HospitalStayDataProcessing.ipynb

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  "metadata": {
    "colab": {
      "name": "HospitalStayDataProcessing.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "250VrWyh2ivz"
      },
      "source": [
        "data = ''# paste your data location here"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN3FmIh_4eWh"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ikl5SKEU4hag",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "75025e1c-3cf4-4dbb-a3ed-f4e4c1fc99ee"
      },
      "source": [
        "df = pd.read_csv(data)\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>case_id</th>\n",
              "      <th>Hospital_code</th>\n",
              "      <th>Hospital_type_code</th>\n",
              "      <th>City_Code_Hospital</th>\n",
              "      <th>Hospital_region_code</th>\n",
              "      <th>Available Extra Rooms in Hospital</th>\n",
              "      <th>Department</th>\n",
              "      <th>Ward_Type</th>\n",
              "      <th>Ward_Facility_Code</th>\n",
              "      <th>Bed Grade</th>\n",
              "      <th>patientid</th>\n",
              "      <th>City_Code_Patient</th>\n",
              "      <th>Type of Admission</th>\n",
              "      <th>Severity of Illness</th>\n",
              "      <th>Visitors with Patient</th>\n",
              "      <th>Age</th>\n",
              "      <th>Admission_Deposit</th>\n",
              "      <th>Stay</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>c</td>\n",
              "      <td>3</td>\n",
              "      <td>Z</td>\n",
              "      <td>3</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>R</td>\n",
              "      <td>F</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31397</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Emergency</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>4911.0</td>\n",
              "      <td>0-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>c</td>\n",
              "      <td>5</td>\n",
              "      <td>Z</td>\n",
              "      <td>2</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>S</td>\n",
              "      <td>F</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31397</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>5954.0</td>\n",
              "      <td>41-50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>e</td>\n",
              "      <td>1</td>\n",
              "      <td>X</td>\n",
              "      <td>2</td>\n",
              "      <td>anesthesia</td>\n",
              "      <td>S</td>\n",
              "      <td>E</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31397</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>4745.0</td>\n",
              "      <td>31-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>26</td>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>R</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31397</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>7272.0</td>\n",
              "      <td>41-50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>S</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31397</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>5558.0</td>\n",
              "      <td>41-50</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "   case_id  Hospital_code Hospital_type_code  ...    Age Admission_Deposit   Stay\n",
              "0        1              8                  c  ...  51-60            4911.0   0-10\n",
              "1        2              2                  c  ...  51-60            5954.0  41-50\n",
              "2        3             10                  e  ...  51-60            4745.0  31-40\n",
              "3        4             26                  b  ...  51-60            7272.0  41-50\n",
              "4        5             26                  b  ...  51-60            5558.0  41-50\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lg5XDW9y4ogp",
        "outputId": "f886c344-c640-436d-f4c1-960035244bf6"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(318438, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGLwFvHz5e5g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2dac911-023a-4d92-ed1a-18b9a4bc9a00"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "case_id                                 0\n",
              "Hospital_code                           0\n",
              "Hospital_type_code                      0\n",
              "City_Code_Hospital                      0\n",
              "Hospital_region_code                    0\n",
              "Available Extra Rooms in Hospital       0\n",
              "Department                              0\n",
              "Ward_Type                               0\n",
              "Ward_Facility_Code                      0\n",
              "Bed Grade                             113\n",
              "patientid                               0\n",
              "City_Code_Patient                    4532\n",
              "Type of Admission                       0\n",
              "Severity of Illness                     0\n",
              "Visitors with Patient                   0\n",
              "Age                                     0\n",
              "Admission_Deposit                       0\n",
              "Stay                                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1zzx4-9Rbq12",
        "outputId": "c22b548d-17ad-47eb-f497-9a9c82dbe59a"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 318438 entries, 0 to 318437\n",
            "Data columns (total 18 columns):\n",
            " #   Column                             Non-Null Count   Dtype  \n",
            "---  ------                             --------------   -----  \n",
            " 0   case_id                            318438 non-null  int64  \n",
            " 1   Hospital_code                      318438 non-null  int64  \n",
            " 2   Hospital_type_code                 318438 non-null  object \n",
            " 3   City_Code_Hospital                 318438 non-null  int64  \n",
            " 4   Hospital_region_code               318438 non-null  object \n",
            " 5   Available Extra Rooms in Hospital  318438 non-null  int64  \n",
            " 6   Department                         318438 non-null  object \n",
            " 7   Ward_Type                          318438 non-null  object \n",
            " 8   Ward_Facility_Code                 318438 non-null  object \n",
            " 9   Bed Grade                          318325 non-null  float64\n",
            " 10  patientid                          318438 non-null  int64  \n",
            " 11  City_Code_Patient                  313906 non-null  float64\n",
            " 12  Type of Admission                  318438 non-null  object \n",
            " 13  Severity of Illness                318438 non-null  object \n",
            " 14  Visitors with Patient              318438 non-null  int64  \n",
            " 15  Age                                318438 non-null  object \n",
            " 16  Admission_Deposit                  318438 non-null  float64\n",
            " 17  Stay                               318438 non-null  object \n",
            "dtypes: float64(3), int64(6), object(9)\n",
            "memory usage: 43.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ov2TB2wmy93X"
      },
      "source": [
        "# Feature by Feature Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nxLGIpwzJu-"
      },
      "source": [
        "## case_id"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKEsJceVzK35",
        "outputId": "44bbfaed-8830-4f25-8364-c4739dfdd0af"
      },
      "source": [
        "df.case_id.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    2\n",
              "2    3\n",
              "3    4\n",
              "4    5\n",
              "Name: case_id, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0B8TjlfqzE4O"
      },
      "source": [
        "## Hospital Code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPukTS6qzjOV",
        "outputId": "45513770-96a4-4bd2-925f-237a10295c22"
      },
      "source": [
        "df['Hospital_code'].nunique()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TkpO0a-iQ98V",
        "outputId": "13de2ca5-8a3a-4908-f811-2d1894d7cf81"
      },
      "source": [
        "sns.catplot(x='Hospital_code',data=df,kind=\"count\") "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe2741a3dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQ0MIkLf00Nm"
      },
      "source": [
        "## Hospital_type_code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "we745TGaVh81",
        "outputId": "128eca9b-4b67-4485-cf93-157830b2911c"
      },
      "source": [
        "sns.catplot(x='Hospital_type_code',data=df,kind=\"count\") "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26c0b4fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VUBgVIm3-pv"
      },
      "source": [
        "## City_Code_Hospital"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQmSAMAl4dti",
        "outputId": "6949d028-686b-4d3f-af43-1f1804f4b1eb"
      },
      "source": [
        "df.City_Code_Hospital.nunique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KY43vFwCVii3",
        "outputId": "b4db319d-ca3e-483c-c482-4bc0752265b4"
      },
      "source": [
        "sns.catplot(x='City_Code_Hospital',data=df,kind=\"count\") "
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26bbab650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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glqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1ZrDgTrI8yb1Jbhxr2yHJpUlubc/bt/YkOTXJqiTXJ9lnbJ9lrf+tSZaNte+b5Ia2z6lJMtRrkaS5ZMgz7k8CS9ZpOx64rKr2AC5r6wCHAnu0xzHAaTAKeuBEYH9gP+DEibBvfd42tt+6tSRpXhosuKvqn4G16zQvBc5sy2cCh4+1n1UjVwDbJdkZOAS4tKrWVtX9wKXAkrZtm6q6oqoKOGtsLEma12b6GvdOVXVXW74b2Kkt7wLcMdZvdWvbUPvqSdolad6btTcn25lyzUStJMckWZlk5Zo1a2aipCQNZqaD+552mYP2fG9rvxPYdazfota2ofZFk7RPqqpOr6rFVbV44cKFm/0iJGk2zXRwrwAm7gxZBlw41n5Uu7vkAOCBdknlEuDgJNu3NyUPBi5p2x5MckC7m+SosbEkaV7bcqiBk/w98DJgxySrGd0d8mfAOUmOBr4DvLZ1vwg4DFgF/BB4M0BVrU1yEnB16/fBqpp4w/OdjO5ceQJwcXtI0rw3WHBX1evXs+mgSfoWcOx6xlkOLJ+kfSWw1+YcoyT1yE9OSlJnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHVmsG95lySAc86/b9rHfO2rd5z2MXviGbckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpM34AR5I2wT0fuWqQcXc6br9H7eMZtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpM90Hd5IlSb6VZFWS42f7eCRpaF0Hd5IFwEeBQ4E9gdcn2XN2j0qShtV1cAP7Aauq6raq+jFwNrB0lo9JkgaVqprtY9hkSV4DLKmqt7b1NwH7V9W71ul3DHBMW3028K2NLLUjcN9mHu5crDXT9eZrrZmuZ63+6m1qrfuqasm6jY+JLwuuqtOB0zd1/yQrq2rxNB7SnKg10/Xma62Zrmet/upNd63eL5XcCew6tr6otUnSvNV7cF8N7JFk9yRbA0cCK2b5mCRpUF1fKqmqh5O8C7gEWAAsr6qbBii1yZdZ5nitma43X2vNdD1r9VdvWmt1/eakJD0W9X6pRJIecwxuSeqMwb0BSZYnuTfJjTNQa9cklye5OclNSd49YK3HJ7kqyXWt1h8NVWus5oIk1yT5/AzUuj3JDUmuTbJy4FrbJTkvyTeT3JLkhQPVeXZ7PROPB5McN0StsZrvTnJj+39kWmtN9ruVZIcklya5tT1vP2CtI9rr+o8k03pL4HrqnZTk+vbf7otJnr5ZRarKx3oewEuBfYAbZ6DWzsA+bfkpwP8B9hyoVoAnt+WtgCuBAwZ+fX8AfBb4/Az8LG8Hdpyh/0fOBN7alrcGtpuBmguAu4H/NGCNvYAbgScyuonhS8CzpnH8R/xuAX8OHN+Wjwc+NGCtX2H0YbyvAIun+Wc3Wb1txpZ/H/jY5tTwjHsDquqfgbUzVOuuqvpGW/4+cAuwy0C1qqp+0Fa3ao/B3qVOsgh4OfDxoWrMhiTbMvolPQOgqn5cVd+bgdIHAd+uqu8MWONXgCur6odV9TDwVeBV0zX4en63ljL6h5D2fPhQtarqlqra2E9Qb069B8dWn8Rm/r4Z3HNQkt2A5zM6Ex6qxoIk1wL3ApdW1WC1gI8Afwj8x4A1xhXwxSRfb9MdDGV3YA3wiXYZ6ONJnjRgvQlHAn8/cI0bgZckeWqSJwKH8fMfdhvCTlV1V1u+G9hp4HozKsnJSe4A3gC8f3PGMrjnmCRPBs4HjlvnX+lpVVU/raq9GX3adL8kew1RJ8krgHur6utDjL8eL66qfRjNGnlskpcOVGdLRn8Sn1ZVzwf+ndGf+INpHzR7JXDukHWq6hbgQ8AXgS8A1wI/HbLmOvWLAf8KnA1V9b6q2hX4DPCuR+u/IQb3HJJkK0ah/Zmq+oeZqNn+tL8ceMRENtPkRcArk9zOaPbGA5N8eqBaAFTVne35XuACRrNIDmE1sHrsr5XzGAX5kA4FvlFV9wxch6o6o6r2raqXAvczet9lSPck2RmgPd87cL3Z8hng1ZszgME9RyQJo2ult1TVhweutTDJdm35CcBvAN8colZVnVBVi6pqN0Z/4n+5qt44RC2AJE9K8pSJZeBgRn/2T7uquhu4I8mzW9NBwM1D1Brzeoa/TAJAkl9oz89gdH37swOXXAEsa8vLgAsHrjdjkuwxtrqUzf19m853U+fbg9EvyF3ATxidXR09YK0XM/rT8HpGf5ZeCxw2UK3nAte0WjcC75+hn+fLGPiuEuAXgeva4ybgfQPX2xtY2X6W/whsP2CtJwHfBbadof9e/4vRP0TXAQdN89iP+N0CngpcBtzK6C6WHQas9Vtt+SHgHuCSgV/b+e137Xrgn4BdNqeGH3mXpM54qUSSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuDWnJHkaUnOTvLtNs/IRUlemuS8tn3vJIdtxvhHtWlKb2hzi7xnI/bdbVOm951svyQf2JjaU6jx9I39GSV52UxMsathGNyaE9onRy8AvlJVz6yqfYETGE1b8ZrWbW9Gkx1tyviHAscBB1fVrwIHAA9s/pHPvqr6t+n4GakfBrfmil8DflJVH5toqKrrGH2k/MY2udIHgde1yehf1ybcXwiQZIskqybWJ3EC8J6q+rc29kNV9T/avnsnuaJNdH/BxAT+SfZtXzZxHXDsxEBtZsW/SHJ12+ftm/qiN1D79zP6Uo3rk5zd2j6Q5FNJvtZe+9ta+24b+Bnt1/pfk+R/j308Xx0zuDVX7AWsdwbBqvoxo6kwP1dVe1fV54BPM5oiE+DXgeuqas0mjH8W8N6qei5wA3Bia/8E8HtV9bx1+h8NPFBVLwBeALwtye4beG3PzNi31wC/O4XaxwPPb+3j/Z8LHAi8EHj/+DeprOdn9E3gJTWavfD9wJ9s4DjVCYNbPVsOHNWW38IoaDdK+zKE7arqq63pTOClbRKu7Wo0KT7Ap8Z2Oxg4qoXwlYzm2BifRGhd325BuneNptL92IZqt+Xrgc8keSPw8NhYF1bV/62q+xjN6vhoMx9uC5zbrrOfAjznUfqrAwa35oqbgH03ZoequoPRVKAHMgqwi6dz/A0IozPxiTDevaq+OE1jT3g58FFG08RenWTL1r7u5EKPNtnQScDlVbUX8JvA46f1KDUrDG7NFV8GHjf+jTVJnsvPf+vK9xl9H+e4jzO6ZHJuVW1oov8/Bf4iydPa2FsneWtVPQDcn+Qlrd+bgK/WaJ7y7yV5cWt/w9hYlwDvaPOnk+SXNuWbb9ZXO8kWwK5VdTnwXkZnzU9ufZZm9GXPT2U02+LV6wy77s9oW+DOtvw7G3uMmpsMbs0JNZqm8reAX2+3A97EKGzvHut2ObDnxBtvrW0Fo1Db4GWSqroI+BvgS23sbwDbtM3LGIX69Yzuyvhga38z8NF2SSRjw32c0XSn32iXIP6O0bfhbIrJai8APp3kBkbT755aP/suy+sZ/RyuAE6aeLN1zLo/oz8H/jTJNZtxjJpjnNZVXUuyGDilql7yqJ07l+QDwA+q6i9n+1g0u/wXWN1KcjzwDn7+MoY073nGrXklyfuAI9ZpPreqTh647q/y83eeADxUVfsPWVePTQa3JHXGNyclqTMGtyR1xuCWpM4Y3JLUmf8H1ZNrQqUgwV0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cswXL5C4wOH"
      },
      "source": [
        "## Hospital_region_code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRxZEgF64x0O",
        "outputId": "ed78120e-7f93-4443-a2ed-0a7dd3fe3d60"
      },
      "source": [
        "df.Hospital_region_code.nunique()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GB5fknchViyx",
        "outputId": "4594c914-caea-4f97-ea83-70bdf4b15e35"
      },
      "source": [
        "sns.catplot(x='Hospital_region_code',data=df,kind=\"count\") "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26bb83690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cBbyewR+Ry5KcDvw9gy9uALgSeBkwA3yrtaWqtiU5G7i+tXtHe90swBuBi4B9gE+2SZKmwkSCu6puBlbOseq4OdoWcMZO9rMOWDdHfRNw5FPspiTtlvzkpCR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6M7HgTrIoyU1J/rwtH5Hk2iQzST6aZO9Wf1pbnmnrlw3t4+2tfkeSE4bqq1ptJsmZ4z43SRqlSV5x/wbw5aHldwPnVtWzgYeB01v9dODhVj+3tSPJCuAU4LnAKuAD7Y/BIuD9wInACuC1ra0kTYWJBHeSpcAvAB9sywFeClzemlwMnNTmV7dl2vrjWvvVwKVV9e2quhuYAY5p00xV3VVVjwGXtraSNBUmdcX9v4G3Ad9py88EvlZV29vyFuCwNn8YcC9AW/9Ia//P9R222Vn9CZKsTbIpyaatW7c+1XOSpLEYe3An+UXgwaq6YdzH3lFVXVBVK6tq5ZIlSybdHUlakMUTOOaLgZcneRnwdGB/4L3AAUkWt6vqpcB9rf19wOHAliSLgWcADw3VZw1vs7O6JHVv7FfcVfX2qlpaVcsYPFy8uqp+GfgUcHJrtga4os2vb8u09VdXVbX6KW3UyRHAcuA64HpgeRulsnc7xvoxnJokjcUkrrh35reBS5O8E7gJuLDVLwQ+nGQG2MYgiKmqzUkuA24HtgNnVNXjAEneBGwAFgHrqmrzWM9EkkZoosFdVX8N/HWbv4vBiJAd2zwKvHon258DnDNH/Urgyl3YVUnabfjJSUnqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1ZuzBneTwJJ9KcnuSzUl+o9UPSrIxyZ3t54GtniTnJZlJckuSo4b2taa1vzPJmqH60UlubduclyTjPk9JGpVJXHFvB36zqlYAxwJnJFkBnAlcVVXLgavaMsCJwPI2rQXOh0HQA2cBLwCOAc6aDfvW5g1D260aw3lJ0liMPbir6v6qurHNfwP4MnAYsBq4uDW7GDipza8GLqmBa4ADkhwKnABsrKptVfUwsBFY1dbtX1XXVFUBlwztS5K6N9F73EmWAc8HrgUOqar726oHgEPa/GHAvUObbWm1+epb5qhL0lSYWHAn+X7gY8Bbqurrw+valXKNoQ9rk2xKsmnr1q2jPpwk7RITCe4kezEI7T+uqo+38lfbbQ7azwdb/T7g8KHNl7bafPWlc9SfoKouqKqVVbVyyZIlT+2kJGlMJjGqJMCFwJer6n8NrVoPzI4MWQNcMVQ/tY0uORZ4pN1S2QAcn+TA9lDyeGBDW/f1JMe2Y506tC9J6t7iCRzzxcDrgFuT3NxqvwO8C7gsyenA3wOvaeuuBF4GzADfAl4PUFXbkpwNXN/avaOqtrX5NwIXAfsAn2yTJE2FsQd3VX0O2Nm46uPmaF/AGTvZ1zpg3Rz1TcCRT6GbkrTb8pOTktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnZna4E6yKskdSWaSnDnp/kjSrjKVwZ1kEfB+4ERgBfDaJCsm2ytJ2jWmMriBY4CZqrqrqh4DLgVWT7hPkrRLpKom3YddLsnJwKqq+o9t+XXAC6rqTTu0WwusbYvPAe4Ya0d3vYOBf5h0J+TvYTcxDb+Hf6iqVTsWF0+iJ7uLqroAuGDS/dhVkmyqqpWT7seezt/D7mGafw/TeqvkPuDwoeWlrSZJ3ZvW4L4eWJ7kiCR7A6cA6yfcJ0naJabyVklVbU/yJmADsAhYV1WbJ9ytcZia2z6d8/ewe5ja38NUPpyUpGk2rbdKJGlqGdyS1BmDu3NJXpHk5h2m7yQ5cdJ925MkOTzJ3UkOassHtuVlk+3ZniUDnxv+7z/Jq5P8xST7tat5j3vKtA8V/TLws1X1nUn3Z0+S5G3As6tqbZI/BL5SVf9j0v3a0yQ5EvgT4PkMBmDcxOADeX830Y7tQgb3FEnyo8DVwIuq6p5J92dPk2Qv4AZgHfAG4HlV9U+T7dWeKcnvAd8E9gO+UVVnT7hLu5TBPSVaaHwReE9VfXTS/dlTJTkB+Avg+KraOOn+7KmS7AfcCDwGrKyqb0+4S7vUVI7j3kOdDWw2tCfuROB+4EjA4J6Qqvpmko8C/zhtoQ0G91RI8hLgVcBRE+7KHi3J84CfB44FPpfk0qq6f8Ld2pN9p01Tx1ElnUtyIPAh4NSq+sak+7OnShLgfOAt7fnCe4Dfn2yvNK0M7v79GvCDwPk7DAn8pUl3bA/zBuCeofvaHwD+TZJ/N8E+aUr5cFKSOuMVtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwa2JS/KPOyyfluR9u/gYX2g/lyX5DwtovyzJbbuyD3Mc48okB4zyGAuV5KIkJ0+6H1oYg1t7hKp6UZtdBnzX4H4yknxPr46oqpdV1dd2ZV+0ZzC4tVtrV75XJ7klyVVJfqTVX53ktiRfSvKZVjstyRVJ/jrJnUnOGtrP7FX9u4Cfbp8ufWvb/2eT3NimFz2xF3P267Qk65NcDVyVZL8k65Jcl+SmJKtbu32TXJbk9iSfSHJtkpVt3VeSHNzm/3M7n9uSvGXo3L+c5I+SbE7yl0n2madPz07yV+3f5MYkz2pfLPCett9bZz9R2+rvS3JHkr9i8Onb2f0cneTTSW5IsiHJoQv9fWlMqsrJaaIT8Dhw89B0D/C+tu7PgDVt/leBP23ztwKHtfkD2s/TGLyZ75nAPsBtDF7pCYO3xAG8BPjzoWPvCzy9zS8HNrX5ZcBt8/T5NGALcFBb/u/Ar8z2B/hbBu+C/i3gD1v9SGD7UJ++AhwMHN3OZz/g+4HNDL4EYFlr/7zW/rLZY+ykT9cCr2jzT2/n9ioGbylcBBzS/m0PBV45VP9h4GvAycBewBeAJW0/vwSsm/R/I07/evLtgNod/L+qet7sQpLTgJVt8YUMQgbgw8DvtfnPAxcluQz4+NC+NlbVQ20/Hwd+Ctg0z7H3At7X3uz3OPCjT6LfG6tqW5s/Hnh5kt9qy08HfqQd/70AVXVbklvm2M9PAZ+oqm8O9fungfXA3VV1c2t3A4Mwf4IkP8DgD9kn2rEebfWfAj5SVY8DX03yaeDfAj8zVP+/7f8cAJ5DeyXt4L1ZLGLwx1C7EYNbXaqqX0vyAuAXgBuSHD27asem32VXbwW+Cvwkg1uHjz6JbnxzaD7Aq6rqjuEGLfyeiuF3ST/O4P8kRikM3uv+whEfR0+B97i1u/sCcEqb/2XgswBJnlVV11bV7wJbgcNbm59PclC7F3wSgyvzYd8AfmBo+RnA/TX4fs7XMbjC/F5sAN7cXu9Kkue3+ueB17TaCuDH59j2s8BJ7X74fsArZs9zoWrwSt8tSU5qx3pakn3bfn4pyaIkSxhcaV8HfGaofijws21XdwBLkryw7WevJM99Mn3R6Bnc2t29GXh9u8XwOuA3Wv097WHbbQzC/Uutfh3wMeAW4GNVteNtkluAx9sDvLcyeP3qmiRfAn6Mf30V/WSczeC2yy1JNrdl2v6XJLkdeCeD+9ePDG9YVTcCF7W+Xwt8sKpu+h768Drg19u/1ReAHwI+weCcv8Tg+0jfVlUPtPqdwO3AJQy+9o6qeozBve53t3+Tm4EFPbDV+PhaV02N2XvjVfWmSfdlVpJFwF5V9WiSZwF/BTynBaT0PfEetzRa+wKfyuDLnAO80dDWU+UVtzSPDL61/d07lO+uqldMoj8ASd4PvHiH8nur6kOT6I/Gz+CWpM74cFKSOmNwS1JnDG5J6ozBLUmd+f9llciHXRFFoQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jM6zF7Pe5CSS"
      },
      "source": [
        "## Available Extra Rooms in Hospital"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtS3WNPL5BSm",
        "outputId": "8cdad1e2-d04f-4272-d94b-069c48e99ac1"
      },
      "source": [
        "df['Available Extra Rooms in Hospital'].unique()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  2,  1,  4,  6,  5,  7,  8,  9, 10, 12,  0, 11, 20, 14, 21, 13,\n",
              "       24])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hxXSte4a5KXm",
        "outputId": "0ccdd933-ab87-4a63-e39c-037d2f6046e1"
      },
      "source": [
        "sns.catplot(x='Available Extra Rooms in Hospital',data=df,kind=\"count\") "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26bb74b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QkTF88b5Rhu",
        "outputId": "3e0435f5-f0f7-4876-ebda-6e6e29f3a96f"
      },
      "source": [
        "df['Available Extra Rooms in Hospital'].value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     98311\n",
              "4     91709\n",
              "3     91520\n",
              "5     19288\n",
              "6      7786\n",
              "1      5582\n",
              "7      2876\n",
              "8      1027\n",
              "9       218\n",
              "10       67\n",
              "11       17\n",
              "0        15\n",
              "12       13\n",
              "21        3\n",
              "13        2\n",
              "20        2\n",
              "14        1\n",
              "24        1\n",
              "Name: Available Extra Rooms in Hospital, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQmJQBOx8kax"
      },
      "source": [
        "## Department"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdzxYEYw8uNG",
        "outputId": "b7464345-941f-4119-c8a9-70900bdeb70f"
      },
      "source": [
        "df['Department'].unique()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['radiotherapy', 'anesthesia', 'gynecology', 'TB & Chest disease',\n",
              "       'surgery'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOBGNvhk9vm-",
        "outputId": "422a374b-fb69-49b6-ebfb-feb35ac3617d"
      },
      "source": [
        "df['Department'].value_counts()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gynecology            249486\n",
              "anesthesia             29649\n",
              "radiotherapy           28516\n",
              "TB & Chest disease      9586\n",
              "surgery                 1201\n",
              "Name: Department, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "6x6SREjWVjB_",
        "outputId": "c6dbd15f-81d2-4794-b2f5-0c486ae6d838"
      },
      "source": [
        "sns.catplot(x='Department',data=df,kind=\"count\") "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26ba96810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LKTnaDw9qxj"
      },
      "source": [
        "## Ward_Type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_msqvge-Blu",
        "outputId": "604f6d8d-06bb-4785-9c5e-ce059e7315a0"
      },
      "source": [
        "df['Ward_Type'].value_counts()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R    127947\n",
              "Q    106165\n",
              "S     77794\n",
              "P      5046\n",
              "T      1477\n",
              "U         9\n",
              "Name: Ward_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Q8OW9VJkVjQ2",
        "outputId": "2bd94829-91fb-4d79-fa49-9fb89122f601"
      },
      "source": [
        "sns.catplot(x='Ward_Type',data=df,kind=\"count\") "
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe269b58b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzuudm6MAJyy"
      },
      "source": [
        "## Ward_Facility_Code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "2qm1TN4yVjgg",
        "outputId": "c7b7eeb8-2af0-496a-975a-7c6ba100d8dc"
      },
      "source": [
        "sns.catplot(x='Ward_Facility_Code',data=df,kind=\"count\") "
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26bafa350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clxqeWQ4AfsS"
      },
      "source": [
        "## Bed Grade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1ODgkDeAkVd",
        "outputId": "fbf7f40b-0b74-4f50-8cda-2b6258c14fbd"
      },
      "source": [
        "df['Bed Grade'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0    123671\n",
              "3.0    110583\n",
              "4.0     57566\n",
              "1.0     26505\n",
              "Name: Bed Grade, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKHZwUrABvTn"
      },
      "source": [
        "## patientid"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd7AcwXHBwkh",
        "outputId": "104c3622-0c09-4186-b9a1-ff4c7ce407e5"
      },
      "source": [
        "df['patientid'].nunique()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "92017"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcf_cHHDB30x"
      },
      "source": [
        "## City_Code_Patient"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZojZmtvJB5aa",
        "outputId": "7583ff0c-03dd-4a9c-ec70-000fd16fb19c"
      },
      "source": [
        "df['City_Code_Patient'].nunique()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9IEUmDZCj5X"
      },
      "source": [
        "## Type of Admission"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JyXR_N6MVjvG",
        "outputId": "a5bb3403-3cbd-400c-9d7e-5b049e006697"
      },
      "source": [
        "sns.catplot(x='Type of Admission',data=df,kind=\"count\") "
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26b9d5e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbSZMkwgCsxm"
      },
      "source": [
        "## Severity of Illness"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MF8nIRlQVj9I",
        "outputId": "ebdd998e-d131-42e3-8e2c-5ef5e87a8ed5"
      },
      "source": [
        "sns.catplot(x='Severity of Illness',data=df,kind=\"count\") "
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe269a76750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lm9PxPEbC6GW"
      },
      "source": [
        "## Visitors with Patient"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mc-wStLlC7E_",
        "outputId": "2f3676f6-7340-4982-da80-66960969f1cc"
      },
      "source": [
        "df['Visitors with Patient'].nunique()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tUcdrtKtDE4G",
        "outputId": "f5854e65-84f9-406b-8a50-2788e1013474"
      },
      "source": [
        "sns.catplot(x='Visitors with Patient',data=df,kind=\"count\") "
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe26995fdd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4y585j7DXdr"
      },
      "source": [
        "## Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csLSMcW3DYlu",
        "outputId": "411773c1-da8c-42ee-a572-30855578d92f"
      },
      "source": [
        "df['Age'].value_counts()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41-50     63749\n",
              "31-40     63639\n",
              "51-60     48514\n",
              "21-30     40843\n",
              "71-80     35792\n",
              "61-70     33687\n",
              "11-20     16768\n",
              "81-90      7890\n",
              "0-10       6254\n",
              "91-100     1302\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3CbWGWdjDfCm",
        "outputId": "c38e4efc-0968-45ba-877d-9876a3e529db"
      },
      "source": [
        "sns.catplot(x='Age',data=df,kind=\"count\") "
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe2698e1dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jlhk-G-hDpHG"
      },
      "source": [
        "## Admission_Deposit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KfVQ4Te1GE1A",
        "outputId": "82da8c9d-2eed-4336-abfc-4d23b43d7780"
      },
      "source": [
        "df['Admission_Deposit'].nunique()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7300"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "E_4ZkL7bGRw3",
        "outputId": "f4aba49d-e443-457a-85dd-799dcea8f016"
      },
      "source": [
        "sns.histplot(data=df['Admission_Deposit'])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe269800c10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Mp1jxCANHkmZ",
        "outputId": "435280af-87aa-4f44-9bd1-461f923de239"
      },
      "source": [
        "data = [i for i in df['Admission_Deposit'] if i > 8000]\n",
        "sns.histplot(data=data)\n",
        "print(\"Total:\",len(data))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total: 4952\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "XHIKQk36Gn0_",
        "outputId": "e6e68fa3-c184-47f6-c215-0d0de91d12ac"
      },
      "source": [
        "data = [i for i in df['Admission_Deposit'] if i > 9000]\n",
        "sns.histplot(data=data)\n",
        "print(\"Total:\",len(data))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total: 1154\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzVzmRrKIyrM"
      },
      "source": [
        "## stay"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2Pv6zBaI0wG",
        "outputId": "99e9dff7-f3a1-42c6-e535-2c4d8ec7f308"
      },
      "source": [
        "df['Stay'].value_counts()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21-30                 87491\n",
              "11-20                 78139\n",
              "31-40                 55159\n",
              "51-60                 35018\n",
              "0-10                  23604\n",
              "41-50                 11743\n",
              "71-80                 10254\n",
              "More than 100 Days     6683\n",
              "81-90                  4838\n",
              "91-100                 2765\n",
              "61-70                  2744\n",
              "Name: Stay, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vZsgx61shonk",
        "outputId": "18cf9f14-df52-4e60-ba4d-e51d7dfd99ac"
      },
      "source": [
        "corr = df.corr()\n",
        "sns.heatmap(corr, annot=True)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe26b2d74d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h94UzHRv2xH/PAVzaaLYYr1S9AjFPY9Ks22cxz6ikrVuXNo0J3HsQgMB9B3XXplkbZwL2apy5S+3WNK31HU1rfYePZwAzRv+SamQh5kkMhUtYY1HUAiNjI+y+teXw/sN6Mof3H6bhd44AfNP0G84EnwHgROAJSpQtQfYc2TEwNKDyV5W4feU2BoYGmObXbLJjaGTIVw2+4ualW1w6c5miJYtiXdQKI2MjnFwdCdyrX6+Be4Nx0ZalgYsdxw6e1IU7uTpinM0Y66JWFC1ZlPOn/smwzouWLKL7/+mTp/jvC/qgvx8f70BqflUVQ0NDcuTMQdUaFQEyzmRm+A8M46uevSIrLADmSil3CiHsgIkAUsqZQojdaD7pGCyEcELTm58hpfzzU2U2I0ZMmMmxU2d59OgJDZp3pF+PTrRq5vTO+gL2H8TO0Qa/Yx7Evohl1KCJurhd/htoZq95GJkwcgazF0wie47sBPqGEOATnI5GDU4uDrRo60J8XDyxsS8Z1HO0Li7IJ4RvGtRl9+HNxL54yc+Dk94u2OSzhjaOXQCYNnoOU+ePI3uO7Bz0O8xB30MAODjbMmbaUPIXzMeidb9y8fxl+rbXvJrnfWwbJia5Mc5mhEPj+ozsMJpbV26TkJDI/J8XMMdtJgYGBni77+Hm5Vt0G96FS2cuE7L/EF4bvRk7fzRuB9fw5NFTJveblmEZC1oUYMxvozAwNMBACPw9Aznke0RPJiEhgTk/zeP39b9gYGjAro1eXL98k94juvPPmYsE7Qth5wYvJv3+E1uD3Xjy6Ck/9U2aOdpxZCO5teWxdbJhUPvh3LhyK2VWdBz0OYRNgzrsPLyJ2BexTBw8XRe30Wc17Ry7AjBj9K9Mmv8T2XNkJzhZ3a5asJZZS6fQ/HsXwu6GM7L3z2mZ0WPFjsWU+qIEOXPnIPZ5LHO3/sKrV3Hsc9/Hrcu36TysE5fPXubw/iPs2biXkfNGsCpoBU8fPWV6/5kAxDyOYduybSzwnI9EctTvGEf9jpE9Z3amr5uKobERhgYGnDx4Cu/1e0hMTGTW2Lks2jAXA0MDdm7czfXLN+gzogd/n7nIgX3B7NjgyZQFP+MRspHHj54wps9EAK5fvsH+XX5sCdSMNM0cO1e3GHD64onUqFuVfAXy4X1iG0t+WYHHht0M+qkPxUsXQyYmcvtOKD8P07SND/X7uXblBgf8Qth9wB2ZmIj7uh38PG34+TdejDfxCYfnM4v4UHNFis8L7TD+dqCOlPK+dhjfF+gppTwhhFgFlJRS2gkhSkspr2nTbQHWofmG8xSggZQyRghRGIiTUkamYy9GSmkihGgJ/IDmwcEczTB+Lynllozyq7a4/bCoLW4/DmqL2w/P+9ji9uXZvZm+32Sv7JRle++C6tkrMoWU8oIQYhoQKIRIQDMcPxHYLIR4iGZ4vaRWfLAQwh5IBC4A3lLKl0KIcsAhIQRADNARSNPZJ2M7mimEv4HbwKH3WjCFQqHIIlJmfovbT4Vy9opMI6VcA6xJEeyRhtzAdNLPB+Zn0paJ9q8EBrxdThUKheIjkvBpRn7eBuXsFQqFQqHICv+BOXvl7BWfDCFEQTTz/ilpIKW8n0a4QqFQ/PtIVMP4CkW6aB161TcKKhQKxb8Z1bNXKBQKheIzR+1nr1AoFArFZ47q2SsUCoVC8ZkTr1bjKxSfhE/xgZuLFzP8zs8Ho2fNER/dZsDT1LvxfQzyGn+aj/kYCcNPYjcs7tFHtxn3iRabFctR8M1C/1LUe/YKhUKhUHzuqDl7hUKhUCg+c9ScvUKhUCgUnzmqZ69QKBQKxWeO6tkrFAqFQvGZo76Nr1AoFArFZ85/YBjf4FNnQKH40IyfPgK/ox7sDnSnQuWyacpUrFIOrwPu+B31YPz01K+y9ejXkWvRJ8lfIB8Ajs627A50Z5f/Bnb4rKPGV+/21d9x0+dSv2k7mnfs807pM6KSbVVm+v7O7ICFNO3bIlX8l7XLM8lzDiuvbqKm89e68IKFzZjkOYfJXr8wfd887Ds0eiu7E2eMIvCYJ3sObKFi5XJpylSsUo69QVsJPObJxBmj9OK69mqP72EP9gdvY8yEIRnaGjV1CJ6HNrPFby3lKpVJU6Zc5S/Z6r8Oz0ObGTU1SV/DZg5sC3TjdGgw5aukbheWhS04fM2XLn2/p679V2wLWo9HyEa6DuiYStY4mzEzl0zCI2Qja3YvxaqIpS6u28COeIRsZFvQeurY1QbAwtqcP7f8zpbAtWwOWEv7nq118n1H9sTddzUb9q9iycZ5mFkU+qBl/aJcadZ6LmVboBtb/deRLXs2vfix04ax58hWdgS4Ub7Sl2naLV+5LB4B69lzZCtjpw3ThQ+fMJDdwZvYEeDGgtWzyWNqoqkvYyOmzf8Zj4D1bPd3o1bd6nr6atnVZE3gStYdXE37/m3TrO/xi39i3cHVLN71OxZFLPTiza3N8Lq0kzY/JL2Cu+HQWlb4LGXZ3iUs2b0ozXK8M4mJmT8+EcrZKz5r7BzrUaJUMRxqu/LT0KlMnjMmTbnJc8YwdshUHGq7UqJUMWwb1NXFWVlbYGNXh3t3wnRhIQeO0tS2Lc3s2zNq0CRmzPv5nfLXvElDlsyd+k5pM0IYGNB5ci9+7TqNMQ0H8/W3Nlj/r4iezP3QKJYPX8hhjyC98EeRD5nScgzjmwxnUvPRNO3bgnzm+TNl197RhpKlimNby4UxQycz9ZdxacpN+2Uco4dMwraWCyVLFceugQ0AdWxq0dDZHuf639GwXkuWLkq5o3ISNg3qULxUUVzqtGby8JmMmzUyTblxs0YyadgMXOq0pnipotg4aB5srl68xtDuYzhx+HSa6UZMGsRBv8MIIRg1fSgDOwynlW1HGjd3pGSZEnqyzdu78OTxU1zrtsNtqTs/jusLQMkyJXBydeQ7u04M+H4Yo2cMw8DAgIT4BH6btJDvbDvRpWlv2nRtqdP51+L1tG3QlfYNu3FgfzA/DO3+wcpqaGjIjEUTmTJyNi1tO9C9ZT/i45KGpOs3qEvxUkVp/FUrJgybwfjZ+g9mr5kwexTjh02n8VetKF6qKN841AEgJPAo39ZvT3O7Dty8dpveP3YFoHWn5gC42n1Pj9YDGDXpR4QQABgYGPDj1IGM7jSWrvY9aeBqT/EviunZa9KuMU8fx9DRpiubl23jh7E99eL7TejDEf9jqfI5pPVwejn1oU/T/mmW452RiZk/PhHK2f9HEELEpDjvKoRY+J5thGj/lhBCfJ8J+RJCiPPvMw/JdNsJITyzqsfR2Y7tmzRqTp84h2nePLqe0mvMLAphkic3p0+cA2D7Jk8aNrHXxf80dRizJs1DSqkLe/7she7/XLlykizqrahZtRJ5TfO8W+IMKFX1f0TcCifqTgQJcfEc2XWQ6o1q6clE343izsVbJKbIfEJcPPGvNDd8o2xGGGhvwpmhobM9W913AXDq+FlM8+bBPEV9m1sUwiSPCaeOnwVgq/suGmnru2O3Niyev4JXr+IAuB/9IF1b9k712bXJG4CzJy+Qx9SEQub6H2YpZF4QE5PcnD15AYBdm7yxb2wLwI0rt7h57XbauhvX597tMK5duo5lYQvu3rzLvduhxMfFs9fDBzsnGz15u8Y2eGrz4usZQK1vamjCnWzY6+FD3Ks4Qu+EcffmXSpWK0d05H0unrsMaNrSjSs3MbfU1NOzmOc6vTlz5QTkBytrHbvaXP77Kpf/vgrA44dPSEzW+3Rwro/HJi8Azpw4r/n9pLBrZl4Qkzy5OXNCcyvw2ORFgyYauyEBR0hISNClt7A2B6B0mZIcOXgcgAfRD3nyOIYvq2hGK8pW/ZLQm6GE3Q4nPi4eP48A6jWqq2ezXqO67N28D4DA3QeoblMtKc6pLmF3wrl5+Waq8n4wVM9e8V9CSvn6F1UCeKOz/y9gYWVO6L0I3Xl4aCSWVmZ6MpZWZoSHRurOw0IjsbDS3JQcnW2JCIvk4oXUX4xr1MSefYe2snzDfEYPmvSBSvBu5LcowIPQaN35g7AH5LfI/BfKClgVZKr3XH47tJTdS3bwKPJhptJZWpkTei9cdx4eGqGry9dYWJkTHpp0TcJCI7DUypQsXZzaX9dgxz433HeupHK1CunaMrcy09MTERaFeYpra25lRkRYZDKZyFQyKcmZKyfdB3Tkj19WAGCSJzfh95J0RIZFYW6pr8PMMqkNJSQkEPPkGfkK5MXc0oyIZG0rIjQKsxRprYpY8mWlMpw/+bcurP/o3ngd30rTVo1YNHvZBytriVLFkFLyx4bfcN+3mm79O+jFW1jqX6vw0EjMU1xPcytzfbuhkVhY6ssAtGzfjCDfEAAuXriCvVN9DA0NKVzMmgpVymJurclrIatCRIZF6dJFhUdTyEr/gbGQZUGdTGJCIjFPnmGa35QcuXLQvl9b1sxdm8q+lJI562fyp9ciXDo0ybBe3pqE+MwfmUAI0VgIcUkIcVUIMTqN+GJCCH8hxCkhxFkhxBsLpBbofQYIIUoAK4FCQBTQTUp5WwjRGpgAJACPpZT1hRBdgRZAXqAwsE5KOUmrJ0ZKaQLMBMoJIU4Da4DtwFrg9bdKB0gpQzKRL0NgFtAYSASWSSkXCCEaAL+gaX/HgL5SypdCiMbAPOA5cDCZntzAAqAiYAxMlFJ6vEtdvQ05cuag7+DudPku7SG/fV7+7PPyp1ad6gwZ0/dDZ+ej8iDsPuOch5LPPD8/Lh3FMe9DPIl+/MHtGhkZkS+/Kc0bdaBK9YosXvELNtWdP7jd5PQb0ZO1S9158fzFm4WzSM5cOfllxTR+HT9fr0e/aOZSFs1cSpcBHWjf/cN9+tnQyJDqX1WhfePuxL6IZdnmBZw9/Q+Hg1IPgWeFHwZ3IyEhgV1b9gCwbf0uSn9Rks371xB6J4zTx86SkJD1Xm/XoZ3Zsmwrsc9jU8UNajmE6PD75CuYj182zOT21TucPXIuyzaB9zo8r71vLgIaAneBY0KInVLKv5OJjQM2SSn/EEKUB7zQdNLSRTn7/w45tc73NQWAndr/FwBrpJRrhBDdgd+B5sB4wElKeU8IkS9Z2tpoHOdzNA1pt5TyeLL40cBwKaULgBAiF9BQShkrhPgC2ADUzESee6NpgFWllPFCiAJCiBzAaqCBlPKyEOIvoK8QYgmwDHAArgLuyfT8BPhJKbtry3FUCOEjpXyW3JgQojfQe/To0WYefmsxEEacO30B68IWnNDKWFqbE56s1wAQHhaFpXVST8TKWtNTKVaiCEWLFWZ34EZd2p1+brRo1JnoyPs6+WOHTlK0eGEQ8f+a920fRjyggHVSb6iAVQEeRtzPIEXaPIp8yN3LtylTqxzHvQ+nKdO5R1vadWoFwNlTF7AunLQ4zdLaQq/XB5oep6V10oIqK2sLwrUyYaER7PH0BeDMyfMkJiZSoGB+HtzXjCy07daKVh2+BeDC6X/09FhYmen1CEHTC08+smBhZZ5KJiWVqpXH0cWeIT/3J4+pCQbCgKjwpDTmVmZEhuvriArXtKHIsCgMDQ0xMc3NowePiQyP0g1dA1hYm+l0GRkZ8suKqXht24ef14FU+WjTtSUu3zWmSHFrPLfs+SBljQiN5MTh0zx6oHmQC/I9RIcerRk56UcAzp/6W8+upoz61zMyLFLfrrU5EeFJMs3bNsWukQ3dWvXThSUkJDBz/G+68/W7l3P3+l0AosOi9UYkzCwLER2WNEoFEB1+H3MrM6LDojEwNMDENDdPHj6hXLWy2Db9hh9+6oWJqQmJMpFXL+PYsdqD6HBN+390/xFBe4IpW/XL9+fs3+/wfG3gqpTyOoAQYiPgCiR39hIw1f6fFwh9k1I1jP/f4YWUsurrA40jf00dYL32/7XA6wnFYGC1EKIXkHwnj/1SyvtSyhfAtmTy6WEMLBNCnAM2A+UzmWdH4E8pZTyAlPIB8CVwQ0p5WSuzBqgPlNWGX5GayfF1yfQ0AkZrH3YCgByA/oodjf6lUsqaM2bMKO7q0Ilm9u3Z5xVAizYuAFStUYmnT2KIitC/cURFRBPz9BlVa1QCoEUbF3y8A7j8z1Vql3PEtroLttVdCA+N5FuHDkRH3qd4yaK69BUql9WsYP6XOHqAG2euYuzeNNsAACAASURBVFHCikJFzDE0NuKrZjac2n/8zQmB/JYFMNauyM5lmpsyNcsRfj39e8lfK9xpYteGJnZt2OflR6u2zQCoVrMyT588JTJFfUdGRBPzNIZqNSsD0KptM/Z7+wOwz8uPOjaatQUlSxfHOJuxztEDuK/aShvHLrRx7ILfngM0a6Pp9VeuXoGnT5/pPYQBREfeJybmGZWra6YDmrVxxn9vaseanK7N++JcqyXOtVritsydpfNWYZw9G9ZFrTAyNsLJ1ZHAvcF6aQL3BuOizUsDFzuOHTypC3dydcQ4mzHWRa0oWrIo50/9A8D4uWO4ceUWbn+66+kqWlKzkHLT6m1s+msbB3yCP1hZgwOO8EXZ0uTImR1DQ0Nq1qnGlnUetHToSEuHjvh6B+LaRjNCXKVGRc3vJ4XdqMj7xDx9RpUaFQFwbdMEP2+NXRv7r+kxoBP9Og0j9sVLXZocObOTM1cOAOra1iYhPoFbVzRrCi6euUThkoWxLGqJkbERDq52hOw/pGczZP8hnFpr3hKxbVqfU8GaftCPrYbSvk4n2tfpxJYV23BbsIEdqz3IkTMHOXPn1NrOQc36Nbhx6WaGdfNWvN85+8LAnWTnd7VhyZkIdBRC3EXTqx/4JqWqZ/8ZI6XsI4T4CmgKnBBC1HgdlVL0DaqGABFAFTQPiKnHyD4sAmglpbz0tgkD9h/EztEGv2MexL6IZdSgibq4Xf4baGbfHoAJI2cwe8EksufITqBvCAE+welo1ODk4kCLti7Ex8UTG/uSQT1H47Ym7ZXnGTFiwkyOnTrLo0dPaNC8I/16dKJVM6e31pOSxIRE1o5fzoi/fsbA0IADm/y4d+UOLYa04+a5q5zyOU7JyqUZ9OcocufNTbUGNWk5pB1jGw3G+n9FaP9TVyQSgcB72U7uXkp7IVtK/PYHYd/wGw4c382LF7EMH5j0loJXwCaa2LUBYNyIafy6cCo5cmQnwPcg/j6aWZtNbtuZs2Ay+w5uI+5VHMP6p1+nQT4hfNOgLrsPbyb2xUt+Hpz0VsMmnzW0cewCwLTRc5g6fxzZc2TnoN9hDvpqHIeDsy1jpg0lf8F8LFr3KxfPX6Zv+9Sv+kkpmTV2Los2zMXA0ICdG3dz/fIN+ozowd9nLnJgXzA7NngyZcHPeIRs5PGjJ4zpMxGA65dvsH+XH1sC15EQn8DMsXNJTEykau3KuLRuzJW/r7Jh/yoAFs74k2C/wwz6qQ/FSxdDJiYSejecKSNnExke9UHK+vTxU/76cwPr96wEKQnyPURgsrYf6BNMfce67D26jdjnsYz9cYoubpvfOlo6aF5DnDxqNjN+H0/2nNkJ8g3hgHZuftzMEWTLlo0VmzVric+cOM+kETMpUKgAy91/JzExkcjwKEb1n6DTm5iQyO8/L2S22wwMDAzwdt/Lzcu36Da8C5fOXCZk/yF2b/Rm7PzRrDu4miePnjKl37R02wlAfrN8TFmuuSaGhob47PDnWEDmHn4zxVus0H09ApksaKmUculbWmwPrJZS/iqEqAOsFUJUlDL9HoeQ77qMWPFRSTaf/vq8K1BTSjlACLET2CylXKsNd5VSthBClJZSXtPKHwN6AVWB6WiG8V8AR4DuUsrjr21oHwrmSilttWl/A+5qG1Y3YKWUUmjXCnhKKSumk+c+aHr37V4P46OZOrgMOEgprwohVgOngD+14fZSymtCiA1AHimlixBiOpohq4FSY7ialPJURvVVulD1j96w1Ra3H57/b1vcxn+CrVM/1Ra3ltnyfhK7/nf3Z/51k3R4sWFCpu83OdtPytCe1nlPlFI6ac/HAEgpZySTuQA0llLe0Z5fB76WUkamoRJQw/ifCwOBbkKIs0An4Edt+BwhxDnt63EhwBlt+FFgK3AW2Jpivh5teIIQ4owQYgiwGOgihDiDZrj9GZljOXAbOKtN+72UMhboBmzWTgskAku04b2B3UKIk0DyRjsFzVTCWW0jn4JCoVD8W3i/q/GPAV8IIUoKIbIB7Uhan/Wa20ADACFEOTRTmxku0FDD+P8Rkvfqteer0Sx0Q0p5C83CtpRpWqYM03644q6Usnl6NqSUcWnoq5zs/1FauZtoRgjSy3M8MFR7JA/3BaqlIb8HzcNEyvAXwA/p2VEoFIpPyntcoKcdBR0A7EWz1mqllPKCEGIycFxKuRMYhmYd1RA007Bd5RuG6ZWzVygUCoUiK7zn6XAppReahXfJw8Yn+/9voN7b6FTO/v8ZyUcE3hdCCCc079Mn54aUMvUH2RUKheJz4z+wEY5y9oosI6Xci2bISaFQKP7/oZy9QqFQKBSfNzLh07zB8DYoZ69QKBQKRVZQPXuFQqFQKD5z/kVfz0wP5ewVnyW5jbJ/dJuf4uM2AMuPz/noNsuW/XCbs2RETPzH/nijhnyf6GM+CZ/AieQ1yvXRbQLce5m5nRX/lST++z9Op5y9QqFQKBRZQQ3jKxQKhULxmaOcvUKhUCgUnzlqNb5CoVAoFJ85as5eoVAoFIrPHLUaX6FQKBSKzxzVs1coFAqF4vNG/gcW6Kn97BWfPaOmDsHz0Ga2+K2lXKUyacqUq/wlW/3X4XloM6OmDtGFN2zmwLZAN06HBlO+StLuu3nzm7J860IOX/NlzPRhGdqvZFuVmb6/MztgIU37pt4b6Mva5ZnkOYeVVzdR0/lrXXjBwmZM8pzDZK9fmL5vHvYdGr1t0dNl3PS51G/ajuYd+7wXfeOnj8DvqAe7A92pUDnVLsUAVKxSDq8D7vgd9WD89NTfJOjRryPXok+Sv0A+XdhX9Wqwy38D3gc3s37nsjTsjsTvqAdeb7DrfWCT1u7INOx24nr0KZ1d07x52Oy1in/uHeGfu4cZNzX19TXOZszMJZPwCNnImt1LsSpiqYvrNrAjHiEb2Ra0njp2tXXhde2/YlvQejxCNtJ1QEdd+Iodi9iwfxUb9q9i76kd/LpqOgA16lTj8BVftvquZavvWtbvXoH34S1s819HuUpfplnW8pXLsj3ADe/DWxgzLWln6WHjB7LroDvb/Ncxf9Us8pjq7ZiNVWELjl33p2vfDnrhX9vVxj3oLzYHu9FpwPdp1sPUJePZHOzGCs/FunowzW/Kos2/4XfFm2HTftRL07C5A+t8V7LOZwW/uc0mb4G8aZZl3PTh7D+6nZ0BGyhfOe3yVqhcll2BG9l/dDvjpg/Xhf84ug87Azbg4e/Gyk0LMbcopJeuUtXy/B12GKdmDdLU+9Ykyswfnwjl7BWfNTYN6lC8VFFc6rRm8vCZjJuV+mYPMG7WSCYNm4FLndYUL1UUGweN07168RpDu4/hxOHTevKvXr5i0ayl/DppYYb2hYEBnSf34teu0xjTcDBff2uD9f+K6MncD41i+fCFHPYI0gt/FPmQKS3HML7JcCY1H03Tvi3IZ57/basgTZo3aciSuVPfiy47x3qUKFUMh9qu/DR0KpPnjElTbvKcMYwdMhWH2q6UKFUM2wZ1dXFW1hbY2NXh3p0wXVgeUxMmzR5D745DcLZpzcDu+tfOztFGZ3fs0KlMmTM2TbtT5oxlzJApyewm7QxqZW3BN3Zf69ntP7Qnpb8oSaO6LXF17Ejbji0oWaaEns7m7V148vgprnXb4bbUnR/H9QWgZJkSOLk68p1dJwZ8P4zRM4ZhYGCAgYEBo6YPZWCH4bSy7Ujj5o46nT2a96d9w260b9iNsyfO4+d1QGfnxJHTtGrQiXnT/+DJ4yc4f/0dE4fPZPzstNvx+NkjmTBsBs5ff0fxkkWxcagDwKHAozS3/Z6W9h25de02vQZ10Us3ctJggnwP6YUZGBgwfPqPDOkwivZ2XWjk6kCJL4rryXzbvglPHsXQul4HNizbQv9xvQF4FfuKpXNWsmDyH3ryhoaGDJk8kP6th9DRsQfX/rlG626pH4BtHetRolRRGtZuwc/DpjFpdtptatKcMYwbOpWGtVtQolRR6mvb1PKFa/nWrj2u9h3w3x9E/+G99Ms1fiDBAUfS1PlOJCRk/vhEKGefRYQQlkKIjUKIa0KIE0IILyFEfSHEFm18VSFEkyzo7yyEOC+EOCeEOCWEGP7mVLq0JYQQ59/BZqp0QoiJb2M7Ezas37aOhBB2QgjPt7Fj71SfXZu8ATh78gJ5TE0oZF5QT6aQeUFMTHJz9uQFAHZt8sa+sS0AN67c4ua126n0vngey6mjZ3n58mWG9ktV/R8Rt8KJuhNBQlw8R3YdpHqjWnoy0XejuHPxFokp9sROiIsn/lU8AEbZjDAQ4i1KnjE1q1Yir2me96LL0dmO7Zs0l+X0iXOY5s2DWYqelJlFIUzy5Ob0iXMAbN/kScMm9rr4n6YOY9akechkdfBtK2f2efoRdi8cgPvR+l9Yc3S2fUe7drr4cVOHM3PSfD271WtV5taNO9y5dY/LF6/y6lUcTVo01NNr19gGT2278vUMoNY3NTThTjbs9fAh7lUcoXfCuHvzLhWrlaNitXLcvXmXe7dDiY+LZ6+HD3ZONno6c5vkola9GgR4HyAlDo3rs3Ozth2fOE8e0zxptuPcJrk5e0Lz09252ZsGzpp2HBJ4hAStozlz4jwW1uZJup3rc/d2KFcvXdfTV75aWe7evEfo7TDi4+LZ7+FHfSf9LdS/caqH1+Y9APh7BlLTRlMPsS9iOXP0HK9evtIviAAhBDlz5gAgl0luosKjU5W3QWNbtrt76fKbJ28ezCz0y2tmURCTPLk5oy3vdncvHJ3tAHgW80wnlytXTr3r26lXW/Z5+nE/+kEqu+9MYmLmj0+EcvZZQAghgO1AgJSytJSyBjAGkFLK198TrQq8k7MXQjgDg4FGUspKwNfA46zn/NMjpQx9H3X0JsytzAgPjdCdR4RFYW5llkomIiwymUxkKpl3Jb9FAR6EJt3MHoQ9IH+Km1ZGFLAqyFTvufx2aCm7l+zgUeS/75OiFlbmhN5LquPw0EgsU9SfpZUZ4aFJdRwWGomFlcbhODrbEhEWycULV/TSlCxdHNN8prh5LMXD140WbZqm0GmuexDQ2I3A0so8lUxyu8llHJ3tCA+L5OKFy3ppoiLvk0PrjCpXq4Bp3jwULaU/GmNmmVSehIQEYp48I1+BvJhbmhGRzF5EaBRmlmYa+XtJ4ZFhUZhb6teRnXN9jh48zrOY57qwqjUqsc1vHQ2a2GJgmHS7jgiLxCJFHVukaMfhoWm345bfN9P14nPlykmPAZ3545flqeTMLM2IDI3Sy7NZCn1mlmZEaGU09RCT7rA8QEJ8ArNH/4ab30o8T22lZJni7NrglUrOwsqM8NCkaxsRGoGFpf61tbA0T/HbjtCrkyFj+xF42pNmrZyZP2uJNo0ZDZvYsX7VlnTz+E6oYfzPHnsgTkq55HWAlPIMcEfbG88GTAbaCiFOCyHaCiGuCCHMAIQQBkKIq6/P02AMMFxKGarV/VJKuUybtqoQ4rAQ4qwQYrsQIr82vIYQ4owQ4gzQ/7UiIYShEGKOEOKYNs0P71roDGwPEkL8rQ3fqA2bKIRYK4Q4pC17L214iQzqqLZW/pQQIkQIkfaEXep89RZCHBdCHH/wPOLNCf4DPAi7zzjnoYy07Y9NKztMC6V/I/0vkiNnDvoO7s5vM5ekijM0MqRilXL0bD+Irq37M2B4L0qWLvbe7PYb3J15M/9IFbd/tz/ZjI3x9N9Il17tuHcnDPkRbtKNmzuyZ4eP7vziuUs0rOFKS4eO3LsdxpCx/bJso/fgrsTHJ+C5VdMb7zeiF3/9uYHnz19kWXdmMDQypGXnb+ncqBcu1Vpx9Z/rdBnY4c0J34Hfpi/GtqoLu7Z606lHGwDGThvGnMkL9Hr67wWZmPnjE6FW42eNisCJ9CKllK+EEOOBmlLKAQBCiLJAB2Ae4AickVJGpaMiI/1/AQOllIFCiMnABDSjAKuAAVLKA0KI5Duk9AAeSylrCSGyA8FCiH1Syhvp6C8thEg+UW0J/PIG26OBklLKl0KIfMnSVkYzKpEbOCWE2P2GOjIFvpFSxgshHIHpQKt08qlDSrkUWAr0v3j+cg2AC6f/wdLaQidjYWVGZJh+dUeGRel6mRoZ81Qy78rDiAcUsE4aWi5gVYCHEfffWs+jyIfcvXybMrXKcdz78HvJW1bo2L0NbTtp5lrPnb6AdWELXUO1tDYnPEX9hYdFYZls6NjK2pyIsEiKlShC0WKF2R24UZd2p58bLRp1Jjw0gkcPH/PieSwvnsdyNOQkvQd2pVKV8gCcPX0Bq8JJC+MsrS0IT9az1diN1LP7WqZ4iSIUKVaY3YHuOru7/NbTvFEnbt64w53b9+jaRvOsfOpqIDev6k/lRIVryhMZFoWhoSEmprl59OAxkeFRekPkFtZmRIVr6sKycFK4uZUZkeFJdZSvQF4qVC3HsO5J6w6afteY5h1cADh/+m+KFLMmX4G8PHrwGAsrcyJS1HFEinb8On+vad62KbYNbejxna4PQOXqFWjkYs+wnweQJ28eZGIiIk6yZdV2osKjMLdO6oeYW5kRlcJmVHiUpoy6ejDh8YP0Bx/LVPgfAPduhQLgu9OfztqFfx26t6ZNp+YAnDv1N5bWlsAZbT1aEBGuf20jwiNT/LYtUtUJwM4t3izb8Du/z15KxSrl+G2pZgFk/oL5Xq/faA7sSDfTmeE/8Oqd6tl/fFYCnbX/d0fjnN8KIUReIJ+UMlAbtAaor3Ww+aSUryf91iZL1gjorHXgR4CCwBcZmLkmpaz6+gCWZGRb+/9ZwE0I0RGIT6bLQ0r5QkoZDfgDtcmYvMBm7bqB34AKb5BPyaI2jl1o49gFvz0HaNbGGdDc2J4+fUZ0pL6zjY68T0zMMypX15hp1sYZ/72p503fhRtnrmJRwopCRcwxNDbiq2Y2nNp/PFNp81sWwDh7NgBymeamTM1yhF8PfS/5yirrVm6imX17mtm3Z59XAC3aaJxS1RqVePokhqgI/XnYqIhoYp4+o2qNSgC0aOOCj3cAl/+5Su1yjthWd8G2ugvhoZF869CB6Mj7+HgHUvOrqhgaGpIjZw6q1qjIqiVuuNi3w8W+Hfu9/N/RbiCX/rlK7XINqF+9KfWrNyU8NJJmDt8THXmfG9duUbJ0cYoUs+b7rq2QwP5d/np6A/cG46JtVw1c7Dh28KQu3MnVEeNsxlgXtaJoyaKcP/UPF05fpGjJolgXtcLI2AgnV0cC9wbr9DVwsSPIJ0Rvjtt3dwCtGnSiVYNOXLt8g1wmuXj04DGVa1Qk5mlMmu34WcwzKteoCMC3rZ3x26Npxzb2X9O9fycGdB5O7IukdSadXX+gUa0WNKrVgrVLN7J0/hq2rNoOwD+nL1G0ZBGsilpiZGxEQ1cHgvaF6NkM2hdCk9aNAbB3seW4th7SIyo8mpJlSpBPO9Rfu35Nbl65BYDbys242nfA1b4DPt4BtGirmdmrUqMiMU9iiErxkBwVcZ+Yp8+ooi1vi7ZN8N2juS0VL1VUJ+fobMf1qzc19VzTFYca3+JQ41v27vJl4qhZkFVHD8j4hEwfnwrVs88aF4C32utTSnlHCBEhhHBA4/QyGsO6ANQA/N49izoEmt743vegKz2aonH8zYCfhBCVtOEpH3vf9Bg8BfCXUrYQQpQAAt41Q0E+IXzToC67D28m9sVLfh6ctAJ9k88a2jhqViVPGz2HqfPHkT1Hdg76Heagdk7TwdmWMdOGkr9gPhat+5WL5y/Tt73m1TzvY9swMcmNcTYjHBrXZ17naYRevatnPzEhkbXjlzPir58xMDTgwCY/7l25Q4sh7bh57iqnfI5TsnJpBv05itx5c1OtQU1aDmnH2EaDsf5fEdr/1BWJRCDwXraTu5dSLxZ8F0ZMmMmxU2d59OgJDZp3pF+PTrRq5vROugL2H8TO0Qa/Yx7Evohl1KCJurhd/htoZt8egAkjZzB7wSSy58hOoG8IAT7B6WjUcO3KDQ74hbD7gDsyMRH3dTu4fPGaLt5fa9f/2E5iX8QyMpldT/+NuNi3A2C81m6OHNkJ9A0mwOdghnZLli6OcTYjfA7vID4ujpVL3Lh++QZ9RvTg7zMXObAvmB0bPJmy4Gc8Qjby+NETxvTR2L5++Qb7d/mxJXAdCfEJzBw7l0TtoqxZY+eyaMNcDAwN2LlxN9cvJw2qObk6snrhOr18OLrY0apzcxISEoiNfUmQTzDeR7YS+yKWcT9O0clt9V1LqwadAJgyajbTfh+vace+hwjy1Tjnn2YMxzhbNpZvWgBoFr1NHjkrw3pISEjgl5/mM3/9HAwMDfDc6M2NyzfpNaIbF89cImhfCLs2eDHh97FsDnbjyaMn/Nx3si799iMbyWWSC+Nsxtg62TCo/XBuXrnFirlrWLL9d+Lj4gm/F8HkwTNT2Q7YH4ytYz18ju7gxYtYxgyapIvz8HfD1V5z25w4ciYzF0wkR47sHPALIVDbpob/PJCSpYuTmJhI6N0wJgyfkWFZs8x/oGcv3vvcxf8jtAv0DgMrtEPICCEqo+mZ/iGlrCiEaAV8K6XskixdK2ABsFZKOSoD/U3QOL6mUspw7fx2Zynlcu2c/AApZZAQYiKQV0o5RAhxFugnpTwohJilTVtRCNEbzSK41lLKOCFEGeCelPJZGnZLAJ5SyorJwiYCMVLKX9KyDQwDikkpbwohjIFbQHk0w/vNSTaMr/0/22sbKetICLEdWCel3KrV31VKWUIIYYdmDYPLm65NZcs6H71hV8th/bFNAv+/9rOXb3xO/DB8qv3sXybGfXSbeQxzfnSbAA/iYj6J3ctRx7P8mkvMcNdMN0yTXzze32s1b4Eaxs8CUvOk1AJw1L56dwGYAYQnE/MHyr9efKYN2wmY8IYhfCmlF7AQ8NHqPgmYaqO7AHO0zr0qmkVuAN2ARdrh+uSNajnwN3BSOzz+J+8+spOWbUNgnRDiHBqH/ruU8pFW/iyaejgMTHm94DAZKetoNjBDCHEqC3lUKBSKj8N/YDW+6tl/AoQQNYHfpJTffOq8fGiSjwh8TLuqZ/9hUT37j4Pq2X943kfP/ungZplumHnm7fokPXvVa/rICCFGA33JeK5eoVAoFP8V/gNz9srZf2SklDMBvRUpQoifgNYpRDdLKad9yLxoF9CtTRH8Ukr51fuyIaWc+L50KRQKxb+ST7jKPrMoZ/8vQOvUP6hjT8fuOTRz7gqFQqF4V1TPXqFQKBSKz5v/wto35ewVCoVCocgKqmevUHwaChh9/NXTAU+vvFnoA/ApVsZfvPieNxLJJPmLvaf9x9+SfHk/zWp8I2H40W1aG5u+WegDcCL60/x+3gvv2dkLIRoD89G80rxcu9YrpUwbYCKaj5SdkVJ+n5FO5ewVCoVCocgC73OjJCGEIbAIaAjcBY4JIXZKKf9OJvMFmo3S6kkpHwohzNPWloRy9gqFQqFQZIX499qzrw1clVJeB9DuIOqK5qNor+kFLJJSPgSQUkam0pIC9QU9hUKhUCiygEyUmT4yQWHgTrLzu9qw5JQBygghgrXbjTd+k1LVs1coFAqFIiu8xTC+dp+S3smClr7eW+UtMEKza6kdUAQ4IISolOwT5WkmUCgUCoVC8a4kZl5U69gzcu73gKLJzotow5JzFzgipYwDbgghLqNx/sfSU6qG8RUKhUKhyALveRj/GPCFEKKkdqfTdmg2T0vODjS9eoQQhdAM61/PSKnq2SsUCoVCkQXke1ygJ6WMF0IMAPaiefVupZTyghBiMnBcSrlTG9dICPE3kACMkFLez0iv6tkrPmtq29Xir8BVuB1cw/f926WKN85mzPjF43A7uIbFuxZgWcRCL97c2hzvS7to+4Nm64Js2Y35w3Mhy/f9ySrf5XQd1jlT+Zg4YxSBxzzZc2ALFSuXS1OmYpVy7A3aSuAxTybOGKUX17VXe3wPe7A/eBtjJgxJ18746SPwO+rB7kB3KlQum64drwPu+B31YPz0Eanie/TryLXok+QvkE8X9lW9Guzy34D3wc2s37ksM0VOk3HT51K/aTuad+zzzjrSY84vEzhzzp/DR7ypUrVCmjITJg7n4uVgwiPP64UPGNiD4yf2cfiIN56719H++5acPO3LmXP+DB2WOq/G2YyZuWQSHiEbWbN7KVZFLHVx3QZ2xCNkI9uC1lPHrrYuvK79V2wLWo9HyEa6DuioC69Vrzpu+1awyf8vJs3/CUNDzbv1zi0bEnDBi2O3Azl83Rcn17S/MVCu8pds8V/LrkObGDU1qW2Y5svDEvd57AxxZ4n7PPLkzQNAnrx5+G3lDDb7/YWb93L+V7YUAMVLF8PdZzW/es3jV695rDu/kR+m9WOB32IWBf5Ji76tUtk2ymbEsIUjWBT4JzN3zMGsiOYNMCNjIwbMGcRve39nrvd8KnxdEYAcuXPq9P/qNY/Vp9bRfXzPNMuVnN/mTuafvw9y8sR+qlWtmKbM5MmjuH7tGA8fXE4zvkWLJsS9ukeN6pXfaO+tSXyLIxNIKb2klGWklKVf75EipRyvdfRIDUOllOWllJWklBvfpDNTzl4I0VwIIYUQad89MqdjuRCivPb/m9qhh4zk09zvUAixWgiR6a+ICCEmCiHuafdKf33ky0DeTghRN7P609FRQgjxQmvrbyHEX0II46zozCrJ6z+T8l2FEAtThAVot+d9X3n6VrsL4Os29sb8aa/n8MzoNzAw4MepAxnVaSxd7Hvg4GpP8S+K6ck0aedMzOOndLDpwpZlW+k9tpdefP8JfTjif1R3/uplHEPbDKdnox/o6fTD/7F33mFVHF8DfodiB4xKVVQ0Gk0sWLAgCCiKBUss2GNLjDGa2KPR2Gs0lhiNMRp7FKwodgRBxN41dkVFOjZQTATm+2Mvl3tpoqJ8yW/f59nn3p09c87s7uzOzJmy1HN14OPaWRfeabi5O2FXoRwuDp6MHT6FaXPHZyk3fe54xgybjIuD+mOsRAAAIABJREFUJ3YVyuHa1AmAhk4ONGvpRsvGnWjWqAPLFq/OMr6reyPKVyhLk3rtGDd8GlPmjM1SbsqcsXw/bBpN6rWjfIWyuDRNz+7WNpY4uTbkwf1IbZiJaTEm/ziWAT2H0dKpM0P6jc7xfHOifatmLJ037Y3jZ0dzD1cqfliemtXdGDJ4LAsWZm1j9y5/XBq3zxR+4fxlnJ3a0qB+S3x99zJ/wRQ6tO9D3drN6dy5LXaVy+ufRzdPnj5JoJ1jV9Yv8+bb8V8BYFe5PB7t3Onk2ovB3UcwZuYIDAwMMDAw4LsZwxnSYyQdXXrSor07dpXLI4Rg8sJxjB04CS+3z4gMj8LTSxlYXdK8JNcu38ChrAuLZi1j0rys7+f42aOYPGIWbRp6UbZCGRo1aQBAvyG9OHH4NG0du3Di8Gn6D+kFwOfffsbVyzfo3OQzxg2ZyuipQwG4e+seXdz7MKLVUEZ5DufvpL+p7Vabab0n86371zi3bUyZSrZ6tt27NCPxSSJfu3zJzhU7+GxMbyW8W3MAhnl8w+SeE+gzvh9CCF48S2JEq6HaLfZBDMf2Hs3x3rZo0YQPP7Sj6sdOfPXVd/zyy8ws5Xb5HcCxUessjxUrVpQhg/tz/PiZHG29KTI191t+kduWfTcgRPP7RkgpP9ddFOA9M19Kaa+zZTtiEaUfJMvCXgjxOt0et6SU9kB1lAEWXq8RN8/J5+ufJVLKHTorQ7UHcl0ZyQ1V7D/iQVgEkfciSX6ZTIDvIRo1b6Qn06i5I3s37QcgaFcwdZxqaY85eTgSeT+KsOt39eIkPX8BgJGREUZGRq9cF7tZSze2eO8E4OypC5iamWBhqV/XtbAsRTGTYpw9dQGALd47ad7KDYCefb1YsnAF//yjfNs8Pu5hlnbcW7qyzccPgHOnL2JqZoJ5BjvmlqUoZlKUc6cvArDNx49mGjsA46aNYPbkBXrn1LZjS/b7BRD5IEpj/1GO55sTde2rY2Zq8sbxs8PTsxkb1m8F4OTJc5iZmWJpZZ5J7uTJc0RHxWYKDw4+RlKScl+fPXtOcnIyYWH3efnyJZs378TVw0lP3rWFE34+ewA46HcIB+c6SriHE/t8/Xn5z0si7kcSHhZOtVpVqVarKuFh4Ty4F0Hyy2T2+frj6uGEWQkzXr5M5t5tZabV8eCTNG3tCkC5irZs+1O5nzu8d1OgYAFKWZTUS0cpi5IULVaUi2cuA7DTZy9NWjQGwM3DmR0+u5X4Prtxa+EMQIXKdpwIOQ1A2M272NhaU6LUB3p6qzeqwZP4J4TfDCf6fjTJL5MJ2XmYes30P4jp0Kw+gVsCADi6+wjVG9UEwLaSLRdDlbz8JP4Jz54+o2KND/XiWtvZYFbSjL9OXM50P3Rp28aDdeuVFRuPnziDWXEzrKwyryFz/MQZoqKynm4+edJo5sxdwosXL3K09cbkccv+XfDKwl4IUQxwAvqjDBRACNFCCLFJR8ZVCOGn+f+rEOKUEOKyEGKyjkyWrUIhxHYhxGmN/IAMx+Zrwg8KITI9uUKIOkKIIE38fUII69yeuBBimBDiD83/6kKIS5qW5UBgmKZV7qzxJCwVQhwHfhRC1BNCHBVCnBVChAohPsrJjpQyBTiBZp6kEKKpJu5FIcQfQoiCrwgPE0LM1KTnlBCituZcbwkhBmpkrIUQwRqZS0II5yzOV3v9hRCJQojpQojzmjmalhnlc3H9umnSekkIMVsTZqi5Xpc0x4bp2F6ok756mvA+QohfNJ6UtsAcjUxFIcQXQoiTmjRuEUIUed00mluXIjYy/eGPjYrF3Fr/ZWluVZLYSOXln5KSSuLTZ5h9YErhIoXoNqgrq+etyaTXwMCA5fuWsv38Zk4dPs2Vs1dzTIeVtQURmoISICoiGktr/ZeVpbUFURHR2v3IiGisNDJ2FctRr0Edtu9fj/eOP6hRK2sXtaW1BREP0nVERcRgZa3/2FhZmxMVkX5NIiNitGlxb+lCdGQMVy/rL1tqV7EcpsVNWe+7DN+D6/nUK+vWU35ibWNJeHi6NyLiQSQ2NlY5xMieTp3bcPv2Pe3+gwdRWGSoOJhbpV/HlJQUEp8+o3gJMyyszInWub7REbGYW5kr8g/Sw2MiY7GwMudx/GOMjAypWlN5jTT1dMPSRrkfFlaliNbkiU+7e/Iw7hEWGe6nhbU50Tp5PDoyRitTwrwEcTFKN25cTDwlzEsAcP3yDZq2cgGgWq2qWJex1NpMw6ltY26ev058ZJw2LD4yjhJW+s9PSauSxEcoMqkpqTxPeIbJByaE/RWGQ7P6GBgaYGFrScVqFSllo1/xdGrjzBG/EF6FjY0V4fcjtPsPwiMp/Rr3tpZ9NcrYWrNnz8Fcx3ld/ist+3bAXinldSBeCFEH8AfqCyHSFozuAqT1GYyTUtYFagAuQohXdZD0k1LWAeoC3wgh0nJTUZTBCJ8AQcBE3Ugat/gioJMm/h9k/5nYtML7nBAiUBO2EPhQCPEpsBL4UtPyXUq6J+CwRrYM4CilHA5cBZyllLWACcCMnE5OCFEIqA/s1fxfBXSRUlZHGSD5VXbhOmruabwEhzVynYAGQFplqjuwTyNTEziXU5pQru0xKWVNIBhlNaas6KJz3c6h3COEEDbAbKAJyidyHYQQ7TX/S0spq2nOY6WOriKa9A1CuVdapJShKKNNR2mu+y1gq5TSQZPGKyiVzfdGn+Gfsen3LdpWvC6pqal87jGQzg5dqWpfBbuPyr/TtBgZGVH8A1PaN+/BjEnzWLJibp7bKFS4EF8N7cf8WUszHTM0MqRazap83u0b+nT+msEjvwCDfO2Vemd06doeO7tyXL6UcwUuLxk7cCIjJ3/Dmt3LeJ74nNQU/RLBoVFtPu3Whvt3wt/OkMZb88eitZiameDtv4pu/Tpz9dINPZtGxkY4uNfj2plrb2zqoM8B4iPjmLNzHv0mfM7VM1cznZdTW2cO+wa/sY3cIIRgzpyJjB495Z3a+Te07HPjlu6GUjCCUqB3k1KeFkLsBdoIITYDrYG0jjwvTQvdCLBGcc1eyEH/N5oCF5S5hZWAeJTL4q0JXwdszRDvI6AacEAIAcqoxUiyZr6UUu8NKaVMFUL00aTtNynlkRzSuEnTQgcwA1YLZW1iCWT31quoKSDtgF1SygtCiJrAHU3FCWA18DUQmE34As1+2rSLi0AxKWUCkCCE+Fso4w9OAn9oKkDbpZSvKuz/Afw0/0+jrMGcFd5SysFpO0KIQ5q/DsAhKWWsJnw90BiYClQQQiwCdgH7dXRtAJBSBgshTEUO4yY0VBNCTAOKA8VQRp/miNBZrKJS8SrERsZhrtOCNrcyJzZSf8BqbFQ85tbmxEbGYWhoQDHTojx59JSqtari0roxA8d9QTHTYqTKVP75+x+2rfLVxk18+oyzoeeo5+rAnWtheno/69+Frr2UAU0Xzl7GpnR6S8TKxlKvNQZKi8zKJt3BYm1jSZRGJjIimr1+Sqvk/JlLpKamUqLkBzyMf8Rn/bvQrZcyhOXiucvYlLbktNaOBVGR+i7rqMhYrHRacdY2FkRHxlC2fBlsy5ZmV9BGbdwdAev5tPlnREVE8/jRE5KevyDp+QtOhJ6hQ9uGSE23Qn4x4Mte9OmrDLo8ffoCZcqkO/ZsSlsTERGVXdQscXVrxOjRX/Pd6CkMGtRXG166tBUxGVz/sVHKdYyJjMXQ0JBipkV5/PAJMVGxeq1kSxtzYjVxrUqnh1tYm2t1Xjh9mf7tvwZg9PShWNtaseHASi6fv0JNh+p06tWer7sP5+c1PxKT4X7GRMbqeYksrS20Mg9jH1LKoiRxMfGUsijJQ033y7PE50wYmt4u2n1yC+F306dx13Ktw+1Lt7h/4x6OrdO7vUpal+JhlP7zEx8VT0mbUsRHxWNgaEARk6IkPEoAYOXUFVq5GVtnE3EnvXVevmp5DA0NuX3pVsbbAMBXA3vTv38PAE6dOkcZWxvtsdJlrHmQy3trYlKMTz6pgv8BpRvAysqcrVtX0qFDX06fyalYej1Sk/NM1Tsjx5a9EKIESuttuRAiDBiFUpgLlILfS3P8lJQyQQhhB4wEmkopa6C88AvloN8VcAcaalpwZ3OQz9gxKoDLOv3w1aWUzXM828xUAhIBm1fIPdP5PxUIlFJWA9rkkN60PvuKQB0hRNvXTJsuf2t+U3X+p+0bSSmDUQrbB8AqIcSrhoi/lOmdsink0RRMzTrNNYFDKN0hy3UPZxR/hbpVwGCNh2AyOeQjHfvLpJR1pZR1bYqW5tr5a5SxK42VrRVGxkY0aedK6IFQvTihB0Jp0VnJNi6tG3PmiFJP+qbjMLo27EnXhj3ZvGIr6xdtYNsqX8xKmFHMVHFoFShUgLrOdbh38x4ZWbPCm1auXrRy9WL/7gA6dmkDQK26NUh4mkBMdJyefEx0HIkJidSqqzjCOnZpw4E9ihNq/+4AGjo5AIpL3biAMQ/jH2nttHHrRhu3buzffYhPvTwBsK9TnYSnicRmsBMbHUdiwjPs61QH4FMvT/z3HOL6lZvUq+qOS21PXGp7EhURQ9smPYiLicd/TxB169tjaGhIocKFsK9TDZnyz6tuxztn2W9rcWzQGscGrfHbuZ9uPToA4OBgz9OnCVn2zWdHjZof8/Oi6Xh1/oKD/oep+GF5ypUrg7GxMZ06tSFon35bIGjfETy9WgLQ1NOVkyFntOEe7dwxLmCMja01tna2XDp7hcvnrmJrZ4uNrTVGxkZ4tHPX6vygpFLvNS5gTIVK5RnVfxzdmvXl/ImLDBzRn3GDJ2Na3JTEhGdat3wacTHxPEt8RvXaStdOG68WBO5THJKH9ofQ1qsVAG29WmnDTUyLYWSsPPIderTlzLFzPEt8rtXp3NaZkB3B3Dx/A2s7GyxsLTEyNsKpjTMnDxzXs3/S/wRuHZsA0LBVI20/fYFCBShYuCAANZ3sSUlOJfxG+gqwTm0bc3hH9q36X5eupq5Dc+o6NMd3xz569lAqtPXr1ebpk6fZ9s1n5OnTBKxtqlOpcgMqVW7A8eNn8rygh3+HG/9VL/lOwFop5ZdpAUKIIMAZxbX+B4oLOM2Fb4pSMD7R9AO3RHnxZ4cZ8EhK+VwoI/0b6Bwz0NjfiOKmzti5cw0wF0I0lFIe1bRqK0spcx7tkX4eZsDPKIXkL0KITlLKzUCC5jxySnNaNbjPq+xIKeOEMuJ8LOAGlBdCfCilvAn0QrmO17IJzxVCiHJAuJTyd01ff20gc2dz3nEC+FkoMyoeoXh/Fmn2/5FSbhFCXEPxyKTRBQgUQjgBT6SUTzQemTQSAN2RWyZApOa+9iDzClKvJCUllYU/LGLO+lkYGBiwx3svYdfv0ndkb66dv07ogaPs3riH7xeOYX3Iap4+TmDKoOx6ghRKWpZg7PzvMDA0wEAIAv2COHrweI5xAg4cxq2ZM8GndpGU9IKRQ37QHtt9yIdWrsrYzfGjpvPTL9MoVKgghw6GEOivZHmf9duYs2gK+0O28vKfl4z4OuvR/IcOhODq7kTASV9eJL3gu28maY/tDNxAGzdlfO3E0TP5cdFkChYqSNDBUA755+TUgls37hAcEMquYG9kaire67YzZliHHONkx6iJszh59gKPHz+lafueDOrfi45tPN5Ily779gbi4eHGhUuHSHqexMCB6TMGQo/twrGBMs5g6rQxeHVpS5Eihbl2I5TVq7yZMX0h06ePpVjRoqxdvxiA2Nh4tu9Yg6GhAWvXbOL29TsMHNWfv85fJXj/EbZv8GPqoh/wDd3Ik8dPGTtwEgC3r9/hwM4ANgetIyU5hVnfzyM1VXnDz/5+Hos3zMPA0IAdG3dx+/odAHoP6o5zM0eEMGDzmm2cPKJUHOzr1cDQ0IDlW38hNVXy4F56y9jbfxVd3PsAMH3MXKYuHE/BQgU5EnCUkIPK6PY/Fq1lzrJptO/uSWR4FKMGKPnGrlJ5pv08Hiklt67dYeLw9NHthYsUoqazPUu/X0JqSirLJ/zGhDWTMDA04KCPP/dv3Kfr8O7cunCTk/4nOOh9gG/nD2dx0G8kPk5g3uA5AJiVKs6ENZOQUhIfFc/Pw+bp3S9HTyem95lMbtiz5yAtWzTh6pUjJCUl8fnnw7XHTp3cT10HpbI+c+Y4unb5lCJFCnPn9in+WPknU6fOy05t3iLFq2XyGZHTSGJN//ZsKeVenbBvgKpSyq+EMjWrD2AhpXyuOb4KZTT7feAJsENKuUrjAh4ppTyl8RLURXnBbwfKoxR4xYFJUspDQpl6twxoDsSg9GfHavT7SSk3CyHsUQpsM5SKywIppd4kYCHEJJQKiW41vz1Kf/s5KeXPQghbFFe6oyYNm1FazUNQ+or9NBUBhBANUdzsz1A8Fz2llOUz2CyviVNNsy9Q+tEHAwWAuZr0ngS+klL+LYRomk14GFBXU2noo/k/WKM37Tq2RvG6vETxVHwmpbyTIU261z9RSllME94J8JRS9skgr2crCx3dgO9RPCy7pJTfabopVpLuMRorpdyjiXcOcEHp9ugnpTyha0MI0Qj4HcVz0Qnlvo/W3LfjgImUso/mfiZm7JbJiGsZ97z9wHQuuJOUu9ZGXmP0WpNE8ob/te/ZVzbL+B2S90NKPjQFKxbMcVb0O2Nn5OlXC70DXv7z4K1L6qjGrrl+31gFH8qXmkGOhb2KSl6gW0l4XzbVwv7dohb27we1sH/35EVhH+nkluv3jXVIYL4U9upyuSoqKioqKm9BfvbF5xa1sFd550gpXfM7DSoqKirvitSU//999mphr6KioqKi8hbIVLWwV1FRUVFR+U/zbxj6phb2KioqKioqb4HasldRUVFRUfmPoxb2Kir5xPPU97+Uq5lx0VcLvQMSk9/Rl7xyIL+mwD269+4+ZpIT9au/alHK/w75NR27mVXNfLGbF6gD9FRUVFRUVP7jyH/BCnpqYa+ioqKiovIWqPPsVVRUVFRU/uOkqi17FRUVFRWV/zaqG19FRUVFReU/jjoaX0VFRUVF5T+OOhpfRUVFRUXlP86/oc/e4NUiKir/Xhq41mPT4bVsObKezwZ3z3TcuIAx05dOZMuR9fzh9yvWZawAMPvAlCWbFnDoxh5GTv82S91zV81gQ8DKbG1/N20Yfkc3sTlgLVWrV85SpmqNj9gSuA6/o5v4btowbXizNk3YGrSecxFH+LhmlUzxrEpbcuzWQXp/pX9OE2aMJuCEL7uDvPmkRuZ4ANVqVmVPsA8BJ3yZMGN0puP9B/XidtxZPihRHABTMxN+Xf0Tu4O82bZ/LZWrVMz2nAHmzJ3I+YuBHDu+h5r2n2QpM3HSSK5eP0JUzCW98MFD+nPq9H6OHd+D36512Nq+/adlx8+YR+PWXWnfc+Ab63B0q8/Ww3/iG7qRPoN7ZjpuXMCYWUsn4xu6kdW7lmnzEUDfIT3xDd3I1sN/0tC1njZ84ryx+F/ciU/gmkz6uvTryJbD6wm6sofD1/axKWANVXLIQ5sD17LzqI9eHjItbsJS7wXsCPVmqfcCTMxMAChmUpSf1/yIz8HVbA1aR7uurbVxlvw5j3UXNzBu5QQAarnU5pfAX1kS/BsdBnXKZNuogBEjFo9mSfBvzPadi3kZCwAMjQz5Zt5QFuxfxKKDS+jwdXpcz35tWHjgFxb6L8azf9ssz6mOSx2WBS5jefByOg/qnKXdMYvHsDx4OfN952Ohseva3pVFexZpN78wPyp8XEEv7oQVE1hyYEmWdt8UKUWut/xCLez/hxBCpAghzgkhzgshzgghHF8z/iQhxMhsjvUUQlwQQlzW6F8uhCj+lulNfJv4BgYGjJ4xlG97jKaLa2882jXFrlI5PZm23VqT8DiBjo16sOH3TQwe/yUAf7/4h9/mrODnKb9mqdu1pTNJz5Kyte3UtCHlKtji2bAzU0bOYvzszIUqwPjZo5k8YiaeDTtTroItTk0aAHDz6i2G9xvL6WPnsow3avI3hAQc00+TuxPlK5SlSb12fD98GlPnfJ9l3KlzvmfssKk0qdeO8hXK4tK0kfaYtY0lzq4NeHA/Uhs2aFh/rly6RiuXLowY9AMTZozK9rybe7hS8cPy1KzuxpDBY1mwcFqWcrt3+ePSuH2m8AvnL+Ps1JYG9Vuyffsepk0fk62t3NK+VTOWzss6HbnluxnDGdJjJB1detKivTt2lcvr2+jmydMnCbRz7Mr6Zd58O/4rAOwql8ejnTudXHsxuPsIxswcgYGB8trd6bObwd1HZLJV17EWrh7O/DztVy6d/Yt2jboyZeRsxs/O+rqPnz2KySNm0aahF2UrlKGRJg/1G9KLE4dP09axCycOn6b/kF4AdOnbkdvXw/Bq2pv+HQYzYuIQjIwVJ++qJetZMGweoDw/A6YNZGrvSXzT9Guc2jamTCVbPdvuXZrz7Ekigxp/yc7lvnw2tg8Ajq2dMCpgzNDmQxjRehge3VtgXsaCspXL0qybB6PajGCYxxDqNnXAqpy1nk4DAwMGTRvEhN4TGNh0IC5tXbDNYNejiweJTxL5vPHnbFu+jX5j+wFwaPshhrQcwpCWQ/hp6E9E34/m9l+3tfEcWzjy4lneL0IlZe63/EIt7P+3SJJS2kspawJjgZl5oVQI0QIYBrSUUn4C1AZCAcssZA3zwmZu+KRWVcLDHhBxL5Lkl8ns9w2gsYeTnoyLRyN2bdoHQIBfEA5OtQF4kfSC8ycu8vff/2TSW7hIYbp/6cUfCzK3yNJw82jMTp89AFw4cxkT02KUsiipJ1PKoiTFihXlwpnLAOz02YNbCxcA7ty4S9ite1nrbtGYB/ciuXXttl64e0sXtvn4AXDu9EVMzUwwtyylJ2NuWYpiJkU5d/oiANt8/GjWylV7fPy0kcyavFBvFbVKH1Xg6OGTANy+GUZpWxssLPT1puHp2YwN67cCcPLkOczMTLG0Ms8kd/LkOaKjYjOFBwcfIylJeRmfOHEWm9JWmWRel7r21TEzNXnj+MKoIOFh4Ty4F0Hyy2T2+frjmiEfubZwwk9zvw/6HcLBuY4S7uHEPl9/Xv7zkoj7kYSHhVOtVlUAzhw7z5NHTzPZ69T7U1b+sg6npg3Z6bOXh3GPuJhDHiparCgXtXloL01aNAbAzcOZHT67Adjhsxu3Fs6AskJekWJFAChStDBPHj8lJTkFgBMhp0lKVCqxlewrERkWSfS9aJJfJhOyM5h6zevr2a/XvD6Bm5VVDUN3H6FGo5paG4WKFMLA0ICChQqQ/DKZpITnlKlky/Wz1/jnxd+kpqRy+dglGrRsqKezsn1lIsIiiLoXRfLLZIJ3BtOwub5Mg+YN8N/sD0DI7hBqNsq8+p5LOxeCdgRp9wsVKcSnX3zKhkUbMsm+LalS5HrLL9TC/n8XU+BR2o4QYpQQ4qSmdT5ZJ3ycEOK6ECIE+CgbXeOAkVLKBwBSyhQp5R9SymsaHWFCiNlCiDNAZyHEFxpb54UQW4QQRTRydkKIo0KIi0IIvaZYdunLCXOrUkRHxGj3YyJjMbcula1MSkoKiU+fYVbCLEe9A0f348+lPrxI+jtbGQtrc6IiorX70ZGxWFibZ5KJjozRkYnJJJORwkUK029wT36duyLTMStrCyIfRGn3oyKisbK2yCQTpXNNdGXcW7oSFRnD1cvX9eJcuXQdD88mANSo9Qmlba2zLYStbSwJD0/3CkQ8iMTG5s0K7N69u3Bgf9CrBd81BkZEPdDPRxYZKjDmVuba65qWj4qXMMPCylwvD0ZHxGKeReVHl3IVbKldvwbN2jah7+AefGKvVA5eNw+VMC9BXEw8AHEx8ZQwLwHAxj+2UKFSOfzP72Bz4Fp+/GFBlkvklrAqSVxEnHY/PjKekpb6lY2SOjKpKak8T3iGyQemHN19hBfPX/DHqTUsO/YH25dtI/FJIveu3eXjep9gUtyEAoUKUsetLqUyPJMlM9iNi4zL0m5sRKyO3eeYfmCqJ9O4TWOCfNPzT6+Rvdi6bCt/5/DcvimqG1/l/xuFNW78q8ByYCqAEKI5UAmoB9gDdYQQjYUQdYCumrBWgEM2ej8BzrzCdryUsraUciOwVUrpoPEwXAH6a2QWAr9KKasD2hIju/S95rnnCZU++ZDS5UtzaO/h/DDPoFGfs3aZN0nPs+9CeBMKFS7EoKH9WDArc7fF0oUrMTU1wS9wI72/6MpfF6+RkpKSp/Yz0qVre2rVrs6C+cveqZ3/jxgaGWJa3JTzJy7gvWoLc5ZNzRvFmgLd0a0+Vy/dwL1mW7ya9mbsjOEU1bT084pK9pVJTUmlv0NvBjb6nHZftMeyrCXhN8PZ+usWJq6fwoS1k7jz121SU/N++bmP7D/i76S/uXv9LgAVPq6AdTlrju47mue2AFJSRa63/EIdjf+/RZKU0h5ACNEQWCOEqAY012xnNXLFUApXE2CblPK5Js6OVxkQQlQH1mrifi+l9NYc8tYRq6ZpuRfX2NqnCW8EdNT8XwvM1vzPLn3BGWwPAAYAlDOrRGxUHJY26S1bC2tzYiPjdKNoZWIiYzE0NKSYaVGePHyS7fnVqPMJVWt8xPbjGzE0NKREqQ/4dfMCvuo0lC59O9KxhzLg6PK5K1jZpPdiWFqbExOp77aOiYzFUqflbWltkUkmI9VrfYy7pxvDfvgaE9NiGBsb0f0LLx7FP+bCuctY67S4rWwsidJp9QFERcZgpXNN0mTKlS9DmbKl2RXkrQm3YGfAn7Rv3ou4mHhGfzNJGyf4zC7C7tzX7g/4shd9+nYF4PTpC5Qpk94Ha1PamoiIdG9DbnB1a8To0V/TwqMr//yTuRvlvZOajFVp/XwUk6ELIjYqFqsM+ejxwyfERMXq5UFLG3Nis+i+SMOrTwesS1vSqEkDToae4XniC1JTJR+ULP7aeehh7ENKWZQkLiaeUhYleRinOPLadW3NH4vWAnA/7AEP7kViV6kcl87mrpvaAAAgAElEQVRe0dP9MCqeUjbpre6S1iWJj47Xk4nXyMRHxWNgaEARk6IkPHpK43bdORt0hpTkFJ7EP+HqqStUrFGJ6HvRHPQ+wEHvAwD0GN2L+MisdaZRyrpUlnbNbcx17BbhqU6XSOO2jTnke0i7X6V2FSrVqMTKIysxNDLErKQZs7xnMabL248JgX/Hojpqy/5/FCnlUaAUYA4IYKamP99eSvmhlDKznzh7LqP00yOlvKipUOwBCuvIPNP5vwoYrGnBTwYK6SYtC/25Sp+UcpmUsq6Usq5FEWv+OncVW7sy2NhaYWRsRPN2TTi8/4henOD9R2jd2QOAJp4unAo5m1GtHlvW+NK6dkfa1+/KgPZDuHf7Pl91GgqA98oteLn3xsu9NwF7g2nj1RKAGrU/ISHhmdalmkZcTDyJic+oUVsZsd7GqyWB+4LJiT7tv6KlQwdaOnRg/e/eLJmznEY1WuDp1pUDuwP51MsTAPs61Ul4mkhsdIbKTXQciQnPsK9THYBPvTzx3xPEtSs3qVe1KY1rt6Zx7dZERcTQpkl34mLitZUKgC69PuXE0TMkJKSPnVz221ocG7TGsUFr/Hbup1uPDgA4ONjz9GlCln3z2VGj5sf8vGg6Xp2/IDY2/tUR3gMy+W9s7WyxsbXGyNgIj3buBO3Tz0dB+47gqbnfTT1dORlyRhvu0c4d4wLG2NhaY2tnm6lQ1cVn1VYWTFvCnm0HOLTnMF6922NsbESZcqVJzCYPPUt8RnVtHmpB4D7F63RofwhtvVoB0NarlTY86kEU9Z3rAlCi1AeUr1iW8LsRmdJy4/wNrO1ssLC1xMjYCKc2jTl54ISezMkDx3HrpHwB0bFVIy6GXgAgNiKW6o41AChYuCCVa3/Eg5vhAJiVVLrJStmY06CFI8G++l01189fx8bOBkuN3cZtGnPsgP5g1OMHjuPeyR0Ap1ZOXNDYBRBC4OzpTPDO9Gdp97rd9HLoRd9GfRnZcSQP7jzIs4Ie8r7PXgjRQghxTQhxUwiRbUKFEB2FEFIIUfdVOtWW/f8oQogqgCEQj9KyniqEWC+lTBRClAZeorScVwkhZqLklTbAb1momwnMFUK0k1KGa8IKZyGXhgkQKYQwBnoADzThR1C6DdZpwtPIMn1SSv1mawZSUlKYM24BP/85FwNDA3Zu3M3t62EMGNWPK+evcnh/KDs27Gbyz+PYcmQ9Tx8nMO6r9OEA249vpGixohgXMMLFw4lvuo3kzo27OZnUctg/FOemjuw6tokXSX/zw9D0IQg+/qvxcu8NwPQxc5i2cDwFCxUkJOAYIQcVN2OTli6MnT6cD0oWZ/G6n7h66TpfdRuWpa00Ag+E4OruRODJHbxIeqHXGvcL3Iinm9L6njB6Jj8umkyhQgUJOniEQ/4hOer9sHIF5i6egpSSG1dv8d232Q+Z2Lc3EA8PNy5cOkTS8yQGDkyfhRB6bBeODZRpXlOnjcGrS1uKFCnMtRuhrF7lzYzpC5k+fSzFihZl7frFANy/H0GXzl/kmL5XMWriLE6evcDjx09p2r4ng/r3omMbj9fSMfv7eSzeMA8DQwN2bNzF7et3GDiqP3+dv0rw/iNs3+DH1EU/4Bu6kSePnzJ24CQAbl+/w4GdAWwOWkdKcgqzvp+ndVvPWDKJOo72FC9RnD2nt7J07gp8N+zCd8MuJs0fS5NWLpQo9QHJyclM/GkME4ZO16bH238VXdz7ADB9zFymavLQkYCj2jz0x6K1zFk2jfbdPYkMj2LUgPEALJu3iqkLx7M5cC1CCBZMW8JjjTdr5fYlVKxkR6GihfgtdAX71u1h4trJGBgacNDbn/vX79FteA9uXrzByQMn8Pc+wNAFw1kS/BuJjxP5afCPAOxZvYshP33LQv/FCAEBPv7cvRoGwOjfxmLygQnJL1NY9sOvPH+q2w5Q+uB//eFXpq2dhoGhAfu993Pv+j16Du/JjYs3OH7gOPu89zFywUiWBy8n4XECswfP1savVr8acRFxRN17PY/S25CXg+w1g5gXA82AcOCkEGKHlPKvDHImwLfA8Vzpza9vF6u8f4QQKcDFtF0UN/suzbFvgc81xxKBnlLKW0KIcUBvIAa4B5yRUs7NQndvYCRKBeIxcAmYKKWMFEKEAXWllHEa2a+A0UAsSkY1kVL2EULYAX+iuOl9gaFSymI5pS+7c61n4/LeM/aL1PxxOefH9+xjnj9+7zbhf+979in58Dm1CgVKvlroHfA373YcSHbsvrf7rX3wodYdc/2+cYzckqM9TRfrJCmlh2Z/LICUcmYGuQXAAWAUygDpUznpVVv2/0NIKbOd9ialXIgyQC5j+HRgeuYYmeRWA6uzOVY+w/6vQKaRYFLKO4DuHJvxr0qfioqKSn6T8hp99rpjizQsk1LqjkQtDdzX2Q8H9OY8CiFqA7ZSyl1CiOwXvtBBLexVVFRUVFTeAknuC3tNwf7G00yEEAbAPKDP68RTC3sVFRUVFZW3IDVvOw0fALpLBpYhfVwTKGOeqgGHhBAAVsAOIUTbnFz5amGvoqKioqLyFqS+Rss+F5wEKmnGMD1AGbSs/QiGlPIJykwqAIQQh8hFn7069U5FRUVFReUtkIhcb6/UJWUyMBhlFtIVwEdKeVkIMUUIkfWXg3KB2rJXUVFRUVF5C/J6zoSUcjewO0PYhGxkXXOjUy3sVVRUVFRU3oKUvHXjvxPUwl7lP8nL1OT3btPo/X3QT4/ixkXfv02z928T8m+++/GL2X/h8F2SH+d7/2X+rKGQmqdL07xf3v9qCK+PWtirqKioqKi8Ba8z9S6/UAt7FRUVFRWVtyAfP2aXa9TCXkVFRUVF5S3I46l37wS1sFdRUVFRUXkL8mdV/9dDLexVVFRUVFTeglShtuxVVFRUVFT+0/wb5hGohb2KioqKispb8G+Yeqcul6vyX0SMnjYU36PeeAespkr1ylkKVa3xET6Ba/A96s3oaUO14abFTfjVewG+oRv51XsBJmYmevE+tq/CyfAg3D1dtWG//PkTITf3E3LzAL6hG+kzuGcme8YFjJm1dDK+oRtZvWsZ1mWstMf6DumJb+hGth7+k4au9QCwtLHgt80/szloLZsOraXb550BcHSrT8DlXRy7G8C+s9tZvHEepSxLvrGdNJ1bD/+ZKe0rti9mw4GVbDiwkoDLuwi5uR/f0I2M+3E0Qdf2ao99MaxPntp1aFSb9ftX4BO4hskLx2FoqKxh8M34gRwLC+DY3UB2ndhMpY8/fKtrDDBx3lj8L+7EJzDzXPou/Tqy5fB6jIqXwaBIiUzHc2L8jHk0bt2V9j0Hvla8rMjuOqWRV3kLoKFbPbYcXs+20A30HtwjS1szlk5iW+gGVu36Tc9WnyE92Ra6gS2H19NA5xoXMy3G7N+nsvnwOjYFr6V6nU8AqPRxRfae207o3QCCr++jVr0aWZ5/1Rof4R2wGt/QjYya+q023LS4CUs2zmf7kQ0s2Thf71kdNfVbfEM34n1wld474NvxX7Hp0Fq2BK9L05Xmg+8GXAQuAHvRWX/+VaSK3G/5hVrYq7wxQog+Qggbnf3lQoiP31CXqxDCUWd/oBAi04oiQojyQohLr1DXsmyFMrRr2IVpI3/k+9kjsxT6fvZIpo6YTbuGXShboQyNmjQAoO+QXpw4fIp2jl05cfgUfYekv1wNDAz4dvwgjgWd1NO1dukGnicmcfnsZTq69KRFe3fsKpfXk2nfzZOnTxJo59iV9cu8+Xb8VwDYVS6PRzt3Orn2YnD3EYyZOQIDAwNSklOYP/kXOrn0onfrAXj16UDFKnZ8N2M4AzoNwenD5jyKf8xf564yYHjfN7ZjYGDAdzOGM6THyExp79/+a7o160sPj/4UKFiApXP/oKNLT+o51+H6pet0a9aXbs368vv8VXlmVwjB5IXjGDtwEl5unxEZHoWnVwsMDAxo1cGDz1p/idOHzUhJTWXqovFvdY0BdvrsZnD3EZnyR13HWrh6ONO1aR+SH4eTmvR6i820b9WMpfOmvVacrMjp/mht5UHesqtcXmvrmx4j6ezSC48sbLXr1pqEJwl86tiNP5f5MGT8QK2t5u2a4uX6GUO6j2TMzOHaazxy6jeEBh6nk3NPujXty50bdwGYtWwKUQ+icSzXhI0rNjNz6eQsr8HYWSOYNvJH2jl2pWwFWxzTntXBPTkRcpr2jbpxIuQ0fTUVoUZNGlC2gi3tHLsybdQcxs5S3gE16lajpkN1ujTpTWfXz/jEviqAC4qXeyHgBtRAKfAH5/YepSJyveUXamGv8jb0AbSFvZTycynlX2+oyxXQFvZSyqVSyjddtqydn89eAC6euYyJqQmlLErqCZSyKEnRYkW5eOYyAH4+e3Ft4awkxMOZnT57ANjpswe3Fo218br278TBXYd4GPdIT1/SsySiI2J4kfQ3yS+T2efrj6uHk/4JtnDCT6P3oN8hHJzraOw5sc/Xn5f/vCTifiThYeFUq1WVuJh4rl68DsDzZ0ncuRGGQ6M6hIeFc/PKba2dqjU+Qkr5xnaq1apKeFg4D+5FZJv2uo61KFDAmG3rdpD8MpmTIacpYa7f0s0ru2YlzHj5Mpl7t+8DcDz4JE1bu1KtVlVuXbvN9cs3SH6ZzO4t+7DRaVW+SRoAzhw7z5NHT8lIp96fsvKXdbz856USIF/PWVvXvjpmpiavFnwFubk/eZG3LKxKUa1WVe6HPeDBvUiSXyaz3/cgLhlsubRwJu35Ouh3iHoaWy4eTuz3Pai1dT/sAZ/UqkpRk6LUalAT3z/9AEh+mUzi00QAbGyt2fC7DwBb1vpSvKRZ1s+qic6zumkvbppn1cXDWXvefj570p/hFs74bdJ9BxRT9EpJwUIFMS5gRIGCxhgZGwFEo7TuBVBU82sKROT2HqWI3G/5hVrYq2jRtJqvCiHWCyGuCCE2CyGKCCEmCCFOCiEuCSGWCYVOQF1gvRDinBCisBDikBCirkZXcyHEUSHEGSHEJiFEMU14mBBisib8ohCiihCiPDAQGKbR5SyEmCSEGKmJU0cIcV4IcR74OhenUjoqIka7Ex0Zg4W1uZ6AhbU5MZG6MrFamZLmHxAXEw9AXEw8Jc0/AMDcqhRNWjVm06ptmQxaWJvzMD69AhATGYuFlb5Ncytz0tKVkpJC4tNnFC9hhoWVOdG66Y2IxTxDXOsyVnxUvTIJTxKIeqDIfj1mAL0GduNj+yr8OmfFG9sxtzLX6swu7a4tnYmOjOFZ4nMAHsU/xsbWmo3+q1i0fi4VKtvlmd3H8Y8xMjKkas2PAGjq6YaljUUm+bJ2ZYiOjM2za5yRchVsqV2/Bqt3LcPQzBphVDBH+XdFbu5PXuStS2f+wtzKnOhMtvS92RZWpbQ602yZlTDTCweIiYjBwsqc0mWteRz/mIkLvmf9/hWMn/sdhQoXAiDpeRLWttYAuLdxw9DAEHNrfXvm1qWIiUi/zzGRMdo0ZfesZkpLZAzm1qW4cPoyJ4+cYf85X/ad8+XooROgfFnuJfAVihs/AvgYSH+oXkHqa2z5hVrYq2TkI2CJlLIq8BQYBPwipXSQUlYDCgOeUsrNwCmgh5TSXkqZlKZACFEKGA+4Sylra+SG69iI04T/ivId5jBgKTBfo+twhjStBIZIKWu+ixN+FWmt5lFTv2Xh1F/1WtHvg8JFCjN3xXR+mrCQF0l/a8MXz1rGTxN/5t6t+3Tt2+GdpsHeoQbhd9MbOpH3I/Hz2UtX9z5sXLGZeStn5Km9sQMnMnLyN6zZvYznic9JTdF/TdZ1rEWdhvZcOPWqHp03x9DIENPipvRuPYDUZw8xNLF4Z7byC928lVaRy2sMjQz5qHplNq/eTo/m/UlKSqLPEGUswPW/buHW0pm1+5ZTtGgRUt/y2XpVdNvypbGrVI4WtTvQotanODSqDeAMGKMU9rVQvJUXgLG5tvsaW36hFvYqGbkvpTyi+b8OcALchBDHhRAXgSbAJ6/Q0QClZnxECHEO6A2U0zm+VfN7GiifkyIhRHGguJQyWBO0NhvRr2NiYu5fvXr1+caNG+sXLZXeCrO0tiAmQwswJjIWC2sLHRlzrUx87COtK7GURUkexil9tR/XrMKs3yaz6+Rm3D1dGTtrpNZtGBMZS4mSH2j1WVibExOlbzM2KhYrG8WmoaEhxUyL8vjhE2KiYrG00UmLjTmxmrhGRobMXTGN3Vv3E7A7WNFR2kLPzplj52jS2vWN7WSlUzftxUuYUbqcjV4lx6yEGRHhkQAcCTiGkbERj+If5ZndC6cv07/913zWagBnjp3j7u37WvlKVSvyw09j2Lv9IOF3H7z1Nc6OmMhYAnYHASCTNZUs8f5fma+6P1qZt8xbaXosM9mK07MVExWn1Zlm68nDJ3rhABY2FsRExRITEUtMZCyXzyo9fKkpqXT9vDPrD/zB/Tvh/LnMh14en7N3uz+pKSnERurbi42Mw8Im3RthYW2hTVPmZ/VRpjSmxYmNjMOtZWMunrlM0vMkkp4ncSTgGEBDwF4jegulTPZBp1vxVagD9FT+jWSsfEpgCdBJSlkd+B0o9AodAjigaaXbSyk/llL21zme1jxNIe+mfy62sLCwrVKlSpGuXbv26dazCwDVa39CYkKi1tWXRlxMPM8Sn1G9tlJv8fRqQdC+EACC9ofQxqslAG28WnJon+Jo8KzXmdYOnWjt0Al/v0PMHDOXQ3uVY5fPXcXC2pyChQpiZGyERzt3gvYd0bMZtO8Inhq9TT1dORlyRhvu0c4d4wLG2NhaY2tny6WzVwCYMG8sd27cZf1v3lo7tna21HGspbWTkpJK2M27b2wnTaeNrXWWaW/q6UrQvhDKlCutlWnV0UMr84l9VYSBAQf9gvLM7gcliwPKyO8+X/dgy5rtXD53lfIflmf+6llMGjaD+s518+QaZ0fg3mDqKi0/MDAGxGv32+cFr7o/kDd5K91WGa2t5u2aEqx5LtII3heCp1eLTLaC94XQvF1THVtluHz2CvGxD4mOiKFcRVsAniU8Y+vaHfRo1o9TR87QqnMLhBCMnPINcdHxWT+rCTrPaucW2ucueH+I9rw9vVoSpHlWg/aF4NlZSaPuOyDqQTR1GtTC0NAQIyND6jS0B8WN/wClgZJWq2imCc8V/wY3vjrPXiUjZYUQDaWUR4HuQAhKDTdO0+/eCdiskU0AshqBdAxYLIT4UEp5UwhRFCgtpbyeg90ElEExekgpHwshHgshnKSUIUDmuUCZ2R1+N4Idx3x4kfSCSUPTXcwb/VfR1b0PADPH/MTkheMoWKggRwKOEXLwKAArF61l9rKptO/uSWR4FKMH/PBKg8u2LKJQkYLUbVSb0NsH2b1lH7ev32HgqP78df4qwfuPsH2DH1MX/YBv6EaePH7K2IGTALh9/Q4HdgawOWgdKckpzPp+HqmpqdjXq4Fn5xbc+OsmGw6sBOCXmb8x+/t5LFw9G+MCxjx5/JRKVStyPyycxs0bvZEdgNnfz2PxhnkYGBqwY+Mubl+/oz03j3burPplHfu2+2tl7ly/w+xlUyj+gRkJTxMZO3AiVy9ezzO7vQd1x7mZI0IYsHnNNk4eUQqUOzfCqN/YgV+95/Pk0VOm/DyOkINH3/gaA8xYMok6jvYUL1GcPae3snTuCnw37MJ3wy4mzR+LT+AajEwtSElM7wPODaMmzuLk2Qs8fvyUpu17Mqh/Lzq28XgtHaD0i2d1nd5F3joScIw5389n0YafMNTaCuPLUf25orHlu2EXUxaNZ1voBp4+fsr3Wlth+O8MYFPQWlKSU/hR5xrPGbeAqYsnYGxszIN7EUzWPJMflCxOjTqfEHo3gMQnCQzumT5zZsOBlXRrpswymTn2JyYvUJ7V0IBjaS1yVv6yjtm/TaF9t9ZEhkfz3ZfKsxpy8ChOTRvie9RbeQcMU+z5+x3CwakOPoGrkVISGnices51d2pMTgaCUfrv76IMQM4V/4Z59uJ99z+q/P9FM1BuL0ofex3gL6AX8D3KHNQo4DpwV0o5SQjREZgBJKG4wvag9MGfEkI0AWYDaf708VLKHUKIMKCulDJOM5hvrpTSVQhRGaUSkQoMAZoCiVLKuUKIOsAfKF6G/UArzfiBbKll1ei9Z2zxL1gyU+XN+F/6nr1BPk0Py6/v2Z+JDHnrE15i2zPXiR90f12+XGC1Za+SkWQpZcZVO8ZrNj2klFuALTpBrjrHAgCHLOKU1/l/Ki2OptWvu6LGYR2504Du4LzRrzwLFRUVlffEv6Flrxb2KioqKioqb8G/wT+uFvYqWjRT4HJ0j6uoqKio6JOfo+xzi1rYq6ioqKiovAWqG19FRUVFReU/Tkp+JyAXqIW9ioqKiorKW6C68VVUVFRUVP7jqG58FZV8wsI40/o875zIl6/3CdS8IiUfVnUzEobv3WZ+kh/z3SF/5vfXqZabdavyHhPDwvliNy9QR+OrqKioqKj8x8mvBYFeB7WwV1FRUVFReQv+DW589UM4KioqKioqb0HKa2y5QQjRQghxTQhxUwgxJovjw4UQfwkhLgghDgohymWlRxe1sFdRUVFRUXkL8vITt0IIQ2Ax0BLlS3zdhBAfZxA7i/KNkRoo3xT58VV61cJeRUVFRUXlLUhF5nrLBfWAm1LK21LKf4CNQDtdASlloJTyuWb3GFDmVUrVwl5FRUVFReUtkK+x5YLSwH2d/XBNWHb0R/niaI6oA/RUVFRUVFTegtcZoCeEGAAM0AlaJqVc9iZ2hRA9gbqAy6tk1Za9yn+auq51WH7od1YeXoHXoM6ZjhsXMOb7JWNYeXgFC3fMx7KMhfaYXZXyzN8+j2X+S1l6YAnGBY0B+NFnNssP/c6Svb+wZO8vmJU0y9L2d9OG4Xd0E5sD1lK1euUsZarW+IgtgevwO7qJ76YN04Y3a9OErUHrORdxhI9rVtGLU6lqRdb6LWNr0Hq2BK6jQMEC2mNjpw9nz7HNbA1cR9XqH2Vp8+MaVdh2aD17jm1m7PTh2vARE4awM8SbrYHrWLhyNiamxfTiWZe25OTtQPp81UN7fjuP+rApYA1Vcji/zYFr2XnUR+/8TIubsNR7ATtCvVnqvQATMxMATMxMmP/HTDYFrGH9nuV8WKUCAOUqlsXbfxXe/qsIvLyLsxEhHDjnm2d2i5kU5ec1P+JzcDVbg9bRrmtrbZwlf84j6OoeFq6ZDYCjW322Hv4T39CN9Bmc8WvQSp6atXQyvqEbWb1rGdZlrLTH+g7piW/oRrYe/pOGrvUAsLSx4LfNP7M5aC2bDq2l2+eZ8+nrMH7GPBq37kr7ngPfSk8a7yIfN2jswMZ9K9kSuI6N+1ZSr1EdPX31XR3YELwa75C19Py6WyZ7xgWMmfLrD3iHrGXZzsVYlbEEwMG5Div2LGWN/3JW7FlK7Ua1tHGMjI0YPXs4Gw6v5s+gVbi2cn6r66LL67jxpZTLpJR1dbaMBf0DwFZnv4wmTA8hhDswDmgrpfz7VWlUC3uV/ywGBgZ8Pe1rxn/2A180+RK3dq6UrVRWT8aja3MSHyfS17k/W5dvp//3/ZS4hgaM/nk0i8YuYoD7QEZ1/o6Ul+ljaWd/8yODWgxmUIvBPIl/ksm2U9OGlKtgi2fDzkwZOYvxs0dnmcbxs0czecRMPBt2plwFW5yaNADg5tVbDO83ltPHzunJGxoaMnPxJKaO/pEOLj3o12EQyS+TAXBu6kg5O1taNujEpJGzmPBj1jYn/DiaiSNm0rJBJ8rZ2eLUpCEAR4NO0N6lOx3cenL31j2++Ka3XrzRk4dy+OBR7fmVrVCGNg29mDJyNuNnj8rm/EYxecQs2jT0omyFMjTSnF+/Ib04cfg0bR27cOLwafoP6QXA599+xtXLN+jc5DPGDZnK6KlDAbh76x5d3PuwaOZvXDl/jYdxj5gx5qc8s9ulb0duXw/Dq2lv+ncYzIiJQzAyVhyfq5as54ch0wAlT303YzhDeoyko0tPWrR3x65yeT3b7bt58vRJAu0cu7J+mTffjv8KALvK5fFo504n114M7j6CMTNHYGBgQEpyCvMn/0Inl170bj0Arz4dMul8Hdq3asbSedPeOL4u7yofP374hCGfjaKjW0/GfzuV6b9M1B4zMDBgxPRvGdFzDD3c+uLevgnlK+kPNvfs1pKEJwl0ceqF9++bGTRugFbvd33G8Zn750wbOosJC8dq4/T+pgeP4h/Tzbk3PVz7cvbo+Ty5RpDno/FPApWEEHZCiAJAV2CHroAQohbwG0pBH5MbpWph/44QQlgJITYKIW4JIU4LIXYLIRoLITZrjtsLIVq9hf7PhBCXhBAXhRBnhRAjXyNueSHEpTewWV4IkSSEOKeZ9rFUCJFtHhJCtNcdRSqEmKKpjb42b3K9PrKvTERYBFH3okh+mcyhHUE0bN5AT6Zh84Yc2OwPwOFdh7FvZA9AncZ1uHPlDrev3AEg4XECqam5d9a5eTRmp4/SjXbhzGVMTItRyqKknkwpi5IUK1aUC2cuA7DTZw9uLRRv3J0bdwm7dS+T3oau9bj+102u/3UTgCePnmrT1aRFY3Zs0tg8fQkTU5MsbRYtVpQLp5Xbv2PTHpr+H3vnGV5F0QXg96RQQ1HSAWmCiPQmJUDovYmACChNQaQXEUVAuvIJIkVAmtKbGgg1jQChg1RBQJqQHgghFIFkvh+7udybjsklgvvy3Cf3zp6ZMzM77NmZOTPTXNO5L/AgcXHa4+jE0dO4uD8Z5WjQvC7XrwVz8Y9LevnqsHnddgBOpVK+3A65OWUq33YaNKtrir9p3VYtD+u2Ur+Z1ssqXqoYh/YeBeDKxau4F3bjZceXzOq1DmdPneevKzcI2L470/QqpcjlkAuAXLlzcjs6hrjHWl0c2nuUu7GaL1TZSq9z/cp1blwL5vGjx+zw8sWzqYeFfs9mHnjr997PexfV6mi9Vs+mHktXoyQAACAASURBVOzw8uXRw0cE/xXC9SvXKVvpdSLDozh36jwA9+7e5/KFKzi7OvJPqVqxHPny5vnH8c2xVjs+d/o8EWGRAFw8d4kcObJjn00bOXu9UmmuX7lB8LUQHj96jJ+XP3Wa1rKIX6dJbbau3wnAri2BVPGoDMCFMxeJDIvSdP9xhew5spnSbflOc5bPXgVo9/v2rZgM1s4TMtNBTyn1GBgA7ADOAuuUUmf052cbXWw64ACs15/Hm1JIzoRh7K2AiAjwC7BLKVVCKVUFGA0opdTbulhF4B8ZexFpDgwBmiilygE1gKTdS+vwp1KqIlAebVlIu1Rk2+kyACilxiqlfP+h3qeurwKujkQER5h+R4ZE4uia6EHlWoCIYO2hEx8Xz90798j7Ul4KFS+IUorJKyYxZ+tsOvZ72yLe8G+GMm/7HN4dnHSIEcDZzYnQ4DDT77CQCJzdnJLIhIWEm8mEJ5FJTNHir6CU4vvVM1m7cxk9P36ytamzmxOhN8x1huOSKD2XRDpDg5PX+da7rU29+Fy5ctJ7wHt8/79FlnnPQPlednqZyHDtoRwZHsXLTi8DcP7MBRq20AxF2Uqv41bIxeKlw9nNiVJlSrD9V59M1btmyUaKlyyC74lNbAhYztdffItSSR/MTq5OhN54knZ4SATOrk5JZYI1mbi4OGJj7pL/5Xw4uzoRFmyWr+AInBLFdSvkymvlSnH62O9JdGcF1mrH5jRuVZ+zp/7g0cNHADi5OhIebF7HkUnqyVwmLi6euzF3yfeS5RbZni3r8sfpCzx6+AiHvLkB+OCTnizZvoCJC8bxktlLZEbJZAc9lFJblVKldPsxWQ8bq5TapH9vpJRyUUpV1D9tUk/RMPbWoj7wSCk1PyFAKXUC+EvvjWcDJgCd9beyziJyQUScAETERt9MIaX/MaOBEUqpYD3tv5VSP+hxK4rIAX2zhV9E5CU9vIqInBCRE8DHCQmJiK2ITBeRw3qcvukpoP72uQ94VUQ+0OOfEJGNIpJLRGoBbYDpehlLiMgyEXnbLD+B+qjHDhFx08N3ichXInJIRM6LSJ3k6iud9+EfY2tnS9lqb/DVwK8Z/tYIajWrZer1fzXoa/o17s/wDiMpW70sjTo0tHZ2LPJV+c0KjP54PO+37UuD5vV4s07VTNXx4ZAePH4ch/dGrefef+QH/LRgNffu3c9UPRbohnXJ7OXkzZeHtb7L6NKrI+dOXyA+7smIiohQsXp5dm7yz1S9teq/ybnTF2hUoQ2dGr7P6CnDyK339J8VOXPl5H+LJ/PN2FmmkYQXnRKvFWPImP5MGPlVpqZbrFRR+n/2IdNHzQS06S8Xd2dOHTlDr2Z9OX30DAPGZo5PA2gOeun9ZBWGN751KAscTemiUuqhiIxF2xRhAICIlAa6At8CjYATSqmIFJJILf2fgIFKqUARmQCMQxsFWAoMUErtFpHpZvK9gdtKqWoikh0IEpGdSqnLqRVQRHIBDYGxwCGzl41JQG+l1Gx9aMlbKZUwdZEQ1x6YDbRVSkXoxnsy0EtP3k4pVV0fth+nlGqUuL5SyJPJy7VM/jeICo3Eyf3J+5KjmyORoVEWcSJDo3BydyQyNBIbWxty58lFzK0YIkIiOXXwNDH6UN/hgMO8WrYEx4OOE6Wncf/ufQJ+DeC1iqXw3ehH554d6NBVe8E+c/wsru4uJj0ubk6Eh1jezvCQCFzcnM1knJPIJCYsOJyjB44TfVMbyIm+eZtpc8ZzM/IWp4//jmtBc53OhCVKLyyRTld3S53tOrekXmMPer9teh+kfOU3aNKqPmO/+kRzaFNw6rczuGSgfDcjbuLoXIDI8CgcnQtwM/IWAHdj7zF2yGRTnK2HN1K9ThUmzPpcixcZTeiNMJN8Zult+05LlsxeDsBfV25w41oIxUoW4fRvZy3SjgiNwLWg5UhDeGhEUhm9Xm1tbXHIm5vom7cJD42wGKVwcXciQo9rZ2fL/xZPYuvPO/Hfupus5Fm044S0Zi6ZxucDJ3L96g3TQTgRoZE4W4zmOJrqKYEEmYiQSGxtbcidN7dpWN7JzZEpi79k4uCp3LgaDGjTXffv3Sdw6x4AArwDaf3OP55FTYJ6DvbGN3r2/x6WAAlHa/VCM85PhYjkA/IrpQL1oB+BuiKSXw9PeIosN4vWBHhPRI4DB4ECQMlU1JTQZYOALUqpbUBZEdkjIqfQXljeSCOrr6G9sPjoaY3BclOIn/W/R4GiaaRlwtzLtZBDYf44cZ6CRd1xKeyCnb0dnm3qccDngEWcAz4HaPy25kZQp2UdTgRpTjtHA49StHRRsufIjo2tDeXfLMe1C9ewsbUhrz5caGtny5sN3+TKH1cBWLt0I50avU+nRu/jv303rTs1BzRjeefOXdPwcQKR4VHExt6lfGWtulp3ak7AjtQf9EG7DlKydAly5MyOra0t2XNkZ9zwqXRo2B2/bbtp01HXWaUssXdik9V5N/Yu5auUBaBNx+b4b9d0etSvQa+PuzPgvRE8uP/Eufe9tn1pUq09tV9vyvwZS5gxaS6LZv1E607NAChX+Q1iUyjf3di7lDOVrxkBO7SH7a6de2nTSXvYtunUwhSeJ6+DyTHura5tOHbgOMvnr6Fzox50btSD7NntTS+Nmak39EaoaYTkZceXKFriFa7rhsKcM8fPUbhYYdwLu2Fnb0fTto0I3BFkIRO4I4hW+r1v2MqTw3uPmcKbtm2EfTZ73Au7UbhYYdPLxNgZo7l84SorF6xNovNZ8yzacZ68DsxZ8Q2zJs/j+OGTFtfOHT9HoWIFcSvsip29HQ3bNmDvzv0WMnt37qNFxyYAeLasx9Gg3wBwyJub6T9NZf6URZw6csYiTpDPfirV0kbnqnpU5vKFq09TLanyGJXuT1Zh9Oytwxng7TSlzFBK/SUiYSLSAG0HpdTOmTwDVAEyYyxT0EYCdqRTPmHO3pxlQDul1AkR6QF4pkPnGaVUzRSuJ1iaODLQRuPj4pn7xfdMWTEJG1tbdq7dydXz13hveHfOnzzPAZ+DbF+zg0++HcnSPYu5E32HKR9PAyD2diw///Azs71noVAc8j/MIf/DZM+ZnSkrJmFrb4etjQ3H9v7GtlXbk+je47uPOg1rseXAeh7c/5svhjzxjl7n+yOdGmme7pM/nc6kWWPIniM7e/0PsFefJ2/QvB6jJw/jpQL5mbviG86dPs9HXYZy5/YdflqwmlXbl4BS7PHbz25fzdjs9g2ibsNabDu4kQf3HzBm8ESTzo1+y+nQUPM8nzjqayZ/N1bT6befPX77APh86gjss2Vj0brZgOakN+GT5IdX9/juw6NhTbwPrOfB/QcWvfG1vsvo3KiHXr7/MVEvX5D/flP5lsxezvSFk2j3bitCrocy8sMxABQrWZRJ341BKcWff1xm3LCppnRz5spBideK47c1MNP1LpyxjImzxrAhYDkiwreT5plGT5b+Oo9iJYuQM1cuvA+tZ+NyL+aunoGNrQ2b1mzh0vnL9BvZm99PnGP3ziB+Xe3NxNlf4LVvDbejYxjdbzwAl85fxmezPxsCVxD3OI5pn80gPj6eitXL06pjMy78fpHVPto7/pypCwjyt3wxTS8jx03j8G8niY6OoWG7bvTv3Z0OrZv+o7Ss1Y7f6fU2rxQrRN9hveg7TBvQG/7up0RHRRMXF8/MMbOZseorbG1s8V67jcvnr9BnRA/OnTjPXp99eK/ZyhfffcbavcuJib7DuP5aW+/Qsz2FirrTc2h3eg7V2vuQLp8QHRXNvMk/MPa70Qwe35/om7eZMjTNHWbTzb+/Xw+SnBOKQcbQHfQOAIsT1lCKSHkgH/C9UqqsiHRAWzbxvlm8DmjD28uVUqNSSb8FMBFoqZQK1ee031NKLdLn5AcopfaIyHggn1JqqIicBPorpfaKyFd63LL60HcLoKNS6pGIlAJuKKXuJqO3KNqwfNlE4ZFojni3gK16/B4iMhs4ppRaqsstA7zRlpH8DnRXSu3Xh/VL6R6nu9D8EY6IiCNwRClVNLn6So2mhZs/84ZtnGf/4mKb8qITq2KcZ299gm74p2PH+tTpW7Rjup83C66sz7C+f4IxjG8FlPYG1R5oJNrSuzPAVCDUTCwAKJPI4WwT2nKKVIfwlVJbgTmAr572MSDBFfV9NKe4k2ge7BP08J7AXH3Y3LyxLUIzvMf05XgLePre9BdoUwBBwDmz8DXASH1pYAmz/D9EG/n4Sn85OQ5Yrq1JSnL1ZWBgYJDlPA8OekbP/l+EiFQFZiqlMm9rp/8oRs/euhg9+2eD0bO3PpnRs+9T9O10P28WXdmQJT17Y87+X4JoZxZ/ROpz9QYGBgYG/zKysseeXgxj/y9BKTUNmGYeJiKfA4k3yl6fsMmCtRCRclh67AP8rZR605p6DQwMDJ5H4p4DFz3D2P+L0Y26VQ17CnpPoc33GxgYGBikQfxzMB1uGHsDAwMDA4MM8O839YaxNzAwMDAwyBDpOeAmqzGMvYGBgYGBQQZ4HrbLNYy9wQtJxOM7z1zno/h0nladyeSze7YHtgC42+dNW8gKZNVS4b+yaFllViyDO3p65TPXCZDT/fldcWx44xsYGBgYGLzgxD0H5t4w9gYGBgYGBhng32/qDWNvYGBgYGCQIZ6HnWgNY29gYGBgYJABDG98AwMDAwODFxxjGN/AwMDAwOAF53lw0DOOuDV4EWn2855VeO1bQ48B3ZJctM9mz7T5X+K1bw0/blmIWyFX07WeA7vhtW8NP+9ZRU3P6qbwcTNG43tqM+sCLE8h++iTPqz1W8Zqn6UsWvcdTi6OpmufTR7O9oMb+XXXSsqUey3ZjJYpXxqvXavYfnAjn00ebgofMW4gW4LW8euulcxe9jV58jpoebe3Y/KsL/DatYpfAlZSrVZlU5wantVZu+cn1getpPuAd5Mt96T5Y1kftJLF3vNM5c77Ul7mrp+J/4VtDJ882CJO43YNWOG3hBW+i5m58mvyvZwvSbqV6lVmtv885gYuoP1HHZJct8tmx/A5I5kbuIBpv07HqZCzFm5vx4Dpg5i54ztmbJvFGzXKApAjd06+2fqt6bPstxX0GtsnWb1zAr5n3u4FvNX/7eT1zv2EebsX8JXX/0x6be1sGTRjCN/unM1sv3m89fGTuK16tWaWzxxm+c6lVe82pvCa9auzcc9Kftm3mvcHJF0OZ5/Nninzx/PLvtUs27LAok31GNiNX/atZuOeldQwa1MOeR346oeJbNizgvW7l1OuyhsAlCxTgiWbv2eN/zJm/zSd3A7a0spRk4bivX89G/yX83q5UknyAPB6+dfYGLAC7/3rGTVpqCm8cesG/By4kuPBQZSpUNoUXqNuNdbsWMrGgBWs2bGU6rWrJJtuehgzZQZ1W75Du279/nEaKTFzxgTO/b6XY0d9qFSxbLIyEyeM4vKfh4m+ed4i/L3unQi5cZIjh3dy5PBOevXskun5U0ql+5NVGMb+KRCRABFpmihsiIh8LyJt9JPrUopbVUS+0797ikha57dbBRHZKiL59U9/s3BPEfFOR/xlInJZP1f+mIjUTEU2sQ53EdmQgbwPEZG0FpXbAnMHdh1Bh3rdaNauEcVKFbUQaNelFTG379C21jusXLiWwWM+AqBYqaI0bduItz27M+Dd4Xw6dTg2Ntp/kc3rtjLg3eEk5qd5q+jcsAddGvdk18699B+hGaW6DWtRpHhhmr3ZgXHDpzL261HJZnbc16MYO3wKzd7sQJHihanTQKvOfYGHaFO3C+08u3Llz2t8OLgHAB27twOgree79O44gFFfDkZEsLGxYcSUwQztOoounu/TpG0DipYsYqGrTZcWxETH0rF2V1b/sIGPx3wIwMMHD1k4fQmzJ3xvWZG2tgydMJCPOw6lW6Pe/Hn2Tzr2bG8hY2NjwwcT+zLp/S8Z3Ohj6rSpS6GShS1kGnVuTOztWD6u15fNizfx3qfva+FdmgAwtOkgvuw2lh5jeiEiPLh7n+Ethpg+ETfCObB9fxK9H07qx8T3xzOo4cd4JKu3CXdvx9K/bl82L/LivdFaHdZq6YFdNnuGNBnI8JZDafpuM5wKOfNKqVdo3KUpI1sPZ2jTgVRtWA3XIm7Y2NgwasowBnUdQcd63WmaTJtq26Uld27foX2tLqxauI6BYzSDV6xUUZq0bUgnz/cY+O4IPp06zNSmRkwcxL6Ag7xdpxtdGvbk8oWrAIz5ZhRzpizgnQY98NsWSI/+3fBoWJMixQvTqmZHJoyYxpivPkmuOTHmq0/4cvhUWtXsSJHihfFoUAOAi+f+ZFiv0Rw9cNxCPvrmbQa+N5IO9bsxZvBEJs8Zl2y66aFdi8bMnzHpH8dPiebNGlDy1WKULuPBRx+NYu6cqcnKeXv7ULN2y2SvrVu/iarVmlC1WhOWLF2d6Xl8Hs6zN4z907EaeCdR2DvAaqXUJv3kumRRSh1RSg3Sf3oCT2XsRSRTplyUUi2UUtFAfqB/WvIpMFIpVRH4FFiQipyFDqVUsFIqaRcs/QwB0jL21YGLN64F8/jRY3Z4+eLZ1MNCwLOZB97rtgHg572LanW03oxnUw92ePny6OEjgv8K4fqV65St9DoAxw6c4PatmCTK7sbeM33PmSsn6G/uDZrXxWvdVgBOHD1N3nx5cHIuYBHXybkADnlyc+LoaQC81m2lYYt6AOzbdZC4uDhTfBd3rVdaolQxDu49AsDNyFvE3I7l9QqvUaZSaa5fuUHwtRAeP3qMj5c/dZvWttBXp2lttq7fDkCAdyBVPbRyP7j/gBOHTvHw74eWhRMQEXLmzAFALofcRIRGWoi8WrEkIVdCCPsrjMePHrN38x6qN7Y8HLFa4zcJ2OgPwP6tQZSrXQGAwiULc2rfSQBuR93mbsxdSpR/1SKuWzF38hXIx++HzliEl0zQey1B726qN7HUW73JmwRs8NPqc2sQ5XW9Sily5MqBja0N2XNk4/Gjx9y/c49CJQtz/rc/ePjgb+Lj4jlz4DQ1mtekZMWS/HXlBjf0ut3p5Ue9RG2qXrM6eK/T6tbPexfV9TZVr6kHO738TG3qrys3eKPS6+TOk5tKNSrgtUp7v3786DGxMbEAFClemGP7NaO8P/AQjVp5Ur9pXTbrbfbksTPkyeuAY6L25OhcAAeH3Jw8ptXV5nXbqN9Ma0+XL1zlyp/XSMy50+eJCNPu6cVzl8iRI3sSmfRStWI58uXN84/jp0Tr1k1ZvlLrIxw8dIx8+fPh6uqcRO7goWOEhoZnuv70oJ7iX1ZhGPunYwPQUkSyAYhIUcAd2CMiPURkjh7eUUROi8gJEdmth3mKiLcepx8wVO8d1xGRoiLiLyInRcRPRF7R4ywTkfkichD4WkTq6XGOi8hvImLxP0tERorIIP37TBHx1783EJGV+vcrIuKIdpxuCT2t6XoSDiKyQUTOichKEZE06mM38KqIOOj5PiYip0SkrX7dQodeztN6Pmz1sMN6ufua1dOuxPnQy+UOBIhIQCp5Kgj8lfAjPCQCZ1cnCwEnVydCg7WHQlxcHLExd8n/cj6cXZ0IC37ysAgLjsApUdzk+PjTD9l6ZCOtOzTju6+0dx8XV2dCg8NMMqHB4Ti7WT6gnN2cCQsx1xeOSzIPsbe6tGaP3z4Azp25QP2mdbG1taXgK+68UaE0Lu7OOLk6ER4cYYoTHhKBk1vScofpMlq5Y5Mdlk8g7nEcX386k5X+S/D+bSPFShVh8+qtFjIFXAsQFfLkBSAqJJKXXQsklQnWZOLj4rl35y55XsrDld+vUK3xm9jY2uBc2IUSZUvg6O5oEdejdR2CvPcmydvLrgWIDDbXG0UBl6R6I5Pozcv+rUE8uPeAJUd+YuGBJfy68Bdib8dy7Y+rlKn+Bnny5yFbjuxUqV8VRzdHXnYtQNiNJ/dJa1OW+XR2dTS1nYQ2le/lfBbhAOHB4Ti7OlHwFTeio6IZ9+1nrNy5mDH/G0UO/aXqzz8uU6+Ztptck9YNcHV3xtnNyaI9hYVE4Jzo/jq7OVm2p5DwJDKp0bhVfc6e+iPd8s+Kgu6uXP8r2PT7xvUQCrq7phIjKW+1b8Gxoz6sXbOQQoXcMzuLxKPS/ckqDGP/FCilbgKHgOZ60DvAOpV0ImYs0FQpVQFoY35BKXUFmA/MVEpVVErtAWYDPyqlygMrge/MohQCaimlhgEjgI/1XnUd4H4ivXv0cICqaMbbXg/bnUj2U+BPPQ8j9bBKaL3nMkBxoDap0xo4BTwA2iulKgP1gW/0F4XkdCTQG7itlKoGVAM+EJFiKeVDKfUdEAzUV0rVTyNfz5S50xbSomoHNm/cTtfeHTM17b5DehIXF8fmDVqv8edVmwkLDme9z4+MnjiU44dPEhdvncFBWztb3nqvDe81+YBWlTpw8ewl3h+Yedu3+q3zISokkumbZ9BrbB/OHTtHfJxlWTza1GGPV+KmmzFKVixFfFw8vau9T7/afWj7QTtcXnHh+sXr/Pz9RsatnMDY5eO5/Psl4q1Yt6+VK8WGH3+la5Pe3L9/nx563U4YNo2OPdqxfMcicjvk4tHDx1bJgzklXivGkDH9mTDyK6vretZ4b/GhRMkaVK7SGF/f3Sxd/G2m6zDm7F9MzIfy39F/JyYIWCYiH6DNIadFTWCV/n05YD5GuF4plbDpehAwQ+/l5ldKJX4KHAWqiEhe4G9gP5rRr4P2IpAWh5RS15VS8cBxoGgKctNF5DjwIZrRFmCKiJwEfNF61y5p6GoCvKencxAoAJR8ynxYICIfNmrUaOLevXs7Rt4LBbTeTnhohIVcRGgErvqwuK2tLQ55cxN98zbhoRGm4XIAF3cnIhLFTQ1bWxv6DHiPn/1XEBEWiav7kypwdXcmPMRyiDE8JBwXN3N9zoSZDUO269wSzyYejPzoC1NYXFwc08bO5K0G3Rjw/kjy5MvDtT//IiI0Amf3J704ZzcnIkKSlttFl9HK7cDtm7dTLE+pN7Qh9RtXtV6V36YAylV9w0ImKjSKAm5PerkF3By5GRqVVEbvsdvY2pArT27u3LpDfFw8SycuZniLIUz7YDK58+Ym+PKTHlzR14tia2vLpdN/JsnbzdAoi1GAAm4FiApLqtcxid4Y6ratx2+Bx4h7HMftqNucO3KWEuW1pue31ocRLYcypuNoYm/HEnwpmJuhUbgUfHKftDZlOZ0RHhppajsJber2zdsW4QDO7s6Eh0YQHhxBeEgEZ377XdPrvYvSuhPn1YvXGPDOcDat3kKzto3JniM7kYnak4ubE+GJ7m94SIRle3JzTiKTHC5uTsxcMo3PB07k+tUbaco/Cz7q977JoS4kNIxChZ/0xgsWcuNGcGi607p58xYPH2pTVIuXrKJy5XKZnt844tP9ySoMY//0eAENRaQykEspdTSxgFKqHzAGKAwcFZECiWWegrtm6U4D+gA5gSARKW0uqJR6BFwGegD70Ax8feBV4Gw6dP1t9j2OlJdmjtR7642VUqeBroATUEUfdQgDcqShS4CBejoVlVLFlFI7nzIfFiilFvr6+pbz8PCIKv9aJezs7WjathGBO4Is5AJ3BNGqkzY407CVJ4f3HjOFN23bCPts9rgXdqNwscKc/i31aitcrJDp+52Yu+zy2ctbDbrhty2Qtp1aAFChSlnuxMQSEW5pjCLCo4i9c5cKVTTv4radWuC/TevFetSvQe8B3enffTgP7j+pjhw5s5Mzl1a1tepVJ+5xHFcuXOXs8T8oXKwQboVdsbO3o3HbBuzZuc9C356d+2jRsRkA9VvV44he7pSICI2kWKmi5NeH+qvXrcoV3YksgYsnLuBWzB3nwi7Y2dvh0boOh30OWsgc9j1E/Q4NAKjZorZpnj5bjmxkz6nNEVfwqEjc43iuXzDNwODRpi57NiXfq7+QRG9dDvscstTrc5D6bzfU6spMb0RwBOVqlQcge87slKr8GjcuXgcgXwGtrI7uTtRoVovdXoFcOHGBwsUK4V7YDTt7O5q0bcjuHZZTC7t37KVVJ61uzdvU7h17adK2oVmbKsSZ384SFXGTsOBwipTQnAqre1Th0vkrALxUID8AG378lTMnzjL182/w376b1nqbLV/5De7cuUtkovYUGR5FbOxdylfWXshad2pOwI7UR0Xy5HVgzopvmDV5HscPn0xV9lny/fwfTQ51mzbtoHtXzdXnzeqVibkd81Rz8+bz+61bN+HcuYuZnt94pdL9ySqMdfZPiVIqVp8zXkLyvXpEpIRS6iBwUESaoxl9c+4A5seG7UMbJViOZjiT7YXr6Z4CTolINaA0cC6R2B604f5eaEPsM4CjyUw13AEyy5smHxCulHokIvWBBDfw1HTsAD4SEX89XikgrW5FQnqRqcg8BgbMXT1ji42tDZvWbOHS+cv0G9mb30+cY/fOIH5d7c3E2V/gtW8Nt6NjGN1vPACXzl/GZ7M/GwJXEPc4jmmfzTAN406ZN54qtSqS/+X8bDv6M/P/txiv1VsY9Hk/ipR4BRUfz/W/Qhg/UvPRDPQNom6jWuw49DMP7j3gs8ETTRn82X8FbzXQlgROGPU1U78bS/ac2dnjt4/d+tz8mGkjyZYtG4vXzwE0J70vR07jZceXWbT2O+Lj4wkPjWDUx5r3dFxcHP/7fBazVk3HxtYG7zXbuHz+Ch+M7Mm5E3+wZ+c+Nq/eyrjvPmN90EpiomP44qMJpjz9cnANuRxyYZ/NnnpNPRjUZQRXLlxl8Ywfmf/Ldzx+9JjQG2FMGGLpgxofF8+isQsY+9N4bGxt8Fvny18X/uKdYe/y58mLHPY9hN9aHwbPHMbcwAXERt9hxgDNRSSfY37G/jQepRRRoVF8N3SGRdq1WnkwuceXyd7k+Lh4fvhiPuOWf6npXevLX+ev0WVYVy6eusBhn0P4rvVhyLfDmLd7AbHRsXwz4GsAtv24hYHfDGaW71xE415zZAAAIABJREFUwH+dL1fPXQHgkwWjyfNSHh4/imPhF99zL0Z7157+2Uxmr/4GW1ObukLfkb05q7cpr9VbmDB7DL/sW01MdAyfmdrUFXw3+7M+cLnmA2HWpqZ//i0T547F3t6eG9eC+XLIFACatm9Exx5vAeC7NYBfV2tOfHUa1mLLgfU8uP83Xwx54vW+zvdHOjXSVjhM/nQ6k2aNIXuO7Oz1P8BeP20VQ4Pm9Rg9eRgvFcjP3BXfcO70eT7qMpR3er3NK8UK0XdYL/oO66UlKDagnr4HOnLcNA7/dpLo6BgatutG/97d6dC6adoR02DrNj+aNWvAH2eDuHf/Pn36DDNdO3J4J1Wraas6pk39nHc6tydXrpxcuXSEJUtXMWHiDAYO6EWrVk14/DiOWzej6dVnSIbzlJh///55IM/Dnr7/NkSkHfAL8LpS6pwe1gOoqpQaICI/ow1JC+CHNv9cDxihlGqlG7YNaCsxBgLXgKWAIxAB9FRKXRORZYC3UmqDrmM2Wk89HjgD9FBKmfeCEZGGwHa0Yf67InIemK+UmqFfv6LnM1JEVgHlgW3AloT86XJzgCNKqWWJ0rfIkx7mCGwGHIAjQA2guVLqSiIdc/W4ZUXEBpiENu8vernboc3XJ5sPERkIDACC05q3r+zm8cwb9v24h2kLWQHjiFvrk1VH3D5MMlNnff5rR9w+fngjLUfkNKldsEG6G2bQDf8M6/snGMbe4IXEMPbWxTD2zwbD2FufzDD2NQvWT3fD3H8jIE19ItIMmIXm87Uo8bJuEckO/ARUAaKAzrrzd4oYc/YGBgYGBgYZIE7Fp/uTFiJiizYK2hxtRVIXESmTSKw3cEsp9SowE0hzGYVh7A0MDAwMDDJAJm+qUx24qJS6pJR6CKwB2iaSaQv8qH/fgOY0nuqIgWHsDQwMDAwMMkAmr7O32BgMuK6HJSujL8G+jbZ8OUUMY29gYGBgYJABnmYHPRH5UESOmH0+fBZ5NJbeGRgYGBgYZICncRxVSi0EFqYicgPL5dqFSLosOUHmun5uSj40R70UMXr2BgYGBgYGGSCT98Y/DJQUkWL6OSzvAJsSyWwC3te/vw34J7OXigVGz97AwMDAwCADpMfLPr0opR6LyAC0jcdsgSVKqTMiMgFtz5FNwGJguYhcBG6S9DTWJBjG3uCF5Paje2kLZTKv5MjIrsj/nBt/33rmOo9GXnjmOgEau1bIEr1ZdVpZHtucz1xnVq13vx+cnuM7/p1k9tG1SqmtwNZEYWPNvj8AnurULcPYGxgYGBgYZICs3PM+vRjG3sDAwMDAIANkds/eGhjG3sDAwMDAIAMYPXsDAwMDA4MXHKNnb2BgYGBg8IKTmd741sIw9gYGBgYGBhlAGcbewMDAwMDgxSarlmY+DcYOegYvPGOnjMT/kBdbAtfyRvnSycqUrfA6W3evxf+QF2OnjExyvXf/bvwZeYyXXs5vCnuzdhU2B6xm2971rNr0g4V8Nc+q/Bi4hBV7l9Hl485J0rPPZs/YeZ+zYu8y5m3+DpdCLhbXnd2d2PrHJjr1fdsUtnr/chb7LuSHHfOZv2VuiuUdM2UEPod+YdOu1ZQp/1qyMm+UL83mwDX4HPqFMVNGmMIHf9qPTbtW4xWwkiXr5uDs4mgRr1zFMvwecoC33mqZov6ZMyZw9ve9HDvqQ6WKZZOVmTBhFJf+PMytm+eTvd6+fQsePbxBlcrlU9RTpV4VFgYsZNHuRXTsn3TJsV02Oz6d+ymLdi9iptdMnAs5A+DZzpPZ22abPt5XvCleprhF3LGLxzLPZx59v+zLot2LWOu3jNLlSiWbj9fLv8Za/x/x2reGkRMHm8Lz5s/DvDUz+TVoNfPWzCRPvjymayMnDsZr35ok6Q4e8xHrdy1n4+4VFmn1G92HgEvbCbi8g9W7fyTfS3kt8mCfzZ4J33/B2r3LWbh5Lq56e6pWpwqLt83nJ99FLN42n8q1Kz2pH3s7PvlqGKv3/MiqwGV4tkh7ff3MGRM4l8a9nThhFJf/PEx0onv7XvdOhNw4yZHDOzlyeCe9enZJU19ajJkyg7ot36Fdt34ZTisjZPJBOFbBMPaZhIi0ExElIslaExHZJSJVnyK9qiLy3T/Ix1YRyZ+2ZLrTWyYil0XkhIicF5GfRKRQZqWfhm5THYiIp4jUeto0PBvVpmjxV2hQvS2fD5vEhOmjk5WbMH00nw2dRIPqbSla/BXqNXyiys3dBQ/Pmtz4K8QUlievA19+PZoPuw2luUdHBvb6xHTNxsaGwZMG8mn3z+hRvw8N29anSMlXLPS1eKcZd27H0s2jB+t/+Jm+n/WxuN5/XD8OBhxOks+hHUfwQdN+9Gv5cbLlqNeoNkWLF6Zx9fZ8MXwyX36dfHm/nD6aMcMm0bh6e4oWL0xdvbyL5iynjWcX2tbvSoDPHj4e8YFFuUaMHUjQroPJpgnQrFkDXn21GK+X8eCjj0YxZ87UZOW2ePtQq3byLwwODrkZOKA3Bw8eS1GPjY0N/Sf1Z+z7Y+nXsB/12tSjcMnCFjJNOzcl9nYsfer24ZdFv9BrdC8Adv26i4HNBzKw+UC+GfINYX+Fcen3S6Z4tZrV4sHdB+TMnZOCRQvSp24fJo2czuhpI0iO0dOGM2nE17St9Q6vFC9MrQY1AOg5oBuH9h6lXe0uHNp7lJ4DugFQu0ENXilemLa13rFIt3zVslSoVo7ODd6no+d7vFHxdSrVrIC9vR3vftSZfm0H0rhUS/Lkc6D38B4WeWjVpTl3bt+hs0d31v6wgf6fa2erRN+8zagen/Neoz5MGjKNsbOetIf3B3XlVlQ0Xeq8T1fPnvy2/0SK9Q3QvFkDSr5ajNL6vZ2bwr319vahZgr3dt36TVSt1oSq1ZqwZOnqVPWlh3YtGjN/xqQMp5NRMnm7XKtgGPvMowuwV/+bYZRSR5RSg/5BvBZKqejMyIMZI5VSFYDXgN8Af33PZquSqA48gac29o2ae/LLOm8Ajh89Rd58eXBK1Ft1cnHEIU9ujh89BcAv67xp3KK+6frnk4bz1ZffWryVt+nQnJ3e/oTcCAUgKvLJLnalK75G8JVgQq6F8vjRY/y9dlG7iWXWazepxY71OwEI3LKbyh5Pely1m9Yi5K9Qrpy/8rTFpWGzevyyVtt468TR0+TJlwcnF8ud/ZxcCuCQJzcnjp7Wyrt2K42aewJwN/auSS5XrpwWZe7+QWd2evsTFXkzRf1tWjdlxcoNABw8dIx8+fPh6uqcRO7goWOEhoYnm8aX4z9h+v/m8eDBgxT1lKpYiuArwYTqdbx7825qNqlpIVOjSQ18N/gCsHfrXirUTrr7Xr229QjcFGj6nSNXDtp/0J7Vs1eTyyEXfhv9ADh17Ax58jrg6GxZl47OBcidJzenjp0BwHv9duo303rI9ZrWwXvdNi183TY89XDPZnXwXr89abpKkT1Hduyz2ZEtuz129nbcjLjF65VKEx8XT/TNGB4/ekzwXyE4ulrmo06T2mzV29OuLYFU8agMwIUzF4kM085HufzHFbLnyIZ9NnsAWr7TnOWzVwFaz/T2rZgU6xugdeumLM/gvc1sqlYsR768edIWtDJx8fHp/mQVhrHPBETEAfAAeqPvUSwiOUVkjYicFZFfgJxm8rEiMl1EzoiIr4hU13v+l0SkjS7jKSLe+vd6InJc//wmInlExE1Eduthp0Wkji57RUQc9e/D9GunRWSIHlZUz9MPuv6dIpKuPTmVxkwgFGiup9dERPaLyDERWa/XRUI+vhaRUyJySEReNdPvLyInRcRPRF7Rwzvq+TwhIrvN60BEigL9gKF6edO9n6eLmzPBN8JMv0ODw3F1c7KQcXVzIjT4ycMpJDgcFzftIdaoeT3CQsI5d8Zye9hiJYqQN39eVnotxMtvJe07PenJOLo5Eh4SYfodERqJo5vlC4ajawGTTHxcPLExd8n7Ul5y5MpBl/6d+XHG8iRlUUoxfdU0FmydS6uuLVIorxOhwaGm32HBYbgkeiC7uDoTGvykTsJCwnAxq5Ohn/Un8Lg3rTs0Z9ZX8/U4TjRu4cmqpRuS1ZuAu7sr1/8KNv2+cT2Egu6uqcYxp1LFshQq7Ma2bX6pyhVwLUBkcKTpd2RIJAUSvdQUcC1ARPCTOr535x55Ew1/121dl0CvJ8a++4ju/LzwZ/6+/ze2drZEmN3H8JBwnBLdRyc3R8KDLWWcXTWZAk4vERmuGdrI8CgKOL0EgLOrI2Fm7S0h3ZNHz3A46Bg7j3ux47gX+3cd4urFa7zs9DInDp5iud8ivI6txyGPg8mAm/Lh6ki4nmZcXDx3Y+4mGer3bFmXP05f4NHDRzjkzQ3AB5/0ZMn2BUxcMI6XHF9KvrJ1Cmbw3gK81b4Fx476sHbNQgoVcn+quP9m1FP8yyoMY585tAW2K6XOA1EiUgX4CLinlHodGAdUMZPPjXZK0RvAHWAS0BhoD0xIJv0RwMdKqYpAHeA+8C6wQw+rABw3j6DnoSfwJlAD+EBEErqPJYG5uv5ooMNTlvcYUFp/qRgDNFJKVQaOAMPM5G4rpcoBc4Bv9bDZwI9KqfLASiBhqmIs0FQfQWhjrkwpdQWYD8xUSlVUSiW7ibb5OdExDyKTE3kqcuTMwUdDejFz2vwk12ztbClb4XX6dBlEj44fM2DEBxQqVjDDOnsMe48NP2zkwb2kvdpBbw2lb/P+jOr+Oe3eb0P5N8tlWF9yzJwyj3oVW7F54za69+4EwGeThzN9wmyrzjmKCNOnj+OTT5L7L5D5vFbxNf6+/zdXz18FoHiZ4rgVcWP/jv1W0ZdW1RUuWpBiJYvQrPJbNKvUnmq1K1OhejlsbG0oUvIVejbtS9vKHQkPjqB0+eT9B1KiWKmi9P/sQ6aPmgmAra0tLu7OnDpyhl7N+nL66BkGjLXuvLf3Fh9KlKxB5SqN8fXdzdLF36Yd6TnBmLP/79AFWKN/X6P/rgusAFBKnQROmsk/BLbr308BgUqpR/r3osmkHwTMEJFBQH6l1GO0YxB7ish4oJxS6k6iOB7AL0qpu0qpWOBntBcFgMtKqYSXg6Mp6EwN0f/WAMoAQSJyHO3IxSJmcqvN/iaMsdYEVunfl+v5TCjjMhH5AO2kp6dGKbVQKVVVKbU0cL8PmwNWExEWgXvBJ85vru7OhJr11gBCQyJwdX/S+3VzdyYsJJxXihai8CsF2RK4hsBj3ri6O7PJfyWOzgUIDQ5jT8B+7t97wK2b0Rzad4wSZUoAWi/T2ayn7OTqSGSI5ctHZGiUScbG1gaHvLmJuRXD65VK0/fzD1i9fzlv936LrgO70K5HW1McgOioaPZsD6J0Rc35rmuvjngFrMQrYCURYZG4mvW2XNxdCEs0pBoWGo6r+5M6cXFzISxRnQBs2rCNJq0aApoD48yFU/A/uommrRsy+7sptGnTFICP+r1vcroKDQ2jUOEnPbaChdy4YTbSkBp58jjwxhul8fXZwIXzB3jzzcr8/PPSZJ30okKjcHR/0st2dHMkKlFvNyo0Cif3J3WcK08uYsyGquu2qcsur12m36Url6bsm2XxuujFosBF5MiZg0FTn8ykObs5E5HoPkaEROLs7mQhEx6qyURF3DIN+zs6F+CmPtUTHhqJi1l7S0i3fvO6nDp2hvv37nP/3n2C/A/wRpUy5MyZg2zZs3HjqtarDr4WgkOioeuI0Eic9TRtbW3InTe3aVjeyc2RKYu/ZOLgqaY0bt+K4f69+wRu1d6bA7wDea1syST1bH5vQzJwbwFu3rzFw4cPAVi8ZBWVK1vnZTUrMObs/wOIyMtAA2CRiFwBRgKdeGIQk+OR2dnD8cDfAEpbrJlkOaRSahrQB20qIEhESiuldqO9UNxAM5LvPUW2/zb7HpeczjSoBJxFK6OP3tuuqJQqo5TqbZ71FL4nQSnVD22UoDBwVEQycoTc3Nb1u9C6fhd2bt1F+06tAKhYpRx3YmKJCEv0wA6LJPbOXSpW0R4+7Tu1wnfbLs6fvUj11xtRr3Ir6lVuRWhwOG0adCUyPArfbYFUfbMitra25MiZg4pVynL14jUAzp34g4LFCuJa2BU7ezsatPVkn49lb3Gfz36admwCQL2WdfktSHv3GtxhGF1qdqdLze5sWPwzK2ev5tdlXuTImYOcubXZlhw5c1C1bhUu/3EFgJVL1tO2flfa1u+K77ZdtO+sDfFXqFKW2JhYIhIZwYiwKGLv3KVCFc2bun3nFvht14ayixR/4uTWqLknly5qOhpWbUuDKm1oUKUNOzb7MXDQZ2zatAOA7+f/aHK68tq0g25dtRUEb1avTMztmHTP38bE3MHNvRwlS9WgZKkaHDx4jLfe6snRYyeTyJ4/cR73Yu64FHbBzt6Ouq3rcsDngIXMQZ+DNHq7EQAeLTw4ue9JOiJCnVZ12L15tyls64qtdCrbibavtqVPvT5EhEQQfkPLe7nKbxB7J9Y0LJ9AZHgUd+/cpVzlNwBo1bEZu7ZrBnT3zr206tRcC+/UnMAdWnjgjr206tgsSbqhN8KoUqMStra22NnZUqVmRa5evMbhPUdxyJOb18qVxM7ejjpNa3Fa9xFIYO/OfbTQ25Nny3ocDfoNAIe8uZn+01TmT1nEqSOWcYJ89lOpVkUAqnpU5vKFq0nq2fzebtq0g+7/8N4CFvP7rVs34dy5i+mO+2/neejZG+vsM87bwHKlVN+EABEJROsxv4vmzFYWSHkNURqISAml1CnglIhUQxtCvw9cV0r9ICLZgcrAT2bR9qC9BExDM8rtge7/NA96PgQYCLihjUzkA+aKyKtKqYsikhsoqE9nAHQGpul/E6zdPjS/huVAVz2fCWU8CBwUkeZoRt+cO0BenpJdPnvxbOSB/2EvHtx/wKhB403XNgespnV9zZ9y3CdT+Xr2l2TPkZ1Av33s8g1KNd0/L1xmt/8+tuxei4qPZ+2KX7miG9/4uHi++2IOX6+cio2NDdvW7uDK+av0HPE+f5w4zz6f/WxZs43PZn3Kir3LiIm+w8T+k1PV95JTfiYu0vJua2uL768BHN51JJnyBlGvUW18D/3K/fsPGD3oS9M1r4CVtK3fFYDxn0xj2uzx5MiRnd3++wjUyzvii4EUK1GE+Ph4gq+HMG5E8h7XKbFtmx/NmzXg3Nkg7t+/T58+T2Z1jhzeSdVqmkGaOvVz3uncnly5cnL50hGWLF3FxIkz0q0nPi6e77/4nknLJ2Fja8POtTu5dv4a3YZ148KpCxz0OciOtTsY8e0IFu1exJ3oO3w14CtT/LJvliUyOJLQayn3TO/fvU/otVAW71lM7L17jB86xXRttc9SujTuqZVl9Dd8+e3nZM+RnX3+Bwjy1146ls5ZwVcLJtCuS0tCrocxqu8XAOz1249Hw5p47V/Lg/sPTOn6eu+imkcV1gX8iFKKfQEHCdJfEn9ZvokFm+YAcONaCHMmzKfPiB6cO3GevT778F6zlS+++4y1e5cTE32Hcf0nAtChZ3sKFXWn59Du9Byq/fcf0uUToqOimTf5B8Z+N5rB4/sTffM2U4Z+nWqdb93mR7NmDfjjbBD3Urm308zu7RX93k6YOIOBA3rRqlUTHj+O49bNaHr1GZLWbU6TkeOmcfi3k0RHx9CwXTf69+5Oh9ZNM5zu0/I87I0vWfmm8SIgIgHAV0qp7WZhg9B6vznR5tPPAgXR5t2PiEisUirBkW08EKuU+p/+O1Yp5SAinsAIpVQrEZkN1EcbBTgD9EAzmCOBR0As8J5S6rI+ulBVKRUpIsOAXnq2FimlvtWd3byVUmV1fSMAB6XU+BTKtwyoB8QAuYADwGil1HX9egPgKyC7HmWMUmqTno+1aI58fwNd9BeCIsBSwBGIAHoqpa6JyM9ovgQC+AFDdL0JdVAK2KDXwcCU5u0TKOFY+Zk37P/SefaXb4ekLWQFsuo8+9BHqXuqW4ucNlZf9JKEgxF/PHOdkHXn2ds7Fk9tFDZd5HMoke7nze3YPzOs759gGHsDq2D+0pEV+g1jb10MY/9sMIy99ckMY583d/F0P29i7l7KEmNvDOMbGBgYGBhkgOdhGN8w9gYAiMhcoHai4FlKqaX/JD2lVNEMZ8rAwMDgOcA44tbguUEplfz+qwYGBgYGqWL07A0MDAwMDF5wngffN8PYGxgYGBgYZIB44zx7AwMDAwODF5vnoWdvLL0zMDBDRD5USi009L54ev9LZTX0GiTG2C7XwMCSDw29L6ze/1JZDb0GFhjG3sDAwMDA4AXHMPYGBgYGBgYvOIaxNzCwJKvm/Ay9L6ZOQ++Lr/e5wHDQMzAwMDAweMExevYGBgYGBgYvOIaxNzAwMDAweMExjL2BgYGBgcELjrGDnoFBFiMiOYFXlFJZc5C4FRGRYaldV0rNsLJ+D6CkUmqpiDgBDkqpy9bUmZWISEel1Pq0wjJZp59SqmFaYVbQW1spFZRWmIGGYewN/rOIyFupXVdK/fwM8tAa+B+QDSgmIhWBCUqpNlbQtRlSPovTGjqBPFZIM12IyDigKvAasBSwB1aQ9CjnzNR5itTruLy1dOuMBhIb9uTCMoyI5AByAY4i8hIg+qW8QMHM1pcMs4HK6QgzwDD2Bv9tWut/nYFagL/+uz6wD7C6sQfGA9WBXQBKqeMiUsxKuv5npXRTRCn15bPWaUZ7oBJwTM9LsIhY++Wjlf434cjo5frfrtZUKiLNgRZAQRH5zuxSXuCxldT2BYYA7sBRnhj7GGCOlXQiIjXR/r86JRo5ygvYWkvv845h7A3+syilegKIyE6gjFIqRP/tBix7Rtl4pJS6LSLmYVZZD6uUCrRGuulB7wX2Bt4AcpjlqZcV1T5USikRUXoecltRFwBKqau6rsZKqUpmlz4VkWPAp1ZSHQwcAdqgGd4E7gBDraFQKTULmCUiA5VSs62hIwWyAQ5o9sv85S0GePsZ5uO5wjD2BgZQOMHQ64QBrzwj3WdE5F3AVkRKAoPQRhWshq5nKlAGS8Nb3IpqlwPngKbABLSe7lkr6gNYJyILgPwi8gHQC/jByjoTEPP5YxGphRUdopVSJ4ATIrJKKfXIWnpS0D1bL19RzGyKUuonK+kLBAJFZFnCy5VB2hib6hj85xGROUBJYLUe1Bm4qJQa+Ax05wI+B5qgDYPuACYqpR5YUedeYBwwE20qoydgo5Qaa0WdvymlKonISaVUeRGxB/YopWpYS6eutzFmdauU8rGmPjO9VYAlQD5d9y2gl1LqmJX11kabGiqCZngFUNZ8kROR5UAJ4DgQpwcrpdQgK+n7Vik1JCUfFCv5njz3GMbewACTs14d/edupdQvWZkfayIiR5VSVUTklFKqnHmYFXUeUkpVF5HdQH8gFDhk5dGELEdE8gEopW4/I33n0Ibtj/LE8KKUirKizrNo02DPxJiISBWl1FERqZfc9aycrvo3YwzjGxhg8rx/Fg55QJZ5xifwt4jYABdEZABwA20O1Jos1D22xwCbdH1fWEORiNwh9brNaw29uu5uSqkViZccJvhkWHupIXBbKbXNyjoScxpwBULSEswMlFJH9b8mo663rcJKqZPPIg/PI4axN/jPIiJ7lVIeyRiHhKFPqxkFnnjGv4X2oFyh/+6C5jNgTQajLZkaBExEW33wnpV1+imlbgG7geIA1lp1oJTKo6c/Ec0ALUe7p10BN2voNCPBCTA5r/9n0fMNEJHpaC+uf5sUW3f6wBH4XUQOJdJp1eF0EdmF5pBohzaSES4iQUqpVPd2+K9iDOMbGKSBiLykGyprpH1EKVU1rbBM1pkVG68cU0pVThRm7amDE0qpCmmFWUl3lmz4IiIByQQrpVQDK+rMkuF0Mz+QPmi9+nEJPiHW1Pu8YvTsDQzSxg/rbdSRW0SKK6Uugam3a+0lYs9y45XSaMvt8iXaxCgvZisBrMRdEekKrEHrVXcB7lpZZwJZsuGLUqq+NdNPQWegiBRB26nQV3c6fRbr3e30ZbKd0JxcDVLBMPYGBmkjaYv8Y4YCu0Tkkq6nCPChNRRl0cYrr6FtNJOfJ5sYgbb++wMr6UzgXWCW/lFAkB5mNbJ6wxcRcQGmAO5KqeYiUgaoqZRabEWdH6C12ZfRvPILAvMBq26Xi7aEcwcQpJQ6LCLFgQtW1vncYgzjGxikQXJD0JmcfnagtP7znFLq79TkM6CnAlAR7SFpvszuDhBgrakKXXdNpdR+a6X/b0Ef0vYE+qEZvATuAJuVUlY1RiKyDW1r4M+VUhVExA74LWHVhZV0HkfbBfJgwkZC5is9DP4dGD17A4MsRF9v3heoqwftEpEF1tgYxWzjlZVKKWv15C0QkU+UUl8D74pIl2TyZJW12LruZ75r379gwxdHpdQ6ERmt5+exiMSlFSmD/K2Uepiw4kB/wbB6L1JECqFNjSScdbAHGKyUum5t3c8jhrE3MEgbaw7jf492QMs8/Xd3PaxPZisSkXVKqU7AbwlbyJpjJcemhF3yjlgh7bTIil37EsguIgtJuquc1RzldO6KSAF0YysiNQBrr/EPFJHPgJz6Jkb9gc1W1gnaCMYqoKP+u5se1vgZ6H7uMIbxDQxI/ShUEXlZKXXTSnqfmce4iLgppUJ0Z6okPIueqIjk1VSpO89AV5bs2qfrPoE2jJ94c5ujKUbKHL2V0Xq7ZdHWvzsBb1tz/bm+Z0NvLHeBXGTtTXZE5LhS6v/t3XmcXlWV7vHfw6AMTZhEUEGEKCAXQSZlcgAaFRsQGUXACcXGgShiC3S3NOptRLQFaScUEFBRuIKiLVPLPCmEKaigCI2zCC0SQUSS5/6xz5u8VVQqILXPSdV5vp9PfVLnvKlau0hRu87ea6/1woXdiyJP9tF7Wkgr1FoTfWOOpOm2f9aMZW2GJoeJNKj/b/tuSatR9lkNXGf7tzViDkjajPLfdrlyqfsp5WNrTn6DrZD7JW1AqdptTc29AAAd+ElEQVT39Irxhj1q+7MtxZrH9g1N3sC6lIn39tq18m3PpfQcaKvvwMB9kvZjfpnrfYBqlQInu0z2Ed20Qh14P6UQynA2/ptrBmzOJX+Q0tJXwAmSPmT75IphTwbeYfuKZgzbUCb/mmeiW6vaN4ZvS3oHcA4jC83UWiHazvbFo443AqwjaVAhcqJjnml7L0mzGLtGfe3z7m+hrGJ8srm+isr/70xmmewjOmiFOmD7eypd6NZtbt1eKxt/yPuBjQf10ps93qspE3ItcwYTPYDtKyVVSxJslpYfGF21r0VvbP58/9A9VxzHyyi/vO08xmumTinoGc2fO1X43AvVbDul6c3jlD376D1Jh1K63u1Aaf36FuCrbqFHt6R3Al+xfX9zvSKwj+3PjP+RTyrm1cDLbT/SXD8FuNT2VhVjHgcsTVlyNaWz4MM0ZYJrlHOtXYlwUSRprUGuyXj3JjjmMbY/sLB7FeKuTamhsAXle+oa4L2DAlUxUib7CDpthTpWktGNg/PKlWKeBrwA+Bblh+RrgFuatyrNWhZQxnWgSjlXSR8F7gW+zlDlvMo5GIPYywCHAM+2feBg9cb2dyrH7aIs8Vgxq5etlXQt8Gnm79m/Dni37RfXjDtZZRk/eq9Ztr/Y9kWS1gXWlbRk7cSmxuKSNMhclrQ48JTKMX/WvA18q/mzWp5CF2VcKasHAO8cHgrtLOmfQsnEH6yW/IpSjrjKZN9FWWJJB1GO2a0taTjbfznK/nlty9g+fej6y5Lev8C/3XN5so/ekzST0st+ReBKypnwR2zv20LsYylJeZ9vbr0d+IXt99WO3cRfjHLM8IHKcWZQJsDZlKztTYDDbF9YM25XBlsIw6s0tY5UNp/7NcCulD3sc4demg18zfbVFWIuT/l/5mjgsOGYLa2eHAP8gfm9D/ZuxnMstLOCM5lkso/eGyxDSno3sLTtj7V1XreZbA8E/r65dRHljHK1qmeSvkop5zoHuI7y9He87WMrxry5Kd/6yib2vwCn1yhD3JzlX3VQmlbSnpR8AShbNLVbCA/yIran1G3fRNJ04AzbL6oct7OyxJKezshKhT+vHG+8PATbbjspc5GWZfyIcu57S0qFtQOae2107RqcUf4cI+uoDw/sG7Z3n+Cw69t+QKUj3HmUp7KZNE9ElQyqEL4aOM32DzWorzrxPk45XTCoQ3805etcmrKs/o+V4g47EjgfWEPSVyg1G97UQtwbm6TP1koES9oZ+A/gmcA9lJWqHzdjqMb2WjU//1SzWNcDiFgEzKC0eD2nmYTWBsZLKGtTjaeTJZtqcrsC5za5CbWX+GZKupAy2V/Q1DGYWynW5sCpQ9ezbb/b9lspleWqaxI8d6NM8GcAm9m+tIXQpwOrUUoEXwasTlnKr+kjlIz4nzQT8PbAtZVjImkZSf/SlCVG0vMkdXIMcDLIZB+9Z/ty27vYPqa5vtMVG7Q8QTUm4c8D/wMsC1zelM+tumdPWTE5DNjc9kOUJMRaBVCWGFWqdf+h91eoFBOYlyg3KFu7JvAb4NfAs5t7tT3X9r8CD9o+FfgHoHZ2+l+bmg2LSVrM9iWUipS1nQI8wsgkyI+0EHdSyjJ+9F5TC/+feOzSZ+2mJZ2w/SlguJ/93ZKqZsvbnqvSpez1zer9ZbZrNUuZK2m1QQlg27cCSHoW9VYTBg6h5GB8YozXDNT+nuqiRPD9kv6OUrzoK5LuYeioY0XTbe+tppui7Ycqbg1NepnsI+ArlLPYO1H2c98I/L7TEc034T+8mizqI5nfVvcySle4at3RmjPvm1P+WwMc3CSTHVEh3LGUcrXvA25s7m1C2cuvmZeA7QObd3e0/fDwayotd2sblAj+V9orEbwLpUDSeyl5L8tTvp9qe0TS0szv8DedodLEMVKy8aP3BkVHhguBSLrO9uaLwNheMdHH0yR9g9IRbbCvvT+wke3RddUnMuYtwAubhMRBPYEbaxVekfQq4AjKao2BHwIftX1ejXhjxB+r0Mxj7k1wzF2B5wKzbF9QK85QvBcDJwLTgVnAAbZ/VDvuUPwdKKc61gcupEmCbCk3YtLJk33E/KXP30j6B8oe60o1Ay6oeQjlSd6DSbDSOfTpozL8j5J0U4U4o60ADM4+L18zkO3zKdnwrVLpJvgsSm/3jZm/MjMNWKZi3M9QfrG5GviwpBfZ/nCteI1PA4dSlu93oTSkeWXlmPM0RbBuoCQHCphh+9624k82mewj4CPN0vb7KF20pgHvqRyzy6zhP0vaxvaVAJK2Bv5cOebRlGNhl1B+ML+UkYVYpopXUjLwV6ccRxuYTVlpqOWllNWZOU2p3iuA2pP9YkNlpc+SdHjlePNIWgLYEVivufVj4P624k9GWcaP3pN0KuWpYNCMZiXg4zXPJndJ0gspS/jLUybe/6Usf95cOe4zKPv2AD8YJNBNRZJ2t/2NFuON2CKovWXQxLiT8mQ/8PHha1doq9vEfRalw99vKDkZorSoXg3Y1vava8Sd7DLZR++N1XhmrHuVYm9BWU14PuU42uKUY1PTWog9DaBmqdyFHTdzhW53i4pmS2j0CY8qiWuSHgLuGFxS9tHvYNS20ATHPGWcl13rl2VJXwJusn3cqPsHA5vafuOYH9hzWcaPKOeDV3TpfT54sm/r/43/pHTrOotyNvkNwDo1Akk6ZAH3gTrd7hh5BG1TSt+BwT52laNoC/o6Byp9naPH8DnKHv22wBeBPYAfVAz5/Iqfe0y2a9VJWJgtbL9p9E3bn5J0ewfjmRQy2UeUCekaSWc113sC/7et4LbvkLR4Uw//FEk3Uir6TbThrnZvZ37znWqGu901qyVt1C4YfJ3rUrYNBo1hdqbuhDtsK9sbNic8jpL0CUrJ3ips3/14/p6ka2xvWWscLRkvv+Sh1kYxyWSyj96zfZqk65n/lLlbi0eIHpL0FOAmSR+j7ENWqWxp+6jB+5J2Hb5uSSt7hoOvS9LlwCa2ZzfX/wb8VxtjYP6E9JCkZwL3Ac9oKfZ42jjrX9voNr4DoiTXxhgy2UcAzeTe2hnhIftTJvd3UYqSrEGpqV5bH5J1VqWUUx14pLnXhu9IWgH4GKXJEJTl/K5NhX/3yyirNGO5vM2BTCaZ7CO6tavt4ykVyAZPpDOA4zsd1QSRdALzJ5jVJQ2X6aVyD4LTgB9IOqe53pWRDXJq+jhwEPAS4BrKUbjPthS7dZK2Ap7D0Jxi+7QasR5vroCkNzb9AYJk40d0agGV1qqcBBhVyOe5jMzerpWxPW5mdO0fxs1pgJc0l5fbvnG8vz+Bcc+knK3/cnPr9cDytvdqI/6C1PjeknQ6Jfv/JmBOc9tdN5Nq4/jhZJIn+4gONM07Xg+sJencoZeWY36VuYnWeiGfReDJahngAdunSFpF0lq272oh7ga21x+6vkRSK9tETRfD59n+76Z2/BKDvAVGdgCcKJsB63vRe3JMU5whmewjunE1JRnvaYw8njYbuKVGwJ5lbCPpSMpEtC6lHeqSlCftrVsIf4OkLWxf24zlxZRjh1VJehul695KlKft1YHPUXrMz+sAOMFupRS0+U2Fz/1kLGq/fHQqk31EB5qJ925gUZxUp0LGNsBrKZXVbgCw/WtJy43/IRNmU+BqST9vrp8N3D7YSqnVAAh4J/Ai4PuUQD+VVKXFraRvUybU5YAfSfoBQ13nbO9SI+4TkCf7IZnsIzog6Urb20iazcgnkMH+eZdHiKbKE9Ejti1p0AJ12RZjv6rFWMP+YvuRQaGkpoZ8rX/Pj1f6vBPlqq4HsCjJZB/RAdvbNH+29aTZKUmrAG/jsRnbNfsPnCnp88AKzfL2W4AvVIw3z+PdMqngMklHULru7QC8A/h2jUC2LwOQdIztDwy/JukYyhG5aiQ9Fdidx35Pfaj581014082ycaP6FiTMb4N5QnsyrYyxscZT42M7aspx89mMj9jm9rNYpoJ7xWUFZMLhrq0TUmSFgMOYOhrBr5YM3luASdKbqm4VTGIcT7wRx77PfWJBX5Qj2Wyj+iQpA9SyvMOOoTtCpxl+yOV4y4wY1vSBhOdyCXpJtsvnMjP+QTjPw24bxHMGJ+0JB1EWTlYG/jZ0EvLAVfZ3q9y/Fttb1AzxlSSyT6iQ03jjo1sP9xcL03p6LVuxZjzMrZtT5f0POBztrevGPMjwNW2v1srxlCsLYCPUo4wfhg4nXLqYTHgDbbPrz2Gto2qofAYlWooLA+sCBwNHDb00mzbtY6PDsc/ETjB9qzasaaCTPYRHZJ0CfBa2/c31ysAZ9dsGCPpJpqM7cFyvaRZtl9QMeZsYFlKtvZfqZiI2PQ5OAJYHjgR2NH2tZLWA85oo3Vx25qVGijZ+FB+wQHYj/Lf+bDHftSTjjnN9gNNl8jHqD3hN3ULngvcRfm+qlYcairIZB/RIUnfpHRmu4jyZLYDpTPbL6FOOVlJ37f94sHefJOxfcNU+SE5vGUg6ce2nz/0WpXqhIuKsb6+WpXkJH3H9k6S7qJ87w4fdbPttSc65qj4a451v8PkyEVasvEjunVO8zZwaQsxW8vYlrSe7duaJMTHsH1DhbBzh94f3Q51qj/dSNLWtq9qLraiXhfFnZo/16rx+R9H/LslbcT8cshX2L65i7FMBnmyj1hESFoRWMN2lQp6Q3Fay9iWdKLtA5vtitFcY7tC0hzgQcrXtjTze5wLWMr2khMdc1EhaVPgZMoWhoA/AG+p9EvVIObplG5zV9i+rVacMeLOoBznHCS3vhY40fYJbY1hMslkH9EhSZcCu1BW2WYC91AymQ/pclwxuTXJc9j+YwuxtqU8Xb+EUqL3RkrToaqdGyXdAmxp+8HmelngmqmyHTXRsowf0a3lmySntwKn2T6y+SE24brI2I52NZP8kcBLm+vLgA/VnPRtXyLpckruybbAPwL/h/ptmsXQ+frm/ZTIXYBM9hHdWkLSM4C9gH+uHGvQ9W7MjO3KsaMdJ1Ma0wxa6e5PaQK0W62Akr5HOWlxDaVw0ua276kVb8gpwPclDXJedgVOaiHupJRl/IgOSdoT+FfK0v1BktYGjrW9e8WYrWVsR7vGKl5Uu6CRpE9SGv/8hVKP/nLKcvro5MgasQfVJ6HkDHRafXJRlif7iA7ZPgs4a+j6Tkq975pay9geCrg1pVjQg5L2AzYBjs8xqQn3Z0nb2L4S5v13rzrp2n5vE2s54E2UJ+7VgKfWiDfqfP//NG+D11Zqo6DPZJQn+4gOSVodOIH5PdavAGbY/mXFmF1kbN8CbARsCHwJ+CKwl+2X1YrZR81RtNMo/7ZQ/m3fVPNImqR3UZLzNqVMvFdQnrIvrhRv9Pn+eS/Rwvn+ySqTfUSHJF0EfJWR++f72t6hhdhtZmzfYHuTphfAr2yflK2DeiRNA7D9QAuxDqVpcmT70drx4m+TyT6iQx3tsY7I2Ka0Iq2asd1khZ8PvLmJew9wc80SvX0i6RDgj7ZPGnX/AGA528d1M7J6FrA1dJztn3c8tEVS1X26iFio+yTtJ2nx5m0/4L7KMU8GZlMytvcCHqDss9a0NyWB6wDbvwVWB46tHLNP9qUs3492OvCWlsfSls8CDzVbF++jdN47ffwP6a882Ud0qKnvfQKwJWX/8Wrg4JpPJ12sJgzFmcZQYnCSqSaGpJttb7SA16o2OepKtoaemGTjR3SoyUbfpeWwrWdsS3o7cBTwMPOTqkzphR5P3mKSVrX9u+GbklatHbipXPdn23MlrQOsB5xn+6+VQ8+WdDglz+WlTRnoKVsK+cnKk31EBySdwPjV7Ca8291Q7C4ytn9KKW16b60YfSbpDcDBlOXswamKTSlbJf9p+9SKsWdSsvFXpJyzvw54xPa+tWI2cVcDXg9cZ/sKSc8GXm57rO2M3suTfUQ3rh96/yhKwlwrmkl9ozYztin7qQ8t9G/F38T2aZJ+D3wI2IDyi+QPgQ/aPq9yeNl+qEkG/Iztj0m6qXJMKHknx9ueM7SicEYLcSelPNlHdKytHutdZmxL2pimvCklUQ+ou4IRjyXpcNtHT/DnvJHSJvmTlATMH7aRJ9DVisJklWz8iO619Rt3lxnbnwcuBq6ldPcbvEW79qzwOWcAhwPnNBP92sBYLY0nmmw/RKn7/xnbe1JWNWIMWcaP6I8lxkqasv2IpNrdwpZM295FwoT+O0taHNjF9rwk06bkcxsrNpK0JeWX2AOae3mAXYBM9hEdkDSb+U/0y0ga7JsPSn5OqxC2s4xt4DxJBwLfZuQyfo7etWtCV5Ga/fJtFv43q3gP3awoTErZs4/oiY4ztu8a43bqmLesRn6IpM8Cz6I0dHpwcN/22RMZJ56cPNlH9ESXGdu216r5+aOQtLLt8SownjXOa3+rpShVH7cbumegymQv6Tjb75H0bcZYqRjeUoj58mQfESNUytheEjiI+fX4LwU+30LhlV5p6hncRDn5cJ6n4A94SZvanilpzI6Jti9re0yTQSb7iBihRslRSV+kVDcbbBXsD8yx/daJjNN3TaLl31NOV2wOnAl8yfZPKsZsvU1zPHGZ7CNihEr7uo+p3T5ePfd48iRtC3wZWBa4GTjM9jUV4nTSplnSTsCHgTUpW9I1k1snvRxTiIjRajwBzJE0fXDRZE7PqRCn1yStLGmGpOuBQ4F3A0+jJGV+tVLYVWyfYvvR5u1LwCqVYg07DngjsLLtabaXy0S/YEnQi4jRapy5fz9wiaQ7m8+/JqW3fUysayhP2LuOWka/XtLnKsW8r2nNPChVuw/12zQD/AK4dSrmJdSQZfyInllYxrakI2z/e4W4TwXWbS5vt/2X8f5+PHGS9rJ95qh7e9qukYU/+PxjtWl+t+1f1IrZxN2csox/GSNrN/xHzbiTVSb7iJ7pImM72fjtGCu5snaPd0lb275qYfcqxL0Q+BMwC5g7uG/7qJpxJ6tM9hE901HGdrLxK5K0I/BqYC/g60MvTQPWt/2iirFb/wWjiXGr7dTCf5yyZx/RM82T/EXARUMZ2++QVC1jG9h8VOb9xU28mBi/prRN3oWRDYZmA++tEbCpS78VsErTUXFgGrB4jZijfFfSK2xf2EKsSS+TfUTPSFqZcjxqf+B3lIztc4EXUiqs1ah2N0fSdNs/a8aQbPwJZPtm4GZJX7H9aEthnwL8HWUeWW7o/gPAHi3EPwg4VNJfgL+So3fjyjJ+RM9I+gklY/uU0YVPJH3A9jEVYm5PyREYkY1vO41LJoCkM23vJWkWY5eQ3bBi7DVt313r88fEyGQf0TNdZGw3MUZk4wOb1U7i6gtJL6EkqY2uWrcG8Fvbd1SI2UmNeknj5gLYvmG81/sqk31Ez7SZUNX0O9+L0hXtvKYV6U7AEcDSE12pr68kfQc43PasUfdfAPy77Z0rxOykRr2kwWrQUsBmlOqAAjYErre9ZY24k1327CN6Yihj+1mSPjX00jSg1j7vSZSnyx8AJ0j6NaWt7uG2v1kpZh+tOnqiB7A9S9JzagS0PbP5c96kLmlFYA3bt9SI2cTbtol1NrDJ4OuWtAHwb7XiTnaZ7CP6o/WMbcqT14a250paCvgtMH0hbVjjiVthnNeWrhlY0qWU76klKN9X90i6yvYh437gk7fu8C84tm+V9PzKMSetTPYRPdFRxvYjtuc28R+WdGcm+iqul/Q2218YvinprYz8xa6G5W0/0MQ6zfaRkqo92Q+5panf8OXmel/Kkn6MIZN9RE8MMraBGyW1lbG93tAPfgHTm+vBMalqWeI98x7gHEn7Mn9y34xyPO61lWMvIekZlNyMf64ca9ibKcfvDqZ8P82kzrHRKSGTfUR/nCBpa2CnUffXoCyv15Bl1RbY/h2wVVMkaVBV7r9sX9xC+A8BFwBX2r6uqaHw09pBm5WiS4FnUn7RWAH4Ru24k1Wy8SN6oouM7YiJJmkdSme9fYB7KeWBD7W9ZqcDW8TlyT6iP1rP2I6pS9I/2f6YpBMY+5z9wZVC3wZcAew0qB8gqVaC6ZSRyT6iPzrL2I4p6UfNn9e3HHc34HXAJZLOB75G2bOPcWSyj+iPLjO2Y+rZUdIfbJ+68L86cZr6DN+UtCzwGkpy4tMlfRY4J41xxpY9+4iekLQqcA7wCGNkbNuulaSHpOcBRwPrUyqfAWB77Voxoy5JMyhP2M+gtEk+w/aNHY1lRWBPYG/b23cxhkVdJvuInhmVsf3DNjK2JV0JHAl8EtiZcmxqMdsfrB076pK0JmXSfx1lO+gMysT/k04HFiNkso+I6iTNtL2ppFm2XzB8r+uxxcSRtDFwMqVqYhs97eNxyp59RLThL5IWA34q6V3Aryi90GOSk7QEsCPlyX574FJSo36Rkyf7iKhO0ubAjyknAj5Mab5zrO1rOx1Y/M0k7UA56/5qSqOjrwHfsv1gpwOLMWWyj4iqmja3x9g+tOuxxMSRdDHwVeAbtv/Q9XhifJnsI6I6Sdfa3qLrcUT0VfbsI6INN0o6FzgLmLfMa/vs7oYU0R+Z7COiDUsB9wHbDd0zkMk+ogWZ7COiDV+0fdXwjaYDX0S0IHv2EVGdpBtsb7KwexFRR57sI6IaSVsCWwGrSDpk6KVpQIquRLQkk31E1PQUSvGcJYDlhu4/AOzRyYgieijL+BFRnaT1bN826t7TbN/b1Zgi+mSxrgcQEb1wpqR55+wl7Q5c3eF4Inoly/gR0YZ9gZMlXQo8E1iZkcfwIqKiLONHRCsk7QqcDswGXmr7jo6HFNEbebKPiOoknQRMBzYE1gG+I+kE25/udmQR/ZA9+4howyxgW9t32b4AeDGQM/YRLckyfkRUI2ma7QcW8Nqzbf+87TFF9FGe7COipksH70j63qjXvtnuUCL6K5N9RNSkofdXGue1iKgok31E1OQFvD/WdURUkmz8iKjp6U1NfA29T3O9SnfDiuiXJOhFRDWSjhzvddtHtTWWiD7LZB8RETHFZc8+IiJiistkHxERMcVlso+IiJjiMtlHRHWSVpV0kqTzmuv1JR3Q9bgi+iKTfUS04UvABZT2tgA/Ad7T2WgieiaTfUS04Wm2zwTmAth+FJjT7ZAi+iOTfUS04UFJK9NUzZO0BfDHbocU0R+poBcRbTgEOBeYLukqSvW8PbodUkR/pKhORLRC0hLAupRSubfb/mvHQ4rojUz2EVGNpN3Ge9322W2NJaLPsowfETXtPM5rBjLZR7QgT/YRERFTXLLxI6I6SStL+pSkGyTNlHR8k50fES3IZB8Rbfga8Htgd0oW/u+Br3c6oogeyTJ+RFQn6VbbG4y6N8v2C7oaU0Sf5Mk+ItpwoaTXSVqseduLUj43IlqQJ/uIqEbSbErWvYBlacrlUh40/mR7Wldji+iTTPYRERFTXM7ZR0QrJK0IPA9YanDP9uXdjSiiPzLZR0R1kt4KzABWB24CtgCuAbbrclwRfZEEvYhowwxgc+Bu29sCGwP3dzukiP7IZB8RbXjY9sMAkp5q+zZKU5yIaEGW8SOiDb+UtALwTeAiSX8A7u54TBG9kWz8iGiVpJcBywPn236k6/FE9EEm+4ioRtJK471u+3/bGktEn2Wyj4hqJN3F/KI6o9n22i0PKaKXMtlHRERMcUnQi4hqJK1n+zZJm4z1uu0b2h5TRB/lyT4iqpF0ou0DJV0yxsu2naI6ES3IZB8RETHFZRk/IlohaQNgfUbWxj+tuxFF9Eee7COiOklHAi+nTPbfBXYErrS9R5fjiuiLlMuNiDbsAWwP/Nb2m4GNKIV1IqIFmewjog1/tj0XeFTSNOAeYI2OxxTRG9mzj4g2XN/Uxv8CMBP4E6XFbUS0IHv2EdEqSc8Bptm+peOhRPRGJvuIqE7SucDXgG/ZfrDr8UT0TfbsI6INnwC2AX4k6f9J2kPSUgv7oIiYGHmyj4jWSFoc2A54G/Aq29M6HlJELyRBLyJaIWlpYGdgb2AT4NRuRxTRH3myj4jqJJ0JvAg4H/g6cFlzFC8iWpDJPiKqk/RK4L9tz2mutwH2sf3ObkcW0Q9Zxo+I6mxfIGljSfsAewF3AWd3PKyI3shkHxHVSFoH2Kd5u5eyhC/b23Y6sIieyTJ+RFQjaS5wBXCA7Tuae3faXrvbkUX0S87ZR0RNuwG/AS6R9AVJ2wPqeEwRvZMn+4ioTtKywGsoy/nbAacB59i+sNOBRfREJvuIaJWkFYE9gb1tb9/1eCL6IJN9RETEFJc9+4iIiCkuk31ERMQUl8k+IiJiistkHxERMcVlso+IiJji/j9l+5eyoXMtdwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vq492wGTKVZ8"
      },
      "source": [
        "# Acting on the inferences drawn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "nZZzMCGOKY8-",
        "outputId": "74a8e9bb-ad24-433a-e3e9-3402a41dda24"
      },
      "source": [
        "df = df.drop(['case_id','Hospital_code','patientid','City_Code_Patient','Admission_Deposit'],axis=1)\n",
        "df.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hospital_type_code</th>\n",
              "      <th>City_Code_Hospital</th>\n",
              "      <th>Hospital_region_code</th>\n",
              "      <th>Available Extra Rooms in Hospital</th>\n",
              "      <th>Department</th>\n",
              "      <th>Ward_Type</th>\n",
              "      <th>Ward_Facility_Code</th>\n",
              "      <th>Bed Grade</th>\n",
              "      <th>Type of Admission</th>\n",
              "      <th>Severity of Illness</th>\n",
              "      <th>Visitors with Patient</th>\n",
              "      <th>Age</th>\n",
              "      <th>Stay</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>c</td>\n",
              "      <td>3</td>\n",
              "      <td>Z</td>\n",
              "      <td>3</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>R</td>\n",
              "      <td>F</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Emergency</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>0-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>c</td>\n",
              "      <td>5</td>\n",
              "      <td>Z</td>\n",
              "      <td>2</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>S</td>\n",
              "      <td>F</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>41-50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e</td>\n",
              "      <td>1</td>\n",
              "      <td>X</td>\n",
              "      <td>2</td>\n",
              "      <td>anesthesia</td>\n",
              "      <td>S</td>\n",
              "      <td>E</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>31-40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>R</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>41-50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>radiotherapy</td>\n",
              "      <td>S</td>\n",
              "      <td>D</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Extreme</td>\n",
              "      <td>2</td>\n",
              "      <td>51-60</td>\n",
              "      <td>41-50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Hospital_type_code  City_Code_Hospital  ...    Age   Stay\n",
              "0                  c                   3  ...  51-60   0-10\n",
              "1                  c                   5  ...  51-60  41-50\n",
              "2                  e                   1  ...  51-60  31-40\n",
              "3                  b                   2  ...  51-60  41-50\n",
              "4                  b                   2  ...  51-60  41-50\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UY-aM8bRaxH",
        "outputId": "acdb0bd2-8469-4b1c-9d5a-773e21e56296"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Hospital_type_code                     0\n",
              "City_Code_Hospital                     0\n",
              "Hospital_region_code                   0\n",
              "Available Extra Rooms in Hospital      0\n",
              "Department                             0\n",
              "Ward_Type                              0\n",
              "Ward_Facility_Code                     0\n",
              "Bed Grade                            113\n",
              "Type of Admission                      0\n",
              "Severity of Illness                    0\n",
              "Visitors with Patient                  0\n",
              "Age                                    0\n",
              "Stay                                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JZuYpeaQ2l2"
      },
      "source": [
        "df.dropna(inplace=True)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3U2TN6VRy2G"
      },
      "source": [
        "df['Department'] = df['Department'].replace('surgery','anesthesia')\n",
        "df['Ward_Type'] = df['Ward_Type'].replace('U','T')"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLXSiFADVMvW"
      },
      "source": [
        "df['Severity of Illness'] = df['Severity of Illness'].map({'Extreme':2, 'Moderate':1, 'Minor':0})\n",
        "df['Visitors with Patient'] = df['Visitors with Patient'].apply(lambda x: 1 if x > 0 else 0)\n",
        "df['Age'] = df['Age'].map({'0-10':0,'11-20':1,'21-30':2,'31-40':3,'41-50':4,'51-60':5,'61-70':6,'71-80':7,'81-90':8,'91-100':9})"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1XmETC4LHPg"
      },
      "source": [
        "df_Hospital_type_code = pd.get_dummies(df['Hospital_type_code'])\n",
        "df_Hospital_region_code = pd.get_dummies(df['Hospital_region_code'])\n",
        "df_Ward_Facility_Code = pd.get_dummies(df['Ward_Facility_Code'])\n",
        "df_Type_of_Admission = pd.get_dummies(df['Type of Admission'])\n",
        "df_Department = pd.get_dummies(df['Department'])\n",
        "df_Ward_Type = pd.get_dummies(df['Ward_Type'])\n",
        "\n",
        "df = pd.concat([df,df_Hospital_region_code,df_Hospital_type_code,df_Ward_Facility_Code,df_Type_of_Admission,df_Department,df_Ward_Type],axis=1)\n",
        "df = df.drop(['Hospital_region_code','Hospital_type_code','Ward_Facility_Code','Type of Admission','Department','Ward_Type'],axis=1)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZefjUjxXKgJ"
      },
      "source": [
        "df['Bed Grade'] = df['Bed Grade'].astype(int)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wHp5vt9GZS_w",
        "outputId": "237b5f6e-aa33-497e-f5ce-c37c02a9ba37"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City_Code_Hospital</th>\n",
              "      <th>Available Extra Rooms in Hospital</th>\n",
              "      <th>Bed Grade</th>\n",
              "      <th>Severity of Illness</th>\n",
              "      <th>Visitors with Patient</th>\n",
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              "      <th>F</th>\n",
              "      <th>Emergency</th>\n",
              "      <th>Trauma</th>\n",
              "      <th>Urgent</th>\n",
              "      <th>TB &amp; Chest disease</th>\n",
              "      <th>anesthesia</th>\n",
              "      <th>gynecology</th>\n",
              "      <th>radiotherapy</th>\n",
              "      <th>P</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>41-50</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <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>0</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   City_Code_Hospital  Available Extra Rooms in Hospital  Bed Grade  ...  R  S  T\n",
              "0                   3                                  3          2  ...  1  0  0\n",
              "1                   5                                  2          2  ...  0  1  0\n",
              "2                   1                                  2          2  ...  0  1  0\n",
              "3                   2                                  2          2  ...  1  0  0\n",
              "4                   2                                  2          2  ...  0  1  0\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bh8duLFPQRG",
        "outputId": "dc78ff6f-4234-4185-ad86-fbfe7b75f26d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 318325 entries, 0 to 318437\n",
            "Data columns (total 35 columns):\n",
            " #   Column                             Non-Null Count   Dtype \n",
            "---  ------                             --------------   ----- \n",
            " 0   City_Code_Hospital                 318325 non-null  int64 \n",
            " 1   Available Extra Rooms in Hospital  318325 non-null  int64 \n",
            " 2   Bed Grade                          318325 non-null  int64 \n",
            " 3   Severity of Illness                318325 non-null  int64 \n",
            " 4   Visitors with Patient              318325 non-null  int64 \n",
            " 5   Age                                318325 non-null  int64 \n",
            " 6   Stay                               318325 non-null  object\n",
            " 7   X                                  318325 non-null  uint8 \n",
            " 8   Y                                  318325 non-null  uint8 \n",
            " 9   Z                                  318325 non-null  uint8 \n",
            " 10  a                                  318325 non-null  uint8 \n",
            " 11  b                                  318325 non-null  uint8 \n",
            " 12  c                                  318325 non-null  uint8 \n",
            " 13  d                                  318325 non-null  uint8 \n",
            " 14  e                                  318325 non-null  uint8 \n",
            " 15  f                                  318325 non-null  uint8 \n",
            " 16  g                                  318325 non-null  uint8 \n",
            " 17  A                                  318325 non-null  uint8 \n",
            " 18  B                                  318325 non-null  uint8 \n",
            " 19  C                                  318325 non-null  uint8 \n",
            " 20  D                                  318325 non-null  uint8 \n",
            " 21  E                                  318325 non-null  uint8 \n",
            " 22  F                                  318325 non-null  uint8 \n",
            " 23  Emergency                          318325 non-null  uint8 \n",
            " 24  Trauma                             318325 non-null  uint8 \n",
            " 25  Urgent                             318325 non-null  uint8 \n",
            " 26  TB & Chest disease                 318325 non-null  uint8 \n",
            " 27  anesthesia                         318325 non-null  uint8 \n",
            " 28  gynecology                         318325 non-null  uint8 \n",
            " 29  radiotherapy                       318325 non-null  uint8 \n",
            " 30  P                                  318325 non-null  uint8 \n",
            " 31  Q                                  318325 non-null  uint8 \n",
            " 32  R                                  318325 non-null  uint8 \n",
            " 33  S                                  318325 non-null  uint8 \n",
            " 34  T                                  318325 non-null  uint8 \n",
            "dtypes: int64(6), object(1), uint8(28)\n",
            "memory usage: 27.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "egUlQZsUFnDv",
        "outputId": "978958b1-b2f7-4b72-a0e6-79b0fafd6b76"
      },
      "source": [
        "corr = df.corr()\n",
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(corr)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe26b2e9510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dlMyGVlbVDquKNquqQ5K8PLNKp8Pr3+90eW2SW+2gz1dMj0/dUZzuvirJM5P83tTno6rqrtPLv5DZcdzW8p3dlzsl+bfu/h9JXp3Z+7I3/XNmw2SPqKrVmVUVfmBKAq7q7r/NrIJvcT8ePfX1fkmu6e5rttjmlsf9Vkm+Mg2Zfdxe2g8AAABgju0oMfaYJG/fYtnfZjacclNmw/EeMv2b7r4osyGUn0jyxiTbG56YzIZj7ldVlyd5cWbDKTf7VpJ7T0MWfzTJ8xev2N03ZZY4e8k0FPPCbONukvmPc4xdOFV6/UmSv5iGLz4xyYur6jZJ3p3kEVO775jEPskfJXlRVV2Qna+0ekeStZkNQXx8ZkMxL8ms4utV3X3j1pbv5LaTWTXVRVOfHp3Z/Gl7TXd/JbNk31lJLkpyXne/M7MbDJw9DSF9fWbJwM1unPr3qmx9vrA3J3nGdAOCuyR5TpJ/yuwc+sRe2xkAAABYTnph5f4sQ/XvI+pg75iGyD69u9ftqO2ecuYdTtnrJ/bjLnr+jhvtIa84/rlD4txxw8q6Hnz+gBoW667rx1zkN9SYfVo9JEpy8MKmHTfaQ9YPOnbX185M37n7Rr1H160a93t0/ZhDl7WDvpOtGfQd65rV496j22wYc/DWDvrifO2qUb9JyQGDzof9BsW5cr8xx+7QTeP+E/WtVWMuQlftkUlMduw+G68fEudTA28ef9/DrhwS5+Db3Tgkzls/ceSQOB9dNeZcSJIHbRhzPpzy5deP+/BbAus/9eGV9R+zRdZ83/2W3Xs36CsoAAAAACwvg/5ewTzr7pOXug8AAAAAW5IYAwAAAFguBk43gqGUAAAAAMwpiTEAAAAA5pLEGAAAAABzyRxjAAAAAMtFLyx1D+aKijEAAAAA5pLEGAAAAABzSWIMAAAAgLlkjjFWpB4Q4xXHP3dAlJmnnP/8IXF+7cTfHRLnQesPGBLne28acSbMfFdvGBLnkv3XDInzPRvGzGvwr/uN+xg6bNOmIXHW9JjzbmPVkDiHDzpuSXLwpjH79M3VY/4uuGZhzLlwq4w5bknyyTVjjt3d1g8Jk1stjDu/Vw/5dpKsO3D/IXEesP7GIXGu7XGfEzVoSp+7LVw/JM5HVx80JM5tN477vnXJVYcPibPha2Ouq3cYNI/UT626xZA4SbLfoO9BK96COcZGUjEGAAAAwFySGAMAAABgLkmMAQAAADCXJMYAAAAAmEsm3wcAAABYLgbdmIEZFWMAAAAAzCWJMQAAAADmksQYAAAAAHPJHGMAAAAAy8WCOcZGUjEGAAAAwFySGAMAAABgLkmMAQAAADCXzDE2R6pqU5JLklSSTUme0t0fuRnrPy/Jdd39sq28dkqS/5JkdZKNSc5N8vTu/sZu9Pe67j5oV9cHAACAfU33pqXuwlyRGJsvN3T3cUlSVQ9K8qIkP7K7G62qByf5rSQP6e4rqmp1kl9K8t1JvrFF29XttxwAAABYBgylnF8HJ7l685OqekZVnVtVF1fVHyxa/qyq+lRVfTjJ3baxrWdlVh12RZJ096buPqO7Pzlt4wtV9ZKqOj/Jz1XVk6ZYF1XV31bV2qndnavqo1V1SVX9t8UBttU/AAAAgF0lMTZfblFVF1bVJ5K8OskLkqSqfjLJ0UnuneS4JCdU1QOq6oQkPz8t+6kk99rGdn8wyfk7iP217j6+u9+c5O+6+17dfc8klyd54tTmz5K8sruPTfKVzStuq39bBqiq06pqXVWtO/tbn97hwQAAAADmm6GU82XxUMqTkvx1VR2T5Cennwumdgdlloi6VZK3d/f10zrv2lGAqjo2yeumdf9rd79leukti5odM1WEHTrFev+0/L5JfnZ6/LokL5keb6t/H1wcu7tPT3J6krzmDqf0jvoKAAAAy04vLHUP5orE2Jzq7o9W1RFJbp3ZZPwv6u6/Wtymqp62k5v7eJLjk5zV3ZckOa6qXpHkFovafGvR4zOTPLy7L6qqU5OcvLhrW9n+VvsHAAAAsDsMpZxTVXX3zO4g+bXMKraeUFUHTa/doapuk1lF1sOr6hZVdaskD93G5l6U5GVVdcdFy26xjbbJrJrsK1W1f5LHLVp+TmZDN7PF8m31DwAAAGCXqRibL7eoqgunx5Xkl6Y7RP5DVX1/ko9WVZJcl+SU7j6/qt6S5KIkX01y7tY22t3vrapbJ/n76Y6U30hyaf59iOSWnpPkn5JcOf17q2n5byZ5Y1X9bpJ3Ltr+Vvs39QkAAABgl0iMzZHuXr2d1/4ss8nvt1z+wiQv3IltvzbJa7fx2lFbPH9lkldupd3nk5y0aNGzd9Q/AAAAgF0lMQYAAACwXCyYfH8kc4wBAAAAMJckxgAAAACYSxJjAAAAAMwlc4wBAAAALBdtjrGRVIwBAAAAMJckxgAAAACYSxJjAAAAAMwlc4wBAAAALBcLm5a6B3NFxRgAAAAAc0nFGCvSEZv2fob9oIVxeeVfO/F3h8T5y3UvGRLnncc+Z0icI+uGIXGS5PL91g6Jc/RNG4fEuXbV6iFxbrHQQ+Ikyfoa8zs7ao9qUJwbBx23JFndY47eoZvG3OlpQ415l265MO7OVSfcOCbWdYPOu6tWj/sqPOp39nZjPibyrR5z7K5ePebzKEnWDPpMumLjmO8Mhww6dAcMunYnyagamv0H7dN1q8Zc60Z+Tmwc9NkHe5KKMQAAAADmkooxAAAAgOWix1X5oWIMAAAAgDklMQYAAADAXJIYAwAAAGAuSYwBAAAAMJdMvg8AAACwXCyYfH8kFWMAAAAAzCWJMQAAAADmksQYAAAAAHPJHGMAAAAAy0WbY2wkFWMAAAAAzCWJsb2oqp5VVR+vqour6sKqus9ejvfkqvrF6fGpVXX7PbTd+0/7cWFV3WKL166b/j2qqi6dHp9cVe/ZE7EBAAAA9hZDKfeSqjopyf+X5PjuXl9VRyQ5YC/G26+7X7Vo0alJLk3y5T2w+ccleVF3v34PbAsAAABgWVAxtvfcLslV3b0+Sbr7qu7+cpJU1QlV9YGqOq+q3l9Vt6uqu1fVP29eearAumRb7aflZ1fVn1bVuiS/WVXPq6qnV9Ujk5yY5A1TlddPV9U7Fm37J6rq7Vt2uKp+rKouqKpLquqMqlpTVb+c5FFJXlBVb7i5B2Hq0xlTXz9XVU9d9NopVfXPUx//qqpWTz9nVtWlUz9+a2r71Kq6bKq+e/PN7QcAAADsExYWVu7PMiQxtvf8Q5Ijq+pTVfWXVfUjSVJV+yf58ySP7O4TkpyR5IXd/YkkB1TVnaf1H53kLdtqvyjOAd19Ynf/980LuvttSdYleVx3H5fkvUnuXlW3npo8ftrOt1XVgUnOTPLo7j42s2rCX+3uVyd5V5JndPfjdvFY3D3Jg5LcO8nvV9X+VfX90z7ed+rjpswq045LcofuPmbqx2umbTwzyX/q7nskefLWglTVaVW1rqrWve/6z+xiVwEAAIB5ITG2l3T3dUlOSHJakiszS3KdmuRuSY5J8o9VdWGSZye547Ta32SWLMr071t20D5Tmx31pZO8LskpVXVokpOS/P0Wze6W5PPd/anp+WuTPGBn93cH/ld3r+/uq5J8Ncl3J/mxzI7PudN+/ViS703yuSTfW1V/XlUPTvLNaRsXZ1YBd0qSjdvYz9OnJOGJD1571z3UdQAAAGClMsfYXtTdm5KcneTsaVjkLyU5L8nHu/ukrazyliRvraq/m63en66qY7fTPkm+tZPdeU2Sdye5Mclbu3uryaW9ZP2ix5syO+8qyWu7+/e2bFxV98yswuzJmQ3jfEKSn84sUffQJM+qqmMH7wMAAACwwqgY20uq6m5VdfSiRccl+X9JPpnk1tPk/JmGFf5gknT3ZzNLHD0n/14Jts32O3BtklttfjLNb/blzCrOXrOV9p9MclRVbS61+oUkH9iZfd1F/yfJI6vqNklSVd9VVXeablKwqrv/durr8VW1KsmR3X1Wkt9NckiSg/Zi3wAAAGBpLPU8YHM2x5iKsb3noCR/Pg1d3JjkM0lO6+6bpsnxX15Vh2T2Hvxpko9P670lyUuT3DlJdqL9tpyZ5FVVdUOSk7r7hiRvSHLr7r58y8bdfWNVPT6zirX9kpyb5FVbtttTuvuyqnp2kn+YEl8bkvx6khuSvGZaliS/l2R1ktdP+19JXt7d39hbfQMAAADmQ82mn2IeVNUrklzQ3f9zqfuyt737to/Z6yf2jTWu4PIf1tw0JM5frnvJkDjvPPY5Q+IcWTcMiZMkl9faIXFuu3HMCOJrV60eEmekGhRn1KfqStufJFk96DvJqKv3hhrzLu0/8Lvc2h7zl97rBn3GXr9q3Gf5qN/ZDYMC3X7Q59HXVo/7PFqzMOZ3adQeXbN6zPl9yKZxFSCbBsUZdWX41qBr0C0HVulsHPTZ98ivvGHUZXVJ3Pih163YRM2B9/+FZffeqRibE1V1Xmbzkf3OUvcFAAAAYDmQGJsT3X3CUvcBAAAAYDmRGAMAAABYJrpHDRwmcVdKAAAAAOaUxBgAAAAAc0liDAAAAIAlV1UPrqpPVtVnquqZW3n9e6rqrKq6oKourqqf2t2Y5hgDAAAAWC4WFpa6B0uiqlYn+YskP5HkS0nOrap3dfdli5o9O8nfdPcrq+oHkrw3yVG7E1fFGAAAAABL7d5JPtPdn+vum5K8OcnDtmjTSQ6eHh+S5Mu7G1RiDAAAAIC9rqpOq6p1i35OW/TyHZJ8cdHzL03LFnteklOq6kuZVYv9xu72yVBKAAAAAPa67j49yem7sYnHJDmzu/97VZ2U5HVVdUx37/L4U4kxVqT1tbKKIR+0/oAhcd557HOGxHnYJS8YEmfU/iTJLbuHxLlu1eohcWpIlFkd9Eoz6tiNMnJ/FmpMtFGzdow6dhsHHbck+WaNuQaNcuCga3cy7np3i4Uxka4e9Hm0/8APipV2DRp1LmwYeA0aZdR7tGbQNWjk5wR7yK7nePZ1VyQ5ctHzO07LFntikgcnSXd/tKoOTHJEkq/uatCVlT0AAAAAYF90bpKjq+rOVXVAkp9P8q4t2vxLkh9Lkqr6/iQHJrlyd4JKjAEAAACwpLp7Y5KnJHl/ksszu/vkx6vq+VX1M1Oz30nypKq6KMmbkpzavXvll4ZSAgAAALDkuvu9mU2qv3jZcxc9vizJffdkTIkxAAAAgOViYW7nGFsShlICAAAAMJckxgAAAACYSxJjAAAAAMwliTEAAAAA5pLJ9wEAAACWizb5/kgqxgAAAACYSxJjAAAAAMwliTEAAAAA5pLE2M1QVWdV1YO2WPa0qnplVf1MVT1zO+ueWFUvnx6fXFU/vLf7u41+vLeqDp1+fm3R8pOr6j07sf6ZVfX5qrqwqs6vqpO203bLGLevqrftRt+fVlVrd3V9AAAAWPYWFlbuzzIkMXbzvCnJz2+x7OeTvKm739XdL97Wit29rrufOj09OcnNSoxV1R65UUJ3/1R3fyPJoUl+bUftt+EZ3X1ckmcm+avttPsPMbr7y939yF2MmSRPSyIxBgAAAOwREmM3z9uS/HRVHZAkVXVUktsn+VBVnVpVr5iW/1xVXVpVF1XVB6dlJ1fVe6Z1npzkt6aqq/tX1VFV9X+r6uKq+j9V9T3TOmdW1auq6p+S/FFV/ci0zoVVdUFV3Wpx56rqGVX11Onxn1TV/50e/2hVvWF6/IWqOiLJi5PcZdrWS6dNHFRVb6uqT1TVG6qqdnA8PpjkrlV10NTv86vqkqp62PT6f4gx7eelUz9WT8vOnfb7VxYdp7O37Me0X7dPclZVnXUz3zcAAACA77BHqpDmRXd/var+OclDkrwzs2qxv+nu3iKH9NwkD+ruK6rq0C228YWqelWS67r7ZUlSVe9O8trufm1VPSHJy5M8fFrljkl+uLs3Te1+vbvPqaqDkty4RRc/lOR3pvVPTLKmqvZPcv/MkliLPTPJMVPlV6rq5CT/KckPJvlyknOS3DfJh7dzSB6a5JKpH4/o7m9OSbePVdW7thLjqEXrPjHJNd19r6pak+ScqvqH6bXv6Ed3v7yqfjvJA7v7qq11pqpOS3Jakpx28L3zE2vvup2uAwAAAPNOxdjNt3g45c9Pz7d0TpIzq+pJSe8s8NEAACAASURBVFbvxDZPSvLG6fHrktxv0Wtv7e5Ni7b7x1P11KHdvXGL7ZyX5ISqOjjJ+iQfzSxBdv/MkmY78s/d/aXuXkhyYZKjttHupVV1YWZJqCcmqSR/WFUXJ/nfSe6Q5Lt3EOsnk/zitJ1/SnJ4kqNvZj/+g+4+vbtP7O4TJcUAAADYJ/XCyv1ZhlSM3XzvTPInVXV8krXdfd6WDbr7yVV1nyQ/neS8qjphN+J9a9F2X1xV/yvJT2VWYfWg7v7Eotc3VNXnk5ya5CNJLk7ywCR3TXL5TsRav+jxpmz7/HhGd397Ev2qOjXJrZOcMPXhC0kO3EGsSvIb3f3+/7BwVrm2s/0AAAAA2GUqxm6m7r4uyVlJzsjWq8VSVXfp7n/q7ucmuTLJkVs0uTbJ4vnBPpJ/r0J7XLZR3TVt95LufkmSc5PcfSvNPpTk6ZkNnfxQZvOZXdDdvYM+7I5Dknx1Soo9MMmddiLG+5P86jTUM1X1fVV1yx3E2ZN9BgAAAOacxNiueVOSe2YbibHMhhpeMk00/5EkF23x+ruTPGLz5PtJfiPJ46ehiL+Q5De3sd2nTZP6X5xkQ5K/30qbDyW5XZKPdve/ZTb/13ck2rr7a5lVnV26aPL9XfWGJCdW1SVJfjHJJ3YixquTXJbk/Ok4/VV2XBl2epL3mXwfAAAA2BPqOwuJYN/3tts9bq+f2CN/c3Z0e9B9zcMuecGQOO889jlD4iTjzoeVdi74PQLmxajr3epB3+037PDm5XvGzkzWy9b5bsJK9sivvGFFn3o3/P3LV2yi5hYPeeqye+9UjAEAAAAwlyTGAAAAAJhLEmMAAAAAzCWJMQAAAADm0o7uAggAAADAKAsLS92DuaJiDAAAAIC5JDEGAAAAwFySGAMAAABgLpljDAAAAGC5aHOMjaRiDAAAAIC5pGKMFWn/7r0e45NrxuWVv/emvb8/SXJk3TAkzjuPfc6QOA+75AVD4iTJu4959rBYI6xfVUPi7D/m1E6SjAq10v6+t2bA9XSzUcduocac35uGRElWD4qTjPl8TZLrV+A1aKU5YmHjkDiXrtl/SJwkuc3GMSfEYZvGXB0+c8CY/+rdY8P6IXGS5N/qgCFxNo65BA27Bo26difJTYM+Y2FPUjEGAAAAwFxSMQYAAACwXCystDEIy5uKMQAAAADmksQYAAAAAHNJYgwAAACAuWSOMQAAAIDlos0xNpKKMQAAAADmksQYAAAAAHNJYgwAAACAuSQxBgAAAMBcMvk+AAAAwHKxYPL9kVSMAQAAADCXJMYYrqoeXlVdVXdf6r4AAAAA80tijKXwmCQfnv4FAAAAWBLmGGOoqjooyf2SPDDJu5P8flWtSvKKJD+a5ItJNiQ5o7vfVlUnJPnjJAcluSrJqd39lSXpPAAAAOxtbY6xkVSMMdrDkryvuz+V5GtT4us/JzkqyQ8k+YUkJyVJVe2f5M+TPLK7T0hyRpIXbmvDVXVaVa2rqnXvv/4ze3cvAAAAgH2eijFGe0ySP5sev3l6vl+St3b3QpJ/raqzptfvluSYJP9YVUmyOsk2q8W6+/QkpyfJO2/72N4rvQcAAABWDIkxhqmq78psuOSxVdWZJbo6ydu3tUqSj3f3SYO6CAAAAMwRQykZ6ZFJXtfdd+ruo7r7yCSfT/L1JD9bVauq6ruTnDy1/2SSW1fVt4dWVtUPLkXHAQAAYIiFhZX7swxJjDHSY/Kd1WF/m+S2Sb6U5LIkr09yfpJruvumzJJpL6mqi5JcmOSHx3UXAAAAWMkMpWSY7n7gVpa9PJndrbK7r6uqw5P8c5JLptcvTPKAoR0FAAAA5oLEGMvFe6rq0CQHJHlBd//rUncIAAAAWNkkxlgWuvvkpe4DAAAALLllOhfXSmWOMQAAAADmksQYAAAAAHNJYgwAAACAuSQxBgAAAMBcMvk+AAAAwHLRvdQ9mCsqxgAAAACYSxJjAAAAAMwliTEAAAAA5pI5xliRPnfA3s/53nX9wl6Psdl39YYhcS7fb+2QOLccNGb+3cc8e0icJHnopf9tSJwjjvqJIXHuc9jRQ+L8zQNuGhInSf74nNsNiXN5Xzckzp1qzO/rM7//y0PiJMkrL7vjkDiHLtSQOJevHnN+35hxn0edMdfvry+sHxLnjb98yJA4SbLx0/86JM6bzh5zrbv1hjHn9xEb9x8SJ0nWLIw5v/cf9Ht08KBLwxWrDhgTKMn1q8Zcv9cMOnb3OuzKIXFec/3hQ+IkyfduHPMerXgL4z7bUTEGAAAAwJySGAMAAABgLkmMAQAAADCXzDEGAAAAsFyYY2woFWMAAAAAzCWJMQAAAADmksQYAAAAAHPJHGMAAAAAy0WbY2wkFWMAAAAAzCWJMQAAAADmksQYAAAAAHNJYgwAAACAuWTyfZa1qjoyyQeTnNDdX6+qw5Kcn+SB3f2FJe0cAAAA7GkLJt8fScUYy1p3fzHJK5O8eFr04iSnS4oBAAAAu0vFGPuCP0lyXlU9Lcn9kjxlifsDAAAArAAqxlj2untDkmdkliB72vT8O1TVaVW1rqrWffS6Tw/tIwAAALDvkRhjX/GQJF9Jcsy2GnT36d19YnefeNJBR4/rGQAAAOwp3Sv3ZxmSGGPZq6rjkvxEkh9K8ltVdbsl7hIAAACwAkiMsaxVVWU2+f7Tuvtfkrw0ycuWtlcAAADASiAxxnL3pCT/0t3/OD3/yyTfX1U/soR9AgAAAFYAd6VkWevu05Ocvuj5piTHL12PAAAAYC9aWFjqHswVFWMAAAAAzCWJMQAAAADmksQYAAAAAHPJHGMAAAAAy4U5xoZSMQYAAADAXJIYAwAAAGAuSYwBAAAAMJckxgAAAACYSybfBwAAAFgu2uT7I6kYAwAAAGAuqRhjRXroQVfu9RjnXX3EXo+x2SX7rxkS5+ibNg6Jc92q1UPijHTEUT8xJM5VX/jHIXEecfxvDInz2g/dfkicJPnt+14xJM5TPnLYkDg/cuOQMPnDy283JlCS3zj0q0PifOPqtUPifCa3GBLnJQ/+5pA4I6068jZD4vzC6d8YEidJLrjuqiFx3rzmkCFxPnLAmN+j22/oIXGSZFPVkDhfXL3/kDiHbhpTcbI6496jQzaNibX/oH36n9cfPiTOf95ww5A4SfKtjVIM7HtUjAEAAAAwl6RzAQAAAJaJXhhXiYmKMQAAAADmlMQYAAAAAHNJYgwAAACAuWSOMQAAAIDlYmHMXWWZUTEGAAAAwFySGAMAAABgLkmMAQAAADCXzDEGAAAAsFy0OcZGUjEGAAAAwFySGAMAAABgLkmMsazVzIer6iGLlv1cVb1vKfsFAAAA7PvMMcay1t1dVU9O8taqOiuzc/YPkzx4aXsGAAAA7Oskxlj2uvvSqnp3kt9Ncsskf93dn13ibgEAAMCet9BL3YO5Yigl+4o/SPLYJA9J8kdba1BVp1XVuqpa9+arvzS0cwAAAMC+R8UY+4Tu/lZVvSXJdd29fhttTk9yepJ85gceJMUOAAAAbJeKMfYlC9MPAAAAwG5TMQYAAACwXCyoBxlJxRgAAAAAc0nFGPuM7n7eUvcBAAAAWDlUjAEAAAAwl1SMAQAAACwX5hgbSsUYAAAAAEuuqh5cVZ+sqs9U1TO30eZRVXVZVX28qt64uzFVjAEAAACwpKpqdZK/SPITSb6U5Nyqeld3X7aozdFJfi/Jfbv76qq6ze7GVTEGAAAAwFK7d5LPdPfnuvumJG9O8rAt2jwpyV9099VJ0t1f3d2gKsYAAAAAlovupe7BXlNVpyU5bdGi07v79OnxHZJ8cdFrX0pyny028X3Tds5JsjrJ87r7fbvTJ4kxAAAAAPa6KQl2+g4bbtt+SY5OcnKSOyb5YFUd293f2NUNGkoJAAAAwFK7IsmRi57fcVq22JeSvKu7N3T355N8KrNE2S6TGAMAAABgqZ2b5OiqunNVHZDk55O8a4s278isWixVdURmQys/tztBJcYAAAAAWFLdvTHJU5K8P8nlSf6muz9eVc+vqp+Zmr0/ydeq6rIkZyV5Rnd/bXfiVq/gSd2YXy/7nlP2+ol91IZxvzv7D/o9valqSJwxUZINowIlec3q3boW77QDavWQOG8//8+HxHn0CU8bEidJblsHDonzx296xJA4X3nyGUPivOUb3z0kTpIctDAmzi0GXb4f86zDhsT5oxd/fUicka6vMSfDxoz7LP+VA68ZEucj1x4+JM5hm8a8Rwf2oAtDkmtXjfmMXT/o+9ZhmzYNiXPdqnG1FgcO+k58zeox+7Rq0CXo9ptuGhMoyZdXHzAkzilffv3Ab/rjXf/HT1qxiZq1v/0/lt17p2IMAAAAgLkkMQYAAADAXJIYAwAAAGAu7bfUHQAAAABgsrBipxhbllSMAQAAADCXJMYAAAAAmEsSYwAAAADMJXOMAQAAACwXvbDUPZgrKsYAAAAAmEsSYwAAAADMJYkxAAAAAOaSOcYAAAAAlouFXuoezBUVYyxrVfWIqrpwi5+FqnrIUvcNAAAA2LepGGNZ6+63J3n75udVdVqSxyV5/5J1CgAAAFgRJMbYZ1TV9yV5bpIf7nb/WgAAAGD3GErJPqGq9k/yxiS/093/so02p1XVuqpa97HrPj22gwAAAMA+R8UY+4oXJPl4d79lWw26+/QkpyfJy77nFLMVAgAAsM/pBQOkRpIYY9mrqpOT/GyS45e4KwAAAMAKIjHGslZVhyV5TZLHdve1S90fAAAAYOWQGGO5e3KS2yR5ZVUtXv6i7Q2rBAAAANgRiTGWte5+UZIXLXU/AAAAYIgFU2aP5K6UAAAAAMwliTEAAAAA5pLEGAAAAABzyRxjAAAAAMtFLyx1D+aKijEAAAAA5pLEGAAAAABzSWIMAAAAgLlkjjEAAACA5WKhl7oHc0XFGAAAAABzSWIMAAAAgLlkKCUr0v03Xr/XY1yzcMBej7HZv+435lf1FoNKdkcVBu8/sAL5bx5w05A4r/3Q7YfEefQJTxsS5y3n/emQOEly/3s8YUicxzz6jUPiHLXq8CFx1teYcztJbrtq/yFxDukaEuc3X/KvQ+Lcc2HNkDhJcsr9rhgS57Hn3GJInN+7ae2QOEmSA8eEudvCDUPiXL7fmPdoYeDf8Q8a9D3okIVNQ+J8c/WYY7dm4JCvG2vM9fuIjRuHxLlVjYlz9oHjPie+/yZDANn3qBgDAAAAYC6pGAMAAABYLhYWlroHc0XFGAAAAABzSWIMAAAAgLkkMQYAAADAXDLHGAAAAMByMfBur6gYAwAAAGBOSYwBAAAAMJckxgAAAACYS+YYAwAAAFguemGpezBXVIwBAAAAMJckxgAAAACYSxJjAAAAAMwlc4wBAAAALBcLvdQ9mCsqxtgnVNU7quq8qvp4VZ221P0BAAAA9n0SY+wrntDdJyQ5MclTq+rwLRtU1WlVta6q1r3j+s+P7yEAAACwT5EYY1/x1Kq6KMnHkhyZ5OgtG3T36d19Ynef+PC1dx7eQQAAAGDfYo4xlr2qOjnJjyc5qbuvr6qzkxy4pJ0CAACAvaAXFpa6C3NFxRj7gkOSXD0lxe6e5IeWukMAAADAvk9ijH3B+5LsV1WXJ3lxZsMpAQAAAHaLoZQse929PslDlrofAAAAwMqiYgwAAACAuaRiDAAAAGC5WOil7sFcUTEGAAAAwFySGAMAAABgLkmMAQAAADCXzDEGAAAAsFyYY2woFWMAAAAAzCWJMQAAAADmksQYAAAAAHPJHGOsSOeuXrv3g6xO7rRh096Pk+SwTWPirK+VlysfNTr/j8+53ZA4v33fK4bE+eRHbz0kzm+e+Mycf9NXh8T60MVnDInzrBOfNSROknxXrx4SZ9Og36SDF2pInLWDLgw/tLAmX16994Ndvaqzvsbs1Ms+OuZa93dve8SQOEnyu4/62yFxjrr+8CFxDh30UX7bQd+BkmRjjbk2rBp0rduUMfszKs71qyprFxaGxFrbY+KM+k/ytT0m0gk3bson14yJdf3K++/E0hh0rjPjtIVdNCopxq4zZeXyNyopthKttKTYSjQiKZZkWFJsJRqVFGPXjUqKsetGJcXYdaOSYrCvkhgDAAAAYC5JjAEAAAAwl9RUAgAAACwXC6ZJGEnFGAAAAABzSWIMAAAAgLkkMQYAAADAXJIYAwAAAGAumXwfAAAAYJlok+8PpWIMAAAAgLkkMQYAAADAXJIYAwAAAGAumWMMAAAAYLkwx9hQKsZY9qrqqKq6dKn7AQAAAKwsEmMAAAAAzCWJMfYV+1XVG6rq8qp6W1WtXeoOAQAAAPs2iTH2FXdL8pfd/f1Jvpnk17ZsUFWnVdW6qlp3znWfHt5BAAAA2G0LCyv3ZxmSGGNf8cXuPmd6/Pok99uyQXef3t0ndveJ9z3o6LG9AwAAAPY5EmPsK7a8LYfbdAAAAAC7RWKMfcX3VNVJ0+PHJvnwUnYGAAAA2Pftt9QdgJ30ySS/XlVnJLksySuXuD8AAACw5y0YIDWSxBjLXnd/Icndl7ofAAAAwMpiKCUAAAAAc0liDAAAAIC5JDEGAAAAwFwyxxgAAADAcmHy/aFUjAEAAAAwlyTGAAAAAJhLEmMAAAAAzCVzjAEAAAAsE93mGBtJxRgAAAAAc0nFGCvSNav2fob9+hqXV14z6C8Go/4uUYPiLAyKkySX93VD4jzlI4cNifPqNz9iSJwkecyj3zgkzrNOfNaQOC9c98Ihcf7y+OcOiZNUNg6KtP+gOKsHXeye/pf3GhMoyZ/+2rphsUb4w0e9Y0icWw38G/H3rd80JM6V+60eEmd9jfk0X9vjPs2vXTXm2G0c9EXoVgtjjt2Ng86FJFk7aJ++tnrMufDNVWOO3Qk3rR8SJ0m+uGrNsFiwp6gYA2DJjEqKsetGJcXYdSstKQYAMJKKMQAAAIDlYsEcYyOpGAMAAABgLkmMAQAAADCXJMYA+P/Zu/c4Se+qTvyfM51JMiEhQLglgETlosCSkAT4eUFAEXEVkAXlogus+BtZxOtLVxSMGFQQ/XmXxdFFgoCwwg+EFUEUEBXQTCAkhJsBEUiAkMgt5DbTdfaPrtFmnElCZvpb1f2833n1K9VPPf2c81RVV1WfOd9TAAAAk2TGGAAAAMCyMGNsKB1jAAAAAEySwhgAAAAAk6QwBgAAAMAkKYwBAAAAMEmG7wMAAAAsiTZ8fygdYwAAAABMksIYAAAAAJOkMAYAAADAJJkxBgAAALAszBgbSscYm0ZVPb6qzq+qd1fVHy86HwAAAGBzUxhjU6iquyd5RpJv7u5TkvzoAfbZWVW7q2r3OVdcNDxHAAAAYHNRGGOz+OYkf9rdlyVJd//r/jt0967uPqO7z7j3sXcaniAAAACwuZgxBgAAALAsZotOYFp0jLFZvCnJd1fVCUlSVbdYcD4AAADAJqdjjE2huy+sql9K8jdVtZrkXUmeuNisAAAAgM1MYYxNo7vPTnL2ovMAAAAAtgZLKQEAAACWRM96y35dn6p6SFV9oKouqqqnXcd+j6yqrqozDvX2VhgDAAAAYKGqaiXJ7yX59iR3S/LYqrrbAfY7LsmPJvmHwxFXYQwAAACARbtPkou6+8PdfW2SlyV5+AH2e1aSX0ly9eEIqjAGAAAAwIarqp1VtXvd1851V98uycfWff/x+bb1P39akjt0958frpwM3wcAAABgw3X3riS7bszPVtW2JL+e5ImHMyeFMQAAAIBlcQOG1G9RFye5w7rvbz/fts9xSe6R5C1VlSS3TfKaqnpYd+++sUEtpQQAAABg0c5Jcueq+sqqOjLJY5K8Zt+V3f257r5ld5/c3ScneUeSQyqKJQpjAAAAACxYd+9N8tQkb0jyviT/u7svrKqzquphGxXXUkoAAAAAFq67X5fkdfttO/Mg+z7gcMRUGGNL+t6bX7rhMXZ/6tYbHmOfvWvrpzfcmChb0x3rmCFx7n9YPpD4+n3iyS8YEufkbScMiZMkt+iVIXGed9oBX7cPu6e886whcZLkBaeOOaejBo3TOGJQnLOffEhd/TfYzZN850mXDIn16ktOGhLn6EGve7OBr3wnrFw5JM7lGfN6NGrZyZ5Bj4WRVgc97rZlNijOuPvospUxf74eMxtz291k0G33r9k+JE6S3Cljnuu2vDEPQeYspQQADmpUUYwbb1RRDABgK1IYAwAAAGCSFMYAAAAAmCQzxgAAAACWRM8GDUQliY4xAAAAACZKYQwAAACASVIYAwAAAGCSzBgDAAAAWBazRScwLTrGAAAAAJgkhTEAAAAAJklhDAAAAIBJUhgDAAAAYJIM3wcAAABYEj3rRacwKTrG2HSq6plV9ZOLzgMAAADY3BTGAAAAAJgkhTE2hap6elV9sKr+LsldF50PAAAAsPkpjLH0qur0JI9JcmqS/5zk3gfZb2dV7a6q3S+9/OMjUwQAAIDDY7aFv5aQ4ftsBvdL8qruvjJJquo1B9qpu3cl2ZUkHzn1W00rBAAAAK6TjjEAAAAAJklhjM3grUm+q6p2VNVxSR666IQAAACAzc9SSpZed7+zql6e5N1JLk1yzoJTAgAAgA3RSzqLa6tSGGNT6O5fSvJLi84DAAAA2DospQQAAABgkhTGAAAAAJgkSykBAAAAloUZY0PpGAMAAABgkhTGAAAAAJgkhTEAAAAAJklhDAAAAIBJMnwfAAAAYEm04ftD6RgDAAAAYJJ0jLEl/dRnjtnwGN+xrTY8xj4nrK4OiXN1jamVj7rljuoeFCl52tdeMiTOL7/vxCFxLvzsbYbEuaauHRInSVYz5vGwd9Aj/AWnnjkkzvefd9aQOEny4lPGnNMoewfFefUlJw16dI8z5lUvecrPnjAoUvLM54w5q9OvGfNoWBn0qPv8tpUhcZLkyEHvG7YPirM66PVoZGPLbNCbyKu2ba3+kZv1nmGxPpwdQ+Lcd0gUpmJr/cYDAEzMViuKAQCMpGMMAAAAYFmYMTaUjjEAAAAAJklhDAAAAIBJUhgDAAAAYJLMGAMAAABYEm3G2FA6xgAAAACYJIUxAAAAACZJYQwAAACASTJjDAAAAGBJmDE2lo4xAAAAACZJYQwAAACASVIYAwAAAGCSFMYAAAAAmCTD9wEAAACWhOH7Y+kYY1Ooqu+rqn+sqvOq6veramXROQEAAACbm8IYS6+qvjbJo5N8Q3efmmQ1yfceYL+dVbW7qnZ/+IqPDM4SAAAA2GwspWQz+JYkpyc5p6qSZEeSS/ffqbt3JdmVJN99x4f3yAQBAACAzUdhjM2gkpzd3T+z6EQAAABgQ3UtOoNJsZSSzeCvkzyqqm6dJFV1i6q644JzAgAAADY5hTGWXne/N8kzkvxlVZ2f5I1JTlxsVgAAAMBmZyklm0J3vzzJyxedBwAAALB1KIwBAAAALImeLTqDabGUEgAAAIBJUhgDAAAAYJIUxgAAAACYJDPGAAAAAJZEz2rRKUyKjjEAAAAAJklhDAAAAIBJUhgDAAAAYJIUxgAAAACYJMP32ZJ+7YQvbniMP//0cRseY5+bro4ZvrjSPSTOrMacz2xIlDX/8723HxLnh2926ZA4f/6vtxkS57bbtg+JkyQ3HTTEdNQZHTXm1zUvPuXMMYGSfN+7zxoS5xX3/LkhcXb0uMG5j/n+vUPivPQFY946jvo9euEvXT4oUnKLbSuDIo15ctiTMY/vowa9N0mSbd4H3SgjOy1WBj0cLh70JLRn0O/rt919zPvHJPnUe8a8J97qeuQfMugYAwDYzEYVxQAAtiKFMQAAAAAmSWEMAAAAgEkyYwwAAABgSfTAOaXoGAMAAABgohTGAAAAAJgkhTEAAAAAJsmMMQAAAIAl0bNFZzAtOsYAAAAAmCSFMQAAAAAmSWEMAAAAgEkyYwwAAABgSfSsFp3CpOgYAwAAAGCSFMbYVKrqR6rqfVX1kkXnAgAAAGxullKy2TwlyYO6++OLTgQAAADY3HSMsWlU1fOTfFWSv6iqH190PgAAAMDmpjDGptHdT05ySZIHdvdv7H99Ve2sqt1Vtfull108PkEAAAA4RN1b92sZKYyxZXT3ru4+o7vPeNwtb7fodAAAAIAlpzAGAAAAwCQpjAEAAAAwST6VEgAAAGBJ9KwWncKkKIyxqXT3yYvOAQAAANgaLKUEAAAAYJIUxgAAAACYJEspAQAAAJaEGWNj6RgDAAAAYJIUxgAAAACYJIUxAAAAACbJjDEAAACAJdG96AymRccYAAAAAJOkMAYAAADAJFlKyZb0mcuP2fAYx8w2PMS/+fzKmBr2zVbHnNSom25W4z7m+GaDPlL5s5/Z+Md2kuwY1L59fI+7j44ZdE4rg+IcsQVb7F9xz58bEudR5z9rSJw/OeXMIXFe+b+OyH++x8eGxDqq7zAkzuoWfHyPsmfQa9/KkCjJjtm4N1xXbdMzsOyOGLS+7KaD3teduGfM+Vxw/m2GxEkSv0ZsRh62AACb2KiiGADAVqRjDAAAAGBJ9KCuRdboGAMAAABgkhTGAAAAAJgkhTEAAAAAJsmMMQAAAIAl0QM/uR0dYwAAAABMlMIYAAAAAJOkMAYAAADAJJkxBgAAALAkerboDKZFxxgAAAAAk6QwBgAAAMAkKYwBAAAAMElmjAEAAAAsiVnXolOYFB1jAAAAAEySjjE2har6uSTfl+TTST6W5Nzu/rXFZgUAAABsZjrGWHpVde8kj0xySpJvT3LGQfbbWVW7q2r3K6/4l5EpAgAAAJuQwhibwTck+bPuvrq7v5DktQfaqbt3dfcZ3X3GI4+949gMAQAAgE3HUkoAAACAJdGG7w+lY4zN4O+TPLSqjq6qY5N856ITAgAAAA6vqnpIVX2gqi6qqqcd4PqfqKr3VtX5VfXXVXXIy8UUxlh63X1OktckOT/JXyS5IMnnFpoUAAAAcNhU1UqS38vabPG7JXlsVd1tv93eleSM7r5nklckee6hxlUY/yBUwAAAIABJREFUY7P4te6+S5JvS3LHJOcuOB8AAADg8LlPkou6+8PdfW2SlyV5+PoduvvN3X3l/Nt3JLn9oQY1Y4zNYte8Unx0krO7+52LTggAAAAOt55t3RljVbUzyc51m3Z196755dsl+di66z6e5L7XcbgnZW1V2SFRGGNT6O7HLToHAAAA4MabF8F2Xe+O16Oqvi/JGUnuf6jHUhgDAAAAYNEuTnKHdd/ffr7tS1TVg5I8Pcn9u/uaQw1qxhgAAAAAi3ZOkjtX1VdW1ZFJHpO1D+L7N1V1ryS/n+Rh3X3p4QiqYwwAAABgSXQvOoPF6O69VfXUJG9IspLkBd19YVWdlWR3d78mya8mOTbJn1ZVkny0ux92KHEVxgAAAABYuO5+XZLX7bftzHWXH3S4Y1pKCQAAAMAkKYwBAAAAMEmWUgIAAAAsiZ7VolOYFIUxtqRX57gNj3HXgRMRj5qNibWnxjwBj3qaXx0UJ0net3LtkDgXZceQOM/+mZsPifOjv/LJIXGS5CvqyCFxfvJ59x4S5+wn7x4SZ++QKGt29Jhnhz855czr3+kweOy7zxoSJ0med9qYc8rKmDDXDHqhmI0JkyS5tMb8Np22urXe3lfGvd8aFcmSnRtv+6D33yuDXo9GvVddHfbuO9mrnsMm5HkZAGATG1YUAwDYghTGAAAAAJgkhTEAAAAAJmlrDSEAAAAA2MRmg+bcsUbHGAAAAACTpDAGAAAAwCQpjAEAAAAwSWaMAQAAACyJNmNsKB1jAAAAAEySwhgAAAAAk6QwBgAAAMAkmTEGAAAAsCS6F53BtOgYAwAAAGCSFMbYFKrqu6qqq+prFp0LAAAAsDUojLFZPDbJ383/DwAAAHDIzBhj6VXVsUm+MckDk7w2yc8vNiMAAADYGLOuRacwKTrG2AwenuT13f3BJJdX1ekH2qmqdlbV7qrafe4VF43NEAAAANh0FMbYDB6b5GXzyy/LQZZTdveu7j6ju884/dg7DUsOAAAA2JwspWSpVdUtknxzkv9UVZ1kJUlX1U91+xBbAAAA4MbTMcaye1SSP+7uO3b3yd19hyT/nOR+C84LAAAA2OR0jLHsHpvkV/bb9sr59reOTwcAAAA2Thu+P5TCGEutux94gG2/vYhcAAAAgK3FUkoAAAAAJklhDAAAAIBJspQSAAAAYEl0LzqDadExBgAAAMAkKYwBAAAAMEkKYwAAAABMkhljAAAAAEti1rXoFCZFxxgAAAAAk6QwBgAAAMAkWUrJlvRTT9r4GH/8B+PaW4/LmFg3mc2GxNlbY85nZUiUNVdnzG33Kw/5/JA4z33OmFvvlNlRQ+IkySdXxtxHv/mU3UPi/NeTLhkS51WXnDQkTpI85vv3Dolz1T98Ykic55125pA4T3nnWUPiJMlvDTqnPRnzOfVjnhXWjHqdmA16e3LEbMx91IPeAyXJSo85p6u2jelNOHbQ+ewZEmXNF1fG3HZXDGof2bN9TKC77fjskDhJcvsxDzs4rBTGAAAAAJZEmzE2lKWUAAAAAEySwhgAAAAAk6QwBgAAAMAkKYwBAAAAMEmG7wMAAAAsiZnh+0PpGAMAAABgkhTGAAAAAJgkhTEAAAAAJsmMMQAAAIAl0YtOYGJ0jAEAAAAwSQpjAAAAAEySwhgAAAAAk2TGGEuvqlaTXJCkkqwmeWp3v22xWQEAAMDhN+tadAqTojDGZnBVd5+aJFX1bUmeneT+i00JAAAA2OwspWSzuWmSzyw6CQAAAGDzUxhjM9hRVedV1fuT/GGSZx1op6raWVW7q2r3C3ZfNDZDAAAAYNOxlJLNYP1Syq9L8qKqukd39/qduntXkl1J8sWzvrf/42EAAABgubUZY0PpGGNT6e63J7llklstOhcAAABgc1MYY1Opqq9JspLk8kXnAgAAAGxullKyGeyoqvPmlyvJE7p7dZEJAQAAAJufwhhLr7tXFp0DAAAAsPUojAEAAAAsidmiE5gYM8YAAAAAmCSFMQAAAAAmSWEMAAAAgEkyYwwAAABgSXRq0SlMio4xAAAAACZJYQwAAACASVIYAwAAAGCSzBgDAAAAWBKzXnQG06IwxpZ02as/teExbr3n1hseY58PHDWmufP0q2dD4ny+VobE2d7jXlE6Xr1ujO/7xouHxfq1t584LNYIr77kpEWncNi99AVj3pYc1XcYEidjnuryW6edOSZQkh9951lD4jz39J8bEmcreuBJnxgS53mfvs2QOHdcHfSLlOSYMW+DcuWgNTs3Xx1zQtsHvgU6ftB9dLPVMXfSp44YE+ejn7vpkDhJ8umVMa/lXzskClNhKSUAAAAAk6QwBgAAAMAkWUoJAAAAsCRmqUWnMCk6xgAAAACYJIUxAAAAACZJYQwAAACASVIYAwAAAGCSDN8HAAAAWBJt+P5QOsYAAAAAmCSFMQAAAAAmSWEMAAAAgEkyYwwAAABgScwWncDE6BgDAAAAYJIUxlh6VXXbqnpZVX2oqs6tqtdV1V0WnRcAAACwuVlKyVKrqkryqiRnd/dj5ttOSXKbJB9cZG4AAADA5qYwxrJ7YJI93f38fRu6+90LzAcAAAA2TKcWncKkWErJsrtHknNvyI5VtbOqdlfV7pdedvEGpwUAAABsdgpjbBndvau7z+juMx53y9stOh0AAABgySmMsewuTHL6opMAAAAAth6FMZbdm5IcVVU7922oqntW1f0WmBMAAABsiNkW/lpGCmMste7uJI9I8qCq+lBVXZjk2Uk+udjMAAAAgM3Op1Ky9Lr7kiTfs+g8AAAAgK1FxxgAAAAAk6QwBgAAAMAkWUoJAAAAsCSWdUj9VqVjDAAAAIBJUhgDAAAAYJIUxgAAAACYJDPGAAAAAJZEpxadwqToGAMAAABgkhTGAAAAAJgkSynZkn7osqM2PMZTe9yH6N71mjFxrqitVSu/ctu4FuR/nY25k7bd4dZD4lxZe4bEedzf7xgSJ0n+/1c8YkicX/6eVw+Jc3SNeXyvDomyZvugOKs9Js41g56C9mTQCSV57uk/NyTO/zj3WUPinHnGM4bESZLP994hcf7m4hOHxLnvbMz7oMq491ujHD/oifWyI8b8qXfT1XH30dGD3n9/9Y4vDInzwdnxQ+Jc0uP+7D9i3EsSHDYKYwAAAABLYmbE2FBbqz0EAAAAAG4ghTEAAAAAJklhDAAAAIBJMmMMAAAAYEnMYsjYSDrGAAAAAJgkhTEAAAAAJklhDAAAAIBJUhgDAAAAYJIM3wcAAABYEr3oBCZGxxgAAAAAk6QwBgAAAMAkKYwBAAAAMElmjLHUqmo1yQVJtifZm+RFSX6ju2cLTQwAAAA2gD92x1IYY9ld1d2nJklV3TrJS5PcNMnPLzQrAAAAYNOzlJJNo7svTbIzyVOrqhadDwAAALC5KYyxqXT3h5OsJLn1/tdV1c6q2l1Vuz96xUfHJwcAAABsKgpjbBndvau7z+juM77i2K9YdDoAAADwZZtVbdmv61NVD6mqD1TVRVX1tANcf1RVvXx+/T9U1cmHensrjLGpVNVXJVlNcumicwEAAAAOj6paSfJ7Sb49yd2SPLaq7rbfbk9K8pnuvlOS30jyK4caV2GMTaOqbpXk+Ul+t7t70fkAAAAAh819klzU3R/u7muTvCzJw/fb5+FJzp5ffkWSbznUGeQ+lZJlt6OqzkuyPcneJH+c5NcXmxIAAADw5aqqnVn7UL19dnX3rvnl2yX52LrrPp7kvvsd4t/26e69VfW5JCckuezG5qQwxlLr7pVF5wAAAACjbOXlUfMi2K7r3XEgSykBAAAAWLSLk9xh3fe3n2874D5VdUSS45NcfihBFcYAAAAAWLRzkty5qr6yqo5M8pgkr9lvn9ckecL88qOSvOlQZ5BbSgkAAADAQs1nhj01yRuSrCR5QXdfWFVnJdnd3a9J8r+S/HFVXZTkX7NWPDskCmMAAAAALFx3vy7J6/bbdua6y1cn+e7DGVNhDAAAAGBJzBadwMSYMQYAAADAJCmMAQAAADBJCmMAAAAATJIZYwAAAABLYlaLzmBaFMbYkj541ac2PMYXjrjZhsfY57jZ6pA4l62MeUo4untInO1jwiRJXvoDxw+J8193fXZInJPrmCFxfubaMXGS5Ke/55VD4hw3qBl7ljHvmJ7ysycMiZMkL/yly4fFGmHU4NytOKD3zDOeMSTOWbt/cUicJHn86T8xJM72Qa+xR/aYR95lR4z7c2XHbMxtd/Sg227U+Vy1bdxf8Ht7ZUic91590yFx7jrosXB1jbuP9gyMBYeLpZQAAAAATJLCGAAAAACTZCklAAAAwJIYNTKDNTrGAAAAAJgkhTEAAAAAJklhDAAAAIBJMmMMAAAAYEn0ohOYGB1jAAAAAEySwhgAAAAAk6QwBgAAAMAkmTEGAAAAsCRmtegMpkXHGAAAAACTpDAGAAAAwCRZSsnSq6rVJBes2/Sy7n7OovIBAAAAtgaFMTaDq7r71EUnAQAAAGwtCmMAAAAAS2K26AQmxowxNoMdVXXeuq9HH2inqtpZVburavfnrv706BwBAACATUbHGJvBDVpK2d27kuxKkrvc6oze8KwAAACATU3HGAAAAACTpGMMAAAAYElY/jSWwhibwY6qOm/d96/v7qctLBsAAABgS1AYY+l198qicwAAAAC2HjPGAAAAAJgkHWMAAAAAS2JWi85gWnSMAQAAADBJCmMAAAAATJLCGAAAAACTZMYYAAAAwJKYLTqBidExBgAAAMAkKYwBAAAAMEkKYwAAAABMkhljbEl/f+pNNjzG2y7oDY+xz0rGxKohUTLobMba+0+fHBLnXVdcNiTOs2910pA4OXpMmCQ5+coThsS5yzWrQ+KcsHLlkDjPfM6Y80mSW2xbGRZrhEtr75A4V2/BSSSf7zG33eNP/4khcZLkRef++pA4v3+vM4fEudW1Yx53N10d9/jear9Jewe9sds28I3dFdvGnNRRPeakvnrH54fEedXq8UPiJMntxzx9w2GlMAYAAACwJLZaoX7ZWUoJAAAAwCQpjAEAAAAwSQpjAAAAAEySGWMAAAAAS6JHfSoaSXSMAQAAADBRCmMAAAAATJLCGAAAAACTZMYYAAAAwJKYLTqBidExBgAAAMAkKYwBAAAAMEkKYwAAAABMkhljAAAAAEvCjLGxFMbYFKpqNckF6zZ9V3d/ZEHpAAAAAFuAwhibxVXdfeqikwAAAAC2DjPGAAAAAJgkhTE2ix1Vdd7861UH2qGqdlbV7qra/aKPf2J0fgAAAMAmYyklm8X1LqXs7l1JdiXJp7/1/j0kKwAAADiM/DE7lo4xAAAAACZJYQwAAACASVIYAwAAAGCSzBhjU+juYxedAwAAAGy0WS06g2nRMQYAAADAJCmMAQAAADBJCmMAAAAATJIZYwAAAABLYrboBCZGxxgAAAAAk6QwBgAAAMAkKYwBAAAAMElmjAEAAAAsCTPGxtIxBgAAAMAk6RhjS3rHBSdteIwjujc8xj67j94+JM6Je4eEyY7ZuNtulD95y4lD4rzsqOOHxHnbF44ZEueus6uGxEmSmw36p6BPH7EyJM7lGXMfnX7NyN/XMbH2VA2Jc9rqmLdZszGnkyR54EmfGBLnby4e85y6feBr+e/f68whcX7wXWcNibPjpPsNifOYE+87JE6SPPTaHUPiXHzEmBeku1475o3dngx8EhrU13Hz2eqQOH+352ZD4px27ZjzSZKrtum9YfPxqAUAAABgkhTGAAAAAJgkSykBAAAAlsTWGzyz3HSMAQAAADBJCmMAAAAATJLCGAAAAACTZMYYAAAAwJKY1aIzmBYdYwAAAABMksIYAAAAAJOkMAYAAADAJJkxBgAAALAkZotOYGJ0jAEAAAAwSQpjAAAAAEySpZRbWFWtJrlg3aaXdfdzFpUPAAAAwDJRGNvaruruUzfq4FVVSaq7LYEGAACAw6AXncDEWEo5QVX1kap6dlWdV1W7q+q0qnpDVX2oqp68br+fqqpzqur8qvqF+baTq+oDVfWiJO9Jcoeq+rn5tr+rqj+pqp+c7/vVVfX6qjq3qv62qr5mvv2FVfXbVfW2qvpwVT1qXcyfrqoLqurdVfWc+THeue76O6//HgAAAODGUhjb2nbMi1/7vh697rqPzrvJ/jbJC5M8Ksn/k2RfAezBSe6c5D5JTk1yelV90/xn75zked199yS3TvLIJKck+fYkZ6yLsSvJD3f36Ul+Msnz1l13YpJvTPKdSZ4zj/ntSR6e5L7dfUqS53b3h5J8rqr2db79tyR/dKCTraqd80Lf7tdfedGXczsBAAAAE2Qp5dZ2XUspXzP//wVJju3uLyT5QlVdU1U3S/Lg+de75vsdm7WC2EeT/Et3v2O+/RuS/Fl3X53k6qp6bZJU1bFJvj7Jn66tuEySHLUu/qvnSzDfW1W3mW97UJI/6u4rk6S7/3W+/Q+T/Leq+okkj85ase4/6O5dWSvG5bW3fazuUwAAAOA6KYxN1zXz/8/WXd73/RFJKsmzu/v31/9QVZ2c5Is34Pjbknz2Ogpz62PWQfbZ55VJfj7Jm5Kc292X34D4AAAAANfJUkoO5g1Jvn/e+ZWqul1V3foA+/19kodW1dHzfb8zSbr780n+uaq+e/7zVVWnXE/MN2atM+yY+c/cYn6sq+f5/M8cZBklAAAAbAWz9Jb9WkYKY1vb/jPGnnNDf7C7/zLJS5O8vaouSPKKJMcdYL9zsrYs8/wkf5G1pZmfm1/9vUmeVFXvTnJh1uaHXVfM18+PtbuqzsvaXLJ9XpK1bra/vKHnAAAAAHBdLKXcwrp75SDbT153+YVZG75/oOt+K8lvHeAQ99jv+1/r7mfOO73emuTc+c//c5KHHCD+E/f7/th1l5+T+TD+/Xxj1uaPrR7onAAAAAC+XApjHA67qupuSY5OcnZ3v/NwHryqXpXkq5N88+E8LgAAADBtCmMcsu5+3AYf/xEbeXwAAABYFrNFJzAxZowBAAAAMEkKYwAAAABMksIYAAAAAJNkxhgAAADAkuhFJzAxOsYAAAAAmCSFMQAAAAAmSWEMAAAAgEkyY4wt6TMrKxseY7bhEf7dN11z9ZA4X+wxTwmf2bbx90+S3HK2d0icJLnVnmuHxHnbkccMifOV1455hL/viB1D4iTJbfesDolzTdWQOKP+ZWtl4JSLPRlz2415BhrniNm4++h5n77NkDj3nY15Djqyx72a32rQ8+qOk+43JM5Vl/ztkDgPu9cPDYmTJNv76CFxTttzzZA4798+5nyOGfgcdNSgWJeujHlP/NXX7hkS5xNHbB8SJ0l2DHw8bGUj/9ZExxgAAAAAE6UwBgAAAMAkKYwBAAAAMEkKYwAAAABMkuH7AAAAAEtiNubziJjTMQYAAADAJCmMAQAAADBJCmMAAAAATJIZYwAAAABLYpZedAqTomMMAAAAgElSGAMAAABgkhTGAAAAAJgkM8YAAAAAloQJY2MpjHGDVNUJSf56/u1tk6wm+fT8+/t097ULSQwAAADgRrKUkhukuy/v7lO7+9Qkz0/yG/u+7+5rq0qRFQAAANgQVXWLqnpjVf3T/P83P8A+p1bV26vqwqo6v6oefX3HVRjjRquqF1bV86vqH5I8t6ruM38Avquq3lZVd53v98Sq+t11P/d/quoB88tXVNWvzh+0fzU/xluq6sNV9bD5PidX1d9W1TvnX1+/iPMFAAAAFuZpSf66u++ctRVtTzvAPlcmeXx33z3JQ5L8ZlXd7LoOqjDGobp9kq/v7p9I8v4k9+vueyU5M8kv34Cfv0mSN80ftF9I8otJvjXJI5KcNd/n0iTf2t2nJXl0kt8+0IGqamdV7a6q3W/+4j8dyjkBAADAQsy28NcheniSs+eXz07yXfvv0N0f7O5/ml++JGv1hFtd10Etf+NQ/Wl3r84vH5/k7Kq6c9bmBW6/AT9/bZLXzy9fkOSa7t5TVRckOXm+fXuS362qU7M22+wuBzpQd+9KsitJXnS77zOvEAAAAJZIVe1MsnPdpl3zv+VviNt09yfmlz+Z5DbXE+s+SY5M8qHr2k9hjEP1xXWXn5Xkzd39iKo6Oclb5tv35ku7E49ed3lPd+8rYs2SXJMk3T1bN7fsx5N8Kskp8+NcfRjzBwAAAAZY39ByIFX1V1n7wL/9PX2/43RVHbQhpqpOTPLHSZ7Q3dfZrKYwxuF0fJKL55efuG77R5I8paq2JbldkvvciON+fF4se0KSlUPMEwAAAFgy3f2gg11XVZ+qqhO7+xPzwtelB9nvpkn+PMnTu/sd1xfTjDEOp+cmeXZVvStfWnT9+yT/nOS9WZsP9s4v87jPS/KEqnp3kq/Jl3apAQAAAFvfa5I8YX75CUn+bP8dqurIJK9K8qLufsUNOaiOMb5s3f3Mg2x/e750/tcz5ts7yfce5GeOPdhx9103H5x3z3VX/fSNSBsAAACW3ixGZh/Ec5L876p6UpJ/SfI9SVJVZyR5cnf/wHzbNyU5oaqeOP+5J3b3eQc7qMIYAAAAAEutuy9P8i0H2L47yQ/ML784yYu/nONaSgkAAADAJCmMAQAAADBJllICAAAALAkTxsbSMQYAAADAJCmMAQAAADBJCmMAAAAATJIZYwAAAABLYrboBCZGxxgAAAAAk6RjjC3ppqtbq8b+hR7zq/qZlZUhcbYP+piV9xy1fUygJLfcOybWSXvG3HhH95jfodnAf5/ZWzUkzjGDbrs9g87n89vGPC8kyVE95vG9YzbmPqpBnynVGfNYSJI7ro55PNSgfyu/7Ihxb4VHvTd5zIn3HRLnYff6oSFxXvOu3xsSJ0lee49nDIlzSR01JM7N926t16ORRp3RlTXmOfWY2bjPONw26LUcDicdYwAAAABMko4xAAAAgCUxG9R1zhodYwAAAABMksIYAAAAAJOkMAYAAADAJCmMAQAAADBJhu8DAAAALAmj98fSMQYAAADAJCmMAQAAADBJCmMAAAAATJIZYwAAAABLYrboBCZGxxgAAAAAk6QwBgAAAMAkKYzxZauqk6vqPftte2ZV/eQGx/2xqjpmI2MAAAAA02HGGBumqo7o7r2H8ZA/luTFSa48jMcEAACApdHpRacwKTrGOKyq6i1V9ZtVtTvJj1bVvavq/Ko6r6p+dV+nWVWtzL8/Z379D863P2B+jFdU1fur6iW15keSnJTkzVX15gWeIgAAALBFKIyxEY7s7jO6+/9L8kdJfrC7T02yum6fJyX5XHffO8m9k/y/VfWV8+vulbXusLsl+aok39Ddv53kkiQP7O4HHihoVe2sqt1Vtfsvr7xoY84MAAAA2DIUxrgxDtbXuW/7y5Okqm6W5Ljufvt8+0vX7fvgJI+vqvOS/EOSE5LceX7dP3b3x7t7luS8JCffoKS6d80Lcmc8+Jg73eCTAQAAAKbJjDFujMuT3Hy/bbdI8s/zy1+8AceoJD/c3W/4ko1VD0hyzbpNq/E4BQAAYCJmi05gYnSM8WXr7iuSfKKqvjlJquoWSR6S5O/22++zSb5QVfedb3rMuqvfkOS/V9X2+THuUlU3uZ7QX0hy3GE4BQAAAACdONxoj0/ye1X16/Pvf6G7P1RV++/3pCR/UFWzJH+T5HPz7X+YtSWS76y1H/p0ku+6npi7kry+qi452JwxAAAAgBtKYYwbpbvfm+Q/FKe6+wH7bbqwu++ZJFX1tCS75/vNkvzs/Gu9t8y/9h3vqesu/06S3znk5AEAAACiMMbG+46q+pmsPdb+JckTF5sOAAAAwBqFMTZUd78880+pBAAAAK7bLL3oFCbF8H0AAAAAJklhDAAAAIBJUhgDAAAAYJLMGAMAAABYEiaMjaVjDAAAAIBJUhgDAAAAYJIUxgAAAACYJDPG2JL2Vm14jGsGxNinZmPiHDUbs5p9Nui2u/XecavzR912q4Nuuy9sWxkS59hBt1uSbBs0rWHUbTfKkT3wPhoU66ptY/5dcNQttzLwPjpm0OvRKDsGPgeNuukeeu2OIXG299FD4rz2Hs8YEidJHvqeXxwS50Wnnjkkzl+tfHFInAfvPXZInCQ5btAv0hGDnlf3DHpfN+p8kjF/h03BzJSxoXSMAQAAADBJCmMAAAAATJLCGAAAAACTZMYYAAAAwJLYYiM9l56OMQAAAAAmSWEMAAAAgElSGAMAAABgkhTGAAAAAJgkw/cBAAAAlkSnF53CpOgYAwAAAGCSFMYAAAAAmCSFMQAAAAAmyYwxAAAAgCUxW3QCE6Nj7EaqqhOq6rz51yer6uJ13/f8/++uqndW1dcf5Bi3rKo3V9X5VfWPVXXsdcS7bVW9rKo+VFXnVtXrquouVfWAqvo/h+F8HnCwPA+w70eq6pbzy2871NgAAAAAi6Bj7Ebq7suTnJokVfXMJFd096/Nv7+iu/dd921Jnp3k/gc4zH9P8tbu/vmqOinJtQeKVVWV5FVJzu7ux8y3nZLkNofxlB6Q5IokX1ahq7tvUDENAAAAYNnoGNt4N03ymYNcd22S2ydJd1/S3QcsjCV5YJI93f38fRu6+93d/bfzb4+tqldU1fur6iXzQlqq6vSq+pt5h9kbqurE+fYfqar3zjvVXlZVJyd5cpIfn3e63W998Hl33F9W1YVV9YdJat11V8z/f2JVvXX+8+/Zd4yqenBVvX3eOfen+7riqurMqjpnvu+udTl/SW7zbTepqhfMu+reVVUPv0G3PAAAAMB1UBjbGDvmBaL3J/nDJM86yH4fSvJfqurJ13O8eyQ59zquv1eSH0tytyRfleQbqmp7kt9J8qjuPj3JC5L80nz/pyW5V3ffM8mTu/sjSZ6f5De6+9R1Bbd9fj7J33X33bPWufYVB8jhcUneMO+UOyXJefPlls9I8qDuPi3J7iQ/Md//d7v73t19jyQ7knzngXKbb3t6kjd1932yViT81aq6yf4JVNXOqtogkeBWAAAgAElEQVRdVbvfeOVF13FzAQAAwHLqLfzfMrKUcmNctW4p5dcleVFV3aO7/+1RUFW3S/IzSe6U5A1V9enufmVVnZ/kft39uS8j3j9298fnxz0vyclJPpu1gtob581YK0k+Md///CQvqapXJ3n1DTj+NyX5L0nS3X9eVQfqgDsnyQvmBblXd/d5VXX/rBXr/n6ew5FJ3j7f/4FV9T+SHJPkFkkuTPLag+T24CQPq6qfnH9/dNaKc+9bn0B370qyK0leceL3LudvHAAAALA0FMY2WHe/fd45daskl6676huSXNDdl1fVdyT566q6TZKPHKAodmGSR11HmGvWXV7N2v1aSS7s7q87wP7fkbVi10OTPL2q/tOXdVIH0N1vrapvmh/7hVX161lbQvrG7n7s+n2r6ugkz0tyRnd/bD6j7ejryK2SPLK7P3CoeQIAAADsYynlBquqr8lat9bl+111fta6pk7q7k8l+fEkv5fkpQc4zJuSHFVVO9cd9577zwLbzweS3GresZaq2l5Vd6+qbUnu0N1vTvLTSY5PcmySLyQ57iDHemvWlkqmqr49yc0PcJ53TPKp7v6DrC0fPS3JO7K2rPNO831uUlV3yb8XwS6bzxx71Pz6g+X2hiQ/vG4O2b2u47wBAAAAbhAdYxtjx3xJY7LW7fSE7l5dv0N3v7+qnp61ZZR7knwqyWOSPKeq3tndH1y3b1fVI5L8ZlX9dJKrk3wka3PFbnegBLr72qp6VJLfrqrjs3Zf/2aSDyZ58XxbJfnt7v5sVb02ySvmg+1/eL85Y7+Q5E+q6sKsfWrlRw8Q8gFJfmp+LlckeXx3f7qqnjj/2aPm+z2juz9YVX+Q5D1JPpm1ZZjJWgHxQLk9a577+fPi2T/n32eSAQAAwJYxW3QCE1Prxl7BljFixtg1Vde/02Fy7GzMU+Pq9e9yWMwG3XZ7xt1FOWo25rl0deDjboS9A09n1O/R1aUZ+8ZaGfSeZNRz0Kh3WKNutyS5YmXM4/v41TG/r9cOfE4ddT/tGXRO27fg3xAPfc8vDonzolPPHBLnr1auGBLnwXuPHRInSY4b9X5rSJS1roARjhj4+7p30HPQoz7xkq31png/Tzj5kVvvSXbu7I+8cunuO+/eAQAAAJgkhTEAAAAAJklhDAAAAIBJMnwfAAAAYEnMtuAcx2WmYwwAAACASVIYAwAAAGCSFMYAAAAAmCQzxgAAAACWhAljY+kYAwAAAGCSdIyxJV2xrTY8xudWNjzEv7nr7MohcS7ee8yQOLMhUZKbr64OipRsH/TvOh9b2T4kzhGD/pnq+Nm4+2g1G/+8kCR7x4QZdj7bB34q0qwG3XiDjPrXx6u2jft3zisHhTp+0FPD0T3qFWmci48YcyedtueaIXEuqaOGxEmSF5165pA4jz/vrCFx/i979x0uSV3lf/z9mSADzBBVQFdFgrCoMMKAecUcVhEDKmZFMYKrq7u6JsSwmNY1rAFxEV3MAdQ1oChghhkyCMKCuuzPAIikgWFm7vn9UXWhud4ZBqa6buj3a55+bld1d51v9/TtrnvqfE+9aYfH9xLnAYt27yUOwEY9/cqO9fR1tMFYP9+xfX7SWemkmciKMUmSJEmSJI0kK8YkSZIkSZKmiTFr73plxZgkSZIkSZJGkokxSZIkSZIkjSQTY5IkSZIkSRpJ9hiTJEmSJEmaJsoeY72yYkySJEmSJEkjycSYJEmSJEmSRpKJMUmSJEmSJI0kE2OSJEmSJEkaSTbflyRJkiRJmibGpnoAI8aKMUmSJEmSJI0kE2OSJEmSJEkaSSbGtF6SbJvkmQPLz0/ykQ62+9Ikz13f7UiSJEmSJK2JPca0vrYFngl8rsuNVtXHu9yeJEmSJEkzwRg11UMYKVaMjYgkxyRZluScJAe2665J8s4kZyT5RZKt2vV3SPLVJKe0lwe26x+S5PT2clqSRcBhwIPbda9uw90pyXeTXJDkPQNjeFSSnyc5NcmXkyxs1x+W5NwkZyZ5X7vukCSvba+/uB3HGe24NurthZMkSZIkSbOWibHR8cKq2gNYAhycZEtgY+AXVbUbcBLw4va+HwQ+UFV7Ak8BjmjXvxZ4RVUtBh4MXAe8HvhxVS2uqg+091sMPB24N/D0JHdJcnvgTcAjqmp3YCnwmnYcTwLuWVW7Au+YZOxfq6o923H+CjhgsieY5MAkS5MsPeHaC27bqyRJkiRJkkaGUylHx8FJntRevwuwI3AD8K123TLgke31RwC7JBl/7CZtdddPgX9LcjRNsuqSgfsMOr6qrgRIci5wN2AzYBfgp+1jbgf8HLgSuB74VJJvDYxn0L2SvKPdxkLge5MFrarDgcMBPn3nZ1t7KkmSJEmS1srE2AhIsjdNsuv+VbU8yQnAAmBlVY0nkFZz0/thDnC/qrp+wqYOS/LfwONoElyPXkPIFQPXx7cb4PtVtf8k49sLeDjwVOCVwMMm3OXTwL5VdUaS5wN7r+35SpIkSZI0U5U9xnrlVMrRsClwRZsU2xm43y3c/zjgoPGFJIvbn9tX1VlV9W7gFGBn4Gpg0TqM4RfAA5Ps0G5r4yT3aCvRNq2qbwOvBnab5LGLgN8nmQ88ax1iSZIkSZIk3SIrxkbDd4GXJvkVcD5NkmptDgb+I8mZNO+Rk4CXAv+Q5KHAGHAO8J32+uokZ9BUdl0x2Qar6tK22uvzSTZoV7+JJrF2bJIFNFVlr5nk4W8Gfglc2v5cl0ScJEmSJEnSWpkYGwFVtQJ47CQ3LRy4z1eAr7TXL6Npnj9xOwdNXNeabOrj+GMeP3D9h8Cekzx+r0liHTJw/WPAx9YQW5IkSZIk6TYxMSZJkiRJkjRNjE31AEaMPcYkSZIkSZI0kkyMSZIkSZIkaSSZGJMkSZIkSdJIMjEmSZIkSZKkkWTzfUmSJEmSpGmiqqZ6CCPFijFJkiRJkiSNJBNjkiRJkiRJGkkmxiRJkiRJkjSS7DGmWWnL1auHHmPnWjH0GON+PndhL3E2ndtLGDYc62fO/IW36+8jbpOxfuJstrqfQPN76mtw1dz+js+sJr3EWTTWz//RHPqJ09frBvT0jGafhT32Idm8p8+gy+b18/nd1/cRwKqefpV2umFVL3HOm7+glzibr+rvk+EHc6/tJc6bdnh8L3F+d+G3eomzw0779hIH4APz79VLnA16+mxYPqef/aAV6e+7fIG9sToxhq9jn6wYkyRJkiRJ0kgyMSZJkiRJkqSRZGJMkiRJkiRJI8keY5IkSZIkSdOEfVf7ZcWYJEmSJEmSRpKJMUmSJEmSJI0kE2OSJEmSJEkaSfYYkyRJkiRJmiaKmuohjBQrxiRJkiRJkjSSTIxJkiRJkiRpJJkYkyRJkiRJ0kgyMSZJkiRJkqSRZPN9SZIkSZKkaWLM5vuTSrIF8EVgW+A3wNOq6oo13HcT4FzgmKp65dq2a8WYepHkhCRLpnockiRJkiRpRno9cHxV7Qgc3y6vyduBk9ZloybGJEmSJEmSNN09ETiqvX4UsO9kd0qyB7AVcNy6bNTE2CyW5M1Jzk/ykySfT/LPSU4duH3H8eUkv0nytiSnJjkryc7t+o2T/GeSk5OcluSJ7fq5Sd6X5OwkZyY5qF3/8PZ+Z7WP22CSce3f3n52kncPrD8gya/bWJ9M8pEki5JcnGR+e59NBpclSZIkSdLMkOTAJEsHLgfeiodvVVW/b6//gSb5NXH7c4D3A69d143aY2yWSrIn8BRgN2A+cCqwDLgyyeKqOh14AXDkwMMuq6rdk7yc5k30IuCNwA+r6oVJNgNOTvID4Lk083oXV9WqJFskWQB8Gnh4Vf06yWeAlwH/PjCuOwHvBvYArgCOS7IvcDLwZmB34Grgh8AZVXV1khOAvweOAZ4BfK2qVk7ynA8EDgR4+aIlPGajHdbrNZQkSZIkqW9Vs7fHWFUdDhy+ptvbfMPWk9z0xgnbqSSTvVAvB75dVZckWacxmRibvR4IHFtV1wPXJ/lmu/4I4AVJXgM8Hdhr4DFfa38uA57cXn8UsE+S8WzrAuCuwCOAj1fVKoCq+nOS3YCLq+rX7X2PAl7BQGIM2BM4oaouBUhyNPB37W0nVtWf2/VfBu4xMOZ/okmMvQB48WRPePAX7Jtb7z97P0kkSZIkSZqFquoRa7otyR+TbFNVv0+yDfCnSe52f+DBbcHPQuB2Sa6pqjX2IzMxNnq+CryVpiJrWVVdPnDbivbnam56bwR4SlWdP7iRdc28dqGqfppk2yR7A3Or6uzegkuSJEmSpOngG8DzgMPan8dOvENVPWv8epLnA0vWlhQDe4zNZj8FnpBkQZKFwOMB2gqy7wEf4+bTKNfke8BBaTNhSe7Trv8+8JIk89r1WwDnA9smGZ/D+BzgxAnbOxl4SJLbJ5kL7N/e55R2/ebtNp8y4XGfAT63jmOWJEmSJEmzy2HAI5NcQDOL7TCAJEuSHHFbN2rF2CxVVack+QZwJvBH4Czgyvbmo4EnsW5naHg7zVTIM9smdhfTJNmOoJnqeGaSlcAnq+ojSV4AfLlNbp0CfHzCuH6f5PXAj2iq0f67qo4FSPIumsTZn4HzBsY7PuZ3AJ+/VS+EJEmSJEkzyNhUD2Caame8PXyS9UtpeqRPXP9pmj7oa2VibHZ7X1UdkmQj4CSa3mEADwKOrKrV43esqm0Hri8F9m6vXwe8ZOKG295ir2kvg+uPB+4zyf33Hrj+eSZPcH2uqg5vk2pfp+kpNu5BwFeq6i9rfrqSJEmSJEnrzsTY7HZ4kl1oGuYfVVWnJvk6sD3wsKkd2qQOSfIImvEeR5sYS/Jh4LHA46ZwbJIkSZIkaZYxMTaLVdUzJ1n3pKkYy7qoqteuYf1BfY9FkiRJkiTNfibGJEmSJEmSpomipnoII8WzUkqSJEmSJGkkmRiTJEmSJEnSSDIxJkmSJEmSpJFkYkySJEmSJEkjyeb7kiRJkiRJ08SYzfd7ZcWYJEmSJEmSRpIVY5qVVpKhx/h1Nhp6jHFbr+rniMHtqp84KzP8/x+AXVeu6CUOwP/NuV0vceb2dPTomjn9HDfZYKzPo2H9xLq+p/f3nB4+5wDGeonS8GjdbbOyx1jze/qV3WR1P++86+b083sEMKen166PfSCAjXr6/O5rnwHgUasW9hLnAYt27yXODjvt20ucC88/ppc4AMfe+829xLmqp8+GeT19Liwa6+/bvM/fWakr7oNKkiRJkiRpJFkxJkmSJEmSNE1UTzN51LBiTJIkSZIkSSPJxJgkSZIkSZJGkokxSZIkSZIkjSR7jEmSJEmSJE0TYz2dTV0NK8YkSZIkSZI0kkyMSZIkSZIkaSSZGJMkSZIkSdJIsseYJEmSJEnSNFH2GOuVFWOSJEmSJEkaSSbGJEmSJEmSNJJMjEmSJEmSJGkkmRgbMUn2TvKt9vo+SV5/C/ffN8kuA8snJFky7HFKkiRJkiQNm833Z4kkAVJVY+v6mKr6BvCNW7jbvsC3gHPXY3gAJJlXVavWdzuSJEmSJM1WY2Xz/T5ZMTaDJdk2yflJPgOcDXwqydIk5yR528D9HpPkvCSnAk8eWP/8JB8Z2NYPk5yZ5Pgkd03yAGAf4L1JTk+yffvQ/ZKcnOTXSR7cPn5ukvcmOaXdxkva9Xsn+XGSb9Am15Ick2RZO84DB8ZzTZIPtOuPT3KHJNu34x6/z46Dy5IkSZIkSbeVibGZb0fgo1V1T+Afq2oJsCvwkCS7JlkAfBJ4ArAHsPUatvNh4Kiq2hU4GvhQVf2MpqLsdVW1uKr+p73vvKraC/gH4K3tugOAK6tqT2BP4MVJ7t7etjvwqqq6R7v8wqraA1gCHJxky3b9xsDS9rmcCLy1jXllksXtfV4AHDnZE0hyYJsYXHrc8gvX4aWTJEmSJEmjzMTYzPfbqvpFe/1pbTXVacA9gV2AnYGLq+qCqirgv9awnfsDn2uvfxZ40Fpifq39uQzYtr3+KOC5SU4HfglsSZO0Azi5qi4eePzBSc4AfgHcZeB+Y8AX2+v/NTCGI4AXJJkLPH1gnDdTVYdX1ZKqWvKojXZYy/AlSZIkSZLsMTYbXAvQVme9Ftizqq5I8mlgwZBirmh/ruam91CAg6rqe4N3TLL3+BgHlh8B3L+qlic5YS3jHJ9Y/VWayrQfAsuq6vL1fwqSJEmSJE0/dhjrlxVjs8cmNAmoK5NsBTy2XX8esO1Af7D91/D4nwHPaK8/C/hxe/1qYNE6xP8e8LIk8wGS3CPJxpPcb1PgijYptjNwv4Hb5gBPba8/E/gJQFVd327/Y6xhGqUkSZIkSdKtZWJslqiqM2imUJ5HM9Xwp+3664EDgf9up1n+aQ2bOIhmuuKZwHOAV7XrvwC8LslpA8m1yRxB01z/1CRnA59g8orE7wLzkvwKOIxmOuW4a4G92sc/DDh04LajaaZaHreWMUiSJEmSJK0zp1LOYFX1G+BeA8vPX8P9vkvTa2zi+k8Dn26v/5YmGTXxPj+l6VU2bu+B2y6j7TFWVWPAv7SXQSe0l/HHrOCmarbJxvqaNdz0IODIqlq9psdKkiRJkiTdGibGNO0l+TqwPZMk7iRJkiRJmk3G7DLWKxNjmjaqauEa1j+p77FIkiRJkqTZzx5jkiRJkiRJGkkmxiRJkiRJkjSSnEopSZIkSZI0TdhjrF9WjEmSJEmSJGkkmRiTJEmSJEnSSDIxJkmSJEmSpJFkYkySJEmSJEkjyeb7mpWunDv8nO/fbX7p0GOMO+uyLXuJs7qXKP35Y27XW6zlc9JLnE1X99OIc0FPDT+vTz+vG8BGNdZPnLF+4lw2t5+v8LH+/ouY21Of2XnVT6D5PcW5tofvvHGb9vP2ZkFPv6+ram4vcQCu6el7oq/j3huMzb7G0It6en9v1FOcD8y/Vy9xjr33m3uJA/DEs97eS5xP3OctvcRZ3dPHwoIev8w362lfdbarnvYh1LBiTJIkSZIkSSPJxJgkSZIkSZJGkokxSZIkSZIkjSR7jEmSJEmSJE0TYz31+1XDijFJkiRJkiSNJBNjkiRJkiRJGkkmxiRJkiRJkjSS7DEmSZIkSZI0TZQ9xnplxZgkSZIkSZJGkokxSZIkSZIkjSQTY5IkSZIkSRpJ9hiTJEmSJEmaJqrsMdYnK8YkSZIkSZI0kqwY04yQZDVwFs179lfA86pq+dSOSpIkSZIkzWRWjGmmuK6qFlfVvYAbgJdO9YAkSZIkSdLMZmJMM9GPgR2mehCSJEmSJGlmMzGmGSXJPOCxNNMqJ952YJKlSZaecO0F/Q9OkiRJkqT1NEbN2st0ZGJMM8WGSU4HlgK/Az418Q5VdXhVLamqJXtvvGPvA5QkSZIkSTOLzfc1U1xXVYunehCSJEmSJGn2sGJMkiRJkiRJI8mKMUmSJEmSpGmianr24pqtrBjTjFBVC6d6DJIkSZIkaXYxMSZJkiRJkqSRZGJMkiRJkiRJI8keY5IkSZIkSdPEGPYY65MVY5IkSZIkSRpJJsYkSZIkSZI0kkyMSZIkSZIkaSTZY0ySJEmSJGmaKHuM9cqKMUmSJEmSJI0kE2OSJEmSJEkaSU6l1Kx0aQ/v7E22uX74QVorL08vceZXPyW7Y71EgVX9vGwAbNDTk5rfU1n1ZXP7OW5y+1WreokD/X3hXT53bi9xNhrr50133Zz+jqH93/x+4mwy1s+Hw9zqJ841PR7m3Gx1P8G23/DqXuKce/0mvcQB2KCn79jNx1b3EudPc/v5VO3xq5x5fe0H9fSkNhjr5/lcNae//6VP3OctvcR5yWmH9hJnp52f0k+cDbfuJQ7A0+ds2VssqStWjEmSJEmSJGkkWTEmSZIkSZI0TYz1VMGqhhVjkiRJkiRJGkkmxiRJkiRJkjSSTIxJkiRJkiRpJNljTJIkSZIkaZqons5Er4YVY5IkSZIkSRpJJsYkSZIkSZI0kkyMSZIkSZIkaSTZY0ySJEmSJGmaGCt7jPXJijFJkiRJkiSNJBNjkiRJkiRJGkkmxjStJfmbJMcmuSDJRUk+kmSDqR6XJEmSJEma+UyMadpKEuBrwDFVtSOwI7Ah8J4pHZgkSZIkSUNSs/jfdGRiTNPZw4Drq+pIgKpaDbwaeG6ShVM6MkmSJEmSNOOZGNN0dk9g2eCKqroK+A2ww1QMSJIkSZIkzR4mxjRrJDkwydIkS395zQVTPRxJkiRJkjTNmRjTdHYusMfgiiSbAFsD50+8c1UdXlVLqmrJfRfu2NMQJUmSJEnSTDVvqgcgrcXxwGFJnltVn0kyF3g/8JGqum6KxyZJkiRJUufGano2qZ+trBjTtFVVBTwJeGqSC4DLgbGqeufUjkySJEmSJM0GJsY0rVXV/1bVPlW1I/A44DFJdp/qcUmSJEmSpJnPqZSaMarqZ8DdpnockiRJkiRpdjAxJkmSJEmSNE0U9hjrk1MpJUmSJEmSNJJMjEmSJEmSJGkkmRiTJEmSJEnSSLLHmCRJkiRJ0jQxVvYY65MVY5IkSZIkSRpJJsYkSZIkSZI0kkyMSZIkSZIkaSTZY0ySJEmSJGmaKOwx1icTY5qVHnTDdUOP8eXz7jL0GOPuXGO9xLlmTj9FpBv01Exyfo/fJ3tufmkvcT61fMte4tzzhl7CsCir+gkEXF39fOVdNSe9xNmYfuL0aWVPO4HbrOwnzupeosDK+f1NAPjjvH5i/Xps017i7NTT9yvA9hte1Uucn6zcrJc429+wspc4yzO3lzgAK9PP5+oGY/18Bi3vab9uXo/7W6t7+urbaeen9BLn/PO+2kucvXd7US9xoL//I6lLTqWUJEmSJEnSSDIxJkmSJEmSpJHkVEpJkiRJkqRponqc6i8rxiRJkiRJkjSiTIxJkiRJkiRpJJkYkyRJkiRJ0kgyMSZJkiRJkqSRZPN9SZIkSZKkaWKMmuohjBQrxiRJkiRJkjSSTIxJkiRJkiRpWkuyRZLvJ7mg/bn5Gu531yTHJflVknOTbLu27ZoYkyRJkiRJ0nT3euD4qtoROL5dnsxngPdW1d8CewF/WttG7TEmSZIkSZI0TVTZY2wNngjs3V4/CjgB+OfBOyTZBZhXVd8HqKprbmmjVoxp2kuyOsnpSc5O8s0km031mCRJkiRJ0q2T5MAkSwcuB96Kh29VVb9vr/8B2GqS+9wD+EuSryU5Lcl7k8xd20atGNNMcF1VLQZIchTwCuCdUzskSZIkSZJ0a1TV4cDha7o9yQ+ArSe56Y0TtlNJJiutmwc8GLgP8Dvgi8DzgU+tKaaJMc00Pwd2nepBSJIkSZKkblXVI9Z0W5I/Jtmmqn6fZBsm7x12CXB6VV3UPuYY4H6sJTHmVErNGG3548OBb6zh9htLMo9dfnG/g5MkSZIkqQNj1Ky9rKdvAM9rrz8POHaS+5wCbJbkDu3yw4Bz17ZRE2OaCTZMcjo3zSH+/mR3qqrDq2pJVS154kZ373WAkiRJkiRpqA4DHpnkAuAR7TJJliQ5AqCqVgOvBY5PchYQ4JNr26hTKTUTXFdVi5NsBHyPpsfYh6Z4TJIkSZIkqSdVdTnNLLKJ65cCLxpY/j63ogWTFWOaMapqOXAw8I9JTOpKkiRJkqT1YnJBM0pVnZbkTGB/4LNTPR5JkiRJkrpUtd69uHQrmBjTtFdVCycsP2GqxiJJkiRJkmYPp1JKkiRJkiRpJJkYkyRJkiRJ0kgyMSZJkiRJkqSRZI8xSZIkSZKkaWLM5vu9smJMkiRJkiRJI8nEmCRJkiRJkkaSiTFJkiRJkiSNJHuMSZIkSZIkTROFPcb6ZMWYJEmSJEmSRpKJMUmSJEmSJI0kp1JqVvpTbTD0GD+fs3zoMcY9bs6GvcTZeGyslzirkl7izO/xNMdHLt+ylzhPXnldL3GuXj2/lzgnLBj+7+q4hdXP+26PG1b0EufP9PN/tFmt7CUOwKPv+ade4px15la9xFlNP++5XTb8Sy9xAH535Sa9xPl/1c8u6vU9fR8BfH31pr3E2f2G1b3E+f28fj6DNhrr77t8Xk/7Df3sbcGKnt7fi3rafwRYMNbPc9ppw617ibP3bi/qJc4JZxzRSxyAz+/2lt5iSV0xMSZJkiRJkjRNVI8H+OVUSkmSJEmSJI0oE2OSJEmSJEkaSSbGJEmSJEmSNJLsMSZJkiRJkjRNjGGPsT5ZMSZJkiRJkqSRZGJMkiRJkiRJI8nEmCRJkiRJkkaSiTFJkiRJkiSNJJvvS5IkSZIkTRNVNt/vkxVjkiRJkiRJGkkmxiRJkiRJkjSSTIxpRkjyxiTnJDkzyelJ7jvVY5IkSZIkSTObPcY07SW5P/B4YPeqWpHk9sDtpnhYkiRJkiR1bsweY70yMaaZYBvgsqpaAVBVl03xeCRJkiRJ0izgVErNBMcBd0ny6yQfTfKQye6U5MAkS5MsPW75hT0PUZIkSZIkzTQmxjTtVdU1wB7AgcClwBeTPH+S+x1eVUuqasmjNtqh51FKkiRJkqSZxqmUmhGqajVwAnBCkrOA5wGfnsoxSZIkSZLUtbLHWK+sGNO0l2SnJDsOrFoM/HaqxiNJkiRJkmYHK8Y0EywEPpxkM2AVcCHNtEpJkiRJkqTbzMSYpr2qWgY8YKrHIUmSJEmSZhcTY5IkSZIkSdPEGPYY65M9xiRJkiRJkjSSTIxJkiRJkiRpJJkYkyRJkiRJ0kgyMSZJkiRJkqSRZPN9SZIkSZKkaaLK5vt9smJMkiRJkiRJI8nEmCRJkiRJkkaSiTFJkiRJkiSNJHuMSZIkSZIkTRNj9hjrVWzqptnolDs/aehv7PNr42GHuNEGPf2eppco/enz0+2quf28etuvXNFLnEvmbtBLnAU9fgctn9PP/9Hcnp7SDizvJc5FbNhLHIA5Pb12fZXLr+rpQ3Xxxlf0Ewg49doteokzG/dOV/b0flg01s+r19f/0dwevyfG0s9/Ul/PaLbt1wGs7inOdT3tM6zu6T9pfo8fqrKun/cAACAASURBVPufcWgvcebffrvZ+Ba/0cKN7j4bvwoBuGb5xdPu/86plJIkSZIkSRpJJsYkSZIkSZI0kuwxJkmSJEmSNE3UrGwqMH1ZMSZJkiRJkqSRZGJMkiRJkiRJI8nEmCRJkiRJkkaSPcYkSZIkSZKmibGyx1ifrBiTJEmSJEnSSDIxJkmSJEmSpJFkYkySJEmSJEkjycSYJEmSJEmSRpLN9yVJkiRJkqaJsvl+r0yMaVpLsiVwfLu4NbAauLRd3quqbpiSgUmSJEmSpBnPxJimtaq6HFgMkOQQ4Jqqet+UDkqSJEmSJM0K9hiTJEmSJEnSSDIxplkjyYFJliZZ+vVrfzPVw5EkSZIk6VarWfxvOjIxplmjqg6vqiVVteRJG2871cORJEmSJEnTnIkxSZIkSZIkjSQTY5IkSZIkSRpJnpVSkiRJkiRpmqianr24ZisTY5oxquqQqR6DJEmSJEmaPZxKKUmSJEmSpJFkYkySJEmSJEkjyamUkiRJkiRJ04Q9xvplxZgkSZIkSZJGkokxSZIkSZIkjSQTY5IkSZIkSRpJJsYkSZIkSZI0kmy+L0mSJEmSNE3Yer9fVoxJkiRJkiRpJJkYkyRJkiRJ0miqKi9evFQBHGic6RtnNj4nXzvjzObnNNvizMbnNNvizMbnNNvizMbnNNvizMbnNNvizMbn1Odr58XLZBcrxqSbHGicaR2nz1izLU6fsYwz/WMZZ/rHMs70j2Wc6R/LONM/lnGmf6zZFkealIkxSZIkSZIkjSQTY5IkSZIkSRpJJsakmxxunGkdp89Ysy1On7GMM/1jGWf6xzLO9I9lnOkfyzjTP5Zxpn+s2RZHmlSqaqrHIEmSJEmSJPXOijFJkiRJkiSNJBNjkiRJkiRpqJLMm+oxSJMxMSZJs0SSbyfZdqrHIUmSJE3i5KkegDQZE2PSLJLkQUle0F6/Q5K7DznenCSbDDPGbJHkrmu57cEdhTkSOC7JG5PM72ibk0ry6LXctt8wY2t0Jfls+/NVUz2WYWo/y/9jqsdxWyTZIckDJ1n/wCTbT8WYpK4l2TDJTlM9Dk29JBsl2bW9bDDV45kBMtUDkCZj831pSJK8Zm23V9W/dRzvrcASYKequkeSOwFfrqq/+gNlPeN8DngpsBo4BdgE+GBVvbfLOG2s46vq4be0biZIchHwceD9VbW6XbcV8H5g56pa0lGchcCbgccAnwXGxm/r8j2XZDVwEvDsqvq/CbedWlW7dxVrwrY3B3YEFoyvq6qTOo6xAHg58CCggJ8AH6uq6zuOcxTwqqr6S7u8Oc3744Udbb/Xz6A2ZoBnAdtV1aFtQnjrqurkCHGSc4FHAN8B9mbCDnZV/bmLOAPxJnsNrwSWVdXpHce6D/BMYD/gYuBrVfXhLmNMEvP2wOXV4c5gkm8Bb6iqsyasvzfwrqp6QlexJmz/DgBVdekwtt+HJHetqt9N9Ti6luR+wIeBvwVuB8wFrq2qGXlgLckTgPcBt6uquydZDBxaVftM8dBusyRzx/dNZrr2O/ylwA7AWcCnqmrVEOLMB94LPJfmMzvAVsCHq+qwJIu7/p6YZAxzgP2r6uiOtrcn8L9V9Yd2+bnAU4DfAod08R2b5BJgjfsfw9g3kdaFFWMaSUm+meQba7p0FGbRLVy69iRgH+BagKr6f0OKs0tVXQXsS/PH6d2B53QZIMmCJFsAt0+yeZIt2su2wJ27jNXG2yrJp5J8p13eJckBHYfZA9geOD3Jw9qKl5OBnwN7dRjnBpr3wAYM9z13JvA54BdJnjrhtqEcDUzyIppk3PeAt7U/DxlCqM8A96T5Q+4jwC40Scau7TqeFAOoqiuA+3S4/fH/9yXAy2h+d+5M8wfDUBKXwEeB+wP7t8tXA11WPn0cOB7YGVg24bK0wzjjltC8XuOv3Utoks6fTPJP67vxJPdI8tYk59G8335Hc9DyoV0nxZLcL8kJSb6W5D5JzgbOBv6Y5DEdhtpqYlIMoF23bYdxSOOQJJcB5wO/TnJpkrd0HOefBq7vN+G2d3UY6piB7X61w+1OKsmyJK9ok/LD9BGaz4QLgA2BF9Ht58KNkhy/LuvW0yE039t/AWiTH0Op0G/3h16R5KNJ/nP8MoRQFyR5b5JdhrDtm+nhfXcUzWf3WcBjaQ5ADsP7gYXA3apqj/aA4N8C2yX5GPD1rgIl2STJG5J8JMmj2s++g4CLgKd1FQf4BM1+JEn+DjiMZp/oSuDwjmLMpXnd+vr7SFo3VeXFy8hdgIes7TLV47uNz+nk9uep7c+NgTOHEOccYD7w5fHXCjij4xivojn6toLmS//i9nIG8MohPKfv0OxYnNEuzwPOGtL/06toqrguAf6m420/BjiXZkdmoyG/38bfZ/egqRw8cjzm+G1DiHkWTaXY6e3yzjRVNV3HOXdd1nUQ5wxg84HlLYbxvqNJJi4aWF4EnDTk98Vpg89zCHE+Nozxr+G1WziwvBA4keaP+/V+T7SfBScCOwysu2hIz2Up8CiairQrgPu163ce/P/qIM4Fa7ntwo6f02uA7wN3H1i3HU3S/NUdxjl1suuTLa9nnNMmuz6sC01FzTuBC4EvAI+mnU3ScZyl7c8zB9Z1+vza74Ytxj9X2+tb0CRjz+s41i8m+f/qfH+r3e6XgbcD/wM8DziOpkq/6ziLgBcDPwN+ARwIbDIT33eD36M0+3PD2ie5cLJx0yR+bvyM7SjWscCnaQ7OfAk4of3uWNzxczpj4Pp/0FSJjS+f3lGMofx/ePGyvhfPCqGRVFUn9hWrLek+gKYCZXD6VyfTpQZ8KckngM2SvBh4IfDJjmNAczTpNzQ7nycluRtwVZcBquqDwAeTHFRDnkrUun1VfSnJG9r4q9qpgp1JshnwbuC+NAmsxwHfSfKqqvphR2HeCOxXVed0tL1bVFW/TnJ/4B3AaW3Z/bBcX1XXJyHJBlV1XobT4+XUJPerql8AJLkvw6lGej/w8yRfbpf3o/ljoWtb0R4Bbt3QrhuGlUnm0kxBHZ/eNrb2h9x6VfWyrre5BnekSdCPW0lTEXVdkhVreMyt8WTgGcCPknyX5o/EYfVfmVdVxwEkOXT8/d3+HnUZZ2mSF1fVzb5/2orPZV0GoqlWfmRVXTa+oqouSvJsmgTCBzqKkzVcn2x5fdQarg9FVV0IvDHJm4HHA/8JrE5yJE3ypaupycuT3I6mYvo9wO/pftbKS4B/AO5E8z4b/3+5iqZirUvnJHkmMDfJjsDBNAmlYdihqvZL8sSqOipNO4sfdx2kqq6m2Wf8ZJKH0FSEfyDJV4C3t++VrmIN+323ciDWqo4/3waNVdVf/Z5W1eokl45/xnZku6q6N0CSI2h+h+5aHbd4oHlPz6tm6unDaRKk47rKG9hjTNOSiTGNtHaH5l9ppkoNJq226zDMZ4HzaI6IHUrTf+dXHW4fgKp6X5JH0uwE7gS8paq+P4Q4HwI+NLDqt0ke2nWcNtaHkzyA5ojvvIH1n+k41LVJtuSmP+bvR1M23qVTaaaZvaLd4TguTV+Sjyb5bVXtv/aH37Kq6qqJ/7q4ccemfT6vb/+w/zxwhyHFvKRNMB4DfD/JFTR9LzqR5Cya98B84GdJftcu343md7hTVfWZJEuBh7WrnlxV53Ydh2YaxMlJxqd17Etz5HkYPkQzfeSOSd4JPBV405Bi9eFo4JdJjm2XnwB8LsnGNNWZ66WqjgGOabf3RJo/7O84Pg1nPJHVkcEE5XUTh9JhnH8Avp7kWdyUCFtC01vqSR3GAZg/mBQbV1WXptsTkKwtYdXla7dbkqtoPl83bK/TLlcNoSdXkl2BF9AcrPkqzXv+QcAPgcUdhXkOTRXNK4FXA3eh6VvUmZ4PqB1EcyBqBc133vdoqrqGYTzJ85ck9wL+QJOw71R7QOPvad4L29IcuDkaeDDwbZrq8C7jDfN9t9uE350NB36vuvw9OjfJcyfuk7aJ+a738weTfauTXDKEpBg07+cT2+np19EmYZPsQHf7xTOuT7BGg833NdKS/AR4K81R5SfQfEnPqarO+pMkOa2q7pPkzKratd1Z/3FV3a+rGH1K0xvrSJreQUfQ9ER6fcd/wI3H+ixtXy6aZv/Q7NQc3HGc3Wn6+9yLpufOHYCnVtWZHcb4m6q6ZA23/VV1xXSXZN/2j/qJ6zcHXlJVhw05/kOATYHvVtUNt3T/ddzm3dZ2e1V1loTrW/seH0+cnlRVpw0x1s40O74Bjq+qzg8E9CnJEmD8JCY/raphVA8Oxtucpnrw6dXhiUbaKthraf9QBJaP3wQsqKpOz2TbHjC5V7t4ToeVsYMx1niij7Xddhvi9Pra9SXJMpo+WZ8CvlpVKwZu+1pVPXnKBrceejqgNh5rLrBxNb1Xh7H9F9Ekjnal2fdaSHPg8+Mdx7kI+BFNo/qfTbjtQ13ud82W912SOwNfo0kgDR4E2BB4Uk04MdF6xhr/DIKbfw51njRvDw5vAxxXVde26+5B01bg1K7iSNONiTGNtCTLqmqPJGcNlCgvq6o9OoxxclXtleQkmjPd/YGmH1gnVWlJrmYtR6y7PsKc5Iyq2i3Jo2mmLrwZ+GxXf4BMiPUrmmb/Q/+gSjKPptIuwPlVtfIWHiJJmkIT/li82U3M4IRVX5JsV1UX9RDn8TQVVXejSVYNswJu6AfU0uPZufuSZGFVXdNTrF7ed31J8jCadinQ9J7s+mQPknrgVEqNuhVpTnV8QZJXAv9HczSuS4e3FQBvAr7Rbv/NXW28qhYBJHk7Tc+Bz9LsdD6L5ohP18an0D2OJiF2TobXwOFsYGua5zU0SSYenbxHkitpGrj+aZixJUm3TVXNneoxzHAvSvKeas+O2+6r/GNVdT39+d9p+umd1cOBriUM/4DaLlV1VTtd+DvA62kqhjpLjCV5dlX9V5LXTHZ7Vf1bV7Fa75pkV+5KmhMnHDvJ/dfHlUk+RDN1soCfAIdW1eUdx+lFWw3beUWspH513fhSmmleBWxE0zh1D+DZQNfNw4+vqiuq6qSq2q6q7kjTFLhr+1TVR6vq6qq6qqo+RtOzpmvLkhxHkxj7XpJFDKG5duv2ND0cvpfkG+OXIcQ5gGZa6LPayyeBfwZ+muQ5Q4gnSdJUe+x4Ugygqq6g+W7v2v8CZ/dR/c1NB9SGaX7bFmNf4BtthXnXz23j9ueiNVy6toCmt9cF7WVX4G+AA5L8e8exvgBcStNn7qnt9S92HEOSbhUrxjTqtq2qU4BraPqLkWQ/4JcdxvgqMHGa4VdoEnFdurY9evkFmh20/Zl8isn6OoBm5+miqlreNq1/wRDiABwypO1ONA/426r6I0CSrWgalt8XOImmCk+SpNlkbpoz/K4ASLIhsMEQ4vwT8O0kJzJwltchVD3BTQfUTp4Qa58OY/Rxdu5PtD/f1uV212JX4IFVtRqgPQHIj2mqus7qONY2VTV4soJ3JHl6xzEk6VYxMaZR9wbgy+uw7lZrG1DfE9h0wlS9TRg4A2aHngl8sL0U8NN2XdeK5iyej6c5y+bGDOf5UFUntjucO1bVD5JsRHNmq67dZTwp1vpTu+7PSew1JkmajY4Gjk9yZLv8AuCoIcR5J80ByAU0ZycdpkOGvP1ez86d5D3AO2gavH+XJoH16qr6r45DbU7T6mP8zIMbA1u0Z0BcseaH3SbHJXkG8KV2+ak0Z/aUpClj832NpCSPpZku8DRuXr69CU3viL06iPFEmjL7fWh6i427GvjCxLP+zBTtUcQx4GFV9bdtT5LjqmrPIcR6MXAgzc7Z9kl2BD7e5Zna2jgfBe7KTQnRpwCXAK8DvlVVQ9nhlSRpKrX7Q+Pfqd+vqs4TFEnOrqp73fI9O4v3VwfUqurqjmP8Pc3BzxsPDFbVoV3GaOOcXlWLkzyJ5oDka2jOLLxbx3EOoOmFewJNL9m/A94FfB44pKpe12Gsq2kSb+NtOOZw0wyHoZyUQZJuiYkxjaQku9FMBzwUeMvATVcDP2r7bHQV6/5V9fOutreWOAtopjlO3FF7YcdxTq2q3ZOcVlX3aded0fVOWrvd04G9gF8OxLrxDKIdxglNY+AHtauuALaqqld0GUeSpFHTVj39oKqG0V91YqyhH1BL8nGa/rQPpelP+lSas40f0FWMgVhnV9W9khwBfKWqvjvEfa5taPa5AE6pqv/XdQxJmq6cSqmRVFVnAGckObqqVg0jRpJ/qqr3AM9Msv8kY+js1OGtzwLnAY+mSfg9C/hVxzEAViaZS9toNskdGF7z/RVVdcP4mZKSzKP7BrdUVSW5CLgfsB9wMU1vOEmSZqW2zcO7gTvSVAmF4VTsvAx4bTslb+UQ4wC8gvaAGk2QC5LcseMYD6iqXZOcWVVvS/J+mrNTDsO3kpxHM5XyZe0+1/VDirUn8OD2+hgwtMRYO9tgR25+IPekYcWTpFtiYkwjKcmXquppwGlJ/irRUlW7dhBmPCm1tINtrYsdqmq/JE+sqqOSfI6mcWrXPgR8HbhjknfSHCnt+tTu405M8i/AhkkeCbwc+GZXG09yD5qTFOwPXEYzrTZOnZQkjYD3AE+oqmEcRLtRVQ3jLIpr0scBtevan8uT3Am4HNim4xgAVNXr24q7K9t+X8sZwhnHkxxGkxg7ul11cDvj4V+GEOtFNGeF/xvgdJqDkj8HHtZ1LElaVybGNKpe1f58/LACVNU3259HASTZpFnsts/FgPEm8X9Jci/gDzRHgTtVVUcnWUbTkyTAvkPcqX49zfTQs4CXAN+mmbbQlfNokoePr6oLAZK8usPtS5I0Xf1x2EkxgCR/N9n6IVUIDfWAWutbSTYD3gucSpN463Lf5EZtj7SX0/RBPRC4E7AT8K2OQz0OWFxVY23co4DTgM4TYzT74HsCv6iqh7Ynq3rXEOJI0jqzx5hGXpKtacrui6anwh863v4S4EhgEU0i6S/AC6tqWcdxXkQz/e/ewKdpzi705vFTfnccay6wFQPJ9ar6Xddxhi3JvsAzgAfSnO3pC8ARVXX3KR2YJElDluSDwNbAMcCNZx6sqq91HGcwMbWAZp9rWVV1XiGUZA7NAbVH0exzfY/me30of/Ak2QBYUFVX3uKdb9v2vwgsA57b9hrbCPhZVS3uOM6ZwN5V9ed2eQvghI5mUEyMdUpV7dn2kb1vVa1Ick5V3bPrWJK0rqwY00hrk0lvAX5IswP14SSHVtV/dhjmP4GXV9WP25gPokmUdbaz0e4IXtWeNOAkYLuutj1JrIOAtwJ/BFbT9gqh2+fzpap6WpKzmGQKRFc7alV1DHBMko1ppib8A80U0Y8BX++jUbAkSVNkE2A5TRJpXAGdJsaq6gmDy0nuAvx7lzEGYo0Bn2wvQ9Emp/4RuGtVvTjJXZM8uKq6ruIC2L6qnj7eq7aqlmd8nmi3/pWmvciPuOmslK8fQhyAS9qKu2OA7ye5AvjtkGJJ0jqxYkwjLcn5NE1UL2+Xt6Q5ErdThzFuPHvjwLpTq2r3rmK021xaVUu63OYa4lxIc4Tv8iHG2Kaqft+ecv2vVNXQdqDahrD7AU/v8ixWkiTpxjNBn1NVuwxh25MdULuSpt/rO7rYd+mriquN9TOa1hU/bc8Ivj3w+ara6xYeeltibUMzxRGas2x2OoNiDTEfAmwKfLeqbhh2PElaEyvGNOouBwZ7fl3druvSiUk+AXyeZmft6cAJSXYHqKpTO4rzgySvpWkgf+34yvGy+A79L81O5tBU1e/bqy+vqn8evC3Ju4F//utHdRb7CuDw9iJJ0qzUnoDmY8BWbYJnV2CfqnpHx3E+zE3JqjnAYpreXMPwHZpq9s+1y88ANqLpu/pp4AmTP+xW6auKC5oK/e8Cd0lyNE3rh+d3tfHxfdEBl7Q/75TkTh3uo47Hm0uTFN0ZoKpO7HL7knRbWTGmkZbkMzQ9uY6l2Wl7InBme6Gq/q2DGD9ay83VVY+NJBevYfudTqtM8imaxq//zc17kqz3azVJrL+qrGtPj955zwtJkkZJkhOB1wGfGK9sT3J2Vd2r4zjPG1hcBfymqn7aZYyBWJPtN5zaVludVVX37iBGb1Vcbbwtac7cGJqG9Zd1uO1e9lEnxDwWOGgm9qaVNHtZMaZR9z/tZdyx7c/OTi1eVQ/talu3EKevhvG/ay+3ay+dS/IymrMwbdc2hB23CBjKzrQkSSNmo6o6eUKx06qug4yfnbsnc5PsVVUnAyTZE5jb3tbVcxtqFdckFgBX0PzdtkuSzs7o2dc+6gSbA+ckOZmbz3DYZwrGIkmAFWPSjdoG9gur6qqOt/sqmmb7V9M0g90deH1Xjd2TbEIzDeKCdnk/YMP25u9V1R+7iNOnJJvS7Dj9Kzdv/nr1EKaGSpI0cpJ8B3gl8OW28umpwAFV9diO4+xI832+C02SB4CuK9rbWONnAl/Yrrqa5iyV5wJ/X1Vf6ijO0Kq4JsR5N00LjnOAsXZ1dZ1ESjIfeBlN032AE2gqCVd2GaeN9ZDJ1jutUtJUMjGmkZbkc8BLafpRnEJzhqYPVtV7O4xxRlXtluTRbaw3AZ/tqvl+ksNpmr5+ul2+kKbHxobAqqp6aUdxvskkZ4gcN8wjfUnuyM13pi2/lyRpPSTZjqaf5gNoKpIuBp5dVb/pOM5PaKqsPkDT4+sFwJyqekvHceYCB1fVB9oDbFRV5z1RkzwQOL2qrk3ybJoDnh8cxomB2pNE7VpVK27xzusX5whgPjBe3fccYHVVvWhI8e4G7FhVP2hPXjC3qq6+pcdJ0rCYGNNIS3J6VS1O8izaSi5gWZc9rMZ7YiX5IHBCVX19sjNVrsf2TwN2r/aXeXDbSX5SVQ/qKM6kR/jGDeNIX5InAP8G3An4E3A34FdVdc+uY0mSNIqSbEyTqBpKYiLJsqraY7DH1/i6IcQ6eVi9vgZinAnsBuxKU532KeBpVbXW/aTbGOs7wH5VdU3X254Q54yq2u2W1nUU68XAgcAWVbV9W1H4cc8ELmkq2WNMo25+Wz6+L/CRqlqZpOts8bIkxwF3B96QZBE3lcN3YV7dPMP9nIHrm3UVZIpK3N9BM1XhB1V1nyQPBZ49BeOQJGlWSfKaCcvQnHV6WVWd3mGoFW27iguSvBL4P26a6ti1nyb5CH99hu4uz664qqoqyROB/6iqTyU5oMPtD57JczlwepLjufkJjw7uMh6wOsn2VfU/bfztaGZTDMMrgL2AXwJU1QXtzABJmjImxjTqPgH8BjgDOKkt7e60xxhNb4vFwEXtKb23pJlG0JWxJFtX1R8AqupsgCR3psMEXJKzWPtUymGcKXJlVV2eZE6SOVX1oyT/PoQ4kiSNmiXt5Zvt8uNpzsr90iRfrqr3dBTnVcBGwMHA24GHAs9b6yNuu8Xtz7e1P0Oz79Ll2RWvTvIGmgN1f9cm/eZ3uH2Ape3PZcA3Jtw2jOk+rwN+lOQimtfsbnS7rzpoRVXdMH7ShyTzGM5zkqR15lRKaYIk86qq07MyJdmHmxqanlhV31zb/W/ltp9Ns9P5j8Bp7erdgfcBH6qqz3YU525ru31IvTV+QFPN96/A7WmmU+5ZVQ/oOpYkSaMkyUnA48an6SVZCPw38BiaqrFdOogxF3h3Vb12fbd1C3HGq9/GT7FZwKXAT6rq4o5jbQ08Ezilqn6c5K7A3lX1mS7jtLFeVVUfvKV1HcXaANipXTx/WH3NkrwH+AvwXOAgmrOQn1tVbxxGPElaFybGNNLa5qxvZSBpBRzaZbPWJIcBewJHt6v2p9mZ+pcOYzwG+BfgnjQ7g+cAh1XVd7qKMRXahqzXA3OAZwGbAkdX1eVTOjBJ+v/t3Xm0JWV19/Hvr7tRUGkQQYwTSssQIjMoCA6AxhcjRBFRArqcjRpBiXF8ExwSnKJGMRoHBIWgwitENCKoDDIqdDM5gCYY1GiiggoiitD7/aPq0qcvty8Nt+oUt8/3s1avPvVUd+3NXOzzPHtL81ySK4GtpyYPtoWRy6pqyy56oU590ZjkwqrapYucZ4l1+AzLGwBPBt5cVZ/pM35fkiybPqypyz61I898Bc371a/a6/sCB1bVh7qM0z57Ac1pij+lKWSeBny8/J9SSQOyMKaJluRzwLdYeQrPtlW1X4cxLge2q6rl7fVC4JKejh72JskNzLzVPTSjwxd3GOvRNJOylgBX0IyP/05Xz5ckadIl+Vvg6cDn26V9aI7tvQf4aFUdNMfnL6uqHZJ8GHgQcCIr9/06aS7PX80cNqDpUzrnSeBTA41meB/q4z3oQJpdabsD54zcWkwzLbLTRvVTw6imrXVegJOkuyt7jGnSLamqZ4xcvyVJlw1np6wPXNd+Xq+H5/euqtYdY7h/Bl4DfB3Yl2bE+5PHGF+SpDVWmgZPxwCnAru1y39ZVVO9reZUFJtmbeBamj5fxYq+X70Xxqrqukw1s5r7s3Zvfx7H+9D5wE9p2ki8Z2T9Bpo+cF1bmCQjE84XAvfoIQ5JdgPeTNPHbBErCoub9hFPklaHhTFNupuS7F5V58Jt/7G+qeMYbwcuSXImzX/8Hwe8vuMYa5oFVfWV9vOJbZNbSZLUgXaq4peqamtWNHrv2v3b3l/fYkVB7LYUeoq5knaa9S87etYGs92vqutmu39ntH1br0nyROCmqlqeZHNgS5qd9F37MvDZJB9pr1/arvXhKODVNIMF+pp8KUl3ioUxTbqXAZ9se42FZlfX87oMUFWfTnIWTZ8xgNdNTZDUKq2fZL9VXY/j+IUkSWu4ZUl2rqqLenr+QuA+rFwQm9JpYWwVk7M3AH5C0+S9C0tZUeB7KE3BLTSnAn4IPLyjOKO+Djy27fl1OnAR8Cy63dEH8DqaYtjL2uuvAB/vOMaUX8/3HriS1jz2GJOAJIsBqur6Dp85az+LqlrWUZzDZrtfVe/tKM49+5pQNEOso2e5XVX1gnHkIUnSmqptvr8Z8F80vb+mjrR1suqMiQAAHdJJREFU0gN1psbxfZlhcnYB11bVjTP9+jnG+hhwclV9qb3eG3haVb20h1hTfdpeCaxTVe+aqR9YR7HWAR5aVVd1/ez2+VN/LxxAUzQ9CbjtvbKr92JJuivcMaaJtKpi0lQbio6KSaM9IXakOaowOkZ8zw5iAEz1utiCZlfaKe31PsA3O4oBcAGwQ5Jjq+o5HT73dqrq+X0+X5Ik9d67s5PeXqujPXo4LrtU1YtHYp+a5F09xUqSXWl2iL2wXVvYQ5B9gXfT9BV7eJLtaKa079thmPdMu95p5HOX78WSdKdZGNOkGm2c+lLgI6v6hXdVVe0x9bmd7NPLf/Cr6i1tjK8DO1TVDe31m4F/7zDUPZL8BfCYacccp/LweKMkSfNEVV2TZHdgs6o6OslGNEcfu9Lp5MS7kZ8k+b/Ace31QTRHNvvwKuANNDvUvp1kU+DMHuIcDjwKOAugqi5N0unR0Kn34iSbVtXVo/faPy5JGoxHKTXxxjGOehzHCZJcBWwzddwxyT2By6tqi46evzvNy98BrNiVNsXjjZIkzSNJDqfZtbNFVW2e5IHAiVW12x381onWNuE/nGaYUtH0AXtrl833Z4h5r6r6bY/Pv7Cqdhl9J05yeVfHaqfFut07cZKlVbVj17EkaXW5Y0wa02SkMfgU8M0kJ7fXTwM+2dXD28md5ya5uKqO6uq5kiRpEE8HtgeWAVTVT5KsO/tvUVsAO3QcsdpjlEfR7OR7aJJtgZdW1cs7DvXt9lTAwiSbAYcA53cZIMmWwJ8A6007ebAYWLvLWJJ0Z1kYk3qS5EhWFN0enOQDo/er6pAu41XVPyQ5FXhsu/T8qrqkyxitY5McQvNNKcDZwL9U1R96iEWSxwAPY+TfV1X1qT5iSZI0QW6uqkpSAEnuPXRCup1/oukFdwpAVV2W5HGz/5a75JXAm2ia4X8aOA14W8cxtgCeSjPFc5+R9RuAF8/4OyRpTCyMaSJNG+v9iCSXT92iu4lMF498XtrB81bHvYDrp3qFJHl4Vf2g4xgfAtZqfwZ4DvBh4EUdxyHJscAS4FLg1na5aHbHSZKku+6EJB8B1k/yYuAFwMcGzknTVNWPpoZDtW5d1a+dQ4zf0hTG3tT1s0difB74fJJdq+qCvuJI0l1hYUyT6ql9B6iqzo4xro7RXiHA0TTFq+OArnuF7FxV245cn5Hkso5jTNkJ2KpshihJUqeq6h+TPAm4nubd4e+q6isDp3W3l2S3qjrvjtY68qN253wlWYvmCOd3uw6SZHPgNdx+h34fg6N+1Lb9mHo/PQc4tKp+3EMsSVotFsY0kVZ3rHeSC6pq177z6ci4eoXcmmRJVf0n3DZJqPNvL1vfAh4A/LSn50uSNLGq6itJvkH7/wRJNuizifwa4khg+kClmda68JfA+4EHAf8NnA68ooc4JwL/Anyc/t7pphwNHA88s70+uF17Us9xJWmVLIxJs5tPzUDH1Svkb4Azk1xNc/R0E+D5XQZI8gWaI5PrAt9J8k2avhcAVNW+XcaTJGnSJHkp8Bbgd8By2nYSwKZD5nV31TbCfwywUZLDRm4tBhb2EbOqfkEzEbxvt1TVh8cQB+D+VXX0yPUxSV41ptiSNCMLY9Ls5tMRvrH0Cqmqr7UTi7Zol66qqt/P9nvugn/s+HmSJGllrwEe2RZfdMfuQTMdchHNF3dTrgf27yNgko1oGtM/jJWPOL6g41BfSPJy4GRW/iKyj92Dv0hyME2Tf4ADgWt7iCNJqy227pFWLcmyqprT1vgxvtTQ9gr5U5pvfU+b771Ckryzql53R2uSJOnOSfJlYL+28bpWU5JNplpyJFkA3Keqru8p1vk0PbiWMnLEsao+13GcmQY1VVV1vnswySY0R093pfkC+nzgkKr6YdexJGl1WRiTZpHkkqrafo7PGMtLzbSYGwLXzvem9TMVJpNc3tHUUEmSJlaS7Wl6O32DlXcJHTJYUvNAkuNpen/dClxEc5Ty/VX17h5iXVpV23X9XEnSyjxKqYnXfnO1WVV9Nck6wKKquqG9/ZwOQtyrzx1OSXYB3gFcB7wNOBbYEFiQ5LlV9eW+YvclycuAlwObJrl85Na6QB9TnyRJmjQfAc4ArqDpMabVs1VVXZ/kIOBU4PU0X352XhgDvpjkKVX1pR6efZskS4GjgOOr6lc9xXhtVb0ryZHM0KrEgqykIVkY00Rre3G9BNgAWAI8mGYqz14AVfWtDsL0/VLzQeCNwHo0L7h7V9WFSbak6d/QaWEsyUk0L0+nVlVfL9LH07xsvp3mhXPKDU7LkiSpE2tV1WF3/Ms0zVpJ1gKeBnywqv4wNfioB4cCb0zye+APtAMSqmpxx3GeRTNI6eIkF9PsJDy945MH321/vrjDZ0pSJzxKqYmW5FLgUcA3po5MJrmiqrbuMMYNwL1pjil0/lIzus0+yXer6o9H7s35KOgM8Z5I8/K0C81476Or6qqOYyxuv43dYKb7FsckSZqbJEcA/wV8gf4brq8xkhwCvA64DPgz4KHAcVX12J7ibQBsxsik9Ko6u6dYC4CnAh+mOSp6NM0xUf+ekLRGc8eYJt3vq+rmJAAkWUTHkyirat07/lVzMrpr66bp4bsOVlVfBb6aZD2aSUJfTfIjmgmYx1XVHzoIczzNi9lSmj+GjKaAo+QlSZqrA9uf3zCy5n9j70BVfQD4wMjSNUn26CNWkhfR7Bp7MHApzZeS59OebOg41jY0E833Bj4H/CuwO81phDn3OUvyBWZ5L62qfecaQ5LuKneMaaIleRfwK+C5wCtp+lp9p6re1MGzt6yqK5PMONWyqpbNNUYb51bgRpri0TrA1HSpAGtX1VpdxJkW837AwTQ92H7CipenravqCV3HkyRJujtIsjFwBPDAqto7yVbArlV1VA+xrgB2Bi6squ3aNhlHVNV+HcdZSvM+/HHgpKr6/ci9k7qIl+Tx7cf9gAcAx7XXBwL/W1WvnmsMSbqrLIxporVbxl8I/ClNIek04ONd9FRI8tGqekmSM2e4XVW151xjDCHJycAWNE3+j6mqn47cu7iqduow1rHA14FzqurKrp4rSdKkmmqC3n5+ZlWdOHLviKp643DZ3f0lOZXmiOGbqmrb9rTBJV224RiJdVFV7dy2/nh0Vf0+yber6k86jvNa4JZpy78GllbVpR3Hut27Ytfvj5J0Z1kYk3SnzDRIIMk9R79d7DDWHsBj2x9LgEuAr1fV+7uOJUnSJEiyrKp2mP55pmvd3kix6rY+rqP9XjuOdTJNX9dXAXsCv6QZmvCUjuMcD+xE028OmnYWlwMPA06cKqR2FOu7wJ9V1dXt9cOBL432yJWkcbPHmCZSuzV9tj4H24wxnfnm74HpEzYvADp/ka6qM5N8neYYwR7AXwJ/AlgYkyTprskqPs90rdu7sW0pUQBJdqHZXdW5qnp6+/HN7QmE9eh42njrwcAOVfUbgCSHA/8OPI6m32tnhTHg1cBZSa6m+fttE+ClHT5fku40C2OaVE9tf35F+/Ox7c8H00PD+jVBkgcADwLWSbI9K16eFwP36inm12gmel4AnAPsXFU/6yOWJEkTolbxeaZr3d5hwCnAkiTnARsB+/cdtK9JlK37MzKZlGaK+sZVdVOSTk8EVNWXk2wGbNkuXdnHqQNJujMsjGkiVdU1AEmeNLUNvvW6JMuA1w+T2d3ak4Hn0Xyr+N6R9RuAvvqRXA7sCDyS5tvYXyW5oKqmT9+UJEmrZ9sk19MO7Wk/016vPVxa80NVLWsbyW9B8+fsqo4mcg/pX4FvJPl8e70PcHySewPf6SHeZjR//tam+fuRqvpUD3EkabXYY0wTrW1m+oqqOq+9fgzwoS77RCTZDbi0qm5McjDNkcP3TxXn5pskz6iqz4055ro0RbnXAA+oqnuOM74kSdKU9n3xYYxsMpjvhZ0kOwG7tZfnVdXFPcU5HHgCsBVNa469gXOrqvddd5K0KhbGNNGS7Ah8gqZnQ2iamr6gqpZ1GONyYFtgG+AYmlHYB1TV42f7fXc3SQ6uquOS/DUzHLWoqvfO8NvmGvOvaBrv7wj8F81xynOq6oyuY0mSJN2RdmL2EuBS4NZ2uarqkOGymj/aPr/b0kzy3DbJxsBxVfWkgVOTNME8SqmJVlVLabZwr9de99E89ZaqqiR/Dnywqo5K8sIe4vTt3u3P9xljzLVpjm0urarpY8QlSZLGbSdgq3J3wV31u6panuSWJIuBnwEPGTopSZPNwpgmWlsQO5xm6g5Jzgbe2nGB7IYkb6Bp7P+4JAuAtTp8/lhU1UeSLASur6r3jSnmP44jjiRJ0mr6FvAA4KdDJzLfJAlweZL1gY/RTLz8Dc2QJUkajEcpNdGSfI7mBeeT7dJzgG2rar8OYzwA+Avgoqo6J8lDgSfM114USb5ZVY8aOg9JkqRxSfIFmlYS6wLbAd9kZJJjVe07UGrzSpIrqmrr9vPDgMVVdfmgSUmaeBbGNNGSXDq90f5Max3FWszKTVqv6zrGOCR5H82Ot88CN06td9mXTZIk6e6knUS5SlV19rhymc+SfJKmtchFQ+ciSVM8SqlJd1OS3avqXLhtguRNXQZI8lLgLcDvWNG0voBNu4wzRlNFw7eOrBWwZ9eB2jHhN7W9KDYHtgROXQPGokuSpPnlv4GNpyaZT0myOx6rvDMeDRyU5BqaL1hDM7xgm2HTkjTJ3DGmiZZkW+BTNFMpoZlK+byquqzDGN8Hdq2qX3T1zEmRZCnNVMr7AucBFwE3V9VBgyYmSZImSpIvAm+oqiumrW8NHFFV+wyT2fySZJOZ1qvqmnHnIklT3DGmidYWwLZtjzlSVdf3EOY/gd/28NxBtGO1jwAeWFV7J9mKpvB3VB/hquq37RTPD1XVu5Jc2kMcSZKk2Ww8vSgGUFVXtL2ytBosgEm6O7IwpomU5DDg11PFnKmCWFuAWbeq/qnDcG8Azk/yDVZu0npIhzHG6RjgaOBN7fX3aPqN9VIYS7IrcBDwwnZtYQ9xJEmSZrP+LPfWGVsWkqTOLRg6AWkgB9EcoZzuWOAFHcf6CHAGcCHNWOqpH/PVhlV1ArAcoKpuAW7tKdahNIXFk6vq20k2Bc7sKZYkSdKqXJzkxdMXk7yI+f1eJ0kTzx1jmlSLZmrgXlU3J0nHsdaqqsM6fuaQbkxyP9pBAkl2AX7ddZAkC4F9R8efV9XVwHzdaSdJkuavVwEnJzmIFYWwnYB7AE8fLCtJ0pxZGNOkWpBk46r639HFtn9W105N8hLgC6x8lPK6HmKNw18DpwBLkpwHbATs33WQqrq1nfQkSZI0qPad8TFJ9gAe2S7/e1WdMWBakqQOOJVSEynJc2l2Hv01sKxd3hF4N/DBqvpkh7F+MMNyVdWmXcUYtySLgC1oRmxfNdPuu47ifBh4EHAizUhvAKrqpD7iSZIkSZImi4UxTawkewOvp/nWr4BvA++oqlMHTexuLsnlwGeAz1bVf/Yc6+gZlququu4DJ0mSJEmaQBbGpFkkeUNVvX2Oz1gLeBnwuHbpLOAjfe2y6luSTYBntT+W00ykPKGqfjhoYpIkSZIk3UkWxqRZJFlWVTvM8RkfB9YCpo5nPge4tapeNNf8hpZkM+BvgYOqamEPz38wcCSwW7t0DnBoVf2461iSJEmrK8m6VXVD+/kRVfUfQ+ckSbprbL4vza6LCZU7V9W2I9dnJLmsg+cOZtqusVuB1/YU6mjgeOCZ7fXB7dqTeoonSZK0Os5t+8geD7wdWDJwPpKku8jCmDS7LrZU3ppkyVQ/riSb0hST5qUk36DZAXci8MyqurrHcBtV1WifsWOSvKrHeJIkSbeT5F7AzVV1C0BVbZvkZcCngWcPmpwkaU4sjEmz62LH2N8AZya5un3eJsDzO3juUJ5bVVeNKda1SQ6meekEOBC4dkyxJUmSppwBPA34H4AkT6fpIftk4NU0XxhKkuYhC2OaaEnuV1WzFVrm/JJTVV9re3Ft0S5dVVW/n+tzB/SrJEcBD6yqvZNsBexaVUf1EOsFND3G3keze+984Hk9xJEkSZrNOlU1VRR7CfBiYK+q+nmSdwybmiRpLmy+r4mW5PvApTR9q06tHv6BWAOnUp5K8+frTe0xgkXAJVW1dQ+xdquq8+5oTZIkqU9JzgDOBh4CPB14RFX9MskfAadV1TaDJihJussWDJ2ANLDNgY/STIr8fpIjkmzecYwPAzsCH2p/7NiuzVcbVtUJwHKAttdGXz3TjlzNNUmSpD49k+Z953vAS4DTk3yCZje7O8YkaR7zKKUmWrtD7CvAV5LsARwHvLydGvn6qrqggzBr2lTKG5Pcj3YwQZJdgF93GSDJrsBjgI2SHDZyazGwsMtYkiRJd6RtvfH3U9dJLgB2A945xt6rkqQeWBjTRGsLPAfT7Bj7X+CVwCnAdjT9xR7eQZg1aiolcBjNn6MlSc4DNgL27zjGPYD70Pw7at2R9et7iCVJknSnVNVPsOG+JK0R7DGmiZbke8CxwNFV9eNp915XVe/sIMZeND25VppKWVVnzvXZQ2n7im1B88dzVV/90pJsUlXX9PFsSZIkSZIsjGmiJTmg7Zc1uvbMqur0G8Ak92RkKiWw03xrIJ9kZ+BHIxOZngs8A7gGeHNVXddhrH+qqlcl+QLtkc1RVbVvV7EkSZIkSZPLwpgmWpJlVbXDHa3dxWcvBA4AHkQz8fLbSZ4KvJFm5Pf2c40xTkmWAU+squuSPA74DM3R0+2AP66qzo44JtmxqpYmefxM96vq7K5iSZIk3RVJNgSu7WOquSRpfCyMaSIl2Rt4Ck3h6rMjtxYDW1XVozqIcQzNSO9vAo8GfkIzkfINVfVvc33+uCW5bGqIQJJ/Bn5eVW9ury+tqu16jn9f4CFVdXmfcSRJkqZrhw29A7gOeBtNK44NgQXAc6vqywOmJ0maA5vva1L9BLgY2BdYOrJ+A/DqjmLsBGxTVcuTrA38D7CknWo0Hy1MsqiqbgH2ohlVPqWXf5ckOYvmr9Eimr9OP0tyXlUdNutvlCRJ6tYHaXb9rwecAexdVRcm2RL4NGBhTJLmKQtjmkhVdRlwWZJ/bQs9fbi5qpa38X6X5Op5XBSD5qXv7CS/AG4CzgFI8gjg1z3FXK+qrk/yIuBTVXV4EneMSZKkcVtUVacDJHlrVV0IUFVXJhk2M0nSnFgY00RKckJVHQBckmSm5u7bdBBmy5EiToAl7XWaEJ3EGJuq+ockXwP+CDh9pJ/GAppeY31YlOSPaI68vqmnGJIkSXdk+cjnm6bdszeNJM1jFsY0qY5Mshvw1GnrD6E58tiFP+7oOXcbU9+OTlv7Xo8h3wqcBpxbVRcl2RT4fo/xJEmSZrJdkutpvuBcp/1Me732cGlJkubK5vuaSEm+SNME/4pp61sDR1TVPsNkJkmSpLubJJfMt4nikqTV444xTaqNpxfFAKrqiiQPG386GpXktVX1riRHMsPxhKo6ZIC0JEnS5HI3gSStoSyMaVKtP8u9dcaWhVblO+3PFw+ahSRJUuP+SVY5Fbuq3jvOZCRJ3bEwpkl1cZIXV9XHRhfb6YdLB8pJK+yd5JdV9cmhE5EkSQIWAveh6SkmSVqD2GNMEynJxsDJwM2sKITtBNwDeHpVddWAnySbAW8HtmKkOWtVbdpVjDVNkkOBZ9NMwDwB+HRVXTJsVpIkaVIlWVZVOwydhySpexbGNNGS7AE8sr38dlWd0UOMc4HDgfcB+wDPBxZU1d91HWtNk2QTmgLZs2mOuH6apkjW5yRMSZKkldh8X5LWXBbGpJ4lWVpVOya5oqq2Hl0bOrf5JMn2wCeAbapq4dD5SJKkyZFkg6q6bug8JEnds8eY1L/fJ1kAfD/JXwH/TdOjQncgySJgb5odY3sBZwFvHjAlSZI0gSyKSdKayx1jUs+S7Ax8l2YS5tuAxcC7q+rCQRO7G0vyJOBA4CnAN4HPAJ+vqhsHTUySJEmStEaxMCb1KMlC4J1V9Zqhc5lPkpwBHA98rqp+OXQ+kiRJkqQ1k4UxqWdJLqyqXYbOQ5IkSZIkrcweY1L/LklyCnAicNtRwKo6abiUJEmSJEmShTGpf2sD1wJ7jqwVYGFMkiRJkqQBWRiT+vfxqjpvdCHJbkMlI0mSJEmSGvYYk3qWZFlV7XBHa5IkSZIkabzcMSb1JMmuwGOAjZIcNnJrMbBwmKwkSZIkSdIUC2NSf+4B3Ifmn7N1R9avB/YfJCNJkiRJknQbj1JKPUuyZVVdOW1tw6r6xVA5SZIkSZIkWDB0AtIEOCHJLlMXSZ4BnD9gPpIkSZIkCY9SSuNwEPCJJGcBDwTuB+w5aEaSJEmSJMmjlNI4JHkacCxwA/C4qvqPgVOSJEmSJGniuWNM6lmSo4AlwDbA5sAXkxxZVf88bGaSJEmSJE02e4xJ/bsC2KOqflBVpwGPBnYYOCdJkiRJkiaeRymlniRZXFXXr+LeQ6vqh+POSZIkSZIkreCOMak/Z019SPK1aff+bbypSJIkSZKk6SyMSf3JyOcNZrknSZIkSZIGYGFM6k+t4vNM15IkSZIkacycSin15/5JDqPZHTb1mfZ6o+HSkiRJkiRJYPN9qTdJDp/tflW9ZVy5SJIkSZKk27MwJkmSJEmSpIlkjzFJkiRJkiRNJAtjkiRJkiRJmkgWxiRJkiRJkjSRLIxJPUuycZKjkpzaXm+V5IVD5yVJkiRJ0qSzMCb17xjgNOCB7fX3gFcNlo0kSZIkSQIsjEnjsGFVnQAsB6iqW4Bbh01JkiRJkiRZGJP6d2OS+wEFkGQX4NfDpiRJkiRJkhYNnYA0AQ4DTgGWJDkP2AjYf9iUJEmSJElSqmroHKQ1XpJFwBZAgKuq6g8DpyRJkiRJ0sSzMCb1JMl+s92vqpPGlYskSZIkSbo9j1JK/dlnlnsFWBiTJEmSJGlA7hiTJEmSJEnSRHIqpdSzJPdL8oEky5IsTfL+dkqlJEmSJEkakIUxqX+fAX4OPINmGuXPgc8OmpEkSZIkSfIopdS3JN+qqkdOW7uiqrYeKidJkiRJkuSOMWkcTk/y7CQL2h8HAKcNnZQkSZIkSZPOHWNST5LcQDN9MsC9geXtrQXAb6pq8VC5SZIkSZIkC2OSJEmSJEmaUIuGTkCaBEnuC2wGrD21VlVfHy4jSZIkSZJkYUzqWZIXAYcCDwYuBXYBLgD2HDIvSZIkSZImnc33pf4dCuwMXFNVewDbA78aNiVJkiRJkmRhTOrf76rqdwBJ7llVVwJbDJyTJEmSJEkTz6OUUv9+nGR94N+AryT5JXDNwDlJkiRJkjTxnEopjVGSxwPrAV+uqpuHzkeSJEmSpElmYUzqSZINZrtfVdeNKxdJkiRJknR7FsakniT5AVBAZrhdVbXpmFOSJEmSJEkjLIxJkiRJkiRpItl8X+pJki2r6sokO8x0v6qWjTsnSZIkSZK0gjvGpJ4k+WhVvSTJmTPcrqrac+xJSZIkSZKk21gYkyRJkiRJ0kTyKKU0BkkeCWwFrD21VlWfGi4jSZIkSZLkjjGpZ0kOB55AUxj7ErA3cG5V7T9kXpIkSZIkTboFQycgTYD9gb2A/6mq5wPbAusNm5IkSZIkSbIwJvXvpqpaDtySZDHwM+AhA+ckSZIkSdLEs8eY1L+Lk6wPfAxYCvwGuGDYlCRJkiRJkj3GpDFK8jBgcVVdPnAqkiRJkiRNPAtjUs+SnAJ8Bvh8Vd04dD6SJEmSJKlhjzGpf+8Bdge+k+T/Jdk/ydpDJyVJkiRJ0qRzx5g0JkkWAnsCLwb+T1UtHjglSZIkSZImms33pTFIsg6wD/AsYAfgk8NmJEmSJEmS3DEm9SzJCcCjgC8DnwXOrqrlw2YlSZIkSZIsjEk9S/Jk4KtVdWt7vTtwYFW9YtjMJEmSJEmabB6llHpWVacl2T7JgcABwA+AkwZOS5IkSZKkiWdhTOpJks2BA9sfv6A5Rpmq2mPQxCRJkiRJEuBRSqk3SZYD5wAvrKr/aNeurqpNh81MkiRJkiQBLBg6AWkNth/wU+DMJB9LsheQgXOSJEmSJEktd4xJPUtyb+DPaY5U7gl8Cji5qk4fNDFJkiRJkiachTFpjJLcF3gm8Kyq2mvofCRJkiRJmmQWxiRJkiRJkjSR7DEmSZIkSZKkiWRhTJIkSZIkSRPJwpgkSZIkSZImkoUxSZIkSZIkTaT/DxMiZzu+sbQ9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Q8pc122el9W"
      },
      "source": [
        "# Saving the Processed dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbC8rVgseqAF"
      },
      "source": [
        "df.to_csv(\"StayDataset_processed.csv\")"
      ],
      "execution_count": 50,
      "outputs": []
    }
  ]
}