWDGZio+3BGy/3Z0CfUi7dOuLfdrlazgFFmGnzs6OGis+ps6+b4nbsI/EPSfuWrbCXElMwd9YfADwdy9FdGLOo6xnY5OIS01nyO+76P3Tdv48mrt4ZQ1Xrl4joKxv1m1/P1+irsbkWOb+KpVZ/c9WAA4cOcGly9FERV/LscyytRvp3Lal+QNncyU6hgC/Mlm3/f18uRKdR/YNN7MfN2a/ir+fLwN7dqXdE08T0WMQHu6uNG8YZrns12II8MvW7mV8iLp6PWf24Iqs3qSPRj1w9BSXoq5m/W0++OI7XhzyJHYW/IJ/05W4RAK8b40S8vdy48qNvEf4JadlsPnYBdrVqpx1X6bBwBMf/Uabd76jSWg5alW0XGHryvU4Any8sm6X9fEk6nrOgyW92jfhdGQ07Ya/z2PjPmVMv4ewMxYpPpi/lBd7dcQujwKvJfgFlCE6MjrrdvSlaPwCyuRartugR9m0cgsxV3J+HiqElMPdy51Pfvkfs5Z+Toce7c2e+V7j6e/Djchb67+4yzF4FnCAyc7BnjrdWnByfdFH8gphLgWNpPgG2AEkAFuBo0BnYDnwdUEvqpR6Rim1Uym1c2VS7vPQAtqHkXo1jtj9/+bxbF3ZiNrEHjzL0jovsKbta9SZMhAHdxf8WtbEu3awfnRw9RT8WjyAW6XibTzKd23KhT+KV6Swdy1F4y9fZP9b35GRkHsYY+Synaxq+QpbBn1EjVcfB7jj7MrBHtdyvlzbcYy1HV4nZucJao3vc1ezx+4/w/IGI1jb9jVOfbWSpnNftqnshb0fzWDAt8F9NJ4zQj8SO3UwzmW9i/R6ysEO9+AA/uk+ie3PfUq9/z2d9eWn2Awaa9uNY1nYMHzCquBZrXyOh2tP7M/BiT/kOlJtK+1eWH8HCOhQj2s7jmed6mET2Qtp95LY329X9ZnObO7zAcvqDefsj/9Q+52+AJQNr8V9wx+hzeoptPz9DexLOeJazreQV7vFzsEe38b3s+OFz1j/6DsEdW6IX4sHTH5+QX1GOdhTunYwm/tOZdOT71HtxW64hwSUgHbPvdNfpMEQBgOHO77I/oZDcKsbivP9Fe9etLsg06Bx5Eo8M7rU5bNH6jJnx7+cvZ5k7Vh5tvHt33sH9+5OXEICjw15ie//WEq10GAc7G/t4qSnp7Nu8w46tG5m5rQ5aXkMD749+5DePYiLT6DH4FEs+H0J1UJDsLe350Z8An9v2s6KH2ex9revSU5O4c+V6ywTnPzaPWf4wT27EBefyGPPvcb3i1dQrWplHOzsWL91Nz7eXjwQGmyhtDmZ0mdu+ufwWepW9sfL9VYRz97Ojp9f6sGKN3pz8Hw0Jy/H5P1kMzCl3TcfOEG1ioGsnvEqP09+gXe//ZOE5BTW7zmKj6cbNYLLWShtHvJo6NsPSPr6+xL+UGv+mJt7Dih7B3vur30fY/u/zpg+Y+k/qg/lrfl+SqC8+np+B4UBHp44iLPbj3J2h+nz5wlhbgXNSeGhadpMAKXU85qm3Zx56iul1LCCXlTTtNnAbIDfA3rn+lT4NryPwA718G9bF/tSjji4u9Dg0+fZOezzrGUq92qddUQw8UwUieei8QgNAgXnfv6HQ1N+yvGaQZ0bUO1lfSKt3S/PyTH5W168alRE2dsXWCjJj3Kwp8lXL3L+901EFjKfxbWtR3GrXBYnH487zp4WE09GUgqRS3cCcPHPrVTuHX5Xs2f/MhG1Zi/qvUE2k92U9+Pg7kJ6XGKOeUcA/Yj6ysmAPhfFkQ9+zfc1kyNjiNmtD9dOOhdN/KlLuIcEcH3v6TvOmx6XRPTmI/hH1CHu6K3z2EvXCabRLP2czVI+Hvi3rauf3mQD7W5qf6/waFPOZzvVwxay35Rfu9tafw8Z1J7KffSh0Zv7fEBKVGyByzv5euD1QKWsESIXFm2h+Q+vGh9VbBv8Sa5TOep/MhSvmpVIiYplc58P8n3t5MgYrm45knW6RNSavXjXDiZ646FC30dhfSY58hppMfFkJqWSmZTK1a1H8Hqgkk31mTxf/9I1nAJvHRF0CvAlvRhfXjLjEonfchCv8DBSjp27a/kKUtbNmaj4lKzbUQmp+LnlHFlT1r0U3s6+uDja4+JoT70gb45fi6dS6Tss0t4hfz9fLl+5dVQwKvoaZX1zjmBxd3Nl0qv6OlTTNDo9+SzlAm/NIbBh2x6q3xdCGR9vi2S+yd/Pl8vRt0bfREVfy3XKhrubK5PGjgD07B17PUP5QH827dhDucCy+HjrR9XbtmrK3kNH6dIh3DLZy/hwOdtolKirMZS9bX4AdzdXJr0yNCt7pwGjKBfgx7L1W/h76y427NhLalo6iUnJjH3/c9579XnLZPdy43Jswq3sNxLxu23ug5uW7z1Fp7AqeT7m6VKKBiGBbDp6gaoWGgrv7+PJ5Zhbp31eiYmjrHfO0wwW/bObp7q0QilFRX9fyvmV5t/Iq+w9fo51u4+ycd9xUtMzSExO5bWZv/Duc4+bNXPXAY/wcG99jryj+47jF+SX9ZhfoB9Xo3KOagp9oCrlKgexYOM8AEq5lGLBxm/p02IA0ZeuciMmjpTkFFKSU9i37QBValThwr+mzTf2X9WoX3vqP6nvP1zcdxqvoFsHJTwDfIjPZ38ifGR33Hw9+HHoV5aIWaIVVOgRd19BIykMSqn7lFINAVelVAMApVRVwP5O/tFDU35iWb3hrGg4ku3PziB606EcBQqApIvXKNtSP8+xVBlPPKoEknj2CtEbDlHu4cZZQ3gdvd1wKV+GyGU7WdtuHGvbjSu0QAFQvlszLizcXOhyean38TPEn7jIyVlL83zcrfKtHSPvWpWxc3QgLSb+rmS/tHIPfs2qA/rR3jgTJ4k0NXspv1tDDEuHVUEpZTPZTXk/GQnJJJ6LplyXW+eJe9WomHVEfW27cQUWKAAuLd+JX/MaADj5eOAeove94nLy9cgaiWHn7EjZljWJPxmZY5kVjUaxouFIVjQcycW/trF37FwuLd9pE+1eWJ8B/Wo8ZZpWz5owFrB6dlPa3db6++m5q7Jev7ACBUB6bCKOHq76qRlA2Va1iD+uv8cr6/ZTZXDHrGW9aupXC9g1ahZr240rsEABELVuP17VK2Lv4oSyt6NM0+rEH79Q4HNuKqzPXFqxC9/G96Ps7bB3caJ0varEn7ho9T5TmMR9J3AODsSpQlmUowM+j7YgdpVpV+Fx8PHE3vglSTk74dmiDiknLbfT/YC/B+duJHExLpn0TAMrTkQRHpxzCHZ4sB97LsWSYTCQnJ7Jwag4gkvn/cXOkmpWq8rZi5e4cCmK9PR0lq3dSHizhjmWiUtIJD1dn6PktyWrqV+7Bu5ut4ory9ZuoHOb4p0aeidq3h/KuQs5s0c0a5Rjmbj4hGzZV1G/zgO4u7kSWNaP/YePk2yce2Db7v2EVCqf1z9jpuwhnL14mQuXr5CensGydVsJb5JzngO93fUJdX9b9jf1a1bD3c2VUU/1Ys2CT1kxbxpTXxtGozo1LFagAHiggh/nrsZxMSaO9IxMVuw9ResauUcuxSensev0ZSIeqJR1X0xCMnHGCSdT0jPYdvIiwWW9cj3XbNlDynHu8jUuXIkhPSOD5VsP0LpetRzLBPh6s+2QXpy+diOBM5evUr5saUb27MCq6WNY9vErvP/CEzSsEWL2AgXAwm8XM6Tjswzp+Cwbl2+i42P6KRo16lUnMT4x1ykdW9duo3u9J+jVtC+9mvYlNTmVPi0GALBxxWZqNaqJvb0dpZxLUaNuNc6dtEwxtyTb/t0qZj44jpkPjuPoyp3U7a6f2lY+rCop8ckkRMfmek69nuFUbVWLX4Z/Kl/Ahc1R+XVKpVRb4HPAADwNvAjUATyBpzVNW2TKP5DXSIrsyjSrTuhzD7Gl34cE99dnrP533hqc/b2pP+1ZnP29QSmOz1jM+d82AVDu0SbcP/wRlJ0dhvRM9r42N/eVEoCOO6bh6O6CnZMD6TcS2djrvawZ5jtu+4RNfT4g4bYvLYXxbXQ/rReP58bhc2gGfSLPQ+/+nDWM+t95a7hvWBcqPt4SQ3oGmSnpHJzwfdYlSO80u0v5MjSc8RyOXm6kXotj16hZWZPP3Y3sIU91IGRAOwwZmRhS0tg/fj4xO09YPXtR3k/8iYuEvfcUpfy9sXO058LCLXleVrLmm09SoVsznANKk3L5Ome+X8eRD/VLVNZ6uy/+bWqjZRo49skiLiwq/twlntUr0GD6cyh7O7BTXFy8laMf/ZGjv2dXf9pQLq3ak3UJUlvvMwAVe7bCP6IOO56dkeP51sxuSrvbcn8v5edFmxWTcPBwQTNoZCamsKrVGDISkmm2YAy7X5pNSlQsQZ0bUH3MY2gGjfQbiewaNZukc1ewc3akzoT++DQMBaVIOh+d51VMgjo3oM7kATj5epIel8SNg2fZ9OR7gD6J5f0jHkXTNKLW7NVPSSqEqX0m9PmHqdSrFZpB48yCvzk1Z7lF272iQ/FOY/BqU58Kbz8FdvZc+2k1l2b8il9fvRgUPX8FDn7e1Fj6IfburmgGDUNSMgcjhlOqQlmCPx4J9nYopYj5axOXPvm5WBlqvJr3ZMCF2XDmKh9uOI5Bg0drBDKkQTC/HNQLT4/X1L/8frv7LIuORGKnFN1qBNGnrv7FbuyKg+y6eJ3YlHR8XJx4tnEI3WoUPYdDjxeKlf2frbv44LOvyTQY6Na5Lc/0fYyfF68A4IlHOrL30DFef3c6dnZ2VKlcnndGv4CXhz4nQXJKKu17Ps2yBTPxcC9+0UWp4s01/s/WnfrlUw2ZdOvcjqH9HuenRXp/7/loJ/YeOsq4KdOwt7MjpHIFJowZlpX907k/sGLtRuzt7akWGsyE0cNwcir6BKZaqulXXMqRfftePvjiO73dO7Tmmd5d+fkvfe6kJx5ux97DJ3h96ky93SuV450Xn8HLI2cb79h3mG9+XVLsS5Bm7l9XrOdtOHKOqcZLkD7a6H6ebhvGL1v0q9M83lQ/ELFox3E2HzvP+31vXTHleOQ13vxpPQaDhkHT6FAnhKHt6xUrgwqoVPhCeWXfe4wPFuiXIO3aqj5PPxrOz2v0fYIn2jbiyvU43pz9G1djE9A0jae6tOLh5nVzvMaOI6f5dummYl+CtFN3068SdbuRk4bTKLwhqSmpvP/SVI7t169i9N68yUwd/RHXbhtZsezYnzkuQdrz2Sfo/ERHNIOBJT8s49evcl7quTCr9s4udvb8jB7/Hjv27Cc2Ng5fH2+eH9yPHl06Fv7EIprY4M278joPTRhIaOva+iVIR88i8oBe4O87dzSLXp1D/JVYxp+cx42LV0lN1EfZHVm+g3XTTb8M++0mnFlgnYlQLKSiT617spJzLuaATf7d8i1S5LmwUmWA65qmmXwR3cKKFEIIIYSlFbdIYQuKW6SwBcUtUtiC4hYpbEFxixS2oLhFCltQ3CKFLbiTIoW1maNIYSl3q0hhDVKkKJlstUhRpC2upmlXNU3LVErJVLtCCCGEEEIIIYS4qwqaOLMgXwG2NS25EEIIIYQQQghxlxnyuFqTMJ98ixRKqcX5PQSYfh07IYQQQgghhBBCCBMUNJKiJdAXSLjtfgU0yr24EEIIIYQQQgghRPEVVKTYCiRpmrb+9geUUsfMF0kIIYQQQgghhBD/RfkWKTRN6wyglKqhadrh2x5+y6yphBBCCCGEEEIIG1CUK2KKO2fK1T1+Vkq9qnQuSqkZwLvmDiaEEEIIIYQQQoj/FlOKFI2BCsBmYAcQCTQ3ZyghhBBCCCGEEEL895hSpEgHkgEXwBn4V9M0g1lTCSGEEEIIIYQQ4j+noIkzb9oBLAIaol96dJZS6jFN0x4zazIhhBBCCCGEEMLKDDInhUWZUqQYrGnaTuPvl4FHlVL9zJhJCCGEEEIIIYQQ/0GFnu6RrUCR/b7vzBNHCCGEEEIIIYQQ/1WmzEkhhBBCCCGEEEIIYXamnO4hhBBCCCGEEEL8J2nInBSWJCMphBBCCCGEEEIIYROkSCGEEEIIIYQQQgibIEUKIYQQQgghhBBC2AQpUgghhBBCCCGEEMImyMSZQgghhBBCCCFEPjRNJs60JBlJIYQQQgghhBBCCJsgRQohhBBCCCGEEELYBClSCCGEEEIIIYQQwibInBRCCCGEEEIIIUQ+DMicFJakzD0JSHmfmiX2L+rn5GXtCMXWyDnI2hGKzQ5l7QjF5liCszdPLbk1y3+dSm67j3je0doRiu3jz9OsHaHYriuDtSMUW6SWYu0IxVZGOVk7wn9ShuxcW4VzCR6wPK76JWtHKLbpR8pbO0KxvblzorUjFJtjmZCSuzNmAj+v++/JFWn0jWM2+XcruWtPIYQQQgghhBBC3FOkSCGEEEIIIYQQQgibUHLHdwshhBBCCCGEEGZm7ikSRE4ykkIIIYQQQgghhBA2QYoUQgghhBBCCCGEsAlSpBBCCCGEEEIIIYRNkDkphBBCCCGEEEKIfBhkTgqLkpEUQgghhBBCCCGEsAlSpBBCCCGEEEIIIYRNkCKFEEIIIYQQQgghbIIUKYQQQgghhBBCCGETZOJMIYQQQgghhBAiH5pMnGlRMpJCCCGEEEIIIYQQNkGKFEIIIYQQQgghhLAJUqQQQgghhBBCCCGETZA5KYQQQgghhBBCiHwYkDkpLElGUgghhBBCCCGEEMImSJFCCCGEEEIIIYQQNkGKFEIIIYQQQgghhLAJMieFEEIIIYQQQgiRD02TOSksSUZSCCGEEEIIIYQQwiZIkUIIIYQQQgghhBA2QYoUQgghhBBCCCGEsAkyJ4UQQgghhBBCCJEPg8xJYVE2XaSY8O5rtGnfkuTkFF584XUO7j+S53JjXh/Bw492INNg4Luvf+Lr2Qvo0DmC0eOGYzAYyMjI5O1x77Fj2x6LZR8zaRTN2zYlJTmF8SMnc/TA8VzLvDPtdeo3rUtCXCIAb42czPFDJwjv2ILnXn0azaCRmZnJ1DensXf7fotl7zl+EDUj6pGWnMo3r3zG+UP/5lrmqU9GUKlWFTIzMjiz7yTzx83GkJGJq6cb/ac+j19Ff9JT05k35nMij5+3WPYnxg/igYgw0pJTmffK53lmH/TJ8GzZT7EgW/Z+U5+jTEV/MlLT+W7MTItm7zF+IDWM2Re8MpMLeWTv/8lwKtQKITMjk3P7TvLjuDkYMjIBqNqkBt3fGoC9gz2J1+OZ3vMds+YNm9ifwLZ1yExOY/uoWVw/cCbXMm4V/Gj6xTCcvN25fuAM24Z/jiE9k0rdm1HthS4AZCSmsGvsXGIPnwPg4e2fkJ6QgpZpQMvMZFWnN836Ptq93Y8qEXVJT05lySuziTqY+33UG9Cehk91onRlf6bVfZbk6wkAVGxSne5zXuTG+WgAji/fwabpC82a9ya74Jo4te0NdnZk7PuHjG1Lcz5e4X5K9RiBFnsVgIzju8jYvFh/sJQLTp0HYVemPKCRtvRrDJGnLJL7pg5v96dKRB3Sk9P465VZXM6j3RsY292ncgAf1R2a1e5Nhj5EzUebA6Ac7ChTtRwfhz1Lyo1Ei2R/dPwAqkfUJS05jZ9emcnFQ7mz9/7kBcrXCsGQkcm5faf4ddyXWZ9VgAq1Qxj+x0TmD5vG/mXbLZIboP/bg6kbUZ+05FS+eGUGZw6ezrXM0x+8QEitKiiluPRvJF+8PIPUpBSqN3mAl+e8xpXzVwDYsXwrf0z/2WLZS9o68l7J/rhxu5pewHZ14G3b1e+N29V2z3ShYdeWANjb2xFQtTxj6g0myUKf1TvJ7uzhwqCPR1C6nC929vasnvMnW39ZZ5HcAN3GD6C6sc/8kM96ps8nw3L0mV/yWM+M/GMS84ZNY/+ybRbJ7Vi/EW7PDkfZ2ZGyfAnJv3yf43GnJs1x7T8YDPo2PnH2p2QcOgCOTnhNnY5ydAR7e9I2ridp/lyLZM7Pg+P7E2rcTv3xyiwu5fE36PHJ85SrFUxmRiYX951i8bivc/wNbMEbUz7in03b8SntzcL5X1g7jhAmsdkiRZt2LQmuUpEWDR6kXoPavPu/N+nSvneu5Z7o3ZWgcgG0btwFTdPwLeMDwMZ/trJy2d8AVK9xHzO//pDwJo9YJHuLtk2pGFKeR5v2pFa9Bxj3/iv0f/CZPJf9ZMJnrP5rXY77tm3YxboVGwEIrV6F92dPpHvL3O/dHGqGh1E2OJA3w4cTHBZKn8lP817XcbmW275wA1+Pmg7A4OkjadGrLf/MX0nnF7pz4fC/fDF0Kv5Vgug9YQgf95lgkewPhIdRNjiA8eEjCA4L5cnJQ/ig6+t5ZN/I3FEzAHhq+kha9GrDP/NX0emFblw4fIZZQz/Ev0oQvSYMZlqfiRbJXiO8Ln7BAUwMH0nlsFCemDyYj7q+kWu5nQs3MM+YfcD0ETTr1YaN81fh4unKExMHM3PAFK5HXsPd19OseQPb1MEjJIClzV7Gt15V6r83iNUPjc+1XO03enFs9jLOL9pK/fefIvjJcE7NW0PCuWjWdp9I+o0kAtrUocHUwTme//djk0iLSTDrewAIiahD6eAAZrV+maCwKnScNJB5Xd/OtdzFncc5tWYPvX/M3Z8u7DjGr0/9z+xZc1AKp/b9SP3pQ7T4GJwHvEXmyb1o1yJzLGY4f5zU36blerpT2z5knj5I2sLPwc4eHJ0slRyAKhF18AkOYGbrlwkKq0qnSYP4pmvu/nN+53FOrNlD3x9zfha2zlrC1llLAAhtG0ajIZ0tVqCoZvysvhf+IhXDqtJj8mCmd81dSNu9cBPfj/oMgD7Th9O4VwRb5q8GQNkpHhrbm2P/7LNI5pvqRtQjIDiIl1o/T9Ww+3hq0lDe6vpqruXmT/ia5IRkAPq+OYgOAx7kz5m/A3B0xxE+fGqyRXNDyVtH3ivZb25X3w4fQeWwUHpNHsLUPLarOxZu5Btj9kHTR9K8Vxs2zF/F6tl/snr2nwDUalufNoMfsliB4k6zt+7XiUsnLzBzyPu4+3gwfu00dizcQGa6+b+AVg+vS5ngQKaEj6JSWFUemzyEaXn0md0LN7Jg1KcA9J0+nCa92rB5/ipAX888bOn1jJ0d7i+M4sa4lzFcjcZ72izStm0i89zZrEXS9u4mbesmAOwrh+Ax7m1in+kP6WncGPsipCSDvT1eH36Kw85tZBw9bLn82YSG18E3OIBp4S9TPqwqXSYPYnYe26n9Czfx26jPAXhs+gvU7xXOjvlrLB23QF0fbE/vHo8wbuKH1o4ihMkKnZNCKTVMKVXaEmGy6/BgBL/+qB/1271zP56eHpT1L5Nruf6DevLJ1JlZl4W5djUGgKTE5KxlXNxcsOQAndYdW/DXz8sBOLD7EB6eHpQp62vy85OTsmV3dbboJW/qdGjI1t/XA/DvnhO4eLjh6eeda7mD626NSjmz7ySlA/T3FxhaniObDgIQdSoS3/J+eJTxMn9woE6HBmz9/R9Az+6aT/ZDt2X3NmYPCC3P0U0HAMtnr9WhIduN2c8U0O6H1+3N+v3svpN4BehFufqPtGDf8u1cj7wGQMK1OLPmLdepPmd+2QDAtd0ncfR0xbls7rz+LR7gwl/6UeIzP/9Duc4N9OfsPEH6jST9910ncAn0MWve/IS2r8/B3/SCYOSeU5TydMMtj/cRdegsNy5ctXC6/NkFhqDFXkG7EQ2GTDKObMc+NMy0Jzs5Y1fhPjL36/0NQyakJhf8nLvsvvb12f+b3n8i95zE2dMV92K2e41Hm3Fo0RZzxMzTAx3qs/N3Pfu5PSdx9nDFI4/P6tFsn9Vz2T6rAC0GdmL/sm1m/5zern77Rmz4TS/en9xzHFdPN7zL5t683yxQADiVcgIbGOJa0taR2ZXk7LU7NGBbtuymbFfPZtsnyK7BI83ZuXiT2bLe7s6zazi7OQNQytWZxNgEDBkGc8cGoGaHBuw0Zj+75yQu+axnjuRYz5zKsZ5pObAT+5dtJ96C/cXhvupkRl7EcPkSZGSQun4tTk1a5Fwo5db6RTm7kGMH/eZjDg4oBwerrnuqdajPXuO6/oJxXe+ex9/gxLpbRaCL+07hGWCd/ZmCNKhbCy9PD2vHEKJITJk4MwDYoZT6WSnVSSmlzB0KICDQn8iLl7NuX4qMIiDQP9dylYIr0KVbZ5as+Ynvfp5JcEjFrMc6PdSWdVsXM+/Hz3l5uHmHjGdXNtCPy5FXsm5HXbpC2UC/PJd9YexQflr7LS+/MwJHJ8es+yM6t+L3Dd8zff6HvPPiFLNnvsnb34cY444QQOzla5QuYIVr52BPk26tOLRe38hfOHKGep0aA1C5TlV8yvnlubNiDt7+PlyPvPWF5vrla3gXkr1xt5YcXr8XgItHzlLXmL1SnSrG7JbZ2Hj5lyb2tnb3KiR7w26tOLJe3ziWDQnE1cuN4T++xeg/36Vh91ZmzesS4ENStrzJl2JwCcz5ZcfJx520G4lomfpOXdKlGFwDcn8hCnkynMtrb23kNU0j/MextF8xiZC+EWZ6BzqPgNLEZ3sf8Zdj8PAvWk22XL2qPLVsMo9/O5oyoeXudsQ8KY/SaHExWbe1+BiUe+7cduWq4jzoHUo9/iKqTJD+XG8/tKR4nB4cjPPAt3HqNMjiIyk8AnyIy9buccVodwAHZyeqtK7NUQueLuHl75Pjs3rjckyhn9X63VpyzPhZ9fQvTc2ODdmyYLXZs96udIBvjvV7zOVrlPbPO/vQqcOYuXMugVXLseKbJVn3h9a7n3eXfcSYb9+kXGgFs2e+qaStI7MrydmLs11t1K0lh4zb1ZscnZ2o0boue5ZtNVfUXO40+7pvlxNQtRzvbp/F6yv+x6/vzLXYQSPP29YzsSasZxp0a8lRY5/x8i9NrY4N2bxgldmz5shRpgyG6Fv7v4ar0dj55j7A6NSsJd6z5+E54T0SPn4/2wvY4f3pl/j+sJC0PTvJOJb3ad6W4Onvw43btlOeeezD3GTnYE+dbi04ud5yp2cLcS8rtEihadobQCjwFTAQOKGUmqKUqpLfc5RSzyildiqldiamxuS3WIHyqoXktXFwcnIiNTWVh9r25Pt5v/HhjFvD85cvWUN4k0cY3HcEo18bVqwcxWFq9hmTv6Bbiyfp22kIXqU9GTSsb9Zjfy/7h+4te/PSoLE8/+rTZs2bXd7Z81++98QhnNh+hJM7jgKwfOZCXL3ceGPpVCIGdOb8oX8xZFro3Ly86mcFhH9y4hBOZsu+wph93NIPsrJnZlrmqEmetb8Csj8xcTCnth/htDG7nb0dFWqFMGvQ+3zefwodh3fHLzjQXHEhr1LlbXlN6Utlm9UgpHc4+yb/mHXfmkfeYWWHN/in9weEDmyPX5NqdyNx3kz8rObn8sEzfN5sFF93fp1d36yk+5wX72a6IsqZ2xB1luSZr5Aydzzpu9ZQqtsIAJSdPXYBlcjY8zcp37yNlp6KY5OHLJo07+5e9J3/0Hb1uLDzuMVO9QDT1+83dZ/4FKe3H+XfHccAePSt/ix573s0g+WPEOZ5iCGf7LNGf8rzjQYTefICTbvoR0LPHDzNiGbP8Frnl1j5zRJenjPWjGlzKnHryGzutewF9fdexu3qKWP2m2q3q8/pnccsdqoH3Hn2Gq3qcP7wWV5rNJR3HxzNExMG4+zuYra82RXlswrw2MSnOL39CP8asz/61gD+ssp6xrTjmGmbNxD7TH/iJryOa/+nbj1gMBA7bAgx/R7H4b7q2FcKNlPOwhV1O/XwxEGc3X6Us8Z1vbj3aPfof7bKpDkpNE3TlFKXgctABlAa+FUptUrTtDF5LD8bmA1Q3qemye9+wOBe9O7/GAD79hwkqFxA1mOBQf5EXb6S6zmXIi+zdLFeKV7212r+92nuOQS2bdlFpeAKlPbx5npMrKlxiuSJQd3p3kef8+LQ3iMEBJXNesw/sCzRl3MPWb56Ra/Qpqels+jHJfR/7slcy+zeuo/ylcvh7eNFbMwNs2QP79eRFk+2A/TTH3yCfLk5hZ53gC+xUXkXmh4e+Rgevp58MfTWOW4pCcl8O/rzrNuTN37G1fO5/253S+t+HWn+ZFsAzu47RemgMoC+gSgd4Ets1PU8n/fQyMdw9/Vk9tDZObJ/N3pm1u1JGz/lmhmzt+zXgabG7Of2ncI76NaIE+8AX27kk72TMftXQ2/NgxB7OYbE6/GkJaeSlpzKqe1HKFe9EtH/XrpreasObE9IH31kQ8y+07hmy+sS6EPy5dgcy6dei8fJyw1lb4eWacA10IfkbO/Jq3oFGv5vCOv7fEDa9VvzT6RExRqfH8eFZTvxqRtC9NacO7x3ol7/dtTppb+PS/tP45HtfXgE+JBwJTafZ+aWlm1Y/Om/92E/cSAupd2zJng0Fy3+Osrz1lE15eGDlhB7W7iUrF8Np/dDh37g4o4hPgYt/jqGS/qEiZnHdlikSFG/f3vCjO0euf80ntna3bOI7X7TA12acGix+U/1aNavPY2fbAPA+X2nc3xWvQJ8iMvns9p+ZA/cfT34duiXWfdVqB1C3xl6wcittAfVw+uSmWng0MqdZsnevn9nInq1B+D0fn39fpNPgC/Xr+SdHUAzGNjy5yYeHtqV9b+szXEayN6/dzNo4lA8SnsQfz3eLNlL2jryXsneqpDtan7ZHzTuE2Tfrt5Uv0tzdizeaJa82d3N7E0fj2DFzIUARJ+N4tr5K/hXCeLsPvNMMty8XweaZK1nbu8zPvlm7zCyB26+nvwy9KOs+yrUDqHfjJHArfWMITOTg2Zaz9xkuBqNnd+t/V+7Mn4YruV/yl7Gwf3YB5ZDeXqhxd3ax9USE0jfvwenBo1IPpt7slNzadSvPfWf1LdTF/edxuu27VS8cf/kduEju+Pm68GPQ7+yREwh/hMKLVIopUYAA4CrwJfAaE3T0pVSdsAJIFeRori+/epHvv1KP6Lapn0rBj39JIt+X0a9BrWJj0vgSlTuFd2KpWtp3qoxPy34g6bNG3L6pD45T+XgCpz5V78yQ83a1XFydDRbgQLg57m/8/NcfWKxFu2a0uupHixfuJpa9R4gIT4hqyCRXZmyvln3R3Rqxamj+peGCpXLcf7MRQCq1boPR0dHsxUoANZ9t4J1360AoGZEPSIGdGLH4k0Eh4WSHJ9EXHRsruc079mGGq3q8nHvCTkqyy6erqQlp5GZnkGLXm05se0IKQnmO999/XcrWJ+VPYzwAZ3YaUL26q3qMC3P7KlkpmfS3ALZN3y3kg3frQSgRkQYrQZ0ZPfizVQOCyUln+xNe7aheqvafNp7Yo7sB1bu5LEJg7Czt8Pe0YFKdUNZ99XSXM+/Eye/WcXJb/SCYGDbuoQ+1YFzC7fgW68q6fHJpOTxJfPKpsOUf7gR5xdtpfITrYhcvgsA13K+NP9qFFuHzyTh9K3TuuxdSqHsFBmJKdi7lCKgdS0OffzHXX0fu+etZvc8fah9lTZ1qTegPUcWbyEorAqp8UkkFuHLspufF4nR+mczsE4I2CmzFygADJf+RZUui/IqgxZ/HYfqjUj9c9Zt4TwhUT8X2S4wWD8slKxn0+JiUD4BaDGXsa9UA8PVyNv/ibtu17xV7Jqn95+qberSYEAHDi/eQlBYVVLjk4tcpCjl4ULFJtVZNGpm4Qvfoc3frWLzd3r26hFhNB/Qgb2LN1MxrCop8UnE5/FZbdQzgvtb1eaL3pNyfFantByZ9XvPD5/lyJrdZitQAKyat4xV85YBULdNfToMeJAtizdSNew+kuOTiM2jSOFfKYCos/rnsl67BkSeugCAl583N4zvtUqdUJSdMluBAkreOvJeyf7Pdyv4J9t2tbVxu1q5gO1qs55tqJHHdhXA2cOF0MY1sianNKe7mT0m8irVmtfi1I6jeJTxwj8kiKvnzHfgYtN3K9lk7DPVI8JoMaAjexZvplIB65nGPSO4v1Udvritz0xuOSLr914fPsfhNbvNXqAAyDh+FPug8tj5B2C4dpVSrdsQ/37Og4d2geUwXNL3ce2rhIKDA1rcDZSXF2RkoiUmgJMTTmENSLrtyiDmtv27VWw3ruvvi6hL4wEdOLB4C+XDqpISn0xCHn+Dej3DqdqqFt/0nmLROeSEuNepwj5QSqkJwFeapp3N47HqmqYVeMJYUUZS3G7SB68T3rYFKcnJvDTsTfbvPQTAvJ8+Z/TI8URdjsbT04MZs9+nXPkAEhOTGPvSRI4cOsbzI56iR69HyEjPICUlhUlv/a/IlyD1cyr+pIlj332JZhFNSElO4e1RUzi8Tz8SPGPBh0x46T2io64y69fplPb1RinFsYMnmDxmKslJyQwc1oeHH+9MRnoGqSmpfDzhsyJfgrSRc1Cxsz85YTAPtNYvr/ft6M84e0Avngyb+xrfvfoFN65c5/OTPxJzMZqURP1o7Z7l21gy/VdC6t3HwP8NQzMYuHTiAvPGzCQprmjDO+1MHC6Yl14TBlOjdR3SktOYN/pzzhmzvzB3LPNfncWNK9f59OQPObLvXb6NpdN/I7heKAP/NwyDMfv8MV8UObvjHWR/fMJTVDdmXzB6JueN2YfOHcsPr84i7sp1Pj75PdezZd+/fDvLp/8GQJtnutDk8XAMBo2tP61l3ddF24ltnlq0i/3UmzKQwIjaZCSnsf3FWVzfpx/taDl/NDtenkNKVCxuFf1o+sVwnLzdiD14lq3DPseQlkHDD4dQ/qFGJBonRbx5qVG3in60+Fo/ZUI52HP2j80cmbao0Cz/OhW/3dtPHEBI69qkJ6ex9JXZXD6gv4/Hv3mFZWO+JOFKLPUHdqDxsw/j7udF4rU4Tv+9j2Wvfkm9Ae0J69sWLSOT9JR01k5awMVdJ4r074943rHwhfJgF1Ibp7ZPgrIj48AGMrb8hUPdcAAy9q7DoV5bHMIiwJCJlpFO+tofMVw8CYAqWwGnToNQ9g4YYqNJW/oVpCYVOcPHn6cVKztAx4kDqWJs979emcUlY7v3/GY0S8bMIeFKLA0GdqRptnY/9fdelryqj0qo/VgrQlrXZuHwT4v1719XxT+Vq9uEQdzfug7pyan8NHoWF4yf1cFzx/DLq3OIu3Kd90/O5/rFq6QaJ3E+uHwHq6b/nuN1bhYpinoJ0kgtpfCF8jFw4jPUaR1GanIqs16Zwb8H9KPCY755g9ljPuNGdCxv/ToZF3dXlFKcO/IvX78+i+SEZDoM6Ey7vp3IzMgkLSWN+ZPmcmJX0YY2l1HFn//E2uvIO2Ht7Bl3MKS3Z7bt6nfZtqvPzx3LAuN2dUYe29VlxuxNHmtNjdZ1+Xp47isNmdudZPcqW5r+Hz6PZ9nSKAUrZy5i+8INRfr3nU2a+i1v3ScMolpr/fLYP4z+Ims98/TcV/np1dnEXbnO1JMLcqxnDizfzsrb1jM3ixRFvQTpuOrFG6nj2LAx7s8MB3s7UlYuJfnH+Tg/qI80Tlm6GJfHn6RU246QkYGWlkbiVzPJOHRAv9LHK+PAzg6UInXDOpK//7ZYGaYfKV+s593uoQkDCTVup/4YPYtI43aq79zRLHp1DvFXYhl/ch43Ll4l1dh/jizfwbrpxT+48ubOu39VudHj32PHnv3Exsbh6+PN84P70aNLx7v+7ziWCbHIvIXW4uZa+Z6sQiUmnbHJv1uhRYo7dSdFCmu7kyKFtd1JkcLa7qRIYW13UqSwtqIWKWzJnRQprK24RQpbcCdFCmu7kyKFtd1JkcLa7qRIIYrvTooUovjupEhhbcUtUtiCu1WksAZzFCks5V4vUri4VLonV6TJyWdt8u9WcteeQgghhBBCCCGEuKdIkUIIIYQQQgghhBA2QYoUQgghhBBCCCGEsAkl9yR0IYQQQgghhBDCzOTqLZYlIymEEEIIIYQQQghhE6RIIYQQQgghhBBCCJsgRQohhBBCCCGEEELYBJmTQgghhBBCCCGEyIeGzElhSTKSQgghhBBCCCGEEDZBihRCCCGEEEIIIYSwCVKkEEIIIYQQQgghhE2QIoUQQgghhBBCCCFsgkycKYQQQgghhBBC5EPTZOJMS5KRFEIIIYQQQgghhLAJUqQQQgghhBBCCCGETZAihRBCCCGEEEIIIWyCzEkhhBBCCCGEEELkQ+aksCwZSSGEEEIIIYQQQgibIEUKIYQQQgghhBBC2AQpUgghhBBCCCGEEMImyJwUQgghhBBCCCFEPmRGCsuSkRRCCCGEEEIIIYSwCVKkEEIIIYQQQgghhE2QIoUQQgghhBBCCCFsgirp13xVSj2jadpsa+coDsluHZLdOiS7dUh265Ds1iHZrUOyW4dktw7JLoT53QsjKZ6xdoA7INmtQ7Jbh2S3DsluHZLdOiS7dUh265Ds1iHZhTCze6FIIYQQQgghhBBCiHuAFCmEEEIIIYQQQghhE+6FIkVJPq9KsluHZLcOyW4dkt06JLt1SHbrkOzWIdmtQ7ILYWYlfuJMIYQQQgghhBBC3BvuhZEUQgghhBBCCCGEuAeU+CKFUmq5UipWKfWXtbMUhVKqrlJqi1LqkFJqv1Kqp7UzmUopVUkptUsptdeY/1lrZyoqpZSnUuqiUupTa2cpCqVUprHd9yqlFls7T1EopSoqpVYqpY4opQ4rpSpbO5MplFIR2dp8r1IqRSnV1dq5TKWU+sD4OT2ilJqulFLWzmQqpdT7SqmDxh+bX0fmtz1SSgUrpbYppU4opX5SSjlZK2N+Csg+TCl1UimlKaXKWCtfQQrIvkApdczYf75WSjlaK2N+Csj+lVJqn3H/4FellLu1MuansP0vpdQMpVSCpXOZooB2/0Yp9W+29X1dK0XMVwHZlVJqslLquHF9P8JaGfNTQPYN2do8Uim10EoR81VA9rZKqd3G7BuVUlWtlVGIe0mJL1IAU4F+1g5RDElAf03THgA6AZ8opbytG8lkl4BmmqbVBRoDY5VSQdaNVGQTgfXWDlEMyZqm1TX+PGLtMEU0D5iqaVp1oBFwxcp5TKJp2t832xxog/7ZXWndVKZRSjUDmgO1gZpAQ6C1VUOZSCn1EFAPqIu+nhmtlPK0aqjC5bc9eh/4WNO0UOA6MNiiqUyTX/ZNQDvgrGXjFEl+2RcA1YBagAswxJKhTJRf9hc1TaujaVpt4BwwzLKxTJLv/pdSqgHgbdE0RVPQvuPobNvZvRbMZKr8sg8EKgDVjNvZHy0ZykR5Ztc0rWW27ewW4HdLBzNBfu0+E+hjzP498IYlQwlxryoxRQqlVEPjEQVnpZSb8chgTU3T1gDx1s5XkLyyA06app0A0DQtEv0Lm59Vg+Yhn+z3aZqWalykFDbaj/LrM0qp+oA/NvxFM7/s1s5linyy1wYcNE1bBaBpWoKmaUlWjpqLCe3+GLCspGQH7AFnwAn9s+oIRFkzZ17yyV4PWK9pWoamaYnAPvSCrtUVZXuklFLoxa1fjXd9C3S1bOIceYq0LdU0bY+maWcsnzS3YmRfqhkB24HyFg9tVIzsccbnKfQCi9UmECtqdqWUPfoXujEWD5s7yz2171hI9ueACZqmGQA0TbPagYDitrtSygN9fbnQUlnzyFDU7Bpws4DuBURaLKyJlFITlVIjs92erGxwpI0Q2TlYO4CpNE3bofTh7ZPQN9jzNU07aOVYJiksu1KqEfqXiFNWipiv/LIrpSoAS4Cq6EcdbG6lnFd24DCwFr0a3taK8QpUQLs7K6V2AhnAe5qmLbRmzrzk0+4hQKxS6ncgGFgNjNU0LdN6SXMzYT3TC/jIKuEKkU/2DUqpv9FHPyngU03TjlgzZ17y6TO7gPFKqY8AVyAC/fNrdUXcHvkCsZqmZRhvXwDKWSBmnu7lbWl+lH6aRz9gZGHLmktxsiul5gIPovf7l82fMm/FyD4MWKxp2iVl5bPLitlnJiul3gLWoG+nUgtZ3iyKkb0K0FMp1Q2IBkbcPBhmaXewnukGrLlZpLOGYmQfAixVSiUDcUATC8Qsqq/QR6dMU0rZoe/LNLJuJCEKoWlaiflB/yK/D9gG2Ge7Pxz4y9r5ipk9EDgGNLF2xqJmNz4WhH6Eyt/aOU3Jjr7zNMb42ED0L21Wz2lquwNBxv+HAGeAKtbOaWK7PwbcMOZ2AH4DBls7p6ntbrw/EH3Hz9HaGYvQ7lXRi4nuxp8tQCtr5zS13YHXgb3AKvSh+yOtndOEfpJje4Q+Qu5kttsVgAMlIfttzzkDlCkp7X7bc+YAn5TQ7PbA58CgkpAdfZ9gI/rIOYCEktTuxvW8Qh959i3wVgnKngC8bPy9O7ChpGTP9tgyoEcJ6zO/A42Nv48GvrR2/nze0yogDH1E4q/WziM/8lPYj00O0y+AD/qOtgf6EOaSJFd2pZ9fvQR4Q9O0rVbMVph8213TR1AcAlpaIZcpbs/eFBimlDoDfAj0V0q9Z714BcrV7sb2RtO008A69A2OLbo9+wVgj6ZppzX9iPJC9OH8tii//v4E8IemaelWSWWa27N3A7Zq+uk1Ceg7gLZ4lAfy7u+TNf085fboXxysclQwH6Zuj64C3kqpmyMXy2P94cD31La0IEqp8eiFopfMnMsURW53TR9t9hPQw4y5TGFq9jD04uhJ43bWVSl10vzxCmRyu2uadknTpQJzsf7R5qL0mQvoBwAA/kCfi8iaivpZ9UVv7yVmzmUKk7IrpfyAOpqmbTPe9RPQzPzxiuVL9INzg4CvrRtFiMKVtCLFbOBN9CNq71s5S1HlyK702d3/AOZpmvaLVZMV7vbs5ZVSLgBKqdLoE/Mds2K+guTIrmlaH03TKmqaVhl4Bb39x1ozYAFub/fSSqlSAEqfZb85NjL8PQ+3f1Z3AKWNG3TQzzktKdlvehL4wSqJTHd79nNAa6WUg3HIe2vA5k73MLq9v9sbd1pR+pwmtbGteWRM2h5pmqYBf6OPJgIYACwye7qC3TPb0oIWVEoNAToCT2rG8/StzKTsSlf15u9AF+CoRRLmz9T+vkTTtABN0yobt7NJmqZZ+2oHRekzgcb/K/S5Y6x9KlRRPqsL0betoK/rj5svlkmKup55HH2UQopZU5nG1OzXAS+l1H3G2+2x3W3sH+ijKBoCK6ycRYjCWXsoh6k/QH/gd+Pv9uhDsNoAG9CHYCejV5E7Wjuridn7A+noQ5lv/tS1dlYTs3cE9qMPhdsPPGPtnEXpM9keH4iNnu6RT/ZxwAFjux/Adk+XyO+z2t7YXw4A36BPHmv1vCZmrwxcBOysnbGI2dsCs9B3mg4DH1k7ZxGy3zwX/zCw1ZbWj0XdHqGf5rQdOAn8ApQqQdlHGG9noI8AsdpQ5mJkz0Cf62mv8cdqQ/eLkh39ANIm47ryIPoXJc+SkD2P51r1dI9i9Jm12dp9PuBegrJ7o49COIB+al+dkpLduNw6oJM1+0sx270bt/bN1gEh1n4PBby3L9DnM7N6FvmRn8J+lKZZbcJoIYQQQgghhBBmZJwwczfwuGalCVWFKIqSdrqHEEIIIYQQQggTKKVqoI/kWyMFClFSyEgKIYQQQgghhBBC2AQZSSGEEEIIIYQQQgibIEUKIYQQQgghhBBC2AQpUgghhBBCCCGEEMImSJFCCCGEEEIIIYQQNkGKFEIIIYQQQgghhLAJUqQQQgghhBBCCCGETfg/ehuasH/cnwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 20))\n",
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "698dab3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "print(np.stack((a,b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4ad35e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.stack(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e065b456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.    ,   2.66  ,  26.    , ...,   7.9024, 247.3   ,   7.87  ],\n",
       "       [  8.    ,   2.66  ,  26.    , ...,   7.881 , 272.5   ,   8.86  ],\n",
       "       [  8.    ,   2.66  ,  26.    , ...,   7.6398, 290.3   ,   8.9   ],\n",
       "       ...,\n",
       "       [ 11.    ,   3.    ,  79.    , ...,   8.967 , 213.9   ,  22.5   ],\n",
       "       [ 11.    ,   3.    ,  79.    , ...,   8.9588, 113.6   ,  21.5   ],\n",
       "       [ 11.    ,   3.    ,  79.    , ...,   9.2255,  64.6   ,  19.3   ]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1ae3606a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.92, -0.87, -1.12, -1.05, -1.21, -1.46, -2.16, -1.75, -1.28,\n",
       "       -2.01, -2.34, -1.16, -1.17, -1.45, -1.33, -1.41, -1.75, -2.54,\n",
       "       -2.08, -1.53, -2.36, -2.73, -1.2 , -1.1 , -1.29, -1.1 , -1.43,\n",
       "       -1.6 , -2.26, -1.82, -1.43, -2.09, -2.42, -2.11, -2.07, -2.4 ,\n",
       "       -2.67, -2.09, -2.35, -3.31, -3.37, -2.09, -3.  , -3.76, -1.2 ,\n",
       "       -1.15, -1.4 , -1.29, -1.41, -1.58, -2.23, -1.68, -1.39, -2.03,\n",
       "       -2.25, -1.49, -1.39, -1.57, -1.29, -1.69, -1.73, -2.27, -1.66,\n",
       "       -1.56, -2.08, -2.22, -1.46, -1.29, -1.33, -0.89, -1.59, -1.47,\n",
       "       -1.83, -1.24, -1.3 , -1.64, -1.66, -3.58, -3.46, -3.63, -3.83,\n",
       "       -3.46, -3.44, -4.16, -4.3 , -3.16, -3.8 , -4.45, -1.9 , -1.84,\n",
       "       -2.06, -1.9 , -2.02, -2.26, -2.84, -2.24, -2.11, -2.67, -2.85,\n",
       "       -1.92, -1.77, -1.85, -1.53, -2.11, -2.02, -2.42, -1.81, -1.87,\n",
       "       -2.25, -2.3 , -2.93, -2.79, -2.93, -3.01, -2.86, -2.81, -3.39,\n",
       "       -3.35, -2.58, -3.1 , -3.56])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bc35658c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "X, y = shuffle(X, y)\n",
    "X_train, y_train = X[:-30, :], y[:-30]\n",
    "X_test, y_test = X[-30:,:], y[-30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9699b84a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.92, -2.42, -1.66, -3.1 , -2.22, -1.58, -1.92, -1.17, -1.28,\n",
       "       -1.41, -2.06, -2.02, -2.67, -4.45, -3.16, -3.8 , -1.39, -1.85,\n",
       "       -1.29, -1.47, -1.49, -3.31, -1.1 , -2.85, -3.44, -1.05, -1.9 ,\n",
       "       -0.89, -1.43, -1.39])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e75a3aea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred_train_lr = lr.predict(X_train)\n",
    "y_pred_test_lr = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "49330c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'ML')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.scatter(y_train,y_pred_train_lr, alpha = 0.5, color = 'blue',\n",
    "            label = 'training')\n",
    "plt.scatter(y_test,y_pred_test_lr, alpha = 0.5, color = 'red',\n",
    "           label = 'test')\n",
    "plt.legend()\n",
    "plt.xlabel('DFT')\n",
    "plt.ylabel('ML')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f2aa898e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE(training)0.221\n",
      "RMSE(test)0.198\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rmse_tr_lr=mean_squared_error(y_train, y_pred_train_lr, squared = False)\n",
    "rmse_te_lr=mean_squared_error(y_test, y_pred_test_lr, squared = False)\n",
    "print('RMSE(training)%.3f'%rmse_tr_lr)\n",
    "print('RMSE(test)%.3f'%rmse_te_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ae8dfc13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 times mean RMSE: 0.253\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, ShuffleSplit\n",
    "\n",
    "cvf = ShuffleSplit(n_splits = 100, test_size = 0.25)\n",
    "score_lr = cross_val_score(lr, X, y, cv = cvf,\n",
    "                           scoring = 'neg_root_mean_squared_error')\n",
    "\n",
    "print('100 times mean RMSE: %.3f' % -score_lr.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "47381493",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbr = GradientBoostingRegressor()\n",
    "gbr.fit(X_train, y_train)\n",
    "y_pred_train_gbr = gbr.predict(X_train)\n",
    "y_pred_test_gbr = gbr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2c1c75ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GBR')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.scatter(y_train,y_pred_train_gbr, alpha = 0.5, color = 'blue',\n",
    "            label = 'training')\n",
    "plt.scatter(y_test,y_pred_test_gbr, alpha = 0.5, color = 'red',\n",
    "           label = 'test')\n",
    "plt.legend()\n",
    "plt.xlabel('DFT')\n",
    "plt.ylabel('GBR')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2ba4898c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE(traing)0.036\n",
      "RMSE(test)0.155\n"
     ]
    }
   ],
   "source": [
    "rmse_tr_gbr = mean_squared_error(y_train, y_pred_train_gbr, squared = False)\n",
    "rmse_te_gbr = mean_squared_error(y_test, y_pred_test_gbr, squared = False)\n",
    "print('RMSE(traing)%.3f'%rmse_tr_gbr)\n",
    "print('RMSE(test)%.3f'%rmse_te_gbr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "11bec4da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(lr) 100 times mean RMSE: 0.253\n",
      "(gbr) 100 times mean RMSE: 0.156\n"
     ]
    }
   ],
   "source": [
    "cvf = ShuffleSplit(n_splits = 100, test_size = 0.25)\n",
    "score_gbr = cross_val_score(gbr, X, y, cv = cvf,\n",
    "                            scoring = 'neg_root_mean_squared_error')\n",
    "print('(lr) 100 times mean RMSE: %.3f' % -score_lr.mean())\n",
    "print('(gbr) 100 times mean RMSE: %.3f' % -score_gbr.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8892e52",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}