Codigos em Python/Aprendizado_Supervisionado_Arvore_de_decisão_Covid.ipynb

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    "# SME0829 Aprendizado de Máquina\n",
    "\n",
    "\n",
    "por **Cibele Russo**\n",
    "\n",
    "**ICMC/USP - São Carlos SP**\n",
    "\n",
    "\n",
    "\n",
    "## Exemplo Árvore de decisão\n",
    "\n"
   ]
  },
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>asma</th>\n",
       "      <th>cardiopatia</th>\n",
       "      <th>diabetes</th>\n",
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       "      <th>IGNORADO</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2510490</td>\n",
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       "      <td>0</td>\n",
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       "      <td>73.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>0</td>\n",
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       "    Unnamed: 0  asma  cardiopatia  diabetes  doenca_hematologica  \\\n",
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       "12     3889751     0            1         1                    0   \n",
       "13     1869966     0            1         1                    0   \n",
       "\n",
       "    doenca_hepatica  doenca_neurologica  doenca_renal  imunodepressao  \\\n",
       "0                 0                   0             0               0   \n",
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       "11                0                   0             0               0   \n",
       "12                0                   0             0               0   \n",
       "13                0                   0             0               0   \n",
       "\n",
       "    obesidade  outros_fatores_de_risco  pneumopatia  puerpera  \\\n",
       "0           0                        1            0         0   \n",
       "1           0                        0            0         0   \n",
       "2           0                        0            0         0   \n",
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       "\n",
       "    sindrome_de_down  idade  obito  FEMININO  IGNORADO  INDEFINIDO  MASCULINO  \n",
       "0                  0   76.0      1         0         0           0          1  \n",
       "1                  0   86.0      1         1         0           0          0  \n",
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       "4                  0   75.0      1         0         0           0          1  \n",
       "5                  0   82.0      1         0         0           0          1  \n",
       "6                  0   86.0      1         0         0           0          1  \n",
       "7                  0   37.0      1         0         0           0          1  \n",
       "8                  0   68.0      1         0         0           0          1  \n",
       "9                  0   80.0      1         1         0           0          0  \n",
       "10                 0   33.0      1         0         0           0          1  \n",
       "11                 0   66.0      1         0         0           0          1  \n",
       "12                 0   68.0      1         0         0           0          1  \n",
       "13                 0   62.0      1         0         0           0          1  "
      ]
     },
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     "metadata": {},
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    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import random\n",
    "\n",
    "random.seed(1)\n",
    "\n",
    "# Considere os dados de comorbidades em pacientes internados por COVID no estado de São Paulo em 2021:\n",
    "\n",
    "# Carregando os dados\n",
    "dados = pd.read_csv('https://raw.githubusercontent.com/predict-icmc/covid19/master/leitura-dados/ae.csv')\n",
    "dados.head(14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>0</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",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29995</th>\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",
       "      <td>0</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",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\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",
       "      <td>0</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",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\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",
       "      <td>0</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",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\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",
       "      <td>0</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",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\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",
       "      <td>0</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",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       asma  cardiopatia  diabetes  doenca_hematologica  doenca_hepatica  \\\n",
       "0         0            0         0                    0                0   \n",
       "1         0            0         0                    0                0   \n",
       "2         0            0         0                    0                0   \n",
       "3         0            1         1                    0                0   \n",
       "4         0            1         1                    0                0   \n",
       "...     ...          ...       ...                  ...              ...   \n",
       "29995     0            0         0                    0                0   \n",
       "29996     0            0         0                    0                0   \n",
       "29997     0            0         0                    0                0   \n",
       "29998     0            0         0                    0                0   \n",
       "29999     0            0         0                    0                0   \n",
       "\n",
       "       doenca_neurologica  doenca_renal  imunodepressao  obesidade  \\\n",
       "0                       0             0               0          0   \n",
       "1                       1             0               0          0   \n",
       "2                       0             0               0          0   \n",
       "3                       0             0               0          0   \n",
       "4                       0             1               0          0   \n",
       "...                   ...           ...             ...        ...   \n",
       "29995                   0             0               0          0   \n",
       "29996                   0             0               0          0   \n",
       "29997                   0             0               0          0   \n",
       "29998                   0             0               0          0   \n",
       "29999                   0             0               0          0   \n",
       "\n",
       "       outros_fatores_de_risco  pneumopatia  puerpera  sindrome_de_down  \\\n",
       "0                            1            0         0                 0   \n",
       "1                            0            0         0                 0   \n",
       "2                            0            0         0                 0   \n",
       "3                            1            0         0                 0   \n",
       "4                            1            0         0                 0   \n",
       "...                        ...          ...       ...               ...   \n",
       "29995                        0            0         0                 0   \n",
       "29996                        0            0         0                 0   \n",
       "29997                        0            0         0                 0   \n",
       "29998                        0            0         0                 0   \n",
       "29999                        0            0         0                 0   \n",
       "\n",
       "       idade  obito  FEMININO  IGNORADO  INDEFINIDO  MASCULINO  \n",
       "0       76.0      1         0         0           0          1  \n",
       "1       86.0      1         1         0           0          0  \n",
       "2       42.0      1         0         0           0          1  \n",
       "3       73.0      1         1         0           0          0  \n",
       "4       75.0      1         0         0           0          1  \n",
       "...      ...    ...       ...       ...         ...        ...  \n",
       "29995   35.0      0         0         0           0          1  \n",
       "29996   45.0      0         1         0           0          0  \n",
       "29997    1.0      0         0         0           0          1  \n",
       "29998   27.0      0         1         0           0          0  \n",
       "29999   48.0      0         1         0           0          0  \n",
       "\n",
       "[30000 rows x 19 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = dados.drop('Unnamed: 0', axis=1)\n",
    "dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1683160167444,
     "user": {
      "displayName": "Cibele Russo",
      "userId": "07272141402608887263"
     },
     "user_tz": 180
    },
    "id": "onZfrc2YKLiF",
    "outputId": "bceb040f-be2d-4045-c7a0-faf9ac6cdfde"
   },
   "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>col_0</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obito</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  Total\n",
       "obito       \n",
       "0      21000\n",
       "1       9000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(dados['obito'],columns='Total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "XK0UhefCIxvS"
   },
   "outputs": [],
   "source": [
    "# Separa as preditoras (X) e a resposta (y)\n",
    "X = dados.drop('obito', axis=1)\n",
    "y = dados['obito']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "IAjlOTjWIxvS"
   },
   "outputs": [],
   "source": [
    "# dados de treinamento e teste: 80% para treinamento e 20% para teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "yiGFd7TRIxvT"
   },
   "outputs": [],
   "source": [
    "# Cria uma árvore de decisão\n",
    "arvore = DecisionTreeClassifier(max_depth=3)\n",
    "\n",
    "# Treina a árvore de decisão usando os dados de treinamento\n",
    "arvore.fit(X_train, y_train)\n",
    "\n",
    "# Usa a árvore de decisão para prever os rótulos da base de teste\n",
    "y_pred = arvore.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "mFFDeOShIxvT",
    "outputId": "f04e17f0-a015-4248-86ff-1a4f1d250a6c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uoxw6c5nAJv2TbH/p04mUnhvM0i97WLddDgunYb8fyJctgNHvtsVIZhE5EREREZEnRSGwiIiIiKSKkIAM2NvZsuPIGYIDfAG4HR3LgdOXCA7IkKrXftx2EKXzBfPF9BWcD71BoJ8XAKt2HsbR3o6iObMke8ygTvXo0axqkm3lX/+c4V0b0qBsQeu2S2HhvPje9+TN5s/P/dpj96/AW0RERETkSVMILCIiIiKpws3Zkfa1SzN43EJ8PVzx9/Hgi+m/YyaapPbE18dtB1G9eB7yZfOn25dT+bBrQ67fus0HPy+gY92yeLg6AbD98Gm6fTGNH/u0oUSebGTO4EXmDF53nSsog5c1BL947SYvvPc9mXw8+OS1xly7GWkdl8HTDVtbm0euWUREREQkJQqBRURERCTVDO/akMjoWNoMHYerswPdm1ThyvUInOzt07q0e7K1tWHG0FfoM2oWdft8i5ODPS2qFWf4yw2tY27HxHH03BVux8Q98Hn/2HGY4+dDOX4+lIIdhyfZt3v8QLL5+zyxexARERER+YvxML3VREREROS/5+vpumvC+52LVCqcM61LeWwxsfEU6jycHs2q3dU6QZ5NedsNuXUpLLykaZpH0roWEREREUmeZgKLiIiISKrZfewcR85epkSerNy6HcPI3/4g4nYMTSsXTevSRERERESeGwqBRURERCRVjZqzhmPnQrG1taFQ9sws/vwN62JrIiIiIiKS+hQCi4iIiEiqKZIziNXf9ErrMkREREREnmtaflhEREREREREREQkHVMILCIiIiJPpdOXw/Cq14udR86mdSkiIiIiIs80tYMQEREREXlE8zfs4ZdFG9lz/DwxcfHkyepP79Y1qV+2YLLjZ67eQdcRk6lTOj8zhnZNsm/swg18M3MVl8PCyZstE5+81pjyBbNb98fExjNw7HxmrdlJdEwclYvm4ss3miXpr3z2ynX6jJrFut3HcHK0p3nVYnzYtSEO9nrbLyIiIvI800xgEREREZFHtGHvcSoXzcWvw7qy9rte1CqVj/bDf2HjvhN3jT118RofjF1AuX8Eu3+ZvWYn/X6cQ+9WNVj7XW9K5wumxaCfOHvlunVM/9FzWbBhDz+/157FX7zJrdvRtBoyloSERAASEhJpNXgMEVExLP7iTX5+rz3z1+/h/THzU+8LICIiIiLPBE0JEBEREXnObdh7nME/L+Dg6UvY2tiQM8iP73q2Jn9wAGHhkbz7/Ww27TtB2K1IgjP58mazarSvXdp6fIO+o8iTxR9nJ3umLP8TWxsb+rSpRZf65Xl/zDx+XbUdDxcnBnaqT+saJQFLq4cinT9kTN92/LxwIzuPniWrvw8jujWheok8KdZ66PQlPvh5ARv3ncDJwZ4qRXPx8auN8PfxAGD/yQv0Hz2PnUfOkGiaBAf48slrjalcJFeqfO1GdGuS5HW/dnVY/ucBFm3am2QWb1x8Ai+PmMTATvVZt+cYYeGRSY4bNWcNbWuVolO9cgB83r0pK7cfYtyiDQx+6QVuRkYxafkWRvVsTbXilq/P6HfbUqjTh6zedYQaJfLyx47DHDx9mb0TBhLk5w3A0Jdf4K2vf2VQp/p4uDqlytdARERERJ5+CoFFREREnmPxCQm0HTaODrXLMKZve+ISEth97By2NpYHxqJj4yiSM5C3W1THw8WJ1TuP0PPb38ji50WVYrmt5/lt1Xa6N63Cyq/fYfHm/fQfPZeV2w9Ro0ReVn/Tk6krtvHW1zOoWiw3me4EtgCDf17IR682okBIAGMWbKDtsJ/Z8fMAMmfwuqvWS2Hh1O87ig61SzO8a0PiEhIYPn4xbYeN4/ev3sLGxoZXPptMwZDMrBz5Dna2Nuw/eREnB/sU7//L6Sv4asaKe36Nfhv+apJA934iomLwcnNJsm34hMVk9fehba1SrNtzLMm+2Lh4dh09R49mVZNsr148D1sOnAJg19FzxMUnUL343wF5kJ83ebJkZMuBU9QokZc/D54iT5aM1gAYoEbxvMTExbPr2NlUC8JFRERE5OmnEFhERETkOXbrdjQ3I6KoWyY/IZkzAJA7i791f+YMXrzVvLr1deeAcqzdfZSZa3YmCYHzZstE//Z1AXizaRW+/nUldra2vN64MgDvta3NyN/+YMv+kzSqVMR6XJcG5WlSuSgAI7o15o8dhxm3aCMDO9W/q9afF26gYEhmhr78onXb6D5tCW45kJ1Hz1IiTzbOXr7Om02rWe8he2a/e95/lwblaFK5yD3HBPh63nP/P41ZsJ4LV2/SqnoJ67Y/th9mztpdrBvVJ9ljroVHkpCYiJ+Xe5Ltfl5uXLl+C4Ar18OxtbHB19M16Rhvd65cD78z5hZ+3knP4evpiq2NjfU8IiIiIvJ8UggsIiIi8hzzdnelba1SNBv4E1WK5qJy0Vw0qliELBkts0kTEhL5368rmb12Fxev3SQ2Lp7Y+AQqFsqR5DwFQgKsfzYMAz8vNwoE/73N3s4WLzdnQm9GJDmuVL5g659tbGwokScrh85cTrbW3cfOsXHfcQKb9Ltr38mL1yiRJxvdm1bhrZEzmLZyK1WK5KJhxcJJQu3k7t/b3TXF/Q9j3vrdfPDzAsb160hWfx8Art6IoPtX0xj7Xnu83JyfyHVERERERB6WQmARERGR59z3vdrweuPKrNx2iCWb9/PhhMVM+aALNUrk5dtZq/hu9mo+7daE/MEBuDk7MGz8YkJvJA1z7e1sk7w2DAM7O5u7tiUmJj5ynYmmSe3S+fmw64t37ftrFm3/9nVpWa0Ev287yB/bDzNi6nK+erM5HeqUSfacT6odxLx1u+n25VR+7N2WemULWLcfPHOJS2HhNOr/Y5L7APBt0IfNo/uSzd8HWxsbQm8kna0beiOCjHdm9mb09iAhMZFrNyPJ4OX295jrtyhXIPudMe5sOXAyyTmu3bTMMs74rxnCIiIiIvJ8UQgsIiIiIhTKHkih7IG807IGzQf9xLQVW6lRIi+b9p+kbpkC1gXdTNPk2PlQPF2fzKzWbYdOU6VoLuu5dxw+Q6OKybdnKJIjiDnrdpElo89dofM/5Qj0I0egH90aVabXtzOZtGxLiiHwk2gHMWftLl7/cio/9G6bpNUFQPHcWdj4w7tJtn04cQk3bt3mizeakc3fBwd7O4rmCmLVjiM0rlTUOm7VziM0rFAIgKK5grC3s2XVzsO0qGZpNXE+9AaHz16hTP5gAErnC+aL6Ss4H3qDQD+vO+c4jKO9HUVzZrnnPYiIiIhI+qYQWEREROQ5durSNcYv3kS9sgUI8PXk1KVr7D95gS4NKgCQM8iPOWt2sWnfCXw9Xflp/nrOXAqjUI7AJ3L9cYs2kjPQj/whAYxduIGzV67TpUH5ZMd2fbECE5Zu5qVPJvJOi+pk8HTj1KVrzFm7iw9faYidrQ2Dxi6gUaUiZPX3IfT6LTYfOEGJPNlSvP7jtoOYtXonr30xheFdG1K+YHYuh1n68zrY2+Lt7oqrkyP5/9EWA8DT1ZmEhMQk299oUoXXvphKiTxZKZM/hHGLN3Lp2k1eql/eekyH2mUY/PNC/Lzc8XZ34f0x8ygQEkDVopbezNWL5yFfNn+6fTmVD7s25Pqt23zw8wI61i2Lh6vTI9+jiIiIiDz7FAKLiIiIPMdcHB04dj6Uzh9P4NrNSDJ6u9OiWgneaWFZDO7d1rU4fSmMFh+MwcnBnra1StGiWvEU+/Y+rMEvNWDUnDXsPnaOLBm9mTzoJess1n8L8PVk2Zc9GPrLIpoN+omY2DiC/LypVjwPjvaWt7U3Im7T/ctpXA4Lx8fDlTql8zO8a8MnUmtyxi3eSHxCIv1Hz6X/6LnW7RUK5WDRZ2888HmaVilG2K3bfD7tdy6HhZMvOIBfh71i7S0M8MlrjbG1teGlTyYSHRtH5SK5+LFPW2xtLW03bG1tmDH0FfqMmkXdPt/i5GBPi2rFGf5y6t2/iIiIiDwbDPNOTzIREREReTr5errumvB+5yKVCudM61KemNOXwyjS+UNWjexJsdxqVfAsy9tuyK1LYeElTdM8kta1iIiIiEjybO4/RERERERERERERESeVQqBRURERERERERERNIx9QQWERERkf9cNn8fbiz5Kq3LEBERERF5LmgmsIiIiIiIiIiIiEg6phBYRERE5DnWoO8o3v1+VlqXcV+fTF6KV71eeNXrxf9+XZnW5TxXXv9ymvVrP2/d7rQuR0REREQegdpBiIiIiMgzIVdQRhaO6I6bi6N12/wNexi/eBO7j53jWngkC0Z0p1LhnHcdu/3waYZPWMLWg6cwDIP8wQFMG9wFX0831u05xovvfZ/sNccP6EjjSkUBOHbuCh+MW8jm/SeJjYsnb1Z/+rWvQ82S+azjdxw+w9Dxi9h59CwARXNmYfBL9SmRJ9sD3+cnk5cyYsryZPcdnTYUPy931u05xvdz1rDj8BnCb0cTEpCB1xtXpkOdMtaxl8LCGThmHruPnef4hVBaVS/JD73bJDnfhCWbmL5yGwdOX8I0TQrnCOT9DvUoVzC7dcyn3Roz5KUG5Gk35IHvQURERESeLgqBRUREROSZYGdrg7+PR5Jtt6NjKZ0/mJbVS9Dti6nJHrft0GmaDhzNW82q8fGrjXCws+XA6UvY2dkCUCZfMIenDElyzOj56/hp/vokAW+rIT8TnMmHeZ90w9XJgXGLNtF26Di2jH6PkMwZiIiKodmgn6hdOj8rujfFNOHL6b/TdOBP7JswCHcXpwe6zx7NqtGlfvkk27p8OgnDAD8vdwD+PHCKAsEBvN28Opl8PFi5/RDvfPMbTg52tKhWAoCYuHh8PFx5p2V1JizZnOy11u85TpPKRfk0fwguTg58P2cNzQb+xLpRvckR6AeAp6sznq7OD1S7iIiIiDydFAKLiIiIPIPGL97ER5OWcGjyEGxt/+7w1XXEJCKiYpk+5GVOXrjKgDHz2H74DBG3Y8gZ5MeADnWpW6ZAiuct1Gk4r75YkR7Nq1m3Neg7ivzBmfi8ezMAYuPi+WjiEn5btYPrEbfJmzUTAzvVo0aJvKl3wyloXaMkANduRqQ4ZsBP83jlhYr0aVPLui1nUEbrnx3s7e4Kl+et30OzKsVwc3a0nv/4+VD+16M5hbIHAjCkSwO+n7uGPcfPE5I5A0fPXuH6rdv0b1+H4Ey+lmt3rMevq3Zw7FwoxXJneaB7cnN2tF4X4FzodTbtP8HoPm2t23q3rpnkmJdfqMC6PceYv2GPNQTO5u/DZ683BWD++j3JXmvMe+2TvP7qzeYs2rSPFdsOWUNgEREREXn2KQQWEREReQY1rlSE936cw6qdh62zVSOiYli8aT+jerW2vI6OoVbJfAzsWA9nR3tmr9lFhw/Hs+H7PuTO4v/I137jq+mcvHiVMe+1JzCDF8u3HqD1kJ/5Y+Q71oD0376cvoKvZqy453l/G/4q5f/RhuBJCL1xiz8PnqJ51WLU7f0tx86HkivIj37t6lClWO5kj1m35xjHz4cytu/fAamPhyt5svgzY+V2iufOirODPeOXbMbN2ZEyBUIAyBnkRwZPNyYv28K7bWoDMHHpZoIyepM326N/vSct24KXmwsNKxS557hbt6PJnMHrka8DEBuXQHRsHF7umvkrIiIikp4oBBYRERF5Bnm5u1CrVD5+XbXDGgIv2rQXO1sb6pW1zPQtlD0wSSjbp00tlm7Zz7z1e3j3H7NiH8bJC1eZuWYne8YPJEtGbwBebViJ1buOMn7xJr58s3myx3VpUI4mle8dYgb4ej5STfdy6uI1AD6ZvIxhL79I4RyBzFu/m6YDf2L1tz2TDa0nLNlEoeyBSWbuGobBnI9fo/3wX8jSbAA2hoG3uwszh79KpjuziN1dnFg4ojvthv/CV3cWr8ua0Ye5H72Gs6PDI9WfkJDIlOV/0qp6CRwdUn7rvnTLftbsOsqyL3s80nX+8uHExbg5OVKvTMHHOo+IiIiIPF0UAouIiIg8o1pWL0H3L6dxOzoWFycHflu1gxcrFMbJwR6AyOgYRkxZzrItB7h0PZz4+ASiY+MpEJL5ka+5+/g5TNOk7GsjkmyPiYuncpFcKR7n7e6Kt7vrI1/3USWaJgAv1S9nXTStSM4g1u0+xi+LNvFVj6ShdVh4JAs27OWjVxsl2W6aJr1HzcLHw5Uln7+Jk6M9k5ZupuOH4/lj5DtkzuBFVEwsb/5vOiXzZmPMu+1ISEzk21mraTtsHKu+6YmrkyMPa8X2Q5wLvUGnumVTHLN5/0leGTGZEd2aPNQCdP/2w9y1jF+8ibmfdMPD9cH6F4uIiIjIs0EhsIiIiMgzqk6p/Nja2rB48z6qFM3F6p1HmPXha9b9g8YsYMX2Qwzv+iI5Av1wcbSn2xfTiI1PSPGcNjYGJmaSbfEJf49PTDQxDIM/RvbE3s4mybi/wufkpFU7iL96/ebJmrQdQ56s/pwLvX7X+Gkrt2FrY0OLasWTbF+76yhLtxzg5K8f4uVmaZVQ9M3mrNp5hCm/b+XdNrX4bfUOTl68xrIv37L2aR77XnuCWwxk4ca9tKpe8qHrH79kE2XyB5M3W6Zk92/ad4KWH4yhf4e6vPxChYc+/1++n7OGjyct5bdhrzxWkCwiIiIiTyeFwCIiIiLPKEcHOxpXLMJvq3Zw7WYk/t4eVCqcw7p/84ETtK5RkkYVLW0YomPjOHnx6j0X/Mrg6calsFvW19GxcRw5e4XCOSxtEwrnCMQ0TS5fD7/nzN9/S6t2ENn8fQjw9eDoudAk24+fDyV/cMBd4yct3UyjSkXwdE3aE/d2TBwANoaRZLuNYZCYmAhAVHQchmFgY/P3GBsbA8OwhOcP6+K1myz/8yDfvNMy2f0b9h6n1eCx9Gtfh+5Nqjz0+f/y3ezVfDp5GTOGdqXcEw7hRUREROTpoBBYRERE5BnWsnoJGvX/gdOXrtGsajFsbP6enZsj0I+FG/dSv1xB7G1tGDFlOTGx8fc8X+UiOZm8/E/qlS1ABk83vpz+OwkJidb9OYMy0rJacbp/NZ2PujakSM4grt+6zfo9x8gW4EvDCoWTPW9qtYO4fiuSs1ducDMyCrD0LPZ0dcbf2x1/Hw8Mw6BHs2p8OnkZBUMCKJwjkDlrd7P10Gk+6940ybk27TvBoTOX+fqtu0PX0vmy4e3uwhv/m0bftrVxdrBnwtLNnLp0jTplLD2YqxXPzQc/L6D3d7N4rVElEk2T//26Elsbm4cKzP8yefmfuDo50KRS0bv2rdtzjFYfjOXlF8rTompxLoeFA2BrY0MGLzfruD3HzwMQfjsawzDYc/w8Dna21pnF38z8g+ETlvDTu+3IGehnPY+To/1dQbiIiIiIPLsUAouIiIg8w8oXzE6AryeHzlxmbL8OSfZ99Eojenw9g/p9vsPLzZnXG1cmJu7eIXDPljU5c/k67YaOw9XZgd6ta3HxWniSMaN6teGL6b/zwbgFXLh6E293F4rnzkqlIjmf+P3dz+LN+3njq+nW12+N/BWA99rVpn/7ugB0b1KF2Lh4Bo6ZT1j4bfJm82fm8FfvWhRuwtLN5MniT9kCIXddx9fTjVnDX2X4hMU07PcD8fEJ5M7qz5QPulA0ZxAAubP4M33Iy4yYspxavUZiGAaFsmfmt+GvEOjnZT2XV71eSepLjmmaTFq2hRbViuPidPeiclN/38rtmFi+nbWab2ettm7PktGbvRMGWV9XfvPLJMct3bI/yZgxCzYQF5/AS59MTDKuTc1S/NC7TYr1iYiIiMizxTDNh380TURERET+O76errsmvN+5SKXC/33I+rT4ZPJS5q/fw6Yf+6Z1KY/l1KVrFOvyMUs+fzPZsPlp5lWvFxMGdKJRpaRtPfK2G3LrUlh4SdM0j6RRaSIiIiJyHzb3HyIiIiIikvYOn71MYJN+fDd7dVqX8sh+33qQ1jVKPlMBcM9vfyOwSb+0LkNEREREHoPaQYiIiIjIU69bo0q0ql4CAB8Pt/uMfnq98mLFtC7hoQ3oUJcezaoC4O/jkbbFiIiIiMgjUQgsIiIiIk+91FpYTu7Pz8sdPy/3tC5DRERERB6D2kGIiIiIiIiIiIiIpGMKgUVERERERERERETSMYXAIiIiIvKf8qrXi3nrdqd1Gf+JMQvWk7X5AEbNWcOs1TsZPn5xWpckIiIiIs8hhcAiIiIiIg+hUKfheNXrxcZ9J5Js/2TyUsp1+yzJtvkb9vDbsFf488ApPpm8lBbViv+XpYqIiIiIAFoYTkRERETkoTk52DFk3EKWf/XWPcct+LQ7AGXyh/wXZYmIiIiIJEshsIiIiIg8UaZp8t3sNfyyeCPnrlwng6cbrWqUYPBLLyQ7fsi4hSzcuJdzodfx83KnSeWiDOhQFycHewDOhV7n3e9ns2nfCWLi4gny86Zfuzo0q1oMgBFTljFp+Z9cCQvHy92FasXzMLpP21S9x071yjFhySbmb9hDwwqFkx1z8sJVBoyZx/bDZ4i4HUPOID8GdKhL3TIFrGNu3LpNv9FzWbJlPzGxcZTJH8Kn3ZqQL1umVK1fRERERJ4vCoFFRERE5IkaNn4RPy/ayEevNKJCoexcvRnJnuPnUxzv4uTAdz1bE5DBk8OnL9Hru5k42NsxsGM9AHp/N4uYuHgWjOiOu4sTx85dsR47b/1uvpu1mrH9OpA/OICrNyLYeujUPesLbNLvnvvLFczOzOGv3nNMUAYvXm1YiWG/LKJ+2QLY2dreNSYiOoZaJfMxsGM9nB3tmb1mFx0+HM+G7/uQO4s/AK9/NY1j50KZ+kEXvNycGT5hCc0H/sS2sf1wdnS4Zw0iIiIiIg9KIbCIiIiIPDERUTF8P2ctn7zWmA51ygCQPbMfpfMFp3hM37a1rX/O5u9Dr1Y1+XbWKmsIfPbKdRpWLEyh7IEABGfytY4/e+U6/j4eVC+eB3s7W7Jk9KZY7iz3rHHdqN733P/XDOT76dWyBhOXbmbi0i10aVD+rv2Fsgdaawbo06YWS7fsZ976PbzbphbHz4eyZPN+Fn32BhUK5QBg9LttKdRxOL+t2kHHumUfqA4RERERkftRCCwiIiLyFDMMo5iXm3Pg/Uc+HQ6fuURMXDxViuZ64GPmrdvND3PXcuLiVSKjYkhITCQh0bTu79a4Er2+ncnKbYeoUjQXL5QvRNFclqC3caUi/DhvHUU6f0j1EnmoWTIv9coUxNEh5be52TP7PfoN/oOXuwu9WtVgxJRltKpR4q79kdExjJiynGVbDnDpejjx8QlEx8ZTICQzAIfPXsbGxkgSkHu6OpM/OIBDZy4/kRr/C6ZpugD1DcM4Y5pmdFrXIyIiIiJ3UwgsIiIi8pQxDMMLaAN0BTIA8WlaUCraevAUXT6dxHvtavNxiUZ4ujmzePN+Bo2dbx3TsU5ZahTPy+9bD7J61xFq9/qGnq1q0L99XYL8vNk2ph9rdh1l9c4jDBwznxFTlrPi67dxdXJM9ppPoh3EX15tWImf5q9n1Ow1d+0bNGYBK7YfYnjXF8kR6IeLoz3dvphGbHzCfc9rGA90+aeDYcQBLYCBhmFMAcaaprk3jasSERERkX9QCCwiIiLyFDAMwwAqYQl+GwLLgQHAChsbYzvwTKwUljuLP472dqzZdZQcgfefcbv5wCkCMngmaQlx9krYXeMC/bzoXL8cneuX4+tfV/LjvHX0b18XsLRvqFM6P3VK56dnyxrkbjuYLftPUb1EnmSv+aTaQfw1dkCHerz3w2xa1Sj5r3s7QesaJWlUsQgA0bFxnLx41fp1yZPFn8REkz8PnrK2gwiPjObAqYu0rV3qgWtIawbEAS9h+WXFS8ASwzDOAz8D003TDE/L+kREREREIbCIiIhImjIMIxPQCXgZS4g2FuhtmmboX2N8PV3TqLqH5+7iRLdGlRg6fhEO9nZUKJidsFuR7Dp6jpdfqHDX+JyBfly8epNf/9hO6XzBrNx+iFmrdyYZ896Pc6hVMi85AzMSfjuaFdsPkSerZWG1Kb//SUJCIiXyZMXV2ZE5a3dhb2dL9sAMKdb4pNpB/KV1jRJ8N3s1k5dvISTg7+vmCPRj4ca91C9XEHtbG0ZMWU5MbHyS/fXLFaTnt7/x9Vst8XR1YviEJbi7ONGiavEnWuN/wTTNE8AgwzCGAHWw/ELjM8MwZmP573qTaZrmPU4hIiIiIqlEIbCIiIjIf8wwDDv+DsmqAbOwBMGb00NINvilBni5u/D5tOX0vHqTjF7utP7XLNm/1CtbgLeaV6X/6LlEx8ZRrXgeBnSoS+9Rs6xjEhNN+v4wh/OhN3BzdqRK0Vx8+EpDwNJDd+RvfzBw7Hzi4xPJk9WfSQM7J1k8LrXZ2NgwtMsLtPhgTJLtH73SiB5fz6B+n+/wcnPm9caViYlL2tnj+56t6Td6Lm2G/kxMbBxl8ocw88NXcXZ0+M/qf9JM00wAFgOL7/ySoyPwC5BoGMZYYOI/f8khIiIiIqnPSAefM0RERESeCYZhZAe6YHlk/iyWx+Vn3O9xeV9P110T3u9cpFLhnP9BlSIPJ2+7IbcuhYWXNE3zSEpj7rQ7qYjlFx+NgN+x/Pf/+53QWERERERSkU1aFyAiIiKSnhmG4WQYRhvDMFYAfwJuQF3TNMuapjlG/VLleWBarDNNsxOQDVgJfAicNAxjiGEY2dK2QhEREZH0TSGwiIiISCowDKOwYRgjgXNYZv+OAYJM03zHNM29D3Uyk8TERD29JU+nRNM0gAf+D9Q0zZumaf5ommZJLLOCMwA7DMNYZhhGC8MwHFOrVhEREZHnlUJgERERkSfEMAwPwzBeNQzjTyw9UcOBUqZp1jJNc4ZpmtGPcl7TJPRymCYMy9MnPiGB8MgoB+DaoxxvmuZO0zTfBIKAiUB34KxhGF8ahpH/CZYqIiIi8lxTCCwiIiLyGAyL8oZhjAPOAHWBIUA20zQHmaZ58nGvcT3i9m8zV++IfNzziDxpa3YdxdHe/qRpmmGPcx7TNKNM05ximmY1oDwQA6wwDGODYRhdDMNweyIFi4iIiDyntDCciIiIyCMwDMMP6IhloSsbYCww0TTNy6lwLS8XJ4eDPVvW8OvepLKtq5Oelpe0ZZomWw+dpvWQsbdvRkR3iU9ImPGkr2EYhh1QH8v/Y5WA37D8f7bV1IcYERERkYeiEFhERETkARmGYQvUAl6+8+95WEKp9akdShmGkdXT1WlKdGx8qZAA32gnB/vUvJxIihISE7lw9aZdTFx8eHRsXN+4+ITJqX1NwzAyA52w/L8XheX/u8mmaT5SGwoRERGR541CYBEREZH7MAwjG/ASlgXermAJoKaZpnkzDWrxBXIDmg4saSURuAwc+a9n5BqGYQNUwRIGvwAsAX4G/jBNM/G/rEVERETkWaIQWERERCQZhmE4Ag2xPIpeEpgK/Gya5q60rEtELAzD8AbaYfl/1BNLGDzeNM1zaVqYiIiIyFNIIbCIiIjIPxiGkR/LLMMOwH4ss35nm6YZlaaFiUiyDMMwgOJYwuBWwCYs/98uNE0zLi1rExEREXlaKAQWERGR555hGG5ASywhUjAwHhhnmuaxNCxLRB6SYRiuQHMsv8jJDUzAMoP/SJoWJiIiIpLGFAKLiIjIc+nO7MHSWILf5sBaLI+TLzZNMz4taxORx2cYRl4sfbw7AYexzA6eaZrm7TQtTERERCQNKAQWERGR58qdhdXaYwl/nbEEvxNM07yQpoWJSKowDMMeyyJyXYFywAwsgfCO/3phOxEREZG0ohBYRERE0j3DMGyA6lhCoLrAQiwh0FrTNBPTsjYR+e8YhpEF6IxlhvANLN8HppqmeT0NyxIRERFJdQqBRUREJN0yDCMIeAlL4HMTS+AzRYGPyPPtHr8YWqPZwSIiIpIeKQQWERGRdEWPfovIw/hHi5hXAEdgHDDeNM2LaVqYiIiIyBOkEFhERETSBcMw8gAvAx2BI2gRKBF5CCksFjkWWKLFIkVERORZpxBYREREnlmGYbgALbCEv7mBicDPpmkeTtPCROSZZhiGG9ASSyAcDIwHxpmmeSwNyxIRERF5ZAqBRURE5JlyZ7ZecSzhTCtgE5bZegtN04xLy9pEJP0xDCM/ll80dQD2AT8Ds03TjErTwkREREQegkJgEREReSYYhuENtMMS/npiCWLGm6Z5Lk0LE5HngmEYjkBDLN+DSgJTgbGmae5O08JEREREHoBCYBEREXlqGYZhA1TBMgvvBWApllm/f5immZiWtYnI88swjGzAS0AX4AqW70vTTNO8maaFiYiIiKRAIbCIiIg8dQzDyAx0whL+RgNjgMmmaV5L08JERP7BMAxboBaW2cE1gblYnlJYb+qDloiIiDxFFAKLiIjIU8EwDDugPpYwpRIwE8vsuj8VpojI084wjIxY+gZ3BWywfP+aaJrm5TQtTERERASFwCIiIpLGDMPIieWR6s7AKSzBya+maUakYVkiIo/kzuKV5bGEwU2AP7B8X1tmmmZCWtYmIiIizy+FwCIiIvKfMwzDGWiKJSQpAEwCfjZN80CaFiYi8gQZhuEBtMbyvS4A+AX4xTTNk2lamIiIiDx3FAKLiIjIf8YwjKJYwpA2wDYss+Pmm6YZk5Z1iYikNsMwCmPpc94O2Inl+99cff8TERGR/4JCYBEREUlVhmF4Ygl9uwIZgXFYZsKdTtPCRETSgGEYTljaRHQFCgNTgLGmae5L08JEREQkXVMILCIiIk/cnZ6YFbGEHI2A34Gfgd/VE1NExMIwjOxYeqK/BJzFMjt4hmmat9K0MBEREUl3FAKLiIjIE2MYhj/QCcsjz4lYAo1JpmleSdPCRESeYoZh2AF1sXzvrAbMwvL9c7OpD2wiIiLyBCgEFhERkcdiGIYtUAfLrN/qwGws4cUmhRciIg/HMIxMWH6Z1hWI5e9fpl1N08JERETkmaYQWERERB6JYRghWB5hfgm4gKXdw3TTNMPTtDARkXTgTludSljC4IbAciyB8ArTNBPTsjYRERF59igEFhERkQdmGIYj0BhLKFEMmIplQaM9aVmXiEh6ZhiGF9AWS7sIX/5eYPNsWtYlIiIizw6FwCIiInJfhmEUxBL8tgN2Y5mNNtc0zeg0LUxE5DljGEZxLGFwG2ALlu/HC0zTjE3TwkREROSpphBYREREkmUYhjvQCkv4GwSMB8aZpnkiLesSEREwDMMFaIrle3Q+YBLws2maB9O0MBEREXkqKQQWERERqzs9KMtiCRWaAquxzDJbZppmfBqWJiIiKTAMIzfQBcuCcsexfN/+zTTNyDQtTERERJ4aCoFFREQEwzD8gA5YHjG2xxIgTDRN81KaFiYiIg/MMAx7oD6WX+RVBH7F8v18m6kPfiIiIs81hcAiIiLPKcMwbICaWMKC2sB8LGHBOoUFIiLPNsMwArHMDH4ZiAB+BiabphmWpoWJiIhImlAILCIi8pwxDCMr8BKWR4evYgl+p5mmeSMt6xIRkSfvzi/8qmL5hV99YDGW7/urTdNMTMPSRERE5D+kEFhEROQ5YBiGA9AQy4yw0sA0LAsI7UzTwkRE5D9jGIYP0A54BXDDMjt4vGma59O0MBEREUl1CoFFRETSMcMw8mEJfjsAB7HM/pplmmZUmhYmIiJp5s4ioCWx/HxoCWzAEggvMk0zLi1rExERkdShEFhERCSdMQzDFcuH+q5AdmA8MM40zaNpWZeIiDx97vzMaIHlZ0YOYAKWJ0X0M0NERCQdUQgsIiKSDvxjVldXLB/m12OZ1bVYs7pERORBGIaRF8vs4I7AIf5+euR2mhYmIiIij00hsIiIyDPsTn/H9lg+tP/V33GC+juKiMijutNH/kUsP1vKANOxzA7ekaaFiYiIyCNTCCwiIvKMSWal90VYZmut0UrvIiLyJBmGkRXoDHQBwrD8vJlqmuaNNCxLREREHpJCYBERkWeEYRiBWD6IvwxEAGOAKaZphqVlXSIikv7d+QVkDSy/gKwDzMfy9MlaUx8qRUREnnoKgUVERJ5ihmHYAw2wBL8VgF+xfOjepg/dIiKSFgzDyAB0wBII2/N3K6JLaVqYiIiIpEghsIiIyFPIMIxcWILfTsBxLI/f/maaZmSaFiYiInLHnUVJy2AJg5sBa7D8vFpqmmZ8WtYmIiIiSSkEFhEReUoYhuGC5UN0VyAvMBHLQjyH0rQwERGR+zAMwx1oieVnWFbgF2CcaZon0rQwERERARQCi4iIpDnDMIpjmfXbGtiC5bHaBaZpxqZpYSIiIo/AMIyCWH6utQf2YJkdPMc0zeg0LUxEROQ5phBYREQkDRiG4QW0xTJjygcYB4w3TfNMWtYlIiLypBiG4Qg0wvKzrjgwFRhrmuaeNC1MRETkOaQQWERE5D9yp3diZSwfhl8ElmGZHbXSNM3EtKxNREQkNRmGEQK8dOefi1h+/k03TTM8TQsTERF5TigEFhERSWWGYWTCssDby0Aclg++k0zTvJqmhYmIiPzHDMOwBWpj+YVoDWAOlp+LG019OBUREUk1CoFFRERSgWEYdkBdLB9yqwCzsHzI3aIPuSIiImAYhj/QEcsvSU0sPfEnmqZ5JU0LExERSYcUAouIiDxBhmFkB7pgedz1DJbg91fTNG+laWEiIiJPqTvtkipg+cVpY2AFlp+fv5ummZCGpYmIiKQbCoFF0hHDMLyBAoBjWtci8hyJAo4AtbB8eC0MTAZ+Nk1zX1oWJiIi8qwxDMMTaI3lZ6o/8Mudf9yAjICRdtWJPFcSgQvAET3FJpI+KAQWSQcMw8jk4Ww/LSYuoVyIn3u0s4NtWpck8lxINLGJjIlzPHMt0sHOxib0dmz8u1gWuYlJ69pERESedYZhFLWxsf2fnYNjFScX10Qvv8wxNnZ2cWldl8jzIDEhnmsXz9onxMfdiLkd8VZiYuKstK5JRB6PQmCRZ5xhGC4uDnYHulbNlfmdOvntXZ3s07okkefOjduxDJm9M2bBjrMHI2LiS5immZjWNYmIiDzr7Bwcu7h6eH/79jczXbLkKYyla4SI/FdM0+Tozo1817NVVFREeHPTNBendU0i8uhs0roAEXlsDQoEefm836iIAmCRNOLl4sBXbUs7erk65ATKpXU9IiIi6YG9g9OQ1z+b5JI1bxEFwCJpwDAMchevQIf3v3F28fAanNb1iMjjUQgs8oxzd7Jv0rRkNve0rkPkeWdjY9C0ZDZnOxujXlrXIiIi8qwzDCMLmBmyFy6d1qWIPPcKV6pLdGREccMwHNK6FhF5dAqBRZ5xdraGv7+nU1qXISJAJk9nWyd724C0rkNERCQd8HHz9ImzsdFHVpG05ujsgr2DYzzgmda1iMij009UkWecATbPw+NxS3afo+yQhWTuMYO3Jm5O63JS3a7TYfi/MZ0z1yJS9TolB83n+xWHUvUazxMbGwPDMPSzVURE5PEZxnMSAO9avYj3GxfltdLejBvcLa3LSXWnDuzglRIeXL1wOlWv0++Fgiyb+E2qXuN5YhiGCaT/D54i6ZhdWhcgIk+vDUcu03TkKg6MaIKvm2Oa1tJryp+0LZ+DrlVz4er4YN+6mny9krwBXnzSqkQqV/fsWtq3Ni4P+PVMD2LiEhgyZxdzt50mKi6BSnn8GdGqJJm9XVI85vNFe/li8f4k2/zcndj3aeNUrlZERERSy+Ft6/jitQZ8teIk7t6+aVrLhGFvULFxJ6q3fg1HZ9cHOubzV+sTmCMfbd/7MpWre3a9P2k1Dk4pv8dLb+JiY/jt6/fZunQmsTHR5Ctdhbb9vsLHPzDFY+aP/pgFP32aZJuHb0a+XH4stcsVkTTw/HzyF5FUExufgIOdbaqd/+btWMIiY6mWPxMBXv/9G7nUvr+08Nc9ZXB/uluJnAuLJMjnwT4MPYhBM3eydO85fnypPN6uDgyevZP2P6zl9361sb3HbKOc/u7Mebu69bWNjSZBiIiIPA/i42Kxs0+9Nqi3b90g4mYYBcrVwDtj5lS7TkpS+/7Swl/35O6dIa1LuadrF8/iG5DliZ1vxhf92LVmEa98PA5XTx9+/d8Avn2nJYMmr8XGNuXPMpmy5aLPT4utr+81VkSebQqBRdKpmLgEhs/bzZxtp7kVFUeBIG+GNClKmZx+QPKzfM9ci6DUBwtZ1rc2Pm4ONB25CoD8780BoFWZYL7pWJYmX68kl78nLo62/LrlFFl8XFn2Xm02Hb3C0Dm7OHD+Bu7O9jQtmY1BjYtYA9RNR68wbO5uDl24ia2NQQ5/d75uX5p8mb1SvI+/6gRoduffs9+uRr7MXvT/dTtbjodyPTKWbL6uvF4zL23KZQfgrYmb2Xg0lI1HQxm39igAW4e9QFZft/vWmdL9Hb54k2FzdrHpWChO9rZUyuPP8GbFyOjpDMCB8zcYNHMHu86EkZgIwX5uDG9ejIq5/e/79/XH/osMmrWDs9ciKZrNh06Vct41ZuuJq3w0bze7Tofh6eJAnUKBDGpcBHdn+/ueP6V7KjloPl2q5KZ7zbwATFx3jB/+OMz5sEhcHe0onNWHKa9Xxs7WEpDO2HyS71ce4sSVW3g421M9fwDfdiwLWALbgTN3sPbQZQCq5M3ERy2K33OWbXIio+NYuOscv245ycajoVz8rtVDHZ+S8KhYpm46wdftS1MlXyYAvutUlhKDFrD20GWq5U+5la+tjY3171lERETSVlxsDLO++YA/l80kKiKcLLkL0eKdj8hVrByQ/CzfqxdO0//FQrw/aTVuXr588VoDAHrVDAGg3Att6TL0Rz5/tT4BwXlwdHZh48Kp+GbOysBJaziyYwMzvx7I2aP7cHbzoEzdFjR7a5g1QD2yYwMzRw7iwvGD2NjY4h+ck84ffE9gzvwp3sdfdQJ82e0FAPqMXkRgzvxM/awPR3duIvJmGBkCg6nT4S0qNGwPwLjB3TiyfT1Htq9n1a9jAPhkwV4yZM523zpTur8LJw4x8+uBHNm5EQdHJ/KWrkKrXp/imcHyPvbc0f3M+LIfpw7swExMxC8ohFa9PyVvqcr3/fvat/F3ZnzRj6sXzxCcvzhVm79815hju7cw57shnNq/AxcPL4pUrk+zt4bi7OZx3/OndE/9XihItZavUqfjWwCsmTWO5ZO/JezSOZycXcmaryhvjZyJrZ0lFtm4YArLJ33L5TPHcHb3pGC5mnQZNhqwBLbTv3iPg3+uBiB/mWq0fveze86yTU707Qh2rJzHpkXTObx9HT9tvfFQx6fk9q2brJ83kc6Dvyd/WcvEhZeH/US/FwpwYMsqCpavmeKxNnZ21r9nEUnfFAKLpFPD5u5m/o4zfN2+DNkyuPLjysO0HrWGzUMa4P8AYVagtwvjXqlAlzEbWDuwHt4uDjg5/P1b4ZlbT9GhQg7m9ayBicnFG7dp8/0aWpQO5puOZTgVGkGvKVuxMQyGNitGfEIinUavo2357PzQuSxxCSZ7zoZhe58ZlaWyZ2DtwHpU/nAJ416pQKmQDHi5OnAtIobCWbzpUSsfbs72rD10iXenbSPQ24XKeTPxYYviHL9yi5z+HrzfsDAAvu6O960zpfu7fDOKxv9bSdvy2RnctChxCSafzN9Dx9HrWNynFjY2Bq+P30SBQC+WvlsbOxuDgxdu4vgAM4jPX4+k80/raFchB10q5+LA+RsMnr0zyZgD52/Q8tvV9G1QkK/aleZ6ZCyDZu3gnclb+PmVive9RnL39G+7TofR79ftfNOhDGVy+BEeFcu6I1es+yeuO8bAmTsY0LAwNQtmJjImnvWHLYFvYqJJp9HrcLK3ZfadGbP9f91O55/Wsaxvbe7Xtzox0WT9kcv8uuUUi3adw9fdkWalsjGidckk40J6zrznecrm9GPaG1WS3bf7zHXiEhKpeicABgj0diW3vwdbT1y9Zwh85moEhQfMxcHOluLBvgxoWJjgDG73rEVERERSx6yRg9j2+xw6fzAKv6AQfp/8HSN7NOXDOTvx8st03+N9/IN4/fPJ/PBue4b+9ieuHt7YO/79dNTmJTOo3KQzfccuA0yuX7nAyB7NKNugNS8N/ZErZ08w8cMeGIYNLXt9TEJ8PKN6taFiow50/XAsCfHxnDm0674zKnMUKcPQ3/5kcIvSvP75ZHIULoOrpze3rl8lW96i1O3UE2dXdw7+uZpJH72NT6Yg8pWuSus+I7hy5hiZgnPT5I3BALh7Z7hvnSnd343QS3zetS4VGnekRc+PSIiPY86oYYzq1Zp+41diY2PD2PdfJih3QQZMWIWtnS3njh7A3vH+7eLCLp1jVO+2VGrSiWotXuXc0X38+r8BScacO7qfr99oTMPXBtBx0LdE3rzOjC/7MX7YG7z+2aT7XiO5e/q3Uwd2MHVEb14a8iO5ipXj9q2bHNq6xrp/zaxxTP/iPZq8MZjCFesQExXBoa1rAUhMTGRU7zY4ODrRZ/RCAKaO6MP3vdvy/qTVD/A+N5HD29ayceFUdv6xADfvDJSp14J2/b5KMu7NivdeWzhXsXK8/e3sZPedPriLhPg4CpSrYd3mkymITCF5OL5nyz1D4KvnTtGnTm7sHBzJXrAETd4YjF9QyD1rEZFnk0JgkXQoMiaeCeuO8VW7UtQqaHms7PM2JVl/5DLj1h6l/4uF73sOWxsbvFwsMwYyuDvd1RM4q69rktD04/l7yOTpzIhWJbGxMcidyZOBjWJ4d/o23nuxEDFxCdyMiqN2oUCC/dwByJXp/r/Z/2fLAi8XB+tszAAvF96olc86LrhiTtYfucKc7WeonDcTHs4OONjZ4OJgl2QG5y9rj92zThcHu2Tvb8TCveQP9GJQ46LWbd91Kkued2ez60wYxYN9ORcWSfcaea33FZLR/b73BzB+7TECfVz5uEVxDMMgVyYPjl+5xYiFe61jvl9xiEYlsvD6nRm7AJ+1KkmNT5cReisavwdo6/Dve/q389cjcXGwo27hQNyc7AFXCgR5W/d/tXQ/r1TLQ7caf9dQJKsPAOsOX+bA+ZtsGdqArL6WcPSHl8pRdshC1h6+TJW8yX8gO345nOmbTzJr62luRcfxYrEsTO1embI5/ZJ9Q/1H/zr3vMd//qLi366ER2FrY9z133IGDyeuhEeneFzxYF9GdihDzkweXL0VzddL9/PCFytYO7AePmncK1tEROR5ExMVyeqZP9Nx0LcUrlQXgPYDvubQ1jWs+u0nmnT/4L7nsLG1xdXD8h7H3dvvrp7AGTJnSxKazhk1DC+/TLTr9xU2NjYEhOSh6ZtDmfzx2zR6fSDxsdHcvnWDwpXrkTGL5am0gJDc963D0rLA8pSeq4e3dTamd8bM1On4tnWcX1AIh7au4c+lM8lXuiou7p7Y2jvg4OScZAbn6t/G3rNOR2eXZO9v3g8fEpS7EM3fGmbd1mXYaN6plo3TB3YQUrAk1y6dpXaHHtb7ypglx33vD2D1zJ/xyRREm3c/xzAMAkJyc/nMMeb98KF1zLJJIylZuym1O/SwbmvX/38Mb1uR8LBQPHz87nudf9/Tv4VdOoejkytFq9THydUd3wDIkruQdf/CsZ9Rs013ard/07otWz7L++ZDf67m3NF9fDxvNxkyZwPglY9+5v3GRTn452ryl6mW7DUvnT7KxgVT2bJ4BlGRtyhZszFvfTOTXMXKJ/s+94Np6+95j/aOKU/kCb92GRtbW9y8kv637OGTkfBrV1I4CkIKlqTzkB8ICMlNeFgoi37+nE+71GLor1vuOpeIPPsUAoukQ6euRhCXkEjp7H/3wbK1saFkSAaOXAx/Itf4K/z7y9FL4ZQI9k3SK7VMDj9i4xM5GRpBgUAvWpcNofV3q6mUx59Kefx5oViWR+73mpCYyDfLDzJv+xku3YwiJi6RuIREyue695vEB6kzufvbcyaMzcdCk52JeupqBMWDfelWPQ+9pvzJjC0nLfdXNMsDBd1HL1tq+uebwZIhSd907TkTxsmrEczbfta67a/ZvKdCIx4oBP73Pf1blbyZCPJxodQHC6iaL4Cq+TLRoGgQbk72hN6K5uKNKCrnSf5RsSOXwsnk6WQNgAGCM7iRydOZIxfDUwyB+0zbysajobSvkJ3hzYtbQ/iUPGiw/iTVKJC0P1/JYF9KDV7Ir1tOJgnERUREJPWFnjtJQnwcOYuUtW6zsbUle+HSXDxx+IlcI1u+okleXzx5mOyFSmHzj/UDchUrS3xcLKHnThCUqyDlX2zH1282IV+pKuQtXZUSNRo9cr/XxIQEloz/iq3LZ3Mj9ALxsbHEx8WSp8S9n/56kDqTu7/TB3dxdMeGZGeiXjl3kpCCJanV7g0mDu/BxoXTyFe6CsWrN3qgoPuvmv75PjdH4dJ3XT/07Am2Lf97lqtpWt7nhp478UAh8L/v6d/yl6mGT0AW+r9YiPzlalCgbHWKV2+Ik6s74WGh3LhygXylk3+a7OLJw3j5BVgDYLAE855+AVw8cSjFEHjSR29zZPt6KjXpTKven1pD+JQ8aLD+JBWqUDvJ6+yFSjGgYWE2LpyWJBAXkfRBIbDIc+av9182d/7w1xssgPiEux+dSsn9wrok17zz75EdyvBqtdz8ceASy/ae55MFexn/asV7Poafku9XHObHlYf5sHkx8mX2wtXRjo/n7+FqRMozOh+0Trj7/hJNqFkwM0OaFL3rOD8PSwD7boNCNCsVzMr9F1l98CJfLt7PZ61L0rZ89keu6Z/Xb1c+O69Vy3PXvkxeD9ar9n5/Z25O9qzoV4dNx0JZc+gS3yw/wMfz97Csb21sbR99IbR7PSE3vFlxpmw6wbztZ1ix7yJNSmalRengJDOQ/+lx2kFk9HAmIdHkWkRMkgXxroZHUzbH/T9c/MXVyZ48AZ6cuBLxwMeIiIhI6vsraDSsIejf720T4uMe+Dz3C+v+dVUAXhryAzXbdmffxhXsXruYud8Po/sXU+/5GH5Klk36huWTv6V1nxEE5iyAk4srs78bxq3roQ99rn/XCXffn2kmUqhiHVr0/PDfB+HhkxGAhq8NoEy9luzb8Dv7N61kwU+f0n7A11Rs1OExarpz/cREKjbuSK12b9y1z8vvwRbLu9/fmZOrO4OmrOPIjg0c3LKKJb98xZxRw3h/0mpsbB8jFrnHG91WvT9l/byJbF02i73rl1G6TnPKNmidZAbyPz1OOwgPX38SExKIuHEtyYJ44WFXrL2yH4STixuZs+fjypnjD3yMiDw7FAKLpEPBGdxwsLPhzxNXra0XEhIT2XbyKk1LWn6D7etueYz9cni0NRDbd+56kvP8tVBaQuL9w+FcmTyYv+MMiYmmdZbtluOhONjZEOz39+zQAkHeFAjypkftfLQZtYYZW04+Ugi85XgotQtlpkUZS78q0zQ5fuUWni5/L5Jmb2tzV+0PWue/FcrizfwdZwjydcXe1ibFcdkzupM9ozuvVMtN32nbmLLx+H1D4Fz+HizadQ7TNK0fXrafunbX9Q9fvJnqM2HtbG2sM7X7NihIgX5zWb7vPB0r5iTAy9nS2iHf3bN6c2fy4NLNaM5ci7DOBj51NYJLN6PIfY/Z0AWzePNJlhIMa1aM3/dd4NctJ6n7+e/kyOhO89LBNC2ZLcnCco/TDqJIVm/sbW1Yc+gSzUoFA3Dh+m2OXA6nVPYHXz06Oi6BY5fDqZg74wMfIyIiIk+GX1AIdvYOHNu92dp6ITEhgRN7/qR03RYA1hDs5tVL1j+fPbw3yXls7yyUZiYm3PeaASF52Pb7bBITE62zbI/u3IydvUOS3qlZchciS+5C1Ovck5E9mrJp4aOFwMd2baJIpXqUa9DGUqNpcvnMMVzcPa1j7OzsSUxIfKQ6/y1r3qJs+302PpmyYmef8oLD/llz4p81JzXavM7kj3uyfu6E+4bAASF52PHHvCTvc0/s3fqv6xfhwolDqT4T1tbOjnylq5CvdBUadhtAr5o52LNuKZWbvoRXxswc/HONdVG1f9/DjdCLXL1w2jobOPTcSW6GXiRz9pSfCsuapzBt+35By56fsHf9MjYunMrHHavhny0XZeu3onTdFkkWlnucdhDZ8hXF1s6eA5v/oEy9lgCEXT7PpZOHyVG4zD3P+09xMdFcOnWEPCUrPfAxIvLsSDnJEJFnlqujHZ0q5WT43N2s2HeBI5du0nf6NkLDY3ipci4AQvzcCfR24YtF+zh+OZzVBy/yv6UHkpwnyMcFw4AV+y5w9VY0kdEpz6B4qXJOLt2M4r0Z2zhy6Sa/77vAh/P20KVyLlwc7Dh9NYLhc3ez9cRVzl6LZP2Ryxw4f4PcmTxTPOe95MjozrrDl9lyLJSjl8Lp/+t2zlyLTDImi68rO09f48y1CK5FxJCYaN63zpR0qZyL8Kg4Xv15I9tPXuPU1QjWHLpE76l/EhEdR1RsPP1mbGPDkcucuRbB9pPX2HIilNwB97+/TpVycjYskoEzd3LscjgLdpxl4rpjSca8WTsfO0+F8e60rew9e52TV26xfO95+kzdmsJZH97yvef5adVh9p69ztlrkczeepqI6HhriPtOnfyMWXWYH/84zPHL4ew7e50fVhwCoHJef/IHetJ9/GZ2nQ5j1+kwuo/fROEs3lRKoYXEP9nb2lC/SBDjX63E7o8a0aFCDhbsOEuJQQuSjAvJ6H7PfwK8Up4F4uHsQNty2Rk+dzdrDl1i79nrvDFhM/kze1E57981Vhi2iJ9XH7G+HjJ7JxuPXuH0Vcvf68tjN3A7Np6WZbRghoiIyH/N0dmVKs1fZvY3g9m7fhkXTx5m8ic9CQ8LpWqLVwDwC8qOj38Q80d/wqXTR9m/aSWLfv48yXl8A7JgGAZ71i/j1vWrRN9O+Qmfqi26ciP0ElM+7cXFk4fZs24ps78bTLWWr+Lo7ELo+VPM+mYwx3Zv4drFMxzaupZzR/cTcI+A8F78s+bk4NY1HN25iYsnjzB1RG+unT+dtP7M2Ti5fztXL5zm1vVrJCYm3rfOlFRr+QpREeH81L8zJ/ZuJfTcSQ5sWcXED98iOvIWsdFRTPm0F4e3rePqhdOc2LuVY7s2ERBy//ur0qwL1y6cYcYX73Hp1FG2r5jLmlnjkoyp17knp/ZtZ9LH73Dm0G6unD3O7rVLmPTR2ymc9eHtXruEFVO/58yh3Vy7eIYtS34j+vYtMoVYnrJr0KUPK6Z9z+9TvuPS6aOcObyH5ZO+BSBfmWoE5SrI2IFdOXVgB6cO7GDswK5kzVuUvKWSfwLtn+zs7SlW7QXe+HIqny89TOWmndm2Yg79XiiQZFzGLDnu+Y93xpRnRbu4e1KxUUdmfvMBB7as4syh3Ywb9CqBuQomaVcxqGkJ/pgx2vr6t/+9z+Ht6wk9f4oTe7fyQ98OxETfpvyLbR/q6ysizwbNBBZJpwY1KgLA25O3EB4VR8Egb6a/UQX/O4uk2dvaMLpLed6bvo3qnyyjQJAXAxoWpv0Pa63nCPByoW+DgnyyYA+9pv5Jy9LBfNOxbLLXC/ByYVr3Kgyds4sanyzDw9mepiWzMaChZRE6Zwc7Tly5RdexGwiLjMHP3YlmpbLRo3a+ZM93Pz3rFeDMtUjafL8GJ3tbWpcNoVmpbBy5dNM6pnuNvPSYtIXKw5cQFZfA1mEvkNXX7Z51piSTlzMLe9fko3m7aTNqNTHxiQR6u1AlXyYc7Cy/T7txO5a3J23hcng03q4O1CqYmSFNUl6I7S9BPq6Me6Uig2ftZNL6YxTO6sP7jYrwxoTN1jEFAr2Y27MGny7YQ+OvV5KQaJItgxv1iwQ90tcvOZ7ODizZfZ6vluwnKjaBbBnc+KpdKcrmtMx47Vw5F/Z2NpY2HHN34+XiQI0CllnchmEw4bVKvP/bDpqO/AOwBMMftyhx3xWT/83HzZGXq+bm5aq5k/x9PgnDmxfD1tbgtXEbiY5NoGIef77rVAbbf/TOO3b5FmGRMdbXF25E0e2XjYRFxOLr5kiJEF8W96lFFt9H62ctIiIij6fZnQXMfhnanahbN8mSpzBvfzsbLz/L00p29va88skvTPm0F8PaVCBL7kI0eeMDvn2npfUc3hkz0/C1AcwdNYyJw9+kbIM2dBn6Y7LX886Ymbe/ncXMrwcyrE0FnN09KVO3BU3eHAyAo5MLl88cY/R7HYm4cQ0Pn4yUqdeSup16PtL9Nej6LlcvnGbkW81wcHSi/IvtKFOvBRdO/t3zuHaHHvwyuBuDm5cmNiaKTxbsJUPmbPesMyVefgG8N245s78bysgezYiLjcYnUxD5y1bHzsHy9ODt8Bv8MuR1bl69hKunD4Ur1aXFO3e3j/g334AsvP75ZH793wDWzP6FbPmK0vTNIfw86BXrmKBcBXl37BLmfj+cz1+tT2JCAn6BwRSr9sIjff2S4+Luxa7Vi1g4dgSx0VH4BYXQadB35C5WHrAE/bb2Dvw++VtmfTMYV09va79cwzB448tpTP+8L1+8ZqkpX+mqtOn7+UO/z3Xz8qV6q9eo3uo1Lp58Mj2s/9Kqz6fY2NnyU//OxEVHk7d0FboMG42N7d9Pyl06fZSIG38/cXj9ynnGDOhibSORvVAp+o9fiW9A1idam4g8HYx/9gMVkWePr5vjqi/bla76JMNAEXk0v6w9ykfz9owPj4p9Ka1rEREReZYZhlHULyhkzcfzdt9/lV0RSXU9KmWOir4dEWya5pW0rkVEHo3aQYiIiIiIiIiIiIikY2oHISJprs2oNWw+lvxqx2/Xycc7dQsku+9Z8e60rcz883Sy+5qXzsbnbUo91vnPhUVSafiSFPevG1SPIB+1LhARERH5r43s0ZSjOzclu69el9406NLnP67oyZr08TtsWTwj2X1l6reiw4CvH+v81y6eZXCL0inuH/rbn/gGZHmsa4iIPC8UAotImvuqXSmiY5NfmdnL1eE/rubJ6/tCIbrXSH7hDDfnlFdgflCZPJ35o3+de+4XERERkf9ex0HfERcTlew+Vw/v/7iaJ69Rt/ep06FHsvucXB+/k4eXXwAfTFt/z/0iIvJgFAKLSJoL8Ep5teL0wM/dCT93p1Q7v52tDSEZ3VPt/CIiIiLyaLwzZk7rElKVh48fHj5+qXZ+Wzs7MmbJkWrnFxF5nqgnsIgI0OTrlfSfsf2hjik5aD7frziUShWJiIiIiDyez1+tz9QRvR/qmH4vFGTZxG9SqSIREUkrmgksIgKMe6Ui9rYP93uxpX1r4+KYut9Gz4VF0m/GdtYfuYyzvS1NSmZjSNOiONjZJjv+emQMny3ax9pDlzgXdhsfVwdqFQyk34uF8HFzBCAx0aTTT+vYf+4GV29F4+niQKU8/gxqXMQ6K/vqrWjemLCZgxducD0ylgxujtQpHMiAhoXxcH72W3SIiIiIPA+6fz4ZW7uHaz/2/qTVODil7pN61y6eZeqI3hzauhYHRydK121Bi54fYWd///eZpmnyzVvN2LdxBd1GTKREzcZ3jYmLiebjTtU5d3Qf709aTXD+4gDcun6VsQO7cv7YASJvhuHu40eRyvVo8sZgXNw9n/Bdiog8XRQCi4gA3q6OD31MhlRs8QCQkJhIux/W4uPqwPyeNbgeGUuPSZsxgU9alkj2mEs3o7h0I4pBjYuSJ5MHF29G0W/6Nrr9spFfe1SzjquY25+36+TH38OZSzejGDJ7J51/Ws+yvrUBsDEM6hcJYkDDwvi4OnIy9Bb9f91O7ylbGdO1Qqret4iIiIg8Ga6ePg99jLt3hlSo5G+JCQl8+3YLXL186Dt2KZE3wxg3uBsmJm37fnHf45dP+hbDuPfkjd++fh/vjJk5d3Rfku2GYVCs2os0fXMwbl6+XDl7gqmf9mbi9bfoNmLCY92XiMjTTiGwiKR7kTHxvDd9G4t2ncPF0ZZXq+XhzxNX8XV14JuOZQFLO4i8AV580soSrpYcNJ+25XNw4fpt5mw/jbuTPa9Uzc0btfJZz1ty0Hy6VMlN95rJL/r2uFYfvMThizfZPvxFAr1dAfigcVF6TfmTAS8Wxj2ZReXyZfbil1crWl+HZHTngyZFaf/jWm5FxeHubI+NjcFr1fNYx2TxdaVH7fx0Gr2O6LgEnOxt8XFzpFOlnEnGdK6Ui2+WH0iVexURERGRhxMTFcnkT3qy848FODi7ULNNd47t3oybly9dhv4IWNpBBObIR9v3vgQsrR4qNu7I9cvn+XPZTJxc3anZ5nXqdHzbet5+LxSkWstXqdPxrVSpe//mlVw4cZBPF+7HJ1MQAM3fHsaE4T1o0v0DnN1SXlDu5P7trJz+AwMnr6V3reR7Be9avYhD29bx+meT2LtheZJ9bl6+VG3+svW1b0BWqrboypJfvnoCdyYi8nRTCCwi6d6Q2TvZdPQK41+tiL+nM18t3c+WY6HULxJ4z+N+WnWYd+sX5I2adVh54CLv/7aD0jn8KJX9wWZHbD52hTaj1t5zzNt18vFO3QLJ7tt28hq5/T2sATBA1XyZiIlPZPfZMCrm9n+gOm5Fx+FoZ4uzQ8otJGZtPUXxYF+c7JMfc+lGFIt3n6VcrowPdE0RERERSV2//u99jmzfQPcvpuDpF8CisSM4tnMTRau9cM/jVkwdRcPXBjCo41r2bvid6Z/3JWfRsuQoXOaBrntk50a+6dHsnmPqdelNgy59kt13Ys+fZArJYw2AAQqUq0l8bAynD+4ib6nKyR4XHXmLse+/TIf3R6a4GF3Y5fNM/qQnb387C3vH+z+1dyP0IjtWLSB3CT3pJiLpn0JgEUnXIqPjmLbpJN92LEOVfJkA+F+70hR7f959j62SNxMvV80NQNeM7oxdfYR1hy8/cAhcJKsPf/Svc88xXq4p9z27Eh5FBo+kb1593RyxtTG4Eh79QDXcvB3LiIV7aVc+O3b/6nk8fO4ufl5zlKjYBEqE+DL59bvfcL82biPL9pwnKi6BWgUzM7J96Qe6roiIiIiknujbEWyYN4kuw0aTv2x1ADp9MIq+9fLd50jIX7Y61Vu9BkCN1jn4Y/qPHPxzzQOHwMH5ivHBtPX3HOPq4Z3ivpvXruDhk3RigZuXLza2tty8djnF4yZ9/A4FytekUIXaye5PTEjg54Fdqd2+B1lyF+LqhdMpnuunAS+xe/ViYmOiKFypLp0H/3DP+xERSQ8UAotIunbqagRxCYkUC/a1bnN1tCNv5vsv/JA/0CvJ60yezly99WDhK4Czgx0hGd0fePyTFhkdR4cf1xLg5cwHTYretb97zXy0LZedc2G3+WLxPt4Yv4lpb1TBMAzrmOHNitGnfkGOX7nFx/N2M2jmTr5oW+o/vAsRERER+bfQcydJiI8jpMDf60Q4OruSOcf9Q+CgnAWTvPbyC+BWWOgDX9vByZmMWZJvxZBaNi2axrmj+xg4aU2KYxaP+wJbewdqtX/zvudr1etTXnylP5fPHGP2d0OY8WU/Orw/8kmWLCLy1FEILCKSAvt/zZw1DDDNBz/+cdtBZPRwZuvxq0m2XYuIISHRJKPHvR9vi4yOo+33lmtP7lY52TYPvm6O+Lo5ksPfg1yZPCg2cD5bjodSNuffMzMyejqT0dOZXJk88HZxoOH/VtKzXv4kLSpERERE5Nlha/evdSUMA9NMfODjH7cdhKdvRo7t3pxkW8SNayQmJODpm3y7s4N/ruHiiUO8WSkgyfbR/TuTY2pp3hu3nINb13B050a6lUm6GN4nnWtQslZTXvno579ryOCPZwZ/AkJy4+rhzWdd69Dg5XeTtKgQEUlvFAKLSLoWnMENe1sbdp0OIziDGwC3Y+M5dOGm9XVqedx2ECVDfPnf0v1cuH6bzN4uAKw5dAlHOxuKZEl5peeI6DjajFqDCUx/owquTncvIPdviXfS7Zj4lD8A/DUmNu7BPySIiIiIyJPnFxSCrZ09pw7swC8oBICYqNtcOH7Q+jq1PG47iOyFS7Po588Ju3weH3/LGh0HNv+BnYMj2fIVTfaYJm98QJ0OSReqG9KqLC3e+ZCiVRoA0Hnw98RG3bbuvxF6ka/fbELXD8eSo0jZFOv5KwCPj4u55z2JiDzrFAKLSLrm6mRPm3IhfDh3Fz6uDvh7OvO/pftJNEnS9iA1PG47iKr5MpEnwJM3J25maNNihEXGMGzObtpVyIG7syXY3XHqGj0mbubbjmUpHuxLRHQcLb9bTURUHONfq8TtmHhux8QDlsDZwc6WrSeusvfsdcrkyICHswOnrkYwYuFesvi6UiaHZZGN5XvPcz0ylsJZvXF1tOPwxXCGzdlFiRDfNG1xISIiIiLg5OJGhUYdmPXNYNy8fPHMkIlFYz/DTExM9fe4j9sOokDZGmTOno9xH7xGy54fEXEzjJkjB1GpSSec3TwAOLlvG+M+eI0uw0YTUrAk3hkz450x813n8vEPsobefoHBSfY5ulieXPMLCrGGzbvXLiHyZhjZ8hXD0cWVC8cPMnPkILIXKvWft7gQEfmvKQQWkXRvSJOi3I6Jp+Podbg62vFa9TyEhkfjaHd3i4Snia2NDVNer8x7M7bz4pcrcHKwpWnJbAz+R3/fqNh4jl2+RVSsJejdfSaM7SevAVBu6KIk55v9djUq5PbH2d6WBTvP8tmivdyOiSejpzPV8wXwU5fy1rYRjva2TFh/jKOXwomNTySztwv1iwTSo3b+/+bmRUREROSeWrzzIbFRkXzXszWOLq7UavsG4WGh2Dvcu21YWrOxtaXHyN+Y8mkvRnSpjb2TE2XqtqT5Ox9ax8RGR3Hp9FFio6Oe6LXtHZ1YM2scF08eIT4uBm//QIpVe5F6nXs+0euIiDyNDPNhGlyKyFPH181x1ZftSletX0T9qx5UTFwCJQYt4I2aeXm9Zt60LkfSkV/WHuWjeXvGh0fFvpTWtYiIiDzLDMMo6hcUsubjebs90rqWZ0VcbAz9XihAnQ5vU7tDj7QuR9KZHpUyR0Xfjgg2TfNKWtciIo9GM4FFJN3be/Y6Ry7dpHg2XyJi4vn294NExMTRqETWtC5NREREROSRnDm0m4snDxNSsATRkREsnfA/oiMjKFm7aVqXJiIiTyGFwCLyXBj9x2GOXb6FnY1BgSBv5vWsYV1sTURERETkWfT7lFFcOn0UW1s7suQuRN+xS6z9b0VERP5JIbCIpHuFsniz/L06aV2GiIiIiMgTkzVvEQZOXpPWZYiIyDPCJq0LEBEREREREREREZHUoxBYROQhnLkWgf8b09l1OiytSxERERERSTVXL5zmlRIenDqwI61LERGRJ0DtIERE0pnLN6MYPHsne89e58SVCFqUzsY3HcveNe5WVByfLNjDwl1nuR4ZS2YvFwY0LGxdMO/zRXv5YvH+JMf4uTux79PGyV63z9StTNpwnMFNitK9Zl7r9is3oxg6ZxdrDl0mIjqOkIzuvFEzL81LBz+xexYRERGR58+4wd3YtHDqXdsdnFwYteHSXduP7tzEF6/VJ1Nwbob+usW6fcP8KYwf+vpd47/feAV7RycAFo/7kh2r5nP59DHs7B3IXqgUTd8cQmDO/E/wjkREUo9CYBGRdCYmPgEfV0d61MrHpA3Hkx0Tl5BIi29X4e3qyJguFQjwdubi9Sgc7JM+IJLT3505b1e3vraxMZI934IdZ9l5+hqZPJ3v2vfmxM3cuB3LhNcq4evuyJJd53hz4mYCvV0olyvjY9ypiIiIiDzPWvcZQbMeQ5NsG9GlFrmKV7hrbGT4dcYNfo28papwI/TiXfsdnFz4eN7uJNv+CoABDm9fR7UWrxCcvzimaTLvx4/4qntDhv32J66ePk/ojkREUo9CYBF5Km06eoVhc3dz6MJNbG0Mcvi783X70uTL7EVYRAz9f93OluOhXI+MJZuvK6/XzEubctmtxzf5eiW5/D1xdrBl+uaT2NoYvFM3P50q5mTw7J3M2noadyd7+r9YiBZlQgBLq4dSHyzk+85lGb/2GLvPhJHF15WPWhSnar6AFGs9fPEmw+bsYtOxUJzsbamUx5/hzYqR8U4geuD8DQbN3MGuM2EkJkKwnxvDmxejYm7/VPnaZfV14+OWJQBYsOtcsmOmbTrBtYgY5veqgYOdrfW4f7O1sbHeR0rOXotk4Mwd/PZWVdqOuntxkq0nrvFxy+KUCPEF4PWaeRm7+gg7T4cpBBYREZHn3pEdG5g5chAXjh/ExsYW/+CcdP7gewJz5ifixjWmftaHozs3EXkzjAyBwdTp8BYVGra3Hv/5q/UJCM6Dg5MzGxZMxsbGlgYvv0uV5i/z61f92bLkN5xd3Wn8xiDKNWgDWFo99H+xEF0/HMvq38Zy6uBOMgRkpfW7n1GgXI0Ua71w4hAzvx7IkZ0bcXB0Im/pKrTq9SmeGSzva88d3c+ML/tx6sAOzMRE/IJCaNX7U/KWqpwqXzsXd09w97S+PrZrM6HnT9Fl+Ji7xk4Y9iblXmgDpsn2lfPu2m8YhvU+ktNz1Nwkr18e/hNvVQni2O4tFKlc79FvQkTkP6IQWESeOvEJiXQavY625bPzQ+eyxCWY7Dkbhu2dWagx8QkUzuJNj1r5cHO2Z+2hS7w7bRuB3i5UzpvJep5Z207RrXoelrxbi2V7zjNo5k5WHbhEtfyZWN63NjO2nKTX1K1UzpsJ/38EncPn7mZo02LkD/Ri3NqjdBq9ns1DGhDg5XJXrZdvRtH4fytpWz47g5sWJS7B5JP5e+g4eh2L+9TCxsbg9fGbKBDoxdJ3a2NnY3Dwwk0c7wSvyfl66X5GLjt4z6/RtDcqUzbnoweoS3efp3T2DAz4dQdL95zH29WBhsWz8E7dAtjb/j0b+MzVCAoPmIuDnS3Fg30Z0LAwwRn+DovjExLp9stGetbNT+5MnsldijI5MjB/x1nqFArEy8WBZXvPcy0ihsp5UicEFxEREXlWJMTHM6pXGyo26kDXD8eSEB/PmUO7sLG1vFeMi40hW96i1O3UE2dXdw7+uZpJH72NT6Yg8pWuaj3PlqW/UqvdGwyY8Ae71yxhxpf92L9pBQXK1WTg5NVsXDCVicN7kK90Nbz8/n6/PPObD2jZ82OCchVk1W8/Map3Gz6auwvvjJnvqvVG6CU+71qXCo070qLnRyTExzFn1DBG9WpNv/ErsbGxYez7LxOUuyADJqzC1s6Wc0cPYO/omOL9Lxr3BUvGfXnPr9Fb384id7HyD/T1XDtnPJlz5CNnkTJJtq/6dQzhYVd44eW+LBw7ItljY2OieK9BARITE8iSuxCNXx9I1rxFUrxWdGQEZmIiLu5eD1SbiEhaUwgsIk+dW9Fx3IyKo3ahQIL93AHIlcnDuj/Ay4U3auWzvg6umJP1R64wZ/uZJCFwngBP3m1QCIBuNfLw7e8HsbM1eLVaHgB61y/Id78f4s/jV3mxeBbrcZ0q5bT2xf2oeXFWH7zE+HXH6P9i4btqHb/uGPkDvRjUuKh123edypLn3dnsOhNG8WBfzoVF0r1GXus9hGR0v+f9d6qUk0bFs95zTCave8/OvZ/T1yJYfySSJiWzMaV7Zc5ei6TfjO1ExsQzpGkxAIoH+zKyQxlyZvLg6q1ovl66nxe+WMHagfXwcbO8mf9s0T583BzpXDlXitca83IFXhu3kXzvzcHOxsDBzpYfXipHwSzej3UPIiIiIs+66Mhwbt+6QeHK9ciYxfJUW0BIbut+74yZqdPxbetrv6AQDm1dw59LZyYJgTNnz0vD1wYAUKv9mywZ/xW2dvbUbNsdgBde6cfSCV9zfPdmStRsbD2uavOXKVW7KQCt+3zG/k0rWT1zLE26f3BXrWtmjiUodyGavzXMuq3LsNG8Uy0bpw/sIKRgSa5dOkvtDj2s95AxS4573n/VZl0oVavJPcd4+d0dSCfn9q2bbP99Dk3eHJxk+7mj+1kw5lP6j19pDdf/LVNwTjp/MIqg3IWIvh3Bymk/MKJLbT6YvgH/rDmTPWb6F++RJU9hchQu/UD1iYikNYXAIvLU8XZ1pHXZEFp/t5pKefyplMefF4plIcjHFYCExES+WX6QedvPcOlmFDFxicQlJFI+l1+S8+TP7GX9s2EYZHBzJN8/ttnb2uDpYs/ViOgkx5UMyWD9s42NQfFsPhy5GJ5srXvOhLH5WCghPWfete/U1QiKB/vSrXoeek35kxlbTlrupWiWJKF2cvfv7ZryjIknIdGEDO5OfNWuFLY2NhTJ6kNYZAwfzNrJ4CZFMQyDGgWSvuEuGexLqcEL+XXLSbrVyMuGI5eZsfkkK/vXuee1Plmwh2uRMfzWoyq+bo4s2X2eHhO3MK+nKwWCFASLiIjI88vV04fyL7bj6zebkK9UFfKWrkqJGo3wDbBMUEhMSGDJ+K/Yunw2N0IvEB8bS3xcLHlKVExynqBcBa1/NgwDdx8/AnMWsG6zs7fHxcOL8LDQJMdlL/R3gGljY0P2giW5eOJwsrWePriLozs28GbFu9ukXTl3kpCCJanV7g0mDu/BxoXTyFe6CsWrN0oSaid3/0+qn+6WJTNINBMp16C1dVtcbAw/9e9Mi3c+xC8wOMVjcxQuQ47Cf88ezlm4DMPaVOCP6aNp0/fzu8bP+Ko/x3Zt4r2fl6UYLIuIPG0UAovIU2lkhzK8Wi03fxy4xLK95/lkwV7Gv1qRavkD+H7FYX5ceZgPmxcjX2YvXB3t+Hj+nrvC3H+2NQDAMLC3TbqwmYFBYqL5yHUmmlCzYGaGNCl61z4/D8tCEu82KESzUsGs3H+R1Qcv8uXi/XzWuiRty2e/6xj4b9pB+Hs4YWdrg63N31+j3Jk8iIpN4FpEDBncne46xtXJnjwBnpy4EgHAxqNXuBweReEBf/dUS0g0GT53Nz+tOsyujxpxKvQWP685yh/961gD3wJB3mw+HsrYNUf5XzvNnBAREZHn20tDfqBm2+7s27iC3WsXM/f7YXT/YioFy9dk2aRvWD75W1r3GUFgzgI4ubgy+7th3LqeNMy1tUv60d4wjGS3mWbiI9dpmokUqliHFj0/vGufh4/lfWnD1wZQpl5L9m34nf2bVrLgp09pP+BrKjbqkOw5n2Q7iLVzJlC8esMkofLNq5e4ePIw44d2Z/xQy6xoMzER0zR5rbQ3b42cmWwPZBtbW7LlL8aVs3cvsjzjy35sXTaL3qMX4RcUct+6RESeFgqBReSpVSDImwJB3vSonY82o9YwY8tJquUPYMvxUGoXymxd0M00TY5fuYWni/0Tue72k1epdKdfrWma7DwdxgvFsiQ7tlAWb+bvOEOQr+vdofM/ZM/oTvaM7rxSLTd9p21jysbjKYbA/0U7iFLZMzBn22kSE01s7vRaPn7lFs4Otvi6JT8LOTougWOXw6mY2/Imv3PlXHd9XVp/t4YmJbPSvoLl0b/bsQkA1mv8xdbm8cJ3ERERkfQkS+5CZMldiHqdezKyR1M2LbSEwMd2baJIpXrWBd1M0+TymWOWBdGegBP7tpKvdBXruU/u306JGo2THZs1b1G2/T4bn0xZsbNP+X23f9ac+GfNSY02rzP5456snzshxRD4SbWDOLlvG+eO7KV170/vOnbIjM1Jtq3+bQwHtqyi+xdT8c2c/Htu0zQ5d3Q/WXIXTLJ9+ud92fr7bPqMXnTPGc4iIk8jhcAi8tQ5fTWCieuPU7dwIJk8nTl9LYID52/QqZKlH1eOjO7M23GGLcdC8XFz5Oc1RzhzLZJCLl5P5PoT1h0jR0YP8gV68svaY5wLi6RzpeR7gXWpnIvJG47z6s8bebNWPnzdHTl9NYL5O84wtGkxbG0Mhs7ZxYvFspDF15XQ8Bi2nAileLBvitd/Eu0g9p29DkBEVBw2huW1vZ0NeQIsHxg6V87JuLVHeX/mDl6ukouz1yL5fNE+XqqUC8OwBLZDZu+kdqFAAr1duHorhq+W7ud2bDwt74Tvfu5O+P1rxrC9rUFGD2dy+lvaXeTK5EGInxv9pm9ncNOi+Lg6sGT3edYcusSE1yo91j2KiIiIPOtCz59i7axfKFKlPt4ZAwg9d4pzR/dTpfnLgCVQ3fr7bI7u3ISbly9/zPiRa+dP45L37rUqHsWamT+TKWtOAnMWYPVvY7h28az12v9WreUrrJsznp/6d6Zup3dw985A6PlTbPt9Di17foSNrR2/ff0+JWs2wTdzVsKvXeHYrk2EFCyZ4vWfVDuItbPHkzFrDvKUTPr+0s7ensCc+ZNsc/fxw87BMcn2+T99QvaCpfDPmoOoyFv8Mf1Hzh/dR/v+X1nHTPm0F5sXz6D7F1Nxcffi5tXLADi6uOLk4oaIyNNOIbCIPHWcHew4ceUWXcduICwyBj93J5qVykaP2pbF4HrWK8CZa5G0+X4NTva2tC4bQrNS2Thy6eYTuf77jYrw4x+H2Hv2OkE+rvzyakUye7skOzaTlzMLe9fko3m7aTNqNTHxiQR6u1AlXyYc7Cwzg2/cjuXtSVu4HB6Nt6sDtQpmZkiTYk+k1pTU+HRZktfL914gi48L24Y3BCDQ25UZb1Zl8Kyd1PhkGX4eTrQpl52edf9+M3zhRhTdftlIWEQsvm6OlAjxZXGfWmTxdX3gOuxtbZjavQofzttNhx/XEhkTT4ifOyPbl6FOocAnc7MiIiIizyhHJxcunznG6Pc6EnHjGh4+GSlTryV1O/UEoEHXd7l64TQj32qGg6MT5V9sR5l6LbhwMvm+vQ+r6ZtD+H3Kd5w+tBvfgCx0/2IKPv7Jv0fz8gvgvXHLmf3dUEb2aEZcbDQ+mYLIX7Y6dg6WCQy3w2/wy5DXuXn1Eq6ePhSuVJcW79zdPuJJio68xdbls3jhlfce+RxRt24y6aO3Cb92GWc3D7LkKcy7Y5ckCbBX/zYWgK9efzHJsS++2s+6KJ+IyNPMME09jivyLPN1c1z1ZbvSVesXCUrrUp55Z65FUOqDhSzrW5ui2Z7MAhXyfPll7VE+mrdnfHhU7EtpXYuIiMizzDCMon5BIWs+nrc75dV05ZFdvXCa/i8W4v1JqwnOXzyty5FnQI9KmaOib0cEm6Z5Ja1rEZFHk3IDSxERERERERERERF55ikEFhEREREREREREUnH1BNYROSOrL5uXB7VOq3LEBERERFJVRkyZ2PM9vC0LkNERP5DmgksIiIiIiIiIiIiko4pBBaRp06Tr1fSf8b2tC7jvj5ftBf/N6bj/8Z0vll+IK3Lea6UHDTf+rW/FhGT1uWIiIiIPJLPX63P1BG907qM+5o/+mNeKeHBKyU8WPLLV2ldTroxbnA369d1+4q5aV2OiKRzagchIvIYcvq7M+ft6rg62Vu3Ldp1lonrj7P37HWuRcQw++1qVMjtn+zxpmnS9vu1/HHgImNfrsCLxbMAkJho0umndew/d4Ort6LxdHGgUh5/BjUuQoCXCwD7z13nm+UH+fN4KGGRsQR6u9C2fHa618iLjY0BwOGLN+k3YztHLt3kVlQc/p7ONC6RlXcbFMTBzvaB73PRrrNMWHeMfeduEB2XQO5MHrxTtwB1Cwdax0zacJzftpzk0IWbmEDBIG/6vVCIMjn9rGNGLjvA4l3nOHYlHEc7W4oH+/J+o8Lky+xlHfPWxM3M2HIqyfWLB/uy5N1a1tdL+9Zmy/FQuozZ8MD3ICIiIiKPLlO2XPT5aTFOrm4AxMfFMfeH4ezb8Duh507i7OpOnpKVaNpjKL4BWe463jRNvnmrGfs2rqDbiImUqNk4yf59G1ew4KdPOHdkH7b2DmTNW4Q+oxda979SwuOuc7br/z+qNn/5ke7n8pljDG9XGUyT79ZfTLJv1a8/8ceMn7h28Qw+mYKo36UP5V9om2TMiqnfs2bmz1y7dBZXTx+KVqlPs7eG4eRi+fr0e6Eg1y6eueu6hSrU5q1vZgLQus8ImvUYSp86uR7pHkREHoZCYBGRx2BrY0NGT+ck227HxFMqJAPNS2XjzYlb7nn8DysPcyevvUvF3P68XSc//h7OXLoZxZDZO+n803qW9a0NwO4z1/F1c+S7TmUJ9HFl56lr9J66lfiERN6pWwAAB1sbWpUJplAWbzycHdh//ga9p/5JQqLJB02KPvB9bjwaSsU8/vR7sTDeLg7M2nqal35az5x3qlE2Z0bLmCNXaFQ8Kx+18MPZwZbRfxym1ajV/NG/Ltkzut85zxU6V85J0Ww+mCZ8tmgvLb5ZzbpB9fB2dbRer3Jef0Z1LGt9bW+X9MGVDO5OeLk4PHD9IiIiIvJ4bOzs8Mzw98SG2OjbnDm0mwYv9yFL7sJERdzk1/+9z8geTRk8fRO2dknjhuWTvsUwkn8YeeeqhYwf2p3G3QeRb0hVTDOR0wd33TWu48BvKVyprvW1s9vdwfCDiI+L5af+XchdrDxHdiSdVLD6t7HM+mYwHQZ+Q/aCJTm5fzsTP3wLVw9vilSuB8CWJb8y65sP6DjoW3IVK0/ouVNMGP4mcbExdP5gFADvT1pNYkKC9bw3r17iw/aVKVmriXWbi7snuHs+0j2IiDwshcAi8sRMXH+MEQv3sufjRtja/P0Gr9svG4mMiWdSt8qcCr3FB7N2sePUNSJi4sjp70HfBgWpXSgwxfOWHDSfLlVy071mXuu2Jl+vJG+AF5+0KgFAbHwCIxbuZdbW09yIjCVPgCf9XixEtfwBqXfDKWhRJgTgvm0Kdp6+xphVh1nerw4F+81Nss/GxuC16nmsr7P4utKjdn46jV5HdFwCTva2tC2fPckxwRnc2Hv2Oot2nbOGwCEZ3Qm5E8D+dZ6NR4PZfDz0oe7poxbFk7zu06Agv++/wJLd560h8A8vlUsy5rPWJVmy+zx/HLhoDYFnvFk1yZhRHcuSs89s/jxxlTr/+G/Awc72rnBdRERE5GmxdvYvzP3hQ75YegQb27+frhozoAsxUZG8+b8ZXDl7gl//N4CT+7YRHRlBpmy5aNhtgDVITE6/FwpSreWr1On4lnXb56/WJzBHPtq+9yVgCTDn/vAhW5b8yu2b1wnIkZfGrw+iYPmaqXfDyXBx96TX9/OSbOvw/kgGtyjNxZOHCcpVwLr95P7trJz+AwMnr6V3rRxJjklMSGD6531p9tYwKjftbN0eEJKHf3Nx90wSRD+qWd98QFCuAuQuXvGuEHjz4ulUatKJMnVbAOAXFMKp/TtYOv5/1r+743u2kL1QKco1aANYFtor16A1O/6Ybz2Pu3eGJOddP28iTq4elKzV9LHrFxF5FAqBReSJaVg8KwN/28Gag5epXsASvkZGx7F0z3lGti9jeR0TT40CAfR7sRDO9rbM3XGGLmM2sGpAXXJlerTf5AO8PelPTl2N4IfO5QjwdmHlvgt0+HEdy/rWokCQd7LHfL10PyOXHbzneae9Udkacj5JEdFxvP7LJr5oWwo/d6f7jr8eGcOsracoHuyLk33KbRxuRcfheY8Zsiev3GLVgYv3DN0fVGR0/D2vFRufSEx8wj1n7EbExJNomng5Jx3z5/FQ8r83B08XB8rl9KN/w8IP9HUSERER+S+UqNmYaZ/35cCWPyhY3tKyKvp2BLvWLKbz4O8BiImKpGD5WjR+fRAOTk5sXT6bH95tz+DpmwgIyf3I1/5lyOuEnjvJKx/9jHfGzOzdsJzverbi/UmryZK7ULLHLBr3BUvGfXnP87717SxyFyv/yHUBREWGA+Dyf/buOz6Kagvg+O+m914JIQkdQu9I772IFBERUEFRBEQBlV5FERWxAIqoiCBIL9J77713AqGE9N7u+2N1cR9JACEuhPP9fN7nZWfuvXNm32N29uydc13cjNuSE+L4YchrdB0yGRcP73v6XD55kMibYVhZWzPmpVpER4STv3AoL/QdTYHiZU3azv1sML+O749nQBA127xC7XY9sLB4uKWOjmxdxZGtqxj22zb2r1tyz/601FSsbUzvO63t7Lh4fD/paWlYWVtTuFx1dq34nfNH91CodBXuhF/l8JY/KV2jcZbH1FqzbcksqjXviI2dTHQQQpiHJIGFEI+Nm4MNDULzsWDvJWMS+M8j17CysKDJX7VjQ/O7myRl320aypqj11l28CoDmoVmOe79XLodx6L9l9k3uhX5PRwBeK1uUbacvskv287zyYuVsuzXrVZh2lQokOPYfm65c5M2cM4+6pX0p0FovhzbjVl8iBmbz5KUmkHFEE9+7V0727ZHrkTy+66LfNu9+j37Wny2lqNXo0hJz+TlGgUZ0rrMI8X/4+azXI9OpEOV4GzbTFh2FEdbK5MZvv9v6PwDlMrvRqWCnsZt9Ur607xcIAU8HbkamcCEZUd5YfJG1g5ujG0OCXAhhBBCiP+Ko4s7pWs0Zvef84xJ4EOblmNpaUW5Os0BCCxa2iQp2+K1gRze8if71y+m5euD/tVxb129wN7Vf/DxsmPGurv1O73Byd2b2LLgR7p8+EWW/eq+8CqV/1GGICtu3jnfl95Peloq878YQtnazfDw/ce6EeP7E/pcw2wTpLevXQJgydTxdHx3HF4BQWyc9z2f9WrB6AX7cPP2A6DNm0MoVqk2tg6OnNqzmflfDCE++s5DvZfRt8P5ZWxf3vpstrF27/8Lrd6AbUt+oXz9VgSXrMDlkwfZtvgXMtLTiI++g5u3H1WatCchJpKJrzcDrcnISKdaixd5oe/oLMc8sWsDEdcuUev57g8cqxBCPG6SBBZCPFbtqwTxzi+7SUxNx8HGigV7L9OiXH7j7NWElHQmrTzG2mPXuRmTRFqGJiU9g5IB/74W1pGrUWgNtcb8abI9NT2DmsWyf1zM3dHWpA7tf2X+7oscvxbNmsFZ3wj/01sNS/BS9YKERSby2cpjvP3TTua8XQelTAsJn7sZS5fvttCzXjFalr93IY7prz1HfHI6x69FM3rRIaasPUm/JiX/VfzLD15l9KJDTHvtOQI9HbNsM33jaX7Zfo7579TD2d46yzbDFxxk94XbLB3Q0KR8yPOVgox/lwxwo2ygBxWHLWXd8eu0KHfvuQkhhBBCmEO15p34ccSbpCQlYmvvwO4/51GhQWusbQ2zSFOSElg2fQJHtq4iJuImGelppKUmk79IqX99zCunDqO1ZkSHKibb01NTKFY5+8kCjq4eOLp6/Ovj3k9Gejo/DO1JYlwMfT6fa9y+c8Ucws4eY+iszdn21ZmZALR47X3jYnFdh3zFyd2b2LliDs26vwtAy56DjX0KFCtDZmYGK2d89lBJ4BnDelG3/WsULF052zYtXx9E7J2bfNKjERqNi4cP1Vu+xOqfvzTOOj69fxvLf/iULh98TkipSty6eoHfPxvM0qnjaNN76D1jbl30M8GhFbKdqS2EEP8FSQILIR6rhqH5sLJQrDpyjVrFfNly6gZz/1EHdtTCQ2w4Gc7I58sR4uOMg7UlfX7ZRVp6ZrZjKqXQWptsS8u4+zpTg1KwenAjrP/vcTA7m+xnjpqrHMTW0zc5cyOGggP+MNne68cdVNroybL37tZz83SyxdPJlkK+LhTxc6H80KXsPn/bJKazN2JpN3kDbSsWYFhb00fm/hbgbkjWFvN3JTNTM2D2Ht5uWBwry4d7fG7Zgau888suprxSLdsZvtM2nOaT5UeZ81YdKgR7Ztlm2B8HWLz/Cgv71SfYK+tZGH/zc7PH392BC7fiHypWIYQQQojcVLpmEywtrTi0eQUlqtTl5O5N9Pt6kXH//C+GcmznOjr0H4tvgULY2Nnz4/A3SE9LzXZMZWEBmN73ZqSnGf/WOhOlFENmbbpn4TVr2+yfYMvNchAZ6el8/9GrXDt3nPenr8TJ7e7938k9mwm/cIo+tUzX6Zj2YXcK/VaFwT+uwdXLMNPXP+Tu+h+WVlb4FChE5I2r2R43pFQlkhJiib1zCxfPB7tfP7V3M2cObGPZ9xMAQ5kGnZnJG1Xc6fLB59Ru1wMbO3u6j/iWlz+aTGzkLdy8/NiycCZ2js44/VXnd/G3Y6jSpD21nu8GQP4ioaQmJ/DzmHdo2fMDk/9tYiNvc2jzCmNNZyGEMBdJAgshHitba0taVQhkwd7LRMan4ONiR40id2/Kdl+4TccqwcbZqslpGVyKiKfQPxYv+3+eTrbcjE02vk5Oy+DczVhK/1VWonR+N7SGW7HJ1Cz64AtFmKscxIety9D7H4vcAdQdt4oR7crRtEz2pRMy/0qEp/wjYX46PIYXJm+kdYVAxrSvkF3Xe8ZJz9RkZGqsHqK6wpL9V+g7azdfda1KqwpZz8iduv4Un644xuzetala+N6abwBD5h9gyf4rLOxf74HqQN+JT+FGdBK+rlITWAghhBBPDmsbWyo2bMvuP+cRH30HFy9filWqZdx/7tBOqrd4kYoN2gCQlpLMrbCL+BQonO2Yzm6eREfcML5OS0nmxqUzFChmKOUVWKwMWmtiIm5SPIeZv/8vt8pBpKel8f1HPbh2/gTvT1t5z6Jtz789nCZd+5psG9mpGh36j6VcnRYABJUoh5WNLTcun6VIeUNZs8zMTG6HXSS0eoNsj3319BGsbe2wd37wJwpH/r7L5PWhzStYOeMzPvplI24+polqK2trY1mLvWsWUKZmE+NM4NTkJCwsTG+klYUl/N/EFYAdy2ZjZWNLlabtHzhOIYTIDZIEFkI8du0rB9P+q41cjYjn+UpBWFjcLV1QyMeZlYfDaFomACtLCyatPEZKWvazgAFqFvNlzs4LNCmdD08nO75cfZz0f8wELuTrwguVg+j3y25GtitP6QLuRCeksuPsLYK8HLMtIZBb5SCiElK4FplITJJhlsfF2/G42tvg42KHj6s9/m4O+Ls53NMvwM3BOCt274UIjl6NomohL1zsbbgUEc8ny48S6OlI1UKG5Oqp6zG88NUGahT1pX+TktyKSTKO5eNqSF7P330RW2tLSuRzw9rKgsOXIxm35Agtywc+VH3dRfsu0+fnXYxoV47qhb2Nx7K2sjC+h9+sPcnHy47yTbdqFPJxNraxs7HE5a+F3z74fR/z91zip161cLO3MbZxtLXC0c6ahOQ0Jq48Rotygfi62nH1TgLjlh7By9mW5mXzP/j/CEIIIYQQ/4FqzTvx+ZutiLh+mSpN2pssUuYbVJiDG5dTrk4LLK2sWfb9BNJTUnIcr3jlOmxbOotytZvj5O7FyhkTyUzPMO73CypC1WYdmTmyNx3fHUeB4mVJiI3i9L5teOcPpkL91lmOmxvlIDLS05k2+BUunThAny9+RylFTMRNAOydXLCxs8fdJx/uPvcmlz188+OdP8TYts4Lr7J02njcffLhlS+IDfOmkRgbTbXmLwJweMufxETcpFCZKljb2nF631aWTh1P7ee7Y23z4PfzAYVNy6FdOnEQZWFhsv3G5bNcPLaPgqUrkxgbzdrZX3Pt/Al6jJpqbFO2dlPWzv6GoJLlCSlVidtXL7Dku7GUqdXUZBaw1pqti3+mSuMXsq1BLIQQ/xVJAgshHrtqhb3xd7Pn9I1YvnvV9JGyUe3K8+7sPbT+Yj1uDjb0qleM5BxKQQD0a1ySq3cS6DZtG462VvRvWpKb/0h4AkzuWpUvVx1n9OJDhEcn4eZgQ/lgD2oU/fc11/6t1Ueu0e/XPcbX7/22F4D3m4cysMWD1QGzt7Zk2cGrfLriKIkp6fi42lO/hD/TX33OWF952cErRMSlsGT/FZbsv2LS/+Y3hhtmS0sLvlpzkgu34tAaAj0c6FG7MG/UL2Zse+VOPJWHL2fyy1V4sXrBLOP5Zds50jM1w/44yLA/Dhq3P1fEm0X9DTM0Zm45S1pGJr1+3GHSt1PVYL56pdpfbc4B0P6rjSZt/n5vLCwUJ6/HMG/3JWKT0vB1saNGUR++f+05nOyyri0shBBCCGEuRco/h5tPPsIvnKLX+B9N9nUcMJ6fR/fh09eb4uDiRsPOb5GWkpzNSAbNegwgIvwy3wzojK2DI81ffd9kZjBA9xHfsfLHifzx1XCibl7D0dWdkNCKFK9cK5tRc0fUrWsc2rwCgLEvm85K7j7iO2q07vLAY7XvNxYraxtmjnyT1OQkChQvy3vTlhsXhbO0smLTHz8w74uP0JmZeAcE0/rNj6jXsZfJOD0rutCq1we0fuOjf31eOjOTtbO/4eals1haWVOsUi0++HEdXvnurlvR4rVBoBRLvhtL1K3rOLl5UrZWU9q+PdxkrNP7tnLrynleH/P9v45HCCEeF/X/dTaFEE8XTyfbjZO6VKkrsyT/exNXHGXZwTC2DG1m7lAeybYzN+ny7RY2D2123/q8T5LtZ27SbvJGTnzyPJ5O//0Cf1mZueUs45Yc+Sk2KbWHuWMRQgghnmZKqXLe+UM2j19y+P61o0SuWzptPPvXL2HUvN3mDiVbt69dYkibsgz6YTWFy1UzdzgPpWdFF9785BfjwnhPondq5UtKTowP1lrfMncsQoh/5+FWBBJCCGHi7I1YQt79g6nrT5k7lH9t/bFw+jQq/lQlgGuPWclL324xdxhCCCGEEM+M8Iun6VPTnzW/fm3uULJ0bPsaqrXo/FQlgGeN70+fmv73byiEEI+BlIMQQoh/6fW6RWlfORgAjydkJuq/MaJdOXOH8NBmv1WH9AxDGRF3BxszRyOEEEIIkbc1ePFNqjXvBICTm6eZo8lavY69qGfuIB5SmzeH0KTrOwC4evmZORohRF4nSWAhhPiXcmthOXF/gZ6O5g5BCCGEEOKZkRsLywlw8fDGxcPb3GEIIZ4RUg5CCCGEEEIIIYQQQggh8jBJAgshhBBCCCGEEEIIIUQeJklgIYQAfN+ey7IDV80dxn/ix81nKfL+AqZuOM2ifZcZv/SIuUMSQgghhBC5pGdFF/avW2zuMHLVs3COQgjxqKQmsBBC5AGVhi3lamQiS96tT7XCPsbtE1ccZdnBMLYMbWbctvzQVX57qw7TNpzmxLVoZvaqaY6QhRBCCCGEuK+rZ46yZOo4Lh7bR2JcDC7u3gSHVqDjgPF4+hcA4LPVZ3FwcTNvoEII8YSTJLAQQuQRdtaWjFl8mBXvN8qx3cJ+9QGoXNDrvwhLCCGEEEKIfyUuKoLP32xFyeoN6Dt5Po6uHtwJv8rRbatJio8ztnP18jVjlEII8XSQJLAQ4pmgtWbq+tP8vO0c16IS8XSypX2VYIa2KZtl+zGLD/Pn4TCuRSXi7WxL6woFGNSyNHbWlgBci0rgw98PsPv8bVLSMgjwcOD95qV4vlIQAJNWHuO3HRe4FZeMq70NdUv48XW3arl6ji/XKMSv28+z4tBVWpQLzLLNpdtxDF9wiAOX7hCfkkZhXxcGtShF49IBxjbRiakM++MAq49cIyU9k8oFvRjbvgLF87nmavxCCCGEEOLhaa1Z++vXbF4wg8gbYTi5e1G9+Yu0e2dklu0XfDWCg5uWEXkjDBcPHyo1ep42bw7B2tYOgMgbYfz26fucPbiD9JQUPPzy0+qND6nSpD0Ay6ZPYNuSWcTeuYmDixslq9XntdHTc+Xczh3aRUJcND1GfoeVtQ0AXvmCKFbR9Em2nhVdePOTX6jYsC0R1y/zYavS9Bz/I5v/mMHF4/vxCy7Kq6OmopQFs8b15eqZYxQoVoZXx0zHOyA4V2IXQognjSSBhRDPhHFLj/Dz1nOMalee6oW9iYhP4VhYVLbtHW0t+fLlKvi52XMmPJZBc/dhY2XBB63KADB47n5S0jNY2K8eTnbWnL95dybC8oNX+Xb9Kab2eI4S+VyJiEth/8WIHOMLefePHPdXK+zNnLfr5NgmwN2B1+oUYdySIzQpHYCV5b1l3xNS0mkQ6s8HrUpjb23J4gNXePX77Wz8qClF/FwA6PvLbs7fiuXnN2rh6mDDx8uO0PmbTewY0QJ7G/nYEEIIIYR4kiz6ehSb/phBxwHjKVrhOeKiIrhyKvs1H2ztHeg+/FvcfPwJv3CKX8e/i5W1DW3fGgbA7AkDSEtN4f1pK7BzdObm5bPGvvvXL2HNr1PoOW4GAYVDiYu6zYWje3OMr09N/xz3FylfnX5TFma5z8XLB52Zyf51i6nStANKqRzH+qel08bTacDHeAWEMPvjd5n+0au4uHvT9q3hOHt4M3PEm8ydOIh3vpz3wGMKIcTTTL7NCyHyvITkNKZvOM2Y9hV46bmCAIT4OOdYDmFAs1LGvwt4OtGvSUm+XXfKmAQOi0ygZblAQvO7AxDk5WRsHxaZgK+LPXVL+GFtaUF+D0fKBXnkGOOGD5vkuN/OxjLnk/xL3yYlmb3jArN3XKBbrcL37A/N726MGeDdpqGsOXqdZQevMqBZKBduxbH66DUW969P9SKG2sLfdKtGhaHLWLD3Mi/XKPRAcQghhBBCiNyXnBjP2t++odN7E6jZpisAPoGFKFSmarZ9WvYcbPzbK18QzV59jzWzvjImge+EX6VCg9YEFi0NYDJT9k74VVy9fClZrQFW1tZ4+gcSXLJCjjEOn7Mtx/3WtvbZ7itUugrNX32fmSPe5LdP3ie4ZHmKVqpFtWYdjfWAs9OoSx9K1zTcYzd6uQ9fv9uJthOHUrxybQDqdezFnE/fz3EMIYTISyQJLITI807fiCUlPZNaxR68VtiyA1eZvvE0F2/Hk5CSTqbWZGRq4/6e9YoyaM4+NpwMp1YxX5qXzU/ZAoZEb6sKgXy/8QyVhy+jbgk/6pf0p0npAGyts0/khvg4//sT/Ac3Bxv6Ni7BZyuP0b5K8D37E1LSmbTyGGuPXedmTBJpGZqU9AxKBhhKPZy5EYuFUlT6R4Lcxd6GEvlcOXMj9rHEKIQQQgghHo/wC6dIT02hRJWcnxj7p/3rFrPut2+5FXaBlMQEMjMzyMzIMO5v0Lk3s8f35/iOdRSvUocK9VoRVKI8AJUatmX9nO/4sHVpQqs3oFT1hpSt0xxrG9tsj+cT+GiTCJ5/eziNurzNqb1buHB0L9sX/8LKGZ/R54u5lKhSN9t++YuEGv928TRMbggoXPIf27xJSUogJSkRW3uHR4pRCCGeBpIEFkKI/7PvYgRvzNzB+81DGV3SH1d7G1YfucbIRYeMbbo8V4h6JfxZd/w6W0/dpOWkdfRtXIKBLUoT4O7I9hEt2Hr6JltO3WDkwkN8tvI4fw5shKNt1pfdx1EO4m+v1S3KjM1nmbbh1D37Ri08xIaT4Yx8vhwhPs44WFvS55ddpKVn3nfcB3/4TgghhBBCPInOH93D9I960KrnB3R87mMcnF05vPlP5n85xNimVttXCK3egGPb13Bi9yYm9GhEsx4DaP3GR3j45Wfswv2c3LOJk3s2Mf+LISz7fgIf/bwBW3vHLI/5KOUg/ubk5kmlRs9TqdHztHtnJKNfqsnyHz7NMQlsaWVt/PvvMhJZbdP6/vfBQgiRF0gSWAiR5xX1c8HWyoKtp29S8AFm3O49H4G/q71JSYirkQn3tMvn7sArNQvzSs3CTFlzku83nWZgC8Njc3bWljQqlY9GpfLxTuOSlP5wMXsv3KZuiaxvgh9XOYi/jz24ZWk+mr+fDv83G3j3hdt0rBJMy/KGheOS0zK4FBFPob/el6J+LmRqzb4LEcZyEHFJaZy8HsOL1UMeOAYhhBBCCJH7/EOKYWVjy8k9m/EtcG8psP93/tBu3LzzmZSEuBN+5Z52Hr4B1G7Xg9rtevDnT1+wfs53tH7jIwCsbe0oU6spZWo1pVn3AbzXuDDnDu0itHqDLI/5KOUgsmJlbYNP/hCib994qH5CCPGskySwECLPc7Kzpme9ooxbchgbKwuqF/YmMiGVI1ci6V67yD3tC/o6Ex6TxB97LlGpoBebToSzeL/pzfGQ+QdoUNKfgr7OxCelseFEOEX9DCUV5u68QHqmpkKwJ462Viw5cAVrSwtCvLNPQD+uchB/61AlmO/Wn2LOzosm9YoL+Tiz8nAYTcsYFo6btPIYKWl3Zz8U9HGmaZkABs7Zx2cvVcLF3rAwnLOdFe0qBT3WGIUQQgghxKOxc3SmQefeLPp6FNY2thQp/xwJMZFcPnmIuh1ev6e9b1Bhom9fZ9fK3ylUpgrHd65nz2rTJ9LmThxEqRqN8C1QmKSEOI7vWEe+gsUB2L50NpkZ6YSUqoStgyP71izE0soanwLZl3x4lHIQh7f8yd41C6jc+AV8gwqDNmw7un2NMSkthBDiwUgSWAjxTBjSuiyuDjZ8/udxwqOT8Ha2pUPVrGe2NikdwFsNizN8wUGSUjOoW8KPQS1KMfj3/cY2mVrz0fz9XI9KxMnOmprFfBnVrhwALg42fL32JKMWHSItI5Nifq782LOGSTI2t1lYKIa1LctL324x2T6qXXnenb2H1l+sx83Bhl71ipH8f6UgJnetyrA/DvDK1K2kpGdSuaAXc96ui72NfGQIIYQQQjxp2vUZiaOzG8t/+JSom9dw8fSheovOWbYtW7sZTbr24/dJH5CWkkzJavVp8+YQZk8YYGyTqTOZ8+lAIm9ew87BiRJV6tDh3fEAODi7surnL5j/5VAy0tPwL1iM3hN/NVk87nHKV7A4tg5O/PHlUCJvXsPS0grPgCA69B9Lg85v5coxhRAir1Ja6/u3EkI8sTydbDdO6lKlbvOy+c0dihDPvJlbzjJuyZGfYpNSe5g7FiGEEOJpppQq550/ZPP4JYddzB2LEALeqZUvKTkxPlhrfcvcsQgh/h0LcwcghBBCCCGEEEIIIYQQIvdIEliIp5yGTJnRL8STISNTo2WJaSGEEOJx0DpTPlKFeFJk6kwFyBdPIZ5ikgQW4imXlpEZHh6dZO4whBBAeHRiRnJaxnVzxyGEEELkARFx0XdsMiURLITZJSfGk56aagXEmDsWIcS/J0lgIZ5y8cnpC+fvuRQns4GFMK+MzEwW7r2clJ6pV5g7FiGEEOJpp7W+ZmFhcePsge3mDkWIZ97hzSuxc3Tao7VONXcsQoh/T5LAQjz9Vp69EXtjyPwDqZHxKeaORYhn0q2YJHrP3Jkcm5x2DNht7niEEEKIvCA1OWnI1EFdk84e3IlMeBDiv5eZkcHRbav5dXz/pMTY6BHmjkcI8WiUfJgK8fRTSnm52FvPTE7LaOTnap9iZ22plbmDeow0oDX2Gm1loVSCAnku8CmiwTJTa0eFSlWK5Lz2/82ElHSLiLhkKxsry8VxyWk9tdYJ5o5LCCGEyCusrK27Wtvaj7ewsPBw8fBJU5aW5g7p8dLaUmvtqBSpKItkc4cjHoIGrTPtASulLBJQees7SmZGOtG3b9haWFiGJcXH9NdannYT4mknSWAh8hCllBNQGLAzdyyPkS8wHrgFjAESzRuO+JfcgbFAOjCcvFVPLBE4o7WWL25CCCFELlBKKSAY8CZvPc3aAegBjAOk7sXTqznwDvA5sNbMsTxOmUC41vqquQMRQjwekgQWQjyxlFINgV+BScBnWi5YTzWllBWGhH5HoIPWeq+ZQxJCCCGE+M8ppRyB6UAo0E5rfcHMIYlHpJQqBywAlgMDpXauEOJJlJd+RRVC5BFKKQul1EfAL0BnrfVESQA//bTW6VrrQcB7wEqlVM+/ZvYIIYQQQjwTlFJFgV1AGvCcJIDzBq31IaASUAjYoJTKZ96IhBDiXpIEFkI8UZRSbsBioCVQWWu90awBicdOa70AqAn0B2YopezNG5EQQgghRO5TSrUFtgFfAz201lLmLA/RWkcBrYFVwD6lVB0zhySEECYkCSyEeGIopcoAe4FLQF2t9TXzRiRyi9b6NFAVcAC2K6VCzBySEEIIIUSuUEpZKaUmAJOBllrrafKUW96ktc7UWo/FUOv5d6XUe/LkmxDiSSFJYCHEE0Ep9TKwHhipte4rdbTyPq11PNAZ+BnYpZRqbuaQhBBCCCEeK6WUD7AaqAhU0lrvMXNI4j+gtV4NVMNwrztPKeVs5pCEEEKSwEII81JK2SilvgZGAPW11rPNHZP472iDycALwPdKqZFKKflsEkIIIcRTTylVFdiHoQZwU631bTOHJP5DWutLGEqgRQF7lFIlzBuREOJZJ1+0hRBmo5TKD2wG8mOYGXHUzCEJM9Fab8OwmEZ9YIVSysPMIQkhhBBC/CvKoDewDHhHaz1Ea51h7rjEf09rnay17gVMBLYopTqYOyYhxLNLksBCCLNQStXDUP93CdBOax1j5pCEmWmtw4EGwAlgv1KqgplDEkIIIYR4KEopB+AnoDdQQ2u9xLwRiSeB1vpHoAnwqVJqklLK2twxCSGePZIEFkL8p/6aGTEI+A14WWs9QWudae64xJNBa52mtX4PGAysVkq9au6YhBBCCCEehFKqELATsASqa63Pmjkk8QTRWh/AUBu6JLBeKeVn5pCEEM8YSQILIf4zSikXYAGG+q9VtNbrzRySeEJprecBtYGBSqnpSik7c8ckhBBCCJEdpVQrDAng6UBXrXWCmUMSTyCtdSTQAtgA7FNK1TRzSEKIZ4gkgYUQ/wmlVCiG8g83gNpa66tmDkk84bTWJ4EqgDuwTSkVZOaQhBBCCCFMKKUslVJjgW+BNlrrb7TW2txxiSeX1jpTaz0S6AUsUEr1U0opM4clhHgGSBJYCJHrlFKdgU3AOK31W1rrFDOHJJ4SWus4oCOG8iF7lFJNzBySEEIIIQQASikv4E+gBoZFjneaOSTxFNFarwSqAd2AOUopJzOHJITI4yQJLITINUopG6XUZGAs0Ehr/Yu5YxJPH23wOYZk8Eyl1DCllHx+CSGEEMJslFKVgX3AQQz3uTfNHJJ4CmmtL2L4ESER2K2UKmbmkIQQeZh8iRZC5AqlVD4Mta4KYpgZcci8EYmnndZ6M1AJaAosVUq5mzkkIYQQQjxj/lrkuCewAhigtR6stU43d1zi6aW1TgJeA77EUAKtnXkjEkLkVZIEFkI8dkqpOhhmRqzCUBstyswhiTxCa30dqAecx7CYRjnzRiSEEEKIZ4VSyh6YAfQHammtF5o3IpFX/PXk2/dAc+BzpdSnSikrc8clhMhbJAkshHhs/poZ8R7wO9BDaz1Wa51p7rhE3qK1TtVa9wOGAmuVUq+YOyYhhBBC5G1KqRBgO2APVNVanzZzSCIP0lrvxfDkWzkM97m+5o1ICJGXSBJYCPFYKKWcgXnAixhujFebOSSRx2mt52CYFTxEKfWdUsrW3DEJIYQQIu9RSjUHdgE/AS9prePNG5HIy7TWEUAzYBuGJ9+qmzkkIUQeIUlgIcQjU0qVAPYAURgejbts5pDEM0JrfQyoDPgCW5RSgWYOSQghhBB5hFLKQik1EpgOvKC1/kprrc0clngGaK0ztNbDgLeAJUqpPkopZe64hBBPN0kCCyEeiVKqA7AFmKi17qW1TjZ3TOLZorWOBV4AFgB7lVINzRySEEIIIZ5ySikPDIu/1cOwyPE2M4cknkFa62XAc0BPYJZSytHMIQkhnmKSBBZC/CtKKWul1CTgE6CJ1vpHc8cknl1/LabxKfAShhvkD5VS8hknhBBCiIemlKoA7AdOAA211jfMHJJ4hmmtzwHVgUxgl1KqiJlDEkI8peQLshDioSml/ID1QEkMMyMOmDkkIQDQWm/AUB6iDbBIKeVm3oiEEEII8TRRSr0KrAYGaa3f01qnmTsmIbTWiUA34Ftgu1KqjZlDEkI8hSQJLIR4KEqpmsA+YAPQQmsdaeaQhDChtQ4D6gBhGMpDlDZzSEIIIYR4wiml7JRS04GBQG2t9XxzxyTEP/315Nt3QGtgilJqvFLK0txxCSGeHpIEFkI8EGXQD0Pd1Z5a65Fa60xzxyVEVrTWKVrrt4HRwAalVBdzxySEEEKIJ5NSKgjYCrgDVbTWJ80ckhDZ0lrvAioBVYHVSilvM4ckhHhKSBJYCHFfSiknYA6GR5Cqaa3/NHNIQjwQrfUsoAEwSik1RSllY+6YhBBCCPHkUEo1AXYDc4GOWus4M4ckxH1prW8BTYC9wH6lVBUzhySEeApIElgIkSOlVDEMN8aJQA2t9UUzhyTEQ9FaH8EwWyII2KSUCjBzSEIIIYQwM6WUhVJqGDATQ/J3ktZamzsuIR6U1jpda/0h0BdYrpR6UymlzB2XEOLJJUlgIUS2lFLtgG3Al8BrWusk80YkxL+jtY4G2gLLMdQJrmfWgIQQQghhNkopd2AphpmUlbTWW8wckhD/mtZ6MVATeBuYqZRyMG9EQognlSSBhRD3UEpZKaU+BT4Hmmutv5eZEeJpp7XO1FqPx1DWZI5SaqDMlhBCCCGeLUqpchgWOT4H1NNaXzdvREI8Oq31GaAaYA3sUEoVMnNIQognkCSBhRAmlFK+wFqgLIaZEXvNHJIQj5XWei1QBegA/KGUcjFzSEIIIYT4DyilXsFwnztUa91fa51m7piEeFy01gnAy8AMDInglmYOSQjxhJEksBDCSClVHcPMiG0YZgBHmDkkIXKF1voKUAu4jaE8RKiZQxJCCCFELlFK2SqlvgWGAHW11nPMHZMQuUEbTAGeB6YqpUYrpSzNHZcQ4skgSWAhBMqgD7AEeEtrPUxrnWHuuITITVrrFK31m8DHGBaM62TumIQQQgjxeCmlAoEtgB9QWWt93MwhCZHrtNY7gIpAbWClUsrTzCEJIZ4AkgQW4hmnlHIEZgGvA9W11svMHJIQ/ymt9U9AI2C8UuoLpZS1mUMSQgghxGOglGoA7AH+AF7QWseaOSQh/jNa65tAQ+AIsF8pVcnMIQkhzEySwEI8w5RSRYCdQAbwnNb6vJlDEsIstNaHgEpAUWCDUsrfvBEJIYQQ4t9SSlkopT4EfgW6aK0nyiLH4lmktU7XWg8E3gf+VEq9bu6YhBDmI0lgIZ5RSqk2wHbgW6C71jrRzCEJYVZa6yigFbAG2KeUqm3mkIQQQgjxkJRSrsBCoDWG8g8bzBySEGantf4Dw3oYA5RSM5RS9uaOSQjx35MksBDPGKWUpVJqPDAFaKW1niozI4Qw0Fpnaq3HAK8B85VSA5RSytxxCSGEEOL+lFKlMSxyHAbU0VqHmTkkIZ4YWutTQBXACdimlAoxc0hCiP+YJIGFeIYopbyBVUBVoKLWereZQxLiiaS1/vvfSRfgd6WUs5lDEkIIIUQOlFJdgA3AKK11H611qrljEuJJo7WOB17EsCbMLqVUMzOHJIT4D0kSWIhnhFKqCoaZEfuAJlrr22YOSYgnmtb6ElADiAV2K6VKmDciIYQQQvw/pZSNUmoKMBJooLX+1cwhCfFE0wZfAu2BH5RSI5RSkhsS4hkg/9CFyOOUwZvAcqCv1vpDrXW6ueMS4mmgtU7WWr8OfA5sUUq1N3dMQgghhDBQSgUAm4ACGOr/HjFvREI8PbTWWzEsjNwQWKaU8jBzSEKIXCZJYCHyMKWUAzATeBuoobVeYuaQhHgqaa1/AJoCE5VSnymlrMwdkxBCCPEsU0rVA/YCy4DntdbR5o1IiKeP1jocqA+cxrAwcnkzhySEyEWSBBYij1JKFQJ2ANZANa31WTOHJMRTTWu9H8NsiVLAOqWUn5lDEkIIIZ45fz3lNhCYA7yitf5Ya51p7riEeFpprdO01gOAD4A1Sqke5o5JCJE7JAksRB6klGqJIQH8A/Cy1jrBzCEJkSdore8ALYDNGGZL1DBzSEIIIcQzQynlAvwBdACqaK3XmTkkIfIMrfU8oA4wWCk1TSllZ+6YhBCPlySBhchDlFKWSqnRwHcYHov7WmutzR2XEHmJ1jpDaz0CeANYpJTqq5RS5o5LCCGEyMuUUqEYyj/cBmppra+YOSQh8hyt9QmgCuAJbFVKBZk5JCHEYyRJYCHyCKWUJ7ASqA1U0lrvMHNIQuRpWusVQDWgBzBbKeVk5pCEEEKIPEkp1QnDAnAfa63f1FqnmDkkIfIsrXUshtn2vwO7lVKNzRySEOIxkSSwEHmAUqoSsB84DDTUWt80c0hCPBO01heA54BUYJdSqqiZQxJCCCHyDKWUtVLqC2A80Ehr/ZOZQxLimaANPgNeBH5SSg1RSkn+SIinnPwjFuIpp5R6HfgTeE9rPUhrnW7umIR4lmitkzDMBp4CbFNKPW/mkIQQQoinnlLKH9gAFMHwlNsh80YkxLNHa70JqAw0BxYrpdzMGpAQ4pFIEliIp5RSyl4pNQMYgKEu2gJzxyTEs+qv2RLTMCwa96VSaoJSysrccQkhhBBPI6VUbWAfsAZorbWOMnNIQjyztNbXgHrAJQwLI5c1b0RCiH9LksBCPIWUUiHANsARw8rIp8wckhAC0FrvBSr+9Z81SikfM4ckhBBCPDWUwQBgPvCq1nqM1jrT3HEJ8azTWqdqrfsCw4F1Sqmu5o5JCPHwlNba3DGIf1BK2QD5ABtzxyKeWLWBCcA04OdcGD8dCP/rEXchxP/5a4ZvAGCbQzMLoC/QFuiHoV63yPuitda3zB2EEE8LpZQthvtea3PHIp4IjsA4IBDDZ+i1RxwvHbiptU541MCEyIuUUgrDNdjxIbsWAb4GdmCo1532mEMTT75MIEJrHW3uQMTDkSTwE0Ip5elsZ/V1anpmawcbK21jZSG/eIt7aLQtGmulVBKQkRvHSM/ItIhLTreys7HcFJuU9rbW+nxuHEeIp41Sytna3umrzIz09lY2dhZWNrb3/TeowQqt7VAqFUhV/0Gcwjy01io1Mc7awtLqRkZaysfpqSnTzB2TEE8qpZS/o7PLt6kpKU0dHJ0yrKxt5L73macttNb2oDKUIhke/RMzIyNdJcTFWNva2e+Mj415R2t97DEEKsRTTyllaWljPwql3lAWFo6Wtg7p/+LfnAJtB1iASgQksfQM0TpTpSfE2Fra2p9MS4gZqLVeY+6YxIORJPATQCnl6GhrdeCFivmD321c3Mbfzd7cIYlnXFRCKr/suJj55drTkUmpGRW01lfNHZMQ5qSUsrG2d9oVUq1pyerdPrJ18S1g7pDEE0hnZnL9+C7WfPpmYnJc1Ii05MTPzB2TEE8apZSHvYPjoTYv9/Lv1LOflYe3r7lDEnlYfGwMqxb8qqd/Miw2JTmpitb6jLljEsLcrB2cf3bKV6R96Z6fOTgHlsAwIViIh5ORlsKtg2s5PLV/UkZyQltJBD8dJAn8BFBKvVytkOd3i/rUcpILsHiSDJ5/KG3O7ssTU9Iyhpg7FiHMSSnV0jO45G+dv9nkrCyknL7IWfT1C/zWu3ZCRmqyu9ZaHpEU4h8sLCz61GjU8tNx0+fJrAfxn/l+4oiMP2Z+80NSQvyb5o5FCHNSShWwtHU43fC7o3ZWdg7mDkfkAeF7lnP0+/cPp8ZHlzN3LOL+5JvsE8DV3vrFTlWCJAEsnjjtKua3tre27GjuOIQwN2t7x/YlGnV2kgSweBBu+Qri7JM/A6hm7liEeNI4ubq91KRdF0kAi/9Ug9YdLS2Uet7ccQjxBGjuU6FRpiSAxePiU74RGSlJJZRSbuaORdyffJt9AlhYKP/87nIvLJ48+d0dSMvI9DR3HEKYm6WVbX5nn/zyS514YH+VDJHn3IX4f1r7+gYEmjsK8YzxCyhAakqKm7njEOIJ4OPoGyLJB/HYWFrbYuXomgp4mTsWcX+SBH4yWFqYYRbw9rO38eu/iDvxKWY9xrJD1/Drv+iRj1Vnwjom/nnykccRd1laKLRcJ4QAhaWysHysQ66d1IdlIzrn2GbZiM6sndTnkY5z88xBpjTzIvbmlUcaRzwcC0srkOunEPfQYGHxmK+nj1vTkl78OX+W8XWdYHs2rVxoxojumvnFWLo3rmjuMJ46FpaWaLT8mCsEWCgLy1z9t3B4aj/2TuyaY5u9E7tyeGq/RzpO9IVDrHjJn8TbsnyNuSlloZH73qeClbkDEHlb5RBPjoxuhoejjblDeSJprfls1Sl+3XmJmKRUyhfw4OP2ZSnu75Jtn7m7L9N/zoF7tl+a2Bo76yf7S5UQ4q7ab44HqcufpaToCNZM7M2dSydIio3Cwc2LkGpNqd59KLaOhutjemoyG6e8z+1zR4i6egb/klVo9+nS+469cFBrrh3dYbKtSO22NP3wh1w5FyHE02/hnos4u7r/p8cMv3qZF2sVZ9rSbRQvczfp26lXf9p17/2fxvI4HNq1lW/GDubSmRN4+vrT+Y0BtHm5Z4596gTfO1lxwNiv7ttPCGFeJV8ZI/e42bi6+XeOTOuf5b4aY/7ErVA5Yi8f5/zSr4k8vYfUuEjsvQIIrNuZgi16k1NpusNT+xG2ZZ7JNrfCFagxesXjPAXxlJMksMhVNlYW+LjYmTuMxyYiPgUnW6vHlmz9ev1Zpm46x+SXKlDIx5nPV5+i03fb2f5RQ5zsrLPtZ29jye6hjU22SQJYiKfL38nMvCLuVhjOPvkfz2AWFhR8rjnVuw/BzsWTmOsX2fTtIJImv0uzj2YAoDMzsLKxpUyr17i0bx2p8TEPPHyJRi9Rvfvd9S6tbOWpSCGeNenp6VhaWvIga3J4+vj9BxE9GAdHJ3B0ytVjpKenE33nFl6++R7LeOFXLzG4R1uadXyFIV/+yNG9O/hiWD/cPL2o0yznMr0DJ3xL9frNjK+dXFwfS0xCiNxj7ZC37nGTIsKw93o897j5qrfGu2w9k22nfhtN1Jl9uBYsC0DMxSPYuHhS7q0p2HsGEH3+IEd+eB+dkU7htjnPnvYqVZuyb00xvrawyj6nIJ5NkgTOw1LSMxi79DiLDoQRl5xGaIArI9qUompB01It+y9H8smKk5y7FUcxPxcmdipH2cC7sx32XrzDuOXHOXwlGlcHaxqH+jOsdSjOfyUpd56PYMzSY5wKj8XSQlHIx5kvOleghL8L28/e5oVvtnF8bHM8nWwBmLfnCp/8eYLI+FRqFPGifgnTG+tLEfGMWHyUA5ejiE9Jp7CPEwOblaBxqL+xze24FN7//QCbT9/C08mW95oUv+f8Y5PSGL30GH8evU5yWial87sysk1pyhV4uJkcqemZrDkezrw9V9hw8iY7hzYm0OPRC+lrrfl+yzneaVCUlmUDAPjqpYqUGraShfvDeKVGSLZ9FeSp5LoQz6K1k/qQHHuHVqPmAJCWnMimbwZxftsyrOwcKNum1z19Tm2Yx+HF04kKO4uVjT0BpZ+j1hvjcPK6e328vG89W6YNIe7mVXyKlqd0i+73jBN+Yg87Zo7h1tlD2Dq5ElK1KTVeHYGNo/NDnUN8RDinN8zn1PrfsXfzot0nSx7uTciGvYsHpVv0ML528Q2kTMtX2ff7l8Zt1naO1HtnEgARl04Q+RBJYCs7exw9pFyvEE8TrTXzfpjMktk/cOv6Vdw8vGj8/Ev0GjwGgGkThrJ1zVJuXruKh5cPdVu+wKvvDsfWznC/NPOLsWz+cxGdevbnlykfcyPsMiuO3iTy9k0mDu7NiYN78M1fgLeGTLjn2HWC7Rn17WzqNm8HwPlTx/hmzCCO7tuJrZ09NRq24J0RnxkTlB+/15OYqDuULF+FhT9/R3JiAnWat2PA2MnY2hl+dNq9aQ2zvvmEi6dPoJSieJmK9BkxkeDChnvaF2sZ/vuN1jUBKFe1FpN/X2M8j5/W7Afg5OF9/PDZSM4eO0RaWiqFipfizQ/HU6riw69Lefb4YVYvnM26Jb/T9uVedO8/5P6dHsCSX7/H09ef/qO+ACC4cHFOHtrL3Olf3jcJ7OTi+kQl4YUQ93d4aj9S4yKpPNBQVicjJZFjMz8kfPdyLG0dCGn6+j19wrb9waVVPxB//RyWNnZ4FK9O6CujsfO4e4976/AGTvwynKSIMFwLliWoYbd7xok8s5fTc8cTfeEQ1o5u+FZoTPHOQ7F2eLh73OTIcK5tW0DY1vnYuHpRfeiCh3wXsmZpY4+lzd3JBxkpidw8sJZCLd8y/igZWNe0XJyDbxAxl44SvmfFfZPAFtY22Ln5PJZYRd4kSeA8bMzS4yw9FMYXnSsQ5OnAtE3n6Dx1BzuHNMbX9W4CcfSSY4x5vgx+bvZMWnWSrt/vZNfQxjjYWHHyegydvtvOwGYl+PzFCkQnpDJs0RH6zznAjB5VSc/IpPsPu+hcLYhvu1YiLUNzNCya7KoMHbgUSb85+xncrCStyuVj+9kIPl5x3KRNQkoG9Uv48UHzkthZW7Lk4DVe+3E3GwY1oIiv4eLd77f9hEUlMq93TextLBm+6ChXIxONY2iteXn6DpztrZnVszruDjbM23uF9t9sY/tHjUzOPzsHLkXy+94rLD0YhpWlBc9XyM/Kd+uaJIBrT1hH2D+O+//yeziw5YOGWe67cieRW7Ep1Cl+9yJtb2NJtUKe7L10J8ckcHJaBhVHrSIzUxMa4Mrg5iUpnd/tvuckhHhybf9hBFcPbqLZ0Jk4efqzZ/ZErh3dSaEaLY1tMtPSqPryYNwDi5AUG8mOH0ex+pOevDBxOQBxt6+xYvQrhDbtSulWr3Hn4nG2Th9mcpyIiydYMqQDVV4eRIP+X5IcF83WaUNY90Vfmg+ded8405ITubBjBafWz+PqoS34FClLqRY9KFrn7hf5a8d2smzYizmOU7FTfyq/+O4DvTfxd8I5v305AaWfe6D293N28yLObl6Eg5s3QZUaUKXLQGwe8suBEOK/9f2nw1ny6/e8NewTylapSfSd25w9cdi4387BkcGfTsXLN4DL504yacg72NjY8tp7I4xtwq9eYt3S3xn17WysrG2wtrZh6BudcHZ145uFm0hJTmTKqIGkpma/lkVSYgIDX2lFibKVmLpkK3HRUUz88C0+GfQGY6bONbY7tHsrNnZ2fD57JRE3rvPJoDeYOmEI/UZ+DkByUgIdXu1DoeKlSUlO4pevJ/DRay/w89qDWNvYMHXJVt5sU4uJPy+lUInSWNtkXVotKSGexs+/xDsjPkOhWPTLdwzu8Ty/bT6Gq/v91/aNvH2TtUvmsvqP2Vw+f4oqdRrTb9TnPNeghbHNrG8+ZfY3n+Y4zic/LaZslZpZ7jt+cDeVa5neD1eu3ZBVC34lPS0NK+vsZ6pNGfU+nw/pi19gEC06dqfVS69hkcPj0EKIJ8+J2aO5fXQLFfv/gJ2HP2cWTCLy1C78Kjc3ttHpaRR9YSBO+QqTGhfJybljOfh1b6oPXwxA0p1r7P/8VQLrdSG4UXdir57k5K8jTY4Te+Ukez5+kaLtB1Km5yRSE6I58ctwjkx/l4r971/2KyMlkRt7/yRs63zuHN+Ga0gZghp2I1/1NsY2kad2seeTLjmOU7hN3/sma/92fdcyMlISyV8353VC0pPisHZ0u+94kaf3sPbNUlg7uOJRohrFOn6Iraus1ybukiRwHpWQks7P2y8wqVMFGoUafj3/tGN5tp2NYOa2C3zQoqSx7buNi1OvhGFG1OSXKlJ+xCoW7Q+jS/Vgvtlwljbl89O7XhFDY2/4pEM5Gn62kdtxKVhZKGKS0mgc6k+wl+HRtL8TtVn5fst5ahXxpn/jYgAU8nHm0NUoftt12dgmNMCV0IC7j3r1b1yMNcfDWX74Gu82Ls75W3FsOHmTpX1rU6Wg4eb2qy4VqTpmtbHP9rMRHLsWw/GxLbC3MZRJGNy8JGuO3WD+viv0aVA0y/iuRycxf+8V5u+9wrWoJJqU8ufrlytRt7gvlhb3ZrZn93qO9IzMbM/XyjL7m9RbcckAeDvbmmz3drLlRkxytv0K+zjxRecKhOZzJT4lne+3nKf15C2sH1Sfgt65+3igECJ3pCbFc3z1bBq+O5mgivUBaDDgK2Z2LWPSrmSTuzedrv7B1O3zGbN7VSf+9nWcvPNxdMVMnH0CqN37Y5RSeAQWIfraeXb98rGx34EFX1OkdlsqvPC2cVvdPhOZ26ceidG3cXDzzjLGa0e2c2r9PM5uXYK9iwdF67Wndu/xuOcvck9b3yLlePGbjTmes53z/Z/KWDWhJxd3rSI9JYngKo1pOGDKffvcT9G6L+DsG4ijhx+Rl0+x86exRFw8Qdvxfzzy2EKI3JGYEM/8GVPoM3wiLToaZn7lDy5kMtu1W98PjX/7Bwbx8luD+P37L02SwGlpqQz5fAYe3ob73r1b1nH57Enmbj2Jb0ABAPoM/5R3OmT9Az7AuiW/k5yUyJAvfsTByXDP+/74b+jfuQlhl86TP7gQAJYWlnwwcToOjk4ULBbKGx+M49PBb9Jr0BjsHRzvmQH7wcTpNC/lw8nDeylTuQZuHoZrsYu7R44zYSs8V9fkdb9RX7D5zyXs3rSGxs9nnVRIS01l65qlrF4wm71b11E0tBwtO/egfqsOuHncmyxo0+V16rV4IdsYALz9si8dEXn7JhVr1DfZ5uHlS0Z6OjFREXj6+GfZ79UBwylfvTb2Dk4c2LGRb8d9QEzUHV5554McYxFCPDnSkxMI2zSHMr0+N5ZBKPvml6zvU8Gk3T9nvzr4BlG6xwQ2D6xN0p3r2Hvm4/Lan7H3DCC021iUUjgFFCEh/Dxn5t/9gerC8m/xr96Ggi3eBMARKPXqBLZ91IiUmIhsk6F3Tu4gbOsf3Ni9DGsndwJqtCO021ic8hW+p61rwbLU+nhdjuds7eT2IG8NAFc2/IpP+YY5zt6NuXiEsC3zKPf21zmO5V2mHn6Vm2PvXYCk21c5Pf8Tdo1rT81xq7G0ts2xr3h2SBI4j7p8J4G0DE2Vgh7GbZYWikrBHpy5GWvStlLw3TaOtlaUyOfCmRuGNkfCorl0O4ElB8OMbf4u8X45Ip5KIZ50qlKAzlO3U7OoN7WKeNOyXAD53bMul3D2ZpwxKf3P4/8zCZyQks6k1adYe/wGt2KTScvIJCU9k5L5XI1jWCgoH3Q3gRDo4YCf693HKg6HRZGUlkHoUNMi6CnpmVyOSMj2fZuw4gTz9l6hSSk/lvStbSxhkZ3HURbiYVUK8aRSyN2ZHZVDPGkwcQMztpxn3Atl//N4hBCPLib8EpnpqfiVqGzcZmPvhGdwSZN2t84dZs/siURcOEZyXJTxghx3Owwn73xEXTmDb/FKJjUu/YpXNhnj9tnDRF+/yNkti43b9F+Ld8SEX8o2CbxwcBssbeyo8dpIyrR6Lcc6mla29rjlK/hA556TWr3GUqXLIKKvnWPnzLFsmTaE+n0/f6QxSzW/++igV0hJXPyDmd+/MbfOHcansFxDhXgSXT57ktTUFCrWqJdtm00rF/LHj19z7dIFkhLjycjIIDMzw6SNt1+AMQEMcPncKbz88hkTwAAly1XJcabp5XOnKFi8lDEBDFCqYjUsLCy4fPakMQlcsEQpQ/3ev4RWqEpaairXL1+gUInSXLt8gRmTRnHy0F6iIyPQmZlkZmZy8/rDrXIfFXGLGZNGc3DXZqIibpGZkUFKclKO4xzbv4tRfbri7ZePz35ZToXn6uR4DBc3D1zcPHJskxv+mdgvElqWjIwMfv3mU0kCC/EUSbxpuMd1L1LJuM3KzhHnQNNyjjEXj3B24efEXj5Gany0cWG5pDvXsPfMR/z1s7gVrmBy//nPMf8eI/HmJcJ3/rM8mTbGkV0SeNeYF7CwtqPES8MIatwjx3tcSxt7HP2yf2L3YcSFnSb67D4qD/w12zbx18+xd2JXQpr2xL9Ky2zbAeR7rq3xb5cCJXAtWIYNfStz6+A6/Ku0yL6jeKZIEvgZpLj/Ahh/05mal6oH8Uade38F+zvpOvmlivSqU5gNJ2+y+tgNJqw8wcxXqxlnFz+sUUuOsfHUTUa0KUVBbyfsrS15Z/Z+UtNNZ9zmdBaZmYYZtkveqX3PvpwWXHu3cTF8XexYsP8qNcevpVW5/LSvFGiccfz/HqUchI+zoSTF7bgUk6T57fgUvF0e/Jc6SwtF2UA3LtzOPrkthHj6pSUnsGRIRwLL16bR+99i7+ZNcswdFgxsSUZ66gOPo3UmoU1fptzzb96zz8kz69lYAC1H/capdb+z/YcRHF81i+L1O1C03gtZ9nlc5SAcPXxx9PDFI7AIdk7uLBjYksqd38PZOyDHfg/Dt0g5lIUl0dcuSBJYiKfU8QO7Gf3OK3TrN4S3hzXC2cWN7euW8+24D03a2Ts45m4gD7DI3N8+eLUd3v4BvDf+a7z88mFlacUrjcqTnpr2UIcc/15PoiJu0WfYp/jlD8LaxpYBXZqRnpr950KJcpUY9Ml3rFrwK++/0pLy1evQ+PmXqNWktUni+m+PWg7Cw9uXqIhbJtsiI25iaWWFq/uDP6ZcslwVEuJiibx90ySZL4R4uqUnJ7JnQmfDoma9p2Dr4kVqXCQ7R7dFpz/4NVHrTALrvURIs3vX1bDzyP6JikoDZ3FtyzxO/jaaKxtnE1CzPQE1nsfO/d4+j7McxJX1s7DzzHfPQnF/i792ll3j2pOvehuKd374Gu127n7YefiTeOPiQ/cVeZckgfOoIE9HbCwt2HMh0limISNTs+9SJO0qmq5suf9yJEFehpvihJR0ToXH0qFyIAClA904HR5HyH3KDPxdwuGdhkXpPHUH8/ZeyTIJXMTXmQOXo0yPfynS5PWei3foULmAcbG05LQMLkUkGEsdFPZxJlPDwStRVP5rRmxYVCI3YpKMY5QJdON2XAoWShnP7UGEeDsxpFUoH7Yoybazt5m39wovTt2Ot7MdL1QKpH2lQJOSC49SDqKApwM+LrZsOX2L8n8tVpeclsHu83cY3rrUA8estebk9VhK5stbq7AK8Sxx9Q/GwsqaG6f24eofDBiSvpGXTxpfR109S3LsHap3H4qrXxAA566cNhnHvUBRzm9bhtbaOIvhxql9Jm28C5Uh8vKph56pG1KlMSFVGpMSH8OZzQs5tX4eO2aOIX/ZmhSr14FCNVoY6+o+rnIQ/6S14VqbkZZ9rc5/I+LSCXRmhiwUJ8QTLKhwcWxsbNm/fSP5Q+6dmHBs/068/PKZzBy9ce3KA40bceM6t65fxSef4d735OG9ZGZmf28XVLg4f877hcT4OONs4GP7d5GZmUlQ4bsz2y6cOk5SYoIx8Xzi4G6sbWzIF1SQmKg7XDl/mnfHTDbOwj1z7CAZ6enG/tY2hkkLmRmms5n/39F9O+g7YhLV6zcDDKUX7ty6kWMfO3sHWnTqTotO3Qm/eonVC2bz05dj+XzIO9Rq0ppGbTtTqVYDLC0NJdUetRxEaPmqbF2z1GTbvm0bKFa6Qo71gP/fuROHsbG1w8nF7YH7CCHMy8E3GGVpTdS5/Tj4Gu5f05MTibt6GkffYAASrp8lNS6SYp0+xMHH8GRG+B7TJ3qd8hXhxt6VJve4Uef2m7RxDS5NXNjph56p61u+Ib7lG5KWEMP1nYsJ2/oHp+eOw7NkDQJqvoBf5eZY2RtyAI+rHERGajLXti0guMlrqCyePokLO83ucR3wr9aakl1HP9T5/C019g7JkTewlYXixD9IEjiPcrS1oluNEMYuP4aHkw0FPByYvvk8t+OS6V7D9Iv/F2tO4+lki6+LHZ+vPoW1pQXPVzDcCPdpUJQWX2xm0LyDdH0uBCdbK87eimPtsRtM7FSey3cSmLXjIk1K+ePnas/lOwmcDI+hWzaLmr1euxAtJ2/mq7WnaVkugB3nbvPn0XCTNoW8nfjzyHWalvLH2lLx2epTpKTfvQEu7OtMveK+DJx3iM86lsPO2pIRS45iZ21pbFO7qDdVQjzpNmMXw1qFUtjXmduxyWw4dYvaRb2pVijnWQcWForaxXyoXcyHhJR0lh26xry9V/hyzSn2DG9inLn7KOUglFL0rF2Yr9adprCPEwV9nPlyzSkcba1MEvXtv9lG+QLuDGkVCsBnq05SMciDgt5OxCWn8cOW85y4HsOEDjKDTYinlY29EyUbd2HHj6Oxd/XC0dOPPb9NNPny7+ydH0trW44s+4EyLV8j8uoZdv+j1i9A6ebdObjgW7ZOG0Lplj24c/Ekx1b+ZNKmYse+zH+3KRunvEepZt2wdnAi6upZLu5e/UClFmydXCndogelW/Qg+tp5Tq2fx+5fP+HU+t95fsIi4NHLQVzcvZrk2Ch8ipTF2t6RO5dPsf2HkfgVr2QybuTl02Skp5Icc4e05ARunz8KgHeh0gDcOH2AtZ+9RaP3v8WvWAVirl/k9MY/CKrcEHtXTyIvn2bbD8PxLlQa/5JV/3W8Qojc5eDkzAs93mb6p8OxtrGlbNWaxEbd4fTRg7Tt2ov8IUWIuHGdtYvnEFqhGns2r2X90vn3HbdizfoUKFSMcQNep8+wT0lJTuLrMYOwtMr+K1Kjti8y84sxjBvwGq8OGE58TDSThvShdtM2xlIQABkZ6Xwy8A269fuIiJvhTPtkGC1f7IG9gyO2dva4enixfO6P+OTLT8SN63z38Ycmx3Xz9MHWzp49W9bhlz/or+Sn6z3xBIYUYe3iOZQsX5mkxASmfjwEa+usF5HLin9gMN37D6F7/yEc2budVQtmM/qdV+jYs58xqf6o5SDavNyTRb9MZcqo92nV5XWO7dvJqj9mMfyrn41tFv78HYt+nsqsDYbF/ravW0Hk7ZuEVqiKrZ09B3du5scvxtCq86vY2EptSyGeFlZ2jgTW7cypOeOwcfHEzt2Psws/B333HtfOKwALa1surfmR4EY9iLt+1qTWL0BQw1e4uHIaJ34ZRlCj7sRdPcWVdbNM2hRq3Yftw1twdMYgCjToipWdE/HXz3HrwBpKvz7xvrFaO7oS1LAbQQ27kRB+gbCt8znzx0TCts6n2hDDZ8rjKgcRvmc5aYmxBNa998m5uLDT7BrbHs+SNSjUpi/J0XefpPi7dnByZDi7xnWg+Isf4Ve5OenJCZxZ8Bn+lVtg6+5L0u2rnJo7HltXL5MF+ISQJHAeNrS1IWnY/7cDxCalUSq/K3PefA5fVzvTdq1CGbn4KOdvxVPM35lZvarjaGv4v0bJfK4seqcWn6w8wfNTtpKhNUGejjQrbXj818HGkgu34+n50x4i41PxdralXcXAbBdeqxjswecvVuCzP0/y+ZpTVC/kzXtNizNkwRFjm5FtSzNg7gHaTNmCm701PesUJiXNdBbEV10q8N7cg7T/dhsejra816Q4EXF3Z4cppZjdqzoTVp7k/d8PEhGfgrezHZVDPOj41yznB+Voa8WLVYN4sWoQVyMT8XB88Bvr++nToAjJaRl8uOAwMYlplA9yZ27vGiYlKy5FJJDP7W6949ikNN6fd5DbsSk421tROsCNxe/UokLQf1+rTQjx+NTsOYq0lERWjumGla09ZVq/Tnry3XIz9m5eNHzva3b+NI6jy37EM6QkNXuNYenQjsY2zj75aT7sJ7ZNH8axlT/jU6Qsz/UYxpqJvY1tvEJCaffpUnb98jELBrVGZ2bi6h9EweoPf4PoFlCIaq98SNWuHxB97dyjvQH/YGljx7GVPxF59QwZaak4e+ejYPUWVOxo+mjd0uEvEnfrbt3LuX0Mj9O982cEAOkpiUSHnSM9xfA+WljbcPXQFg4vmU5qUgLO3gEEV2lElS4DsbC0RAjx5Oo1eAzOrm78MuVjbg+5hruXD03aGR7JrdGwBS/2epcpoweRmpxEpVoNePXdYXwxLOfHcS0sLBg77XcmfvAWvZ+vjU++QN4eMoEx/bpn28fO3oGJvyzj69EDebNNLWxs7ajZqCXvjPjMpF25qrUILlqS/p2bkpKUSO2mbXnzw/HG4478ehZfjXyPHo0rEhBciLeGTGB477sLI1lZWdF35CR+/mo8P08eR5nKNZj8+5p74hn86VQ++/BterZ8Di9ff7r3H0pMZMSDvq0mylSuQZnKNeg38nMib9/8V2NkxT8wmE9mLubrMYNYMvt7PH386TtiksnieDGRd7hy4YzxtZWVNYtnTeebsYPRmZn4Fwjh1XeH0faVe0sZCSGebCW6jCAjJZH9n7+Kpa09wY1fJSPl7j2urYsXZd+czOnfP+by2p9wDixByZdHsueTl4xt7L3yU+HdGZz8dSRXNvyKa0gZir/4EYe+7WNs41KgJNWHL+LMvE/YNaYdOjMDB58gfCs1e+iYHf0LUqzjYIp2GERC+PlHewOycHXDbLzL1MXeK/89+8J3LSM1NoLwXUsI37XEZF+L3wwT6DIz0kkIP09aYhwAysKCuCsnubZ1PmkJsdi5++BZsgYV+k0zzmIWAkD9vRiMMB8PJ9tDP3SvUrZGkawX4xHCXG7EJFF93NqYxJR0N3PHIoQ52bt6bqzf78u6hZ6TX9LFg1k24qXYS3vW9NRazzN3LEI8SZzd3C9++duq4CKhefcJpo/f60lM1B0m/LjQ3KGIvyQnJdK8tG9aelra45vNIcRTSCk1ski790YUbf++uUMReci6t8rFpUTfrKS1PnP/1sKcsi9YKoQQQgghhBBCCCGEEOKpJ0lgIYQQQgghhBBCCCGEyMOkJrAQQgghhBBCPCYfTvre3CEIIYQQQtxDZgILIYQQQgghhBBCCCFEHiZJYJHrnp+ylQ//OPxQfSqNWs23G87mUkRCCJE3LBzUmk3fDn6oPj91K8+BP77OpYiEECLv6tepMV8O7/9QfTrVKMbc6V/kTkBCCJGH7RzTjmMzP3qoPhv6Vub88u9yKSIhnn5SDkLkuh9frYq1pXqoPqveq4uDjWUuRWQQFpXIh38cZtvZ29hbW/J8hfyMaFMaG6vsfxuZteMiiw6EcSwshtjkNPYMa0wBT0eTNkeuRjN22TEOXYnG0gJalA1gVNvSONrKPzchxOPVfNjPWFg+3LWl0+S1WNk55FJEBnG3wtj0zSDCDm/DytaOonVfoObro7C0zn5R9vWT+xN2aBsJkTewtnPEv2RlnusxHI8CRQGIvXmFvb9NIuzINhIib+Lo4UuR2m2p8tL7WNnaG8e5efoAO34ay62zhwDwKVyW6j2G4VesQq6esxAi7xszbS5WVtYP1Wfa0m3YOTjev+EjuHntCl8Mf5eDOzZha2dPgzYdeeujCVjbZH/N/ZvWmkHd27Jn8xpGfTubus3bGfddvXCWqR9/xNF9O0lNTSGkSAm69x9K1bqNAYi+c5ux/V/lwuljxEZH4ubpTY2GLek5cBROLq65dr5CiGdDxXdnYGH5cNfcGmP/xMo2d+9zkyLCODbzI+6c2IaFtR0BNdpRostwLKyyv+ZeWT+LazsXE3vpGOmJsdSbvAcH70CTNhv6ViYpIsxkW6FWfSjeeQgAsZePc37p10Se3kNqXCT2XgEE1u1MwRa9URYyv1M8GMlKiVzn7nj/G9D/5+VkmwuR3JWRqXl5+k7cHWxY8k5tohJT6Tt7PxoY/0LZbPslpWZQt5gPTUv5M3zx0Xv234hJouN322hVNoDxL5QlLiWd4YuO0Pe3/czoUTUXz0gI8Syyc3Z/6D72bl65EMldmRkZLBvRGTtnd174bBnJsVGsm/Q2aE2dtyZk28+3SDmKN+iEs3cAyXFR7Pn1UxZ/1I5uPx3E0sqaqKtnyczMoG6fibjlK0jk1TNs/GoAybGR1O9nmGWXmhTP0mGdCKrSiA5vrQat2Tf3c5YO7UD3nw9h4+Ccq+cuhMjbXNw8HrqPm6d3LkRyV0ZGBoNfbYermwdT5q8jJiqSj997Ha01/Ufdfwby799/iUU2yYMPXmtHvsAQPp+9EjsHR5bO/p4hvTrw89qDBAQVRFlYUKtJa3oOGoWruyfXLl/gy2H9mRh5m1HfzH7cpyqEeMbYOD38fa6tS+7e5+rMDPZO7Iq1kzvVhy8mNT6Kw9/1Q2tNqe7jsu2XkZqEd+k6+FVswolZI7JtV6TdAAo07GZ8bWV390fEmItHsHHxpNxbU7D3DCD6/EGO/PA+OiOdwm37PZ4TFHmeJIHFI0lISWfw/EOsPHIdBxsretUpxJ6Ld/BwtOWrLhUBQzmI4v4ufNzekFytNGo1XaoHcT0qiUUHwnC2s+L1OoV4u35R47iVRq3m1VoFeat+kVyJe9Opm5y+Ecu+4U0IcDf8UjisdSjvzT3Ihy1K4myX9S+OveoWBuDQlags9689fgMLpZjQoRyWFobZz592KEe9Tzdw8XY8Id5OuXA2Qoi8KC05gY1fD+TC9hVY2TlQru0bhJ/YjZ2LJ43eM5RzWDioNR7BJaj71ieAodRDaNOXibt9jTObFmLj4Ey5tr2o0P4d47g/dStPmVavUaF9n1yJ+8qBjdy5fIruPx/C2TsAgOdeG8GGL9+lerch2DhmnYgt1by78W8X3wJU6/YRc96qQ+yNS7jnL0JQpQYEVWpgbOPqH0zciwPY/cvHxiRw1NWzJMdFUfXlwbj6BQFQ9ZUPOb3xD6LCzuFbtHyunLMQ4umXlJjA50P7snXVEuwcHGnfow/H9u/E1d3TuNBbv06NCSlWkv6jvwQMpR5avNiDW9fDWL9sHo5OzrzQ4206vzHAOG6nGsV4vtubvNjr3VyJe++WdVw6c4J520/jk88wq+zND8czcXBver4/Ckdnl2z7njy8jz9mfsP3y3bQtlKQyb7oyAjCLp7jvXFfU7hkGQB6DR7L/BlTOHv8EAFBBXF196TNyz2NffzyB9Gmay9mf/tZLpypECIvSU9O5NiPg7mxdyWWtg6ENOtJ1Jm92Dh7UPbNyYChHIRz/uKU6jEeMMyUDaz3Esl3rnN952Ks7J0JbvI6hVq9ZRx3Q9/KBDV+lUIte+dK3LePbCIu7DT1v9qLvafhPrf4S0M5+v37FOv4AdbZTDgIadYLgOgLh3Ic39LOCTs3nyz3BdbtbPLawTeImEtHCd+zQpLA4oFJElg8klFLjrLzfAQzX6uGr4sdX6w5xe4Ld2hWOl+O/aZvOsf7zUqwtn491p+8ydCFR6ga4kmlEM8HOu6u8xG8NG1Hjm36NSpGv0bFsty371IkRXydjQlggLrFfUlJz+Tw1WhqFvl3szZS0jOxslTGBDCAnbWhrMXuC3ckCSyEeGDbvh/O9aM7aD7sZxw9/dj722dcP7aLgs+1yLHfoUVTqfLyYF78ug+X965ny9QP8Q+thn+Jyg903GvHdrJs2Is5tqnYqT+VX8w6oXHj5F48AosaE8AAQRXqk5GWwq1zh8hfttZ9Y0hLTuDkmt9w9smPi0+BbNulJsZh6+RmfO2evzD2rl6cWD2bKp3fA+D4qlk4++THM6j4fY8rhHh2fTv2Aw7v3sqYab/j5evPL199zJG926nVuHWO/ebPmEKPd4fy4hs72b1pNV+NfI/SlZ6jVMVqD3Tcw3u2Mbh72xzbdHl7EF3fHpTlvuMHdxNUuLgxAQxQpXZDUlNTOH30IBWeq5Nlv8T4OMb0687747/B3evehIOruydBhYuzZtFvFC9bEVs7e5bNmYGDozOlK1XPcsyIm9fZumoJ5arWzPF8hBDi5OyRRJ7aScUBP2Ln7sfZhV8QeWo3fpWb5djv4p/fU/SF96nZ8i1uHd7AiZ+H4lGsCu5FKz3QcSNP7WLPJ11ybFO4Td9sk6pRZ/fjlK+IMQEM4F2mLplpKcRcPIJXaI0HiiM7F1d8x/mlU7D3zIdf1ZYUavlWjmUm0pPisHZ0e6RjimeLJIHFv5aQks6c3ZeZ0qUSdYoZbh4/f7ECFUauum/fOsV8ea1WIQBe93ZixpbzbD17+4GTwGUD3Vk/sH6Obdwcsr9Y3o5LwdvZtOSEp6MNlhaK27HJDxRDVmoW8Wbk4qNMWXeaN+oWITE1nbHLjwNw6xHGFUI8W1KT4jmx5jcavfcNBSrUBaDBu5OZ2bXMffsGVqhL2davA+DWpiCHl04n7NCWB04C+xYpx4vfbMyxTU5lKBKjbuHgbvpDmp2rJ8rCksSoWzmOe2T5j+yYMYq05ATc8hem7ccLsbTJujxQ7M2rHFzwDZU69Tdus3Fwpt0nS1gxpiv7530JgLNPAdqM+8OkbrAQQvxTYkI8K+f/zEeTfqByLcMTB4M+nUr7aoXv27dSrQa062aYcZa/+1ss+OlbDuzY9MBJ4OJlKvLDyt05tnFxy/6aG3n75j1JXFcPLywtLYm8fSPbfpOGvEOVOo2oVq9JlvuVUkz6dTlD3+hE81I+WFhY4OzmwSc/LcbTx9+k7ah3XmH72uWkJCdRvUFzPvhseo7nI4R4tqUnJ3B101zK9f4K79KGH6rK9Pqc9X3uv36Dd+k6BDd5FYAQv9e4tGoGEce3PnAS2LVgWWp9vC7HNtb/mGDw/1Kib2Hranqfa+NsuM9Nicn5Pvd+gpu8hktwaWyc3Yk+f5BTc8aRdOsqZXpNyrJ9zMUjhG2ZR7m3ZcFn8eAkCSz+tUsRCaRlaMoXuHtj6mhrRXH/7B87+1vJfKZtfF3tiIhLeeBj29tYPpGzaov7u/BVl4qMWHyUj1ecwMrCgtdqF8Tb2RalHm5xPCHEsysm/BKZ6Wn4/mMxM2s7RzyCSty3r1dIqMlrRw8/EqMjHvjYVrb2uOUr+ODBPkbF6rWnQPk6JETe5OCCb/hz3Gu0n7QC6/9byC4x6hZLh3UksHwdyj1/93G/9JQk1n3RF9/ilWg8cCo6M5MDC75hxeiudPpqHdZ2ubs4kxDi6XT98gXS09IoUe7uj2X2Do6EFCt5376Fipc2ee3l40/UnQdPBNja2ZM/uNCDB/sYrF74G+dPHmXa0u3ZttFa88XQ/ri4eTJl/jps7exZPvcnhvfuzLSl2/D2uzsLrs+wT+nebwhhF88y/dPhTBk1kPc/lqSEECJriTcvoTPScCt0t0yXlZ0DzoFZP8X7T84FTO+F7dx9SY2988DHtrSxx9Ev5MGD/Q8VbPGm8W+XAiWxsnfm4FdvULzzEGycTWvSx18/x96JXQlp2hP/Ki3/61DFU0ySwMIsrCxNF6BQKDL1g/d/1HIQ3s627Llo+mFxJyGVjEyNt4vdgweShXYVA2lXMZDbcck42Bj+iU3bdI4gL0k+CCFyn4Wl6Ue7UgqtMx+4/6OWg3Bw9yH8xB6Tbckxd9CZGTi4Z13j7G+2ji7YOrrgFlAIv+KVmN6hMOe3L6d4g47GNgmRN1n0wfN4BpWg8cDvTH5gO71pATHhl2g/aSUWloZSPE0GTzOMs2MFxet3vOeYQgjxKKyss7jmZj74NfdRy0F4ePtybN9Ok20xkRFkZGTg4e2XZZ8D2zdy6exJmoWaLqA0qk9X/qjwNV//sYEDOzaxY/0Klh26jrOrGwADxpZn37b1/Dl/Fq+884Gxn6ePH54+fgQVLoazmzvvdGjIK+8MNilRIYQQj4Oy/L+1ex7ymvuo5SBs3XyIOrPXZFtqnOE+19Y15/vch/V3kjzh5iWTJHD8tbPsGteefNXbULzzkMd6TJH3SRJY/GvBXo5YWyoOXokyJjgTU9M5FR5LkGfuJjwftRxEpWAPvlx7muvRSeRzMzwivOX0LWytLCgb6PZYYvR2NiSTf9t1CVtrS+oUzd3VoYUQeYerfzAWVtbcPHMQV/9gANKSE4m8fNL4Orc8ajkIvxKV2Tv3c+JvX8fJ21Af/srBTVha2+JTuNyDB6I1oMlIu/uUSELkDRYObotnUHGafDD9noR3enISSinUP1a6VxYWfyVlHuKXRiHEMyVfUEGsrK05dXgf+QoYZoglJyVy8fQJAgrk7pMRj1oOIrR8VWZNmcCt8DB8/PMDsHfbemxsbClWOuvFMF8fOJJOvfqbbOvRpBK9P/qYmo1bAYbzB7CwMJ24YWFhQWYOCZe/r7Wpqak5npMQ4tnl4BuMsrQm+sIhHHwNi1JmpCQSd/U0jr7BuXrsRy0H4V6kIucWf0nSnevYexrucyOObsHC2hbXkPuXbXsYsZcNZSX/uVBcXNhpdo/rgH+11pTsOvqxHk88GyQJLP41R1srOlcNYuyy43g62eDjYseXa06TqTW5XfngUctB1C3uSzE/F96ZvY+RbUoTlZDK6KXH6FI9GGc7w6+LBy5H0nf2fr7qUpEKQYZf3m7FJnMrNpkLt+MBOHMzjtikNALcHXB3NCSdZ2w9T6VgD5xsrdh8+jZjlh5jSMtQXHNISgshxD/Z2DtRsvFL7PhxNPYunjh6+LJ37iR0Zmaul5Z51HIQBSrUwzOoOGsnvUXN10eTFBfF9hkjCW3aFRtHw4rJN04fYO1nb9Ho/W/xK1aB6OsXOL9tOYHla2Pv6kV8xHX2z5uMpbUNwVUaAxB/J5xFg9vg6OFHrTfGkRRz92kOe1cvLCwtCaxQl+0zRrLp64GUbdMLrTPZP28yysKCwAdYkE4I8WxycHSieYduTJswFFcPLzx9/PhlygTDUxS5fM191HIQlWs3JLhoScYPeJ23hkwgNjqSqeM/okXnHjg6G8qvnTy0l/EDXuejz3+gRLnKePsFmJRz+JtPvvzGJHhohaq4uHkwYWAvuvX96K9yED9y/cpFnmtgWLhpx/qVxEZFUrR0eewdnLh09gTfjf+IkuWr/OclLoQQTw8rO0cC677IqTljsXH2wNbNh3OLJ4POBHL3mvuo5SC8y9TFOX8xDn/XlxIvjyAtLoqTv40hsF4XrB0M97nR5w5y6Lt3KNd7Cm6FDT/GJUffIiX6FgnhFwCIDztDWkIM9l4B2Di5E3VmH1Hn9uNZsgbWDi5EXzjEiVkj8K3YBHsvww98cWGn2TW2PZ4la1CoTV+So++WHvpnoliInEgSWDySEW1Kk5h6iFd+2IWjjRVv1C3E7bgUbK0szR1ajiwtFL/2qs4H8w/RevIW7KwtaFcxkOFtShnbJKVmcO5WPEmpGcZtP2+/yKTVp4yvX55uePzuy84VeLGq4VfMg5ej+OzPkySkZFDY14lPO5ajQ+XsV7cXQois1Hh9FGnJiSwf9TLW9o6Ue/5NEqNuY2md9UJpTwoLS0tajZrDpm8G8sf7LbCysaNovfbUfG2ksU16SiLRYedITzHMNLO0tiXs6HYOLvyWlIQYHNy8yVeqOu0/X4Wjhy8AVw9sIvraBaKvXeCnV8qaHLPbTwdw8S2AR2ARWo6czZ7ZE5k/oClKKbwKlqL1mN+Ns5KFECIrvYd8TFJSAh+93h57Ryc6vNqHqIhb2Ng+Wpmw3GZpacknPy7ki2H96dO+PrZ29jRs24neH35sbJOclMSVC2dITkp64HHdPLz49Ocl/DBxJO++1Iz09DSCChVj3PR5FC1lSGrY2NqxZPb3XDl3mtTUFHzy5adW49Z0eev9x36eQoi8pUSXEWSkJLJvUjcsbR0Jad6LlJjbWDzh97nKwpLKA2dxbOaH7BjZGksbewJqtKP4S8OMbTJSk0gIP09G6t1r7pV1v3B24d0F3vZOfBmAMm98SWCdTlhY2xC+aylnF35OZloq9l4BFKjXhUKt3jL2Cd+1jNTYCMJ3LSF81xKTuFr8Fp5bpyzyGKW1PB5pbh5Otod+6F6lbI0iT3+5gJT0DCqNWs1b9YvQu14Rc4cjHtGNmCSqj1sbk5iS7mbuWIQwJ3tXz431+31Zt9Bzzc0WQ0ZqCj91L0/5F96mwgtvmy0O8WCWjXgp9tKeNT211vPMHYsQTxJnN/eLX/62KrhIaNn7Nzaj1JQUOtUoxotv9KdTz/7mDkc8ouSkRJqX9k1LT0uTR/PEM00pNbJIu/dGFG3/ZP1Yk5GWwsa+lSnY8i2TBdLE02HdW+XiUqJvVtJanzF3LCJnMhNYPJKjYdGcvRlH+QLuxKek8/X6M8SnpNOmfH5zhyaEEE+12+eOEHn1DL7FKpCWGM/++V+RmhhP0drPmzs0IYTIc84cO8Tl86coUbYyifFxzJk6icSEOOq1bG/u0IQQIs+JuXSU+GtncStUnvTkeM4v/Yb05Hj8q7U2d2hC5GmSBBaPbOqmc5y/FY+VhSI0wJXF79QyLrYmhBDi3zu06Duiws5hYWmFV8FSvDBxqZQ1EEKIXDL/hylcuXAGS0srCpcsw1fz1hoXWxNCCPF4XVw5jfjw8ygLK1yCQ6k+bJFxsTUhRO6QJLB4JKXzu7HmvXrmDkMIIfIc78Jl6PTVenOHIYQQz4Sipcoxfdl2c4chhBDPBNfg0tQct9rcYQjxzLEwdwBCCCGEEEIIIYQQQgghco8kgYXZXLmTgF//RRy6EmXuUIQQ4pkXe/MKU5p5cfPMQXOHIoQQeU741cvUCbbn1JH95g5FCCHytMTbV1nxkj/RFw6ZOxQhnjhSDkKIHNyMSWbkkqMcDYvmwu142lcqwFddKpq0mbv7Mv3nHLin76WJrbGztgQgI1Pz2aqT/LHvKrdik/FxseOFioG837Q4VpaG32L8+i/KMobuNUOY0L6c8fX5W3GMW36cbWciSMvIpLCvE9+8XImifi6P6ayFEOLJlZGWyt45kzi9YT7xd27g4O5NhRfepmybXgAsHNSaa0d33NPPo0AxukwzPOp97M9fOLV+HpGXT6K1xrtQaap1/ZB8par9p+cihBBPkoM7t9C/c5N7tv+y7hBBhYsB0K9TYw7t3npPm+AiJfh57d374c1/LmLGpNFcv3KBfAUK8vr7I6ndtI1xf53grNcPadv1Dd4d8+UjnokQQjzZkqNucnL2KGIuHiHhxkXy12pP2Tcnm7TZOaYdkSd33tPXKaAodSZuvmf7tR2LOPT1W/iUb0jlgbOM28/88RlnF04yaWvr6k3D7448prMRTxNJAguRg5T0DDwcbejToCi/7ryUbTt7G0t2D21ssu3vBDDA1+vPMHPbBb56qSLF/V04GR5L39n7sbGyYECT4gAcGd3MpP+hq1G88v0uWpe7uyDJ5TsJtJq8hQ6VC/DH28Vxtbfm7K04HG3ln7IQ4tmwakJP4iOuU6/v57gFFCQx6jbpqUnG/c2H/UxGWqrxdUZaKr/1rkXh2neTD9eObKdI7bbkKzkeKzt7Di2aypKhHen8zUbcAgr9p+cjhBBPmp/XHsDZ1d342s3T2/j3mGlzSUu9e41NS02hR9PK1GvxgnHbsf27GNWnKz3eHUatpm3YumoJI9/uwtd/bKBk+SoALNxz0eSYp48e4MPXXjAZRwgh8qrM9FRsnD0o1Podrmz4Ncs2Fd+dQWZ62t0+aSls/aA+/tVa39M28eZlTv02Bo/iVbMcy9G/ENWGLTS+VhZSFOBZJZmjZ8DO8xGMWXqMU+GxWFooCvk480XnCpTwdyEyIYWP/jjC7gsRRCWmUsDTkd71itC5apCx//NTtlLE1xl7G0t+33MZC6V4t3ExXqkRwojFR1m4LwxnOys+aFGSDpULAIZSD1XGrOHbrpX4adsFDl+NJtDDgbHtylC3uG+2sZ6+EcvopcfYdf4OdtYW1Criw+jnS+PjYgfAyesxDFt0lENXosjUmmAvR0Y/X4aaRbyzHfNRFPB0ZNwLZQFYfvhatu0UGGPMyt6Ld2gU6kfjUv7GcRuX8uPA5Uhjm//vv/poOIW8nXiusJdx24QVJ6hb3IdRbUsbtwV5OT7UOQkhzOva0R1snzGKyMunUBYWuOUvTMN3v8IzuARJsZFs/nYw14/tIjkuCle/IMq/8DYlG79k7L9wUGvcA4tiZWfPyTVzUBaWVO48gNLNu7P1+2Gc3vgHNg7OVO82hOINOgKGUg8/d69A40FTObp8JrfOHsLZN5A6b35MgYrZL+4Zefk022aM4PqxnVjZ2JO/XC1q9RqLo4fhOh5x8QRbpw0xlJDQGhf/YGq/MZb8ZWvlynt3Zf9Gwg5t4ZUf92Hv6gmAi28BkzZ2zu4mr09vmE96ShIlG3cxbmsyeJpJm7p9PuPCzj+5vG+DJIGFeMod3r2NqR9/xMUzJ7CwtCSwYBEGfzqNgsVCiYm6w5fD3+Xo3u3EREWSr0AInXr2p3nHV4z9+3VqTIHCxbGzt+fP+bOwsLTklT6Dad2lJ9+MHcy6xXNxcHbm9fdH0aSd4docfvUyL9YqztDJM1kyazqnjxzAL38QfUdOonLthtnGeunsSb4b/xGH92zD1s6eCs/Vpc+wT/H08QPg/KljfD16IKeO7EdnZpKvQEH6DJ9Ihefq5Op76ObpjZuHV5b7XNw8TF6vXTyHlKREk/fwjx+/plz1OnTtMxiA4D7FObhzM/N//JoRU34BMJ7j37atXU5gwSKUq5Y7nx9CiMfvzsmdnJozlrirp1AWljj6F6LsG1/gHFic1LhIjv80hMjTu0mNi8LBpwAFW/QmsO6Lxv47x7TDKV8RLG3tCdv8O8rCgsJt+1Og4Suc/HUk17YvxMreiWIdPyB/rQ6AodTDxn5VKPf2N1xe+xMxF49g75Wf0G5j8S5TN9tY48JOc/K3MUSe2oWljR1eobUo0XUUdm4+AMReOcmJWcOJuXAInZmJg28wJbuOxiu0Rq68dw7egYR2GwvAjT3Ls2xj42R6T3tt2wIyUpJM3kOAzPQ0Dn7dm2IdP+DOie2kxkXy/5SllfFcxbNNksB5XHpGJt1/2EXnakF827USaRmao2HRWCrD/pS0TEoHutKnQRGc7azZcuYWg+YdJL+7PbWK3r1ILNx/lTfqFmblu3VZfSycYYuOsuHkLeqX8GH1e3WZt/cK7809SO2iPvi63k1mjll6jJFtS1Mynyszt12g+4xd7BzSGH+3ex8BuxmTzPNTttK5ahAjWpciPVPz8YoTdPthFyv618HCQtF71j5C87ny54C6WFkoTobHYmeV/a9Yk9eeZvLa0zm+R7+98RzVCmV9o/ugktMyqDhqFZmZmtAAVwY3L0np/G7G/VULevLTtoucvRlHEV9nTt+IZfvZCN5pWDTL8RJS0ll88Brv/TVLGCAzU7Pm+A3eaVCUzlO3GxPrvesVoW2F/FmOI4R4smRmpLNidFdKNu5Ck0FTychI5/a5wyiLv0rHpKbgU7gMFTv0xcbBmasHN7Nxyns4e+cnsHxt4zinN/5B+Xa96fjlai7sWsXWaUO4vH8DQRXr0+mrdZxa9zvrv+xPYPnaOHrc/aK9fcYoavUag2dIKEeXzWD56K68MmMvTl7+98SaEHmDBYNaUbJxF2q+PprMjDR2/jSOFaO70uHzVSgLC9Z8+gZeIaF0nLwWC0tL7lw8iaVNDj+Izf2C/b9/meN71GrMXAJKVc9y34WdK/EpWp6Di77j1LrfsbK1I6hSQ6p3H4KNvVOWfY6vmkVQpfo4ewdke8zMtFTSU5OxdXbNMTYhxJMtPT2dj3p2oEWnbgyd/BPpaWmcOX4QC0vDNTY1JZmipcrx0pvv4ejswr5tG5g0pA++AYFUrHH3B7F1S+bS8bW+TF28he3rljNl9EB2b15L1TqNmLZsO6sX/MrED3pTqWY9PH3uXj+nfTyEt4Z+QqHipVk0ayof9ezAb5uP4e137/Xnzq1w+nZsRPOO3eg95GMy0tL4/rORDOnZgW8XbcbCwoIx/bpTuERppi7eiqWVFRdOHcPG1jbb85/1zafM/ubTHN+jT35aTNkqNXNs80arGqSmphJcpDhd+3yQY9J5+ZyZVKnTGJ98gcZtxw/upl233ibtKtduyKJfpmY5RmJCPBuWzad7v49yjEsI8eTIzEhn36QeBNbrTLm3v0GnpxFz6Sj8NcM0My0Fl+DSFGz1Ntb2zkQc28LRGYOw9wrAq9TdH3uu71hISLM3qDF6BTcPrOHErOHcPrIR7zL1qDl2FWFb5nH0+/fxKlUbO/e7k8lOzRlLiZdH4hJYgktrf2LfpB7U+2IHdh733tMmR91k5+jnCaz3EiW6DEdnpHP69wnsn9Sd50YtR1lYcOibt3AuUJIao1eiLK2Iu3oSS+vsr7fnFk/m3JKvcnyPqgyejUfxx1dq7MrG2XiXrYe9p+lnyul5E7D3DiR/7Y7cObE9y76Jty6z7q1yWFjb4FaoAsU7fYiDb1CWbUXeJkngPC4uOZ2YpDQah/oT7GX4glzE19m439/Nnrfr301EdvUKYdvZ2yw6EGaSBC7m58zAZiUAeLNuYaasO4O1paJnncIADGhSnK/Xn2HPxTu0Knf3otStRkHalDckKMc+X4ZNp27y8/aLfNCi5D2x/rT9AiXzuTKsdSnjtildKlL8oxUcuhpFhSAPwiIT6V2vsPEcQryz/tL/t1eeC6F1uey/+AP4uWZdk+xBFfZx4ovOFQjN50p8SjrfbzlP68lbWD+oPgX/iq9Pg6LEp6RTe8I6LJUiPVPTv1ExetQsmOWYC/dfJS09k45V7s5wi4hPISElncnrTjO4WQmGtAxl29nbvP3rPhxtrWgU6pflWEKIJ0dqYhwp8TGEVG2Ca74QADwCixj3O3n5U6H9O8bXrv7BhB3eypnNC0ySwJ5Bxan6smGGVfl2b7F/3ldYWlpRru0bAFR+6X32z/+K8ON7KFzr7iNjpVv0oEjttgDUfnM8Vw5s4OiKmVTvdu8X76PLZ+IVEkqN10YYtzV6/1u+71iYm2cP4VesArE3r1K+3dvGc3DLl/U17e7xu1PkH2UZsuLkee/N+99iblwm/PhuLK1taD70J1ISYtjy3Qck3LlB86Ez72kfFXaOa0d30GL4Lzkec+cv47G2c6Rg1WY5thNCPNkS42OJj43muQYtCAgyXI/+rmUL4O0XQOc3Bhhft37pNQ7u2MT6pfNMksDBRUrQ492hAHR8vR+zv5uElbU17V/tA0C3vh/x29RJHN23k7rN2xn7tXm5F/Vbtgeg74hJ7N2yjiW/fs/r74+8J9bFv35PoRKlefPDccZtQz7/gZZl83H6yH5KlKvMzWtXeLFnf+M55A/O+UmFNl1ev285BW+/fNnu8/TxY8DYryhetiJpqamsWTSHAV2aMfn3NVkmjq9eOMuh3VsZN32eyfbI2zdx9zJ98s/dy5fI2zezPO66Jb+TnpZK0xdezjF2IcSTIz0pjvTEGHwrNMLRNxgAp4C797R2Hv4UavWW8XUB365EHN/O9R2LTZLATgHFKNr+fQBCmr/B+aVTUJbWhDTrCUCRdgM4v+wbos7sxb9qy7vjNXyFfH+VRQh9ZQwRRzZxed3PFOv4wT2xXl73My5BoZToPNS4rVzvr1jTqwQxFw7jVrg8SRFhFGzR23gOjn4hOZ5/gYavZFmW4Z/sPB7f9/P48PNEntxJxQGm97u3j2wifNdSan28Ltu+boXLU/bNyTjlK0xqTARnF3/JjpGtqP3pJmycPbLtJ/ImSQLnce6ONnSqUoDOU7dTs6g3tYp407JcAPndHQDDgmVT1p1hycEwbsQkkZKeSVpGJs8VNi2vUDLf3dlRSim8nG0pke/uQmTWlha42tsQEZ9i0q9S8N2LioWFokKQB2duxmYZ65GwaHadj6DgoKX37LsckUCFIA/eqFuY9+YeZN7eK9Qq4kOLsvlMktpZnb+7o00O79CjqxTiSaUQT+PryiGeNJi4gRlbzhtLSSw5eI35e6/yXdfKFPNz5ti1GIYtOkIBTwdeqhZ8z5i/7rxEk9L+eDnd/fUxU2sAmpby5816hg+nUvndOHw1mh+3XpAksBBPATtnd0o06sySoR3JX64WgeVqU7hma5x9DD+WZWZksH/eZM5uWUzCnXAy0lLJSE8loLTpo2ieIXd/SFNK4eDmhWfw3W2WVtbYOrmRGBNh0s+vRKW7/Sws8C1WkcgrWT8tcevcYa4f28nU5++dJRAbfhG/YhUo3643Gyb359T6ueQvW5tCNVuZJLWzOv//L9fwMHRmJihFk8HTsXU0fAbV6f0JS4Z2IDHqFg7upo+5HV81C0cPX4KrNM5qOAAOLZ7GsZU/0/bjhdg4Zv95IoR48rm4edC0fVcGvtKKCjXqUaFGXeo2ex7fAMOP6hkZGfz23WdsWP4HETeuk5aaQlpaKuWq1TYZp1Dxu2W3lFK4e3pTsFiocZuVtTXOru5E3blt0i+0wt1ajBYWFpQoV5lLZ09mGeuZowc5vGcbTUve+zTatcsXKFGuMh1f68unH/Rm1YJfqVijHrWbtjVJamd1/v9fruFhFChUlAKF7k4OKVWxGjfCLjN3+pdZJoGXz/0RTx8/qtV/tB/Qls/9kRqNWprUHhZCPNlsnNzJX7sTeya8hGdoTbxCa+JftSX2XoZ7Wp2ZwbmlUwjfuZTkqBtkpqWQmZ6GZ0nTp71cCpQw/q2UwsbFC+fAu0/DWlhZY+3oSkqs6T2texHTe1q3wuWJu3Ymy1hjLh4h8tQuVvW494e0hFuXcCtcnpBmb3Dk+/cI2zIPz1I18a/cwiSpndX5/3+5htx0dcNsbN188Sl/t8RQSmwEh6f2p/w732LtmP3TbD7lGpi8ditSkY39qxK2ZR4FW7yZazGLJ5MkgZ8Bk1+qSK86hdlw8iarj91gwsoTzHy1GvVK+PLthrNM3XSWMc+XoYS/C462VoxfcYKIONNkrpWlackFBVj9XzFxpe4mKv+NzExoWNKPEW1K3bPP29mQDB3YrAQvVApkw4mbbDx9k0mrT/JJh3JZJlLhvysH8U+WFoqygW5cuJ1g3DZ66TF61ytsLNtQIp8rYVGJfLXuzD2xHwuL5vDVaD5qGWqy3cPRFisLRVE/0yRFEV9nFh8Me2zxCyFyV8MBUyjb9g2u7NvAxV2r2PnzeFoM/4WgivU5uOAbDi78ltpvjsczuATW9o7s/GkcSdGmiQYLK2vTQZXCwsrq/zYpQ9L0X9JaE1SlETVfH3XPPgc3wxf1qi8Ppli99lzet57L+zew57eJ1OvzGSWbdLmnDzx6OQhHD1+cPP2NCWAA9wKGhEXcrTCTJHBGWiqn1v1OaNOuWFhmfbtzaNFUds36mNajf8evWIUc4xJCPB0+/Gw6HV7tw57Na9ixdgU/TBzJuOnzqFKnEb9P/4Lfv5/MOyM+o2CxUOwdnfh+4nCiI0yvsVbW915Prf7vuqt4tGtsZmYm1es1pfeQCffs8/AyXMt6vDuURm1fZNem1ezdso6fJo9jwLgptOjYLcsxH1c5iH8qWa4y65fNv2d7WmoqqxbMpuWLPbD6v88fD29foiJMZ/1GRdzEw/vedUHOHj/M6SMH6Dlw9APHJIR4MpR980tCmvXk9uGN3DywhtPzPqHSgB/xLluPC8u/4+KKaZR8ZTTOgSWwsnPk9O8f35PMVZZZ3NP+3zallCFZ8G/pTHzKNaBElxH37LJ1NdzTFm3/PgE12nHr8AZuH9nE2QWfU/q1Twis2znLIf/LchCZ6amEbZ1HYL0uJve08WFnSIm+ye5xHY3btDa8Tytfzk/tTzfhlK/wPeNZ2TnilL8YCTcu3rNP5H2SBH5GhAa4EhrgyjsNi9J56g7m7b1CvRK+7PlrwbK/F3TTWnPhVjwu9tb3GfHB7L8cSc2i3saxD16OomW5rB9DKxPoytKD18jv4YC1ZfZ1fgt6O1GwjhOv1ynEoHmH+G3X5WyTwP9FOYj/p7Xm5PVYSv5jpnRSajoWFsqknaVSWSbNZ+28RAFPB2oXNZ0NYWNlQbkC7py/FW+y/cLteOPMbiHE08G7YCm8C5aiYse+LBnWiVPr5hJUsT7Xj+8ipGoT44JuWmuir503SXo+ihun9hNYrrZx7JunD1C4ZtaPsvkUKsPZrYtx9gnE8v+Tzv/gFlAIt4BClG3Ti41T3uf46l+zTQI/ajkI/5JVOLdtKalJ8cYawNFh5wFw9g00aXth50qSYu9kG8vBhd+y+9dPaDVqDvlKPb56bUII8ytcsgyFS5bhpd7vM7BbG1Yt+JUqdRpxZN9OnmvQ3Ligm9aasAvncHJ5PPXAjx/cQ4Xn6hrHPnV4H3WaPZ9l26KlyrFxxQL8AgpgZZ39NTZ/SGHahxSmfY+3mTSkLyvmzsw2Cfyo5SCycvbE4XsWcQPYumYpMZERtOjU/Z59oeWrsm/bBpPSG/u2bSC0wr3X2mVzfsQ/MJhKNes/VFxCiCeDS1AoLkGhFGrdhz2fvETY1vl4l61H5Ok9+FRoZFzQTWtNwo0LWDk8nnva6LP78QqtaRw7+vwh/Kq0yDrG4NKE71qGvVf+eydS/IOjf0FC/AsS0vR1js4YzJWNv2WbBP4vy0Hc2LeK1LhICtR9yWS7a8Fy1P5ko8m20/MmkJYQQ6keH+PgY7p48t8yUpNJuH4Or5K5s+ideLJJEjiPu3wngVk7LtKklD9+rvZcvpPAyfAYutUw1Lgp6O3E0oNh7L4QgYejLTO2nudKZAKlAtwey/F/3n6Rgt5OlPB34aftFwmLSqRbjaxrRvaoWZBfd17ijZ/30Kd+UTydbLl8J4Glh64xsk0pLC0sGLX0KK3KBhDo4UBEXAp7Lt6hQlD2j2E8jnIQx8KiAYhPTsdCKY6FRWNtZUExP8MH2GerTlIxyIOC3k7EJafxw5bznLgew4QOZY1jNAr15+t1Zyjg4WgsBzF10zlj8v1vianpLNx/lbfrFzX84vl/3q5fhF4/76FqQU9qFvFm+7nbLD4QxszXJIEhxNMg5sZljq38mYLVmuLo6U/sjUvcuXic0i16AOCWvxBnNy/m+rFd2Ll6cmTp98TeuIx3odL3GfnBHFsxE/eAQniGlODo8pnE3QozHvv/lW71KsdXzWLVx69TsUNf7F09iblxmXNbFlOz52gsLK3Y9sMICtdqjYtvARKjbhN+Yje+OcyofdRyEEXrvcDeOZNY/3lfqrw8iJT4GLZM+4jCNVsbZycbz/XPXwgsVxtX/+B7xjnwxxR2/jyexgO/wy2gEAmRhhlrVrb2jy3hLoT474VfvcTS2T9Qo1ELvHzzcf3KJS6cOkqbl3sBEBhSmI3L/+DI3u24unux8OfvCA+7RJGSZe8z8oNZ8ut0AkMKU7BYKRbPmsbNsCu0eblnlm2ff+UNls+dycg+L/PSm+/h5unN9SsX2bhiAW8PmYCllRXfjvuQus3b4Zc/iKiImxzdt4OS5Spne/xHLQcxf8YU/PIHEVK0JGlpqaxdNIdta5YxZuqce9oun/MjFWrUI1+Be+tmtn/1bfp2bMTsbydSs3Frtq5eysGdm/l6/nqTdslJiaxbMpfObwzI8r5XCPHkSrx1hSvrZ+FTsTF27n4k3rpC7JWTBDV8BTAkVMN3LSXy1G5snD24tOZHEm9dwSX43qd+/43L637B0b8QzoHFubz2J5IiwghqmPUPZMGNe3B142wOTHmDQq36YOvsSeKty1zftZSSL49EWVpy8n/s3WdgFNUah/Hn7GbTew8ECL333nvvVQQEFEWlS+9FUEAEaSIIoiBNVEBEeu+9d5BeEkJJSK/nfti4kEsSEkiYlPP7cC/Zndn5bxwOs++eec/yL/Gq2BwrtxxEBPrz7OpRHPMmfk2bGu0gAm+dB4z9lRE6Am+dR2dmwM47ftufOzuW4Vq0+isLuZlZWsdrnQFgsHFAxsbEe/zi8gl4lKmPlYs3Ec8fc33td8REhJI9rkCvZC2qCJzJWZvrueEfzCe/HOVpcCRudha0KZuDPnWNt89+0aAgd5+G0GnBISwNOt6rkIs2ZXNw1TcoVY4/qllRFuy+zrl7AXg7WbP4o4pkc0x45q2ngxV/96/JVxsu8P6Cg0REx5Dd0ZqahdwxNzOu6hwYGkX/FSd59DwcJxtz6hdNuH1Eaqr3bfxv17Ze8MXbyZrj4xoC8DwsisGrT+H/PAI7KzOKZ3dkXd/qlMn14iL867YlmLrxEsP/OM2T4Ajc7S3pUtmHgQ3jD9p/nbpPaGQMHSsm/K1d4xLZmNahNLO3X2HM2rPkdrVlTueyqh+womQQBgsrAu7/y6avPyIs8CnWTm4UrN2OMu37AVC+4yCe+95h/dj3MDO3onD9jhSs3S7Rvr0pVeXDMZxa+wP+189i5+5NkzFLsHVLeFaYrYsX7ab/w8GfJ7F+TAeiIyOwc8tOzjK1TaslRwQHsH16X0Ke+mFl74RPhQZU+zjtbuk1t7Kl1eQ/2TNvBKv718fC1oE8lZtQ5cMx8bYLfHiLe2f20Wj4wgRf5+zfi4mNjmLz5I/jPV6oXkfqD5qbZvkVRUlbFpZW3L15jXG9OhP47AlOru7Ua9WRTp8NAqBr3+E8vHuLod1bYWFpRaN2XajXsiO3E+nbm1KfDpvI6kWzuXb+NB7eOZm44DfcvbwT3NbVIxvf/7GTH78Zw5BuLYmMCMcjew7KVa+Lwdw4xgYFPmPK4E944u+LvaMzles2odfIyamSNSFRUZH8MHkk/g/vY2FphU+Bwkz9eS2VajeKt92DOzc5eXA3Y+ckvOhmsbKVGTtnKT99O4HF300kW848jJ/7K0VKV4i33c6//yA8NITG7T9Is/ekKEra0FtYEez7L/dm9SQq6CnmDq5kr9qGvM2NC2jmbzWAUP87HP2mM3pzS7xrvEe2qm0ITqRvb0oV6jiSGxsX8PzWOaxcvSn7xU9YuSR8TWvp5EmVceu5/NvXHJ3SidioCKxcs+NavCY6g3HCWFRIIGfm9yci4BEGWyfcS9dLsH1Eato/sn68nx+d3IqVqzd1Zh8zPRbqd5snF/dTuu/8Nz5O+JOHnJrTi8igp5jbu+CUrwxVJmzA2i3H63dWMh0h36KHq5I6nG0tTi/qXqFk1fyZZzGEO09CqDBxK5sH1qJUznfXMF1JXb6BYVT+altgaES0o9ZZFEVLVg4uu+r0n1krb5UmWkd5I8/97rCkexk6zNqGR4HSWsfJEv4e1+n5raNbP5FSrtY6i6KkJ3aOTjdnrtjsk79o6sy+TQ8e3r1Nx+qFWLB+P4VKlNU6jpKA8LBQmhT3iIqOikrbFaMVJZ0TQozP32bQuALtBmsd5Y2E+t9lV/8KVJ20Ccc8pbSOo8TZ3qtUUESAXzkpZepU+ZU0k3jjVUVRFEVRFEVRFEVRFEVRFCXDU0VgRVEURVEURVEURVEURVGUTEz1BFbSRE4XG3xnJrwasqIoivJu2XvkpO+mx1rHUBRFyZS8cuRiz60wrWMoiqJketZuOWi64qHWMRQlw1IzgRVFURRFURRFURRFURRFUTIxVQTO5FrP2ceIP85oHeO1pm26hOeAtXgOWMuc7Ve0jpOllJuwxfS7fxIcoXUcRcly1gxtwe55w7SO8VpHlk1lTmNX5jR25fjqWVrH0dzLv4+Tf8zVOo6iKED/9xowc+wArWO81s/fTaKmjxU1faxYPm+aX22jMwABAABJREFU1nEyjf7vNTD9Xi+fPaF1HEXJcg5NbMP5n0dqHeO1rv7xLf908uKfTl5cXz9H6zgZws5+5U2/s8jnT7SOo7wF1Q5CSTfyuduypk91bCxenJb/nLnPrwdvcfZeAE9DIvmzdzWq5ndLcH8pJZ0WHGLXZT8Wdq9A81LZTc/N3HqFHZd8OX8/kLDImARbVZy684yvN1zgzJ0AAErkcGRksyKUyeX8yrY3/IOp/+0upJTc+KZFit6nX2A44/86x7l7AdzwD6ZduZzM7vzqStJB4VFM+eciG8484FlIJNmcrBjRtAgtS3sDEBwexdSNl9h47gFPgiMolt2RiW1KUDqnEwBRMbFM+eciOy/5cetJCHaWZlTN58ao5kXxdrI2HWfzoFoc+fcxPX4+mqL3oShK1uPonY82U//C3NrW9Nj1Axu4sHEJj66fJfz5E1pPXYd3iWrx9tsxawD3Tu8n5KkvBksbvIqUp8qHY3HOWcC0zaPrZzi4+Ev8rp5Cp9OTt2ozqvWciLmVLSkxp7HrK4/V6jON4k0/BODp7SvsnjeUp3euEhnyHBsXT/LXbE3FzkPRG4yLxt8/e4CDv0wi4N51oiLCsHP3pmjDLpRp18f0mqXb9qZYk+6s7l8/RfkURVEAcuYpwMxVW7C2tQMgOiqKRd+O58ierTy4fQNrW3tKV67Bp8Mm4pE9p2m/b4b34tTB3Tz2e4iVjS3FylSi5/CJ+OQrBMDDu7dZOmcypw7t4ckjX1zcPanTrB3d+o/EwtLK9Dp+9+/w3dgvOHVwNxaWVtRt2YFeI6dgMDdP9ns4fXgfP34zhrs3rhEeFopn9pw07didjj2/MG2z658/WTl/Bvdv/Ut0dBTePvlo36Mvjdp1MW0TExPDLzMnsXXtSp4+8sXZ3ZP6rTrSfcBozMyMnwtq+li9cnyAVh98yhcTZwIwccEq7t++wWctqyf7PSiKkjXZeOWl0pg1mFm+uM58ePQf7uz4lee3zhEZ9JRKo//EpUiVBPeXUnLsm874n9lFmf4L8arYzPTczn7lCXt8L972eZv3odD7o1KU8cKS0Ty7eoyge1ewcHCjzuxjCea4tXkht7cvJcz/LgZbR7yrd4h3rCeXDnLx1/EE37+KhaMHeZv3Ile9bvFe59a2X7ixYR4RAY+wzV6Aol2/xLlQJdPzVSdt4unlI5yc+XGK3oOS/qgisJJu6HUCd3vLeI+FRsZQLrczbcvloO/ypL/R/2HXdXSJzG2PiI6hSYlsVMnnyqxtV195PiQimk7zD1C3iCf/fFETKSWztl3l/fkHOTGuIbaWBtO2kdGxfLbkGJXyunDoesp7bEZEx+BsY06fugVYduhWgttExcTSYd4BHK3N+bF7ebwcrHgYGIa5md60zcBVp7j0IJDZncqSzdGKP47fpcO8/ewdXg8vRyvCImM4dy+A/vULUiy7A8/Do5jw13nen3+QXUPrYKY3/rJcbS1wtE7+Bb+iKFmXTm+GjbNHvMeiw0PxLFKegnXas+3bXgnu55G/FIXqvoedW3bCg55xdNk3rBvZhm6/nEJvZiD4yUPWjWhLvuotqNlrCpEhQez7cTTbp/elyeifU5yzTv/v8KnQwPSzhY39i/dgMFC4Xkdc8xbHwsaBxzfPs3PWQGRMNFV7jAfAYGVDyRaf4JK7CGYWVjy8eIRdswdjZmlNiWYfAWBuZYu5lS1Cp0dRFCWl9GZmuLh7mn4ODwvl6oXTfNB7KPmKlCQkKJDvvxrOkG4tWbz5mKkYWqh4GRq26YS7Vw6CAp/y88yvGNS5Cb/tv4KZwcCdf68QGxPDwEmz8c6dj9vXL/PtiN4EBjxlyOTvAWPRddhHbXBwdGbO79sJfPaUyYM+RkrJgAnfJfs9WNnY0LZ7L/IUKoallTXnjh9i+sg+WFhZ0/qDTwFwcHLhgz7DyJm3IGYGA4d2bOSbYZ/h6OJKpdqNAFgxfzprly5gxPSF5ClYjBuXz/H1oE8wmFvQrd8IANYcvRnv2FfOnWREj7bUbtrW9Ji9ozMhQUFv8F9DUZSsRujNsHR0j/dYTEQoTgXKk71aW8780C/J/W/8Mx9E4jfW528zkJwvFVrNLG1SnFFKSfYaHQi6c4nH5/YkuM2lZeN5dGo7hTqNwT5HYaLCnhPx7JHp+dBHdzj2TRe8a75Pqd5zeXblKOd/HoG5vQteFYyF6weH/uLi0jEU+3AyTgUrcHvbEo5O7UzNaXuwcjVOQLOwd8Xc1inF70FJf1QROJ369eBNpm68xJkvG6PXCdPjny89RkhENEs/qcytx8GMW3eOk7efERwRTT53W4Y0LkyDol6Jvm65CVv4qHoeetXJb3qs9Zx9FPKyZ3K7koCxyDl140XWnLhLQGgUBTztGN6kCLULeyT2smmmfXnjzIfXtSk4decZi/b+y5ZBtSk+ZuMrzw9rUgSAv0/fT3D/a35BPAuNYkjjwuRyMQ7QQ5sU5s8Td7n+KJhSOV8MeJP+Pk/hbPZUzuv6RkXgnC42fNXW+LvecCbhPKuO3OZJSAR/9auBuZnOtN9/wiJj+OfsA376sIJpZvSQxoXZdsGXJQduMrxpEeytDKzuFX823jcdSlFzyg6u+QVROJtDirMrivLC+Y1LOPzrFD5adh6d/kUhcMvUnkSFhdBs/HICH9xk38Ix+F05SWRoME7eean4wXByV2yY6Ov+0q00JZr3iDfrdM3QFjj7FKZWr6kAxERFcnjpZK7s+oOI4ACccxakUreR5CpbJ+3ecCIK1e0AQFhg4reGFWvS3fRne4+cVOo2kpW9avLc9xZO3vm5dWQrQqejVu9ppt9lrT7fsrJXDQIe3MAxW54UZbKwcXilWP0fx2x54r2evUcO7p89wIPzh02PuecvhXv+UqafHTxz8e+Bf3hw/pCpCKwoSupZv+InFs/4kj+P3ED/0nj6Zb9uhIWGMHnRH9y/fYPvJw7j0uljhIYEkSNPAT4aOIYqdZsk+rrvVS1I626fxZuZ2v+9BuQuWIQBX84EICoykp+mT2D7X6t4HvAMnwKF+XjQeCrUfLez/G3tHZix7J94jw3+ei7d6pfh9vXL5C1UDIAWnV/MwvLKkYuPB43jo8YVeHDnJjnzFqBirQZUrPXiS7BsOXPTpc8wFk//0lQEPrZ3O7euXmT1gSu4Z8sBwGcjvmbasM/5ZPAEbOzsSY6CxctQsHiZl/L4sHfzOs4ePWAqApepUivePu0+6sPmP5dz9ugBUxH4wonDVKnbhKr1mpreV9V6Tbl0+sWst5cL5gD7t20gR578lKqkZv0qytu6s+NXrvz+DfXmnY735fapub2IDg+h/OAlhPjd4tKy8QRcP0l0eDA2Xnkp0G4oHmUSHyt39itPrgYfkbfZ56bHDk1sg513IYp9+DUAsdGRXPn9Gx4cWENk8DPsvAtSsP0w3ErWTrs3nAjv6u0BXtvuIODf09zavIhqX21h++fFE9xGb2n7SpE5pYp1/wqAfzf8kGAROPjBdW5tXUz1KTuwy/7i7jp8Xvzx9o6lWDh6ml7LLnsBAq6f4saG+aYi8M2NC/Cu0YGcdbqYjut/Zhe3ty+hUMeUzV5W0j9VBE6nmpfKzug1Z9lz5RF14oqvIRHRbD7/kJnvl4n7OYY6hT0Z3qQIlgY9f526T4/FR9g5tC75Peze+NgDVp7g1uMQ5nUtTzYHK7Zf8qXrokNsHlibotkTLhzO2naFWduS7uW74tMqVMr76m26bys4PIpeS48xrUMp3Ows3ug18rnb4mJrzsrDt/miYUEAlh+6RXYnKwp6vrgQ3nbBl20XfNk+pE6iBeXUsOncQyrkdmHUn2fYfP4hjtbmtCiVnQENCmLQ64iJjSUmVmJhFn8GmqVBx5Ebif+jFRweDYCDmvmrKG8tX/WW7J0/krundpOrXF0AIsOCuXFoM/UGzjb+HB5CrnJ1qdR1JGYWllzbs46Nk7rz/ry9OOfIn9TLJ2n7jL4EPrxFw2ELsHXNxq1j29kwvjMdZm3DLU+xBPc5tuo7Tvw2M8nXbT5xFdmLVX7jXMkRFR7Cpa0rsHP3xt7d+EVfTFQkOr1ZvGK6mYXxzpCHF46kuAi8d8FIds0djL1HToo07Eyxxt0QidwqEvDgBreP7yR3pUaJvp7/9bP4XjpGhc5DUpRDUZTkqd20DXPGD+L4vh2mAmZoSDAHtm1g2LQFAISFBFOxVgN6DB6HhaUVuzb8wZjPOrJ40zFy5Sv4xseeMqQnD27fYMysJbh5Zefwrs2M+LgtC/7aT74iJRLc59fvv2H5998k+bpTf1lHyQrVktzmdUKCngNg5+CY4PNhoSFs+n0pHtlz4OmdK9HXCQ16Hu81Lpw6Qq58hUwFYIAKNeoRGRnBlXOnKFOl5hvlvXr+NBdOHKH7gISLBlJKTh7czd0bV/l48HjT48XLVWbdsh+5ff0KufIV5Na1S5w8tJvOvRIec0NDgtn59+9075/+e48qSkbgVbE5F5aOwf/cHtxLGicURIeH4HdiMyU+nQlATHgIbiVrU6D9MPTmljw4/BcnvutBjSk7sM3+5te0Z+YPIPTRbUr1/h4r52w8Or2DY992o9qkTdjnKprgPtfXzeL6X7OTfN0Kw5bHa2eQWqLDgjk1txfFP/4GC4fEaxs3//mBf9fPwcolG54Vm5G3WS90Zqn7GdzvxBas3XPhf2YXx775AGQszoUrU7jTWFO2gGvHcSsRf0x3K1GLe/tWExsdBUgCb54lT9PP/2+bmjy7ejxV8yrpgyoCp1OO1ubULeLBmhN3TUXgTeceYKYTNCxmnOlbNLtDvKLsgAYF2XrhIRvO3OeLBoXe6Li3Hgez9uQ9jo1taOob26N6XvZd8WfpwZtMbV8qwf26VslNi5d68CbE0yHhXl5va+jvp6ld2IO6RTxfv3EibC0NrOlTnQ9/OszsuIXpcjjbsPrzqliZG4sSvoFhDP7tFD9/VDFe3+K0cOdJCAeu+dO6jDfLelbm7tNQRvxxhpDIaMa3LI6tpYFyPs7M3HaFQl72uNtbsvbkXY7fekpu14T7Z0ZGxzL+r3M0KOpJNse0+W+hKFmJpZ0jucrX48quP0xF4BuHNqLT600FRbc8xeIVZcu/P5CbR7bw7/71OL8/6I2OG/jgJlf3rKH7L6ewczfeolWyxcfcPb2HCxuXUKtPwosMFW/anfw1Wib52rYuid9J8rbObljMwZ8mEBUegqN3PlpNXoPe3PjFnXep6uxfOIbjq2dRuvXnRIWHcnDxRABCnvql6DgVPxiOd4lqGKxsuHt6L/sXjiP8+VPK/9/v+/eBjfG/fpaYqAiKNvqAKt1Hv/Jai7sUJyzwCTI2mgqdhpj6CiuKkrrsHJyoWLsh2/5aZSoC79/6N3ozM6rWM85UylekRLyi7Ad9hnFw+z/s2bSWrn2Hv9Fx79++wY71q/lt/2VT39023T7nxP6drF/xEwMnJbwIZsvOH8drQ5AQN89sb5TpP1GRkcz7ajhV6jXF3cs73nNrf13AgsmjCAsNIWeeAsxYvglzi4QnQvjeu81vC2fRufeLgupTfz+cXOPPUHNwdkWv1/PU3zfFWdtVykvA08fEREfTrf8oWnb5JN7zwc8DaVcpL5GREeh1egZMnEml2i/uiOn0+WBCQ4LpVr80Or2emOhoPugzzDSb+P9t/+s3oqMiadS2S4LPK4qSMgZbR9xK1eHBgTWmIrDv8U0InRkeZYxjsn2uovGKsvlbDeDRyW08PLqB/K2/SPB1XyfE7xYPDq2jzqyjprYDPg0/4vH5vdze8SvFP5qS4H4563XFq1LS6/JYOr95bSAp534ailvJ2riXqpvoNj4Ne2DvUxxzOycC/j3F5ZVfEfboLiV6Tk/VLKGPbhP2+B4PD/1Fyc9mIoTg0vIvOf5tV6pM2IDQ6YgI8Me1WI14+5k7uCJjookMegpIZGwM5v9X0LZwcOPx+X2pmldJH1QROB1rWzYH/VacJDQyGmtzM/48fo+mJbJhaTAWJUMiopm+5TLbLvjy6Hk4UTGxRETHUuQtbvM/ey8QKaHG5O3xHo+MjqVaIguyATjZmONk8+5nl/5+7A4X7geyZdDb3S4SFhnDFytPUiaXM99/UJ7YWMm8XdfovugwmwfVxsbCjD7LTtCtam7K+Ly6UFxqi5USV1sLpncsg14nKJnDiWchkYxdd45xLYohhGBul7IMWHmS0uM3o9cJins70rpMDs7effbK60XHxNJ72XECw6JY8nHqfyOqKFlVwTrt2T69N1HhoRgsrbmy60/yVm2OmblxFmtUeAhHl0/j5pGthD7zIzY6mujIcFxzF3njYz769yxIyfJPq8Z7PCYqAu+Sid8Wa2nnhKWddr28CtZuR87SNQl56sepP79n01c9aDf9HwyW1rjkKkS9QXPZv3Ash5d8hdCZUbLlJ1g7uSOS6LeWkAqdBpv+7Ja3ODI2huOrvnulCNxoxCKiQoN5fPM8+xeN58Tvsyn33oB427T9dgNRYSH4Xj7OwcVfYu+Zy9T+QlGU1NWg9ft8PegTwsNCsbSyZtu6VdRo1AoLS+N4GhYawi+zvuLQjk08eeRLTHQUkRHh5Cmc8K24yXH1/CmklHSrXybe45GREZSpXCvR/ewdnbF3TLvrwejoaCZ98SHBzwP5etEfrzxfv2VHylery5NHvqxaOJNxvTrz/Z87sbSyjrfdU38/hnRrSdlqdejQI+n+lm9jzu87CA0J5uKpoyyYMhqvHD40bNPJ9Ly1rR2LNh4hLCSYkwd38f2kYXh656JsVeP1+86/f2fLmuWMmfULPgWKcP3iWeZMGIxXDh+avtf9leNtWLWYqvWb4eiS+GcTRVFSJnvVtpyZ35+YiFD0FtY8OLAGzwpN0cdd00aHh3JtzXQendpG+LNHyJgoYqMisMtR+I2P+fzmOZCSPUPiz1SNjY7EpUjVRPYCc1snTfrT3tv3O8/vXKTapM1Jbpen6WemP9vnLIKZlR2nZn9KofdHYW6Xev92yNhYYqMiKNlrDrZeeQEo2WsOewZVI+DGaZzylXnNKyhZkSoCp2P1inpiphNsOfeQagXc2Xf1Eas+ezEYTvjrPLsu+zGuZTHyuNliZdDTd/kJIqNjE31NnQ4kMt5j0bEvto+NlQgBmwfWwqCP/8H7v+JzQrRqB7Hvqj9X/YLIO+zveI9/uuQoC/c4s75/8m5nW3vyLrceh/B3/5qmHsw/fFCegiM3sOncA9qVy8n+a/4c+vcx07dcBoy3tMVKyD5wHVPaleSDKrlT7X2521ti0Ovi9YPO72FHWGQMT0IicbW1wMfVlnV9axASEU1weDQeDpb0/OUoOV3jN52Pjonls6XHuPzwOWv6VMfZ5s1aZiiK8qrc5euj05tx8/AmvEvV4N6pPbSYtNr0/P6F47hzYidVP56AY/Y8mFlYse3b3sRERyX6msa2BfHH6diYaNOfZWwsCEGHWdvQmcX/Z9zMPPFZ/lq3g7CwscfCxh7H7HnxLFSOH9vn498DG0xF1YK121GwdjtCnz3CzNIaIQSn1/6AvVfitzgnh2fBskSGBhH67BHWTi9mvtm5Ge9ecc5VkNjYGHbO/IIy7fqg07/4nTp4Go/tmrsIYQH+HFn2jSoCK0oaqVS7MXq9Gfu3/k3ZqrU5cWAn3y59cX0376sRHN2zlV6jJuPtkw8LK2u+HtiD6MjIRF9T6HRI+X/XvS+NvzI2FiEE89fvx8zMEG+7/4rPCUnLdhDR0dF82a8rNy9fYOaqLTg4ubyyja29A7b2DnjnzkeR0hVoVtKLPZvWxSu8PnnkyxedGpO7YBFGfbcYIV5cUzq7eXD++KF4rxn49DExMTE4u6V89pxXDh8A8hYqxrPHj/hl5qR4WXQ6Hd4+xgJF/qIluX39Csu+/8ZUBP5h8kg6fjKAui06mF7H7/4dls+b9koR+NqFM1w5e5JPhnyZ4pyKoiTOvXQ9hM4M3xNbcC1ancfn91Fh+ErT85eWT8D/7C4Kdx6LjWce9OZWnP6hHzIm8WtahA7+bwyWL1/TSuM1bdVJm9Dp44/B/xWfE6JVO4gnF/YTfP8qWz7KF+/xk7M/xSl/WaqMX5/gfo55SwPGmc+pWQS2dPJA6M1MBWAAG888CJ2e8Mf3IV8ZLBzdiAj0j7dfZOBjhN4sLotE6PREBsZf6ygi0B8LB/VFW2akisDpmIWZnualsvPnibs8CYnE3d6SKvleFFGP3nxC+/I5aVbS+EE2PCqGW49DyOOWcDsAABcbCx4Fhpt+Do+K4bpfEMWyOwJQ3NsBKeFRUESSM3//n1btIEY0LcLndeL3IKo9dQfjWhanUbHk39YcGhmDEPBSzRWdAAH8VyPfNSz+LR9bzj1k1rYrbBxYCy+HxP+RehMVcruw5sQ9YmMlurhQN/yDsTLX4/J/M65tLMywsTAjIDSS3ZcfMabFi9t0omJi+WzJiwKwu33q5lSUrE5vbkG+ai24susPwgKfYu3kjneJFx/6H148QsG6HchXrTkA0ZHhBD68hWP2vIm9JFYOLvFaIERHhvPs7jVc8xpnu7nlLQ5SEvrML8mZv/9P63YQ8UgJSGKiXl30879C7cUty9EbLMlZutZbHcr/xnn05pZY2CR+l4yMlcTGRCNjYkCf8KWRjI1NMK+iKKnD3MKCWk3bsP2v3wh89gRnNw9KVXpxC+u54wdp2KYzNRu3BiAiPJwHd26SI3fivSgdnV158uhFe4OI8HDu/HuV/EWNC/TmL1oKKSVPH/mlqA9uWrWDiI6KYkLfD7h59SKzVm15ZTG0hEgpkVISFflifHry6CEDOjbCp0Bhxs5eitn/fWFYtHRFfp0zhUcP75laTRzbvwNzcwsKFi+d4twvi42NjZclOdtEhIXF6wkPoNPpiY19dWLL3ysX45XDh3LV3v1CqIqSmekNFnhVbGZcoC3oKRYO7rgUrmJ6/tnVo3hXb29aTCwmMpxQv1vYeiW+boOFvQsRAS+uaWMiwwl+cB37XMZWafY+xUBKY8uCoonP/P1/WrWDKNhh+Cu9c/cOq03hzmPxLJv42hLPb18wZnrLheL+n1OB8siYaEL8bmHj4QMYW0TI2BhTew3H/OXwO7Yp3n7+5/bgkLskurgvPx1yl8D/3B68KjU3bfP43F48KyS+8KqScakicDrXtlwO2n+/nztPQ2lVxttUEATI62bLprMPaFTMC4Ne8O2Wy0RExyT5etXyu7HyyG0aFPPCxdaCWduuEB374tu5vO52tC2bg/4rTjC+ZXFKeDvyLDSSg9cfk8vFmqYlEy70plU7iGchkdx/FkpgmPEbxluPQ3CwMuBub4m7vSVejlZ4JdDfNpujFblemhF771koASGR3H0aCsD5ewEA5HazxcbCjJoF3Zm4/jzDfj/DxzXyECth7o6r6HWCagWMxfDCXvFXSj5z5xlCvPp4cvx3/ODwaHRCcP5eAAYznWkRum5Vc7N43w1Grz3LR9XycPdpKNM2XaJ71dymmRy7LvkRKyX5POy49TiEL/86Tz4PWzpWNM5ci46J5ZOfj3L67jOWflwZIeDRc+MXAHaWBlOvY0VR3k7BOu1ZN6INz33vUKBWm3gLkDlmz8uNgxvJU7kxOr2Bo8unERMZnsSrgXfJ6lzcuoLclRph5eDK8VUz4s0EdvLOR8Ha7dg+oy/VPv4St3wlCA96xv2zB7D38iFf1WYJvm5atYMID3pG0KN7RIQEAsaexRY2Dlg7uWPj7EHAgxv8u38DOUrXwMrBleDHDzixehZ6gzk+FV6sYH9m/SK8CpfDYGXL3VN7OPDTeKp8OAYL2+S3OLp5eDMhzx7hVbg8ZuaW3Du7nyO/TqFY466m/sOXd6xGb26Bi08R9GYGHl07zaFfJpKvWnPTNmf+Woi9Z06cvI0zPe6fP8TJP7+neLOPUuvXpihKAhq0ep8vOjfm4d1b1G3RAd1L42mO3PnYt3U91Ro0Q29mYMmsr4iMSHo8LVOlFht/X0rVes1wdHHl17lTiXlpPM2RJz/1W3VkypBP6DVqCgWKleZ5wFNOH95Ltpy5qdGoVYKvmxbtIKKjoxnXqxOXz55g8k9/AsJUwLa1d8DC0op7t/5l76a1lK1WB0dnV/x977P8h+kYzC2oXKcxAI/9HtC/Y0Nc3b3oO3YagU9fzO5ydHFDr9dTvkY9fAoU4euBH9Nr1BSeBzxl/tcjafr+h9jYJf+69s9f5uGVw4eceYyr0p85up/fFs6kVZeepm1+nTuVwqXKky1nbiIjIziyazNb166g//gZpm2q1G3Cih++xSuHDz75i3DtwmlW/zQ73mxigPCwULb/tYr3Px0Yb2azoiipI3u1thz5ugOhj+6QrUqreNe0Np558D22CY+yDRF6A9fWTCf2NV+OuxStyt3dq/Ao2xBzexeur5sVbyawrVdeslVtw9kF/SnceTwOPsWJDHnGk4uHsHbPiVeFpgm+blq1g4gMfkbY4/tEhxoX5Qzxu4mZtT0Wju5YOrpj6eyFpfOrEyasXLJj7WH8DP7s6nGeXT+BS5GqGKztCbhxmou/jsOjbENTYTa5QnxvEh0eQsQzX2Kjowi8dR4AO+8C6MzMcS1WA3uf4pxd8AVFuhrvjri4dCyO+crgkMf4ZWeuul25vXUxF5aOIWfdD3h29Rj39q6mdN95puPkbvIpp+f1xTFfaZwKlOfO9qWEP/MlZ92uKf8lKumeKgKnc5XyuODpYMVV3yDmdy0f77nxrYozcNVJWs7Zi6OVgU9q5iMiKukicL/6Bbj7NJTuPx3GxsKM/vUL4hsY/wJ6ZqcyzNx6hYl/n+dhQBiO1uaUzulE1fxvttjc29hy/iEDVp40/Tzot1PG/29YiCGNk99/6JuNl1h97I7p53rf7gLgz97VqJrfjfwediz9pDLTN1+i2cy9CGFceG/Fp1VStIjanSchVJi4lZnvlzEVYxPy3/H/s/WCL95O1hwfZ1wkI7uTNb99XpVx685R79uduNlZ0rFirngL/j0Pj+LrDReN/41sDDQtkZ0RTYuY2ng8CAhj8/mHADSYHv94r8unKEryZStWGRsXL57euULD4T/Ge67aJxPZMbM/fw5ujoWtA6VafUpMVNJFi3IdBvDc7w7/TPgAg5UN5ToOJORJ/IV66g6cw/FVMziweALBjx9gaeeER4HSeJd8u5Xo38TNw5vZPqOv6eeds4yLg1ToPISKXYahN1hw79wBTq2ZR0RIINaObmQrVpl2MzZj4+xh2s/v6kmOLptKZFgITjnyU7vv9FdaL/zSrTTZS1Sl/qC5CWbRmRk4t+Fn9i8cg4yVOHjlouIHwynRvMeLbfR6Tvw2k4AHN0CCnbs3xZv1oHTrF/3bZGwMBxd/yXO/u+j0ehy8fKjy0ViKN+meGr8yRVESUaJCVdw8snHr2iXGzl4S77neo6fyzbDP6du+HnYOjrT7qA+REUkXIDr3GsLDe7cZ1bM9VtY2dOkzjCd+D+NtM3zaj/w6dyrzJ4/C3/c+9g7OFCpVjtKVkz8zODX4P7zP/m0bAPikWZV4zw2f9iON23+Aubk5pw7v47dFswl+HoCTqzslK1Rj3prdplnDx/bu4N7N69y7eZ32VQrEe51V+y7jlSMXer2eqYvX8N2YAfRpVwcLSyvqtXqPz0dMNm378O5tOlYvZDp2QmJjYlgwZTS+926jNzMjW8489Bw2kZadXywMFxYSzIzR/fB/eB8LSyty5i3AyOmLqNfyPdM2/SfM4KfpE/huTH+ePfbHxd2TZh0/pFu/kfGOt/PvPwgPDUk0j6Iob8e5UCUsnTwJvn+V0n1/iPdc4S7jOfvjIA592QqDjSM+jT55bRE4b4t+hPnf4/j07ugtbcjXqj/hz+Jf05b8dCbX183i0oqJhD99iLmtIw55S+NapEoir5p2/E5s5eyCAaafzy00rjORv80gCrQbnMhe8ekM5jw8vJ5ra2YQGxWJlWt2ctbuTN7mveJtt7NfeVyKVKHkZwkvQApwduEgnl560bpn/8j6ANSedRRrtxwInY7yQ37lwpLRHPqyNXpzS1yL1aDIBxNMBXxr95yUH7qMi7+O4872pVg4eVC020TTjG6AbJVbEhn8jOtrZxIR8Ahb74KUH7oMa7ccyXrPSsYi/r9PlvLuOdtanF7UvULJqilov5DZTNt0iQ1n7rNneD2to7yV/df86fLjQfYMqxdvJnJ6d+CaP22/38+FSU1wsX3RM9g3MIzKX20LDI2IdtQunaJoz8rBZVed/jNr5a2SdW+LOrJsKtf3/03n+fs1OX5UeCgL3ytAvS9mUaBW0rdhv0u/dCtNieY9KNOuT7zH/x7X6fmto1s/kVKuTmRXRcmS7Bydbs5csdnnv5YMWdHP301iz6a1/LL1hNZREnXy4B6GfdiKJdtOki1n6q17kdb+K14vWL+fQiXKmh4PDwulSXGPqOioqHe/krWipCNCiPH52wwal9yiZmZ09Y9veXh0AzW/2a3J8WMiQtnasyglPp1B9iqtNcnwJp5cPMjhSW2pP/885vbx+9Vv71UqKCLAr5yU8qpG8ZRkStmS24qShq75BZFn6Hrm77qmdZQ3tuOiL73rFMhQBeAaU7bT+ceDWsdQFCUDeHb3KvNb5+LUmnmv3ziV3Tu7H8+CZdJNAfjYqu+Y3zoXQf73tI6iKEoGdPv6ZRoVceW3RYnPAtPSkd2b6fTZoAxVAB7SrSXdG5TROoaiKBlA8P1rbP4wLzf+mf/Oj/344kEc85XOUAXgPUNqcnRqZ61jKKlAtYNQ0oWPa+SlXTnj7QbOadBb+F0Z17K41hFSbHnPKkTHGBfecLLOuL97RVHSVsmWPSlYpz0AVvavrlaf1nJXaEDul3oIa+3lhfa0+H0oipJxtf2wF/Vbvw+Ag1P6HD8+Hzn59RulM0OnziMi3Nhy6b8F7xRFUf6fT6MeZK9mnFRgbpe6/d2Tw6N0PTxKZ6w7oMsPXWbq52xIg37MyrujisBKupBWC8spr5fD2VrrCIqiZABptbBcRqV+H4qivKm0WFhOATfPhBewVhRFeVlaLSyXman+wJmHagehKIqiKIqiKIqiKIqiKIqSiakisJLqPAes5e/T97WO8U4s3neDAsM3sGD3ddadvMfkfy5oHUlRlCxmTmNXru9br3WMNJUV3qOiKBlDTR8rdm9co3WMNJUV3qOiKBnDP528eHhkg9Yx0lRWeI9K+qHaQSjK/yk3YQv3noWyrm91KuV1NT0+bdMlNpy5z57hL/r3/HP2Pss/rcyC3de59OA5i3tU1CKyoihKhuV/4zxHfp2C35WTRAQHYuXoikeB0lT7ZCL2HsZbzz5afgFLW0dtgyqKomQC1y+eZfGMiVw6fYzg5wE4urpRqERZeo+eiqd3LgDWHL2JnYO6VVpRFOVtPb99gat/TCPg+kmiQp9jbu+CY55SFO4y3tRioe68MxhsHDROqmQVqgisKAmwNOiY9PcFNgyomeR2f/auDkD53OlzUQ9FUZT0LCzgMetGtCFnmdo0n7ACS3tngvzucuvYNiJDg0zb2Th7aJhSURQlcwh44s/Azk0oX6MeU35eg72jC37373Bo1yZCgl+MuS7unhqmVBRFyRwinj/m8FftcStRi/JDfsVg50zY47s8OrWD6LBg03aWju4aplSyGlUEVlJMSsn83ddZeuAm95+F4WJrTrtyORnVvGiC20/6+zybzj7kfkAorraWtCidnaGNC2Np0ANw/1koI/88w5EbT4iIiiW7kxWDGxWmVRnjqr7TN19mxZFb+D+PwMHaQM2C7sztUi5N32OXyrlZdugm/5y5T9OSCS8ycetxMOPWnePk7WcER0STz92WIY0L06Col2mbgNBIxqw9y9bzvkREx1A+twsTW5egkJd9muZXFCXzkFJyas08zm/8haBH97FycKFQ3Q5U+XBMgtsfWPwlNw7+Q5D/fawd3chfoyUVPxiOmbklAEH+99kzbxgPzh8mJioCW7fsVOw8lAK12gBwdPk0Lm5dTsjTR1jaOZKjTC0aDJ6XJu/twcWjRAQHUG/gHPQG4+Kg9h45yV6iarzt5jR2pfHIxeSr3oLnfndY0r0MDYf9yLl/fsbv6imcvPNTf/BchNCxc/ZAHt+4gFve4tQfMg8Hz1xpkl1RlMxJSsnqRbP4a/kiHj24i6OzKw1ad6LnsIkJbr9gymj2bV2P3/27OLu6U6tZWz76YiwWlsYx99GDu8wcO5Czxw4QGRGOR7YcdB8wirotOgDwy6yv2bj6F576+2Fn70S5GnUZNeOnNHlv544fIijwGcOn/YjB3DjmeuXIRalK1eNtV9PHignzllOrSRse3r1Nx+qFGDt7CX8tW8ilM8fJmbcgI6cvROh0fDuiN/9eOkf+oiUZ9d1ivHL4pEl2RVEyJyklNzcu4Pb2pYQ/uY+5vQvZq7WlUMdRCW5/eeVX+B7fRNjj+1g4uOJVqQUF2g1BH3edG/bkPhd+GcXTy0eIiQrHyiU7BdoOJluVVgBcWzODu7tXEBHgj8HGAdfiNSnVa06avLdnV44RFRJIyc9mojMzjrnWbjlwKVwl3nb/dPKiTP+FeFVsRqj/XXb1r0DpPj9we/sSAv49jW22fJT8fBZC6Di7aAhBdy5gn6sYpXrNxdo9Z5pkVzIvVQRWUuzrDRdZcuAm41sVo3JeV54ER3DufmCi21ubm/Hd+2XwdLDkql8Qw1afxsJMx7AmRQAY/scZIqJi+LN3dWwtzfj30YtvxTacuc8Pu67xQ9fyFM5mz+OgCE7cfppkvjxDk+4bWTGPKys/q5LkNtkcrfioel6+3nCRhsW8MNO/2j47JCKGOoU9Gd6kCJYGPX+duk+PxUfYObQu+T3sAOi/4gT/Pgrml48r4WhlYPI/F+m04CAHRtbHylyfZAZFURSAQ79M4tw/P1P9k4lkK16ZsMAn+P97LtHtDZbW1P1iNrauXjy9fYVdcwejN1hQqesIAHbPHUJMVAStp67D3NqOgHvXTfte3/83J//8nobDf8TVpwihAf74Xj6RZL75rZMusnoVq0TLib8l+JyNkzsyNpbr+9dToFZbhBBJvtbLjiybSvWek7D38mH33MFsmdITK0dXKncbiZWjG9un92bvDyNoPmFFsl9TURRl4Tdj+WvZQnqNmUrJCtUIeOLPtYtnEt3e0tqGYd/Mx9UjO7evX2L6qL6Ym1vQY9A4AGaMGUBkRDgzV27Gxs6eO/9eNe27Z9Nafls4k7Gzl5CnYFGePfHn4qmjSeZrVMQ1yeeLl6/KtCV/Jfics5sHsbGx7N60hnot3kvRmPvzzEn0GfMNXjlyM2N0P77s1w1HFzc+HjwBJ1c3Jg/6hFnjBzHlpz+T/ZqKoihXfvua29uXUqTLeJwLVSLy+RMCb59PdHu9hRUles7A0tmL4HtXObd4KDozcwp2GAbA+cUjiI2KoNLoPzCzsiP44Yvr3IdHN3Djnx8o1ecH7HMUIuL5YwKunUwy3+YP8yb5vHOhilQYlvC1poWjO8hYHh7ZQLYqrVM05l7981uKdJmAtXtOzv08nFNzPsfcwZWCHYZj4eDKmR/6cWHJaMoPWZrs11QUUEVgJYVCIqL5cc91vmxdgk6VfADI7WZLuSTaIQxsWMj055wuNvSrX5Afdl0zFYHvPQ2laclsFM1u7IOTy8XGtP29p6F42FtSq5A7Br0ObydrSuVMukfZjiF1knz+vxnIr9OvXgFWHL7F8sO36VY19yvPF83uYMoMMKBBQbZeeMiGM/f5okEhbvgHs+W8L2v7VqdyXG/huV3KUXbCZtacuEvnyj7JyqEoStYVGRbM6bXzqf7pJIo07AyAY7Y8eBUun+g+FToNNv3Z3iMn5d4bwKk/55mKwEGP7pG3WjPc8hQDiDdTNujRXWycPchZpjZ6MwN27t54FCidZMaO3+9K8nkzc6tEn/MsXI5y733B9ul92DNvGO75S5O9RFUK1m5n6gecmNJtPsenQv24P/diw/jONO46Au+SxhltJZp/zJ55w5N8DUVRlJeFhgTz+09z6DN2Gk07dAPA2ycvxcpWSnSfbv1GmP7slSMXXXoN5beFM01FYL/7d6jZqBX5ipSI28bHtL3v/Tu4uHtSvno9zAwGPLLnpFCJsklmXLTxSJLP/zcDOSFFy1SkS++hTB70CTPHfEGhkmUpVakG9Vu+Z+oHnJgOPfpRqXYjAN77pD8jerRl4qCxlKlibJ3WuutnzBr3RZKvoSiK8rLo8BBublpIkQ8mkKPW+wDYeObGqUDid/3mbzPQ9Gdrtxzka9mPG//MNxWBwx7fw7NCU+xzGe9SfnmmbJj/PSwcPXArXhOdmQErV28c85RKMmP1yduTfP6/GcgJccpflrwt+3Fmfn/O/zwSx7wlcSlchWxV25j6AScmd5OeuJeuC0CeJp9x/NuulGk/FNeixrvlcjX4iAu/jEzyNRQlIaoIrKTIFd/nRETHUr2AW7L3+fv0fRbuuc7NxyGEREQTKyUxsdL0/Cc18zJ09Wl2XfKjegF3GpfwomQOY6G3eansLNz7LxW+3EKtQh7ULuxBw2KeWJglXsjN7Wb75m/wJY7W5vStV4DpWy7Rrtyrg3RIRDTTt1xm2wVfHj0PJyomlojoWIpkMxaGr/kFoRNQzsfZtI+9lYHCXvZc9X2eKhkVRcncnt65SkxUBDlK1Uj2Ptf3ref0ugUEPrxJVFgIsbExyNgY0/MlW/Vk95zB3Dm+E+9SNchbpQnu+UsBkK96S8789SNLupchV9k65CxXhzwVG6E3t0j0eI7Z8rzx+wOo3H0Updp8zr0z+/C9fJyLW5ZzfNV3NBu3jBylE3/fLrlftCCydjT+m+TqU9j0mJWjG1HhIUSFh2KwtH6rjIqiZA23r10iMjKCslVrJ3uf3RvX8Mfiudy/dYOw0GBiYmKIfWnMbfdhb6aP6suRPVspW7U21Ru2oGDxMgDUbtKGP3/+no7VC1G+Rn0q1KxP1XrNMLdIfMz19kl6VtrrfDJkAh0+7sfJg7u5eOooG3/7hWVzp/L1oj+SfN95ChUz/dnJ1di/Mk/B+I+FhYYQHhaKpZUacxVFeb3ge1eJjYrAtWj1128c5+GRDdzc9COhfreIDg9BxsbGu871afQx5xcPw//MLlyLVcOzXGMc8pQEwKtSc25tWcSuARVxLVEL9xK1cS/bAL0h8THXxvPVyWApUei9EeRp8ilPLhzg2fUT3N29kut/zaLcoCW4Fkv8fdvnKGL6s4WDW9xjhV96zJWYiFBiIkLRW6gxV0k+VQRW0tSJW0/5bOkxBjUsxITCHjhYGdhy/iET/npxi0enSj7UKuTBjou+7L3qT/OZe+lbrwBDGhcmu5M1B0bWZ99Vf/ZdfcSEdeeYvvkSG7+ohY1FwqdvarSD+E+P6nlZvPcGC3Zff+W5CX+dZ9dlP8a1LEYeN1usDHr6Lj9BZHTs6184BbeCKIqiJJfvpeNsnvIJFToPIVfZiVjYOnDj8GYOLBpn2qZowy7kKlOHW8e2cff0Xn4f2IRy7/WnYpdh2Lllp8vCw9w9vZd7p/awf+FYji6fRoeZWzBY2iR4zLdpB/EfK3tn8ldvSf7qLanSfQyr+tTm2MpvkywC6/Qv/RsQN6bqzAwvPWR8TMpkjMmKoihv4MLJI3zZtyvd+o+i95j62Nk7cmD7BuZ99WJ2cNP3ulO+Rj0O79rCiQM76d22Np0/H8KHX4zGPVsOft1xlpMHd3Fi/07mfTWcJbO+5od1e7GyTnjMfZt2EP9xcHKhdtO21G7alp5DJ/Jx00osnT05ySKwmeHV8VWfwJgbG6vGXEVR0sazayc4Necz8rcZiFvJOhis7fE7uZVLyyeYtslZuxNuJWrhf3oHj8/v4+D4FuRt0ZcC7QZj5ZKdmt/u5/GF/Tw5v5eLy8dzdc10qn65EbNEJgy8TTuI/5jbOeNVqTlelZpTqONI9o2oz7W13yVZBBZmL1/n/veY4aXH/rvOlShKSqgisJIiBTzssDDTse+qP3mSMeP26M0neDpYxmsJce9p6CvbZXO04oMqufmgSm7mbL/Kor3/MqSx8ZsuS4Oe+kU9qV/Uk771ClB8zCaO3XxCrUIJrxafWu0g/tt2aJPCjPrzLO3Kx58NfPTmE9qXz0mzuIXjwqNiuPU4xPR7ye9hR6yE47eemtpBBIVHcenhczpWVAsVKYryes458qM3WHD39F4cs79+9teDi0ewdfWK1xIi6NG9V7azdctGsSbdKNakGydWz+bMXwuo2MV4G52ZuSW5KzQgd4UGlO3Qn586FeHhhaPkLJtwceBt2kEkRG8wx8HLh5CnvinaT1EU5W3lylcIc3MLThzYhXfufK/d/vyJQ7h6ZovXEsL3/p1XtnP38qZFpx606NSDFT98yx8/f8+HX4wGjO0bKtdpTOU6jen0+WBal/fh/PFDlK9RL8Fjvk07iIQYzM3JlisPT/wepmg/RVGUt2WbPT86gwWPL+zDxuv1d5Y9u3oMS2fPeC0hwh6/ep1r5ZKNnHU/IGfdD/h3/Vxubl5EgXbGa2O9uSUepevhUboeeVv0ZfvnJXh29ShuJWoleMy3aQeREJ2ZOdYePkQ8U9e5ijZUEVhJEVtLAx/XyMvXGy5gbqajcl4XnoZEcvZuAN2rvTpw53WzxTcwnD+P36WcjzO7Lvux9mT8gXr0mrPUKexBXjdbgsKj2HXZjwKexoXVVh25TUyspEwuJ6wtzFh/6h4GvUiy5UNqtYP4T/tyOZm/6zqrjtyO1684r5stm84+oFExLwx6wbdbLhMR/eJWlDxutjQq5sXQ304x7b3SOMQtDGdnaaB1maR7ACmKogCYW9tRsmVPDv0yCb3BguzFKhMW9Az/a6cp3uyjV7Z3yp6X4McPubLzdzwLl+fOiZ1c3b0m3jZ7548kV7m6OGbPS2RoELdP7MApZ0EALm1bSWxMNB4Fy2KwsuHa3nXozAw4ZE/8wvxt2kHcPLKFa3vWkr9m67git+Tm4S3cOrbdVJRWFEV5V6xt7Wj7YW9+/GYsBnMLSlasxvNnT7hy7hStPuj5yvbeufPz2PcB29atpGiZShzds40d63+Pt83s8YOoWKshOfLkJyToOUf2bMMnv3Giw6bffyUmJprCpcpjbWPLzr9/x8xgSLIA/TbtIA7u2MjOv3+nTvP25MidHyklB3f8w5Fdm/nwizFv/LqKoihvwszKFp9GH3N51WR0ZhY4F65EVNAzAm+eJVf9bq9sb+OZh/Cnvtzf/ydO+cvhf3Y3Dw6ujbfNhSWjcStVB1vPvESFBfHo7C5ssxcA4O6e35Cx0TjmLYOZpQ0PDv+F0Buw8Uz8WvZt2kH4ndzGg0PryFa5lbHILSV+J7fhf3oH+dsNfv0LKEoaUEVgJcVGNSuKo7WB77ZeZmhAGG52lrQvn3BRs0ExL3rVzs+YtWcJj4qhViF3hjYuzPA/XqyyHCslo/48w4OAMGwszKhewI3xLYsD4GBlYO6Oq0z46zxRsbEU8LDjp48qxivGpjWdTjC6eVE6/3go3uPjWxVn4KqTtJyzF0crA5/UzEdEVEy8bWZ2KsOYtWfptugwEdExlM/twopPq2BlnvzZyIqiZG1VPhyDhZ0jx1ZOZ9fjB1g7ulGo7nsJbpu7UiPKtOvD3gWjiYkMJ0eZWlT6YBi7vx9q2kbGxrLnh+EE+z/AYGVLjlLVqfbJRADMbRw4+fts9i8aR2x0NM45C9Bk9C/xFo9LTc45C2KwsmH/onEE+z9Ap9dj75mLah9PoGSrT9PkmIqiKEnpOWwidg6OLJ0zGf9R93Fydadhm84Jblu1XlM69vyCOV8OJTI8jHLV6/LRF2P4bkx/0zaxMpZZ4wfi/+AeVrZ2lK1Si16jpwBga+/AivnT+eGrEURHR5ErfyEmzl8Vb/G41OSTvzBW1rb88NUIHj28h97MDC9vHz4fOZl2H/VJk2MqiqIkpdB7IzHYOHBt7XeE//QQCwc3sldvl+C2HmUbkKfZ51z8dSwxkeG4lqhJgXZDOf/zi4WApZRc+GU04U8fYGZpg0vR6hTpYmyLZrC259+/v+fS8i+RMVHYZi9A2S9+ird4XGqyy14AM0sbLi2fQPjTBwidGdbuOSnceSw+jT5Jk2MqyusI1UNEe862FqcXda9Qsmr+5C+2pijvgm9gGJW/2hYYGhHtqHUWRdGSlYPLrjr9Z9bKW6WJ1lGUDOLvcZ2e3zq69RMp5WqtsyhKemLn6HRz5orNPvmLltQ6ipKFhIeF0qS4R1R0VJS51lkURUtCiPH52wwaV0DNRFVS0fZepYIiAvzKSSmvap1FSZpO6wCKoiiKoiiKoiiKoiiKoihK2lFFYEVRFEVRFEVRFEVRFEVRlExMFYHTh+iYWNWWQ0l/omIkAmK1zqEompMyWsZEa51CyUBioiMBYl63naJkNQJiYtR4qrxj0VFRCCHUNa2iQIyMiVLFByVVydhogbruzRBUETgdiImJvXXzcYjWMRTlFbcfh2DQ63y1zqEoWouJirwR8OCG+vCoJFvgg5sCuK91DkVJd4S4e//Wv1qnULKYB3duYG5h+VjrHIqSDtwPundFFR+UVBMdHkpUaJA58EjrLMrrqSJwOvA8PHrlskM3g9VsYCW9WX74VkRwRPRyrXMoitaiwkNWX9yyPDQmKlLrKEoG4H/9LKHPHsUCx7TOoijpTVDAs2V/r1wcqhanVt6ljauXRkdHR6mFOhUF/vE/u8cQ+fyJ1jmUTOLh4fWYWVidkFIGaZ1FeT1VBE4fNtz0Dzn/0eIj4afvPENdFCtaklJy3S+IEX+cjt56/qFvTKxcoHUmRUkHdocG+B/+e2zHsIeXjiFj1aRg5VWRYcFc3bOWtSPbhsZGR/WVUqrb4hTlVauunD15Y9KADyP/vXROXfcqaUZKyYM7N5k/eVTMpt9/fRIZHj5D60yKojUppZ/Q6WYdHN885NHpHcRGqwkOypuJCHzMrS2LubBkZEhUSOAXWudRkkeoC6/0QQhhbWnQjTXT6bqGRkZ7mOl1memDowGIBjLryWYAorQOkVpiYqXO3EwXJOD30MiYMVJKP60zKUp6IIQw1xvMR+oNFh9FhgV76/SGaCG0TvUKAZiRicak/6MD9KTD9yelRMbG6Myt7Y9EBAdMllJu0DqToqRXQggHSyvr8ULoOkeEh7nozcwy6HWv1AE6EJm0ybHUG/9fZMj/PjEx0TqDwTxEp9evDQsJHiOlvKt1JkVJD4QQAqH72GBt1y8q9HkRoTfEpL9L2gSZYVyvJrPOxsgw70/KWAEiRm9htS069Pl4KeUJrTMpyaOKwOmQEMIcsNA6RyqZjbEAPFDrIGmoN9AEaKp1kFQSJaUM1zqEoqRn6XScNgAHgMnAWo2zpKVVwBmM7zO9CZFSpvsLd0VJT4QQFoC51jneQB5gJ1ATuK1xlrRiBxwFegL7NM7yJqKllGFah1CU9EwIoQestc6RDO0x1hSqY6wvZEZlgNVARSC99+uIBUKlKihmOKoIrKQZIUQt4FegqJTyubZp0k7cP5yHgB+llIu0zqMoStYkhBgLlANaZuYLMiGEN3AKqCWlvKB1HkVRsh4hhAB2ABuklJm6xYAQojnwHVBcFVQVRdGCEMIVOA80l1Jm6vUWhBDTAQ8pZRetsyiZkyoCK2lCCGGFcabWYCnleq3zpDUhRAlgO1BSSvlQ6zyKomQtQogiwB6gtJTyntZ50poQ4jOgG1BN9d1VFOVdE0J8BHwGVM4KY5AQYhVwS0o5XOssiqJkPUKIpYC/lHKQ1lnSmhDCBjgH9JZSbtI6j5L5qCKwkiaEEJOBPFLK97TO8q4IISYBhaSU7bTOoihK1iGE0GG8TXeZlPIHrfO8C3HveQ/wm5RyrtZ5FEXJOoQQXhgnOtSXUp7ROs+7IITwwFiUaCilPKV1HkVRsg4hRCNgHsa7EUK0zvMuCCHqAwuBYlLKYK3zKJmLKgIrqU4IUQrYinGgzjKLigkhLDF+KBgupczM/TgVRUlHhBC9gfeBGlmpH60QohCwHygjpbyjdR5FUbIGIcTvwFUp5Sits7xLQojuQF+gopQys/bjVBQlHRFC2GJsA9FTSrlV6zzvkhDiFyBQStlf6yxK5qKKwEqqEkKYAYeB76WUP2ud510TQtQAVmLsgxygcRxFUTI5IUQO4CRQXUp5Wes875oQYjRQGWiWmfsgK4qSPgghWgFTMbb/ylKL6Mb1Qd4KbJVSTtM6j6IomZ8Q4jvAWUrZTess75oQwgVjAby1lPKw1nmUzEMVgZVUJYQYBDTGeItcljy5hBA/ADop5adaZ1EUJfOK+0C+HjgipZykdR4tCCHMgePAZCnlSq3zKIqSeQkhHIALQCcp5V6t82hBCJEHOApUklJe1zqPoiiZlxCiIrAOY0uEJxrH0YQQ4j1gDMa73iK1zqNkDqoIrKQaIURe4AjG28T+1TqPVl76kNBZSrlH6zyKomROQoiOwCigbFa+MBRCVMBYDC8mpXysdR5FUTInIcR8ACnlZ1pn0VLchI8mQL2sOuFDUZS0Ffcl/wngKynlKq3zaOWlCR9HpZQTtc6jZA6qCKykirgBahuwWUr5rdZ5tCaEaAlMw3i7YJjWeRRFyVxeukWslZTyiNZ5tBZ3u6CLlLKr1lkURcl84tp9rcDY7itQ6zxaeqn12zwp5WKt8yiKkvnEtfuqBDTP6l82vdT6rYaU8pLWeZSMTxWBlVQRt1hEH4y3h6nFIjAtHHJNSjlS6yyKomQucYtFBEgpB2gcJV2IWzjkHPCZlHKL1nkURck8Xlr4d5iUcp3GcdKFlxaBLiGl9NU4jqIomUjcwr/7MN7pphb+xbQIdCeMa4BkmUWglbShisDKWxNCeABngYZSytMax0k3hBCeGH8v9aWUZ7TOoyhK5iCEqA8sxNj+IFjrPOmFEKIhMB8orn4viqKkFiHEJKCglLK91lnSEyHE10A+KWUHrbMoipI5CCF0wB5glZTye63zpBdxv5d9wHIp5Tyt8ygZmyoCK29NCPEbcENKOULrLOmNEOIj4HOgspohrSjK2xJC2GCc8dpLSrlZ6zzpjRBiKfBYSjlQ6yyKomR8QogSwHbUjNdXCCGsMM6QHiKl/EvrPIqiZHxCiM+BD4BqasZrfEKIIhgL5GWklHe1zqNkXKoIrLwVIUQLYDrGi2PV+/b/xPVK3g5slFJO1zqPoigZmxBiOuAupfxA6yzpkRDCFWOv5BZSyqNa51EUJeMSQugx9r6dL6X8Ses86ZEQoiawDOOdKVm6V7KiKG9HCOENnAJqSikvap0nPRJCjAXKY7zOVYU85Y2oIrDyxoQQ9sAFoKuUcpfWedIrIUQ+jB8iKkgpb2idR1GUjEkIUR74G+OH7cda50mvhBCdgOFAOSllpNZ5FEXJmIQQXwDNgbrqw3bihBA/AjFSys+1zqIoSsYUN3HqL+CElHKC1nnSKyGEOcZF4iZKKX/TOo+SMakisPLGhBDfA+ZSyk+0zpLeCSGGAvWBBuqDhKIoKSWEMADHgW+klMu1zpOexX2Q+Ac4IKX8Sus8iqJkPEKI3MAxjAseX9c6T3omhHDEOCmko5Ryn8ZxFEXJgIQQ7YHxGFsdRGgcJ10TQlQG1mCcFPJE6zxKxqOKwMobEUJUBVYDRaWUARrHSfeEEGbAUWCWlHKJ1nkURclYhBAjgOpAU/VF0usJIXJinClRTUp5Wes8iqJkHHFfJG0Gdkgpv9E6T0YghGgNTAZKSSnDtc6jKErGIYRwxtjKq62U8pDWeTICIcQswF5K+aHWWZSMRxWBlRQTQlhi7NczSkq5Rus8GYUQogywCWP/ZD+t8yiKkjEIIQoCB4CyUsrbWufJKIQQfYEOGHvLqcVFFEVJFiFEV2AAxjZealHfZBJC/AlcklKO1jqLoigZhxBiMRAspeyndZaMQghhh7Fw/rGUcpvWeZSMRRWBlRQTQnyJ8faDNlpnyWiEEFOBXFLKjlpnURQl/RNC6IBdwJ9Sytla58lI4hZ12g8skVLO1zqPoijpnxDCHTgHNJZSntQ6T0YihMgGnMHYQ/ms1nkURUn/hBD1gJ8w1haCtM6TkQghGgPfA8WllCFa51EyDlUEVlJECFEc2IHxdq8HWufJaIQQ1sBZ4Asp5d9a51EUJX0TQvQEPgKqSiljtM6T0QghigK7Mf6bdV/jOIqipHNCiBXAPSnlUK2zZERCiE+AT4DK6t8sRVGSEve5+BzQV0q5Ues8GZEQYjnwUEo5WOssSsahisBKssXNqjoA/CSlXKh1noxKCFEbWILxG8/nWudRFCV9emlWVS0p5QWt82RUQojxQGmgleqnrChKYoQQTYFZGNt2hWqdJyOK66e8E/hLSjlT4ziKoqRjQohvgOxSys5aZ8mohBBuGAvpzaSUx7XOo2QMqgisJJsQoj/QGqij+iu+HSHEIiBCStlb6yyKoqQ/cR+k1wBnpZTjtM6TkQkhLDD2sR8npfxd6zyKoqQ/L/VX/FBKuVPrPBmZECI/cAgoL6W8qXUeRVHSHyFEWWAjxklR/lrnyciEEF2AIUA5KWWU1nmU9E8VgZVkEUL4AMeBKlLKqxrHyfCEEE7ABaC9lPKA1nkURUlfhBBtgUkY2xhEaJ0noxNCVAH+wPhh46nWeRRFSV+EEHMAayllD62zZAZCiOFAbaCRugNDUZSXCSEMwFFghpTyV63zZHRxE0c2AXuklJO1zqOkf6oIrLyWGljShhCiHTARVeRRFOUlcV8SnQc6qC+JUo8QYi5gpYo8iqK87KUviYpKKZ9pnSczUEUeRVESI4QYBtRBfUmUatSEPSUlVBFYeS0hRGeMtxiUV7cYpJ6Xbvc+J6Ucq3UeRVHSByHEQiBStYtJXXG3e1/AeLv3Dq3zKIqivbh2MSeB8apdTOp66Xbv4lLKR1rnURRFey+1iyknpbylcZxMJa51ZxugtmrdqSRFFYGVJKlm42lLCJEdOI1xsD6vcRxFUTSmFo5MW0KIZsBM1MJPiqJgWjiyFNBazUhLfUKIaRgXfuqkdRZFUbSlFo5MW0IIPXAQ+ElK+aPWeZT0SxWBlSQJIZYBvlLKwVpnyayEEJ8CHwJVpZQxWudRFEUbQggrjF+6DZBSbtA6T2YlhFgJ3JVSDtU6i6Io2hFCFAV2Y2zLdV/jOJmSEMIa479r/aSU/2idR1EU7QghPgZ6ApXVZ960IYQojrHQXlJK+UDrPEr6pIrASqKEEI2B7zHexhWidZ7MSgihw/gh5A8p5WyN4yiKohEhxBTAR0rZUessmZkQwh1jUaKxlPKk1nkURXn34mZM7QeWSCnna50nMxNC1AN+wniHS5DWeRRFefeEEF7AGaCelPKs1nkyMyHERIw97ttonUVJn1QRWEmQEMIW48JEH0spt2udJ7MTQhQEDgBlpZS3tc6jKMq7JYQoDWzG2KbAT+s8mZ0QohswAKiget0rStYjhOgDdABqqd6JaU8I8TMQJKXsp3UWRVHePSHEH8BlKeVorbNkdkIIS4ztJkdKKddoHEdJh1QRWEmQEGIWYC+l/FDrLFmFEGIkUB1oovrSKUrWIYQwA44As6WUS7TOkxXE9aXbAuyQUk7VOo+iKO+OECIncAKoJqW8onWerEAI4YxxcklbKeUhrfMoivLuCCFaA5Mxtt4J1zpPViCEqAb8hvEOjGda51HSF1UEVl4hhKgErMU4aDzROk9WIYQwYPxQMlVKuVzrPIqivBtCiCFAA6CB+gLo3RFC5AaOYexNd03rPIqipL24L4A2AAellF9pnScrEUJ0AMYDpaWUERrHURTlHRBCOAIXgI5Syn0ax8lShBDzAIOU8hOtsyjpiyoCK/EIIcyBk8BEKeVvWufJaoQQFYD1GPsw+2udR1GUtCWEyAccxtiW4IbWebIaIcRAoDlQRxXgFSXzE0K8D4wAykkpI7XOk5XEFeD/Ak5IKSdonUdRlLQnhFgAxEopP9c6S1YjhHDAeAdGVynlLq3zKOmHKgIr8QghxgAVgBbqA7E2hBAzADcp5QdaZ1EUJe3EfSDeDmyUUk7XOk9WFNeK4xAwX0r5k9Z5FEVJO0IIV4yLQraUUh7VOk9WJITwBk4BNaWUF7XOoyhK2hFC1ASWYby7OFDrPFmREKIFMB3jmiNhWudR0gdVBFZMhBCFgb0Yb9O6p3WerEoIYYPxW7vPpZSbtc6jKEraEEJ8CPTC2I4gWus8WZUQoiSwDSgppXyodR5FUdKGEGIJ8ERKOVDrLFmZEOJz4AOMPZnVonyKkgnFLU52FhgspVyvdZ6sTAjxG3BDSjlC6yxK+qCKwAoAQggdxgLwCinlPK3zZHVCiAbAjxi/OQ3WOo+iKKlLCOGJ8eK4vpTyjNZ5sjohxFdAQSllO62zKIqS+oQQDYH5GK+rQrTOk5W99JljlZRyrtZ5FEVJfUKIr4F8UsoOWmfJ6oQQHhjvgmkopTyldR5Fe6oIrAAghOgFdAaqq2/l04e4GSvPpJQDtM6iKErqEkKsBq5LKUdqnUUxzVg5AwyXUq7VOo+iKKlHCGGL8QPwZ1LKLVrnUUx3H+4Dykgp72idR1GU1PPSHVYlpJS+WudRTHcf9gEqqrsPFVUEVhBC5MC4GFwNKeUlrfMoRkIIF4xtIVpJKY9onUdRlNQhhGgJTMPYfkD150onhBA1gBUYZwoGaBxHUZRUErfWgquUsqvWWZQX4tYhqQQ0U+uQKErm8NJaCz9IKRdrnUcxiluHZBuwWUr5rdZ5FG2pInAWFzcgrAeOSiknap1HiU8I0REYBZRVq1grSsb30kq9XaSUe7TOo8QnhJgPIKX8TOssiqK8PSFEBeAvjF/uPNE6j/KCEMIcOAF8JaVcpXUeRVHenhBiINAUqKe+3ElfhBB5gSMYZwP/q3UeRTuqCJzFCSHeA8ZgvB1LFRnTmbgi/d/AEVWkV5SMTwjxA6CTUn6qdRblVXFF+gtAZ1WkV5SMLa7IeByYLKVcqXUe5VVCiIrAOlSRXlEyPCFEHuAoqsiYbgkhBgONUUX6LE0VgbOwl9oNtJZSHtY6j5IwIUROjO06qqt2HYqScQkhqgMrUe0G0jUhRCvgG4y97MI1jqMoyhsSQowCqqDaDaRrQoiZgJOUspvWWRRFeTNxE5e2AlullNO0zqMkLK5dxxFgrpTyZ63zKNpQReAsTAjxM/BcStlf6yxK0oQQfYCOGPs2q4X7FCWDiVt47DQwQi08lv4JIf4ArkgpR2mdRVGUlBNCFMK48FhZtfBY+ha3cN95oKeUcqvWeRRFSTkhRDegH2rhsXRPCFEKY8FeLdyXRakicBYlhKgHLMI4Iy1Y6zxK0oQQOmA/8KuU8get8yiKkjJCiIlAYSllO62zKK8nhPAEzgL1pZRntM6jKEryxV0z7QZWSynnahxHSQYhRCPgB4yfS0K0zqMoSvIJITwwXjM1klKe0jqP8npCiMlAHinle1pnUd49VQTOgoQQNhgH6j5Syk1a51GSRwhRBNgDlJZS3tM6j6IoySOEKAFsB0pKKR9qnUdJHiFED+AzoLKa1aIoGYcQ4jOgK8Y2WjFa51GSRwjxK+AvpRyodRZFUZJPCLEKuCWlHK51FiV5hBBWGOtBg6SU67XOo7xbqgicBQkhvgU8pZRdtM6ipIwQYhxQDmih+tspSvonhNADh4AfpZSLtM6jJF9cf7sdwAYp5Qyt8yiK8npCiOwYW+/UklJe0DiOkgJCCFeMbSFaSCmPap1HUZTXE0I0B2ZgbC0QpnUeJfmEELWBpRjvwAjUOo/y7qgicBYjhCgHbACKSyn9tc6jpIwQwgLjInETpJSrtc6jKErShBADgJZAHfXFTcYjhMgHHAYqSClvaJ1HUZTExX1xsw44JaUcr20a5U0IIToDwzD2co7SOo+iKIkTQthj/OKmm5Ryl9Z5lJQTQiwEoqSUvbTOorw7qgichQghDMAxYJqUcrnWeZQ3I4SoDKwBikopn2qdR1GUhAkhfIDjGNsJXNM4jvKGhBBDgXpAQ1XIV5T0SwjRDpgAlJFSRmidR0m5uEL+P8B+KeXXWudRFCVxQoi5gIWU8hOtsyhvRgjhCFwA3pNS7tc4jvKOqCJwFiKEGAHUAJqoD7IZmxBiNmAnpfxQ6yyKorwq7oPsZmCnlHKq1nmUNyeEMAOOArOklEu0zqMoyquEEM4YZ6S1lVIe0jqP8uaEELmAE0BVKeUVrfMoivIqIURVYDXGVgLPtM6jvDkhRBvga6CUlDJc6zxK2lNF4CxCCFEAOIjx9qrbWudR3o4Qwg7jh50eUsrtWudRFCU+IcQHwECMbQTULa0ZnBCiDMaifnEppZ/WeRRFiU8I8RMQKqXsq3UW5e0JIfoB7TD2do7VOo+iKC/EtSc8DYyWUv6pcRwlFQgh1gAXpJRjtM6ipD1VBM4ChBA6YCewVko5S+s8SuoQQjQB5mAsSoRqnUdRFCMhhDtwDuNdFye0zqOkDiHEN0AOKeX7WmdRFOUFIURdYDHGGWlBWudR3l7coqoHgJ+llAu0zqMoygtCiAlACaCNurs4cxBCZAPOYFzD5JzWeZS0pYrAWYAQ4hOgB8bbqmK0zqOkHiHEcuCBlHKI1lkURTGK+3t5X0o5VOssSuoRQlgDZ4EBUsoNWudRFCXe38v+Usp/tM6jpB4hRFFgN8ZblO9rHEdRFEAIUQzj5LJSUsoHWudRUk9czehjoIqqGWVuqgicycV9q3Ma47c65zWOo6QyIYQbxrYQTaWUx7XOoyhZnRCiKTAbNUM/UxJC1AF+wTjj8LnGcRQly4uboe8tpeykdRYl9cXNOCwJtFYzDhVFWy/N0F8spfxR6zxK6nrp7vF1UsqZGsdR0pAqAmdycf1dzkspx2qdRUkbcb1HBwHlVe9RRdHOS726P5JS7tA6j5I24nqPhkkp+2idRVGysrhe3Zswfun2SOs8Sup7qffoGCnlHxrHUZQsLa5Xd1ugturVnTm9tI5UOSnlLY3jKGlEFYEzsbiVHr/CeLtGhNZ5lLQhhBAYFyzaJaWconUeRcmqhBCzAVsp5UdaZ1HSjhDCGWOxv72U8oDWeRQlKxJCGICjwHdSyqVa51HSjhCiKvA7UFRK+UzrPIqSFQkhcgEnMLaXvKJ1HiXtCCFGADWBxuoOjMxJFYEzKSGEI3ABeE9KuV/jOEoaE0L4AMeBylLKaxrHUZQsRwhRGfgTY5uAp1rnUdKWEKI9MAEorb5kVZR3TwgxFKgLNFIfUjM/IcT3gIWU8mOtsyhKVhM34egfYL+U8mut8yhpK+5L1mPAt1LKZVrnUVKfKgJnUkKIhUCklLK31lmUd0MIMQBohbH/s7pFR1HekbjbVU8C46WUv2udR0l7cR+I1gJnpJTjtM6jKFmJECI/cAhjG6ybWudR0p4Qwh7jHRjdpZQ7tc6jKFmJEKIzMBRjiwDVejALEEKUw1j4Lyal9Nc6j5K6VBE4ExJC1AaWYrxtSi1ck0XENes/BCyUUi7UOo+iZBVCiHFAGaCVmpGWdQghsmPsVVlbLbyqKO9G3BcwO4H1UsrvtM6jvDtCiGbATKCEWnhVUd6NuEXIzwHNpZTHtM6jvDtCiG8BLyllZ62zKKnrjYrAQghbwAPQp3oi5W1ZAH8DUzBeJGdWkcADKWXkuz5w3Ky/bIDhXR87GQoCS4AWgFokJXUFZLaFZ4QQ1oAnYKZ1lgwsL7AcaAn4aZxFAk8ycjuKuAJPNsBG6yzJ1BFoE/f/6g6MtBWC8d/9d/5Fi7ruTVfaYfz71oHM83funV3TCiGcAFdApPWx0sh3wH3gW62DZEERGM9TzWeCxl0reAJ2WmfJAr4BnmKsLWQE6eY8TQ4hhA7jda+11lkSYAVsAL4E9micJbMJA+5rdfd2iorAQojSDlaG2WGRMRXsrcwi9TqhZjylM1JiAeiEIEzrLGkpMjpWFxoZozM30/0TFB7d613cpiCE8DK3tp8XHRXeyGBlG6M3M6TLDx/Gc0DqhBCZ+hx4l6SUIjI0yKDTm/nGREVMjomMWKB1prchhChk5+A4NzwstJqtnUOUmZmZGsvfkJTSGogSQmh+sRkbGyOCngeaW1ha/Rv8/PnI2NiYdVpnSi4hhN7MwmoCQvep0OlsDBbW0RmmRCGlNYhoBO/8S8ksQ0JURKiZjI0JRbIwOiJ0tJQyJq0PK4Qo7ejoODs0NLSCo4NDpF6vV2OltoSU0ibu+ibN//u/K5GRkbrgkBCdhYXFP8+fP0+Ta1qdTt/M3MZuSnREeH4LG/tIodNl1HM5U54DGUFMVKQuKjwUvZlhTWRYcF8pZeC7ziCEEDpzy+E6nVk/iXQ0WNpEITLKxUIGJDGTSEshRAjGiQbpXkxUhC4mIgydmWFNtEbnaXIIIQx6C+upUsruOr3BUmduGZ0+z2VphsSSDHQOpHtSEhsZbhYbExUhhFgaExE65F1PbEx2EVgIUdTKoD80vkURu7ZlvLG1VBPHFG35BoYze8e1qN+O3b0bEhlTOi1bXwghnM0sbU4XadLdq3irXmbWTu5pdSglnZKxsfhePMyubz8PDQ96Ni46IjRDzkIRQuSxtLI60XvYeIdW73cX9g6OWkdSUlFUVBSHdm9jZJ8Pw0KDg7pGR0f/oXWm5DC3tlvilKNAuxp9Z1g7+xRBpMsLYUVLUkqe3r7Evu+HhD69dXFDZGjQe2l5PCFEUWsrq0PTpnxl1/m99tjZqQlnStp58PAhU76dEbXk1xV3g0NCUvWaVqfTtzBY2axqPHSelU+5uugN5qn10koW8/zRPQ4tnRpxdf/6a1FhIeWllOHv8vgGK9uZ1m7eH5ftNcvGMU9Jda2gJCj08T0urp4W8eDwhmvR4e/+PH0dIYTQW9r+ZZ+nZN1CH3xtbZM9v9aRFA2EPLjGlWVjwwKvn9gfEx7c8F3e6ZbsIrCdpWFZ3zr5OvWvl1+Ntkq6IaWkw4LDIfuuPe4npVycVscRQtcnV6VG3zQYtdQqrY6hZAyBD27wZ98aITGREU4Z5Vajl1lZ28x6r/unvQaOm6K+ycvEjuzbycCP3rsV9Dwwt9ZZXkcIkdPM0vpK12UXLQ2WGaULhKKVqPBQfv2gaHhUWHBRKeWNtDqOvb39smGDBnQaMWSQuu5V3gkpJQ2btw7ZsWt3ql7TWto6XGky4scCucvXS62XVLIwKSXL+9YLenTtTHcp5Zp3dVwhhLPOYPGgyYLTFua2Tu/qsEoGJaVk14iGQQE3zr7T8zQ5hBDFDLbOR6rPOmGtM1hoHUfRUGx0JPsHlA+JfO5fQ0p58l0dV5fcDaNjY5u3Kp1dXQgr6YoQgvfK57BxtDZ0TMvjWNg6dMpf5z1VAFZwyJYHW7ccMUAlrbO8Cb2ZWdvGbTqqAnAmV75qLaSUnkKInFpnSYYmuco3iFUFYCU5DJbW+FRuAtA0LY8THR3dvGP7tuq6V3lnhBB07fy+jbOTU6pd0wohPGNjonPmKlMrtV5SyeKEEBSt/76duY19+3d86AauhSpGqgKwkhxCCHLVes/OzNruXZ+nySCaeVRsYaYKwIrOzBzPyq0shE7f/J0eN7kbhkfF2nk7qRqYkv54O1khwCstjyGRHrZu3ml5CCUDsfPICcZFgjKcqMgIJ6/sObSOoaQxnU6Hi7tHBJARete422fLoy4wlGRz8MpjKYTOMy2PERYWZpczhxorlXcrV84cIFL1mtbdysElUqdX3/0qqcfewxud3uxdfzByt/H0SY+LcivplLVrDnR6Q7r7AK8zt8xu5Z5T9eVRALB0y2mmM7d8pxecyS4CA0KvyxwTIlrPO8iINedStE+5SduZt+vfNEqkvA29sR9USs7lN6HT6dL6EK+3YURLDswflqJ9VvYow9k136dRoqxJp9dD2p9zaUJKdHH535kerevz9Yj+KdqncbkCLJk3I40SZQ36jHOe6nQ6fbq7wFg/vCX7f0jZeLv8ozKcUeNtmhN6HUKnS+uBTOjf8ViZWuo0akrfgUNStE+ewsWZPnNOGiVSkisNxm2dSPO/Kqlj9ZAW7JibsjF3UdfSHP99bholUhIjdHrEu7++0AmdPl1e0+wd35rTP41I0T6be5fj6vp5aZRIARDGz+7p7pwRQpgJof24fPzrtlxeOjJF++wfWIHbG39Io0RZk9Dp4/7n3cmSXwsv7l4OQwoLepsHVMfaPG3/29x7FsqINefZf/0xVmY6WpfxZlzzIpibJZ7110O3WXvqPufvB/I8PJqjo+qS09n6le12XX7Et1uvcvFBIAa9juLeDvz5eZVXtnsSHEHd6XvxfR7OhQkNcLFVtymkJ/VG/oJOn7IvwVvN2IrB4tVzIjUFP7rHgfnDeHB2P3pzS/LVbEPFjyYkuvhIeNAzTqyYyv1Tewj2v4elvTM5yzegXJcRWNo7m7Z7fP0MR5dMxP/aKYROT+4qzajU40sMVrbxXu/art85u/Z7Au9dx8zShhzl6lF7oCrEpBczFv+GmSFl5+3yzQewsk7b9gAP793h6xH9Obp/N5ZWVjRu/R6Dxk3FYP76L+ellPTu1IIDu7by7cKV1G/exvTcpbOnmDlpJBdOn0Cn11OvaSsGT5iGtc2L83bq6IGcPnqI61cu4OrmyabjV9PkPSpvrsGolI+3bb7bilkaj7dBj+6x/wfjeGtmYRxvKyUx3gLsmf0FD87uJ+SpLwZLGzwKl6di9zE45Shg2iYiOIADC0Zy+8hmAHJVbETVTydjYetg2ubuiZ0cXzGNZ3cuoTdY4FG4ApU+Go9j9rxp9n6VlPtjxTIMhpRd4h/Zuwsbm7Q9d+/cvUufLwaza88+rKwseb9DO6Z9PQnzZI65TVu3Z8u27fy2bAntWrc0PXfy1GmGjxnP8ZMn0ev1tGnZgulTvsLW9sWYu2PXHsZN/IpzFy5iY21N187vM2n8GMzMsuRHoXSp+Zgl6FP436PT7G0YLNP2vH3+6B475w7lzmnjmFuodltqfpL0mLv1uwHcPbOf4Ce+mFvZkK1wear1GItLzgKvbBsdGc6Kfg15fPMCneZsw7NA6XjPX9yxmhN/zOPp3WsYrGzIXb4ejYeq4mF6UmnwYkQKrxdqT96MPo2vF0If3+P0ohH4X9iP3tyKHFVbU7zrOHRmCZ+7kcHPuLh6Go/O7iXU/x4W9s54lqlHkY7DsbBzfmm7AM78PJqHx7cA4FWuISU/+gpzG+P1gv+FA1z/50eeXj9FdGgQNp4+5GvyCT51OqXp+1VSpmS/RSk+byuM34TeIm1v3gt/fI/LS0fy9OIB9OaWeFZuTf73xyZ63gLERkVwdeWX+B1eR0xkOM5Fq1Go22QsnbOZtnl6YR///vkNwfcuo7ewxqtae/K2G87Ld8o8ObubG2unE3z/Mjozcxzylyd/xzHYeGWu69x0983Iu+BkbY6tZcouMlxtLbA2T7sLxZhYSZdFRwmOiOav3lX4oUtZNpx9wPj1F5LcLywqhloF3RjcsGCi22w695DPlp2kXVlvtg2syYZ+1ehUIeE2kQN+O0PR7PZv9V6UtGNp54S5te3rN3yJlYMrZml4gRwbE8PmLzsRFRZM8ynrqTNkATcP/s3hxWMT3Sf0qS+hT3yp+OE42s7ZQ+2B83h4/hA7p31q2ibkiS8bx7TDziMXLb/dQuPxv/HszmX2zOwb77XOr/+RIz+Pp0Tr3rSdu5dmX6/Fp2KjNHu/Sso5ODljY2uXon2cXd2wsk678zYmJoY+XVoRGhzML3/tZMoPS9m+YS3fjh+arP2X/vAdCd0d8Mj3AT07NCZ7rtws27iPeSv+5t8rlxjT7+N428XGxtK8Qxeat++cKu9HSX1vOt6mZUEiNiaGzROM422LqeupO2QBNw78zaGfEh9vAdzyl6LWgNm898MBmnz5G0jJhlFtiYl+sbbljmmf8vjfszSe8BuNJ/zG43/PsnN6L9Pzz31vs2VSV7yKVqLtrJ00nfQHMZHhbBqfpi35lTfg7OyEnV3Kxlw3N1es03jMbd7mPYKDg9mzdSPLf/6JP9euZ/CIUcnaf8asuQmOuQ8ePqRB81bkye3Dod072LjuTy5eusyHn744d8+cPUezNu2pV6cWJw7uZeXSxfy9cRMjxoxPrbenpAIreyfMrVN23lo7pv2Yu3bM+0SGBfPe9L9pOvxHru1bz54fkx5zPQuUouGgOXRfeJA2X61GIvljeJt4Y+5/9vw4Dju3hDuAnFz3I/sWjadsu150XbCP9t+sI2/lxqny3pTUY27r9MoEldexsHdN0y+NZWwMByd3ITo8mBoT/qJ8/x+4f2QDZ5eOT3SfsKe+hD/1pViXMdSbvotyfefy+NJhjs36PN52x2Z/TsDNs1QdtZKqo1YScPMsx+f0MT3/5Mpx7HMWptLARdSbvps8Dbpx6sch3N2frtZny/IMtk6YpfC8Nbd3SdMvL2RsDKdmdCU6PIRyo9dSrNc8/I79w9UVE5Lc78rycTw6vpFiveZRbvRaosOCOT2jKzI2BoCgOxc4Nf0DnIvVoOLErRTvPR//k1u5vvor02uE+d/hzKwPcSxYgYoTt1Jm2G/ERoVzevoHafZ+tZLpvv4OiYhm2J/n2HjuIdbmenrWyMPRm09xtjFn9vvGb1ZbzztIIU87JrcpDhhbPXSumJMHAWGsPfUAO0szPq6em96185let9yk7XxUNTe9aqfNtwC7rzziil8Qx0fVI3tc7+UxzYowaPUZRjQphJ1lwt/S9KyRB4DTdwMSfD4mVjJ63XnGNCtMl0q5TI8X8Hj1Imvh3huERcYwoF5+dlx69JbvSEmpqPAQ9s8byq1DGzBY2lCsRU98Lx7F0t6ZWl8Yb3XbMKIlTrkKUfWzqYCx1UPBBp0J8X/Av3vXYG5tR9EWPSnZ5sU/xCt7lKFo0x6UaNM7TXLfP7WLZ3cu8/5Pp7B1yw5Ahe7j2DfnC8p/MCrBC3rnXIWpP/IX088O2fJQ8aNxbPmyM5GhQZhb23Hn2FYQOqp+/s1/7Reo1utb/uxbk8AHN3DIloeI4ECOLf2K+qOW4F261ovX9ymSJu9VeVVoSAhfDevLjo3rsLK2oUvPvpw6ehAnZ1cmzl4EGNtB5C1UhJGTZwHGVg+tO3+I74O7bF67Ghs7ezp/3JvuvQeZXrdxuQJ0/OgzuvUamCa5D+3exr9XLrL5+DU843okDxjzNRMGfUbfEV9ia5f4l2HnTx1n+aLvWbnlEHWKx2/htHfbRnQ6HaOmzPnvtl5GfzOXdrXLcufmdXLmNv67MuLrmQAsmTeDQ7t3pME7VJISFR7Cvu+HcjNuvC3eoie+l4zjbe248Xb98JY45ypEtc+N4+3yj8pQuEFngh8/4Poe43hbrEVPSrV9Md4u/6gMxZr1oGQajbf3Tu3i6Z3LdF78Yryt+OE49s7+ggpdEx5vAYo07mb6s51HTsp/MII/+tYiyPc2jt75eHb3KndP7KTlNxvwLFwegOq9v2X9sOYE3LuOo3c+Hl8/Q2xMFBW6jTaNyaXa92fDyNaEBT7BysElTd6zEl9ISAi9+g9k7foN2Fhb07/35xw4fARXF2d+/tF4i2adRk0pWqQIc2ZMA4ytHnp078rde/dZ9fuf2NvZ0a/XZwz+op/pdfMULk7vT3syaEDfBI/7trZu38mFS5e4efkcObyNLRqnTJpAz979mDRuDPb2iY+5x06cZPa8+Rzbvxuv3PnjPbdh0xZ0Oh3fz5xuGnPnzZpBqYpVuf7vDfLlzcPqP9dSpFBBxo823vaaL28epkyaQMcPPmTsyGEpLpgrKRcVHsL22UO4fuAfDJbWlGn9KfcvHMHKwYVGg41j7uohLXDJVZi6fYxj7qKupSnWqAtB/ve5sts45pZu1ZPy7V+co4u6lqZU8x6Ua98nweO+rdsnd/Hk9mU+WXoaO3fjmFv943Fs++4LqnYfhYVNwudOiabdTX928MxJ1W4j+fXzmgQ+vIVzjhfn8PWDG7l3Zj/NxvzMzaPb471GeHAg+xdPouW4peQqW8v0uFtudY37LkWHh3B60TDuH9mImaU1+Zr05MmVo5jbOVOu92zA2A7CPkchSvWYDBhbPfjU6UzokwfcO7AWMys78jX5mAItXlwbbO5djjwNP6JAi14JHvdt+Z3ZzfN7V2j0/XGsXY3nbrHOYzi5YBBFO47AkMD1gkPOwlQavNj0s61nbop3GcvBqR8QFRqEwdqO5/eu4nd6FzW/XI9LgXIAlO45jb1jWxL04Dp22fJRqE38FnB5GnTH//wB7h/+hxzV2qCkvZiIUC79Mhz/4xvRW1iTo+EnBF49hsHOmaI9ZwLGdhC23gUp1PVrwNjqIVvNTkQ8fYDvoXWYWdmRo0EPfJq+OEf3D6xAjnofkqvJ5wkd9q09ObeHkPtXqDbjKJYuxvM2/3ujubR4MPnaD8fM6tXzNjr0OQ/2rKTIJ9/hUqwmAMU+nc3+gRV4en4fLiVq4XdkPTbZ8pO3jbFVlrVHbvJ3HM25uZ+Rp9UgzKxseX7zLLHRUeTrMNLUncGnWV9OTmlPZNATzO0yz3VupisCT/j7IoduPOHn7uXxcLDku21XOXLzKY2LJb1+yI97bzC4YUG21c7HjkuPGL3uPBVzO1POxznJ/f5z+MYTOi08kuQ2/evmp3+9/Ak+d/z2M/K725oKwAC1CroRER3LmXuBVMvnmqwc/+/MvQDuB4Rj0OuoP2MPvs8jKOJlx+imRSju/eIWz3P3Apm76zqbB1Tnhn/IGx1LeTuHfxqH7/mD1B+5BGtnT079Nh3fi4fxqdQkyf3O/7WAsp2GUqLNDu6e2MGhH0fiWaQiHoXKJ+u4Dy8cYvNrZnKVaj+A0h2+SPA5v8vHcfQuYCpIAHiXqU1MVASPr58hW4lqycoRFRqM3mCBWdwtJjFREejMzHi5f63e3NJ4zItHcMiWh3undiFjYwgL8Of3XlWJDHmOW4EyVOoxAXtPn2QdV3k70ycM5cShfXz382rcPLLx43dfc+rIAeo0bpnkfst+nM3ng8fQfdtA9u/YwtTRAyldsSoly1VK1nFPHt5Pr04tktzm4/7D+Lh/wv0Fzxw/Qu78hUwFYIAqteoTGRHBxTMnqVCtVoL7hQQHMaJXV8ZO+x4Xt1fXXIuMiMDMzMDLvUQtLI3n7akjB01FYEVbhxaN4+H5gzQcZRxvT66aju+Fw/hUTnq8PfvXAsp1GkrbWcbx9sAC43j7X+H0dR6eP8TG14y3pTsMoEwS461TjvjjbY648db/+hmyJ2O8jQoP4cr2ldi6eWPnYTz//S4dw2Blg0fhCqbtPItUxMzSGt9LR3H0zodbgdLo9AYub11GoQZdiI4I4+qOVbjlL60KwO/Q4BGj2bv/AH+uXEY2L08mTZ3G/oOHaNW8aZL7zZw7j/GjRjB4QD82b91G/8HDqFqlEpUrVkhyv//sO3CQpq2TXmR9xJCBjBgyKMHnDh89SuFCBU0FYICG9eoSERHBiVOnqV2zRoL7BQUF0eXDj5k/Zybu7m6vPB8ZEYHh/8ZcKyvjdcT+g4fIlzcPERERWMaNw6ZtLK0IDw/nxKnT1KpRPcn3pby9PQvGcu/cQVqMXYKNiydHVnzL/fOHyVc16fP25Jr5VP5gGOXm9uHW8R3smjeC7EUrka1I8sbce+cOsXZ00mNuhY4DqPh+wmPug4vHcMlRwFQABvApW4eYqAj8rp0mZ6nXnztR4SFc2LoCO3dv7D1e3IkZ5P+AHXOG0GbSb5iZW76y3+0TxmvckIBH/PJJFSJCnuNZsAw1e36Jo5fPa4+rpI5zSyfgf/EQlYb8jJWTB5f//I7Hl46QrULSM7Kv//MjhTsMpkCLbfid2sGZn0fjUqiiqXD6Oo8vHebA10m3TyjYuv8rBdf/PL16HLvs+U0FYACPUrWIjYog4MYZ3Iol8/NZWDA6MwtTC4CnV49jZmmDc8EXfwddClZAb2HNkyvHsMuW8HVuVFgwVi5puo678pKrKyYQcPkQJfr/hIWjJzf/+o5nV4/gXjbp8/bu5oXkaTOIihO38PjMTq4uG4NjgQo45k/eefvsyhFOf5v0XY4+zfuRu0W/BJ8LvH4cm2z5TQVgAJfixvP2+c2zOBep+so+z2+dRcZEmQrAAJYu2bHJlp+A68dwKVGL2KhI9Ib446zOYElsVDjPb53FuXAV7POUQmdm4P7uFWSv1YmYiDAe7v8d+zylMlUBGDJZETgkIpqVR+8w5/3S1CxovFCc0aEkZSZuf82eULOAGz2q5Qbg4+q5+Wn/TfZde5zsInDJHI7sGFQzyW0crRPvueIfFIGbXfz+uy425uh1Av/nEcnKkJA7T0IB+GbLFcY3L0JOZ2t+PnCLNj8cZP+w2njYWxISEc1ny07wVetieDlYqSKwBqLCgrm6fQW1vvjeNKO1Rr+ZrOhe8rX7epeuRdFmxlvNHbLl4cLfC7l/Zm+yi8Bu+UrRZtauJLexsHNK9LmwgEdYOcX/YGZp74LQ6Ql9lrwZ5RHBgRxfNplCDbqY+vJkK1Gdwz+N5fTvsyje6nOiI0I5tmQiAKHP/AAI8r2NlLGc+m0GlT+ZhIWdEydXfss/I1vTft6BNG2DoUBoSDDrVi5h0pzFVK5ZD4DxMxbQoEye1+5buWY93u9h/Ga508f5WPnT9xzZtyvZReAiJcuyesfRJLdxcEx8/H7i74uLm0e8x5xcXNHr9Tzx90t0v0lD+1CldgOq1U245UiFarWZPn4oP82ZRtdP+xMWGsKsSaMBePzIN8m8yrsRFRbMle0rqP3SeFuz/0yWdUveeFus+Yvx9tz6hTw4szfZRWC3/KVoN/vNx9vQZ4+wckx4vA17zXh74Z/FHP55AtHhoTh656PZV3+iNxivO0IDHmFp74oQL9bnE0Jg5eBmel079xw0nfg726b0YP8Pw5AyFtc8xWkyYVWSx1VST3BwMD8vXcYvC+dTv25tABbNm0POAq+fGVi/Th16f9YTgD6ff8qcHxawc/eeZBeBy5UpzclD+5Lcxtkp8XPX1+8RHm7xz11XVxf0ej2+fomfu5/3G0jD+nVp3LB+gs/XrlmDQcNHMfXb7/iiX29CQkIZMXa88Zi+xrG8Qb26zJw7j2Urf6Nj+7b4PXrEpCnG2aYPfRMf75XUERkWzPmtK2g05HvTjNYGA2fxY+cSr903V9lalG5pHHOdsufh1LofuXN6b7KLwB4FStFlXtJjruVrxlzr/7vGtXJI3jXu6b8Xs2/RBKLCQ3Dyzke7KWswMzeOubExMWyc+ill2/bCLW8xAn3vvLJ/YNw17pEVM6j12VdY2jlxePk0fh/aiu4LD6Z5L2TFOAv41q6VlOszB48Sxs/5ZT6bwabPy7x2X/cSNcnbqAcAto0/5vqmn/A/ty/ZRWCnvCWpOy3pO8XMbR0TfS48wB9Lh/jnrrmd8dwND/BPVobIkEAu/jaV3HU7mz6fhQf4Y27v8sr1goWDKxGJvO7DE1vxP7+PmhPXJ+u4ytuJDg/hwd5VFP10lqkwWuTjGezrX/a1+zoXr0GO+h8BkLNBD+5uW8zTi/uTXQS2z12CipO2JbmNwcYx0eciA/0xt49/3hrsnBE6PZGBCY+5kQGPEDo9Brv4n/vM7d2IDDSeky7Fa3Jny488PPAnHpVaEhnoz82/vovb33gdYOXqTemhqzg3tydXlo5EyljschWj9ODlSb6fjChTFYFvPQkhKkZSOqej6TEbCzMKeb7+Nq8i2eLfhuZhb8Hj4MhkH9vKoCe3a9ouYvQmYqUEYEDd/DQvaWyM/W37Euy75s/q4/foWycfo9edp0JuZ5qVyJbUSylp6LnvLWKjo3B7aTEIg6UNTrkKvXbf/299YO3sSXjA42Qf28zCCodsry/apZWosGC2TOyMjYsXFT4cZ3rcOVchag2Yy+GfxnB82dcInRnFmn9iLIAIY19AKWOJjY6iSs+v8S5j/EBcZ/B8lnUtyu1jW8hbvbUm7ymruHvrBtFRURQv/eLCwNrGhnyFir523wJFisf72c0jG08fJ78NjaWV1TufVfv378u5cuEsK7ccSnSbfIWKMHH2T3w7bihzJ49Fb2ZGpx69cXHzQIgs2YY/3Xn+0DjeuheMP946J2O8dfm/8dbGxZOwwIwx3uar1Q7vUjUJeebH2TXz2DalBy2/+SfZhYTQZ37smT2AAnXeI1/N1kSGBXN82VS2TfmY5l+v/W8VbiUN/XvjJlFRUVQo9+KDnI2NDcWKvL4IXKJY/HE5m5cnj/yTVwgA4+zafHnf7bn764pVnD13nqP7Ey/iFS1SmJ9//IHBw0cxesJEzMzM6Pv5p3i4u5t6CDeoV4dpX0+k78AhfPRpLywsLBg9bAj7DhxKsM+wkroCHxjHXM+CLwpnBksbXHMVfu2+brnjn7c2Lp6EpuAa12BhhVN2bcbcwnXakatMTUKe+nH8j+/Z8FUPOs4wjrlHV32H3sycsm0TbwUgY43XuLV7TcanrPEat8nwBczvWIQbh7dQsJa6xk1rwb63kDFROOd7cb1gZmmDfY7XXy845Io/Lls5eRCRgusFvbkVtp65kx82lUWHh3BoalesnD0p1mXMG7/Ok8tHOTa7FyU/nIRzvtcXz5W3F/bIeN7a53lx3uotrLH1TnwNqf/Y5oh/3lo4ehD1PGXnrbWHdudtYlyK1yJ/x7FcXjqSiwsHIMzMydNyAAFXjpjqChEBj7i0aBBeVdvhUakVMeEh/LtmGmfnfkrZ4b9nquvcTFUEfhtm//cfVQhhKqAmx9u2g3Czs+DozafxHnsSEklMrMTN3iLBfZLD3d447b2Ax4um32Z6Hbldbbn/LAyAfdce8yAgjNXH7wHGFZgBSkzYRp/aeRnR5PUXaYp2/n/1eiEEUsYme/+3bQdh5eiO78X4MzLDnz9BxsZg7fTq7fIviwoLZvOE9wFoOHb5K7fD5avVlny12hL67JGxUCEE5/76AXtPY39ra2fjTE7HnC/+UTO3scfa2ZNg//tJHlvR1v+vyC6EIDY2+eft27aDcHHz5NTR+MXcZ08eExMT88oM4f8c3beLG1cvUTlv/G+ah37amRILK7FkvbFQ0aRNR5q06cgTfz+srG0Awa8LZuGdK/1dFCkpozP7/zt6BDIF5+3btoOwdnLH71LC463Va8ZbCxt7LGzsccieF4+C5filY35uHtxAgTodsHZ0J/z5Y6SUptk9UkrCAv1Nr3thw2LMLK2p9NGLL+vqDP6B5d1L4nvpKF5FkzeLX9GGwfDqtUJsbPKvc9+2HYSnhzsHD8e/Tn78+AkxMTF4eiR87u7cvYeLly9j/9Kt+ADvd/2Q2RUrsHf7ZgA6vdeeTu+1x8/vETY21ggh+G7O9+TO7WPa54t+fRjQtzcPfX1xcnTk1u07jBw3gTwvbaOkPy+v2g5x17gpGHPfth2EtZM7Dy7EH3PDApN3jfvfmOuUPS9ehcrxfdt8XNu/gSL1OnDn9F7unz/MzCbx2xWu7N+IgjVb0WT4AmzirnFdXrrGtbCxx9bFk+f+95I8tqI98X/nLin8fPa27SAsHd14ciX+uRsZZDx3LR1fba3zsujwEA5MNt7SX2X4MlM7vv9eN/L5k1euFyICH2Pxf6/7+PIRDk7uTJEOQ8nToHuSx1TSh/8fc1N63r5tOwhzBzcCrh2L91hU0FNkbAzmDgmPueaO7sjYGKKCnmJu/6JtQ+RzfxwLvrjbKVfjT8nZqCeRAX6Y2TgQ7n+P679PxsrdWFe4t/0XdBZW5O/44kuPYp/NYf+AcgReO4ZjwYpJv/kMJFMVgX1cbDDoBafuBJDLxTgrNzQymsu+QeRySdtbZt62HUS5XE7M3H6NBwFhZHM09tzZe9UfCzMdJV/q3ZviXN4OWJjp+Nc/hIp5jH8pYmMlt56EUCuuZcZvn1YiMvrFX+7TdwP44rczrPm8MnncUrZipPJm7D190JkZ8L92ytTLNjo8lGe3L6d5b9u3bQfhUagcp1bPIPjxA2xdjbPJ75/ag95ggWu+xG+vjgwNZvP49wBJo/Grk1xV978L7SvblqM3WJK9VC3jsQsbB+PA+9dNx44KCyb0qR+2bt4JvpaSenL45MHMYOD8qRN45zLOtAkLDeX65QvkyJW2M2/eth1EyXIVWThzMn4P7uGRzXiuHN67A3MLC4qUTHimQp8RE+j6efwPiu1ql2HguCnUbtT8le3/KyavXfEL5haWVKpZN8m8yrth72Ucbx9dfTHeRoWH8vT2ZezTuM/i27aD8ChUjpO/xR9v78WNt25JjLevkoAkJsrYbsqjcHmiwkLwu3wMz7i+wH6XjxEdHmr6OToizLRQxn/+mxWRkg8IypvLmyc3BoOBYydOmoqXoaGhnL94Mc2LmW/bDqJShQp8NfVb7t2/j3d2Y1F3285dWFhYULZ0qQT3mTR+DIP6x1+ormSFKkz7eiItmr3aS9Yjrpi8eMmvWFpaUr9OrXjPCyHI5mXsSbnq9z/I4e1NmVIp+XujvAmHbMYx1+/KKVMv26jwUB7fvoRDNp80PfbbtoPIVqQ8R1bOIMj/AXZuxjH39snd6A0WeOQvlewcxgk2L8bchoPmEBUeano++Ikva0a2p8mw+WQrary2zV7UOPY+u3fddOzIsGBCnvph7x5/UVolbdh6+iD0Bp79ewobD2OhKDoilOd3L5t+Titv2w7CuUA5Lq+ZSeiTB1i7GM+fR2f3ojNY4Jgn8XEvKiyYg193QiKpOnIlZpbx73R2LlCO6PAQnl49jktcX+CnV48TExFq+hng8cVDHJzShcIdhpCvac/XvV0lFVn9j72zDIzi6sLws7txdzdihBghBCe4u7sVd4fiwd2d4lLc3d0lBIdAQpQIcff9fizdsESKFtpvnz/tzNy5c2Y4++bOmXPPNZL4bVKgH2ofApy5mWmkhL1C1cjmh177W8tBaNt78fboMjLi3qGiJ/HbuKdXECoqo1Wi8BJCWjbuCESKxD29gkllycKDGXHvSH33Gh172dJBAoEAZV3Jx7fI24dQ1jNDy0YyOzU3q7BxrmRb/AXJof8G/lNBYHVlBTqWt2LmiRfoqythpKXC0vOvyfvoS9WP4lvLQdQoaURJY02G7HrI1KYuxKdlMf3YCzpXsEJTRRI89g2JZ+jOhyzvVAZPK8mAJTopg+jkTALfpwDgH5VMUno25rqq6KopoamiSLdK1iw48wpTbRUs9dTYdP0tienZtPWSBD7sPgn0xqVKymDYG2mgr/H1WchyPh9FVQ0c63Ti7pYZqGjpo6ZrzMM9ixGL8364737r9GTzMjXRtXLi8pJBVOw5jczkeO5snkrJ+l2kK9VH+/tyefEgaoxchZGjJ1lpKZya0pastGTqTdpKTmYqOZmSWtTKGrqIFJUAeHZ8A0ZO5VBU1SDc7zJ3Nk2jfPdJKGtIPozomNthXaEht/6YSNVBC1HW0OHBznmo6hhgXa7eNz4ZOX+HmroGLTp2Z9nMiejq62NgZMr6pXMkWTo/2G+/tRxEpRp1sSvpzMQhvRg1dR6J8bEsnj6eVp17oqEpKQ/0xPcek4b2ZObyTbh5lsPY1BxjU/MCfZmYWUqD4AC7Nq6mtFdF1DQ0uH3lAktmjGfoxJloaetI24S8fUNaairRURFkZ2fx8ukjAOwcS6GopPTV9yXn71FU1aBknU7c+aC36nrG+O5ZDOIf77ffqrcWZWqiZ+XEpcWDqNRrGhnJ8dzeNBWnj/X2lS+Xlgyi5ohVGJX0JPFdIG9vHsfcozoqWvqkxr7Db99yhApKWJeX6KSupSOWZWtxbeVovAcvAuDaytFYlauHjoXkd2ZVri6Pj6zlwa6F2FeTlIO4u20WGobmXxiAlvO1aGho8Fu3Loyf7IOBvj6mJsbMmreQvLx/YJz7jeUg6tWphUupUvTo3Z8Fc2YSGxfP7xOn0Pu3bmhpSTT37v0H9OjTny3r11LeqyzmZmaYmxUsVWZhYSET9F619g8qViiPpoYG5y9eYuzEKcyZ7oOOjo60zcIly6lftzZCoZBDR48xb9FSdm/fLLOgnJwfg5KqBq71OnFt03RUtSWae3vnIsR5eQj4sX77reUgrD1rom/txOkFA6nedzrpSfFc3TAVt4ZdUVaXaG7ES19OLxhIgzGrMXXyJD48kNfXj2PtWQ1VbQNS3r/j7t5liBSVsK0g0VxtE9kAouKHQJu2mY004KtrYY9dpYZcWjOBOsMWoaKhw83t81DTMZD2I+fHoqCijk3Njjz9cyZKmvqo6Brx8sDSf2Sc+63lIIxL10DLoiQPVg7BrdtUspLjebJ9Oja1O6P4YbwQ98aX+yuH4jV4OXr2nmSnp3BjZnuy05OpNGYLuZlp5GZKPlYoaeggVFBCy8IRY4+aPPxjDGX6LQTg4R9jMPGsK10U7v2zG9yc2wXbej2wrNqKjARJuTeBUIiy1tctdi/n81FQUcesWgfe7J2FkqYeSjrGvD2yVPLB/se67TeXg9B3q466eUmerRuGQ8cpZKfE83rPTMyqd0JBVeK3iQEPefbHUFz6LkfbrgwKalqYVe/I6z2zUNQyQFFDl9e7pqFhWQo91/zFO4NOrMbAvSYIhETfP0nQ8VW4DV4rDfQaeNQm5MwfBB5ejEnFFuRkpBCwb64kUFxEAPrfyn8qCAzg09SZtKwcum2+h7qSiH7VbHmfnImywq9dw0MkFLCjd3nGHXhCs5XXUVEU0crTgilN80sxpGfl8uZ9KulZudJ9W28Fs+isv3S7ywZJZtzS9h50KC/5SjylqTOKIiFDd/uRnpWLm4U2BwZUwlir4Eq0cn4eFXtO5XpGGmdndEVRVR3XZv1IT3gvMwXnV0QoEtFgyk6urxnL0bFNUFBWwb56ayr0nCptk5OZTmL4G3IyJSVIYgIeEf3qPgB7+8lOIW48+zBmbpKVP6P9H/Jg53yy01PRsXDAe9BCHGq1k2lfY+Rqbm+czNkZXRCLxZg4V6DxzAPyReH+IUb5zCM9LY2h3Vqjpq5Bl35DiX0fhbLyr/0BSSQSsXLHYWaNG0qPZjVQVlGlUasOjJwyV9omIz2NoDf+ZKSnFdNTQZ4+vM+ahTNIS02hhH1JJs1fRdO2slOjpo0cwP1bV6Xb7etIMn5O3n2FuZXN19+YnM+iUq+p5GSmceaD3ro170da/HsUFP8Feusj0dsjY5sgUlLBoUZrKvacKm2Tk5lOQli+3ooUlXn35AaPDq0hKzURVR1DTF0q0WLhKdR080uf1B6zjutrx3NyikRjrSs0oGr//N+DeWlvao9ex6ODK/E7sAIFJVWMSpal0bQ90gCGnB/PgtkzSE1NpUW7jmioqzN88ECio6NRUfm1fVckEnHs4B4GDR+Nd50GqKqq0Kl9W+bPmiFtk5aWxiv/16SlfZnm3r3/gKmz5pCSkoqTowNrli+hayfZEgCnz55j9oKFZGZmUdrNlUN7dha52Jyc70+1vtPIXpHGYZ8uKKmq49mqP2kJ7xEp/dpjBaFIRMsZu7iwYgy7RzZGQUkFp1ptqNZ7qrRNTmYa8WFvyPkQLFNQVCbs8Q0eHFhNZmoiajqGWLhVouOS09ISD59Lw7GrubxuMoendAbEmLtUoM3cg/JF4f5B3Lr5kLM+jVvzu6Ggoo59435kJr6XLqz6qyIQiqg8fgd+G8ZxZXIzREoqWFZthWvXKdI2uZnppLx7Q+6H8UJC4CPiXj8A4OywyjL9efscwNBF8n5WbugaHm2ayI1ZEp01LVuf0r1mS9sGX95DbmY6r4+t4fWxNdL9aoYWNFh1/8fcsBwZHDpOITczDb8lPVBQUceqfh+ykmIQ/uLjXIFQRJmR23i5dQL3ZzZHpKiKSeWWMiUacrPSSYsIIDcrXbqvZOdpCIQinq4aQG52OnrOVXHpu0wmszf28SWCji0nLzsLDStnSg/fjEHpWtLjes5VcR2wiuCTawg+sRqhkgradp6UGbMTkfJ/S3MFn5vaLBAIxJGLCk53/dXJzMnFa+YFBtawY0ANu59tjpwfwL23cXTbdPd5XGrW369G9ZUoa+q8bTLrkI2+rdvfN/5O5GZnsqtnGdxbDca9ZdELR8j55zkzvVNSyL1zfcRi8d6fbcuXoqSskn7xSYjKx1mp35OszEwaeDnQfeAIug8ovMaenH+GFt7uiW9fv6onFouLr5vxkxEIBFPLdhzj49V57E+zITc7kz9/K0PpVoMp3Uqut786vnuX8ODP+fNyc7LH/ahrCAQCcW5qwo/q/ruRmZlJCSc3Rg8fwshPSifI+fdx8/Ydmrft8Dw2Nu67jGkFAoGHtqnNlV5b7mv9fet/jpysTDZ0K4NXm0F4tRn0s82R84UE3j3HmQWDbqYlxlb5p64pEAiG29b/bZ5Hrzk/dbpUbnYmpwd64dhsIA5NB/xMU+T8DZG+57m/aujNzKR/zk8/BwUV9TV2rX/vb9Wgzz92zbzsTK6PLI91owFYN+z/j11Xzt8Ten4Lb/bN2ZKTlvTbP3XN/1wm8JOwRF5HJ1PGSpeUjBxWXnpDSmYOzT0KTieTI+dXIibgMQmhrzF0LEN2egqPDqwgOz0VW+8WP9s0OXKK5MUTP96+folrGS9SU1LYvHIhqSnJNGhe/AJCcuT8TGICHhMf+hqjD3rrt1+it3bVWvxs0+TIKZaHfo948cqf8l5lSU5OZv6SZSSnpNCuTaufbZocOUUS/eYxsSH+mJSUTDm/t3c5WWkplKze8mebJkdOsSS8fUJy+Gt07cuQk56C/5GV5GSkYF65+c82TY6cIkkKekLau9do2Un8NvjEKnLSUzCuUPyi2nL+P/jPBYEB1l4JJOB9CgpCIS5mWhweWFm62JocOb8yT46sITH8DQKhAvq2rjSZe1S6+I8cOb8q29cuIyjAH5GCAiVdSrP58AXpYmty5PyqPD6cr7cGtq40myfXWzn/DpauWMWr129QUBDh4e7G5bMnpYutyZHzq+J7cA1xYW8QihQwtHWl/aKj0vq3cuT8yrw+vpaUdwEIRApo27hQbdph6WJrcuT8qgSf/oO0yAAEQgU0rV3wmnhIutianP9v/nNBYDcLbc6OqPazzZAj54sxsHOn5ZLzP9sMOXK+iFJuHuw6e+tnmyFHzhdhYOdO66VyvZXz76OMR2nuXr/8s82QI+eLMLJ3p/PKCz/bDDlyvhidEm7Umnv2Z5shR84XoWXjRoXpp3+2GXJ+UX7t1dLkyJEjR44cOXLkyJEjR44cOXLkyJEjR843IQ8C/w0hcWmYjDqGX2jCzzZFjpzvSnJUCOubGvL+td/PNkWOnG8iPCSI0ibKPPN78LNNkSOnSJKjQljXRK65cn5tgoKDEanrcN/34c82RY6czyYxMoTF9Q2I9Jf7rZx/N6nRIRxsZ0J8gN/PNkWOnM8m/X0o57uZkRT46GebIucz+M+Vg/h/JCopg6lHn/MkLIHAmFTalLVgeccyMm123w1l+B6/AucGzW2EiqIIgAVnXrHorL/McUNNZZ5MrSfdPvE4gu23gnkcnkhcahYHBlSiir1BgX59Q+KZe/Il94PjEQiglIkWW3uWQ19D+TvcsZz/FyKe3ODutlkkhr8hJzMdDUMLnOp1wb2V7ErSWWnJ3N8+m7c3j5GRFI+GoRleXSdi99Gies9PbOLRwVWkx0eha1WSin1mYupS6R++IzlywPfODXq3qouNfUkOXpF9YU1JTmLlXB/OHz9EQnwsJmYWDBk/g/rN2/wka+X8v5GbnYXvnsW8vrSP1NhI1HQNcW85ELdmfQF4cXo7/hf3EBf8EhBjYOuGV5dxmLpUlOknNS6Su1tmEHL/AtnpKWiaWOM9cD5mbr/UIt1y/oVcuXadiT7TefX6NWlp6VhbWdKrezdGDR8ibbNl+5/06j+owLmpsZGoqKgAMHfBYg4dPcar129QVlaiQjkvZk/zwdXFWdo+KiqacZN9OHfhEgmJiXhXqczyRfNxsLf78Tcq5z/F6+vHeXxiC9EBT8jJykDfqiQVOo7ArlJDaZuYoJfc2j6P6DePSYwMpmKXMVTu+rtMPxu6lSEpKrRA/yXK16HljN0A5OXmcmvHfF5c2EdqXBTqesaUqtWGSl3HIhTJX//lfDuh1w/if2QVKRGBKKhqYORWDbduPqjoGAFwdWpLYp4XLBenaeFI3cVXP7uNHDnfk8ibBwk6uZq0yEAUVDXRc/HGocMUlD/4LUBOejIB++cRde8E2SnxqOiZYd92nHRRPXFeLoEHFxFx8wBZidEoaRthUrkVti1H/Wv09d9hpZxiyczJQ09dicG1HNhxO7jIdqpKIu6MryWz768A8F/YG6pzcGBl6bZQKJA5npaVg5eNLq3LmjNkl1+h1/ENjqfDH7cZWNOOac1dUFIQ8jIiGUWRPPFczpehoKqBa9M+6FqXQkFZlagXd7m+ajQKyqo4N+4JQF5ONicnt0FZQ4faYzegbmBGasw7RIr5HxwCrh3i5vqJVB0wH2PnCjw/uYnTUzvQdtUNNIzkC5jJ+edISohn0pCelPeuSXTEO5lj2dnZ9GvXCG0dXeb/8SfGpuZERYSjpCT/eCbnn+P8/L6kxryj2uBFaJvZkpbwntzMDOnxd09uYOfdgir9yqOgrMbjw2s5OaU9bZZfRNtcEhjLTEnkyJgmmDhXoKHPTlS09UmKDEZVx/Bn3Zac/xAaGhoMHtAPNxdn1NRUuXHrDgOGjkBNTZUBfXtL26mpqfH6ieyHtr8CwACXr12nf59elCvriVgsxmfmbOo1acHTB3fQ09NFLBbTqkNnhEIhB/f8ibaWFktWrKJek+Y8fXAHdXX1f+ye5fz7CXtyE0sPbyp3n4CKpg4vL+3n6PTutJ1/BAs3SVJCTmYaWsaW2FdpzM2tcwrtp9Pyc4jzcqXbqXFR7BhcG8dqLaT77u1djt/RjTQYsxIDG2di3j7j9MLBiBSVqNh59A+9Tzn/fWJf3uXeisG4dfXBrHwDMhNieLhxHPeWD8R7yn4AKo7eRF5OtvScvOxMzo+uiUWlZtJ9n9NGjpzvRYL/XZ6uG4pDxykYlW1AVuJ7Xm4dz9O1gyk7bi8giSv4zuuAooYO7oPWoqxnRmbcOwQfxRWCjq8i9MIWXPouRcOiFCmhz3n2x3CECkrYthjxs27vi/hlgsC3AmKZcfw5LyOTEQkF2BlqsKR9aUqZahGXmsWEg0+48zaO+NQsrPTVGFDDjo7lraTnt1x9EwcjDVSVROy5G4pQKGBEHQe6VbbG58hzDvqGoamiwLiGTrT1sgQkpR7Kz7rA6s5l2HIjiEdhiVjqqjKzpSs1ShoVZSqvIpOZfvw5twNjUVEU4W1vwPTmLhhpSQaWLyKSmHz4GX6hCeSJxdjoqzO9hQtVC8mY/R5Y6akxq6UrAMcfvyuynQCkNhaFSCQsts1fzy42JbPINlOOPOO3KjYMr+Mo3WdnqFHsdf/LRDy9yd0t04kLfoFAKELH3J5qw5ahZ12KjKQ4bq4bR+Sz22Qkx6NpbI17q4GUrNNJev7x8c3RsXREQVkV//O7EAhFlGk/glINe3B7w2TeXDmAoqom5bpOwKFWO0Ay7Xh377LUHLWW5yc3EfPmERpGllTuOxsLz5pF2hof8oo7m6cS+ewWIiVVzEt7U7H3DNR0jQGIC3rOrfWTeP/6IWJxHlomJajUZyZm7lV/yLMztC+NoX1p6baWiTVBt04Q+fy2NAj86vwuMhJjaTr3GCJFJQA0ja1k+nlyeC2OtTvgVL8rAFX6zSXswUWen9pM+e6Tf4jt/0Ue3LrGkhkTePPyGSKRCGs7R6Yt+QOHUi4kxMUyZ8JwfO/cIDE+FnOrEnQfMIIWHbtLz+/Vsi4lHEqioqrGkT3bEAlF9Bkxjrbd+rLQZwwnD+5GXVOLweOm0bRtZ0BS6qFR+ZLMWb2VPVvW8fzRA8wsrfl95mIq16hbpK0Br16wZPo4Hty+jrKKKhW8azJm+gIMjEwAeP3iKfMnj+KZ3wPy8vKwtLFlzPSFlK9a44c+Q5+R/WjWritisZhzxw/KHDuyeyvxse/ZcuQiikoSXza3svmh9vwXeff0Jnc2SzRXKBShbW5PjWHL0LORaO71tfmaq2VijXvLgTjVzdfco+Oao/tBc1990FzP9iNwbtSDmxsm8+byAZTUJJrr+JHm7uxVllqjJZr7/vUjNI0lmmv5N5p7e9NUIj7S3Mp98jU3Nug5N/+Q1dzKfWdi/oM0N9T3Eu8eXaXD+nuoausDBfW09pi1MtvegxYQdPskob75QWC/AytQ0zOi1qhV0nZaJtY/xOZ/K1ev32DcJB+ePn+BSCSipIM9G9asxNXFmdjYOIaMHMP1m7eIjYvDtoQNI4cO5rduXaTn12rQGKeSJVFTVWXLjj8RiURMHDuafr17MmrcRHbu2YuWphYzfCbRtVMHQFLqwc65NNs3rWft+o3c932IjbUVSxfMo16dWkWZyvMXLxk7cQrXbtxEVUWFWjWqs3jebExMJH765OkzRo4dzz3fh+Tl5WFXwobF8+dQs/qPWai5bBkPypbxkG6XsLHh0NFjXLtxSyYILBAIpDYWxumjshq8bcM6dE2tuHH7Nk0bNeT1mwBu372H761rlHZ3A2D1ssWYlXBk174D9O7R7fve2L+AsCc3ubphGrFBLxEIheha2FN/1HIMbEqRnhTHxVW/E/70NulJ8eiYWFO2zSBc6+fr694xzdCzdERRWZWnZ3chFImo0HEk7o17cOWPyby8uB8lNU2q9JiIcx2JviZGhrCxuycNf1/Lo+ObifL3Q8vYkpoD52BTtmh9jQ1+xdUNPoQ9uYWCkipWZbyp0W8m6noSn3j/9jmX104kyv8h4jwx2qY21Og/EysP7x/y7GoOmC2zXanLWALvnCPg5klpENikpCcmJT0BuLt7aaH9qOnIvk8+Pf0nymqaOFZrLt337vld7CrWx65iAwC0Taywq9iAiJfysll/EfP8Fk/+nEFSyEsEQhGaZnZ4DliCtlUpMpPjeLRxAjEv75CVHI+6sRUOTQdgU7Oj9PyrU1uiae6ASFmV4Et7EAiFOLUeQYm63Xiy1YfQ6wdRUNXEpeM4rKq1BSSlHs4MLk+5oasJPLOF+MBHqBlaUvq3mRiXrlGkrUlhr3iyfTqxL24jUlLB0NUb9x7TpVm3iSEveLxlMvEBfojz8tAwscG9+3QMXX/MWCHW/z6q+qY4NOkHgLqRNXYNevJo00RpGyUNXZlzQq4dIDczHeuPnuHntJEjS/zL27zeM5PUsJcgFKFuaodz78VoWDiRlRzHq+0TSXh1l+yUeFSNrLBu2B+zah2k59+f3Rp1MwdESqq8uybx2xLNhmNRqyv+O6cSeesQIhUN7NuOw7SKZBZi+vtQboyqgGv/lYRe2Epy0GNUDCwo2WUG+m41irQ1Jdyf17tnkPDqNkJFFfRcquLYaZo06zYl9AWv/vQhKdAPxHmoGtng2Hkaes4/ZsZY4psHqOiZYt1AMrNN1dAKy7o9ebV9krTNu2t7yE6OxWvSIYQKSh/aWcr28/o+hh51MSxTT3rcsEw9kgL+PeWIfokgcE5uHj0236NjeUtWd/YkOzePJ+GJiD5koWbm5OJmoc3gWvZoqihw1T+GsfsfY6GjirdjflbJQd9w+lW35eSwqpx5FsXkI8+4+CqaWiWNODOiGnvvhTJq72OqORpi/FGgc8bxF0xt5oKzqSabbwTRY9M9bk2oham2agFbo5IyaLnqBh0rWOHT1JmcXDFzTr2k++Z7nBhSFaFQwIAdvriYaXFqWFUUREJeRCSholB0Fuyy869ZduF1sc9oZ58KVLTV/9JHK0NGdi5lZ54nL0+Mi5kWvzdwws1CW6ZNSGwqpaedRUlBiKeVLhMaOWGt//mZDu+TM7kfHE9LT3OarbhOQEwq9oYajK7nKPNv9f9CXm4OZ2d2o2TdztQctYa8nBxiAh4jEEoysHOzM9G3c6d06yEoqmkS7neV66tGo2FogXnp/JenN5f349ZiAM0XnSb4zhlurZ9E6IOLWJatRYvF53h9cQ9XV4zA3KMaanom0vPubplGhV7T0bdx4dnJjZyd1Y32f9xFXd+0gK1pcZEcG9eMkvU6U6HnNPJysrm/fTZnZ3aj+YJTCIRCLi7sj34JF1osOotAJCIu+AWiYrIUH+5dgt++pcU+owZTd392WYaYgMdEvbiLZ6ex0n3Bt09iXKo8N9eNJ/jOKZQ1dbGt2pwy7UYgVFAkNzuLmDePcG85UKYvizI1iHpx77OuKwdycnIY1qMNLTv2YM7qLeRkZ/PiiR+iDxn+mZkZlHLz4LfBo9HQ1OT21YvMGDsIUwtLKnjnBxdOHtxN137D2HHyGpfPHGf+5NHcuHiWKrXqsfPMTY7u3cG0Uf2pWK0Whsb5frpkxgRGT52Po7MruzevZXiPNhy79RxjU/MCtr6PiqBny9q06NiDkT5zycnJYcWcKQzr3obtJ64iFAoZN6Abji7u/HnqOiIFBV6/eIayStEfwDYsm8eGZfOKfUardx7Fs2LRA+49m9cS9z6aPiPG88fi2QWOXzp1FI/ylZkzcTiXTx9HW0eXes3a0Hv4OBQVFYu9thwJebk5nJnRDad6nak1+iPNFX3Q3KxMDOzc8WgzBCU1TcL8rnLtg+ZaeORr7uvL+3FvMYCWi04TdOcMN9dPItT3IpaetWi15Bz+F/ZwZblEc9U/0tw7m6dRqfd09GxceHZiI2dmdqPjH3dRNyioualxkRwd14ySdTtTsZdEc+9um82ZGd1osfCD5i7oj14JF1ouPotQJCIu6IXMLIdP8d27hId7lxb7jBpN3Y2pa+GaG3T7FIYOZXh8eA2vL+5FpKSClVdtynebiKJq4R9z83KyyM3ORElDR6YfS89anJvXm3ePb6CuZ4xTvS64NOmFQCAotJ//J3JycmjZvhM9u3Vl+6b1ZGdn4+v3CNEHP83IzMDTozRjRw5DS0uL85cuM2DoCKwsLalds7q0n5179jFiyEBuXb7AsRMnGTF2PKfPXaBB3drcvXaZbX/upO+godSpWQNT03w/HTfJh4VzZ+Hu6sLqP9bTsn0n/J/4Ym5mVsDWiIhIatRvRM9uXVkwewbZ2dlMmjaDlu07cePSOYRCIV1+64O7myu3r1xAQUGBJ8+eyWTcfsqcBYuYs2Bxsc/oxKF9eFepXGybv3jo94hbt+8yZeI4mf3p6emUcHIlNzeP0u5uTJ88gTIepYvoBZKTU8jLy0NXRweAzExJwsPH9yIUClFWVubGzVv/d0HgvNwcjkztimv9zjT6fS15OTlEvXkkHdPmZGViZO9OuXZDUVLTJOThFc4vH4WWkQVWZfL19eWl/Xi2GkCn5WcIuHWay2snEnT/IjZetei04jzPz+/h3NLhWJWphoZ+vt9e2ziN6n1nYFjCBb9jGzkytSs9N99DsxB9TYmNZM/oprjW70y1PtPJy8nmxpZZHJnalY5LTyMQCjk5tx+Gti50WnYOgUhETNALFJSK9ts7u5YUGZj9i5Yzd0sDup9DdnoKypo6n93+U8RiMU/P/IlTrbYoKue/t5q7VuTRsU3EhbxGz8qB2OBXhPhdo3yH4V99rf8Sebk53FrQA5uaHSk3ZDXi3GwS3j6R+nJediY6tm44thiMoqom0U+u8vCPsagZWGDklv+RIPT6Qewb96Pm7JNE3D/D4y2TifK7iLFHLWrOPUPw5b34rh2FoVs1VHXzP0g92TED925T0bZ2JuDMZm7N70H9FbdQ1Svoy+nxUVz1aYlNzY64dfVBnJvDs11zuDW/OzVmnkAgFHJv2QC0rV2oOfsUApECiSEvEBbjyy8PLuPVoWXFPqMqE3ZiUKpiocf0ncrzbNccIu6fxaRsXbKS4wi7eQSTMrWL7C/owg6MPWqiZlBw3P4lbf6fycvN4dHS3zCr3gHX/isR52aTHPQEgSDfbzWt3bBpPAiRqiZxz67xYvPvqOibo+eS77eRNw9i1aAv5X2O8/7hWfz/nELsk0vou9Wk/LRTRFzbx/ONo9Fz8UZZJ99vX++ZhWMnHzQsSxF6fguPlvak8oIbqBTit5kJUTyY1RKz6h1x7DCFvNxsAvbP5dHS3yg35RgCoZAnawahaeVM+aknEIgUSAl9ibCYMe7bo8sJOra82GfkMfpPdEtWKPSYtkM53uyby/uHZzHwqEt2ShyRt4+gXzrfb98/OI22QzlebZ/Ee98zKKrrYFS+KSWaDUOoIHkX03EsR+iFraS+e426mQMp4f7EvbiOTZMhhV73V+SXCAInZ+aQmJ5NPRdjbAwkAUcHY03pcVNtVQbVtJdud62kzvU3MRx6+E4msFjSRJMx9UsC0L+6OisuvkFRKKRPNVsARtZzZOWlN9x9G0fT0vkD3e6VrWnuIdme2cKVy6/es/VmMOMaOhWwdcvNIJzNtJncJL9e2IqOHjhNPoNfWAKeVrqExaczoIad9B5KGBQfRO1W2ZpmHgUH3h9jol18Bu/fYW+kzpL2HriYaZGSmcP6a29ptvI6F0ZVx/ZDlq6nlQ5LO5TBwUiDmJRMlpx/TZMVN7gypgZ66kqfdZ2QuDQAFp55xeQmzriZa3Ps8Ts6rL/D2RHeuJhp/00P/y2y0pLJSk3Eunw9tExLAKBj6SA9rq5vSulWg6XbWg1sePf4GgFXDsoEgXWtnCj7IfDp1mIAj/YvR6igiGszyRdYzw6jeXRgBZEv7mJbJX8KTamGPaR1cSv3mU2Y7yWen9xMua4TCtj6/OQW9Eu4UKHHFOm+GiNXsa2jA+/f+GHk6ElKdCjuLQdK70HbzLbY+y/VsAe2VZsX26awgPSn7OzhTnpiLOK8HDw7jMG5YQ/psaTIYFIeX8eueivq++wiJSqEG2t/Jzs9lYq9ppGRFIc4L7fANGRVHSPSH8nrTX0uqclJJCcmUL1eYyxtJNl+JRzyNdLY1Jweg0ZJt9t0teXu9cucOrRXJghsV9KZAWMk2dfd+g9n84qFKCgq0rmP5A9nv5ET2bxyIX53b1G3aSvpee2695XWxf195mJuXj7Hvq1/MHjctAK27t3yB47OboyYnB9onbViE95OJjzze4CbZzkiwkLoPmCE9B6sStgX6Odj2nbrQ71mrYttY2RS9KD19YunrF00i+0nrkoDPZ8SFvKWuzcu07BlB1buOMy70GBmjx9GWmoKo6YWH4CWI+FjzdX+oLm6H2uugSkerfM117mBDe8eXSPg6kGZILCulRNenSWa695yAH77lyMUKeLW/IPmdhyN34EVRD2/i23VfM11bpSvuVX6SjT32anNlC9Cc/VsXKj4W77m1hq1ii0dHHj/2g+jkp4kR4fi3mqg9B7+TnOdG/bA7hs0NykyiMjndxApKlF3wmayUhK5sW48qbGR1JuwudBz7m6fg6KKOjYVGkj3JUcG8/zkZtya96PM9KHEBD7lxrrxALg27V1oP/9PJCUlk5CQSJNGDbCzlfipU8n82VPmZmaMHjFUut23RA8uXbnK7n37ZYLALqWc8Jkoea4jhg5m3uKlKCoqMHTQAAAmj/+d+YuXceP2Hdq0zPeL/n160q51SwCWLpjH2fMXWbt+EzN88rNh/mLtho2UdnVl7sx8rd26fh0GFjbc931Iea+yBIeGMnLYYOk92NsV76f9evWkbauWxbYxN/v7sYGVgzPvY2LIyclhyoTf6d+7p/RYSUcHNqxZSWk3V5JTUli+ei3edRrw8Pb1Iuv5Dh87Dg93NypVKA9I/k2sLC2YOHU6f6xcjoaGOktXrCYsPJyIyKi/te+/RmZqMpkpidhWrI+OmcRv9azy9VXTwJRybfNfgnVMbQj1u8bLywdkgsD61k7SOrdlWw/k3t7lCBUU8Gwp0deKnUdzb+9y3j2/i6N3vr6WbvwbJau3ACRZtUEPLvL4+Gaq9Cior4+Ob8bQ1oVqvX2k+xqMWc3qNvZE+vth6iTRV682g6T3oGtevN+6N+4hk21bGBqFBKSLwu/oRpJj3uFcu91nn/Mpwb6XSYwMxr1hV5n95doNJSsthS19KyMUisjLzaFCx5F4NO1ZRE//X+SkJ5OdmoiJVz00TGwA0DTP92VVPVMcm+XXFC9h3JX3T68TeuOQTBBYy6Ikzu3GAGDfpD+vDq9AIFLEvlEfAEq1GYn/kZXEvbqLecWm0vNs63XHorLEl0r3mEn0o8sEnt2KSwfZD1kAb89uQdvaGdcu+TMXvQav4HhPJ+ID/dCz9yQtJgyHpgOk96BhUqLY+7et1w2LysWXXFD96AP3p+g7elF++FrurRhIblYG4twcjNyr4zW48ABd8rsAYp7fouKYLUX2+Tlt/t/JTU8mJy0RwzL1UDO2AUDdLN9vVfRMsWmcn/SkZmRN/PPrRN4+LBME1jAviV0rSVkYqwb9CDq+EoFIAav6kvFZiRYjCDqxigT/exiXbyI9z6J2N2ld3JJdZhD35AphF7dh30a2bjlA2IWtaFi54NA+f1zh0m85VwY4k/T2Edp2ZciICcO6YX/pPagZF++3FrW6YlyhabFtlHWL9lsdBy/cBq7h6ZrB5GVL/FbPtRoufZdK26S/Dyb+xQ2MK7bAY+R20mNCebVtArmZqTh2lPw9sW4ymJyMVG6Nr4FAKEKcm4NNs2FY1ulRrG2/Er9EEFhXTYn25Szp+McdqjoY4O1gQBN3Uyx01QDIzROz4uJrjvi9IzIxg8ycPLJz86hsJ5sZ62yaHzgWCAQYaChR6qN9iiIh2qqKxKRkyZznZa0n/X+hUICntQ7+UcmF2vo4LJHbgbHYjj9Z4FhwTBqeVrr0q27LqL2P2Hs/FG8HAxq7mcoEtQu7f121zwuyfi1eNnp42eTfZzkbPWovusLG60HSUhK1S8lOmStrrUv52RfYez+U/tU/bwGMvDwxAF0rWdOpgmQKqZuFNjfexLL1ZjDz27h/j9v516CiqYtj7Q6c8mmPWWlvzEpXw7ZyU2kd2rzcXB7tX0bg9SOkxkaQm51JXk42pq6y0yD0bPI/OggEAlS0DdCzLiXdJ1RQRFlDh4yEGJnzjJzK5Z8nFGLkWJaEUNnF//4iJuAREc9usbltwSm7SRFBGDl64tZiAFdXjMD/4h7M3b0pUbmpTFC7sPtX0dQt8vjn0mTuMXIyUol+9YC7W6ajaWwlLX0hFuehom2A9+AlCEUiDO1Lk5Ecz+0Nk6nQc+o3X1uOBG1dPZq178aAjk2oULUm5b1rUrdJK0wtJL/z3NxcNq1YwJkj+4iOfEdWZibZ2Vl4VZadDuzg7Cr9f4FAgK6BIQ6l8vcpKiqipa1LXEy0zHnuXvlfdYVCIW6e5Qnwf1GorS8e++J7+zoVbfUKHAsLDsTNsxxd+w1j2qj+HN27gwreNanTuIVMULuw+9fWLdjf55CVmcnYvp0Z6TMXC+uiBzh5eXnoGRjhs2gNIpEI59KeJMTHsmDKGEb6zJVnUH4GKpq6ONbpwMkp7TEv7Y156WqUqNIUzY8012//MgKuyWrup4uV6ZeQ1VxVHQP0bPI1V/RBc9MTZTXX+FPNLVmW+JAiNPfNIyKf3WJjm0I0NzIIo5KeuLcYwNXlI/C/sAfz0hLN1f2RmisWg0BArTHrUFbXAqBK/7mcnNKOtPho1HRlS2U9ObKOF6e20mSmpERGfjd5GNp7UKGH5KXVwM6dxHeBPDuxSR4EBvT0dOnepRMNm7emVo3q1K5RjdYtm2NlKZlymJuby7xFS9i7/yDhERFkZmaRlZVFdW/ZmQZuri7S/xcIBBgZGuLmkr9PUVERXR0d3r9/L3NexfL5fioUCinvVZbnL18WauuDh4+4euMmWkYFP3IFBL6lvFdZRgwZSN9BQ9n+5y5q1axOq+bNZILahd2/nt63jw2unDtJSkoqt+/dZ/xkH2ysraWlLypVKC8N5gJUrlgBz4rerFy7jmUL5xfoa9TvE7hx8zZXz5+WfqhTVFRk/64d9BkwGEPLEohEImrXrEGDenURi8XfbP+/DVUtXVzqduTghHZYlfHGyqMaDt7N0PpIX+/tWcarq4dJiYkgNzuL3JwsLN1l9dXgE31V0zbA4KNxrkhBERUNHdI+GdOaOnvlnycUYupUltiQV4XaGv3mEeFPbrGieUF9TYx4i6mTJ56tBnBuyXCen9stuZeqTWWC2oXdv6rWt/stgP+1Y1zdMJXGE9ajZWz59ycUwZNT2zF2LIOhnavM/ldXDvH8/B4ajVuHvrUT7wOecmnNBLRMrHBr0KWI3v5/UNLQxapGe27M6oiha1WM3Lwxr9gENQOJL4vzcnl1eAVhN4+QERcpHSsYusjOTtCylvVlZW0DtK1k38+UNLTJ+GSsoOco68u69p4khxU+VogPfEzMi9sc6VrwI0VqZDB69p7YN+6H77pRhFzZi6GbN+YVGssEtQu7/09LMXwJSWGveLRpIk6tR2BcuiYZ8VE82TGdh3+MwWvwygLtgy7sQEXXGBPPOkX2+Tlt/t9R1NDF1LsdDxd0Qte5KnrOVTEu1xiVj/w26PhKou4cJTMukrwcid/qlpKdnaDxkY8KBAKUtAzQsJD1W0V1bbKSZP1W275s/nlCIVp2ZUgNL9xvk4KekPDqNpf6FEyySY8OQtuuDFYN+vJi02giru9Dz6UqRl6NZILahd2/4jf4bUq4P692TKJE8+Hou9UgKyGa13tm8GLz77j2+/ABI0+MoqY+zr0WIhCK0CrhTnZKPP5/+uDQYQoCgYCoO0eIuLEP1wGr0DAvSXLIM17tmIKqoSXm1TsVb8Qvwi8RBAZY1sGDvtVKcPHle848i2LuyZds/q0cNZ2MWH05gLWXA5nRwoVSplqoKysw++SLAsFchU8WHhMIBIXuy/uGgVueWEydUkb4NHMpcMxQQ5K+PqZ+SVp7mnPxZTSXXr1n0Vl/5rV2lwZFC9z7P1QO4mNEQgGlLXUIjEkpso26sgIljTUJfJ/62f3+VU/Y8ZOgt6OxBuEJ6V9n7L+c6sNX4Nq8H6EPLhJy5zT3t8+m7sStWHrW4vGhVTw5vIZKfWahZ1MKBRV17m2bVWCwIFSQ/akKBALplISPEYvzvtpOcV4eVl51Cw2cqn6o3VO201jsa7Qm9P4Fwh5ewnf3QqoOXEDJup0L7fN7lYP4q5akno0zaQnRPNi1QBoEVtM1RqigiPCj7EodSwdyMtPISIpFRUsPgVBEeoLsS3B6QrT0vuR8HjOWradL3yHcuHiWK2dOsHKuD0s276NKzXpsXb2EbWuXMnbGIhxKuaKmrs6K2VOIi5F97gqf+K1AICh0X17e1/tyXl4e3nUaMtJnboFj+oaSj10DxkymUesOXL94hluXzrF20UwmzVtJy049Cu3zW8pBvI+KIPD1S3yG98FneB+pjWKxGE9zNVb+eYTKNepiaGSKgqKCTKawrYMTGelpxMfGoGfw/1dS52uoOXwF7s36Eep7kaA7p7m7fTb1J27FsqxEcx8fWkPlvhLNVVRR5+62WaR/EmwouLqvAKGoEM39Bj8Vi/OwKleXij2nFjj2lzZ5dR6LQ43WhDy4QJjvJR7sWoj3wAU41Stcc7+1HISarhHq+ibSADCArqUkmJfyPlwmCPz4yFru75hLw6m7MfpQwzK/H2N0rWSDgLqWjjw9ur5Y2/6f2LRuNcMGDeDMuQscO3mKSdNmcnD3n9SvW5tFS1ewePlKlsyfi5uLMxoaGkycOr1AMPfTMjECBCgqFhwvfKueNmpQjwWzZxY4Zmwk0SSfiePp1L4dp8+e48z5i0yfPY/VyxbTs3vXAufA9ysHUcLGBpAEw6Ojo5k+e640CPwpIpGIsp4evH4TWODYyLHj2bP/IBdOHcO2hI3MsbJlPPC9fZ3ExESysrIxNDSgUvXalPUsU6xt/1Xqj16BZ8t+vL1/kYDbp7m+ZTbNfbZh41WL+/tXcf/AamoOmI1BCYm+3tg8i7REWb8toKUCQYFxLgLBt+lrnpgS5etSrU/B2ULquhK/rdz1d0rVasPbexcIfnCRW38uoM7QhbjWL1xfv1c5CP9rRzm9YBANxqyS1uz9GtIS3hNw6xS1BhUcm1xdPxWvNoNwqiGZUWVYwpmk6FDu7l4mDwJ/wGvgMuwb9SXK7yIR98/wbNdcKo3ZjLFHTfyPrub1sbWU/m0GWlaS97NnO2eTmVT8WEEgECAoZPzAN7yfIc7DpEwd3Lr5FDikrC3xZed2Y7Dybk3kw4tEPbrEi32LKNNnHja1Cg9IfWs5iFeHVqBrX0aaLa1t7YxIRY2rU5rj3HECavr5M5zzcrIIubIXm9pdChlbfX4bORJc+izFqn4fYh9f4v3DswTsn0fpYZvQd69B8Mk1BJ9aR8ku09GwcEKkos6bfXMLBHML89EC+wTf7rcGpWvj0HFKgUNKWhK/tWs1GtPKrYh5fJHYJ1cIPLQYpx5zMa9eeE3oby0HEXRsBVq2HvnZ0lbOiJRVuT+rJfZtx6GiZ4aSjhFCkYK0NAxIsq3zstLJTo5DSUuf17tnYN1wACYVWwCgYVmK9Jgwgo6tlAeBvwYXM21czLQZUsuejutvs/d+GDWdjLj7No66LsbSRcnEYjGB71PRUv0+NRIfBMdT1cFA2vfDkASauBc+ncfdXJujjyKw0FVFUVR0nV9bQw1sDTXo7W3L2P2P2XknpMgg8D9RDuJTxGIxLyKScDbVKrJNRnYub6JTqPIFC9pZ6alioqVCQLRscDnwfSpOpkVnQ//X0S/hin4JVzzaDOWUT3teX9iDpWctop7fwap8vY+yWsUkvgtAWf37lM2IfnUf89Le0r7fv/alROXCp1EY2LkTeP0ImkaWhQaY/0LbzA7tZna4NuvL9dVjeHn2zyKDwN+rHIQMeWLysvMXJjRxLs+bKwcR5+UhEEp+k4nhASgoq6GipS+ZFWBfmnC/KzK2hPldoUTlJgW6l1M8JV3cKeniTs8hoxnYsSnH9u6gSs16PLx7g+p1G0sXdBOLxQQHvkZTS+e7XPfJg7tUqFpT2vfTh/eo26RVoW1LuZfh7NH9mFpYF1tL19rWAWtbBzr3HszMsYM5tHNzkUHgbykHYWRqzv5LvjL79m5Zy+2rF1i8aR/mVpKPHB7lK3Hq4B7y8vIQfvDloMDXqKiqoav/YxYW/a+ib+uKvq1Ec0/6tMf/4h4sy9Yi4tkdrMvXky7oJhaLSQwPQOk7aW7UJ5ob7e+LbZWiNTfg2hE0jCwRFae55na4mdvh1qwv11ZJNLeoIPC3loMwdq5A4I1jZKenSGsAJ4QHAEizqQEeH1rD/Z3zaOCzE1OXgi+JJs7lSQh7I7MvMTxAOgtGjoTS7m6Udndj7KjhNGrRhm1/7qJ+3dpcv3WLJg0bSAOaYrGY16/foKPzffz0zr371KpRXdr3vQe+tG5RuN94epRm38FDWFtZFqunDvZ2ONjbMWRgfwYOG8mmrduLDAJ/r3IQH5OXl0dmZlaRx8ViMU+ePsPdTTZjcvjo39l74BAXTh0rNntZW1vy7F+/CeC+70OmTZ5YZNv/OoZ2rhjauVK+/VAOTmzPs3O7sfGqxbtnt7GrWF+6oJtYLCY+PABljaLfNb6EiBcPsPpQtkcsFhP5yheHqoVPaTeyd8f/6mG0jIvXV11zO3TN7fBs0Zfzy0fz5NSOIoPA36McxKsrhzm9cDANRq+UKXXxNTw7uwuRohJONQuOhXIy02WCGABCoejbgjr/QXRsXNCxcaFkiyHcmN2R4Ct7MfaoSezLu5iWrStd0E0sFpMSEYii+vfx5Tj/Bxh9WLRNLBYT/+Yh5hULfyfRKeFO2K2jqBlYFPt+pmFqi72pLfaNevNw/ViCLu4sMgj8reUgcjPTpe9cfyH1t0987N3dU2QmxxVpy+e2kZOPppULmlYu2DQZzMOFnXl3fS/67jVI8L+LoUdd6YJuYrGYtMgAFNS+z9gh8Y0ves75fpsU+BCjcoX7raa1G1F3j6GiX7zfqpnYYmVii1W93rzYMo53V3YWGQT+1nIQuVkFdZG/tj98dNRxLEfkrUMycYW0yACESqooakpmhOZlZhTu//8iff0lgsDBsWlsvxVMfRdjTLRVCI5L48W7ZLpXlrwY2xqqc9TvHXcCY9FTV2Lj9SBC4tJwNf8+Dr31ZhC2huqUMtViy80gwuLT6V7ZptC2v1UpwY47IfTb/oDBNe3R11AiODaNo4/eMbWpCyKRgGlHn9O0tCmWemrEJGdy920cnlZFp65/j3IQT8MTAUjJzEEoEPA0PBFFkZCSJpLA68IzryhrrYutoTrJGTlsuPaW5++SmNvaTdrH1KPPqOdijLmOGrEpmSw+509aVi7tvPJf2uLTsgiPTycxPRuAoJhUtFUVMdJUxkhLBYFAwMCadiw484pSZlq4mWtz1O8dD4Ljmd1KduD9/0BSZDAvT2/FukID1PRNSY4MJi7oOaUa9QAkL/eB1w4T+ew2Klr6PDu+nuSoEJRt3Yrv+DN5cWoL2uZ26Fk78/zkJlKiwyjV6LdC2zo37sXLszu4ML8PpVsPQUVbn+TIYAKvH6FCz+kIRSLubPKhRNXmaBpZkp7wnsjndzBy9Cy0P/j2qclPj61H09gaHQtJOZKIp7d4fGgVzh/dQ6mGv/Hs+EZurp+AS+PeJEeH4LtzPs6NfpNOn3dr0Z/Liwdh6FAGY+cKvDi1hbS4SEp9VFtYTvGEBb9l//YN1KjfBCMTM8KC3+L/4intuktWWLW2deDM0f343rmBrp4+uzauJjwkCCdXj+9y/b1b/8Da1gGHUi7s2bKOiLAQ6bU/pf1v/TmwYxNj+3Xmt8Gj0dM3ICz4LWeP7mfU1PmIRAosnvY7dZu2xszSmtiYaB7evYmbZ/lC+4NvKwehqKiIQynZ2SN6BkYoKinL7G/XvS+7N61h3qSRdOw5gPDQYNYsmEH7Hv3kpSA+k6TIYF580Fx1fVOSIoOJffsclw+aq2NuR8C1w0R80NynHzRX/ztp7vOTW9Axs0PPxplnJySa61yE5ro07sWLMzs4P68PHm2GoKqtT1JkMAHXjlCpl0Rzb23ywa5KczSNLUn7S3NL/jjNdajeCt/di7i8dChlO40lKzWRm39MxLZKU2lddb8DK7m3fTa1Rq1Gx9yOtHhJbVSRkqo0g9iteX+OjGmE757F2Hm3ICbwCU+Pradct//fwNnHvA0K4o+NW2jauCHmZqYEvg3iydNn9O8jqdfpaG/P3gOHuH7zFgb6+qxc+wdvg0Moo/N9/HTt+k042Nvj5uLMmj82EBwSKr32pwzs15sNm7fSodtvjB05HEMDAwLfBrHv4CEWzpmJgoICYyZMpk3L5thYWxEV/Z4bN29TvlzZQvuDby8HsXLNOmxsrCnpIJk2evXGDRYtW8mAPr2kbabPnkuFcuVwsLcjKSmJFWvW8fjpM1Yty89AHjxiNDt27eHg7h3o6ugQ+aHOr4aGOhoako8g+w4exkBfD2srK548e8aIMeNo3rQx9erU4v+NxMhgHp/Yil3FBmgYmJIQEcT7t88o3USicbrmdry6epjwp7dR1dLn4dH1JEYGY2T/ffz20fHN6FrYYWBTikfHN5MUFSa99qd4NOvJk1PbOTG7N+XaDUVVW5/EiGD8rx6met/pCEQKXF3vg6N3M7SMrUhLeE/4szuYOhWtr99aDuLl5YOcnj+Qan2mYeFWidQ4ib8JFZSk/eZmZ0lLXORkZZIWF010wBMUVdRlahaLxWKenN5ByeotUSpk0U7bivW5t3cZ2iZW6Fs7ER3whAcH13xT/eH/EqnRwbw9tx1Tr/qo6pmQGhVMYvALbOt1B0DDzJawm0eJeXkHZU09Ak5tJDU6BJ0S3+dd9u25rWia2aJlVYrAM1tIiwmjxIdrf4pt/d8IurCDu0v74dh8MMpa+qRGBRN26yhu3aYiFIl4sm0a5pWaomZoSWZiDLEv76LrULQvf2s5CFOvuviuG03g2S3SchCPtk5Gp4S7tKSG9F4v7MDI1Rt144KlWb6kjRxIfx9C2MXtGHrWQ1nXlPToYFJCX2BeS7JIqZqJHVF3jpLw6g6KmnqEnttE+vtQNK2/T8ws7OJW1Exs0bB0IuzCVjJiw7GoXfgCqZZ1ehB++U+erOqPTZNBKGrqkx4dTPTdYzh09EEgEvF613SMyjdF1cCSrKT3JPjfRduu6Fk231oOwrBMXV5sGkPYha3oudUgKyEK/z990LRxk5bUsKjVjdBzm3m1YzKWdX8j430YgQcXYVm7u/RdzKBMXYKOr0LV0Ap185IkBz8l5PQ6TKu0/Wrb/ml+iSCwmpKIwJgU+mwLIy41C0NNJVp5mjO4lqSGyIg6DoTGpdFpwx1UFEW097Kklac5/lFFlzL4EiY2LsW6K4E8CU/EQleVTT28MNNRLbStibYKxwZXYdbJl3Rcf4fM7FzMdVWp7miIksKHLMT0bIbt9iM6KRNddUXqOhvj09S50P6+F3UWyy5wdfZ5FBa6qtyfJKmrk5SRzej9j3mflImmqgJuZtocHlRZJjgdkZjBgB2+xKVmoa+uTFlrHU4MrYqlnpq0zZmnUQzf4yfdHrXvseS/9Ryli/L1rWZLZk4e044+Iy4tm5LGmuzsU+H/blE4AAVlVRLfBXJ+bi8ykuJQ1THEvkYbPFpLFnwp034kyVEhnJ7WAZGSKo61O2BfvQ0JoYXXOPtSynefzJPDa4kNeIyGkQV1J2xBw6DwrHN1fROazT/Ova0zOeXTntzsTDQMzTEvUwORouQjRWZKIleWDiEtLgoVLV2sytWjQs+CU+2+F+K8XO5umU5KdCgCkQgtExvKd58sE7zVMDSn4fR93N44hYPDaqKqY4RjnU6UaT9S2sbOuyWZSfE83LuEtLgo9KydaOCzC02jr6/D9v+GqpoawYGvGd2nEwlxMegbGtO4VQd+GyxZWKDviPGEhwYxqFMzlFVUad6+K41adSDQv/A6k1/KsIkz2b5uGS+ePMTUwoolm/ZibFZ4VqGRiRlbj11i+azJDOzYlKzMDEzMLalUvQ5KSpKyPUmJCUwZ1of30RHo6OpTrW5DRvr83MXXTMwtWbvnBAt9xtKuTnn0DY1p0aE7fUeM/6l2/ZtQUFYlITwQ/zkSzVXTNcShRhtKt5FormeHkSRFhXBqqkRzS9bugH2NNsQXUVfyS6nQfTKPD68l5oPm1ptYvOa2mH+cO1tncnJKvuZalMnX3KyURC59ormVev04zVVU1aDJzAPcWDeeQyProaSujU2lhlTonj+d79mJTeTlZHN+Xh+Zcx1rt6fmCEktQCPHMtSbtJW7W2fju3sxGobmeHUZh0tj+aJEAGqqavi/eUP7Lj2IiY3F2MiITu3bMnbkcAAm/j6Gt8HBNG7ZFlUVFbp36USn9m15UUTd3i9l9nQflq5Yha/fI6ytLDmwawcW5oXPZDAzNeXahTNM8JlGoxatycjIxMrSgrq1a6KsLNHT+PgEevYbSERkFPp6ejRuWJ8Fs2d8F1sLIzc3l/GTpxIUHIKCggJ2JWyYM92Hfh8tDJeQkEj/IcOIjIpGW0sLj9LuXD57kvJe+cHpNX9sAKBuY9nszikTfpcuuBcZGcnocROJio7G1MSYrp06MGnc2B92b78yCsqqxIcHcGxWT4m+6hhSqlYbyrWT6GuFTqNIjArh4KT2KCip4lKvA6VqtSmybu+X4t1rMg8OrCH6zWO0jC1o5rMVTcPC9VVD35QOS05wfdNMDk5sR05WJlpG5lh71kT0YfX5jOQEziwaQmpcFCqauthWqEe1PtO/i62F8fjEFvJyc7i8diKX1+Z/ELNwr0y7BUcBSImNZMfAmvnnRLzl8cmtMm0AQh9dJyE8kIZj1xZ6rVoD53Bj61wurBxLWkIMGnrGuDXoSsUuo3/Q3f27ECmpkRIRyJ3FfchKjkNZ2xBL71Y4NpcsHOvUagRp0aHcnN0JkZIKVjXaY+ndqsi6vV+KS6eJvD6+joS3T1AzsKDi6E0yJRQ+RlXPhOozjvF05yxuzO5IblYmagbmGJWunj9WSE3kwephZMRHo6Spi4lnXdy6Fiwf8b2wrtGBnPQUAk5v4sm2aSioaWLkWhWXzrKLi6ZGBfP+6XXKDyvcTz+3jRwJQiVV0iIDebKyH1nJcShpGWBSqSU2jSVlOUo0H0b6+xAeLuqCUFEFM+92mFZqRcq77+O39u0mEnJ6HcnBT1HRN8d96EZU9Ar3W2VdE8pNPsKbfXN4uKAzedmZqOiboedaHeEHv81OTeT5+uFkJkSjqKGLoUedQstHfC/MvNuTk55C6PnN+O+ahoKqFnrOVbBvn6/HKvrmeI7dhf/OqdyZVA8lbUPMqrWnRPPh0jYlu84k4MB8Xm4dT1ZSLMo6RpjX6EyJ5iN+mO3fG8HnLmwgEAjEkYuKT7/+txESl0b5WRc4PdwbD0udn22OnK/k3ts4um26+zwuNatgoebvhLKmztsmsw7ZfK9ssR9NclQIu3uXpcXicxg6ePxsc/5znJneKSnk3rk+YrF478+25UtRUlZJv/gkREVLW+dnm/JZhIcE0ah8SXaevomLR9GZZXIK0sLbPfHt61f1xGLx3Z9tS3EIBIKpZTuO8fHq/O8NrCRHhbCzV1laLZFr7j+B794lPPhz/rzcnOyCS6l/JwQCgTg3NeFHdf9TCAoOxs65NHeuXcLr/7Sm7a/Ozdt3aN62w/PY2LjvMqYVCAQe2qY2V3ptuf995rH/BBIjQ9jY3ZNOK85h4ij321+BwLvnOLNg0M20xNgqf9/6+yAQCIbb1v9tnkevOT92NfUfSGp0CGcGl6fmnNPo2nn8bHP+80T6nuf+qqE3M5P+OT/9HBRU1NfYtf69v1WDPn/f+Bcg/X0oN0ZVoPzUU2jZlv7Z5vznCD2/hTf75mzJSUsqfGrLD6DoorZy5MiRI0eOHDly5MiRI0eOHDly5MiRI+dfjzwILEeOHDly5MiRI0eOHDly5MiRI0eOHDn/YX6JmsA/Cys9Nf5rJS7kyAHQNLaiz7H3P9sMOXK+GXMrGx5FZv5sM+TIKRZNYyv6HZdrrpxfGxtra/5rJS7k/PfRNrFi5JmYn22GHDnfjLqRFa32Rv5sM+TI+SJUDS2ps+3dzzZDzndEngksR44cOXLkyJEjR44cOXLkyJEjR44cOf9hfokgcMvVNxl/8MnPNuNvWXDmFSajjmEy6hgrLrz+2eb8X9Fy9U3ps/cLTfjZ5nxXjo9vzo21v/9sM/6WBzvns76pIeubGuK3b9nPNueH4n9+l/Re/w3/Nr8KvVrWZfb4YT/bjL9lzYIZlDZRprSJMhtXLPjZ5nw1R3Zvk97Hv+G5/wyOjmvO9TW//m/4/p/zWdfEkHVNDHn4H9fXz+Hj5/Ho4Kqfbc53p1aDxgwZOeZnm/G3TJs1B5G6DiJ1HeYtXPKzzfnPUKtBY+lzve/78Geb803sHdOMCyt/fY29uX0ei+sbsLi+AXf3yDX2czi9cLD0mflfO/qzzfmhXJ3aEr+N43+2GX/L870LONjOhIPtTHh1eMXPNuenE3x5t/R5/Bv+/f4J7s9uzcttE362GX9LwMGFnO9mxvluZgQd+2/78rtre6T3+qv82/xfl4P4GuwN1Tk4sDLqyvmP7sTjCLbfCuZxeCJxqVkcGFCJKvYGhZ4vFovptOEOl16+Z323sjQtbSY9tvS8PxdeRPP0XRLpWbmFlqqYdPgpd9/G8SoyGUNNZe5PqlOgzRG/dyy/8JrA9ynoaSjTs4oNg2raf9F9RiVlMPXoc56EJRAYk0qbshYs7yi7Iu/RR+9YefENQTGpZOeJsTVQp281W9qXs5S2ScnIYd7pl5x8Gklsciau5trMaOFKGSsdaRuTUccKtaFHZRvmtnYDYFMPL4Ji0mi47NoX3Yec74u2uT1N5hxGUVVDZn9CeAD3ts7g3eNr5GZno2NhT83Ra9G1dAQkge6IpzdlzrH1bkHtses/+9rJUSH47llExOMbpMVHoaZrjK13czw7jEZBWVXaLvzRVe7vmEt88HMUlNVwqN2Bcl0nIBQpSPvZ3btsgf4bTN2NZdnaUtssytbi3Ox/bJFOOf8wNvaObDx4DjV1TQCys7NZOdeHGxfPEBoUiIamFuWqVGfYxJmYWlhJz8vKzGTRtN85fXgvGenpVPCuycS5yzE2s/jsa7969phNKxbw8O5NEuJiMDG3pGXHHnQfOBKhMP/brFgs5s/1K9i3bT3hIUFo6+jRtF0Xhk+aBUD95m2pUqseI3u2/05PRc7PRMfCnqaf6GvgzeO8OLWVmIAnZCTF0nT2YczcZRe5fn56GwFXDhIT+ISs1CQ6bXyAprGVTJv3bx5xZ8sM3r9+iEAookTlJlTuPb2Alv8d65oYFtjnPXABzo16SLdjg55zY+04ov0foqyhg3PD7nh2GIVAIAAkgflP/x4A6FqVpN3q6wCUbjUQ50bdOTii3hfZJ+f7U9LRgYunjqOpme8rB48c5Y+NW3j46BExMbFcOHWMGtW8Zc6LjIxi7MTJnL94maTkZBzsbBk9YhidO7QDIC8vj5btO/Ho8VOi379HV0eHWjWqM3fmVMzN8sfGInWdAjatWraY/r17fvY9ZGRkMGDoCB76PeLFK3+qVKrAxdMnCrTLyspi1rwF7Ni1h3cRkRgbGTFq2GCGDOwPSP5OzF24mG1/7iL8XQQlHeyZM2MaDeoVHIsDzF2wmIlTpzOwXx9WLM7/4Lh/5w4C3r6lYrVan30Pcr4dXQt72i04gpJa4bp3btlInpzcRrXeU/FqO1i6PzUuiqsbphLse4WstGR0zGwp124wpWq1lbbZ0K0MSVGhMv2VazcU715TvsjGS2sm8O7ZHWKCXqKuZ0TvbQU/EojFYh4eWsejE1tIigpBRVMH5zodZK7ld3Qjfkc3kBgVipaRORU6jMS5bv5Ywf/qEe7tXU7Cu7fk5uSga26LZ6v+uNTtIG1Tc8BsvHtOZl1Hly+6Bzk/Fg0ze6pNPYiCirp0X/idE7w9v52EwMdkJcfh7XMAQxfZsUJudiZPtk8j7MZhcrPSMXT1xqP3XNT08/U2LSYMvw3jef/sOiIlVSyrtMStmw9CBaXPtu/+qqGEXNlbYL9IWZXm299+1zYWlZtj7FGL2ws//++BnF8HNVM7yo4/gEIRY9EXm8cSfmkHDh0mY91ogHT//dmtSXh5S6atcYVmuA1a+0XXT40I4M2emST43yUvJwt185LYthyFgXtNAJJDnhF0fCUJ/nfJTo5HRd8Ms+odsW44AMGH97WUcH9ebZtAarg/OenJKOsYY1yxObYtR0l/N8YVmqHvVpPHy3t9kX0/EnkQ+AsRiYQYaanI7EvLysHLRpfWZc0Zssuv2PPXXA5E+OFF6FMyc/Jo5GZKZTt9ll14U2ibvDwx7bwseRmZxOVXBesPXngRxcA/fZnZwpVaTob4R6Uwet8jVBRF9Kpa4vNu8oMteupKDK7lwI7bwYW20VNTYkQdB+yNNFAUCTn3PIqRex+hr6FEnVLGAIzc+4gXEUks7+CBmY4q+x+E0W7dLa6OrYGptiRw99inrky/fmGJdNt4l2YeptJ9umpKJGvkfLb9cn4MQpECarrGMvuSIoM5NrYxDrXa0XjmQZQ0tEkIe43iR4MTAMc6HSnXbaJ0W0FJlS8hIew14txcqgycj7aZLQmh/lxbOYrM5Hi8By8GIPbtU05P7YhHm6HUGLGStNgIrq8egzg3l4q9psn012DaHvRL5A9slTV0821TVkVBWRWRguIX2Sjn34NIpICBkYl0OyM9jZdPHtJ72DicXN1JTkpi0bTfGdixKfsuPUBBQfLncv7kUVw+c5y5a7aho6vPQp+xDOnakl1nbyMSiT7r2s8f+aKrb8CsFZswtbDkycP7TB81gNzcXHoPy8+oWjh1LFfPnWTklDk4lHIlOSmJmKgI6XEVVVVUVFVRVPz8wbmcXxeBsKC+5mSkYVyqPA4123Jp8aBCz8vJTMOiTA2sKzbg1vrJBY6nxkZyYlIbbKs2o2r/uWSlJXNz/UQuLRlCvQmbv9jOakMWY10+PzirpKYl/f+stGROTGqDqWslWi0+S0LYay4vG4qCshqlWw0EoN7ELeTlZEnPyc3OYt+gathWbS7dp6iqgaKqBgLh5/2m5Pw4FEQKmJjI+mVqahqVK5anc4d29OjTv9DzuvfpT3x8PIf27sTQwIDDR4/RvXc/LC3MqVZVEpyoVb0a48eMwtTEmPB3EYydMJnWHbpw++pFmb7WrVpOkwb1pdva2lp8Cbm5uaioqDCwf19OnTlLYmJioe06du9JePg71q5YhoO9LVHR70lPT5cenzxtJtt37uaP1cspVbIkZ85foHXHLly/cIYyHqVl+rp99x7rN2/B3bVgAE1PT5ek5KQvugc5345QpIC6nnGhx/yvHSXy1UPU9U0KHDu9YBAZyfE0n7odVW193tw4wan5A9E0NMfCrbK0XcXOoyndJD95QFFVvUBff4c4Lw/nuh2IefucYN/Lhba58sdkAu+cpVrvqRiUcCYrNYmUuCjp8UfHNnFt43TqDl+MiVNZIl/5cm7pCJQ1tbGr2AAAFS1dKnQahZ6FA0IFBQLvnOXs4mGoautjW17yXqasroWy+pf91uT8eIQiESo6RjL7cjPT0Hf0wsq7NfdXDin0vMdbJhNx/wzlhq1BSUOXJ9t8uDW3K7XmnUUgFCHOy+XmnC4oaepSbdoRslLiebBqKGLEePSc/dn2lf5tJq6dJ8nsuzK5KfqlKn73NiIlVURKqgjl72v/SgRCBZQ/8eW/iLp7nMSAhyjrFtRkAFPv9ti3zc/+FimpFNquOB4t7o6qkRWe4/YiUlYj7OI2Hi39jUpzLqNmbEPS28coaerj0m8FKvrmJAU+5MUmSVyhRLOhAAgVFDGt2hZNa1cU1LRJCXn2oU0ODh0mf7BN4qeCL/iY8qP5piDw9lvBzDv9kkc+9RAJ8wObA3b4kpqZw7Ze5QmKScXn6DN8QxJIyczB3lCdMQ2cqOdc+B9hAK+Z5+lZpQQDa9pJ97VcfRMnE03mtJJkhmbl5DHv9EsO+oaTkJaFo4km4xo4UdOpcEf6kbT1kmS+xqYUv3jRw5AENlwL5MyIarhNPVvg+O8NnAA49qjowtuzP9z/6ksBhQaB9z8Ip56zMb9VsQHAWl+dIbUcWHXxDT2r2Egzcf4OKz01ZrV0BeD448Ltqeogm+3cp5ote++HcicwjjqljEnPzuXEkwg2dveSZkaPqV+Sc8+j2HozmHENJff7aVD9zNNX2BmqU9mu8GzqX4UXp7fx4M+5dNryBOFHwZ+LC/qRnZFK/ck7SIp4y+2NU4h+9YDs9FS0ze0o23mczAv0p+zq5YlL4164t8p/4T8+vjm61k5U6T8PkLw03/9zLgGX95ORnICuVUm8uo7H0vOfzyq5v3025mVqULHXdOk+LRObAu0UlFULBDi+BMuytaWZun9do0y7Edz/c640CBx47TC6Vo6U7SwJpGmb2VL+Nx8uzOuNZ8cxMtkfKpp632TPf4n92zewat40zj8KkglkjhvQjbTUFJZvO0hoUAALfcbyxPceqSnJ2Ng7MnDMFKrXa1xkvw29HOnQsz/dB46U7uvVsi52Ts5MmCOZjpmdlcXKeVM5eXA3SQlx2Dk6M2jcVKrU/GczADW1tFm395TMvsnzV9GqugdvX7/8EIRN5NCuLUxfup5K1SWZX7NWbqKBlwO3r174bJtbduohs21hbcvLxw85f+KQNAgc9OYVuzeuZt/F+9g6lspv7Obx1ff4b+L56W3c3zGXLltl9fXCgn5kp6fSYMoOEiPecmtDvr7qWNjh9Tf6+mdPT1yb9KL0R/p6dFxz9KydqDogX1/v7ZjLm8v7yUyR6Gu5LuOxLPvP66tjLUnWZHpibJFt3JtLgnDvX/sVejz43lkEQiFVB8yXPkvvQQvZP7g6ie8C0Taz/SKblNW1i9TO15f3k5OZTs0RK1FQVkXPphQJYa95fHgN7i0HIBAIUNHUlT3nkuQcp7qdvsiOn8Efm7bgM30WYQEvZbSyc4/epKSmcGTfbgIC3zJq3ATu3ntAckoKJR3smTp5Ak0aNiiyX9tSbgzq15dRw/Nf3ms1aIyLs7M0gzQrK4sp02exc88+4uLjcSnlxPQpk6hft3ZR3f4wunaSZAvGxBTtl7fu3GXZwnlUKOcFwMhhQ1ixZh337vtSrWoVhEIhwwYPlLa3trJi7KjhtGzXiYyMDFRU8seGOtraBQLRX4K6ujprlkvKWTx5+qzQIPDZ8xe5ePkqr588xMBAH5AspPcxO3bt4fdRw2n8ISA9oE8vLly6zOLlq9i+6Q9pu8TERLr27MOGNSuZMWfeV9v9I3l8cis3t86l786nMhp7Yk5fsjNSaTHtTxLeveXKuslEvPIlKy0FPUs7Kncdh23F+kX2u6FbGTya9pLJpN07phn61qWoPThfY29sncPLS5IxrL51Sap0n4CN18/JjE6KCuXSmgm0mXuQQ5MKzqx59/weNQfOwdRJMovMq80gHh5ZT+RLX5kgsJKqRpFB5s+l1qC5ANzft7LQIHBc6Gv8jmyg69qr6Fs5Svd//Pb7/MI+3Bp2walmawB0TG2IevWQe3tXSIPAVh7VZPr1bNmP5+d3E/70tjQI/G/g7fntPN8zj0brHsl8NLy7bAA5GalU/n0bKZFBPNnmQ9xrX3IyUtAws8e53RhMyxY9Vjg9yAvb+j1xbJavUVentkTL0gmPXnMAyMvJ4vnueYRcP0h2SgKalo64tB+HsUfNH3fDRWBVTZKVnplUuCZnpyURdHEXZQcuxdi9OgBeg1dyepAX0Y+vYuxRk6hHl0kKe0WDVfdRMzAHwLXzZHzXjcKlw3gU1TQ/yxZFNS0UP/o4HPvyLqlRwXgNXvnd2/yXCLu0g8AD8/Fe/lDGl5+sHkhuZioeI7aSFhWE/86pJAU+JCc9BXVTO2xbjcGwTNG/2esjy2NZ57cCmbQaFiVx6iYJ7uflZBFwYD6RNw+RnRqPhnlJ7Fr/jr57jR92v8WRHhOG/59T8By7h4eLOhfaRqSsWmQA+XPISo4lLSoQp9/moWkl+Vhr324iIafXkxz8FDVjG8yrd5Q5R83ImuSgJ0TfPyENAqsZl0DNOD/RUtXAgvgXt0h4dferbfsn+KYgcNPSpkw6/JQr/u+p9SH4mpqZw+lnkSxt7yHZzsqhlpMR4xo6oaIo4ohfOL223OPiqOo4GH+emBTG8N1+BMWmsrqzJ2Y6Kpx/EU23TXc5PdwbFzPtQs9Zdv41y/6mlu/OPhWoaKv/1XYVRUpGDgP/9GVBW3cMNZW/e/9/kZWTi7KCbKlnVUUh7xIzCI1Px0pP7YdcVywWc/11DG/epzKuoR4AublicvPEBexRURBy521cof2kZuZw2C+cUXVL/hA7vye2VZtx648JhPtdlgYns9NTCL5zmmrDPgS4MlKxKFsbry7jESmpEHjtMOfn9KD18ivoWDp89bWvLBtKcmQQNUevRd3AjND75zk7owstFp9Fv4Rroec83LsEv31Li+23wdTdmLpU+mw7xHl5hNw7Q+nWQznl046YN4/RMLLEvdVA7LxbyrQNuHqYgKuHUdUxxLJs7QJB2a8hKy0ZZY3833tudhYiRdmPCgpKKuRmZRAT8Agzt/ypUedm9yA3OxNtsxK4Nu+PbZVm32TLv5l6TVszb9JIbl85T5Vakpe7tNQULp0+xvSl6z9sp1KlVn0Gj5uGsooKZ47sZ2Sv9uy/eJ8SDk5ffe0pw/sQGhTI3NVbMTYz59r50wzt1oqdp29S0sW90HM2LJvHhmXFv1iv3nkUz4pVv9ougNQUSaaWlrYOAM8f+5KTnS0NAAOYmFtSwsGJR/duf1PgOiUlSXodgEunj2FuXYIbl84yuEsLxHliylbyZsSUOegb/vMfO/9p7Ko24+a6CYT5XcbqI30Nun2aGsMl+pqTnopV2dqU6zoeBSUVAq4d5uzsHrRZcQXdb9DXy0uHkhQZRK0xa9EwMCPk3nlOz+hCq8Vn0bctXF999y7h4d6lxfbbaOpuTF0/X1+/F3nZmQhFCjKBHoUPGRORz+98cRD4xh8TubZ6DJrGVjjV7UypBt2k0+KiXtzH1KWiTIkeC89a3Nsxl+SoELRMrAv09+LMdizL1kLD0Pxrbu8fpW3LFgwf/TvnLlySlgBISUnh6ImTbFy7SrrdoF5dZkyZhKqqKnsPHKRNx6743bmBU0nH4rovlp79BhH49i07Nq/HwtycU2fO0rxtB+5cvUhpd7dCz5mzYBFzFiwutt8Th/bhXaVysW2+hiqVKrLv4GGaNW6Erq4Ox06e4n1MLLVrVi+0fVxcPDv37KNCOS+ZADDAiDHjGDhsBCWsrfmte1f69uwhUzrne3Dk+AnKeZZhyYpVbN+5G1VVFRrUq8OsqVPQ0JCMVTKzMlH+xDZVFVVu3JKdltpvyHBat2hOzerVftkgsKN3cy6tnkCw72VKlJNobFZ6CgG3TlN/1HIAstNTsSlXm8o9JqCgpIL/lcMcndGDbmuuomf19Rp7ZtEQEiKCaDRuHRoGZry9e57DPp3pvPwchnaFa+ydXUu4u3tpsf22nLkbC7cv09i83BxOzOlLxY4jZYKqH2PmUgH/q0ewq9QAFQ0dAm6fJj0xFitPWV++f2A1d/csQ9PQHEfvZni1HYzoO8/UCbh1Cm1Ta4LuX+Dw5I6IxXlYuFWmWp+pqOlIyvXkZmcWyIpTUFYl8pUvuTnZBWa5icViQv2uERcaQJUeE/k3YV6xKY82TyLq8RVMPCQfEXIyUom4f5qyA5ZKt409auHcYRwiJRXCbh7h9sJe1Fl4EU3zr/fjB6uHkxIVRPmhq1HVNyPy4XluzutGzTmn0bEpvITGy4PLeHWo+JrUVSbsxOCjbNfvQXzgY8S52RiXzvdZNQNzNM0diPW/h7FHTeL876Np7iANAAMYe9QgLzuThMBHGLp+3Zj67YUdaFmWRL9kuR/e5t+Mcfkm+O+YTOzTq9JyBDkZqbz3PYNLH8lHzNzMVAxK18K+ze8IlVSIunOUx8t7U3HWedTNvt6Xn68fQVp0MK4DVqGsZ0rsowv4LelO+WknpQHST3l7dDlBx5YX26/H6D/RLVnhi2zJy83h6eoBlGg2DPVifp9Rt48QdfsIStqG6LvXxLbFqCLLShSGooYe6mYORNzYj5atByIlFcIv7UBBVQMdx6J9LCc9BUW1wmONAGlRb4l9cgmDMr92ObNvCgLrqClRu5QRB33DpEHgU08jURAKqO8i+RLqYqYtE5QdXseRs8+iOP44ghF1vy4IHBSTyiG/cO5NrI2FriSo2atqCa75v2fbrWDmtS48cNCtsjXNPMwKPfYXJtpfnkr+OYzd/5iaJQ2pXerHZh7WKGnElCNPufwqmmoOhryNTWXtlUAAopMyvnsQOCk9G4/p58jKyUMkFDCnlZv0HjVUFPCy1mXp+dc4mWpipKnCoYfh3A+Op4RB4VOkDvqGk50jpl25z6+v+bNQ1tDB0qsOby4fkAaBg26fQiASYV1B8qVdv4SrTFC2TPuRhNw7S+DNo3i2H/VV102KeEvA1YN03OCLhpHkObk06U2431VenNpG1YHzCz2vVMMeMtNtC0Nd37TY45+Snvie7PRU/PYto2yXcZTvPpnwx9e5tHAAiirqWJWTCKBd9daUNrJAXc+E+JBX3N06k7ig5zSase+LrvcxydGhPD60Go92w6X7LMrU5MmRtby+tA+7ai1JT3iP7+6FAKR9mCqnqKJOhZ7TMC5VHqFIgeA7p7k4vw+5wzNxqNm2sEv959HS0aVq7QacOLhbGgS+eOooCgoK1KjfBICSLu4yQdk+w8dx5ewJzh0/RN8RX7cYQ2hQAKcO7eHUPX9p3d2OvQZy59pF9m9bz8R5hS8U0LZbH+o1a11s30Ym3xZQys7KYtHU36ler7G03m9sdBQikQhdfdlZCvqGxsS8j/zqa714/JCje7YzZ9VW6b6wkLdEhIVw+vA+ZizbgEAgYNG0cQzt1ortJ65+9wDIr8bH+vpXEPjt7VMIP9ZXW1eZoKxn+5EE3z3L2xtH0e3wdfqaGPGWN1cP0mmjL5of9NW1aW/CH13l+elteBehr84Ne2D3nfX1e2Hm7s2tDVN4uG8Z7i0GkJOZxp0tM4B8XfxcvDqPw8y9Coqq6oT7XePWRh8ykmLx/PC80xOiC9yn6ofARFp8dIEgcEJ4ABFPb1J/0ravvb1/FF1dHRrWr8vOPXulQeDDx06goKBAs8YNASjt7iYTlJ0wdjTHT57mwOEjTPz96xaDCwh8y+59+wl88RgrS8nss0H9+3L+0mX+2LSFVUsXFXpev149aduqZaHH/sLc7Mf45Z7tm+nYvRdGVrYoKCigrKzMn1s24FFadow+bpIPq9atJy0tjYrly3F0/x6Z49MmT6BGNW801NW5ePkKY8ZPIjY29qufZVEEvg3i+q3bKCkrs2/nNhISEhk2eizvIiLZ96fEP+vVrs3yVWuo4V0VB3s7Lly6wqGjx8jNzZX2s37zVgICAtm+8Y+iLvVLoKKpQ4nydXh5ab80CPzm5kmEIhF2lSQaa2jnKhOUrdBpJAF3zuB//SgVO32dxia8e8vLywfpve0hWh80tkzz3oQ8vMLjk1upPaTwxVrdG/fAsVrxGqth8OW+fHPbPFS19SjdtOiaok0mbuTEnD6saeuIUKSASFGZRuPWYWSX/zsv07wPhnZuqGrpEfnKl2ubZpAYFUy9Ed93EbrEiGCSosJ4dfkQ9UevAARcXe/D4Smd6bj0NAKhEJuytXh65k8cqjTG2LEMUa/9eHJ6B3k52aQnxqLxoeRFZmoSf3RyIzc7E4FQRK3B8yhRrvD61r8qSho6mJSpTei1g9Ig8Lu7pxAIFTD1koxndWxcZIKyTq2GE/HgLOG3j+PUesRXXTclMojQG4dosOoeagYSP7Zr0Ivox9d4e34bZXoX/vHHtl43LCoXn3Siqlf49PdvITMhGoFQhJKmbKKbirYhGQmSmcUZCe9R0Zat+6+kqY9AKJK2+VKy05IIv3UMl05FL4b1vdr821FU10HfvRaRNw9Kg8DvH5xGIFKQBhQ1rVxkgrIlmg3j/cNzRN07gW3z4V913bSoICJvH6bqojuofPBltbo9iXt2jfCLO3DqMafQ8yxqdcW4QsH1qz6mqFIOxRF4cCGKmnpY1O5eZBuTSi1R1bdAWdeYlPBXvNk7h5TQF3iO3f3Z1xEIBJQZu5vHy3txuZ8jAoEQBXUdPEbtQFmn8HhdUtBjIq7vxaV/wWz0e9Obkhz8lLzsTMxrdJYpVfEr8s01gVt7WjB090PSsnJQU1LggG8Yjd1MUVGUZJykZuaw6Kw/515EEZ2USXZuHpk5eTibfX2NocfhiYjFUG3+ZZn9WTl5VC1iQTaQ1JXVVfvna3Hsux/Ks4gkzgz3/vvG30iXilYExabSY9M9svPEaCor0Nu7BAvP+hdZi/hb0FBW4MKo6qRm5nDtdQw+R59hqauKt6Pkj8jKTmUYvsePMtPPIxIKcDPXpmUZcx6HFV6PbcedYOq7GmOg8eOypb8n9jXacGXpYHIy0lBQUePN5f2UqNxEmmWVnZGK766FhNw7S1pcFHm52eRmZaJn4/zV14wJeAxiMfsGfVrwPwsz96K/0qpo6haYhvutiPPEAFhXaIB7C8k0E31bN2Je+/Hs+EZpELhUg27Sc/RsnNE0sebIqPrEvHmEgX3pgh3/DWnx0Zz2aY9Fmeq4Nc+vR2jhWZMKPadxY+3vXFk6BJGiMmXajyTy2W0EH0rWqGjr494yf3qXoYMHGUlxPD648v82CAzQuHUnJg/tRXpaGqpqapw8sIvajVtKs57SUlNZt2gmV8+d5H10JDnZ2WRlZuDoXHjWzufw4rEfYrGYltU8ZPZnZ2VSrmqNIs/T1tVDW1fvq6/7d+Tk5DBhUA+SEhNYtvXAD7sOSMo+DO7Sgs59hlCnSX6wRpyXR1ZmJrNWbsLGTpKdNGvlJppXceOp333cPcv/ULt+BRxqtuHyksFkZ6ShWIS+Pti5kOAfoK97B8rqa95P0NfvhZ61EzVGrOTWhsnc2z4bgVAB12Z9UNUxlGbwfi5lO+YHfgxs3RDn5fJw7xJpEPhLeXFmO2p6xliV+/dMP+7coR2/9R1IWloaampq7Nyzj1bNm0qzV1NTU5k+ex4nTp8hIjKS7OwcMjIycCukNuzn4uv3CLFYjGtZ2eywzMxMalavVsRZktqzeno/xy8nT59JbGwsZ48fwUBfjyPHT9CjzwAunzkhEyQfPXwoPbt3JTgklBlz5tG1V19OHt4vLV82adxYaVuP0u7k5uYxe8Gi7x4EzsvLQyAQ8Ofm9WhrS5JXli9aQMPmrYiKisbY2IilC+bSd/BQXMtWQCAQYGdbgh5dO7N52w4AXvm/ZtLU6Vw9dxpFxV+/RmWpWm05vXCQVGNfXjyAQ9WmMhp7a8cCAu+cJTUuirycHHKyMjAs8fUaG/1GorFb+xRctMrSo+j3JFUtXVS1vq8vhz66zrNzu+i6+nKx7W5snU16Yixt5h5EVUuPN7dOcnrBINovPCYNkpdt/dG40tYFJTVNTszujXcvH1S1vt94RSzOIzc7k4ZjV6NrIVn0u+HY1WzuVZFI/4eYOpWlQudRpMZHs3tEI8RiMWq6hjjXac/9fStkNF9JVYMuqy+RnZFKyMOrXFk3GW1jK6zKFK0pvyKW3q15sGooOZlpKCirEXr9AOYVGkuzoXMyUnmxfxGRD86RkRBNXk42edmZaFt9vR8nvJX48bkRss8qLyer2IxZJQ1dlDR+zbHCjyDk6n7E4jysqrX54W3+C5hWbs2z9cPIzUxDpKxG5K2DGHk1kvpybmYagYcWEeN3nsyEaMS5El/WtCz1Nz0XTXLwExCLuTW+hsz+vJwsdEtVKfwkQFFDF8Xv7MtxL24ScX0vFWacK7adRc0u0v/XsCyFqqE196Y1JinoMVo2hSeDfopYLObl1vEoaujiNfEQQiUV3l3exeMVfSg/9SQqerIfFVMj3uC3qBuW9XpjXK5gKUS3QWvJyUglJeQZr3fPJOjEKko0Lbw+96/ANweB6zgboSAUcuZpFFUdDLjmH8PuvvmD1GnHnnPpVTQ+TZ2xNdBAVUnEkJ0PycrJK7JPoUCAGLHMvpzc/PZ5eWIEAjg93BtF0SelBhSLfqH5WeUgrr2OwT8qGbsJsvUm+21/wPqrgRwd8m1Tlj9GIBAwuYkzExqVIjo5A311Za69lny9s9L//qUghEKBNKvX1Vyb11EpLLvwRhoEtjFQ5/CgKqRm5pCSmYOxlgp9tz0o1Jan4Yk8Ck1kQsOvF7J/GqtydREIFQi6cxrz0t6EP7pKw2n5K5ne2TSV0AcXqdhzKlpmtigoq3J5yWBys7OK7FMgEBbw/7zcbOn/i8V5IBDQYvE5hCLZn/DHU3A/5UeUg1DR0kMgUkDnkyl0OpaOBFw9VOR5hvYeCIQiEiMCvzgInBYfxYmJrdC1dqLGyNUF6ly7txiAW/P+pMVFoayhTXJ0KPe2zUTL2KbIPo1KeuJ/YdcX2fFfo1qdhogUFLh85hjlq9bkzrWLrN59XHp88bTfuXHpLKN85mFla4+KqiqThvQiOyu7yD4FQgFi8SdanpPf/q+X7p2nb6DwyQuzskrRvvwjy0Hk5OQwrn9XXr98ysaD59DRy/97oG9kTG5uLvGxMegZ5GdLxL6PwrNC0QOlonj7+iW9W9enQYu2DJ80S+aYgZEpCgoK0gAwgLWtAyKRiMiw0P+LILD1B30N/ktf/a7SaHq+vt7eOJVQX4m+an/Q10uLB8ssOvYpAoGwgE/K6GueRF9bLSlEX4tZzPJXLgcB4FCjNQ41WpMWH42iihoIBDw5vAbNQsozfAlGJT3JSksmLT4aNV0jVHWMSP8kY+ivbTXdTxayyc7C/8IeStXvUuBZ/8o0blAfBQURR46fpHaN6ly4dJlTR/I/Fo2ZMJkz584zf/YMHOzsUFNTo0ef/mRlFe2XQmFBv8zOzl8M9y+tvHP1YoHgoqpq0TPYflY5iIDAt6xc8we+t65JA76l3d24fuMWK9f+wfrV+bM8DAz0MTDQx9HBnlJOjlg7unD95q0ibSpfrixJSUnSwOz3wtTEGHMzU2kAGKCUk0R/Q8LCMDY2wtDQgEN7dpKRkUFsXBxmpqaMnzwV2xI2gKQOckxMLG5e+e9Bubm5XL1+k3UbNpH8/h3Kyr9OkkOJ8nURihQIuHUKqzLVCHl4hVaz8zX2yh8+BN2/SLU+09A1l2js6QWDyM0p5u9+YWPYnHxf/ktjO634sjHsjygHEfr4BqlxUazrmP+BRpyXy7VN0/E9vI6+fz4h4d1b/I6sp+vqy9KAr6GdK+FPbvPw6PoiM33/qh+c8O7tdw0Cq+sZIxQpSAPAADrmdgiEIpKjwzB1Kouisir1Ry2nzrBFpMW/R13PmCcnt6KkpoGadn6ylEAoRNdcUg7IyM6NuFB/7uxe8q8LApt41kEgUiDi3hkM3aoS/eQaVSbmZwQ+2T6NKL9LuHX1QcPUFpGyKvdXDil2rIBACAXexT5aoPzDu1jNOacLLEpW3AJVP6schLKOEeK8XLKSY1HWyveBjMT36JeSTNdX0TEk9pM6plnJsYjzclHRkc0Q/lyCLvyJeYXGxQa+v1eb/wIGHrURCBV473sGPRdv4p5do8yYndLj/rumE/v4Eg4dp6BmXAKRsirP1g0l7+80+ZPxhbiQcW/5qacQKMhqslCxaF/+EeUg4l/cJDMhimtDPT6yL5fXe2YRcmYD3sseFHqeVonSCIQi0iLffnYQOP75dWL8zlF99XMU1SV/97V6uBP77Crvru2RyaxOffeaB3PaYlyxOQ7tCy+Zo6IvmYGqYe6IOC+PF5tGY91owC87vv1mq5QVRDQtbcoB3zBiU7Mw0lSmsl3+S/Pdt3G0LWtJE3dJGYaM7FyCYlOxNSx6xVR9DSWik/IXWcvIzuVNdAqu5pJ/IDcLbcRiiE7OLDbz91N+VjmI8Y2cGFDDTmZfzYVX8GnqTAPX7z/lA0AkFGCqLRlMHXr4Di9r3X8kuzZPLCYrJ7fAfnVlBdSVFUhIy+Lyq2gmNyn49XX77WCs9NSo5vhrLwj3MSJFZWyrNuPN5f1kJMWipmskU3c28vkdHGu1o0QVyXSJnKwMkiODiq3BqKKtLzNFNycrg4SwN+jbSl6k9G3dQSwmPT662My0T/kR5SBEikoYOpQhMSxAZn9ieIB0KnVhxAU/R5yX+8ULs6XFRXJ8Qkt0rUpSa8wfRQqrQCCQrvAccOUg6gbm6NsV/UchNvDp//0icUrKytRr2ooTB3YRHxuDvpEJ5Srn1w57ePcmTdt2kWarZmZkEBoUiLVt0fWadPUNeR+dXyYhMyODt29eUdJVEvh3ciuNWCwmJjqK8sVk/n7KjyoHkZ2dze/9u/Dm5TM2HjyHgZGsPju7e6KgqMjtqxdo1EqyMFLUuzDevn5J6XJfNmAPePWCPm3qU69Za8ZMX1jguEf5SuTk5BAaFICljeTvR1hwILm5udLSGf91/tLX1x/0VbUQfXWo1Q7bj/Q1KTIIbfOi9VW1CH01+KCvBnYSfU2Lj8b8C/T1Vy4H8TF/BWJfnv0TkaIKFh41vqm/2MCniJRUpLXZjUt5cWfzdHKyMqTZhGF+l1HTM0HTWNZvg26fJCMpFqd6hS/68auirKxMm5Yt2LlnH7GxsZgYG1OjWn4G442bt+naqQOtW0j8ISMjg4DAtzjY2xXVJYYGBkRE5mtlRkYGL/39paUTypR2RywWExkVVWzm76f8rHIQaWlpADKL54FkVfu8vKKTQP46lplZdHDm0eMnqKiooKNTdE2+r6FypYrsP3SElJQUaQ1g/9eSsY31hxIcf6GiooK5mRnZ2dkcPHJU+oxbNG2Ml2cZmba9+g/C3s6O8WNGoqT066wMDqCgpIyjdzNeXNxPelIcarpGWH6ke+HP7uBcpx2O3vkamxgRhK5F0b6sqq1P6icaGxf6GsMPpROM7N1ALCY1LgqrYjJ/P+VHlIPwaNoTR2/ZqfkHJrTFqUYr3Bp2BSA7Mx0AwSe+LBCJpLPhCiM64AnANy8U9ylmLhXIy80h4d1bdMwkixElRgQhzstF00jWT0UKimgaSt57X145RIny9Yqd/SHOExebpPKrIlJUxrxiU0KvHyArORYVHSMMnfM/IsW+vItV9baYV5SUN8vNyiA1KggN06LHCspa+mTER0u3c7MySAl/g46N5EOAto3EjzMTor+oVu7PKgeha+uOQKRI9OOrWFZtBUBa7DuSw1+j/6H+qZ6jFy8PLiUt9h1q+hK/iX58FaGiMjq2Xz5rM+6NL4nBz3DvMf2Ht/mvIFRUxrh8EyJvHiIrOQ4lbUN0nfJ9OcH/LqZV20gzUXOzMkiPDkbNpGhNVtTUJytB1pfT3gWgaS3xZU1rV4kvJ0aj5/z5CS0/ohyEZZ0eGJdvIrPv4YJOGFdsjnmNoseKKWEvEOflFlnGoTBysz5o+yeaKBAIJB95/uo73B/fuW0xLt+Ukp2nfVbfYnEe4twcyMuF/2oQGCQlIdquvUVIXBotypgjFOZn5tkZqnPqaQQNXI1RFAlZeNafzGKygAGq2huw624I9VyM0ddQZtn51+R89IfWzlCD1p7mDNvtx9SmzrhbaBOfls3NN7FY66vR2L3wQcCPKgcRn5ZFeHw6iemSrypBMaloqypipKmMkZYKptqq0oDsx5jpqGKtnx8MD4tPIyEtm9A4iVM+DZeUTChhoI66suSf6m1MKqmZOUQlZZCdmydt42isiZKCkNiUTI49jqCynT5ZOXnsvhfK8UfvODjoy7M8/uo7JTMHoUDA0/BEFEVCSppIajkvPe+Pp5Uu1vpqZObkceFFNPsfhDGrZf708Esvo8kTi7E30iQoJpXpx59jb6RBh/KyA5W0rBwO+oYzqKZdgczOXx37Gm04Mak1yVEh2FVrJSMm2ma2BN0+iXWFBggVFPHdtYCcrIxi+zNzr4r/+V1YV2iAipY+fnuXyHx91jG3+1CGYggVek3DwM6dzOQEIp7cQNPEmhKVmxTa74+arly61WAuzO+NiUtFzNyrEvHkBgHXDlFvoqS+aVLEW0ndZK86qGjpER/6ijsbfdC3dcO41Od/HUyNjeT4hOao65lQqc8sMj5aAVdFy0C66NGjgyux9KwFAiFBt07w6MByao/dID3uf2E3QpEi+nZuCARCQu6e4fnJTZTvPuU7PpV/J41bd6Jv2waEhwTRsEU7mbqz1nYOXDx1hJoNmqKgqMjahTPJyizel8tXrcHhXVupUa8JuvqGbFg2l9yPMoJs7Bxp1LojU4b1YdTUeZRy9yAxPp77N69gbm1LncYtCu33R5SDyMnJYUyfjjz1e8CKbQcRCATEfAhga2hqo6KqiqaWNi079mDJjAnoGRiiravPQp+xODq7UbFa7c++1puXz+nTpj7lqlSn97DfpdcBpIHnitVqU8q9DD7D+zFmhiRIvGDyaNw8y+PiUfY73vmvjUPNNpyYKNFX+0/11dyWoFsnsfmgrw92LiD3M/T11fld2FRogIq2Pg/3LpEM1D7wl75eXjKESr2mYWAv0dd3H/TV9h/W14zkeFLeh5H1YZHCxIhAlDS0UNM1kn64SouPIi0+moRwScAqPuQVmamJaBhaSG16emwDxqXKoaiqQdjDy9zZPI3y3SfJLKz5dwTdOUN6fDTGTl6IlFV49/g69/+cR6n6XREpSj4y21dvzYOdC7i8ZAie7UeS8C4Av33LKdtxTIG/7S9Ob8e8dDW0TGy+9TH943Tu0I66jZsTFBxMh7atZbTSwcGOw0eP06xxIxQVFZk+Zx4ZmZnF9AY1q1dj87YdNG3cEEMDA2bPX0TORx/UHR3s6dS+HT37DWTBnFl4epQmLj6eK1evU6KENa2aFx5U+FHlIOLi4gkJDSUhUTJOfBP4Fh1tbUyMjTExMcappCP2drYMHjGK+bNnoq+nx5Fjxzl/8RKH9kqymm7ductDv0dUqVQRHR1tAgLf4jNjNjbWVlStLPmoduzkKSKjoqlUvhyqqqpcunoNn5lz6PNb9y/OqH3+4iVZWVnExMaSkpKK36PHANJAe6d2bZg1dwE9+w/CZ8I4EhITGTFmHK1bNsfISJIFd+fefcLfvcPD3Z3wd++YPmsueXl5jBkhWSVcR0cHHR0dmeuqq6uhp6eLq8vXTz3/kZSq3Zb9v7ciKTIEp5qyGqtrbsebmyexq9QQoYIit3f8/RjW0sObZ2d2YlexAaraBtzZtVhmDKtrYY9TrTacWTSE6n2nY2TvTkZyPGGPbqBtaoND1cI19keUg1DTMZQupvYXIgVF1HWN0PuwuKiepQM6ZiW4sGIM1fpMR1VLlzc3TxLse5nmU7cD8O75PSJe3seydFWU1bWIfPWQy+smYVexgbTu8ecSHx5IdkYqKXGR5GZnSYPJ+lYlESkqYV2mOkb27pxZPJSa/SUziC6tnYiJU1lMHD0kfYS9IeKlL6alypKRnIjvwdXEBr2kwehV0uvc2bkYE6eyaJtak5udxdu753hxYS81BxZe//NXx8q7NddmtCU1OgSLKi1k/FjD1I53d09h6iUZK7zYt5DcrOI12dC1KsGXdmHqVQ9lLX1eHlwm48eaZnZYVm3N/dXDcO82FZ0S7mSlxPP+2U3Uja0xr1Bwujj8uHIQWSnxpMWEk5364d09MghFdW1UdIxQ0TFCUU0Lm1odebJjBspaBihp6vJ4mw/aVs4YuUs+LBqXroGWRUkerByCW7epZCXH82T7dGxqd0ZR7cvXcXp7fgcaprYYuhQdWPxebf5LmFRuje+8dqTHhGBSUdaX1Uxsef/gNIae9RGKFAk8vJjc7OJ9Wc+5Cu+u7sbAsx5Kmvq8PbqMvLx8X1Y3tcOkciuerx+OQ0cfNG3cyElJIP7lTVQNrTEq16jQfn9EOQglLQOUtGSTAQUiBZS1jVA3lcx+SIsKIvLWQQzca6OoqUfqO3/8d05D09q12AXdPkXbviyK6jo8Wz8C2xYjECqq8O7yn6S/D8HA48PCv2Gv8J3bFt1SlbFpOpTMj4LpyjqSpIqIG/sRKiqjYeGEQEGJ5LePCNg3B6NyjREq/jozfz7luwSBK9rqYaKtgn9UCmu7yL6cTm3mwsi9j2i+6iY6qor08bYlM7tgpujHDK1tT2hcGj0230NdSYFhdRyITJIddCzt4MHS86+ZcfwFEYnp6KgpUcZShyr237eUw+dw5mkUw/f4SbdH7ZMMLEfVc2RM/ZKf3c/806/Yez9Mul1n8VUADgyoRJUPGc8j9z7iVkBsgTZ3J9aWLvq2734Y0489Rwx4WetycGBlPK3yf6QhcWmUn3WBpe09CgRjP+avvv/i7PMoLHRVuT9J8sNIzczl9wNPiEhIR0VRhL2RBis6lqGlZ34GXlJGDrNPviAiIQMdNUUau5syvqFTgTIeR/zekZaVS4dy/74MNxOXSqjrm5IQ+opaY9bJHKvYewZXlw/n2LhmKGto49qsHzl/M/DwaDuclOhQzs7siqKKOmXajSAtTnbRqerDlvNw7xLubp5Oauw7lDV0MHT0xPQLMte+FzaVGuE9aBEP9y3l1vqJaJvZUmPEKmk9YKGCEuGPrvL02B9kp6eiYWiGpVddPDuOkVmt/vh4SYZHkzlHCr1O+MNLJL0LJOldILt6esgc67DhgTTLLPTBBfz2LiE3Owv9Ei7Um7gNSy/ZhS4e7l1MSnQYAqEQbXM7qg1d9n9dD/gvPCtWxcjEnED/F8xbu13m2Oip85k6sh89mtdCS0eXLn0Gk/k3QeBeQ8fyLjSY4T3aoKquQZ9hv/M+MkKmzfSl69mwdC5LZownKiIcbR09XMt4Ua5Kje99e8US9S6MS6ePAdChnmxW7/Sl62neQVLXeuyMRYgUFBjbrwuZGemUr1qTWSs2ymS8NfRyxKtyNWYs31Dotc4dO0BcTDRnjuzjzBHZxREfRUr0QSgUsmL7IeZNHEnPFrVRVlGlUvXajJ42/z+/KNzHmLpUQk3flPiQV9T+RF8r9Z7BlWXDOfq7RF/dmvf728FwmXbDSY4O5czMriioqOPZfgSpsbL6WmP4ch7uWcLtj/TVyNHzi2ZefC+C75zm8tKh0u2rK0YCULbjGLw6S2qlPj+5lQe78hdTOjWtEyC5j5J1OgIQ7f+Q+zvnk52eio6FA96DFuJYq53Mtf7s6YmZW2Vqjii44AWAUEGBZyc3cWvjZMR5YrRMrPHq/DsuTXpJ2yira9F45n6ur/mdgyPqoqyhjXvLgbi3HCDTV1JkEOGPr1Fn7K+9eFZReFepjLmZKc9fvOTPzbK/80VzZ9FnwBCq12uEro4OwwYNICOjeL8cN3oEQcEhtGzfGQ11dSaMHUXEJ1q5ad0qZs9fyLhJUwgLf4eeri7lvDypUf3HrzfxKUdPnKRX/0HS7X6DJD46ZcLv+Ewcj6KiIscP7mP8lKk0b9OBlNRU7G1LsHHtKpo2kiygp6qiwoHDR/CZOZvU1DRMTYypX7cOu7ZtktZXVlRQZO0fGxg9biJ5eXnY2tgwbdJ4BvbrI2OPSF1Heu2iaNKqLcEhodLtspUlgY/c1AQANDQ0OHv8MENHjaVCtVro6ujQvGlj5kz3kZ6TkZHBlOmzCHwbhIaGOg3r1WXrxnUFAr//JsxdK6FhYEpsyCsajZf9PdboN4Ozi4exZ1RTVDS18WzR72+DwOXbDycpKoQjU7uiqKpOhY4jSf1kDFt/1Aru7FrMtQ3TSI55h4qmLiYly2Dp8c9r7N8hUlCk5czdXNs4gyM+nclKT0XHrAT1R63ArqJkAT2RohKvrhyWBMmzs9AyssCtYVfKtZWtB7mhWxks3KvQYHThGgtwbulwwh7flG7vGChZIKrXVl+0TawQCIW0mL6TS2smsGd0UxSUVLH2rE71fjOkwaK8vDweHFxDfNgbhCIFLEtXpcOSk2ib5L9jZWWkcmHFaJJjIlBQUkHP0oEGY1bhVLP4WVa/KvqlKqKqZ0JymD/lh62VOebefSoP1o7kqk9zFNV1sG/Uh7y/GSuUbDGUtOhQbs3vgYKKOiVbDSMjXtaPyw5cysuDS3myYwbpsREoaeiga18GQ9d/PlgZcf8MD1YPl24/XCep0+/UZhTO7ST10917zEAgUuDu0n7kZmVg6FoVr0ErEAgl41eBUETl8Tvw2zCOK5ObIVJSwbJqK1y75ifJpEaHcGZwecoOXIp1jQ5F2pOdnkLYjcOUajPyh7f5r6FTsgLKuiakhvvjOmC1zDHHTlN5sWEk92e2RFFdB6v6vcn7m7jC/9q7z7iorqeB478FlqU3aQoC0lQUG/aKDUvsUZOYqEnUGI3RxF5jjcYYS+wllmii+Rt7jV2DFXsXG4qgomChL2X3ebEK8giokSbO9xXZe/bes3zGyWH23jluLSAV6+0AADTeSURBVL8lIfIOZ2d8gYGR6bNiZsZNgX26Tydk069c/98EEh/dQ2lmhYV7hWx7AucXPQMljy4e5M6OxaSo4zCyKYZthYa4t+mfFssAJybqclnl4Znv7WJoXoSKA1dyfc1PnJrUEU1qMqbFvCjfb0laS4mIoM0kRUcScWwTEcc2ZXh/o+V3Ad2/m1ubZxEfEQJaLUa2zjg3/ByXpj1eumZBovj/PUKyHKhQaO9Pzf6W78Juyo5gtpy7x4FB/vk9lbdy8Hokny06xoHB/hnuRC7onhev//muDhWKW6W9fjzkEV2WBF16FJf033ddeQWVuVVIix/Xuz1vyfA+OrnyZ0IObab9nMBcOf+qLytSullXKnT4LlfO/19sGdYaa9dS1Po6Y//ZHeM6RYce39VDq9WuzuKtBZahyihh7/lQIwtLq/yeSr6ZN2U8u7asY92B0zl+7oT4eOr5FGXs9IU0a/tRjp8/M93aNsajlA/DJ2XsM9emTrmnIdeCA7RabVAWby0QFArFGL9PBo1+XtR8H53482duHtpMx7m5k19fJTkxnt87lcS/36941muXL3PIzJ9fVqJsi26Ub/dNhtdPrZ7OyT9/npyakjw0t66tUCi0zwuE76uxP05i7fpNnDtxJL+nkqWQW7fwKluRA7u2U6tGzvbRzE23bt/Gw6c8xwL3ZWghcfjoMVp3+PhSVNSjHFnTKhSKCpZF3Q50W3biv+/IXQgcXjGZa4Gb6brwYL5cPzkxnrkdvGnS/9d3rtA6rYktLUYuydA642bQLnZM+eZw/NOoPKsSKRSK79ybfDG5QrdJBauvSh66tHoKd49todHUA/ly/YcXDnJo0mc0nnYAU4e321MgJ/07pi0WxUtRoVvGO9nvn9rNiTl9D6uj8y5OX4eBkek8jw+HfF3QC4W56ca6X3hwfCs1Ju3LlfMf/L4KTg26FKjN2U5M/BAz55KU6jIxw+t3di/j+t+TlqXER3+RV3N5f24nyiHXImJwH7aN+QduvHpwAbXn8gO+aeD5ThWAP1l0lHpT9uf3NN57T8KusrSDK+c2zMvR8z66fQU9pSG+bXq/enAeuL5/DUs7uHL/0tH8norIJSHXrlDd3Ybl82fk6HmPH9qPb8WqeVIA3rp2FdXdbTh1LH/+qBU560nYVRa3d+Xc+pzNr6/j7vmD2HtXKjAF4FOrp7O4vSuxD8NePVjkqsvBwVjYOzF9ZtZ3L+an7Tt20bnTx+9UAbh5m/b4Vs6fTSLfZ4/uXGVWa1dOrp376sE57M7ZgxQtVemdKgDv/nUAs1oXnEKf0IkOu8bGzu5c2zL/1YNz2P3Te/Bu/U2BKQCHBq5lY2d3Ii8fy++piP8g7u419vXw5Pb2Ba8e/AZiw4LRM1Dh2qxnjp73v7p3eB37enjyJLjgxGnB7FRcQHWvU4L2frreTjam7+6XkKNbFsy+ZNmZ1rE8icm6XtLFrHJ+8z7xamVa9sDTvz0ARhY523bFxrUUHy0oOInRpWpT2nlXAkBlmrObz4j816n7N3zQXveYvJVNzm5EWbdxc+o2zrx/Vk7zb9IC30q6/lfmFlZ5ck2RO8q26oFX/dzJr6/DtUoArs9a+BQEL260lx+/D6Hzba+v+fRj3RdatkVytg97Tvn/rSHeBYvmziQhQddWobjzm29iKt5cxdZfUbqBru2XsWXe5xT3agG4Vys4OfZ11Ow6FL/2uqcwzIq835snFxSezbvjUle3VjA0z/uc7Nt59KsH5aGilZtg46X7e01p8l4/7PDOcQnoRtFaui/FlGY5G8tmziWpOaXg3CBjVzEASw/dEz8GBSROpQj8BnJrYznxapltrCfyVm5tfFQQGZqYYWhilt/TELkkNzaWyw+mZuaYmr35Zh2i4Hmf8uvrkN9HwZBbG8u975yKFcvvKbx3cmNjucIus43zRP7KrY3l3lVKYzOUxvL32rsoNzaWK6gMjM0wKGBxKu0ghBBCCCGEEEIIIYQQohB7b4vAjgM2s/ns3fyeRp5YcjAE7xHbWXDgBhtOhzNp2+X8npJ4C4ta2nHz0KZXD3yHvQ+f8X1T3lHFrs3r8nsauep9+IzvmwUt7Lh5sHDnovfhM75v9E2tWLN+Y35PI0/MXbAIm2IuzJg1h7/+XsvIMePze0riLUxrYsvVwMKdj96Hz/g+WtfRkfCjm/N7GrnqffiM75vdXYoREbQlv6eRq96Hz/impB3EO6ryhN2EPU5gwzc1qe6e3ttqyo5gtpy7x4FB/mmvbT1/jz+7V2PBvze5fC+aJZ9XyYcZC6ETFXKBk39O5kHwSZLiojGytMXOqwLVu4/H3L44AJ8uv4DKzCp/JyrEC4IvnmPuz2M5f+o4MdFPsLG1p0wFPwaO+ZlixXUbZOw5dxsLy/fj0Sbxboi6eYHjL+RbYytdvq3xQr7tvELyrShY3Ev7cjv0Dvt3bqNOrZppr4/9cRJr12/i3Ikjaa+t27CJLev+ZsbsuZy/cJG1q/7IjykLAcDDGxc4vPwn7gWfQh37FBMrWxy8K+LfczwWDrqc23PVRcm5osB5cusil1f/zKNrp0iOj0ZlYYu1RwXKdR2DiZ0udpsvPIdS9koRBUhM6EVurJ1C9M3TpMRHozQvgoV7Bbw7jcHYVreXV52ZZyRu/x8pAr/DjAz0mLDlMlv61s523NpeugV0lRLvfg9M8W5LeBrJ1hEf4lypPk1Gr8LI3JrYB3cIPbGb5PiYtHEm1rIBhSg4HkU+5KsOTanp35jZf2zA0tqGe2GhBO7eTlxsetza2jvm4yyFyCjhaSRbRn6Ic8X6NBujy7cxD+4Qenw3SZJvRQFnZGTEsFFjOLh3Z7bjdm/X3ZVWs3q1vJiWEFmKfxLJ30Pb4eZXn7bjV2JkbkP0gzuEBO1C/ULONbWRnCsKFnV0JAfHd8ChvD81h/2BoZk18ZFh3D+1m+SE2LRxRlb2+ThLITJKio7i1E8dKeLrT4X+K1CaWZMYFUbkmd2kJqTnXJXE7UsKbRFYq9Uy/8BNlh+5TfjjBIqYGdLez5kRH5TOdPyELZfZfuEe4Y8TsDVX0ap8MQY3LYmRUh+A8McJDF9/nmM3H6FOScXJypiBTUrSpqJuV9+pO6+yMiiUh9FqLE2U1PO2Y3anirn6GT+r4cofR26z9dw9PihXNNMxtyLjGL3pIqdCnxCrTsHTzpRBTUsR4JO+AHkSn8SojRfZeTECdXIqVUrYML5NWUo5yoZD+UGr1XJ+wzwub19G7MNwjC2L4Fm/A1W7jsp0fNCycdw6uk031soO99qt8ft0CAaGRgDEPgzn8IKh3L94lJQkNWZ2Tvh1GoxH3bYAnFr1C8G7/iT+8QNUZpY4VaxP/f5zcuWzRVwKIinuCfX6zURfqdtk0dzBhaK+tTKMW9TSjoZDF+NeqxUxEaH81d2PBoMWcmnbUh5eO42Vsyf1vpuNQk+PwNkDeBRykSLuZfHvPxcLR9dcmbvInlarZfn8GaxZvoh74XewLmJHi/ad6DdiQqbjZ0wYwd7tG7kffocitg4EtPqQ3oNHozLSxe398DtMGv4dp44dIkmdiKNTcb4eOIpmbToCMH/qj2xYuYzIh/exsLSmRr1G/Dh7Sa58tjPHDxP95DHjZixCaaiLWycXNyrXrJthXHlHFb8sWkXjlu0ID71F86ol+Wn+Cv5etpALZ47j5lmSCTMXo1DoMX5Qb4IvnqOUbwV+nLUEZ9cSuTJ3kT2tVsu59fO49EK+9arfgWqfZ55vjy0bR8iR9HzrUbs1lT/LmG8Pztfl29TkZ/n2k8F41tPl25OrfuHKzvR861yxPg0G5E6+vX8pCHXsE/y/y5hvi/2/fLughR2Nhy7GvbYu367s5kfDQQu5tH0pD67q8m3972ejUOjx7+wBRIVcxNajLPUl3+YbrVbL9JmzWbB4KaF3wrCzteWzTz5i4rjMd24fNmoMGzZvIfROGA72dnRo15axo4Zj9Czf3gkL49v+gzh4+AiJiWpcijvzw/ChfNxBt3P3+EmTWfL7Cu5HPMDayorGDevz+28LcvUz9viiK4uW/s66jZto17pVpmNu3AxhwNDhBB0/SUxsLCW9PBkzajgtmjVNG/P48RO+HzyUzdu2k5ioplb1akyf8hNlfDL/e0DkLq1Wy8m1czm3dRkxz3Ju6YYdqfNl5jk3cPE4rh/eSvSDcEyt7fCu25qaXYam5dyYB+HsnTOEsAu6nGtu50SNzoMp5d8OgCN/TOHCjuc51wrXSv40Gzw3Vz7b3Wc5t8mAWWk519LRheLlMubcaU1saTFyCd51WvH0fiiLu1ai+bCFnN2ylPvBp7Ep7kXTgbo17q4Z/Xl48yL2nr40GzwXS8m5+Uar1XJ9y3xu7lpOQmQ4hhZFcKnbnrKdRmQ6/sKfE7gbtJ34yHCMrGxxqtEKn46D0X8Wu/GR4ZxdMpzIy8fQJKsxtnWidIeBFK/VBoDLa6Zya+9K1E8eojS1xKF8PSr3mZ0rny3qynGSYp/g13sGega62DW1d8HOp2aGces6OlKt/yKcqrck7kEoO/pUpUq/+YTsXMaj62cwd/Kk8jczQaHg9MJBPL19ESs3Xyp/OwtTe4nd/KDVagn9ZwFhe1eQGBWOoXkRitb6EM+OwzMdf+1/P/Lw5HYSo+5iaGmLQ9WWuLcblBa3iVHhXFk+gidXg9AkJ2JUxAn3tgNwrN4GgJsbpnH3wCrUT3Vxa1O2HmV7zsyVz/bkWhDJcU/x6TE9LW6N7YpjXapGhnG7uxTDt89CHKq2IOHhHQ4NqEbZ3nMJ27Oc6JtnMCnqQZmvfkWhp8flJYOICb2EuWtZyn49C2M7l1yZe34rtEXgiduu8PvhW4xpVYYaHkWIilVzPjw6y/EmKn2mf1QBR0sjrkbEMGTNOVQGegxpVgqAoevOo05OZW3vGpiplNx4mP6t2JZzd5m3/wbzPqtE6aLmRMYmcfL242zn5z5sW7bHq7nbsKpH9WzHFLM05svaJZi47TJNyjhgoP9yi+e4pBQalLJnaLNSGCn12XgmnG7LjrN3QD28HHRF3n5/neHGg1iWfVEFKxMlk7ZdodPCoxwa1gDjZ0VwkXeOL5/A5e3LqN5tHI5lapAYHUXUjfNZjjcwMqVu318xLVKUx3eCOThnEPpKQyp/NgyAQ/MGk5qs5oOJ61Eam/M0/Hrae0MObebc+jk0GLQAG1cfEp5G8iD4RLbzW9oh+/+JO/pUp9nY/2V6zNjaHq1GQ8ihzXjUa4dCocj2XC86uXIy1buPx8LBjYPzBrF3Sk+MrWyp0nk4Rpa2HJjRhyMLh9Pkhz9f+5wi58ycOIq/f1/IgDE/41ejNo+jIrly/kyW441NTBk7fSH2jsW4efUyE4Z8i1Klos+QMQD8OLQvSepEflu7E1Mzc27fuJr23t1b1rN83nR+mrccr9JleRT5kHMnj2U7v+ru2T8JUalaLeauyrzPma29IxqNhl1b1tGs7UdvFLfzpoxj0NgpOLuWYMKQvgz5ujM2tvb0GToWG1t7RvXtxuSR/Zm1Yv1rn1PknKDfJ3Bp+zJqdBtH0bI1SHgaRdTNbPKtyhT/fs/ybWgwgXN1+bZKZ12+DZyry7ctJ65HaWLOkxfy7c1Dmzm7bg4NB+vybeLTSCKuZJ9vF7fPPt8WLVOd5lnkW5Nn+fbmoc14vmG+PbFyMjW7j8fc0Y2DcwexZ0pPjC1tqdJlOMaWtuyb3odDC4bTbLTk2/wwYvQ45v+2mF8m/Ujd2rV4GBnJmbPnshxvamrCb/Nm41SsKJcuB9O7X39UKkPG/TASgG++G0Biopo92zZjYWFO8NX0uF27YSNTf53Nn8t+w7eMDw8eRnIs6Hi287Owd8r2eO2aNdi2YU22Y4o7O9Pn668YMXocrT5ojoHBy3+yxMbG0jSgMeN/GImxsTGr166j/SedOXPsEKVKegPwRc9eXL12nfX/W4m1lRUjx46neZv2XDl7AmNj42znIHLewaUTOLdlKfV6jsfpWc59kM0aV2lkQkD/mZgVKUpUaDB7Zg5EX6miVlddzt0zexApSWo6/rwBQ1NzHt1Jj92rgZs5uXYOzYcuxLaED/FPHnLv8sls5zerdfY516lsddr9mH3OvRq4iVL1P3yjnHtkxWTq9ZyAlaMbu2cNZNukrzC2sqXW58MxsbJjxy/fsG/uMNqMW/na5xQ56+KqiYTs/B3fLmOw9amBOjqKJyFZx66+ygS/XtMxsnEkJuwqpxcNQc9ARZmPhwBw5rehaJLV1Bm9FqWJGTF3b6S9N/zoFq5tnkeVfvOwdCmN+mkkj65lH7sbO7tne9y2dDVqDV+V6TEjK3vQagg/ugXnWm3fKHYvr55Cua5jMXVw5fRvQwj69WtUlraU+XgoKktbTszpy9klI6k5dMVrn1PknBt/TyJs73K8PhmNdanqJEVHEXP7Qpbj9VUm+HSfhsq6KHF3r3J5qS5uPdoPBuDK78PQJKvxG/Y3BsbmxN1Lj9uI41u5vW0+vr3nYuZciqToKJ7eyD5u9/XwzPa4VclqVByY+VpTZamL2wdBW3Co8WZxe3PdL3h/OhZjOxeu/D6MC/N6Y2hhi0f7oRha2HJxYT+CV4ykQv/lr33Od0mhLALHqVNY+O9NxrUuQ6dquup9CVtTKrtlXQTo39g77WcXGxP6NvRi3v4baUXgsMfxfOBblDLFdP1EXIuYpI0Pe5yAg4UK/5J2KPX1cLY2oUJxq2znuGdAvWyPGylfb8++vg09WXkslD+PhdK1pttLx8sUs0ybM8B3jbzZeTGCLefu8X1jc24+jGXHxQjW965JDQ9db+HZnSriN2E3606G8Wl1+dYuLyUnxHJh4wKq95hAycafAmBZzB2HUln3ca708YC0n80dXKjQ8TvOr5+TVgSOfRiGW80WFClRFiDDnVsxD8MwsXHAuWJ99AyUmNk7Y+dVIds5tvt1X7bHDVRZ/1HlUKoyFTp8x/4ZfTg0fwh2XhUp6lsTT//2af0ps+LbuhculRvrfm7Tm53jP6XyZ0MpVk7XDqXMB904tGBotucQuSM+LpY/Fs5k0LhfaNvpcwBcSnhSvnLWX2T17J/+DbSTixvd+g5m+bzpaUXge2GhNPqgLSXLlAPIcKfs3bDb2Do4UsO/MUqlkqLOLpSp4JftHFfvCcr2uMoo67gt51eN7v2GMKpvNyYO60eZCn5UqVmP5u0+TusHnJXOPftRp1EzALp83Y++XdrxzZAxVK3tD8DHX/Zi0vDvsj2HyB3JCbGc37iAmj0mUCogPd86ls463/p9kjHfVuzwHWfXz0krAsc+DKNEzRYUcX8538Y+SM+3+gZKzF8j37af+Yp8a5h9vq3Y8Tv2T+/DwXlDsPfW5Vuv18i35dr0wqWKLt+Wa9ubf8Z9SuXhQ3F6lm/LtujGwfmSb/NDbGwsM2bPZdrPk/iya2cAPD3cqVGtapbvGTl0cNrPbq6uDB3Un2m/zkorAoeG3qFdm1aUL+cLQAk3t7TxoaF3KOroQEDDBiiVSlyKF6dypeyfdDt1JDDb48bGRtkef27owP4s/n05vy1bztfdv3zpePlyvmlzBhg+eCBbtv3D2g0bGTFkENeu32Dz1u3s27GVurV1d2Mu/20BbqV8+fN/f9P98y6vNQ+RM5ISYjm1bj7+X0+gbBNdzrV2cqeYT9Y5t/qnA9N+tnR0Iebj7zixZm5aETj6QRhetVtg51H22ZgX1rgP7mBq44Crny7nWtg74+idfex+Nve/r3GLla5M1Y+/Z8cvfdg7ZwiO3hVxLleL0g3ap/UDzopfu164V9Xl3Mof9mbD6E9p2XUYLhXqAFChVXf2zpGcm19SEuO4vnUh5bqOw61BJwDMHEtQxLtylu8p3b5/2s+m9i6UbNuXa5vnpRWB4yPDcKr2AVZuZZ6NSY/d+MgwjKwccCjnj56BEhNbZ6w9KmQ7x4ZT9mR7/PmdnJmx8fajZNt+nJjTlzOLh2HtUQFbn5q41GmX1g84K54teuJYqREAXi2+5sjkLvh8NAS7srr1gkfTLzmzOPO7TkXuSkmMI3THIrw/HYtTvU8AMHEogZVX1nHr3ub7tJ+N7YpTolVfbm+bn1YETowKx75yc8xdyjwbk36nbGJkGCore2zK1kPPQImRrTMW7uWznWO1CbuyPa6nzDpuLT39cGvZl4uLvuPK8hFYuJfHulRNHGu2S+sHnBWXpj2xLd8w7eez07vi0W4wNj66tULxxl8QvDzzu/wLg0JZBA6OiEGdoqGOl+1rv2fz2bss+vcmIVHxxKlT0Gi0pGq1acd71HFn8Jpz7At+SB0vW5qVdaT8s0Jvy3LFWBQYQtUf9+Bf0o76pexpUsYBlUHWd9GWsDX9z5/vRVYmhnzb0JOpO6/S3u/lYI9TpzB151V2XY7gQbSa5FQN6hQNPsUsALgWEYueAiq7pW9mZGGspLSjOVcjYl86n8hdj+9cJTVZjVP5Oq/9npuHNnFh4wKi74WQnBiHVqNBq0lNO16mZQ8Ozh1E2Mm9FCtfB7caH2DnqUvI7rVacXHTQv7q7odTpfoUr9QA12pN0VeqsryeZbHsv2l+lSpdRuDbphd3zwXyIPgkwbtWcmb1DAJGrcCpfN0s32dTwiftZxMrO91rbumPcxpb2ZGSGE9KYjwGRiYvvV/knhvBl0lSq6lWp/5rv2fX5nX8sWgWd0JuEB8Xi0aTSmpqetx26tGHHwf34dC+nVSrU58GzVrjU74SAAEtP2Tlojk0r1qSmv6NqVU/AP8mLTBUZR23LiWy/6b5Vb4dNo7OPfsRdHA/504eY/3KpSya8RMzl6+lWp0GWb7P2ye9QFHETteGx6t0mRdesychPo6E+HiMTSRu89Lj0P+Qbw9u4vymBTy9m3m+9W3Vg8A5g7hzci9OFepQ4sV8W7sV5zctZFU3P5wr1ae4XwPccjnfVu0ygnJtehF+LpAHV04SvHMlp/83gyajVuBcIZt865aeb42f5dsiri/n2+TEeJSSb/PUpSvBqNVqGvpnfzPBi9as38jMOfO4fuMmsXFxpKZmzLfffvM1vfv2Z8euPTTwr0ebVi3wq1gBgPbt2jBz7nw8fMoT0KgBTRo3otUHzVBlk289Pd4ubp+ztrZi6ID+jJ84mc6ffPTS8bi4OMZNnMzWf3Zw7/59kpNTSExMxLesLsdeDg5GT08vQ4Hc0tIS3zI+XL58JUfmKF5f1G1dznWpmHXu+f+uBm7i1PoFPLkbQnJCHBpNaoacW7HNV+yZOZBbJ/biUqEunrWa4/DsyzXvOq05tWEhi7tWwtWvASUqN8C9elMMDLOOXWunt4vd2l+MwO/DXtw5E8i9yye4sONPjq2aTpuxf2T7uW1LpK8LTKztnr1WOsNryYlxknPzSXRYMJpkNfa+r79eCD+6metbFxF7P4SUTNYLns17cHrRYCLO7MPOtw7FqjbD+lnBzKl6S65vW8Q/fariUN4fhwr1KVq5SbbrBTPHt2srVuaTYXi26MnDCwd5dO0kt/etJHj9DGoMXp7t57Z0TV8vqCx1sWvpUjrDa6nqeFLU8RioJHbzUlz4VTTJamx8st8/6kURQVsI3bGIhAe3SM0kbosHdOPKsqFEnduPdZna2Ps1w6KE7oYdh6otuLPzNw4NqE4R33oUKVcfu4oB6GUTtyYObxe3nh2G4tL0Kx5fPsTT66e4++8qQjb9SoXvl2FTJuu4NSueHqOGz+LWrHip9NcsdHGbqo5HvxDGbaEsAr+pk7cf8/UfpxgQ4M3YkvZYGhuw42IEYzdfShvTqZoL/iXt2HP5Af9ee0jLWYf4tqEng5qUxMnamEND6hN4LZLAaw8Zu+kiU3deZVvf2piqMv8V50Q7iOe61S7BkoMhLPj35kvHxm6+xL7gB4xu6YO7rRnGhvp8u/I0SSmaV5/49e+oF/kk4soJ9v78FZU+GUTxSg0wNLPg9rEdHFuS3hewVMBnOFdqwJ0Tuwk/c4BNg5pToUM//DoNxszOiQ7zj3D37L+En/mXo4tHc2rVL7Se+g9Ko8y/qHibdhDPGVnY4F67Ne61W1Oly0jW9WvA6b+mZlsE1tNXpv/Hs8c9MntNq32N2Bb56tzJYwz5+jN6DhhJrbFTMLe0ZP+OLUwbm36XS7tOX1DLvzGBe/7h2L976dKyHt2+HUyvQaNwdCrOxkPnORa4l2OBe5k6dgjzp07gj20HMTHNPG7fph3Ec1Y2RQho9SEBrT6k34gJdGxUlYXTJmVbBDYwSI/R548pZfaaxG3BF3HlBLt//gq/TwZRo3sDVKYW3Dq2g6OZ5NvQZ/l2w8DmVOzQj8qf6vLtRwuOEP5Cvj258hfaTss6375NO4jnjCxs8KjdGo/aranadSRr+zXg1F9Tsy0C62cSo3oGL+dbJG4LvKNBx+nU9Ut+GD6EqZMnYmVpyeat2xg0PL0Ha7euXWjSqCHbduxiz7791G4QwNCB3zN6xDCKOztz+cwJ9uw/wJ69+xk0bCTjJ07myIHdmGaRb3OiHcRzfXp9xez5C5k+6+Xe2YOGj2LHrt38PHE8Xh4emJiY8HmPr0lKSnrled/ksVGRP+5ePsHWiT2o8dkg3HqOR2VmyY0j//DvovSc69v0M9z8GhByfBehp/7lr++bU+WjftTsPARzeye+WHyU0NP/Enr6AAcW/sCRP6bQaeaOLHPu27SDeM7Ywgbvuq3xrtua2l+O4o/e9Tm68pdsi8B6L7Y7yWyNi6wV3iWPrp4kaMbXlGo/AN+uYzE0teTeiR2cXzE2bYxbg07Yl/cn4vQeHpz7lwMjW+Ld5lt8Og7CxNaJgBmHeHAhkIfnAjm/fCxX1kzF/8dtGGQRu2/TDuI5lbkNzjVa4VyjFWU7jWDP4EZcWTst2yKwnn567D7Pqwr9l+OZF26uEwXT0+snuTC3FyXa9KdIufooTSx4eHon11aNSxvjVK8TRXz9iTy7l0cXAzk+vhVuLfrg0W4gRkWcqDE5kMeXDhJ1MZCrK8dyc/00qo7ZmmUh9W3aQTxnaG6DQ9WWOFRtiWfHYRwbFcDNjTOyLQK/mF8VPI/bzOoKhTNuC2UR2NveHJWBHoHXInG3M3vl+KCQRzhaGmVoCRH2+NZL44pZGdO5hiuda7gya+91fgu8yaAmJQEwUurT2MeBxj4OfNvAC98xOzl+6xH+JTPfjTCn2kE8v/bgpqUYsf7CS3cDB4U8ooNfcVqUKwZAYnIqt6LicLfT/Q/Ey8EMjRZO3Hqc1g4iJjGZy/dj+Lhq4WyEXZBZOXujr1QRfjYQy2IerxwfcTkI0yJFM7SEiH1w56VxZrbFKN20C6WbduHMmplc3LwQv066xzoMDI1wqRKAS5UAyrfvy59dyhBxKQjnSpnf1fk27SAyo680xKKoG/GP7r/R+0TB4e5dCkOVimOB+3B193rl+NNBh7F3dMrQEuJeWOhL4xyKOdO+c3fad+7Oklm/sPK32fQapCtcqIyMqNu4OXUbN+fLbwfRwNeFM8cPU9O/cabXfJt2EJlRGhpS3M2dh/fvvdH7RMFhVfyFfOv06nx7/5Iu377YEiL2Yeb51qdpF3ye5dvzmxZS+dP0fOtaJQDXKgFUaN+XFZ3LcP9SEMWzyLdv0w4iM/pKQywc3YiTfPvOKl3SG5VKxZ79B/DyfHXcHj5yFKdiRTO0hLh95+W4dXZy4qsvP+erLz/n56kzmDl3PqNH6B65NzIy4oOmTfigaROGDPieYu7eHDpyjIBGmX8BllPtIJ5fe+yo4fQbOITP/t/dwIcOH6Vzp4/5sE1rABITE7lxMyTt91K6ZEk0Gg1HjgWltYOIjo7m/MVLdO3c6bXnIHJGERcv9JUqQk//i/Vr5Ny7F49hZls0Q0uI6AdhL40ztytGueZdKde8K0H/m8npDQuo2Vn3yL2BoRHu1QJwrxZAlY/6seBjH8IvBuHml3nOfZt2EJnRVxpiWcyNuCjJue8ycydv9JQqHpwPxKzoq+8WjwoOwsjGMUNLiPiHL8euSZFilGjUmRKNOhO8YRY3tv+GT8dBgK59Q9FKjSlaqTHebb5l21e+RAUfx6G8f6bXfJt2EJnRMzDE1MGNxMcSu+8q02Je6ClVPLp0EBPHV8ftk2vHUVk7ZmgJkRj5ctwa2RTDuf5nONf/jFtbZhO6czEe7XR5Wt/QCNsKjbCt0Ai3Fn0I/LY8T64GUcTXP9Nrvk07iEzHGxhibO+K+nHEG73vfVMoi8BmRgZ0r1OCiduuYGigRw33IjyKT+Jc2FM+z6RvroedKfefJrL2ZBiV3azZF/yQ9afDM4wZueECDUrZ42FnSkxiCvuuPMD72cZqfwXdIVWjoZKrNSaGBmw6E45SX0EJ26wL0DnVDuK5Dn7OzD9wg7+CQnF94dwedqZsv3CPpmUdUOrr8cvOq6hfuAvY3c6MpmUcGLzmHFM6lMPSWLcxnLnKgLaVsr+TQ+Q8QxMzyrT6iuO/T0BfaUjRMjVIjHlM5PWz+DT/4qXxlk4exEXd4/r+NdiXqkzYqX3c+DfjBlOHFw6nuF9DLJ08SI6PIezUXqyK6768uLp7FZrUFOxL+mFgZMrNwA3oGSixyOYR5Ld5PPl20E5uBq7HvU5bXdFFqyU0aAd3TuxOK0qLd4+pmTmduvdh5sRRGBqq8KtRmyePHnH53Ck6ft7zpfGuHl48uB/O1rWrKF+5Gof37eKf9aszjJk8sj+1GzTB1cOL2JgYDu3bibu37jGdjX8tJzU1Bd9KVTE2MWXHpjUYKJXZtnx4m3YQB3Zu5Z+Nf9O0dQdcPbxAq+XAzq0c3PMPvQb98J/PK/KXoYkZZVt9xbHfJ6CnNKRo2Rqoox/z8MZZymSTb6/tW4ND6crcObWP6wcy5ttDC4ZTvHJDrJw8SIqP4c7JvVg/y7fBL+RbpZEpN57l2+xy6tvm2+v/rsezbnq+vR20g9ATu9OK0uLdY25uTt/eXzNi9FhUhobUrV2LqEePOHn6DL16dHtpvJeXJ+F37/HnX6upUa0qO3bv4a+/12YY893AITQNaIy3lyfR0dHs2L0bn1K6uF224k9SUlOpVtkPMzNTVq9Zj1KpxMsz69jMqXYQz3Xu9DHTZ85m6fI/8CiR/uiol5cHGzZtodUHzVEqlYybNJlEtTr9uKcHrVo0p1ff75k/awZWlpaMHDseC3NzOnXskKNzFK9maGJOxTZfcXDpBPSVKpx9a5AQ/ZgH185QvuXLPZ+tnT2IjbzH5b1/U7R0FW6f2Evw/nUZxuybNxy3yg2xdtbl3Fsn9lDERRe7F3fqcq5jKT8MjU0JPqDLudm1fHibdhA3j+7gyoH1lKrXFmtnD7RaLTeP7iAkaHdaUVq8m5TGZng2687FVRPRUxpiW7oGSbGPeHLzHO4Bn7803qyoB4mP7hMauJYi3pWJOLuPO4cyrhfOLh2JQ8UGmBf1IDkhhoiz+zB31t2Qdnv/X2hSU7HxqoSBkQlhhzeh0FdiVjTrR+ffph3EvZM7CTu0EedarTEr6gFouXdiJxGn91D6WVFavHsMjM0oHtCN66snoWdgiFXJ6iTHPibm1jmcG3Z9abyJozvqx/e5d3gdlp5+PDq/n/tHN2QYE/zHKIqUa4CJozupCTFEnd+PWTHdDUB3A/+HNjUFS49K6BuZEnF0Iwp9ZbYF6LdpB/Hw9C4ijm3EoVrrZ9fQEnl6F1Fn9+LebuAr3/8+K5RFYIARzUtjZaxk+q5rDH56DjszFR0qZ94gOqCMI739PRi18SKJyan4l7RjcNOSDF2bvuOnRqNlxPoL3H2SgKnKgDpetoxppeuBY2lswOx9Nxi7+RLJGi3eDmYs/rxKhs3jcpuenoKRH5Tm098y3u02plUZ+q8+S+s5h7EyVtKjjjvq5NQMY2Z8XIFRGy/Sdclx1MmpVClhw8qvqmOszLqnscg9VbuMRGVqyem/pnEw6i7GVnZ41e+Y6VjXqk0o1+4bjiwaSUpSAs4V6+P36RAOzUv/A1+r1XB4wTDiIu+iNDajWPk6VO+me6zD0NSSs2tncWzpGDQpKVgV96bRsGUZNjPKSdYu3hgYmXJsyWjiIu+i0NfH3MGFal+OoWyrl4uF4t3Rb8QELKysWTh9IhGDwyli50DLDp9mOtY/oAVde/dnyqiBJCYmUMO/Eb0H/8CPQ/umjdFoNPw04nvu3w3D1NScqnXqM2DMZADMLS1ZOnsq08YOJTk5GQ/v0kxb/L8Mm8flJI+SpTExNWXauKHcDw/DwMAAJxc3+o/+iU97fJsr1xR5o1rXkajMLDn11zTinuVb7waZ51u3ak0o3+4bDr+Qbyt/NoSDczPm20Pz0/OtU/k61Hgh355ZM4ujS3T51trFm4DhuZtvlUamHF0ymtiHd9F7lm+rdxuDr+Tbd9rEcaOxtrLix8lT6NX3exzs7enc6eNMx7Zs3oyB3/Wl/5BhJCQk0rhhfcaMHE6f79LvaNdoNfQbOJg7YeGYm5vRwL8ev0yaAICVlSVTpv3K4OEjSU5OwadUSdasWpFh87jcpqenx6TxY2nRLmPhdupPP9Kj17fUC2iOtZUV/b7pRWKiOsOYJfPn8v3gobTp+AmJiWpqVa/Gtg1rMDZ+szs6Rc6o8+UojMytOLZyKrsj72JiZYdPo5f7PQN4VG9K5Q592D9/JCnqRFz9/KnZeQh7Zr+QczUa9s0dSszDuxiamOFSoQ51vxoPgMrMkuOrZ/LvotGkpqRQxNWbVqOWZdg8LifZuJbE0MiUA4tGE/Ms51o6ulKvx1gqtpWc+64r02kESjMrrqydTkLUYIys7HCpm/mXSUUrB+DVqjfnlo0iNSkRh/L++Hw0mDO/pbc902o1nF0ygoSouxgYmWLvWwffLmMAUJpYcnXjbC6sGIsmNRlzZ2+qD1ycYfO4nGThXBIDI1POrxhHQlQ4Cj0DTO1d8O08Go/mPXLlmiJveHYYjtLEkpCNM0h8dA9DS1uK1so8bu0qBuDavBdX//wBTVIiNmXr4dFuEFd+H5Y2RqvRELxiJOpHd9E3MsXGpzZen+ha9BiYWHB7y1yu/TUeTUoyZk7elOv7W4bN43KSqZM3+ipTrv01jsSouyj0DTC2K47Xxz9QvEn3XLlmYaF43T4XCoVCe39qy1yejhBv7njII7osCbr0KC6pzKtH/zcqc6uQFj+udyvi7vvqwaLQ2zGuU3To8V09tFrt6lePLlgMVUYJe8+HGllYWuX3VEQua1On3NOQa8EBWq02+14Y+UyhUIzx+2TQaLk7VbyuU6unc/LPnyenpiQPffXo/0ahUGhT457k1umFyNTho8do3eHjS1FRj3JkTatQKCpYFnU70G3ZCYucOJ8QADeDdrFjyjeH459G1cqrayoUiu/cm3wxuUK3SYZ5dU3xbrt/ajcn5vQ9rI7Ouzh9HQZGpvM8PhzytUtTKbALuLN7Gdf/nrQsJT765ccQc8nrN54VQgghhBBCCCGEEEII8c6RIrAQQgghhBBCCCGEEEIUYq9dBNZToFGnpL56oBB5TJ2qAUjOzWsoUCSnJifl5iXEOyQlSQ3wTgaEnp4iJTlJ/eqB4p2XlJSk4N2I0+TU5ETNq4cJoZOalIQmNSUxN6+hp6enUaslV4q8pVarUaDIyTVtcmpykiIHzycEqclJaPN+fZGcmqyWYoR4bakpSVAA18FaTWqiJkXWF0JHk5IEmtRcXdP+f69dBDZVGYRfDI/OzbkI8Z9cCH+qTdFoL+TmNbRazeWom7l6CfGO0Gq1PAq5qA9cy++5/BcqldHtKxfO5vc0RC6Lj4vl4f27KuB2fs/lNVyLuHwiLr8nId4dEVeOx5DLOdjM1DT8zLnzrx4oRA46c+68NjklOScXnKEJTyMN1XHyN5zIOQ+un0tNUSfk9WLy2uNrp3L1ph9RuDy9eT41Ne/j9JU0yerLT2+cknWvACD6xun4VHVCnhaaXrsIrE7RrJi7/4b6dTeSEyIvxCamsDgwJD4mMeWP3LxOUlz07+c3zo9LSYzPzcuId8CtI1tJTUp8DFzK77n8F7Ex0Ut/nzs9ITVVbqYozNYs/02jUhmf0Gq1j/N7Lq9hW8TVk8qoW+/kPymRxx6HBnPv4hFDYEtuXkedlLRi6oyZsu4VeSYmJobZ8xbER0fH5NiaVqvVxhgYGh8+u2WpPG0hckRC9CPObf1dnaJO+CuPL70v9n6IXtTVE3l8WfEuUsc8ImT3cnVqUp7H6evYEHVun0F8REh+z0Pks4SHoTw8tUMftOvz8rqK113cKhQKCzOVQaCfq5VnlxpuJh72Zij15ekikT/UyRpOhT5m7r4bcRHRiWviklK/1Gq1ubbAVSgU+koj01Vm9s7Nfdv2NrX1KI++oVFuXU4UNBoNCU8fcvvYP8mXti9Tp6oTArRa7ZH8ntZ/oVAojE3NzHd7+fhW+OTL3iYeJX1QGirze1oiB2hSU4m4F86ODX+rt29YHZuYEF9Dq9W+E3esK/T0PlIamy0p37a3yrlSfX2VqSUoZI0hntFqSYqLJuzM/tSz6+aokxPiemlSU5bn5iUVCoWFublZYLUqVTx7dv/CpKSXF0ql5EqR8xITEzl24iRTZ8yMu3fv/prYuLgcXdMqFAp3A5XJMe+6rcxL1f9QZW7nhJ6ebAsjXp9WqyUlKZG7l4K0J1bPjo9/GrkoRZ3QX5vH35IpFIoP9FUmq70+6KlyqNRQ39DMCpC1gnhOS2pSIlHBx7XXNs2JV0dHLUrNhzh9HXoGhj31jUynuX3Q28jGp46evrEpEsvvCy2pCXE8unxQc3vr3MSUxLihmmT1rLycwWsXgQEUCoUZ8JmVibKrRqN11oJB7k1NiKwpFCQpUJx/mpC8DFiXmwXg9Gsq9IH2hmaWX6DV+mi1Wvlr8P2hVejpP01VJ2xKTVb/9q4U1rKiUCiMgE8trKy7ajSaEmi1kssLB42evn5UXEzM6tTUlKVarTY8vyf0JhQKRRUDI9PuegYGDbUajWl+z0cULAo9vThNSsr+lMS437Ra7dE8ueazda+1tXVXTWqqsxbJlSLn6Sn0klBw/smTp8vIpTWtQqEoqtA3+EJlYvaRJjXVFtkcXLwhhUJPrUV7OikuegmwJb8KawqFory+yri7noFhU61GY5YfcxAFl0JPT63Vak+nxOdvnL4OhUJRW9/ItLtCz6COVqsxye/5iLyjUOglaDUpB1MT4xZrtdoDeX79AvzvQgghhBBCCCGEEEIIIcRbkm+BhRBCCCGEEEIIIYQQohCTIrAQQgghhBBCCCGEEEIUYlIEFkIIIYQQQgghhBBCiEJMisBCCCGEEEIIIYQQQghRiEkRWAghhBBCCCGEEEIIIQoxKQILIYQQQgghhBBCCCFEISZFYCGEEEIIIYQQQgghhCjEpAgshBBCCCGEEEIIIYQQhZgUgYUQQgghhBBCCCGEEKIQkyKwEEIIIYQQQgghhBBCFGJSBBZCCCGEEEIIIYQQQohCTIrAQgghhBBCCCGEEEIIUYhJEVgIIYQQQgghhBBCCCEKMSkCCyGEEEIIIYQQQgghRCEmRWAhhBBCCCGEEEIIIYQoxKQILIQQQgghhBBCCCGEEIWYFIGFEEIIIYQQQgghhBCiEJMisBBCCCGEEEIIIYQQQhRiUgQWQgghhBBCCCGEEEKIQkyKwEIIIYQQQgghhBBCCFGISRFYCCGEEEIIIYQQQgghCjEpAgshhBBCCCGEEEIIIUQhJkVgIYQQQgghhBBCCCGEKMSkCCyEEEIIIYQQQgghhBCFmBSBhRBCCCGEEEIIIYQQohCTIrAQQgghhBBCCCGEEEIUYlIEFkIIIYQQQgghhBBCiEJMisBCCCGEEEIIIYQQQghRiEkRWAghhBBCCCGEEEIIIQoxKQILIYQQQgghhBBCCCFEISZFYCGEEEIIIYQQQgghhCjEpAgshBBCCCGEEEIIIYQQhZgUgYUQQgghhBBCCCGEEKIQkyKwEEIIIYQQQgghhBBCFGJSBBZCCCGEEEIIIYQQQohCTIrAQgghhBBCCCGEEEIIUYhJEVgIIYQQQgghhBBCCCEKMSkCCyGEEEIIIYQQQgghRCH2f48sT1qmurEGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,10))\n",
    "a = plot_tree(arvore, \n",
    "              feature_names=['asma','cardiopatia','diabetes','doenca_hematologica','doenca_hepatica','doenca_neurologica','doenca_renal','imunodepressao','obesidade','outros_fatores_de_risco','pneumopatia','puerpera','sindrome_de_down','idade','obito','FEMININO','IGNORADO','INDEFINIDO','MASCULINO'],\n",
    "              class_names=['Não', 'Sim'], \n",
    "              filled=True, \n",
    "              rounded=True, \n",
    "              fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "VKQt817jIxvU",
    "outputId": "68640a8b-f585-4ac7-d9a7-ff8df2d94e6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas de desempenho para aprendizado supervisionado em classificação binária\n",
      "\n",
      "Acurácia: 0.837\n",
      "\n",
      "Precisão (para a classe 1): 0.819\n",
      "Recall (para a classe 1): 0.58\n",
      "F1-score (para a classe 1): 0.679\n",
      "\n",
      "Precisão (para a classe 0): 0.841\n",
      "Recall (para a classe 0): 0.946\n",
      "F1-score (para a classe 0): 0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Acurácia\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('Métricas de desempenho para aprendizado supervisionado em classificação binária')\n",
    "print(\"\\nAcurácia:\", np.round(acc,3))\n",
    "\n",
    "# Para a classe 1\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(\"\\nPrecisão (para a classe 1):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 1):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 1):\", np.round(f1,3))\n",
    "\n",
    "# Para a classe 0\n",
    "precision = precision_score(y_test, y_pred, pos_label=0)\n",
    "recall = recall_score(y_test, y_pred, pos_label=0)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=0)\n",
    "print(\"\\nPrecisão (para a classe 0):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 0):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 0):\", np.round(f1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores parâmetros: {'max_depth': 9, 'min_samples_leaf': 5, 'min_samples_split': 4}\n",
      "Acurácia da melhor árvore: 0.8590416666666666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Uso da validação cruzada para encontar a melhor árvore\n",
    "param_grid = {\n",
    "    'max_depth': range(1, 11),\n",
    "    'min_samples_split': range(2, 11),\n",
    "    'min_samples_leaf': range(1, 6)\n",
    "}\n",
    "\n",
    "# Realiza a busca em grid com cross-validation para encontrar a melhor árvore\n",
    "grid = GridSearchCV(arvore, param_grid, cv=10)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "# Usa a árvore de decisão para prever os rótulos da base de teste\n",
    "y_pred = grid.predict(X_test)\n",
    "\n",
    "# Melhor árvore escolhida\n",
    "print(f\"Melhores parâmetros: {grid.best_params_}\")\n",
    "print(f\"Acurácia da melhor árvore: {grid.best_score_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas de desempenho para aprendizado supervisionado em classificação binária\n",
      "\n",
      "Acurácia: 0.859\n",
      "\n",
      "Precisão (para a classe 1): 0.782\n",
      "Recall (para a classe 1): 0.729\n",
      "F1-score (para a classe 1): 0.755\n",
      "\n",
      "Precisão (para a classe 0): 0.888\n",
      "Recall (para a classe 0): 0.914\n",
      "F1-score (para a classe 0): 0.901\n"
     ]
    }
   ],
   "source": [
    "# Acurácia\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('Métricas de desempenho para aprendizado supervisionado em classificação binária')\n",
    "print(\"\\nAcurácia:\", np.round(acc,3))\n",
    "\n",
    "# Para a classe 1\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(\"\\nPrecisão (para a classe 1):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 1):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 1):\", np.round(f1,3))\n",
    "\n",
    "# Para a classe 0\n",
    "precision = precision_score(y_test, y_pred, pos_label=0)\n",
    "recall = recall_score(y_test, y_pred, pos_label=0)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=0)\n",
    "print(\"\\nPrecisão (para a classe 0):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 0):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 0):\", np.round(f1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exemplo Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas de desempenho para aprendizado supervisionado em classificação binária\n",
      "\n",
      "Acurácia: 0.852\n",
      "\n",
      "Precisão (para a classe 1): 0.787\n",
      "Recall (para a classe 1): 0.691\n",
      "F1-score (para a classe 1): 0.736\n",
      "\n",
      "Precisão (para a classe 0): 0.875\n",
      "Recall (para a classe 0): 0.921\n",
      "F1-score (para a classe 0): 0.897\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Inicializa o classificador por árvore de decisão\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "# Inicializa o classificador por Bagging \n",
    "bagging = BaggingClassifier(base_estimator=tree, n_estimators=20, random_state=42)\n",
    "\n",
    "# Ajusta o classificador por Bagging aos dados de treinamento\n",
    "bagging.fit(X_train, y_train)\n",
    "\n",
    "# Prediz a resposta para a base de teste\n",
    "y_pred = bagging.predict(X_test)\n",
    "\n",
    "# Avalia a performance do classificador por Bagging nos dados de teste \n",
    "score = bagging.score(X_test, y_test)\n",
    "\n",
    "\n",
    "\n",
    "print('Métricas de desempenho para aprendizado supervisionado em classificação binária')\n",
    "print(\"\\nAcurácia:\", np.round(score,3))\n",
    "\n",
    "# Para a classe 1\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(\"\\nPrecisão (para a classe 1):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 1):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 1):\", np.round(f1,3))\n",
    "\n",
    "# Para a classe 0\n",
    "precision = precision_score(y_test, y_pred, pos_label=0)\n",
    "recall = recall_score(y_test, y_pred, pos_label=0)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=0)\n",
    "print(\"\\nPrecisão (para a classe 0):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 0):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 0):\", np.round(f1,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dt59T6pMIxvV"
   },
   "source": [
    "# Exemplo Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "FRlV-sBPIxvW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas de desempenho para aprendizado supervisionado em classificação binária\n",
      "\n",
      "Acurácia: 0.862\n",
      "\n",
      "Precisão (para a classe 1): 0.804\n",
      "Recall (para a classe 1): 0.707\n",
      "F1-score (para a classe 1): 0.753\n",
      "\n",
      "Precisão (para a classe 0): 0.882\n",
      "Recall (para a classe 0): 0.927\n",
      "F1-score (para a classe 0): 0.904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# Inicializa o classificador por árvore de decisão\n",
    "tree = DecisionTreeClassifier(max_depth=3)\n",
    "\n",
    "# Inicializa o classificador AdaBoost\n",
    "adaboost = AdaBoostClassifier(base_estimator=tree, n_estimators=50, learning_rate=1, random_state=42)\n",
    "\n",
    "# Ajusta o classificador AdaBoost aos dados de treinamento\n",
    "adaboost.fit(X_train, y_train)\n",
    "\n",
    "# Prediz para a base de teste\n",
    "y_pred = adaboost.predict(X_test)\n",
    "\n",
    "# Avalia a performance do classificador AdaBoost nos dados de teste \n",
    "score = adaboost.score(X_test, y_test)\n",
    "\n",
    "\n",
    "\n",
    "print('Métricas de desempenho para aprendizado supervisionado em classificação binária')\n",
    "print(\"\\nAcurácia:\", np.round(score,3))\n",
    "\n",
    "# Para a classe 1\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(\"\\nPrecisão (para a classe 1):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 1):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 1):\", np.round(f1,3))\n",
    "\n",
    "# Para a classe 0\n",
    "precision = precision_score(y_test, y_pred, pos_label=0)\n",
    "recall = recall_score(y_test, y_pred, pos_label=0)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=0)\n",
    "print(\"\\nPrecisão (para a classe 0):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 0):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 0):\", np.round(f1,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exemplo Florestas aleatórias\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Métricas de desempenho para aprendizado supervisionado em classificação binária\n",
      "\n",
      "Acurácia: 0.855\n",
      "\n",
      "Precisão (para a classe 1): 0.789\n",
      "Recall (para a classe 1): 0.702\n",
      "F1-score (para a classe 1): 0.743\n",
      "\n",
      "Precisão (para a classe 0): 0.879\n",
      "Recall (para a classe 0): 0.92\n",
      "F1-score (para a classe 0): 0.899\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Ajusta um classificador por florestas aleatórias\n",
    "clf = RandomForestClassifier(n_estimators=200, random_state=10)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Obtém predições para a base de teste\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# Avalia a performance do classificador por florestas aleatórias nos dados de teste \n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "\n",
    "print('Métricas de desempenho para aprendizado supervisionado em classificação binária')\n",
    "print(\"\\nAcurácia:\", np.round(acc,3))\n",
    "\n",
    "# Para a classe 1\n",
    "precision = precision_score(y_test, y_pred, pos_label=1)\n",
    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
    "print(\"\\nPrecisão (para a classe 1):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 1):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 1):\", np.round(f1,3))\n",
    "\n",
    "# Para a classe 0\n",
    "precision = precision_score(y_test, y_pred, pos_label=0)\n",
    "recall = recall_score(y_test, y_pred, pos_label=0)\n",
    "f1 = f1_score(y_test, y_pred, pos_label=0)\n",
    "print(\"\\nPrecisão (para a classe 0):\", np.round(precision,3))\n",
    "print(\"Recall (para a classe 0):\", np.round(recall,3))\n",
    "print(\"F1-score (para a classe 0):\", np.round(f1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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