4_final_result.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier, RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>solidity</th>\n",
       "      <th>hist_hue5</th>\n",
       "      <th>std_v</th>\n",
       "      <th>canny_edges_mean</th>\n",
       "      <th>hist_hue2</th>\n",
       "      <th>canny_edges_std</th>\n",
       "      <th>mean_H</th>\n",
       "      <th>feature_181</th>\n",
       "      <th>...</th>\n",
       "      <th>NaI</th>\n",
       "      <th>energy</th>\n",
       "      <th>std_R</th>\n",
       "      <th>hist_hue39</th>\n",
       "      <th>b_sqrt</th>\n",
       "      <th>hist_hue44</th>\n",
       "      <th>NBI</th>\n",
       "      <th>hist_value1</th>\n",
       "      <th>mean_b</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.541988</td>\n",
       "      <td>-0.636399</td>\n",
       "      <td>0.027090</td>\n",
       "      <td>-0.319468</td>\n",
       "      <td>1.110275</td>\n",
       "      <td>-1.034481</td>\n",
       "      <td>-0.374966</td>\n",
       "      <td>-0.971115</td>\n",
       "      <td>0.035082</td>\n",
       "      <td>-0.594504</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.222307</td>\n",
       "      <td>0.260809</td>\n",
       "      <td>-0.589118</td>\n",
       "      <td>-0.250197</td>\n",
       "      <td>1.402781</td>\n",
       "      <td>0.344540</td>\n",
       "      <td>0.007380</td>\n",
       "      <td>-0.142360</td>\n",
       "      <td>1.413693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.461202</td>\n",
       "      <td>0.631790</td>\n",
       "      <td>-0.125862</td>\n",
       "      <td>-0.490414</td>\n",
       "      <td>-0.327106</td>\n",
       "      <td>0.484966</td>\n",
       "      <td>-0.498869</td>\n",
       "      <td>0.597380</td>\n",
       "      <td>0.200445</td>\n",
       "      <td>-0.594504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.253234</td>\n",
       "      <td>-0.256399</td>\n",
       "      <td>0.694681</td>\n",
       "      <td>-0.304659</td>\n",
       "      <td>-0.480920</td>\n",
       "      <td>-0.517704</td>\n",
       "      <td>-1.450854</td>\n",
       "      <td>-0.139556</td>\n",
       "      <td>-0.490484</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.368511</td>\n",
       "      <td>-0.301142</td>\n",
       "      <td>-1.210674</td>\n",
       "      <td>-0.479651</td>\n",
       "      <td>0.168901</td>\n",
       "      <td>0.373189</td>\n",
       "      <td>-0.493399</td>\n",
       "      <td>0.511859</td>\n",
       "      <td>-0.114343</td>\n",
       "      <td>0.425434</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.096522</td>\n",
       "      <td>-0.177009</td>\n",
       "      <td>0.121255</td>\n",
       "      <td>-0.298097</td>\n",
       "      <td>0.599403</td>\n",
       "      <td>-0.514652</td>\n",
       "      <td>-0.748915</td>\n",
       "      <td>-0.137873</td>\n",
       "      <td>0.590018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.510080</td>\n",
       "      <td>-0.717689</td>\n",
       "      <td>0.184322</td>\n",
       "      <td>-0.504343</td>\n",
       "      <td>-1.299392</td>\n",
       "      <td>-0.576578</td>\n",
       "      <td>-0.501485</td>\n",
       "      <td>-0.384116</td>\n",
       "      <td>0.373520</td>\n",
       "      <td>-0.084535</td>\n",
       "      <td>...</td>\n",
       "      <td>2.449737</td>\n",
       "      <td>-0.161403</td>\n",
       "      <td>-0.989295</td>\n",
       "      <td>-0.305315</td>\n",
       "      <td>-1.752813</td>\n",
       "      <td>-0.517704</td>\n",
       "      <td>-3.257197</td>\n",
       "      <td>-0.142360</td>\n",
       "      <td>-1.722720</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.827898</td>\n",
       "      <td>-1.036748</td>\n",
       "      <td>0.983762</td>\n",
       "      <td>-0.497378</td>\n",
       "      <td>0.192236</td>\n",
       "      <td>-1.225424</td>\n",
       "      <td>-0.494350</td>\n",
       "      <td>-1.263919</td>\n",
       "      <td>0.770605</td>\n",
       "      <td>0.425434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907521</td>\n",
       "      <td>0.250593</td>\n",
       "      <td>-0.419422</td>\n",
       "      <td>-0.286286</td>\n",
       "      <td>-1.576289</td>\n",
       "      <td>-0.511600</td>\n",
       "      <td>-1.176523</td>\n",
       "      <td>-0.142360</td>\n",
       "      <td>-1.554277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        std      mean  solidity  hist_hue5     std_v  canny_edges_mean  \\\n",
       "0 -1.541988 -0.636399  0.027090  -0.319468  1.110275         -1.034481   \n",
       "1 -0.461202  0.631790 -0.125862  -0.490414 -0.327106          0.484966   \n",
       "2 -1.368511 -0.301142 -1.210674  -0.479651  0.168901          0.373189   \n",
       "3 -1.510080 -0.717689  0.184322  -0.504343 -1.299392         -0.576578   \n",
       "4 -1.827898 -1.036748  0.983762  -0.497378  0.192236         -1.225424   \n",
       "\n",
       "   hist_hue2  canny_edges_std    mean_H  feature_181  ...       NaI    energy  \\\n",
       "0  -0.374966        -0.971115  0.035082    -0.594504  ... -1.222307  0.260809   \n",
       "1  -0.498869         0.597380  0.200445    -0.594504  ...  0.253234 -0.256399   \n",
       "2  -0.493399         0.511859 -0.114343     0.425434  ... -1.096522 -0.177009   \n",
       "3  -0.501485        -0.384116  0.373520    -0.084535  ...  2.449737 -0.161403   \n",
       "4  -0.494350        -1.263919  0.770605     0.425434  ...  0.907521  0.250593   \n",
       "\n",
       "      std_R  hist_hue39    b_sqrt  hist_hue44       NBI  hist_value1  \\\n",
       "0 -0.589118   -0.250197  1.402781    0.344540  0.007380    -0.142360   \n",
       "1  0.694681   -0.304659 -0.480920   -0.517704 -1.450854    -0.139556   \n",
       "2  0.121255   -0.298097  0.599403   -0.514652 -0.748915    -0.137873   \n",
       "3 -0.989295   -0.305315 -1.752813   -0.517704 -3.257197    -0.142360   \n",
       "4 -0.419422   -0.286286 -1.576289   -0.511600 -1.176523    -0.142360   \n",
       "\n",
       "     mean_b  label  \n",
       "0  1.413693      0  \n",
       "1 -0.490484      0  \n",
       "2  0.590018      0  \n",
       "3 -1.722720      0  \n",
       "4 -1.554277      0  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('main1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(df, test_size=0.3, random_state=42)\n",
    "X_train = train_data.drop(columns=[\"label\"])\n",
    "y_train = train_data[\"label\"]\n",
    "X_test = test_data.drop(columns=[\"label\"])\n",
    "y_test = test_data[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9832\n",
      "Precision: 0.9833\n",
      "Recall: 0.9832\n",
      "F1-score: 0.9832\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
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yAT8tXse0r18+6biOdc8r9TGLcia/tmVBfcHZBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleW+8m1rWQbHEBfn3DMr0AkVYvv02wOdUC5MLjwCrF69mueee47vv/+ezMxMYmJieOSRR3jwwQcLjPvuu+9o3LgxZ599th+zuC88iohI2Slq4dES14VHERERERERERGxTguPgaaFx9ObyVutAnTs2JFPP/20xHHdunWrgBoREREREREREREREREpd8H78YdnhJBAB4iIiIiIiIiIiIiIiIjI6U8LjyIiIiIiIiIiIiIiIiJGLVmyhL59+9KoUSM8Hg9z5szJ35ednc2f/vQnLrzwQiIjI2nUqBH33HMP+/btK3CMI0eOcPfdd1OjRg1q1arF4MGDSUtLKzBm/fr1dOvWjYiICM466yzGjx9/yq1aeBQRERERERERERERERExKj09nXbt2jFp0qRC+44dO8bq1at57rnnWL16NbNnz2bLli3ccMMNBcbdfffdbNy4ka+++or58+ezZMkShgwZkr8/JSWFq6++mmbNmrFq1SpeffVVXnjhBd59991TavV4vV6vf08zGGwNdICIiPzG6kPbAp1wUh3rnhfoBBERERERERERKVctAx1wxjvn3lmBTqgQ2/95u19f5/F4+Pjjj7npppuKHbNixQpiY2PZtWsXTZs2ZdOmTZx//vmsWLGCiy66CIAFCxZw3XXXsXfvXho1asTbb7/NqFGjOHDgAGFhYQCMGDGCOXPmsHnz5lL36YpHERERERERERERERERsSHEc0Y8MjMzSUlJKfDIzMwsk1OYnJyMx+OhVq1aACxbtoxatWrlLzoC9OzZk5CQEH766af8MZdffnn+oiPANddcw5YtW0hKSir9y1cmz0BERERERERERERERERESmXcuHHUrFmzwGPcuHHOx83IyOBPf/oTd955JzVq1ADgwIED1KtXr8C4SpUqUbt2bQ4cOJA/pn79+gXG+P7sG1MalVziRUREREREREREREREROTUjBw5kqeeeqrAtvDwcKdjZmdnc9ttt+H1enn77bedjuUvLTyKiJxBvOQEOuGkrH+G4p70LYFOKNZZkTGBThAREREREREREZFSCg8Pd15o/C3fouOuXbtYuHBh/tWOAA0aNCAhIaHA+BMnTnDkyBEaNGiQP+bgwYMFxvj+7BtTGrrVqoiIiIiIiIiIiIiIiNhg4PMXK+RRhnyLjtu2bePrr7+mTp06BfZ36dKFo0ePsmrVqvxtCxcuJDc3l4svvjh/zJIlS8jOzs4f89VXXxETE0NUVFSpW7TwKCIiIiIiIiIiIiIiImJUWloaa9euZe3atQDExcWxdu1adu/eTXZ2Nv3792flypW8//775OTkcODAAQ4cOEBWVhYArVu3pnfv3jzwwAMsX76cpUuXMnToUO644w4aNWoEwF133UVYWBiDBw9m48aNzJo1i4kTJxa6HWxJPF6v11umz/60sjXQASIiFcr6rVY9hAY64aR0q1URERERERERkWDXMtABZ7xzBv870AkVYvvUW0s9dvHixVx11VWFtt9777288MILtGjRosivW7RoEVdeeSUAR44cYejQocybN4+QkBBuueUW3njjDapVq5Y/fv369Tz66KOsWLGCunXr8thjj/GnP/3plJ6XFh5FRM4gWnh0o4VHEREREREREZFgp4XHQNPC4+lNt1oVEREREREREREREREREWeVAh0gIiIiIiIiIiIiIiIiAuD1BLpAXGjh0VF6+nGmTp3NunVb2bBhK8nJaYwb9wT9+vUMdBoAWVnZTJz4PnPnLiIlJY2YmOYMGzaArl07BDoNsN1nuQ3UF6xtYKcv7+fbHNav28qGDdtITk7jr+Meo1+/HgXGtYq5qdhjXHppO977x5hyLi2oos/f5o27+WreStau3M7BfUeoXiuS1hc25f5HrqVJs+gCY3Nzc5n/0Y98+tGP7NmVQHhEGOec15CHn7mRc1o2KjDu39O/Zd6HP3D4UCpNmkZz5/3d6d67/L4H1q/fypw5C/npp/XExydQq1Z12rWLYdiwgbRo0bjc5j0VVt4bxbHcZ7kN1OfKcp/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W20TOdKfNrVazs7NJSUkhOzs70CkFJCWlMGnSTHbs2ENMTNEf3hlII0ZMYNq0OfTtewWjRj1AaGgIQ4aMYeXKjYFOA2z3WW4D9QVrG9jpS0pK4a1Js9ixYy8xMc2LHTd+/LBCj3vu6QNA167tKyb2Nyr6/M2atojvFm6gQ+x5PPLHm7i+3yVsWB3HQ3e9Ttyv+wuM/duYfzHp1Tmc17oJQ4ffzMAHelGvYRRHj6QVGPfepAX87xuf0vHilgwdfhP1GtTir8++z6Iv1pTLcwCYMuUjvvzyB7p0aceoUQ9w2229WblyI/36DWPr1l3lNu+psPLeKI7lPsttoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y22gPleW+yy3gfpcWe6z3CZypvN4vV5voCOKcuLECaZNm8a//vUv1qxZw5EjR/L31a5dm/bt23PbbbcxaNAgKleu7OcsW507s7KySU5OIzo6ig0bttG//1Nmrnhcv34rt976NMOH38fgwf0AyMzMok+fodSpU5OZM19V32nYpr7gbauIPi85pR5b8Ofbr9za/5kir3gsyp9H/Q8fffQNixb/Lw0a1C31nB5CSz22KOV9/vakbym0beO6nbQ8vwmVK//3JgJ7dyfywG1/5/IebRk59i4AFn+5lr+MmMELf7uXy7pfWOwchxKSGdDnr1zf72IeG5H3HLxeL0/94S0O7DvCjPmjCA0t/HtDZ0XGOD231as30abNuYSF/fffqTt37qNv36Fcc01X/va3p52O7+pMf+8Ga5v6grvPcpv6grvPcpv6grvPcpv6grvPcpv6grvPcpv6gruv/NtaukeKk7P/8O9AJ1SIHVNuDXRCuTB5xeOhQ4eIjY1lyJAh7N69m2uvvZZnnnmGP//5zzzzzDNce+217NmzhwcffJDY2FgSExMD1hoWVpno6KiAzX8yCxYsJTQ0hNtv752/LTw8jP79e7FmzWb27w/ceQPbfZbbQH3B2ga2+vz9+ZaVlc2XXy6jc+cLTmnRsSwE4vxd0K55gUVHgCZNo2l+dn12xx3M3/bhjCW0anMWl3W/kNzcXI4fzyzyeEsX/8yJEznccNul+ds8Hg99b72UxIPJ/LK+fK4+7NixdYFFR4DmzRtx3nlN2bFjT7nMeSosvTeKYrnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y22gPleW+yy3iYjRhcennnqKXbt28dVXX7F582amT5/OK6+8wpgxY3jllVeYPn06mzdv5quvvmL37t0888wzgU42adOmHTRv3phq1aoW2N62bcv/7I8LRFY+y32W2/LmV5+/LLflzW+7rzS+/XYVKSnp9L3higqf28r583q9JB1Jo0atSADS0zLYsnEPMeefxdQ3P+PGy5+jb9dRDOj7VxZ/ubbA1/66ZR8RVcJo2qJ+ge2tLjjrP/vjK+Q5QN7zOHToKFFRNSpszuJYeW2LY7nPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPcpuUkRDPmfEIUiYXHj/99FP++Mc/0qPHyW/n16NHD5555hnmz59fQWWnl8TEI0VereTblpBwuKKTCrDcZ7kN1OfCchvY7yuNefO+JSysMtdc06XC57Zy/r75bDWHEpK58ur2AOzbexiv18uiL9ey4JMVPPDE9Ywcexe1oiIZO/J9li/dnP+1RxJTiKpTHY+n4H981K6bt/h3ODGlQp4DwCefLObgwcNce223CpuzOFZe2+JY7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y20iApVKHlLxsrKyqF69eqnGVq9enaysrHIuOj1lZGQVum0e5F127tsfSJb7LLf55leffyy3+ea33FeStLRjfLt4FVdc0YkaNapV+PwWzt/uuATeeOVjzm/bjKv7XpQ377G826qmHD3Gm/98jNYXNgPg0isuYECfv/L+1K+J7doKgKzMbCpXLvxZl2Hhef/KzsrILvfnALB9+x5efPEdOnRoxc03d6+QOU/Gwmt7Mpb7LLf55lef/yz3WW7zza8+/1nus9zmm199/rPcZ7nNN7/6/Ge5z3Kbb371+c9yn+U23/zq85/lPsttImL0iseuXbsyceJE4uNPfju5+Ph4Jk6cyGWXXVbiMTMzM0lJSSnwyMwM7h9AERFhZGUV/gtq3/OOiAir6KQCLPdZbvPNrz7/WG7zzW+5ryRffLEs78O8+14ekPkDff6OHEph1BNTiawWwehX7yE0NO9fs2ERef8x3KBx7fxFR4AqVcO55PLz2fLzHnJO5OSNDa9MdnZOoWNnZZ4ocKzylJiYxIMPvkj16lWZOHEEoaGFF0IrWqBf25JY7rPc5ptfff6z3Ge5zTe/+vxnuc9ym29+9fnPcp/lNt/86vOf5T7Lbb751ec/y32W23zzq89/lvsst4mI0SseJ0yYQLdu3YiJiaFPnz5cdNFFNGzYkPDwcDIzM9m/fz8rV65k/vz5VK1alddee63EY44bN44xY8YU2Pb880N54YXHyutpBFx0dG0OHix8WXliYhIA9erVqeikAiz3WW4D9bmw3Ab2+0oyf963VK9elauu6hyQ+QN5/tJSjzPysSmkpR5nwtRHqBtdM39fnei826RG1S58NX+t2tU4cSKH48ezqFa9CrWja7Bu5a94vd4Ct1s9ciilwLHKS2pqOg888AKpqem8//7L1K9v43vO+nvDcp/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W20B9riz3WW6TMvK7jyCS04vJKx5btWrF2rVrueeee1i8eDHDhw9n4MCB3HbbbQwcOJDhw4ezaNEi7rnnHlavXk3r1q1LPObIkSNJTk4u8Bg58sEKeDaB06pVC3bujCct7ViB7evWbQGgdesWgcjKZ7nPchuoz4XlNrDfdzIJCUf46aefufrqLkXe7qIiBOr8ZWVm89yw94jflcjYCffT7OwGBfbXja5J7brVOZSYXOhrDyemEBZeiaqR4QCc27IRGRnZ7I47WGDcpp935+8vL5mZWTz00Evs3BnPO++M5txzm5bbXKfK+nvDcp/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W20B9riz3WW4TEaMLjwCNGzfmrbfe4sCBA+zdu5cVK1awZMkSVqxYwd69ezl48CBvvfUWTZo0KdXxwsPDqVGjRoGH757Pwap3767k5OQya9aC/G1ZWdnMnv017drF0LBhdADrbPdZbgP1BWsb2O87mc8++57c3Fz69r0iYA2BOH85Obm8NGIGv2zYxXPj7+H8ds2LHHdlr/YkHjjKqh+35m9LTkrnh2830r7zuYSE5P0r+dIrL6BSpVA++dcP+eO8Xi/zP1xG3Xo1iz2++/PIYdiw8axdu5mJE0fQoUOrcpnHX9bfG5b7LLeB+lxZ7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9ym4gYvdXq7zVq1IhGjYq+wiM1NZWkpCSaNg3cVRkzZswnJSWdhIS8y7sXLVrOgQN5/zxwYB+qV48MSFe7djH07t2V116bzuHDyTRr1pCPP15IfHwCY8c+HpCm06XPcpv6grfNYt+MGZ+SmpJOQsIRABYtWsHB//x8GzDw+gI/3+Z98i316tUm9uI2Fd7pE4jzN/m1eSz7diNdLj+f1ORjfP3pqgL7e17fCYA77+/Ot1+tY8wf/8ktd19OZLUqzP9oGTknchg89Lr88dH1a9Hvrm78a/piTpzIJeaCs1i66Gc2rIlj5Ni78j83sqy9/PJ7LFz4E1ddFcvRo6nMnbuowP4bb7yqXOYtLWvvjdOpz3Kb+oK7z3Kb+oK7z3Kb+oK7z3Kb+oK7z3Kb+oK7z3Kb+oK7z3KbiIDH6/V6Ax3hYuzYsYwePZqcnBw/vnpryUNKoXv3wcTHJxS575tvptCkSf0ymccfmZlZTJgwg3nzFpOcnEZMTHOeeGIA3bp1DFjTb1nus9wG6gvWNijfPi+n9rOye/cH2BefWOS+r7+ZnP/zbceOeK679lEG3XcDI0bc73efh1C/v9anPM/fnvQthbY99cBbrF+1o9iv+Xr13/L/ed/ew7z7+jxWr/iVnBM5nH9hMwY/fh2tLij4yzO5ubnMnLaITz/6kSOHUmjctC533teDHtcV/xzOiozx4xn918CBI1m+/Odi92/ZMs/p+GXhTH7vBnMbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcBupzZbnPchuoz5XlPsttoD5XlvvKt61lGRxDXJz94EeBTqgQOybfEuiEcqGFRxGRM8ipLjxWtLJYeCxPRS08WuG68CgiIiIiIiIiIqCFx8A7++HZgU6oEDve7hfohHJh8lar06dPL/XYNWvWlGOJiIiIiIiIiIiIiIiIiJSGyYXHQYMG4fF4KO3FmB6Pp5yLRERERERERERERERERORkTC48RkVF0b59e8aPH1/i2KlTpzJ58uQKqBIRERERERERERERERGR4phceIyNjWXz5s106tSpxLELFiyogCIREREREREREREREREpdyGBDhAXJl++2NhYdu3aRUJCQolja9WqRdOmTSugSkRERERERERERERERESK4/GW9oMUK1B6ejqHDh2iUaNGVK5cuRxn2lqOxxYRscdLTqATTspDaKATTmpP+pZAJxTrrMiYQCeIiIiIiIiIiASBloEOOOOd/ejsQCdUiB2T+gU6oVyYvNVqZGQkkZGRgc4QERERERERERERERERkVIyeatVERERERERERERERERETm9mLziUUREyof1W5laZ/l2pj8n2b59eJso3aZERERERERERERKweMJdIE40BWPIiIiIiIiIiIiIiIiIuJMC48iIiIiIiIiIiIiIiIi4kwLjyIiIiIiIiIiIiIiIiLiTJ/xKCIiIiIiIiIiIiIiIjaE6DMeT2e64lFEREREREREREREREREnGnhUUREREREREREREREREScaeFRRERERERERERERERERJzpMx4dZWVlM3Hi+8ydu4iUlDRiYpozbNgAunbtEOg0QH3B2gaQnn6cqVNns27dVjZs2Epychrjxj1Bv349A50G2D5/lttAfS70vihs944D/GvKF2zfvJejh1MJj6hMkxYNuPHuK+nc7YIiv+bEiRyeHvB39u48yD2P9eHGu68qsP/Df3zNto272LZxN8lJadw2+Gpuf+CacnsOPpa/98B2n+U2UJ8ry32W20B9riz3WW4D9bmy3Ge5DdTnynKf5TZQnyvLfZbbQH2uLPdZbhM50+mKR0cjRkxg2rQ59O17BaNGPUBoaAhDhoxh5cqNgU4D1BesbQBJSSlMmjSTHTv2EBPTItA5hVg+f5bbQH0u9L4oLPFAEsePZXLVdRdx/5M30f/+XgC8/Mf3+HLOsiK/5rN/fc+hg0nFHvODyZ/z66Y9tIhpXC7NxbH8vQe2+yy3gfpcWe6z3Abqc2W5z3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcBupzZbnPcpu483o8Z8QjWHm8Xq830BEuZsyYwXvvvcfChQv9+OqtTnOvX7+VW299muHD72Pw4H4AZGZm0afPUOrUqcnMma86Hd+V+oKzzScrK5vk5DSio6PYsGEb/fs/ZebKLsvnz3Kb+tydye+Ln5NK/++0nJxchg96naysbN6cNaLAvuQjqQy97WVuuOtKZr67oMgrHhP2HaFeo9qkHE3jvt7Pl+qKxzZRLUv/ZIpg/XvPcp/lNvUFd5/lNvUFd5/lNvUFd5/lNvUFd5/lNvUFd5/lNvUFd1/5t7n9HYa4a/HE3EAnVIi4iTcGOqFcnPZXPO7atYtvv/02IHMvWLCU0NAQbr+9d/628PAw+vfvxZo1m9m/PzEgXT7qC842n7CwykRHRwU6o0iWz5/lNlCfK70vSic0NIQ69WpxLDWj0L4Zb31K46b1uLx3x2K/vl6j2uWZVyRL568olvsst4H6XFnus9wG6nNluc9yG6jPleU+y22gPleW+yy3gfpcWe6z3Abqc2W5z3KbiATBwmMgbdq0g+bNG1OtWtUC29u2bfmf/XGByMqnPv9ZbjsdWD5/ltvy5ldfsAr0ucs4nknK0TQO7D3EvA++Zc2Pm7nwonMLjNm2cTeLP1vJfU/eiAdbt3sI9PkrieU+y21586vPheU+y21586vPheU+y21586vPheU+y21586vPheU+y21586vPheU+y21586vPheU+y20iApUCHVCU0NDQQCeUSmLikSKvrPFtS0g4XNFJBajPf5bbTgeWz5/lNlBfMAv0ufvnG/P48uO8z3QMCfFw8ZUX8odn+uXv93q9TP37x1zasz0xFzYnYd+Rcu05VYE+fyWx3Ge5DdTnynKf5TZQnyvLfZbbQH2uLPdZbgP1ubLcZ7kN1OfKcp/lNlCfK8t9ltukjOiSudOa2YXHc845h549S/5MrpUrV7J8+fISx2VmZpKZmVlgW3h4FuHhYX53ZmRkERZWudB23zEzMrL8PnZZUJ//LLedDiyfP8ttvvnVF5wCfe6uv70bl1zVlqRDKfzwzVpyc7ycyM7J37/o0xXs2r6fZ8bdU64d/gr0+SuJ5T7Lbb751ec/y32W23zzq89/lvsst/nmV5//LPdZbvPNrz7/We6z3OabX33+s9xnuc03v/r8Z7nPcpuIGF14bNu2LSEhIbz55psljh07dmypFh7HjRvHmDFjCmx7/vmhvPDCY353RkSEkZWVXWh7ZmZW/v5AUp//LLedDiyfP8ttvvnVF5wCfe6aNK9Pk+b1Abjyuot48fHJjPvjVF6e+gTHj2Xy/lufceOAK6lb3+ZnZAb6/JXEcp/lNt/86vOf5T7Lbb751ec/y32W23zzq89/lvsst/nmV5//LPdZbvPNrz7/We6z3OabX33+s9xnuU1EjF6wGhsby/r16wtdoVgcr9db4piRI0eSnJxc4DFy5INOndHRtUlMTCq03betXr06Tsd3pT7/WW47HVg+f5bbQH3BzNq5u6R7W379ZQ/7dicy9/3FnDhxgq4925Ow7wgJ+45wOOEoAGkpx0nYd4Ts7BMV2vd71s7f71nus9wG6nNluc9yG6jPleU+y22gPleW+yy3gfpcWe6z3Abqc2W5z3IbqM+V5T7LbSJidOHxvvvuY+TIkaSkpJQ4duDAgSxatKjEceHh4dSoUaPAw+U2qwCtWrVg58540tKOFdi+bt0WAFq3buF0fFfq85/lttOB5fNnuQ3UF8ysnbuszLzfDDyWlsGhA0mkpRxn2J2v8nC/sTzcbyx/fmgSALP/+Q0P9xvL3riDFdr3e9bO3+9Z7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y20iYnThsXPnzjz//PNER0eXOLZp06ZcccUVFVBVWO/eXcnJyWXWrAX527Kyspk9+2vatYuhYcOS+8uT+oKz7XRg+fxZbgP1BbNAnbvkI6mFtp04kcO3n60iLLwyTVrU5/rbujH8lUEFHg+O6A/AVdd3Zvgrg6jXqHa59JWW9e89y32W20B9riz3WW4D9bmy3Ge5DdTnynKf5TZQnyvLfZbbQH2uLPdZbgP1ubLcZ7lNykiI58x4BCmTn/F4KlJTU0lKSqJp06YVPne7djH07t2V116bzuHDyTRr1pCPP15IfHwCY8c+XuE96jsz2n5rxoz5pKSkk5BwGIBFi5Zz4EDePw8c2Ifq1SMD0mX5/FluU1/Z0PuioHde+ZDj6Rmc3/5sakfX5OjhVJZ8sZr4XQnc+/gNVKkaztmtmnB2qyYFvi5h3xEAzjq7PhdfcWGBfYs/X8mh/Ulk/ueqyV/W7uDD974C4PJrO1GvYdkvUlr/3rPcZ7lNfcHdZ7lNfcHdZ7lNfcHdZ7lNfcHdZ7lNfcHdZ7lNfcHdZ7lNRMDjLc0HJBo2duxYRo8eTU5Ojh9fvdV5/szMLCZMmMG8eYtJTk4jJqY5TzwxgG7dOjofuyyoLzjbfLp3H0x8fEKR+775ZgpNmtSv4KL/snz+LLeB+lydqe+Ln5OK/nfa91+t4ZtPfmL39gOkJqdTJTKcs2OacN2tl9H58jbFHi9h3xEe7jeWex7rw413X1Vg3+iH32Ljmu1Fft2YSQ/TptO5hba3iWp5Cs+maNa/9yz3WW4D9bmy3Ge5DdTnynKf5TZQnyvLfZbbQH2uLPdZbgP1ubLcZ7kN1OfKcl/5trn/HYa4afH0J4FOqBBxf78h0AnlQguPIiIiQaC4hUcrymLhUURERERERESk/OnvMAJNC4+nN5O3Wp0+fXqpx65Zs6YcS0RERERERERERERERKTCeIL38w/PBCYXHgcNGoTH46G0F2N69E0oIiIiIiIiIiIiIiIiElAmFx6joqJo374948ePL3Hs1KlTmTx5cgVUiYiIiIiIiIiIiIiIiEhxTC48xsbGsnnzZjp16lTi2AULFlRAkYiIiIiIiIiIiIiIiIicTEigA4oSGxvLrl27SEhIKHFsrVq1aNq0aQVUiYiIiIiIiIiIiIiIiEhxPN7SfpBiBUpPT+fQoUM0atSIypUrl+NMW8vx2CIiIhXn5yTb/05rE9Uy0AkiIiIiIiIiIqWgv8MItBbD5wc6oULEje8T6IRyYfJWq5GRkURGRgY6Q0RERERERERERERERERKyeStVkVERERERERERERERETk9GLyikcRERE5NdZvZbruiO1bwbatfV6gE07KgyfQCSIiIiIiIiIiIiXSwqOIiIiIiIiIiIiIiIjYoN+/Pq3pVqsiIiIiIiIiIiIiIiIi4kwLjyIiIiIiIiIiIiIiIiLiTAuPIiIiIiIiIiIiIiIiIuJMC48iIiIiIiIiIiIiIiIi4qxSoANEREREREREREREREREALwhnkAniANd8SgiIiIiIiIiIiIiIiIiznTFo6OsrGwmTnyfuXMXkZKSRkxMc4YNG0DXrh0CnQaoL1jbQH3B2gb2+9LTjzN16mzWrdvKhg1bSU5OY9y4J+jXr2eg0wDb589yGwSmb8+OA/x7yhfs2LKXo4dTCY+oTJMWDeh715Vc1O2C/HGTXvqAbz9bWejrGzWNZsKsEfl/PpKYzIxJ89m+aQ9Jh1IICfHQ8KxorrmlK1dcdxEeT/n9xtzPP//KhNf/H2vWbMbrhfYdYvjjHwfRuvXZ5TZnael960bnLzjbQH2uLPdZbgP1ubLcZ7kN1OfKcp/lNlCfK8t9lttAfa4s91luEznTmb7iceHChUyaNIlZs2aRkpJS5Jgff/yR+++/v4LL/mvEiAlMmzaHvn2vYNSoBwgNDWHIkDGsXLkxYE2/pb7gbAP1BWsb2O9LSkph0qSZ7Nixh5iYFoHOKcTy+bPcBoHpSzyQxPFjmVxx3UUMevImbrmvFwDjh7/H13OWFRhbOawSQ5+/q8BjwNC+BcakJqdzJCGZS65qy8ChfbljyLVE1a3BW3+ZyQfvfF5uz2Pjxu3cfdcI9uw9yKND7+DRR29n1859DBzwLDt27C23eUtL71s3On/B2Qbqc2W5z3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcBupzZbnPcpvImc7j9Xq9gY74vczMTK677joWL16ML69mzZq88sorDBkypMDY999/n3vuuYecnBw/Ztrq1Ll+/VZuvfVphg+/j8GD+/2nPYs+fYZSp05NZs581en4rtQXnG3qC96206EP8n6jLDk5jejoKDZs2Eb//k+ZufLH8vmz3FYRfeuOlP7fubk5ufzpvtfJzszOv5px0ksf8OOi9fy/heP8mv/lZ6aycfWv/POrsYSEFv69q7a1z/PruD4PDnmRtWs3s+CLd4iKqgFAQsIRel/zMF0va8+bb450Or4Htys19b51o/MXfG3qC+4+y23qC+4+y23qC+4+y23qC+4+y23qC+6+8m9r6R4pTpqP+izQCRVi59jrAp1QLkxe8fi3v/2Nb7/9lhdeeIH169fzxRdfcNFFF/Hwww/z4IMPkpubG+hEABYsWEpoaAi33947f1t4eBj9+/dizZrN7N+fGMA69QVrG6gvWNvAfh9AWFhloqOjAp1RJMvnz3Ib2OoLCQ2hTr1apKdlFNqXm5PLsfTC20tSr2EUWRnZnMj25xeVSrZy5Ua6dGmXv+gIUK9ebTrHXsDiRStITz9eLvOWlt63bnT+gq8N1OfKcp/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2ETG68Dhz5kwGDRrEc889R5s2bejVqxdfffUVL730ElOmTKFfv35kZmYGOpNNm3bQvHljqlWrWmB727Yt/7M/LhBZ+dTnP8ttefOrz1+W2/Lmt91nneXzZ7ktb/7A9mUczyTlaBoH9h5i/gffsvbHzVx40bkFxmRlZHNvz1EM6jmK+67+M1Ne/YiMY0X/90BWRjYpR9NI2H+ExZ+uYNGnK2jZphlhEZXLpT8rK5vwiLBC26tEhJOdfYJt23aVy7zBINDfe6c7y+fPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPclve/OpzYbnPcpuIQKVABxQlLi6OYcOGFdr+7LPPcs4553DvvffSq1cv5s2bV/Fxv5GYeKTI3373bUtIOFzRSQWoz3+W20B9Liy3gf0+6yyfP8ttEPi+6W/My/9MR0+Ih4uvuJD7n+6Xvz+qTg1uGHAlLVo2wev1svbHzXw5+wd2/bqPFyY9Qmil0ALH++xfS/i/t/97W44LLzqPh/98e7n1t2jRmHVrt5KTk0NoaF5LVlY269fn3WL24MEj5Tb36S7Q33unO8vnz3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcBupzZbnPchuoz5XlPsttImJ04bF27dokJCQUue/222+ndu3a9OvXj8svv5wBAwaU6piZmZmFrpIMD88iPLzw1QmllZGRRVhY4SsnfMfMyMjy+9hlQX3+s9zmm199/rHc5pvfcp91ls+f5Tbf/IHsu/72blxyVVuSDqWw7Ju15OZ6OXHiv7dFveuR6wuM79qrAw3Pimbm5M/5cdF6uvbqUGj/2a3OIuVoGquX/kLykTSyMk+UW/9dd13HCy+8zahRb/KHP/QjN9fLO2//i8TEJAAyMwJ/pwarAv29d7qzfP4st/nmV5//LPdZbvPNrz7/We6z3OabX33+s9xnuc03v/r8Z7nPcptvfvX5z3Kf5TYRMXqr1Q4dOjB//vxi9/fq1Yuvv/6affv28eyzz5bqmOPGjaNmzZoFHuPGTXbqjIgIIysru9D2zMys/P2BpD7/WW7zza8+/1hu881vuc86y+fPcptv/kD2NW5en7axLbniuosY8fc/kHE8k1eemYrX6y32a/rccQWeEA8bVmwttC+6YW3axrbksqs78viYAdRrXIeXHn+HrIzCz7Es3HHntTz40K18On8Jfa4fyg19H2P37v35H3JfNbJKucwbDAL9vXe6s3z+LLf55lef/yz3WW7zza8+/1nus9zmm199/rPcZ7nNN7/6/Ge5z3Kbb371+c9yn+U2KSMez5nxCFImFx5vvvlmli1bxo8//ljsmIsvvpglS5bQsGHDUh1z5MiRJCcnF3iMHPmgU2d0dO38Kxl+y7etXr06Tsd3pT7/WW4D9bmw3Ab2+6yzfP4st4G9vkuuasv2TXvYv7v4D4QPi6hM9ZqRpKUcL9XxDh88yi9rt5dlZgFPPjmQ75dO5/33X2buJ2/w4UevkevNBaB580blNu/pztr33unG8vmz3Abqc2W5z3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcBupzZbnPcpuIGF14vPfee0lNTaVTp04nHde6dWt++eUXduzYUeIxw8PDqVGjRoGHy21WAVq1asHOnfGkpR0rsH3dui3/6WvhdHxX6vOf5TZQnwvLbWC/zzrL589yG9jry8rM+83FY2kZxY45np5B6tF0atSKLJPjlYWaNavR6aLziYlpDsCyH9bRoEFdzj67SbnOezqz9r13urF8/iy3gfpcWe6z3Abqc2W5z3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcJiJGFx49Hg+RkZFUrlz4Ps2/5/V68QToktTevbuSk5PLrFkL8rdlZWUze/bXtGsXQ8OG0QHp8lFfcLaB+oK1Dez3WWf5/Flug8D1JR9JLbTtxIkcvv18FWHhlWnSoj5ZmdkcTy+8YPjRP77C6/XS/pJW+dtSktKKnGfhvJ/weDycHVNxC4CfffYdGzZs4557+xISYvI/uUyw/t6wzvL5s9wG6nNluc9yG6jPleU+y22gPleW+yy3gfpcWe6z3Abqc2W5z3KbiEClQAe4euONNxg9ejQ5OTkVPne7djH07t2V116bzuHDyTRr1pCPP15IfHwCY8c+XuE96jsz2tQXvG2nQ5/PjBnzSUlJJyHhMACLFi3nwIG8fx44sA/Vq5d8BVp5sHz+LLcFsu/dVz7keHoGrTucTe3omhw9nMr3X6wmflcC9zx+AxFVw0nYf4Q/3fsaXXt1oFGzegCs+2kLa37YRPtLWnHR5RfkH2/2tK/ZsmEn7S6OoW6DKNJSjvHTovVs37SH3rdeRoOz6pbL81ix4mfemjSLrl3bU6tWddat28rs2V/TrVtH7rnnhnKZ81TpfetG5y+42tQX3H2W29QX3H2W29QX3H2W29QX3H2W29QX3H2W26SM6Pe3T2ser9frDXSEi7FjxzosPG51nj8zM4sJE2Ywb95ikpPTiIlpzhNPDKBbt47Oxy4L6gvONlBfsLaB/T6A7t0HEx+fUOS+b76ZQpMm9Su46L8snz/LbVC+feuOFP3v3KVfrWHhvJ/Yvf0AacnpRFQN5+xWTbj21su4qFsbANJTj/Peax+z7eddJB1KITc3lwZN6nLZ1R3pe/eVVKoUmn+89cu38Nm/viduy15SjqYTFlaJpuc2pMcNF3PFdZ2LvUtC29rnOT2/3bv3M2bMO/yycTvp6cdp0qQ+N93cnUGDbiQsrOQ7OJTEg/vdHfS+daPzF3xtoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y22gPleW+8q3rWUZHENcNB/9eaATKsTOF68NdEK5MLnwOH369FKP/eSTT/j4448DtvAoIiIiJStu4dEK14XH8lYWC48iIiIiIiIiUhpaeAw0LTye3kzeanXQoEF4PB5KuyYaqM94FBEREREREREREREREZE8Jhceo6KiaN++PePHjy9x7NSpU5k8eXIFVImIiIiIiIiIiIiIiIhIcUwuPMbGxrJ582Y6depU4tgFCxZUQJGIiIiIiIiIiIiIiIiUO93l8rQWEuiAosTGxrJr1y4SEhJKHFurVi2aNm1aAVUiIiIiIiIiIiIiIiIiUhyTC4/Dhw8nLi6OqKioEsc++uijxMXFVUCViIiIiIiIiIiIiIiIiBTH5K1WIyMjiYyMDHSGiIiIiIiIiIiIiIiIiJSSyYVHEREREREREREREREROQOF6DMeT2cmb7UqIiIiIiIiIiIiIiIiIqcXXfEoIiIi5a5t7fMCnXBSPyb8GuiEk+pSz/b5ExERERERERERAV3xKCIiIiIiIiIiIiIiIiJlQAuPIiIiIiIiIiIiIiIiIuJMt1oVERERERERERERERERG0I8gS4QB7riUUREREREREREREREREScaeFRRERERERERERERERERJxp4VFEREREREREREREREREnOkzHh1lZWUzceL7zJ27iJSUNGJimjNs2AC6du0Q6DRAfcHaBupzkZ5+nKlTZ7Nu3VY2bNhKcnIa48Y9Qb9+PQOdBtg+d6C+YG0DO31579GPWb9uCxs2bCM5OY2/jnuCfv165I/Jzc1lzpxFfPXlMjZt2kFycipNmtTnuuu6cf/gmwkPDyuXtk+mf8Xs//2cxi0aMHb68AL7tm2I419vz2fX1r1EREYQe1U7+g+5noiq4flj4uMO8PF7X7Bryx6Sj6QSFhFGo+b1ufbOq+jQ9YJyaQY7r21x1OfGcp/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W28Sd16PPeDyd6YpHRyNGTGDatDn07XsFo0Y9QGhoCEOGjGHlyo2BTgPUF6xtoD4XSUkpTJo0kx079hAT0yLQOYVYPnegvmBtAzt9SUkpvDVpJjt27C32PXr8eCbPjpzIkaRk7rijNyOf/QMXXngeb775AQ/8YQxer7fMu44kHGX+//uG8CqFFzV3bYtn/LC3ycrI4o6hN3JFn4tZPO9HJo3+Z4Fxhw4cIeNYBl2v7cxdj9/EDff2AmDiiKks/mRZmTf7WHlti6M+N5b7LLeB+lxZ7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wmcqbzeMvjb+bKwIEDB5g+fTpHjx6lR48e9OiRd5XDW2+9xfvvv09ycjKdO3fmz3/+M+ecc46fs2x1aly/fiu33vo0w4ffx+DB/QDIzMyiT5+h1KlTk5kzX3U6viv1BWeb+txlZWWTnJxGdHQUGzZso3//p8xc8Wj93KkvONsqos9L6f9z4/fv0Vv7P13oisesrGx+/vlXOnZsXeBrJ/3PTN588/947x8vcuml7Us9548Jv5Y45q3np5N6NJ3c3FzSktMLXPH42h/fZfe2fYx7fwRVIiMA+Hbej/xj/L945u8P0iY2ptjj5ubk8vwfXiM76wQvvz+iyDFd6p1X6ufye2f695769HNPfWden+U29QV3n+U29QV3n+U29QV3n+U29QV3X/m3tXSPFCfN/vJVoBMqxK4/9wp0QrkwecXjrl27aNeuHSNGjODll1/m6quvZtKkSYwfP55nnnkGgLCwMGbMmEGXLl3YvXt3QDoXLFhKaGgIt9/eO39beHgY/fv3Ys2azezfnxiQLh/1BWcbqM9VWFhloqOjAtpQHOvnTn3B2Qa2+krzHg0Lq1xo0RGgZ69LANi+fW+ZNm1Zu52V367nrsdvLLTveHoGG1dspcvVnfIXHQG69r6IiCrhLF+49qTHDgkNoXa9WhxLO16mzT6WXtuiqM+N5T7LbaA+V5b7LLeB+lxZ7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4mI0YXHMWPGEBISwsqVK0lMTOT6669nzJgx/Otf/+Lnn39m6dKlrF69mqVLl3Ls2DHGjh0bkM5Nm3bQvHljqlWrWmB727Yt/7M/LhBZ+dTnP8ttefOrL1hZP3fq85/ltrz5bfeV1qFDSQBERVUvs2Pm5uQyY8JsLu9zMWed06jQ/r3b95OTk0uLVmcV2F6pciWanteIXdviC31N5vFMUo+mkRB/iC9mfcuGnzZzfif/r2o8GeuvrfrcWO6z3JY3v/pcWO6z3JY3v/pcWO6z3JY3v/pcWO6z3JY3v/pcWO6z3JY3v/pcWO6z3CYiUCnQAUVZvHgxQ4YMoWPHjgCMHj2a2NhYnn32Wc4+++z8cbGxsQwaNIjPPvssIJ2JiUeKvCLDty0h4XBFJxWgPv9ZbgP1BTPr5059/rPcBvb7SmvqlNlUq1aVyy/vVGbHXDj3Bw4dTOKPrz9c5P6jh1MAqFmnRqF9NevUYOu6HYW2fzDpExbPzftMR0+Ih06Xt2Xgk/3KrPm3rL+26nNjuc9yG6jPleU+y22gPleW+yy3gfpcWe6z3Abqc2W5z3IbqM+V5T7LbVJGTF4yJ6VlcuFx3759tGjRIv/PzZo1A6B168K3VGvbti1TpkypsLbfysjIIiyscqHt4eFh+fsDSX3+s9zmm199wcn6uVOf/yy3+ea33Fca77zzL374YR3PP/8QNWpUK5NjpiWn8/HUBdxw79XUiCr6mFmZ2QBUrhxaaF/lsMpkZWUX2n71rZfT+cp2HD2UzPKF6/Dm5nIiO6dMmn/P+murPjeW+yy3+eZXn/8s91lu882vPv9Z7rPc5ptfff6z3Ge5zTe/+vxnuc9ym29+9fnPcp/lNhExum5cq1YtkpKS8v8cGhpKeHg4ERERhcamp6dTpUqVEo+ZmZlJSkpKgUdmptsPoIiIsCL/MtF33IiIMKfju1Kf/yy3+eZXX3Cyfu7U5z/Lbb75LfeV5LPPvmPihPfp378Xd951XZkd96P//ZxqNarS65bLih0TFp73f3ayi1g4zM7KLvL/DDVqVp8LLmpJ196deXL8H8g4nsmEEVPwer1l1u5j/bVVnxvLfZbbfPOrz3+W+yy3+eZXn/8s91lu882vPv9Z7rPc5ptfff6z3Ge5zTe/+vxnuc9ym4gYXXhs1aoVGzZsyP9z7dq1OX78OFdccUWhsRs3bqR58+YlHnPcuHHUrFmzwGPcuMlOndHRtUlMTCq03betXr06Tsd3pT7/WW4D9QUz6+dOff6z3Ab2+05m6dI1/Gn461xx5UW8MOaRMjvugT2JLJ63jJ63dCPpUAqJ+4+QuP8I2VnZ5JzIIXH/EdJS0qn1n1usJv/nlqu/lXw4hVp1a5Y4V+cr2xG3aQ8H9iSWWb+P9ddWfW4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W20B9riz3WW4D9bmy3Ge5TUSMLjwOGDCgVFcxHj16lFmzZnHVVVeVOHbkyJEkJycXeIwc+aBTZ6tWLdi5M560tGMFtq9btwWA1q1bFPVlFUZ9/rPcBuoLZtbPnfr8Z7kN7PcVZ926LTw2dBxt2pzLhAnDqVSp8O1O/ZV0KBlvrpf3J37MH2/7S/5jxy+7ObAnkT/e9hfmTvuSxmc3IDQ0hLjNewp8/YnsE+zeto+m5zUqcS7f7VqPpx0vs34f66+t+txY7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9ym5QRj+fMeAQpkwuPf/jDH5g0aVKJ46pXr87evXt55ZVXShwbHh5OjRo1Cjx893z2V+/eXcnJyWXWrAX527Kyspk9+2vatYuhYcNop+O7Ul9wtoH6gpn1c6e+4GwD+31F2b59Dw8OeZHGjevxzuTRRESEl+nxm7RowGNj7yv0aNyiAXXqR/HY2Pu4/PqLqVqtCudf1JJlX67i+LGM/K9f+sVKMo5n0vmqdvnbUpJSC81z4kQOSxesJCy8Mo2aNyjT5wD2X1v1ubHcZ7kN1OfKcp/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2EYFKgQ5wERoaSkhICPv376dp06YVPn+7djH07t2V116bzuHDyTRr1pCPP15IfHwCY8c+XuE96jsz2tRXNmbMmE9KSjoJCYcBWLRoOQcO5P3zwIF9qF49MiBd1s+d+oKzzWLfjBnzSU1JJyHhCJD3Hj144BAAAwb2wePx8IfBz5OSks7gwf34dvGKAl9/VtOGdOjQyqmheq1qdLr8wkLbv/z3EoAC+2554Dr+8sgbvDx0Elfc0IWkxKMsmLmYNp1jaHtx6/xx0179N8fTM4lpfzZRdWuSfCSVZV+tYv+uBO549AYiqpbt4inYe23Vd+b0WW5TX3D3WW5TX3D3WW5TX3D3WW5TX3D3WW5TX3D3WW4TEfB4vV5voCNcjB07ltGjR5OTk+PHV291nj8zM4sJE2Ywb95ikpPTiIlpzhNPDKBbt47Oxy4L6gvONlCfq+7dBxMfn1Dkvm++mUKTJvUruOi/rJ879QVnG5Rvn5dT+8+N7t3/wL5i3qNff/O/APTs8UCxX3/Tzd15+eVhpZ7vx4RfSz123GOTSEtOZ+z04QW2b12/g3+9PZ9dW/cSUTWC2O7t6P/g9VSpGvHfeb5ew3ef/sSeHftJT04nomo4zWPOouctl9HhsjbFztml3nml7ivKmfy9VxbUF5xtoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y22gPleW+8q3rWUZHENcNBv3daATKsSukT0DnVAutPAoIiIi5e5UFx4r2qksPAaC68KjiIiIiIiIiJSWFh4DTQuPpzeTt1qdPn16qceuWbOmHEtERERERERERERERESkwoR4Al0gDkwuPA4aNAiPx0NpL8b0ePRNKCIiIiIiIiIiIiIiIhJIJhceo6KiaN++PePHjy9x7NSpU5k8eXIFVImIiIiIiIiIiIiIiIhIcUwuPMbGxrJ582Y6depU4tgFCxZUQJGIiIiIiIiIiIiIiIiInExIoAOKEhsby65du0hISChxbK1atWjatGkFVImIiIiIiIiIiIiIiEi5CvGcGY8gZXLhcfjw4cTFxREVFVXi2EcffZS4uLgKqBIRERERERERERERERGR4pi81WpkZCSRkZGBzhARERERERERERERERGRUjJ5xaOIiIiIiIiIiIiIiIiInF5MXvEoIiIiwcWD7fvWd6l3XqATTmpr8tZAJxSrZc2WgU4QEREREREREREjtPAoIiIiIiIiIiIiIiIiNtj+/XUpgW61KiIiIiIiIiIiIiIiIiLOtPAoIiIiIiIiIiIiIiIiIs608CgiIiIiIiIiIiIiIiIizvQZjyIiIiIiIiIiIiIiImKCN0Qf8ng60xWPIiIiIiIiIiIiIiIiIuJMC48iIiIiIiIiIiIiIiIi4uy0vNVqdnY2mzZtokWLFlSvXj2gLVlZ2Uyc+D5z5y4iJSWNmJjmDBs2gK5dOwS0y0d9wdkG6gvWNlCfK8t9ltvAdt/69VuZM2chP/20nvj4BGrVqk67djEMGzaQFi0aBzqP9PTjTJ06m3XrtrJhw1aSk9MYN+4J+vXrWW5z7tp+gA/+9wt+3byXpMOphEdUpmmLBvQbeCWx3S7IH7d1426+nr+CrRt3s3PbPnJycpm3/O+Fjpd4MImvP1nOiqWb2LfnECEhHpqd05Db7+9J+9iW5fY8wPb3HqgvWNtAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W20B9riz3WW4TOdOdllc87tu3jw4dOrB48eJApzBixASmTZtD375XMGrUA4SGhjBkyBhWrtwY6DRAfcHaBuoL1jZQnyvLfZbbwHbflCkf8eWXP9ClSztGjXqA227rzcqVG+nXbxhbt+4KdB5JSSlMmjSTHTv2EBPTokLmTDyQxPFjmfS4/iKGPH0TdwzuBcBLT7/Hgo+X5Y9buXQTX839CQ/QoHGdYo/307cb+XD6Iho2qcvAh3pzx+BeHD+WyXNDJ/P1vOXl+lwsf++B+oK1DdTnynKf5TZQnyvLfZbbQH2uLPdZbgP1ubLcZ7kN1OfKcp/lNpEzncfr9XoDHfF7r7322kn3JyUlMXbsWO677z4uuCDvN/qfeuopP2ba6sfX/Nf69Vu59danGT78PgYP7gdAZmYWffoMpU6dmsyc+arT8V2pLzjb1Be8beoL7j7LbadD3+rVm2jT5lzCwirnb9u5cx99+w7lmmu68re/PR3AurzftExOTiM6OooNG7bRv/9TZXrF49bk0v03S05OLk/e8zpZWdm88+8RACQdTqVqZAThEZV559XZfPrvpUVe8bhr+wFq1alGzVrV8rdlZ53g8QF/J+NYJv+YP7rIOVvWdLsa0vr3nvqCs019wd1nuU19wd1nuU19wd1nuU19wd1nuU19wd1X/m3le9cfKVnT1xcHOqFC7H7yykAnlAuTVzw+88wz/PGPf+SZZ54p8jF27Fg8Hg//+Mc/8scGwoIFSwkNDeH223vnbwsPD6N//16sWbOZ/fsTA9Llo77gbAP1BWsbqM+V5T7LbWC/r2PH1gUWHQGaN2/Eeec1ZceOPQGq+q+wsMpER0cFOoPQ0BDq1q9FempG/raoOtUJj6h8kq/K0+ycBgUWHQEqh1XioktbcyghmWPpGcV8pRvr33vqC842UJ8ry32W20B9riz3WW4D9bmy3Ge5DdTnynKf5TZQnyvLfZbbRMTowmOrVq2oWrUqL730Ejt27CAuLq7AY8mSJXi9Xt59913i4uLYsWNHQDo3bdpB8+aNqVataoHtbdu2/M/+uEBk5VOf/yy35c2vPn9ZbsubX30uLPdZbsub33ZfUbxeL4cOHSUqqkagUwIq43gmyUfT2L/3EHP+71tWLdtMu87nltnxkw6nEB4RRnhEWJkd87esf++pz3+W2/LmV58Ly32W2/LmV58Ly32W2/LmV58Ly32W2/LmV58Ly32W2/LmV58Ly32W20QEKgU6oCjr16/njTfe4KWXXuLTTz9lwoQJxMbGFhpXr149mjVrFoDCPImJR4q8usG3LSHhcEUnFaA+/1luA/W5sNwG6nNluc9yG9jvK8onnyzm4MHDPP743YFOCaipE+blf6ZjSIiHLldeyEN/7Fcmx9635xDLFm+ga/d2hIaWz++rWf/eU5//LLeB+lxZ7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wmIkaveKxUqRJPPfUUW7ZsoVWrVlx66aUMHDiQffv2+X3MzMxMUlJSCjwyM7OcOjMysgrd9g3yLuv27Q8k9fnPcptvfvX5x3Kbb371+c9yn+U23/yW+35v+/Y9vPjiO3To0Iqbb+4e6JyAuuHObrz0Pw/y5PN30rFLK3JzvWRn5zgfNyMji1dG/pOw8MrcO/T6Migtfh7L33vq85/lNt/86vOf5T7Lbb751ec/y32W23zzq89/lvsst/nmV5//LPdZbvPNrz7/We6z3CZlJMRzZjyClMmFR5969erx3nvv8cMPP7B161ZatmzJiy++yPHjx0/5WOPGjaNmzZoFHuPGTXbqi4gIIysru9B234JmRDndkqy01Oc/y22++dXnH8ttvvnV5z/LfZbbfPNb7vutxMQkHnzwRapXr8rEiSMIDQ0NdFJAndW8Pu1jW9L9+ot4/vU/cPx4Ji89PRWv1+v3MXNycnl11P9jd9xBRoy7lzrRNcuwuCDr33vq85/lNt/86vOf5T7Lbb751ec/y32W23zzq89/lvsst/nmV5//LPdZbvPNrz7/We6z3CYixhcefWJjY/npp5948803efvtt7nsssvweE5tNXjkyJEkJycXeIwc+aBTV3R0bRITkwpt922rV6+O0/Fdqc9/lttAfS4st4H6XFnus9wG9vt8UlPTeeCBF0hNTWfKlDHUr2+jy5Ku3duy7Zc9xO9O9PsY//PXf7Hi+00MG30H7TqfV4Z1hVn/3lOf/yy3gfpcWe6z3Abqc2W5z3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcJiKnycKjz3333cfWrVt55JFHuOeee2jatGmpvzY8PJwaNWoUePguvfZXq1Yt2LkznrS0YwW2r1u3BYDWrVs4Hd+V+vxnuQ3U58JyG6jPleU+y21gvw/yfnPxoYdeYufOeN55ZzTnnlv6/w44k2Rl5v3W57G0DL++/r035vH1vBX84ckbuOKajmWZViTr33vq85/lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W20TkNFt4BKhevTovvvgi//jHP2jfvj2pqans3r07IC29e3clJyeXWbMW5G/Lyspm9uyvadcuhoYNowPS5aO+4GwD9QVrG6jPleU+y21gvy8nJ4dhw8azdu1mJk4cQYcOrQLaY8HRI6mFtp04kcPCz1YRFl6Zs1rUP+Vjzv5/i/h4xmJuHdSDG+64vCwyS2T9e099wdkG6nNluc9yG6jPleU+y22gPleW+yy3gfpcWe6z3Abqc2W5z3KbiEClQAe4euONNxg9ejQ5OTkVPne7djH07t2V116bzuHDyTRr1pCPP15IfHwCY8c+XuE96jsz2tQXvG3qC+4+y22nQ9/LL7/HwoU/cdVVsRw9msrcuYsK7L/xxqsCVPZfM2bMJyUlnYSEwwAsWrScAwfy/nngwD5Urx5ZpvNNGvchx9IzaNPhbGpH1+To4VQWf7GavTsTGPzEDVSpGg5Awv4jLPpsFQC/btoDwKypXwEQ3TCK7tddBMCyRRv4x5vzaXRWXc5qUZ9Fn68qMF/72JZE1aleps8B7H/vqS8429QX3H2W29QX3H2W29QX3H2W29QX3H2W29QX3H2W26SMnNon7YkxHq/X6w10hIuxY8c6LDxudZ4/MzOLCRNmMG/eYpKT04iJac4TTwygW7fyvzVZaagvONtAfcHaBupzZbnPchvY7hs4cCTLl/9c7P4tW+ZVYE3RuncfTHx8QpH7vvlmCk2anPoViD5bkwv/N8uSL9fw1Sc/sfPXA6Qmp1MlMpxzWzWhz22XcfHlbfLHbVj1K88+/HaRx23T8RzGvfMIAP/37hd8MOXLYhv++vbDXNjp3ELbW9ZseapPpxDL33ugvmBtA/W5stxnuQ3U58pyn+U2UJ8ry32W20B9riz3WW4D9bmy3Fe+be7/P1fcNH3j20AnVIjdj19R6rFLlizh1VdfZdWqVezfv5+PP/6Ym266KX+/1+vl+eef53//9385evQoXbt25e233+a8887LH3PkyBEee+wx5s2bR0hICLfccgsTJ06kWrVq+WPWr1/Po48+yooVK4iOjuaxxx5j+PDhp/S8TC48Tp8+vdRjP/nkEz7++OOALTyKiIiIlLeiFh6tKIuFRxERERERERE79P9zA00Lj4V9/vnnLF26lE6dOtGvX79CC4+vvPIK48aN45///CctWrTgueeeY8OGDfzyyy9EREQAcO2117J//34mT55MdnY29913H507d+b//u//AEhJSaFly5b07NmTkSNHsmHDBu6//34mTJjAkCFDSt1qcuExJCQEj8dDadM8Ho8WHkVERCRoaeFRREREREREpKLo/+cGmhYeT87j8RRYePR6vTRq1Iinn36aZ555BoDk5GTq16/PtGnTuOOOO9i0aRPnn38+K1as4KKL8j52Z8GCBVx33XXs3buXRo0a8fbbbzNq1CgOHDhAWFgYACNGjGDOnDls3ry51H0mP+MxKiqK9u3bM378+BLHTp06lcmTJ1dAlYiIiIiIiIiIiIiIiJSnkJBAF1SMzMxMMjMzC2wLDw8nPDz8lI4TFxfHgQMH6NmzZ/62mjVrcvHFF7Ns2TLuuOMOli1bRq1atfIXHQF69uxJSEgIP/30EzfffDPLli3j8ssvz190BLjmmmt45ZVXSEpKIioqqlQ9JhceY2Nj2bx5M506dSpx7IIFCyqgSERERERERERERERERKRsjBs3jjFjxhTY9vzzz/PCCy+c0nEOHDgAQP369Qtsr1+/fv6+AwcOUK9evQL7K1WqRO3atQuMadGiRaFj+PaVduHR5LpxbGwsu3btIiEhocSxtWrVomnTphVQJSIiIiIiIiIiIiIiIuJu5MiRJCcnF3iMHDky0FnOTC48Dh8+nLi4uFKtnj766KPExcVVQJWIiIiIiIiIiIiIiIiIu/DwcGrUqFHgcaq3WQVo0KABAAcPHiyw/eDBg/n7GjRoUOhivxMnTnDkyJECY4o6xm/nKA2TC4+RkZE0a9aMypUrBzpFRERERERERERERERExKQWLVrQoEEDvvnmm/xtKSkp/PTTT3Tp0gWALl26cPToUVatWpU/ZuHCheTm5nLxxRfnj1myZAnZ2dn5Y7766itiYmJKfZtVMLrwKCIiIiIiIiIiIiIiImcej+fMeJyKtLQ01q5dy9q1awGIi4tj7dq17N69G4/Hw7Bhw/jLX/7CJ598woYNG7jnnnto1KgRN910EwCtW7emd+/ePPDAAyxfvpylS5cydOhQ7rjjDho1agTAXXfdRVhYGIMHD2bjxo3MmjWLiRMn8tRTT51Sa6VTe2oiIiIiIiIiIiIiIiIiUlFWrlzJVVddlf9n32Lgvffey7Rp0xg+fDjp6ekMGTKEo0ePctlll7FgwQIiIiLyv+b9999n6NCh9OjRg5CQEG655RbeeOON/P01a9bkyy+/5NFHH6VTp07UrVuX0aNHM2TIkFNq9Xi9Xq/j8z2NbQ10gIiIiMhpLf3E/kAnnFRkpYaBThAREREREZHTSstAB5zxWkz6NtAJFSLu0SsCnVAudKtVEREREREREREREREREXGmW62KiIiIiIiIiIiIiIiICaf6+Ydii654FBERERERERERERERERFnWngUEREREREREREREREREWdaeBQRERERERERERERERERZ1p4FBERERERERERERERERFnlQIdcLrLyspm4sT3mTt3ESkpacTENGfYsAF07doh0GmA+oK1DdQXrG2gPleW+yy3gfpcWe4LRNux9Az++Y8v+Xl9HBs3xJGScowX/jKIG26+NH9Mbm4u8+f+yMKvV7Nl8x6Sk9Np3Lgu11zbmYH3XU14eOUCx0xNPcbUyZ+x6Ju1JBxMIqp2dS7u0pohD/ehYaM65fZcLL+2YLvPchuoz5XlvvT040ydOpt167ayYcNWkpPTGDfuCfr16xnoNMD2uQPbfdZfW+t9ll9bUF+wtoH6XFnus9wG6nNluc9ym7jzeDyBThAHuuLR0YgRE5g2bQ59+17BqFEPEBoawpAhY1i5cmOg0wD1BWsbqC9Y20B9riz3WW4D9bmy3BeItqNH0/jft+cTt2M/LWOaFDkm43gWL/x5GklJadxy2+U886fbueDC5rwz6RMee3AiXq83f2xubi6P/GEC/575LVf1aM/wZ++g93WxfP3FKu4b8Arp6Rnl9lwsv7Zgu89yG6jPleW+pKQUJk2ayY4de4iJaRHonEIsnzuw3Wf9tbXeZ/m1BfUFaxuoz5XlPsttoD5Xlvsst4mc6Tze3/6NjmG5ubl8+OGHfP311xw5coTo6GiuvfZabrjhBoejbnVqWr9+K7fe+jTDh9/H4MH9AMjMzKJPn6HUqVOTmTNfdTq+K/UFZ5v6grdNfcHdZ7lNfcHdV95t6Sf2F7k9KyublORj1I2uyS8/72TA7X8tdMVjdtYJftm4i3Ydzinwte++NZ93Jn3C21OGcXGX8wFYt2Y79w14hT+NupPb77oqf+zcj5cy5s//5G8TH6Z7z8K/2RpZqaHT87P82lrvs9ymvuDvy8rKJjk5jejoKDZs2Eb//k+ZuerM+rmz3mf5tQXbfdZfW/UFZ5v6grvPcpv6gruv/NtaukeKk3PeXhLohAqx/eHLA51QLkxe8Xj++efz6aef5v85PT2dK6+8kjvvvJP33nuP7777jnfffZebb76ZPn36kJOTE5DOBQuWEhoawu23987fFh4eRv/+vVizZjP79ycGpMtHfcHZBuoL1jZQnyvLfZbbQH2uLPcFqi0srDJ1o2uedEzlsEqFFh0BrurZHoC4HQfyt6WlHQegTp0aBcbWrZs3x+9vy1pWLL+2YLvPchuoz5X1vrCwykRHRwW0oTjWz531PsuvLdjus/7aqi8420B9riz3WW4D9bmy3Ge5TUSMLjxu3ryZ5OTk/D//6U9/4vvvv+cvf/kLaWlpHDx4kOTkZJ5++mk+++wz/v73vwekc9OmHTRv3phq1aoW2N62bcv/7I8LRFY+9fnPclve/Orzl+W2vPnV58Jyn+W2vPnV58Jyn+W24hw+lAJArVrV8red36YZVaqE89b/zGX5j5tJOJjEqhVbmPjaR1zQpjkXd2ldLi3Wz5/lPsttefOrz4X1PsusnzvrfeI/66+t+vxnuS1vfvW5sNxnuS1vfvW5sNxnuU3KhsdzZjyClcmFx9/74IMPGDRoECNHjiQiIgKAatWqMX78eK699lpmzJgRkK7ExCNF/iajb1tCwuGKTipAff6z3Abqc2G5DdTnynKf5TZQnyvLfZbbivPP976gWrUIunZrk78tKqo6L//9AdJSj/PQ4Nfo3f1PPDDo70RH12Lye09RqVJoubRYP3+W+yy3gfpcWe+zzPq5s94n/rP+2qrPf5bbQH2uLPdZbgP1ubLcZ7lNRE6DhcfU1FSSkpLo3bt3kft79+7Nr7/+WsFVeTIysggLK3xbr/DwsPz9gaQ+/1lu882vPv9YbvPNrz7/We6z3OabX33+s9xnua0oU9/9jJ+WbeKxJ/tRvUbB316NiqpOq9Zn8egTN/Ham4/w4KN9WbN6Gy/8eVq59Vg/f5b7LLf55lef/6z3WWb93FnvE/9Zf23V5z/Lbb751ec/y32W23zzq89/lvsst4kIVAp0QHE8/7nONDIykqpVqxISUvwaaWhoyb/hnpmZSWZmZoFt4eFZ+T+M/BEREUZWVnYRc2Xl7w8k9fnPcptvfvX5x3Kbb371+c9yn+U23/zq85/lPsttv/fF5yt464253HTLZdx6x5UF9u3dk8iQ+//OS3+9jx5XdwLgyu7tadSoDs+PmsbS7zbQtduFZd5k/fxZ7rPc5ptfff6z3meZ9XNnvU/8Z/21VZ//LLf55lef/yz3WW7zza8+/1nus9wmIoaveBw8eDA1atSgVq1aZGRksHr16iLHbd68mUaNGpV4vHHjxlGzZs0Cj3HjJjs1RkfXJjExqdB237Z69eo4Hd+V+vxnuQ3U58JyG6jPleU+y22gPleW+yy3/daPP/zC6JH/4LLLL+TZ0XcX2j9vzg9kZWbT7cq2BbZfcVU7ANau3l4uXdbPn+U+y22gPlfW+yyzfu6s94n/rL+26vOf5TZQnyvLfZbbQH2uLPdZbhMRowuP9957L7fffju33HILt9xyCwMHDsy/AvK30tLS+OCDD7jkkktKPObIkSNJTk4u8Bg58kGnzlatWrBzZzxpaccKbF+3bgsArVu3cDq+K/X5z3IbqM+F5TZQnyvLfZbbQH2uLPdZbvPZsH4HTz/+Nudf0IxXXhtS5Oc1Hj6cgtcLOTm5BbafOJEDFN5eVqyfP8t9lttAfa6s91lm/dxZ7xP/WX9t1ec/y22gPleW+yy3gfpcWe6z3CZlw+M5Mx7ByuTC4z/+8Y9Cj7FjxxYaV7lyZdasWcMbb7xR4jHDw8OpUaNGgYfLbVYBevfuSk5OLrNmLcjflpWVzezZX9OuXQwNG0Y7Hd+V+oKzDdQXrG2gPleW+yy3gfpcWe6z3AawY/t+nnj4TRo1rsPEt4YWe0ucZs3r4/V6+WrBqgLbF3y2AoCY1meVS5/182e5z3IbqM+V9T7LrJ87633iP+uvrfqCsw3U58pyn+U2UJ8ry32W20TE8Gc8lkZ4eDi1a9cmKSmJmjVrVvj87drF0Lt3V157bTqHDyfTrFlDPv54IfHxCYwd+3iF96jvzGhTX/C2qS+4+yy3qS+4+wLZNvP9haSlHicx4SgASxavI+Fg3q1vbr/7KkJCQhg6ZAIpKce4575r+G7JhgJf3+SsaNq1PweAvjddyv/7x5eMHTODLZt3c/Y5jdi8aTdzPvqec85tRPceHcrlOVh+ba33WW5TX/D3AcyYMZ+UlHQSEg4DsGjRcg4cyPvngQP7UL16ZEC6rJ87631g97X1sdpn/bVVX3C2qS+4+yy3qS+4+yy3iQh4vF6vN9ARLsaOHcvo0aPJycnx46u3Os+fmZnFhAkzmDdvMcnJacTENOeJJwbQrVtH52OXBfUFZxuoL1jbQH2uLPdZbgP1ubLcV55t6Sf2F7vv+l4j2b/vcJH75n/5VwD6XP1ssV/f98YujPnrffl/TjiYxNv/8wkrl28h4eBRataKpNsVbRk67CaioqoXeYzISg1L8zROyvJrC7b7LLeB+lxZ7+vefTDx8QlF7vvmmyk0aVK/gov+y/q5s95n+bUF233WX1v1BWcbqM+V5T7LbaA+V5b7yretZRkcQ1ycN3lJoBMqxLYHLw90QrnQwqOIiIiI+O1kC48WlMXCo4iIiIiIiJxJtPAYaC3/98xYeNz6QHAuPJq81er06dNLPXbNmjXlWCIiIiIiIiIiIiIiIiIipWFy4XHQoEF4PB5KezGmx+Mp5yIRERERERERERERERERORmTC49RUVG0b9+e8ePHlzh26tSpTJ48uQKqRERERERERERERERERKQ4JhceY2Nj2bx5M506dSpx7IIFCyqgSEREREREREREREREREROJiTQAUWJjY1l165dJCQklDi2Vq1aNG3atAKqREREREREREREREREpDx5PGfGI1h5vKX9IMUKlJ6ezqFDh2jUqBGVK1cux5m2luOxRURERIJf+on9gU44qchKDQOdICIiIiIiIqeVloEOOOPFTFkS6IQKseUPlwc6oVyYvNVqZGQkkZGRgc4QERERERERERERERERkVIyeatVERERERERERERERERETm9mLziUURERERERERERERERM48IUH8+YdnAi08ioiIiIjfrH+GYpWmzwc64aSO7x4T6AQRERERERERkTKjW62KiIiIiIiIiIiIiIiIiDMtPIqIiIiIiIiIiIiIiIiIMy08ioiIiIiIiIiIiIiIiIgzfcajiIiIiIiIiIiIiIiImODxBLpAXOiKRxERERERERERERERERFxpoVHEREREREREREREREREXGmW606ysrKZuLE95k7dxEpKWnExDRn2LABdO3aIdBpgO2+9PTjTJ06m3XrtrJhw1aSk9MYN+4J+vXrGeg0wPa5A/UFaxuoz5XlPsttoD5Xlvsst0H590VWDefJh/rSuf25XNT+HGrXqsYDT73NjA+XFBrr8Xj4w909GHx3D1qe04hjxzPZ8Mtuhr84nQ2bduePGz70Jjp3OJfO7c+lfnRN/vL6h4x9/aNCxzvv7IY8MKAnndufS/s2zYmICCPm0sfYvfdQmTy3bdt28eabH7Bx468cOpREREQ4557blMGD+9G9e2yZzOHiTP/ec6W+4GwD9bmy3Ge5DdTnQn+H4UZ9biz3WW4D9bmy3Ge5TeRMd9pd8Zibm0tqamqgM/KNGDGBadPm0LfvFYwa9QChoSEMGTKGlSs3BjoNsN2XlJTCpEkz2bFjDzExLQKdU4jlcwfqC9Y2UJ8ry32W20B9riz3WW6D8u+rU7s6o4bdQqtzG7Hhl10nHTv5bw/y9zH3smZDHE+Nnsa4ibPZs+8Q0XVrFhg3ZvjtdGp3Nus27jzp8S7ueB6P3NebatUi2PzrPtenUsi+fYmkpx/n5pt7MGrUEB555A4AHn74JWbNWlDm852qM/17z5X6grMN1OfKcp/lNlCfC/0dhhv1ubHcZ7kN1OfKcp/lNnHn8ZwZj2Dl8Xq93kBH/N6GDRtISEigR48e+du+/PJL/vKXv7B8+XKys7OJiIigR48e/PWvf6VNmzZ+zrTVqXP9+q3ceuvTDB9+H4MH9wMgMzOLPn2GUqdOTWbOfNXp+K6s92VlZZOcnEZ0dBQbNmyjf/+nzPy2oPVzp77gbFNfcPdZblNfcPdZbquIvipNnycsrBJRNSM5mJhMx7Zns3T+2CKveLylzyXMeOsJbn/g73zyxcqTHrdpk7rs3nuIOlHV2bvu3WKveIyqGUn2iRzS0jMYNuR6xv15QIErHo/vHuP0/IqSk5NDv35PkpmZxYIF75T58UvrTP/eU59+7qnvzOuz3KY+d/o7DPWp7/RqU19w95V/W0v3SHFywT8K36EoGG287/JAJ5QLk1c8Dhs2jPfffz//z//+97+59tpr2bx5M3fffTfDhw/nlltuYcmSJVxyySWsXr06IJ0LFiwlNDSE22/vnb8tPDyM/v17sWbNZvbvTwxIl4/1vrCwykRHRwW0oTjWz536grMN1OfKcp/lNlCfK8t9ltugYvqysk5wMDG5xHGP/+E6Vqz5lU++WInH46FqlfBix5b2VqlJyemkpWeUurUshIaG0rBhXVJT0yt03t/T954b9QVnG6jPleU+y22gPlf6Owz/qc+N5T7LbaA+V5b7LLeJiNGFx3Xr1tGhw3/vxTxixAhiY2OJi4tj6tSpjBs3junTp/Prr7/SpEkTRowYEZDOTZt20Lx5Y6pVq1pge9u2Lf+zPy4QWfms91lm/dypz3+W2/LmV58Ly32W2/LmV58Ly32W2/Lmt9FXvVoVLmp/DqvWbWfM8Ns5uHEqh7dM45fvJ3BLn0sqpMHFsWMZHDmSzO7d+5k2bQ5LlqzikkvaBbTJymtbHPW5sdxnuS1vfvW5sNxnuS1vfvUFK+vnTn1uLPdZbsubX30uLPdZbhMRqBTogKIcO3aMyMjI/H+Oi4tj7Nix+dt86taty4MPPshzzz0XiEwSE48U+dtuvm0JCYcrOqkA632WWT936vOf5TZQnyvLfZbbQH2uLPdZbgM7fWc3q09ISAj9b7iUEydyGPXX/yMl9RiP3n8t0//nMVJSj/PVt+sqpMUfL788Nf8zHUNCQujVqwujRz8U0CYrr21x1OfGcp/lNlCfK8t9lttAfcHM+rlTnxvLfZbbQH2uLPdZbhMRowuPMTExLFu2jPvvv58qVapQrVo1UlJSihybkpJC5cqVSzxmZmYmmZmZBbaFh2cRHh7md2dGRhZhYYXn9h0zIyPL72OXBet9llk/d+rzn+U23/zq85/lPsttvvnV5z/LfZbbfPNb6KsWmXdb1bq1q3P5DX9mxdrtAMz/ahWblr7BiMdvMr3weO+9N9C7d1cSEo7w+effkZubS3Z2dkCbrLy2xVGfG8t9ltt886vPf5b7LLf55ldfcLJ+7tTnxnKf5Tbf/Orzn+U+y21SNjweT6ATxIHJW60OGTKE6dOn88knn+DxeHjsscd44YUXWLlyZYFx33zzDa+//jo9evQo8Zjjxo2jZs2aBR7jxk126oyICCMrq/Bf6GRmZuXvDyTrfZZZP3fq85/lNt/86vOf5T7Lbb751ec/y32W23zzW+g7npHXELf7YP6iI0D6sUw++3o1F7U7l9BQk//pDMA555zFpZe256abujN58vMcO3achx56Ca/XG7AmK69tcdTnxnKf5Tbf/Orzn+U+y22++dUXnKyfO/W5sdxnuc03v/r8Z7nPcpuIGF14fOihh7jzzju56aab6NKlC6mpqWRnZ3PxxRdz7rnn0rVrV5o3b87VV19NZGQkf/vb30o85siRI0lOTi7wGDnyQafO6OjaJCYmFdru21avXh2n47uy3meZ9XOnPv9ZbgP1ubLcZ7kN1OfKcp/lNrDTt/9g3nwJicmFWw4nExZWiciq4RXSUhauuaYrGzZsIy4uPmANVl7b4qjPjeU+y22gPleW+yy3gfqCmfVzpz43lvsst4H6XFnus9wmIkYXHj0eD9OmTWPmzJkATJo0icOHD+P1etmxYwfLli0jKyuLxx9/nDVr1tC8efMSjxkeHk6NGjUKPFxuswrQqlULdu6MJy3tWIHt69ZtAaB16xZOx3dlvc8y6+dOff6z3Abqc2W5z3IbqM+V5T7LbWCnb//BJPYnJNGoQe1C+xrWj+J4RhapaRkV0lIWfLcW+v15rUhWXtviqM+N5T7LbaA+V5b7LLeB+oKZ9XOnPjeW+yy3gfpcWe6z3CYiRhcefW677TaWLVvG0aNHWb16NUuWLGHFihXEx8ezb98+Xn/9derVqxewvt69u5KTk8usWQvyt2VlZTN79te0axdDw4bRAWsD+32WWT936gvONlCfK8t9lttAfa4s91luA1t9H837kbMa16V7twvzt9WJqk6fXhexeOnGgN62tDiHDx8ttC07+wRz5y4kIiKMc845q+Kj/sPSa1sU9bmx3Ge5DdTnynKf5TZQXzCzfu7U58Zyn+U2UJ8ry32W26RseELOjEewqhTogNKoXr067du3L3JfamoqSUlJNG3atGKjgHbtYujduyuvvTadw4eTadasIR9/vJD4+ATGjn28wntOtz6AGTPmk5KSTkLCYQAWLVrOgQN5/zxwYB+qV48MSJf1c6e+4GxTX3D3WW5TX3D3WW6ryL6H7r2amjUiaVg/CoDre3akccO82++8PW0BKanHeXXSXG7pcwkfvDOMN6Z8RkrKMf4woCeVK4fy/PiZBY53Z7/LaNo4mqpV8u6gcVlsa/702M0AfDD7O3bHHwKgRvUqPDyoNwBdLmoJwMP3XsPRlGMkp6Q7P6/RoyeRlnaMzp3bUL9+bRITjzJv3mJ27NjLiBGDiYys4jyHv/S9pz61qe9M67Pcpr6yob/DUJ/6Tp829QV3n+U2EQGP1+Kvbp+CsWPHMnr0aHJycvz46q3O82dmZjFhwgzmzVtMcnIaMTHNeeKJAXTr1tH52GXBel/37oOJj08oct8330yhSZP6FVz0X9bPnfqCsw3U58pyn+U2UJ8ry32W26B8+6o0fR6AzUvfoNlZRf/Wa8ylj7F7b95CYfOm9Xh51N1c2bUNlSuH8tPqbTw37gNWrd9R4Gu+mPUcl3c5v8jjXX3bi3z34yYAmjapy5Yf3ixy3K49iTQ7q/DnkpyKTz9dwocffsXWrTs5ejSVyMgqXHDBuQwY0IcePS52OnZZOJO/98qC+oKzDdTnynKf5TZQnyv9HYb/1OfGcp/lNlCfK8t95dvWsgyOIS4unP5doBMqxIZ7ugU6oVxo4VFEREREgpZv4dGq47vHBDpBRERERERECtDCY6Bp4fH0ZvJWq9OnTy/12DVr1pRjiYiIiIiIiIiIiIiIiIiUhsmFx0GDBuHxeCjtxZgej6eci0RERERERERERERERKS8acnn9GZy4TEqKor27dszfvz4EsdOnTqVyZMnV0CViIiIiIiIiIiIiIiIiBTH5MJjbGwsmzdvplOnTiWOXbBgQQUUiYiIiIiIiIiIiIiIiMjJhAQ6oCixsbHs2rWLhISEEsfWqlWLpk2bVkCViIiIiIiIiIiIiIiIiBTH5MLj8OHDiYuLIyoqqsSxjz76KHFxcRVQJSIiIiIiIiIiIiIiIiLFMXmr1cjISCIjIwOdISIiIiIiIiIiIiIiIhXI4wl0gbgwecWjiIiIiIiIiIiIiIiIiJxeTF7xKCIiIiJSFo7vHhPohJN6fFl8oBNO6o0ujQOdICIiIiIiIiKnEV3xKCIiIiIiIiIiIiIiIiLOdMWjiIiIiIiIiIiIiIiImKDPeDy96YpHEREREREREREREREREXGmhUcRERERERERERERERERcaaFRxERERERERERERERERFxpoVHEREREREREREREREREXFWKdABIiIiIiIiIiIiIiIiIgAhnkAXiAstPDrKyspm4sT3mTt3ESkpacTENGfYsAF07doh0GmA+vy1fv1W5sxZyE8/rSc+PoFatarTrl0Mw4YNpEWLxgFt87F67nws91luA/W5stxn/WeL5XMHkJ5+nKlTZ7Nu3VY2bNhKcnIa48Y9Qb9+PQOdBtg+f5bbwHZfIN+3KTt38+uc+SRt+5Xc7GyqRkfT5MrLaNare/6Y3BMn2Pn5V+xb+iPHDx2mUtUq1GjejAsG3U1E7SgANvzvNPYt/bHYea54fRwRUVHl8hy2bdvFm29+wMaNv3LoUBIREeGce25TBg/uR/fuseUy56mw/nPFep/l967lNlCfK8t9lttAfa4s91luA/W5stxnuQ3U58pyn+U2kTOdyVutXnPNNbz22mvs27cv0CklGjFiAtOmzaFv3ysYNeoBQkNDGDJkDCtXbgx0GqA+f02Z8hFffvkDXbq0Y9SoB7jttt6sXLmRfv2GsXXrroC2+Vg9dz6W+yy3gfpcWe6z/rPF8rkDSEpKYdKkmezYsYeYmBaBzinE8vmz3Aa2+wL1vj308y/8+JfxZKWkcs4N19HqrtuIbnchGUeS8sfknshh9ev/w455n1Pnwgtofc+dtLj2akLDwzhx/Hj+uLOuupwLh9xX8PHAIELDwohs1LDcFh0B9u1LJD39ODff3INRo4bwyCN3APDwwy8xa9aCcpu3tKz/XLHeZ/m9a7kN1OfKcp/lNlCfK8t9lttAfa4s91luA/W5stxnuU3kTOfxer3eQEf8XkhICB6PB4/Hw5VXXsndd9/NLbfcQo0aNcp4pq1OX71+/VZuvfVphg+/j8GD+wGQmZlFnz5DqVOnJjNnvloWkeoLgNWrN9GmzbmEhVXO37Zz5z769h3KNdd05W9/ezpgbWD73Fnvs9ymvuDvs/yzxfq5g7zfZkxOTiM6OooNG7bRv/9TZq78sXz+LLedDn3l/b59fFl8oW0njh/nuz89T63zzqb9o0PwhBT9u4Jxn33Bto8+IXbUM9Q6+9QWpZK2/sryv/6N8265kbP7XlvsuDe6lP1VnTk5OfTr9ySZmVksWPBOmR//VFj+uQK2+yy/dy23qS+4+yy3qS+4+yy3qS+4+yy3qS+4+8q/raV7pDjp+H/fBTqhQqy+q1ugE8qFySseAR5//HEGDRrE6tWrGTx4MA0aNODWW29lzpw5ZGdnBzoPgAULlhIaGsLtt/fO3xYeHkb//r1Ys2Yz+/cnBrBOfS46dmxd4C8YAZo3b8R55zVlx449Aar6L8vnDmz3WW4D9bmy3mf5Z4v1cwcQFlaZ6OjyuzLLheXzZ7kN7PcF4n27f9kKslJSOO+WG/GEhHAiMxNvbm6BMd7cXHZ9uZD6ndpT6+wW5ObkkJOZVfo5flwOHg8NL+lc1vklCg0NpWHDuqSmplf43L9n+ecK2O6z/N613Abqc2W5z3IbqM+V5T7LbaA+V5b7LLeB+lxZ7rPcJmXD4zkzHsHK7MJj586dmTJlCgcOHOCjjz7iuuuu49NPP+WWW26hfv36DBkyhG+//TagjZs27aB588ZUq1a1wPa2bVv+Z39cILLyqa9seb1eDh06SlRUWV95e+qsnzvLfZbb8uZXnwvrfUWx8rPldDx3llg+f5bb8ua33VeU8n7fHv5lE5WqRJCZdJTvRjzPNw8+wTcPP8kv//w/crLyfgEvbd9+Mo8mU61JYzb+YwZfP/gEXz/4OEv//BKHN2056fFzT+RwYPkqap17NlWi65bLc/i9Y8cyOHIkmd279zNt2hyWLFnFJZe0q5C5pXxYfu9absubX30uLPdZbsubX30uLPdZbsubX30uLPdZbsubX30uLPdZbhMRwwuPPmFhYdx88818+OGHHDx4kClTptCxY0fee+89unfvzllnncXw4cMD0paYeKTI30D2bUtIOFzRSQWor2x98sliDh48zLXXBv7yZ+vnznKf5TZQnyvrfUWx8rPldDx3llg+f5bbwH5fUcr7fZt+MAFvTi5rJr5N3Tbn037ogzTudil7Fi3h56n/BODYwQQAdn35DUc2b+WCe++izeB7yM3OZtXf3yR1z95ij3/o541kp6XTsEtsufQX5eWXp9KlywB69RrCK6/8g549uzB69EMVNr+UPcvvXcttoD5Xlvsst4H6XFnus9wG6nNluc9yG6jPleU+y20ichosPP5W9erVue+++/j666/Zu3cvf/vb36hfvz5///vfS/zazMxMUlJSCjwyT+F2VEXJyMgqdOstyLus27c/kNRXdrZv38OLL75Dhw6tuPnm7oHOMX/uLPdZbvPNrz7/We/7PUs/W063c2eN5fNnuc03v+W+797a/AABAABJREFU36uI921ORiY5WVk06noJrQfcTv2LOtB6wO00ubIbB35aSfqBg+RkZAJwIiOTi4YPo3G3S2nc7VIuGj4MvF7iPvuy2OPv/3EFntBQGsR2Kpf+otx77w384x8v8corT3L55R3Jzc018/EJ4h/L713Lbb751ec/y32W23zzq89/lvsst/nmV5//LPdZbvPNrz7/We6z3CYip9nC4281aNCAJ598kpUrV7Jp06YSx48bN46aNWsWeIwbN9mpISIijKyswn9h4lvQjIgIczq+K/WVjcTEJB588EWqV6/KxIkjCA0NDXSS+XNnuc9ym29+9fnPet9vWfvZcjqdO4ssnz/Lbb75Lff9VkW9b0PD8p5zg999/qLvCsWj2+MI+c//yY467xyq1KmdP6ZKndpEtTyXo7/uKPLYJzIySFy9jrptziesWrXyyC/SOeecxaWXtuemm7ozefLzHDt2nIceegmv11thDVK2LL93Lbf55lef/yz3WW7zza8+/1nus9zmm199/rPcZ7nNN7/6/Ge5z3KbiJzGC4+/1bJlyxLHjBw5kuTk5AKPkSMfdJo3Oro2iYlJhbb7ttWrV8fp+K7U5y41NZ0HHniB1NR0pkwZQ/36gW8C++fOcp/lNlCfK+t9PhZ/tpwu584qy+fPchvY7/OpyPdteK2aef9bo3qB7WHV8/58Ij2d8Fq18rb9boxvXHb6sSKPnbB6HTlZWRV6m9WiXHNNVzZs2EZcXHxAO8R/lt+7lttAfa4s91luA/W5stxnuQ3U58pyn+U2UJ8ry32W26RseDxnxiNYmVx4zM3N5a677irTY4aHh1OjRo0CD9+l1/5q1aoFO3fGk5ZW8C921q3bAkDr1i2cju9KfW4yM7N46KGX2LkznnfeGc255zYNaM9vWT93lvsst4H6XFnvA7s/W06Hc2eZ5fNnuQ3s90HFv29rNM87fkbS0YIdR/P+XLl6dao3aYwnNJTM343xjQurXvTVjPuXLSc0Ipx6HdqVZfIp89366Pevu5w+LL93LbeB+lxZ7rPcBupzZbnPchuoz5XlPsttoD5Xlvsst4mI0YXHU5Gamsru3bsDMnfv3l3Jycll1qwF+duysrKZPftr2rWLoWHD6IB0+ajPfzk5OQwbNp61azczceIIOnRoFbCWolg+d2C7z3IbqM+V9T7LP1usnzvrLJ8/y21gvy8Q71vfZy/GL1laYPveJUvxhIZQu1VLKlWJILptG47+uoO0fQfyx6Tt28/RX3dQ54LWhY6blZLK4V82Ub9je0IdfwGvtA4fPlpoW3b2CebOXUhERBjnnHNWhXRI2bP83rXcBupzZbnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wmIlAp0AGu3njjDUaPHk1OTk6Fz92uXQy9e3fltdemc/hwMs2aNeTjjxcSH5/A2LGPV3iP+srOyy+/x8KFP3HVVbEcPZrK3LmLCuy/8carAlSWx/K5s95nuU19wd9n+WeL9XPnM2PGfFJS0klIOAzAokXLOXAg758HDuxD9eqRAemyfP4st50OfYF439Zo1pTG3S4l/rsf8ObmEhVzHkc2b+XgitW06NObiKhaAJzX/0YOb9rMyvGv07RnXsfurxdROTKSs/v2LnTc/ctX4s3JrdDbrI4ePYm0tGN07tyG+vVrk5h4lHnzFrNjx15GjBhMZGSVCmspjtWfKz5W+yy/dy23qS+4+yy3qS+4+yy3qS+4+yy3qS+4+yy3iQh4vF6vN9ARLsaOHeuw8LjVef7MzCwmTJjBvHmLSU5OIyamOU88MYBu3To6H7ssqM8/AweOZPnyn4vdv2XLvAqsKZrVc+djuc9yG6jPleU+6z9bLJ87n+7dBxMfn1Dkvm++mUKTJvUruOi/LJ8/y21gu6+837ePLyv6Mw5zT+SwY/7nxH/3A5lHk6lStzZndb+S5tf0KDAuZedutv57Nkd/jQOPhzqtY2h5ez8iGxR+L/z40iscTzzElRNewRNSuhufvNGl8ak/qd/49NMlfPjhV2zdupOjR1OJjKzCBRecy4ABfejR42KnY5cVyz9XwHaf5feu5TZQnyvLfZbbQH2uLPdZbgP1ubLcZ7kN1OfKcl/5trUsg2OIi87/+j7QCRVixW2XBTqhXJhceJw+fXqpx37yySd8/PHHAVt4FBERERHxV3ELj1a4LjyKiIiIiIicfrTwGGhaeDy9mbzV6qBBg/B4PJR2TdTj8ZRzkYiIiIiIiIiIiIiIiIicjMmFx6ioKNq3b8/48eNLHDt16lQmT55cAVUiIiIiIiIiIiIiIiIiUpxSLTyeyq1Pi3LPPfec0vjY2Fg2b95Mp06dShy7YMECf7NEREREREREREREREREpIyUauHRd+vTU+X1evF4PH4tPH7xxRckJCRQr169k46tVasWTZs2PeU2ERERERERERERERERsUWfrnd6K9XC4+jRoyv0cxSHDx/O/fffT1RUVIljH330UR599NEKqBIRERERERERERERERGR4pRq4fGFF14o54yCIiMjiYyMrNA5RURERERERERERERERMR/IYEOEBEREREREREREREREZHTX6mueDyZI0eOsGrVKg4dOkSzZs249NJLy6JLREREREREREREREREzjD6jMfTm98Lj4mJiTzxxBN8+OGH5OTkAHDvvffmLzxOmTKF4cOH88knn3DZZZeVTa2IiIiISBB5o0vjQCecVGbO0UAnFCs8tFagE0RERERERETkd/y61eqRI0e49NJLmTlzJm3atOGRRx7B6/UWGNOvXz9SU1P58MMPyyRUREREREREREREREREROzya+Fx7NixbN++ndGjR7N69WrefPPNQmNq165N27Zt+fbbb50jRURERERERERERERERMQ2vxYe58yZQ8uWLXnhhRdOOu6cc84hPj7enylERERERERERERERERE5DTi12c8xsfHc+ONN5Y4zuPxkJKS4s8UIiIiIiIiIiIiIiIicobxeAJdIC78uuKxRo0a7N+/v8Rx27dvJzo62p8pREREREREREREREREROQ04tfCY+fOnVmxYgVxcXHFjlm3bh1r166la9eufseJiIiIiIiIiIiIiIiIyOnBr1utPvbYY3z++efcfPPNfPDBB7Ru3brA/l9//ZWBAwfi9XoZOnRomYRalZWVzcSJ7zN37iJSUtKIiWnOsGED6Nq1Q6DTAPUFaxuoL1jbQH2uLPdZbgP1ubLcZ7kN1Odi27ZdvPnmB2zc+CuHDiURERHOuec2ZfDgfnTvHluhLbt2HmDSmx+yevVWUpLTadCwDtdd34V777uOKlXCiY9P5NpeTxX79f36X8kLLw6uwGLbry2oL1jbQH2uLPdZbgP1ubLcZ7kN1OfKcp/lNlCfK8t9lttEznR+XfHYu3dvhg8fzvr162nTpg2tWrXC4/HwxRdf0K5dO1q3bs3PP//Ms88+y2WXXVbWzaaMGDGBadPm0LfvFYwa9QChoSEMGTKGlSs3BjoNUF+wtoH6grUN1OfKcp/lNlCfK8t9lttAfS727UskPf04N9/cg1GjhvDII3cA8PDDLzFr1oIK6ziw/zB33fE869dt5867ejF8xN20a3cub/3PbP70zFsAREVV568vP1TocX2fSwG49NI2FdbrY/m1BfUFaxuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9ym7gL8ZwZj2Dl8Xq9Xn+/+N///jdjx45l/fr1Bba3atWK5557jjvvvNPvsDlz5vDPf/6TKlWqMGzYMGJjY9mxYwejRo1i6dKlnDhxgo4dOzJy5EiH27lu9bsPYP36rdx669MMH34fgwf3AyAzM4s+fYZSp05NZs581en4rtQXnG3qC9429QV3n+U29QV3n+U29ZWPnJwc+vV7kszMLBYseMfpWJk5R0s17n8nf8KbE//N7LnjOPe8JvnbR42czLy53/P9sneoUTOyyK994P6X2fjzDhZ99z+Eh4eVui08tFapxxbF+murvuBsU19w91luU19w91luU19w91luU19w95V/W0v3SHFy6ezvA51QIX7oF5wX7vl1xaPPrbfeytq1azl48CA//fQTy5YtY8+ePfzyyy9Oi46fffYZ/fr145tvvmHx4sV0796dn376icsuu4yvvvqKjh070qFDB5YsWcJVV13FkiVLXJ6G3xYsWEpoaAi33947f1t4eBj9+/dizZrN7N+fGJAuH/UFZxuoL1jbQH2uLPdZbgP1ubLcZ7kN1FceQkNDadiwLqmp6RU2Z3r6cQDq1K1ZYHt0dC1CQjxUqhxa5NclJh5lxfJf6NHzolNadCwL1l9b9QVnG6jPleU+y22gPleW+yy3gfpcWe6z3Abqc2W5z3KbiDguPPpER0fTuXNnLr74Yho3bux8vPHjx9O+fXvi4+PZt28f9957LzfccAP16tVj27ZtzJkzh08//ZRffvmFBg0a8NJLL5XBszh1mzbtoHnzxlSrVrXA9rZtW/5nf1wgsvKpz3+W2/LmV5+/LLflza8+F5b7LLflza8+F5b7LLflza++snDsWAZHjiSze/d+pk2bw5Ilq7jkknYVNv9FnfM+8/35P09h86ZdHNh/mAWf/8i/Zn7DXQOupmrViCK/bsFny8jN9ebfbrUiWX9t1ec/y21586vPheU+y21586vPheU+y21586vPheU+y21586vPheU+y20iApVcDxAfH8/333/Pvn37AGjUqBFdu3alSZMmJXxl8X7++WdGjx5N9erVAXj88cd5++23GT9+PFFRUfnjmjRpwsMPP8zLL7/s9iT8lJh4hOjoqELbfdsSEg5XdFIB6vOf5TZQnwvLbaA+V5b7LLeB+lxZ7rPcBuorKy+/PDX/Mx1DQkLo1asLo0c/VGHzX9atLUMfv4Up785j8aLV+dsfePAGHnvi1mK/7tP5y4iOrkXsJedXRGYB1l9b9fnPchuoz5XlPsttoD5Xlvsst4H6XFnus9wG6nNluc9ym4g4LDzu27ePoUOH8sknn/D7j4n0eDz07duXN998068FyJycHCIi/vtb0b5/9i1E/laNGjVO+fhlJSMji7CwyoW2+24TlZGRVdFJBajPf5bbfPOrzz+W23zzq89/lvsst/nmV5//LPdZbvPNrz539957A717dyUh4Qiff/4dubm5ZGdnV2hDo0bRdLwohp69OlOrVjW++3YtU96dR926tbjz7l6Fxu/cuZ9fNsYx8N7ehISUyY1YTon111Z9/rPc5ptfff6z3Ge5zTe/+vxnuc9ym29+9fnPcp/lNt/86vOf5T7LbVI2PJ5AF4gLvxYe9+3bR5cuXdizZw9Vq1bl6quvpnnz5gDs2rWLL7/8krlz57Jq1Sp+/PFHGjVqdErHb9WqFZ9++ilDhgwBYN68eQDMnz+ffv36FRg7e/Zszj333BKPmZmZSWZmZoFt4eFZTp8lExERRlZW4b/QyczMyt8fSOrzn+U23/zq84/lNt/86vOf5T7Lbb751ec/y32W23zzq8/dOeecxTnnnAXATTd15/77n+Ohh17i3//+O54K+H9sn3+2jBdfeI9PPnuVBg1qA9CzV2dyc728/tpMrr3+EmrVKvhLhJ/O+wEgILdZBfuvrfr8Z7nNN7/6/Ge5z3Kbb371+c9yn+U23/zq85/lPsttvvnV5z/LfZbbRMTPz3h89tln2bNnD3fffTe7d+9m9uzZvPbaa7z22mt89NFH7N69m4EDB7J3715GjRp1ysd/8sknmTdvHpdccgk33HADTz31FD169KBq1arceOONTJ06lXfffZcePXqwePFiBg8eXOIxx40bR82aNQs8xo2b7M/TzxcdXZvExKRC233b6tWr43R8V+rzn+U2UJ8Ly22gPleW+yy3gfpcWe6z3AbqKy/XXNOVDRu2ERcXXyHzzfrgG1q1bpa/6OhzZfeOZBzPYvMvuwp9zeefLqN5i4acf0GLCmn8Peuvrfr8Z7kN1OfKcp/lNlCfK8t9lttAfa4s91luA/W5stxnuU1E/Fx4/Pzzz2nRogXTpk2jdu3ahfZHRUXx3nvv0aJFCz799NNTPv5tt93GxIkTSUpKYsWKFdxyyy188MEHvPLKK2RlZfHAAw/w0EMP8e233zJ48GAeeqjkz68ZOXIkycnJBR4jRz54ym2/1apVC3bujCct7ViB7evWbQGgdevA/CWKj/r8Z7kN1OfCchuoz5XlPsttoD5Xlvsst4H6yovv1kK/7y4vhw8nk5OTW2j7ieycvP/93b71635l9+6DAbvaEey/turzn+U2UJ8ry32W20B9riz3WW4D9bmy3Ge5DdTnynKf5TYR8XPhMS0tjUsuuYTQ0NBix4SGhnLxxReTnp7uV9hjjz3Gli1b2L9/Px988AF169YlMjKSzz//nO3bt/PDDz9w4MAB3n333VJ9Jkx4eDg1atQo8HC5zSpA795dycnJZdasBfnbsrKymT37a9q1i6Fhw2in47tSX3C2gfqCtQ3U58pyn+U2UJ8ry32W20B9rg4fPlpoW3b2CebOXUhERFj+7VfLW7PmDdi8aRc7d+4vsP3zz5YREuKhZUzBjs8+XQbAddd3qZC+olh/bdUXnG2gPleW+yy3gfpcWe6z3Abqc2W5z3IbqM+V5T7LbVI2PCFnxiNY+fUZj61bt2bfvn0ljtu/fz+tWrXyZ4qTatGiBS1a5P3WQmpqKklJSTRt2rTM5ylJu3Yx9O7dlddem87hw8k0a9aQjz9eSHx8AmPHPl7hPeo7M9rUF7xt6gvuPstt6gvuPstt6nM3evT/Z+/e43uu//+P394b2xznNOeYyEgRHymmksPHFCopPoXIJx0+kko+dNBRdKY++lTfSH58PqSPYwflrBJaCSFTCHPYDJsNO//+WO/VbE7v17bXY2/36+Xyvnw+vV6vvZ63PV9meO71ek8iOfk4V155GTVqVCE+/igLF65gx469jBo1mHLlyhRLx8C7b+SbrzYysP8L/O2OLoRWKs+qFev5+quN9OrdgerVK+cem5mZxRefr6V5i0ZcVK9GsfQVxPq1VZ9/tqnPv/sst6nPv/sst6nPv/sst6nPv/sst4kIeLKzs7PP94OmT5/OXXfdxWeffUbXrl0LPObLL7+kW7dufPDBBwwYMMBx6OmMHTuWMWPGkJmZ6cNHxzgePzU1jQkTprNw4QoSE5OJiAjnoYf6cc01rRyfuzCozz/bQH3+2gbqc8pyn+U2UJ9Tlvsst4H6nPj001V8/PFiYmJ2cfToMcqVK0OzZo3o1687nTpd5fj8qZlHz/nYTRt/5d+T5vDz1t84ejSZOnXD6HnTNQwafCOlSv3xpJRvvt7I/UNeYdTj/bmj3199bgsOrOTzx3pZvragPn9tA/U5ZbnPchuozynLfZbbQH1OWe6z3Abqc8pyX9G2NS6Ec4gT7ed/7XZCsfj6pvZuJxSJc1p43L17d75tr7zyCu+99x59+vShT58+1K9fH4DffvuNjz76iFmzZjFkyBBGjBhRpHcjur3wKCIiIiLir85n4bG4FcbCo4iIiIiISH5aeHSbFh5LtnNaeAwICMDj8eTbnp2dXeD2P+/zeDxkZGScV9S0adPO+dgFCxYwd+5cLTyKiIiIiBQyLTyKiIiIiMiFRwuPbtPCY8l2Tu/xeO211552gbEoDBw4EI/Hw7k+BbY420RERERERERERERERKRoaMmnZDunhccVK1YUcUZelStX5oorruDll18+67GTJ0/m3XffLYYqERERERERERERERERETmdc1p4LG5t2rTh559/5i9/+ctZj120aFExFImIiIiIiIiIiIiIiIjImQS4HVCQNm3a8NtvvxEXF3fWYytVqkS9evWKoUpERERERERERERERERETseTfa5vpFiA48ePs3z5crZv386xY8cKfE9Gj8fDU089dV7nTUlJ4dChQ9SuXZvSpUv7mncOYorw3CIiIiIiJVtq5lG3E04rOLCS2wkiIiIiIuKXGrsdcMG7duE3bicUi1U9It1OKBI+P2p16tSpPPzwwyQlJeVuy87OxvOnd/30/vf5LjyWK1eOcuXK+ZomIiIiIiIiIiIiIiIiIsXMp0etLlmyhMGDB+PxeHj88cdp27YtAO+++y6PPfYYjRo1Ijs7m6FDhzJlypRCDRYRERERERERERERERERe3x61Gq3bt1YvHgx33//PS1atGDQoEFMmzaNzMxMADIyMhg5ciTvvfcea9as4bLLLiv08MKhR62KiIiIiJREvyZtczvhjBpWjHA7QUREREREfKJHrbpNj1ot2Xy64/G7777j6quvpkWLFgXuL1WqFK+++irVq1fn6aefdhQoIiIiIiIiIiIiIiIiIvb59B6PycnJ1KtXL/e/g4ODATh27BgVKlQAICAggKuuuoqlS5cWQqaIiIiIiIiIiIiIiIj4O4/H7QJxwqc7HmvWrMnhw4dz/7tWrVoAxMTkfXTp4cOHOXHihIM8ERERERERERERERERESkJfFp4bNKkCdu3b8/973bt2pGdnc3LL7+M9y0jV69ezbJly4iI0HubiIiIiIiIiIiIiIiIiPg7nxYeb7zxRnbu3Mm6desA6NSpE82bN+fjjz+mTp06/OUvf+H6668nKyuL4cOHF2aviIiIiIiIiIiIiIiIiBjk03s8DhgwgMaNG1OjRg0g5/0cP/30UwYPHsySJUs4cOAAoaGhjBw5kn79+hVqsIiIiIiIiIiIiIiIiPgnvcdjyebTwmNoaChdu3bNs61OnTosWrSI48ePk5iYSPXq1QkMDCyUSBERERERERERERERERGxzaeFxzMpW7YsZcuWBWDKlCns3buXMWPGFPYwZqSlpTNx4gzmz19OUlIyERHhDB/ej8jIlm6nAerz1zZQn7+2gfqcstxnuQ3s96WknGDy5Dls2BDDpk0xJCYmM27cQ/Tq1dntNMD2/FluA/U5ZbnPjbaYzbtZ8mk0G6N/5eD+w1QMLUfE5fUYcF836tYPy3Pswo++5pPZq9kfm0BopXJc0+UKBtzXlZAywac9//LPf+CVMf8hpEwQc1a9WGSfx8aNMcybt4y1azcSGxtHpUoVaNEiguHD+9OgQZ0iG/d86Nee79TnjOU+y22gPqcs91luA/1Z3inLfZbbQH1OWe6z3CZyofPpPR7P1f/93//x7LPP+vzxn376Kf379+fSSy8lNDSUoKAgqlSpwtVXX81TTz3F3r17C7HWN6NGTWDq1Hn06HEdTzxxD4GBAQwZ8izR0ZvdTgPU569toD5/bQP1OWW5z3Ib2O87ciSJSZNmsmPHHiIiGridk4/l+bPcBupzynKfG22zpy3nm2WbuKLNJdz76M1E3XI1m3/YybD+b7Drl/25x0156xP+/co86jesyb2P3kRkx+YsnPU1L4z88LTnPnE8lSlvfUJImaAi6/d6//3/8eWXq2nbtgVPPHEPt98eRXT0Znr1Gk5MzG9FPv650K8936nPGct9lttAfU5Z7rPcBvqzvFOW+yy3gfqcstxnuU3kQufJzs7OLqqTt23blnXr1pGZmXleH3f8+HFuvfVWvvzyS/6cV7p0aVq2bMmePXvYv38/5cqV49133+WOO+7wsTDGx4/LsXFjDLfd9igjRw5i8OBeAKSmptG9+1CqVg1l5sxXHJ3fKfX5Z5v6/LdNff7dZ7mtJPRBzk8zJiYmExZWmU2bttO79yNmfkra8vxZblOff/cVdduvSdsK3L5lwy4uubQupUv/8XCX2N3xPPC312jfsTmPPX8Hhw8lcVf3F7iua0tGPPu33OMWfvQ1/35lHk+/Noirrm2W79wfvPUp3678iUuaXsS3K3864x2PDStGOPjs4IcftnLZZY0ICiqdu23Xrn306DGUrl0jefXVRx2d36kL+dee+tRXEtvU5999ltu89Gd5/+yz3KY+/+4r+rbGziPFkQ6ffuN2QrFYcWOk2wlFokjvePTVU089xdKlS3nzzTeJj48nJSWFzz//nNq1a9OhQwdiY2P56aefuO666xgwYACrVq1ypXPRom8IDAygT5+o3G3BwUH07t2F9et/Zv/+eFe6vNTnn22gPn9tA/U5ZbnPchvY7wMICipNWFhltzMKZHn+LLeB+pyy3OdW26UtwvMsOgLUqRdG/YtrsGfXQQC2bvyNzMwsrvvrFXmOu/avOY9lWvnlj/nOG7s7nrn/XcU9D/ckMLDo/xrVqlXTPIuOAOHhtbnkknrs2LGnyMc/G/3a8536nLHcZ7kN1OeU5T7LbV76s7zvLPdZbgP1OWW5z3KbFA6P58J4+SuTC4+zZs3igQce4B//+AdVq1alTJkydO3albfffpvXX3+duLg4Lr30UhYuXEjr1q154YUXXOncunUH4eF1KF++bJ7tzZs3/n3/TjeycqnPd5bbcsZXn68st+WMrz4nLPdZbssZ33afdZbnz3Jbzvjqc8Jyn6W27OxsjhxOpmKlcgCkp2cAEBScd2EvOCTnv3/5Of9bOrz3+nxa/KURV0Y2LeLa08vOzubQoaNUrlzRtQYvS9f3VJbbcsZXnxOW+yy35YyvPics91luKwmsz5/lPsttOeOrzwnLfZbbRMTowuOhQ4e49NJL822/9NJLycjIYPv27QB4PB7uvPNO1q1bV9yJAMTHHy7wp7W82+LiEoo7KQ/1+c5yG6jPCcttoD6nLPdZbgP7fdZZnj/LbaA+pyz3WWpb/vkPJMQlcm2XKwCoWz8MyHks659tXp/zDxQJ8Ul5tq/7egs/rInhnod7FHnrmSxYsIKDBxPo1u0aVzvA1vU9leU2UJ9Tlvsst4H6nLLcZ7mtJLA+f5b7LLeB+pyy3Ge5TUSMLjxefPHFLFu2LN/25cuX4/F4qFmzZu620qVLk5WVddZzpqamkpSUlOeVmprmqPPkybR8jz+CnNu6vfvdpD7fWW7zjq8+31hu846vPt9Z7rPc5h3fcp91lufPcpt3fPX5znKflbY9u+J4++W5NL28Pp1ubA1AoyZ1ibisHh9PW86XC9ZxcN9hvvtmK2+N+5hSpQJJTU3P/fj09Azee2MBN9zalnoX1zzdMEXu11/38Nxz79CyZRNuuaWjax1eVq5vQSy3ecdXn+8s91lu846vPt9Z7rPcVhJYnz/LfZbbvOOrz3eW+yy3iYjRhccHHniAjz76iEGDBrFo0SJWrlzJiy++yEMPPURkZCQNGzbMPfbHH3+kUaNGZz3nuHHjCA0NzfMaN+5dR50hIUGkpaXn2+5d0AwJCXJ0fqfU5zvLbd7x1ecby23e8dXnO8t9ltu841vus87y/Flu846vPt9Z7rPQdvhQEk8Pn0y58iE8/tKAPO/N+MRLd9HgklpMeP4jBt30Is8++gHXdG7BxRG1KVPmj7Z5/1lF0tEU+g3pWuS9pxMff4R7732OChXKMnHiKAIDA11r8bJwfU/Hcpt3fPX5znKf5Tbv+OrzneU+y20lgfX5s9xnuc07vvp8Z7nPcpsUjgDPhfE6H5mZmTz11FM0aNCAMmXK0LBhQ55//nmys7Nzj8nOzmbMmDHUqlWLMmXK0Llz59wniHodPnyYO++8k4oVK1KpUiUGDx5McnJyYVy2XKXO5aDi/ovt0KFD2bNnD2+88QbTpk0DcibsqquuYtasWXmODQkJ4aGHHjrrOUePHs0jjzySZ1tw8G5HnWFhVTh4MP9t2/HxRwCoXr2qo/M7pT7fWW4D9TlhuQ3U55TlPsttYL/POsvzZ7kN1OeU5T6321KSTzDmofdJST7BK+89QNWw0Dz7q1UP5dX3hxK7O54jCceofVE1qlSrSL9uz1GnXljuOWZOWcqNvdtyPOUkx1NOAnDiRCrZ2dkc3HeY4JDSVKpSocg+j2PHUrjnnmc4diyFGTPGU6OGjd+P3b6+Z2K5DdTnlOU+y22gPqcs91luKwmsz5/lPsttoD6nLPdZbhMpKi+99BL//ve/+fDDD2nWrBnR0dEMGjSI0NBQhg0bBsDLL7/Mm2++yYcffkiDBg146qmn6Nq1K1u2bCEkJASAO++8k/3797N48WLS09MZNGgQQ4YM4T//+U+htZ7THY/Z2dk+v3z10ksvsXv3bj7++GNmzJjB999/z7fffkutWrXyHDdhwgTuuuuus54vODiYihUr5nl5b732VZMmDdi1K5bk5ON5tm/YsA2Apk0bODq/U+rzneU2UJ8TlttAfU5Z7rPcBvb7rLM8f5bbQH1OWe5zsy0tNZ1nHplC7O54nnn97jM+IrVOvTAua3kxVapVZPeOAxw+lMQVbS4BIDnpBCeOp/LxtBUMuunF3Nc3yzaRejKdQTe9yJsvflxkn0dqahr33fc8u3bF8s47Y2jUqF6RjXW+9GvPd+pzxnKf5TZQn1OW+yy3lQTW589yn+U2UJ9Tlvsst4kUldWrV3PTTTdx4403Eh4eTu/evfnrX//KunXrgJx1vAkTJvDkk09y00030bx5c6ZNm8a+ffuYN28eAFu3bmXRokW8//77XHXVVbRv35633nqLmTNnsm/fvkJrPaeFx6ysLJ9fmZmZPsfVrFmTW265hb59+9KyZcsCjzl27Bi7dzu7c9FXUVGRZGZmMWvWotxtaWnpzJmzhBYtIqhVK8yVLi/1+WcbqM9f20B9Tlnus9wG9vusszx/lttAfU5Z7nOrLTMzi/GPT+fnjb/x+PgBNG0efk4fl5WVxeS3PiU4pDQ33NoWgNAq5XnylYH5Xs1bNyQouBRPvjKQ2wcWzfstZmZmMnz4y/z4489MnDiKli2bFMk4vtKvPd+pzxnLfZbbQH1OWe6z3FYSWJ8/y32W20B9Tlnus9wmcj5SU1NJSkrK80pNTS3w2Hbt2rF06VJiYmIA2LBhA19//TXdunUDYOfOnRw4cIDOnTvnfkxoaChXXXUV3377LQDffvstlSpVonXr1rnHdO7cmYCAANauXVton9c5PWrVsjfffJMxY8Y4WuD0VYsWEURFRfL669NISEikfv1azJ27jNjYOMaOHVbsPeq7MNrU579t6vPvPsttJaHPa/r0T0hKSiEuLueRKsuXr+PAgZz/379/dypUKOdKl+X5s9ymPv/uc6vt/QkLWbNqM1ddcynHEo+z7LPv8+zveMNfAHjn1Xmkp2VwcePaZGRksuKL9cRs3sMjz/Sles3KQM57w7TrcFm+Mdas+ImYzXsK3FdYxo+fwrJla7n++jYcPXqM+fOX59l/003XF9nY50K/9tSnvpLVpj7/7rPc9mf6s7z/9VluU59/91luEzkf48aN49lnn82z7emnn+aZZ57Jd+yoUaNISkqiSZMmBAYGkpmZydixY7nzzjsBOHDgAAA1atTI83E1atTI3XfgwAGqV6+eZ3+pUqWoUqVK7jGFwZPt5HmoBowdO9bBwmOM4/FTU9OYMGE6CxeuIDExmYiIcB56qB/XXNPK8bkLg/r8sw3U569toD6nLPdZbgP7fQAdOw4mNjauwH1Ll75P3bo1CtxXHCzPn+U2UJ9TlvuKsu3XpG0Fbv/nvW+z6Ycdp/24z757FYDFC79j/n+/Yt/eQ3gCPERcWo8+d3eiRetGZx379Wdm8vWyjcxZ9eJpj2lYMeKs5zmT/v1Hs27dT6fdv23bQkfnLwwX6q+9wqA+Zyz3WW4D9Tlluc9ym5f+LO87y32W20B9TlnuK9q2xoVwDnGiy6Jv3E4oFp9c3zrfHY7BwcEEBwfnO3bmzJk89thjvPLKKzRr1owff/yR4cOH8/rrr3PXXXexevVqIiMj2bdvX563LLz99tvxeDzMmjWLF198kQ8//JBt2/L+fbp69eo8++yz3H///YXyeZlceJw2bdo5H7tgwQLmzp3r2sKjiIiIiIgUv9MtPFrhdOFRRERERETcooVHt10oC4+LoyLP+diLLrqIUaNG8Y9//CN32wsvvMD06dP5+eef2bFjBw0bNmT9+vVcccUVucdcd911XHHFFUycOJEpU6bw6KOPcuTIkdz9GRkZhISEMHv2bG655ZZC+bxMPmp14MCBeDweznVN1OPxFHGRiIiIiIiIiIiIiIiISPE7fvw4AQEBebYFBgaSlZUFQIMGDahZsyZLly7NXXhMSkpi7dq1uXcytm3blqNHj/L999/zl7/kvA3JsmXLyMrK4qqrriq0VpMLj5UrV+aKK67g5ZdfPuuxkydP5t133y2GKhEREREREREREREREZHi1aNHD8aOHUu9evVo1qwZ69ev5/XXX+fuu+8Gcm7QGz58OC+88AKXXHIJDRo04KmnnqJ27drcfPPNADRt2pSoqCjuuece3nnnHdLT0xk6dCh9+/aldu3ahdZqcuGxTZs2/Pzzz7krrmeyaNGiYigSERERERERERERERGRohbgMfcOga576623eOqpp3jggQeIi4ujdu3a3HvvvYwZMyb3mJEjR5KSksKQIUM4evQo7du3Z9GiRYSEhOQeM2PGDIYOHUqnTp0ICAjg1ltv5c033yzUVpPv8fj000/z/PPPc+DAAapXr37GYydNmsSrr77Kzp07fRhJ7/EoIiIiIlIS6T0eRURERESkaOg9Ht3W9Yuv3U4oFl90be92QpEwufCYkpLCoUOHqF27NqVLly7CkbTwKCIiIiJSEmnhUUREREREioYWHt2mhceSrVAetbp9+3YOHTpE1apVadzY+RdluXLlKFeuXCGUiYiIiIiIiIiIiIiIiEhxCPD1A1NTU3n88cepVq0aTZo0oX379owfPz53//Tp02nVqhU//vhjYXSKiIiIiIiIiIiIiIiIiGE+LTyeOHGCDh068NJLLxEUFMQNN9zAqU9s7dixIxs2bOCjjz4qlFARERERERERERERERHxbwGeC+Plr3x61OrLL7/M2rVrGTx4MG+99RYhISEEBORdw6xduzaXXnopS5Ys4cUXXyyUWBEREREREbD/HopLYn9xO+GMOtdp5HaCiIiIiIiI+CGf7nicNWsW9erV49///jchISGnPS4iIoI9e/b4HCciIiIiIiIiIiIiIiIiJYNPC487d+6kdevWlCp15hsmg4KCOHLkiE9hIiIiIiIiIiIiIiIiIlJy+PSo1TJlypzTguLOnTupXLmyL0OIiIiIiIiIiIiIiIjIBcanO+bEDJ+u3xVXXEF0dDTx8fGnPWbnzp2sX7+eK6+80uc4ERERERERERERERERESkZfFp4vOeeezh27Bh/+9vfOHToUL79R48e5e677yY9PZ0hQ4Y4jhQRERERERERERERERER23x61Orf/vY3Fi5cyMyZM7n44otp164dAN988w033XQTK1euJCkpiQEDBtC9e/dCDRYRERERERERERERERERe3xaeASYMWMGLVu25JVXXuHLL78EYPv27Wzfvp3Q0FDGjh3LqFGjCi3UqrS0dCZOnMH8+ctJSkomIiKc4cP7ERnZ0u00QH3+2gbq89c2gJSUE0yePIcNG2LYtCmGxMRkxo17iF69OrudBtifP8t9lttAfU5Z7rPcBupzQt8zfLd9+2+89dZ/2bz5Fw4dOkJISDCNGtVj8OBedOzYptg6Fk1fzMIpn1ErvCZPTvlngcccTz7BswNeJPloMoOfvotW112RZ396WgafTv2cdYujOX7sBLUvrkWPu2+gaeuIIm23fH0tt4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9ym8iFzuf36PR4PDz22GPs37+ftWvXMmvWLP773//y1VdfcfDgQUaPHo3H4ym00PT0dJKSkkhPTy+0cxaGUaMmMHXqPHr0uI4nnriHwMAAhgx5lujozW6nAerz1zZQn7+2ARw5ksSkSTPZsWMPEREN3M7Jx/r8We6z3Abqc8pyn+U2UJ8T+p7hu3374klJOcEtt3TiiSeG8MADfQG4//7nmTVrUbE0HIk/yhf/WUJQSNAZj/vkg89JO5l22v3/76X/sHT2Clp3+gu9h95MQEAAb49+j1827Sjs5DwsX1/LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcJs4FeLIviJe/8mRnZ5v87DIyMpg6dSofffQR69ev5/Dhw7n7qlSpwhVXXMHtt9/OwIEDKV26tI+jxDhq3Lgxhttue5SRIwcxeHAvAFJT0+jefShVq4Yyc+Yrjs7vlPr8s019/tvmlZaWTmJiMmFhldm0aTu9ez9i5u4V6/Nnuc9ym/r8u89ym/qc0/eMwpWZmUmvXg+TmprGokXvODrXkthfznrMlOencexoMtlZWSQnphR4x+O+nfsZN+RVbhjQlU8++DzfHY+7tv7GK/+YwC339qRzn+sBSE9L54W7X6ZCpfKM+NdDBY7duU4j3z6x31m+vpbb1OfffZbb1OfffZbb1OfffZbb1OfffUXf1th5pDjSY/FXbicUi4VdrnE7oUj4fMdjUTp06BBt2rRhyJAh7N69m27dujFixAiefPJJRowYQbdu3dizZw/33nsvbdq0IT4+3pXORYu+ITAwgD59onK3BQcH0bt3F9av/5n9+93p8lKff7aB+vy1zSsoqDRhYZXdziiQ9fmz3Ge5DdTnlOU+y22gPqf0PaNwBQYGUqtWNY4dSynysbZv+JX1KzfQ+x83n/G42f+aS4v2l9Pw8osL3L9+1QYCAgKI7N42d1vpoNK0u+Eqdm7ZxZG4I4WZncvy9bXcBupzynKf5TZQn1OW+yy3gfqcstxnuQ3U55TlPsttIuLjezzefffd53ysx+Nh8uTJ53X+Rx55hN9++43FixfTqVOn0x63dOlSbr/9dkaMGMGHH354XmMUhq1bdxAeXofy5cvm2d68eePf9++kVq2wYu/yUp/vLLfljK8+X1luKwmsz5/lPsttOeOrzwnLfZbbcsZXn78qKXN3/PhJTp5MJTn5OMuWrWXVqu/p1q1of+o0KzOL2W/Nod2NV1Hn4tqnPe6HFT+yc/Munpo6ioQDhws8Zu8vsVS/KIwy5ULybK/fpF7u/srVC39x2vL1tdyWM776nLDcZ7ktZ3z1OWG5z3Jbzvjqc8Jyn+W2nPHV54TlPsttIuLjwuPUqVPPeozH4yE7O9unhcdPP/2Uxx577IyLjgCdOnVixIgRvPrqq+d1/sISH3+4wJ8w926Li0so7qQ81Oc7y22gPicst5UE1ufPcp/lNlCfU5b7LLeB+vxZSZm78eMn576nY0BAAF26tGXMmPuKdMyvFq7m8MHDPPjq/ac9Ji01jTnvLOD63tdRtWaV0y48JiYkUbFKxXzbQ3/fdjQhqXCiT2H5+lpuA/U5ZbnPchuozynLfZbbQH1OWe6z3Abqc8pyn+U2KRwBHrcLxAmfFh6XL19e4PasrCz27NnDl19+ycyZM3n44Yfp0aPHeZ8/LS2NChUqnNOxFSpUIC0t7bzHKAwnT6YRFJT//SWDg4Ny97tJfb6z3OYdX32+sdxWElifP8t9ltu846vPd5b7LLd5x1effyopc3fXXT2JiookLu4wn3/+FVlZWaSnpxfZeMmJKXw69XOi+v+VCpXKn/a4L/+7lKzMTLreeeb360xPTadU6fx/rSsdVCp3f1GwfH0tt3nHV5/vLPdZbvOOrz7fWe6z3OYdX32+s9xnuc07vvp8Z7nPcpuI+LjweN11151x/4ABA7jxxhu566676Nmz53mfPzIykokTJ3LzzTdTp06d0x4XGxvLxIkTad++/VnPmZqaSmpqap5twcFpub8Z+SIkJIi0tPx/kU9NTcvd7yb1+c5ym3d89fnGcltJYH3+LPdZbvOOrz7fWe6z3OYdX33+qaTMXcOGF9Gw4UUA3HxzR+6++ynuu+95Zs9+DY+n8H/MduGUzyhboSwdbjn941wTDhxmyazl9Bl2KyFlgs94vtLBpclIz8i3PT0tI3d/UbB8fS23ecdXn+8s91lu846vPt9Z7rPc5h1ffb6z3Ge5zTu++nxnuc9ym4hAQFGd+G9/+xvNmjXjmWeeOe+PnTBhAkeOHCEiIoK+ffvy6quvMmPGDD7++GNmzJjBq6++St++fYmIiCAxMZHXX3/9rOccN24coaGheV7jxr3rw2f2h7CwKsTHH8m33butevWqjs7vlPp8Z7kN1OeE5baSwPr8We6z3Abqc8pyn+U2UJ8/K6lz17VrJJs2bWfnzthCP3fc3ni++fRbOtxyDYkJSSQcOEzCgcOkp2WQmZFJwoHDpCSl8MkHn1OpWiiXXNEo95ikwzmPTE0+mkLCgcNkZWUBEFq1Yu6+P0v8fVulqvkfw1oYLF9fy22gPqcs91luA/U5ZbnPchuozynLfZbbQH1OWe6z3CYiRbjwCHDJJZcQHR193h/XpEkTfvzxRwYMGMCKFSsYOXIk/fv35/bbb6d///6MHDmS5cuXM2DAAH744QeaNm161nOOHj2axMTEPK/Ro+/15dP6U2cDdu2KJTn5eJ7tGzZsA6Bp0waOzu+U+nxnuQ3U54TltpLA+vxZ7rPcBupzynKf5TZQnz8rqXPnfSzTqd2F4eihRLKzspn9r7mMueP53Neurb8RtzeeMXc8z+fTvuRI3BHiYw/x9J0v5B7zwQv/D4BZEz9mzB3Pc/J4ztNc6jSsQ9yeeE6knMwz1q6tvwFQt9HpnyDjhOXra7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttEpAgXHrOysti4cSMBAb4NUadOHd5++20OHDjA3r17+e6771i1ahXfffcde/fu5eDBg7z99tvUrVv3nM4XHBxMxYoV87ycPGYVICoqkszMLGbNWpS7LS0tnTlzltCiRQS1aoU5Or9T6vPPNlCfv7aVBNbnz3Kf5TZQn1OW+yy3gfr8mfW5S0g4mm9benoG8+cvIyQkKPfxq4WpdoOaDHnu7nyvWuE1qVy9MkOeu5u2N1xF97tvyHdM90HdAOjStyNDnrub4N8fH9XyuhZkZWXxzSff/vF5pGWwZtE6wpvWp3L1yoX+eYDt62u5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynLfZbbpHAEXCAvf+XTezyeyfHjx4mJiWHcuHFs376d7t27Oz5n7dq1qV27diHUFa4WLSKIiork9denkZCQSP36tZg7dxmxsXGMHTvM7Tz1+Wmb+vy37c+mT/+EpKQU4uISAFi+fB0HDuT8//79u1OhQjlXuqzPn+U+y23q8+8+y23qKxz6nuGbMWMmkZx8nCuvvIwaNaoQH3+UhQtXsGPHXkaNGky5cmUKfczyoeVp0f7yfNuX/28lQIH7vMqUz+mpF3FRnuMaNK1Py+taMP/9Tzh29Bhhdaqx9ovvSDhwmDtH9C3kz+APlq+v5Tb1+Xef5Tb1+Xef5Tb1+Xef5Tb1+Xef5TYRAU92dnb2+X5QYGDgWY/Jzs4mLCyMr776isaNG5932JYtWxg/fjxbt26lWrVq9O3blwEDBuDxePIcN2PGDAYMGEBmZuZ5jwExPnxMXqmpaUyYMJ2FC1eQmJhMREQ4Dz3Uj2uuaeX43IVBff7ZBurz1zavjh0HExsbV+C+pUvfp27dGsVc9Afr82e5z3IbqM8py32W20B9Tul7hm8+/XQVH3+8mJiYXRw9eoxy5crQrFkj+vXrTqdOVzk+/5LYX8752AkP/4vkxBSenPLP0x4T8+MvTHxkEoOfvotW112RZ196WjoLp3zOd0uiOX7sBHUurk33u7tx6ZVNTnu+znUanXPf6Vi+vpbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcV7Rt57+eIYXrliVfuZ1QLOZ2vsbthCLh08JjeHh4vgVAr6CgIGrVqsV1113HP/7xD6pXr37eUdu3b6dVq1ZkZGTQrFkzDh48SGxsLJGRkcyePZuaNWvmHuv2wqOIiIiIiMipzmfh0Q2FsfAoIiIiIuKftPDoNi08lmw+PWp1165dhZyR15NPPkn58uX56quvaNQo5y/E06dPZ+jQobRt25ZFixYRERFRpA0iIiIiIiIiIiIiIiJSvAIKvu9NSgif3r9ywYIFfP7554XdkmvNmjU8+OCDuYuOAP369WPNmjUEBATQvn171q1bV2Tji4iIiIiIiIiIiIiIiMj58Wnh8ZZbbuHNN98s7JZcCQkJeR6n6tWkSRNWr15N3bp16dSpE1988UWRNYiIiIiIiIiIiIiIiIjIufNp4TEsLIzKlSsXdkuu8PBwNm7cWOC+GjVqsHLlSlq2bEnPnj2ZPXt2kXWIiIiIiIiIiIiIiIiIyLnxaeGxQ4cOrFu3juzs7MLuyT3/7NmzycjIKHB/xYoVWbx4MVFRUSxYsKBIGkRERERERERERERERETk3Pm08Pj8889z6NAhHn74YU6ePFnYTQwcOJB27doRHR192mOCg4OZO3cuw4YN49prry30BhERERERERERERERESleHk/2BfHyV55sH25bfO655/j555+ZNWsWYWFhdO7cmXr16hESEpJ/AI+Hp556qlBiC1+M2wEiIiIiIuKHlsT+4nbCGXWu08jtBBERERERoxq7HXDB671sldsJxeLjjv55U905LTxefPHF3Hbbbbz00ksABAQE4PF4zulRqx6Ph8zMTOelRUILjyIiIiIiUvi08CgiIiIiUlJp4dFtWngs2Uqdy0G7du0iPj4+978/+OCDIgsSERERERERERERERERkZLnnBYeT3XXXXcVdoeIiIiIiIjfsH5H4S9J29xOOKNGFSPcThAREREREZcEeNwuECcC3A4QERERERERERERERERkZJPC48iIiIiIiIiIiIiIiIi4tg5P2r1xx9/5LnnnvNpkDFjxvj0cSIiIiIiIiIiIiIiIiJSMpzzwuOGDRvYsGHDeZ08Ozsbj8ejhUcRERERERERERERERERP3fOC48NGzYkMjKyKFtERERERERERERERETkAqb3CCzZznnhsX379kyZMqUoW0RERERERERERERERESkhDrnhUcpWFpaOhMnzmD+/OUkJSUTERHO8OH9iIxs6XYaoD5/bQP1+WsbqM8py32W20B9Tlnus9wG6nPKcp/lNlDfqX779QAz/u9Lftm6l6MJxwgOKc1FF9fg1n4duOraZnmO3b3zIP/3+gK2bNhJqdKBXBnZlHse7klo5fJ5jps5ZQnbftpNzObdHD2czB33dOHOIV2LpP/PdG2dsdyXknKCyZPnsGFDDJs2xZCYmMy4cQ/Rq1dnt9MA23MH6nPKcp/lNlCfU5b7LLeB+pyy3Ge5TeRCpztWHRo1agJTp86jR4/reOKJewgMDGDIkGeJjt7sdhqgPn9tA/X5axuozynLfZbbQH1OWe6z3Abqc8pyn+U2UN+p4g4c4URKKp27t2bIozfRd3AXAJ579AM+n7Mm97hDB4/yzyFvs3/vIe56oBu9+nXgu2+28sQ/3iM9PSPPOf/fvxexfcseLm5cp0iaT0fX1hnLfUeOJDFp0kx27NhDREQDt3PysTx3oD6nLPdZbgP1OWW5z3IbqM8py32W20QudJ7s7Ozssx0UEBDAwIEDTT5qdfr06UyZMoVly5b58NExjsbeuDGG2257lJEjBzF4cC8AUlPT6N59KFWrhjJz5iuOzu+U+vyzTX3+26Y+/+6z3KY+/+6z3KY+/+6z3KY++CVp2zkdl5mZxUP9J5Cels67H/8TgEnj/8fST6J55+ORVK9ZGYD1a2N4cuh7DB3dm269rs79+IP7DlOjdhUSj6ZwR5enz/mOx0YVI3z4rHJc6NfW3/vS0tJJTEwmLKwymzZtp3fvR8zc8Wh97tTnv32W29Tn332W29Tn331F39bYeaQ4cseKlW4nFIv/dLjO7YQiUeLvePztt99YudKdX4SLFn1DYGAAffpE5W4LDg6id+8urF//M/v3x7vS5aU+/2wD9flrG6jPKct9lttAfU5Z7rPcBupzynKf5TZQ37kKDAwgrEYoycdO5m5bvXwTV17TNHfREaDlVY2pUy+Mr5ZsyPPxNWpXKZbOP7Myd6ejPmeCgkoTFlb57Ae6wPrcqc8Zy32W20B9Tlnus9wG6nPKcp/lNhE5x4XHrKwsk3c7um3r1h2Eh9ehfPmyebY3b9749/073cjKpT7fWW7LGV99vrLcljO++pyw3Ge5LWd89Tlhuc9yW8746nPCcp/ltpzx1Xc6J0+kkng0hf17DzH3P6uI/nYbV1zZCIBDcYkcPZzMJU0vyvdxjZtdxI6Y2CLrOle6ts5Y77PM+typzxnLfZbbcsZXnxOW+yy35YyvPics91luExEo5XZAQQIDA91OOCfx8YcL/ElL77a4uITiTspDfb6z3Abqc8JyG6jPKct9lttAfU5Z7rPcBupzynKf5TZQ35m8P2Fh7ns6BgR4aHv95dw38hYAjhxKAqBKtYr5Pq5KtYocSzxOeloGpYPc+6umrq0z1vsssz536nPGcp/lNlCfU5b7LLeB+pyy3Ge5TUQMLzw2bNiQzp3P/h4N0dHRrFu37qzHpaamkpqammdbcHAawcFBPneePJlGUFDpfNu95zx5Ms3ncxcG9fnOcpt3fPX5xnKbd3z1+c5yn+U27/jq853lPstt3vHV5zvLfZbbvOOrr2A3/e1aIjs25/ChJL5asoGszCwy0jMBSE1NB6B06fw/LOpdbExNTXd14VHX1hnrfZZZnzv1OWO5z3Kbd3z1+c5yn+U27/jq853lPsttImJ04bF58+YEBATw1ltvnfXYsWPHntPC47hx43j22WfzbHv66aE888yDPneGhASRlpaeb3tqalrufjepz3eW27zjq883ltu846vPd5b7LLd5x1ef7yz3WW7zjq8+31nus9zmHV99BbsovDoXhVcHoNONrXly6Hs898gUXp86jODgnH/gSf99IfLP0tMyAHKPcYuurTPW+yyzPnfqc8Zyn+U27/jq853lPstt3vHV5zvLfZbbpHAEeNwuECfO6T0ei1ubNm3YuHFjvjsUTyc7O/usx4wePZrExMQ8r9Gj73XUGRZWhfj4I/m2e7dVr17V0fmdUp/vLLeB+pyw3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1Hc+Ijs2J2bLHmJ/i6fy749YPfz7I1f/7PChJCqElnX1bkewNXcFUZ//sj536nPGcp/lNlCfU5b7LLeB+pyy3Ge5TUSMLjwOGjSI0aNHk5SU/y+zp+rfvz/Lly8/63HBwcFUrFgxz8vJY1YBmjRpwK5dsSQnH8+zfcOGbQA0bdrA0fmdUp/vLLeB+pyw3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1Hc+0n5/vGpKykmqVQ8ltHI5tm/dk++4mM17uPiS2sXWdTqW5q4g6vNf1udOfc5Y7rPcBupzynKf5TZQn1OW+yy3iYjRhccrr7ySp59+mrCwsLMeW69ePa677rpiqMovKiqSzMwsZs1alLstLS2dOXOW0KJFBLVqnb2/KKnPP9tAff7aBupzynKf5TZQn1OW+yy3gfqcstxnuQ3UV5Cjh4/l25aRkcnSz6IJDi5NvQY1gJw7IL/7aivxB47mHvfjuu3E7o6nfefmhd51vnRtnbHeZ5n1uVOfM5b7LLeB+pyy3Ge5DdTnlOU+y20iYvQ9HkuKFi0iiIqK5PXXp5GQkEj9+rWYO3cZsbFxjB07zO089flpm/r8t019/t1nuU19/t1nuU19/t1nuU19BfvXuP9xPPkkl7W6mKphoRxJOMbyRT+wd1ccfx/egzJlgwG4fWAnvl6ygdH3/5uefa/h5PFU/jd9BeGNatGlR5s851z22ffE7T9C6smc99v5af0OZk5eAkDHG1pRvVaVQv88dG39uw9g+vRPSEpKIS4uAYDly9dx4EDO/+/fvzsVKpRzpcv63KnPf/sst6nPv/sst6nPv/sst0nhMHnHnJwzT/a5vEGiC7Zs2cL48ePZunUr1apVo2/fvgwYMACPJ++7is6YMYMBAwaQmZnpwygxjjtTU9OYMGE6CxeuIDExmYiIcB56qB/XXNPK8bkLg/r8sw3U569toD6nLPdZbgP1OWW5z3IbqM8py32W2+DC7vslaVu+bSu/XM+X89ex65cDHEtMoUy5YBo1qUuP29tz9XXN8hz7268HeH/CAjb/uJNSpUtxZWRT/j68B5WrVshz3Kh732bTDzsKbBj3zn00/0ujAvc1qhjh42eW40K+toXBel/HjoOJjY0rcN/Spe9Tt26NYi76g/W5U58zlvsst4H6nLLcZ7kN1OeU5b6ibWtcCOcQJwasXOl2QrGY5tLTPIuayYXH7du306pVKzIyMmjWrBkHDx4kNjaWyMhIZs+eTc2aNXOPdXvhUUREREREpKQpaOHREqcLjyIiIiIivtPCo9u08Fiymbxj9cknn6R8+fJs2rSJ6Oho9uzZw7Rp09i0aRNt27Zl2zbbf0kWERERERERERERERERudCYXHhcs2YNDz74II0a/fHYnX79+rFmzRoCAgJo374969atc7FQRERERERERERERERERP7M5MJjQkJCnsepejVp0oTVq1dTt25dOnXqxBdffOFCnYiIiIiIiIiIiIiIiBSFAM+F8fJXJhcew8PD2bhxY4H7atSowcqVK2nZsiU9e/Zk9uzZxVwnIiIiIiIiIiIiIiIiIqcyufDYoUMHZs+eTUZGRoH7K1asyOLFi4mKimLBggXFXCciIiIiIiIiIiIiIiIipzK58Dhw4EDatWtHdHT0aY8JDg5m7ty5DBs2jGuvvbYY60RERERERERERERERETkVJ7s7OxstyPcE+N2gIiIiIiISLH7JWmb2wln1KhihNsJIiIiInLBaux2wAXv7q9WuJ1QLKZc08HthCJh8o5HERERERERERERERERESlZSrkdICIiIiIiIsXL+h2Fu47ZvSMzvILtuRMREREREXGT7ngUEREREREREREREREREce08CgiIiIiIiIiIiIiIiIijulRqyIiIiIiIiIiIiIiImJCgMftAnFCdzyKiIiIiIiIiIiIiIiIiGNaeBQRERERERERERERERERx7TwKCIiIiIiIiIiIiIiIiKO6T0eRURERERERERERERExATdMVeyaeHRobS0dCZOnMH8+ctJSkomIiKc4cP7ERnZ0u00QH3+2gbq89c2UJ9Tlvsst4Htvo0bY5g3bxlr124kNjaOSpUq0KJFBMOH96dBgzpu5wG25y8l5QSTJ89hw4YYNm2KITExmXHjHqJXr85upwH2+yxfW+tfG5bnbvv233jrrf+yefMvHDp0hJCQYBo1qsfgwb3o2LGN23mA7flz4+t22+bdLP4kmg3f/8rBfYepGFqOJpfXY+D93ahbPyzPsSsX/8icGavYsyuOgMAAwhvW5LYBHbiq/aV5jvvP5CVs+2k3P2/ezdHDyfS7pwv97+1aZJ+Dl+VrC+rz1zbr3zPA9vyB3b6ScG315z3fae6c0fw5Y7nPcpvIhc70wvGyZcuYNGkSs2bNIikpqcBj1qxZw913313MZX8YNWoCU6fOo0eP63jiiXsIDAxgyJBniY7e7FrTn6nPP9tAff7aBupzynKf5Taw3ff++//jyy9X07ZtC5544h5uvz2K6OjN9Oo1nJiY39zOA2zP35EjSUyaNJMdO/YQEdHA7Zx8rPdZvrbWvzYsz92+ffGkpJzglls68cQTQ3jggb4A3H//88yatcjluhyW58+Nr9uPPlzON8s20fLKS7h/xM3ccMvV/LR+J//o9wa7ftmfe9z8mV/z4ujpVKxUjruH3sAdgzuTknySMcOn8PWyTXnO+eG/FxGzZQ8NI4r3H+YtX1tQn7+2Wf+eAbbnD+z2lYRrqz/v+U5z54zmzxnLfZbbRC50nuzs7Gy3I06VmprKDTfcwIoVK/DmhYaG8tJLLzFkyJA8x86YMYMBAwaQmZnpw0gxjjo3bozhttseZeTIQQwe3Ov39jS6dx9K1aqhzJz5iqPzO6U+/2xTn/+2qc+/+yy3lYS+H37YymWXNSIoqHTutl279tGjx1C6do3k1VcfdbHO/vylpaWTmJhMWFhlNm3aTu/ej5j7KV+rfdavreWvDetzV5DMzEx69XqY1NQ0Fi16x9UW6/NX1F+3u45ty7dt84ZdNL60LqVL//Hgntjd8dzb9zWu6dScfz5/BwB39xpPufJlePPDYXg8HgBSkk9y5w3P06J1I559fVDuxx/Yd5iatauQeDSF2zs/fU53PIZXiHD0uVm/turzzzaw/T0D7M+f5T7r1xb05z0nNHfOaP78s6/o2xo7jxRHhny9wu2EYvFe+w5uJxQJk3c8vvrqq6xcuZJnnnmGjRs38sUXX9C6dWvuv/9+7r33XrKystxOBGDRom8IDAygT5+o3G3BwUH07t2F9et/Zv/+eBfr1OevbaA+f20D9Tlluc9yG9jva9WqaZ5/SAEID6/NJZfUY8eOPS5V/cH6/AUFlSYsrLKrDWdiuc/6tbX8tWF97goSGBhIrVrVOHYsxe0U8/PnxtdtsxbheRYdAerUC6P+xTXYvfNg7rbjKalUqlI+d9ERoFz5EMqUCSI4OO/XS83aVYo2ugDWr636/LMNbH/PAPvzZ7nP+rUF/XnPCc2dM5o/31nus9wmIkYXHmfOnMnAgQN56qmnuOyyy+jSpQuLFy/m+eef5/3336dXr16kpqa6ncnWrTsID69D+fJl82xv3rzx7/t3upGVS32+s9yWM776fGW5LWd89Tlhuc9yW874tvsKkp2dzaFDR6lcuaLbKSVy/uTclMRra+Vro6TM3fHjJzl8OJHdu/czdeo8Vq36nquvbuF2VomZP7dlZ2dz9HAyoZXK5W5r3qoh0d9uY/7Mrzmw7zC7d8Xxr5fmkJJ8kpv/1t7F2hzWr636fGe57XSsfM8A+/Nnve9Ulq6tdSXt2lqiuXPG+vxZ7rPcJoUjwJN9Qbz8VamzH1L8du7cyfDhw/Ntf/zxx2nYsCF33XUXXbp0YeHChcUf9yfx8YcL/IkZ77a4uITiTspDfb6z3Abqc8JyG6jPKct9ltvAfl9BFixYwcGDCQwbdqfbKSVy/uTclMRra+Vro6TM3fjxk3Pf0zEgIIAuXdoyZsx9LleVnPlz27LPf+BQXCID/vR41Aceu4nExBTefnUeb786D4DQSuUY/+97ubR5uDuhf2L92qrPd5bbTsfK9wywP3/W+05l6dpaV9KurSWaO2esz5/lPsttImJ04bFKlSrExcUVuK9Pnz5UqVKFXr16ce2119KvX79zOmdqamq+uySDg9MIDg7yufPkybR8j7LIOW9Q7n43qc93ltu846vPN5bbvOOrz3eW+yy3ece33HeqX3/dw3PPvUPLlk245ZaObueUuPmTc1fSrq2lr42SMnd33dWTqKhI4uIO8/nnX5GVlUV6errbWSVm/tyUcyfjXJo2r0/n7q1ztweHBHFR/TDCqodyVfumHD+eytz/fMXzj33Iq+//gzoXVXOx2v61VZ/vLLcVxNL3DLA/f9b7/szatbWuJF1bazR3zlifP8t9lttExOijVlu2bMknn3xy2v1dunRhyZIl7Nu3j8cff/yczjlu3DhCQ0PzvMaNe9dRZ0hIEGlp+f9RIjU1LXe/m9TnO8tt3vHV5xvLbd7x1ec7y32W27zjW+77s/j4I9x773NUqFCWiRNHERgY6HZSiZo/OT8l6dpa+9ooKXPXsOFFtGt3BTff3JF3332a48dPcN99z5Od7e5jb0rK/Lnl8KEkxjw0mXLlQ3jqpQEEBv7xV9sXRk0j7sARRjzTl2s6t6Brzza8/O79pGdkMvXtz12szmH92qrPd5bbTmXtewbYnz/rfV4Wr611JeXaWqS5c8b6/Fnus9wmIkYXHm+55Ra+/fZb1qxZc9pjrrrqKlatWkWtWrXO6ZyjR48mMTExz2v06HsddYaFVSE+/ki+7d5t1atXdXR+p9TnO8ttoD4nLLeB+pyy3Ge5Dez3eR07lsI99zzDsWMpvP/+s9SoYaOrpMyfnL+Scm0tfm2UlLk7VdeukWzatJ2dO2Nd7Sip81ccUpJP8OSw90lOPsHYt/5O1bDQ3H379yYQvXobV1/bLM/HVAwtS7MWDdiyYVcx1+Zn/dqqz3eW2/7M4vcMsD9/1vvA7rW1riRcW6s0d85Ynz/LfZbbpHAEeC6Ml78yufB41113cezYMf7yl7+c8bimTZuyZcsWduzYcdZzBgcHU7FixTwvJ49ZBWjSpAG7dsWSnHw8z/YNG7b93tfA0fmdUp/vLLeB+pyw3Abqc8pyn+U2sN8HOT+5eN99z7NrVyzvvDOGRo3quZ2UqyTMn/imJFxbq18bJWHuCuJ9LNOp3cWtpM5fUUtLTWfMw1PYuzue5964m/oX18yz/8jhYwBkZWbl+9jMjEwyC9he3KxfW/X5znKbl9XvGWB//qz3Wb621lm/tpZp7pyxPn+W+yy3iYjRhUePx0O5cuUoXTr/c5pPVb58eerXr18MVflFRUWSmZnFrFmLcrelpaUzZ84SWrSIoFatMFe6vNTnn22gPn9tA/U5ZbnPchvY78vMzGT48Jf58cefmThxFC1bNnG151TW5098Z/3aWv7asD53CQlH821LT89g/vxlhIQE0bDhRcUf9SfW588NmZlZjB09na0bf+PJ8QO4tHl4vmNqX1SNgAAPKxdvyPO43PiDR/npx500jKhdjMUFs35t1eefbWD7ewbYnz/LfdavrXWWr611mjtnrM+f5T7LbSICpdwOOJ0tW7Ywfvx4tm7dSrVq1ejbty8DBgzA48l7/+mMGTMYMGAAmZmZxd7YokUEUVGRvP76NBISEqlfvxZz5y4jNjaOsWOHFXuP+i6MNvX5b5v6/LvPcltJ6Bs/fgrLlq3l+uvbcPToMebPX55n/003Xe9SWQ7r8wcwffonJCWlEBeXAMDy5es4cCDn//fv350KFcq5mWe2z/q1tfy1YX3uxoyZRHLyca688jJq1KhCfPxRFi5cwY4dexk1ajDlypVxtc/6/EHxf92+N2Eha1Zt5uprLuVY0nGWfvZ9nv2dbvgLlSqX568927Bo3lr+ef87RF5/OSeOp7Lw49WkpqbTd2DHPB+z5NPvidt/hJO/vx/QpvU7+M/7S3LOd2MratSqUqifA9i/turzzzaw/T0D7M+f5T7r19ZLf97znebOGc2f//VZbhMR8GT/+cdAjdi+fTutWrUiIyODZs2acfDgQWJjY4mMjGT27NnUrPnH43ScLTzGOG5NTU1jwoTpLFy4gsTEZCIiwnnooX5cc00rx+cuDOrzzzZQn7+2gfqcstxnuQ1s9/XvP5p163467f5t2xYWY03BLM8fQMeOg4mNjStw39Kl71O3bo1iLsrLcp/la2v9a8Py3H366So+/ngxMTG7OHr0GOXKlaFZs0b069edTp2ucjsPsD1/ULRft7uObcu37bEhb7Pxh9O/zcYX0a8COY9U/eR/3/LF/HXs25vzj4qNL63LHX/vwhWtG53zOV9+5z5anHI8QHiFiHP+PE7H+rVVn3+2Wf+eAbbnD+z2lYRrC/rznhOaO2c0f76z3Fe0bY0L4RzixAOrl5/9ID/wdjsbPxxU2EwuPPbp04dVq1bx1Vdf0ahRzl/0pk+fztChQ6lcuTKLFi0iIiLnL3tuLzyKiIiIiIhI4Spo4dGKwlh4FBERERHLtPDotqHfXhgLj/9q658Ljybf43HNmjU8+OCDuYuOAP369WPNmjUEBATQvn171q1b52KhiIiIiIiIiIiIiIiIiPyZyYXHhISEPI9T9WrSpAmrV6+mbt26dOrUiS+++MKFOhERERERERERERERERE5lcmFx/DwcDZu3Fjgvho1arBy5UpatmxJz549mT17djHXiYiIiIiIiIiIiIiIiMipTC48dujQgdmzZ5ORkVHg/ooVK7J48WKioqJYsGBBMdeJiIiIiIiIiIiIiIhIUQi4QF7+yuTnNnDgQNq1a0d0dPRpjwkODmbu3LkMGzaMa6+9thjrRERERERERERERERERORUnuzs7Gy3I9wT43aAiIiIiIiInGLXsW1uJ5xWeIUItxNEREREpEg1djvggjfs2+VuJxSLN9te73ZCkTB5x6OIiIiIiIiIiIiIiIiIlCxaeBQRERERERERERERERERx0q5HSAiIiIiIiLyZ5YfZ5qZneZ2whkFeoLcThARERERcSTAcwG/Q6Af0B2PIiIiIiIiIiIiIiIiIuKYFh5FRERERERERERERERExDEtPIqIiIiIiIiIiIiIiIiIY1p4FBERERERERERERERERHHSrkdICIiIiIiIiIiIiIiIgIQ4HG7QJzQHY8iIiIiIiIiIiIiIiIi4pjueHQoLS2diRNnMH/+cpKSkomICGf48H5ERrZ0Ow1Qn7+2gfr8tQ3U55TlPsttoD6nLPdZbgP1OWW5z3Lbxo0xzJu3jLVrNxIbG0elShVo0SKC4cP706BBHbfzAEhJOcHkyXPYsCGGTZtiSExMZty4h+jVq7PbaYDt62tl7rZv382kf33Els2/cujQUUJCgmnYqC53330T13e8ssCPSU/P4JabH2XHr3sZ8dgA7h58U7E2g535Ox3Lv/Yst4H6nLLcZ7kN1OeEfk92Rn3OWO6z3CZyodMdjw6NGjWBqVPn0aPHdTzxxD0EBgYwZMizREdvdjsNUJ+/toH6/LUN1OeU5T7LbaA+pyz3WW4D9Tlluc9y2/vv/48vv1xN27YteOKJe7j99iiiozfTq9dwYmJ+czsPgCNHkpg0aSY7duwhIqKB2zn5WL6+VuZu3754jqec4Kabr2f043dz/wO9AfjHA+P5aNaXBX7MjOmfsX//oeLMzMfK/J2O5V97lttAfU5Z7rPcBupzQr8nO6M+Zyz3WW4TudB5srOzs92OKMiBAweYNm0aR48epVOnTnTq1AmAt99+mxkzZpCYmMiVV17Jk08+ScOGDX0cJcZR48aNMdx226OMHDmIwYN7AZCamkb37kOpWjWUmTNfcXR+p9Tnn23q89829fl3n+U29fl3n+U29fl3n+U2gB9+2MpllzUiKKh07rZdu/bRo8dQunaN5NVXH3WxLkdaWjqJicmEhVVm06bt9O79iJk7HKxf36Kcu8zsNGcfn5lJ71tHkpaaxqefv5VnX0JCIjdEDWXgoJ689eZMn+54DPQEOeoD/drzxzb1+Xef5Tb1Oaffk9WnPjfaGjuPFEdGrF3mdkKxePWqjm4nFAmTdzz+9ttvtGjRglGjRjF+/Hj++te/MmnSJF5++WVGjBgBQFBQENOnT6dt27bs3r3blc5Fi74hMDCAPn2icrcFBwfRu3cX1q//mf37413p8lKff7aB+vy1DdTnlOU+y22gPqcs91luA/U5ZbnPchtAq1ZN8yw6AoSH1+aSS+qxY8cel6ryCgoqTVhYZbczCmT9+lqeu8DAQGrVrErSseP59r3+2nTCG9ShR89rXSj7g+X5s/xrz3IbqM8py32W20B9Tun3ZN+pzxnLfZbbRMTowuOzzz5LQEAA0dHRxMfHc+ONN/Lss8/y0Ucf8dNPP/HNN9/www8/8M0333D8+HHGjh3rSufWrTsID69D+fJl82xv3rzx7/t3upGVS32+s9yWM776fGW5LWd89Tlhuc9yW8746nPCcp/ltpzx1eeE5T7LbaeTnZ3NoUNHqVy5otsp5pXE6+um48dPcuRIErt3H+DDqQv56qv1XH315XmO2bhxO/PnrWD06EF48LhUap/lX3uW23LGV58Tlvsst+WMrz5/ZX3u1OeM5T7LbSICpdwOKMiKFSsYMmQIrVq1AmDMmDG0adOGxx9/nIsvvjj3uDZt2jBw4EA+++wzVzrj4w8X+BNH3m1xcQnFnZSH+nxnuQ3U54TlNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22ns2DBCg4eTGDYsDvdTjGvJF5fN7380oe57+kYEBBA5y5X8eRTf8/dn52dzdgXJtOtWzuuaBlB7N44t1LNs/xrz3IbqM8py32W20B9/sz63KnPGct9lttExOjC4759+2jQ4I83S65fvz4ATZs2zXds8+bNef/994ut7c9OnkzL93gmyLmt27vfTerzneU27/jq843lNu/46vOd5T7Lbd7x1ec7y32W27zjq893lvsstxXk11/38Nxz79CyZRNuucU/32ejMJW06+u2AXfdyF+7Xk183BEWfb6arKws0tMzcvfPnbOc7TG/MWHiCBcrSwbLv/Yst3nHV5/vLPdZbvOOrz7/ZH3u1OeM5T7LbSJidOGxUqVKHDlyJPe/AwMDCQ4OJiQkJN+xKSkplClT5qznTE1NJTU1Nc+24OC03N+MfBESEkRaWnoBY6Xl7neT+nxnuc07vvp8Y7nNO776fGe5z3Kbd3z1+c5yn+U27/jq853lPsttp4qPP8K99z5HhQplmThxFIGBgW4nmVeSrq8FF19cl4svrgvATTd34O93P8c/7h/HzI/Gk5JygjfemMGgu2+iVq1qLpfaZ/nXnuU27/jq853lPstt3vHV55+sz536nLHcZ7lNCkeA3nmgRDP5Ho9NmjRh06ZNuf9dpUoVTpw4wXXXXZfv2M2bNxMeHn7Wc44bN47Q0NA8r3Hj3nXUGRZWhfj4I/m2e7dVr17V0fmdUp/vLLeB+pyw3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7ntz44dS+Gee57h2LEU3n//WWrUsNFlXUm5vlb9tevVbNr0C7t27uODKfNJT8+g2w2RxO6NI3ZvHAcO5jweLCkpmdi9cQX+w9qFyvKvPcttoD6nLPdZbgP1+TPrc6c+Zyz3WW4TEaMLj/369TunuxiPHj3KrFmzuP7668967OjRo0lMTMzzGj36XkedTZo0YNeuWJKTj+fZvmHDNgCaNm1Q0IcVG/X5znIbqM8Jy22gPqcs91luA/U5ZbnPchuozynLfZbbvFJT07jvvufZtSuWd94ZQ6NG9dxOKjFKwvW1zPvT+MeSj7N/3yGSEpPp2X04XTrfT5fO99P/zicBeO/dOXTpfD+//rrXzVxTLP/as9wG6nPKcp/lNlCfP7M+d+pzxnKf5TYRMbrw+Pe//51Jkyad9bgKFSqwd+9eXnrppbMeGxwcTMWKFfO8nDxmFSAqKpLMzCxmzVqUuy0tLZ05c5bQokUEtWqFOTq/U+rzzzZQn7+2gfqcstxnuQ3U55TlPsttoD6nLPdZbgPIzMxk+PCX+fHHn5k4cRQtWzZxtaeksX59rUhISMy3LT09g/nzVhISEkTDhnXp1/9G3vzXyDyvZ57N+SHZm2+5njf/NZK6dasXd7pZln/tWW4D9Tlluc9yG6jPn1mfO/U5Y7nPcpuIGH2Px3MVGBhIaGioa+O3aBFBVFQkr78+jYSEROrXr8XcucuIjY1j7NhhrnWpz7/b1Oe/berz7z7Lberz7z7Lberz7z7LbQDjx09h2bK1XH99G44ePcb8+cvz7L/pprM/VaU4TJ/+CUlJKcTF5Tx6c/nydRw4kPP/+/fvToUK5Vzpsn59wcbcPfP0OyQnn6B160upUaMKhw4d5ZOFq9ixI5aR/7yLcuXKcGmzi7m02cV5Pi52bxwAjRpdROfOVxV5Z0EszF9BLP/as9ymPv/us9ymvsKh35PVp76S0yaFw+PJdjtBHPBkZ2ebvIJbtmxh/PjxbN26lWrVqtG3b18GDBiAx5P3XUVnzJjBgAEDyMzM9GGUGMedqalpTJgwnYULV5CYmExERDgPPdSPa65p5fjchUF9/tkG6vPXNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y239+49m3bqfTrt/27aFxVhzeh07DiY2Nq7AfUuXvk/dujWKuegPlq8vFN3cZWannfOxn336Nf/731JiYnaTePQYZcuVoVmzi7mz3w107HjlaT8udm8cXTrfz4jHBnD34JvOqy/Q4+zJPl76ted/baA+pyz3WW4D9Tml35N9pz5nLPcVbVvjQjiHOPHP75a6nVAsXrqyk9sJRcLkwuP27dtp1aoVGRkZNGvWjIMHDxIbG0tkZCSzZ8+mZs2auce6vfAoIiIiIiIiF47zWXh0Q2EtPIqIiIhcuLTw6DYtPJZsJt/j8cknn6R8+fJs2rSJ6Oho9uzZw7Rp09i0aRNt27Zl27ZtbieKiIiIiIiIiIiIiIiIyJ+YXHhcs2YNDz74II0aNcrd1q9fP9asWUNAQADt27dn3bp1LhaKiIiIiIiIiIiIiIiIyJ+ZXHhMSEjI8zhVryZNmrB69Wrq1q1Lp06d+OKLL1yoExERERERERERERERkaIQ4LkwXv7K5MJjeHg4GzduLHBfjRo1WLlyJS1btqRnz57Mnj27mOtERERERERERERERERE5FQmFx47dOjA7NmzycjIKHB/xYoVWbx4MVFRUSxYsKCY60RERERERERERERERETkVCYXHgcOHEi7du2Ijo4+7THBwcHMnTuXYcOGce211xZjnYiIiIiIiIiIiIiIiIicypOdnZ3tdoR7YtwOEBERERERkRIkMzvN7YQzCvQEuZ0gIiIiUsI1djvggvdE9FK3E4rF2Nad3E4oEibveBQRERERERERERERERGRkqWU2wEiIiIiIiIiJYX1OwqPZ8S7nXBGZUuFuZ0gIiIiIiJFSHc8ioiIiIiIiIiIiIiIiIhjWngUEREREREREREREREREcf0qFURERERERERERERERExIcCT7XaCOKA7HkVERERERERERERERETEMS08ioiIiIiIiIiIiIiIiIhjWngUEREREREREREREREREcf0Ho8iIiIiIiIiIiIiIiJiQoDH7QJxokQuPKanp7N161YaNGhAhQoVXG1JS0tn4sQZzJ+/nKSkZCIiwhk+vB+RkS1d7fJSn3+2gfr8tQ3U55TlPsttoD6nLPdZbgP1OWW5z3Ib2O9LSTnB5Mlz2LAhhk2bYkhMTGbcuIfo1auz22mA7fmz3Abu9B1POcmHHyzip407+GnTTpKSUnj2hbvpeUv7PMf9tHEHC+Z9w0+bdrA9Zi8ZGZms3zwl3/kO7D/M/Llf8dXKjezefZDAgAAaXlKHv9/bnavbNiuyz0NfF86ozxnLfZbbQF+7Tlnu07V1Rn3+2SZyoSuRj1rdt28fLVu2ZMWKFW6nMGrUBKZOnUePHtfxxBP3EBgYwJAhzxIdvdntNEB9/toG6vPXNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y21gv+/IkSQmTZrJjh17iIho4HZOPpbnz3IbuNN39Ggy7/17ATt27KdxxEWnPe7rrzYy93+rwAN16lY77XErlq1n6uTPuahedf7x4C3cc18Pjqec5P6/v8b8uV8VxacA6OvCKfU5Y7nPchvoa9cpy326ts6ozz/bRC50nuzs7Gy3I071+uuvn3H/kSNHGDt2LIMGDaJZs5yfpHzkkUd8GCnGh4/5w8aNMdx226OMHDmIwYN7AZCamkb37kOpWjWUmTNfcXR+p9Tnn23q89829fl3n+U29fl3n+U29fl3n+W2ktAHOT/FnZiYTFhYZTZt2k7v3o+YuYPA8vxZbiuOvuMZ8QVuT0tLJynxONXCQtn800769Xm+wDseEw4lUq58GUJCghj/wnRm/XdZgXc8/vpLLFWqVqRy5T+eQpSWlk7fW5/h+PGTLFr6WoEdZUuFOfjs9HWhPvWVtDYvfe36b5+urfr8s62x80hx5OkflridUCyebeX+75VFweQdjyNGjOCxxx5jxIgRBb7Gjh2Lx+Phgw8+yD3WDYsWfUNgYAB9+kTlbgsODqJ37y6sX/8z+/cX/Be+4qI+/2wD9flrG6jPKct9lttAfU5Z7rPcBupzynKf5Taw3wcQFFSasLDKbmcUyPL8WW4D9/qCgkpTLSz0rMdVrRZKSEjQWY9r2KhOnkVH7xiR11zOwQNHSEk54XPrmejrwnfqc8Zyn+U2L33t+s56n66t79Tnn20iRSk2NpZ+/fpRtWpVypQpw+WXX050dHTu/uzsbMaMGUOtWrUoU6YMnTt3Zvv27XnOcfjwYe68804qVqxIpUqVGDx4MMnJyYXaaXLhsUmTJpQtW5bnn3+eHTt2sHPnzjyvVatWkZ2dzXvvvcfOnTvZsWOHK51bt+4gPLwO5cuXzbO9efPGv+/f6UZWLvX5znJbzvjq85Xltpzx1eeE5T7LbTnjq88Jy32W23LGV58Tlvsst+WMb7vPOsvzZ7ktZ3zbfU4lHEoipEwQISHBbqcUO+vXVn3OWO6z3FYSWJ8/632WWZ879fnOcpsUjgDPhfE6H0eOHCEyMpLSpUvz+eefs2XLFl577TUqV/7jhz9efvll3nzzTd555x3Wrl1LuXLl6Nq1KydPnsw95s4772Tz5s0sXryYTz75hFWrVjFkyJDCunQAlCrUsxWSjRs38uabb/L888/z6aefMmHCBNq0aZPvuOrVq1O/fn0XCnPExx8u8Cd6vNvi4hKKOykP9fnOchuozwnLbaA+pyz3WW4D9Tlluc9yG6jPKct9ltvAfp91lufPchvY73Ni928HWbbkezp3bU1goMmfcy5S1q+t+pyx3Ge5rSSwPn/W+yyzPnfq853lNpGi8tJLL3HRRRfxwQcf5G5r0KBB7v/Pzs5mwoQJPPnkk9x0000ATJs2jRo1ajBv3jz69u3L1q1bWbRoEd999x2tW7cG4K233uKGG27g1VdfpXbt2oXSavJvAqVKleKRRx5h27ZtNGnShHbt2tG/f3/27dvn8zlTU1NJSkrK80pNTXPUefJkGkFBpfNtDw4Oyt3vJvX5znKbd3z1+cZym3d89fnOcp/lNu/46vOd5T7Lbd7x1ec7y32W27zjW+6zzvL8WW7zjm+5z1cnTqQy8pF/ExwcxLCHe7ud4wrr11Z9zljus9xWElifP+t9llmfO/X5znKbyPkoeN0qtcBjFyxYQOvWrbntttuoXr06LVu25P/+7/9y9+/cuZMDBw7QufMf7xsZGhrKVVddxbfffgvAt99+S6VKlXIXHQE6d+5MQEAAa9euLbTPy+TCo1f16tWZMmUKq1evJiYmhsaNG/Pcc89x4sT5v1fEuHHjCA0NzfMaN+5dR30hIUGkpaXn2+5d0DyX98UoSurzneU27/jq843lNu/46vOd5T7Lbd7x1ec7y32W27zjq893lvsst3nHt9xnneX5s9zmHd9yny8yM7MYNeJddvy6j1feeIDq1fPfgXAhsH5t1eeM5T7LbSWB9fmz3meZ9blTn+8st4mcj4LXrcYVeOyOHTv497//zSWXXMIXX3zB/fffz7Bhw/jwww8BOHDgAAA1atTI83E1atTI3XfgwAGqV6+eZ3+pUqWoUqVK7jGFweSjVk/Vpk0b1q5dywcffMDjjz/Om2++icdzfg/AHT16NI888kiebcHBux11hYVV4eDB/Ldtx8cfAaB69aqOzu+U+nxnuQ3U54TlNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y21gv886y/NnuQ3s9/ni+aen8tXKDYx96R7aXN3U7RzXWL+26nPGcp/ltpLA+vxZ77PM+typz3eW26RwBLodUEweK3DdquD3Ss/KyqJ169a8+OKLALRs2ZKffvqJd955h7vuuqvIW8+H6TseTzVo0CBiYmJ44IEHGDBgAPXq1Tvnjw0ODqZixYp5Xt5br33VpEkDdu2KJTn5eJ7tGzZsA6Bp0wYFfVixUZ/vLLeB+pyw3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN7PdZZ3n+LLeB/b7z9carHzF/7teM+Gdfut14tds5rrJ+bdXnjOU+y20lgfX5s95nmfW5U5/vLLeJnI+C160KXnisVasWl156aZ5tTZs2ZffunBvsatasCcDBgwfzHHPw4MHcfTVr1iQuLi7P/oyMDA4fPpx7TGEoUQuPABUqVOC5557jgw8+4IorrnC1JSoqkszMLGbNWpS7LS0tnTlzltCiRQS1aoW5WKc+f20D9flrG6jPKct9lttAfU5Z7rPcBupzynKf5Taw32ed5fmz3Ab2+87Hh1M+Z9oHixg85Ebu6N/F7RzXWb+26nPGcp/ltpLA+vxZ77PM+typzz/bRIpKZGQk27Zty7MtJiaG+vXrA9CgQQNq1qzJ0qVLc/cnJSWxdu1a2rZtC0Dbtm05evQo33//fe4xy5YtIysri6uuuqrQWs0+anXLli2MHz+erVu3Uq1aNfr27cuAAQPyPWJ1xowZDBgwgMzMzGJvbNEigqioSF5/fRoJCYnUr1+LuXOXERsbx9ixw4q9R30XRpv6/LdNff7dZ7lNff7dZ7lNff7dZ7mtJPR5TZ/+CUlJKcTF5TxKavnydRw4kPP/+/fvToUK5Vzpsjx/ltvc7ps5YynHjh0nPu4oACtXbODgwZxHkvW9sxMVKpRl375DfLrgWwC2bN4FwP+9sxCAWrWr0r1nOwCWLfmeCa/Npl79GjS4uDafLvw2z1hXt72UqtVCi+Tz0NeF+tRXctr+TF+7/tkHurbqU5tIcXj44Ydp164dL774Irfffjvr1q3jvffe47333gPA4/EwfPhwXnjhBS655BIaNGjAU089Re3atbn55puBnDsko6KiuOeee3jnnXdIT09n6NCh9O3bl9q1axdaqyc7Ozu70M5WSLZv306rVq3IyMigWbNmHDx4kNjYWCIjI5k9e3aeWz6dLTzGOG5NTU1jwoTpLFy4gsTEZCIiwnnooX5cc00rx+cuDOrzzzZQn7+2gfqcstxnuQ3U55TlPsttoD6nLPdZbgP7fQAdOw4mNjauwH1Ll75P3bo1irnoD5bnz3IbFG3f8Yz40+67octj7N+X//2QAD798mVq16lG9LqfuWfQywUe85crI3h/6j8BeGfSPN59e8Fpx/q/D0bSuk2TfNvLlnJ+B4K+LnynPmcs91lu89LXru+s9+na+k59VtsaF8I5xIkX1i9xO6FYPNmy83kd/8knnzB69Gi2b99OgwYNeOSRR7jnnnty92dnZ/P000/z3nvvcfToUdq3b8/bb79N48Z//Jo+fPgwQ4cOZeHChQQEBHDrrbfy5ptvUr58+UL7vEwuPPbp04dVq1bx1Vdf0ahRIwCmT5/O0KFDqVy5MosWLSIiIgJwf+FRRERERERExIozLTxaUBgLjyIiIiJFSwuPbnvxx8VuJxSLx6/wz7crMPkej2vWrOHBBx/MXXQE6NevH2vWrCEgIID27duzbt06FwtFRERERERERERERERE5M9MLjwmJCTkeZyqV5MmTVi9ejV169alU6dOfPHFFy7UiYiIiIiIiIiIiIiIiMipTC48hoeHs3HjxgL31ahRg5UrV9KyZUt69uzJ7Nmzi7lORERERERERERERERERE5lcuGxQ4cOzJ49m4yMjAL3V6xYkcWLFxMVFcWCBad/M3sREREREREREREREREpOQI8F8bLX5lceBw4cCDt2rUjOjr6tMcEBwczd+5chg0bxrXXXluMdSIiIiIiIiIiIiIiIiJyKk92dna22xHuiXE7QERERERERKTQHM+IdzvhjMqWCnM7QUREROQsGrsdcMEbv2Gx2wnFYlSLLm4nFAmTdzyKiIiIiIiIiIiIiIiISMmihUcRERERERERERERERERcayU2wEiIiIiIiIiUjisP8q0yiVvup1wRoe3D3M7QUREROSCF+Bxu0Cc0B2PIiIiIiIiIiIiIiIiIuKYFh5FRERERERERERERERExDEtPIqIiIiIiIiIiIiIiIiIY3qPRxERERERERERERERETEhUO/xWKLpjkcRERERERERERERERERcUwLjyIiIiIiIiIiIiIiIiLimBYeRURERERERERERERERMQxvcejQ2lp6UycOIP585eTlJRMREQ4w4f3IzKypdtpgPqcSEk5weTJc9iwIYZNm2JITExm3LiH6NWrs9tpgObOCctzB+pzynKf5TZQn1OW+yy3gb5vOGW5z3IbqM8py33Wf1+x3lfU17Zc2SAe/Htn/tIinFbN61O5Ujn+8c//x3/nrM1z3L9e6scdva7O9/Exvx7g6qgXTnv+3j1b895rA0lOSaXeFY+e9rhSpQL4auFoIhrVYsz4ufxr8lLfP6nfWf66APt9F/rXhhPW5856n+VrC7b7LLeB+pyy3Ge5TeRCV2LueMzKyuKjjz5iyJAh9O7dm/vvv58FCxa4ncWoUROYOnUePXpcxxNP3ENgYABDhjxLdPRmt9MA9Tlx5EgSkybNZMeOPURENHA7Jx/Nne8szx2ozynLfZbbQH1OWe6z3Ab6vuGU5T7LbaA+pyz3Wf99xXpfUV/bKpXLM/LBG2jcsCY//Rx7xmNPpqZz76Mf5nk9/fK80x5frmwQzzx2M8kpqWftGNK/A3VqVTnf/DOy/HUB9vsu9K8NJ6zPnfU+y9cWbPdZbgP1OWW5z3KbOBfguTBe/sqTnZ2d7XbEqS699FJeeeUVbrzxRgBSUlLo1q0b33zzDR6Ph6pVq3Lo0CEAunXrxvz58wkMDPRhpBhHnRs3xnDbbY8ycuQgBg/uBUBqahrduw+latVQZs58xdH5nVKfM2lp6SQmJhMWVplNm7bTu/cjZn4aT3PnO+tzpz7/7bPcpj7/7rPc5qXvG/7ZZ7lNff7fZ/n3FbDdV9TXtsolbxIUVIpKFcsQd+gYV1xWj2VzR572jseeXVue8a7FU40Z0ZMbOjfnx592c0PnFqf92GpVyvPd4jFMmrKMx4d3z73j8fD2YT5/bta/Lqz3wYX9teGU5bkD233Wr63lPstt6vPvvqJva+w8Uhx546fFbicUi4cv6+J2QpEwecfjzz//TGJiYu5///Of/+Trr7/mhRdeIDk5mYMHD5KYmMijjz7KZ599xmuvveZK56JF3xAYGECfPlG524KDg+jduwvr1//M/v3xrnR5qc+ZoKDShIVVdrXhdDR3vrM+d+pzxnKf5TZQn1OW+yy3een7hu8s91luA/U5Zb3P8u8rYLuvOK5tWloGcYeOnfPxAQEeKpQPOetxF9cP4/5B1/PkuDlkZGad8dinH7uJX3bG8dH8786542ysf11Y7wN9bThhee7Adp/1a2u5z3IbqM8py32W20TE6MLjqf773/8ycOBARo8eTUhIzl82ypcvz8svv0y3bt2YPn26K11bt+4gPLwO5cuXzbO9efPGv+/f6UZWLvX5L82d76zPnfqcsdxnuS1nfPU5YbnPcltJYH3+LPdZbssZX31OWO8T31m7tmXLlOa39a/y2/pX+fW7l3j56dspVzaowGNffPJWvl6znSUrt5zxnK2a16fvLVfx+Av/ozAfAmVt7k5lvc86zZ//sn5tLfdZbssZX31OWO6z3CYiUMrtgLM5duwYR44cISoqqsD9UVFR/POf/yzmqhzx8YcL/Gkt77a4uITiTspDff5Lc+c763OnPmcs91luA/U5ZbnPcltJYH3+LPdZbgP1OWW9T3xn6doejEvizf9bwsbNewgICKDjtU35e79ruaxJHXr0m0jmn+5q7NKhGddHNuXanuPOet6XxtzG3M9+4Lsfd3JRncJ7j0dLc1cQ633Waf78l/Vra7nPchuozynLfZbbpHAEeMy9Q6CcB7MLjx5PzjtrlitXjrJlyxIQcPqbM8/l/R1TU1NJTc375vLBwWkEBxf8k5Ln4uTJNIKCSufb7j3nyZNpPp+7MKjPf2nufGd97tTnjOU+y23e8dXnO8t9lttKAuvzZ7nPcpt3fPX5znqf+M7StX3+tQV5/nvOp9/z6844nnq0JzdFtWTOp98DULp0IGMfv5UP/vs12345cMZz3nHr1TRtXJuBQ98v9F5Lc1cQ633Waf78l/Vra7nPcpt3fPX5znKf5TYRMfyo1cGDB1OxYkUqVarEyZMn+eGHHwo87ueff6Z27dpnPd+4ceMIDQ3N8xo37l1HjSEhQaSlpefbnpqalrvfTerzX5o731mfO/U5Y7nPcpt3fPX5znKf5baSwPr8We6z3OYdX32+s94nvrN+bf/9wXIyM7O4rl1E7rb7B11P1crlGP/mp2f82ArlQ3jq0Z786/0lxB44Wuht1ufOep91mj//Zf3aWu6z3OYdX32+s9xnuU1EjN7xeNddd+Xb5r0D8s+Sk5P573//S48ePc56ztGjR/PII4/k2RYcvNv3SCAsrAoHD+a/bTs+/ggA1atXdXR+p9TnvzR3vrM+d+pzxnKf5TZQn1OW+yy3lQTW589yn+U2UJ9T1vvEd9av7cnUdA4fTaFSpZz3dKpQPoRH749iyn++okL5ECqUDwGgfNlgPB64qE4VTpxI49DhZIYO7kRQ6UDmfvZD7iNWa9esBEBoxbKOH7tqfe6s91mn+fNf1q+t5T7LbaA+pyz3WW4TEaMLjx988ME5HVe6dGnWr19PpUqVznpscHAwwcHBp2x19pMPTZo0YO3ajSQnH8/zRrYbNmwDoGnTBo7O75T6/JfmznfW5059zljus9wG6nPKcp/ltpLA+vxZ7rPcBupzynqf+M76tS1fLpiqlcuRcDgZgEqhZalQPoSHhnThoSFd8h2/YcVzfLp4A/0f+D/q1K5M5Url+PbzJ/Md9+gDXXn0ga7Ab0Bqvv3nwvrcWe+zTvPnv6xfW8t9lttAfU5Z7rPcJiKGH7V6LoKDg6lfvz6hoaGujB8VFUlmZhazZi3K3ZaWls6cOUto0SKCWrXCXOnyUp//0tz5zvrcqc8Zy32W20B9Tlnus9xWElifP8t9lttAfU5Z7xPfWbm2wUGlKF/u1B8ehhH/iCIgIIClq7YAcCjhGP3ufy/fa9W32zhxMo1+97/HhHe/BOC9D1fkO+7hJ/8LwH/+t4Z+978H5H9s27myMnenY73POs2f/7J+bS33WW4D9Tlluc9ymxSOAM+F8fJXJu94BNiyZQvjx49n69atVKtWjb59+zJgwIB8j1ydMWMGAwYMIDMzs9gbW7SIICoqktdfn0ZCQiL169di7txlxMbGMXbssGLvUV/hmz79E5KSUoiLy7l1f/nydRw4kPP/+/fvToUK5Vzp0tz5zvrcqc9/+yy3qc+/+yy3/Zm+b/hfn+U29fl/H9j9fcXLal9xXdu/97uW0IplqFk95weJozpenvvY0/emraRSaFlWzh/F/z6JZvuOgwB0vKYpf+1wGUtWbuazJZsAOHEync+WbMx3/hu6NKdV8/A8+zZu2cvGLXvzHOd9vOrP2/f/fmwHnz8n618X1vu8LvSvDSeszp2X1T7r19Zyn+U29fl3n+U2EQFPdnZ2ttsRp9q+fTutWrUiIyODZs2acfDgQWJjY4mMjGT27NnUrFkz91hnC48xjltTU9OYMGE6CxeuIDExmYiIcB56qB/XXNPK8bkLg/qc6dhxMLGxcQXuW7r0ferWrVHMRX/Q3PnO+typzxnLfZbbQH1OWe6z3Oal7xu+s9xnuQ3U55T1Psu/r4DtvqK8tlUueROAH5c/S726Bb//UosOY0hMOsFLY26j9RXh1KweSmBgADt/i2f2gmj+NXkJGRlZZxznXy/1o2fXltS74tEzHndRnSpsWPEcY8bP5V+Tl3J4u7N/rLT+dWG9Dy7cr43CYHnuwHaf9Wtruc9yG6jPKct9RdvWuBDOIU68teVLtxOKxYOX/tXthCJhcuGxT58+rFq1iq+++opGjRoBMH36dIYOHUrlypVZtGgRERERgPsLjyIiIiIiIiJybrwLj1Y5XXgUERERf6CFR7dp4bFkM/kej2vWrOHBBx/MXXQE6NevH2vWrCEgIID27duzbt06FwtFRERERERERERERESksAVeIC9/ZXLhMSEhIc/jVL2aNGnC6tWrqVu3Lp06deKLL75woU5ERERERERERERERERETmVy4TE8PJyNG/O/OTxAjRo1WLlyJS1btqRnz57Mnj27mOtERERERERERERERERE5FQmFx47dOjA7NmzycjIKHB/xYoVWbx4MVFRUSxYsKCY60RERERERERERERERETkVCYXHgcOHEi7du2Ijo4+7THBwcHMnTuXYcOGce211xZjnYiIiIiIiIiIiIiIiIicqpTbAQVp3br1OT1CNSAggAkTJhR9kIiIiIiIiIiIiIiIiBS5AI/bBeKEyTseRURERERERERERERERKRk0cKjiIiIiIiIiIiIiIiIiDhm8lGrIiIiIiIiIuJ/Dm8f5nbCGb226Te3E07r0cvru50gIiIiInJWWngUEREREREREREREREREwI82W4niAN61KqIiIiIiIiIiIiIiIiIOKaFRxERERERERERERERERFxTAuPIiIiIiIiIiIiIiIiIuKYFh5FRERERERERERERERExLFSbgeIiIiIiIiIiIiIiIiIAAR63C4QJ7Tw6FBaWjoTJ85g/vzlJCUlExERzvDh/YiMbOl2GqA+f20D9flrG9jvS0k5weTJc9iwIYZNm2JITExm3LiH6NWrs9tpgO35s9wG6nPKcp/lNtDvK05Z7rPcBupzynKf5TZQn1PF3bfvpxg+eebNAvfd9OKj1GjcIPe/M9Mz2LhgKTEr15Ecn0BQ2TJUa1iPa+7tS/mqlQFY8a//R8yKtacd7853X6Bc1UqF+jl46XuuM5b7LLeB+pyy3Ge5DdTnlOU+y20iF7oS96jVrKwsjh075nZGrlGjJjB16jx69LiOJ564h8DAAIYMeZbo6M1upwHq89c2UJ+/toH9viNHkpg0aSY7duwhIqLB2T+gmFmeP8ttoD6nLPdZbgP9vuKU5T7LbaA+pyz3WW4D9TnlVt9lN3Tg+mED8rxCa4bl7s/KyGTRuH+zfs4XXNSyKZF/70OLmzpTOjiItOMnc49r2iUy33k6PDiAUsFBVK5bs8gWHUHfc52y3Ge5DdTnlOU+y22gPqcs91luE7nQebKzs7PdjjjVpk2biIuLo1OnTrnbvvzyS1544QXWrVtHeno6ISEhdOrUiRdffJHLLrvMx5FiHHVu3BjDbbc9ysiRgxg8uBcAqalpdO8+lKpVQ5k58xVH53dKff7Zpj7/bSsJfZDzE2WJicmEhVVm06bt9O79iJmfkrY8f5bb1OfffZbbvPT7in/2WW5Tn3/3WW5Tn/2+1zb9lm+b947Hzo8O5uK2p7+L4sd5i4me+Qk9n3+Y6peEn9e4B7b+yoKn3uDKv/Wg5a1dCzzm0cvrn9c5C6Lvuf7ZZ7lNff7dZ7lNff7dV/RtjZ1HiiOTt33hdkKxGBxR8J/7SjqTdzwOHz6cGTNm5P737Nmz6datGz///DN33nknI0eO5NZbb2XVqlVcffXV/PDDD650Llr0DYGBAfTpE5W7LTg4iN69u7B+/c/s3x/vSpeX+vyzDdTnr21gvw8gKKg0YWGV3c4okOX5s9wG6nPKcp/lNi/9vuI7y32W20B9Tlnus9wG6nPK7b60EyfJyszMtz07K4ufPltBeJsWVL8knKzMTDJS0875vL98HQ0eD42uaV2Yufnoe67vLPdZbgP1OWW5z3IbqM8py32W26RwBHgujJe/MrnwuGHDBlq2/OOnCEeNGkWbNm3YuXMnkydPZty4cUybNo1ffvmFunXrMmrUKFc6t27dQXh4HcqXL5tne/PmjX/fv9ONrFzq853ltpzx1ecry20549vus87y/FluyxlffU5Y7rPcVhJYnz/LfZbbcsZXnxOW+yy35YyvPifc7Fs5aTpT+49g8t8eZuHTE4n/5Y+7I4/sPcDxw4lUqV+bVe/8hyl3PsqUOx/h40deZN9PZ37aUlZGJr+u/oEaEQ2oUL1qkfVbp197vrPcljO++pyw3Ge5LWd89Tlhuc9ym4gYXXg8fvw45cqVy/3/O3fu5KGHHsrd5lWtWjXuvfdeVq9e7UYm8fGHC/xJQe+2uLiE4k7KQ32+s9wG6nPCchvY77PO8vxZbgP1OWW5z3JbSWB9/iz3WW4D9Tlluc9yG6jPKTf6AkqVosHVV9B2UG/++s8hXPm37hzevY8FYyZwaMceABJ/v7Ni0yfL2b95O9fc25fr/tGPzPR0PnvhbRJ2xZ72/Ht+3ELqsRQuuebKQm8vSfRrz3eW20B9Tlnus9wG6nPKcp/lNhExuvAYERHBt99+C0CZMmUoX748SUlJBR6blJRE6dKlizMv18mTaQQF5R87ODgod7+b1Oc7y23e8dXnG8tt3vEt91lnef4st3nHV5/vLPdZbisJrM+f5T7Lbd7x1ec7y32W27zjq893bvTVbHIxXUb8nSad2hJ+ZXOuuOWv3PziCADW/WcBAOknU3P+90QqNz49jIjrrybi+qu5ccyDQDYb5i857fl/+TqagFKBXNyu5WmPuRDo157vLLd5x1ef7yz3WW7zjq8+31nus9wmIkYXHocMGcK0adNYsGABHo+HBx98kGeeeYbo6Og8xy1dupQ33niDTp06nfWcqampJCUl5Xmlnsf7LRQkJCSItLT0AsZKy93vJvX5znKbd3z1+cZym3d8y33WWZ4/y23e8dXnO8t9lttKAuvzZ7nPcpt3fPX5znKf5Tbv+OrznZW+0FphhF/ZnH0/bScrM4tSv//jZ80mF1O+2h93YJQPq0LNJg05uG1HgedJP5HKb99tom6LpoRUKF8s7VZZubanY7nPcpt3fPX5znKf5Tbv+OrzneU+y20iYnTh8b777uNvf/sbN998M23btuXYsWOkp6dz1VVX0ahRIyIjIwkPD+evf/0r5cqV49VXXz3rOceNG0doaGie17hx7zrqDAurQnz8kXzbvduqu/zeDOrzneU2UJ8TltvAfp91lufPchuozynLfZbbSgLr82e5z3IbqM8py32W20B9TlnqK1etMlkZGWSkplK2cigAZUIr5DsupGJ5UlOOF3iOXd9tICM1jUbXtC7S1pLA0rUtiOU+y22gPqcs91luA/U5ZbnPcpsUjgDPhfHyVyYXHj0eD1OnTmXmzJkATJo0iYSEBLKzs9mxYwfffvstaWlpDBs2jPXr1xMeHn7Wc44ePZrExMQ8r9Gj73XU2aRJA3btiiU5Oe9fIDZs2AZA06YNHJ3fKfX5znIbqM8Jy21gv886y/NnuQ3U55TlPsttJYH1+bPcZ7kN1OeU5T7LbaA+pyz1HTt4iMCg0pQOCaZK/doElAok5fDRfMcdP5JImYoF3834y6poSocEE35l8yKutc/StS2I5T7LbaA+pyz3WW4D9Tlluc9ym4gYXXj0uv322/n22285evQoP/zwA6tWreK7774jNjaWffv28cYbb1C9evVzOldwcDAVK1bM8/I+89lXUVGRZGZmMWvWotxtaWnpzJmzhBYtIqhVK8zR+Z1Sn3+2gfr8tQ3s91lnef4st4H6nLLcZ7mtJLA+f5b7LLeB+pyy3Ge5DdTnlBt9JxKP5duWsGsvv0Vvom7zJngCAggqE8JFLZtxcNtOjsYeyD3uyN4DHNy2kzrNmxR43r2bfia8TQtKOfz3CX+gX3v+2Qbqc8pyn+U2UJ9Tlvsst4kIlHI74FxUqFCBK664wu2MfFq0iCAqKpLXX59GQkIi9evXYu7cZcTGxjF27DC389Tnp23q89+2ktDnNX36JyQlpRAXlwDA8uXrOHAg5//379+dChXKudJlef4st6nPv/sst/2Zfl/xvz7Lberz7z7LbeormX1L3/iAwKDS1IhoQJmKFTiy9wA/L/mGUkFBtOl3U+5xbe7owb5N2/jkmTe57IYOAPz02QqCy5elZa+u+c776+ofyM7MotG1xfuYVX3P9b8+y23q8+8+y23q8+8+y20iAp7s7OxstyMKsmXLFsaPH8/WrVupVq0affv2ZcCAAXg8eR98O2PGDAYMGEBmZqYPo8Q47kxNTWPChOksXLiCxMRkIiLCeeihflxzTSvH5y4M6vPPNlCfv7aB/T6Ajh0HExsbV+C+pUvfp27dGsVc9AfL82e5DdTnlOU+y21e+n3Fd5b7LLeB+pyy3Ge5DdTnVFH2vbbpt3zbfvp0Bdu/+o6kA/GknThJmYrlqX15BH+57QZCT7mj4tCOPaydPo+DMTvxeAKofVljrh5wM6G18j+tad7jr3LsYAJ3vjeWgMCzP5Dq0cvr+/6J/Ym+5/rOcp/lNlCfU5b7LLeB+pyy3Fe0bY0L4RzixP/75Qu3E4pF/0b5fzjNH5hceNy+fTutWrUiIyODZs2acfDgQWJjY4mMjGT27NnUrFkz91i3Fx5FRERERERExD8UtPBoRWEtPIqIiMjZaOHRbVp4LNlMvsfjk08+Sfny5dm0aRPR0dHs2bOHadOmsWnTJtq2bcu2bdvcThQRERERERERERERERGRPzG58LhmzRoefPBBGjVqlLutX79+rFmzhoCAANq3b8+6detcLBQRERERERERERERERGRPzO58JiQkJDncapeTZo0YfXq1dStW5dOnTrxxRcXxu22IiIiIiIiIiIiIiIiItaVcjugIOHh4WzcuLHAfTVq1GDlypV0796dnj170q1bt2KuExERERERERERERERkaIQ6Ml2O0EcMHnHY4cOHZg9ezYZGRkF7q9YsSKLFy8mKiqKBQsWFHOdiIiIiIiIiIiIiIiIiJzK5MLjwIEDadeuHdHR0ac9Jjg4mLlz5zJs2DCuvfbaYqwTERERERERERERERERkVOZfNRq69atmT179lmPCwgIYMKECUUfJCIiIiIiIiIiIiIiIiJnZHLhUURERERERERERERERC48Jh/VKedMC48iIiIiIiIiIsCjl9d3O+G0thyNcTvhjC6t1NjtBBERERExQAvHIiIiIiIiIiIiIiIiIuKYFh5FRERERERERERERERExDEtPIqIiIiIiIiIiIiIiIiIY3qPRxERERERERERERERETEhwON2gTihOx5FRERERERERERERERExDEtPIqIiIiIiIiIiIiIiIiIY1p4FBERERERERERERERERHH9B6PDqWlpTNx4gzmz19OUlIyERHhDB/ej8jIlm6nAerz1zaAlJQTTJ48hw0bYti0KYbExGTGjXuIXr06u50G2J4/y22gPqcs91luA/U5ZbnPchuozynLfZbbQH1OWe7Tn5WdUV/Jatu94wAz/+8Ldvy8lyMJxwgOKc1FDWpyc78OXHlNs9zjvpy3hpWLvid2VxwpySeoUi2Uy1o1pM/f/0r12lXynXfJgrXMm7GCuH2HqVa9Ejf2ac+Nt19TZJ8H2L62YLvPchvo92Untm//jbfe+i+bN//CoUNHCAkJplGjegwe3IuOHdu4nWd67kB9Tlnus9wmzuk9Hks2k3c8du3alddff519+/a5nXJWo0ZNYOrUefTocR1PPHEPgYEBDBnyLNHRm91OA9Tnr20AR44kMWnSTHbs2ENERAO3c/KxPH+W20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyy3Kc/KzujvpLVFr//CCePp3L9ja0Z/MjN3HZ3FwBeHDGFL+d+m3vczphYatSuwi39r+fekbdyXVQrfvj2Zx4bNIHD8Yl5zvnFnG+ZNPYj6jWoyd8fvYWIy+vz/mvzmDNtWZF9HmD72oLtPsttoN+Xndi3L56UlBPccksnnnhiCA880BeA++9/nlmzFrlcZ3vuQH1OWe6z3CZyofNkZ2dnux1xqoCAADweDx6Phw4dOnDnnXdy6623UrFixUIeKcbRR2/cGMNttz3KyJGDGDy4FwCpqWl07z6UqlVDmTnzlcKIVJ/aCpSWlk5iYjJhYZXZtGk7vXs/YuanBS3Pn+U29fl3n+U29fl3n+U29fl3n+U29fl/n/6srD5/bNty9Nz/DSMzM4sRd71Belo6//po1GmP+3XrHkYMnEC/B27g1rs65TSfTOeens/R+LL6PPn633OPfePpGaxb+RP/t+Apylcsm+9cl1ZqfB6fTX6Wr631PsttXvp9uXBlZmbSq9fDpKamsWjRO651WJ879flvX9G3OfueJs59vNP9H6woDr0bRLmdUCRM3vEIMGzYMAYOHMgPP/zA4MGDqVmzJrfddhvz5s0jPT3d7TwAFi36hsDAAPr0+eMXR3BwEL17d2H9+p/Zvz/exTr1+WubV1BQacLCKrudUSDL82e5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynrffqzsu/U5x9tgYEBVKtRiZRjJ894XNjvj1hNST6Ru+2n73/hWOJxut0amefYbr0jOXkijehvthZ+MLbmryCW+yy3een35cIVGBhIrVrVOHYsxdUO63OnPmcs91luExHDC49XXnkl77//PgcOHOB///sfN9xwA59++im33norNWrUYMiQIaxcudLVxq1bdxAeXofy5fP+pF/z5o1/37/Tjaxc6vOd5baSwPL8WW7LGV99Tljus9yWM776nLDcZ7ktZ3z1OWG5z3Jbzvjqc8J6n2XW5059vnO77eSJVJKOJrN/7yEW/HclP3z7M82vbJTvuKTEFI4ePsYvW/fwr+dn5jS2viR3/46YWAAaNq2b5+MaNqlLQICHnb/vL2xuz9/ZWO6z3FYSlJT5O378JIcPJ7J7936mTp3HqlXfc/XVLVxtsj536nPGcp/lNhGBUm4HnE1QUBC33HILt9xyC8eOHePjjz9mxowZTJkyhcmTJ1O7dm3+9re/8fLLLxd7W3z84QJ/Wsu7LS4uobiT8lCf7yy3lQSW589yG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1PW+yyzPnfq853bbR9MXJj7no4BAR6u6nA594zole+4v3d/jvS0DAAqhJbl74/ezBVXReTuP3IoiYDAACpVqZDn40qXLkX50HL53g+ysLg9f2djuc9yW0lQUuZv/PjJue/pGBAQQJcubRkz5j5Xm6zPnfqcsdxnuU0KR6DH3DsEynkwv/D4ZxUqVGDQoEEMGjSIAwcO8N///pcZM2bw2muvnXXhMTU1ldTU1DzbgoPTCA4O8rnn5Mk0goJK59vuPefJk2k+n7swqM93lttKAsvzZ7nNO776fGe5z3Kbd3z1+c5yn+U27/jq853lPstt3vHV5zvrfZZZnzv1+c7tth59r6Fdx+YcPpTEN0t+JCsrm4z0zHzHPfXGPaSlpbN3VxyrFn3PyRN5u9JS0ylVKrDAMYKCSpGWWjRvf+P2/J2N5T7LbSVBSZm/u+7qSVRUJHFxh/n886/Iyspy/e2orM+d+pyx3Ge5TUQMP2r1bGrWrMnDDz9MdHQ0W7ee/f0Fxo0bR2hoaJ7XuHHvOmoICQkiLS3/N/jU1LTc/W5Sn+8st5UElufPcpt3fPX5znKf5Tbv+OrzneU+y23e8dXnO8t9ltu846vPd9b7LLM+d+rzndttdcNr0KJNY66/oTVPvv53Th5PZeyIyWRn571j4PLWjfhLu6bcdMd1jHhxAB9N/pLPZn+duz8ouDQZGfkXLAHS0jIICs7/D72Fwe35OxvLfZbbSoKSMn8NG15Eu3ZXcPPNHXn33ac5fvwE9933fL6v8eJkfe7U54zlPsttIlKCFx7/rHHjxmc9ZvTo0SQmJuZ5jR59r6Nxw8KqEB9/JN9277bq1as6Or9T6vOd5baSwPL8WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcBupzynqfZdbnTn2+s9bWrmNzftmyh3274097TK261WjQuA4rF/2Qu61ytYpkZWZx9PCxPMemp2eQnJhClbDQIum1Nn+nstxnua0kKKnz17VrJJs2bWfnzqJ539VzYX3u1OeM5T7LbSJidOExKyuLO+64o1DPGRwcTMWKFfO8nDxmFaBJkwbs2hVLcvLxPNs3bNgGQNOmDRyd3yn1+c5yW0lgef4st4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tllvc8y63OnPt9Za0v9/ZGoKcknz3hcWmo6x/90TINLagPw69a9eY77desesrKyc/cXNmvzdyrLfZbbSoKSOn/eR0me2l2crM+d+pyx3Ge5TQpHgOfCePkrkwuPJUVUVCSZmVm5b+wMkJaWzpw5S2jRIoJatcJcrFOfv7aVBJbnz3IbqM8py32W20B9Tlnus9wG6nPKcp/lNlCfU9b7LLM+d+oreW2n3pkIkJGRyYrPvicouDQXNahBZkYmyUn5FydiNu/mt18P0Khp3dxtl7e+hPIVy7Jozuo8xy7637cEhwTxl8hLC/+TwPa1Bdt9lttKAuvzl5BwNN+29PQM5s9fRkhIEA0bXlT8Ub+zPnfqc8Zyn+U2EYFSbgeczpYtWxg/fjxbt26lWrVq9O3blwEDBuDx5F0GnjFjBgMGDCAzs+D3HyhKLVpEEBUVyeuvTyMhIZH69Wsxd+4yYmPjGDt2WLH3qO/CaPuz6dM/ISkphbi4BACWL1/HgQM5/79//+5UqFDOlS7L82e5TX3+3We5TX3+3We5TX3+3We5TX3+3wf6s7L6Lpy2d8Z/zPGUkzRreTFVwkI5mnCMlV/8QOyuOAY+1JMyZYNJOXaCe3o+T2TnK7jo4pqEhATx26/7WfbJd5QtF8Jtd3fJPV9wSGnuuDeK916Zw8ujP6Tl1RFs+XEnKxd9z533d6NCaNki+TwsX1vrfZbb/ky/L/tmzJhJJCcf58orL6NGjSrExx9l4cIV7Nixl1GjBlOuXBnX2qzPnfr8t89ym4iAJ9vNdyA+je3bt9OqVSsyMjJo1qwZBw8eJDY2lsjISGbPnk3NmjVzj3W28BjjuDU1NY0JE6azcOEKEhOTiYgI56GH+nHNNa0cn7swqM8/27w6dhxMbGxcgfuWLn2funVrFHPRHyzPn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbaA+p6z36c/KvlOfzbYtRwv+N4yvvlzP0oVr+e2XAxxLTKFMuWAaNqnLDbe1p821lwE5d0dNe+sTfvr+F+L2HyEtNZ3KYRVpceUl3DaoC9VrV8l33i/nrWHBf1ZwcN9hqtWoxA2929O97zX5fiDc69JKjR1/jpavLdjus9zmpd+XffPpp6v4+OPFxMTs4ujRY5QrV4ZmzRrRr193OnW6yu0803MH6nPKcl/Rtjn/nibOzP/tc7cTisVN9bu5nVAkTC489unTh1WrVvHVV1/RqFEjAKZPn87QoUOpXLkyixYtIiIiAnB/4VFEREREREREpKidbuHRisJYeBQREbFB39PcpoXHks3kezyuWbOGBx98MHfREaBfv36sWbOGgIAA2rdvz7p161wsFBERERERERERERERkcIW4LkwXv7K5MJjQkJCnsepejVp0oTVq1dTt25dOnXqxBdffOFCnYiIiIiIiIiIiIiIiIicyuTCY3h4OBs3bixwX40aNVi5ciUtW7akZ8+ezJ49u5jrRERERERERERERERERORUJhceO3TowOzZs8nIyChwf8WKFVm8eDFRUVEsWLCgmOtERERERERERERERERE5FSl3A4oyMCBAzl48CDR0dFcffXVBR4THBzM3LlzeeSRR9iwYUMxF4qIiIiIiIiIiIiIiEhh8+f3P7wQeLKzs7PdjnBPjNsBIiIiIiIiIiJnteWo7X/DuLRSY7cTRERECom+p7nt0z2fu51QLG68qJvbCUXC5KNWRURERERERERERERERKRk0cKjiIiIiIiIiIiIiIiIiDhm8j0eRURERERERETkD9YfZZqSsd/thDMqV6qW2wkiIiIiFwQtPIqIiIiIiIiIiIiIiIgJgR63C8QJPWpVRERERERERERERERERBzTwqOIiIiIiIiIiIiIiIiIOKaFRxERERERERERERERERFxTO/xKCIiIiIiIiIiIiIiIiYEeLLdThAHdMejiIiIiIiIiIiIiIiIiDimOx4dSktLZ+LEGcyfv5ykpGQiIsIZPrwfkZEt3U4D1OevbaA+f20D9Tlluc9yG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gTt9x1NO8uEHX/LTxp1s3rSTpKTjPPPCQHre0i73mKysLD6Zv4ZlS35g2897SExMoU6danTtdiX9B/2V4ODSec6ZcCiJN9+Yw9erNnE85SQNLq7JoHu60aVr6yL7PMD29bXcBupzIiXlBJMnz2HDhhg2bYohMTGZceMeolevzm6nAbbnDtTnlOU+y20iFzrd8ejQqFETmDp1Hj16XMcTT9xDYGAAQ4Y8S3T0ZrfTAPX5axuoz1/bQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbeBO39Gjyfzfvz9h5479NI6oW+AxJ0+k8cyTUzlyJJlbb7+WEf/sQ7PLw3ln0gIevHci2dl/PG4tOfkEd/d/mWWLf+DW265l+IjelC0Xwj8feY/PP1lbZJ8H2L6+lttAfU4cOZLEpEkz2bFjDxERDdzOycfy3IH6nLLcZ7lN5ELnyf7zn94MmTdvHh9++CFlypRh+PDhtGnThh07dvDEE0/wzTffkJGRQatWrRg9ejSRkZE+jhLjqHHjxhhuu+1RRo4cxODBvYD/z959h1dR5v8bv1NIAxKKgYBUKQEEIqAooqKAgFLUgOh+AcuyClawLoiyqMuCZRFkseyChR8oWFCKUlTariJNKdKCUg0loaRD+u+PeKIxoXgmyXw4vF97nesiM5N57szJxCUPMwOZmVn06vUg1atHMGvWS47275T6fLNNfb7bpj7f7rPcpj7f7rPcpj7f7rPcpj7f7rPcpj7f7rPcVh596TkHS1yelZVNSnIGF0RGsPWHPQy87R/FrnjMzsph65a9xLRpVORz//3aAt6YMo/Xpw7n8g4tAHj3rcVM+ufHvDHtUdpf0QwouGLyzj+N5/ChY3z2xXgqBBW/wVfFwFqOvj7L76/lNvU5l5WVTXJyGpGRVdm8eSf9+j1q5opH68dOfb7bV/ZtTZ1HiiNL4j93O6FcdLvwRrcTyoTJKx4///xzYmNj+eqrr1i+fDmdO3dm9erVXHXVVXzxxRe0bduWNm3asHLlSq677jpWrlzpSueiRV8TEODPbbf1KFwWHBxEv37X8/332zl4MNGVLg/1+WYbqM9X20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y23gXl9QUAUuiIw47TYVggKLTToCXNf1EgB27zpUuOz79TupWq1y4aQjgL+/P9f3uJQjR1JYv87ZPzI/Fcvvr+U2UJ9TQUEViIys6mrDqVg/dupzxnKf5TYpHf7nyctXmfzaXnzxRS655BLi4+M5cOAAd955J3369KFGjRrs3LmTTz/9lM8++4ytW7cSFRXF888/70rntm27aNDgQipVCiuyvHXrpr+s3+1GViH1ec9yW8H46vOW5baC8dXnhOU+y20F46vPCct9ltsKxlefE5b7LLcVjK8+Jyz3WW4rGF99Tljus9xWML7tvpIcPZICQJUqlQqXZWXlFHvmI0BISBAA27bsLZMWy8fPclvB+OrzVdaPnfqcsdxnuU1EjE48/vDDD9x1111UrlwZgIcffpjExEQeeeQRqlb99V/41KlTh/vuu481a9a40pmYeKzEf3HkWZaQcLS8k4pQn/cst4H6nLDcBupzynKf5TZQn1OW+yy3gfqcstxnuQ3U55TlPsttoD6nLPdZbgP7fSV5963FVKoUQserWxYua9AwioTDxzlwoGjv9+t3ApCQkFQmLZaPn+U2UJ8vs37s1OeM5T7LbSJidOIxNzeXkJCQwo89f/ZMRP5WeHh4uXX93smTWQQFFf9XdsHBQYXr3aQ+71lu84yvPu9YbvOMrz7vWe6z3OYZX33es9xnuc0zvvq8Z7nPcptnfPV5z3Kf5TbP+OrznuU+y22e8S33/d60f3/O6lXbeOiRWCqH/3pFy819r8Lf358Rj77Jxu9/Yv++BN76z0KWffU9AJmZ2WXSY/n4WW7zjK8+32T92KnPGct9lttExOjEY7Nmzfjss88KP54/fz4ACxYsKLbtnDlzaNy48Rn3mZmZSUpKSpFXZqazH0AhIUFkZRX/P7Se/Xpu8+EW9XnPcptnfPV5x3KbZ3z1ec9yn+U2z/jq857lPsttnvHV5z3LfZbbPOOrz3uW+yy3ecZXn/cs91lu84xvue+3Fi9cy2uvzuXmvldx6+3XFlnXNLoO/3jxL/y8P5G7B77ATTc8zawZX/H4iNsACA0NLpMmy8fPcptnfPX5JuvHTn3OWO6z3Calw9/v/Hj5qkC3A0ryyCOPcPvtt3PFFVdQo0YNFi1aRJcuXQgLC+Omm26iT58+5ObmMnv2bJYvX87kyZPPuM9x48bx7LPPFln2t789yJgxD3ndGRlZjcOHi1+2nZh4HIAaNap7ve/SoD7vWW4D9TlhuQ3U55TlPsttoD6nLPdZbgP1OWW5z3IbqM8py32W20B9Tlnus9wG9vs8vv1mK6NHvs1V17TiqdEDStyma/d2dLouhrgdP5Obl0fz5vVYt3YHAPUb1CyTLsvHz3IbqM+XWT926nPGcp/lNhExesVj//79mTRpEsePH2ft2rX07duX999/nxdeeIGsrCzuuecehg4dyooVKxg8eDBDhw494z5HjhxJcnJykdfIkUMcdTZr1pA9e+JJS8sosnzjxoL/s9u8eUNH+3dKfd6z3Abqc8JyG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfqcstxnuQ3s9wFs3rSLxx5+nRYX1+eFCfcSGBhwym0rBAVycasGtI65iApBgaxetQ2Ayzs0L5M2y8fPchuoz5dZP3bqc8Zyn+U2ETE68Qjw0EMPsWPHDg4ePMj777/PBRdcQMWKFVm4cCE//fQT33zzDYcOHeLf//43/v5n/jKCg4MJDw8v8vLc89lbPXp0JDc3j9mzFxUuy8rKZs6cL4mJiaZWrUhH+3dKfb7ZBurz1TZQn1OW+yy3gfqcstxnuQ3U55TlPsttoD6nLPdZbgP1OWW5z3Ib2O/b9dNBht03mdoXVmfSaw/+odvk7dt7mI8/WMnVnVqX2RWPlo+f5TZQny+zfuzU54zlPsttImL0Vqtn0rBhQxo2dP9fLcTERNOjR0cmTJjO0aPJ1K9fi08+WUp8fAJjxz7sdp76fLRNfb7bpj7f7rPcpj7f7rPcpj7f7rPcpj7f7rPcpj7f7rPc5nbfrJlLSUs9QWJCEgArl28k4XDB7fBuG3Ad/v7+PHjvRFJSMrjj7u78d+XmIp9fp24kMZc0Kvy4b++/0bV7O2rVqkb8z0f4aPYKwiMqMupvJd+atTRYfn8tt6mvdMyYsYCUlHQSEgpuLbls2RoOHSr486BBvahcuaIrXdaPnfp8t89ym4iAX35+fr7bESXZunUr48ePZ9u2bVxwwQXcfvvt3HHHHfj5FX3i5syZM7njjjvIzc31YpQ4x52ZmVlMnDiD+fOXk5ycRnR0A4YNG8jVV7d1vO/SoD7fbAP1+WobqM8py32W20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5Dcq2Lz3n4CnX9bx+JAcPFH8WF8CCJf8AoFe3p075+b1v6sCz/7i78OORj/+Hjd//yNGjqVSpWolO18Uw9IHeVKsefsp9VAysdaYv4Ywsv7+W20B9TnXuPJj4+IQS13311VTq1CmbK33PhvVjpz5nLPeVbVvTUtiHOLHi4OduJ5SLTrVudDuhTJiceNy5cydt27YlJyeHiy++mMOHDxMfH0/Hjh358MMPiYqKKtzW7YlHEREREREREZHz3ekmHi0ojYlHERE5X2ji0W2aeDy3mXzG49NPP02lSpXYvHkz69atY//+/UyfPp3NmzfToUMHduzY4XaiiIiIiIiIiIiIiIiIiPyGyYnHb7/9loceeojGjRsXLhs4cCDffvst/v7+XHXVVaxZs8bFQhERERERERERERERERH5rUC3A0py9OjRIrdT9WjWrBnffPMNPXr0oEuXLnz00Ucu1ImIiIiIiIiIiIiIiEhZ8Pcz94RA+QNMXvHYoEEDNm3aVOK6mjVrsmLFCtq0aUOfPn348MMPy7lORERERERERERERERERH7P5MTjtddey4cffkhOTk6J68PDw/niiy/o0aMH8+bNK+c6EREREREREREREREREfk9kxOPd911F1deeSXr1q075TbBwcF88sknPPzww1xzzTXlWCciIiIiIiIiIiIiIiIiv+eXn59/Ht8sN87tABERERERERGRc156zkG3E06rYmAttxNEROSc0dTtgPPefw995nZCubg6qqfbCWUi0O0AEREREREREREREREREQB/P7cLxAmTt1oVERERERERERERERERkXOLrngUERERERERERFHrN/K9O/f73M74bSeblPP7QQRERGRUqErHkVERERERERERERERETEMV3xKCIiIiIiIiIiIiIiIiboGY/nNl3xKCIiIiIiIiIiIiIiIiKOaeJRRERERERERERERERERBzTxKOIiIiIiIiIiIiIiIjIOWD8+PH4+fkxfPjwwmUnT57kgQceoHr16lSqVIm+ffty+PDhIp+3b98+evbsSVhYGDVq1OCJJ54gJyen1Ps08SgiIiIiIiIiIiIiIiJi3Nq1a3nzzTdp3bp1keWPPPII8+fP58MPP2TFihUcOHCA2NjYwvW5ubn07NmTrKwsvvnmG959913eeecdRo8eXeqNgaW+x/NMVlY2kybNZO7cZaSkpBEd3YDhwwfSsWMbt9MA9flqG6jPV9tAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfqcstxnuQ3U55TlPsttoL7fO7Qlji+en1Tiuh7PP05kk4YA5OXksvnTRexauZqMY8mEVYug0bUdaHlTN/wDAv7w/spCevoJpk2bw8aNcWzeHEdychrjxg0jNrZrmY15tjZtiuPTT5eyevUm4uMTqFKlMjEx0QwfPoiGDS90Ow/QueGrbWC7T+eG77aJc7pirmRpaWkMGDCA//znP/z9738vXJ6cnMy0adN477336Ny5MwBvv/02zZs359tvv+WKK65gyZIlbN26lS+//JKaNWtyySWX8Pzzz/PXv/6VMWPGEBQUVGqdev8cGjFiIu+88ym9e3di1Kh7CAjw5957n2Xdui1upwHq89U2UJ+vtoH6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfafSrMe1dHzgziKvyjUjC9f/71/vsOnjhURdHM1ld/ajRrPGbPxgAaunzfZqf2Xh+PEUpkyZxa5d+4mOLrsJTm9MnfoxS5Z8Q4cOMYwadQ/9+/dg3botxMYOJy5ur9t5gM4NX20D2306N3y3TeRsZWZmkpKSUuSVmZl5yu0feOABevbsSdeuRf9h0fr168nOzi6yvFmzZtSrV49Vq1YBsGrVKlq1akXNmjULt+nevTspKSls2VK6541ffn5+fqnusRR99tlnzJo1i/Xr1xMfH8+JEyeoVKkSTZs25frrr2fIkCHUqVPHwQhxjvo2bYrj1lsf48kn72bw4IJLVjMzs+jV60GqV49g1qyXHO3fKfX5Zpv6fLdNfb7dZ7lNfb7dZ7lNfb7dZ7lNfb7dZ7lNfb7dZ7lNffD37/cVW+a5QvGa4YOpf0XbEj/vyE97WTjqRVrF3sAl/XsVLl///+aw9fOl9Bo/kqr1Lzzr/Z3K023q/aHtfy8rK5vk5DQiI6uyefNO+vV71MwVj999t42WLRsTFFShcNmePQfo3ftBunfvyMsvP+Zinc4NX207F/p0blhua+o8UhxZnfCZ2wnlYuFra3n22WeLLPvb3/7GmDFjim07a9Ysxo4dy9q1awkJCeHaa6/lkksuYeLEibz33nvcfffdxSYt27dvz3XXXccLL7zAvffey969e1m8eHHh+oyMDCpWrMjnn3/ODTfcUGpfl8krHjMyMrjhhhvo06cPM2fOZPv27aSmpuLn50fTpk3Zv38/Y8eOpXnz5rz33nuudS5a9DUBAf7cdluPwmXBwUH063c933+/nYMHE11rA/X5ahuoz1fbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbaC+M8k+cZK83NxiyxO2/whAgyvbFVne4Mp2kJ/PnlXr/9D+ykpQUAUiI6uW23h/RNu2zYtMrAA0aFCbJk3qsWvXfpeqfuX2996ZWO6z3Ab2+3Ru+GabyB8xcuRIkpOTi7xGjhxZbLv9+/czbNgwZs6cSUhIiAulf4zJicdnnnmGr776ildffZXExETS09NZuHAhtWvX5tprryU+Pp4ffviBTp06cccdd7By5UpXOrdt20WDBhdSqVJYkeWtWzf9Zf1uN7IKqc97ltsKxleftyy3FYyvPics91luKxhffU5Y7rPcVjC++pyw3Ge5rWB89Tlhuc9yW8H46nPCcp/ltoLx1Xcq37wxg1l3P8Z7g4az5LmJHP3p11sc5mbnABDwu8mBgOCC5yAd2138SsrT7U8K5Ofnc+RIElWrhrudonPDActtBePb7iuJzo2zY7lNSoef3/nxCg4OJjw8vMgrODi42PFYv349CQkJtG3blsDAQAIDA1mxYgWvvvoqgYGB1KxZk6ysLJKSkop83uHDh4mKigIgKiqKw4cPF1vvWVeaTE48zp49m/vvv58HHniA6tWrExoaSvfu3XnttdeYMGECCQkJtGjRgvnz53PppZcWeYhmeUpMPFbivybzLEtIOFreSUWoz3uW20B9TlhuA/U5ZbnPchuozynLfZbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6SuIfGEi99pdw2Z39uPbxIVxyW2+S9h9g8ZhXOLa74GqjiNoFz0FK3LGryOd6roTMOJb8h/YnBebNW87hw0e54Yar3U7RueGA5Taw31cSnRtnx3KbSFno0qULmzdvZsOGDYWvSy+9lAEDBhT+uUKFCnz11VeFn7Njxw727dtHhw4dAOjQoQObN28mISGhcJsvvviC8PBwWrRoUaq9gaW6t1Jy5MiREr/QFi1akJOTw86dO6lRowZ+fn4MGDCAZ5555oz7zMzMLHZ/2+DgLIJ/+Rdq3jh5MqvY5fAF+w0qXO8m9XnPcptnfPV5x3KbZ3z1ec9yn+U2z/jq857lPsttnvHV5z3LfZbbPOOrz3uW+yy3ecZXn/cs91lu84yvvqJqRF9EjeiLCj+ue2lr6l1+CQue/Affz5pLl5EPcuElF1PxgmqsnzGHwOAgqjWsy5Ef97Bh9nz8AvzJzcr6Q/sT+Omn/Tz33Bu0adOMW27p7HaOzg0HLLd5xrfc93s6N86e5TaRslC5cmVatmxZZFnFihWpXr164fLBgwfz6KOPUq1aNcLDw3nooYfo0KEDV1xxBQDdunWjRYsWDBo0iBdffJFDhw7x9NNP88ADD5R4laUTJq94vOiii1i6dGmx5cuWLcPPz6/IZZ8VKlQgLy/vjPscN24cERERRV7jxr3pqDMkJIisrOxiyzMzswrXu0l93rPc5hlffd6x3OYZX33es9xnuc0zvvq8Z7nPcptnfPV5z3Kf5TbP+OrznuU+y22e8dXnPct9lts846vvzMKjalD30tYc2rKTvLw8AoIq0Pmv9xFcuSIrJvyHTx4azdevTadV7A0EV6pIYMjpf1H3+/2d7xITjzNkyHNUrhzGpEkjCAgIcDvJzPfeqVjus9zmGd9y32/p3PhjLLeJuOWVV16hV69e9O3bl2uuuYaoqCjmzJlTuD4gIIAFCxYQEBBAhw4dGDhwIHfccQfPPfdcqbeYvOLx/vvv5+GHHyY0NJTbbruN0NBQvv76a1544QU6duxIo0aNCrfdsGEDjRs3PuM+R44cyaOPPlpkWXBw8fvw/xGRkdU4fLj4ZduJiccBqFGjuqP9O6U+71luA/U5YbkN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcBur7I8KqVyUvJ4eck5kEhYVSpW5ter/0NMk/HyQrPYOIOrUICKrA+ukfU7P5mX8n9fv9na9SU9O5554xpKamM3PmeGrWdPd7zsPS915JLPdZbgP7fR46N/44y20i5WX58uVFPg4JCWHKlClMmTLllJ9Tv359Pv/88zIuM3rF44MPPsgTTzzBzJkz6dmzJ507d+bpp5+mRYsWzJ49u8i2ISEhDBs27Iz7LPkhnc7+5UOzZg3ZsyeetLSMIss3btwBQPPmDR3t3yn1ec9yG6jPCcttoD6nLPdZbgP1OWW5z3IbqM8py32W20B9Tlnus9wG6nPKcp/lNlDfH5F2+AgBFSpQ4TdXM/r5+VGlbm1qNGtMcKWKHN4SR35+PlEtm3m1v/NNZmYWQ4c+z5498bzxxmgaN67ndlIhS997JbHcZ7kN7PeBzg1vWW6T0uF3nrx8lcmJR4AXXniBffv28eGHHzJz5kzWr1/PqlWrqFWrVpHtJk6cyJ133ulKY48eHcnNzWP27EWFy7Kyspkz50tiYqKpVSvSlS4P9flmG6jPV9tAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfqcstxnuQ3U55TlPsttoL6SnExJLbbs2N6f+Xn9Zmq1boaff8m/NsvJymLDBwsIrRpBw46XOt6fr8vNzWX48BfZsGE7kyaNoE2bM0/WliedG77ZBvb7dG74ZpuIGL3VqkdUVBSxsbFuZ5xSTEw0PXp0ZMKE6Rw9mkz9+rX45JOlxMcnMHbsw27nqc9H29Tnu23q8+0+y23q8+0+y23q8+0+y23q8+0+y23q8+0+y23qK9nKSW8RGFSByKYXERJemaSfD7Jz6dcEBAfR9v9u/nW7iVMJrVqFiAujyD5xkp+WryI14Qidn7yPCqEhf3h/ZWXGjAWkpKSTkFBw+8Fly9Zw6FDBnwcN6kXlyhXLvKEk48e/xdKlq7nuuvYkJaUyd+6yIutvuuk6V7o8dG74Ztu50KdzwzfbRAT88vPz892OKMnWrVsZP34827Zt44ILLuD222/njjvuwM+v6AWoM2fO5I477iA3N9eLUeIcd2ZmZjFx4gzmz19OcnIa0dENGDZsIFdf3dbxvkuD+nyzDdTnq22gPqcs91luA/U5ZbnPchuozynLfZbbQH1OWe6z3Abqc8pyn+U2OL/7/v79vmLLti1cxu7/rSX18BGyT5wgJLwyURc3pXW/GwmPqlG43ZZ5X/Dj8lWkJx4jIKgCNZo1IubWnlRrUNer/ZXk6TbOb6/YufNg4uMTSlz31VdTqVOnpuMxvDFo0EjWrPnhlOt37JhfjjUlO5/PDV9uA9t9OjcstzUthX2IE2sTP3M7oVxcFtnT7YQyYXLicefOnbRt25acnBwuvvhiDh8+THx8PB07duTDDz8kKiqqcFu3Jx5FRERERERERMS2kiYeLSmNiUcRESktmnh0myYez20mby7/9NNPU6lSJTZv3sy6devYv38/06dPZ/PmzXTo0IEdO3a4nSgiIiIiIiIiIiIiIiKlzM/v/Hj5KpMTj99++y0PPfQQjRs3Llw2cOBAvv32W/z9/bnqqqtYs2aNi4UiIiIiIiIiIiIiIiIi8lsmJx6PHj1a5HaqHs2aNeObb76hTp06dOnShcWLF7tQJyIiIiIiIiIiIiIiIiK/Z3LisUGDBmzatKnEdTVr1mTFihW0adOGPn368OGHH5ZznYiIiIiIiIiIiIiIiIj8nsmJx2uvvZYPP/yQnJycEteHh4fzxRdf0KNHD+bNm1fOdSIiIiIiIiIiIiIiIlIW/M+Tl68y+bXdddddXHnllaxbt+6U2wQHB/PJJ5/w8MMPc80115RjnYiIiIiIiIiIiIiIiIj8XqDbASW59NJLz+oWqv7+/kycOLHsg0RERERERERERERERETktExe8SgiIiIiIiIiIiIiIiIi5xaTVzyKiIiIiIiIiIiUlqfb1HM74bS2J8W5nXBKzao0dTtBREREziGaeBQRERERERERERERERET/Pzy3U4QB3SrVRERERERERERERERERFxTBOPIiIiIiIiIiIiIiIiIuKYJh5FRERERERERERERERExDE941FERERERERERERERERM8HM7QBzRFY8iIiIiIiIiIiIiIiIi4pgmHkVERERERERERERERETEsXPmVqvZ2dmcOHGC0NBQKlSo4HZOoaysbCZNmsncuctISUkjOroBw4cPpGPHNm6nAerz1TZQn6+2gfqcstxnuQ3U55TlPsttoD6nLPdZbgP1OWW5z3IbqM+JnTv3Mnny+2zZ8iNHjhwnJCSYxo3rMXhwLJ07t3c7j/T0E0ybNoeNG+PYvDmO5OQ0xo0bRmxsV7fTANvvLdju27Qpjk8/Xcrq1ZuIj0+gSpXKxMREM3z4IBo2vLBMxty36xDv/2cxP23/meNHUwkOqUDdhlHcMvBa2l99ceF2cVv2sfSztcT9sI89Px4gNzePuav/ecr9Jh1N5b1/L2Lt11tJTc6gavXKtL60CQ89fVuZfB3Wz1uw/b1n/eeK+nyzzcPyuWG5TeR8Z/aKx5ycHKZOnUq3bt2IjIwkJCSEqlWrEhISQmRkJNdffz3/+c9/yM7OdrVzxIiJvPPOp/Tu3YlRo+4hIMCfe+99lnXrtrja5aE+32wD9flqG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfqcOHAgkfT0E9xySxdGjbqX+++/HYD77nue2bMXuVwHx4+nMGXKLHbt2k90dEO3c4qx/N6C7b6pUz9myZJv6NAhhlGj7qF//x6sW7eF2NjhxMXtLZMxEw4e50RGJp17Xso9j97MbX++HoCxj7/F4k9WFW63/pttfDF3NfhBzQurn3afiYeP89jdE1m/ajs9brmSoU/Ecn2fy0lOSiuTrwHsn7dg+3vP+s8V9XnPcpuH5XPDcpvI+c4vPz8/3+2I3zty5AjdunVjw4YNNG3alPbt21OrVi1CQkI4efIkBw8eZM2aNcTFxRETE8OSJUuIjIz0YqQ4R52bNsVx662P8eSTdzN4cCwAmZlZ9Or1INWrRzBr1kuO9u+U+nyzTX2+26Y+3+6z3KY+3+6z3KY+3+6z3KY+3+6z3Ka+spGbm0ts7CNkZmaxaNEbrrZkZWWTnJxGZGRVNm/eSb9+j5q5esX6e2u977vvttGyZWOCgn69C9eePQfo3ftBunfvyMsvP+Zo/9uTzu53VLm5eTx25ytkZWXz2gcjgIIrGEMrhhAcUoE3X5rD5x99fcorHp8b/h9+3pvAy+8MJzyi4lmN2axK07P7Iv4AS+et9e89yz9XQH2+2ga2z42ybyv9n3vyx2w8tsDthHIRU62X2wllwuQVj48++ih79+7liy++YPv27UyfPp0XXniBZ599lhdeeIHp06ezfft2vvjiC/bt28fjjz/uSueiRV8TEODPbbf1KFwWHBxEv37X8/332zl4MNGVLg/1+WYbqM9X20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5DdRXFgICAqhV6wJSU9PdTiEoqAKRkVXdziiR9ffWel/bts2LTDoCNGhQmyZN6rFr1/5y6wgI8OeCmlVITz1ZuKxK9coEh5z5sUQ/7znM+lXbuWXgdYRHVCQrM5ucnNyyzD0lS+et9e89yz9XQH1OWG4D2+eG5TYRMTrx+Nlnn/HEE0/QpUuX027XpUsXHn/8cRYscGf2e9u2XTRocCGVKoUVWd66ddNf1u92I6uQ+rxnua1gfPV5y3Jbwfjqc8Jyn+W2gvHV54TlPsttBeOrzwnLfZbbCsZXnxOW+yy3FYyvvtKQkXGSY8eS2bfvIO+88ykrV67niiti3M4yzfp7a72vJPn5+Rw5kkTVquFlOs7JE5mkJKVx8OcjzH1/BetXbaf1ZY3/8H42rt0JQJVqlXjmgde59ZoR3HrNCJ4d/h8OHzhW2tnFWD1vz8XvPZHyYPncsNwmIhDodkBJsrKyqFy58lltW7lyZbKyssq4qGSJicdK/FcpnmUJCUfLO6kI9XnPchuozwnLbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcBuorLePHTyt8Npy/vz/XX9+B0aOHulxlm/X31npfSebNW87hw0d5+OEBZTrOW5PmFz7T0d/fjyuubcWQx2P/8H4O7DsCwJRxH9KkRT2eGDuIxEPHmTXtC0Y/9Aavznyc4JCgUm3/Lavn7bn4vSdSHiyfG5bbRMToxGPHjh2ZNGkSN998MxdeeOEpt4uPj2fSpElcddVVZ9xnZmYmmZmZRZYFB2cRHOz9/6E6eTKr2G02CvYbVLjeTerznuU2z/jq847lNs/46vOe5T7LbZ7x1ec9y32W2zzjq897lvsst3nGV5/3LPdZbvOMrz7n7ryzDz16dCQh4RgLF/6XvLw8srOz3c4yzfp7a73v9376aT/PPfcGbdo045ZbOpfpWH1uv5orO7fm2JEUvv5yA3l5+eRk//FbpJ48UfB7sarVw3lmwmD8/QtuhFa9RhX++cwMViz+jm43XVGq7b9l9bw91773RMqL5XPDcpuUDj+3A8QRk7danThxIsePHyc6Oprbb7+dl19+mZkzZ/LRRx8xc+ZMXn75ZW6//Xaio6NJTk5mwoQJZ9znuHHjiIiIKPIaN+5NR50hIUFkZRX/P0iZmVmF692kPu9ZbvOMrz7vWG7zjK8+71nus9zmGV993rPcZ7nNM776vGe5z3KbZ3z1ec9yn+U2z/jqc65Ro7pceeUl3HxzZ958829kZJxg6NDnyc/PdzvNLOvvrfW+30pMPM6QIc9RuXIYkyaNICAgoEzHq9OgJpe0b0rnGy/lmQl/4WRGJn9/fNof/n4PCi74JX3HLjGFk46ejwMC/Nm+aU9pZhdj9bw9l773RMqT5XPDcpuIGL3isVmzZmzYsIGxY8cyZ84cPvjgg2LbREZGcscdd/DUU09Rp06dM+5z5MiRPProo0WWBQfvc9QZGVmNw4eLX7admHgcgBo1qjvav1Pq857lNlCfE5bbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1FdWunfvyOjRU9i9O56LLjrz7wjOR9bfW+t9Hqmp6dxzzxhSU9OZOXM8NWuWf9eVnVvz2viPiN+XSJ36Nc7686pFFjyLskq1oo83Cgjwp3JERdJST5Rq55lYOW/Ple89kfJm+dyw3CYiRq94BLjwwgt57bXXOHToED///DNr165l5cqVrF27lp9//pnDhw/z2muvndWkI0BwcDDh4eFFXk5uswrQrFlD9uyJJy0to8jyjRt3ANC8eUNH+3dKfd6z3Abqc8JyG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfrKiueWar/vll9Zf2+t90HBlTRDhz7Pnj3xvPHGaBo3rudKR1ZmwVU+GWkn/9DnNWpW8Du0o4nJRZZnZ+eQkpxORJVKpRN4lqyct+fC956IGyyfG5bbRMTwxONv1a5dm3bt2nHVVVfRrl070tPTWbduHRkZ7v4fkx49OpKbm1f4YGyArKxs5sz5kpiYaGrVinSxTn2+2gbq89U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tl19GhSsWXZ2TnMnbuUkJAgGjWqW/5R5wjr7631vtzcXIYPf5ENG7YzadII2rRpVuZjJh1LLbYsJyeXZZ+vJyi4AnUb1vxD+2vVtjERVSuxcvF3hZOXAEsXrCUvN49LLm/quLkk1s9b6997Im6xfG5YbhMRo7daBZg6dSoTJkwgKSmJrl27MnnyZDIzM+nTpw9r164FIDQ0lLFjxzJs2DBXGmNiounRoyMTJkzn6NFk6tevxSefLCU+PoGxYx92pUl9vt+mPt9tU59v91luU59v91luU59v91luU59v91luU59zo0dPIS0tg8sua0nNmtVITExi/vzl7Nr1MyNGDKZixVC3E5kxYwEpKekkJBTcAm7ZsjUcOlTw50GDelG5ckVXuqy/t9b7xo9/i6VLV3Pdde1JSkpl7txlRdbfdNN1pT7ma+M/4kT6SS5ucxHVIiNIOprKisXf8fOeBP48rA+hYcEAJBw8xvKF6wH4cdt+AD546wsAIqOqct2NlwJQISiQux7uzaRn3+epoVO49oZ2JB5KYsHs/9Likou44tpWpf41gP3z1vr3Htj9ueKhPt9ss3xuWG6T0uHv53aBOOGX7/YTnEuwYMEC+vTpQ0xMDHXr1mXhwoXExsaSm5tLamoq/fr148SJE7z77rts2LCBefPm0bNnTy9GinPcmpmZxcSJM5g/fznJyWlERzdg2LCBXH11W8f7Lg3q8802UJ+vtoH6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPic8+W8lHH31BXNwekpJSqVgxlIsvbszAgb3o0uVyt/MA6Nx5MPHxCSWu++qrqdSp88euUitNlt9bsN03aNBI1qz54ZTrd+yY72j/25OK/45q5ZLv+XL+avb+eIjU5HRCKwbTqFkdet56FZdf07Jwu83rf+Tp+18vcb8t2zZi7Ov3F9vvnOlL+XlvAhUrhdKxS2sG3ncjYRVDStxHsyrOroQ8F85by997YPvnCqjPCcttYPvcKNu2srkCXM7eD8cXuJ1QLlpW7eV2QpkwOfHYqVMn/Pz8WLZsGX5+frzyyis88cQT3HjjjcybN69wu5ycHFq3bk3dunVZvHixFyM5n3gUERERERERERFxoqSJRyucTjyKiJx79HPPbZp4PLeZfMbj1q1b6du3L35+BdfT3nTTTeTl5dG/f/8i2wUGBjJgwADWr1/vRqaIiIiIiIiIiIiIiIiI/MLkMx4zMjIICwsr/DgiIgKA2rVrF9s2KiqK1NTiD9sWERERERERERERERGRc4se8XhuM3nFY1RUFAcOHCj8ODQ0lCFDhlCnTp1i28bHx1O9evXyzBMRERERERERERERERGR3zF5xWO7du1YtWpV4cdhYWG8/nrJD8leuXIlrVq1Kq80ERERERERERERERERESmByYnHMWPGsHfv3jNul5iYSHh4OLfffns5VImIiIiIiIiIiIiIiIjIqfjl5+fnux3hnji3A0RERERERERE5Dy3Pcnu76iaVWnqdoKISDnTzz23bTm+wO2EcnFx1V5uJ5QJk1c8ioiIiIiIiIiIiIiIyPnHz8/tAnHC3+0AERERERERERERERERETn36YpHERERERERERERF0VXaeJ2wim9tnWP2wmndX+LBm4niIiIyG/oikcRERERERERERERERERcUxXPIqIiIiIiIiIiIiIiIgJesTjuU1XPIqIiIiIiIiIiIiIiIiIY5p4FBERERERERERERERERHHNPEoIiIiIiIiIiIiIiIiIo5p4lFEREREREREREREREREHAt0O+Bcl5WVzaRJM5k7dxkpKWlERzdg+PCBdOzYxu00QH2+2gbq89U2sN23aVMcn366lNWrNxEfn0CVKpWJiYlm+PBBNGx4odt5gO3jZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Y/7vQnj0HeHXSTNav30pyciq1akXSq1cn/jz4FkJDg8tkzJ9/2MnHz7xa4rr+4x+lVnTDYssz0zN49/7nOZGSxo1P/JkmV/763i559f+xbdmaU443eOrzVKpexXH37+3cuZfJk99ny5YfOXLkOCEhwTRuXI/Bg2Pp3Ll9qY/3R61evZk77niqxHWzZ7/EJZc0K+eiotLTTzBt2hw2boxj8+Y4kpPTGDduGLGxXV3t8rDeZ/nnHtjus/7eijN+bgeII7ri0aERIybyzjuf0rt3J0aNuoeAAH/uvfdZ1q3b4nYaoD5fbQP1+Wob2O6bOvVjliz5hg4dYhg16h769+/BunVbiI0dTlzcXrfzANvHz3IbqM8py32W20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyw/HehgwcT6X/rY2zYuIMBA3sy8qm/cEmbZkye/B6PPfpSmY9/Sc9OdB92R5FXlVqRJW676v3PycnKKnFdq25XFdtPt2GDCAwOolrdqDKZdAQ4cCCR9PQT3HJLF0aNupf7778dgPvue57ZsxeVyZjeGDSoNy+++GiRV716tdzO4vjxFKZMmcWuXfuJLmGy2W3W+yz/3APbfdbfW5HzmV9+fn6+2xFOzJgxg7feeoulS5d68dlxjsbetCmOW299jCefvJvBg2MByMzMolevB6lePYJZs8r+/9yp7/xrU5/vtp0Lfd99t42WLRsTFFShcNmePQfo3ftBunfvyMsvP+Zine3jZ7lNfb7dZ7lNfb7dZ7lNfb7dZ7lNfb7dZ7lNfc6V9d+F8vH+13NvvPEBE1+ZwfwF/6JJk3qFy//611eY++kyVq95j4iISl7v//WtJU+seq54/P2Vi6dyZO8B3n/sBdr3v4Fv3//srD4vfutPfDRqIh0G9KJ9v+4lbnN/iwZnHPuPys3NJTb2ETIzs1i06I1S3/8f4bnicdKkEfTo0dHVlpJkZWWTnJxGZGRVNm/eSb9+j5q66sxyn/Wfe9b7yva9bVoK+xAntictcDuhXDSr0svthDJxzl/xuHfvXlasWOHK2IsWfU1AgD+33dajcFlwcBD9+l3P999v5+DBRFe6PNTnm22gPl9tA/t9bds2L/IXbYAGDWrTpEk9du3a71LVrywfP8ttoD6nLPdZbgP1OWW5z3IbqM8py32W20B9Tlnus9wG6nPK8t+F0tNOAHDB764IrBFZDX9/fypUKPsnLWWdOElebu5pt1kx7WMaXR7DhS0anfV+d/x3Hfj50eyaS50m/iEBAQHUqnUBqanp5TrumaSlZZCTc/rjXN6CgioQGVnV7YxTstxn/eee9T7L763I+e6cn3h007Ztu2jQ4EIqVQorsrx166a/rN/tRlYh9XnPclvB+OrzluW2gvFt95UkPz+fI0eSqFo13O0U08fPclvB+OpzwnKf5baC8dXnhOU+y20F46vPCct9ltsKxlefE5b7LLcVjK++0mbl70Lt27cEYNSoV9m2bRcHDyby+ef/5f33FzJoUC/CwkLKdPwvJs/k9f97gn/1f5SPn3mVwz/uK7bNzq+/5+CO3Vx1501nvd/cnFx2fv09taIbEl6jemkmlygj4yTHjiWzb99B3nnnU1auXM8VV8SU+bhna+TISbRrdxutW8cyaNBTbN680+0kccj6zz3rfeLb/P3Oj5evKvt/8uSFgIAAtxPOSmLisRL/VYVnWULC0fJOKkJ93rPcBupzwnIb2O8rybx5yzl8+CgPPzzA7RTTx89yG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfrKgpW/C119TTuGDRvAm29+yNKlawqXDx3an+GPDCyzcf0DA2jc4RIatG1BaHglju4/yHdzl/LhqIn0H/cINS6qC0BOZhb/ffcT2vS+lvAa1UlJOHZW+9/7/TZOpqbTrFP5XO04fvy0wmc6+vv7c/31HRg9emi5jH06FSoE0r37lVxzzaVUrRrOTz/tY9q0TxgwYASzZr1Iiz9wBanYYv3nnvU+EbHL7MRjo0aN6Nr1zPdjXrduHWvWrDnjdmXh5MmsYrfZgIJLzj3r3aQ+71lu84yvPu9YbvOMb7nv9376aT/PPfcGbdo045ZbOrudY/r4WW7zjK8+71nus9zmGV993rPcZ7nNM776vGe5z3KbZ3z1ec9yn+U2z/jqKz3W/i504YU1uPTSi+nW/UqqVKnMiuXrePPND7kgsgoDB5bN86NqN7uI2s0uKvz4ovataHJlG2YOH8c3M+Zz8+j7AVg75wvycnK5rG/Jz2g8lR3/XYd/YABNrmxbqt2ncuedfejRoyMJCcdYuPC/5OXlkZ2dXS5jn07bts1p27Z54cddulxO9+4d6dPnIf75z+lMm/asi3XihPWfe9b7RMQukxOPrVu3xt/fn8mTJ59x27Fjx57VxGNmZiaZmZlFlgUHZxX+oPRGSEgQWVnF/w9IZmZW4Xo3qc97lts846vPO5bbPONb7vutxMTjDBnyHJUrhzFp0ggTV6tbPn6W2zzjq897lvsst3nGV5/3LPdZbvOMrz7vWe6z3OYZX33es9xnuc0zvvpKh7W/C3322UpGj57CosVvEBV1AQDdul1JXn4+/3z5XXr2vKbcbgdbpVYkF7VvxU/fbiIvN4+0o8f57tOvuPbe/gSFBp/1frJOZLJrzWbqX9Kc0PCKZVj8q0aN6tKoUcFVmjff3Jk///kZhg59ng8//Cd+frbuh1e/fm26dLmCJUu+ITc31/XvQfGO9Z971vtExC6Tz3hs3749mzZtKjZReCr5+fln3GbcuHFEREQUeY0b96ajzsjIaiQmHi+23LOsRjncf/501Oc9y22gPicst4H9Po/U1HTuuWcMqanpTJ36LDVr2uiyfPwst4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6ivtFj8u9D7731O8+YXFU46enTu3J4TJzLZtm1XufZUvqAquTk5ZGdmsur9z6hYvQp1WjYmJeEoKQlHST+eAsCJlDRSEo6Sn5dXbB+7Vm8iJzOL6GvK5zarJenevSObN+9k9+541xpOJyrqArKzczhx4ux+fyr2WP+5Z71PROwyOfF49913M3LkSFJSUs647aBBg1i2bNkZtxs5ciTJyclFXiNHDnHU2axZQ/bsiSctLaPI8o0bdwDQvHlDR/t3Sn3es9wG6nPCchvY74OCf9k2dOjz7NkTzxtvjKZx43puJxWyfPwst4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6ivNFj9u9CRI8nklTB5l5OdC0BuTm659iQfPkpAUAWCQoJJTTxO8sFE3hn6LG8PGcPbQ8awaMI7ACx78wPeHjKGrBMni+1j+8q1VAgJ5qL2rcq1/bc8t5H8/fekFT//fIjg4CDCwkLcThEvWf+5Z71PfJvfefLyVSYnHi+77DL+9re/ERkZecZt69WrR6dOnc64XXBwMOHh4UVeTm6zCtCjR0dyc/MKHzwNkJWVzZw5XxITE02tWmfuL0vq8802UJ+vtoH9vtzcXIYPf5ENG7YzadII2rRp5mrP71k+fpbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5b8LNWhYm61bdxW7Mu+zz1bi7+9P0+gGZTJuRnJqsWWJu39m19rN1I9php+/Px3+rxe9RvylyKvD//UEoN0tXek14i8EBgcX2+/+TTtodEVrKjj83d3ZOHo0qdiy7Owc5s5dSkhIUOHtV91y7FhysWXbt+9m6dI1dOzYBn9/k7/elbNg/eee9T4RscvkMx7PZOfOnSQnJ9OiRQvCwsJc64iJiaZHj45MmDCdo0eTqV+/Fp98spT4+ATGjn3YtS71+Xab+ny37VzoGz/+LZYuXc1117UnKSmVuXOLXnF+003XuVRWwPLxs9ymPt/us9ymPt/us9ymPt/us9ymPt/us9ymPucs/11o8OBb+O/K9QwcMIIBA3pSpUo4y5evZeXK9dx6a7cyux3swpffJjC4ArWiLyIsohJHfz7ED0u+ITAoiI6D+gBwYYtGxT4vuGLB7/JqNq5Ho8tjiq3f+fV35OXm0eyay8qk+/dGj55CWloGl13Wkpo1q5GYmMT8+cvZtetnRowYTMWKoeXScSrDh79ISEgQbdo0o3r1Kvz44z4++GAxISHBPP74na62ecyYsYCUlHQSEo4CsGzZGg4dKvjzoEG9qFy5fJ7TeSpW+6z/3LPeB3bfW5HznV/+2Twg0QVTp05lwoQJJCUl0bVrVyZPnkxmZiZ9+vRh7dq1AISGhjJ27FiGDRvm5ShxjjszM7OYOHEG8+cvJzk5jejoBgwbNpCrr27reN+lQX2+2Qbq89U2sN03aNBI1qz54ZTrd+yYX441JbN8/Cy3gfqcstxnuQ3U55TlPsttoD6nLPdZbgP1OWW5z3IbqM+Jsv67UD7Ofj23aVMc/5r8Ptu27SIpKZULL6zJzbdcx1/+0pfAwABH+359694Sl29YsJztK9eRfDCRrBMnCQ2vRN3W0Vx+2w1UOc2VUD//sJOPn3mVG5/4M02ubFNs/ey//pPkw0f5y7S/4x9w5qv57m/R4Ky/lpJ89tlKPvroC+Li9pCUlErFiqFcfHFjBg7sRZculzvad2mYPn0e8+evYN++g6SlZVC1agQdOrTmwQf/RP36td3OA6Bz58HExyeUuO6rr6ZSp07Nci4qynKf5Z97YL+v7N7bpt5HSanYmbzA7YRy0SSil9sJZcLkxOOCBQvo06cPMTEx1K1bl4ULFxIbG0tubi6pqan069ePEydO8O6777JhwwbmzZtHz549vRjJ+cSjiIiIiIiIiIiIE04nHsvSqSYerXA68SgiUpwmHt32Y4r7FzeUh8bhvd1OKBMmJx47deqEn58fy5Ytw8/Pj1deeYUnnniCG2+8kXnz5hVul5OTQ+vWralbty6LFy/2YiRNPIqIiIiIiIiIiLs08eg9TTyKSOnTxKPbNPF4bjP59OGtW7fSt29f/Pz8ALjpppvIy8ujf//+RbYLDAxkwIABrF+/3o1MEREREREREREREREREfmFyYnHjIwMwsLCCj+OiIgAoHbt4vctj4qKIjU1tdzaRERERERERERERERERKQ4kxOPUVFRHDhwoPDj0NBQhgwZQp06dYptGx8fT/Xq1cszT0RERERERERERERERER+J9DtgJK0a9eOVatWFX4cFhbG66+/XuK2K1eupFWrVuWVJiIiIiIiIiIiIiIiImXEz+0AccTkxOOYMWPYu/fMD65OTEwkPDyc22+/vRyqRERERERERERERERERORUTE48tmjRghYtWpxxu8jISObMmVMORSIiIiIiIiIiIiIiIiJyOiaf8SgiIiIiIiIiIiIiIiIi5xaTVzyKiIiIiIiIiIicL/wMP83q/hYN3E44rf3pO9xOOK26FaPdThAROef42f3PopwFXfEoIiIiIiIiIiIiIiIiIo5p4lFEREREREREREREREREHNPEo4iIiIiIiIiIiIiIiIg4polHEREREREREREREREREXEs0O0AEREREREREREREREREdAVc+c6vX8iIiIiIiIiIiIiIiIi4pgmHkVERERERERERERERETEMd1q1aGsrGwmTZrJ3LnLSElJIzq6AcOHD6RjxzZupwHq89U2UJ+vtoH6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9TlluS89/QTTps1h48Y4Nm+OIzk5jXHjhhEb29XtNMD2sQPbfXpvnVGfM5b7LLdB+Z+727fs44v569iw7icOHzhG5SoVad6qHn++/wbq1I8ssm1eXh4LPv6Wzz7+lv17EwgOCaJRk1rc9/hNNGpaG4B9uxNYNHcN67+N48DPRwkNC6Jxswu5c2h3olvULZOv4bcsv7+W20B9Tlnus9wmcr4zfcXj0qVLmTJlCrNnzyYlJaXEbb799lv+/Oc/l3PZr0aMmMg773xK796dGDXqHgIC/Ln33mdZt26La02/pT7fbAP1+WobqM8py32W20B9Tlnus9wG6nPKcp/lNlCfU5b7jh9PYcqUWezatZ/o6IZu5xRj+diB7T69t86ozxnLfZbboPzP3dnvLOO/SzfTpn0T7n/iZnrGXsHm73Yz9P9eYfePB4ts+/KzHzDlpU9p0rwODz55C4PuuZ4ataqSdCytcJuFn67m809W07RFHYY80pu+A67h5z2JPHTnZNavjivzr8fy+2u5DdTnlOU+y23inJ/f+fHyVX75+fn5bkf8XmZmJjfeeCPLly/HkxcREcELL7zAvffeW2TbmTNncscdd5Cbm+vFSM7+j8GmTXHceutjPPnk3QweHPtLexa9ej1I9eoRzJr1kqP9O6U+32xTn++2qc+3+yy3qc+3+yy3qc+3+yy3qc/3+7KysklOTiMysiqbN++kX79HzVwVZ/3YWe/Te6s+9Z1bbR5lee7uT99RbNmWjXto2qIOFSr8erO3n/clck//f3JNl9aMHPt/ACxfsoG/j5jBmJfv5KrOrU45RtzWn6nbIJLQsODCZclJ6Qzu+yIX1o9k0lsPnvJz61aM9ubLKmT5/bXcpj7f7iv7tqbOI8WRvWnz3U4oF/Ur9XY7oUyYvOLx5ZdfZsWKFYwZM4ZNmzaxePFiLr30Uu677z6GDBlCXl6e24kALFr0NQEB/tx2W4/CZcHBQfTrdz3ff7+dgwcTXaxTn6+2gfp8tQ3U55TlPsttoD6nLPdZbgP1OWW5z3IbqM8p631BQRWIjKzqasOpWD921vv03npPfc5Y7rPc5lHe5+7FMQ2KTDoC1KkXSYOLarJv9+HCZR/NWEmzlnW5qnMr8vLyOHEis8T9NW1Rp8ikI0BElYq0bHMR+3YnlP4X8BuW31/LbaA+pyz3WW4TEaMTj7NmzeKuu+7imWeeoWXLllx//fV88cUXPP/880ydOpXY2FgyM0v+PwLladu2XTRocCGVKoUVWd66ddNf1u92I6uQ+rxnua1gfPV5y3Jbwfjqc8Jyn+W2gvHV54TlPsttBeOrzwnLfZbbCsZXnxPW+yyzfuys91lm/dipzxnLfZbbLMnPz+f4sTTCq1QEID3tJDu27Ce6RV2mTf6cm655ht4dRzGw9z9YvmTDWe3z+NFUIn7ZX1mx/P5abisYX31OWO6z3CYiRiced+/eTYcOHYotf+qpp3jvvfdYtGgR119/PcnJyS7U/Sox8ViJ/1rLsywh4Wh5JxWhPu9ZbgP1OWG5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynrfZZZP3bW+yyzfuzU54zlPsttlnz1+XccSUjm2m6XAHDg56Pk5+ezbMkGFs1byz3DejJy7P9RpWpFxo6cyZqvt592f5u/28XWTXu5tltMmXZbfn8tt4H6nLLcZ7lNRCDwzJuUv2rVqpGQUPJtCm677TaqVatGbGws11xzDQMHDjyrfWZmZha7SjI4OIvg4CCvO0+ezCIoqEKx5Z59njyZ5fW+S4P6vGe5zTO++rxjuc0zvvq8Z7nPcptnfPV5z3Kf5TbP+OrznuU+y22e8dXnPet9llk/dtb7LLN+7NTnjOU+y21W7NudwKsvfEKL1vXp1vtSAE5mFPyeMCUpg8nvPkTzVvUBuLLTxQzs9Q9mTvuS9h2blbi/48dS+ceomURdWI3b7ryuTNstv7+W2zzjq897lvsst0np8HM7QBwxecVjmzZtWLBgwSnXX3/99Xz55ZccOHCAp5566qz2OW7cOCIiIoq8xo1701FnSEgQWVnZxZZnZmYVrneT+rxnuc0zvvq8Y7nNM776vGe5z3KbZ3z1ec9yn+U2z/jq857lPsttnvHV5z3rfZZZP3bW+yyzfuzU54zlPsttFhw7ksKoYdOoWCmE0S/dQUBAwa9Dg0IKJi2iLqxWOOkIEBoWzBXXtGDHD/vJzckttr8TJzJ5ethbZKRn8tyEu4o9+7G0WX5/Lbd5xlef9yz3WW4TEaMTj7fccgurVq3i22+/PeU2l19+OStXrqRWrVpntc+RI0eSnJxc5DVy5BBHnZGR1UhMPF5suWdZjRrVHe3fKfV5z3IbqM8Jy22gPqcs91luA/U5ZbnPchuozynLfZbbQH1OWe+zzPqxs95nmfVjpz5nLPdZbnNbWuoJRj40lbTUE4z/11+4IDKicF31yHAAqlarXOzzqlSrRE5OLidOFL1qKjs7hzGPvcuunQd57pW7adj47H4v6YTl99dyG6jPKct9lttExOjE45133klqairt2rU77XbNmzdn69at7Nq164z7DA4OJjw8vMjLyW1WAZo1a8iePfGkpWUUWb5x445f+ho62r9T6vOe5TZQnxOW20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyy3meZ9WNnvc8y68dOfc5Y7rPc5qaszGyeGf4W8XsTGTvxz9S/KKrI+gsiI6h2QWWOJCYX+9yjiSkEBQcSVvHXqxnz8vJ44ZlZfL/2R54aO4CYdo3K/GsA2++v5TZQn1OW+yy3iYjRiUc/Pz8qVqxIhQrF79MMsHPnTtatW0dGRgaVKlWifv36JW5X1nr06Ehubh6zZy8qXJaVlc2cOV8SExNNrVqRrnR5qM8320B9vtoG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5DdTnlPU+y6wfO+t9llk/dupzxnKf5Ta35Obm8fyIGWzdvJdnXryDFjENStzu2usvIfFQEuu/jStclnw8nW9WbOGSyxrj7//rr07/9cKnLF+ygYdHxHJ1l1Zl/SUUsvz+Wm4D9Tlluc9ym5QOP7/z4+WrAt0OOJWpU6cyYcIEkpKS6Nq1K5MnTyYzM5M+ffqwdu1aAEJDQxk7dizDhg1zpTEmJpoePToyYcJ0jh5Npn79WnzyyVLi4xMYO/ZhV5rU5/tt6vPdNvX5dp/lNvX5dp/lNvX5dp/lNvX5fh/AjBkLSElJJyHhKADLlq3h0KGCPw8a1IvKlSu60mX92FnvA7236lPfudT2W+V57r45YT6rVmyhwzUtSE3O4MvP1hdZ37VnwV3W/vTnzqz4YiPPPvEufQdcQ8VKoSz4eBW5ObkMfvDGwu0/nrmSeR9+Q4vW9QkJqVBsfx07tyQ0tGye9Wj5/bXcpj7f7rPcJiLgl5+fn+92xO8tWLCAPn36EBMTQ926dVm4cCGxsbHk5uaSmppKv379OHHiBO+++y4bNmxg3rx59OzZ04uR4s68yRlkZmYxceIM5s9fTnJyGtHRDRg2bCBXX93W8b5Lg/p8sw3U56ttoD6nLPdZbgP1OWW5z3IbqM8py32W20B9Tlnv69x5MPHxCSWu++qrqdSpU7Oci35l/dhZ79N76z31OWO5z3KbR1mdu/vTdxRb9ug9r7Fp/akfzfTldy8X/vnAz0f59yvz+W7tj+Tm5NKiVX0GP3wjzS6uV7jNi3+bxZL56065vxkLniKqdrUS19WtGH02X8ZpWX5/LbeB+pyy3Fe2bU1LYR/ixP70+W4nlIu6FXu7nVAmTE48durUCT8/P5YtW4afnx+vvPIKTzzxBDfeeCPz5s0r3C4nJ4fWrVtTt25dFi9e7MVIziceRURERERERERExB0lTTxaUhoTjyJS3jTx6DZNPJ7bTD7jcevWrfTt2xe/X25ye9NNN5GXl0f//v2LbBcYGMiAAQNYv359SbsRERERERERERERERERkXJi8hmPGRkZhIWFFX4cEREBQO3atYttGxUVRWpqarm1iYiIiIiIiIiIiIiISNnwcztAHDF5xWNUVBQHDhwo/Dg0NJQhQ4ZQp06dYtvGx8dTvXr18swTERERERERERERERERkd8xecVju3btWLVqVeHHYWFhvP766yVuu3LlSlq1alVeaSIiIiIiIiIiIiIiIiJSApMTj2PGjGHv3r1n3C4xMZHw8HBuv/32cqgSERERERERERERERERkVPxy8/Pz3c7wj1xbgeIiIiIiIiIiIiIl/an73A74bTqVox2O0FE/rCmbgec9w5kzHc7oVzUDuvtdkKZMPmMRxERERERERERERERERE5t2jiUUREREREREREREREREQcM/mMRxEREREREREREZEzsX4r03d37nY74bTubNLQ7QQREfExuuJRRERERERERERERERERBzTFY8iIiIiIiIiIiIiIiJigp/bAeKIrngUEREREREREREREREREcc08SgiIiIiIiIiIiIiIiIijmniUUREREREREREREREREQc0zMeRURERERERERERERExAQ/v3y3E8QBTTw6lJWVzaRJM5k7dxkpKWlERzdg+PCBdOzYxu00QH2+2gbq89U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG0B6+gmmTZvDxo1xbN4cR3JyGuPGDSM2tqvbaYD942e5z/J7u3r1Zu6446kS182e/RKXXNKsnIuKK+/jt3fTTmY+NbnEdXe+/AgXNmsIwK7vtrHtv98Tv2MvR38+RPgFVXngrTHFPif1aDJL357LwZ37SDuWjJ+/P9VqR9Ku19W06twePz+/Mvk6Nm2K49NPl7J69Sbi4xOoUqUyMTHRDB8+iIYNLyyTMf8oy+ct2O6z/HMFbB87kfOdbrXq0IgRE3nnnU/p3bsTo0bdQ0CAP/fe+yzr1m1xOw1Qn6+2gfp8tQ3U55TlPsttoD6nLPdZbgP1OWW5z3IbqM8py32W20B9Tlnus9wGcPx4ClOmzGLXrv1ERzd0O6cY68fPcp/19xZg0KDevPjio0Ve9erVcjsLcO/4Xdq7E30eG1TkVbVWZOH6LSvWs2XFekIqhlCpWsQp93MiJY3UI0k063gJnf98M50G9qRStXAWvDKTFdMXlFn/1Kkfs2TJN3ToEMOoUffQv38P1q3bQmzscOLi9pbZuH+E5fMWbPdZ/7li+diJnO/88vPzTV6zeujQIaZPn05SUhJdunShS5cuALz22mvMnDmT5ORkLrvsMp5++mkaNWrk5Shxjho3bYrj1lsf48kn72bw4FgAMjOz6NXrQapXj2DWrJcc7d8p9flmm/p8t019vt1nuU19vt1nuU19vt1nuU19vt1nuU19vt1nuc0jKyub5OQ0IiOrsnnzTvr1e9TM1SvWj5/1PsvvreeKx0mTRtCjR0e3c0pUlsfv3Z27iy3zXPF4y4i7aX7Vqa/OSj2aTFhEJQICA/jg2TdJ3HuwxCseT+WDZ99k7+adPDb7RfwDSr7+5M4m3k8offfdNlq2bExQUIXCZXv2HKB37wfp3r0jL7/8mNf7Lg3Wz1vrfZZ/rpT9sWvqPFIcOXRintsJ5SIqtI/bCWXC5BWPe/fuJSYmhhEjRjB+/Hi6devGlClTePHFF3n88ccBCAoKYsaMGXTo0IF9+/a50rlo0dcEBPhz2209CpcFBwfRr9/1fP/9dg4eTHSly0N9vtkG6vPVNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5ZbnPcptHUFAFIiOrup1RIuvHz3qf5ff2t9LSMsjJyXU7oxg3j19mxknycks+JpWrRxAQGOD1viNqVic7M5vcnByv93E6bds2LzLpCNCgQW2aNKnHrl37y2TMP8L6eWu9z/LPFevHTuR8Z3Li8dlnn8Xf359169aRmJhIz549efbZZ/nggw/44Ycf+Prrr/nuu+/4+uuvycjIYOzYsa50btu2iwYNLqRSpbAiy1u3bvrL+uL/oqk8qc97ltsKxleftyy3FYyvPics91luKxhffU5Y7rPcVjC++pyw3Ge5rWB89Tlhuc9yW8H46nPCcp/ltnOB9eNnve9cMHLkJNq1u43WrWMZNOgpNm/e6XaS6z6b9B7/7P8kL9zyGDNGvsrBnc4ursjOzCIjOY2kw0fZ9NVqNn35LXWaNaBCcFApFZ9Zfn4+R44kUbVqeLmNeSrWz1vrfZbp2Pk+v/Pk5asC3Q4oyfLly7n33ntp27YtAKNHj6Z9+/Y89dRTXHTRRYXbtW/fnrvuuovPP//clc7ExGMl/qsPz7KEhKPlnVSE+rxnuQ3U54TlNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5ZbnPctu5wPrxs95nWYUKgXTvfiXXXHMpVauG89NP+5g27RMGDBjBrFkv0qKFt49QOncFVAgg+soYGl/agtCIShzZd4jVnyzl//11Ene8NJyoRnW92u/aeStY/u78wo8bxDSl1/ABpZV9VubNW87hw0d5+OHyHbck1s9b632W6diJ2GZy4vHAgQM0bPjr/cXr168PQPPmzYtt27p1a6ZOnVpubb918mRWsdsJQMFl3Z71blKf9yy3ecZXn3cst3nGV5/3LPdZbvOMrz7vWe6z3OYZX33es9xnuc0zvvq8Z7nPcptnfPV5z3Kf5bZzgfXjZ73PsrZtm9O27a+/0+vS5XK6d+9Inz4P8c9/TmfatGddrHNHneYXUaf5rxdXNL28Fc06XsLUh8az/N353P7c/V7t9+JO7ajVpB4ZyWn8uOYH0pNSyc7KLq3sM/rpp/0899wbtGnTjFtu6Vxu456K9fPWep9lOnYitpm81WqVKlU4fvx44ccBAQEEBwcTEhJSbNv09HRCQ0PPuM/MzExSUlKKvDIznf0ACgkJIquE/3h79hsSUn63MSiJ+rxnuc0zvvq8Y7nNM776vGe5z3KbZ3z1ec9yn+U2z/jq857lPsttnvHV5z3LfZbbPOOrz3uW+yy3nQusHz/rfeea+vVr06XLFaxevYncUzzf8HxTrXYkTS9vxd5NO8nLzfNqHxE1qtHwkmgu7tSOm564kypRF/D+01PIdvg70LORmHicIUOeo3LlMCZNGkFAgPfPpiwt1s9b632W6diJ2GZy4rFZs2Zs3ry58ONq1apx4sQJOnXqVGzbLVu20KBBgzPuc9y4cURERBR5jRv3pqPOyMhqJCYeL7bcs6xGjeqO9u+U+rxnuQ3U54TlNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5ZbnPctu5wPrxs953LoqKuoDs7BxOnMh0O8WM8AuqkpuTS3Zm6RyTZh1jSEk8zv4ffiqV/Z1Kamo699wzhtTUdKZOfZaaNW2cD9bPW+t9lunY+T4/v/Pj5atMTjwOHDjwrK5iTEpKYvbs2Vx33XVn3HbkyJEkJycXeY0cOcRRZ7NmDdmzJ560tIwiyzdu3AFA8+YNS/q0cqM+71luA/U5YbkN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcdi6wfvys952Lfv75EMHBQYSFFb/D2fnq+KEjBAZVICgkuFT2l/PLFWEnM06Uyv5KkpmZxdChz7NnTzxvvDGaxo3rldlYf5T189Z6n2U6diK2mZx4/Mtf/sKUKVPOuF3lypX5+eefeeGFF864bXBwMOHh4UVenns+e6tHj47k5uYxe/aiwmVZWdnMmfMlMTHR1KoV6Wj/TqnPN9tAfb7aBupzynKf5TZQn1OW+yy3gfqcstxnuQ3U55TlPsttoD6nLPdZbjsXWD9+1vssO3Ysudiy7dt3s3TpGjp2bIO/v8lfT5ap9OTUYssO74pn55ofaNgmGr8/eExK2h/AxiXfgp8fUY3qetV5Jrm5uQwf/iIbNmxn0qQRtGnTrEzG8Zb189Z6n2U6diK2Bbod4I2dO3eSnJxMixYtiIiIcK0jJiaaHj06MmHCdI4eTaZ+/Vp88slS4uMTGDv2Yde61Ofbberz3Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5bbfmjFjASkp6SQkHAVg2bI1HDpU8OdBg3pRuXJFV7qsHz/rfWD3vR0+/EVCQoJo06YZ1atX4ccf9/HBB4sJCQnm8cfvdKWpJOV5/D594R0CgypQp3lDwiIqc2T/ITYs+oYKwUFcd1efwu0SdscTt/oHAI4dTCQz/QT/m7UYgJoNa9Pk8lYAfDN7CT9v281FbZsTHlmVk2npbP96Iwd37uPS3tdQrXbZTMKMH/8WS5eu5rrr2pOUlMrcucuKrL/ppjPfpa4sWT9vrfeB3Z8r58KxEzmf+eXn5+e7HVGSqVOnMmHCBJKSkujatSuTJ08mMzOTPn36sHbtWgBCQ0MZO3Ysw4YN83KUOMedmZlZTJw4g/nzl5OcnEZ0dAOGDRvI1Ve3dbzv0qA+32wD9flqG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gfqcstxnuc2jc+fBxMcnlLjuq6+mUqdOzXIu+pX142e9z+p7O336PObPX8G+fQdJS8ugatUIOnRozYMP/on69Wu70lSSsjp+7+7cXWzZ2nkr2LJ8HccPJpKZcZKwiEo0iGnKVX+6ocgk4aYvV7Ng4swS99uqS3t6PzIQgN3fb2ftvBUc+ulnMlLSCKwQSI2GF3JJtw606tIev9M8SOzOJt7fknLQoJGsWfPDKdfv2DHf632XFuvnrfU+qz9XoKyPXdNS2Ic4kXByntsJ5aJGSJ8zb3QOMjnxuGDBAvr06UNMTAx169Zl4cKFxMbGkpubS2pqKv369ePEiRO8++67bNiwgXnz5tGzZ08vRnI+8SgiIiIiIiIiIiJSkpImHi1xMvEo4rs08ei2xPNk4jFSE4/lp1OnTvj5+bFs2TL8/Px45ZVXeOKJJ7jxxhuZN+/Xb7icnBxat25N3bp1Wbx4sRcjaeJRREREREREREREyoYmHkXORZp4dJsmHs9tJp/evHXrVvr27Vt4G4CbbrqJvLw8+vfvX2S7wMBABgwYwPr1693IFBEREREREREREREREZFfmJx4zMjIICwsrPDjiIgIAGrXLn7f96ioKFJTU8utTURERERERERERERERESKC3Q7oCRRUVEcOHCg8OPQ0FCGDBlCnTp1im0bHx9P9erVyzNPREREREREREREREREyoDJK+bkrJmceGzXrh2rVq0q/DgsLIzXX3+9xG1XrlxJq1atyitNREREREREREREREREREpgcuJxzJgx7N2794zbJSYmEh4ezu23314OVSIiIiIiIiIiIiIiIiJyKiYnHlu0aEGLFi3OuF1kZCRz5swphyIREREREREREREREREROR3dKldEREREREREREREREREHDN5xaOIiIiIiIiIiIiIiIicf/z83C4QJzTxKCIiIiIiIiIiIlIG7mzS0O2E05r10y63E07ptka2j50fmhkRkfIzbtw45syZw/bt2wkNDeXKK6/khRdeIDo6unCbkydP8thjjzFr1iwyMzPp3r07r732GjVr1izcZt++fdx3330sW7aMSpUqceeddzJu3DgCA0tvulC3WhURERERERERERERERExasWKFTzwwAN8++23fPHFF2RnZ9OtWzfS09MLt3nkkUeYP38+H374IStWrODAgQPExsYWrs/NzaVnz55kZWXxzTff8O677/LOO+8wevToUm31y8/Pzy/VPZ5T4twOEBEREREREREREXGFrnj0nq549GVN3Q447x3LnOd2QrmoFtzH689NTEykRo0arFixgmuuuYbk5GQiIyN577336NevHwDbt2+nefPmrFq1iiuuuIKFCxfSq1cvDhw4UHgV5BtvvMFf//pXEhMTCQoKKpWvS1c8ioiIiIiIiIiIiIiIiBF+58UrMzOTlJSUIq/MzMyzOkLJyckAVKtWDYD169eTnZ1N165dC7dp1qwZ9erVY9WqVQCsWrWKVq1aFbn1avfu3UlJSWHLli1nNe7Z0MSjiIiIiIiIiIiIiIiISDkaN24cERERRV7jxo074+fl5eUxfPhwOnbsSMuWLQE4dOgQQUFBVKlSpci2NWvW5NChQ4Xb/HbS0bPes660lN7TIkVERERERERERERERETkjEaOHMmjjz5aZFlwcPAZP++BBx7ghx9+4H//+19ZpTmiiUcRERERERERERERERGRchQcHHxWE42/9eCDD7JgwQJWrlxJnTp1CpdHRUWRlZVFUlJSkaseDx8+TFRUVOE2a9asKbK/w4cPF64rLZp4dCgrK5tJk2Yyd+4yUlLSiI5uwPDhA+nYsY3baYD6fLUN1OerbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcBrb7Nm2K49NPl7J69Sbi4xOoUqUyMTHRDB8+iIYNL3Q7D7B9/Cy3gfqcstxnuQ3s96Wnn2DatDls3BjH5s1xJCenMW7cMGJju575k8+DthWzlrB0+mfUqB/FA6+PLFyem5PLytlL2PjVWlKOJBF+QRXaXH85V/XvSkBAQOF2mScy+frjr4jfsZf4Hfs4kZbBzY/8H22uv7zUGguO0yds2riDzZt3kpycxj/GDSM2tkuxbX/6aT/j/jGV777bRoUKgXTqdCkjRg6mWrWIUus5Gzt37mXy5PfZsuVHjhw5TkhIMI0b12Pw4Fg6d25fri2nYvnctdwmUhby8/N56KGH+OSTT1i+fDkNGzYssr5du3ZUqFCBr776ir59+wKwY8cO9u3bR4cOHQDo0KEDY8eOJSEhgRo1agDwxRdfEB4eTosWLUqt9Zx8xmN2djabNm0iNTXV7RRGjJjIO+98Su/enRg16h4CAvy5995nWbeu9B7E6YT6fLMN1OerbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcBrb7pk79mCVLvqFDhxhGjbqH/v17sG7dFmJjhxMXt9ftPMD28bPcBupzynKf5Taw33f8eApTpsxi1679REc3PPMnlCO325KPJPHf2V8QFBJUbN3HL/0/Vry3mIatm3DDkFjqt2zE0v/3OZ9N+bDIdhkpaax4bzGJ+w5T86LaZdJ5/HgKr02Zxa5dP5/2OB06dISBA0ayb99Bhj8yiLv/fDMrVqzjz3ePJisru0zaTuXAgUTS009wyy1dGDXqXu6//3YA7rvveWbPXlSuLadi+dy13CbO+Z0n//sjHnjgAWbMmMF7771H5cqVOXToEIcOHeLEiRMAREREMHjwYB599FGWLVvG+vXrufvuu+nQoQNXXHEFAN26daNFixYMGjSIjRs3snjxYp5++mkeeOCBP3zl5Wnfv/z8/PxS21s52bt3LxdddBGffvopvXv3drCnOEcdmzbFceutj/Hkk3czeHAsAJmZWfTq9SDVq0cwa9ZLjvbvlPp8s019vtumPt/us9ymPt/us9ymPt/us9ymPt/us9x2LvR99902WrZsTFBQhcJle/YcoHfvB+nevSMvv/yYi3W2j5/lNvX5dp/ltnOhDwqunEpOTiMysiqbN++kX79HzVzxWNZts37addr1H45/h/TkNPLz8slISSu84jE+bi//Hj6BTn/qTudBNxZuv3jqp6z6ZDlD//UEUb9cKZ+TncOJ1AwqVwsnPm4f/x7+z7O64vG2Rmc/0fr743Rrv8dKvOLx2TGv88knX/H5wtepXTsSgG++2cCf7x7Ns8/dz2239TjrMf/opMHZyM3NJTb2ETIzs1i06I1S3/8fYfncLfu2ps4jxZHjmQvcTigXVYN7nfW2fn4l/8x5++23ueuuuwA4efIkjz32GO+//z6ZmZl0796d1157rchtVPfu3ct9993H8uXLqVixInfeeSfjx48nMLD0bpBq8orHCRMmnPY1depU8vPz+fTTTwuXuWHRoq8JCPAv8h+k4OAg+vW7nu+/387Bg4mudHmozzfbQH2+2gbqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbWC/r23b5kUmHQEaNKhNkyb12LVrv0tVv7J8/Cy3gfqcstxnuQ3s9wEEBVUgMrKq2xklcrNtz+Yf2fq/jdxwb2yxdXt/KJiwbNmpbZHlLa9pS35+Pj+s/L5wWWCFQCpXCy/T1rM9TkuWfMO1115WOOkIcOWVl9CgwYUsWvh1WSaelYCAAGrVuoDU1HS3U0yfu5bbRMpKfn5+iS/PpCNASEgIU6ZM4dixY6SnpzNnzpxiz26sX78+n3/+ORkZGSQmJvLyyy+X6qQjGH3G4+OPP46fnx+nuxjTzwHQDeUAAHN0SURBVM+Pt99+u/DPjz76aHnlFdq2bRcNGlxIpUphRZa3bt30l/W7qVUrsqRPLRfq857ltoLx1ecty20F46vPCct9ltsKxlefE5b7LLcVjK8+Jyz3WW4rGF99Tljus9xWML7tvpLk5+dz5EgSTZrUczvF9PGz3FYwvvqcsNxnua1gfNt9UrK83Dw+f+Nj2na/gpoNi98eNSc7B4AKv/vHKhV+uSXrwR/d/8cqv3f48FGOHk2mZcvGxda1bt2EFSvXu1AFGRknOXkyk7S0DJYuXc3Kleu54YarXWn5LcvnruU2ETE68disWTP279/PiBEjGDBgQLFLSPfv388111zDv//9b66//nqXKiEx8ViJ/5LGsywh4Wh5JxWhPu9ZbgP1OWG5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynLfZbbwH5fSebNW87hw0d5+OEBbqeYPn6W20B9Tlnus9wG9vukZGs//5rkhOPc+Y8HSlx/QZ0aAOzbuouqUdULl+/94ScAUo4kl33kH5SQcAyAyMhqxdZFRlYlOSmVrKzsYlf+l7Xx46cVPtPR39+f66/vwOjRQ8u1oSSWz13LbVI6/PxM3qxTzpLJicdNmzbx6quv8vzzz/PZZ58xceJE2rdvX2y7GjVqUL9+/bPaZ2ZmJpmZmUWWBQdnERxc/MHIZ+vkyawS/0Pk2efJk1le77s0qM97lts846vPO5bbPOOrz3uW+yy3ecZXn/cs91lu84yvPu9Z7rPc5hlffd6z3Ge5zTO+5b7f++mn/Tz33Bu0adOMW27p7HaO6eNnuc0zvvq8Z7nPcptnfMt9UlxGSjrLZnzONX/qRsWISiVu0+SyFlSpUY0l0+ZSITiI2o3r8vOOvSyd/hn+Af7kZGWXc/WZZWYWfK+d/vsxs9wnHu+8sw89enQkIeEYCxf+l7y8PLKz3T9+ls9dy20iYvQZj4GBgTz66KPs2LGDZs2aceWVVzJo0CAOHDjg9T7HjRtHREREkde4cW866gwJCSKrhP+Iev4jFhLi/aRmaVCf9yy3ecZXn3cst3nGV5/3LPdZbvOMrz7vWe6z3OYZX33es9xnuc0zvvq8Z7nPcptnfMt9v5WYeJwhQ56jcuUwJk0aQUBAgNtJpo+f5TbP+OrznuU+y22e8S33SXFfTf+M0MphXN77mlNuUyGoAgOevZfQyhWZPfYtXrn7WT755ww6/ak7oZXDCAoNLsfis+OZlDr992P5dzdqVJcrr7yEm2/uzJtv/o2MjBMMHfr8aR9DVh4sn7uW20TE6MSjR40aNXjrrbf45ptviIuLo2nTpjz33HOcOHHiD+9r5MiRJCcnF3mNHDnEUV9kZDUSE48XW+5ZVqNG9WLrypP6vGe5DdTnhOU2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D+30eqanp3HPPGFJT05k69Vlq1rTRZfn4WW4D9Tlluc9yG9jvk6KOxiewftE3XN6nE6nHkjl++CjHDx8lJyub3Jw8jh8+SkZqOgA16tfigddH8MDrI/jzSw/z2IznaNejAxkp6VSvbe/5ejVqFNxiNTHxWLF1iYnHiahSudyvdixJ9+4d2bx5J7t3x7vaYfnctdwmIsYnHj3at2/P6tWrmTx5Mq+//jpXXXVVsec+nklwcDDh4eFFXk5uswrQrFlD9uyJJy0to8jyjRt3ANC8eUNH+3dKfd6z3Abqc8JyG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3gf0+KLhaYOjQ59mzJ5433hhN48b13E4qZPn4WW4D9Tlluc9yG9jvk6JSjiaTn5fPwjc+ZuLdzxW+ft6xl6PxCUy8+zlWvLe4cHs/Pz9q1K9F/YsbEVa5Irs37SQ/L5+L2kS7+FWUrGbN6lSrFsEPP/xYbN2mTTtp3szG96LnNqG/P2fKm+Vz13KbiJwjE48ed999Nzt27OD+++/njjvuoF49d//y06NHR3Jz8wof/gsFl+rPmfMlMTHR1Krl7r/sUZ9vtoH6fLUN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9ltvAfl9ubi7Dh7/Ihg3bmTRpBG3aNHO15/csHz/LbaA+pyz3WW4D+31SVI36tbj96cHFXjXqRxERWZXbnx5M225XlPi52ZlZLP1/n1O5WjitOrUt5/Kz061bB5YvX8vBg4mFy1at2siePfH06NGxXFuOHk0qtiw7O4e5c5cSEhJEo0Z1y7Xn9yyfu5bbpLT4nScv3xTodsAfFR4ezqBBg0hOTqZp06autsTERNOjR0cmTJjO0aPJ1K9fi08+WUp8fAJjxz7sapv6fLdNfb7bpj7f7rPcpj7f7rPcpj7f7rPcpj7f7rPcdi70jR//FkuXrua669qTlJTK3LnLiqy/6abrXCorYPn4WW5Tn2/3WW47F/o8ZsxYQEpKOgkJRwFYtmwNhw4V/HnQoF5UrlzxvGirGFGJ5le2Lrb827krAIqs++Afb1O5egSR9aLIzDjJ90u+5fihowx49l6Cw0KKfP7q+Ss5mXaC1GPJAOxY/QMpR5IAuLzPNYRUDHXcPmPGAlJT0klIKLiV6rJlazh86AgAA385TkOG3sqiRV9z5x2jGHRHHzIyTvDWtE9o2rQ+sX27Om74I0aPnkJaWgaXXdaSmjWrkZiYxPz5y9m162dGjBhMxVI4Jk5YPnctt4kI+OW7/ZTaU5g6dSoTJkwgKSmJrl27MnnyZDIzM+nduzfr1q0DIDQ0lLFjxzJs2DAvR4lz3JmZmcXEiTOYP385yclpREc3YNiwgVx9tY1/1aM+32wD9flqG6jPKct9lttAfU5Z7rPcBupzynKf5TZQn1OW+yy3ge2+QYNGsmbND6dcv2PH/HKsKZnl42e5DdTnlOU+y21gvw+gc+fBxMcnlLjuq6+mUqdOzXIu+lVZts36addZbff2XyeTkZLGA6+PLFz2vw+/4vsvV5N0+BgVgipQr+VFXDfgBmo1qlPs81+561mSEoo/WxFg+NujqVrCs4Rva/THbpfZufNfOHCK4/TlV/8pPE47d+5j/PhpfLd+KxUqBNKp06X8dcSfueCCqn9oPD+HVyt99tlKPvroC+Li9pCUlErFiqFcfHFjBg7sRZculzvad2mxfO6WbZu7FzwJJGUtdDuhXFQJusHthDJhcuJxwYIF9OnTh5iYGOrWrcvChQuJjY0lNzeX1NRU+vXrR0ZGBtOnT2fDhg3MmzePnj17ejGS84lHERERERERERERkXPR2U48uuGPTjyWN6cTj2KZJh7dponHc5vJicdOnTrh5+fHsmXL8PPz45VXXuGJJ57gxhtvZN68eYXb5eTk0Lp1a+rWrcvixYtPs8dT0cSjiIiIiIiIiIiInJ808eg9TTz6Mk08ui05a9GZN/IBEUE93E4oE/5uB5Rk69at9O3bFz+/gh/eN910E3l5efTv37/IdoGBgQwYMID169e7kSkiIiIiIiIiIiIiIiIivzA58ZiRkUFYWFjhxxEREQDUrl272LZRUVGkpqaWW5uIiIiIiIiIiIiIiIiIFGdy4jEqKooDBw4UfhwaGsqQIUOoU6f4g4nj4+OpXr34w4dFREREREREREREREREpPwEuh1Qknbt2rFq1arCj8PCwnj99ddL3HblypW0atWqvNJEREREREREREREREREpAQmJx7HjBnD3r17z7hdYmIi4eHh3H777eVQJSIiIiIiIiIiIiIiImXLz+0AccDkxGOLFi1o0aLFGbeLjIxkzpw55VAkIiIiIiIiIiIiIiIiIqdj8hmPIiIiIiIiIiIiIiIiInJuMXnFo4iIiIiIiIiIiIiUrdsaNXQ74ZTWJPzodsJpXV6jidsJIiImaeJRRERERERERERERERETPDz0806z2V690RERERERERERERERETEMU08ioiIiIiIiIiIiIiIiIhjmngUEREREREREREREREREcc08SgiIiIiIiIiIiIiIiIijgW6HSAiIiIiIiIiIiIiIiJSwM/tAHFAVzyKiIiIiIiIiIiIiIiIiGO64tGhrKxsJk2aydy5y0hJSSM6ugHDhw+kY8c2bqcB6vPVNlCfr7aB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynLfZbbQH1OWe6z3AaQnn6CadPmsHFjHJs3x5GcnMa4ccOIje3qdhqbNsXx6adLWb16E/HxCVSpUpmYmGiGDx9Ew4YXup1XaMuWH5k8+X2++24rmZlZ1K0bRf/+3bnjjj6udu3Zc4BXJ81k/fqtJCenUqtWJL16deLPg28hNDS4XBrmTf+Cj/6zkAsbRjFu+pNF1u3cvJtZry9gb9zPhFYMof11Mdx6b09Cwn5t27VtH/9btJZt3/1I4qHjVAoPo/HF9en7lxuoVa9GmbZbPnctn7dg+9iJnO/OmSse8/Ly+OCDD7j33nvp168f9913H/PmzXM7ixEjJvLOO5/Su3cnRo26h4AAf+6991nWrdvidhqgPl9tA/X5ahuozynLfZbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbQDHj6cwZcosdu3aT3R0Q7dzipg69WOWLPmGDh1iGDXqHvr378G6dVuIjR1OXNxet/MA+N//vuO2257g2LFk7r//NkaNupdrr72MQ4eOutp18GAi/W99jA0bdzBgYE9GPvUXLmnTjMmT3+OxR18ql4ZjCUnM+39fERwaVGzd3p3xjB/+Olkns/i/B2+iU6/LWT7/W/41+t0i2302cylrl2+iRbsmDHz4Zq7r04EdG3cx+i8T+HnXwTLtt3zuWj5vwfaxEznf+eXn5+e7HfF7LVq04KWXXqJnz54ApKenc8MNN/D111/j5+dH9erVOXLkCAA33HADc+fOJSAgwIuR4hx1btoUx623PsaTT97N4MGxAGRmZtGr14NUrx7BrFnl8x9Y9Z1fberz3Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5Tb1+Xaf5TaPrKxskpPTiIysyubNO+nX71EzV0599902WrZsTFBQhcJle/YcoHfvB+nevSMvv/yYi3WQlpZB9+5DaNOmOa++OgJ//9K9jiQf7381/MYbHzDxlRnMX/AvmjSpV7j8r399hbmfLmP1mveIiKjk9f7XJPx4xm2m/G06qUnp5OXlkZqcXuSKx5ef+Df7dh7ghZkjCK0YAsDy+d/y1osf8MQ/h9CqfTRQcFVkw2Z1Cazw680BD+1PZNRdL3FZp9YMHT2wxLEvr9HE668N7J+7ls/bsj92TZ1HiiOp2V+5nVAuKlfo4nZCmTB5xeP27dtJTk4u/Pivf/0r//vf//j73/9OWloahw8fJjk5mccee4zPP/+cf/7zn650Llr0NQEB/tx2W4/CZcHBQfTrdz3ff7+dgwcTXenyUJ9vtoH6fLUN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcBupzynKf5TaPoKAKREZWdTujRG3bNi8y6QjQoEFtmjSpx65d+12q+tX8+Ss4ciSJRx4ZhL+/PxkZJ8nLy3M7C4D0tBMAXFC9SpHlNSKr4e/vT4UKZfuUr+0bfmLtik0MePimYutOpJ9ky9o4ruzWrnDSEeCqHpcSEhrMmqUbCpc1adWwyKQjQFTdSC5sEMWBvQll1m/93LV83lo/diLnO5MTj7/3/vvvc9dddzFy5EhCQgr+Q1GpUiVefPFFbrjhBmbMmOFK17Ztu2jQ4EIqVQorsrx166a/rN/tRlYh9XnPclvB+OrzluW2gvHV54TlPsttBeOrzwnLfZbbCsZXnxOW+yy3FYyvPics91luKxhffU5Y7rPcVjC++pyw3Ge57VyVn5/PkSNJVK0a7nYKq1ZtoFKlMA4fPkr37kNp0+ZW2rW7jb/97TUyM7NcbWvfviUAo0a9yrZtuzh4MJHPP/8v77+/kEGDehEWFnKGPXgvLzeP/zdxDp16XU7dRrWLrd//00Fyc/No2KxukeWBFQKp16Q2e3fGn3b/+fn5JB9PpVJExVLt/i2du97TsROxzfzEY2pqKsePH6dHjx4lru/Rowc//njmy+7LQmLisRL/1YdnWUKCu/dZV5/3LLeB+pyw3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9y27lq3rzlHD58lBtuuNrtFPbsOUBubi733/93rr66DZMnj6Rv367MmrWQkSMnudp29TXtGDZsAN98s4Fbbh7OddcO5tFHXmLgwF6MfOovZTr20rnfcPTwcfoOvqHE9UlHUwCoUr345HGV6uEcP5JcbPlvfbNkPccTk7m8yyWOW09F5673dOxEbCvb690d8PPzA6BixYqEhYWd9v7lZ/N8x8zMTDIzM4ssCw7OIji4+IOHz9bJk1nFbsVQsN+gwvVuUp/3LLd5xlefdyy3ecZXn/cs91lu84yvPu9Z7rPc5hlffd6z3Ge5zTO++rxnuc9ym2d89XnPcp/lNs/46vOe5T7Lbeein37az3PPvUGbNs245ZbObueQkXGSEycyuf32G3j66SEAdOt2JVlZOcyevYiHHx5AgwbFr/grLxdeWINLL72Ybt2vpEqVyqxYvo433/yQCyKrMHBgrzIZMzU5nY+nLeKmO7sRXrXkZ0hmZ2YDEFih+O+NKwRVIDsr+5T7P7D3MNNfmUPjlg24usdlpRNdAp273tOxE7HN7BWPgwcPJjw8nCpVqnDy5Em+++67Erfbvn07tWuf+T+u48aNIyIioshr3Lg3HTWGhASRVcJ/pDy3OQgJ8X5SszSoz3uW2zzjq887lts846vPe5b7LLd5xlef9yz3WW7zjK8+71nus9zmGV993rPcZ7nNM776vGe5z3KbZ3z1ec9yn+W2c01i4nGGDHmOypXDmDRpxFld7FDWPO9fr17XFFneu3cnADZs2F7uTR6ffbaS0aOn8PexD9G/f3e6dbuSsf94mJtv6cw/X36X48dTymTcj/+zkErhYVzf96pTblMhuGBSKic7t9i67KxsKpQwaQUFV0pOeHIqoRVDeOj5O/EPKLtfn+vc9Z6One/zO0/+56tMXvF45513FlvmuQLyt9LS0nj//ffp3bv3Gfc5cuRIHn300SLLgoP3eR8JREZW4/Dh4pdtJyYeB6BGjeqO9u+U+rxnuQ3U54TlNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynLfZbbziWpqencc88YUlPTmTlzPDVr2jhuNWpUY+fOfVSvXqXI8mrVIgBITk5zoarA++99TvPmFxEVdUGR5Z07t+eTOV+xbdsurrzyklId89D+RJbNX8WAh27m+JFfJzazs7LJzckl8eAxQisGF95i1XPL1d9KOppC1Qsiii3PSDvBy0/8h/S0Ezz9rwdL3KY06dz1no6diG0mr3h8++23i73Gjh1bbLsKFSrw/fff8+qrr55xn8HBwYSHhxd5ObnNKkCzZg3ZsyeetLSMIss3btwBQPPmDR3t3yn1ec9yG6jPCcttoD6nLPdZbgP1OWW5z3IbqM8py32W20B9Tlnus9wG6nPKcp/lNlCfU5b7LLedKzIzsxg69Hn27InnjTdG07hxPbeTCl18cWOAYpMsCQnHAKhWrfgzDMvLkSPJ5OXlFVvuucowN6f41YZOHT+STH5ePjMmfcJj/f9e+Ppp6z4O7U/ksf5/59N3llDnoigCAvzZvX3/79py2LfzAPWaFL2DXlZmNq+MmMah/Yk8+sJfuLBhVKm3/57OXe/p2InYZnLi8Ux27tzJunXryM3NpX79+kRElO2/PjmVHj06kpubx+zZiwqXZWVlM2fOl8TERFOrVqQrXR7q8802UJ+vtoH6nLLcZ7kN1OeU5T7LbaA+pyz3WW4D9Tlluc9yG6jPKct9lttAfU5Z7rPcdi7Izc1l+PAX2bBhO5MmjaBNm2ZuJxVxww0FtxP96KMviiz/6KMlBAYG0L59KzeyAGjQsDZbt+5i9+74Iss/+2wl/v7+NI1uUOpj1mkYxbCxdxd7Xdgwiuo1qzJs7N106nk5YZVCufjSpnyzZD0nMk4Wfv7Xi9dx8kQm7a+LKVyWl5vHlDHT+fGHPTz03B00aVn63SXRues9HTsR20zeahVg6tSpTJgwgaSkJLp27crkyZPJzMykT58+rF27FoDQ0FDGjh3LsGHDXGmMiYmmR4+OTJgwnaNHk6lfvxaffLKU+PgExo592JUm9fl+m/p8t019vt1nuU19vt1nuU19vt1nuU19vt1nuU19vt1nuU19vt1nue23ZsxYQEpKOgkJBVfuLVu2hkOHCv48aFAvKleu6ErX+PFvsXTpaq67rj1JSanMnbusyPqbbrrOlS6PFi0a0bfv9Xz88Rfk5uZy2WUtWbNmM4sWfc2QIbe6ekvYwYNv4b8r1zNwwAgGDOhJlSrhLF++lpUr13Prrd3KpK1ylUq0u6b4ZOviD1cCFFnX754bef7+V/nHg1O4rk8HjiUmsXDWclpeFk3ry5sXbvfelLl8/78ttOl4MWkpGXy9eF2RfXfsfmmpfx1wbpy7Vs/bc+HYiVPn5DVz8gu//Pz8fLcjfm/BggX06dOHmJgY6taty8KFC4mNjSU3N5fU1FT69evHiRMnePfdd9mwYQPz5s2jZ8+eXowU57g1MzOLiRNnMH/+cpKT04iObsCwYQO5+uq2jvddGtTnm22gPl9tA/U5ZbnPchuozynLfZbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7nNo3PnwcTHJ5S47quvplKnTs1yLiowaNBI1qz54ZTrd+yYX441JcvOzuHNNz9kzpwvSUg4Ru3akfzf//XkrrtucrzvfJz9anjTpjj+Nfl9tm3bRVJSKhdeWJObb7mOv/ylL4GBAY72vSbhx7Pe9h8PTSE1OZ1x058ssnzHpl188PoC9sT9TEhYCJd3juHWIT0JDQsp8rnbN/x0yn1P/++EEpdfXqPJWfedivVz1+p5C2V97JqWwj7EibTs5W4nlItKFa51O6FMmJx47NSpE35+fixbtgw/Pz9eeeUVnnjiCW688UbmzZtXuF1OTg6tW7embt26LF682IuRnE88ioiIiIiIiIiIiJyLnE48lqU/MvHohtKYeBSrNPHoNk08nttMXq+6detW+vbti5+fHwA33XQTeXl59O/fv8h2gYGBDBgwgPXr17uRKSIiIiIiIiIiIiIiIiK/MDnxmJGRQVhYWOHHERERANSuXbvYtlFRUaSmppZbm4iIiIiIiIiIiIiIiIgUF+h2QEmioqI4cOBA4cehoaEMGTKEOnXqFNs2Pj6e6tXde4iyiIiIiIiIiIiIiIiIlA7P3TDl3GRy4rFdu3asWrWq8OOwsDBef/31ErdduXIlrVq1Kq80ERERERERERERERERESmByYnHMWPGsHfv3jNul5iYSHh4OLfffns5VImIiIiIiIiIiIiIiIjIqfjl5+fnux3hnji3A0RERERERERERERckY/dXw2vSfjR7YTTurxGE7cTpMw0dTvgvJees8LthHJRMbCT2wllwuQVjyIiIiIiIiIiIiIiInI+0jMez2X+bgeIiIiIiIiIiIiIiIiIyLlPVzyKiIiIiIiIiIiInIf8DF9VZP1WppUajHU74bTS9oxyO0FEzlO64lFEREREREREREREREREHNPEo4iIiIiIiIiIiIiIiIg4plutioiIiIiIiIiIiIiIiAmWbwMtZ6YrHkVERERERERERERERETEMU08ioiIiIiIiIiIiIiIiIhjmngUEREREREREREREREREcf0jEeHsrKymTRpJnPnLiMlJY3o6AYMHz6Qjh3buJ0GqM9X20B9vtoG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5sXPnXiZPfp8tW37kyJHjhIQE07hxPQYPjqVz5/Zu55k+dps2xfHpp0tZvXoT8fEJVKlSmZiYaIYPH0TDhhe6nQdAevoJpk2bw8aNcWzeHEdychrjxg0jNrar22mA7ff3fD92FcOCGT7kRi695CLaxVxEtSqVGPL4f5j50f+KbPfGy39hYL+ri31+3E8HaNtlZJFlNSMjePqRWK67+mJqRkZw8HASn33xHS/9ax7HktKLbBvbsz0P/qU7TRvVIjc3n21xP/PKG5+zeNnGUvn6LH/vWW4TOd+dc1c85uXlkZqa6nZGoREjJvLOO5/Su3cnRo26h4AAf+6991nWrdvidhqgPl9tA/X5ahuozynLfZbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1OfEgQOJpKef4JZbujBq1L3cf//tANx33/PMnr3I5Trbx27q1I9ZsuQbOnSIYdSoe+jfvwfr1m0hNnY4cXF73c4D4PjxFKZMmcWuXfuJjm7odk4xlt/f8/3YVa9WmZHDbia6UW1+2Lb/tNuezMxi8PA3i7xG/WN2kW0qhgWz9JNn6N29Le/P+ZrH/zaDJcs2MuSOrsyf+Vf8/PwKtx16Z1emT3mAo8fS+NsLH/LC5LmEVw7l47cfpU/3dqXy9Vn+3rPcJqXB/zx5+Sa//Pz8fLcjfm/z5s0kJCTQpUuXwmVLlizh73//O2vWrCE7O5uQkBC6dOnCP/7xD1q2bOnlSHGOOjdtiuPWWx/jySfvZvDgWAAyM7Po1etBqlePYNaslxzt3yn1+Wab+ny3TX2+3We5TX2+3We5TX2+3We5TX2+3We5TX2+3We5TX1lIzc3l9jYR8jMzGLRojdc67B+7L77bhstWzYmKKhC4bI9ew7Qu/eDdO/ekZdffszFugJZWdkkJ6cRGVmVzZt30q/fo2au2rP+/p7Px65Sg7EEBQVSJaIiCYnJtGnVgP/Of/aUVzzefMNlRF085LT7vLXPFbz96n30vXtCkasWRz1yCyOH3cyVPZ9h05Z9AHy/9AWSUzK49uZnC7erXCmEuG8nsmLVNnpdX8nR12f5e6/s25o6jxRHMnK+djuhXIQFdnQ7oUyYnFIdPnw4M2fOLPz4ww8/5IYbbmD79u0MGDCAJ598kr59+7Jy5UquuOIKvvvuO1c6Fy36moAAf267rUfhsuDgIPr1u57vv9/OwYOJrnR5qM8320B9vtoG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/WVhYCAAGrVuoDU1PQzb1yGrB+7tm2bF5l0BGjQoDZNmtRj167TXyFWXoKCKhAZWdXtjBJZf3/P92OXlZVDQmLyWW/v7+9H5Uohp1wfXjkUgIQjRfd5KCEJgJMns3+zbQiJR1OKbJeadpL0jExOnsw666ZTsfy9Z7lNRIxOPG7cuJE2bX69F/OIESNo3749u3fvZtq0aYwbN47p06fz448/UqdOHUaMGOFK57Ztu2jQ4EIqVQorsrx166a/rN/tRlYh9XnPclvB+OrzluW2gvHV54TlPsttBeOrzwnLfZbbCsZXnxOW+yy3FYyvPics91luKxhffU5Y7rPcVjC++kpDRsZJjh1LZt++g7zzzqesXLmeK66IcbXpXDl2v5Wfn8+RI0lUrRrudop55+L7a4W1YxcWGsTBH97k4A9vsm/DFCY8N4iKYcFFtvl69Q5yc/N48W8DuaxNI2pHVaXbta154oHezF+8nrifDhZu+99vt3N9p1YMvbMr9epcQNNGtZjw3CDCK4fy2ttLHPdaO36/ZblNRCDQ7YCSZGRkULFixcI/7969m7FjxxYu87jgggsYMmQIzzzzjBuZJCYeK/Ff9HiWJSQcLe+kItTnPcttoD4nLLeB+pyy3Ge5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynLfZbbQH2lZfz4aYXPdPT39+f66zswevRQV5vOlWP3W/PmLefw4aM8/PAAt1PMOxffXyssHbtDCcm88ubnbPxhL/7+fnTt1Ip77+hKy+b1uOH2ceTm5gGw/ccDPDTybcaOup1ln4wu/PwZH/2XB/76VpF9PjFmBtWrVublZwfx8rODADhyNIVeA15gzXc/OW62dPx+z3KblA4//M68kZhlcuIxOjqaVatW8ec//5nQ0FAqVapESkpKidumpKRQoUKFEteVtZMns4rdKgIKLuv2rHeT+rxnuc0zvvq8Y7nNM776vGe5z3KbZ3z1ec9yn+U2z/jq857lPsttnvHV5z3LfZbbPOOrz3uW+yy3ecZXn3N33tmHHj06kpBwjIUL/0teXh7Z2dln/sQydK4cO4+fftrPc8+9QZs2zbjlls5u55h3rr2/llg6dmNe/LDIxx/NX82Puw4x5slbueXGy/ho/urCdQcOH2f9xl0sWbaRffFHufKyptx31/UcPZbGqH/MKtwu40QWO3cdJP7QMRZ9tYFKlUJ58M/dee+Nh+l261jHzZaO3+9ZbhMRo7davffee5k+fTrz5s3Dz8+Phx56iDFjxrBu3boi23311Ve88sordOnS5Yz7zMzMJCUlpcgrM9PZD6CQkCCysor/n0vPfkNCghzt3yn1ec9ym2d89XnHcptnfPV5z3Kf5TbP+OrznuU+y22e8dXnPct9lts846vPe5b7LLd5xlef9yz3WW7zjK8+5xo1qsuVV17CzTd35s03/0ZGxgmGDn2e/Px815rOlWMHkJh4nCFDnqNy5TAmTRpBQECA20nmnUvvrzXWj92/pi0mNzePazteXLjsinZN+GjaIzz70ke89vYXLFjyHU+NncWL/5rPQ3/pTrPGtQu3/X+vPUid2tUZ+vhUPl24jhkf/pcbbh9HhQqB/O2Jfo77LB8/y20iYnTicejQofzpT3/i5ptvpkOHDqSmppKdnc3ll19O48aN6dixIw0aNKBbt25UrFiRl19++Yz7HDduHBEREUVe48a96agzMrIaiYnHiy33LKtRo7qj/TulPu9ZbgP1OWG5DdTnlOU+y22gPqcs91luA/U5ZbnPchuozynLfZbbQH1OWe6z3AbqKyvdu3dk8+ad7N4d71rDuXLsUlPTueeeMaSmpjN16rPUrGmjy7pz5f21yPqxO5mZzbHjaVSr8uvjxf78f9eRcCSF7zfvKbLtZ19+h7+/P5e3awJAg7qRdLu2NZ9/+X2R7Y4np7NqXRxX/LKdE5aPn+U2ETE68ejn58c777zDrFkFl45PmTKFo0ePkp+fz65du1i1ahVZWVk8/PDDfP/99zRo0OCM+xw5ciTJyclFXiNHDnHU2axZQ/bsiSctLaPI8o0bdwDQvHlDR/t3Sn3es9wG6nPCchuozynLfZbbQH1OWe6z3Abqc8pyn+U2UJ9Tlvsst4H6nLLcZ7kN1FdWPLfz+313eToXjl1mZhZDhz7Pnj3xvPHGaBo3rud20jnjXHh/rbJ+7CpVDKF6tUocOZZauKxGZDgBAcWfbVchsOCJaYGB/oXbAQQEFP/1foXAgMLtnLB8/Cy3iYjRiUeP/v37s2rVKpKSkvjuu+9YuXIla9euJT4+ngMHDvDKK69Qo0aNs9pXcHAw4eHhRV6eez57q0ePjuTm5hU+VBwgKyubOXO+JCYmmlq1Ih3t3yn1+WYbqM9X20B9Tlnus9wG6nPKcp/lNlCfU5b7LLeB+pyy3Ge5DdTnlOU+y22gPqeOHk0qtiw7O4e5c5cSEhJEo0Z1yz/qF9aPXW5uLsOHv8iGDduZNGkEbdo0c7XnXGP9/bXMyrELDq5ApYohxZb/9aE++Pv788WKzYXLftx1iJqRVbj6iqLnya19rgBg45a9AOzak0Bubh59e11eZLvaUVW58rKmbNyyz3G3leN3rrVJ6fDz8zsvXr4q0O2As1G5cmUuueSSwo937tzJunXraNGiBWFhYa51xcRE06NHRyZMmM7Ro8nUr1+LTz5ZSnx8AmPHPuxal/p8u019vtumPt/us9ymPt/us9ymPt/us9ymPt/us9ymPt/us9ymPudGj55CWloGl13Wkpo1q5GYmMT8+cvZtetnRowYTMWKoa61WT9248e/xdKlq7nuuvYkJaUyd+6yIutvuuk6l8qKmjFjASkp6SQkHAVg2bI1HDpU8OdBg3pRuXLF0316mbH+/oKO3ZA7uhIRHkatmlUAuLHLJVwYVQ2AN979gioRFfn6s+f4aN63xP10EIAu17SkR+dLWLJ8EwuWfFe4rzenf8nAW6/mg6nDeePdL9kff4SrLm9G/5s68NXKH1i3YRcAR46lMv2Dldz9p2v57L2/Mm/ReipVCuGegZ0JDQnin68toNu1zp7zaPl7z3KbiIBfvptPvz6NqVOnMmHCBJKSkujatSuTJ08mMzOTPn36sHbtWgBCQ0MZO3Ys/7+9Ow+P8dz/OP6ZSDLJiARBYgmhilKxFlXEEppaYomtloqmLaqkR0vr+LVUi+NU0XJoe2hpq5ZSWxRFbccW+15RRCNIYktCZJ3790eaqTGTZDKPzH2Lz+u6cl3yzPK8MyP5ZubOMxMREWHnXqI1d6anZ2D27B+wfv0OJCXdRe3a/oiIGITWrRtrvu5HgX3Fsw1gX3FtA9inlcp9KrcB7NNK5T6V2wD2aaVyn8ptAPu0UrlP5TaAfVqp3KdyG8A+LTZs2IWVK7cgOjoGd+6koGRJd9SrVxODBnVFhw7NC76CIqbybTd48HhERZ3K8/Rz59Y7sCZv7duHIy4uwepp27YtQJUqPg4u+pvK9y/w5N52Hv5TAACn/zcD1apYP8Kubqt3kJScihmTBuG5Rk+hok8ZlCihw8WYBCxfuw+ff70RWVnZZpd5uoYvPnwnFE0bPgWf8l64lnAbqzccxJRZq3H/r5d3BnJeZvW1ge3xSr82qFEt51UBj5y4hOlz1mLXvt9xN2aC5q9R5f97RdtW6xFcB2mRlr1PdoJDuJV4XnZCkVBy4TEyMhIhISFo0KAB/Pz8sHHjRvTq1QvZ2dlISUlB7969cf/+fSxevBjHjh3DunXr0KVLFzv2pH3hkYiIiIiIiIiIiIieLLkLj6p6FAuPTy4uPMrGhcfHm5ILj4GBgdDpdNi+fTt0Oh1mzZqFsWPHonPnzli3bp3pfFlZWQgICICfnx82b95sx5648EhEREREREREREREhcOFx+KMC4+ypWXvl53gEG4lWshOKBJOsgOsOXPmDEJDQ01vrtm9e3cYjUb07dvX7HzOzs4YOHAgDh8+LCOTiIiIiIiIiIiIiIiIiP6i5MJjamoqDAaD6XMvLy8AQKVKlSzO6+vri5SUFIe1EREREREREREREREREZElJRcefX19cfXqVdPn7u7uGDZsGKpUqWJx3ri4OHh7ezsyj4iIiIiIiIiIiIiIiIge4iw7wJomTZpg376/3zzUYDBg/vz5Vs+7a9cu1K9f31FpRERERERERERERERERGSFkguPkyZNwuXLlws8X2JiIjw9PdG/f38HVBEREREREREREREREVFR0qn5Yp1kI50QQsiOkCdadgARERERERERERERPWY8/KfITsjX3ZgJshMeY7VkBzzx0rOjZCc4hL5EM9kJRYLLxkRERERERERERERERESkmZIvtUpEREREREREREREpCrVjyi8n3VDdkK+3J3LyU4goiLChUciIiIiIiIiIiIiIiJShE52AGnAl1olIiIiIiIiIiIiIiIiIs248EhEREREREREREREREREmnHhkYiIiIiIiIiIiIiIiIg048IjEREREREREREREREREWnmLDuAiIiIiIiIiIiIiIiICAB0Op3sBNKARzwSERERERERERERERERkWY84lGjjIxMfP75Eqxdux3JyXdRu7Y/3n57EF54oZHsNADsK65tAPuKaxvAPq1U7lO5DWCfVir3qdwGsE8rlftUbgPYp5XKfSq3AezTSuU+ldsA9mmlcp/KbQD7tLh37z4WLvwZx49H4+TJaCQl3cW0aRHo1StIdhoA9ftUvm8BOX2p99Kw6NuNOHXiIk6dvITk5Hv46JNwdO/ZyuK8Fy9cxYzpS3H0yHm4uDijdWAA3hnXH2XLelqcN/bPBPxnzs84sP8MUu+loYJPGXQKboZREaFF8nWoft8SPcmUPOLxxRdfxMyZM3H16lXZKQV6//3ZWLRoDbp1C8SECa+jRAknvPHGRzh06LTsNADsK65tAPuKaxvAPq1U7lO5DWCfVir3qdwGsE8rlftUbgPYp5XKfSq3AezTSuU+ldsA9mmlcp/KbQD7tLh9Oxn/+c8yXLwYi9q1q8vOsaB6n8r3LSCn7/adu/h6/jpcungNtWr75Xm++Ou3ED5kGmL/TMCot0PxytBg7N55AsNfm4HMjCyz8/5+9k+83HcSos/F4pUhL+K9fw7ES51bIDHhTpF9Harft0RPMp0QQsiOeJiTkxN0Oh10Oh3atm2LgQMHIjQ0FJ6eln9JoU20pkufOBGNPn3ewbhxQxEe3gsAkJ6ega5d34K3txeWLfv0UUSyj23se0La2Fe8+1RuY1/x7lO5jX3Fu0/lNvYV7z6V29hXvPtUbmNf8e5TuY192mVkZCIp6S7Kly+DkyfPo3fvMcodUahqn+r3bVH33c+6YXV7RkYmkpNSUa68F06fuoSB/SZbPeJxyuTvsH7tHqxePxUVK3kDAPbvO43hr83A/00cgt592wIAjEYj+vb6EO7uevz32/fg5uZqU5+7czm7v7aiv29rabw8aZVhPCw7wSFcnZrITigSSh7xCACjR49GWFgYjhw5gvDwcPj6+qJPnz5Ys2YNMjMzZecBADZt2oMSJZzQr1+waZte74revTvi6NHfce1aosQ69hXXNoB9xbUNYJ9WKvep3AawTyuV+1RuA9inlcp9KrcB7NNK5T6V2wD2aaVyn8ptAPu0UrlP5TaAfVq5urqgfPkyUhvyo3Kf6vetrD5XVxeUK+9V4Pm2bT2M1oENTIuOANDi+Xqo5u+LLZsPmrbt23Maf5yPw7A3u8PNzRX376cjO9tYJO25VL9viZ50yi48Pvfcc1iwYAGuX7+OVatWoXPnztiwYQNCQ0Ph4+ODN954Azt37pTaePbsRfj7V4aHh8Fse0BArb9OvyQjy4R99lO5LWf/7LOXym05+2efFir3qdyWs3/2aaFyn8ptOftnnxYq96nclrN/9mmhcp/KbTn7Z58WKvep3Jazf/ZpoXKfym05+2cfyaH6fatyX3z8bdy6mYy69fwtTnu2fnX8fvay6fMD+3Ne2tTVxRkD+n6E55sOR4smw/Deu/ORdOdukfSpfNsRkcILj7lcXV3Rs2dPrFy5EvHx8ViwYAEaN26Mb775Bu3bt4efnx/GjRsnpS0x8ZbVv+jJ3ZaQcNPRSWbYZz+V2wD2aaFyG8A+rVTuU7kNYJ9WKvep3AawTyuV+1RuA9inlcp9KrcB7NNK5T6V2wD2aaVyn8ptAPtIHtXvW5X7biTe+aultMVp5cqVRlLSPWRk5Lwq4Z+X4wEA496ZD//qvpgxaySGhnfGti2HMXrk5yiKd3pT+bYjosdg4fFBpUqVwtChQ7F161ZcuXIFM2bMgI+PDz777LMCL5ueno7k5GSzj/T0DE09aWkZcHV1sdiu17uaTpeJffZTuS13/+yzj8ptuftnn/1U7lO5LXf/7LOfyn0qt+Xun332U7lP5bbc/bPPfir3qdyWu3/22U/lPpXbcvfPPvup3KdyW+7+2UcyqH7fqtyXnp6zqOji6mxxml6f05yWlnOe1NR0AEC9Z6tj6vRhCOrUFG+O6ok33+qJ48f+wIH9Zx55n8q3HRE9ZguPD/L19cU//vEPHDp0CGfPni3w/NOmTYOXl5fZx7RpX2lqcHNzNf1lx4NyFzRtfSPdosI++6nclrt/9tlH5bbc/bPPfir3qdyWu3/22U/lPpXbcvfPPvup3KdyW+7+2Wc/lftUbsvdP/vsp3Kfym25+2ef/VTuU7ktd//sIxlUv29V7stdXMzMyLI4LXdR0s0t5zz6vzqDOzc3O99LXVoAAI4f/eOR96l829GjoYPTE/FRXBWLr6xWrVoFnmf8+PFISkoy+xg/fpim/ZYvXxaJibcttuduq1DB2+I0R2Kf/VRuA9inhcptAPu0UrlP5TaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3AawTyuV+1RuA9inlcp9KrcB7CN5VL9vVe4r99dLrCb+9ZKrD7px4w68vEqajjjMfTlWb29Ps/OV/evz5OTUR96n8m1HRIouPBqNRgwYMOCRXqder4enp6fZR+6h1/aqU6c6YmLicPeu+Q/P48fPAQCeeaa6puvXin32U7kNYJ8WKrcB7NNK5T6V2wD2aaVyn8ptAPu0UrlP5TaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3Aawj+RR/b5Vuc/HpwzKlC2FM6djLE47dfISatepavq8br1qAICEBPOFwMS/Pi9TttQj71P5tiMiRRceC3L+/HkcOnQIqamP/q8lCiM4+AVkZxuxfPkm07aMjEz8/PNWNGhQGxUrlpdYx77i2gawr7i2AezTSuU+ldsA9mmlcp/KbQD7tFK5T+U2gH1aqdynchvAPq1U7lO5DWCfVir3qdwGsI/kUf2+Vb0vqGNT7N55HNev3TRtO7D/DC7HXEfHF58zbWvbrjFcXZ2xdvX/YDQaTdt/XrULANDi+bqPvE31247oSWf57rCKWLBgAWbOnIk7d+4gKCgIc+bMQXp6OkJCQnDw4EEAgLu7O6ZMmYKIiAgpjQ0a1EZw8AuYOfM73LyZhGrVKmL16t8QF5eAKVNGS2liX/FvY1/xbWNf8e5TuY19xbtP5Tb2Fe8+ldvYV7z7VG5jX/HuU7mNfcW7T+U29j0aP/wQieTke0hIyFkA2r49Ctev5/x78OCuKFWqpMw8ZftUv29l9i1bshUpKalISLgDANi14xgS4m8BAPoPDEKpUgaEv94FWzYfxOtD/40BgzsiNTUNi7/ZhKdrVUH3nq1M11WuvBdee6Mb5s1djTeHzUS79o0Rfe5P/LxyF4I7N8ez9Ws88n7V71t6FHSyA0gDnRBCyI54WGRkJEJCQtCgQQP4+flh48aN6NWrF7Kzs5GSkoLevXvj/v37WLx4MY4dO4Z169ahS5cuduwpWnNrenoGZs/+AevX70BS0l3Uru2PiIhBaN26sebrfhTYVzzbAPYV1zaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3AawTyuV+1RuA9inlcp9KrcB7NNK5T6V2wD2adW+fTji4hKsnrZt2wJUqeLj4CJzKvepft8WZd/9rBt5nvZSx3dx7epNq6dt+PVTVK5cDgDwxx9x+Gz6Uhw9eh4uLs5o3SYA74ztD+9yXmaXEUJg+Y/bsPTHbYi7kohy5bzQrfsLeGNECFxcrB/75O5czs6vLEfR3re1HsF1kBaZxmOyExzCxamh7IQioeTCY2BgIHQ6HbZv3w6dTodZs2Zh7Nix6Ny5M9atW2c6X1ZWFgICAuDn54fNmzfbsSftC49ERERERERERERERCrJb+FRBVoXHosWFx5l48Lj403J93g8c+YMQkNDodPlHE7bvXt3GI1G9O3b1+x8zs7OGDhwIA4fPiwjk4iIiIiIiIiIiIiIiIj+ouTCY2pqKgwGg+lzL6+cQ7crVapkcV5fX1+kpKQ4rI2IiIiIiIiIiIiIiIiILFl/gWXJfH19cfXqVdPn7u7uGDZsGKpUqWJx3ri4OHh7ezsyj4iIiIiIiIiIiIiIiIqADjrZCaSBkguPTZo0wb59+0yfGwwGzJ8/3+p5d+3ahfr16zsqjYiIiIiIiIiIiIiIiIisUHLhcdKkSbh8+XKB50tMTISnpyf69+/vgCoiIiIiIiIiIiIiIiIiyotOCCFkR8gTLTuAiIiIiIiIiIiIiOiRup91Q3ZCvtydy8lOyEct2QFPvCzjcdkJDuHs1EB2QpFQ8ohHIiIiIiIiIiIiIiIievLodHyPx8eZk+wAIiIiIiIiIiIiIiIiInr88YhHIiIiIiIiIiIiIqJiRO2XMgVWXLwoOyFPfWvwpVaJtOARj0RERERERERERERERESkGRceiYiIiIiIiIiIiIiIiEgzvtQqERERERERERERERERKYLHzD3OeO8RERERERERERERERERkWZceCQiIiIiIiIiIiIiIiIizbjwSERERERERERERERERESa8T0eiYiIiIiIiIiIiIiISAk66GQnkAZceNQoIyMTn3++BGvXbkdy8l3Uru2Pt98ehBdeaCQ7DQD7imsbwL7i2gawTyuV+1RuA9inlcp9KrcB7NNK5T6V2wD2aaVyn8ptAPu0UrlP5TaAfVqp3KdyG8A+rVTuU7kNAO7du4+FC3/G8ePROHkyGklJdzFtWgR69QqSnQZA/dtPdt+Opb9i23cbUKGaL0Z9Od603Wg04tDGvTj4y17cupoIFzdXVKrph7Yvv4iqdaubzvfzZ0twdGtUntc/9vuP4FmudFF+CURPJGVfanXNmjXo2bMnBgwYgKionB8OFy9exMsvv4yqVauiUqVK6Nq1K/bs2SO18/33Z2PRojXo1i0QEya8jhIlnPDGGx/h0KHTUrtysa94tgHsK65tAPu0UrlP5TaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3AawTyuV+1RuA9inlcp9KrcB7NNK5T6V2wDg9u1k/Oc/y3DxYixq165e8AUcTPXbT2ZfUuId7Fq+Ba5urhanbV64Fuvn/gQf/4oIfr0nXujVHjeuJGDhuC9w5dxl0/madm6J0LGDzD/eHQQXvSvKV/XloiNREdEJIYTsiIf98ssv6Nq1Kzw8PODh4YHk5GRs27YNPXv2REZGBlq1aoXMzEzs3r0baWlp2Lp1K9q0aWPHnqI1dZ44EY0+fd7BuHFDER7eCwCQnp6Brl3fgre3F5Yt+1TT9WvFvuLZxr7i28a+4t2nchv7inefym3sK959Krexr3j3qdzGvuLdp3Ib+4p3n8pt7CvefSq35crIyERS0l2UL18GJ0+eR+/eY5Q54lH126+o+1ZcvJjv6cunLUJq0l0YjQKpyXdNRzxmZ2djSuh7qPVcPfSfMNR0/tvXb2Lm0Mlo0b0NugwPzfN6L5+6gAVjv0DQkC4I7N/J6nn61gi24yuiR8ko1Fh8L2pOunqyE4qEkkc8/vvf/0bDhg0RFxeHq1evYsiQIQgJCUGFChVw/vx5rFmzBhs2bMCZM2fg6+uLjz/+WErnpk17UKKEE/r1+/sHkV7vit69O+Lo0d9x7VqilK5c7CuebQD7imsbwD6tVO5TuQ1gn1Yq96ncBrBPK5X7VG4D2KeVyn0qtwHs00rlPpXbAPZppXKfym0A+7RSuU/ltlyuri4oX76M7AyrVL/9ZPbFnPwDZ/53HC8N62VxmjErG5npmfAoU8pse8nSHtA56eDi6pLvdZ/YcRg6nQ4B7Zo80mYi+puSC4+nTp1CWFgYSpXK+eExevRoJCYm4h//+AfKlPl7UFSpUgUjRowwvRSro509exH+/pXh4WEw2x4QUOuv0y/JyDJhn/1UbsvZP/vspXJbzv7Zp4XKfSq35eyffVqo3KdyW87+2aeFyn0qt+Xsn31aqNynclvO/tmnhcp9Krfl7J99Wqjcp3Jbzv7Zp4XKfSq3PQ5Uv/1k9RmzjYicvwpNglvAt3oli9Nd9K6oUrsajm45gOO/HcKdhFu4fikOP3/2I9w9DGj6Uss8rzs7Kxundh+D3zP+KOPjXST99KjonpCP4slZdoA12dnZcHNzM32e++/chcgHeXp6OqzrYYmJt6z+xUzutoSEm45OMsM++6ncBrBPC5XbAPZppXKfym0A+7RSuU/lNoB9Wqncp3IbwD6tVO5TuQ1gn1Yq96ncBrBPK5X7VG4D2KeVyn0qtz0OVL/9ZPUd/GUPkhJuo8PUkXmep/e4wVgxbRFWfvq9aVsZX2+89lkEylYsl+fl/jh8FqnJ99CgXdNH2kxE5pQ84rFOnTrYsGGD6fP169cDACIjIy3O+/PPP6NmzZoFXmd6ejqSk5PNPtLTMzR1pqVlwNXKodt6vavpdJnYZz+V23L3zz77qNyWu3/22U/lPpXbcvfPPvup3KdyW+7+2Wc/lftUbsvdP/vsp3Kfym25+2ef/VTuU7ktd//ss5/KfSq35e6fffZTuU/ltseB6refjL7U5HvY9v0vaPtyJ5Qs7ZHn+fTuepSvVhHNu7XGy//3KrqN7AOj0YgfJy/EvaS7eV7u+I7DKOFcAs+2afTI24nob0ouPP7jH//A+vXr0aJFC4SEhGDMmDHo0KEDDAYDunfvjoULF+Lrr79Ghw4dsGPHDoSHhxd4ndOmTYOXl5fZx7RpX2nqdHNzRUZGpsX23AVNNzdXTdevFfvsp3Jb7v7ZZx+V23L3zz77qdynclvu/tlnP5X7VG7L3T/77Kdyn8ptuftnn/1U7lO5LXf/7LOfyn0qt+Xun332U7lP5bbc/bPPfir3qdz2OFD99pPRt3XxBriXMqB5SJs8z5OdnY1v/zkPbgY3dH2zN+q+0ADNurbC0Klv4va1G/jfyt+sXi79fjp+33cKNRvXgcGz5CNvJ6K/KflSq3379kV8fDzmzp2Ly5cvIzQ0FHPmzIG7uzt69+6N119/HQDg5OSE8PBwDB8+vMDrHD9+PMaMGWO2Ta//U1Nn+fJlER9veUh5YuJtAECFCnJfJ5p99lO5DWCfFiq3AezTSuU+ldsA9mmlcp/KbQD7tFK5T+U2gH1aqdynchvAPq1U7lO5DWCfVir3qdwGsE8rlftUbnscqH77ObrvZlwCDm3ai85v9ELKrSTT9qyMTGRnGXE7/ib0BjdcvxCHhJhreOn1HmaX965cAeX8fPDnmYtWr//svhPITM9AAF9m9bGg0xXf9z98Eih5xCMAjBo1CufOncO1a9ewdOlSlCtXDiVLlsTGjRtx4cIF7N27F9evX8fXX38NJ6eCvwy9Xg9PT0+zj9zDwu1Vp051xMTE4e7dVLPtx4+fAwA880x1TdevFfvsp3IbwD4tVG4D2KeVyn0qtwHs00rlPpXbAPZppXKfym0A+7RSuU/lNoB9Wqncp3IbwD6tVO5TuQ1gn1Yq96nc9jhQ/fZzdF/yzSQIo8CGL1dhZthk08eVc5dxMy4BM8MmY8eSzbh7JwUAIIzC4jqMWdkwZhutXv+J7Yfh6q5HnRbPPtJuIrKk7MJjfrKysuDs7AyDwSC1Izj4BWRnG7F8+SbTtoyMTPz881Y0aFAbFSuWl1jHvuLaBrCvuLYB7NNK5T6V2wD2aaVyn8ptAPu0UrlP5TaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3AawTyuV+1Ruexyofvs5uq9CtYp4+YNwi48K1XzhVaEMXv4gHI1fbAHvyhUAACd3HjG7/NU/YnEjLgEVn6picd337tzFhaPn8EzLALjyJYCJipySL7UKAAsWLMDMmTNx584dBAUFYc6cOUhPT0e3bt1w6NAhAIC7uzumTJmCiIgIKY0NGtRGcPALmDnzO9y8mYRq1Spi9erfEBeXgClTRktpYl/xb2Nf8W1jX/HuU7mNfcW7T+U29hXvPpXb2Fe8+1RuY1/x7lO5jX3Fu0/lNvYV7z6V2x70ww+RSE6+h4SEnJcN3b49Ctev5/x78OCuKFVKzvv9qX77ObqvpJcH6rYMsNi+b81OADA77alGtXF0axTSUtNQs3FtpNxKxoF1u+Hi6oLnewZaXMfJXUdgzDaiQbsmj7ybiCzphBCWxyRLFhkZiZCQEDRo0AB+fn7YuHEjevXqhezsbKSkpKB3795ITU3Fd999h2PHjmHdunXo0qWLHXuK1tyanp6B2bN/wPr1O5CUdBe1a/sjImIQWrdurPm6HwX2Fc82gH3FtQ1gn1Yq96ncBrBPK5X7VG4D2KeVyn0qtwHs00rlPpXbAPZppXKfym0A+7RSuU/lNoB9Wqncp3JbrvbtwxEXl2D1tG3bFqBKFR8HF/1N9duvKPtWXLT+XowPWzhuDlKT72LUl+NN2zLTM/C/VdtxcucR3Im/iRLOzqhWrwY6vNLZ6hGPX/9jFm5dv4FxP3wMpxIFvwhk3xrBtn8hVCQEzspOcAgdnpGdUCSUXHgMDAyETqfD9u3bodPpMGvWLIwdOxadO3fGunXrTOfLyspCQEAA/Pz8sHnzZjv2pH3hkYiIiIiIiIiIiIiIbGfrwqMMXHiUT+Cc7ASH0KG27IQioeR7PJ45cwahoaHQ6XQAgO7du8NoNKJv375m53N2dsbAgQNx+PBhGZlERERERERERERERERE9BclFx5TU1NhMBhMn3t5eQEAKlWqZHFeX19fpKSkOKyNiIiIiIiIiIiIiIiIiCwpufDo6+uLq1evmj53d3fHsGHDUKWK5eszx8XFwdvb25F5RERERERERERERERERPQQZ9kB1jRp0gT79u0zfW4wGDB//nyr5921axfq16/vqDQiIiIiIiIiIiIiIiIqIjroZCeQBkoe8Thp0iSMGjWqwPMlJibC09MT4eHhDqgiIiIiIiIiIiIiIiIikuM///kP/P394ebmhubNmyMqKkp2kgWdEELIjpAnWnYAEREREREREREREdETZcXFi7IT8tS3RrDsBHpi1m5qFercy5cvxyuvvIIvv/wSzZs3x+zZs/HTTz/h3LlzqFChQhE1Fp6SRzwSERERERERERERERERUY6ZM2fi9ddfx9ChQ1G3bl18+eWXMBgM+Oabb2SnmeHCIxEREREREREREREREZEDpaenIzk52ewjPT3d6nkzMjJw+PBhBAUFmbY5OTkhKCgI+/btc1SybQQ9EmlpaWLixIkiLS1NdopV7LOfym1CsE8rlftUbhOCfVqp3KdymxDs00rlPpXbhGCfVir3qdwmBPu0UrlP5TYh2KeFym1CsE8rlftUbhOCfVqp3KdymxDs00rlPpXbiAoyceJEAcDsY+LEiVbPGxcXJwCIvXv3mm0fO3asaNasmQNqbfeEv8fjo5OcnAwvLy8kJSXB09NTdo4F9tlP5TaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3AawTyuV+1RuA9inlcp9KrcB7NNC5TaAfVqp3KdyG8A+rVTuU7kNYJ9WKvep3EZUkPT0dIsjHPV6PfR6vcV5r169isqVK2Pv3r14/vnnTdvHjRuHnTt34sCBA0Xeaytn2QFERERERERERERERERET5K8FhmtKVeuHEqUKIH4+Hiz7fHx8fD19S2KPLvxPR6JiIiIiIiIiIiIiIiIFOXq6oomTZpg27Ztpm1GoxHbtm0zOwJSBTzikYiIiIiIiIiIiIiIiEhhY8aMwZAhQ9C0aVM0a9YMs2fPxr179zB06FDZaWa48PiI6PV6TJw40ebDYh2NffZTuQ1gn1Yq96ncBrBPK5X7VG4D2KeVyn0qtwHs00rlPpXbAPZppXKfym0A+7RQuQ1gn1Yq96ncBrBPK5X7VG4D2KeVyn0qtxE9av369UNiYiI+/PBDXL9+HQ0bNsSmTZvg4+MjO82MTgghZEcQERERERERERERERER0eON7/FIRERERERERERERERERJpx4ZGIiIiIiIiIiIiIiIiINOPCIxERERERERERERERERFpxoVHIiIiIiIiIiIiIiIiItKMC48apaen47333kOlSpXg7u6O5s2bY8uWLbKzAAB3797FxIkTERwcjLJly0Kn02HRokWys0wOHjyIt956C/Xq1UPJkiVRtWpV9O3bF9HR0bLTcPr0afTp0wc1atSAwWBAuXLl0KZNG6xfv152Wp6mTJkCnU6HZ599VnYKduzYAZ1OZ/Vj//79svMAAEeOHEFISAjKli0Lg8GAZ599Fl988YXsLISFheV52+l0OsTFxclOxPnz59G/f39UqVIFBoMBderUweTJk5Gamio7DQBw+PBhBAcHw9PTE6VKlUKnTp1w7Ngxh3cU5mfw2bNnERwcDA8PD5QtWxaDBw9GYmKiEn1RUVF488030aRJE7i4uECn0xVpl61tRqMRixYtQkhICPz8/FCyZEk8++yz+OSTT5CWlia9DwD++9//IjAwED4+PtDr9ahevTqGDh2KmJgYJfoelJmZibp160Kn02HGjBlK9OX187BOnTrS24Cc/4Pz589Hw4YN4e7uDm9vb7Rv3x7Hjx+X3pffHOnYsaP0PgBYsWIFWrRogdKlS8Pb2xuBgYHYsGFDkbUVtm/u3Ll45plnoNfrUblyZYwZMwb37t0rsrbC/F7s6Jlha5uMeWFrn8yZYevtJ2tm2POYzFEzw9Y2GfOiMH2AnJlha5+smVGY28/RM6MwbY6eF0Dhnq+Q8TjD1j5Zc8OWPllzw9bbTtbMsOe5Mkc+zrC1T9bcKMzt5+i5YWubrJlRmNtOxuMMIrLkLDvgcRcWFoaVK1fi7bffxtNPP41Fixahc+fO2L59O1q1aiW17caNG5g8eTKqVq2KBg0aYMeOHVJ7HjZ9+nTs2bMHffr0QUBAAK5fv465c+eicePG2L9/v9QFtMuXLyMlJQVDhgxBpUqVkJqailWrViEkJARfffUV3njjDWlt1ly5cgVTp05FyZIlZaeYGT16NJ577jmzbTVr1pRU87dff/0V3bp1Q6NGjfDBBx/Aw8MDFy5cwJUrV2SnYdiwYQgKCjLbJoTA8OHD4e/vj8qVK0sqyxEbG4tmzZrBy8sLb731FsqWLYt9+/Zh4sSJOHz4MNauXSu178iRI2jVqhX8/PwwceJEGI1GzJs3D4GBgYiKikLt2rUd1mLrz+ArV66gTZs28PLywtSpU3H37l3MmDEDJ0+eRFRUFFxdXaX2/fLLL1iwYAECAgJQo0YNh/xxiC1tqampGDp0KFq0aIHhw4ejQoUKpv+L27Ztw2+//VZkT17YetsdPXoU1atXR0hICMqUKYNLly7hv//9LyIjI3H8+HFUqlRJat+D5syZgz///LNIeh5WmD69Xo8FCxaYbfPy8lKi7dVXX8WSJUvwyiuv4K233sK9e/dw9OhRJCQkSO/7/vvvLbYdOnQIn3/+OTp16iS9b86cORg9ejS6dOmCf/3rX0hLS8OiRYvQtWtXrFq1Cr169ZLa99577+Hf//43evfujYiICJw5cwZz5szB6dOnsXnz5iJps/X3Yhkzw9Y2GfPC1j6ZM8PW20/WzLDnMZmjZkZh2hw9LwrbJ2Nm2Nona2bY2idjZtjaJmNeALY/XyHrcYatfbLmhi19suaGrbedrJlhz3NljnycUZg+GXOjMH2Onhu2tsmaGbb2yXqcQURWCLLbgQMHBADx6aefmrbdv39fPPXUU+L555+XWJYjLS1NXLt2TQghxMGDBwUA8e2338qNesCePXtEenq62bbo6Gih1+vFwIEDJVXlLSsrSzRo0EDUrl1bdoqFfv36ifbt24vAwEBRr1492Tli+/btAoD46aefZKdYSEpKEj4+PqJnz54iOztbdo5Ndu/eLQCIKVOmyE4RU6ZMEQDEqVOnzLa/8sorAoC4deuWpLIcnTt3FmXKlBE3btwwbbt69arw8PAQvXr1cmiLrT+DR4wYIdzd3cXly5dN27Zs2SIAiK+++kp63/Xr10VqaqoQQoiRI0cKR/zqYEtbenq62LNnj8VlP/roIwFAbNmyRWpfXg4dOiQAiGnTpinTFx8fL7y8vMTkyZMtfq+R2TdkyBBRsmTJIm2xt2358uUCgPj555+V7LMmPDxc6HQ6ERsbK73v6aefFs8995wwGo2mbUlJScLDw0OEhIRI7bt69apwdnYWgwcPNts+Z84cAUCsW7euSNps/b1YxsywtU3GvLC1T+bM0PKYxxEzo7B9jpwZtrbJmBdC2N4na2Zo+b/niJlha5+MmWFLm6x5kRdrz1fIepxha5+suWHNw30y50ZBbXlxxMywJr8+Rz/OsLVP1tywxlqfrLnxMFv/7zliZlhjrU/W4wwissSXWtVg5cqVKFGihNlfpLi5uSE8PBz79u1DbGysxLqcv97x9fWV2pCfli1bWvyF3dNPP4169erh7NmzkqryVqJECfj5+eHOnTuyU8zs2rULK1euxOzZs2WnWJWSkoKsrCzZGSY//vgj4uPjMWXKFDg5OeHevXswGo2ys/L1448/QqfTYcCAAbJTkJycDADw8fEx216xYkU4OTkV2V/N2mr37t0ICgqCt7e3aVvFihURGBiIyMhI3L1712Ettv4MXrVqFbp27YqqVauatgUFBaFWrVpYsWKF9D4fHx+4u7sXWYc1trS5urqiZcuWFtt79uwJAEU6R7TMV39/fwAo0llS2L73338ftWvXxqBBg4qs6UGF7cvOzjb97ClqtrbNnDkTzZo1Q8+ePWE0Gov8JdVy2ft/Lz09HatWrUJgYCCqVKlSBGU5bO1LTk5GhQoVzI4U8PT0hIeHR5H+vLGlb9++fcjKykL//v3Ntud+vmzZsiJps/X3Yhkzw9Y2GfMCsK1P5szQ8pjHETOjsH2OnBmFbXPkvABs75M1M+z9v+eomWFrn4yZYUubrHmRF2vPV8h6nGFrn6y5Yc3DfTLnRkFteXHEzLAmvz5HP86wJr8+R88Na6z1yZobtrQ9zFEzwxprfbIeZxCRJS48anD06FHUqlULnp6eZtubNWsGAFLeU+xxJ4RAfHw8ypUrJzsFAHDv3j3cuHEDFy5cwKxZs7Bx40Z06NBBdpZJdnY2Ro0ahddeew3169eXnWNh6NCh8PT0hJubG9q1a4dDhw7JTsLWrVvh6emJuLg41K5dGx4eHvD09MSIESOK/D1+7JGZmYkVK1agZcuWpgcSMrVt2xYAEB4ejmPHjiE2NhbLly/H/PnzMXr0aOkv95uenm71l0mDwYCMjAycOnVKQlXe4uLikJCQgKZNm1qc1qxZMxw9elRC1ePt+vXrAKDMHAGAmzdvIiEhAYcOHcLQoUMBQJlZEhUVhcWLF2P27NkOe1+dwkhNTYWnpye8vLxQtmxZjBw50qF/QGBNcnIyoqKi8Nxzz+Gf//wnvLy84OHhgRo1ajj8STxb/fLLL7hz5w4GDhwoOwVAzizZtGkT5syZg5iYGPz+++8YOXIkkpKSEBERIbUtPT0dACxmicFgAJDzPsKO8vDvxSrNDNV+Z3+YrX2yZkZ+fSrMjLz6VJgZebWpMi8e7lNtZtjyvSFzZljrU2VmPNymwrzI7/kKFWaG6s+n2NPnqLlha5usmWFLn8yZYUufzLmRX5/suVHY7wtHz4yC+lSZGUQEvtSqFvXq1RPt27e32H769GkBQHz55ZcSqqxT8aVWrfn+++8FALFw4ULZKUIIIYYNGyYACADCyclJ9O7dW/pLST5o7ty5wsvLSyQkJAghhDIvtbpnzx4RGhoqFi5cKNauXSumTZsmvL29hZubmzhy5IjUtoCAAGEwGITBYBCjRo0Sq1atEqNGjRIARP/+/aW2WbN+/XoBQMybN092isnHH38s3N3dTd8bAMSECRNkZwkhhKhfv76oVauWyMrKMm1LT08XVatWFQDEypUrpXTl9TM4d/t3331ncZmxY8cKACItLU1a38NkvARSYedXUFCQ8PT0FLdv3y7Srly29On1etP3ire3t/jiiy8c0lZQn9FoFM2aNRMvv/yyEEKIS5cuOfwlkPLre//998V7770nli9fLpYuXSqGDBkiAIgXXnhBZGZmSms7cuSI6b708fER8+bNE0uWLBHNmjUTOp1ObNy4scjb8uuzJjQ0VOj1eod9XwiRf198fLzo0KGD2RwpV66c2Lt3r/S+w4cPCwDi448/Ntu+adMmAUB4eHg4rPHh34tVmRnW2h4m+yXzbH1M4eiZkSu/PpkzI78+FWZGXm2y50V+farMjLz6rJExM3JZ61NhZlhrU2Fe5Pd8hQozozDPp8iYG/Y83+OouWFrm6yZUVCf7JlRUJ/suZFfn+y5UdjvC0fPjIL6VJkZRCSEc2EWKcnc/fv3odfrLba7ubmZTifb5f4VyvPPP48hQ4bIzgEAvP322+jduzeuXr2KFStWIDs7GxkZGbKzAOT8ZduHH36IDz74AOXLl5edY6Zly5ZmL0sSEhKC3r17IyAgAOPHj8emTZuktd29exepqakYPnw4vvjiCwBAr169kJGRga+++gqTJ0/G008/La3vYT/++CNcXFzQt29f2Skm/v7+aNOmDUJDQ+Ht7Y0NGzZg6tSp8PX1xVtvvSW17c0338SIESMQHh6OcePGwWg04pNPPsG1a9cAqPdzObenoFli7XSyNHXqVGzduhXz5s1D6dKlZeeYbNy4EWlpaTh79ix++OEHaS+V87BFixbh5MmTWLlypewUq6ZNm2b2ef/+/VGrVi1MmDABK1eutHhpM0fJ/UvomzdvYv/+/WjevDmAnFlXvXp1fPLJJwgODpbSZk1ycjI2bNiAzp07K/N9YTAYULt2bVSpUgVdu3ZFSkoKZs2ahV69emH37t2oWbOmtLbGjRujefPmmD59OipXrox27drh7NmzGDFiBFxcXBw2R6z9XqzKzFDxd/YH2dona2YU1Cd7ZuTVp8LMyKtNlXlhrU+lmWHL94bMmZFXnwozw1qbCvMiv+crVJgZKj+fAhS+z5Fzw9Y2WTOjoD7ZM6OgPtlzI78+2XOjMN8XMmZGQX0qzAwi+ovslc/HGY94fHSuXbsmatSoIfz8/ERcXJzsnDx17NjR4k2KZRk+fLioWbOm2Zveq3LEY1769+8vXF1dzY5Gc7R69eoJAGLnzp1m23fu3CkAiMWLF0sqs5SSkiIMBoPo2rWr7BSTpUuXCnd3d4s3DQ8LCxMGg0HcuHFDUtnf/vnPfwoXFxfTX7c1bdpUTJgwQQAQq1evltLEIx7tZ2vbsmXLhE6nE+Hh4Y4J+0th5+sff/wh3NzcxJw5c4o27C959SUlJQkfHx/x4YcfmrapdsSjNampqcLJyckh93NB37fVq1e3uMzQoUOFi4uL1CMyH/bNN99IOeI7v77g4GCL2Xbz5k1RtmxZ0bdvX+l9V65cES+88IJpjpQoUUKMHTtWNGvWTHh5eRV5W16/F6swM2z9nV3WEY+29smaGYV9zOPomZFXnwozo7C3nSPnhRAFf9/Knhm23n6yZkZ+fbJnRn5tsufFwx58vkKFmZFf38NkHykvRP59suZGLluei3L0zHjQg30qzIz8+vLi6LnxIGvfu7LnhrW2h8maGQ96uE/2zCCiv/E9HjWoWLGi6SiaB+Vuq1SpkqOTHktJSUl46aWXcOfOHWzatEnp26137944ePAgoqOjpXacP38eX3/9NUaPHo2rV68iJiYGMTExSEtLQ2ZmJmJiYnDr1i2pjdb4+fkhIyND6tE+uf+/fHx8zLZXqFABAHD79m2HN+VlzZo1SE1NVeY9uQBg3rx5aNSokcWbhoeEhCA1NVWJ9yScMmUK4uPjsXv3bpw4cQIHDx6E0WgEANSqVUtynbmKFSsCQJ6zpGzZsjza0QZbtmzBK6+8gi5duuDLL7+UnZOvp556Co0aNcKSJUukdsyYMQMZGRno16+faYZcuXIFQM7PwZiYGKX+Ij2Xu7s7vL29pc64vOYIkDNLMjMzlTmqFQCWLFkCLy8vdO3aVXYKAODixYvYtGkTQkJCzLaXLVsWrVq1wp49eySV/a1y5cr43//+h+joaOzatQtXrlzBv//9b8TGxhb5HMnv92LZM0P139lt7ZM1M+y5/Rw5M/Lrkz0z7LntHDkv8utTYWYU5vaTMTPy65M9Mwq67WTOC2sefL5C9swoqE9FefWp8FjDlttO5uOMB/tkz4yC+vIi83HGg30qzI282h6mwuOMB/tkzwwiMseFRw0aNmyI6OhoJCcnm20/cOCA6XTKX1paGrp164bo6GhERkaibt26spPylftyJUlJSVI74uLiYDQaMXr0aFSvXt30ceDAAURHR6N69eqYPHmy1EZrLl68CDc3N3h4eEhraNKkCYCc2/BBV69eBQClXrZ2yZIl8PDwsPilSab4+HhkZ2dbbM/MzAQAZGVlOTrJqjJlyqBVq1aoX78+AGDr1q2oUqUK6tSpI7nMXOXKlVG+fHkcOnTI4rSoqCjOERscOHAAPXv2RNOmTbFixQo4O6v/KvL379+XPkf+/PNP3L59G/Xq1TPNkNatWwPIeRmp6tWr48yZM1IbrUlJScGNGzek/qyuVKkSfH19LeYIkDNL3NzcUKpUKQlllq5du4bt27cjNDRUmT9iiI+PB4A8Z4kqcwQAnn76abRu3Rq+vr44c+YMrl27hqCgoCLbX0G/F8ucGar/zm5rn6yZoeX2c8TMKKhP5syw97Zz1LwoqE/2zCjM7SdjZhTUJ3NmFOa2c/S8yMuDz1eo+DhDledT8mKtT5XHGrbedrIeZzzYp+LjDFtuP5mPMx7skz038mt7kCqPMx7se5weZxA9CbjwqEHv3r2RnZ2Nr7/+2rQtPT0d3377LZo3bw4/Pz+JderLzs5Gv379sG/fPvz00094/vnnZSeZJCQkWGzLzMzEd999B3d3d+lPtjz77LNYvXq1xUe9evVQtWpVrF69GuHh4dL6EhMTLbYdP34c69atQ6dOneDkJO9HT+57JS5cuNBs+4IFC+Ds7Iy2bdtKqLKUmJiIrVu3omfPnjAYDLJzTGrVqoWjR49a/LXb0qVL4eTkhICAAElleVu+fDkOHjyIt99+W+r/vbyEhoYiMjISsbGxpm3btm1DdHQ0+vTpI7FMfWfPnkWXLl3g7++PyMhIuLu7y04yycrKsnoEdVRUFE6ePImmTZtKqPrb6NGjLWbIV199BQAICwvD6tWrUb16dWl9aWlpSElJsdj+8ccfQwgh/T0U+/Xrh9jYWGzZssW07caNG1i7di3at2+vzM+aZcuWwWg0KnXkfM2aNeHk5ITly5dDCGHafuXKFezevRuNGjWSWGed0WjEuHHjYDAYMHz48CLZh62/F8uYGSr/zg7Y3idrZtjSJ3Nm2NIna2bY0iZzXtj6f0/WzCjs966jZ4YtfbJmhr0/9xwxLwDbn6+Q9ThD9edTbO2TMTdsaZM5M2zpk/k4w5Y+mXPD1v97MuZGYb9vHT0zbOl7HB9nEBVn6h8WoLDmzZujT58+GD9+PBISElCzZk0sXrwYMTExFosassydOxd37twxHc21fv1600scjBo1Cl5eXtLa3nnnHaxbtw7dunXDrVu38MMPP5idPmjQIEllwLBhw5CcnIw2bdqgcuXKuH79OpYsWYLff/8dn332mdQj9gCgXLly6NGjh8X22bNnA4DV0xypX79+cHd3R8uWLVGhQgWcOXMGX3/9NQwGA/71r39JbWvUqBFeffVVfPPNN8jKykJgYCB27NiBn376CePHj1fmZcOWL1+OrKwspZ4sBoCxY8di48aNaN26Nd566y14e3sjMjISGzduxGuvvSb99tu1axcmT56MTp06wdvbG/v378e3336L4OBgREREOLzHlp/B//znP/HTTz+hXbt2iIiIwN27d/Hpp5+ifv36GDp0qPS+y5cv4/vvvwcA019Mf/LJJwCAatWqYfDgwVLanJyc8OKLL+L27dsYO3YsNmzYYHb5p556qkifHC+oTwgBPz8/9OvXD/Xq1UPJkiVx8uRJfPvtt/Dy8sIHH3xQZG229DVu3BiNGzc2u0xMTAwAoF69ekU+Rwrqu337Nho1aoSXX37ZdKTy5s2b8csvvyA4OBjdu3eX1ubl5YXx48djxYoVCA0NxZgxY+Dl5YUvv/wSmZmZmDp1apG12dqXa8mSJahUqZJD/6imoL7y5cvj1VdfxYIFC9ChQwf06tULKSkpmDdvHu7fv4/x48dL7fPy8kJERATS0tLQsGFDZGZm4scff0RUVBQWL16MqlWrFkmXrb8Xy5gZtrbJmhe29KWkpEibGbb03b17V9rMsKVP1sywpe369evS5oWt3xuyZkZhH287embY0idrZth628mYF4Dtz1fIepxha5+suWFLn6y5YUvbnTt3pM0MW/pkPs6wpS8mJkba3LD1e0PG3Cjs86COnhm29Hl4eEh9nEFED5H4/pLFwv3798W7774rfH19hV6vF88995zYtGmT7CyTatWqmd7o/OGPS5cuSW0LDAzMs032f82lS5eKoKAg4ePjI5ydnUWZMmVEUFCQWLt2rdSuggQGBop69erJzhCff/65aNasmShbtqxwdnYWFStWFIMGDRLnz5+XnSaEECIjI0NMmjRJVKtWTbi4uIiaNWuKWbNmyc4y06JFC1GhQgWRlZUlO8XCgQMHxEsvvSR8fX2Fi4uLqFWrlpgyZYpD3+A8L3/88Yfo1KmTKFeunNDr9aJOnTpi2rRpIj09XUqPrT+DT506JTp16iQMBoMoXbq0GDhwoLh+/boSfdu3b8/zPIGBgdLaLl26lO8MGTJkSJG12dKXnp4uIiIiREBAgPD09BQuLi6iWrVqIjw83CHz1575n3ubfvrpp9L7bt++LQYNGiRq1qwpDAaD0Ov1ol69emLq1KkiIyNDaluuCxcuiJ49ewpPT0/h7u4u2rdvL6Kiooq0rTB9v//+uwAgxowZU+RNhe3LzMwUc+bMEQ0bNhQeHh7Cw8NDtGvXTvz2229K9H377beiQYMGomTJkqJUqVKiQ4cORd5WmN+LHT0zbG2TNS9s6ZM5M2zpkzkz7H1M5oiZYUubzHlRmNtOxswoTJ+MmWFrn4yZYWubjHkhROGer5DxOMPWPllzw5Y+WXPDljaZM8Pe58oc9TjDlj6Zc6Mwt5+j50Zh2mTMDFv7ZD7OICJzOiEeOPaYiIiIiIiIiIiIiIiIiMgOarwBDRERERERERERERERERE91rjwSERERERERERERERERESaceGRiIiIiIiIiIiIiIiIiDTjwiMRERERERERERERERERacaFRyIiIiIiIiIiIiIiIiLSjAuPRERERERERERERERERKQZFx6JiIiIiIiIiIiIiIiISDMuPBIRERERERERERERERGRZlx4JCIiIiIiIiIiIiIiIiLNuPBIRERERFSM6HQ6sw8nJyeULl0arVu3xoIFCyCEkNq3aNEi6HQ6TJo0yWx7WFgYdDodduzYIaXLXm3btoVOp0NMTIxN58/9+sPCwoqkJyYmBjqdDm3bti2S63+Qv78/dDpdke+HiIiIiIiIHh9ceCQiIiIiKoaGDBmCIUOGYODAgahbty727NmD119/HQMGDJCdVmTyWtQkIiIiIiIiIsdwlh1ARERERESP3qJFi8w+37JlCzp37oxly5Zh4MCB6Nq1q5ywPEybNg3vv/8+qlatKjuFiIiIiIiIiOzEIx6JiIiIiJ4AHTt2xODBgwEAa9askRtjRcWKFVGnTh0YDAbZKURERERERERkJy48EhERERE9IRo1agQAiI2NNW3T6XTw9/dHRkYGJk+ejDp16kCv16NHjx6m86SmpmLatGlo1KgRPDw84OHhgRYtWmDx4sV57mvPnj0ICgpCqVKlULp0abz44os4cOBAnufP7z0e7927h+nTp6Np06bw9PREyZIlUadOHYwcORLR0dEAct5rcejQoQCAjz76yOx9Lh8++vPs2bMICwuDn58f9Ho9fHx80L9/f5w+fdpqW3Z2NmbMmIE6derAzc0Nfn5+iIiIQHJycp5fz6Ny7NgxjBs3Dk2aNEH58uWh1+tRo0YNvPnmm7h69Wq+l01OTkZERAT8/Pzg5uaGZ555BrNmzYLRaLR6fnvuZyIiIiIiIqIH8aVWiYiIiIieECkpKQAAvV5vtt1oNKJHjx7YtWsXAgMDERAQAG9vbwBAQkICOnbsiBMnTsDX1xeBgYEQQmDv3r0ICwvDoUOHMGfOHLPri4yMRM+ePZGVlYVmzZqhRo0aOH78ONq0aYOwsLBCNV+7dg0dO3bE6dOnUaZMGbRt2xZ6vR4XL17El19+iaeffhq1atVCcHAwsrKysGfPHjRo0AANGzY0XUfNmjVN/16zZg369++P9PR0NGzYEC1atEBsbCxWrFiB9evXY+PGjWjTpo1Zw6BBg7Bs2TIYDAZ06tQJzs7OWLx4Mfbs2QMXF5dCfT2F9a9//QurVq1CQEAAWrVqBSBnMXL+/PlYs2YNDh06hEqVKllcLj09He3bt8eFCxfQvn17ZGRkYNu2bRgzZgyOHz9usRhrz/1MRERERERE9DAuPBIRERERPQGEEIiMjAQABAQEmJ0WGxsLvV6Pc+fOoXLlymanDR06FCdOnEBERASmT59uWrSMj49H165dMXfuXHTp0gXBwcEAchY3X331VWRlZeGbb74xHYUohMD48eMxffr0QnUPHjwYp0+fRt++fbFw4UJ4eHiYTouJiTEddfj+++/D19cXe/bsQY8ePTBp0iSL64qJicGgQYPg4uKCyMhIBAUFmU7btGkTQkJCMGjQIPzxxx9wdXUFACxfvhzLli1D1apVsXPnTvj7+wPIWajr0KEDDh8+XKivp7CGDRuGzz//HD4+PqZtRqMRn3zyCSZOnIj/+7//wzfffGNxuf379yMgIADnz59HuXLlAAAXLlxAmzZtsHjxYvTo0cPsqNbC3s9ERERERERE1vClVomIiIiIirHs7GycP38er776Kvbt2we9Xm9aDHzQtGnTLBYdjx07hl9++QXPPfccZs6caXakpI+PD77++msAwPz5803bV65cicTERLRp08ZsPzqdDh9//DGqVKlic3tUVBS2bduGChUqYMGCBWaLjgDg7+9vsYian9mzZ+PevXuYNm2a2aIjAAQHB2PEiBGIjY3Fhg0bTNvnzZsHAJg0aZJp0REAKlSogE8//dTmfdurXbt2ZouOAODk5IQPP/wQlStXxrp16/K87IwZM0yLjgDw1FNP4YMPPgAAzJ0717TdnvuZiIiIiIiIyBoe8UhEREREVAzpdDqLbaVKlcLixYvx1FNPWZy3W7duFuf/9ddfAQA9evSAk5Pl3yzmvhdgVFSUadvu3bsBAP3797c4v4uLC3r37o3Zs2fb9DVs3boVAPDyyy+jVKlSNl0mP7lfT69evaye3rp1a3zxxReIiopCz549kZmZif379wMA+vXrZ3H+4OBglClTBrdv39bclp+bN29i3bp1OHXqFO7cuYPs7GwAQGZmJm7evIlbt26hbNmyZpcpW7YsOnbsaHFdL7/8MkaMGIG9e/fCaDTCycnJrvuZiIiIiIiIyBouPBIRERERFUNDhgwBkHN0nKenJ+rXr49evXqhTJkyFuetUKGCxfs+AjkvTQoAEyZMwIQJE/LcV1pamunfV69eBQBUq1bN6nkfPGqwILGxsQBgsVBqr9yv5+EjOx9248YNADkLfhkZGShfvjwMBoPV81arVq1IFx6XLl2KN954A3fv3s3zPCkpKRYLj3nd/l5eXihdujTu3LmD27dvw9vb2677mYiIiIiIiMgaLjwSERERERVDixYtsvm8bm5uVrcbjUYAQKtWrR7Z4p9MuV9P7qJsXpo3b+6InAJdvnwZYWFhAHJeJrZLly6oXLky3N3dAQAtW7bEvn37IITQtJ/idj8TERERERGRPFx4JCIiIiIiq3Lfj7FHjx545513bLpMxYoVAeQsmlmT13Zr/Pz8AAAXLlyw+TL5qVKlCi5cuIDPPvsM3t7eBZ7f29sbrq6uSExMxP37900Lfg/6888/H0mbNb/88gsyMjLw7rvvIiIiwuL0ixcv5nnZvLqSk5Nx584duLu7o3Tp0gDsu5+JiIiIiIiIrLF8Aw8iIiIiIiLA9B6Bq1evtvkyrVu3BgCsWLHC4rSsrCysWrXK5usKCgoCkPNyo/m91GguV1dX036sKezX4+LiYjr60drX8+uvv+LWrVs2XZc9cl/CNXdh8EG7du1CfHx8npe9efMmtm3bZrF92bJlAIDnn38eJUqUAGDf/UxERERERERkDRceiYiIiIjIqubNm6Njx47Ys2cPRo4cieTkZIvzHD9+HJs2bTJ93qdPH3h7e2PHjh1YvHixabsQAhMnTizUEYLNmjVDu3btkJCQgDfeeAP37t0zOz0mJgYnT540fV6pUiUAwLlz56xe3zvvvAN3d3e8++67+Pnnny1OT09Px8qVK3HlyhXTthEjRgCARfuNGzcwduxYm78We9SqVQsA8MMPP5h97XFxcRg+fHiBl3/33Xdx8+ZN0+eXLl3C5MmTAQAjR440bbfnfiYiIiIiIiKyRie0viEIEREREREpQ6fTAYDN7/un0+lQrVo1xMTEWD09ISEBwcHBOHr0KEqXLo2GDRuiUqVKSEpKwokTJxAbG4uIiAjMnj3bdJm1a9ciNDQU2dnZaN68OWrUqIHjx4/j/PnzCAsLw3//+19MnDgRkyZNMl0mLCwMixcvxvbt29G2bVvT9ri4OHTo0AHnzp1D2bJl0apVK+j1ely4cAHHjh3DZ599hrfffhsAkJaWhmrVqiEhIQGBgYGoUaMGnJyc8Oqrr6Jly5amtgEDBiA1NRU1a9bEM888g5IlSyIuLg5HjhzBvXv3cPToUTRs2NDU0LdvX/z0008oWbIkOnToAGdnZ/z222+oUaMGXF1dsX//fly6dAn+/v4F3t6LFi3C0KFDUa5cuXzfT3H16tXw9vZG48aNcfr0afj6+uKFF15AWloatm/fburbu3ev2b5jYmJQvXp1tGjRAhkZGbh48SLat2+PzMxMbNu2DampqRg0aBC+//57zfezv78/Ll++rPk9JomIiIiIiKj44BGPRERERESUpwoVKmDv3r344osvULduXRw9ehQrV67EiRMnUKNGDXz66ad49913zS7TvXt3bN++He3atcOpU6ewYcMGVKxYETt37jQtANqqcuXKOHjwICZPnowqVapgy5Yt2LhxI1JTU/Hmm2+ia9eupvO6ublhw4YN6NixI44dO4ZFixZh4cKFiI6ONms7ceIE3nzzTeh0OmzZsgUbNmxAQkICunXrhhUrVqBu3bpmDT/++COmT5+OypUrY9OmTdi/fz8GDBiA3377DXq93o5bNeeIyQMHDuT5kZ6eDldXV+zevRsjRoyAm5sbIiMjcfbsWYwaNQpbtmyBi4tLntev1+vx22+/YcCAAdi/fz82b94MPz8/zJgxA4sWLbI4vz33MxEREREREdHDeMQjEREREREREREREREREWnGIx6JiIiIiIiIiIiIiIiISDMuPBIRERERERERERERERGRZlx4JCIiIiIiIiIiIiIiIiLNuPBIRERERERERERERERERJpx4ZGIiIiIiIiIiIiIiIiINOPCIxERERERERERERERERFpxoVHIiIiIiIiIiIiIiIiItKMC49EREREREREREREREREpBkXHomIiIiIiIiIiIiIiIhIMy48EhEREREREREREREREZFmXHgkIiIiIiIiIiIiIiIiIs248EhEREREREREREREREREmv0/4lOArUIlCI8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(C=100, gamma='scale', kernel='rbf', random_state=42)\n",
    "\n",
    "svm.fit(X_train, y_train)\n",
    "y_pred = svm.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='weighted')  # Adjusted for multiclass\n",
    "recall = recall_score(y_test, y_pred, average='weighted')  # Adjusted for multiclass\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')  # Adjusted for multiclass\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1-score: {f1:.4f}\")\n",
    "print(\"Confusion Matrix:\")\n",
    "\n",
    "plt.figure(figsize=(25, 15))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='YlGnBu', cbar=True, annot_kws={\"size\": 12})\n",
    "plt.title('Confusion Matrix', fontsize=18)\n",
    "plt.xlabel('Predicted Label', fontsize=15)\n",
    "plt.ylabel('True Label', fontsize=15)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()  \n",
    "# 9824 | 61 9828, 65 9830, 69 9832, 73 9828"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.9851 ± 0.0015\n",
      "Mean F1 Score: 0.9851 ± 0.0015\n",
      "Mean Recall: 0.9851 ± 0.0015\n",
      "Mean Precision: 0.9852 ± 0.0015\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "import numpy as np\n",
    "\n",
    "# Giả sử df là DataFrame đã được định nghĩa\n",
    "X = df.drop(columns=[\"label\"])\n",
    "y = df[\"label\"]\n",
    "\n",
    "# Định nghĩa mô hình SVM\n",
    "svm = SVC(C=100, gamma='scale', kernel='rbf')\n",
    "\n",
    "# Sử dụng StratifiedKFold để tạo k-folds\n",
    "kf = StratifiedKFold(n_splits=10)\n",
    "\n",
    "# Để lưu trữ các kết quả của từng fold\n",
    "accuracies = []\n",
    "f1_scores = []\n",
    "recalls = []\n",
    "precisions = []\n",
    "\n",
    "# Thực hiện cross-validation\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    # Huấn luyện mô hình\n",
    "    svm.fit(X_train, y_train)\n",
    "    \n",
    "    # Dự đoán trên tập kiểm tra\n",
    "    y_pred = svm.predict(X_test)\n",
    "    \n",
    "    # Tính toán các chỉ số\n",
    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
    "    f1_scores.append(f1_score(y_test, y_pred, average='weighted'))\n",
    "    recalls.append(recall_score(y_test, y_pred, average='weighted'))\n",
    "    precisions.append(precision_score(y_test, y_pred, average='weighted'))\n",
    "\n",
    "# Tính toán trung bình và độ lệch chuẩn\n",
    "accuracy_mean = np.mean(accuracies)\n",
    "accuracy_std = np.std(accuracies)\n",
    "\n",
    "f1_mean = np.mean(f1_scores)\n",
    "f1_std = np.std(f1_scores)\n",
    "\n",
    "recall_mean = np.mean(recalls)\n",
    "recall_std = np.std(recalls)\n",
    "\n",
    "precision_mean = np.mean(precisions)\n",
    "precision_std = np.std(precisions)\n",
    "\n",
    "# In kết quả\n",
    "print(f\"Mean Accuracy: {accuracy_mean:.4f} ± {accuracy_std:.4f}\")\n",
    "print(f\"Mean F1 Score: {f1_mean:.4f} ± {f1_std:.4f}\")\n",
    "print(f\"Mean Recall: {recall_mean:.4f} ± {recall_std:.4f}\")\n",
    "print(f\"Mean Precision: {precision_mean:.4f} ± {precision_std:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# repeated hold out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 0.9824069732491734 ± 0.000971889806163889\n",
      "Mean F1 Score: 0.9823862148140193 ± 0.0009725927772862195\n",
      "Mean Recall: 0.9824069732491734 ± 0.000971889806163889\n",
      "Mean Precision: 0.9824402766710552 ± 0.0009715853247962772\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "\n",
    "X = df.drop(columns=[\"label\"])\n",
    "y = df[\"label\"]\n",
    "svm = SVC(C=100, gamma='scale', kernel='rbf')\n",
    "n_repeats = 50\n",
    "scoring = ['accuracy', 'f1', 'recall', 'precision']\n",
    "results = {metric: [] for metric in scoring}\n",
    "\n",
    "for _ in range(n_repeats):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True, random_state=None)\n",
    "    \n",
    "    svm.fit(X_train, y_train)\n",
    "    y_pred = svm.predict(X_test)\n",
    "    \n",
    "    for metric in scoring:\n",
    "        if metric == 'accuracy':\n",
    "            results[metric].append(accuracy_score(y_test, y_pred))\n",
    "        elif metric == 'f1':\n",
    "            results[metric].append(f1_score(y_test, y_pred, average='weighted'))\n",
    "        elif metric == 'recall':\n",
    "            results[metric].append(recall_score(y_test, y_pred, average='weighted'))\n",
    "        elif metric == 'precision':\n",
    "            results[metric].append(precision_score(y_test, y_pred, average='weighted'))\n",
    "\n",
    "mean_results = {}\n",
    "std_results = {}\n",
    "for metric in scoring:\n",
    "    metric_values = results[metric]\n",
    "    mean_results[metric] = np.mean(metric_values)\n",
    "    std_results[metric] = np.std(metric_values)\n",
    "\n",
    "print(\"Mean Accuracy:\", mean_results['accuracy'], \"±\", std_results['accuracy'])\n",
    "print(\"Mean F1 Score:\", mean_results['f1'], \"±\", std_results['f1'])\n",
    "print(\"Mean Recall:\", mean_results['recall'], \"±\", std_results['recall'])\n",
    "print(\"Mean Precision:\", mean_results['precision'], \"±\", std_results['precision'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC Mean Accuracy: 0.9851 ± 0.0015\n",
      "SVC Mean F1 Score: 0.9851 ± 0.0015\n",
      "SVC Mean Recall: 0.9851 ± 0.0015\n",
      "SVC Mean Precision: 0.9852 ± 0.0015\n",
      "--------------------------------------------------\n",
      "RandomForest Mean Accuracy: 0.9616 ± 0.0019\n",
      "RandomForest Mean F1 Score: 0.9611 ± 0.0019\n",
      "RandomForest Mean Recall: 0.9616 ± 0.0019\n",
      "RandomForest Mean Precision: 0.9618 ± 0.0019\n",
      "--------------------------------------------------\n",
      "LogisticRegression Mean Accuracy: 0.9564 ± 0.0026\n",
      "LogisticRegression Mean F1 Score: 0.9562 ± 0.0026\n",
      "LogisticRegression Mean Recall: 0.9564 ± 0.0026\n",
      "LogisticRegression Mean Precision: 0.9564 ± 0.0027\n",
      "--------------------------------------------------\n",
      "KNN Mean Accuracy: 0.9501 ± 0.0023\n",
      "KNN Mean F1 Score: 0.9496 ± 0.0023\n",
      "KNN Mean Recall: 0.9501 ± 0.0023\n",
      "KNN Mean Precision: 0.9504 ± 0.0023\n",
      "--------------------------------------------------\n",
      "DecisionTree Mean Accuracy: 0.8500 ± 0.0058\n",
      "DecisionTree Mean F1 Score: 0.8500 ± 0.0059\n",
      "DecisionTree Mean Recall: 0.8500 ± 0.0058\n",
      "DecisionTree Mean Precision: 0.8510 ± 0.0060\n",
      "--------------------------------------------------\n",
      "XGBoost Mean Accuracy: 0.9741 ± 0.0036\n",
      "XGBoost Mean F1 Score: 0.9740 ± 0.0036\n",
      "XGBoost Mean Recall: 0.9741 ± 0.0036\n",
      "XGBoost Mean Precision: 0.9742 ± 0.0036\n",
      "--------------------------------------------------\n",
      "ExtraTrees Mean Accuracy: 0.9575 ± 0.0023\n",
      "ExtraTrees Mean F1 Score: 0.9568 ± 0.0024\n",
      "ExtraTrees Mean Recall: 0.9575 ± 0.0023\n",
      "ExtraTrees Mean Precision: 0.9577 ± 0.0022\n",
      "--------------------------------------------------\n",
      "CatBoost Mean Accuracy: 0.9810 ± 0.0017\n",
      "CatBoost Mean F1 Score: 0.9809 ± 0.0017\n",
      "CatBoost Mean Recall: 0.9810 ± 0.0017\n",
      "CatBoost Mean Precision: 0.9810 ± 0.0017\n",
      "--------------------------------------------------\n",
      "HistGradientBoosting Mean Accuracy: 0.9195 ± 0.0547\n",
      "HistGradientBoosting Mean F1 Score: 0.9205 ± 0.0535\n",
      "HistGradientBoosting Mean Recall: 0.9195 ± 0.0547\n",
      "HistGradientBoosting Mean Precision: 0.9233 ± 0.0508\n",
      "--------------------------------------------------\n",
      "GaussianNB Mean Accuracy: 0.7007 ± 0.0092\n",
      "GaussianNB Mean F1 Score: 0.7057 ± 0.0088\n",
      "GaussianNB Mean Recall: 0.7007 ± 0.0092\n",
      "GaussianNB Mean Precision: 0.7567 ± 0.0080\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, HistGradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "import numpy as np\n",
    "\n",
    "X = df.drop(columns=[\"label\"])\n",
    "y = df[\"label\"]\n",
    "\n",
    "models = {\n",
    "    'SVC': SVC(C=100, gamma='scale', kernel='rbf'),\n",
    "    'RandomForest': RandomForestClassifier(n_estimators=100),\n",
    "    'LogisticRegression': LogisticRegression(max_iter=10000),\n",
    "    'KNN': KNeighborsClassifier(n_neighbors=3, weights='distance', algorithm='auto', leaf_size=30, p=2, metric='manhattan', n_jobs=-1),\n",
    "    'DecisionTree': DecisionTreeClassifier(),\n",
    "    'XGBoost': XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),\n",
    "    'ExtraTrees': ExtraTreesClassifier(n_estimators=100),\n",
    "    'CatBoost': CatBoostClassifier(verbose=0),\n",
    "    'HistGradientBoosting': HistGradientBoostingClassifier(),\n",
    "    'GaussianNB': GaussianNB()\n",
    "}\n",
    "\n",
    "kf = StratifiedKFold(n_splits=10)\n",
    "\n",
    "results = {model_name: {'accuracy': [], 'f1': [], 'recall': [], 'precision': []} for model_name in models}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    for train_index, test_index in kf.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "        \n",
    "        model.fit(X_train, y_train)\n",
    "        \n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        results[model_name]['accuracy'].append(accuracy_score(y_test, y_pred))\n",
    "        results[model_name]['f1'].append(f1_score(y_test, y_pred, average='weighted'))\n",
    "        results[model_name]['recall'].append(recall_score(y_test, y_pred, average='weighted'))\n",
    "        results[model_name]['precision'].append(precision_score(y_test, y_pred, average='weighted'))\n",
    "\n",
    "for model_name in models:\n",
    "    accuracy_mean = np.mean(results[model_name]['accuracy'])\n",
    "    accuracy_std = np.std(results[model_name]['accuracy'])\n",
    "    \n",
    "    f1_mean = np.mean(results[model_name]['f1'])\n",
    "    f1_std = np.std(results[model_name]['f1'])\n",
    "    \n",
    "    recall_mean = np.mean(results[model_name]['recall'])\n",
    "    recall_std = np.std(results[model_name]['recall'])\n",
    "    \n",
    "    precision_mean = np.mean(results[model_name]['precision'])\n",
    "    precision_std = np.std(results[model_name]['precision'])\n",
    "    \n",
    "    print(f\"{model_name} Mean Accuracy: {accuracy_mean:.4f} ± {accuracy_std:.4f}\")\n",
    "    print(f\"{model_name} Mean F1 Score: {f1_mean:.4f} ± {f1_std:.4f}\")\n",
    "    print(f\"{model_name} Mean Recall: {recall_mean:.4f} ± {recall_std:.4f}\")\n",
    "    print(f\"{model_name} Mean Precision: {precision_mean:.4f} ± {precision_std:.4f}\")\n",
    "    print(\"-\" * 50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC Mean Accuracy: 0.9823 ± 0.0005\n",
      "SVC Mean F1 Score: 0.9822 ± 0.0005\n",
      "SVC Mean Recall: 0.9823 ± 0.0005\n",
      "SVC Mean Precision: 0.9823 ± 0.0005\n",
      "--------------------------------------------------\n",
      "RandomForest Mean Accuracy: 0.9568 ± 0.0013\n",
      "RandomForest Mean F1 Score: 0.9561 ± 0.0013\n",
      "RandomForest Mean Recall: 0.9568 ± 0.0013\n",
      "RandomForest Mean Precision: 0.9567 ± 0.0013\n",
      "--------------------------------------------------\n",
      "LogisticRegression Mean Accuracy: 0.9539 ± 0.0011\n",
      "LogisticRegression Mean F1 Score: 0.9537 ± 0.0011\n",
      "LogisticRegression Mean Recall: 0.9539 ± 0.0011\n",
      "LogisticRegression Mean Precision: 0.9538 ± 0.0010\n",
      "--------------------------------------------------\n",
      "KNN Mean Accuracy: 0.9445 ± 0.0017\n",
      "KNN Mean F1 Score: 0.9440 ± 0.0017\n",
      "KNN Mean Recall: 0.9445 ± 0.0017\n",
      "KNN Mean Precision: 0.9446 ± 0.0018\n",
      "--------------------------------------------------\n",
      "DecisionTree Mean Accuracy: 0.8374 ± 0.0033\n",
      "DecisionTree Mean F1 Score: 0.8377 ± 0.0031\n",
      "DecisionTree Mean Recall: 0.8374 ± 0.0033\n",
      "DecisionTree Mean Precision: 0.8384 ± 0.0030\n",
      "--------------------------------------------------\n",
      "XGBoost Mean Accuracy: 0.9715 ± 0.0010\n",
      "XGBoost Mean F1 Score: 0.9713 ± 0.0010\n",
      "XGBoost Mean Recall: 0.9715 ± 0.0010\n",
      "XGBoost Mean Precision: 0.9714 ± 0.0010\n",
      "--------------------------------------------------\n",
      "ExtraTrees Mean Accuracy: 0.9540 ± 0.0011\n",
      "ExtraTrees Mean F1 Score: 0.9533 ± 0.0011\n",
      "ExtraTrees Mean Recall: 0.9540 ± 0.0011\n",
      "ExtraTrees Mean Precision: 0.9542 ± 0.0011\n",
      "--------------------------------------------------\n",
      "CatBoost Mean Accuracy: 0.9791 ± 0.0009\n",
      "CatBoost Mean F1 Score: 0.9791 ± 0.0009\n",
      "CatBoost Mean Recall: 0.9791 ± 0.0009\n",
      "CatBoost Mean Precision: 0.9791 ± 0.0009\n",
      "--------------------------------------------------\n",
      "HistGradientBoosting Mean Accuracy: 0.8719 ± 0.0705\n",
      "HistGradientBoosting Mean F1 Score: 0.8745 ± 0.0674\n",
      "HistGradientBoosting Mean Recall: 0.8719 ± 0.0705\n",
      "HistGradientBoosting Mean Precision: 0.8805 ± 0.0611\n",
      "--------------------------------------------------\n",
      "GaussianNB Mean Accuracy: 0.7010 ± 0.0040\n",
      "GaussianNB Mean F1 Score: 0.7059 ± 0.0042\n",
      "GaussianNB Mean Recall: 0.7010 ± 0.0040\n",
      "GaussianNB Mean Precision: 0.7566 ± 0.0033\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, HistGradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "X = df.drop(columns=[\"label\"])\n",
    "y = df[\"label\"]\n",
    "\n",
    "models = {\n",
    "    'SVC': SVC(C=100, gamma='scale', kernel='rbf'),\n",
    "    'RandomForest': RandomForestClassifier(n_estimators=100),\n",
    "    'LogisticRegression': LogisticRegression(max_iter=10000),\n",
    "    'KNN': KNeighborsClassifier(n_neighbors=3, weights='distance', algorithm='auto', leaf_size=30, p=2, metric='manhattan', n_jobs=-1),\n",
    "    'DecisionTree': DecisionTreeClassifier(),\n",
    "    'XGBoost': XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),\n",
    "    'ExtraTrees': ExtraTreesClassifier(n_estimators=100),\n",
    "    'CatBoost': CatBoostClassifier(verbose=0),\n",
    "    'HistGradientBoosting': HistGradientBoostingClassifier(),\n",
    "    'GaussianNB': GaussianNB()\n",
    "}\n",
    "\n",
    "n_repeats = 10\n",
    "scoring = ['accuracy', 'f1', 'recall', 'precision']\n",
    "results = {model_name: {metric: [] for metric in scoring} for model_name in models}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    for _ in range(n_repeats):\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True, random_state=None)\n",
    "        \n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        for metric in scoring:\n",
    "            if metric == 'accuracy':\n",
    "                results[model_name][metric].append(accuracy_score(y_test, y_pred))\n",
    "            elif metric == 'f1':\n",
    "                results[model_name][metric].append(f1_score(y_test, y_pred, average='weighted'))\n",
    "            elif metric == 'recall':\n",
    "                results[model_name][metric].append(recall_score(y_test, y_pred, average='weighted'))\n",
    "            elif metric == 'precision':\n",
    "                results[model_name][metric].append(precision_score(y_test, y_pred, average='weighted'))\n",
    "\n",
    "mean_results = {model_name: {} for model_name in models}\n",
    "std_results = {model_name: {} for model_name in models}\n",
    "\n",
    "for model_name in models:\n",
    "    for metric in scoring:\n",
    "        metric_values = results[model_name][metric]\n",
    "        mean_results[model_name][metric] = np.mean(metric_values)\n",
    "        std_results[model_name][metric] = np.std(metric_values)\n",
    "\n",
    "for model_name in models:\n",
    "    print(f\"{model_name} Mean Accuracy: {mean_results[model_name]['accuracy']:.4f} ± {std_results[model_name]['accuracy']:.4f}\")\n",
    "    print(f\"{model_name} Mean F1 Score: {mean_results[model_name]['f1']:.4f} ± {std_results[model_name]['f1']:.4f}\")\n",
    "    print(f\"{model_name} Mean Recall: {mean_results[model_name]['recall']:.4f} ± {std_results[model_name]['recall']:.4f}\")\n",
    "    print(f\"{model_name} Mean Precision: {mean_results[model_name]['precision']:.4f} ± {std_results[model_name]['precision']:.4f}\")\n",
    "    print(\"-\" * 50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55448 entries, 0 to 55447\n",
      "Data columns (total 70 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   std                 55448 non-null  float64\n",
      " 1   mean                55448 non-null  float64\n",
      " 2   solidity            55448 non-null  float64\n",
      " 3   hist_hue5           55448 non-null  float64\n",
      " 4   std_v               55448 non-null  float64\n",
      " 5   canny_edges_mean    55448 non-null  float64\n",
      " 6   hist_hue2           55448 non-null  float64\n",
      " 7   canny_edges_std     55448 non-null  float64\n",
      " 8   mean_H              55448 non-null  float64\n",
      " 9   feature_181         55448 non-null  float64\n",
      " 10  hist_hue11          55448 non-null  float64\n",
      " 11  std_U               55448 non-null  float64\n",
      " 12  hist_hue10          55448 non-null  float64\n",
      " 13  entropy             55448 non-null  float64\n",
      " 14  harris_formula_sum  55448 non-null  float64\n",
      " 15  H_sqrt              55448 non-null  float64\n",
      " 16  corner_area_mean    55448 non-null  float64\n",
      " 17  hist_hue3           55448 non-null  float64\n",
      " 18  hist_hue6           55448 non-null  float64\n",
      " 19  a_sqrt              55448 non-null  float64\n",
      " 20  std_a               55448 non-null  float64\n",
      " 21  std_H               55448 non-null  float64\n",
      " 22  hist_hue4           55448 non-null  float64\n",
      " 23  mean_a              55448 non-null  float64\n",
      " 24  correlation         55448 non-null  float64\n",
      " 25  brightness_hsv      55448 non-null  float64\n",
      " 26  hist_saturation2    55448 non-null  float64\n",
      " 27  std_b               55448 non-null  float64\n",
      " 28  hist_hue1           55448 non-null  float64\n",
      " 29  lbp_std             55448 non-null  float64\n",
      " 30  contrast            55448 non-null  float64\n",
      " 31  hist_hue45          55448 non-null  float64\n",
      " 32  hist_hue7           55448 non-null  float64\n",
      " 33  hist_saturation3    55448 non-null  float64\n",
      " 34  sobel_std           55448 non-null  float64\n",
      " 35  NHI                 55448 non-null  float64\n",
      " 36  roberts_std         55448 non-null  float64\n",
      " 37  mean_R              55448 non-null  float64\n",
      " 38  hist_hue9           55448 non-null  float64\n",
      " 39  std_B               55448 non-null  float64\n",
      " 40  NRI                 55448 non-null  float64\n",
      " 41  NSI                 55448 non-null  float64\n",
      " 42  gabor_std1          55448 non-null  float64\n",
      " 43  fft_mean            55448 non-null  float64\n",
      " 44  log_std             55448 non-null  float64\n",
      " 45  NGI                 55448 non-null  float64\n",
      " 46  R_sqrt              55448 non-null  float64\n",
      " 47  hist_hue8           55448 non-null  float64\n",
      " 48  hist_hue43          55448 non-null  float64\n",
      " 49  S_sqrt              55448 non-null  float64\n",
      " 50  sobel_mean          55448 non-null  float64\n",
      " 51  mean_S              55448 non-null  float64\n",
      " 52  hist_saturation4    55448 non-null  float64\n",
      " 53  roberts_mean        55448 non-null  float64\n",
      " 54  hist_value2         55448 non-null  float64\n",
      " 55  v_sqrt              55448 non-null  float64\n",
      " 56  hist_saturation1    55448 non-null  float64\n",
      " 57  mean_v              55448 non-null  float64\n",
      " 58  entropy_std         55448 non-null  float64\n",
      " 59  NvI                 55448 non-null  float64\n",
      " 60  NaI                 55448 non-null  float64\n",
      " 61  energy              55448 non-null  float64\n",
      " 62  std_R               55448 non-null  float64\n",
      " 63  hist_hue39          55448 non-null  float64\n",
      " 64  b_sqrt              55448 non-null  float64\n",
      " 65  hist_hue44          55448 non-null  float64\n",
      " 66  NBI                 55448 non-null  float64\n",
      " 67  hist_value1         55448 non-null  float64\n",
      " 68  mean_b              55448 non-null  float64\n",
      " 69  label               55448 non-null  int64  \n",
      "dtypes: float64(69), int64(1)\n",
      "memory usage: 29.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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