21..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib; import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "matplotlib.rc('font',family = 'NanumGothicCoding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>EVAL</th>\n",
       "      <th>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>64</td>\n",
       "      <td>51</td>\n",
       "      <td>54</td>\n",
       "      <td>63</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>84</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81</td>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>83</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EVAL  DISSATIS  PRIVILEGE  CHANCE  ADVANCE  CRITISM  PROMOTION\n",
       "0    43        51         30      39       61       92         45\n",
       "1    63        64         51      54       63       73         47\n",
       "2    71        70         68      69       76       86         48\n",
       "3    61        63         45      47       54       84         35\n",
       "4    81        78         56      66       71       83         47"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('./data/직무능력평가.csv', engine='python')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>51</td>\n",
       "      <td>54</td>\n",
       "      <td>63</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>84</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>83</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DISSATIS  PRIVILEGE  CHANCE  ADVANCE  CRITISM  PROMOTION\n",
       "0        51         30      39       61       92         45\n",
       "1        64         51      54       63       73         47\n",
       "2        70         68      69       76       86         48\n",
       "3        63         45      47       54       84         35\n",
       "4        78         56      66       71       83         47"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_x = df_raw.drop('EVAL', axis=1)\n",
    "df_raw_y = df_raw['EVAL']\n",
    "df_raw_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.600</td>\n",
       "      <td>53.133</td>\n",
       "      <td>56.367</td>\n",
       "      <td>64.633</td>\n",
       "      <td>74.767</td>\n",
       "      <td>42.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.315</td>\n",
       "      <td>12.235</td>\n",
       "      <td>11.737</td>\n",
       "      <td>10.397</td>\n",
       "      <td>9.895</td>\n",
       "      <td>10.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>37.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>34.000</td>\n",
       "      <td>43.000</td>\n",
       "      <td>49.000</td>\n",
       "      <td>25.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>58.500</td>\n",
       "      <td>45.000</td>\n",
       "      <td>47.000</td>\n",
       "      <td>58.250</td>\n",
       "      <td>69.250</td>\n",
       "      <td>35.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65.000</td>\n",
       "      <td>51.500</td>\n",
       "      <td>56.500</td>\n",
       "      <td>63.500</td>\n",
       "      <td>77.500</td>\n",
       "      <td>41.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.000</td>\n",
       "      <td>62.500</td>\n",
       "      <td>66.750</td>\n",
       "      <td>71.000</td>\n",
       "      <td>80.000</td>\n",
       "      <td>47.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000</td>\n",
       "      <td>83.000</td>\n",
       "      <td>75.000</td>\n",
       "      <td>88.000</td>\n",
       "      <td>92.000</td>\n",
       "      <td>72.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DISSATIS  PRIVILEGE  CHANCE  ADVANCE  CRITISM  PROMOTION\n",
       "count    30.000     30.000  30.000   30.000   30.000     30.000\n",
       "mean     66.600     53.133  56.367   64.633   74.767     42.933\n",
       "std      13.315     12.235  11.737   10.397    9.895     10.289\n",
       "min      37.000     30.000  34.000   43.000   49.000     25.000\n",
       "25%      58.500     45.000  47.000   58.250   69.250     35.000\n",
       "50%      65.000     51.500  56.500   63.500   77.500     41.000\n",
       "75%      77.000     62.500  66.750   71.000   80.000     47.750\n",
       "max      90.000     83.000  75.000   88.000   92.000     72.000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_x.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd95b6f1198>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_raw_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DISSATIS</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <td>0.558</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHANCE</th>\n",
       "      <td>0.597</td>\n",
       "      <td>0.493</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADVANCE</th>\n",
       "      <td>0.669</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRITISM</th>\n",
       "      <td>0.188</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.377</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROMOTION</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.283</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DISSATIS  PRIVILEGE  CHANCE  ADVANCE  CRITISM  PROMOTION\n",
       "DISSATIS      1.000      0.558   0.597    0.669    0.188      0.225\n",
       "PRIVILEGE     0.558      1.000   0.493    0.445    0.147      0.343\n",
       "CHANCE        0.597      0.493   1.000    0.640    0.116      0.532\n",
       "ADVANCE       0.669      0.445   0.640    1.000    0.377      0.574\n",
       "CRITISM       0.188      0.147   0.116    0.377    1.000      0.283\n",
       "PROMOTION     0.225      0.343   0.532    0.574    0.283      1.000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_x.corr().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Data : \n",
      " [[-1.192 -1.923 -1.505 -0.355  1.771  0.204]\n",
      " [-0.199 -0.177 -0.205 -0.16  -0.182  0.402]\n",
      " [ 0.26   1.236  1.095  1.112  1.155  0.501]\n",
      " [-0.275 -0.676 -0.812 -1.04   0.949 -0.784]\n",
      " [ 0.871  0.238  0.835  0.623  0.846  0.402]]\n",
      "Shape : \n",
      " (30, 6)\n"
     ]
    }
   ],
   "source": [
    "x_cols = df_raw_x.columns\n",
    "scaler = StandardScaler()\n",
    "nd_scaled = scaler.fit_transform(df_raw_x)\n",
    "print('Scaled Data : \\n {}'.format(nd_scaled[:5].round(3)))\n",
    "print('Shape : \\n {}'.format(nd_scaled.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>DISSATIS</th>\n",
       "      <th>PRIVILEGE</th>\n",
       "      <th>CHANCE</th>\n",
       "      <th>ADVANCE</th>\n",
       "      <th>CRITISM</th>\n",
       "      <th>PROMOTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.261</td>\n",
       "      <td>-1.923</td>\n",
       "      <td>-1.938</td>\n",
       "      <td>-2.116</td>\n",
       "      <td>-2.649</td>\n",
       "      <td>-1.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.619</td>\n",
       "      <td>-0.676</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>-0.624</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>-0.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>0.281</td>\n",
       "      <td>-0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.794</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.787</td>\n",
       "      <td>2.483</td>\n",
       "      <td>1.615</td>\n",
       "      <td>2.286</td>\n",
       "      <td>1.771</td>\n",
       "      <td>2.873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DISSATIS  PRIVILEGE  CHANCE  ADVANCE  CRITISM  PROMOTION\n",
       "count    30.000     30.000  30.000   30.000   30.000     30.000\n",
       "mean      0.000     -0.000  -0.000   -0.000    0.000      0.000\n",
       "std       1.017      1.017   1.017    1.017    1.017      1.017\n",
       "min      -2.261     -1.923  -1.938   -2.116   -2.649     -1.773\n",
       "25%      -0.619     -0.676  -0.812   -0.624   -0.567     -0.784\n",
       "50%      -0.122     -0.136   0.012   -0.111    0.281     -0.191\n",
       "75%       0.794      0.779   0.900    0.623    0.538      0.476\n",
       "max       1.787      2.483   1.615    2.286    1.771      2.873"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = pd.DataFrame(nd_scaled,columns=x_cols)\n",
    "df_scaled.head().round(3)\n",
    "df_scaled.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA Data : \n",
      " [[ 1.67606983 -2.73856419 -0.12843595 -0.50321261  0.24330658  0.22576117]\n",
      " [ 0.21834397 -0.12615302  0.43115839  0.08726221  0.19566103  0.1941439 ]\n",
      " [-2.10515127 -0.51500683 -0.27683087  0.49131136 -0.44691698 -0.63174369]\n",
      " [ 1.35994032 -0.86064574 -0.91799066  0.03173793 -0.40137177  0.44095403]\n",
      " [-1.5120347  -0.35277019 -0.32684445 -0.32877858 -0.32709718  0.21246099]]\n",
      "Shape : \n",
      " (30, 6)\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=6)\n",
    "nd_pca = pca.fit_transform(df_scaled)\n",
    "print('PCA Data : \\n {}'.format(nd_pca[:5]))\n",
    "print('Shape : \\n {}'.format(nd_pca.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prin1</th>\n",
       "      <th>Prin2</th>\n",
       "      <th>Prin3</th>\n",
       "      <th>Prin4</th>\n",
       "      <th>Prin5</th>\n",
       "      <th>Prin6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.676070</td>\n",
       "      <td>-2.738564</td>\n",
       "      <td>-0.128436</td>\n",
       "      <td>-0.503213</td>\n",
       "      <td>0.243307</td>\n",
       "      <td>0.225761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.218344</td>\n",
       "      <td>-0.126153</td>\n",
       "      <td>0.431158</td>\n",
       "      <td>0.087262</td>\n",
       "      <td>0.195661</td>\n",
       "      <td>0.194144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.105151</td>\n",
       "      <td>-0.515007</td>\n",
       "      <td>-0.276831</td>\n",
       "      <td>0.491311</td>\n",
       "      <td>-0.446917</td>\n",
       "      <td>-0.631744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.359940</td>\n",
       "      <td>-0.860646</td>\n",
       "      <td>-0.917991</td>\n",
       "      <td>0.031738</td>\n",
       "      <td>-0.401372</td>\n",
       "      <td>0.440954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.512035</td>\n",
       "      <td>-0.352770</td>\n",
       "      <td>-0.326844</td>\n",
       "      <td>-0.328779</td>\n",
       "      <td>-0.327097</td>\n",
       "      <td>0.212461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prin1     Prin2     Prin3     Prin4     Prin5     Prin6\n",
       "0  1.676070 -2.738564 -0.128436 -0.503213  0.243307  0.225761\n",
       "1  0.218344 -0.126153  0.431158  0.087262  0.195661  0.194144\n",
       "2 -2.105151 -0.515007 -0.276831  0.491311 -0.446917 -0.631744\n",
       "3  1.359940 -0.860646 -0.917991  0.031738 -0.401372  0.440954\n",
       "4 -1.512035 -0.352770 -0.326844 -0.328779 -0.327097  0.212461"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca = pd.DataFrame(nd_pca, columns=['Prin1','Prin2','Prin3','Prin4','Prin5','Prin6',])\n",
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "        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>PrinNo</th>\n",
       "      <th>EigenValues</th>\n",
       "      <th>EigenValueRatio</th>\n",
       "      <th>CumEigenValueRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.279</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.041</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.032</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PrinNo  EigenValues  EigenValueRatio  CumEigenValueRatio\n",
       "0       1        3.279            0.528               0.528\n",
       "1       2        1.041            0.168               0.696\n",
       "2       3        0.789            0.127               0.823\n",
       "3       4        0.572            0.092               0.915\n",
       "4       5        0.328            0.053               0.968\n",
       "5       6        0.198            0.032               1.000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca_stat = pd.DataFrame()\n",
    "df_pca_stat['PrinNo'] = [I for I in range(1,7)]\n",
    "df_pca_stat['EigenValues'] = pca.explained_variance_\n",
    "df_pca_stat['EigenValueRatio'] = pca.explained_variance_ratio_\n",
    "df_pca_stat['CumEigenValueRatio'] = np.cumsum(pca.explained_variance_ratio_)\n",
    "df_pca_stat.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9580ab278>"
      ]
     },
     "execution_count": 19,
     "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": [
    "plt.plot(df_pca_stat['PrinNo'], df_pca_stat['EigenValues'], label='고유값')\n",
    "plt.ylabel('고유값(Eigenvalue)')\n",
    "plt.xlabel('주성분 번호')\n",
    "plt.legend(loc='bset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>PrinNo</th>\n",
       "      <th>EigenValues</th>\n",
       "      <th>EngenValueRatio</th>\n",
       "      <th>CumEigenValueRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.278507</td>\n",
       "      <td>0.528204</td>\n",
       "      <td>0.528204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.041048</td>\n",
       "      <td>0.167724</td>\n",
       "      <td>0.695928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.789216</td>\n",
       "      <td>0.127151</td>\n",
       "      <td>0.823080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.571569</td>\n",
       "      <td>0.092086</td>\n",
       "      <td>0.915166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.328186</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>0.968040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PrinNo  EigenValues  EngenValueRatio  CumEigenValueRatio\n",
       "0       1     3.278507         0.528204            0.528204\n",
       "1       2     1.041048         0.167724            0.695928\n",
       "2       3     0.789216         0.127151            0.823080\n",
       "3       4     0.571569         0.092086            0.915166\n",
       "4       5     0.328186         0.052874            0.968040"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca_stat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd952a33b70>"
      ]
     },
     "execution_count": 29,
     "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": [
    "plt.plot(df_pca_stat['PrinNo'],\n",
    "        df_pca_stat['EigenValueRatio'], label = '고유값')\n",
    "\n",
    "plt.plot(df_pca_stat['PrinNo'],\n",
    "        df_pca_stat['CumEigenValueRatio'], label = '누적 고유값')\n",
    "plt.ylabel('고유값 비율')\n",
    "plt.xlabel('주성분 번호')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Feature</th>\n",
       "      <th>Prin1</th>\n",
       "      <th>Prin2</th>\n",
       "      <th>Prin3</th>\n",
       "      <th>Prin4</th>\n",
       "      <th>Prin5</th>\n",
       "      <th>Prin6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DISSATIS</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-0.313</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.316</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRIVILEGE</td>\n",
       "      <td>0.395</td>\n",
       "      <td>-0.309</td>\n",
       "      <td>0.217</td>\n",
       "      <td>-0.815</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHANCE</td>\n",
       "      <td>0.461</td>\n",
       "      <td>-0.217</td>\n",
       "      <td>-0.272</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADVANCE</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.365</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CRITISM</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.457</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.289</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PROMOTION</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.321</td>\n",
       "      <td>-0.687</td>\n",
       "      <td>-0.206</td>\n",
       "      <td>-0.255</td>\n",
       "      <td>-0.416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Prin1  Prin2  Prin3  Prin4  Prin5  Prin6\n",
       "0   DISSATIS  0.439 -0.313  0.445  0.316 -0.192 -0.612\n",
       "1  PRIVILEGE  0.395 -0.309  0.217 -0.815 -0.038  0.190\n",
       "2     CHANCE  0.461 -0.217 -0.272  0.225  0.776  0.118\n",
       "3    ADVANCE  0.493  0.116  0.006  0.365 -0.460  0.631\n",
       "4    CRITISM  0.225  0.802  0.457 -0.100  0.289 -0.058\n",
       "5  PROMOTION  0.381  0.321 -0.687 -0.206 -0.255 -0.416"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cols = pd.DataFrame({'Feature':df_raw_x.columns})\n",
    "df_eigenvector = pd.concat([x_cols, pd.DataFrame(pca.components_.T*-1, \n",
    "                                                columns = ['Prin1','Prin2','Prin3','Prin4','Prin5','Prin6',])], axis=1)\n",
    "df_eigenvector.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Prin1</th>\n",
       "      <th>Prin2</th>\n",
       "      <th>Prin3</th>\n",
       "      <th>Prin4</th>\n",
       "      <th>Prin5</th>\n",
       "      <th>Prin6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prin1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin3</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin4</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin5</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prin6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prin1  Prin2  Prin3  Prin4  Prin5  Prin6\n",
       "Prin1    1.0    0.0   -0.0   -0.0   -0.0    0.0\n",
       "Prin2    0.0    1.0    0.0   -0.0   -0.0    0.0\n",
       "Prin3   -0.0    0.0    1.0    0.0    0.0   -0.0\n",
       "Prin4   -0.0   -0.0    0.0    1.0   -0.0   -0.0\n",
       "Prin5   -0.0   -0.0    0.0   -0.0    1.0   -0.0\n",
       "Prin6    0.0    0.0   -0.0   -0.0   -0.0    1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_pca)\n",
    "df_pca.corr().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   EVAL   R-squared:                       0.733\n",
      "Model:                            OLS   Adj. R-squared:                  0.663\n",
      "Method:                 Least Squares   F-statistic:                     10.50\n",
      "Date:                Thu, 09 May 2019   Prob (F-statistic):           1.24e-05\n",
      "Time:                        11:55:04   Log-Likelihood:                -97.250\n",
      "No. Observations:                  30   AIC:                             208.5\n",
      "Df Residuals:                      23   BIC:                             218.3\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         10.7871     11.589      0.931      0.362     -13.187      34.761\n",
      "DISSATIS       0.6132      0.161      3.809      0.001       0.280       0.946\n",
      "PRIVILEGE     -0.0731      0.136     -0.538      0.596      -0.354       0.208\n",
      "CHANCE         0.3203      0.169      1.901      0.070      -0.028       0.669\n",
      "ADVANCE        0.0817      0.221      0.369      0.715      -0.376       0.540\n",
      "CRITISM        0.0384      0.147      0.261      0.796      -0.266       0.342\n",
      "PROMOTION     -0.2171      0.178     -1.218      0.236      -0.586       0.152\n",
      "==============================================================================\n",
      "Omnibus:                        2.386   Durbin-Watson:                   1.795\n",
      "Prob(Omnibus):                  0.303   Jarque-Bera (JB):                1.255\n",
      "Skew:                          -0.081   Prob(JB):                        0.534\n",
      "Kurtosis:                       2.011   Cond. No.                     1.34e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.34e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "df_raw_x_const = sm.add_constant(df_raw_x)\n",
    "reg_model = sm.OLS(df_raw_y, df_raw_x_const)\n",
    "reg_model = reg_model.fit()\n",
    "print(reg_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   EVAL   R-squared:                       0.624\n",
      "Model:                            OLS   Adj. R-squared:                  0.580\n",
      "Method:                 Least Squares   F-statistic:                     14.36\n",
      "Date:                Thu, 09 May 2019   Prob (F-statistic):           1.02e-05\n",
      "Time:                        11:58:43   Log-Likelihood:                -102.38\n",
      "No. Observations:                  30   AIC:                             212.8\n",
      "Df Residuals:                      26   BIC:                             218.4\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         64.6333      1.440     44.890      0.000      61.674      67.593\n",
      "Prin1         -4.5451      0.809     -5.620      0.000      -6.208      -2.883\n",
      "Prin2          3.3489      1.435      2.333      0.028       0.399       6.299\n",
      "Prin3         -4.0601      1.648     -2.463      0.021      -7.448      -0.672\n",
      "==============================================================================\n",
      "Omnibus:                        1.252   Durbin-Watson:                   2.154\n",
      "Prob(Omnibus):                  0.535   Jarque-Bera (JB):                1.044\n",
      "Skew:                          -0.237   Prob(JB):                        0.593\n",
      "Kurtosis:                       2.219   Cond. No.                         2.04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "x_var = ['Prin1', 'Prin2', 'Prin3']\n",
    "df_pca_const = sm.add_constant(df_pca[x_var])\n",
    "reg_model = sm.OLS(df_raw_y, df_pca_const)\n",
    "reg_model = reg_model.fit()\n",
    "print(reg_model.summary())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA 실습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>SEPAL_LENGTH</th>\n",
       "      <th>SEPAL_WIDTH</th>\n",
       "      <th>PETAL_LENGTH</th>\n",
       "      <th>PETAL_WIDTH</th>\n",
       "      <th>SPECIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH SPECIES\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('./data/IRIS.csv', engine='python')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SvbG6NeVQW4UKqChAfV5UdDzEpIR3HrNMREIXo96uG9ECuw6GalJ5eNTD3PPePVTUVgCwtXQr97x3D69lvyaOh9CYFthiOGNpuOOt7/jv64C0JVdDxvqeT6c6H7qu/wX4C4CiKC8A6zvzekJRVmEnNtpMTFTTP5ex8iGKV0Jvwnep3JiAGsXq1lbBi6M9X7BYwVnvoM/b3aabNEHoVHzt2pc22rXRp17Lfs2vT72W/ZqEoQjBaSdbbI7A8d+tuzEpJgmZEpqkS+R8KIoSDQzTdf1AZ19LMMoq7KQ1k2wOoJpNxFstkvMh9DpCxeq63C6qtWoc9QmxWuZkap74Fi1zcidfsSB0HMEkSEPJkhr7AYl/F7oFxvi/auoqNkzfwKqpq0LaqkNzeOYETSJEejNdwvkAfgL8NXCnoig/UxTlS0VRvjx79mwnXJaHExV2UprJ9zBIsqkSdtWL6So22xn4xupm2AbgspdTVFHElDVTKLKfpubxbym+4ynu/OBeim97TByQLkJvttmI8+A7kPMBuJ1w4ShcOIpWWUbxhYN+EqShZEkD9wMNfUocDy9is2EQxBa5cNQTntVO1DhrmPb+tJAFCR2ao2FOqCgSB6QX01WcjweAdwJ36rr+uq7rN+i6fkNaWlonXJaHExfszSpdGSTFREnCeS+mq9hsZ6GaVWLUGFzuOopqTpG7MZdyRzm5G3M5pFXwyckvPFKM2570OCByM9Xp9HabjSiqDfJ/BEuugxdHo637V4orviNnwz97JUiLLhRRWlXaSJbUoTmCypUCxKgx4nj4IDYbBgG26N2C5YW0EcNpnrlhJuWOcmZumNlI6cpwPHznBHFAei+d7nwoijICKNV1vbqzryUYVQ6NSoez2RofBok2VcKuhF6NQ3NQZD9NbuGjftKLuRtzyRqQxZxr53ikGLc9SXHt+VZVwxWEro6WOZni2x4jZ9uTfv1g5oaZnKw5SVb/LO++nPU5FFUUkbc7r81ypYLQkYQjtevreNiddqLN0d45QRyQ3kmnOx9ALpDX2RcRipMXPZ0i/JUPj/Oh61LlXOh9aC4NFy7mbJoTVHpx5YGVPHjVg0y+YrJHinHTY2huubESuinR8Z6EXmOLH+jZb7Gi3b2EWZ8+HbQfPLXtKRbcsoBoc7R335xNc5h/03zvPmP/W3vfos5dJw6I0DShbLGdaU5qV3P7zwljB49l/fT1jB081mv3Llwdcq1C16HTnQ9d13+l6/qXnX0doThRL7PbJ8yVj+SYKOqcbirtzva8LEHoeGrK/WOHA2KIjSdgqw6uYsmEJd4qtQbZQ7KZMXIGU9ZM4R+G/wPZQ7JbLcUoCF2CmBRIvtxz42eQ8wE89BfUqpMsHf9io35gs9h4fszzLNixgFpXrXffkglLWPjZQu8+8O8zsgIitJicDzy5H+2IIbUbzM6N8d2MmSUTlpA9JJsHRjzA9Pen88CIB8geks2SCUswY27XaxS6HhGT2lUU5TGCOzMuXddfidR5OpqyFjofibZ6ud0qB4kxclMl9CCakHLUouP9lt7nXjuXvIl53mX27CHZ5Fyd433/xJYnyJuYR3pCusSyC92fIH1DBTJ+8TH5Y17whl7ZLDaWT1pOjCWGnad2Ap6btEXjFvF52efkXJ3D9rLtQftMzvocUbwSQhNqfM75oF1Pq5pV0hPS/cZ7m8XmN76rZpXhicODzgHDE4djrVdDFHoPkVz52AV8U7/l+LzfHcFzdDhlFXbMJoUkW3iDfbJUORd6GZpZ9XM8os3RvLTrJT4v+5y8iXlMHTrVb9KBhhyQksoSeZor9FhUNY6MpGHkT/qTVy43MzmTAbEDyJuYR4o1hUXjFrHywEpe2vUSb+97m7yJeUzPmB60z0gOiNBi4gf6h2P5rtJFAM2lUVJZQv6efBaNW+S16fw9+d7xXXNpHKk6EjTn40jVEbHnXkjEnA9d17frur5V1/WtQIWu6x/Xv98WqXN0Bicu2EmNjcJkUsI6vqHKuTgfQvcgVL2BsLBY0awJ3phf33jel3a9xKnqUzxz8zMhc0CMeHZJOBR6JCYLasJAMpKvZN20daQnpKO5NZy6kxX7V7D6rtWsPLCSraVbGTt4LL/O+jV1rjp+/YNfs2L/iibj6AUhLEwWT2igsYVZcDDcecHI+Sg8Vsjus7spmFbA7rO7KTxW6LVV37yQwJyPWR/OapTT1KY5SegWRMz5UBRlqqIodymKcjeQVv/6LkVRpkbqHJ3BiQo7qWEmm4NPlXOp9SF0A0LVGwgbpwPVUcnS219pFM8799q59I/tz/OfP99sDogonghdnmA5T1Unofywp5ZCzgcN29xdfjUW1MoyVPtFSi4e4s41d3K08ig/H/1z7nnvHq/j8cCIB/jDF38gyhzFlDVTmDFyBtlDsv0uwTeOXhD80N3+78Op8xEij0+ruRD2vGDkfGQPyWZ02mj+4b1/YHTaaL+cPt9jguV8PPvps83WwBF6FhHL+QCSwZs19If690D3ljEoq7AztE9c2MfbVDPRFhNnKmXlQ+jaBEoktjamXHVppFvTyBn1U6+8rhHPm78nn8JjhdRoNeRlL/N+HhjPnrsxl7yJeWQmZUr8r9A1CRZTn/OBp5ZCIL/8wm9/oOzuzA0zWT5pOW9NfoslXy9heuZ0VhetZnrm9EZ9AqDwWCE2i01yPoTQBDofRp2PQObtblj9CGLTWuZkirOfJmfjI2HNC0bOR3M5faGO+bzsc9YeXsum45tYNXUVNc4aZm6YKXlOPZxIOh/X6br+LxFsr9Opc7o5edFB1tDUsL+jKArJMVESdiV0aZrSZg852BtSjoFtRSdQUl0atK7HonGLqHPXUXisEIC8iXms2L+CGSNnBM0BEQdE6BGYGtR7mqr3sXzScmZfO5tXd73K9MzpPLHliaB9IsocxYyRM0SgQQhNVIx/cnlsvxY34bXVescDmp8XjJyPYOO58R0g5DGLxi1i7OCxAJysOcnjmx8Pf04Sui2RTDi/NoJtdQnKKuy4deiXEJ7SlUFijCoJ50KXJhxt9kYYsqLGFh0PKGgWlVmbHgva1vzt8731DAqPFfLng39uMgdENN+FLoslyj+0KueD5m/wmqn3Mfuj2SRbk5l/03zmb58fsk88c/MzrCleg5uAp9tC7yUwZKqttcWasdVQ80I4c0lTxxhzxIJbFvDUtqckz6mXEEnn4xpFUdYoivJu/b/G61URPEeHcrS8BoB+8S17CptkUyXnQ+jShKPN3iy1VfDiNah/eYSlE14O2tbC2xZ66xnYLDZmXHk/iq4EzQExah2I5rvQJamr8YSx+G7Vp5v+jtOB+t4clt78XFB7f/X2V6msrWThZwtZeNvCoMe8NP4lVh1YxX1X3ie5HkIDRsiUsdVeapltBtKMrYaaF8KZS5o6xpgjFuxYwPNjnm/bnCR0GyLpfOzRdX2aruv31P9rvL4/gufoUI6drwagb0ILnQ8JuxK6OKpZJSMpg/zJ+d7BvrUx5WrRejIsCeSPeQGbxUa0Odqj8569jNVFq9lautXT9pgXyFCTiYmKITMpk7yJeX7nlpAroSeiFq0nY/vL5P/Qv38YsruD4weTOzqX1UWrWTRukV+fWJa9jJ0nd3LTwJsk7ERod7y2OvGNRrbaVM5Hc3NJqGPyJuZ554idp3YyIGYAyyctb/OcJHR9IpnzcTCCbXUJjp6vIcps8tbuCJekGJUqhxOH5sKqylNcoWviOyHM+nAWS+9Y2upBXgUyohL43ykrSbQmcdFRwUCzjdyrZ/LVma9YOuFlMtRkUK3UaDWoJtXrgMzZNIclE5aI4yF0bZQgz+o0O8z5yqN6BZ6aCiaLJ9nXNz/KrDIkOt7bPyprKxkcPxjVrKK5NIbED+GRax7h/+37f3594svTX3LLoFvk5ksIg4CwK83uCQ00bNLAt85HkDw+FciITmDV1FUkRCf42WogDs2BCxdmzEHnEsA73gebb9IT0skdneuZI+5YyuD4wQARmZOErk0kVz6eNl4oivJPiqLMrd/uiOA5OpSj5TX0TYhGUcKr8WFgOCuieCV0dYwJYd20dc0P8oExxm5nw2cXj6GdOUi1s4Zp70+j2lmD+8h2MqJTPW2njID4fhQ7znglFM0mM5lJmRRMKxDHQ+j6BHM+3r7X43gYoS6KyZMPFdfXmxulJQykWKvguc//09s/apyekF5D+GHqu1OJscTwm5t/w/DE4RRMK6CvrS93D7ubGEtMB/9HhW5J9Rn/92/fG1ztypfAPD5vPl8cNQG2GohDc1BUUeSVSne5XX5zCdBIMjdwvrGq1kbzT4vmJKHbEknnY43P69k0VDh/NoLn6FCOnq+mbwvzPQCS6wsNnrxob+ZIQeh8VLNKjBrT/CDvG2P82g+hqsz7UU3S5RwaMIJHNj5CuaOcRzY+wqEBI9Dq2wa86lrVWrW3UrPZZCZWjRXHQ+gZBMidGs5F3u48fjzsx97+MXPDTIouFFFaVertE/evvZ/jlccxKSaOVx7nrvfuYtLqSdy/9n6pdyC0nqqygNyQqiYPN2x25oaZVGvVzNwws5H9GY5H7sZcyh3l5G7M9TogTY33hgPiO98Em3/CnpOEbksknQ/fLKFNuq5/DGyn0Vpg90DXdY6X21usdAWQFu/5TukFcT6E7o+32mz9ROAY9xTVT+7DEZsGOR9Q86tDHNIqgsoofldzikt1l7w3WVn9s1g/fT1Z/bP8JiRB6PIYISrzdsPcrz21PH75BSQOaVC/impYpfB1PILJ6M7cMJOTNSeZOWom66ev5zc3/YbBCYM5az/L67tfx+60U+uq9cqNSl8R/PC1x3m7IXU4zPm7p8DlvN2e1zkfeMKvwsRXgj1wrC66UMSlukvU1NV4HY/A8b64orjZ8d6hOaR6uRBR52NpvcLVDcDi+n9XAa9F8BwdxtmqWuyai34tTDYH6BPncT5OVIjzIXRv/KrN1p6nZtoyiq6607PUTh2OhEHo0TEhpXMf2/QYta5aztScYeaomX7VbbP6Z4mEotD1McINjSfGutsja1pxBF75gedpshF25azzfk1za8zbPK9JGd2ntj3FQ1c9xANrH2BIwhCe/fRZyh3lPH3T00Sbo/2Olb4iNImzzpPbYU30hE4pJo9Nvn2v/3GBxQh9MCRxs/pnNRqrZ380m1pXLW7crR7v39r7Ft9d/E6qlwuRSzjXdf1PiqJ8BkwHBgBlwDO6ru+P1Dk6kmOGzG4rVj7U+iT10gvBYyUFoTsQWIjwk7LPcA680b8Cc/Yyhp6tZMmEJX5PwsCjVPL8mOf57Y7fsvPUTpZlL+PNvW9S7ijniS1PsHj8YgbY+omEotC1CVbZHPwLugVBNam8OP5Fr4yu78oHNMjo/vaT33Kq+pS34Nqbe98k5+ocHrn6EV795lXvsSI3KvgRyi6NCuahnIwmnA/VpJI/Kd+v2J8xVidEJfDbHb8lyhTV7HgfZYrip6N+yqP1hWef2PIEL094mRRrCjMKZkjxQCFyKx+KojwI3AAcAz4HjgM/qN/f7Th6vnU1PgzS4qNbFnZ1oABWPQTv5EBxYavOKQiRItDxmHPtHLIGZHknnGhztMcBKXyUwxaFoU69kXTu4vGLWbF/BVtLt2J32nm08FGmZ05n7OCx2J12Ht/8ODUuWR0UeiZG4uyjox9lddFq/njHHxvJjL65900Kj3nGe+MmbXrmdPL35HPLwFuY/b3ZIjcqtB1L+PcxNc6aRlXGH9/8uFe1qvBYIfl78kOO9wDTM6d7HQ+jjcc2PcYZ+xmy+md590k4Ye8lkmFX5+u3s/VbIvAL4OcRPEeHUXLuEialIX+jpfSJi+Z4eZgrHx//Hv73ATj2KRzeCm//BAp+DW6pZiu0P96cDp8JwLci7Z1X3MmDVz3I3M1zsTvtjB08lvXT13udiDmb52LqM5z0S5XkTcwjxZrCi+Nf9DoeBoEVzz0Vnn9JnbvOe+5g1yIIXZLYfv7Vzuf83Stjatixy+3i8oTL+dX1vyIzOZNXbn+FFGsKf5r0J1bsX+F1PAyMPjL/pvn86uNf8fCoh3lr8lt+sqXSN4SwiIqBnA/QfvExNf96CO0XHzfKSwpEc2vM/mh20JCqNcUbxOv1AAAgAElEQVRreO6257h9yO0UHitkxf4VLJ+03G+8/+zkZyy4ZUGTYYbG+G/se2vvW35zAMg80BuImPOh6/p64AtgOPATIAaYoev6DyN1jo7k0JlL9E+0YjG37idKi4/m5EUHLncz+fYlW2DTc5A+DqblwbQ34Kq7Yefr8PF/t+rcguAlUB7X2GrKoaYcrbKM4vIDnhjc8gNolWVQU+6tSJs9JJu7h9/NqoOrWDJhCdlDsv3ieLOHZLNkwhJWHVzFMbNOetUFCqYV0M/Wj52ndvpdSrCK50smLOHZT5+luPwAjkunKb5wUOKBha7Jg+/4OxsAsX09CecGrlq0ilKKyw/wH5/+B0UVRdy55k7O1Z7juc+eIzk6mb/e/Ve2lW7jx8N+HLKi+btF7/LC2Bdwu90MSxoGNJYtFXo5lih/ezQ2S5T3EM2aQLEJ7nz3RxSbQEsc4slXCjYfELpauTHuT1kzhXsz7yV7SDYPj3qYoQlD/cb7WlctC3YsYOFtC4Pa9vNjnveO/0a7M0bOYMqaKV679sszFFvvsUQy7God8CGQDLwH7AdGK4oyNVLn6EgOnbnEoCRb8weGoE9cNE63zulKR+iD3C5Y+yQkDIKb53iSxcwq3JALw26HLc/DvvfCPufxquP8+/Z/5xcf/oL3Dr2HrndLoTEhkvjK4wbILWpOB8UV35FTL/+Zs/ERiiu+Q3M6UM0q6Qnp5FydwxNbnmDRV4sorSz1vjfyNnKuzqG0spRFXy0iZ9uTHLOYiLJf5LKEyxpXsw2oeJ6XvYz8PfmsPbyWvD3LKao5Rc6Gf/ZciyzHC10N1daQWJ7/I0+y+StZcPGY5/2S69Cqz1BceZi8Pcv58bAf+0mR/njYj3l116uUVpVSfKGYlQdWsnj8Yr8+YuR83DjwRtIT0omvX0kxQiClbwhe6mr87dHY6owaMi6K6yrJKXzUYzeFj1JcdQyt5mzQ+QA8oYJXxF/hF1KVPSSbnKtzeLS+HWPcT09IJyYqhlg11m+831q6ldVFqxuFZS2ftJwBMQO8D6WMdo0+YihqGUpZYus9m0iGXb0NvAAcAZLqt+T6f7sVdU43R8/XtMn56FsfrmXkjgRlzxo4fwiuexgsPuFdigI3PwZ9MuFv8+DS2WbPd/jiYe77231sPLKRQxcO8fQnT/O7nb8TB0QIimZWKa49T862J/3icnO2PUlx7XkcmoOSyhJvjsfYwWNJtCYGlVdMtCZ6Q7Bytj1JcZ3nKZpRzTbFmkL+pD+RGdOf3KtnkmJNIW9iHvn18e5jB49leuZ0cutjhI2QLJl4hC6BIWkaP7DJw7TMyRSb4M3DHwSV1jXyOZbvWU7O1TnEqXEMiRviDVVcNG4RKw+spPBYIbkbcympLKGmrsYv98poS/qGELToZf1+zaVRrF0IPr6bPLYaDIfm4NDFQ+TvyWfRuEVMHTqVn476adBxv6SyxGt/vtXLU6wp5I7OJTMps2H8n5xPZnImg+MHkz85n7uH3e11PILJT0teSM8nks7HNl3X39R1/U1gpc/r4gieo0M4er4ap1tnYBucjwGJnu+WnLsU+qDPX4PEwTDk5safmVW4ZZ7nicT6f2vyXJpb4/HNj2NSTCy4ZQH/edt/cseQO1hxYAXvfRf+yonQS7BY0awJzNr0WNC43FmbHsOFi3mb53mdgabieAPzOGZ99Es0d0A12+Qrscb1IyNlBAXTCrzx7oFtB+aTiLyo0OkYVaBNTYhDWqxody9hRdE7/OaW3zTZV+bfNJ8ntz7J0zc9TbItmS9Pf8nqu1az8sBKb46U3WlnzqY56IrOr7f+Ong/lb7RuwnpfJg9OXuhxvfNc9HuXhI0Cd2Fizmb5lB4rJC95/byzM3PeHP9GrUTYH/hVi9PT0jnqRufYsX+FWHnhYit9zwi6Xws93ld4PP6dxE8R4dw6IzHYWjLykdqXBTRFhMlZ6uDH3B6H5z4AjImhR5Eki+Ha+6DPauhZGvwY4B3Dr5DycUSckbl0DemLybFxD+O+EcykjL4w5d/4ILjQqv/H0I3IzDHw+1sfIzTgeqoZOmEl4PG5S6d8DJmzLw4/kVsFluzcbyBeRxLb3/FKwkarJptlCmKh0c+2KjtYPkkIi8qdDpGn3I7/WPrH3zH83lsP3joL5hqyrnvyvt49tNneWn8SyH7ysLPFvL6Ha9ztPIoU9ZM4YZ+N/D7nb/3E2cw8qEUXeH3Y38fvJ9K3+jdhMz5UD25G02M7+p7c8DZOCTcjNmb2zeqzyie//z5kLYczP6aq16uuTRKKkuYsmYKM0bOIHtIdqN2A/NCxNZ7JpF0Pjqi3Q7BcD7asvJhUhT6J1o5fC6E8/HtO6CYYdiEphu65ieepMbCZ4KqX9W56nh99+uMSBnB99K+53N+E/901T9RVVvF8r3LG31P6KEE5nhUlQU9THVpZESnkj/mBb+43PwxL5ARnep9auUbx7vv3L5Gcbx5E/PYd26fN48jf8wLZESlNCkJqppVv3MbbQfLJ0lPSBd5UaFzMfrUkuv8Y+vV+vmh+jTajpcp0WvJ3ZjL2sNreXPvmyzLXtYon2N10WrmfH8ONc4aZm6Y6c0HefCqB703Yka/ykzKJCYqxq8fGp+L9K7QVM6HalbJUJODj++WBNSi9UGbtKpWhicO947F64+s5829bzYa91tjf77y7Ybd51yd42f3gXkhYus9l0g6CZcpijJXUZR5ga8jeI4O4dDZS6TFR2NVzW1qp3+Cle/OBgm70nXY9y4MGO2pRtoU5ij4/kNw8hvYu6bRxxuObOC84zxThk5BURS/zwbHDyZrQBYr96+k3FHelv+K0F3R7A0yoPN2N2zR8agWKxlJw8if+IYnLnfiG2QkDQOLjRrNk6tk3PhMHTqVGwfe6I0FNmLU8/fkc+OALKYOnUr+mBdIL/kEzZrQbHyu77mNtpuLKxaELkW91K5msVF822Pk+NQ1qHPXMTh+MG/U9628iXn87bu/+Tkegbaec3UOU4dO5Y2Jb5CZlIlV9YTFBMbTy82YAISMmNAsVs/4bYkiIyqB/OxlHrvJXkZGVILHbnzngvr5ADwOwpGqI+RuzCWrfxbrp6+nzl3nrevRWvsLrBsFje0+MC9EbL1nE0nnYybwDbALyPV5PTOC5+gQDp25xMDE1hUX9GVgko3Scjt1zoAVi7MHoPwwDLklvIbSx0FyOnz0/4Gz1u+jlQdWMiB2AFelXhX0q1PTp+JwOVhT3NhxEXoBb9/reRpmsnjC+IwtJgViUlATBpKRMsITl5syAmLTKLaf9MocgscBeebmZ7yxwCsPrPTGqBceK2TO5rk8c/MzpB/5nJL0W7nz3al+sr1B8Tm30bbEtQudSihZ6iYqQhPbD63vCGZ9+rTXfscOHssDIx5g2vvTUBSFgmkFZCZl8pubf0O/2H4h6yjM2TSHZ25+hjhLHGaT/4OvwHh6uRnrZQSzzSB2qWVOpriu3DN+208DkFGnse5H75Dx4ULUP/4QXJr/XGDMBzTUd8rqn+UXAlvnrmPF/hUUTCtolf351o3yxdfuffNCxNZ7PpGs87Fd1/WtgRuQEalzdAQut853Z9sms2swINGKS9c5ej4g9OrQR55/B10fXkOKCa7PgYpj8FW+d/eRi0f49ty3jBk0BlOIpyCD4gYxInkE7xx8B5fb1fL/hNDjMeJyIbikJ0CUKcobQ7y1dCuTV0/2hlotvfk5zJ8upeSKG8nZ9mQj2d7mzh1ligqqLS+xvkKHEkqWOpTzUX0aXvkB6l8eYenNz2Gz2LyOhxE++M8b/pmjF49iNpk9se8h6igYse7/vv3fub/g/qDqPoHx80IvIphtBoTUapmTPStwvtLpdZVgUYlZdE3IUCtfVJNK/qR8Zoyc4RcCO2PkDH4++udEmaJaZX9N2f3SO5Y2aldsvefTEbkZ/9QB54gYh89dwqG5uTw1ts1tDU723NAdPF3l/8F3myDxMojrG35jg66DflfD9kXe1Y+1h9eioHDjgBub/Oq4y8ZRVl3GjrIdLbp+oZsTRM0kFKGWxX0dEN8YYiO53Ai1MhwPP6ncetneZkOwfMJKJK5d6G6oRevJ2P4yq+5YxoyRM/g/2/6Pfx/a0CAVGsrWF49fzIr9K/jo2EciLyq0GK/jEUpaN2Ni2G3VOGt4fPPjfmP545sfp8bZRNmAZpAxXgikWyeGtwd7yyoBuDw1ps1tDUqyYVJg/8nKhp2aHY5uh4Hfb3mDo++HqpPwzUrAk+8xImUEydbkJr92Xb/rSIhK4M8H/9zycwrdi/qaBNq/7KfmX79D+5f9fjG9oWhqWdwIfVJddWRsf5n8MS/UxxC/TkZUIu5bHvOGnTSSyt30WFhhUxLXLnRZomL84+Pn7vLkUGkNfUUt2UK/hMt4/9D7vHv3u4wdPNb7WWD4YKCtvzj+RVbsX+GndiUhh0KzaHb45RcwswDtJ3l+oX8GhrRu3bQ/hjUXaG7NGxYYOJbP/mh2m+xRxnjBl0hWOF+lKMqagO1dIHgyQsP37lIUZYeiKFsURbkyUtfTWvadrMRiUiISdhVlMTEoycaBkz4rH0d3eFYuWuN8DLjWU3hw2wuUXjzK4YuHubbvtc1+zWKyMGbQGD4+8TGnqk+1/LxCt8IoIHjnu1M9Kw9hDO5NLotPeBnVXgHoqLc8RobJ5okhrnOiOi6i1lSwdMLLwaVyJ7wcXthUTTlqZRkZahLr7llLhpqE2lTOiCBEmlDhVZrDPz4+qn5V/O17G45xOjB9/jozRs7w2r/hgBiyuabaKm+8vlpb5Y1r7x/T36vuYyAhh72IULlGzY19b9/rCf1bPgX1L7ksDSGJu2TCEp799FnPXKDGgP0CnP/Of7t0BmiYB9pL9lzyOQSDSOZ83K/r+rSA7R5d1/uH+o6iKIOAacAPdV0fp+v6wUhdT2vZV1bJZSkxWMyR+WmGpMSwz3fl47tNYFKh3zUtb0xR4Jr7oeIY279cAsDoPqPD+uptg27DrbvZcGRDy88rdBs0p4Piiu/8437DzL0Iuiw+5gUyCp9D/b8jvVKjqtPuiSF22j3vF48i3WQlZ9RP/aVyR/2UdFvf8CaY+phm9f+OJOa/h3nO9+Joz35B6AhCOR+B+2urgsbbl6TfSu7GXK/9GzdthipcSXkR2rp/9dq1EdduqPtIOEovJVSuUQvGPrVoPRluGknr5mUvI39PPmsPr/XMBfZTaBvme8Zy363+XEYRwPaUPZd8DgE6MOxKUZSCILtnACeArYqi/GdHXUsodF1nb1kll6e0PeTKYEhqLCcvOqioqfPsOLoD0jJBbaWa1mVZkDyUj48U0jemL/1i+4X1tX6x/bgi4QrWlqxt3XmFLo/m0iiuPR887reFuRde6d3tLzdOVIwfCLN2eP6l/sbLVU1uvdSoESecW/goJY6zLY9bb8ZREoR2ISomeNG2qCDzgSFhnfMB2i8+pviO+Y363RNbnuCno37K7rO7KTxWyM93zKf4tsfQMif7NSXhKEKz1IfTNgr/Sx3ufa/G9CUjYahXOj1vYh75e9+k8FghUD8XbHwkqA0aGEUADdlz71gusudChOnInI9gcUxDgQRd128FnIqi+GVFKYryM0VRvlQU5cuzZ8+2+wWeraqlvLouIsnmBoYjc+BUlaco0KndkDay9Q0qCo6r7manyckN0S1IWAeyBmSxv3w/RyuPtv78QpN0tM36ork1Zm16LHjcbwtzL9ZNW0dGdGpwhRSTBVKHef61WNHuXsKszfOC53x89EuJW+/idKbNdimcdcGLtjnrGh9rSFj/z0/Q4gcwa/PcoP1u7ua53Hvlvdw+5Hbevftd3jqyFu3uJYB/TSYJR2kZvc5mY1Iay+OmDIX4AQ3vEwagJg0mI2UEBdMKWLF/hdfxMLA77cz69GmPDQYRJPHN/Ws0lksOkhBBOtL50IPsuwSsrn/9PvA93w91XX9d1/UbdF2/IS0trb2vj7314VFXRCDZ3GBIfVv7T1ZC2d/B7YS+TabBNMsXSWnUmkzcfqZlTkRW/ywUFAoOB1uEEiJBR9usL6pJ9crh+uLN2wgzXte7LB7OUy6nA/W9OSy9+bngccK3v+I5b2vjmoV2pzNttlvgdvrbrC8+9h+s3y28bSHvHHyHezPvZfr705kx4gFMX/yJYNOhhKOEj9hsaAz58odHPhh8Lrj5OdT35gRdYVZNKktvf6XpsVwQIkBnq119Boypfz0G2NeJ18LeExeBBochEiTZVBJtqsf5OPaZZ2faiDa1ua3iANGYGHtiL7bzJWF/L8WaQmZyJgUlBeh6MF9Q6M6oZpWM6NRGcb/5Y17wrGK05abGmhT6vEXrSS/5JHjOhzXNc94IxDULQqdQVeZvswEYUrv5E99oFG+/79w+RqeN9vaL3MJHKUm/NSwRCEFoLapZ9ZNGh/q5IFQorc/30q1pTY/lghABLB14rmAi0e8CP1YUZRuwB3ixA6+nEbuOVzAoyUZMVOR+FkVRGpLOa3d66ntYE1rdnq7rfFy+h5Fxg7GYyui3ezVHxv867O/fOOBG3tr3FgfKDzAytQ3hX0KXRLVYyUgaRv7EN5i16TGWTnjZ43i0oOaHl/o4Y4dqxRUdh7n2ElbNgRadgKbVoEYnoM7bjWZWKak9T+7GR/xi3nMLH/VMdrYUmp2yjJjmYPsFoSMItEG30+N4aP7hVETFeqR2fVCBDIuV/DEvMOvTp1l683NcceogDLzRGz8PDTlY+ZP+RIZLbuYE2m3sU81mMqISyM9exqzNc1k6/iUybP1QJy2ESQuDnktzaZTYz3jz96BhLF8+6U9cHhWLRbFgbW3OahA0l+aRcjep0h96Ee2+8qEoykwAXdd/FPiZ7mGmrutjdF2fpXfi43hd1/n6WAXD0iKX72FweWoMxacq0Y9/Bn3bdsN/xH6GE47zXJM4nHNDskgt/gi1+nzY37+h3w2YFTPrDq9r03UIXZSYFNSEgWSkjPDEj6eMQE0Y6IkZbo7A0CjHRRzmaIrsp5myZgpF9tPUqDEU15zgzjV3Umw/iZYwEM2WFEauiRL8nD7X3SimOfny8K47DNxunbNVtZy4UMPZqlrcbln5iwQ94nc17N5V63E4jE1RILYf9LnSk1z+4Due4521npwn381kQV10FRnbX/bIUH/6Ggwbx5xNc4L3C8mF6nS6le1eOtNYHtdHIjcozjrUP/6QjA8Xemzyw4Wof8hoyNnz3eoLHjeVNzj7o19S66qluKIYhxYZURCjuO2da+6Uopq9DKlwXk/pBTvnq+sY3jcu4m1fnhrLZe5SFMfFtiWbA9vK9wBwTfwVnE4fg+J20Xfve2F/Py4qjlF9RlFwuAB3KGlJodvTqvjxgNAox+5VFDlOe+VDczfmcsh+krxv/+SR8a2vwmzCFEauSedN7G63zsHTVdzz6ifc+l+buefVTzh4uqpr32x0A3rM72rY/flD/vKjL30fXvkBXDzmSS5Xm6/9pBat98hQ7/8b6ntzePX2V4L2i1dvf1Xi5zuRLmW74YSk1lY1lsf1kchtCq9Nhgi18ju2ibzB58c8z293/JZHNj5CUUVRmx0Qw/HIWZ/jN5+IA9I76Oycjy7DruMVAAzvG/kwj8tTYrjBVOR508aVj4/L9zAoOpU+UQnUxqVRMeBq+u79G6YWDAQ39r+R0zWn+ebsN226FqHn4hj3FEVX3dl4+X1jLtMzp3sVUHLW51BSWUK6rW/75JpEgPPVdTz61peUXvD8P0ov2Hn0rS85Xx1ExUgIG/ldQ1CfyKtdPZ1os5XF4xf79YvF4xcTbY6Wm6xOpNfZbpjy5aHyBhePX8yK/SvYWrrVOw+0xQHxdTz8QhLFAek1RCy5QVGUVdAotFsBRkXqHO3JruMVRJlNXJbS9srmgQxMsmE2F1NjTiAmYVCr26l2Ovjq4iHu6NNQ1fzUsLEkn/yW1KKNnB11V1jtXNv3WlSTyrrD6/h+31ZUWhd6FN6YW7Pq6cDWJFy3zmHOmilBl9/nb5/P6rtWM3n1ZK8E47ppBWQkDQ+da9KJOR11Tpf3JsOg9IKdOqer3c/dk+l1v2v8QI8NB7PZRvZtRo9L5pE1U7imzzUsGreI+dvns/C2hazYv4Jvz31LwTRRHewseoXttnLMbcgb/BOzNv2S3435HW/vf5utpVu9x9idduZsmkPBtAJqtJoW52v4Svr60jCfrOv0h1ZC+xIx50PX9fsj1VZnsOt4BUPTYrGYIr8YZDYp3GguZr8pk+uVZmLfm+CzigM4dRej46/w7ruUMpRLSUPo/81qzo6cCiZzs+3YLDZGp41m45GN/NsP/g1zGN8ReibGE6hZH87yOAyZk1GL1mP+ZAlLxr/kt/IBDfKhC3YsoNZV6wmtuv0V1JoKVKeDDDWZdfcUoDoueqR6DecjJiVi+RvBcLt1zlfXUed0EWUxkxobhcnk6WtRFjODk21+NxuDk21EWcKz+6ba7s2E+l1tUWbOVtV6f69km8oFu9a5v19NefAQlWA3Yg++4wmxqi+iCXgSzFWbJ/ncpTWW3DXa8rFxvfYSSyYsIXdjLgCr71rNgh0L2HlqJ3kT81B0saHOIpjtTryqL4qicOJCjddOgfbv+5YoT05RsP0Gqq3xManDPbXDzn/nvz8qDuL7tX7MjfEIhGS40iiYVsDJSyfZeWqn3yE2i42XJ7zM6UunmblxJkvvWNqi+jSqSWXpHUv9Vj6MdpfesVRCEnsB7aZ2pSjKlcAE4DZd1x9sr/NEAs3lZs+Ji9w+Mrxq4S3F4ihnsF7GGu2HXKfrKK10QLaV78VqimJ4rM+kqCicGj6O4V++RdLRz6gYemtYbWX1z+Kr01/x5ekvuXHAja26HqF7E7j0nbPxEU+oFGDd8jyZQF72Mq8DYrPYPFVz9+Tz2cnPGkKrnC7Ulz21a1RAnVkAy6d4TjJvd7s6HdAQv22EUQxOtrHs4Ru4sl88JpNCamwUyx6+odHnxs1FW9ruzQT7Xd/65yxOV9Z69028qi9zb8/kF//zVef+fkZcfSDBngyrNk+ORyA5H3j2z/m7J94+WFv1tq65NI5fOs7nZZ+zLHsZjxY+yuTVkzEpJvIm5jE8cTgxwSqnCx1CoO0adnrfa5/62Wm0xcTDf9rZvrZbVxPc3nwV1TR742N++YUnJyno99p+L6OaPasZg+MGkzcxz6vaZrPYeGPiG8RaYvnHgn/ErbvJWZ9D/uT8sB0Qo6hm/uR87/xjs9ha1IbQvYnYY35FUYYqivKIoij5iqKsAdYApcCcSJ2jvThwsopap5vhaZFPNgeIO7cLgE9qMzhd07qENl3X2Va+h1HxQ7Ao/k9sLwy4htqYFPrv+nPY7Y1OG020OZr1R5pPQhN6HiFjbrc9SfEd89F+8THWUfeQaU0jb2IeKdYUz03TyQPMueqnbJi+gVV3LPNoxjsq/RvvYCGD5uK3TSaFK/vF8+7sW/nk38bz7uxbw76B6HWx4S0g2O8aZ7X4/V7Tr7/M63hAF/39jPCU1OGeGzffFY9WoLk0SqtKSYtJ45tz37CjbAfLspcRq8aSNzGP9IR0cTw6mUDbXXDX1UHt9Oj5mva3XSXEbZjv/lDHdABW1UpmUqZ3HvB1PLL6Z7F++nqy+me1OF/D1wFJsaaI49HLiKRFfw3cDCzQdX0asFvX9b/put7lSxjvOn4BoF2UrgDiz+3CjYnd+lD2nWtdTGlR9QnO1F1kdPzQxh+azJxK/yHxp/cSeyq8Oo3R5miuTbuWwiOFIvnYC2ky5nbzXLT4AXDxONby78iM7kvBtAIyo/ui7llNjcvOtPenUeOyh2jdhw5wRMKJ3zaZFNLioxmUHENafHTYTy57RWx4Gwj8XTWn2+/3SrKpXfv3czs9Eru6G5x1HhnScFamcz5ovNWHydS6ajlVc8pbHXrv+b3sKNtBwbQCvjz9JaZ2CO0VWo6v7eq6HtROY6LMjfZF3HZDORa6u0H2vDXjaKB0urHVtPyWzHBACqYVMDRxKDM3ziSrf5ZfFfSs/lnM+nBWi+4nDAdk3bR14nj0MiIZdjUIGA88rijKIGC0oih3Ax/run4hgueJOF8fryDJptInrvkwjNYQd/Zr7HFDsDus7DvvZsLlLW9jW/lewCOxG4xzl9/IoIMb6L/7Hb7r/9uw2swakMXnpz7n85Ofc9ug21p+UUK3xZBUzPEpDAj1Mbc3P4f63hy45THI/xFWAGsSWs77FN/2GDM3z8XutDNz81xP2FVUQugigh3gfLQ1p6Oz2u6JBP5eFXata/9+VWWef31DWn75RfPfCxEmo7k0jlYeZd7mediddp7Y8gSLxi1i5YGV3PXXu1gyYYnEs3dBQvXzmjp/R6NDbbeqrMHOguWENEdToYatCIU1CgtqLo38SfmcrDnJ45sf99r54vGLGRAzoMX2bYR3Cb2LiD2C0XW9Wtf1D3Rdf1zX9XuBO4A04KVInaO92HWsgmF941qdi9Ekbhfx53ZRmzScgbGw73zrnppsK9/D5ba+JKnBiyC6LdGcueIWkku2E32xLKw2r+5zNTGWGCk42AsJJamYP+YFTyhVgCa8NuQmik2Qs+3JxmFaJtAyJ3f4/8HAiN8enOz5f7Qkp6Mz2+6JBP5eq786zh8fur5X/H6aOZriimJmbpjp10ee2PIEM0bOIC87T57udlFC9fPLU2N6he22lBpnjdfxAI+dP775cWqcNZ18ZUJ3IZJSu8OAZ4Eq4P/oun4cyKvfuiwXazRKzlVz/xXtkxRru3gIs7OamsThDK2Gva0Iu7qo1fBN5WHu7HtDk8edSR9D/0Nb6P/NOxz94bxm21VNKt/v+30+OvYRv3X9liizDKq9CdWlkbH9ZfLHvMCsT59m6fgXyXArqJMWwp3/7QlJAbBY0e5ewqwP7g0ZprXuJ2tRL54EdLBENzypC6MwW1vxjVexZXYAACAASURBVN+OtCpNe7bdEwn2eyXb1M7//Yy8DrezYbUDPIm8gTZq2G/CINB9cvRUm2dVJCrIAyCLFS0miVnvPhi0jzy17SmRD+3ChOrnQPvbbjBJXLcTyn1UrDS7xybjB3pCA8Fjp3O+AnfA6nIw+4wgmltj9kezQ1RBny12LoRFJMOuXgaeAAYCvwdyI9h2u7Gr1Cgu2H75HgD2xAzSL8EnJ3Uu1enERYU/gH16YR8u3H4Su8HQrAmcG/ID+hxYR9l1D6LF9Wm27az+WXxS9gnbT2xnwpAJYV+T0E3wlRi1RENddcNnqg110kIyzNGsu+cDVEcVau2lxjHvTgfqe3NYettzfisf4BOmVX0eXgniHPsqtrSAlsrbGvHb7XGutrTdW/D9DRVFwazg91t2+u9nyI5eOOoJZXnwHUi6HFA88uS//AKi4zw3eUbMussZWk3INwxGs8Pb96I6KkPKh0pF866H0+nmzKVaNJcb1Wyib1x0UDvtFNtVFOhzZYOdJQ4BZ63/2FxXDUuub/zduV+366WJTK4QCSLpfFh0XT8AHFAU5d8j2G678s3xChQgPa19nhbEn/07TjWeupj+pCd69h0od3FD//B/+q3le4gz20iP6d/ssSczbqfPsS8YsOt/OXbbY80ePzJ1JHFqHOsPrxfnoyfiG/cbKM1YLx2q4iksxUN/8Y9l97nBUovWkwHkj3nB64D4hWllPxuxS+5IeVuR0m07wX7D/5o+mjd3HOaJ7Cu75m+p2uCVLP99hpSu7/tg+Mbi+xynujSRD+0mOJ1uDpyu8pOA/uND1zOiXzwWSwcLAoTKzfC1x0DbNPYFQ2+doma4iEyuEAki6Xz0VxTlLjxVzfvWvwZw6bq+NoLniSjfHK9gYLKNmKj2KXkSd+5r7InDQFFIT/Ds23/ezQ3N+xEAOHUXH5fvYXTCFZjCkNuri03l/GXXk7Z/LSe//49osU2vflhMFm7ofwObjm+iqq6K+Kj2rzgtdEGcjsb7Apb6VSDDrJI/6U/M+uiXLL39FTKiUlDv/O/6EIAgqxytqGAeSt723dm3RvwpZEeeq6cS7Df8t9W7eeZHV3W939I3/Ko5fO3foKrMs98Xn8rnvjdmsz6c1eLia0LHcOZSbSNp3V/8z1f8+ec3MzCp/UNFI4Jm9zxQqj7tv1+tl3FuZYXzcBA7F9pKJO+4fwck17/+g8/rMEb5zkHXdb4prWDkgIR2ad9cV0nMxUOcHnYvAKlWiI9qWdL5roslVDpruDYhPezvnMzMps/xL+m/688cv3V2s8ffOvBWthzfwsYjG5meOT3s8wg9nLc9dsu83ZDskWgzKt+um7YO1dQ+KiUdKW8rUrptJ9RvaMjsdqnf0jf8qjkM+5/zd0gd1hCyFYjJ4u0f4C8f2l59RGgbmssd1Gadro6tUdQm3r43+IqI8RCotRXOw0TsXGgLkXQ+tuu6fhRAUZQoXdfr6l9nNf21zqPsooNzl+rasbjgNwDYE4cDnnDNoQktcz62lO/GopgZFRe+Pm9tbCrnBl9P330fcOra+9FiU5s8Pj0xnQGxA3jv0HvifAjNElQa0Te3xJfo+LAnQCMGW4dm5Vmby9MIFs8dLJxCpHTbTqjf0JDZtZhNlFXYm/1bdFvcTo9j4mPrIh/atVHNpqA2azH726WmuThzqRanW8diUugbF42qdpOxIQJjcnOInQutJZIzwHKf1wU+r38XwXNElN3HPcnm6e3kfMSf+xodBXvCMO++ofFw8Lwblzu8uMwt579lROxgbC1UojqZeQeK20n/b5qveq4oCrcOvJWvz37N0cownggKQiBG3HLgFmzyC4IRg33fa58yZ8XX/P4no0NKXBo5Bve8+gm3/tdm7nn1Ew6ersJd36d82xr7+y3c99qnHDhdhdPZ+KmmSOm2nWSb2khO97+mj2b1V8f5/U9GU+nQWPD+nmb/Ft2WqrIW2brQ+fSJUVkaYLNLH7qePjENN9Ka5uLAmUvc//pnjP39Fu5//TMOnLmEpnWhlbymaOOYLAjtSfskOkTWqWk3dpVWYDEpXJ4a0y7tx539mtq4wbjVhvaHJoLDBUcq3QxLavoJyuGa0xy1n+HBgeNbfO7auDTOD76evnv/xqnv3dts7sfNA29mTfEa3jv0HnOvm9vi8wldFN+438C8DNXmea+YPJslyv+9bxvtjG8MdukFO/+9/iDP3n01w9JisUVZ/FY2msvTaEk8t0jptp0Ldo2XPirimR9dRUbfONw62OucTL/+Mv57/UHOXqrlmR9dxcZ9Z7pObH10PMzd5V8EU7U1KAX5Ju0a9t+UXK/QrThXo/HBrlKW5/wAs0nB5db5y5fHSL01nYH1+Z9nLtUyK2AcmfU/X7HqZzcxKDmC9wzBcjN0N6hWn7E7yNisu4Pn2nXAeC0IbSWSzsdliqLMxZNw7vc6gueIKLuPX2RIagyquR18Jd1N3LldXEq7zm+3b9J5c87H1vOegefahKGtuoSyKyeSUvoVA/6+kmNj5jR5bLI1mav7XM2a4jXM+t4sWUrtKbRz3G+kCIzB/vp4BTPzv+DjX49rlKxc53SRFhfNMz+6iiSbSoVd449bvvPmFrQ0nrtLSMF2Y+qcLjbuO8PGfWdY9bOb+N26A/xi3DCSbCq/GDeMP275jiRbw3jSJWLrW9MvAuV6hW6L5nKz80gF112R6h1Ddh6p4MGbGuzS6daDjyNhRi2ETTcZowUhkkTS+ZgJGHfTucDlwAAgJ4LniBhut87uExXcMqz5WhitwVZRjFp3kZrEDL/9Q+LArMD+8y5+NKzpG/zN53czxJpGalTrEuJrY1M5d/mNpO1fy6lr76Muvl+Tx08YMoHFf19M4dFCpqRPadU5BaE1hBuDDWCLMvOvk6/k13/Z7ZXJ/P1PRmOLMre4LaHt+OZ8uHU96N/G7bOSIH8LobOxqcHHEKtPPofFpAQfR2RVVBDaTCRngF/pur5V1/WtQA3wQyAV6JIZzCXnLlFd62JYO9X3SDjzOQDVySP99qtmuCzes/LRFBXaJXZVljC6laseBicz7wBg4Ff/0+yxV/e5mv4x/Vl5YGWbzikILaVvXHSjvIE/PnQ9feMar0g43br3pgE8TyN//Zfd3ieSLWlLaDu+eTOuEH8bpb44mvwthK5CMDv1pW9cdNC8ELFdQWg7kVz5SARQFMUC5Oq6nlv/fksEzxExdh2/CMCwdko2Tzi9kzprKpqtb6PProhvXvFq6/k9uNH5fgskdoNRZ0vm7BU30/fgBk5+/x+pTRwU8liTYmL8kPGsPLCSfef3cVXqVW06t9CLaKOmvMViYkS/eP7885txutxYmlBF0pzBw6q0+iTmlrQltB3fvJmaOmfQv83ARCsf/3pcz/hbtGP9BKFjCBWaqfmEA6qqmRF941j1s5u6p9qV2KnQhYmk83FaUZQ5wFXAEp/9XVI2Zs+Ji0RbTAxMbIekR10n4fTn1CSN8OjrBpCeAFtO6FQ4dJKswZdwC8/9nVQ1nitsTYdKhUNZxu30OfoZA798i8O3P9XksbcOvJU1xWt4a99b/G5MlxUqEzqClkg1RiBu2WRSUM0mdF3HYlaorNWwVzVOAg9HHtdiMYVMaG5OpldoOUbezJkqPejfxhZl6bp5NS2VJJUY/W5HYJ9XLcFDMwMltlXVHNnk8vakA6R1BSFSRNL5eBiYDPxN1/UjAIqiqMC/RPAcEePAyUouS4lpl5sOa2UJUY7znAsIuTIYWp/Cse+8i1sGNf4TVDnt7Liwn/Gp3/OGK7QFpzWBM0Nvo3/xJk5+fwaOlNA1Q2LUGMZfNp51h9fx89E/Z2hi28K+hG6MIdUYyLzdEZ/MDPlcQ8XKiME21JKWPXwDV/aLx2RSvGE+vseGK48b7Dy+bQutx+3WqdVcvPrgdcx+++/e33fpQ9eTZG0vYcUI0IF2LnQ8wfr8WzOz+OND13tV8bqFnTaH2LHQjYjY2reu63W6rr9vOB71+zRd1z+L1Dkiha7//+zdeXxb1Zn4/8/RLu+7s+9OQhICJE4I0AXKFoalpdAyLVBCC4EwZdqZ+dLOr9/pq51vl2lLO0NLh7C1pEDboSWlDBRC2JdASJyFJGSxs9tJvMa7ZW33/P6QpVi25EWWLMl+3q+XX0FX914fo0fn6uie5zmafbXtTC9IzDcaOXWR8z2CZuYG/o029eqtpl14tZ9lfZLVR6J2ziUYFhuTK9YNuu/KGSuxmqw8uuvRuP1+IQYSqXzufc/u4u6LZ4dK6TZ1eoDwaT6bvn0Jz91z0ZAHD9HK9AbPLWLX1OnhYH0nv36jiu9es4BnVq/gu9cs4MHXK2mQ/78iSSK957/yxBZcHr/EqRBJksbD/NjVtblpdXmZlqDBR27t+3jt+XgyJkR8Pt8OBfboSecbG3dQYM1mZpTjY+GzZ1E761NMrnyVk40HcRXNibpvjj2HS6ZewktHXmL14tVy90MknMfnjzgHO1iitabZFSqlC7GXx432e3qfW8TG4/OTYTOHyu729m9Xj6FFBUVaifaeN7Tmrqe2hW2XOBVidIzLwce+U20AiRl8GD7yTr5He/F5EfM9gmblwu6G/h94Onwu3j+9l08Xno0pDlOuequbczETDr/LxJ3PcPiy/zvgvitnrOTN6jf5z4r/5MFLHxxwX5GmkjxHuPc8bKUUVywo4YalU0N199dvq6bF5QX6z8fuO4c7126modMzaGLoUPJFRGxsFjNdHn/odZyU68BhDWwzmxR1rS5MJhP5TivNLm98cm5knrsYRLT3fJbdwqv/9KmwRQbNJsWxpk6sPYURILDYoNdvYDWbKM600dLtS3y+mMS1GOPG5+CjNjD4mJqAwUd2404s3jY6is4ZcL/5+fD7AwZtbk2O/Uzn9fbpPXi0L65TroL8Vif1M1Yw4dDb1Cz/Kp6ciVH3zbHncN3s6/hz5Z95q/otLp56cdzbI5IsiXOE+87DvuuTM7j30rmhFYWn5Dt56OYlPP3BsX7zsSMde825U8KOXXvLUuaXZPUbgIwkX0QMLM9hYXZJJl//TBm/fqOK2y6cyZpeuR8/vWEx7xyo49pzp4TNtR9Rzo3McxeDiPSe/+Od59Pi8nH7uq1hfcb2Y03c+z+7mJLv5A93nk+by9cvL+TB1yvZuLc+sfliEtdijBuXg4/9p9opyrKRaY//n5934m20MtFRcPaA+52VDxrYWe/nU1PPtGNjw3byrVnMyog+MBiJulmfovTQO0zYtZ7jn/j6gPtePv1y3j/5Pv/x4X9w/sTzcVoSUBlMpK4ElmrsOw97yYzC0OABAtMi7vn9dp766nIuXVDKg69X8v3rFjEpz9nv2BvLp4U+RASPXfP0Np5ZvaJfpZre+SJS7Sq+Gjo9mJTint9v57vXLODb68PXUfj2+l08sWpZv9fqzicreO6ei5JXDUtKko5pkd7zHp+/X3+z5ultPLFqWeixx6dDA4/e+3z3mgVs3FufGrHbm8SxSCPjcvCx71Qb0woSs7hg3sm36cotw7AOfP65+YFs/211vtDgo9PXzXunP+ZTBYviPuUqyOvM4/SUpRTtf5kT5bfid+RG3ddisnDLWbfw060/5RcVv+DfVvxbQtokhmG4t+MH2n8wwywpOlgJ297PA1w4q5BLF5SS57RSkm2nOMseutB/cekU7vzULJRJMasokzynDZ/f4ERzF36tw6ZQmE0q4pzu4KKDfcWaLyL6C76mhmGgtcak4IlVy7CYFN+9ZgEPv3WIHdUtQOA1ifZaxT3nRhvQfCz8sTbAlgG+XknFwfeNfJs8pvV9zx9r6qQ4y853r1kQmub58FuHsFlMPLN6BS0uL2YTA+ahARRn2fH4/Jxo7or9i4yO+sh99HBJHIs0ktTBh1JqGrAJONSzaVXvalmJ0O31c7ihk2vPmRT3c1u76sk6vYe62V8cdN8MC8zIge11Zy667/RMuSrPi/+Uq95q51xMUfVWSj5+gVNLbxlw33kF87hi+hU8c+AZykvLWTlzZULbJgYx3NvxA+0fR4OVsO37/BULSvj6Z8rCSrIGS+uWlWRxywXTw6ZEPHTzEtq7fVz94Hs8sWpZ2BxuvxF5bQmL3M1IqOBr+l+vHuC2C2fyu/ePcNuFM0N3PIJTrX7+ygF2VLcwJd+J2aRGJ+dGG/Crc/tvX/UirLvmzGOZxjIuOS0mvrVyXmiV82D/U9/m5qZHNzMl38nv7zg/YqwGFyI8b2oe31o5j5se3TyyKYTudnhwSfi2VS/G488UImUle5lZE7Bea31xz8/RRP/Cg/Ud+LVOSLJ54fENALSXlA9p//n5sKPOj7/nG9qNjdvJs2QyJyP+A6PeXDkTaS2ZT8neF8AY/BvHG+feyJy8OXzv/e9x4PSBhLZNJInFMaLDByth2/f5G5ZODQ08gvvf9+wu/vHSMu781Kx+z93z++04bIEPqL96vYr7b1zMlPzANMBnK46z9palocfBudnBhFGRGMHX9IalU/n2+l2hf/tOtbr74tmhD3dOm5mH+7xWknMjRptPExp4wJn+x91zB66m2cWP/raXh25eEhar99+4GHvPQPkfLy3rdw4p2y3E0CR72pUGrlRKvQls0lqHzetRSq0GVgNMmzYtLr9wf23g9ua0wgQMPo69RHfWVNxZU4a0/1kF8NIxqGw2mJ7n5d3Te/hE/sKETbnqrX7GhZRt+S15xz6gZeYnBtzXYrJw9zl386PNP+LOjXfyxMonmJ03O+FtTEeJiNmE6Zkj7DVb8TpysHa3YfV7Y5ojPFgJ277P5zmtEfefWuDEYjZFfC74rthR3cLPNhzgmdUrAELVrp5ZvWLQaleiv1hjNviaBl/LaK9pWUkW371mAT/bcIAH/v5c5scz5ybaPHdL4gczXr8Xr+HFarJiNVsHP0DEzUj7Wa/fiBirVvOZ72M37q3n3s+UhU3N+tmGA/zyS+ex6duX9Jv+GTxHXKYQel1w73Yw9fmIlqD8DYllMdqSfefjOHCe1voSwK+Uurb3k1rrR7XW5Vrr8uLi4rj8wv2n2rCZTUzMGdk3vX1Zu+rIrt9KW8n5Qz5mUc/d/k0nfLxz+mPcho/yBFS5iqSl9Cw8zjxK9vzvkPYvcBRw37L70Gi+9srX2Fm/M8EtTE+JiNmEMHzgbsdrtlLlOc1Vz11NlbcFb86kmKahBMtZ9nbFghIgML8aCHu+xeXtt/+UfCeHGjrx+oyIz1nMgfnYj9y6lOJsGzaLmcn5GRRn27HZLEzOz2B6YSaT8zNk4DEMscZs8DUPvpbRXtOq+g7uemobDR1uLCaFxWKiONseeu1GlOyfUQD50/v/eLtjP+cQeP1eqlqquOovV1HVUoXX703o7xPhhhuzPp/ByRYXx5o6Odniwtoz/a+3YAz3ftzY4eGup7Zx06ObQzHstAb6HafVEvEccZlC+PsvBP7tG9cJmCIosSySIamDDx0QvEq8BMxL9O/ce6qNKQXOuFe3KTy+AYWmtXTog4+SDJiWDW9X+9jYsJ1cSwZlmYmdchViMlM//QJyT2zH3lIzpEMmZE7gvvL7UEqxasMqfrP7N9JRpav2k3hf/hZVLYdY9cpXOd19mlUbVsV88cl3WsOm01yxoIR7L53LTY9u5tP3v8W/v/Bx2NSo9duq+02/WXvLUtZvq+axdw73m+6w9pal/P6DI9z06GZ+8OJe/vHSueQ75Ru6ZAqWMF2/rZqf3rA49G/faSoPv3Vo9KfC6cQtFhf8sLZqw6oRv29E4vl8Bvvr2vniIx/w6fvf4ouPfECX1x+1/wk+fuwr5UwvzIg6RTAY/+k8hVBiWSSL0jpyRZhR+eVKmbQOXCWUUr8AntdavxNp3/Lycl1RUTGi36e1ZskPXuWcKXnc9en4Ths6+2+fxexp5fAFPxnWcY9/DC8e85A7/wdcmH8Wt07+TFzbNRBLdxvnbPx/1C++geoL7hrycV3eLp74+Am21W1jUuYkvnb217h61tVkDlLhK0mSlnUcj5jtZyTVrgwftJ8EwGtxUmW4WPXuv+DynZk64LQ4WbdyHWV5ZcO6/d7Q7ub/PrcrtEjgxDwnX35sc9i0hCsWlPC9axcCgW/N+y42l+ewBBYK9Btk2s24fRqf38BsUjz5/hEeefdo6FxT8p2pU+Iy/tImZntXu/IZmhaXF5vZRIfbR6fbh91ipiDThsNqGt2pcO2noOngmceZJYACexb0/mA1zEXben9Yi8f7ZgxJ2Zg92eLii4980C9x/KGbl+C0mkOLDG6qqufyhYES98HpgMCQK/jFPIWwvRa62/pvd+RA9oThnWsYJJaTF7Mi+TkfZyul1gIG8HK0gUe8NHS4ae7yxj3fI+P0x2Sd3s2pebcN+9ilJfBCwwHchjchCwsOxOfIoWXi2RTt30DNstvRQ5wnnWHN4J5z7mF3426eP/Q8P9j8A3629WdcPv1yrpt9HcsnLMdskmkvCTHccoq9928+Fqj0Y3Hg/afdrHnxC2EXHQCXz8Wa19bw8udfHtaFx+Pzk+e0MasoE7NJYVKElc6FwBzqf7t6AdMKzwxSew8efL7At9UacPs0JVl2LBYTJ5q7wgYekKDyrGLYepcwPdbUyb89t4e7L55NntNKp8fPLzZW8q9XzWdirgPVk8sWlw9sg/F5wqtaBX1jV2D6Soy8hpc1r62J2/tGJF60/A6Xx891v94U2nbe1DwuWzgx7BPpYGW541K22+eG/17Wf3ucKxL2JbEskimpgw+t9UfAhaP1+/afCnwDPD3Ola5Kq/4Hw2SjZeJFwz52UQHYc3dj1ZnMzZwc13YNRcP0Cyg4+RH5R97jdNnQ77oopVhcvJizi87mcOthNp3YxBvH3+DFwy9SklHCl+d/ma8s+Ip0XqnI1431+XtZ+4kfRrzzsfaytVhNw3vdMu3mfuVxg6Vzg+s8TMl3hiV0hjWpZ2pE79WEH75lKfNLs0O5BQkvzypGxGk1RyxfalKKQw2dtHX7mFeSxcHGzqglmVOd1WRl7WVrI35bHMv7RiSezWyK2H90ec58eREsm/v3Iy2bm0YklkUyJTvhfFTtOxW4tTk1joMPk7eLoiPP01a6HMOaNezjDeXBnLUff8dCVBJejrbiOXRnFFK876WYjldKMTtvNl9Z+BX+6+L/4u5z7qbYWcwD2x/g5pduprazNs4tFvFgrdxA2Xu/Zt0nfxFauX4kt9u7PEbU0rlwZv6/zRr5Ql7f4e63mvDdT2+jvsM9JuZWjwd+Q0csX1qcbedXr1eFXs+BSjKnOqvZSlleGetWrovL+0Ykns2qwkpzB7/YKMi0hraNx7K5EssimZI97WpU7a9tpyDTRrYjfm+qkkPPYvF2cHrKZTEdv71zP1p56Dx9DkeaLcwq8MWtbUOiTDROW86U/S9jbz2BOzf2uy9Ws5XlE5azfMJyttdt5zd7fsNtL9/G7676HRMyEzd3VQxRn7KkVqDMbGXdlb9lzev/wNrL1sZ80Yk2tWFqgTO0YvDPNhzg118+DyKkBkU73uc3MJkU8+JZnlUkhCfKa9jc5Qnd/fIZCSxP2lu0ErxxKFXa+0PbmtfWjOh9IxKv22Pwsw0Hwkrm5jot/OMfd4a2FWbZRicuI0lgrA5GYlkky7gafOw71RbfxQUNPxP3PU5XbhmuvLkxneL9jp1kmTLp6JrBlhMuZhV0xK99Q9Q4bTmTD2ygeP/L1Jx/R1zOuaR0CfmOfH5e8XO+/vrXefrvnsYxwoXsxAhFyBexAmX+4sD83hHUeLdGmdpwqKGTu57aFnoMgdwAq9kUyukY6HhLzzStuMytFglj9KyvEuk1rG93h/472j5xn0I33NyoYQp+aBvp+0Ykns1ipqHDHeqHAF7750+HbXvk1qXJm9qZ4FgdjMSySIZxM+3K4zM4WN8R18FHQfUrODpqaJx+dUzHd/pd7Og8wELnTGble/iwJjkfrrzOXFpKF1C4/xWUP353XmbmzmT14tUcaD7Azyt+Hrfziviymq1kWDNGdNEpybIPWrpy7S1L+fcXPg6Vu9xf1x5KMo90/MOySnlaMAzNgbp2nnz/SMQVoXuX2i0eQ1Po4vG+EYkXadpmpt3Ur/T32j79T7rGZSwklsVoGzd3Pg7Wd+AzdPwGH1ozefdDuDMm0F5SHtMptnZ+jA8/C52zySzu4PnKYk60mZmcM/pVfBqmryD/w9+Qe/xDWmYOP3E+mnOKz+Hy6ZfzzIFnuHLGlSybEKGqh0h7FouJ+aXZ/OmuC/D5DSxmE8WZNn50/WK+d20gnv/9hY/ZuLceOJPT8ae7LmBSnjPi8b3vjIjU1dTpCeVxNHf5eGLVMmwWE6dau9Fa869XzafF5eXB1yv50fWLZQqdGFWRpm36/AYPvl4ZNhXrxZ01PLN6BYDEpRAJNm4GH8Fk8xmF8VmLoqD6FbKa91Kz8G5QsX1Aer/9I/LNOUyyFpNZ3MnzlcVsOeHg+pzOuLRxOFpL5uNx5FK876W4Dj4APl/2eXbU7+AnW37Cn6/9M6YY/3+J1GaxmJiUF77ib3HPug7HmjpDA4+gYE7HQMeL1Ofx+UPTVf60rYY/bQt8iPv7Rzf32/d71/plCp0YdX1jLtgf9e2Tbl4xI6wUuBAiMcbV4MNmNjEhNw55B9pg6s4HcGdMpHVCbB/UW30d7HEd5IKsc1BKkefwMzWnmw9r7Fx/1ugPPjCZaZy6jIkH38Da0YA3qzhup7ab7Xy+7PM8uutR/nb4b1w7+9q4nVukrt7rOVhMiisWlIQWIWxxeVm/rTqU09F3/77fPI7K2hBiUJFeh96lkL+4dAp3XTwbi0nxxKpl/Or1qrBSy1IeWaQCq9kUsT8ymxQnmrukjxEiwcbN4GPvqTamFjgxx6EzKTz6NzJaK6le9HWIcTG9zR27MdAscp5ZaX1hcScbDhXS1GWiMMMY4OjEaJx+PpOqXqPofVxdHwAAIABJREFUwCucWnpLXM+9fMJyNh7dyC+3/5IrZlyB3SzffI5lwTyA4HScuz45g3svncuaXut4BHMAIu3fu84+EPU5+XAweqK9RmXFWTz2lXL+ur2aa86ZzG2/3dJvrZeGDve4mkMvUluh0xqxP3r141N8/8X90scIkWDjYv6L1pq9p9qYVhCH26mGj6m7fkl31lTaJqyI+TSbOnZSbMmnxHqmysWi4sAdjy0nklMVyp1ZSGvxXIr3vQw6voMfkzLxhblfoK6rjv/Z/z9xPbdIPb3zAACWzCgMXeghMOVqzdPbaOn2Rdy/d539gZ4Toyfa69Ds8jKvNJuvXDiTNRHWevnVl87jT3ddIB/kRMpo7PJE7I8uKisJPZY+RojEGReDj7o2Ny1dXmYUjjzZvOjI/+JsO0z9rBtizvU45WmksvsYZzvnhG0vyfRSmpm8qlcQuPth76gjp2Z73M99VuFZnFVwFk/seYJuX3fczy8iMwxNQ7ubE81dNLS7MQyd8N/ZOw8AIM9pHbCOft/9ez8/0HNi9Az2OkRbw8PrN9Bay8BDpIxosWqzmHhm9QoeuXUpxVl26WOESJBxMfjYe6oVgGkjHHwow8vUXb/ElT0j5gpXAG+3b0OhWJxR1u+5hcWd7Guw0eZOzoW6ecLZeG2ZMa94PpjrZl9HU3cT66vWJ+T8Ilxwqsz1D23iop++yfUPbeJAXXvCByDBPICgFpc37DGE5wD03b/38wM9J0ZPtNfBaTNzoK6dww2dEZ83NPJaiZQSXG+mtyn5Tg43dHLTo5v5wYt7+dbKeThtErdCJMK4GHzsO9UOMOIyu8WH1uPoqKZ+9o0x3/UwtMHbbduYbZ9Ctrn/NLBFxR0YWrEtSVOvtNlC09Ry8o6+j8XVHPfzzyuYx9z8ufx2z2/x+OWWdqIla8pS39r667dV91vHo3cOQKRa/MHnB3pOjJ5or4PP0Nz5ZAW/er2Kn96wOOz5h25egt2i5LUSKaUky95vXY/7b1zMr16vAs5MGfSNwl1iIcajcZFw/lF1CxNzHWTYYv9zTT4XUz96gK7cMjqKzov5PLu6qmj2t3FpzvKIz0/O9pDv8PHhCTuXzHJF3CfRGqadz4RDb1N04FVqz/1i3M9/7exr+UXFL/jrwb/yxXnxP784I1lTliLV1s93WqOu7xBp/97Py9oQyRftNTrV6qKmOfDz81cOhNZOmJzvJMtuJschr5VILVarmfklWTyzegU+Q2M2Ke79w45QZTbomTLoG/3CL0KMB2N+8KG1Zkd1S6hqTqwm7vstNlc9JxbeDSr2C+mbbRVkmBzMdUyL+LxSsLC4gw9P5OLyKpzW0f/mpTtnAu0FMyna9xK153xhRH9vJAsKFjAnbw6P7X6M6+dcL6uqJlDvMqhB8Zyy1Lv0qtNmxmdovD4j9MG0d239waZ6DbT+g6wNkRoivQ42i7lf2dLfvHeY71+3SAYeIi0ooDg7/O6cTO0UInHG/LSrU63dNLS7mVOSFfM5LN1NTN7zMG3F5XTlz4/5PA3eZrZ27uEc51zMKnqntqi4E6+h2HEqeR+2Gqafj7O1JiGJ50oprpl1DbWdtbxw+IW4n1+ckcgpS73zSb7+hx0cqG3n8w+9HzG3JFm5JyLx8hwW7r10Lj94cW9ovvy9l85l456T8hqLlOT1+tlf38FNj27m0/e/xU2PbubeS+dyxYJAtSuZ2ilEYo35wcfOntuos4tjH3xM2f3fmHwu6ubcNKK2bGjdBMCyrIUD7jcjr5ssm58tJ5I3+Dg9eQkeew4TPnomIec/u+hsZubO5LFdj+EzfAn5HSJ8qsymb1/Cc/dcFLeSp73zSe6+eDb3Pbsram6JlMsduxo6o5ctlddYpKL6DnfEmP3etQvj3k8KIfob84OPHcebsZgU02OsdGVvP0Zp5dM0T/40nqzJMbejy9/N661bWeCcRa554IGQScGCok62n7TjTVKlP222UDfrk+TWbMfZeDDu5w/e/ajpqOGlI4mprCUCglNlJudnUJxtj9sFtXc+yUhK6Yr05vUbEV9bs0nJayxSUrRSuz5Dx72fFEL0N+YHHx8camJuaRZWc2x/6vTtPwFlpmHWjSNqxxttW+jWbs7PPHtI+y8s7sTlM7G7Lnm3fRtmXojfYmfijsQsCnhu8blMy57Go7sexW/IB5R007v06khK6Yr003vtmGhlS/2GltdYpIS+ax1Fi1mLDDiEGBVjevDR0uXh45NtLJiUG9PxeSfepPD4KzTM/Bw+R37M7eg23Pxvy9vMsE1ikq14SMeUFXThsBh8WJOckrsAfquTulmfpPDQWwm9+3Gs7RivHH0l7ucXidU7n+T1vXU8dPOSsNySh29ZSr7T2m/f4PMypzo99c3fefL9I/3Klj508xKerTgur7FIukj5ZobW/WJ27S1LKcmSohZCjIYxXe1q8+HTaGDhpJxhH2vydTNzy/fozpxM0/SrR9SOF1vepc3fyQ35lw75GIsJ5hd2svVkBqsNiPHGzYjVzrmEkiPvM+XD31B19X/E/fxLSpcwOWsyj+x6hCtnXInZJN+Spou+pVf//YWPQ2VWW1xefvV6JT+6fnFoCoOUyx0b+ubvPPLuUQCeWb0Cf0/ZUrvFxB2fmiOvsUi6SPlmX3rsQ16498JQqV2LSVGSZcdqleuPEKNhTN/5eKeqAYfVxJwYks0n73kIR0cNtfNXoU2xj9FqvU083/wWZzlmMsVWOqxjFxV30u42s78xeaVo/VYnp8o+Q171VnKqt8b9/CZl4rrZ13G49TB/rvxz3M8vEqt36dWNe+u566lt3PToZu56ahsb99aHzfdPVO6JGF2R8neCA5BphZlMzs+gKNshr7FICdHyzbrcBpPzM5jeE7My8BBi9IzZwYff0Lyyp5ZzpuRhGeZtg8zGj5i052FaJlxEZ8HAlakG4tN+Hqr7EyYUV+ZeMOzj5xV2YTFptiRx6hVA3axP4soqYcbbD2Dyxn/hw/LSchYULuCB7Q9Q31Uf9/OLxJOcjvFDXmuRTiRehUg9Y3bw8eGRJpo6PayYVTis40zeDsre+yY+ey6182+L+fdrrfldwwtUdh/j7/I+SbY5c9jnsFs0cwu6eL/akbSqVwDabOXoOV/A3lHH5C1PxP38SiluPetWPH4PP9nyk7ifXySe5HSMH/Jai3Qi8SpE6hmzOR/PbT+B3WLi3Kl5Qz/I8FP23r/gaK/maPn/xW+NbW0Qn/axruEFXmv7kAsyF7PIOTum8wBcOKWVx3dmsum4g4tndsd8npHqKJpN3cxPMGH3X+goXUDznIvjev7SzFKum30d66vW81zVc1xfdn1czy8SS3I6xg95rUU6kXgVIvWMycFHfXs3f915govnleAY6jxOrZlZ8f8oqHmVU/Nuoyv/rLCnG70t1HobafF3YGg/VmUlw+wg25RBljmDDJODNn8ne12H+VvLu5zyNnJB1mIuzV4+or+lrMDFhCwPf92XySendyct8RygetF1ZLSeYOZb9+PJLqGzdEFcz3/VzKvY27SXH27+IVOzp1I+oTyu5xeJ1Tv/Q4xt8lqLdCLxKkRqGZODj0fePozPr/m7RROHtL8yvMza/B1KDq2ncfrVnJ52JYY22Os6zKaOnXzUWcVpf+uQf3+JpYC/L7iSMse0WP+EM21TcMWsJp7cNZFXDmbwd3O7RnzOWGmThUPLbmP+e79m3ovfpmrlD2iffG7czm9SJtacs4b/2PIf/MPr/8CvL/01yyYsi9v5hRBCCCFEco25wcf24808sekIl8wvYULu4InaztaDzH7/PrIbP6Ju5vV8MHk57ze8wAcdu2jxt2NTVmbbp7AscwEl1gKyTBmYlAmf9tFteHAZ3bi0G7fhwWGyM8FaRIklH6Xid0t3YVEXcwu6+MOuLM4q9jAz3xe3cw+X15HD/ov+gXkfPMy8F7/FyaW3cuq8v0eb41ORK8uWxb+U/wu/qPgFq19dzTfO+wY3n3Uz1jidXwghhBBCJI/SWie7DUNSXl6uKyoqBtxn69HT3PG7CmwWEz/5/Nlk2KKMrQw/2Y07KK36I9ZjL/KRM4s3Jp3LO7qJRl8LZkzMcUxlkXMOZfZpWEdQajde2t1mHtw6BQPFP13QyqJST1LbY/a6mL7rLxTWbMOdVULd4hs5PfvTeDOHl+AfTae3k9/s/g07G3YyNXsqX1v0NS6ddil5jmHk8AQkbWLvUGJWiAgkZkW6kZgV6UaSfpIorQcf7d1eXtp9ihPNLrYdb2bTwSZKsu185+/OojTHgbP5AFWnXqbb24LH14nP04rRXUuzu4Fa5eeY1cYRqwWtwIyJmfbJLHDOYq5jOk5T6s0Pre+0sm7XBBq7bMwv8nBWsYdCp59FpR4m5ySnHFZO3T4mV75K1umjAHTnTKaraDbu3El4HXn47VkYJivdBdPoKiob1rm11uxu3M2zlc9S01GDWZmZVzCPuflzmZA5gTx7Hrn2XBxmB4uLF1OSURLpNHJRFOlGYlakG4lZkW5k8JFEyf9KfwRcXj/fXr8bk4KpBRncfuEMblo2NXTHI6fyDf617llOWHv9mVZwWp2UWLLJz5jEZ7NmUpY1i1mZM3CYU2/A0dss4JwZmlcqvbx3xMTz+20YGv6/z2SytChJbS9aQPPCG2hvPobz+AfYGqvIaTyI+egmlHFmQNS29DZap39y2KefmDWRy6dfzqGWQ2w6uYnK5krernmb5u7msP0euOQBLp029BXkhRBCCCHE6EubOx9KqQbg2BB2LQIaE9ycZBirfxck9m9r1FqvTNC5BzSMmI1VqsZEqrYLUrdtvds1lmM2klR8TVKxTZCa7SoC9qdozKbi/6+BpFt7If3aHGxv0vpZkUaDj6FSSlVorcdcjdax+nfB2P7bEilV/7+larsgdduWqu0aDan4t6dimyA125WKbQpK5bZFkm7thfRrc7q1d6wasyucCyGEEEIIIVKLDD6EEEIIIYQQo2IsDj4eTXYDEmSs/l0wtv+2RErV/2+p2i5I3balartGQyr+7anYJkjNdqVim4JSuW2RpFt7If3anG7tHZPGXM6HEEIIIYQQIjWNxTsfQgghhBBCiBQkgw8hhBBCCCHEqJDBhxBCCCGEEGJUyOBDCCGEEEIIMSpk8CGEEEIIIYQYFTL4EEIIIYQQQowKGXwIIYQQQgghRoUMPoQQQgghhBCjQgYfQgghhBBCiFEhgw8hhBBCCCHEqJDBhxBCCCGEEGJUyOBDCCGEEEIIMSpk8CGEEEIIIYQYFWkz+Fi5cqUG5Ed+hvuTNBKz8hPjT9JIzMpPjD9JIzErPzH+iCRKm8FHY2NjspsgxLBIzIp0IzEr0o3ErBDpJ2GDD6XUD5VS66M8N00pVa2UeqvnZ0ai2iGEEEIIIYRIDZZEnFQptQBwA+You5iA9Vrrbybi9wshhBBCCCFST6LufPwf4OcDPK+BK5VSbyqlfpigNgghhBBCCCFSSNwHH0qpm4D/1Vq7BtjtOHCe1voSwK+UujbKuVYrpSqUUhUNDQ3xbqoQcScxK9KNxKxINxKzQqS3RNz5uAD4nFJqHbBEKXV/3x10QHfPw5eAeZFOpLV+VGtdrrUuLy4uTkBThYiv8R6zTa4murxdyW6GGIbxHrMi/UjMCpHe4p7z0TuPQyn1V631fX33UUqZtNZGz8MvAs/Hux3pwjA0TZ0ePD4/NouZwkwbJpNKdrOEGLbHdz/OL7f/kkJHIQ9f/jDzC+Ynu0kiCaRPE2OFxLIQiZGQhPNe3FG2n62UWgsYwMta63cS3I6UZBiaA3Xt3PlkBTXNLqbkO3nsK+XMK82WDk6klV0Nu/jV9l8xN38upzpP8cPNP+Spq55CKYnj8UT6NDFWSCwLkTgJXedDa30TgFLKrpQ6qJSy9mz/SGt9odb6E1rrHyWyDamsqdMT6tgAappd3PlkBU2dniS3TIjheWzXY2RaM/nmkm9y/Zzr+ajhI7bXb092s8Qokz5NjBUSy0IkzqgsMqi1dgPLtdbe0fh96cLj84c6tqCaZhcenz9JLRJi+Oo663i75m0unnoxDouDFRNXYDVZ2XBkQ7KbJkaZ9GlirJBYFiJxRm2Fc6316dH6XenCZjEzJd8Ztm1KvhObJdryKEKkng1HN6DRXDTpIgAcFgeLixfz2vHX0FonuXViNEmfJsYKiWUhEmfUBh+iv8JMG499pTzUwQXnlBZm2pLcMiGG7rVjrzEjZwalmaWhbYsKF9HoauRo29HkNUyMOunTxFghsSxE4iQ64VwMwGRSzCvN5rl7LpJqGiIttXna2N24m6tmXhW2fV5BoHp2RV0FM3NnJqNpIgmkTxNjhcSyEIkjg48kM5kUxdn2ZDdDiJhsObUFv/azqGhR2PbSjFJy7blsq9vGF+Z+IUmtE8kgfZoYKySWhUgMmXYlhIjZ+yffx2FxMCt3Vth2pRQzc2bycePHSWqZEEIIIVKRDD6EEDHRfj+bjmzkPJVJRlf/ehIzcmZwrO0Ynd7OJLROCCGEEKlIBh9CiJjUvPl9TnpbueRUFfP+9z7M7o6w56fnTkej2de0L0ktFEIIIUSqkcHHKDIMTUO7mxPNXTS0uzEMKUMq0tTpI+zY+VsACs76HI62E5Tufi5sl2nZ0wCobK4c9eaJ5JA+TqQLiVUhkkcSzkeJYWgO1LWHVkwNlu2bV5ot1TNE+nn/QXY67DhNVvImnEtL6TZK9vyVU+d9CW0OdCt59jycFieHWw8nubFiNEgfJ9KFxKoQySV3PkZJU6cn1NFBYKXUO5+soKnTk+SWCTFMXhfs/hM7s/OZlTERkzLRMP0CrN2tZJ/8KLSbUopJmZNk8DFOSB8n0oXEqhDJJYOPUeLx+UMdXVBNswuPz5+kFgkRo8pXaPd0cBAPZZmTAGgtmYvfbCf/yLthu07MmsihlkPJaKUYZdLHiXQhsSpEcsngY5TYLObQSqlBU/Kd2CzmJLVIiBhVbWR3dh4amJ0RGHxos4224jJyaraF7ToxcyKnu0/T6m5NQkPFaJI+TqQLiVUhkksGH6OkMNPGY18pD3V4wTmmhZm2JLdMiGEwDKh6lZ0FUzChmJVRGnqqvWgOjrZT2DrqQ9smZQUGJzL1auyTPk6kC4lVIZJLEs7jyDA0TZ0ePD4/NouZwkxbKHnNZFLMK83muXsuivi8EGmhbjd01rNn6gwmKQtO85nVf9uKZgOQffIjmuZeDgTufAAcbjnMeSXnjX57xagJ9nF/uedCur0GZgVOm3yTLJIv0rVZrsdCJI8MPuJkKNUzTCZFcbZ9kDMJkcIOvoYGPva3c1ZPKd0gV85E/GYbGQ1VocFHkbMIq8kqdz7GkaYOj1QREiljoGuzXI+FSA6ZdhUnUj1DjAuH3qChcCanfZ1Mc5aEP6dMuHImktFYFdpkUiYmZk7kUKsknY8H0g+KVCMxKUTqkcFHnEj1DDHm+X1QU8HewqkAzHCW9tulK3cyGY2HQBuhbRMyJ3C09ehotVIkkfSDItVITAqRemTwESdSPUOMefUfg6+bfc5MFDDVUdRvl87cyVi8XdjbakPbijOKqe2sxWf4RrGxIhmkHxSpRmJSiNQjg484iUf1DMPQNLS7OdHcRUO7G8PQiWquEMNXsxWAj/Ew0V6Aw9w/trtypwCQ0XgwtK3YWYxf+6ntrO23v0hvffusfKdVqgiJlBLt2pzvtMr1VogkSVjCuVLqh8BZWusbojz/M+DTwDat9T2JasdoGWk1q6EkrAuRVDUV4MxnX3c9szImRtzFlTMBrUxkNB6kefangMDgA6Cmo4Yp2VNGrbkisaL1WWXFWVJFSKSMSNfmfKeVqoYOud4KkSQJufOhlFoAuIGI9zWVUmcDZq31+UCtUuqiRLRjtAWrWU3Oz6A42z6sTkyS4kTKq95CY9Ec6j2tzOibbN5Dm624skvD7nyUZAT2rW6vHpVmitERrc9qdnlj7geFSIS+1+Zml1eut0IkUaKmXf0f4OcDPP8J4CWl1O+Bl3se96OUWq2UqlBKVTQ0NCSgmalDkuLGhjEbs12n4fQh9uUFksynRxl8QKDkrrP5SOhxviMfszJT016T8GaK4Ys1ZqXPEsky0n5WYleI5Ir74EMpdRPwv1pr1wC7FQCtPb+/BSiMtJPW+lGtdbnWury4uDjeTU0pkhQ3NozZmD2xDYC99kBd/GnO6H+bK6sEe0cDJm83ECi3W+QsksFHioo1ZqXPEsky0n5WYleI5ErEnY8LgM8ppdYBS5RS90fYpwXI1Vp/CcjreTyuxSNhXYiEqdkKysQ+3U2pLT9sZfO+urMCd0XsrSdC24ozijnefjzhzRSjR/oska4kdoVIrrgnnGutvxn8b6XUX7XW90XYbStwE/A6cBXwQbzbkSp8PoP6Djdev4HVbKIky47F0n/MN9KEdSESqnoL5E2nsusUU539S+z21p0V+CbS0VqDq2g2EEg6r6itiHqMx2fwi40H+MuOExRm2vi3qxfwibKBf49Irt59lmEY+DVorWnq9IT1XYYR2Cb9mkgVQ73eDvX6LYQYnoRVu+rhjrRRa71FKXWrUupdoBL4YYLbkRQ+n8H+unbufnpbqKLGw7csZX5pdtQBSHF29G+UhUgKw4AT2+ietoIT3Qc4L2f2gLu7MwODBkfLmQTz4oxi2r3ttLpbybXnhu2vteYf/2cHG/bUsnxGAdXNXXx13Vb+dPcFnDs1L/5/j4gbk0lRmGmLWqkPkCp+IiUNdr0d7vVbCDF0CX0Haa1vAlBK2ZVSB5VS1l7P3au1/qTW+mta91oOeQyp73CHOi4IJLTd/fQ26jsijsmESE1NVeBu41j+JAw0kxwRU7RCDIsdtzMfR8uZHI+SngT1SHkfv910lA17arn5/Gn80+Vz+f51C8nLsPLPz+zE5x+TXcOYMlClPqniJ9KVXL+FSJxRGb5rrd3Acq21dzR+X6rw+o2IFTXkA5VIKz2LCx5yZgEwyV4w6CHdWcU4Ws7keBRnBKZiVXeEl9uta+vmFxsPcN7UPK4+O7B2SI7Dyi3nT+dwYyd/2XECkdoGqhwkVYVEupLrtxCJM2r3DrXWp0frd6UKq9kUsaKGxTw+btl6/V66vF14/d4hbRcpqmYr2LI4pD0oFKX2wadCBQYfNaADqwYX9eSJ9L3z8cBrlXh8BrddOAOlzkzDKZ+Rz8yiTB5/9zBay8rDqWygykFSVWh0SF8bf+P9+j0Ug8WXxJ+IRt5FI+T1+jnR3MWxpk5ONHfh9Z75Rq8ky87DtywNq6jx8C1LKck6M8/UMDQN7W5ONHfR0O7GMMbGBy2v30tVSxVX/eUqqlqqQp1PtO0ihVVvhaK5HO6qpdSWh9U0eKpYd1YJFm8XFlczAE6LkxxbTtjgo/p0F3+uqOEz80sozXGEHa+U4tKzSqis62BH9bgvhpfS+lYOumJBCX+443wMw8DrN/j9Hefz2j9/mhe+fhFPrFrGH+88H40ec31eskhfG5u+116v18/JFhfHmjo52eKiKMPKutuX8cSqZTyzegVPrFrGutuXhV2/x7PB4kviTwwk0QnnY5rX62d/fQdreiWkrb1lKfNLsrBazVgsJuaXZvOnuy7A5zew9KmWYRh6TCZjBjudVRtW4fK5WLVhFetWrmNWziwOtx3ut70srwyr2Tr4icXoc7dDwz5YfBMHuw4y0TH4lCvoXfHqBB0ZgWOKncVhq5z/dlNgIcLrzpkU8RwXziri6c3H+HNFNUum5Y/krxAJ1LfqVWOnhx/+bS+3XTiTb6/fFerbfnrDYjYfaqA4287dj304pvq8ZJG+NjaRrr1rb1nKg69XsnFvPVPynay7fRlen8F3n99zJlZvLZc4JXrcBeNrsOeFkDsfI1Df4Q4NPCAwH3RNn4Q0i8XEpDwn0wozmZTnDKuSMRaTMft2OgAun4vHdz1OZUtlv+2rNqySb0VS2ckdoA28RWVUdzcwaYiDD3fPgMPediq0rTijOHTno73by58qqjl/ViGFUb5JdNrMnDctnw17avHLt+MpLVg5yGQycddT27hh6dTQwAMCfdu31+/ixvJp/ZJ4073PS5Zofe2qDauobKnk8V2PS18bRaRr75qnA3EbfFx92sWdT/WJ1ackVgeKu6qWKrq93QM+L/EnQAYfI+IzdOSEtCF+UBqLyZhew8ua19aEOh0Au9nOd1Z8h3vfuDdsOwQ6pTWvrcFrSIeUknqSzY9nFuDXBpPsA1e6CvI4C9Ao7O21oW1FziJqu2rxGl7+XFFDp9vPVYsmDHie82cU0NzlZcuRcZcylpaCfVqe0xqxbzOb1Jjr85IlUl8LgT713jfu5TsrvoO912Kg0teeEe3am+c88618hs0ssRrBQHG35rU1+PHzjTe/Idd6MSAZfIyAxaQiJ6QN8bbsWEzGtJqsrL1sLU7Lmb/L7Xfz480/5sHPPBi2HQK5AGsvW4vVJLdiU1JNBeRO4ZCvDRhapSsAbbbgdeZibzsz+Ch2FmNog5Pttax7/yhzS7OYXZw14HnOmZqHzWxi497aAfcTqSHYp7W4vBH7Nr+hx1yflyyR+loI9KkPfuZBfrz5x7j97rDt0tcGRLv2trjOfDDu8vglViMYKO7WXrYWM2Z+eckv5VovBiSDjxEoybKztk9C+do+CeUD6ZuoGZz/XJhpS1ibE81qtlKWV8a6letCnY/T4uSOxXcwN29uv+0yDzSFaR1Y2bxoHoe6alHABMfQcy/cGQXY206GHgcrXr28/2OOn+5i5cKJg57DYTUzf2I271U1Drv5YvQF+7T126r56Q2Lw/q2n96wmGcrjvcrwpHufV6yROtr161cx9y8udyx+A7pa6OIdO1de8tS1m+rDj2eWhDI8ZBYDTdQ3JXlleGwOgZ8XuJPAKh0KWNZXl6uKyoqkt2MfjweHw2dHnyGxmJSFGfasFjMNHUHshLCAAAgAElEQVR68Pj8WC0mLCaFy+PHZjFTmGkLS1gzDB3aN9Lz6So4L3TNa2tYe9nafolofbcnUNL+Z6ZqzA5Z81H45Tmw4h+4zzhJRWsVP5v/1SEfPnP7H8g5fZSPbv0fABpdjXzrnW8xW63iyJGz+fWXz8NiGvz7jxd3neT3Hx7nw+9c2q8q1hiV1jHbt080mxSGBrMCk8lEvtNKs8s75vq8ZEmRvjbtYtbr9VPf4Q7FaVGGjSaXN6w4DARyOyMVjBnvBouvJFzrh0s6nSSSalcjYBiaQ01dYRUznvzqctw+I2zb/Tcu5mcbDtDQ4e5X2SWYqDnWBL8defnzL2M1WUOdTrTtIgXV9FzQi+dx8PC2Ied7BLkzCrBWb0P5PWizjXx7PiZlYn/jMS4ru2RIAw+ARZNzAXivqpEblk4ZVhvE6PJ6/Rxo6IxaATBoLPZ5ySJ97fD5fAYH6jtCxQ+CZfDnl2aP+WqU8TJYfEn8iYHIEH4EIlXMONZrMBLcdt+zu7j74tnjrrKL1Wwlw5oRsVOKtF2kmOotYHHgy53CMVf9kCtdBbkzClFobO31AJhNZhwqHyynuXheyZDPM60ggxyHhU0HZepVqhtKBUARf9LXDk99h7tf1bW7+8TpWKxGGW+DxZfEn4hGBh8jEKliRrQKGcEqGlItQ6SN6s1QVMYJTwte7RtysnmQOzNwp8TeHii3q7XG7conI7OVyXnOgQ4NY1KKhZNzefdgo6x2nuJGWgFQiNHg9RuR49RvhB6PxWqUQqQKGXyMQKSKGdEqZASraKRrtQyv30uXt0tqdI8X7nao3Q0lCznUGRg8DHWBwdApQmt9BCpVHaz14XLlYbY2D7s5Z0/OpaHdTWVdx7CPFaNnpBUAxeCkLx45q9kUOU7NZz4SjcVqlOlCYnzsG3TwoZT6ulLqHyP8/MNoNHC0GIamod3NieYuGtrdGBG+qeu9z8kWFwrNI7eGV26ZXpjRr4rG/Tcu5uG3DqVttYxg4thVf7lKFgkaL2q2gjagdCGHuwKDj+He+fA6cjBMltBaH5squ8GbT7duxe3vHta5zu7J+5CpV8k1WD9ZlGHrV81qOBUAxcCkL45N37gtzuwfpw/3idOxWI0yHUiMjw9DSTjfCQSH+v8FfJNAlQAj6hFpZiiJZZH2+ekNi3nnQB1/uON8zCYVqtwC8Nw9F4VVu/r1l89Ly8oufVczXbVhlZTMGw+ObwZlguJ5HDq0k3xrFk7zMD9AKlNPud1T+A3NpgPdlBYX0gw0uWuZlDFjyKcqyrJTkm2n4thpvvqJmcNrh4iLwfpJn8+gqrGTX71eyXevWUBhpo3ibDsTsuxhyeYiNtIXxyZi3N5aTo7Twg8+u4gMm5kujx+HNfy7WJNJMa80O3QtT8frd7qRGB8/Br3zobV+T2v9ttb6baBFa/1Oz+N3R6F9o2IoiWWR9vn2+l0smVHIlx//EJvFTHG2HZNJhSpYTc7PoCTbQUFm4L+Dz6eLvh0BEOoQ5BuJMe7Y+1AwC6wZHOysHfZdjyBPRj72tlPsP+mlpUtTVhxY66Ope/iLBs4tzWbrkWbJ+0iSwfrJYBLvxr313PXUNm58+ANufvxDmlzST4yU9MWxixi3T1VwqL6T29dt5aZHN3P7uq2semJrv8IIva/l6Xb9TjcS4+PLUKZdXa2Uuk4p9VmguOe/r1NKXT0K7RsVQ0ksi7ZPntM6ZpPQvIaXNa+tCXUEQS6fizWvrcFrSGcwJvm9gWlXJWdhaIOjrlomOYZXZjfInVGIvb2WTQfcWM2waGJg8NHYfWrY55o3IZuGDjfHT3fF1BYxMoP1k0NJ4hWxkb44dtHiNsNm7rdNYjV5JMbHl6EknOf3/OQBP+/1eOhLHae4oSSWRdunxeUds0loVpOVtZetDa1SGuS0OFl72VqsJrkNOiad2gW+bihZyCl3M92GN+Y7H+6MAizudvZUnWbeJBN59hwsykqjO4bBR2k2AFuOnI6pLWJkBusnh5LEK2IjfXHsosVtl8ffb5vEavJIjI8vQ3mnLdFa/y7Cz9MJb90oGUpiWaR9fnrDYtZvqx6zSWjBRYLWrVwX6hCcFqfMwRzrjn8Q+LdkAYeCyeax3vnoKbeb767jrMkmlFLk2ApojGHa1eR8J1l2CxVHh18tS4zcYP1kSZZ90CReERvpi2MXMW5vLWdqgVNiNYVIjI8vQ0k4P3e4J1VK5QDre87fAazSWjf12WcasAk41LNpldb66HB/VzwMJbGs9z4Kjdtn4DM037t2IQ6riermLpw9SZVevxHxHIahaer0pFXyWu8OYc1ra1h72VrpCMa64x9A9kTIKOBw0zYAJo7gzgfANFMDsyfMBSDXWhDTtCuTUswtzWbLUbnzkQwD9ZM+n0F9h5sch4VnVq/ApEAphdZQ196dNv1dKpO+ODbR4tbr9fPM6hX4DI3FpCh02qjvcOP1G1jNJkqy7KHVzqNJx2t6KpMYHz+GMvg4Wyn1FwIVroKZngrwaK1vinSA1rpNKbVSa+1XSq0E7gF+0Gc3E7Bea/3NGNseV8HEssH2yXda2V/XHloddUq+k4duXsLb++spn1nAfc/uilgJZigVtVJVsEN4+fMvYzVZpSMYyww/HHkHpq0A4FBXLbmWDLIsjphOFxx8LMpqxGENxHmOrYDK1o9iOt+8Cdn8cctxmjrcFMq3lKMuUj/p8xn9+sTfriqnzeXjm8/sTLv+LpVJXxybvnHr9fqpbOxkTU/MXrGghHsvnRt6HLwTMr80O+oAJJ2v6alMYnx8GMq0qz1a689rra/v+Tf43xEHHkE9Aw8b8AngQKRdgCuVUm8qpX4YQ9uTIljRpXfljHt+v53PLpkSGngEt/euBDOUilqpzGq2kmHNkI5grDu5A9xtMPEcAA51nYr5rgdAvTeLdu1kvvPM+hy51gJc/g66fO3DPt/8CYG8j4pjMvUqVUTqE080d4cGHsFt6dTfpTLpi0euvsMdGmgA3LB0atjjmmYXdz+9rV/1q97S/ZqeyiTGx76hDD4iDRwGpZT6HHAcmERgClZfx4HztNaXAH6l1LURzrFaKVWhlKpoaGiIpRlxF62ii9Z6wEowQ6moJdJfKsbssBx+M/DvxHPRWnO4K/ZKVwA7ax3U6CKmm8/8v8i1BQYzseR9zCzKxGpWbJWk87gZacxG6hMzbGbp70TCjDRmfUb49TpYtbK3wapfyTVdiNgNZZ2Pu5VSk5RS9yqlfqKUuq8nX2Ow4/6qtZ4APAd8I8LzWmsdXOb4JWBehH0e1VqXa63Li4uLB/9rRkG0ii5KqQErwQylotZwef1eurxdeP3esP8WyZOKMTssh96EgtngyKXe00qnvzvmSlcA20/ZqTcVUuipD23LtQUGM7HkfVjNJmYXZ7FV8j7iZqQxG6lP7PL4497fjTV9+2zpw4dupDFrMYVfr4NVK3sbrPpVIq7p44HEvYChrfNxK/AosA94AtgBPKiUumaAY3pPePQC/b46VUr1/t1fBLYMsc0JYRiahnY3J5q7aGh3YxiB9Bafz+Bki4tjTZ2cbHFRlGHtV9Fl7S1L8fv9PP218/nzXRfwzOoVPLFqGU9+dTl5DgsnW1x4/X4e6XPcoFWyuk5D87H+P12nQwvyXPWXq6hsrqSmvYar/nKVLMYjYufphOotYVOuACY5Yht8+A3YXWej055PlqsBehYHzLEGztfkHv6dDwjkfXx8so0ujy+m40V8RapyNbXA2a+/e+TWpZhNgT41Ul+bNgbol4eqd/9d1VJFt7c77LH04YlVkmVn3e3LeGLVMp5ZvYKCDFvESm1FGdaw67/Pd+ZOyFCqZKacOMTuSEjci6ChJJx/Ezhfax280h9QSr0HvA68GOWYS5RS3wcMoB64M8I+Zyul1vbs87LW+p1htTyOoiWOzSnK5EB9R1gi5brbl5HjtLDu9uWYFVjMJp7+4AiPvHuUKflO7r9xMT/bcICGDjdP3r487PgrFpTw+zvOx2JSQ6uM4W6HXy7ut9n7z/uoctWGVgK9/ZXbeeCSBzi76GxWbVglpelEbA6/DYYXJi0JPOwZfMSa83G81YLLZ8LIysHW5MLm7cBjy8ZhzsBucsZ05wMC6308b5xkZ3ULF84uiukcIn4sFhPzS7P5010X4PMbaOBHf9tLQ7uHH3x2ETOKMmjt8uL1Gzzy1kGuPXdKWJ+adkm6UfplvrELMgZ/r/RdyXnVhlU8fsXjrNuzjtPdp6UPHwV+v6bba/Dd5/eEXdv/tHoF3T4DQ0OO00xlQ2dYrPZOQh9KlcyUM8LYHQmJe9HbUHI+DGCBUmpx8IfAFKmow3ut9Rta609prS/WWn9Ra92qlLIrpQ4qpaw9+3yktb5Qa/0JrfWP4vPnxCZa4likRMrq0y6+/NiHXPafb1NZ38GXHtvMI+8eDT1/37O7uPvi2dQ0uzh2uivs+I1767n58Q9RKlB5I5ZOyjt3JVXuptAbGAIrgH7zzW/ypflfYvmE5azasEq+RRDDV7kBrBlQuhCAQ521ZJkd5FgyYjrd/sZAF2HNDSSJZ3cFpl4ppci1xVZuF2BuaTYKZL2PFBKsCOTxa25+/EM27q1nR3ULt6/byq2/2UKm3cLX/7CDG8un9etTx1OSbt8PYBDov+/YeAc3zL2BT0/5dOiDmfThidPk8vSLw1VPbEUDn/nF21z2n2/T5vL326dvEnqwitbk/IyYr+njgcS96Gsog4/ngev7/HyuZ/uQaa3dwHKtdcpFVbTEsb5JaRCeSBktSS3Pae23b9h5B0hiG5DFgfezD7Lmja+H3sBBLp+L77z3Hb5/4fcxtMGa19bgNVLuf7VIVYYRGHxMOg96vnU63FPpKnwW5dDtb7CS5/ChsnMAyOo6k/eRM4LBR6bdwrSCDFnpPMV4/QYmRcQ+z2xSYf/2fX68JOl6DS9rXlszYP9tN9tx+VzShydQpGt78JofFC2WY75+j2MS96KvoSSc/1Br/e8RfoZdHldrnZKfFqIljvVNSoPwRMpoSWotLm+/fcPOO0AS24B83Vifv5e1n/l1aAXQIKfFyY8/8WO+//73MSkTay9bi9Ukty7FENV+BB11MHU5AFprDnadijnfQ2vY12BjRq6LTkceQCDvo0eutYAmdy1axzbff+6EbLYfb5YPAinEajZhaCL2eX5Dh/3b9/nxkqRrNVlZe9naAftvt99Nnj1P+vAEinRtD17zg6LFcszX73FsqHHvtDgl7seJoSSc/yHK9g3xb05yREscGyyR8uG3DnH/jYvDnr//xsU8/NYhpuQ7mV6YETGJrWQEi6NZKzdQZi9k3cp1oTey0+LkgUse4I/7/8iW2i0Dzpvs9nbT6e2k29vd7zkxjh14GVAwuRyA094O2nxdMZfZbegy09xtZkZeN16LE4/ZQVZXXej5HFsBHsNNuze2qVPzSrPp8vjZd2r4a4WIxCjJsmOzKB66eUl4QY6bl/BsxXF+esNinq043q9PTPkk3TjqvYJz7/778SseZ33let6ueZvLp13Oy59/mRnZM8L6cKkKFD+FThtrIxSOqWvtCj22W1Tcr99jXbQYHUrcOy3O0GcXv+GXzyljnBrsm0elVDOBCld9Vzg/R2ud2AylXsrLy3VFRUXCzm8YmqZOT7/EMZ/PoL7Djc9vYDGbKMmyo7UObDM0VrMJi0nR7fVjNZuwWRXdHiN0DsPQ/Y6PtmJqP12nAwlifdmz8dqzqWqpYs1ra3jo0v/GbnbwtY1f48HPPMhcZykOk7VfAlm3t5vKlkrufePewH55c3FYY1u5Oo0kbRJuomM2brSG/14OFgdc+WMAtrZU8tVdD/AvM69nYfb0YZ/ynaMOHvwwj39aXs3EbA+X73yIjsxJvH7+dwA41LaHvx7/Df+6+L+ZlbNw2Odv6nDz9T/u4LvXLOBrn5g57ONTXNrGrNfrp7HTg8dvYDYprCaFMoHXpwnM3lMUZ9po6falT5JuX737ZW0EfgBsGeDryV2xZw+YwBucA7/mtTWsvWwts3JmcbjtME9+/CRfPuvL/frovvunYFJu2sVsd7ePJpcHn6GxmBSFThvtXn9YXPa9fqd97HbUR/1MQVbJiE49lBiNFve9j/Eb/tH6nJJGL9zYM5RqVxu01l/qu1Ep9UoC2pM0wcSxviwWE5PyztwqDFTG6girjNW7wlXfyi0mkwo7flgyCqJewKxAWV4ZL1//N+qaD/Hg7odYf916frz5x9wx+3rK8mbT+20fHHjcsfGOUKLX41c8Pl4GIGIg9fugsRLOvye06dAIK13tb7ThsPgpzQp8GOu055HlirDWh7uWWQx/8FGYZac0x86WI01jcfCRlgxDc7Cxs1/VQLvFxFd+uyV9q1v11btfbj4Gvzq3/z6DVA8KfhP88udfxmqyYjVbmZE9g1sX3Mrtr9we1kfPyZ3D0fajYVWCpCrQyBiG5khzV79Y7RuXva/f0apiplUse13w4JL+27+xa2SnjVDJKlKMRor73o+DAw/5nDL2DSXno9/Ao2f7lfFvTuqLVBmrd4Wr0a7ccqz1KDe9dievHn+VletX8urxV1n17r9Q5W4K3frsO/CAM5UmKlsq5dbmePfxc6BMMP2C0KZDXbU4TTbyrVkxnfJAo5XpuW6C1+Que14g4bzPWh+xJp0DzJ+Qw4dHTsecNyLiK1rVwGNNXeO2utVArGYrGdYMrGYr3d5uDrYeDA084EwffbD1IJtqNoVtl6pAIxMtVgeKy1iOGQ+iVbKKFqO94773474Dj+B55HPK2DSknA+l1O+UUj9WSq1WSl2hlJqrlBqXEx+jVcYKVrgazcotXsPL/8/eu8dHVZ37/+89M3tuuQcSwh2BRC6KVRSvFEFB0KrVaD2i0qDYFiSi9fS0Uo+HX2vh9LRWJShtBUlt1fq1gFoFBAUvCAh4R8QEwv0WyD2ZTLJnZv3+2NmTueyZJJN7st++5pVkzdprb8yTZ69n9vp81s82P+D/Q63zqhaAPuFj7ub5fscIL15yN+fqOk3kbs7FS+9wmjHQQQjYuxYyzgdHir+5qOYk/e2xOV3Ve+FYpYWBCY2WlDX2FGRvHbb6SgCsZhtOSwIl7tg2GgQYlZFAuUthf3F1zGMYtB2RcqPTag5r6y3uVs2lqRw9c8xMEq2J2Mw2f7vhChQ7kWI1WlzGckxvIJqTVUti1Jin9C6a8+RjJvBzYAvQF5gLbAIq2vfSuiaRnLE0h6uOdG6RTXKY89WkQZPYkL2B/GkvYMKES3EhCYm8KXm6ThPPXfMckpCaFDMaYsceSvFeOFsIQ68Maj7gOsWAWDcXLLfgE1Jw8WHTd7w6Wxf7k4/R/VUL308My90uQaTc6Kr3hrX1FncrjdD8GfqzGXPEHL1syjLqvfXcPfpuNmRvYNKgSYYrUCuJFKvR4jKWY7ozzbnnK14Fn/Dx3DXP6cauXoy66l3UKDW46l1B7dH+BvKm5GGmZ/5/7q0058nHq0Ah8Bhq8bEamA4ktO+ltR8+n+BMVR3Hy1ycqarD5xNBbcWVboor3RwuqeFEeS0eT6Odp54z1lM/usDvcNUmzi2uUnUtcejLFTzJks2y6nw18UkcFgeTBk1i5uiZZL+Zjctby8nq48xYM4NDVYcYmTSSFdNWhDlN/H3v3zlYeZBjlUeYsWYGhaX7UCpPBJ1Te6w6Y80M41F/T0NbcjXkCn9ThVJDiVIZs9PVwTL1ZhP65AMgIcTxqjXLrtITbKTGWY39PjoRLW+erqjF5/P5nQBBzY1/uWc8Wf3iWZVzCRcOTu4Z7laB+dnnUdvueg1y3mp8+TwR86dbcYflU7tsJys5KyxHr5y2kjhLHD9844dcMeAK/rjrj8wcPZNXb3jV0Hy0gj5xVv56T3Cs/vWe8VHjMpIrZpeJ5dB5Q9VJKDkApQfD47WJcZTKExSW7gueE4TMP7S4vmHtDTgtTlZdtyoodvU0H656F/sr9nP9muvZX7E/qACJ9DdgaD56Js1xuzIBGcD5wMUNr/FAfHd0u9ITjb147wTqPL6IIvI/3z2eUf0SsFhM+HyCQyU1HC5x4bSacdV7GZzqIMFmwWQytY37RdlheGZcePuCryAlxHnIVYricXPMW8up2mIWbFlArafWb7/78rcv++13hyUMY3/Ffr+LRP6efDYd2RTed+KTZG5dhlywAeXn31KolPvXc0ZKKl2YbufC0mEIAXnjVaeTaY3b9nxecYBZXz7JQ8NuZlxiy8Xcf92dyNbDNhZ9/xDaqi2Lx82tn/yO3aPvYc/IHwLw0am3+LTkA5694h1MUmyfai3dXEjRmWp2PHpNzJshdkG6RcxqufSpTd/x4yvO4W/bDjJv8kjKahR/bkyNk1n05l7OVNfxl7vH0z/ZTrKjmzkEhRKYn+96DWQHxPWDZy8J6xqaP6cOmUrOeTn+de2B+RSguKaYs+6zPLjlQfKm5BFniePOdXf6+z519VOsLljNfeffR1ZKVlfKwd0iZjXq6jwcqajlWGmtP1YHpToYkuTAZovswxPJFbNLEDpvyHkL8n8Q3Oeu1yB1BJhC/o0B7mxK5QkKyw+Q89EjjTE68UnVxCZxgNonROfhsDh49YZXcXlczHtvnq7blVZ4BMa+ZqjgtDr9/TrQlbOL/OJ6J83xfP078HtgGlDT8HM2cG47Xle7oScaO1ziiioi/9k/PqW4us5//KwXdjI7fxd3/HUHs/N3kbNqFyaTibQEW8cnImcqxKXh8tb6Cw9Q10k+tOUh7hx1JxMyJpCzIYdDVYcYnjict29521946Pb96BEKr5qP++pHKawrabaQzKCbcXoPlB6AYVcFNfudrmLcYLCo1MKAhHoCawGPxU6dxRm0y3mSNRWv8FBWdzam8wCMzkjgdGUdR0trm+5s0KZouTR7/GB+uforsscPZv7Lnwflxgde/tyfR3/6j0/x+ug6k7W24KXb1Qlezemwt5Ss6UH5c9KgSWRnZYcJanM25HCs6hgFZQXc8u9b2HZiG+tuXYfFZPEXHlrfh99/mOysbFZ+vdLIwa3grKue2at2BcXq7FW7OOuKLh7XXDEHpjg7537fWl66XS08UoYGv7TCw6uoMdtQeEBDjAaY2EQSmN/x9h04LU7W37q+ycJDO0YzVNB7ArLu1nXGE48eTHM0H3cJIe4RQjwihHhaCLFGCLFLCHEGQJKkde1/mW2HnmjMaTU3KSLXdlLuiqIzxacw770HdIVaC7cuZNEVi/AJHwu2LEAySdzy5i3+wiNi352/wXtlLnM3z2+1kMygi/LNWpDMMOTyoOYDrlNYJQt95MQWD+nxwZEKmQEBS640auzJQcWH3/GqFbqPURma7qMk5jEMYkPLhckOOehrIJ1lxtHpWOwoN+f586fNbGPRFYtYuHVhWD71CR+JtkTmvTePWk8tK/es9OfYSDl94WULWbBlgZGDY8TjE7qx6vH1buc8xadEvuc3mNhEE5jnvJMDEPZETkgiqphcSMH/3+2ynTg5zig8ejDN3O0uKjFuYtE56InGXPXeJkXkFrMp4vGdLToLFZ5rjigOi4PFVy1m0bZFmCQTz0x+BjNmnpn8jK6oa8nEJY19JzyO+eO8MEG71tcQO3ZzhIBv1kD/cWBPCnqryKU6XZliWMZ0vNKC4gsWm2u4Iuz1UdIK3cfAFAcJdouh++gEtFxYXqsEfQ2ks8w4Oh2PG/mNXH/+rPPW8bsdv2PxVYuD8qnNbMMkmaiqq+K5a54j2ZZMnbeORdsWhfWFxpy+eMdinpn8jJGDY8RiknRj1dLdnmS0MdHmEsunLFP35jDJLL92eYvmBdFMb/Km5CGJ3v3/vTfSFsVHt/qoQE80NrSPM6ztD7eN84vI/3z3eNLjbRGP72zRWaDwfOqQqWzI3sDUIVN5evLTvLLvFb/mIzM5E7PJzJCEIWHisD9f+2fG9R3HBX0v8Gs+7O8vUcednt+kkMygm3F6jypEDHG5AnXZVaxOV4fK1LXEA+N1nnzYUoL2+kiQUwCJs62w2zVJEqMyEgzHq05Ay4WrPz3K77PH+b9GyqOdnSc7Grlggz9/Th0ylV9f9mv2nt3rF9RqzoRrb1xLelw65ySew/pb1zN1yFQ+OPYBqwtWh4lvNc3HnHFzjBzcCvo6rSwPMUdYfvd4+jp7T3zqEWkukT/xSTJtfZDNjRsD6s0LhicOV5+OhCwHdFqdEU1vhicOBwljCWEvo0nBeZMDSNJmIcSUNrqeiLSleFdPNObzCYqr6/B4fchmE2aThFvxYpfVT+oUrw+71US9IhAIbf7UPqIzVynUVYW3B4jC9I5x+xQKak/7hVp9bKnMXH+XKv6yp4PiplApY+7m+eRPewGXp5Z5mx/guWuexWa2c9/G+8ibksdIR3+cwgOeerAloNgSKCwvZO67c3WFZF2cbiWE7DA2PwEfPQk/+nvQk49qTy2Xb3uEWzOu4AfpE1o87KrPEth0wMFvrz5I6J/EyJM7uKjobV6duoLaBverv373G8YmX8K95y6M+Z+y7uuT/H3HYXY8eg0ZST3iMX23iVktl/p8Pjw+gUlSa0uPT2AyScgmCa8QgER6vA2LpS0+7+oktLwsfOoLVLG5p2Hzs8B7qexQd5O2OnFLpqC8PDJpJKddpznlOsWvPvoVeVPy2H16Nxf3u9jf5+VvX2bW2FkMTxxOUWURc9+dG9TeBXNwl45ZvXt+fb2Xktp6PD6BxSTRx2HFbo8sNu/yVBcHzxtkJygukKTg2LTGQ0K/yOPozCWyHP2wm+Sg+Yem/dDmBYGxGmmeUF1XTVFlkX/ccxLP4Wj10c6aWxiPWzqRtvhLczXdpWuhicY0fD5B4ZnqILer52ddTGZavL89Ld7Gf00/l1/86yt/H9UFy972ojNnauQiIwKKLYGi8kK/oGvOxjmsum4Vb9/ytvrotOo0hZUH/UKyO9bdyavXPogQGX0AACAASURBVM+6W9dxsuoEM9fN9B+3YurzZDkzsDc4a8lAZnIm629drz527Vo3PYNY2PsG9DsvbMnVAZf6FGKgLTab3SMVFjLi68MKD1CffIC614dWfCTJqZTUxf7kA9TNBgF2HirlpgsGtGosg5YRmksVxcu+4mrm/uNTf578ffY4/rbtIA9PPZdz+yV0P5GuRl1VuAuhnqNQQLuSNZ2iq+YzpyHvannZYXb4DULy9+Tz47E/DsvdwxOHY5ft/txrwsTjlz9u5OAWoudw+cr9l1Je6wmK0+V3j2dUejyy3E2XBiq1kHdR48+RYjP3MyBy8aE3l/Cvdgjopz0B0WKzqLLIL0LP2ZATtkJC8SocqT5C/p58Vt+0msU7FpNzXg75e/IpdZfqHmPQc4n5YyhJkmYDCCF0ort7oeeAdf+LuymurvO3/+zqEf7CQ+sT6ILVmURyn5j9zmyOVB3B6/NSqJSFOVjk7f0bReVFzFx/V7ADxab7Kag9jVtx+88hm2WcstNICj2B4n1wtkB/yVXNCYCY9/g4VmkhPU7fMabG3rDRYKDo3JrCmVZoPgCG9onDIZvZaYjOO53i6jr/hA7UPKk5Yd3/4m5KaqK7CfUklKzpFF41Pyzvzn5nNqdqTzEhY4LfAev+TfeH9SmqLELxKv7ca5ftRg6OAb37e51HhMXp3C5yP+9MIs0lIjlcymZVAxJYeOgdEzjupiObmL56OpuObGLOxjlkZ2UzadAkw0mzl9GaZ+D3tNlVdDKRHKwUr8/fHsnJRXPB6kyiuU/84oNf4MUb5GBhM9uwmW0svGwh8yM4W+RuzsVLL3Gm6W3sfQOQwlyuAPa7TiJLFtKsLXe6qq6XKHeb6Renf+Nw2cKLjyS5DxX1Z1F8sU9KzSaJrIx4dhQZuo/OJpKLkJY/e4XblcUOkgnl5mXM3f5YUN4FVdT76EePsuiKRREdsAxHwbZD7/5ukjDcrnQInUskNtwHosVjtPmHdky0PprLps1sM+K+F9GNF+C2HZEcrGSzyd8eyclFc8FqDxSvgktx4VbcuBSX/xME7XuNSO4TU4dM5dUfvIrP5/M7WGgix8v6X8biHYubdKCIdt6WXLdBF2LvG5A+Rndp3wHXSQbYUzFJLY/r45XqKs5ITz48ZhtuOS5srw+BoLSuWPeY5jI6I5H9xdWc7eWfXHY2kVyEtPzZ092ulKzpuB7+Gnd8Oj5J4rmJvw/Ku0uuWsLG2zbywrQXWLRtUVRXq+eueQ6f8OGqdwXl0ebk4pbk656O3v3dJ+jVbleR4iNwLjHvgnlsvG0j8y6YF9HJSjs+/7r8qDFswuQfV/tbmDRoUpAjZ523znDS7EU0qfmQJOlVIDQSJGBsu1xROxEoOHNYzXh8AsXjw2oxk+KQeX7WxWGaj/R4Gy/eO4HDJS6SnTIv3juB/13/LRv3Fvs1HzZZwtcgrgwjknDcYof66kZRooZkUl+yA8XnobCuhLmb5/uFhveMuRun2UHOxntZPmWZ6j5hsSM7U/3uE6E76d6w9gbypuQx3DmQV699npPeGrLfzGbJxCWk29PYX3GAFdNWBO86OvV5Rjr7c6jqEHPfnUv+dflRdy4NRHu8+uI3LzJz9ExyN+d2R5F6z6W0CIq/gUvu1317f80JRjj7xzT0sYbio58z8lOMSHa7Z90n6ecYFNN5AcYOSAKOsqOohB+MM3Qf7U2ogDfFIVNWq2A2SfzzJ5dRUl3PqUo3qz896t/9vNu4XWl522xVBbsakgQP7AJ7oio413J3zlsAKPZECk2CuW/drubs3Sv56Xn38c/rX+ZUbTHZb2aTNyWP327/LTNHz+SCvhew9IulWE1Wnp/6vH/plcPiYNV1q7CZbf78vfv0bi7rf1mzRL2hQuDennv7xFl55f5LqfOohgg+AYkOM6tmXxK0w/nw9Dhks4njZa6ut3u53lxC+EC2q8YwoMZr7mfq9z4vmMxqbCq16uaCDSgWW2N8XPMsmdZU5IYiQgYy5WTW3LiGEncJ16+5nqWTl3Lj8BvpF9cvKI4C4+y5a57jn9f/k/9Y9x9BMey0OLlh7Q1+Qfo/r/8np2pP+ecgD130EM998RwfHPvAcNLsZbTa7Up3UElKBFajFjfVQI4QImxBtiRJ/wdMAj4VQsyLNmZrnIMCBWd6wnFNXF5Wq4Q5YO07XcXPAkRpf7l7PElOmcLT1Sx9r5Az1XU8P+tifSFl2eFwgSKoCSLvooiCMOWhb4LE4YEWi7edexsvf/uyap878Ukyk0cgJ6qTrdCJf2BBsXLaSuIsTv6jYcdch8XBsinLeHHvi1zQ9wIuHXBpoxuLsz+Hak+T8869TMiYwMzRM3loy0P+4yIlCO38K75aQXZWNg+//3CTx3QAXdqFpcP55C+w/r/glr9CYvAkvcpTyxXbHiE740puSL+kxUP/7fME3tmv73Slcfm+f5LoLmXtlGUAVNaX8XzBb7hrxM+Z1P+mFp9Tw+sT/OTvu/nhhQNZfMv5MY/TRejSMRsq4J02Jp0Hr8li6XsF/PiKc/jl6mBTDptF4liZm6F9nAzrE9d1JnSR0PJ2JMHuA7vUHc0D3gvVd2g5e+/ZvVw24FLuC8jFWi7POS+H3ad3Mz59PJ8Wf8r49PE8uOVBnrvmOWxmm98ExGFx8PzU5zledZxBiYOC8rqeqDdwzX4H5t4uG7Mejy/sPv7y/ZdSWevxt00bk07uNVlBAvSI9/XOINJcIjBGm4pXGgpki5mcd+5tjI8Ga325YAMA1bPepCghJfgDyQZL3HhbvDqOTpytnLYSSZJ44L0H1A83HelsObKFpV8sxWFxsPbGtZTWlXLfxvv8x7xw3Qs4zU5mb5xtuF31Mlq8tkKSpHMlSZorSdJLkfoIISqB6UKIycCzQFhhIUnS+YBZCHEpcEqSpHD1axsRKDjTE47f/+JuymoV0hJsDExxkpZgw2SSKK6u8ycnre9P//EphaermZ2/i8+PlvuPbyshpZI1XVcc/vD7D5Odlc2/vvsXd466kwkZE8j56BEK60r8jz5ls8yQ+CHcM+Yef+LQjr9v432cqj3NhIwJ/rb5m+dz56g7+fLsl3xy4hPW3bqO4YnDOeQ+4y887hx1p7/w0I7TE4VFKjyiHWPQCRRuhMSBYYUHqEuuAAbGLDY309epRCw8AGrsKcTXnvFblcbLSZgkMyWt2OUcVN3H6IxEtu0/26pxDJomVMCbPX4wP/vHp2SPH+wvPKDRlKPorIvZ+buY9cLOHik4jyQsX12wmgn9J/gLD61dy+X5e/K5uN/F1HpquWnETWw7sY3Xb349qPDQjvnbN38LKjy09kiiXiP3NqJ3H6/3iKC27PGDwwToPcYgoaFQVrYto7C+0l94QEN8fPQIhVfNR8maHlZ4aH3mbJxDUWUR1XXVEePsvo334VJcrLlpDS9/+zIz181kQv8JzLtgHrPHzuas+6y/8NCOufede6nx1PD2LW8bTzx6GU0WH5IknSNJ0n2SJOVLkrQGWAMcA3KjHSeE8EqSZAWuAr7T6XIVsK6hiFnf8HPouX8iSdJuSZJ2nzlzphn/HH0CBWeRhON6QshAwXlgX6fVHNbWJkJKix3l5rwgcbiGJsxaeNlCFm1XhYo+4WPu5vlB4izJJDHvvXm6x2siR034GCj2+svXf+FH//4RkkliwZYF+ISvRWJIxaewYMsCFl62sFcLKNsqZtuFehcc/AgGXqz79oEatQAYEKPN7rFKC/0i6D00amzJmH0eHHXlAJgkE4lySqs2GtQYMyCRQyUuTpTXNt3ZwE9LYzZUwKvl1Ei5Ndkh+7/vcYJzLWcHCMsBv6HHg1sejJrLH/ngEc5PO5/b/307K/esxCu8QRO0wLFyN+fGLOrtabm3JTGrdx8PFZy3ZF7QLYkQp9AQH9sfQ7l5GdLgiyPGWe7mXCSTFN3g5sNfIBDsOLmDWk8tD255kFljZzFr7KyIfwvzN89HkiSj8OhlNOfJx+fA5cAiIcStwFdCiH8LIaJay0iS9EPgCDAAdQlWKKlARcM1lANhMx4hxF+FEBcLIS5OS0trxqXqEyg4iyQc1xNCBgrOA/u66r1hbW0ipPS4kd/I9YvDA9GEWYt3LGbR5apQ0SSZWD5lWZA4SxJSRBH5kolL/MKuwDG1sf4w6Q+YMfPM5GcwSaaoYshQUZhsknlm8jMs3rG42cf0RNoqZtuFQx+Btw4G6Rcf+10nsUoW+sbgdOX2SJx1WUiP4HSlUdOwv0eQ3a6cyhn3iRafM5SxA9Tr3n7AsNxtCS2N2VABr5ZTI+XW8lrF/32PE5xrOfvyJ4JyXp23jsU7FrN08tKoufyP3/8jL37zImdrz/qPCc3fkdq1sbS8Gsl4pCfm3pbErN59PFRw3pJ5QbckQpxCQ3xc/gTyG/MRR3dHNaERPhE1zkLF40snL+XFb17kxW9ejPi3kDclDzM95P+zQbNpTvExEHgdeEiSpNeAiyRJulmSpJRoBwkhXhdCZABrgQU6XcqBJCHEnUByw8/tQp84K8/Pulhdg/z+Af5w2zh/otHWduoJIdPjbfz57vFBfZffdRF9463NOj4W5IINZMop5E980v+HGqr5eGXfK42aD1sfZLPsd6+QzTIjk0ayYtqKoONXTltJhi2Vnad2+tuenvx041gN64K1Ta3yp+ez89ROXtn3Ck9PfjporFXXrWJIwpDg627YcGjOuDmsLljNU1c/FXSMISTrAhRuVM0O+p2n+/aBmhP0t6diklq+FPZ4pXrzaM6TD4CEgOIjxZZGce1xWqs/G5zqJMFuYXuRUXy0J4H5FGD1p0f5893jWf3pUX6fHZxb/3DbOP78/oE2z5NdCblgA5lbl4Xl7OysbHae3MmKqc/r5vKc83JItaey6ptV/vdyzsvhnMRzwvL3j8f+mGOVx8LaA3OxloPzp+cbuTcAvfu41SKxPKBt9adHg37uifHqj9NpK4PjI0DzEf/iTQyvKguLs0DNR6Q4WzFtBasLVvvF4yumrWDnyZ089+VzrPpmFX3tfXXHzUrOwi7bO+d/ikGn0WLBuSRJg4HrgElCCN29PiRJkkTDwJIkTQcmCiF+HdJnAnCHEOIRSZIeB7YLITZFOm9rxbuB7ix2q4l6RaB4fVjMJtLjbVgs+nWYx+OjuLoOT0Nfp9VEneLDK0AIEd0Voym3K2sc1NeEv2+NRxFeCutLmfveA41uV6PvxmkJcLuSU5B9iupe0eCMpblgKWaZ/a6TjSLyuIHI9bUUKmXM3Tyf5655ttE5S0foFehkEeh29dw1z+G0OMl5JyfqcV3I7arLCiE7FCHg6XGQ2B+m/Ldulyk7HmWkcwD3D7muxcN/eMhO3ifJPHLpEfrFR376YfbWk73jt3x27n/wVZbqwPLp2fd5/9QbPHnp6yTIyS0+dyBPv1vA0TIXH/9yClIMRVQXocvHbCS3K6/Ph9cn8PoEFpOETTbhVnxdxz0oUk62JTRaT2t9LDY1P4e5Ejb8Gyz2oHbFYqNQKQ/K2bNG38VwRzpFtcVBzoUzR88kKzkLgILyAn+e1lythiUMY3/F/rD2QLerSLm4k9yuunTMht7H0+Nt+Hw+ztTU42mI1b5OK5X13iDTmU6PVw29uNXiMxRtXiFJat4PiV/Flkhh3RldtyuNSlsih2qO+uNvWOIwEm3BT8RD42xI/BCKKov8xwxPHM6R6iNBcej1eYPivZMLjy7yy+2dNFl8SJI0AvgtUAX8SghR1uSgkjQFWAT4gGLgfiFEhU6/POB7QEFDn4g79rXVRC7UqaVTXC2aclNZ8BWkDEWpPIFitmDa9QK+S+5FfkOV2Si3rUT+133IBRt0XVa0TzKU87IRo29A+vZtnGtUa1UlazrKzXnIXg/EpaH4FPWRvc7NSfEq/vdB1XWcrjnNHW/fEdVJRTvOhAkfvojjdxBd+qbYYZwpgGcvgcvmwbnXh71d6XFx5bb/5LaMK7k+Bqerl7+K5419cTxxdRER6ng/P9j1R06kf4+tFz4IwIHKb3j9yAp+Oe5ZRiS2zsF7095TvPDxIT74xdUM7RPXqrE6kW4Zs10itzZFJNeghpyr2zdSng5tz3kLZdsylJvzMAmBz5bgz31aTkSAkARmzP5Jl6vehZAEL+992e8MlD89n2EJwxCSQBISSISNFS0XB+buDsq93Spmu0WsNkXJAdU1M5Qm3DRZ8BVK4oCI8eFW3BSUF1Cr1HJe2nnsObMHh+zQLRRC40yLZUlIOK1O3Th0K268eIP+BjqJbvKL7pk0Z9nVMuA3wKvAH5ozqBBisxDi+0KIq4UQPxJCVEiSZJMkab8kSXJAv1whxEQhxH3RCo+2JNSppSu7WsheBedT52N/fwnOp85HLtiAXLABZ8VJ3cIDgt0r5D2rias84y88QH306nzqfGSvgmyWccrOiDen0PcPVx723+z859JxUtGOs8v2qOMbdCCHPlK/9v+e7tt+sXnMTlcW+jqUJgsPgCpHKok1je5WKTZ1zXZx7bGYzh2Iut8HbDN0Hx1Od8qt7YWWX+2KOyj3aTnRaXUSJ8f5J12KV+FQ1SFuev0mln6xFGjMq4eqDmE1WXFanWF5tKlc3FRu7+309liNFB9a4TFn4xzmbJrDrW/eypxNc5izcQ4F5QW4FXfUcbT4dlqdEc9jl+1BfwMGvZPmFB8WIcQ+IcRm4JxYTySEqAMmCCE61XIj1KkFOtnVwqL3BxhQkHvcwV8BEH73igU7f+O/AWkuVrJJbnCvyEN9+BSCxx3e1gSKT+Fn7/4szK3CJ3w9zkmlR3L4Y3D2gQT9DQT3t9Zmt8IScWfzUKrtfUisbhSYJ8mpSEicdh+N6dyB9E+yk+KUjeKjE+hyubUboDkHFTdooBIbzB6iOVQpPiWic5CRi5tHr4tV3XlGOF68QW5XJbVqHtXcrrz00P8/Bh1Oc4qPDEmSbpIk6WYgveH7myRJuqGlJ2vKIasjCHVqgc5ztVDsibge/hola3pjW9Z0XHGpTXuye9yYdr3A2pvWMHXIVCYNmsSG7A0suWoJG2/byMtTVzQs02qbTST1HC60c+Zfl6/rpKKJ4Hurv3yXQQg49DGkj21crx7CgZoT2EwyfeSWO10pXjhdY27S6UqjytEXu1KNtV5dw2w2WUiy9qG49niLzx2KJEmMGZDEtv1nWy1gN2gZXSm3dhZK1nRcD3+NW7YH5T634qZGqQn/5Dggr867YB4bb9vIvAvmRXWoMmFi7U1rmTpkalC73jFGDtanN8WqFpOB84xImDH73a60+/ukQZP8pjVAUCyFxnVovDUn/owY7Z00p/j4XyAF1ZHqjw3faz93O0KdWjrL1ULJmk6h2cSMt26n8NqFKD/7EOVnH1J47UJmrL1BfXwe6ZG5yYySNZ2i4Vdyw9ofMO+CucwaM4vsN7MZkjiE327/LSUeF94BF+off9dr4POoa5oDX67ItWGow8WkQZOYOXom2W9m4/K4wv99DWK0GWtm9OoNrroEpUVQfQoy9F2uQH3yMcAWm9PVySoLPiE16XSlUW1Xhb2BS6+SrX05Xdv6Jx+gWu6W1NRTWFzdJuMZNI+ukltjxlWq5sGqk+p6ep9HXTcf10/9etdrUQ9Xks+hcOpjzHjrdgpqT/Ob7b+hsOw7XPUuCsoLuH7N9WFLV7S8uvbGtVwx4AquX3M9Vwy4grU3rtUViitehaLKIm5YewM55+X4C5Bou50bOTicbherWmwGvmSHGpehL609rp9/afaMt273byRIlBXudtlOVnIWL1//sv/+PnP0TFbfuJo6bx3Xr7neH0vaEi0trl31rqB4cyvuJuPPiNHeS3ME50OFEIcbvrcKIeobvp8ghNjZAdcItK14N9SppaNdLRRXGYU1x8hp2EzKYXGwavJSnGYHd7x7f6OA8LoXGp0ofB6oUpeqKCnDKaw7S86m+5mQMYGZo2f6dyEPtXLMsqdjLylUT5wwAEwWdSw9oZqe6DL02r0Kx6qOccp1igVbFkQUOwbugNrJdo/dSgjZLnz2d3hzPtz8HCQP0e1y9fZfMSp+IPcNbrnT1fajNv60LYUFE44yMKHpAiTBdYYZny/lwwsfpGjQJAA2n1jD3vLdLL18Xatdqs5UuXnwn1+w6MYx5FwZ80rRzqTbxmxn59YmieZ2VVcVXWCe+5n6NdRFqOoEStIQCpUycjb9JCgP7z27l0sHXOrfMVrPXjRwnX2kPoBuXl0xbYXqqjV2lm7h0YE5uNvFbJeP1UD0jBIe/ByW6nzA2CA4V+a8R6FVJmdTwJxi6vNkOvsjx6dHPJXiVSgoK2D2O7Op9dQydchUcs7LCYrPVdetwma2MXPdTN0+esdEKo47cZ7QRX/ZvYPmPPlYFfD9uoDv/7eNr6XDMJkk0hJsDExxkpZg69jCw6tQWHvSX3iAup5y9pYHOemtYULGBH9bzjv3UqiUoyQOUA/O/wHKtmUUVhT5C487R93pLzy04x5+/2Gys7LJ35NPQe1p3Ic+Vm+mJotaXJgsrfo3uDwuf+Hhv9YGsaP2aYeWUELfNz7Z6AQObwVbEiQN1n27QqmhRKmMfWfzCgsSgjRn8363NfYUfEhBuo9kW1/qfLVUKq1fmZmWYCc9wWboPjqBzsytzcKZqubA0JdmsxsNkwX6jADJrH5486fRauGxbRmFSrm/8AA1560uWM2E/hP8EzCtPVC8G1p46PWB8IlaYL97xtzD8MThESd1Wl8jBwfT5WO1KSojLFOtOqE+8fDV+gsPaIiBTfdTWHs6YgxosaMVHpMGTSI7KzssPme/M5vi2mImZEwI6xPpmMD4M2LUoDnFR2v6G4SgCQz1xIKPfvQoi65Y5BeOBwsIJb/IfO72x/AJH4uuWMTCrQt1x1q4dSELL1vIIx/+J94rc8HeNqvkFJ/CvPfmRRQ7evEGFSah7xtiyE7g0MfQL7Leo9Vi80oLKQ4PVnPzNBY+kwWXPSXY8cqqOV61XvcB6tKrTw6W4vUZug+DdkQyqTk5RABuM9tYeNnCiMJwTbwbKvDV6wPR7xvz3puHL8BYJFpfIwf3ArSY3P6Yfgxsnh8xBgJjx2a2RZ1jaPOVwD5NHaPFnxGjBs0pJgZLkvSgJEkLQr9v52vrkegJt0Fds7tk4hIWbVsU1NYoIBTgcSO/kcvyy5/AJJlYtG0Ri69arDvW4qsWs3jHYp78/h8xf5wH7rbZQD7a9S+/djlmzDwz+ZmI7+sJKA3akfKjUHE0qt6jtTa7RystpDtbZlFZZe+jW3y0ne4jiYpahW9PVrbJeAYGugifmpMnLw3KeXXeOhbvWMzSgHbtQyWHxUHelDzMDf9pAt9AAvtA03k3MK+2pK9BD0SLycuf0I+BKcsixkBg7NR568LmGIExrM1XAvvoHRN07ob4M2LUoDnFx2zgS+ALYA5QDtiBnPa7rJ5LqHAb8Gs++pvjsJqsbMjewNQhU3XXP8oFG8jcuoz8iU+y89ROXtn3Ck9PfjpoLL/mY+yPydq7Hvv7S9r9+rVrtcv2qO8bvvMdzOFt6td+UYoP10lsJplUOaHFw3t9cKrK0mynK41qba+PBs1ZojUFE2aK3a3f6wNgzADVtWvbgbNtMp6BQSTkgg1k+iB/4pNBOe/e8+/leNVxVkxbwdQhU/15fcW0FYxMGokPH2aTmazkLFZMWxF0bKjmo6m8G3SPaEFfg55J4DwhKAYmPkmmrU/Uvb0CY+eDYx+wumB1WAyvum4V6Y50dp7aGdQn9JhI8WfEqEFzBOevCyF+2PD9JcBPgVLU/T9+3v6XqNJlxLuRiCZkDF1P7CpF8bgprC9l7nsPsHzKMjLlFBSLjf21J8ndnEvelDxGxg3E6a4EyaQ6VGguFbIDxeejUClj7ub5rJq2ihpPDfM3zydvSh4vf6s6VYx09sdZ7wq/lpZcawS0NZtz353L8muX6+5yHu39DqTbCSHblDdzYc8auOMlMOnbSM756hmK68r578w7Wzz8ySozD65L4/bRxVwyQCemIjDyxA4uOvg2/5y2ErdNXRK4qvB/GRp/LnNH/6bF16HHf772JSPS4njxvkvbZLwOpHfHbEdTXazmw1AhufazhuwEb736fn2Nmo+1viE5OTAPD4sbzKGao/68fk7cII66Tqi5vyE3en1eCsoL/H30dpOGluXVDs7BRszGSnPux1qMBmKNU+MwlIB2xWyj0FPeOM9w9EOur1HnFJHORaMJghaPwxKHcajykP/n4YnDsZgsQX1GJo3kUNUhf7wNTxxOUWVR1Pjr5HlCNxP59CyaU3xsEUJMliTJAjwrhPhpQ/v7QoirO+AagW6QYPTcKEDfQaqhr5I1HeXmPOQ3cvEOuJCCsdeHO558sy78yUXDmIpXXTt5uuY0eZ/nsfCyhew8sZNJQybxwZEPGJY8rF3/mLXzyyZZ9xxNvd9B9O6b4tILwdkXrnk8YpdJ23/J6PjB3Dd4WouH33Xcxv9tTeGBi48xNKmu2cdllBXy/b0vsu6K31LcZwwAaw8/T523lv+5aFUTRzePv207xJbvivnyf6Zhl7uVf3/vjtmOpuSAvvvfA7vg2UvC26O4AroVNx7h4fGPH2fTkU26rj8rpq0gf08+m45sCvq01+tTNSBmzFF3f25JXu3AHGzEbKw0Z+6g1yeSK1tIu/Lzb1EcyWoMVJ5o8lxaQbDiqxUsvGwhawvW6jq2jUwaiUkyBcVsaLw1J/46cZ5gFB+dSHOWXZ2WJCkXyGt4aXRRQ+zug1ywAedT56uFx5gZ+o4nY2bgvvrRiGMcrjzMHW/fwaYjm5i+ejq/3PpLbnr9JpLsSaz4akW7OkfIZhmn7Iz6CDfa+wbtTNUpdY+PKEuuypVqSpWqmMXmxytV57SWLruqcqjnC9V9FNcexxfFh74lXDA4iTqPj50HO31vU4NegLYHx4w1M9h0ZFNE1585G+eQNdVBqwAAIABJREFUnZXNpEGTghx+zCYzcXJc1MIDWpZXjRxsIHuVZsdAoAvVpiObeGLHE4zpO0Y3hvdX7McnfEExGxpvzYk/I0Z7J80pPmYBh4HfCyH2AEiSJAMdtuSqR2Ox470yl9xIrihbHtRxq1ILdsWn8LN3f+Y/rs6rfvJc7Cr2u10t2LLAcI7orWh6j2ibC2pic1vzltuFcqzSQqLNg8PSsoLBZUvGK5lJqmm02021ZaCIekrqTsV0LaGM7p+IbJb4sOBMm4xnYBANzcGnvK68SdefhVsX+p0NDYcfgzbHEr2AjUSo29Vjlz0WMYZzN+ciJMNN0CA2miw+hBD1Qog3hRCHAtoUIcSOdr2y3oK7HPPHeeSFuKVAg+PJ5KVBblVK1nRccakoXiWqY4TmdvXM5Gc6zTlC8Sq4FJfh2d1ZHP5YXZOeOiJil/0udfIfu82uudk7mwciJBPVjj4kVTUKzPvY+wFw0nUopmsJxWYxMyojkQ+M4sOgA2jKKUhDy8+Lti2izlvnd/gxYWrXfGnk496BYk/E9fDX6m7mLaQlMZw3JQ9JtHzlkhGHBmDs29FxuErVdZtlh9UdxgOwv7+ErL3r9R1PJJtf86FkTafwqvnMWHsDheXqruV6jhGa29WccXM6TeitPb6dsWaGsWlQZ3FoK6SNjig0ByisOYHTbIvJ6UoIddlVS212NSoc6aRUNVrr9rFlAHDCdTim8fQYNyiJwuJqTlbUNt3ZoOcSmH8DX66AJXl3vaaul9dekf5ufB7dMaI5BYXm9dUFq/ng2Ad+zYcmzm2vfGnk4y6MxRocd9rLErCy3WJTNUiBr4QBYUMpWdMpNMGMt26n8Kr5LS5AWhLDI5NG4rQ6WzS+EYcGGk0KzrsKXV5U1pRjRaBg7K7X1E+kAeL6Qc1pSOiP2+ehQNSRu+VB8iYvVW1yz78dTBYUs0xhXYl/Z/RAoSLgd4zQXFZmjZ3V6YWHtntpJ1vo9U4hZE0J/GE4XDgLxv0oYrd7vvgjLm8dvxpxe4tPcdZlYu6/07nl3DNcPqjl+2mMObKFsUe38I8ZL+G1qP7xf9m3iPNTL2N2VmSdU0s4Uuril6u/4v+yx/GjS7rN1kS9M2bbk2iiXtnRmLsDhedang6c5FWdUN2tXro9eIwAAXqQg881zzLM0Y/9rgAXQ2d/DtWe9rtdaYVHe+XLDsrHRszGSulBWPq98PYHv4DUc9Tv9UwR7npNfaptUnV3islCYX1p8Bxh2koy4wYhO1PUY5rpdBnqQjUkfghFlUVBzlaxFh5dZF4AhuC8UzGefLQVzlT1BhT60rOufel21Y0i/wdq4ZH/A6g6if3ZS8nau551t65r3J/DZEFJHEChUu5PKkCQUBHUJyDrb11PVnIWj1/+eJcpPEKv1fiko4M4sl39GkXv4RM+CmqOM8jeN6ZTHKvQxOYxPvlwpiMhSK5uXHqVau/HiTZadgUwOMVBapyVDwqNpVcGEYhPhz6Nkzg/Wp42WRrfz/9BcOGhg/bp8fpb15OZci7OuDSykrPUvJ6chTMujcyUc1l/6/qwwgPaNl8a+bgbEGp721S7hhaHKUPVOYKnInyOsPE+CmtPNv6emzlPCYrh5EzibfGMTBrJulvXtUnh4b8+Iw57LUbx0cWwv7+EuCfHBNjrSkEisEAChYqaY4Rdtneqc0RzrtWgAzj8MZit0CczYpcT7lJc3rrYi48Gp6tYNB8Alc50AJIDll71tWVw0nW4zRyvJEni/IFJbC08i9fXPZ7yGnR/Qh187LJd1xXIh69d86WRj3sH7fF7Do1hp9VJnBzX4sKjva7PoHtjFB9dhcBPORrE5SoiqrB8+bXLO01Qrkd3utYezaGPIW0URClCCxvE5oNiFJsfrbAQb/USb42tUKh2pOKVzEHFRx9bP+p9bkrrTsc0ph4XDEqiolbhy2PlTXc2MGgJMboKabR3vjTyce+gq/+eu/r1GXQ8RvHRQtrDqUHJmo6r/7iI4rBQERgQtl6yudfV3k4TzblWg3bGXQGnv4Z+Y6N2K6g+DsDAGJ98HKmwxPzUA0BIZqocaUHFR2qD6PxkG4rOzxuYhAS8/52x9Mqg7VCypquuQlFymltxU6PU4Fbcuu9Hy5fDE4ej+JRW5WojH/cOWvt71psXhLY1p097XZ9Bz6Ndig9JkgZLkrROkqT3JUl6QZKkMGGPJElDJEk62tDnfUmShrXHtbQlYU4NrrKmHVQ0ZAfkfhb2UvqOonDqY8xY+wMKpz6G8tA3qtBswVfqy6a6EAX+8abaUxv/aOuqUCpPUFi6T72u0n0olSd0r6GjnCYiXquRYDqGI5+A8EG/86N2K6g5Tro1CYe55fuFCqEuu2pN8QFQ6UwjueqI/+e+drX4OO4qatW4gSTYZc7NSODdvW33NMWgi9AcFytQ86iWUwNftoTGMSw23RztNwcJGEP5+bdq3n7rdgrrSxtzacD1uKtPU1BewPVrrqegvKBZBYiWL9vS/crIx10cvdj8+T4QXlVoXnIAJEmNxZ9/Gxyb1jh/vMmVJ8iUk8m/7gX193zdC2TKyequ5lH+NvTmBaFtbsXdZJ+WFCBGHBq0i9uVJEnxgBBC1EiS9ASwQQixNaTPMOAhIcRDzRmzsx0tdJ0apq0kc9MTyAUbgjuHuJ8Aum4rStZ0Cq9dSM6m+5vt/qB4FVXjYZLVJx6VJygsP0DOR480jjHxSTKTRyAnDgg6rqOdJkKvtZPofS4smx6H7c/Cnf+Muizkxl2LSJETyB12Y4tPcabGxLy3Yne60hh99APOP/IuL01/EUWOA2DFd0+QmTSOn4z6n5jHDeWtr07w0idH2PrLyQxKafma5Q6m98VsrERzsQrNwU2NkfOWKihvYqyoubTyBDwzDvfVj1IwZgZzAnL7imkryErOiriDuZYvTZjaxf2qnfOxEbNtiZ67FagFR5+GfZsixL7y829RHMnIteXIfxodPkZAPOvF8qs3vIrL42L2O7Op9dQydchUcs7L8e9y7rA4WHXdKpwWJ3e8fUeLY7SLzAvAcLvqVNrlyYcQoloIUdPwYzVQodcNuE6SpC0NBUqXJaJTw8b7YvLShsY9O7TCwz9mE+4PgSIwxauo9rsNhYd/jI8eobCuJOhxaWc4TYQK1gw6iEPboG9W1MLD7a3nSO2ZVovNM1r55KMsvj8AqRWH/G1pjgEcqS5o1bihjB+qWk1uMp5+GLSCJnOpWQ4rPLQ+czbOafIJiGyS2839ysjHvQPZq6i/52YswQ6N5QkZEzjpOukvPCYNmkR2Vra/8AA1Hme/M5uTrpNMyJjgb2tujBpxaADtrPmQJCkZGCyE+Frn7SPAhUKIyYBXkqSwj18lSfqJJEm7JUnafeZM563XjurUsP0xlJvzWiY8tNhRbs5j7vbHWuX+oPgU5m6erz/G5vn+MQyniY6j02O2vgZOft6k3uOA6xQ+BINjLD6ONtjs9otvXfFRHqcWH30qD/rb+tkHUew+Tq2nJtJhLaZ/koNBKQ6j+NCh02O2G9FkLrUn4r0yl9wtD+r2yd2cixdv7OMbuRowYrYtCI01m9nGoisW8ehHj1LrqfX/vHDrQt14fPSjR1l0xSJsZpu/zYhRg+bSbsWHJElWYDHwuN77QkX7CGgdcK5On78KIS4WQlyclpbWXpfaJFGdGi5/AvmNXPDof5qli8eN/EYuyy9/olXuD7JJZvmUZfpjTFnmH8Nwmug4Oj1mj+5Ud2DuF3l/D1D1HgCDHLEXH4lWD065dZa4bmsCtdYEUisai490xyAAjtUcaNXYoYwfmsInRaWUu1pXMPU0Oj1muxFN5lJ3JeaP88ibvFS3T96UPMxE2Dm9OeMbuRowYrYtCI21Om8di7YtYsnEJTgsDv/Pi69arBuPSyYuYdG2RdR56/xtRowaNJf2EpzLwDPAk0KIkgh9As/9I2Bne1xLWyCbZTId/cmftjLYqWHaSjK3LgvXfOgRsm+BXLCBzK3Lwsdswdpe2SyTaetD/sQng8eY+CSZtj7+MQyniV7Eoa0gmSFdZ61vAAU1x7BKFtKtSTGd5milhfRWPvXQKI/rT2pFo8A83T4QoM2XXl08NBWvELz3bXGbjmvQBRG+6EJ0V6m6s3TJAbVYf2BX8E7mEWgyl3oV7O8vIWvvelZMfT6oT1Oaj2aNb+Tqnkd1caOwXHtFIty7J2b0Ym3nqZ30d/Zn1XWrcFgcfHDsA1YXrGbFtBVB8bjqulX0d/Zn56md/jYjRg1agqXpLjHxa+BaYHSD0dUyIcS/QvqcL0nScsAHrBdCfNhO19ImyHWVZG56gvyJTzJ3+2Msv/wJMuvqkK9bDDP+L7hzg0NVEBabKmgMHBPIdKSTP20lczfPZ/k1z7b4j1e22MlMHtE4xpRlauERsgwsMNHMfXcuy69dbiSKnkjRB6reQ44uqv6m6giDHWmYmtpFVwefUHc3v2SAK9arDKIsLoNRx7di9tbjNVuJl5OIsyRypKawTcbXGJEWR3qCjTe+OE72+EFtOrZBJ6E5BYViscKTo8LbF3yl7uZcVwVLvxf83l2vqUWIqeHJhGRSXyH5PGoubbgeO5Al21kxbQW5m3PJm5LXZOHRrPENeh51VeHici0Wa0KWiQY+gYgU+1q8NvU++rE2KEHNjVrbnHFzGJ44PCweA/sYMWrQUtql+BBCLAIWBbZJkmQDvgFGCyEUIcSXwBXtcf72Qi7YQCaw/ubXkN/IVZ94NNdVRanVdVKRcz8jM3UU629dH5v7gzNVLWK8aU2OoSWamM9l0LVxV8KJz+C826J28wof31Yf5cqUMTGd5qzLTJ3X1GqbXY3yuP6YhI/kqiOUJI8E1Kcfh6u/a5PxNSRJ4ooRfXjzyxOcqaojLcHWpuMbdALOVPUVSlkM+8S8dLv6AVHSkCZzesRcGnA9diDLlsS6W9dhxtyswqPJ8Q16B1oshs4ZAouJSLHf3PcbiBRroW3N6WNg0Fw6bJNBIUQdMEEI0a3VSHLBBpxPnd+8pVbNHbMN3B+aO4bhNNGDObxN9Ybvf0HUbodcp3H76hnmSI/pNEfKG5yu2mjZVVm8utylb3njcoP+zmGcdB2mxlPVJufQuHJkX3xCtd41MGgNzcmldtlOnBzXosKjJeMbGLQFerEW2tacPgYGzaVDdzgXQujsvtcNaYm43MCgozj4IZitkK6z3CSAb6rVT4WHOfvFdpoyCxKC/m1UfNTYUnDLcfQtb1xmNShuOALB/go9o7zYGZTiZFgfJ69/frxNxzUwMDAwMDBoHh1afBgYGLQjB9+H9DFqARKFvVVHsJlk+ttSYjrNgTKZ9DgFq7mNNiiVJEriB5Fe2rjMqr9jKGbJQmHll21zjgCuHNmXL49VcPBs21n5GhgYGBgYGDSP9hKcdxt8PkFJTT31Hi9Wi5k+cVZMJh1HCU28ZbFCfYDQVnNVsSVEX19pjVd3J9Vrbw2uUlWwpne9zVjvadBDqDkLp7+BC2c12fWb6iMMsccmNgc4WCYzLKltxOYaJYmDGXj4Xaz1VdRbE7CYZPo7hlJQ0T7Fxz93HeXlTw7z6xti070YBNPsPNpRyA79fCs3CHZtCfDgF2EuhMgO1VEoUDOi5VIj1/ZoOi2GI84N4sIF44HmB9XFkeMxPrYltQYGHUWvLj58PsF3p6u4/8XdHCurZVCKg+dnXcy5/RLCk44m3io7HO5MAY0uKpHwuCMf1xrqquCZcS2/HoOexcEGs7j+OrEQgEd42Vd9jKtiFJuXu02U1pq5YlBdTMdHoiR+MABpZYUc76f+nQyMG86uM+/h9rqwm6O7d7WEFKeVS4al8P92H+PnU8/FYY2874JB07Qoj3YUSm30fKsnxi07DH/SsagOdMgycm2PpFNjONrcIJrxgZ5LFqiFjFF8GHRxevWyq5Kaen+yAThWVsv9L+6mpMbYhMygm1G0BeQ46JMZtdu+6qO4ffWMbNhZvKUcLFM/rxiU0LbFR1nCQHxIpJU37u0xKG4EPnwcqNzTpucCmDYmg4pahTe/NLQfrcXIowbdHSOGDQw6ll5dfNR7vP5ko3GsrJZ6j7eTrsjAIAaEgIJ3YMCFjXsURODTCtVRKjNuYEynKipTXU0GtHHx4THbqIjLIL10n79toPMcLJLM16U72vRcAKMyEhic6uBv2w4jRBtpV3opRh416O4YMWxg0LH06uLDajEzKMUR1DYoxYHV0rWWYSheBZfiQvF2a5dig/bi5JdQfRoGX9Jk108rCulnTSZFjk1rdLBMpq+zHrul7SfsZxKHkl76HaaGOJdNVobEZ/FFydY2LxAkSWLamAz2nqxk+4GSNh27t9Fd8mhnYOTu7kFPjmEjBg26Ir26+OgTZ+X5WRf7k462zrNPXHS3oA7DVYpSeYLC0n3MWDODwtJ9KJUnVKFZ2WH15fN09lUadDYF7wASDLw4ajef8PFZxQEy4wbEfKqiUgsD49v2qYdGcdJwLL76oKVXmYnnU1pfzMHqb9v8fN/PTCM1zsqf3i0wnn60gi6fR5uLxapu7Bb6sjTx7/B51FzsCnaSV7wKheWFau4uLzQmf12YTo3hUNODptqbS6T5g6tn7Hhg0L3p1YJzk0ni3H4JrJ13ZfMdLjTXK732aMRwnOJxU1h+gJyPHqHWU0vOxvvIn/gkmYnnIGvCx7teU2+QAAkDwGRp3vUY9BwKNkDauWBPitqtyHWKCk8NWTEuuaqqkzjjsnDJgPZZB30maRg+JPqf3cPpPmMByEy8gPdOrGZH8UaGJ7StM5XVYuLmCwawatshPt5fwlWZfdt0/N5CTHm0vYklTyvu8B2lQXXFChzT54GqgE0qSw+oO1IHCM+1wiNnQ46auzfkkD89n8zkTGNDti5Ip8awbG+8h4e2R8OWoO+S1RDjEecPySMwItCgs+nVxQeoSSctwdb8A/RcUtrhOMWrUFhX4k8cgJpAPnqE/Gkrycyaru6y/tLtjQc15Y5h0PMoPwonPoOLmrbY3V2hbuIXa/FRUKJ+Cjg4sX022VQsDsrjB5Bxdg+cewcANrOdzMRx7Ch+hx8OnYPT0kpr6hAmj0rnzS9P8KdN33HlyD5IUidOmLsxLc6j7U0seTqS9bTWHuh4qFekNBBaeABGAdIN6LQY9tTrx1NTTpjx6RFdrZqcP3jTjBg06FR69bKrroziU5i7eb4/cWjUemqZu3k+ys15YGnikxGDns/e19WvQyc22fWDkj2kW5NJs0Z/QhKJfWdlTJJgcGL7LLsCKE46h7Sy77B4Gguc8X0n4fbW8sHJN9r8fLLZxC0XDuSzI+W8+eWJpg8wMGgCxacw9925+rn73bkoPmP5lUH70uT8wYhBg07GKD66KLJJZvmUZTgswSI4h8XB8inLkN/IVf3BDXo3e9ao9rqJ0a1zazxuPinfx/cSh8f86f6+M1YGJdS13c7mOpxOHoFZeMk4+7W/rZ9jMMMTxrLu2D8orStu83NOPjedEWlxPPHWt1S6jZuyQeuQTTLLr12un7uvXY5sMj5xNmhfmpw/GDFo0MkYxUcXRTbLZNr6kD/xSX8CcVgc6ppNOUVdcmXQuyk9qC65GnZVk123lu1FEV4uShoR06kULxwolRmW3L4F75nEYShmG0NO7w5qn5zxQ4QQPLf3MaqVijY9p8kkMfvKczhbXcefNhY0fYCBQRRks0xmcib50/ODc7ex5Mqgg4g6f7D1MWLQoNPp9ZqProxssZOZPIL8aSuZu3k+y6csUxOHyRKb6N2gZ/HVq4AEw5pecrWl5EsSLA5GOmPbXHDfWSuKT+Kc5NqmO7cCn8nCqeSRDDq9W3V7aVhvn2zry/WD7ubfR/N5/NNZXNT3+8RZEhEIJCQynEP5XuqVOCxxMZ13RFo8U8f042/bDjFtTD+uGGmIz3sdzRWpN6NfYAEy9925LL92uVF4GOgTq4lNE0ScPxjLtQ26AEbx0ZVxpiIDmd401t+6HtkkGzcvAxWfFz79m7qxYATRoUatt573S77mwsThmCKJapvgq9NWTJJgREr7Fh8Ax1NHMbjkG/qWH+BsSuOO7SMSx3Ln8AfZVvwOO8+8R53XjSRJCCEQ+HCY45k5YgGXpk+N6bx3ThjCNycqeejVL9jw0PdJ7W5WsQato7ki9Wb20woQI3cbRCVWE5tmjGvMHwy6Kkbx0Q2QzUbSMAhh/7uq3ef4HzfZdeOZz6jxurkyJXar2q9OWRma5G6XzQVDOZWShQ8TQ059ElR8gKr/uGXonKA2IXycqj3Kh6f+zcqC31HjqWLKgFtbfF67bCZ3ykgee30PP3/1C1bmXIK5M+1iDbo9Ru426GyMGDToihiaDwOD7sjuleBIgcGXRu0mhODlE1vob0uJ2WK33G3iYJlMZmr7P/UAqJednE4ZwfDjHzVroy1JMtHfOZTbzpnLiITzeLVoGd+Vfx7TuYf2iWPW5cN4v+AMv9+wL6YxDAwMDAwMDCJjFB8GBt2N4m/VXc2zpjduKhmBbWXfsrf6KNf1HR+zy9WuYzYEEmPTamI6PhYOp11AfO1ZMkr2NvsYs2Tm+kF3kWLry1+/+/+oUspjOvfUMf2YNqYff/2wiFd3HYlpDAMDAwMDAwN9jGVXLcVVCnVV4e22hPZZt2lgEMrHz4DFAaNujNrNI7z86eBa0qxJXJ4yKubTfXLcTl+HQkZc++xsrsfx1NEoZhvDj33Aqb7nNfs4q9nODwb/mH/s/xP/Oric2VmPxnT+WZcP43Slm0fXfE2iXWbG+bEJ9Q26CEbeNuiqGLFp0Atpl+JDkqTBwF8AJ1AE3CeECFssLknS/wGTgE+FEPPa41ranLoqeGZcePuCr4xEYdD+lBbB16/BqBvAnhi1a/7RdymoOc68ITeoDmkxUO42see0lYlDyunIzb+9ZivH+ozlnBPb2DU2B0VuvotVmn0Al6RNZnvxO1yePp1RyRe2+Pxmk8RD12axZP235L7yOSttFiZlpbV4HIMugpG3DboqRmwa9ELaa9lVGXC7EOJq4ARwZWgHSZLOB8xCiEuBU5IkhfUxMDAIYdP/qEutxmZH7ba9bB/LDv2bi5MyGZ80MubTfXDQgVdIXNxf55O5dqaw/6XIXjdZR95t8bGXpk0l2dqXlw88hSfG3Xztspn/um4Ug1Ic/PTvu9lRVBLTOAYGBgYGBgaNtEvxIYSoFkJoC8SrAb1dwa4C1kmS9BKwvuHnICRJ+okkSbslSdp95syZ9rhUA4M2pV1j9vB2+PZNOC876idihTUneGTv8/S3pzJ70NSYtR5eH7xb5OCc5FrS4zp+5+/y+AEUJ53DmKK3kXyeFh0rm6xcnfFDTtUeYfOJ1TFfQ5zNwq9mjKZPvI2cVTvZWng25rG6KkaeNehuGDFrYNC9aVfBuSRJycBgIcTXOm+nohYlJqAc6BPaQQjxVyHExUKIi9PSjCUPBl2fdotZxQ1v5kJcGoy9JWK3b6uPMvvLpzBLJnKH3YjDHPteFduO2jlVbeGqwW27o3hL+G7AlcS5Sxh57P0WHzsicSzDE8by5pF8yupin6AkOWT++4Yx9Eu0c2/+Lt779nTMY3VFjDxr0N0wYtbAoHvTbsWHJElWYDHweIQu5UCSEOJOILnhZwMDAz22/A5KCuHyXIiwQ+2eqkPc9+XTWCQTvxxxO2nWpJhPV++F/7cnnoz4+g51uQrlZEoWZxOGcOG+V7B4Wm71O7n/D/EKD/86uLxV15HkkHns+jEMTnXw079/ytrPj7VqPAMDAwMDg95KewnOZeAZ4I9CiEgLpXcBdwDvATOA7e1xLW2OLUEVgum1Gxi0B/vehm1LVWvdgRfpdvmi4gA/2/MsTrONXwy/lb4BhYcQUKNI2C0CSzM/bnh1Tzynqi3M+d4JOnWfPUnii3Omc+1Xf2Vc4Wo+G313iw5Ptvblkr5T2HFmI1eV38Do5PExX0q83cLC60fzp00FPPzqlxwucbHgmsyYl7UZdCBG3jboqhixadALaS+r3V8D1wKjG27My4QQ/wrsIITYKUnSPZIkfQQUAE+007W0Lc5Uw4HCoOM4+RWsuR/6ZsEl9+t2+bDkax75diVJljh+MfxWUq0J7C+xsO2og89PWjlVbcHjk5AQJNt9jEqr54J+9Vw0oI4UR/AmfkLAO/udvLkvnksHVpDVp2M2FoxGacJgDqZfxHn7X+d42oWc7ju2RcdPSLuG7yo+Z1XBEh6/cCXxcuxPhJxWC7+aPooVWw/y9LuFHC5xseTW87HL5pjHNOgAjLxt0FUxYtOgF9IuxYcQYhGwKLBNkiQb8A0wWgihNPTLbY/zGxj0CE5+CS/eDLITrl4IFltYl3+d3MpvC19hiCOd3KE3ceBMKk/tc7LvrA2zJBieUstVg6uJt3pxe0yU1srsLXaw/agDgJGp9VzUv44BiV7qPRLbjtr44pSd0X1r+GFW1xFXf37O9fStOsykz55i3VWLqXamN/tY2WTlhsH38HLRM7xQsJgHxvwOsxR76rOYTfz0+8NJT7Dx2qfH2HO8gmUzL+LcDOOTSgMDAwMDg6bosE0GhRB1kiRN0AoPAwODKHz9L3hzPlgT4LrFENc36O3/n703j4+quhv/35+ZTDZ2XECkKAqpuLaWUrUiQg0gVKxEnxZsFRS1KqA+rV1sa/H7WPX5tRaEqlVRUh8NIgYqVVYVcCsgdrEoNiwqUhbZAiHLZJbz++PeO8yeSZhkZsLn/XrNK3fudj4zc3Lu+ZzP1hBo5KEt86nc9Q5ndTyVM4Nl3PdaV/5T46FboY8x/fcysNchCvNiyutgDOyqzeejPcV8tLcDL354ZNLcMd/PqH77uKRPdWbdraLw5xXw7pe/x9ANzzDy3XtZduGvqemQeuG/HkVfYmjPq3h950uUVz3ExJKf45KWWytEhLHn96bfiR15fNUWvj3rLW6+5DRuH9qP4nyt3aoIf5RgAAAgAElEQVQoiqIoiWjTp6QxZn9btqcoOcfuD+H1/4GqJdDjLLjkpxEm+aAJsnLfB/xu6wK2N+zlVC5iwz8vZ02jh5M7eRl31m7OPfEw7iSxHSJwUsdGTurYyLf6VtPgF6ob8shzGboX+bNK6QjnYIeerD7rei758E9c8ebdrD/zOjZ9aRgmxQKKXznum3gDdbz9xWKqG/cxseTndC9I3YISj3N7d+XBsedQsXYbj67cwrz3Pue6C09l/Df6cHzHWEuVoiiKohzrSJzC41nJwIEDzfr16zMthpJ7ZGwqnbTPGgN+L9TthT0fWy5W/14K29eBpwjO/R5mwBjqCXKgsZZ/HNjF2/uqePfQ39kf2IPLdwKHd3yHYN1pnHVCLRd/6SB9uza0aRXyTFHcUM2gTQs48dAnHC46nk96fZPdx53JwY69OVx0fJPKyL8OrGXlzoUYE2TQCZdxXveL+FLHfnTNP4G8FlaCB6jaXcOCv2/nn58fxCVw/ind+Obpx3NWr86cclwHTuxUQNdiTyoB6tnZZxUlMdpnlVwjY33WDkN4FugBHAJeBH4G7Ab8wGSgJzAHcFIrTjTGfCIi3wF+hVWqYqoxZoOIvACMBJYaY74nIoOjr8Uq+B1q0xgzpvU/aWJU+VDaO9n1UNy7GZ4uBW8NRFfePq4f9L0E+o+gdPUKdhUsjzhsjItA3SlwaBC93f05+4RGzjmxji6FgVb+JFmIMfTc/2/67XiXE6q34jJHvgO/y8PBzqew5NLfJby82ruXtXte58MD7+ELNgIgCL86/0lO6VhyVKJtP1DHu1v28f5nB/hsXy3BsCE2zyUU5bvJd7t49NrzueC0mPJGligZQsdZpYVon1VyjUwqH1cCpxpjHrHfTwCqjTF/FpEBWCUqngC+YoyZEXZdN2ABMNoYUxd1z1XGmEvt7UvjXBvRZqbJGeVDRPYAn6Vw6vFA9kTKpo/2+rmgdT/bXmPMyFa6d1Ka0WdbSrb2iWyVC7JXtnC52nOfjUc2/ibZKBNkp1zHAx9naZ/Nxu8rGbkmL+SezI68mRxn+2JZOiYbY3xRyscwYBzwPLEKxPWAzxhTEeeeTSkfEW222odLkZxRPlJFRNYbYwZmWo50014/F7Tvz9aaZOv3lq1yQfbKlq1ytQXZ+NmzUSbITrmyUSaHbJYtHrkmL+SezNkir4icA0wFngLOBH4M7AE+Ae4GzgFmY7lOBYFRwJ3ASmPM2jj3i1Y+Iq41xjSEt2mMWdean68pNC2LoiiKoiiKorQRxph/icjNWErCNuCXxpg/O8fDauSFWy92AKcBMcpHHCKujW5TRHYbY9rayh0ixXrHiqIoiqIoiqIcDSKSD2As16P9QGOKl/4FuElEitPQZq/m3iOdtEfl48lMC9BKtNfPBe37s7Um2fq9ZatckL2yZatcbUE2fvZslAmyU65slMkhm2WLR67JC7knczbI+zUR+auIvAl4sNyjojPHBIDJIrLKfl1gjDkAzADeFJGVthsVIrIc+Ir9N+610W0aY/7aFh80Ee0u5kNRFEVRFEXJXkSkgzGmthXvPwS4FugHGGAz8Lwx5s003T9l+UXkRKCfMebdFM8XoNAYU380MmYz7dHyoSiKoihKGyMiz9sZedpNuyLyuIhUicjk1rh/WyAiM0VkTZzXeyJyd4r36C4ib4nI2yKyUUR+bG+/LSLrRGRWguu+KSLfE5H+UYdSmojb97gxjuzrROSQiJwc5/xrsdLVPgWMBq4AyoHfiMj3Um037H6T4ux+N+qcCVHy/V1ErrEPnwmMj3PfMSLyZ/s1X0ScOOyvAo81V85cQi0fiqIoiqJEICIPYU2YGoBHU6kPICKdgDpjTIuKD4nIN7EKrm21d60FfmKMCWayXTsVatfoAN4k97sCWGOM2dNCeb5vt/cH+/1sYIYxZkNL7peknb7ATGPMFc28bgpQa4x5xn7fE3jCGHNl2Dku4CXgc+AjYDjwjjHm9/bxDcaYs1so9wXAXVhF9ebEOT4XmBVtabCtITcaY65rRlv5wD+MMWdG7U8qv4jcC3xijPk/O/vU1caYyWHHpxLfALAVqyDgZGPMhFTlzDXU8qEoiqIoSgTGmJ8BzwAPpFqYzBhT01IFwMYDzDfGDDbGDAYKgRuztd0klAExK/LNII/IbKTR79NFs+eAtlIxBtgeZhE4Hvgi6tRRwOfGmDuMMU8AVwPfFZG4lVWbaNMtIl8TkXtF5DXgUaAIaBSRU+Jc8gpwn31Ngf36OjANeLWZzXcH9jVT3kJgrC1HIv4FbMBSNvZjKWgbgE+bKV9OosqHoiiKoihNIiJX2y5IN4vIIhF5R0SetY/9JZ57kojcISJr7XNnNrPJV7DqHSAik203ofdE5Kdh949pN5mczW03ESJyg4h8ICL/EJHr7H2XiMjbwOXAHNsl6avNaDcptgvT78Le/0JEvm1vX2p/zg9EZGLUdc/Zbkpvh7+wXJGSTZDj0RN4HKt+xBn2vkbguajz+gBVzhs7y9Jme3/KiMjFtozjgDXA/cAPgZvtU+6xLQshjDHPA/cBtwBL7NeNwC+MMfOa0z5wNpb1JlV53VjVyR+3A8Qdrrbdsa60ZVyJpaBdB5wP/DfQxRjzQTPly0m0zoeiKIqiKE1ijHlJRDoCvwYuM8ZssSdbGGOucNyTnPNFpDPwfWCQaaaPt4h0AK7HcocC2AgMwlo0fUdE5htjtsZrN5mcLWg33jknA7fa8gjwtogst4OZLxaRciw3qX805zOngIfIeZsHyLO/54eBoYAX6/t53RizzT6vN1ahuRZVIheRr2FZG8LpDHS1lQPnPIDbjTHvA28CvxeRJ+0q3icBZ2H9jiljjHGUOaeNO4HDxpj3sKqAPx92rD+WwuFwCPhb2PuxIjLW3n7CGLMpBRGGAUNEpMAY4012om2FeQpYa1t7wnkpyu3qLOBkY8zV9nsPlrtfpX3KWSLyY2CVMWZ9CnLmFKp8KIqiKIrSHN4wxmwBaMLd6TDgx3K5mZ/iva+2rQU+4E/GmIV2O6/bxwMishIriHdrgns0V86E7SZgJPB/xpgGsALesVaxn2mijeYwWUS+Y2+fgZViNRHfBl41xhyy5XkJGIE1EQZr5X65iMSrJ3HIGDM8mSC2MnGB896eOHez3x40xvwrzjUbbGvTKhGpAQqAm53vLBUSKD09sfpAdBD4bVhuSynF5RDrJhav/Q5YsSq/x6ou/r9Jzr0XK7j9Z7ZVoyl2Av3s+JVPgG8RaWHZg2Xp+U8K98o5VPlQFEVRFKU5VDV9ChhjgiIyAviVHaQ8NQVrwEvGmDujd4rIcOB2rBX3U0mtynNKciZrNwEnYrkQOfwH6NuMtlIhVKHatqTEw2P//RLwPRG5xH7fkTBFyBjzA2c7zHIw+yhkO8NuE6xJdIzyYbf7HPCciLhbEpMTrfSkyHYRuQdLGYymM5bS+NsU7/ULrO/xj1jK2+vhVgivP3Dh3prGh/3B4Elbdx3Y1+u4TlMK8tzx6mdsAOrCdxhj9ovIf2EpTd8HHiQyI9YXttUnISIyzxjz3RQ/S1ahyoeiKIqiKM0hqftJOPZq/N32ankFcF5zGxORs4HfAVcaYz4RkZQC4JsjZzPZg7UC79AL2B32Pml2rqOgHgivbt0feA/YBTxrjLm/ldoFQKw0tTEph0XkN8AEZ7IsVsrYfljKUSHQUUS6Y31Pp2JNxpvTbhfgl8BgLDc3l/13JfA/jsXHwRjzAPBAnPsMAVJKtSsi38VSsn5pK9E/AF4RkTJjzKeXDh1aVLX78KJbn3v/+O0H6undrejUmf919tKf3Hzt56+/9trhqNt1BZ4G1tn3dmEpQvuAR7Bcu94ALhGREqzfMxUKUjwv61DlQ1EURVGUtGPHWYgxxo81Oc9v4Sp4CbDeVjx6AddgTTwzxWJgkYg8g6VofB/L5cbhIBCT1UlEhmLFKJwRPWFOkS3A1+3v9TSsyXiFLc8bIvKEMWaP2AXwErgtuW1Zot2WnFiNhBhjXgBeCPs8g4GbgLewXIQcumLFXgSxVvwPYbk5VQHL7c/xo5Q/tVXz4i3gp2Hpj93AVGA6UZnJROSXWH2kJuo+QazJfip0AL7vtGeM2SkiI7EyU/GzX/+m563PvV+8/YBVB3D7gXqmvrih87wXFx06uVvRRVHyXIrleuhQAszEUqAasb6XSVgWtP8APwa+JCJLsDKL/RHLsrYbmIDlCvYj4GwRWYUV77MKK4HAccA2rN8lDyuGpBCoN8ZcbiuGf8JSnncDtxpjDqb4naQNVT4URVEURYnAdvUZBjSIyBnGmJ+JyADgHqwg535OAK2IFGNlFDrJPnYlcBnWBHmxiOzHmm/8sgnFw2+/olkMjBORtViTWSfDVqJ2S+LJ2dx2RaQHVq2KHva9xgClxpgdIvIg8FcgANxnjAlfrZ4LPC0i+4AfhU3qfwNMSUHxiJbHD/iNMXtFpBJ4G0vBedTev0dE7sJSiIKAX0SGttBtKSF2DMQ3sALAz8VaeQ/Y8gwQkY+MMQE7sP2uJu7VnKa3YwWrnyYin2JN2k/DivvZGef8U7DiS1JxzYuLU8Mkal+obkvX4473bD8Q6dW3/UA9/mDwpBTu/TGWAhGDiJyGFa/zoTFmgojcDLxujKmwrS/jjDHlwF9E5M/GmO/Y1/03MM8Y86KI3IaVHexNYIMx5sdhbftFZB1W4cUBWErR003JnG60yKCiKIqiKEorIiKXAzcYY65p8uQsREQGAv8PS9FYaoz5m72/EzAEuAT41BiTUmVuEVlvjBmY4rluLOvSSCzFIgB8BvwlXupc2z3sNuKXk9icjuJ9y99ac/j/vVvXwbF8APTuVsS8my/8axzLxxCsIoNTUrm3WOmT78YKcP82VireBiw3tkXGmP+1zwtXPh4HphljdtuLBD8wxtwjIqdj/Ta7jTGLReQqrN/rbqzv83STYvHMdKLKh6IoiqIoiqKkiNcfuDAq5oPHv/+1vSU9Oo6JDjq3XZ2Km+tqZ8c2bQYO2MH70cdfNcaMtrfvBP5jjJkvIj/EqkD/f1H3ehxLmdlgjFkqIi8Aa1T5UBRFURRFUZQsJzzbVZ7LtfP4Tvk/SpDtKmXs1LuPAQYr/uO7wJNY2cW8wPWOi5+dUvkE4CEsF6tnsDKxfYIVc/O1OPfqBszDiv15Gagzxsw6GplbgiofiqIoiqIoiqK0CfH84bKSkSNHGiztTV/6as4rY2if1VcLXxlD+6y+WvjKGNpn9dXCl5JBckb52Lt3b6ZFUJRmoX1WyTW0zyq5hvZZRck9ckb5UBRFURRFURQlt1HlQ1EURVEURVGUNkGVD0VRFEVRFEVR2gRVPhRFURRFURRFaRMyqnyIyNUissp+/csukqIoiqLkKJu/OMwPnl7L5i8OZ1oURVGU1uHw7jXs2/JpzOvw7jWZFi0csWh2HQ8RKRaRN0TkndaQK681bpoqxpiXgJcARORhYGkm5VHSiy/gwxf04XF58Lg9mRZHSRH93ZSj4aX3t/PWpr289P52fnb5GZkWR1GULKLdPF+8h3sy6/xTYvZP+Rt07JEBgeJjrGJ+U1pwXR0wTET+nH6pssTtSkQKgNONMR9nWhYlPfgCPjZVb+LyBZezqXoTvoAv0yIpKaC/m3K0/Ke6HoAd9l9FURTQ50sqiMgdIjLM3i4VkSki8rRthZgrIgUiMkFEtojIf4vIO/b+DiKy1PYkWmJfX2BftyWqjftE5G0RWSkiXUWkk4hU2tc+a8/J48kWc148WVL5nFmhfABXAzHalYjcLCLrRWT9nj17MiCW0hKcAWbC0gnsb9jPhKUTjpmBJpf77LH8ux3LpLvPbj9QZ/9V5UNpHXJ5nD1W0edLyiwArrK3y4DuwOvGmGHAYmCcMaYcWA3UG2O+aYwZB/QANhhjLjXGXA5gjPHa1/3LubmIXA50MMZcbIwZaoypBm4C5hljLgXWAOMSyBZzXgJZmiRblI9xwPzoncaYJ40xA40xA0844YQMiKU0l/ABpt5fT4G7gHp//TEz0ORqn43+3YBj6nc7lkl3n3UsHv+prjvqeylKPHJ1nD1W0edL6hhjPgd6iogH6IKlVEwVkVXAD+33YM3fF4RdtxV4XEQmisioJE2cAyyM2tcfS4EAWAmUJLg20XkRsqRCxpUPETkD2G6Mqc20LMrR4wv6uPW1W6n31zOk9xCWli1lSO8h1PvrufW1W/EFdZDJRsJ/t3Di/W6+gI86X13WPTCyVa5jjYN1voi/iqK0T5oac0PHm/F8UQD4K3A3sALYCPzBtmh80xjzv2HnecMvMsZsMcbMAUbYc+t4/AP4r6h9/wYusbeH2G3GI9l53tjTE5Nx5QOYBMzOtBBKevC4PDx+2eOU9ill3BnjKFtUxrgzxlHap5THL3scjyuHA8zaMc7vVpRXFLG/KK8o4nfLVp/dbJXrWMMXCNLgD1KQ56LBH8TrD2RaJEVRWoGmxtzw47trd/PYtx5r8vmSUxR03MWUv30W8yrouCsNd58P3IllTXgKS5lYJSLLRKSniAwBRgKviEgfABG5QET+JiLvA32Bz0TkZNticrF9/TnGmOVAtYj81d7X1W7jahFZCQwCXhCRIWHXrhSR7onOi5YlFcQKhM9+Bg4caNavX59pMZQUqGusY/PBzUxaPol6fz1FeUXMHj6bfl36UZxf3NbiSFs36JBrfTbaNF6UV0T5yHL6d+2Px+1p8ni2yp2D5GyfPVDbyFf/ZwUndy3iP9X1rPvFtzixU2EaJVSylJzts0rzacmzYt7oedT565i4bGK2jNMZ67NKdlg+lHZEg68hQvEAy7Q6afkkNh/cTIOvIcMSKonwuD3079qf8pHldC/snvRhAvF9dtva9Ul9ibOLmgY/AMd3zAfgUL1+/4rSnkg25lYdqKLGW0PVgaqY49999bsU5xUzZ8ScmOeLcuyhyoeSVgIEmPLGlLi+nVPemEIAdcPIZhwFZMnYJREPhlR8djPh+qS+xNnFoQbr+z6hk5Wp8WC9P5PiKIqSZpKNube9fhsul4s7V90Z9/iEZRM4pfMpMc8X5dhDlQ8lbfgCPqrrq5k5dGZc385Zw2bhxp0h6ZRU8bg9FHuKIx4MTcWEuHBlJI1iqrEqSttw2GspG8d1tJQPRxlRFKV9kGzMfXDwgzz74bPcd9F9CcfkAndBzPNFOfZQ5UNJC86q91V/uYp3d7zLU6VPhQYfJ+ajpGsJhR71/85Fwl2ywn/X8pHlnNb5NLYe2poR16douQrcBdngS3zM4rhddSu23K7qG9XSqSjtiURj7oyhM6jYWMFj/3yMio0VzBg6I+ZZoWOy4pCXaQGU9kG4Kfaxfz4GwFOlTzF15VRmDp1J3859Q4qHL+DDF/ThcXl0IMohwh86t752K49f9jj9u/Zv0vVpydglrfo7O3LNGz2PzgWdOeQ9RO9OvbVvZYAa29LRrdj67utU+VCUdkf0mLuzdid//OcfWb3dKgHh/H1k6CP87K2fhZ4V7XhMLgK0qmozUMuHkhaiTbGP/fMx3t3xLovHLub4wuMpzDuieGhK1NwlXkyIx+Xhj5f9MeOuT3X+OsYuGkudX4vbZQrH7eqI5UNjPhSlveKMucYY8l35EcfW7VpHz+Ke7T6+o6ax5qpaX+0XNY01VzV9tuKgyofSbOJlNIrnljPnwzl8fuhzTuxwYkzGpLaMC1DSS7yYkFM6n8K80fMyYmZ3+tXEZRPZ37Cficsmar/KEI7bVVe1fChKTpMsc2H0mHvj8huZcPYESvuUAkfG/96derfr+I6axpqrPjn4yZOjFozq+MnBT55sLQVELGa15rVH00ZLUOVDaRbJLBfxUrWe3vX0ZqVqVXKL8P5Q569j3uh5bZpGUftVdtHgCyBAh3zLo1eVD0XJPZI95xONuZOWT2LC2RMY3Xf0MRHf4Sgek5ZPOn5/w34mLZ90fGspIMZiSmteezRttARVPpSUScVycTSpWpXcIro/TFw2kTp/Ha9e9WqbPXi0X2UXDb4A+XkuXC4h3+2iwafKh6LkEk0955ONuVPemMKvLvzVMaV4RClgR62AiMgdIjLM3i61378hIlvCzpkgIltE5L9F5B0RmSsinURkoYi8KSKzROQHIlKQ4Non7GrpS0WkMN559rn3icjbdoXzriKSJyLPi8jrIlIhIl1a+jlV+VBSojkrzC1J1aopUXOLRP1h4rKJbKvZ1mZyaL/KLhp8QfLzrMdKgcellg9FySFSec43Nebmu/LbteIBFLnE9eyUN6Ycn0ABO94lrmexgtBbwgLAUV7KgBeNMcOAfzknGGPKgdVAvTHmm8aYccCNwNPGmEuAToDbGOONvtZmtzFmBLAYKI13nohcDnQwxlxsjBlqjKk2xviBdYABBgBXt/AzqvKhpIYv6OOOlXfErHYETTClFeZkqVrb+ypJeyTZ6tcdK+9oM4uD9qvsosEXIN9tKx95qnwoSi6RiiVZx1zqgyZ43axhs/YmqGe2N2iC19HC7FfGmM+BniLiAboYY3YmONWFpag4nAGstbfXxp4ewd/tv58A3ROccw6wMHyHiFwF9AUuB+7FUnJahCofSkq4cLFwzMJQQBnAkN5DWFq2lPIR5XFXmKMD1uLFhBwjg1W7wvk9y0eUx6x+lfYpZeGYhbhaYWhJFACp/Sp7aPCHWz7c1Ps025Wi5ArJrBrlI8oBaxw+mjE3WSB7c87JJJ3yOy3s26XvzbOHz94broDNHj57b98ufW/ulN9pYRO3aIq/AncDK5o4zxu2vQ34hr19QRPXmbC/kuCcfwD/FbXvdGCpMcYHXNtEG0lR5UNpEl/Ax9ZDW7lq0VWhjBZDeg9h/IDxlC0qi5vaNFHAWqKYECU3iBdg7gy+pX1KrYDDhaPZemhrWh8cTaVo1n6VHXh9ATxq+VCUnCRRAcF5o+dR56+LGH9bMuamkmo/V9Lxhysg3Qu7p1PxAJgP3AksEJGTRWQVcLGIrBKRc0RkCDASeEVE+tjXPAFMFZHXsFy+Dsa7FgjYL5zteOcZY5YD1SLyV3tfV6AC+IWIvItlPWnxAC/GmKbPygIGDhxo1q9fn2kxspbWKtznDASzP5jNPRfcwwNrHuC2r9zGAe8Bbn/9dur99TEm12i/0QybZBNp9a1Oe+uz8X7XOSPmUJxXzBMfPMH4AeOZtHxS0t88up+m0m+zrD+1BTnbZ697Zh07quv5nyvP5r6/fEjnIg8v3nJhGiVUspSc7bNKLL6Aj+012+lc0JkGXwPVjdVMXDbxqMbfVMbxNh7r09JnaxprrnKJ69mgCV6XJsWjRYiI2xgTEBEBXgT+23bhykoybvkQkTEi8q6tWX050/LkIq21UhCueJSVlFG2qIyJZ1t5vR3FAyID0hp8DZr6tB2SLMDcGMMPzvxBSPFwjsVkSbHvceXLV0b0lVRWwbQ/5QYNjQE8buuZXuhxU6dFBhUlJ6nz1/Hb937LPu++kOIBLRt/o8fxAndBzH1ydazvlN9pYQdPhxMzqXjYfEVE3gbeAlZls+IBGVY+RORkYCxwiTHmUmPMvzMpTy5yNIX7mvKr9AV9PPvhs5SVlHHXqruo9dVyUseTuPvNuxMGGgcIaOrTdkiiQMSgCdKlsAu3vX5b0t88XJGdO3ousz+YTVV1FbM/mJ2032oq3dxCA84VJbdxxuofvvZDfvz1HzPljSkpj7+J5hTh47gTKzqk95DIZ0Ruj/UtCi5PJ8aY9+3MVBcbYx7NtDxNkWnLx3jgP8BqEflNhmXJOY5mpaApa4kv4ONA/QHGnTGOu1bdFbr/tHen8eDgB+MGpD0y9BHcuDX1aTskUSCiS1wc8h7isW89lvA3d+GKsaCVlZRRvqGcspKy0EMobtpmTaWbUzT4A6GA8/w8rfOhKLlE+Jyi2lvNtHen8cDFD6Q0/iYtQGyP46V9Shl3xjjKFpUx7oxxlPYpDd3HhYtZw2bFbWvWsFmtksREyRyZ/jX7Ap2NMd8E/CIyPPygiNwsIutFZP2ePXsyI2EW09RKgTfgjVmF8AV8HG48TNWBqsRFhAI+NldvpsBTwNSVUyNWKwAqNlbwyNBH4qbZK/QUHtNp+HKxz6aSWSRZesXenXpzepfTmT18NlGZPzit82kECUZY0PY37OeuVXdRVlLGX7b8hWkXTQuZ4aNXuDStY+uTzj7b4AtGWD4afMF0iKgoEeTiOJtNpGKhAFi9fTVzP57L9EunJx1/E3lgNPgaqPNZCWlO63waE86eEPEMmHD2BE7rfBoet4cgQSo2VsS0Nf3S6VRsrCCIjiXtiUwrH4eBSnt7EXBe+EFjzJPGmIHGmIEnnHBCmwuX7TSVFu+zQ59FrEI4QWQf7v0woQ+n44d//dLrQ6se0asV+a58ehb3ZM6IOXHT7B3LqU9zrc82J14o0e8KsPXQVrYf2s7s4bOxM3+w/dB2th7aCgbGDxgfYUGr99dz16q7GHfGOF7e9DLegDehNeNY7k9tQTr7rFPhHNAK50qrkWvjbDaRioUifE6xevtqKqsqQ2N7U0HizkKS41p7+YLL2V6znS0Ht8TEBU5aPimUGdHj8nDdWddRWVXJ9Eun07NDT6ZfOp3KqkquO+s6tXK3MzKtfKwBBtvbg4GPMihLzpFoVdhJizdx2cTQKsT2mu1sOrCJroVd6detH4N6Doq4l7PqHCAQKia4evtqPtjzAdefdX3MasVJHU6ipFtJwjR7mvo0+2lJvFC839UX9LFm5xpO7nQy5RvKqRxTSfmGck7udDLv7XoPIyah3/Dcj+fyvQHf467z70qqVGh/yg0awlLt5ue5afAFyJWMiorS3mlqzPe4PZzW+bQYK/aEsyfQr0u/uONvvHiO2867jbKSMiYtn0Str5ZOBZ2ajAt0xvhJ507io70f8ecr/8xHez9i0rmTdMxvh2Ra+VgInCYibwFfxqPFUGUAACAASURBVCr1rjSD6FXhcMXD+Ucf1HMQO+t28s5/3uHzms8Zu2gs4weMZ0jvIaH7FOUV8dDgh9h9eDdPXvYkRXlFDOk9hHNPOJebVtwUd7UCoNhTnHBQ8Lg9SY8rmeNo4oViflcDA3sM5KYVN7Fi2wpGVo5kxbYV3LTiJr564lcxQRPXQlfap5TxA8YzasEoBvYcyKmdTk3aV7Q/ZT+NYUUG8/NcBA34Aqp8KEqmSWXMd2p6lW8oZ/ql0+le2J3pl06nfEM5n9Z8Gjclerx4jot6XURlVSX1/nq8AW/SWNFwa7ej/Hyj1zcYtWAU3+j1jZBbltK+yKjyYSwmGmMGG2NuNbpE1iLCV4V7dOjBT978SWhwKXAXMO2iaXy8/2MGnTSIScsnsb9hP3euvDOkgBTlFTFj6Aye3/g831v8Per8dbw4+kWmXTSNe96+J1ezTyhJSGdmkWjLhjfgDd1ryhtTEJfErKY5BQmd/jhp+SQ2H9xMg68hTZ9QaWuMMVaF87CYD4B6db1SlIyTypjvnLNi2wrmfjyXyjGVzP14Liu2rUj4XHAUBieeo9ZXy00rbgolEylwF7B6+2r++cU/ear0qbhxgeEuXFsPbY14LqS7YK2SHWTa8qE0k0SBYoFgAIMhEAzw4hUvMvUrUwFrIrhmx5rQynT4isedK+/k2gHXUj6ynIqNFazevjpUu6HWX4vX5+WhwQ9ppqF2SFPxQkDCAb/B10CtrzakKIiRhFlKnh7+NAA7a3eGVtNG9x0dUjyiLWqbqjdxuPGwPmxyEK/fCgj12EqH437lVeVDUdqMRHOEVDIHhp+zevtqRlaOZPX21Umf+eEKw6Ceg1hatpRBPQdx16q7uOXcW1hx9QpuO+82zjvxPP704Z9iLCr76vdR66ulrrEuJ+t8KC1DlY8cIlGgWIOvgarqKkYtGMUnhz7hwbUP8o1e32DqV6ZyfNHxDO0zNKHP/c/e+hmndD6Fv3/x94j9t71+G92KunHO8ecwZ8QczTTUzmgqXihRAHp4X6uqrqKusY5Paz5l/e71MX7CFaMq8Aa8jFowil11u2gMNvLnzX/mVxf+KmF/nPzGZLwBL5urN+vDJsfw2pmt1PKhKJkhaTB5CpkDnXOcZ36Bu4CivCLmjJiT8JnvWEsG9RwUkZhm4lkTOew7zHde/g6Dew9m0eZFMRaV/t36s7dhL995+TsECeZynQ+lmajykSMkChSra6yjqroqZKYs31DOTwf9lLU71vKNXt9gwRULqPHW8Idhf4i74vGHYX+g4qMKDjUeitjvrHJ0zO9ISbcSzTTUDkkWLxQvGNFRPKJdpd7Z/g7T35/O2h1reWbEM3Qv7M7cUXPxBryhe92x8g7GDxhPvb+e5z96nplDZ8btjw8OfpBfv/trrl96va525RgNfkvJCI/5ADTdrqK0AakkEEk1c2BxXjFzR81l+dXLmTtqLsV5xQnb9bg8lI8oD2U03N+wn8qqSi7qdRF3rLyD/Q37uWHZDYwfMJ7SPqUhi8pZx53FRb0u4qYVN7Grdhf3vnNvQgu6elq0P1T5yAESBYrN/mB2RPq6Ib2HUFZSxrcXfptBJw1i7Y61bKvZBkCHvA7MGDojYsVjxtAZ5LvzGdpnaMQqR6LUuZppqP3h/LavXvVqTKKCcJN3uJIb7So16KRB3HbebfiNn0J3IQvHLIx7LyfO6IO9H7Bu57oYS8mMoTOo2FjBmp1r1NyegzhpdfPdkcqHWj4UpXVpTgKRZM9z5z6vffYatf5aRi0YRa2/ltc+ey3pWFznr+POlXdS76/nsj6XUVZSFjdRzYSzJ1Dap5Sbz7k5pHg456zYtoLyDeUxzwVd8GyfqPKRA8QLFCtwF/CLC35Br469GNRzEEN6DwlVI9/fsJ+bVtzEhSdfyMLNC+lU0ImJyyeGCvg4/pYVGyu4ecXNHFd0HHNHzWVZ2TLmjZ6XMHWuZhpqn3jcHlziSpoK0YjhR6t/FPf41JVTue6s67i418VMXDaRIEEmvzE57rk/f+vn3HfRfVzc+2L6denH08Ofpnthdx4Z+ggVGysAWFq2NFT1XM3tuYNj4Qiv82HtV+VDUVqT5iYQSfQ8d9KmhyencRaY1uxcE3cs9gV9oWfHt/p8i/svvp+/bPlLXFmmvDGF+y66j2vPvJa5H8+NOWfFthVUbKxIWENMaT+o8pEDxAsUu+CkC9hSvYWxi8Zy3ZnXccu5t8QUcbth2Q2M7T+Wf+35Fw8OfpB1u9ZF+Fuu27WOBy5+gBc+foF9DfsYu2gsdf66TH1MJYM0FYwoRnh4yMNxj88aNot9dfu4ccWNVHurmf/v+QndqmYNm0W+K59+XftRnF9M/679WTx2MT2KepDvyo/wGS7tU6rm9hwi2vKhMR+K0jakEkyeEmFp08PnEjetuImBPQZCnHykLlzMGjaL0j6lXFNyDaMWjAqN39GylI8oZ1vNNkYtGBVyw4o+Z/yA8fTt3Fc9Ldo5qnzkANGBYkN6D2H8gPFMfmMy+xv2M/mNyRz2HY5bOHDKG1Po160fL/77RR4Z+gjrdq1jZOVI1u1ax/RLp/PBng8494RzQ/eauGyiurocgyQLRjyt82kg0K9LvxiT+Ozhs+nbuS8/WPqDUHXba758De9/8X5MWsWnSp9i/e71uFxHhp1CTyEdPB3o1bFXKFVjeDFLzfGeOzjKhycq5kOzXSlK65JKMHkqJCsIO+WNKRgxMXODIEHW714fUYz4phU3hVysHFmiYwrD3bCcc2YPn01J1xKK84vV06Kdo8pHjuAMLvNGz+PaAdeG/CvBGhicgN7owoEPXPwA096dxrs73qVncU/mjJhDB48V/+EoHtEWE/W1PzaJF4x4WufT2HpoK5cvuJxPaz4NKSDdC7sze/hs+nXpxyHvoZClwxvwMv/f8/naiV+LSav4pw//xMAeA5n373kR/Ss8VWO8YpbaD3MDJ9WuxnwoStuTajB5Mty4EwZ9zxo2K2bsBsvyEc9a4igXo/uOjlv8OPocR/Eo9BSm4dtQsh3Jlbp+AwcONOvXr8+0GBmnzmelQd3fsD/mWPfC7lSOqWRk5Uhc4mLG0BnM//d83tnxTmggAstHc3ftbjoVdKJsUVnCey0Zu4RiT+IsFzmCZKrhXO2zvoBVbMqFi62HtoaCGJ2VtFM7nYoRgxs3hZ5CfAEfDf4GPjn0Cbe/fjsLr1wY6ldDeg9h2kXTmPbuNFZvXx3qo1e9fBVPlD5B/6798QV9Sft0O+mHzSEn++zyD3dx8/+9zwNXnUPf4zuw77CXyXP/zoNjz2HcoD5pllTJMnKyz7ZHnPE7XjXyVAjPauiM+7OHz2btjrXM/MfMGItKU3OSxWMXI0iT5zjPkzYkY31WUctHzpHMt/N3Q37HzsM76eDpwB+G/YEeRT345QW/5IVRL8SsgJzU4SRqvDVaRFCJweO2ik2FKx5wxCr2ac2n5LvyQw8Kj9tDp4JOlHQtoWJUBUV5RTx3+XNxC1U5lrhqb3XIwubClR5/ZSWjNPij63y4AahvVMuHorQVR5scptBTSEnXkggLt6N4QKR3hFNotnxEecLxO9+VH4oLSWRRyYDioWQYVT5yjES+nY8MfYTnNz7PEx88QeWYSt7f/X4oiLw+YE0ewwsQbT20lZM6nBRRRDBRql3l2CM6e0qBuwBouuDTAe8BvvPydzjgPcDCKxZG9NHpl05n7sdzWb19dcS9ggTT4q+sZJZQwHme2H/tbFd+VT4UJZdwFJDFYxezfvf6kOIR/hx49sNn2XJwC5cvuJw6fx3zRs9LOH4HCVKxsSKi3lioztjGCoJoLaBjDVU+soy6xjpqfbXUNdbhC/io89XF+LxH+3bOGTGH3h1788sLfgnA/WvujwkirzpQxfaa7REFiLYe2kqBu4CSbiXMGz0vaapd5dgi3MI2pPeQUPrborwiKi6vwGBCq15wxFRfvqGcuaPnUr6hnH3efSy6chHLypZRMaqCyqrKkOIBkZaNdPgrK5nFCSz32JYPj9tSQhrU8qEoGSPRPKIp3C43gjC099CY50Bpn1LGDxgfCh6fuGxiSAGJN357XB5uOfcWij3FzBg6g+6F3ZkxdAbFnmJuPudmgiaosX3HGGlTPkRksohMjfO6PV1ttHfqGuvYfHAzoxaMYkftDqoO/JvLF1zOpv0f4zu0Ew58Zr3q9kcUCjq9y+kcajzEuFfH8cPzfsj4M8bHBJFPXDaRnXU7Qxmxwk2ngWCAOn+dptpVQnjcHvoXncQLo+YyfsB4yhaVMX7AeBZduYh93n2MWjCKquoqGnwNEYpHWUkZZYvKKCspo3xDOV/Uf8HLW17GG/Ay+SuTI1a95oyYo8Us2xGhgHPb4iEiFOS5Qu5YiqK0LeHeDpuqN+GrO3BkHhH+qtuf8Lo6fx0LrljA+AHjQ3OMiWdPjEkQ4iggr171atzxu85fxw3LbqBiYwWVYyqp2FjBDctuoNZfy/1r7tckN8cY6bR8/AP4p/2aEPb+gzS20S7xBXzUeGvYfHAzk5ZP4pzjz+GL+i+YuOwGy0qx/EaqGvfR8M+58Mi54K0BIn3zn/7X08wdPZeTO57M3W/enbDA27SLpsW40PiNnx++9kNNtduOaM5qV4OvgVpfbYQlAyAQbKTWX8udK+9kf8N+7lx5J1/Uf0H5hvJQqsTN1ZsJEKBiYwVlJWURqXLLSsqo2FjBd7/8XaaunEqtv5ZnRjxzZNUrLzaIXItZ5i6O8uFYPsCq9aExH4rS9oRXPXe8HTbVbse35CfWPCL8Zc8p4l03cdlE9nv38+GeD0NzjERFZG97/TZc4opbvNApRBgeB1jvr2fyG5P58dd/zC0rbtG5xzFE2pQPY8zbxpjVxpjVQLUx5k37/VuJrhGRPiLyuYissl+npkueXMEXsDJPbT1opRod1HMQ484YF5NKd+KyG6g683IaLv05TpIGZ5CY/cHs0Irz9prt/PaS38YN7Hpw8INMe3ca3oA3tG/WsFnc+869VHurQ21pqt3cJma1K8nv6Fgtwi0Zof31u7kxTvrbspKyUAXyG5bfQI23hvEDYq1td626i/EDxrNy20q+qPuCG5ffSJ2vjgVjFlhKyavf1X7WjvD6AgiQ5zqSRCY/z6UVzhWljQlXICIShiy/kU0XT8ZXMjLygqAfDnyG79AONu3/OOY6p8r57977XdI5xqxhs3DFmVZGB5yHz0FmDp3J/H/Pj0hCos+E9k863a5Gi8gYEbkSOMHeHiMio5tov9IYc6n9+jRd8uQCDb4GvAEv3Yq6MXXlVIImyLSLpnHP2/fEL/Kzcir+iyZT16F7KJ3esx8+G7HifOPyGynKK+LRbz0aE9jVOb8z63atC+2bM+IZKjZWsGLbipi2kgUVK9lL3NWuBIN5eEpFx5LhKCABAgmLTd3z9j0hC1rQBPG4PUkLU13Q6wIK3AXU++u5+827MRjW7Fyj/ayd4fUH8eS5EIlUPrTOh6K0LdEJQxzq/fXc+tdf4rtyFuSFZZeq2QGPDsLnzuPWBFaNqSun8uOv/5jbXr8t7hzDKSIbL3jcCTiffun0uElIrvnyNaFnhD4Tjg3S6XbVzX51BX4X9r5bkmsMMEJEVorI/WmUJetxJn6jF47ms0OfWSsG4mLau9N44OIH4q4q/PaS3/LpoU+5fOFoqg5UgSHuivONy2+ka35XHr/s8VCBtxc+foEOgQBzhs60AsIGP8zpG17hugHXxk+R961HNcVpjpFwtSuOAhKdy90511FATNAkTI3opMv1Bry4xMWBhgM8FvYgSnRuvPeaSrf94PUHQ2l2HfLdLhp8GvOhKG1J0tS2Q2fieu8Z8Ee62eJvwPPyFB6/8P6kY3m1t5obl99It4JuPD38aboXduep0qdYt3MdA3sMjGv58Lg8XHfWdVRWVUYUnq2squSK06/QZ8IxSDqVj/ONMX+K83ouyTXbgK8aY4YCARG5IvygiNwsIutFZP2ePXvSKGpm8QV8bDm4JbTiPGHpBPJcecwePpt1u9Yx9+O5zBg6I2KF4JkRz3Bc4XHcYMeB/PC1HxIkmHDFedKKSZza+VQqx1RSWVXJpLMn0vv1ByhZNZ0l355P/7f/QOGqB+nv6Ub54IcjU+QNfpj++d3V774FZLLPJl3tilpNSmbZmPLGFMQl9Cs+idnDZ0f0jdnDZ4eyVjnve3XoxemFJzC79Knk55Y+FfFeM1plB+nqs15/IJThyqFA3a6UVqC9zg3SRTJLQ8XHcwl+/YZIy4eNp2opp1bvjBn346VJv3H5jfTq2IvKMZX86cM/cebxZyZMm+skE5l07iQqqypD85IJZ0/QZ8IxSjqVj6809wJj4ajfi4EvRx1/0hgz0Bgz8IQTTkiHjBnHWZ2euGxizOq0o4D8a++/6FHUI7SqMHv4bJ776Dl21+8OZauq9lZz7zv3MnPozIS+lw2+Bsa9Oo5J506if8FxeKqW4qlaSvH0c/BULQXAE/DSP78z5aVPhSwi/d/+Ax71uWwRmeyzyQpQRq8muXEnLfokRvi0fjflG8ojVqq2H9rOhLMnhFa71u5Yy+7anWxp2MPanet4yu5HT5U+FXFueemTlLiLmXT2ROv98Kfp7+mKJyzQUckM6eqzDb5gKNOVQ36eW5UPJe20x7nBUVO3P5S9ylNfzXUDro1rabhuwLV4An64fR1M+RtMeAV81lyk4dKfs7nnl1m7Y21oLA9fRHJwYkh//e6vGVk5khXbVoRi/OJZPiBSAXHmJSVdS5h07iRNr34Mkk7l4xwRWSAiC+2/zva8RBeISHj7/wWsS6M8WUlTq9N9O/dl8djFnPT5+5y+cyOvXvUq5RvKefWTV7lz5Z2MO2McQ3oPAWDFthXM/Xguz4x4JnZ1Y2MF3Yq68fKVL1v/0OHKRLi5tWYHnj9eQv/XHmDJVa9YioetmCi5RaIClPEG9UJPISVFPWKtFaVPUVLUAwRuff32UB+rHFPJB3s+oEthF8o3lIdWu84+/my6FnXn/z56jjOPP5M/ffin0LEuhV1Yv3s9i8cupn/QTeGsgfRfcb9leVtxP57fD4jIsqLkNpblI/KR4nGLxnwoSlvgrQllr/L8fgD9V9zPpNOvirA0TDr9Kvp7uuHp3Au6nQKuPCj/Njx/DRR2JfDNKVR8PDdiLF+7Yy3Xn3V9TFHjio0VrN6+OhQ87ljNkxUMdJ5Rzryk0FOo6dWPUfLSeK8NxpixzbzmHBF5HAgCS4wxb6ZRnqzEWZ0O98uHIyvOHpeHwsN78W2oZNPFk7llweURmajuWnUX0y+dDsC6Xeu44vQreGv7W8wYOoOfv/VzfnvJb3nh4xeYdPbEUPE2AAo6wR121uOg3wowg9CKh6dqKZ6dH4AqHjlNuAJy62u38vhljycc1At9DZR8tITZpU8xZeVUZg2dSclHSyg8bxy+ou6hfrp6+2p+s+Y3XF1ydSi+6M3/vIk34OXtHW9TeUUl4weMD8WPhB+bPXw27r8+jqfPNyz5qpbimX5OrL+xkvN4fcEY5SM/z8XBerWiKkpb46laSn9g0mX3MO7VcTwy6F5rcXHEA0dOCp8XAMbXEHcsL91fyuzhs5nyxhQe+9ZjFOcVh5LXOKQar+FxeyKeR9HvlWODdCof/27uBcaYfwIXpVGGrCd8cugoIE7BtdO7nE6hpxAI4rtyFre+ck1I8XBwsg1Vjqlk0/5NPP/x86zevpohvYewYMwCAG7qV0a/lsRsmLAVCzv1HgWdoLj7UX5qpS0JL9YXoYDGoXDVg5QAi8cuxv3OLApXPQjnjYvop7esuIVfXPALyhaVhRRmZ7UraIIUe4r5/pLvxxxzVsKWXPUKHNx1xLz//DVHBHD6mYP2t5zF6w/GxHzkuzXblaIcNXX741uJw8dLE2VxuHY+Hk8R/T1deXnMAjz7t+K5aDJ4iiLH3LD7iK82IhbQGctXbFtBvjufxWMXk+/KB4iZw6jblNIc0ql8/NLZEJEfcCTL1UfGmNfS2E7O0/TqtLGyTlx8PxPe+lGMheShwQ+xq3ZXSPEAywqy+cBmTizsTj9PRzyuKI86xyQbzYRXjmx36HHk/f4t1iTxjg90MpiDpLSaZK96FQIc3g/njbNeBZ1C9+jftT9PlD7B7977HbOGzYrIjgXgEheHGqp5bPD/MnHl1Ji++viF9+N5adIRi1p4fwPLAlf+7SPvtb/lLF5/gDxXvJgPzXalKEdFoud3+HgZrXx4iqD823gAT17hEWvz7evg0UFx7+PEAkaP80V5RYwfMB437tBzJVULu6LEI53KxwLgEnv7NuCnWNXwHgJU+YiiqdVpx2RaPvjhkAJSlFfEnOGzOaVzX/bU7oqs2TF0Jqd0PZ2CP99uxWxM+Zu1upGXD411qQlVuztyIqi0b4q7NznRd/rpvRfei6v+UEx/LB/8ML3Jg2Ae5aVPMWHFTUeOlT5F/0afxhAdI8QPONdsV4rSaoRbjj1F8RcTo63NtV8kvF14LOCksLHciQW0PDPs5pphYVeUaNIZcB6eNucNO37jbaxaHu0GX8BHna8uLRU4PW4PxZ7i2H9ae0Xac/n/R/+up1NuZ70qH/wwJSt+Q8ff9qP30l9SPvjhI/tXTafj7886MtGr2WGtlOzbDLPOPxLjEU2nXtaqx5S/heI/lGOLpvq0008LCUZmRit9iv75na2VNX89/V97IKJP9n/tATxRiRVCD8Tb34vIsqLkPl5/IKbOh5Nq15h29RhQlOzAec4/cq41lpZ/+8jr0a9bfz1FTd7G5/aEngGFLg8lxT2ZPXx2KNtVSXFPCuPEciScwyhKE6TT8vG4iCwAHgBmiMhA4GfAE2lsI6M4aXJTNjMm8tP0FFkDhQlGmkrzi8HfaCkf3U6Bw1/g8dbQ39ONJVe9iuelG4+kyLUtI0uufhXP/i2WL+fXb4xc4UgFV57V1oHPmn+tkn2k4hscRrP6tL/ByoxWMpIlV87Hc2CbpVy4i6DzyUf65JXz8bw8xeqrF01OLGu4le3a+db/hcaA5CyJYj6CBhoDQQry3BmSTFGOAYIpWhg79YqwkPgKO7OpcT+3LhkXegYUuj2UFHSxYgFxR1g8FCUdpE35MMY8IyJrgDLgJGAH8CtjzMZ0tZFJoqtHT1g6oekAq0R+mlP+ZlkjopnwijUhc/w4vTUw63xrZXni4phMVJ6qpXgO7oQ/XnLkeuXYJhXfYJtm92l79TqUser7Lx1RIG5/L/JYomxWjmtfdF+1/ZNTkVvJThJluwLLJUuVD0XJAsLi7HwlI9l08WQmvP6jmGeAKhxKa5I25UNErgXcWFXLt9m7vy4i5xtjnk9XO5kgepIGpK6AODgruw7O5CvaHzMR0cFkDi7XkXt16mVNAl1ucEqodOljHe9+uqX0OIhYk0lHpqiUeyHs4GMlR0lgUfAVdGZT/c74fXr401ZRSscNy7E+RPsUd+oVv81wxaNTryP9LhgA5//EV2/dq1Mvy/oW9KfrEysZosEfwBMn5gOgwRegS5G6ZihKi/AURT6/wXqG5xUeGZNdCZT78EQyHXpA9adAmOIRltSm2fMaRWkh6XS72oelfDjOvf2A7wF+ILeVjyYKAy4Zu6Tpf9JEK7tHa60IBpLf1+9NfNyxskBKwcdKDhKv3+UV4vvJlsR9+o3JLPn2fDwzv2opEo71wfEpdrCtHU0SbuVz+qWjcN/xwRG3PyWnsdyuYmM+AA06V5SjwVef3FvC2Y5HeCKZCa9YY29eYSid/1HNaxSlhaTT7WqpiJyIpXB8BagCxhtjPk1XG5kiWWHAVIrqpAVnpRgsK4b3sLXtcse3ojhWEHc69UulXeBvwNNwKHGfvvB+PC9PiXWdkqj8FAUdI1fdHGUkrwAO2sbPFIIdlfZBoy9IfpyYD0BrfShKurl2fpRV40RrDC7oaKXK79TLcrGKl9TD35A0nX+bzWuUY5Z0ul0tAU4GKoGX7d3nishZxphX09VOJkhUGLBNTZPhrllT/mZlsogmfOXD8euMNtUqCuAJ+OL36cEPW1Vw46XHjVY+oi0hDuGrcdr/jgn8gSABY5LGfCiKkkY8RfHnAbe/Z42/zt9ElydI568uV0pbkM5l8eex3K4AuobtbxfO3E0XBoxDeBxFIp/2cJ/4/GLrfCfOoqBT/MmbSOw+RYHY2J0ksRQxfXrYH+i/4v7EdTmi720SrGZ3OumIIhxt+XBSO4ffM969o48rWY3XbykX8ep8ANQ3quVDUdoUt+dIDKiDpwim/h3E2ucB+rs9lI94hltfv12LBSptRjqVj7eMMZ8BiEi+MabR3h6U/LLcodlFdcLjKMJ92p0g4OP6RRYAdLYDviPnu8J+IifwV/3jlUREx+400Vci+nR9daTi0VT62/1bE9w1TDmOrh3ipHZuSm4lp3CUj4QxH35VPhSlxeTlx8Z0dD45/rmOq/XMr1h/p/ztyDwi4IPCrhFjrQfoHzhBiwUqbUo6lY85wDB7ezFwmb39UNj+nMfjbuE/Z7QVZNb51qpEPLNpolS8TaUdDff/dPw8HWtKvJoi4VYWpX2SgkUh1Kd9DbFWk2T9MFHhuPAkCFP/Ht/SobQrvLZyEa18OO8b1PKhKC3H1xDrQpUo4YcxkRkGxRWZfj3OPKLF8xpFaSGtFY2czsrp7YPwld39n1h/wwN2wwlPaZpqKl6IzGrh4G+Mv9KsHBs0x6IQfa7TTx3iWUKcKuXJ+qj2v3aP1+dYPiJdQp3aHmr5UJQEpFIYNjreDpLPHzr3ssboeItHipIFpFP5+JKITMXyuYjYTmMb7QPHApEoYHfK35Knz4u3mm2C4CmM3a8rzUpLia4tkyhd9O3vRfbTvIKweySwjijtCke5yE8QcF7fqAHnihKXZhSGjSDR/GHq39Mnm6K0EulUPiZyJOB8UtT+hIjI/cAAY0xZGmXJPg5/EX914ApCZQAAIABJREFUIxHhaXUnvGIVCwz6Yd8WaxXEWQkJXx1RlOhVNMfdLr/YsoI5OEpp+Ll5+VbcUXj/SoVoi9vR1q5Rco4jlo/ERQYVRYlDogLC4fvjnRNM8D+lCz5KDpDOOh9vx9svIhOBRMfOBLwcUVraL96aIybQVCZnzmTOSZcXnr40nKZWR5Rji0SraNH9x7GQhZ8bfY4qEUqKhALOo7NdaZ0PRUlOS5UPRclh2qIC3Q+wgtHj8WPgdmBuG8jRuqTit+ngFAxMJWWuq/3rZUqGaOqB5vTTTr2sVbZEfbFTL8vUH7DT+uYVhKXaLU6fvErWciTgPHJM87gFAbyqfChK6lw73/rrxNZ5imMXgxIVEBaxrsvLt1y4oy3Z6oqtZAEZK38tIt8FFhlj6iXBJFxEbgZuBujTp08bStcCmuO36QTnJlpZrtmRXtmUNiOn+mxTykd4P3Wsb4kQNzz61dj98TJtKVlFOvpsIrcrESE/z6WWDyWt5NQ42xI8RZHB4vEKBiYaj42BmVHZrTTph5JlpLPC+TyslNERu4EzE1xyIdBVRL4DnC8ivzXG3B1+gjHmSeBJgIEDB7Y/R0ZfvbUy4awqO0qHkyYXgPb3sdsz7brPOpaQDj2sOA+H/GJwF2iRwBwlHX02VGTQHRsrVJDn0grnSlppV+NsfhyrRoceTV/nq7eUkvCxGGILuypKFpLOmI/vNvP8O51tEflztOLR7ohn3Xn+Grh9HTw6KP7KBkBQH9pKmnBS5ULSyucJibaEONzxgVXVXGOPjlkct6voCufOPrV8KEoC/I2xz/47PohUSOK5vD5/TfxY0ET1PxQli2gztysRWWyMGZXgsLet5MgYeUXxzaR5hfbfggRpdTtaA5Hjvwmx2a4UxSE6DXN4tqvGukhT/rXzIy0ZXfpYDy53nuVGFfRb1rgISxxWjIcWDlTCSFThHKxaH5rtSlESEC91ftAfu8ATYx050brWmReA5UWhxQKVHKAtYz4S2gKbazXJSZqKLT+4LXE2K/XXVFIlWVHBmp0JFOCC2Addt1OstM7x+iRon1QicALKowPOAfLzRJUPRWku184/Ym1ONj847nRrW4sKKjlEWyofue2X2RTxVi+c/WCtOicqKOhYNqb+I7a+gq4qK+miqT7o4PS56FW16OOKYtOQxPKR73ZrzIeiJCJZenSH8MyDrrBpW/hY3NQcRFGyiIxlu2p3JFtxTgV/IxR2Ub95JTPEs2R0PNF6JaI56aWVdk2ibFcAnjzRmA9FORocC0i4J4Qz/kaPwTr+KjlAWyofdW3YVu4QDBwxlWrBQCWXaE56aaVd4/UHcLsEtyuO25VbYz4UJe3o+KvkMK2ufIjIRGPMHGNMAufxHKOlq73R7lQOLvcR82rQb/lt6sqFkoijsTYk6oPOfrVkKC3E6w/GTbMLVsxHdZ0qH4oSl0T1lqITe4C6UCnthkxXOM89WrrakGjiV7Mjfpo9newp8Tia1a6mlA9dSVNaiNcfiJtmFzTblaIkJVmxV03sobRTNOajrQgPBnNSmHboAdWfZlQs5RhCAxKVVsLrC8bNdAVWnY86VT4UJT6eBGn4tVig0o5piwrnZ6WrjZwmPCD9wGeWtWPCK0cCyRSltTnapAiKkgCvPxg32BygMM9FfaMqH4oSF1994iyEitJOyViF82MaZwW6JVWmFSVbUEuKYuP1BxIrHx43Xn8QfyBIXoJzFEVpJjr+KjlMq7ldiciXgWHAxcaYa1urnTbDCcZ1exIUastP/V7OCvSBz9Inn6LEIzyI3Kl2DlbFc3+jtd3SgHK1pCg2DUncrgo9bgDqfAE6q/KhKJG01O1Kx18lh0mn21VfLGVjMNAZ+DLwM2BKutrIKE4w7u3r4ptIp/6j+ffUlQuluTS3zyQrYOX0YyegXPuj0kKSWT4KPNb+Wq+fzoXRnrmKcozjb0gwp/h728uiKG1EOi0ffwdeAqYZYz4VkbnGmL+k8f7ZQe0X8fcnyiSUDF25UJpLa/YZ7Y9KC7ECzuOPgUW25aPWq3EfihKDMc3bryjtgHQqHycDQ4E7ReRk4FwRuRJ40xhzII3tKIqiKFmE1x8MuVdFU5hnu101aoyboiiKkt6A81rgFfuFiHwJGAHMxKr1oSiKorRDGvwBOhXGf5wUhtyu1PKhKIqiQNqi/0Tk9+HvjTGfG2NmG2NU8VAURWnHeH3BxEUGPWr5UBRFUY6QTrerr6TxXtmHE4ybl38k/7anyMrRDVYWISd7VUuzBylKugkPIo/OdvWjj6GxLrLvOtdo/1WagdcfJD9Jql2Aw15VPhQlhoJOsTU9PEXg98ZmxNSxWWknpFP5OEdEFmAVFnQipQRoTFQDREQ6A5W2HIeBCcaYfWmUKX3EC8Y98BnMOj/2XCd7kKJkmmRB5Np/lTSRLNtVUcjyoW5XihJDxxOtVzgHPoOZcdZzdWxW2gnpVD42GGPGNucCY8whERlpjAmIyEjgNuB/0iiToiiK0srUNwZCKXWjKQxLtasoiqIo6az49O+WXGQrHvnAxdH3EJGbRWS9iKzfs2dPOmRUlFZF+6ySaxxtn/UFgviDJqHbVUGeWj6U9KLjrKLkNulUPh50NmxlwtkelOwiEfkOsA3oheWCFcIY86QxZqAxZuAJJ5yQRlEVpXXQPqvkGkfbZxt8llLhKBnRuF1Cvtullg8lbeg4qyi5TTqVjzlh24vDth9KdpEx5s/GmJ7AQuCONMqjKIqitDL1tvKRKNsVWK5XtZrtSlEURSG9MR/hpKTUiIgYEyrj6QOOayV5WofwTELR+xUl29H+q6SBhkYrg1pBEuWjKN9Nndb5UJTU0LFZaeekU/n4kohMwcpw9SURmWpv905yzVARmQYEgS+Am9IoT+uTLJOQomQ72n+VNFAfcrtKrHwU5LnV8qEoqaJjs9LOSafyMRF4EagBfgLst/ffkOgCY8wbwBtplEFRFEVpQ1J1u9KAc0VRFAXSq3z8AhiGFTg+3hgzKY33VhRFUbKQ+sbULB+HG9TyoSiKoqQ34DzPGPOxbc3om8b7KoqiKFlKQ8jyET/bFbSzgPNgALatge3rIRSyqCiKoqRKOi0fPUVkDFacx4n2NkDAGPNqGttRFEVRsoRUYj4KPW52VDe0lUitx4HP4MUfwM5/Wu/7D4fvPgd5BZmVS1EUJYdIp+XjIaAb0BX4nb3tvFcURVHaIY7bVfKYDzd1uW75qP4cnh4O+zbDN++Cr90Am5bD6/8v05IpiqLkFGmzfBhjnk/XvRRFUZTcICXLR56L2lwOOK+vhuevhsYaGPm/0O1Ua/+h7bD2CRh0M3Q7JaMiKoqi5ArptHwoiqIoxxgNKWW7ctPoD+ILBNtKrPQRDMCL11sWj0vvOaJ4AJw3Hv5/9s48PIoq6//fW92V7qyEsGNkTxAEXEB22ZQ1IqNBGXCZoHEBRXF8fef3Mo6Td0ZxfB1GWQQHgokLKA4BRdkFRAERQWWRJYGgGNlJQrbuTnXX/f3RqaKX6k530t3pTs7nefpJd9WtW7dS59yqc+855zIG7PpXgzWPIAgi0iDjgyAIgqgzvrpdAYjMdLtf/gM4/SUwcCbQ7ibnfbEtgU7DgMOrgeqqBmkeQRBEpEHGB0EQBFFnTJINeoFBL3hJtSva90Vc3MfJL4CvXge63WkPLtei2yigugI4/nlo20YQBBGhkPFBEARB1BmTZFONC09E18x8VFoiaObDVAqsfRJI7AgMeNJzuTa9gLg29tkPgiAIolbI+CAIgiDqjFmyweBljQ8AMOoV4yOCZj62/x2ougIMnQ3ojZ7LMQG4vr/dNYtcrwiCIGqFjA+CIAiizpiqbV7jPQD7IoMAImehwbM/At8tB7qnAS261V4+eQBgtQCFX/p1Gs45rHKE/E8IgiACRCAXGSQIgiCaGJXVNq9pdgHAoAScR4rb1ZevAoY44JYHfSvf5kZAjAXyNwE3TPBY7MeLP+KzU5/h8OXDOFN+BpVSJQAgISoB/dr0w5QbpmBw+8GBuAKCIIiwhYwPgiAIos5UmK1qNitPKPsjYubj/GG7EXHzA0BUrG/H6ESgbW/g9E7N3SarCVl7srDh9AYYdAZ0S+yGAW0HIC4qDgIEFJuL8cPFH7D91+2Y1HUS/jr4rxAFMYAXRRAEET6Q8UEQBEHUmXKLhJhajI+ICjg/8C6giwJ6TPTvuHZ9gH17gdIzQGIHdbPFZsEz25/Bt+e+xd1d78b4zuNh0BncDpdkCZ+d+gyfnvoUFVIF5g2fB53g/f9KEAQRiTRozAdj7HrG2AbG2JeMsXcYY6wh20MQBEH4R4XZiuio2mY+IiTVrrUaOLIauH4AEBXn37Ft+9j/nv7aafNbP7yFvef24pFej+B33X6naXgAgCiIuDflXvy++++x7cw2LDu8rC5XQBAEEfY0dMB5CYD7OOcjAJwFMKRhm0MQBEH4Q4XFqs5seMKgj5CZj5NfAKYSoOso/49N7AgYE4HTX6mbjlw+gnePvothycMw5DrfHm+jO47GwHYDseTgEhy6dMj/dhAEQYQ5DWp8cM4rOOeVNT8rAFx13M8Ye5wxtp8xtv/SpUuhbyAAWea4VG7BbyVVuFRugSxzv/YTTYtwkNlIpT66RHpYd+orsxWW2mc+dAJDlE4I/5mPgx8CxmZA+1v8P5axmriPrwBul7/5389HnBiHKd2n+FENw0M9H0JCVAJe2fsKbHKYG2wNQCD6WV/6DOpXCCI4hEXMB2MsEcD1nPPDjts550sBLAWAfv36hVzrZZnjxIVyPPbefhSVmJDcPBrLHu6H7m3iIQis1v1E06OhZTZSqY8ukR7Wj/rIrGSTYZbkWmc+ALvrVVgHnJtK7YHmqWMBoY6PxrZ9gJ+/BooL8aOtHHvP7cX93e9HtD7ar2qi9dG4P/V+LD28FOtOrcM9KffUrT2NlPr2s770GdSvEETwaGi3KzDGogDMBfBSQ7fFlSuV1WrHAwBFJSY89t5+XKms9mk/QRC+UR9dIj1sOCrMdmMippaZDwCIjtKFt9vV0U8AWzXQpQ4uVwrtlLiPncg5koM4MQ4jk0fWqaoB7Qagc0JnLDm4BJJNqnubCDd86TOoXyGI4NHQAecigPkA5nHOrzRkW7SottrUjkehqMSEaqvNp/0EQfhGfXSJ9LDhqKhZsbw2tyvAHvcR1iucH/wQaJbs26KCnohvD8S0xMVTX2Bn0U7cft3tMOi1A8xrgzGGSd0m4VzlOaw9ubbubSLc8KXPoH6FIIJHQ898/BnAnQCW12S8mtzA7XEiSq9DcnPn6fLk5tGIqgmerG0/AFitMs6WmvDLlUqcLTXBapWdypNPKdFUcZR9xlituuQJT3rIGCO9CjLlNTMf0WLtbkpGUUBVdZi+uJX8DJzZa5/1qE/SxZq4j08uH4CN2zAseVi9mtW7ZW90bdYVSw8tRbWNRtwDhS99hqgXqF8hiCDR0AHnWZzzFM75iJrP6oZsjystYqOw7OF+agek+Hy2iI0CADSPFvH2g32d9r/9YF80j7YvDmW1yjh+oRz3//sbDH/9S9z/729w/EK5aoAoPqX3LN6NIa/twD2Ld+PEhXLq0IhGj6vsZ607giVedMkbWnq45MG+eHd3IelVkCk3292BfHG7Muh16kxJ2HHoY/vfLiPqXRVv0wufGhh6xndCm9g29apLmf24UHUBn576tN5tI+z40mdUmK2aZbLWHaF+hSDqSVgEnIcbssxxpbIa1VYbWsdH4eMnBkGyydALDK3jDLDZZJy7aoFV5midYMCKzAGwyRw2mWP1/jNoM6wbWsUbcLHCgic/OODkM/rkBwfw8ROD0D4x2qNP6dqZQ9Aqvm5T9QTRUDjqTZReh+bRIkpMkuZvxhje2HpClf0tRy+ic4sYrHxsIKw2GTqBoaSyGmfLTIgW9WgRG+UxyLPEJOGzH4uQk3EbdAJT9fDWTi2Ar38mvQoixTX+7/HG2h8l0VE6XKmwBLtJ/sO53eWqTW8grnW9qzvarBXOnBfxjK5ZABoH3NjiRnRu1hnvHH4H93S7B/q6BsMTKiUmCcUVJqx6fCCsModeYDh1scypz3j4nX3494N91X5FrxOw4pvT2HL0IgD78/qNrSeQdXcvcM4Rpdd57acIgrgG9WIuOGa4aBVnwH+P644XVh9Ss13kTr8NZknGkx8c0Nz/WnofyLJ9ZkOyyZo+o1abfT/5lBKNBdfMMGN6tsYzd6Sqxrcyk7FgWz62HL2o6sql8mr88Gspbrk+EcO6t8G0ZXs19cpblhlZljGsextMz/3OSQ8THF6ISa+Cw+Ua4yPBhxmq2CgdfjaH4czHbweA4kJg8DMBqW5T5c/QcY7RpZcRiGTbjDHc1eUuLPxhITae3oiJXf1ceZ1wQ9RxNI+LxpSle9U+Y8mDfdEt9lqf0SrOAJlzt35l38+lap/1h8Gdcf+/v6FsWAThJ2R81OA4aqu8QP3lrp7qCxBg74wApr5Que4vKjHhT3mHsPrJQTV+7MBHjw8E5xwCYyg1Scg78Cv0Oru3m+J36miA+OrnThDhhOssXnrf67FgWz7+cldPJEaLKDVJWLAtH+l9r8eWoxdVXfnLXT3xxPsH8OSIrvjqxAXkZNwGo6jD1GV7vc4ISpINFyss6qjlu3tOu+lhTsZtavsc9cp1hqa20Up/yzcliiv8mfnQqzEiYcXBjwBdFNCx/mvccs6x6dIB3MYNSD53BJe4DLD6ezff1OomXBd3HbIPZyOtSxqEANTZlLBaZVyssECyyRB1AsA5Frr0Twu35eOvE2/EqscHotQkoVm0iIXbC5zKvLvnNJ4c0VXts/6Ud4g8FwiiDpDxAedR23n33aR2JonRovr9lusT8V9ju6O0qlpzv0KrOAMulVswY8X36mjI65P74B8bj+NShQVLHuyLljH2UULF79R1dNgXP3eCCCdcZ/HaNzPiD4M7qw9nT7MRSvxU11axaNfMiOm53znpoGNZZeZCkmw4frECMxz0xnEWRSkv1cwwOsZq+Zu7n3L9e6e40oJYgw56ofaX4dgoHUyS7doLYDggmYEjq4HrBwBRsfWu7mD5aZy3lOD3cSnQW04i+sppmFp2rXe9AhOQ1jkNSw8vxY4zO3BHxzvqXWdTQYm9dHzOfvzEQM3+SRCAKUv3Irl5NFY9rl0mseb53SI2ijwXCKKOhMkToGFxHLUtNUlqgJnjd2WU40plteZ+hWfuSFEND8DeGb2w+hCeHNEVRSUmzPjgAC5X2YM0S2pGg/9yV0+senwg/nJXTyzYlo8SE+V0JyIL1+wxRlHnNir4p7xDMDosRpfcPBrtE6Ox+08jES3qMLNGb7T0SskyAwAXKyyq4eFY95MjujqVbxYtYvefRmLtzCGqseBv7n7K9e+dy5XVaObjYIkSlB5Wsx/HPwdMJUDK2IBUt+nifohMh55t+wEAEs7+GJB6AeC2trehdUxrLD28FJxTkLOvaMVeyhya/VONx7TXMok1/Ur7xOg6Z+gjiKZOk5z5cHWjkGUZg7u0wGPDukCvY/jg0QGYu+Eo3v7yFN6adguKKyV0bBGDf9zbG/FGPXIy+qGoxIzEGBHvPdIf/9h4TPVj79AiRnM0JDFaxC3XJ+LJEV0h2eSaNH0ythy9qAawKfx1Io2cEJGBokuyLCN3+m34tdiEmCgdRJ2AVnEGJ11oFWeAqBOw6vGBqKq2oVvrWOh1DJKVg9fsLyox4e0vT+G19D5OI47z7rsJAgN+uVIJAJo6psyiKCOUeoGhTTPnlwOtOKtWcQZUW234raTKza2K4rK8U1xRjXiDr8aH/XFTbpaQVHOvGpwDOUB8u2uLA9YDG5ex+fL36BXfCfq4NjDHtkL82R9xoU96ABoK6AQdJnSegNyfcvHN2W8w+LrBAam3sSPZZLSKMzi5T9lk7rbt7S9PwSZz1e2KMa6p+zaZo0OLWMgyx7KH+7nNirYIF9kmiDCmyRkfWm4UudNvw0ODOjoFlr39YF+0TjDgUpkFf/n0iLr9rWm34KrJ6rRt8QO3YtaoFJy9asalcotmHIdkk/FfY7s7vVD9+6G+GNOztZPxQSMnRKTgqEuDu7TAQ4M6OunF65P74P82nVCDM/97XHc8uPxb54D0Zd9qlv/n5hP4+6Re6NIqFr8WV8EoCpj8tj2wMyfjNk0da51gVF8c3t1zGq/c4/5C6RpnpbTLMfDU0a2K4rK8c6HcjJZxvvm3KzMfZaYwmfm4fBL4eRdw6x8CEpfx/dWTuFxdhslt7bEjZS27IunsIUC2AUJg5GVQ+0H49NSnWHp4KRkfPhIt6twSWOQ9Ocht2+uT+6DUJKluV28/qP181tUMTAgCQ/c28Vg7cwjFgxGEnzQ5t6srldX45PtfkZNxG7Y/PxzvPdIfok5wc5V68oMDkKwynnCZri2ulDB71Y9O22au+B5nr5rx98+PIloUMO++m5xyg78+uQ90NUGxji5W87/Ix4tpPT2uI0IQgSBYC1k6uiQ9NqyLprvhM3ekALC7IzomZ0jve72bK4Rj+VbxUejSyu6D37FFLN7acVItu2BbAV6f3Mct//6Kb05jytK9+PvnR/Hc6O7QCXC7Zte1e1zb5epWVdtaP00ZWbaPDLf2Mbg2xnBt5iMs+GahPdC8250BqW7TpQMwCCJuSugCAChvmQJ9dSViL+UHpH4AEAUR4zqNw4ELB/Dd+e8CVm9jxiZzNx03W2W3bS+sPoS4GhlV3gH+7PJ8XvJgX7R2MLYFgaFVvAHXNY9Bq3gDGR4E4SNNbuaDgSPtpuucZjnefaS/dkpc2X3aNSZKp1m2R9t4/OPe3qiqtiHOoEdOxm2osFjROt6As6VmtG1m0AxeM+gFGjkhgkYwA6YdXZL0OqapF11bxWLnCyPA4OwqpZWsoajEhM4tY7HrTyNQWmXFA9nfOumKElD+w6+l+L9NJ/DR4wNhq8l21So2Cm2HdcPDgztD1AuoMFtx96LdmtfsOFpp49quFYpbFY1ueuZCuRnVVhmt440+lVdnPsLB+LhaBPywAkgZA0Q3r3d1kmzD5kvfo098JxgEuxva1dap4Iyh2Zl9qGzTo97nUBiWPAybf96M//vu//BR2kfQBWhWpbFSrZHyXvag966/BQbkTu8PgQEyBwx6Bl24JEsgiAimyWmRxSqrga2AvYM5c6VKM3BMJzC37VXVNs2yYAz/b81hTF32LSYu2o3Rb3yFWR/+gPwLFbjv39/ALMmawWs2DnXkpEVsFK5UVgd8hJpougQzYNoxyFwnCB51qGOLWDdd8hRUfuJCOUzVstusiGtA+aUKCwRmr/u65jGIitKresTA8PA7+9yu+XyZGb+VVOFKZTVaxEbhuuYxiBb1tQaN0uimNr9cqQIAtEnwbeYjvmZUuaQqDIyPbX+3/+0VmHiMb0qP4aq1EgMTb1C32aJiUdG8ExJ/2RuQcygYdAZMTp2M48XHadVzHxB17n2T1rbk5tGwOTxzlSQXd/5rJ0bN24k7/7UTU5d9S8kmCCIANDnjQ2s2Y8G2Aix54Fan6dXFD9yKPQWXsNhle3Jzo5tb1WvpfWCx2txcQf79YF8s2FYAAKiwWDVHVpSsJcoI9T2Ld2PIaztwz+LdOHGhnAwQol4EM2Da0SWpyiLhtfQ+bnqhV/yjGZz25x341U23ljzYF3kHfoVO0J5FcQwof9vF/cGXaz5banLTLXKrqjunL9uD/9sk+DbzoWTFulhmgVmyOb3ohZSfdwOHPgJuvCcgK5oDwPoL3yFWZ0Tv+E5O26+26YHYywUQK68E5DwKA9oOQLfEbpj//XxctVwNaN2NjdZxBrz9YF8nHY/SMc3+Z/X+M+rv19L7AC5ZxSjZBEEEhibjdqVk5dHXjMA6vpxcqrCgwmLFX+7qiRaxUWiTYMTCbQX4+EAR7u+bjNzp/SHqGKJ0AgQGvL75hNvCQ3+deCO6torDmhmDIdlkROl10An2ugHgoodAdGWE1dMINS1YRNSHYAZMu7ok/WtrvpteKEHfMoca86TsX3/wN6x6fKDazkSjHll39wLnXLPN7ZoZ8dULI6DXCWgdZ4Berz124umalRFLV90it6q6caioFLEGnc8xH3qdgHijHqcuVWDIP7bjts5JePvBvkFupQumUmDN40B8e6D3/QGpsspmwfYrBzEgMRV6Fxeo0jY9kHxsA5r9ug+XbxgfkPMB9lXPH+jxAF7e+zL+vvfveH3Y62oqasIZvV5A99ZxWPX4QHVRUsnGsf7gb8jJuA06gcEmc+wuuIjJ/TpgVI+2av/13+Oc3eUo2QRBBIYmYXy4ZuVZ/MCtquuVY5adSxUWLHu4HySbjD2F9pGqPYVXMH1oZ3RpafcXv3DVpBm7oRcYWruMADqm4nv7y1N4fXIfp+wajiOslNKTCAbKyH6w0kEqLklWq2zPXuVhwUy9wDzqjWM63PaJ0bBaZc3FN9vEGz0aHLVd82vpffDPzSfUMq5xHWTg+8/ewmKktI7366W3eYyIdQfPAgA2HTmPMrOEBGOIFlW1VgMfPwxUnAPG/R8g+jZjUxs7rhyCWa52crlSMCW0R3V0IhJ/3hNQ4wMAOiZ0xKRuk7CmYA1ubX0rpvWYFtD6GwuyzHHycqVTf/DpU4Nx183JTrGfSx7si9c3H1fT5r/9YF8Y9NcGK2lWlCACR5MwPhxnFT4+UAQATrMZUSLDomm3gDEGHQOYwLDu6SEwVbuPhAqC4DaC6ymtp+vIcHSUDmtmDoZkld3qpZSeRDAIVcC044KZil4s2JaPV+7pUxMn4bve6PUCbmgTj4+fGASrTYZeJ6BVbBRKTJJP1+B6zYwxZK07oq5+DpBu1ZfDRVdx+nIlpg/p5NdxreONOFN8rY878ttVDO7aMsCt04Bz4LNngNM7gSHPAa26B6zqvHO70TIqASmx17nvZAzF7W9C69O7obOUw2bfR0XNAAAgAElEQVSID9h5AWBC5wkoLC3Ea9+9hhbRLTC2U2AWS2xMaHkVlFRZceD0Zax8bCA452CM4edLZfjrxBvxYlpPdXZVEBjNihJEEGhw44MxlgJgHYD7OOdHgnEO11mFjw8U4eMDRdj9p5Folxhtnxkp8y0jUIvYKDw3urvPI8m+jqoGe4SaaLqEYmS/2mrzumCmv3qj1wton2ifEalLxi7Ha5ZljudGd8fRc+WkWwHi7a9OIVrUYWg3/wyHbq3jsP+XEtzaIRHfnylF4aXK4BsfnANbXgQOfgjc/ADQ7Y6AVX266gK+u5qP9LaDIXiYAbpy3a1oe2onmhfuwuUegZ39EJiAJ/o8gX8d+Bde2PkCzleex0M9H4IQgHVLGgtaXgUbD53FiB5tMG3ZXqeZj9ZxBoii86AEzYoSROBpUOODMaYDMBvA+mC2pbZZBX/iLYI1kkwpPYlIpjYdq4981zceinQrsJy8WI4Nh85h4k3t1VXLfWVk99YoqZIwsU87/HT2IAovVQaplQ7seAX4ZhHQPQ3o8/uAVv3xua+hg4ChzW/0WKYqMRnm2FZIOrk94MYHABj0Bvyx3x+x7NAy/HP/P7Hz153IGpyFDgkdAn6uSESrb+qVnIiFLjO1C5WZWpFmRAki2DTo8Ajn3MY5fwpAhdZ+xtjjjLH9jLH9ly5dqvN5asto42+8RbBSb1JKz8gnUDIbafiSNaqu8h2IeCjSLc/4K7Nrvv8NgsCQ1rud3+dKiBaRMbgTWsQZ0D4xGoWXNbv+wLHzdeCr1+3reQx4AghgUPZVqRJ553bhtsRUNBNjPRdkDFeSb0HCbz9CrAhOn2DQGfDUzU9h+o3TceTKEfzu09/hzQNvokqqCsr5Ghp/ZFarb+rcMhZbjl7EE+8fwJSle/HE+wew5ehFirEkiBDR4G5X3uCcLwWwFAD69etX57yMtY18UrwFESgCJbORRjBnF0g/g4u/Mrv75GV0axWHhOj6BYq3bWbEqYtBND72LAR2vAx0GQkMfAoIsCvSyrNfwiRXY0LrfrWWvXz9bWh/YitaHVuPs7dlBLQdCowx3J58O3q36o3V+aux/MhyrDu1Dn/s90ekdU5rVNmw/JFZrb6JQzujHvUpBBEamoxjqLeRT8r1TxD1J1izC6Sf4UOZWcLh367ixvYJ9a6rXTMjfis1wRKM0eZvl9rjPDrdDgyZDQR4FfAr1eV4r2gbbknogmRj7TEr1bEtUNq2J1r/9DmYLbiL1CUaEpHZOxN/HvBnxIqx+J+v/wd/2PQHFJUXBfW84Yxr39Qy1kB9CkE0IGE98xEqyCecIMIX0s/w4bvTxZA5AmJ8tG8WDZnbV0pPbRPALFBf/wvY9r9Ah0HA7c8H3PAAgAU/fwqTrRqT2w71+ZgLXYah+Z4laHVsAy72+l3A2+RK18SueHHgi9j12y58fOJjTPl8Cl69/VUMSx4W9HOHO9SnEETDEi4zH7aaT4NBPuEEEb6QfoYHe05dQZROQLfW9TcW2jWzr7NReClArleyDdj8Z7vh0WUEMPxPgBD48bVdxT9hzfk9GN3yFrQzJvl8XHnLbihr0RXtDqyAIJlqPyAACEzAsORheGnQS0g0JOKpbU9hwfcLYJMptoH6FIJoOMLC+OCc/51zfrih20EQBEF4ZvfJy0htE4coHxZ7rI12NYtLngpExquqYmDFffasVjdMBIb+MSiGx2/mK5hz4l0kG1vid20H+XcwYyjqmYYoUwmu25cT8LZ5o3VMa8wZMAdDrxuKZYeXYcYXM1BiLglpGwiCIBTCwvggCIIgwpvLFRYcP1+OG9s3C0h90VE6JMVG1T/d7qntwNu3A6e/AgY9XZPVKvCPtnPmYmQemo9qWcKTHSYgqg7GTWVSJ1zoNARtDq9Fwpl9AW+jN6J0UXik1yPIuDED+y/sx/2f348jl4OytBZBEIRXyPggCIIgamX7cfsCkn2SA2N8AECnFjH47ufiuh1cegZYOwN4/x6AARj3DyB1XMDapsA5x/bLB3H/96/iSnUZnut8D9r74W7lStGNd6GqWXt03foy4s6FfsJ/WPIw/L/+/w9W2YoHNzyIf373T1RKIVhvhSAIogYyPgiCIIha+ezgWbSMi0Lnll7WtPCT3tcl4kxxFU5f9vHlV5aBX/cB654BFtwKHP4P0GsycNd8oFX3gLULAMqtJnx24Vv84eC/8OzRfyNOH42/pExFl5i29apX1htQMOBRSIY4dP/sBbT/7l3oLOUBarVvdG7WGS8NfAlD2g/Bu0ffxZjVYzBv/zwcLz4OmcshbQtBEE0PynZFEARBeOTX4ios2FaArwsuY8pt1wd0vYjbOjXHim9/wQurD+Jf992MDi1i7DsunwROfgFYTYBkAiouACW/AGd/AMylgM4AdLsD6DMFiG1Vp3MXV5djy+XvYZGtqJYlVMtWXLVW4rylBGdMl1BYdR4cHK2jEjGt/QiMaNEbehaYzFlSdCKO3T4LHQ+twXUH3ke7Hz5EZeseMCV1hBSTBJsYA1NSZ5Rd3zcg59MiLioOGb0yMPz64dh4eiPeO/oecn/KRaIhEV0Tu+L6+OvRwtgC0fpoxIgxEAURd3a8Ey2ja08tTBAE4Q3GeWSsg8YYuwTgFx+KtgRwOcjNaQga63UBwb22y5zzwPti+IAfMltXwlUmwrVdQPi2zbFdjVlmtQjHexKObQLCs10tARwPU5kNx/+XNyKtvUDktVlpb4P1s0QEGR++whjbzzmvfcnZCKOxXhfQuK8tmITr/y1c2wWEb9vCtV2hIByvPRzbBIRnu8KxTQrh3DYtIq29QOS1OdLa21ihmA+CIAiCIAiCIEICGR8EQRAEQRAEQYSExmh8LG3oBgSJxnpdQOO+tmASrv+3cG0XEL5tC9d2hYJwvPZwbBMQnu0KxzYphHPbtIi09gKR1+ZIa2+jpNHFfBAEQRAEQRAEEZ40xpkPgiAIgiAIgiDCEDI+CIIgCIIgCIIICWR8EARBEARBEAQREsj4IAiCIAiCIAgiJJDxQRAEQRAEQRBESCDjgyAIgiAIgiCIkEDGB0EQBEEQBEEQIYGMD4IgCIIgCIIgQgIZHwRBEARBEARBhAQyPgiCIAiCIAiCCAlkfBAEQRAEQRAEERLI+CAIgiAIgiAIIiSQ8UEQBEEQBEEQREgg44MgCIIgCIIgiJAQMcbHuHHjOAD60MffT4NBMkufOn4aDJJZ+tTx02CQzNKnjh+iAYkY4+Py5csN3QSC8AuSWSLSIJklIg2SWYKIPCLG+CAIgiAIgiAIIrIh44MgCIIgCIIgiJBAxgdBEARBEARBECGBjA+CIAiCIAiCIEICGR8EQRAEQTQ5fjp7FeVmqaGbQRBNDjI+GgmSTUKVVAXJ5r0j9bVcoI4jCG+YJTMqpUqYJXOtZUkGiaaAJzlXtpslM+lBAKi0WJG2YBee/ejHhm4KQTQ5yPhoBEg2CQWlBRi/ZjwKSgs8PpR8LReo4wjCG2bJjPzSfExYMwH5pfleDRCSQaIp4EnOle1/++ZvyC/NJz0IACculAMA9hZeaeCWEETTIyjGB2PsesbYBsbYl4yxdxhjTKNMB8bYrzVlvmSMdQpGWxo7ykMpY1MGis3FyNiUoflQ8rVcXesnCH9QDI/MLZkoNhcjc0umRwOEZJBoCniSc7NkRkFpAbIPZWNi14mqzpAe1I+rVfb/m1Wm9eYIItQEa+ajBMB9nPMRAM4CGOLh3Hmc8xE1n5+D1JZGi+PDymQ1AQBMVpPbQ8nXcnWtnyD8wdHwcJQrLQOEZJBoCniT8/zSfOwu2o301HQ89+VzpAcBoqraBgDgnIwPggg1QTE+OOcVnPPKmp8VAK5qFQMwljG2gzH2slY9jLHHGWP7GWP7L126FIymRjSSLGHGFzPUh5GCyWrCjC9mQJIlv8rVtX7iGiSztWODDbO2z9KUq1nbZ8EGm7qNZDD4kMw2PN7kfNb2WZjWcxqyvskiPaghEDJbVW0FANho5oMgQk5QYz4YY4kAruecH9bYfQbALZzzkQBsjLGJrgU450s55/045/1atWoVzKZGJKIgYsmdSxCtjwYAGHQGAEC0PhpL7lwCURA1yym4lqutfl+Pa8qQzNaODjosHLVQU64WjloIHXTqNpLB4EMy2/B4k/OFoxZi5dGVyBqURXpQQyBk1iTZBznI9iCI0BM044MxFgVgLoCXtPZzO4p/xQYA3YPVlkjCn4w+ok5ESmIKcsflYnSH0diUvgmjO4xG7rhcpCSmQNSJbuWUh1e0PtqtnLf6/TmOaNz4I6NaZY2iEamJqcgek+0kV9ljstGtWTfIkNXyJINEY6A2nRF1IjrFd0LO2Bw3OU9NTMWQ5CHIy8/DGyPeID0IEIrbFUEQoSdYAecigPkA5nHONVNJMMYcz30/gH3BaEskUZeMPqJORJeELsjolYH0denI6JWBLgld3B5GSrnsMdlIMiYhe0y2Zjmt+pWXvyRjEj3smjj+yKi3so4GiCKP3Zp1w8/lP7uVJxkkIhlfdMYsmXHy6km8f/R9VScUOTeKRqQkpiCzTyY+O/WZ237Sg7rhaHzINP1BECElWDMffwZwJ4DlNZmsJmuU6c0Y28MY2wWgmHP+VZDaEhHUJxtVYVmhU9agwrJCzWxXhWWFyD2Si7y785B7JFeznBbKy9/GezfSw64J44+M+lJWMUA23LtBNTw8lScZJCIRX/TAMQHD+tPrkXskF+vvWY9O8Z3cZq9fGvQSUhNTSQ8CgFm6ZnxU2+QGbAlBND2CFXCexTlPcchktZoxZmCMnayZFQHn/CDnfDDnfCjn/JVgtCNSCHY2KsdyW89sxbi8cdh6ZqtfmVJEnYgYMYYedk0Uf2TUn7JG0YgoIUo1PLyVJxkkIglf9EAr89vWM1sxfs14nLx60inzmyL/RtFIehAAlIBzALBYyfggiFASskUGOecWAP05500rLYcP1Ccb1bM7nq1Xtqtndzzb5DKlEP7jj4y6llUSIXiSZ3/LE0Qk4EmuZS6rcu0p81uppdQt8xsRWBzdrqrJ+CCIkBLSFc4558WhPF+kUNeMPgIErL17LUZ3GO123OI7FmtmuxqePByb0jdhePJwjO4wGmvvXgsBAsySGZVSpddVpv0lGHUSwUcrONZXGTVLZnBw5IzJcZO3aH00csbkgIOjqrpKs27X8k0xkw/RONCS65k3zcSm9E1YOX4lZG5/4V0+ZrnXzG+u+uhPwgdH6npcY8XkYHxYrGTkEUQoCanxQWhTl4w+SgxH2to0ZPTKUA2QaH005o+cjxh9jFv9q9JWYVqPaUhfl46Hez6MmTfPRNraNJy6egpFFUWYsGaCx1Wm/UVxJwhknUTw8RQc64uMOt7zSmsl1kxco8rbtB7TsGbiGlRaKzFhzQScvHpSNUC05HNaj2lYlbaK/NqJiMVVrv/53T8xuP1gpK9Lh8lmwrHiY5iwZgIsNgtWTljppFfLRi9DC0MLAHDSR2W1c3+SkgB1S2bS2KGZD4JoOMj4CBP8yejjGsSYuSUTGb0ykNY5DW+OfBMrjq3AlPVT3B4yVdYqzN4xG8XmYjy9/WlcNF1E75a9MX3zdPW71irT/uLox6y0jwyQ8Ke24FhvMup6zx/d8iiKLcVYfWI1is01fy0122tkwtEAAZzlc/aO2aiyVnlqKkFEDFXWKqw+sRoTu07EY1sfQ++WvXHRdBFPbXsKxeZiTN88XTVAkoxJWDZ6Gfac3YN5B+YhvzTfSR/zS/ORfSjb76QkdUlm0tgxUcA5QTQYZHyEEb5k9PEUxJi5JRMP9nwQBy8exM6inU6Bjcpo2fTN052Omb1jNqbeMBX92/Z3+l4fY0ErgFJpHxkg4YuvQeJaMurtnqenpmPmTTORnpquuf/k1ZOosFRoyuf0zdPpJYmIWBSdWn54OdJT0/Hcl8+hf9v+mHrDVMzeMdtN1s02M9bfsx57zu7BT1d+8qgz6anpGJ48PKBJSZoiksNsh0Ui44MgQgkZH2FGbRl9vAX+PrXtKdzX/T6ngN1ndzwLG2wej5mzaw6yBmdB5rLT97oGO3oKoDRZTRRAGcb4E1DuKqPe7vmcXXPw8I0PI+ubLM39z+98HkxgdUq4QBDhjJIQZM7AOZizaw5kLiNrcBbm7Jrjsf8228z44NgHXssp/bRBZ/ApKQnpljbVNhmijqnfCYIIHWR8RBjeAn9fvf1VZO3Jcto2f+R86KDzeMzcoXORtScLAhOcvivBjv6igw4LRy30GkBJhB91TXoAeL/nc4fOxXs/vYesQVma++cNnwcu8zqfmyDCFVEQMX/kfMzdOxdzh86FwARk7cnC3KFzPfaPr+97HWXVZV7LKf20xWapVUfqo9eNnWqrjGhRp34nCCJ0kPERYXgK/M0Zm4N2Me0QJURhU/omjO4wGjljc9AhvgN0gk7zmDdHvokPj3+Ifef3OX3PHpON1MRUGEWj3+1zXLna8Vz1qZMIPv4kPXDNmuPtnufl52HxwcXIy8/T3N+tWTfEGeL8TrigBWXzIcIJRacy+2QiLz8Pb4x4A/vO78OHxz/EmyPfdOu/uzXrhr8M+gtGdxiNnUU7PepMXn4edhbt9FlHOsR3QM7YnHrpVmNEssmIjiLjgyAaAjI+IhCtwN/U5qloF9sOGb0ykL4uHRm9MpAYlYi0tWkoKC0AAKdjFGPl8OXDyBmbg9bRrXH48uGAGAlG0Yhuzbohe0w2koxJ6ksmGR7hjS9JDzxlzdG6510SuiCzTyaSjEnI7JOJLgld3GQiJirG53N7g7L5EOGIowHy2anPkD0mG4cvH0br6NZ464631L64a7Ou+Ln8ZzV7YVpn+9/9F/arOpM7LhepiamqTtWmI4pOpK1NQ4w+Bjljc+qkW42VapsMY83MB6XaJYjQwjjnDd0Gn+jXrx/fv39/QzcjrJBsEiRZUqfOHQMLlZmNlcdWYt/5feoDB4DTMcp3m2yDDTbooKu3kaA89HYX7ca0ntOw8uhKDEke0lAPPBbqEypEqsw6ypWW4eEoY45ypXXPuyR0gQxZrauqugqccTDOVMPDl3PX1l5P7YrQFyyS2UaGItcCBMiQIUCAlVshMAECBBSWFTrJb87YHOz5bQ8W/LgAozuMxt+G/A16podRNPqkI1o6sSptFdrEtvFLt/wg4mT2tle+QIvYKBw/X475v78Zk26+LgitI8KYBpNZgmY+Ihol8BeAZkYTxwxWSnYTAGqwsGPgsFE0IlaMDZjhkbEpAwt+XIAxq8dgwY8LKLtKBKGV9MBb1pz8knwUlRdp3vPCskKnl52YqBjEirGahoenc3uDsvkQkYAi10bRqP6Ni4qDKIhOhgdwLftVz5Y9MTx5OLae2Yrxa8ajsKwQkk2qPSmJB52Ysn4Kfin7JWTXHO5INor5IIiGgoyPRoC3jCaOGaxCkd3EtS1l1WVqW5p6dpVIxpuMzdw2EwmGBHXFZsd9wb7nlM2HiGR86bsNOgNKLaU+y7O3Op/84knSiRqqrddiPixkfBBESCHjoxHgLaOJYwarJXcugQBBDcp1DNDVCtatSwCvKIhYfMdiyq7SyPAmY4vvWIwySxkEJmDmTTOxZfIWzLxppt39aWwuAHhdh6A+QeKUzYeIVBSZzxmToym/rw973Smr1eI7Fvskz9QH+wbNfBBEw0HGRyNACWp0zWiyaNQiNYNV7rhcdEnogsKyQoxfM151lRm/ZjyKyouQX5LvFKxbnwDeGH0M5o+cT9lVGhGeZCxnbA5Sm6ciOT4ZayauweD2gzFhzQQMbj8Y6yatQ5W1yqMMBSJI3J8sXQQRLjjKfqW1Eu+Oe9dJft+64y3VpVZJmR6j13ZV1IL6YO9wziHZ+LVsV7TOB0GEFDI+GhEx+hi8OfJNJBmT1L9xYpyT4ZGxKQPF5mJM3zwd56rO4ffdf49zVecwffN0FJuL3Xz4lW2+vBwqD9Qp66dgxbEVahtyxubQQ6+R4CpjyguRZJNQbCnGY1sfQ7G5GO/+9C4umi46yZWjDDn6pfsjY1rUN1MWQYQSV9l/dMujkGQJ74x9R9Uro86Idw6/gwd6PIB3xr6DFcdWYMr6KbXqCPXBvmGV7Yl21GxXtMI5QYQUMj4aAY4PnJXHViLv7jysPLYS0zZMw0M9H0Kn+E6aQY2rT6zG4PaDMXvHbLdgx3NV59C/bX91W20vh65BjjuLdqpt8WfEjghPPMnYlPVTkF+Sj7OVZ5G5JRMmqwnDk4cjPTVd/Q04y5BZMgc8SFwxQDbeu5FesoiwxVMweOaWTFRJVVhz9xqsPLYSj255FOmp6fjPif+gSqpSy3nTEeqDfUeqmemI0gkQ2LXfBEGEBkq12wiokuyuLcXmYgCAQWeAxWYBACQZk7Dh3g343ae/w/nK8+oxBp0Bm9I3IX1dunqcI0nGJOTdnYdxeeOc6tp470bVHcBbGxzPEyvGejwuBERcCshwpDYZW3P3GozNGwsAtcqVljw67m9AWQkXSGYbKZ76ScC9z1V+T10/FR+mfei0XUtHGrgPjiiZvVol4aa/bcHDgzpi1Xe/ImNwJ/zPhB5BaiERplCq3QaEZj4aAa5Bt8pLYbQ+Gm/f+TZ00Dn5/ypl5u6diwUjF2gGJr56+6tqsKOyzVuwIgX+Nm68ydjiOxajxFwCgQmw2Cx4Ze8rmDt0rkdZ0JJHx/0kK0RjRRREvH3n216TgygB5nOHzsXcvXORNSjLabsnHaE+2HcsNvuignqBQa9jFPNBECGGjI8GRMn0Y5bM9cv44yHo9qMJH6FDQgcA0Nw/uftk7Dm7B2+OfNMtiLhdTDvsO79P3VabH71WG0Z3GI2N925El4QubmtG1Od6icDiy/3wFtid2jwV7WPbI3tMNkZ3GI0/D/wzjl4+iuwx2W5llUUHuyR0CXiQOMkVES649u3KX5tsQ8eEjliVtspJ9peNXoa8/DzsLNqJaH003hjxBvLy85DRK8NpuycdURYedNUrT31wU0ey2T0+9IIAvSCQ2xVBhJigGB+MsesZYxsYY18yxt5hjGlObzHG/o8x9i1jbHEw2hHOOGY7yS/Nx9+++Vu9FkVzDbr9aMJHqLRWYsKaCcgvzYdNtjntVwyMj058hHYx7ZAzNkcN1lWyF/kbwOvYhrTOacjolYG0tWnq4liu102LwDU8/twPUSeiU3wnZI/JRpIxCdljstEpvpN90bOoGHRJ6IKMXhlIX5eOAe0HoHNCZycZcsy2VlhWqL4oBSJInOSKCBe0+vb80nx8dOIj5JfaswpWWauwKm2VqkcHLh5ARq8M9fdnpz5DZp9MpCamIrNPplcdcTyfo1556oMJQKpJravXMegFRql2CSLEBCXmgzEWB4BzzisZYy8D2MQ53+VSpjeADM7584yxlwBs45zv9lRnY/JFdg0MdBzpyuyTWe+XMIvNgtNXT+PRLY+q9WePyUZqYip0gg6SLKlT8FrflXMro2mO23zBLJlx6uopTN88XT2/a8Ytx+1BDhCOKF/kUKIlh97uh1kyI780H7lHcjFn4BzM3TsXGb0yVLnSqkuZ6RAgaN57Zb+/Mlaf64gASGYjFE99+9HLR9G/XX88tvUxdXvO2Bx0TOgIPdOrOuL4V9EJb/2wJ9nvFN8Jp8tOu/XBQdSJiJLZggvlGP3GV3hmVApW7T+DwV1b4o0pNwephUSYQjEfDUhQZj445xWc88qanxUArmoUGwpgA2NsBYCNNb8bPZ6ynTz35XNIT01H9qHseo3c2mSbk+Gh1J+5JVOdAYkRYyDq7A8yre8KWtt8ub7CskL1oaecP/tQNvJL8wOa4YioO57k0NP9UAyPzC2Z2HpmK8bljcPWM1uRuSUTRRX2dWK06iosK3QzPFz3B9LwqO06CCJYeJLFvPw8J8ND2T5983ScKT8DnaBDjBgDo2h0+qvohKd+2Jvsn7x6EssPLyed8IAS42Gf+RAo5oMgQkxQYz4YY4kAruecH9bYnQS7USIAKAXQQuP4xxlj+xlj+y9duhTMpoYMSZYw44sZ6kNBwWQ1Yc6uOZgzcA5e2PkCJLmOxgdseHr705r1z9o+CzbY6tx2X9C6PoPOgDkD52DW9lma7ZrxxYw6X2+4ESky63qfDDoDAM/3wwab5v2TuYzmxuaYuW2mx3trgw3P7ng2KPfemz41JrkKJpEis+GOt77vmR3PBFxGvcn+rO2zMGfgHFWvA3G+cKK+Mqu4WekFBlHHVDcsgiBCQ9CMD8ZYFIC5AF7yUKQUQDPO+VQAiTW/neCcL+Wc9+Oc92vVqlWwmhpSvGUkmTt0Ltbmr8Wqu1YBfnrDKQGOXOZYfMdizfoXjloIHXT1vQS3czqOpGldn5JZa+GohY0+E0ukyKzjfRqePByb0jdhePJwzftRVW1fZ2D5mOVu5QUmoMRc4lHmgp3dijL81J9IkdlwR0sWB7YbCAaGRaMWacro4jsW+y2jSr8rQPAo+wtHLcTcvXPVrHTK9saiE/WVWTXgXCdAJzAKOCeIEBOsgHMRwHwA8zjnVzwU+w7AhJrv42t+N3o8ZQ1S/IIHtB+AtLVpOHn1JMyS2ac6XQMOvz3/rVumISXmwygaA3IdngJ8PV2fEjwZ6AxHRN1Q7tOqtFWY1mMa0telY1qPaViVtsrpflRVV+Hk1ZOYsGYCzDYz8ibmOZVfOWElkuOSkdrc8701ikaPmbLqe++9ZeEiuSJCiassDk8ejmk9puHedfciWh+Nt+54y0lG54+c7/fif56Cy90y0NUEqpNOaKMYG3qB3K4IoiEI1szHnwHcCWB5Tcarya4FOOf7AEQxxr4G0BHAtiC1JexwzUyVPSYbJ0pOYED7Acjckolic7Eao1GbAeLo96sc1715d3x79lunrETBMDyUc7r6Erten9ZLaCAyHBH1p8pahdk7ZqPYXIzZO2ajylp1bV+N4aHI5CObH1hpETQAACAASURBVEGJpQSrT6xWy1tsFugEncd77ui3Hqx7H8y6CcIfHI36B3o8oOrWtA3TYNQZsWjUIiQZk7Bo1CKsOLYCU9ZP8TkOQ6vf9ZQ1jvpa7zgaHzqBQbJGxmLLBNFYCNkK54wxA4CfAPTgnPvtdNoYs7AoWUxkWcYv5b84BWkDtc9YaAUcKscpMylTe0yFAAExUYFZ2dbbOV0fcJ6ytNQ1i1YdiagsLKGitvvYIa4DCssKkbklU1O2Pjz+oebaA7Xd22De+xDLVTAhmY1gJJuE/JJ8zf58+ZjlSI5PRlF5Ef596N+1rt/hWKc3ffWUNS6EOhFRMrv16AU89t5+vPK7Xli1/1cIjOGTp4YEqYVEmELZrhqQkC0yyDm3AOhfF8OjsaJkMWEC8xiw+/zO5z0GidcWvH5P6j14aMNDAVUxfwJ8PWVpqUsWLSKw1HYfmcDw/M7nPcpW1uAsGHQGt/te270N5r0nuSLCAUmWNPtzmct4evvTsHEb/rjzj046VFuSkdr0VYZMfa0fWNVsVwJEHS0ySBChJqQrnHPOi0N5vkhBB51mMPboDqOx9u614DJXA7sdg7wFCB4DfV8f9jrW5q/F+xPeh+Bwm+u6CrRjkKO34OLGEMzYFKgtUJvLHPOGz/OYGCFrTxYsNosaNCtzGWbJjApLBSqlSlRYKgC4yxutQk40ZsySGTKX3fpIJUnDolGLMHfvXGQNylJ16Jmbn8HHEz+GLMsedUUURI/97uI7Fjv18UTtKDEeOsG+yKCFsl0RREihHisMMIpGpCamOgWJj+4w2ml12r998zfkl5xAUXmRfeXcknycqzyH94++7xZcPn/kfMRFxWFkh5FOq9vWdRVox+NOXT2FxKhEp+xFFMwYeSi+6Tljc5zuY87YHKQkpiDOEIduzbqpGa6U/dljspGXn6e6i+SMzYFBZ0Da2jSUV5ejsKwQE9ZMQGFZIcot5dfkreQEzBUXUFB83P67+DiksrNAFY1HEI2AqmKYKy4gvzQfaWvTYNAZ1ABzJfA8fV06TFYTZt48U9WhZ25+BgPaD1B15qMTH6GgtABmyezUV9tkG2L0MW79bvaYbLx/9H1awdxP1GxXNcYHzXwQRGgh4yNMcDRA0jqnIaNXhlPw+cSuE7H88Ds4V3UOvVv2xvTN03Gu6hwqpArkHslVg8vfHPkmVhxbgYxNGbhouojeLXsjY1MG8kvyUVRe5DFI3BOuQY7TN0/HLxW/4MeLP+LNkW8iyZikvrCS4RF5xOhj1Pv45sg3nbLviDoRsfpYdf+iUYvQ3NAck7tPVssbdAYs/nExXrv9NZyrOucks6fLTmN30W67vG1+BPlV55F9JMf+e8ujKCg9BcnqW0Y3gghnzLKE/KrzqvwrAea543KdAs+f3v40LDYLZt0yC8/1fc4tyUjf1n3xc+nP6oKsxeZidYHWKeunYMWxFao+Lh+zHLlHcrH+9HpaQNBPrI7ZrnQCrfNBECGGjI8wwiga0a1ZNzzU8yGnQF/HFdBXn1iNqTdMRf+2/TF7x2xMvWEqUpqnQGAC8u7Ow8pjK7GzaCdMVpO6v3/b/qqx0r9tf7XO2h5YnlbQnb1jNvq06oODFw8i7+48v9NFEg2Pcm+nrJ+ClcdWqrKjZN9RRl5/v+H3WHlsJT6Z9Ami9dFI/yzdqfy0DdPwfN/nESPGuMls5pZM9G/XHzNvsvu/Z259DOmp6RiePNwuf18/jwLLFXphIiIas2RGvukCMl1WMM85kgOrbHVaYFNZ2bzKWoVh1w1z05nHtj6G5IRkfHv2W5isJgxPHo701HS13M6inar+McZQLVerx5IB4juSi9uVMhNCEERoCFm2q/rSVLKwVElVGL9mPIrN7u4oScYk5N2dh6nrp+LDtA8xLm8cmhub45NJn2DCmgmolCqdFpVyPGZc3jjEirHqd6VckjEJG+/diBjR3YDwpS2TPpkEvaD3WEcYEFFZWEKF67016AyqTLSNbavKVLG5GAadAZvSNyF9Xbpb+ZbRLfH5PZ+rZV1JMiZhw70bMGb1GJRVlznJo8Vm8Sp/TRiS2QiiUqp0k38tnXEkyZiENXevwdi8sZp99oZ7N+CutXfhPxP/o1mHQWfwuz8PMhEls+/sOo2/fX4Uyx7uh9UHirC38AoO/nVMkFpIhCmU7aoBoZmPMEMURCzxsBru3KFznYIVBSYga1AW3vvpPbx6+6sQmKB5jFL21dtfVYMclf3egsRrW409a08WJFmiQPMIxPXeOsrE/JHzoYNO3W+xWZC1Jwtzh851K18pVaLUVOpx9foFIxfgvZ/eQ1l1mWaw+pJRi0h2iIhGK2GIxWbB3L1zsWDkAo9B4iXmEs0+e+GohVh5dCUumy676Z1CXfpz4hrOiwwyWmSQIEJMrcYHY+xpxtgzGp+nQtHAcCPY2XpEnYgUQwvk3j7PKbDwjRFvIC8/D5O7T8aHxz/EvvP78ObIN/Hh8Q+R81MOEqISsMjBaInWR6v7953fh5yxOWgX0w77zu9T99cWJO5p9WjHeinQPDKpbWVw11XJdxbtRF5+nltyg+wx2Zh3YB6qpCrNffvO7cPig/YsPdmjlzkFq+fePg8phhYkO0REojwLdIIOqdFtkD16mZP8p6emY9+5fW56kTsuF6nNU5Ecl+y2b9noZSgqK8KA9gO86l1d+nPiGlbZIeBcRwHnBBFqfJn5+BHAwZpPhsPvQ8FrVnhS12xR/iLqjUhJ7IrcMcvVFco/O/UZHu39CNrFtMPhy4fVh4/y3agzYk3BGjXw3HF/7rhcpMa0R7JgUOvMHbMcKWIiREu597a4rB7tWi897CIXUSeiS0IXVWayx2SjS0IXj6uSZ/bJRGpiqtOqyd2adUNGrwz86es/oV1MO6e6OscmY8h1g+xlx76D1Ji2yOw1/Zr8JXaFqHdfPJMgwh3XZ4FOiEJqTFsn+f/s1GcY0H4AusW0u9bvjn1H7TMdk4woxxy4eACdEjs56ZmW3qU2T0VyfDKtYF5Hqq3OMR9WG0ekuKATRGPAr5gPxth2zvmoILbHIw3ti+wafB2KkSZldVoBgrp6LQB1xVrH7zbZBhtsYJwBDG77xbKzwPw+kFLHQZq0EOKnsyDmbwKePQQ07+hzW9zqDf+HXUT5IocSRaazD2VjzsA5mLt3LjL7ZLrJtOsqya6/zZIZNtig270Q1g6DwK7vB/7rfsS9dzekPx6DFJ3o8VhCE5LZMMbbs0Dph7lkBhON0O1eCOOXr17rd21WiAntnepT9Mex79bSFS3dCSN9iiiZfX3zcby9sxAfPDoAn/zwG1bt/xX5L49HlJ480ZsQFPPRgOhrK8AYSwOgg/1GtWKM3V2zy8Y5Xx/MxoULnrI+ZWzKCKoBIuq0HyiO2xxHqT3V4fQ7fxPEN3oDfqY4dW0LvThGNq4y/dVvX8Fis2DX2V1uMq117x1/G0UjUPIr8OWr9g2JHYDSM/ayNgmiGOPxWIKIJHx5Fhh1RqDiMrDgVsBcCsCh331qn1udRlF79q82vfO0jagdycahF+zvnnodq9kmk/FBECHCF01rXvNJBPBPh9/Ng9iusEKSJcz4Yob6sFEwWU2Y8cUMSHKEpTaktRWaPK4yrQSt1l2mHXymKy4GqJUEEV749SyoMTxUqN8NG6qt8jXjQxDUbQRBhAZfjI9bOefvanw+CHrrwgRvWZ+aSnaRYAfaE6ElWDItpY5D1XOHIaWOC0QzCSKsCOWzgPrc4GGVZeg0Zj4IgggNtbpdAbg56K0IcxwDb0MZ84GKi4BWQLghHohrbf9eVey5TExSQJqhuBrM+GIGlty5hAIbGwE+ybSfsiWljkPB0Kcx4/P7sGToy0gB4FFKXOvWRwHVVQAT7J9azkUQDYGoE5ESbQ8gz9jy6DW9GbMcKYaW9tg6AOA1L7IP/AcQHQwV2QqU/FKrXFOfG1wkK4deZ+9nlBkQSrdLEKHDF+OjN2NsDewxH0p0OgNQzTmfErSWhRmOL2sheyBYyoGFt7pvn/X9NePDUg7M7+Ne5tlDzg83Q7x9myuGeK9NcPVxDnacCxE6apVpX2ULgGRIQMHoF9UXsoyvn695IUvQNkBc6874HMi9y6dzEURDIlrKkLL1ZeTePg8zvnkRSwa9jJStL0Mc/PQ1GX7+uF12Zat2H+5FrqnPDT6S7OB2VWOE0CrnBBE6fDE+jnDO7w16SyIA5WVt470bwyG7iH/EJPn9EtdQgfZE6AiETEs2CQWmc6rhAdTIyZZH7XJiiCM5IRoVYv4mpADYOOk/1zIHDn76WgFrtT2LYMkvftVLfW5okGxcdbsSlZkPivkgiJDhS8zHiaC3IoIQdSJixJgm8QBodIH2hCb1lWmSE6IpIuZvQswbve2GR4AgXQoNVtu1mQ8dxXwQRMip1fjgnD/JGGvPGJvFGPsHY+wFxliHUDSOcMCXxdgCvGAbBdoTvkByQjRZfMlgpfTLPvTPpEuhQbI5BJwr2a7I+CCIkFGr8cEYewjAUgDHAOQA+AHAQsaYhoM2EQx8ySAUjCxDjjEBysMwJIH2RL0JZqYcSSc61U1yQhDaSDoRVc8dhnnE//jUP5MuhQbJ5h5wLpHbFUGEjFpXOGeMHQAwgHNuddhmBLCNcz7Ey3EpANYBuI9zfkRjfwcAuwGcqtmUwTn/2VN9EbnyrqdsQWI0IJnctysZUJTj9Ea7D7BUghnbn8aSUYuQIja3P4CUETddFCTZplFGD1RXXssepI8CJLNzJiFf2oKIz7wSUSvv1puqYkhWMwosV67Jg6EFRL3RPeantmxWrvu5DEkQUWAtxYxtT2HJHW8hRZ8IUZbssuooh+q+asCxj4mKBawW9+xWlO3KkaYls+GKJ/3QG4HqCvt3x/6T1dw2zgExxq4P1lK8d/QDTOsxDbO2z/Kujw5EYJ8bUTI7bdleXKmoRtbdN+L4+TL872dH8cGjAzA0pWWQWkmEIbTCeQPiS8C5DKAnY073iQGI8nQAY0wHYDaA9V7OIQDI45zP9q2pEYinbEGzvveeAaXmOOnJr1BQXYaMr5+/FsB7+zykJHSGWFOvxzJRCRDfHnatbk/ZhGprCyI80L6JIVnNKCg95S4PiV3ds07Vls3KJUmBVHbWue7Nj9jr3rUI4uCnIebehZTUcfYg3PJzEBfd6F73U98Bb93mfK7mHQNx6QQRWHzpvz30q0q/nH1qLdJT05G5JdM5CUNsiuc01KA+N9hINlld30PUKW5XtoZsEkE0KXwJOP8UwD0un9/VbNeEc27jnD8FoMJLvRzAWMbYDsbYy743uWkgpY5DgQD1RQ+AmsK0QCqBlDrOexkBAXfBaiqB9pGKZJNQYLmiLQ+WK/VywfJa99CnIRkTADgE4ZrL6n9BBBGBKP2yYng89+VzbpmrCkoLatVH6nODh2Tj0DEl5kPJdkWpdgkiVPgScP4y5/x/NT71NRjOALiFcz4SgI0xNtG1AGPsccbYfsbY/kuXLtXzdJEEgzRpIWbseEYz68mzO/8IadJCSJMWeSwzY8czkCYtDHgQOuGdhpRZSZYwY/vT2vKw/el6ZcrxWvc3L0JK6nJN1nwJwiXChqbbzwYBvRHSpIV49qv/wpyBczBn1xzKXBUE6iuzjjMfSsA5ZbsiiNDhS8D5Sg/b65VfkNtR3lI2AOiuUWYp57wf57xfq1at6nO6CIND/HQWloxc4Jb1ZHSH0Vh791oI370D8dOnNctE66OxZOQCiJ/OohfBENOQMisKIpaMWqQtD6MW+ZwpxzWgvNa6B70MsbiQZC1Cabr9bBCwmiF+Ogvzh/0Tc/fOxdyhc33OXBXMJBGNjfrKrGSTVaNDT6l2CSLk+OJ2NZ4xtr3GPWq78h1A//qcmDGnyOf7AeyrT30NTlWxfUEpx49srf04D4j5m5AiA7m3z1MfXqM7jEZGrwykrU1DYc/xwKg5SDG2cioTrY+2++HL9jrwwH/sfsnx7TyfLONz++eB/1zbJlvt11BVXOdrIEKLqBORYmihLQ+GFj65b0ip41BguYLxa8ajoOQEpLKzQMkvEMvOIiW6NXLHLHeve9ciiIbEa3KU8TkQ28ZdpgBA0Dn/5vTAJxoYrb675Bd32XzmR3vMEnCtX1Xk3KEPFfM3IUVMROaNf0Befh7eGPFGrZmrlADz8WvG++SSRdQPx0UG1WxXZHwQRMjwJdvVh5zzqRrbN3POx9Zy7F8AfMI5P6yx7yYAS2APaN/IOX/FW11hn4Wl5Bf34MQH/gMkdQUEl5h7n7NdRUGy2TMIvXdsBabdMBWZWx+DyWq69uLXrDPA4ZztKqo5RCZcO8fCW+1tEWtG4GLbAJUX7N8lE7DiPvv3p75z3x75AcERlYWl3tQj25WkE+1xHTUrlTsZF/mbgIzPIe1ZhIKhT2PGNy9iycj5SJEZRF00oNMDC25xb8+s74Hys9d+J3UFik9d+92im3fDuGnStGS2odHquwHg+eP2lcoVZKu9L33mR7thopWoQ+lDJROk75ajYPSL9r5byXalkbnKdVXzCE2tG1EyO/S17ejcIhYzR3bDVZOEJz84gL9PuhEPDeoUnEYS4Qhlu2pAas12pWV41Gz3anjUlPm78p0xZgDwE4AenHOJc34QwGA/2hp51PXl3SHLkAigi9QSD/V8CNM3T3cL9s0dsxwpSTcgBW20M6OU/HKtLQqeMl9VXtDeTkQOMUkQAaTYWtWeKcdBztQXoBrDA3CQsdvnIQV2WRTzNyEFsGe0+nSWapQgto32OWSbs0y5yt6zh+p7xQQRHKzVzn33lRqjecHNdjnWwqEPFQGk3PUGXhr0EgQImvroangA14LSI9AAiRicFxmsCTi3UcA5QYQKn2I+GGPvMsbm1gR5jWGMpdYYEz7DObcA6M85p/lkP5EhY+a2mV4DiSkzCuGIv/IgyRJmfDHDc0D5pIXq+htqRqv8eoV9EUSjR7RJiBFjYBSNmvroVe8oKD1oWG38WsA5xXwQRMjxJdvVNAB/BLADQEsAMwBsBXDV35NxzimAoA4EKpCYIDwhCiKW3LnEc0D5p7OcfeApuJwg6o1XvdMISicCg33mQ1nhvCbbFa1wThAhw5eZj1UACgC8CLvxkQdgHID44DaNUBB1IlLE5tqBxMqK5wRRD5RFzXLH5WoHlNMsB0EEHI96Ry5XQUWSuepuJTC78381zXwQRMjwJeBcANAWQG8A/Wo+fQHEcc6TvB0bSMI+ENIleFdFCSKvuHhtvxgDSFVAVCxQXel+TFQcEO/iQ19+we4f7BhYLjaHKFcDyj10DGSPirH7LOuNQHUFwJhzOavZ/psJ9g+XAdHoHGDp2P7IJaICIQOOo9w5Yoi3J0Jw2SfpRBRUF2PGtqew5I63kKJPhGiz2HcqcqvIkvLXNYGCo1xHxdXImmz/KHLp2I7Ilq9g0LRlNpho9dNK3wdml9va5LxmcTrojc6/ncrU9Ks+yrcS+zHjixmaQekRQETJbLc/b8CEXu0wtX8HAMAf3tmH6UP/f3t3Hh9VfS5+/PPMlj0hK0gAWURQEKkJuKHiUreq2Gpdcb0uqJdq9br87KJtvdalt61LtYq3eluXaqEurWuL4i67oIIgKAIKhCWEhCSTWb6/P85MmCQzySSZ5UzyvF8vXsxylidnvt8z+eac5/kO5/+dtF8yQlT2pAnnadRlwjnwF6yKVDXAhtDzO4F1yQsrA0Uk70blrd9THSWcdHvNQvjDpI7LzlwCtBt8+Jtx3zeB0fueyKtn/hP37Ms6/jU6Mpk3/HjmkuhVWa5dDiUZXcVKxSOy3UWaucQafLSr8uMGRl+/0kqObdqJ+7cRX8axChW0fz2yXWd+tTTVl3jro1e2unb5nmpW8bbz8PPI169dDqWjuh1W+ApIl0UiVK8ZY9rkfICV9+HTGc6VSpl4ql2d39n7IvKKMebkxIWkOuNe/RruTctBb4NRSeIO+HC7c6FBZ7tWKlXcTh10pII/aA0ynBIx+HCIJpwrlULxTDLYlZyuF1EJpROzKaWUUt0WHmS4nHt+/XE5HTr4UCqFEjH40GuVSimllLI9X2g+j3DCefhxi1a7Uipl4sn5UIngztkzMVV4QjZHJ2O/8IRWWQWQXwEuT8f1lUqkH30MAZ917/v2tV0vH4vDuaetujyJiU2pZDn/b1abD5+j4z2/5g20li8Ztae9B/3WxK5aSMG2Wq98RAw+3E6HVrtSKoUSMfhoTMA2+j6/d09S4vl/s76sPPlWcm57nlz4n7HW45lLrMGHr7nj+gCFlW2rWM1csmcb1y63Xos2i3SWVkruF7IK9rSJ9q87XG3bRtDftgBCuJ0VDIb6b6FomNVeHU7rebjthdtdMGCtJ449bfVHHyfvZ1Oqu7IKOp4Pw4nm1yyw2m243RdWQsBvtXew2vk1C8Hpgl3fwM518NQP96wX6drlOviwKX/oyofTqTkfSqVLjwcfInKJMeZxY0yUsiCqA4m4yvHUD63/O6uqEs/6oFWrVOfyK6x/sUT+gtT+ake4nV2zMHqVn7Brl1sDmWhVtSQRd3YqlSDRqhLWfm39v7vG+j+e83Pk6+H1VEaIduXD5ZTW27GUUsnXm98MLkhYFEoppZRSSbZn8BGRcK5XPpRKKf2zpFJKKaX6hfAVDmfElQ+nw4FXE86VSpkub7sSkWex5h5r8zIwLikRZZKuZjWPpOVxVbq1b69OT9uZnMG6pcTX1PbWPqXsqjvn4FjrBv3JiU3ZUtTbrvTKh1IpFc8kg2enIpCM1Nlsue2/+NzZHXM5ioZZibr131pJva3LRiSOhxPDoyVKRr6vVFfat9dY97TPXNI2Sd2TZ7U9E7T+hYsZhIXboLZPlWrdOQfHWjecYB5ZUCEY2FOsQRzWv2jt3wStc3v7tq/t3rbCgwxnu5wPrXalVOp0O+FcRMYAxwBTupr9XEXwt3SdvHjtciiOkTweLVFSqWQpHdX9dbR9qkzU/ipf+Jx87fKe9QNla63zfEROMuhwsNurV8CUSpV4brsagTXYOAIoBMYAtwAzkxuaUkoppVTiRLvtyukUzflQKoXiSThfChwK3G6M+QGw3BjzD2PMjuSGppRSSimVOOGZzN3O9tWutNSuUqkSz21XlcDRwHUiUglMEJFpwDvGmNqkRqeUUkoplSBevzUZqrvNJIM6w7lSqRRPwvlu4J+hf4jIUOAE4H46metDREYDLwE/NMZ8GmOZe4CjgMXGmKu7HX0qhSujuLKgZbf1WmRieDgpEaInG0YuG8mTt+d1E7AmevPkWjki4WRGf0vbdeKp5KL6pq6q+7R/3+WBlsZQ2zR72po7x/p/5pI91a7C/7tzrYnXIhNsI9ugtj9lF7EKcbiy206a2dq2c6xqbu4c63zb/pzszgHEet3ptrYRPrdru+8TvFGufLidWu1KqVSKJ+fjt8aY68PPjTEbgMdC/2Kt4wSuA16OtQ8ROQBwGmMOFpGfi8jhxpj3u/sDpEy4Mso1C+EPkzq+P3MJlIyIvb6vKfoM0NEqDkW+Fu39eCq5qL6pq+o+8Va0inx95hIrsbb2a7g/yrajzWiu7U/ZQaxCHNvXdn6+7axfQPT3tN33CV5frNuudPChVKrEk/MxsbsbNcYEjDHXAA2dLDYFeEVEngJeDT1XSimllEoKbyA8+IgstetozQVRSiVfPDkfB4jI37EmFgxnZAnQ0ss5QEqAOqwB0E6gtP0CInIFcAXAsGHDerErpVJD26zKNNpmVabpTZv1+sI5HxFXPpxWwnkwaHBEVMFSSiVHPIOPT0NVrhJtJ1BkjDlXRCaFnrdhjHkUeBSgurpaS1Eo29M2qzKNtlmVaXrTZqPlfGS5nAA0+wPkero9/ZlSqpviue1qVZL2vRA4OfT4pNBzpZRSSqmk2DP42HOFI9tt/Sq02xtIS0xK9TfxDPF/HX4gIh5jTEvo8WRjzIIu1g2E/nVgjFkgIheIyLvAauCOOGNOj3BVFVdW9KpV0SpcRVs/Uria1bXL91QWAqu6UPi18Pvd2Zfqu2JV9wm3ifbvuzyhilahvzOE21hk9bVY60ZWu4p8Xdufsrusgrbn6chqVzOX7Pk/skph+HwL7daNqHalMl6LP4jbKYhEDD5CVz4aW/xAVpoiU6r/iGfw8TjWDOcArwDHhR7fFfF6VMaYX4Ufi0gW8BmwnzHGF3o/c2ZJj1VVJVXrKwVdt6PetDNto6qvyK+w/inVjtcfaHPLFUC22xp86JUPpVIjntuuerN8K2OMF5gcHngopZRSSqWS1x+MMviwnltXPpRSyRbPlY+hIjITq8LVUBH5UejxkO7uzBizo7vrKKWUUkolgtcXxONsW9Gq9cpHi175UCoV4hl8XAI8B9QDNwHhAcSlyQpKKaWUUirRWgIdr3xkuUJXPrx65UOpVIhn8PETrNyOwcB5xpjLkhuSUkoppVTieX0BXLFyPvTKh1IpEU8Oh8sY87kx5k1gRLIDUkoppZRKBm+o2lWk8OBDcz6USo14rnwMEpHTsPI8KkKPAQLGmJeTF5pSSimlVOJ4/dGufOg8H0qlUjyDj7uA4tDj30Q81j8RKKWUUipjNLYEyHa1HXx4nA5cDmFXsxbjVCoVuhx8GGOeSkUgSimllFLJtNvrpyy/7USCIkJRrptt9d40RaVU/9LjeTuUUkoppTJJY0ugNccj0oAcN1sbdPChVCro4EMppZRS/UKswUdRjputeuVDqZTQwYdSSiml+jxjDLu9/tYE80hFOR5qdPChVEro4EMppZRSfV5LIIg/aMh2dbzyUZzrZnuDF69fK14plWw6+FBKKaVUn9cYKqUb7barYaW5BA2s3FSf6rB6Z+MieOgw+FU5PHkGbPks3REp1SUdfCillFKqz2vwWjMERLvtap/yfAA+Xl+b0ph6ZdMy+PNp0LgNRp9gDUQeOxZWv5HuyJTqlA4+eiAYNGytvQK60wAAIABJREFU9/JNbSNb670EgybdISnVp2gfy3z6GSq7aWyJfeWjJM9DWb6H+V/tSHVYPRPwwwvXgCsbTroHDr4STnsACivhuemwYWG6I1QqpngmGVQRgkHDqi31XP7nRWysbWJIcQ6zLqxmzMACHA5Jd3hKZTztY5lPP0NlR7tbwlc+Og4+RIRxg4t4f+02gkFj/3a67GnY8gkcdQvkllqv5RTDcb+Al2+A2ZfAjPcgZ0B641QqCr3y0U3bd7e0fqECbKxt4vI/L2L77pY0R6ZU36B9LPPpZ6jsqK7JmsE819Nx8AEwuiKfXU3+1nZrW8EgvH8flIyCvQ9v+152ERx5I9R/C//8cXriU6oLOvjophZ/oMOJaWNtEy1aIUOphNA+lvn0M1R2FJ7BvCjHHfX9vUtzAVi5eRcA/kCQD9duJ2C3WwbXzoXta2DcD0CiXKEpHwMTzoHP/g5r30p9fEp1QQcf3eRxORlSnNPmtSHFOXiilO5TSnWf9rHMp5+hsqNtDdaVt1iDj6EluQjweaji1ax3v+LcWR/x9IL1qQoxPkuftK5w7H1Y7GXGnwEFe8Hrt1pXSpSyER18dFNpnodZF1a3frEOKc7hkQuqCAaDmlSpVDfESkiO1sdmXVhNaZ4nneGqboh6npxehdOBniNV2mxr8JLtdkTN+QDIcjmpKMxizdYGAN5eXQPAO6u3pizGLjXVwqpXYMSR4Iw+iALA6YGJ50PNCvj8n6mLT6k4JC3hXETuAY4CFhtjro7y/jDgfWBt6KWLjTHrkhVPojgcwpiBBTx/9eG0+AMEgoY7Xl7BGytqNKlSqTh1lZAc2cc8LieleR7tUxkk/Bn+/erDaPQG+Grbbn76wqdsbfDqOVKlzbYGb8yrHmF7FeWwtqYBY0zrnB9fhgYjtvDp3yHQAqOO7XrZ4UfAsmfgvd/B/qclPzal4pSUKx8icgDgNMYcDGwWkcOjLOYA5hhjpob+rUtGLMngcAjlBVl4XE7Oe2w+b6yw/jqiSZVKxaerhORwH6sszqW8IEt/Uc1ADocgCNP/dz6XPLGQpRt26jlSpdXW+q4HH4MH5PDltga21nupa/LhEPimtgljbHLF7uOnYcBwK9m8Kw4njD0Fvl0C3y5NemhKxStZt11NAV4RkaeAV0PP2zPACSLylojcEW0jInKFiCwSkUVbt9rosmeIJlWq9uzeZu1C+459JLPN6ueskqGnbXZNTQMDC7M7XWbwgGyafUHmhW61mji0mGZ/0B4D5m1fwDeLYNQx0RPNoxl1NDizYNHjyY1NqW5I1uCjBKgLbX8nUBplmfXAd4wxRwMBETm1/QLGmEeNMdXGmOry8vIkhdpzmlSp2rN7m7UL7Tv2kcw2q5+zSoaetNm6Rh819V6GFud2ulxlkdVeX/t0MwDfGWbNk2GL8rvLngFxwMip8a/jybfyQz75GzTvSlZkSnVLsgYfO4EiY8y5wIDQ8zaMpTn09BVgTJJiSRpNjFWqZ7Tv9A/6OSu7WF1j5W8MLcnpdLnBA6z33/y8hoJsFyPK8gCo2dXc2WrJF/DDx09B5UGQW9K9dUcfD75GTTxXtpGshPOFwNnAXOAk4MP2C4iIwxgTrv92FvBikmJJuGDQsH13Cy3+ABUFHp678lB8gSAuh1CR3/b+dJ8vQE2DF3/QtL7vjqi0EbktTaxV/UW0pPIB2S4272rGFwjidjqoyM/C5Yr+95H2/aY4x01tk6/NtrbubolrW13RPtq1yGOUm+Wg0RtsPecV57p59opDcAgYhPI8jx5PlXKrNluDjyFdXPkozHFTmO1iV7OfkWV5FOdaA+Wa0BwhafPFG1C/Gaov6/665WMhf6CVrD7xvG6vXt9Sz6LNi6hrqWNg7kDGl42nwFPQ/TiUCknK4MMYs0BELhCRd4HVQLScjgNE5GEgCLxqjHknGbEkWmSVnvL8LG46cQw3zl7eWrHnj9OrGDuwAJfLgc8X4POaBq56cnHr+w9Pr2JsRT5ut7PLij9K9WXhpHIAvz/I51vqmRHRVyL7UqT2/eb4/Sv40bH7tln34elVPDB3dWsVuljb6or20a5FHqPDRpYy/dC9ufqpJa3H6+4zJvB/H3zFRYeN4J1VWzh14pA2n5UeT5UKX2ypJ8ftjOuqW0VBFrua/Ywoy6cwx4VgJaun1eInILcUhkzq/roi1kzoK1+Cxh1xXznxBXzcv/R+nvn8GbyBPT+/x+HhqKFHcfo+pzOlcgoO0VkbVPckrcUYY2YaY44wxvyHMSYoIlkiskZE3KH3lxljDjPGTDHG/Hey4ki0yCo9M6aOah14gHVP6IwnF1PTYHXSmgZv68Aj/P5VEe93VfFHqf6ipsHb+gspdOxLkdr3mzOqhnZY96onF3NG1dAut9UV7aNdizxGlx85snXgAdbxunnOcs6oGsrNc5ZzZvWwDp+VHk+VCl/UNDCkOAeJI1H7tImVDC/NZeqYclwOB4U5brb24PyRMLXrYM2/YNRxVgWrnhhxJAT9sPIfcS1e31LPxa9dzBOfPUH1wGpunnQzv57ya26ovoEjhxzJ/E3zuWbuNZzy/Ck8ueJJ6lvqexaX6peSNs9He8YYr4hMNsb4UrXPZIis3jIgxx21kos/YN1N5g+a6O+HJtnSSjBKWXyBYKd9KVL7fhOrHw6IKKkZa1td0T7atchj5HRIzM9iY21TzPf1eKpkW7d9NyPL8uNadtLwEiYN33N1oCjHnd4rH+/9DsQJY07q+TZKRlkznn/2PFRd1OmivqCP6+ddz2fbP2PGgTOYPGhy63sD8wYyrnQcZ405iyVblvDv9f/m7oV388DSB5i2zzTOGXMOIweM7Hmcql9I6bUyY8yOVO4vGSKrt+xs8kWt5OJyWofV5ZDo74duL9BKMEpZ3E5Hp30pUvt+E6sf7mzytXkebVtd0T7atchjFAiamJ/FkOKcmO/r8VTJ1OIPsrmumYrCrB6tn9bBx7YvYOlTVtJ4XlnPtxO+9Wrdu9atV514cOmDfLTpIy4cd2GbgUckl8PF5L0mc+vBt/KzQ37GxIqJ/G3135j24jROef4U7ll4Dx988wFNfhtUCVO2I7aZOKcL1dXVZtGiRQnfbmSipIjgFHA4HK1JkNESW9fXNvL19kbK8j0YaHN/c3dzPtZt383X2xvJ9ThpbAmwd2kuw0vz9P7nxEnbgUxWm+2uVCVM92Y/7XM+jt+/gp98b39EwOVomzAeT87HH6dXcX/m5nxkVJsNBg3f7GzE6zfkuK3jW9fkY0NtE3MWb+Ciw0bwfx98xcxjRuMQwYDmfPQ9tm6z67btZupv5jHjqFEctW/3y0k/9NYavty2m/dvOaanYfZMwA//dwps/gROfxhyinu3vW2r4eXrYdpD8J3zoy7ycc3HXPTaRUwZPIWLx1/crc3XeetYuHkhy7cuZ1XtKnxBHy6HiwPLD+TwwYdz6qhTGZQ3qHc/Q+LoCSeNUnbblR1F+8UinBz54++OYXR5Pl9sbejwi0eWy8HPXvy09Refpy47mKAxBILgce1pz263k7EV+Tx7xSExq115/cHWbYW3r1SipOqX597ux+EQinJcPHHJZLJdQl2Tn/Mfmx91UN++Upbb6aDZH+BX08a3DuKz3Q5+/YMD+On3Arh6Ue0qWlUurc7UVjBoqGvyc//c1Vx02AhunrOnAMcj06soK/Bw6ZSR/PzFz9ja4OXPl07m71cfhs8f1OOpUmJDbSNgJZL3RFGum5r6ZowxceWMJEQwAK/cAOs/hCk/7v3AA6B0NORVWInnUQYf/qCf2z+4neKsYs4ee3a3N1+UVcRxex/HcXsfh9fv5YudX7Bi+wpW7ljJ/Uvv54GlD3DqqFOZ+Z2ZdhqEqDTo1yUKoiWThpMjL//zImoavFGTTb/e3tj62hsrajj/sfms3tLAcb99m/NmzW+T2Op2O6kszmXv0jwqi3PbDDw0mVUlW6raWG/3s313C+fOms9xv32bJl+QK7tIPg9XyqoszsUfNJw3az6XPLGQsx/9iEueWMjFjy/E6zcMK81j8ICcHpfZbb+v8oIs/UW5nXCxgHBSeeTnduWTi1nxbT11TT6WbtjJxtomLvzTAgTR46lSZv2O3g0+inM9+AKGXU3+RIYVW+MOePpsq8LV+DNh1LGJ2a4IDDsU1r4ZdcLBOavnsLZuLeeMPYccV+fzoXQly5XF+LLxnDXmLG479DbuPuJujh9+PK9+9So/ePEHzP16bq+2rzJbvx58xEomDSdHxkqCzfU4o64TfhxvYqsms6pkS1Ub6+1+4klajtWvupOsrhIvfPxjJf7nepwdkv/1HKdSaWNtkzXnTA8ntywKtd+tDSmYaHDDAvjjEfDlW3DI1XBQ58nh3bb3YRBoseYNibCrZRcPfvwgY4vHclDFQYndJ1CeW87ZY87mV4f/irKcMq6bdx1PrXwq4ftRmaFfDz5iJZOGkyNjJcE2tgSirhN+HG9iqyazqmRLVRvr7X7iSVqO1a+6k6yuEi98/GMl/je2BDok/+s5TqXS5rpmSvI8OHp4y9SAXGvwkfSJBte+CU+cYpXEPekeGHOydbUikSr2s+YLWdF2XudHlz1KnbeOc8aek9RbyypyK7jl4Fs4qOIg7lpwF0+vfDpp+1L21S8TziNnHQf475dXsLW+hR8dO5rhZbk4RPC4HGAMOxp9XPmXPcmRj1xQRUG2i7U1u8n1ODHAsJIcvP4ggpCX5cQXCGIMGMAT+gWoyRcgP8uJ129aZ10uz/OwZtvuNvfJP3JBFWV5njZJ76pXbJ0ImWzJzvkIJ5kHg0G27W5p01cevaCK0jwPzf4gLodQmuNhe1NLa/5TaY6Hel/AyttwOchyQUNzEBGo3e1rvfUq3C/K87No8lk5HmW5brY1+vAFgnhcDnyBYGufbGwJMLQkh+EleW2S1GMlw9twBnPbttn2x2pAtotdXh87Gn3s3O2jotBD0IBDxDqPOoUghmAQRAyrNu9mn4o8stxOzfnoW2zbZgHOefRDaht93H7quB7t45vaJv5r9jLuO2ci0yZW9mgbXdq+Fh45EvLK4fg7ILsoOfsB+Ohh+PJNuPFL8OSyoX4D016YxsF7Hcyl4y9N3n4jBIIBHlr2EB/XfMzvjv4dxw5L0K1l8dOTThr1u4TzaBWonrhkEt6I+8yHFOdw75kTeH7JN5x/yDB+NW08A3LdFOW4eWfVFqqGl7ZJEr/3zAnc89oqtjZ4+dPF1exq8nPdsx+3eX/RVzs4amxFh8pYYyryW5NZA0HDHS+vaK3Qo1VgVG8lM2G6/czWlx05ok3StztUmOGNFTXcfspYqkaUtfa74/evYOax+3aoBBeelfz4/Sv486WTqWvysbPRh8fl4H/fXcsj766Lue4zC77e03cuqG4zuIg1AAN0BvM4RasyNvPYfVn81TaqR5Tx7xWbOGXikDafy71nTiA/y8UDb37BzGP3xSlB6pr8zJg1X4+3SplNdc0MLup5DkNR6MpH0srtBoPw9yusqxzH/Cy5Aw+wbr1a9TKs+Tfsfxr3L7kfhzj4/j7fT+5+IzgdTq6YcAX3LryXW9+9lWdPeZbhRcNTtn+VXv3uvoRos45v2NHUIcH1xtnLufzIkVzz9FIueWIhNfVeLvzTAg4fXcFV7WbwvXH2cmZMHcXG2ia+qW1uHXhEvj/toCEdZv6d8eRitu5uobwgC4/LyXmPzeeNFTWt72vyuUqEZCVMt5/Z+tInFrVJ+r7k8YWts4wfs/9ebfrdGVVDO/TDyFnJ31hRw4V/WkBNvbd1W2dWD4tr3Y21TVz+lz19p7NkeC36EL9oM8tf9eRijtl/L2Y8uZgzq4d1+FxunL2cbQ0trcuOqijUGc5VShljWm+76qk8jxO3U5I3y/knz8E3i6D6MsivSM4+Ig0cD1mFsPIllm1dxmvrXuOE4SdQnJ2AilrdkOXM4uoDr8YhDq5/+3qa/SnIqVG20O8GH9FmHc/1OKMmSkYmvnY1Q284oTLWtoyJMdt5KClWk89Vpol3ZmuAYLv2H++s5JGFHJyhQVO864b7Tmd9S/td/GLNLB/+bGO1gXDC+cbapqjnXz3eKpnqmnx4/cFeDT5EhOJcD1t3JWHwEfDDm3dYZXBHHZ347UfjcMKwQzCrXuU3C+5mQNYAThx+Ymr23U5JTgmXH3A5a2rXcNeCu9ISg0q9fjf4iDbreGNLIGqiZGTia1cz9IYTKmNtSyTGbOehnBBNPleZJt6ZrcHKAejJrOSRhRwCoRyteNcN953O+pb2u/jFmlk+/NnGagPhhPMhxTlRz796vFUybaqz/ppe2ovBB4RmOU/GlY+VL0LdBphwDkgKfyUbeTSvu4N8vO0TTt/ndLJd2anbdzsHlB/AySNOZs4Xc3hj3Rtdr6AyXr8bfFTkZ/Hw9KrWL8AhxTkMKbHyLyJfu/fMCcx650vuPXOClZ8xby33njmB2YvW89D5B3VY9o/z1jKkOIfK4mx+f/bEDu+/uGRjh/X+OL2Kinyr7nhpnodZF1a3eX/WhdW9PmEqlSyRbXbWO192aN8PT69izuINALy5YlObfjdn8YYO/TBy+fb96uHpVcxetD7udSP7Tmd9S/td/Nofq/DnEP5sZy9az8NRzo1l+Z7WZdfW7OpwrtXjrZJpc2jw0ZsrH2ANPhJe7coYeP8+KKyEoZMSu+0u1JWM5K6yUsYGHUypnJLSfUczbZ9pjCwaye0f3s6mhk3pDkclWb+pdhVZpSU3y0GjN9hm1nGgtQKW2+nA5RSaWwLkeKyqLL6gIc/jpMVvVeMJGuuvvS6nA5dAkz+I22FVyWqJUu2q2RcgL1Ttyh8IRp112YZVd/oCW1dhyXSRbbYgx0F9U7BNNav21a1iVbvyuJwUuJ2t77sdgsvpoMkXiLpuWa6H7U2+1r5UnudhZ7M/Zt+JjDPH48QfNK3Vlopz3NQ2+ezU72zbZiOPo4iQ7RaaWoJkux00+4K4nYI/YPAFDU6H4HEIgVC1q7wsB76A2PF4q96zbZt9ev56bn3+Ex489zuU5vdskkGAx979kqUbdrLkZ9/t8TY6WPcePPE9OPQ/Yd/U3vb0s1V/4aUtH/LXbzbhP/1hmkuGp3T/0dQ01vCLD3/B2JKxPH7C4zgdSb0iqiedNOoX1a7iLTdaWZwbc73y/CxuOnEMN85e3qZa1diBBbhcjoSUNA0nBiuVKcJt1u8P8vmW+tZk4vDViH9+vJFH3l0Xsz9kZ7c9BVVmdzwlxdu3yt2xv6jCcSa79HBf53AIpXmeqMdwn7I8VtU0tGkD9545gfKCrDZljwE9z6mU2VzXhENgQG7vrnyU5HnYsbuFxhY/uZ4E/er0wf2QPQBGpijXI+SfWxbwwpYPOa1kImO+/pbty+ewbuoNKY0hmorcCqbvN51Zn8zi0U8e5aoDr0p3SCpJ+sVtVz2taBO53oypo1oHHuFtzHhyMTWhe0C1ao7qz2oavB2qGF0VqoAUft7T/pDIvqX9tPdiHcNobeDG2cvZsKOp9TypVKptqmtmQK6ntWBFTw0J/XHyiy0NiQgLtq6C1a/D2O+BK3WD8U/r1/HLL55m37xKTq08kq3DDqZ09b/w1G9JWQydOXTwoRy616H8cdkfWVqzNN3hqCTpF4OPnla0iVwvVoUdrValFPgCwZgV4yKf96Q/JLJvaT/tvVjHMFYlq1yPs/U8qVSqbd7VTHGuu+sFuzC0xMpTWrW5vtfbAuCDB8DpsWYxT5FVDRuZ8ckfyHflMGPYyTjFwebRxwBQufCJlMXRlen7T6csp4yb37mZXS270h2OSoJ+MfjoaUWbyPViVdjRalVKgdvpiFkxLvJ5T/pDIvuW9tPei3UMY1WyamwJtJ4nlUq1r7c3UlHQ+0pOAwuyyctyMv+rHb0Pqu4bWPZX2Oe7yZ9QMOSd7Z9w4bL/wSHC9SO+zwB3HgAtucVsHjWVstX/ouDbZSmJpSs5rhwuP+Byahpr+OWHvyRTcpNV/PrFN0JPK9pErheudqXVqpTqqCI/q0MVo8gKVb3pD4nsW9pPey/WMYzWBu49cwJDS3Jaz5NKpZIvEOSb2iYGFvZ+8OFwCBOHFvPvlVvY7fX3bmMfPggEYfwZvY6rK/X+Jm5f/RTXfPYwZe5CfjLqbAZmDWizzKZ9j6U5r4wRc+/C2VyX9JjiMWrAKE7f53ReX/c6s7+Yne5wVIIlrdqViNwDHAUsNsZc3dNlwhJZ7ao7FVYi18v2OGjxGXxarSqT2LYKS1/j9wetinFxVqDqjkT2rQzop7Zvs7GOod8fZGuDl5ZA0Kp25XRQkutpc55UfZIt2+xX23Zz9G/mMeOoURy1b3mv97V6Sz23vfQZZ1UP4e4zJiDSgx+7oQbumwDDDoMpP+51TLE0BVr467dv878b3qDe38iJ5VVMG3gIbkf0ZPnc2g3s994DNJaOYtWp9xD05CUttngFTZDfL/49K3es5L6j7+OooUclcvO2Oun3N0mpdiUiBwBOY8zBIvJzETncGPN+d5dJpJ5WkurOelqtSvVnLpeDwQPa3nbTWQWq7khk39J+2nuxjqHL5WCvdm1AqXRZU2Mlh+9VlJgJ9PYdWMDpEyt5btFGKgfkcvFhw7lx9jLmf7WDG08Yw/RD9u56I3N/CQEfTDg7ITG11xL0MXvT+8xa/xrbfLsYn783Zww6nL1zKzpdr7F4KGsmXcToBY8z5h83sebEX+DLK0tKjPFyiIOrJl7FvQvv5Ya3b+CR7z5C1cCqtMakEiNZf46aArwiIk8Br4aed3sZEblCRBaJyKKtW7cmKVSlEkfbrMo02mZVpom3zS7fuBOHwN6luTGX6a6zqodw5Ogyfvfv1Rx211ze/LyG/CwXP3/xU5Zt2Nn5yuveg6VPwthToHBwwmIC60rHM9++zfcW3s6v1z5HqaeAW0b9kOtHfr/LgUdY3aBxrJl0ETk71jFu9lUM+Cppfw+OW44rh+sOuo7i7GKu/NeVvLX+rXSHpBIgWYOPEqAutP2dQGlPljHGPGqMqTbGVJeX9/6SqVLJpm1WZRptsyrTxNtml67fyZDiXLISWFBCRLj8yJGccVAl4yqL+Pkp+/PLaeMozHHzsxc/JRiMcSv7rm9hzuXWoGPi+QmLp9bXwCNfv8rx83/CnWueJc+ZzfUjvs9NI89k37zKbm9v514HsOLIa/G5sxn9+m3s89rPya5dn7B4e6Iwq5BbJt3C4LzB/OitH/Hbxb/FG9Dy3ZksWZMM7gSKjDHnisik0POeLKOUUkop1S11TT4++nI7J44flPBtuxwOzqwa2ua18yYP46F5a3lu0QbOmTys7QpbV8Ez50LzTjjh1+Du3a2JTYEWPtr5OS9t+Yh52z/BbwJMKBjOleUnsW9eZc9yUSI0Fw5ixVHXM3DtPAav+jfjv/6IHaOmUjPuNBoGjYNebr8nCrMKuWnyTTz7+bM8/unjvP7V61x54JWcPOJksl2Jua1OpU6yBh8LgbOBucBJwIc9XEYppZRSKm7BoOE3r6/CHzQcNio1eQtT9injrc9ruPOVlQzIcTDSuY19ZSOsfg2WPwvuXDj2NigdFXV9YwxBDH4TIGAC+IJBvMEWan0N1Poa2NC8jS8bN/F5w0aW7foSnwlQ6MrlmNIDOaJkHJXZ0W4w6TnjcLJ59LFsG3Ywg9a8ScW69yld8ybNhXuxa0gVDYPG0VxYiS+vlKDLg3G4kYCPQFYBxpmcXy2znFlcOO5CJg2axLOrnuW2D27jrgV3MXnQZKoGVjGiaAR75e1FUVYR+e58sl3ZOMXZ68GYSryktBBjzAIRuUBE3gVWA3f0ZBmllFJKqe6Y+3kNf/noa06ZsBcHVKZmHg2AG44fw23/+IwXn3mUhz33WS+6c2DfE6DqYsiNPUB4cM1sHv3qpU63n+VwMySnguMHHsyEolGMKxyBy5HkOYpyitlRfRE7Dzybwg0LKNy4hNIv5lKx4p9RF19z3pN4S0cmNaSqQVUcNPAgVu5YyUfffsTyrct5e+PbUZedfepsxpSMSWo8qvuSVmq3w45EsoDPgP2MMb4erL8V+DqORcuAbd3dfgboqz8XJPdn22aMOTFJ2+5UN9psT9m1Tdg1LrBvbJFx9eU2G40dPxM7xgT2jKsM+NymbdaOx6szmRYvZF7M4XjTdp5VKRx8AIhIiTEmAdODdrqPRcaY6mTuIx366s8FfftnSya7Hje7xgX2jc2ucaWCHX92O8YE9ozLjjGF2Tm2aDItXsi8mDMt3r4qpTM/JXvgoZRSSimllLIvnXZWKaWUUkoplRJ9cfDxaLoDSJK++nNB3/7Zksmux82ucYF9Y7NrXKlgx5/djjGBPeOyY0xhdo4tmkyLFzIv5kyLt09Kac6HUkoppZRSqv/qi1c+lFJKKaWUUjakgw+llFJKKaVUSvS5wYeI3CEic9IdRyKJyGki8oGIzBORPjFbjogMFJHXQz/TCyJSmO6YMoWIDBWRV0LH7k9ik+lbRaRQRP4lIm+JyD9EJLFT7vaSHc8NIjJMRDaEPst5IjI83TGlil3bMdi2rdjqe8Du53ARuUdE5ovIQ+mOJV4iMlpEVorI+HTH0hk7991Y7P791N/0qcGHiOwPeIEkT/mZOiJSCfwAONIYM9UYsyrdMSXIZcCdxpipwN+B76c3nIxSC/wwdOy+BQ5PbzgWY8wu4ERjzNHAH4Cr0xxSKxufGxzAnFDfnmqMWZfugFLIlu3Yjm3Fpt8Dtj2Hi8gBgNMYczCwWURs0bY6IyJO4DrgZcCV5nC6Ysu+2xk7fz/1R31q8AH8F/CbdAeRYOcB3wBvi8h/pzuYBHoXOEZE8oCpwPvpDSdzGGMajDG7Q08bgLp0xhPJGBMQEQ8wBbDDL0hhdj03GOCE0F/j7kh3MKlk43Zsx7Zix+8BO5/DpwCviMhTwKuh57ZmjAkYY67B6gu2ZuPi5Q/RAAAF/ElEQVS+2ykbfz/1O31m8CEiZwMvGWOa0h1Lgo0ACo0xhwN+ETk+3QElyIdALvATYCWwNr3hZB4RGQAMNcZ8ku5YwkTkdGA9MBiwxW0rNj83rAe+E/prXEBETk13QKlmp3Zs47Zix+8BO5/DS7B+IXYAOwG9xSYJ7NR342HH76f+qs8MPoBDgdNF5AngIBG5N83xJEoDezrJS8CBaYwlke4E/mCMuRWYC9yU5ngySuivN3cCP093LJGMMS8YYwYBzwPXpjueENueG4ylOfT0FSDt9/Knkg3bsV3bih2/B+x8Dt8JFBljzgUGhJ6rBLJh3+2STb+f+iW731cYN2PMdeHHIvKCMebGdMaTQB8BRwDzQv+vSGs0iTMMCP/StRvYJ42xZBQRcQP3Ab8xxmxPdzxhIiJmz8RBPmzy10Y7nxtExGGMCYaengW8mM54UsmO7djGbcWO3wN2PocvBM7GGhSdhHWVRiWIHftuV+z6/dRf9ZnBRzvedAeQQM8Dp4rIu8CnWB2+L7gDeEREarH+MnV9muPJJD8BjgP2CxUZedAYMzu9IQFwtIjcDgSBGuDy9IYTld3ODQeIyMNYx+xVY8w76Q4ohezajsPs1Fbs+D1g23O4MWaBiFwQOl6rsWLNFIHQPzuze9+NJhO+n/oNneFcKaWUUkoplRJ9KedDKaWUUkopZWM6+FBKKaWUUkqlhA4+lFJKKaWUUimhgw+llFJKKaVUSujgQymllFJKKZUSOvhIMBHJEpFnRWSeiLwkIkeIyJrQ83kiMkJEpovIpyIyV0ReF5HRoXXzRaROREZFbO+5OPf7XLvn0fZ7gYi8Fnr/JBG5IPT4UhFZJCLviUiFiDwSWmdb6P/fh9Y9NWLbMxN1zJQ9pKPtisjTIuIUkefE8kzo9b+KyE4R+Wvo+c9CbXSpiFwUek3baT8VpR1Ga6sd2kdoXT3PqrRJR9vV86yym746z0c6nQh8YIy5D0BEpmLVwP59eAEROQr4qTHmBRHZD/glcC5wNNYstscAa0OLe+Lcb/vlnDH2O0REBoXed4rIOOA0YDJgAIwxV4aWf8EYc3ro8cWhdcLbDj9WfUc62q4BBgHHA2WAH8AYc46IzDPGnBNazglchjW52gsiMlfbab/mom07fBO4O7KtAh3aR4ieZ1U6paPt6nlW2Ype+Ui85cD+Ys0AGo+9gIbQ4+OwJkOakozAQuZgzfwadgZwpzEmaEKSuG9lb+louzXAAcAnwHeAbzpb2BjTAvwe+EE396P6rsh2GA89zyq7SFXb1fOsshUdfCSYMeYr4EHgIRGZHHr5P0OXK98UkezQa3eIyFvAdOCW0GuVxpgvgcIEhRNtv0uBiRHLDAa+FpE7ReSj8KXWGO4QkXlYJyXVx6Sp7a4HqoHFwCRgXZzrDOnkfW2n/UP7dhitrUaj51mVbqluu3qeVbaig48kMMZ8AlyBdel0b6zL8lONMccYY5pDi/3UGHO0MeZSY8x2ERkGTAjdezk2dDm2t6LtF+ALIHzP6LfAMGPMrVgnweJOtvdTY8xU4LoExKZsKA1tN/yluIT4vxSHAts7eV/baf/Q2g6x2kOs810rPc8qm0h129XzrLIVHXwkmIh4AEKX1Xdg/cUrHicAN4fuvbwF697OZHkGCN/jORu4RUQ0/6efS1Pb3QBUYf1F7iC6+FIM/VVwJvBsN/ahVJieZ1Wm0vOs6jN08JF4VSLyoYi8A7iB99hzSXWeiBwCBEL/Ik0F3gk9fg84IvT4sIh1/6OT/bZfLhBrv6Hba8KPVwD/AN4F7sZKNAvzRTyOjDkYJX6V+dLRdtcBBVh/JR4EfA0gIm8AE0P/E9rnY8BbwGPGmHUR29B22v+0b4fRzndhke1jKnqeVemVjra7Dj3PKhsRzXtTSimllFJKpYJeAs4wIvIykBfxUrMx5sR0xaNUvLTtqkyhbVVlKm27KhPolQ+llFJKKaVUSmjOh1JKKaWUUioldPChlFJKKaWUSgkdfCillFJKKaVSQgcfSimllFJKqZTQwYdSSimllFIqJf4/i4IOrflHarcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 806x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_raw, hue=\"SPECIES\", markers=[\"o\", \"s\", \"D\"])\n",
    "plt.title(\"Iris Pair Plot, Hue로 꽃의 종을 시각화\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>SEPAL_LENGTH</th>\n",
       "      <th>SEPAL_WIDTH</th>\n",
       "      <th>PETAL_LENGTH</th>\n",
       "      <th>PETAL_WIDTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843</td>\n",
       "      <td>3.057</td>\n",
       "      <td>3.758</td>\n",
       "      <td>1.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828</td>\n",
       "      <td>0.436</td>\n",
       "      <td>1.765</td>\n",
       "      <td>0.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100</td>\n",
       "      <td>2.800</td>\n",
       "      <td>1.600</td>\n",
       "      <td>0.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800</td>\n",
       "      <td>3.000</td>\n",
       "      <td>4.350</td>\n",
       "      <td>1.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400</td>\n",
       "      <td>3.300</td>\n",
       "      <td>5.100</td>\n",
       "      <td>1.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900</td>\n",
       "      <td>4.400</td>\n",
       "      <td>6.900</td>\n",
       "      <td>2.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH\n",
       "count       150.000      150.000       150.000      150.000\n",
       "mean          5.843        3.057         3.758        1.199\n",
       "std           0.828        0.436         1.765        0.762\n",
       "min           4.300        2.000         1.000        0.100\n",
       "25%           5.100        2.800         1.600        0.300\n",
       "50%           5.800        3.000         4.350        1.300\n",
       "75%           6.400        3.300         5.100        1.800\n",
       "max           7.900        4.400         6.900        2.500"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_x = df_raw.drop('SPECIES', axis=1)\n",
    "df_raw_y = df_raw['SPECIES']\n",
    "df_raw_x.head()\n",
    "df_raw_x.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd9531d9a90>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_raw_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled Data : \n",
      " [[-0.901  1.019 -1.34  -1.315]\n",
      " [-1.143 -0.132 -1.34  -1.315]\n",
      " [-1.385  0.328 -1.397 -1.315]\n",
      " [-1.507  0.098 -1.283 -1.315]\n",
      " [-1.022  1.249 -1.34  -1.315]]\n",
      "Shape : \n",
      " (150, 4)\n"
     ]
    }
   ],
   "source": [
    "x_cols = df_raw_x.columns\n",
    "scaler = StandardScaler()\n",
    "nd_scaled = scaler.fit_transform(df_raw_x)\n",
    "print('Scaled Data : \\n {}'.format(nd_scaled[:5].round(3)))\n",
    "print('Shape : \\n {}'.format(nd_scaled.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>SEPAL_LENGTH</th>\n",
       "      <th>SEPAL_WIDTH</th>\n",
       "      <th>PETAL_LENGTH</th>\n",
       "      <th>PETAL_WIDTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "      <td>150.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.003</td>\n",
       "      <td>1.003</td>\n",
       "      <td>1.003</td>\n",
       "      <td>1.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.870</td>\n",
       "      <td>-2.434</td>\n",
       "      <td>-1.568</td>\n",
       "      <td>-1.447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.901</td>\n",
       "      <td>-0.592</td>\n",
       "      <td>-1.227</td>\n",
       "      <td>-1.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.132</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.675</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.492</td>\n",
       "      <td>3.091</td>\n",
       "      <td>1.786</td>\n",
       "      <td>1.712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEPAL_LENGTH  SEPAL_WIDTH  PETAL_LENGTH  PETAL_WIDTH\n",
       "count       150.000      150.000       150.000      150.000\n",
       "mean         -0.000       -0.000        -0.000       -0.000\n",
       "std           1.003        1.003         1.003        1.003\n",
       "min          -1.870       -2.434        -1.568       -1.447\n",
       "25%          -0.901       -0.592        -1.227       -1.184\n",
       "50%          -0.053       -0.132         0.336        0.133\n",
       "75%           0.675        0.559         0.763        0.791\n",
       "max           2.492        3.091         1.786        1.712"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = pd.DataFrame(nd_scaled,columns=x_cols)\n",
    "df_scaled.head().round(3)\n",
    "df_scaled.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA Data : \n",
      " [[-2.26470281  0.4800266  -0.12770602 -0.0241682 ]\n",
      " [-2.08096115 -0.67413356 -0.23460885 -0.10300677]\n",
      " [-2.36422905 -0.34190802  0.04420148 -0.02837705]]\n",
      "Shape : \n",
      " (150, 4)\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=4)\n",
    "nd_pca = pca.fit_transform(df_scaled)\n",
    "print('PCA Data : \\n {}'.format(nd_pca[:3]))\n",
    "print('Shape : \\n {}'.format(nd_pca.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Prin1</th>\n",
       "      <th>Prin2</th>\n",
       "      <th>Prin3</th>\n",
       "      <th>Prin4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.264703</td>\n",
       "      <td>0.480027</td>\n",
       "      <td>-0.127706</td>\n",
       "      <td>-0.024168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.080961</td>\n",
       "      <td>-0.674134</td>\n",
       "      <td>-0.234609</td>\n",
       "      <td>-0.103007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.364229</td>\n",
       "      <td>-0.341908</td>\n",
       "      <td>0.044201</td>\n",
       "      <td>-0.028377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.299384</td>\n",
       "      <td>-0.597395</td>\n",
       "      <td>0.091290</td>\n",
       "      <td>0.065956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.389842</td>\n",
       "      <td>0.646835</td>\n",
       "      <td>0.015738</td>\n",
       "      <td>0.035923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prin1     Prin2     Prin3     Prin4\n",
       "0 -2.264703  0.480027 -0.127706 -0.024168\n",
       "1 -2.080961 -0.674134 -0.234609 -0.103007\n",
       "2 -2.364229 -0.341908  0.044201 -0.028377\n",
       "3 -2.299384 -0.597395  0.091290  0.065956\n",
       "4 -2.389842  0.646835  0.015738  0.035923"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca = pd.DataFrame(nd_pca, columns=['Prin1','Prin2','Prin3','Prin4'])\n",
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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