Swimming_model_Hackathon_codedex.ipynb

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        "<a href=\"https://colab.research.google.com/github/benny-png/CODEDEX-HACKATHON-PARIS_OLYMPICS_PREDICTION-Team-Hackathletes/blob/main/Swimming_model_Hackathon_codedex.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "Exploration of the swimming performance data from the Olympic Games (1912-2020). Here's a breakdown of the code pipeline:\n",
        "\n",
        "**1. Setting Up and Data Loading**\n",
        "\n",
        "* We install the required libraries: `researchpy`, `pandas`, `scikit-learn`, `statsmodels`, `seaborn`, `matplotlib`.\n",
        "* We mount the Google Drive to access the dataset file/ could directly upload the file to the colab notebook folder to run this code (replace the location indf to df = pd.read_csv('/content/Olympic_Swimming_Results_1912to2020.csv')).\n",
        "* We download the \"Olympic_Swimming_Results_1912to2020.zip\" file from Kaggle and extract it to the mounted drive (\"datasets\" folder).\n",
        "* We load the CSV data using `pandas.read_csv`. <br><br>\n",
        "\n",
        "---\n",
        "Link to the Kaggle dataset (https://www.kaggle.com/datasets/datasciencedonut/olympic-swimming-1912-to-2020)<br>\n",
        "\n",
        "\n",
        "The \"Olympic Swimming Results 1912 to 2020\" dataset on Kaggle provides a comprehensive collection of swimming event data spanning over a century of Olympic history. It includes detailed records of athletes' performances across various swimming disciplines, such as freestyle, backstroke, butterfly, and breaststroke, for both men and women. The dataset encompasses key metrics like race times, event types, athlete details, and competition locations from the 1912 Stockholm Olympics to the latest events in 2020. This valuable resource allows for in-depth analysis of trends, performance metrics, and historical comparisons in Olympic swimming, making it an essential tool for sports analytics and historical research alike.\n",
        "\n",
        "**The 'Results' column in the dataset represents the swimming times**\n",
        "\n",
        "---\n",
        "<br>\n",
        "\n",
        "**2. Data Preprocessing**\n",
        "\n",
        "* We filter the data for the men's 100m freestyle events. This same process can be repeated for any swimming style with varying gender and distance but chose not to pursue that because of time constraints.\n",
        "* We define a function `convert_time` to handle non-numeric values in the \"Results\" column (e.g., \"Did not finish\") by converting them to NaN.\n",
        "* We drop rows with missing values in the \"Results\" column.\n",
        "\n",
        "**3. Exploratory Data Analysis (EDA)**\n",
        "\n",
        "* We define a function `cramers_v` to calculate Cramer's V for categorical-categorical correlations.\n",
        "* We create a correlation matrix to explore relationships between features. The matrix uses different correlation methods depending on data types (categorical vs numerical).\n",
        "* We visualize the correlation matrix using a seaborn heatmap.\n",
        "\n",
        "**4. Performance Over Decades**\n",
        "\n",
        "* We create a new \"Decade\" column to group data by decades and avoid overlapping data points.\n",
        "* We filter data for each stroke type (freestyle, backstroke, butterfly, breaststroke).\n",
        "* We define a function `create_aggregated_plots` to visualize performance trends over decades for each stroke. This function creates box plots and line plots with standard deviation bands.\n",
        "\n",
        "**5. Feature Engineering**\n",
        "\n",
        "* We convert the \"Distance (in meters)\" column to a numeric data type.\n",
        "* We create a new binary feature \"Relay?\" to indicate relay events.\n",
        "* We perform one-hot encoding for categorical features which seemed the most relevant in influencing the results (swimming times): \"Location\", \"Stroke\", \"Gender\", and \"Team\".\n",
        "\n",
        "**6. Model Training and Evaluation**\n",
        "\n",
        "* We define the features (`X`) by selecting all columns except \"Results\" (target variable).\n",
        "* We split the data into training and testing sets using `train_test_split`.\n",
        "* We train a linear regression model (`LinearRegression`) to predict winning times based on the features.\n",
        "* We evaluate the model performance using mean squared error (MSE) on the testing set.\n",
        "* We save the trained model using `joblib`.\n"
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      "cell_type": "code",
      "source": [
        "# Install researchpy library for statistical analysis\n",
        "!pip install researchpy"
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from statsmodels.tsa.api import SARIMAX\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import researchpy as rp\n",
        "from scipy.stats import chi2_contingency, pointbiserialr, pearsonr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive to access dataset\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "id": "8DfQwZV_7J5V"
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      "cell_type": "code",
      "source": [
        "# Extracting Paris dataset zip file and saving to datasets folder\n",
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile(\"/content/Olympic_Swimming_Results_1912to2020.zip\", 'r')\n",
        "zip_ref.extractall(\"/content/drive/MyDrive/datasets\")\n",
        "zip_ref.close()\n"
      ],
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      "execution_count": null,
      "outputs": []
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      "source": [
        "# Load the dataset SOURCE https://www.kaggle.com/datasets/datasciencedonut/olympic-swimming-1912-to-2020\n",
        "df = pd.read_csv('/content/drive/MyDrive/datasets/Olympic_Swimming_Results_1912to2020.csv')\n",
        "# df = pd.read_csv('/content/Olympic_Swimming_Results_1912to2020.csv')"
      ],
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      "execution_count": null,
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      "source": [
        "# Display dataset columns and shape\n",
        "df.columns\n",
        "df.shape"
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        "# Filter data for men's 100m freestyle\n",
        "event_data = df[(df['Distance (in meters)'] == '100m') &\n",
        "                (df['Stroke'] == 'Freestyle') &\n",
        "                (df['Gender'] == 'Men')]"
      ],
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        "event_data.head()"
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            "text/plain": [
              "   Location  Year Distance (in meters)     Stroke  Relay? Gender Team  \\\n",
              "24    Tokyo  2020                 100m  Freestyle       0    Men  USA   \n",
              "25    Tokyo  2020                 100m  Freestyle       0    Men  AUS   \n",
              "26    Tokyo  2020                 100m  Freestyle       0    Men  ROC   \n",
              "27    Tokyo  2020                 100m  Freestyle       0    Men  FRA   \n",
              "28    Tokyo  2020                 100m  Freestyle       0    Men  KOR   \n",
              "\n",
              "               Athlete Results  Rank  \n",
              "24      Caeleb Dressel   47.02     1  \n",
              "25       Kyle Chalmers   47.08     2  \n",
              "26  Kliment Kolesnikov   47.44     3  \n",
              "27     Maxime Grousset   47.72     4  \n",
              "28        Sunwoo Hwang   47.82     4  "
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              "type": "dataframe",
              "variable_name": "event_data",
              "summary": "{\n  \"name\": \"event_data\",\n  \"rows\": 196,\n  \"fields\": [\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Tokyo\",\n          \"City\",\n          \"Seoul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 1912,\n        \"max\": 2020,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          1988,\n          1956,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance (in meters)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"100m\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stroke\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Freestyle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Relay?\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Men\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"JPN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Athlete\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 164,\n        \"samples\": [\n          \"Albert Schwartz\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Results\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 155,\n        \"samples\": [\n          \"51.680\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Results' to numeric values, handle 'Did not finish' and 'Did not start'\n",
        "def convert_time(result):\n",
        "    try:\n",
        "        return float(result)\n",
        "    except:\n",
        "        # Return NaN for 'Did not finish' and 'Did not start'\n",
        "        return np.nan"
      ],
      "metadata": {
        "id": "vgY43Ussv53S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "event_data['Results'] = event_data['Results'].apply(convert_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XigXqG75wcVe",
        "outputId": "d5b59f25-d32e-42d3-e9c7-e5eb14893e97",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-58-ce729cbeef2c>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  event_data['Results'] = event_data['Results'].apply(convert_time)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with NaN values in 'Results'\n",
        "event_data = event_data.dropna(subset=['Results'])\n",
        "event_data.head()"
      ],
      "metadata": {
        "id": "SricWxjcwfwk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to calculate Cramér's V for categorical-categorical correlation\n",
        "def cramers_v(x, y):\n",
        "    confusion_matrix = pd.crosstab(x, y)\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n * (min(confusion_matrix.shape) - 1)))"
      ],
      "metadata": {
        "id": "ZUFaPty650d1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the correlation matrix\n",
        "correlation_matrix = pd.DataFrame(index=event_data.columns, columns=event_data.columns)"
      ],
      "metadata": {
        "id": "UiIC3mkkb1qs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col1 in event_data.columns:\n",
        "    for col2 in event_data.columns:\n",
        "        if event_data[col1].dtype == 'object' and event_data[col2].dtype == 'object':\n",
        "            correlation_matrix.loc[col1, col2] = cramers_v(event_data[col1], event_data[col2])\n",
        "        elif event_data[col1].dtype != 'object' and event_data[col2].dtype != 'object':\n",
        "            correlation_matrix.loc[col1, col2] = pearsonr(event_data[col1], event_data[col2])[0]\n",
        "        else:\n",
        "            if event_data[col1].dtype == 'object':\n",
        "                # Calculate point biserial correlation for each dummy column and take the average\n",
        "                dummy_cols = pd.get_dummies(event_data[col1], drop_first=True)\n",
        "                correlations = [pointbiserialr(dummy_cols[col], event_data[col2])[0] for col in dummy_cols.columns]\n",
        "                correlation_matrix.loc[col1, col2] = np.mean(correlations)\n",
        "            else:\n",
        "                # Calculate point biserial correlation for each dummy column and take the average\n",
        "                dummy_cols = pd.get_dummies(event_data[col2], drop_first=True)\n",
        "                correlations = [pointbiserialr(event_data[col1], dummy_cols[col])[0] for col in dummy_cols.columns]\n",
        "                correlation_matrix.loc[col1, col2] = np.mean(correlations)"
      ],
      "metadata": {
        "collapsed": true,
        "id": "tzDChjGB56sP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert correlation matrix to numeric\n",
        "correlation_matrix = correlation_matrix.astype(float)"
      ],
      "metadata": {
        "id": "xm_AnUCE522e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot heatmap\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "9zHfVQM66YCa",
        "outputId": "35cec044-b140-4c49-fcc3-af546ae68dac",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Convert 'Results' to numeric values, handle 'Did not finish' and 'Did not start'\n",
        "def convert_time(result):\n",
        "    try:\n",
        "        return float(result)\n",
        "    except:\n",
        "        # Return NaN for 'Did not finish' and 'Did not start'\n",
        "        return np.nan\n",
        "\n",
        "\n",
        "# Drop rows with NaN values in 'Results'\n",
        "df['Results'] = df['Results'].apply(convert_time)\n",
        "df = df.dropna(subset=['Results'])\n",
        "\n",
        "# Add a new column for decade to avoid overlapping (TOO MANY DATA POINTS TO VISUALIZE)\n",
        "\n",
        "df['Decade'] = (df['Year'] // 10) * 10"
      ],
      "metadata": {
        "id": "4wxTn8XX-A-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data for each stroke type\n",
        "freestyle_data = df[df['Stroke'] == 'Freestyle']\n",
        "backstroke_data = df[df['Stroke'] == 'Backstroke']\n",
        "butterfly_data = df[df['Stroke'] == 'Butterfly']\n",
        "breaststroke_data = df[df['Stroke'] == 'Breaststroke']"
      ],
      "metadata": {
        "id": "RL-xdmqJ-UP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to create aggregated plots\n",
        "def create_aggregated_plots(data, stroke_name):\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    plt.suptitle(f'{stroke_name} Performance Over Decades', fontsize=16)\n",
        "\n",
        "    # Aggregate data by decade\n",
        "    agg_data = data.groupby('Decade').agg({'Results': ['mean', 'std', 'count']}).reset_index()\n",
        "    agg_data.columns = ['Decade', 'Average Time (s)', 'Standard Deviation (s)', 'Number of Competitors']\n",
        "\n",
        "    # Box plot\n",
        "    plt.subplot(1, 2, 1)\n",
        "    sns.boxplot(x='Decade', y='Results', data=data)\n",
        "    plt.title('Box Plot of Times per Decade')\n",
        "    plt.xlabel('Decade')\n",
        "    plt.ylabel('Time (seconds)')\n",
        "\n",
        "    # Linear plot\n",
        "    plt.subplot(1, 2, 2)\n",
        "    sns.lineplot(x='Decade', y='Average Time (s)', data=agg_data, marker='o')\n",
        "    plt.fill_between(agg_data['Decade'],\n",
        "                     agg_data['Average Time (s)'] - agg_data['Standard Deviation (s)'],\n",
        "                     agg_data['Average Time (s)'] + agg_data['Standard Deviation (s)'], alpha=0.2)\n",
        "    plt.title('Trend of Average Times per Decade')\n",
        "    plt.xlabel('Decade')\n",
        "    plt.ylabel('Average Time (seconds)')\n",
        "\n",
        "    # # Violin plot\n",
        "    # plt.subplot(1, 3, 3)\n",
        "    # sns.violinplot(x='Decade', y='Results', data=data, inner=\"quartile\")\n",
        "    # plt.title('Violin Plot of Times per Decade')\n",
        "    # plt.xlabel('Decade')\n",
        "    # plt.ylabel('Time (seconds)')\n",
        "\n",
        "\n",
        "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "TwGTagCy-WJr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create aggregated plots for each stroke type\n",
        "create_aggregated_plots(freestyle_data, 'Freestyle')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "OAzOQSAk-X_z",
        "outputId": "8c194cfe-4214-4a3c-a062-20f6fa3101c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "create_aggregated_plots(breaststroke_data, 'Breaststroke')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "Rr3HB_N-C2Gd",
        "outputId": "dd2846d1-4385-45f5-8d47-e21ef877ee33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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fvn0FAPH000/blN/qnC+53ZdffrnUsiNHjtw0kfX111/bTc7eirXnz99//21Tbn1NWrVqVSpJJYQQzz77rN3z0doz7J133inX9mfMmCGA4p5+9ly6dEkoFArh5+dnN44b/frrr9JxspcItmfYsGFSMtoqNzdXuLi4CLlcLi5dumRT32AwCD8/PwHAJuF46tQpIZPJRHBwsM17TEm9e/cWAGwS9CUTUT///HO5Yr5RRRJRM2fOFABEo0aNpLJr164JjUYj1Gp1qf21mjBhggAgli9fLpUtXbpUeq8umVy7XR999JHdvwFr8mzo0KF213v00UftJqJu573i999/FwBEv3797nh/iIio+uAcUURE95Do6GiMHj1aevTp0wchISH4448/MGXKFMTHx5e57uDBg+2W//DDDwCAQYMGwdm59M1W5XI5OnXqBAA4ePBgqeV//fUX3n33XUyePBlPPvkkxowZgzFjxsBkMsFiseDcuXNS3TZt2gAAFi9ejM8++wyZmZnl33k7/Pz8EB4ejuPHj2Pq1Kk3nVfldpR1zPbu3QsApe5gaDVu3DgAxXfPsjdJcUZGBrp3746NGzdi4MCB2L17N/z8/ErV+f3336HRaBAbG2t3O126dAFg/3W5lbVr10pzMmk0GrRu3Ro7d+6Eu7s7Pv/8c+m1sp4fQ4cOtduOm5sbWrduDZPJhD/++KPU8h49ekCr1VY4PpPJhAMHDgC49XHes2eP3eVlvX4l9e7du1RZyYnTb7a8rHnQzp07hw8++AAvvvgixo0bJ/1NpKamAkCZc0X17dvXZl40q0aNGgEALl++LJWlpKTg2LFjkMvl0nG4lVu9lrVq1UL9+vWRnp5+0/eSOyGEKFXm7u6OwYMHw2Kx4LPPPrNZ9sMPPyA9PR0PPfQQGjduLJX/+OOPEEKgV69ecHd3t7utm/19+Pv7o2PHjnewJ+VjsVgAwOZ13bNnDwoLC6V5m+yxF/u2bdsAFJ/3Tk5O5Y4hPz8fmzZtwiuvvILx48dL5+PXX38NoPT5aH1/e/zxx+22N3r0aLvlt/Ne0bBhQ7i7u+PHH3/Em2++Kc0ZR0RENVvpbxRERFRjPfXUU6W+lJtMJsydOxcLFy5E586dcebMGbtfzMqaiNc6afCcOXMwZ86cm24/PT1d+r9Op8OoUaOwefPmm66Tm5sr/b9Lly6YMWMGlixZgtGjR0Mmk6F+/fqIjo7Go48+itjYWMjlFfsN5bPPPsPgwYPx7rvv4t1334W3tzeioqLw8MMPY9SoUfD19a1QeyWVdcysCYE6derYXR4REQEAKCoqwrVr1+Dv72+zfNasWTCZTIiJicGmTZvs7vOFCxcghEBhYSFUKtVN4yz5upRXREQEOnToAABwcnKCp6cnmjdvjn79+sHT01OqZz0/Ro0ahVGjRlU4jtudAPratWvS5NW3Os4lEzQV3XZoaGipMjc3t5sut/593Ti5ttlsxqRJk/DRRx/ZTbhYlfybuFUsAODh4VFqe9a7DQYFBZU70Wd9LcuTgElPT0eDBg1uWqfk31ZqaioiIyNv2W5aWhoAlEq8Pvnkk/jss8+wZs0azJo1SyqPi4sDUHxzgZKs+/LJJ5/gk08+uek2K/O8rKiMjAwAgLe3t1RmjX3Xrl12E48llYzdOvF8w4YNy73977//HmPHjrV7wwmrG8/HS5cuASj7766s8tt5r3B3d0dcXBzGjh2LV199Fa+++iqCgoLQtm1bPPLIIxgxYoTN3yMREdUMTEQREd3jnJ2dsWDBAnz88ce4evUqPvvsM0ycOLFUPY1GY3d96y/2HTp0kL7Yl6Vkj4RZs2Zh8+bNaNiwIRYtWoQ2bdrA19cXSqUSANC+fXscOnSo1BfyRYsW4dlnn8X333+PX375BQcOHEBcXBzi4uLQpk0b7NmzB66uruXe/44dOyIxMRE//PAD9u3bh4MHD2L79u3YunUr5s2bh82bN6N79+7lbq+kso7ZnXrsscfw7bffYufOnVizZg2efPLJUnWsr4ubmxsGDRpU6TF06NDB5q6JZbHG8cgjjyAgIOCmdcPCwkqV3a1jWB7l2fatEp8VSYwuW7YMq1atQmBgIN599120b98eAQEBUKvVAIARI0bgv//9b5lJqoomYSvK+loOHjz4ln9jPj4+t2yvTp068PLyQlZWFn777Tep52RZTCYTjhw5AuDfuzNaderUCRERETh79iwOHjyI9u3bIy0tDT/++CPUajWGDRtmd19atGiB5s2b33S7UVFRpcqq6ry07m/Tpk2lMmvs9erVQ3R09E3Xr0jS6UaXL1/G0KFDUVhYiOnTp2PkyJEIDw+Hm5sb5HI5fvrpJ/Ts2fOmSdOKuN33ikGDBqFHjx7YsmUL9u/fjwMHDmDz5s3YvHkz5s6dix07dtgcPyIiqv6YiCIiug/I5XKEh4cjIyMD//zzT4XWDQkJAQA8+uijmDZtWrnXs976+8svv0SzZs1KLb/Z0J7w8HBMnjxZut37H3/8gccffxx//PEHFi9ejNdee60iuwCNRoPBgwdLQ7HS09Px6quvYvXq1XjyySft3sL+TtSqVQsJCQk4f/48mjRpUmq5tWeAWq226QlhFRMTg2effRZ9+/bFU089hfz8fDz//PM2dayvi0wmw6effnrXkxRlCQkJwenTpzFu3LhyDXWrLD4+PlCpVNDr9Th//rzdc8x6nMsa3lTVrH8TH330Efr161dqeWUOd7P2nrp69SpycnLK1SsqJCQE8fHxmDFjBlq3bn3HMcjlcsTGxuKzzz7D559/jmnTpt20h8+WLVuQk5MDuVyOPn362CyTyWQYM2YM5syZg7i4OLRv3x7r1q2DyWTCkCFDbHrpWfcFKB6u/MEHH9zxvtwNf//9N44dOwag+G/eyhp7ZGRkuZLBVqGhofjnn39w+vRp9OjR45b1v//+exQWFmLAgAF4++23Sy0v63y0vr8lJiba/PhglZiYaHe9O3mv0Gq1Nj2pLl68iMmTJ+O7777DpEmTsG/fvgq1R0REjsU5ooiI7gMWi0X6clDRYQy9evUCAGzatKlCv4xb53ey1wtm+/bt0pCU8mjTpg0mTJgAANIXNyuFQgGguDdFefn5+WHx4sUAiocwZWVlScusPbYq0t6NrPO3lPUl8tNPPwVQ3FvL3rxbQHEPkF27dsHLywsvvPAC3nrrLZvlwcHBaNasGfLy8qS5YRzBen5YkyxVxdnZWRo6eKvj3LVr16oK66Zu9jdRMilRGQIDA9G8eXNYLBbpONzK3XgtX375ZTg7O+PEiRN4//33y6yXk5OD6dOnAyieQ8he78sxY8ZALpdj48aNKCgoKHNYHvDvvmzZsqXUEMnqwGAw4NlnnwVQ3KupZGKye/fuUCqV2Lt3rzRUsTweeeQRAMXnvb255250s/NRCIEvvvjC7nqdO3cGAKxfv97u8hvn8bKqzPMrJCRE+kGiMv9uiIioajARRUR0jzOZTHj11VelxI+9nhg38+ijj6JNmzb4/fffMXbsWLvzqWRlZWHVqlU2yRvrBMrLly+3qXvmzBnpC9iNNm/ejJ9//lkawmFlNBqlZMuNX5pq164NoPiL/I2SkpLwn//8x+6cO99//z0AwMvLS5pj51btldcLL7wAZ2dnfPvtt1i3bp3Nsp9++gkfffQRANyyh1mbNm2wd+9eBAYGYvbs2Zg5c6bN8gULFgAo/iJu3Z+ShBD47bff8NNPP932vtzK+PHjERYWhk2bNmHGjBnIy8srVSclJQUff/xxpW976tSpAICVK1di165dNsvWrFmDLVu2QKFQ4IUXXqj0bd8O69/EihUrbM7xq1ev4oknnrij5Kc98+bNAwDMnj1bmni6pFOnTtn0kHz55Zfh6emJd999F++88w4MBkOpdS5cuFDqnL6ZJk2aSL1tXnrpJSxevLjUflp78CQkJCA8PLzMHky1a9fGww8/jNzcXLzyyis4efIkQkND0a1bt1J1W7ZsiUGDBuHixYsYOHCg3V46Op0O69evlyaJryoHDhxAx44d8csvv8DNzQ3r16+36dEYEBCAyZMnQ6fTITY2FidOnCjVhl6vx5YtW3D69Gmp7KmnnkLt2rVx9OhRPP3009DpdDbr5ObmYufOndJz6/n41Vdf4erVq1K52WzG3Llzy7zJweTJk+Hk5ISNGzeWmgNww4YN+Pbbb+2udzvvFUePHsWXX36JwsLCUnWt73n2EmlERFTNOep2fUREVHmst5qPjo4Wo0ePlh59+/YVISEh0u3IZ8+eXWpd663G9+zZU2b7ly9flm4F7+rqKtq3by+GDRsmBg4cKFq0aCGcnJwEAFFYWCit8/XXXwuZTCYAiKZNm4phw4aJbt26CYVCIbp16ybat29farsvvPCCACB8fX3Fww8/LEaOHCn69esn/P39BQBRq1YtcfHiRZvYpk2bJq0zZMgQMW7cODFu3DiRkZEhjh49KgAIhUIh2rRpI4YMGSKGDBkiWrZsKQAImUwm/vOf/9i099dffwm5XC7kcrno0aOHGDt2rBg3bpz47rvvpDrW43kzH330kZDL5QKAePDBB8WIESNEdHS0dEzmz59fap3Ro0cLACIuLs6mPD4+XoSGhgoAYsKECcJisUjLli1bJpydnQUAUa9ePdGnTx8xYsQI8fDDD0vHbcaMGTeN1V4Mo0ePLvc6J0+eFOHh4QKA8PT0FJ06dRIjRowQ/fv3Fw888ICQyWQiICDAZp158+aV6xb31nP7woULdpe/+uqr0mvZoUMHMWLECPHggw8KAMLJyUl88sknpdYpzzl/q+3e7By4cOGC3dvX//rrr0KpVEqv1ZAhQ8QjjzwiNBqNaNy4sRgwYIDd17+s88IqLi6uzNfszTfflM65hg0biqFDh4p+/fqJBx54wG6b+/btE76+vgKA8Pf3F926dRMjR44Uffv2FREREQKAiIqKshvHzSxbtkzadx8fHxEbGyuGDRsmoqKipPhatWpV6u/7Rhs2bJCOPQAxd+7cMuvm5uaK7t27CwBCqVRK7wGPPfaYaNOmjRTPP//8I62zZ88eAUB07ty5wvtoZT2/mjdvLr0XjxgxQjzyyCMiMDBQir158+bi6NGjdtswGo1ixIgRAoCQy+WiZcuWYtCgQWLo0KEiOjpauLq6CgBi69atNusdOXJE2oanp6fo06ePGDp0qGjfvr3QaDQ2+2U0GkWrVq0EAOHm5ib69OkjhgwZIsLCwoRCoRAzZswo81gsXrxY2o+oqCgxYsQI0aZNGwFATJkyxe75L0TF3ys2b94sAAiNRiOio6PFsGHDxODBg0VkZKT0ut54DIiIqPpjIoqI6B5g/dJ840OpVIqwsDAxdOjQMr90l+dLuRBCFBUViVWrVomuXbsKHx8f4ezsLPz9/UWLFi3ExIkTxfbt20ut8/PPP4vu3bsLX19f4eLiIpo0aSLefPNNodfr7W736NGjYubMmaJDhw6iVq1aQqlUCj8/P9GqVSvx1ltviYyMjFLbKCwsFNOnTxf16tWTvlhaEwi5ubli6dKlYsCAAaJ+/frCzc1NuLq6igYNGognnnhC/Pnnn3b3dfPmzSI6Olq4u7tLX5JLJkzKk4gSojjxMHjwYBEYGCicnZ2Fj4+P6NOnj/jpp5/s1r9ZwiE5OVk0aNBAABBPPPGEMJlM0rITJ06I8ePHi/r16wu1Wi1cXFxE3bp1Rc+ePcX7778vLl++fMtYb4yhIokoIYq/9C9evFi0a9dOeHp6CoVCIYKCgkSbNm3Eyy+/LA4ePGhTv7ISUUIIsXXrVtG7d2/pvAwMDBSPPfaY+O233+zWd1QiSgghjh8/Lvr16yeCgoKEWq0W9evXF9OnTxe5ubllvv53kogSQohDhw6J4cOHi1q1agmFQiG8vb1F8+bNxfTp00VSUlKp+qmpqWLOnDniwQcfFO7u7kKpVIratWuL9u3bi3nz5onjx4/b3c6tJCYmipdfflk0b95caLVaoVQqRXBwsOjXr59Yv369MJvNt2yjqKhIeHt7S8nH8+fP37S+2WwWX3zxhejdu7cICAgQCoVC+Pj4iCZNmoixY8eKzZs3C4PBINWvzERUyYdGoxGBgYGiXbt2YtKkSWLXrl02CeWy/Pjjj2LgwIHSa+fp6SkaNWokhg0bJr744guh0+lKrZOeni5effVV0bRpU+Hq6io0Go2oW7euGDp0qNi2bZtN3by8PPHKK6+IyMhIoVarhb+/v+jfv7/4888/b3ksvvvuO9GhQwfh6uoq3NzcRPv27cVXX3110/NfiIq9V1y9elUsWrRI9O7dW9SpU0e4uLgIDw8P8cADD4iJEyeK06dP3/IYEhFR9SMTopJuhUFERERERERERHQTnCOKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFBERERERERERVQkmooiIiIiIiIiIqEowEUVERERERERERFWCiSgiIiIiIiIiIqoSTEQREREREREREVGVYCKKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFBERERERERERVQkmooiIiIiIiIiIqEowEUVERERERERERFWCiSgiIiIiIiIiIqoSTEQREREREREREVGVYCKKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFBERERERERERVQkmooiIiIiIiIiIqEowEUVERERERERERFWCiSgiIiIiIiIiIqoSTEQREREREREREVGVYCKKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFBERERERERERVQkmooiIiIiIiIiIqEowEUVERERERERERFWCiSgiIiIiIiIiIqoSTEQREREREREREVGVYCKKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFBERERERERERVQkmooiIiIiIiIiIqEowEUVERERERERERFWCiSgiIiIiIiIiIqoSTEQREREREREREVGVYCKKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFBERERERERERVQkmooiIiIiIiIiIqEowEUVERERERERERFWCiSgiIiIiIiIiIqoSTEQREREREREREVGVYCKKiIiIiIiIiIiqBBNRRERERERERERUJZiIIiIiIiIiIiKiKsFEFNF9IDExETKZDGvWrHF0KDa2bduGFi1aQK1WQyaTITs7+47aGzNmDMLDwyslNqo51qxZA5lMhsTEREeHQkREd9H8+fMhk8kqtc3KvhahW6uu16V094WHh2PMmDGODoOqASaiiCrA+oW35MPf3x9du3bF1q1bqzyevXv32sSiUChQt25dPPHEEzh//nylbOPgwYOYP39+pV+YXbt2DUOGDIFGo8GKFSvw+eefw9XVtVS9G493WY+9e/dWanz3kzFjxtgcSzc3N9StWxeDBw/G119/DYvF4ugQiYjoLrpfP2vLey1yow8//BAymQxRUVFVEGXNYE0S3urRpUsXR4daY1kTeCWv+319fdG+fXu88sorSE5OdnSIROXm7OgAiGqi119/HXXq1IEQAqmpqVizZg169+6N77//Hn379q3yeJ5//nm0adMGRqMRR44cwerVq/HDDz/gxIkTCA4OvqO2Dx48iNdeew1jxoyBp6dn5QQM4I8//kBeXh7eeOMN9OjRo8x6n3/+uc3zzz77DDt27ChV3qhRI3z88cdMmtwmlUqF//znPwCAwsJCJCUl4fvvv8fgwYPRpUsXfPfdd/Dw8HBwlEREdDdU5LP2XlLea5EbrV+/HuHh4fj9999x7tw51KtX7y5GWTMMHDjQ5jjk5+fjueeew4ABAzBw4ECpPCAgAGFhYSgsLIRCoXBEqDXe8OHD0bt3b1gsFmRlZeGPP/7A0qVLsWzZMnzyyScYNmyYo0MkuiUmoohuQ69evdC6dWvp+bhx4xAQEID//ve/DklEdezYEYMHDwYAjB07Fg0aNMDzzz+PtWvXYtasWVUeT3mkpaUBwC2TW48//rjN819//RU7duwoVU5lE0KgqKgIGo2mzDrOzs6ljumCBQuwaNEizJo1C08//TS+/PLLux0qERE5wO1+1hYUFMDFxeVuhnZXlfdapKQLFy7g4MGD+Oabb/DMM89g/fr1mDdv3l2K0D6LxQKDwQC1Wl2l272ZZs2aoVmzZtLzjIwMPPfcc2jWrJnd86g6xV6d6HS6W/bKe/DBB0sd06SkJMTExGD06NFo1KgRmjdvfjfDJLpjHJpHVAk8PT2h0Wjg7Gyb29XpdJg6dSpCQkKgUqkQGRmJ//u//4MQAkBxz5OGDRuiYcOGKCwslNbLzMxEUFAQ2rdvD7PZXOF4unXrBqD4Yulmdu/ejY4dO8LV1RWenp549NFH8c8//0jL58+fj5dffhkAUKdOHakr8K3m4tm0aRNatWoFjUYDX19fPP7447h8+bK0vEuXLhg9ejQAoE2bNpDJZJUyXvzGOaKsXZj/7//+DytWrEDdunXh4uKCmJgYXLx4EUIIvPHGG6hduzY0Gg0effRRZGZmlmp369at0nFyd3dHnz598Pfff9vUSUlJwdixY1G7dm2oVCoEBQXh0UcfveWxGjNmDNzc3HD+/Hn07NkTrq6uCA4Oxuuvvy6dJ1YWiwVLly5F48aNoVarERAQgGeeeQZZWVk29cLDw9G3b19s374drVu3hkajwUcffVSxg3ndzJkzERMTg02bNuHs2bMVPi4AcPr0aQwZMgR+fn7QaDSIjIzE7NmzpeVJSUmYMGECIiMjodFo4OPjg8cee8zusfv777/RrVs3aDQa1K5dGwsWLCizF1x54yMiolvr0qULmjRpgsOHD6NTp05wcXHBK6+8AgDQ6/WYN28e6tWrB5VKhZCQEEyfPh16vd6mDZlMhkmTJuHbb79FkyZNoFKp0LhxY2zbtq3U9n755Re0adMGarUaERERFf4cu1vXIuvXr4eXlxf69OmDwYMHY/369dIyo9EIb29vjB07ttR6ubm5UKvVmDZtmlRW0eO2fv16NG7cGCqVSjpm//d//4f27dvDx8cHGo0GrVq1wldffVVq+4WFhXj++efh6+sLd3d39OvXD5cvX4ZMJsP8+fNt6l6+fBlPPvkkAgICpNfo008/veWxKS97c0RZr4eSk5PRt29fuLm5oVatWlixYgUA4MSJE+jWrRtcXV0RFhaGL774olS72dnZePHFF6Xr7nr16uHtt98udZ2wYcMGtGrVCu7u7vDw8EDTpk2xbNmycsX8f//3f3jvvfcQFhYGjUaDzp074+TJk6Xqnz59GoMHD4a3tzfUajVat26NLVu22NSxTvmxb98+TJgwAf7+/qhdu3Z5D6ONsLAwrFmzBgaDAYsXL76t42KxWLBs2TI0bdoUarUafn5+eOSRR/Dnn39KdeLi4tCtWzf4+/tDpVLhgQcewMqVK0vFI4TAggULULt2bbi4uKBr165lXoOVNz66t7BHFNFtyMnJQUZGBoQQSEtLw/Lly5Gfn2/z64QQAv369cOePXswbtw4tGjRAtu3b8fLL7+My5cv47333oNGo8HatWsRHR2N2bNn49133wUATJw4ETk5OVizZg2cnJwqHF9CQgIAwMfHp8w6O3fuRK9evVC3bl3Mnz8fhYWFWL58OaKjo3HkyBGEh4dj4MCBOHv2LP773//ivffeg6+vLwDAz8+vzHbXrFmDsWPHok2bNli4cCFSU1OxbNkyHDhwAEePHoWnpydmz56NyMhIrF69WhrmGBERUeH9LK/169fDYDBg8uTJyMzMxOLFizFkyBB069YNe/fuxYwZM3Du3DksX74c06ZNs7nY+vzzzzF69Gj07NkTb7/9NgoKCrBy5Up06NABR48elRJfgwYNwt9//43JkycjPDwcaWlp2LFjB5KTk285gbrZbMYjjzyCtm3bYvHixdi2bRvmzZsHk8mE119/Xar3zDPPSMf3+eefx4ULF/DBBx/g6NGjOHDggE0X9zNnzmD48OF45pln8PTTTyMyMvK2j9+oUaPw008/YceOHWjQoEGFjsvx48fRsWNHKBQKjB8/HuHh4UhISMD333+PN998E0Dx0IiDBw9i2LBhqF27NhITE7Fy5Up06dIFp06dkn5tT0lJQdeuXWEymTBz5ky4urpi9erVdnt6lTc+IiIqv2vXrqFXr14YNmwYHn/8cQQEBMBisaBfv3745ZdfMH78eDRq1AgnTpzAe++9h7Nnz+Lbb7+1aeOXX37BN998gwkTJsDd3R3vv/8+Bg0ahOTkZOm65cSJE4iJiYGfnx/mz58Pk8mEefPmISAgoFxx3s1rkfXr12PgwIFQKpUYPnw4Vq5ciT/++ANt2rSBQqHAgAED8M033+Cjjz6CUqmU1vv222+h1+ulYVMVPW67d+/Gxo0bMWnSJPj6+kqfY8uWLUO/fv0wcuRIGAwGbNiwAY899hj+97//oU+fPtL6Y8aMwcaNGzFq1Ci0bdsW+/bts1lulZqairZt20rJLz8/P2zduhXjxo1Dbm4uXnzxxXK9BrfDbDajV69e6NSpExYvXoz169dj0qRJcHV1xezZszFy5EgMHDgQq1atwhNPPIF27dqhTp06AIp753Xu3BmXL1/GM888g9DQUBw8eBCzZs3C1atXsXTpUgDAjh07MHz4cHTv3h1vv/02AOCff/7BgQMH8MILL9wyxs8++wx5eXmYOHEiioqKsGzZMnTr1g0nTpyQzs+///4b0dHRqFWrlnS9snHjRvTv3x9ff/01BgwYYNPmhAkT4Ofnh7lz50Kn09328WvXrh0iIiKwY8cOqay8xwUoHuGxZs0a9OrVC0899RRMJhP279+PX3/9VRoJsnLlSjRu3Bj9+vWDs7Mzvv/+e0yYMAEWiwUTJ06U2po7dy4WLFiA3r17o3fv3jhy5AhiYmJgMBhsYq5IfHSPEURUbnFxcQJAqYdKpRJr1qyxqfvtt98KAGLBggU25YMHDxYymUycO3dOKps1a5aQy+Xi559/Fps2bRIAxNKlS28Zz549ewQA8emnn4r09HRx5coV8cMPP4jw8HAhk8nEH3/8IYQQ4sKFCwKAiIuLk9Zt0aKF8Pf3F9euXZPK/vrrLyGXy8UTTzwhlS1ZskQAEBcuXLhlPAaDQfj7+4smTZqIwsJCqfx///ufACDmzp0rlVmPpTXG8po4caIo661r9OjRIiwsTHpu3W8/Pz+RnZ0tlc+aNUsAEM2bNxdGo1EqHz58uFAqlaKoqEgIIUReXp7w9PQUTz/9tM12UlJShFarlcqzsrIEALFkyZIK7Ys1ZgBi8uTJUpnFYhF9+vQRSqVSpKenCyGE2L9/vwAg1q9fb7P+tm3bSpWHhYUJAGLbtm3ljsHV1bXM5UePHhUAxJQpU4QQ5T8uQgjRqVMn4e7uLpKSkmzqWiwW6f8FBQWltnno0CEBQHz22WdS2YsvvigAiN9++00qS0tLE1qt1uYcrUh8RERUmr3P2s6dOwsAYtWqVTbln3/+uZDL5WL//v025atWrRIAxIEDB6QyAEKpVNpcA/31118CgFi+fLlU1r9/f6FWq20+O06dOiWcnJzKvAawupvXIn/++acAIHbs2CGEKP4sq127tnjhhRekOtu3bxcAxPfff2+zbu/evUXdunWl5xU9bnK5XPz999+lYrrxM9RgMIgmTZqIbt26SWWHDx8WAMSLL75oU3fMmDECgJg3b55UNm7cOBEUFCQyMjJs6g4bNkxotVq7n9n2pKenl2rbyt51qfV66K233pLKsrKyhEajETKZTGzYsEEqP336dKm233jjDeHq6irOnj1rs62ZM2cKJycnkZycLIQQ4oUXXhAeHh7CZDKVaz9ujFmj0YhLly5J5b/99pvNNZIQQnTv3l00bdpUup4Uovhcad++vahfv75UZj3/OnToUK54rDHc7Hrz0UcfFQBETk6OEKL8x2X37t0CgHj++edLtXmra7aePXvanNtpaWlCqVSKPn362Kz7yiuvCABi9OjRUll546N7D4fmEd2GFStWYMeOHdixYwfWrVuHrl274qmnnsI333wj1fnxxx/h5OSE559/3mbdqVOnQghhc5e9+fPno3Hjxhg9ejQmTJiAzp07l1rvZp588kn4+fkhODgYffr0gU6nw9q1a23msSrp6tWrOHbsGMaMGQNvb2+pvFmzZnj44Yfx448/lnvbJf35559IS0vDhAkTbMb+9+nTBw0bNsQPP/xwW+3eqcceewxarVZ6br3LzeOPP24znDIqKgoGg0Hqur9jxw5kZ2dj+PDhyMjIkB5OTk6IiorCnj17AAAajQZKpRJ79+4tNUyuvCZNmiT93/orpMFgwM6dOwEUDzHQarV4+OGHbWJp1aoV3NzcpFis6tSpg549e95WLDdyc3MDAOTl5QEo/3FJT0/Hzz//jCeffBKhoaE2bZa8/XbJHk1GoxHXrl1DvXr14OnpiSNHjkjLfvzxR7Rt2xYPPfSQVObn54eRI0fatF3e+IiIqGJUKlWpYWebNm1Co0aN0LBhQ5v3XOs0ATe+5/bo0cOm51GzZs3g4eEh3e3XbDZj+/bt6N+/v81nR6NGjcr1uXY3r0XWr1+PgIAAdO3aFUDxZ9nQoUOxYcMGaSqFbt26wdfX12ZexaysLOzYsQNDhw6Vyip63Dp37owHHnigVEwlP0OzsrKQk5ODjh072nx+WofxTZgwwWbdyZMn2zwXQuDrr79GbGwshBA2cfXs2RM5OTk27d4NTz31lPR/T09PREZGwtXVFUOGDJHKIyMj4enpaXOH6E2bNqFjx47w8vKyibtHjx4wm834+eefpTZ1Op1Nr6GK6N+/P2rVqiU9f+ihhxAVFSVdO2dmZmL37t0YMmQI8vLypDiuXbuGnj17Ij4+3maIKAA8/fTTtzUCwp4br9nKe1y+/vpryGQyu/OdlXXNZh0h0rlzZ5w/fx45OTkAikddWEcilFzXXm+68sZH9x4OzSO6DQ899JBNkmf48OFo2bIlJk2ahL59+0KpVCIpKQnBwcFwd3e3Wdd6x5mkpCSpTKlU4tNPP5XmQoiLi7N5476VuXPnomPHjnBycoKvry8aNWpUar6qkqzbtjdcq1GjRti+fXu5JkusSLsNGzbEL7/8UqH2KsuNSRBrUiokJMRuuTWZFB8fD+DfObduZL2LnEqlwttvv42pU6ciICAAbdu2Rd++ffHEE08gMDDwlvHJ5XLUrVvXpsw6BM46T1J8fDxycnLg7+9vtw3rhKtW1q7qlSE/Px8ApHO5vMfFeoHYpEmTm7ZfWFiIhQsXIi4uDpcvX7aZG8t6UQMUn1/2bpV94/lW3viIiKhiatWqZTPcDCh+z/3nn3/KHLZ/4+fTjZ/JAODl5SV99qanp6OwsBD169cvVS8yMvKWP5bdrWsRs9mMDRs2oGvXrjZzcEZFReGdd97Brl27EBMTA2dnZwwaNAhffPEF9Ho9VCoVvvnmGxiNRptEVEWPW1mf6//73/+wYMECHDt2zGZuqZLXkUlJSZDL5aXauPFuf+np6cjOzsbq1auxevXqcsVVmazzEpWk1WpRu3btUtfFWq3W5se/+Ph4HD9+/JbHc8KECdi4cSN69eqFWrVqISYmBkOGDMEjjzxSrhjtnZcNGjTAxo0bAQDnzp2DEAJz5szBnDlzyoylZDLrbl+zlee4JCQkIDg42OYHansOHDiAefPm4dChQygoKLBZlpOTA61WK/0N3nis/Pz84OXlZVNW3vjo3sNEFFElkMvl6Nq1K5YtW4b4+Hg0bty4wm1s374dAFBUVIT4+PgKfSg1bdq0Qrcdvt+U9StTWeXWRIh1ksTPP//cbkKpZLLvxRdfRGxsLL799lts374dc+bMwcKFC7F79260bNnyTncBFosF/v7+NpOilnTjB/jN7pBXUdZJOK0XrBU5LuUxefJkxMXF4cUXX0S7du2g1Wohk8kwbNiw25qosrLjIyKiYvY+WywWC5o2bSrNc3mjG3/0udVnb3W1e/duXL16FRs2bMCGDRtKLV+/fj1iYmIAAMOGDcNHH32ErVu3on///ti4cSMaNmxocyezih43e8d+//796NevHzp16oQPP/wQQUFBUCgUiIuLszuZ961YPz8ff/xxaSL3G5W8M15lu93rNaA49ocffhjTp0+3W9f6A5+/vz+OHTuG7du3Y+vWrdi6dSvi4uLwxBNPYO3atXe4B/8ew2nTppXZg+/GBGBlX7P5+/tLP7qV97iUR0JCArp3746GDRvi3XffRUhICJRKJX788Ue89957t33NVlnxUc3Cq3GiSmIymQD8+0tEWFgYdu7ciby8PJteUadPn5aWWx0/fhyvv/46xo4di2PHjuGpp57CiRMnbIaTVSbrts+cOVNq2enTp+Hr6yv1hqpIz6yS7d7YG+XMmTM2+1wTWIcO+Pv7lyvRFxERgalTp2Lq1KmIj49HixYt8M4772DdunU3Xc9iseD8+fM2H7bWO9RZJyONiIjAzp07ER0dXakXLOXx+eefQyaT4eGHH5ZiAW59XKy9vOzdTaakr776CqNHj8Y777wjlRUVFSE7O9umXlhYmNTbqaQbz+OKvm5ERHT7IiIi8Ndff6F79+4VumYoi/UOq+V5v7fnbl2LrF+/Hv7+/tJd3Er65ptvsHnzZqxatQoajQadOnVCUFAQvvzyS3To0AG7d++2uVssUDnH7euvv4Zarcb27duhUqmk8ri4OJt6YWFhsFgsuHDhgk0vlXPnztnU8/Pzg7u7O8xmc437/IyIiEB+fn654lYqlYiNjUVsbCwsFgsmTJiAjz76CHPmzCmVJLqRvfPy7Nmz0vWa9dpHoVBU+TE8dOgQEhISbG6eVN7jEhERge3btyMzM7PMXlHff/899Ho9tmzZYtOz8cZhpNa/sfj4eJse/+np6aWmsKjI60b3Fs4RRVQJjEYjfvrpJyiVSmnoXe/evWE2m/HBBx/Y1H3vvfcgk8nQq1cvad0xY8YgODgYy5Ytw5o1a5CamoopU6bctXiDgoLQokULrF271ubL/smTJ/HTTz+hd+/eUpk1IXVjUsCe1q1bw9/fH6tWrbLpHr5161b8888/du/OUp317NkTHh4eeOutt2A0GkstT09PB1B8x4+ioiKbZREREXB3dy91C+aylDxPhBD44IMPoFAo0L17dwDAkCFDYDab8cYbb5Ra12Qylev1uR2LFi3CTz/9hKFDh0oXr+U9Ln5+fujUqRM+/fRTJCcn29Qp+Sumk5NTqV/Cly9fLs23YdW7d2/8+uuv+P333222dWMvsfLGR0REd27IkCG4fPkyPv7441LLCgsLK3wXMCcnJ/Ts2RPffvutzWfHP//8I/Uev5m7cS1SWFiIb775Bn379sXgwYNLPSZNmoS8vDxs2bIFQHFP+cGDB+P777/H559/DpPJZDMsD6ic4+bk5ASZTGbzeZmYmFjqjnvWnjkffvihTfny5ctLtTdo0CB8/fXXdn9Eqs6fn0OGDMGhQ4fsniPZ2dnSD8bXrl2zWSaXy6VeXuW5Zvv2229t5nj6/fff8dtvv0nX9f7+/ujSpQs++ugjXL16tdT6d+sYJiUlYcyYMVAqlXj55Zel8vIel0GDBkEIgddee61UPes1mrVn2o1TKNyY+OzRowcUCgWWL19uU9feHfDKGx/de9gjiug2bN26VerZlJaWhi+++ALx8fGYOXOm1BU2NjYWXbt2xezZs5GYmIjmzZvjp59+wnfffYcXX3xR6rVhHde/a9cuuLu7o1mzZpg7dy5effVVDB482CYpVJmWLFmCXr16oV27dhg3bhwKCwuxfPlyaLVazJ8/X6rXqlUrAMDs2bMxbNgwKBQKxMbG2p0/SqFQ4O2338bYsWPRuXNnDB8+XLplcnh4+F1Nrt0NHh4eWLlyJUaNGoUHH3wQw4YNg5+fH5KTk/HDDz8gOjoaH3zwAc6ePYvu3btjyJAheOCBB+Ds7IzNmzcjNTVVuk3zzajVamzbtg2jR49GVFQUtm7dih9++AGvvPKKNOSuc+fOeOaZZ7Bw4UIcO3YMMTExUCgUiI+Px6ZNm7Bs2TIMHjz4tvfVZDJJPbeKioqQlJSELVu24Pjx4+jatavNXBHlPS4A8P7776NDhw548MEHMX78eNSpUweJiYn44YcfcOzYMQBA37598fnnn0Or1eKBBx7AoUOHsHPnTuk23lbTp0/H559/jkceeQQvvPACXF1dsXr1aoSFheH48eO3FR8REd2ZUaNGYePGjXj22WexZ88eREdHw2w24/Tp09i4cSO2b99e5s1TyvLaa69h27Zt6NixIyZMmACTyYTly5ejcePGNu/39tyNa5EtW7YgLy8P/fr1s7u8bdu28PPzw/r166WE09ChQ7F8+XLMmzcPTZs2lX6otKqM49anTx+8++67eOSRRzBixAikpaVhxYoVqFevns1xatWqFQYNGoSlS5fi2rVraNu2Lfbt2yf1vi7ZI2vRokXYs2cPoqKi8PTTT+OBBx5AZmYmjhw5gp07dyIzM7PCx68qvPzyy9iyZQv69u2LMWPGoFWrVtDpdDhx4gS++uorJCYmwtfXF0899RQyMzPRrVs31K5dG0lJSVi+fDlatGhR6jWyp169eujQoQOee+456PV6LF26FD4+PjZDy1asWIEOHTqgadOmePrpp1G3bl2kpqbi0KFDuHTpEv7666872tcjR45g3bp1sFgsyM7Oxh9//CFNNv7555/bDJ8s73Hp2rUrRo0ahffffx/x8fF45JFHYLFYsH//fnTt2hWTJk1CTEyM1JvsmWeeQX5+Pj7++GP4+/vbJN38/Pwwbdo0LFy4EH379kXv3r1x9OhRbN26Fb6+vrf1utE9yAF36iOqsay3WS35UKvVokWLFmLlypU2tygVovg28lOmTBHBwcFCoVCI+vXriyVLlkj1Dh8+LJydncXkyZNt1jOZTKJNmzYiODhYZGVllRnPnj17BACxadOmm8Zt7za5Qgixc+dOER0dLTQajfDw8BCxsbHi1KlTpdZ/4403RK1atYRcLhcAxIULF266vS+//FK0bNlSqFQq4e3tLUaOHGlzq1shKn7LZCt7t5S2Gj16tAgLC5Oel3Wb27KOW1kx7dmzR/Ts2VNotVqhVqtFRESEGDNmjPjzzz+FEEJkZGSIiRMnioYNGwpXV1eh1WpFVFSU2Lhx4y33Z/To0cLV1VUkJCSImJgY4eLiIgICAsS8efOE2WwuVX/16tWiVatWQqPRCHd3d9G0aVMxffp0ceXKFalOWFiY6NOnzy23XTKGkue0i4uLCA8PF4MGDRJfffWV3TjKc1ysTp48KQYMGCA8PT2FWq0WkZGRYs6cOdLyrKwsMXbsWOHr6yvc3NxEz549xenTp0VYWJjNLX6FEOL48eOic+fOQq1Wi1q1aok33nhDfPLJJ3bPy/LGR0REtux91nbu3Fk0btzYbn2DwSDefvtt0bhxY6FSqYSXl5do1aqVeO2116TbyAshBAAxceLEUuvbe7/ft2+faNWqlVAqlaJu3bpi1apVYt68eWVeA9yoMq9FYmNjhVqtFjqdrsw6Y8aMEQqFQmRkZAghim95HxISIgCIBQsW2F3nTo+bEEJ88sknon79+kKlUomGDRuKuLg4u8dJp9OJiRMnCm9vb+Hm5ib69+8vzpw5IwCIRYsW2dRNTU0VEydOFCEhIUKhUIjAwEDRvXt3sXr16psep5LS09MFADFv3rxSy+xdl1qvh25U1nln71onLy9PzJo1S9SrV08olUrh6+sr2rdvL/7v//5PGAwGIYQQX331lYiJiRH+/v5CqVSK0NBQ8cwzz4irV6/edH9KXlO+8847IiQkRKhUKtGxY0fx119/laqfkJAgnnjiCREYGCgUCoWoVauW6Nu3r/jqq6+kOhW9FrbGYH04OzsLb29vERUVJWbNmiWSkpLsrlee4yJE8fePJUuWiIYNGwqlUin8/PxEr169xOHDh6U6W7ZsEc2aNRNqtVqEh4eLt99+W3z66aelrsPMZrN47bXXRFBQkNBoNKJLly7i5MmTdv/Wyxsf3VtkQlTzmQGJiO5hY8aMwVdffSXNLUZERERUFY4dO4aWLVti3bp1GDlypKPDqdYSExNRp04dLFmyBNOmTXN0OEQ1HueIIiIiIiIiuocVFhaWKlu6dCnkcjk6derkgIiI6H7GOaKIiIiIiIjuYYsXL8bhw4fRtWtXODs7Y+vWrdi6dSvGjx+PkJAQR4dHRPcZJqKIiIiIiIjuYe3bt8eOHTvwxhtvID8/H6GhoZg/fz5mz57t6NCI6D7EOaKIiIiIiIiIiKhKcI4oIiIiIiIiIiKqEhyaZ4fFYsGVK1fg7u4OmUzm6HCIiIjoLhNCIC8vD8HBwZDL+TvdneB1FBER0f2lotdRTETZceXKFU7aR0REdB+6ePEiateu7egwajReRxEREd2fynsdxUSUHe7u7gCKD6KHh4eDoyEiIqK7LTc3FyEhIdI1AN0+XkcRERHdXyp6HcVElB3WbuQeHh68gCIiIrqPVKehZJcvX8aMGTOwdetWFBQUoF69eoiLi0Pr1q0BFHeDnzdvHj7++GNkZ2cjOjoaK1euRP369W/a7ooVK7BkyRKkpKSgefPmWL58OR566CFpeVFREaZOnYoNGzZAr9ejZ8+e+PDDDxEQEFCuuHkdRUREdH8q73UUJ0EgIiIiqmaysrIQHR0NhUKBrVu34tSpU3jnnXfg5eUl1Vm8eDHef/99rFq1Cr/99htcXV3Rs2dPFBUVldnul19+iZdeegnz5s3DkSNH0Lx5c/Ts2RNpaWlSnSlTpuD777/Hpk2bsG/fPly5cgUDBw68q/tLRERE9w+ZEEI4OojqJjc3F1qtFjk5Ofwlj4iI6D5Q3T77Z86ciQMHDmD//v12lwshEBwcjKlTp2LatGkAgJycHAQEBGDNmjUYNmyY3fWioqLQpk0bfPDBBwCKJxYPCQnB5MmTMXPmTOTk5MDPzw9ffPEFBg8eDAA4ffo0GjVqhEOHDqFt27a3jL26HUsiIiK6uyr62c8eUURERETVzJYtW9C6dWs89thj8Pf3R8uWLfHxxx9Lyy9cuICUlBT06NFDKtNqtYiKisKhQ4fstmkwGHD48GGbdeRyOXr06CGtc/jwYRiNRps6DRs2RGhoaJnt6vV65Obm2jyIiIiIysJEFBEREVE1c/78eWm+p+3bt+O5557D888/j7Vr1wIAUlJSAKDUvE0BAQHSshtlZGTAbDbfdJ2UlBQolUp4enqWu92FCxdCq9VKD94xj4iIiG6GiSgiIiKiasZiseDBBx/EW2+9hZYtW2L8+PF4+umnsWrVKkeHVsqsWbOQk5MjPS5evOjokIiIiKgaYyKKiIiIqJoJCgrCAw88YFPWqFEjJCcnAwACAwMBAKmpqTZ1UlNTpWU38vX1hZOT003XCQwMhMFgQHZ2drnbValU0h3yeKc8IiIiuhUmooiIiIiqmejoaJw5c8am7OzZswgLCwMA1KlTB4GBgdi1a5e0PDc3F7/99hvatWtnt02lUolWrVrZrGOxWLBr1y5pnVatWkGhUNjUOXPmDJKTk8tsl4iIiKginB0dABERERHZmjJlCtq3b4+33noLQ4YMwe+//47Vq1dj9erVAACZTIYXX3wRCxYsQP369VGnTh3MmTMHwcHB6N+/v9RO9+7dMWDAAEyaNAkA8NJLL2H06NFo3bo1HnroISxduhQ6nQ5jx44FUDzh+bhx4/DSSy/B29sbHh4emDx5Mtq1a1euO+YRERER3QoTUURERETVTJs2bbB582bMmjULr7/+OurUqYOlS5di5MiRUp3p06dDp9Nh/PjxyM7ORocOHbBt2zao1WqpTkJCAjIyMqTnQ4cORXp6OubOnYuUlBS0aNEC27Zts5nA/L333oNcLsegQYOg1+vRs2dPfPjhh1Wz40RERHTPkwkhhKODqG5yc3Oh1WqRk5PDeQ6IiIjuA/zsrzw8lkRERPeXin72O3yOqMuXL+Pxxx+Hj48PNBoNmjZtij///FNaLoTA3LlzERQUBI1Ggx49eiA+Pv6W7a5YsQLh4eFQq9WIiorC77//fjd3g4iIiIiIiIiIbsGhiaisrCxER0dDoVBg69atOHXqFN555x14eXlJdRYvXoz3338fq1atwm+//QZXV1f07NkTRUVFZbb75Zdf4qWXXsK8efNw5MgRNG/eHD179kRaWlpV7BYREREREREREdnh0KF5M2fOxIEDB7B//367y4UQCA4OxtSpUzFt2jQAQE5ODgICArBmzRoMGzbM7npRUVFo06YNPvjgAwDFd4QJCQnB5MmTMXPmzFL19Xo99Hq99Dw3NxchISHsUk5ERHSf4HCyysNjSUREdH+pUUPztmzZgtatW+Oxxx6Dv78/WrZsiY8//lhafuHCBaSkpKBHjx5SmVarRVRUFA4dOmS3TYPBgMOHD9usI5fL0aNHjzLXWbhwIbRarfQICQmppD0kIiIiIiIiIiIrhyaizp8/j5UrV6J+/frYvn07nnvuOTz//PNYu3YtACAlJQUAbO7kYn1uXXajjIwMmM3mCq0za9Ys5OTkSI+LFy/e6a4REREREREREdENnB25cYvFgtatW+Ott94CALRs2RInT57EqlWrMHr06CqLQ6VSQaVSVdn2iIiIiIiIiIjuRw5NRAUFBeGBBx6wKWvUqBG+/vprAEBgYCAAIDU1FUFBQVKd1NRUtGjRwm6bvr6+cHJyQmpqqk15amqq1B4RVS9FRUVITk52dBjVTmhoKNRqtaPDICIiIiIiqjQOTURFR0fjzJkzNmVnz55FWFgYAKBOnToIDAzErl27pMRTbm4ufvvtNzz33HN221QqlWjVqhV27dqF/v37AyjuebVr1y5MmjTpru0LEd2+5ORkjB8/3tFhVDurV69GgwYNHB0GERERUSlGs6X4YRIwWor/bzILaF0U8FArHB0eEVVjDk1ETZkyBe3bt8dbb72FIUOG4Pfff8fq1auxevVqAIBMJsOLL76IBQsWoH79+qhTpw7mzJmD4OBgKckEAN27d8eAAQOkRNNLL72E0aNHo3Xr1njooYewdOlS6HQ6jB071hG7SUS3EBoaKv3dO1JSUhLefPNNzJ49W0qIO1JoaKijQyAiIqL7kDWpZLAmm254bjILlHXv9ewCI9zUzgjSqqFWOFVt4ERUIzg0EdWmTRts3rwZs2bNwuuvv446depg6dKlGDlypFRn+vTp0Ol0GD9+PLKzs9GhQwds27bNZrhKQkICMjIypOdDhw5Feno65s6di5SUFLRo0QLbtm0rNYE5EVUParW6WvX8CQsLq1bxEBEREVUWk9kCo/l6LybT9f9LCafi/5eVZCqv/CITzunz4e2qRICHGk5yWeUET0T3BJkQd/o2c+/Jzc2FVqtFTk4OPDw8HB0OEVWRs2fPYvz48RwSR3Qf4md/5eGxJHIcs6U4kWQwFyeZTBYBg6lyk0wV5SSXIVCrhrersmo3TERVpqKf/Q7tEUVERERERES3Zk0ylUwqGa4nm6z/r45dDMwWgctZhcjU6RGk1cBVxa+gRPc7vgsQERERERE5kMVScj4mAZO1V1OJYXMWi6OjvDOFBgvOp+ug1SgQqFVD6Sx3dEhE5CBMRBEREREREd0l1iSTySKuz8lUnGQymf8dRlfTk0wVkVNoRG6REf7uKvi6qSDn/FFE9x0mooiIiIiIiG6DEOLfnksmS/EE4OZ/E05Gs4DZUg3HyzmYEEBqrh6ZBQYEeWigdVE4OiQiqkJMRBEREREREd3AmmQq2XPJOmyueE4mJpnulNEkkJxZABedE4K1GmiUTo4OiYiqABNRRERERER0XxFCSPMvmcwl52ey2JRT1SjQm3EuLR/ebkoEuKvg7MT5o4juZUxEERERERHRPUVKKpms8zP9+3+j2QKzRVTLO8zd7zLzDcguMCDAQw0fVyVkMs4fRXQvYiKKiIiIiIhqjBt7LlkTTsXzMxX3ZGKSqeayWICr2UXI1BkQpFXDXc35o4juNUxEERERERFRtWC6nmCy9lyynZ+JSab7id5oQWJGAdzVzgjyVEPlzPmjiO4VTETd44qKipCcnOzoMKqd0NBQqNVqR4dBREREdN+wJpmMFsv1u8qJUr2bmGSiG+UVmZCfmg9fNxX83FVwknO4HlFNx0TUPS45ORnjx493dBjVzurVq9GgQQNHh0FERER0TzBbSvRcMllgsggYTEwyUeUQAkjP0yOrwIBADzW8XJWODomI7gATUfe40NBQrF692tFhICkpCW+++SZmz56NsLAwR4eD0NBQR4dAREREVCNYk0wlk0qG68km6/+ZZKKqYDILXMoqxDWdAcGeargo+XWWqCbiX+49Tq1WV6ueP2FhYdUqHiIiIqL7mcVS1nxMAqbr/7dYHB0lka1CgxkJaTp4uigQqFVD4SR3dEhEVAFMRBERERER3YMsFuud5MT1OZksMFr+/T+TTFTTZRcYkVNohL+HCn5uKshknD+KqCZgIoqIiIiIqIYRQpTquWRNOJksFhhMAmYLx8vRvU8IIDVHjyydEYFaNbQahaNDIqJbYCKKiIiIiKgaEULY3FHOUGLYXPGcTEwyEd3IYLIg+VoB3NTOCNKqoVY4OTokIioDE1FERERERFWkZJLJZP53fqaSE4GbzEwyEd2u/CITzunz4e2qRICHGk5yDtcjqm6YiCIiIiIiqiRSUslUnGQyWf79v9FsgdkieIc5ortMCOBavgHZBUYEeKjg7ark/FFE1QgTUURERERE5WCvF1PJ5yYzk0xE1YnZInAluwiZOgOCPDVwU/HrL1F1wL9EIiIiIrrvma4PjSuej+nfYXIl52dikomoZioyWnAhXQetRoFArRpKZ7mjQyK6rzERRURERET3NJPZApPles8lk8VmInAjk0xE942cQiNyi4zwc1fBz00FOeePInIIJqKIiIiIqMYyW0r3XDKYrg+VswgYTEwyEdG/hADScvXIKjAg0EMNTxelo0Miuu8wEUVERERENUahwYyU3CIp4cQkExHdDqNJ4GJmIa7pDAjWaqBROjk6JKL7BgfHEhEREVGNYTBbkF9kgt7IJBQR3bkCvRnn0vJxKasAJrPF0eEQ3RfYI4qIiIiIiIjua1k6I3IKjfB3V8PXTQmZjPNHEd0t7BFFRERERERE9z2LBUjJKUJ8Wj5yi4yODofonsVEFBEREREREdF1eqMFSRkFSMzQochodnQ4RPccDs0jIiIiIiIiukFekQn5+nz4uCnh766Gk5zD9YgqA3tEEREREREREdkhBJCRZ8DZ1Dxk6gyODofonsBEFBEREVE1M3/+fMhkMptHw4YNpeUJCQkYMGAA/Pz84OHhgSFDhiA1NfWmbYaHh5dqUyaTYeLEiVKdLl26lFr+7LPP3rX9JCKqKUxmgctZhTiXlged3uTocIhqNCaiiIiIiKqhxo0b4+rVq9Ljl19+AQDodDrExMRAJpNh9+7dOHDgAAwGA2JjY2GxlH3r8T/++MOmvR07dgAAHnvsMZt6Tz/9tE29xYsX372dJCKqYQoNFpxP1+FiZgGM5rLfc4mobJwjioiIiKgacnZ2RmBgYKnyAwcOIDExEUePHoWHhwcAYO3atfDy8sLu3bvRo0cPu+35+fnZPF+0aBEiIiLQuXNnm3IXFxe72y2LXq+HXq+Xnufm5pZ7XSKimiq7wIicQiP83VXwdVNBzvmjiMqNPaKIiIiIqqH4+HgEBwejbt26GDlyJJKTkwEUJ35kMhlUKpVUV61WQy6XS72mbsVgMGDdunV48sknIZPZfnlav349fH190aRJE8yaNQsFBQU3bWvhwoXQarXSIyQkpIJ7SkRUMwkBpObqEZ+Wj5wCo6PDIaoxmIgiIiIiqmaioqKwZs0abNu2DStXrsSFCxfQsWNH5OXloW3btnB1dcWMGTNQUFAAnU6HadOmwWw24+rVq+Vq/9tvv0V2djbGjBljUz5ixAisW7cOe/bswaxZs/D555/j8ccfv2lbs2bNQk5OjvS4ePHi7e42EVGNZDBZkJxZgPPp+Sgymh0dDlG1x6F5d1FqaipycnIcHUa1kJSUZPMvAVqtFgEBAY4Og4iIqqFevXpJ/2/WrBmioqIQFhaGjRs3Yty4cdi0aROee+45vP/++5DL5Rg+fDgefPBByOXl+43xk08+Qa9evRAcHGxTPn78eOn/TZs2RVBQELp3746EhARERETYbUulUtn0ziIiul/p9GacS8uHl6sSAe4qODux3weRPUxE3SWpqal4fNQTMBr0t658H3nzzTcdHUK1oVCqsO7zz5iMIiKiW/L09ESDBg1w7tw5AEBMTAwSEhKQkZEBZ2dneHp6IjAwEHXr1r1lW0lJSdi5cye++eabW9aNiooCAJw7d67MRBQREf1LCCAz34CcAiP8PVTwcVWWGgJNdL9jIuouycnJgdGgR2HdzrCotY4Oh6oZeVEOcH4fcnJymIgiIqJbys/PR0JCAkaNGmVT7uvrCwDYvXs30tLS0K9fv1u2FRcXB39/f/Tp0+eWdY8dOwYACAoKqnjQRET3MbNF4Gp2EbJ0BgR5auCm4ldvIiv+NdxlFrUWFldfR4dBRERENci0adMQGxuLsLAwXLlyBfPmzYOTkxOGDx8OoDiZ1KhRI/j5+eHQoUN44YUXMGXKFERGRkptdO/eHQMGDMCkSZOkMovFgri4OIwePRrOzraXgQkJCfjiiy/Qu3dv+Pj44Pjx45gyZQo6deqEZs2aVc2OExHdY4qMFlxI18FD44xArRoqZydHh0TkcExEEREREVUzly5dwvDhw3Ht2jX4+fmhQ4cO+PXXX+Hn5wcAOHPmDGbNmoXMzEyEh4dj9uzZmDJlik0b1qF7Je3cuRPJycl48sknS21TqVRi586dWLp0KXQ6HUJCQjBo0CC8+uqrd29HiYjuE7mFJuQV5cPXTQV/dxXkcg7Xo/sXE1FE9zlOqv8vTqpfGifVJ3KMDRs23HT5okWLsGjRopvWSUxMLFUWExMDIYTd+iEhIdi3b1+5YyQioooRAkjP0yOrwIAgrRqeLkpHh0TkEExEEd3HOKm+fZxU/1+cVJ+IiIiocpnMAhczC5GRb0CwpxouSn4tp/sLz3ii+xgn1aeb4aT6RERERHdPocGMhDQdPF0UCNSqoXCSOzokoirBRBQRcVJ9IiIiIiIHyS4wIrfICH93NXzdlJDJOH8U3duYciUiIiIiIiJyIIsFSMkpwtnUfOQWGR0dDtFdxUQUERERERERUTVgMFmQlFGACxk6FBnNjg6H6K5gIoqIiIiIiIioGskvMuFcWj6uZBfCbLF/t1OimoqJKCIiIiIiIqJqRgjgWr4BZ1LykKkzODocokrDRBQRERERERFRNWW2CFzOKsS5tDzo9CZHh0N0x5iIIiIiIiIiIqrmCg0WnE/XIflaAQwmi6PDIbptzo4OgIiIiIiIiIjKJ6fQiNwiI/zdVfB1U0Eulzk6JKIKYY8oIiIiIiIiohpECCA1V4+zaXnIKTA6OhyiCmEiioiIiIiIiKgGMpoEkjMLkJCej0KD2dHhEJULE1FERERERERENViB3oxzafm4nF0Ik5nzR1H1xjmiiIiIiIiIiO4BmfkGZBcYEOChho+rEjIZ54+i6oc9ooiIiIiIiIjuERYLcDW7CPFp+cgr4vxRVP0wEUVERERERER0j9EbLUjMKEBihg56E+ePouqDiSgiIiIiIiKie1RekQnxqflIySmC2SIcHQ4RE1FERERERERE9zIhgPQ8Pc6m5iFLZ3B0OHSfYyKKiIiIiIiI6D5gMgtcyirEubR8FBhMjg6H7lMOTUTNnz8fMpnM5tGwYUNpeUJCAgYMGAA/Pz94eHhgyJAhSE1NvaM2iYiIiIiIiO5nhQYzEtJ0uJhZAKPZ4uhw6D7j8B5RjRs3xtWrV6XHL7/8AgDQ6XSIiYmBTCbD7t27ceDAARgMBsTGxsJiufkfSlltEhEREREREVGx7AIjzqTkIS2vCEJw/iiqGs4OD8DZGYGBgaXKDxw4gMTERBw9ehQeHh4AgLVr18LLywu7d+9Gjx49KtxmWfR6PfR6vfQ8Nze3AntAREREREREVDMJAaTm6JGlMyJQq4ZWo3B0SHSPc3iPqPj4eAQHB6Nu3boYOXIkkpOTARQnh2QyGVQqlVRXrVZDLpffsodTWW2WZeHChdBqtdIjJCTkzneMiIiIiIiIqIYwmCxIvlaACxk6FBnNjg6H7mEOTURFRUVhzZo12LZtG1auXIkLFy6gY8eOyMvLQ9u2beHq6ooZM2agoKAAOp0O06ZNg9lsxtWrV2+rzbLMmjULOTk50uPixYt3Y3eJiIiIiIiIqrX8IhPOpeXjSnYhzBYO16PK59Cheb169ZL+36xZM0RFRSEsLAwbN27EuHHjsGnTJjz33HN4//33IZfLMXz4cDz44IOQy8vOn92qTXtUKpVNzysiIiIiIiKi+5UQwLV8A7ILjAjwUMHbVQmZTObosOge4fA5okry9PREgwYNcO7cOQBATEwMEhISkJGRAWdnZ3h6eiIwMBB169a97TaJiIiIiIiI6NbMFoEr2UXI1BkQ5KmBm6papRCohqpWZ1F+fj4SEhIwatQom3JfX18AwO7du5GWloZ+/frdcZtVRV6Y7ZDtUvXG84KIiIiIiGqKIqMFF9J10GoUCNSqoXR2+HTTVIM5NBE1bdo0xMbGIiwsDFeuXMG8efPg5OSE4cOHAwDi4uLQqFEj+Pn54dChQ3jhhRcwZcoUREZGSm10794dAwYMwKRJk8rVZlXTXPjZIdslIiIiIiIiqkw5hUbkFhnh566Cn5sKcjmH61HFOTQRdenSJQwfPhzXrl2Dn58fOnTogF9//RV+fn4AgDNnzmDWrFnIzMxEeHg4Zs+ejSlTpti0YR26V942q1phnU6waDwdsm2qvuSF2UxSEhERERFRjSMEkJarR1aBAYEeani6KB0dEtUwDk1Ebdiw4abLFy1ahEWLFt20TmJiYoXarGoWjScsrr6ODoOIiIiIiIio0hhNAhczC3FNZ0CwVgON0snRIVENwYGdRERERERERHRbCvRmnEvLx6WsApjMFkeHQzVAtZqsnIiIiIiIiIhqniydETmFRvi7q+HrpoRMxvmjyD72iCIiIiIiIiKiO2axACk5RYhPy0dukdHR4VA1xUQUEREREREREVUavdGCpIwCJGboUGQ0OzocqmY4NI+IiIiIiIiIKl1ekQn5+nz4uCnh766Gk5zD9Yg9ooiIiIiIiIjoLhECyMgz4GxqHjJ1BkeHQ9UAE1FEREREREREdFeZzAKXswpxLi0POr3J0eGQAzERRURERERERERVotBgwfl0HS5mFsBotjg6HHIAzhFFRERERERERFUqu8CInEIj/N1V8HVTQc75o+4b7BFFREREVM3Mnz8fMpnM5tGwYUNpeUJCAgYMGAA/Pz94eHhgyJAhSE1NvaM2AaCoqAgTJ06Ej48P3NzcMGjQoFu2S0REdLuEAFJz9TibloecAqOjw6Eqwh5RRAR5YbajQ6BqiOcFkWM1btwYO3fulJ47Oxdftul0OsTExKB58+bYvXs3AGDOnDmIjY3Fr7/+Crm87N8Zy2rTasqUKfjhhx+wadMmaLVaTJo0CQMHDsSBAwcqc9eIiIhsGE0CyZkFcNU5IdhTA7XCydEh0V3ERBQRQXPhZ0eHQFQjFBUVITk52dFhVDuhoaFQq9WODuOe4+zsjMDAwFLlBw4cQGJiIo4ePQoPDw8AwNq1a+Hl5YXdu3ejR48eFW4TAHJycvDJJ5/giy++QLdu3QAAcXFxaNSoEX799Ve0bdu2EvaKiIiobDq9GefS8uHlqkSAuwrOThzEdS9iIoqIUFinEywaT0eHQdWMvDCbScobJCcnY/z48Y4Oo9pZvXo1GjRo4Ogw7jnx8fEIDg6GWq1Gu3btsHDhQoSGhkKv10Mmk0GlUkl11Wo15HI5fvnll5smospqEwAOHz4Mo9Fos37Dhg0RGhqKQ4cOlZmI0uv10Ov10vPc3Nw73XUiIrqPCQFk5huQU2CEv4cKPq5KyGScP+pewkQUEcGi8YTF1dfRYRBVe6GhoVi9erWjw0BSUhLefPNNzJ49G2FhYY4OR0pkUOWJiorCmjVrEBkZiatXr+K1115Dx44dcfLkSbRt2xaurq6YMWMG3nrrLQghMHPmTJjNZly9evW22nR3d0dKSgqUSiU8PT1t1gsICEBKSkqZ7S5cuBCvvfZaZe06ERERAMBsEbiaXYQsnQFBnhq4qZi+uFfwlSQiIiontVpdrXr+hIWFVat4qPL06tVL+n+zZs0QFRWFsLAwbNy4EePGjcOmTZvw3HPP4f3334dcLsfw4cPx4IMP3nR+qFu1ebtmzZqFl156SXqem5uLkJCQ226PiIiopCKjBRfSdfDQOCNQq4bKmfNH1XRMRBERERFVc56enmjQoAHOnTsHAIiJiUFCQgIyMjLg7OwMT09PBAYGom7durfdZmBgIAwGA7Kzs216RaWmppY5rxQAqFQqm2GCREREd0NuoQl5RfnwdVPB310FuZzD9WoqzvxFREREVM3l5+cjISEBQUFBNuW+vr7w9PTE7t27kZaWhn79+t12m61atYJCocCuXbukOmfOnEFycjLatWtXOTtCRER0B4QA0vP0OJOah+wCg6PDodvERBQRERFRNTNt2jTs27cPiYmJOHjwIAYMGAAnJycMHz4cQPHd7H799VckJCRg3bp1eOyxxzBlyhRERkZKbXTv3h0ffPBBudvUarUYN24cXnrpJezZsweHDx/G2LFj0a5dO94xj4iIqhWTWeBiZiHOpeWjwGBydDhUQRyaR0RERFTNXLp0CcOHD8e1a9fg5+eHDh064Ndff4Wfnx+A4p5Ks2bNQmZmJsLDwzF79mxMmTLFpg3r0L3ytgkA7733HuRyOQYNGgS9Xo+ePXviww8/rJqdJiIiqqBCgxkJaTp4uigQqFVD4cS+NjUBE1FERERE1cyGDRtuunzRokVYtGjRTeskJiZWqE2geEL+FStWYMWKFbes6ygyACpnOUwWAbNFODocIiKqBrILjMgtMsLPXQU/NxVkMs4fVZ0xEUVERERENUKhwQS1wglergpoNQpkFxiRXWCE3mRxdGhERORgFguQmqNHls6IIE81PNQKR4dEZWAiioiIiIiqPb3RjFX7ziPu4AXkFprgoXHG2PZ18EznuriUWchkFBERAQAMJguSMgrgpnZGkFYNtcLJ0SHRDZiIIiIiIqJqrdBgwqp957FsV7xUlltokp6PjApFaq7eUeEREVE1lF9kwjl9PrxdlQjwUMNJzuF61QVn8iIiIiKias1JLkfcwQt2l8UdvABPFwW/YBARUSlCANfyDTiTkodMncHR4dB17BFFRERERNVaXpERuYX2b8+dW2hCer4B3x27DHe1Ag0C3BDooeZEtUREJDFbBC5nFSJTp0eQVgNXFVMhjsSjT0RERETVmrtaAQ+Ns91klIfGGV4uCmw6fEn6tdtD7YwGAe4lHm5w56S1RET3vUKDBefTddBqFAjUqqF05iAxR2AiioiIiIiqNbPFgrHt69jMEWU1pn04LmYWoH2ED+JT85GQno/cIhP+TMrCn0lZUr1grRoNAt3RwN8dkYHuqOPrCoUTv4AQEd2PcgqNyC0ywt9dBV83FeQc3l2lmIgiIiIiompNo3TGhC4RAFDmXfOe6VS83Gi24EKGDmdS8nA2LQ9nU/JwJadIeuw9kw4AcJbLUNfPFQ0C3BF5vedUkJZD+oiI7hdCAKm5emQWGBDkoYHWhT1nqwoTUXeZvCjH0SFQNcTzgoiIqGJUCic807kuJnath5xCI7QaZ2QXGHEpsxB6k0Wqp3CSS0PyrPKKjIhPzceZ1DycTc3DmdQ85BWZcDY1H2dT8/E/XAUAuKucUT/AHZEBbmgQ4I76Ae7QavjFhIjoXmY0CSRnFsBF54RgrQYapZOjQ7rnMRF1l2i1WiiUKuD8PkeHQtWUQqmCVqt1dBhEREQ1hovSGbmFRmTpDEjP08NsEeVaz12twINhXngwzAsAIIRAaq7+38RUSh7OZ+QjT2/CkeQsHEn+d0hfkFYtzTPVIMAddX3dOKcIEdE9qEBvxrm0fHi5KhDooYYzh2/fNUxE3SUBAQFY9/lnyMlhzxcASEpKwptvvonZs2cjLCzM0eFUC1qtFgEBAY4Og4iIqEYRgE0PqNshk8kQqFUjUKtG5wZ+AIqH9CVm6HA2NQ9nr/eeupxdiKs5RbiaU4R9Z/8d0hfu6yoN54sMcEeQpxpyDukjIronZOmMyCk0IsBDDR9XJYds3wVMRN1FAQEBTDTcICwsDA0aNHB0GEREREQ2FE5y1L8+HK/P9bL8IhPOpuUh/vpwvrOp+cgpNOJcWj7OpeXjhxPFQ/pcVU5o4O+OBoH/zjfFIX1ERDWXxQJczS5Cps6AIK2ad16tZExEERERERHZ4aZ2xoOhXngwtMSQvjx9cWIqpXhYX0K6Djq9GUcvZuPoxWxp3QAPFSKvJ7YiA9xR188VKmfOO0JEVJPojRYkZhTAXe2MIE8138crCRNRRERERETlIJPJEOihRqCHGh3rFw/pM5ktSLxWIE2CHp+ah4tZhUjN1SM1V4+f4zMAAE5yGer4uKJ+gFtxr6lAd9Ty1HBIHxFRDZBXZEJ+aj583VTwc1fBSc737jvBRBQRERER0W1ydpKjnr8b6vm7oXfTIACATm9CfFq+lJg6k5qH7AIjzqXn41x6PraeTAEAuCqdUF+aa6p4MnRPF6Ujd4eIiMogBJCep0dWgQGBHmp4ufL9+nYxEUVEREREVIlcVc5oEeKJFiGeAIqH9KXn6XE2LV8a0ncuPR86gxnHLmbjWIkhff7uKmkS9PoBbojwc4NawaEgRETVhckscCmrENd0BgR7quGiZFqlonjEiIiIiIjuIplMBn8PNfw91OhQzxdA8ZC+5MyC65OgF0+EfjGzAGl5eqTl6fHLueIhfXIZ/r1L3/UJ0Wt7cUgfEZGjFRrMSEjTwdNFgUCtGgonuaNDqjGYiCIiIiIiqmLOTnLU9XNDXT839GpSPKSvwFA8pO9sSh7OpuXhbEo+MgsMOJ+uw/l0HbaieEifi9IJ9f2Lh/JZe09xiAgRkWNkFxiRU2iEv4cKfm4qyPhDwS0xEUVEREREVA24KJ3RvLYnmtf2BFA8pC8j33C9x1TxXFPn0vJRYDDjr0s5+OtSjrSur5tKmmcqMtCdQ/qIiKqQEEBqjh5ZOiMCtWpoNQpHh1StMRFFRERERFQNyWQy+LkX36Ep+vqQPrNFIDnz37v0nU3JQ3JmATLy9cjI1+NAwjUAxUP6wnxcr/eaKr5TX20vF97piYjoLjKYLEi+VgA3tTOCtGr+IFAGJqKIiIiIiGoIJ7kMdXxdUcfXFT0bBwIoHtKXkJaPM6n5Uu+pazoDLmTocCFDh+1/F6+rURQP6atf4i59Pm4qB+4NEdG9Kb/IhHP6fHi7KhHgoeaPADdgIoqIiIiIqAZzUTqjaW1PNL0+pA8AruXrbSZCj0/LQ6HRjOOXc3D8cskhfUrU9y8eztcgwB31/NygUfIXfCKiOyUEcC3fgOwCIwI8VPB2VXL+qOuYiCIiIiIiusf4uKnQ3k2F9hH/Dum7eP0uffHXh/UVD+kzICP/Gg6d/3dIX6i3izQReoMAd4R6c0gfEdHtMlsErmQXIVNnQJCnBm4qpmF4BIiIiIiI7nFOchnCfV0RXmJIX6HBjIT0/H/nm0rNQ0a+AYnXCpB4rQA/nUoFAKgVctTzc0NkoLvUe8qXQ/qIiCqkyGjBhXQdtBoFArVqKJ3ljg7JYZiIIiIiIiK6D2mUTmhSS4smtbRS2bV8Pc6m5eNsSnFiKj4tH4VGM05eycXJK7lSPW9XJRpY79IX4I56/m5wUfKrBRHRreQUGpFbZCy+GYWbCvL7sMcpPy2IiIiIKpHRaERKSgoKCgrg5+cHb29vR4dEVG4+biq0c1OhXV0fAMVDSi5lFUhzTZ1NzUPiNR0ydQb8ej4Tv57PBADIAIR4uyDy+nC+yEA3hHq7ckgfEZEdQgBpuXpkFRgQ6KGGp4vS0SFVKSaiiIiIiO5QXl4e1q1bhw0bNuD333+HwWCAEAIymQy1a9dGTEwMxo8fjzZt2jg6VKIKcZLLEObjijAfVzz8QHFZkbHkkL7if9Pz9EjOLEByZgF2/FM8pE/lLEc9f7cS8025wc9Nxcl6iYiuM5oELmYW4prOgGCt5r65WQQTUURERER34N1338Wbb76JiIgIxMbG4pVXXkFwcDA0Gg0yMzNx8uRJ7N+/HzExMYiKisLy5ctRv359R4dNdNvUCic0DtaicfC/Q/qydAacTcvDmRJD+goMZvx9JRd/lxjS5+WikBJTkQHuqB/AIX1ERAV6M86l5cPLVYFADzWcne7t+aP4rk9ERER0B/744w/8/PPPaNy4sd3lDz30EJ588kmsWrUKcXFx2L9/PxNRdM/xclUiqo4PouoUD+mzCIHLWYXSJOjFQ/oKkFVgxG8XMvHbhX+H9NX2dkED/+LJ0BsEuCPM2+We/xJGRGRPls6InEIj/N3V8HVT3rM9SJmIIiIiIroD//3vf8tVT6VS4dlnn73L0RBVD3KZDCHeLgjxdkGPRgEAAL3JjIR0XfFE6Nd7T6Xl6XExswAXMwuw63QaAEDpLEeEnxsiA/4d1ufvziF9RHR/sFiAlJyi4vmjtGp4qBWODqnSMRFFREREdJfk5uZi9+7diIyMRKNGjRwdDpFDqZyd8ECQBx4I8pDKsgoMiE+1zjeVh/jUPOgMZvxzNRf/XP13SJ+nRiHNM9UgwB31A9zhpuJXGSK6d+mNFiRlFMBd7YxArRpqxb0zfxTfvYmIiIgqyZAhQ9CpUydMmjQJhYWFaN26NRITEyGEwIYNGzBo0CBHh0hUrXi5KPFQHW88VKf47pIWIXAlu9BmIvQLGTpkFxrxe2Imfk/MlNat7aVBA393NAgsnm8q3IdD+ojo3pNXZEK+Ph8+bkr4u6vvibuRMhFFREREVEl+/vlnzJ49GwCwefNmCCGQnZ2NtWvXYsGCBUxEEd2CXCZDbS8X1PZyQbeGxUP6DCYLzqfnX59vqjg5lZJbhEtZhbiUVYjdZ64P6XOSo66fqzQReoMAdwR4cEgfEdV8QgAZeQZkFxgR4KGGt6vS0SHdESaiiIiIiCpJTk4OvL2Le3Zs27YNgwYNgouLC/r06YOXX37ZwdER1UxKZzkaBnmgYYkhfTmFRmkS9LPXE1T5ehNOp+ThdEqeVE+rUaC+dSJ0/+LklJuaX4GIqGYymYtvBJGp0yNIq4FrDR2iXDOjJiIiIqqGQkJCcOjQIXh7e2Pbtm3YsGEDACArKwtqtdrB0RHdO7QaBdqEe6NNeHHiVwiBK9lFOJuWh7MpxfNNXcjQIafQiD+TsvBnUpa0bi1PjTTXVIMAd9TxdYWCQ/qIqAYpNFhwPl0HTxcFAjzUUDrXrPcwJqKIiIiIKsmLL76IkSNHws3NDWFhYejSpQuA4iF7TZs2dWxwRPcwmUyGWl4a1PLSoGukPwDAaC7+onamRM+pqzlFuJxdiMvZhdhzJh0A4CyXIcLPTUpORQa6I9BDzSF9RFTtZRcYkVNohL+7Cr5uKshryPxRTEQRERERVZIJEybgoYcewsWLF/Hwww9DLi/+hbJu3bpYsGCBg6Mjur8onOSIDCxOLFnlFhqlXlNn0/JxNiUPeXoTzly/ax9wFQDgrna2mWuqvr8bPDT33i3UiajmEwJIzdUjs8CAIA8NtC7V/72KiSgiIiKiStS6dWu0bt3apqxPnz4OioaISvLQKNA6zButw/4d0nc1p8hmrqmE9HzkFZlwOCkLh0sM6QvSqhEZ4I761xNUdf04pI+Iqg+jSSA5swCuOicEe2qgVjg5OqQyMRFFREREdAdeeumlctd9991372IkRFRRMpkMwZ4aBHtq0KXEkL4LGTqcvd5L6mxKHq7kFOHq9cfes/8O6avr51o8CXpgcXIqSMshfUTkWDq9GefS8uHlqkSAuwrO1TBh7tBE1Pz58/Haa6/ZlEVGRuL06dMAgISEBEybNg2//PIL9Ho9HnnkESxfvhwBAQE3bXfFihVYsmQJUlJS0Lx5cyxfvhwPPfTQXdsPIiIiun8dPXrU5vmRI0dgMpkQGRkJADh79iycnJzQqlUrR4RHRBWkcJJLE5n3vV6WV2REfGq+zXxTuUUmnE3Nx9nUfOBE8ZA+N5Xzv3NNXe89peWQPiKqYkIAmfkGZBcYEOChho+rslolyR2eGmvcuDGuXr0qPX755RcAgE6nQ0xMDGQyGXbv3o0DBw7AYDAgNjYWFoulzPa+/PJLvPTSS5g3bx6OHDmC5s2bo2fPnkhLS6uqXSIiIqL7yJ49e6RHbGwsOnfujEuXLuHIkSM4cuQILl68iK5du1ZoeN78+fMhk8lsHg0bNpSWJyQkYMCAAfDz84OHhweGDBmC1NTUm7a5cOFCtGnTBu7u7vD390f//v1x5swZmzpdunQptd1nn322YgeE6B7krlbgwTAvDH8oFPNiG2PduCh8PKo1psVEol/zYDQMdIfCSYZ8vQlHkrOx4Y+LeO1/p/D4J7/h6c/+xJLtZ7Dlr8s4fTUXBlPZ32WIiCqTxQJczS7CubR85OtNjg5H4vChec7OzggMDCxVfuDAASQmJuLo0aPw8PAAAKxduxZeXl7YvXs3evToYbe9d999F08//TTGjh0LAFi1ahV++OEHfPrpp5g5c+bd2xEiIiK6773zzjv46aef4OXlJZV5eXlhwYIFiImJwdSpU8vdVuPGjbFz507pubNz8WWb9ce65s2bY/fu3QCAOXPmIDY2Fr/++qs0QfqN9u3bh4kTJ6JNmzYwmUx45ZVXEBMTg1OnTsHV1VWq9/TTT+P111+Xnru4uJQ7ZqL7hUwmQ6BWjUCtGp0b+AEoHtKXmKGTJkE/k5qHy9mFSMktQkpuEX6O/3dIX7iv6/VeU8W9p4I9NZBXo94KRHRvKTJakHRNh8bBWkeHAqAaJKLi4+MRHBwMtVqNdu3aYeHChQgNDYVer4dMJoNKpZLqqtVqyOVy/PLLL3YTUQaDAYcPH8asWbOkMrlcjh49euDQoUNlxqDX66HX66Xnubm5lbR3REREdD/Jzc1Fenp6qfL09HTk5eVVqK3K/rFu27ZtNs/XrFkDf39/HD58GJ06dZLKXVxc7G63LLyOIiqmcJKj/vXheH2aBgEA8vUmxF8fynfm+mToOYVGnEvLx7m0fPx4onhdV5UT6vv/e5e+yEAO6SOie5dDE1FRUVFYs2YNIiMjcfXqVbz22mvo2LEjTp48ibZt28LV1RUzZszAW2+9BSEEZs6cCbPZjKtXr9ptLyMjA2azudQcUgEBAdK8U/YsXLiw1FxVRERUfaSmpiInJ8fRYVQbSUlJNv8SoNVqbzmHZFUYMGAAxo4di3feeUean/K3337Dyy+/jIEDB1aorcr8sc4e69+Ut7e3Tfn69euxbt06BAYGIjY2FnPmzLlpryheRxGVzU3ljJahXmgZWtxLUgiBtDx9cWIqJQ9n0/KRkJYPnd6MYxezcexitrSuv7sKkYHu0nxVEX6uUDlX37tgERGVl0MTUb169ZL+36xZM0RFRSEsLAwbN27EuHHjsGnTJjz33HN4//33IZfLMXz4cDz44INldjm/XbNmzbK5401ubi5CQkIqdRtERHR7UlNT8fioJ2A06G9d+T7z5ptvOjqEakOhVGHd5585PBm1atUqTJs2DSNGjIDRaARQ3LNp3LhxWLJkSbnbqewf625ksVjw4osvIjo6Gk2aNJHKR4wYgbCwMAQHB+P48eOYMWMGzpw5g2+++abMtngdRVR+MpkMAR5qBHio0bF+8ZA+k9mCxGsFiE+7npxKzcPFrEKk5emRlqfH/vgMAICTXIZwHxcpMRUZ4I5aXhzSR0Q1j8OH5pXk6emJBg0a4Ny5cwCAmJgYJCQkICMjA87OzvD09ERgYCDq1q1rd31fX184OTmVmqwzNTX1pl3MVSqVza+KRERUfeTk5MBo0KOwbmdY1NVjXDtVL/KiHOD8PuTk5Dg8EeXi4oIPP/wQS5YsQUJCAgAgIiLCZg6m8rjbP9ZNnDgRJ0+elG4SYzV+/Hjp/02bNkVQUBC6d++OhIQERERE2G2L11FEd8bZSY56/m6o5++GXk2Kh/Tp9CacS/v3Ln1nUvOQXWBEQroOCek6bD2ZAgBwVToVDwf0d5N6T3m5KB25O0REt1StElH5+flISEjAqFGjbMp9fX0BALt370ZaWhr69etnd32lUolWrVph165d6N+/P4DiX/x27dqFSZMm3dXYiYjo7rKotbC4+jo6DKJycXV1RbNmzSqtvTv9sa6kSZMm4X//+x9+/vln1K5d+6Z1o6KiAADnzp0rMxFFRJXPVeWM5iGeaB7iCaB4SF96vh5nU/NxJiUP8Wl5iE/Lh85Qekifn7vKZiL0CD83qBUc0kdE1YdDE1HTpk1DbGwswsLCcOXKFcybNw9OTk4YPnw4ACAuLg6NGjWCn58fDh06hBdeeAFTpkxBZGSk1Eb37t0xYMAAKdH00ksvYfTo0WjdujUeeughLF26FDqdTrqLHhEREdHdotPpsGjRIuzatQtpaWmwWGxv037+/PnbavdOf6wDir/ITp48GZs3b8bevXtRp06dW2732LFjAICgoKDbipuIKodMJoO/uxr+7mp0qFf8d2+2CCRd0+FMah7iU4t7T13MLEB6nh7peXocOFc8pE8uA8J9XKXhfPUD3BDi7cIhfUTkMA5NRF26dAnDhw/HtWvX4Ofnhw4dOuDXX3+Fn1/xeOkzZ85g1qxZyMzMRHh4OGbPno0pU6bYtGH9NdBq6NChSE9Px9y5c5GSkoIWLVpg27ZtDu+qT0RERPe+p556Cvv27cOoUaMQFBQE2W1+0bsbP9ZNnDgRX3zxBb777ju4u7sjJaV4aI9Wq4VGo0FCQgK++OIL9O7dGz4+Pjh+/DimTJmCTp06VWrvLiKqHE5yGer6uaGunxt6XZ/qrcBgQnxaPs5eH9J3NiUfmQUGnM/Q4XyGDtv+Lv671yicUD/ADQ383dEgsDhB5e3KIX1EVDUcmojasGHDTZcvWrQIixYtummdxMTEUmWTJk3iUDwiIiKqclu3bsUPP/yA6OjoO2rnbvxYt3LlSgBAly5dbOrFxcVhzJgxUCqV2Llzp9SbPCQkBIMGDcKrr756R/tCRFXHRemM5rU90by2J4DinpDXdAZpEvQzqXk4l5aPQqMZxy/l4Pilf+9I6+umQoMAN0Renwy9nn/Fh/Q5yWVwlstgsgiYLaIyd42I7iHVao4oIiIioprMy8sL3t7ed9zO3fixToibfykMCQnBvn37yhUfEdUMMpkMvm4q+NZTIbrEkL7kzAIpMRWfmofkzAJk5OuRka/HwYRrAIqH9IV6uxQnpgLd0cDfHSHeLnCSl+7pqXKWw9NVAU+NAjmFRmg1CmQXGJFdYITeZClVn4jub0xEEREREVWSN954A3PnzsXatWvh4uLi6HCIiEpxkstQx9cVdXxd0bNx8Z3FCwwmJKTl42xavtR76prOgMRrBUi8VoDtp4rvSq5ROKGef/Ek6NbeU8GeGtT21mDVvgSsOZiI3EITPDTOGNu+Dp7pXBeXMguZjCIiG0xEEREREVWSd955BwkJCQgICEB4eDgUCoXN8iNHjjgoMiKisrkondG0tieaXh/SBwDX8vXXe00VzzllHdJ34nIOTlz+d0hf3Jg2+P74FSzffU4qyy00YdmueADAyKhQpObqq2xfiKj6YyKKiIiIqJL079/f0SEQEVUKHzcV2rmp0C7i3yF9l7IKcCY1D2dT8nA2LR95RUZE1fXGC18etdtG3MELmNg1Ahn5Bs4ZRUQSJqLucUVFRUhOTnZ0GEhKSrL519FCQ0OhVqsdHQYREd1j5s2b5+gQiIjuCie5DGE+rgjzcUXMA8VD+mQA8opMyC002V0nt9CEnEITnOUyJqKISMJE1D0uOTkZ48ePd3QYkjfffNPRIQAAVq9ejQYNGjg6DCIiukcdPnwY//zzDwCgcePGaNmypYMjIiKqfHK5DF4uCnhonO0mozw0znBVOeHbo+loE+5td6JzIrr/3FYi6sKFC9i/fz+SkpJQUFAAPz8/tGzZEu3atWMvk2omNDQUq1evdnQY1U5oaKijQ6hW5EU5t65E953qdl7IC7MdHQJVU9Xp3EhLS8OwYcOwd+9eeHp6AgCys7PRtWtXbNiwAX5+fo4NkIioEpktAtmFRoxtX0eaE6qk0e3C8Ut8Bt788R+EeGkwIioM7SN8IJcxIUV0P6tQImr9+vVYtmwZ/vzzTwQEBCA4OBgajQaZmZlISEiAWq3GyJEjMWPGDISFhd2tmKkC1Go1e/5QmbRaLRRKFXCet+sm+xRKFbRaraPDAABoLvzs6BCIbmny5MnIy8vD33//jUaNGgEATp06hdGjR+P555/Hf//7XwdHSERUubJ1RjzTuS6A4jmhbrxr3n/2X4CbyhkXswrx9rbTqOPrisejQtEm3BsyJqSI7kvlTkS1bNkSSqUSY8aMwddff42QkBCb5Xq9HocOHcKGDRvQunVrfPjhh3jssccqPWAiqjwBAQFY9/lnyMmpXj1fHCUpKQlvvvkmZs+ezWT6dVqtFgEBAY4OAwBQWKcTLBpPR4dB1ZC8MLvaJCq3bduGnTt3SkkoAHjggQewYsUKxMTEODAyIqK7Q2+y4FJmIUZGhWJi1wjkFJqg1Tgju8CIS5mF6Brpj4fCvbHlryvYfPQyLmTo8MYP/6BBgBtGRoWhZYgnE1JE95lyJ6IWLVqEnj17lrlcpVKhS5cu6NKlC958800kJiZWRnxEdJcFBARUm0RDdREWFsaehNWQReMJi6uvo8MguimLxQKFQlGqXKFQwGKxOCAiIqK7T2+yIDVXj4x8A5zlMqTn6W0mJ3dVOWP4Q6Ho0zQIm49exvfHr+Bsaj7mbfkbjYM98HhUGJrUqh49sIno7pOXt+LNklA38vHxQatWrW4rICIiIqKaqlu3bnjhhRdw5coVqezy5cuYMmUKunfv7sDIiIjuPrNFQG+ylHmHPA+NAqPbh+PjJ1qjX/NgKJxk+PtKLmZtPoE5353EmZS8Ko6YiByh3Imoko4cOYITJ05Iz7/77jv0798fr7zyCgwGQ6UFR0RERFSTfPDBB8jNzUV4eDgiIiIQERGBOnXqIDc3F8uXL3d0eERE1YKXixJPd6yLj0e1Rq8mgXCWy3DsYjamffUXXv/f30hIz3d0iER0F93WXfOeeeYZzJw5E02bNsX58+cxbNgwDBgwAJs2bUJBQQGWLl1ayWESERERVX8hISE4cuQIdu7cidOnTwMAGjVqhB49ejg4MiKi6sfHTYUJXeph4IO18eUfydh9Og1/JGbhj8QsREf4YERUGEK9XRwdJhFVsttKRJ09exYtWrQAAGzatAmdOnXCF198gQMHDmDYsGFMRBEREdF9SyaT4eGHH8bDDz/s6FCIiGqEQA81XujeAIMfDMF//0jGz2fTcSDhGg4mXEPnBn4Y/lAogj01jg6TiCrJbQ3NE0JIE27u3LkTvXv3BlD8K2BGRkblRUdERERUgzz//PN4//33S5V/8MEHePHFF6s+ICKiGqSWlwb/3969x0VV5/8Df525zzAww/0iF0UElMS7qKmZmqamaZalpOlaWmm1srbppmu2ldVu6W/TdCtvpaaUaVZGeclb3hLvl1RIRBJQQC4DzAww5/eHX2djBYVh4Azwej4e89A558xnXh/OYTi8+ZzPmTkoCh+M7YSe4d4QAey6cB3PrU3Gv3dexLVCs9QRicgJHCpEde3aFW+88QY+++wz7N69G8OGDQMAXLp0iXffIiIiomZr48aNuPfee29b3qtXL3z55ZcSJCIianzCvN3wt6FtsXBMR3QN84RNBLadzcbUNclYtjsVuSaL1BGJqA4cKkQtWrQIR48exfTp0/Hqq68iIiICAPDll1+iV69eTg1IRERE1Fjk5ubCYLj9FuQeHh4cNU5EVEsRfnrMGx6Df46ORYdgA8ptIr47lYkpnyVj+b7fkF/CG2URNUYOzREVGxtb6a55t/zzn/+EXC6vcygiIiKixigiIgJJSUmYPn16peXff/89wsPDJUpFRNS4RQd64I2R7XEqIx+fHUrHucxCbD5+FUlnsjA8NgijOrWAu0YpdUwiqiGHClHV0Wg0zmyOiIiIqFFJSEjA9OnTcf36dfTv3x8AsGPHDrz33nu8mQsRUR21DzbinRYGHEvPx2eHLiPlmglfJGfgu1OZGNmxBR7uGASdyqm/4hJRPajxd6mnpycEQajRtnl5eQ4HIiIiImqs/vSnP8FiseDNN9/EP/7xDwBAy5YtsXTpUkyYMEHidEREjZ8gCOgc5olOoUYcupSHtYcuIy23BOsOp+ObE1cxukswhrUPhEbJK3WIXFWNC1F//Ctebm4u3njjDQwePBg9e/YEABw4cAA//PAD5s6d6/SQRERERI3Fc889h+eeew7Xr1+HVquFXq+XOhIRUZMjCAJ6hHujeysv/JySg7WH0vF7filW7U/D5mO/47GuwXgwJhAqhUPTIhNRPapxIeqpp56y/3/06NF4/fXXK81/8OKLL2Lx4sXYvn07ZsyY4dyURERERI1EeXk5du3ahdTUVIwbNw4AcPXqVXh4eLAoRUTkZDJBQJ82vujV2ge7L1zDusPpyC604OO9l/DV0d/xeLcQDGzrD6WcBSkiV+HQd+MPP/yABx988LblDz74ILZv317nUERERESN0eXLl9G+fXs8/PDDmDZtGq5fvw4AeOeddzBz5kyJ0xERNV1ymYD+0f5YFt8F0++PgI9ehdxiKz7clYrn1iZj+7lsVNhEqWMSERwsRHl7e+Prr7++bfnXX38Nb2/vOociIiIiaoxeeukldO3aFTdu3IBWq7UvHzVqFHbs2CFhMiKi5kEhl2FwTAD+82RXTOkTDqNOiexCC/7fjouYtu4o9ly4DpvIghSRlBy6pcD8+fPx9NNPY9euXYiLiwMAHDp0CElJSfj444+dGpCIiIiosdi7dy/2798PlUpVaXnLli3x+++/S5SKiKj5USlkGN4hCA+088fWU5n48mgGfs8vxT9/PI/EI1cQ3yMMPVp51fiGXETkPA4VoiZOnIi2bdvi3//+N7766isAQNu2bbFv3z57YYqIiIioubHZbKioqLhteUZGBtzd3SVIRETUvGmUcjzSORgP3hOAb05cxaZjv+NyXgne2noOEb56xPcIRZfQmt8hnojqzqFCFADExcVh7dq1zsxCRERE1KgNGjQIixYtwkcffQTg5l2dTCYT5s2bh6FDh0qcrmlwU8nhpVfhRrEVvLqGiGpKp1Lg8W6hGNY+CJuP/44tJ64i5boJ8785i7YB7niyRxhig41SxyRqFhwuRNlsNqSkpODatWuw2WyV1vXt27fOwYiIiIgam/feew+DBw9Gu3btYDabMW7cOFy8eBE+Pj74/PPPpY7XJCjkMrQwauHtpsK1QgsKSsukjkREjYheo8CTPcIwvEMQNh7NwHcnM3Euqwivbj6N2BYGPNkjDG0DPaSOSdSkOVSIOnjwIMaNG4fLly9D/J8/RQmCUOWQdCIiIqKmLjg4GCdOnMCGDRtw4sQJmEwmTJ48GfHx8ZUmL6e60yjlCPXWocRajqwCM4otPP8kopozaJX4072tMLJjC3xx5AqSzmTh5O8F+OvGk+gS5okn48IQ4aeXOiZRk+RQIerZZ59F165d8d133yEwMJDX0xIRERH9H4VCgfj4eMTHx0sdpVnQqRQI99Wj0FyG7AIzzGW2u7+IiOj/eLmpMPW+1hjVuQUSf7mCbeeykXz5BpIv30CPcC/Edw9DSx83qWMSNSkyR1508eJFvPXWW2jbti2MRiMMBkOlBxEREVFztHr1anz33Xf253/9619hNBrRq1cvXL58WcJkTZ+HRok2/u4I9tRCqeAfSYmodvzcNZjevw2WxnfB/VG+EAAc/C0PL64/hn/+8CsybpRIHZGoyXCoEBUXF4eUlBRnZyEiIiJq1N566y37JXgHDhzA4sWL8e6778LHxwczZsyQOF3z4OmmQpS/OwIMGshlLEgRUe0EGbVIeCAKS8Z1Ru8IH4gA9lzMwbR1R7Fw+wVkFZiljkjU6Dl0ad4LL7yAv/zlL8jKykL79u2hVCorrY+NjXVKOCIiIqLG5MqVK4iIiAAAbN68GY8++iimTJmCe++9F/369ZM2XDMiCAJ83dXwclPhepEFOSYL77BHRLUS4qXDKw9GY0yOCWsPpePQpTzs/PUadl+4jgfa+uPxbiHw0auljknUKDlUiBo9ejQA4E9/+pN9mSAIEEWRk5UTERFRs6XX65Gbm4vQ0FD8+OOPSEhIAABoNBqUlpZKnK75kcsEBBg08NarkF1oRn5JGQtSRFQrrXz0mDOsHS5kF2Htocs4mp6PpDNZ2PFrNobcE4hHOwfD000ldUyiRsWhQtSlS5ecnYOIiIio0XvggQfw9NNPo1OnTrhw4QKGDh0KADhz5gxatmwpbbhmTCmXIdhTBx99BbILzSgsLZc6EhE1MpH+7pg/4h6cuVqAzw5expmrhdhy4iqSzmRheGwgHukUDA+t8u4NEZFjhaiwsDBn5yAiIiJq9JYsWYI5c+bgypUr2LhxI7y9vQEAycnJGDt2rMTpSKOUI8zbDcWWcmQVmlFi4Sh+IqqdmCADFoxqjxMZBVhz8DLOZxdh49HfsfVUFh7uGISHO7aAXu3Qr9lEzYbD3yGpqalYtGgRzp07BwBo164dXnrpJbRu3dpp4YiIiIgaE6PRiMWLF9+2fP78+RKkoeq4qRVo7atHQWkZsgvNsJTZpI5ERI2IIAjoGGJEh2ADjly+gTUHL+O3nGKs/+UKvj2ZiVGdWmB4bBC0KrnUUYlckkN3zfvhhx/Qrl07HD58GLGxsYiNjcWhQ4cQExODbdu2OTsjERERkctKT0+v1fa///57PSWh2jJolWjjp0cLTy0Uct5hj4hqRxAEdGvphYWPd8SsB6MR4qWDyVKOzw5exjOfHcHmY7/DUs6Rl0T/y6FC1KxZszBjxgwcOnQI77//Pt5//30cOnQIf/7zn/HKK684OyMRERGRy+rWrRumTp2KX375pdptCgoK8PHHH+Oee+7Bxo0bGzAd3Y0gCPByUyHK3x3+BjVkDp0dE1FzJhME3Bvhgw+e6IS/PBCJQIMGBaVlWP7zJUz5NBnfnbyKsgqOvCS6xaFL886dO4fExMTblv/pT3/CokWL6pqJiIiIqNE4e/Ys3nzzTTzwwAPQaDTo0qULgoKCoNFocOPGDZw9exZnzpxB586d8e6779onMCfXIpMJ8HPXwEunwnWTBbkmK++wR0S1IpcJ6Bflhz5tfLHz12x8/ssVXC+yYNme37Dx2O94olsI+kf5QSFnxZuaN4e+A3x9fXH8+PHblh8/fhx+fn51zURERETUaHh7e+P9999HZmYmFi9ejDZt2iAnJwcXL14EAMTHxyM5ORkHDhyocRHqtddegyAIlR7R0dH29ampqRg1ahR8fX3h4eGBMWPGIDs7+67tLlmyBC1btoRGo0FcXBwOHz5cab3ZbMa0adPg7e0NvV6P0aNH16jdpkQhlyHQoEWkvzuMOt4Bi4hqTy4T8EC7APznyS549r7W8HJT4XqRBR/sTMHz647ip/PXUGFjpZuaL4dGRD3zzDOYMmUKfvvtN/Tq1QsA8PPPP+Odd95BQkKCUwMSERERNQZarRaPPvooHn30Uae0FxMTg+3bt9ufKxQ3T9uKi4sxaNAgdOjQATt37gQAzJ07F8OHD8fBgwchq+basg0bNiAhIQHLli1DXFwcFi1ahMGDB+P8+fP2PyTOmDED3333Hb744gsYDAZMnz4djzzyCH7++Wen9KkxUSlkCPHSwde9AlkFZhSZy6WORESNjFIuw7D2gRjY1g/fn87Cl8kZyCww4/1tF/DFkSuIjwtDz9bekAmco46aF4cKUXPnzoW7uzvee+89zJ49GwAQFBSE1157DS+++KJTAxIRERE1RwqFAgEBAbct//nnn5GWloZjx47Bw8MDALB69Wp4enpi586dGDhwYJXtvf/++3jmmWcwadIkAMCyZcvw3XffYcWKFZg1axYKCgqwfPlyrFu3Dv379wcArFy5Em3btsXBgwfRo0ePeuqpa9Mo5Wjp4waTpRxZBWaUWjnxMBHVjlohx8iOLTC4XQC+PXkVXx37HVdulOLtpF8R7uOG+LgwdGvpCYEFKWomHLo0TxAEzJgxAxkZGSgoKEBBQQEyMjLw0ksv8ZuHiIiIyAkuXryIoKAghIeHIz4+3n53PovFAkEQoFar7dtqNBrIZDLs27evyrasViuSk5MrFalkMhkGDhyIAwcOAACSk5NRVlZWaZvo6GiEhobat6mKxWJBYWFhpUdTpFcrEOGnR6iXDmol53chotrTquR4rGsIPpnQFWO7hUCrlOO3nGL847uzePnLkziWfgMiJ6ejZsChn6KXLl2yz3vg7u4Od3d3ADdPmNLS0pwWjoiIiKg5iouLw6pVq5CUlISlS5fi0qVL6NOnD4qKitCjRw+4ubnhlVdeQUlJCYqLizFz5kxUVFQgMzOzyvZycnJQUVEBf3//Ssv9/f2RlZUFAMjKyoJKpYLRaKx2m6osWLAABoPB/ggJCalb512cQadEGz89gowaKOT8AywR1Z6bWoFxcWH4ZEJXjO4cDLVChvPZRfj7ljOYvekUTv9eIHVEonrlUCFq4sSJ2L9//23LDx06hIkTJ9Y1ExEREVGzNmTIEDz22GOIjY3F4MGDsXXrVuTn5yMxMRG+vr744osv8M0330Cv18NgMCA/Px+dO3eudn6o+jR79mz7CPmCggJcuXKlwTM0NEEQ4K1XI8rfHf4eakjwZSeiJsBDq8TEXi3x8YSuGNEhCEq5gDNXCzF70yn8/evTuJBdJHVEonrh0BxRx44dw7333nvb8h49emD69Ol1DkVERERE/2U0GhEZGYmUlBQAwKBBg5CamoqcnBwoFAoYjUYEBAQgPDy8ytf7+PhALpffdge87Oxs+zxUAQEBsFqtyM/PrzQq6o/bVEWtVle6TLA5kckE+Hlo4OWmwrUiC/KKreBVNURUW546FZ7pE45RnVog8cgV/Hg2G8eu5OPYlXx0b+mF+LhQhPvqpY5J5DQOzxFVVHR7dbagoAAVFZzAkYiIiJqvzz77DPfeey+CgoJw+fJlAMCiRYvw9ddfO9ymyWRCamoqAgMDKy338fGB0WjEzp07ce3aNYwYMaLK16tUKnTp0gU7duywL7PZbNixYwd69uwJAOjSpQuUSmWlbc6fP4/09HT7NlQ1hVyGIKMWbfz1MOqUUschokbKR6/G8/0isOzJLhjY1g8yATicloeXNhzH29+fQ3peidQRiZzCoUJU3759sWDBgkpFp4qKCixYsAC9e/d2WjgiIiKixmTp0qVISEjA0KFDkZ+fbz9XMhqNWLRoUY3bmTlzJnbv3o20tDTs378fo0aNglwux9ixYwHcvJvdwYMHkZqaijVr1uCxxx7DjBkzEBUVZW9jwIABWLx4sf15QkICPv74Y6xevRrnzp3Dc889h+LiYvtd9AwGAyZPnoyEhAT89NNPSE5OxqRJk9CzZ89me8e82lIr5Ajx0iHCTw+9xqELD4iIEOChwUsDIrFkXGf0beMLAcDPqbmYvu4o3tt2HlfzS6WOSFQnDv2EfOedd9C3b19ERUWhT58+AIC9e/eisLAQO3fudGpAIiIiosbigw8+wMcff4yRI0fi7bffti/v2rUrZs6cWeN2MjIyMHbsWOTm5sLX1xe9e/fGwYMH4evrC+DmSKXZs2cjLy8PLVu2xKuvvooZM2ZUauPWpXu3PP7447h+/Tr+/ve/IysrCx07dkRSUlKlCcwXLlwImUyG0aNHw2KxYPDgwfjwww8d/XI0W1qVHK183FBkLkN2oRmlVpvUkYioEQr21OHlwVEY0zUYaw+l48Bvudh1/jr2XLiOgW398XjXEPh5aKSOSVRrgujg/SGvXr2KxYsX48SJE9BqtYiNjcX06dPh5eXl7IwNrrCwEAaDAQUFBfDw8JA6DhE1kAsXLmDKlCn46KOPEBkZKXUc+j+39ktxuxGwuflIHYdckKw4B25nt9Tpe9dZP/u1Wi1+/fVXhIWFwd3dHSdOnEB4eDguXryI2NhYlJY2/b9i8zzqdvklVmQXWmAtZ0GKiByXcs2EtYcu48jlGwAAhUzA4JgAPNYlGN765jlXH9WcTAbEBBnqpe3a/ux3eMxwUFAQ3nrrLUdfTkRERNTktGrVCsePH0dYWFil5UlJSWjbtq1EqUhqRp0KBq0SucVWXCu0oMLGGc2JqPYi/PSYNzwGv2YWYs2hyziRUYDvTmVi29lsDG0fiEe7BMOg5Tx15PocLkTt3bsX//nPf/Dbb7/hiy++QIsWLfDZZ5+hVatWnCeKiIiImqWEhARMmzYNZrMZoiji8OHD+Pzzz7FgwQJ88sknUscjCQmCAB+9Gp46FXJMFlwvsvAOe0TkkOhAD7wxsj1OZuRjzaF0nMssxObjvyPpTCaGxwbhkU7BnKeOXJpDR+fGjRsxfvx4xMfH4+jRo7BYLABu3jXvrbfewtatW50akoiIiKgxePrpp6HVajFnzhyUlJRg3LhxCAoKwv/7f/8PTzzxhNTxyAXIZQL8PTTwclPhWpEFN4qtLEgRkUNig414p4UBR9PzsebgZaRcN+GL5AxsPZWJkZ1aYESHIOhULEiR63HornlvvPEGli1bho8//hhK5X+H/t177704evSo08IRERERNTbx8fG4ePEiTCYTsrKykJGRgcmTJ0sdi1yMUi5DC6MWbfz1vJSGiBwmCAK6hHni/TEd8OrQtmjprUOxtQJrD6Xj6U+P4KujGTCXVdy9IaIG5FB59Pz58+jbt+9tyw0GA/Lz8+uaiYiIiKjR0+l00Ol0UscgF6dWyBHqrUOJtRxZBWYUW/gLIxHVniAI6BHuje6tvPBzSg7WHkrH7/mlWLk/DZuO/47HuoTgwZgAqBQOjUUhciqHjsKAgACkpKTctnzfvn0IDw+vcygiIiKixig3NxfTpk1Du3bt4OPjAy8vr0oPouroVAqE++rR0kcHjZK/KBKRY2SCgD5tfLFkXGfMGNgG/h5q5JeU4eO9v2HqmiP4/nQmyip4B0+SlkMjop555hm89NJLWLFiBQRBwNWrV3HgwAHMnDkTc+fOdXZGIiIiokZh/PjxSElJweTJk+Hv7w9BEKSORI2Mu0YJd40SN4qtyC4yo6ycE0gRUe3JZQL6R/ujbxtfbD93DRuOpCPHZMWHu1Kx8WgGxnYLRb8oP8hl/DlFDc+hQtSsWbNgs9kwYMAAlJSUoG/fvlCr1Zg5cyZeeOEFZ2ckIiIiahT27t2Lffv2oUOHDlJHoUbO000Fo06JHJMV14ssqLCxIEVEtaeQy/DgPQHoH+2HH85kITH5CrILLVi04yK+SM7AuO6h6N3GBzL+4YQakEOFKEEQ8Oqrr+Lll19GSkoKTCYT2rVrB71e7+x8RERERI1GdHQ0SktLpY5BTYQgCPB1V8PLTYXrRRbkmCy8wx4ROUSlkGF4hyA80M4fW09l4sujGfg9vxT//PE8vki+gnFxYejRyosjealB1OkCdJVKhXbt2iE6Ohrbt2/HuXPnavX61157DYIgVHpER0fb12dlZWH8+PEICAiAm5sbOnfujI0bN9apTSIiIqL68uGHH+LVV1/F7t27kZubi8LCwkoPIkfIZQICDBpEBbjD000J/p5IRI7SKOV4pHMwPpnQFfFxoXBTyZGWW4K3tp5DwhcnkHz5BkRWvKmeOTQiasyYMejbty+mT5+O0tJSdOvWDZcuXYIoili/fj1Gjx5d47ZiYmKwffv2/wZS/DfShAkTkJ+fjy1btsDHxwfr1q3DmDFjcOTIEXTq1MmhNomIiIjqi9FoRGFhIfr3719puSiKEAQBFRW8Ixo5TimXIdhTBx99BbILzSgsLZc6EhE1UjqVAk90C8VD7YOw6fjv2HLid6RcM+G1b86gbYA7nuwRhthgo9QxqYlyqEKzZ88evPrqqwCATZs2wWazIT8/H6tXr8Ybb7xRq0KUQqFAQEBAlev279+PpUuXonv37gCAOXPmYOHChUhOTr5jIepObVbFYrHAYrHYn/MvlkREROSI+Ph4KJVKrFu3jpOVU73RKOUI83ZDibUcmQVmlFhY4CQix+g1CozvEYYRHYLwZXIGtp7KxLmsIry6+TRigw14Mi4MbQM9pI5JTYxDhaiCggL7LYiTkpIwevRo6HQ6DBs2DC+//HKt2rp48SKCgoKg0WjQs2dPLFiwAKGhoQCAXr16YcOGDRg2bBiMRiMSExNhNpvRr18/h9usyoIFCzB//vxa5SYiIiL6X6dPn8axY8cQFRUldRRqBnQqBVr76lFoLkN2gRnmMt6SnYgcY9AqMbl3K4zseLMglXQmCyczCvDXjJPoGuaJ+LgwRPhxTmhyDofmiAoJCcGBAwdQXFyMpKQkDBo0CABw48YNaDSaGrcTFxeHVatWISkpCUuXLsWlS5fQp08fFBUVAQASExNRVlYGb29vqNVqTJ06FZs2bUJERITDbVZl9uzZKCgosD+uXLlS4z4QERER3dK1a1eeR1CD89Ao0cbfHcGeWigVHIVHRI7z1qsx9b7W+M+TXTConT9kAnDk8g3MSDyOt7aeQ1pOsdQRqQlwaETUn//8Z8THx0Ov1yMsLMw+QmnPnj1o3759jdsZMmSI/f+xsbGIi4tDWFgYEhMTMXnyZMydOxf5+fnYvn07fHx8sHnzZowZMwZ79+6t9n3u1mZV1Go11Gp1jXMTERERVeWFF17ASy+9hJdffhnt27eHUqmstD42NlaiZNQceLqpYNAqkVNswfUiC2wcIEVEDvLz0OCF/m0wunMw1v+Sjl3nr+PAb7k4+Fsu+rTxxbjuoWjhqZU6JjVSDhWinn/+ecTFxSE9PR0PPPAAZLKbA6vCw8PxxhtvOBzGaDQiMjISKSkpSE1NxeLFi3H69GnExMQAADp06IC9e/diyZIlWLZsWa3bJCIiIqpPjz/+OADgT3/6k32ZIAicrJwajEwmwM9dA283Na4VmZFrsoI3wCIiRwUZtUh4IAqPdgnBusPp+DklB3suXse+lOu4P8oPT3QPRYBHza+KIgIcLEQBQJcuXdClS5dKy4YNG1anMCaTCampqRg/fjxKSkoAwF7kukUul8NWiz/v/LFNIiIiovp06dIlqSMQAQDkMgGBBi283dTILjQjv6RM6khE1IiFeukw68Fo/HbdhHWH03HoUh52/HoNuy5cx6B2/hjTNQQ+el5lRDVT40LU22+/jZdeegla7d2H3x06dAg5OTl3LUzNnDkTw4cPR1hYGK5evYp58+ZBLpdj7NixMBqNiIiIwNSpU/Gvf/0L3t7e2Lx5M7Zt24Zvv/3W3saAAQMwatQoTJ8+/a5tEpFrMpvNSE9PlzoGLl++XOlfqYWGhtZq3j0ikl5YWJjUEYgqUSlkCPHSwde9AlkFZhSZy6WORESNWLivHnOGtcOF7CKsOXgZx67k4/vTWdh+LhtD7gnEo12C4alTSR2TXFyNC1Fnz55FaGgoHnvsMQwfPhxdu3aFr68vAKC8vBxnz57Fvn37sGbNGly9ehWffvrpXdvMyMjA2LFjkZubC19fX/Tu3RsHDx60t7t161bMmjULw4cPh8lkQkREBFavXo2hQ4fa20hNTUVOTk6N2yQi15Oeno4pU6ZIHcPuzTfflDoCAOCjjz5CZGSk1DGI6C62bNmCIUOGQKlUYsuWLXfcdsSIEQ2UiqgyjVKOlj5uKLaUI7PAjFIrLxMlIsdF+rvj9YfvwZmrBfjs4GWcuVqILSeu4oczWXgoNgiPdGoBD63y7g1RsySIYs2vGj9x4gQWL16ML7/8EoWFhZDL5VCr1fbL6Dp16oSnn34aEydObNR/xS8sLITBYEBBQQE8PDykjkPU5LnKiChXwxFRN124cAFTpkxBcbsRsLn5SB2HXJCsOAduZ7fUqXhbl5/9MpkMWVlZ8PPzu21KgT9qLnNE8TyqcSgoLUN2oRmWMs5oTkR1I4oiTmQUYM3ByzifffNu9VqlHA93DMLIji3gpnZ4RiByIpkMiAky1Evbtf3ZX6sjokOHDvj444/xn//8BydPnsTly5dRWloKHx8fdOzYET4+/AWBiGpPo9Fw5A8RNVo2mw3p6ekQRbFW81gSScmgVcJDo8CNkpsFqfIKzmhORI4RBAEdQ4zoEGzAL2k3sPbQZfyWU4z1v1zBtycz8UinFngoNghalVzqqOQiHCpNymQydOzYER07dnRyHCIiIqLGp1WrVsjMzISfn5/UUYhqTBAEeLmpYNQqkVNswfUiC1hLJSJHCYKA7q280LWlJw6k5mLt4XRcySvBpwcv4+sTV/Fo52AMaR8AtYIFqeaOY+SIiIiI6qgWMx0QuRyZTICfuwZeOhWumyzINVnBQ5qIHCUTBNwb4YMe4d7Ye/E61h1OR2aBGct/voRNx37HmG4hGNTOH0p59ZezU9PGPU9ERETkBIIgSB2BqE4UchkCDVpE+rvDqOMkw0RUN3KZgH5Rflga3wUv9o+Ar7saeSVWLNudimfXJGPb2SxU2Fj1bo44IoqIiIjICebOnQudTnfHbd5///0GSkPkOJVChhAvHXzdK5BZYIbJXC51JCJqxOQyAQ+0C0C/KD/8eDYbib9cwbUiC/69MwVfJGdgXPdQ9GnjC7mMf9BpLliIIiIiInKCU6dOQaVSVbueI6aosdEo5Wjl4waTpRxZBaUotXICKSJynFIuw7D2gRjY1g/fn87Cl8kZyCww471tF5CYnIH47qHo2dobMv68bPLqVIhKSUlBamoq+vbtC61WC1EUeZJFREREzdKmTZs4WTk1SXq1AhF+7igoKUNWoRnWchakiMhxaoUcIzu2wOB2Afj25FVsPJaBK3kleDvpV4T7uCE+LgzdWnqyttCEOTRHVG5uLgYOHIjIyEgMHToUmZmZAIDJkyfjL3/5i1MDEhEREbk6nixTc2DQKRHpr0eQUQOFnMc8EdWNViXHY11D8MmEbniiWwi0Sjl+yynGP747i5e/PIlj6Td4M5AmyqFC1IwZM6BQKJCenl5pLoTHH38cSUlJTgtHRERE1BjwRJmaC0EQ4K1XI8rfHf4earAGS0R1pVcrEB8Xhk8mdMXozsFQKWQ4n12Ev285g9mbTuHM1QKpI5KTOXRp3o8//ogffvgBwcHBlZa3adMGly9fdkowIiIiosZi5cqVMBgMUscgajAymQA/Dw283FS4VmRBXrEVrMcSUV14aJWY2KslHu4QhC+PZmDrqUycuVqIWV+dQqcQI57sEYZIf3epY5ITOFSIKi4urvKuMHl5eVCr1XUORURERNSYPPXUU1JHIJKEQi5DkFELb70K1wotyC8pkzoSETVynm4qPNMnHKM6tUDikSv48Ww2jl3Jx7Er+eje0gvxcaEI99VLHZPqwKFL8/r06YNPP/3U/lwQBNhsNrz77ru4//77nRaOiIiIiIhcn1ohR4iXDhF+erip5VLHIaImwEevxvP9IrDsyS4YEO0HmQAcTsvDSxuO4+2kX3Elr0TqiOQgh0ZEvfvuuxgwYACOHDkCq9WKv/71rzhz5gzy8vLw888/OzsjERERERE1AlqVHOG+ehSZy5BdaEaplXfYI6K6CfDQ4M8DI/Fol2B8fvgK9l68jp9TcnAgNQf3RfpibPdQBBq0UsekWnBoRNQ999yDCxcuoHfv3nj44YdRXFyMRx55BMeOHUPr1q2dnZGIiIiIiBoRd40SEX7uCPHSQqVw6FcOIqJKgj11eHlwFP79RCf0DPeGTQR+On8dz65Jxgc7L+JakVnqiFRDDo2IAgCDwYBXX33VmVmIiIiIGr38/Hx8+eWXSE1NxcsvvwwvLy8cPXoU/v7+aNGihdTxiBqUUaeCQatEbrEV1wotqLBxRnMiqpuWPm7429C2SLlmwtpDl3Hk8g38eDYbO3+9hgdjAvBY1xB4uamkjkl34HAhymw24+TJk7h27RpstspDbkeMGFHnYERERESNzcmTJzFw4EAYDAakpaXhmWeegZeXF7766iukp6dXmmOTqLkQBAE+ejU8dSrkmCy4XmThHfaIqM4i/PSYNzwG5zILsebQZZzMKMC3pzLx49lsDG0fiEe7BMOgVUodk6rgUCEqKSkJEyZMQE5Ozm3rBEFARUVFnYMRERERNTYJCQmYOHEi3n33Xbi7//cW00OHDsW4ceMkTEYkPblMgL+HBl5uKlwrsuBGsZUFKSKqs7aBHnhzZHucyMjH2oOXcS6rCJuP/46kM5kY0aEFRnVsAb3G4TE4VA8cumD7hRdewGOPPYbMzEzYbLZKDxahiIiIqLn65ZdfMHXq1NuWt2jRAllZWRIkInI9SrkMLYxaRPjpOVqBiJymQ7AR74yOxWvDYxDhq4e5zIbEI1fw9Ke/YP0v6Sixlksdkf6PQ2XB7OxsJCQkwN/f39l5iIiIiBottVqNwsLC25ZfuHABvr6+EiQicl0apRyh3jqUWMuRVWBGsYV/0CaiuhEEAV3CPNE51IiDl/Kw9uBlXM4rwdpD6dhy4ioe7RyMoe0DoVHKpY7arDk0IurRRx/Frl27nByFiIiIqHEbMWIEXn/9dZSVlQG4eUKcnp6OV155BaNHj5Y4HZFr0qkUCPfVI8xHB42Sd9gjoroTBAE9w73x77Gd8PKgKLQwalFkLsfK/Wl45rMj2HLiKqzltrs3RPXCoU/6xYsX46uvvsLEiRPx3nvv4d///nelBxEREVFz9N5778FkMsHPzw+lpaW47777EBERAXd3d7z55ps1bue1116DIAiVHtHR0fb1WVlZGD9+PAICAuDm5obOnTtj48aNd2yzZcuWt7UpCAKmTZtm36Zfv363rX/22Wdr/4UgcoCHRok2/u4I9tRCqRCkjkNETYBMENA30hdLxnXGnwe0gb+HGvklZfh472+YuuYIkk5nobyCBamG5tCleZ9//jl+/PFHaDQa7Nq1C4Lw3x8UgiDgxRdfdFpAIiIiosbCYDBg27Zt2LdvH06ePAmTyYTOnTtj4MCBtW4rJiYG27dvtz9XKP572jZhwgTk5+djy5Yt8PHxwbp16zBmzBgcOXIEnTp1qrK9X375pdJcnqdPn8YDDzyAxx57rNJ2zzzzDF5//XX7c51OV+vsRHXh6aaCUadEjsmK60UWVNg4ozkR1Y1cJmBAW3/0jfTF9nPZSDxyBTkmK5bsSsHGoxkY2z0E90X6QS5jEbwhOFSIevXVVzF//nzMmjULMhmHzxIRERH9Ue/evdG7d+86taFQKBAQEFDluv3792Pp0qXo3r07AGDOnDlYuHAhkpOTqy1E/e8cVW+//TZat26N++67r9JynU5X7ftWxWKxwGKx2J9XNUcWUW0JggBfdzW83FS4XmRBjsnCO+wRUZ0p5TIMuScQA6L9kXQmC18kX0FWoRkLt19E4pEMxMeF4t4IH8gEFqTqk0OFKKvViscff5xFKCIiIqI/qG6KAkEQoNFoEBERgb59+0Iuv/skqRcvXkRQUBA0Gg169uyJBQsWIDQ0FADQq1cvbNiwAcOGDYPRaERiYiLMZjP69etXo5xWqxVr1qxBQkJCpZHtALB27VqsWbMGAQEBGD58OObOnXvHUVELFizA/Pnza/S+RLUllwkIMGjgrVchu9CM/JIyFqSIqM5UChlGdAjCoHb++O5UJjYmZ+D3/FK8+8N5tDxyBfFxYYhr5XXbz0hyDocKUU899RQ2bNiAv/3tb87OQ0RERNRoLVy4ENevX0dJSQk8PT0BADdu3IBOp4Ner8e1a9cQHh6On376CSEhIdW2ExcXh1WrViEqKgqZmZmYP38++vTpg9OnT8Pd3R2JiYl4/PHH4e3tDYVCAZ1Oh02bNiEiIqJGOTdv3oz8/HxMnDix0vJx48YhLCwMQUFBOHnyJF555RWcP38eX331VbVtzZ49GwkJCfbnhYWFd+wbkSOUchmCPXXw0Vcgu9CMwlLehp2I6k6jlGN052AMuScAW05cxaZjvyMttwRvbj2HCD89nowLQ+dQIwtSTuZQIaqiogLvvvsufvjhB8TGxkKpVFZa//777zslHBEREVFj8tZbb+Gjjz7CJ598gtatWwMAUlJSMHXqVEyZMgX33nsvnnjiCcyYMQNffvllte0MGTLE/v/Y2FjExcUhLCwMiYmJmDx5MubOnYv8/Hxs374dPj4+2Lx5M8aMGYO9e/eiffv2d825fPlyDBkyBEFBQZWWT5kyxf7/9u3bIzAwEAMGDEBqaqq9P/9LrVZDrVbf9T2JnEGjlCPM2w3FlnJkFZpRYqm4+4uIiO5Cp1LgiW6hGNY+EJuO/Y5vTl5FyjUTXvvmDNoGemB8XCjaBxuljtlkOFSIOnXqlH3+gdOnT1dax0ohERERNVdz5szBxo0bKxVtIiIi8K9//QujR4/Gb7/9hnfffRejR4+uVbtGoxGRkZFISUlBamoqFi9ejNOnTyMmJgYA0KFDB+zduxdLlizBsmXL7tjW5cuXsX379juOcrolLi4OwM1iWnWFKCIpuKkVaO2rR0FpGbILzbCU8a5XRFR37holJvRsiYc7tsCXyRnYeioT5zIL8bfNpxEbbMD4uDBEB3pIHbPRc6gQ9dNPPzk7BxER0R3JzAVSRyAX5UrHRmZmJsrLb79kqLy8HFlZWQCAoKAgFBUV1apdk8mE1NRUjB8/HiUlJQBw21ydcrkcNtvdfxlfuXIl/Pz8MGzYsLtue/z4cQBAYGBgrfISNRSDVgkPjQI3Sm4WpMorOIEUEdWdQavE5N6tMLJjEL5IzsAPZ7JwMqMAL2ecRNcwT8THhSHCTy91zEbLoUIUERFRQzEYDFCq1MBvu6WOQi5MqVLDYDBIHQP3338/pk6dik8++cQ+evzYsWN47rnn0L9/fwA3R5a3atXqju3MnDkTw4cPR1hYGK5evYp58+ZBLpdj7NixMBqNiIiIwNSpU/Gvf/0L3t7e2Lx5M7Zt24Zvv/3W3saAAQMwatQoTJ8+3b7MZrNh5cqVeOqpp6BQVD4NTE1Nxbp16zB06FB4e3vj5MmTmDFjBvr27YvY2FhnfYmInE4QBHi5qWDUKpFTbMH1IgtqUJMlIrorb70az97XGo90aoENR65g+7lsHLl8A0cu30DPcG/Ex4UizNtN6piNTo0LUY888ghWrVoFDw8PPPLII3fctiZDvYmIiGrC398faz77FAUFrjPqRWqXL1/Gm2++iVdffRVhYWFSx3EJBoMB/v7+UsfA8uXLMX78eHTp0sU+h2Z5eTkGDBiA5cuXAwD0ej3ee++9O7aTkZGBsWPHIjc3F76+vujduzcOHjwIX19fAMDWrVsxa9YsDB8+HCaTCREREVi9ejWGDh1qbyM1NRU5OTmV2t2+fTvS09Pxpz/96bb3VKlU2L59OxYtWoTi4mKEhIRg9OjRmDNnTp2+JkQNRSYT4OeugZdOhesmC3JNVt5hj4icws9Dgxf6t8HozsH4/Jd07D5/HQd+y8XB33LRp40vxnUPRQtPrdQxG40aF6IMBoN9/idX+IsjERE1H/7+/i5RZHA1YWFhiIyMlDoG/UFAQAC2bduGX3/9FRcuXAAAREVFISoqyr7N/ffff9d21q9ff8f1bdq0wcaNG++4TVpa2m3LBg0aBLGa38xDQkKwezdHHlLjp5DLEGjQwttNjexCM/JLyqSORERNRJBRi788EIXHuoRg3aHL+Dk1F3suXse+lOvoH+2HJ7qFwt9DI3VMl1fjQtTKlSvx+uuvY+bMmVi5cmV9ZiIiIiJq1KKjoxEdHS11DKJmTaWQIcRLB1/3CmQVmFFkvn3+NiIiR4R66TBrSFv8dt2EtYfScTgtD9vPXcOu89fxQDt/jOkaAh897yhbnVrNETV//nw8++yz0Ol09ZWHiIiIqFHLyMjAli1bkJ6eDqvVWmnd+++/L1EqouZLo5SjpY8bTJZyZBWYUWqtkDoSETUR4b56zH2oHc5nFWHNocs4fiUf35/OwvZz2RhyTyAe7RIMT51K6pgup1aFqOqGchMRERERsGPHDowYMQLh4eH49ddfcc899yAtLQ2iKKJz585SxyNq1vRqBSL89CgoKUN2kRmWMs5oTkTOERXgjn88fA9O/16ANYcu48zVQmw5cRU/nMnCQ7FBeKRTC3holVLHdBmyu29S2a15ooiIiIiostmzZ2PmzJk4deoUNBoNNm7ciCtXruC+++7DY489JnU8IgJg0CnRxk+PIKMGCjl/tyEi57mnhQELRrXH6yNiEOmvh6Xcho1HM/D0p0ew7tBlFFt4iTBQyxFRABAZGXnXYlReXp7DgYiIiIgaq3PnzuHzzz8HACgUCpSWlkKv1+P111/Hww8/jOeee07ihEQE3PzjurdeDU+dCjkmC66bLLBxgBQROYEgCOgU6omOIUb8knYDaw5dxqWcYnz+yxV8czITj3RqgYdig6BVyaWOKplaF6Lmz5/Pu+YRERERVcHNzc0+L1RgYCBSU1MRExMDAMjJyZEyGhFVQSYT4OehgZebCteKLMgrtoKzkRCRMwiCgO6tvNC1pScOpOZi7aHLuHKjFJ8evIyvT1zFo12CMeSeAKgVza8gVetC1BNPPAE/P7/6yEJERETUqPXo0QP79u1D27ZtMXToUPzlL3/BqVOn8NVXX6FHjx5SxyOiaijkMgQZtfDWq3Ct0IL8kjKpIxFREyETBNwb4YMe4d7Yc/E6Pj+cjswCM5bvu4RNx37HmK4hGNTOH0p5rWdOarRqVYji/FBERERE1Xv//fdhMpkA3BxFbjKZsGHDBrRp04Z3zCNqBNQKOUK8dPDRVyCr0AyTmfO5EJFzyGUC7o/yQ58IH+w8fw3rf7mC60UWLNudiq+OZuCJbiHoH+0Puazp11141zwiIiIiJ6ioqEBGRgZiY2MB3LxMb9myZRKnIiJHaFVytPJxQ5G5DNmFZpRaOYEUETmHQi7DoHYBuD/KDz+eyULikQxcK7Lg3ztT8GVyBsZ2D0WfNr5NuiBVq7FfNpuNl+URERERVUEul2PQoEG4ceOG1FGIyEncNUpE+LkjxEsLlaL5XDZDRPVPKZdhWGwQPprQBZPvbQUPjQJXC8x4b9sFvLD+GH5OyYGtiQ4G4qcpERERkZPcc889+O2336SOQUROZtSpEOmvR6BR06RHKRBRw1Mr5BjZqQU+mdAN43uEwU0tx5W8Eryd9CtmJB7HL2l5Te7qNBaiiIiIiJzkjTfewMyZM/Htt98iMzMThYWFlR5E1HgJggAfvRpRAe7w81CD0+cSkTNpVXKM6RqCTyZ0wxPdQqBVyvHb9WK8/u1ZvPzlSRy/kt9kClK1vmseEREREVVt6NChAIARI0ZUusmLKIoQBAEVFRVSRSMiJ5HLBPh7aODlpsK1IgtuFFvRRH43JCIXoFcrEB8Xhodig7DpWAa+OZmJ89lFmPv1adwT5IEne4QhJsggdcw6YSGKiIiIyEl++uknqSMQUQNRymVoYdTCR69CdoEFBaVlUkcioibEoFViYq9WeLhDC3x5NANbT2Xi9NVCzPrqFDqHGhEfF4ZIf3epYzqEhSgiIiIiJ7nvvvukjkBEDUytkCPUW4cSazmyCswotnDkIxE5j6ebCs/0CcfIji2QeOQKtp3LxtH0fBxNz0dcKy/Ex4WilY9e6pi1wjmiiIiIiJxo7969ePLJJ9GrVy/8/vvvAIDPPvsM+/btkzgZEdUnnUqBcF89WvrooFHy1ywici5fdzWm3R+BZfFd0D/aDzIBOHQpDy+uP453kn7FlbwSqSPWGD8hiYiIiJxk48aNGDx4MLRaLY4ePQqLxQIAKCgowFtvvSVxOiJqCO4aJdr4uyPYUwulgjOaE5FzBRg0mDEwEkvGdUbfNj4AgH0pOZj++VG8v+08MgtKq3ydK93xk4UoIiIiIid54403sGzZMnz88cdQKpX25ffeey+OHj0qYTIiamiebipE+bsjwKBxqV8AiahpCPbU4eXB0fj3E53QI9wLNhH46fx1PLsmGYt3XsS1IjMAQK2Qwd+gRriPG3JNFljLbSixlkuanXNEERERETnJ+fPn0bdv39uWGwwG5OfnN3wgIpKUIAjwdVfDy02F60UW5JgsvMMeETlVKx83vDq0HS5mF2Ht4XQkX76BH85mY8ev1/BUz5ZIGBSJ/+xJxar9aSgsLYeHVoFJvVrh+X6toVbKJcnMEVFEREREThIQEICUlJTblu/btw/h4eESJCIiVyCXCQgwaBAV4A5PNyUEDpAiIidr4++O14bH4J3RsYhtYUC5TUSP1l5YtjsV/96RgsLSm6OgCkvL8f92XMSHu1IlGxnFQhQRERGRkzzzzDN46aWXcOjQIQiCgKtXr2Lt2rWYOXMmnnvuOanjEZHElHIZgj11iPDTw0PLi1OIyPnaBXrgzVHt8c9HY9E7wherD6RVud3K/ZegkElTEuKnHxEREZGTzJo1CzabDQMGDEBJSQn69u0LtVqNmTNn4oUXXpA6HhG5CI1SjjBvN5RYy5FZYEaJpULqSETUxHQINsJkKbOPhPpfhaXlKDKXwVuvbuBkLEQREREROY0gCHj11Vfx8ssvIyUlBSaTCe3atYNer5c6GhG5IJ1Kgda+ehSay5BdYIa5zCZ1JCJqIsptIgxaJTy0iiqLUR5aBdw1yipeWf94aR4RERGRk6xZswYlJSVQqVRo164dunfvziIUEd2Vh0aJCD89WnhqoVRwAikiqrsKm4j80jJM6tWqyvWTerVCuU2a4jcLUUREREROMmPGDPj5+WHcuHHYunUrKip4uQ0R1YwgCPByUyHSzx3+BjUkmrqFiJqQ/OIyTL0vHC8NaGOfl85Dq8BLA9rg+X6toVNJc5EcL80jIiIicpLMzEwkJSXh888/x5gxY6DT6fDYY48hPj4evXr1kjoeETUCMpkAP3cNvN3UuFZkRq7JClGUOhURNUaWchsy8koRHxeKafe3RpG5HO4aJcptNqiVcslysc5ORERE5CQKhQIPPfQQ1q5di2vXrmHhwoVIS0vD/fffj9atW0sdj4gaEblMQKBBi0h/dxh10szjQkSNn6XchuxCC37LKYa3Xg2VQibZSKhbOCKKiIiIqB7odDoMHjwYN27cwOXLl3Hu3DmpIxFRI6RSyBDipYOvewWyCswoMld9BywiojupsLnO0EpJR0S99tprEASh0iM6Otq+PisrC+PHj0dAQADc3NzQuXNnbNy48a7tLlmyBC1btoRGo0FcXBwOHz5cn90gIiIisispKcHatWsxdOhQtGjRAosWLcKoUaNw5swZqaMRUSOmUcrR0scN4b5u0Kqku6SGiKiuJB8RFRMTg+3bt9ufKxT/jTRhwgTk5+djy5Yt8PHxwbp16zBmzBgcOXIEnTp1qrK9DRs2ICEhAcuWLUNcXBwWLVqEwYMH4/z58/Dz86v3/hAREVHz9cQTT+Dbb7+FTqfDmDFjMHfuXPTs2VPqWETUhLipFYjw06OgtAzZhWZYyqS56xURkaMknyNKoVAgICDA/vDx8bGv279/P1544QV0794d4eHhmDNnDoxGI5KTk6tt7/3338czzzyDSZMmoV27dli2bBl0Oh1WrFhR7WssFgsKCwsrPYiIiIhqSy6XIzExEZmZmVi8eHGlItTp06clTEZETY1Bq0QbPz1aeGqhkAtSxyEiqjHJC1EXL15EUFAQwsPDER8fj/T0dPu6Xr16YcOGDcjLy4PNZsP69ethNpvRr1+/KtuyWq1ITk7GwIED7ctkMhkGDhyIAwcOVJthwYIFMBgM9kdISIjT+kdERETNx61L8uTym5fNFBUV4aOPPkL37t3RoUMHidMRUVMjCAK83FSI8neHv4caMsl/uyMiujtJP6ri4uKwatUqJCUlYenSpbh06RL69OmDoqIiAEBiYiLKysrg7e0NtVqNqVOnYtOmTYiIiKiyvZycHFRUVMDf37/Scn9/f2RlZVWbY/bs2SgoKLA/rly54rxOEhERUbOzZ88ePPXUUwgMDMS//vUv9O/fHwcPHpQ6FhE1UTKZAD8PDaL83eGtV0HgACkicmGSzhE1ZMgQ+/9jY2MRFxeHsLAwJCYmYvLkyZg7dy7y8/Oxfft2+Pj4YPPmzRgzZgz27t2L9u3bOy2HWq2GWq12WntERETU/GRlZWHVqlVYvnw5CgsLMWbMGFgsFmzevBnt2rWTOh4RNQMKuQxBRi189GpkF5qRX1ImdSQiotu41OBNo9GIyMhIpKSkIDU1FYsXL8aKFSswYMAAdOjQAfPmzUPXrl2xZMmSKl/v4+MDuVyO7OzsSsuzs7MREBDQEF0gIiKiZmj48OGIiorCyZMnsWjRIly9ehUffPCB1LGIqJlSKWQI8dKhjb8eeo3k96ciIqrEpQpRJpMJqampCAwMRElJCYCbczz9kVwuh81W9Z0hVCoVunTpgh07dtiX2Ww27Nixg3esISIionrz/fffY/LkyZg/fz6GDRtmnyOKiEhKGqUcrXzc0MrXDVqVS/3qR0TNmKSfRjNnzsTu3buRlpaG/fv3Y9SoUZDL5Rg7diyio6MRERGBqVOn4vDhw0hNTcV7772Hbdu2YeTIkfY2BgwYgMWLF9ufJyQk4OOPP8bq1atx7tw5PPfccyguLsakSZMk6CERERE1B/v27UNRURG6dOmCuLg4LF68GDk5OVLHIiICAOjVCkT4uSPUSweVggUpIpKWpOM0MzIyMHbsWOTm5sLX1xe9e/fGwYMH4evrCwDYunUrZs2aheHDh8NkMiEiIgKrV6/G0KFD7W2kpqZWOtF7/PHHcf36dfz9739HVlYWOnbsiKSkpNsmMCciIiJylh49eqBHjx5YtGgRNmzYgBUrViAhIQE2mw3btm1DSEgI3N3dpY5JRM2cQaeEh1aBvGIrrhVZUF4hSh2JiJohQRRFfvr8j8LCQhgMBhQUFMDDw0PqOERERJVcuHABU6ZMwUcffYTIyEip4zQJ9fGz//z581i+fDk+++wz5Ofn44EHHsCWLVuc0rYr43kUUeNgs4nIMVlwrcgC/kZI1PTJZEBMkKFe2q7tz36OyyQiIiKqB1FRUXj33XeRkZGBzz//vFavfe211yAIQqVHdHS0fX1WVhbGjx+PgIAAuLm5oXPnzti4cWOd2gQAs9mMadOmwdvbG3q9HqNHj77tJjBE1DTIZAL8PDSIDnCHt14FQZA6ERE1F7yFAhEREVE9ksvlGDlyZKU5LmsiJiYG27dvtz9XKP572jZhwgTk5+djy5Yt8PHxwbp16zBmzBgcOXIEnTp1cqhNAJgxYwa+++47fPHFFzAYDJg+fToeeeQR/Pzzz7XKTkSNh0IuQ5BRC2+9CtkFFhSUlkkdiYiaOBaiiIiIiFyQQqFAQEBAlev279+PpUuXonv37gCAOXPmYOHChUhOTr5jIepObRYUFGD58uVYt24d+vfvDwBYuXIl2rZti4MHD6JHjx517BERuTK1Qo5Qbx1KrRXILChFsaVC6khE1ETx0jwiIiIiF3Tx4kUEBQUhPDwc8fHxSE9Pt6/r1asXNmzYgLy8PNhsNqxfvx5msxn9+vVzuM3k5GSUlZVh4MCB9mXR0dEIDQ3FgQMHqm3TYrGgsLCw0oOIGi+tSo5wXz1a+uigVfHXRSJyPn6yEBEREbmYuLg4rFq1CklJSVi6dCkuXbqEPn36oKioCACQmJiIsrIyeHt7Q61WY+rUqdi0aRMiIiIcbjMrKwsqlQpGo7HS6/z9/ZGVlVVtuwsWLIDBYLA/QkJC6v4FICLJuWuUiPBzR4iXFioFf20kIufhpXlERERELmbIkCH2/8fGxiIuLg5hYWFITEzE5MmTMXfuXOTn52P79u3w8fHB5s2bMWbMGOzduxft27d3qE1HzZ49GwkJCfbnhYWFLEYRNSFGnQoGrRK5xVZcK7SgwsZb7BFR3bAQRUREROTijEYjIiMjkZKSgtTUVCxevBinT59GTEwMAKBDhw7Yu3cvlixZgmXLltW6TQAICAiA1WpFfn5+pVFR2dnZ1c4rBQBqtRpqtdrxzhGRyxMEAT56NTx1KuSYLLheZIHIehQROYhjLImIiIhcnMlkQmpqKgIDA1FSUgIAkMkqn8bJ5XLYbDaH2gSALl26QKlUYseOHfZtzp8/j/T0dPTs2dMJvSCixk4uE+DvoUFUgDu89CoIgtSJiKgxYiGKiIiIyMXMnDkTu3fvRlpaGvbv349Ro0ZBLpdj7NixiI6ORkREBKZOnYrDhw8jNTUV7733HrZt24aRI0fa2xgwYAAWL15cozYBwGAwYPLkyUhISMBPP/2E5ORkTJo0CT179uQd84ioEqVchhZGLSL89DBolVLHIaJGhpfmEREREbmYjIwMjB07Frm5ufD19UXv3r1x8OBB+Pr6AgC2bt2KWbNmYfjw4TCZTIiIiMDq1asxdOhQexupqanIycmpcZsAsHDhQshkMowePRoWiwWDBw/Ghx9+2HAdJ6JGRaOUI9RbhxJrOTILzCixVEgdiYgaAUEUeXXv/yosLITBYEBBQQE8PDykjkNERFTJhQsXMGXKFHz00UeIjIyUOk6TwJ/9zsOvJVHzVWguw/UiCwtSRC5IJgNiggz10nZtf/ZzRBQRERERERHVmYdGCQ+NEiXWcuQUWVFoLuOk5kR0GxaiiIiIiIiIyGl0KgVCvRWwlFcg12RFXrGVBSkismMhioiIiIiIiJxOrZAjyKiFv4cGucUW5JqsKK9gRYqouWMhioiIiIiIiOqNXCbAz10DX70a+SVlyDFZYC6zSR2LiCTCQhQRERERERHVO0EQ4OmmgqebCiZLOXKKLCgyl0sdi4gaGAtRRERERERE1KD0agX0agXMZRXIMVmQX8KJzYmaCxaiiIiIiIiISBIapRzBnjr4e9iQV2xFrsmKChsrUkRNGQtRREREREREJCmlXAZ/j5vzSN0osSK32AoL55EiapJYiCIiIiIiIiKXIJMJ8Nar4a1Xo6D05sTmJZYKqWMRkROxEEVEREREREQux6BVwqBVotR6cx6pglLOI0XUFLAQRURERERERC5Lq5IjxEsH/3IbcostyCu2wsar9ogaLRaiiIiIiIiIyOWpFDIEGrTwc9fcnNi82IKycg6RImpsWIgiIiIiIiKiRkMuE+DrroaPXmWfR6rUyiFSRI0FC1FERERERETU6AiCAKNOBaNOBZOlHDlFFhSZy6WORUR3wUIUERERERERNWp6tQJ6tQLmsgrkFltxo9jKic2JXBQLUURERERERNQkaJRytDBq4e+uRl6xFTkmKypsrEgRuRIWooiIiIiIiKhJUchl8PPQwEevRv7/zSNlKeM8UkSugIUoIiIiIiIiapJkMgFebip4ualQaC5DTpEFxZYKqWMRNWssRBEREREREVGT56FRwkOjRKm1AjkmCwpKyziPFJEEWIgiIiIiIiKiZkOrkiPESwf/chvyiq3ILbbAxqv2iBoMC1FERERERETU7KgUMgQYNPBzVyOvxIockwVl5RwiRVTfWIgiIiIiIiKiZksmE+CjV8PbTYXC0nJcN1lQauU8UkT1hYUoIiIiIiIiavYEQYBBp4RBp0SxpRw5JgsKS8uljkXU5LAQRURERERERPQHbmoF3NQKWMorkGOy4kaxlRObEzkJC1FEREREREREVVAr5Ghh1MLfXf1/E5tbUV7BihRRXbAQRURERERERHQHCrkMfh4a+LqrkV9ShhyTBeYy3mqPyBEsRBERERERERHVgCAI8HRTwdNNhSJzGXJMVpjMnEeKqDZYiCIiIiIiIiKqJXeNEu4aJcxlFbheZEFBaRnnkSKqARaiiIiIiIiIiBykUcoR4qVDQIUNuSYrcostsPGqPaJqsRBFREREREREVEdKuQwBBg383NXIK7Ei12SFtZwVKaL/xUIUERERERERkZPIZAJ89Gr46NUoKL05sXmJpULqWEQug4UoIiIiIiIionpg0Cph0CpRYi1HTpEVhWbOI0XEQhQRERERERFRPdKpFAj1VsBSXoFckxV5xVYWpKjZYiGKiIiIiIiIqAGoFXIEGbXw99Agt9iCXJMV5RWsSFHzwkIUERERERERUQOSywT4uWvgq1cjv+TmPFLmMk5sTs0DC1FEREREREREEhAEAZ5uKni6qWCylCOnyIIic7nUsYjqFQtRRERERERERBLTqxXQqxUwl1Ugx2RBfgknNqemiYUoIiIiIiIiIhehUcoR7KmDv4cNecVW5JqsqLCxIkVNBwtRRERERERERC5GKZfB3+PmPFI3SqzILbbCwnmkqAmQSR2AiIiIiCp77bXXIAhCpUd0dLR9fVZWFsaPH4+AgAC4ubmhc+fO2Lhx4x3bXLBgAbp16wZ3d3f4+flh5MiROH/+fKVt+vXrd9v7Pvvss/XSRyIiqhmZTIC3Xo1If3eEeuugU8uljkRUJxwRRUREROSCYmJisH37dvtzheK/p20TJkxAfn4+tmzZAh8fH6xbtw5jxozBkSNH0KlTpyrb2717N6ZNm4Zu3bqhvLwcf/vb3zBo0CCcPXsWbm5u9u2eeeYZvP766/bnOp2uHnpHRESOMGiVMGiVKLXenEeqoJTzSFHjw0IUERERkQtSKBQICAioct3+/fuxdOlSdO/eHQAwZ84cLFy4EMnJydUWopKSkio9X7VqFfz8/JCcnIy+ffval+t0umrfl4iIXINWJUeIlw7+5TbkFluQV2yFjVftUSPBS/OIiIiIXNDFixcRFBSE8PBwxMfHIz093b6uV69e2LBhA/Ly8mCz2bB+/XqYzWb069evxu0XFBQAALy8vCotX7t2LXx8fHDPPfdg9uzZKCkpuWM7FosFhYWFlR5ERNQwVAoZAg1aRAd4IMCggVIhSB2J6K44IoqIiIjIxcTFxWHVqlWIiopCZmYm5s+fjz59+uD06dNwd3dHYmIiHn/8cXh7e0OhUECn02HTpk2IiIioUfs2mw1//vOfce+99+Kee+6xLx83bhzCwsIQFBSEkydP4pVXXsH58+fx1VdfVdvWggULMH/+/Dr3mYiIHCeXCfB1V8NHr0JBaRlyTBaUWjlEilwTC1FERERELmbIkCH2/8fGxiIuLg5hYWFITEzE5MmTMXfuXOTn52P79u3w8fHB5s2bMWbMGOzduxft27e/a/vTpk3D6dOnsW/fvkrLp0yZYv9/+/btERgYiAEDBiA1NRWtW7eusq3Zs2cjISHB/rywsBAhISG17TIRETmBIAgw6lQw6lQwWcqRU2RBkblc6lhElbAQRUREROTijEYjIiMjkZKSgtTUVCxevBinT59GTEwMAKBDhw7Yu3cvlixZgmXLlt2xrenTp+Pbb7/Fnj17EBwcfMdt4+LiAAApKSnVFqLUajXUarUDvSIiovqkVyugVytgLqtAbrEVN4qtnNicXALniCIiIiJycSaTCampqQgMDLTP2SSTVT6Nk8vlsN1hplpRFDF9+nRs2rQJO3fuRKtWre76vsePHwcABAYGOh6eiIgkpVHK0cKoRXSAO/w91JDLOI8USYuFKCIiIiIXM3PmTOzevRtpaWnYv38/Ro0aBblcjrFjxyI6OhoRERGYOnUqDh8+jNTUVLz33nvYtm0bRo4caW9jwIABWLx4sf35tGnTsGbNGqxbtw7u7u7IyspCVlYWSktLAQCpqan4xz/+geTkZKSlpWHLli2YMGEC+vbti9jY2Ib+EhARkZMp5DL4eWgQHeCOFp5aqJUsB5A0JD3yXnvtNQiCUOkRHR0NAEhLS7tt3a3HF198UW2bEydOvG37Bx98sKG6RERERFRnGRkZGDt2LKKiojBmzBh4e3vj4MGD8PX1hVKpxNatW+Hr64vhw4cjNjYWn376KVavXo2hQ4fa20hNTUVOTo79+dKlS1FQUIB+/fohMDDQ/tiwYQMAQKVSYfv27Rg0aBCio6Pxl7/8BaNHj8Y333zT4P0nIqL6I5MJ8HJTIdLfHWE+Orip5VJHomZG8jmiYmJisH37dvtzheJmpJCQEGRmZlba9qOPPsI///nPShN4VuXBBx/EypUr7c85bwERERE1JuvXr7/j+jZt2mDjxo133CYtLa3Sc/EuE4OEhIRg9+7dNcpHRERNg4dGCQ+NEqXWCuSYLCgoLeM8UlTvJC9EKRQKBAQE3LZcLpfftnzTpk0YM2YM9Hr9HdtUq9VVtklERERERERElWlVcoR46RBQYUOuyYrcYgvuMO0gUZ1IflHoxYsXERQUhPDwcMTHxyM9Pb3K7ZKTk3H8+HFMnjz5rm3u2rULfn5+iIqKwnPPPYfc3Nw7bm+xWFBYWFjpQURERERERNScKOUyBBg0aBvggUCjBkoFJzYn55O0EBUXF4dVq1YhKSkJS5cuxaVLl9CnTx8UFRXdtu3y5cvRtm1b9OrV645tPvjgg/j000+xY8cOvPPOO9i9ezeGDBmCioqKal+zYMECGAwG+yMkJKTOfSMiIiIiIiJqjGQyAT56NaL83RHqpYNWxXmkyHkkvTTvj3M9xcbGIi4uDmFhYUhMTKw08qm0tBTr1q3D3Llz79rmE088Yf9/+/btERsbi9atW2PXrl0YMGBAla+ZPXs2EhIS7M8LCwtZjCIiIiIiIqJmTRAEGHRKGHRKFFvKkWOyoLC0XOpY1MhJPkfUHxmNRkRGRiIlJaXS8i+//BIlJSWYMGFCrdsMDw+Hj48PUlJSqi1EqdVqTmhOREREREREVA03tQJuagUs5RXIMVlxo9jKic3JIZLPEfVHJpMJqampCAwMrLR8+fLlGDFiBHx9fWvdZkZGBnJzc29rk4iIiIiIiIhqR62Qo4VRi+gAd/gb1FDIOY8U1Y6khaiZM2di9+7dSEtLw/79+zFq1CjI5XKMHTvWvk1KSgr27NmDp59+uso2oqOjsWnTJgA3C1kvv/wyDh48iLS0NOzYsQMPP/wwIiIiMHjw4AbpExEREREREVFTp5DL4OeuQXSAO4I9tdAoXWqcC7kwSS/Ny8jIwNixY5GbmwtfX1/07t0bBw8erDTyacWKFQgODsagQYOqbOP8+fMoKCgAAMjlcpw8eRKrV69Gfn4+goKCMGjQIPzjH//gpXdERERERERETiYIAjzdVPB0U6HIXIYckxUmM+eRoupJWohav379Xbd566238NZbb1W7XvzDRalarRY//PCDU7IRERERERERUc25a5Rw1yhhLqvA9SILCkrLOI8U3calJisnIiIiIiIiosZNo5QjxEuHgAobck1W5BVbUWFjRYpuYiGKiIiIiIiIiJxOKZchwKCBn7saN0qsyDFZYS23SR2LJMZCFBERERERERHVG5lMgLdeDW+9GgWlZcgxWVBiqZA6FkmEhSgiIiIiIiIiahAGrRIGrRIl1nLkFFlRaOY8Us0NC1FERERERERE1KB0KgVCvRWwlFfY55FiQap5YCGKiIiIiIiIiCShVsgRZNTC30OD3GILck1WlFewItWUsRBFRERERERERJKSywT4uWvg+4d5pEqtnNi8KWIhioiIiIiIiIhcgiAIMOpUMOpUMFnKkVNkQZG5XOpY5EQsRBERERERERGRy9GrFdCrFTCXVSDHZEF+CSc2bwpYiCIiIiIiIiIil6VRyhHsqYO/hw15xVbkmqyosLEi1VixEEVERFRDZrMZ6enpUsfA5cuXK/0rtdDQUGg0GqljEBERUROnlMvg73FzHqkbJVbkFlthKeM8Uo0NC1FEREQ1lJ6ejilTpkgdw+7NN9+UOgIA4KOPPkJkZKTUMYiIiKiZkMkEeOvV8P7DxOYllgqpY1ENsRBFRERUQ6Ghofjoo4+kjuFyQkNDpY5AREREzZRBq4RBq0Sp9eY8UgWlnEfK1bEQRUREVEMajYYjf4iIiIhckFYlR4iXDv7lNuQWW5BXbIWNV+25JBaiiIiIiIiIiKhJUClkCDRo4eeuuTmxebEFZeUcIuVKWIgiIiIiIiIioiZFLhPg666Gj15ln0eq1MohUq6AhSgiIiIiIiIiapIEQYBRp4JRp4LJUo6cIguKzOVSx2rWWIgiIiIiIiIioiZPr1ZAr1bAXFaB3GIrbhRbObG5BFiIIiIiIiIiIqJmQ6OUo4VRC393NfKKrcgxWVFhY0WqobAQRURERERERETNjkIug5+HBj56NfL/bx4pSxnnkapvLEQRERERERERUbMlkwnwclPBy02FQnMZcoosKLZUSB2ryWIhioiIiIiIiIgIgIdGCQ+NEqXWCuSYLCgoLeM8Uk7GQhQRERERERER0R9oVXKEeOkQUGFDrsmK3GILbLxqzylYiCIiIiIiIiIiqoJSLkOAQQM/dzXySqzIMVlQVs4hUnXBQhQRERERERER0R3IZAJ89Gp4u6lQWFqO6yYLSq2cR8oRLEQREREREREREdWAIAgw6JQw6JQotpQjx2RBYWm51LEaFRaiiIiIiIiIiIhqyU2tgJtaAUt5BXJMVtwotnJi8xpgIYqIiIiIiIiIyEFqhRwtjFr4/988UrkmK8orWJGqDgtRRERERERERER1pJDL4Oeuga9ejfySMuSYLDCX8VZ7/4uFKCIiIiIiIiIiJxEEAZ5uKni6qVBkLkOOyQqTmfNI3cJCFBERERERERFRPXDXKOGuUcJcVoHrRRYUlJY1+3mkZFIHICIiIqLKXnvtNQiCUOkRHR1tX5+VlYXx48cjICAAbm5u6Ny5MzZu3HjXdpcsWYKWLVtCo9EgLi4Ohw8frrTebDZj2rRp8Pb2hl6vx+jRo5Gdne30/hERETU3GqUcIV46RAW4w9ddDblMkDqSZFiIIiIiInJBMTExyMzMtD/27dtnXzdhwgScP38eW7ZswalTp/DII49gzJgxOHbsWLXtbdiwAQkJCZg3bx6OHj2KDh06YPDgwbh27Zp9mxkzZuCbb77BF198gd27d+Pq1at45JFH6rWfREREzYlSLkOAQYPoAHcEGTVQKZpfWab59ZiIiIioEVAoFAgICLA/fHx87Ov279+PF154Ad27d0d4eDjmzJkDo9GI5OTkatt7//338cwzz2DSpElo164dli1bBp1OhxUrVgAACgoKsHz5crz//vvo378/unTpgpUrV2L//v04ePBgte1aLBYUFhZWehAREdGdyWQCvPVqRAW4I9RbB51aLnWkBsNCFBEREZELunjxIoKCghAeHo74+Hikp6fb1/Xq1QsbNmxAXl4ebDYb1q9fD7PZjH79+lXZltVqRXJyMgYOHGhfJpPJMHDgQBw4cAAAkJycjLKyskrbREdHIzQ01L5NVRYsWACDwWB/hISE1LHnREREzYtBq0RrXz1a+7nBoFVCaOJX7bEQRURERORi4uLisGrVKiQlJWHp0qW4dOkS+vTpg6KiIgBAYmIiysrK4O3tDbVajalTp2LTpk2IiIiosr2cnBxUVFTA39+/0nJ/f39kZWUBuDnvlEqlgtForHabqsyePRsFBQX2x5UrV+rQcyIiouZLp1Ig1FuHNv56eOtVTbYgxbvmEREREbmYIUOG2P8fGxuLuLg4hIWFITExEZMnT8bcuXORn5+P7du3w8fHB5s3b8aYMWOwd+9etG/fvkGzqtVqqNXqBn1PIiKipkytkCPIqIW/hwa5xRbkmqwor2g6t9pjIYqIiIjIxRmNRkRGRiIlJQWpqalYvHgxTp8+jZiYGABAhw4dsHfvXixZsgTLli277fU+Pj6Qy+W33QEvOzsbAQEBAICAgABYrVbk5+dXGhX1x22IiIio4chlAvzcNfDVq1FQWoYckwWlVpvUseqMl+YRERERuTiTyYTU1FQEBgaipKQEwM05nv5ILpfDZqv65FSlUqFLly7YsWOHfZnNZsOOHTvQs2dPAECXLl2gVCorbXP+/Hmkp6fbtyEiIqKGJwgCjDoVIvzc0crXDe6axj2mqHGnJyIiImqCZs6cieHDhyMsLAxXr17FvHnzIJfLMXbsWBiNRkRERGDq1Kn417/+BW9vb2zevBnbtm3Dt99+a29jwIABGDVqFKZPnw4ASEhIwFNPPYWuXbuie/fuWLRoEYqLizFp0iQAgMFgwOTJk5GQkAAvLy94eHjghRdeQM+ePdGjRw9Jvg5ERERUmV6tgF6tgLmsAjkmC/JLyiA2sqv2WIgiIgJQUVGBkydPIi8vD15eXoiNjYVc3nxuoUpEriUjIwNjx45Fbm4ufH190bt3bxw8eBC+vr4AgK1bt2LWrFkYPnw4TCYTIiIisHr1agwdOtTeRmpqKnJycuzPH3/8cVy/fh1///vfkZWVhY4dOyIpKanSBOYLFy6ETCbD6NGjYbFYMHjwYHz44YcN13EiIiKqEY1SjmBPHQI8bMgttiLXZEWFrXFUpARRbGy1s/pXWFgIg8GAgoICeHh4SB2HiOrZnj178OGHH1a6K1RAQACef/559O3bV8JkRNRQ+LPfefi1JCIiang2m4gbJVbkFlthKbv9Un2ZDIgJMtTLe9f2Zz/niCKiZm3Pnj2YN28ewsPDsWTJEmzduhVLlixBeHg45s2bhz179kgdkYiIiIiI6I5kMgHeejUi/d0R6q2DTu26V3ewEEVEzVZFRQU+/PBD9OzZE2+88QZiYmKg0+kQExODN954Az179sTSpUtRUVEhdVQiIiIiIqIaMWiVaO2rR4SfHkadEoIgdaLKWIgiombr5MmTyMrKQnx8/G13n5LJZIiPj0dmZiZOnjwpUUIiIiIiIiLHaFVyhHjpEOnvDh+9Wuo4dixEEVGzlZeXBwBo1apVletvLb+1HRERERERUWOjUsjg76GROoYdC1FE1Gx5eXkBAC5dulTl+lvLb21HREREREREdcNCFBE1W7GxsQgICMDatWths1W+s4TNZsPatWsRGBiI2NhYiRISERERERE1LSxEEVGzJZfL8fzzz+PAgQOYM2cOzpw5g5KSEpw5cwZz5szBgQMH8Nxzz0Eud907ThARERERETUmCqkDEBFJqW/fvpg/fz4+/PBDTJs2zb48MDAQ8+fPR9++fSVMR0RERERE1LSwEEVEzV7fvn1x77334uTJk8jLy4OXlxdiY2M5EoqIiIiIiMjJWIgiIsLNy/Q6deokdQwiIiIiIqImjXNEERERERERERFRg2AhioiIiIiIiIiIGgQLUURERERERERE1CBYiCIiIiIiIiIiogbBQhQRERERERERETUIFqKIiIiIiIiIiKhBsBBFREREREREREQNgoUoIiIiIiIiIiJqEJIWol577TUIglDpER0dDQBIS0u7bd2txxdffFFtm6Io4u9//zsCAwOh1WoxcOBAXLx4saG6RERERERERERE1ZB8RFRMTAwyMzPtj3379gEAQkJCKi3PzMzE/PnzodfrMWTIkGrbe/fdd/Hvf/8by5Ytw6FDh+Dm5obBgwfDbDY3VJeIiIiIiIiIiKgKCskDKBQICAi4bblcLr9t+aZNmzBmzBjo9foq2xJFEYsWLcKcOXPw8MMPAwA+/fRT+Pv7Y/PmzXjiiSeqfJ3FYoHFYrE/LywsdLQ7RERERERERERUDclHRF28eBFBQUEIDw9HfHw80tPTq9wuOTkZx48fx+TJk6tt69KlS8jKysLAgQPtywwGA+Li4nDgwIFqX7dgwQIYDAb7IyQkxPEOERERERERERFRlSQdERUXF4dVq1YhKirKfuldnz59cPr0abi7u1fadvny5Wjbti169epVbXtZWVkAAH9//0rL/f397euqMnv2bCQkJNifFxQUIDQ0lCOjiIiImolbP/NFUZQ4SeN362vI8ygiIqLmobbnUZIWov4411NsbCzi4uIQFhaGxMTESiOfSktLsW7dOsydO7decqjVaqjVavvzW19EjowiIiJqXoqKimAwGKSO0agVFRUB4HkUERFRc1PT8yjJ54j6I6PRiMjISKSkpFRa/uWXX6KkpAQTJky44+tvzSmVnZ2NwMBA+/Ls7Gx07NixxjmCgoJw5coVuLu7QxCEmneAqlVYWIiQkBBcuXIFHh4eUschqhKPU2oseKw6nyiKKCoqQlBQkNRRGr36Po9qLsc/+9m0sJ9NC/vZtLCfdVfb8yiXKkSZTCakpqZi/PjxlZYvX74cI0aMgK+v7x1f36pVKwQEBGDHjh32wlNhYSEOHTqE5557rsY5ZDIZgoODa52f7s7Dw6NJf3NT08DjlBoLHqvOxZFQztFQ51HN5fhnP5sW9rNpYT+bFvazbmpzHiXpZOUzZ87E7t27kZaWhv3792PUqFGQy+UYO3asfZuUlBTs2bMHTz/9dJVtREdHY9OmTQAAQRDw5z//GW+88Qa2bNmCU6dOYcKECQgKCsLIkSMboktERERERERERFQNSUdEZWRkYOzYscjNzYWvry969+6NgwcPVhr5tGLFCgQHB2PQoEFVtnH+/HkUFBTYn//1r39FcXExpkyZgvz8fPTu3RtJSUnQaDT13h8iIiIiIiIiIqqepIWo9evX33Wbt956C2+99Va16/93VnZBEPD666/j9ddfr3M+ch61Wo158+ZVmhSeyNXwOKXGgscqNWfN5fhnP5sW9rNpYT+bFvaz4Qki71NMREREREREREQNQNI5ooiIiIiIiIiIqPlgIYqIiIiIiIiIiBoEC1FERERERERERNQgWIgiIiIiIiIiIqIGwUIU1diCBQvQrVs3uLu7w8/PDyNHjsT58+crbWM2mzFt2jR4e3tDr9dj9OjRyM7OrrRNeno6hg0bBp1OBz8/P7z88ssoLy+vtM2uXbvQuXNnqNVqREREYNWqVfXdPWoinHWcvvjii+jSpQvUajU6duxY5XudPHkSffr0gUajQUhICN5999366hY1Mc44Tk+cOIGxY8ciJCQEWq0Wbdu2xf/7f//vtvfi5ylJxdXPG5z1Gd5Q/fzqq6/wwAMPwNfXFx4eHujZsyd++OGHO2ZLS0uDIAi3PQ4ePOiy/dy1a1eVmbOysu6Yr7Htz4kTJ1bZz5iYmGqzueL+rK/zpZp837tSP3ft2oWHH34YgYGBcHNzQ8eOHbF27dq75qtqf9bkzvJS9dPRY7Cx7c/XXnutyn66ubndMZ8r7c/6PE90yuetSFRDgwcPFleuXCmePn1aPH78uDh06FAxNDRUNJlM9m2effZZMSQkRNyxY4d45MgRsUePHmKvXr3s68vLy8V77rlHHDhwoHjs2DFx69atoo+Pjzh79mz7Nr/99puo0+nEhIQE8ezZs+IHH3wgyuVyMSkpqUH7S42TM45TURTFF154QVy8eLE4fvx4sUOHDre9T0FBgejv7y/Gx8eLp0+fFj///HNRq9WK//nPf+q7i9QEOOM4Xb58ufjiiy+Ku3btElNTU8XPPvtM1Gq14gcffGDfhp+nJCVXPm9w5md4Q/XzpZdeEt955x3x8OHD4oULF8TZs2eLSqVSPHr0aLXZLl26JAIQt2/fLmZmZtofVqvVZfv5008/iQDE8+fPV8pcUVFRbbbGuD/z8/Mr9e/KlSuil5eXOG/evGqzudr+FMX6OV+qydfP1fr55ptvinPmzBF//vlnMSUlRVy0aJEok8nEb7755o75AIgrV66stD9LS0tdtp+OHIONcX8WFRVV6l9mZqbYrl078amnnrpjPlfan/V1nuisz1sWoshh165dEwGIu3fvFkXx5g9UpVIpfvHFF/Ztzp07JwIQDxw4IIqiKG7dulWUyWRiVlaWfZulS5eKHh4eosViEUVRFP/617+KMTExld7r8ccfFwcPHlzfXaImyJHj9I/mzZtX5Q+oDz/8UPT09LQft6Ioiq+88ooYFRXl/E5Qk1fX4/SW559/Xrz//vvtz/l5Sq7Elc4b6vMzvL76WZV27dqJ8+fPr3b9rV8ajx07Vsde3a6++nmrEHXjxo0aZ2kK+3PTpk2iIAhiWlpatVlcbX/+kTPPlxz9fqiJ+upnVYYOHSpOmjTpjtsAEDdt2lTj/DVVX/105BhsCvvz+PHjIgBxz549d9zOVffnLc44T3TW5y0vzSOHFRQUAAC8vLwAAMnJySgrK8PAgQPt20RHRyM0NBQHDhwAABw4cADt27eHv7+/fZvBgwejsLAQZ86csW/zxzZubXOrDaLacOQ4rYkDBw6gb9++UKlU9mWDBw/G+fPncePGDSelp+bCWcdpQUGBvQ2An6fkWlzpvKE+P8Prq5//y2azoaioqNL3fHVGjBgBPz8/9O7dG1u2bHG4b39U3/3s2LEjAgMD8cADD+Dnn3++Y5amsD+XL1+OgQMHIiws7K6ZXGV/1oQj+8aRr19N1Vc/q3uvmnx/Tps2DT4+PujevTtWrFgBURTr9L633huov37W5hhsCvvzk08+QWRkJPr06XPXbV15fzrjPNFZn7csRJFDbDYb/vznP+Pee+/FPffcAwDIysqCSqWC0WistK2/v7/9uv6srKxKH0K31t9ad6dtCgsLUVpaWh/doSbK0eO0JmpyLBPVhLOO0/3792PDhg2YMmWKfRk/T8lVuNp5Q319htdnP//Xv/71L5hMJowZM6baPHq9Hu+99x6++OILfPfdd+jduzdGjhxZ5+JFffYzMDAQy5Ytw8aNG7Fx40aEhISgX79+OHr0aLV5Gvv+vHr1Kr7//ns8/fTTd8zjavuzJhzZN662Px2RmJiIX375BZMmTbrjdq+//joSExOxbds2jB49Gs8//zw++OADh98XqN9+OnIMNvb9aTabsXbtWkyePPmu27ry/nTWeaKz9qeixlsS/cG0adNw+vRp7Nu3T+ooRNXicUqNgTOO09OnT+Phhx/GvHnzMGjQICemI3KO5vJ53FD9XLduHebPn4+vv/4afn5+1W7n4+ODhIQE+/Nu3brh6tWr+Oc//4kRI0Y4/P712c+oqChERUXZn/fq1QupqalYuHAhPvvsM6e/35001P5cvXo1jEYjRo4cecftGuP+dCUN1c+ffvoJkyZNwscff3zHyecBYO7cufb/d+rUCcXFxfjnP/+JF1980eH3r89+1tcx6IiG2p+bNm1CUVERnnrqqbtu66r70xXPEzkiimpt+vTp+Pbbb/HTTz8hODjYvjwgIABWqxX5+fmVts/OzkZAQIB9m/+9a8Gt53fbxsPDA1qt1tndoSaqLsdpTdTkWCa6G2ccp2fPnsWAAQMwZcoUzJkzp9I6fp6SK3DF84b6+Ayv737esn79ejz99NNITEy87ZKKmoiLi0NKSkqtX3dLQ/Xzj7p3737HzI15f4qiiBUrVmD8+PGVLnWpKSn3Z004sm9cbX/Wxu7duzF8+HAsXLgQEyZMqPXr4+LikJGRAYvFUuvXAg3Xzz+62zHYmPcncPOyvIceeui2UUA14Qr709nnic7anyxEUY2Joojp06dj06ZN2LlzJ1q1alVpfZcuXaBUKrFjxw77svPnzyM9PR09e/YEAPTs2ROnTp3CtWvX7Nts27YNHh4eaNeunX2bP7Zxa5tbbRDdiTOO05ro2bMn9uzZg7KyMvuybdu2ISoqCp6ennXvCDVpzjpOz5w5g/vvvx9PPfUU3nzzzdveh5+nJCVXPm9w5md4Q/UTAD7//HNMmjQJn3/+OYYNG1arnLccP34cgYGBtX5dQ/aztpkb6/4EbhYuUlJSanTZT1Wk3J814ci+cfQ4qUpD9RMAdu3ahWHDhuGdd96pdPlTbRw/fhyenp5Qq9W1el1D9rOqzHf7/myM+xMALl26hJ9++qlO359S7s/6OE902udtraY2p2btueeeEw0Gg7hr165Kt6QsKSmxb/Pss8+KoaGh4s6dO8UjR46IPXv2FHv27Glff+v2nYMGDRKPHz8uJiUlib6+vlXehvnll18Wz507Jy5ZsoS3G6cac8ZxKoqiePHiRfHYsWPi1KlTxcjISPHYsWPisWPH7HeIyM/PF/39/cXx48eLp0+fFtevXy/qdDqHbhVNzY8zjtNTp06Jvr6+4pNPPlmpjWvXrtm34ecpScmVzhs++OADsX///vbnzvwMb6h+rl27VlQoFOKSJUsqvU9+fn61/Vy1apW4bt068dy5c+K5c+fEN998U5TJZOKKFStctp8LFy4UN2/eLF68eFE8deqU+NJLL4kymUzcvn17tf1sjPvzlieffFKMi4urMour709RdM750ldffVXpjlu1+fq5Sj937twp6nQ6cfbs2ZXeJzc3t9p+btmyRfz444/FU6dOiRcvXhQ//PBDUafTiX//+99dtp81OQabwv68Zc6cOWJQUJBYXl5+WxZX35/OOk+sr89bFqKoxgBU+Vi5cqV9m9LSUvH5558XPT09RZ1OJ44aNUrMzMys1E5aWpo4ZMgQUavVij4+PuJf/vIXsaysrNI2P/30k9ixY0dRpVKJ4eHhld6D6E6cdZzed999VbZz6dIl+zYnTpwQe/fuLarVarFFixbi22+/3UC9pMbOGcfpvHnzqmwjLCys0nvx85Sk4krnDfPmzbvte8NZn+EN1c/qfi499dRT1fZz1apVYtu2bUWdTid6eHiI3bt3r3S7b1fs5zvvvCO2bt1a1Gg0opeXl9ivXz9x586dldpoCvtTFG/+QqfVasWPPvqoyiyNYX8643xp5cqV4v+Oj6jJ18+V+vnUU09Vuf6+++6rtp/ff/+92LFjR1Gv14tubm5ihw4dxGXLlokVFRUu28+aHINNYX+KoihWVFSIwcHB4t/+9rcqs7j6/nTWeWJ9fd4K/9dRIiIiIiIiIiKiesU5ooiIiIiIiIiIqEGwEEVERERERERERA2ChSgiIiIiIiIiImoQLEQREREREREREVGDYCGKiIiIiIiIiIgaBAtRRERERERERETUIFiIIiIiIiIiIiKiBsFCFBERERERERERNQgWooiInGDXrl0QBAH5+flSRyEiIiJqdHguRdR8sBBFRI3GxIkTIQgCBEGAUqmEv78/HnjgAaxYsQI2m03qeEREREQujedSROQKWIgiokblwQcfRGZmJtLS0vD999/j/vvvx0svvYSHHnoI5eXlUscjIiIicmk8lyIiqbEQRUSNilqtRkBAAFq0aIHOnTvjb3/7G77++mt8//33WLVqFQAgPz8fTz/9NHx9feHh4YH+/fvjxIkTldr55ptv0K1bN2g0Gvj4+GDUqFH2dZ999hm6du0Kd3d3BAQEYNy4cbh27Vql12/duhWRkZHQarW4//77kZaWdlvWffv2oU+fPtBqtQgJCcGLL76I4uJip39NiIiIiGqK51JEJDUWooio0evfvz86dOiAr776CgDw2GOP4dq1a/j++++RnJyMzp07Y8CAAcjLywMAfPfddxg1ahSGDh2KY8eOYceOHejevbu9vbKyMvzjH//AiRMnsHnzZqSlpWHixIn29VeuXMEjjzyC4cOH4/jx43j66acxa9asSplSU1Px4IMPYvTo0Th58iQ2bNiAffv2Yfr06fX/BSEiIiKqBZ5LEVFDEkRRFKUOQURUExMnTkR+fj42b95827onnngCJ0+exEcffYRhw4bh2rVrUKvV9vURERH461//iilTpqBXr14IDw/HmjVravS+R44cQbdu3VBUVAS9Xm//y+GZM2fs28yaNQvvvPMObty4AaPRiKeffhpyuRz/+c9/7Nvs27cP9913H4qLi6HRaBz/QhARERE5gOdSROQKOCKKiJoEURQhCAJOnDgBk8kEb29v6PV6++PSpUtITU0FABw/fhwDBgyotq3k5GQMHz4coaGhcHd3x3333QcASE9PBwCcO3cOcXFxlV7Ts2fPSs9PnDiBVatWVcowePBg2Gw2XLp0yZldJyIiIqoznksRUUNRSB2AiMgZzp07h1atWsFkMiEwMBC7du26bRuj0QgA0Gq11bZTXFyMwYMHY/DgwVi7di18fX2Rnp6OwYMHw2q11jiPyWTC1KlT8eKLL962LjQ0tMbtEBERETUEnksRUUNhIYqIGr2dO3fi1KlTmDFjBoKDg5GVlQWFQoGWLVtWuX1sbCx27NiBSZMm3bbu119/RW5uLt5++22EhIQAuDmc/I/atm2LLVu2VFp28ODBSs87d+6Ms2fPIiIiog49IyIiIqp/PJcioobES/OIqFGxWCzIysrC77//jqNHj+Ktt97Cww8/jIceeggTJkzAwIED0bNnT4wcORI//vgj0tLSsH//frz66qv2k6B58+bh888/x7x583Du3DmcOnUK77zzDoCbf2FTqVT44IMP8Ntvv2HLli34xz/+USnDs88+i4sXL+Lll1/G+fPnsW7dOvtdZm555ZVXsH//fkyfPh3Hjx/HxYsX8fXXX3OCTSIiIpIUz6WISHIiEVEj8dRTT4kARACiQqEQfX19xYEDB4orVqwQKyoq7NsVFhaKL7zwghgUFCQqlUoxJCREjI+PF9PT0+3bbNy4UezYsaOoUqlEHx8f8ZFHHrGvW7dundiyZUtRrVaLPXv2FLds2SICEI8dO2bf5ptvvhEjIiJEtVot9unTR1yxYoUIQLxx44Z9m8OHD4sPPPCAqNfrRTc3NzE2NlZ888036/VrRERERFQdnksRkSvgXfOIiIiIiIiIiKhB8NI8IiIiIiIiIiJqECxEERERERERERFRg2AhioiIiIiIiIiIGgQLUURERERERERE1CBYiCIiIiIiIiIiogbBQhQRERERERERETUIFqKIiIiIiIiIiKhBsBBFREREREREREQNgoUoIiIiIiIiIiJqECxEERERERERERFRg2AhioiIiIiIiIiIGsT/BwVCiYBrOuwFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "create_aggregated_plots(backstroke_data, 'Backstroke')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "NxFnQY4Z-ZZD",
        "outputId": "653b24d4-ae78-4d1d-8c92-cf5a4139eadb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "create_aggregated_plots(butterfly_data, 'Butterfly')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "6uPcnRZx-asr",
        "outputId": "5542b9fe-3a4d-42ae-a900-5e9d4c8ec88b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#trend shows that over the past DECADES the average time for swimming competitions has been decreasing\n",
        "\n",
        "#that is athletes keep improving significantly by shortening time taken to swim\n",
        "\n",
        "#this is a notable important analysis"
      ],
      "metadata": {
        "id": "iIf1ydbMK-vP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing data\n",
        "# event_data['Distance (in meters)']\n",
        "event_data['Distance (in meters)'] = event_data['Distance (in meters)'].str.extract(r'(\\d+)').astype(float)\n",
        "event_data['Relay?'] = pd.to_numeric(event_data['Relay?'], errors='coerce')"
      ],
      "metadata": {
        "id": "oQjCWRdzC2ON"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "event_data = event_data.drop(columns='Athlete')\n",
        "# event_data = event_data.drop(columns='Location')"
      ],
      "metadata": {
        "id": "PK0DKz1MFDcK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One-hot encoding categorical variables\n",
        "event_data = pd.get_dummies(event_data, columns=['Location', 'Stroke', 'Gender', 'Team'])\n",
        "event_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "myxqGjI_wn6W",
        "outputId": "d85ea2d8-4f8c-4db7-a669-cf2490571dc5",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year  Distance (in meters)  Relay?  Results  Rank  Location_Amsterdam  \\\n",
              "24  2020                 100.0       0    47.02     1               False   \n",
              "25  2020                 100.0       0    47.08     2               False   \n",
              "26  2020                 100.0       0    47.44     3               False   \n",
              "27  2020                 100.0       0    47.72     4               False   \n",
              "28  2020                 100.0       0    47.82     4               False   \n",
              "\n",
              "    Location_Angeles  Location_Athens  Location_Atlanta  Location_Barcelona  \\\n",
              "24             False            False             False               False   \n",
              "25             False            False             False               False   \n",
              "26             False            False             False               False   \n",
              "27             False            False             False               False   \n",
              "28             False            False             False               False   \n",
              "\n",
              "    ...  Team_ROC  Team_ROU  Team_RSA  Team_RUS  Team_SUI  Team_SWE  Team_UKR  \\\n",
              "24  ...     False     False     False     False     False     False     False   \n",
              "25  ...     False     False     False     False     False     False     False   \n",
              "26  ...      True     False     False     False     False     False     False   \n",
              "27  ...     False     False     False     False     False     False     False   \n",
              "28  ...     False     False     False     False     False     False     False   \n",
              "\n",
              "    Team_URS  Team_USA  Team_VEN  \n",
              "24     False      True     False  \n",
              "25     False     False     False  \n",
              "26     False     False     False  \n",
              "27     False     False     False  \n",
              "28     False     False     False  \n",
              "\n",
              "[5 rows x 57 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0bc88647-d2a5-4ac8-a539-a4ef6426b1ba\" class=\"colab-df-container\">\n",
              "    <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>Year</th>\n",
              "      <th>Distance (in meters)</th>\n",
              "      <th>Relay?</th>\n",
              "      <th>Results</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Location_Amsterdam</th>\n",
              "      <th>Location_Angeles</th>\n",
              "      <th>Location_Athens</th>\n",
              "      <th>Location_Atlanta</th>\n",
              "      <th>Location_Barcelona</th>\n",
              "      <th>...</th>\n",
              "      <th>Team_ROC</th>\n",
              "      <th>Team_ROU</th>\n",
              "      <th>Team_RSA</th>\n",
              "      <th>Team_RUS</th>\n",
              "      <th>Team_SUI</th>\n",
              "      <th>Team_SWE</th>\n",
              "      <th>Team_UKR</th>\n",
              "      <th>Team_URS</th>\n",
              "      <th>Team_USA</th>\n",
              "      <th>Team_VEN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2020</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.02</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2020</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.08</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2020</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.44</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.72</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2020</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.82</td>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 57 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bc88647-d2a5-4ac8-a539-a4ef6426b1ba')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0bc88647-d2a5-4ac8-a539-a4ef6426b1ba button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0bc88647-d2a5-4ac8-a539-a4ef6426b1ba');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e60ad222-4e44-466b-8990-ff6254244b07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e60ad222-4e44-466b-8990-ff6254244b07')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e60ad222-4e44-466b-8990-ff6254244b07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "event_data"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "event_data['Year'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFbZ-8BJCmUa",
        "outputId": "08627816-0f22-4c05-f44d-204115e94414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select features and target\n",
        "X = event_data.drop(columns='Results')  # Add more features if needed\n",
        "X.to_csv('/content/input_data.csv', index=False)\n",
        "y = event_data['Results']"
      ],
      "metadata": {
        "id": "4NRBmHpWwlZA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "collapsed": true,
        "id": "2uMLfZQxxc3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.columns"
      ],
      "metadata": {
        "id": "JCu75xqUM7mR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "mCKe4SxCxhr6",
        "outputId": "5ae5860f-cb8d-4c91-f7bd-6303254af813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24      47.02\n",
              "25      47.08\n",
              "26      47.44\n",
              "27      47.72\n",
              "28      47.82\n",
              "        ...  \n",
              "4019    59.20\n",
              "4020    59.50\n",
              "4091    58.60\n",
              "4092    59.80\n",
              "4174    59.00\n",
              "Name: Results, Length: 167, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "4uP3UPuDxk1_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit a linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "dufUwEdNxnuL",
        "outputId": "19dc96ac-ab83-48fb-e536-2e805706fb79",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict winning times\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "gfngQIvixqOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4bLYha_xsKT",
        "outputId": "3ec6e1b5-da1f-40fc-9a3f-89399d83ea37",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([52.67081616, 53.96587019, 51.36240646, 49.03019802, 52.2642155 ,\n",
              "       47.97888427, 52.97740488, 51.51962281, 52.58341451, 53.96587019,\n",
              "       48.81436408, 48.77992593, 48.07469258, 50.17479575, 47.01491012,\n",
              "       48.38134852, 57.2158666 , 49.58436863, 58.42269837, 47.4306506 ,\n",
              "       51.36240646, 48.12080938, 47.66962696, 47.57847248, 57.2158666 ,\n",
              "       51.20557758, 57.2159204 , 49.72417129, 49.84045524, 58.03349437,\n",
              "       48.08341089, 58.44833266, 55.39064911, 58.19740586])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate model performance\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error:\", mse)\n",
        "print(\"Predicted winning times:\", y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csep2lu5xu4J",
        "outputId": "c43c30aa-db61-4853-d778-243d99a5ba37",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 0.14337334783858943\n",
            "Predicted winning times: [52.67081616 53.96587019 51.36240646 49.03019802 52.2642155  47.97888427\n",
            " 52.97740488 51.51962281 52.58341451 53.96587019 48.81436408 48.77992593\n",
            " 48.07469258 50.17479575 47.01491012 48.38134852 57.2158666  49.58436863\n",
            " 58.42269837 47.4306506  51.36240646 48.12080938 47.66962696 47.57847248\n",
            " 57.2158666  51.20557758 57.2159204  49.72417129 49.84045524 58.03349437\n",
            " 48.08341089 58.44833266 55.39064911 58.19740586]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "# Save the trained model\n",
        "joblib.dump(model, '/content/drive/MyDrive/output_model/swimming_time_taken_predictor.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82naVsuB94s8",
        "outputId": "110befda-5ad4-446c-8fab-f9d4b4ea1652",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/output_model/swimming_time_taken_predictor.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 344
        }
      ]
    }
  ]
}