diff --git "a/notebooks/.ipynb_checkpoints/feature_exploration-checkpoint.ipynb" "b/notebooks/.ipynb_checkpoints/feature_exploration-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/.ipynb_checkpoints/feature_exploration-checkpoint.ipynb"
@@ -0,0 +1,3818 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9d9cb688",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(23, 102)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>log</th>\n",
+       "      <th>variant_entropy</th>\n",
+       "      <th>normalized_variant_entropy</th>\n",
+       "      <th>sequence_entropy</th>\n",
+       "      <th>normalized_sequence_entropy</th>\n",
+       "      <th>sequence_entropy_linear_forgetting</th>\n",
+       "      <th>normalized_sequence_entropy_linear_forgetting</th>\n",
+       "      <th>sequence_entropy_exponential_forgetting</th>\n",
+       "      <th>normalized_sequence_entropy_exponential_forgetting</th>\n",
+       "      <th>n_traces</th>\n",
+       "      <th>...</th>\n",
+       "      <th>entropy_k_block_diff_1</th>\n",
+       "      <th>entropy_k_block_diff_3</th>\n",
+       "      <th>entropy_k_block_diff_5</th>\n",
+       "      <th>entropy_k_block_ratio_1</th>\n",
+       "      <th>entropy_k_block_ratio_3</th>\n",
+       "      <th>entropy_k_block_ratio_5</th>\n",
+       "      <th>entropy_knn_3</th>\n",
+       "      <th>entropy_knn_5</th>\n",
+       "      <th>entropy_knn_7</th>\n",
+       "      <th>Log Nature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BPIC15_2</td>\n",
+       "      <td>2.405122e+05</td>\n",
+       "      <td>0.627973</td>\n",
+       "      <td>2.858769e+05</td>\n",
+       "      <td>0.602371</td>\n",
+       "      <td>1.505466e+05</td>\n",
+       "      <td>0.317217</td>\n",
+       "      <td>1.853129e+05</td>\n",
+       "      <td>0.390473</td>\n",
+       "      <td>832</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>7.105</td>\n",
+       "      <td>5.545</td>\n",
+       "      <td>5.039</td>\n",
+       "      <td>4.721</td>\n",
+       "      <td>Real</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BPI_Challenge_2018</td>\n",
+       "      <td>1.156384e+07</td>\n",
+       "      <td>0.712079</td>\n",
+       "      <td>2.114626e+07</td>\n",
+       "      <td>0.570688</td>\n",
+       "      <td>1.414023e+07</td>\n",
+       "      <td>0.381612</td>\n",
+       "      <td>1.557608e+07</td>\n",
+       "      <td>0.420362</td>\n",
+       "      <td>43809</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.010</td>\n",
+       "      <td>1.855</td>\n",
+       "      <td>0.511</td>\n",
+       "      <td>1.403</td>\n",
+       "      <td>3.572</td>\n",
+       "      <td>2.001</td>\n",
+       "      <td>7.849</td>\n",
+       "      <td>7.371</td>\n",
+       "      <td>7.067</td>\n",
+       "      <td>Real</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Receipt_WABO_CoSeLoG</td>\n",
+       "      <td>2.382326e+03</td>\n",
+       "      <td>0.689363</td>\n",
+       "      <td>1.829627e+04</td>\n",
+       "      <td>0.235532</td>\n",
+       "      <td>7.814868e+03</td>\n",
+       "      <td>0.100603</td>\n",
+       "      <td>1.072870e+04</td>\n",
+       "      <td>0.138113</td>\n",
+       "      <td>1434</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.672</td>\n",
+       "      <td>2.966</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>1.484</td>\n",
+       "      <td>2.966</td>\n",
+       "      <td>2.966</td>\n",
+       "      <td>3.260</td>\n",
+       "      <td>2.845</td>\n",
+       "      <td>2.584</td>\n",
+       "      <td>Real</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BPIC15_3</td>\n",
+       "      <td>2.981464e+05</td>\n",
+       "      <td>0.661781</td>\n",
+       "      <td>3.975043e+05</td>\n",
+       "      <td>0.605676</td>\n",
+       "      <td>2.241393e+05</td>\n",
+       "      <td>0.341521</td>\n",
+       "      <td>2.657571e+05</td>\n",
+       "      <td>0.404934</td>\n",
+       "      <td>1409</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.007</td>\n",
+       "      <td>6.780</td>\n",
+       "      <td>6.780</td>\n",
+       "      <td>6.780</td>\n",
+       "      <td>6.780</td>\n",
+       "      <td>6.780</td>\n",
+       "      <td>5.701</td>\n",
+       "      <td>5.212</td>\n",
+       "      <td>4.900</td>\n",
+       "      <td>Real</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BPI_Challenge_2019</td>\n",
+       "      <td>1.690369e+06</td>\n",
+       "      <td>0.645530</td>\n",
+       "      <td>7.477256e+06</td>\n",
+       "      <td>0.328029</td>\n",
+       "      <td>7.298458e+06</td>\n",
+       "      <td>0.320185</td>\n",
+       "      <td>7.300663e+06</td>\n",
+       "      <td>0.320282</td>\n",
+       "      <td>251734</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.041</td>\n",
+       "      <td>1.530</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>3.244</td>\n",
+       "      <td>1.913</td>\n",
+       "      <td>7.333</td>\n",
+       "      <td>6.882</td>\n",
+       "      <td>6.601</td>\n",
+       "      <td>Real</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 102 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    log  variant_entropy  normalized_variant_entropy  \\\n",
+       "0              BPIC15_2     2.405122e+05                    0.627973   \n",
+       "1    BPI_Challenge_2018     1.156384e+07                    0.712079   \n",
+       "2  Receipt_WABO_CoSeLoG     2.382326e+03                    0.689363   \n",
+       "3              BPIC15_3     2.981464e+05                    0.661781   \n",
+       "4    BPI_Challenge_2019     1.690369e+06                    0.645530   \n",
+       "\n",
+       "   sequence_entropy  normalized_sequence_entropy  \\\n",
+       "0      2.858769e+05                     0.602371   \n",
+       "1      2.114626e+07                     0.570688   \n",
+       "2      1.829627e+04                     0.235532   \n",
+       "3      3.975043e+05                     0.605676   \n",
+       "4      7.477256e+06                     0.328029   \n",
+       "\n",
+       "   sequence_entropy_linear_forgetting  \\\n",
+       "0                        1.505466e+05   \n",
+       "1                        1.414023e+07   \n",
+       "2                        7.814868e+03   \n",
+       "3                        2.241393e+05   \n",
+       "4                        7.298458e+06   \n",
+       "\n",
+       "   normalized_sequence_entropy_linear_forgetting  \\\n",
+       "0                                       0.317217   \n",
+       "1                                       0.381612   \n",
+       "2                                       0.100603   \n",
+       "3                                       0.341521   \n",
+       "4                                       0.320185   \n",
+       "\n",
+       "   sequence_entropy_exponential_forgetting  \\\n",
+       "0                             1.853129e+05   \n",
+       "1                             1.557608e+07   \n",
+       "2                             1.072870e+04   \n",
+       "3                             2.657571e+05   \n",
+       "4                             7.300663e+06   \n",
+       "\n",
+       "   normalized_sequence_entropy_exponential_forgetting  n_traces  ...  \\\n",
+       "0                                           0.390473        832  ...   \n",
+       "1                                           0.420362      43809  ...   \n",
+       "2                                           0.138113       1434  ...   \n",
+       "3                                           0.404934       1409  ...   \n",
+       "4                                           0.320282     251734  ...   \n",
+       "\n",
+       "   entropy_k_block_diff_1  entropy_k_block_diff_3  entropy_k_block_diff_5  \\\n",
+       "0                   7.105                   7.105                   7.105   \n",
+       "1                  -0.010                   1.855                   0.511   \n",
+       "2                   2.672                   2.966                   0.804   \n",
+       "3                  -0.007                   6.780                   6.780   \n",
+       "4                  -0.041                   1.530                   0.840   \n",
+       "\n",
+       "   entropy_k_block_ratio_1  entropy_k_block_ratio_3  entropy_k_block_ratio_5  \\\n",
+       "0                    7.105                    7.105                    7.105   \n",
+       "1                    1.403                    3.572                    2.001   \n",
+       "2                    1.484                    2.966                    2.966   \n",
+       "3                    6.780                    6.780                    6.780   \n",
+       "4                    0.620                    3.244                    1.913   \n",
+       "\n",
+       "   entropy_knn_3  entropy_knn_5  entropy_knn_7  Log Nature  \n",
+       "0          5.545          5.039          4.721        Real  \n",
+       "1          7.849          7.371          7.067        Real  \n",
+       "2          3.260          2.845          2.584        Real  \n",
+       "3          5.701          5.212          4.900        Real  \n",
+       "4          7.333          6.882          6.601        Real  \n",
+       "\n",
+       "[5 rows x 102 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "dmf = pd.read_csv(\"../output/features/real_event_logs.csv\", index_col=None)\n",
+    "#dmf = dmf.drop(['Unnamed: 0'], axis=1)\n",
+    "print(dmf.shape)\n",
+    "dmf.head()\n",
+    "# Include complexity features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "851d06cf",
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>variant_entropy</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.072162</td>\n",
+       "      <td>0.209197</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000385</td>\n",
+       "      <td>0.015079</td>\n",
+       "      <td>0.025995</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_variant_entropy</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.579834</td>\n",
+       "      <td>0.205284</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.457637</td>\n",
+       "      <td>0.604760</td>\n",
+       "      <td>0.686986</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sequence_entropy</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.088289</td>\n",
+       "      <td>0.222556</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.006431</td>\n",
+       "      <td>0.013057</td>\n",
+       "      <td>0.028613</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_sequence_entropy</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.620993</td>\n",
+       "      <td>0.343005</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.413454</td>\n",
+       "      <td>0.629387</td>\n",
+       "      <td>0.970122</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sequence_entropy_linear_forgetting</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.088578</td>\n",
+       "      <td>0.230392</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.005289</td>\n",
+       "      <td>0.010100</td>\n",
+       "      <td>0.023332</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    count      mean       std  min       25%  \\\n",
+       "variant_entropy                      23.0  0.072162  0.209197  0.0  0.000385   \n",
+       "normalized_variant_entropy           23.0  0.579834  0.205284  0.0  0.457637   \n",
+       "sequence_entropy                     23.0  0.088289  0.222556  0.0  0.006431   \n",
+       "normalized_sequence_entropy          23.0  0.620993  0.343005  0.0  0.413454   \n",
+       "sequence_entropy_linear_forgetting   23.0  0.088578  0.230392  0.0  0.005289   \n",
+       "\n",
+       "                                         50%       75%  max  \n",
+       "variant_entropy                     0.015079  0.025995  1.0  \n",
+       "normalized_variant_entropy          0.604760  0.686986  1.0  \n",
+       "sequence_entropy                    0.013057  0.028613  1.0  \n",
+       "normalized_sequence_entropy         0.629387  0.970122  1.0  \n",
+       "sequence_entropy_linear_forgetting  0.010100  0.023332  1.0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "scaler = preprocessing.MinMaxScaler()\n",
+    "min_max_scaler = preprocessing.MinMaxScaler()\n",
+    "\n",
+    "def scaleColumns(df, cols_to_scale):\n",
+    "    for col in cols_to_scale:\n",
+    "        df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
+    "    return df\n",
+    "\n",
+    "scaled_dmf = scaleColumns(dmf, dmf.drop(['log', 'Log Nature'], axis=1).columns)\n",
+    "stats = scaled_dmf.describe().transpose()\n",
+    "\n",
+    "#print(stats.index)\n",
+    "stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "741de31b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "THRESHOLD= 0.25\n",
+    "sorted_features['var'] = stats.apply(lambda x: x['std']**2, axis=1)\n",
+    "sorted_features = sorted_features.sort_values(\"var\", ascending=True)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15, 10))\n",
+    "plt.xlabel(\"sorted feature\")\n",
+    "plt.ylabel(\"std\")\n",
+    "#plt.xticks(sorted_features.index)\n",
+    "\n",
+    "plt.plot(range(len(sorted_features)), sorted_features['std'], marker='o')\n",
+    "plt.axhline(y=0.25, color='r', linestyle='-')\n",
+    "\n",
+    "filtered_feat = sorted_features[sorted_features['std']>=THRESHOLD].index\n",
+    "len(filtered_feat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "1ef30b30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_activities_max</th>\n",
+       "      <th>end_activities_variance</th>\n",
+       "      <th>trace_len_skewness_hist</th>\n",
+       "      <th>trace_len_skewness</th>\n",
+       "      <th>activities_max</th>\n",
+       "      <th>end_activities_q1</th>\n",
+       "      <th>activities_q3</th>\n",
+       "      <th>activities_std</th>\n",
+       "      <th>trace_len_hist1</th>\n",
+       "      <th>end_activities_std</th>\n",
+       "      <th>...</th>\n",
+       "      <th>entropy_k_block_diff_3</th>\n",
+       "      <th>ratio_top_10_variants</th>\n",
+       "      <th>trace_len_median</th>\n",
+       "      <th>entropy_k_block_ratio_1</th>\n",
+       "      <th>entropy_k_block_diff_5</th>\n",
+       "      <th>ratio_top_20_variants</th>\n",
+       "      <th>trace_len_q1</th>\n",
+       "      <th>ratio_top_50_variants</th>\n",
+       "      <th>ratio_top_75_variants</th>\n",
+       "      <th>ratio_unique_traces_per_trace</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>start_activities_max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.983424</td>\n",
+       "      <td>0.510680</td>\n",
+       "      <td>0.510680</td>\n",
+       "      <td>0.628179</td>\n",
+       "      <td>-0.090011</td>\n",
+       "      <td>0.582388</td>\n",
+       "      <td>0.654280</td>\n",
+       "      <td>0.384826</td>\n",
+       "      <td>0.992254</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.353923</td>\n",
+       "      <td>0.409484</td>\n",
+       "      <td>-0.309794</td>\n",
+       "      <td>-0.410932</td>\n",
+       "      <td>-0.294408</td>\n",
+       "      <td>0.405747</td>\n",
+       "      <td>-0.280090</td>\n",
+       "      <td>0.394243</td>\n",
+       "      <td>0.394256</td>\n",
+       "      <td>-0.393977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>end_activities_variance</th>\n",
+       "      <td>0.983424</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.458959</td>\n",
+       "      <td>0.458959</td>\n",
+       "      <td>0.525272</td>\n",
+       "      <td>-0.071941</td>\n",
+       "      <td>0.473409</td>\n",
+       "      <td>0.548543</td>\n",
+       "      <td>0.363145</td>\n",
+       "      <td>0.973173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.289558</td>\n",
+       "      <td>0.397381</td>\n",
+       "      <td>-0.300372</td>\n",
+       "      <td>-0.342097</td>\n",
+       "      <td>-0.234241</td>\n",
+       "      <td>0.370588</td>\n",
+       "      <td>-0.276479</td>\n",
+       "      <td>0.352681</td>\n",
+       "      <td>0.352185</td>\n",
+       "      <td>-0.351606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_skewness_hist</th>\n",
+       "      <td>0.510680</td>\n",
+       "      <td>0.458959</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.860331</td>\n",
+       "      <td>-0.020142</td>\n",
+       "      <td>0.231172</td>\n",
+       "      <td>0.827151</td>\n",
+       "      <td>-0.142547</td>\n",
+       "      <td>0.492269</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.319538</td>\n",
+       "      <td>0.166790</td>\n",
+       "      <td>-0.004567</td>\n",
+       "      <td>-0.385021</td>\n",
+       "      <td>-0.328756</td>\n",
+       "      <td>0.121233</td>\n",
+       "      <td>0.019730</td>\n",
+       "      <td>0.101141</td>\n",
+       "      <td>0.100460</td>\n",
+       "      <td>-0.099458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_skewness</th>\n",
+       "      <td>0.510680</td>\n",
+       "      <td>0.458959</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.860331</td>\n",
+       "      <td>-0.020142</td>\n",
+       "      <td>0.231172</td>\n",
+       "      <td>0.827151</td>\n",
+       "      <td>-0.142547</td>\n",
+       "      <td>0.492269</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.319538</td>\n",
+       "      <td>0.166790</td>\n",
+       "      <td>-0.004567</td>\n",
+       "      <td>-0.385021</td>\n",
+       "      <td>-0.328756</td>\n",
+       "      <td>0.121233</td>\n",
+       "      <td>0.019730</td>\n",
+       "      <td>0.101141</td>\n",
+       "      <td>0.100460</td>\n",
+       "      <td>-0.099458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>activities_max</th>\n",
+       "      <td>0.628179</td>\n",
+       "      <td>0.525272</td>\n",
+       "      <td>0.860331</td>\n",
+       "      <td>0.860331</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.083272</td>\n",
+       "      <td>0.612303</td>\n",
+       "      <td>0.996429</td>\n",
+       "      <td>0.029984</td>\n",
+       "      <td>0.617851</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.348155</td>\n",
+       "      <td>0.190647</td>\n",
+       "      <td>0.044931</td>\n",
+       "      <td>-0.421804</td>\n",
+       "      <td>-0.349853</td>\n",
+       "      <td>0.164998</td>\n",
+       "      <td>0.080486</td>\n",
+       "      <td>0.149258</td>\n",
+       "      <td>0.149243</td>\n",
+       "      <td>-0.148344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>end_activities_q1</th>\n",
+       "      <td>-0.090011</td>\n",
+       "      <td>-0.071941</td>\n",
+       "      <td>-0.020142</td>\n",
+       "      <td>-0.020142</td>\n",
+       "      <td>-0.083272</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.046639</td>\n",
+       "      <td>-0.063032</td>\n",
+       "      <td>0.401540</td>\n",
+       "      <td>-0.047169</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.318846</td>\n",
+       "      <td>0.283839</td>\n",
+       "      <td>-0.329279</td>\n",
+       "      <td>-0.318022</td>\n",
+       "      <td>-0.216124</td>\n",
+       "      <td>0.271773</td>\n",
+       "      <td>-0.319079</td>\n",
+       "      <td>0.258102</td>\n",
+       "      <td>0.257585</td>\n",
+       "      <td>-0.257085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>activities_q3</th>\n",
+       "      <td>0.582388</td>\n",
+       "      <td>0.473409</td>\n",
+       "      <td>0.231172</td>\n",
+       "      <td>0.231172</td>\n",
+       "      <td>0.612303</td>\n",
+       "      <td>0.046639</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.672378</td>\n",
+       "      <td>0.626451</td>\n",
+       "      <td>0.633719</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.459399</td>\n",
+       "      <td>0.296510</td>\n",
+       "      <td>-0.203190</td>\n",
+       "      <td>-0.472406</td>\n",
+       "      <td>-0.383402</td>\n",
+       "      <td>0.350791</td>\n",
+       "      <td>-0.186218</td>\n",
+       "      <td>0.357892</td>\n",
+       "      <td>0.359259</td>\n",
+       "      <td>-0.359788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>activities_std</th>\n",
+       "      <td>0.654280</td>\n",
+       "      <td>0.548543</td>\n",
+       "      <td>0.827151</td>\n",
+       "      <td>0.827151</td>\n",
+       "      <td>0.996429</td>\n",
+       "      <td>-0.063032</td>\n",
+       "      <td>0.672378</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.092188</td>\n",
+       "      <td>0.649804</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.374559</td>\n",
+       "      <td>0.219661</td>\n",
+       "      <td>0.010179</td>\n",
+       "      <td>-0.446197</td>\n",
+       "      <td>-0.366750</td>\n",
+       "      <td>0.198607</td>\n",
+       "      <td>0.053483</td>\n",
+       "      <td>0.183560</td>\n",
+       "      <td>0.183629</td>\n",
+       "      <td>-0.182776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_hist1</th>\n",
+       "      <td>0.384826</td>\n",
+       "      <td>0.363145</td>\n",
+       "      <td>-0.142547</td>\n",
+       "      <td>-0.142547</td>\n",
+       "      <td>0.029984</td>\n",
+       "      <td>0.401540</td>\n",
+       "      <td>0.626451</td>\n",
+       "      <td>0.092188</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.436749</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.447970</td>\n",
+       "      <td>0.374526</td>\n",
+       "      <td>-0.497937</td>\n",
+       "      <td>-0.419779</td>\n",
+       "      <td>-0.299042</td>\n",
+       "      <td>0.456175</td>\n",
+       "      <td>-0.486918</td>\n",
+       "      <td>0.471813</td>\n",
+       "      <td>0.473146</td>\n",
+       "      <td>-0.474428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>end_activities_std</th>\n",
+       "      <td>0.992254</td>\n",
+       "      <td>0.973173</td>\n",
+       "      <td>0.492269</td>\n",
+       "      <td>0.492269</td>\n",
+       "      <td>0.617851</td>\n",
+       "      <td>-0.047169</td>\n",
+       "      <td>0.633719</td>\n",
+       "      <td>0.649804</td>\n",
+       "      <td>0.436749</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.392985</td>\n",
+       "      <td>0.446302</td>\n",
+       "      <td>-0.348237</td>\n",
+       "      <td>-0.442602</td>\n",
+       "      <td>-0.324465</td>\n",
+       "      <td>0.449856</td>\n",
+       "      <td>-0.313528</td>\n",
+       "      <td>0.440095</td>\n",
+       "      <td>0.440227</td>\n",
+       "      <td>-0.440039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_hist2</th>\n",
+       "      <td>0.273830</td>\n",
+       "      <td>0.280674</td>\n",
+       "      <td>-0.152002</td>\n",
+       "      <td>-0.152002</td>\n",
+       "      <td>-0.011685</td>\n",
+       "      <td>0.034058</td>\n",
+       "      <td>0.404388</td>\n",
+       "      <td>0.035337</td>\n",
+       "      <td>0.473301</td>\n",
+       "      <td>0.339153</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.471400</td>\n",
+       "      <td>0.529464</td>\n",
+       "      <td>-0.411308</td>\n",
+       "      <td>-0.423462</td>\n",
+       "      <td>-0.344250</td>\n",
+       "      <td>0.488012</td>\n",
+       "      <td>-0.379066</td>\n",
+       "      <td>0.466637</td>\n",
+       "      <td>0.465813</td>\n",
+       "      <td>-0.464762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_hist3</th>\n",
+       "      <td>-0.134197</td>\n",
+       "      <td>-0.114261</td>\n",
+       "      <td>-0.165286</td>\n",
+       "      <td>-0.165286</td>\n",
+       "      <td>-0.205869</td>\n",
+       "      <td>-0.143193</td>\n",
+       "      <td>-0.153692</td>\n",
+       "      <td>-0.203849</td>\n",
+       "      <td>-0.113372</td>\n",
+       "      <td>-0.115093</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.221953</td>\n",
+       "      <td>0.489331</td>\n",
+       "      <td>-0.398227</td>\n",
+       "      <td>-0.286088</td>\n",
+       "      <td>-0.320360</td>\n",
+       "      <td>0.464521</td>\n",
+       "      <td>-0.322516</td>\n",
+       "      <td>0.455329</td>\n",
+       "      <td>0.454387</td>\n",
+       "      <td>-0.453468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std_variant_occurrence</th>\n",
+       "      <td>0.515343</td>\n",
+       "      <td>0.455728</td>\n",
+       "      <td>-0.123401</td>\n",
+       "      <td>-0.123401</td>\n",
+       "      <td>0.125167</td>\n",
+       "      <td>-0.059618</td>\n",
+       "      <td>0.725710</td>\n",
+       "      <td>0.185710</td>\n",
+       "      <td>0.766575</td>\n",
+       "      <td>0.572387</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.265147</td>\n",
+       "      <td>0.207817</td>\n",
+       "      <td>-0.352143</td>\n",
+       "      <td>-0.255289</td>\n",
+       "      <td>-0.161265</td>\n",
+       "      <td>0.367574</td>\n",
+       "      <td>-0.337717</td>\n",
+       "      <td>0.412857</td>\n",
+       "      <td>0.415973</td>\n",
+       "      <td>-0.418762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>start_activities_iqr</th>\n",
+       "      <td>0.551099</td>\n",
+       "      <td>0.504501</td>\n",
+       "      <td>0.917979</td>\n",
+       "      <td>0.917979</td>\n",
+       "      <td>0.794155</td>\n",
+       "      <td>-0.014051</td>\n",
+       "      <td>0.202410</td>\n",
+       "      <td>0.767316</td>\n",
+       "      <td>-0.181574</td>\n",
+       "      <td>0.550830</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.358504</td>\n",
+       "      <td>0.341720</td>\n",
+       "      <td>-0.136349</td>\n",
+       "      <td>-0.418042</td>\n",
+       "      <td>-0.350923</td>\n",
+       "      <td>0.301336</td>\n",
+       "      <td>-0.051488</td>\n",
+       "      <td>0.281930</td>\n",
+       "      <td>0.281314</td>\n",
+       "      <td>-0.280110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>end_activities_kurtosis</th>\n",
+       "      <td>-0.084362</td>\n",
+       "      <td>-0.016109</td>\n",
+       "      <td>0.004839</td>\n",
+       "      <td>0.004839</td>\n",
+       "      <td>-0.080614</td>\n",
+       "      <td>-0.259556</td>\n",
+       "      <td>-0.408587</td>\n",
+       "      <td>-0.115391</td>\n",
+       "      <td>-0.429077</td>\n",
+       "      <td>-0.136003</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.791028</td>\n",
+       "      <td>-0.646266</td>\n",
+       "      <td>0.646009</td>\n",
+       "      <td>0.764827</td>\n",
+       "      <td>0.830060</td>\n",
+       "      <td>-0.694035</td>\n",
+       "      <td>0.742203</td>\n",
+       "      <td>-0.697840</td>\n",
+       "      <td>-0.698061</td>\n",
+       "      <td>0.698135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>activities_q1</th>\n",
+       "      <td>0.130966</td>\n",
+       "      <td>0.001644</td>\n",
+       "      <td>-0.108268</td>\n",
+       "      <td>-0.108268</td>\n",
+       "      <td>0.193729</td>\n",
+       "      <td>0.280098</td>\n",
+       "      <td>0.564967</td>\n",
+       "      <td>0.239929</td>\n",
+       "      <td>0.368785</td>\n",
+       "      <td>0.161317</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.300322</td>\n",
+       "      <td>0.071864</td>\n",
+       "      <td>-0.208967</td>\n",
+       "      <td>-0.311057</td>\n",
+       "      <td>-0.216334</td>\n",
+       "      <td>0.232969</td>\n",
+       "      <td>-0.218867</td>\n",
+       "      <td>0.275571</td>\n",
+       "      <td>0.278782</td>\n",
+       "      <td>-0.281242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>skewness_variant_occurrence</th>\n",
+       "      <td>0.412067</td>\n",
+       "      <td>0.340044</td>\n",
+       "      <td>0.729327</td>\n",
+       "      <td>0.729327</td>\n",
+       "      <td>0.794430</td>\n",
+       "      <td>-0.027120</td>\n",
+       "      <td>0.242875</td>\n",
+       "      <td>0.769150</td>\n",
+       "      <td>-0.300462</td>\n",
+       "      <td>0.358910</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.153580</td>\n",
+       "      <td>0.015473</td>\n",
+       "      <td>0.192436</td>\n",
+       "      <td>-0.224267</td>\n",
+       "      <td>-0.181436</td>\n",
+       "      <td>-0.057777</td>\n",
+       "      <td>0.186335</td>\n",
+       "      <td>-0.088299</td>\n",
+       "      <td>-0.089221</td>\n",
+       "      <td>0.090874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>end_activities_skewness</th>\n",
+       "      <td>-0.085638</td>\n",
+       "      <td>-0.011941</td>\n",
+       "      <td>0.110247</td>\n",
+       "      <td>0.110247</td>\n",
+       "      <td>-0.030496</td>\n",
+       "      <td>-0.415923</td>\n",
+       "      <td>-0.492149</td>\n",
+       "      <td>-0.078236</td>\n",
+       "      <td>-0.549613</td>\n",
+       "      <td>-0.146411</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.781047</td>\n",
+       "      <td>-0.618607</td>\n",
+       "      <td>0.680351</td>\n",
+       "      <td>0.757497</td>\n",
+       "      <td>0.765602</td>\n",
+       "      <td>-0.684222</td>\n",
+       "      <td>0.763603</td>\n",
+       "      <td>-0.691484</td>\n",
+       "      <td>-0.692054</td>\n",
+       "      <td>0.692535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>end_activities_median</th>\n",
+       "      <td>0.295714</td>\n",
+       "      <td>0.270227</td>\n",
+       "      <td>-0.120207</td>\n",
+       "      <td>-0.120207</td>\n",
+       "      <td>0.041583</td>\n",
+       "      <td>0.682742</td>\n",
+       "      <td>0.600396</td>\n",
+       "      <td>0.104515</td>\n",
+       "      <td>0.776963</td>\n",
+       "      <td>0.375178</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.402268</td>\n",
+       "      <td>0.332030</td>\n",
+       "      <td>-0.442353</td>\n",
+       "      <td>-0.391401</td>\n",
+       "      <td>-0.264005</td>\n",
+       "      <td>0.412936</td>\n",
+       "      <td>-0.437024</td>\n",
+       "      <td>0.426404</td>\n",
+       "      <td>0.427833</td>\n",
+       "      <td>-0.429005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kurtosis_variant_occurrence</th>\n",
+       "      <td>0.512580</td>\n",
+       "      <td>0.423723</td>\n",
+       "      <td>0.832497</td>\n",
+       "      <td>0.832497</td>\n",
+       "      <td>0.896302</td>\n",
+       "      <td>-0.064885</td>\n",
+       "      <td>0.332804</td>\n",
+       "      <td>0.872668</td>\n",
+       "      <td>-0.222644</td>\n",
+       "      <td>0.466580</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.257033</td>\n",
+       "      <td>0.116612</td>\n",
+       "      <td>0.090582</td>\n",
+       "      <td>-0.341894</td>\n",
+       "      <td>-0.276999</td>\n",
+       "      <td>0.068433</td>\n",
+       "      <td>0.106727</td>\n",
+       "      <td>0.045392</td>\n",
+       "      <td>0.044911</td>\n",
+       "      <td>-0.043619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_entropy</th>\n",
+       "      <td>0.788014</td>\n",
+       "      <td>0.682984</td>\n",
+       "      <td>0.479331</td>\n",
+       "      <td>0.479331</td>\n",
+       "      <td>0.712292</td>\n",
+       "      <td>-0.027996</td>\n",
+       "      <td>0.780574</td>\n",
+       "      <td>0.750551</td>\n",
+       "      <td>0.399449</td>\n",
+       "      <td>0.814673</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.613474</td>\n",
+       "      <td>0.584718</td>\n",
+       "      <td>-0.484330</td>\n",
+       "      <td>-0.654473</td>\n",
+       "      <td>-0.558782</td>\n",
+       "      <td>0.636138</td>\n",
+       "      <td>-0.395248</td>\n",
+       "      <td>0.639575</td>\n",
+       "      <td>0.640517</td>\n",
+       "      <td>-0.640714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_trace</th>\n",
+       "      <td>-0.246336</td>\n",
+       "      <td>-0.263862</td>\n",
+       "      <td>0.246474</td>\n",
+       "      <td>0.246474</td>\n",
+       "      <td>0.259748</td>\n",
+       "      <td>-0.194485</td>\n",
+       "      <td>-0.156561</td>\n",
+       "      <td>0.221683</td>\n",
+       "      <td>-0.543472</td>\n",
+       "      <td>-0.301778</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.507782</td>\n",
+       "      <td>-0.739612</td>\n",
+       "      <td>0.829063</td>\n",
+       "      <td>0.525860</td>\n",
+       "      <td>0.465916</td>\n",
+       "      <td>-0.818711</td>\n",
+       "      <td>0.771976</td>\n",
+       "      <td>-0.835934</td>\n",
+       "      <td>-0.836628</td>\n",
+       "      <td>0.837496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_global_block</th>\n",
+       "      <td>-0.065534</td>\n",
+       "      <td>-0.045121</td>\n",
+       "      <td>0.303671</td>\n",
+       "      <td>0.303671</td>\n",
+       "      <td>0.230746</td>\n",
+       "      <td>-0.274134</td>\n",
+       "      <td>-0.276976</td>\n",
+       "      <td>0.179784</td>\n",
+       "      <td>-0.592811</td>\n",
+       "      <td>-0.133919</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.539261</td>\n",
+       "      <td>-0.726966</td>\n",
+       "      <td>0.848891</td>\n",
+       "      <td>0.549797</td>\n",
+       "      <td>0.499204</td>\n",
+       "      <td>-0.807659</td>\n",
+       "      <td>0.691131</td>\n",
+       "      <td>-0.825738</td>\n",
+       "      <td>-0.826435</td>\n",
+       "      <td>0.827099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_1_variants</th>\n",
+       "      <td>0.611446</td>\n",
+       "      <td>0.610239</td>\n",
+       "      <td>0.377794</td>\n",
+       "      <td>0.377794</td>\n",
+       "      <td>0.427250</td>\n",
+       "      <td>0.285483</td>\n",
+       "      <td>0.364196</td>\n",
+       "      <td>0.447450</td>\n",
+       "      <td>0.308390</td>\n",
+       "      <td>0.608020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.532604</td>\n",
+       "      <td>0.702380</td>\n",
+       "      <td>-0.565136</td>\n",
+       "      <td>-0.592046</td>\n",
+       "      <td>-0.595418</td>\n",
+       "      <td>0.639142</td>\n",
+       "      <td>-0.548979</td>\n",
+       "      <td>0.603939</td>\n",
+       "      <td>0.601899</td>\n",
+       "      <td>-0.599992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>activities_mean</th>\n",
+       "      <td>0.689734</td>\n",
+       "      <td>0.570561</td>\n",
+       "      <td>0.608014</td>\n",
+       "      <td>0.608014</td>\n",
+       "      <td>0.903445</td>\n",
+       "      <td>0.000567</td>\n",
+       "      <td>0.882116</td>\n",
+       "      <td>0.934870</td>\n",
+       "      <td>0.343345</td>\n",
+       "      <td>0.705968</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.450543</td>\n",
+       "      <td>0.272442</td>\n",
+       "      <td>-0.093455</td>\n",
+       "      <td>-0.503690</td>\n",
+       "      <td>-0.406734</td>\n",
+       "      <td>0.287503</td>\n",
+       "      <td>-0.062349</td>\n",
+       "      <td>0.281803</td>\n",
+       "      <td>0.282532</td>\n",
+       "      <td>-0.282288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>n_unique_activities</th>\n",
+       "      <td>-0.307299</td>\n",
+       "      <td>-0.231862</td>\n",
+       "      <td>-0.255983</td>\n",
+       "      <td>-0.255983</td>\n",
+       "      <td>-0.310645</td>\n",
+       "      <td>-0.274822</td>\n",
+       "      <td>-0.429265</td>\n",
+       "      <td>-0.344947</td>\n",
+       "      <td>-0.417079</td>\n",
+       "      <td>-0.342842</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.752915</td>\n",
+       "      <td>-0.803954</td>\n",
+       "      <td>0.858650</td>\n",
+       "      <td>0.804961</td>\n",
+       "      <td>0.731855</td>\n",
+       "      <td>-0.838799</td>\n",
+       "      <td>0.661169</td>\n",
+       "      <td>-0.837176</td>\n",
+       "      <td>-0.837057</td>\n",
+       "      <td>0.836612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_hist8</th>\n",
+       "      <td>-0.264695</td>\n",
+       "      <td>-0.211164</td>\n",
+       "      <td>-0.277963</td>\n",
+       "      <td>-0.277963</td>\n",
+       "      <td>-0.301216</td>\n",
+       "      <td>-0.192024</td>\n",
+       "      <td>-0.331976</td>\n",
+       "      <td>-0.313881</td>\n",
+       "      <td>-0.310399</td>\n",
+       "      <td>-0.274991</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.462180</td>\n",
+       "      <td>-0.093397</td>\n",
+       "      <td>0.106830</td>\n",
+       "      <td>0.428788</td>\n",
+       "      <td>0.320296</td>\n",
+       "      <td>-0.109544</td>\n",
+       "      <td>0.194200</td>\n",
+       "      <td>-0.101583</td>\n",
+       "      <td>-0.102420</td>\n",
+       "      <td>0.102941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_harmonic_mean</th>\n",
+       "      <td>-0.252755</td>\n",
+       "      <td>-0.280504</td>\n",
+       "      <td>0.144213</td>\n",
+       "      <td>0.144213</td>\n",
+       "      <td>0.242428</td>\n",
+       "      <td>-0.304785</td>\n",
+       "      <td>-0.062527</td>\n",
+       "      <td>0.215553</td>\n",
+       "      <td>-0.476595</td>\n",
+       "      <td>-0.285225</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.681330</td>\n",
+       "      <td>-0.760508</td>\n",
+       "      <td>0.881421</td>\n",
+       "      <td>0.665910</td>\n",
+       "      <td>0.602829</td>\n",
+       "      <td>-0.802513</td>\n",
+       "      <td>0.967328</td>\n",
+       "      <td>-0.804672</td>\n",
+       "      <td>-0.804297</td>\n",
+       "      <td>0.804721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_sequence_entropy_linear_forgetting</th>\n",
+       "      <td>-0.126553</td>\n",
+       "      <td>-0.094836</td>\n",
+       "      <td>0.334540</td>\n",
+       "      <td>0.334540</td>\n",
+       "      <td>0.216887</td>\n",
+       "      <td>0.290060</td>\n",
+       "      <td>-0.277255</td>\n",
+       "      <td>0.176753</td>\n",
+       "      <td>-0.418566</td>\n",
+       "      <td>-0.168434</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.467868</td>\n",
+       "      <td>-0.555968</td>\n",
+       "      <td>0.634342</td>\n",
+       "      <td>0.439906</td>\n",
+       "      <td>0.450746</td>\n",
+       "      <td>-0.645306</td>\n",
+       "      <td>0.632278</td>\n",
+       "      <td>-0.668865</td>\n",
+       "      <td>-0.669891</td>\n",
+       "      <td>0.670929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_prefix</th>\n",
+       "      <td>-0.287911</td>\n",
+       "      <td>-0.276583</td>\n",
+       "      <td>0.167972</td>\n",
+       "      <td>0.167972</td>\n",
+       "      <td>0.134730</td>\n",
+       "      <td>-0.215524</td>\n",
+       "      <td>-0.287850</td>\n",
+       "      <td>0.087938</td>\n",
+       "      <td>-0.601110</td>\n",
+       "      <td>-0.343492</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.600463</td>\n",
+       "      <td>-0.793830</td>\n",
+       "      <td>0.912679</td>\n",
+       "      <td>0.627180</td>\n",
+       "      <td>0.552172</td>\n",
+       "      <td>-0.875814</td>\n",
+       "      <td>0.784246</td>\n",
+       "      <td>-0.892214</td>\n",
+       "      <td>-0.892898</td>\n",
+       "      <td>0.893662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_k_block_ratio_3</th>\n",
+       "      <td>-0.295388</td>\n",
+       "      <td>-0.238604</td>\n",
+       "      <td>-0.253862</td>\n",
+       "      <td>-0.253862</td>\n",
+       "      <td>-0.256823</td>\n",
+       "      <td>-0.537589</td>\n",
+       "      <td>-0.420225</td>\n",
+       "      <td>-0.289894</td>\n",
+       "      <td>-0.551796</td>\n",
+       "      <td>-0.345309</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.886059</td>\n",
+       "      <td>-0.828878</td>\n",
+       "      <td>0.855525</td>\n",
+       "      <td>0.916695</td>\n",
+       "      <td>0.822976</td>\n",
+       "      <td>-0.865079</td>\n",
+       "      <td>0.818149</td>\n",
+       "      <td>-0.861356</td>\n",
+       "      <td>-0.861200</td>\n",
+       "      <td>0.861102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_k_block_diff_1</th>\n",
+       "      <td>-0.191208</td>\n",
+       "      <td>-0.167327</td>\n",
+       "      <td>-0.235865</td>\n",
+       "      <td>-0.235865</td>\n",
+       "      <td>-0.221442</td>\n",
+       "      <td>-0.054772</td>\n",
+       "      <td>-0.187839</td>\n",
+       "      <td>-0.226468</td>\n",
+       "      <td>-0.092049</td>\n",
+       "      <td>-0.204120</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.524131</td>\n",
+       "      <td>-0.431399</td>\n",
+       "      <td>0.384808</td>\n",
+       "      <td>0.490237</td>\n",
+       "      <td>0.606014</td>\n",
+       "      <td>-0.393154</td>\n",
+       "      <td>0.425139</td>\n",
+       "      <td>-0.375995</td>\n",
+       "      <td>-0.374354</td>\n",
+       "      <td>0.374178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_sequence_entropy_exponential_forgetting</th>\n",
+       "      <td>-0.232849</td>\n",
+       "      <td>-0.195929</td>\n",
+       "      <td>0.243782</td>\n",
+       "      <td>0.243782</td>\n",
+       "      <td>0.127185</td>\n",
+       "      <td>0.143459</td>\n",
+       "      <td>-0.350854</td>\n",
+       "      <td>0.083429</td>\n",
+       "      <td>-0.499940</td>\n",
+       "      <td>-0.282837</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.580380</td>\n",
+       "      <td>-0.686515</td>\n",
+       "      <td>0.745713</td>\n",
+       "      <td>0.571463</td>\n",
+       "      <td>0.557078</td>\n",
+       "      <td>-0.780019</td>\n",
+       "      <td>0.729359</td>\n",
+       "      <td>-0.802473</td>\n",
+       "      <td>-0.803516</td>\n",
+       "      <td>0.804556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>n_unique_end_activities</th>\n",
+       "      <td>-0.210656</td>\n",
+       "      <td>-0.143129</td>\n",
+       "      <td>-0.201001</td>\n",
+       "      <td>-0.201001</td>\n",
+       "      <td>-0.226001</td>\n",
+       "      <td>-0.304703</td>\n",
+       "      <td>-0.395401</td>\n",
+       "      <td>-0.252694</td>\n",
+       "      <td>-0.423647</td>\n",
+       "      <td>-0.254563</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.884300</td>\n",
+       "      <td>-0.784948</td>\n",
+       "      <td>0.745131</td>\n",
+       "      <td>0.877965</td>\n",
+       "      <td>0.913235</td>\n",
+       "      <td>-0.821402</td>\n",
+       "      <td>0.804332</td>\n",
+       "      <td>-0.820785</td>\n",
+       "      <td>-0.820518</td>\n",
+       "      <td>0.820448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>start_activities_skewness</th>\n",
+       "      <td>-0.014424</td>\n",
+       "      <td>0.041578</td>\n",
+       "      <td>-0.173669</td>\n",
+       "      <td>-0.173669</td>\n",
+       "      <td>-0.125777</td>\n",
+       "      <td>-0.291153</td>\n",
+       "      <td>-0.359154</td>\n",
+       "      <td>-0.144055</td>\n",
+       "      <td>-0.301608</td>\n",
+       "      <td>-0.092610</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.856504</td>\n",
+       "      <td>-0.860033</td>\n",
+       "      <td>0.774297</td>\n",
+       "      <td>0.833720</td>\n",
+       "      <td>0.820198</td>\n",
+       "      <td>-0.862088</td>\n",
+       "      <td>0.756824</td>\n",
+       "      <td>-0.862883</td>\n",
+       "      <td>-0.863043</td>\n",
+       "      <td>0.862941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_sequence_entropy</th>\n",
+       "      <td>-0.389177</td>\n",
+       "      <td>-0.351216</td>\n",
+       "      <td>0.068333</td>\n",
+       "      <td>0.068333</td>\n",
+       "      <td>-0.026130</td>\n",
+       "      <td>-0.130382</td>\n",
+       "      <td>-0.421818</td>\n",
+       "      <td>-0.070924</td>\n",
+       "      <td>-0.581672</td>\n",
+       "      <td>-0.445538</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.703591</td>\n",
+       "      <td>-0.823207</td>\n",
+       "      <td>0.847782</td>\n",
+       "      <td>0.725650</td>\n",
+       "      <td>0.665065</td>\n",
+       "      <td>-0.911287</td>\n",
+       "      <td>0.818041</td>\n",
+       "      <td>-0.928235</td>\n",
+       "      <td>-0.929146</td>\n",
+       "      <td>0.930042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_mode</th>\n",
+       "      <td>-0.266332</td>\n",
+       "      <td>-0.260106</td>\n",
+       "      <td>0.004591</td>\n",
+       "      <td>0.004591</td>\n",
+       "      <td>0.061684</td>\n",
+       "      <td>-0.307516</td>\n",
+       "      <td>-0.187181</td>\n",
+       "      <td>0.036688</td>\n",
+       "      <td>-0.454778</td>\n",
+       "      <td>-0.297675</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.824287</td>\n",
+       "      <td>-0.786654</td>\n",
+       "      <td>0.842892</td>\n",
+       "      <td>0.801922</td>\n",
+       "      <td>0.802496</td>\n",
+       "      <td>-0.820828</td>\n",
+       "      <td>0.985198</td>\n",
+       "      <td>-0.819121</td>\n",
+       "      <td>-0.818473</td>\n",
+       "      <td>0.818907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_most_common_variant</th>\n",
+       "      <td>0.215971</td>\n",
+       "      <td>0.199184</td>\n",
+       "      <td>-0.110800</td>\n",
+       "      <td>-0.110800</td>\n",
+       "      <td>-0.083176</td>\n",
+       "      <td>0.218752</td>\n",
+       "      <td>0.237795</td>\n",
+       "      <td>-0.049509</td>\n",
+       "      <td>0.477613</td>\n",
+       "      <td>0.267916</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.644150</td>\n",
+       "      <td>0.831908</td>\n",
+       "      <td>-0.846697</td>\n",
+       "      <td>-0.696758</td>\n",
+       "      <td>-0.622967</td>\n",
+       "      <td>0.890116</td>\n",
+       "      <td>-0.787607</td>\n",
+       "      <td>0.896724</td>\n",
+       "      <td>0.897069</td>\n",
+       "      <td>-0.897362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_k_block_ratio_5</th>\n",
+       "      <td>-0.360969</td>\n",
+       "      <td>-0.287937</td>\n",
+       "      <td>-0.422165</td>\n",
+       "      <td>-0.422165</td>\n",
+       "      <td>-0.426998</td>\n",
+       "      <td>-0.461655</td>\n",
+       "      <td>-0.455464</td>\n",
+       "      <td>-0.448613</td>\n",
+       "      <td>-0.452048</td>\n",
+       "      <td>-0.393977</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.929262</td>\n",
+       "      <td>-0.765697</td>\n",
+       "      <td>0.736494</td>\n",
+       "      <td>0.960882</td>\n",
+       "      <td>0.876058</td>\n",
+       "      <td>-0.786888</td>\n",
+       "      <td>0.784698</td>\n",
+       "      <td>-0.777933</td>\n",
+       "      <td>-0.777599</td>\n",
+       "      <td>0.777367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>start_activities_kurtosis</th>\n",
+       "      <td>-0.073691</td>\n",
+       "      <td>-0.018361</td>\n",
+       "      <td>-0.262776</td>\n",
+       "      <td>-0.262776</td>\n",
+       "      <td>-0.195049</td>\n",
+       "      <td>-0.312515</td>\n",
+       "      <td>-0.387442</td>\n",
+       "      <td>-0.210601</td>\n",
+       "      <td>-0.359485</td>\n",
+       "      <td>-0.142239</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.849717</td>\n",
+       "      <td>-0.822708</td>\n",
+       "      <td>0.758849</td>\n",
+       "      <td>0.832276</td>\n",
+       "      <td>0.827590</td>\n",
+       "      <td>-0.824496</td>\n",
+       "      <td>0.760201</td>\n",
+       "      <td>-0.824777</td>\n",
+       "      <td>-0.824862</td>\n",
+       "      <td>0.824708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_5_variants</th>\n",
+       "      <td>0.424220</td>\n",
+       "      <td>0.430879</td>\n",
+       "      <td>0.208612</td>\n",
+       "      <td>0.208612</td>\n",
+       "      <td>0.221417</td>\n",
+       "      <td>0.290360</td>\n",
+       "      <td>0.262169</td>\n",
+       "      <td>0.246490</td>\n",
+       "      <td>0.313113</td>\n",
+       "      <td>0.453253</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.766608</td>\n",
+       "      <td>0.979523</td>\n",
+       "      <td>-0.793067</td>\n",
+       "      <td>-0.798745</td>\n",
+       "      <td>-0.777589</td>\n",
+       "      <td>0.899778</td>\n",
+       "      <td>-0.746087</td>\n",
+       "      <td>0.860405</td>\n",
+       "      <td>0.857955</td>\n",
+       "      <td>-0.855618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_geometric_mean</th>\n",
+       "      <td>-0.303338</td>\n",
+       "      <td>-0.309011</td>\n",
+       "      <td>0.070028</td>\n",
+       "      <td>0.070028</td>\n",
+       "      <td>0.132227</td>\n",
+       "      <td>-0.320670</td>\n",
+       "      <td>-0.159491</td>\n",
+       "      <td>0.097244</td>\n",
+       "      <td>-0.513081</td>\n",
+       "      <td>-0.341088</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.701625</td>\n",
+       "      <td>-0.847353</td>\n",
+       "      <td>0.988025</td>\n",
+       "      <td>0.714997</td>\n",
+       "      <td>0.640710</td>\n",
+       "      <td>-0.894544</td>\n",
+       "      <td>0.921866</td>\n",
+       "      <td>-0.897428</td>\n",
+       "      <td>-0.897151</td>\n",
+       "      <td>0.897381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_lempel_ziv</th>\n",
+       "      <td>-0.372166</td>\n",
+       "      <td>-0.298315</td>\n",
+       "      <td>-0.218838</td>\n",
+       "      <td>-0.218838</td>\n",
+       "      <td>-0.297340</td>\n",
+       "      <td>-0.237743</td>\n",
+       "      <td>-0.459104</td>\n",
+       "      <td>-0.330634</td>\n",
+       "      <td>-0.443945</td>\n",
+       "      <td>-0.414385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.863986</td>\n",
+       "      <td>-0.908732</td>\n",
+       "      <td>0.883271</td>\n",
+       "      <td>0.894343</td>\n",
+       "      <td>0.860245</td>\n",
+       "      <td>-0.955315</td>\n",
+       "      <td>0.822784</td>\n",
+       "      <td>-0.956670</td>\n",
+       "      <td>-0.956585</td>\n",
+       "      <td>0.956493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_k_block_diff_3</th>\n",
+       "      <td>-0.353923</td>\n",
+       "      <td>-0.289558</td>\n",
+       "      <td>-0.319538</td>\n",
+       "      <td>-0.319538</td>\n",
+       "      <td>-0.348155</td>\n",
+       "      <td>-0.318846</td>\n",
+       "      <td>-0.459399</td>\n",
+       "      <td>-0.374559</td>\n",
+       "      <td>-0.447970</td>\n",
+       "      <td>-0.392985</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.800000</td>\n",
+       "      <td>0.719356</td>\n",
+       "      <td>0.976078</td>\n",
+       "      <td>0.901504</td>\n",
+       "      <td>-0.798434</td>\n",
+       "      <td>0.819318</td>\n",
+       "      <td>-0.782107</td>\n",
+       "      <td>-0.781403</td>\n",
+       "      <td>0.780828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_10_variants</th>\n",
+       "      <td>0.409484</td>\n",
+       "      <td>0.397381</td>\n",
+       "      <td>0.166790</td>\n",
+       "      <td>0.166790</td>\n",
+       "      <td>0.190647</td>\n",
+       "      <td>0.283839</td>\n",
+       "      <td>0.296510</td>\n",
+       "      <td>0.219661</td>\n",
+       "      <td>0.374526</td>\n",
+       "      <td>0.446302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.800000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.869299</td>\n",
+       "      <td>-0.832724</td>\n",
+       "      <td>-0.805754</td>\n",
+       "      <td>0.968464</td>\n",
+       "      <td>-0.812285</td>\n",
+       "      <td>0.944425</td>\n",
+       "      <td>0.942833</td>\n",
+       "      <td>-0.941302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_median</th>\n",
+       "      <td>-0.309794</td>\n",
+       "      <td>-0.300372</td>\n",
+       "      <td>-0.004567</td>\n",
+       "      <td>-0.004567</td>\n",
+       "      <td>0.044931</td>\n",
+       "      <td>-0.329279</td>\n",
+       "      <td>-0.203190</td>\n",
+       "      <td>0.010179</td>\n",
+       "      <td>-0.497937</td>\n",
+       "      <td>-0.348237</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.719356</td>\n",
+       "      <td>-0.869299</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.744796</td>\n",
+       "      <td>0.672842</td>\n",
+       "      <td>-0.914434</td>\n",
+       "      <td>0.883990</td>\n",
+       "      <td>-0.916323</td>\n",
+       "      <td>-0.916030</td>\n",
+       "      <td>0.916115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_k_block_ratio_1</th>\n",
+       "      <td>-0.410932</td>\n",
+       "      <td>-0.342097</td>\n",
+       "      <td>-0.385021</td>\n",
+       "      <td>-0.385021</td>\n",
+       "      <td>-0.421804</td>\n",
+       "      <td>-0.318022</td>\n",
+       "      <td>-0.472406</td>\n",
+       "      <td>-0.446197</td>\n",
+       "      <td>-0.419779</td>\n",
+       "      <td>-0.442602</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.976078</td>\n",
+       "      <td>-0.832724</td>\n",
+       "      <td>0.744796</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.903243</td>\n",
+       "      <td>-0.830485</td>\n",
+       "      <td>0.801267</td>\n",
+       "      <td>-0.812599</td>\n",
+       "      <td>-0.811931</td>\n",
+       "      <td>0.811224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>entropy_k_block_diff_5</th>\n",
+       "      <td>-0.294408</td>\n",
+       "      <td>-0.234241</td>\n",
+       "      <td>-0.328756</td>\n",
+       "      <td>-0.328756</td>\n",
+       "      <td>-0.349853</td>\n",
+       "      <td>-0.216124</td>\n",
+       "      <td>-0.383402</td>\n",
+       "      <td>-0.366750</td>\n",
+       "      <td>-0.299042</td>\n",
+       "      <td>-0.324465</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.901504</td>\n",
+       "      <td>-0.805754</td>\n",
+       "      <td>0.672842</td>\n",
+       "      <td>0.903243</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.794496</td>\n",
+       "      <td>0.768813</td>\n",
+       "      <td>-0.778036</td>\n",
+       "      <td>-0.776764</td>\n",
+       "      <td>0.776143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_20_variants</th>\n",
+       "      <td>0.405747</td>\n",
+       "      <td>0.370588</td>\n",
+       "      <td>0.121233</td>\n",
+       "      <td>0.121233</td>\n",
+       "      <td>0.164998</td>\n",
+       "      <td>0.271773</td>\n",
+       "      <td>0.350791</td>\n",
+       "      <td>0.198607</td>\n",
+       "      <td>0.456175</td>\n",
+       "      <td>0.449856</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.798434</td>\n",
+       "      <td>0.968464</td>\n",
+       "      <td>-0.914434</td>\n",
+       "      <td>-0.830485</td>\n",
+       "      <td>-0.794496</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.851612</td>\n",
+       "      <td>0.996390</td>\n",
+       "      <td>0.995989</td>\n",
+       "      <td>-0.995577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trace_len_q1</th>\n",
+       "      <td>-0.280090</td>\n",
+       "      <td>-0.276479</td>\n",
+       "      <td>0.019730</td>\n",
+       "      <td>0.019730</td>\n",
+       "      <td>0.080486</td>\n",
+       "      <td>-0.319079</td>\n",
+       "      <td>-0.186218</td>\n",
+       "      <td>0.053483</td>\n",
+       "      <td>-0.486918</td>\n",
+       "      <td>-0.313528</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.819318</td>\n",
+       "      <td>-0.812285</td>\n",
+       "      <td>0.883990</td>\n",
+       "      <td>0.801267</td>\n",
+       "      <td>0.768813</td>\n",
+       "      <td>-0.851612</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.851273</td>\n",
+       "      <td>-0.850790</td>\n",
+       "      <td>0.851144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_50_variants</th>\n",
+       "      <td>0.394243</td>\n",
+       "      <td>0.352681</td>\n",
+       "      <td>0.101141</td>\n",
+       "      <td>0.101141</td>\n",
+       "      <td>0.149258</td>\n",
+       "      <td>0.258102</td>\n",
+       "      <td>0.357892</td>\n",
+       "      <td>0.183560</td>\n",
+       "      <td>0.471813</td>\n",
+       "      <td>0.440095</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.782107</td>\n",
+       "      <td>0.944425</td>\n",
+       "      <td>-0.916323</td>\n",
+       "      <td>-0.812599</td>\n",
+       "      <td>-0.778036</td>\n",
+       "      <td>0.996390</td>\n",
+       "      <td>-0.851273</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999987</td>\n",
+       "      <td>-0.999954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_75_variants</th>\n",
+       "      <td>0.394256</td>\n",
+       "      <td>0.352185</td>\n",
+       "      <td>0.100460</td>\n",
+       "      <td>0.100460</td>\n",
+       "      <td>0.149243</td>\n",
+       "      <td>0.257585</td>\n",
+       "      <td>0.359259</td>\n",
+       "      <td>0.183629</td>\n",
+       "      <td>0.473146</td>\n",
+       "      <td>0.440227</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.781403</td>\n",
+       "      <td>0.942833</td>\n",
+       "      <td>-0.916030</td>\n",
+       "      <td>-0.811931</td>\n",
+       "      <td>-0.776764</td>\n",
+       "      <td>0.995989</td>\n",
+       "      <td>-0.850790</td>\n",
+       "      <td>0.999987</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.999988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_unique_traces_per_trace</th>\n",
+       "      <td>-0.393977</td>\n",
+       "      <td>-0.351606</td>\n",
+       "      <td>-0.099458</td>\n",
+       "      <td>-0.099458</td>\n",
+       "      <td>-0.148344</td>\n",
+       "      <td>-0.257085</td>\n",
+       "      <td>-0.359788</td>\n",
+       "      <td>-0.182776</td>\n",
+       "      <td>-0.474428</td>\n",
+       "      <td>-0.440039</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.780828</td>\n",
+       "      <td>-0.941302</td>\n",
+       "      <td>0.916115</td>\n",
+       "      <td>0.811224</td>\n",
+       "      <td>0.776143</td>\n",
+       "      <td>-0.995577</td>\n",
+       "      <td>0.851144</td>\n",
+       "      <td>-0.999954</td>\n",
+       "      <td>-0.999988</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>53 rows × 53 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    start_activities_max  \\\n",
+       "start_activities_max                                            1.000000   \n",
+       "end_activities_variance                                         0.983424   \n",
+       "trace_len_skewness_hist                                         0.510680   \n",
+       "trace_len_skewness                                              0.510680   \n",
+       "activities_max                                                  0.628179   \n",
+       "end_activities_q1                                              -0.090011   \n",
+       "activities_q3                                                   0.582388   \n",
+       "activities_std                                                  0.654280   \n",
+       "trace_len_hist1                                                 0.384826   \n",
+       "end_activities_std                                              0.992254   \n",
+       "trace_len_hist2                                                 0.273830   \n",
+       "trace_len_hist3                                                -0.134197   \n",
+       "std_variant_occurrence                                          0.515343   \n",
+       "start_activities_iqr                                            0.551099   \n",
+       "end_activities_kurtosis                                        -0.084362   \n",
+       "activities_q1                                                   0.130966   \n",
+       "skewness_variant_occurrence                                     0.412067   \n",
+       "end_activities_skewness                                        -0.085638   \n",
+       "end_activities_median                                           0.295714   \n",
+       "kurtosis_variant_occurrence                                     0.512580   \n",
+       "trace_len_entropy                                               0.788014   \n",
+       "entropy_trace                                                  -0.246336   \n",
+       "entropy_global_block                                           -0.065534   \n",
+       "ratio_top_1_variants                                            0.611446   \n",
+       "activities_mean                                                 0.689734   \n",
+       "n_unique_activities                                            -0.307299   \n",
+       "trace_len_hist8                                                -0.264695   \n",
+       "trace_len_harmonic_mean                                        -0.252755   \n",
+       "normalized_sequence_entropy_linear_forgetting                  -0.126553   \n",
+       "entropy_prefix                                                 -0.287911   \n",
+       "entropy_k_block_ratio_3                                        -0.295388   \n",
+       "entropy_k_block_diff_1                                         -0.191208   \n",
+       "normalized_sequence_entropy_exponential_forgetting             -0.232849   \n",
+       "n_unique_end_activities                                        -0.210656   \n",
+       "start_activities_skewness                                      -0.014424   \n",
+       "normalized_sequence_entropy                                    -0.389177   \n",
+       "trace_len_mode                                                 -0.266332   \n",
+       "ratio_most_common_variant                                       0.215971   \n",
+       "entropy_k_block_ratio_5                                        -0.360969   \n",
+       "start_activities_kurtosis                                      -0.073691   \n",
+       "ratio_top_5_variants                                            0.424220   \n",
+       "trace_len_geometric_mean                                       -0.303338   \n",
+       "entropy_lempel_ziv                                             -0.372166   \n",
+       "entropy_k_block_diff_3                                         -0.353923   \n",
+       "ratio_top_10_variants                                           0.409484   \n",
+       "trace_len_median                                               -0.309794   \n",
+       "entropy_k_block_ratio_1                                        -0.410932   \n",
+       "entropy_k_block_diff_5                                         -0.294408   \n",
+       "ratio_top_20_variants                                           0.405747   \n",
+       "trace_len_q1                                                   -0.280090   \n",
+       "ratio_top_50_variants                                           0.394243   \n",
+       "ratio_top_75_variants                                           0.394256   \n",
+       "ratio_unique_traces_per_trace                                  -0.393977   \n",
+       "\n",
+       "                                                    end_activities_variance  \\\n",
+       "start_activities_max                                               0.983424   \n",
+       "end_activities_variance                                            1.000000   \n",
+       "trace_len_skewness_hist                                            0.458959   \n",
+       "trace_len_skewness                                                 0.458959   \n",
+       "activities_max                                                     0.525272   \n",
+       "end_activities_q1                                                 -0.071941   \n",
+       "activities_q3                                                      0.473409   \n",
+       "activities_std                                                     0.548543   \n",
+       "trace_len_hist1                                                    0.363145   \n",
+       "end_activities_std                                                 0.973173   \n",
+       "trace_len_hist2                                                    0.280674   \n",
+       "trace_len_hist3                                                   -0.114261   \n",
+       "std_variant_occurrence                                             0.455728   \n",
+       "start_activities_iqr                                               0.504501   \n",
+       "end_activities_kurtosis                                           -0.016109   \n",
+       "activities_q1                                                      0.001644   \n",
+       "skewness_variant_occurrence                                        0.340044   \n",
+       "end_activities_skewness                                           -0.011941   \n",
+       "end_activities_median                                              0.270227   \n",
+       "kurtosis_variant_occurrence                                        0.423723   \n",
+       "trace_len_entropy                                                  0.682984   \n",
+       "entropy_trace                                                     -0.263862   \n",
+       "entropy_global_block                                              -0.045121   \n",
+       "ratio_top_1_variants                                               0.610239   \n",
+       "activities_mean                                                    0.570561   \n",
+       "n_unique_activities                                               -0.231862   \n",
+       "trace_len_hist8                                                   -0.211164   \n",
+       "trace_len_harmonic_mean                                           -0.280504   \n",
+       "normalized_sequence_entropy_linear_forgetting                     -0.094836   \n",
+       "entropy_prefix                                                    -0.276583   \n",
+       "entropy_k_block_ratio_3                                           -0.238604   \n",
+       "entropy_k_block_diff_1                                            -0.167327   \n",
+       "normalized_sequence_entropy_exponential_forgetting                -0.195929   \n",
+       "n_unique_end_activities                                           -0.143129   \n",
+       "start_activities_skewness                                          0.041578   \n",
+       "normalized_sequence_entropy                                       -0.351216   \n",
+       "trace_len_mode                                                    -0.260106   \n",
+       "ratio_most_common_variant                                          0.199184   \n",
+       "entropy_k_block_ratio_5                                           -0.287937   \n",
+       "start_activities_kurtosis                                         -0.018361   \n",
+       "ratio_top_5_variants                                               0.430879   \n",
+       "trace_len_geometric_mean                                          -0.309011   \n",
+       "entropy_lempel_ziv                                                -0.298315   \n",
+       "entropy_k_block_diff_3                                            -0.289558   \n",
+       "ratio_top_10_variants                                              0.397381   \n",
+       "trace_len_median                                                  -0.300372   \n",
+       "entropy_k_block_ratio_1                                           -0.342097   \n",
+       "entropy_k_block_diff_5                                            -0.234241   \n",
+       "ratio_top_20_variants                                              0.370588   \n",
+       "trace_len_q1                                                      -0.276479   \n",
+       "ratio_top_50_variants                                              0.352681   \n",
+       "ratio_top_75_variants                                              0.352185   \n",
+       "ratio_unique_traces_per_trace                                     -0.351606   \n",
+       "\n",
+       "                                                    trace_len_skewness_hist  \\\n",
+       "start_activities_max                                               0.510680   \n",
+       "end_activities_variance                                            0.458959   \n",
+       "trace_len_skewness_hist                                            1.000000   \n",
+       "trace_len_skewness                                                 1.000000   \n",
+       "activities_max                                                     0.860331   \n",
+       "end_activities_q1                                                 -0.020142   \n",
+       "activities_q3                                                      0.231172   \n",
+       "activities_std                                                     0.827151   \n",
+       "trace_len_hist1                                                   -0.142547   \n",
+       "end_activities_std                                                 0.492269   \n",
+       "trace_len_hist2                                                   -0.152002   \n",
+       "trace_len_hist3                                                   -0.165286   \n",
+       "std_variant_occurrence                                            -0.123401   \n",
+       "start_activities_iqr                                               0.917979   \n",
+       "end_activities_kurtosis                                            0.004839   \n",
+       "activities_q1                                                     -0.108268   \n",
+       "skewness_variant_occurrence                                        0.729327   \n",
+       "end_activities_skewness                                            0.110247   \n",
+       "end_activities_median                                             -0.120207   \n",
+       "kurtosis_variant_occurrence                                        0.832497   \n",
+       "trace_len_entropy                                                  0.479331   \n",
+       "entropy_trace                                                      0.246474   \n",
+       "entropy_global_block                                               0.303671   \n",
+       "ratio_top_1_variants                                               0.377794   \n",
+       "activities_mean                                                    0.608014   \n",
+       "n_unique_activities                                               -0.255983   \n",
+       "trace_len_hist8                                                   -0.277963   \n",
+       "trace_len_harmonic_mean                                            0.144213   \n",
+       "normalized_sequence_entropy_linear_forgetting                      0.334540   \n",
+       "entropy_prefix                                                     0.167972   \n",
+       "entropy_k_block_ratio_3                                           -0.253862   \n",
+       "entropy_k_block_diff_1                                            -0.235865   \n",
+       "normalized_sequence_entropy_exponential_forgetting                 0.243782   \n",
+       "n_unique_end_activities                                           -0.201001   \n",
+       "start_activities_skewness                                         -0.173669   \n",
+       "normalized_sequence_entropy                                        0.068333   \n",
+       "trace_len_mode                                                     0.004591   \n",
+       "ratio_most_common_variant                                         -0.110800   \n",
+       "entropy_k_block_ratio_5                                           -0.422165   \n",
+       "start_activities_kurtosis                                         -0.262776   \n",
+       "ratio_top_5_variants                                               0.208612   \n",
+       "trace_len_geometric_mean                                           0.070028   \n",
+       "entropy_lempel_ziv                                                -0.218838   \n",
+       "entropy_k_block_diff_3                                            -0.319538   \n",
+       "ratio_top_10_variants                                              0.166790   \n",
+       "trace_len_median                                                  -0.004567   \n",
+       "entropy_k_block_ratio_1                                           -0.385021   \n",
+       "entropy_k_block_diff_5                                            -0.328756   \n",
+       "ratio_top_20_variants                                              0.121233   \n",
+       "trace_len_q1                                                       0.019730   \n",
+       "ratio_top_50_variants                                              0.101141   \n",
+       "ratio_top_75_variants                                              0.100460   \n",
+       "ratio_unique_traces_per_trace                                     -0.099458   \n",
+       "\n",
+       "                                                    trace_len_skewness  \\\n",
+       "start_activities_max                                          0.510680   \n",
+       "end_activities_variance                                       0.458959   \n",
+       "trace_len_skewness_hist                                       1.000000   \n",
+       "trace_len_skewness                                            1.000000   \n",
+       "activities_max                                                0.860331   \n",
+       "end_activities_q1                                            -0.020142   \n",
+       "activities_q3                                                 0.231172   \n",
+       "activities_std                                                0.827151   \n",
+       "trace_len_hist1                                              -0.142547   \n",
+       "end_activities_std                                            0.492269   \n",
+       "trace_len_hist2                                              -0.152002   \n",
+       "trace_len_hist3                                              -0.165286   \n",
+       "std_variant_occurrence                                       -0.123401   \n",
+       "start_activities_iqr                                          0.917979   \n",
+       "end_activities_kurtosis                                       0.004839   \n",
+       "activities_q1                                                -0.108268   \n",
+       "skewness_variant_occurrence                                   0.729327   \n",
+       "end_activities_skewness                                       0.110247   \n",
+       "end_activities_median                                        -0.120207   \n",
+       "kurtosis_variant_occurrence                                   0.832497   \n",
+       "trace_len_entropy                                             0.479331   \n",
+       "entropy_trace                                                 0.246474   \n",
+       "entropy_global_block                                          0.303671   \n",
+       "ratio_top_1_variants                                          0.377794   \n",
+       "activities_mean                                               0.608014   \n",
+       "n_unique_activities                                          -0.255983   \n",
+       "trace_len_hist8                                              -0.277963   \n",
+       "trace_len_harmonic_mean                                       0.144213   \n",
+       "normalized_sequence_entropy_linear_forgetting                 0.334540   \n",
+       "entropy_prefix                                                0.167972   \n",
+       "entropy_k_block_ratio_3                                      -0.253862   \n",
+       "entropy_k_block_diff_1                                       -0.235865   \n",
+       "normalized_sequence_entropy_exponential_forgetting            0.243782   \n",
+       "n_unique_end_activities                                      -0.201001   \n",
+       "start_activities_skewness                                    -0.173669   \n",
+       "normalized_sequence_entropy                                   0.068333   \n",
+       "trace_len_mode                                                0.004591   \n",
+       "ratio_most_common_variant                                    -0.110800   \n",
+       "entropy_k_block_ratio_5                                      -0.422165   \n",
+       "start_activities_kurtosis                                    -0.262776   \n",
+       "ratio_top_5_variants                                          0.208612   \n",
+       "trace_len_geometric_mean                                      0.070028   \n",
+       "entropy_lempel_ziv                                           -0.218838   \n",
+       "entropy_k_block_diff_3                                       -0.319538   \n",
+       "ratio_top_10_variants                                         0.166790   \n",
+       "trace_len_median                                             -0.004567   \n",
+       "entropy_k_block_ratio_1                                      -0.385021   \n",
+       "entropy_k_block_diff_5                                       -0.328756   \n",
+       "ratio_top_20_variants                                         0.121233   \n",
+       "trace_len_q1                                                  0.019730   \n",
+       "ratio_top_50_variants                                         0.101141   \n",
+       "ratio_top_75_variants                                         0.100460   \n",
+       "ratio_unique_traces_per_trace                                -0.099458   \n",
+       "\n",
+       "                                                    activities_max  \\\n",
+       "start_activities_max                                      0.628179   \n",
+       "end_activities_variance                                   0.525272   \n",
+       "trace_len_skewness_hist                                   0.860331   \n",
+       "trace_len_skewness                                        0.860331   \n",
+       "activities_max                                            1.000000   \n",
+       "end_activities_q1                                        -0.083272   \n",
+       "activities_q3                                             0.612303   \n",
+       "activities_std                                            0.996429   \n",
+       "trace_len_hist1                                           0.029984   \n",
+       "end_activities_std                                        0.617851   \n",
+       "trace_len_hist2                                          -0.011685   \n",
+       "trace_len_hist3                                          -0.205869   \n",
+       "std_variant_occurrence                                    0.125167   \n",
+       "start_activities_iqr                                      0.794155   \n",
+       "end_activities_kurtosis                                  -0.080614   \n",
+       "activities_q1                                             0.193729   \n",
+       "skewness_variant_occurrence                               0.794430   \n",
+       "end_activities_skewness                                  -0.030496   \n",
+       "end_activities_median                                     0.041583   \n",
+       "kurtosis_variant_occurrence                               0.896302   \n",
+       "trace_len_entropy                                         0.712292   \n",
+       "entropy_trace                                             0.259748   \n",
+       "entropy_global_block                                      0.230746   \n",
+       "ratio_top_1_variants                                      0.427250   \n",
+       "activities_mean                                           0.903445   \n",
+       "n_unique_activities                                      -0.310645   \n",
+       "trace_len_hist8                                          -0.301216   \n",
+       "trace_len_harmonic_mean                                   0.242428   \n",
+       "normalized_sequence_entropy_linear_forgetting             0.216887   \n",
+       "entropy_prefix                                            0.134730   \n",
+       "entropy_k_block_ratio_3                                  -0.256823   \n",
+       "entropy_k_block_diff_1                                   -0.221442   \n",
+       "normalized_sequence_entropy_exponential_forgetting        0.127185   \n",
+       "n_unique_end_activities                                  -0.226001   \n",
+       "start_activities_skewness                                -0.125777   \n",
+       "normalized_sequence_entropy                              -0.026130   \n",
+       "trace_len_mode                                            0.061684   \n",
+       "ratio_most_common_variant                                -0.083176   \n",
+       "entropy_k_block_ratio_5                                  -0.426998   \n",
+       "start_activities_kurtosis                                -0.195049   \n",
+       "ratio_top_5_variants                                      0.221417   \n",
+       "trace_len_geometric_mean                                  0.132227   \n",
+       "entropy_lempel_ziv                                       -0.297340   \n",
+       "entropy_k_block_diff_3                                   -0.348155   \n",
+       "ratio_top_10_variants                                     0.190647   \n",
+       "trace_len_median                                          0.044931   \n",
+       "entropy_k_block_ratio_1                                  -0.421804   \n",
+       "entropy_k_block_diff_5                                   -0.349853   \n",
+       "ratio_top_20_variants                                     0.164998   \n",
+       "trace_len_q1                                              0.080486   \n",
+       "ratio_top_50_variants                                     0.149258   \n",
+       "ratio_top_75_variants                                     0.149243   \n",
+       "ratio_unique_traces_per_trace                            -0.148344   \n",
+       "\n",
+       "                                                    end_activities_q1  \\\n",
+       "start_activities_max                                        -0.090011   \n",
+       "end_activities_variance                                     -0.071941   \n",
+       "trace_len_skewness_hist                                     -0.020142   \n",
+       "trace_len_skewness                                          -0.020142   \n",
+       "activities_max                                              -0.083272   \n",
+       "end_activities_q1                                            1.000000   \n",
+       "activities_q3                                                0.046639   \n",
+       "activities_std                                              -0.063032   \n",
+       "trace_len_hist1                                              0.401540   \n",
+       "end_activities_std                                          -0.047169   \n",
+       "trace_len_hist2                                              0.034058   \n",
+       "trace_len_hist3                                             -0.143193   \n",
+       "std_variant_occurrence                                      -0.059618   \n",
+       "start_activities_iqr                                        -0.014051   \n",
+       "end_activities_kurtosis                                     -0.259556   \n",
+       "activities_q1                                                0.280098   \n",
+       "skewness_variant_occurrence                                 -0.027120   \n",
+       "end_activities_skewness                                     -0.415923   \n",
+       "end_activities_median                                        0.682742   \n",
+       "kurtosis_variant_occurrence                                 -0.064885   \n",
+       "trace_len_entropy                                           -0.027996   \n",
+       "entropy_trace                                               -0.194485   \n",
+       "entropy_global_block                                        -0.274134   \n",
+       "ratio_top_1_variants                                         0.285483   \n",
+       "activities_mean                                              0.000567   \n",
+       "n_unique_activities                                         -0.274822   \n",
+       "trace_len_hist8                                             -0.192024   \n",
+       "trace_len_harmonic_mean                                     -0.304785   \n",
+       "normalized_sequence_entropy_linear_forgetting                0.290060   \n",
+       "entropy_prefix                                              -0.215524   \n",
+       "entropy_k_block_ratio_3                                     -0.537589   \n",
+       "entropy_k_block_diff_1                                      -0.054772   \n",
+       "normalized_sequence_entropy_exponential_forgetting           0.143459   \n",
+       "n_unique_end_activities                                     -0.304703   \n",
+       "start_activities_skewness                                   -0.291153   \n",
+       "normalized_sequence_entropy                                 -0.130382   \n",
+       "trace_len_mode                                              -0.307516   \n",
+       "ratio_most_common_variant                                    0.218752   \n",
+       "entropy_k_block_ratio_5                                     -0.461655   \n",
+       "start_activities_kurtosis                                   -0.312515   \n",
+       "ratio_top_5_variants                                         0.290360   \n",
+       "trace_len_geometric_mean                                    -0.320670   \n",
+       "entropy_lempel_ziv                                          -0.237743   \n",
+       "entropy_k_block_diff_3                                      -0.318846   \n",
+       "ratio_top_10_variants                                        0.283839   \n",
+       "trace_len_median                                            -0.329279   \n",
+       "entropy_k_block_ratio_1                                     -0.318022   \n",
+       "entropy_k_block_diff_5                                      -0.216124   \n",
+       "ratio_top_20_variants                                        0.271773   \n",
+       "trace_len_q1                                                -0.319079   \n",
+       "ratio_top_50_variants                                        0.258102   \n",
+       "ratio_top_75_variants                                        0.257585   \n",
+       "ratio_unique_traces_per_trace                               -0.257085   \n",
+       "\n",
+       "                                                    activities_q3  \\\n",
+       "start_activities_max                                     0.582388   \n",
+       "end_activities_variance                                  0.473409   \n",
+       "trace_len_skewness_hist                                  0.231172   \n",
+       "trace_len_skewness                                       0.231172   \n",
+       "activities_max                                           0.612303   \n",
+       "end_activities_q1                                        0.046639   \n",
+       "activities_q3                                            1.000000   \n",
+       "activities_std                                           0.672378   \n",
+       "trace_len_hist1                                          0.626451   \n",
+       "end_activities_std                                       0.633719   \n",
+       "trace_len_hist2                                          0.404388   \n",
+       "trace_len_hist3                                         -0.153692   \n",
+       "std_variant_occurrence                                   0.725710   \n",
+       "start_activities_iqr                                     0.202410   \n",
+       "end_activities_kurtosis                                 -0.408587   \n",
+       "activities_q1                                            0.564967   \n",
+       "skewness_variant_occurrence                              0.242875   \n",
+       "end_activities_skewness                                 -0.492149   \n",
+       "end_activities_median                                    0.600396   \n",
+       "kurtosis_variant_occurrence                              0.332804   \n",
+       "trace_len_entropy                                        0.780574   \n",
+       "entropy_trace                                           -0.156561   \n",
+       "entropy_global_block                                    -0.276976   \n",
+       "ratio_top_1_variants                                     0.364196   \n",
+       "activities_mean                                          0.882116   \n",
+       "n_unique_activities                                     -0.429265   \n",
+       "trace_len_hist8                                         -0.331976   \n",
+       "trace_len_harmonic_mean                                 -0.062527   \n",
+       "normalized_sequence_entropy_linear_forgetting           -0.277255   \n",
+       "entropy_prefix                                          -0.287850   \n",
+       "entropy_k_block_ratio_3                                 -0.420225   \n",
+       "entropy_k_block_diff_1                                  -0.187839   \n",
+       "normalized_sequence_entropy_exponential_forgetting      -0.350854   \n",
+       "n_unique_end_activities                                 -0.395401   \n",
+       "start_activities_skewness                               -0.359154   \n",
+       "normalized_sequence_entropy                             -0.421818   \n",
+       "trace_len_mode                                          -0.187181   \n",
+       "ratio_most_common_variant                                0.237795   \n",
+       "entropy_k_block_ratio_5                                 -0.455464   \n",
+       "start_activities_kurtosis                               -0.387442   \n",
+       "ratio_top_5_variants                                     0.262169   \n",
+       "trace_len_geometric_mean                                -0.159491   \n",
+       "entropy_lempel_ziv                                      -0.459104   \n",
+       "entropy_k_block_diff_3                                  -0.459399   \n",
+       "ratio_top_10_variants                                    0.296510   \n",
+       "trace_len_median                                        -0.203190   \n",
+       "entropy_k_block_ratio_1                                 -0.472406   \n",
+       "entropy_k_block_diff_5                                  -0.383402   \n",
+       "ratio_top_20_variants                                    0.350791   \n",
+       "trace_len_q1                                            -0.186218   \n",
+       "ratio_top_50_variants                                    0.357892   \n",
+       "ratio_top_75_variants                                    0.359259   \n",
+       "ratio_unique_traces_per_trace                           -0.359788   \n",
+       "\n",
+       "                                                    activities_std  \\\n",
+       "start_activities_max                                      0.654280   \n",
+       "end_activities_variance                                   0.548543   \n",
+       "trace_len_skewness_hist                                   0.827151   \n",
+       "trace_len_skewness                                        0.827151   \n",
+       "activities_max                                            0.996429   \n",
+       "end_activities_q1                                        -0.063032   \n",
+       "activities_q3                                             0.672378   \n",
+       "activities_std                                            1.000000   \n",
+       "trace_len_hist1                                           0.092188   \n",
+       "end_activities_std                                        0.649804   \n",
+       "trace_len_hist2                                           0.035337   \n",
+       "trace_len_hist3                                          -0.203849   \n",
+       "std_variant_occurrence                                    0.185710   \n",
+       "start_activities_iqr                                      0.767316   \n",
+       "end_activities_kurtosis                                  -0.115391   \n",
+       "activities_q1                                             0.239929   \n",
+       "skewness_variant_occurrence                               0.769150   \n",
+       "end_activities_skewness                                  -0.078236   \n",
+       "end_activities_median                                     0.104515   \n",
+       "kurtosis_variant_occurrence                               0.872668   \n",
+       "trace_len_entropy                                         0.750551   \n",
+       "entropy_trace                                             0.221683   \n",
+       "entropy_global_block                                      0.179784   \n",
+       "ratio_top_1_variants                                      0.447450   \n",
+       "activities_mean                                           0.934870   \n",
+       "n_unique_activities                                      -0.344947   \n",
+       "trace_len_hist8                                          -0.313881   \n",
+       "trace_len_harmonic_mean                                   0.215553   \n",
+       "normalized_sequence_entropy_linear_forgetting             0.176753   \n",
+       "entropy_prefix                                            0.087938   \n",
+       "entropy_k_block_ratio_3                                  -0.289894   \n",
+       "entropy_k_block_diff_1                                   -0.226468   \n",
+       "normalized_sequence_entropy_exponential_forgetting        0.083429   \n",
+       "n_unique_end_activities                                  -0.252694   \n",
+       "start_activities_skewness                                -0.144055   \n",
+       "normalized_sequence_entropy                              -0.070924   \n",
+       "trace_len_mode                                            0.036688   \n",
+       "ratio_most_common_variant                                -0.049509   \n",
+       "entropy_k_block_ratio_5                                  -0.448613   \n",
+       "start_activities_kurtosis                                -0.210601   \n",
+       "ratio_top_5_variants                                      0.246490   \n",
+       "trace_len_geometric_mean                                  0.097244   \n",
+       "entropy_lempel_ziv                                       -0.330634   \n",
+       "entropy_k_block_diff_3                                   -0.374559   \n",
+       "ratio_top_10_variants                                     0.219661   \n",
+       "trace_len_median                                          0.010179   \n",
+       "entropy_k_block_ratio_1                                  -0.446197   \n",
+       "entropy_k_block_diff_5                                   -0.366750   \n",
+       "ratio_top_20_variants                                     0.198607   \n",
+       "trace_len_q1                                              0.053483   \n",
+       "ratio_top_50_variants                                     0.183560   \n",
+       "ratio_top_75_variants                                     0.183629   \n",
+       "ratio_unique_traces_per_trace                            -0.182776   \n",
+       "\n",
+       "                                                    trace_len_hist1  \\\n",
+       "start_activities_max                                       0.384826   \n",
+       "end_activities_variance                                    0.363145   \n",
+       "trace_len_skewness_hist                                   -0.142547   \n",
+       "trace_len_skewness                                        -0.142547   \n",
+       "activities_max                                             0.029984   \n",
+       "end_activities_q1                                          0.401540   \n",
+       "activities_q3                                              0.626451   \n",
+       "activities_std                                             0.092188   \n",
+       "trace_len_hist1                                            1.000000   \n",
+       "end_activities_std                                         0.436749   \n",
+       "trace_len_hist2                                            0.473301   \n",
+       "trace_len_hist3                                           -0.113372   \n",
+       "std_variant_occurrence                                     0.766575   \n",
+       "start_activities_iqr                                      -0.181574   \n",
+       "end_activities_kurtosis                                   -0.429077   \n",
+       "activities_q1                                              0.368785   \n",
+       "skewness_variant_occurrence                               -0.300462   \n",
+       "end_activities_skewness                                   -0.549613   \n",
+       "end_activities_median                                      0.776963   \n",
+       "kurtosis_variant_occurrence                               -0.222644   \n",
+       "trace_len_entropy                                          0.399449   \n",
+       "entropy_trace                                             -0.543472   \n",
+       "entropy_global_block                                      -0.592811   \n",
+       "ratio_top_1_variants                                       0.308390   \n",
+       "activities_mean                                            0.343345   \n",
+       "n_unique_activities                                       -0.417079   \n",
+       "trace_len_hist8                                           -0.310399   \n",
+       "trace_len_harmonic_mean                                   -0.476595   \n",
+       "normalized_sequence_entropy_linear_forgetting             -0.418566   \n",
+       "entropy_prefix                                            -0.601110   \n",
+       "entropy_k_block_ratio_3                                   -0.551796   \n",
+       "entropy_k_block_diff_1                                    -0.092049   \n",
+       "normalized_sequence_entropy_exponential_forgetting        -0.499940   \n",
+       "n_unique_end_activities                                   -0.423647   \n",
+       "start_activities_skewness                                 -0.301608   \n",
+       "normalized_sequence_entropy                               -0.581672   \n",
+       "trace_len_mode                                            -0.454778   \n",
+       "ratio_most_common_variant                                  0.477613   \n",
+       "entropy_k_block_ratio_5                                   -0.452048   \n",
+       "start_activities_kurtosis                                 -0.359485   \n",
+       "ratio_top_5_variants                                       0.313113   \n",
+       "trace_len_geometric_mean                                  -0.513081   \n",
+       "entropy_lempel_ziv                                        -0.443945   \n",
+       "entropy_k_block_diff_3                                    -0.447970   \n",
+       "ratio_top_10_variants                                      0.374526   \n",
+       "trace_len_median                                          -0.497937   \n",
+       "entropy_k_block_ratio_1                                   -0.419779   \n",
+       "entropy_k_block_diff_5                                    -0.299042   \n",
+       "ratio_top_20_variants                                      0.456175   \n",
+       "trace_len_q1                                              -0.486918   \n",
+       "ratio_top_50_variants                                      0.471813   \n",
+       "ratio_top_75_variants                                      0.473146   \n",
+       "ratio_unique_traces_per_trace                             -0.474428   \n",
+       "\n",
+       "                                                    end_activities_std  ...  \\\n",
+       "start_activities_max                                          0.992254  ...   \n",
+       "end_activities_variance                                       0.973173  ...   \n",
+       "trace_len_skewness_hist                                       0.492269  ...   \n",
+       "trace_len_skewness                                            0.492269  ...   \n",
+       "activities_max                                                0.617851  ...   \n",
+       "end_activities_q1                                            -0.047169  ...   \n",
+       "activities_q3                                                 0.633719  ...   \n",
+       "activities_std                                                0.649804  ...   \n",
+       "trace_len_hist1                                               0.436749  ...   \n",
+       "end_activities_std                                            1.000000  ...   \n",
+       "trace_len_hist2                                               0.339153  ...   \n",
+       "trace_len_hist3                                              -0.115093  ...   \n",
+       "std_variant_occurrence                                        0.572387  ...   \n",
+       "start_activities_iqr                                          0.550830  ...   \n",
+       "end_activities_kurtosis                                      -0.136003  ...   \n",
+       "activities_q1                                                 0.161317  ...   \n",
+       "skewness_variant_occurrence                                   0.358910  ...   \n",
+       "end_activities_skewness                                      -0.146411  ...   \n",
+       "end_activities_median                                         0.375178  ...   \n",
+       "kurtosis_variant_occurrence                                   0.466580  ...   \n",
+       "trace_len_entropy                                             0.814673  ...   \n",
+       "entropy_trace                                                -0.301778  ...   \n",
+       "entropy_global_block                                         -0.133919  ...   \n",
+       "ratio_top_1_variants                                          0.608020  ...   \n",
+       "activities_mean                                               0.705968  ...   \n",
+       "n_unique_activities                                          -0.342842  ...   \n",
+       "trace_len_hist8                                              -0.274991  ...   \n",
+       "trace_len_harmonic_mean                                      -0.285225  ...   \n",
+       "normalized_sequence_entropy_linear_forgetting                -0.168434  ...   \n",
+       "entropy_prefix                                               -0.343492  ...   \n",
+       "entropy_k_block_ratio_3                                      -0.345309  ...   \n",
+       "entropy_k_block_diff_1                                       -0.204120  ...   \n",
+       "normalized_sequence_entropy_exponential_forgetting           -0.282837  ...   \n",
+       "n_unique_end_activities                                      -0.254563  ...   \n",
+       "start_activities_skewness                                    -0.092610  ...   \n",
+       "normalized_sequence_entropy                                  -0.445538  ...   \n",
+       "trace_len_mode                                               -0.297675  ...   \n",
+       "ratio_most_common_variant                                     0.267916  ...   \n",
+       "entropy_k_block_ratio_5                                      -0.393977  ...   \n",
+       "start_activities_kurtosis                                    -0.142239  ...   \n",
+       "ratio_top_5_variants                                          0.453253  ...   \n",
+       "trace_len_geometric_mean                                     -0.341088  ...   \n",
+       "entropy_lempel_ziv                                           -0.414385  ...   \n",
+       "entropy_k_block_diff_3                                       -0.392985  ...   \n",
+       "ratio_top_10_variants                                         0.446302  ...   \n",
+       "trace_len_median                                             -0.348237  ...   \n",
+       "entropy_k_block_ratio_1                                      -0.442602  ...   \n",
+       "entropy_k_block_diff_5                                       -0.324465  ...   \n",
+       "ratio_top_20_variants                                         0.449856  ...   \n",
+       "trace_len_q1                                                 -0.313528  ...   \n",
+       "ratio_top_50_variants                                         0.440095  ...   \n",
+       "ratio_top_75_variants                                         0.440227  ...   \n",
+       "ratio_unique_traces_per_trace                                -0.440039  ...   \n",
+       "\n",
+       "                                                    entropy_k_block_diff_3  \\\n",
+       "start_activities_max                                             -0.353923   \n",
+       "end_activities_variance                                          -0.289558   \n",
+       "trace_len_skewness_hist                                          -0.319538   \n",
+       "trace_len_skewness                                               -0.319538   \n",
+       "activities_max                                                   -0.348155   \n",
+       "end_activities_q1                                                -0.318846   \n",
+       "activities_q3                                                    -0.459399   \n",
+       "activities_std                                                   -0.374559   \n",
+       "trace_len_hist1                                                  -0.447970   \n",
+       "end_activities_std                                               -0.392985   \n",
+       "trace_len_hist2                                                  -0.471400   \n",
+       "trace_len_hist3                                                  -0.221953   \n",
+       "std_variant_occurrence                                           -0.265147   \n",
+       "start_activities_iqr                                             -0.358504   \n",
+       "end_activities_kurtosis                                           0.791028   \n",
+       "activities_q1                                                    -0.300322   \n",
+       "skewness_variant_occurrence                                      -0.153580   \n",
+       "end_activities_skewness                                           0.781047   \n",
+       "end_activities_median                                            -0.402268   \n",
+       "kurtosis_variant_occurrence                                      -0.257033   \n",
+       "trace_len_entropy                                                -0.613474   \n",
+       "entropy_trace                                                     0.507782   \n",
+       "entropy_global_block                                              0.539261   \n",
+       "ratio_top_1_variants                                             -0.532604   \n",
+       "activities_mean                                                  -0.450543   \n",
+       "n_unique_activities                                               0.752915   \n",
+       "trace_len_hist8                                                   0.462180   \n",
+       "trace_len_harmonic_mean                                           0.681330   \n",
+       "normalized_sequence_entropy_linear_forgetting                     0.467868   \n",
+       "entropy_prefix                                                    0.600463   \n",
+       "entropy_k_block_ratio_3                                           0.886059   \n",
+       "entropy_k_block_diff_1                                            0.524131   \n",
+       "normalized_sequence_entropy_exponential_forgetting                0.580380   \n",
+       "n_unique_end_activities                                           0.884300   \n",
+       "start_activities_skewness                                         0.856504   \n",
+       "normalized_sequence_entropy                                       0.703591   \n",
+       "trace_len_mode                                                    0.824287   \n",
+       "ratio_most_common_variant                                        -0.644150   \n",
+       "entropy_k_block_ratio_5                                           0.929262   \n",
+       "start_activities_kurtosis                                         0.849717   \n",
+       "ratio_top_5_variants                                             -0.766608   \n",
+       "trace_len_geometric_mean                                          0.701625   \n",
+       "entropy_lempel_ziv                                                0.863986   \n",
+       "entropy_k_block_diff_3                                            1.000000   \n",
+       "ratio_top_10_variants                                            -0.800000   \n",
+       "trace_len_median                                                  0.719356   \n",
+       "entropy_k_block_ratio_1                                           0.976078   \n",
+       "entropy_k_block_diff_5                                            0.901504   \n",
+       "ratio_top_20_variants                                            -0.798434   \n",
+       "trace_len_q1                                                      0.819318   \n",
+       "ratio_top_50_variants                                            -0.782107   \n",
+       "ratio_top_75_variants                                            -0.781403   \n",
+       "ratio_unique_traces_per_trace                                     0.780828   \n",
+       "\n",
+       "                                                    ratio_top_10_variants  \\\n",
+       "start_activities_max                                             0.409484   \n",
+       "end_activities_variance                                          0.397381   \n",
+       "trace_len_skewness_hist                                          0.166790   \n",
+       "trace_len_skewness                                               0.166790   \n",
+       "activities_max                                                   0.190647   \n",
+       "end_activities_q1                                                0.283839   \n",
+       "activities_q3                                                    0.296510   \n",
+       "activities_std                                                   0.219661   \n",
+       "trace_len_hist1                                                  0.374526   \n",
+       "end_activities_std                                               0.446302   \n",
+       "trace_len_hist2                                                  0.529464   \n",
+       "trace_len_hist3                                                  0.489331   \n",
+       "std_variant_occurrence                                           0.207817   \n",
+       "start_activities_iqr                                             0.341720   \n",
+       "end_activities_kurtosis                                         -0.646266   \n",
+       "activities_q1                                                    0.071864   \n",
+       "skewness_variant_occurrence                                      0.015473   \n",
+       "end_activities_skewness                                         -0.618607   \n",
+       "end_activities_median                                            0.332030   \n",
+       "kurtosis_variant_occurrence                                      0.116612   \n",
+       "trace_len_entropy                                                0.584718   \n",
+       "entropy_trace                                                   -0.739612   \n",
+       "entropy_global_block                                            -0.726966   \n",
+       "ratio_top_1_variants                                             0.702380   \n",
+       "activities_mean                                                  0.272442   \n",
+       "n_unique_activities                                             -0.803954   \n",
+       "trace_len_hist8                                                 -0.093397   \n",
+       "trace_len_harmonic_mean                                         -0.760508   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.555968   \n",
+       "entropy_prefix                                                  -0.793830   \n",
+       "entropy_k_block_ratio_3                                         -0.828878   \n",
+       "entropy_k_block_diff_1                                          -0.431399   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.686515   \n",
+       "n_unique_end_activities                                         -0.784948   \n",
+       "start_activities_skewness                                       -0.860033   \n",
+       "normalized_sequence_entropy                                     -0.823207   \n",
+       "trace_len_mode                                                  -0.786654   \n",
+       "ratio_most_common_variant                                        0.831908   \n",
+       "entropy_k_block_ratio_5                                         -0.765697   \n",
+       "start_activities_kurtosis                                       -0.822708   \n",
+       "ratio_top_5_variants                                             0.979523   \n",
+       "trace_len_geometric_mean                                        -0.847353   \n",
+       "entropy_lempel_ziv                                              -0.908732   \n",
+       "entropy_k_block_diff_3                                          -0.800000   \n",
+       "ratio_top_10_variants                                            1.000000   \n",
+       "trace_len_median                                                -0.869299   \n",
+       "entropy_k_block_ratio_1                                         -0.832724   \n",
+       "entropy_k_block_diff_5                                          -0.805754   \n",
+       "ratio_top_20_variants                                            0.968464   \n",
+       "trace_len_q1                                                    -0.812285   \n",
+       "ratio_top_50_variants                                            0.944425   \n",
+       "ratio_top_75_variants                                            0.942833   \n",
+       "ratio_unique_traces_per_trace                                   -0.941302   \n",
+       "\n",
+       "                                                    trace_len_median  \\\n",
+       "start_activities_max                                       -0.309794   \n",
+       "end_activities_variance                                    -0.300372   \n",
+       "trace_len_skewness_hist                                    -0.004567   \n",
+       "trace_len_skewness                                         -0.004567   \n",
+       "activities_max                                              0.044931   \n",
+       "end_activities_q1                                          -0.329279   \n",
+       "activities_q3                                              -0.203190   \n",
+       "activities_std                                              0.010179   \n",
+       "trace_len_hist1                                            -0.497937   \n",
+       "end_activities_std                                         -0.348237   \n",
+       "trace_len_hist2                                            -0.411308   \n",
+       "trace_len_hist3                                            -0.398227   \n",
+       "std_variant_occurrence                                     -0.352143   \n",
+       "start_activities_iqr                                       -0.136349   \n",
+       "end_activities_kurtosis                                     0.646009   \n",
+       "activities_q1                                              -0.208967   \n",
+       "skewness_variant_occurrence                                 0.192436   \n",
+       "end_activities_skewness                                     0.680351   \n",
+       "end_activities_median                                      -0.442353   \n",
+       "kurtosis_variant_occurrence                                 0.090582   \n",
+       "trace_len_entropy                                          -0.484330   \n",
+       "entropy_trace                                               0.829063   \n",
+       "entropy_global_block                                        0.848891   \n",
+       "ratio_top_1_variants                                       -0.565136   \n",
+       "activities_mean                                            -0.093455   \n",
+       "n_unique_activities                                         0.858650   \n",
+       "trace_len_hist8                                             0.106830   \n",
+       "trace_len_harmonic_mean                                     0.881421   \n",
+       "normalized_sequence_entropy_linear_forgetting               0.634342   \n",
+       "entropy_prefix                                              0.912679   \n",
+       "entropy_k_block_ratio_3                                     0.855525   \n",
+       "entropy_k_block_diff_1                                      0.384808   \n",
+       "normalized_sequence_entropy_exponential_forgetting          0.745713   \n",
+       "n_unique_end_activities                                     0.745131   \n",
+       "start_activities_skewness                                   0.774297   \n",
+       "normalized_sequence_entropy                                 0.847782   \n",
+       "trace_len_mode                                              0.842892   \n",
+       "ratio_most_common_variant                                  -0.846697   \n",
+       "entropy_k_block_ratio_5                                     0.736494   \n",
+       "start_activities_kurtosis                                   0.758849   \n",
+       "ratio_top_5_variants                                       -0.793067   \n",
+       "trace_len_geometric_mean                                    0.988025   \n",
+       "entropy_lempel_ziv                                          0.883271   \n",
+       "entropy_k_block_diff_3                                      0.719356   \n",
+       "ratio_top_10_variants                                      -0.869299   \n",
+       "trace_len_median                                            1.000000   \n",
+       "entropy_k_block_ratio_1                                     0.744796   \n",
+       "entropy_k_block_diff_5                                      0.672842   \n",
+       "ratio_top_20_variants                                      -0.914434   \n",
+       "trace_len_q1                                                0.883990   \n",
+       "ratio_top_50_variants                                      -0.916323   \n",
+       "ratio_top_75_variants                                      -0.916030   \n",
+       "ratio_unique_traces_per_trace                               0.916115   \n",
+       "\n",
+       "                                                    entropy_k_block_ratio_1  \\\n",
+       "start_activities_max                                              -0.410932   \n",
+       "end_activities_variance                                           -0.342097   \n",
+       "trace_len_skewness_hist                                           -0.385021   \n",
+       "trace_len_skewness                                                -0.385021   \n",
+       "activities_max                                                    -0.421804   \n",
+       "end_activities_q1                                                 -0.318022   \n",
+       "activities_q3                                                     -0.472406   \n",
+       "activities_std                                                    -0.446197   \n",
+       "trace_len_hist1                                                   -0.419779   \n",
+       "end_activities_std                                                -0.442602   \n",
+       "trace_len_hist2                                                   -0.423462   \n",
+       "trace_len_hist3                                                   -0.286088   \n",
+       "std_variant_occurrence                                            -0.255289   \n",
+       "start_activities_iqr                                              -0.418042   \n",
+       "end_activities_kurtosis                                            0.764827   \n",
+       "activities_q1                                                     -0.311057   \n",
+       "skewness_variant_occurrence                                       -0.224267   \n",
+       "end_activities_skewness                                            0.757497   \n",
+       "end_activities_median                                             -0.391401   \n",
+       "kurtosis_variant_occurrence                                       -0.341894   \n",
+       "trace_len_entropy                                                 -0.654473   \n",
+       "entropy_trace                                                      0.525860   \n",
+       "entropy_global_block                                               0.549797   \n",
+       "ratio_top_1_variants                                              -0.592046   \n",
+       "activities_mean                                                   -0.503690   \n",
+       "n_unique_activities                                                0.804961   \n",
+       "trace_len_hist8                                                    0.428788   \n",
+       "trace_len_harmonic_mean                                            0.665910   \n",
+       "normalized_sequence_entropy_linear_forgetting                      0.439906   \n",
+       "entropy_prefix                                                     0.627180   \n",
+       "entropy_k_block_ratio_3                                            0.916695   \n",
+       "entropy_k_block_diff_1                                             0.490237   \n",
+       "normalized_sequence_entropy_exponential_forgetting                 0.571463   \n",
+       "n_unique_end_activities                                            0.877965   \n",
+       "start_activities_skewness                                          0.833720   \n",
+       "normalized_sequence_entropy                                        0.725650   \n",
+       "trace_len_mode                                                     0.801922   \n",
+       "ratio_most_common_variant                                         -0.696758   \n",
+       "entropy_k_block_ratio_5                                            0.960882   \n",
+       "start_activities_kurtosis                                          0.832276   \n",
+       "ratio_top_5_variants                                              -0.798745   \n",
+       "trace_len_geometric_mean                                           0.714997   \n",
+       "entropy_lempel_ziv                                                 0.894343   \n",
+       "entropy_k_block_diff_3                                             0.976078   \n",
+       "ratio_top_10_variants                                             -0.832724   \n",
+       "trace_len_median                                                   0.744796   \n",
+       "entropy_k_block_ratio_1                                            1.000000   \n",
+       "entropy_k_block_diff_5                                             0.903243   \n",
+       "ratio_top_20_variants                                             -0.830485   \n",
+       "trace_len_q1                                                       0.801267   \n",
+       "ratio_top_50_variants                                             -0.812599   \n",
+       "ratio_top_75_variants                                             -0.811931   \n",
+       "ratio_unique_traces_per_trace                                      0.811224   \n",
+       "\n",
+       "                                                    entropy_k_block_diff_5  \\\n",
+       "start_activities_max                                             -0.294408   \n",
+       "end_activities_variance                                          -0.234241   \n",
+       "trace_len_skewness_hist                                          -0.328756   \n",
+       "trace_len_skewness                                               -0.328756   \n",
+       "activities_max                                                   -0.349853   \n",
+       "end_activities_q1                                                -0.216124   \n",
+       "activities_q3                                                    -0.383402   \n",
+       "activities_std                                                   -0.366750   \n",
+       "trace_len_hist1                                                  -0.299042   \n",
+       "end_activities_std                                               -0.324465   \n",
+       "trace_len_hist2                                                  -0.344250   \n",
+       "trace_len_hist3                                                  -0.320360   \n",
+       "std_variant_occurrence                                           -0.161265   \n",
+       "start_activities_iqr                                             -0.350923   \n",
+       "end_activities_kurtosis                                           0.830060   \n",
+       "activities_q1                                                    -0.216334   \n",
+       "skewness_variant_occurrence                                      -0.181436   \n",
+       "end_activities_skewness                                           0.765602   \n",
+       "end_activities_median                                            -0.264005   \n",
+       "kurtosis_variant_occurrence                                      -0.276999   \n",
+       "trace_len_entropy                                                -0.558782   \n",
+       "entropy_trace                                                     0.465916   \n",
+       "entropy_global_block                                              0.499204   \n",
+       "ratio_top_1_variants                                             -0.595418   \n",
+       "activities_mean                                                  -0.406734   \n",
+       "n_unique_activities                                               0.731855   \n",
+       "trace_len_hist8                                                   0.320296   \n",
+       "trace_len_harmonic_mean                                           0.602829   \n",
+       "normalized_sequence_entropy_linear_forgetting                     0.450746   \n",
+       "entropy_prefix                                                    0.552172   \n",
+       "entropy_k_block_ratio_3                                           0.822976   \n",
+       "entropy_k_block_diff_1                                            0.606014   \n",
+       "normalized_sequence_entropy_exponential_forgetting                0.557078   \n",
+       "n_unique_end_activities                                           0.913235   \n",
+       "start_activities_skewness                                         0.820198   \n",
+       "normalized_sequence_entropy                                       0.665065   \n",
+       "trace_len_mode                                                    0.802496   \n",
+       "ratio_most_common_variant                                        -0.622967   \n",
+       "entropy_k_block_ratio_5                                           0.876058   \n",
+       "start_activities_kurtosis                                         0.827590   \n",
+       "ratio_top_5_variants                                             -0.777589   \n",
+       "trace_len_geometric_mean                                          0.640710   \n",
+       "entropy_lempel_ziv                                                0.860245   \n",
+       "entropy_k_block_diff_3                                            0.901504   \n",
+       "ratio_top_10_variants                                            -0.805754   \n",
+       "trace_len_median                                                  0.672842   \n",
+       "entropy_k_block_ratio_1                                           0.903243   \n",
+       "entropy_k_block_diff_5                                            1.000000   \n",
+       "ratio_top_20_variants                                            -0.794496   \n",
+       "trace_len_q1                                                      0.768813   \n",
+       "ratio_top_50_variants                                            -0.778036   \n",
+       "ratio_top_75_variants                                            -0.776764   \n",
+       "ratio_unique_traces_per_trace                                     0.776143   \n",
+       "\n",
+       "                                                    ratio_top_20_variants  \\\n",
+       "start_activities_max                                             0.405747   \n",
+       "end_activities_variance                                          0.370588   \n",
+       "trace_len_skewness_hist                                          0.121233   \n",
+       "trace_len_skewness                                               0.121233   \n",
+       "activities_max                                                   0.164998   \n",
+       "end_activities_q1                                                0.271773   \n",
+       "activities_q3                                                    0.350791   \n",
+       "activities_std                                                   0.198607   \n",
+       "trace_len_hist1                                                  0.456175   \n",
+       "end_activities_std                                               0.449856   \n",
+       "trace_len_hist2                                                  0.488012   \n",
+       "trace_len_hist3                                                  0.464521   \n",
+       "std_variant_occurrence                                           0.367574   \n",
+       "start_activities_iqr                                             0.301336   \n",
+       "end_activities_kurtosis                                         -0.694035   \n",
+       "activities_q1                                                    0.232969   \n",
+       "skewness_variant_occurrence                                     -0.057777   \n",
+       "end_activities_skewness                                         -0.684222   \n",
+       "end_activities_median                                            0.412936   \n",
+       "kurtosis_variant_occurrence                                      0.068433   \n",
+       "trace_len_entropy                                                0.636138   \n",
+       "entropy_trace                                                   -0.818711   \n",
+       "entropy_global_block                                            -0.807659   \n",
+       "ratio_top_1_variants                                             0.639142   \n",
+       "activities_mean                                                  0.287503   \n",
+       "n_unique_activities                                             -0.838799   \n",
+       "trace_len_hist8                                                 -0.109544   \n",
+       "trace_len_harmonic_mean                                         -0.802513   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.645306   \n",
+       "entropy_prefix                                                  -0.875814   \n",
+       "entropy_k_block_ratio_3                                         -0.865079   \n",
+       "entropy_k_block_diff_1                                          -0.393154   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.780019   \n",
+       "n_unique_end_activities                                         -0.821402   \n",
+       "start_activities_skewness                                       -0.862088   \n",
+       "normalized_sequence_entropy                                     -0.911287   \n",
+       "trace_len_mode                                                  -0.820828   \n",
+       "ratio_most_common_variant                                        0.890116   \n",
+       "entropy_k_block_ratio_5                                         -0.786888   \n",
+       "start_activities_kurtosis                                       -0.824496   \n",
+       "ratio_top_5_variants                                             0.899778   \n",
+       "trace_len_geometric_mean                                        -0.894544   \n",
+       "entropy_lempel_ziv                                              -0.955315   \n",
+       "entropy_k_block_diff_3                                          -0.798434   \n",
+       "ratio_top_10_variants                                            0.968464   \n",
+       "trace_len_median                                                -0.914434   \n",
+       "entropy_k_block_ratio_1                                         -0.830485   \n",
+       "entropy_k_block_diff_5                                          -0.794496   \n",
+       "ratio_top_20_variants                                            1.000000   \n",
+       "trace_len_q1                                                    -0.851612   \n",
+       "ratio_top_50_variants                                            0.996390   \n",
+       "ratio_top_75_variants                                            0.995989   \n",
+       "ratio_unique_traces_per_trace                                   -0.995577   \n",
+       "\n",
+       "                                                    trace_len_q1  \\\n",
+       "start_activities_max                                   -0.280090   \n",
+       "end_activities_variance                                -0.276479   \n",
+       "trace_len_skewness_hist                                 0.019730   \n",
+       "trace_len_skewness                                      0.019730   \n",
+       "activities_max                                          0.080486   \n",
+       "end_activities_q1                                      -0.319079   \n",
+       "activities_q3                                          -0.186218   \n",
+       "activities_std                                          0.053483   \n",
+       "trace_len_hist1                                        -0.486918   \n",
+       "end_activities_std                                     -0.313528   \n",
+       "trace_len_hist2                                        -0.379066   \n",
+       "trace_len_hist3                                        -0.322516   \n",
+       "std_variant_occurrence                                 -0.337717   \n",
+       "start_activities_iqr                                   -0.051488   \n",
+       "end_activities_kurtosis                                 0.742203   \n",
+       "activities_q1                                          -0.218867   \n",
+       "skewness_variant_occurrence                             0.186335   \n",
+       "end_activities_skewness                                 0.763603   \n",
+       "end_activities_median                                  -0.437024   \n",
+       "kurtosis_variant_occurrence                             0.106727   \n",
+       "trace_len_entropy                                      -0.395248   \n",
+       "entropy_trace                                           0.771976   \n",
+       "entropy_global_block                                    0.691131   \n",
+       "ratio_top_1_variants                                   -0.548979   \n",
+       "activities_mean                                        -0.062349   \n",
+       "n_unique_activities                                     0.661169   \n",
+       "trace_len_hist8                                         0.194200   \n",
+       "trace_len_harmonic_mean                                 0.967328   \n",
+       "normalized_sequence_entropy_linear_forgetting           0.632278   \n",
+       "entropy_prefix                                          0.784246   \n",
+       "entropy_k_block_ratio_3                                 0.818149   \n",
+       "entropy_k_block_diff_1                                  0.425139   \n",
+       "normalized_sequence_entropy_exponential_forgetting      0.729359   \n",
+       "n_unique_end_activities                                 0.804332   \n",
+       "start_activities_skewness                               0.756824   \n",
+       "normalized_sequence_entropy                             0.818041   \n",
+       "trace_len_mode                                          0.985198   \n",
+       "ratio_most_common_variant                              -0.787607   \n",
+       "entropy_k_block_ratio_5                                 0.784698   \n",
+       "start_activities_kurtosis                               0.760201   \n",
+       "ratio_top_5_variants                                   -0.746087   \n",
+       "trace_len_geometric_mean                                0.921866   \n",
+       "entropy_lempel_ziv                                      0.822784   \n",
+       "entropy_k_block_diff_3                                  0.819318   \n",
+       "ratio_top_10_variants                                  -0.812285   \n",
+       "trace_len_median                                        0.883990   \n",
+       "entropy_k_block_ratio_1                                 0.801267   \n",
+       "entropy_k_block_diff_5                                  0.768813   \n",
+       "ratio_top_20_variants                                  -0.851612   \n",
+       "trace_len_q1                                            1.000000   \n",
+       "ratio_top_50_variants                                  -0.851273   \n",
+       "ratio_top_75_variants                                  -0.850790   \n",
+       "ratio_unique_traces_per_trace                           0.851144   \n",
+       "\n",
+       "                                                    ratio_top_50_variants  \\\n",
+       "start_activities_max                                             0.394243   \n",
+       "end_activities_variance                                          0.352681   \n",
+       "trace_len_skewness_hist                                          0.101141   \n",
+       "trace_len_skewness                                               0.101141   \n",
+       "activities_max                                                   0.149258   \n",
+       "end_activities_q1                                                0.258102   \n",
+       "activities_q3                                                    0.357892   \n",
+       "activities_std                                                   0.183560   \n",
+       "trace_len_hist1                                                  0.471813   \n",
+       "end_activities_std                                               0.440095   \n",
+       "trace_len_hist2                                                  0.466637   \n",
+       "trace_len_hist3                                                  0.455329   \n",
+       "std_variant_occurrence                                           0.412857   \n",
+       "start_activities_iqr                                             0.281930   \n",
+       "end_activities_kurtosis                                         -0.697840   \n",
+       "activities_q1                                                    0.275571   \n",
+       "skewness_variant_occurrence                                     -0.088299   \n",
+       "end_activities_skewness                                         -0.691484   \n",
+       "end_activities_median                                            0.426404   \n",
+       "kurtosis_variant_occurrence                                      0.045392   \n",
+       "trace_len_entropy                                                0.639575   \n",
+       "entropy_trace                                                   -0.835934   \n",
+       "entropy_global_block                                            -0.825738   \n",
+       "ratio_top_1_variants                                             0.603939   \n",
+       "activities_mean                                                  0.281803   \n",
+       "n_unique_activities                                             -0.837176   \n",
+       "trace_len_hist8                                                 -0.101583   \n",
+       "trace_len_harmonic_mean                                         -0.804672   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.668865   \n",
+       "entropy_prefix                                                  -0.892214   \n",
+       "entropy_k_block_ratio_3                                         -0.861356   \n",
+       "entropy_k_block_diff_1                                          -0.375995   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.802473   \n",
+       "n_unique_end_activities                                         -0.820785   \n",
+       "start_activities_skewness                                       -0.862883   \n",
+       "normalized_sequence_entropy                                     -0.928235   \n",
+       "trace_len_mode                                                  -0.819121   \n",
+       "ratio_most_common_variant                                        0.896724   \n",
+       "entropy_k_block_ratio_5                                         -0.777933   \n",
+       "start_activities_kurtosis                                       -0.824777   \n",
+       "ratio_top_5_variants                                             0.860405   \n",
+       "trace_len_geometric_mean                                        -0.897428   \n",
+       "entropy_lempel_ziv                                              -0.956670   \n",
+       "entropy_k_block_diff_3                                          -0.782107   \n",
+       "ratio_top_10_variants                                            0.944425   \n",
+       "trace_len_median                                                -0.916323   \n",
+       "entropy_k_block_ratio_1                                         -0.812599   \n",
+       "entropy_k_block_diff_5                                          -0.778036   \n",
+       "ratio_top_20_variants                                            0.996390   \n",
+       "trace_len_q1                                                    -0.851273   \n",
+       "ratio_top_50_variants                                            1.000000   \n",
+       "ratio_top_75_variants                                            0.999987   \n",
+       "ratio_unique_traces_per_trace                                   -0.999954   \n",
+       "\n",
+       "                                                    ratio_top_75_variants  \\\n",
+       "start_activities_max                                             0.394256   \n",
+       "end_activities_variance                                          0.352185   \n",
+       "trace_len_skewness_hist                                          0.100460   \n",
+       "trace_len_skewness                                               0.100460   \n",
+       "activities_max                                                   0.149243   \n",
+       "end_activities_q1                                                0.257585   \n",
+       "activities_q3                                                    0.359259   \n",
+       "activities_std                                                   0.183629   \n",
+       "trace_len_hist1                                                  0.473146   \n",
+       "end_activities_std                                               0.440227   \n",
+       "trace_len_hist2                                                  0.465813   \n",
+       "trace_len_hist3                                                  0.454387   \n",
+       "std_variant_occurrence                                           0.415973   \n",
+       "start_activities_iqr                                             0.281314   \n",
+       "end_activities_kurtosis                                         -0.698061   \n",
+       "activities_q1                                                    0.278782   \n",
+       "skewness_variant_occurrence                                     -0.089221   \n",
+       "end_activities_skewness                                         -0.692054   \n",
+       "end_activities_median                                            0.427833   \n",
+       "kurtosis_variant_occurrence                                      0.044911   \n",
+       "trace_len_entropy                                                0.640517   \n",
+       "entropy_trace                                                   -0.836628   \n",
+       "entropy_global_block                                            -0.826435   \n",
+       "ratio_top_1_variants                                             0.601899   \n",
+       "activities_mean                                                  0.282532   \n",
+       "n_unique_activities                                             -0.837057   \n",
+       "trace_len_hist8                                                 -0.102420   \n",
+       "trace_len_harmonic_mean                                         -0.804297   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.669891   \n",
+       "entropy_prefix                                                  -0.892898   \n",
+       "entropy_k_block_ratio_3                                         -0.861200   \n",
+       "entropy_k_block_diff_1                                          -0.374354   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.803516   \n",
+       "n_unique_end_activities                                         -0.820518   \n",
+       "start_activities_skewness                                       -0.863043   \n",
+       "normalized_sequence_entropy                                     -0.929146   \n",
+       "trace_len_mode                                                  -0.818473   \n",
+       "ratio_most_common_variant                                        0.897069   \n",
+       "entropy_k_block_ratio_5                                         -0.777599   \n",
+       "start_activities_kurtosis                                       -0.824862   \n",
+       "ratio_top_5_variants                                             0.857955   \n",
+       "trace_len_geometric_mean                                        -0.897151   \n",
+       "entropy_lempel_ziv                                              -0.956585   \n",
+       "entropy_k_block_diff_3                                          -0.781403   \n",
+       "ratio_top_10_variants                                            0.942833   \n",
+       "trace_len_median                                                -0.916030   \n",
+       "entropy_k_block_ratio_1                                         -0.811931   \n",
+       "entropy_k_block_diff_5                                          -0.776764   \n",
+       "ratio_top_20_variants                                            0.995989   \n",
+       "trace_len_q1                                                    -0.850790   \n",
+       "ratio_top_50_variants                                            0.999987   \n",
+       "ratio_top_75_variants                                            1.000000   \n",
+       "ratio_unique_traces_per_trace                                   -0.999988   \n",
+       "\n",
+       "                                                    ratio_unique_traces_per_trace  \n",
+       "start_activities_max                                                    -0.393977  \n",
+       "end_activities_variance                                                 -0.351606  \n",
+       "trace_len_skewness_hist                                                 -0.099458  \n",
+       "trace_len_skewness                                                      -0.099458  \n",
+       "activities_max                                                          -0.148344  \n",
+       "end_activities_q1                                                       -0.257085  \n",
+       "activities_q3                                                           -0.359788  \n",
+       "activities_std                                                          -0.182776  \n",
+       "trace_len_hist1                                                         -0.474428  \n",
+       "end_activities_std                                                      -0.440039  \n",
+       "trace_len_hist2                                                         -0.464762  \n",
+       "trace_len_hist3                                                         -0.453468  \n",
+       "std_variant_occurrence                                                  -0.418762  \n",
+       "start_activities_iqr                                                    -0.280110  \n",
+       "end_activities_kurtosis                                                  0.698135  \n",
+       "activities_q1                                                           -0.281242  \n",
+       "skewness_variant_occurrence                                              0.090874  \n",
+       "end_activities_skewness                                                  0.692535  \n",
+       "end_activities_median                                                   -0.429005  \n",
+       "kurtosis_variant_occurrence                                             -0.043619  \n",
+       "trace_len_entropy                                                       -0.640714  \n",
+       "entropy_trace                                                            0.837496  \n",
+       "entropy_global_block                                                     0.827099  \n",
+       "ratio_top_1_variants                                                    -0.599992  \n",
+       "activities_mean                                                         -0.282288  \n",
+       "n_unique_activities                                                      0.836612  \n",
+       "trace_len_hist8                                                          0.102941  \n",
+       "trace_len_harmonic_mean                                                  0.804721  \n",
+       "normalized_sequence_entropy_linear_forgetting                            0.670929  \n",
+       "entropy_prefix                                                           0.893662  \n",
+       "entropy_k_block_ratio_3                                                  0.861102  \n",
+       "entropy_k_block_diff_1                                                   0.374178  \n",
+       "normalized_sequence_entropy_exponential_forgetting                       0.804556  \n",
+       "n_unique_end_activities                                                  0.820448  \n",
+       "start_activities_skewness                                                0.862941  \n",
+       "normalized_sequence_entropy                                              0.930042  \n",
+       "trace_len_mode                                                           0.818907  \n",
+       "ratio_most_common_variant                                               -0.897362  \n",
+       "entropy_k_block_ratio_5                                                  0.777367  \n",
+       "start_activities_kurtosis                                                0.824708  \n",
+       "ratio_top_5_variants                                                    -0.855618  \n",
+       "trace_len_geometric_mean                                                 0.897381  \n",
+       "entropy_lempel_ziv                                                       0.956493  \n",
+       "entropy_k_block_diff_3                                                   0.780828  \n",
+       "ratio_top_10_variants                                                   -0.941302  \n",
+       "trace_len_median                                                         0.916115  \n",
+       "entropy_k_block_ratio_1                                                  0.811224  \n",
+       "entropy_k_block_diff_5                                                   0.776143  \n",
+       "ratio_top_20_variants                                                   -0.995577  \n",
+       "trace_len_q1                                                             0.851144  \n",
+       "ratio_top_50_variants                                                   -0.999954  \n",
+       "ratio_top_75_variants                                                   -0.999988  \n",
+       "ratio_unique_traces_per_trace                                            1.000000  \n",
+       "\n",
+       "[53 rows x 53 columns]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "\n",
+    "selected_per_category_highest_variance = ['ratio_unique_traces_per_trace', # simple_statistics\n",
+    "                                         'trace_len_hist8',# trace_length\n",
+    "                                         'skewness_variant_occurrence',# trace_variant\n",
+    "                                         'activities_mean',# activities\n",
+    "                                         'start_activities_kurtosis',# start_activities\n",
+    "                                         'end_activities_median',# end_activities\n",
+    "                                         'entropy_k_block_diff_1',# entropies\n",
+    "                                         'n_traces', 'n_unique_traces'\n",
+    "                                         ]\n",
+    "def plot_miner_correlation(input_df):\n",
+    "    #df = input_df.loc[:, input_df.columns.isin(selected_per_category_highest_variance)]\n",
+    "    df = input_df.loc[:,:]\n",
+    "    #df = input_df.loc[:, sorted_features]\n",
+    "    #df = input_df.loc[:, input_df.columns[3:]]\n",
+    "\n",
+    "    corr = df.corr()\n",
+    "    fig, ax = plt.subplots(figsize=(20,20)) \n",
+    "    sns.set(font_scale = 2)\n",
+    "    b= sns.heatmap(corr,\n",
+    "                ax=ax,\n",
+    "                vmin=-1,\n",
+    "                vmax=1,\n",
+    "                xticklabels=corr.columns.values,\n",
+    "                yticklabels=corr.columns.values)\n",
+    "    #b.set_yticklabels(b.get_yticks(), size = 15)\n",
+    "    plt.show()\n",
+    "    return corr\n",
+    "\n",
+    "#scaled_dmf = scaled_dmf.drop(['log', 'Log Nature'], axis=1)\n",
+    "scaled_dmf = scaled_dmf[filtered_feat]\n",
+    "corr_df = plot_miner_correlation(scaled_dmf)\n",
+    "stats = stats.loc[filtered_feat]\n",
+    "corr_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "634cfe00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: xlabel='disconnection', ylabel='std'>"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#feature_metrics= corr_df.loc[:,:]\n",
+    "feature_metrics = pd.DataFrame()\n",
+    "feature_metrics['disconnection'] = pd.Series(min_max_scaler.fit_transform((corr_df.abs()).sum(axis=1).to_numpy().reshape(-1, 1)).reshape(len(corr_df))).apply(lambda x: 1-x)   \n",
+    "#TODO: MinMaxScaler ONLY before std computation\n",
+    "#feature_metrics['std'] = pd.Series(min_max_scaler.fit_transform(stats['std'].to_numpy().reshape(-1, 1)).reshape(len(corr_df)))\n",
+    "feature_metrics['std'] = pd.Series(stats['std'].to_numpy().reshape(-1, 1).reshape(len(corr_df)))\n",
+    "#feature_metrics['score'] = feature_metrics.apply(lambda x: x['disconnection'], axis=1) #TODO mean or prod?\n",
+    "feature_metrics['score'] = feature_metrics.apply(lambda x: x.prod(), axis=1) #TODO mean or prod?\n",
+    "feature_metrics.index = corr_df.index\n",
+    "#pd.concat([feature_metrics, stats['std']], axis=1)\n",
+    "#feature_metrics = scaleColumns(feature_metrics, feature_metrics.columns)\n",
+    "\n",
+    "\n",
+    "feature_metrics.plot.scatter(x=\"disconnection\", y=\"std\")\n",
+    "\n",
+    "#top_k_score = feature_metrics.sort_values([\"score\"], ascending=False).head(TOP_K).index[:5].tolist()\n",
+    "#other = ['variant_entropy', 'normalized_variant_entropy', 'sequence_entropy', 'normalized_sequence_entropy','sequence_entropy_linear_forgetting','normalized_sequence_entropy_linear_forgetting','sequence_entropy_exponential_forgetting','normalized_sequence_entropy_exponential_forgetting'] \n",
+    "#selected_highest_score = top_k_score+other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "cb5bfed6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9051285781090895\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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r6rqqiqoqiYWvwqSbz3nOc3D8+HGcP38em5ub+NSnPoUbb7wx+HdFUfAzP/MzOH36NDzPw/ve9z685CUvKWo5pWJKnqMXuG7mMGPRI+boGbqKqQmDpJsEIQGWOCHXTYIgCIIgqkBhgd6ePXvw2te+Fj/1Uz+FH/mRH8F//s//Gddeey1+/ud/Ht/5znegqip+67d+Cz/3cz+HH/zBH8T09DR+9md/tqjllIrsOXqsz26qLm7G0rZdGLraI5sNs23KJDMWgpCATa6bBEEQBEFUiMKkmwCCGXlh/vIv/zL438973vPwvOc9r8glDAUW6IkEZEmw8Qp1U4OuKYJmLG4w7iEKGppeDMdPLODIsZNYXGlhbqaGw4f24+CB+WEviygQGphOEEQR0POEIIisFBrobVVMvTMwXdKBj1UGDV1FzdCExytEjVZgzDZMnFocj0H1VeH4iQXcfvTuQHK7uNLC7Ud9d1l6OI8vFOgRBCEbep4QBJGHwqSbW5lAuimrotfZ4E1Dg2loQvP5LNtJDvSma1hea8ONcEklsnHk2MmBz6htuzhy7OSQVkSUgU3jFQiCkAw9TwiCyANV9AogkG5K6tFrW91ZeH6gJyjdTAj0tjVMOK6HtU0LM5Nm7rUSfsZV5OvEeMB69GRV8gmCIOh5QpQJyYTHD6roFYCuqVAVRdqBj7n4GbqKmilZutkZmr5MfXrSmIsZqRH3dWI8IOkmQRCyoecJURZMJsySCEwmfPzEwpBXRuSBAr2CMHRVqnSTOWeKSjfbthv0DEbBAj1y3pTH4UP7gxEbDFNXcfjQ/iGtiCiDrnSTAj2CKILjJxbw+nd/GT/zfz6H17/7y1viAErPE6IsSCY8npB0syAMXZU2T8uy3GCjrxlaIOXk+lknRbo55cs1KdCTB5M5fOCT30Oz7WB2ysTLn38lyR/GGM/zaLwCQRTIVjUlYX/b+/75LjiuR3I6ojBIJjyeUKBXEKYhL9Br2Q5Mw6/K1QwNG5sW989atovJWvzHPBsEeiTdlMnBA/O479FlfP7fH8Xrf/ypuGiuMewlEQXiuF0DFgr0iFGmqj06SdWGKqyvSA4emMc/ful+tCwHb3/1DcNeDjGmzM3UIoM6kgmPNhToFYShqRIHpnercn4Ayf+6dkqPnqFraNR1LFNFTzpMwscqPcT4YgXOuL5k2/M8KIoy5FURVaWqwVSVq2ZbvdpgOS49S4hCOXxof8/9D5BMeBygHr2CMHRNWkWvbTlBn51paEK9f+2U8QoADU0vCpLybR1YRa9u6j3/TRD9VNnwoKo9OktrLehadOJkq1QbLNslR1+iUA4emMetN1+DiZp/3pydMnHrzdcMPclD5IMqegUhU7rZtl2YRqhHT3S8gpYc6G2bMqmiVwAswJN1HRDVhX3GdVPDyrr/2esp9x1RLFWtmlVZgljFqtn3HrqA93z0BDzXg64pPVWtrVRtsGwXDgV6RMEcPDCPR86s4ei/PkRtJ2MCBXoF4Us3ZZmxOD1mLCKBQ9p4BcCv6H3voaU8S4ylqoetMiC7/a0D+4zrnV5aklgNF5IgZmPYPTr9z4srH7cN/3b3WezaPoHX/dhT8PCZNfz1x++C7Ww9UxLLduG4HsnCicJhzy+HnmNjAaWcC8IQ7KVLwq/odaWbLcE5eknjFYBORW+9Bc+Te1NXWaJUBiTd3DoEgZ7p32uUeR8uVZUgAtWeizZMK/+o58W/3nUGl+yZwv/n1mdg364pHDwwj6svnsX+x83g7a++YcsEeY7rBnJwSiIRRWO7/t5JLQjjAQV6BWHK7NELVeVqpgbbceFyBGWe58GyXehpFb1GDbbjYb1pS1kvo8qHrTLoSjdpsxx32OGr1unRo8PYcKly1azKc9FYjw7rh5ubqZXWoxP1vACA1Y02JkLO0Zqmbrn7y7bJ1Zcoj8BIzqVrbRygQK8gDF2edLMdkm6yyh5PEOm4HrzOWpKYne4MTV+Vewiq8mGrDEi6uXXor+jRA3K4VLlqxoKpmsEMD8oLpng4eGAel85Po2ZopVbNeJ8XuqZuuT01bMKy1f52onxIujleUKBXEFIHpoekm+xwwDM0nblz9meP+9nW6MzSW5cbgFX5sFUGFOhtHQYCPXpADpUqV80AP5h65hN2AwDe8BNPrUyQx7Adr/R9i/d50W/IshUInyW22t9OlI9D0s2xggK9gjAlBnrhEQkiFT2WBUyr6N2/sAIA+MMPfxuvf/eXpfXQVf2wVTRMskmB3vjDpC416tGrBKxq1qj7kr+pCb1SVTOguy9U8TBlO13jj7LgfV7omhrcb1uFcL8/jVggiqZb0aNrbRygQK8gfOmmJDMWq2uoUuuMWeCRhbKHQ9J4heMnFvD3x74f/LdMwxR22NJUv99j+3S1JEpFw7JilIEdf+y+OXr0mQ+fgwfmccuNVwAAfrSC7oxBH0wFr5XgoFdiEHrwwDx+6gevDv47rj9Q19QtJ40OJ3bp8E0UTaBGqmASihCHxisUhKFrsCw3txUyc9sK5uiZ/NJN9nAwjPhAr+iZTgcPzOMfv/wATp/fwG/8t6dh57aJ3K85KrD3n+bojT/s0D4RSDfpM68C7N5rteUk3WRSZVdedj07jocU02apPOmKOQDAj7/wKrzoGRdHfo+uKVuudyhcxaPnCVE0LJmw1e6zcYUqegVh6io85M+IdvvsuuMVAN6KXifQ0+Kf1GUYpjRbvpunyFiIcaBrh721/u6tCMt8knSzWrB7r1nJQK/C0k2mRii5cnah89zZMV2P/R5dU7ecfLEdenZWsQJMjBfdiv7Wus/GFaroFQTri2tbLvQE6WQaQbDW36MnUtFL6NErY0DuZtsP9HiqkOOEFUizaLMcd1gFpOu6SYexKsDuwWYF954g0Kvg/jAsWen5lSYAYEfC80fX1KG8Z/3D3Msc1k6um0SZ0By98YIqegXBmsrzDk1nfX6BdLMT6LVEKnoJgV7RhimO6wbZyK0W6FGP3taBHcRqBuvRo8NYFWD3XhWlm5ZT3f2BJSrKDqjOd0b87JhJquj5rptlGsVEDXOX1cvOQ6/rJu0tRLHQeIXxgip6BWHo/O6YSfRLN2sCrptM3pk0XoFlJG//xN1oW670TGVYMtXaYoEeuW5uHdiBuF5j0k16QMogbxWl0tJNu7ryKHtIaoTFlSZ0TcX0pBH7PUwh47heMNi9aIruZU/DzhnoDbMaSYweQY9eBfcmQhwK9AoikG7mDPSsvmDNFJijx1PRA/xg7/5TK/jydxbw9lffkGe5A2x2+vOA3j6DUSDPw9HzunOotlo/yVbEcpjrJpmxyIJVUdgeyqooALjvw8CMpYJJJruiFT3P8wLJVtnSrfMrTeyYrkFNMDBjgZ5l52uLEKGMXvYk8szRk3EfEVuLYbjuEsVBgV5BdKWbOSt6gXSzU9EzBcxYnM54hZRADwCm6gY2WzYc14Wmynt4NlujWdHL+3AMb5BbbebTViSo6Blbb2B6UdUCGVUUlmRpte2U7ywfq6JmLOFrt/QevdVWYn8egKCKV+b7VkYvexJ5evSGXY0kRg+bXDfHCurRKwg20kCWdDOTGYvFV9EDgMaEL5VZb8o9EG2GDlijFOglPRx5sKl5fkvBDmLBHL0tInkpsndJRhUlkG5WcO/pVvSqda0Mc++6sNLE9gTHTaBb0StzbUX3sqcRPkeInimGXY0kRo8qOwIT4lBFryDYkPK8Q9MHzVj8/8tlxuKwQC99EFKj7l8K65sWZibNTGuNYjNU0Rsl6Wbeh2M4E25RVmzssR0XCroJnlHKhOapyBVZLZBRRWHV9Gr26FU/0CvzOnZdDxdW2xwVvU6gV6JSgl3L7/2n78L1gO3TNbzseSW6bubo0Rt2NZIYPWi8wnhBFb2CEJl3l0S3z85/PV1ToSh8bp7dOXrDq+g1R7SiF/cQ5H049h6WaLMcd2zHg66rwb02Kp953opckdUCGVWUKg9MtyraB9Mr3SzvOl5aa8H1PMwlOG4CXelm2SNMDh6Yx7Ypf///lZddW6rsMZwwFpXTHj60f8C0psxqJDF6sABvlBKWRDwU6BUEk0vmzTqyKlit83qKosDUNa7qWGDkYnAEevVOoLdpZV1qJL1mLNU7bMVx+ND+AcmryMMxfEAiM5bxx+4YQwzrEJqVvBLl2al8CZEkDh6Yx603XwPmyzE3U8OtN18zPq6bFe2DCScpypQgd0crVK+ix2DPVLbWsn8vIB58Hzwwjx94+r7gv7PcR8TWgiUTRuU5RiRD0s2C6LpuypFuGkZXfmnoKlelsG37cjJNTbegbkx0pJtN2YGev35NVUaqonfwwDw2mhY++Ol7AUBY0taTFSczlrHHdlzomhIYGVVNjhdH3orczm01LK31fq/MasHBA/P4m0/dg12zdbzlVc8S/nlWNava3uN5XmWlm73GH+Ud9IJh6bw9ekOQlbHP6kJnrWVh2S5URYEbcnMW4crHbcMn8TB+9NAV+KGDl8lfIDFWdHv0qrU3Edmgil5BsLl3eaWb3Tl63Y+qZqhcZiy27cLQVSgJVtWMbkVPvnRTATDTMCt32Erjuv07AQAHD+zB2199g1glIYcdNjF62I4HXVOhqgpUVRmZzzyPRPn7j63gvkdX8MTLtvf8nOxqgeO4md/PYMSJ7Vbq0OK4HrzQ/64S4QpjmRLk8yu8Fb1O1dwu/31j11PpFT3HxUQtu6OvFboPCCIJ1/XgdS6xqqkNiGxQoFcQhqTxCqwXLywjNHSNb7xCJ9DjYbKmQ0ExFb16TUPN4JObVong4Zhhswtnm0m6Of6wih7gVxxGpUcvqg9OUYBbbrwi8ec8z8P//ey9mGmY+MUfPuC/1o1XCCdEeLAcl6snOYpwwqXVrs5nUmVX3vB+VWYQen61ibqpYaKWLDQahusmwGaj+u/H+ZIdKy3bhWlo0FQl09/NgmIK9Ig07CHd/0RxUKBXELICvbbtQlOVnsGwpqFy9bu1bYc70FNVBZN1XXpFb7Nlo27qqBnayFX02GeXRXrJHqymoVbuIEfIxw/0/HvN0Eanosf64FiQOlnTg2xuEv929xnc9+gyDt94BaY6Rk5FXOcsu5x1Hw0HLVXaf4Y5qy4NZ0hmLOdXWtgxU09VoAwr0At/ThdWy5VuMnWOpimZ7gX2XuVVGBHjT/g6p0BvPKBAryCCHr2ch4u2NViVM3kreg5/RQ/w5ZvSK3ptGxM1nTs4rRJBQ3KWDGrnZyZMvXIHOUI+tuMFjpu6ro7UHL2DB+Zx8e4pPOmKHXjnr/wnXPm4bfjQZ+4d6L1jtC0HH/n8fbhk9xSe++SLoCgKNFUp5FDAArWs9xDrbQJ6HYCHTfiwXiVJKTDcHr0d0+mSYV3vSDdL3lfDz4FhmLEYHVffLHI6dr1lrYwTW4fws2tUlClEMhToFYSq+FW4vLI9y3aCUQ0MQ1e5snr+wyF9hh6jMWFgTXKg12zZmDC1TkVvtDaNPAON2c/UTY3MWLYAluNCY4HeCEk3GZbtwuj0GL7qJdegbbv4wCe/By+ivPfJf3sYiyst/NgPXAW1Y/SkqUoh/RxOzt4i23Ex1TGaqpLz5rBm1fHgOMM56J1fbaX25wGAPiTDI/YsN3UVF1ZbkfdGkb/b1NXMZwqq6BG8OBVWGxDZoECvQAxdhZUzuGlZ7kAPjd/vxjdHT6iiN1GAdLPtoF7TYXKuuUoEWdBMD1Z/g6ybOkk3twCO48II9eiN2gMyvFdcNNfAj/yny/HNe8/ha3ed6fm+C6stfPz4g3j643fhmku7JiyaVkwV08pRVfd/zgtmhFZplt6o9OiVdR1btouV9Xaq4ybgV8yBIUg3O8+DXbMTsGwXa5JHESXRthwYnfEtWf7uoN98xJKtRPn09ujR9TIOUKBXICbnGIQkrIg+O97xCqKB3lQR0s2goqdWqkeGh0A2lsHdLZBu1rTKHeQI+fRX9EbtM7cdN5CeAsBNz7wEl180gw9++h6srLeDr//9F78Px3Xx8uf3jk8ouqLnuB7cDNJQy3aDHsJmhfafXulmtZICPRn9kg56F9aY4yZHoKcOV7q5e/sEAD/pURasDSPr3hK4z47YvkSUD5mxjB8U6BUIr8QyiXbHbSuMqatcWnsmx+KlUTekD0xvdip6vFXIKmHnqOixn6mbOj1ctwC24wVJFUPP1kczTPqTQqqq4Gd+6Alotm38zae+BwB4YGEFX/7OKbzwGRdj9/bJnp/XNKWQ7G/40JFVsjZVyYrecAxPeBhGRe/8cmeGHod0UxuSGQurLu+a9QO9Mp03WRuGrmdTC7BzyKg9g4ny6TFjGbHnGBENDUwvEIMzIEuibTkD0k2Ts9/Nsl006vwfcWNCx0bThut5gYFBXvyKng5Fwej26OVwOavXtKHMeyLKJey6mVVeNUwsxw0kcYzH7Wzgvzz3cvzdse/jl//4X7C2aUFRgD2dikYYXS1GrhoebWLZLmoGf8+x63lwXA/Tk51Ar0KH3Cr36PWurZzr+PwqC/TSK3rGkKWb7Po/X6LzpmX792fmih6NVyA4oYre+EEVvQLhdcdMwrIHe/R4K4Ui4xUAv6LnwQ/OZOB6HpptBxM1LejRK7OBPS+yevRcL5vsjBgdbLt3jp49Yp+3ZXuRe8XsdA0KEPQjeR7woc/ci+MnFnq+z6/oFSfdBMQP9uxgznr0qmTG0lM1q9i1Mgx7dVYd287huqkNWbq5Y6YOTVXKlW521Dl6xvEK7HojMxYiDafn/qfrZRygQK9ADKMg6aahom2nB01ZzFgASJNvMqmUP0dPhYfRyijKct3M+hrE6GA7Xreip4+W66Y/CDpa5v0PX/w++neZtu3iyLGTPV8rymk0HBCJ7h3sZ7vSzeqMVwirBKp2rQzDKOb8agtTEwZXxXZoFb2Q6+bslInzKyVW9JzweAVy3SSKo8pqAyIbFOgViKFJCPSswaqcqWvwvPSMpv9wEBivUPcPRGuSnDdZZXCyrgfBapXkU2nkcfyjQG9r4Us3OxW9gmSMRcGuzaik0GJMH1L/1zW1mCHxeYZ3s2CqZmjQNbVSFT32txQ1fzAP4bWV1qO30uTqzwPCFb3hjFfQdRXbZ+qlVvTYwHR/vEJ2czCb5ugRKTADJlNXK7c3EdmgHr0CMXU1t4tl23Zh6oNmLEC0I2cYK2LYehJM4iTLeXMzqOh1198eoT69wKksk+um/zMTpn+LZXk4E6NDT4+eLt5Hc/zEAo4cO4nFlRbmZmo4fGg/Dh6YL2KpA7BkVFRFb26mFhnszfUdyvWCpJsyKnqGpqJuatVy3QzP2axYEqgrO9fK69FbaWLntsHezygURen0wZYs3ew8B3RNxY7pGh44tVra72YtHFl79AIzFqroESmw+8o0NJJujglU0SsQw9ByV/Qs24Vp9PXodapjaZs2k3vwwoxbZEk3m52K3kRND/6Gkaro2V3ppmhvYVBNYBU9esCONT3STcFD6PETC7j96N1BQLW40sLtR+8e6IMrCpaE6DdjAYDDh/YPmkHpKg4f6h+vUMxIiXCgIdora4f+rpqhVct10+4GU1Wr/rK9yjTKW9v5Fb5h6QxtCCNM7FDiYMd0HedLGprueR7aQUUv3xw9CvSINNieWzM0km6OCRToFYgs6WZcRS/JKtnzPPHxCkFFT5J0s9MTM2HqQe9Fe4SkI3ncp1iFxxiSFThRLr2um6pQJvTIsZMDB7CoPriiYM7AUXvFwQPzuPXma4IK3txMDbfefM1AtbGoil7PGALBvdS2eyt6lQr02GHK1CuXNbdd/1rWNaWUOXrNto2Nls3luMnQ1fKdbXulmzXYjovVEoams3vA0NVMagEgNCpohBQ1xHBg11vN1Ei6OSaQdLNAfNOU7BtrOJPX+7rpFb2gcdwYZkWvI92saWjbnR69Ch220ggH6ZbdPcjz4Fd4lKBKQrP0xhdm4x/06Akexnj74IoikG7GVP8PHphPlZFqqgKrgHvbliDd1Csv3azWYcq2O3uXVs48SOa4uYPDcZORdZ5cHliwpGtKsNYLKy3MTJqF/t6wtDpr/y/7GX+vcqGplOMnogmSUIaKjVZ19kwiO3S3F0jegelxwZoRVPTiXzup7yYOTVUxUdOwJqtHr9Wt6HXNWEYn4MkzrJlVeNjhnyQQ44sTCigA/54TOYz197ulfV024YpBVrSCRkrkuQfDAWytohW9MvvgeGEVvaLkuP2IzNBj6CWtLUyPdLOz1jJm6QW9pjkqeuFzyCj1yRPlw6p4vnSTrpVxgAK9Ask7MJ0FRf3SzVrIjCWOtCx9HI26gXVZrpudg9VETetKNyuUVU8j/HAUlo11XBjZ4Z8qeuNL0AsWlm4KfN68fXBFYdm9gWoWNFUp5FAQDphFk2Z2EIArqBlatVw3Q46gVZNHOZ29q6jZiP0EFT2BxEbWgCcP4V5WNu+vDOdN9pzXc/To5amME1uLbkWvensTkQ2SbhaIqfuyHNf1oHYsoUUIgrUMZizdQI9/vALQCfQkV/T8OXr+a46SGUueeVLdil6nR48ermNLOOMOiMvKmCzyff98FxzXG4LrZqdHL0dFz+9LLLail+UeBPzPo25qaFZojp7leFDgH6aq1r9r2V63R6+Mit5KEwqA2SmBQG8Yrpuhyv1ETYemKkGQWiThpK3f9y/+d4cTjaPUJ0+UT9hIjgK98YAqegVi5uzPYhtyVLYfSK6OtbNW9CZ0qYFezdCgqkq3r3CEAr3wSATR8QiW4wUPZoDMWMYZ9mDUWI+epgqbWBw8MI+dsxPQNRVvf/UNpQV5wGCgmgWtoKDAtrNXIsLy9ZqpVyrJZDsudF3tvG/VOkw5zIylpHmQ51da2DZlClWUs44ZyEPY3EdVFMxO1XChDOlm8Hu1wG1U2AU65MBNFT0iCSbBN0m6OTZQoFcges6NtR0j3eQxY6mCdLPZtlGv+WutjWKPXg7ppuP4De+67h/+Sbo5vrAHoxFIN5VMPZm27cB2XLglWLb3/t5e6WkWihr8He77Ew06esxYKjdeoVvxr1rW3LK7/cVlHPTOrzYxJ9CfB6C0tYWxHBeqogTqnB0ztXIqeqFefYP1fIu6QNsuJmqdma4U6BEJkHRz/FC8MgbBFMR/vO330L6wJP11DVOHJUHms7TWxunzG7jicTNCpiiMzbaDhxZWsW93A426EaxtY7ON7z+6gvm5SWxrRDt+dX92KnDT5OH0hU2sbrRx5eO2Ca016j177Nw6WpaDyy+agQfgnoeWsHNbHXPbxB7qecn6eT58dg0bnaD3kvlpTJj8MthHzq7BcT3M75jEA6dWsXdnA9OThrS1lQGtjY+W5eKBUyu4aGcDM5MGLqy1ceb8Bh5/ySxEBNv3PboMx/Fw1cWzyKD0TiXuPVvdsPDYuXVcdtF0kJARZeH8BtY2LeF9I21ti8tNnFv2qya7tk8IOTMur7exsLiBK/bOYHm9jcXlpvBnkrS2PJw+v4HVTQvTkyZW1tu4ap/c9y0Pj5xd9xNVnerRZfPTha7t/lMrqBka9u5scL/2w2fW4AG4ZPdUYevq58zSJpbWWnj8vlkAwGOL62i2HFyxd0b4tUTWttGy8fDpNVy8ZwrNtoOzFzZx1cXboCr8V/K9jyxD1xS0LVf4WZa0tqpAa8tG1NrOLTexuNzEjpkaLqy28PiLZyuxLiIec/ssnvT//lrsv1OPXoGww1rWUJrF4Erfhs42eDch2+K57GfFfqfMzLzrdXsTlc5ayq5W5CG8VNF8iOcBCpTgs/NQnb97ZaONs0tNv6qgq9g1Wy/cInycYZ8tu9eCe84DRKIKdol5nid+4+Ygbp8RQVGUzPtcEuGXzHIPAv7a2D7keV6uv1MWLvx1KQoqtDP4sPeorLfJst1ghis3SvcZVxZsT2cYmoo1p/g5euHrWAl/TWhv8aB2RiqU/b4Ro4V//3f29GEvhpDCSAd6F/3iqxODnazs2jWNs2dXc7/O1+8+gzv+4T/wWz/zLOzLkHm88+Qi7vjIt/HGn3o6Lt67LVjbI48t4bf+4Bhe9rz9uPb6S2N+9hzu+MidPT/Lw3e/9hA+/Ln78Ke/cmMg9eAh6j37wAe+AUNX8dwffyoA4Pf+6Iu4/onzeNqLH8/9ujLI+nm+//1fxyNn/arkr77yOlx8+Rz3z37gb74BQ1Pxqpdcgzv+7DhedfM1OHDdXmlry8rxEwu4/ejdaNe78h1TVyOHYJe9NhGqtLbvP7aCO97/dfzKy67FxVfuxIMnTuOOj53An75G7B767d//AizbxR/89xsCVz+ZxL1nx771KO74xPfw+69+jpDFfZgvf/ZeHPv2Y/izXz0kdW3HP38fPvuNR2DbLn7oOZfhKTdewf2ad3/9YXzoM/finb/yn/Dg987gjk98D3/wavH3tohr7eP/eAL3n1rB06/ehU//28P4i9c/P9PrFLG2D/7NN6CqCmYaJh46vYa3/cL1ha1tbdPCB//4X/BjP3AVnv7Mi7lf+8hHvo0Lay285VXPKmRdUXz26N349n3n8I5fei4A4Htffxh3fOZe/NF/fy5mYpQ1Mtb27/ecxR1HvoO3vOqZePSxFdzxye/hD//HDdzmNZ7n4X/97ufxpCt24D++fx6/8rJrcfWVO6WsrSrQ2rIRtbYvfeZe/Mudj+GmZ12Cj37pfvzV658vVD0ual1EPGlmj9SjVyDBvLvMPXrMjKVXZmFwmLEwHX7/z6bBJKIyhqZvtu2eg65paJUyREjDctygx1C0r8GXPimVM2M5cuzkwPXYtl0cOXZySCsafcJufOH/K1IZ9zwvuMbKdsWzQ7bxWfHHKxTQo9fpFzMy2OmHB1zXgx7hauw/tu32DMCuUgeF5Xi+UYxavOvm+ZXODD3B4FvXyxnmHoY5KTO2T/tJkaJHLIT77dlcVpGecbYPTVKPHsFBd44mzQAeFyjQKxCTY95dEt1grfdjUhUFhq4mBpB5XDcBYL2ZXx/dbNk9vQCmoY2UtbNlu8HDUdgIwvb8g1zgvFqNzXIxxjwg7utEOuF5bUA3YBI5JIeDQqtkw6KwO2VWNE2FI+g0yoPtejA68yiFXTdDbqK1zj5UFUMWqxM0aBnNNYrEcVzoqtr5TItdFzMzEe3b1jW1dIMr5pTKYHP/WLBaFOH7M8tcVvbzFOgRPLAkdXdvoutl1KFAr0DYDLvMrptsvEKEQYKpq4kHwqyHN1bRW5MwYmGz5aAequjVDLUyBy0ebMdF3ewEepkGpofm6FWkojcXM5Q47utEOuF5bQACZzyRzzy8R2RVAGRFyhw91e/Rky2lt23fFCRTRc9xoSiApqpBRa8qs/T8oEHJVP0tGqsT0JQxwuD8asaK3jBcN203uLeB7prPF13RCyUs2PNcpMrCfn6iY8o2SslWonxsx/MTPWr19iYiGxToFUh+6WZ8Vc6v6KVLN8UrenKkm57njbx003Y8TDDpZqaB6SHpZkWyqIcP7Y+cy3j40P4hrWj0CaSPap90M8NhDMiuAMiK5XidgCh7H0ZR2V/bdYNKhmjCzO5U1QGg1knYNCuSaOpKN5k8qhr7A+Bft7qmQFeLn/G3uNKEpiqYFuxx84PQsqWbXo90c7ph+kPTC56l1yvdFK/osWcPS+KWnUgiRgt2diHp5vgw0mYsVcc0cs7RixmY7r+2VsgcvalO1i9voNe2XHgegkAJ8OeybLaqkVHnwQrNHspSTdA1FaqqQFWUyszRY4Yrf/3xu2A7HuZmajh8aH+pA7rHjf6KXhbppj3Eih4LOvK4UbLsr+14MCQ+VfyKnu+amWVguh4EehXr0XM9TBr+AGz231XB6kg3dU0tPAC9sNLC9umasNnDUAam90k3VUXB9ulaCT16rOKudXv0RPYWh6SbBD9OJ6GhV1BWTmSDKnoFwrLJWaUSScGaqaspZizZ5FiTgXQzX0C22ZFITZhh6eZoVfQsJxToCUs3vZA5RzFGFVk5eGAel837s5/+z20HKcjLCbtPgx49rRv0cL9G6ODWHkKPXh7ZJoDCDgW24wUVPdGDvRU6mHelm9XYf5jJTFAJrdD+4HTeN02TN2onjvMrzUxOr7pWfLWxH8txB1ohdkwXPzQ9vL/oGRQirD88q7EYsbVgSepAulmRJDWRHQr0CsQwcvboWS5MPTrTbuha4utaQX+KWKbU0FXUDC13RY9V7uq1sBlLcnBaNWzbDQLVbNLNrpSvKhU9Bjs023Z1DpijCjsMG1qvdNMWkDGG7+XypZtOLsdNAN3KlOTr3Ha7PXrC0s3QwZztQ1XpEQ764NRi3rc8WI4HXfWDCsf1Cp19en61lak/eCgVPbvXdRMAts/UcaEE6abROQcEagGBAJwFhWanIkg9ekQSgXSTKnpjAwV6BdJ13cwu3YzLtNdSgibLdmHqWiY5VmNCx3pOMxaWOR+s6FXnQJOE63lwXA/1juRLNHtsd/pcAF/KV6WDHND9e6oWgI4i3Yw7M2PJ0KM3VDMWL5fjJoBQr5l8Mxa/11Xc6j8stauxil5FEk1+EFrNwxSr6OkFVxtd18OF1VbGil7xQWg/7AAcZkdHulnkOiy7m7DI0vPdY+aia6W7+hKjhe140ELjFaokKyeyQYFegeQ2Y7HdSMdN/7WTe/TaOeRYjbqB9c2c0s1ORa/HjEUfHekmkyuYhvg8Kc/zeip6hqZUxoyFwf4ekvHkR8Z4hfD3lv2ZWE5+6SYLWESqmDzYrhfM0cvSo2eE5LS6plSmomcHfTBVrOgxM4Zi17a83objesKOm0BIKlzi+2Y53sB9sn26BtvxsLaR36U6/vd27092n2UxY9E11W/5oD2fSMCfo6eQdHOMIDOWAtFUBYqSXYrVtpxIIxagM14hxYwle6CXv6K32fL/5npojl7N9KuQnuflMn4og/7ZRSKHTJadD8w5KizdLFsmOI4MDkxnhgnZKnplB3q2Pdh7JEoWp1EemHxaVQDLEduTLKdXalcztMqMV7BsNxhKDlSnR891PXgeSpnxx9wqt2es6AHyzX+SiJJusmrk+dUmZgSdQ3kJP8uzqAXY/mQECRPa88vg+IkFHDl2EudXWtgxQqZntuOhUa/mjE8iG1TRKxBFUWCm9NIl4W/w0RU901ATq2N2nkBvwsg9MD2qolczNHhetbLXcbAGdiZhEsqgRlR4yjYOSINt3lUZ5D7KBOMV+nr0RDKh4Xui7B4aKRU9VdwNkAcmgc7Uo9e3B9ZNrUIVPTY2ophKaFasUNKi6GrjhY6JSbaKnviYgbzYTkSPHpulV6AhSzjQyzMwXdez3UeEOMdPLOD2o3djcaUFD8DiSgu3H70bx08sDHtpqTiOC01VunJ8CvRGHgr0CsbIIZVo2y5qRvRHlGbGkl+6mbOi146WbgIYiT69sNzF0FUh6eXAwV+tXo8eu3aqJikdRWzHhar4IwAAZDJMGGZFz4qoVIhS1HBddrjONjC9d+5ZzdQr1aPnV82KqYRmxQkHegUF74zzK35Fb25bNtdNoNz3LWzuw2AVvSJHLIR79LKMV7BCFT0zpeWDkMORYycH3ue27eLIsZNDWhE/bN/UKjjjk8gGBXoFY+hq5ubnthVvxmIayQGklUOOxcxYvBwN5k3mutkj3fT/9yg4bwZyF13c2n1AyqeLG0kUDRtsTdnd/PimH10pspGhEjI24xVkSzc7AVGWgen9lcqaUY2Knt/D2x1KDlRH5dBNUnWt/IuTbrZQM7RgvpsIw+httPrucwCYnjSga0oQtBb1ew2jf0anuHRT11UYBlX0ymAxpsIb9/Uq0XXdLPb+J8qDAr2CMfXs/VlJZiymrqXO0ct6eJuqG7AdL9eBc7PtBEESgw2QHwVDlnCPnqGrQhLHbjVQCV6japUzqzNWgfo18mPbXmCTD2Q7hA51vIKEQC/I/kqWIDpuV7op7HzbV6msm1olKnrs7zB0tXKHqXCSSsvQayrC4koTO2Zqmfq1tQyVrbzYtjdQ+VYVBbNTxQ5NtyxncHSLUOKxO/7FN2MZ/j0w7sSNDMkySqRsHLfXdbMqexORHQr0CsZIGWyehD8iId6MxXG92IOV5cT/bBqNCX9oeh5DlmbLxoTZG6TWdFbRq1bQE0V/r4qQdNMd7NmqUi+c53mBHKNqJjGjiO26PXPo2P8WM0zoVlLKllZF9R6J0p2jJ/c6Z7LSrBW9sB1+VXr07J69pWoVvcHh3EVJt86vtDL15wHhqnk5+6rbGeUQpZLZMVMvvqLXeXYG14vIeIUe183sngEEP4cP7R+YYWzqKg4f2j+kFfHTlZVXyyiKyA4FegWT1kuXhC/djDNjSQ6aLCveyCWNRt2X0qzl6NPbbDuo90lyTJP16A3/sJVGV+6iiEs3Q9VAYDjDfZNwPQ9s66aHfn7YrDdGnoreZN0Ywhw9eeMV5Ff0uj16mQamh6WbFQn0wkmkroV5NQ5TVqi/uGuwU5zrZpYZekA4sVDOvWKF5I/97Jiu4XzRPXpsvIKqQlHEzHvCgZ5OZiylcPDAPK7YOx3899xMDbfefM3IuG7qavHjVYjyoPEKBZM2BiEJX7oZZ8bSHcY+EZEUtRw38qHEQ6POKnrZnTc3W3bPsHSgW9EbiUAvLN0UHNYcPsgB1RuYbtvdgxs99PPDZr0xsoxXYNfHZE2HVfL9IcN1Uy8oYLHsjnRTU+F6voJBU/nW2j82om5URLrJ9ha9eq6bTp+SASjmoGc7LlbW2pkDvSx9sHno77sOs32mhgt3+0PT1QLGBvUnYvxWgAzjFfSOdHMEFDXjAFPI3nT9pXjl86pfyWM4rKJH0s2xgSp6BZPHddOyncCpsh8my4zT2yfJPtOYYtLNHBW9ZsvGRK137SxoHQUzlnAGVxfss3RCMjwAwoFi0YQPlRTo5ac/oGCHMpHqVreipw+nopdbuskCFnmHAiYxZhU9AEIHXGvAdbMac/TC8siquW72StaLc91bWvVt57NKN7NIGPPQTfwNBnI7putwXA+rBQ1Nt/rcPkXnsoavN5Pm6JWC53lYOL8OYPSesbbjQdeVwlQaRPlQoFcweQaUtq34il6adDPXeAUJPXqbbadntALQdd0chYoeMyvJMjC9v6KnadWaoxc+HFGPXn4sxw0eigBCkhexrLumKqgZ5ffQ2Dmq/4wi+rkc15cYh/vFROeHhf+umqGhbblwh5yhDssjq+a6GU5SBfLIAt6vxU5PW9aKnl7g2qLo39PD7Ahm6RXTp9df0dMzKEx0TYGiKDBovEIpLK21sdnyzzlVM2JLgvWi6mp4vEJ1zi5ENijQKxgz48HNcV04rhc/XiEk3Ywi13gFGT16LRv1PunmSM3RCz3YDcFAzek7FBgZjCSKJPy3jNJDqKo4fRl3VVWgqYKHsU5QYpTsiud5npyKXgH9XEHQoas9UnUe/BEGfdLNzn407ERTWBZeNdfN6Iqe/LWxnrYdGV0Iyx6vYIeuxX62d/6Gopw3BwI9wVaA8JxMk8YrlMKpxfXgf49SMpVdV5qmFDYblSgfCvQKxtCySTdZpS5Ousnm6iRJN42YamAapqHB0NVcPXrNtjMg3awZozlHT3Rgelf22RmgXTEzlh7pZoXWNapYjhcc2BmapggdkJk8K09PbxbCVv956M5ck7d2dp3qqni/GDuc9FT0KqIoCM81y9LPWSS9A9OLC6ZY9WvHdNaKXrnvW3jcTj/sbyivoieWeLSdbsLY0Hy37mFXtcedU4sbAIBtU+ZIBdZ22IxJox69cYECvYLJOqCU/UysdDNhVIEbkc0WpVHXM/foeZ7nm7H0u26O4By9wNo9w9widlCq2sD0HunmCD2Eqopf0evt3dFV8ay7n1TQSjVLCCc08lBERS88j1K0ohd1MK93Ek3Ddt4MAr1O5RcoboSBKEyy7ks3izOKOb/aQqOuB8G3KOVX9OKlm1OdoelFVPRc1xtQ9giP+wlV9NISxIQcHltcx0RNw85t9Uo9+9MIkmshWXlV9iYiOxToFUzWDD2resVKNxM27LCrW1YaE0bmip7t+LLTet9DnDk5jYLrlxU6AItKZcIZewCB9NPzqpEZCx/GKdDLjxUxh07XFKH+IZaYMQ2xpEJewgmNPOgFZH/D2eXAjIXzvelKEHvn6AG+2mCY9Bg9ldxrloYTOugVaRRzYaWVuT8PCJn/lLR/BfeJPmjGoioKthc0YsGKeJaL9uiFk75Bgpj2/UI5dW4dF801Kte2kQa710m6OV7QeIWCYT03nudBEbBeZhtxrHSz8/WoTYQdJOJ+lodG3chc0WNNyP0VPcCXb45CRa93cLDgg9XuPWR2ZW1ez8FzWJDrZjTHTyzgyLGTWFxpYW6mhsOH9nPNPbL73B0BZsCTrUevTFe8qINkFrrz4CRKNyOs/nmv16hkV2Wkm3ZIHlWxrLkV2ruKNIpZXGlmdtwEQuMVSjqEBpXvmITI9ulihqZbEb/XEN1bQmZLXffaalxv48qpxQ086YodWFprj1ZFL1AbqIU4KRPDgSp6BWPoGjxPPCuSJt2s6fEySBmHt0Zdz+y6udmxMO+fowf4f8+wD1o8hA+KflZOoDrjdg9y4f9blaCKXDcHOX5iAbcfvRuLK35WfnGlhduP3o3jJxZSfzbKtVLXFKHDOxvRYJQ85ypcuc5DEYeCcGVctKIXJbWrBRW94Y5YCAcNiuLLN6uSNXdCe1dXHlmAGctK9mHpQGhgelnjFRKkm4BvKlOEdDPqWa5pauDcyvsaLFBkr0MVveLYaFpYXm9j7whW9HpGv1QsCUVkhwK9gklzx4yDBUPxZizxFb32kKWbzU5Fr14bXLtvcV79QM9yXCiKX6lgZiq80ku7Tw7XNQ6oxoYZPoyP0kOoSI4cOzlw+GnbLo4cO5n6s3bHvjyMqGGCn3VXYOpaqWYJYQfIPBRxKOj2uirB+rh79CJMZliPXpWkm4AfJFdlb+jtTZb/mR4/sYD/+adfxnrTxle/e5orkRJFt6JXlnSzN3nXz47pOi6s+kPT5f7ewRYOQzSJ1NlbgND83RF4Bo8qzIjlorlGpe5tHpyQXL5qSSgiOxToFUzWDFpaVa67YUdINyUEelO5pJvxFT02y6rq2LYXHCZ0XSyz3W1oVjL9fNGQGcsgrJLH+/UwkdJNQTMWVtEL7uuS5Jv9QUdWijgURFX0eCvQ/ckWICTdHLYZS7+0W63OnM2w66bs0Q+sas562TZbNnfVvJ+ye/T6+6772T5d84emr7el/t7us7ybNBU2B+up6MUniAk5PNYZrXDR3KSwcc6wYWcXdn9RoDceUKBXMMEBRTCDxg56bCRB3OsWZ8aio227mTJ/QaAX0aNnjkiPXthgI8geZzxkBtWIimT22KFSQXWqjMNmW8OM/Pocx5yvsKsdQ9fEHpCsj6ZsaZWsHj1AfmUq7E7JgiL+it5goMfm6DWH3aPX13elCV4rRRI2sZGtRMhTNe9HU5XO/lV2j150jzWbByjbkCWqR883BxPZW7qJKJOkm4VzanEDuqZg52wduqZU5rnPQ9gACxAfE0RUEwr0CoZJL0Vvdlb1ijuAsUb+aOlmsmMnD426AQCZ5JusRy9auqmOhGzEdrqzi0SrCZbjQUFXzhb0ulTk4cqc9eo1jTK78D9rLeIAZ+oqDh/az/Xz0dJNETMWr+O62dkvSqp6J80HE0VTVamHAjtwgBMfmB5pxlKR8QpMVsqqQ1WasxmWbqmK3GAqT9W8H0VRfMOjsqSbaT16wSw9yYEeu45DvfqiVaLwwHT2OrTvF8epc+vYs2Oy2/YxQu+103eda6oqdTYqMRwo0CsYI0FimQQL1syEYM00oo0bZBzeGhMs0BOXbwaum5FmLCNS0Qs9HHVBmZDjuNA6Gnf/58ud+ZQGuz7qpk4PfAD/fPxBnF9p4UXP2Ida5yA0N1PDrTdfk9l103dqFRyY3lPRK1e6KaOiJzpSIo3wjD/ReyjKJdHQ/eTYsPeffmMPTRXruSoS23WhKgpUVekEU4q0YCquOs5TNY9C15TAwbRoApVGnHQzqOjJdd5sRzzLdcHPpCdpGfS6Vv8ZPKqcWtzARXMNAB2Z7Qg9Y7sVPZJujhMU6BVMVjOWrutm/IgEQ9ciD4Q8P5tGo+4HaVn69Jir3USsGUv1N76wk6LoIdM/tHcrPF0JVDU2TLZxT9b0kZKVFMEDCyv4p688gOsP7MGPv/DxuOHJF6FR1/H2V9/AFeS5rgfX8waSKpqmZnLdzLpfZEWWGQsgP2Bh95umZhiYHvRU9VZa66Y2dDOW/vdc09TKHKZs2+t5z/zrWM7aDh/aP5C45K2aR6GXWNFje3fcfTI9YUDXVOnOm1HSakN0YHqoDYGdCUi6WQyW7eDs8ib2zk0CEJ95OGz6K9ck3RwPaI5ewXQPKII9einSTcB/SBZW0csj3Ww50FQlUuYyShU91o/RlW7ybXiO4wVzxYCwGUs1NvygolcbjaC7KCzbwXv/6S5MTxr4iRc9HoB/fYocgoJgpF+6qYpVt7o9euUexGT26OmSAxY75JwpavUfNwi+ZmpDH69gOd2qGSBe/S0S23Ghh/cuVd5BlSVOPvDJ76HZdoRmVUbhV/TKlW7G3SeKomDHdE36LL0oCbLweIVQ4rHsRNJWY+H8JjwPI1vRc0LJNfZ/Sbo5+lCgVzBZD26BdDNmjp7/b1pkAClljt5E9oreZtvGRE2PHBA/OgPTveD9E+2xs0JSGaB6ZixsM58wdaxvyh/yOyr8w7/cj0fPreM1L78uSGzUDL9v0fU8qBHXbz9xg5T1vBW9ku6RtN4jETSJQQEQDqJV4YSZHXMwrxna0Hv0wnb3gPz3LQ/9MyFFx4SkcfDAPE4+uoyv3XUGb3/1DbleS3Rtx08s4Mixkzi/0sIOwSCT7f3sABzF9mn5s/TiKnqie0vQo0fjFQrlVMhxE+gmv3ifJ8NmwIxFrY7agMgOSTcLJo90U1OVnspQP4auRgaQMvpu8lT0mi0bdTNaNsr6Cnln0g0Ly3YGBp7zHsYcx+05EFTNjIVVmuq1rdujd98jy/jE1x7CjdddhGv3zwVfZ4kVXjMUK2QYEkbL0KOn62pglkAVvd4gWlMVKAp/VT1Oalc3teG7btpeX89VdQ5Tfr9pr+xcdka/2XZinw8iiJjYsNEOiystePANYERGOzD5Y1TykrFjpibfjCXSdVPMyTGceBQ1FiPEOLW4AQXA/I6udBPAyMgfwwPTAfHnGFFNqKJXMFldrlqWk1jNA5h0s5iKXt3UoKlKZjOWyYjRCoCfUXc9r1Mxq26GK1zRMwRtxq3QzwIVHJhus4qetqUe+Cyjv7jSgqoqmKzpeOULrur5HuaS27KdYO5aEk5MUkXkEOp5XqiiV+6cK6njFVS5/Rxd103fGESkNylWulmBil54dAvgyyMrY8bStzaZPXqMzYREoAgi91jSaAeeql5/33UU26frWFo7I7V6E3V/6qoKz/Pdk5MSwQzbDo9XKNfVd6txanEdc9vqQS9kOEksY48tGpZwqqJRFJGd6l95Iw7LxIm66Fm22zMkNQrTiLbHlzFeQVEUNOo61jKasdRjAj22AVZdvhnOguqCVdn+uWpVy6IyZ71azPUzjoQz+oBvotKyHHzrvnM938eSK7zSJqsvA8oQmaPnuB48+NdZ2T00Ml03Zdvdd+fodasR3APTYySpdVMfeqAXFUxVJWvev7YiRj80207s80EEkXss72iHKGfdfnbM+EPTVyQOTY8M9IKe7/S/vd8sSlUVaKpCZiwF8di5Dezd2Qj+u2qO22mE5fIASTfHBaroFUxWl6u25SaOVgD8it7yWoIZS87DW2PCyNaj13IwOxU9gLoWPkh3RjhUEdtxg4cj26y5D5lu71w1tmnKzIyHq1OipgZMnmXo1ZnfVTRRGX3b8QYy+mzWGq9JTX9PA0NT+d/bsHlS2eMVeHqPeJHt0Nbts+uOKeF23bR7f5ZRq4J0s68PTtMUNNvVuA/7pZt+/6Dcg16z7QQ94HkQuR7mZmqRQR3vaIf+5F0UZy9sAgB+9U++nNtohmFFJG3DwUMtxVm76z7b/XnTUEvbX7YSruth4fwGDly+Pfha1Ry30xgYryCQTCGqC1X0CoZt0KL9WW3bSR2PYOhqrBlLWn8fD40JI/PA9IkE6SYwAhU9u3sYMwQyqEBERU+yGUt/dUq436SzPv/6qX6/pAx4M/pmYJ7EafoRIxEU6W0IV9W6gV55FT1DT+494kW2BNHu638USUzEV/QqIN0MOfoCfsWyKgfBwYqevDl6jGbbRj1ixqoousZ/veUd7RBO/EVx/MQCPvfvjwb/Lbonx9GOeJYHrQQce0SUhNnQtcr0i48T55Y3YTtu4LgJjF5Fz+lTUVRJVk5khwK9gsl6cPOlmykVvRgr+HCQkoeperaKXrOVLt2suq2/f+DpjFcQNFOxXW/gsCTy82kk9Zvwr08RDmBHGd5hzV3ppmhA0S/d5HfGC1uoD6NHT8YMPYBJN+VW9DRVCfqdhCp6nfu3P4CtGVWo6PXuD1qFZm2V0aPXbDuYkNSjx2vOc/DAPG69+RpMdubDTk0YuPXma7grbpaT/Ew9cuzkQCJPZE+O/b0Rz3JN49+3o9xnDS3axI3Ix2OLGwCAvaFAT6tYf34a/eOCaGD6eECBXsGoigJdU4SlEm3LQY1DuhlnxiLj8Nao69nMWBIe5KNS0bPtHNJNe7DPBZAXUOXuN7Fd37Ke/V1b4KHPm9HvJiLEbPz7K0cis9HCznqGYI9gXvplhHmQb8bi9swnFKro2dE9Vayi5w6xih01wqAqh6kB180CRj/4ZixypJsi1YaDB+bxkusvBQC88gVXCskq/T09vuqdd0+Ow4qoJBoCVaKu4qC7dl+6Of57ftkEoxV2TgZfMyQ/+4smUFGwOXqSk3fEcKBArwQMXdz0om27MFKkm2bM61q2m+rYyUNjwsCaoHTTdlxYthtb0asJHqSHheV4wWFMdOB5/2FJthkLb3UqDtt1e/rBqmISUyQHD8zjpmddHPz33EwtMqPPgsFWzh49XVXheh5cjodkcBjT1SAxNIoVPV26GcvgGAKxit7g38WcVIe5//Qf3qskjxpwBJUchHqeh1bbwURNRkVPbMwA0E1qid5fadLNvHtyHFHKHl1g344az2DoamlzOrcSp85tYKZhBqOpgHD1tRr3dxq+v0BXyi87eUcMBwr0SoD1QonAZcbSycz191i1bUdaRa/VdoQ2qWan/yWuoscCUN6D9LAIH4BFeiKACPmTKle6efjQ/oHssli/iQdNU7qVyi3SmM8ewO/4Hzfg7a++ITKjHyQiON+TuIHjrBLFM4Os/zBm6NGS7CLgkYjzUkxFr++AKuB8G/V31ZmiYIh9ev0Vf01TKpM1dwp23WxZDjxATkVPF5eVsvtauJUixXUzbw9gHFHXsa7yywGjElFmifvLVuLU+XXsnZvs+ZpI9bUKOBFmTLLnaBLlQ4FeCfgSS9HMI58Zi/+9va8t6/DWmBAfmr7Z8r83zYylyhU9z/N6gjVh6WbfYUnpVGlkVTsOHpjHoev2Bv8dV52KXV/IjAXYGtJNALh/YRXbp2vYNhWfZReVbgbN6xE9egBnH43dOYx1HCLNGJOlIpAZ6OmSe83CfbKAn3ARMWOJSnaxit4w+/T6/64i+uCyYvUf9CQ7qW62/Pddyhw9VbyCzObHid5faRJn1gM41XET3dYwhfbkOJIqejx7S5QDt8iYEoIPz/Nw6twG5kP9ecDouW4WXdEnhgONVyiBLBtrm8eMRe+ObggHhb6TXv4HKauArG9a2NaIHpfQDwv04jK2ozBHLxgaOiDd5HTddAb7OXRNDQ70MrhkzzQA4OXP34+bn32p0M8yecaWC/ROreCy+enE7xE1Y4mr6Im4rQUW6iF3ybIy7v1JiTzInrnk9FVRDF3D6gZfz3CceUbN8PelYVb0LMcbGIBdlYx/0RW9ZrvzfJAh3dTVDG7W/veLJl5t24UxmXyfHDwwj0t2T+HN7/0aXv78/KMVgOgevWBv4fjbgx7ivkBvI4ObNhHPynobGy0bF/VV9EbRdVPrr+iNSJBKxEMVvRLIoolvWw6XdJN9b5g42ZIobNaRiCFLt6IXZ8ZSrtlEFsJzzQDfUEdT+fum7FB/H0P6ganz/mUJ0pg8Yyv16K03LZy5sInLL5pJ/D7R8QrsITjojMefybX6XsM0tKDyUDSyK3oye80Gs8v8PVlx5hkswGgOMdDrD6aqNKvK6q82Sp6jx953KdLNDGvL2qPX/77EMT83CVNX8eDCmtDrx/7eqIqegJNjVI+eqdMcPdlEOW4Coxfo2Y4XjFYA2N40Gmsn4qFArwSyaOIt2w0OnUmvy743DE81kIduRU9Ausl69FLGK1S5oheea8bQBWd46X0zDGXLZVhFIssDhB2gRcdGjDIPLKwCQGqg51vy8/eQRh2kgO4cIp7Apz+xUGZFj83Rk0ExFb1eUyMRM5Yo6Wa9AvtPVABblYNgfxVV9hy9JksEShqvIPq+sep5S7J0k6GpKi7ePYWHTq8KvX4cbXsw4SsmC4+eo7dVVBxlEThu9lX0gl7tEamKDcjKJe/pxHCgQK8ERA/5nud15JicFb2IHr20aiAP3R49/opeM5BuRj/IdU2FpiqVbgaPsqQ2NP7P0D8U9Es35R7m2EE1ixyUVRSYvHcrVPQeOLUCALjsomTppqIo/nxK3vEKdu/cIUaQdedx3exLLJTeoydtjp5cU5H+gEhsvEJ0ABv06A3ZjCX8nmuqCs8Dl0Nr0fS/57L7B6VW9HTxih577ohWzPtnHyZxyZ5pPHRmVcoID7+i1/ssFTH4iOrRMw1xczgimVPnNlA3NWyf7u3/DkYYjcgztj/RQ9LN8YACvRLwpZv8N3rU5hz9utHGEbLkWFOd4bIiQ9PTKnqAX9UbZo9MGlGz0XRN4ap8+UYu3kBFz+/Rk1/Ry/IAsew+6eYWeOg/cGoVu2cneqyv46gJVNTYQTO2jyZDRc/M4NKbFalz9CRLN6P6xfgrejFz9AwW6A2vR8mXdodm1Qk4tBbNwHuuqlLXFQR6Mnr0BEaYMNj1IypdFEmIXDo/jc2Wg7NLm0K/I+73DrQBCEjuu8+y3qRllROto8hji+u4aG4yGEvAGMWB6Vq/GVMFElBEPijQKwFRTTzbhNOlm3EVPTnjFeo1HYoCoVl6XWlOfKBXM9SKSzc7h/e+ocY8m3W/kUvvz0vMjOfp0euYsehbKNC7f2EltZrHEKroxbhuikh2+g0TSh+vIGuOnupXf/rHvWSlvy/Kr+iJGCLFV/SGOl5hYPyKmNlTUQRJKq03CJW5rs12slmXCKLzTYF8c/R4K3qXdoyyHjqdv08v6v4UGa8QOUfPKE8xsFU4tbiOi/r684ARHJju9lf05CZ6iOFAgV4JiGri2femSzdZj14xFT1VUdCoG2JmLG0bqqIkrt00qj3Hx7YjHo66GgSASfRXZxjS51Hl6NFjOvytUtFbXm/j/EortT+PkSXQ02RU9IJAr8QePZlz9DQFHiBFsgYMyuWEKnoRlRCgKykfVqLJ9Tw4bu8g+Kpk/YMkVZ90U67rZvKcVRFEAh5G1jl6UXL8OPbubEBTFTy4kL9PL6qHVsQF2o4wizJ1DbYjVgkl4tlo2lhaaw/05wGjNzDdcdzgvgJIujkuUKBXAqID09khk7ui1ycLlTVeAfCHpgtJN1sOJmragIQhTK3i0k2rr8IC8Esv2WGpv2dLZAYYD0GPXqZAz+sxYxmV/oGs3N/pz+MO9ASlm5qqQO273sWGGvdKhcvs0YubN5cFtn5ZB4N+GaGhq51Aie89jfq7dE2FqihD69EL+n/79hYAQ5dIxUnWZfYPbrb8RKCM5ILo2BsgVNETCPRZpZP3PjF0FY/b2ZBiyBI5MF1kvEKEGYu5RRJ8ZbFwPtpxEwjJskckWLIdrydpGSTvKCkw0hQa6H3sYx/DS17yErzoRS/CBz/4wYF/P3HiBH70R38UP/zDP4xf/MVfxMrKSpHLGRoiB0egm21M7dELzFh6H1ptS16WvjFhCAV6zZadKssxKy7djHo48lbkYit6kl032UE1y8OayZC2SkXvgVMrUBTgkj1TXN8v0kMaJ+kKMrkcD8gB101DE57zlRWpFb0guJUl3exz3QwOuDwjK1wYERUYRVFQM4eXaIoMpjJUpoogqP70jVfw/03O2prt9EQgL1ms67s9evw/031f+O+TS+an8eDp1dwyZmnjFfrm6IX/jcgHc9ycj6jo6SOWTLX7Ksiy739iOBQW6J0+fRrveMc7cMcdd+CjH/0oPvzhD+O+++7r+Z63vvWt+OVf/mX84z/+Iy6//HK8973vLWo5Q8UQdLligVt4CHoU4YHpDNftSINkBXp1Q6hHb7PzIE+iZmiVnuPT74II+BU5ns+QmVEMujDKHZjOAuUsDxDWcL1Vxis8sLCKvXMN7r4g0+Dvqe23o2Z0q1t8hzFNVaB2HqplmbF4nhcrccxCtzIlZ+0DxiAiJhR2fE9V3dSCHteyCfp/+wwPgOFn/aMrenJ7jJptO9aRWZQsh9DAdVMo0Bt8X9K4dM80VjcsXFhtcf9MP+z+zGP0FNeGAFR7lu0o8djiOjRVwe7tEwP/xq5RmSZVRcIUKgzWPzxstQGRj8ICva985Su4/vrrMTs7i8nJSdx00034xCc+0fM9rutifd3PhmxubqJerxe1nKFidKpBvL0rLJvPPzC9u4mwQ5CM8QqAPzRdTLppo57guAkw6WZ1N76oqhzvHL24uWoGZ0WQl6BHL1NFz5chsZ6Tca7oeZ6H+0/xG7EAQE3nr6jFVfS6WXe+ip7el3Fv2440U5M4HNeDh3TlAC/yK3puZEWP53pNssOvm9rwpZtRwdSwpZuJa5NU0Ws5UoxYgO51KybdZD16/J9/VFUsDRmGLLbj35/9/e6aqkAB+HrGHReq0k0iAfHzd4lsnDq3gT07JoOgKIzSkSmPSkWPGbUxgiQUBXojjZwdN4IzZ85g165dwX/v3r0bd955Z8/3vOENb8CrXvUqvO1tb8PExAT+9m//Vuh3zM3xSbGysGsX/8Ewje2zfkl/2+wk10PuwXO+5nv3rqnIdbCvbWO9fDU9+NrqRhsAMDs7IeVv2LWjge+cXOR6rV27pmG7HrZN1RK/f3qqhoULm1LfY5618TLx6ErnZ7rvf2PSRNNqpr7OWidA2LG90fO9jYYJ9/xG4ucpAntwKKoq/POO42Jmuo75PdugqQqM0PUjY21lwbO2Mxc2sLph4clX7eb+W6ana3jk3DrX9+uGjpqpDXzvrp3+3jTZSL4XAMAwdZh69zW2b5uA5wHbd0xJC8KCdYXWstExWZqdkbNXzG7zM9qzs5PYtWNQxiSyNgBwXWB6qh58fcd2/zVntk1gV0Q/TBjLcbFtph75dzUmTXgRv09kbVmx4B+cdmyf7P5dHdOOmYyfg6y1tTtr2x5aW/gznds2WLEQXZsDYLphSlkze9+mBd43q6OqsB2P+2eUzpiE7QLP1OmZCSgKcG61Jfy3su9nCdbZbZMDr6HrKkwzft9mGKYOw+h9RszN+e9bYzr6/uBZWxUZ1trOLG3isr0zsb9f11SYplHZ9y68LtfzzypR9//2mXILMVV9v0aRwgK9qGx0WJffbDbxxje+EbfffjuuvfZa/PVf/zV+/dd/HX/xF3/B/TsWF9cKaRLdtWsaZ8/mb6RmtFv+hn1qYQVTE+lzvM6e87OAG2utgXWE1+Z5HhQAF5Y2g68xqUiraUn5G1TPw3rTxsLp5ciMVf+6Vtfb2D5lJv9u18WGpPXxIPp5nr/gV5lXVzZxtpPRch0XzZad+jrBZ7fe+9k5loN2e/Dns15rGx057cam2PvouC5cD2i1/J/TdRXLK83I15B9H8iEd23fuPsMAGBn2jUZwnNcbHJ81gCwtt6Coig937tr1zRWVvzD4YULG6mvs7LahK51X6PdGVHy2KllTNblbdH979lKJynUbsm5Fzc7r3fm7CoUR6xiFvV5tm0HVuieYa9/+swqtIQKk+v6joJWzGeoK8BqxN4qsrasnD7b2R822sFrrq35e/bZc2uYNsUCe5lrO3PGX9tmaO8KPtMzq3AFZw9GrW11rYV6TZey5o11sffNdb1AVdFsO9xrOHPBT7xuhj4zHuZ3TOK7318U+pnwe7a83rk/m4O/V9cUrKxF79thVlab0NXe/am54b9vp8+uYirF2TtubVVjWGuzbBenFtfxtMfviv39hq5yfVbDoP99a1sObMsZvP/PrsJu8Su7ZK+LSEZVlcTCV2HSzT179uDcuXPBf585cwa7d+8O/vuee+5BrVbDtddeCwB45Stfia997WtFLWeoiJpeMFmJkbIJK4oCo6+fiElTpEk3OwfNDc4+vc02jxkLv339MIgagm1wum52Z6L19ehxjmfgwfO8zOMV+v82QxsdWUkW7l9YgaYquHg3f/Xf1PmvT8uO6dELZqPxXTPh12BSraKdN4P+HYnjFQB5EkSnb3gv7z4a5ZobpmYMr0cvau5iVQamM3lmv+um/2+SXDfbjrwePUEzFnbdqIrYvRU1V5WHS/dM46Ez2Q+rbI1R1zHvXFZ/LMRgGwEw/r3ZZXD6wgY8D9gbYcTC0DV1hHr0+md8yr3/ieFQWKD3nOc8B8ePH8f58+exubmJT33qU7jxxhuDf7/00kuxsLCA73//+wCAz372s3jyk59c1HKGSnewOd/DhXdgOvuesBlLdyaXpPEKnQrkOmeg12xxmrGU5CqYBfYe9lugc5lAMIe2vuon73gGHsL9nqJBWtcspjuzbZx7NR44tYp9u8UkkKahom25XD1yjhvdCyZyQPad9br3TGCWUPDnYkUEHXkIGvclHGqiLO27PVnJr59mnjFU1017MImkV2RgOltbOEmVxdkyiWbbxoSsHj3RQK/zfY0JU2iOXFTvIg+X7JnG+ZVWUDkXpX++Zhje54llD46FMIxBEzdCnOMnFvB7d3wTAPDhz92H4ycWIr/P7+8fjUDJdryBOXrA6JjJENEUJt3cs2cPXvva1+KnfuqnYFkWXvayl+Haa6/Fz//8z+OXf/mX8eQnPxm/8zu/g9e85jXwPA9zc3N429veVtRyhopo87PFzFg4ZBWmoQbfD4RGM0iajdWodwI9DkMW1/XQspzUB7lpaHA6MhrRh2cZ5DFjsWOqCTLNWFrWYGDPS7/rn6HLC0Crhut5eGBhFc9+wu70bw5hGlow2DotCIpyxQPEqg123xiBKDfdIpCdFJLZuN+dRzloDJJa0Qvu3+jPbrium4NBg1aVih5bmzqY0ZflCLrZklnREzP/YVX6qUkDqxttWLaLGsdasrhuAsClnXEuD51exZMunxP6WSB8HQ+uUdcULoOcqGds3Pxdgp/jJxZw+9G7gz16eb2N24/eDQA4eGC+53tlG7EVSb/TMdt/yYxltCks0AOAl770pXjpS1/a87W//Mu/DP73oUOHcOjQoSKXUAn0jNJNHvmloWt90s3Ow0FAe59EY8K/RNab6YHeZqeHg8d1E/BHBFQx0IsbHCw2/LpfuqlIk0g2O+8z72y/MFuponfmwiY2WzYu4xyUzqiF7MfTrk8ndrwC/wHZsp2erH13oHGxwUjczMesiMz3SiMYcZKlohdRkQ9TN/Xhz9GLGJg+7Kx/1NpE5ZFJeJ7nj1dIUXzwIiKPBrrX+/SkgVPwn7MigV5c4iCOS+a7zpuZAr0Et0/eil5UoGeUtL+MM0eOnRxIxLVtF0eOnRwM9EasoheWy8tO9BDDoXqn7DHEDB0ceWhbLhTwZRBNXY0cryDr8DYVVPTSpZubHROJiZSHZ9RYiCoRFawZmho4tiX/bPRwXV1T4XlysvbskDo9aQgfwPrleryS1FHk/lO+e+rlgoGeGSQieMZpxEk3+Q+hVr9E0RBLDGUlSRqWha50M/+hgN1HWobxCml7oD/epfjxFVFEzTWrijwqamC6zOC9bbvwPEiTbuqcgX/49wPA9KQJQEBhk9LzGUejbmDntjoeXMjWp5fUQ2tw9uhFDVyn8Qr5WVyJno8Y9XVeNdCwcV0PrucNJLgBquiNOoVW9AifYGMVyDwahtrjUhr72v1mLJbcwxvr0VvjqOg1W/46JgQqelXE6gxbDr//rHrmeV7i5xLXzxHO2ueNwZnsrFE3sLoh5oTVH4iOc0XvgVOrMHUVe3eKWf0HiQiOjHec/FhkmLNlu0HlHChPuhlXfc6KzENBVEWPd2B6XLKFUTc1ePATTTwVHZlE9UVWRR6VNDBdxtqanUSgLOmmaBDK9rmpCT/Q472/gt7FDBv3pXum8eDpbIFet1c/WhrOqzDpr0SW1QM8zszN1CKDurmZ2sDXipBuHj+xgCPHTmJxpYW5mRoOH9o/UEkUxXEj9qZgYDpdK6MMVfRKIJBKcFawWrbDZcQCRJixZBjumsRkJ2jj6dHrSjdTKnrsIFvVQM9xYejRD8e0LGqsdFOiBIpV9KYm9CD45MXpO8z5lcrx3MTvX1jBJXumE8eCRNG9PnmlUYOBkqIo0FSF64DsH8YGJYq8+0VWpPfoCUrpkmAJk96Knv+/067XuD5ZBgvuhtGnFy3dlFc1y0NUoCfaB5cEG1KfJu3nxdD49mQGkypOT/rJS97nT1TSgZdL5qcDCbkoSRV3g7OVwIpy3RRsJSEG+cFnXzLwNVNXcfjQ/oGv8zqk8sL6A1mgubjSwu1H7441g+ElUFEU2KNLDAcK9EpANINmWYNyizhMvdeMRfZ4BVVVMFnTuVw3N1lFL0WaU+vMPKpqRc+OkOPxBmpxhwJ2SJVhfNIN9Ixgvbz0m0EY+nhKNx3XxUMLq7jsIvGhq0y6yXMQjLIvZ/D2UPbLq7r7RUk9epL2CqkVPTdixEknIE17T9P+rkBRIDgXTgZRo1uq4roZ7A0h1z1dopNqEOgNabwCe/5OTYpV9LJKN4FeQxZREl03Ofdt2x58lokoFohoHjm7DgXA7JR/Lc3N1HDrzddEVtUMydLNpP7APNgRRQKZBlvE8CDpZgmYghm0tu0Eh800DCPGjEVSlh7wDVl4zFiaGcxYqogd0ddghGRjE0k/G/QWRQeKMoIq9r51Az3+xIDT14czrq6bp85toG27wv15AFALekh5Aj1vYJQGwzfw4Zx1FbpeRPeLrMiu/ovIVdOIM0QC8vfosUCjOQRDFitC2l2Vw1SwN0RVGyWsbTOQbsqq6IlVGwPpZqeiZ/FW9ILPTFzifOmeriHL1ZdsF/rZJLMkXVO5+uatiGeDpqrQVIUqehl59Nw6vvjtx/ADT9+H//qix6d+fxbTtCRE+gNFiOqLJunmeEAVvRJgc2t4Xa7alstdkes3Y2lLztIDfi+YVDMWAWncMLAi+q50zopcXEWvaxwgQQLFevQ6gZ7IAzuyojeGD/ysRixAyIyF19Uu5l7TOAflDlb0SurRy3GAjUJmPxe7jyIHpvO6bibM0QOGk2hK6oMbtnQzevSDvLWxwDptziov4hW9Pukmb49eDunmtqkatk2Zmfr0Ul03ecYrxIx/0cd03y+Dj3z+PtRNDS+94TKu75ftuhnVB5j0dV7KGK9CDAcK9EqAbbTcUhHb4Q/0DK0ngExy6spKY8LAGk+PHq8ZyxAPWjxEPRxFpZtafwM8+/lCpJv8rxnZozeG0s37F1YxUdOwe3tS/TUaYemmGh0oZa7oMdfNgu+P7kFSVo+ezIpehMSR8x5KM5mpG/7+NIwRC115VPUszPur/UBXxikn0JNd0RML9FiLw5Sw6+ZgpVOErIYsyQPTFb6B6TFmUaaukhlLBu568ALuPLmIHzp4WeDemobsit7hQ/sHzodx/YEiRO2bVVEbEPkg6WYJmIJ26W3b5ZZumrraU3mQPRsLABp1HWeXNlO/r9m2oQCpTnZVl25GPRy71YTkDc9yPCjoHt4YgamBxPEKLNATCdT6LdTHuaJ32fwMVA7n2n66c/R4KnpefI+eypd176/oBeNYyhqvIGmvkOke2TVj6c0uK0r69Z7Wo1cF6WbvIPiKDUyPGpgsIQjdlNyjp3auB27pZufvmw5cN8XMWLLOfL1kzzT+4/vn0bb4WzKArgIofrxCdsWB39tfzedvVXE9D3/7ufuwY6aGFz59H/fPye7RY32A7/2n78L1IM91M8KtOEjeSdybinAMJZKhil4JsAMK78GtHdEjFofRZ8bStl1oqgI1psqQhcaEwee62XJQr2mph+tRmKPX//6LVBN0fXA0RreiJ0e6aehqcGgQqRJ2K47jK920bBePnFnLZMQChCp6KQdBz/Ng29EZc8AP7tMOoZ7nDSQWdE2FgvLGK/Q7zGYlyP7KmKPnDgahiqJ0ekrTki3JAWxtiIGeP15F6dkjtYqYsQR7Q9iMRWJGn1X0ZM3RA8SqJe2goico3cyZELl0zzRcz8MjZ9eFfi6qn5OhcTo59jv6Mow+t24inX/97mk8eHoVh2+8Qihg1wsYmH7wwDymJ01oqoK3v/oGKYES23N7evQkJnqA4hxDiWQo0CsBdkDh79ETk266nhc87CzbDQIpWTTqBjaaNtwUG//Nts0ly6l8Rc8etMwXkW5GScakmrG0HdQMrWtOIVTRY4eW0MD0MXvgP3J2DY7r4fJ58f48gD8R4bgePMRLBHWOHj3H9eB5vVl70f0iK0kHySzoEgd/s2AuSgKdPl4hWWo37B69/nV1K1PDruh50DVlYH6o/2/517bZcqAokPp8Egn0uuMVOtJNzkSj5bhQlezJU+a8KSrfZJX+qLmtvBU9y3ahRyRyzDFM8BWJZTs4cuwkLtkzhesFgypdU6Tsif20bQeO60nbN6LmjwZ7uiTpZlGOoUQyFOiVhKlr/D0BtsvdN2P2ycysmAxeHqbqOjwgdRZQs2Wn9ucBCDLaVZ2jF3UY45/hNWhnDYTNWCQEepaDuql1Z/sJVAmjBqa7njd02ZhMmBFL5ooe55xHJ6KPLAxPj17sOI4SemisTjUy6iCZhcAco6CB6QCfrXyaGUvdYBW98scrxO3PuqYOvQ+mv1cUkDvjr9lJBMq63gD+PljAP1CqioKJut75b37pZlSwxMvctjoadV14xIIVY6QCALqupN4HnufBdryYil7xiaRx4jPfeASLKy288vlXCrcDGLpWSB88O/PJUkYF/ftqWG0gL3kHFOcYSiRDPXolIXJwa9tuYPGeRteK3QGgw+rI+mTC3B3XNy006kbs9222nVTHTcCvWNRMtcIVvcGHI+8Mr6jDEiDfjKVmasFrZqnohQM9wD9UaOZ45H0eOLWK6UkDczP1TD+vqgp0TUUr5SDE3vf+URoMjSPrHtdPZhpaKQPTZe4VMg8FUTIigLeilyy186skQ6roRagFABawDLuiN7h3Bfbqkgamy+rPY4hV9FwYhjqQHE3DjngeiKAoCi7ZM40HFwQDvYSxOb5aIC2JNFihYZSRSBp1wr1kCoCLdzfwhMt2CL+OX9GTm8SxHTdIDLUsB5P1/Ef5qNFQMpN3gN9PGBXU5XUMJZIZj5PdCCDSC9W2HP6KntFrxW45LnSJM/QA4JEzawCAN/z5V/H6d385Vk/dbNmpM/QYpqFVuqI30KOn88kk46Wb4jLLOJqWg7qhdeWgQj16fWYsgTRr9F21jp9YwOvf/WV86Tun0Gw7+Op3T2d+rZqhpks3+2Sw/ehqumSnK5/sC2hKGGTv9+/Iq650XTdlmLFEH1J5jA2C9zSmCqMoCuqmNqQevehEkKamH9yLJmrvUjvqCxlmDM2WXUCgxx8gswpZVxrNL93MK2++dM80Hjm7JhTMJyViWAU4qZ0iyUTGNKhHL4n+XjIPwMLiZqZeMkPXUj8rUXpGakk6R0WaMUl2BD58aP/AHiPDMZRIhgK9khDRxIv02RlBdtLp/qzELP3xEwv47L8/Evx3UvMsb0UPAGq6VlkzFiZpC6NzBkSx0k2JTc2ttu/elqV/JsqMBSh+OHfR9D+YLdvN1eTNk4iImjsWxp91xSndjLDLLjoRIruipygKNFWR47rpRr+3PD2lrKdKixlkD/h9wsMYr2A5XuR7rmnK0OXTcXsXj6kQD822wyXtF0HnNCUBfKkme6764wUEpJs5A71L5qdgOx5OLW5w/0xyoJc+1zXJfXYcTbhkEtVLZjnZeskCQyOJibuwGkGWMqE/CQyEAj1Je9PBA/N41hP2BP89N1PDrTdfQ66bBUOBXkn4Lld8c7kc1xMYmN5X0ZN8eDty7OTAgzSueXZTsKJXVelm1IOdV3oZ58Io04yFSaCCHr1MZiz9gV41PwteZDd588yZSpJGsa9zV58iKldFH8SKqP7LmhkVN8ydp6LH01NVM/UhSjcj9gdVvjOfKHEBjSxZaVHSTd4DdLj3XUS6GDeiQIRL9/j9wiLyzcQePY4kX1wSiX1t1Pf8IpHZS9Z9Tkus6NnyAz0nIrlWxBy9XbP+bNsfee7l0hxDiWQo0CuJ/jEIcXSzcLzSzd6KTDvh4ZAFkQ1vs2VzW2dXuUcvypKaN1CLPSxJNGNpW36Pnp6hGmc7HhQFgYMcu85GPbsru8mbp6IXSF1iDoEaR29GMBi83/ynBPtz/yApT7oJQF5FLyaI5qno8fRUVU66qcl53/LAXDf70VQ5RjG8rswi6Fq6KQkjrHYRNkfLeZ/s2TGJmqEJGbKk9egBycFDV3EQ7bpJ0s144nrGsvSSZemlTyOshpJX0Ru8XpjxjExZ+VpnVFfRrQlEFwr0SsLk7Llhmy+vdLPfIZA1nMuCd8NzXa8jzeF1C62wdDOyRy/feAWDQ2rDC+vRy2rGEj4EF/EQGgYyH8yAf/9xB3oxtusiFb3+wMQsY7xCwkEyK7qkgCWuv4irR89xUqV2dWN4gV5U0CCrEpqHpIqeDNlZs8Uv7eeFx5SEEZ5Py3N/M+IkrSKoioJtUya+8K1H8TP/53OJve6MJHUOj5ojyX3W0Is3explDh/aP6CqytpLxs4OMoOl8LUr6xwVlVyTKcdnsJnMw97vthIU6JWEoaebOwCA1bmBTc6KXtCjF5ZuSqzo8W54zKqcN2Nbq7B0M2r2EG+gltajJ22Ontmdoyc6MD3sqjUuPXoyH8z+z2po5ZzXpqvpvU3BcO+Bil7xGXdb8l4B8DmN8hCVXQb4XDctO7oPLkzNHFaPXrQM0DfuGXZFL152LqdHr4iKHr9pUbiiZwhU9GRIN4+fWMC5pWbwPvIMirbseFM2HnOvOLUAQBW9NA4emMetN18TvHd5esmCZ6zUil4RPXrRLtKy+4fXgkBv9A3gRgUar1ASvJr4lmhFjw13tllFT+54BbaxfeTz92FprY3Juo6feNHjBza8jaYf6HFX9AQyqmXiuC48L1oyBvBJN6MMB3jNXNJwPQ8tyx+YnkX73y/PCg4MI/7QZ9fjHZ++B+tNG7NTNbz8+fsz6/9rhob1ppX4PWnz2jRNTXUrjOvRK2u8guyeKU1SwOK4HjRVGZi5xjVHj8M8o25qOD2UHj0PjXqM4cmwzVhiRj/ICN49z1d81DmfD7zomso9D9GyHUxN+MPSRQIdGbNpjxw7OeC6yHqI4/aoNNdNIEW6GaMWALqVcdfzhOfCbRUOHpjHv3z7Mbiuhzf8t6dnfp2uEZtEM5bQtVukGQsg3xF4lSp6pUMVvZIwOYdmsmCQN1jrSje74xV4q4G8HDwwjz/8H8/FRXOTuGx+OvLBtNE5FPO6qlW1osds3aMGNQM8A9OjD0uBTXnOzY0Fx35FT9xIpf8QPC49eoB/nd5y4xUAgLe86pm5mrxNjvEKcVUnBs/8pDjDBEPAFTArMmzj+9E0VUr2N8r5FuCfo5f2d/mum+UPTI/q/wU679uwK3punBohf/DOTMaKGK9g2eLSTcPgl0bHGeiIkKWHmCvQS3LdTHAFDpKEY7DvF8lm20EtZxW6CNVMj3RTkjKhOzC9P8ldkHSTrr3SoECvJAyOgyPQDdjYfLw0+s1YZLtuhrnuyp343kNL2GwNHpA2Wlmkm9W70YOHY997qHa06lnHK/ivmT/QY3KzuqH5FQ/4lu28OH2B6LhINxlxbo2imBwuuUW6boqMY8lKEXuFrOHATowxiKGnm2/4vYdprpvDSTQlSTeHneGOC2g0CWvbbHX2rQKkm7yJBcvqNWPhHpgek7wTIUsPcVIlUcR1M+p663frJqKR4RQbVPQkBktFmrFoAxW9YqSbo+4LMEpQoFcSBmcvATtcCo9X6Nzs7QIDvadcuROO6+E/7j8/8G9Mujk54gPTk+QuOq+1e8zD2dDUoGKYlVaooscG/woN4e0LRLOMaKgyabPteBGr6MW7bqYnBqIremZZrpuS9woZQQEQX200NI1rxAmv66YncYgxD3FBg6bJcbbMg1/RizGKybk2Jq/klfbz4lf0BCSYQaDHXzGPm30owuFD+yNnZSb1EHPN0eMwY4mUbhrjleArCr+vVE6gJ/MZ2zteQa4Zi6YWJ920HTcwwaIevfKgQK8kTINzvAKr6PGasQQ9ev7P2QUGevsfN4NGXce37j038G+bnUCPtwejZvgHm6oFGEmHd55gPamaIGIcEAfbJGuGHrymiATC6ZduauP1wHdSDFJ4ERqvECfdVP0ZX0nBRNocvSIDkUKkm5Ks+PuvU4bOWdFL+/zrpg7PK/+6jwtCq1zR841i5Oxbsit6IgFy2+r2rwvN0ZMg3Tx4YB4/ddPVwX/zmHvk7tFLrOj19vYT0TDjszwUIZNlSUhNVaQlzG3XT0L190XLHP3CqnnA+CSXRwGu3evhhx8GAKytreG73/1uoQsaVwxdg+ulBzai4xVURYGu+dlJx/X7IIoK9DRVxZP3z+E731+E23fjBz16AtJNAJWr6nXlLlGZbSV1s3Ycb8C1qvvz+U0NWEWPZRl5zCnC9JuxFOEINkwsx4XW6YfMAzNr6DdQ6P9dQHTGHPCvFw/ge43+bH8JGfcikkKyhmtbcWNK9PSquG2n2+Gz/afsEQtJrrzD79GLm/GXv6K3GUj75Vb0DIE91Xfd9H+/KTBegKfnk4cbnnwRaoaGFz/zYq5B0TzjFZL27a46JWqO3vj0ZheF53lotZ3cyYlgNJNU6aa/b01NGPIGpsecXTRVkbb2cKBH1155pO5eH/jAB/DqV78aAHDhwgX80i/9Ej7ykY8UvrBxg7dywm5gkQOY2RndEBiJFBToAb58c23TwsnHlnu+znr0+F03/e+rWp9e9+E4+Hdw9Vwl9VUIyiyjaAUVPa2zzvTgM8ygGct4VfT88RH5XeTY+5v0vgRSl9Q+mviHZJy8yiihh6aIOXqyZi45jhfdy6apcD0vsWckzvAkDAs4miUnmuKs+n3XzSEHejGum34QKqeix2vWxYvGmVjwPK8ncDINAddNiWNIGhN6qpsv4M+ldVwv9vcyeV3S3p+kTuE1F9vKtCwHHpB79iP7DGVW9Fq2Pyu0LrHX2HbcyJmwmpr//mesbfjXvqJQRa9MUnevD3/4w/jQhz4EALj44ovxD//wD3j/+99f+MLGDd4MfVDRE3DOZA5iTIYhezZWmCddPgdNVfCt+3rlm6xHT8SMBaheRa9bYYmuJqQZnzgJgYZIP0kcgXQzqOjxubkyxj/Qiz8ciWByXJ9x/XUMFgAmPSStmAqyWcLnUowZi5yZa5bjDri/AWEZVLIcNk26yfafsmfpxcllZR6mshJfbUzvNU2jK90soqKXvjbb8eCh+xwOjxdI/9nBuapZmawZwbMyiSDhGKPsCe6DxIRHvIw9kG5W7PlbJVqSrtliKnouaoaKmsFvKpRG3P1fhHRzW8OkQK9EUp/yjuNgamoq+O/p6ekBDS+RTnewefLGaglKNwGg1jFu6P6s3IdpmMm6jsdfPItv37fY8/WNpoWaoUGNyAhFwf6+qo1YSJqNltYP53leYqChSzBcCJuxAH5FTyQY6K94Bc6dYxPoyZFZdQ9C6Rnz/uZ1RpB1T5l15Tu6Dvbo+f9ezP0RNy8yL7Ic2vrdYRk88yx55p6xw1uZgR57z6OkdLyVqSJJlG7mdd1siyUCedE6e3JaL6vVlwQ1OSr2wc9K7GVt1PXAXj7tdwJJsnC+hEfca5B0M53+pGpWgoqe5IHppqHBlFjRi+2LlqTSAIC1TjV7dqrGPRaFyE/q7nXFFVfg93//9/Hwww/j4Ycfxh//8R/jsssuK2Fp4wVv5YRl2EQrem3LTX04yOK6K3fisXPrOLO0GXxts2ULDcPtVvSq9aBJssxPM1NJk/LxzABLI+jR67x/ohWU/kCUOXeOS49enPxMlK60OKmilyyV5nLGi5FPdhNDxXwuwSGwgIqejF6zuOwyzz4aJ48MUwukm+XN0mOH8jhJ6tBdN20vujdZzT8yo9kqqqKX3gcLdO8jg0neOZ/H3eBclnTTwHrEeKJ+0u5PHifHuP7f8NdovEI8sgyEgoqeTDMW2x8VInMecZzrribBjInBkhyzU7WhJ7a2Eqm712/+5m/igQcewI/8yI/gZS97GR544AG85S1vKWFp4wVvBq1td8wkOCtj/mv7ZiysubzIHj0AeMqVcwCAb4fcNzeaNrcRC8B3kB4GaQ9Hnp6IJHMOaT16ZvfAIvKafkWvd326hAC0KsQNfRbFDNxsEwI9O7mixzM/yYoJTIvOuCdl+/Mgq9csbgyBwVHR45l7NgzpZiDTjZCk6hwzOovEdT24nhe5Nv8zzSs594ObvNWRfngqW0BIKRMarwCkSxeTgvMsTNZ1Tulmcq++yHiFqP1p3CT7RdBsyzEQCip6ks1YTEPr+DNI7NEryIyJsbphwTRUTNR0CvRKJPVkvnPnTvzJn/xJGWsZa0zODFrbdoSll8xBLClIkcnu7ZO4aG4S37rvHF70zIsB+NJNkRlJVe3RS5w9pCmJLn2pdvu6ihaHbCeJZtuBgu71JDqywTeq6F0fs/IfB2yO/iweTI6KMwso4qTsWp6KnsF3EM1KURU9Wb1meSp6lp3epxmYsZQY6CUNsNY0eVnzLCStTUaVttmxqc/rhttPEOi5LmqIf/60+6533oHhsuZyMrilm8F6o/8mLqOnzsE9an+i8QrpyKroFTNewYFpqKjJNGOxo/0FNFVBsy0n0FvftDA1YcDgGJNDyCP2Cn7rW9+KN77xjbjtttsi//0973lPYYsaR3gzaFanJC/02oaK9U2rsMNbFNdduROf/reHsdG0gyylyIZY1R69pNlDflAV/5BOkn0CTLqZv0fP7AxLB/zPeoNDChReY39FT7QqWGXiTDxEqenpiYi4YITB1pF0SLZiZnQVbcaSNuw9K7Ia9+Oyy2kOdl5nhE3aHsj2qlIDPTs+ETRs6Wawd0VUf3Q1//7QbNu53Quj6Fa2OHv0QnP0AI6KXqDSkBOgNupG0E+fdI2mtWFwjVdImOlqCPQoblVk9eiFkxGyaHVGhfjSTUlmLDFqGFl914BvxjI1YQjP/yXyEXsyP3jwIADgpptuKm0x44wRSLGSHyzhoa68mLqGC3ar67pZQqD3lCt34hP/+hD+4/5FPOsJe7DRtDA3U+f++VpFpZuJhzE9uR8u7fAsY45es+0E/XlZXjOq4XqsKnqSHPK6iYi0il5CoMfxgLec6LmXvBWHrBTboyejohddmdVTDrhpyRYGO7yVuf8kHd7ZWArP84ZidpZWbcw/Ry//PLIotJTAn9GVbnbm6HG6YCeZc2WhUfffg42mhW1TtdjvS7s/mRwz6V6zE8ZC8JhNbXWYdDP3eIWCKnpTdUNqj54TN15BYhKqJ9Ab8tzQrUTs7vWCF7wAAPDggw/illtu6fl/J06cKG2B4wLvxtq23SAI4n5tQ/WlmxlGM2Rl/+Nm0Kjr+HZnzMJGSyxjyyONGwZdy/zBv8VIyUKlSjdl9OhZTk+GMa1vsB/LGWy4lmESUxWkj1dI6tFL6QXjqTbEHcZ4XXqzUpTMW5PUaxY30ymtosdbqTR1FQrKlm7GB6HBKI4hVfUSZ65JmqMnIu3nxeCsluSWbkrr0TMAAGspfXppgZ6iKP64npSKXty6eSqCWx1Z4xXUjrO1zMCmbbkwDRWmoaHddlJdZ3mIUvsAzIxFbqA3TiqiUSA2xfbOd74TKysr+PjHP461tbXg65Zl4XOf+xze9KY3lbLAcYF3Y80y28rUVbRsJ3g4yHooJaGpKq7dP4c7Ty7CcV1fuikwDLeqFb2uSUW0vIrHdbPIil5roKInpnWPreiNyaZrOy4mJQxl5jFrSKvo8c7Ri67odfaLghIhzGRCthmLLk26GX3oSKvo8QawiqL4/S0V6dELm2vIltPykJSkYnP08lQbm20xaT8vQR9sWsDWZ1RmBBW9NOmm3PukMdGt6CXRH5hG4cvfkhQm8dJyTVWgKMWNbxkHZEk3FUXpGJrIdN10OtJNFR46LT85x2rZjouJiGenL8eXL910OgZQsvt2iUFid97rrrsO3/nOd6CqKmZnZ4Ova5qGd73rXWWsbawQGa8g3KPHzFhK7NED/D694ydO4+SjK9gUNGPRNQWqolQv0EvKbKdkoVKlmynSTx4GKnqCWncrogo1VtJN24U+WY4Zi5XWo8dR0Yvr0TOMoqWbxci8NQn9XAAzDRLv0UuSXvfjGxmUN14hyemU9XMOS86UWG1kMsEY+3UeNlsOds0a2RcYQ3dGWUqPntPvusmnKEkKzrPQ6FT01lMqejZvoJc0MD1BuqkoCkxdzrDt4ycWcOTYSSyutDA3U8PhQ/tx8MB87tcdNs2230bTP+M0C4aupDrDihCu6AGdcQu5A72E8QoSkneu62GjaXcCva70WC1BgbbViQ30Dh06hEOHDuHGG2/EtddeG3zdsiwYhvwNe9wxOV0m23Z0ViX5tdWegelFz9FjPOnyOWiqgq/ddRquB6HxCoqi+OuunHQzadZVsvSyeyiIaYAXdMiMotl2gqwwW6fFeTh0XS9ySLauq1jfLO/AWyRxs4BEqXGMV4gbMMvgmXUVV4Es2oxFtiSNwSp6eXvN/IpedJ8skL+iB/izKIfiuhkzMB2ornQT8E2Fsp7Jiqvopd9jQPe5yxIovPeX7GfqZJ2voseTtNU1JTHJlyTdZK+dd385fmIBtx+9O0hILa60cPvRuwFg5IM9/5qVE4RoquSKXme8QnhMzNREvnO54w6OXgKYk3L+fWm9acGDP0vS6+xzlu3BkL8tEH2k7l7tdhvvfve70W63ccstt+AZz3gGPv7xj5extrGCZ/4T0MnUZDBjsR03qI6xRvOimazrePzFs/jaXWcAQEi6CUBqI7EsbKczxzDikJr2YEwazQAwqY2EHj2jr6LHGTzGHebGqkdP0ngFXfN7uJLMWJJc7YBuJSRTRa/oHr2i5uipcgKWtEHycderiHlGtaSb6TLfIulW9KLNGADkkm812w7qhfTopY8wAQavd977K63vWpSgopeSWEtz3fTXlLz3WwkVPYAliPNd/0eOnRxQHbRtF0eOncz1ulWgaTnSAj3RXvokXM/rGZgOyGmBies5l+WkvNYZKzI9YXAnaAg5pD4N3/72t+MpT3kKPvOZz2Dnzp3453/+Z7zvfe8rY21jRdATkGrGkmGOXue12SDWsip6gO++yW5g0WZ7GQ8a2VgJgYLRcYqKa3xmznRRWTGgW+1wczROt9r2gOumZbtczdixgd4Y9ejJGq+gKApMU0uswDsxfWQMneOAHBfQ6JoKVVEKr+hJl25KMBVxPQ+O60UPek5JmIn0VNVNvVzXzYS+yG5SYFiBXlJFj2+EQRIyqyNhugfG5LWxYIQ9K3nNwGRLN1n1fl1CRc9IUXOkmUXJSPAtrrSEvj5KNFsOapLKTbJMqoDutVELV/SkBHpe5LNT1ngFdk5kZiz+7xyPc0fVSb2KHcfBc57zHLzpTW/CC1/4Quzbtw+uxBL0VkHtuGSlunxlMmPxb3b28CjDjIXhet2/50OfuReexy/ZqBnlZtR5sGJ6g4DeIbVRlRyeih7gBwhqxhEAbPBw8Jp692CdlnWOy9r7fX7lfA5F93PE2fJnoaarifdr0rUC9BpsxL5GTEUP8JNDhQV6BVX09LDte8aeESYTSqroxWXHRSSpdVPD8no70xqzkBRMjYJ0M+uhzLJd2I4nJO3nhXdtrCeVqWV4K3pJwXkWVFXBRE0PkrLxvzc90PMldclJpJoZL+czdC33/jI3U4sM6uZm4kdHjArNti2tCu2bj8jZy9uBckvrthhICPScuJE2mhzpJgv0GhMGVjb8fXdcEsxVJ3X3cl0Xd955J77whS/ghhtuwD333APLSs5GEdHwaOLblhMMaxZ5XcBv8GaVgDI4fmIBf//F+4P/Xt2wcPvRu3H8xALXz9eM5IrJMLDt+Cxo2qGiW9HL9vM8tCx3wIwF4OvlSq7oFX/AZP0c7GDA+jl4rxce4hrKs2CmXJ+27cZ+1kBvYiCOONdNwP9sizJj4TF7yEJQYckRsLDrNMoEgX22sT16AgFs2YmmROnm0M1YEoJQVm3M+JmyeWRFVPT4B6b3/n285mi84zpEaNR1jopeulmSoaeMV7CTR82w3v48HD60f2CNpq7i8KH9uV63CrQkSjd1CdVTBqtCm7oKU+I8UL+iV5wZS7iiFzwbx6RlpOqk7l633XYbXve61+FlL3sZ9u3bh9tuuw2vec1rSlja+OFn0NLNWAzBHjsmR1lvWqU5bgL59fmmoaFVsRs9TkoHpI/ISKvo5Z1dZDsubMftkW6KvGZcv4lekutmGf0cMu3pUwO9lJl9POMVEocaGyqsghIhRY1iCSpTOQKWpL6otIqeiNTOd92sxsB0GUmgPCT16OXtH2wG88iGV9Frd5QyzCBIVRQYKRX78OvKvE8adSPVddNyXKiKkuj4mNbznZREAvxAIe/+cvDAPG5+9iXBf8/N1HDrzdeMvBEL0OkrzelkyZA1dgboVqFNQwuKAjJM7Ww3+tnJAr28s/pYX+rUhMHtlkvIIXXnffGLX4wXv/jFwX9/+tOfhqaRHWoWzJQHi+d5/jyUrNLNTbvUQC+vPr9maFjfrFZ1OElKF2SP02RjGX8+DRZ01EIHJpHXjLNQL2u8QtH9HJ7ndSqykgK9lPs1LajkGq+QmFjQihuvwGH2kAUtLN3MSJLzraZ2ehczJlvClO66mTD6oSrSzbiByf73ZK3o+e9xEQPT2TXCY8bS/1z1A52UnyvgPpmsp0s321Z6wjd1rmvKXmjoGlY38kuX9+2aAgBctW8bfuO/PT3361WFZtuRlpyQmUwNKnqG5IqeHe10HO67zqOWWd1sQ1MV1E0t1T2ZkEvsVfzWt74Vb3zjG3HbbbdF/vt73vOewhY1rqQdqNm/CZuxBNJNSzhIzENefb5pqBV03Yyv0qRlj52ESgTPz6cRDHANHQBEmpqTXDdtx81tiZ9G0f0crufBQ/Sw+yykV/SSzQ4COV5Cb4Zv/hP9GmaBAXhRFb3gGpcg3Ywz1dH1eJMaK+UeDMNcN4u+7hlJs+p0CQFyHuwgoImv6GXdtzZbTLpZQEWP08TGsp2Ba92v6KW4bgrMZeSlUdfx6Ln1xO9J6//116QG720UaYkoWSZcrM+1aqOS8iLTQEhXFWn3dqunR09OoOd2TOIS9yY3+3gVAFjvDEtXFIWkmyUTu/MePHgQAHDTTTeVtphxJy3QY9l7YTOWzs2+0bQxPVnejMPDh/b3zNABxPT5plFcxSIrSbOHujLJ6EOslVCJ4Pn5NFpBRa/XdZP3NWPNWELBolHg8NK810sabCCtPOlm8nzBtIOUllLRc1wXnhdfLTA5DqJZsTpBqux+XjkVPRaExs+jjBs+zGNiwaibGlzPK/y6ZyQ5nfK6RxZFYhCas9oYSDcLreilu24OVvTSzUiS3pesNCY4pJscpmw8c10Te/Q4Kpo8LK35ybuqOWjnwfM8qSNBZM6qZe9zTe+aseQN9JhRTNzAdKAjx89xvFzbtDHVOZ8aOZNHhBixu8ALXvACAMCDDz6IW265pef/nThxorQFjgvHTyzgsXPr+NZ95/D6d3850oAicFMSDPSMUEWvzNEKBw/M49abr8HcTA0KxPX5VXTdTOqZSstCOSmVCNZvkTWLFRyYQpbPhkBmLE6exQ65Rcs32fXCYgtTV6X2c6RJZ0Wp6Vri4cU3fklyxUsOetKqajw9RFmx7eS1Z4XHgCaN4HAdW9FTYTnRn4uIeQbLhpcl32T3aNTYiK50c7gVvWhH0Lw9ekVW9ESkm72HdoPDjKSIMSS+dNNK7HmyuQI9NbMsHOCraPKwvDZ+Fb227SfharJ69FIcUkXokW6GBqbngV1HUT2hMuZoAsDaRhtTnTmSLIlH0s1yiN153/nOd2JlZQUf//jHsba2Fnzdsix87nOfw5ve9KZSFjgOMLdBdjMxt0GgdxRBXumm50HYyCUvBw/M4+CBeezaNY2zZ1eFfraSc/QcFxMxg9/TjE+stEqEztzr8vboDY5XEDFj6Q9keR3oZPCsJ+zGX33suwCAiZqO65+4R9prB4G2pEOZaagc0s3436V0RqrEHcbSZr6ZhobNtWLs/9MOgVmRMTA9zQDDn/+V/J7yjVfw7/Nm28H0ZJaVisGulyiZ6PBdN5PMWCT16BXqupke6EW5Q/JKN6OC86w06gZsx0O7z0E5DF9FL31getyzCOCraPKwtN7q/L5qPcvzINtASNeUXHL2MOHxCqqi+NdxziA7yQArb48uY61p46Idk53fQ2YsZRK7k1x33XWYnZ2FqqqYnZ0N/t/8/Dze9a53lbnGkYfXbbCVsaIXDgzLrOjlpWZosB1vaFnsKPJU9GzHgwLEyuHy6tK7D5/Bz1vEjKW/4TqwrC8h0FtZt+ABuGT3FJbX2zi7tCnttUX6s3jwe/RSzFhS5iFqCYcxvopeUa6bTiGBHs+Q+DSSDh1Acm+RyHgFdh+VpSrwg+vov0njDFiKosg5es0ie/RCs0mTaFuD17uhJ9/fQHJwnpVGPX1oOk+PXtJ4BSYLT+3RkxHorfrJqKo5aOehJXkkiK4nO6SKwM6S7Jzou5fLqegljVfJXdHbtAalm2N0zVSZ2J330KFDOHToEG688UZce+21Za5p7OB1G+z2l2Sr6GX52WESNBK3XUzWqxGgst6lKNJ05WxYd9yhIG8WK+jRM/JV9PolcXnHPojA+jmecc1uPHRmDfc+sozd2+WUU2T305gJ0k3P8zpzh1Ky7qoSewhNc/Qr2oyliKSQjOxvmnTTSLCVF5HvsmpKsyRDqCSpbzdArvAcvYyf6WZEgkoWqqokurAyLNsd+P2mrqbPs0sIzrPS6MjXNpo2dszE/F7eil5c0pFj0DuThuc1I1pmFT2reEOvsohKquZBV9XMSp5+wmYsgG/O1s6ZrGJqmGjXzfwqDc/zAjMWIOwtQIFeGaSm2GZmZvDbv/3b2NjYgOd5cF0XDz74IP7v//2/ZaxvLOB1G2SZmpqg/DIc3JU5XiEvbKNq2w4m0y/FUkiao5emK0+3s863uXVdN7NW9KIrSIZWTo8e0A30nnDZdjS+puPeR5Zww5MvkvLaItb6PPjSzbiMOZ9EUNfiH/BpxiFFjlewHU+64yYgp6KXJsHVEyp6Ii6JNUn9Lbwk7Q+87pFFYXfmtqkREsW8n2mzbcM01MjXloGuxydTGJbtYnrS7PmaaWi4sJY82iWtDzcLkzwVPdsN5uPG4e8tyUmkpHucvX4eMyLbcbG6YQUS9bwW/FVBunRTZkUvSPiGKno5k1XsOopO9PBVzZPYbDlwXC9IcrDrclguw1sNxUuZgvijP/qjeNKTnoSvfe1r+KEf+iF8/vOfx4EDB/Bbv/VbZa0xlv942++hfWFJ+usapg6rLcchCQBWNtpYWNxA+J1WFGB+bhIzoYfP2qaFR8+u45L56dh+hqi1eQDueWgJADDTMHHRXAkNJxzrSmNlvY1Tixu4fO9MoWMhRNZ28tFlNCYMzO8YfA9bloMHTq1i785GpLvp6QubWN1o48rHbYt87aifF1nbhdUWzlzYxJX7tgVZ9rQ1hYl7v9ebNh45szZw3cm+DwBgaa2N0+c3cMXjZnD6/CYs28HlF8WktROIWluz7eDBhVU8blcjyBzmYXGliXNLTTz+kln0H11cz8O9Dy9j1/YJ7JjuTdiE13bysRVM1vWgN0FkvWcubGJ5vY2r9kVfT6KE1/XI2XU4jotL56elvDYj62cQXhvbBy+dn47MqD90xu8Zv2T31MC/nV3axIXVFh5/8Wz6Wi0HD55axd5dDUwnrFXWfXBqcQObLRtX7B283m3Hw8lHl7FnxyRmp8yIny52bUnvW9t2cf9jK7hobhIzDfG1LZzfwNqmFbsv5uXeR5Yx0zCxZ/tE7Pfcf2oFNUPD3p2NYF1Jnwdj4fwG1ps29id8jyhJ9whb2wMLqzA0FY/b1Yh9nbNLTZxfbeLqiM+M53qKep4kEXWtWY6L7z+6EiTFeF9LNrKfVetNC4+cST6L8WKYOh49vSptLz+33MTichNXXzILAHhwYRWapmJfwrWStDarbSeeI1Y3LDx2bh2XXTSd2ZyG7SHzc5PY1jDhuB7ue2QZu7dPYPv04HilIs4e44y5fRZP+n9/LfbfU0/X6+vr+M3f/E0897nPxY033oi//uu/JtdNQWYmTczPTfZk12anaz1BHoAgEBTdJ5Xg/wNGSTWhdP5QNznXUCoeECs9YV+Py42kyVaCn0e2v5e9T+HMeNqaetYX/Ez/usD9GnkJS8Qman6PjCy5Glu+LOkQ67V0I9YX/K6U11AUIO7j7n4ecddbcZ9JYRIrdi3leInu5xjzKxLeF8/j//zZ5+uVJJd0k97z4H0bzl6Y9L4pOT9T1/MKq+YB7B5LXp0b8ffx3F+el36Pi8JjWOTfn8mvw/aWqFdhf1fSa3SfHcm/JwlW5WEVwQo9ynPBiteyLluZe7nrecHZCfDPUXnPUEl7bvd8kP312TOUXfsyrj2Cn9S69OzsLADg0ksvxb333otrr70WbkXMMy76xVdHHsLyksVBkhfbcfE///TLuGrfLP774Sf3/NtX/uMU7vinu/B/fvH62L6luLW97Q+PodV28PynPQ7PfPHVhaw9iSzv2Xe+v4g7/vbb+H9/8um4uKBsLyC2trf94TEcum4vnvEDVw382+JyE3f82Vfw0zdfgwPX7R3496MfO4GTjy7jd297TuRrn13axB3vOY6feckTcODai4TX9rVjJ/GJf30If/lrzw++dn6libe++yu49QevxoGnPC7x5+/790dwx6fuwTt+6bnYFsrM3/foMu74wDfwq6+4DhdfMRd8vYj74LNH78a37j2LP/rl/4Tmw0u444P/jl/60SfjqVftEnqdqLXd9eAF3PGhb+LXfvypuPjS7bnXevKbj+KOT34Pf/g/bsDsVG/W8cJqC3f86ZfxUzddjWuf2vu+h9f253/1r9g7N4lX39J7rwPA3Snr/fcv3Y+Pful+/NX/fL6Ug3J4XR/4wDdg6Cpu+PGn5n7dMI+eW8cdf/WvuO2/HMATn8DvqBpe2/ETC7jjY9/F237h+sjK+t///+7E+ZUm3vIzzxr4t89/8nv4xvfO4I9/+T+l/s7ltRbe+idfxk+++PE48LR9XGvLw5GPfBsX1lp4y6sG173RtPG//+iLeOULrsS1z7qE+zVlre3zn/we/v17Z/BHEe/b8loLv/0nX8ZP3nQ1nvTU5D0mam1/95FvY3mtjUOvembudUbxx+/+Mp5w6Xb87A89MfZ7/uCd/4KnXb0bz7rp6mBdX/zUPfjqdxfwrtfcGPtz//j338GpxQ389s89W9p6N5o2fvuPvohXPP9KPOnZvZ81W9u7/uwruGrfLJ790vi/6dtfeQBHvvh9/PmvPm9A/n1qcR13/OW/4hd++Il40hOjx9c89J1TuOOf78L/ue0gds/GV0P71xbmm/ecxR1HvoMfePo+fPYbj3C/lmxkP6u++O3HcMfRu/F7/89B7NyW7+/ZtWsaX/m7b+Hjxx/CX4We3Vn5wie/h6+H9ri/+8i3sbzexv/6afH7i71vJztngNf2nQEA4M6Ti7jjI9/GG3Oc09hrsLOe63r4zd/7PH7kuZfjuudeHrsugo+080FqRe/SSy/FW9/6VjztaU/D3/zN3+ADH/gA2u1iLL+3Arqm4voD8/jWfeewstH7PrYzmrEAQI05MI1Qj17QI1OSGQIPSfOLusN5s/Xo5XavazsDUjbegcFAaKB7/8B01hhdUo8eC5ouv2gauqbg3keWpbx2EeMVAESOWLATmtfD6Gr8eIW0GV1Fjr3gMXvIgh4erpuRfK6b/G6igRlLWT16CU6KMgwP8mDbbux9o0nYtyYKGJbO0DU13XUzYmA6zxw9f0+XW9ObqPm2+ButFNdNjv5fIPpz4XGfDfaXHM/fpXX/DLOrE9wljaMZJaT36KkqXM+TUphoW07PTEh/vEJe101m1FaMGcv6pn+tM6kyr4kSIYfUJ+Jb3vIWPOMZz8ATn/hEvPzlL8dXv/pV/O///b/LWNvY8txrL4LjevjqidM9Xw8PwhSFBYejZcYSf5AeBq7Hmsnj3f6A+IN3WuN+XjOWVtsZmLEoEqQ5IdmkzHWJsLzWxrZOoGfoGi67aAb3PrIk5bWlj1fo3FNRhixxMwn70RLMWLrGIcmBXhEjFgqboydhTECae6quxbuRiphnmIYGBeUFelai6+aQzVhcN35AvZYveG+27EJGKzB0LT7wZ8TN0bNsN1H2lhScZ0VRFEzWdaw343uQeAamJ+3bwYzOJDMWtr/lSCQtr7WgANi5re6vZUzs8osYrwDIub9bfUY9NUOTEOix0UsR41WC5F32ta/1BXqAb6I0rP1uq5G6g/35n/85brrpJgDAf/2v/xV/+qd/io9//OOFL2yc2bdrCpdfNIMv3flYj26bDRw1M1T02I0/anP0gOpU9NIc+4KB50njFRIrevkOc01rsKInEqTFVUrKHJjuV/S6stGr9m3DA6dWpQT70scrsOszItDi/V26xjFeIeYwZhZa0Stmjl7g0CZjYHpCAJzkusm7B6qKAtPM71jHCxu/ErcWBUMcmJ5Q0ZOiRCiyopcwwgTw3UId14uYo5d+fyUF53mYrOtBlSPy93IEelpCAM7OEomJRyP//rK01sZ0wwwMS6qStM1Ls+1A11Rpn70uaeg40KnohRK+/h4ma2B6gutmjj19ddOConQdZwE2Joea9MogNs32zne+EysrK/j4xz+OtbW14OuWZeFzn/sc3vSmN5WywHHluddehA988nt4YGE1cB1sWy4UZKtImEFFb/Tm6KUNrS2LtCqNllrRc2EkfHZ5B6a32s6A61Uw44rjNS3Hb/DX4uboFRzoOa6LlY1uRQ8Arto3i6NffQj3n1rB1Zfk66vjrbLxUgsqzvEVvfRAL/vAdBkZ9ziKsI0H5FSmnBiJMSNtjp7I31U3tPKkm7YLYzJ6bYqiQNOU3EOJs+LPhIwZ5p5z9MNmu+CKnh5/jwHd+6w/gcr+27LdWDdB23ExUZO/9kbdwEZMRc/zPK45l4GaI6Gil3QvyEgkLa21MNswYRjF7VXDIKpNIg8yK3ptywladQD/HJV7vELCnhuoNHJKNxt1IzDA8n9XeiWekEPsLnDddddhdnYWqqpidnY2+H/z8/N417veVeYax5JnP2E3DF3Fl75zKvha23ZgGPEDt5Ng2bnRkm5Wq6JnpchdVEWBltBzZTtepPSBwQ5MVsasXqttDzx8FEVJDCbCODGHYD0lgJXFyroFzwO2hyp6zHJdRp9emhRSlGDOY0KPnp4yTFnTOHr00qSbBdwfRfXoycj+pg09T+7Rc1M/kzC1Eit6fhAavzaNo9esKJKqjUpn38v6mco+NPfj98HG713d3vc+6SZH64BIhViERoJ003F971XuHr2IfTtNLRD+tzzScCbFN4O9ajwO7s2IZ20e8lbFw7Rtt6eiVzM0X4KcY89lCaao84smoe96LTQsncF7biHyE5uqOnToEA4dOoQbb7wR1157bfB1y7JgGPlnVG11JusGnn71LvzridN45fOvhGlonYbxbJsLy/CMUqBXq1iPHo/cJSl7bDlujzShHxaUZdW6tyw3ciZS0sE3jF/FGTxoGhKzjUksr/vDicMOllMTBh63syEn0JPeo8cOQhEZcxZUxvQ1MXQ1/vO2Yw6gwe+XIK2Kg6dikAU5Fb3OoSOmwqTrSkpVXayiV9rA9IRgCkgPWIrEdtzYih7gJyyyHPRsx4Vlu7lnkSWh62pigGFZMYFeqKIX+7MpwXlWJus6zi5tRv5b0KufGujF32vB3pLYM57+96extN7CxXumukmxAvqJh4Hs5ERQFZdkxhJ+htZC733WynnSs5MFf3nUBpGBXkolnpBH6hOx3W7j3e9+N9rtNm655RY84xnPoB49STz3yRdho2Xj3+89C8B/IGUxYgFG04xF11QoSnUqeoF8ISkLmiA3cBw3dVisoWd3mmpaTuAU2LsmhUu6GddDWJbr5tKq79C2rW9UwVX7tuG+R5dzO5JZHJ+fCIkVPZfvd+maEvtwT6/oFSeHKqyiJ8GhjR2u45QNhqbC86IPHmnBVD81U0OzpMG8aXJZTVOH57rpeClBaLZDmWz3wijSJGDs3wZcNxMSOQzR64mXxoQRW9HjqcYB4SpRRI8ehwNx3iqc63pYWW9jNlTRGxczlmY7+lmblSCZKuH9afedE9n/ztOnF5x9IhKXOsfcxzSiAj1Di0/YEXJJ3cHe/va34ylPeQo+85nPYOfOnfjnf/5nvO997ytjbWPPNZdux9xMHV++05dvtm0nc48du9lHabyCoih+JbMicg+eLKieEFRZzmDD/+DPq5kbslttO7KXROeu6EUHeqrqS7OK1ssvBRW93qrkVftmsdmy8ei59VyvH+cqmpXEQI/jWmFrSRrHAcTLP7uHJ7mJEMf1nQaLqOjJMBVxUgIiPeFQadlif1ep0s2UKqquDbmil7a2DAe9pmT3wijSVBLs/o2VbibcX0X1sjbqOjaaduQQ7UBZkvYsSVBipBmLAfn3l9WNNjzP38+r1oaRF7+iJy85wSTtclw3e8cryDC1c5IqeoVKN8mMpQxSdzDHcfCc5zwHX/nKV/DCF74Q+/btq8zA9FFHVRQ899qL8N0HLuDc8qafqckYqJkjWNED5DQSy4InC5p2cO83Oon8+axmLLEVPb4N006oOBp6vGW9LJZWfSvumUZ/oMf69JZyvb788QrxmVJ26E2bo+dL3uKlvn7fZ1qPntzPhTmdFbFXyDAVSTNUMRIqGWkBS5jjJxZwz0NLuP/UKl7/7i/j+ImFbAvmJDWYUofbo5ekRtAy9tOwil4RhiYMXVMS+56DmXJ9SdRAupgk+yxI4jxZM+B6XqQRkJUi6WYkm7HwSDfzVeGW1joKjUatVOfmMmhFOFznoevYLUO6OTheAQDaOSToSeMVSLo5+qTuYK7r4s4778QXvvAF3HDDDbjnnntgWfG2wIQYNzxpHh6Ar3xnAZbtZJdujuB4BcDv06tOjx7fwzHuUGG7brChx/58xgOT1zkURD18dM4gzU6oOJYR6C2vtzE9aQwcdue21bF9upa7T8923EhX0awkZfzFKnox14vtJRqHsCy57M+FJ6GRh7ymIo7jJgbQyRU9Pknq8RMLuP3o3YFsb3GlhduP3l1osGenmrFkq5rJIGlvANiYkAyBXotJN4ur6GkJfbBA2HWzv0ePU7pZUEUPQOSIhe5zKPk9645XiDJjSU/m5JWGL611FRpFGkcNA9lmLFqQnJLkuhkeryChosdmvUY9z7Sc0s2W5cCyXTQmepM9SW0whFxSd7DbbrsNr3vd6/Cyl70M+/btw2233YbXvOY1JSxta7BzdgJPuHQ7vvSdU2hZ2c1Y2EPLiLGJriqmkX8GjCx4zDySrN1tO/1QwCuz7MeyXXgeoqWbnMGjn7UfXqC3tNoa6M8D/CrQVfu25a7o2Y5cOaKmqtA1JfIByj7DtIpeUm9TWrWgexCVe3jiSWjkIW2uWRppn2NaJYPnYH7k2MmBA27bdnHk2EnB1fLheV56j15KwFIkSXsDkF1m1ZVuFlfRM1IGLweum31JVINDuuj36Mk3Y2l0qhtRfXq8PXrd3uroyjaQYiym+TLrrIHe8jrruTahKgoMXR2f8QotB3VD3jWr5xxRwrAdfyZkOGlRC3r0ZFT0EsYrZNzTWTJjerJXyZNH3USIkfpEfPGLX4xPf/rTQXD36U9/Gj/wAz9Q9Lq2FM+99iKcW27igYWVTHKq4ycW8MVvPwYA+NMj3ylcgiSTKkk3bY5KR1KgZrvp/RxZD8HsPYo6MBmaSEUv3uCi+B69do9bWJgrH7cN51daWFxuZn5923YTx1tkwdSje0gdDmkUkFylsVKMHoqSQ/EeJLPim4rkMQZI/hyT3hde183FlZbQ1/PicJj36AmjOIrGdpLVCFpGR9DAjKXAgelaShDKArn+68JMmePKgvOixisAwEZzsKKX5sbLSLLsT5vRCfgJNsNQM/fosYretoa/p5t6ddQ5eQjUMxKv2W4/ZU7DMVadjqjo5ZH4Jzkddyt62V5/rRPoNer9PXrD60neagjvYFqKnIAQ5+mP3+U7Jzoe7jy5KNQvwiRImx2JzPJ6u3AJkkyq9HBgmdFkw4ThVPSYBXxURc/g1LonHaBLqeittbAtYjwE4BuyAPn69NIG1mfBjJEW8zp8JhlFpFX0Cgv0OA+SWUmaNcmDf7hOrqoD8fPDeCowczPRCYe4r+eFp4o67IHpiTNAMzqCbraKN2NJk8O3Iw7HQHrFnGfoeFYm6wkVPe5AL2G8QqfnUk2ZyWvqWvaK3lobUxNGsE42ImrUsR3frErqHD02XzRnYMOeRf1z9ID8Fb04p+NgNmrGPX21E+hN9Us3dTJjKYvRaugaU75xz1mE73+RfpGyJUiyqVJFz3Ki3dnCGDFZKM/z4LjRc+rCZJUrNDvvUZQZi85d0YuflcX7GlkJW3FHsW93A3VTy9Wnl2YRn4W4w0vgUpZqvuMHPVHuen4VJUm6Wcx4BdmD5fvxh2uXUNGLqWTw/F2HD+2P7Nk6fGi/4Gr54JGF+zLfIVb0UmR+mXr0SjBj0VIqA7E9ekby/cUjf8xK0KMXUdFrx6y3n7SKHs+68yT4+hN3VUra5mEzIamaFT1hzxKhFXFtyAn04vfcvCNz1oNAjwamD4vidl+CmyPHTsLtOwiyYO3ggfnEny1bgiQb3968mJv9+IkFHDl2EudXWtgxU8PhQ/sT30/mRpjc16DCciLkNpzZX0NTIp3W0kiq6PFKIGzHi50NxFsVzMpKyIo7Ck1Vsf9x23IGevKNE3zpZnyPXlr1qOtYNpgESKvosbEX0nv0CpZu5rXNTg86oiudjuv3sfL8XWwf+NBn7sHapo3ZKRMvf/6VqfttVnhmdGox/aBl4Ff05AehmyWMV2Cuw57nRVYk4mSMaRXzIu8TJmPbyFPRS5ADpiWRGPkCvTZmQw7Khq6NhetmEbMf9cA4J18ihz2LwucA9kzPG+jFJYFVRYGi5JduTkX06JEZSzmk7gSbm5v41re+BQC4/fbb8Ru/8Rt47LHHil7XliJPsFa2BEk2cQfpvDBJ6+JKCx74qqRcDewxcgPe7G/Wza0Z9OhFSzeT7MUZjuPGVqCKlm4ud6y44yp6gD9m4dGza5F9Kzyk2fJnIc4V1nZcKECqNCrpAc8zjNk01ET79ywUbcbiSxBzBHp2sjtlMHy47z6yOaTXYQ4emMfPvOSJAIBf+tFrCwvygFDQkCYLH0JFz+9F45jxl+Gg12w7MHRVmhNuFOFkShQsURI7MD3m+cMziy4rpqFCUxWsRex1vPennjCbzUq5h4J15KjCLa/3mmtVyUE7D612/LM2K0kGUiKwPrze8Qr5HU/5jKKy7U1rG6xHrzdwTppJTMgldff9jd/4DXz2s5/FnXfeife///3Yu3cv3vzmN5exti1DnmCtbAmSbGpGMYFeFkkrTwY3znWTR5oFxAeKaST26GkqbI6qT5L5B6+hS1YusMb9mIoe4PfpeQDue3Ql0+9ICxCyYBpaIJfp+V0dmWhUBSFMcBiLOCTzzOgycvTQxFF0j16S0ygPtpvcLxZX0bMySO0mOoYLrJesKHjkspqaTR6ZFyeYCZnco5fNddPBRIHVPCC5Vw2Iv95VRUmUrBcp3VQUBY0JI7qix+u6qSf36PFJN7NV4VzPw/JarxR/XFw3A6dYiWYsQTJCVo9eyJ1d11QoSv6B6WmjX7Im79Y2LUzUtIHrkQaml0fqTvDwww/jda97HT7/+c/jlltuwS/90i9haWmphKVtHfIEawcPzOPWm68JgsK5mRpuvfmaQrPTMjFNtRDpZpYqKc9stLiKHI80i/18lixWK6Gip3NX9OJ7CI2MJjG8LHcCve0JFb0rLpqBpiqZDVlsV75DXlwigsd4BwgfQjNW9PTsrnhxWBxBRx5kVPR4TGoGKnoZ5gOy3jFmZlUUPEFDVsOTvPAM104yFUqi2bILHa0AhHvVYtxtbd+YJKqqaCYEJzyz6PLQqOu5zFi0mIQH4K+9SOnm2qYFx/V6e/SMaIfiUSOQbsocr5BzRAEjqE6HEr6KouR+71OTazlG5qw1B4elA8W3ixBdUq9kNhz9S1/6En79138djuNgY2Oj8IVtJVhQduTYSSyutDDH0U/W//OjEtj1U9M13+XK9aDGaMTjYD144ffsmku24+i/Phj7M0lVUp6+q7jNKTjI8ZhzZJBABRW9GDMWnuDRdtOkm8Uddpc60s2ZRnxFr2ZquGTPdOY+vULGKxhq5APU5jDeAZIzuZbtYnoyLWsvP0tuc1YMsqLnrEz5hw7xgelZJKn1TqDHsvhF0a3SJPxdQ7IbT5qhxcjqpNpsO4X25wHJpiSAL3eLu9aTxgsUbVo0WddjBqanm4IBfkUy7nNJS5YwTF2NDDbTiJLi+0Hz6Es3mwVIN9OuUV6ipJtAflO7tApwHoOttY3oQE/vJLZcz0ttgSDykRroPe1pT8NLXvISaJqGpz3tabj11lvxnOc8p4y1bSlGOVjLgxlyjBJxZmM9eOwQvLjSwnv/6S548KBAwVX7ZvDAwlrPYTCtSmo7LhQFqYOD4+Z3AenmHEkD15NoyhivYCdINwvv0WthetJIPTRdtW8bPv/NR31Zo2AgYjuu9EOlbz8uo6IXlXXnqejJNzgofLxCzib7tH6xuH4X3nswDJMVblRAujks102+il62g16zbQfBdFGkSjcdN9bBMqmiV6R0E/ANWVjAFEak4h7nXMijFgCyJ/iWgxl6vRW98TBjkW8glHaN8tKKGK8A+H16+aSbXqwZC9CRbueQbk5NRgV6nffEdgf+HkIuqTvwm9/8Znzzm9/E1VdfDVVV8bM/+7M4dOhQGWsjtgDhRmKRQC+qB8/1PNQMDf/7Z5+FnbMTOH5iAR/6zL1Y27SwrWHiFS9IdtWz7XS5i65HZ90D6WZqRY9PZtlPK8Jtq+c1bTfWdS68xrjN3NCKfUgvrbWDwbpJeJ4Hy3bxi7//BeHKdlpDeRZiK3opPQ2MJFkZV49eAQYHxQ9MV9Bs5wv0kqpLcW6JWSp6bM9pFh3ocbjy5pW8ZoXtZ4kVvYz9NJstJ7EvVwap0k3LgaFHHySTJG/dALiYakOjruOxc+sDX2emUmn9v0B8FZh3vELW2XdLQUVv/MYrNBPUM1nRVL+PLsuzPwz7rGp6REUvg5s3Iy0xoOWRbm5auGhucuDrRqjKSYFesaTuBJqmwXVdfPWrX8WnPvUptNttfPrTny5jbcQWwMw4Ayau165lOdg5OwHAr5L+P//lAADgF3/4QGrAwHXwDll5h+HtD9Iz6tJbbQemrkbKWw1NgYf0OTdJ8oyie/SW1lqYnU4+8B0/sYAvfKvr6CsyTxIoaLxCXI8e5+/SEpzxfAv0tIHG8iutRUvS9BwObQAbmJ5e0Rvs0RMfcK1rKgxdDWZnFQVPcO2/b8OTbhbSo9e2C5duxl0PjHaCOiBJbmhx7ulZmawb0T16CVLTfuKeJ35VPD1QzKrkWF5n5loh6eaYDEwvYrwCkP0eChM1MB3Ib2qXNF4ByCfdXG9aaERJN4PZgmTIUjSpV/Kv/dqv4atf/SouvfTS4GuKouDFL35xoQsjtgbdYZ9im8jcTC0y2OvvwZvszCvi6UPgsecPZ4/Dh3RuS+yOdDOt+tZP03ISZuBpnTUlr993iowZmK4rBVf0Wti3ayrxe44cOzmwBt55kgCfFFIUJu3q7yNIC0YYwXiFiCDcTywkH4JNXcPqRrZxE3GUUdHLKvMB0nst2TU8WNFzOv8u9ndNmFrxFT0e100tWx9cXrjGymTs0dtsiyk1sqBxuG7GSTcNXYsdX8IzVzUPjbqOzZY90J+eJDXtJ07uazkuprTBw3U/WQO9pdU2Jmp6j8LE0KPVD6NGq+1AUxXp+6OuKRLGK0T3b5pGvnnEaUZmWY2ibMfFZsuJ7dEDQCMWSiB1B/7617+Oo0ePotFolLEeYovBMlOi2ajDh/b39OgB0T14bHYLz2w2nipNuKk6vNk6wWEpfa4aq76JjAJotZ1I2Wb4d1q2i3pM0cztND3HmrFoKjzPH4oqe+aV63pYXm+nVvTyzJME0i2is8Dec8t2e95/y+EzfklqwvcD05SezgIqeoXP0cttxpJi9a2qUJVByRpPZSqKek0vvkePY3/QMvbB5YUr0NPUjHP0yqzoxbluOjCM6L8tyYyEp3cxD8HQ9JbdcxAW6U+Or+jxuW5mNVBZWm/1yDYB/1nudmYyFhUcl0FR16xf0csv3dRUZeD9rRlapLEPL47jYiKhgpnVdZOtaTrKdVOSQQ2RTurduHfvXgryiMJgPXqi0k02VoI9zOLGSohU9PqDtyiC/qC+zcnilI2xzU1002xZ8e51rIKRlHFPk5ayqmARVb3VjTY8D6k9ennmSQL+Z1CEdBMYTEQ4nNIoTU0Yr8DRE1qEGyqzmxd1ueUl73wk207/HKMC4KyVyglTD+RaRcE1MF2NloUXTVfymhKECn6mjuuhbSUfIGWgpVQG2gmS/KT7q3jpZnQSUiTQM7RoJYZlO5xmLH5FU/SaW15r9xixAN2B9KNe1SvKKTbOOEeEluVE9rOZOc1YLDs58Zy1f3itE+glSzdH+3oZBbhcN1/72tfi+c9/Pur1evB1km4SMmByxCwPh4MH5vFPX3kAe+ca+O+Hnxz5PfWaBlUBNlrp2S6eBvawU1QYhyMrHv53y3FRA//DpNW2Yyt6cS6EYbpmMfFz9IDkqmBWliKsuKPgrdLGweuEKULc4cWXV6V/fnoQ2PddL64vB01bb5IrYFZ4HfmykqefI6g8c9yHTFrHyNp7OFHTih+YzjFnMyzzlV2ZToLLEbQj3RKRnLP3tPjxCp09Oeaaa9vuQFDCSOor674vBZmxTEQnIXl6xRlxkjpeYypTV+FhsBUhjaW1Fq7ct23gtQAmoS42uC8SP9CTv34Z41PaljswWgHIP17BcZMVKpqqZtrTWaAXLd2U40RKpJN6NX/zm98EAHzkIx8JvkY9eoQs2GE5yybleR7OLTdx7f652O9RFQWNieim934sDnOMOCmexSHNAsLVN7HNLblHL13rzg5BcZt5nJOhDJY6Vtz9Up9+WDX2/3v0bli2m8F1k/+AxEtQ0evL+tuOh0adw4wlZlAuC1LSK3ryDQ5EDpJZyDP4mx0m0u4j3zyo/zPJdjCfqOk4u9QU+hlR+Hr0OkkB1wNHDkEatsuxNlU8CN3s7LnFj1dIVjTYCRWyJGl08dJN/31Z76/ocShLGEbMuB/eezy87/P+Ts/rSPH7FBqBsdqI91y1CpRuyhiYXovYHGp5e/RS2h6yum4mBXqBdNMuV8GwFUndgT/wgQ+UsQ5ii1LL6LoJACvrbVi2i53bJhK/b2rCxAaPdJOjIhQXEDmc0s24imAarbaDmcnoQCmoEiYFeimz03iqglnpBnrpEsyDB+bxrXvP4eEza3jbL1wv9HuSzGayYgbjPwZntvHOuWLfH8birACbhhprFpGVLDMKRchq3AH4EiKArzIeO15B8G+rm3rhA9O5ZtWxYMpxgRLtxgPTkcRh7t3rmLdiutlRUZQ2MD1Wuukku27GPHssjipsHlhbQf+zSahHL6ZKxNOGAGSrwm20bFi2OzA2I3g2jviIhaKkm5qaX7oZW9Ez87puxvfvA37CMovqKrmiR9LNsoi9s9/61rfijW98I2677bbIf3/Pe95T2KKIrYMZmqMnytllPwu/a7ae+H2NSWMgaxqF7XiRm2iYuMZ/3oN71oAq6eGTZPjBYC6IGod0UzZsKDDvPK0sIwXSzGayEjf+w08K8M25AgZlZbxBiaGr0g0Oiqh8hsljKsJTXQJYRS96xIno31aGdLPb75U8qw5I7rUtgqASmnLQ87+Xf20bgXSzpIHpCdLN2IHpSdLNwgemdyp6m4M9eo0JvvdM11W0NgevXd79gvVmi6gGlmL28676YbQP7s220zM2QhaGnt9Vtx3bo6fBcbM/J9KMzPwgVXyPTAz09Oi2BkI+sbvJwYMHAQA33XRTaYshth55KnrnljYBAHOpFT0DK2vpzo2W7QYN8nHENRBzz9HLeJhrWw5qMQcmg0PrnlbR0wuWbk5NGNwPoCwz/YoyTqjp8dJNrjl6MeY7POYcQFfazDsAmYfCK3o53OV4+6KMzpiSMFkrMBM1HZstR3jkiQi240JRkOhoq4V69MqEZ2B6XK9pEoF0s7SKXtzAdDd+YHonqRT12Qd7ZuHSzf6KngND40uK6RFVIj8xxCexDRKtAvv+MlNo9Es3g35mquhFoUkwY2lbTmTSohZ677M8J2zHS+nRy27GYupqZHBapIqI6CX2VPuCF7wAAPDUpz4Vl112WfB1z/Pw3ve+t/CFEVsDQ1ehQHyOHgCc61T0dm5LruhNTRg4dW499fV4Kh1xMiEe57qen8/Qo1ePM2PhcMwMDnNx4xUKDfTaqf15YfQMTpO8ZjiiJEs3OSp6anQQnhZ4M9i/t20XE5KSzEXMGwzDDgVZAidWeear6MUkW4Qrejpcz0O7b4SGTGw7fe4iq6iVbU7ANTA9wT02DlYlLXqOXrCnxlT0kubShfe9/sOo5bhQleLcaQ1dg6mrkdLNqINxFFHjFdheyJPM6f79/PstU2jMTkf36I1+Ra+YHj1DhutmjLGQaXbnEU8mH4ciSR1pk8N1c2oyep5jFpUAkY3UneAXfuEXcPr0aQDAY489hp/8yZ/E0aNHC18YsTVQFMWXz2Sp6C1vYqZhph7OpidNrjl6PAfguNkvvIdMJt0S2fDdjk15nKw0GCCd8JqOmxyIFjnTZmmtxdWfxzB1TTjgtDgDbVHixiuI9+j1VfQ4HSKDHhqJWfIyzFiAbJUpXufMxB490UCvc0gqcmi6xTWjczgudGlGTUB6MBXFRmkVvfi+Z8/zEivYZoJ00eaYc5mXybqOtSgzFl71Q8R4BdbnKmrGwsvSul/RG+fxCnHGZ3nQtOKkm3mUUYCvOEkzY8oisVzbsDBVjw70jJikOSGf1J3gV3/1V/FzP/dz+OAHP4jDhw/j+uuvx9/+7d+WsTZiC3D8xALatoNP/dvDeP27v4zjJxa4f/bsUhO7Uqp5ADA1aWCjaafOCuKq6MUEVbyOf1k2N7Z5x/W68MhBg8CCI7Mtm+X1tlCgZ+i+O5krMNspq7V+Gmaootbz+zilm4Flfsz1klrRyyCtSkPE1S8LXVORDIEe730UUcmwnWzzAZkrZJFD03lGWuQJkPPAU13Okn0vvaIXsba0Xlh2f0XtezxV2Lw0JoxcZixRDre8bQRAcqAbx9JqGzVDG/hc4xyKRwnLduG4XiF9pTIqem3LDWSaYYJAL8M8UNfzOm66SYFeNifltaYVOUMPIDOWMkm9mn/wB38Qtm3jDW94A9773vfi2c9+dhnrIrYAx08s4Pajd4Od5xdXWrj96N0AwGWpv7jcxGUXTad+39SEAcf1OkPH4y95y04/jMUdKnh6cMI/328kkQQL9PKMVwikjXFmLBzOnVlwXc8frisg3QwHnbxSOlZpKGq8woAZC2dFLzDYcLNW9OQPsrdsF5MFHr61UPVHZFYkEK48c0g31wcrelkCfXZgLXJoum27QS9tHHkC5DywvSzOqMn/N/EevY2SXTejDIDYITJu5mU3kTP42fNUYfPSqOk5B6bHV7b5zFiYYkCgR2+9FbmfmwUmC8uim1StaI+eLb+i53CoYbJLN21cuic6GZ911BQhTuzTvt9tc9u2bXjLW96CSy+9FAC5bhL5OXLs5EAmsW27OHLsZGqg57oeFleaeOYTdqf+nsaE/1DaaNqJgR5P31VcRc62eSs84psby9LF9uhxZMa60saUip7kTXd104LrecIVPUAw0LPTDSWyUItwhe1mQPldN7NW9JIOolmxi5Zu5ghYeA+pesShibdvsh8m3SzSeZMnaNBS3COLguda1DMYxWw2beiaUniwxN63qACDyQjTpJtRgY5Mp9s4JutG0GvOEBuvEH0fAHxJryz7y9JaG7NRfWIxMvdRotkqTm5c9MB0/9/F3/u0/n3AT0JlkW6ub8ZX9IwEyTUhl9hTb7/bJrlvErJZXIl2woz7epgLqy04rpdqxAIgaAZeb9rYMRP/fRaHVCcuIOLv2RLvw0mr6PHM0UszKylKurm0yjcsPe9aeAwlsqBrvllQuO9ExPhFVRQoGKwA8/aTGTHS0TzwVK7zkKfXrPvecgxMH5DTZvu7WEVvs1VgRc/x+KWbQxqvkFTRy5Kg2mwlJ9ZkoSpKrCsgMxlJGpgOJPXoFS3d1PHQmW5Fz3H9JBLvPqZH9H2JOBAbhrhiYHmthUvnB5U0RexVZcOq+kVct3kHpnue13HdjBqv4L/3WUzt0vr3gWzSTdf1sL4Z36NH0s3yiL2ab7nlluB/X7hwAV//+tehqiqe9axnYXo6XS5HEGnMzdQig7q5mfTqz7llf7RC2rB0oDvDJcmQxevMKkvLpMYFVX5/H0eFJ0MQwx4+aYEezxy9uM28qPEKy+v8w9IZvUN8+ShqvEJgFhRai51SHe3/eU1TB+foca7XzHAQS0PE7CELgcwvg9QnrfLM0LVB182sJjP1INAruEcv1XVzSGYsHSv+JIdUPUOP3karGPfCKKLMeYDufRPnYtm9vyKkmxxy27w06kbPeIW0wLQfVtELO9yKVPSySPaX1tq4tjG4nweB3ihX9AqUbvpjZ7Lf25btwgOkV/R4VBSapgy0H6Sx0bLhAbGum13pJrluFk3qTvDpT38aL37xi3H77bfjr/7qr/CiF70IX/3qV8tYGzHmHD60f8D22tRVHD60P/Vng9EKKcPSgW6g1z+vKAzv4T0uqEqbQ8NgD1aRQ3BQ0Ysdr5D+sA7MSuIy2wX16MUN102CZ1xEP0WNVwD8B2u4ohfMwOM+jCmDc/SCih6fVFh2j16xc/RkVPTSK51Rc/Sy/F2sX3GzXaB0kyMIHZoZi+Om7l3daqNARS9FKi+TqHsM6FaX4qWb8ZWQMnr0Jus6Wm0nuFfYPsObsAqGToeuGTZPkMcxVHSO3mbLRstyIhUaqqLA0NURr+gVK90U6c3vp52QtIjrJeeB3dNJbQ++66bY2pOGpQPdSjz16BVP6i78jne8A3/zN3+Dq6++GgBw4sQJvOlNb8Lf//3fF744YrxhfXh/+7n7sLzexvSEgR974VVcRixnlzahAJib4ZFu+g+l9YSKHu94BCNmPAKPY2f49UUO7mk9ejwH6+DvS5ujJ3nTXVpjVtwCPXqauAQoqJAVkIE39d7xH6L9gIl9NDEmEQwjokcwL0W7buYJWHg/RyOiomdnNGOplzBegad/cGjjFbjmh2Zz3ZyolVjRi3jfUl03E9QDdsESZ8Cv6AF+//hMwwzWETf3r5/wWJzus8Xp+bckRNUTy+vJiTtTV4WMXapGs5WcVM1D3opeOyHhm8eMhWd2qW/GIrb2tY3kQI/9zlE27xkVUneCer0eBHkAcODAAeEhuAQRx8ED83jbL1wPAHjRMy/mCvIAv6K3fabGdbALKnqb8Qc53iqNFhOo+VlxHnMO8V6XNOmmoiidbGF26aamKlCUYip6UxOGUGCRZHkeR5DFLqii1wqthb2XvDLBqPlJXblM8jVThOtm1oCIlzzukQ5vZb3ToxcemZLVPEPXVBi6is0iXTc5+r30wNmyfNfNtL0ryzD3jZJ69ABW0YswY0kJnJKGfNtOCeMV6v77w5KQaeYx/USNveCVP4e/h3f23TJL3MVI8U1DQ2uExysEPXoFuBKnPaPTYEFc1LVcM9We7xGBJ8mtqSo8D0Ijj9Iqev7vpIpeGaTuBM9+9rPxF3/xF9jY2ECr1cKHP/xhXHXVVVheXsbS0lIJSyTGnYmajrmZGh5bXOf+mXNLm9jJUc1jr68oXbvvKHhmSQFhuUH/eAW+Q0FcRTCJNDMW/3XVINiJIk26qXRkN7I33eW1lpARC5BNrijSlyKKaURX9HiDCl0dzOQG5jFpc/QkGxy4zOyh0IpedvdI7sq6psDzequGeSSpE6ZWrOsmh9HTMF03edcm6rpZao9eQkUvbbxCZH9fSa6bQLetoB306PG9b1F7Ja+jL9Dd93kDECbFj+u5jjJJGiWKHK+ga51gKaM0mwXjUdJNTVWha0q+8QqJ41XEk3dcgV4BZw5ikNS0xfvf/344joM//MM/7Pn6Rz/6USiKgrvuuquwxRFbh4t2NvDYOYFAb6WJay7ZzvW9qqpgsqYn9uiJSP+iNieLo88FiK8IJhE8fBLkJHEHHQY7PMZJN4HomUx5WVprxWZ/4zCzVPQ4+gyyUtPV3kBPUCaqRzSyM6lU+hw9cWOaJET7C7Og5ahM8Q9M14LvD1fJsx7QJmr60M1Yhum6ydujJ+a6aZUq3Yx636yUBFeSgUjWcR0iNCb8IxgzCmOyR37XzcHPhTdZwhCRWzIpflzyrl/mPmqwHr2JggI9wP98TFX89YPqdIQZC8De++zJtaQ9oJvocWGk14cA8AV6/pmDzFiKJjXQO3HiRBnrILY4e+ca+N5DS3BdD2pCZgnwN6YLKy2u0QqMRt3ARpIZi8iQ2YigyuF03YyrCCbRbDtQkDbnatCcIky3opfgrFdANnZprY29OxtCP8MzF7Afq+CK3kYoCBBx3WTfN5gY8KAoyZb2QPggKudzERmmnJW42YE8BO9tqvttd3ZavXPmtBwXU1r8oSKJek0vdmA6T4/eEF03uYe5i1T0SpVuRlcGgipI2hy9KOlmwaZFQLdHb7Cix2/0BPReM2nBbT++gQpnj95aG7qmBgZG/ZjGqJuxOFCVYmY/9gR6GXoAu9dy9M/WTC2ndDPhbJDBSXlt04KmKonJt7j7lpBL6i7sui7e+9734otf/CJs28YNN9yA2267DbpezgZObA327mzAsl2cW2li92zyyITFlSY88I1WYEzW9RQzFv6+K11TIhz/+Id7i25urbaDmqkl9sYaKa9pOx4U+IFm0mvIDPRcz8PKeltotAKQzabb4QwQsmAaWpDJBsQz5lqEIyAbWp7W7xxIqyR9LiKyrqzkGa+QZhrEYOsPJ0x4ZtXFUbh0U6SiV0HXTdHeYtfzsNlySpRuRvf6sARQXKBnJLhOljMwnVX0/GvPEuzR634u4R49saSXqWvc+8vSui/Fj9u3zD71w6jRbPvXbBE+FFkMjcIkmbEAgy0GvPCasQDi0s2pCSNlbAsFemWQuhP8wR/8Ab761a/i1ltvxate9Sp885vfxO/+7u+WsTZiC8GqPo+dTZdvstEKuzhGKzAadT2xoiciaYvqZfNnUfFnYcV69OzE/jy2psTxCq5/mEsMFgV6NXhY27DguF6GQK+TZc9Q0StjvIK4dDNa6st7mDMFMu5p8A5qz0OUQQQvvBLcKLdEFjxnwZduFljR43BwzHsQzEoRrputAgdPRxE3jNqykitkasfIKur+sgT29KwEZiwdmZtwRU8fDMC76hS+/ckQqMItr7UTR+X4M0dH9+DebNuoFyQ3zmLEFqaVIt2sGWpw34nAM9JGy1DRX+8EekkYupi6ichG6i78L//yL/i7v/s7GIb/gT3vec/DD//wDxe+MGJrsXduEgDw2OI6nnLVzsTvPbfEPyydMVk3cC5iODtDRNIWZQkskv0VbUButp3E/rxgTUkVPdsLjGDiiJpNlofuaAVBM5YMw9tFgy8RTL3XSS74XbyHsYhZQZaA86XM2VRpdvMy6B4KxNdsdSrPaZLWwEq+r5KRuaJXeI9euhlL13WzfOlm2n0jOkev615YXkUvSnrbnaMXv4643qY8iQNeNFVF3dS60k3hHr1B6Sav0RNDRMmxtNZKlOKPQ0WviNEKQP7xKanSTSOrdDPZkRsI913zr31100IjJdDTqKJXCqk7ged5QZAHAKZp9vw3Qchgsm5gdsrkMmQ5t9yEpirYPs1fKfIrehxz9HgqehHZY57DUvjnRRqQmXQzCV1PrhLarhts1rHr0lVpph9AyKFN4HNi6wBExysUXdELB3rMpYxXuqkOmLHYQhU9fmlVGqKy0yxEycl4cZz0yjMQkm6G3heeoeRxTJh6YMRQBHxmLMx1s/yKXuo4C8FDatfUYrgVvbbtQkHyIdYwovc9fyRG8aOkws+moKIXU7XpJ5ijF74PspixcO77S2ttzCbMRDUk7lXDwJduFnPNRiWnRGDPoPiKnoZWwWYsInvT+qaF6bSKXkqCmpBD6k5wzTXX4G1vexseeughPPTQQ/id3/kdPP7xjy9jbcQWYy+n8+bZpU3smKmlmraEmeyYsXgxc2BsAUmbHiFxFJlNJtyjZ6VX9AwOMxauIdQFVPRmS6no8VWCslAztGjppkAfTX8mVKiiZ8jLkpdS0dOyV/RsJ73yDEQb9uTpqarXNGy2nNj9IQ+u54+0SK2aBRbmZVf00t831tvLK91iMtgyxytE7am27cIwkhMHZkzFXERenYfJupGjohdR2e4Et7x7ocEpt2xZDjZbdqJ0szbiZiytdnF9peyzynp/s/c1zsjFl83mGK+QWNET35vWOCp6aSZyhBxSd5P/9b/+F5aXl/FjP/ZjeMUrXoHFxUW8+c1vLmNtxBZj71wDpxY3UodyLi43hWSbgJ81dVwvVtpgCRzeozYnEStu8R49B2ZqRS9FusnRb2LomtTsWtpw3TiS+mbisDkrQVkwdRWO6wWfGe+wc4YeMTBdrKInL+tZRqDXHa6drUcvrfIMRCcD8lRgJmo6XM8r5JDKO6NTyeDIKwOegelK557kXRur6JUV6Gkxe2rbdlKDJlPXBsYLOK4Lzyu28s1ohIzCLGHXzcHgwe5ImHn3Ql9umX7dX1jxe+OTAj1jDMYrFBfoyTFjibs2svboBWefhH1X1GDL87zAjCUJGpheDqk16qmpKTJfIUph784GWpaD8yvJgdzZ5SaecuWc0GuH3c2ipBldkwqeasJgP4hQj16MzCiOZtvBXMpw+DQ5qOOmr8+Xbso7ZC6ttdGo65mCClEJkJ99L0ZmxXo22pb/HrKHHW/WPaoPQaiip/HPuUqjyDEUjCzDtRm8AXC/sYHreVx9cHFMdOziN1u29B4dEbmspimZKqF54DFjAcT6adj+OBFjwy+bKDk94FdB0qzso3pgbVvsHs9Do25g4fwGgFBFT3C8Qk9lW1DCzGvCdb4T6G1PSNyZxmgPTG+WUNHL06Nn6mqsc3Yto+tm14wloaInuKc32w4c1+MwY1EzS1kJflJ34W984xv4kz/5EywuLvbIWj72sY8VujBi6xE4b57biA30WpaDlfU25oQret15RTtmBv9d5DDmG5/09vsJuW4KmrG0LI4evZRDGE9goWuK1Ire0lpLuD+PIWoM4xTokMcOim3bwSR04Vl0etR4BYGKnmFoiY6xIvBWl/KQ50DjV/Q4ki19FT12WMn6d7EByZstW9glNg0rkEZx7C2qWBJIBn5fZPp7rquD13EczNimvIpegnQz5ZqI6lEr0sW3n8m6jrWsFb0o101BUyLe3uzzQUUvIdALqR/KeO9kU0aPXh7XzaSkhZm5R8+/p5N69II5mpz3P8+wdICkm2WRekW/+c1vxite8Qo84QlPEJZFfexjH8Of/dmfwbIs/PRP/zR+4id+Ivi3u+66C294wxuC/z5//jy2bduGf/qnfxL6HcT40A301nHt/uiKXTBaQWBYOtC1sY4zZOEd1My+J0qKJ1ShEZyjVzeSb9W08Qo8PUJ+cCXXjEW0Py9YiybW68EzpywrrPmdZUuFxyuoKuy+Ko0/6JvvEGzqKpZljVcQ7C/MgiZ4KAjDmzAJepNsJqcVG2LfT71TeSpiaLpIEKprSulz9CzOSqiuDV7HcTRLHq8QN0e0zRHoGcbgDEXRPtw8NCaMIJHTtl2oisIlXwZCZix9PXo8yhRGnOtoP+c5pJssEBFRLFSJIit6mqChUT9ty4k1YgG6FT3P84TO6uye5urR47z/hQI9km4WTuoubJomfvqnf1r4hU+fPo13vOMdOHLkCMz/f3v/HiBHWef74++69WVumWQySQgEhICEzQriBYgiEY/IIldz8CyuPxFxWXVRz7K7HHQPrnvw7Mp6WVwP6q58d/ewLoqgQcQFREVyJIwoKgIDQUxAAslkJnOfnr7U7fdH9VNd3V3d/TxV1d1VPZ/XP5ow6Xmmprvq+Tyf9+f9TqVw2WWX4fTTT8fxxx8PADjppJNw9913AwDy+Tze+c534m/+5m+Evw/ROwxkNQz1p3BgurEhy/R8OVqhRah6LX2ejp4fIvlitd0mXrMFhqrIQlbuBR7XzZaB6TzSzWgd0+ZzRWwcWR3o34pKgERmJIXXolakm+x7AWIdvdqDAd20MKjyFcFJi1dQQ5mx8G0QtZpORlg3UW9HL2p0gYOBbtiNM6fTVojM07iumx2KV1AazA+WDLNhWDojpcqYX6o31wLaE9dSS3/GUQmUdBMl3RT6bCo+XSLRQ69Wh4SM2YUiFFlqunln17qkmx2T7UaFYVowTP4DOFH8inIRHOlm47WlUwpsOIcFIvJzg0Nx4L7POA+heAu9Rgc0RLS0vBscd9xxePLJJ4Vf+JFHHsEZZ5yB4eFh9PX14dxzz8X999/v+7X//M//jNe//vV43eteJ/x9iN5i40gfDjZx3pyaC9fRyzXo6IkEptdm1pkC0izn6/g3TIZpwbTs1oHprQo9XtfNiG66lm2Xw3UDSjcFHUBFpLOisFNqlqXH82D0ojR03eQ1S4iuAO9IYLqbuRS0o8chI6zp6BkCn18/vDN6USMS/aHIUsddN3WBGT3e32mhZEKRpY51dbQGcnhHutn83pnSlIbSzU7M6HkPIUULPda58x48GqYt1IlMaTJKRmvH2ZmFAob6Uw1nxJzXYjL35G3eWRc63aYutF9RLkKrjp63yBaB3W+aSeZFVRpuodfXoqOnRjsuQvjT8B194YUXAgByuRze9a53YdOmTVDVype3mtGbnJzE6Oio++d169bhiSeeqPu6hYUF3HHHHTTzRwBw5Jtj4xMN5QeH5/PQVBlDgpJA9jBtNOvEHpRc80E1RYh4h4d/AJm5hLY6oWs1UG9YNvp4XiOiB/RSXodp2RhuIvNpupYG2VaNEIm3ECXtSjcrRYUsSdzxHmFdNyONVwhZEPEgyxIkCdwyPy+880XujF6NE2p4M5bopZtugDVvMdVh6abJ4boJMOkmZ6FXNNGXUdviguuHIjuSV8u2qwqRkmG17Cz5mrEIHuaEwTtWoHNITb1U4hWqn0dCZiyKDNtuLe+fXSi0nF/VAhYbcaBYam8kiBZWutnCWIjtEYq6iUGB1zVMG4osNf2sikbmLC2LzOiRGUu7aXgHDBuh4Hc65PdGuueee/DWt74VIyNiLooAMDIyEGhtPIyOinxUOktc1xbFul75ihE8+MuXIac0X3nmQt7A+jV9WLfOx1GlCZuOHIYsAZBl33Wm0o47JM/rDg6mYVq2+zosRmB4VZbrGgz0p2Cjcr2a/pvZslR1TX/TrxsaysAwrCZfI6G/L9X0NYaHMjBMGyMjA24RE/R3unRgHgBw9MbhQK/Rn01VXWM/vP9NVmRkM2pbPhuz+bIMrXz9tJQKTfN/H/mtbWgwA9Oq/t2YFjDQn+Za76rBDHSz+bXgJZ12HrxHrB9y59LagarISKc1oTWPjg5CkiX0aa3/3WBZGsi+x3J5s7BmTV+g65TuczawSkrx/fdhrv10ztn0jIw0/wwDziZTUf3X0Igwa7MsR3a+aqj1vSudUqAozd/3DFuWkE235/Pox6oh51mxZk1/VQfPsoEBn/ue98+rBjMwzOrPJ/vM8/zOwrJxxrnHa5kUSrqFjMB1Y4cC6UzlZ5RkCdkM/2dv9XAfAGDVcJ97IOrHzEIBG1pcj9FJR43TP5jp+D4l7PdbLhf369YORL720dFBoNwoyfbx3fdrseD/Xna/R3k/3D8gdu1TaRWpFs+zpfIhJ+9rW+XDvqOPWt308HzVUBaWbWPNyEDd18V1n5tEGj7pTzvttFAvvH79ejz22GPunycnJ7Fu3bq6r/vhD3+ID3zgA4G+x/T0Eqw2nH6Ojg5iamox8teNgriuLap1DZVnOp78zSH8/rH1xf+BySWsHkgLfa/R0UFMTy8hm1YxNZPz/bfziwWoisz1unrJgG6Y7tfOLjqFXqFQ4vr3pmGiWDIwNbXY8rqxAHm9qDf9ulJRh2UDE4fmfQf5CyUDpmfNvq9R3jwfnJhHSlNC/U6f3z8LAJAtK9Br2JaN3HLj61m7tuV8CbDstnw2lpccufDUtPPeWVwsQpEk7rUVCzoM08bk5IJ72FXSzZa/D4ZRnt3x/vsgjI4OYq484zo3l+M2fAiCLEtYXCpy/z7YNcsXDPRn1Zb/jt335xbymJpaxORh5+uXc/zf0ws7ZT88XX9/CHtvmzq8BADI8VwPG03f97WEXRvrmhd57l22jeV88/sQY26h4NxvO/SsKpYl+QcnFqo6ePmCDrvmHlR7zUzdRLFU/Vlkv7NlgfdwUPSis/aXJ+ZRMkzIEoS+pyxJmC9/DgAgl9ehcT7LAOfZAQAHJhawqolSZmahiGOPGGr6uvll51l4aHIRw5nOzehFsf84OLEAAChxPsd5YWtbyJUAALNzy4FeP7esoy+lNPy3hYLz+hOTi8hyjgWMjg5icbEIucnzDAAWys+N2Vm+tU9O59Cf0TAzvdT069h77+DEfJVqKa773Lgiy1LTxlfbnvRveMMbMDY2hpmZGeTzeTzwwAM466yzqr7Gtm2Mj4/j1FNPbdcyiIThjVjw4/B8HmsF5/MY/RmtqXST16mMZTaxrrXoPIeIpbAr3eSY0QPQUAbBZcbiIwMKytwiC0sPJt0UDQkXnUsRWoubo1ee0bPE7MvdUGPPoZSIK11KDTfb4UU3xVz9gqLKwYJwDdNqGtzLkGUnXNyd0QuZe6YqMlKq3CbpJv/9Qemw66YucN3EcvSMpt2hqFEbyOJ0s7mBBVCRbnpVSKLOumHoz1bGCnTBDDzAmXOqilcQlLG7MugmckvDtLC43NpFmV3rJGbpFfT2OsWGDkxvEa/gSjcFnYMNjoxdUelmLq+77+tmhI2cIPho29N+/fr1uOaaa3D55ZfjkksuwQUXXICTTz4ZV111lWvuMjMzA03TkE5Hm1tEJJfBPg0DWc3tZHlZLhjIFQysHQ5W6PVl1MaumwLZQ5Wbk3PDdoeZBVw3eW/27txAi/k6VW1epPG5brJiMYJCr3x6GXhGT3BesL3xCjWFnoCRCuD/MBOZ0XOH7CP4vYjOAAUl6KyZSAaY12lWNH/Mj0xaRb7URtdNlTerrnObHjZHyeW6KbC2fLGzrouVPLnq91xJ58jR0/w/n97XbScVozBxMxagPixe5N4CeObqmtxf5pec+3mrgzt2LYPkuXWbQrG9M3qRmLE0+b16Z/RE4HGsFjFjGRufwC9/cxiHZpZx7Zd3Y2x8ouHX+pkJEdHT1jvxhRde6Jq6MG655Rb3/4+MjGD37t3tXAKRMCRJwsaRPt+IhcNl+cCoYFg6oz+jNnTdFAo899ywvQUJ70lsI4c4PwqCHb1GxRGPm2FtCHUY5paK6M+oLR3vGqEKFnrtDOhlD1e2eTEsMYdPpeYkl81Fcb9f3ELTQn+wMw4XXXATGBQRZ1kvIjEZ3s+eSCh5I7Kp+jy1KBAx9lAE3WbDwjZYvG7Dywbf9SmUjM4WerL/JprnYIPdo0oeh06RTmdYsmkVEpwuSJCDmNpOq87pXMvg6cLN5cpz6JxmLCJGWnGBRYK0P14haKHX3IzFzXsVvH84ZkwtOnrMSbnF4d3Y+ARuvW+P+zNOLxRx6317AADbtm6o+3o/MyEiepKXaEn0PBtHB3DwcK7O0IeFpQfv6GkNO3qGwAO21vGP3fx4TsWBaoe4VrCOXivXzVYSCMNonZUV5U13fqnUclPQjJRwoSe2uRFai/sArXT0RDaArnSTdZ8EnS9TEW6eRGIdwsDe46IYps0l3QSqi8mwOXqA09FrR2C6IXAQ5Eg3O9nRK9+7OBxkRcKNCyXHdbNTsG6pX6HXzJIe8NrSB3dSDoMsSejLqFguGCgZpnBxqSlSdbyC4GukOA745haZQqP5PT1N8QoNcd2IQ0k3OTp6gvcwHjVMJRu1+dp37tpb97svGRZ27trr/7pq8Cgegh8q9IjYsXGkD7mC4Q4vMw7PlR0oQ3T0lpvk6PE+1GsljkE6et5/34yizicnabTRYRhW6+Ik6o5eUNkmW0spJvEKiixDVSR3I6ibFrdMF3Akb0DlAc+ur+j7LYrfi6isKygi8mQvItJNb0cvCqlduzp6ukDRoMrBrltQRDp6IvOD3evoVUsYLbt155xtnr0HKZ2UbgLlsYKiHki6WRt7oQvOK1ekm/7327HxCfzf+54BAHzx2080l+IlOV6B81kbhqAB4aZlwTBtpJsFpgeUbpqm7T6jGlHJRm2+9umFotDfU0evM1ChR8SOiiFLtXxzar6ATEpxZxpE6SubsfhFf4gMwbPTLb2um8A/o+f8u9abpsopYyvpZnP5DU9WVpQFxfxSMXBYurMWJyS8VYgvo53STcCRN7HNiymYU+X+vq1g4d48MzS86BwB0lEQNPjb4Nh0MLx5lJXDluDdymxabZN0k79oCCp5DQr7XjzmPIpcnwfpxyNPHUS+aOK7P9nXckYnKvwUDe57olVgulrfhepkYDpQVpvkjfJModjns9bcS1Rx0CzknEnxmBJmdtGR4jX6nbLXSqQZS8mAJKHpHFxYRAyNvLBDRh4zFtEi2zBbq30Uzo7eyJD/M7/R3zcyUSKihQo9InawQu/lmkJver6AtauygS3m+zMqTMv2PfESmQ1yNxVuN0FsPkjEaarI8sJahp1XF59eLJtvJqzVnB8vtm1jLqR0U1MrIb48iIYEi5LSKh1GnWOmwUvtjJ5oRy9KJ7sgrn5BCGrGYgp29Nyuusm3qW+GU+i1U7rZ+v7S6cB0N8ydxyhGkVsW72PjE/j3+591/8xmdNpd7PkpGtjnpZV0U/OTbrqf0c4Evg+U1Sa6EayjVxuYLtTRq3meeRGV4rG1i3aV4kChaCKTUkJF2LTCOcgR/3yXON7Lmhbs2ouYsRgt7k07tm+uK5RTqowd2zf7fn0rt3AiGqjQI2LHqv4U+tIqDkxXRyxMzecxGnA+D4A7M+IXseBI2vg2iVpNR050nsOVbvIUerpzE26toW/8sOZ1Ba2dPQzC2PgE/vLLj8C0bDz0+MuBN3iiRafB0bEMQ0pT3I2g09ETd900azrA4h298JsnZxPY/s1rUPdIXcSMRZFduV0UG/NsSnUNGaJETLrZYddN994QTbdRtDCICj/pJvu8tJRu+szAih7ehYXNj/O4hNbijVewbVvYgVjTGt9fRKV4siQJOybHhYJuti1agSEy5+qFdemaRYXIkoSUKosXehzmYhXXzeZr37Z1A/7wv5zg/nlkKI33nrfF14gFIOlmp+iciJ4gOJEkCRvX9ldJN23bxuG5Ak46ZnXg1+0v5zrlCgbWDFX/NxGTitooA3HpZuPuWy3Fktmymwc0L9J4Ny1hpZtM5sM2essFo6njVjMqczMWshyNwXbPnqVUxX2A6qaFAYU/I6w2P0m4o8euRQSW5Z3s6ImeXFuWDdsWOzDR3cOW8BvzTFpBvmjCtu1IT/XdtXHNwXV4Rk8o46/12kQLg6iomDr4SDdbdfR8pIuihklhYfPjpQCum965TrP8GRI5iPKTrjJGhtK+v7tGUjzn9eSq7mhSKJTMts7nAc6zIMhBjlvotTIW8hxI8mKaVsufW5IkboOtk48bAQBccd4WnHXKxqZfqwocehPBoY4eEUs2ru2rKvQW8zqKuhk4WgHwdvTqDVl00+Z+wNZLN8UG95t132op6EbL+bzq1/SZP+TsKIQt9KI8zWcbT54uFpOmtvP0Pa3J7s8mEsUB1OcniW4itSYbMVE6OqMnKEEU6XwB1REcUWzM+9IqLNuOfJNqmBYUWYLMUTx2OjCdFQg83XBVlls6gorO6ESF3+GZO9fUckavsXSTx400ClhHTw/guql64nqCmMhUAtPrf7c7tm9G7SVoJsUDysVGQuMV2l/oVQ6nRKhIN5uvL60pAaSbfFE/ztw1h1O4gKlNM9kwER1U6BGxZONIP5byOhaWHefN6ZDRCkB1R68WEdfG2jwc98Se2xae34yFu6PXZO7PdLsdLaSbIXN+ojzN1zT+otMU7KgGwTkpLcsEBWdg1BrZi8jMFuANTI8gXkFAGhmGIBIl9/fIubn2OtjphgUJ4TbmmbJLZNSh6brAvcXpznReuslz7+KZL9qxfXPd+6tVYRAFFXm0132SFXq8B1zVrpuqIrd1XstLf1atdOOCBKbXzIsHiVfwu7+89pWjUGQJaU2GhNZSPIA5Jidv417gfNaGgWfO1Q/27Em3eG+kU0EKvdZmLIBzGGRwRL8U3e4jzwE1v7qp3YyNT+DaL+/GlTc+2DETqU5B0k0iljBDloOHcxg6OoWpkNEKQKWj5xeaLiL9U2uMTwJ39Dhubs7cAMcNs4l0s1MdvSAyn4ZraeEi6oWFG7fXdVN2ZztFjHsAr+tmWbopaBzCk3PFi0heZBiCdfT4JY5AfbyCqobbmGfLn7N80QhlJFSLmNFTpzt6/PcuRZFbnuhv27oBv31pDj/+1QEAzmd/x/bNwtJtUfxmffTyhrPV+72R6yaPQU1UsENIQLzQUxWpcm8xxJ5F3q/1u788/tvD0E0bf/nOU7D99cdgamqx5et5HYqTRLFkYmAo+EEyD7XGObwUOVw3AUd5EihegccoSuYzimI5fhmeQi8m0s3asZNWQe9Jgzp6RCypjVhww9JXhenoNTZjETl1rzNjEezQaO7MVoQzek3MS0zOQq/ZA58HUcetZogUnZ0IN/bKkUSlm7WFfWUzxvl+YRvRKGb0OpSjpwSICeB9nzK8XcMochRZRy/q0HSRDnCQ6xYG3m4/4HRaLduG1WKzt3HtAADga3/zB/jsn76xIxslP5t2tmlrGa/gox4Q/YyHpc+TORgoR69WwiywdllyzL787rVjT01g9WAaW47mn41PaUnt6BnIpDsxoxdAusnZJUtrCkqC9y/Dsrg6+rzSzQLrPopIN7scmN4tE6lOQYUeEUtWD6aRSSk4cNhx3jw8X8BAVgsVwptJq5DQQLopYM/vnkKxboLF5lzE/j2XGQunE1izkzFDULoZVEYh6rjVDJEulqiLZRBSmlyRbgoWFW4GUY1xCO/7zS/QOSidmtFTA8QEiJoa1Xb0wmToAZXN9nLEWXq6wTcDAzgSSttGy2IqKkQKg0qWVvPPJJuBHujjNywKi58c3o1X4O7oeaSbERwciNCfDdPRq3SJKu6zYq+R8pFbLuRKeHLfDM7Yuh6ygCQ6pcpuNzVJFEomVxcqDEE7euy9yWPGUhQ8EDR4O3qK1PKzD3hkplzSzejUKmHololUpyDpJhFLXOfN6XJHby4fqpsHOCeXfWV3s1p0Adv52oKo0tETNWPhnNHjOhmTqtbihbfjFbajB1Rkmtf90ak4UeAUuBaRqIdK6HN7Z/TYAzSwdNPt6DkPQu4ujyxBkqIzY+nEjB7v6a8XV7oZoKMXRaeSSaQLERd6bN6LB28xJcvtL8gr0Ss8M3qVYkprsnPIFQykNaWjhVLzwPRWrps+ZixtzuWshalNgACFnioHjm5xv6cm1x0kPfrMIVi2jTcIOyYrmM+VhP5NHCiWOhOvECTChdtYKIARjsk7o8cpx2eKCJFCr9vSzSjHTuIIdfSI2LJxpBKxMDVfCF3oAc4sRK1007SssiW1WKHmdhMsC5IE7lNP0Rm9dItTPKBVvAJfoedIeKRQhd6+AwuQJOAVG4YCvwbgH2LcCFYgtLOjl1ZDSDdZ2GzAjp4kSUipSjQzeh2Sbqqcg/teRKWb3o6eiPS6EUwtEHVoulCh55MH107czw6PdJPzvrVcMNx56E7hL93km9Fj9z3vBlkXNFwKi/d6sflkXlRZcn+Pos61le9ZL9185KkJHLN+EEeODoi9VgJz9EzLQsloHTMQlsCB6W6XrIUZS4AZPV6DLkVuPaMLVMxYuA6oVf4xlnYS5dhJHKFCj4gtG9f2Yz5XwuJyCdPzeawdDm7EwujLqHXSTdEB9toNjyEgzXL+vWiOXutNk9JEAsEr3QTCP6T3HljAUaMDXDf5VusAAN1s/dAKKlcSwZFuWjAtC5Yt9vt24xWs6hk9kYIrCic7y7K5rbTDwrsp8FLZpPIfmOimBdt2fq6wG/Nsu1w3BYw9/AqWdiLS0VOU6gOLRuQKelWHqhP4STd5LekBZ45Pr4lX6ETnmxHKjEWtnlUFxHL0ANYJqvz8Lx/O4XcTi9j2+0Fk98kzY3ENRDoQrxDks10UiVcQnNEzOQ8ueaNfigIdvWb7lk6ybesGvPe8Le7vP5NSAo+dxBEq9IjYsnFtHwDgmd/NwjBtjEbS0auXboqGLdeeQumc0gf333OejNu2zS3dlMuBpn6bMBGzEi3gDAHg5NntO7CAzRvDdfOA5tlOtbACqt3xCkCl28OTO8aondETDUwHWAEebvPUyRBo3nkOL+z68H6WNNWZZzMtW6hr1oiMx3UzSkTmvdjP3innTZEZPWbY0MoefrlgVJmLdAKlJsIEEJPU186odVq6mUkpbs5iUDMW27YrhyWi0s2aA76xpyYgSxJO/731Qq8DJNOMxZUbdqSjF2BGTzehyFLL+0hacEbPspwMWp6xB0eOz+cr4Ky19Ws227d0mm1bN+DUE0YBACdvHumZIg+gQo+IMRtHHOfNJ/ZOAwBGQkQrMFgwrRddYEMA1J9CmYJGEBXjlOY3t5JhwQb/KaP3ZNeLUKEXoqA4NLOMfNHAsREUemwWgWtGrxMdvfLvjB0SCHVw5erCPkjBlYpADiX6Pg+DGqKjx7s+74GJboTfmKuKjJQqoxC5dJNf6luR+XZmo2yYNrfsXHXnB1t19Az0ZTpnxAI48mZVkaoD09n7nVP6rldJNzvruimV58fZWkRQFQk2nIM2I2DUTEqtmE1Zto2x8Qn8/nFrsKo/JfQ6zmslr6NXcDt67T2gUBQ5oHTTamnEAjiFnmFa3GZOpsX/7BSJV0hpCnfUTaN9Szdg0Vtzi71hwsKgQo+ILWtWZZDSZDy5zyn0RkOEpTP8O3piG+/aUyjReY6KGUvzm1tRwL0K8J+zAMSkm6qqBD5d2/vyAgBg88ZVgf69F7F4hQ7k6JV/B+yQQKSDWyvHMwKEe2uqEjpegTdXLAqUALMoFRkhv+sm4LxHooqNyKTVtkg3RQxmAASyYA+CSOdKUaoPLBqRL3ZeugmwMGqvdNOEqkhup6wZtdJFkUiMqGDOm6IHFu6Bh2EHNmPxHiQ9+7tZzC4W8YYAsk3A6eh1W4onSqFD0k0toHSzZJgtjViAynOKd05PRF3CLd3kzP5lqHJnI2Wa4RZ6S8kzE2oGFXpEbJElCUeM9GNx2fnwRWHGwjp6tl1vwy1ksOE5hTJNmyuHhsEr3RTRugPlU+mwHb0GxSIP+w4uIJtWsWGkL9C/r1oHM2OJTY5euaNXlvWJbKRqN++sKBEJ9075uOKJwq5lJzawCstcs/kLFlGTGm+hF5UdfjalRC/dFHBpdefgOiTdNARk57WmQo3oRkcPqLeuF4kSqe2YGxF0iEXpD9zRqxhxBcnRc75nxezpkfEJZNMKXn38WqHXYKRU2ZVTJwXmhNnuQi/IARjgSDf5OnrMxIzvWWG4cnm+HE1e6SbPXKz7unHq6OWd98HcUrFqj5h0qNAjYg2Tb64aSEWS/9WfUWFadp2VNiA4M+XZVIh29BROMxbRAfFG+n9DoFOiqcFdN/e9PI/jjhjkOkFvBYsUEAtMb9+MXrr83mOOrSLfSy7/LGyWMEhR4pdzJQp7+HdEuhmgM1V5n/J2vyqdUtHIi0Zk02r0gekGf7dR4ZyDiwrHnIe3CK02FfLDtCwUSmZXOnqKUr0R1QWuu1e6CPA7EUYJk262yv2rhT17zLKEGQjgulm+vxR1E489O4XXnbhOaLNe/Vpl2X2CunrFDkk3A3f0dIvr9yHa0TMEDgZ44xWKgnmEzuFyPIoq1tErGVbkeardhAo9ItYYZdfF+aUSrv3yboyNT4R6PfYwzXnkm5WZKREpneyZ0bPd024eFFmChNYdvYKATbGzJn8LfpFOiXPTFd/oFksmXprK4dgIZJuAM7OiqXJLeSvQGZMR9gBlsl/RjZTqmc0IIjPUVCV0odeJYHkGb7i2l8qmg1e6WdlQ6qYdyc+VTavcD/ix8Qlc++XduPLGB5vem0SMYirFaww7ejWmQn6wg5BOxysA9RvGkm5xF02aVn3v7LR0c2x8Ar95cQ4A8Onbfin0nGPPHr184AGId+1T5RnFX/1mCsWSGVi2CXi6Sgkq9Dol3VQCu27ySTfTbqHH9z3Y85XnEFhRZC6lQZEzEooR1Ik0amzbRi5vuCNCvTSnR4HpRGwZG5/Ar5477P55eqGIW+/bAwCBHZGYjXWuYGBN2TNENPAcqO6eiXb0JEkqyxWa3zRFpZuqIjUNTOfZ0Gmq7BaYIrwwsQDLtiNx3GSkVL7wV7MjM3rMjIV19MQNE7wzekE6emFPyEsdnNFTA+TBic5aeouiqKR2mZSCxbnW8xlj4xO49b497ma22b1JxNgjSIEcBpEZPZ4cvW4WeooiV103kQOVlCpjfslb6HXOjKX2vTS7KPac85p7BXmWAU6hWzIsPPLUBEaGMjhh07DQv696rXJBEoUhy9j4BHbu2ovphSJGhtLYsX1zW9wQOyXd1BSp7BRsud17Hkq6xVU8sUNh0Y4enxmLxJ2jN5DlN/GJS6FXKJmwbBtHjQ5gaq6AuaUSjhzt9qqigTp6RGzZuWtv3UaxZFjYuWtv4NdkG5Bln46eqKEKe6gahiXU0av9940QPWVsJAsRkTY26gq2Yt9Bx4glCsfNylr4ipugIcEipGvMWERlXd5cuUAdPU0OvXEqBdwEBqFSsLRPullrxhJFByabVrkC03fu2lvXsWh0bxIpQoMUyGEwTJvb/IYnRy/nFnqdn9HTauafdN3k/pzVuW52cEZP5L3kR5X7bAB1CnuN5YKB8RdmsO3314eS36ci6uixAnh6wemssMOUsKoePzoXrxDs813inHtLi5qxiBR6NQcpjSiUxDp6mipxuWu3m1ze2RMeOeqMC832UEePCj0itrAbPO/f8+Dt6DGCWFJrHimeYYlvMjWOPJ2SoHTTMSPwy9Hj//nUgJ2jfS8vYHQ4g6E+cTvuRvAaw1TiFdqfoxdcuunpAHepo+fO73Sio+fO6AlINwXDntkGVzcjNGNJq+7pfjNE7k2ODJA/BB7o4IxegCK02WaPfT66M6NXfdBVMiwuuRvA1AM10s0OFXphn3NK1ayqWBYl4BRUDz9xAKZlw7adz0AYUhF19MIWwCIUSiYk8KtngsLrXFuL815u/Tt1i2zOOWP32ckTr8LZ0SvpfNm/7utyHHp3ArYnPHLtAADHkKVXoEKPiC0jQ2mhv+fBb0ZPZCCZoXocLg1DXObj/feNKIjGKzQoBlzbeo6beVDXzX0HFyKJVahaC6dNd0fiFcoP2Vxg6Wb3Z/Q6Gq/AHBpFOnqW2CZV9XT0ogq4zqYV5ItmS8c1kXuTSLxCx103Lf4ZPYVnRq/YzY5edaEnYsZSe68RKc7DEvY5541X0A0LiswXKQFUumZ5T2Fw90+eD9U1Y8VG2IOpdhz0NqJQcooTESfkIGgBZ3CFO3qcc/YiKgrHjIW3oydY6MUgMH2pvCdcPZhGf0alQo8gOsGO7ZvrTrFSqowd2zcHfs1+V7pZObUPYuZRJd20xE9/eW5u4jN6/tJNttHkeYg1imhoxsxCAbOLxUhlm0C1s2kzOhOvEK6j55W9OF0UsQ1FSo0uXqEj0k2ZFQVBzFg4N+dKZUOpR7Qxz6ZUWLbdMrNwx/bNdR1kv3uTaVmwbf6fKch1C4PIe5FnRo8dhHQnR696Rlnn7IIAzNXW+XzZtl12I+3M9ijsc877ezEED5Ha0TWLqqPXjoPeRhRKRtvn84BKQSX6+RYt9HgzV3UBNYyiSJxmLJZQRy/InqMdMOlmf0bF8EC6p7L0qNAjYsu2rRvw3vO2uDf2kaE03nvellDD2Jm0CgnV0s0gltRanblGO2b0nDVyF3oN8mhM0+ZeH6/TpZd9B5z5vMg7eirfXFpH4hVqcvTEf99SyI6eczBghej0sEKxs9JNsRk9SXLiKHhwsxZ1U6iYakamLFtrFZq+besGvH7LOvfPwwP+9yZXFs55zd3r1rGOnh1pjl7XpZue61Yy+Gf0UqoCXbfcIg9o78GRF+9zToL4c85b6Il0j4H2dM2imtFrx0FvI4q6iXSboxWAarm5CEVu6Wa5o8cr3RQyY5Fb3s/ZYYN4Ry8GhR47pMpqGB5I9dSMHrluErFm29YNkbpsyZKEvoxaZcYSxJJaUxUslk+Agji0qUrrAeRiOSSVe+PbQHYpMm8S5HRt34EFqIqETesGhP5dK1KcDqAs86qdshtVkasOCIR/33LlYaYbFgYEpW3sAa6bFtJysJNnvRsdPSEzFrEuCvsduMV3JGYszrXNFw0MDzTvHKzqr/z3959/ErYeu6bua0SNgrzzVp3AMCxkOTe4PDl6uYIBVZEjyTwVRVNkLNZ09LgD0zUZNsrOlR1QCNTCnnOjo4OYmloU+resk81mVUUOkUaG0r5FXZiumfcAJgzsuf8v//kMLMvG8EAK7zz7+Da5bpod6uiJSzdt226bGYtIYDpPjh77nYvk6DVyC+80VR29wTQOTM92eUXRQR09YsXhFHpeMxYxEwigukMTZHBfU+SW8g3h4NEG7lUiYdKsWGw1o+Rl34F5HLN+MPLZL618yt4K0+TvSgRFkiSkNKUSryAaauwJczYCZL55ZYpBYXKezuToiZsOGAb/vBhQ+TnyxWDFtx+s6OFx3pxZLKCv3AE8cDjn+zWi2YDdcN3kvTfw5ejpXenmAfXSMt2w3O5SKyqZjGZHcjmjxOvkKBr03o6umSvdjGDzvm3rBgxknUOxD13y+20p8gCgUDSQ7UCh5/6uBK6NYTomOTxOlqrizGdyu266Ziw8rputZ/RYfl9KRLoZm46ejpTmHFIND6Qxv1SCJbAPijPJuJMRRIT0ZbRq6WaQGT3PzJRIIeX++wYOmV6KEblXiXQcVVUu5/zw3eAM08ILE4uRz+cB/N1FPSIjjlakNNktKkS/n+I1YzFMcZdWLfwpOXu/djYwXcyMReRz5BZ6pWC/Ez+ynNJNAJhZLGLTugH0Z1QcnG5Q6AnKwtUA1y0MpsB8Me+MXjcy9IDyhtFz/ysJOIqyYqdkWB1x8Y0Sb7yCqPtsO8YjUhHcqxhOiLXTacnlW38mgyJqIBIUlaMrXgubHeVxkHUOJOUAOXo8Hb3W0k03j1Cko6e23gt1glzecF3ZhwfSsGwbi7nemNMj6Sax4uivkW6yUy2RboLXTCWQdFOtFA2NiMq9Ski6Kdg5enkqh5JhRT6fB/Dn6AWZkQxCSlWwaDnvG97sMYaqVE5Zgxg9sI1oqI5egFnUoFTmucQ6eiJrY1/Lum9RFLBMvlVo8dkEgNmFAl65aRimbTfs6IlmdAa1Xw+KSNQHTxG6XKhsljqN09HzSjdN94CkFX5yw05KN8PgygENK5BaIOrxiJTbHQ3/Hi7plvt+W8rrLb46OAXddOdz24nq+V3xwpQYvN3ptKZwm7GIjK0w6aZt2w3HJNj3Fd63xEG66VEjrB50Dj7mlkpY1ULCnwSScScjiAip7egZpi1kSQ1Uz8OJOp2xf9/q5iba0WtUGInIs9wQas6N5r4D8wCA49rU0eOR/wQptIPgfdCKFmpV8QqGeO5iFHIo3XDMTnhiNsLiussJdfTECnbNLfSik24yKWYr6aZl2ZhdLGHNUAYbR/pxYHrZ9+vY71zcdbNTHT3+e4Mity5Cl7vd0StfN6tsqsKdo6dVPl+su5AU6WZtYHqn3EIbrqd8n+TtKjXDG4Pk/f9R06kZvUpHj//zzQ4feGb0AFboiebo8Uk3ATSVM7oGckmUbub1qo4eAMz2SMRCMu5kBBEhAz5mLOIzV87NybJtmJYtvHlWeMxYRGf0FBmWXe/MaJj8s09sc8N7wrbvwAKG+jSsXZXhXicv3B29DoUbex+0oh1ERa7M6AXZjLkdhxARCyXdcSFsd1YU4Dm5FopXECvYZVmCIkuRFnqu62aLjt58zpnfWDOYxsaRPizldSws18t8RI09Kl2zDnb0OO99sixBkprPD+a6OqNXOTxzjYd4c/S8mYwd7HxHAfv9GaYt3BVvB7IkOaMJEXRpvLP0be3odSheoeJGzH9tWMEscmjRHulm60MoNqMnUugpHr+DbpIrGOjPskIvBQCY6xHnzWTcyQgiQlhHjxmOBNl4q6oTr2AGmO8DWp9ijY1P4IWJRYy/MItrv7ybK8BWbdCNEwmT1gQlgnsPLOC4javaUjzErdBLe37HouYvqiK73S1RZzzAI93klOT4oQvMLIWFdX+E4hUCbFK9Emgtghw9ttlrNaM3s1AAAKweymDj2n4AwEEf+WZFGiXaNevgjB7HaT5DbWEi5XT0uiPdVD3STdFCL+WRbnbDdTMMqqfTqgc4tGwHaY1PjdGK6o5ee2b0LMvJzezMjF7ZIdUQ6OgZTA7JKd1M8c/osb0CX2B6a5UG+74i17LRAXWnWfIcUg31pyABPROa3v07AkF0mP6MCtOqhCLrATbeTCbEbtiKwGYJaB6YPjY+gVvv2+PeUKcXirj1vj0tiz22MaktjoSkmwIzermCjomZ5bbINgHnBJN1TZvh5NJ1YEav/PASlfkCzKXVgmU5HWDhjp4WjXSzU5tAt6Mn0JkyBIxBGJoiR9rRUxXZifVoId1kGUtrBtM4YsQp9PzkmxVHX/6umSxJHZMy6YYtFDSvNjl9t2wb+aLhyl87jTPr46zNlbvxFnpaZa5M1Cm129TFK8SgQOXNQG1FJzp6rDjJdCBHr5KTKTKjJy7d5O7osZxPzsB0oEWhVwoQryA4LtIOHNOfSkdPVWQM9aeo0COIpMJmSNhpYVDXTKDykBAtFBuFmwPAzl176zb0JcPCzl17m76mK7v06eiJSjd5brrPl4PS21Xo8cpIOxGvAFQetEEKCua6KWrO4X5vt9MaQrppmB3s6InPmgUx1dGqOnrR/GyZtMrd0VszlMGaoTTSKcW3o8fmvUTeM46NeftPt23bhmmKdfQUWW5YvOeLBmx0JywdcK4x6wywzxm33M3juhn0M9otFFl2JbVx6eilNCUS6Sbr4g0PpFz3zagpsOIk3cmOXvvMWFKqgmJJ0IyF4/6kuvf0xq/tykwF3cK9a+kGpfIBj/feNTyQxuxib7hudv+OQBAdhg3cstPCIJKx+gyvYB0eP/wCbJv9vfc1gfrCqF3SzX0HFiABOPaINhV6SmXz1YxOxisAwWzXmeSt0i0IOqMXoqOni3eug1I5uRaNVxDtjEtYLnffopLaZdNqyxm9mcUiUqqM/owKSZKwcaQPB3wiFoLIAJvdG6LEtGzYECtovHmQtbBNeTelm0BZwiiYGemdga10OZKzPWIz4869vvudyJTKLx9sBjuMXTecbWOhJx4JEJQg90WReAXAmY/jneV2Hcc5PAZ4DLaKAQLTtQb7lk7ihqVnK/eu4QHq6BFEYqnv6IlbUrvW7qVgsjHHtdP/hskyjXj/3vuaQH03zul4Re+6uffAAjau7Xezx6KGube1Kjo7ZUDAHrRBvpcqS2Wpb9COXnjL8u509NoXrwA4QdeFgNmGjcimlJaumzMLBaweyrizqUeM9OOgn3QzQHeIJ68qCkyTX7bFcA4s/NeWLxd63ezoAc79vCQ8o1f+fOnBD2O6CbOoF4nLaCdRdvQkAGuHs1hqk+um29HroHRT5CDH7ZJxxyuI5egpssQ1Y8/u6c0cQwslExIEM4kb7Fs6CTv0H/AcUq0eTFOhRxBJpbajpwfYALMbWSFgN0Epn8DaPvNnO7ZvhlxzwpZSZezYvplrTbUPWJGOV6M5v1ps28a+A/Ntk20CjQvXWkRmEMNQ6egFKPQUR/ImOrPFiCYwvXMdPcWz6eYlyO9RVSSw7xCVZC3LI91cLGLNYOXgZePafswuFqtmigCPKYjAz+VIN9u/6dEDdBsVRW640WMHZ92KV/CGUeuG2Iye+/lKoHQTcN5fTkfPjsW6U5HN6Onoy6gYyGptC0yvFHodjFcIJN3kd93kjlcQMDLjObwrlSOhRMzZvK6x3YLdu2qlm4vLeiyiH8LS/TsCQXQYthFhJ4R6kMDz8sYtqHTTlSv43Ny2bd2A1QMp9zVHhtJ473lbWobaqg0216ZpCXf0Wj2IJmfzyBWM9hZ6rtNk84dWx1w32YxegI2Uokgwq2b0xN4vjz83BQD4+g+f43ZhraWjhR7bFAhIlMwgZiyenyeqDkwmpbQMTJ9dLGKNp8N+xEgfAODgTLV8M5B0U25s1BQlZqC1NZaVLrsdvS5LN8udLcDp+PKQqpJu8lvOxwVn5tuORY4eUC42IopX6Muo6M9qKOpmJF3CWlzpZgdn9IRy9MqHFmle6abmzOj5HSLXIjIXzSM7LZRMYfdSLUDxGzVL5UOEKummG5qe/K5ed47eCKKLsFMbd0bPtITlh+ymx04Dg9jCs+9dS75oYGaxiAu2vQLvOOu40K8pkk/GO6O3r2zEsnnjKu71ieLKqVp29Dol3Qw5o+dxetUU/ofh2PgEvv6D59w/MxdWAC2Lfy8l3ayKiGgnQXL09CBmLJ7fe1SdjL602lS6aVoW5paKWD1YyY5kEQsHDueqPhOsYBOdg+vEKXKloyfYbWxQhManoxdSuqklULopl2f0OniY0wxNjSpewUB/RsNAeQOeK+humHVUMKfIzsQrBO/oadzSTQWWbZdHUpp/tnUBozYegy3W0RMhDtLNSkfPO6PHCr0S1q7KdmVdUdH9OwJBdJhMWoWEinlAEEtq9vVBZ/SaSST3HpiHbQMnbBIrohpFI4gUQjxyybHxCfz7958FAHzhW78O1F3iWoubbdW60OtkvEIw6aazPnZ6LLLeoC6stXS2oyduOmAGMWOp6uhF8x7ItDBjmVsswbZR1dEbXZWFqsh1c3p6AKmu4slcbCdmAEdQZvrhx7JrxtLlQs+wKgYWnJtjWZagyJIj3Qw4R9tN1HJhFeQz1A5SKr98sBnL5WwzdjjbjoiFTs7oufEpgvEKmipzR/qw5xTPnJ6I47jidiMbrz1IR4+pW7opkXQLvaxXutk7oenU0SNWHLIkoS+jYtmVbopbUrOvLwSWbrKirP5m/Nz+eUiSeLeskZGKUI5ei44ey/hjhcdMwO6S0Fo4ZvQ6Ga8Q5KSfFT5sUyHyGkFdWGvRDbNjhZ4sS5AksbyoIEYSVR29yFw3FeRLBmzb9p01mVksRyt4OnqyLGHDmj4cOByFdLNx1yxK2OdK5L3YTLqZKxhQZKkjnRE/qlw3AxTYjtzQhG4GP9DpFqoiuYdIcZCcpiIKTF8qGFgzlKl09Npa6HXmfasqkuvsykNJt7hnTYFKsHpJN4Fscxm1YfAfDLiHd03uTcUAHT0R6ebY+AR27tqL6YUiRobS2LF9cyT7jly+/t7FpJuzPSDdTM6djCAixCn0mBmLuCV1nXQzoGunX0H13EtzOHr9YGA5qV+8QlTSzai6S1GshdGxeIVQ0s3yTCfbjAm8X4K6sNZS6nCYsqqIzZqZFv8sKcNbuEZmxpJSYduNO8luWHrN9d+4tg8HpxsVeiLyyMZZdVHCNmwi17yZGcty0ZmnEjFiiBLvjLJb6AkUnSlVLgemOz9foqSbilw5ROKc5WonmiqHyvxkVDp6TsGy1AZDFlYgixYoQWnWFfejaJjcRixAawRxogAAZ3xJREFURYLK39ETlG62iFcQ7ui5KqLmzwp2yMwOONkIQxSKolxBR39Wq7p3DWY1KLLUEzN6ybmTEUSE9GW0inQzwIyXm6PHCj2B0GGgcXCqYVrYe2ABJxwlPvvmNxdl2baQnKdVFy2q7pLQWmISr5AOJd2s6egJFCU7tm+uO9HlcWGtRdc7G6asCHamdMMOLKFWypKoKGAHLI2cN2fK73VvRw8ANo704/BcoUqyppelUSLFT7M5uCgJ1NFrEq/gOCR2x4gFqLaud50KBd7vmiqjVI5XkCWpzvk4zqiK7MqN45Cjl9aUlpL7Vti2XTZjqZ7RixomN4zq/tEKVZHEzFj0YIUez/U3TMsNQm8FOxBqptIo6qZwHiFv5EQ7D5lzeb0uFkaSJAwPpDHXA6HpVOgRK5J+j3QzSI4e2xy50s2AHb3am9vvJhahGxZeedSw0OsB/oWRaFaWIjuzAI2Kq6i6Szyk3J+n8cmk7Ray8Y5XYA/JIHEc27ZuwHvP21J1jbe/eqOwZCVIjEgYVIHOlG3bZXfYYBLqKAtY5r7XaE5vZqGAdEpBtsal74i1/bABTMxU5vREpFGMZvLIKGGumyLXvFVgercy9IAa6SYrYgXeF072m+PsKOqK2200RQpsDNYONNWZMw3zPi7qJkzLdjp65dmpdkk3OyXbBMoOqYJmLCImWqkUf0dPzIyFQ7pZEitKgebGdF7aecicKxhVjpuM4cHeCE3v/h2BILqAt6MXZDYobLyC2qBb9ZuX5gAAJ2waFno9wKN199wwg8wIaU0eRDu2b0btAWCQ7hLfOlqHhBsBDCWCUglMD+a6CXjNWMTWu23rBnz2T9+IW/7Hm3HESB+een5GOGut1GFHPpGOnmnZsCHejWDv+SgL2GzZlKGR8ybL0Kvt0m0sRyx45/SCqAWYQ2u7CdLRU+TG3Yjlgo4+Qbl5lFRLN01IUkVuxgNzijRiElEgguLp6MXBRCbFce9uhRvXkdWQ1hSoitSW0PRCyehsoSeLSbNL7ZRuCjgd80o3Ra+l1kDdVEs7D5lzeb0qLJ0xPNAboendvyMQRBeo7uiJb4BdM5YA5hrer6+9uT23fx7rV2exqj8l9HreNXm17oHMIJTGHb1tWzdgoE9zrxdvxl8QXNfNpoWe+M8XlFSIHD22PlY8BN1IKrKM/7p9Mw5OL+PhJw5y/zvbtjvqugk07/7UUskuC9jRi7Cj21q6WcCaoUzd369f0wdZknDAM6enB7i3KB3q6BkBZvSazRflypln3cLN+SpLN1OqWHAzC/nuVFxLlGjeGb0YrD2ltb53t4IdxPalnbnP/ozWlo5esSRuIBIGRZGEO3q87rFARQnDYiOaITSjp3AUekFcNzmlm1GNMPiRK9RLNwFgdY8UeuS6SaxI+jIqcgVDeIaNURuvICw584T7MizbxnMvzeHUV44KvVblNeuljkE2c5oqQzf9HxLLBR0LOR3vOOs4XPiGVwRap8g6gOZuXEHMLoLiSjcF5zGdf1MdrxDm1P3UE9Zi85FD+M7Dz+OMrRu4HqydLIgZiszfmQq6PrejF2EBywq9RqHps4tFbFo3UPf3qiJj3eosDh72SDcF7Mu9r9OJGb0ggelKk+J9uZx51i3cueeydFP0PZFSnWJJDyC37Tbe+0msOnohIhZYUcc24ANZrU1mLGZHohUYmqBJVUk33RlFHljRWuIww9ENy3XpbEUlR8//82+V8yuD5ui1uibsMPlf//MZmJaNgayGd731hEgOmZcaSjfTyBfNctc3ueVS9+8IBNEF+jMaTMt2HybCHT13Ro+dogaUbnpumgcP55ArGIHm85w11N8wjQDyLK3sPufHCxOLAIBjjxgMtEYR+Dp64oHUQQnT0VPcg4Hwp+6SJOGdbz4e80sl/PCx/Vz/Ri/beXdUuikQ/O1mlwX8HEW5Mc+WNyrLPoWeYVpYyJV8O3oAcMRIX1VHL4hRkCJo1hCUIIHpjZxUK8YZcejo2dB18UJPUxVXuhmHYkkE7+8wTh29YhQdvfLhQX+2PR29Ts/oibrqFg2xjl5FuslrxsLb0St/vhrcm5hUtF0dPcAp9kaHnfDyc0/bFEmRZ5gWiiXTt6PnZuktJduQpft3BILoAmxDMp9zPsBBA88LQQPT5fru229emgcAvFIwKJ3BQn/9ZvTEOnpKw0Lv+YMLAIBXbBgKtEYRZElqKiMFgs0ZBeXJvYcBAA/96mVc++XdQrbO9YHp4db7yk3DePXxa3HvT3/HFSIcxJwiLCIdPd19nwbs6EX4+8+4Hb36E/HZxSJsAKsH/edCNq7tx+Rs3v3cORmWotJNmVvyGoZAgemyv3SzUDJh2XaXO3qVw7OSYQo5bgKV7DdnRi9ZZize32EscvTcGb3gHT02WsE24P0ZtSdm9DRh6WbAGT1u6Sbf+0VtMaPH3IaFc/QEA9OZimpqLi/0fRqR88yC1rJ6wLnPJz00nQo9YkXCNiSs0BM2gVBZLlowpzM/M5bn9s9hVX/KPbEKgqpUd+OCZEJpSuOO3vMHF7FuOCskJQlDs+4i4HUObO/mZmx8Anf8uGLjLJrhU9sBFjGJaMR/3X4cCiUT33vkhZZfyzZcnZSkqYrELVFiGx/hWdd2uG6WNyp+M3ozC+Ww9AYGABtH+mFaNg7NOpsQPZAZS2dm9PSg0k2fjd6y233pvuumWQ5MF82TS6ll180EzuhVFXox6EZqbmh3dB29gXZ19AIYiIShWRalHyXdRFrgvVyJV+AxYxEJTG8u3Sy4HT1RBYO/X0Ej2DN0aq4g9H0aUZEI+0s3ASR+Ti+5olOCCAE7JVxYCtbR896cgmQu+TlNPffSHE7YNBwqcNiZr/Pr6AlKNxvczJ8/uIBXBnAEDYpTdDZ+YOkBCwRRdu7aW/cgYhk+PPIRxdPR01Q5klDpI0cH8MZXHYEHf/kS3vrao7C2yQGBW/B3WLrJ6wyqByzY2c8T5e9fVWSkNNm3ozez6J+hx9i4th+AI8M+cm0/DEO8O6R0yHVTNHrF+Vp/6SbLN+uq66bHjCqI8ZCmlXP0OmxaFAXe938cpJusMOGZE2vEclGHLElujEl/eUbPtm3u++fY+AR27tqL6YUiRobS2LF9c939utMzet7MQx6KgmYssixBVWSBeAXeHL2yxLKRdLPECj2xaylLTInU+p5nWpb7c03ORtXRq+4cexkud/RmE17odf+OQBBdgJ3ezOWcD7Dog53JCoFwdvusEJueL2B6oYhXBghKr37dallIkM1co3iF+aUiZheLOHZD++fzKmtpLCMFOhevEDbDh0l180Uz0rVecuaxkCQJd/3k+aZf16mC2Isi85sOBF0f+/qouxjZlOrb0ZtlhV6Djt6GNeWIhfKcXpB5L5Vz0xOWylykQEdPdsxYbLt6fa4Vfjc7em7Ol4WSYYlLN8vqgSBy227j3azHoUh156tDdvT6Mqpb1A1kNRjl3y0PY+MTuPW+Pe492k+FYdk2ip3O0RNQOljlLELRbLq0xlfoCbluuh29FjN6qQBmZWpjN18vzFk2m1Yws1iIRPmQyzeWbmbTKtIpJfGh6dTRI1Yk7oxewI4e+zeGGWzjXivddPPzAhqxVK+pcvMLMsOmqbI7S+bl+YOOEcsrjmj/fJ53LVzxCm3e3IwMpX2LOt4MH7WmoxcVa4YyeOtrj8J9j76Ip1+YwXyu5Hty7RZSHY5X4NlsAME6z0Dl9x51AZtJq76n7jMLBfSl1YYdgHRKwdpVGRycdpw3ddNCvyImc3ZcNzswo2cFi16x4WyQFU9XpVZm1w1Uz6yPbpi+G7dmOGYsjnSzmxLUIHjf/3EoUivxCmFm9KrNfdghQi6vcxl+7Ny1t+7ZUavCCDpXFoZmESW1sOsn0tFzvl7hKrJFzKIqOXr+r8vu9RnBjh5QVu5wXBN2T960bhC/2T+H6fkC1pcP14LSrKMH9EaWXvfvCATRBVzpJjNjCZGNFqSjV5uj99xL88imFV/bdqHXrZlpM4NINxvM6O07uABJAo5Z37mOXqrFjJ5b6EUw89aMsBk+FfMeM3Kjh9HVjmSTzZv6nVy7M3odNmMRn9ELFpgetQFFNqX4BqbPLBSxukVxf8RIvxuabpi2cBHaaA4uaoI4nTayQq81zugGsiRBQkW6mRKe0ZNh285nNA7FkghV8QoxWDu79qE6evnqbDM2F85jPgXwqTBYh6jT0k3uQq98/UTfy2lNEejo8Uo3m5uxMOmmaFEK1CuRGsHk9Eevd/ZJkxEYsrgzeg0OhlYPpEi6SRBJJJNWIcFrxiL+UQhjBKHWFnr757D5yFXCs351a6qZoQna0fMrrl44uIAj1/Z39PSz2bwg0LmO3ratG/De87a4HTzRoHjvQ1IVfGi34j99zFjYyTUjiClPWJzAdDHppnhHryyfjlq6mfaXbs4sFhrO5zE2ru3DxMyyI7syxKWbiuwUepbd3mLPtGwosiQ0L+parNd8JlkURTc7epIkOfONZXlfkBw9AMgX9Fg4V4rgPeiKg3STbfZ5DTb8cKSblfcTG7fgNWRppLYY6qu8ZqXQ66x0k/cgh3UcRYsnsUKP77Vlqbl0M8y15C1+2T356HXOYXMUzptLBQMSKvmptQwPphPvupksfQJBRIQsSejLqG5LPsjDsTKjJ/5vFY8Zy1Jex8uHczj999YLv07dmmoKIzNAYLrqI6OwbRvPH1zAawKGuQdFU+WmobtuPlwHCphtWzcEzu3xvkei7ujxnFx3Q7rpFCx8G73QgekR//6zaRWLs8t1fz+zUMSxLaTLR4z0QzcsHJ7Plx0cxQPTAeezK6vtKzj0AEUo+1lqO3q5ggFJAjLpzm2Y/dBUyQlMD2TGUslPjIOhiQje32Mc1l7p6IWLVxgdrhyqsI4ekwm3Ysf2zfi3e5+pe68uLOv4xzt/jRM2DeMHP3dySG//4XMAEEkuWyvUFioVLyyHUDSbLq3JLa+9bdswTJvbAVoq+xK0jFcQXCvADnRbF79MZbFhpA+aKkdiyJIr6OjLqG4hW4sj3SwJmQDFje7fEQiiS/Rl1NAzekH/LXOa0g0Tz7nzeeGMWNhavBKIIBtov47e1HwBuYLRcpMbNZqqNO3osUJCtBPUaaoKvYiLrUYn16oi4fmDCxgbn8C/3vsMAOCL33pCKP8vDM6smVhHL3BgeuRmLErdjJ5umFjK61jTIEOPwZw3D0wvlzPZxKWbQONZmKgwTVtY8uwWoVa9dLMv3Xiz1CmcDEIm3QzW0TNMOxYRBSJUxyt0fzPqxitE2dETlG7WHsyNDKXxvrdvwaVv3oynX5jBtx7a6yp6FvO6UFxOGFRZ5v5sux09URkyR0ePfYbFzJgad94KIeYda/ctjWD35L60itHhbCQdvVxebzrPOzyQhmFa3AcMcYQ6esSKpS+juVksYebsgsp8HKcpG8+9NA9VkXDcxvBFlKZWWzdXsrLEXDdrC70XykHpnS/0mp9+6gFnuzqN99Q06hmaHds349b79lRtqpSyxfanbn0MsgSwffl8roRb79sDoP2n10nu6DlmLNUbJTdaYaiFdHPEMQc4eDgnJI1iMPfIdjtv6gEcQdn7uHazV2uc0S2Y1LtkmOLSTU8nIg5zbiKw978kOZvxbuO4UrfuKjXCtm0sF4yqGT32/3kLPQAYWeV8Vv/5L99c9X740S9ech10GSJxOWFQFclVorQijHRzpoXcMMg9l8nK/ajM6LVfuplNq1gXVaFXMHwz9BjDAykATmh6p/KDo6b7dwSC6BLeh0gg6SbrJgTcFLCMuOf2z+EVRwwJB/w2ek3/eIVwHb19BxagKjKOHO0PvUYRUryumzHfmLWzo+c3P3jl+Sfh81e/EZmUgtrncu38XrtQGmSu+eF29ETldiE/g41gM3reGIGZshR2dYuOXl9Gw6qBFA5M56Ab4t0htYXpQVSYpuUWlbzUxsIwarsv3cLJ47Kg6+KB6VrM5I8isC5wnNbd6t7djELJhGXbVRvwlKYgpcquSyIPc0slDGS1untubZHH4I3LCQNz1a2NKPGDXT/R4slx3WxeZBsBxjqaGUUVdRMpTQ7U1dcUiS9eoViZA3Q6egWu69iM5YKO/mzjQ6rVPRCa3v0jOILoEt6NSSAzlhDSTeffScjlDbwwsYi3nbYp0Gv4vaZfYLpQoVcObLY8N/QXDi7gmPUDHS+oWs0zdCpHLyzeh2k71tpofpANyNfSiQ2NiHtkUPfUJ/dNAwDu/env8OjTE76ByEHIphXYdrVr4MyC0/1v1dEDgI0j/ThweDlQR6+R4UnUhOno1Upylwt6Vx03GZoqo1gyYQOBpZtAPOSPIrid7RhJTlOa7Lr9isKKudousROaLlDoLRbd0GsvYeNywtAoosSPinRTsKOXai3dDNzRa3BfKupWoPk8wHnO85jH5IuOcUo6pWB0OIOibmJhWceq/lSg7ws4OXrrVzeOaHBD0xNsyBKfuwJBdBjvxiSQc6bbTQgo3VRkjO87DNOy8cqQ+XneNelVM3rBAtOBiuzTsmz87tBSR/PzvGvhileIeaHHZjKBzm7GGm1cOrKhaTLPUYsRoKM3Nj6Bb+/a5/7ZL1YiKNmy3fqyRwbNHvStOnqAU+i9fHgJgLisuJE8MmpM0w5sFGPUSHKXi3Hp6FWk6+KFXnKlm2HVJe0gpfJlufmxXJ6Hqj08GMhqbsA1D7NLRd/Pa9i4nDCw35XBId9k1y+IGQt3oSdwuKbIchPpphG80FNkruuRLxnIpBXIkoR15VihqZCGLLmCzifdTHBHLz53BYLoMN7TwiAPyPAdPRmH5wuQEI0RC1uTEbKj54a5lx8UB6ZzKOomjj2ic/l5DN4cPS0BJ/DdkFd1c0Mj0tFzZ0kFpIQ7d+2te29EJUtl7pEFT8TCTHlGg2czc8TaPneTJi7d9Dc8iRo9yPxgE9fNeHT0JLc4F3bdTLB0k23WY1XoacGlm8z4ovbwoD+jYklIull0N+pewsblhMGdweWYXy4awebe0uXA9GYRLUHGOppLN63A0UvcM3pFw41BGB0uF3oh5vQsqzwL2kS6qakKBrIa5srGfUmk+3dmgugS/SGlm2rIjTu7wR45OhDZaTgzeGGwmyevhTJQ2fCwh/TzXTJiYWvhMWOJu+smwEwSxOVyYWAbl5279mJmoYg1Q+nI5I2tUDnnLoBgrps8sRJB6StvJryGLDMLhZaOm4yNI5VZVmHppuxfTEWNGSD6wU9W6hhn6LEwY1EUGUt5FpkjOtfkkW4m4H7ipV3us2HQ1NZzYo1YLhdzfh29lw/nuF7DtCws5Eq+0k0gXFxOGNzDEo4i2A1MFzRjYYWh3qT4cvcGIjN6zaSbJQOZwB09/hk9prZYuyoLCeFC05eLBmygaUcPcLp6Se7odf/OTBBdoqqjF6AjxB6qIjdKL6wLdcKmaLp5QNngpaqj58izRPJfWOFa0k0oAJ4/uIhsWsH6NY117O1CU2RYtt1w1omFPnfb1p2HsAcDQWEbmtHRQUxNLXbs+yqyDNt2Tk3lFgcNrnRT4Nq0c84mU95MeEPTZxaK3K99xNpKoSf6+25keBI1umkHWFt5k+o51S8ZFgzTdovjbqLKkluci26OtaoZvfgUTDyw90ycOpHpCDp6tRvw/qzGHZi+kNNh23xS605S+XzzSDeDxSsw1UFRN5sUeuIZtE2lmyE6erVKpEYw6SbgfF5XD6VDdfTYLGizjh7gzOnRjB5BJBD2EHHmp4J09II/XMfGJ7B/0pnh+fkzk5Hl9/jl6Il2u9wZPU9H7xUbhrpSTLFT+UZdPd0Ql591C/f9krBNZFBc4w4OiZJuWpAlqWVB6KWdslQmD8oXvDN6BS4jFgAY6tPcbkRQeWQnXDdF7w2qT0dvucGmvBuonngZ0fty9Yxe/A+OvFTuLfFZd7iOHpNu+szoFQwup0XWgWnU0esWIgc5JcOEJIm/H9khR7PrbwTIoG0m3SyUzFBmLLyB6ayjBwCjq7KhOnps3rNlR28wneiO3srYcRCED+whEtRhjW0kRDdLY+MTuPW+Pe6J2lKEYa2aWnbMLD8IgwQ2ezt6umHhpcklvKIL83lAvTFMLUYA+Vm3UGI4R9NORE6udcMS/hy2c84mWz6ZZh29YslErmBgDWdHT5IkNzhd9OeqOFu2t6NnBAhM95OVNnJI7Aaqp+OgCcvdEjyjp8Tv3uK4bgbt6OmQJQmZmg5Rf0aDadkN3YS9zJU7MMODwR0Z24FrxsJT6OkWUpoipMgBqjt6jTADGLWpTaSbpSbdw5avyxmYXigZyHiUA6Orw2XpVTp6raSbacznSlVO5Emi+3dmgugS7MQ97Iyd6L/fuWtvnaQlqrBWr/4/pSkwTFtYWurt6B2ezsO0bBy7ofPzeUDFOU9v4N7mSFPjs7lpxsrt6LV+OBoBMt2A9s3ZZMv3BpbbNLNYjlYY5OvoAXA74F/97tP49kN7uWcjFYECOQxGkHgFn25E3Dp6DFG5W5JdN8Mag7WDFKdlvh9OLqNaV+AwiV0ur7td90bEtqMnMINb0k2kAzwvKoVetI7VzQLTC3rwjh63dLNouIdwgGPIMr9UciSqAb43kwG3MpJaPZCCbQPzuVLspMA8xOeuQBAdhhmgBJ3HYCf1oif27TSRYA981gEL1NFzzVjMrhqxeNfSvKOXjNtY5dQ9GR3IsLgSRI4HuGGKB4u3k0xNR2+m3B3g7eiNjU/gty/Pu38WiX6ouG62u6MX3HXTm6PXSGbXDbyfLdEDFVmuRKDE6b3IgxLDQ6SUpgTu6DXKZRwoP7N5nDdnl4qQJQlDfcnt6BXLHT1ReDp6wQLT5ar5XC9Biy3A2UM1esZ7yRfNqgJ/XUjnTXcWtFVHL+Gh6fG5KxBEhwnb0Qt6itrObDP2oGcyiCAzeuxBVNItPH9wAUP9Ke4NbtS4RWeDB1aQrkS3qGzGgj0Mk4ZIZ0o3zFgVwIosI6VV5r1YWDrvae7OXXvrTr55ox865boZRPbsWsN7NmW5Bg6J3cB7Lw5S9DD5ZtKkm+xnjdO6NVVGKXBgun8uI9uQ82TpzS2WsGogJTT32wlUgZzMkmEGKvRSXNJN8Ugbx3Wz/r5k2zZKYWb0FMe4q9nhlmXZKOrVhV7YiAXejh7rClOhRxAJI5tWISG43IU9XEX/fTtNJNSajl6QUGS3K2g4hd6xGwaFZwSiwjVjadjRE//5ukXFdTMZ6w2LiBmLYdiBpJvtJJtSXQfH2QUWls4n3QzTta9k1XVgRi8C183lBpln3cD784gGpgOV+01S7ikMVy0Qo0MvluUWhOWC7uuEyAq9JQ7nzUYZet1G5ACspFuB3sdpLjMW8Rk9R7pZ/zstGRZsoG6mkhe252gWms4yTb3SzbCh6UsFHdm00tKMzy30Euq8GZ+7AkF0mEefPgQAmJhZxrVf3i1shqK6Hb3gJhISojWRqMhCnBtmkFBkVsAu5IqYmF7ummzTu5ZGg9qJkm7K8cu6aieKgHukbpqxuy6ZtOpuLmYWixjq07i7RGG69m5WXZsH/4N8dvxy9FwzlpjEKzCCdM7Zpjpu78VW/OLZKQDOMy3Is6wdMGOwIBLkXMHwnfkcEC704jdPpQlIN0t6sI4el3QzQKSNovjHKxRLwYLdGbUH1H6wQzevGUt/RkU2rWJqrhDo++by/u+zWlb1pyBJwGxCQ9O7f2cmiC7AnC/ZLYvN0ADgLrjUgNJN9j3akW3m7cYBwUKR2YPo6ednYAN4RRcLvZQ7L9g4XiFOcqVmdCtHr1tUZH4cZiyGuANku+lLK+7mYmaxgNWc0QqA07W/9b49Ve9b3q69iLQrDEG64ZVuY3VHL5tWYiGR8xZoQaSbcZRAtmJsfAJf+/6z7p+DPMvaATO3KekWsmmx67lcNmOphUnscjwzeotFnLBpWOj7dgIRaXbJMFvOj/nxxPPTAIB/u3cPvvvw875GUMED030KvXJBGbSjxzO3yOalvdJNSZIwOpwJHLGQK+hchZ4sS1jVn9zQ9OTczQgiQpo5X/ISVLrZTmpvmI6FumhHz7lZj+9zHhbHdilawVlLdeFaS6LiFWJomNBOKh09jhw9I4YdvZTqbi5mF4pYI+C2Fib6odI1i19Hz88oJlcw0JfuvmwTqFZXBJG8pVzpZrzei82I4lnWDtwsN0FDFsu2yxvw+kJPVWRkUkrLjp5uOHEoye/oWUgLdqbHxidwx49+6/65kRFURbop0tHzl26yjl7wGb2KW3gj2Lx0Nl39PdYNB49YyDWQCPsxPJDcLD3q6BErkiicL+Pooljb0TNMq0rqwPcazs9zaGYZa1dlMNhF1zKvA6gfhmkHfrh0mjAd4CQikqMXJNOt3WTTKhZmlwE4Hb0tR68W+vdBox9EJK9BsWwbpiU+o+fXjcgXjVgYsQDRmbHE7dChGe10cQ6De0gnGLFQKJqwbTQ8POjPaC3NWObKErt4z+jxuG6aVfmOPPDGN0UZr8A6ekFz9Grdwv1g6gpvYDrgGLI8/tvDsCxbWFWQyxvcc9fDA2kcng+e2ddNknM3I4gIicL5Mo4bd1Z06lUdvWDSTaB7sQoMvo5efK5/M1zpZoI2kWEQCf6Oo3tqNqWgUDSwXNCRL5odc571c7aMGtdxT/CQSpKcCIJa1804RCsA1ffkIAZSqQRKN9vp4hwGdgAn2tFbbuHiOpDVWko3ZxeZeVL8Onpirpvi8Qq8hT9TDCgC+wNVkX0P7gp62I5e60NBNi9de3A9ujoLw7Td37kIuYKOAc571+rBtHuAkDSSczcjiAiJwvlyXzkn61/+85kYDcA7N1pvvILoBtpbOHW/0Kv+eWqJY4HQCObslZTCNCzuw5vHjMUwY3ddsmnHdfNwWRa0ulOFXgc6emxDFeSaK4pUl6MXB8dNoPLzBJFtAsl03Wyni3MYWqkxGtEq22wgq7q2+I2Ia1g6UG+Y1oySbrpyYl54C39WaIocPDozej6um2GlmzwzesV6100geMSCbduOGQvnDOTwQApLeT1wNmQ3ideTlSA6RJgZGsDRwT/4q5fdP4sEIreTWmv2IB2vn5bdSAHg/kd/19WfiZ2sNzoVTtKM3srt6PFJN0VOljtBJu3M6LENxBpOiU9YlA7EK+gBZFsMVZZj3NErf8YE5W4MV7oZs0OHZoR9lrUL1okSjVho1dHrz2otZ/Qq0s0YFnqcHXvbtp14BcH3Mm/h75qxCNx3FdnfdbMQUrrpKpGazuiVpZs1HT0Wmi5qyFIombBsm8uMBUh2ll487s4E0QWCztAAjg6+9kTOTwffaVypY0DpJnMjZSws6111cGMPucbSTfE5o27B1pkkWVgYXOMOjoJFN8zYFcDZtALbBl6eXAKAjkk3ZUmChPYGpptuR0+8uFYVqS5HL24zekE+Y2PjE/j1bw8DAD7xL4/iv/o4FcaVMM+ydpEK2dFr1CXuz2ru1zRibqkIVZFj8770oql8BzmmZcOybWHpJnsf3PHgbzGfK2Egq+Jdb31l3fvDmdGVhCTOjhlLkxm9gB09jeNZUSgZkFBfTK4ZSkORJeGOHm9YOoPJgOeWim4XMSnE68lKEAkhrgPwbh5NQOlm3BzcFNnZ9DYq9JIUr6Cs1I4eV46e3TK0ttOwof/fTSxCQue6A5IklfOq4tnRUxTZ3ZDphoWSYcVIuum850Q3x+yAq1juPs3ERKGRZJjkUBfs6OVadfQyzoyeZTe+r8wtFrF6MBVoTrPd8LrqsrDzdIDnxbatG/D5q9+IbFrB605c53sIEChHs1G8QmjXTXZA3fiaLBcNZNIK5JrfqSLLGBnKiBd6LSTCtVQ6esmb04vXk5UgEkJcB+C1Gv2/YVpC8QpxK2AlSYKmyQ0LPdOyhHKAusXY+AQefuIgAODvv/6rFbGBFJEgGobpnnTHBSYR2n9oEUMDqY52jmvn4KLGDCPdVCT3/tJKZtdp3I6e4OY4bgdcvQBTYxQFO3rLbkevsRmLbVdmtvyIa1g64HTsFVlq6jAJwD10CBpCLssSTjhqGM/un/P970HUMIoswbJt2DVFdsV1M9g9smLG0qSjVzSRSfm/J0ZXZzE5K1boLQneu4bLHb0gpi/dhgo9gghAXAfg3Rum4ZFuCmyg41jAakrjQk837Nh39Fi3oFA+9ZxbWhndAvfkmsuMxXa/Pi6wvKYXJxY6Np/HUGucLaOGfZ4CdfTkSrdxudh8U95pgpqxxO2AqxcI3tEzoMhSw+4Q25g3m9ObXSrFttAD+A5ymORVdEbPy4lHD+Pg9DLmfebKzABGZo3u6cWSY6YVVJXBG5heO5/HGA2QpedKNzk7ev0ZFaoiJ3JGL15PVoJICHEdgK/NoxGVZ8SxgNVU2XfOw7btRMQrrNRugYiNuGHGT4LLTo9zBaNj83kMVfE3PYgK0wo5o1fepLrzVDELTBft6MXxgCvpaAED05fL5j6NZJcD5Y15s0Ivzh09wDFkadXRYyY2oq6bXk7c5GR/+nX1gnT01AYGWwXdRCagEQtQye5tZsZSKBp1jpuMdcNZ5AqGqzDggXWOec1YJEnC8EAqkYVePI7hCCKBxHEAnnXvDMOCXQ5FFnHVYj/Pzl17MbNQxJqhNHZ02ZQgpSq+D0V3sxrzmbeV2i0Q6+jFT4LrPT3udEdPUTrU0Qvw2VGUiutmXKWbopvjHds349b79lQVJd0+4Eo6aZXl6ImbsTTbfLMOTKPQ9HzRQLFkxjJDj6GqckuTKjajF1S6CQDHbBhAOqXg2RfncNpJ66v+W5DDtcrctQWgsq5SyUQ6ROeRR7qZL5lNOnrO/XlqroBjNvAVbq1mQWsZG5/A3FIRPx0/hOf2z3V9XyRCPO7OBEFEgiLLkCVH/89O3UVPt1kBOzo6iKmpxXYsUwhN9ZduGu6cUbwKhFpGhtK+RV2vdwsUt6PXvNBzO7NxM2NJVzYynd40qrLcmRm9AJEWqlLJ0sq1mKfqNEFn9LwHXNMLRYzE4IAr6bgdvQDxCs023wNuoeffvalk6KWEvm8n8XbFG+GasYQooBRZxglHrWrQ0bOExjqAyuFdbTZqQTeRbjA/xwNPtmC+aGDNkP+B26gnYuGYDYNc3zOXN5BSZa5Cmo1fsPWxOC2gO27kosTj7kwQRGSoqgTdsDw5OfHaQIvSuNALHvrcSVZqt6AS/N3aRhyIX2e2qqPX4aJcqYkwiBrDCv7ZcXL0mBmLmPyp3QQt9IB4KjSSjCxJUBV/2X0zcgUDg32NizR3Rq+BTG9uMb5h6QxVkVt27ItGODMWxombhvHtXfuwkCthqL9yXYOasQD10s2iHq6j546cNM3RayzdDBKavlTQuefzmo1fJOGeEa8nK0EQodHKD5EwczhxolGhx/4ubrNdtcR1nrPdKHLrU1rnv8ezM+udOWl0ktwuFLm1tCsMRggzFtWTpcXkT/Hp6JXjFWJ2aLBSSamycEcv16Kjxw4VGnX0ZllHL87STY5Cz5VuhnwvbznamdP7TU1Xz7CCxSsA9Yd3xZIZOFoBCC/dzKZVDPZpQs6buXzz95mXpI9fxOPuTBBEZKiq8xAJM4cTJzRVdu2bvbCNcNxmu/xYid0C9ntpVbDEtTOryDJSmrNRXdNp6SaHtCsMRohDIEWRYZQ7ecsFA2lNic3vTnE7euG6IEQ0pDQZeoB4hWYHB7IsoS+tNpzRYzlnsZZuys0/32PjE/j6D34DAPj8Nx/HO88+PvDz45gNg0hrzpze67asc//eNG3hzrd7T6913dRNrA7RQVVbRPFYlo1iqbnhyzpB581Ws6Bekj5+EY+7M0EQkeHEEdieOZxkf8xTquLf0TOT0dFbqciSBFmSWpqxVDp68fo9jo1PuO+7v/uPX3Q0DkNtY2D62PgEvvZ9Z77k8998XPjnUmSpSroZl24eADy17zAA4AeP7ce1X97d8xEmcSelKkIdPcu2y++p5hvwgazWVLqZTSsNM9fiADuM9YPNg7H517mlUqg4HlWRcfxRq7Bn/2zV3wcLTPcPey+WTKRDuG5KkgRVaZwtyKKJGnX0APGIhZyAdDOObuQixOvJShBEaFTFsW5mN03Rgeu4oSZ8Rm8lo3LkRVVkhPF5n7LNFssFnlnobPah0uLEPyjs51rKB99EeovQXNkKPw6MjU9g5/973v3zdId/Z0Q9KU0WilcoFA3YAAZavKf6s1pTM5Y4z+cBzXMy2xHHc+KmYbw8lcPicsn9OydjN6h0029GL1wXXVVkGIb/Pa9Qcu5XrQq96YUCt1uxiHQz6eMX8bhDEwQRGZoqwzAsd4Od9I5eo8D0uHaCiAqOqUgL6WYIY5B20e3he1WR3FPsKIni51I90Q/LBQP9TTZfnWTnrr1194kkGSb0IpqqCJmxVFxcm3da+rMqlpYbz+jFvtBrMrvYjnmwE48eBuDM6b32REe+aQaJV2hgsFXUw3X0gOZzi8tFvkLPtoHphQLWr+5r+f1yBYO7owcke/wiPk9WgiAigd0w2QY7ThvoIDhzHk0KvYR3LHsZhSMmII4Fe7eH7xVFbovrZhQ/l5OjVwlMb7Up7xTd/p0R9YiasVRcXJsfHgxkNdcIqJa5xVL8C70mRU2jua8w82DHHjGElCrj2Rfn3L8zrCCum/XSTdu2USxZoTt6mto4RL5QLEs3m83orS47b3IYspR0E7phxSb/s93E58lKEEQkaEo5XsFgtvXJLoQadvQS4rq5klEUqeWsWRxdN9ux2RJBkaW2uG5G8XOpSsURdLnIL39qN93+nRH1pDRFyIxlidPFtT+jufJjL7ZtO9LNwfgasQDNC712zIOpiozNR67CHm+hF2RGz8eMxTAtWLbd1CiF67WbyFnzZelmpkVHD+CLWGCdY5GOXpKhHRJB9Bhs0NvoETMWrcGch14+VVSo0Ist3sy1RsRx1rLbw/eq0vq6BSGKn0uVvfEK8enodft3RtQTvKPX2owlXzTqDpEW8zpMy05AR6/xDC6bB5PK515RzYNtOXoYL08tYak822gGCExX5Xp3zKIeTd4fGznxI8+km02KyVUDKWiqjEmeQq98DQZicu9qN/E4iiMIIjI0RcaSqXukjfHZQAeB5QLatg1JqjyYTHLdjD2KwuG6GSLTrV2wTdXOXXsxs1DEmqE0dmzf3LEZDZ5OaBDY+v+/7z0N23Y2kaI/FytCTctCsWTGpqPn/Z1NLxQD/WxEtDhmLCIzerwdPbX89QaGPOHqLCw9jNV/J1Dk5jl6r3nlKGwbeMdZx+HCN7wiku954tGrYeN5PLd/Dqe+cjRYYHr56733dGaUkonCjKVB8cvjuilLEkaHs1xZeux9Fpd7V7tZGT8lQawgnI6e7emUxEcSFwSW9aMbVtWpoR5DyR9RTTM5DqMySxqv3yMbvh8dHcTU1GJHvzdPJzQor33lKG6xgXe86Vhc+MZjhf+9UjZjYd2XbIw2S0k2TOhFNMF4hWVOSd1AthKaXlXosQy9GIelA4CmNr8vHp4vAABGV2Ui+57HHjEETZWx50VW6AUwY2Gum557E+votdWMpdDajAVwrtfUXKHl92KyX5JuEgSRSJyZNrNnpJupcvhx7aB2r3QsexlnnqtVRy9+0s1uo3J0QoPibiLLMy2iKGXpZo7TOINYuYjGK+QKOlRFqpPg1tLvFnrVc3pzS05HL85h6UC1oZEfbM4s6GfUD02VsXnjEJ4t5+mFi1fwSDfL3bbQZixK4+KXdQ1bFZOjq50sPdtufu+sdPSo0CMIIoEwCUSvFELejp4Xo0fiI3oZReaIV4ih62a3cdxK2xOYzjaRawNuItnvaSHndE/iMqNHxA8nMJ1fusnC0r0SfT9YR2+pJkuPSTfjPqOnNeleAcDhNhR6gCPf3H9oCbmCHtCMpV66WSz/fsOasahNXDfzRROZlAK5xftidDiLom5isUH0BoNXItwr0JOVIHoMrRwwXimE4iWJE4UVerUnw71SyPYyXB29mEo3u4nSxKwhLGE7erWFHnX0iEakNBmmZXPPm/KGWFdm9GoKvaUiBvu02B8atfp8T80VkNJkDPZFe4iy5ehh2HDy9MwA8Qqqn3Sz3NELa8bSLDA9XzJayjYBYF35ntbKkCWXN6DIUujiNCnQHZogegxVcU7G2AY66a6UDTt6brwCFQhxhScmgKSb9aiK3BYzFsDp6KU0GUMBN5HMYn1xmTp6RHOY7L6kW8imW3++HRfX1tvShh29pfhn6AFsBrfeYIwxNZfH6HC2ZWdTlOM2DkFVZDz9giPf1AJKN70qjag6es26nIWiwfX6B6ZzAIC/+9ovmpox5QrOgULU1zeu0JOVIHoMVZVgGFalEEp4x6tS6FVLgJjMI+mFbC/DM2tW6ejR75HhmNjYLWdNgjA1l8faVcE3kW5Hb3llOdcR4qQ0/0O6RiwXDK65qUxahSTVd/Rml4rJKPTUegmkl8PzeYyuila2CTjmOMdtHML48zPOOiKUboad0Wsq3SyZ6GvR0Rsbn8DdP3ne/fP0QhG33rcHY+MTdV+by+srxogFoEKPIHoOTXHkMm4hlHDppmvGUrNZYPIRileIL61MBwBvvEKy36dRovoEE0fF1FwhlJsfk2+5M3ockipiZeLK7jnn9HIFnaujJ0uSb2j63GIRq2Melg5UPt9+HSzbtjE1V8Da4egcN71sOXoYEzPLzjoEc/R8XTeZGUto180mgelFo2lYOuDEqtSOd5QMCzt37a372hzngUKvQDskgugx2MO1UDSr/pxUGkk3ddOCLEmQE17I9jKOQ2MrMxaSbtai+pycR4Ft2zg8nw9sxAJUz+ipihx6NofoXViXh9d5k7ejBzjyzZxHumlaFhZyCZFulj9Dfodgi8s6iroZuREL48RNw+7/F45X8DmAKkTU0dOU5oHpzcLSAaeDx/v3vLOgvUJbn6z33HMP3v72t+Occ87BbbfdVvff9+3bh/e85z246KKL8P73vx/z8/PtXA5BrAjYQyRftiROekevmRkLdYHijVLuLjeDXDfrqZycRzunlysYKJTCbSLZZm9+ubSiNkuEOJV7d+uOnmXbyBcN7vdUf1atmtFbyOmwEX/HTcBzkOPz+Z6ab4/jJuO4I1e59xdh6aZPvEJJN6HIUuj7t+Mt0DgwvVVHb2TI//fu9/e5Akk3I+HQoUO46aab8PWvfx133303vvnNb+K3v/2t+99t28aHPvQhXHXVVfjud7+Lk046CV/96lfbtRyCWDGwG26h5NyAkz5wzDYLtad9hinuGkZ0lmZyHIbhzlom+30aJUqTE/8wuPlcIaSbilzp6K0Ue3IiGKzbyxOani8asMFv7jOQqe7ozSYkWgGoSDf9ZtKi+Iw2I60pWFt+7S/e8Tiu/fJu3zk2P9hn3yvdLJTM0N08wJnRaybdzKaa32t2bN9cl7+YUmXs2L657mtJuhkRjzzyCM444wwMDw+jr68P5557Lu6//373v4+Pj6Ovrw9nnXUWAOCDH/wg3v3ud7drOQSxYqhIN42eiB5onKNn9cTP18uwcO1GjI1P4Ac/3w8AuO4rj3BvOHqdds3oRRHEzNa2kCutqM0SIU6qwb3bD1a08Xf0tCozFhaWvnowCYVefcHEmJpz4k/CyKubMTY+4UasAM1NS2rxk24WdTP0fB7A4hWsOgMqy7ZRKJnIppt/j21bN+C9522p6uCd8/pNda6bhmmhUDLRn105h1Rt+0knJycxOjrq/nndunV44okn3D+/+OKLWLt2La677jo8/fTTeOUrX4lPfOIT7VoOQawYNFe6aSY+Qw/wWHTXyH8Mw6JohZijNjFjGRufwK337XEluWzDAcDXEnslwU7OW3VDRamEpYcwY/EoBqijRzSjEq/QWrqZKzijBrzvqYFstRkLK/SGB5JgxlIugH0+34fn8ljVn4qkS+bHzl176w6QmGlJq/uuLEmQpGrpZjGijp6mSLBRf7jFvAZ4cvS2bd2AbVs3oKSb+Isv7cah2fo8veXy+2wlHVK17S7tZwvtlZAZhoGf/exn+I//+A+86lWvwhe+8AXceOONuPHGG7m/x8jIQCRr9WN0dLBtrx2WuK4trusCVtba1qxZAODcMNMpJdTrx+G6pfucE7p0JlW1HkVTkEqpsVhjLXFcE9D5dQ30p2Hbtu/3/c7DY74uad95+Hlc9OYTOrVELjp93VavdubVh1ZlW35vkbUtFU0M9aew6cjVgdc24jE3WNNiffQ5EKeX1lYof7wzfamW//alGWdTfuSGVVzfZ91IP4q6ieHVfQCAkgXIsoRjjxmJ3Vx67c8zcmgJADA4VP/5mV/WccTa/ra9D2YamJbMLBS5vqeqyEinNfdrbUnCQJ8Wer3D5TiJ4dX9ACrXbKpcrI2ODAh9j3NOPwb3/GQflLSGNUOVg62CtQgAOGLdYKw/a1HStkJv/fr1eOyxx9w/T05OYt26de6fR0dHccwxx+BVr3oVAOCCCy7ARz/6UaHvMT29BKsN9tOjo4OYmlqM/HWjIK5ri+u6gJW3tnzZ9nxxuQQJUuDXj8t1Y/bNs3PL7npGRweRy5UgA7FYo5e4XLdaurGuUtFAybB8v++Uz2kr+/s4Xb9uXLd8ztmMTR1eQrrJnlV0bfsnFjAylAn18ywtVmRfChp//uhzIE6vrW1p0fmMH57Otfy3Bw45B5R6ocT1faRyV+mF/bM44di1ePnQAlb1pzAzvSS0xnbjd91yns/3qhpJ4suTSzhh06q2vQ/WDKV9nSjXDKW5vqcsS1hcKrpfu7hUhCwF32cwCmXp7sShBRx79Br39V6ecn6fRkkX+h6nnziK7+zai+88+Btc+MZj3b9/6YBziGbpRmw/a6LIstS08dW2AZc3vOENGBsbw8zMDPL5PB544AF3Hg8ATj31VMzMzGDPHkeq8+CDD2Lr1q3tWg5BrBhYNk6haPSEK2WzeAUyY4k3qiL5zqEAYi5pKw2/vKooODxXwGjIfC7vPYWkm0QzUgLxCsuudJNPUsdcE9ls39xSMqIVgMaum4ZpYWax0JawdIaIaYkfqlx9Ty/oJjJRzOg1eM7nS/zSTS/r1/Rh6ytWY9evD1Q1hJbKc53kuhkB69evxzXXXIPLL78cl1xyCS644AKcfPLJuOqqq/Dkk08ik8ngS1/6Eq6//nqcf/75ePTRR/Gxj32sXcshiBWDO6NXNHvCrESWJSiyVDfPQPEK8UdRpIYRAWE3HL1MxXUzuhk9y7IxvVAIbdvO5gcBfuMMYmWSEohXYMYqImYsANyIhbmlYiLm8wBvYHr1Qc7MQgG2HW6GthVe0xIJzsHae8/bwj0XXZuNWtLNSLI0XQOqmnteoegcALRy3fTjzacehZmFIn6997D7d6KmP71AW3/SCy+8EBdeeGHV391yyy3u/z/llFPwrW99q51LIIgVhzdHb1Ru38lgJ9FUuc6im+IV4o8qy7DhFBm1wfZsaP7W+58F4Gw4dmzfvOKNWAB/d7uwzCwWYFq2a60elOqO3so5FSfEYWYsOke8Qq5gQFVk7qJhoPzeYyYuc4tFvNITBh5nGpmxMMfNdW1y3GQw05IgclylxmCrUDKRicSMxf+aLLNCr4Xrph+vPmEEqwfT+PGvXsapJzjmkOz9spI6eiunpCWIFQKTOto2eqbjlVJl346eqJyD6CyKe3JtISXXP6iPPWIIAHDd5a/DiRuHOrq2OMPccqPs6B0ubyJDd/QU6ugRfMiyBFWRUOTo6C0XdKH3E7PHX8rrKOomcgUj8dLNdoelR4FfRy+SHL0G2aGFgNJNwFEfnHXKRtz98POYnF3GutV9yOV1SAFfL6nQcThB9BjeLlevdLw0VYZeY9FtGFZPxEf0Mm7AboPO1GTZkOWIkf6OrSkJuBvBCDt6lWiFcJtI72eOZvSIVqRUhbujJ/J+6nc7ejpmF5xDjNWJKfT8A9On5vJQZCnWBauiyFX3pUIpohw91V+uni939DIBpJsAcNYpGyFLEh56/AAA5/3Sl1EhSytn79Abu0CCIFy8c3m90tHTVKW+o2fZPTGD2Mt4O3p+sOLjiLVU6Hlpdd2CMDVfgCxJWBMyUNp7eETSTaIVmiZzzegtFwwhOV0mpUCRJSzldUyXA8CHB5Myo+cfmH54roC1qzJ1Mvc44TVjMUzLiXHSwj+HXelmrRmLW+gFKyZXD6Zx6glr8fATB6EbTud3Jck2ASr0CKLn0Hq1o1fzAHAC03vj5+tVWnWmDs3mMZDVqGCoQZX9N4JhODyXx5qhdOh7gvfwiKSbRCvSqsLlupkr6OgXkNNJkoSBrIZc3sAMK/Ri3AnzojYwW5qay4fuuLcbR7rp3JeKZZVNOmC3zYva4HCLdQzDFL9vfs2RWMrreGzPFHJ5fUWFpQNU6BFEz6GpvVno1W4WKF4h/igtZs2m5vJYtzreG5tu4Hb0rCg7evnQRixA9YweSTeJVmhavZGWH8sFQ/jApz+rIZfXMc2kmyG71Z2ikevm1Fw+1vN5gHNvYvcllnEbRUevmXQzG1IaetIxq7F+TR9+/KuXnQOF7Mq6b9EuiSB6DO+Ju9Ij0s2UT0fPpHiF2KO2cI+cnKVCzw+lgbQrDE6GXvhrzYp3RZYiMWEgepuUqnDHK4h2iAcyKpbyOmYWCtBUGX0JMdjw6+gtFwzkCkbonMt2o8iye1+qdPQidN00qu95+aIR2jhFliSc/eqN+O3L83h5Kuc6tq4UqNAjiB7D2+XqFWmj6lPo6RSvEHuUJhJE3XDCgdttJZ5EonbdLOom5nOlSGRhrNDry6iQVpChARGMlE80Ti2WZSNfNIU7xP1ZDbmCjpn5AoYHUol5P/rJFA8zx802hqVHga90M1LXzfrA9CgcMt/wqiMgS0DJsPDTpw/h2i/vxtj4ROjXTQK0SyKIHkORJbDnndIjhVBKVaDXnAobpkVmLDGn0dwF4GxsbDveVuLdImrXzcPzLFohfLdAkhzLfJqrJHhIafX37lpYVpro7FR/VnPMWBbyiZnPAyrPZa90kxlTxf1+qCiVeAUm3YwiR6+RdLMQgXQTAJ7cN1315+mFIm69b8+KKPZol0QQPYYkSW4nr1c6erVmLLZtO/EKJN2MNc3iFdjGhqSb9SgNZniC4m4iI+gWjI1PwDBtHJpZXlGn4kQwUj7z1bXkCjoACM9ODWQ05AqOGUtS5vMAR0qoyFJVUTM1F91hTDvxk26mopRu+nT0MhF09Hbu2ovax1DJsLBz197Qrx13emMXSBBEFawj0CszeppSvVmwLBs2esdspldxZ/R8ChaWobdudV9H15QEKq6b0Ug3D0fULRgbn8Ct9+1x/7ySTsWJYDhmLC06egWnoyduxqJCNyxMzi4nqqMHOM+uqkJvPo++tBr7TrlXulmIsqPHDrd84hWyEbh6Ti8Uhf6+l6BdEkH0IMx5s1cKIU2r7uix/98rHctehc1zmT7ukZNzeaQ1BUN98d7YdIOK62ZUHb0CUpqMwZDXeueuvXXdmZVyKk4EI8URr+B29ALM6AFO5zt5hZ5UJ92Mu2wTYNLNds7oRW/GAgAjQ/7vj0Z/30vQLokgehB20+wVaaOmyFWSDvb/e2UGsVfxm0VhTM46G5ukGCh0ErdAjqqjN5/H6Krw13oln4oTweAxYwna0fO6Jw4PJCMsnaHUdPQcV9x4yzaBckfPrIlXiEC6qfpINy3bRqFkIpsO//o7tm9GqmamP6XK2LF9c+jXjju0SyKIHkTttY6eKkP3bBYMt6NHRUKcUZTGHT3K0GuM5M7wRNfRi6JbsJJPxYlg8Jix5PLhOnpAcjL0GJpSmdGzbBuH5+Mflg6UZ/Ta0tGrl266Zi8RSDe3bd2A9563xb1XjQyl8d7ztmDb1g2hXzvuJCN0hCAIIVgB1CuFXkqVYdk2TMuCIldknL3y8/UqbNastmCxbBtTcwWccvzabiwrEaiK7Fsgi2LbNqbm89hy9HDo19qxfTNuvW9PlRRvpZyKE8FIqTIM04Zl2ZBl/4O5XIG5bgqasWS9Hb1kFXpOR8+5L84tFmGYdiKlm5JUGRUJA3Pz9XY582U31ig6eoBT7K2Ewq4WKvQIogepzOj1RsdLU50bvW5YUFIVGScVevFGaRCY7mxsLMrQa0JUHb2lvI5iyYxkE8k2STt37cX0QhEjQ2ns2L55RW6eCD5S5W5PyTAbdmaWCwY0VXbv87x4C8OkFXqaR7oZZfxJu1GrpJsWMiklMvm96il+AcdxE0AkM3orGbp6BNGDVGb0eqMQYoVrybCQSVXkHZSjF2+UBsHfh8qOm6Mk3WyIqkiRzOgx2/a1EW0iV+qpOBEM996tO/duP3IFXbibBwDjL8y4///6/++niTp0UBTJdSOOMv6k3VRLNw23kI+CWifSgtvRo1IlDLRLIogepFcLPVbgVaSbvdGx7FXc4O+azpSboUcdvYYoihyJ6+bh+WQEMRO9SUpjh3SN5/SWC4ZwWPrY+ARue+A37p+TFvXhNRibmstDAjCyKv4dPUWR3PtSUbciiVZgaGq16Zor3YxgRm8l0xu7QIIgqug96WalowdQvEJScDt6NbNmk7N5KLKENWTi0RCvu10YWFG9NgGbSKL3SJXlmM2cN3MFHX2CHb2kR30oiux+vqfmClgzlE7EwWyt62YURiyMuhk9ZsYS0YzeSiX+7yqCIITReqyjx2yRWYGnm84DgOIV4k2jjt7kXB5rV2WgyPT7a4RjxhK+ozc1V8BgnxaJcx1BiMI6enqTLL1cgI5e0qM+VEWqdPTm81ibANkmUC05LepmJNEKDFWRq1w3qaMXDfSUJYgepBfjFYDKZsEwnAcNdfTijWvGYtZ29JZpPq8FihKNGcvh+WQEMRO9CevoMSt+P5YDdPSSHvXhNR5JSlg64Dgp2wAsy8m4i7Kjp9WYsRQidt1cqdAuiSB6ENWNV+gV6SZz3TSr/ldVe+Pn61VYvIK3M2XbtpOhl5CNTbdQZbnOxCYIU+XuKUF0g+demgUA3HjbL3Htl3f7ztDlCoZwoZf0AGxmPFLSTcwvlRLhuAlUZ6OWou7o1c7oRZijt5KhQo8gehBWGPVqR8+NVyDpX6xhmwKvqchSXke+aGLd6r5uLSv2jI1P4OXDS3hi73TDzTEPpmVhZqGYmG4B0VuMjU/gvp/ud//sZ5hiWhYKJRMDgtJNbwC2hOQFYKvljj2LVkhCWDoAV25vmNF39Pykm+mU0jB/keCDymSC6EHUHgtMZxLNkivdpHiFJMDMWLzSzUly3GzK2PgEbr1vjythYptjAMKb2NmFIkwrGUHMRO+xc9feqg4NUDFMYe/l5XJYumhHD6hEfYyODmJqajH8gjuIWjZjcaMVEvIZde/plh35jJ6mSCiUKhLffNFANsLXX6nQLokgehBWGCk9It2sHeineIVkIEmS49Lm6ehNUoZeU6J0E5xiQcwk3SS6AI9hCiv0RM1Ykg4zY6mEpSfjfliRbpYLvYg7erXSTcrQCw9dQYLoQVgnr1fMStjPUSfd7JGfr5dR5GrL7ClW6FHx4UuUboJutEJCNpFEbzEylPZ93yqyhLHxCVi2jW/92DnAuP3B5wBJvGudVJyOnjOvnNJkDPUlo9BlHT3dMKEb0eboqWq9GQvN54WHdkkE0WOMjU/gh794CQDwD3c8npgA2WZUZvQcWYcr3aRCL/Yo5Q0NY3Iuj9WDaaQi3CD0Eo1cA1cPirsJHp7PQ5Yor5DoDn6GKYoiYbBPwy33PI1/+d4zmM+VAACLy3qiAs/DwrpXU3N5jK7KQpKSoU5hM3qsExvlfVyrndErGeS4GQG0SyKIHoLN97D8mbmlUk88PCuumxSYnjQUWaoyY5kkx82m+G2OASBf0PHUvmmh1zpcDmKmvEKiG3gNUwDnEOPKt5+Ez139Rgxm6ztYSQo8D0tlRq+QGNkmUJFusj1GJsIZOsWTLQgAhSJJN6OAriBB9BDN5nuSLIlxO3pmjXST4hVij6pI1WYss3mcvHmkiyuKN+xzunPXXkwvFDEylMabTz0SPx0/hH+449f4g9OOxpGj/fjOT/ZhZqGINUNp7Ni+2ffznaR8LqI3YYYptSzmdd+vT0rgeViY6+bUfB5bjh7u9nK4YdLNXLmjF32OXuVZsVw0KCw9AugKEkQPEeV8T5xQFQkSgJJe6ehJAOSEyF1WMopcmbsolAws5ErU0WuB3+b4ra/bhG8++Fvc/7MXIQFgPdJmrpxT8wW8+ngqqon40Wh+LymB52FhYwfFkpmow5ha6WakOXo1hV6hZCBD0s3QkJ6DIHqIRg/JpD88JUmC5glTNQwLqionZq5hJaMqEkzL+b1NzTkOc+vIcVOYtKbg8nNPxEBWg13z3/wkb8WSiYVcCWtX0bUm4kfSA8/D4p0vT1ShV5ZuLhecjmykrpuqDN1w7m6WbTvSTerohYYKPYLoIXr54ampMnS9It0kI5Zk4DVjYdEKVOgFZ4lT8nZ4Pln5XMTKwm9+L0mB52HxRh+tHU6OA7Falm4uF9vT0TNNC7Zto1gyYQM0oxcBdAUJoofwm+9pNL+TNJyOnuO6qRsWZeglBNWTozc5twyAwtLDwCt5Y93TJG0iiZVFo/m9lYDXSGw0QV339s7oSbDhZPSx4HSSboaHCj2C6DF69eGpqbLrtmkY1NFLCopSydGbms2jP6Oib4WFI0fJju2bcet9e+pMl7afsrHqz1Oso5egTSRBrBRYR2+oPxVpV6zdKEobZ/TUSl4u6xj2UUcvNLRTIggiEaRUxd3c6oZF0QoJQVFkT0cvj3Wr+7q8omTjlbxJAIYH0ujPKPjBL17Cwemc+3WH5wpIawoGExLETBArCfb8Gl2VrI476+i1ZUZPqRR6BTe+gQq9sNAVJAgiEaiejp5umlUzDkR8UeVKR29yNo/NR67q8oqSD+vaj44OYmpqERMzy7jxP36Bz93+OD7+/3sN1q7KYmouj7XDGTIsIogYsu/AAgBg74EFXPvl3YkZsWDP3RwrxCKOVwAA3TCRLzmvT4Hp4aEjcYIgEkG1dNOmjl5CYB09w7QwvZCscOCksGFNH/78D1+NYsnE525/HD/6xX48uW8aL0/lcO2Xd2NsfKLbSyQIoszY+AQeevxl988sIiUJn9PaeAVNi+45XN3Rc2b0yHUzPLRTIggiEaS8HT3DdPX8RLxRZAmmaWN6vgDbJiOWdnH0+kH82X87BdPzeXz9B8+5ctkkbSIJYiWwc9deN1uU4ReREke80s20pkSaZauqzmvphoU86xhSRy80tFMiCCIRaIpXuklmLElBVWQYloXJOYpWaDfHH7kKfRm+nD2CILqDn2tus7+PE5UcPQPpCLt5QEW6aZgW8mXXTTJjCQ/tlAiCSASaKqNkULxC0mAdPcrQ6wyLy3w5ewRBdIfaKJRWfx8n1LJ0s2RYkbuFeqWbeTJjiQwq9AiCSASaqlRm9KijlxhYvMLkbB4pTcaq/lS3l9TTJHkTSRArgR3bNyNVM3qQUmXs2L65Syvix2uCFqXjJlAdr5AvGo40VKYD3bDQTokgiETgBKZTvELSUGXHjGVqLo/R4Sy5QLaZJG8iCWIl4I1IAZxDmPeetyUZrpuewivqjp7XdbNQMmg+LyKoJ0oQRCJIqTJ0vVLoUbxCMlAUCabpzOitJ9lm22GbxZ279mJ6oYiRoXRirNsJYqXAIlKSBnPdBKLv6LFnutPRM8lxMyLoKhIEkQi8HT3DpI5eUlBlGbrpdPReddyabi9nRZDUTSRBEPGmndJNzTujVzIoQy8iqNAjCCIRsBw927YdMxaKV0gEiiK5g/UUrUAQBJFc2indrDVjyZLjZiTQTokgiESgqRXrZd2wXPcvIt54T4BHSbpJEASRWLyFXqaNZiwFkm5GBu2UCIJIBJrqPFR0w3JcN1Wa0UsC3pmOdav7urgSgiAIIgySJLnFXqpN0k0nR4/MWKKCCj2CIBIBcxIsGeWOHs3oJQKWd6jIEln8EwRBJBxW6GUil26SGUs7oJ0SQRCJgEk3i7oJy7LJjCUhsE3ByFCmqrtHEARBJA8mx2/fjJ6JQtFAhmb0IoGeugRBJAJW6DFjD4pXSAbs4U3zeQRBEMmHHdhF7rpZfsYvLuuwAfRRoRcJVOgRBJEI3EKv4BR61NFLBqyjt44KPYIgiMTD7umR5+iVX3d+qQgANKMXEbRTIggiEbBCb7loAgDFKySAsfEJ3P3w8wCAR8cPYWx8ossrIgiCIMLgSjcjLvQkSYKqyJhfKgEAzehFBF1FgiASAevgMekmmbHEm7HxCdx63x6UDCfkfrlo4Nb79gAAhXkTBEEklHaZsQCApkqYzzkdPQpMjwbaKREEkQiYlXOl0KMZvTizc9det8hjlAwLO3ft7dKKCIIgiLCwGb2o4xUA5wB3odzRy1BHLxKo0CMIIhFQRy9ZTC8Uhf6eIAiCiD9MutmOjp6qyG5Hj8xYooF2SgRBJAJNYzN6VOglgUaZeZSlRxAEkVzaZcYCOAe6y2XDNTJjiQbaKREEkQioo5csdmzf7IbcM1KqjB3bN3dpRQRBEERY3HiFdnT0PM+MLHX0IoGuIkEQiaA2R0+jGb1YwwxXdu7ai+mFIkaG0tixfTMZsRAEQSQYtU2um97XBtojDV2JUKFHEEQiSKnOTX/ZDUynjl7c2bZ1AxV2BEEQPUQ7pZtMqZPSZLdzSISDriJBEImgrqNHOXoEQRAE0THGxiew9+UFAMB1//RI5NmorNCjDL3ooJ0SQRCJQJYlKLJUCUynjh5BEARBdASWjaqbTmzO9EIRt963J9Jij41k0HxedNBOiSCIxKCpMuXoEQRBEESH6UQ2qtvRI8fNyKBCjyCIxFBd6NHtiyAIgiA6QSeyUZnrJoWlRwftlAiCSAwpVYZePlGkQo8gCIIgOkMnslE1t6NHhV5U0E6JIIjEoKoVOQeZsRAEQRBEZ+hENmrFjIWkm1FBJTNBEIlB83TxmMUzQRAEQRDtpRPZqEy6SR296KArSRBEYkhplUKPOnoEQRAE0TnanY3KTNYyVOhFBu2UCIJIDNTRIwiCIIjeRCPXzcihQo8giMSglTt6qiJDkqjQIwiCIIhegQLTo4cKPYIgEgM77SPZJkEQBEH0Fm68AnX0IoN2SwRBJAZW4FGhRxAEQRC9w9j4BL7/6IsAgG/88DmMjU90eUW9AfVGCYJIDKlyvAIVegRBEATRG4yNT+DW+/agVM7JXVzWcet9ewCgreYvKwHaLREEkRhYgUdh6QRBEATRG+zctdct8hglw8LOXXu7tKLegXZLBEEkBpJuEgRBEERvMb1QFPp7gh/aLREEkRio0CMIgiCI3mJkKC309wQ/tFsiCCIxkHSTIAiCIHqLHds3I1VzgJtSZezYvrlLK+odyIyFIIjEQGYsBEEQBNFbMMOVnbv2YmahiDVDaezYvpmMWCKACj2CIBIDSTcJgiAIovfYtnUDtm3dgNHRQUxNLXZ7OT0D7ZYIgkgMJN0kCIIgCILgg3ZLBEEkBuroEQRBEARB8EG7JYIgEoOmsEJP6fJKCIIgCIIg4g0VegRBJIaURh09giAIgiAIHmi3RBBEYmAdPZrRIwiCIAiCaA7tlgiCSAwaxSsQBEEQBEFwQbslgiASA5mxEARBEARB8EG7JYIgEgPFKxAEQRAEQfBBuyWCIBLDU89PAwC+9eBzuPbLuzE2PtHlFREEQRAEQcQTKvQIgkgEY+MT+Paufe6fpxeKuPW+PVTsEQRBEARB+ECFHkEQiWDnrr3QDavq70qGhZ279nZpRQRBEARBEPGFCj2CIBLB9EJR6O8JgiAIgiBWMlToEQSRCEaG0kJ/TxAEQRAEsZKhQo8giESwY/tmpGpiFVKqjB3bN3dpRQRBEARBEPFF7fYCCIIgeNi2dQMAZ1ZvZqGINUNp7Ni+2f17giAIgiAIogIVegRBJIZtWzdg29YNGB0dxNTUYreXQxAEQRAEEVtIukkQBEEQBEEQBNFjUKFHEARBEARBEATRY1ChRxAEQRAEQRAE0WNQoUcQBEEQBEEQBNFjUKFHEARBEARBEATRY1ChRxAEQRAEQRAE0WNQoUcQBEEQBEEQBNFjUKFHEARBEARBEATRY1ChRxAEQRAEQRAE0WNQoUcQBEEQBEEQBNFjUKFHEARBEARBEATRY7S10Lvnnnvw9re/Heeccw5uu+22uv9+88034+yzz8bFF1+Miy++2PdrCIIgCIIgCIIgCDHUdr3woUOHcNNNN2Hnzp1IpVK47LLLcPrpp+P44493v+app57CP/zDP+DUU09t1zIIgiAIgiAIgiBWHG3r6D3yyCM444wzMDw8jL6+Ppx77rm4//77q77mqaeewi233IILL7wQN9xwA4rFYruWQxAEQRAEQRAEsWKQbNu22/HC//zP/4zl5WVcc801AIA777wTTzzxBD71qU8BAHK5HP7sz/4M119/PY488kh87GMfw5FHHul+PUEQBEEQBEEQBBGMtkk3/epHSZLc/9/f349bbrnF/fOVV16Jv/qrvxIq9Kanl2BZ0depo6ODmJpajPx1oyCua4vrugBaW1BobcGI69riui6A1hYUWps4cV0XQGsLCq0tGHFdW1zXFVdkWcLIyEDj/96ub7x+/XocPnzY/fPk5CTWrVvn/vnAgQP41re+5f7Ztm2oatvqToIgCIIgCIIgiBVD2wq9N7zhDRgbG8PMzAzy+TweeOABnHXWWe5/z2Qy+OxnP4v9+/fDtm3cdtttOOecc9q1HIIgCIIgCIIgiBVD21po69evxzXXXIPLL78cuq7j0ksvxcknn4yrrroKH/3oR/GqV70KN9xwAz70oQ9B13W85jWvwfve9z6h7yHLUusvCkg7XzsscV1bXNcF0NqCQmsLRlzXFtd1AbS2oNDaxInrugBaW1BobcGI69riuq440upatc2MhSAIgiAIgiAIgugObQ1MJwiCIAiCIAiCIDoPFXoEQRAEQRAEQRA9BhV6BEEQBEEQBEEQPQYVegRBEARBEARBED0GFXoEQRAEQRAEQRA9BhV6BEEQBEEQBEEQPQYVegRBEARBEARBED0GFXoEQRAEQRAEQRA9BhV6BEEQBEEQBEEQPYba7QXEiXvuuQdf+cpXoOs6rrjiCrz73e/u9pJcLr/8ckxPT0NVnV/ZDTfcgFNOOaVr61laWsJll12Gf/qnf8JRRx2FRx55BJ/+9KdRLBZx3nnn4ZprronN2j7+8Y/jF7/4BbLZLADgwx/+MM4555yOr+vmm2/GfffdBwDYvn07/sf/+B+xuW5+a4vLdfvHf/xHfP/734ckSbj00kvxvve9LzbXzW9tcbluAPD3f//3mJ2dxY033ohnnnkG119/PZaWlvC6170O/+t//S/3ftLttd1888349re/jaGhIQDAf/tv/60r91+/++yLL74Yi+eC39o+//nPx+K58OCDD+Lmm2/G8vIyzjzzTFx//fWx+Yz6rS0On9E777wT//Ef/+H++aWXXsLFF1+Mt771rV2/bo3Wls/nu37d7r77bnz1q18FAJx11lm47rrrYnNv81tbXO5tX/3qV/Htb38bqVQKb3/72/GhD30oNtfNb21xuW49gU3Ytm3bExMT9tlnn23Pzs7auVzOvvDCC+3nnnuu28uybdu2Lcuy3/jGN9q6rnd7KbZt2/bjjz9uX3DBBfbWrVvt/fv32/l83t6+fbv94osv2rqu21deeaX90EMPxWJttm3bF1xwgX3o0KGurIexe/du+w//8A/tYrFol0ol+/LLL7fvueeeWFw3v7U98MADsbhujz76qH3ZZZfZuq7b+XzePvvss+1nnnkmFtfNb2179+6NxXWzbdt+5JFH7NNPP92+7rrrbNu27fPPP9/+1a9+Zdu2bX/84x+3b7vtttis7QMf+ID9y1/+smvrsW3/+2xcngt+a4vLc+HFF1+0zzzzTPvgwYN2qVSy3/Wud9kPPfRQLD6jjdYWl88o4ze/+Y19zjnn2AcOHIjFdfNb2/T0dNev2/Lysv3617/enp6etnVdty+99FJ79+7dsbi3NVpbHO5tu3fvti+44AJ7cXHRNgzD/sAHPmB///vfj8V1a7S2OFy3XoGkm2UeeeQRnHHGGRgeHkZfXx/OPfdc3H///d1eFgBg3759kCQJV111FS666KKqk7ZucMcdd+CTn/wk1q1bBwB44okncMwxx2DTpk1QVRUXXnhh165d7dqWl5dx4MABfOITn8CFF16IL37xi7Asq+PrGh0dxcc+9jGkUilomobNmzfjhRdeiMV181vbgQMHYnHdTjvtNPz7v/87VFXF9PQ0TNPEwsJCLK6b39rS6XQsrtvc3BxuuukmfPCDHwQAvPzyyygUCnj1q18NANixY0fXPqO1awOAp556CrfccgsuvPBC3HDDDSgWix1fl999Ni7PBb+1xeW58IMf/ABvf/vbsWHDBmiahptuugnZbDYWn1G/tW3ZsiUWn1Evf/M3f4NrrrkG+/fvj8V181tbJpPp+nUzTROWZSGfz8MwDBiGAVVVY3Fv81tbOp2Oxb3t6aefxplnnomBgQEoioI3velN+NrXvhaL6+a3th/+8IexuG69AhV6ZSYnJzE6Our+ed26dTh06FAXV1RhYWEB27Ztw5e+9CX83//7f3H77bdj9+7dXVvP3/7t3+J1r3ud++c4XbvatU1PT+OMM87A3/3d3+GOO+7AY489hm9961sdX9cJJ5zg3lBfeOEF3HvvvZAkKRbXzW9tb3rTm2Jx3QBA0zR88YtfxPnnn49t27bF6v1WuzbTNGNx3f76r/8a11xzjSt7qb1mo6OjXbtmtWvL5XI46aSTcN111+Guu+7CwsICvvzlL3d8XX732QMHDsTivea3tvvvvz8Wz4Xf/e53ME0T73//+3HRRRfh61//emw+o35rKxaLsfiMMh555BEUCgWcd955sblufmuLw7N0YGAA//2//3ecd955OOuss3DkkUdC07RY3Nv81nbiiSfG4t62detWPPzww5ibm0OxWMSDDz4IVVVjcd381jYxMRGL69YrUKFXxrbtur+TJKkLK6nn1FNPxWc+8xn09fVhzZo1uPTSS7Fr165uL8slztdu06ZN+NKXvoSRkRFks1m85z3v6eq1e+6553DllVfiuuuuw9FHH13337t53bxrO+6442J13T760Y9ibGwMBw8exAsvvFD337t53bxrGxsb6/p1u/POO3HEEUdg27Zt7t/F5TPqt7b+/n7ccsstOOaYY6CqKq688squvNf87rNf/OIX676uG9fNb23z8/OxeC6YpomxsTF89rOfxR133IEnn3wSL730Ut3XdeO6+a3tscce6/pn1Mvtt9+O973vfQDi8zlleNcWh2fpnj178O1vfxs//vGP8fDDD0OWZd/DjW5cM7+13X777bG4t23btg07duzAe97zHvzxH/8xXvva18I0zbqv68Z181tbNpuNxXXrFajQK7N+/XocPnzY/fPk5KQr/+s2jz32GMbGxtw/27bdVROFWuJ87Z599ll8//vfd//czWv3i1/8AldccQX+4i/+Au94xztidd1q1xaX67Z3714888wzAIBsNou3ve1tePTRR2Nx3fzWdu+993b9ut17773YvXs3Lr74Ynzxi1/Egw8+iDvvvLPqmk1NTXXlmvmt7eMf/3hVZ6Bb7zW/++yRRx4Zi/ea39r27NkTi+fC2rVrsW3bNqxZswaZTAb/5b/8F+zevTsW181vbXfddVfXP6OMUqmEn//853jLW94CIF7P0tq1xeGZ8PDDD2Pbtm0YGRlBKpXCjh076p4H3bq3+a1t165dsbi3LS0t4ZxzzsE999yDr33ta8hms3X3tm5dN7+1rVmzJhbXrVegQq/MG97wBoyNjWFmZgb5fB4PPPAAzjrrrG4vCwCwuLiIz3zmMygWi1haWsJdd93VNRc/P0455RQ8//zzrkzme9/7XmyunW3b+Lu/+zvMz89D13V885vf7Mq1O3jwIK6++mp87nOfw/nnnw8gPtfNb21xuW4vvfQSrr/+epRKJZRKJfzoRz/CZZddFovr5re217/+9V2/bv/2b/+G733ve7j77rvx0Y9+FG95y1vw6U9/Gul0Gr/4xS8AAN/5zne6cs381nbttdfis5/9LPbv3w/btnHbbbd15b3md5/97Gc/G4vngt/aTj/99Fg8F84++2w8/PDDWFhYgGma+MlPfoI/+IM/iMVn1G9tb33rW7v+GWU8++yzeMUrXoG+vj4A8Xkm+K0tDs+ELVu24JFHHsHy8jJs28aDDz6I0047LRb3Nr+1HX/88bG4t7300ku4+uqrYRgGFhcXceedd+LSSy+NxXXzW9s73vGOWFy3XoFK5DLr16/HNddcg8svvxy6ruPSSy/FySef3O1lAXAeVr/+9a9xySWXwLIs/NEf/RFOPfXUbi/LJZ1O48Ybb8RHPvIRFItFbN++HX/wB3/Q7WUBcG6+f/Inf4J3vetdMAwDb3vb23DBBRd0fB3/8i//gmKxiBtvvNH9u8suuywW163R2uJw3bZv3+6+9xVFwdve9jacf/75WLNmTdevm9/aPvzhD2P16tVdv25+fO5zn8P111+PXC6H3/u938Pll1/e7SUBANasWYMbbrgBH/rQh6DrOl7zmte4crFO4neffe1rXxuL54Lf2t773vdC1/WuPxdOOeUU/PEf/zH+6I/+CLqu441vfCPe9a534bjjjuv6Z9Rvbe95z3ugqmosPqP79+/Hhg0b3D/H6Vlau7Y4PEvPPPNMPP3009ixYwc0TcOrXvUq/Mmf/AnOOeecrt/b/Nb2P//n/8QZZ5zR9Xvbli1b8La3vQ0XXXQRTNPEFVdcgde+9rWxeCb4re20006LxTOhV5BsP1E4QRAEQRAEQRAEkVhIukkQBEEQBEEQBNFjUKFHEARBEARBEATRY1ChRxAEQRAEQRAE0WNQoUcQBEEQBEEQBNFjUKFHEARBEARBEATRY1ChRxAEQfQUb3nLW/Dkk0925HstLS3hsssuw/nnn18VKA0AzzzzDN761rfiHe94B1566aVAr3/zzTfjhz/8YRRLJQiCIFYYlKNHEARBEAF55plnMD09jR/84Ad1/+1HP/oRTj/9dPzt3/5t4Nd/9NFHcfzxx4dZIkEQBLFCoUKPIAiC6CiPPvoobrrpJmzatAnPPfccSqUS/vqv/xpnnHEGPvaxj+GEE07A+9//fgCo+vNb3vIWXHDBBXjooYcwNzeHj3zkI/jlL3+J8fFxqKqKr3zlK1i/fj0A4Otf/zr27NmDUqmE973vfbj00ksBAA8++CC+8pWvQNd1ZDIZXHfddTj11FPxf/7P/8Hjjz+OyclJnHjiifjc5z5XteYf/vCHuPnmm2GaJgYGBvDxj38cAwMD+Ku/+iscOnQIF198Mb75zW8ik8kAAL773e/iG9/4BkzTRKFQwOc//3nceeed+MY3vgHLsjA8PIxPfOIT2Lx5M55//nnccMMNWF5exuTkJLZs2YIvfOEL+Na3voWnnnoKn/nMZ6AoCn70ox81vTYnn3wynn32Wfz5n/85Tj75ZNxwww04ePAgdF3H+eefjw9+8IMwDAOf+tSn8Mtf/hKapuGoo47Cpz/9afT393fq108QBEF0CCr0CIIgiI7zxBNP4JOf/CROOukk/Ou//ituvvlmnHHGGS3/XbFYxHe/+13ce++9+Iu/+Avcdddd2LJlC66++mrcdddd+OAHPwgASKfTuOuuu3Do0CFccsklOOWUU6BpGm666Sb8+7//O1avXo3nnnsO73vf+/DAAw8AAF5++WV873vfg6pWPxr37t2LT37yk7j99tuxadMmjI2N4U//9E9x//3343//7/+NT33qU7j77rur/s1FF12E3/3ud5idncVf//Vf42c/+xm+853v4LbbbkM2m8XDDz+Mj3zkI7j33ntxxx134JJLLsHFF18MXdexY8cOPPTQQ3j3u9+N+++/H+9+97txzjnn4Ec/+lHTa3PCCSfgC1/4AgDg8ssvxxVXXIG3vOUtKBaLuOqqq3D00Udj3bp1+NnPfoZ7770XkiThs5/9LJ599lm85jWv4f3VEQRBEAmBCj2CIAii42zcuBEnnXQSAOD3fu/3cNddd3H9u7e97W0AgE2bNmHt2rXYsmULAODoo4/G/Py8+3WXXXYZAGD9+vU488wzMTY2BkVRMDk5iSuuuML9OkmS8OKLLwIAXv3qV9cVeQDw05/+FGeccQY2bdoEANi2bRvWrFmDp556CpIkca37oYcewu9+9zt3XQAwPz+Pubk5XHvttdi9ezduueUWvPDCC5icnMTy8jLX63p53eteBwBYXl7Gz3/+c8zPz+Mf//Ef3b/bs2cPzjzzTCiKgne+850488wzce655+Lkk08W/l4EQRBE/KFCjyAIgug4TOIIOMWWbdt1/x8AdF2v+nepVMr9/5qmNXx9Wa54jdm2DVVVYZomtm3b5na9AODgwYNYt24dfvCDH6Cvr8/3tbzr8f6dYRhN1+DFsixcfPHFuPbaa90/T05OYtWqVbjmmmtgmibOO+88vPnNb8bBgwd9v2era8PWb1kWbNvG7bffjmw2CwCYmZlBOp1Gf38/7r77bvzyl7/ET3/6U/zZn/2Z2/0jCIIgegty3SQIgiBiw+rVq/HUU08BcIqTxx57LNDrsA7hgQMH8Mgjj2Dbtm0444wzsHv3buzduxcAsGvXLlx00UUoFotNX4v9u/379wMAxsbGcPDgQZxyyinc63njG9+I//zP/8Tk5CQA4Bvf+Abe+973AgAefvhhXH311Xj7298OSZLw61//GqZpAgAURYFhGAD4r83AwABe/epX49/+7d8AAAsLC3jXu96FH/3oR/jxj3+MK664Aqeeeio+8pGP4JJLLsGePXu4fw6CIAgiOVBHjyAIgogN73nPe/CXf/mXOPfcc3HUUUfhtNNOC/Q6xWIR73jHO6DrOq6//noce+yxAIAbbrgBf/7nf+52+b7yla807OQxjj/+eHzyk5/Ehz/8YZimiUwmg3/6p3/C4OAg93re9KY34aqrrsKVV14JSZIwMDCAm2++GZIk4ZprrsHVV1+NVatWIZvN4vWvf70rJz377LPx93//99B1XejafO5zn8OnPvUpXHjhhSiVSrjgggtw0UUXwTRN/L//9/9wwQUXoK+vD6tWrcKnPvUp7p+DIAiCSA6S7acPIQiCIAiCIAiCIBILSTcJgiAIgiAIgiB6DCr0CIIgCIIgCIIgegwq9AiCIAiCIAiCIHoMKvQIgiAIgiAIgiB6DCr0CIIgCIIgCIIgegwq9AiCIAiCIAiCIHoMKvQIgiAIgiAIgiB6DCr0CIIgCIIgCIIgeoz/P5F7YQZPov8gAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.neighbors import NearestNeighbors\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from random import sample\n",
+    "from numpy.random import uniform\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "# Clustering tendencies\n",
+    "# function to compute hopkins's statistic for the dataframe X\n",
+    "def hopkins_statistic(X):\n",
+    "    #X=X.values  #convert dataframe to a numpy array\n",
+    "    sample_size = int(X.shape[0]*0.05) #0.05 (5%) based on paper by Lawson and Jures\n",
+    "    \n",
+    "    #a uniform random sample in the original data space\n",
+    "    X_uniform_random_sample = uniform(X.min(axis=0), X.max(axis=0) ,(sample_size , X.shape[1]))\n",
+    "    \n",
+    "    #a random sample of size sample_size from the original data X\n",
+    "    random_indices=sample(range(0, X.shape[0], 1), sample_size)\n",
+    "    X_sample = X[random_indices]\n",
+    "    #initialise unsupervised learner for implementing neighbor searches\n",
+    "    neigh = NearestNeighbors(n_neighbors=2)\n",
+    "    nbrs=neigh.fit(X)\n",
+    "    #u_distances = nearest neighbour distances from uniform random sample\n",
+    "    u_distances , u_indices = nbrs.kneighbors(X_uniform_random_sample , n_neighbors=2)\n",
+    "    u_distances = u_distances[: , 0] #distance to the first (nearest) neighbour\n",
+    "    \n",
+    "    #w_distances = nearest neighbour distances from a sample of points from original data X\n",
+    "    w_distances , w_indices = nbrs.kneighbors(X_sample , n_neighbors=2)\n",
+    "    #distance to the second nearest neighbour (as the first neighbour will be the point itself, with distance = 0)\n",
+    "    w_distances = w_distances[: , 1]\n",
+    "    u_sum = np.sum(u_distances)\n",
+    "    w_sum = np.sum(w_distances)\n",
+    "    \n",
+    "    #compute and return hopkins' statistic\n",
+    "    H = u_sum/ (u_sum + w_sum)\n",
+    "    return H\n",
+    "\n",
+    "#https://github.com/prathmachowksey/Hopkins-Statistic-Clustering-Tendency\n",
+    "def hopkins_wrapper(dmf, k):\n",
+    "    selection = feature_metrics.sort_values([\"score\"], ascending=False).head(k).index\n",
+    "    selected_df= dmf.loc[:,dmf.columns.isin(selection)]\n",
+    "\n",
+    "    imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
+    "    imp_mean.fit(selected_df)\n",
+    "\n",
+    "    selected_df = imp_mean.transform(selected_df)\n",
+    "\n",
+    "    selected_df = Normalizer(norm=\"l2\").fit_transform(selected_df)\n",
+    "    return hopkins_statistic(selected_df)\n",
+    "\n",
+    "print(hopkins_statistic(norm_data))\n",
+    "\n",
+    "hopkins_stats = []\n",
+    "for k in range(99):\n",
+    "    hopkins_stats.append(hopkins_wrapper(dmf, k+2))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15, 10))\n",
+    "plt.xlabel(\"number of features\")\n",
+    "plt.ylabel(\"hopkins statistic\")\n",
+    "plt.xticks(np.arange(0, 100, 5))\n",
+    "#ax = plt.axes()\n",
+    "\n",
+    "plt.plot(np.arange(2, 101),hopkins_stats, marker='o')\n",
+    "plt.axhline(y=0.75, color='r', linestyle='-')\n",
+    "plt.axhline(y=0.85, color='r', linestyle='-')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "28f1d19d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TOP_K = 6\n",
+    "selected_highest_score = feature_metrics.sort_values([\"score\"], ascending=False).head(TOP_K).index\n",
+    "\n",
+    "feature_metrics.loc[selected_highest_score,:]\n",
+    "corr_sel_df = plot_miner_correlation(scaled_dmf[selected_highest_score])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "6fb147f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 0 columns in 23 rows\n",
+      "After: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## import numpy as np\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "df= dmf.loc[:,dmf.columns.isin(selected_highest_score)]\n",
+    "\n",
+    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
+    "imp_mean.fit(df)\n",
+    "imp_df = imp_mean.transform(df)\n",
+    "imp_df = pd.DataFrame(imp_df, columns = df.columns)\n",
+    "print(\"Before:\", len(df.loc[:, df.isna().any()].columns), \"columns in\", len(df.loc[:, df.isna().any()]), 'rows')\n",
+    "print(\"After:\", len(imp_df.loc[:, imp_df.isna().any()].columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "1b6e1d43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andreamaldonado/miniconda3/envs/py39/lib/python3.9/site-packages/sklearn/base.py:457: UserWarning: X has feature names, but PCA was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import Normalizer\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "X = imp_df\n",
+    "scaler = StandardScaler()\n",
+    "minmaxscaler = MinMaxScaler()\n",
+    "scaled_X_train = scaler.fit_transform(X)\n",
+    "normed_X = Normalizer(norm=\"l2\").fit_transform(X)\n",
+    "minmaxscaled_X = minmaxscaler.fit_transform(X)\n",
+    "\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2).fit(minmaxscaled_X)\n",
+    "minmax_pca =  PCA(n_components=2).fit(X)\n",
+    "scaled_pca = PCA(n_components=2).fit(scaled_X_train)\n",
+    "norm_pca = PCA(n_components=2).fit(normed_X)\n",
+    "\n",
+    "\n",
+    "X_train_transformed = pca.transform(X)\n",
+    "X_train_minmax_transformed = pca.transform(minmaxscaled_X)\n",
+    "X_train_std_transformed = scaled_pca.transform(scaled_X_train)\n",
+    "X_train_norm_transformed = norm_pca.transform(normed_X)\n",
+    "\n",
+    "\n",
+    "first_pca_component = pd.DataFrame(\n",
+    "    pca.components_[0], index=X.columns, columns=[\"without scaling\"]\n",
+    ")\n",
+    "first_pca_component[\"with scaling\"] = scaled_pca.components_[0]\n",
+    "first_pca_component[\"with normalization\"] = norm_pca.components_[0]\n",
+    "first_pca_component.plot.bar(\n",
+    "    title=\"Weights of the first principal component\", figsize=(30, 8)\n",
+    ")\n",
+    "\n",
+    "_ = plt.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "f58a2a1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.preprocessing import Normalizer\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "\n",
+    "# Normalizer: By default, L2 normalization is applied to each observation so the that the values in a row have a unit norm. \n",
+    "    # Unit norm with L2 means that if each element were squared and summed, the total would equal 1. \n",
+    "    #Normalizer does transform all the features to values between -1 and 1. \n",
+    "#norm_data = Normalizer(norm=\"l2\").fit_transform(df.drop([\"log\"], axis=1))\n",
+    "norm_data = Normalizer(norm=\"l2\").fit_transform(X)\n",
+    "\n",
+    "#StandardScaler standardizes a feature by subtracting the mean and then scaling to unit variance. \n",
+    "    #Unit variance means dividing all the values by the standard deviation.\n",
+    "scaler = StandardScaler()\n",
+    "sca_data = scaler.fit_transform(X)\n",
+    "#norm_data = Normalizer(norm=\"l2\").fit_transform(norm_data)\n",
+    "\n",
+    "sns.set_theme()\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(24, 8))\n",
+    "\n",
+    "#X_new = pca.fit_transform(X)\n",
+    "X_new = pca.fit_transform(minmaxscaled_X)\n",
+    "df_pca = pd.DataFrame(X_new, columns=[\"PC1\", \"PC2\"])\n",
+    "sns.scatterplot(ax=ax1, data=df_pca, x=\"PC1\", y=\"PC2\", palette=\"bright\", hue=[0]*len(df_pca), alpha=0.9, s=100)\n",
+    "ax1.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
+    "ax1.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
+    "\n",
+    "X_new_norm = pca.fit_transform(norm_data)\n",
+    "df_pca_norm = pd.DataFrame(X_new_norm, columns=[\"PC1\", \"PC2\"])\n",
+    "sns.scatterplot(ax=ax2, data=df_pca_norm, x=\"PC1\", y=\"PC2\", palette=\"bright\", hue=[0]*len(df_pca), alpha=0.9, s=100)\n",
+    "ax2.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
+    "ax2.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
+    "\n",
+    "X_new_sca = pca.fit_transform(sca_data)\n",
+    "df_pca_sca = pd.DataFrame(X_new_sca, columns=[\"PC1\", \"PC2\"])\n",
+    "sns.scatterplot(ax=ax3, data=df_pca_sca, x=\"PC1\", y=\"PC2\", palette=\"bright\", hue=[0]*len(df_pca), alpha=0.9, s=100)\n",
+    "ax3.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
+    "ax3.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
+    "plt.tight_layout()\n",
+    "# plt.show()\n",
+    "# plt.close()\n",
+    "\n",
+    "#ratio number of activities and traces... "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fee9585",
+   "metadata": {},
+   "source": [
+    "# Clustering\n",
+    "## TODOs\n",
+    "- [x] Finetune choosing top k std, where correlation is not too high: \n",
+    "    Multiplication maximizes std and disconnection at the same time\n",
+    "- [x] Include complexity features\n",
+    "- [ ] Investigate different clusterings\n",
+    "\n",
+    "### Other thoughts\n",
+    "- Project clustering label to classification task to evaluate oversampling in MA\n",
+    "- Include sector labels/ related tasks from literature for classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "99c102a2",
+   "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>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>agglomerative_9_label</th>\n",
+       "      <th>agglomerative_8_label</th>\n",
+       "      <th>agglomerative_7_label</th>\n",
+       "      <th>agglomerative_6_label</th>\n",
+       "      <th>agglomerative_5_label</th>\n",
+       "      <th>agglomerative_4_label</th>\n",
+       "      <th>agglomerative_3_label</th>\n",
+       "      <th>agglomerative_2_label</th>\n",
+       "      <th>...</th>\n",
+       "      <th>kmeans_7_5_label</th>\n",
+       "      <th>kmeans_6_5_label</th>\n",
+       "      <th>kmeans_5_5_label</th>\n",
+       "      <th>kmeans_4_5_label</th>\n",
+       "      <th>kmeans_3_5_label</th>\n",
+       "      <th>kmeans_2_5_label</th>\n",
+       "      <th>dbscan_3_label</th>\n",
+       "      <th>dbscan_2_label</th>\n",
+       "      <th>optics_3_label</th>\n",
+       "      <th>optics_2_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.600024</td>\n",
+       "      <td>0.058897</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.491109</td>\n",
+       "      <td>-0.091375</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.702456</td>\n",
+       "      <td>-0.120056</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.114739</td>\n",
+       "      <td>-0.335209</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.494107</td>\n",
+       "      <td>-0.116327</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PC1       PC2  agglomerative_9_label  agglomerative_8_label  \\\n",
+       "0  0.600024  0.058897                      1                      1   \n",
+       "1 -0.491109 -0.091375                      2                      5   \n",
+       "2  0.702456 -0.120056                      1                      1   \n",
+       "3 -0.114739 -0.335209                      4                      4   \n",
+       "4 -0.494107 -0.116327                      2                      5   \n",
+       "\n",
+       "   agglomerative_7_label  agglomerative_6_label  agglomerative_5_label  \\\n",
+       "0                      4                      4                      4   \n",
+       "1                      5                      5                      0   \n",
+       "2                      4                      4                      4   \n",
+       "3                      0                      2                      0   \n",
+       "4                      5                      5                      0   \n",
+       "\n",
+       "   agglomerative_4_label  agglomerative_3_label  agglomerative_2_label  ...  \\\n",
+       "0                      1                      0                      1  ...   \n",
+       "1                      2                      2                      0  ...   \n",
+       "2                      1                      0                      1  ...   \n",
+       "3                      2                      2                      0  ...   \n",
+       "4                      2                      2                      0  ...   \n",
+       "\n",
+       "   kmeans_7_5_label  kmeans_6_5_label  kmeans_5_5_label  kmeans_4_5_label  \\\n",
+       "0                 3                 3                 3                 3   \n",
+       "1                 1                 1                 1                 1   \n",
+       "2                 3                 3                 3                 3   \n",
+       "3                 5                 5                 1                 1   \n",
+       "4                 1                 1                 1                 1   \n",
+       "\n",
+       "   kmeans_3_5_label  kmeans_2_5_label  dbscan_3_label  dbscan_2_label  \\\n",
+       "0                 0                 0               0               0   \n",
+       "1                 1                 1               1               1   \n",
+       "2                 0                 0               0               0   \n",
+       "3                 1                 1               1               1   \n",
+       "4                 1                 1               1               1   \n",
+       "\n",
+       "   optics_3_label  optics_2_label  \n",
+       "0               0               0  \n",
+       "1               3               4  \n",
+       "2               0               0  \n",
+       "3               2               2  \n",
+       "4               3               4  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import OPTICS, DBSCAN, KMeans, AgglomerativeClustering\n",
+    "import re\n",
+    "\n",
+    "clustering_methods = [\"OPTICS\", \"DBSCAN\", \"KMeans\", \"AgglomerativeClustering\"]\n",
+    "distortion = []\n",
+    "df_pca_norm = pd.DataFrame(X_new_norm, columns=[\"PC1\", \"PC2\"])\n",
+    "\n",
+    "\n",
+    "def parametrize_clustering(clustering_methods=[\"KMeans\"]):\n",
+    "    result = []\n",
+    "    for method in clustering_methods:\n",
+    "        if method == \"KMeans\":\n",
+    "            for k in range(2, 10):\n",
+    "                result.append(f\"KMeans(n_clusters={k}, random_state=5, n_init='auto')\")\n",
+    "        elif method == \"OPTICS\" or method == \"DBSCAN\":\n",
+    "            for k in range(2, 4):\n",
+    "                result.append(f\"{method}(min_samples={k})\")\n",
+    "        elif method == \"AgglomerativeClustering\":\n",
+    "            for k in range(2, 10):\n",
+    "                result.append(f\"AgglomerativeClustering(n_clusters={k})\")     \n",
+    "    return result\n",
+    "\n",
+    "def cluster_features(features, method=\"KMeans(n_clusters=4, random_state=5)\"):    \n",
+    "    clustering = eval(f\"{method}\")\n",
+    "    clustering.fit(features)\n",
+    "    #clustering.inertia_\n",
+    "    params = re.findall(r'\\d+', method)\n",
+    "    clus_name = clustering.__module__.rsplit(\".\",1)[1].replace(\"_\",\"\")+\"_\"+\"_\".join(params)\n",
+    "    df_pca_norm.insert(2, clus_name+\"_label\", clustering.labels_)\n",
+    "    \n",
+    "\n",
+    "par_methods = parametrize_clustering(clustering_methods)\n",
+    "#print(par_methods)\n",
+    "for method in par_methods:\n",
+    "    cluster_features(norm_data, method)\n",
+    "\n",
+    "df_pca_norm.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "a06a55ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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dBwBQJx0HAFAnHQcAUB8NBwAw/JqzvfD1r38997nPffKTn/wk3/rWt/Lwhz88f/M3f5NHPOIR+eIXv7iYewQAYDfoOACAOuk4AID6aDgAgDrpOACAOuk4AIA66TgAgPpoOACAOuk4AIA66TgAgDrpOACA+mg4AIA66TgAgDrpOACAOuk4AID6aDgAgDrpOACAOuk4AIA66TgAgPpoOACA4dee7YXXve51eec735kjjjgif/Znf5a3ve1tOe6443LppZfmuc99bk488cTF3CcAAAuk4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAA6qTjAADqpOMAAOqk4wAA6qPhAADqpOMAAOqk4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAAhl9zthdarVaOOeaYJEm3281xxx2XJDniiCMWZ2cAAOwRHQcAUCcdBwBQHw0HAFAnHQcAUCcdBwBQJx0HAFAfDQcAUCcdBwBQJx0HAFAnHQcAUB8NBwBQJx0HAFAnHQcAUCcdBwBQHw0HAFAnHQcAUCcdBwBQJx0HAFAfDQcAMPyas71w0EEH5ayzzkqS3P3ud8+XvvSlJMmXv/zlrFmzZlE2BwDA7tNxAAB10nEAAPXRcAAAddJxAAB10nEAAHXScQAA9dFwAAB10nEAAHXScQAAddJxAAD10XAAAHXScQAAddJxAAB10nEAAPXRcAAAddJxAAB10nEAAHXScQAA9dFwAADDrz3bCy972cvy9Kc/PW95y1tyyCGH5F3veldWrVqViYmJ/P3f//1i7hEAgN2g4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAA6qTjAADqpOMAAOqk4wAA6qPhAADqpOMAAOqk4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAAhl97thd+6Zd+KR/72Mfyox/9KD/+8Y/zu7/7uznwwANz/PHHZ3x8fDH3CADAbtBxAAB10nEAAPXRcAAAddJxAAB10nEAAHXScQAA9dFwAAB10nEAAHXScQAAddJxAAD10XAAAHXScQAAddJxAAB10nEAAPXRcAAAddJxAAB10nEAAHXScQAA9dFwAADDrz3fgqOPPjpHH330YuwFAICCdBwAQJ10HABAfTQcAECddBwAQJ10HABAnXQcAEB9NBwAQJ10HABAnXQcAECddBwAQH00HABAnXQcAECddBwAQJ10HABAfTQcAECddBwAQJ10HABAnXQcAEB9NBwAwPBqLvUGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGC7t2V64z33uk23btv3CxweDQRqNRv793/99n24MAIA9o+MAAOqk4wAA6qPhAADqpOMAAOqk4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAA6qTjAADqpOMAAOqk4wAA6qPhAADqpOMAAOqk4wAA6qTjAADqo+EAAIZfe7YX3v72t+fUU0/NG9/4xhx88MGLuScAAPaCjgMAqJOOAwCoj4YDAKiTjgMAqJOOAwCok44DAKiPhgMAqJOOAwCok44DAKiTjgMAqI+GAwCok44DAKiTjgMAqJOOAwCoj4YDAKiTjgMAqJOOAwCok44DAKiPhgMAGH7t2V44+uij89SnPjUf+tCH8upXv3ox9wQAwF7QcQAAddJxAAD10XAAAHXScQAAddJxAAB10nEAAPXRcAAAddJxAAB10nEAAHXScQAA9dFwAAB10nEAAHXScQAAddJxAAD10XAAAHXScQAAddJxAAB10nEAAPXRcAAAw68914snn3xyzjvvvMXaCwAAheg4AIA66TgAgPpoOACAOuk4AIA66TgAgDrpOACA+mg4AIA66TgAgDrpOACAOuk4AID6aDgAgDrpOACAOuk4AIA66TgAgPpoOACAOuk4AIA66TgAgDrpOACA+mg4AIDh1pzrxVarleOOO26x9gIAQCE6DgCgTjoOAKA+Gg4AoE46DgCgTjoOAKBOOg4AoD4aDgCgTjoOAKBOOg4AoE46DgCgPhoOAKBOOg4AoE46DgCgTjoOAKA+Gg4AoE46DgCgTjoOAKBOOg4AoD4aDgBguDX35C+dddZZpfcBAMAi0HEAAHXScQAA9dFwAAB10nEAAHXScQAAddJxAAD10XAAAHXScQAAddJxAAB10nEAAPXRcAAAddJxAAB10nEAAHXScQAA9dFwAAB10nEAAHXScQAAddJxAAD10XAAAMOhuSd/6T/+4z9K7wMAgEWg4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAA6qTjAADqpOMAAOqk4wAA6qPhAADqpOMAAOqk4wAA6qTjAADqo+EAAOqk4wAA6qTjAADqpOMAAOqj4QAAhkN7T/7S6aefXnofAMCtRKvVzFQ2pzXoJFu2Zby9PDPdwVJvi/+k4wCA2YyNNTPZ25jctDkrWs3saKxMt9tf6m3xn3QcADCbsVY7/e3NbFnfSztj6Te76fe9HzcMNBwAMJtms5Fmv5XBTDNb1ncz1mynO+hmIOOGgo4DAGbTajXTmGll0Gtk64Ze2u1Wut3eUm+L/6TjAIDZOBs33HQcADCbZqudLdNjuXpD0sh4xpodZ+OGhIYDAGbTbDYy2ZzOWH9bsnlLJttTme61nI0bEjoOAJhNq9XM9Nj2dBvdrO900h4bT7cj4oaFjgMAZtNuN7NtbGuunZlOfzKZ7E45GzdEdBwAMJuJ5iBjW25K76otmWqOZXps0tm4IaHhAIDZNJuNjHen096+Pb3u1kyMTWYmzsYNCx0HAMym1WpmYvuWNDsz6fW3ZWx8eTqeqTo0dBwAMJt2u5mJLZvSu2pLlvcbmV7uvnHDRMcBALNpttrZ6r5xQ0nDAQCzaTYb6Q3Gsr3TytUbkrHWWNLvOBs3JHQcADCbVquZyWxJezCdbNmW8bHlmXHfuKGh4wCA2Yy1m5nsb/zPZ6o2sqOxytm4IaLjAIDZTIwlE71NyebNWd4ey/b+lLNxQ0LDAQCzaTQamWrNZKy/Ndm8JVPtyezotZ2N20eau7P4rLPO2lf7AABuBVa1t2b1+q9m/FNPS+u9D0v++fFZ8aN3ZW17U1qt3coOCtNxAMBsms1G1rY3ZeXF783YR56c/ONvZfxfTs2qq7+Q1e2taTSWeoejTccBALNpN5tpbZ/ID987nX95/g350DOuyzfP2JIdV7bTHowt9fZGmoYDAObSTjud68by7TO35mPP3pCPPPv6nPOPO9LYMpF2s7XU2xtpOg4AmMtYfzw3/rCRL73qpnzsWRvy8edfnx9/upux7oSzcUtMxwEAs2k2G1nTvikrL/7nW56Nu+rzzsYNAR0HAMym2Wpl88zynPnRiTzhZc08/IXJ/33rWC65dll6GV/q7Y00DQcAzGWqNZM1Oy7J5FdenNb7Hpm855FZ9u3XZO3gmoy33D1uKek4AGAuvWUzOSf/kRdf9bqccun/ydMv/tN8ZPunM718m7NxS0zHAQCzaTYb6Szfnk/PfDHP+enLc9KFz8sLrnh1/rX3vXSXTzsbt8R0HAAwm3YzWb15Q3Lmm7Pl8U/Oxof+djoveFGWX3xBlvWml3p7I03DAQBzmezPZMXlP07vz16eLY9+QjY+8vEZ/OXrs/KGazPW6C/19kaajgMA5rKisy1T3/lWpv/4OdnyiMdl0+NPTuvd/5TV229yNm6J6TgAYDbNZiOrpjdn7MMfzvaTT82ND3lMtj/1mZn44uezcsZ945aajgMAZtNqtbK5szx/+/GJPP5lzTzshckL3zKWi65ell4mlnp7I03DAQBz6TfGcsUNy/LSd4zlMX/azKNelPzlWeO5YdvyNJrtpd7eSNNxAMBcVra3ZfX138jEp5+R1nsfnvzz47Pi/H/I2vZGZ+OWmI4DAGZz8zNVL/tAxj56cvLuh2T8E3+QVVec7ZmqQ0DHAQCzGWsla5sbsvzf35D2+x+b/ONDM/G552fNlh9meXvHUm9vpGk4AGAuU+2ZrJ25LJNf/dOdz1T9p/96purVGW+7b9y+MOuJw89+9rO/8LG3ve1tWbt2bZLkQQ960L7bFQBQnVVj2zL21dMzOP9DP//g9uuTr5yexjn/lFWPf282NtZlMBgs3SZHhI4DABaq0UhWN25M40MnJddftPODzUayZX3y8aeldccHZ9UD/iKbOsuXdqMjQscBAAvVajUyfU07n3jB+sxs2fl+W7PZyE++sSM/+caO3PsZq3P7E9vpNrpLvNNbPw0HAOyOdtq56tu9fOX1GzL4z7NwzeYgP/zo1lz4mW156F/sn2WHDtLrOyi3r+k4AGB3jPfH8523b81Fn91288d2bBzkW393U877xNY8/HX7pzE142zcItBxAMBC/fxs3MnJ9Rfu/OB/nY276ulpHfHArHrg65yNWyQ6DgBYqFarmZ9eP5knviK5aevOjzWbydXfTT773eQlTxnLb983aWVmaTc6AjQcALA7Jlszmbr8c8nZ/zs/PxzXSH7wj8l5H8iKx/5TNq+6azq9pd3nKNBxAMDu6C2byZnXvTP/csMXb/7Yjf1NeeNV78yHrj87f3vEn2fF9lXp952N29d0HACwUI1Gsn1qa57741fkkh0/TZI0m82sn7khL/rp63LfVb+SF9/mWWlvnVjinY4GHQcALFSr1ciyn/04G3/nKRlsumnnB5uNdD/3xcx87otZ/qcvzNTjH5vtLR23r2k4AGB3TAw6aX7py9n4/P+b9H9+Nm7Hu96bHR/4SFb/49szOPrO6Q4aS7vREaDjAIDdsaKzPTOvfX12nPXfnqm6YUM6r/7L7Pin92X1+96VzWsOcDZuEeg4AGChGo1k5dZN2fx7T033gp33jWs2G+lduz6b//j5GT/xN7Lir16TzWPuG7cYdBwAsFCtVjM/2zCZJ70i2bhl58eazeTzG5LP/1vyopPaefz9Bu4btwg0HACwOwaNdr76H+N5/puS3s3PVE3e/Znkg19K3vmnE7nzbQfp9904bl/TcQDA7ljZ3paxb742+Y/3/fyD269PvvqaNM55d1Y9/qxsah7obNwi0HEAwEI1Gsnq5sY0PnJKct15Oz/YbCSbr0s++ay07nD/rHrw67Ops2JJ9zkqdBwAsFCtVjMrd1yavP93kx037vxgs5Fs/nxy2eczcd//mxzzpGztTS7tRkeAhgMAdsdkq5upK76cfOo5yeA/z781Gxn84N3JDz+YFY99Vzavvls6PfeNK6k92wuve93rsmXLltzpTnfKYLDzB9kbNmzIu9/97jQaDTEHANys3W6mfdV3Mjj/Q7tesPEnaXzlVZm6/2uzrTu+uJsbQToOAFioyVY3zW/+dQbXX7TrBZd8Jq0jHpixOzwmnY5fWN3XdBwAsFCNHWP5/Ok3ZGbLrn8Z9Ztv2ZTb/sq6NFYnA7+vuk9pOABgdwy2t/LVv96QQf8XX+tsH+Rzr7ghj3nzAemNTS/+5kaMjgMAFqrdbmb9Od1c9Nltu3z9pqt6+dZbbsqvP3dFuuks8u5Gj44DABZqstVN81tvyOD6C3e94NLPpXXE5zJ2+GOdjVsEOg4AWKitMxN59huSm7bu+vVXvTu53/FjOWjljLNx+5iGAwB2x7L+puQzL8gsh+OSTzw9y5/8qWzM6sXf3IjRcQDAQrVazZyz48L8yw1f3OXrV85ckzOuentedNCz09wx621rKUTHAQALNt7PO677QC7Z8dNdvvy1m76br676dh40/r/S7e7i/TqK0nEAwEJNbb0pm5/1/Aw23bTL17e+6nVZc+JvZMdBt3U2bh/TcADA7pjcfFNu/D8vTvq7eK9t2/ZsfvppWfWpj+SmqVWLv7kRo+MAgIVqtZoZfO+c7Dhr189U7f/simx9+asy8ZevyfbWxCLvbvToOABgocYHvWw/8y3pXrDr+8bNfPGrGfv059L+7d92Nm4R6DgAYKG2diZy2t8kG7fs+vXX/FNy/+PHcvBq943b1zQcALA7tkyP5/+8Oent4q227TPJM/86+eRrJzLV2vXzuihHxwEAC9VqNTO2/pzkP9636wWbrkjjiy/N1APPyNb+5OJubgTpOABgoSZavTS+8+bkuvN2veDHX0rr4k9m7MgnptNxNm5f03EAwEItz8bkk89Odty46wVf+4tMHH5ito3d4eauYN/QcADA7pgabEo+/bxk0PvFF7vbk48/PctPPjsbs2bR93ZrNuuTzT70oQ/lT/7kT3L00UfnOc95ThqNRh796Efn3e9+92LuDwCowORgc/K9t8y96JLPZOJ/vSTbsv/ibGqE6TgAYKEmB5szuOBjcy/6t7dm6rD/lU48zGFf03EAwEI0m41sWz/Ipit38UPV/zJIzv/4ttzt9ybT6XUXb3MjSMMBAAs11m7lws9tT3+OjNt2Qz83/rSb1Uc10uv5RYd9SccBAAs23co5779pziU//daO/PozViWeq7rP6TgAYKF2no37yNyL/u1tmbrdic7GLQIdBwAsRKPRyNU3NHPZVbOvGQySfzw7ecHvtBNn4/YpDQcALNT4WDM590NJf44+27o+zesvSOuAe6e3q6evUoyOAwAWanpse/7pyrl/pvr1Td/L9CE7MpUVi7Sr0aXjAICF2t7enk/d+KU517x3/cdywu1/JRPdqUXa1ejScQDAQjQajTSuuSa9Sy6bc92Od/5Txv/khZlOc5F2Npo0HACwUGNjrUx/9BNJd/azcf3rN6T3w/PSutcJzsbtYzoOAFioiR1bsuMtb59zzcwXv5Ll27dm+wo3jtvXdBwAsFAT2zZn04fmfqbqjre/M8sf+JvZMrVykXY1unQcALAQjUYj193YzCVXzL3uHZ9OXvTEsWTQWZyNjSgNBwAs1NhYKx//UiOdOW4bt2FT8sPLknsd3XQ2bh/TcQDAQk1mc/Ldt8y96Mdfznh/S7ZmcnE2NcJ0HACwUJODm5LzPjj3on97e5Yd8ZBs8kzVfU7HAQAL0Wg00t5+XbLhornX/fs/ZPLeL8v2bmuRdjaaNBwAsFDj4600zvt40p/jdxe235DmteekddD9nY0raNY7Ka9evTpvectbMj4+nlNPPTUbNmxIo9FYzL0BAJVoDTrJhkvmXtTvprHjpsXZ0IjTcQDAQjW6O5Lu9rkX3XBZmg1vxi0GHQcALESz2cgNP5njjiP/acNlnQxmtMS+puEAgAXrNbL+4vlv7Lvhkm6azVmPdFGIjgMAFqzXyI0/m7vj+r1kerOfqS4GHQcALFSjuyPpzHc27tK0GoPF2dCI03EAwEK0Wo1cdPn86y78WTLT9TPVfU3DAQAL1RrMJNf9cP6F1/0wzaae2Nd0HACwUN1mNz+dvmLuNellY8994xaDjgMAFmpmMJPt/R1zrvnZ9FVpNJ2NWww6DgBYiFarkd7Fl867rveji9Oanl6EHY02DQcALFSrO5PeufOfjeud9yNn4xaBjgMAFqo100nv0h/PvajXS3/DDZET+56OAwAWbHo6g61b51zS+/FP0oz7/y4GHQcALESz2cglV86/7qLLk2n3jdvnNBwAsFC9fjPnzv8rDjnvJ420WnpiX9NxAMBCtQczyQ2XzL1o0Etj2wZn4xaBjgMAFqrZm0lmtsy9aONPnY1bJDoOAFiIZrOR3DD/D1UH11+YVn/7IuxotGk4AGChmukm1547/8Jr/8PZuMLa8y145jOfma9//es55ZRTMu3mywDALgzSSCZXJdPzPDh1bGpxNkQSHQcAzG/QHMu8b7VNrFqMrfDf6DgAYC6DQTK5av4fmE6saCbtQTJYhE2h4QCAeTVaycTK+TtucnUzg4GIWyw6DgCYV2Pne23Tm3tzLhubdNuRxaTjAID5LPRs3MAPVBeVjgMA5jIYJGtXDpJ5Sm7lsqTVjLNxi0TDAQDzGTTH0phcM3+eTe0XR+MWj44DAObTGDSyorU8N3bnvm/csubkIu2IRMcBAPNrN+Z9pECWN6e8F7fIdBwAMJfBYJDGmtXzrmusXJFBq7UIOyLRcADA/Aatdhpr18y7rrHfGu/HLSIdBwDMZ9BopLFqZXL9hjnXNZYv13GLSMcBAPNptMeSRiNzRVpjxYrMd08SytJxAMDcBlmzYv5VK6aSdtObcYtFwwEA82k1B1m7cv51a1bM+XYdhek4AGA+gzSTidXJ1vVzLxx3Nm4x6TgAYD6DZjuNNDLng7bGl2fgbNyi0nEAwFwGg0EGE2vmL7SJlekv4D7BlKHhAID5DNLyTNUlsqAqvs997pPDDz88n/nMZ/b1fgCACk231qR958cn3zpj9kXrjk63tSzpLd6+0HEAwNy6zcmMHXKP5Kp/m33RMY/JjsaqxdsUSXQcADC7Xq+fdUdNZGyqkc722X9yeudHLsug2fd+3CLScADAXLr9Xo55+PJc9Nnts65ptpPb3H083a4D94tJxwEAc2lM9XKnBy3L9961edY1+92+nebkIP1F3Bc6DgCYW7c5lbHb/Epy5XdnX3Tnx2RHYwFPfKAoHQcAzKbX6+euhyfLJ5OtO2Zfd/KDk7FmNz1n4xaNhgMA5jLTTZYd9+Tkh++ffdH/Z+++w+yq6v2Pf9Yup05ND2kkIaGHXqV36UVARVEEBG75qdeLXAuiFxAVvCpYAFGkiYIgilQFadJCgNBCQk1II236qbv8/hjIMGZKktk5Z/bM+/U89/HJXuvmLL/OeeaTvb97LctRuPl+8jxCXCWR4wAAQF8yflZHNR6kq5ff0uucqalJSitdwVVBIscBAIC+JfyEdsxuoxc7Xut1zhEjDlDKT/d/4AMiRY4DAAC98f1QznbbyNTUKGxv73Ve8rOfUiGRknzeVq0UMhwAAOhLOZCyp52qwu//2Pskx1HiwP2UpzeuoshxAACgL8XaeiVPPlG5H/xfr3PsLaYpqKmp4KogkeMAAEDfysmU3D12Vfnp3veNS55wjIrpbAVXBYkcBwAAeuf7obaeEqombdTe+3FcnfvG2ewbV0lkOAAA0Bff9/XpQ6Rb/977HMeWDt5FKpcJcZVEjgMAAH0p2A2q2e5k6bHLep/UOE2+WycR4yqKHAcAAPpSNmklJu8pLXqq1zlmq+NUNLzjUGnkOAAA0JsgCKVRW0rJWqnY1vvEHU5XyWQksW9cpZDhAABAX0rlUOGs06S5vZ/FJWNL0w+lNy5iVm8DpVJJV111lb7+9a/r0Ucf1WabbaYzzjhDknTxxRdXbIEAAGDwK5ZDhdt/Uqod3/MEY0sHXKScaajouoYrchwAAFhfOdVJB3xbstyeJ2RGKtzlTBX9Xm8hIULkOAAAsL7ChK9dPlvb6/i4bRNqnOrI51DVTY4MBwAA1lcQhKoZazR592Svc3Y8pUZK0hxXCeQ4AACwvsqery2PSKtmtN3juLGlvc6rl1JehVc2PJHjAADA+sqpVjrgQslO9DwhPVLhzmep6Pec8xAtchwAAFhfabek/zq19/GdZ0rbbB7QG1cBZDgAALC+giCUVztJmnZQ75N2/aKKVu/vQCA65DgAALC+/FKooxsP1jh3dI/jtix9ZbMzlSlxIFclkOMAAMD6SpbT+vJmX5BrnB7HG+w6fWb08VLJVHZhwxQ5DgAArK9CulaZ//5/vY47O+0ga/vt6I2rADIcAABYX0EQKtxsMyUOOaDXOemzP6dSht64SiDHAQCA9VX2QyVPOk7WxM16nmBZyl70dRVq6iu7sGGKHAcAANZXMV2jzLcukJI97xtnRjQqfc6ZKhn2jasEchwAAFhfabek//5U7+M7bCHtMJ194yqBDAcAANZXEIQaP8LXIbv2PucLR0nZRLlyixrGyHEAAGB9lcqhwq1PkOon9jzBWNKBFylnGiq6ruGKHAcAANZXXjXSfhdKdrLnCalGhbv/mwqcqVoR5DgAALC+cqZW2veC3ieMnSV/3E7yPHrjNjUyHAAAWF9hGKqcGS9tcXjvk3Y+Q0WrrnKLGias3ga++93v6t1339X06dP1rW99S9dee+3aseeff74iiwMAAPHRqpHSKbdJm+/f2RD3oZEzpJNuVL5xFjfkKoQcBwAA1pfvB8rVzJBOuVUavdVHRow0eS/p1D+qzYxWGFZticMKOQ4AAKwvL/Q0/dCk9v1SvTIjuu7F2QlpyyMyOuTCBnlOqYorHD7IcAAAYEN4Tkn7fbVe2x6XlZs2a6+nGyzteU6dtj4hLU9eFVc4fJDjAADAhghSZR39o5GatFuyW2tc4xRHR146QvXTRW9chZDjAADA+vL9QLnsFtLJt0qjt/7IiJEm7SV98o9qs8bQG1ch5DgAALDeAk/H7+PpsnOksY1dl5OudNL+0tVfDZWyCtVb3zBChgMAABuiza9RcOjlMjt+TnIzXQPpkdJ+35C30xeV83o56AGRIscBAIANkSnU6JdbXKK96naR9ZGtaackJ+jH0y7UVtYMeZ5fxRUOH+Q4AACwvnw/0IRgM/1y+iWamZ669rqR0c412+raLb6n2mIdvXEVQo4DAADrqyhL1nHHquaHl8gaO6ZrIJlQ6hPHqebaq9Seqq3eAocRMhwAANgQHcmsMj+4VOkvfFYm09UbZ0aOUPYb58v94lnK24kqrnD4IMcBAIAN0V7TqPo/3KjEQftLtr32uj19qup+e4287Xdg37gKIccBAID15fuBipOnqv73N8jZ9iP7xhkjd49dVX/7zepoHEVvXIWQ4wAAwHoLPB27l6cfnieNG9F1OeFIJ+wnXXt+qJTNvnGVQIYDAAAbImEVdNkXA33hKCmb6ro+olb6n9Okc47xZalUvQUOI+Q4AACwIVrDEQpP/oM07WDJdPXGqXGadML1yo/aRWV64yqCHAcAANaX7wfqyEyVTv2DNHa77oMT95A+eTtnqlYQOQ4AAKyvomerNP0Y6fArpJpxXQN2QmabExUe/2u1BXXVW+AwQoYDAAAbot3PKjjkMmmXM6VEtmsgPULa9wJ5u/2HOnzOVI2a09vASy+9pL/85S8yxujjH/+4PvOZz2jixIk68sgjFXJXFAAA/AvfD9RsjVP68CuV9NsV5lZLmTp5Vq1ypoFDVSuIHAcAADZEwXfk1e+ozAm3yPHapHKblByhkpVVLswq8MkPlUKOAwAAG6JsSpp8oK1Je45SqTWUAqNkvRQmfJVNkZccKoQMBwAANkQYSiW7qB1PT2nHT2aVWxPITVhyaySlPZU9No6rFHIcAADYEL4fyKopad+v1UrFeuWaAqVrbZlUIKV8eZ5X7SUOG+Q4AACwITp743ZQ5oSb6Y2rMnIcAADYEI6KOmYPXwfu5Kqp3ZIfSCNqQ2USZVlhmd64CiHDAQCADRGGUrNXq/Qe/6PU7v8uk1shOa6CRKPypl5Fz1R7icMGOQ4AAGwI3w9Ul2/QReO+rOKEvNZ4Lapzs0oFaWVKWXklDlWtFHIcAADYIGVL051p+vHki5Q3OXWEOdVbdUoFKbnFpIKA/FAp5DgAALAhOpyU3KOPUe0B+8k0NcnyPYUjRqqYyarNcskPFUKGAwAAGyIMpdZEVqn/+rIazj1LwYqVshOugoZGFbJ1yvNItWLIcQAAYEP4fqC2htFK/egHyuY7FKxaLaeuVuVsjQo19fI8glylkOMAAMCGKBlbwVbbKnPDr2R1tMtqa1PY0KhyJqOOVFY++8ZVDDkOAABsCFtFHbm7r/12cNXcbsn7cN8415OlkgJux1UEGQ4AAGyIMJQSJq8vnejqi8e4WtFslHClhmyobLKkgLO4KoYcBwAANoTvB2qxxih96I+VCNplcqulVI18u1Y506AyvXEVQ44DAAAboujb8mu3U+a4G+T47VKpVUo2qmTVKC964yqJHAcAADZEu5dWYupxykzZX1axSQrLClMjVTA1yvsJ8kOFkOEAAMCGCEOpuVyj9K5fVWqXcz44U9VRkBihvFWvYpkzVTcFp69BYzqLPmHCBF111VU688wzNWXKlLXXAQAAPioIQnUEaXUoLZMZrVGjatW6sk2SX+2lDTvkOAAAsCE8L1CraiVTq9ETarVmZdsHEY4HepVGjgMAABui7PmS68saKY0eXauVK9s6B4hxFUWGAwAAG6ocepLrKTFOGjXqgxzHvnEVR44DAAAbIghCBaYspaTkZtKIUSlyXJWQ4wAAwIagN27wIMcBAIAN4fue0randP2HvXE5KSDFVRoZDgAAbKi8ZyuvBplUg0aNqlXzh+84oKLIcQAAYEMEQSi74CojV1lTp1F1ne84eOJQ1UojxwEAgA3heYESXkoJpbTF6Clr9xsJeKpaceQ4AACwIcp+qHK6TkrXsW9cFZHhAADAhiqEtgrpepnN67v2jeORasWR4wAAwIYIglA5JyXVpmTqRmrUqFo1rWyTPIJcpZHjAADAhvC8QO3JGilZo9Fbzuh6purzULXSyHEAAGBD+F4P+8aFtMZVGhkOAABssKCstF3W5qM+PFM1p4CzuCqOHAcAADZEEITqCFLqUEomPUqjRtWqZWWbeMmh8shxAABgQ6w9U1W1Gj1hJmeqVhE5DgAAbIhSOVRJ9ZJTr9GjP3hPFRVHhgMAABsq7znrnqnqV3tVQ5fV28D222+v//mf/9HChQslSdttt52+/e1v6+yzz9b7779fsQUCAIB4CnmWWjXkOAAAgHgixwEAAMQPGQ4AAAwEz1SrhxwHAAAGghxXPeQ4AACAeCLHAQAAxA8ZDgAADATPVKuHHAcAAAaCHFc95DgAAIB4IscBAADEDxkOAAAMBM9Uq4ccBwAABoIcVz3kOAAAgHgixwEAAMQPGQ4AAAwEz1SrhxwHAAAGghxXPeQ4AACAeCLHAQAAxA8ZDgAADATPVCvD6m3goosu0rhx47RkyZK114466ih9+9vfVm1tbUUWBwAAgA1HjgMAAIgnchwAAED8kOEAAADiiRwHAAAQT+Q4AACAeCLHAQAAxA8ZDgAAIJ7IcQAAAPFEjgMAAIgnchwAAED8kOEAAADiiRwHAAAQT+Q4AACAeCLHAQAAxA8ZDgAAIJ7IcQAAAPFEjgMAAIgnchwAAED8kOEAAAAGP6e3AWOMvvzlL69z/YgjjtARRxwhSSqVSkokEptscQAAANhw5DgAAIB4IscBAADEDxkOAAAgnshxAAAA8USOAwAAiCdyHAAAQPyQ4QAAAOKJHAcAABBP5DgAAIB4IscBAADEDxkOAAAgnshxAAAA8USOAwAAiCdyHAAAQPyQ4QAAAOKJHAcAABBP5DgAAIB4IscBAADEDxkOAABg8LN6GzjvvPP0t7/9TUEQrDMWhqHuu+8+nXvuuZt0cQAAANhw5DgAAIB4IscBAADEDxkOAAAgnshxAAAA8USOAwAAiCdyHAAAQPyQ4QAAAOKJHAcAABBP5DgAAIB4IscBAADEDxkOAAAgnshxAAAA8USOAwAAiCdyHAAAQPyQ4QAAAOKJHAcAABBP5DgAAIB4IscBAADEDxkOAABg8HN6G7jyyit1xRVX6LLLLtMee+yhKVOmyPd9LV68WM8884z22Wcf/fSnP63kWgEAALAeyHEAAADxRI4DAACIHzIcAABAPJHjAAAA4okcBwAAEE/kOAAAgPghwwEAAMQTOQ4AACCeyHEAAADxRI4DAACIHzIcAABAPJHjAAAA4okcBwAAEE/kOAAAgPghwwEAAMQTOQ4AACCeyHEAAADxRI4DAACIHzIcAADA4Of0NpDNZnXRRRdp+fLleuihh/T222/LGKPttttOX/7ylzV27NhKrhMAAADriRwHAAAQT+Q4AACA+CHDAQAAxBM5DgAAIJ7IcQAAAPFEjgMAAIgfMhwAAEA8keMAAADiiRwHAAAQT+Q4AACA+CHDAQAAxBM5DgAAIJ7IcQAAAPFEjgMAAIgfMhwAAEA8keMAAADiiRwHAAAQT+Q4AACA+CHDAQAADH5OfxPGjRun0047rRJriRXLMgrDUGFY7ZUAAAD0jBy3LmMkY4yCgBAHAAAGL3LcuowxMkbkOAAAMGiR4XrW+UxVCnmoCgAABilyXM/ojQMAAIMdOW5d9MYBAIA4IMeti944AAAw2JHhekZvHAAAGOzIcT2jNw4AAAx25Lh10RsHAADigBy3LnrjAADAYEeG6xm9cQAAYLAjx/WM3jgAADDYkePWRW8cAACIA3LcuuiNAwAAgx0Zrmf0xgEAgMGOHNczeuMAAMBgR45bF71xAAAgDshx66I3DgAADHZkuJ7RGwcAAAYDqxofevfdd+vII4/UoYceqltuuWWd8Xnz5umkk07S4Ycfrm9+85vyPK8Kq1yXbVsqpwtqyqzWa9Y8LU0uVSmTl+OYai8NAACgIuKa4xzHKBG0KFizQPlFz8rOL1EibJdtVyUOAwAAVFxcc5xr+0oGzSove1GF955TorxKCVOU4XYcAAAYBuKa4YyREqaoRHmVCu89p/KyF5UMmuXaQbWXBgAAUBFxzXG2bakYpLSqI6sX38lo4aqsCn5atm1Xe2kAAAAVEdsc5xiVMnktTS7Ra9brasqsVjldoDcOAAAMG3HNcVYiVDGb01vOW3rdmq9ctk1BypOhOQ4AAAwDcc1wxkiBSaitlNFLCzOatySjnJ+RsdxqLw0AAKAi4prjbNtSImHkOL4KhVaFYVHJpJHj8EwVAAAMD3HNcY5jKZk0kkoqFFrlOL4SCUNvHAAAGDbimuOSTqBGu1mN7XNV3zxbjWaVsk6R3jgAADAsxDXDGSNlnaIarVWqb35OjW0vqNFuVsrxq700AACAiohrjrNtS7V2u0YES1Tf9KwaCwtUb7fI5UxVAAAwTMQ1x7mOUb3dqsbCG6pvekYjgsWqtdvojQMAAMNGXHPcur1xK+mNAwAAw0ZcM5wxUsYp0RsHAACGrbjmONs28tJFNWfW6DXrdS1JLFYpk5Ptci8OAAAMD7HNcWvPVF2q16x5asqs4kxVAAAwrMQ1x7mWI7ecVH6RrbY3Ldn5pJzQpTcOAAAMC3HNcMZIrikp4a1W4b3nVFr2gpJBs1yL3jgAAFAdTqU/8P3339ePf/xj3XnnnUokEvrkJz+pPfbYQ1tsscXaOeeff74uueQS7bjjjvrGN76h2267TZ/+9KcrvdRuLEd6312ui9+7Uq/l3lx7fbPEGJ0/4Rxtk9xSVrHi5QQAAKiYuOY415TUuuBpvXDzd5RbtXjt9RHTd9RuZ/1ITt0kedybAwAAQ1hcc1zS5PTu32/U/PuvlZdvlyQZ29HEXY7QrNMuUslqUBiGVV0jAADAphLXDGdZRq7XpJd+979aPOd+hX5n056brtXMI7+ozQ/4rIphpqprBAAA2JTim+NsLW1O6fxfSHO7WuM0cYyl756R0s5blGWpVL0FAgAAbGJxzXFh0tML3jz96O1faWlpxdrr22Zm6MJJ/0+jy6MVDI73agEAADaJuOY4P1PS7c336Xcr/qz2ICdJcmTrgIY99ZXxZyqVzyoI6I0DAABDU1wznGUZ5by0LrnR6L6ntfad1Jq00VlHJ/TZw2w5KlR1jQAAAJtSXHOc41gKw5Jmz56jpqamtdczmYxmzdpBtbUNKpe5FwcAAIauuOY41zVqb2/W3LkvKpfLrb3e2NionXfeWbadkO+T4wAAwNAV1xxX5+TkvHqLNPtqqdgmI0mWo+QWhylx4HfVEjTQGwcAAIasuGY4Y4wa7GaZRy+WFtyrD19KNYkaZXY5S4kdPqfWcraqawQAANiU4prjHMeorrhQeuC/pWUvdN6Lk2TXTVDtgd9VfuyeyvuJqq4RAABgU4prjks7JaVXzpEe+qbUsnhtjnPH7aD6wy9XW3JzlTlTFQAADGFxzXGdvXG/+6A3rrWrN276oUoc9F21BI30xgEAgCErrhmuqzfuEmnBPev2xs36nFo9euMAAMDQFdccZ9tGqxKrdPHiK/Vyx/y118e5o/WVCWdqp+R2MkWniisEAADYtOKa48KEp5eDN/SDt6/W0tL7a69vndlC35r0nxpXHqfA45kqAAAYuuKa49wwoTfvLejF37er2N6Z1yxbmrJXSnv/e538ZIneOAAAMGTFNcMZY5QMWvTyHy7VomfuVuh39sY56RrNOPQMTTv0CyqG9MYBAIDKsir9gU8++aT23HNPNTQ0KJPJ6PDDD9f999+/dnzJkiUqFAracccdJUknnnhit/FqMEZqS7bqnDe/oddyb3YbW1paoa++c6neCN6W41S8nAAAABUTxxznOLbaFz6vJ3/6ReVWLe42tuatF/WP750sq7hKxvTyFwAAAAwBccxxrinpzft+qVf/9H/y8u1rr4e+p/ee/aueuvJsJcK2Kq4QAABg04pjhpMk12/VU1eerfee/eva5jhJKufb9OodP9Kb918j15SquEIAAIBNK445zhipuZDSqRdJc7u3xmnxCunsy6WX33XpjQMAAENaHHOc41iaH7yl89+5TEtLK7qNvZp7Q+e8+Q21JVvpjQMAAENaHHNcmPT0uzV36drlt6o9yK297snX35v/qfMXXqZSKl/FFQIAAGxaccxwklTwUzr3R0Z3/1Py/K7r7XnpJ7dL195ty1eiegsEAADYxOKY44yRLMvX448/pqampm5juVxOTz/9lDo6WuU4dpVWCAAAsOnFMcc5jqVcrk1PPfWkcrlct7GmpiY99thjsu2A3jgAADCkxTHHpZ2ynBevk564XCp+ZH+4wJMW3Ctz15mqtVqrt0AAAIBNLI4ZTpLq7DaZu8+VXv9LZ3b7UKldeuoncp77pTIO+8YBAIChK645rjZYKf3hFGnZC90HWpdIfz5b6VVz6I0DAABDWhxznONYSje9Iv3pC1JL9zNVtXyu9IdTVBusoDcOAAAMaXHMcWm7JGfu9dITP5SKH+mBCzzpjfvojQMAAENeHDOcJNXabTJ3nye9/udeeuN+roxdrN4CAQAANrG45ri2VKvOeesberljfrfry8sr9T/v/kCveq/LdemNAwAAQ1ccc5zjWHo7fFf/9fbFWlp6v9vYvNybOvfNb6o12UxvHAAAGNJimePkaP7dBT1zXZuK7eHa64EvvfNEQQ9c2CS7zFlcAABg6IpjhpMkN2jVM7/4Ny188k8K/a7eOC/frnl/uUoL7r5KrqE3DgAAVJbT1+Cdd96ppUuX6pBDDtFWW2219vo111yjc845Z6M+cMWKFRo9evTaP48ZM0YvvfRSr+OjR4/W++93f5i5PkaOrNmo9fWkGBR13ZK71R7mZFlWj3OuWv5b/Xz6/2q02xjZ5w7U6NG11V7CkEI9o0U9o0U9o0U9o0U9qyOuOS7KDCdJ+ab39fQfLpUxoUwPXXBeR5PefugGzTr1G7LdwfOAle9NtKhntKhndKhltKhntKhn9ZDjOrWvWKi3/na9LKvnNxma35mr3LIFGjdr/0g/dyD43kSLekaLekaLekaLekaLelZHXDOcFH2OWzZ3jprffanXHPfW336jmYd9Xg1jRkb6uQPB9yZa1DNa1DNa1DNa1DNa1LM6yHGdiiXpypuklg6pl9Y4ff8W6XcXZTVmdM/j1cD3JlrUM1rUM1rUM1rUM1rUszrimuOivhe3qtykq978rWRJltYNcm1hh25fc6++tNnnlbDcSD97IPjeRIt6Rot6RodaRot6Rot6Vg85rtOS4nL9YfVfe31P9fXCW3rHX6S9R+8S6ecOBN+baFHPaFHPaFHPaFHPaFHP6ohrhpOiz3GPvijNfbP3Z6q/vV8646ikJo9NRvq5A8H3JlrUM1rUM1rUM1rUM1rUszrIcZ1839crr7wiz/N6vR/36quvaN9991U6PXh+VvneRIt6Rot6Rot6Rot6Rot6Vkdcc1zk+8bl85oz59VeM5zv+3rnnbe13XbbyXH63FK5ovjeRIt6Rot6Rot6RodaRot6Vg857gMt70nP/0rqZb8RrXxZdtM8jZ52cLSfOwB8b6JFPaNFPaNFPaNFPaNFPasjrhlO2gQ57t0XpOXP957jXvytsrt+QdnRU6L93AHgexMt6hkt6hkt6hkt6hkt6lkd5LgPeCXp8Rul4prec9zjl6rx1NukxrHRfe4A8b2JFvWMFvWMFvWMFvWMFvWsjrjmuMjvxXWslB67VDKB1MOZqiq1SM9fp1EHXiS5qWg/ewD43kSLekaLekaLekaHWkaLelYPOe4DLe9Jc67pozfuFdmrX9XoLQ6N9nMHgO9NtKhntKhntKhntKhntKhndcQ1w0mbIMctfElaPqf3HDf3BmV3O0vZEaOi/dwB4HsTLeoZLeoZLeoZLeoZLepZHeS4TqWgrJuX3a6WoK3XPUd+tvxGXTPjUo12R0T2uQPF9yZa1DNa1DNa1DNa1DNa1LM64prjor4Xt7rcpKveukFhL2eqdoQ5/W7Vn/XViWcpZXGOw1BFPaNFPaNFPaNDLaNFPauHHNepbbmnubetkdXLM9U1b3tqesvX1I8Nnp9VvjfRop7Rop7Rop7Rop7Rop7VEdcMJ0Wf41a89rJWvzG71xz39j9u0pZHnq2GsfTGDVXUM1rUM1rUM1rUM1rUc9Pq9fSoyy+/XHPnztWMGTN0+umn68ILL9QxxxwjSbr//vs3OsyFYbjONfORjTz6G19fq1e3KwjW/bs2RjGT0wNrHlMQBL3Omd/xttrKHQqbB8eBXKNH12rlyrZqL2PIoJ7Rop7Rop7Rop7Rop4DZ1lmg29SxTnHRZnhJMkttqr5vfl9znn3iTs17ZAzVbIbIvvcgeB7Ey3qGS3qGR1qGS3qGS3qGQ1y3MZzHEurX31KXrnU57w3HrpRqYk7qOzbkXzuQPC9iRb1jBb1jBb1jBb1jBb1HLjhluGkaHNcwvb0xt9v6PPvC0pFvf/aUyq7I+V5vT97rRS+N9GintGintGintGintGingNHjhuYnJfWXY9Z6qM1Tq+9K7W0S8briOQzB4rvTbSoZ7SoZ7SoZ7SoZ7So58ANtxwXdW9cW7ZDC3Lv9DnnvtWP6FONxyqZy0T2uQPB9yZa1DNa1DM61DJa1DNa1DMa5LiN5ziWnvNeUdHvuzfu9hX3aqZmKCxu+L3DqPG9iRb1jBb1jBb1jBb1jBb1jMaG5rg4Zzgp2hxnLFc3PZDo85lqsSQ9MVf6+G55euOGIOoZLeoZLeoZLeoZLeo5cMPtXpwU8TsOCWnRokV97hvX0tKiYrGk9nYvks8cKL430aKe0aKe0aKe0aKe0aKeAzfcclzUvXGJRKimpqY+5yxcuFBTp05Xqe9HrxXD9yZa1DNa1DNa1DM61DJa1DMa5LiNZ9uW6pc8I5WLfU98/kblR+ysfLn65zjwvYkW9YwW9YwW9YwW9YwW9YwGvXEbL+UGyrxwk9TX3xeUpHceV+vmo+iNG4KoZ7SoZ7SoZ7SoZ7So58ANt3txUrQ5rt5qlv3qXX3nuJULpHyr1uTZb2Qoop7Rop7Rop7Rop7Rop4DN9xyXNS9cSNMq7T85b4nvfZn+bv8m1qChsg+dyD43kSLekaLekaLekaHWkaLekaDHLfxbNtS3ZLZMuVC3xNfvEn5kbso77mRfO5A8L2JFvWMFvWMFvWMFvWMFvWMBr1xGy/lBsq+cJPCPnvjytLbj6p16kn0xg1B1DNa1DNa1DNa1DNa1HPghtu9OCnaHFdM53T/6kf73DfurfwitZTaFTZX/16cxPcmatQzWtQzWtQzWtQzWtRz4IZbjou6N64jm9OrHW/0Oef+NY/pcyM/wZmqQxT1jBb1jBb1jA61jBb1jAY5buPZtqWVr0jlQt9/36t/6VDjllI5rP45DnxvokU9o0U9o0U9o0U9o0U9o0Fv3MZLOKHefOimvv++oKxlcx+Rt9vJ9MYNQdQzWtQzWtQzWtQzWtRz4PrLcL2eOvDoo4/qzjvvVCKR0Mknn6wzzzxTo0eP1p577tlj4FpfY8eO1XPPPbf2zytWrNCYMWO6ja9atWrtn1euXNltvCpMqJyf73eaNwhuxgEAAJDjuvjl/k9L9Yq5CqwEAACgf+S4DxmVO1r6neXlO6TAk2Rv+iUBAAD0ggzXJQx8eYWOfueVc62SNryZDwAAIErkuI8y6ujnHAdJKtMaBwAABgFyXJdyUO53Ti7IS4puwzoAAICNRY77kFGb3/8z1XxQkC9fVu+v/wIAAGxyZLgufmDUvh6vobblJHrjAABAtZHjuvO8/hvfOg9etTb9YgAAAPpAjuvSmc/65nmeNuLMCQAAgMiR4zoZI6nY/75xKudkAk99HI0BAACwyZHhuliBJ5Xa+59YbNuoQ2ABAACiRI7rYqT1y3FekVtxAACg6shxHxGsx8a+5Q564wAAwKBAjutkjGSKrf1PLHXIhJ4kd5OvCQAAoDdkuC4m9BWW2vqfWGylNw4AAFQdOa5LaMIPztrqWzEsVWA1AAAAfSPHdfFCv985eT/PEQ4AAGBQIMd1MkYqdfT/37eUCxT4hmMcAABAVZHhPiLwVM73v99IuYPeOAAAUFm93j6yLEuJREKStM022+iHP/yhvvrVr2rx4sUDCix77723nnrqKa1Zs0b5fF4PPvig9ttvv7XjEyZMUDKZ1Jw5cyRJd911V7fxarACW9PTk/uck7KSSplUhVYEAADQO3JcFzddK9vtO6PVT5ip0GLDEQAAUH3kuE5BEKhx6qx+542YvqNkcz8OAABUFxnuI+yURkzbsd9pDZtvryAINv16AAAA+kCO62JbgbbsuzVO6YSUTW38CyAAAABRIcd1yVhppUyyzznTU5NlhXaFVgQAANA7clynIAi0VXp6v/O2zsyUG/KOAwAAqC4yXJeEE2jHGf3P226a6I0DAABVR477KKO6uro+Z1iWJcdxKrQeAACA3pHjuti2I9vuu++trq5O3IoDAACDATmuUxCECsds1//EsTvIt9KbfkEAAAB9IMN18aykNH6n/ieO3V6+zw05AABQXeS4Lp5xpdFb9T3JSSlM9t0/BwAAUAnkuC6hk5HcTN+TRm0pn33jAADAIECO6xQEoTRm2/4njp1FbxwAAKg6MlwX3ySl8Tv3P3HsLHrjAABA1ZHjujiBo+mpKX3OSRhXtVZNhVYEAADQO3Jcl5RJKtPP89Lp6SkywcbXBQAAICrkuE6+H2rk9P7PZxizZUKWyzNVAABQXWS4LqGd1Mgt+u+Na5y2A71xAACgoqzeBqZMmaKf/vSnWr16tSRp33331VlnnaUzzjhDa9as2egPHDt2rL7yla/o9NNP1/HHH6+jjz5as2bN0tlnn62XX35ZknTFFVfosssu08c//nHl83mdfvrpG/15UUiXMzp9zEl9zvl44wFKe/1sTAIAAFAB5LgugVujyXsd2+ecrY7+d4VubYVWBAAA0DtyXKcgCJUeNUk1Y3p/YdXYjqbsc7LKXljBlQEAAKyLDNel7IXafL9TZezeX3aoGTdV6ZGTOjcMBgAAqCJyXJdsoqRzj+t7zrH7SplEuTILAgAA6AM5rkvKS+uwEX2/NHv6mJOULvOOAwAAqD5yXKcgCDXeGaPJyc16nePI1rEjDpZf4pkqAACoLjJcF8/zdPIBkmP3PmfKOGnSmIDeOAAAUHXkuC5BYDRjxpZ9zpk0abLCsNdt+AAAACqGHNclCIwmTZrc55yZM7ckxwEAgEGBHNcpCEIFNROlxmm9TzK2NOtTKpZ5pgoAAKqLDNelVA6lbT4hWb3vG6f6SfLrN6c3DgAAVB05rkvB1Eq7ndfnHLPl0SqYmgqtCAAAoHfkuC4FUyuzzYl9T9rtXOWt+sosCAAAoA/kuE5BEMqv2UwasUXvk4wtzfq0ihzjAAAAqowM16VUDhRufYJkub1Pqp8ov2EavXEAAKDqyHFdkuW0Th/T9zPVgxs+ppSXqtCKAAAAekeO65LyUjpyxIF9zvns6BOUKWcrtCIAAIDekeM6hWGo9Eijxim97zdibGnrozPyAr+CKwMAAFgXGa5LuRxo0p7Hy7J7743LjJqo7Ljp9MYBAICKMmEY9pg+1qxZo8suu0yHHXaYDj300LXXr7/+el155ZV64YUXKrbIjbF6dXukwcrPlPSLFTfqL2v+vs7YVunp+tHm31Qyl1HP1ay80aNrtXJlW7WXMWRQz2hRz2hRz2hRz2hRz4GzLKORIzdsg9o457ioM5wxUsJv1j//7/NqXvTaOuPTDjxNW594vophJrLPHCi+N9GintGintGhltGintGintEgxw2MbRsFTW/p0e9/SqX2pm5jxrK1+zk/0YhtDlI57GNjkgriexMt6hkt6hkt6hkt6hkt6jlwwy3DSdHnONeUtea1h/TsNV9R+C8vMyRqR2j/C26V1ThNvj84HqryvYkW9YwW9YwW9YwW9YwW9Rw4ctzAeUrp8t/b+v1D645tO1X69QWhMk6O3rghinpGi3pGi3pGi3pGi3oO3HDLcZuiN66Yyem/3r1E8/NvrzN+/MjDdO7oz8jOJSL7zIHiexMt6hkt6hkdahkt6hkt6hkNctzA2LbRssRy/fubF6rZb+0+JksXTf6S9kzsKlOyI/vMgeB7Ey3qGS3qGS3qGS3qGS3qGY0NzXFxznBS9DkuNK4eezmhr1wl+UH3scZa6ZZvS5NH5uX/62CV8L2JFvWMFvWMFvWMFvWMFvUcuOF2L06KPsclk5YWLJind955Z52x+vp67bXX3ioWQ3rjhijqGS3qGS3qGS3qGS3qOXDDLcdtkn3jEkbPPPOUmpub1xmfMmVzbbXVNioWB8e9OInvTdSoZ7SoZ7SoZ3SoZbSoZzTIcQPj2EZ1xXek2z4l5Vd3HzS2dMQVyk86THmffeOGIuoZLeoZLeoZLeoZLeoZDXrjBiZle8os/Yd075ekwPuXwQbp5FvVmt5CHvvGDUnUM1rUM1rUM1rUM1rUc+CG2704KfocV+vk5D7zI+nFm9YdHL21whNuUHPQQG/cEEU9o0U9o0U9o0U9o0U9B2645bhN0RvXYLfI3HWm9P5L645ve7LK+35TrWXOVB2qqGe0qGe0qGd0qGW0qGc0yHEDY9uW6otv99Ebd/kHvXGDY/9fvjfRop7Rop7Rop7Rop7Rop7RoDduYFK2p8yyR6V7/rOX3rjfqTU9g964IYp6Rot6Rot6Rot6Rot6DtxwuxcnRZ/j/ExJ1638vf64+t51xqanpuinU7+tVC5Lb9wQRT2jRT2jRT2jRT2jRT0HbrjluE3RG1fK5PXVhZdqXu7NdcaPHHGg/t+YMzhTdQijntGintGintGhltGintEgxw2MbVvyVjn66/mrlW/uvsevsaT9v9qgzXa35Rmvl7+hsvjeRIt6Rot6Rot6Rot6Rot6RoPeuIFxrLJa5j+hp3/x7wr97lnNzdZr/wtulT1ihvwIP3Mg+N5Ei3pGi3pGi3pGi3pGi3oOXH8ZzoThhrd0lctlue7gOLSgN1GHOamzSe7t8kLdtPIuLSkuU71Tq5NHHqndsjsokU9H/nkDwZcnWtQzWtQzWtQzWtQzWtRz4DbmwWpfBnuO2xQZzrKMXL9FK199XG89dKOKHc2qHbu5tjzyPGXGz1RJg2fjOInvTdSoZ7SoZ3SoZbSoZ7SoZzTIcQPn2JJVWq2Fj/9Ri2ffo8Ara9TM3TXziDNl1Y5XORg89eB7Ey3qGS3qGS3qGS3qGS3qOXDDLcNJmybHuaakoH25Ftz/a61a8Kwsx9Wk3Y/W5H1OUpAYKc+P9OMGhO9NtKhntKhntKhntKhntKjnwJHjolFWSm8stnXNX6SFy6XGWumzh0sf2z5U2s7TGzeEUc9oUc9oUc9oUc9oUc+BG245blP1xhXTec3ueFF/XH2fWrw2TUiO1+mjT9BUZ7Ls/ODZOE7iexM16hkt6hkdahkt6hkt6hkNctzAWY7UkWzXvU3/0N+bn1A59LRDzTb69KhjNTIYIRXtSD9vIPjeRIt6Rot6Rot6Rot6Rot6RiPKHDfYM5y0aXJcYFytak3o1/dIs+dJjiMdtZd00v6h6pJFBcHgaY7jexMt6hkt6hkt6hkt6hkt6jlww+1enLRpclwyadTR0a433nhDHR3tSiQSmjp1mkaPHq1SSfTGDWHUM1rUM1rUM1rUM1rUc+CGW47bVL1xiYS0atUqvf32WyqVSspmazRjxgxlszUqFgdPhpP43kSNekaLekaLekaHWkaLekaDHDdwji3Vao3Ma3dK8/8i+WWZzXZRuOsXlU+MU94fPO848L2JFvWMFvWMFvWMFvWMFvWMBr1xA5e2y0qXl8vM+bXCxc90vvQw8yiF256sdjNS5cHTGsf3JmLUM1rUM1rUM1rUM1rUc+CG2704adPkuFonJ7d5gTT7aqnpHSnVIO3wWQVT9lNrUE9v3BBGPaNFPaNFPaNFPaNFPQduuOW4TdUbV2e1ynrvn9KLN0j5NVL9FGm3c1QesZXavGyknzdQfG+iRT2jRT2jRT2jQy2jRT2jQY4bOMcxqg1Wy8z71964s5VPjlfeozduqKKe0aKe0aKe0aKe0aKe0aA3buDSTlnp0vsyc66jN26YoZ7Rop7Rop7Rop7Rop4DN9zuxUmbJsf56ZIW+Yt144o/aVFxiersGp048gjtWbOTkvkMvXFDGPWMFvWMFvWMFvWMFvUcuOGW4zZVb1wpnddzHS/p9tX3qtlr1WaJsfrMmBM03ZkiJ5+M9PMGiu9NtKhntKhntKhndKhltKhnNMhxA+fYlkze1ZsPFfTmP/Lyy6HGbpPQrE9k5TYG8uRF+nkDwfcmWtQzWtQzWtQzWtQzWtQzGvTGDZxreQral+vNB3+tFfOelrFtTdrtKE3Z9xQFyZHy6I0bsqhntKhntKhntKhntKjnwPWX4Zy+/p+vvPJK7bbbbtprr70kSV/72tc0efJk/cd//Ee0q4wJO5fQVvZMfWfcl1VWWZYspb2svA5fgQZPcxwAAAA5rksQhCqaOo3Y8ViN3GZ/KfRl7KQ8K63SIHrBAQAAQCLHfZTnS7JHavIh52jyvp+SFEpuVl7oyA+qvToAAIAuZLjuymFCpmaytjr5QqncIclIyTqVPEmDqDkOAACAHNedq4K2n2L0o39zVfIs2VaobMKT5/kKuB8HAAAGEXJclyAI5XaktJ/zMe02YUcFCuQqIbfsys/TGwcAAAYXclyXwJPSXo1OqTlWx9QdosCESgcpqWApJMYBAIBBhAzXnRWWNbaurK9/2lW+ZEuSalKefM/jmSoAABhUyHHdFYuhkskazZq1k/TBPnFhaKtQ4AUHAAAwuJDjugRBqEJBamgYo112GSlJMsbI9zvzHQAAwGBCjuvi+VKTRiix7VlKbn2KFIby7KwKvqvQJ8cBAIDBgwzXXd53VbAnKbXXhXKCdklGJbtexTIZDgAADC7kuO7avIysup2UPuQnssKiQtkqWPXyPF/iTFUAADCIkOO6BEGo5qBWzqQjlZqwj0zoK7CSKigt3yPDAQCAwYUc18Xzwt5748hxAABgECHDdZf3XBXsiUrt9S05QeeZqvTGAQCAwYgc152dT2i6NV0Xjv2SyqYkS7YyfkblDl8BvXEAAGAQIcd1CYJQTkdK+zh7apcJOyiQL1eu3HKCM1UBAMCgQ47r4vmBlChqi2McTTukXgolKxnKU1keh6oCAIBBhAzXXTlwZLITNfOkb2pmqaPzYrJBJS+UOI4LAABUgdXbwK9+9Sv985//1IQJE9Ze+8xnPqPHH39c119/fUUWNxj5fiir4CpZyMgtpD7YOA4AAGDwIMf1zPN8la0ale16lZRSEPBQFQAADC7kuJ6VPals16ls16scOKI3DgAADCZkuJ6FYWejXNmu78xyXrVXBAAA0B05rme+H8pRSRmnoKRVpDcOAAAMOuS4nnmeL7eQUrKQkVVw5Ps8VAUAAIMLOa5nfilUopBWKp9RWLTojQMAAIMKGa5nYSgpKCvtFJR2CvI9muMAAMDgQo7rme+HKpdDlctSuSx64wAAwKBDjuuZ5/lrM1ypFNIbBwAABh1yXM9KntQW1KktrFfecxTSHAcAAAYRMlzPwlDKe7bagnq1BXUqlslwAABgcCHH9SwIQnX4KbUF9WoPauiNAwAAgw45rmeeF6g9qFFbWK8OP0VvHAAAGHTIcT2jNw4AAAxmZLiedfbGOfTGAQCAQYsc17MgCGUVHSULGbmFpMpleuMAAMDgQo7rmecFcgvJD85UdemNAwAAgw45rmdlz5fvluUnyiqHHr1xAABgUCHD9SwMpbLvqGzXd/6fR4YDAADVY/U2cPfdd+vaa6/V5MmT116bNWuWfv7zn+vOO++syOIAAACw4chxAAAA8USOAwAAiB8yHAAAQDyR4wAAAOKJHAcAABBP5DgAAID4IcMBAADEEzkOAAAgnshxAAAA8USOAwAAiB8yHAAAQDyR4wAAAOKJHAcAABBP5DgAAID4IcMBAADEEzkOAAAgnshxAAAA8USOAwAAiB8yHAAAwOBn9TZg27bq6+vXuT5q1CjZtr1JFwUAAICNR44DAACIJ3IcAABA/JDhAAAA4okcBwAAEE/kOAAAgHgixwEAAMQPGQ4AACCeyHEAAADxRI4DAACIJ3IcAABA/JDhAAAA4okcBwAAEE/kOAAAgHgixwEAAMQPGQ4AACCeyHEAAADxRI4DAACIJ3IcAABA/JDhAAAABj+rr8FSqdTjNc/zNtmCAAAAMHDkOAAAgHgixwEAAMQPGQ4AACCeyHEAAADxRI4DAACIJ3IcAABA/JDhAAAA4okcBwAAEE/kOAAAgHgixwEAAMQPGQ4AACCeyHEAAADxRI4DAACIJ3IcAABA/JDhAAAA4okcBwAAEE/kOAAAgHgixwEAAMQPGQ4AAGBws3obOOCAA3T55ZcrDMO118Iw1BVXXKE999yzIosDAADAhiPHAQAAxBM5DgAAIH7IcAAAAPFEjgMAAIgnchwAAEA8keMAAADihwwHAAAQT+Q4AACAeCLHAQAAxBM5DgAAIH7IcAAAAPFEjgMAAIgnchwAAEA8keMAAADihwwHAAAQT+Q4AACAeCLHAQAAxBM5DgAAIH7IcAAAAIOf09vAeeedp/POO08HH3ywdthhBwVBoJdfflnTpk3TVVddVck1AgAAYAOQ4wAAAOKJHAcAABA/ZDgAAIB4IscBAADEEzkOAAAgnshxAAAA8UOGAwAAiCdyHAAAQDyR4wAAAOKJHAcAABA/ZDgAAIB4IscBAADEEzkOAAAgnshxAAAA8UOGAwAAiCdyHAAAQDyR4wAAAOKJHAcAABA/ZDgAAIDBz+ltIJFI6Ne//rVmz56tV155RcYYnXHGGdpxxx0ruDwAAABsKHIcAABAPJHjAAAA4ocMBwAAEE/kOAAAgHgixwEAAMQTOQ4AACB+yHAAAADxRI4DAACIJ3IcAABAPJHjAAAA4ocMBwAAEE/kOAAAgHgixwEAAMQTOQ4AACB+yHAAAADxRI4DAACIJ3IcAABAPJHjAAAA4ocMBwAAMPg5fQ0uWLBATU1NOvLIIzV27NhKrQkAAAADRI4DAACIJ3IcAABA/JDhAAAA4okcBwAAEE/kOAAAgHgixwEAAMQPGQ4AACCeyHEAAADxRI4DAACIJ3IcAABA/JDhAAAA4okcBwAAEE/kOAAAgHgixwEAAMQPGQ4AACCeyHEAAADxRI4DAACIJ3IcAABA/JDhAAAABjert4E77rhDn/nMZ/SrX/1Kxx57rJ544olKrgsAAAAbiRwHAAAQT+Q4AACA+CHDAQAAxBM5DgAAIJ7IcQAAAPFEjgMAAIgfMhwAAEA8keMAAADiiRwHAAAQT+Q4AACA+CHDAQAAxBM5DgAAIJ7IcQAAAPFEjgMAAIgfMhwAAEA8keMAAADiiRwHAAAQT+Q4AACA+CHDAQAADH5ObwM33XST7r77bo0dO1YvvPCCfvzjH2ufffap5NoAAECFGWMkhQrDaq8EA0GOAwBg+DHGKCTExR45DgCA4cUYSSLHxR0ZDgCA4YccNzSQ4wAAGH7ojRsayHEAAAw/9MYNDeQ4AACGF56pDg1kOAAAhh9y3NBAjgMAYPihN25oIMcBADD80Bs3NJDjAAAYXnimOjSQ4QAAGH7IcUMDOQ4AgOGH3rihgRwHAMDwQ44bGshxAAAMLzxTHRrIcAAADD/kuKGBHAcAwPDDM9WhgRwHAMDwQ44bGshxAAAMP+z/G39kOAAAhh964+LH6Wtw7NixkqSddtpJTU1NFVkQAACoLGOktF1SMmiTaV2s0E4orBmvvGlQ0av26rCxyHEAAAx9tm2UUbtcr1Vqe19K1ctPjVSHaZDnBdVeHjYSOQ4AgKHPsS2p6KjYIpXaAmVH27JSgQLXUxDwkDWOyHAAAAwPru1IBVsdKwP5XqjasbZM0pdnPF5cjSlyHAAAQ58xkhO6CouW2t73ZbtG2VGWlPJV9mmOiytyHAAAQ19vvXE506AyvXGxRY4DAGDos21bHaWE1rRbammXxo2QalK+knaR3riYIsMBADA8JN1QmaBFal8m45cV1k1Q0apV3k/QGxdT5DgAAIY+Y6Qg6alg57WstFKu5WiMM0qZckZ+qdqrw8YixwEAMPTZtlExUVTOdGhFeY3qnRrVmzplSln2jYsxchwAAEOf41hKt7XINK1R0NIia9xY+dlaFdJZ+T4PVeOIDAcAwPCQNKGSuVaFy5YrLJdlT9hMpUytCrZLb1xMkeMAABj6jJFSflmJXJv8JUtlXFdm/DgVs3UqBqbay8NGIscBADD02bZRKt8hu71Nwfvvy6qvV9g4QvnaenrjYowcBwDA0Oc4ttqL6+4bl3KK9MbFFBkOAIDhwbIddZQSWr5GKnlGE0aFyiQ82SrRGxdT5DgAAIY+Y6S0XVIyaJNpXaLQdhXWjFfeqlexTG9cXJHjAAAY+izLKGPalfDbpNblnKk6RJDjAAAY+lzHUiZsll1YLRVapJqxKjt1yqmG3riYIsMBADA8JN1Q6aBFpn2ZjF9SWDeRM1VjwultwJjuD8Rt297kiwEAAJVlWUb1VrPMo5dIC+6RAk9GkkmPUHb3f1diq5PU5mWqvUxsIHIcAABDn2tLtaWF0oMXSEvnrL1uN05T3cEXKzdiRxV8t4orxMYgxwEAMPQ5ctT0Wqh//myNWpf5a6+Pn5XQfl9pkN1QokkuZshwAAAMD4kgofn3FvTSbe0qtnfmNcuVZhyY1m5fqFXZLSmkSy5WyHEAAAx9lmVklxJ69ldteuvRvAKv83qyztJOn6rR9EOSKptSdReJDUaOAwBg6HNsqa60SPrbBdKS59ZetxunqnZtb1yiiivExiDHAQAw9AXG1QtvJnTRb6SFy7uu7761rcvOyWhMbV5BwEbAcUKGAwBgeKhzcnJevVV69mqp2CJJMnZC6a2OUXKfr6vZr6c3LmbIcQAADH3GGBUzOf1s+Q36W/MT8sLO5rh6u1anjz1JR9UdJDvHM9W4IccBADD02Y7RSnelvrf455rbMW/t9UnJ8Tp/wjnaKrGFTKnX4wIwSJHjAAAY+lJBWc5zL6r9WxfLX7ho7XV3912U/cHF6hg7gX3jYoYMBwDA8FBTzsu//Q61/PJXCps7e+Pkukoec4Rqv/41taXr6I2LGXIcAABDnzFGtflW5S/7oTruvl8qlzuvN9Qrc97Zqjn5JLW76SqvEhuKHAcAwNBn20bZ5UvUccGFKs/+yJmqm09R9uJvyZu1owoWZ6rGDTkOAIChL5Cr595M6KJfS+9+ZN+43bbq3DduXH1evs++cXFChgMAYHjwlNIf/2Hrl3+Smts7r7mO0dF7ufqfzzhK23l642KGHAcAwNBnjFGD3SLr8e8pnP9XyS/JSDKpBmV3P1fJrT+pVi9T7WViA5HjAAAY+hzbqK68uPNM1cXPrr1uN0xR7UEXKz9qZ+U5UzV2yHEAAAx9KbukzOoXpYculJreWXvdnbCr6g/9gVqTk+V5PFONEzIcAADDQ62Tk/va7dKzv5AKTZIkY7lKb3m0kvt9Q81+A71xg5i1vhP/NdwNZ06i8xC6XLZNxWyHlAyoDwAglmqtVpk/nyW9/mcp8LoG8mukRy+W+8rNSjte738BYoGc0skYo4RVVjJoUsJbpUTQogTnzQEAYsgYqTZYKf3hFGnpnO6DTW9Ld35OmdZ5cpz1vu2DQYoc18m2LSWUU9JvUsJbrUTQKtflwTMAIH4cx1bzG6Huv3CNWpf53caWvVTSX/5rlUyOlxzijgzXxXVtJYJWJbzVnVlOOdk2/04BAMSPEzp65Y68Zv+mTcX2ria4oCzNfzCvh77XLMfjIIe4I8d1sW1HeT+ttnJGOT+j0HKpDwAgluyyq799p0lvPJTv1hpXbA309DWtmn93QY6heSjuyCmdjDFS0lcx29H5jkMmJ5vbzQCAGOrqjTtZWvJc98Gmd6Q7PqdMy2v0xg0B5LhOtm3JT5VUyHYon21TKZPn5xsAEEuOY+mVdxP6wvelhcu7jz07Tzr1Iqm1mKrO4hAZMlwX17VV8NNqL2fU4WVUDpP0xgEAYiltl+S8eJ30+A+kYkvXgF9S+OodMn/9N9XabdVbICJBjuviuraSSSmR0Af/aVEfAEAsldJ5XbDw+7qv6RF5YVdzXIvfpquW/lZ/bLpHYdLv429AHJBTOhlj5LrWR3KcUSLBvTgAQPwYI7UlW3TuW9/Q3I553cbeKy7TV97+X70Vvkvv0BBAjutk25Zq7Jwa7SY1WqtVb7fK5ecbABBDjmPJee0VtX7+XPkLF3UbKz87Ry0nf1Y1zaurtDpEhQzXxXVt1dmtarRWq9FeoxqbfeMAAPGUCsryfnujOi67QmHzR3rjymUV77xbHf/2JdUU6I2LO3Jcl6QrNdjNarRWqdFuVsYpUx8AQCzVFNvVcd6XVLzzbqlcXns9bG5Rx2VXyPvtjUoFpSquEFEgp3QyxijteGqwm9RorVKD3awk21sDAGKqpnmVWk75rMqzu5+p6r+7UK2fP1fOa6/SGzcEkOM62balrJ2nNw4AEHuOY+n1xQl94TLp3X/ZN27269IpF0nN+bSIAPFGhuuybm9cB71xAIBYCoyrGx+0ddlNUnN71/WyJ/3pcenf/s+oELD/b9yR47rYCaNiJtd5Fle2Q2HSpz4AgFiqtdtk/vpvCl+7U/I/0gNXaJYe+76cF69T2qY3Lu7IKZ2MMTLJoOtM1SxnqgIA4qs2XNV5puriZ7sPNC+U/vR5pde8RG/cEECO62TbRk7gyikl5RSTcsoJfr4BALHkOJYyLa9Jd3xOanqn++CS56Q/nKxafwW9cTFHhuviurYSQasS3mol/DVKhOwbBwCIp7RTlvvyDdJjl0qFpq6BoKxw3p9k/vJF1dmt1Vsg+uX0NjB//nztvPPOa/9cKBS08847KwxDGWP0/PPPV2SBg4llGRXTOf1+zX26c/UDavJalDQJHdS4t84a80k1lBoUeP3/PQAADAa2bcle+Yq0fG7vk575uVLbnqy8Giu3MAwYOW5dji2Z3DK9etdP9N6z9ygoF5WsG6lpB56maQedrrJdryAIq71MAADWS9L2pWeulfJrep4QeNIjFyt97G/UptrKLg4DQo5bl2tKKixfoFf+eLlWvv60FIaq3WwLbXXUeRoz62AVw0y1lwgAwPor2Hrq6iaFQc/D+aZA8/6a0zanJFT2/cquDRuNDNezpMlp5QsP6/W//lKtS9+QjNHoLffQdp84X6mxW6oc8uYqACBGSrZeuqO91+FlL5XUtiRQZnPD86YYIcety7KMcl5atzxgdMvfpNUtnYesHrlnQl86OaGR2YKCgH+rAADiwbYtNb3ha8Xr5V7nvPD7Nm15xBgpQfN3nJDj1mU5UkuiSb9a8Xs93PSkimFJjU6djh95hE4e8XEl8xn+rQIAiI2kHcjM/nXvvXGhLz1yidLHXU9vXMyQ49YVJny9o4X65dKbNaf9FYUKtXlyoj475gTtk91NVo5nqgCA+GgvJXTxDVLQS2/cqhbppgeN/v1YR4HP/bi4IMP1zFNKDz1n6xd3SW8uloyRdt/a0X9/0tH08WXZ4tASAEB8pMI2afY1vU9Y8qycpjdk1e/M86YYIcety7KMEgnp3Xff1jvvvK1isSjLsjRhwgRttdXWCgJbvs/POAAgHmzb0vzSQr2Sm9/rnBtX3KnjRhyqVLGmgivDQJHj1uU4lozx9frr87RkyWIFQaBEIqHNN5+qadOmqVQS/1YBAMSGcUP9ftVf1eT1fFiDr0A/Wfob/Xjyt+V6qQqvDgNBjltX2i4r3f6G9MQPpUVPSQplj5iu2l3PkTftMLWW2TcOABAfqfZW5b57mdTLnnDhqtUq3HCzkv/vP1UMOJwzLshwPat183IXPSI98zNp9RuSjBITd1din68pX7el8j7vOAAA4iOZa1PT1b/udbw8+3mFb74le9aO9A3FCDluXZZlVGe1yJp7izT3Jim3SrKTSs38uFJ7fUVtzjiVff6tAgCIB9s2CucvUPm53n+n567+tRo/fYoKWe5TxAk5bl2ObVQXvC/zxE8Vzv+r5OVl0iOU3f5Tyuz0ebUEDfTGAQBiI2mFKtxws8JVq3ue4PvKXfx9ZW64Tu0p3nGIE3LcutJOWem2N6V/Xi4t/KekUHbjNNXudo68aYfTGwcAiJWOYue+cX4v+8ataZVuuF/6fyc4Ctk3LjbIcD2rdT7sjfu5tHqBJHX2xn3sayrUb6UcvXEAgBjJl1xdfVfv43PmSwves7TjVENvXIyQ49ZljFEpk9PtTQ/qj6vu0xqvWa5xdFD93jp73CfVUBqhkH+qAABiwraNnKYF0pJne5/03LVKzTpNeY2s3MIwYOS4ddm2UUuyWb9ecZv+3vSECmFR9Xatjht5qE4debRS+Sy9cQCA2Ei6ocyc33a+s9eTMJAe/V9lTrhZraqr6NowMOS4dTnGVn6JpdnXt2nJC0WFgVQ/0dEOJ2c1ee+ESoazuAAA8ZEJW6RH/lcKe943Tvk1Ms//RondzlfRsyq7OGw0MlzPkiavVS89qnl3/0ytizt740Ztubu2O+l8pcdvpXKYrPIKAQBYf6mwTXr2l71PWPaC7NWvyx6xh/zeXoRAVTm9Dfztb3+r5DpioZjO6b/evUSv59/quhaWdN+aR/TPlud03YwfaGQwioerAIBYSJii9NLNfU/y8tKy52WPP5QwFyPkuO4syyhsW6yHLzlRpfamtdeLras1789Xavncf2jvr1yvoqGBAAAQD6mwTXr9L31PWj5XbpCXVFuRNSEa5LjuHDtUy/wn9dTPzlMYdDUStC19U7N/9VVtccjnNPPYr6gkNpADAMRDUDRqWtj3Tgtv/D2vrY5JS4lemugw6JDh1pVQXvPv/qnefPD6rothqJWvP61HLj1Ze/7HL1W/5f7yAhpBAQCDn+NYWvJ0SUE/G2bN+2tOu/9nRoHYWSsuyHHryvtpnfVDo5e7WuNULEt/elz6xwvS7RenNK4uR28cACAWrMDSa3d39DnHL0nLXy1pzK4WvXExQo7rzrKM1iRW66w3LlCL37b2epPXquvfv01Ptj6nH29+odyOdBVXCQDA+kuGrdK8P/c96f2X6I2LIXJcd5Ybao73ir7+7g/kq+vfI+8WF+vi967SSSOP0NmjPi07z6FcAIB4KJRsLXiv7zl3PSZ97nBXGYdnqnFBhluXp6R+fpet39zTdS0MpWdek079jnTVl13ts22gfpsMAAAYBBzHkln4pBSU+54492YlD5ylfGBXZmEYMHLcuhIJ6ZlnnlZTU9d+I0EQ6L333tPy5cu13377y7JceuMAALEQOL7uWHFfn3NKYVlzc/O0t80mwHFCjuvOsowkT48++ohKpa6Dt0qlkhYsmK/331+uvfbaW4VC9dYIAMCGyDt5PdD0WJ9z5uffVtEqylWqQqtCFMhx3SWcUOn3n5b+/MXuB9CteUt68GtyZs1V7Z7nq81n3zgAQDxYhby81xf0Oaf4p7tV+4XPqZipr9CqMFBkuHVl7bzc2VdJc677yNVQWvyM9IeTlT7qZ/I3O1Aln94BAMDg5ziWyk/Plj7yjKknxZtvlfv97eSL329xQY5bV53VKusvZ0vLXui66BeleXdJbz+s2k/dqZbEFPk+vXEAgMHPDTwVb/l935NKJZWfmS3nsCPkefTGxQU5rjvLMqrzlkq3nqgwv7prIL9GevbnMu88rPoTb1JTwJmqAIB4SLS3qu3Ou/uc4732uqxCXkrVVGhViAI5rruELaVXPCv9+ezue4o0vS09eIGc7V9Q7V7/Q28cACA2Cp6t197te86fHpO+cCT7xsUJGW5dWTsvd84vpOeu6T6w+FnptlOUOvKn8iccoiK9cQCAGHAcS8+9YlTsZ9u4W/4mbX+2I6mfiRg0yHHrKmfy+trC7+uV3Pyua6GnB5of0z9bn9OvZnxfY8Ox9MYBAGIhYXnS3Jv7nuSXZBY/LWfyMfTGxQg5rjvLMmpKrtFZb/6PmryWtddb/DbduOJOPdk6R1dO/Q5nqgIAYiPlt/R/purK1+X4OUn0fscJOa47x7bU9Jr0wLdXKfjItnEtiz099uMWbflaRrucmZVn9f3eNgAAg4UT5KX3X+l70ut3KbXLOSqqoSJrwsCR4dblKq837/+l5t9zdbfrq+Y/q0cuO0V7nPMTNW53mDzOnAMAxIBtW9KSOZLXzwFEc29U4qCdlJdTmYVhg/T6v8qECRMquY5Bz3Et3dvyhF7Pv9XjeKvfrv9bcp2+M/4rsguJCq8OAIANZ4WeVGzvd54ptsqYCiwIkSHHdWcHOb1wy3dUam/qcbzp3Ze1+Nm7tdnHPqsyjaAAgLgotfU/J/AlclyskOO6s8uteu43Fyj8aHfcR7z59xs09YBPy9RPUxjywioAYPDziv3/virlAyJczJDhujPGqNy6XG8+eH2P42EYaM5vLtChlz4oz2qo7OIAANgIxhgV23q+N/FRxY5QIY+ZYoUc153j2PrT40Yv99wap+Z26eLfSj/+94QcU6zo2gAA2Bihb1TOr8f9uI5Qhua4WCHHdVdOlPTjpb9Wi99zD8H8/Nt6oOVxHZ8+XF6ZZ6oAgMHPSOvXG+d7krWpV4MokeO6KyTy+t6Cn8tXzzeW71h9v04YebjGmc3ojQMAxEJhPfY7bc9v+nUgWmS47owxWtXi6Df39DweBNI3rpEe+FFCaZsDhAEAg58xRiq09j+x1CYT+JLYHDUuyHHdua6lZcuWqqmp5/1GyuWyXnrpJe200y4qcZYDACAGfHnqCPq/2dbqdcgkK7AgRIYc151tSy+//LJKvYS0lpYWLV68WOPHT1a53P97PwAAVJ2ROvxcv9PKIc+Z4oYc110mbJEe+JoU9pLRXrpF7g6nySRn0BsHAIiHYv97K4Qd/ec8DC5kuO6MkZKlldKc63qeEAbS3y5Q9nMPqcQBwgCAWDAKW1r6nRW2tsl4fueDKcQCOa4717FkvXW/tOyFnicUW6WHL1T6iF+qXZnKLg4AgI1gPF9hW/9nqoYtreIwrnghx3WXNnnp0Uuk/OqeJ6ycJzPvDiW2+YJK7BsHAIgBIyno6Oh/Ii/vxQ45rruMWqQH/lsKeunzfPn3cnf4rKz0TAUBOQ4AMPiVyv3P6SiIMxxihgzXXWdv3CrpuWt6nhAG0t++rszn91CR3jgAQCwYNff/SFXtOckPDLvGxQg5rjvHsfRw27N6JTe/x/H2IKfLl1yj7024QHY+UeHVAQCw4UzgScX1OIur0ML9uJghx3XnJcq6atlv1eT1/E7Pm4WF+mvzw/pE5hj55Z7P6wIAYDDpPFN1PW7IeUXJ2dSrQZTIcf+i4OqRK1Yp6GXbuPkP5LTNsRklxxt64wAA8eCvR3NcsV3ciosXMlx3xkhBx2rNv+fqnieEoebc8E0dftle8qyGiq4NAICNYYxkiv3vG6dSu0zoiRtyg5NV7QXERYfTrjtW39fnnGfb5qpk978pNgAAg4FnpaUx2/U/cdRWPGxCrBkvp/dfebzPOW89dKNUbKrQigAAGBhftjRqZt+T3IxCJ1WZBQGbgG1bal70ikrtfWe0t/7+W7k2LzkAAOIhUWNkuX3PaZziSjb34hBfrh3orb/f0OecUkezmt99WbbNo2oAwODn+4FGbdFPiJM0eqYrQ45DjLUVXN10f99znnhZKng0gQIA4sEk1i/HjZzm0huHWCvZRT3T+kKfc+5Yda86nPU4oA4AgEHAD9enNy6t0E1XZkHAJmDbRm8U3lWz39rnvNtW3SOToDcOABAPdZlQyX5ux82YKNkW9+IQX5Zt68YH+p7T0iHNfcvQGwcAiAXfD6Sx2/Y/cewseVZy0y8I2ESMCfT222/1OWflyhUyhn+vAADiIREmtFV6er/zZqY3pzcOsWZZod5/f3mfc95++y0Z08uJXQAADDLGtzQtPbnPOSmTVNqwbxziy7KMzOr5Un51n/PMC9craZPjAADxYGprpGSizzn2jGkKbPZhQHwlHCPz4k19Tyq2ySx/gd44AEAsBEEge7tt+p1nz9pOfoLeOMRXOmiWXuh7/18telqJMFeR9QAAMFB+Iil7+/7fcXC23VpBwD4MiK9E2CG984++J829SWm/uSLrAQBgoHzbljNji74npZIy2WxlFgRsApZlZNYskHL99cb9Rknbq9CqAAAYmGwqVKrv1jhtMVGyDPfiEF8Jx8jMvbnvSaV2adkceuMAALEQBIG22bz/edtNkxIOOQ7xlXM7dNuqe/qc80L7aypahQqtCACAgfGslDRuVv8Tx2zbuVcwEFMlu6AnWp7rc86dq+5Xzm2v0IoAABgY3zjSyH564+ykwmRdZRYEbAKWZdTynq98U9//Fnnlzg7Zsiu0KgAABiZ0M5LTz7n3o2bKD/ndhvhyHaN3/nFLn3O8fLtWL3hWjkNvHABg8AuCUOGY/veN05jt5Fuc4zBYkTrWlxVqRanvl1UDBSoExQotCACAgSl5knb4jGT6iAMjpsuvmcDBqog1v5hTGPb9YDW/ZrmM+DkHAMRD3jRIu57b5xyz9QkqGBrkEF/GGHWsWNTvvI5VixX6pQqsCACAgTMJX9P27btBbsdTs1KSQ8MRX6FfVm7V4n7nta9YJMsyFVgRAAADEwSh6ibaqh3b+0sMli3NPCytskeOQ3yFsrRsTd9zgkDqYO84AEBMeL6vrY/O9NkaVz/BUWaMoTcOsVYIi/LVd2/civJq3hoBAMRG3qqXdj2nzzmdvXG1FVoRED1jjJaU3u933rLyCpXFwaoAgHhIu56O3LPvOeceL2UT9H0jvkq+pcUr+p/33vuiNw4AEAtBEMqvnyrVT+x9kuVI252iUplnqoi3fD7f75xyuVyBlQAAMHBeKdTxIw6T3UdD0MTEOI13xtIbh1jzfV9h2PfPcD6flzHciwMAxEPGy+gzo0/oc85hI/ZV2uvn0C5gELMsI9PyXr/zwpbFskPOcQAAxEMxnVXy6I/3OSd93tkqZnnHAfFlhSWFrf3nODUvpDcOABALQRDKTJkia3IfvXG2rdQnP6GSzzNVxJeRL7Uv62dWqLDYVpH1AAAwUCU/VOqTJ0t2H/v/Tp4oTZ5MbxxizfhFKehnL5G25TKGn3MAQDwUs3VKn3dWn3NSxxypYqamQisCorchvXGWT28cACAeMomyjv5Y33POO4594xBvnb1xi/qdZ+iNAwDERBCEmjQm0JRxvc+xLemTB0u+x972iK/ABJ1nbfUhVKg2v6NCKwIAYGBK5VDa7tTOPX57Uz9Rfv1UeuMQa8WwJE9+n3NWlFdL3IoDAMRE3tRJu/1bn3PMlkepYOiNQ3xZllHb8r4znCS1r/AVctsZABATBVMrs/WxfU/a9RzlrfrKLAjYFPyy2lf23xvXvmIh5zgAAGIhCEIFNROlhs17n2RsadZpKnLU5KDV+0lq6C4wGuU29jnFkqWUlazQggAAGJgwlPLOSOmQ76nHzqBUo3T0L9Qhbsgh3uxkRsb0HXvTDWMkbsgBAGLC83x5k/eVtj6u5wljtlWw55dU8HvfJBgY7MIwVHb0pH7nZUZsJmMnKrAiAAAGzjO+dj+rVo1Ten5hdesjMxq1tSPPCyq8MiA6xnaVGTmh33nZ0RN5MRsAEBthqqxDL2pUIrvusyRjSwec3yCT4Q0+xJtRqLF9t8bJGCmbqsx6AAAYqDCUrKyv/b7coJ7ahpK1Rod8u1FhkrccEG8pk5DVzysho9wREredAQAx4XmBvEn7Sluf0POE0Vsr2OvLKvh9bBIMDHJhKI1PjO533lh3tBLiZx0AEA+WSvrap0PNmNjz+CkHSrvM9OmNQ6wl7EAT+49xmjhG9MYBAGKjQw3SsddKybp1B40tHX658lY/TUXAIBeGUirVf+Ob67oVWA0AANGo8Wr1jUn/0WPfUJ1do8s2v0DpYqYKKwOiY9t2v4c0pNNphSH34gAA8eB5gXZJb68jRxzY4/gWqSn64phPSSWOC0B8hWEo1fW/34ip20w+5zgAAGKiaLnKfO0rsrec0eN48uQTZHbbld44xFpgJWRq+89xYf0keuMAALGRy9Sp7uorZep76I2zLNVccamKtQ0VXxcQpVCWVDOun1lGJllTkfUAABCFYl2Dai6/VLLWfW5q6jszXi7TQ8YDYiR0kp3v7PSlZqzCkDNVAQDx4HmBzO67KfmJ43sct7eaqfR/f1lFi720EF9BECpcn9642s0U0BsHAIgJE5b136eG2mpKz+Mn7i/tvjX7xiHeApOQqe1lc8SPqp/Eu6oAgNjIukX94r+k+uy6Y5Ylff9cqS5dqvzCgAiZ0NJot//9D2vsHr4IAAAMUnmrUTr8cvV8qGqddMw16lB95RcGRChhEnLUd2/cKLdR4lYcACAmPC+QN343mW1P7nnCqJkK9rlABZ+97RFfQRCqdmz/ex/WjLZlaAMFAMREwXcU7P1VafTWPU/Y+kR5kz5GbxzizU4oO6r/3rjs6Mn0xgEAYqND9dKxV0uphnUHjSUd9n3l7REVXxfWH7eP1lPWy+pzYz+hFeVV2qlmGwUK5YW+Hm1+Wg82Pa6OIK9da2cp4fOyKgAgPvJ+Qmba0UqdvqM0+2pp6RzJTkpbHqNwu1PUbkbK87lJgZhz0pp+6OmqnzBTtWM3VxgGKufa9O4/79SyFx9WGPiafvBnFSYaJI+fdwBAPLSWs6rb7ztytj5Jeu5qqWWRlBoh7Xi6gs0PUGtQz8MmxJrvB2qYsp0m7XW8Ju16uNxMrYyx1Lb8Hb3z2G1a8/ZcSdIWh35eZd8SbzsAAOIgDEMFqZKO/MEILXqqqHn35lRsC1Q/wdEOp9SoYaqlsmHTEcRb2be0xaFnyFiWJuxymIxlyxhbK19/Wu88cYdyqxbLzdarceoOKvo0gwIA4sH3A6XGSZ+4ZrTm3ZPTO08U5JdDjd8+oR1OqZHbGMgLvWovExiQ2lRJ5x2f0pJV0h7bGAWB5PnSg7ND/eUJqT0v7b2dlHL5WQcAxIcnTxP2dnTilqP10m3tWv5qSXbCaPp+KW358YzCTFk+vXGIuYSf0smjjtTU9ERNTm6mQKHa/Q7du/oRPdk6R558fWLUx5X1svJ4pgoAiInWckZ1+31bzjYnSrN/+ZHeuM8q2PxAeuMQe74faGZ2mo5o2E8HNO6pGjsrS0aLisv019UP6ZXcAknSqaOOUliiNw4AEA9hKNUk8rrlwrT+Psfo1r9LTe3S5uOkc46Vtp7sy1Wh2ssEBiTwfZ1+RGc6O2w3I8fuPHDumddC3fGotHiFVJuRdtwilE9vHAAgJjw/VFt6umpOv1/mpd9Jb9wn+SWZCbsr3O0c5VObKe9xkAPiztLMmVsqn89p1KhRCsNQYRhq6dKleu+99+R53gfXjbgXBwCIC1OytW9qD900c5puXnmX5nbMU8K4Orhhbx074lDVFOvojUPshaHRtGnTVVtbo9raWoVhqHK5rEWLFmn58uUKw1BTp05TGFqSuB8HAIgHO5fQf475vI5o2F83rbxTS4rLVe/U6cSRR+hjNbsomc8ooDcOMeb7oYJRW8va5kSZLQ6VSdZKxlLYvFDhy7dJy16QJIU7fUEl3xb34wAAcRCGUltNo2pv/o28h/6hwu9uU9DUJGfKFKXOO0vaeiu1u5lqLxMYkFJZCnf6vEzoy8w4TLIcydgKFz+r8NXbpZbFUqJGGr8LvXEAgNjwg1C5KdPUcN+fVLj1dpXue1BhqSR3t52VPucslcaOV8GiNw7xlrcbVbv7uTLNC2Um7SWFvhT4Ct/6m8J5f5aKbdLE3VUy2WovFQCA9VawXKUOPkgN9/1J+WuuU/m5F2QSCSU+fphSnzpFHfUj5Ac8Y0K8lUxWyZ0+JzNiqsyI6VIYSMV2Ba/9SXr7ISnwpFmnKW83SPy8AwBiot3NqOYbX1PyhGOU/8V18hctktXYqNRpp8o56AC1ZeoU8nsNMRYEocKRW8psfaLMFofIpOokYxQ2L1L4yu3S0uclSeHOX1AxcERvHAAgDsJQyrp53fiNtB5+3uiWv0tNbdKUsdI5x0nbsG8choCSFyrc6XMyQemD3ji3szduyWyFr9y2tjcu3Gw3eR69cQCAePD9QJNHFXTv5Un9/iGj+56WimVp1y2lLx4rjWssywrL1V4mMCDZclanjzlJ75WWauea7RQqlB/6erxlth5oekxtfod2zG6jpJ+s9lIBAFhved+VJh+q9On3y8y+RuGSZyU7Ic34uMJZn1a7NUqexzMmxFvST+mU0UdpQnKcpqYmfnCmak4PrHlUj7fMlidfJ4w8XBkvK59nqgCAmGj1sqrd5xtytz6+80zV5nelVKM06zQF0w7hTFXEXhCEapjsaMbBaW2+V1rJWksyUusyT/MfzOn9V0uSpO1OyMqXV+XVAgCwfsJQagkaVXfiTbLe/Yf04k1SYY3UMEXa9Vx5o7ZTa5k9GBBvZS/QtINOl1fKa+Iuh8tyXBljadUbc/TOY7epY+V7clJZjdpyDxXpjQMAxITnh2pLTVPNZ++Tefn30oJ7JL8os9muCnc7R4XUROX9RLWXiT441V5AXAQKtX3NTH1/8cO6fMm18uWrxsromJGH6IfTvq6fLPmNvrrZmXKLSQVVerBq25Ycx6rKZwMA4ivnJZRPTFdqv0vl+h0KjVHJblCxPPQfqCYdKR02ywSeZDkqmowKQVIBG60MLZaj8dvvrxduvFCr3+zcZCQ7epJmHP4FTdnrWM2/79eauOfxKlaxGdRxbNm2EX0MAIAN0eplZY/aW6kjZskOSgqMo6Jdr3LZ11DeSMsYo7RdUjLskAJPoeUob9WrWDbVXhoiZmxXjZO31DNXf0m5VYslSSO32FnbHPcltW37MRXamuTWj1epiiEqkbBlDD97AID1FwShAqeoSQdamrhnvRQaGSdU6Poq+0O76du2LZmSLXmWwlAydiiT9lSmSWpIMUZKZOsUlAt65NJTVM63ydiOJux8mHb9/KVa8MD12ny/k+U7dVKV/qc3xsh1bRHjAAAbwgsCKVXUNqcktNUxKUlGVjKQp7K8If6Ax3EsqeBInpGMJCdQmPA5JH2ICQJpxxlGdzwqfec3kudLtRnpEwcaXXO+dMmNob5zhpSwSgqq9D89vXEAgI3hyZMzymi38zIKSzWSCWWlApX9ouRXe3WblmvbCguOQl+ybEluIN/y6I0bYmxZ2qt+J12++Fd6KTdPkrRZYqxOHXW0jhixn25+/y4dUb+/vA564wAA8dLqZWWP3EupI7b/oDfOVtFuGBa9cSm7pFTYLgV+Z2+cqVPJt/hdOsS4xtaM7FRdtOinWlpaLknaPrOlzhx3qnYtzFJTuUWjzaiq5nd64wAAGyoIQiWtnI7by9YhO7vyAyPXCZRyykP+2aJtWyp4rkq+rSCQHDtUbbIkzxviNyGHGWOkuqxU8ow++R2pLSc5tnTIrkaXnC399t5QJ+4npd0SvXEAgFgp+0ZNGqXkjv9PiVmfkwmlsp1RwU8oHOKHcbmOpXTYLDssSbLkmYTypo5D0oeYMJRGjhyhF19cpLlz5yoMQ7muq8mTJ2uvvfbS3LlztcMOO8qvYnynNw4AsDFM0dZYM17/NepsFcYUZGSU8bPyO8Kq7YVaKU7CqMPpkG982bKU8JKyyy69cUOMMUbjxo3V3LlztXr1aklSJpPR9OnTNXnyZM2fP1+TJk1SoVC9/E5vHABgYzi5pLazt9HF46epbMqyQ1sZP6tyhz+kc5wxRmHCU8EpyJcvR47S5bTCMr9LhxzLlTV6S+m+L0st70mSzPidZPb6ksIp+0jt76uUnaCgivef6Y0DAGyoIAjVmqqVc9zxSh98kEzgK3CTyqcyw6I3LlnokFsqSoGvwHFVqG1g37ghxhjJJGtkQk+67VSp2CZZtsz0Q2UOuVTB8zdIWx+nnGqruEZ64wAAG84LpJbakUqce44yn/m0jEJ56azaLFfhEL8p5bq2kq3Nsr2yZBl5TkLFbC29cUNN4Msat4P06u3SP74jBZ6UrJXZ5iSZ465R8I9LpIMvVl5ZVevdbHrjAAAbo2AlVJywuZLf+baS+ZxCGZVratUSmKq9t1cpCVtKtrXI8j3JtlVKZVR0OFN16LFkTTtA+vuF0tLZnZfqJsja+QyF21yp8NmrFW57ikpVPEeY3jgAwMZodzOyd9pNyau2lVUuKrQdlWrqlCv70hDOM8YYJYOyEvkOGc9T6DgqZmtVEvvGDTm2K2vs1tL9/yW1LJIkmXE7ftAbt6/Uslilmkn0xgEAYuXDfeOO3cvWQTsNz33jyr4tn33jhixjjEwiK6NAuu2TXb1x0w6ROeQSBS/cJG15tHKqqeoa6Y0DAGyowPdV4+Z0zlGOTjvEURhK6YQvS96w6I1rKyTkBUbGSI4VKJso0Rs3xPgKtE12C/216WH939Lr5IW+slZGR404UD+YeoF+uuR6nT/hi0qW0/Kr1Ehg20aOY1flswEA8ZX3Eiq4U5Xa93/l+jmFRipZ9Sp6GvK9cUlXSgfNMoEnWbZKJqN8kKI3boixZLRX/c76weKrNbej80zVce5onTr6aF0y4qu68f07deyIQ+S30xsHAIiXNi8re8QeSh2+neygqNDYKlj1H+y7MXR/qfR0pmrBqlPRozduqLEcafSMhB79UbNalnQ+Mx+9patdPlOjSTsn1brcV2asUbmK+d11bVkWD1UBAOsvCEI1B3Vyp56o5OSDZIW+fCupgrLyh/izRdu2lFa7XBWl0FdgXOWtRpXKQ/u/93BjjJGbyshIevT7n1I51ypj2Rq/48Ha+fSL9ebDN2vibkcqcGo5UxUAECtlX2rSSCV3/Hcltv+MTBiqbGc7z1T1h/ZNKde1lQ6aZIdlSSaWZ6o61V5AHNi2pWX2Up09/+vqCHNKWK4KQaj2IKdbV96tl9tf17UzLlMmX1uVB+qdm8aFamtr0bJly2SM0eTJk5RMplUui4f8AIB+hWGovOcqr4bOZ6lD/HeHZRnVWy0yL/5WeulWKb9aslyltjhcqX3+W23OeJV97o4MBY7la+VLD2r2r74qBZ7sREp+qaCOle9p7i0Xa4tDPqe9v3ytSlZ9VX7uHceSbYdasWKZmpqalEwmNGXKFCUStkqlof09BABEw/cDdSgjKfNBjhvaG284tlTnL5N57McKF9wr+UWZZL2ysz6pzM5nqSVo4D7IEJFQTi//4RIteuKPCn1PluMq8Mpa/ebzeuInZ2rv/3ettjjiHOX9dFXWFyih9qKrvzwsvbdC2nKydNhuGWXcsiyVq7ImAEC8eF4guR95oDi0Y5wcOepYaPTsb1q17OWSFEp1423tcEqNpuyTUMmUqr1ERMT1m/XYDz+rtqULJGNkLFuh72nx7Hu18vWndegl9ysxcpqKfuUPxDJGKqeKWh026b6mR9QR5LSHt4NmZbdWsphR6FV8SQCAGCr7vpToDG9DvC9OkuSGCS35Z1kv/qFJzYs6f1mO3Tah3c6oVd3mljzxC3QosG2jxWtS+sSFodryRglX8gOpLSddf6/0/Hzp9ouNahP5qvXGdZSSWrDQ0v3PSrYlHbuPNHl0WmmnwD1hAEC/wjDszC2Jzj/7Q/xenGUZ2aWEXrszp3n3NKvQEshypM33TmnXz9fKafDkBfF52QG9M26oJwqz9d1FP5VvAiVNQsWwrKWl9/WTpdfrE6M+riumfUPJjoyCKmyyY9xQhURej7W/qJdb5qneqdMxiYNUk6qVXUhUfD0AgPgZnr1xy2Ue/7HC+fd09cZtf6oyu5yllqCR+yBDhJ8u6arlN+ivTQ/LD305xpUXlvVybr6++vb39IOpF+i0kcdJ7W5V1hckPeWdDv2p+QktKS3X9Pxk7V+7p9LljEyJwx0AAP3zPF9J25c++LUx1O/HhcbVgqUJ/fBW6ZnXpDCUJo0x+uKxKR25RyBH+WovERHJeWmdfon0xuLOXjTbljxfuv8Z6dl50j0/NJoypiz5lX+OboxUDFJa1Wzrzsek9rz0se2lXbfMKOMWFQ7xf08BAKJR9KSi6jv/4ElD+RAHSap183IXPiw983NpzZuSJHezneV+7HwVRmyvnMfzrKHAto2CoKjHHntMnufJtm35vq9yuay33npLa9as0f77769yWfK86vTG1apZ1pr5Mm/cJ1m2tNUxqq/dXG1hPfeEAQD9CsNQKtpKKStJ8od4hrMso2I6p9+vuU93rn5ATV6LHNnar34PnTvuNI30Ripgq4chwXUtrV69Us8995ykcG2Oy+VyeuWVV7T55ptrr732VrFYnZ/5hB0oq1aZdx+XWfacwnSjtN0nVOuMUJuXqcqaAADx4vuBbD8h+4OXHMpDfMMR2zZqSTbr2hW36uGmJ1UMS6q1szpmxCH69KjjlM5nuQ8yRNTaOdmPf0/hq7fJhJ5kuVJQlpa9IP3pTJljfylv5zPVVqxOZso4RaX8VumVu2Wa35VGzVTjFkeoYOqU97knDADon+cF8lI1XRf8of2eZjIoK/HWW8r94P/U/vRsKQxlTZ6o9BfPUOrIj6vN5T7IUFFvtSi847Myq+ZLMpKxO9/heeN+6b1nZJ1+n/K1W6hYhfcJjJFqrA65hRXS3Dtlim3SlL3VOH53taues0QAAOulFBiVMnVdF8KhfS+qxisoeOgxdfz8GvlvvCVJcnbaQZnzvyR/2+2Ut7gPMhRYllFtaaHC350gU2qT7KQUBlKxTXrht9KS52V9+k61aKT8KmyYaNuWatQse80CmTfulYwlbX2s6us2V1tAbxwAoH9hGKpgXCnzwTsOQ/tWnIwxqi20qnTzrWq9+Q8KV62WHEfJQw9UzflfUX7MeJVD7oMMBQknUHLxIwrv+U8Z+ZKTkryi1LpEeuRSmVmflo6/Ts1+varxXk/CDpVVi8zCJ2SWzO7WG9ceZIb6P6cAABHw/UA5NyW5qc4L5aHfG1ezZoVyP/6Zmu+5XyoUZepqlTrlJNWdfYbaajhTdaiodXKynviBwpdv7d4bt/xF6a6zZI75ufwdz1BbqUq9cXZRqYDeOADAxiuXh9++cW8uT+gHv5OefrXzEfLEMUZnH53SUXsFctk3bsios1oU3vk5mZXz1K037s0HpMUf9MbVzFCx7FR8bcZINXaH3Dy9cQCAjef7ntLOB/ufhkN91zjJU0oPz7H18z9Jby7uvLbjDFtfPTWtbaeUZatU3QUiEpZltNJdobPmX6C2sEOuScgPi+oIcrpt1b16sX2erp/5Q2ULtfKr8F6PbVsqJDu0oPyeHl7zpIwxOtzsqwmZzZQspLknDADoVxiGynuu8qr/4Cyuaq9o0zLGqN5ulTX3ZmnuTVJulWQ5Sk4/RMmPna/2xESVuA8yJBhXerb4or658IrOM1WtpIpBScvLK3Xl0t/quJGH6vKp31CqI1uVM1UtN1Q+kdcTHXM1t2We6u0aHZU4SLXpOrmFBL1xAIB+dZ6pmpaU/iDHDe0g59hGdcH7nWeqLrhH8goyyTpltj1Z6V3PUasaq/LeIqLnBK6eu6Fdr9+TV+BLttt57NbK+WU9+J0mHfyNBm19XFpFFauyvpRfUrKjVcW771OwcKFyW0xX/RGHqZitVcGmNw4A0L9y2VdZtZ1/8KWhfkMuZZeV6XhLevz70qKnJIWy6yeqZpez5c88Vi3lbLWXiIi4foue+L/Pq2XRazKSLNtW4Pta+vyDWrXgWR168X1KjpmholedfeNsP6FSszT/obyK7YEm7Bhq7HZJhcmyvCH+7ykAQDSK5WF4puqiR6VnfiatXiBJcsfvKHfvr6o4akd1eMkqr3D9VL4rP4aKbl5XLP2VckFeRkYZk1baSSmUZCQtK6/U35v+qePTR1T8ny+2bcmYsp544kl1dHSsvf7uu++otrZWe+yxlzzP0CQHAMBH1FmtMnd9QXr/pa6LQVla8Fdp4aOq/eQdaklO5eHqEGB7rXrhpm93vpFsbLnpWrnpGn0Y5N579h7NPOY/ZBoaK7421zXq6GjRs88+I8/z1l5/4403NGHCRG277XYqFLgpBwDAhyzLqK68RLr1BIWFpq6BYos0+xqZd/6h+pNuUVNQX71FIhKWZVRavUQLn7hDkpFsV4lsg9a+kW2MXvr99zR21kGSla74+jyldNODtq66o+sMPMuSLr3R6DtnJHTkHoYXpwEA+AjHsrX6lVAPfne1wo9skte6zNfjP23Ritcz2vXMrMoWvz/jznWMFj1yh1qXviHJyHJTSrrJD+7NSZLR3N9/T7uc+wtV+jG1MVIpk9cli3+mJ9vmrL3+l6a/q87U6EfTvqnNzRQF5YouCwCAQc0JXL30+5xevrOj2/X3Xy3pr+ev1oFfa9D43W154RDfCXkYKHgJfed6qT0vGYXKpowyKa19prp0lfTXf4Y67eCu+2GVYtuWVral9fnLpIXLu67f/KA0c5Kl676WVn0qT28cAAAfYZcTevA7TVr5eteNjsCT3n6soPeeK+q4H4+SO8aryqawiFYhkdMV71yrUKGs0ChrZ5VVuPYdh4ebntTnR39CE1T5F5RNItCrwXx9Y/4P1RF0HR5y08o/6ZD6ffRf486U3RGPF24AAKgEyzKq85Z29sbl13QNFFuk566VeecR1X+C3rihwLKMloWrdM+ah2UkOXJUZ9fooy9jX7X4Bn1sy12VlFvx9QXpsu5ouVe/Xv4H+R+8KWtZlq4MfquvTDhLh2T3kVWo/LoAABisLNvWs/MT+uLl3Z+jvbdCuvA66cU3LH39tJRcU6jeIhEJ27H154eN3lgsSaGSrpRwTVeMM9L3bgr1sy9JboXP7jBGKgQZXXC10T+e77r+h4el+qzRdV9LaeZmBQUBz/YBAPhQ1snLnf0zac6vug8sfV66/dNKHfEj+ZOPUNFna7a4syzpxRdfkud1Pj+3bUe2/eFmv0a5XF6LFr2nzTabrEpvmGjbluq9JdKdn5Oa3+369Lk3yh4xU/UnXK8WazS9cQAAfEQpndfXFn5fr+Tmr73mydfDLU/qmbYXdO0Wl2l8sBm9cUOAZYWaO/dFffhSg+O4cpyufL506TLNnLmlLCtV8bUlHV/Z1XOkv5wrldq7ctzsX8qdeYzqD/iOWso1FV8XAACDlWUZrUmu0dlvXqAmr3Xt9Ta/Q79b+Wc91fa8fjb1u0p0ZKq4SkTBGCM3v0zhq7dLMpJxZVIN+uh9t/DR78tMPUxS5f/3rnXycl+5SXryx1Lod67KMjKPfk/pAy6UNeMEdXiVz5cAAAxWriW5z81Ry9n/IX1k7/xg0WJ1fOtiJefMVfZb/6OOBDku7hKOkXn9LmnVAgUyMnZKJp3UR5vjwkculTnyalVj37h6u03W374mvf2Q9OGqXrlVJlGv2uN/rfb67VTyKty0BwDAIJYpF1S+5lrlr/lNt+veC3PVetqZqrnie0oedpiKqvyB6YhW2nRI//hfhaV2hTIyblbG7TqvIWxfpvC1u2S2Pavia7NtS/X+EunOz0tN73TdIXzpJtkjZqj++OvVYo2hNw4AgI+oLbar/Yv/Ke/5F7suep6K9/1NpcefUv3tNymYMp3euCEgG7ZKD31LYefOcTKJWplEV79Z+ObfFO7xn1Km8meqJm1f2TXPS385p3tv3HO/lLvFUao/6H/VTG8cAABrWZZRdtVyNZ/4aYWrVq+9Hra2KX/db1V65DHV/e63aknXVXGViEJnb9xy6eXfK+ytN+6xH0rTjlDVeuNevUX654/W7Y3b/5uyZp5EbxwAAB9hWbbmvJXQWT+QvI9sybV4hXTRb6TnF1i68HT2jRsKEq4la/7d0srX++mNu0ZV6437+wXSW3+X9K+9cdepvW57lXx64wAA+JCnpK79q61r/tz9+otvSKdfKn3/HFeH7xrIhF7PfwFio5wo6SdLf6P2ICcjo4xJKe0k157Ftcpr0j2r/6FTa45TNfaNa0qu1lfeuViLikvXXr9zzQPaPDFB/zf1QtXm6+mNAwDgI+rtNll3nyMtfa7rYuBJb9wvLXxcNaf+US3pGfTGDQHFRE4/ePfqtWeq1lgZZa302hz3ePNsnTH6ZE2pxpmqbqjX9Ya+Nv+y7meqrrpLB9btpf8ef7acDp6pAgDwIcsyqvOXSbeeqDC3qmug2Co9/2uZdx9R3cm3qkkNVVsjomGMUanJaN49uc4/21Ky1pJCrc1xz/6mVVP2Ga0qHKmqrFeQ/4fb1HTFlWv3vilZRsFlP1L2G/+tzAknKOckK78wAAAGKdeRMqvmSH/6Quc9uA+1LJYevkj20jmq3f+7avMqf38G0XIdS0ufvEfNi+ZJMjJuSgk3KYUfPKc0Ri/ecrF2/49fSabyvXGul9QTP23Vu0929WHOvy8vNyMdfvEI1Uwx8jhTFQCAtbJ2Qe4L10rP/Lz7wLIXpTs+q+RhP5C/+TEqxOBMVavaC4iDkl3Ui+2vdbtmQiMrNDKhkULpztX3q8PJVXxtjhPqySf/qY6OjnXGWlpa9OyzT8sZ/D+HAABUjOtYst68V3r/pZ4nFNukh7+ttNb93Yp4sW1La956QV6+fe21UEahLIXG6vxPSW///Qa5dmWbIY2RwtDT008/Jc9b94Wa995bpLfeekOJBHEdAIAPpU1eevRiqdDU84RVC2Re/aMSLps9xJ1t+Xrzb9d3u9Y9xxmVcq1qfvcl2XZl85Lj2HrsJVs/uV361/dpPF/61nXS6++5FV8XAACDmSk6euz/mhX20nc0/4GccitDWRY5Lu5MsVlvP3zz/2fvvsPsqsq3j3/XLqdOTe8hJIGE3juC0gXpiCIoiF1RXwXsvf3sFcVeQMSCFBUVO9J7DyUkkJBepp6223r/OCHJZGbOBJlyhrk/18WlOfuZnT2b5JqHte61Vo/PLGarsTjD2kW3Y6Lhn1ON0xHfWv0zbuu6t9e19riTdz/9SbpTXcP+XCIiIvXKGAg7DQ//vp85Uwu3fLsDEyiU9FJQDj3u7BGNq47GGVP9X7D88m/QXUkN+7MVggznfQ6eXd372uPPwtu+YijpIAcREZHNPM9h2W0V1j0e9nk9LFpu/U4HJnCH+clksLmuw2Olp+hOtoy1GbvNGgfgN+v/BP7wbrRrDLR77Vy89HM9No573k1tN3PF+msxaW1gKCIi8rxqNu6zUNrYd8GGJzGP/IaUhuNGP9/y2/V/2uoDu7mPe/6f7qTAY6UnRyAb53Bn6T5+sPpXxPTs1SJivrzi+zwZLVE2TkREZCuFIM0HLu+dK3/eNf+BFRtcZeNeArrLKa68qednm+dUN82r3vlYde51uMWk+MIVhn/d1/taWxe84fPQUdacqoiIyPOMgXRlPdz7w34qLPzzY+To7ue6jCaOY1m3bt1Wn9ht/r9l6dIlGDP8c5eNtME150H7M70vrn8Cc/2baDCdw/5cIiIi9crzHG7uuotHik/0eb2QlPjyiu9TSfXOK8no4roO7e3thOHzOUhLzz6u+tmSJU/jecM79moM5MO1cO2FEPTx3wyP34B792Vkvd57yomIiIxVUSrkO6t+RlvU9zjH0vJyrm/7O25Kc6qjXdqzmAd+vvnXFkgwJDib/7FBN2bVfcOeQfN9F/+5m+HWr9Br85skgn9+gvT6B/E8ZeNERESely100n3pRzcfSL6tyrU34Dy3XNm4l4BM0g4Pbtk3rncfZ7DP3UnKDv++cVk3wL3l87DkH70vltvh968nH68f9ucSERGpV8aA176R0vd/0neBtRQ+9hkyRWXjXgpStgTLbt3862oft9VYHAYeuops0jHsz1bNxr0e2pb2vrj+Scz1F9KobJyIiMhmvu8S/eNfRPc90Od1291N4aOfJlNSHzfaua6DWfdIdWxrE7vtnCpg7vspaXd4M2jGQD5eB9e+se9s3JN/xL3r28rGiYiIbCUdVSh9/ivY9Rv6vB4vXkLlV78h5WpOdbRLeRbzwC82/7q/bBwr7xmZbNyKW+GWL/WdjfvXp0ivu1/ZOBERka0UwjSXfq96/nhfrr8Flq3VvnEvBZm4DR64YvOv+87G3TVy2bhbvwhP/733xXI7XPN6GhJl40RERJ5nDLQXPL5/fd/XrYVP/hRK4fCfsSmDL3Ar3NX1YI/Ptj6Ly1i4bsNNFL1+ztgdQqV0gf+39DMsq6zsdW1JeTnvX/o5Kpnh7y9FRETqle+7OM/+C1be03dBUIC/f5gcypaPdq5reKq8lI64a8uHfZyp+uv1f8T6w7v/rzHQ6XfwviWf7fNM1X+038pP1v1WZ6qKiIhsJeNU4L9fhGI/81Ubn8Y8eCVpf3ifSwaf57o89oee41kWi60erIo1lqBkWf1wMOwZNM9z4Y47Kf7f13rvfRNFFD79fzgPPqBsnIiIyFbytgP+emk1R96Xx2/A71iibNxLQaWNp/95ZY+PLAZrnOo/GNY/dQ82HP45Vdd63PPTbp65rdzrWqXbcuOHNmILw3/Wq4iISL0yBtLRRrjzu/0X/fOTZOnq/3od0UjNdigmZWyvQ7h62hC2Y4f5YFXPc+joaKdY7D8A197eTqVS1n9UiIiIbJJN2mGrjSj6tPxOUrbvQRpjDFkvosVtp8Vtp9Ht1uRXnXIcQ3HDqgHrihtXYuNgGJ5oC993WLx4MUnSf/+4dOlSjKndg4qIiIwlKVuApf+uXfTQlWTj9j4vua4h75Y29XEd5L2KxkvqVRxSal8zYFlxw0qMGd5/h92VFN++pnbNN34LpUgLp0VERKA6PtP2bESpvfYc2iO/L+Di9nnN9128KIUfpvHCNL7jMcwtgGwvYyl39r3Z82bWEge9Q2pDreQW+XvbLf1eLyQlbmz7F25Kf7hEREQAfM9j0R9rb8gVFi1rHg37nSv1PRcvfL6PS1UXHkpdKgXVDQFrWdcO1g5vr+S6Dg8vNTy3tv+aR5bCyg2OxnpFRESeV3J59PraixNXPRxgg757OGMMvuNVx+HCNF6UUjauThljWBMOMBYHrAs3EDHMB5imEn617g+Etv/f99oNf6Xs9d5YTkREZKxK2SIs/VftooeurK6F6IPrGvLe1tk4rSWsVzER67ajj1sdrB/2bFwxVeAna35bs+ZHa66mklIfJyIiAtVs3OIVhvUdtet+eiNY+t64y/NcynGGUpyjGGUxjq9sXL0yA/+7thaKleF5nK0VKj5/uK3/690luOY/4HraQE5ERAQg5RvMQ1fWLgoKmBV39jtXmvKg2emgxe2g2ekg5auJq1dxHA9YUy6Xh70Pd10HZ92j0LGs/6K1j+F1LddYr4iIyCZFv8Bv1/+pZs0DhUWUnb7XLhoDJm2p5IpU8kXCbFnZuDpljKFcHnhOslQqYQdaCDHI0m4M9/0Y4hoDgQ/9iowdHZtRi4iIDIfALXNLxz01a67d8BeKXt/rIFzXEGcCKvlqH5dkIo2X1CknCbDdqwcu7Fo57P8Os0kb3PGt2kW3f52s7RyeBxIREalzjmNInl5Csm59zbryj35GKgn7vOZ5LvlKN02lThqLHaRNomxcnXKwUKz97xqAYPgPVk0nndhF1/dfEBQwj/6WtD98zyQiIlLPfM+hctVvatbYYpHwjrv6z8a50FjqpKnUSUOpE99TE1evTFwBO8B5qYW1w37mles6OOsXQfuz/Retexy3a5nGekVERDZJdbVT/lntNQ7hvffjFvs7UxUyJt7cx+UrOlO1XhkDdA08p2oLq3GS4T1TNe3GcP9Pa2bj7MNXKxsnIiKylVSpSOXvtfeNK195NenuvjeqcBxDLirTVOqkqdRBNtaZqvXKseF2ZeNM14oRysZ9o3aRsnEiIiKbOY7hmdWGtW216378J0i2Y9+4Uqx94+pZNRu3buDCSvfQP8w20kkX9rFr+y8Ii/DI1aS94Z3vFRERqVee53H1P2rXlCpw26Om37lS1/MoRdnNfZzOVK1fgQ1IqJ2N2xC2wQjsG7ckeJZllZX91jxdfpYV0RqN9YqIiGySidurmaRaVt2PH/c9PmMMZL1401lc7TS6XcrG1anqmaobB6xbF24gpO91yUPF+JbfbriRiu0/k/eHDX+n5BWH8alERETqW9oWYPFNtYseuopMP2eqOo4h75Y293F5V2eq1q0YCusHPsehe20y7GeqZrrbKX3zspo1xa9/h3RBaxxERESg2oM5Hc/AQLn3e39I2ul7nMTzXBrdrs19XNaLlY2rUwZLqWPtgHVhaQR6pYrLk//of6wtKlsev7GI72rOXkREBMD3XXj4aqBGbjwqYZbdUvtMVbc+zlTVqenbIe9kcXBqhuQmpyZg7PBOjjuOYcWK5wasW7NmFbNmzSUIBh5YFBEReakzxAMPyGGxlW7wJ/b4NOVZGqJVmFsvwz71ZwhLOJN2xT/wXYRTDqAryg7dg8sLliSWhsmzB6zLT9oB46YhGoaH2ixh7do1NSuiKKJcLuE4GYb53FcREZG6VN3cf4Cxje41fW7un/cqpDuehNu/Cc/dCcYhvcPLSB/8XkrZWZRinZ5UT4ybIj9x1oB1+ck7YAc68GGQVSKHJf2vVQXgrkUQxQ4pjbyKiIjgOIbuNdsRfF8XY0MDW2WTjAE/TrPitoAHf9tJ+7MRqQaHea/IsvvpeUxDSBQPby8gtVlryI2bStfqJf3WGMfFS+cYzllL1zU8WVpKNMDvekvn3ZzSdCw+mWF6MhERkfplI+haPfBP7M5VMZNNz0EQ1zU4lRRP/KHEohs7KG2MyU902e2UPHNfkSHyA5JEk1/1JJ+2uI6hVns9fQKYATaYG2yu63DjHQPX/f0e2PkUR9k4ERERAGu2ayOKoJDgbhN1cx0X2+lx76+7WXJziahsGT/PZ+/XNjBx1xShGd7DOaU2axNmpKYMWDc9NQXf+sS1Fr8MspJT5o6u+2vWFJMSa8L1zDZ5ZeNEREQA4jIkA4TZu9fQ135wea9CuvOpajZu+R2bsnGHkz7ovZTysylFysbVEx+fadvRx81MT8UOc6MUmpBnKrXXqj5YWETsRHikh+mpRERE6pfjGJ7bjnM2V66HIDZsu59ISJZ/3ufwgxvgqeegMWc4+dAUbzopRUu2TJJo7quu2IRp410Wr+i/xHEgnxneHs51DU8ss0Rx7Q1r/n4vvOYVLhl3WBfRioiI1CUnCbGdA+/XRfsyzMyeP2Mdx9DstGMeuRoe+hUU1kLjVBr2egPJgtPoTJqUjaszrutijKk51pbL5YZ9ztLzDObJPw48i/v0TXj77q5snIiICJCYhHXbcdhmV1SggeYenzk+tPtt/Hzd7/ln262Ukwo75ebwhklnslduV5yi5lTribUJ+Xx+wLp8vmHTgVzD18ylkm545ubaRWEROp/DNIwb9jlfERGRehTYcMB9HtaHbX0esmXTEUvsc/xo1a+5r/sRHBwOaNyTN005m6l2ClR0eFI9iZ00pnnWwN1Zyw7DPo7qJhXY8FTtopX34REOzwOJiIjUOccxJCsG2HQVSFatwQ9Ctt14tTEsEv/rVkqX/5joycWYxgbSJ7+Spje/kULrBCJtG1dXEhycxqm1+yXjYFONDOeWI45jMOufHHitzZJ/4O92HhUah+fBRERE6pgThcTLlg9YlyxbvmmebauvdQyNxQ4qv/k9Xb/8NcnadThTJ5M57xyyp59Md1bZuHpjvQzG8Wr3S43TSOzwHo7ruQ7myT8NPE64+K94++2hbJyIiAjgJDHxmrUD1tn2dkzrxB7Zd9+B7IY1lL/7Qzpv/Cu2WMLbZQHZd7yZzAEH0O3rTNV6kiQW2zyLgTo00zSL2EkznAc5+Ek3LP1P7aKwhOlYhmlUNk5ERATAVioQ1Z7LStatx/QxUpJNArzFT1P6xmV033EXGEPqsEPIv/edhDNnU3a0xqGeJCa1ndm4OSOQjQtg/ZO1i1Y9oGyciIjIJo5jWLEd+8at2gBB5JByen4ekuVf9zv88A/wxDJoyBpedWiKt7xK+8bVo/j5bFylq/8i42DTw5+NY8OTkAzQoy35J/7u5ysbJyIiAkSJYfnAU6osX0Of2bhilOW3fzX86u+wpg2mjDecd2yG0w63ZL2SsnF1Jm1SeLg11yZPTk0c1h4OwPMc/rHxtgHr/t1xBzs1zyMItP+viIiIQwxdqwess6V2TG5Kz2yca2mM12Buvxz7xB8hLOJM3AX/gLcTTTuYzig3hE8uL1SSWKanJg9YNy01hRT+sLZyJbfE7Z331awp2wrPBWuYbxqVjRMREQFMXBl4Lqu4HqePj3NeQKZrMdz2DVh+OxhDetahpA9+L6WGOZSi1FA8svyPjGtpmjbwXn7NM9zhz8aFAdHjtbNx0YMP40UBpAfew1hEROSlzhgDnQPvG0f36ur+rPRcs9DkFfCeuwXu+T6sexxSDeQWvIrsfm+ly51EqGhcXbHGIT9+Gu2Fjv6LjCGVayYYvsfCcQzty6IB/3Ni2Z0VFpycAV9/sERERBwbQseygQvbn8WZ09eZqh2YR38DD/0SutdA4xQa9jwPu/AMOpLmYR/T6WvMULaRitMc0LhnzZpXTziJfFivg17Du6mdiIhIPbM40DB1gCqDSfdcIOi5hobuJ+GKV2If+U11AWQSweoH4fo349/9TRrc4tA9uLxgcZzQMns3Uo3jatbNPer1GkwVEREZBaybBserXdQwBbvN5v5ZNyD99A1w9Rnw7M0QVyAqweK/wi9fRXbNraRcNQP1JIwd5h39hpo16abxNM9YQBwP72Dq9oyyGUOfB/yKiIiMRUliaZg8cPC9cbKL8Xv+XPfiFLd9p5N/fbGdjUsikhjKHQmPXFvg2netJ9ro4br6oVtX0q3MO+b8miVT9ziSxK3PhcZGc6oiIiKbGQ8apw7cxzVN67mA0RhwSin+ePFG7vl5F4V1MUkMXatjbv9+J3/9WBtuoMWq9Sbtx7ysdjSO1x8PTdn6PDBBY3EiIiJbWGPJTxygjzOQaui5hMB1DZVVDte8fR2P/7lIULAkMax7IuSmT7bx4C+L+IkO5KoncWyZn5lDq9dUs+6sCa8ccOHoyFEjJyIispmb2a5sXNJXNm7JH+FXZ8Az/9kqG3dTNRu36hbSysbVlTio9mi1tLhN7JSZQxwP82kO2019nIiICFSzcTMnDVw3fQKk3J7ZuNBm+NTPHP7ft2HRsxDF0NYFP/8LnPIhWN2RUTauzjRmQt5wQu2aI/aETJ1uzqY/TSIiIlskjo9pnjVwYcvsHocgGQPNZiPmt6+BW78KXSur+410LIf/fBbnuvNpdmpsNCsjIkkMkydPqVmz445zsbZet+FTJyciIvI8xzpMSo0fsK7Ja+jxa89zWOGu4PVPvo8bNvyN7qRIRMxjxcV84Jn/44frfkWUHc7jAGQgcWxpbGwinU7XrNtxxx0Jwzo9uFSLHERERDZLGR+P2mscJvrj6HUeeSrmP+U7ePPiD3FH1/0ENqRsK9zceRcXPHkJ94QPgl+v+aqxKYgsdq/X1y7KtJJM2n0EsnHqz0RERF6IJLE4M6YPWOdMm0KS6rnutCEsUv705+h+9yVEjz0OUYRta6f886tof9WZ5FY9h+vW69zc2FRyWmCfN9Yumn04ganPfeOqvZ76PREREYDE83F2GDgb58ye1Ssb19jdTuc5F1D88jdIVq6CKCJZvoLi579M9/lvpaHYOZSPLv+DwORhzstrF+39Bspu6/A80NbUnomIiLwgiePiTpk8YJ1paekxr+p5Dtlnl9DxyjMoX/07bGcXRBHRQ4/Q9bb3EHztm+TD0hA+ubxQSWKx43eG3ISadXafC6hEA+8JPSKUjRMREdnMpNPg1d43zpk0EbvNYEnaRpib/kbH6ecQ/OcWqARQrhD8/V90nHI27q23kLL1uW/FWBVEFrvnebWLMi3YyXsMfzZO7ZmIiMgLkiSW6RMHrps6HlJez5/roc3wuV84vPdb8OjS6r5x7d1wxV/hVR+Ele0ZZePqTHl7snGzDqvfbJzG4kRERDbzHMusgadUmT2FXtm4QpjldZ8xfOVqWLG+2sc9txa+cCVc8H+GYpQdwieX/0UqznBY8341a86c8EryUX6YnuiFURsnIiKyRYILjVMHrDPZntk413VoLC2BK16JfegqqHRW9/9d8xD84e14t3+JRq84hE8uL1SSWOakZzDeq71+4TUTTsIG9TmOqjZORERkC+umwRngDPvcBJJtfoJm3JDMs3+Fq06DZ/696UzVMiz5B1x1Ctnn/k3GjYbuweUFC+OYhSfWni9NNxgm7uxr3zgREZE6Z62FpoH3jaNhKrHTc+//Rq+A/9/Pwp8ugjWPVMfiyu3YB66AK06ksfKMsnH1Jt3KvGMuqFkyeZdDsH69ZuNG+gFERETqR2J8aJk9cGHLDiTJNmeqOm2Y350Dt3wJOldsOlP1Obj5C5jfn0ez0z50D94PdY3bwQ/SXDz9zbR6TX1e3yO/kCMbDySKhndALo4t06fPGLBuypQpw/5sIiIi9arktMJeb6hdNPOgXgsY83TAje+GoND319z3E1KF5TiORlHqSew1s/+bvoJx+95oZucT34bbMLH3AWxDzmHy5NorbjzPI5PJjsCziYiI1KfA5GGHI2sX7XkuJbelx0eZpAP++Ym+65MI/vw+8nQNyjPK4LDW4jVNZqcT3tLndeN67P/mrxL7zcP8ZNVNbeZOq12z/wJwHY3FiYiIQHUBY+tsj2xr7SnJ3U7LE7NlQ1/XdVj/eMSSm8t91pc7Ev77jQ5MUHtzYRleYZQwbf8TGT9vnz6vZ5onsMc5Hyd2hnfDmDi27Jydg0ftAyQObz6ATKLNbERERADCKBpwAaOfM0zapecCRs943P+rAh3P9b0gdd2TIU/8uYTv1enBTmNUygn4+Pkwvp/htn13huMPtITh8B7CEccJJx40cN3R+6FsnIiIyCYmG7PrybU3fJ26ewrj9/zZaco+//xiO2Gp76DSI9cVKKxB2bg6kw1yfHzmu/sd9zp30qm02pYeGz4Py3MlGQ5q3LtmTd7JMtkfr2yciIjIJoGTgx1fUbtoz3MpOT0HcDJJJ/zjY0AfP1RtDH95Hzk6B+9B5UWz1tJqWzlv0ul9Xvdw+fisd5MJhn/e0rc+czIza9bslV+Im2h8V0REBKrZuLnTLBMGiLSf/0owbJk/dV2Hh5a4/OHWvus3dsGHvg+lMN13gYyIKIo57gDLvjv3fX18M3z8/Orc63CKY8vOs2CgKfij94N8enjne0VEROpVEFrsHq+rXZRqwE4/oEcmKePGOHddBhuf7vtr1jyMefgqUlriUFfiGHbffXfS6b7769bWcUybNo0wHN78WRRZ7M6vGrhw3rHKxomIiGySC/O8evyJNWv2yu9COu75c7+YKvCJZd+gkJT6/JrfbbiRlfEqZePqTBwb9t13P0w/J83Pmzcfz0sNezYucBpghyNqF6Xy0Dh92J9NRESkXqXjDIc171+z5rTxx5GLeq6DKPpFvrj8e9g+snExCZ9e9i3Kqb57PBkZ1kIlNRH2f3vfBY4Hx3+ZIn2f8TCUYicF43eqXTRtXyJSw/NAIiIidS5JLM6Oc3AmTaxZl7nwDQSOv/nXruvAw49Que5PfdbbtnYKl36UTFH7/9aTMEpI5h4H0/vp27Pj4eWfomSH92DVJLHYCTvDVn/G+jT3KEK3cXgeSkREpM6FUUL6ta+uWWPyefwD9++RSUqRUP7eD4mf6jsbFz3yGMEvryalZYV1pZRk4MiPQW5C3wVT9iTZ6WSC4c7GxQl2p5MGLpx3nLJxIiIimwSNLWTOP7dmjb/fPsS5nnOqma4Out57Kba7u8+vKV95Nc4zzygbV2cKNMMJX6vOn/Zl3wsJMlOGPX8Wuo0w58jaRX4W2zRT2TgREZFNgkyO9LG1943LnPsaKg3b7BvX3Un3hz9Jn5uxxjFdF3+YbElzqvWkmo2bAAe+s+8C48JxX6YwEtk4k4IJ/WyE8rypeysbJyIiskmSWHaYkjC5tXbdhSeBs82+cY8963Ltf/uub++GD1wOxVA/c+tJGCUkc46F6Qf0XZAdB6/4FCVb+1yPwZYktrq+YaBs3JxXKBsnIiKySRRFvOao2jW5DBy0q+2ZSXJ8fvAHw+Ln+v6aR5fCFX81OJ42jqsnXuDz7qkXMN7ru3FfmJvHcc2HE4XDO28ZRQlHNR86YN2RTQcRRdr/V0REBKDstsDeF9QumrYPodvQ46M87fDn90LQdzaOh67C73ha2bg6kwlyfGLWe/BM3/312RNOYoIZP/xnqsZZDm7ap2ZNxqSZnhr+3J6IiEi9qpg8zDuudtGe51Le5kzVrO2Emz5I32eqJvDXS8iibFw9sRa8Rss+r2vo87px4Yj3t5Ckoj6vD6XY9/EW1s7GeXvtTuRpnl5ERASqmaSkeTY0TKlduN+bqSRbfn66roO/YRH2sWv6rq90wF8vIUfnID6tvFhhGDN5z6OZuODAPq+nGlrZ67zPEDvDn41rmeUNGI2bdUAaJ6P9RkRERKD6c53dXgPUmPv0s9hZh/bcN85NMPf8EDY82ffXrFuEuf9npIc5GucM7283OiWJZVxlPD+d/2VOHX8sWScDwCR/PO+c+nq+OOsD+MXssD9XHCe0tLSQy/W/+XBLSwvpdKa6KEJERESqCxjnHQdT9uq7IN0ER32aElsGaYwxuIXV0La09s3v/SFpJxy8h5UXLUoMTTseyCs+di1T9jgSY6rtb8vsXTn4Xd9j3vFvJ2T4+7gwTJg3bx6O0387PmfOjiSJApciIiLPK9lsdXP/7Pi+CyYuwO5yJsFWCxhTKRfz+PWQ1AhSBd2Y527H8zRMVk8Cm2X+K9/JQe+8jJZZuwBgHJepe76coz5+PY1z9ieKh79XakgHXHRm7Zr3vhqyXjA8DyQiIjIK2HTEEe9rwenn8KwFJ+TITDA95rJM4PLQ7wo177vq4YCkrB6u3gSmiYMv+iG7n/UBsq2TAfDSOXZ8+Tm84uPXY/PTRmTeMhPnOKb18H6vNzg5jm85gjjQnKqIiAhUFzCmmi17ntV3sN048LL3NGO3WcBoyy5P/b1Y896P/aGALelk1XqSJJaJjSWu+xycc0x1Y0CAKePg0tfC995vyTilYX+uOE7YbU7CrMn91+w+F6aOi5WNExER2SSKEmYfkmbyLn1v5pDKGw67qGcfZ4yh3GZpX1Z7c4qHf9eNk6iPqydJCLt5u/Djnb7EIU374m5aGrJTdg6fn30J5407A7c8Aht7BA6vnXgyKdP/itXTJhxPJhr+3J6IiEi9KiVZeNlHINdPNm7CzthdzyLYqmVLpVzMEzcMkI0rYJbfpmxcnXHLPueOO43Pz76EnbM7AuDgcHDjvvx4/pfYzV1IEg5/Ni4fNnDh5FfXrHnTlNeSDtTHiYiIPC+fqvCld4DXz7DZWS+H6eN7zmWVI58f/KH2fe95AgoVjcXVm4xT4nvvt3zgnOpcKlTnVs85Bq77HExsLI3IvGU+HXHyYf1fb8jC6S+zxNHwb1AsIiJSj6yFij8e9n9bPxUGjv4cRXpu/p+2ndjHfl/75g/8gpxtH5TnlMGRJJYk8TjiiCOZM2cO3qaDbzOZDLvssisHHnggwQgsBY3jhGTCLtAyu/+iybsRNcxQNk5ERGSTKEo4rHF/ds8v6PN63sly6Yy3kg63zGUZAxuTdpaWl9e89y/XXU/sa+yknkRRQj7fxJFHvpzJk6dgTHX+tLm5mf33P4C5c+cTjMBa0Erswr4Xgpfpv2iP11E2TcP3UCIiInXODXwumvoGxnktfV6fm5nFyeOO6bHPg++7/L3jViLifu9bSsrc1f2gsnF1phBnCfd5G7zqezB5t+qHxoEdjoTX/p7S5IMJRmDfuJLTCge/p3bRIe+jZBqH54FERERGgVK+iYYvfw68vk/PSp95KsnMWT3mstLlAqUf/KTmfcP7HsAtdA3qs8qL1xE1kpx0ORz+QWicWv3Qz2H2OAdedz2dqekjMm9ZcRoxu5zWf0GqAbvLWVRCzamKiIhANRsXtbSSfceb+y4whvxnP04pu002rtBF+XfX1bx3+cqrSXd1DNKTymBIEkunOxV7zg2Yvc6D1Kb9AvOT4NBLSE75CZ3J8M9bxnFCMn4BtOzQf9GkXYgbZykbJyIiskkYxnivOAJ/v336vG4aGsh/9uOUt+rjjAFn4wbiJxfXvHf5Bz8mFVUG9XnlxQljKI7fD865AXY8CsymtcQTF8KJ3ybc7yK64/7Ppx8qlciBvc+vnY3b/RzKmlMVERHZrOKnyX7wYpwJfe8b586fS/o1ZxHEPbNxwY1/hbDGuffFEuF/bsHrbyMTGRGFOEu411vgVZdvk407As65ltLkQwji4c8zFk0rHPze2kXKxomIiPSQ94Oa+8adfgTMmvjC94174CnoLquHqzcdcSPJSd+Dl314q2xcFrP7a+F1N9Dpj2A2btcz+i9I5bG7nU2lxn86iIiIjCXWQnMu5B39RMuNgU9fCDmv52ZihcDnt/+qfe+r/g6FSv9nK8nwSxJLS6WVn8z/EmeMP4G8U91HZoLXylunnMNXZ3+EdGn451TjOGHH1Cxmpaf1WzMvM5tp3hRl40RERDYJw5hk9pEw/YC+C1INcNTnKLIl924MeOUNsP6J2je/+/tkHGXj6kkSwkJnJ346/8sc1rT/5jNV52Vm85lZ7+eNE16NWxr+M1VtaDhrwolkTLrfmlPGH6MzVUVERLZSTtJw+AcgN6HvgvHzsXu8rsdclu+7mMV/qTYF/YlKmKX/wvc1r1pPIidk4alZjv1EKxPmV8dKjQMz9ktz6jcmMGF3h9j2vx/gUCk3tJB977tq1uTedxGVvLJxIiIizyuYZjjuy+D0vW8cC08jbJzTYy4rQwHuubz2jVc/iB+2D96DyqAITCMHvOO77HH2h8mNq85huuksc454DUd98gZM48icd2XSMTsfk+/3up81LHhljjAe/h5TRESkHlkLFa8VDrqonwoDr/g0JXqOgWRtJzzy69o3f/hXZIf5TNV+OlHZVhxbGorNvGvc+bxp0muwWIw15MIG4kKCZWQCaGFoOPTQw7jttlspFAo9rjU3N3PggQfVXB8tIiIyFnUmzTSd8iOch66EB6+E4npwUzD/BDjkfXR5U4i32nTEcQy241kGPLap7RncpAw0DFQpwyi0Pu6EBez9pm/iJNUAozUuid9MJUpG5JmsBWs9DjroYO66606iqOeBvLNmzWLu3HmUyyPzfCIiIvUoSSyd3lSazrkOc8c3sU/8EaIyZFphj9dg934jHUkzbDVG45Bg1i8aeNRm/eM4s08ayseX/0HFZmnZ9TgOmX8QhggwJE6a2MkRjtBi0CiKOXz3iPed7fHN30K8Vbvme/DJC2DhzJA4Vh8nIiLyvCiJGbfQ4+SvT+Dun3ax4oEKWGie7rHnq/PMOiRFYHpuOmJjQ9eqgYNKxY0x2QaDtdoool5YaymbRma8/E3MPPRMjE3AGBK/kTBxsfHI/LtyKx4XTXkDXXE3t3Te0+Naq9fE1+Z8jIagEXVxIiIiW4QmZLdX5xi/o8/9V3fT9mx1Pmvq7in2O7+RxlkQ2Z5zXEkEYan2z/vixgTsgDOvMsziOKE5U+CDr/F59xk+1oIxloZ0SBxFjFTLnU9V+PlHslzweXhmdc9ru+wAl7/fkvUqJGrkRERENgu9gGM+0cLjfyrx6B8KlNoSHB92PDzLvuc14jaHRNtk4zq3YyyuY2WMDQ1oH+D6UnGY4czg41PeSzQ9BCxO4pINc0TFkcvGtUTNfHXOR/jgM1+kkJR6XH9l65GcO/5UbGH4DwsTERGpV9Vs3DSazrkec/s3sU/8YUs2bvezsftcSEfSQq9s3LrHBs7GrVuEs8MpoJmwuuIUfQ729mevWbuSODFg8GIfP0iN2EEJYRizf3Yv3jbldfxw9a+It/oz4xmP909/E/PdHYlD/VkSERF5XhLH7DM34HefSfHlX8Ftj1THRnaYAm85GY7bP8Gj3ONrwthh+dqB772mDcbllI2rJ9Za0qbIecd4nP4yn8QajIGcH4INGam92VwCPvg6j45uwz/u7XltXCP8+IPQnK2QaO84ERGRzQpxFmeft+NPXAh3XgYbnqxemL4/HHox5dZdqUQ9t2UzcQhBd+0bF9djNA5Xd+I4IUkM8+cvZKedFgB2034fzoju59FFK81n/AJ+fwG0Lel5ceJC7Mk/oNs2wQjtayciIlKP/GKWL876ANe23cQ16//Mxqgdz3i8ovlg3jzltYwLxvXY58FxHFYGawa873PBKkITkkKHctWTMLQ4Tpo999wbs2kJirWWJHGoVEYuG1dwJ5I/7Sdw/Vt6/zfCwtOI93s7pVB/lkRERJ6XJJbWyjh+PO+L/Hjtb/h72y2UbYUmt4GTxx/Na8efTKaYI9lqDMQ4sLi0dMB7P11+lqPTL0PZuPrSFWXxph1D7pQDcKlOUgakKdkcSTRy2bhw+mH4h10Kt32tuhj6eY4PL/84lfF7EI3Q/sQiIiL1KEzA2Xtvmn9/FaUvf4PgltvBWtzZs8i89Y14xx9Ll5/t8TVOGBAvWz7gvZPVazHjpyobV0estbRHjaR2vZDswjNwSLAYKqaRcuxiR6iPK8UpUod+AKfcDotv6nkxOw5O/TEFdzxEfX65iIjImFT0MjS8+Y14C3em+J3vEz/xFAD+fvuQvfg9xAsWEpiec1kmDLHdtbNxyfoNONrkq+5EsaXdTCBz4IdIH3ARBkuCQ8k0E0QwUtmzzdm4ay+AjU/3vDhxAfaUH9GlbJyIiEgPXelGGi//JsFVv6H8i6tI1m8A3yd9/NFk3/duSpOmbJONM8TPrRjwvvEzy0hVKpBNDeXjywtUjj2C7Hyyx3ydlC0DlhiPomkhikZmIai1UPAmkT/tp3DDW6DS1bNg4SnEB7yTUuj1fQMREZExKEkshfGTab7uakrf/h7lG26EUhnT0kzm7DPIXPgGuvLNsNV+Yo6BeNHjA947fvwJnFN0jkO9qWbjjiZ7yv54z2fjTJpSkiMZobO4oigmmHYIqcM/CLd+pXc27siPUpmwl7JxIiIiW0mSmD3nVLjms2m+ejXc8jAkCcyaXN037vgDEvxe+8YZlg28VJXVG2BigxmxPWWlt83ZuF0uILvgtK2ycQ2UYw87QvuyFaMUqUMuwSm1weK/9ryYaYVTf0TBnaBsnIiIyFY8Ai58pcOC2S6X/R6eWFb9fL+d4X1nw8KZIWxzpmoUQ1ex9n03dkKcGLTdSH2JY0u+2Mjbx72eN046iwSLYw3ZKE9csD3WJA+nbCXPN+Z8nPct/SzPVJ7rcW1uZjZfmfMR0qXsiD2fiIhIPeqIG2k+6Xs4D18FD/4CCuuq81jzjoND30eXP71HNs4YA50DZ+PoeAY3KQPKxtWVwGGaM42PTXk34fQQi8VJHHJhA1Fx5LJxTUETX9/xY1z6zBfoigs9rh/TchjnTzwLdKaqiIjIZkli6XSn0HjO9Th3fgv7+A0QliDdvOlM1Tf1PlPVMdt1pqpZvwgzT9m4ehOagAl7Oxy7sBkSp7oHsJeQeBHRCGbj0gceQP7Dl1D40tch2mr81/PIf/RSkj20b5yIiMjWwgiK4/cld851cMuX4dn/gk2geRbs9xbi+SfSFeZ7fI1rA2hfNuC9bddqnNaZysbVEWstFRqZfsQFzDj4NIxNwBgSv4EoGblsXEjEvufnKXfFLP1vzyxmutFw3KfHY/KRtpIWERHZSiHO4Ox5If6EneGOb8H6J6oXpu0Lh76fyrjdKW97pmoSQaWz9o1LGzHD3BRoN4oXwFqg4pCu5DZ/Fo9wl1SdvPc47LCX0d3dxapVqzDGMGvWTFKpDGGI/qNARERkG0liaU+aSO/5DrK7n1NtwIxDhTylJI3dZrLNWovJTRj4xrlxJI6vQZQ6lCSWhCw4Wx3SMcKTllFkyeWaOOqoY1i/fh0bN24glUqzww6zsdahXNYfJBERkW1FMbSZKWQO+xSZQy8BG2ONR8lpphIatt083xoXGqYMfOOGqegsrvoURQk4jT0/HOGxLo8K5x1tOe1wnxvvgGfXwILZcNQ+llwqxLHhiD6fiIhIPYqIyM50OOJDjRA1V9s2x0I2IoiC3l9gLJkWh+51tSdO040O1mq3r3oURhac5i0fjFAw7nnWQqqY5SNTL6Jtajt/bfsP3XGRA5v3Ypf0TmQreZIROvRVRESknoUmYOohLlP2aoXYwKYFjDYVE8W957IcDxwXkho/+/2cqS6ElLpjLWBDsu6W8a14hNvtOE6Y2FDi159M89QKh7/dDa4LrzoUpo9PyHplZeNERES2Ya0lcCvsfJrPTidMgGo0DpuKSZyAKOmdjcu2Dryzb6bZwWjlQV1KEotb9nHxN38WjXCI0QYOC72d+fXOl3FP4SEeKiyi1WvmleOPJB834Ba0CaGIiMi2otjSZiaTOeyTpA+9GDNANi7BxTZOG/jGjdOwCsfVpShK8KNMj89G+pAEp+RzeuMJHN96BP9ov5UVwWrm5mZzeMP+ZMMclHUqiIiIyLaMDZk3JeKbF6UIYhdrwXUsjemAKOo9ceoay7gmWLGu9n1bGlAfV6fiKCLjbplItSO8DNRayDhF/u+tGTZ0ulx/C3QW4bA9YO95lpxfIYlHOMAnIiJSh7qiLP6sk8jOOBTXhoAhMmmKNBL3tQ+F61cDckmNQFUqj0UHKNUjayEItv33OvL7xnW402k469e4bYsxi28CY2Dhq4jzs+iyTcrGiYiIbMNai1/I8tqGUzml5RhiE+PgkI7SuGW/189Oay2tXnM/d9ui1WvGVR9Xl5LEEvRagjyyfVwldrGt+5A//x+Y5bdjVt6DzY6D3U4ncFroDnMD30RERGSMiWNLY6mF94y/kLdNPoeYBBeXbJgjKfSRmbKGSamB9/+d5I/XnGqdiqKETrbZN26Es3FdUZbsLueRWXAK5skbMR3PwISdsDseS9k0Uoq0xkFERGRbFeMTzduZ9De/Sj6ogE2IXY9yYwulPuZUrePhjBtHsnxFzfs6rS2MdG8gfQtCS8DAY6rDxVroiBtpeMUX8Q+9GBZdh6l0wuxDsFP2o5tmwkgb2IiIiGyr28viH3Mc+YMOxI0iMBD5aUrZhk1nXW7D88D3Iex/X32Ty2FdzanWI2uhFHmUaBnpR9msmo2bRsOZz2fj/lrdv3DhycrGiYiI9MNaS2e6kdSbLqTx7DNxkhjrOITpHN1+utfPziQBZ9y4Ae9rxo8j8bRxXD1KEkshyVBg6z1HRnYdaCVysK17k3/DPzDLb6tm4zKt1Wyc26psnIiISB/ixNLZMon0Rz9My/veg4ljrOtSaWiiIzG9ztq0GJxpA5+p6kybSqRsXF2KooSuOsvGdUdZsgvPJbPzq7Zk48bPw849Xtk4ERGRfhgbMXdyzNffmaquFxxo3zinum/csjW179vSqH3j6lW9ZeNgm2zc49diysrGiYiIDMSjzFF7uhy4wCdKHIwB30nI+kGf2TjPAd+DsMa2cdk0KBpXn6wFKoZUZcs8ZTzCY3FxnNBSGcdlcz7DsnAF/+q4Hcc4HNN6KFOdyaRLOWXjREREtmGtpT1qJLX7W8ntejZm06GqFZOnnKRJ4m33jQObG8eAIyPZ8STGH6hKRkCSWJyyT7rHmaojm42zoWEnfx6/2unb3Fd4mAcLi2jyGjlx/JE0RI06U1VERKQPUWxpN5PIHPIJ0ge/H2OjzWeqBpHpNS9qrcU2DHymqtWZqnUrihLwthlnHeGjrgpumsyrz6L1xOMJ/nwT8TPP4M/bEe/YY6jkGyg56uNERES2VY49wtzOZI79FinKYBMS41F0WgnD3nOqCS7kxkH7MzXva7Kt6uPqVBhZcLbKxtXBcaWhW+HgdzWy3+sbeeofJYJCwvS9MkzaxcOmQ6JkhA9+FRERqUNdURZv+vHkTjsQl+fPVE1RMk3VcZttOS64KYh7Hei0hZfFGnfInrnP33JYfzcZEnGcEMeQyTQyb14LAM3NWdat6xrZBxMREalzlRAqvTaF7T2gliSWpGVHnNwEKK7v/4Z7v5GKzTLSB3bK6BFFliiC1tZJTJgwBWstTU059XEiIiI1WGspRT4lWrd82M9kWxBE2N1eA/f8sP8bOh527tGEYR3M2Mmo4RDQlA4472gXxzE0NWVZt6440vvaiYiI1LU4TsBJYOsseT+biphszMITc/z3Gx393q91toff0O8tRHqxFrxSmklmCm9sOgdj2NTHdZGokRMREelXGMbgx7D1PiH9DKUZP2HWQRmeubXc7/12OioH6VhTqrLd4jgh45bYa45h33nVcGU1G1dCaxxERET6F8Z99HF9/OxMEkvjVJfcOIfixv5/uO52ah7rxXWxEFJGBxsZ0lGOI7xDeMW4w7DW0pxVNk5ERKSWF5KNC8MIdns13H15/zd0POzcY5WNkxfEVDzylSbOyJ2E02A2z6mKiIhI/+LY4psK/lY7d0T9BNsaMiHnHuPygaf7v9+86dCS10SYbD9rIWXKTGsxvPs0d6tsXBGrP0oiIiL9CsOYkKZtPu37h2fF5MnMPQae+nO/9zMLTqViGgfxCeWlLo4TOmjGbd0P96ADgWo2rmNdF1qsKiIi0r84sKSDXI/P+lofmCSWad5kJnitrI/a+r3fqyecSCrMEGuRg2ynIHYIaMWbdRLunJOr2bjmHN2aVxUREemXtRYqDulKfvNn/XVfYRhxUutR/HzNNf3ez8PlyOaDCAvKxsn2K8VpSkzE3+WNm/eNa1MPJyIiUlMcW4p+Fvzslg/7OowLCBqbyJz3WroffLjf+7k77oBtbUXnqsr2sha64jzGb8Df9+LN2Tj1cSIiIrWFYUyY2SbLFvfTx2VzpI99BZU//bXf+6VPPYkg16BIk2y3ajauCbd1X9yDDgCUjRMREdkeQQxBrrnnh0nvn53WWpgxHWfKZJLVa/q9X+aNr6eSzfc7pieyrWo2rkXZOBERkRfAWksZj/LWfVw/7VcYRjSecRrF7/yAfidNXZfUCcdS0r5x8gKU4pSycSIiIi9QHFs8U8Hbjn3j8qmI847zeOCp/u+3wxQY15goGyfbrZqNy2H82fj7KBsnIiKyvaIoJuPG4G75LO5nKC3jRxx3gM8fb+v/ficfClk/1Jmqst3iOCFVzLKzO59dWhYA0NxYPcehr71vREREpCqIIKBlm0/7zsbZhmmYxqnQtar/G+5zARWTR42cbK8khHSY43DvYI4cd6jOVBUREdkO1TNVPUpb93H9nqkawy6nwx3fot/1g8aFnU7UmarygpTdFOWm8fjnnovjGHI6U1VERGRAcWwpkKXAVvvG9bPfSNlpJrXXG2Dlff3fsHkWcXYCVm2cbCdrIXIDnHGw2znprc5U7dKQroiISA1RFNP5Qs5UnX8CPH59v/czC07aNKc6fJxh/d3GGN938X2z6Z+hf9VxbAmCiCDoZ6WNiIiI/M+Kpgle8WnA9F0w8xDiibsR9zOoJ6PL831cKmXwPHfgL3iRoiihUokIAo3oioiIDCZrIUhNgD1e13/RQe+mbLYd4JPRyBiDcX1CmwLHx3H66d0HURjGVCoaixMRERlsUZQw66A043f0+7zuuHDYRc2Q0c/hlwLXdSCdEKUDnBSYIW7jrK3OqaqPExERGXyxG3HAhY2kGvr+gZ4b57Dn2Xkiq5/DLwW+7xLZFKFN4W29S/QQUTZORERkCGUiDn1Xc7/RuKl7pBg/31M27iXCTRmidECSjpSNExERGcWshYo/HvY8t/+iAy+i4jQO30PJkDHG4KQtUTqAdIzrKhsnIiIyWoVhzMv3sew6p+/rngufuhAa0sHwPpgMCdd1sE51jYPj+srGiYiIjGLlJA2HXQrpftai5ieSHPAOyvHQz7/J0PN9l5xbJu8USQ19NE7ZOBERkSGUDXNcMuMtOP1su7tXfld2Sc9XNu4lIuVB3imSc8vKxomIiIxi1kJj0sBZE17Zb80bJp9JNsoN41PJUDHGkPFi8m6RrBcqGyciIjKKhWGMd+TL8Hbbpe8CzyP/2Y9Tamge3geTIeG6DlkvJO8WyfiJsnEiIiKjWMVLk73k/2Ga+87GORPGk33nW6igbNxLgbJxIiIiLx3lfBP5T38MnL6zcf4B++LsuQdRpGzcS4GycSIiIi8N1kLU3ELm/P7PVM2+480Eee0b91JgjCHjJ8rGiYiIvAREUczhe1h237Hv654Ln74QGrVv3EuC65ptsnFD28dZi7JxIiIiQ8QzIe8/G1oa+r4+oRnedToYnan6kuD7Lkk6IkoHuKmhH4tTNk5ERGToFEwLHPUZMH1n45i2H/GUfZWNe4nY+kxV31c2TkREZLSyFipeC+xzQf9F+7+VstPPGQ8yqhhjSBtLNiyRsZGycSIiIqNYFMUkMw+DyXv0XWBcOPqzFE3LsD6XDA3XNfjGw0s8fMdTNk5ERGQUK8VpOPR9kGntuyA7HnvQeyjHqWF9rmHY0mzscV2D78OaNatYseI5kiRh0qTJzJw5kyQxhKHt92sdx2CtxfZfIiIiIiMgiAzl6S8jc8bP4ebPw7rHqxfSTbD7a0j2fSudcSOgH+KjmecZHCdh2bKnWb9+PY7jMGvWbCZMmEAYmn4Pzq2O2VX7OBEREakv3XGWpkMuxmudA/f8AAprqxeaZ8BB7yHc4VhK0fAOyMngi2yatR0eP/0TPLMaxjXBBSek2HFaTNop9zvWpj5ORESkfsWpgOM/18p9VxZ46h9FonL15/XkhSkOemsT+ZlWi1VHOWMMYbbME8Ez/Gbtn+iMupmbmc1ZE15Jq23FKfc/lV0N0WlOVUREpN4kicVriTjlGxO44/udPHdvBZuA48LsgzMc+JYmbD7Aas+IUc3zHLoqGf5yn+EPt0IYweF7wKmHp2jKBNik/4UIysaJiIjUpyiOmbi7xys/P447f9jFhiUhAKm8YcEJefY4K0foBYrGjXImldDtd/Onjf/k3sLDpIzPCa0vZ7/8HmTL+ZrZOGMMSaI/ACIiIvWmEGdxD74Yr2UO3PsD6F5TvdA8Aw58N+GOx1EM0yP7kPKixZmQjWYjV6//I8vKK2jxmjh7wknskJ6JX0orGyciIjIKZd0SP/5Alm9dY7juv1AsVz/faz585DyYP61CFGlSdTQzxlBJMjyx1OEXf4H2bpg3A84/PsX4phCP/g/OfX6DOfVxIiIi9SVJLF3+NBrPuRb+9Rl49maq4TgP5h4NL/swXc4kbKyf4aOZ6zo0mTbM0v/A49dBEpGedTh2l9MomHEEcT8HsqFsnIiISL1KQtg7vTvf2PHjfGfVz3mytBSABifHyeOP4dwJp+IXs1iF40a1tBuTSzZiHv4dLL8d3BSZhadjZx1Gp21RNk5ERGQUcssp3jThNUxPTeWqddexNtwAwLTUJN4w+UyOzB+MU9TRCqNd3i2RrqzG3PUz7ManITuO7N4XELXMpytpUDZORERkFOrONtL4k8spffu7VK65AVssAuDttTv5j36QYKedtG/cKGcMNLrdeBsehwd+DuU2zLj55PY+n0p6MoU4U+NrlY0TERGpR0liKU6cQiCi9NsAAQAASURBVPO1V1P8zP8R3HwrxDF4HqmjjyT34UvobpmI1ZzaqLY5G/fMzbDoOkhC0rMOxe5yBgVnHEGkbJyIiMhoEybgHnAATVf8kOLnv0L06CIATEMDmdecQfptb6Ir04h+iI9uW7Jx18Dy2zZl407blI1rVTZORERkFCp6GRouegfurJmUvv8TktXVfeOcGdPIvfOtOMcfS7erfeNGu2o2bg3mrp9uycbtdT7RuJ3oipWNExERGY2ybokffSDLt68xXHszFDbtG7fHXPjo62H+9ED7xo1ym7NxG5+A+3+2KRs3j9zeF1DJTKYQKRsnIiIy2iSJZUJDmWs+m+Fzv4D/PABxAp4Lr9gHPnwetOZKxNo3blRzXYdSpsAthQf4S9t/CG3Ifg17cmLrkeSDRmxo+v3aah+nbJyIiEi9CSMoTTqI7BlXwn8/D2seqV5INcBur8bu/3Y64yZ0qOroZnxLIdXFn9r+zT3dD+Ibn+Nbj+CA/F46U1VERGSUKsRZ3APejdc8C+6+HLpXVy80TYcD3kE490SKNebcZHTIRWW89Wsp//QKgsVLcFpbyZ5/Lman+XSn88rGiYiIjEKdSRPNp/4Y585vYx+7BoJC9cLkPeDIj1Jq3pUw0s/w0cwY8KIUHUsTHr2+QLkjoWWmx66n5km1WCInrPG1ysaJiIjUI2stXe5UGl/7e/jPZ2Hpv8HG1TNV57wcjvgonc7kYT9TVadGDDLXdTAm5N///i/lcnnz5+vXr+eJJx7n4IMPJZttIAyTrb7G4LpgbUypVCKVSpFK+cSxow2hRURE6kgxShFMOITcaVfiJWVIYhIvQ8k0U4kcFI4b3TzP0N3dzl133UmSbOnB1q5dSy6X47DDDsdap0cIzvMcHCchikKCICCbzWKMSxyjRS8iIiJ1pDPMk1rwenI7n4ITFcEYYjdLyWklCDX2MtpFZPjhjS6XX9fz87/cCS/f2+VL78iSotTjmnE9ikGKVRsgCA0zJllyfoRrAi1cFRERqRNJYrGpCvu8McPer8sTB+C41QWONh0R9bOQUUYHYyDIlfjIsq9wf+GRzZ8/UHiMazb8mXdMPY9TGo/FKfs9viZJRZS9EiuDtXjGY4o/gWyYIwn636BEREREhleUJLjjQg6/pBHCZuIA3BSQTkicQHNoo5zrOqzpyHLOp2H1xi2f3/kYXHat4ScfTLPrLItNtmz07DiGcpymUHZZtQGaG2B8Y0JDWhtCi4iI1JPIRLQsdDju8y3YwJAkm/q4TEyQVBSNG+VMKuHheBGXPv0FQhtt/vyOrgeYlprEd3b8NM3l1l7ZuGKqQIftpCPqYmJmPHmbJR3k+t1oTkRERIZfZ5gjteA8cjufvCkbB7GbUzbuJSLJhlzddj0/W/u7Hp//u+MODmncl4/NuAiv0HODQDcFRb/I2nA9gQ2ZmppENsriBJ6ycSIiInUiSSw5r8gHXuNz0ek+5cDguZD2Y/KpQPtLjHLGQDnJ8p5vGm5/dMvndy2Cq/4GF7/G55yjDC6VHl8Tk6IYeDy31uB5lukTIJcKsHHUx+8iIiIiIyGMDe3eLLLHfpOULWLiCtbNEDh5SkmGRNm4Uc11HZqj5+C3r4WuVVsuLL8Dc+d3aDjtJ3S37k0QO5svOY4hZ7pJxZ3QsRrSTcTZCRRNizaEFhERqSOm4rG7twtfn/UJAqdCbBMyJlVdl1gwWIXjRrW0G5PfcDdc92aIt4y58czNmOYZNJ95FR3u1B5rWXzPIWfbccsboNwODZMJvSaKNCkbJyIiUkecYoqTc8dy9NxDqWyaW8uQJhfmiYrq4Ua7Rq+E/+CP4I5v9+zIn/oL3g5H0Hz812gPG3t8TdqzZG0HpntVdXy2aQaB00gxTisbJyIiUieSxNKZayJ96SU0v+vtUC5hPI8onaWUb1Q27iWg2e3CufEiWH7b5s/sc3fBQ78kfcj7Mbu/ge5oyxoHYyDrBKRtF6ZzOdbxoXE6RdO06VwPERERqQdRAt2Tp5P52pfIl4vYSoDJpAkyObr9TI99KGT0qWbjVmzKxq3ccmH5HZg7v0vDqT+ie/y+BFFf2bgu6Fi1VTaulVB9vYiISN0ouym8ffYn9/Mf4pbL1bWI6QyVfBOdOGgSbXTbnI27/i0QlbdceOZmTNMMms/qnY3zPIe8snEiIiJ1r9vP4Z/9GhpPPAFTKoEBm8lSbmzWusSXgAavROqhn8Lt3+idjZv9MppP+PoLyMZlsOrrRURE6kKSWLJukUvO9nnnado37qWo2e3C+fN7YNktmz+rZuOuIn3I+zC7n99PNq57UzbOg8ZplEwzZWXjRERE6kaSxExqLPLlt6cohy5BaEj7lowf4TuhzlQd5VzX0JbewDsXf5zV4brNn9/X/Sg/X/M7vjLnwyz0d8aGW868dxxDkCpTckqsCdfT4OYZ57SQDXLEGp8VERGpG6U4RTj+AHKn/BwvKUESY70MJdNEJXY1hzbKGd+yyD7BxY9/nooNNn9+Z9cDTPEnctncT9NaGdcrG9fzTNVx5GyOjM5UFRERqSudYY7Uzq8jN/+VOFEJjCF2s5ScFoJQPdxol48rxFdcSfvXvtPj88pf/45/2ME0fuPLdKYbelxLOZZMoRO7ajW2UsGdMZ0g10DZUzZORESkXiSJpd02kznwg2QOeCcmKoPjEjpZSqZZ2biXAD9K8+8vdfDcvVvOcFj1cMCiG4vse14DC07OEJpw8zVjwMPDll261sQ4rqFhkgPpmDDRmaoiIiL1Ioyh3ZtB9uivbXWmaprA5CnZ7IicqeoN++/4Eue6CbfffgflcrnXtTiOueOO2zjqqKO3qncwJuTee+9jw4aNmz615PN59txzL/L5JkIN1IqIiNSNKEropAloAgPEI/1EMlgcJ+Guu+4kSXoPrhaLRe6++y723/8ggk3ZOd83FAodPPjgAxQKRdi0Vcm4cePYZ5998TxfA7UiIiJ1JIggoBlMc/WDGFCgfdTzPId7nnC5/Lq+r//rfvjZnx3edpJHElcnTSMy/O6fLt+7Dtq7DRbwPcMrD0rxkfM8cl5JB3yIiIjUCWshJIJUBKmthuI0Jjf6pSw/Xvtr7i880ufl7666gj3zC5nvziWOLcYYglyR76z+BTe1/7f65wJLo9vAuRNP5bSW43AKqeH9HkRERKRfSWJJnBDSQBo2R9k1HDfqFYI0b/kKrN7Y+1qpAhd+Ef7+9TR5rwiA4zis68rywe/DXYvAUt1UbuYkl09fmGHvHUMcgt43ExERkRERRQl4weYVBhGoh3uJ6Pa7+cDT/0doey80XRms5SPLvsJXZ34Uv1Q9zMGmYh5JHudLS7/P8soqrKnOn+6e25mPzbyICcFEtGZVRESkfigb99Lkug6PhE/ys7W/6/P6bV33cvWGG3h906tJNg2xxbmAP3T+k5+vuYaOpAuLxcPlFS2H8t6pF5Ap5pWNExERqRPWAjYk54XkttrxI9KYy6hnjc93fm+4/dG+r3/lathvgcdus4LN2bhSnOWLVxn+eCsEkcFiaMrDm09K87qjfTxKw/tNiIiISL+SxFIgS4Fsdb+RZNM/aMxltGugHa5/C3St6n0xKsF1byR//j8JGA+A5xqaohVw06WY5+6iulLV4jTPoukVn6E4cX9KsdY4iIiI1IsoSkhFGVJkNn+mGdWXhpxtg+veDHGl98WO5+CP7yR/ys/opBGAjBuS2/AA/OOjmLalmE1/EtJT9yF13FfoTM0i1BpmERGRuhEHlnSQI01u82eRxuJGPdd18Nc/CHd8u++CZ/6Dc+8PSe/zXiqRA0CjV8J//Hdw12WY0sZqH+d4ZHY6kdQRH6cjaVE2TkREpE5YC2VcyrlmyDVvuaC9+ke9rBfj3PUdWH5b3wW3fZXU9P1xW/cjjhOMMTS7Hbi3fhEWXQ9xBQcL6SYa9nszmd3fQEeY6/teIiIiMuySxFL0MtCQgYatL2jMZbTL0wE3vAW6Vva+GJXg+jf1kY1buSkbd+dW2biZNL3iUxQnHqRsnIiISB2JooTudAOkGwYullElZ9uqaxyicu+Lnc/BH95O/tRf9MzGbXwI/v4RTNuSHtk4/7gv05WarWyciIhIHQljS5htgmzTlg8jjcWNdq7rkNrwCNz+jb4Lnr0Z557vk9n3fZS3zsY98Tu4c5ts3PwTSB35SWXjRERE6oj2jXvpyngxzj3fhWW39F1w29dITd8Pt/WArbJxnbi3fgkWXQdxeVM2rpH8Pm8ivecFysaJiIjUkSSxeFRo8AF/689H7JFkkJTTJT747BdZHa7rda1iAy5Z+gV+veA75MLqnKrrGtrSG/ncc9/hvu7HNp3FZZmWmsT7pr2JPdO7Yiper3uJiIjIyIiiZFM2qrG6/6+yTy8ZhVQ3Fz/+eSq293n2q8N1fOjZL/GNWR/HL2WrH6YSFtmn+OLSy3m2shJrqs38brmd+MiMdzE5nEKi+XYREZG6EYSWgBYwLdUPYiDWz+rRznUdzMOPUfzad/q8Ht5yO5XLf0j6Pe+hQjUbl49KJNffQMe3LsdubMPYBDyP1HHH0PTJD9OVVzZORESkXlgLpcijRGt1LM6yaTxOk6qjne94PHR1kefu7eMMB+DeK7qZsluapp2cTdk48KM09/y0m6f+WSQJDFhI5Q27n5FnwclZAtP3vURERGT49XmmKjBSZ6o6I/K7vkQ5jqFcLtHV1dVvTRRFrFq1Cs+rvnrPS/jvf29mw4aNWJsQhgFBENDW1sYtt/yXUqmLdFoBOREREZGh5Psuy5Y9S1JjxUpbWxtRFGAMeJ5DqdTN7bffTqFQJEligqDax61Zs4Z///tfuG6Mo25bREREZEgVgxSX/b52zVV/g+6gejiDNT5X3OTyhSthY5ehuwQbO2H1Brjir/CmLzoUY206IiIiIjLUyl6RP7X9s2bNT9f8lsCvht6CbIkPL/sKf2r/FxUqdMcF2qJOllVW8pWVP+SKjb/H5rSroIiIiMhQchzDig0Oi5/rv6ZYhr/cafB9F4DOSoZXfwLuXGQIIkNHN2zogIcWwzmfNjz0TErZOBEREZEh5qYMN7b9i8CG/dYsKi6mnQ6MMXiew1Ke5f8t+QzLgpWUbYWOuIu2qINbuu7l/KcupjPToWyciIiIyBAr+yV+tuZ3NWuu3XATJb9Q/UUq5vftf+ZbK39GW9JJISnRHnWyLmrjmg1/5n3PfJZyrjAMTy4iIiIythVDn9/9u3bNd6+FSlxd41CO07z7m4bf/8dQCgydxeoah2dWwad+Zrj8BpfEyQz9g4uIiIiMYY5jcLtXwoYn+y8KS5gn/0TKr06UNtr18OuzcJ67E5NUoNwOxQ2w+kHMb19DfsM9ysaJiIiIDLGUbzCPXQNxjcMX1jyMV163ORuX61yE+f0bcNqWYKIilNqqfdzSf2J+eRJNyRocxwzfNyEiIiIyBmXpgjv7PlR1s4euIms7qvVeiP/IFZj/fAantB4TdkNpI3SvwTx4Be51b6DFbR/6BxcREREZ4zK2Ex79Te2iu75Dhm4AGp0unBvfDY/8BhMVMZVOKG6E9mcw//oU/t3fpskvDcOTi4iIiIxdjmPwCith/RP9F4UlzBN/3Cobt2FTNu4OTBJslY17CPObc8ivv5tMyh2eb0BERERkjEr5BrPoOojK/RetfRSvtAZjDK7rkOt8HHPNeThtT/fKxjm/PInmZLWycSIiIiJDLEsX3PGt2kUP/4rM5mxchP/YVZh/95GNe+iXuL9/vbJxIiIiIsMgazvh4V/XLrrz21uycW4Xzp/fDY9cjYkKW2XjnsX85zP4d31T2TgRERGRIWaMYX2ygadKz/RbU7YV/tZ+C55fnSctZLp429Mf4d7Co4SEdG46i+ux4mIuWvJJHo4fI6VsnIiIiMiQ8nzDTe3/pWKDfmueLC1lvd24ORv3LMt499Of5JngOcq2vPlM1Vu77uWCxZfQkWlTNk5ERERkiKXLBUrf/l7NmvLV15AudlbriYl/8zsKn/wCbNgAhW7sxjbsmrVUfvVrul//JpqKHcPx6CIiIiJjW8Vl0Y21z7K/75ddmKA6T+pFKf71xQ6e+EuJuGwIuhNKHQkdK2Nuv7yLB64skiI1HE8uIiIio5Az0g/wUuK6Dhs2bBiwbt26tTgO+L7DkiVLqFQCoigkCAKSJMFai7WWMAy57777MCbS5KqIiIjIkEpYv379gFXt7e24roPjWB555GGsTQjDgDAMN/dwSZJQKBR44oknyGS00EFERERkKMXW4aGna9e0dUE5qI6tFUOfy6+HODFs7IRSBZIErIUghH/eB/c/acBND8PTi4iIiIxdnUk35aRSs2ZRcTGhE+G6hmXhCu4vPEpgA9qjTgIbYEmwJFSSCt9ddSWdTieOP0zfgIiIiMgY5LoO9zw+cN1tj0AYOziux5U3GdZ1GLqL0NENYVQdi4sT6CrAJd+D9d0ZZeNEREREhlBoAu7rfnTAukcLT+G6hpJf5Bsrf0JoIjriLgpJkdjGWCyxjXguWM2P1lxNkg+H4elFRERExq7EjXms+FTNms64m/KmTYLLfpmfr7mG2CS0Rx2UkzLJpjnV0Ibc1nUvD5UWYXLJcDy+iIiIyJjVWTAUy7VrHlwMQeTgOIZn1rjc+ZihEsLGTqgE1TUOSQLlCnz5V9DW7WEcrVUVERERGSqu68DKewYuXH4brq2Q9sA89EucwloIuqDcAUkIWLBx9bO/XExDtFrZOBEREZEh5CUleO7OgQtXPYDrGrK2E/79GUwSQLkNgu5q/4aFJIKOZZjbvkqzXxzyZxcREREZyzxCWP1Q7aJKJ05UAiBjuzF3XoaxERQ3QFgEmwAW4gCW/htnxZ00pIKhf3gRERGRMcwEXRDUPliVVQ/g2wDHMXidz2CW34qJK1DaCHEFSKq9XFSGW79CKtxAyrXD8vwiIiIiY1E1G3fvwIXLb8NLyqR9MA9fhVNYsykb194zGxd2w18vJh+vUTZOREREZAh5SRmeu2PgwlX347qGHJ3wn1rZuOVw61dpUTZOREREZEh5hLBmoGxcF05Y7csytgtzx7f6z8Y9+x+c5bfRkNb+vyIiIiJDyQTd1TG1WlY/tFU27lnMslv6z8bd9jVSwQZSrvb/FRERERkqruvwUOGJAevu7X6I0AlxU4YbNv6dtdFGCkmRzriLyEZYLAkJhaTIZ5Z/h/ZUm7JxIiIiIkModELu6354wLqHCk/gug6VVIlvrvppP2eqxqwM1vC9Vb8kzleG4elFRERExi43DIgeerRmje3uhsKmbFypm9I3v4eJY+yGjVAsVQ/ishaCkPC/txHdejsNKBsnIiIiMpSikiUs1t7jbd2TISTVM1ULaywr7wuIA0u5IyEKqrE4m0BUsdx7RRdhp4OnM1VFRESkD85IP8BLjesO/Epdt9qYGZOwfPlyrE2I47jP2ra2NsrlMp43qI8pIiIiIttwnO3r46wFSGhv7yCKIpKk7wWpzzzzDOVyCc9Tyy0iIiIylFL+wDWOqY7b3fuEoRQYOgps6ut6++mN0FXyMEYLVkVERESGimcGnvxMOT5gwbP8fuNfsMbSHfd9WENoQ/7RfitRJhjkJxURERGR51kLmdTAdSm/Oh7XXfG57r8QRVDup017dAmsbYMg2Y4bi4iIiMj/xGDwnYHH49JOqrqviFNhUWkxxaREbPte4/CHjf9gdbQOz1c2TkRERGTIWEvKDByOczC4rsPDxccpU6Er7sbSdzjuN+v/RLfTrWyciIiIyBDyt2OPt81rIByPX95UnYvt7DsaRxjBH2+DiPSgPaOIiIiI9GStBS8zcKGXBhwySQdm0bWQhBCV+65d9ximezVZ00+jJyIiIiKDwAF3O9YibOr1/KSEWf0gBAVIor5rH/0tbvdzpJSNExERERla29PHGad63sOq+zFREcod0E82jvt/RjpsVzZOREREZCg527H5r5fBAinXYh68AoOFSmfftUkIj/+BvNfPnKuIiIiIvGjWWvCzAxd6KaxxSMcdmEW/HyAbtwjTvUrZOBEREZEhZI0D7vaNxwF4SQmz6oEBsnG/weleoWyciIiIyBCyFnC3Y28Qx8V1HczqBzFhcdOcaj/ZuAd+TjrYqGyciIiIyFDanrE4d6ts3EO/3I5s3PXKxomIiIgMKUvGGXidaspJ4eBQdAv8qe3fRDaiklT6rH26/CxrovWEqb6vi4iIiMiL5+Dgb8da1YyTBiyBU+Hh4hMUk3K/Z6r+cePfWRtvwPM1pyoiIiIypNLbs/9vNRsXPfQIFArYzo5Nwbreyj//JW5bm7JxIiIiIkPIuAP3Wm7KgLV4rsuiPxXBQlCwfbZxSQSL/1HEs9uRuRMREZExRztZDKIwjJk8ecqAdbNmzSaOwRiIoogo6mfDkc33DTGmn8WsIiIiIvKiWWuYNWt2zRpjDOPGjSOOE5IkAeym/+1bFEUEQYDj9F8jIiIiIi9O2os4dv/aNTvNhIwfYwx0FCBJIO57jQMA3SV48GnAeIP6rCIiIiKyRZ4cU1MTa9a8vPkQcnGOiIjuuLhpw5H+50y74gLPBatwXU2Bi4iIiAyFKIo5Yi9wBmi3Xv1yMFQH4DqLhsIA+/u2d0M51FiciIiIyFDx4xQntb6iZo2Hy575hcRxQmRjLJZyEvRbX4iLdMRdFL3CYD+uiIiIiGySjjO8rOXAmjVzMjNJk8aY6nxpQtLvBsAA3XGRx4pPQUprHERERESGSj6TMGNS7ZoTDoR8KiKOoasEpYBa0Tg6CrBklVE2TkRERGSIRFECO7wMzAD91q5nE1gfsJhKJwQDzJeW20gnmlMVERERGSqBycCuZ9UucjzsjAOqPV8SYUggqrHIodIF5Q6ySdvgPqyIiIiIbFYxecz842oXjZtH7OYwhupYnI2r//R70y5Y8yAZt/ZZDyIiIiLyv4u9HDTPrFljdjqBwGnA2BCCrtpjcQDlNkzbYmXjRERERIZIFCUw69DtyMa9msCmMCSYStfA2bjSRtJJ9+A9qIiIiIj0EJKGXV9du8jxsDMOIooSjLJxIiIiInUhcBow84+vXdS64+ZsHJWOai4uqZF725yNCwf1WUVERERki9jNQcvsmjVm/nEETiPGRlDp3L5s3MbFeJ6ycSIiIiJDIYoS9mvYA5fa/darWo/CiTyssRSSIqWkdh/XHnVSdgfo9URERETkf+bGHq9qPbp2DQ77NexOFG05U7WSVPqtLyQlOuNuCr72/xUREREZKmG+gfTxx9SscXeYjc1Us3G2sxOSBKL+942z3d3EjzxC2iobJyIiIjJUnLSlebpXs2bOYRlMOsHGEHQnRIHF1jhTtdxlaXs2VjZOREREelF3MMiMcZkxo/9NgJuammhsbCKOE6w1NDY2YGt0co7j4LouQRAMxeOKiIiICNWFDhMmTCCfz/dbM2fOjiSJAcB1XVzXrdnHNTc3093djTFm0J9XRERERKpcQt51OmRT/ddceg40pEOSxLLL7JrZOAAW7gCLn4Mg1tCpiIiIyFDJhTneOuV1/V7PO1leO/FV2MDg2xS75OYR2dqN3LzsbBYVF+M4Go8TERERGSoNmYhTDuv/+k4zYZfZCXGc4DmWnWZCnPRfn/KrY3sbOkHTqiIiIiJDI4oS9s7tysz01H5rThl/LNkoC0DGpMk4aaD/bNz87ByWV1aCU2NFq4iIiIi8KCbweOOks8iYdL81b59yLvmwgSSxzM/uQGxrDMYBO2Xn8Ex5OYHRWlURERGRodKQDrj47BrXs3DhSYAN8V3LnnO3Y43DbHjkaZSNExERERlCFacRs/C0/gvG70QyaTfiOCE2PkzYCWqtcXBT4OewxfXKxomIiIgMkShKSKbtD61z+q0xu59N4DQCYL0M+DlqZeOYuAu27RmcWjUiIiIi8qKU4xT2gHeCn+2/6LBLKJoW4tjCxAWQDDCpOnEX2PAUblIa3IcVERERkc2KTiscekn/Bak8dt83U449YpOGqXtBEtW+6cRdYfVDuK4mVUVERESGSsVtwuxyRv8F4+eTTN5zUzYuBRPmD5CN8yHVoGyciIiIyBCKooRk6r7QumO/NWbXswjcJmBTNi6VZ8BsXLuycSIiIiJDqRz72P3fPkA27tKtsnELtzMb9zRuUh7chxURERGRzYqmBQ67tP8CP4fd/22UY7c6p/oCsnHaN05ERERk6GTjLCeOO6rf6zukZ7BLdn71TNXEY8fsTOIa2TjPuDS6DawPNyobJyIiIjJEoihh1+x8dkjP6LfmxHFHkY2rc65pkyLnZLE1z1TdgRWV1WCUjRMREREZKhVcMm+7EJPL9VuTu/S9lBqbiWOLt2BniGvPqXoLFxAtXopTUTZOREREZMhkIvZ/Y2O/l/2sYc9XNxARgWuZtDA1YDRuwlyP9U8EysaJiIhIL85IP8BLTRhadtttd2bNmoXZJtE2ceJEDj74EMKw+uskMey008417zdz5kxWrVqF53lD9cgiIiIiAoSh4dBDD2PcuHE9PjfGsOOOc9lpp50Jw2rYzVqHmTNn1rzfjjvuyMqVK6m5wZyIiIiIvCjWWiY2VfjFR2H6hJ7XmvPw1XfBPvNCoigmSSzTJybMm97//VwXTnsZPLkcXEd9nIiIiMhQiULLwZl9uWT6W8k7PTcCnp6awmXzPkNT0Iy1EAUJJ407ipTj93u/GakpZEyalPGxauNEREREhoxHhQ+fm3Dmy8Fze147cBf46YcsWa+68DSXCnj7KdTcFO6Uw+Ff91sasqiPExERERlCmXKeb+/4KXbP91y74BmPMye8kjdPeg2mUl2vkIlynNB6RM37nTXhldzcfpd6OBEREZEhZK2lNRzPt+d+kqmpiT2uNboNfHzmu9kztQthWM3GTXEnMifd/xoHB4dXth7J06VleLj91omIiIjIixNFMYfvEfGZN0FDz2gcsybDLz8O4/LlajYuijjjCEjV2Epk5qTqfXxPc6oiIiIiQ6kQZ4lf9hHM7meDs02DNuNA7Ok/pzNpBqBEI/aAt4OpsX3fgpOxS/6NSTeojxMREREZQl22GXvGlTBt354XHA+z13lEB11MIUoDUDZNsOsZtW+49/nYp/6KRQc5iIiIiAwVay3d3hQ485fQNKPnxXQTHPdlKlMPJowSrLXE+WkwocY5DsaBXc/ArnuCpMa+JCIiIiLy4oRhQmXmEXD05yC9zQGrzTPhrF/R7U3GWgjCBLvwdKjVnzXNqPZ/jqc5VREREZEhVIgyxId/CLP7a/vIxh2APf0X22Tj3lE7G7fzq7BLb8aklI0TERERGUpdthl75pUwff+eFxwPs8friA65ZKtsXCPsomyciIiIyEjbko27Cpr7yMYd+yUq0w7Zko3LTR04G7fbmdi1j2GNsnEiIiIiQyWMEirTD4dj/q+PbNyM3tm4BaeCm+7/ho3TIDsOHFdzqiIiIiJDyCn7vGPyuZw2/rhe5y7sld+Fb+74CdKlHADpMMsbJp2BqXGo6lHNh3Jn5/00uDn1cSIiIiJDKF3K8c0dP8Fe+V16fO7hctr443jH5HNxytX50UyU46RxR0GN3NtZE07kX+2316wRERERkRfHWiiNn0TTr36KM3N6j2umsYGG//s09uCDiTZl4+ykSbgLF/R/Q2NIn3Ua0aOPYd0ah3aJiIiIyIsSRQmT9nA54n0tpBp6jp81TnE58YvjcZsirIUwjJn38ixejWhcfqJDttUBR2eqioiISG8a5Rlk1kK5nLBgwa4sWLCQjRs3Yq2ltbUVx/EIQ0iSalcWRQnjxo1j7ty5LF68uNe9mpubmT9/PosWLWLOnLnD/a2IiIiIjClxnGCtw377HUAUhbS3t+O6LuPGjSNJDOVysrk2DBMWLFjIunXr2LhxY697zZo1i0wmQ2NjE0niAEmvGhEREREZJEnEgumWaz6bYvlah6WrYGILLJxtyfkBNok2l+b9CpdfnOOES2BDR8/bOA584a1w052W1xwFvhMRx8P7rYiIiIiMJU7Z57jskRyx84E8UX6atqiTOemZTPEmkqnkSOItSbd82MDnZ1/C/1vyGWJ6NmlNbgOfmf1+rlh9LZ+e/T6igpo4ERERkaGUMiU+fE6K957pc9+TEESw13xozsZkvEqPbNx+O8e88USXH97Q+z4LZ8NbXgVf/w1c+Er1cCIiIiJDKY4TGkvNfHHGh+igk0XFxaSdFLvldiYbZzGFrZaVBIY3TX4Nd3c9zEPFRb3u9crWlzPJH8/M9HSycVbJOBEREZEhZEPY0ZvDj3b8Eiui1SyrrGSC18L8zBzSQRZb3rIZSaaS40tzPsBrn3gPbVHPcJyDw4dmvp2bO+7mlPHH4IUpYnVyIiIiIkPGo8LJB8ccs1+KR5fChk7DvBkwfXxCPlUhjrf0Yo3pgO9dnOYNn6PX+oWmPHzrvfDDGyxfv8gSRZpXFRERERlK7WED+UM+SurA92BW3QtxCFP2JEy1UrCNm7NxcZwQTdkPf983w93f632jCQtg/7djb/0q0d5vBrVxIiIiIkMmji0dziTyJ/0Qv7IB1jyEdTMwbR/KTiPFKLW5thy75A58N+bZW2DF3b1vtsvp2KZp0DKbsmkYxu9CREREZOwJYofOxt3Iv+Y63M5noW0J5CZiJ+5C0TRRiZzNtd200Hry5fDzY6G4vueNjIFjvoBd8i/Y7WwqNov2/xUREREZOoUoSzT3dHI7HoNZ9wgUN8C4ecSNMyjQQhRt2Teu5LSQP/UH8LvzYKt9gQFIN8FJ3yG550fYE75BGGpSVURERGQoVbNxHyZ10EWYlVtn48ZRsA09s3GT98Hf/61w52W9bzRhARzwDuwtXyPa603KxomIiIgMoWo2biL5E7+PH2yE1Q9uysbtTdlp2iYb55E78CLMslvhuTt732zhqdiWWZiu1VRMfhi/CxEREZGxJ4gNnU27kT/7OtyuZbDxachNwE7ctY9sXDOtp3wffnYcFNf2vJExcPTnsEv/A7udTRll40RERESGUiHKEO14Crk5r8Cse7S6dmHcPOLGmRRo7p2NO+UH8LvX9ZGNa4STLiO558fY47+qbJyIiIjIEHMLad4+7jzOn3QmjxSfoJIE7JqbTxNNpEqZHtm4XTLzOW/i6fxs7W973WduZjZvnHwWl6/8JWe1njTc34aIiIjImJIkloZSE1+Y8QE66eTR4lNbzlSNep+pev6kM7iz637uLzza617HthzOrPQ01lTWk4ky2F4VIiIiIjJYwsSQzF9A4++vhuXLiZ9eipkwHnfXhZSzjQRsycYVc000Xf5N2o47BbumdzYu/5mPEfz3NtKvPoNyOgexsnEiIiIiQyUyIdMPczlr/4lseDqi1JbQMtMlP9mBTES0VS9mMxHHfmocf7xkQ69oXCpvOPqjrTz4uy6OvKSVMKwM83ciIiIi9c4buET+F0FgAUNz80SM2bRRXNR7arRUSthtt92ZPHkyTz/9NF1dXaTTaWbPnk1rayv33Xcfe++9D0niMFYWq7qugwlciB0sFiebECURVjPLIiIiMsSSxBIEYIzPuHGTsRYqlQS2ibhZC3HscPjhh7N8+XKeeeYZKpUKjY2NzJkzB2MMjz76KIcd9jLCcGz0cACe52BLHsYarLE42YgwGjvfv4iIiIycJInJuiUWTDfsMtNgrSWOLXabViSOE2ZNDPj711P84i/w5zugEsJ+C+G8Y+GORy2PL4N3nZ4Qj6FwnON6dFd8ADzHkksFROrjREREZBjYwJAOcuzt7onxIYksSWBJtl1yWnE4pGEfrl7wba5cey33Fx7FNz5HNh/Eca0v42erf8cBjXuSjbIj842MAMcxxH5IxQ0ASzpO44b+5s1aRERERIaSS0BjKuAVezqbs3HWQrLNkJJrS3zo3DxH7Gn46Y2weAWMb4IzjoC9d4KP/MDyubdAQzogivr+vV5qXNehFPlEcXVhbz4dgrJxIiIiMgySxOKXMkw0Gab4U6oZuELvOUFroaHcyHfnfZq/tN3MDRv/xsawnVnp6Zwx4XhcXL638pd8b+5nsUUzAt/JyPA8h6JfwBoLFvJRA9EYygaKiIjIyEkiSEVZ5jo7Mt+fW83GFWyvTXzj2DI1mcpvF1zGb9bfyD/bbyWwIXvkF3LmhBO4v+tRnigu4fzxZxEXx04f46ag6BWxWLzEIxNllY0TERGR4ZFEZN2IA3euzqkmiSVJLPG2Z6PakJft7vO3rzn84A9w56OQ8uGY/eG0w+Gy31sO2hVyqTEyoQoYY4jxKYcuABkvxnNCZeNERERkWBSiNAXSuNNP2JKNi2HbPUc6ghzjD/8gZofD4b6fwIanIDsOdj0Tpu1HctOH4ZjPUTStwLZN4EuT6zpkIoObgDUQ+BAksbJxIiIiMuSSxNJFA8ZvwJ09Z9P+cEmv7XuthQ7G03LWVbDoenj4V1BcD61zYM/zsG4K+98vY8+6mkrsjsw3MwJ8zyEdGByqXW8lBUE0NnpYERERGVlRDB004TTugdO85+Z947YVxwnF3BxyF/wT7v8ZPHkjxBWYth/s9Xrsiruxqx4k3u+dxGMoG+Z7QKUdA1jHx/qNysaJiIjIsKhELhVacCcejjGmmo2LLdvOqZYij/SMI/Au+Afc/X1Yfjs4Psw7FnY5neTO78H0A6g4TWPlKC6MMfgmgLAAgPWyxCajbJyIiIgMixeUjTv0Usysw+C+H/fMxk3fj+RvH4WjPkPRtDCWsnFZunBtCEDFaaISO8rGiYiIyJDbnI3zGnBn7zBwNu7MX8Lj18NDv4LiOmjZoTqn6qax//k/ePWvKcfeiHwvI8H3HLJJO45JSKyh7LYSaN84ERERGQZRZKvZuIbdcZr2GDgb98Z/wH0/3Sobty/s9QbsynuxK+4j3vftYyobl/Ygk1SzcbHxKRll40RERGR4bMnGHbYd2bjDN2XjfrApG+fB3GNg19NJ7vo+TNunmo0bG1OqGGPIugF+Us3GRSZLySobJyIiIsPDVDxylUYOdg/AuBCXN52puk0fZwo+b5/yOg5s3JNfrbuBpeXlNHmNnDTuFeyZX8gXl3+f901/E7kgTzRGFjm4rkPFLxM5IQZDNspiQ6NsnIiIiAy5JLF4pTTjzURe7k+ueaZqttjAd+Z+ipva/8t1G25iQ9jGjPRUzhx/Ahknzbef+znfm/cZKDoj8J2MDM9zKPgF2Hymap4oVBMnIiIiQy+OLV2ZRpydd8VZuFvNbFx5xixa//5Hyj+7ksqf/gKVAG+fPcm+4XUE9z1IcPd95N9yYXWNxBiRciDV3YEBEtejklc2TkRERIZHlMTgx7TuahhnDEkSEyVRr3xbZCOm7JHi7J9M5MHfFlhxfwXHM+xwcIb5x2R58LddTFqYxmTGTg9jjMHFhcqm8UffkriRsnEiIiJ9GPadLFauXMkll1zChg0bmDNnDl/5ylfI5/M9atauXcuHPvQh1q9fj+M4XHrppRx88MHD/aiDYnsG0oIAJkyYBECSJERRxKpVq1ixYgX77LMv6XR+zAxI+TbFxkUxD/yqkw1LQryMYd7Ls+x6ah5yIXEyNt6DiIhIPRpLfZy1DNh/hWGMtQ7Tpk0jm82SJAnlcpnFixeTzeY49NDDiGODHQMpf8cxeGGKZ/9b4ZHr2uheF5Of4LLrq/LMeVmayA80MCciIjJCxlIPB9UFDwP1HUkUML7RcPJhHnvNNwQRPL3C8smfwpF7wbffa0k75TGxWNN1HTrKGa74o+H6W6C7BDvPhLedmmXveTEe5ZF+RBERkTFrrPVx2zOn6hbSzMnN5ORxR/Py5oOp2ICHuh/nc89+h9dOPJmjGg/DFMfIQQ7phDXOOn6+5hru6Lofi2W/hj144+SzmGwnQcUd6ScUEREZs9TH9Zb3ShyyewbjGMoVQ3cJ/nmv5aa74QtvhQUzgjGTjYvI8MDTLpddC4uegVwaTjo0zfnHp2nJlkmSMbITsoiISB0aS33c9mXjEvI0clTTIUz2JxDYgA1hG79e+ycm+uP41o6fpKHcRDIGJlWNMYS5Ev/oupPfrPwja4L1TPDHccaEEzi66VDSpZyycSIiIiNkLPVwsH3ZuLgMzZkWjms5nF1z8wmTkGfLK/jq8h9xUOPefH7WJfjFDJaXfv/iug7dmQ6u2fAX/tz2bwpxkbnZ2Zw38XR2zy3ALaZG+hFFRETGrLHWx23PnKpvisyfluXVRzocd4ChEsB9T1ou/R5ceCKcdHCMSzAMT1sHjMeqjjTfvRb++yAkFg7e1eFdZ/hMGxfg2HCkn1BERGTMUh/XW3vSStOMQ3FwICpBUMA+/Q946m9wzBcotu5GFI2NTFiD8XCeLRH+cSXhsgIm7eAdMJ7s0VMoZCzhGDrQQkREpN6MpT5ue7JxUZTQ6Y6nYf4rMQ1TIK5AcT32/p9DfjL2jCvociZh+zjQ66XGGEOTdUnuaSf8+2qSjQFOi0/q5ZPJ7j+OLidWNk5ERGSEjKUeDrYvG1cMDCY1ifTCUzFT9oIkgI1Lsf/8JGb2y7CvupzOuBHGRDbO4FbWs+SmK1l227WEpS6aps9n5xPfQevc/aiQG+lHFBERGbPGWh8Xx5aB+q/2MEdzy3y8XV8Nc4+tjsetvA/7l0thnzcS7nQqxWhsZPs9JyLpXMGjf/wuqx7+DyQJExcexMKTL8IftwNh4o/0I4qIiIxZY6+P255sXAtNMw7BwfTMxi3+Gxz9OUqte4yZbFyjV8Lf8DDc+R1Y+yh4WXI7v4rcPhfQ5U4iHBuvQUREpC6NpT5u+7Nx46rZuPzkrbJxv4D8JDjjSjrHUDau2e3CWfJXuP+n0LkSJz+Jhj3Pxe58Mh1Js7JxIiIiI2Qs9XCwndm4Chh/IumFp/WRjTsce/IP6IybGAvZOM81NNr1mAd+CYuuhXIHzvj5+Ae8nWjK/nRG+YFvIiIiIkNirPVx252Na56Pt+tZMPeYTdm4+7F/+QDscwHRTqdRjNLD88AjLONF5CorMbd9F7v032AT3JkHkT7wXZRzO1CMx0ZGUEREpB6NvT5u4Gxcpphjn9xuJBMsFVuhGJe4teNe/tN2J5fMeCs7mblEwdjYLy3JBjwRLuWnK3/LE8UlZJw0R7UcxmsnnkRjpYUkeumPSYqIiNSrsdTHbU82Lo4T8uUGXt50MBO9cVRsQFvYwTXr/kKr38y35n6CxkozYyES9vyZqv/suovfrPwTq4O1jPdbOW388RzXfLjOVBURERlBY6mHg+3LxpUjcMZNwD/1VXh77oENKsRLn6Xzk18gddjBNPzgO3RnGxkLjZzrGho6N1L+5a/p/v312I4u3Plzyb79TWT225duX/vGiYiIjJSx1sdtTzauYgIap6fZ6dgssw9OEweWNYsC/v2VNnY7Jc/co9MEZmycqeoZj7DN4b6ru1l+dwWbWKbukWafcxrITraERCP9iCIiInXFWGuHdaTnrW99KyeffDInnngil112GcVikUsuuaRHzcUXX8xee+3Fueeey5IlSzjvvPO4+eabcV13u3+fDRu6R9UknDEG3zcYkxCGIa7rYoxDkjgDTs72ZeLERtat6xqCJx06fuLz0K9LPHxNode1dJPDyV8djzch2q7Q4WAbje+znul9Di69z8Gl9zm49D5fPMcxjB/fMNKPAQxPHzfaejgA13VwXQskRFGE7/tYa4gi/qfvZTT+vUnFaW76ZBtrF4W9rk2Y73PcZ1oJvcoIPNnofJ/1TO9z8OhdDi69z8Gl9zk46qWP01hc/zzPpbuSolB2CCJobrDk/RBsyAsdMR2Nf29c17CmM8fZn4D1Hb2vn3MMvO+sGN+Uh/3ZRuP7rGd6n4NL73Nw6X0OLr3PF69eejhQH1eLk7KU/RKdSTcuDnmTIxvliYOxMRaHn/Bg8ggfXPpFInqevOXi8OnZ7+MAf28Itv/PwWAZle+zjul9Di69z8Gl9zm49D5fPPVx9c8YQ2x9SpFHZ8GQTUM2FZNPBWMmGxeR5vIbPH74h97XWhrg6k/CjHElZeNeAvQ+B5fe5+DS+xxcep8v3ljr40ZbDwfVbFwlVaTiBBSTEg1OjlScxg/SYyYbF+ZLXPrs//FI8Yle13bKzuHrO3yMVGFkNh4Zje+znul9Dh69y8Gl9zm49D4HR730cRqL65/nOxT9AkVKhDakyWkgE2UhcMZENs5xDBsz63nr4o+wMWrvdf3U8cfytonn4haH/1Cu0fg+65ne5+DS+xxcep+DS+9zcKiPq3/G9SgGKToKBteBxmxCPh0SRy98w7RR+ffGeNyzOM1bvwxRz2gcjgNfvwiO2L2CscO/gdyofJ91TO9zcOl9Di69z8Gl9/ni1UsPB+rj+mOMIetWSNtuTKUTvAyRm6domsdMNi6PCzetIfzzql7XTN4j+4GFdLU4ysa9BOh9Di69z8Gl9zm49D5fvLHWx422Hg6q2bgcXfhJEYJubLqJwMlRSv63w0RH49+bZjyCyxYTL+793M7MHOn/tzOdTvSC55gHw2h8n/VM73Nw6X0OHr3LwaX3OTjqpY/TWFz/fN8hl7TjRt0QB9h0C2XTQDn2xkw2zims4N+fP5Nyx/pe1+e87GwWnvkhAoZ/jcNofJ/1TO9zcOl9Di69z8Gl9zk41MfVv7RvySUdmEoHOC6J30jRNBP8D1Gw0fj3xnNiupfcwa3ffDM27vlNG+Ow/1u/zrhdjyay/rA/22h8n/VM73Nw6X0OLr3PwaX3+eLVSw8H6uP6o2wcNLglUvdfDnd/r/fFdDOc/Ws6MvOUjXsJ0PscXHqfg0vvc3Dpfb54Y62PG209HPSVjWskcPJjKhvX4nXh/PFtsOLu3hcn7Iw9/QrakxZl414C9D4Hl97n4NG7HFx6n4OjXvo4jcX1z/cccrYNNypsysY1UzaNYyob1xKvhF+fCYV1va6bXc8kOOwjdEX5YX+20fg+65ne5+DS+xxcep+DS+9zcKiPq39p35JNOnAqHWAcklTTmMrGpdyEhvV3wnVvgiTsedE4cPxXKc48lnKsbNxop/c5uPQ+B5fe5+DS+3zx6qWHA/Vx/THGkKRDKm6ZrqRAxqTJ2AzZMDdmsnFJJuBX7Tfws7W/63Wt0c3z3bmfZUY0/X96Hy/WaHyf9Uzvc3DpfQ4uvc/Bpff54o21Pm609XDw/JmqJSpOZcyeqRrly3x42Ve4v/BIr2tzM7P45pxPkinmlI17CdD7HFx6n4NH73Jw6X0Ojnrp4zQW1z/Pc8h2dWCKBQgqmOZmKrkGKmbsZOMa1q+m86xzSdb2zsalzziF9Ec+QLevfeNGO73PwaX3Obj0PgeX3ufgUB9X/3zXg7JL0G3BQKrBQCYi3PaA0e0wGv/eeI5L+xPwl49tJNkmD2gcOOL9LUw70CHihb+PF2s0vs96pvc5uPQ+B5fe5+DS+3zxBurhnGF8FsIw5O677+a4444D4PTTT+cvf/lLr7pjjz2WV73qVQDMnj2bSqVCsVgczkcddtZagiChUoEk8QlDhyBgRMJgI8FxDN2rLA9fU+jzeqUz4V9fasdUvGF+MhEREQH1cbXEcUIQWILAkCQ+lQoEgR11A4v/K99zefzGEmsXhX1eX/9UyCPXFKsDlyIiIjKs1MPVFkUxGbfE+HyBqc0Fcm4Rm4QjEuofCaUwzQe+B+s7+r5+1d/gqRUurmuG98FEREREfdwAksCQKuT+P3v3HSVZVa5//NknVeo0kZkhDTCAgCgqkoOIgICCiSxivF4jQVAxAYZrRkURJSgoKAKSgyBRkIxkZIAhTGJSz3SqeML+/dHAMNJV1f6oqaru/n7W4q7lOW8Xu96+Vew+5zl7a2pxuiYVpyooZBVXJsgkTlI5KOlbL/xsxABcrESnzP+FSn6xBSMDAADM46qz1spRRTmvoJndefWk80o5pQmVjVuw3NNZV418vm9I+tLpUqESNHdgAABAEvO4WuI4kVdMK5fv0rTiOsrkO+WWggmTjXMDoytX3ajHCnNHPP9U8Tn9qfdKOUzjAABoOuZwtUVhoqCQUU9hsqYV11Eqn5MtOxMmG1cJSvregtO1Muob8fzlvTdoXvgC2TgAAFqAeVxtNo6UcQua0ZXXtI680m5RcfT/savqGFWMAh39C2mktfKSRDrhdCnPPVUAAFqCeVx11loVokCr4sla6c3WSs3QQNw5obJx3spI4XUvjnje5iOVzpqnbMS1OAAAWoF5XHVxnGgwzmmlnaaV/kZalUxRPspMmGxc4LmK/7lC8TMjL/idLCgo+tsSBc7oN/QAAACNwRyutjBM1B93aaWZpZXebK2Ke1SM/vtNVccqNx7SA7/7ikr9K0Y8/9w//qKhhY/LdZu69QgAABDzuHrKodGquEcrvQ210llPfXG3KhMnGic3GtTdZ3xBNn7tm7Y20f1nHS8nHGjByAAAAPO46sjGGQWFhdJ9Z4xcUO6Xrj1aOfU1dVwAAGAY87jqXpuNmzqxsnG+kfPExdKi+0YuWDFX5v7fKOVNjHktAADthDlcbWGUqD/uflU2btKEysZlzZD0969K+eUjnrePXyJ/Bdk4AABagXlcbeXQqO/lbJy7/oTLxuU0IF3zeSkJX3vSJtL1X1bGko0DAKAVmMdVZ62VKXlK5zs0rbiOOgs98ovpCZWNW6oVOnfZJSOeH4zzOmn+qSr4+SaPDAAASMzjahneUzU1cfdU9Y2u7b9VD+YfG/H8vNJ8nbf8rzLBxOgHAADthDlcbVGUaDDTqYEpMzQwc0P1Z3tU0sTJxqXLBeW/dpKSZSNn48p/vUJ69DGycQAAtADzuNrCOFLol2UmVWR6Kgq9ssKRNhgdp0zZ043fW6VkhDygTaR/nNonlbymjwsAgHbW1Ksbq1atUkdHhzxv+D/I06ZN09KlS19Tt/fee6u7u1uSdM4552iLLbZQZ2dnM4eKJnMSV49cXDv8tuLpUOGQZNhbFQCApmMeh2ps0dPjV9aexz35t7xsiY1VAQBoNuZwqGWo5Or+ubVrfnulVI795gwIAAC8gnkcqvE8R/cNPaxCUqxaU7Ghbhu4R77P9TgAAJqNeRyqiayvM6+sXfP4c9KqvEs2DgCAFmAeh2oKXl6XrLi2Zs2VvTeq6I//B5cBAGg3zOFQS8Ep6KH8EzVr/rjsUlX8SpNGBAAAXsY8DtV4nqM7HzMaqh6NUyWSrr/XkI0DAKAFmMehmpQchde/WLMmWVCQMxSTjQMAoAWYx6GadEUKb1xSsya8Y5nSIZM4AACajTkcaqoMavnce2qWzL3mDDlx7TWCAQBA4zGPQzWe52jFk3crKg5VrUniUIvuu4ZsHAAALcA8DtWknIp0/29rF62YK6+0gmwcAAAtwDwO1WSSPumh82oXPX6x0kl/U8YDAABWYw6HWoJ4SFpwd+2i+36ttKrfdwUAAGsH8zhU43mOzKJ7pPJg9aIklJl7tYKAbBwAAM3GPA7VJH6sPy2/ombNs6UFWmlXkY0DAKAFmMehmoKf10XLr65Zc+3Km1Xw2FMVAIBmYw6HWvzCkMI7a68bV/zN2UoVWTcOAIBmYx6HajzP0dLHQ1WGbNWaJJbm3VoiGwcAwKt4a+uFr7vuOn3/+99f49js2bNfU2dqpJ3OPfdc/eUvf9H555//X//7p0zp+K9/ZryZNm3sTIDzvbEGFg/IcWqn3wrLE83etDXvayz1cyygn41FPxuLfjYW/Rx7WjmPYw43bCx9bgZejFTutzXncWFecqzTsvc1lvo5FtDPxqGXjUU/G4t+jj1ci2u9sfa5efJhyXFq17ywRDJuStMmp5ozqFcZa/1sd/SzsehnY9HPxqKfYw/zuNYba5+bpxY8K6fORO7J0jwdsU6m5v/frC1jrZ/tjn42Fv1sLPrZWPRz7GEe13pj6XOzbJX0/JL61+MWrZC23oR7quMB/Wws+tlY9LOx6OfYQzau9cbS52ZxuaCVSX/N63F5W5B1E7Jx4wT9bBx62Vj0s7Ho59jDtbjWG2ufm2cH59W9p7owXCInZTWtBYvQjLV+tjv62Vj0s7HoZ2PRz7GHeVzrjbXPzZPz699Tfew5qeuAbN26tWGs9bPd0c/Gop+NRT8bi36OPczjWm8sfW6S/oqKy8py3NoTNNNb0dSNpzRpVGsaS/0cC+hnY9HPxqKfjUU/xx6yca03lj43yYqSKoOxTK15XNnKkyEbN07Qz8ain41DLxuLfo49XItrvbH2uVn6+CN193DIL39egRurZzLZuLGOfjYW/Wws+tlY9HPsYR7XemPtc/PCgifqzuP6XnhMb+gM5LhrbRu5qsZaP9sd/Wws+tlY9LOx6OfYwzyu9cbU5ya/XOp7Tqozj9PAC5r6hjc3Z0z/YUz1cwygn41FPxuLfjYW/Rx7yMa13pj63PT3S/kltedx4ZBcE5ONGyfoZ2PRz8ahl41FP8cersW13pj73Cx4THIkqcY8rv8FZf1EWbJxYx79bCz62Vj0s7Ho59jDPK71xtzn5qF/17+nuuIxdXcGkkM2bqyjn41FPxuLfjYW/Rx7mMe13lj63KwM+7WgsrjuPg6Lo6XaeurmTRrVmsZSP8cC+tlY9LOx6Gdj0c+xh2xc642lz82LlaKWxytrzuOKKss6ZOPGC/rZWPSzcehlY9HPsYdrca031j43lWfnDt9SrfH/E/aF+Uo7iXIteG9jrZ/tjn42Fv1sLPrZWPRz7GEe13pj7XMz97n+uuvG9c4L9ZZcp9zuOhm6tWCs9bPd0c/Gop+NRT8bi36uXWstLb7vvvtq3333XeNYGIbafvvtFcexXNfV8uXLNX369BF//kc/+pFuu+02XXDBBZoxY8Z//e/v7R1Sktj/r7GPB9OmdWr58sFWD2PU3NhXkDN1f2de1mjFiiFZ29zf7VjrZ7ujn41FPxuLfjYW/Xz9HMc0/SJVK+dxE30OJ429z41vUzKeVVypXuO4UqKkJe9rrPWz3dHPxqGXjUU/G4t+Nkaz53Fci2utsfa5MUbKpXNKktp1nVkpCiMtX15uzsBeMtb62e7oZ2PRz8ain41FP1+/iXYtTmIeN9Y+N77varI7SUmdidwUt0cDAyVVKlGTRjZsrPWz3dHPxqKfjUU/G4t+vn7M4yaesfa5iWxKnVmv7vW4rqy0YkWBbNwYRz8bi342Fv1sLPr5+k20edxEn8NJY+9zk+Qk33oq2+rhOE+uFBuyceMA/WwcetlY9LOx6GdjkI2bWMba58YYKZfK1b2n2unkFJUTLe9v7nsba/1sd/SzsehnY9HPxqKfjcE8bmIZa58b33c1pStd957q1G5pcLBMNm6Mo5+NRT8bi342Fv18/SbaPVWJedxY+9xk5chmHCVx7YlcknVb8rsda/1sd/SzsehnY9HPxqKfr99Em8dN9DmcNPY+N92Jp8SVVKkxj3ONImvVTzZuzKOfjUU/G4deNhb9bAzuqU4sY/Fz46Y76/7O/GyPKqFVvsnvbSz2s53Rz8ain41FPxuLfjYG87iJZax9bnzfVdA1te7vLNU1TUP5UOVysUkjGzbW+tnu6Gdj0c/Gop+NRT9fv4l2T1ViHjfWPjc51yqV6pLq/c6CSeojGzfm0c/Gop+NRT8bi36+fhNtHjfR53DS2Pvc9LhWjpuWwhrX2Ywrax2tIhs35tHPxqKfjUMvG4t+Ngb3VCeWsfi5mex11L8Wl+pWWEk0SDZuTKOfjUU/G4t+Nhb9bAzmcRPLWPvc+L6rzszUuvM4k56ioSGycWMd/Wws+tlY9LOx6OfrN9HuqUrM48ba5yZJx+pwRrGPg+lg3bhxgH42Fv1sLPrZWPTz9Zto87iJPoeTxt7nJs5ZpRSomJSq1rhyZBKHPVXHAfrZWPSzcehlY9HPxuCe6sQyFj83Xbls3d+Z09WlcpiwbtwYRz8bi342Fv1sLPrZGMzjJpax9rnxfVfpHlP3d5budjSUL6pSiZs0smFjrZ/tjn42Fv1sLPrZWPTz9as3h3OaOBb5vq9tt91W1157rSTp8ssv12677faaunPPPVf33HOP/vznP/9/TeQw9phUrC0PyNWsyU521LGOI2sn7iQdAIBWYR6HamwQa+PdMjVrZu+UlglqPwgBAAAajzkcqrFWmtadaL2R77O/4oi9pY5U1JxBAQCAVzCPQzVhGGvP7p3k1LnNv//kPRWGzOMAAGg25nGoJuOH+sg+tWum9UjrT7Nk4wAAaAHmcagmHaW156Sda9bs0r2tUnGqSSMCAAAvYw6HaqyVJjndmhWsU7Pu/ZP3USbKNmlUAADgZczjUE0Yxtp3B8mpswLOQXuIbBwAAC3APA7VVDzJf2fta3Gmy5eZlprQizsDANAqzONQTcW38refUrPGe1OPQo85HAAAzcYcDrX4HZPVMX3DmjUbv+MI2aC7SSMCAAAvYx6HasIw1qy3vVvGcWvWzd79EFUqZOMAAGg25nGopmxy0jZH1S7KTlHSsxHZOAAAWoB5HKopmw6ZNxxYu2ijd6gi1hsBAKDZmMOhljg1WZq0Ue2irY9QyelpyngAAMBqzONQTRjG0px9JFM7G2e3PkyVStykUQEAgJcxj0M1QZTSQVP2q1kzyevSBsG6ZOMAAGgB5nGoJh2ltc+k3WvWbN/1FgXsqQoAQNMxh0MttrtH7saza9akDj9ElU7WjQMAoNmYx6GaMIy1wY5p1Vk2Tm/YL6swTJozKAAAxoA625E33kknnaSLLrpI++23n+6//34dc8wxkqQ///nP+sUvfiFrrU4//XStXLlSRx55pA488EAdeOCBWrp0abOHiiaKokQz3+Rr6qb+yAVG2umz3bIpFgAGAKBVmMdhJLFive3ITqU6zYjn/azRth/rVOwwjwMAoBWYw6GajlRFJ39McqpcId5sfWmPt9jhBUoAAEDTMY9DNbkkp4On7V/1/IGT91JX0inLmiMAALQE8ziMJIoSvX2LRFtvUr3mWx+VskG5aWMCAABrYh6HEVVcfXL6Iep2O0c8nXMy+syMI+VWqjwDAQAA1irmcKgmW8nqhHX/R06Vx6c3Tq+vXTq3VRSx6AgAAK3APA7VdKRCfbzGnlwH7yFNykVk4wAAaBHmcRhJFCXSnA65G3dUrUkdtqGKHpM4AABahXkcRlJKEvnvmSXT4Y1ckHHlf3B9lQz3VAEAaAXmcKgm8Xu0zZHfljEjZ+O6Zm2qGW95F9k4AABahHkcqkp1a9N9Pln19OxdPigvN5VsHAAALcI8DiOJokTxjLdJM7apXvSOb6pgups2JgAAsCbmcRhJKfZkt/+8lJk8ckGQk3b7qoo209yBAQAASczhUF3B9EjvPEUy7sgFUzaVnbOPQrJxAAC0BPM4VFNyu6RtP1X1vNnygwrT02QJxwEA0BLM4zCSKEq0VWYzvTG7edWaL876mDJhtomjAgAAr8Y8DiOqOPro9A9oktc14umMk9YXZn5EXiVo8sAAAIDEHA7VFTu6lfv2NyR35Gycu8lG8vfdm2wcAAAtwjwO1Zh0rDcfXH0vrk33zCjosWTjAAB4FWPH6X8Ze3uHlCTj8q2NyrRpnVq+fLBp/z7HMfI8yRirOE7kuo6sNYpjKY5H93twHCO3HOj+84b0zM0FxZXh4z0beNr+k12asqVRpGgtvovqmt3P8Y5+Nhb9bCz62Vj08/VzHKMpU6pf7BhvJvocTmr+58Z1HbmulZQoSaxc11WSGEVRMurNF1zHUbTS052nD2jRQ2XJSjLSrDcF2ulz3QqmRoqS1txY5Xuosehn49DLxqKfjUU/G4N53MTSis+N57kaKgcqVhxZK2VSVh1BRXE8+utnsQI9+ryv754nPb1w+FjKl/bbQfry4VYdQbElv1e+hxqLfjYW/Wws+tlY9PP1m2hzOIl5XCs+N05gVfQLKtmKHBmllVI27FAYxqN+jThb0TUDN+uCZZdrZdQnSZrkdemQqe/V+ybtLTefWkujr43vocain41FPxuLfjYW/Xz9mMdNPM3+3BhjFMtXMfRUqhgFvpT2IqW9yn+VjStEGZ16kdGVt0vFl7Jxm8ySvvJhadtNQ7mqrMV3UR3fQ41FPxuLfjYW/Wws+vn6TbR53ESfw0mtycaVg6LKTlmRjZQyKaWjtEzFG/UDpsaTVvor9JPFZ+u+wYdlZWVktE3Hljp+1qc0I56hJFzLb6QKvocai342Dr1sLPrZWPSzMZjHTSwtycb5jgp+XiWVZCVlTErZMKeoMvrfg01FejJ+Rr9Y/DvNK82XJAXG1549O+uzM45UtthBNm4coJ+NRT8bi342Fv1sDOZxE0tLsnGup3wlULFs5DhS2k/Uma78V9m4UGldfrur31whregfPja5U/roftLh70rkq7iWRl8b30ONRT8bi342Fv1sLPr5+k20OZzEPK4V2biMW1bKDslEJVk3UGiyKqpDcTy6NUIcx6gzdhVdsUjhXSukyvDPOTPSCg7aQNEmWRXs6OeEjcT3UGPRz8ain41FPxuLfr5+E20eN9HncFJrsnFZDcpPClISynoZlUyHSnEw6myc5zrKDVpV/jxf8eN9w+vGSXI361RwxGwVJjkKRzknbDS+hxqLfjYW/WwcetlY9LMxmMdNLK343Pi+o2yySm5clKxV4mVUND0q/xfbLvimovyCh/Twn76j/oVzJUmOF2j97fbXVgd9RZE/hWzcOEA/G4t+Nhb9bCz62RjM4yaWVnxuUr5VJumXExUlYxS7WRVMz3+1oX1KBS28+6+ae81vVOpfLkkKOiZp070+qtl7fERl5dbW8Gvie6ix6Gdj0c/Gop+NRT9fv4k2h5OYx43VbFy30yfnrp/J/vsyKXzpeYZJG0m7flXlmTsqH6XX4ruoju+hxqKfjUU/G4t+Nhb9fP0m2jxuos/hpFZl44bkJQWZpCLrZVQ2HSr+V9k4qStcJN1ysvTC7ZJ9af633nbSHqdoKLOxKrFZe2+iBr6HGot+Nhb9bBx62Vj0szGYx00sLcnGeY6ytk9uXPj/zsZl3IoyKx+WbvuOtPzJ4YNuIG22n+yuX1W/JRs3HtDPxqKfjUU/G4t+NgbzuIllrGbjuryCvKcuk+79tZQfzsYpPUl6y1GK33yU+kOyceMB/Wws+tlY9LOx6OfrN9HmcBLzuFZk42wqVMktq2zL8o2vIA6UCjOjzsYZY1TJFnTWsgt13apbVUrKkqQNUrP0uZlHapvgjXJK/tp8G1XxPdRY9LOx6Gdj0c/Gop+v30Sbx030OZzUuj1VK05ZoY2UMoHSUea/2lPVdY16U7366aKzdO/gw0o0PP/bJreljl93eE9VG5KNGw/oZ2PRz8ahl41FPxuDedzE0pI9VT1HmaF+mWJx+NmETEaljm5V/ottF9JJKO+Jx1Q45fuKnnxq+GAQKLXvXsqeeLwGOyeTjRsH6Gdj0c/Gop+NRT8bg3ncxNKSZxxcTyq5isqSMZITWCkTKfovsnG+DfTcrRU99OdBFVYO/1yq0+iN7+vQFu/NqOKU19bwa+J7qLHoZ2PRz8ain41FP1+/enM4r4ljwTjluo5cN9bjjz+uxYsXvXIjddq0adp66zfJ81KjmtAliZUNytr2Uxm97SMdquStPN/ISVspHSmKWrOpKgAAwHjleUZhWNCDDz6ilStXSpIcx9F6662nLbbYUmFoRnWBNE4SuZND7X5ip1TpVqVgFWSN5CeyQagonrgXWQEAANaGSCnd+ICnn18kLVg2fGxKt9FH903psD19+SqO6nVcVbTtnFjnfyNQMXRUrkidWauMH8lVRcno79ECAACgDmOkMFvSn3uv1GW9N2gwHpIkbZReX5+Z8WG9Ob3lqBcLcQuB3p/dV/tsupvySUFWVh1OVtkwpzi/Nt8FAADAxOO6jlYVMvrBBdIN90lhNDy3235LT9/6qKf1Jpdkk/q5tiSxyrgFnXi4r2M+5GugYJTypUyQqCNVIRsHAADQaEGiF8wCnbrwbD2anytJSplAe07aWZ9Z5wjlSp2KR5Frs5E0JZmmb888TuX1ysonBeWcjII4pVSYHtVrAAAAYPSSTKjbig/orAUXalFliSRpktetg6fur/dP2kduPjWq1zFlT1t7W+m0DU9R2ZRVthV1ODmlorScgqdklIsJAwAAoD5jpLLN6ndXGv3p71L/Sxm2Oes6Ov6wtLbbPJSryqhey1dJh+7h6T07BRosGlkrdWWtOlIVxVG0Ft8FAADAxOO6jrqSpTK3fF965nopCWVkFKy/vYJ3nKShzMaqxPU30koSqwEnUuqDs5Q5YF2pEMn4juLAqOhbhWTjAAAAGirlxsrln5RuOUVa/IAkybgpZTffX+mdv6wBZ8qocm1RnGiwwyj9idnKRpItxjIZV6EnDblWccyCIwAAAI3U4RUVzL9VuvNUqe8FSZKTmaLcW45Seusj1R/lRvU6oQ2Unb2Ddj7+AtmwoLhSkp/tUuJ1KFRKdgJvsgYAANBoxkjd3pCcB8+WHv6TVOqTJLmT56hzlxNUnrmj8lF6VK9VVlazdjlS6253gOLigKys/EyXEr9b5VHclwUAAMDoDWfjlsnc+n3p6b+NkI3bSJXYqfs6SWLVZ7uV3vEbSu9wtEy5X3JTir2cCqZH4Sj2ZQUAAMDoDWfj5kq3niItul/ScDYus/n+Sv1X2Tipz1tPmb1PU2DzMpUhKcipYrIqqpNsHAAAQIN1eCUFC259KRv3vKSXsnHbHKn0mz6igbhDo1nyrRgHCqdsr+z7zpeXFKSoJJvqUtl0qBinZFk3DgAAoGFWZ+N+Jz18/quycZuoc+fjVZ6186izcQNRVsFmRyi76XvkhIOSTZQEXSo63SqHZOMAAAAayXUdDab7dfqLf9DN/XcpspGMjLbp2FLHzfqkZjgzpFHMway1CgoZfXbyUfr49IM1lOTlG08ZZZSt5BSVuKcKAADQUH6i+c5C/XzhOXoo/29JUmB8vbNnJ312xpHqKHWNKtcWx1aTS1N08sxjVVmvrKGkoKyTUSpOKRVmyMYBAAA0WDYqy9x2h4Z+cpri54fXjTOTJylz1OHq/PDhGkrlRpWNKzm+vG3epuwffyenWJBKJZmuTlUyHRpwA9aNAwAAaCBjJD9O6YlLC3r86oLKA8PXzLrX8/T2j3VqnTf5Ck04qtcKTUUb7eVq9i5TFRWsbCL5HUYmHasSl9fm2wAAYEzyWj0AjG3GSJ6X6B//+IeKxeIa55YvX65//OM27bbb7nKcQMkoLqhZK4WKpCCSE0iJhv9RtFaGDwAAMGF5nqNKJa/bb//HGouCJEmi+fPna9WqVdp5511UKo3u9ZLEKnFCKS056VdN3+KGDx0AAGBCS4yvS2719P3z1zze2y/99EJp3iJH3zgyLd+MbiIXRbHSblFpV9LLa5VYiWgcAABAY4WZsk5Z+AvdPfjgGsefKy3Ql5//vr61/he1W7Cj7CgXf4srVqlKVillVx9r6IgBAAAgSQPltA45WVq8YvUxa6W7H5c+9E3pr99Na/3JhVFtymWtZGyorBcq27X6eEQ2DgAAoKE8z9E8Pa/PPPMNhXb1ZKtsK7p25S16PP+Ufr3JdxUMZUb1ekli5ZYCZRUoq85XjsfcVQUAAGisINY1gzfrtMW/X+Pwqqhfv13yJz1XXqhjp39SXjEY1ctFUawgyihQ5lWzOMkyjwMAAGiocpLWl88wumXNaJyeWST970+kH33G1z7bJlIyuhujcRQp40bKdLz6WAMHDAAAAElSl1bIXHSwNLDoVUettOBu6cIPqOOwy9Sf3mTU2bhSHKvkSOqQXlo1jnXjAAAAGszzHOUGn5D+crAUV1afiMuyT1wq8+KD6jr4Iq1S96heL0msCopV8KThm6ovPaXKw6oAAAANlfFCBXMvkW79zponir3SnafK7X1ane/4tgaj3KheL4oSyemSUl1SSipLLy0aRzYOAACgkTrdvJzrj5eeu3nNEyufka78tFJ7/1DRRgeoHLmjer0wkuR0S7nh63dliWtxAAAAa0GXel/Kxi181dFXZeMOvVT9mU0Vx0nd17JWKkaeipokeZOGD8bSKxk5AAAANITrOsoN/fulbFx59YmXs3GL/6WuQy7+r7JxeWWUV0bypr5q+sY8DgAAoJHSXqTgqUulW05e80SxV7rr53J7n1LHHv+nwSg74s//pyhKNKAuSV2Sp1fdTyUbBwAA0Eidbl7ODSdIz9605omV86SrPqPUXt9XtNH7VI5Hl42rRFJF3cP5OGl4Hkc2DgAAoOEK6UF9Zt7Xtbiy7JVjVlYPDj2uTz39VZ216Q+0brLuqLNxKjtKl3NKa/WzrRH3VAEAABrKdR294CzQp5/5mio2fOV4xYb626rb9Fh+rn67yf8pyI/unurLe6r+525cMfM4AACAhkoplr3qKg1967trHLcrV6nws9MVzH1aue+crKFg9Nm4oXSHlO5Y84QlGwcAANBIXhzojp8P6Pk7S2sc718Y6cbvrNIuR3drg908RXZ0G2qFUSz5sdQtGb20DRfZOAAARuS0egAY23zf1bx581QsFkc8H0WRHnvsMXlekwcGAACAmhwn0cMPPyRb5cbn4OCgFi1aJN8f3cOqAAAAaI5ixdepf6l+/vLbpeUDrowxzRsUAAAAanJdo+fDBbp78MGqNT9b/DuVgpHvuQIAAKA1HNfTedcZLV4x8vlCSfq/P0jlKGjuwAAAAFBT0S/ox4vOVFjlYdQXyot0Q9/t8nzuqQIAALSTkl/Ub1+8oOr5G1b9Q722l2wcAABAG3Fdo2cXu7qlejRO3/2DVAy5pwoAANBOUr6VeegP0sCikQvConTrt5VRvrkDAwAAQE0ZOyDd9C0proxcsOo5mX9fpoBsHAAAQFtJ2yHpjh9XL5h7lfz8i3Ic5nEAAADtwnGMvL550nM3Vy+67bvK2sHmDQoAAAB1pTzJPPxHaWDhyAWvZOOGmjswAAAA1JTVgHTzt6S4PHJB3/MyT/yVbBwAAECbydhB6fYfVi946lr5+UVk4wAAANqI4xh5/c9Jz95Uvei27ykrsnEAAADtxA2MLlt5vRZXlo14vmTLOnXx2Sr7xSaPDAAAALWUg6J+uugsVWw44vmFlSW6uu9meYHT5JEBAACglnRhUPkf/LTq+cq1N8gsJhsHAADQThzHaOjFRM/fWapac89ZAzIVt4mjAgBg4uBuF16nWPPnv1CzYtmypTLGNmk8AAAAGA1rE/X399esef7552RM3KQRAQAAoB7Pc3THo0blkZ9xeMX5N0iux81VAACAdhF7kS5ccVXNmsF4SM+Unpfr8qADAABAuxgq+7r41to1/3xMKkVciwMAAGgnJVPS3OKzNWsu771eeS/fpBEBAACgHs9zdN/QIyrbSs26v/b+TW7QpEEBAACgrth6+v11tWsG8tITz4tsHAAAQBtJJ/3SYxfVLpp/l3zLxqoAAADtxE/y0tJHahc9+mel476mjAcAAAD1eZ4js+BOKaq+IZckmYf/oMBlHwcAAIB2kXJC6cHf1y4qD8ose0yuy9ZvAAAA7SKd9EmP/aV20YK75dtCU8YDAACA0fGTgrTk4dpFZOMAAADaiuc5MgvvlqLaz6Gah84jGwcAANBGUk5UPxtXGZJZ+jDZOAAAgDZS8PK6cuWNNWseGnpCFbfcpBEBAABgNMpOWY8VnqpZc0Xv35V3h5o0IgAAANTjeY7C+x6QCrWzceXzzpevpEmjAgAAQD2uXD1+Re21RMKC1bInQ7JxAACsBfzXFa+L4xhVKpW6dUnCBTkAAIB2YYwUhmHdunKZhxwAAADaiTFGS1fVr1u+SooTs/YHBAAAgFGJFKk/Gqhb1xutkjHM4wAAANqFMUarBmvXJIlUiZjDAQAAtJN8Un/j+75oQNawIRcAAEC7MMZoRVQ/HLciWqlYcRNGBAAAgNGoxEYr69xTlaRlfYZsHAAAQBtxZKRivetxViauv7YcAAAAmsdW8vWLCitl2JALAACgbRhjpPyyunU2v0zG1l8nGAAAAM3h2FAqrqxfmF8monEAAADtwxiRjQMAABiLwvrrxqlINg4AAKCdjD4bt5xsHAAAQBsZzsb11i/MLycbBwAA0E7M8F5btVhZVbgWBwAA0FaKSaluTT97qgIAALQVY4zs8hV165LlvTIh1+MAAADahY2NSn31n0EtrkzIxgEAsBY4rR4AxrYkscpmszVrHMeR47hNGhEAAADqsVZKpVJ163K5nCSuyAEAALSLJLHabL36dZttIHkODzoAAAC0i0CB1k+vW7dug9S6ShLmcQAAAO3Dav3ptSvSgZT2mcMBAAC0ky63U06dR2XWTa0j1/KMAwAAQLtIEquNU+vXrds4vYFc6zVhRAAAABiNlGe18cz6dZvMEtk4AACANhJbI3XXuR7npmS9dHMGBAAAgNFJdUumTu6tZ0Ml4p4qAABAu0gSK03etH7hlM2UmGDtDwgAAACjEjspmUkb1y+cvDHZOAAAgDaSWCN1b1C7yA3IxgEAALQZm+okGwcAADDGjD4btynZOAAAgDYSjTobtwnZOAAAgHaSSOsGM2qWeMZT2qSaNCAAAACMRs7JylPtbNy6qRlybO19VwEAANA8SZLI3aT+PVV30zlKUlyPAwAAaBeOb9W9fv1nULvX98jGAQCwFnC3C6+Lta423niTmjXrrbe+rDVNGhEAAABGw1qj6dPXqVkzZ86mShLmcQAAAO0ijhO9eY5VT0f1GseRDnqHFIZR08YFAACA2mzF0aFT31uzZlYwXbO86QTkAAAA2khnqqKP7le7Zt8dpEwQNmdAAAAAGJVUnNJ2nW+uWXP41AOVCbNNGhEAAADqieNEb8jMUY/bVbXGyOjASXspqiRNHBkAAABqSiJ9dN/aJetOldafnpCNAwAAaCNFt0d668dr1pjN9lPZ1HiYFQAAAE1XMVlp9m61i7b9pIpOd3MGBAAAgLriOFEy/Y1SZkqNKiO96TBVIu6pAgAAtIty5Mq+5aO1izpnKeneiGwcAABAGyk6k+pm47TpvmTjAAAA2kxFOWmjd9QueusnVXR6mjAaAAAAjEYcJ0qmbSVl62XjjiAbBwAA0EYqkamfjeuYoaRnY7JxAAAAbSQXdujQae+tWbNH9w5KxekmjQgAAACjkYrT2rHrbTVrDpt2gHIRzzgAAAC0izi2ct6wuZyptbJxUuqIQxTG3FMFAABoF2EcacsDsjVrslMcda/vko0DAGAtcFo9AIxtYRhrvfXW15QpU0c8n8vltMUWWygMmcgBAAC0kyiS3vzmNyuVSo14fubMWZo8eYqiKGnyyAAAAFBL1i/rF0dLvjfy+W98ROpMh80dFAAAAGqy1mqKnayPTv/QiOfTTkrf3uA4ZSq5Jo8MAAAAtURRrPfuaLXdliOfX3+6dPyhVo6NmjswAAAA1ORXUjph3f/RVG/SiOd369pO2+beRDYOAACgzWQqWZ2y4bHyzMjhuKNnfUwdMQsAAwAAtBNrraZ1R/r8B0c+nwmkXxwt5fxKcwcGAACAmsIwUbLZe6T1th+5oHs9JbucoGIcNHdgAAAAqKlos9I7T5E61hm5YOM9Fa+3K9k4AACANpNXt7T/LyS3yvW23b6mkjdZlm0cAAAA2oa1VuXUdGnHY0Yu8DLS/qcNz/UAAADQNsIoUbLpftL6O45c0L2e7K5fUTEeea8uAAAAtEbRZqQ9TpY6ZoxcsNEeijfYTVEUN3VcAAAAqC2vbmm/02pk404kGwcAANBmrJXKwTRpp2NHLvDS0v6/JBsHAADQZqIo0e6dO2jbjjeNeH5mME2fn3mUnLLf5JEBAACgFq/i67hZn9B0f8qI53fsfKt2zL1VYUg2DgAAoJ0Ucp3q+OVPpdTI2bjsV7+kcBLZOAAAgHZireR3Wb39o50jnncDac+vTZJNh00eGQAAE4PX6gFg7CuXrd7+9rdr8eLFevbZecrn80qlUpo9eyPNnj1bYWhkLRtyAQAAtJMksTLG1TvesYeeffZZzZ//gsIwVEdHh+bM2VTTp6+jcpk5HAAAQLuxSaw3b1TWVT9I6Zd/lW59UIoTads3SF/4oLTprEiuKq0eJgAAAP6DW/J12KQD9OaOLXTe0kv0ROEZBcbX7j076KPTP6RJ4STFEU85AAAAtJuUU9Tpx2R03T1G514nLVgmTemSDtlTOmxPq46gpDhmHgcAANBOksRqUnmyfrfpj3RR7zW6duWtGoiHtGFqXR0+7QDt3LGtvEJKzOIAAADaSxJKWwab64+bnarfLb1I/xx4QLGNtXXuDfrEOgdrY3e2TIlHogEAANqNp7I+9m5p2809nX6p9Mg8KfClvd4ufe790rTOspKEjRwAAADaTX/Uqe79z5DzzHXSg7+T+uZL2cnSGw+RfdOHNaDJsglrjgAAALSTJLEacGeq87DLZR46T3r8EqnUL03eSHrrJxVvtJcG4lyrhwkAAID/EMZGhclvVfbDV0v3/Ep69mYpDqV13yrtcIzKk7ZQIUq1epgAAAD4D/k4I/OmjymY9Tbpnl9KLz4sub7MnL1lt/+8hoJZCqNWjxIAAAD/qT/qVPd+p8uZd530r1dn4w5+KRs3hWwcAABAmxnOxs1Q52GXrZmNm7SR9LZPvJSN62j1MAEAAPAfwljKT36LckdeI939K+nZm4azcbPeKu1wtMqTtyQbBwAA0IbycUZm65eycXefRjYOAABgjPALaX1n/eN06+A9+svyq7SwskSTvC69Z/K79IHJ+yhX6lRsycYBAAC0kySx6qlM0jlzfqhLeq/T1StvVn88qPVTM3XY1AO0W+d27KkKAADQhqLEqLLV1uq55lIVf/UbVW68VTasyH/Lm5U5+rNKtthSBZdsHAAAQLuJTKjN9k9p+haB/nXBoJY9WZHjGc3eKa1tDuuQ3xMrirmnCgDA2uC1egAY+6y1KpWkddZZVzNmzJQxRtZaWeuqVIolbqsCAAC0pTi2imNp9uw52mijjUeYxwEAAKAdGRtp3UmxvvsJX6Vw+BKv78ZKe6FibqoCAAC0LacY6M3u1vr+unMUu7FkpXScloqurOWeKgAAQDuy1iowBX1oV1d7vz2QlZGxVh3pUHEUKea2KgAAQFuKY6tMvlMf6zpUh04+QDJWJnGUizoU5mOecAAAAGhXFUfTnXX05en/q/KMsmQkL/YVhGnFIdk4AACAduWprG3nhPr1sYGixJEkZfxQjiLZhKtxAAAA7chaq76oQ/6cQ5TZeB85JpG1UsntUTmUJK7HAQAAtKMotlqlKUq97Vilt/m4jJES66joTlIY8oADAABAuyrFnirBRkrv/n8KditIkiIFKqpDccS1OAAAgHY1FGXkTtlRmf3eKE+hZKWyk1MpDmQjsnEAAADt6JVs3CYvZeOUyMqo5HSTjQMAAGhjZOMAAADGpnLsKfRnv5SNy0siGwcAADAWDEVpuZN3UGa/rYazcZLKhmwcAABAO7PWysun9e5gD+06++2SYyVrlI2yivNSTDYOAACgLUVRonTUoaO6DtJBk/dnT1UAAIAxomJchTPWV+qUb6nrxBNkjBR5gYrpnOKYa3EAAADtKjShujZztMfXu6SX9lQ1QaxIoSLL1TgAANYWr9UDwPgRhi9ffHt58saCIwCA9pZyrPyhARlJse+rnO7gZhImJOZxAICxxHGMUlFFXml4M8kwk1XZCWS5mYQJJkmsHFWU9SqvHIuZxgEA2pjrOkqVhuSGoawxqnR0qcJ/uzABxXEiL069cqPevur/AgDQjjzPUWqwX45NlDiuyh1dili8HhNQGMbKuMVX/ncctXAwAACMAtk4YFhSkVKV7Cv/OyQbBwBoY45jFERl+aXhaxBhOqOymyIbhwknSaxU8pR61ePPbOIAAGhnruuoGPmKYkdGUi4VKuFmEiagOE4UOCUFzksHLMk4AEB78z1HmaRPRomsHBWdHoVk4zABhVGiUF2rD/AxAAC0uVRglXb7JVnFNlAx7CQbhwmpHBqV1bP6QEI2DgDQvowxiuWrFLqSpLQfyzPhcE4ImECSxKqQBCooePXRlo0HAIB6XNdRKfIVko3DBBfHiYaUW30gkUjHAQDamee5Gir7SqyRY6w6UqGiiHtJmHhek43jYwAAaHOO62mo7EuSfDdR2gvJxmFCIhsHABhLjDFK5KtINg4T3HA2zlfh1fM4snEAgDZGNg4Y9ppsnCSycQCAdvaf2bjOdKgw5F4SJp4oSpSKVu/FxacAANDuAkcKhgZkZBV7vsoZ9lTFxBSzpyoAYAwxxiiVVOQX85KkKJVV2U+RjcOEY61VyQlUyr5q3Tj+ngEAtDHXdVT2i4qcSLJG2TirpNLqUQHNF8eJ5CaS+9IB/pQBALQ5z3NU8POyJpGxjrJRTlE4tq5BeK0eAAAAQLN5jpTr71XpvAs0dNV1svm8vC02V+Zzn5Z941bKe5lWDxEAAAAjSNtQwYJFKp7+WxXuvEcyRsEuO6rzs/+j8oxZKhsudQEAALSjjqgoPfCIir8+U9Hcp2U6ckod+B51f/gwDfVMURyTEAIAAGg3jmPUURxUeOXfVTjvAiVLlsqZOUPpjxyh7D7v0lCmk4dWAQAA2pDnGuX6Vqj0hz9p6MprZQsFeZtvOpyN2/qNZOMAAADaVMpGSi0czsYV/3m3JK3Oxs1cl2wcAABAm4qU1oNPu/r1FdLcF6SOjPTenVM6cp9A3ekSGzoAAAC0Iccx6nIG5Mz7m/TgedLQEqljhjq3OUrJnHdrIOkiGwcAANCGPNeoM7VCZsV5Uu9VUjwkN/sGBTM+qzCzjQaLZOMAAADakTW+XuwL9OvLpDseHT6289aOPvs+XzMnVWRs2NoBAgAAYESR0nroGVe/vlyaO1/KpqX37jScjevJlBXHcauHCAAAgP/gOEbFOKOr7zQ672/SkpXS9EnSUfu42md7q4xbJBsHAADQhlzXUX8prfOvMbryDmmoKG2+gaPPvM/TmzaO5anU6iECAABgBNb4WtK/OhtnrbTTGx199v2+ZpGNAwAAaFuR0np4nqvTL5OefEHKZV7OxqXUnS4pScjGAQAAtBtjjMpJRtfcZXTudWTjAAAAxgrXddTRv0Kl8y/U0OVXyQ7l5W2+qdKf+ZTMNm/WEHuqAgAAtKWUIqVefFHFX5+p4u13StYq2Gk7dXz206qsu75K7KkKAADQluJMRU+Gz+ncxRfrqeLzyjgp7T1pVx00ZT91lLuURK0eIQAAAP6TMUZhtqibB+/VRYuv0bJwhab6k/WhKftqj64dlSpmx0w2jquGAABgQnFdR9nF89V38JGyK1e9cjy8+z6Fd9+nzP98TLn//R/lfUJyAAAA7SSwsdy77lLf546TotV3UMuXX63y1X9T129Pk91ue1XktnCUAAAA+E+5sKjKaaerdO75rxyz/QMq/voslS+6VF0X/VH5GesqjsfGzVUAAICJoqMwoKFPfEbRI4+9ciweGFT+6yfLu+QydZz1Kw2kOls4QgAAAPwn13WUfXG++g7+iGzvyleOh/fcr/Ce+5X55FHKffZ/ycYBAAC0mcDG8u6+S32fPXbNbNwV16h8zfXqPOPnstvvqIohGwcAANBOQqV1+mWufn/t6mP9eek3V0gX32p04ckZzewuKo6T1g0SAAAAr9Hl9Mu54pPSkodWHywPSDd9Xc7jF6nrwHPUl3S1bHwAAAB4Ldc16grmS/8+WIp6V58YvEcavEf+Oh9T59SjNVjKtm6QAAAAeA1rPN07N9BnT5WiePXxq/4pXXe39KtjA+3wBitj2ZULAACgnYQ2pTOucHXONauP9Q1Jv71yOBv3l5PTmtlDNg4AAKDdFOOMPv0TowefWn1sIC998xzpoluNzv5yRmmn0LoBAgAA4DVc12jpQEaHnCSt6F99/N5/D/9z1L6uvvD+tHxTat0gAQAA8FqOp/vmBvrMT9fMxl19p/S3e6RfHhNopy2sRDYOAACgrURK6TdXujrrqtXH+vMvZ+OkC09OaxbZOAAAgLZTSjL6zE+NHpi7+tirs3HnfDmjFNk4AACAtuK6Rrmli9R/0IeVrFi9blx47wMK731A6Y8crtwxX2BPVQAAgDYT2Fj+A/ep71NfWHNP1av+pvK1f1fnr36qYOddVDFeC0cJAACA/xRnKrpg1WX647LLXjk2GA/p/GWX6+rem/WbOd/VdDuDbBwAAECbCbNFfWX+D/VI/t+vHBuM8/rxojN15cobdersbyrIj417qk6rBwAAANBM6cKAho77quzKVSOeL575eznz58txTJNHBgAAgFoyhUENHn3CGuG4V0SRBr9wvDKFoeYPDAAAAFU5jpHz7DyVzj1/xPPJil7lj/+a0szjAAAA2krgSOXzzlf0yGMjno8efFiVC/6iwOWeKgAAQDtJFwY19KUTZXtXjni+ePZ5cp5/nmwcAABAm6mbjTv6BGWKg80fGAAAAKpyHKPnl7j6/bUjn+/tl074tVQIg+YODAAAADWlPCvn4T9ISx4auWDJw3Ie/oNSflOHBQAAgDqy/qD0/PFS1DtywdLfy4/nkY0DAABoM8Uw0NGnSVH82nNRLB172nANAAAA2ofjGL2wzNM514x8fuWA9KXTycYBAAC0G9f19Ke/Gz341MjnH50n/eF6I8fzmjswAAAA1FQIU/ryGdKK/pHPn3ed9OyLLtk4AACANlMIAx1TIxt33C+5pwoAANBuHMdo/jJPZ1018nmycQAAAO3J9TxdeJPRA3NHPv/oPOn31xk5Ltk4AACAdpIu5pX/8jeUrBh53bjSH/4k8/TTZOMAAADaTKY4pMEvVNlTNY41eOxXlCkMNX9gAAAAqMpxjF5MluqPyy4b8XxfPKCT5v9cpaDQ5JEBAACgFjcwurLvRj2S//eI5+cWn9Wfe6+QM0YecXBaPQAAAIBmMUZy+/sVPfJYzbrSb89REJWbNCoAAADU4/uuKjfdIpWqz9FsoaDw9n/K89wmjgwAAAC1pKKyir85p2ZN+K+H5A4ONGlEAAAAGI10vl+lP11Us6b0xz8rNdjXnAEBAABgVNyBfkUPPVqzpnTmOUqFZOMAAADahe+7qtxym1QsVS8qFBXedrt8n2wcAABAu4gSX7+9snbNQ09Lg0XmcAAAAO0kY/ulRy6oXfTIBcokfU0ZDwAAAEbHN/3S0IO1i5b+Vmm/xn1XAAAANJXnubrtIaNijccXihXplgcN2TgAAIA2EllfZ9bJxj0yT+ovuDKmOWMCAABAfYNlX+ffULvmzzdK+fIY2VkVAABggsiXXD0wt3bNmVdKYeI3Z0AAAACoy/Nc3f6wUb7G4wvFinTTv8jGAQAAtJPRZOMenSf15cnGAQAAtJPBkq8//K12zYU3SfkK2TgAAIB24g4NKLzvgZo1pd+eo1Sl2KQRAQAAoB7PcxT+807ZoaHqRaWyKn+/iWwcAABAG0n8SOcvv6xmzdzis+rXANk4AACANlLw8rp4+TU1a67svVFFL9+kEb0+TqsHAAAA0CzGGMXzF9Sti+Y9K7fMxqoAAADtwnGM4kceq1sXP/KoXJc7qwAAAO3CKZUUz3uubl2yaLEch3kcAABAuzBhJLtyVc2aZEWvTBw3aUQAAACox3FGm417Tk6FbBwAAEC7GG02LnrkMe6pAgAAtJFy5OjZRfXrFi4T8zgAAIA2YmwkFXprFxV6ZZKoOQMCAABAXY5jZMv1s3EqzpNryMYBAAC0C9c1euTZ+nWPPss9VQAAgHZSDh3NW1y/buEyK8POqgAAAG0jsUYr+mvXrByQQqJxAAAAbcNxjBYur183b/HwM60AAABoD65r9OgosnGPzCMbBwAA0E7KoaNnRrFu3AKycQAAAG1lNNm4VYNSyJaqAAAAbcNxjJJFL9ati+c9J6dSbsKIAAAAMBqO4yh+5PG6dTF7qgIAALSVsqnohVL9cNyi8lKycQAAAG0kMYlWRKtq1gzEQ4rM2AjHsSoKAACYMKyVnJ6eunVOT7es6639AQEAAGBUrJXMtCl168z06bLWNmFEAAAAGA3rujI9XXXrTHc38zgAAIA2Yj1X8urcLw0CyXWbMyAAAADUZa3kTOqpW+d0d8syjwMAAGgb1lo506bWrXOmT+OeKgAAQBtxHKvujvp1PZ1iHgcAANBOjCs5fu0ax5cc7qkCAAC0C2utjNdTv9DrlhXzOAAAgHZhrdX0nvp103q4pwoAANBOXMeqJ1e/rqdj+LlWAAAAtAfHSEGdZeN8r/7ScgAAAGgea616RvGcanfH8HU7AAAAtIfRZuOm93BPFQAAoJ24jtWkzvp1kzoM8zgAAIA24hgpVWfZOM8d/gcAAADtwVor09Ndt870dMu6POQAAADQLqy1MtOn1a0z06ZxTxUAAKCNuHLV6dV/WLXb62AeBwAA0EYcGaVMULPGkyt3jOzF5bR6AAAAAM1irZVZb5acOjdXUx8+TJXsKFaZAwAAQFOEYaTUB99Xu8gYBQfsrzCMmzImAAAA1Bd2dCl9xKE1a5xZM6V1phOQAwAAaCNhOqvU3u+sWZPady9V0pkmjQgAAAD1WGtlZs6Us870mnWpIw9VJdvVpFEBAACgnjCMFXzggNpFxig48L1k4wAAANpIRyrSEXvXrpk5RVpnUkI2DgAAoI2UTU6as0/tok3fPVwHAACAtmCtlHgzpWBm7cJpH1Ypqr8BKwAAAJojDGMduItkTPUaY6T37yqycQAAAG0kG9TPxq0zSZo5xcoSjgMAAGgbaT/SPtvXrtnr7VLai5ozIAAAANRlrTS9J9GsqbXrjnjX8DOtAAAAaA9hGOu9O9fOxknSB3Yf3n8VAAAA7SEbhDpir9o10ydJs6YmZOMAAADaSMaPtO8OtWv22pZsHAAAQDuxVtLUqXLWm1WzLn3EwQo72FMVAACgXYRhrOA9+0qOU7Mu9aH3kY0DAABoI+korQ9OfnfNmslej2b565CNAwAAaCOpKK139uxUs2aX7m0VxKkmjej1qX1VEQAAYJwpZruUO+nEque9rbaQt9MOiqKkiaMCAABALdZKUfckpQ/9UNWa9EcOV9TZJe6rAgAAtI8wjOXtvqvcN2w2coExyp10okod3c0dGAAAAGoqeylljj9GpqNjxPOms0OZY7+gijc2AnIAAAATRTHXpdzJX6u6K5e3xebydt5RURQ3eWQAAACo5pVs3OEHV61Jf/hQRV3dZOMAAADaSBjG2nVrqzdsOPJ5Y6RvflTqCCpNHRcAAABqKyUpaZfjpVTnyAWpTmnnLw3XAQAAoG0Uwm5p/ZMljZyNU+YNSjp3IxsHAADQRqyVurKRjtires1he0pdmYhsHAAAQBuJolg7v9FqixrZuG99TMqRjQMAAGgrrkIde7DUlRv5fEdG+tIhkmfC5g4MAAAANeWCir710arLxmmz9aXdt7EKQ7JxAAAA7cJaqSMd6ch9qtccuqfUnSUbBwAA0E6iKNGOb7TaaqPqNd88imwcAABAu3EU6uiD6mTjDiUbBwAA0G5KHd3KnVR9T1V3szny9ngH2TgAAIA2E3Z2KX3UEVXPpw/+gKKeSWTjAAAA2kgUJXpbbmttntm4as2xsz6uTKXKTVcAAAC0hKl4+uQ6h6jT7RjxfM7J6DMzjpRX8Zs8sv8/TqsHAAAA0ExhIiU776LOs34ld6PZq0+kAqU/9D51/v43Gsp0tmx8AAAAGFnBzyj15eOUPf6LMpMnvXLcTJms7FeOU+roz6vgpVs4QgAAAIxkKNulrvPOVOoD75WC4JXj7pyN1fW7MxRvv4PCmKccAAAA2kmSWBXWmanuS/8kf/ttV58wRv6O26n70j+rMHUdJQnzOAAAgHYSJlKy407D2biNZ68+kQqU+uAB6jzvTA1lulo2PgAAAIys4KWVOv4YZU845rXZuC8fo9SxXyAbBwAA0IYyXlHnnmj1/l2l1KvWFtlklnT2l6Ud3xAqjtnIAQAAoJ0kidWgN1M69K/S+jtIenljLiOtt7106F816M0kGwcAANBmKqFUSu0szTlbSm+y+oRJyUz5gOym52mg2N26AQIAAGBEnso65qBEJxwmTX7VowyTO6UvHSp96ZBEnim3boAAAAAYUcYr6vcnWr1/tzWzcRvPks48Qdppi1AJ2TgAAIC2Yq3V1I6SLv62tP2WkjGrz223pXTxd6TpnSWycQAAAG0mjmNtt3moc74izVl39fHAk96/q3Te16wyXrF1AwQAAMCIfFPWFz+Y6MuHvzYbd9wh0vGHJvJENg4AAKDdZNyizvmK1Qd2Hzkbt/NWZOMAAADajbVWU3IlXfJtaYet/iMbt4V08beldbrIxgEAALSbMLaK376dun7/G7mbvmrduCBQ6v3vVdcfztZQlj1VAQAA2k3RSyv1hc8od+LxMlOnvHLcTOpR9rjPK/XV41XwMy0cIQAAAEYSFDM6dfY3tP/kPZUywSvHN0jN0o9mf1Xbpd+qOOSeKgAAQDux1qqnMllnzfm+tu3YWkarw3Hb5LbUb+d8X1OjqWMmG+e1egAAAADNVnQDebvspo6L3iyTz0vlkkx3j8rZnAYcX3aMTOQAAAAmmkE/q+BjH1P3wR+S+vqGn1rt7lYp16XBpNWjAwAAwEiSxGog16PUSd/UpC9/Sba/T0qnlWRzKnV0K4qYyAEAALSjKDHKr7ehUmecpo5iUcngoJyuToXpjPLpnOKYe6oAAADtqOgG8nbeVbm/vElOIS+VXsrGZXIacMnGAQAAtKvhbNxH1X3Q+6X+geGDZOMAAADaWpJYZb2CTjoq0AmHe+ofMkqnpGyQqCNVURSxqSoAAEA7CmOj/tRGyuz7GwU2L1selEl1qmJyKopsHAAAQLsqlAOF3u7KznmzXOVlk5LkdasUdapY8GUt8zgAAIB25Kuoj+zt6YO7BxrIG1lJ3TmrjlRFcRS1engAAAAYwcvZuG99JNCXD/PUN2SUDqRsOlFHQDYOAACgXdkk1rqTCjr9mECl0NNAQerKSmk/UtqrkI0DAABoU64q2n7zWBd8M1Ch4qhUlno6rNJ+JFehEtaNAwAAaEu+ijpyL08f2I1sHAAAwFixOhvn64RD/dXZuBTrxgEAALQzm8SaNamgXx29ZjYu5cfKeBXFMRs5AAAAtKOSG8jfYSfl/nTuf+ypmtWAm2JPVQAAgDY16GcVHPlhdb//AGlgQEoSqadH5VynBhPT6uEBAABgBElilSpkdcyUj+t/1zlcA/GgUk6gjDLKVnKKStxTBQAAaEdJZDUtma7vzjpBZbeswTivDjerVJxSKsyMqWyc1+oBAAAAtEIUJRpMd0rpzjVPkI0DAABoa5XEqJLpkjJdqw+OnWtxAAAAE5K1UskJVMoFUq5n9YmIiRwAAEA7i2Orgp+V/KzUNeVVJ7ipCgAA0M6iKNEQ2TgAAIAxpxJLlUy3lOlefZBbqgAAAG3NWsmoopxXWTMaF7VsSAAAABiFOLYaUlZSVvKnveo6HDdVAQAA2lkYJeqPuiS9tN5IuaXDAQAAwCglUaSMG62xbFzMPVUAAIC2Zq3kqKKsV1G2Z/VxsnEAAADtLY6tfFOWH5TVGbz6eOvGBAAAgPqiKFbaLSqdkZR56aDlCQcAAIB2RzYOAABg7LFWMjZU1gvJxgEAAIwhZOMAAADGpjCMFY64pyrpOAAAgHY2vKdql9YIx7GnKgAAQFuzVlLZVbqcU1q5V45HTOQAAADaWpJYuaVAWQXKavV91XiMzeOcVg8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7cVp9QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADQXpxWDwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADtxWn1AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANBenFYPAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO3Fa/UAAEiOY+R5rpRI1liFYbzGeWOMPOvJVhxFRSsvZaQgkfVjxXHSolEDAADAdR25jiNJSmyiKEpec95UPNnQKC5beRkjBbFiE8ta24ohAwAAQJLvu0rkyJFVHMdKkjXnZp7naqgcqFhxFCdSLm2VCypK4qhFIwYAAIAkBYGnxMRyrKMwjPWfl9icQCr6BRVsUa4cpZVWLswpDLmnCgAA0CrGGPm+qzgxckwyYjYulq9i6KlQNkr7UsqLlfErZOMAAABayHUdGU8ykmysEbNx5aCoilNW2YbKmJRScVpO2ScbBwAA0EK+78o6iYyM4tC+NhvnOyr4eZVUUqxEWZNRJswqqbRowAAAAJA0nI2LEyPXsQrD6LXZONdTvhIoXzJyHSkTJOpIVRRF8cgvCAAAgLXOGKPAd2SsFEsjZuPScpSKjWwplgkcRa5U9CzZOAAAgBZyXUee48hIiqqsG5eJjbxIspVEJuWq7FmVlJCNAwAAaCHfd2UUyxhHYTRCNs5z5FT6ZMOCkjiWl87JBt2qRKZFIwYAAIA0nI1TEkmOO+K6cb5n5VT6FZfzknHlBFnZoEdhxD1VAACAVlmdjYsVyx0xG5dxy0rZIZmwIHkphSargjrJxgEAALSQ6zryXMnIKkrMiNm4rAbl26IUlWS9rMpOh4pximwcAABAC/m+K1exbJVsnO85yto+uXFBSmJZP6eC6VI5clo0YgAAAEjD2TjHRrLGVWWEbFzKt8ok/XLCvGQcxV5WRWeSKuypCgAA0DLDe6o6SkwikzgjZuOSIFTZK6toS0oZX0GSUqqSIRsHAADQQq7ryPGMJFt9T1W/qIpbUdlWhvdUjdJyQ/81918BAADQPL7vyrGJZIzCeOR14zJD/TLFohRHUi6ncrZTZUs2DgAAoJWCwJOJI1l35HXjUo5VKj8g5QuSY2SzOZU6ulk3DgAmOK/VAwAmMmMkLwpUXGb1yLUFlYespm/ha6Nd0rLpSLGN5bqONOTr7rMH9Nw/S0qi4Z+dsVWgnT7bpfQMo8jGtf9FAAAAaCjXdeSUffU+GWneLQXZRNpo17Smb5FSkq4ojq1c46q0yNGdv+7X0icqkiTHkzbeNaPtPtEpmwt52AEAAKDJjOMqX0np5nuN7n5CyqWlg/eQ1p8eK+WUZa1VrJRu+ZenUy+S5i8d/rmeDqMj9k7pY/v6CkzxNTdiAQAAsHYlqUh5d0hXr7pZC8qLtY4/TQdO2UudSYfcUiBjpDBb0oUrr9KlK67XYDwkSVovmKFPzzxc26XfIqfkt/hdAAAATCzGSJUkrSUrXf35Jql/SNpmjrTvDlYdQUWykVzXUV8xre+fb3TDfVL4UjZu281dfetjGW04raxXAnMAAABoCtd1VErn9VjpWV2/4h9KlGiPrh31ptwblCnlFMdW8hPNdxbq1IVn65H8k5Ikz3h6R/cO+sLMo9RZ6iYbBwAA0GSOb1UMCrpp8H79K/+o0iatAybvpVneOgqKGVlrlaRD3V5+UGcu+JMWVpZIkjrdDn1g6j46dPIB8gspsnEAAABNFivQYMnXX6+Xnl0szZoqHfLOlLqzkXxTljFSOcno3KsdXXCD1DccjdMG6zg67uC0dntTJFfl1r4JAACACcYYKWdduX2xon+8KDsUydsop9zbJ6voWZVtItd11Fk2qly8QIUHVkrx8IU3d06HOg6brdJUV+WEe6oAAADN5LqOOiJH9rmC4rt7pcQqeNtkmTkdynuxotgqcBxlVsSq/PkFVZ4efOkHjby3TVb3QetrMGXJxgEAADSZ51q5YZ+W3HOzlj15p7xUVrN3PUTZabNVcTplrVVgSup99DY9fulPNbT0eUmSn+vWJnscoTl7f1Jl00E2DgAAoMl8U5bK/Xr+lks0uPgZZafM0uzdD5OTmaxQGRkjpeyQnrnud3r25vNVyfdJknLTN9BW7z9WU9/4TlVsprVvAgAAYIIxRupw8/ILS2Tu/5NscZU0YxvZzfZX0XSpFPtyXUdddpnMbT+QnrpOSkJJkj/rrere42Tlc5uqHLstficAAAATi+sadZp+OcufkHnyMimJZefso2TWdhpSz3A2zkvUkX9auuVkadF9kiTjeErP2Uep3b6mAXcdsnEAAABNFrhWOfXLzLtZZsE/JS8ru/Uhijo31GDSJWutcl5RqUV3SHf8WOp7XpJkUl3KvfkIZd7ySfXHnWTjAAAAmizjlpVJ+qWHLpFWPiN1zlRu68NU9qcoHw9n47q8IbkPnSs99EeptEqS5Havr46djlNlg3dqKCIbBwAA0EzGSFG6rBV2pS7rvUED8aC2yG6qPbt3UjbMShVXrutoKN2v01/8o27uv1OhHd4/9Y3ZzXXcup/Q+s56Uui0+J0AAABMLK5rVEwVNK/yvK5bcatiJdq9a3ttk9tS6VJOSWxlfKtF7kL9dNHZeij/hCTJk6vdurfXF2d9VF2lHrJxAAAATeY7Ujbfr/Dafyj8510y6bRyh3xI2nAD5TOdShKrbFSSuf0uDf34F4qfe16SZLo6lT78YHV96uMaTOXIxgEAADRZOq4oNTSg8l8vV/zMPDkz1lHnYQcr6pmkgj+cjeso5xX+4QL1/+FPsqv6JEnOBuspe/TnFLxzD+W9dGvfBACgZbxWDwCYyPwopdt/1q8X7l69wf28W4u695wBvevrkzXljZ5M0dWVx/dqcGm8xs8uebyiK45doQN/PlWpmWzKBQAA0Cyu68gO+rr6xF71L1o9R3vmlqJy01zt94PJCqbGys93dOVxK15e+1eSlETDdUv/XdEBP5ui2C+P8G8AAADAWuF4euyFlP7nx9JQcfXhC2+Stt/S1WlHZ5T2Ql1+u6dvn7vmj/YNSadfKj21wNH/fSqtwJSaOnQAAICJLMmEunLg7zrjxfOVaPU90fOXX6Yjpr1PH5nyASWy+t6i03XHwH1r/OzCyhJ984VTdfy6/6N9Mu+QKiw8AgAA0CzlJKuv/tbopgdWH7v6TunHfzY67ZiUtt/caLDs69BTjBYuW/Nn758rHfRN6eLvpLTx9IRsHAAAQJO4rlF/apWOfvYUzS8vfuX4Datu13R/ik7b+GTNiGboOWeBPv3M11Sxq8NxkY10Y98deqLwlM7c5AdK5bMteAcAAAATk/GlpzRPx8/9nvLJ6nDclStv1Da5rfS9DY9XKkzr7/l/6CeLzlrjZwfjIZ239K96tjhfX5v5OXlFFh4BAABolkgpXXKbpx/+SUpedUv0zCulT77H02cONEoS6cQzHd14/5o/O3+pdMwvpZM+5ukDO8fSSxt1AQAAYO3rtJ6iPz6vyr9WrT54b68qly5Q+tNzZDbNKh06Kv7wCdkVa64pEj8zpOIPHlfmq1spmuaSjQMAAGgS1zXqLBmVTv23kiWr1wsJ7+mV6fGVO34LFXtcZZZFKn7/CSl81Twttoru7VX87JA6T9xSfQ5zOAAAgGbxnESlRY/ojp99XFFx6JXjz932F03ZbFvt8LkzlHgdevGeK/TgH765xs+G+X49efWv1b/gSb31Ez9VWblmDx8AAGDCCkxJC//5Fz36l+/L2tXX0+Zee6Y23ecT2uw9X5C1Vg+ed6IWPXD9Gj+bXzZf9/72WL35iG9p1o6HKkzYWg8AAKBZut0hOTedKD1zvezLB+deLXPHj5Td7zSZmbsqrbzMRYdI/QvW/OHF/5Iu/JByh1ysqGMrsnEAAABN4jhG3clS6dKjpJXzVs/jnrxSTm66uj50voaym6hj8Gnpwg9K0av220oi6alrZJY8pK5DL9UqTWrFWwAAAJiQAteqo/9R6bKPSpWh1fO4x/4ib9bb1PPeM1Q0XUo9c7V049fX/OHygHTvGXKWz1Xn3j/RQNTR5NEDAABMXDmvqNTcS6Tbvie9Khun+89S6q0fk7vd0bKJlXvT16Wnr1vzh/sXSNcdq2D3ryvzhsNVjPzmDh4AAGACC7Nl/Wjxb3RL/12vHPt73x0648U/6pQNjtXbg21U9Av67LxvamFlyRo/+1hhrj79zNf0mznf08beRooisnEAAADN4DhGA+k+HfPsd/R8eeErx29YdbumepN02iYna1Y8UwucRfrU0yeqZFev/xsp1s39d+rxwlM6e84Plc6z3ggAAECz+MYq/e8n1PexT8sOrl43rnTRpfLeuo06f3OaytlO6W/Xa/DEk9b4WTswqOJvzlH85FPK/fQHGgqYxwEAADRLNi7LXnqZVn3vx1Icv3K8eNa5Sh91hDqO/pyslUrfOkXla25Y42eT+Qs19KUTlfvaCUoferBKhmwcAExETqsHAExUvvH0r/PzeuHu8mvOxRXp799eqbjf1dM3FjW4NB7hFYbr7jpjQKbiru3hAmgh33fVURpUV7FfnYV+pUwiY1o9KgCYuJyyp+u/uVL9i147R8svj3XdV1fKFHzdeXq/knDk1xhcEmvu34ryPeZxwHgWuEadxQF1FQfUURyQ73EZBgBaaaCY0id+IA0VX3vuniekU35vVIoC/fjP1V/j7/dJi1a4chz+MAfGK2OM0orUWehXV6FfuUpBHvM4AGgZz3P0eGWuTn/xD0r02gVDLlh+uW7P36sBO6g7Bu6r+jq/fvGPKvsjTAQBjBuu6ygbFtVVHFBXoV+ZpMLfbgDQSo6vX/7V6KYHXnuqHEqfO1VaMRjoyn8aLVw28kuUQ+nbv5eKYbB2xwqgpcjGAUB7KaYKOv7572l+efFrzi0Le/XFZ0/WUHpApy4+WxU7cjhucWWZrlz1d7kBX+jAeBa4Rp0FsnEA0C6GggEd+9x3lE9ee0/0ofzj+vGi36ocFHX6i3+s+hq3D9ynxfFS7q8A45gxRtbxVYyzKsRZlZM02TgAaCHPc/TIs56+f76UjLCX1tlXSzfc76o/7+rG+6u/zo//LBUi7qkC45nrGuXconrcPvW4fcp5Zf52A4AWSjuu4mtfVPSvVa89GVqVznhG6SGr6J5e2RWvXVvu5brKn55XJuL7HBjPfN9VKU4PX4+LMjKuTzYOAFooGzkqnfaUkiWl15yzfaFKP/m3chWjyp9fkMIRLthJsivKiv6xTCnWjQPGNdfzVIwyKsZZFeOMPD7zANBSTrlXt//0KEXFodec633qfj30h2/IjQb16EU/qPoaLz58swrLnuP+CjCOGWMksnEA0DZc19HgCw/pkQu/J2tfe63t6evP0YsPXKu42KdFD1xf9XUeu+THcqLBtTlUAC3mukaRTb10LS6rWAF/uwFAC2W8WO59v5KeGWGOFlekaz6vTGWxzFNXSf0LRn6RuCLdcpKyGli7gwXQUq/Jxjlk4wCglTpNv3TFp6SV8157Mr9MuuTD6kxWSLeeIkWvzc9JkgYWyTxyvlKeXbuDBdBSruepQDYOANpGzvZKl35Eqrw2G6fFD8jc+HXlNCD94/vVX+S5m+X1k40DxrPV2bjMcDYuJhsHAK3kuo5SvY9Kt35HGiEbp3/9Xt4z18iPBqSnr6v+Qv88VWk7wjwQwLjhukaRUipEZOMAoB2YVKI/rrhUt/Tf9ZpzoY30zRd+qhXuCt08cKcWVpaM+BqhjfTTRWep6BfW9nABtJDnucqVhthTFQDaRDld1Fee/6GeLy98zbkV0Sp9Yd7JGkoN6tTFZ6tkR17/d2m4Qn/tvU4u2zgA41rg6j/2VGUSBwCtlB1YpYGj/kd28LX3RKN/PaT8id9SujCg/Pd+XPU1KrfeLj37LPdXgHHMGMmkrMrZgsq5vMJMiWwcALSQ6zpynnhC+W//QIrj15wvnXeB4muukzs0qPI1N1R9nfyppylVyK/NoQJoMdc1itMVlXPD87gkHfG3G17BjB5olbKruddXn4QlsfTIJUMKS7UXFHnx0YoU8lEGxiNjpM6wIP/qq5Q/8hPq22lPDez3funnp6lroFcuEzoAaDrHMRpcnGjVC1HVmqHlsZbPDZW89nrdGuZeX5AtspAUMB65rqPuYr+cM8/U4AEHqW+ndyp/0Ifl/eVCdZWHhhckAQA0leN5uvBmqVipXnP9vVJ/wSgZYU2SV/vD3yQ5XkPHB6A9+Maqc8WLSr79XQ28a3/17fIuFT/5GaXuvEO5qNjq4QHAhFT0Cjp76YU1a85b/lf1xitr1hSSoh4tPCXX5b4qMB5l4rKyjz+i8hePU9+u71LfHu9W9NWvq2P+s0rZ6tfzAQBrT6Hi6+Jbqp+PYunMq4yGquzF9bL750rliHuqwHhkjNRZycu/9uo1s3E/+8VwNs7lnioANJvjGC2OlmpeaX7VmmVhrx4vPqXI1g7HXdl7owouD6wC45HrmuFs3FlnafDAV2Xj/vwndZWHeGgVAFrADYyuWnmjSsnIi/tK0u3992kwySsZacOuV7m49xrJr/08K4CxyTiuevNZfff8QHsdZ7Tb543+5yeu7vx3RpHSrR4eAExI+UqgX1xSu+Y3Vxgt76/9t3ahJD0w15CNA8apnFdW98DDSl3/OTnn7Crnd7srddPx6ik9o7QXtnp4ADAhpSKj8B/LqhfEVuHfXpQp174WF88bkkfMGRiXjJFCZXTd/Wkd9X1Xu3zOaP8vO/rZJYEGyjn+fgOAFnAcI6c3VLKw+sb2ti9U8syQFNWex4W3L1dQ/dYsgDHMdY2KcVbnXJfS+7/haJfPGR18kqM/3ZJW2WbJxgFAC/ie0Qt3XKK4XH3dp8X/+ruiQr9snYXj5t14rlzDBTlgPBopG/epH7v6J9k4AGgZNxrUE5f/vGbNU1f/WpX+GvddJcXlolY8dR+bZQPjVKxA/16Y1TG/8rT7F4z2+KLRiWf5mt+blTV+q4cHABNS2g7KPlpj/d8kknPvGTJhnbVEXnxIflL93iyAsataNu7USwL1l3NyXdaMBIBmcxwjZ2ihtPzf1Yvyy2QW3SvFNTbskqTHLlI66W/sAAG0hVdn4z7wUjbuoG+RjQOAVgo8yTx+iRRWv45mnr1Jptwv1Vn/Vw/+XimHbBwwHhnH1cpCVv93QaC9v+QMZ+N+4uqOJ8jGAUCrZDQo3fXzmjXmvjPkFGpn4xQVZRbeTTYOGKcSE+jJRVkd+ytP7/jicDbuq2f6emEF2TgAaJWSV9LlvTdUPR8r0XnLL1Uhqf4sqyQ9UXhGZYcFR4DxqjMsKLjhOhU/8tKequ8+UPYnp6qrf4X48w0Ams9xjJbGyzW3+GzVmt5olf6Vf0xxnXuqV628SQWPZxyA8chxjLqLA3LO+Z0G33+I+nZ6p4Y+dLi8Cy5QV3mQbBwAtEDgSuXLrpTNV38OtXLzbdJAv1Rn3bjy7/+oICEbB4xHji/1Z1bptJW/02FPf0Hve/LTOn7Bd3Vv8i8l2TrPPwEA1opUcUjFn/+qZk3xN2dLK5bXfqFSWeFd95CNA8Ypm4r0rPucTn7xZ/rgk/+rDz35WX1vyWlaFCyUDeo8/4QJgW9/oAWMMRpcGtddT2TxvyrqmuHVfb24Yhs0MgDtpKOcV/HEb2ro+K8pfvIpKYpke1eqePa56j/gYOWWv8jNVQBoMs9zNP+e+g8nPHdHSVM2rj2PK/UnEtM4YNwxRuoY6NXAQUeq8PPTlSxZKkWR4hfmK3/y/yn/6S+oszTY6mECwIRTLLv6x8O1a+JEeuI5adbU2nW9A1IU8fc4MN64rlH6mbnq3+8DKl18uezgkBSGih58WIOf+Kyi35ypbFj7gXYAQOMlbqwnCs/UrJlfflGBE9R9rd5olYxhHgeMN+m4Il11tfoP+rDCO+6SyhWpUFT5ur+r770Hyb//Pvmm9oNQAIDGMkZ6sVcq1snG/fMxaf1p9ednJZ5ZA8aljkpBxW+crKHjTlwzG3fOecPZuKWLycYBQJN5nqN/Dt5ft+6Wvru0aXp2zZq+eEAyhOOA8WY4G7dSAwd/RIWf/WrNbNy3f6ChT31OHUWycQDQbCWnqLsHH6xZEyvRk4V5mhFMq1nXFw0oEovHAeON6zp6dmla+39FuuQWabAghZH00NPSp34knXGFq0ipVg8TACacxDp66OnaNS8skYL6y41o+SqRjQPGoYxbUWreFdKFH5JeuEOKy1JUlJ65XrrgvcouvUuBRzYOAJrJGEmrKlK59vdv/MSAnKn1/9a2Fb7HgfGokmT0zXMcfelX0pMvSFEsrRyUfn+tdODXpKUDGbJxANBknucofrSvbl30r1Vy1s/WrLGDkfgaB8YfY6SBckaHf9voZxcNPxcVxdL8pdL3/iB96kdGxTjT6mECwMRTGdSSR26tWWJtopXPPaLc1PVq1pUHe6U4bODgALQD13X03LK03vMV6eJXZeMefkb6nx9Jp1/uKrRk4wCg2YwN1fvMv2rW5FcskOP6dV+r1LeMbBwwDiXG17X3+jr4JOn2R6RyOLxO0fX3SgeeKN0zN5CcUQRoAQANY4xk8kulsFC7cMGdUvf69V+w3usAGJMqSVrf+t1rs3HnXjc8j1vSn5br8jccADST5zkyz91Wv/Dpv8lM26J2TXGVuBQHjD/GSIPljI74j2zcgmXD2bhP/pBsHAC0gp/kpefrzONsIr34kNRV53pcsVeOJRsHjDeu6+j55cPZuL/cLA3kV2fjPv1j6ZeXko0DgFbwFUqLHqhd1L9Actz6L5YnGweMR4l8/e0+Xwd9S/rHw6uzcTfcJ73va9LdTwayhmwcADSTMcP72ReSYs26B4Ye1cxget3XKyalRg0NQBvpCAsqnfI9DR39ZUX/nju8p+qqPpXOPV997z1I2SWLyMYBQJO5rqO76uzFJUm39d+jOekNa9b0RwMSX+PAuGOM1Dm0SgOHf1SFn56mZPGLUhQpmb9Q+e/+SEMf/4w6CgOtHiYATDhesajw1tvrVFlFDz4iZ4Pa68YlK1fJhJXGDQ5AW/A8R4vcRfrIU8fpyt6/azDOK7KRnig8o688/wOdveJCxRk++wDQbF4cKby/9vU4u2ixjOPUfa1k2XKyccB4FMS6rXS3PvXMibp78EGVbUUlW9Y/Bu7Vx546QQ9Gj8j47MU30dX/rwSAtcDKjGIXLeNKXqp2neNJbpqJHDDeeJ6j5L77Vbn+phHPJyt6lf/GKUqXWTwSAJrNjOKvqNE8q9o1y5OMff0DAtBWUkmk4s9+qfiF+SOeDx94UOHlV8n3+DsOAJptNJvapwIpqTNF22x9KfC4uQKMN9n8gIa+eIJsYeTrbcXf/l7ukhcJVgBAC5g6T5kaSYGpv7HqRun1lCTM44DxJjU0oPzJ/zfyySjS4NEnKFMYbO6gAAAaxfMLch0pXWeddt+TsinuqQLjjec5Su5/QJXr/j7i+WRFr/JfP5lsHAC0gDuKR4wc49ZdF269YIaM5XElYLxJ2UjFn5+u+PkXRjwfPfiwKpdeLp9FgAGgyYxM3XmcVcrxZVX7Wtvs9PryxaY8wHiTr6R0zGlSocpeLWdfLS3u9eSMJmgLAGgYq+GF2usJ6kfjtOn6IhsHjEOZpF+66aSRTyaRdO0xyonNHACg6Ubz57NjpKDONTvXyKRHsTAJgDHF8xw9+Iyj6+4e+Xxvv/S1M6VyHDR3YACA0S04Ykzdi3bO9JTiBg0JQBsxvn51qdGzi0c+/fAz0l9vNXI9MhUA0FTGjOphVddPydra90s7Z82RvDoPtAIYc/JhSsf+UspXycb97hppEdk4AGgJU3cjByPHr3+/pGvdzcjGAeNQvhzo5N9JdoTHnKJY+tKvpGLIPVUAaLrRPODgeJJb5zqb48kGnY0ZE4C24bqOHprn6tq7Rj6/cmA4G1eKuBYPAM1krf6LTVXr1PVsqMRyTwUYb6zxdfrlRvOqZOMemSddfAvZOABoOmNGN4/zUlKddePMpDmKHP4eB8abfCWlY0+Thoojnz/3OmnhCrJxANASo8jGyR1F7mXK5koS9uMCxpt8JdA3z66djStFZOMAoJmslcwosnGOHKWc2t/Rnlx1OLlGDQ1Am3BdR/bhR1S+4poRz9uVq5T/6reULuWbPDIAwGj2VHWNK1NnoeD1UjOlhHsqwHgTKFHx12cqfubZEc9HjzymyoUXK2D5bwBoKmtUfx8HKykIpDpribhzNlESkI0DxptCkNdJ83+ufDJyOO7iFddqUbyEbBwANJ2tu/6vNUYaxbpx7mZzyMYB41DBL+iHC86QHeE5p1iJTp7/C5WCKg9AYMIYxdNyABrNWqljuiM/W/sP6Q13Sik7pfbHdKNd0lLAllzAeBMUBlU68/c1a8J/3i2/SEAOAJopipLh+Vcdc/bMyM/Unse96YM5mSwbOQDjTVAcUunKa2vWFM/9o9KD/U0aEQBAkrKpWO/ernZN4ElbbyxVwuo1xkiHv0uK46ixAwTQUsYYafGLiucvqFlXOvv3SonPPwA0kxt7emvHVjVrNk3PVpdbe7Otaf5kbeivR0AOGGd831X5r5dJcfXMhB0cUnz/v+R5RKQAoFmslWZMljqztev2epvV9Em152d7vV3KBPwtDow3o8rG3XUv2TgAaLIwTLR79w516/breYcyTqZmzWHTDlAu6mjU0AC0iaAwpNKVIy8A/LLSuRcoPUQ2DgCaKROntUedeZxnPG2Z3VSVpEY4TtIHp+yrpNLI0QFoNWOMlqxy9PyS2nXnXCMl8pozKACAJMl3Yu1YOxqnzTewmlTnMtv0SdKm61qyccA4EwSu9MRfJVtjPaHKkMzCe8jGAUATWStpUiBla++k427TI/X4NWu8t0xSxWcOB4w3hYqvs66qXXPvv6VChWtxANBMYZjI3WZS3TpvpylSpvZcz997hsrsxwWMO/mKr8tvr11z3t+koXLtv/UAAA3md2i9bfetWWJcT5M33kZxWK5Zt8meRymK2VgRGE+MMVq2ytGzi2vXnXM12TgAaDbrBJq+1c41azpnbaqgY9LwIr9VpLunqmvdzRXH3FcFxpMgcHXZ7VJUIxo3VJTuecKQjQOAJrJWstnpUqq7dt3Ge0od69R+sY33VMVhvRFgvCmFvs4cRTYuX66duwAANFYUJdKcverW2S0+IJuqvY+D3vpxFZ36OTsAY0uh4uuy22rXkI0DgOYLnQ5p09rZOOt40sy3SlHtbJx9y0dVicjGAeOJMUbL+h3Nq5ONO/tqKSYbBwBNVTEpacNdatbYKZvKZiZJqjFHy05VMnULxXHS2AECaKkgcHXlP2tn4/Il6c7HyMYBQLNNcXvU5dbOtO3S9XZN9SbXrNmx621KxSw4Aow3qVJepTN/V7MmvO8BuUNDTRoRAEAazsbt2v32unXv7tldHW6uZs0h096rXFi7BsDYk8oPqPzXK2vWlP7wJ6WGBpo0IgCAJEWZnIL99qld5Hny3/YW2UrtDVPTHz1CFXFPBRhPjJFWJn16rrSgZt0Fyy9X4kdNGhUAQJIiP6Vglx1r1rgbbyQzufa6cWbqFLlbbUk2DhhngsDVjf13KFL1cFwxKem+oUfIxk1w/PaBVknF2vp91W+I+hmjLd6bU/eGrtbbduQAXOcMV9t9skuxqZGEBiDPc9VRHFTXqmXqWrJA3fk+pW1U6++klnOjSPH82hfkZK2S3pVt/T4AYLxJEqv0FKN1tgiq1kza0FP3hq62/mBOHdNHXuRzg+1TWnfbQGHIPA6oJXCNuor96lq+ePif4oCCNl8715QrUrFUsyZZuJgLMgDQZHEU6cBdrHpqPK/6oT2kXBDqZ1+QUlXW+PzqEVJ3NpRlPy6gKmOktI3Une9T15IF6lq1VB2lQfl++07kHMcofuGFunXxc/PllGrP9QAAjZUKM/r0jCNkaiwM96l1DlOX7dRnZx458muYQN/b8HhlKjysCtTiOEbZsKjuwZXqenG+ugd7lQ2Lct32vRnpxpHiuU/XrYvnPi3Had/3AQDjUTYI9bH9qp/PpKSPvFvadFai3bcZuWbdadKJH5ZchWtljMB44XmuOkr/mY0L2zpT5oSjzMat6G3r9wEA4421VlOdyXpTbouqNbNT62njYAMdOu29mhlMG7Fmp863aeeObcnGAXUErlZn45YtGjvZuEKxZk2y+EU5IlQBAM0UhVZ79+yqbrezas3+k/dQNsrplA2PUcqM/CzE52Z+RF1xF9k4oAZjJOv4KkRZLR3IaVUxp1Kckee170TOcYxeWFq/7rkXpXLIUw4A0ExpL9Rxh0hOja/fYw6SunOhvnpEldcIpNOOljJ+ee0MEhgnHMco5xY1yenV5Oh5TXZWqMPNt3U2zlEk9c6tX7hiLtk4AGiykmcV7DWzekHKkb/nOrIzUvLe1DNiiZkSKDh4A5XEAsBALZ7nqBSn1VfKaclAToUoq8QEbZ0pixJH8+tcj7NWWtFn2/p9AMB4Y61V0uXK3ayrao0zIy2tm5G/5zoyU0de/9d9Y7fMm3vIxgF1uJ6nYpzV8qGclg3lVIyzcjyv1cOqqRIZFeosJbJkpWRrPO8OAGi8MLJab7v3KOiYVLVm9s4flFLd2u7TP5cbZEaseeNBX5GTnUo2DqjBGEkvZ+MGx1k2bolUIhsHAE0Vuzlt9f7jZEz179+tPnCcTGay3nTI10Y87/gpbf/Z0xV71a/pARieE4U2paFw+J7qYCWnSpJq62xcYh09VWe5EUn69wsiGwcATVZwuqW3fbJ6gZeRfesnFHVtLG30zpFrOmdJ7/imikl67QwSGCdGysbZNs/GhYmjhcvq1y3vk0w7vxEAGGestYrS06X1tq9eNGkjxdPeKLvNR6Tu9Uau2XBXxRvvozDiGQeglleycfmxk40LI6N8nWzcslWStczhAKCZKmEiu9n+UmZy1RqzxftVdrulfU+VvJGzcdr5eJWDaWTjgBrWyMaNoXXjFowiG/fCEtaNA4BmK9mstNOxUo1snHY8RmV/irT710c+76ak/X+pvMjGAbU4jlGkNbNxoU3Jddt3/mOtoydfqF/35HzJqbUAJQCg4TJhVodPO7Dq+ZQJdPjUA7SeN1O7dr19xJrp/hQdN+sT8ioj7/EAYJjnOeooDamr7z/3VG3f+5FOWFG8YGHdumTpsrZ+HwAw3lhrNUk9emvHG6vWrJ+aqTekNtEHp7xbs4J1Rqx5e8eb9I7OHRSRjQNqGt5TdWD1nqqFfgVOe4cRTBjKDg3VrEmWr5CTsG4kADRTGCUK9t1bZkr1bFzqwP1V6exS56k/lLIjZ+OyX/qCwilk44BajJFMKlEpl9eKzDINZFepki3K89r3XqTjOFpcqR+OW1RZooqpNGFEAICXlYKMMsd9QXKrZ6yzx3xOle5Jyn3zKyMXBIE6T/uxijmycUAtjmMUpysq5ga1IrNMhdyAonS5vbNxRnq6+HzduqdLz5ONm+Da+4lrYBwLk0hbvj+r8pDVE1fnZV91bzTT42jvkybL6YgU2ljvOKFb8+8p67FL8+pfHCnd7eoN787oDftllaRD2ZgrckA16bgi76GHlf/ODxU/9cxLB1NKv3c/dZ1wjAZzPUqS9vsMJcYZvmi/ordmnenu4qI8ADRZEoR61zd7dOP3+rT08TVvjEzZxNfeJ09SElSkwOjAn0/RE1cX9NQNBZUGEnWv62nrD+S0/vYphW5Z4jscqKojLCi+8HIN/PYcJS/NiczUKcp88ih1HvwhDfrZFo+wCt+XHEdKqgdgTU+3iMcCQPN1psu64FtpffKH0ouv+nPbGOmAXaTjDk7k2LK2WD/WNT9K6fTLpJsekMJIesum0hc+KG2+fiTXEo4BqnEco858v4qnnqZVV1wtFYdX1XU33US5r50g7y1vVdFtvwe+rbVypk6tW+dMmSTrt9/4AWA8i+NEs731dfIGR+sHC3+jYrJ6xfbA+Pr8rKP05mBLxXmjAzv31lbZTfX7pRfrscJT8o2vXbvfro9N/5AmhZOVRFyMA6rxHCm7dJEKJ31XQ3fcJVkrGSN/x+2UO+UbKsxcT1EbboaQuK6c6dPq1pkZ69S6XAcAWAtsHOqofTz1DTo6/+9r3jaZ3CX99nhpUrYs2Ug/+WxGN//L0TnXSPOXSpM6pYP2kA7b06ozVVJMNg6oKh1X5D38yHA2bu7TLx1MKf2ed6vry8e1bTbOuo6cyZOULFtes850kY0DgGZLlTL6vw2O19fn/0QP5/+9xrlNM7P149knKl3KKi2jszb5gS5beb2uWnmT+qNBrZeaocOmHaBdOt4ur5AiGgfU0BEVFV90uQbOOHvNbNzHj1TnoQe3bzYu8IcfaI+rLw5nOjtk1X7XEgFgvMuWO/SrTb6tLz33XS0LV4fjjIz2mrSr/nf6h6W8q439jfTHzU7Vucv/qtv771NoQ70xu5k+vs7B2sSdLafEI+dANY5jVIgy+tlFRlfcLhVfipLOWdfRiR9O661zQjlqv3yptVZTR7Ge0OQuyfcszzkBQBPFcaKNZ4Y69fO+vnamVFgdjVPgSV/5sLT9FpGSsKyD3iG9aY6nX/1VevDp4fPvfJv0ufdL07rKsizkDlTlu1JnuEC6+WRp/h0aXtzHKFh/BwXvPEWDqQ0Vxu13PcvKk8mtU3961jGDe6oA0GTlOFawxzT5Q5HCm5es8be06fSU/vxmKmSH15fr/NhGch/tV3jDEiVLSzIdnvxdp8nbfboG/UQJ2TigqliBHpnn67t/kObOHz6W8o3229HXVw73lPOLbZmNc4zV5C5pycradd0dhnkcADRZ3k3U+b+bqPKbeYqfGljjnLNeVukvbqZBL5Y8o84Tt1T0j2UKb18uOxjJmZ6Sv/cMmTf3aFBRi94BMDZESuviW1399gpped/wscldRh/fL6VD9/Tlq9jS8VXje1aeaxTVuNzekRHJuP/X3n2HSVqW+d/+VucwEZghg6BkUMAAElQUSTJEUQRZFcGIrPpTDIgJA4KrqKuuvGtAVFyz6xrWdV1zDmtYs6goKCAMEztV1fP+MTrDQPeE7nu6q6bP8zj2OHaqHnruvqDGz1RdXQUwAxo92+ShL7w+X3/D+Vl9x83r3bfrYUuy/6NfkJFmb/p2PDDHXv6Z/PJTb81NP/yvNOuj2WbPg7PfKRdnzi4HZLTqm6HvAFrf33fjrv5wLZ/4ajI0sub2++zckRec25f77zWWzlbdjZu/8eu2mZt0d3oyDmA6NRpVehfvlQc9/c35/jsvSX1k9dr7Orp6ctBZL8jCfY7M8FhndjnysVm4x33zi39/c/76mx+ko7M7Ox1ybPZd8sx0zNkpY01/G4eJdHR05tYVfXnFe5Kv/XjNe/vUasmD9uvKK87vys4Lh9Nswf3SjlqV7Rdu/Lodt43XVAGm2chYLb33PS9dw3ckP3xvUt3lf0f6t0lO+Zes7FqcsUZH5h33+nT+/ovJ9/+/5M4/JH0LkgPPSnXfx2d5tk2z4c0/YSLNWnd+eENPXvXe5Bd/WHNbb3ctJx3enUvO6cqcnhbejZub3PzXDV83f86a5+4AmD4rmnOz4KS3pPaZZyV/+vb6d263T3LaO7OiWpDUknlnfyy1H1+f/PTfkqE7kvm7Jfc/P409jsvyxpwZOT+0i3r68tEvd+ZfPpncunTNbdvMreWJJ/XmnGNbdzeua1N34zwdDzDtVmRh5p31geQTT06W37T+nXuflMbRL8zKem+65x+YOed9JrXvvi3Vrz+fNEeTHQ5ODntWRrc9MKvqduNgIrVaLcON/lz9kVo+/pV1u3H33qkjL3h8Xx7Q5rtxC+3GAUy7RqPK0Ny90v+otySfvyQZXbXuzs6e5KjnZ3Tno7JyuCOD+z4mvTvcN/nWm5Obv590dKd272NTPeiZWdW7U8bq/jIOE+no6MxtK/vy8nev241Lkgft35WXP6kru2wz3JLvvVirVdlhm41ft+M2XlMFmG6N0eS0BcfnzsbyfPi2T6eRdftt8zvn5op7vSDzxxYk9Vou3fmifGPBD/KBWz+RP478OfO65uRR2zwiZ25zfAaH56Xhg7FhQn3NsXT970+z+pVXpP7zX665sbcnvY86PvMu+X9ZMXdhS+7GVR2d6dhmmzT/dPMGr+tYMF/HAUyz7uG+vHLX5+ayP74hP1z50/Xuu3ffbnn9vS5N3/BgkuSae78m/770C/nk7f+VO+vLs1Pv9jl7uyV5yNzD0u0zVWGD5owNpfGx/8jyt16z9nPma9ssTP8TH5+5j39c636malfXmv+rT/zekLXBwVQdHdN4KACSZNXchZn/gXdn+ZOffo+/b/eedFz6X/z8rOjsTdfe+2TBpz+W4bdfk5H//O9UY2Ppvu+B6X/W01MdcEBWdfbO0HcAra9Wq2V0YHX+9dZ/y2eW/k+Gm2uW43br3SkX7fgPuV/fgS35ucRVVWVh18aX4xZ0zUtnOqfhRAD8XbNZZWz3PTL3n/8pK593aapVd9mN6+nJ4PP+MTnqqKxudmTgjNMz/6ADMvTmt2f0+z9MrasrPY94WPovemqGF++UMS+pwoQ6O2u5o/f2/NNN/5pvr/jfNP+2v3Dw4P55/s5PyQ4dO6Q5NsOHHEetqmVx93YbvW5x97ZeU53lWu9vITCLjNZGcr/H9+egRw/mD18fzvDyZhbv153t9upOs3cs9b8tvo12jmTXh3Zm5wcuTC21VFWVjoFGxuojSevtZ0PL6Oqspes738vyCy9a9xMOSTI8kuEPfzz1n/4sc977r1neN3fmDjmB0bnz03fuY7Pqpa+a8Jqu/fdNc8AbDwFMt2azSvpG84iXzs/wHVVu/NZIqmaVXR/Ul4FFtTR7Rv/2wfdVGj0j2f+snux38t/eKapWpdbfyOjYSGzHwcT668MZe+u/ZOid713v9uqvt2f1FW9I301/zsBz/zGru1rvzXvG+vrT85AjM/qlr054Td9jzsjowBx/DgBMs2ajkd23G8rHX92bX/2xI9/5eTJ3IDnugcm8/nq68rd3uWrWs3huIy97QlcueVxXarVaujoa6esaS8OHOMAGzRlakRUXPCP1H6//w0SNX/82y89/eua+/ep0H/3QlltQaDardNzn3qltszDVHUsnvK7v/CdkqLs38WcBwLSqjXTliJ4H5cP7HJTvrvpx/jB8U3bsWZwj5h6avrH+ZHjN8nLHcHf269w3l+/0vDQ61vzgWm+jL7WhLosxsAG1Wi0Dt/8ly854XKo7l627o6oy9o1vZ9kZj8v8f/9wVi7eqeXeeGSsXmXuOY/N0HveP/FF3d3pedjRGdrQpz0AsEV0ZSj/+OieXLCkO5//brJ0RXLwfZKD9qwy0D2y9o1Je2pDWXJ4Zx52cHeaVUeSKnN7x9Jo1NPwxzdMqKurlq7vfj/LL3jmPXfjPvLJ1H/688y57p0tuxvXe+7ZqV/2ygmv6dp37zTntN7ZAbZ2zWaV/qE5ee0uL8gd1dJ8Zfl306waOWLe/bND5+L0DvWn0VyzG9e7ajDnzj0zpy88Yc1eXLMjg/U5GVvVsBIDG9BfH87Y296Rof/vPevdXv319qy+8ur0/enmDDzvuVnd3Xq7caO9/el56FEZ/eKXJ7ym7zFnZMRuHMC0a9aTnaud8u57X5UbRm/MD1b9LAMdfTlm/uGZ05yTrtU9a/5oHuvINvVFec62F+QZix+fJOlsdqV3rD+NVlvogRYz1OjLhVfW8uPfrn/7b25KLrgyecuzu/PQgxppttgT281mlT13qrLt/FpuXzbxdU86Kenu8Lw8wHTrrEZzzH2b+eLVPfn6T2v57U3JrouShx5cpb97LB3Vmne06spI7rv7WN58cXfqzc5UVZX+7no6Uk/VYvs80EpqtVrmNv6cXH9mMnzXnxOokj9+M7n+jMw9999zZ9euLbcbNzrWSHXQ45IfvGviizq6U+35iIyNiTiA6baiqmdgyQ4ZOH6H1H+wNNXKejr3GExtj8Gs7mxm7G8/e7Y89XQfOi+9B85LZ5U0k4z2JCvrde8bBxvQ1dWZ7/+qOxe8bv3VuJGx5ONfSX72+1ree2l/+jpWz9whJzCndyznPLIzL/n/Jr5m712TOX3+EACYbs1mlRWdjQw8bc/0rGik8cOlSVWl88AFaW7bnRVdzTT+9r5xd3Y003vsovQcvSgdtTXpNtybjI1N/IGLQFJPT97x75255lPr337H8uT1H0z+dFtHnv/Y3nTVRmbmgBvQ21XPww7uzhe+P/E1Zz4sGegeS2W9AmBa1RtJ54J752Ev/WRW3PTL/PWX305X32B2vv8J6ehfkNHaQFIl9WZHagO7ZL+zX5H9z7wkSVJ19qTZOSej3isKNmi40ZenXFXLj36z/u2/uSl5ylXJm/+xOw+7b2vuxt1rhyrbza/lrxvZjevptBsHMN3Gqp4s3P/hOf51X8ptP/9Glt/06wwu2jXbH/TQVN1zM1r1JElGq7707XL/HPqUt6bWHE2SVN2DqVc9f/tZVmA8tVotS4f6ctZlyR0r1t1eVcm3f5Y8+rLk46/pyw7zVrfcbtzYWD2PfURv/vU/Jr6mqzN5xP0ru3EAM2D52EAGH/Cc9B765OQ3n0+G7kh2uG+a2x+cVZmfsXotSbKsPifde5ye/t0elo5aM1WVDHcuyMhYkng+DibS2dmZH/6mJ+dfMc5u3FeTn/6ulvdd1tq7cS++ZuJr7rNLMq/fnwEA062qqtzZXJi5J709XcO3rum4qpnc66FpzN01KzN/7WdtLc2C9N7vGek78HGp1ZJm1ZGhzoX+Dg4b0UhPrvlUZ97x7+vffseK5A3/lvzp1o5c8rjedLfgblxfVz0PP3TNe4NP5LSH2I0DmAn1RrK8796Zc/Yn0nHHr5I/fSvpHkzuc3xGuxdmVX0gVZWM1juytHOX9B358vQ8+Pmp1ZJ6ujOUuWnU/eENGzLc7MtT/6mWH/5q/dt/e3PylCuTN13cnYcf3Eyz0Vo/L9RsVtl9+43vxj3xRLtxADNhqN6d5k7HZOCJ/5PaH7+R3P7rZN7OqfY4JsO1uRmqr9mNW1Xvy/D8Q9J3/D+nO6NJlYx0DGa40ZOq3lr7PNBKarVk6VBfHn3Zmp9Nvavv/Cx5zEuTj7+6LzvMb83duMc8ovceP2N7V12dySMfGM/LA8yAztU9edLCx+bs7U7Ol5d9J0sby7Nf/71zYP/e6RsdSHPsb9et6s3Du4/KYbsfnKqjSqpkoD6QxqqkYTcOJtTV1ZGu7/1vlj/xaVnvCauR0Yx87FOp/+RnmfuB97TsZ6r2Pf6xWXnJTye8pvM+e6a5YMH0HQqAJGt24/pWD+Y1Oz8/d+TOfHnZt9OoGnnw3EOzU9f26RvpX7sb17tqMGfPOS2nLHjk2s9UHRgbTH1V00cpwgb0N0Yy9q/vytDb1n8T3eqOpVn9hrek98Y/ZvDFL8iq7v4ZOuHERvsH03vsMRn53H9NeE3v6Usy0j9nGk8FQJLUG1VW7XqvzP3YB1P95rcZ+9Z3UhvoT8/xj0xj/oKs6OlPVSVj6Uh90U7pvezSzPt/z04tSb2rO0P9c9Z2HjC+sYGhXPKHK/LT1b9c7/YbR27OJb+/Iq/Y7Tk5quew/O2tfFpGs1ll5+7ts13Xwvy1vnTC6x6z3aPSM9bneXmAaTbc0Z2eI4/Ogi9+JmPf+nYav/pNOnbeKT3HPCQjA3OzqqM7SbK6szedB9w3vW/6p/SPrfkfm7H+gazo6ElVeTYOJlKrJcv77syFv35RltbX/0GB/131s1z4mxflXXtdmUWNxS25G7dkm0fkvbd+dMJrutKZh80/PGOr7MbNZh0zfQCY7eodY2kOjGTPk7pywON6ss1ByVj3SBrN9f+CPTbWSKN7NPXukTR6RjNW94c3bEz/ymVZ+bJXrf/OcXdR//kvU//q19PV1TnNJ9u4sXoz3Scen8697j3+Bd3dGXzlZRmeM296DwZAkjUvntQ7R9O9uJ59zujOfmf1pG/neupdo/d4gmCs0Ui9Z03H1btG/YAabILuFcsy9K7rJrx/+H0fTNeyO1OrTeOhNtFwT38GXnJJanPGX4Dr2GH79D3pvIx6SgZgRjQazfR3DuXQew/lmacO57xjh7Pt4Kp7fFh3VVWpNccy0DWU/s7V6a6NWI6Djejq6kj9W99O/ccT/MBnVWXVy16dvlXLx79/hg0Pzs2cl704E0Vm99FHpLb3Xv4sAJgpox3pXTWYh3UdmSfNe2yO6zkmfavmJKPrv8bTaDTTNdyb3tWD6V09mIx0Wo6Djehtjmbo6n9Odef476JbrViZoddfnd56632QQ1VVaWy3KH2POWPCawaec1FGBvywKsBM6cpo5veuyjnHDOeiU4fz4H3XPN9WNdd/zXRsrJG+zuEMdK3OQNdQGi32xvPQivpXLt/wbtwvfpX6l7+S7u4W3I0ba6b7hEemc+/7jH9Bd3cGL3+p3TiAGdJsVuke6ssOIzvlnMHTc97cR2f3sd3TvbrvHrtxjdEqvasH0rtqMD1D/XbjYBN0r1yeoXe+d8L7hz/woXQtW9qSu3EjPf0ZePHzJ96N235x+i54ot04gBnSaFTpWT2QA6r988S5Z+WsgSVZMLRNOod7cteXTKuqSkY6176m2jXcax8GNqKrqyPf+XlHfvzb8e+vquSV70lWj/ZO67k21WDPaF72xAlX43Lkgcl+uzX9WQAwU6p6+jtX54T7D+VZpw3nlAcPZ7B7dToytt5ljUYz3bWR9HeueU21Vo3ZjYON6OscTe2bb0qGJ3gT3dGVydeuTF9HK+7GJaN9i1I76OyJL3rwP2a41nofJgYwW6yuGrmzu5GRoxZm7MRFWbVXf5bV6hkb533jVtYaWdbRyIqORka8bxxs1MqRnrzsnROuxuWXNyb/84NaS+7G1euNHPeAKvvsNv79XZ3JK5+czOltsU+hAJglms0qK2uNLJ9fy8ix22X0uEVZsbgzK2qNNBrrP9c2Um9kReeajlvZ0bAbB5tg1Uh3/vXTE9//wf9Olq7uasnduJ6Osbzg8cmcCT7zdfHC5ClLqqQaG/8CALaoRrPKaMf8DNzr8Oxx0rOzyzEXpjGwU0YzcI/duLFmd0Y7F2S0c0HGMmAfBjaiq6sj3/1FR370m/Hvr6rkFe9OVrXybtyTJt6Ne/AByQH3shsHMFPqza6MdCzINvc7OXue/NwseuBZGe3aNmNVz3rXNRrNjNUG1nVcs9tuHGxEs9aVt3w0uWPF+PevHEpef30y1uye3oNtgqpKtplTzznHTnzNs85MBnq99xDATFnV6Msd1eKs2PcJWXnoc7Js+4fnzsaCjDXW/wv42Fgjy5vzcmdjQZY1F2TEyyiwUatGe/Kyd028G/frPyX//f1aS36mar3eyLH3r7Lv7uPf39WZXP7kZE5v6/18BsBsUFVVljfmZGnPvbPivs/MyoOflTvnHJhljXn33I0bq7KsuSB3NhZkeXOe3TjYBKtGuvOv/zHx/R/6n2TpqtbcjevuGMsLzk3mDox//6IFydNOid04gBlSb1S5szE/y7Z5UFYe8uysOODCLO3cOSsb99yNG6p3r+24lY1B+zCwEV1dHfnhrzryw19NfM3l1655zq4VDfSM5BVPnng37rD9k4P2tBsHMFNGGl1Z2liQ5budnJX3f25W3OexWdrcNkONu+/GVVnVGMidjQW5s7kgQ3W7cbAxVa07b/t4csfy8e9fOZS87gPJaIvuxi0crOfxx018zUVnJAM9nosDmCkdw10ZXDU/p/afkCfNfUweWDs0PasG0rzbH81jY430DPWnd9VAelcPpOGtomCj+lcuy6rLXpU0xt9BaPz6txn7/H+nq6v1Ps9qbKyRroc/LF377zv+BV1dGbz8sgzPmT+9BwMgyZrXSruGerP98A45Z/D0/MPcR2eP+r3SvbrvHrtxd/9M1Xrd6yiwMT2rVmToHe+a8P6Rj3winXfc3pK7cSNdvel/wXNTmzf+5zR0bLdt+p9+YUZrrffzGQCzQaNRZUX/vAwdfGiqZz4jjSc+KSsW7ZhV3f332I0bTldWDMzP8oH5Wd3jfeNgY7q6OvJ/Q7/KT1f/csJrrr75XRnqXj2Np9p0faODef4uT0kt40fmwYMHZP/evfxZADBDRmudWTYwP6PHnZDq4melfsaZWTZnmwx3rL+r02hUWd3dnxUD87NiYH6Ga3bjYKN6mrn21o9laX3ZuHevbg7ln//83tR7Wm9JoaqSuc05OWu7kya85gnbn5n++gQ/yMqs0XobATBLjY01Mjra8JdrKKRWS3LbbWn+6eYNXjd83fXpWTXBT0LMsJUD8zLvfe9M32PPSPr71t7eff9DMv/D12Vk730tWQDMsKqq1nZcs+mJNiihp6czo5/6bLKhJ6+rKqMf+2S6u7um72CbqNmsMrTDLpn/sQ+k5+gjko6/PfXS3Z3eRx2f+R9+X1Yt2G6D3x4AW16j0czo6JoPvfdnMpTRs3pVRt77gQ1e07zl1uTmP6fWgj/pMNqspfnQh2beu96Wzn32Wnt7bf689D/1/AxefWVW9s2ZwRMCkCT1+pqOq9d9QAOU0jO0OiOf/s8NXjPy+S+mZ7g1f9BhVXd/el/4vAy+6HnpWLTd2ts7dtslc/7ptel83GMy3NGab2AMMJvYjYOy1u7G/fGmDV43/L4PpntlC+/GXffO9J195vq7cYcenPkfem9G97EbBzDT7MZBed3dnRn99OeS5oY7Z+SjH2/d3bgdd878j1+fnoccuW43rqsrvScdl/kfeX9Wzt/WHgbADLMbB+UNjXXnug2/pJpblyZ/+mutJXfjmo16jj5wLO98YbLPbutunzeYPOWU5I0XV+ntGJ65AwKQxG4cbAm91apUv/r0hi/67RfSW62angNtppX1gdSPeEHykBcng4vW3TF/1+T4qzJ24Hn3+BBmAKaf3Tgoq1ZLbl/RkT/euuHr3v+FZMVw94YvmiH9XUO59sVVzn5E0t+77vZD9kr+7eXJvruM2I0DmGF246C87u7OfPZbG12Ny0e/lJbdjdth3nA+9qrkIQffZTWuMznhsOTDr0zm9w3bwwCYYXbjoLyhsa5c9/kNX/PXZckfb62lo6M1d+OOOnAs73phsu/u626fO5BcuCR58z/ajQNoBXbjoLzhse78x9c3fM1/fz8Zqbfec3FJ0pWRPPexzbz4vGTxwnW377I4ed3Tksc/spHOqvU+FBZgtrEbB+XdsbIjf/jLhq+5/gvJqpHW3Y17z4uqPO5uu3EH75V88OXJfrvajQOYaXbjoLzu7s587jvJxv5q9KEvtu5u3KK5E+/GfeTyZEH/kD0MgBnWaFR246CwTd2N+8Mtrbob18iD9xvLe148/m7cW55tNw6gFfx9N25szG4clDI01p1Pfm3D1/zPD1p7N+7ZZzVz6Ti7cVc8LTnvuEY6YzcOYKbZjYPyancuTeP3f9jgNSMf+Lf0rlg2TSfaPCv752Xue96RvnMek9rAwNrbuw4+KPP/7dqMHXCQ3TiAGWY3Dsrr7u7M6H9+IWls+DnukQ9+OD09rfd8XLNZZfWiHdZ8purDjl7/M1VPeGTmfez6rFywnT0MgBlmNw7KG+0ayUdu/8wGr1laX5Y/jt7cmrtxY1UO6T4ob9rzpdmr/15rb5/TMZDHLTolr9nteeke6p+5AwKQxG4cbAnDXcP53NIvb/Caby7/fkY7R6bpRJunc7gnF2x3dv5xpydlUfc2a2/fqWdxXrTrM/KYhUvSMdJ6zyMyvfwXAMBWqVarpbpz44tv1Z3LUtvIi68zpdmssnxwQXpfcmkWPufiVMNDqXV3p943kNX9cyzUAgBbpVqtluqWWzZ6XfOWW1NrvddVkyRjVS2NnXdP35vfkMHh1alGRlLr689o/0BWdPWm6YccAICtUK1RT3MTno9rLl2aWi0tuZw61NmTrsOPyuD7DkznyHCqej3pH8jI4NwsrzqSFjwzAMBU1ZrNZGQjb642Npa08Acar+geSM95j8+8009JhlYntY5U/f0ZnjM/Y56LAwC2Qpu8G7d0WWqN+jScaPOt2Y2bn95LX5SFz3lWqqG/7cb19mf1wFy7cQDAVqlWq6X6y60bva665bbW3Y1r1tLYabf0vemfMji0OtXo33bj+gayors3zYYXVQGArU+jWcudKzd+3dLlVWo7tuZuXEdGc9jejbz30u6MjHWm3kgGeqsM9IymatRb8swAAFNVqxpJfSMfIN+sr/m/FrWsPpie/Z6YgX1PT62+KrVaRxqdAxnqWJDRMa+pAgBbn1qtlmWb8FzcnSuSZjNJ5xY/0mZrNqsMdK3Oi87pzj8+ujtDo7V0dyZ93Y30d4/ajQMAtkq1Wi23LN34dX+5Y8ufZbKazUZ2XLA6b3xmT4bHOjMyVktfT5X+7nq6amNp2I0DALZCjWZH7lyx8evuWFG17M84dFSjedDejbz3Rd0ZrtuNAwBmh2YzGdrI28bVG8lYo9ayn07ZnaGc8/CunHJkd4ZGOpJaMtDTzJze0dRb+P3uAAAmq1arZfmm/JzqyjU/09rKu3EvPKc7F5/VnaGRdbtxAz2jqXv/XwBgK1Sr1XLrJuzG3XZn636kVdVsZIf5duMAgNml0axt2m7c8jXN14o115nRPOA+9bz3xWs6rt5I+nurDPaMpWqM2Y0DALZKzSoZGtnwNY1mUm/x3bjHPbwrS+zGAQCzRK1WS3PZxp+Ma955Z2pVa+6YNZtVlg/MT++LXpD5//iM5C6fqTo0ONduHACwVarVamneettGr2ve9td0NFvzea16VcvKHXZN3xuuyuDw3z9TtS+j/YNZ2dWbZtOLqgDA1qeeRpY1Nv7DqnfWl6fW0Zq7cbWRrhzUdUDetNvLM9IxkkbVSF+tN/1jA2muqqVqwTMDAExVlSpDzQ1/pmojzdSr1nwuLkk6Vvfk1IET8sh7H53hDKeWWvrSl4GxgdRXazhadq0TAKam2azSsdOOG72uc4/d0+ztnYYTTU5VJcPpzPDggmRwwbo7fKgqALCVajSq9By4/0av6zzogNRbeNGs2ayyuqsvmdOXzLnrHa17ZgCAqWj29KZrzz3S+NVvNnhd5y47p2rhd2Gr1xtZ2Tc36Zu77sbWPS4AwJQ1OzrSsd22af719gmvqS2Yn6q7tVeMRhvJaP+8pH/euhu94QgAsJXarN24nlbfjevK8MCCZGDBujvsxgEAW6lms7l17cbN7bvbHa17ZgCAqejpaubeO3Xmlzdu+Lpdt6+1/G5cX0cjfXd5yrCF3ycFAGDKqlpnaoOLklUb+ECH3vmpOrqTFu6i0XoymvlJbf6aGxrxmioAsNVqNqvssO3Gr9tjpzXP27WqqkpSjWWgaywDd/lRjEYLdycAwFQ0m80csMfGrztoz7T0a6rNZpWujGROdzKn+283VlbjAICtV09XM/feuTM//8OGr9t9+1pLf9B8vd5Ib2cjvZ3rbrMbBwBszTo6qixeWMutSye+Zt5g0t3Zug2XJI16Pf2d9fQPrLutXp+58wAAbElVVWX7bTZ+3b12SLrbYTeucywDOg4AmAWazWb2v9fGrztgj/wtllqT3TgAYLbp7Wpmz52Sn/5uw9ftvsOa5mtV9XozvR3D6fW+cQDALNFRq7L9wlpu2cBu3Jx+u3EAAK2kqqp0bL84qdU2+Jpp5712T7O71T9TtfOen6lab93nDwEApqLZbKZn//02el3n/vulWetIq36QQ7NZZXV3X9LtM1UBgNmht+rJ7r075+erf7PB63bp3THNkdZ9bqteb6a73pfurOu41j0tAMDU1apaFndvm1vHbp/wmsGO/nTVuia8vxU0Rqv0jg6kN+uW4+rxXBxrdMz0AQBgS2nMnZeuQ+63wWv6nvrkjNz9RUsAAGZMvd5I90OPTgb6J76orzc9xz48Y2OtuRwHADAbjXT3pu+p52/wmq4D909z4cJWfg9gAIBZZ2Tu/PSd97gNXtP32DMzMjhvmk4EAMCmaMyZm+77H7LBa/qe+uSM9m7gdVcAAKZVvd5M99FHpHbXT5W/u96e9Bz3CLtxAAAtpKs2lguWbPia/e+VLJjTsBsHANBCVtcWJPc7b8MXHfTYDHXMn5bzAACwaeb0NvLAfTd8zVOWJL1dY9NzIAAANqpeb+aIA6sMbuCtfXu6kuMfVNmNAwBoIV21sVxw8oav2Xf3ZBu7cQAALWVO71jOO37D15x1TDLYOzo9BwIAYJMM9jXyoP02fM1TT0n67MYBALSMer2Zww+oMmcDb+3b3ZWcdLjdOACAVtJZq290N27vXZNt59qNAwBoJXN6xvKEEzZ8zVnHJIM9XlMFAGgl9cHBdB/2gA1e0/+0CzLSt4HP6wIAYFrV6810HfbA1ObOmfii7u70Ljkpo6N24wAAWkVtrDOPW3TKBq/Zs2/XbNOxwG4cAEALGRgbzFnbPWqD15y0zcPTX/eaKu2rY6YPAABbyvDA3Mx5/WtS23abce/ve/zZqfbaK82mZ+QAAFrJUP/czH3j65Kurnve2dmZuW+4IkMDc6f/YAAATKjZrNLc/V7pe9J5495fW7ggc95wRYYG5k3zyQAA2JDRRtJz7mPTdfBB497fdcB+6X3yEzPSrE3zyQAA2JDhgTkZvPJVqW237bj3953zmFT77JNGw24cAEArGeqfmzlvunLi3bh/em2G7cYBALSUZrPK7ovrefIE7zuyYE7yTxclg92j03swAAA2aLSeNA86J9nxkPEvWLRfqvtfmJExu3EAAK2kv3skVzwt2Xb++Pc/9hHJvrs27MYBALSYge7RXH1x0tV5z/s6OpIrn5EM9nhNFQCglTSbVXZdVM8FJ49///zB5A0XJQM6DgCgpTTq9Tz24VUO3Xv8+/fdPbnw5Cpp1qf3YAAAbFBf50he+9Rkuwl24x5zTLLfbo00Gs3pPRgAABs00DWaN21gN+51T7MbBwDQaprNKjtvW89TThn//nmDydXP0nEAAK2m0ajn0cdUecC+49+/z27JU0+pkmpseg8GAMAGDfcOZvCKy9MxwWeq9j76tOSA/e3GAQC0mKGBOZn7ln8a/zNVOzoy58rLMzzoM1UBAFpJs1ll+yzKeYvPGPf+uZ2DecVuz83A6OA0nwwAgA1pjFU5ecHDc7/B/ca9/959u+UJi85MRn2mKu1rnGeaAWDr0Gg0s3rHXbLgUx/O8LXvz8gn/iPVqtXp2mev9D3jwtQOOSQru/tn+pgAANzNaK0ztQcfkQX/8ZEMvfUdGf3qN5KqSs+Rh6f/WU/P6M67ZLQ2zjvLAQAwo1Z192fw4mek56FHZeht16T+s1+mNtCf3tNOTt8Tzs2qBdv5YVUAgBa0sn9e5vzr2zP22c9n+L3vT/Pmv6Rjh8XpO+9x6T75xKzsn5c0q5k+JgAAd9FoVFm14y5Z8O8fzvC178vIJz+dauWqNbtxT78gtUMPtRsHANCCRmudqR12eBZ8+qNrduO+8vWk2bzLbtyuGan5cT8AgFbTlZE847Rajr5vZ972ieRnv08G+pIlRyZPOjGZ3z9sNw4AoAUtb87PvFP+NR2/+Vzyv+9JVtycDG6fHHxemnsvyfLm/CR24wAAWkmjUWX7eUP599f25br/rOUTX01WDiV775o8/bTk0L0a6crwTB8TAIC7a9bzwL1r+Y/X9eRtH0++8qM1P5Z6xIHJRWckO287llpVn+lTAgBwN10ZydNPreWo+3bmbR9fsxvX35uccmTyxBOTBQN24wAAWlFfx1D+5Xn9+fx3a3nPZ5M//zVZvDB5/HHJyUdUGegaSlPGAQC0lGazyuK5Q/nka/vyvs+v2Y1bsTrZa5fkaacl99+7kW67cQAAraeq5wF7JZ++sjdv/dj6u3HPPCPZxW4cAEBL6qqN5KlLajnyoM689ePJz363Zjfu5COS80+yGwcA0Kr6Ooby9uf257++V8u7P5Pc/LfduHMemZxypN04AIBW1GxWWbV4x8z/1Icz/L4PZuRj/55qxcp07rVn+p92QWoPfEBWdg/M9DEBALibsXSm49D7Z8FnP5aht16T0S99NWk2033EYem/6GkZ22W3jHR0z/QxAQC4m87hnjx+4Wl50Jz75t23fji/XH1Dejt6c+yCo/K4RUsyd2R+6nUvqgIAtJru1f25YrcX5Msrvp0P/fU/8pfRv2bb7gU5Y9sTcvz8h6R3aCDNymeq0r66ZvoAALAl1RtVls3dNr0XPytzz39CkqTZ1Z2RgTmejAMAaGEjta6M7bpHel/1ivQNDyVJ6r39Wdndm2bTk3EAAK1qVVd/Oh/04PQedFAG6mNJktHBuVmejlSejgMAaEnNZpXlvXPSfdajM3j8salVVapaR0bmzs9Qvbnmkx0AAGg5jUaVZXO3udtuXFdGBubajQMAaGEjta6M7XKv9F7+ivQNr05iNw4AoB10ZTgP2Ksjb312d+rNjtSSDPSOJc16mo2ZPh0AAONpNqvc2Zyb7vs8Nv17Hp+ONNNMLcOdCzM61kzi+TgAgFbUaDQzp3t1nnlqV55wwpoP3+rqqDLQM2o3DgCghdWqsey8sJ5XPKk7w2OdSZK+7ka6amN24wAAWlhXhvOA+6zbjUuSQbtxAAAtraqq9NZW5/QjOvPI+3enWXWkVqsyt3c09XojTS+rAgC0pGZzzW7cM07pyj8cv2Y3rrOjyqDdOACA1lbVs9OCht04AIA205Xh3P/eHXnbs7sz1vj7blw9aY7ZjQMAaFFVVaWntjqnPrgzjzj0b7txqTK3z24cAEArazSqLJuzTXqe+YzM+YdzU0vS7OzKyKDPVAUAaGUjta6M7rh7el/5sswb+vtnqvZlVXef3TgAgBbWMdSTAzr3z6t3uiT1jnpqqaW/0Z9qdS2VjAMAaElVVaVrVV+O7zkmR+/+oFS1KqlqGawPpL6qStNnqtLmumb6AAAwHUaatYwMzF93g+U4AICW12xWGeroSQZ67nKjJ+MAAFpdo9HM6p6B5O8ZJ+EAANrC2FgzY/3z1t3gNVUAgLZgNw4AoP2s2Y3rTu7acXbjAABaXqPRTE/HSHrWfK5qKh+oCgDQFsbqzYzlLrtxPlEVAKAtNBv1DHTV1/66Xt/AxQAAtIRms0pHRjPw93e9r6zGAQC0A7txAADtqV5vpK+zcZdfz+BhAADYZHbjAADaj904AID21Gg0010bSfffOs5uHABAe7AbBwDQnkYbyajPVAUAaCtVVWW41p1hn6kKANBWGo1muhq96UpvksQzcQAA7aFeb6an3r/u1/FcHFuHjun+DW+++eace+65OeGEE/L0pz89q1atmvDalStX5thjj823v/3taTwhAADj0XEAAO1HwwEAtCcdBwDQnnQcAEB70nEAAO1HwwEAtCcdBwDQnnQcAEB70nEAAO1HwwEAtCcdBwDQnnQcAEB70nEAAO1HwwEAtCcdBwDQnnQcAEB70nEAAO1HwwEAtCcdBwBQTsd0/4aveMUrcs455+Rzn/tcDjzwwLztbW+b8NrLL788y5cvn8bTAQAwER0HANB+NBwAQHvScQAA7UnHAQC0Jx0HANB+NBwAQHvScQAA7UnHAQC0Jx0HANB+NBwAQHvScQAA7UnHAQC0Jx0HANB+NBwAQHvScQAA7UnHAQC0Jx0HANB+NBwAQHvScQAA5XRM5282NjaW7373uzn++OOTJGeccUY+97nPjXvtZz7zmQwODmafffaZziMCADAOHQcA0H40HABAe9JxAADtSccBALQnHQcA0H40HABAe9JxAADtSccBALQnHQcA0H40HABAe9JxAADtSccBALQnHQcA0H40HABAe9JxAADtSccBALQnHQcA0H40HABAe9JxAABldU3nb7Z06dLMmTMnXV1rfttFixbllltuucd1N998c6699tpce+21ufDCCyf1e2277ZwpnXVrsGjR3Jk+wlbFPMsyz7LMsyzzLMs8tw7T1XEabg2Pm7LMsyzzLMcsyzLPssxz6+C5uOnlcVOWeZZlnmWZZ1nmWZZ5bh103PTyuCnLPMsyz7LMsyzzLMs8tw46bnp53JRlnmWZZ1nmWZZ5lmWeWwe7cdPL46Ys8yzLPMsxy7LMsyzz3Dp4Lm56edyUZZ5lmWdZ5lmWeZZlnlsHHTe9PG7KMs+yzLMs8yzLPMsyz62DjpteHjdlmWdZ5lmWeZZlnmWZ59bBbtz08rgpyzzLMs+yzLMcsyzLPLcOnoubXh43ZZlnWeZZlnmWZZ5lmefWQcdNL4+bssyzLPMsyzzLMs+yzHProOOml8dNWeZZlnmWZZ5lmWdZ5rl1sBs3vTxuyjLPssyzLPMsxyzLMs+tg+fippfHTVnmWZZ5lmWeZZlnWea5ddBx08vjpizzLMs8yzLPssyzLPPcOui46eVxU5Z5lmWeZZlnWeZZlnluHezGTS+Pm7LMsyzzLMs8yzHLssxz6+C5uOnlcVOWeZZlnmWZZ1nmWZZ5bh103PTyuCnLPMsyz7LMsyzzLMs8t6yuLfWFP/vZz+a1r33terfd6173usd1tVptvV83m81ceumlueyyy9LX1zfp3//221em2awm/c+3u0WL5ua221bM9DG2GuZZlnmWZZ5lmWdZ5jl1HR21aX+SaiY7brY3XOJxU5p5lmWe5ZhlWeZZlnmWMd0d57m4meVxU5Z5lmWeZZlnWeZZlnlO3Wx7Li7RcR43ZZlnWeZZlnmWZZ5lmefU6bjZx+OmLPMsyzzLMs+yzLMs85y62dZxs73hEo+b0syzLPMsxyzLMs+yzLMMu3Gzi8dNWeZZlnmWZZ5lmWdZ5lmGjptdPG7KMs+yzLMs8yzLPMsyz6mbba+pJjrO46Ys8yzLPMsyz7LMsyzznLrZ1nGzveESj5vSzLMs8yzLPMsxy7LMswyvqc4uHjdlmWdZ5lmWeZZlnmWZZxk6bnbxuCnLPMsyz7LMsyzzLMs8p262vaaa6DiPm7LMsyzzLMs8yzLPssxz6mZbx832hks8bkozz7LMsyzzLMcsyzLPMrymOrt43JRlnmWZZ1nmWZZ5lmWeZei42cXjpizzLMs8yzLPssyzLPOcutn2mmqi4zxuyjLPssyzLPMsyzzLMs+pm20dN9sbLvG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\n",
+      "text/plain": [
+       "<Figure size 12528x432 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import silhouette_score\n",
+    "\n",
+    "cluster_cols = df_pca_norm.columns[2:]\n",
+    "fig, axes = plt.subplots( 1, len(df_pca_norm.columns)-2, figsize=(8*len(df_pca_norm.columns)-2,6)) \n",
+    "\n",
+    "for i, ax in enumerate(axes):\n",
+    "    #fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "    sns.scatterplot(\n",
+    "        ax=ax,\n",
+    "        data=df_pca_norm,\n",
+    "        x=\"PC1\",\n",
+    "        y=\"PC2\",\n",
+    "        hue=cluster_cols[i],\n",
+    "        palette=\"bright\",\n",
+    "        alpha=0.9,\n",
+    "        s=100\n",
+    "    )\n",
+    "    ax.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
+    "    ax.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "#print(silhouette_score(norm_data, clustering.labels_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "b20dea38",
+   "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>method</th>\n",
+       "      <th>calinski_harabasz</th>\n",
+       "      <th>silhouette</th>\n",
+       "      <th>davies_bouldin</th>\n",
+       "      <th>type</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>agglomerative_9</td>\n",
+       "      <td>45.903208</td>\n",
+       "      <td>0.764623</td>\n",
+       "      <td>0.361465</td>\n",
+       "      <td>agglomerative</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>agglomerative_8</td>\n",
+       "      <td>50.754332</td>\n",
+       "      <td>0.823292</td>\n",
+       "      <td>0.384660</td>\n",
+       "      <td>agglomerative</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>agglomerative_7</td>\n",
+       "      <td>10.824340</td>\n",
+       "      <td>0.625600</td>\n",
+       "      <td>0.648804</td>\n",
+       "      <td>agglomerative</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>agglomerative_6</td>\n",
+       "      <td>9.178705</td>\n",
+       "      <td>0.575738</td>\n",
+       "      <td>0.842760</td>\n",
+       "      <td>agglomerative</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>agglomerative_5</td>\n",
+       "      <td>5.827782</td>\n",
+       "      <td>0.431105</td>\n",
+       "      <td>1.052480</td>\n",
+       "      <td>agglomerative</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            method  calinski_harabasz  silhouette  davies_bouldin  \\\n",
+       "0  agglomerative_9          45.903208    0.764623        0.361465   \n",
+       "1  agglomerative_8          50.754332    0.823292        0.384660   \n",
+       "2  agglomerative_7          10.824340    0.625600        0.648804   \n",
+       "3  agglomerative_6           9.178705    0.575738        0.842760   \n",
+       "4  agglomerative_5           5.827782    0.431105        1.052480   \n",
+       "\n",
+       "            type  size  \n",
+       "0  agglomerative     9  \n",
+       "1  agglomerative     8  \n",
+       "2  agglomerative     7  \n",
+       "3  agglomerative     6  \n",
+       "4  agglomerative     5  "
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "\n",
+    "clu_metrics = pd.DataFrame()\n",
+    "clu_metrics['method'] = [(x.rsplit(\"_label\",1)[0]) for x in df_pca_norm.columns[2:]]\n",
+    "clu_metrics['calinski_harabasz'] = clu_metrics.apply(lambda x: \n",
+    "                                                     metrics.calinski_harabasz_score(df_pca_norm, \n",
+    "                                                                                     df_pca_norm[x['method']+'_label']),\n",
+    "                                                    axis=1)\n",
+    "clu_metrics['silhouette'] = clu_metrics.apply(lambda x: \n",
+    "                                                     metrics.silhouette_score(df_pca_norm, \n",
+    "                                                                                     df_pca_norm[x['method']+'_label']),\n",
+    "                                                    axis=1)\n",
+    "#davies_bouldin_score #Measures better separation between the clusters.\n",
+    "clu_metrics['davies_bouldin'] = clu_metrics.apply(lambda x: \n",
+    "                                                     metrics.davies_bouldin_score(df_pca_norm, \n",
+    "                                                                                     df_pca_norm[x['method']+'_label']),\n",
+    "                                                    axis=1)\n",
+    "\n",
+    "clu_metrics['type'] = clu_metrics.apply(lambda x: x['method'].split(\"_\",1)[0],\n",
+    "                                        axis=1)\n",
+    "\n",
+    "clu_metrics['size'] = clu_metrics.apply(lambda x: int(x['method'].split(\"_\")[1]),\n",
+    "                                        axis=1)\n",
+    "clu_metrics.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "55e97fb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[224, 222],\n",
+       "       [  0,  60]])"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics.cluster import pair_confusion_matrix\n",
+    "pair_confusion_matrix(df_pca_norm['optics_2_label'],df_pca_norm['agglomerative_2_label'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "f757a9dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "selected_metric = 'silhouette' #'silhouette'#'calinski_harabasz'#davies_bouldin_score\n",
+    "#fig = plt.figure(figsize=(15, 5))\n",
+    "plot_metrics = clu_metrics.set_index('size')\n",
+    "plot_metrics.groupby(['type'])[selected_metric].plot(legend=True, linewidth = 2)\n",
+    "\n",
+    "#plt.title('Elbow curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4414632c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12620b38",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}