2_6_13_Solar_Energy_Potential_Mapping.ipynb

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        "# 🌞 **Solar Radiation Prediction Using Machine Learning** 🌞\n",
        "\n",
        "## 📘 **Project Overview** 📘\n",
        "\n",
        "In this project, we aim to predict **solar radiation (GHI)** for a given location using machine learning techniques. Solar radiation is a critical factor for understanding solar energy potential and for developing effective energy systems. As environmental engineers and data scientists, we combine data analysis with predictive modeling to understand and forecast solar radiation levels.\n",
        "\n",
        "---\n",
        "\n",
        "## 📍 **Key Steps in the Project** 📍\n",
        "\n",
        "### 1. **🔄 Data Collection from NASA POWER API**\n",
        "We start by fetching solar radiation data (Global Horizontal Irradiance - GHI) from NASA's POWER API. This data contains historical solar radiation values for a specific location and time range. 🌍\n",
        "\n",
        "- **Parameters**: Latitude, longitude, start and end dates.\n",
        "- **Data**: The data fetched includes solar radiation values for each day in 2022.\n",
        "\n",
        "---\n",
        "\n",
        "### 2. **📊 Data Preprocessing**\n",
        "Once the data is collected, we load it into a DataFrame and clean it for analysis. The following steps are performed:\n",
        "- Renaming columns for clarity.\n",
        "- Creating a new column `Date` by combining year, month, and day.\n",
        "- Extracting month and day for later analysis. 📅\n",
        "\n",
        "---\n",
        "\n",
        "### 3. **🔍 Data Exploration**\n",
        "We explore the data by visualizing its structure:\n",
        "- **Summary statistics**: Check for missing values, duplicates, and data types.\n",
        "- **Exploratory visualizations**: Create histograms, scatter plots, and heatmaps to uncover trends in solar radiation over time.\n",
        "\n",
        "---\n",
        "\n",
        "### 4. **📍 Geospatial Visualization**\n",
        "Using the **Folium** library, we generate an interactive map that shows the solar radiation levels for a particular location, such as Nairobi, Kenya. 🌍\n",
        "\n",
        "- **Map Features**: Markers with GHI values, and an interactive map saved as `solar_map.html`.\n",
        "\n",
        "---\n",
        "\n",
        "### 5. **📈 Model Training**\n",
        "We split the data into training and test sets, and train two different machine learning models:\n",
        "- **Linear Regression**: A simple approach for predicting GHI based on time.\n",
        "- **Random Forest Regressor**: A more advanced method to capture non-linear relationships and improve prediction accuracy.\n",
        "\n",
        "---\n",
        "\n",
        "### 6. **📊 Model Evaluation**\n",
        "After training the models, we evaluate their performance using key metrics:\n",
        "- **RMSE** (Root Mean Squared Error)\n",
        "- **R² score**: Measures how well the models fit the data.\n",
        "\n",
        "We also extract **feature importance** from the Random Forest model to understand which factors (e.g., month, day) impact the GHI prediction.\n",
        "\n",
        "---\n",
        "\n",
        "### 7. **📊 Predictions vs Actual GHI**\n",
        "We visualize the predicted vs actual GHI values to assess model accuracy. This helps identify areas where models can improve and how well they generalize to new data. 📈\n",
        "\n",
        "---\n",
        "\n",
        "### 8. **🖥️ Interactive Dashboard with Dash**\n",
        "Finally, we create an interactive web dashboard using **Dash**:\n",
        "- **Features**: Users can select time ranges, visualize GHI predictions, and download the results.\n",
        "- **Visualization Tools**: Interactive plots for GHI data and predictions.\n",
        "  \n",
        "The dashboard provides a user-friendly interface to explore the solar data and predictions in real-time. 🌐\n",
        "\n",
        "---\n",
        "\n",
        "## 🛠️ **Skills You Will Gain** 🛠️\n",
        "\n",
        "- **Data Collection & APIs**: Learn to fetch and process environmental data from APIs.\n",
        "- **Data Preprocessing & Cleaning**: Master data manipulation techniques with Pandas.\n",
        "- **Data Visualization**: Gain experience in creating interactive visualizations using Matplotlib, Seaborn, and Folium.\n",
        "- **Machine Learning**: Train and evaluate models using Regression and Random Forest techniques.\n",
        "- **Interactive Dashboards**: Develop real-time interactive dashboards with Dash and Plotly.\n",
        "\n",
        "---\n",
        "\n",
        "## 💡 **Learning Outcomes** 💡\n",
        "\n",
        "By the end of this project, you'll be able to:\n",
        "1. Collect and preprocess environmental data for predictive analysis.\n",
        "2. Apply machine learning techniques to forecast solar radiation.\n",
        "3. Visualize and interpret model results to provide actionable insights.\n",
        "4. Build interactive dashboards for data exploration and presentation.\n",
        "\n",
        "---\n",
        "\n",
        "## 🌍 **Real-World Impact** 🌍\n",
        "\n",
        "Solar radiation prediction is vital for optimizing solar energy systems and understanding local energy requirements. By utilizing data science and machine learning, we contribute to more sustainable energy solutions, benefiting the environment and society at large. 🌱\n",
        "\n",
        "---\n",
        "\n",
        "**Let's start building powerful models and uncover insights from solar radiation data!** 💡🚀"
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      "source": [
        "### 1. Importing Libraries and Installing Dependencies\n",
        "\n",
        "In this step, we begin by importing the necessary libraries required for data handling, visualization, machine learning models, and dashboard development. These libraries are essential for the functionality of the project. The installation commands ensure that the necessary packages such as Dash, Plotly, and others are available.\n",
        "\n",
        "- **Libraries and Modules Used:**\n",
        "    - `requests`: To fetch data from external sources via HTTP requests.\n",
        "    - `pandas`: To handle data manipulation and analysis.\n",
        "    - `matplotlib` and `seaborn`: To generate static plots and visualizations.\n",
        "    - `folium`: To create interactive maps for geospatial visualization.\n",
        "    - `sklearn`: Provides machine learning algorithms for model building and evaluation.\n",
        "    - `dash`, `dash_daq`: For building interactive dashboards.\n",
        "    - `plotly.express`: For creating interactive plots.\n",
        "\n",
        "Ensure you have the required dependencies installed before proceeding with the project code. This can be done using the `pip install` commands provided at the beginning."
      ],
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        "id": "N2zT36zbzKx4"
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      "source": [
        "import warnings"
      ]
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    {
      "cell_type": "code",
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      "metadata": {
        "id": "IP4uQ16_zK4i"
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      "source": [
        "warnings.filterwarnings(\"ignore\")"
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          ]
        }
      ],
      "source": [
        "pip install dash pandas plotly folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VxsVA8O6zSPj"
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "EUQKzSusuI9B",
        "outputId": "65b0a729-7f13-4ade-8d5d-2075a2432306"
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          ]
        }
      ],
      "source": [
        "pip install dash pandas plotly folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nf8ZN1jdzZ9E"
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        "outputId": "cd432a06-6aac-4e43-dfd1-1fb333a06fd5"
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          ]
        }
      ],
      "source": [
        "!pip install dash-daq"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "sASyaQ4CzaZq"
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      "execution_count": null,
      "metadata": {
        "id": "Pi5BQD5ouJCd"
      },
      "outputs": [],
      "source": [
        "import requests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5xqoaPzfuJE_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xR6rehTtuJHw"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x55LoISruJKm"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-dQ6Q1iMuJNM"
      },
      "outputs": [],
      "source": [
        "import folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "egMELZgcu_04"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yG_Mu6Hru_-5"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mo_BULCRvABv"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIaeNF-zvAEd"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oy1t1pFQvAHS"
      },
      "outputs": [],
      "source": [
        "import dash"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mwpUE6cJvAJ9"
      },
      "outputs": [],
      "source": [
        "from dash import dcc, html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dv2Q_1f1vAMn"
      },
      "outputs": [],
      "source": [
        "import dash_daq as daq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3NvbJbVVvAPk"
      },
      "outputs": [],
      "source": [
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pKbNGpulvS-R"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cdAaI3m6vTBY"
      },
      "outputs": [],
      "source": [
        "from dash.dependencies import Input, Output, State"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lXuwVNg8vTEo"
      },
      "outputs": [],
      "source": [
        "import io"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4IJGMWjNvTH5"
      },
      "outputs": [],
      "source": [
        "import base64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UuSdLLTAvdi8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RGpmnCyZvdmD"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wiLBsAEUvdo_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Data Download from NASA POWER\n",
        "\n",
        "In this section, we fetch solar radiation data from NASA's POWER API for a specific location (latitude and longitude). The data corresponds to solar radiation (Global Horizontal Irradiance - GHI) over the course of the year 2022.\n",
        "\n",
        "- **Parameters:**\n",
        "    - `latitude` and `longitude`: Coordinates of the location to fetch data for.\n",
        "    - `start_date` and `end_date`: Defines the time range for data collection (e.g., January 1, 2022, to December 31, 2022).\n",
        "    - `parameters`: Specifies the data type (GHI) to be retrieved.\n",
        "\n",
        "We use a `GET` request to fetch the data from the NASA API and store it as a CSV file locally for further processing.\n",
        "\n",
        "If the request is successful, the data is saved to `solar_data.csv`. Otherwise, an error message is displayed."
      ],
      "metadata": {
        "id": "dohAliJv4E-g"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KAaD_xygvdsY"
      },
      "outputs": [],
      "source": [
        "# --- Data Download from NASA POWER ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hb8aK5q2vTK3"
      },
      "outputs": [],
      "source": [
        "latitude = 1.2921  # Example: Nairobi, Kenya"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a7IXKIjcvTOQ"
      },
      "outputs": [],
      "source": [
        "longitude = 36.8219"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RToP9HuVvTRb"
      },
      "outputs": [],
      "source": [
        "start_date = \"20220101\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6KjCC0PDvn8r"
      },
      "outputs": [],
      "source": [
        "end_date = \"20221231\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JOtFie1vvn_r"
      },
      "outputs": [],
      "source": [
        "parameters = \"ALLSKY_SFC_SW_DWN\"  # Solar radiation: GHI (kWh/m²/day)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l6T4fUzevoDM"
      },
      "outputs": [],
      "source": [
        "api_url = f\"https://power.larc.nasa.gov/api/temporal/daily/point?parameters={parameters}&community=RE&longitude={longitude}&latitude={latitude}&start={start_date}&end={end_date}&format=CSV\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DnzjaoAsvoGV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "62h9AmKtvoJx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N-Asv_rNvoNC"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_B9Bnhxbvog3"
      },
      "outputs": [],
      "source": [
        "# Fetch data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sPq27yKlvosd"
      },
      "outputs": [],
      "source": [
        "response = requests.get(api_url)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gXdlW6Cvo3O",
        "outputId": "bdf9ed43-32f8-4993-cd84-eaa48211a351"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data downloaded successfully!\n"
          ]
        }
      ],
      "source": [
        "if response.status_code == 200:\n",
        "    with open(\"solar_data.csv\", \"w\") as file:\n",
        "        file.write(response.text)\n",
        "    print(\"Data downloaded successfully!\")\n",
        "else:\n",
        "    print(\"Failed to fetch data:\", response.status_code)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GVxSF7Szv3Kf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LYFellTBv3Nr"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cxb3--4mv7XH"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Loading and Preparing Data\n",
        "\n",
        "In this step, we load the CSV file containing the solar radiation data into a Pandas DataFrame and perform initial data preprocessing:\n",
        "\n",
        "- **Steps:**\n",
        "    - Skip metadata rows in the CSV file by using `skiprows=10`.\n",
        "    - Rename columns to more meaningful names, such as `Year`, `Month`, `Day`, and `GHI`.\n",
        "    - Create a `Date` column by combining the `Year`, `Month`, and `Day` columns.\n",
        "    - Filter the DataFrame to only include the relevant columns: `Date` and `GHI`.\n",
        "    - Extract month and day from the `Date` column for later analysis.\n",
        "\n",
        "This data is now ready for exploratory analysis and visualization."
      ],
      "metadata": {
        "id": "HTVrrhPh4LQM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ft6QV_Hmv3RC"
      },
      "outputs": [],
      "source": [
        "# --- Load and Prepare Data ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fi0uvHnkv3Uu"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"solar_data.csv\", skiprows=10)  # Skip metadata rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X1iwEDx9v789"
      },
      "outputs": [],
      "source": [
        "data.columns = [\"Year\", \"Month\", \"Day\", \"GHI\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BMpHsKjBv8A4"
      },
      "outputs": [],
      "source": [
        "data[\"Date\"] = pd.to_datetime(data[[\"Year\", \"Month\", \"Day\"]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mf_bFY5Sv8FH"
      },
      "outputs": [],
      "source": [
        "data = data[[\"Date\", \"GHI\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CE1eOwKPv8Im"
      },
      "outputs": [],
      "source": [
        "data['Month'] = data['Date'].dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rkJjjd8Xv8MM"
      },
      "outputs": [],
      "source": [
        "data['Day'] = data['Date'].dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RlhaCj5RwMNl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dGhNddtfwMRS"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "19HqCPyGwPgP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Data Exploration\n",
        "\n",
        "Here, we perform a preliminary exploration of the dataset to understand its structure and quality.\n",
        "\n",
        "- **Operations:**\n",
        "    - Display the first 5 rows of the dataset using `data.head()` to inspect the data.\n",
        "    - Generate summary statistics with `data.describe()` to understand the distribution and central tendency of the data.\n",
        "    - Check the data types and non-null entries using `data.info()` to identify potential issues.\n",
        "    - Check for missing values with `data.isnull().sum()` and duplicate rows using `data.duplicated().sum()`.\n",
        "\n",
        "These steps are critical for understanding the data and ensuring it is clean before proceeding with analysis."
      ],
      "metadata": {
        "id": "6nGQDRZr4QFQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nRIPZ3kmwMWH"
      },
      "outputs": [],
      "source": [
        "# --- Data Exploration ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RoNI8ubwMaN",
        "outputId": "0beecd9d-0bb4-4bc3-cc87-bded17a4201b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "First 5 Rows:\n",
            "        Date   GHI  Month  Day\n",
            "0 2022-01-02  7.17      1    2\n",
            "1 2022-01-03  7.30      1    3\n",
            "2 2022-01-04  7.00      1    4\n",
            "3 2022-01-05  6.17      1    5\n",
            "4 2022-01-06  5.56      1    6\n"
          ]
        }
      ],
      "source": [
        "print(\"First 5 Rows:\")\n",
        "print(data.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QPDeKuy3wMeh"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ra2FYKHHwMiW"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGCg5_A4wMqj",
        "outputId": "e7a181b2-aa1f-499a-f9fb-72e081d6a059"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Summary Statistics:\n",
            "                      Date         GHI       Month         Day\n",
            "count                  364  364.000000  364.000000  364.000000\n",
            "mean   2022-07-02 12:00:00    6.230632    6.541209   15.760989\n",
            "min    2022-01-02 00:00:00    2.450000    1.000000    1.000000\n",
            "25%    2022-04-02 18:00:00    5.720000    4.000000    8.000000\n",
            "50%    2022-07-02 12:00:00    6.330000    7.000000   16.000000\n",
            "75%    2022-10-01 06:00:00    6.840000   10.000000   23.000000\n",
            "max    2022-12-31 00:00:00    7.920000   12.000000   31.000000\n",
            "std                    NaN    0.902451    3.445115    8.786448\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nSummary Statistics:\")\n",
        "print(data.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ko7geUmHv8Pn"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f04jjS5_v8dk"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzDIlFT_v3YB",
        "outputId": "5fbc99bd-fb28-4b17-ab0a-b0291d75c988"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 364 entries, 0 to 363\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   Date    364 non-null    datetime64[ns]\n",
            " 1   GHI     364 non-null    float64       \n",
            " 2   Month   364 non-null    int32         \n",
            " 3   Day     364 non-null    int32         \n",
            "dtypes: datetime64[ns](1), float64(1), int32(2)\n",
            "memory usage: 8.7 KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nDataset Info:\")\n",
        "print(data.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vbjB6Ts3v3bR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lCqCtOfDv3eo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0f2D17QXv3in",
        "outputId": "49966300-5778-44f0-9d8a-ffefb46964f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Missing Values:\n",
            "Date     0\n",
            "GHI      0\n",
            "Month    0\n",
            "Day      0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nMissing Values:\")\n",
        "print(data.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ElgNvlX9v3mF"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VUmCHUsLv3pl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAKFBNR6v3tD",
        "outputId": "52698a77-b93d-4117-eaa5-6966fbaa6d25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Duplicate Rows:\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nDuplicate Rows:\")\n",
        "print(data.duplicated().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FbAAgVziwcql"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GWokuubIwcvI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "-s0n5Sbnwc0j"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Data Visualization\n",
        "\n",
        "This section visualizes the solar radiation data (GHI) to identify patterns and trends. Various visualizations are created, including histograms, scatter plots, heatmaps, and line graphs.\n",
        "\n",
        "- **A. Histogram of GHI**: Visualize the distribution of the GHI values to understand how they are spread over the year.\n",
        "- **B. Scatter Plot**: Plot the GHI values over time to observe daily fluctuations and trends.\n",
        "- **C. Heatmap**: Show the variation of GHI across different days and months to detect seasonal patterns.\n",
        "- **D. Line Graph for Monthly Average**: Plot the average GHI for each month to reveal seasonal trends in solar radiation.\n",
        "\n",
        "These visualizations help in identifying important trends in the dataset and provide insights into the solar energy potential throughout the year."
      ],
      "metadata": {
        "id": "HczPCQpE4U20"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xwUR7am2wc4v"
      },
      "outputs": [],
      "source": [
        "# --- Data Visualization ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "nM-Jk7D2wc8m"
      },
      "outputs": [],
      "source": [
        "monthly_avg = data.groupby('Month')['GHI'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "iog8zyR2wdAW"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "0lcc58yOwdD5"
      },
      "outputs": [],
      "source": [
        "# A. Histogram of GHI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "vwadjZ0twdHc",
        "outputId": "b8ec8159-3ea2-455f-f68c-bafa32a342af"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.hist(data['GHI'], bins=20, color='skyblue', edgecolor='black')\n",
        "plt.title('Histogram of GHI')\n",
        "plt.xlabel('GHI (kWh/m²/day)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "3bqKgvwkwdLI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "XXEeqBhvvTUu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "bZ97zjz_vASW"
      },
      "outputs": [],
      "source": [
        "# B. Scatter Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "35prSakhvAVk",
        "outputId": "8c6f96be-800a-4eb6-e685-8e4ae7310166"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.scatter(data['Date'], data['GHI'], c='orange', alpha=0.7)\n",
        "plt.title('Daily GHI over the Year')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('GHI (kWh/m²/day)')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "oxAY7ouvwq5r"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "FXqjsjmKwq9M"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "aaddNlDlwrA8"
      },
      "outputs": [],
      "source": [
        "# C. Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "YwuCAF43wrFK"
      },
      "outputs": [],
      "source": [
        "heatmap_data = data.pivot_table(index='Month', columns='Day', values='GHI')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "y1NThYZqwrJA",
        "outputId": "42b43172-9425-4736-d078-ce365573cda2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(heatmap_data, cmap='YlOrRd', annot=False, cbar=True)\n",
        "plt.title('Heatmap of GHI (Monthly by Day)')\n",
        "plt.xlabel('Day of Month')\n",
        "plt.ylabel('Month')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "xNMvXmyrwrNR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "McdwSJvYwrRI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QvqPi1EkvAYf"
      },
      "outputs": [],
      "source": [
        "# D. Line Graph for Monthly Average"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "lnhxiRzaw1tI",
        "outputId": "4402df30-f3df-4264-d1ca-58448a20d073"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(monthly_avg.index, monthly_avg.values, marker='o', color='blue', label='Monthly Avg GHI')\n",
        "plt.title('Monthly Average GHI')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('GHI (kWh/m²/day)')\n",
        "plt.xticks(range(1, 13))\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9hKZBKaw1xU"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ABO6WXxkw11F"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DikwKSKQ4a2O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Folium Map for Geospatial Visualization\n",
        "\n",
        "In this section, we use the `folium` library to create an interactive map. The map is centered on the coordinates of Nairobi, Kenya, and a marker is added at this location with the average GHI value displayed.\n",
        "\n",
        "- The map is saved as `solar_map.html`, which can be opened in any web browser to explore the geospatial aspect of the solar data."
      ],
      "metadata": {
        "id": "l2ABIzqv4bIC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kmsatYfbw16-"
      },
      "outputs": [],
      "source": [
        "# E. Folium Map Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TVN3MfoDw1_T"
      },
      "outputs": [],
      "source": [
        "solar_map = folium.Map(location=[latitude, longitude], zoom_start=6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mH6OCA9w2DG",
        "outputId": "f5a3e27b-7450-43b6-8043-a74ad7bbe272"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<folium.map.Marker at 0x7c574721a290>"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "folium.Marker([latitude, longitude], popup=f\"Avg GHI: {data['GHI'].mean():.2f} kWh/m²/day\").add_to(solar_map)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kCG9vVJYw2G9"
      },
      "outputs": [],
      "source": [
        "solar_map.save(\"solar_map.html\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wePke2hfxDCf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1kU5qfCWxDHV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7HX_EjXAxDLs"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. Model Training\n",
        "\n",
        "We split the dataset into training and test sets and train two different machine learning models: Linear Regression and Random Forest.\n",
        "\n",
        "- **Data Splitting**:\n",
        "    - We use the `train_test_split()` function from `sklearn` to split the dataset into training (80%) and testing (20%) sets.\n",
        "\n",
        "- **Linear Regression**:\n",
        "    - We fit a Linear Regression model using the training data.\n",
        "    - The model predicts GHI values for the test set.\n",
        "\n",
        "- **Random Forest**:\n",
        "    - We fit a Random Forest Regressor with 100 estimators to predict GHI values for the test set.\n",
        "\n",
        "Both models are evaluated based on the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score, which measure the accuracy of the predictions."
      ],
      "metadata": {
        "id": "BPNB75f74g9l"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FCwMimPsxDPy"
      },
      "outputs": [],
      "source": [
        "# --- Model Training ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nErPXF96xDUr"
      },
      "outputs": [],
      "source": [
        "X = data[['Month', 'Day']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZdvwJnezxDYx"
      },
      "outputs": [],
      "source": [
        "y = data['GHI']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z562WpiVxDdP"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_9JUSc92xOFc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3IzJD9ExxOJf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XFHlyM88xOOZ"
      },
      "outputs": [],
      "source": [
        "# Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FDZuVWWoxOTY"
      },
      "outputs": [],
      "source": [
        "lr_model = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Wn25Rj38xOXo",
        "outputId": "84a01c59-b7ce-4a82-f62d-978ff9e3c40e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q3VRJdOV0Vhz"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X-4-2-wLxObu"
      },
      "outputs": [],
      "source": [
        "y_pred_lr = lr_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sIViKzFNxOid"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RkJHgXpt0XYY"
      },
      "outputs": [],
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xmaDqG6XxOnA"
      },
      "outputs": [],
      "source": [
        "# Random Forest Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u3RGUozRxOr1"
      },
      "outputs": [],
      "source": [
        "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "znhPX8sGxDhp",
        "outputId": "0083a659-01a4-4561-e2f4-2e28b5815520"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(random_state=42)"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9fOKrIfN0WuY"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rLc2MSt-xDl3"
      },
      "outputs": [],
      "source": [
        "y_pred_rf = rf_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BYVvBt7lx4BD"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5S5Ogibcx4F2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DCx2mVcH4m7E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8. Model Evaluation\n",
        "\n",
        "After training the models, we evaluate their performance using the following metrics:\n",
        "\n",
        "- **Linear Regression**:\n",
        "    - Calculate the RMSE and R² score for the Linear Regression model.\n",
        "\n",
        "- **Random Forest**:\n",
        "    - Calculate the RMSE and R² score for the Random Forest model.\n",
        "\n",
        "These metrics help us understand the accuracy and generalization ability of each model.\n",
        "\n",
        "Additionally, feature importance is extracted from the Random Forest model to identify which features (month and day) contribute the most to predicting the GHI."
      ],
      "metadata": {
        "id": "_WvRyIg54nM3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lehVIGMVx4LH"
      },
      "outputs": [],
      "source": [
        "# Evaluate Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qJDrWiLmx4QO"
      },
      "outputs": [],
      "source": [
        "lr_mse = mean_squared_error(y_test, y_pred_lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9uU0umnBx4VP"
      },
      "outputs": [],
      "source": [
        "lr_rmse = np.sqrt(lr_mse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U8IqCx3cx4cp"
      },
      "outputs": [],
      "source": [
        "lr_r2 = r2_score(y_test, y_pred_lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NYwjLMrQyBHu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6jJafOLkyBL9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q5Vho8kpyBRw"
      },
      "outputs": [],
      "source": [
        "rf_mse = mean_squared_error(y_test, y_pred_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fTLsvKmwyBXs"
      },
      "outputs": [],
      "source": [
        "rf_rmse = np.sqrt(rf_mse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VGxZ8iohyBcE"
      },
      "outputs": [],
      "source": [
        "rf_r2 = r2_score(y_test, y_pred_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wda3HH7UyBgg"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MDMivpqHw2K_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qTFxtPLw2PC",
        "outputId": "4444453f-8b61-4b98-cfa5-8846d8f7787a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Linear Regression - RMSE: 0.96, R²: 0.02\n",
            "Random Forest - RMSE: 0.68, R²: 0.51\n"
          ]
        }
      ],
      "source": [
        "print(f\"Linear Regression - RMSE: {lr_rmse:.2f}, R²: {lr_r2:.2f}\")\n",
        "print(f\"Random Forest - RMSE: {rf_rmse:.2f}, R²: {rf_r2:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f6urF89yyMcf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ghapo4yyMhI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3UcR6GsByMme"
      },
      "outputs": [],
      "source": [
        "# Feature Importance from Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UUigPSCEyMrJ"
      },
      "outputs": [],
      "source": [
        "importances = rf_model.feature_importances_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30Emdja4yMwS",
        "outputId": "fd427155-d68f-4f0d-9580-27c9f1dc6921"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature Importances: [0.46961285 0.53038715]\n"
          ]
        }
      ],
      "source": [
        "print(f\"Feature Importances: {importances}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MIVGT1fsyM03"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3yoH3_Aa4sJh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M54iIIXv4sZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9. Visualization of Predictions vs Actual GHI\n",
        "\n",
        "This step involves plotting the actual vs predicted GHI values for both models:\n",
        "\n",
        "- We create a plot that compares the actual GHI values from the test set with the predictions made by the Linear Regression and Random Forest models.\n",
        "- This visualization allows us to visually assess the performance of both models over time.\n",
        "\n",
        "The plot is helpful for understanding the errors made by the models and identifying any patterns where predictions diverge from actual values."
      ],
      "metadata": {
        "id": "8rTCJEcK4ss9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ho5eXOb5uJQi"
      },
      "outputs": [],
      "source": [
        "# --- Visualization of Predictions vs Actual GHI ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "fExCpU8iuJT2",
        "outputId": "9590d2d6-690a-47d3-da5e-ef3476b114de"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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IJBKJRCKR1BCKopCZmYm7uztq9c1jPQ+0CIqJicHLy6ummyGRSCQSiUQikUjuE6KiovD09LzpOg+0CLK2tgbEidrY2NRoWwoLC9myZQs9evTA2Ni4RtvyMCP7oeaRfVDzyD64P5D9UPPIPqh5ZB/cHzws/ZCRkYGXl5deI9yMB1oEFafA2djY3BciyMLCAhsbm//0m+t+R/ZDzSP7oOaRfXB/IPuh5pF9UPPIPrg/eNj6oTJlMtIYQSKRSCQSiUQikTxU1KgI0mq1TJs2DR8fH8zNzfH19eWDDz5AUZSabJZEIpFIJBKJRCL5D1Oj6XCffvopc+bMYeHChTRq1IgjR47w3HPPYWtry8SJE2uyaRKJRCKRSCQSieQ/So2KoP379/P444/Tp08fALy9vVm2bBmHDh0qd/38/Hzy8/P1zzMyMgCR51hYWHj3G3wTio9f0+142JH9UPPIPqh5ZB/cH8h+qHlkH9Q8sg/uDx6WfqjK+amUGsw9mzlzJj/++CNbtmzBz8+PkydP0qNHD7766iuefPLJMuvPmDGD9957r8zypUuXYmFhcS+aLJFIJBKJRCKRSO5DcnJyGDFiBOnp6bc0TatREaTT6Xj77bf57LPP0Gg0aLVaPvroI956661y1y8vEuTl5UVSUtJ94Q63detWunfv/lC4btyvyH6oeWQf1DyyD+4PZD/UPLIPah7ZB/cHD0s/ZGRk4OjoWCkRVKPpcMuXL2fJkiUsXbqURo0aceLECSZPnoy7uzsjR44ss76pqSmmpqZllhsbG983HXo/teVhRvZDzSP7oOaRfXB/IPuh5pF9UPPIPrg/+K/3Q1XOrUZF0Guvvcabb77JsGHDAAgMDCQyMpKPP/64XBEkkUgkEolEIpFIJHdKjVpk5+TkoFaXboJGo0Gn09VQiyQSiUQikUgkEsl/nRqNBPXt25ePPvqI2rVr06hRI44fP85XX33F888/X5PNkkgkEolEIpFIJP9halQEzZo1i2nTpjFu3DgSEhJwd3fnxRdf5N13363JZkkkEolEIpFIJJL/MDUqgqytrfnmm2/45ptvarIZEolEIpFIJBKJ5CGiRmuCJBKJRCKRSCQSieReI0WQRCKRSCQSiUQieaiQIkgikUgkEolEIpE8VEgRJJFIJBKJRCKRSB4qpAiSSKpIeDhcuVLTrZBIJBKJRCKR3C5SBEkklSQ/H958E/z8oFEjOHu2plskkUgkEolEIrkdpAiSSCrB0aMQHAyffgo6HeTkwJAh4q9EIpFIJBKJ5MFCiiCJ5CYUFMD06dC6tYj8ODvDr7+CqyucOweTJtV0CyUSiUQikUgkVUWKIImkAk6dEuLn/fdBq4XBTyjsP5bGc8/B4sWgUsHPP8Pvv9d0SyUSiUQikUgkVUGKIInkBoqK4KOPoEULOHECatWC339X8Br1Kr1XtSI6I5ouXbV0e+dLsI1kzBi4dKmmWy2RSCQSiUQiqSxSBEkearTa0s9DQ6FdO3jnHSgshMcfh9OnFY7XeouvD3zNxZSL7Incw6tbXmWbZio2zz5DZpaWYcOEcYJEIpFIJBKJ5P5HiiDJQ8trr4GlJbz+OmRnwxdfQLNmcPgw2NnBb7/B6tUwN2wGn+77FIAfHv2BEYEjeLnVy1iZWJFhvwfzR77g6FF4442aPR+JRCKRSCQSSeWQIkjyULJokRA9+fnw+edgZSVEUX4+9O4NZ87AU0/BzH8+4v097wPwdc+vGddyHAC+Dr581+s7AAo7TAO3Y3z7LaxbV2OnJJFIJBKJRCKpJFIESR46Tp2Cl14q/zUbG/jzT/DwgC/2f8E7O98B4NNHPmVym8ml1n026FkGBgykSCnEYfSTYJzDc89BVNRdPgGJRCKRSCQSyR0hRZDkoSItDQYOhNzc8l/PyICgINiyM4efjv0EwPtd3uf19q+XWVelUvHjYz/iZuVGiiYMpydfIyUFhg8X5goSiUQikUgkkvsTKYIkDw06HYwcCeHhhmWWljB7tnht40bw9BRObz1DLGh/YRefd/2OaZ2nVbjPWha1WNB/AQDpdX/B0v0q+/aJuYUkEolEIpFIJPcnUgRJHhomTixds9Opk0iNGztWzPnTuzds3B/J6NHi9fmz3Jj97Mvs2HHz/fbw7cGXPb7k8JhD/Pp1bQA+/hi2bbtLJyKRSCQSiUQiuSOkCJL851EUePpp+OEHw7Kvv4adO6FuXcOyxacWE7ygPp1fXsyWLVC7NkREQLduooYoI6PiY0xpO4UmLk0YMgTGjBHHfOopiI+/e+clkUgkEolEIrk9pAiS/KeJiYHmzWHxYsOy8+dh8mRQl3j3/3HmD0auGUmhrpAD0Qfo3l04xI0dK16fNw8CA2HLllsf86k3DuPVaznx8UII6XTVekoSiUQikUgkkjtEiiDJfxJFgSVLwM8PTpwwLM/MFMtKsjp0NU+uehKdomN0s9F811tYX1tbi3qhHTvAxweuXoWePWH0aEhPL/+4B6MPErKkHUntn8PU/QLbtsEnn9ydc5RIJBKJRCKR3B5SBEn+c6SlmTBkiIannhKToBZz6ZKYD6gkGy5sYOiKoWgVLc80fYZ5feehVpX+WHTtCqdPi5oigF9+gUaNhJHCjbT0aEmnOp3I1ebgPuEpUBfy7ruwb181n6REIpFIJBKJ5LaRIkhSI+h0kJgoIjbVycqVKiZODGHt2tJv7XXrwNe39LqbL21m0PJBFOoKGdZ4GL/2+7WMACrG0hK+/Rb27IF69eDaNejTR7jNpaYa1lOr1CzsvxB7M3siCg7TePz7aLXCNjslpern8+WXwrSh+DF4MGzYIC24JRKJRCKRSO4EKYIkNcKkSeDsDI6O0L07vPkmLF8u7KtvRxglJ8OwYTB8uBEZGaYYGRl28vbb0Ldv2W22Xd5GgbaAQQGDWNR/ERq15pbH6dgRTp6EKVOEKFm0CBo2hLVrDet42ngy77F5AJxznIlnu71ERcFzz1X93MLCSj9fuVKci7GxQRi1aAGzZkFSUtX2LZFIJBKJRPKwIkWQpEbw9RUD+JQUYSX96acwdKiIstjbQ0gIvPYaLFsGFy7c3Fxg3TqRnvbHH6DRKDz2WDi1hVM13brB+++Xv91n3T9j/uPzWTpoKcYa40q33cJCRGj27QN/f4iLg/79YcQIgxB5otETPNP0GXSKDl3/pzG2ymDdOvjuu0ofBoC5c+Grr4QpQ0UcPSpS9ZycDMLIwUEItZMnqz/aJpFIJBKJRPKgI0WQpEaYPBkuXhR/bWxKv5aeLuyrv/hCCAt/f7Czg86d4ZVXhNNbaKiI/owcCY8/LqyoAwLgn3+0pKSYcfmyCk9PIaI0JQI8ZxLOkF+UD4BKpeLZoGcx0Zjc1jm0bQvHj8PrrwunuWXLhBhbuVK8Pqv3LLztvInJuUKfaT8BQtgdPVr5Y2g04pxPnRJiRlEgPx9WrYJ+/SreLjVV2IAHBYm2FYuj/v1hzRqxD4lEIpFIJJKHFSmCJDWGr68YqEdHiwhJ/fpl19FowMxMuLrt2QPffCPm/GnYUKTSLVok1nNxgYULYfduFfv3e2BsrPDnnyI6Uszha4dp/2t7Bi0fRF5RXrWcg7m5iGL9+69oU0KCqNsZMgTy0m1YPGAx3/X6jhVTX6F/fygsFBGvm805dCtMTGDAAJGCVyyMdDohyCZNEoKxItauFduamRmEUdOmoh/i4m6/TRKJRCKRSCQPElIESfQcOSJspaOi7u1xra3h5ZdF/ctff0GPHobXtFohll55RaSGPfts+fuIj4dWreCtt0TYp7BQxblzwh67sBCOxx6nx+IeZORnkFWQhU6p3sl7WrWCY8fgf/8Twu3PP0VUKGp/eya0ehmNWs2vv4oJWMPD4cUXqzdNTaUSUZ9vvhFRoGJxFB8vDB2aNat421OnROqcm5tBGNnYiBS7I0fkPEcSiUQikUj+e0gRJAHEgHnAADG5Z+3aYi6dsWNhxQqRdnYvUKvh0Udh82Y4exZeeknU35w9KyIVL70ECxYY1u/RA378UQikG+f+ARg1Sgz+LXxO0/L7R0jLS8PXuD0fBGxAo7Oo9vabmsKHH8KhQ9CkiagPGj4cBg4UURZjyyz6fvwFao2O338XVtt3G2dnIWaOHSudTrdunWhXRWRmCrOFli2FqCsWR489Jt4TJa3HJRKJRCKRSB40pAiSAGKAO2GC4fnFiyLy8sQTIqWseXNRz7J5870ZADdsCHPmiFS56dPLvu7uDu+9By+8INLRXF3FcmNjhfHjjzNlipauXcHK5xxFI7qhNUmB6FaEv7+RTm2ssLYW5/TCC+I4hw5Bbm71tL15czh8GGbMACMjUYMT0EhLoy878MPF1+g142tAiJOzZ6vnmFXBxEQ4zK1cWTqd7tQpmDpVpBlWxF9/ifeElZVBGDVqBJ9/LiaTlSYMEolEIpFIHgSkCJLoeeON0u5lKpWIsCiKqDf54gvo1Uu4t3XuLFzX9u0T6WZ3izNn4Lffyi6PiRHGBK1bg7e3qBeytobjx4vo3v0qn3yiY+6fF7Aa3w0sE/Exa86kWn/zSEcbHByup8gdh59/hnHjxH6srUV9zPPPw/ffizqfnJzba7eJiRBvR46IaFRaioarK8YDsE15m7b9T5KbK2qHbvcY1YlKJRzoPv/cMH+Toogapx9+EBGhijh3TphD1KkjonkmJsYMGdKHl19W8++/d/f9IZFIJBKJRHI7SBEkKcXLL8PSpSKCoShQty5cuiRqhZ5/XqTKFRYK0TF9OnToIOyY+/QRKWunTlVPDUlurqhT6dwZLl8GLy/YulW06dgxURtkYiIiODExYptie+1iErMTySrIoolLEw6/vIVvPrFn61aRphYRISIhb78thJ2Tk6g/OnUK5s8X16FdOyGMGjeGZ54RtTV790JWVuXPo2lTOHhQpMkZnxkNYY9ToCvgcrMncfHI5dw5YWZwK/buFSL06tUqXcY7xslJiMRDhwzCKDcXNm4UBg8VUVBgxLx5Gtq1E/1UHDXq1Qt+/x3S0u7ZKUgkEolEIpGURXmASU9PVwAlPT29ppuiFBQUKGvWrFEKCgpquinVwqZNimJhIYa9bdsqSnKyWK7TKcrFi4oyd66iPPGEotSqVTw0NjycnBRl6FBF+fFHRQkPr/qxDxxQFH9/w/6ef15R0tLKrvfPP2WPbWqqU0JCIpVDh0Q/HL52WInPir/lMXU6Rbl6VVHWrFGUadMUpU8fRXF1Lbt/UBSVSlEaNFCUESMU5csvFWXnzvLbdyNnzihKUPsEhakuCjNQ1I9O0u9z6dKKt4uOVhQrK7GeWq0o/fsrypYtiqLV3vqY9wqtVlFOn1aUN99UFFdXXbnXraKHn5+ifPSRoly4cH+d04PMf+376EFF9kPNI/ug5pF9cH/wsPRDVbSBjARJyqVXLzGJqb29SAvr1AmuXRN38+vVE+5my5eLdKniVLnevcHSUqRT/fEHjBkjnN18fGD0aDGPTnx8xcfMz4e33hIRmPPnhVvZhg3CQMDWtvS62dnCKAFEWtzChcKhLd/0GjvOpNOqlTGdO8PVAy2oZeZ8y/NVqUS06fHHRZrfhg0QGyuiTOvXi/qefv3Aw0MM3cPCRMTs1Veha1dhS+3nB8OGiZSy7duFS1tJGjWCw7uceM5hPgC6Vt+C7xZAXKtLl8pv26uviuiTra2Isq1ZI0whGjQQ0bcbj1MTqNUiYvbxx3D1ahFr1qyloKCQ+HiYN0/0UUVcuCBc9fz8DCYMxsaiXmvPnuqr1ZJIJBKJRCLRcw9E2V1DRoLuPmfOKIq7u7hjX7u2opw/f/P18/MVZc8eRZk+XVE6dFAUI6Oyd/4DAxVl8mRFWb9eUTIyxHZHjihKo0aGdZ56SlFSUso/hk6nKMOHi/Xc3BQlNlYsj8mIUTw/ra8Y/c9O0Xgc1e+rTh1F+fzzivdXVeLiFGXjRkX58ENFGTBAXJeKohw+PooyeLCifPyxomzerCiJiWIfIxaPV5iBwktNFRCREwcHRcnLK32srVsNEaCjRxXl3DlFefllRbGxMRzD3FxRRo0Sr98P3OqzkJ0trsWTT1Y+WlT86NZNUX77zdDnkvL5r34fPWjIfqh5ZB/UPLIP7g8eln6QkSBJtdGokTA/qF9f1KN06ABHj1a8vokJdOwoIif//COiFBs3imhGUJBY5/RpMZ9N375iPhqVClq0EE5pdnawerUwQyhZ31OSH34QUSWNRkSjXF0hITuBbou6EZ17EXsLY3b/bcvbb0OtWhAZKZztPD1FfUtY2J1dExcXEfX63/9g1Sqx/8RE4Zz38cdistS6dcW6ERHCUvqtt6BnT1FjU6cOZK36nFaMp9PVzYAKgJQUUYNUXFOVnw/jhZcC48cL17mAAGFece2acO9r0kRESn75BYKDoU0bMYFsXvXMBXtXsLAQkazFiw3ypqhIvC/eeUf0U0Vs3y4myy05p5Gvr4jenTkj9iORSCQSiURyK6QIktwSb29RmN+8uRjsd+0KO3dWblsrKyEYvvhCpM0lJBhS5cojLU1YVn/2mRBbWm3p1//9VxgmgEg769ABknOSeWTRI4QmheJp7ckH9T6glX9tPvpITPz688/C+SwnR+w7IEAIko0bq28iUEdHMbB/800xUWp4uBA127aJcxk6VAhJEGJy3SpzDs34nj0bXUrtp7BQiLtiI4QLF4To+uCD0sezshIpiSdOiHVHjBApZAcPwsiRQki8/rowlXgQ0GhEOt0HH4g+KxZHMTFC4HXsWPG2ly8Lk47AQHENisXRc8+J65+efu/OQyKRSCQSyYOBFEGSSuHsLIRPSIiYSLNXLxEFqSpOTmKSzlTPpfCmLYxrDE0Wo3G6hLOLmGRmyxZh192ihVh/0CCYPVsM9p94QgiFwYNh8mRIzU2l+2/dOZ1wGjcrNzY/uRlXU1f98czNxaSpJ0+K9vfvLwbIW7YIR7uAAGGHnZlZPdepJPb20K2biEL9/rsQNGlpsGsXfPmlEC4NGoj20HAFOBiKgjp2FFEREIJn506DOCiJSgXt2wv3vuhomDlTOPglJwuRWK+eOM+//iorKB8E3NyEK+GePQZhlJkpnAJHjrz5tgsWQPfuIrpYLIw6dxbuf1euyDmNJBKJRCJ5mJEiSFJpbGzEYHrgQCgoEILkp5+qto/QUGF88OexLWCWAc5nYeDTaMfXRzvFiY5z+/Dx1+n07StSw1JThdgaP14Ig2vXxH66doXzken0XNyT43HHcbZ0Zvsz26nvUL/c46pU0KWLSLULDxfRJFtbIUxefllETqZMufuRE1tbMRCfMkUIl9BQ+HD7lzDkCWpPfgrUZSfVCQ+HAQOEuHFxEQL0f/8TFt8lB/POziLt7vJlWLtWRLsURUS8HntMCKJPPxXRvAcZKyt45BEhcoqFUWGhsDefMUMYcVTEnj1CVPn4CDMHlUpc13ffFZHH+zmNUCKRSCQSSfUhRZCkSpiZiTqcF14QqWRjxog6mFvdVddqRXpXs2Zw+DDY7v8We40HAG0822CiMSE5N5mz6Qd4Y5IN69aJdLIRC16j2XvPQYu54HoC1KLoY/x4CPDXcOaEBWa6WrzlsR1Xo4BKnYOPj4jEREeLKJCfH2RkCKe1evVEtGjnznsXKXiq+RPYmtpyVXeQiSs+uum6xbVHM2eKaJiPj0jF695dpOItXy6EUd++8PffcPGiqMeytxfL33xTCL5nnoEDB/470RAjI5EON326EIGKIt6fUVHCOTAkpOJto6JEGl6LFiJyWBw1evppISAfdNEokUgkEomkLEY13QDJg4dGI2yPHR2FAHr7bTFQ/OILcXf9Ri5eFJOb7t8vnvfuDT/9ZIuHRzRFuiKM1EbkF+Wz9+peHvntEVYc2s+A4PYYGcHe1OVcVa7CYwsAMNJZ4Ka0oDCiDXGHO5D700awjeSVpABeVUPz5hrq1AnA1FRF585iUFsRVlZCTI0dK4TFt9+Kv2vXikdgIEycCE8+efP93Cm1bWszp88cRqwawfenPwDPnhAtPKVVKoNQadoUXnlFGCEcPSomjT192lB7tG2bYZ+2tqKGq3lzYZjw1FNi/Tlz4MgRYTzx229ClI4bB8OHC3vz/xIqlUHwPfOMYXl6urgGy5aJaFJFaYKLF4tHSdq1E2l4nTqJGi+N5q41XyKRSCQSyV1ERoIkt4VKJaIRX30lnn/9tShELyyRzaXTCSezpk2FALK2FiYFf/0l5tsBMFILHW5qZMr2iO0ADPm7A6ZDnqNBcAKav3+A3e9A+CNoCm0oUucQpdlDXL3PaDbpfVb9YcH4oQE0aAC6wEUcSdjPynWe9O5thL29iAB89JEwDKjIOSwlRaTdTZ4sUu9GjhQOZqdPi4iXl5cQetHRd+liAsMDh/Nk4JPoFB0MfApMRJGSkxN8+KFIRTx5UpghFJtHHD0q6mOOHIEffxSvtWwJpqZioL9zp6H2qFkzcX4WFqKGyOj67Y/jx8U5engIgXX+/N07x/sFW1tRq/Xzz+I9oSjCie/ECXGt65efUQmI9/GLL4paMiMj8Tlwdxfvj3377k5tmUQikUgkkupHiiDJHfHKKyLdSKMR1swDBwoXtogIMdCcNElELrp1E6Ji1KjrRgDlMKXtFPyzRgOga7qA8939iUi5CrtmwG9b0X4aB2m19eubRzxBTIw45obNuRgNHAXPd0T1li3G44PJ7zaenSmLeOfrC7Rpo1CrlpgM9bvvhB13cYTl7bdFtKd3b7GvhQtL14YkJ4uIl5eXsLdeufLumAy87Ps9pNcGh8t0+XQSjRsLN71du8S1693bMKFs27bCEtrUVER6XnhBWGYfOiQG4idOCFe1ceOEbbaZmVi+Z48YrN8oCNPThW15gwai3mr16ofLbtrERIj1//1P1IkVp9NFRIiIWY8eFW8bGyveHx06GCzfVSoRXVu9unxDC4lEIpFIJDWLTIeT3DHPPAMODsIoYcOG0mlVlpbCpeyllyoWP8U4Wjhy8sOf6DRiFIecxoHbcegzHpr9isfpb0n0/4QCu6tQYAGL/2b/1Y7sL97YOhWjfo9h7HWAQrM4Cp2OgdMxYDYAJueeJWP5fNatg3XrdWCWhqutAyEh5aeBVWSdffWqqMUppm1bUUdUu3bZh5VVZa+gGCS/9YodXP4Nnu3CrvT5rJg3kWe6B7Ftm0jL+usvITQnTxbRn+bNYdo0UedjbGzYl7GxGNA3bSpMAEAImrAwQxrd0aMiCpSTU7Ytu3aJBwh79DlzhIgteYyHAZVKnL+3t0gnLCYlRVzDlStFOl1FZgq//y4eJWnVSnxe2reHhg2F+JJIJBKJRHLvkSJIUi089hj8+qtIvSqmfn1RnF88cWhlMDWFnb+1wdLqMLScAyHvgPtRrrl3AMBMY8bn7dZjVb8joaFiYB8WBuHh7hQtWQ0oYBsFngfA46D4634Um5ymmHkKdznF4SK83IC4JD+WRreBi63BrQ3EB4LOMNL39BSRq4KC63P7rCub7vTvv+JRHvb2InJUnkCqXVtM8lpcU7JsmUhfMzPrxOTgT+nUoDG96weR8b0QMu++K+pQRo4UJggvvQTr14vlq1YJ2+fiyWjLw8hIzMPTuLHBWlqrFVGPksJoz57S2125IiJQxYwZIyJPwcFiX6amt+zS/xwODsKd7pFHhEAEIYTOnRPW3QsXCte/8jh0SDxK4uQkauZ69RLCtVatu9p8iUQikUgkSBEkqQYURdwRnzy59PKCgttLAzp3DlA0cGgChA6AVz0BMNGYsHb4Wtp4tsDKRIdaZcjmLCgQVtKnT2tZty4LGMyFC0MI+xcycwpIUhdCcb2Sy2nx1/GCeAQtEs8LzSEmGP55Gy71Jjoa3ntPWHOvXm0oko+NFcvnzSu//XZ2om4nNVU8Tpwofz0jIyG0bG1FvQ8Im+vOxq9RuxCyssTgePt2Yac9fLjYl7u7MG5YtkzYe584IWqB3npLzC1U2eiCRiNqWwICDJEOnQ4uXRLC7u23xWSlJfnxR8P/xsZCCAUHGwwYAgMN9UYPE2ZmBiOKN94Qy3Q64VR38CAsXSqc5sojMVFESz//vPTyQYNEdLV5c/D1Ld90RCKRSCQSye3xEA5XJNVJbKyIDmzYIJ63aSNsiidMEKKkfXsRDbpZlKIkycmGdLMOHRVO+75FOoDWmHndV9HDtweDlg8iNjOW2X1mE+QqdmxiIgbz9eopmJpe5NFH62NsrEZRICbGhLAwE33UKDR0MGcXJRKnOWSIFnkcAvM0qLMX9pdwd6izm3/cvsPx8dYQ3YYpw4MZ0MeSWbNEbdHKlcJV7uBBwyaBgcIkolkzISKuXi37iI4WKWpXrpQ+/6tXS0debGtfxcznKDCA6GjhyLdsmYgwde4saoVefllEgz74ANasEVGh4OBKduANqNXCMtzPzxAxOn5cTPi6fXvpdQsLxWvHjxuWaTTQsKERTk7NuHJFTatWIrphYXF77XmQUatFqmS9eqLmrJiEBCFc168XNw+yssrffuVK8ShJ8+ZCsLZtK95n/zVHP4lEIpFI7hVSBEluC0URg/EJE0S0w8REDMJffVUMhPfuFek9J0+Kwfr69SKd62ZotWKwGBkJdf3ycB77LOkX/rh+QBWvfL+Zndc2sCp0FQDBPwYzvuV4Puj6AbZmtuXuU6USzmceHqKuxYAjmZmPcv78o4SFwblQHUfOX+Bs2kESYtuj9wTw2QkNV4kH8JVOw1eLA+FTIYrq6/oxaYwD06aJu/3Ll8M//4hH7drCgnv0aJFCdeO5xsYK0fLyy2JZsfgoFkpp6kukj2hBulEehB6BhMaAiAgVY2RkcNoDIYpatBAC7OefRUqitfXNr/utaNZM2G+np4uapNmzhZi8keII2OnTKqA2O3aI5Wq1EKjF0aLmzcU+q1Iz9V/C2VkYLfToAbNmiWU5OcLoYs8ecY1Pny5/22PHxKMk9vaGNMmmTUWkUCKRSCQSyc1RKcqD61uUkZGBra0t6enp2NjY1GhbCgsL2bhxI48++ijGd1hBrigKEzZOICEngVHNRtHDt0ep1K+aJiFBzK2zSugCgoNFHUSjRqXXS0+Hfv3EwM7MDP74QzyviBkzRJqZmVUevh914mzq4Uq1x8XShfjseAIcA3CxdEGbrqVZ/Wa427jjYuWCi6ULver1QnUrZ4brFBUJV7DQUNh57hQ7rm7mVPJB8PwXbG7ID/vhDCReP3GPg2CRDNGtIddQ2GFqKtLaJk4UxfDF6HTijv6hQzBsmBCVJUlPV+j3e1/2xP2Fh1ET0r84RFaaoQhHo6mcS529fcV1SXXqlK5NqgyKIuqXZs8WIq64DS4uIorVrJmWffsukZVVn2PH1MTFld2HSgX+/mWFkW35WvahRKsVqYlHjghxvW5d1bZv1SqWF190omVLIxo0ePiMLe4HqvN3QXJ7yD6oeWQf3B88LP1QFW0gRVA1UZ1vrrCkMAJ+CCizfGTTkbzR/g0aODao9IC+ulmxQgigpCQRhXj33bLuZCXJzRUD/HXrxED755+FILiRTZugTx8hAOt/3JGL+fsA0Kg0fNvrW2Lji/jm53iyVXHYesTRsa0pk9qNZ8LGCZxPvvnkNramtqS9maZ/PmLlCM4knMHFygVXK1dcLEv/7e7bvdz9REfDiJei+efy9RQ615OweJOoXwIxv0+TJeL/5HpwTUSLiG4D8U1AK4p1nnxSWIvv2QNTpohITVhY+Xfw47PiCZwTSGJOIlPavErEvC9YvVrUiBw+DNnZZVPtfvjhppejDMW1SRUJpdq1K44mXbsGP/0kaoViY8UytVqhVatYpk93pkcPI+LjS5svHD0qtiuPevVK1xg1by5EnMRAXJxIQdy8Wdx8SEur/LZNmgjzkjZtRNTIzu5utVICD8+g435G9kHNI/vg/uBh6QcpgmqA6nxzXcu4xmf7PuO7Q99VuM70ztOZ0WXGHR2nKiQni9S3YsvfJk3EAKwytT5FRWIemwULxPPPPhM1JsVcuSIGu6mpCk1fe52Tll8AwgihQFtAHds6rBq6CpPk5nTuLCyKO3USwkljks9X/37F2zveBmBO7zkcOnkI+9r2JOUmEZcVh5mRGWuHrdUfr9m8ZpyIO1FuW+3M7Eh9I1X//MlVT3I24awQSVYuuFq6kpfswsblrlw64QoRIbi5iQjIPyZvc9FoFTiWI8oKLOGzJCgyE89NMqHAChBidtIkMUdPgwYidczZ2WApvuHCBvou6wvA6gHbmNS3G1evCnG5dGn51uPJyWKfS5YYlj39tJjrqFgsRUYKYVeZaJKd3c2d7hwdRV3Y7NkiSlSMn58QzSNHlhYz8fGG1K5igRQZWf6xfXzKCiNHx1u3+WEiM1Ok0P37r+jzknVat8LaWvRP165CGPn4SBOG6uJhGXTcz8g+qHlkH9wfPCz9IEVQDXC33lzRGdF8sPsDfjz2Y6nlU9tO5fMen3Mi7gQrzq1ge8R2JrWeRIhPCM6WztV2fBD1PGPGiDvQGo1wIZs2rWpznCgKvP46fCH0Da+/Dp98Iib/7NABjh5VcHvqHWLrzQRg3mPzaOfVjv6/9yc8NRwzIzN+6vsTDQqeIiREDPp69RLpWKamEJkWSUxmDC1cW7Bx40Z69urJrqhd9PTtWSZqdi7xHNEZ0cRnxROXFUd8tuGvuZE564Yb8o6C5gZxMv5kuedkqbbH4acUoqLEc8cxT1GrQSi2liZcSookpTC29AYzSnzURrUFuysiSlQcMYppcV0YCdFRLIoaNIBdVmP5O2kuHtYe/NLiFH1CHNBqRRRm9OiKr/v69fDiiyJKo1IJYfThh4aC+uLapPLMG4ofqakV77+Y4tqk2rUhK0vh+PGyymz0aCGImjcvfx9JSWWF0eXL5a9bu3ZZYeTicut2PiwUFhayfv0m6tbtzZkzxqxZU9Zk4Vb06SMmFw4KEi6A5uZ3o6X/bR6WQcf9jOyDmkf2wf3Bw9IPUgTVAPfizVWoLWR7xHaKdEXUd6iPv6M/b217i0/2fVJm3eeDnufxBo/TqU4n7Mzsbut4aWnC9nrhQvE8IED837LlbZ8Cn38uBBAYJvL89Vcw7/U+uW2mAzCr9ywmtJoAQGpuKk+uepJNlzYB8FWPr2ipfYUePUSq3cCBotao2Ja5uB+uuV5j/N/j6V63O98/+j1+tfxuq71nEs4QnREtRNINosnSxJLl/dfz5Zfw8ceQ80xTcD1V4b5ejFXYvh0uXS6Cd2/yHonsCPNvmLDHOBtebA6OFzA9OpX89QY/5fnzxXWo6COQmirS7oojcb6+8MsvwrCiMmRm3lwkFTvdVQVra5g6VUSKiqNJbm5la5NSU0VUo2Q63cWL5e/Tw6O0KAoOfnhNAir6PlIUkYp44oSYEHfhQiE+K0vDhsKYo2VLETVyda32pv+neFgGHfczsg9qHtkH9wcPSz9IEVQD1NSba+W5lby+7XUup1ZwyxxYMnAJIwJHVPh6efz9t7hzf+2aiCBMnQrvvy8MDu6U+fPFvnW66ws6fAKPvAXAlz2+ZErbKaXW1+q0zNg1gzlH5nD4hcP42PuwdauYoLWgQKR5LVggUniK++FCrQtM2zWNfG0+JhoTXmv3Gm93fBsL47vj1RwTAy/NOMn6XdFgFY+RXRzBnePx9I8jKS8eKxMrNozYQFGRmBwz7en6UOtSxTvUR40UmHFDbtLe1yHNG7JdINMNottWuBt/fzFZrYWFSFNLSSn9+ptvivaYmYmHubnh/xuf3/iaiYl4b2i1IkoYGSlEUUSElr17I1GpvImOVhMZWbm6FY2m/Nqkkml41taQkVFWGJ0/X/6cVK6uZYWRp2f5KYT/Jar6fZSeLpwcDx8WNxUOV86TBBDvi2eeESmqQUFC2D6Mc0WVx8My6Lifqe4+0Oq0XEq5RF37uhhrZJ9WBvk5uD94WPpBiqAaoKbfXHlFeey+sptFpxax9PTSUq+dGXuGRs6NWHp6KRsubCA8NZw+9fsQ4hNCK49WmGgMeW2ZmcLm+qefxPP69YXAaNeuetv73nvCDY7gedD3JQA+7vYxb3Z4s8JtknOSqWVhcF1buCKBUcOc0WrhpZdEPUpRkaEfIjMjmbhpoj6KVMe2Dt/1/o5+/jexqLtDjhwR0bN9wtcBT0+R9jd8uBBpX38tojI2fieYvTiK8IQ4pn8WD1Zx4HYMvP4Vk7Z+lCN2YHcFJvvc/KAl0+z6jwSHS5DlKkRSlitkFf91g2utqu1cVaqKBJKO3NxU3NzsMTdXY24u5hRKTBQ20BkZt3/M4nmMfH1LiyMHBxE1io0VUY6jR4W7n15ol8DJyTCxaXCweNSp898SRtXxfVRQIK7h8ePipsgff1Rt+549RUpdUJCoIXwYnf9q+ndBcmd9kJidyKn4U+KRcIrT8ac5m3iWvKI8Tr10ikCXwLvU6v8W8nNwf/Cw9ENVtIG8X/cfwczIjJ71etKzXk+WDFzCxeSLXE69TCPnRnhYe6AoClO3TCU2S9SpHLp2iOm7RPpZT9+ehPiEYJPUjY8nB3H1ishJmjQJZs6s/okuU1PFXCgAXH4E0mrjGvs8z71UsQACSgmgTRc3Me7CYF74fjbzxo1k7lwx78xHHxnWr+dQj79G/MWasDVM+nsSkemRPP7747zR/g0+eaRsCmF10KKFmCPozz9F2l9kpJjcctYsYQbx7rtivS9fC+LJlkEAdLYSA8b8fBg6VLyuH2/m28DRF6Dhn2Iy15vQISSLvUGLKl4h2wk+TzA87/8s2F82iKTroslG7YqrpRvabHsyLU+gTfamKM6f/CxL8vIMmyuKSEnMzb2xbkgN1CI09FZXq+rodOgnvb1dEhOFs9rmzWVfa9pUTPDbvr2wfDc3LxsB+w//dpTCxERcj6ZNhaPj77+LPo+MFEJz/3747TfKtUCH8q+xnx8MGSI+J02b/vfEp+TBJK8oj3OJ56hrX1efPv7Vv1/x6pZXy13fwtiCqIwoKYIkkgccKYL+o9SvVZ/6terrn2cVZNHXr28ZgwWAzeGb2Ry+GRQVxCXj42PP/PkQ2CoFczN7ih3MqgOdTjhRXb4M3t7wyy++DH32BHFRdnQ4Clu2CHeqW7HszDJyCnOYG/8sXT87ws43vuKLL4yxsFCXKrxXqVQMCBhAD98efLjnQ74+8DWDGw6utvMpD5VKDPT69RORn5kz4eBBGHz9sB4ehnooELU5S5bAE0+Iu+0ffiiiFd9/D+Q6wPofxQPALA08DsHTPcXzqDb6/eyNOAjlTEhrYWSJg6Y25qratH1GCIjQUMh0PwzO58qsn3H9cSNueV3wsHPB2tycsNzdtHLuQgfPLvhYBlLXvBl5eUIQZWUVsX//MQICmlNUZKRfnpdneJR8fuqUiBBVhI2NEIj5+Te76tXDyZPiMXt2xetoNJVLF7wbr9V0mplKJT633t7Qv79wegSRZnnypIjArVolXOrK48IF8f4uiZGRSKdr315EjRo2rJ60W4nkRhRFITI9Uh/dOZ1wmlPxp7iQfAGdomPVkFUMCBiAoijEZsaW2d7Z0pm9z+2lrn1dNOoqTK4mkUjuS2Q6XDWxaVMRU6em4erqgK2tGltbMXgr/lvy/xv/WlvfO0taRVE4nXCajRc3suHCBvZF7TO8GNGVsRY7eH9mHusilvLx3o/JzM8kxCeEbj7dCPEJwce+EgrlJsycCf9bvhCjolocXPQYzZuLQvcePYRVtpubuHsceIsbbDpFxwe7P2DG7hkA1NV05PInf0K2C889d4Z58/zLDfcmZifiZOmkf/79oe+p51CPXvV63dF53YzYWGjVShgIFDNtmogUWVkZln3/Pbz8svh/6lSDk165NPkNBj4DOg38sl+kufluhpBpqNxOoKgLS62uQsXigYv1tWF5hflsPn2EoxevsX5HPCcSD0PT30ofQ6eGhECwvgaWN6+et0jsSC2lAe4mAUTb/AGF4OPsQ217d+o4uuDr6oKnrStu1m40cWlS7j5ycoQI/OEHMZgupnlzGDdOWIKr1eULqbw8MWdSRIQYaJ8/L/5euHDTZj9wGBlVTjyZmupISoqmXj0PLC011SLAqjKhLog+OXtWRI127BB27lWhWzfhAFkcjXKuXtPLe8LDkn5yP5KWl8bp+NO4Wbpxdt9ZHn30UdZcWMOQFUPKXb+WeS0+DPmQnMIcfjz6Y6n554JcgxjTfAwjAkdga/YQ5nXeIfJzcH/wsPSDrAmqAV59VctXX93+nSFr64pFUkV/b1xmbl751JLcXHjnHfhqTirU3YKLphGLv2zMI4/AlvAt9Fzcs9ztvO286ebTjT71+zAgYECVznHbNujx6hKUAU+jURlx5MVDBLkGAcJYoGdPERGwsxNzzrRvf+t9rju/jqdXP01GfgY2eJDx00q41prZs4sYO/bmt83PJ50ncE4ghbpCBgYM5Jue3+Bl61Wlc6oMeXlC1F26wQfB3V24yj31lEEEv/WWqCHSaAzz9/j4iHl1cnJKbq3A4OHQ+A9Iro/dH8dIS7iuqIzywPU4eB7AyOcAxt4HyTWNZHm3Ywxu3wydouW5tc/x26nf6OrdlTaebWjp3pKBywdiojGhs2d36hcNJP9UPw7scOTsWaDOHiGGrOKhwRrw3n3b16P7gXw8XE1wc4Od1qPIMY7CzdoVLwcXfJ1d8bR3IS3ala2r3dj8WyN9BMjOTqRljR0r0qpuh8xMiIqq2OkuKqrqTnclsbMTc0a1aydSx24VBavMa4WFtzzsPcHYuOri6cb1TE3FZz0sTMxrFBFRtTbUrSsiqs2bC2FUv37Vxdm95GEZdNQkRboiwpLCOB1/ulR0JypDzF0ws+tMGqY25NFHH+VS2iWazm1KQ6eGNHFpQqBzoPjrEoiblRtaRUudb+oQkxmDpbElIwJHMCZ4DMFuwTU2QfntkpMj5ovzqv6ftCojPwf3B3ezH6KixNQbDg7VutvbQoqgGiAzs5BXXw3ln38CCQsr/WVpbS0G9Lm5oig8Pd3wtzoHOEZGt4462dqKiMuPJbLimjUTlsleXmKdHVf/5t2d73I4pmKLqMf9H2fNsDX65xsvbqStZ1vsze3LXT8qCho98SeZPYeBWsdLwS8xu8/sUj8sqanC8W3/fjF4+vNPUVh9K84nnaf/H/0JSwpDo5ig/f40qpT6LFqk4qmnKt4uIz+DGbtm8N3B79AqWiyMLXi307u80vaVUmYRd8oHH4haIDc3kYa2fbuI9BQPAFu0gG++Ee8RRRED/UUlSnucnMQ1eeMNkWqkxywVxjYB22g4MgY2zNO/ZGws3nd6Nzjbq+B8GvOgdej815BvVKI2qAQO5g50r9udz7p/RmZ0bQ4fhmXLRJqiuzu0aSMGoCYmQrDu2p/N1ezz4BgGjqHgFCrmPMpyBct46PApWCaWPojWCFJ9ITEAAtZUeN1M8l0JOR7L339fX9D3BbCJhmwX3G1c6dnBhZBWrrjbuOBm5UaAU0AleuPmaLVCcBZPJltSIF2+fPO0vZvRq5cQR/XqGcwcKvuVpdWKVMCqiKfsbC0nToTh7d2AggJNlURXyf/vRBDWBHXqCPtuJ6fqSUE0Nb2zKL0c/FUfiqIQkxnD6YTTOFs609xN5D0fiz1G8I/B5W5T27Y241uMxz/Zn0cffRSNkQatTouxxpiE7AQWnljIXxf/Ytsz2zBSi5tmPx/7GZ2iY3jj4VibWt+z86tuHn1UfG8vX6FlYP+avVMgPwf3B9XdD4WFsHYtzJkjov0BAXCubIb9PUeKoBqg+M3Vu/ej7NtnzOzZsHq14W6+qyu88IKYdNTTUyxTFDG4KSmKMjLKCqXK/K3OXjQzEwM0S5d4lLp/k+OxkZRamynSpAPgq+tFG7Pn6FRrCGmmp0k3usDMS4NRoaJxreZ08Q6hp18Inb07YGViRUEBNB68hotBT4CmiGcCn2f+gJ9Qq8qOLnJyRG3Mxo3iDu+CBdxUyBSTkZ/ByDUjqWXmSMy8t9m0yQeNRgipAbcIWJ2KP8X4jePZe3UvAAGOAXz/6PeE+IRU9dKVITxcFNjn5wsxMWyYWJ6XB999J+ojMjPFsiFD4NNPRc1Q376li8oXL4Ynn4RNm0TKXHi4WG7ZeCfZg7qBSoFla+G8wfnu6FE4mLiNnw8v5FT+eoqM0g07zLeGFF8cYkbg1CCMbIeDXMs/h4J4I3Xcn8I/W64L2hZzDOImujWk1qVhQxUjRgjXOx8fMddMaKi4w3/unJZ//kkiNdWZiOyz4LdeiCSnUCGUTLMM7fjnTUgKEIKp7VdgHVf6Ai7YKcRStjNMaACO5ee3meS7MS43Bjc3ITZ/zx5DpuoatR1c8bJ3wc3aFVcrV1wsXXCzdrvteaMAsrIM0aSICNi6VUzaW54T3a3w8xPRjPJswd3cbr8GqLp+7IqKqi7Aquu14u/OmsbU9PZru4yNtVy5EkqzZgH6tMSqCLAHLPhQbRRqCzkWe0wf1Sn+m5Ir7uq80PwFfuwr7uTlFubi8ZUHAU4BNHEWUZ0mLk1o7NwYOzO7Up8FjZGGnRE7+fHYj6wOXU2hTtyFXDts7V11Db3XJCeLGwFK0wVoWv3IoYmbaN6o5tL4pAi6P6iufoiKEg7CP/8s0v2LKZ7CoqaRIqgGKO/Nde2aeKP8+KPhjaLRiIL58eMhJKR6fuQURQzMKiukVqy4jTequlDYN9fZDXveQW+WMGgEBC4rfxutESaJbSi42gSCfwJNIQ7RT/G4sgB7W02FKX4WFvDKKyJiAiJKMmnSrZuoU3Tk5eexedNWVq/uy28r0jAyy2fDMjd6lp/dp0dRFBadXMRrW18jMScRC2MLrk6+WsqRrqooiohsbdwo6hu2bi3b3/Hxoj7o55/F+qamwqJ8wgQhhIprY3x8RCQCxCDx009FKl1+Pqh7vYauzRdY67xQ/XSUjHg70In3YKNXpnLW9ksAXCxdaG07AOuogYTv6MLhA8alB5qmGeB+WAiWw+MNy5/tUjr9LdsRrrXWi6I2Lt14coSaIUNE3UbJz0JhoTEXLxoc3ULDFE5FXONSWhiFtqFwZhjkXK/R6joNOt9QNX8dVZ499hcmYZRdm4TseHA9IZztCs1FpCnLBRbuNGzwsh/UKn9WVUutB2+ZRusF0y8JL5KpxFLbwRVXKxdcra4LJisRYfJ18K24k8uhqAj27hXCaPVqIZZuB41GCOIbBVLJR0W20/+FQUdRUVmBlJ0t0uj+/VfY0J+qeG7iW2JkJNLrdLqygux2BO3doCKxdLuRrcquVzwH2N1Gq9NyOfUyp+JPYWliqa/NTM9Lx+5TuzLrq1Vq/Gr5MThgMB+EfKBfrihKhelqhYWF/L7ud6Kcovj1xK+Ep4brX2vl0YoxzccwtPFQrEysyt3+QWTZMhjxXDpMrAeWSVgktyX8vc242tdMZOu/8H30X+BO+kGnE5HFOXNEuULxd6SLiyhlWLQIHB2F+2pNI0VQDXCzN1dxyPCHH8Qs7cX4+4v6hpEjRS3BvaaoSKT37NsnHjuPxBCXFwExLUBrWmZ9Ly9o3Fjcpc7OFkLqoOtzJDj9ic4o+9YHPDME9r0BvlsgIgRigkGpfJg+MFAM+m6W6mdhUURo6AE6d21Jn6V9yTQ/g+rPFez6rR2dynFOu5HU3FSm7ZxGbdvavN7+df1ynaIrN3J1M9asEVEoY2MxcPP3r3jdEyfE/EE7r4/jXV2F8PvgA0Mt0K5dwkmumEuXRFTo7/3RMMULTbYnKqt4hhRtYOkHPcRKbkehyRKCLQbyzrNt6feYRp/ek5kpxNeU0nPTliVgFdTZg4XfAXJsjoNRgeG1bMfrttsqNBoIGrSFvl1r4WtzjaFP9K7wi1anE+Kg2KkuLAzOXEwnNPksqZrraXXFKXb2ESLS9e0lbHW+NGgAmc0/4JzLu1BkBkl+IpqUGGD4a3sVtW0cOvM4UcdkFSeiTVZxkOl+g2CqeOJaa8WDL9wNgum78LFk6OJwsxYiqTi6VCyYKjIOURQhYosnd92zp2IHtapga1u+OHJ3L+LSpR08+WRXzM3/+4OO4vmhTpwQtYc7dlRt+6FDhXlJUJCoNbKxqZ5oVk6OjvDwazg4eFBQoK5UGuL98Itccg6w6nM3VDhfsJPowtNE5p0iIvs04ZlnyNPmAtDRqyvbntqBsbE4fuufW2Nraquv3Ql0CSTAMQBzY/MqnUthYSGzVszi1QvC7trG1IanAp/iheAX9DWp/zWGj8zk90XW9Bl1nI3OISimaTjldiD8vU1Ym957sSdF0P3B7fRDYqKY4H7ePMONWIAuXcT4tX9/MY4MDhY37UoaQNUUUgTVAJV9c507J+x3Fy0ypEFZWIhUp3HjxI9wTeH9jTeR6ZEAmOkcsLs4gcTl75VJS7GygtatDfOpNGuRz4nUPWy8uJG/Lv7FxRRxB95SY0f2mk+g3mZsbODlFlM4kLqO7XmfA2Cis8UpuzM2KSGYxXSjMKYRmRkqfcTqju7GWsbDyG7gfBa0xrBxFhwdQ716qiqbT4Tm7ubDI5P4oc8PdPKuhFsDQiQGBIiw8dtvl56/qCIUBdatE5Gg4nQ3G5vSUbv8fHGXNio9itVhq1kVuop/Iv9Bh+Fi+cW9w+i6H/D665TB11dEmTw8xBfbpk2G1xwdhdB0cxMD9BuL1i0twb12PvGcIMPmAHgehAIrg303wCteokapwBLHghZ09GnDsA6t6ejTBjdrt0pcOVHHVBw5CguDs+dzOR1zgejjjVG010XzI29Am2/BqALf7O9DIakBgwZB8KBdZBlFYJUXgFFqAGnxtsTGigL92Fi4YryJDKJKCyWr6+IpwwMWlIiC3UQw2ak9+aVRlF4wfXxqHEl58bhaGiJLJVPyvGxqc/WqEEVHjxoEUnXeSVOrFTw8VPoUu6pEkx50MjPFzYcTJ+DAAeFOV5UUu3btoGtXIYqCgsRnp6r1QVUddCiKuGlWnemFVVmvWtDki5sYzqdBUwDHRxlee825bI1goRkkNoLITrD5K9TqOxNgBaYxnFTPB6M8hjh+gJFREWfPHuWY5zya1epEL68ncLC2LHcfRkYPfgqiVqtg8lp9dDnWLOi7lDxdNi/tfwTM0qlv3JnjU//C0sTynrZJiqD7g8r2g6KIG+Nz54qSgoLr9z1tbcVN+5deEuObYv79V3xf1q1rGLvUJFIE1QBV/ZBnZoq5YX74oXSxdbt2QgwNHixSo+4lL6x7gZ+P/1xqWcYUHYcOqdi3T+H7lL6khwdQENESrrWENG9AhVotBs/Fosit8UUOp27iq5n2xG95mu49dCxdmY771y76HOzycLZ05t9R/1LXvi6KYog2ffGFmG+nmNmzxY92eal+aWk6YmKy0emsSM/NIrv7c9Bwpdjw6GjY+H25Ua6b8mxn8N4DgNGZkdQ69hn2Js43FVAzZ4o6GRB3pV1dDa9ZWt78hzY/X9hlv/9+2bTFRx+FLxecJ2B2g1LLm7kEYxU1kP2/DEBrlIGpyob8aMO31DPPiBSJ8ow4+vaFiRNFyl7Jdp04IUwzKo1xDgzvJ+YxMs0s83Jbh778M26d3s2rQFtQJQOK/HwR/dKLo1Atp65GcCktjHzrUEPNkUM4fHlNnxJI/5FQYhJZB2M3Gjg2oKlHAA2dAngu6DlMVJbExwtRVPwoFkklH3E2G9BZRRlEkl44xUOGJ8zfY2jwxHqiLeVQy8iL1Z2u6gXT67vGk5iTiIuVK+ZaF3ISXEmOdCEq1JULx9xIDPeo8LrUqiUErJOT+D89Ha5eVbh6VaGo6Najdhub0rVIZaNKNT8/UXVRVCTs048fF4/164VFf2VxcxPpzM2aCXEUGCg+zxXxIA3+FEUMdqoqnk5mbSUi/wjXik4Rr5wiRX0eRSXUpnmBF11PXNWvG9bgWQrU6RglNYGEQLQxTSiI9yU/9w4L91VakWEQ/CP4rwe1Fgos4MsYyK+8yler7+3cXzcKsOpg2fbTjNjbBIpMSXkzCXtLKyZ+epBZ6d3BNJMWDiHseWlDlSNqd8KD9Dn4L3OrfsjIEPXHc+eKG0jFtGghoj7Dhomb9jeya5e4YSSNEe4xD7IIKkZRRP3A7NmiVqfYjcnJCUaPhhdfFAOTe8Wx2GMMXzmcC8kX8LD2IHqKiG2Gp4RTb1a9UuuaFjmijmtJ7qVWcKmXqBEB8DwAQQuE4NAZ8fff4Nj4JAOW99XblpaHnZkdSa8l6Sehe3v728RkxtDNpxs550KY+KwHBQWilmrNGlGEdyM39kNRkcI7f3/Kp0feFilV0a2ZHrCSBu4elTadSCtIpqDjW6KuCSDXDnZ8BEderFI6XzFqdeVc/PLyFb5YdJo8n5Wg0sFOkQM/aLDCyY7+uFm7MjBgIAMaDKCOnXiTfPH377z271MQHwg/HwStQWSUtN0uSZ8+IvXukUdKi6DVq2HgQJHGd+6cEOvbt4vH7t2iDq1cVFqRyuZ5PVrkeQCczsLhcbifnMWwYTBwaA7dtzrS0KkhbTzb0NqjNW0821DPoV6VrWgVRdTfFafWhYYphIWq9KmFtP0K6m8UbbK5dsPGKoaGZ9PY35wGDWCf6hOSCKOxSwABTgEEOAbgY++jd47SaoW4LU8g3fgo8FkHNlGlI0vFoim9Nsz/x3DJJvui2F2mPJxNa7OsVSSxsUIELkiYQHxmMrmJLsKFL8tV1ERd/7+eqxvNmukwMwsjJMQPFxcjMjPLtwRPTr719VWry69NKimYHuRoUvH75/hxIfz37BEpdVVhwAAxUChOp3N3F5+l/8rgLyM/Q29BHZcVx3td39O/1u6XdvwbXTq3097MXhgUODfhm17f3HJi0WKToKpGveJzrrEv91eO6H4mQ2UovnPKbU/txDHUihtCQY4JsbFpmJvbk5enKrOPezEJc2XQaKpHZE3b9j6ZLabD+b6sHbYOc3NxQ3XIq/uJ794TTLOYFvQTkzqM1m9zt23m/yufgwedivrh5ElR67NkieF33dxcGB+NHSu+227G5s3CATUoSHyP1jRSBNUA1fEhj4sTNRrz5hnyKtVqMUgdPx66d793k6ruiNhBXfu6eNt5A3Ak5ghj1o+huVtzTsaf5GTcyVJRnX61Xsfn0qdsOXOE0I4txcJLPWD5CiiwxsICWrVW8Gt/liKfjYTp/uJgzD60ihiVf9H9C3rX701Dp4ak56WTXZhN+1/bcyXtiv4YXub+xP4bQtGFEIJsu7JlbS2cDPOeAhX3w4oTmxn253C0JqmYxLfl/Ov78Pau/GA7Px92XjzAqzvGcS5VfMp9zYN52mEujvktSkSiRP8V06RJaUF1y3QclU5EUgJWiUdxJCHPBj5PNIgaozw0ihn29qWFk4lDLLsbBZKvSYa9r8O2T8scYtQoaNgQVq4U1tvFNGwoIkJPPy3u9owdK+4ITZwI335beh+FhXD4sEEU7d9/C7t3k0wwzhUubwBe+2BUhzKrOZg70NqjNaObj2ZgwMBbXKxbk5Eh2jdlirCGxzSjtJ23eUrpdL7n20Pt/aX2YYQJta38CHQNYOXwZfoBnVanrXBwpygira88cXSjgMrJQfS1TVTZ+iWreEirA7+KSY3NzKBovA9F1lfKP+FUb/i2RB5j74lgnoKNxoXaDq4EeLoSVN+F9k1dCfByxRLnUvMm3WgLHhVVORv/ktGk8sTSgxhNSk8Xg4MTJ+DIEZEWkpdX+e1btoQOHbTodCd45pkmBAYa86CM/7aGb2XXlV16V7biNGkQ5gRZb2XpIwkf7fmI0KTQUnPueFh73JN5dT7Y/QHv7noXEMJrZNORvBD8Ag2dGurXudVvs04nImB3M9WwoteK04yqlRebg9txWPsLHH++9Gu190LdrbBrBnpzI8Rn804FmKNjxZECKYLuD0r2g1ZrzJ9/CvFTsj61QQOR7vbMM2Bf/ownZVi3Dh5/XEyhUR21rneKFEE1QHV+yIuKRJrG7Nml70j6+oqB6XPP3fsJqYavHM7vZ37H286bD7t+yMCAgZxOOM2ha4c4HHOYpwKfwsnSiU6/hJBZlGrYUFGhTglAF3U9he5aK4hvgkpnSkCzNNw7biXfayPvPzKNzk3qolLBT0d/YsyGMTdvUEIj/LefYcsWMdDKL8rH1Mj0pv1w6GI4nb97irw/5+Jr1ZR//hHpLVVBq9My58gc3tnxDun56SwduJThgcP1ry9eLESEubmInnh7l7gUihjwVuTetybhC3bmfU0mMfpt1DozbBN7orkwkKRdQyuXytdgDQwbAIoKFu6AK12qdpI3EBwMI0bcPHKlUokc4u3bYds2hRMnQFFuNghSxNxF16NFlv4HyHc4RhHituzXPb9mcpvJgIhCfrDnA320KNAlUB+ZqQqFhSKCOHt2aYOSYvz94arZX+TaHith6R0mxBtAuhdOi6/SoIEI++/0eoQMTTgNnAJo7iVS6wIcRQTJwbxyH1BFEamxNxNJMbEKcbGiVg6Ahn+CbZQhDa+kaEr1hV/3Gg4wyQfsr5R/8DRv+oZH0KqVmPtpde4kCjRpuFgaapecLV0xynOhIMWVjDjHcsXSnUSTSj5qwhymqhQUiGjjiRPijufmzSICWVmcnMRNreKIUdOmlR9oVCeKohCXFacXOWcTz/LjYz9irBHfmc+sfobfTv1WahtPG0+9ScEb7d+ocE64u8XV9Kv8evxX2nq2pWc9Yfd5LeMaT61+itHNRjOo4SDMjMzKbHc/D8B1uuq1oL+YeIV9LX1Ap8ZnZRzOVk6l1ivjVGmUJ34nqpoiXgGzZ4sxyo3cz33wMFFYWMgvv+zmwoWuLFyo0c8jaGQksj7GjhXmS1W9f7F8uTCX6dy5/N/We80DI4K8vb2JjIwss3zcuHH88MMPt9z+vyqCSnL+vLgbP38++kGQmZkIU44bd+swZXXx09GfmL5rOrFZsQA0dWnKx90+ple9XqhUKs4knKHTr11IzU8GRYV7bg+M3EK5ml7WH9jx33kkbb4uciwSRaFskj+uLhrat4eU4NfZVfCFfs4aEHcfzYzMMDMyw0JtS9qhx8j68zs8PGDDpnw6/+VMI6dGdK7dGYt4CyYPmoy1edl8uehohU6dVEREiMjHR0t20qtx23J/PG9GfFY880/M5432b+jveB6/Ek7P1j4kJqiZORPeeqvi7fOL8tl2eRtdvLvoi1Tf3/0+03dNx9rEmsf8HmNgwEB61euFlYkV7+16j3k7NhG7Y4Bw2CuBi4sQxsuXl3Bv6fsCBP8M6V4w5yTk3cGARV0E1jEihasCVCpDip+NjUJ6ehbR0VW0Y9UUgOtJ/EIOMLJ9D8YN9cfODhaeWMiza5/Vr2ZuZE4L9xb6NLou3l2qbGV+9qy4A3ajQcnw4TBokBCxIrVOx4mIq4Qlh5KYlg3nBht28qpb2XmNrlPbtAlre53Ez0/s92jMUZwsnfCy8brtO+Q5OSJafLNUvGsxCinJJfbf6I8SgumGtLzk+voIEwCTvcGu7PcxgLedDxGTDOl6k/+eTHp+Oq6WrtibuKDJc0Wb7kJ+kisZsa4kXrXXi6XqiCYV1ybdj+MnRRHnWSyMDhwQVrJV4dFHxc2GYnHk41P9Uf8dETtYd36dXvgk5SSVev3suLP6CMqy08vYdWWXfs6dQOfAey56AIp0RWy8uJEfj/7Ipkub0Ck6evr25O+n/q70Ph6mAfgzP3zLb0mTsUzsRNb3u8tdZ9kycVMLo1yazByAh4sZ7zb4k7Bzxpw9K9Kez5ypvMuXra2YBLpRIzHvnZdX2XUepj64HzHcWNexbZvhi6V2bTF35ahRom75dvntNxE56tGj9PyGNcUDI4ISExPRlsgPOnPmDN27d2fnzp106dLllts/DCKomOxs8eX1ww/ix7aYVq2EGBoyRAzc7iY5hTl8e+BbPt33Ken5QpF1rtOZ55s9z2tbXiMhJwGutaThka0c3muLhYUQC4djDnP42mEOxRzi8LXDbHtmG64EsW8fzDk8h+3m4yDfCmKDDdGiNG+M3cOwa7mRTOfN5KlEdEmtUpP4WiKZibb07qUhNDIRa++LZA4p7dpmqjGlfe32hHiH0L9Bfxo5N9K/FhEBHTpAjPpfVM93JsitKWuHr8LLtpxv70qSlpeG+8f+5F7zxfvsbM7vDsLkhpr/rIIsNl3cxKqwVfx14S8yCzJZ8cQKBjUcBMCVtCucTThLt7rdSomyUpGxw2Phr9m4uUFsYi482xXigoSteUwwJDSmjpcxL4zP4lfjZlxOv8TwxsOZ231pmbqNp56C//1PuM0VR6RSU8X7bPnyEisOeBqaLoYjL9Iy6TsKck3066en34NJLV1O0WDgCrLsD5BofIh8VXqplz8OXE9v38ewsYEUwslUYmjlFYyFcTl5GTdQbFAye3bpQtA2bUQK6uDB4qYDiM/g+fMGY4YTFxM4ExfG1dxQtPahhhQ72yiI6AoLd6BSiXSwuOG1yTONwlRlibdVAwLdAgjyCCDAqQGBzoHUr1W/2i5XQQFERRWyatW/+Pi0IzHRqEyE6fx5yMnVgVJipB24tOKUvCR/vWAKCICrg+qQbVT+BEh17esSPtFgCPHK31OIT8vAtNAVVY4LRWmu5CS4kn7NhcQIV65dttGbiNwMtVoIoYpc7oprk+4Xd6+UFDhypIjffw8jP78h69apK66jK4egIGE0U+xO17jxzb/jdYqOy6mX9bU7pxNO83XPr/Xfa8U3WYpRq9TUd6ivFznPN3seD5uKDTjuJZFpkfxy/Bd+Of4LMZmGyHhX7668GPwiQxsPrfS+HqYBuMubXUgw301Iwdds/2iyfnlRkYgCXbok3LvGjQM8DsJznYXD5rlBsGKZwUzmBlxdhdDx9RWPkv87ONz6M/cw9cH9xLVrosTip5/E/wAqlUKvXgpjx6p59NHqqQf7+Wd44QVhsrRu3Z3v7055YETQjUyePJkNGzZw8eLFSt0tfZhEUDGKIu40zp4tBqrFOcUODkLNv/SSsCm8myTnJPPJ3k+YdWgW+doSVaWxQViv2sHRffbUr2BMV/x2K+7fj//5mA//+ZCcwpyyK2c7wqJtwj7V4yD4r8fOPZEnTH+hfXuRu/rIsvZkGV1GndKQFk3NcXKC/RH7SS2RkvfpI5/q5/xJy0vjStoVTFKb0G74HtJ7DgaLZBzNnfhzyHK6eHe5rWsyd/MOxu5+HEyzUKNmfKvxfND1A9QqNatCV7EqbBWbL20udb08rD2Y2W0mzzR9psL97o/aT5cFXSjUFTKuxThOLXqWvctbYmkJ2XYH4YU2pTcoMqWOWRN6N22Bt10d/rfjf2gVLVuf3opjxiNl3N7s7cWkqy+8UPrOc//+Ym4rHENhgiG/3ji2HRNcVvD6WDdcXcX7MS+vdGpfSkoRO3ceo3795mRlGZVJ/UtPh0OHhOCqMiod1DpvMFzwPAC/bTHUGnV9Fzp/ADoNRslNsExtg0NOa1yK2uBmUh9bG3WFKX2nT4t0xpITcDo6is/Viy+Ku/PlodWKeqNicXQqLItzl1O5fMJLpBtoCuClIHC4CJqiMts757djqv0+GjQQ7+nV8V/gbuNKgGMA/o7+tzWJY2W+j4qKhB13sTC6cAH+/ruCKIbqBsHU5DewiS7HJS8OTWpD6u/Zp3e+W1+vNpnq8g1RfO19uTTxEtnZImL0zp6pJKZnomS5UpDiQla8C6lRriRGuFCY6ios2W+CtfWtne7u5Rjsxn7IyxNRyOI5jXbtKu0Oeivs7cXEhMURo0LX/ay7Op/TCac5k3CG7MLS87WtHbaWfv79ANh3dR8rQ1fqa3caOjW8pw5hVaGk2YKjhSPPBT3H6Oaj8avlV+V9PSwD8OxssGqxGhqupLvmQ+o7eRMeLoRPZKTBdKkU9TbBsP5gVIBlxBBaX1uCXz2jUmKnbt2yDohanZb0/HTszOzIzM/kfPJ5sguyaV+7fbmOnw9LH9wP6HQiLX3OHCFIim9SOjnBs89qqVdvB88916Va++GHH8TUG4MHi9rJmuaBFEEFBQW4u7szZcoU3n777XLXyc/PJ7+ElUtGRgZeXl4kJSXdFyJo69atdO/e/Z59yMUkVmp++klNZKQQFSqVQs+eCi+9pKNnT+Wuur5EZUTx1YGvML7WmW+OfA5LNvLFj1cZ0MsOL5vKR1W0Oi2hSaEciT3CkZgjHI49zOmE02h1Wr50SuT4AXv+/VfFpbpTRQF5zPX6ooTGMOIxUOv0+9JgRIBVA4J9grEyteJa5jXe7vA2QS5BACw9s5Rn1z1LLfNaNLXtzL719chv+j2Y5KBRafis22dMaDmhSilLWi107KjhyPlYar8whas2IoziYunCS8Ev8d4eg5OSr70v/f37M8B/AC3cW9x0AtZrmddo+2tb4rLjGNhgIN+0W8aQIUYcOHB9G7NUqLsd3I+A+xFU7kdRzNL0279Q+zN8fIuwMrGip29Pvjn4DWnngln2ZSsxqajOUFfTsqWOWbO0NG8uUpdcXY3IzFTx+M+jWBv9K+40JzY/HMU0HTLc0axcydD2rXj5ZR3BwaW/Qqr6WSgqgqNHVXz9tZpVqyqfA2RlpeDvr5CRoSIzU4ir3JYfQss5YB1bdoNcO5h70pDWpy4qdQ0qy8CBOuztRdqfweVPwdq65PxS4rXCQoiMVHH+PJw7X8SJyMtcSAkjXhdqqDu62h7+vu48YZQHb1uWek87aLyob9eAIK8GPOLbicf9H79lG6vr+ygnB06eVLFtm4qNG1UcPVqJ/rnxugYtMAimktElqzjM0gMJPvnPdcGksKiWN+mUn4fjbVOPJa1Dr6fYqfg9+XVSsrPJT3YlM9aV7HjX0k55RWUH+Gq1grs7eHkpeHmJv0IgKfr/qzOaVJl+0OlEGuvJkypOnlRx9KiKrVuvX2dNgXifuJwS8+64nII970DU9ch34FIY9KR+X8YqU+rZNCTYK5AmLo3p59ePuvZ3+a7YHRKRFsGvJ37lldav6GvpFp5cyO9nf2dUs1H08+tXJSv9G6mOz4KiiPSwc+dUnD2r4tw5Fbm58O23Whwdb7tpVSYtDcLDVYSHw+XLKsLDVVy+LP6/du3mb1pTU4W6daFuXQVfXwUzM/jsS624ofH4aACsTayJmRyDqZGoEfrt9G9sDt9Mam4qkemRXEi5UPH+NaZ80+MbRjUbVea1mhgfPWwkJ8OiRWI8eOmS4b3QoYOOMWN0DBigoFbfnX745hs1r7+uYfhwHQsX3u3UkFuTkZGBo6PjgyWCli9fzogRI7h69Sru7u7lrjNjxgzee++9MsuXLl2KRXmWJA8JWi0cPerC33/7cOyYi365s3M2vXpd4ZFHrmJjczdsaCAqyorXXutMXr6aAQMucrL1o0TlRdHHqQ+DnAdhbVTFupDrFOgKiM6Lpq6F4Qf8jdB3OJ9/k9umabXBzpCi46frzDv1XsfGRhQk5Ovy2Zy0maVxS8nTVWzz1MW+C2O9xmKqrlyx6ObNdZizzBbjJiuo03sB0YWXcTB2ICbfkMYx2GUwHew6UMesTqUEVoGugP9d+h8Xcy7ipq6L7+6/OLC7PkVFBlXr5JTDm28eYubM1iQnm1PfL5nGHQ6x8WQq+Q4n4PQIWtV24dlnz3LRfCNfR5aYbKnQHKOkJtQ1q8eV/SEUhPZGle1K794RBAUlMHNmG2xs8vn25z/YlPwXLWxaYI41089/Roo6AjZ/Af+KGdj9/VPo2zecNm1iMTK686+TggI1f//tza+/BlZq/X79LtG9eyReXlkUFanIydFwLSuNsKwLhOdd4ErROWJV59EoxgyJ+JfcHBNycow5WudV0q1OYpbUAqO4FihRbSm8FkRulkWl5tepLBqNDguLQiwsivR/jYx0pKWZkpJiRmZmifeZeQqE/M8w55FVQql9mYY+SYOwr/DwyMLdM4Wt7i9Qx9KVurZueJl54mnqiZOJ003F9Z1SUKAmMtKG8HA7wsNtCQ+34+pVm0pfMzOzIvLyroskdWHpFJxmv5SOMFnGi7orqzjMU5vQ9NhK7O3zsbfPY61vF7I1CeUew7bIi54XtpKYaE5SkjmXfT8ip7AQJdPNIJKyXQyiqURhuLl5IU5OuTg65uLklHP9b/EjBweHvGp5n1fEldwrrIxfSUT2VWIKo9Fxw6Di76/gwCvif7sIaPYrJARCfBNIqacXoF5eGfj7p+LtnY6PTzre3hlYWpYXDrj3FOoKOZxxmM3JmzmZeRKA0R6jeczpMUBkDNwLd7kbEa6OZly9ak1UlDVXr9oQFSX+z8kpO2icNu1fgoPLfw/e7vFTU02Ji7MkNtaSuLjSj8zMCsSgUR6YJ4vvD/MUGjS7iIVjPKZ2iRhbJzHcfSgutXSo1bAkdgm7UnaRqc0s93fQQm3BKI9RxOTHsDdtL/EF8bdst72RPXXM6zDGcwzupuWP3yTVj6LA+fP2bN7szd69HhQWivGBhUUhXbpE0bPnFerUKTtnX3WzYkV9Fi9uSLdukbz88om7frxbkZOTw4gRIx4sEdSzZ09MTExYv359hevISNCtuXQJfvpJzYIFalJTxY+IqanC4MEiOtSqlXLHdzmvZV5j5NqRfN75B0b2aURYmIouXXQsWhHPiLVD2Bsl3KnszOyY2mYqE1pOqFR9xq1Iz0vnWNwxjsQe4XDMYY7EHCU6U6TZmBQ6U/BRHNhfFnPCPDpR1BltmoW3cUuaB6lZ59GM1i6d6df4EZwsHIlMj2TXlV3sj/qXQqW0SJzTew5etl6k56XTpU4XnCydyrQnNCmUJUfX8OWmNWidDeb4ZkZmXJ14lblH5/Lxvo8ZHDCYX/r+UqVzXRf2F0+sGoimwJ6iOYchVYjB1q11KAocOqSmcWOFo0eLOHsWunY1Ij1dxeOP65g9W8vHH6uZM0eNVqvC2FjhiYlH0LSex2/nfsHS2IrswtLFCR2iV7H35wHiidNZcDtGrybNWf2zbykb6Mz8TBacXEBb9cv88IOG5ctVFBaKN5Snp8KLL+oYOTKfY8eq57Og08HmzSomTNAQFXXrN26/fjo+/1xbJnWtUFtIRFpEqXSahnMacin1Uqn1zIzMaO7anFaubZnSZCaZmSoyMlQcO6biiy/URESUbYOPj4Kfn3Ld9U+ld//LyLiVQ14lME8x1Bo5hoq5uM49IV5zDIMJAWU2URWZY1voh3/uo8zo/S4uLhqsbXSYWxXiaGd6V2z28/OFG+KxYyqOHxfX6/RpFfn5Zc/fykqhfn0R+SmOAFlbQ0ICxMWpiIuD2FjxNyWlxPaa/NIuVsE/gvW1UoYPGts4dBZx2GW3YmD6Lv0xpqXWJqUopkxbACzz/PDbHEZUFCQlqeCRN8SgsqRIKp6PKdsZNUblRpPEc0XvdFc8T9CNvwuZ+ZmcTTwrUtgSz3A64TTPBT3H04FPA3A45jDtFxhqHG1NbQl0DqSxU2P87QJxye1C6iV/Tp6Ef/9Vc/p05d9jVlYKISEKTZsqNGki/tapc+/qqC6lXOLXk7+y6OQiUUN6ne4+3ZnSZgrdfLpV+zHL6wNFgfh4EdkRD8P/aWk3vxjGxiLb4skndQwcWPXf0+L6nMuXVdejOdejO5ch4rKKnJzrO3Q9DrUu6IWNeKRiapeCqV0Kj6X+TX0fU+rWVZifPZRdCasqPGbkxEjcrIQV6qtbX2XW4VmVamt9h/q82PxF7M3tMdOY8eSaJ2nk1Eg4dXq0obFTY/xq+WFjevPx1/0yPvqvkJUFy5apmTdPzalThjdgUJDCiy9qGTpUwaqczOG71Q/vv6/mww81vPiillmzdLfe4C7zwEWCIiMjqVu3LqtWreLxx2+d6lHMw1gTVFlyc+GPP0Su5pEjhuXNm4uiyOHDy/fzvxVxWXF0XtCZC8kXqJXTjuTP9uLuruLYMeFSpigKGy9u5K3tb3E6QVSau1u7M73zdJ5v9vxtWRvfjGLjhbS8NEY0fooRI3T88YcapniATfmDnmIcVX508+rDkJZdOX1Kw/tzQtFFB+M/eBlnP5lL76U92Xp5KwCBzoF08+lGiE8Inep0YsKmCSw+tdiwM0VNZ+9ODAoYyICAAXjaeAKiwNfC2EIvoq6kXeHwtcO0tR3Ml1+qMDFBXzvh6ipMCtavF24r0RYbRMQmoRVD+lsyaaKaVq1ELY2np0hV2r0bOnUSf3v0EDViY8eKfg8Lg6lTYeNGwDIB9Zg2YBvF3uf2E3nBhhFTj6C4HqV+5yPsHL+MC0c9GDcOwlzeh66igNpcY0kLj+YEuwXTwr0FLdxbUL9WfX2k4dLVTIb8OoWoBR+RFCnqcszMFDp2jOSTTzxo3rz6PgtFRbBjB3z9tahduRUNGoi86IosP5Nzkjl07RAHrx3kQPQBDl47SFpeGiDcD0+8dEK/7pvb3sTezJ4gpzZc/qcFP8+x5Ngxw76Cg8XnquQ8GTqdyNOvzIS8Ff3NyBD7KBezNKj3d2mRVOsCGF0X9Dvfg91iDhXsw+FlfyGmEwMwTg/AiQDsdQ1w1QTgYGlz04l7b/xrbl6ZYmghjI4ehWPHxN+TJ8V3041YWIg6l+Bg8R0VHCwMGIqKuC6Kbj7nUmKiGNgKFCFiSqbDtZgjJsq9npJnZBeHyjqeIrM4nAvaMEq9Bzc3cW4vR7qTri0nnRIg2R9mlfDE7vaWKCy/IbpkqbhSx8mR2p5qFCWSRu0Udpm/QqzuNLF5EWV2O67FOH7oIxxRswuymXVolr52x9PG86ZREa0WLl401BkdOwZbt96sZ8rSoQM0ayb6IChIOGcWG4JUF6m5qbh84aKfX87VypVRzUYxqtkofOwrKLirBmJiCvn114PY2LQlLEzDmTOiLqvYHrgyaDTQrZv4fPfvf3Nb8yJdEbGpaZy6mMK5iFSM4lrrDQlOaJeRYHIAxTTlBnEjBA4zM1HrzKlTB/J6jSTWZVGFx7k25Rru1iLq4vVlHaKzRBaEr60/Lja1UKEiMSeRhOwEBgcMJjk3mfPJ56nnUI93Or6Dvbk9RmojfL4te+27enfFv5Y/j9R9RG/ccyfcb+OjB5UzZ8Rv2m+/GVxNzcyEPfXYscIo62bfy3erH958Ez79FF55Bb76qtp2e9s8cDVBM2bMYN68eURFRWFUhRn1pAiqHIcPCyOFZcsMs2Pb2Qlb5ZdeAr9K1pomZifSZWEXziWew15dm9SvdmOU5c3u3dCuXel1tTotS08vZdrOafqJ9tYMXcPjDSovcm+HwsJCxk84y0/7Q8H9CHaNDpNrd5R85QbjBa1RqSJ1l6gXMd33AVfDnEClY+LLaiz7/Y/fzywjIq30oEWtUqNTdBipjCk63x1CB7Lxm3707lQ2WnQjj//+OOvOr8Nf04Pz38yC5Ftc/B6vQjvxrVJXFUIdSz8O5i4lR5sBgGmhC25O5phoTMjNMiEqwgRWLuPDV/z43//gjzN/8O2O3zlz0pjM2oaKxV7Oo8lJtmPPFxMwy6/D4cOQ73CUTWf3MW3FInA/WmGTTo89jauVq14Qp+SmoFapmeK8ka3z23LymImY1FVRExICkyaJuVGqsz4tLw82bYKlS2HFispt8+23wr2mIoMDnaLjYvJFDl47iJHaiBGBIwBhZ27ziQ0FWiEu1Co1gc6B+Ji0JuFYGw6vak9hrOjH2/lc3YqiIvQ1T+UJpcREMfA9eBBi44tEipRjGKTUh6QGYif1/4InH6v4INtmwt7rnu6m6aL/EwPEwJ6yv6pGRpSohyr7f0V/LS2FaLl0SQzcT58WA/fyhJ6ZmSj+LxZFwcFicH6j6yIIwZWQcOuJaePibnQzVMQ8UIUl7gi1+l4fYTKyj8fINg7FIp4Ck3g8dO0Zkb+boiJxzF/s3MjRlG+TDsCM6z+v5inwxg027joNdind8dZ2J9imD828/MvMm3S7ERpFEedaLIyOHxc3ECozp1Mx9euLQVWxO13TpuDsXPntLyRfYPvl7YxtaZg45ok/nyC7IJsxwWPoU7+Pfl6i6iA5WYibGx+JieWvr1KJon83N+GWWGY9o3zadk2le78UWnRKQTFNISU3hbS8NCa3mXy9Pgdm/vsuB1I2klGYQq6Sgta4tHslH+YYBHn/kRBUsbD5p/81WgW4Y2ICn+/7nL8u/oWDuUO5j971emNqZEpEagTdfhxEVMFpHC9OIeLX9/D9zpeE7PLT9Lr5dGPbM4YJCD/Z+wm1jL34YJI/USf86NHZho0bxff1lbQr1LGtXAr3zbifx0f3O/n54jdu7lzYW2IKOD8/8TszcmTl5428W/3wyivwzTdCDH38cbXt9rZ5oESQTqfDx8eH4cOH88knn1RpWymCqkZysphvaM6cEvPJAN27CzvgPn0qntU9OSeZkEUhnIo/hZOpBylf7Uab6Mu338LEiRUfM78on7lH5rI5fDMbRmzQRxDis+JxsXKpeMPbpLgfMjP78PzzRhQWwiM9ivhwXihnUw9zIOowZtn1cYwaxYZz2zhVtJz8+iX8oFO9K55cEpEecDHlIm082pL+wyZCT9jywguQ/+hI6tnXI8QnhFYercr9cdcpOt7f/T6f7P2EfG0+GkzQ7nkN/nlbDMLsrkC/0bDuZ0jzFhuF/A86zazaRdj4HZx/nL6dPXF64j1+DX+/wlXd1h8i9mhLGjaE4d9/xrQ9b1S4LginprhX45h3dB7jN46vcD2fsPeI/PMddFo1NF6G+rGJWJmbYGdtgpmxCcZqY0w0JphoTPgo5CO61RVpMP9G/cvXB77Wv3bjY3DDwTR3aw6IKNumS5vQFphw+oQJB/cbc/KYCUrRdRGWFAAZIiKHcbaoNdGK14Y9YUKPbiZ062qCh4tJqZS/G8kqyGLukbn6aFF0RunC/cfqDqRT3ErmzIGICEVEZ2Ja0r294y0/V3eLwsJC/vprI0FBjxIebszuPQpb/40lXhtKRNZ1U4biCJJ1LKxcDKevF9j7boGnxWSU5NkKMZQUgFFaA7TxAShRrQ1ufHeImRlYWVEpi+yStGsHXbuKR7t2lZ8eQKcTx6pIJJV8lMi8Fqh04n1UcL3O0SQL3r55zWPnnVoSElKxsrLncJ+b3AVIaASzS9Q7hryDsVkhDiZi0traDq7UdXGhgacrDb0dqFNHhYdH1Z3uMjOF8Dx+XIijQ4dKuyHeClNTCAkpLYzq1zfc4MgrymN16Gp+PPYju67sAuDChAt6C3itTnvTz1plSEsrX+zE3USLurhk06BdNG4NruHolYKJbQqXY1M4eSGFK/GpKMYZsOIPQNjj5z72BCeLKr67Yv9DDqmJxcLmWQhaWHalfBtMihzoFXWIRj5O+PpClM2fJBkfp46TA7UsygobF0uXcq+PoigciD7A+eTznE86T1hyGOeTznMp5ZI+sgYwIn8PS2Z2xPlzZxJzEvGw9sDf0R//Wv40cGyAfy1/Gjo1LHc6iNOnoXVryM1VeGtaPl1H/kOPxT2Y0HIC3/X+DpVKxcm4k2TkZ5BXlEdeUR752nz9/yYaE9p4tiE8JZzw1HDS8tJ4te2rmBubPxDjo/uNy5dh3jz49VfD96NGIyKRY8eKz2FVtend6odx48S4cvp0mDGj2nZ72zxQImjLli307NmT8+fP41fFW6dSBN0eos5CRIf++suQRuLlJe4sjBolUtuKSc1NpduibhyPO46zhSvM30VCqD9Dh4roUlU/iFkFWfh+50sz12Z83O1jmrk1u/VGlaRkP+zcacyAASJtrHVrca61brgZG5MRy7jVb3Ao+jCxRbee+r2f0Q8Mb9GT/YdzmTWtMbVqwa6jMQQuMMyvYWlsSac6nQjxCSHEJ4Qg16BSherHrlziycUvE6a9ntOVVge2fQIdPgHXk3CpJywuke9lHSMEksMl7OtfoND5ANn2B1CMs2HuMeEgpSkQcwapS9zmLjQD4+uFrwWWmB+ahtZnCwUeO8SyxABYtBUyRdsDBqwls84you3+0O+ioVV7wi/ryC8shPU/0aNpE76fpWZ35s/8b8f/KrzbCGBv6siotGvM3reQnEfGVLjeqiGrGBAg6pGWnV7GiFUjKlx3Uf9FPN1U1E6sO7+Ox3+/SWRxwxw48pL432cHjKy43qBj7ue80XEqnTrBxaxj9F3Wt1wRZqw2pne93vg7+nMg+gB/nvuTK2lXeDboWYzVJpwIj+Zw+kbDjjNdsTvzNsM6tGHi03XZn7ymQoHnZeuld/Iq0hVxNf1quetpVJpb3pktFkE2Nn348UcjVq4Un/OLF0UkLDNT3P0ODYUTYelcOm/EpVBLLl6EwrrrRATS/nIpl7pi/EPn01z9LG5uoHI5Q4T5n9S2CMC2IADzXD9yM8xvmeJXlflyqopKBR07iu+wykaobG1FSl7xZdXqdJyMvMK+S6c5Gn2K0OTTROScwja/EcHhK4mJgZhYhfBBjiLKo6iEKUF8E4gPFLb+yfUhvqmhYW2/RGMfg1mteHQWcRSZxqM1j0dnloQmujMmS3fqI0xMdSljiqEnrgnMPYlKJSy/CXkXG9si3KxdqePoQn1XVwJquxBUz5Xazrao1Td/rxQVXZ/36kTpqFFVCOwaBsE/cdl6IdmKCDepVWp61+vNJ498QmPnxlXbIeJ9cu6cEDhnzuo4EZrOucvpJFzwNqzUYDU4n9WnlJk7pGBmn4LKMgWVcTZ/94qkXr1Cdu/eyNz0n/g74q8Kj1f7txxaBpmj1cIuu2dJ814IOrWYaDrXocTDHv6aDfm2uLiAS/MjONZOoK6bA/517Gns60BTPztcnY2r9NtYqC3kQvIFTsaf5GTcSSyMLZjeRaQlK4qC+v1bFPNlO7H90VhCumgYtHwQlsaWGKuNydNeFyxFQrD41/LXp14CtPixBVfTr5KvzScnP48iypooTWo9ia97fo3vd75lMiNuRnH2x4M0PqpJtFoxTpkzR4zRisdmHh5iUtPRo69/5m+Tu9UPo0YJsXarSeLvFQ+UCLoTpAi6c65cEWHWX34x3G0wNhZ+7+PGicn6Xlg/ml+O/4KThRPeu3ZxeGNDAgLEXcTyiu9uxaaLm+j3ez+KdCIdbXjj4XzQ9QN8HXzv+Hxu7IeDB8Vs7CkposZgyxZRTwOQkZ9BXFacvlj+YvJF/L6vpBCPbgUxLbFIbcW0cfVRu5/gSPJOdl7ZWWYm9vEtx/P9o99z/jx8N0thwQLIyQYCVqN6dDKKtWEOFWdLZ1b1OsLZ/V5s3Qrbtok7nyWpW1fU/yQkKqxaKX5lV66ExVc+4VzmfuKLLpCmCi89J01OLfjsert6ToG2153iwh+Bqx1FWl6yn6gZGRMMDpfxCf2OZoUvY28PCxeWnmdizBj48kvR/4XaQs4knKH3kt7EZxuchFwsXYibGkdMSho/LrvG+3EtUIzyoNAc4gPxMmlC31ZNeHvAIDxsxDf7heQLbAnfQoG2oNzH002eJtg9GICD0Qf5bP9nFa47rtG7ZB4cxLJlcCx1BwwZbBCMGsPdUwA2fQsHJ2JkBA16/MOZVp0q7PqPQj7i7Y7Cxv947HGa/9i8wnWrwqTWk/im1zcAXMu4hufXnuWup0LFmOAxzH1sLiDex03mNNGLJCOVCekpxsRGa8jPsYDLj8DeNzEygmsxWt7YPxoTdTkCT2OMn31DgkwHEhYGp0Pz+CN+BtG5F0jXhKOzDQeTbNjyOUS1E+8pnx3w2LhS7TPHnnrWTQn2aMrULi/RyKVBmXPQam+e4lfe3/R0MUi/8fNwxxjlQZEZGo0QRLmDu1PgfACdcVmlZk9d3q8VrhdOZws3UMvUBWdVQ7R5luTkiIh7cYQpJkbHxYvZZGVZVVxwry4SEab8ErMZt/tc3PwonoOp2F7cIkU/Ea+e15zBsvycL3V8EA12H9en2F30mIG1rRYfJxf83F0J8HLB084VFysXrE2s9eK62BK6WBidOCFScRLK02X1N8KTfQzP0z3h2Gg8Ep+nTYCXPmLUtCm4uheSnp9GSm6K4ZGZQwPdE/panbVp7xPNv+SpbqiXUSnixs5HuXh4QKNGEN7yccKNK56dMfXVHDasMWbatGyu+M8QUc5Sgub6I98GzgwXYtYoD4xzIMcJVYENtb2LqBW0HxfPPJxc83B0ycfeKQ8bhzwwyse/lj8964nIaV5RHlO3TC0TJSkWIJ3qdOLDkA8BERUz+uDmIWJlumF4pnrvFoYNp0eRvexnjI3B8TNHknPLz31s6d6SQy8c0j/3/sZbn7JeGfxq+WFmZEZSTlKpiWwBLIwt8LX3xdfBFzcrNz7v/jmWJpYP7PjoXhEba5jUNKrElGo9e4qoT3VlE9ytfnjqKTHp+JdfwpQp1bbb20aKoBrgQf+Q5+WJvNMffhCTsRbTpAmMfCmVzZZP4hH6CfM/aYKVlagzalB2bFNpwlPCmbZzGsvOLAPASG3Ei8EvMq3TtDtKkyuvH86dE6Lh2jXw8Etk7Nfr2J+2im2Xt9HOqx07R+7Ub//dwe9o7tactp5tmTFdw4czC8HjMH4jfuSCeTkpDyVwPPI1j9aaiHerM+S4beVc7k7+idzDWK95nFoyXBTzexyCYY9jmxLC40278fKA1rSaH4iC+BjueXYPHet01O9TqxXGFlu3CgH377/lT3rn5yfuxLRqJURsbn4RPYZcYe+5i1jWvsBzLxTwUuBrxMVB5LU8RkXcIn8o2xG+iRRpep4HIN8aUn2hqGy1dLNmBmOHdPeVrDcaiUYx5usWf9GjUTtcXcHUTEetz2rpjQdKosmvRQvbPmyfsLDMpHzVxfnz8PvvoobowgUARQihYlFUZGaoCzHJAodLoCnA1KKAwKACmjYvoFGTAjxqFxDo0ogAJ+HKFp8Vz5LTSyjQFlCoLdQLsPjsePZe3YudmQNF6U6cTTtAkcn1SuxLPbG0KSDbeWepNvb07cmfT/yJtak1kWmRNJrdSOxXd4Ngo3QhfWJ2Is5fVJyeZhb2DHm/L6RfP1i2IgfLmRVf5EEBg1gxRKQBKYqC5n2N/r15I7XSeqI58DoJzktFal3t/eWup9Ka4pzbmfYWzxFmOY9GLgH8eWUOINIrfex8MDUy1YuxBrUa8HUvg537OzveIT0vvZRYK/7fwdSFIEaycyfs3AnbrvxNITn6tEfxMDb8r6jB5fp8O3V2i/+znWDWhevW0ioY3Ro8D0GRCSQ2FNGdYgvq+EDIcqvw+oFIGSsWSdbWCkVFSXh718LMTE1Ojqh/ys5G/3/Jv7f8NdYUCMGUV6Iyv8Mn1wXTDfMwmaXD5RBYtN2w7k0Ek31uc0brjuoF06ac97C21eHt6IqrlQuuVq5Y6Fw5eDqZM+ezSDzXkGPnE7gYfw2e6i12EtEFrrUSgmXrZ4adPzFEpImaVmDX+2mycP8zyocBT0OdveWvB3zaZRYW5iLV+nDMYaxNrLFQO5CYks+yiG8xwZKiXAt0eZYiOqcuEsLmzDA4OEnsxPaqmMBYky9euyHi+ajzS3zdbQ7e3pBRlITT5xXXez7d5GkWDRB1PrmFuVjMvLnjUFWETcl1m89rTqGuEDMjM0w1ppgZmWGiMWHTpU0AtLuygX3zhRid+c9MCrQFpdY1MzLD1MgURwtH/Gr56dPWMvIz6FWvF6YaUxKyE+gwv8NN26R7V4dKpUKn6Hhv13vUta+Lr4Mv9Rzq4WLpUm6U+kEfH90NFEV8Z82ZA2vWGH7Xa9WC558XE3T73vl94VLcrX544gkxfvz+e1FaUdNIEVQD/Jc+5MeOwQ9ztCxboinXyWn5cvGmrw6Oxx7nre1vsTl8MyAma7s86TKOFrc3A115/RCdEc2v+9fwydpV5DrvLvWD18ipEcdePFbuZHyKIu5qfPMNqNWwaGkeC5JeZFuS+MFz0jYlUXOy9EZXOsHFR8UPb+vvILY5xAaLCTFjWhIw4hdCPd8st+0da3dk7bC12JjaMHzlcJ5v9jy96vUqtU5mpphlvlgUnT9feh/W1iJXuEcPaNsWXnhBuHL5+AgBVZzm+MeZP3hy1ZNMaj2JlNw0/jl7kfC08/oUHM2e95jR9V3s7GBGUgDJqjBQVJjk1KEg1k8U3Sf7iQlrI0JKN8L5DBjlikltr2NrCy4e+dj4nkHlcZQEoyNEFh4RE0BqiiCsH/y+lqlTYfx4hb6bmuBl40UL9xZ6Zzp3a/c7LtBVFJHus2yZEEXR5c/LWSEODqIOpVs3eOQRMaN6ZZqkKArr9oazerELfy62JicH6PgRdHun1HpqlZpGTo1o49mGKW2n0MCxAYqiUKgzCKxCbSGmRqbYmdlRVAQr1xTy+W8nOHq8QC/q3DzzCWh8iSeGOTF9si8JR9vx55/Qt38+3xz4psLoWXO35vpCdq1OS5+lfUq9XrIdnWp34pfHfyE7W6TZtV1nTZ5SQZ7b5RCIC9KbfNyMVu6t2D5yO2ZGZhipjaj9dW2iMqLKXbeRUyPOjDPU0gT8EEBY0q3TWivCCBN9OtDvjQrIzjAmIwMWpj/HNe0JlCJjlCITdEUm6ApM0BaYoM21xmjtEr1bEy3mCpe+UkKshBg7PA692YTHQbBMKGfd649kfyHcQIgflWLYVzmGFaVPJldENHJL5AB3+hCsYoVQsr0qasJMrrtSpNWGLV8Yoi+PlD9peTFmagvydDkVr5BSV0S3FBW4H6t4vRtwzO7E8AbP06apA6naq0zYNKHCdd0uvUXBppnC8MHhEkysX/GOD77MYMvvqFcPHH1imRpbfl6RkdqIUc1G6SOt2QXZtPypZSkhUfx/VkEWdWzr8HO/nwFR96l5/+Y1T4XTCvUuqVO3TCW/KJ+mrk1p6tIUXwdfzI3MMTUyrdRcXxGpEdT/MgitTsecuom8NFrcpCo5z1J2QTZvbnuT8FQheiJSI0rdWBnSaAh/DP5D336rmVbYmzqSeN6Xwvh6BNf1xSrob3ZH7gZgWqdpvN+14jrTcs/5PzQ+ulNSU0Vmxdy5pX+/27UTUZ/Bg6vfmbGYu9UP/foJV9uffhIpezWNFEE1wH/pQ55XlEe/Zf3o6tkH85OTmDKl9B3KkBCRKtevX9ULcytiR8QO3tz2JvVr1WfJwCX65TpFV6WJH8vrhz5L+7DxYok6jZjmmIQPZM6kgTzft+w8KyVRFJH69fPP+iXQYh5PdGnI4k/asPfqXtadX8e3B7+tVPuM89wxNimiiV1HLhbsITmv9F3ZPc/uITQplBc3vAhAiE8ICx5fUG4hKwhHsDZtKj6eubnBktjHR1gUW1dQy+3SdjsJvR4RTz5NQlNQi5fGKhxu3IGw1DNk5GeU3SimOfwonOQsLaHO2AkU5JiRecUTVUoQqRf9yXfbBa4nYPtMUG4YIBjlCSGkqIVYBLCJgim1yxzK3siVRg7BDAp4ggkdRt5xeoBOB/v2CUG0fHnVnLOK8fISgqj44XbzIAEg0rkWLoTv5+VyKeu4iLR5HMS03gHyzQyT/R5/8ThBrkEA/HXhL/ZH7aeNZxtae7ZGm+HMTz/Bjz+KCCcIod63r7gT16lTIX//vRELiz706GGEra0oHL9bP67FKIpCka6IAm0BuQUFXI4sIPRCAZfDNYRezuRk4iGi8kLJ9Z8vJkEtyeYvIccRS7U9toH7ifP5Ekd1fRKUcwB4Wnvi6+CLj70PxmpjCrQF2JjaMCJwBKfjT3Mq/hTfH/5ev7tA50C0ipYCbQGXUsR8UBYaGxy1gagTm3DFaU6F56HJcWfQlWt6y+7/XW7PodjyI122prakvZmGVitqnfou78E/MeV7VKsUNWNTcknLzCc1M4+93o+SaX2k3HUBWDNf/DXKg8fGln6tWFSVFDHGuWCaISIq9xNHXoT9r4rUswEjwa+C2pwCC/jnf8IsBkRd2tCBIlJbZCr+ak0Nzy88BmeHinXN0oSz3/X1zE1M6feoGZ3ba7gacZah3YcQ5B4EiJq7i8kXSwkaU40ppkam5U7jcDn1MqfjT5cxJkjOTcbMyIzst7P1v1PDVgzjj7N/YGFsgX8t/zLGBE1cmtyxOQRAWl4aR8LD6T4iFPw2MHSgJXH5Quh0qtNJ/zuq1Wkx/8i8lPAx0ZjgY+eDr4MvId4hvNruVUMXaAsw0Ziwfbu4mabTicFtVuNveGXzK3Tz6camJzdVydnvvzQ+uh0URWTPzJkjbr7lXS/VtbKCp58WtdhNmtz9dtytfujZU9yUXbRInE9NI0VQDfBf+ZDnF+Uz4I8BbLq0CUtjS06Oukj/R9w4c6bsuu7uQiCMGVO5wd+tUBSF7MJsrExEoVF4Sji9l/RmWqdpjAgccdMfDkVROJNwhj/P/slvh39j26ht+DqKWPL84/P55fgvDAwYSA+vgUx+1pvt24XV7pIl4s7LzdBqxQd72TLDsvR0kfICsCZsLaFnTNix3J9tkZuEHXH9TbDzPRyt7DDxPkyM0+LSO50VBoUWGI/qTqGt4XbQ3uf208DRj4/++YivD3xdapN1w9YR4hOCpUnpdKZhw8ScUM8/L+4kFUeJ9u27XmR9A6+/LnKM27Qx2A2vOfsXI9p1Jdd7FVgkGmakR1j1Tp+uMHhkAhEZF7iYcpELyRe4kHwBe119Ls39lD17AJUW1TuWKJqKB2A/1k0hI8GeuDi4Gp9B0jUb4uLEQD692FlWUyAmCnQ/Iqya3Y+A0zmD6cP+Kai2fomTEzh7pRPf/mk8VMHUt2pBU6dg/NxdS829VJkUu8JCUX+1bBmsXn37hfsBAQZB1KWLuHYVoSiiAH32bFi79rp1s1Usdo0P0qjnQRY//wHetcWA7Pm1zzP/xHzDxqk+EN0aottgl92Gl/oFM/ZFI2rXLj4f8X20enVfFi5U88ILQjDdLyQlibugZ0ILOHAhnNMxYaQe6M+VCBU6HSLU3Khid64JBVGcrfUhOzPnlfu6RqXh0sRLeNt5A+K7RKVS4WzpTIG2QF+rcTUum6NnMkg6X49Tp1ScPFPANd0xEc3M8BTiQ5MPtfdh6hSDmU0Gna3G0NDHAc86BfwV+TubLm2ir19ffQ3Insg9+na092yPZa4lrh6uLDpVsTVyMdYm1mQW3P0Z3muEAkvUGKMzSbv5epd6GCJhDVeJZfumiohztouIYHkegALLUlEzc1MTWjYzYUyPEIb28vo/e2cdHtW9ff3PTGQm7g5JICEkwR0KFHcKxbVUqFHq7krdDUodSlugSHEt7k6QCEQgCXH3ZOT9Y2csmYTQC5fe39v1POcZO3PmzNHv2nvttbG1hYySDM5knuH4keP069MPB3sHizo4HycfYyPv7LJs4nPjSchL4GL+Rd4Z9I4xmzJh+QRWxa6yurrBbsEcve8ovk4iSU0qSMJOaUeQa9A1BfDqQq/Xk1GaYZStOdk5MamNSDA0Og0ObzsY62rrom7dzwf7P8BD7WGUrQW5BDWJiL37Lrz4okg89++HZIcVjGo1Cge7Jtox1uL/yvjoWlFWJveUBQuw6CvXvr3cq2fMaDgoeSNwo/ZDv36wZ8/1VQn9J/iXBN0E/F84yau11Uz6YxJr49fiYOvAxumb+OHVfixZIjKqkydFt7pwoUSGDEWytrYwbpwhAn39uo8/tOEhFhyTaG0733a8O+hdRrYaabwx6fQ6jqYfZVXsKlbFrTJGewE+HvwxT/a2XqFXVSWFfCtWyLp+840QucYQFycDXAMOHBCL2I9/TuC1K53R2ZTDjnmw9wWGDVPwwMPljBxmh6r2WHhi85N8dthEauxLw6g+O1pu7L0/gOD9xs+UOhWByo6kcdjquvQJ7sOgFoN4+daXsVXasm+fuGE5OAiZMDTyKy2VC9PWrdIjpy6cnWWgXtz7UfZUfQl7XsL//DymTZOGpCCDeEMheuvW8NFHQqDM97FeL2TyyWeqyGn+A3gl4BEeg1uLVFJLk9HqTY51YR5hrJm6hiifKFzedcHB1oEIrwgivCJo4doKb2UEObERzJ8XTs4VM/ZiVw5+pyHwOMrMrugu16a/QnfCXXXkeMVBcKUrXOkC8WNxKW9vQYqsPQ8IEKmbQiGZsw0b5Oa1YYMVq+RaqFTyPT8/aVJ76pRlxlSphK5dTaSod++GMzFpaRgzOwarXxsbGDsWZs2CdUkrWHl6I4VOh8H3fL3vFzxXgLvaHYDTmadxsHHg5I447rvvNkpKFOzZI8fIzYBOr6NKU2UkCYYiccPrroFdjYPF3YmH2HD6IPsvHeZw8Sq0WGHx1Y7wbgk8FSi1Lw3gofbPMbptf4aEDeaZrc/w2eHPGpw3/uF4o0HKUxte5pNjbzf8hxYeF5kr4DbqXYq6NSwbe2fAOxQlF9EiugXz9s2rZ69up7QzSqzqGqrUxaToSSgUCpQKJUvPLm103ldvfY0rRVloapQczThMlaYae5zRahVUaqoYYvcaqrKWUBJIfNUuLiv2kWV7hKJsF+kNZVMtNTyOeSLx3fMyJA+C7l+KHbTRYKTO9N0RU2+qAa9Cv7caXskyHygKFmmea3qj/+da0MdjEg/2mE2bYH+2JG7m+b+sy49BCvk7+HUgPi+e/Ir63VN9nXyxt7E37rdpbafR2qs1hZWF/Hz6Z9r4tMHDwcPC8t/exp4Z7WYwoMUAQHrtLD69uEG3yS6BXYj0lm02d8Nclp5banVdgt2COf/QeWMQLPSzULLyKqm8EkaboDAmDwo3mhKEe4b/bUm5OXQ6ubevXQshISKtNjis6vV6DqQeoHdw76su5//C+OhacP68EJ/Fi8XYBeR+MWmSkJ9eva7fOOlacKP2Q8+eokpZs0YUQjcb/5Kgm4DrdXBt3AiXL4t0KTRULjw3WsICElmaumIqK2NXorZVs37aei5sHcScOTIY27FDCI4B1dWwapUYKZg38IqOFqncHXeYMiV/F+U15Xx5+Eve2/+esaC+b3Bf3h/8PipbFWN+H0N6ienmqbJRMaTlEFpWteTFCS/i59qwwYJWK6RtYW0ged48iXhZuzDp9eIwt3lz/c+wqYIRj0FXWdCQoPGsvONnXFSW4Z0LeRdYG7+WjRc3siO5vv+sX9YMcstz0PofFSckMzjn9aHUy7JQ2E5pR9XLVSgUCvR6CB25gssxIXz4ZGeefsp6hO+77xoge9ErYPIksYP9eTeL5/XhtdcgOVmsxWfNEu9/QzPBIUOkK3TbOq63hYXwyiswf74enU6Bi4ue196sYfi0ZFbFLefVXa8CYiH+/uD3G9X6j209lh+H/sn338OXX+lJC/wCCsKwKYxgwsAW3DHDjubNISYllU2XVhNbKDVGBTZxUjdhwPr5cKxWRuSRCG2XCTnK6ALlloMEOzvwaZFBoKcngb4qAgLENvnQIamnagxBQSIJCAwUJ8K//qpfr6VSCREy1BN16VK/gWx1tRTJzp8Pu3db/6077y+i54Sj5Kqlb1FRZRF77jZlH/r93I89l/bgoPOgIqk7ruUdWfBaRzr4t8Pext5IPnR6Hd2Duhu/ty1xG2nFaVZ7gAC8N9jUx+2t3W9xKP2QBakxfKdaW03KYylNiqID5DyTYxywXa1Q/JWIlThlD+JSvBsrFJPJ8f2j0fnR2NNlSxkVt7zEeY8P6n3sbO+Mq8qVv2b9ZRyI/nTyJxadXiSWwTXlKFAQ5dGBkkIVhblq8rLUZGXrKKjMh5bbwM2S2HhuWUOrFmqiWqlZbz+T3Brr9Uw+jj5kP2OyW+v3cz/2XtqLu9rdav8Yc7OIo+lHqdHVGD/3UHs0WZ6k1UoPknPn4MTZUrakL+WM/bdUeB6Fg0/Altq6LYVOssJlch11chL5Z7NmEgy7dEmuEQ3CplqCFwaCpKyxJEy5kVBTG+jwSoDgvWKcUO5dO08VDHtKJH7ZbUT+55wlfa0MOHm3aXltrnIsIBlCN5UH+ZXX2JgK+GXcL8xsPxO4uj3/glELeLDrg02a96MhHxnlaFc7/t/o/wav9pPr6JQ/prL8vNTyONu64WBvSbDu73I/T/aSQGBGSQZ3/nlng/b8A1sMZGK0SCLKqsv44vAXxs/0NQ588uBtXDrnx/DhsH49KJV6Xt7xMu/se4dPhn7CE72esL7Ctfj/gQQZxkQLFkjw0YCwMJG73XUXeP/nvPQ/wo3aD506SRBw82a5D95s/EuCbgKux8FVXCyR97p7JDBQSJG1qVmz+gOpa4VWp2Xm6pksPbsUext71kxdg2f+cPr2lRP7ww/h6acb/n5MjJz4v/xi6vxu0Lo+9FD9wfK1IqMkg8G/DOZ8jikCfnvk7Wy8sBF7G3tGR4xmfOR4RrQagUqhavJ+0Otl0P52bdD3scdkcK+so2BYtQomTJDnrVpJEbg5PvoIbHt8x7O7HqZaW02UdxR/Tv3TGFk2x59xfzJu2Tja+ralR1APNl7YiEKhIO2JNLRaBadO6Xlw20yOV/9m+lJBC9j/jPTD6G7q7+CgcKezby8GRnTnrb1vAKCsdmN02/4Maik9itr4tLEwE/j5Z7j7bnl+992S3VmzL4GDXVvLm4UhsOC0hV1vr14S4fnoIzGJqK6WbfTAA/DGG+BTxzjpyJEaZs4s5cIFSUl16CDHR6sOuUz+YzI7U8QV7cmeTzKz/Uwu5F/gQt4FEvITjDK7ezvdy/tD3gcgtSCD4C/Miph1NlDQAndtBL1aRzBn6FBuixR3qtLqUk5mnOR4xnGOXTnGQx2exVvbnowM+DX2e77Lus+4GHVlKPa5XdBc7kr5xS7iDje71hlpzfdwcna9/ddU3HEHjJlQRlZeJTv3VrL3YCXZ+bVOVLaVoLXHrawr/fsLKdK2XonaM5fyqkpOnqli3+FKUi7Xzl/pIZH4WkQ/PReXFgnYO1hmVKo0VXg5euGmcmPf5X1WXeXM4engSe4zucbjY9DiQVYJOgjprn7F1D9k7NKxrI1v2Jq4+uVq46B8+srpRhdIazD0EgHo8E0HYrIsu3d6qD3oFtSNaO9oXu//Om5qOTb3XtrL+Zzz6KrV5GWrSUvXEpuRwqWiZHI0yZQXq6XWbM2P4HZJMg4zRlssW6G3wV0fRoEyAYAw91YUVuVTUFmATi8GKlHeUZyfa7r2tF/QnjPZZ6z/mVI/+Mis1un2O8HzIvY6T3ycPQny9CQs0JOoUE8ignyY0nay6avVpTjYOlyXehGQKH5KCkbracMUFweVHiegy7fQ7ldQ1Wo/tXbYxsymY/oCIiMliKVWy1RdDYmJpslwnb8amjcXmW5oKPj6yiDQ3t7S/rywUJZ55oxIUq8KhQ4c8oQw2VbK9VGjhiHPQu+PGv/uT7vh0q3wvLu451mBW1UbbitZh6NrNQ5O1exXvoOtnZ72Hr2J8mpLsKc/jk5a0qrPYauqQauvIqM0g9jcWGJzYimpLmHZxGXGps5XIzYrJq1gQrTcYO5bex/fnzQWoOKqknFNtbaaKk0Vbw98mxf6SiMW+zdV1Ojr9/Qx4KW+LxntuONz44n8umE716vZ8w8OnMD+x1ZQUWFqiPn6rtd5Y7fcd74Y/gWP9HikweX/XyZBKSmSwf/hB5M6RqmUjMicORLwqjumuFm4UfshOlp6zu3cKeqSm41/SdBNwPU6uD75RAbdx4+biucag62t2JkaMkd1SZKf39XTrmvi1nD7stuxVdqyavIqenndRufO4lc/frxJNnY1FBUJEZo/X04IA/r2lazLuHGmGpSrobS6lM0XN7MqdhXrE9ZTUl2Cn5MfoyNG89Opn3it32sMDx9Oe7/2qG1NqbK/sx8+/xwef1yez5wpVtOGr+blNR698fMTaVzLlnAo7RATlk/gSskVXFWu/Dr+V0ZHmAZd53PO0+P7HpRWl/JI90d4se+LHEo7ZHQH8nLwwk3tRtAnQfWkMS2qxuK4YyHnvOZBxx/BvhFXJjM42zvzXO/nePlW0yD67bfh5Zdlny5bBmv197Mk9jvj5+6X7qDwp/r1CyNGiBvatm0ykAJxfXv1VXj4YdO+rampYf36jWRmjuKll2wpqE1szZ4N897R8P4JkSY91/s5i+yCOTQ6jbFA+VLhJZ7d/iwJeQnE5SRQqbX8706nn+KFLh9JlssxhwGLBuDj5EOIWwjBbsHG6Wj6URYcW2CRPTRHtGcHzudbuv11LnmNwITXyMpUkJEB6b0noHfIM1nr2kqvEGwrIbMD/LrJ9OWnAi2j1ubIagcLzAb7D7cG7wSrs/rZt+RVt0QWLJABLQ90hoCTVuc1ZBcSL5XQqu8Z9LPrS1V8HH1Q26rxcPCgWluNv7M/PYN6si5hHWpbNSHuIfWsddW2at4e+LaRMG1N3Ep6cbqFW5bKRkWVtoqEvAQmt5lMoIsQ108PfsqTW61LUz0dPFk4eqExCl1eU45er69X92ZAbE4sueW5xh4zBZUFxufejt4WrlXRX7chNre+dNCIKpeGLZvr4K2e87mlVRTRvpEsPr2Y3PLcetkaD7UHar0X+cnBHD9O7aQnNhZ0uvoXUG9vjMYLhsfQ0GuXyeh0oh4wJzrnzsk1uNzaZWLmcAjfYnypLmtFs6z7CSm8E5tKH5KTZWBnrZ7wavDygjfflOxxU3vIZWXJPWbpUktVgVIpAYIxkwsJ7BTD+ax4jl+OJzYnjvTKeEptk6VO8PsDkNZLvtRlIdz2oDjSFYaIRXaFl/Q/SxosMtlqZxjyjMlK3NCHybHWGeXCCPjVzEjneTdQWzGFMaDSrR6h6n5pGZ72/vg6+rHNdSaFiov42oUT6BBGsHMYYe7hRPiEEe0fRuvAIJydmzZQNri+ZZRkEPiJnF/9E/ez8FPvek6PzVybGZsvF1UWsS5hnYWNv/nUo1kPoxNpXnkeAxcPNAYj1LZq/pj0BwWHRzNrlhyfGzbA8OF6Xtn5Cm/vlSji1yO/5qFuD1lZ6/97JEirhU2bJLi3aZMpcB0QII6s991n6kf4T8KN2g9hYZJdPnBAgqY3G/+SoJuA631w1dTIgOfwYZmOHJGb2rXuLQcH6+TI8J6hfuT9fe/TyqsVYyPGM2KEDHQjIsTR5Fo3rV4vNs7z50uRuba2JMTfXy4O99/f8AXij3N/8OuZX9mSuMUowwEIdAlkXOQ4Phv+GRfyLtDMtZlRcrYzeScbL2zkhb4v4GLr8rf2w5IlkhnRaKTm5YsvRM/7xhuW882eDY88Iuvfr58MNkJDYe9eeS+zNJOJyyeyP3U/j/d43ChhKawspNt33biYf5H+of3ZOnMrGy9s5PZltzd5HQ0IdWxLSrkVp4pLfcVhq8UOXDr8RYmvyaHqno730C2oG+Ge4Sw6tYjMfcPZ/v0A7GwVaB8NRaeoJvDc+2S2fQGdXseCQUtxSp7CrFlNW6fw8Np6odFaskoy2bRtE7369iKvSMfHn1eydoOQBVdlAB8+04bmA7bQN6Q3i08vrtdQ0JDZ6BbYjdmdJRtTUVPBqN9GUaGpIKUwhczSOs5ip2ah2rSIwXfvZ4N/430uRrYayW/jf+NA6gEWxyy+an2FAU/0fKKeWYUFzJzyAHisJXgkg9a21s3KzNkqtzX8ZuaQNXKu9Hqp636lUdM50od5I58mOlqykG8uW8f+Y8XoquVzVycVtw1XM3GcivAQR9r6tuWzz7Q88YQNnXoUc2CfyFpyynIoriqmlZfYCDfUkDXELYQezXowPnI8U9pOsfpXDd3tz2SLK5vh8XKRuNt9d9t33NtZvFIPph6k38/9iPSOJNwznFaerRjQYgDtfNsR6BLI4tOLySzNNDXPrDQ10mzp0ZLVU1Ybf7f5p83r1dUYEOkdSexcU/TFPGPjqnI1ERa1J36qltzt/Q2HzqdzNCWOfZpPKC3TU7XtRXEwq/SQ/WfFZc1O604rmyG8FrWcyEjJDhdqMvB18q2XxampqWH16i0EBg7n9GlbTpwQcnTunPV+Xx4elqSoSxcZYCgUpgam5kTn7FmpP2g4M6OHgBPY5HTCQa1ErYbKfk9RGv0VnJsIJ+6DlH5Ys9u2t5fgTliYnN8gxfHH6pjZhYfDnXdK9jMkpKH1sER+vtwbli4VqbVOUSUW197xhHaJ567uk3lwchh+fvDVka94ZJP1LIOLvQu/jPuFMa3HkpMDpy5kczYlk+qMVuzZ4cCmTVa/Zh12ZdLHzTtOeoh5JMo6RdQSouT+KFyFMOlVhfW/XxwIrlfqvw9yLicOgd/Xmd7r+amYOpT6Q5kfzvjjqvTHw8nZ2GeqoceDNd/w7ZU5kNqTZ70O8sQT8pmDw/WpNRm/bDyr41bT2qs1f0z6g3Z+7QDJbHzzjRynJ05ASIie57c/zwcHRGq6cPRC7u9SX3P9f4UEZWVJxufbb0UOasDgwbJtbrvt+jnm3gjcqP3QrJnUIx8/Ltetm41/SdBNwH/jJC8qkhvQkSMmcpSZefXvWYcebCtxc3KwIEc//YQxcn/kCHTr1vhSrob0dFPBd0ZtUNzGRlLFc+dC2x7Z+Dr5GCPM01ZOMw5KW3q0ZELUBMZHjad7UHerTjt6vZ7u33fn2JVjuKnceLrX00TkRzBu9Lhr3g8bNwoBsoZbbhFJmHlWKCND6qQuXhRZ2Z49Ivmo1laz4OgCHur2EHY2dmh1Wkb/PprNFzcT7BbMsfuO4ePkw9bErby681Vyy3NJLEi8pnVtFGt+kKLkayw2bquZxVlbUwao6Pki7HSudOx3mYR208GmCif3SsqrqtDbVJqyIcfmwPbajE4D9tZGHLsf1i+kRw9497N8Bm7xanDW6e2mG21ea7Q12M9rOI3onjmGwm/WiE1us4Mwc2SD897X+T6+ve1bDqcdpucPjfiLN4IvR3xJK89Wxs7pvk6+BLoEUZDU0tiD6Ep2RW2DzusjbzKHufW5OZyd4a234Ouv9Vy8qOCTT4QMWYNWpyU2N5ZDaYc4lCb1Reeyzxmboz7a/VE+G/4ZGaUZHEo7xPyj87m74910D+rOqcxTTF4x2epym7sKURkdMZr8inxyynPIKcuhtLqUGl0NvZr14sBsk910Y8SmtVdr4h429f3p/3N/rpRcsZqFae7W3Ei8AFKLUlHbqnFXuze5XqakRJrpno/V8Vf8Yc5mxpJSFku+Mha9V5xYNiv0FtkChQIUz3mDfRme+ghCnaJoFxDFLa2i6BwcTtKRJMaOHmtxPaqsFPnX8eMYidGZM38v+2IVqiJo/6tI3vxPw6K/TP28nLJFJljujYuLkJiwMBPZMTwPChJzleXLxdJ9v8m7BVdXmDJF6hyaWuRdUiLX0KVLYcvJM2ja/gze8UI6PJJF5lYL8/qbv5L+4t519xotpg2Prb1bE+AccNXeYYZ788CBI9l3tILfNiey/XiiBFKOmNUl3jkAWuxqeEFvl5kaKnf8CfxisC9vQU/fQXQIaYGXm4q/7B+nwiaTwposCjWZlOiyqFJIFsmvaBRRJ9cbZYBJU1zQ21uxoax2hKQhsPRP03s9PpfgSKmfkKZxd4LXBdj2Hux/zjibrW3DxKmxx7rvlWjyeXP3m7w14C2L+taqKrnnHTkiBH3fPlCp9Dy99Wk+OSS1ZD+M+YF7Ot1jdR/8L5IgvV7qM7/5RlQ6hnPUw0MCpw88IAHj/wXcqP3g4yOOn2fPQps2122xfxv/kqCbgJtxkhsig+bZomPHGpBAWH4Tu9FPUuN7VG7kVQ1vOz+/hqV2wcFNj3rU1MgN8OuvYdfJSxC1Sqbg/Tzneobn72mDu7sUZ++7vI8J0RNo59vuqjc4vV7P5oviAGRI33vYejBv8Dzu63pfkwY/NTVycfv8c+sF8KNGSTGotd++fFlB376QmllG6y5Z/PhLJQ5OlhmNhzc+bCQ5J+4/gavKlZWxKy0yH5subrKoefonYOmEpXQL6sbFizBsYyOtq488BBtra5WcsuEZP9CoUNmocVarcVIb5FJqgorHs//t1ygpAYVdJS2fnkmiaqXF4p7v/TwOdg6092vP7ZG3A7Ktl51bVq/7uUGG5a724NLZQD7/HFauBK1eAzpbWrQQqd70WZXk6RLxcfIxWtluvriZu/68i6wy6w5jHfw6cDrrtNXPbml+CwtHL6Stb1sm/TGJledXEuEVYWzu2sm/KxVJnfhzuTMrVkjkuzE88YQMKrOzhVzHx4tUMb3JPFYvkhzXNHBJl8eE26DMl1tugZxOT1Hmfox7HdYTEuBidMSzdctB6ZRPUVU+maWZrIlfw6LTi4xL9XLwIq+iaQ2U9ty1h7a+bfFw8LimBqePbXqM4upiPNWe9ciNr5MvHfw7NHUj3DBoNGICEHO+kv1xCSQl68g505HYWCioKISn/RvszeOU2Z/JVX8RHa0kMhIuqH+jW6sWtPGNoqrI3ZjVOXlSCMfVr98NQQ/NDgvxabPMJJnVqGiR8DF9VHPrkR1v7/oERquV2pxFiyRbY5BlK5XSN+bOO8W10KEBl+RKTSUX8y8SnxvP2cx4dsTEcSYjntLNz1MTM05mCt9UL1DhqnI1kpvZnWbTP7T/tW8BsyahAD+f+pmtF7dyIvkE+QghN0CBgvKXyqmpUBMTA0/vnc3R8qVoc8KhIAzyw0yP+eFQFGJqXNsEdOkimbSA4Ao8mmUREKije6uWBAeDm7uWRzc/QlZZFleKM8ksySK7PJNyjaT0urqO5mHPdcbaqTc1ztQorKf77C6OQ/vbKrGWB2nSrVELWSr1F+JU5ifvNYSgIxC1sjaYpUCtbpgwFRWJeYsBS5eCi4uenzMf54/UL3j3loXM6X4/Li4mid//IgkqLBQ1yDffWMr7e/YUo4PJkxs+B/6puFH7wcVFAiYXLpiyxjcT/5Kgm4B/ykmu0cgN1TxbdO5cHRldn/dgsBRXuuUNpEX5FBTFIZzc0kZ6YyAXO4O1Y0NQKiUNas2wITRUDB0MF8G43Dixso5dxfGM45YLWrcQx9j7mTFDjBQ6dmz8dw0Fy4bMUGFlIRklGfx06ic+PPChxbz3dLyHdwa9g5+zOBydzDjJxgsbqdJKs8Ljpys5fa6K8iqp77A9/Cxtvbpw6hQQsR4GvEpwy0ps1VX1ZFu/T/idSW0mceECdLtrOUVDrUuHzLF6ymrsbewZ9VsDKSdg3oB5dA/qzqG0Q7y//33KappYhdxE2O+bR3XsYLivaVmQSO9ImlUNYfs2JfT83Pi+n5MfQa5BBDtFoMiLIGXbKE6ur00d2lYKIarwwt3JidtGKxgyRJzl9Hp46ilT3yXfgBo6PPMk24ql0eXtkbez+PbF9Rz2morUVJFifvutiXw4Ocng7dFHJWtnjuKqYjFmqDVlSMhP4ELeBd4b/B6uKldC3UPZcnELM1fPvKb1UKCgvV8HnnU9wYwZtQMzhbbBzJC/v/R8mj5drLUNYzm9Xs+5lFzW7Ezj4IZwNqyq3S6Rf4plsXtt0b99nePkwnDI7CSDYkO9Q9xYWLZSMhp+Z2DKhKv+DyVKdOiuOp/+NbnI5JXn8ebuN4n0jsTXybcesXG0c7xqcON/BXq9uCaei9Wy/1wyx1LiiMuNJb06llJ1HHjHQswdsOkL+YKqCF5wNy2gxB9yoyAnSh5Te5maBl8LnLJg1hDZp7UIsotmbq/7eaDnHXg6eF51EbGxQnx++QWumKm6oqPl3Jk5U67p8r/1ZJZmYmdjZ3T2O5x2mOmrppNSmGK8Rltg12tEXHmdadNgwJgr/Jn9Ia29TZkdf2f/Jh0XGp2G1KJUEgsSScxP5GL+RXlekEhKYQrZT2ejslUBcPeau/n51M8W3/d29CbcU+ykPx/+OV6Oko2uqKlAbatGoVCg0Ujd46lTMp08KY9XC2Y0FY6OEkSsO/k2K0XtnYWfn55o/3Dj/527YS5ZZVlklmZyON2ydcJtEbfx55S15OfLsdh5hROVuvpM2qbGDafModivWU5eXu2YoPtX0Pl7yRaCZJYOPSEZ7GuGHpofhNRbAGk5sHOn9MT5p4yPmoLjx6XW5/ffTQEJJyfp6fPgg+KE9r+KG7Uf7O0lmJya+s+ohfqXBN0E/JNP8pISObENpGij1zCqmm2tP+O+Z2H7+zz+OLTrmcWykkfwV4diVx6CPj+UsivB5KYEkHbBk0spSokQOmXLDdi8QLy2waCNupLQyjG4tznK8baDrK5ba/tBZF70paisUupZCltwyy0QNeN7Duo/o0pbWa+/SI2uhgP3HKBXc6nA++TgJzy19SmrywfYOnMrQ8KGAPDt8W95YP0DDc77w5BVjIsah6cn0O43mDCjwXnNJRtfbV3PI7ungkaNvY2KIF81ajvJVJzMtCxiHxc5Did7JxxtHet1LFfbqhnYYiCdAuRKm1eex/7U/cbP5+2dx/akplgowQs+B9mpeoJDaYcs3vd39qerxxDWp/4ib8TeDoUtoMMicPz7d/mvR37NnK4PMX++loc/3Ad395cPNCoo9xLr2wov3Oy8Geh5FxGKkSxdCpeyCqDZYQIHrOVKkPSFivKOYs3UNcb6lb+D8nL47TfJ8Jk3+x0+XJwAhw5tumvPiYwTLD+3nPf3v2/18x/H/IidjR0phSm8svMVyw93vwx5ESgKIlDPmIbaxhHn4q6kH+2C7konMVSodpYi/fDN0OVb7D1y8Hd3R++cRlZlGtVacYFytnems39nMovzSSi0UhdW4VHPYr0ulFpH6Wtl/I57bR2MOwSadfTL6Ahrf5ABusYBAk7g4qDGz8MJl+aXUQaepMzjMNn2h3g06Df6tuiBvz/sLPiZh7ffjaOdI90Cu9EjqAc9m/WkR7MeRrOE/6vIzzfV6xw9CqtW6ygsrhEpE0ijz7H3CDmyVj9y9EHYIOcAdmUwaq5YSedE0cIliki/lrQMtaW6Rk9G+SU0uaHk5cG583rK744SF7xzU+D4/ZDaC7VaQYcOlnVGbdqYTEzy82Wwt2iRrK8Bnp4wbRrMnFWDU0g88XlxxOfGE58XT1xuHPF58RRXFTNvwDxeuvUlNBpYvCGB2adqIwyVrrLeea1x17amf5tI7hvdlRG9QpoknauoqSCpIMlIdOZ2n4u9jaz0XX/eZZGtrIu4uXG09pb12JCwgdOZpylKLmLCwAlE+kYaHdeuFQblxalTorg4fFjs841Nnv9b8D8pgY0yP4iZga0taLJrr5NKDYx+UO7J5sYPNrUarvjb4PdaV8e6hNwc5Z6QOBRWmrk6dl0g1wFDdqnUH8p9QGdrdRFqzxzeWXyEJ0aN+kePj0DuFUuXStbH/Dxo00ZqfWbOlEzY/zpuxH7Q6UwOxdnZ9d1ibwauhRtYP3r/xT8Ker0ejU5DlbYKZ3uT5c7losvklOU02ITwjvZ3YKO0wcUF8v1WUdT1GKEdK5lU7suaWFdKasxSPYUhxiZ3n518E9xfq78iXrVTF/hk6KdMDX2cJza/x7LL1ovFtUDiV7Gwuje08Aan+r0Z4qv/AkMJyd4XoLAFBw7AAUU+DDnX4Dap0pqkJ872zrir3YVM2KipqaxB5aAiqTCJvsF9cVG5oNXCq7+uZ/nWSii+11iA7u+t5pbuarp2UuGsUtOvVXterjVS8yjqj8ueTVxOUuOoUvP5Ryp69zDJsMyjqw8PHU1Pz1IGDhTSGTlCJANGxzRtDc9ue5bPDn/G6rjVjGw1ki/Hf2lscNkQvBy9GNNauo8VVxUbC8/DPML4eOjHnM85z6H0QxxMPUhOeQ7BbsE8WBPPiwsOsNGtB/mz6uupMkszWV8qBCgg+w4yVnzP8GFKNn/7KBSGykx25TDsCej6rbyucAeHwkbX9VDaIcZFjuP++73JcN7O+yn2aKgWcux6xTjoKwJWrx8Ax2qzHcExMHME5kPC2NxYIr4SofWi2xcxq4M4NGSUZPDbmd/wdvTGy9FLHh3k0U3tZlE35ugI994rZhY7dwoZWrdOehls3iwZoUcfbZqTVeeAztRoa4wkaEjLIbxy6ytGN7QhYUPo93M/HLUBuFZGU6w2kzb2E5taPVBROxV4nYPhDQ/kqoHLAHWSO6XVpey5vKfe/H8MOMPuQ8V8tzGRqpGzIGGUuFa1/63evDqbcmz0aqK82jAycgiPt32XjAyMU2Zm7fNsyAyFDJW8rsroTAlQAnA8BDB1X33d/Ad6FsAAF8opYfel3ey+tNv4kadNc16NWE3f8C74+0sNne3/4F2osLC+G9u5c9ZqNJWAyvSyKBgW1wYxVEW1NTGx4BMrj6lmbn7e8dKYtBbJtZM5Tj5fRpsIR5RKBWuPLCXnQghx5R6cKIAThXItMgS/jGukxCSdAkAPzpkow+Jo2y+ecf1a8cKUQahUcC47gbYL2lndBkqFktMX8nhoqbi75eS1hOa7IK81/i5+TJ2iYOq90L17Q/3XTNK1tfFr+TPuTyPpqevgOCRkNM2dWlFVBV7KltgrVQQ5tSDIIYwAdRh+dmH42IbhrQzjxI6WHK2W+pWqqlHYlw8nIyae9Ymt+VNjU/v+fzbprp4YvXHI7GQiyoCFv4bOFtaKxba9vciU7FV67FwKsXHNQmWvxLU71HifILb9OMwNaB01gVTaZKNTaMAxn4g25czqJP3N7O31PF3wJDUW30Bc+Mq9IXEoux5dgr+/zP9t7Hu8e+QFnjwGBY6vcH/H+y2aZv9TEBcnxGfRIlNTcHt7mDhRsj59+tycpqb/S6g2c2lXqRqe75+KfzNB1wmFZYW8sPQFWrZuib+LP3d0uMP42Q8nfiAuN85qE0KFQsGaqWuM8z6w7gG2Jm2tR2gM8gLtq1rjQG/yH5P543zDDeJKXigxkqZ71tzDT6d+avgPfJTBZ/P8cXaGT+Ie4bzzV43+314FX3N3m4c45vIq38Y33BW8p+cobrV5FmVGDw5lbSdWt57SAgfKikzuV0Y3rNhxxsZ8uKeIZMfMJWvwABWzpqsJD1XRNtwdF6f6kYy6kY7CQnFz+eKbUi7fHgbO2SjOTWG4/TxemhPOLbdYXuSOHZObtl4vA+dOnUT/vnu3nOBLl8Lttze8XfbuhaHDNVT2fpFRHk/z56++FoO8JTFLuG/dfVRqKgn3DGfN1DVE+0Q3uq3NkVyQTK8fepFVlsXAFgPZNGOTNLTT60kuTCa9OJ02Ln0JCoLKSj28fpVUR20R7k8PPsrduwahyG6LPn40I8Nv455xYdx1qCOlZGJ/8hGq988ROVxtY1hKAupZQHs7evP6ra+jvKSkc8/OHEo9yeEtLVn5iyfVNvngmIu9Wx49/AaQfLgtaWlIk8QRj4FjrvT/qGP/PX/kfOZ0k4anu1J2MWDRAKt/RalQ8sHgD4yNBy8VXuKtPW9ZEKXqIi92rPdmwwovyjICodoFNzchS3PnipTTHFqdloNpBymoECvmr45+xbErJnssf2d/Xu/zPl/vWMmZ6ob75vxtVLqK8YT/KUjvTkRAIP17eGIbepjsqsukFaeRXpzOlZIrpkFGZgfCt5/EYegTnPGtlS9mtYW4cWLNndVeahxqZXm//CJR/8b6jen1EvG2SpbqPC8sRIrcveOg2SEIOiyPvmdBqYMPsiWCDNDvTWzbrMWttAeBup60cuhJhHc4gQEKY92Svz/GxrX/bRQXi/uageTExEgj3L8LX1/rJgTh4VKfk58v9WBxcXAk/jI7CxZxpeY8paENOBj+sA/bjN4073KWrIG34W8bRYRHFJ2Do+gTGYmXPoqks16cOCHW/3l5gKpY6ka8ak0JvOMtrMJ72N/NOz1+pHVrqNFX0WlJICHOrWju2JoAu9YUJ0Wye1VrMs+FmzJcZhg9Wq6h1TV68qozyNFcJFeXSD6JFCkSKba9SKl9It1PH8GmKJyqKkhp8TpZkXWsOKtcpSYnLwx2vA15tdXntX22rqU+50ZCqZR7g/mkUFi6h90MKBRy7tSV3CU6L+ab9Pup1lUR4hbC8knLjU2TdXod+RX5ZJVmYaO0MTYQrtZWc9+6+8gqFUleVlkW2WXZJtlj7O2MLFnNunWgUOhRzVPV61OmRImvky9Dw4ey6HYTuf/2+Lc42zvj5+SHv7M/fs5+eDp4WjVCuh6oqZEA5YIFco83oEULMTm4+245T/8v4kZkgoqKpL8lSP3gP4EI/SuHuwl4cvOTfHrYlBF55pZnGNJyCH2C+zB26Vi2JW2z+j0bhQ2aV02xHIM1ZUOoeKnC2Bdn7oa5rE1Ya5RRmReKq23VLJ241Jj6/+3MbxxJP2JRWJ6TqeLLT9VoKtW8NmEKr78kvTku5F0gszQTfY2axAQ1sWdUnDut5tQxNVeyqsAhX1LhvT+ATj9eNUNg3j9g/+X9DF0ylBDXUHzsQ3DRhmJXFoI2L5TS9BDyEyJJveDeJO21tSayzZtrSE7eQbduA/j2WzsWLarV9TrmYD/mCapb/wYKPbZKW+7vfD+v9HsFf2d/QAqCe/YUIjRjhthmg5zY06bJhVOpFLe7e+5pcLUYt/Bx/sz8HLKjmVkaw6KfbCxkVycyTjB+2XjyK/I5et9Ro3SjqTiRcYJ+P/ejtLqUaW2nsWT8kno3jNmz4cefdAy++wBtJq3g88OfN7A0sM/qxcd3zeCRTQ9bvK/GDRdHe2Mx8Te9N0PiMB580Gwml3QI3QUTGq+XcbFzw7GwK1knukN6NxwLu/HSI0GMHKlgzx7YulVs1cvKANsKIVfB+6A4iMGdInjy3ub07qMluewsHxz4gLzyPHLLc8mrkMfSanFZMu/UvjN5JwMXD2x0vVwv3EdxTb44ypnJk57q9TQeane2J29nV8quRpdxLZjbbS7hnuGEuIVwJvsMey7t4a/k/2BUbQZ3lTslie3Qpnbls/vH8eSZ/ujQsXD4Iprnz+Kvv8T6PibG+veVSpFGjR37n93IKirESrYuQbqcWcqFkhiqLtxCRobMo5sxBMLqyDvLPSG9B6T1gP3PigQHqVM0EKK6BMn8uafntUdvS0ulJubcOXFrW79ejBD+DlxcxCo6JASaNdNSURHHkCGtadnSFgcHGYRpNDJVV8v1qaxM1sEwGV4nafayz/M+SlTxlj+SFy5SxUu3yus2y2FSIzWJhSHwWYo8N5iXmEOnlF46ea3h4nBL1zT0WLPQBkBZIzVpnhfFUvrcZBPB7f869H/D+vcAftkikiuA5gegxV+WhgTlXlZ/186uPulobLKz05GTk0ZYWBAODjbX9N2GJrXa9LypmUy9XvovGeqMDLVGqdb9Q6wj+g8oDZDaMb0N990nx7tKJSYqly+bJmvukYBs51lDIGE06k2LCPHzIDhYjte6hKlZs4avBVqdlryKPHYdy+SOGXZUp0fx5pvw7AtV3LvuXtKL042Nsc0xLnIcq6asqt0metRvq41yXwNslbZCmMKG8tNYU/D2hxM/CGFyriVMTn64q92bVEt2+bKpqakhY6tUivHRnDkwbNg/p6npjcKNIEHZ2WKgBZIh/Sdkzv4lQTcBOxN3MnBJ/QHXG/3fwN/Znwt5F7BV2uJo54iDnYNFHcjM9jONJ3FsTizFVcVWa0VUtiocbB2uS1Fxfr5oxFNSxNveMMBvCMVVxWy5uIVbPCZw7KiSw4dhUf79XAn4Dkp9Ie52iB0P6d3BJQOP0Es0b5+Ce8glprWfzPQBnXF1FTI2Y1XDdTZfjfiKud3nUlwM20+f55tTn1F0KZQjW0NEqlUUIhmIa7QdbtECXnoJbJudYlH6C+xM3QyAo50jT/Z8kmd6P8OvP7ry0EMy2IqPl0GVARqNRIl+/FFev/8+PPts/d9ZdGoRd625CwDl8lXozo9jzhxxxTPfbbnluZzPOc+tIbde0/8wYGviVkb9NgqNTsPTvZ7mw6GWhhAnTsj+tbWVi39AgDjvjVk6xqL/EgtOMXp8KYs+jsLrg4btqkGyHmfmnMHZzoXpU1SsruXqs2fD6bPVnMw6ijbkL2ixQ0iFbcPdzAH4JJVQz2Z8+CF0HpiMo40b8ac8WbtWmgaboIc2f8DoB2iT9C3dO7jTqn0+t7QLpF+oSLLyyvMY8esIqrRVaHVa8ivyySi1zFKNCB9BbnkuWWVZRlnhtWJK9DRiUtKJLa8vSQNwtHVkWrtpKFDwx/k/KKqSgoFdd+6iX2i/Rpe9JGYJP5/6+W+Tog7FL3L6t4k0U7Xhju/e4N0D7zC1zVR+m/CbxTUjN1cioN99J6TI6v9wlM7wgwaJUUljWaLGoNebBv11H6uq4FRyKnuSDnEi+xAXKw+RbXMcnbJW6lrpBu/nmyL+Xb8B9EKQstqB7tpv4jY2pr5l/0zUuvxVustLzwvwaARUO8GZaVLrc6Ur2FWIHXxJbY2VYy48exUx/uu1t/rWa2Da7ab3r3SBhNFSm5YbKVnChrZt8D5o96uQHs9EqXFSmm3QxdukKSlIjeHY2ULAasmNbUk4IS7SJLRHq1a0aqG2IBRXm+ztr32g+k+vR8nLg9OnLQ0YYmOtHKcKLTwdAE45sGg7JJvqbB0coF076NBBztcOHSRIWFAgGamLlyrITHUwEqSLVQfIP92LBsmtGcyzSdaIkpeXyMnuvlvucZs2CaEAMXW4Y/UdxrYXb/Z7k/HR42njKz7KVZoq7l5ztzG7ZOgXZkBTCJO9jT1+Tn4MDRvK92O+N77/48kfcbJz4fJ5fzb94ceu9f7oK10ABf7+kv2/7z75D/+/4EacC6mpsg3t7eWa/k/AvyToJsBwcHXuGsmevKNsS9rO9qTtLJ+0nFuai1vKyvMruX/9/QxqMYjBLQczpOUQWni0uMqSrz90OiE+GzeKhefx46Z0pjlyy3NZG7+WVbGr2Ja0jWptNQdnH6RnM3EVO5d9jtzyfDzLbuHYERujI11MTP0LuEIBUVHQpXsVYV0u4dvqEgqPFNJKL5FSmMKlInlcOHohI1uJdervZ35n+qrp9dZLobNDX9gctn4s5AskuukdKySpuFmDxZoG2EfsQjnsOSq9jgAwq+IYi98XV6Y334RXXqn/Hb0enn8ePpC+cDz9tDw3jC+PpB/h1p9upUpbxau3vkrrjDeYOVO+98wzQpwa4q87k3fy8cGPWTxucZOcnAB+Of0Ls/6UWplj9x2jS6Clq1Tv3tLB+Y034NVX5b1NFzYx8jfZvgEn5pOxdg7LlkGPoZd4dvuz/HHuD2OfmIYws/1Mtl3cTsWRGRQfG82kHr1Z9rsdFRVSVLp3r5ZV6y5xoTKRUp+dQowLQyHwqBT0Bpl1Wyz1hcyOEG4y6pjWdhrdAruhzQ3nmUNT68njDLCPn8Y43W8MHQoDBmlo+bP1i7qt0pbxUeNZNnGZ8b37192Po50jXg5eeDp4otVr2ZdwjrNn9VyIs0NT6SDHk6FRIjCQecR9+xJXVDvgTutGHwBvD3ybF/u+SFFlEQ9ueNA4AHCxd0Gr11JeY/o/f4z/k+EtxqLRwJgVg9ibvsNiWX4OQYS7tKelczsUmZ34M3kRxX6bocxbtPieiaaiZwM09hYE1BlfWioG46GLwF3bCldNBJ7VHdDV2BndJPdY53T14OIiAQVDJqMhglNTI9M1311sqsEvRuRzdmUWPVCMzWcBahxk8J7WszZr1NPobPk/CadsIQ1dvhOCt7zWNt4lXTLuJYHSTNcgX3O7LH1/Fptl0Z4MkmymzkayOrmtTdskbpwp69JlIYx8GGysdGsFyG4j29YzEYL3w8m74GitQUP0H3B7nTR4jQPkh6EoDMPtzHN4VfTC1RVy8qtIS1U2SlYdHCylgeHhpql5879Pvi1W7x9OgqyhstJkm27IGp3I3UfFtL5Sn/lhdpOCAGFh4D7gJ+KbvcBH0fsY0SOc5s3lPlRVJUYP5tmjS5eamE0yg4ODDILjzZKVr78u/YSCg8E/UMOstVNYFbcKlY2K9dPXM7jl4AaXV62tJrssm8zSTNS2atr6tpVtoqnkzj/vFMJUK8szBJkAxkeNZ+VkOW+ys/UELlChpY4kT6vGS+3P2DZD+W7sQuP7P5/6GVeVq4Ukz7z++v8KbsS5cPGiNIx2cbm6o/B/C/+SoJsAw8E15pVXUJw/D1qtcSipAFAoeORuP74KtqycbenRksEtBjMkbAgjwkfgZO90w9f1rbdkUKxWS18cc0vqnLIclp9bzqq4VexO2W1RzBjhFcFnwz5jRKsRjS6/vFwu3oaC3MOHreujHRzErahHD5m6d5dIk4EoxGTFsCp2lZEgXSq8RGpxKhqd3LwjT6wjbu1ombnt7zCxljDpbPB1CCLAIRQnTQi2paF4pE2lICGa5GRz+YEeolbL4Pyvd00rFngUMjo3OdvUoQMoXDI536cr1ep0PHPG0PbsamxtlOywHNMyZozc2JVK06PCpoa1oeGU2V3GpaYlQ/JX46NrX28+a48HlR/iqgigs+3Mep8vWybuRSCZKJUKPs0dyrnKbbRVTuDsq38AChYuhAS7P1AqoY/HRIp0GcSW7eejPV9RE9jE0TEQ6d6Bjt49AT0X0uJQuduRU1JAUNltOB55nYMHocB/JUyZ2ORlNoqdb8DuV02vO/1AM19XukR70qO9J16Onqh1ntjqnNFoFI0O2s2fZ2dLZBMAdQFMngQt/5Ii4O3vweFHwP0SzVvn4uqfR6HzAZRVXlTZ5lJjk4dT+m2oU25Ho4HcDi9R2umdhv/D6TtgdW2D2lEPQbcFlp9XO5mc9Y4/IJkAkCxA+yW1jm5HofvXIiNyT7l6Bg7gx71CHHS24oLomio1F3kRErVvrKfIzYBCJ/KqoMPSC0ddx5IroyMsNHNhDDghJKDmxl9PDYP3Vq1k0OfiIpk0tRpsbDTExBylT59uODnZWmQ97Ox1HM3dyfdnP2NLsqkRmYfag7Qn03C0c8T3Q1+L3jbmaOvbls1jzrBkiRyvsWX75FgpCCPI35477hDTj6goqfVIK04jMT+RhNxEgulDUhIcuhjLqpLnKHe4cG1/usoN5YXR6PY9JUEMKxmF8HCx1/bwMPWYyc+XQVNioqgQGsvK2dkJ4TaQInOSFBpqMp25Gv4XSZA1PLnlKT499Amcngmrf2HkSCFH5nbmFjC4CxrMNQ4+Dls+xcPDMmPUsaPsp7rbU6+XLJU5KapLlJrarN0vsJrS4RPQ+59lWtUO2jVvYZFd8vL6ezKqSk0lWaVZZJVlobZxoOhCOxYsgBVrKqgZNUsMP1wzsXXPolphqnubEDWBFZNXAHJuqOapjOMKA5zsnPBz9mNY2DDmj5pvfH/x6cW4qlyNcjx/Z38c7P43GgbdiHPh7FnJQvr4yL3zn4B/SdBNgJEE3XcfihzrN60aJRwNgu23t2dbJ1cOpexHozRt/kufQnAREBJCeu92eN02BXV0ezHav07YulUsgvV6+Okn6fhdo60xNhXdd3kffX8yuT519O/I+MjxjI8aT7RP9N+W4mVlmXoXHTkikzVrUV9fEyHq0QO6dbPMUmm0Wv7YcoWvlqRw4M82Yu0LQoIGvCYWsVYGgWunruW21rcBcM/Hy/npyhOSNSoMFbmGQWqHXpr4ZbUVYpQwiqtKBmyq4c6BEjXNiYLvDzXagNYq/E7D1HES6a52hDU/itXt9UTACXigi0SKv7gg2n2HfJG2GDIbiUNg41cymBr8vHzunAW+Z0D9N8M8Z6bBylqXMqUGnvWWWgJrGZ6CFnBitjTvizQzGninWCKfox6CTmYGH2u/k7qI/PAbVyitrBHjhm4L4OwUWPE7xmNCoQP3ZOnP4hcDrddKtH7TF3DyHnHY6/VZ48vf9ywcfFIanI6bJdImxzxLmRHA9ndh3/Py3P8UPNhIw4qstpAyQGpFzkyHXtYdHK2ZW6BXiIuZgRRt/tQs6txIfUgT4OAgU0P1FXUnkHqipCQZdAGyzb0SLE0XUnvLcQtgUwUvuMr2y2ovtUVpPWnl1IMZwyIYNFCalup0pnolc2OHuq/LrqFNl62t6OMNdUp+flpKSi7Qt28rmjWzwd8f9pX+zJtHH6O4yvr5ZG5oM2DRANKL0429dCK9Iwl1ac3FQ61Z9as327cpjE5lDg4wbpxc0/3aneOHk98a++ckFSRZyIg+7vcdDrH3snQp7Lm8C+4aAEXNJINUEEagOpwW7mGk6g+RYb+HGnUaOFsZ4Xx7VOR5IM2vW2009TzKiarXYDQ0VGS6XbrIwMnbW4w0EhOFHBmmpCRL16m6UCplAG2NILVsaWmi8X+BBOn1esK/CCepMAmWrWTPwvH0rb1NZ2WZ5HQGSV1cXixMmgS+56Tea8c8yao2cI20tRUiZE6OOnSQ/dMY6maT9u6VmhsDHBzMskk21XJtM0g4sZzPWt8k89okdQNxmeJiMXf55hvLVgjdukmtz5QpcjyU15QbM0iOdo7GJszlNeXMWDXDSKYySzMtsvUToyfyxyQxoNLpddi/ZV/P6c6QRRoWNowvR35pfH9JzBLcVG74O/vj7+yPr5OvsYfVzcCNOBeOH5e+ds2aXWN92w3EvyToJsB4cA0YgF1GBmzZIlejkyflClUXs2dT8ssP7AmBbWFw0RPWm7nZ3jYNtreEvjUBDBnzBIM/X0eHtBqUB816vjg5iX1Z166ifYqMhLZtG1zHy5cl85KXp2fiQ+doN1mal97S/BZjpEOr03L7stvpH9KfcVHjaOnR8nptIgvodJCQYNnU9fRpicTXRevWwgMvXJCLvDkMPV8GD4YNG+DTzzTsPpYjEXH3S9h4pRDZ8xJvDX+G6ICWaDSw8Nx7fBn7QhNXVCkR8zPTLUlTtVkjT9dUmDVYyMJ3R0wuRtcKhzyYOA3Cags19j8Df73TuLRPoROrXYd8ueFNGwt7XoSSIHnvShe4WJu5u6ePEDUArV19GZUBGns4dbfJBc4akgZClZtk0hrCzjeEmAKcHy834YzO1v+P22WpJTo3GWpqRzBDn4JbaouDqpyF7FzpZr3YusJdBmPxY+DIIw2v03+Ctkul2Wizw5I98T0j5Kduk1IDjj4IF0bK/8qudWXLiRZnq+gV0PcdU3PLGjWcnA27X6l1SKytDXHIM5GivFaQX9sPxDsOBr5s+tw509KCftt7JimZ/0l4sPPf/tvhSR9y8bSvZKMGPy+DGQNByouQ9cqLqB3cKHB3l2LjkSNhwAAJYqhUDddyFBfLIDgxUSQ1W7bIYOraIOSsRQvoN+4i67z7kVddP0TurnbnqV5P8fKtLzd5yaWlTXPFy81FrOW9EmT/GNzX7Cpg2Sqp6dDZwexe0PxQvd8J1N5Cc3UUj7X4joBadzx/fz2urkI69++H7xYXsXJnImX2iSJX80jErUUiNj6JvD3oTR7sdScAO5J3MGixpWTTTmmHpzIUTXY4BdseRBc7Rj5Q1tCrj4bpkxyYONGyFtJ8Gxw5m8fuc3GcuBxLfH4sGTWxVC1ZTk1ZrWzIWiazxkEycrmRsPUjuS4B5mS6eXNTDyMDQfL2lkJ/Q9bInCAlJl6dmAYGmmeNtBQXn2DSpI60bm33P9nvJSYrhg7fdIAaNa7f5JKX6dSgIcOSmCU8uP5BymrKcFX60y/nd7KP9CcmpmnyNnMEBQkpevZZkbc1BVu2wIgREmj98UeR3icl1bB69Ql8fbuSnm7D5ctwqnINeeke5J9o2oL9/CxrkvLz5Z5vHnN2cJBm03PmyHH0d1FaXWqU3TnZO9HRvyMAZdVlTFs5zUiWskqzLNp1TIqexPJJy4GGCZOH2sOYYfps+GfG938/8ztuajdjhsnXydcYnL5euBEk6MABGX6Ghcn5+U/AvyToJqDRgysmRsIqTYQeiHoY4utEYbzLYFAyjLwAs6zwKu64AxYvFuuy4mJYudL4kaZ1a+YrQ1gSVMPF9nEUuJqiv81dm3Pp8UvXnuW5914J+4SFyf9rX5u1atNGrljXeLeprLSU0R05IhHB6w5VEXhdEKLkdslImOQxBX5bD602iI2sXaX1ZZR7Cilat1AG5qoiaLldIqmFobWFzdeyPWsHBQqtZB26f2366MBTYgvrkC9T7DiRRoGpcLohHHkINn4tzk2Phdf/XGOPssoLO40HbhWdUDjmkeW62fhx86rhBGbfyeFdnpJ1q/Dkobs98XFz4ortASLs+5FScJmvt62T2gV3Cdcr9bbMDfyZLzMado3ztPdngO8k/ByaMy30Cexr7+ovvZfB9m0KcZ2LWgktdgoJaAgalfQiAob4zuLZVotISIBNm7VscJiGPqOD1CVd6QqVHsavubpK4f+tt0o8Yc+eWkdAm2oZvPrWZnf8YmDbB0JkALp9DaMetvz9nGghOdltZRDc+XtQ6LG50ouAvSvwsg/E1VVOCUdHIQT2Kh1Zrhs45fo2WXaHUerteEKZhK+6WZOyJIYpNRWGvvcy3Po2SoWSrdN3EGAbwdcfZnH+fHt2nUyDzt/JNjSQJqccEwEDeje7lbM5p1k3bR23/vz3zDoAOH6fSb5nV4Fd6mB6hEfQKdoN3+AiAj3cuXxJSUKCmDLk1m8d1iSMHCkudsOHY6xxqIu04jQOpx3mUNohDqUf4viV41RoKvhg8Ac80/sZAJIKkhi+ZDg9mvWgZ5A0dG3v197YmNMadHodWaVZBLgEGN97dNOj/Bn3J6nF1kOh6uogAuLeRHP0HtKbfYHO+yz4nBc5Z0Zn2V6A9OzJkvPVM1HqgzJrM36hO+Guht0OX+zzEm8Pkp5UWaVZfHzwY5o5hZN5Powjm8PYva45mmqTxLdrV5g6FSZPlm34d6DViqwtLg7Wnd3BkazdXK6IpdA2Fq17gmVW/r0Ck+HD0Keg9TpT1qi2ISy5kVDlRmCgJTHq3FmIjUIhg+usLOsE6eJFU7+XhuDtbT2DFB7+92VZNxpv7n6T13a9BnFjmKRdw/Ll1udbfm45U1aIgmBQi0H8Ov5X/JzFukurlUCieZ3RyZOWJKIxXLwo8sSmGFO8+aYYqxgk923aWI6PdqfsZvAvg1HZqFg3ZQvB9K4nuzOX3zWVvPn6ylDkWrNJfxd6vZ7iqmKjsYOLvYux0XlZdRlTVkyxIEzmtuGT20w21qlqdVpU81T1CJOXg5eRMH0yzOQWtPTsUiOZ8nf2x8fRBxvl1eX7N4IE7dwJAwdKJvFcw60d/6v4lwTdBDR6cF25IqLt8+dNvsvWMGcO3H8/dOqEHoj1kWzQtpawKxRKa7Oo/ZNhp8lmn20toesV8KjEdJeog1vvhr0hptcqnZJhkaMZf0bDbeXN8Py8timmt7fccTp1kiuen5+kYqKi6q/v1e4WK1bAhAnyfNIked2ihSnXHh0ty/f1tVi+Xg/79oktdXr9Xp83DF27go2tHjs7sLNVoHFM43i7fpSrTUxMqbdDpzBdyKYXnSYrpr30Dun9PgwRuZKt1hXbshB0eaFUZ9dmkM5MF3tTc7TcLnalAJ9chuLmIk97tFWD6+lx8SHc939NWRmU6fIoe9hbakfAMitxbqI49p2dBiPnQvf5Epn9/pCR0FDjgCVZ02Pf/k80Qx5D5yIDup7HzlN2KYozhjGzXRnBLw8lVX+I14P30j3gFo4fR5rM2lYy94OdVDqv4qPpH/LewfeMDUYbgq3SluqXqzmZeRK9Xs/ru19nfUJtjUS1M1R4iFRMUXtca1Sy7t8dERLoUCDHl1KHMmkYX434mkmDw8jQnqH9N5ZSUnVZK6qSuqFP6w5JQyAnGhsbaDP0CLpun3M254wYItQpGg888hNXNt4lL3zPwr09THK+pauk8NwcrTbC+BliH1/iD3/8AZf7AFKz5e4utRIeHuDhqacmaCfV7mfop37M+P5uzQf0DRxKt+Yd8fSU95yd6592d72+m0UMAIWeZROXMbnNZIvrUU6OHcuXw2+/WXZDN0KhQ4GSKVN13Da5AF3oNgqrc3lkU+MZNafSdpQ5n2l0nkZxubeJMF0cAedra8VsqiHoCCE+3owa4MXw/p60b2tD8+b/mYVtjbaGM9ln8HPyI8hVMhLW3CrVtmo6B3SmnW87ejbricpGRXxePPF58cTlxpGQl0CNtobyl8qxVQpxn7lqJr+e+RWQSG9BZUG93x/ccjDb7tiGXi9R7IwMiElJY8nFz7hckkhWTSKFiiQ0SrNzePfLsPMtee6eDI+3hDIfkX8arKRrH5X5kfi5euLuLu5i1uDiAo8/LrVC4VZiItcTmdkadpxMZn9CLOevJOMY8xixsUKa9DOGWZihWKAkAL6KlUwzCCGsccTXyZ8unRXGbFHnztYJsHnd0cWLkJCg49ixAgoKPMnKavye5eraMEEKCLh5BGnoL0OlzcafP/Ljo3dz993W56vWVjNg0QBjM+erDYz1eslmmhOjU6eELFmDi4tkhsynNm3q22jXNV86eLCGgwdN46OKmgrGLB3D9qTtuNi7sPWOrUbDJWvrePgwvPiiZV+fvwM/v8ad7ry9G97HOp2llX1JiTxWVUkm5Gp9zPR6PQWVBUZC5KpyNZoZlVaXMumPSUa5XnZZtgUhmtJmCksnisGOVqfFfp69qUcToECBj5OPUZJn7ha7/NxyPB088XPyw0vlxeFdhxk9avR1I0GbN0vmr1MncaX9J+BfEnQT0GSGPXu2yWe5LgzVtefOmQnga5evhMPNhBSFOTXjjo53woQJ5PbpjM+z0oOw6xXoexmUesh3gPkbwL72PHp6KCzsAqMuwPhYGHEBXJpQPw2IkHvdOvj5Zwllv/pqg2TLAmvWiBMAXP3usWkTVQOGs3Qp+N81jGFsJZlQTtOB80RT6erH9Ee9UQb4ccZvMCdOYJwaKsYLD5e+Pz16yM/v3w+rVtW3cfTzE57a0AArLjeOl3e8TFvftrze/3WKq4rZf3k/I38byfuD3+eZW54hKUlBtyfeoyDyE4myW8HpB08T6dGeIUNgT9UX4tLke95yptzWIoHr+w44ZUmUuLiZDPqrXKHMF7eqtrRy7kKLFhDaQk9QcDWBviq8vKDC/jJ37etFbtUVohxv5RH3LWxap2bdzkzo8Tl9A0axd0kf48+1bi21WcXFtf2UDLArg1vnSWPFjWZZKUPGatwd0GGJ1PAsOG0pD6yFq6tIedzcQOWbSmXwei4EP0+NjakW4lb7R7G119DVcwgfXB5Xbxn1oLXjOW0Z0T0vkxUXxidXxpHp/meDs3tUt8PPxYcczpJXVf9Aab75EKkJnhC+GUY+avqg0g2HknZM6N2e3q3aMaTlEMI8wwA5Nd/6KJ/viydKlkqvkEzRgaewIJSeF1HNGkeV+1nQ2mK3bDM1CQ27ylnAvOYnYRTseQnSemFra0mgnLzz2dm6A7il4ZxwD693/gEPD3Bx0RAff4jhw3vg62tnJFCJidLw9/ffJSZjDa6uEoCYMQN69tKSkB/H8YzjHE49xsGU45wvOIla78n03Aus3ptAZlEu3FnH7UlnIzU5xUHg2rRIRm/1/dzaJgJ/b3se22LaFwoUuKvdpemtoxfT207nkR5C0Co1lfxy+hfjZ4bGuJ4Onk2SkhRWFrL83HIWnV7EgdQDuKncLBynGkPyY8mEuocCcCrzFGXVZayMXcnK2JX1LNid7Z3pHNCZKyVXuLPDnUY5XmJ+IuFf1mEjOqUEQ/LD4Ow0QvPvZehQ6NlLR05xKYWZrvWkeNdakOzufvV+SwEBcrm/3gP/igo4eCaTXefOc/xyLBcKYsnQxFKqjpX6tHJP+CAX47k0fTREbBDLdPN6o9xIyI1iSNcwunZRGLNGoaGW62x+b66stDOSI/MsUmLi1esZzJ3s6pKk6+Vk1xAys3QEdD0MuZFcSfIgwCyWtjVxKwNbDDQScvMa378DnU5qy9bWlmRGRAj5OXvWugWyoZ7IQIo6dZIYp15vasMxcqSOe+9dx+jRpvFReU05o38bzc6UnbiqXNl+x3a6BXUzLremRoYdCxbAdjMTxJAQiRXPnm3qT1NVZdknKSXF1Per1qfqhmLyZDEiul7Q6XXklecZs0juane6BkrtXUlVCRP/mGgkTDnlORaEaGrbqfw+4XdALMrt37Kv5/hqq7Rlz1176NW813+8rmvWSAP5nj0l6/dPwL8k6CbgmtKMSUmSFQkOlsKYhAT49dem/VBwsMje5s2D1FRO/vg246oWc0lVP1+s0MPvSYPZt3Qiu20jOFIzEHUDrqg3BAEBcodWq0Xr1giWTFnHHcvE6U1/NRnZ3r3Qp3YgX+sDXeUfQoZnNJcqfDmf7UZyWRCZ+PMLs4xfc7GrxC9YxcXE+ssfMgTmzpVahoa01nq9HoVCQUVNBY7vmMI+ec/m4engSUaGyHNiYstwbnaZ1z+9hFNQitECfOHohbiqXNFooPWTD5HktcD6DwF+qlBmOf4OaT05mLOFpMqjXIl8BdK7wR/Loah5g+51Pm1jyB/bF61dMVH6iTwVvIx7ZwvD++gjaQS5a5c4xj30kOl7NTUS3TKQoqIiKCrSU1yskLqNwot8nTOOyrUfSZ3UnA7gfgnPS3fhvvsn8vOvLkUxwjkTSv0wDnQmTZZeQI1h1WKprzh+P6DH3V1BxNxnSLffSbr+eBN/+BqhU+JwZTht3LrTsbUXPdp70dzLkwEtBqDVKBi78BG2FdTWTp28G9YvAK0pLOrgVkrgnNk4BCRx9KG96GvU5OdL/47GptSyi5z1fYU8v+US4QBI7g97X4KkQch208PkiRC9SupyFp6QzFkDsLU1yz55SEHztWRajXEPpUbsmouCTR8+HiqS0rqocIcr3Qg89yHBXc6RrTpIlt1BtOUuaA48jMY+V+yhB77a5PWY1WEWP439CaVCSXJBMi2/sF636KZyY07XObw7WJwfy6rLGLBoACczT9ZzgjIg6dEkanQ1HE47bLSf7xPch9ZerVl+bjkl1SaHqWauzfBy8MLXyZcJURO4r8t9jPx1JFsStzS6/jPazWDJeFEExMZruOu3p7lwOIyCpDDJ8BSG0qqlPXfeKZf6hvqYaDRyHi9dCsuXy7lbFz17CqkxN4C4ll4eanXDBMn8ua/vf04CdDo4e7GQg+fSqbjUhrg4kdkdbDWY6sCdpvPAHFUu8G4RxutIW4mW+yqimDI4gt49HGjfvoa4uI0WA3BrqKiQBrnWCNJ/y8muLgz3nF9+kexdx46StQHpsfP01qf56uhXPNf7Od4b/N7f+xGL34OHH4b58+V68eefck8EuT/Ex1v2Mzp1igYbm4eEyD41kMshQ1JYvz4Ie3vTPiirLmPkryPZc3kPbvbu/DxwO7Y5XfjkE+tZn/bt5dZfXm7ZXNg8M2NoNvzfxPjx8MIL8p8byybdCGh1WnLLc42SPA+1h5FMFlcVM37ZeONnueUm/fGi2xcxq8OshhbbZCxfLuYT/frJ9eifgH9J0E3Af6y1TEsTP+NJk6x/bmcnVyEDxozh9O09eUO7g80p26lo4Ceb759P6rY53NHvMq/3/ZCdJacZkmpPcFyGZSi4SxepJWrRQu5oXbte+3/4b2HdOhhda419lavNwvuPsya1M0eOwOa8rnTlOFcIIBtfcvAhBx9y8SaDAN7jBRwdJe1+/6h0fILspR232d1dr9dz15q7WHx6sfE9f2d/Xr31Ve7tfC9lJXbcdpvI+dRqUUEZVtUcSblpTH04jqMXUrD3ucSgiSnsyPzDoshyVodZLLp9EXM3zGX+sfn1luGlbIlDVQiRsYvJTGhGcjKU2aTKINXrgkRQbWpg16uwq76ZQP/+YijRooVp8vNreJPOWj2LX2J+kRfnJ8D5CSgmzkSPjj8m/cHE6Imkp0Pr1nrKyhQMG6Zj3jylkVA19njZZgcZ7isp8l+L1jmt/o+nd4XvzLRc93WX+pYaJ5EC1jhCyx3WV/xqqM1aBGsHkm17jEr91V3wHtZdYOrQcLp106N6t04KMa2HRKkrPEXudfgxUGgZ2s+NmXOucEvfKrTU0Mqz1VXr8C7kXeD9/e+z+PRio568nUd3Xmq9FH2ZNzM2jkQXeBi+P8jDE7oYSVR+vo7U1DJqapwpKFBYXDpuCJQ14BMLgcdkCjgO/qelVivlVnw27Obee6Xh8NTtvXCxd6GDbxeci7uSHdOerbEHSSy8gN7jgtRUeSUYpZ3NNLfycqcFhEbnUabPYcLyCaht1YR7hmNvY8+JjMY1GOlPphPoEtgoYTIg4eEEWnm1oqSqhPHLx+Pr5Iu3gzf2NvZ8dPCjRr978oGTFFUWkViQyOy1swHo5N8Jx0pHekf3JsIrgjDPMALsItm9wZ9Fi6So2AA3N6nRufNOIS/WDg2dTr6zdKlcW8wzQP7+EpGeOrXh7+v1Eqi4mslDRoZ1986GoFSKRa41glT3tcPfcBPOyKnir5MXOHAhlpgrsaSUxpKtj6WmxB1+3mWacW4U+MTV/lmFZKoNNUeZHenjOpPp06WGoVWrpssra2pqm45aIUiJidfPyc4c53POM3PVTJaMX8K8x6L5/Xe5N739NiQXJDN5xWSOXTkGwAt9XuDtgW//x43UX3oJ3nlHjp1ff5WMsDn0+vqSsLg4iUvu3WvpztZk2JfCzOFi2lPhAQuPQ2GL/+h/GKBQSAbcxUUezae679nbyzGfn2+a5FoqVuGN7WNzqNWNO901b379a5Mawv7L+2nh0YJAF3Hk23phK7NWzOLtoW9zT+d7/uPjBaQM/c47YehQMcX4J+BfEnQTcF0KzsrK5Gysi+HDYcMGsufeRdnSX2hRKG/H+EGHOfK8RYHI3Cach+7ppnqiKWfhfdUvvL48mp90G3lyv3QBjXAOZXCrYQxpNYz+np1xd/SUq8LmzdL6ubEGAA8+KIQpK0ty5t999/f+738D69fDqFHo9aBQNn7CtyaOBFoDsJc+9GE/eoUCvLxQeHuDjw+fRRXxRGAMChTc0+kediTvILkwGYBw11DmDXiLUa2mM3WKkg0bhD/9+KNE8eqiokIKvHftEq61e7eY+53JOsNnhz7jyV5P0sa3Db+f+Z0vj3zJwTTrueaC5wpwV7uj18PsVXP56ex8FCjRY4qc2qcPpPr4NDg7tdFsgYODRC4NpMj8uVdgMZ+ffp0vDn8heuVqJ7EjttHgofbgzJwzBLkGsWaNhttvl3Ta6tWSKm8q9Ho9Z7LPsC5+PWvj1nM04xAtXaN4t/1aXDVhXM7L5v4Lfk1bmM5G5IUbvxLTggovcWqr2+hUayemCReHQewEed3rE2j/q6nGKrm/fN8hXyRwAMWBYrgQ9Wfj6/F2qalfzdh7LCy+/Zz88HIU+ZaXgxc/jf0JDwcxbziUdoj04nQ8HTwprynnp1M/sTJ2Jf7O/iQ/loxCq8Y/UEOh0xG2/XgLg2sVaVKsXsNvvx3Bx6cHycm2nDkDx441vQj6ukBZI46FtpWSOQQxEHnBvd6szkovWqi64Fc0Bv3huRw/oadQkyFkqNoJrnTDzg4635rB4b71LXYbwz0d72FOtzm4qlyZsmIKpzJPWZ3P2d6ZezvdS2JBItuStlGpaTx7XRftfNvh5+yHt6O3sUEugI+dD8/3exGbjFvYv6oDa9fXUFXiBChQKmHYMBlEjBljnSDo9bLvDBmfNLMYgZcXTJwoxKdv3+sryaqosG4hXpcsZWdjtOluClxdm0aWPDyuHlGvrNSTlKQwZo1+zJpDYlmM1PU51KnLqttLasxscWrMjWJQ+yjuHBXF1JFB2Nld28BQq71+TnYGgpThsoFXTk2jpLqEQS0Gc/KJbeTnC9HI9f6Tu/68i6KqIjwdPPll3C/GBuPm0OstMybmmRJrr9+p086sb1/rGZYbMmK0L4E7hoqxzPqFRivvnj3l3tgQcbH2nvlrB4frl5XZvVvqnAwZVzs7keXl5JhkeBkZTds+5rVJ1iYfn7+/3jq9jnXx6/jgwAccSD3Akz2f5ONhHwM3xhjhu+9kO4wZI9K4fwL+JUE3Adft4EpMhC+/lCzN+fNcHtOP1aNasipnD3sv7WV6jJ4lq2RWPfDRLTAkCTpkmlUj3HsvfP99vUX/2g6+6g5HgkR2boBSB92uwG8roaVXuKXPYceOkiXq0EFG6KGhclUyOL8lJ8PChXJnzMoyPWZny93ho4/gscfIO5fJgTe3c9uqBio6/wGY672M+bmTAIVVSd5fLWDYHaBVwqfdX+PxEa9Tra3m2zuieSsokexabjEp3pZlh4NJLPYhJjeQCazi44/hySeRHH95uVzlvL0pUfswZJwzh48o8POTm1wrK54Id/15F4tOL6r3/ojwEWycsdH4+r619/FLzC8WGSUL1Do0OTpC12dfIbbyLygKoTIzlJLUEJMFeFGIyaraDG5u4N/hDLndHyLPeZ/luoSNZuPMddTU1DBp0iXWrAnHw0MkEw1Jesyh0+u4VHiJmKwYzmSfISYrhh3JOwjzDOPwvYcByC3PxedDn0aXc1urcTjY27H8nJmF0solkNaL9sEtuPeJK5wv38He9B3EV/+FxsmsGGDDV3B0rjx3zBGJV3ZbMWMwHBNDn4aen1qX5wDsfV5c6BzypKnp+m9M350wHdr93uC635ddhperI25usIa7OVT1s9X5Ap2DuLvmOG+/6A3TxtA8tAbb/DaUZAZQkO6JtsSr1vyi9rHUr8nNf/8uevcWiZa5rK+e5b1SI257AcdNWSO/Mya79hP3wNofxPrdIxHmRoPGQYh7tbPUqrlegT9/kgJ6rwQUQ59Db2uSA0e4RxPp0xp3BxcWxyymLtq59cFbEUGIdjCLi2ehUzSiEd7ykRiVdPum3kfdg7rzRM8naO/Xnjbz21zbxtLZ4GrjR5CHN/6uplqmfqH9mNp2Kno9xMTo+eyPY/y1zpvUeC+RfqHA1VVqNqZOFXfDm936RquVgeDV+i1lZFxVFW0Be/umSfH8/OpLmHU6PTGJ2fwVE8uBC+fYeeY0BZdbmvpsKbTwkpPRVdKIKmfJGiUOo2XKPKZPF6lTRGstTo7Xfv5cu5OdHm75GIY8K3r2lH6wfIWYhyhruOW15zmgF5cw36pedLu0FF1BsFVp2A0jLLW4FlKiUGh5/vmrbD+7MjnXawlQWJhlnVHHjiaHwP8mKislQ/bpp7I9Q0OlPLpfv/rzVldb9k2y5nRnUXvbANRqyRhZM29oKJtUpaliScwSPjzwIfF58QDY29jzUNeH+HT4p8CNIUFffQWPPCIipoZcC//b+JcE3QRcz4MrPjeeVbGrWBW3ypjuNmBQi0Fsu2MbivJyk0B32TLpggowezY7Z3zPz4N+YZHeut6zSCVuc9vCJFsU7w0qDRS8Bw6144EfOkGRGgYnQbssUEyeLGLhpCQhWDExUg0ZFSWP0dGmlukqlYQG8/I4E2/Ppz+68dtvEFiVxBwW4E8mXTlGFHGWKzZiBLzyiojWd+2CN+pLuG4Wspwgei7kO8Kdp6DTpqeJ7XwHLbt58ewXzSixh097wYe3wLfrYJqZLMCLXPLx4oUX4O19t6Ko0wRFr1KRpfMhpSaIyc0PsXevXPxYsULy8D4+vF++lY/SlpNbVd916s3+b/JKv1eMr3V6Hdll2by681W+OyFZuns63sNPp35Cv2gbJA/ik0/gL7/RbLiwocH//ERVARnJ7iQnQxx/UqRLN/VKKgqGyNUw9BmTEcTX5/BVRBMaqsPGJpODB00R+7g4kX4YTo1qbbXRhlir09Lv536czjpNaXVpvfXoHNCZ4/dLzU9WaRbdvuvWoBUx2dF4Hv+ITx8azpUWH/DCjuctPy/1RZXTk7FdevLAqJ50b9aNzNJMdiTvYHP8Dkao3yTleAT798OBqoXUDH9Q6lpS+kPyQEgeiCIvmjYdy/DreJIcuyMklB2l0vMoeCaB1hbeLQGNWgb7czpIBDq9u1ipp3cTG/Wx90CbFcZ1Yvv74op2/D6MhKnv29BqU601ep48mrvWvZcvma0pExvch0Z8kioGGyAELmK9iSBVeOHl4Elzby/CgzwZETmILu2caN0aVCqpSSgpkSjf77+L41NDmDRJ+nSMGCGD2LIyLGqgDh+WiHOxQXGoKpbtFL0CNA4EVQyjKr4/eaHfoh/9QMM/VBgsg8Mr3cQ50P+UDF4LQ6HPuyIHDd0Fbo1Uux94SprJOuSJ6YhLbfZbp4SLw1FcGI1n4VB8XTyIH9UcnU05Xmpv7ul8N/d2vpcIL7Gmr9RUEpMVQ155HrnluVzOzWPv8VyOXkwk32uT2EJnt0HR/DB6h0as3oEQ53BmlJ5g9TIXYpML4XkP42dKvR1udl4Eunvj7eTF6IjRPH3L07LKeh1LYpbg5eBlYRLhpnK7LpKXv4PkgmS2J23ntta34e/sj14v+/1qUrzMzIbrTKxBoZA6jIbIko+Phvj4XUyd2g93dzuKi+H02Wre37CMPedjKVHFipTT86KpQfH5CXJ8AaCH57yg1B9yoghxiqJ3RBSdmkfSvWUkbo5OjdanWHtteO/yZbMaLdtKGP0AdKwl7sfuh01fSrYKZP0e7CjZ6QNPSfNkXdPGGo2RlI0bLbNVX3zRtAzLtTg11tTUMH/+Xh5/vL7Fu4cH9OolQ4mEBBnSpKZrYPjjcORhOa9r4e1dnxhFRDRcx/uf4vhxqcszuC3eey988olsi78DvV6ugwZCZI0oNTWb5Otr1jMp/CtOOL1DsT4DAFd7Nx7qNodHezxqYeV/I0jQxx/D00/DzJnStPafgH9J0E3A9Ty4OnzTgZisGECckfqG9GV85HjGRY0j2K1OSF2jkZDS6dMQE0Pl4dPk7ozhK91DlE29ly/0D6NozLbknXdI3beRswn7GGGWAGo/B87Uqo78SmFQkhCiIUnQ7ColE9rVa1ijG8PpV/7gofMP40c2GmyIJYrzRHOeaF5VvIVNHU/8a8bgwTLS2r37hldC6hGS80e02JM3ZDCR4wheFeLQB+LIt66dL6q1j6PNj+JPGndAc6SMoHBH9uyBgCm3WnSNLLeDHztJ9u+Su+k7t0fezuqCYaLJ8PEBHx903l5ExzxAfGkKHw18j7YBHRnx6wj0Vc7w414euL0DD795lrjcODFuKLxESlHtY2EKCoWCoudNRQGjf6tPmNQ6TxTVblSok6U4uar2HAw6IgP9ci+J/HvHS88d/xjUwWfQ+8bgRggPqfcapXazjrUipfgi9jb2RPtE0863He1829Herz3t/NoZNc3G/aHXE58Xz/qE9axPWM++y/vQ6rV4Hvya/C3i9tCxTyat7/kAB88CYnNiOX7lBBq9ZXHMQ9Gv8fWk1wGxKb1ScsVYq/Pung+Yt+dtyrV1DvhSPyFE296H4ua0bClZkPD2uWRq4rDL6MOWXcXED2xn7JtkgRoHcf0LOiLbR6GXfjBLV4vlcYPQQ/RKmFxbN7h4K6TeAl2+heFP1p+91E8i3g758E6pRFkBbr8LOtbPKhrQ/0QqvupmuLnBCZ+nOWv/HU5KT1xtvXCz98RJ4UXBFU8ux3tR9tdjQqYAXNJlgFbhiaLKg3vusmH6dImY2tjApcJLrI5bTXxuPOez4zmdHkeRLsP0w3/+iO+Vu5k4EXy77+L1lAGNbItaZHQUJ7X138h2uNIVpo6tn6Ur9xJr6QovODJXSNLlPpDRSUhK8wMwu4/Vn1Do7LDVutIzbz7t7Mfi5qGh2PkI4Z4RtPQOxNNTgbOzELy1a6X/kaEGy9ZWT+fOmTz9tA9jxtiQWpZISmEKrTxbGYuZR/9ep2hQrxDSVBaEvuUW7BRqavT1UygPdHmAb0ZLhqqgogDPDzzrzWOrtMXTwZPpbacbo8E6vY4X/3qxHmEyvHZXuzep54g5iiqL2HxxM6cyT3Eq6xQnM06SVZYFQK9mvTgw+8BVlmCJqqqmSfGysv6+85eNjQxmDZONfTVn0i9Kj7ByL7hUG+p3TYUnG0llG7KXAOiltiU30qz3UxPgkC81nM0Pgs4Gl/2f4XB2Ltlmlt633grKdstxUqlorxrb5CxMY4Rl82aRMdXUSL3eggXXP9MSEwPz52tZtEhPZaWJrdjaimNrt271f/OJ9S/y2fF3cVUE0D9lF0lHI4iNtb6v1Wpo185Eijp2lNfWKguaipoaCdbMmydDLD8/aYloMIm4kaiutnS6q0uYrGaTDI3Fi4Pg4BNw4j7eeNGVV+v4zdwIEvTOO5Ipmz3bqgDppuBfEnQTcK0Hl06v41DaIVbFrmLTxU0cvvcwzvZy1r679132XN7D+MjxjGk9xtjsrB6OHBH/5wZQ/dLr2Pt5ih/q6tVyVdDrpUNhTIxkdl54QWZOTpZQvb09+vbt+KxzNVvtU9njXkS5reUhMiAZdjQ8hgLge2YzhWW4UD+yXw9RUQ03tviHQaME2ybq32uUEPo4XHEFWy3cewJe3Q0BjWySYoUrv+qnY+Ptyf2571idp0YJy9vb8N59UZzNOctbA97i5Rc3w/79FKnArQpWR8L4qeBeAZfn22OfV0T7j0aQUL0L5xIX5i0azkMzgrErK5Jwaa9eRomeXqulxNsFV3fTcffRgY84kHqAlEJxuzPvg+Js50KzJUXEpRQIEXq1CRfXSjeR5hmyHsH7sNN4EuLcipahdhZmDYbapMZcdwoqCtiSuIUe/v1Y/kMA8+ZBaeQCGP0QCr0tPQP64uvmypr4NfgqosmLbYM24BDKDd/y9O3DeeUV2JG+lrFLx+Lp4EnPZj3pGdSTbkHdOHHchpe+Og4tdmAfvo9qfQUKvZKotfnEnnKTqF3kn2BXDskDcLcNoGcvPSVRX7Lf9THTSma2B49kUNWKygtCYfViITXOMmDkwJNw6VbJHNX2lIqOlkFhuSKbzHHt0TtlyUB+41emZdtUCbHp/b5kpAAqXeGb01AUjKODkqAgGUTk2R+nRBWPTlWbXXLMs8w2/byryYSJj9OgRHruMOZe6PxD/XnSu8mg8twUuL0BOWxRM9j3gmTcPJJkW8aOB13toOnxEOuEErApbU5w7Cckd7NiKhM3Fs5OgYvDjY1y27WTSauVqGxeYTWp7r+T3XWuZZ8tEMJqVwHr58Ox2gLMZofg3lpr2WpHyG8FeRG1Uyu43FeCAECrVnrc3LJo3doXLy8lgYESTa6qEmOD35dqOZx2RJbZ7LA8mrns3RZxG2unraW8ppzc8lye3PIkgS6BhHuG0z2ou7G3SlZpFrP+nEVuea4xI1VWY/ovD3Z5kAWjxY0yvyIfrw+8rO8H6ne9H79sPJ4Onng7euNo50hmaSapxal0CehC7+a9uZB/gW+OfcO5nHNWlzd/5HzmdJvT4O8ZoNeL9Ohq2RPz18XF4tyWkGBZK3VdodBJttG7NmNk/uiUC/ueg+21zmzOmfB0beS9zNto463MiyLCM4pbWnagb6cA/PwsiYrKoYYZW4ZyJucUyycuZ0jYEDQ6Da/ufJVhYcPoF2pFe/UfYt8+KWavqBB3r19/vX41ZZWVImRYsMDS/CMiQk92toLCQiFnhw6J41td5JbnMnDRQM5knyHQJZDdd+2mmWM4Z89a9jM6fVqOg7pQKCRDZN7PqFMnk6V2Y4iNlRreY7UinEmTxC3P+xo47Y3E2axzvLP7I4Z4zcarrA+XL8PWQ2msO7tDan5rM4evvlpfTHMjSNBrr0lz3IceEsfZfwL+JUE3AU05uGq0Ney+tJtVsatYHbeazFKT+YDBXeuasGWLmCYAuLiwP2Qav57twETbPxmo2dbw9/bskapHjUb0LZ99JrYuVuxPqmzgYLiK7ff0Z5t7HscyTzCz+WgWjf0JPD3R6XUMersLPXbHMfJCJT3TTL2JmoRvvpFRwTvvUC9sYQ0ODk1vH30dsDoSBiWD6zXYyprjtB+8OAg2inIGx2p44hA8s1/Iyn8CPbBpZDg9aY7nxp2cCIAutQoipU5UPS/ugbd3AAkJPP/Aeb6PnEOeXwaRObD/R/CsuykNIUOdTu4YWVmmz+ztYehQykYP46BDLutqzrG+7ARJBUm4qzwpLrBHFzcSOv/Y6Hq/0mwPuox2ZKa4s1n5MPn6JCozQ9AXhJrkdoUhUOZn1IeDDBjMjRrqTuYShcxMmPLuz+zRfiADFisYejaVrSuCAAXNm8PwVxawOOuJevVUChS410RTsOg7gpSdWbjhEJcrzjOn2xwKC+V0umd/b/Ica+/2OVFG6Ry2lTBqDqiLxanq9zUiYQk8KnUwJ++RDMqUCTIINkdxkBCI9O6Q2ht6fyD9UrLbwLdHjURl6FBpWNi2LbSO0hBrs4xPjr6NtlzLmcfOYF/rz1teU46jndR5GQacV3Puyy4uJKssm/yKPAoq8rlSnE1WWQZ6mwohTCdnSxTSO84kJ2sI3x6FPu+JWUXXhUK+GsPrtbem1mth2liLj7z0kbR27k5H7+70CrwVVCWsSVvI2fxjxOfH1uuLwR/L4Nxkee6RhNIngehQH1x6/EGc+icKakw2awODhzKx5f10cb6N4kJbErPTqSxxorrIk4IC2JP6F/s9HhRCq7Rysdv4BRyRPkZ4x8p/rkOUFBono+RFoRCnxqlTpf5Eo87kcNphDqUdop1fO6a3mw5AalEqwZ+ZMhKh7qH0bNaTHkE96NmsJ538O6GyNdmzV2mqyKsQQuRi70ILD3HcKqgo4M3db5JbYSJMhvmKq4qZ03UO80fNp0Zbw6rYVUxdObXx/WQFzkovgm264amLwrbaG2WVF46l0Tjn9zWSmZKyGspK7CxIzY3u53Ld4ZgrJKnMV177xUgm0iPF+vwHH4ctnxISAmFtCqlo9xVt/WvldZEeqFxLaO0dQXpxOtNWTmPv5b0EOAeQ8EiCMUh6PXDypBxzxcVizrN69d+38TZHYqKUCP/4oyi5QTI+Y8bo6NjxIM891x1bWztGjZIsVFiYkA139/rLyinLYcCiAZzLOUdz1+bsvmu38Rg2QKeT3zQnRidPSpbQGvz96xOj8HC57el0IgV84QW5Nrq7C/mZOvXmNck1QK/Xs/fyXj488KGxkfjoiNF80XMd774rNUqG7HPfvkJMBg6sv943ggQ9/zy8/z488YRIBf8J+JcE3QRc7eDadGETU1ZMsegz4apy5baI2xgfNZ5hYcNwsne69h+uNSJYfro1U+6USrn9b+3glvLtll6P5n6PBw7IqAmEwr/22tV/Z/lyyMqi4JlHKLWH5rUKoZP+0PlB02zKagf8U1pza5IdDyUl0Ce7iNMvLKPD00NQuLsJ6Zk713LZKpVcga01uvhPMHJk4wUMV8HGVjB6OkTmwsHv/zPSsicEnhsMh5rLa69y+G4tjItr/HvXgodHwtfdLd/7ZRXMiDGZZqS5Qq/ZkOYGvS/DtsWmOrC60CpEdtfSrAxpxnj4vZ0odurCRgc//Qmzxtf/TFXlSNWFUfj7qEj78hdsFDpQKmm/oD1nss9Y/X2VxpuuO3NITpZmtrRZLnUzhbVEqSTQotjfy6u+q11REXzycyI57htkMN3yL+P87f3a84zfNl550peU8jNQGsCw/q488Opp0jjEofRDHEw9aHT/44sEGcwCfR//lrP6FehSelEc2xN9q3UQdBgCToq8zQC9QtZTo5IMTZWLDMgvjrD8s7aV0PFnkcgFHgW/BrxmNSrurD7Kom1HICeaC/s6Eh5S31KsqrqKpWuXMn3sdOzs7CioKKDVl60YFzmO5/o8R7hnuNHuNjvbcsrJgdTsIhKL40mrjCNXF0+xfTyVznFSl7Dvedj1uvyQRxI8ZibhK/UTAwP7Eqn30TiIS5yqVDJh35yW+bp9DaMetv4fAS71hZ92AwoY9gT0+qzheeefEfMKkAbE3RZI1khtOnD9E58h+2Q3dBeHQNdvYPAL9RZjk9mNUfoF3H9bF/r1Aycn0yCiqkoGiq+/LqWYgPwvj2STpbdnrb337ldNUqp2v8GEGfXXuTgIV00r7gh5iZemDSYgQEiLQqEw1srVRXJBMvP2zONw+mHO55yvR/Qe6/EYnw2X7VSlqSK9JJ0W7i0ABVVVjWRTSnSkFCVzofQkFZU6AktGUVXqRGFJNX9EquqvSF0kDZIpsxPMHNHwfGcnw4paabZCC6/Yy7FS4SXSsfLaxwov7HO74X55hlHepfA7i5vKAw+VN+7OqqvKwMzfU6lq2LVrI7fd1rSBX3W1xH6uVreUmUnD1vN25eAVb8oYecfJ8wNPwam7AT3cMQzCzIKVOhvID0Ppno7OVrJ4DkoX3u31A3P7T7pudS/x8TJQzsmRx82bG7bpbgo0GjFh/eYbS4vk5s1NTU29vS3HR3l54rd06ZLI8Vavti7ZyyrNov+i/sTlxhHiFsLuu3YT4h5y1XXKypIskXlPo/h467U2Tk7iWGhOnPr1k8xYUNC1bo3rC61Oy5r4NXyw/wMOp0uQTIGCoc3HY3fkGTZ938MYOBg4UOLI1gwbDLgRJOiJJySO/vzz8O6712WR/zH+JUE3AVc7uHan7Kb/ov4W73UP6o6vky/uandGho9kWjsx5a/SVLHxwkbc1e7GycPBA1eVK0pF/SvF+fPQvbsMaAx9BJqM5GTJ/5qTJPPnn34qV/qgIAmT1MnW5DnAn5FisLC0Xf3Fv7FTJGAgmYt6Y2d7+6Yb8F8rbG2t2FM1gDvusKjqS/CC7veJOcQDx+Cb9f/56uiBNZGSGYr1gaPfQtcr//lyDdAphKTU3Q+dr8Dz+8RC3UYP53ygzz1Q6AC3x8KK5VDgIJbrZ3zlMcYPzvlChR0UvwMutbvokRHwVQ/wL4F22dA+S6Z2WRCVK7VSD42CBdKrjeBCuOwuz/0zguj+y2vcfkc77j7xKMTHs6etCxcCVaR42XDJVUeKuopLtqWkUUy0TzRn5pyB/Hwq9So6LrmF+MIY4/9S6G2xK2+OriAEzZU2lvIwp2zpOaGzA/S4UowOJaUqHYx4zCjx8lE1Y8Gtf/Lolrlc4Sik9YKE0ZAwmon92nAlXUFCeha5qiPyvuEInjgF2lpa4ahKWuOhi0DtVE2ASwBp+sOkVsYSrRhPyW8LSe01EUJrT4bdr8jAMa2nRXNV47o/HCkWv8fvle/03+8AAQAASURBVBoDQzZr45dwZho8J9oMW6Ut7Xzb0S2wG92DutPBpxve+miyM/Vs2HCMkJBu5OfbsiP/ZzapaqVoeiWOSVPQ7nmWqmKX2hoIb0ivldZ6XoRHrVgU1sIufgpB8W/jGJSI2icDN1clLV0iae0dwc9VYzlfvtv6F3U2MK/CVMzd5VtwzoCCMDoGhzOqbzNu7aukRJOPhzaS8lJbTp2Cd/7YQIXvnlrJnqV8T+GUR/NVyZRlBlBUBJrBj0PPzxtcd07cA1kdhIR5J1ifJ70bJA+QbI5eIS6JdrWZrwpPmTQNN/po2VIGVBUVgM85Id+1RMnWLwGNKtc47/pp6xkVIYUGy84uY/qq6YS4hdLCNYJgpwgCVRH42rTCiwjU1c2pKLOhtBRySoq4UHqMpJpDpOkPk2V3iDaXv8D18lRKSyHbYTeXB/VHUe6NPq0npPaU/ZveTYhrwAkxk/A/Jf2cDBLNrLaw6QsJFrTcLtnJCnc48LSQHOdM6LoAkgbLlHoLjvYOODuDk2s1yuZHsHPNw8Y1F4VjHnqHXLSqXDT2eYTb3cpw16dwdgadOpf7Lzbs8jilzRSWThSbca1Oi91bdkbS52zvbFHP1D+kPy/0NZHaledX4qZ2M87jaufKrm27ruvADyRzkJ/fOFk6dsxK/YZduchMzZtDV7mY9oE5DIEXv9PiSpkbRYRnJL1bRzGmVxQDuvnh5tb0NMXly9JsNDUVOneGHTtMRq/XiitXpAbku+9MUkSFQizf58yRGKSBuFkbHx07JrWU1dUiBHmhflwCgIySDPov6k9CXgLRPtGcfvA0tsprZ4RlZSJ4MW/0GhNjXViiVEJkpGWdUceO/31J3MhfR7Lp4iYAVDYqxrW4m4odT7Lu51ZGW/qhQ8VPytA/vjHU3Q96vZ6F+/5g07m9LJzxCv4uvte8jnPmCAF+7TUJEv0T8C8Jugm4GgmKyYqhwzcdGvz+072e5sOhHwJwuegyIZ/Vj3YoUOCqcuWBLg/w/pD3AcjML6P1E3MpznanRYAHj9znjqeDiTgFuwUT6h769/6UVitXeRsbLuZ78tWXenb8kMxjZW8zjtV4Yoq0Lm0LH/SGH9fAgocfI8X2L7ZWnuW3FSantA2t4JmhMCRRTBb6Dbwb16++g2eeEbJ1M9GrFxw8CECxCnreK0Sl92Wpf7KQ+D36qBDCvwmNEnaHiMzOgE97QlgB3BZvhSheIza0gtFWgs8TzsOSVRDrDfkOMHIGPFire/6ip/VlOdTAoe+F6ABccZGaKN9GfCjK7aDzA+I6eHssDEyGlwdChyzY/VPT/l9N0gUKPB3xdQuU5gzr1/PoaBvOBtqS4qYn1aEGjdJ06Wqj92NH8h0ojx1GkZdLj2nZJLoVYFcRhDYvhKn5+2lRpCeg0B6PfE8UpX48MzWRdO9SGejqFfWL6QtDhPjEjofkgXh5mSQe+Jzj9sf3UOF1kITyQyQXXzB+TaG3JWx5MUnxDugcM8QSO68VZHSRgYy5RXaNWgr0kweJfC6js0SOZ4wEtzSx7D07TbJgl/vAb+sk+zD8MQg6aqolMseRh2BjrThbWQNulyUbNfxx6Pqt1e3tlDiTNvG/SFmYbw2/BDvhrPQmSBVJas1JSrWFANgobNDpdcYBqa3SloqXKoyDkhmrZrD07FKC3YIJ9wwnzCNMJs8wgl3CyDrTluVLbVi8uOF9P26cOMyNGmXqm1NZKTU0X38tBgQGtG8Pc+bomTlTgZMTnEw7z8m0WK4U5JFaeIXVl74ntzrdOL8SWx4rqSDpoi3bHO+hvPVPDa/I12eh16fW65xA6pgMDnsbvpamnAD+JyUjWF7HprzCU/T66gIxZvBKgMShJilV33dg0EsNr8/vf0J8rSzQ94zs/7xamV2Zr8iyDJnRDoth3J31l6FXWGYqm4BpPu8xI+Q5qxkXJ6e/V0ei1+vJr8g3yvDqyvLa+bZjRnu5iBVUFND6q9bkV+RLf7I6mNp2Kr9PkHNKo9Ng91b9+6+9wh5fZ19GR4w21kYBvLP3HZztnS2MIQxGEU52TtfNWa+sTEhRTEoajx0aS5r2hPQjW79AJKV2ZXB/V1OjV4Af9ovxCTS8Pyvc5bjb+QYkDUGthn4DaxjQT0m3rjYEBooEzM3NlPlJSJAB/p49UgZ6LdDrhTgtWAB//mmSL3p7S+vABx6QIEBdNDQ+MvSYUSrF4HbQoPrfBUgvTmfkbyP5eOjHDG45+NpWugFkZkqWylwsEhYmEsGG+qk1a2ZJijp2FMXBtTjlNYaCigIc7RyNstZvjn3Di3+9yOTQuWStf5g1v/oZs1mjRgn5aaQsvB4M+2HEiJF8uX4fbx9+jgJHaUL+kN8Svn7QysDhKrjnHvjpJ8kCPf/81ef/b+BfEnQTcDUSpNPrKKwspLCykIKKAuPzwspCCioL6BrYlf6h/QFIKUxhxqoZFvNXaEzhCgNh0uvhtpmX2RDRcHp4dqfZfD9GLDuKq4pp8XkL3O1dca9S4uHkhbuDB+6ocT8dR68CFyYk2kNuLvq8XA44FeBRCXv8H2ZO3PtQ40BLkkgkvN7v9L1bMhGv74IhsVWUKzQ4vSPyvohcuPUSrIoSi2kDbPQKegb1YLBtBINPl9Aj2x67/EJTFurKFQmjPfmk+EFeuCAWLTcQOgXcPhXWtYagYjj2LfjXLbx87DGxtTFUTjaC5VGvMTm2cavvFHdo/TBU2wrpem879LFeA35VGLJtCV5CRMrqKGtsdNLnaP8PYKeVTNQXPeDxERCWLxmd9j5taP/XOdply3s2f+MKcTxAiKTGRiRywy9CmZ0QPRCiuToS7ogxOeldC7QKIWSX3GX72epgqpmCzO9pjH2b6qJNNpydL1bxM8fD+tagOjuWap0jet/zKH1PW/TRGnDOk0f+uJVKVz9iFO14TzPJOHBtRir5eFLuWCFyuGaHJGNQS0Lc3UFzT1dKXcXi28XWkxJNI/6/+5+GbR9KNmjSJAjdI+/HzICtH4lNrxF6sYAOPCquUm2Wy+ucaBxzb0FVHkQr2+Ec6dALO70TNYrGHRTv7HAniQWJpBalEjMnBleVXFOnrZxm0fwTQG2rpqVHS8I8wlh0+yJjg9eCigKc7J0alHQZUFEBGzZISeKqVY2s051CiAYONEWVjx+XQdhvv5miuK6uMu+cOaDwiePb49+y6NRi8iuFtSr0SvxKRqA7dj/Ze0cJWbArl15Q5tklt0siuXTMk2zduDuh9bpG/wsghCmvtRg59HsDBrxufb4qZ1j8l9R5AYRtgcg1QpgqPeS3HfKk1sQxT167XZbs3IIYk11wn3dh8IsNr08dsjMqeCrnig6TUmSKvDzV6yk6+ndk2bllxjoDA2a0m8HgloMZ1GIQzd2aX/3//xeg0+soqiyqR5yauzVnYIuBAJRUlTBm6RiLz2t0Js1aUwiTAXV7sN3555042jrWc9PzcvQi0CWQZq7NGl3/Q2mHGLdsHJmlmXg7erNq8ir6hvQFICtLz+Tl0ziYv56Q0wu5uLrOYNQ9RezwzU0ZPJJN+/iXzZA4TJ63/V0MSHJbG40ZyImSx7wI0KgZO1Zc2eraifv6Wie1+flSd7JwoZAoA/r0kXNuwgRRtTeEhsZHer0QkZ9+EkJ24oQQDWvQ6rTX7FrYEFaskJ7veXkiRnnrLXjqKfnver0Q1rp1RubtE83h6iptFM3rjKKjG98edXG56DKfHvyU7058xxcjvuCeTvcAcOxkFW+/V8OaP5yN5GfsWCE/Xbpc+//OyanhgVfXskX3PeWBmy0+W94tm0kjr5EVAzNmyLX4k09EGvdPwL8k6CbgRmgtzVGlqaKoqojCykKc7Z0JdAnk00/hyZfyUXb7lhmzC3H0NJEqA4Ga0W4Gr/YTCVtDGSYDZp+A79fK82IVuNVJT9tqbXCqVuFXVs7wODUzt7flrGtv7O6J5A53cf95peNjtGnZg9yKfB7e1Ijm3wriW31BRLkDdOtGSXgwzo7uKAyHpyHU8ssvYt3i4nL9a4iA1/rDm/2lb9LeH6WJ7H+E77+n5HQSLl9ad3oDKFTD+73ho552aOzkhj06Ht79C9pmN/g1I4pUcNZXanumTYDHDgvheHao9fk9y6FTJhwLhIeOwj0npUbpjJ+Q1WuCv7+E1Kzg3T7w4mBwroK4ryDIbHc9MQw+6wW9UmH+Buh4lZr6a4VOIb2dDCTpkps87nEPIaDcmaeP9mR42g/oFODwkhDQhrBymUgJwXR8KGpU2Bx5kM1nFjEws5AynMjCj0z8ycKPLPyIV3Vgd/QDZHaZQ6HrXipdzzf8I7mtwSlL+oLEzJT3wrbAHcNN8+SFw097jK5xKHRClAw9TmxqLBbpr2nLvb3uYN7R565t4wFJjyYZi5CXn1vO6czThHlKVifcM5wAlwCr0ty/g6IiiSr//rtlTUFdtGkj9QPR0VLLkpoqvZjrqWlHP2DKeBU1gxP3igFF8d8czDvki3ws4LhkX1rsEKkiyODTMQ/ix8C0MeByRSzha2Ff5Y+WKrT2hcYBa5td51AVR6PVQkbrN8iOfr3Bn7ZZtBdtch+xUm/3G/T6RI6Tq5lQGJDf0uQWaAa7wmii9hzD3dmBlHb3cdnH0tvWQxlMK8cetHXrybgWd9HMyxM3Nxnwubre/AatTYVer6egrIAVG1fQvmd7XB1cifaJBsQo5OmtT1tkoAzEqUpbxbS20/htwm/A1QnT0LChbJlpOnh7/dALext7I1HamriVS0Wmi2vyY8kEuwVTqanEwdYBhUJBcVUxV0quEOktRDcvT0jBiRNC/E+cEBMAI2wrJKPoHSvSRINVff/XoP+b1ldUp4QlmyFpiLx2TxGJY04UVLmhVAoRMpCi1FSRkNXF3XfLgLedFQm8NTQ2PqqoEFncyZPQs6d0vLiaSUNsTixzN87l9wm/N+yeawUFBdLY89df5XWHDjKkaMr/KCkR+Zx5ndHZs2Z9nsxgayvXKfN+Rh06SE8kc8RkxfDhgQ/5/czvxiznxOiJvBD2B2+9JddFAyZMgJdflmVdK0yBIz0VHT+FYU+B1hafksHkZNmiLG1OydL5f6s2bOJEWLlSsvQPPXTt378R+JcE3QTcaBJUF3v3woABko7+6qv6XgNW11Fbw8X8ixQuX0zB5+9RGORFoa6cQn0FBWrong6TzsNhm1u4Q/0uibPvRKEuBHUh2jrjHQNh0j4wh2fHOfDJoYZtQVRaBY9mhrAnWM8xXZrxZI/KEcmZAUHFMOeo1K9MnQgHwuwJs/Fmgv9AJvS+l2Ydb23YpqWqCqqrqVGp2LpqFSGF/qjm3EcrGgjfWEGxShqiprvCotUw63STv3pdcMUF3ugHP3SWbI1CD3echve3SzZKo5QMT4wfnBrRjXNpRznjZ+oZFFACGS4iN3zsEIybKqYOrfKgR7pI2iLypG5n1jj4y4psYfMvMCyx/vt/B1qFGEuMjZe6KvM991lPkciV2YsKbe5ReGvHf+6Yd62oDItkhVM6mU4lXHKXeidz9E1w4dvfAolEBraK1xteVp9LsGUJONZykRJ7mFG9mlx8ycGHA+ooTgZp2d3Mjn3NbNnTypTdddCouY0VrLWdjqOtC+hryNc2wIBP3iXSuYTR0jyxAevoupgS+AL2npmcyDpiYWfcN7gvYZ5hHLtyjLPZphHP4z0eZ97AeVc1bKmpubYGkY29vnytGVCfc9DlOyGOV7rKewHHod+bcPx+scbWX1v0eNgwGbAYJF+OjjLY2bBBSigByda4pkNWrb+vQgfPeYgLoBkUKAhSBTG23VjeHPgm+RX5BLsFGzNlu1N2syN5B3kVeUZ5WH5FPnnleSQXJjN/5ALKq6o5mnaSZfE/N/k/uOeMpNDnKqYwegUUNYddb0gW0+uChXGIER/kmHreRKwHdSGq3B6468Jxc1UYydG1Pjo7Xz8ZUWO41nuzXq+nvKacam21McNZra1m4bGFJrJUkWch4RvcYjA/jBWVQo22Bvt5DY/ib2l+C2umrmHmqpk42jmyK2UXtkrb+hkmBy/a+bVjZvuZxu+eupRESrwrCac9OHnChhMnLDMzgJhOuKfUsfOOk+cOhRYGL9z6FgysrfMt8bfMGuVESc1ijfXz383NenNaw3PDaw8P0Gga3wdJSZLZKCyUscxXX9X/PfP90+P7Hhy9cpRon2h23rkTX6er17Js3SrSrfR0Oe5eeEFKnP8TV7yaGlPPegMxOnlSyJY1hIQIKXLruJMzLh9wosSUjRnUYhBjvJ5l6zdD2LBe7pYKBUyeLOSnbdtrW7eKCli2DL74PpeTF9OlFhIIbpFNyP2v88X0Jzm0MZw5c8RMYdeua///YFSr8/33ktX7J+BfEnQT8N8kQZmZUtSYkSFSkSVL/r6Fo14Pe/fo+eaTcnavK6ab/jDv8CKeNkX4ayUNogdK7SVjYZg8vJrR1ikU/YQJ+FbNI7cyD3uFLSPPayhUS6G9Yd5J5+C7dcCDD5L54WsEfBzQ6Dq5VEHJVVLJfbMd6KcP5i3vyXJVGTeO+Ufno1KqyNi0m7+23UpcQiADBzVj/sJmuKhdUWbnmLqNhYfLRgSpBbpFtNeZzrAySgblFggJkVGasSjkxiHeC+4dA/tqk3aZH4JfGayIhkmTrX9HpYGq2mzGX4ug7yUZ45jXMumB2WPF9e6P5VIX9F4fk2OdAd+vgdknr89/sWqGUYs0V3hqKCyvvbj7lcJHW8XNLpXmBJN6fVaiich1FOe8i15SC/XxFhgTb8pgVdsIOT/UTMimNaz/FUbVlge5PS/7ILQQQgprH4vkMTJXiO3mcLjDipteUzFe/SV7vvOguMaX6o7L6tevaG3FSa8gTEwV8lvRpQu0652OX7szjIi+FW2lI6WlsCD5CTYXfmb1d4Lz7qFZ4qto80LqkZcb5WvSIOzKIXqFGCsE75f3LBpW1kebNqL3z8217Fvi7CzJtLvuEmmPYVCu10vkfelSGUikmh2Knp4SlZ06VQYPWVlSJ7FhZw7bzx8n1/6YELHAY1LXBfgUDuPFkM0MHizrMuSXwQS5BtEloAtdArrg7ehNQl4Cg1oOMtqYz90wl/nH5jf4nya3mYyn2pOssiw2XdzEM7c8w5sDJAPw+q7XeWN34zJcA8a5vMPqkoaldQ7ZfbCLnUnllQiqbx8vA2kDSgLgwkiZkgdK49kmQqGQhP7fJVGGR7W68fvffztAqdFp2J2yu152aWXsSqJ9otHqtcTnxpNeko6t0haNrmHznmFhw9g80zRQdnvPjeKqYhQo8HDwkOa29l7YVnvjWtoN73OvcPw4xMWBPmyzmHoYnPcqPPFqnkeQuw/OTjY4OkJ6y3dJ8vqaKlW69RX4+hzkSOaMVhulFs0gsStoaerj1QhUKvD316NSFRAZ6U5QkNIqeTp2DG6/Xb6zZInIrBrCxfyL9P+5P+kl6bTzbceOO3fg7WjdtaCsTMqOF9SWgrVqBYsXS9bpRkCvF6MIcwOGU6fMAigAM4dD+BbQKbG7MAkOPEPNJUt924wZ0oQ0Kurafv/CBTEq+PGXcgojP5PecaWBTM0/wwP3QWHhBkaNknNh8mSptXzjjaZ1KLGGoUOlQfQvv8DMmVef/7+Bf0nQTcB/60Kr0Ujx4J49cjM9fFiKU68VlZVyg//8czlBDXim4zY+ONWAjsocS5ZAVBT7ZvSl79RynKsg6yNTFNwc+rFjUIwcBWo1Nf6+7Jv/HIWKKgpT4ilUw+FmsOwaoxwghf4rlktmJOqBl1Ar30WHzuq8Ay8p+WuzH4weDd9+y9ilY7FR2OB+4hxuSkc87FzxwAH3Mi0hRXBrhr3Y/7i5UT5uNA6TZ6DQ6eDoUTFRuBrUagkxFRc3OluVjcmN7YyfyaEtt3afGupXQMhR5CPQPhN6ptW6smVLHc+nveCtftA1HY58Z514FKmg7z3yO7Za+HyzZN72hsCb/SwzQ4mfW1pjXw8UqYRk/j/2zju8yfr9/q8k3XtvKJ1AgVL23iDI3gIKKgoILlTcCye4cCAqiAtk7w3K3ptSoBNKC5S2dO82zfj9cTdJS9MCzs/3d3muK1fT5J0nT54kT97nfZ/7nMa38cjdwfDkQEis+g17fw+8fujut1tp54Rlaf3H+W6RZwMTRsHOqoXSF4/AnN3m+6KKrKQy90sUfN1eCE/JB/IdKLICp3paNqrDswSy/sB3GCCqIJKv18bwVQcTmbRTw6Qz1vSL9qMkqz0/6J4ggcZkUP/iA7Y5YtEdusu8w9rhl6RqoLGByKXiLJbWHm62g7wgrK0Vd7QtLiuTVr+bN2URJ7ee9qha8LogxKflUrApkNt0KpGinZpBV7++TJwo8gwHB5FoTJhgflPt2om/yYgRNc+fFy/KeXHlyprSI0dHmaCNGwd9+9a9eqzXyyRkz56qy/EM8u3OSuhqSi9QVuISdZD8oeabu5t4NOGD3h8wsulIFp9dzHsH36OVTyuifKKMl4bODbl06xK7k3ez5+oeDqQeoFhdjJXKiryXpbFarVWz6MwiskqyaOnTEpVCVaPK9HSHp7FUWpKcl8yZ9DN8eOhDcstya/SemoMCRe0Mpmp4xHIHPkUDKCyEaN2vXLP8HYq90Ba7U1noRnmeO6U5buiK3WUSrf7z2TeWlubJkeG6vb2WjIwE2rVrjJubqs6xf+XPdnJeMlO2TOGnYT/R0LkhIBWMz459xiu7X0Gr1xLuHs6aMWvwsPOoZQxh+D/cPdwYNKvRafD8xJP88nyzzzkgdAA7HhQnseJicJ7rhM7yNsl4ubOQotTusOlH0+1d54ik1j5L+uTss8A2B69l18nLdBAr8GGPQqufTY/RWElFyVA5OvoCVDhjaVnVC6mpuyJiDgpFTQvrNm1kgm2u0mRvD4k5ifT8uSfpxem09G7J3of34mbrVmObR49Kr6Chn+eppyTT5s9Ygt8rSitL+Tn6Z3r5DufWFT+io2FH/H5Ol6wlZ+vzxmDl29G6dc0+o8hI+Zyag0YDW7YI0ft9jwaifoJeb4Oj+H43c2/Jjolb8LHzYfv27fS5rw8v7X2Z5S88TkFCKw4fFkniH0GPHjIfXb1aFpT+F/AfCfoX8E+RoBdfFB28o6OsnISH39vjb96UL8rChSYHFBsbcYh++mm4diKdg7P34p92glGsw5/bmmKcnOD++2HmTCgqYsr8+1jcBh49J85wZrFkiQhk27a94/5p539FTP+WrNz+CR/n1+9L3f4GjL0Es/rXvq9JlqkSVW4JI2Nh3WrggQfQL1yI8guXOrfrVQy7lwBdu6J/711arpGmW/dScCkH1zK47gwOamiZYZqw64HNjcEltDkdXVvgUqnEZfEyo1wt2VVIjl+RSA9xcyWxR3Medq97xj/6Ejx9Uq4fD4CX+0nF5K0D0Cpdqj96oPcjpse0vglf7IRuVdKiMgshPvk2UtF7bKjYY4NI6BZtgcGJcKghdJ9s2s5PG4W4xHhLRUSBSPSGVJNfHA+AdIeauUHVTyjD44VAXPSCFlV64R82gb3a9Bg98jpSnWFhWzi7UK5fdqs5Bkz/j4oF+yrCfdJfbL+r3199H0bFyfsGcMoPzvnW3p7h/9GxEiD7Rm+Y2820jXf2yfte/TGjY+W9BCFy37WFeA+53C4f/StgWwkj4uCUPyS533m8Siefsz7J8NZ+BR/o3+I6DcjCkx3cj4a6z1OBgdB3UBEXvF7nJPMBcFJ5srh5Cu5OdsxOGMqhWybDAHdbd9r5t6OdXzsiXNrRqHIAyZctSUwUUpCYKJf61gQsLcVZKjxcLmFhpus+vjqCvwriWoF8qJ10jSg5OAXt6UdNPVL3AHt7WbWcPl0mRKtWCfG5ZFIJYmsrUo9x4+SUZ1O3K7ZZFFUUcfrGWQ5tyMNKM4T9+1XsLf2Cyj71dw8/1e4p5g+cj16vp0xTxtPbn6atX1va+LXh9yu/M//kfDJLaroCutu60ye4D5/3/xw/R79729FqKKssI7s0mwu3LnD8xnGO3ziOv5M/WSVZ5Jbl8nKXl/n5/M9kFGdw/MZxs9vo4N+BcPdwlsYsNXu/AUvu30IHl8EUFsK2Kxv4KWU2tno3rHXuWFS6oVJL9UJf4oZdRj8qsxtSUAAFxRUUFEJxvrXZ3Jc/ClvbP1+VcnCAQ9cOMGr1KHLKcozmCrlluTyy8RG2JMp3Znzz8SwcvBBH6zpKyvVAo9OQW5Zby1XP296bIY2HoNfDrJe0zMvrBnbZOHjlUKLNq0Fe3XIHkPvVDtNGX3Uyb9ENNLXtyWct9rEj/WcO3fyNaO0Ks+NYfEwcNYt9odebWAaewVXbBDddUzwVjVFkO9G6SQvs7FTculXTTjwz896Cch0dhQw5BcdzsW1Pyi0y8Ve05vUGuwkNcMXNTaohP/wgxCogQMwX+v41xnJ3hZzSHBacWsD8k/PJLs3m5S4vM7fvXPR62LdPIhoP3JYmEBAg58i6zpMhITX7jLy9xd1u0SJIS9OLyUqfV40ug4HOgbzf+30mtJiAUqE0zlOzA7J5fOvjUBCA3fcp5Oeq/vAiQIcOcPIkbNokPZv/C/iPBP0L+CdI0Pr1IsMAWeUceQ8ymhMnpOqzZo0pOqdBA9HfPv64rJi89ZapKuTsLG4pzz5b9+oDZWUM/XUQW27uY3/4h/RQ+8nSblaW1IMHDpQAIw8P2fCgQbJEVRcefxwmTqTQUofPtl6UVR1Gt1J4+Lz0EE29xy/Z0yfg499FyuQQ0hRFz5584ZPC87odd3zsPw17dW03t9sRkgtX3OofA5Ljk/6ZVJocXhOXtvpwbZ4E4KY7gN+s+sfqZ5uuj3gANtZTri9/D6y1UmFxu4N9Zu5cMXew0cC0wbCoHs58fR4EVP1QGEwW6kL8fFP16Y3e8EH3usee/U5MIwBaTYPoeubXAxOFWMZ71HQ9/Kug1Ekf8/gLsHyd3GbOsOROcC8Vc4e2N4U4Lo0Ey9wG+N30I0/nzTA2cwtP0vElC0+CSUaNFQk05qplY8qfe4qC9ttoFGjBtKYTwMaG5Rc28OCmMXhbBpNdeQMt1TRxlbYwp9AklQnbJqGpN9uiUDsRGFib5ISFCfEyOMBFZ0SzLGYZH/b5EEuVnAjmHp7L6Zunmdpmqtjk6pUcOiSGCgsX1n8M3nxTzmWrVkkDb2wdHhVWVkJ4xo2TorHDXRQq9Ho96cXpRGdEGy/nMs5xOVeWn2c2nMnHD32MpaUlvyXuZ8TqoXhoolBfiyLzfBT69CiRkXldBN8zBFt0o3/LVrhE7WNL4XtczIqp87mntZnGE22fINI78i8zqrhb/HTuJ06mnSQxN5Fz6efIKzct+1uprFBr69dJvtr1VSa1nESwazDfnvqWmbtm1jl2y/gtDA4fDMDS80uZtHES9pb2uNm442TphqOFO3YKN2z07nS3n4pbRSsKCyGtIJNrxQncSNTgZhtBeZ4HxYUWFBTIRLOgwHxOzB9Gm+9h4AxQabDJbUuzCxvxsPbjbGQPsuwOodJbMVD5FX2cp+LsXHc/1Z0kfvXhww9FRgVCAiZPFme17JI81u/MZsmaHI4fsYGbVfIrhQ7bB6bg5J1NsT6HEl22VIVtc+UklHQ/LNuOk5NUJ470cKRSYf533Lm4Pa5rT5DSqxsEHq5zH/1P/kKYRwjdArvQMKyAFo0d8fRQUlQk1djqsqpRo0zhtOnpZnKXPGPhkZ5SvTozBbbUjgEIDxdb8Lr6lry9/1xvUHWk5Kcw79g8fjj3A6WVsrONXBrxRrc3aZA9mXffhSNHdaBSY2Gt5sFJaqY/VUHLcHdsLGzQ6+F03C12n71MXGIFiclqrqRUkJ2nFrdIVYVITwuq9PJeFyBineR6GeTBwIQWE/hx6I9Gu22Qeeq2bdt4J+MdojOjYfcc7nd85c9kyhMVJcG0O3dKP+X/Au6FG9x74tR/+FeQkCC6dYBZs+6OAKnVYgX51Vc1szW6dpUJwfDhItkYOFCYPMiP/syZ4kp9u5NJLdjasnnKXlLyU6TkX9+PcPfu0khQUSE18pwcIUw5OWRkXeV331ImDpYzt1N0NAPzPchUlDItxorRp0uxKZUf1ClnIXX2cxzsE8KhW6dZHbeWAk3dxGp+B3jumPRhrCWOsV5x1KGYM8KzyklYoa/bZvnvwO0EKDzbdN0gFasv4sOr2LS/ZZZSeUpzvDMBArhvIpz/ThzUOtwQiWJd+LyjvA8Oault6VxVcTL8Zhv2UVHtNtdy6HsFdoeYttPras3xFjohQCDBqwp9zQpTUJ4YOyj00gNlQNNsISR17YOhYgRCpIfH1T3WuQKy7CTjyL+oNgkaESd9Vgq9HKvb+6luH+tQNQ989rgE724NF+kiwJc7hNQG50kP2FFRzeBbJBJIxwqRKl4d1oN4xxIoKsLRKoFeV6VSllwHGW5/A37aJA55LWZAjh30fPT2UdeB69yfBFeSoffVLCIzs2rYlTchASqBjz+p9ripANg3BsZDcUky089Bv2SIcXJlsV9LShU2NHa7TOOGZbiFe/BLk1ncIh4FCsLdGtOhQXva+UnVqKVPS2wspMRSrC5m5dmVLDqziFM3pSmvU4NOjGwqJ7tXutZk0RqtTIjy86WXR1fP9/q99+RyJzz+OLz8MjRsaP5+rU5LYk4ijtaORkvkHZd3MGj5ILPj/R39qdSbPoB9QrtR9Hq+kbAUFYmUZNeeMrbEXCFFUU5y8Ct863AWrsibYVXuT0PrSLKtTpNfWTPApKlHU6J8ogC4UXiD7UnbaevXluZeze9oU/5n8WirR3m0lemDVVpZyuXcyyTmJFKiLmFiy4kkZCdw/MZxJm+eXOvxcw7PYc7hOThaOeJk7USoWyhhbmEk5CTg6+ArWT1W9uSX5xPoHGh8XE6ZrGiUVJZQUlkC1HTTmD5+EIPDWwGw5PwuPt/4MLQz3e9k7YS7rTv+tm4s6TOHng37iXzvegK7ruzEUuuGhdodZYUb+hJ3tCVuqAtdKC5U1SBP1f/mF2rQ9nkBOn4lT3LxAco3/ciZyqrVkUtzYehjaNcvY0t6a+5kul6XxO9Of3/9VbJ3QGRfkycLefjhBxWLFnlw7Zqpb6ZfP6mEDhmixMLC1E+nVgsROX1Gx7Fz+USnqomzlte5f78eKmaLI6JdNirHHOw9s7F0ykFjlU3rMB9+vwyHrn7C7ovn+eD8E2ZfX1r7h0m70YH97x2HSSMh/gRYVbPwnw1ktoAbHbgR5suMN8OY1HIier18Z84n36Qox4GCW45kZkYQk7GXXcWvcvO3TwC9uCnqLDCc5ROv55KYnSsEQlVFJiwqTKQitQceTg74+IBd2CnwP4WDUwV2TmpsHdTY2FdgZavGylbNaz1nGb/76+PWs+T8EtRaNWqtmj1XaxuLLB+5Aodro3lm7vekhNlDbzX0kx8wDfAL8Msa2PngTvqH9kehgPPlm3ntyhRQAWFVl+rIiBSjA42NBDv3rN3/t+P3cp7ZZW2U1EVGCtGLL4knOjMapc4G3dnH6VOHkeDdwuCOd6+V8v8V/FcJ+ovwd1aCSkqk5HjpknCJPXtMK6bmcOuWrIx++62snIB8+MePFx1869ZSjn3zTYm7AZGEPPWUyO3+iVRknV7HnuQ9LDyzkE0Jm9DoNCQ+lUiYu3zbKzQVNVYwKC01kiajhyfA1atkfvsJB4vjORKTw6gjTji7p7GrTRYvdTaRowArD26os6kPT5+ABy5Cl+tiGpBtB6ubSQN/vk39fUs9r0pfx6V6TGouf2nKyen2qMn4wByqr/5/2xZmDK531wnKg4T5YKmD0Gfqrxb1ToZYT8iopsQYlCg9RnO61Rw785hUXA43hN9DTETNtQxeOwSzjnJPeKkffNKlypL7G/CtpzBYZgEfdRXzhgoLsNLAi0flec31nt0L1Cq44ipkZ3CiEDCAqUPg+3ryFy4tEOlck2w41kA+GwYHPvt72KcyC6l6AZz1hTbTat4/IQZ6pMLsnmL1/cVOeOpkzV4vrUKqUN0fNVWigtT2xH9UgpVWessi/4Bl6bdbaxo5mDvWe4Ng1n0iLQR5bx6NhpeO1Owl0ymkx+p4gMnFsDpa4M1Pbo/yfd4elikvUqyVZXlLpSUjmozgpS4v0cav5hty8SL88otM+Kq7szdvLvr/kSNFKrx8uUg06sPIkeDvL1X2tKrecKVSqkCTp5Xg2fwCMbdMFZ6YzBjKNGW83eNtZvecDQj5CPwikCYeTWr077T0bomLlcsdfxf2p+xnwK8DqNDWtEa0zI+gMr4vnJ8kQbvosfG+TtPeZ3BtfpoylzPMG/IuHRtI5tCS80t4eOPD8n6orIj0jqStr8jo2vq1pZlnM2NV7Z9GXlkeSblJnEs/x4qLK8gpy8FKZUViTiLF6npOAlUY3mQ44W7hRHpHMr7FeArKC4xuejmlNZ31DNUlgOUXlvPWvrfIKMygRFs7J2vr+K0MChcCW/343Q4FCpaPWs645uMAOJl2kq9OfIWbrRsWSgs+P/65cWxv/6E8E/YlSdnJBCt6G8lSfoGW4qK6iVRBgUzw/4kZWdeu0nNztxI/rRbi4kxW3afOVnD+UjnlldXJhFys7dW09m5L6zZ6LJvuINNxJ1crThOXGUuBpqDGfrQr/IDzVgtQ29w5i8IxpwdtNM/QLKAhv+j6U6ytv6Hw0JjLBDqGsP3SIb68+BpxJXVXpmoYQPScbZZUGGCz9CT+tMPXFwqbf0KMz0t1jn03ZA+bvujNmTNAu29gkHkbXwulBWvGrKGZZzMKKwpJyU/hlT2vYKWyIjarnmgF4Ov2v7Hm0nrysqyJsavq5SxoAD/vE0OcKigUEBamJ7f3ULJ9tsLZx2DzYs6eFXndH0VwsJg+HDv295lN3Cv+k8P9C/i7SJBeL6Xh5ctl3n/2rJRvzSE6WiRvK1aY2LmPj6z2TJsmJd8jR4T87Nsn91tbi7f7yy/L/XeLgvICKrQVd2VNWR2ZxZn8HP0zi84uIjkv2Xh7p4BOfDngS9r5t6vn0XXj44/lNXh66vn88x2MHN2L2Qff5lDqIU6ln67XgceAXzZID8tFL3EtO95AJnfhORCRZWo8rw6XMjEn6HpNXM2y7KWPpqCqH8lw3UYD7+81PW7IeOntyLcxuboZYJCyGXAnwqTSwbJ18MAleHAkLI+se2xUOpyrkg7d3gP0R9AuDTpflzyjx8+abk91FmLhUi6TaMPkvUIlIarRvjAgCbYvq9s9zoDLbvDM/bCjajWsUZ6YOgxNuLt9jPOAIw2FMCS4C/FJdjX17SR+BWFVv6fvdYe3ekPDfCE3jXOgcbbpepEVdJgieUq/rjf1Gv0ZFFmJY9/WcPi1Zd3jpp2G+duF6FaHTgHvdxfCpNDDkR/FOEOngFv2poykIw1hZfPaJgzdUqXiVB+eOAXfbpPrZRbSAxVYIMd0fnvT51OlEwL/3dbapPCWvWRTnfSX3qyT/rIoYK7q2DQLBibJ56vdTSH5t39OzhFFtqUvrWzjsQnyxf6BISiiWlJs4cLx3wrZvx8+OH0fd/qE+fvDg1MzsbIv59iOQPbsAVyvwDNhZkuvdpZ2PNnuST7u9zEgcrhyTTm2lra1xpoS2u8nuTDZaGbQI7AHMzvOBCC7NBuvT7wIcAqgT3Af+gb1pXdQb3wdfbl6tZrJwp7aSfbu7hKV0Lcv0HgLa699xen002ab56tLylLzU8kvzyfCM+JfI0Ygx+564XVOpp009iAduX6kzvG+Dr680f0NOgZ0pIVXCx5c/yAWSgvC3cMJcwuTv+5huNi4GB9jeA/6D+hPsbbYaA6RW5ZLO/92xt+wvVf3sujMohoGErlluRSppVdm24RtDAwbCNRPmAywtbDl5JSTpBel89SOp3C3dcfN1g13O3fcbKr+2rrRN7gv4e7S3FumriAzr5jyEity8tXkFqopLNRjWe5nJEsJBdHklhZQVKKmqExNcVkFF2KrSIjWCi6OM+1Eqx8kVNVQ8aheBdFY13RUvP8ZCXs2EhrTeAVKAlalG8lRUrshZLnW07P7jsZkTT/6AWi+us6h2S9mk1WaxdrzO3jz8PP1HlO0lrXy0OqCr4MvSU8n0XRBU64X1u002ta3LfN7LcO+PJwl53/ht5uriS3bg53WD8sKXxTFflTme1FaYEtluRWcfgKK/CBiDYx8CM4/LEYTWisxlbAuFMMYrRVkREkIMoB1AeMm5xIZYUWa7S6KrS5TpLhBduV1Uguucr3wOjq9jnD3cKIfS2D1avjmGzjZsh34nwadEqXGEZ1VTSL507CfeCTqEeP/Op2YulQPeo2OrloQd7oBMxuBUgvfRkNmS7y8TCYMhl6j0NC7t6/395de8z9Lpv5K/EeC/gX8XSRowQKp0KhU4uPetWvN+zUaWe388kvJDjKgXTuRvI0ZI1Wgkyel58cQRmhpCVOnwmuvgd8f6KOdd2weL/3+Ei92fpE5fefc1WMOpByg39J+xgRvJ2snJkZOZFqbabTwvsvUNTO4dk1sJEtL4YcfNLi7b6vxPpRWlnL8xnEOpR5ib8peDqYe/MPPZcCzx+GFo1IlifGGS4G2RFwro3G2aYX/blGhqkmaKlVCLPQNGlDpH8TXARmkFidSUHV/vg3G6zYauFTNRfdOhOnUIukNASFiWxvXv2/PHYNFbe7cq2RAjxSppr3R21SdsNBWGUqUy99iK7jqIoYV320Bp4oq2/Wq+w0X1zL5a62V6svGJvDs/WJK0TAfEr6W169WCVEyEJx4D/hgj8na+u2eEnB6O+yr5Hzfbzb1ARVaC3mrq9K0qbHYZJdbSvVn0wqR4/1RaBWg1Jum6RqlVE22hsOWcIj1EqnfjjCRBlpr5PhOOi/GF9Ut0LeFSbVlxu327nVArZJjFZkJ153g+f6wtln9j3nknHzm3+9hus2lzGS0YYButrwmw4+L4fppPzHpmHxO/i+zhAEPyTb7JgvZNfdZcy+VSu3bB2rfdzcocA2EkFB0yZe5ZFnB5x7dOO8DOT43KPa5jMYxC0XMeCKvLKd7d8jK0bIyyFHctDKiUGW1oltYFE+NjmJ495C7Sq6/WXSTXUm7WHp4KYmaRNKKTDbEPQJ7sP+R/cb/U/JTCHQORFFPI4hOJ1UwAyE6cKB2i2XDhtC7j57I7lexCz1NctkZTqef5szNM8Q9GYevo5Tu3tr3Fu8dfA8bCxuifKJo4yvVorZ+bWni0QQL5b+nlK/UVhKTGcOR60dYE7uGASEDcLJ2Iik3iX0p+4xZVvX1HXnZezEobBA/DvvR+Nsc2iGUQLdArFRWNaSCyXnJVGgqUGvVVGir/lb972LjQlu/tuSW5eJk7cSKiysoqijiXMY5NidsrtEHZQ6+Dr6kF6fXO2bpiKV8cvQT4rPj63w9wxoPw83WjcmtJvPU9qc4n3ne/MaKfOAz0/O5PN+VfCfzpNJK78jjWYVGcnW4UX/y3H8zv129At7RYjxTjR0FEevlutZSJvwaa/mrtYL5iaCtUnL0eEfyp4z3y1+l3go3J2ueCPqc994UHfeWhC2czzxvfI8Ky8r59lNPMm5Ygc4C/1FfkqO9SrlFHRlqh18CvRK6za11l7XSlgpdzQawTgGdOPqYSc7Q4PMG3Ci8UeuxSoWSdn7t2DRqDwuO/sj3F+eRUZ5ivL9z1mKKs9yJOeEKFuViMmGTD67JsqCi0ElDtgFT24Df2VrPo6i0Q5/VFMeVpygqlGNtERBN375KGvbexpqbnxg/c10bduXjvh/TqcFduNUi5hPPbHqV1elzIaUH/Ly/zrH29iKfM5CiqCiptNvWXufBw0MEOpcuif/V/wL+I0H/Av4OEnT8uMjfKith3jxJaDYgN1fCqRYsMAUMWliIPewzz0hZUqGQFYC33hL7RMOYRx+V8K26dO93g5bftSQmM4ZvB33LE23N636zSrJILUilrZ90uJdVluE/z59w93CmtpnKA80euGMQ491g5EjYsAG6dYPduyvZsaPm+5BZnElMZgwxmTHcLLqJQqHgs2OfcWnGJR7b/FidLke3w9/RH3c7d1LyU7g04xIB1p5QVMQbR97jg2jRgitQEIQrEcU2RGRBREoJQytDcA1qij6/AE1WLmqlLfrsHBT5udhnX7vDs94brrjC8HFwsVpVz0ILU8+Aoxrm7jbd/ugwkTXl20ChGT2vRwmsXgMHwi15p/Of1J/dhq+2y3PPPG6uX8UElzLI+0gmzHoFPDG4frmaATuXCpG00cCuUKlWGKo5TbKluuNXdOcqlDmc9ZVjfN1Z+naWrpdA2D+Cd3pIReSnTeBVW61Dsis0KJA8oQmjoLiaQtSpXIJtByfC/UngeVvDcII77AuqHVRbHzIchIQdD4DvW/8xw4f2CZ58s6IB83ieUQGzGPV4Rq0xC7dAVAaE447TzRyUbdtBs2Zosm6RcHI7p/xNFaPzPrIw8O5eeLNq/SLNETo/JlWi9mlSMWqTLoTagDILqXo1rFo4rVSC14u1CRtIwWdQImypZnyVaytOgaUKO37UP8o1GuJCPt2dz+PeNpjgJ+7DuqG3LI07O1PpaI+lvWhMtTotbh+7UVhhsnmyVlnTtWFX+gb3pV9wv1oyv3tFZaU49htI0dGjclt1RERIpEKv3jp691Li7Cy3v7r7Vb45/U2N/TPA1sKWuCfjCHSR1ZT88nwcrRzvivjp9DqK1cXGHgnDxUAq3O3caeTSCIByTTnbk7bXIiCG8c28mjG08VDj2Jk7Z7IxfmMtZ7z6YGdph6XSklJ1ac3erKA+hLuHE+4eznO76nbr69moJ/se3mf83+NjD2NPUn0YEj6E/Sn7jVUkc3i81ePklufyYucXmbxpMnHZcXfc7tIRS1kXt4747Hjis+PNjrEo88PLwZ25979NljqVlPwU0ovT2Ri/EV8HX3wdffFx8CHAMYDXu7+Ou6071hbWHL1+lNyyXCMBsVJao9daoS61oqLMCjddOEVFCgoKIKegnMICJSWFlhQWKuqU9xUW3lnip1JJm7BjHUZ5V66IdK+gQOY2X3wBC5fk8sKceErtJAzWLjCOxs4tcTnzIfHxkG51EB7tUXNDlbaQH4iy3AuPyla0sBpKo4aWDGjajaZNpfrx1K4p3Ci6YXTeyynLqfEdcbN1I7esfhleXfAtHoimwJOCdHfUrb4ClUb6nq51hZgHRbpW7E31s3WzZjB+YhlfVjYnSyvKGR9lMwZZzyVMPwitVoFWK4vhhkt9/1+yn88l1w9Qb/oG4qTX0tlZju2doFJJ1Xn79pp28o6Oshhz+bK41/0v4D8S9C/gryZBWVnCwtPShNisXi2kJjZWjA6WLDG52ri7i9xtxgwpTYKsGL79tmjdQUqbEycKIQo2b0t/1zifcZ6ohVFYqazIeCHDmKoNIm/Yn7KfhWcWsj5uPcGuwcQ9GWdc4cwozsDHoQ493x/A9u1iOqdSCeFr3LiS2StmU+xRzKXsS8RkxpBVatKQKFBQ/FoxhRWF+Dj48OnRT4nLkv3LL88nrzyPi7cucqukjpUmpF8h/YV03GzdKSmBLw4vZF3iMq4UXqLIjE658c4rFKYGk5UFmqbLwe+U6I+zIiC7Cc5lKhqRQhBX0aOgECecKMSHDIK4ihu5uJPDSDbU2nYKgXhaF2JfUXNF8lBDsdM+VtW471YKrx6WvhKD+UB16BQiy8q3kZX6IVX5KiqduOu93NdksNAoD1xzvCix1qC0ySXHDt7bC08Mqf+9qg7XMpEYHrkLIt7hhkzohyVIJk99eDBGCM4Dl+CpgdLHZK+uXV1yK4OfN5p+bvY1koBfw/2Gi3MFNcwCDLhlD2PHwIFG8v/sfTJBNze2LhxpIP08OiWsWAvjLtY/PsYbWk43f59CD52uyyTerQxKLKHdVIjzlOrNt9vMv+93QqVSPg+7QoS0trgFe4LkNqhpXGHAtmVCyk4EQKfH699+05vvMznsdUJCwOnD/iz1PYxjngu2+V5Y5AdgrQ6mcWQgdvdV0PLx8fg5NGLPHvho8wb2e9fvDqPSK2ibpuf4YtNtzWfIQkFkppCwVpVuRF3KpUXmvfV0gVRwjzaQfK3dwVVOgV8jJ+QOHRjuu5+bylL6JkMvn8509WqDrbO7nLitrcVPtlUrOTmXl0vzp7Oz2YZPvV6PVq81EgYDaXC2dsbZRpjNzdwCVuyJ4cSZCs6eV3MlRV1D2qTIaEP7hlH06QMtu97krOobEvNiOXr9aC1isXzkcsa3GA9A5x86c+zGsVr75GnniUanYXrb6XzQ5wNAZHaNvmxU5zF7os0TfDv4W0BkgJ6feNY5dmLkRJaMWALIAprdh/UzcpVCxbXnronRwtx7nw/4OvhipbLCUmVpdPYLdQtl38P78HP0Q6lQMmnDpBrW3+392+Nk7cTu5N3Gffio70c83+l5VlxcQbmmHCuVFdYqayO5sLawxsnaybg4CBirD5ZKS7R6LcXqYorVxeSX59eQ5g1vMhyym/Ldd/DV9h1w3ywxKbDNkUl1Nfw64lcejJTE0c0Jmxm2clidr/3r+7/myfbSr3Lx1kXeOfCOSb5XTcbnYu1GiEs4btZedzXp1miEmGdna9iz5xyNGrWmoEBl9EXKzZX5SJcu9W9j40ZRwpjDoEFCYKqPz8wvZK/HKEp9d5t/0PmJsEE+Wyg1MD0SckNNmUdZTSUUtsFRlEFH0HX5wPx2js0UFz27HAjfVvO+y/0hN0TITf8X6jz2lHiKo12j/RB4UHKDtiyCsmqNvUOmQugO2Peu9Ajq77wgUSeUlVIt+wPbsLUV41+3artmZSXv8fXrYvH9v4D/SNC/gL+SBGm1YjW4Zw80bizObocOieRtd7XvdGSkSN7GjzeVKRMSYPZssYLV64U4jRsnhKjxHaRPd4sXdr3AvOPzGB0xmjVjpMSbXZrNL9G/sOjsIhJzTEEy7fzasXXC1nvuHaoPOr2OlPwUTl2PYfpbF8izvMRzjZYy7xNLKisr6f9tf/blmVbvDAF/Q8KH0NavLc90eKaGZrw6Kirg1i09p68mcSDlIHsz1uKuaUlOWTbJ/E6JhUlbrMhqjj6lG7RcAseelxNrhRN4xoFHnFh3uifCsu2mE86o8dBiZc0nLfKpOvFGwJ4PoEImNa1bi8N4eLissISEyA+GRgMjR+jZu0eHylLFsmUwpn8h7NpFWVouP3ySQ/jN/TTlEmcb3+S1PiKrAuiR2pj1lxvjdmhzvce4zEKqLkui5P+xF6VKcLCRVIiO/Ci9UrcjyQ1mDoDtt+VX2WkUlFrIqeZ217e/EqNi4fOd8EVHmNe5/rEGq+9YT2gxXcjI7VDohQxlf2wiOJ92llwiBzV83cE0dnCCGAN0uX5nMpRvA1FPiHztofOwtDa/NYtb9kLuPt8JaU4im9saLgYFDfMh5QuT9KzFDJNRR9s0yclqeBcrfneDDAcJiD0eACf8a1ao5v4u5HH6Hcw8fIpg3i4YX0X+toXB4Adrj7PSQGChggaKpUT/Op7cilvMV04jKeowN51sWGvQd5pBoHMg68auY/HZxbT3b0+AUwA9ArtjVVQqGo7QUNDr0RUVUjHnA0oOxqCPvYJn/mUKrcUyvsJCpIObVffh5ruHLU207K1jIenZ45LjVWEh3yGtUh478bxUrUCO1wfd5fYKFaitlKjRGZ/n3X0wNtcXnJ3ZrY5n8AS53dx35ouAx3k2bCK4uHC4PJFuO+pJK9zzARx6rergR8MTdQv4X+36Kh/2+RAAhw8dqlzYzMOQawSQXpSO3zzRV6sUqhoTfyuVFQ+2eJC5fUWqVKIuYcCyAaYxVUTB2sIaK6UVnRp04vHWwqK1Oi0fHvrQVKW4bbsNnRsS5ROFnaUQpRM3TjB58+R6m8qntJ5CXnkeiTmJjGwykrd7vg2IjNF/nn+NsXaWdjhbuZBeIm+iUqHkjfaf8kjjmcw7N5uvL7yLt20AH7VdRXPnzve0On83Y8rKxOnVEPp5O7p11+MXWEwJOZTpcykjB7uSZliW+6HRQK7tSa77LqDSIpdKixwqLXLRWOWgtcoFhQ6vw79im/QgWi0U+28m//66CRPbv4KTT8v1gOMwehyUusukvcy95vWrvcTFDISMWxdCmeufm8TfCxoegocGgFUp5ITBng9FopYTBrdaiFQtbIf5cOj6UO4sBGfJHrG3B9mWXiWmBCiEaBgQvhUcMoSsul2BiLVgW7eU0rakMS1i1+FU0QwLC9BZ52GttMFKaYuFBcaLSsU9/a9QaFm16hYnT4os9scfa95/p20EBYkvlQE6ndwPYsjlWfeaxj+K/0jQv4C/kgS9/rp4/YP07ezZY0ovVyph2DAhP927m7IErlyR8K1ffzVZxY4aJYSoeT2uZvcKjU5DwLwAMksy2TxuM0MaD2HByQU8/9vzRj2zg5UDD7Z4kKltptLat/Vf8ry7Lu9iXdw6Lty6wMVbF2s5Cp14+ALtGzWnsrKSN5a/QZlnGVG+UTTziERR4caQdd1wt2zAM16bUOd6k5UlX9rql6ysO5WF9VXOMXfwlDzzOCT3FS//0tvOCk02QOAhlN6xqHxiqbQzkSqVwoJLE0oJaWSJhQXM3DmTs+lnaerRlAjPCOPFz9EPtVrBxIkiM1YopIHyiSpVYnZ2zZPR6DFa1iYtkX1fvRaLm1EEcANXVTru2iI8yDVWmzzIJpoofuM+Jo8uICdgHgudfkSrhJcPSxXgtL80qh9bDN4lSAhuamqNru10ByEiH3cVq+g2N6Vf57w3xFQrBLqWiWxte5hUk97oU/+hHXtRnrPAGjJCvChHS5qiiCsGL2rATqukVFW/D/o7+yRwFqQPZX6HeocbCdOqZjCunnkmgPYdIUF64MFR8FvIbdWlclhfpZ0OKJCeLqcKcXMrtK7ZG2VbeWcp25ZwMaa44WQKyNVUSb/ybpN+LV9rIh1/FbQKeKenuPgNTIJvtsnrD39a3vupZyAoX+6vfpytNHDsByGaFRbS37SxiZg4bA8Ta++6sHgTPF7PPA3EPtpdb0uj00n8GlS3LKk6DDa1Wi28tGox85Km1DvescieZsk+YJ/N8dC6Tx6/bJA+LhDSaqi0msM3W2H6abm+r1HNIOTqsNLAR7tFUgry3Ro7Bqz0Cqy8/LC2tMGqohLrK9ewsrJhTEUk7dLbkpTpzOEcJdtbHSFdG0iu1he01jhYKGniX0lgqDODO3ZmZHuZzOeXFvF9zALici4Ql3uBhLwLxn2wVFqxvc8NHFWeaLXwzOl+lGgKaerQmXC7DgRZt8FDFYJOq7wrMvBn/7/9thJVGgWOx8i2PkildzRlrqfRW5ShqLSnwbICtJUqtFooaPsaGvtrKG52hPyGaBr+js4pRRaxXJNrVVn4dTtcvh/sM+HpcLAphKQBcL0z5ITLJTcM1A71fn7+dSh0Qkw01pLlBfJ6w7aZKhy2uabcILsc+P0jiBslYxtvgvHD69y846GvcIp/GgsLPUWuv5E7fAAAKrULqko3LCvdsdC4YaVxxzfrIbwL70elAr11PoVOx7HFDU2ROwd3usniYDVi8cgjUpGoa+JeqSogX3GFU5qfOFb+A5WUMcNjDbEVv3Gg6Af0d8rKqI6978r7qVfCmAeEBGU3qV05yguuRfBc/fIICdVw5YIneXlA480wvubJq7F7EwKc/GtYbI9sOpJ1Y9fd/T7Wg3Pp50jOS2ZgyEAmPniddevCmTRJXDb/DMrKxFkYZO70L0/DjfiPBP0L+KtI0ObNQnJuh4uLZFg8+SQ0amS6PTUV3n9f0pANictDhgghior6w7tRJ7YnbTdmYqjfUGOpsmTv1b30WdKH1r6tmdZmGuObj7/nJGy1Vk1CdgIxmTFcuHWBmMwYvhn0jVE//v7B93lz35vG8RYKKzQ3IyAzkjDnFgwLepDybF8yMnTEx+eg1XqQlaUgO78CHu4FDY7JiWrxcanW1AMLC1nt8PCQMm9RkRlts/0taHgYmq2q1/kGoE3GAtp4dKdDcARhoUpCQ8W1T6GAwopC4rPjicuK41bJLV7s8qLxce2/b2/MS6kOJ2snmns1Z9/Egzz7jIrvvgNscxk71IWRI5RcvCifiVowZidUof/z4H8Cds/l2RHdGD5cAuXat5fS9pIlIqE8kHKAz4/NY1WfbynIvEbnHaO5UpbGk059md9+NhmRwSTkJJCQfJKEVd/QP9WSrtFF2N26xabGekaMM7MvCBn45Ddxyfu+jeQcLd4sxglxnlJtifWEJHeTk9utj029Ly/1E/vwptnQ7JZM/G93V5u3U3qAqptJ5NvA7P1iuADwQTdxTTOMKbKuuY0jPwhRgzsTJocKyP1IpGC/B5s3ZKiOo4uh0w0xKGg7tbYpgKXWJNE7s8iUO7SshUjStoeZpGnfbREzCHu19H8tbi0T7jQzH/cBSfBQjFQY1CoxKjC4zq1vKr04apWpCmKsWqjgx01S6cm1hcjpNbffKM/0uAJruPyVuMgBvNgPPu1S97E4/63I1AAUs+s/bo+fkX6na87SL1Qf7sb9zoAZJ+UzUGgNW6pVzr0qnClVO1LsWK1pescXcOIZFAoFev9j0OnzWs3faK3w1uQSGR9KeLoDnmShck7mRkgcXpp8Gmhv8Z72PRy0aty1xXygfZewHJN1vKGvyUorF2uN/LXQ/bF+NnPYxFBycSOCWDpw0nh7Dm4U4IwOJcU4MJdXWIV8mb2UNxjg8QXZNhbYXmtLAc7kKh0491pP9Ba3NfeXO0sw5+UBcPRF/lUoKyWU1iUV4oebbn+qCXhUa+zTWEN6a7jRUVbwW1RrFjv2HNhlYbFjMcqAU6gf6lbn07lffAOfuPdQqUBhXUyp917sK8KwrwjGSmVtdrVdoZCFz9y7aD2ZNk16e/9IVeD2///IY4orC7hcEE9eeU1HPYPD3pTWU+gV1IvKykreWfEOH1ytQ1YGzL9/Pk+1fwqAQ6mH6P5z95oDdEqs9a5oCt3RHnyJkUGPsXYtZBSnM37deM5nnjfriggi78wozuC5Ts8ZlSy2FrYEuwbTwLkBp2+eJrs02zjeQmnBU+2e4oHmD2Cp9uajV4LE1yBoL0zsJyGy5nDwdTj6gsjamm6AqCqWcfQF+O1TAOzc8rCe3pW+4d0Z1bonTTyasPDMQr4/+z0anQYFCh6Oeph3er4j+Yt/AUavHs26uHU83+F5tjzzBklJrvzyC0ya9Oe2m59vypOsqPjrAmf/LP4jQf8C/iwJ0uvhu++kr6c6mjSRZsBJk8Sxw4C0NKkWff+9qRl2wAB45x2ZxP7V0Ov1HLtxjC4/mmYx+rf1xvuiM6Jp5Xtv/ogHUw+y8MxCzqdfIDE33ugaZ8CQkg04pw/n1i24WnmCdKfNlF2NRJveQsrZujsdZz0MmQZtvkdR4UzwnlME2Ibh5YXx4uoqrnIGklNYKCqZ5GS5lN/BAtnPTxQ1DUILUTQ6iL+fiuuWvxNduJvYnAtmH+Nh58G7Pd9lSpsp9bowXci8wIVbF4jNiiUuO47YrFiScpLQ6rX4WIUyrSKJS5dEJsHkLuB7ToheVoRJXpcVIatTOgvGjhV3QR8fOHyyhK8tG6CzrirJJw6iY8kcXnm0BefPi3yyfXtTyK5erzf2de26vIsBywbQwqsFqQWptZqrZ3WaRdeyrgwcOJCc4gyGrhxKE9uGNHYP542znxrH2estmVrShKk3fRkScIjLdmWMSbLlx2XwC4+ya8gCMtMqyTsfzwa3SBI8YGS13uG7cbczuOEluMskv1H+naVqWoVMgg2OfQbZn12l9H4cD5D+nBtOIk/LsjMZS/ywCT7pLFWvu5H8vXAUPv0N3uoF7/Wof2yTLIhbIA51w8ffeduad+TvwAfht9D6xy5dL1WbfBupXhl6ncxh5RohV2ua1bZ3vx0J803H7/Xe8GH3+sd//Bs8cVqyrswFFbdNk76k1w+KvbZWAZuawI+tpPqY7iiXF45C63QhDRe9YHavup/zofNCKA4Gms8yalAASzZINfMb9UuUYcts3jG7rSIcSKERH/MSR+hCGbaEcAVnCsjGgwKcyceFApwpx4bbqUxDUmnAdTzJwoNsPMkyXtzJYSDbUaLDhXz20YtIzJ9jAA7RFX/SCOZq3S/+HpGDG7m4EUZtXZZOIcf6jC9sadSA/Q1cKXBJQKcSt4pelz1557fulFq5UGrlxJzeO/Es8cNa3xe9zTicCcSaSjwrblBp50ylnTMKS4s/PbHX6zWcO3eKTp3aYWNjYfYxJ7J/42L+cWJyj3M+5wT5FbUZSM+GvRnWeDjP/f4MAE+3f5pP+n1CYk6i6ZIrf5NyksgqzaphHHTs+jE6/ygaXaVCSSOXRjWsvaOc+3B4Q1MWLoSUFNPz9u4tMRd9+sD998s52dcXDh/+8/29/xQM86N+/fvVsik3EKY+QX1o6SOrWHuTjjBuyVNkVaSBfZb5jX58i0/e8aT9mIP0+Ln+k6chgzA5L5lxa8dhqbKktU9rVl1aVaNnGODZ9s8xwGUmCScbMnOmmY1ZlIPbZfCIQ+UTh4VPHFqfU2icquQ6eoX5VPO8IMhqiiKnKcFOTWnt0puyRhvYrX2Lcp3ITQeFDWJOnzl/yi33dlwruEbQl0Ho9DoOPHCWXs2i0OkUf0kPT2amKbJFpzMpk/5t/EeC/gX8URJUUgJLl0q6c/UTX79+MGuW/K3+wcrMhLlzJQjVkAXUu7dUfrrUs8r6R5Ffns/S80tZdHaR0ZoUwMfBh/QXzFt/qtWijEpNL+ZU6kViMi+QkB/DtfILNM96B9WNHty6Bcn2K8nuUW02V+4kOt3MSHFNSRoIBfUv4Xp5QadOJlLj7q4lLe0c/fpFcbh8Me9HT0eBggWdt+NTPIArV0Q6ePmy/L12zVRBMweVSipvoaGmvhzD9aAgUynYHNbHrefMzTOcvHmSo9ePUlpZ077LydqJ+0Luo19wPwKdA+ke2N2YNaLRyP5dulTzEp+kRuOUJHria11NG5vlAw7mXZNU+aFov0iiY0f47Tc4k7MfTztP7CwceGrVHHZkLkav0MrJO7kPFnEPoqEcPOLpNCSBjMoEJkZO5J1eMvGbsG4CKy6aVkYNP+hNPJrQ2L0xfRv1pTK+0ux3QaPTsC52HXOPzCU6IxqQVTdve2+jhfAjge+w/NWRqPVaOnevIOG0ku5Xr/Hg/bmM6pHDxoITFBfnUlKazyVdJtG+Cs7ZFVCskeNrpQG1hZgNnPEVomLobbodG1dAsywIyYVej0Ciu6nioVbJdgBCcyBpvulxUU+Ia9mdEJUumUh1wbECCufAgnbwUys4U49dvUIvUrvZPe9cYbKthJyP4Lu28PyAO+/nn8XE82IZ7l0iLnfexSLxC8kzGTJ81EXyjIqt69+WaxmMjoUnT0pl6M/+rubbCAlOdZGetdXN5DZHtUjvZu+HF++7u21tXGFyAoz2EeOI8BypjP1RlCtsOKTvSil2DKNmr94+erKUiRTgTAXWBHCDbDwoxQ4lOuwpwZ4SGrkX4+2pxcdDSwPbbDwU2SS88D0WlgosLKDJ+w/h+duyOveh8MlXUOq1WBTkYrPshz/+Ym5DpVL60s74SpVyQBV3SnGBoJk1x7qrHGmj9KftnnjuvyzZa9jbG933cHERDdTUqfKA/HzRAbu4mMZUH+vuDvb29/zbrNfruZx7mRNpJzh+4zh+jn40cmnE7uTd/BT9U42xLbxa0DGgIx0DOtLBvwNNPZuiVEjZOq8sD5VShZO1zE0Oph7k+V3Pk5iTaNY1TrnrK3THpNfGKfQCng+8Sc8W4XQMC6eRYzjvPBPO4Z3euLkpOHDgr5W5/92o6z3Q6/Vkl2ZzJe8KV3KvMLTxUOJjHJk0CeJ934Qe5iQNVVh8DFV6R37YHMsrcX3IKK7tQgk1TR8OptZPmPxSn+fmT5/JP56xMKmv9DdV9Tk18HDHx0dBqX0sXYPb8N0wcYXV6XW4zHGjqLKq7J3VRLJ43JPq3v9Sdzj0KvSfBTc6YLH/Ixpb96BJE4yXpk2ll9vhT6gqX9n9Ch8d+YjeQb2ZYb+L0aMtCAvTk5j45xnLtWsQGCg+L3daMP4n8R8J+hdwryfalBSxt168WM7l1bFvH/TsWfO27Gz45BP4+mupXICs6r/3Xu2xfxVm/TaLb059Q5lGbOjsLO0Y12wc09pOo51fuxqZFomJYtCQWHSO4tbvgncMuCXX3uhvn8DRWXLd+RpE/gqZLbDKi8TbtiHeXgq8vKSnpXrFxnCbra30QuXkSL/T229LFS03V0hDQoKGHTuSyHG4xe++fdErNfD7XDjycp2v09a2NsExXDdIDf4sKrWVrI1dy6t7XiW1ILXOcZ6lXVFc707u+S5ornQ1K91zcBDr22bNTJfGTTWs25vMix/HysnbMxbPZpfIsYihqUNHLr2xGSwq8G+oJm1MkHFbAXbB3Cg18z7dhg6Oo5nu9Svrct5lV8EXqPWltcbcr/qYLooXqajQEhefyOEmk9CiRosaDRXG6zqFGp+yPgTnTeWS61yy7PfX/+RXe8IvJqML1SueaG2yzQ5Vprcl+JdV2NrG45lnzfGZ4yh1MT/WAJtKKP5QpF0GA4layG+I/guTnXmfSSJDM8iTql88SyV096mBJqlWhxvihmelFZKlUYpJgV0lvH7I9DS7QsR0wEorEsAyC8nSaZIthKnjDXH8O9IArrhBth2UWsqkMq5aH5ihr2R2T+nXqQ8Pxsj2UlzrHwdibf5qX8ntueZy5/GnFokL29ft4TkzZCwkF3pdhVcOSyVmbldI9JD7HCrkNTx/DHyKaz/2bmCoTuwJkiregUYiOZxyBhZtkb4tjVLMPHqkivxwX5D0Z+0Pqr29gz+a+q7MSfZCc2SiH5wHo+L+OiOKu0UC4TQm8c4Dg4Lg+edl5lJQIB63trayqvPmm/U+dI73F9zItMCWMj7lHuVtVlbQsiW5dgrWFp/kjK8pO6q6rPHFI+JKCUJYP+0sFd02NyFgxCQUbu6S/rhypfnnAcmUmDePyspK9ixbRv/PPkPh4lKTNBmut29v/CFNz7vOs+unsKDnJ3j6BJNYdoOxax+okc3j7+hfI/fJgDERY1g9RuTRer2enLIcPOw8aozR6/UkpWfyzapE1uxJ4mZFovQeHZ1Fe98uTJ8O+ubLmLztodqvSe1AY49w5g1+zxjcWlpZasw0+l+FYX7k3MKZ7Ve2G0lPcl5yDUI4WXuCXz5sj1YLTn3nU9j1GZysnQhxDSHELYRgl2BC3EIIcglm8TvtWb3UCW9vCeo0l3dYVilzF1tLW5Jyknh224uk5xQTXbin5sC0dmBZCkdelPBTEEnb7Tbbt+G7gd/jc/NxvvkGfsv9TkwfUnvQ0M2HadMg5L7fOJMnAcln02vnAg1PyiQ69ygZ+4dRXlY3KQkIqEmMDNd9feuvvpRWltLg8wbkluWy8YGN7F4wmK+/VjF1qpaFC/+8OUVSkhg3OTndnc32P4X/SNC/gLshQXo9HDwoLm+bNpkMDKpj717xYjcgLw8++0weYwjGa99eyM/tVaI/i4LyAhytHY0rWbN+m8Vnxz6jhVcLprWZxkORD+Fs48yRa0f44sQXZBZnSh9L5xdJ3/6Y/Hb6nYap7YzbtCjzxam8Bd76SBpYt6CFczeaeAfVIDZeXjKxv5vX8sILkpkE4jB77Zo44hnswgFQaGFGc/CMh4sPwNoVgAIbG6nqBAbKX8N1T8+azbR304xbWalHrRHLWr3OAjTW4uKjKSVPn1JlY1uBWqdGo1NToVVzM7OC1DNNKL/RRPpzwrbBsMek2bQ+XO4P2+fDgJmgUmNpW4FeKWRCr6oApRrOP4L+wOsy3vkaPBNSu5n3j+LcoxA7SlKwX7qDy9/saqeTt1R1a6ev9IOlVcF8Acfh8ToC3/Ibwo1OsLbaZGfsKAmjMwTzVQ/pyw8yOWABtPsGLEskrC90V81t65TiXFRpi9OP23H0OoqTIo/4qQ+jr37sChrgkNeCB24mEpmpYGSCDidNLg6afJRVcaAH/MfzZfvlWFiAtbKSpausONoARo2FDEfp01m/Cu67AkkNe7N5wLecWpuKQ24qi6ndfJ+FB5VY4kf9QYvVUW4BU4aY+qJOfC+GCTm2mA3aXdFCemUUenEkK7aCj7rWvf3njgl5sK+UviFzpOZ2LN4EKj08Orz+cVe/EKniZTcIe6b2/aE50lvkWiaOcg2qFJhnfaWfqrr9uUu57OMTg4X83C6r8yiBiTGynbtBvg2kOguZGpwoVR89oJxd/+MO/ATdq9Y7fmkpE/nAAnmdgflVf6v+9yz563p8/lbcbahIFW426oSLgxbb9Csocuo+z1Wo5Pie9pNq6JhL0C8ZaNiQ3WEq+nUzSfq8iiUTqu1NuXS+Dh6lSOXHYDVuQGgoOldXyq9exS67nsWQ/v3hl184XZ7M8NUjSSvNYHickPDRY6VP0LNMybLoYPr1ngIvvUR6UTonrh7i+PovOW6RyWndDV7zHMlrYZPB2ZnrlmU03NSDYNdgOvh3oGNAR1xKOnBgZRSrllsbd9PODiZMEMlb6yofoaScJHZd2UVCdiLrDyQKUXJONZ5Pt03YZiRBKy+uZPy68XjaeRqzj6rL7MLcw7CxsLnr9+yPoKiiqAaxuZJ3hSt5cn3NmDW08GjB9u3buexxmVm7Z9V6vLdtACXXgyne8DGkdWD8ePjwswIcnTW42bqZDREuLZU8xAsXRAWzb1/N/JqyMonNWH30BOvSP+a6wwY52R14U/q9xoyR38joSbDpxxqGBsHB8NLrJcwraUNibt0hcMoyL3TfngavS+Adg1+rGL7qu5jhg21QqWDK5iksPre41uMGhw+mtU9rnu34LG62buh00ocbFwfx8aZLXJyYNtUFJydqVI4Ml9BQORaLzy5mypYpBLkEkfR0EpEtlMTGKlixQsO4cX9+dffiRWjRQuZQ9e3nP43/SNC/gPpIUHk5LF8u+T7nTYtJ9O0rX94PPpDJ9dy58HJVwaKwUELB5s0z/ea0aiWVj/79733SXtf/lZV64otO8VvOQo4WruRZ7800tuyDVgu3Kq6Ro04jgI7c0iSzsnwS/nTkqH5e7QNgmPxalMEbdniWdcWnpB+OZc2wrPBBVeGNZbk3erUDWo3iD+13YaH8vSu4XoHeb8Lm76HyDoGsDQ/D5NuaW1dshIQqh4quc6Hvq3U/fscXcOLZurdVHXveh0NVZOUONrX3hGJvOPmkuNHFD4NZ9eiqAMqdsbjVDse4GeT1r5m3YlkchE1xE4pKKyB4b52bsFH7U25VczW0b856giuGcuNGKhZNL2FlYYOl0qpWVoa9hROulj5GTT4qNdYWluRxlc8zB6FDS0ZlEtZ5LanY8zLEjjGaOlhZyWJBWRlcvWrq3zJc6mwotsuGvq9Aa5H7WGpc6F75IaMaTaVJYxVhYbKa+MS2qZzLOEdsVmwtCWOEZwSXZlySf7RaXtn+PDZqHRHujYmI6Em4ezhWaq007OXmcrPgBj0CfueyrbD0lLkOFIYPo8X5XwEoyK7E2bPubtIdDGAgO5g4USrB3mFOshqirHKKCAyUVf2qjmr9zTTmeiVSoqvgnYMWKF1dUSiVoqMFOekUFsLJk1So4Nn7YWFbucuxQiRIu4Nrrsp7FYvtd6Y9HK3q0/UoEbmbWgU3nGvv90uHpbenUT582fHOhKlgjmzvpyiYPLz+sb8vgb7JcLghdJtc/1gAO7WYT2TbyWS7e6pkKVV34XMplyqbIWxVz51JiR6pwO0NgrURsDOs5v1pn0kgL9zZ6KE6YTrUUF5bdZLkU3xvGVT/P+Gsr8hFz/gJUTKYpBiwcIs4EIL06UX7SMXI9x6rh6uawSPDodwSmmbBluVyzFs9AS0zYMW6qvfTwQF+/lmsycrKJKgGqShWqEx5U9vDYJAZy3dLjYKQDCfGnolgrE0EoW1csPZyFgZUtS30evTR53n3S2e+/MWZEqUTy9ZqadY1mcScRLo27Iq7nTsAnx79lBd/r7siZ3BxBTibfpYDKQeMRKmRSyMsVXdWruj0OtKL0o3Epm9wXwKcpKFkwckFPLXjqbqP6+hVjAgfwfbt2/GM8mR13GpCXEMIdg2mkXMIW5Y2YvYbNlRUyCH99lsYO/aOuwRIJaJtWzml3XefqFFOnIATJ3WcL92BruPH0Oig6QEJQ2iQ+io9gjuhDl3HGv0DIgUv8YALD2ITdoxv2h1g4ngbLCzg2R3P8tXJr+5uZ6pwdupZY3/0W/ve4r2D79W4/71e7/Fi5xextriDLrgKubmy0FudGMXHi/LF3EI6yM9BcIiejOEtKbS9wDjXzxgX+DzDh8v96emV+Pj8ORdjgNOnoV17HY5D3yKg33ra+rU1Znv9m/iPBP0LqIsEbdwIU6aInK06nJ1F7nyzWsRFQIB8mQtr9pn/PbAuhBbLoc1C8I023X78Wdj5Re3xPd8Wa+jUrrLiXn1ynNwHluyW65Yl8Ho9Ata4EbBqven/gU+JNrbEWybyxT6m62oH/uj6qEqlx8JCcVfNs+kdHiW/0c81Hh+asIDAWzOwsIBdHVViJVoXfv8Ijrwk1yf2g5DddY/d/D2cfZzAQPDrspfEsCdxs3HH2dIDJwt3HCydsVJZU6LLJbMimQuFB6nUq+ve3m3wULcle85xglumk5tlRf6A4VirbBnXK5IuYREUJjfh1amNqcz35OGHFfz4o2k+fTu++iGLZ0/dV/PzUQ2+N6cwr+989lg9w+LoRcbbxzcbT0R5BI8OfhQ/Zz+zq3j1QafX8ebeN/nyxJemfJK8IJEqRD9isnKtBwqF8IOwMCnXh4ebrqdbHOeZnTM4l3EOgB6BPdj38L5a+6nT67hWcI3YrFjjxdfB1xgMqdPrcJrjVCNDRaVQEeYeRoRnBD0De/JU+6cYtHwQOy7vkAEflGCps+HYCSVt2iCuJjt2kBmbw85lOaRdFKtyN3LxtcqhoGlHhsR8iF4P7k6VZBfWY78zYADskOeprKwEZ2csy8rIcIAie0vClB6yWu7mJg0Fo0ZBXh4LU9fzRNHyu3tz6sDks/BItEkqVh2VSrHqLqhmNlH9+qhYk4PclnCY1wluOkoPT13GC44VtV38zOGXDRJCuy0MRtbhUmjA2e+gVYZMpPtNhPhq8kLfIlMFx6NUJHpB+XLfVRe46ipEyrFC5Iw2GlMuUJYdeL1U/3Onfm6Szr3aB+beto5ipZH7AwvEgtxgNpHhIETUr0gc4/5/R5mF9OIZZHRn/OQ9blXVDrKwjSm02a/QVDFqU1U18jYTd6RTiGzUYEzSNRW2Ljf1eV30EjnqHzm++TbwZuBIirzPkxxwizj/IrKr1uRWrpFQZ4BjATBnfAAdh0yng38H2rlE4OR+20KWvb1Jvjd0KMyZI7fr9RS99gKXHSpJtC0l0bKAJEUuiZWZJJRd59jI7TRpIiXeDw99yOt7Xzdu0kJpQZBLkJEUPd3+aYJcgziXfo5fzv9irO5czb9KucbU9LF69GrGNJOsgE3xmxi+ajiedp4EuwbXkK0FuwYT6R2Jvcq+1vzo6lV4+GHJQAQYOFDaA3zr6aE0IC1NyM7Jk9JPXQMKLUzpAH7CjBU6S1qpHuKJyFmM7hFBbmk+r3x7kE3n91PZ9vNa2947aS9BrkHEZMawPWk7C88sNN2ptgermh8ipUJJY/fGRHpHEukdycTIiWj1Wt7Y+wbLLkgfnqXSkultp/NG9zfwtP9rwnQqKoQI3V49io+vUg3Z5sCEwdKaMC/NlGcE3Hefjq++Uv7h7MhbJbfwsvfiyBFpy1C+4onOJhsHKweKXq3d7/ZP4z8S9C+gLhI0dChs2fL3P//duuYorUu41eY5ChosR2chX2aF1hqvrDEEZE7DvbQLlreRB5WFji1BIRRbptArdxlNKicY79dZFKO0qOSLudJQ0K1vEVY9PqNEkUEJmRSRSbEuk0J9Bmp9Kd0dHmOa72IsLKBSUcxDsXVbaffyGcln7ddVPZeeD6Ofxs3WHS87b7wdvPF18MHH0RsfR29cbB34+eJCAp0aoE3UMGjQ3RtUZJdm8+zOZ7mQeQG1Vo2dpR3rxq4jyDUIvR5GL3+I9ZfraCrWWsIHpSbr6Ul9RXplBkqFEs2bGuNke+SqkWyI31Dnfg1vMpxrBddwt3Un1C2Ub09/e1evh68SWTDPia6dbBg1xIHLiSo8PWV+3KYNbNggSgCtVtwIv/7avBSxrAwCAtXkNlqMy7B3yddUM17Iairhi/Ej8PMDv6nTOc13tbbhbutOhGcEk1tN5pGoRwDRxAPG41BZKT1yiYlySUqSv3Epudxs9iI0XylhdwDFXnDwDWNYn49PTYJjuB4SAjb1KEC0Oi3fnv6WN/a+watdX+XlrnX3jNWFCk0FX5/8WghStpCk6k55wxoP4/7Q+3li2xNYKi3Ro8crfzA3oyNw9Snhu5d7MTSifw2pyhdfSCuDAWFhEna8YwecPq3Hj5t0bZLD20/nEuGdgzF6PSdHXvwUkdZVlpWh9fdHX1ZEr4c0JLrD2tXSf5PmBN+PDcV60qOcyzhHdEY0l3Nru32BTOqDst2IuTEBq4wInDKCeJe5XG9xncNhuRxzL0BTVaKwS21F558+4nOeI0iZit6iGDulLUqlwtTICGgiIim9kYNTYe2eCnPQI31PBTbg/0L9YwdWtcPccJLg2gcuyeT1l5bwyIj6H3ttnsjsNjahTlt3AwyE6ZwPtH6i/rGHf5B9ONIQ3ulhchI0wL0U1qyW6lSDQgh6VsbYq6Uydcu+ZvUj5XMTaXytD8zpBiqduNhVl9sFFoibossfbFg+c9+rbA+cTnSGD9r4JF6/8ihOunxcyMeVPKwwuXn+xCPM52n87Qt4xupb+uWtqXfbiYThTAHe1NTQFPuHY0cpyrQbf2iff4qCzzpDnIf58OPqFbcMB5GQvnCfKbcrLEeO98q1JhOHvxJ6INlVLPT7JouJCEgv3Kt9TeMUeukh7HhDLkMTTGMBsYnbuFGsQS9cEBlJHc/HsKEoNm5Cn53N9xd+5oNtL3PN0Tyjuxj0Cc0mzWJj/EZGrDL/hYnyiWJOnzkMCJXybrmmHLVWbTSAMIfq8yMLC0sWL5aWtOJiKazNmycRIOZ+hwoKpOJw8qTpUn3xGJB+nkpxKRo0CNT3PcGx4uU80fYJHol6mECXQEryHKS14ORnqHvVluTdEZsXYZc8jv4PxWHfYSV9mgvpaerR1GhoZMDWxK0MWSFsfHzz8bzf+32CXf8ZKz+9XkiigRD9duI625Y3qFU1+vpriVy5W9wsusmqi6tYeWkl0RnRZLyQwbljrvTpAx5jZpPd7B287L3InGXeoOmfxH8k6F9AXSQoPx927ZIvd3VC8sEHcOyYjImIgNjYmtt79VXJaLGxubMVqFJZfz+NVqdFpRR9i16vJ+KbCOKz42ns3phpbaYxqeUkY2ndHAye/Y5WjmTMyjCmchug18siVVmZTGBD67DjLVYXU6mtxNVWCFNRRRGfHfuMzOJMMksyySjOILMkk8ziTEoqS3i81eN8P/R741inuXW/xy42LsYJ6Cdhn3DZ8TKe9p54O3jjbe9t/Ovj4IODlYPZ6sStW6Jxvd2RLS/PzBMqtChtSghuXEpUqI/RoKDc+zB6p2uUaYooVhdTpC6iqKKIInURGp2GH4b+QFpRGhnFGSw4tYCdl3dSrC6uFf5aHW62bvRq1It1cX8+OC3EviXHZ+xm1wYPJk4EfdM1tBtxkoF9HXCydsTRyhHHan83fdOeeR/b0Kt/Md1f+ZR5x+YZG1kDdb0oXrqUnKv+YJeNssVqdPebP6vO6T2XCYEvk5QEB2Lj+CinI3YlEegyIii6GoH+VpWld0FDUyBexFoYNhnLKyNwL29HRvCnku9x6FXY8yFOTrIS5uFh9invCpnFmbjaumKlkgrLodRDJOUm8UjUI8beuLuFXq8nrSjNWDUqUZfwwaEPKNOUMavTLD499qnZx4W6hTIxciJv9XjL1PNmUYaXm61RZ92rl2i9V6yQc4pCIXznww+luHM7DOej9j3aMWL1ME6kn6533zsWu7D8bBCPNo7ngHcZ42PgvX1S7dihH8gw1TYGDBC5ydjHHbEsl89rvo2EwW4Nl96MQRkdKf7tGF6BOfjN86NjvgODc9wZom5E40pnFN4+coJwd0dr58iFnWmc+j2PgpQ83MjFmQJcyEXtcZW1EX7E9rJkTdPHCIiOg7lzmdMVXutbe/+7p4jBRL8r5mvHOgUUWZnvjcq3gf5XILRKRnkgEF7vI05y5my6g3Ph+GIxwLgbu3IDYdrfSBwI68Oc3TUnw+bw+BmpCk24IIYTC9rXnZVUnTB92lkI3u39SIH5sj3b+qTG/frB77/Xu18P8zOrGcv97GA9o+ocd8BtOI84bSAlBVoQQwzSxBZHEy7RjOuKQIK9igh3zsSxRSN8GrtgUVIgmqCrV8UQYe5caeosLRVr1JiYGs9RYimyuNN+cKahBScaqLjsWEHORyZSWF9u1ZAE2LzC/H1/B+I8YFconPAXC/7bTUpOLjJVFk/7QZojdEirbRiiUcJ1JzFNueIqMtbmla6wbh0bP5zEiK71k8uy6MHYbNhCyry3CCp6r85xruUKThSMJeyblXDhAgmXT1Dy/QLCVF44ejeQ1SlfX7k0aEClmxvbL16kVauBTJ9uyfbtsp1u3SSwM6jKhEStlreyOuGJj6+Wz1cFlUqK2S06pZMd9iWHy74j/Ngezm5rQ1iLfJ79bh3nso9xLuMc59LP0a/0Bw4s60yFcwy0+gnCduBnHc4nA9+hW2AXwuaHUaG9zeIxParKqTaSEO1QnpsUxkMPSSHudpSoS4jJjKFTA+lt1ev1vPT7S4xvMf4vC4y/FxQVSd/5ihXiCHt7G8GXX2qZMUN1R9OnnNIc1sauZeWllRxIOYC+qgdWqVCyedxmlFcGMXAghN63m8ud+9HcqzkXptdt2/9P4T8S9C/gXtzhdu6U0u/tR75hQzHmefjhmg1+fxTn0s+x8MxCdlzeQfyT8cbVih1JO7CztKN7YPe7kio9vvlxfjj3A5OjJvPDsNr2qVlZYm4A0v9kfXdS13pxt4QpozijRt/GxBYTuZ/7mXCh7kj2ISGjeaHBGi5dgouX9GxSP0XeTQ/KsqpkeNWleWoHlEoFoaE13diaNZOF9/pe66Vbl7hw6wLx2fESJpqdQGJOIiWVJXjbe5Mxy2Tp2W9pP3Yni4zO3dadBs4NGNdsnNhOezQmOS+ZuKw4I6kyEix1ETeLbvJW97f4asd29ibvB4/63aFCbNswqcNQbGOn8tLON4w9MuZwdPR1urQIQK+Hh1c8zy8JNeUDzTyboy/2IOOaI7lLvhWp5Izm4GBa4XVMH0RZXA80qe1F4hjyG/R97fanAkCls+M+3ec8EDqVArc9PHtWZoQbHthAG8dBtHl4FVnH+8l7BHQcv4/m41fwUucXCXMPM7vNu0WltpKohVHEZsXSKaAT3wz6hiifqD+0rXJNOR0WdyAmM4b7Qu5jwwMbOHb9GJeyLvHrsd84VbCtxvjnOj7HJ33nERAAGQU5KF72JNA5CFVuBFdPCllU5EQwuENTtGUOxkmEu7vIQcY+WMKl7AtEZ0QTnRHN2fSzuFe4s2HKBnZe3Wl2VdfWwpZ3e71LlE8UVrlRbF7pwa8rKsl02IUicSDOFNApPJehw8GuWQj798P6dXqeLXwXd3JwJwd/21xCXHLwssjFqigHRZcusHWr2ZXkkFwxFhicKKvxVm07wPHjAOw+mUbCM1Ec9Ctkf5CWW04m3/qJawcwLmAgfYo2kHd+H4XWsmJ/xQ0+7gI/RwkJaG7biPMvXkGZkWneKupPolIp+VG2GnH1Awlr3RtUk1hl2YmUL8UFHj8Ls47K+M2NYdgdCNOGldID82lncQWsDyvWSoXJsQIGTKx7nE0l/L5UgoWXR9Y9btdSsSR3L5XXlOxqIkuB+aZelzshu3kPPC4eqHfMNRrQhz0MY9MdHeYacI3mfX2Z0PQcE+e3lxu9vaV7vWVL6NBBCJGNjdiE3bxZ46Lt0YPLmZn4jB2Os50LLFoEV64wKewiS1vWfr4XjsKcnFZYnjl3dy/4b0CmvVSLjgcI6dm6XBwjQcxPFre5u+188pt8/gDOe0PUdJEJhuTJ9zE4T64H54nkz6UcOa7JyVyrMgNJdJcFgUR3uRhcIUuiB2G3YStMn860G9+xqKq30LdIvp/h1S59fbswu+3nuC3YzoSyHwnEpJu92vlBPm+9lJOnFJw7J0TodgQFiSGU4WLXMJ5voj9lacxS1FrTAyyzWlPpcc58Ts9teLzV43jZe/FT9E+kF1czoCl1gxPPYnH0TUaNVDBjhpA1c1MljU7DD2d/YPaB2ZRrykl+Jtk4Z/mnUV4O27cL8dkUs5/K65Fi701NP5MVK7YyalT/O85TN8ZvZMyaMWh0JgbVpUEXxjUfx5iIMXg7eLNxI4wYAeHD15EYNZquDbty6NFDdW/0H8J/JOhfwN2SoJQUkSRVb97294fXX4fHHvvzibsl6hJWXlzJwjMLOXXzlPH2laNW8kDzB+55e2WVZXh/6k2Ruoj9D++nR6PalpFnzkhzoo+PLNL9kyhWFxP5bSRX86/Szq8dex7aw9YdW4l1jiWtIJsrGZncyM8kpzyTYjLQqkrg9FTYWqXztS6EV80s7VShr99otkxcg42NrO48uf1JPOw88Lb3xsveC41OQ355Pvnl+RSpi/iwz4fGx/b+pTf7UvbV2qaF0oIQ1xDOTTtnJKZX867iaO1Yy071XvDww7BkCcx4OY12435nfdx6ujXsxpW8K2xN3EZaUc1VwNk9ZnP2uBOb96VB2HbsvDJo27AFKoXKSLZOTjnJQ2Oc2LIFmr0yhUs2tZ1ujDg/EXbPkRPvG/UEKAGDSlfi56dE7RzLL6mza90/OHwwjlaONfKIvOy9cLLw4GqKFm1qB0jrAH1fBmtZDh0QOoB3e75LO/92tbZ3N6jUVvLVia+YfWA2xepilAolT7Z7kvd6vYezTd2fEXOYuXMmX574Ek87T2Kmx+Dj4FPj/semlfFj1jRouRSAtn5teSvgIEMH2uLU/AiFo+u2abM68RoDLD8gLl5PUtQYiDBfIbRQWODv5G/Wkr1bw278dN9mtq1z4ZdfZO5ogIeHVHyCg+FMUhorNQ+g2fSNrIoi/GLsWJHptW9/2+RArzfecDU3mW0bP2Zr1hH2lcehxkRsvk9qyuPu/eDLL1kft55Rq2tWDmwqxaShb7JkBt3K60gwybXkUyDSt886yar3aNcu0LIlJb9vY6NNqlEOVwuGJOCCAumz+IdQblF3b1SXa9C0qnf0nA9MHywT4brw3l54s/fdPe+eXyS7aXmLOwfWrlsl7n/L6iFM008JYep/WciRtUbCiEstZSL9P9ufNGIEvwTmURHoz9TnllFoLcf6tJ84Ew5NgPsvI6XXr74SKUBWFjz9dK1NZap82avtwVE68ymzsKae3s0nnpDEc1tbcTiqB4mEcLDzaBo7bSE5PZZkV1Nl54qbVHv8CyV/qa5A5iZZQmyGxwsRVyDBwmqVmYpfx47GxYh64e0NHTpQtn0zV11FrmfAM/fDyuaQVYcHUcEc+KxiNu8wm2/aiaOjgSCF5UDzgnxK9c7YUIadqw3tOyiMhKddO9Mi65FrR3ht72scTD1YY/tutm7kllWbVGU3lt+hBseMN1kr7Wjp05xI70iaezVn5q6ZpvElnhD9MJx9HK/QG+TdP4SpUU8zf+hcswvFer2eDfEbeHXPqyTmyKJjkEsQa8eu/UcrP5WVsGePOMVv2FDVT25ZAs8HoLCsYJrqBM+Oa8Hhw6Ic6NxZx0svbak1Ty2rLGN70nZcbFzoE9wHgIziDPzn+dPSuyXjm49nbLOxBLoE1nj+Vavkd6DxuB9IaPI4g8IGsXXC1n/s9deF/0jQv4C7JUGDBmFcxfX2FtnbtGn19y/cDa4VXGPu4bn8GvOrUa5kqbRkZNORTGszjZ6Net5zgzqY7DcbuTTiyjNXzEqE1q+XHusOHe7uXPpXQa/XM3btWNbGrsXNypuX3U5z45IvBw/mcOuWJ+npZl6vZQmoKmnk40Lz5hDarJAU33monDOptMoku1zkeBnFGZRUljC19VQWDhHClF+ej+tH9a/yFL5SiKO1I3q9HuW7pmPV3r89PQJ70D2wO50bdMbVxvUPvR91HwuZnGZkyEmxt5nJ0ZyDH/Havldq3e5OGDlIqJsCJVE+Lens350Qi+44F3Tl14Ve7NsHWJRh41JAua5ILKqtisGq6nrHzyHgJFTaYn3+aSoyG8Kg2o5Bbhb+aFRFxDwRYzyhGqzY/yp42nny47AfyS3L5csTX+Jg5VBD5mf4/5GoRwhyFR3G9YLrXM69jKO1I4UVhby9/20OXzsMgLe9N5/e9ykPtnjwrt+zuYfn8ua+N9n4wEYGhQ+qdX9xMUS21HPV8ysUA15Ar9DiVt6W3G/WM+OhBsz+OIuLty6yKWETyy8sr5lqvm0BnJpBYCCkPmp+f/oG9SVcE87im4uxs7Tj4ZYPM6X1FC5lJjBp40QqdKUo4oejXyk9aZaWcm5q2lR4wcaNVbr70eOg+SqotKNvyQ+8OWKcNMLem1KQnNIc3j3wrtFp6cXOL/Jxv48BeG3Pa8w5LE3eA3268bzPSDpW+nH5UDHHt+WQdyWHNPwpxInZzMaDbBypx/qrUyc+72bB83aHCMqDlw/Dw+dNoa1G2NjIDMvKSr409eHCBVlObdjw3l74Xww9knOk0okEcW5XOFtP0cuxQown3jgopgDRPnd21Nvzi1QAPu945+yodatgQxOTLXtd8CqG1Wski0mjlArHGV/pgapube5aLn1Qf4cTnlYBL/eTniGVDk4vgqhn54hH9bPP1v3AZ55B/8WXHD0KP31VxOg1D5Cvd6IQJyqsnAhp5UTrXk74hDrKj7qjo0lLvXu39OudPWv67NjbU1FRSoqLkJpkV2iRKccGYFbf9/isa91ZTTNOwoLtUpXc1whGPmDqf7KtyjwzHL8+kyDGW6RzHdxa0DFyIO2X7MH5cP3y2DrRqhWcq7tClm8jVaOLzz7MQc8ATh/ej2/lEX5bahrTbyLsDqn5OJXeCj+LRrTMK2ftfd9h7e0Lbm4UO1ihsbXm0LXD/J78O/NPzq939yx0dmjmXYZiX1CpofsHvP1EJK0iLTmRdpx3e71L9i0LFi+GT07NptDpGJydAglDGdDPihkz4Ib3QmbskEa/17q+xvu9369x3j+UeoiXdr/E8Rsy2fGw8+Ct7m8xre00o6z674ROB0eOSMVnzZqaplsBAdD0oYX8bvMEIa4hJD6diFKhZMIEGf/aa1rat9/KwIEDQQm/J//Oyosr2Ri/kSJ1Eb2DerNnkulceL3gOg2cG9S5L0uWyOJr40fmkdDoBR5s8SC/jvz173z5d4X/SNC/gLslQQ89BAcOwDPPSFO6/R3cm+8Wl3MvEzZf5EAhriFMbTOVR6Iewcv+Drkud8DB1IN8dOQj2vu15+2eb5sdY2jmHjtWVgb+LhQVSe+U4fdlS/6HJDV8XcwJft4H12sLvBs2rC1ja9q0dgKzTq/jesF1o2wtPjuesxlnic2KJeOFDGwtbSkoL8DlI5d69zHnpRzcbN3IK8vD7WO3OseNbTaWVaPlYOn1emZsmyE9TFX9Sz4OPsbrjlaOd5x8X7gAkZHye56bW7dMr0Kj5rG3TrDs+O8QvBtFwEmxCAUJerM10wCV3goWnqF6t8XthgR6/+OsypvFuZwjADioHHih8wu8c+idak/uCOuX4p0/jClThPwHBEhfzs2imzV6qKpf7xbYjV6/9Kr7xZc7g83dZ5cYcPCRg3QLFBuur058xbM765kIATsf3En/0P7suryL+Sfn42jtiIOlQ40eKgcrBwaEDqCRSyOuFVzD0cqRzJJMI/lysHIw9ucdOSLBv7rAvTg8OpZiXQ6cms7kcZ7Eq3dzPuN8Dec5A74OyOX9N1zJyAAGzYB2dRtmTGg+ge+HLCbugi1fL73O2ugdFJdVQOfP4Jc9tAkOISpKije7d0ul2gBnZxg8Jpf4ZuM5UyCZTs93fJ6P+n2EhbJ+MblOryM6I5rdybvZnbybw9cOG0OXAfqH9GfnQzuBmlJQgAZODRgcPpjB4YPp1agX15JtWbAA5puZ/9hSyscv5/LQwFxctFUmEfb2LPRI5c19bxrJo2+5JbPOWDNtf3FNWVenTnD0qKzk5OTA1Kl1v6jGjSXobcAAaVzQas2P+/ln8PMjK1OH58T6vcEXWM7Ey0/FmNQ/vghgcEw75Qcn/eGUPyR4wPV5EFDl0/FuDzFlaJotTmlNsmUVvmGByO4Mcr7ITMmWUullMjtxZP3VqDML4c1esD38zvu5bpVUXLaEw0Xv+sf+sgEmnZfX9shwWN1crM4b5YOiogndM+JplC//d7puyo3SKUQJdfvZssAaJowy7eebB2D2/iqyEBAAN+5sxPAsX1CAMz/zaN2Dhg+Hn34CKysqiwux9BarswxHBW/cZ8EVdyVXnLXcsNPUqOJ0O92ag1ulHJv11ef45D5PoLU3wXEZtWRrobkmK3d++EH6op56Cn2XzuSdO4Zb1ddMD3i9iNGNzgAFCppoXel/MpfP68rK+vhjePFFKTGMr1/DqW/cmGJnfxxP1h2nUB1rIuRzkFQlr7vsqaKi6jfIXg37fhbidsrfZN9fF5yxoaneH+u0YLKjm8L19pRndiAdX0otFTh3WkP4+O85lSm/S12ubeTEkmFVPTJ63N0VTJ4sv0Uh1YjZ/BPzeWanBJa93eNtZvecDQgpCPoyCK1ei52lHS90eoFZnWfVawrxV0CvFx69YoXMsap/XD09xeho/Hjo1ElP5MLmxGbF8nn/z5nZcSZ6vbRmZWbC7t0aTmR8TIpjCuvj15NTZsruaujckAnNJ/Bhnw/verHv++/llDlkqJ4Va0rR6DT3rJr4O/AfCfoXcC89QX8Wl25dYuGZhRSri/lx2I/G29878B6dGnSid1Dve27qvhP0en2dX4znnhMiNGuWZJn8WZSUiO3j7QYFqTVUPXoY/AS0XQRbFuKfMbWK4GjRaGIYN64FkZEW3P6xKFYXk5CdQKR3pDEf4c29b/LZsc9qTNKq4/wT54n0Fm3Irsu7iMmMobFHY5p4NCHIJQhLlSUl6hIqdZXG1O6C8gK+OP5FLcMHQ4XpiTZP8O1gmbzeiTA90OwBVo5eKa+6DsK0dZU3H73pzf19HNm+zfQ+6XRywjQ4rhnc17YZWlKsC6DRfvCMg8OvgONNaHgIxtS0x7I79BmlF/tCZgv6/TwIHycPujXsRvfA7oS7h6NQKNDr9WxJ3MKru18lNvs2p48q2CSPonzJWkCaW4cOlcWAPn3qN/f48viXzNw1E1sLW6KfiCbcPZwff9Tz2GMgUx49tF4MPWeD0+3WQYKRTUcS5R1FsbqYlZdW0sa3DQPDBhLhGcGJGyf4/uz3NciXVm+a5DpZO5H/cj4KhYKvT37N0ztqy2MMqJ7LseT8Eh7e+HCdY230rpRfaw4blsCD9+NQ3IrioNod2d723jze+nEGhw+mvX97svLKmfr5eramLkcXtKtmKG3CEFBVoPK9SJ/iH7h5aAAXLwLNV8BoU6+cTaUPypwISlObyfE7+iL2moYMGyYSh/vuEzKt1Ynd69wj4kDVq1EvVo1eVcPqVa/XS6W0Sg9frinH9SPXGra6Pg4+9A3uS9+gvvQJ7mPMGUkrTGNb0ja2Jm5ld/LuGt9DVxtXMmZlYKWyorBQVh7feMN8XufkyTBzpoT3gaSlLz67mE+OfsKNQpk1uNu681zHmbwW9QyK3FzpGK7u5PLJJ1ICMzjuVXffi4gQEmRAQIDYMJlD48YQH4/m+HFO7txJ53feMTtsPk+RQiPm8QIf8yItiaGd6izWqkoUtjZYWehQ5VRVATduNK0EGeyR60CBtUyUDV+ph4fDkqja46w00DITNq24t1wdtUoIh6G6dsNJerPO+MLBQMi9TQ3b8yr8vBEeGgmHA2/fWm2sWwWWWpF83ckoYvEmeOycGF84mW81rIGVa2DsJSGM8R7S8+RVYgrctdHcXTCDTlHThKC6bC3ZVUjcl8LzybUF99sMKO3VEFxijUemDb0TbHnjnKlHtFIp8sI7okMH8UqOjjZ7d4VKCPLxADgRasPxUBuSFfkADEiCHdVMT8c9oCKwxIKO1/V0mPYeftNfkh+QtWvhgbql9K9ZfMwczYv04zd+o3+9u6t79FGU48bJbP7nnwHpf/Kpvy2s7u3NNr1Xq5tJha/ECvaM6civpXHorOREodJBu0RPBh6Kwi0tDDt/N1r0cCOylxtW3Tpi9Io2BBRaW/P5sc95/rfnAcn2eaP7GwBM3zodhULBWz3eqiVz/qsRFyeHauVK+d02wMkJRo4U4tO7N0aDg93Ju+m3tB8OVg7ceO4GzjbOXLyop0ULBba2cOtWJV0XtuN88XlAJOZjI8YyvsV4OgZ0vOd54/z5sqA/ZgysXv1Xveo/j/9I0L+Av5sElVWWsTZ2LQvPLOTIdVnVUClUXH/uOr6Od2Gs/zdi5EjRo86fD0/VnZlWC2Vl4vxy6VJNV7aUlNqmEQb4+Eg1p3lziIjQo/Y7wENdexpl/Yb3oU33NlzKuURCTkINY4K0Ipm0xM6IpalnU0CkS6/ueRVLpSUhbiFiRuDemMbuQnSifKJqWWD+GdwrYZredjrfDPoGgNyyXNw/rtvJzy9nHJPsVwjhSdIRFzwDbaFnzfwlw/UKR6r/3D/7LHz5JYAehj2Ga/vt5FXeZneptZCU7WrwsvcyEqI+QX0IcQ7hpWUvsbFgI9cLr9cYu2jQD7imTGbBAti/33R748aSmP7ww+ZbNHR6HQOXDSTSO5L3er1nDJp7/30xEwGpgokTsx7PZrFMm5WGT8Rl4rJjWXBqAQ5WDkbZwmt7XmPBqQU1nsPHwYcIzwgiPCJ4vfvrOFs7GwmRr6Ov0RXx+I3jdPqhE10bdqV/SH8hThVFfHNa3qNfR/zKg5GSkvhL9C/M3DWT/PL8Ot8zAD69CcG7YeSkOocsHbGUhyIfYl3sOkavGW1+UFYTKPWAIy9D4mBAD25XoOUvODY/TJH7/jq3720ZQuIz8Tg5WHAo9RCrLq2S41F1OZR6iIc3PkxJZQkNnBqwdMRS0orS2J28mz1X9+Bo5cjFGReN2xuzZgwVmgohPsF9aerR9I4rjGWVZey9upetiVvZmrSVZp7NjBUjgIkbJhLsEoJX/mA2fdea33+r/aPdurWESg8aJERbrVWz9PxS5h6Zy+Xcy4xqOoq1Y9fWux9mUa3fCRC3tMxME0mqTpwCAuCHH4zno6EzZqCo5esryPNuwlN94jhyRBZ6zhNJJOKwpEVJvtKNCns3FO7uWDULxXnjEiyy0sW2cvly9Dm53My1YecBW1IzrdGjYBkP4kQhTYjnO57AhQLSHWR1vXrFKM9WKiwFc019PE/fL+SjXRq0TxNXsgYFfzSxrSYONRTikeIif8/7CHEwYMl6cd3rngoDHoJDd0GYdi2VDKACawieWf/Y6ackZ6nQGpzryb8GIVfjLsr+9XxEjhVA52tSgXJQYzQDMIdBibA1NkpOTEePMqerVN4MVR3PEjPHdMIEOHWq5oz3L8Yte3n/7Sqh91W5LcMBfG9zjW5QAB2qLLr7XIWojNrb+oVJ/MpDxDl2YJNyOGEFp3GiZk5MgbXIK2O84fzQDhxyK+JqZjwzTujY30h6sszZmRvQocyd1zbmGI0ZDEYNDQrFpdGA8OetSTKWyGri7f1S+TOLzz+X1ROQ0KLu3cHenjxvZ9wn3jRW7OYUtOOVQXPQ9+4t57GCAinPuLpK9pqbm0h7/qTMPTVVSM+KFXD+vOl2GxsYMkSIz/33m2+hGLpiKFsSt/BUu6eY1nYaKy+uZNGR1WR9sp9+Hf3Ytq2SV5e9So5LDhNaTKBXUK87VvXrw6efSsHwoYdg6dI7j/+n8B8J+hfwd5GgxJxEvjn1DUvOLyGvXKRKKoWKYU2GMa3NNPoG9/3Lqz4GfHf6OwaGDaShc/06+LZtxRxh0yZZ2b8dFRUmslP9kpxcd+Kxl1dtGVuzZmDtUIKNhQ0qpYqiiiIScxKNJGdam2l42Xqxfft2op2imX1wttlte9p5smbMGqPJQ0ZxBkUVRQS5Bv2pE8LfgdsJU355Pl8e/1KIkoE0FWdyJSNTDAJOzoDtVZN72xx4uW6jhWHB45nsvJwHHoDyCh0+j03nweHe7N7kzfkj3lhVevHW3Gw0bhc4kXaC3xMPoFGWotLZ8VrPFziYepDjN47XsBZ9JOoRFg1cxPbt2+nZryePb3ucZzs8y8qLK42k492e7/JG9zd4fNUrlB9/mC0/RlBU9btpZwcPPijVoaiomvur0WlqvT96vYz97jupWrz8MixfDperMj6GDJE8hIf2dOfQNXGtcbZ2ppFLIyI8I8gpyyE2K9ZYKTAg+8Vso238uwfeZXfybiMZWHFxhVEP3i+4H18P/Jq8sjw6/tARkO9n5wad2Tphq1EmYTBKqA4/Rz/GNRuHS14f3prUE9wu02PqFjp2L+KjI6YEwHD3cJQKJQsGLqB3UG8Oph6kx8+1DUpqYP0SiKmyDAvfAhPMfDHNwM7Srobb4r1ix4M7aOjcEAcrBzzsPGrZ6d8Lbq8upRWmEfC5SZvl4+BDd59BVFwYwqZ5faGytrZ43jypEDk7y+dnbexamnk2o4W3lIuu5l3l8+OfM6vzrDue5/4IjL8L7dtjWVhYu7qUmys796IshaelgU3PDjikXsLajBQylqa0t4+lQwfo0gVeXBqJY0ptS1o1liTQ2EimAF7kYzzJIgd3XILcePljN5Jty7miucV9Lm1kOTkxkYgbrxKnq7n44VUsZKjjDXj94F9DiGrsrwqy7ST01YBZ9wlhS3WRSlP1rKSAAkj9QvqLrLTQ/dE7EyabSjixWCR/Nx3vnDl1r3jqhFjKv1BPMaRlBny9XUw/QCpnN5zAc/QzNO7cHRfPAFTtO9b/RM8/Lw2grVuL9G7fvtpyvi5dpGp4m3V4XSiwhnURVRUjfyEt1YmJoQ8JxNxjTYR8FkJzTZ8FPTU/F5NGCOm9U1+ZOTTLUvBSg3GMe+EnrCzNN0yrVeKkGOsJjw54hcc73CJ2y48cN9PC0jYNTn1f9Y9SycCXA1CmXqvlYudfJNud3x4+7Ab51dY+R8VKX5ty7APS+5WQIPkn1WFpKW4FII3SU6dKz2F2tniBN2gg6aIG0qTTQVgYmRp3Vq8W4mOITgH5SvbvL8Rn6FBpN6sLV3KvEDpfqtqWSksqddV0vzs/Z+7wmTz//F87T/3gA6nMt535Ec17xjOl9RQ6N+j8p7f7Z/EfCfoX8HeRoOra1EDnQKa0nsLkVpP/9upPdEY0rRa2wtbCllsv3sLBykxgRhU8PeU7fvKkmN/cTnYuX65bPu/ubp7seHqaH29wXLO1sK0lX9syfgv9g/qzfft21CFq3j7wtqmqUyVfa+ze+F+zsPy7oNeL5fqlxFJCwitpFuJMWBj4BedzWPMlFRaZ3Co1VZgySzIpVhfzZLsn+Xrg1xw6BIPGZFM0vY6DDkxoMYEXQn6izbDjKLFg/NxfCPLyoqCioFaz6qtdXsUh04GeXXvS5ecu2FrY0s6/XS1HHwAHKwe+6/8LRSdHsmCBVAQN6NRJwtxGj67d46TRabhWcI1g12C0WhmzcaPMJ3fvFkL+0Ufye2RvD2+/q8a1xzI+O/4x8dnxANhY2DA5ajIvdH4BDzsP4rLiiMuOIzkvmXd7vWt8rvuX3c/Oyzu5V1TvOTp6/Sjn0s8R5RNFpHckjtamX7PPPhMpKUivWkwMJGp3MWCZqZ+knV87Tk45CQg52JywmXCr3vy6ooIV64q4erPIZFJhVQxp7aCgalbofR7afG80sghqUoS7XxHlOqlgfdRXenwmbphYpyT0j6C6hCQ2K5YhK4bUMKkwXrdyZGDYQPqHyuyxqKKI35N/r5VZBSJH3XVFLtWztaxV1gy0e4e9775sVio3fbos9obf1rsyfet0vjvzHRZKCyZFTuKVrq/8aav16vhTvwsVFZTeyOXCgVwSjuaQcjaX2AQVq0qHGIcsZCrhJOJvk4OnKhdHdQ6qSlmU0DVuymOdYg2qI2JoQQsumnkiZFKXIUv9MZkxnPxgBqcqkjnlWsYFmwI0VZbDESpfLnm8JStfN27wVt56nC0caKf3pfW73+Nw5br57f9JaJSSjZPiIqRIo4TJ1frzQ5+pWVG6HTHfSEjwFVd4qrZPCTuXinwwy762jblKV5OAmcPS9fBQzN1VmD7dBS8cE9KgnF3/2O83i7sbwMwB8EMrCHRtRCOnhgQGNCfw1y00unCd0FxonH2bjbmHhzjbGfDee5KzZG+PXmVBTqEFV0u8OFYaRUyiDdFXHPlKO4MWXCDFyp3TfnA0QEW2fzKTzsMIOW1ytAF0ecz8/ubOlT6rQ4Ew5LaUikGJQkDjPGBjU5h6GnqmiCHEA6NFJtkjRUKO708Chb29SFWrl0OAHFv4NRIWt5beMlutknT3D3GeMBm8vNAqpL/NWDVyF7njMyfk8dpXXsLW7vOaJKEONM+z4oGwYXj+tIbJ5+5SovgHEKm4wAV9c0AKST17CvEZOdJ8DtztiM2Kpdk3zWrcZqm0ZEDI/ez+Yjxl0UM4ecSeqKi/dp761lvysfJ/rRdpVvv/sAvxX43/UyQoLS2Nl19+mR07dlBaWkpoaCg//fQTbdveoSOO//9IUEJ2AovOLKKdfzvGNZeejPzyfKZsmcJjrR6jX3A/Y1P1343ndz3P58c/Z0zEGFaPqSn2rKwUYnPpklTuP/74zttzcTERnObNTde9vO6+elyiLsF5rnONXg0vey8juZnaZiotPVv+Y71Z/5dRWllKpbbS2MR46FQ+A9/5imIysPPKpHHrTOKuZ1CukgrT+JCnWP7QfHr2hAOnsuClP2e4cTte6/oa7/R8l2NHVXzzjcjQDQFvnp5iH//EExAYKBWB0WtGk16UzvknzuNs40xZmeQ5HjkilvPHjslC6LRpcFhM3mjVCr5bqOOm42bmHJ7DyTQhFcMaD2PjuI219kmv15NakMqymGX8fP5nLudelv4nFFzOvVzjc2gO/UP60yOwh7GCFOwabPb7GxVV7XdeWUnTEZsJGLaI35N/qzFu0eBFTGgyhQ0bRE6/d29t2ahSWXd11QAbG3jtNSk+VJdUnLl5hrbf1z7vvtr1VQKcAjh98zQn0k4QmxXLryN+xdfRlwWnFrA+bn2dz9UpoBOrx6wmJT+Fbj91q3Pcuz3f5c0eomuMyYyh5Xd124293OVl3un5DgdTD7Lo7CLWxpqkbY+0fITCbEeOXLhJZul1+HUXlLsY72/fXn60+/WT886+q/t47+B7Rit7pULJmIgxvNbtNWMf4J/BX704ptPJeffIEdPl6tXqI/TYUkYz7xw6tSonuH8YAQGi2X+Sr2lECu7k4EZujb+qAF8U0dGmCVfz5vJEiDlBtI/I52w0MPWGECaNToPTHCcjcVbqIKLYhnZlrrTX+dHFIogWCh/5Et66JZrXpCRZMdu1CxbXY7d/j8i1hSMNYE0zWNoSXMqk7yfVRXKPNjWRXpE7oVU6zDglhCgkVyRc3iWSkxRYIBK426FViJEESBVhe5g0/qe4QKqz/L1R1Suu1MGeJTL5L7EEh9fr3x9Dzs/dEKbXD8L7Vd4EnmYMEUAIXYcb8PkukTqC5Adl2FgxID8RT1s/OrazZE7KeBrlnEbl64qNp7tR8vWM63HmW5ypf0fM4Ott8OQpOVYbm8CCnnas+bkU96o1l5P+8ho71NFip1NIiPH3bWB9hIKKqgNuWynv8wd7pIpTHcmqMIpW76BZQAGvTcnioZgXsbC2IGhQCMdaOZF09TSJuiwSK9JJ8BCyZECDArGgnzBqNk7KuZRrynEpk2pRaK7IB6ecFVJntAqfNEn02XfpIJlCII1IrXV7wZuf4vzO8/VOjLJLs7lZdNN4jiooL8DrUy/UWjUNnBowu+dsRjQZQdIFVzp0kMXBnBzQ6f7a89HLL8v8z/PNVmSpotnx4A4GhNZvBvNP4P8MCcrLy6NVq1b06tWL6dOn4+npSVJSEiEhIYRUt+qoA/8/kKAKTQXr49az6Owi9qfsB2qu+v4b0Og0+M/z51bJLbaM38Lg8MHG+27dElfZa9fMP9bJyXxlx9f3T0tlAZm0pBak0tSjKY09GhtlYgb8kwYV/78hOVkmh8nJsjC8di288AKcPFeCl6+Gw7udiY6GsZPysO89nwenZdSwFL+Sd8W4LWcLZwo0BXwz8BtmbJ9xT/sxpfUUGtlGEnPMm72bfMhKqeplUjsxZLCCR6YV8eKVKJLzknko8iGWjhAxcm6uKA3i4qSH/fBhOfn/+KNM+PPzhSQ89RS8956es7kH+OjIR7zZ/U1jCX/n5Z28tuc1nKydOJ95vlYvz49Df+TRVo9Soalgc8Jmxq4de9evy93WnawXs4x9McdvHCcj1YkRPUKxVFoxfeknzD/zGXp7kxSpc4POHL1+1Pi/RfR0NFu+kFDaetC2rQ4fn6vk5wdx+LD5ZezQUFiwANybn+HTY5+yJ3lPTSvuKmx4YAPDmww3u43bbeO97b1xsXExkkQFCkpeK0Gj03Dx1kUmrJ9ASn5KjWPSwLkBMzvMZFLLSSgUCmKzYpmyZQpFFRIGbOi5Msgu3+n5Dm/1eAuAi7cu0uLbFnUeh66atzn8/myz9337LUycKJXCY9eP8eHhD9maaMq4+CvsXv+J81F6upCho0fl79mztRPiDRg1CtaZjZUSMVOPHvD44zDS4yB2+TfNy/ecnGDFCkrUJXx14itOLp3DKcci0m77CR4aD5v2S3icXq9n5cWVtHrxM8J/O2OywHZxkaVuNzfR+ixcKD8yTZrISkZaGuzdi3b1KvJtME6aASYPgwteUgHKs6UW9LNN1zs9BsVW0osTXdXLrtKZyEu6AxRbS1bTYZPXEI1mCpEywL20KkA2H1pliPW4AaWWphDde4G+6rHZdhK+a6h2KfQw/qL0EFUqYfAE+C207u38UcKkuMPYb7bC9NN3N9aA8GwhetedYUcYtNV4E6/IpVhlOkDv7oU3a4sDauFFxScEtH+RmfebbotKFxIy4UJVwGtd8PYGDw/Uju5YHa/nySwsmLP9VV47+h5TW0/ly3dOYBMTS5aXA+3GF3HNXmM2k2lkZSjrPhDttf7hSczolEuQxpHwTYcJj75OSC5Ym1kr+5YnmMG3jA8+wfJkM/LH+HiTWUMVCisK2RS/iRUXV/B78u9E+URxaoopC3Jb4jba+rXF28FkvTh3rsSwDBsmSom/+nw0c6b0Ebu8HUy+4irHHjtGx4A7yDn/AfyfIUGvvPIKR44c4dChP5Yw+3+ZBCXlJLHozCJ+Pv8z2aVi9K5UKBkUNoipbaYyKGzQX5ojcy/YlriNwSsG42nnSdrzaUYXNZAf3FatpB/4drRuLa5S3bvL9X+Dg/xHgv4cMjLEAfj8eSEQS5aI8UBMjCxw7dsn6dk3b8KyZdLHWx0l6hLKKso4tOcQDdo2wNvRm5+ifyKjOINvT9dt5XxXOP8QbBDC49fxMDcHSGWhS4MuPNvhWXwcfNAWejN+iDcZqU5066bgt9+k2pGZKYRu2TLAugCP5ucZNi0anVc0D7Z40BgQ1+TrJiTkJBifUqVQ0cK7BVE+UUR5R3F/2P2Eu4um6kbhDVp+15LcslyebPckbrZuPNH2CYoqiojNiuVg6kGuF14nOS+ZuOw4mns1r/Gj1fjrxhK0p7XAsTKMIps4432KxCF8N/wLbl4K5udf9KQ2mAO93xA7+MXHIaNVrcPTooV8/x54ABo2NH0PTp+25LnnJBsUu2xotA9ywyAjCoAekw5wILgnID1B7fzacSD1AO3923PssWN37DlMyklixKoRXMq6hKXSki8GfMFjrR4jMSeRq/lXGdrY1I/U4+ceZmWRgNiJz8o0mpBcunUJO0s7Al0CUSqUVGorKVIXYam0NMrjCsoL2HN1Ty1b9Q3xG/B38mfR4EX42gaxapUYf5iTyj37rDhcBgbC+YzzzDk8h9WXVvNK11eM4ceGn8l7PSf/G+ej0lKp0B85IiHcfxRjx5qasOuy3AfEoCEnh5tp8ZxKO8mprPOcKk5goLoRz1p2hTfe4HrBdRp+ISvkThXSo9Hupsl8IaAQFD4+xMXsJSk3iSu5V0j+4ROulKdzxVWIQUQWnFuIuF24u9P8NRcu5Sea3aWHzsPSDXf/WvVINanIWkiO4bYxYzE+f/5tRKvzNThSjTAFPStkzECSGuVL9ahRvgSCtqid8/uXQY8Epyr1UjXZFgY7Q02EKtVZSJ4Bq6oc8u6GMHVPEYna/kbwae0Uilq4+oW85sjpcKEeK3TncslqCs2V/dBUOeJpFbAuxJ6V6ieYbl9Ju44qStZ+TosZss9TzkCb9JrbOufRj+3Z7fiYl9jTeAatHRJRnqk7CynBHV6f5M8jN70YnGoNx49TbgFl1kpc7T2EiFdDmYWQ7QR3GF1N7RXs3IgrS1zh3DnSHCHgtj4zpQ4oaoQuKwyb2MFsObuZLoqjFLg3wrJdC9yt1VIVLavG7qdOlRWay5cpqyhhe+FpVqRuY9vVXTXcNlv5tOLgowexs7QzqhRuR79+Ig//6ivJ+/2rz0fTp0s/rs1sN8rJq2E49W/i/wwJioiIoH///ty4cYMDBw7g7+/PjBkzmDJlitnxFRUVVFSYmrALCwtp0KAB2dnZ/xMk6Pfff6dfv3539eEavHIwv1XJXfwd/Xm05aM8GvUoDZzqDqb6pzBhwwTWxq3lmXbP8Gm/T2vdX1wMJ04oOHxYwQcfmJfn2drq6dBBT5cuerp2leu3Z/P8HbjX9+E/1EZ+PowcqeLwYSU2Nnq+/FLLp5+qSEpSEBqqp29fHd99p6JjRx0HD9Ze5qrrPTiXcY718evZm7KXM+ln0OnvTWD9UOgzuJ2cx88riigsK4NZdadEWsZOpHL1EoYP1/HxwhTCv627sjy9+ct8OfQ9AB7b8hhLL5hsbkJcQ3i1y6uMazauVhBeWlEaD296mGJ1MQcfPljr/hFrRpCQncDn931O36C+5JXn4WHnQVJuEj+c+4F5J+bV/WIzW8C31ZqaO3wJDY6Kq9+eD0EtX6bQUD0PPKBjzBgdERGm4Yb3oEvPLpzMOMmeq3tYd24vqRXnZYn5xNOwQ4JLUVVg2ecDHu3Zi4+eao+9jRV6vR6tXms0oigoL0ChUNSZh1GsLmbqtqmsjRNp2qTISXw94GtsLGo2Nau1ai7nXiY2O5a4bOnBis2KJSk3CR8HH648Zaom3rfsPvan7sfO0o4m7k1o6tGUCM8Imno0palHU0Jc76wWqA69Hk6eVDB3rpJt22oTu06ddLz/vo6uXfUk5SbibutuNMfYc3UPbx14i5c7v8zgsMF3bUbzb56PCgshMtKCmzcVDB+u4777dBw5omTZsnsz0rG31zN6tHzOevbUG+147wUXbl3g6Z1Pc/TGUbP3v+o5inc9H6BF5lvE58SbHeNeClkfVxni+/qy7jf5/G5J2sKSmCUAdEsVe23Pat4eaiwJtUnhsSG3GP50A5qvehvF+fMo4uJQ5OSYeSbzKLA2EYoUF3ArgwerfCc0SrB9HTR1qNVvJ0yPDgNbTW3C5F3815lO6BSynzHeNS+X3SDpK6mKgRgAvN7H9Dh7tRBVC51Ucu4F91JhArj5qVizZ9mB10u17x+UKLblfZOlKmallWO9uXG1gN0yWFE+jfvUJwjzLMU50zwxBqn4TegZzoHWl9ErdUQW2XE2rgeq7TvqfIze3R3tL7+gSEkBjQblzJm80B8+7yT3/7gRHo0W171v2sFFbzuuOFdwxV1LUTXi2TN7Ar//tBWLkkIyHCBwprwHBlOGsHJ7gn/cRJh7GD72Plj0789D7vtZUa3Q3VjvzthWkxjbYTKN3aVS9FvybwxeOZiRTUaycuRK49jycvDysqC8XEF0dCUREX/9+ejxx1UsWaJAOdsSHVpSn0nF1+HfdSsG4QYeHh7/+yTIpkqQ/vzzzzNmzBhOnTrFs88+y3fffcfDD9fO1pg9ezbvmMlbWL58OXZ2f9yF6N/AkfwjfJIioTp2Sjv8bfzxtvLG28obH2sfgmyDCLWrp/b9N6FYU8yjlx6lUl/JvPB5BNsF1zt+2bImrFnTmODgfLp3v0FsrDtxce4UFdWcECqVOoKDC4iIyCEiIpcmTXJwcTEjsP4P/xOoqFDy6adtOXXKF6VSx7hxCeze3ZBbt+xxcqqgsFDO7p99tp+QkHsPKi3WFLM8Yznbs7cbb/u52c8klCbQzL4ZLyW9xM0Kk6XwyhYr0aHDTmXHB5fncrroJHpF3STKATfK37+FRqOi49jVHI+ou1lTkRvK5JL1DByYjF6p4eOrH3OysLYcdZLvJEZ4jahRDdDqtRRpinCxdKkxNq8yj+cTnidPIzONtk5taebQjLOFZ7lQXM3N69yj2Bx7kxFTN/P/2Dvr8Ciu7/+/NkaEhCREIEgMCRY0uITiDqW4txQoWgpUgBZooUYpWtylaHF3t+ASNCEh7u67e39/3OxuNgIBQtvP79v388xD2L1zd2buzNx7znmf97n+IpYXyaGobR9DVFU4uCL7ANUwtTgY67yFZplOGBfLAIMsPiv3GY2sG+n9fpoqjTkv5vA45TFKoc+NKpFRgeRLw1Gdz1W4BChfPpGRI+9RrZpucagSKmb7zyYmK4aprlMpVSz/2hhCCPZG7WVT6CbUqPGw8ODHCj8WymBQCiVxWXHYm+jEOb57/h2+Kb55jh/A2sia9dXXa/9/Ie4CJgYmlDMth6OJI4aKV+dOxsYW49gxF7Zv98j3+3HjbtOsWTAmJvIe+97ve24lyQz18qbl6enYk6bWTV/7O/8kVq2qwaFDbpQunczChWe05zJwYAeSk00YONCXpCQTHj+2xc/PGqXy9eNUokQ6TZqE0rx5MJUrx+WhOCuFkviseOxMdKqUq4NX8yD5AeGZ4aSr83KXDDBgovNEmtk0Y37gfM7FndP7vp5VPZpaN6V+ifoUVxlhkpyMYXo68aXsWRa8jNOxctXdPqAiA3d7YZ6Yqs11sjeMwdDcgJMrV2BmJh02Tb/5hpKPHuU5DgCVsTFHN27EJD6eDGtr6s6fj/3duxjlcL4WhBRjfSMp0FqXF1QvFBZnr7OVBmA6PX+xBdMs6PYEtuVQcN/rIRP7neOlwWCQz4otoZikXWnqNi2vB1Pa6Ed/cmLfVuiaHfB+aA97qsh6PcZq+D2H4FnFcfC8pIxk1Q8wo36AGU6RVfko4qJef2qFrNMUaC3zZABOukGbghX/8YiCe8vgQGX4ss2rhS1yqtMVZDABWKXDkLuwKPtaZxnAiC7ymq2tk7ft5t3QpWCbicBWrVCbmBDaqBFNvvtO+7kAJnSAxQ2kT2nDHhh0Dw7Sia7sZz1DyTI2plOxtTyzlQINNSJ1eVgXykPzjwv+3cGptdnw6212VoWRXWTu03fnZCSsRJYhIU2bcuvzz0Gh4Af/H7iZeJPOdp0ZXna4to/790vy7bdNsbFJZ+3aY0WSjpAb8+bV5cI1a5gqI/PbPbdTzOBVYeO/B6mpqfTv3//fbwSZmJhQr149Ll/WeYfGjx+Pj48PV3LqBGbj/6dIUFBiEA3XNsyXgw8woPoA1nVdB0jvaY8dPXCxdsHV2hVXa1fcbNxwtXbNkxPzrjgbcJYeO+Vv3Rp+67X0j2HDDNmyxYDZs1V8+aWcZNVqSWm9dMmAixcVXLqk4OXLvP1UqiSjRE2aSO+ri8u75w39FwkqOiiVMHKkIZs2ydl65EgV+/cbEBamG6ShQ9WsXKkfDXqTMdj+cDuD9kkp519a/cLEBhMRQlBpaSUCE3RJo07FnfjA9QNaurRk5KGRKNUFJD1kw0XZjuGmh/j2WwOEWQx8KRfXNqY2OFo4olQrCUuKIEWZBFcmwrHfqVNHzazfg+hy1qXAfgfWGMjCtguxLGZJliqL8cfG42DhgKOFo/ZfzaZQKJh+ZjrLby3P00/HCh15vv1Tnh7qDOrXuNaNUzDvPgWbSr6kWTwmNjMvF/XXVr+Spc5icsPJ9NjZg/KW5Vl+W/6uqZEpfar2oaVLS1q6tKR08dIEB8O33xoWGBUYMEDNzz+rcHSEgPgAvDd6E5ocip2ZHTt67qBp+aYFHu6pF6cYuHcgs1rMYkSdEa8+t9dAqVbiH+efJ3LkZOnE/j77te0qLKnAy0SZqFjMsBiVSlaSkSO7qtQuVZsOFTrk239mJuzZo2DcOEPi4/O+fL74QsX48WqMSkSy8PpClt9cTlKmzMB2t3FnSqMpDKg+QFu3Kjf+qffRjRsKmjQxRAgFR44oadVKN817eBjh76/g/HklDRvKz9PS4OZNBZcvy+3w4dcYRPa+YO9Ls27PsKvoT7yBPy/i/HmZ+BKrYlZEfqGjE3Xb3o0jfnJVqkBBOaty2jnMzcaN0XVHY2RgpKVBrrq1ijFHx+T5SQOFAVXtqjKvzTxaurQkLDmMuisaEpMegcmZ38k4Px5QYGoq6NVLMHKkGi8vgVKpPwaKa9cgKEgWy83eFJp8J4UC1W6d0IehtzcGl/OPXglTU5TR0RAVhbqEDandBmBz4dBrxybZ3JytHql6hlKAtZTtFgrodx/+zM7byh1hyk+pzjle9nN0k4yYGApZOLRPL1kMt1qUNEw0m3O8XJSfdZHbrdI6OWzLDIj9RVcz6loZSVXMLTqgdx2Q/fzaBI5WhLvLoFpPmQea7FCC4gGhKK5c5Wnt2nhs26a3b5d+cDBH6otzPPR6CM3rfUTm3l3Em0qlN88IaOMv24RaQu9eEJf9XbwppObwuQ6/KWXIi6lebTAB9L8HW7KHO8sAHKfookvaSFP2v/VCZd0oDS6Xg29awXkX+f/Jf9Wk10Mzgqq1o+f9/Isja6BSQGA3bzaPb8nsMzPIyuVLWXYQRt2Q7a6WhaY51PjsUrIjR7HSAFtfWz5XD0c9pIKtznE+Y4YBP/1kSN++ajZulHN0Ub+Pevc2ZO/pUPiiHEYGRqR8lfKPpXHkxP9MJMjZ2Zk2bdqwOodKzLJly5g9ezYhBVXizoH/5ZwgDZIykngR/wL/OH+9rXOlzoz2ki+S57HPqbg4f9lWG1MbxniN4YcPJJ1HqVZyLuAcbjZulCtR7q3q3iRnJhMYH0g1h2qvbduihSyi/uefkj9eEF6+lInqFy7ILVt4SA9OTjLfRLNVry4T2d8E/+UEFS3UavjySynjDLLuzuXLMkdag+hofRnPNx2D36/8zqTjkkz954d/0rd6X+6E3+GE/wk23dvEg0h9WV9Xa1fWdVuH9wbv/Du8NhZCGoBJChilQWR1CG7I+pXFyR1gTs5IZc06JTO/sSI+HhQWMTQY+wc1GkYQmynlxAPjA7VFX0fUGcHpgNMUNymOk6UTh58dzvv72RhScwjruq3D4Pu8N7Hjgx+J8GmiK16bYcWriDAJCTIfHeB+xH0WXlvImttr8rQL/DwQ5wV5C6bYmdtR1b4qfar10b5XAK5dE0ycqCAfnxMlSsCPP0pxr/CUELpv786N0BsYGxizvPNyPq5dsCszOjWakmYltRNiZEok9ub2RTZBCiG0fanUKobtG8bDqIc8inqUR+a7cbnGXPr4kvb/44+Mp6RZSaraV6WaQzUq2FbAxNCEW7dg1izYv588aN4c5s6FijXiWHJ9CQuuLSA2LRaA1m6tOTHoRL7H+U+8j5RKqYJ3+7asubU5l7ZD3bpSPOHQISmtnxtqoSYoPoRz9/w5fsOPbcf8UZmFwf4c99uAjlAxfwqRmZEZ4ZPDtdTJ84HnSclMwc3GDRdrlwINxpyIS4vjZthNrodcxyfUB58QH22h6wuDr/HySn2WLYOLluOhwWK4Ng5HlRdD29Rn8rCK2JXUPXPvNAaJifIFl7sgbmyslEn94Qdd2z59ZAHd+Pg8so1pmGJBCjbEkYIFO+hNVw7otck0hCAr+RZwi5ML+ABze6Z0jpLRpddUdlh4BL5qLdXsrNPBWAVt/aBUsjSEKsVIg6bNIDiZi0laIUYKGXgHwEe++Sfz54ZKAburSOPnRhn5mYFaRmHG+ORtn24k27d/LqmEKZiz0Usws2sxPnZsyydlulAhy1Lm4zRvLh/GmBg4fjxvZ8jo193S5jxbOoMHC7/ialm0NYJ2bYeej6RkeccBcClbsM1EbYC3v5qEbOOpxyP46ZT8Ltoc7N/AYDL5Lm8b++xr3+mpVODT4NuWUr7cOl0aLqvqws1cbG7rNPjwEQy+KyNGZtl+vituxkztbsXTMsUITcq/4HJnfyMOWI3ibv9WfPFsMZVsK3F0SyUCblZi9hcV+fJTV4wNjYv8fdS5Mxw6JPhjVQrd+yTiZFkwRf3vxJvYBv9oZcgmTZrw5MkTvc+ePn2Ks3MhykT/fwLLYpZ4Onq+Uo61pFlJ1nVbp2ck+cX5EZkSSVx6nB7d5GXCS1pvag3IpG5na2fpcbOWXjdvF28alG3wymMqblK8UAYQ6FTiXqcKWb68TKLXJNLHxsrEXY1RdOOGTLbfvl1uIBdhTZrojKJ69V6ToPsfihwGBrIqtIODlMM8cECKXmRkyNwwkImR75KA/UWjLwhODGb+1fn0392f36/+jq2ZLXfC7xCZop+gWs+pHk3KNaGFSwvGeo3FzNiMuZclrdTWzJa+1frRpvZ8puyfw/NyM3U7qg0ZerEu2+OaM6pDM5qWb4qtmS3Fi5kzYRT06S6T47dtK8nVX74jpJwssto1h/hbWlYaA/cM5HmsVAO6E35H+52HnQcRyRHagsaAdtG/t/cBjl8NYulLnfERUX0qVM9xYreHwr51WFlBtw8ziao/hirlHFm3uBTxwY4M/taRX78rxaTjk/QUzHLjQeQD1nVbx/2I+5z3PU+sQSwv4l8QnRrN+cDz1Heqr20blRJFuzMVqfpJVVr2rsrt41WJf1ZFUvESy5GQYMCYMVJdb+nSMpwbeo5h+4ax4+EOPtn/Cb5RvvzS+pd8Zb/tzHV0qNi0WBqtaUT9MvVZ3WU1Fib5aPe+IXIaU4YGhmzsIXNC1EJNQHwAvlG+2k3DnQc5hn/4/KGXj2ZkYERF24pUc6hG5+/asnr1p6xYIQVBNDh/Hho0ALDhzz+/5fmYiay7t5LfLv/GIM9B2nbpynQylBla6fl/AkuWSAPIxkYWi80Na2vAKA3fyEA6oqMDfnfmO3Y83MGL+BdkqnJQlbOT4feOnsdnw6wJCwPC6oBpPMS5Qaw7xLlr/05LLoXDDwoWLZJqdM2dm7/xOdiY2dDarTWt3VprP7vyIJQR26fSYeAzki/L+1jRJQMB0GAxEcAvKbB8VQnqOdXDy8mLiY0mYmPyDnXhrKzk5vZqWjigm7hUKvasi+frT2OwIZYPm8dQr2oqVc4b4OsrvUW3qIMp6ZQkBgvDGGLsovB3TOWeI9wpZcBNDwfissJZ3GYqZ386BesP4uME9XMFVr1fwKyzUCMCku2smGCcKCl52d9rjBOAvvdh61+yLo+fLbywgfLxkj7W5KXMSyqf8HoDKMMQ1taG3xqDfzaFzSxL1m364oo04HLigYOs6bPJE2LNYcERmHANLEjlk1sw/GYaxuodwI48vyXMTAnt4s1jFwuKi4qMPjCd0MBMlNVPEv3RYCAVrn8F+mxgnmY75awyYO82uFbLnnZBJhjduVdg4R3rdHj4hy66FP/VBOJXLNT+v2a4rm2KiTQc48ykoIZGOS7KQm6ROV5xWQYw+zW1rfdthXbP5bWvNwJMTC2wiU7JjkZl0fhODB2vSiO2SrQ8vxtOMDf72Rx9WQnPl3D//BJOfwinX5wGD8ADpkfAjDmGuNm48dMHP2GUvexPzEgkPj2eslZlC53jmBuSmKWghFlxnCz/hqTv94B/NBLk4+ND48aNmTVrFr179+b69et8+umnrFy5kgEDBrx2//8fIkHvguTMZF7EvcDa1JpyJaQL5HbYbfrv7s+LuBdaSdmcmN5sujZqFBgfyIc7PtQaSSVMS1C/TH0ZRbIqp6cKlx9UKqm8pVRCUBCULfvK5q9Eaqostqoxiq5c0S2yNShWTHo4NUZRo0bSUMqJ/yJB7w9r1kjhGrVaKo/mVAhMT9cZqK8bg5TMFO5H3udO+B2SM5OZ3HgyaqGm31/92PFQfyI0UBjgYedBrVK1qO9UnwkNJ+hFAa4EXaHxWv0K1VbFrEjMSHzt+UxuNIW5bfWLXB09CqNH62qvfPihVNYpUwa23t9K/939MVAY8G3zb7kUdImT/ifz7XtF5xXUoB+7/rRkyxaISIqGesvA7gmUuS4jQMUjwDIETFKpFPs5v34wn3btIDi14MivBl5OXrR2a02z8s349sy33AzT1e4YUWcE47zGcffSXXp16YVSoeRJ9BN8o3zxsPOgrlNdQHroW6wvYHbOtICzM+DyFPl/owz6jnjJwlmuLH0wm1nnJN3j0zqfsrLLylce6/4n++m5oydKtRJPR092996Nu+2biRoUFZIzk1l+YzkPox5qjaScRVcH1xzMhu4bAEjLyMJjfh1CH1RCGVpVGodR1SCmEihNmToVRo1Np7SjkTbivtRnKVNPTWVs/bFMaDABaxPrv/V9FBQEVapASgqsXAlNuvtyO+y21nHmH+ePj58f6cbSo5zwdYI2YjP60GitgqORgREu1i642bjhbuOOu407w+sMp4RpCRISYNMmmD9fyukXBj16SK9xkyayYG1hA4IqlYxYLV0mOJY6Bz74FrLMcNh7gzG9q9KuTwB+mZfxCfHheuh1boXd0lPQip4SjZWxFYcPHyaufBzBScF4OXnhVcYLW7NXJKG8JWJjZQBj0aL8vhVglc1wScyeLEvfhOENwTAfeq9Q4G38FWvwxCXpPqq4KAKTg3ELS8cgJpvKl5YG0dHcDbrBpod/su3CMkJM8teMnnEWZp6VEZxQSyj/Rf7n4JgsC7RqBA5UCqk0pxFvMFJLJbxwSylWMfY6jLmuL0aRbCIpeavq6KIzIOvvfHtOylvnRkIxSat7ZA+P7WRR1cd2kJitrfLzCfgqO6Draw/VxoBTInhES8PAw9WLKh5N8XCuh5NbTRQeHlJJUAjdDZeeLiNLMTFw8CBxJ25gk6Rf70PZ/ANiwjMZX3YP20/b8zoIIOXhbeIb1SbOVIpPhFjBgHvyGqyrLVXyht7WGVhnXeW+xipJ/3tdhEmDDs/g8Bb59+L6MD5HJLd4Rt4cMKeoUsSXTiRVKQenp0dPKqZX5IPGH3Ay4CS/XpbzXzX7anjYeVDRtiKVSlaiUslKeYp654fmzeV6bedOWbD834L/GXU4gIMHD/LNN9/w7NkzXF1d+eKLLwpUh8uN/+tG0KugFmrCksL0Ikf+cf70r9GfjhXlk3PK/5Q2apQbhgpDfmr1E1OayEVQbFosJ/1Pannctma2BAdDuXJgZCTfLYZFmCOsVEqZZo1RdOGCfvFrkFEKT099Cl3Jkv+ucfj/DXv2SNpj7jxhTTE2Q8O8z8L5wPNcCbrCnYg73Am/w9OYp1pPvFUxK+K/ikehUJChzMBzuSetXFtJSepStajuUB1z41eLnryIe8FJ/5Oc8D/BqRentDQlDYwMjPRziJJKgWU45e4sZ0bnkfTrBy+SH9J9e3eal29Og9LNub23GavmuqJSKrC0hC++D2B+ek0SMxL5rvl3fFr3U3699CuLry/OczzFFfZU2BvGnduvfyA6f7+Eg+pxAHSt3JW0rDRO+OdDrYqpiMIyHGGSxPDaw1nVdRUgI7/50d80GFpzKOu6y9zCdGU64w6Pw7G4zFmyNbMlISOBmNQYYtJiCE0K5VH0I55EP5HV1A8sh5sjZUdlr8LwRqAsRlmzyoRk3UcgmNZsGv1r9NdSygrC+cDz9NrZi8iUSKxNrdnac+u/oqieEILgxGB8o3x5GPWQKnZV6FBR5g89jn5MlT/ykXtVG8jIx42RcGUSLVvC7/PVVK6aQc8dPTnyXNLEzI3NGV5rOJ4pngzuPrjI30dZqixeJrzUe79vPuxH2NL1NPGy5Px5GH14FCtursh3/2JYcWfMNTzsZDToXsQ9IlMicbdxLxSVWgg4dw6WLpXvhYJqE+WGmZmU7m3SRG516+oX7QUp1b96tTTkgsJToesnUEPmk3SyG8+uT+dhapL3+LJUWTyMeohPiA9+cX783Ppn7ftoUfwiTgec1rZ1t3HHq4wX9Z3q41XGi8blGr+RRzw9He7ckc67a9fkv8+fZ39pnAIOD8HxHopS97Bwu0eW7T0yDOIYVHEC6/suwMBAqi5a/2KNlYk1pRSeEO5J2F1Pkp55SoM7WwXSxgZatoRWraBB80TCTS5S3aEaztby2d94dyND9uq4vu6G9njjjHeKAy1iLSkXmR26P3EC1GqiU6JYP9GbgLDHWiGHAGvdIvqri/Bztn/nYnloloP5WjIVYrJfySNvQLfH0CH7vAWS9lb2Cxn1ATBSSfGBCje9MXjZiA9L/sozOxWP7WRx1M7ZwgR3HaHWZ3mvs4FaqqiNuS4jSCANs2QTKPF6vQrJq3+QTam2tJSe1HHjoFkzlJY2tGml5uwFA0BBu3Zw9qyc3wxQ4UIATSrHMKDGXRoXv49l6BOZ8PzypaRHdOsmDdGFC6FMGR5YpVMjn7J4Bmp4vETm8fDHH7JYXfbSW1bo0p3Xeecc0aiqbsRZGROfmUh8XBieEbIWFIMH09DlJNcM8qfHgTSYduyyxSIumNCsWMrOfzMvtaHCkME1B2Ntas2TmCdcC77Gmq5r6ObRTdumQQO4Hn6BVlPW0bOBF5955TOA/wD+p4ygd8F/RtC7ISY1hktBl/CP82f9nfXcjbir9/2armu0vP/TL07TaqNOR9Pa1BoHYzeeXnXDSu3G8QUfvpZm9y4QQhYbz2kU5eeFdHcXODsH0bevE97eRlSoUDRFWv+DDmfPQteukJQzWVahpteo5/QcfYfHUY+olVhL+yx02NKBo8+P6vXhaOFI7dK1qeVYi+9afKdNis6JDGUGacq0NxL/UAs1t8Nua42iX9v8ioWxBV8c/0I/f+def3jaGZ52wdq8OLVHLOOMuf7s5WBaBtWLZsTcbgyNfgebADxtGnFz7HmCEoJwX+SOQOBcwgVXg+Y8CoghwuIEHF4Ct7IdOQbZxQHVxhgZQdu2EBYuuH1HRQMvI8as2MTgvXkllEoXL00rt1a0dm2Nd9kOtG/mwOPH0LNvGuvWK7UeuujUaJb5LCMiJYLQpFD2PN5DSbOSZKoyScpMYlLDSfzWTsrcB8YH4rLQpcBr90ntT1jddTVZqix8o3yZsHcG/lerE/TIUcpz19hW4L4L2i1gcM3B2JjZEJwYzMWXF7UFjTUy2SGJIfTc0ZNrIddQoOCHlj/wTbNv3pqK8b6RkpnC5aDLWgPJN8qXB5EPSciIlw3OzIJz2W5b6xcwwR3HYi5EZL7I09cntT7h62Zf6yUuFwaJGYn4x/lT1b6q1sicf2U+i68v5mXCS1QiL3fJcOVd7h73pFo1WHVzFZvvb9ZGc9xt3Tmw0Y0/l7gzaYwtv80tmpdjaCisWiWNltCC12X5wsREUp0bN5ZCFXfvSqq0UglYhmA4sBsqx5sYKYxY2mkpn9YtnJNUA83cHFY6jAvBF7gecl1LadXAxtSGmC9jtBHmE34nsDO3o7pDdYwNjVGr4ckTnbFz/bo8TqVKDdYBYJgB0dkGs3kUTHGUyR+5YKgwZFDNQazrtk77WUhiCE6WTtrfFgJ8feHUKVnj5eyVRJJsLoLLWbmVvgkGaurG/MYEr0m0agUqiyBmnZuFt4s3LZxbaJkhr0RKCpmh0az6JZZ9a2OwFtE4OQTTsps/ntZZxE8czdzLc9n6YOsruxlxA9r5yXyWYCuoNloXvXkdPr0JK7NTo9KMoOnHMrJT2diJSqqS1Dh3nwoFFBwtagTgTDilcCGAJBM7kqvVp0yn2jh82k3H91erC0xUnn9lPl8czxteW+LvQa90dxzqNpch2smT4YMPZK5ZQMCrD2riRBlyzQfpRrCtOnR/LI2nuO3riT+6l3hTeHjqFjUSX/LBC2DyZETlygxU7yTO9xYhxQ3JdLQmITOR6NRo6fBCvsOfxjzlaezTAhkOAJGTI7G3kBGyWrXgrslS6DSGHh492N1nd4H7/Z14b0ZQfHw8e/bs4cKFCwQGBpKamoq9vT21a9emXbt2NG7c+PWdFCH+M4KKDsP3D2fN7TV8XOtjVnVdRXhyOBbGFlpu+9mAs0w/PR3/OH/CksPy7L+qyyqG15HyjOcDzzNk7xC9XKScm62ZbZEkSIeG6ost3LuXJx8VR0do2lQXKapZs2gjVv9XseucL8NmXCLZ4g6Uvg2O96QQQTbWV1tP/279MTY2ZsHVBVwNvqqN7tQqVYtSxfOXWdYgPj2e7tu6o1QrOTHoRL5G0pui4eqGXAu5pv+hUEBgMwipD1HVcKn3GNNKF/BL99FODjlhsNifYcPUDB4ZxxafQ4T7NOLixtbExmjklUIhzRaU2auAuqswbTOHVnaDaFPfhXuxl9l8+RSZZyexeNA4xo4FnxAf6q+ur/c7SzsuZUTdEdp8Gx8fSf9UqWDrVlkU9VXIyspiz8E9XDW7ytDaQ/F09CQqJYrlN5YTkRJBeHI4ESkRRCRL8YfEjES+avIVP7f+GZCKcK4LXV/5G4q7Q3Co5ku08S3tYrxu6bqUsSrD/ic6dQF3G3eqOVSjql1V3G3d2f9kPweeypXP7Jazmdb8HRLK/mYIIQhPDud+hC8Bd8oz5ZOKJCYCFY7AwHxUBnKga+Wu7Ou7L9/vnkQ/4XLQZb2ojl+cn7aQ9r1R96jhKAuG/HLxF74+9TUglf/cbNwob+nGxf3uJL90Z1yrXiz6seDna84cmD4dPvlERluKEllZUlhi6VI4fVr/O3Nz6ZBKScl/3zwocx2Twd3JLBZGSbOS/NX7L1q4vCa5It9jyjs3x6XFcSP0hlZ4wbKYJZt66OqDlZvnQnByIEbCFPPE2qT5eZEVJmmQGKeCwwMZ5XF8gDBJxim5E6G/yVy9Zs0Fjzs7YqAw0Ob7arYqdlUKJQwBMsLdZ1ef/GuqxbrD1QlwXUaRPTxklKhVK/D2lpGj1+H+fRg8WEazQIpoLFokuBknaVI5F8Jt3dvyvff3eNhWIjDkIQGhvmx6vJ1d4TkGOaA5uORfCDkn7M3t8bDzoKR5SS49OMLkMxl69ZIcUnSRkdKEojQvwYHhe2m4qIAUCQeHPMVN3wvMzSVvH0hzLsOhOhbUMC5LZTsPcHTkuE0s7WIXUsOhBvcjZRmEYobF2N9vP23d2xbcb2am5JY/fSotbaVSntPDh3KAfvxRGkwHC84FBWTV54ULC38+n38Oo0eT4VqeDFWGXg24bQ+2EZ8er9223N9CcGIwkxpNYm6budr1m4cHPLH/CVpPZVitYazttraAH/t7UeRGUGhoKN999x1btmzBycmJ+vXr4+TkhJmZGbGxsTx48ICbN2/i7OzMjBkz6NOn4JocRYn/jKCiQWpWKqV+K0VSZhLnhp57bSJralYqAfEB/LLSn40H/PFo5Me2rz6mZqmaAKy9vZZP9n9S4P6ru6zmkzrye79YP074n9AaSOVLlH8lreZVSEiA8+eVbNjgT3h4BXx8DMjMVYrI0lIuJjVGUf36kp7xH/JHdGo0d8IljW1UvVEUN5H0jHGHx7HEZ4l+4yxTiPCkdumajKnR4J0oQI+iHtF4bWNpDHl0Z1evXfkm4L8polKi+PPKGb5aeZIMpxNgE6D7cl4IJEl1G/uGxzFuP5VQbup3MC8YGiyCpr9CRnEIaiKNqMDmEOqlNX4aNsmkZo8TrEjunO9xKB73IHLxbuyy9QOUaiU3Qm8w+tBoboffBmBk3ZEs76yT1541C2bOlIntDx7IPKWCkJWVxZTNU1j4ciHmxuas77aeXtV6Fdg+LSsNpVoXYcptMIUlRfAkOIKYjAgolggXvoZTP8lrVcmfqP6Fz/EpVbwUaqEmMiWS/jX68yT6iZTvdm1J43KNi1z2/33jyRP4+hvB3uNRYP9QKx+Nw0OMnXzJMpE83mnNpuFg4aA1cH5v+zsVS8rcr58u/MTU01Pz7d/e3J6dvXZqDYCA+ABeJrzE3cad0palMVAY8MUX0mHs6irvjVeVzdOwcXr2hF27Cm73rnj0SIqmrF8vRdZA0t4qVJCL9AsXXtNBq6nQ7CeIqE6JI/tpVt2VJk1kxMjLq/Dv7dfNzYmJUphHE+G5eiuFsJ6Vdfk7hYDpy46krz1E3brS+BMmCYUWx0jMSOTiy4ucDTiLk6UTnzf8HJD0VeufrclQZeBu4463i7cUNnJsQeD9ctpI0c2b+s4/AwMpXtO6tTSKmjTRv1YqlVT7/PZbuf4uWVKO0wedYmm1sZVW8MVQYUif6n0YV38cLtYulCpeiqiUKFbcXMG3Z3KohuRCKYvSrO22hoD4AAITAvnl0i/a72ZWG8eMj2TC1KWXl2i6Lq/UvqlSgVO8IZ9fNmRZfAI79hXDpVIyd8f0xGX78QJrJREWJl+Od+5IvmXupOJ3RJYBnHCHrdVl7abkYjDpMvyWLV6nNAC/xh5U/mgUWRfO0dviMHvdMjDNgoMXy9HKtp5MnHZ0hFKloEMHKYkL+nlLr0FyRhLmkXEYXLosk6f9/eVNMH8+BAXx6FIMVfb+XPgTa9JEvgwmTnzDKyLfNwEVvoamv/B5g8+Z3z7/qNXfjSI3ghwdHRkyZAhDhw6las6y5DmQlpbG3r17WbRoET179mTy5Mlvd/RvgP+MoKKBJuHbxdoFv/F+haanjB4Ny5ZJr2JOpdD49HgeRj7UqdnF61TtQpNCOTnoJK3cJLVu3e11fLxfRzg2UBhQzqqc1iga4zWG2qVrA1IK10Bh8MooUs5xUKmMuXFDFym6dEk3GWtgbCypGBqjqHFjsC36fNn/CYQlhXE56DJ3wu9wO/w2d8LvaGVpAS59fInG5WS0d9uDbay9vZYKxWuxbEYtCKsNsRW1NW+GDHnIqlWV3ulZOB94njab2pCpymR0vdEs6bikyCSWr1+HFl1fkv6ZLp9mdI2pJPp0Ydfz9aRXz5tH4fnsT+792Qdafw11V4Jp3iKxbgbN2dt3L67Oxtj8YlNgPSPnqJFc/HE6Za30edoqtYplN5Yx4+wMTg0+Ra1StbTfZWXJ+/PGDUmrO3q04HkzKyuL7fu3syFlAydfSI/u102+ZvYHs9/JmAwPh6+/TWPDJiVkZCfNmkeB1zJoOUOvrQIFAkHDsg0ZWGMgvlG+HHp2SK/+U34oXbw0Ve2r4mLtQkB8AA3LNpS1l7JzmRyLO1KqeClKFCvxr6hJIYQgJi2G+8H+XNhbiRlfWcsvPDdB628KXEzv67uPrpW7AnDk2RHmX52vEyKwdde+A3N6aPPDrVvSKFCr4cgRaP+aVKstW2DgQLlAPlkw66XIkJwsSyj88YeM1hcaChXmbX9FfXUs6Qn6CdrGxnKhr8kratJEri3zQ845QQhj7t/XGTyX7kTyLPGejGQ73gOH+zKXxygDk6RKdEs9gJWHD2m21/nTT6d2MLXpVGo41sCWirT7qx4klaZ4ghfjetbHu6IX9ZzqFSi8kNPoORtwVi/S4+noyd1ROlr60edHqWZf7ZX0trg4SVE+dUpujx/rf1+smHxvtGoFLi6weLGk9QF07qJm1UoDSpWS87bNL7oQUnWH6qRlpfEi/gVt3NowtdlUWm9snW+EvKZxLzrWq4KnUxVqONTIoy6rGYMOHTpgYiKdnFeDr3Lw6UGtsXTxpX4B1qoBS7j4+xhsbOBy0GWarJVSaMYYUl5hjbOyOC7pxXBONqJTsDl1d17UqfMMGpRXGz4HJvYLp/NAa1pUicSoRxfJbcwH6uwcna3VYVdVXZ4TSGW9cdezc3TyQaYhfNRbFoM1y4ITG6FJUI4GP/wAQ4bAunVSaUSTUGZqCtWqyVymypWhdGlo107+C4w9PJbjfseZ324+nSp1yvO7o0bBrhXRzOtwiiFN/WS/fn5yyy49o+7UCQOAw4d1FvTevTLH6Q3g5ARhdUdBvRXMbDGTGd4zXr/T34AiN4JiYmIoWYCsYFG0f1v8ZwQVDTQ5G981/45ZLV9d5CsnOnWSz9DKlVBILQvSstIwNDDURnsOPzvM8hvLtUZS7hofxwcep417GwA23NnA+KPjddS6XFQ7F2sXUFPgOKhU0kuaM68oLC+zj+rV9cUW3kX17n0gKSOJyJRIUrNSSc1KJSUrRft3alYqrd1aaxfXV4OvsuXeFvmdMpWUTP2289rO0xqkC68u5PNjn+f5vQq2FahVqhZfN/laqyyWEzExaCMaObFokYpx494terPLdxe9d/ZGIPjxgx/5ptk379RfThw+DF2G30fd5nNwO/3a9gCE14QnXeDcDKjxJ3jP0IsmOVg4ED4pHIVCwcDdA9lyfwvlrMoxrdk0MlVZTNqymqyScsLtVLETB/vnT3FIzUrVE4T46cJPOBZ3pKHpUOrWMSA9XS4qR+eThAu691Hb9m2ZcX6GVka8fYX2/Pnhn9iYvYNsMFKC+fPPpXR0ThjW3Ardh6FSZFDdoTo7PtpBWauy2ghTZEokK2+u1NLxdvm+fSjC3cad88POU7p4aVKzUplwdIKekZTTcLI2tS4Sg8k/zp8Tfify0NY0aoQH+h2gU8XOnDwJXb7ZRkaX7OJpGZbZUtJuNKvuzkcfuNOtWnttYvvbQqWChg2lYdy3r6RKvg6HD8t3d5060oH8d8DXV0YbFufVEQEkk6e0SxKb/H/Bf/23pCXpKGMeHlLxrlw5uYa7dEka47nh7q5vFHl4SJXHy5eVbNv1lKC0dB6HBaN82FW308jaUPpOnr4sjC2oVaoW54ae0zoNMlWZKFCQkpWCtak18fHQoNsdnnrXlZnvuY/Hxp0JDSYwou4IPQqc+yJ3/OP0E1rdbNzwdvampWtLBnoOLPhCFgIhITIapYkU6ZdczFaocz6PWavfsHWK4/7Y21iblqDkryX1JP5zoppdDQalX+ObGCdEjDtEVsPhxQSmD6/Jp8MN8whb5EZWVhb7D+3HqpoVx18c5/Dzw/jF+hH7VSzmxuZERkLjSfPwS3oAtz5l4NAMfvi8Ai620vg76X+STw98SlBCUL55cEs6LGFM/TH4+8PcrVdZm9AbopwwT7CnRLwNjsnFaWJbjDZOhrSwVmP+888yGQ1kIbyNG+VElkvdQ2kAZX6yIzJN0lIdUw3ofV9N3wfQMDifiFSXLjIUt0/SXjMM4cM+spjtqQ2yhtNb4cwZ8PYmfsRgytpvIsVE9vfBC2Qor1o1qF0b6tSh8cy2XHlRin37ZO6u3jgkJnJh40aa9eiBcZkyMorUq5ek+e3bJ6Ub3wAlS0Jsy35QYxvz283XRjH/abxXYYSUlBQsLN69zkNR4D8j6N0RlhRG2fllUQs1z8Y9e6PE3Ro1pFFx7Jj0TL8rhBBEpETo1UMaUXeENn9kxpkZfH/++wL3PzbwGC3Lt+Tw4cPYeNpwIeiCnkc1Z/FG+XtyktQYRBcvSmpLbjg76xtFHh55PfBZqixSs1KxMLHQqiq9THjJ89jnekaH1nDJTGF4neGUsZKcpr2P97L29to8Bo1mO9DvAE3LS+rAkutLGHdkXIHX4WC/g1oP0YY7Gxi6b2iBbbf23Erf6jLB5NLLS0w4OoHapWprc3cKI5MJMHu2fl0VDTZskLTmd8Hia4sZf3Q8AOu7rWdIrSGv2eP1EEJwI/QGk7eu5PzmppBRArv+k4hW51LbuDoeVCbgdlJ/sZRiDxa55AqBRQ0PMK5dZ+1vlPm9jH4OXWRVFCZpCOsXbO6xmQGekucenhzOy4SX1C9TP0+fj6MfU2NZDZRqJY3LNaZJ/B/MnVQLMzPpwKyYj5p27vfR1vtb+WT/J6Qp06hgW4G9ffYWuhZYQRBCqoJNnqyTFAegzDUMB3RHZR6Ovbk914Zfw9Xm1flFX534SivXamtmy7Rm07gbcRdXa1eiU6NfaTCVKFYCE0MTolLzjocGOamFyZnJfH708zwGk4WJBSmZKcSkxeipak5tOlVLRdNEzfNDGcsyzG83X0s7jE6N5vSdJ/z6tRU3L1QndzHctm2lYeD66kvzSixeDOPHS3XGx48ly+Z1uHxZGglubtI5/L6QmSnvj2XLpIqcBu7uUnperZZ0vMBApLhAv67geJ9W1iP42H4F27fLyFZWjsBDw4ZSndLLSzq4L12S28OHOShhlqFQ6o4uuuN4D+weg4EKlCZYL02hgZcR9evDZYdhvFBeoraTzNmp4VADT0dPXG1cX8mISEmR43f5MjiUSWHB9tuEG/jgE+rDqRen9Oqb2ZvbEz45HL9YP3rt7KUVIKpTug5jvMbQ2q015Uu8ptDeW+LQ08PsvXKX1fsegd0jKHMjTxvLi4vo4jCO69WbkGDoT1SavoU5zTSUFfNKER2lgOJhlLctzdSpMHTo6+v2RSRHcOjZIQ4+OcixZ8dIVet0tA0UBpwbeg7TyKZ8+KGUdy9eXNojPXrk359SrSQ0KZTA+EBtBMk3NADHkOFc2dlQRrhq/Ak9Cy6xsrjDYsbWHwvA05inrL+zHucSziRlJHIl4CJPw15g9d1azNPiGNY1lkfj7hGWGkHf6n3xPvYEo0NH9AvnxsXJmxmkda4JSY4bB0uWkG4klezsUgs4oMKiXj3mG93gi/ZQLRLuL82/vHYLznLRoAXpLdpifCav0miqvT2mLVpg0L275EwWFEItBIoXh5TusnDyum7rGFpr6Fv3VZR4r0ZQ8eLF6d27Nx9//DFNm+blc/6d+M8IendkqbI47necq8FXtfWDCgMh5MSblCS53x4er9/nXZGWlUZAfICekZSTand31F2cLZ05fPgwN4rfYPbF2Xr7W5pYag2in1r9RGU7WUTxZuhN7kXcIzUrlci4VJ74p/L8ZQovw1KJik+Fkz9BSvaLwusPDBusxNQyBUPTVNSGqWSoU7UUgbuj7moL3845P4fpZ6YXeD4Xhl3QGjYFRWE0ODLgiFZSeP2d9Yw7Mg5zY3PMjc2xMLbQ/m1ubM705tO1tLW74Xf569Ffet/n3K+GY43XihQUBnFxMpczt0yugQHs2CEpx+8CzSLZ3twev/F+hTLM8kNCegJ/3v+TlbdWarnvjuo6RHx/Uyo8NVwIbb7S7RBcH1ZfxdJSQaoiEtXEUjrVp0xzLDIqkWIp+yG9BLxoRWu31vw2ujXVndzY/nA75wPPcz7wPI+iH+kdi5eTF9c/vQ7AlONT+O3Kb3i7ePNN029o49ZGa7BnqbJYeG0hM8/OJCUrBQOFAU7BYwne+D0NapXg4kUpU58T+b2Pbofdpsf2HrxMeMmh/oe0UtDvivR0WRdl9uwcioFWQdCvK6VM3bg4fifubq+n2G6+t5kRB0aQpkzD3cadPX32aMUAQOaJ3Qq7hW+UL7fCb3Ev4h7hyeFaKtG0ZtOIT4/P12AyMjBiV69d2JjZ8CzmGcMPDH+jc5zadCqOxR2JTIlk4bWFNC7XmMZlG1OndB3cbd1xtXbNV7xDMw7e3h1Zs8aYSZPy7//ECUlXepNgVUiIjJAkJUlDY9Sowu3n6ysdx7a2cg1X1AgIkOyANWt0+eoGBtIr/dlnct2lEdhSqeDX7eeZ4duTLONoKV+/bS8uxg0YNUouhs+flxGuM2d0ho6BAbRonULj7g+w8XhA9ImP+fmn7IvXqxdUy8dgTrPBKMaTYcV30LaJg6TQlVK/sTphRoY8l+PHZQrKuXOQanuVPY/2cDbwLDdDb+aJVtz/7D53w+8ycI9+lMfUyJTapWrj5eTFsNrD9OivhUFyZjKPox/zKOoRj6Mfk5SZxKIOkrYnBFT4xQv/jLyGjwYme3eQefdDKPES6i0Hz81glUPeL7ApbN8DqXa4ucHUqZJppgmi5IZKrUIlVHpKhjkV0+zM7OhQsQOdKnairXtb9m23YdQoeU0rV5ZGc5V8VOlzIzYW/vpL3hdnz+rfF81aJ9C4my9udQOJzgqQBlNCgNZw2tFrB50rSUfV7POzC85vCq4PZ2exZ257uneXZUJCk0JxLuGsP/+o1TIhOSZGejQ0ykvHjsmcnZwGU2wsm0oEUCk4jQbn/GQoJSxMqpQcP/7Kc1YpoNI4WaB2xQEYUUAU9wHVOF92AKOD888x1MO5c7LYT+XKUpghPzRtCmPGSEU7Bwe9r4yNQTmkMZS7wu7eu+lRpQDr9W/GezWC9u7dy/r16zl8+DAuLi58/PHHDB48GCdNgtffiP+MoH8OcXG63JmUlFcn4v4d0NzGSqWSw4cPo6yg5ODzg1oDKTgxWK/907FPtUnJmgVoQVhR8z7Bt6rLaJHRLJRNZxbYdpHnZT5u2wgLCykQ8fuV33VGh0kOY8XInM8bfq41xB5EPuBa8DV9IyVH+7JWZV9bL+efxqef5q82ZWws6cYdXy2e9UqohZovT3zJsFrD3iqCcS34GitvrmTbw22kZkmXnLGiGOWSPiLs4AjSHjcD4zRwPQX99TkEmxrdJ7XkVQZ5DuCk/2l2XLxOwJnWXNreAFEiQOYIuZ7OkyPkYOLMz+1nMKz2MNLSwME1imSbi3w0+Tz+qvO0dm3NL21k0vDH+z5m3Z11evtPazaNGS1maIsWBycGM+n4JG1BWUVKKcTR3/ihd3+mT9dfPRf0PopOjeak/0lt9K8oEREB330npZKFQNZKAUwNLZg2DcZPzMDCzOiVOUl3wu/QY3sPAuIDMDc2Z23XtfSpXrDQToYyg2exzwiID9AubADcFrrxIj6vVLUG1qbWDKwxkIiUCHb67szzfd/qfXG3cUelVvHzpYKTjEfVHcWyzrLAaFJGEhOPTdSj4ZU0LcmTm0/o1aEX9pb2gIJz5yQdLTUfz/Avv8jIzuvoRSCLE/71l4yOXLpUoHJvHoSFSS6/gYGMshR2v1dBpZI5asuW6acZlC4t3wuffpo/rXjNrTV8dugzstRZVLOtQ9OgfexcU5bY7FJfJibQuzd89HEwMcV82HnhHtcC7hFnfA9s/XQOiQUvIN6FUqXAqfdPhNptoZyJJ3YqTxKeVcf3dGXiX1Ygt+/c1VVHn2vcWBqHr1IPVamgZ/8k9t26iFl0E04dtqJRI/j29LfMvqBzumnobd4u3rRwaUH5EuWJTo3mQuAFfEJ9uB5ynRuhN0jI0L0z9vbZq63Bcj3kOnsf76V+mfp4OXlpGQMAv13+jeN+x3kU/SjPvGZsYEzqtFTiY40YPRp2Rs2Esleggm6B3cq1FdObT6eFcwsyMxWM3f4Tq1/kWDCn2MHdIXBrOERL76adHaxdK2sVFS+uf01iUmM47necQ88OcfT5Uea2mcuw2sMAqXo4cM9A2rm1o0RECcZ/NJ5iJsXIzIQvvpCUXpBG5caNeYuf50RyslQe3LpV2hc5I4SNGkk6aO/er46GCiFQCzUXXl7gyxNf4hPqU3BjgD8PYBXemRs34Ea6Lgpsa2aLcwlnXKxdtP92qdwFNxu3V3Z34MkBum3rhlUxK04OPkk9p3ryC40yXA5jiZgYuT1/DnPnciDkNF1f/IhNOgTNA4u8qVlvjokTYeTIwnmxz5+XVJhsqNXZz4pJMnefxOPuZIOFyf8eS+yt6wRFRUWxadMm1q9fz6NHj2jXrh0ff/wxXbt2xSi3W/I94T8j6J/D3btSI97OLm8R038SBY1DujJdL4o0ou4Irbdq3e117Hq0S89AyWmADKs1DMfiMhLkG/6ckzde8OSBOQ9uW3DHx5zEGHPItIAsc1CZYGiooE4dHX2uadP8c2b+f8OdO5KWbGQkaNMmgCNHdDwfU1NJbfH2LrrfE0IUOs+j85+dOfTsEAB2oirq6yOIPTtIylm7noLms2UtHCN9OcHqDtVpWq4py28up3yJ8sxtM5deVXuhUCgICoIVK+SiPzJaKet3uJ0E9xOyL8MsGgRvYc/3/blwAfqMfYhlsw3s/KUNzZ2bYmpkqj3+/GSyc+JAvwPaRf4JvxOMPTKWpzFPIaM4hkufc/20I3Xq6NoX9n3kH+fPjLMzWNJhSaHVrF6Hu3fl3HrmTM5PBcWHDqB67RROjNqiVRnMDzGpMfTf3Z/jfsdRoMB3jK+2oKcQgrj0OPxi/fLk5QyvPVxLLzwXcA7vDd4F/sbSjku1hf3qr6qvXQy5WrtSqWQl6pepT1X7qpQ0K8m1kGtaKfGc0uIJGQl6eZRPop/g8UfBi4nP6n3G0k5LAZkcP3L3F9y64MjTW46Q7AjJpWTEOdmR9i2tWbNaQUG+xYMHZfqBoaEURvD0LPBn8yAtTee0SkiAd5k6IyLk4njFimxaWzZatZJRn65dpRMkN5RqJVOOT2HBtQUA9K7Wm3Xd1pGhzOBG0H02HL2H7/b+3L6c7WlrLRWo8iCpFER4wrH5EFUVMzNJtevXT9LVjI3ls3Do0GGqVevI9evGXL4sjcb8SipYWckFtcYwatAA1EZJXHx5kTMBZ1l/5ixRxjfBQMUsj31810c6TK4EXWH1rdV6Rs/roBZqnsc+lzLdIT581fQr0rLSeBT9iH5/9SM5U6du5mjhSMOyDfFy8uL3q7/rFYR2tHDEw86DKnZVqGJfhdIRQxk56wZxtz7AyEgKF/l7DkOgYnLjyZgYmmBhbKEVW9DUAHRVt+bFX5/C426gyp/nZmQEDRoKqre6i9r9EA8yDnMt9KqehPeAGgPY/KG+KEHO91FMjDG9ekn6OUjVy+nT8zfGMzLkvLF1Kxw4IO9dDTw95Tj37SsFH16F6NRo0pXp2lzZK0FXaLxWFsdt5dqKftX70aNKD0oUK0FcehwB8QH4xQTy2/gmXD/jQI0a8NnKtUw7N7nAvKn9fffTpXIXQNLbZ5ydoWckOZdwxs7cjs8Ofcaj6EdYm1pzevBprfDT69BmUxtO+p9kSoMv+LXWFJ2RFBICd+8ibt0i9tRtNqkH0KdNHKWzXsowmZOTbJe7wvmbYscOGWUFOHQI5V/7mLSuGpsYRGCiLZZvR854L/jbi6UuXryYKVOmkJmZiZ2dHaNGjeLrr7/G/D2HB/4zgt4NP134SU7G9UZKUYE3wP79Ukikbl2ZlPtvwd89Dmq1pAPmFFsICsrbzsNDP6/I2fn/zyKuzZrJya1Pn8c0a1aRceMMtQuN4sVlom6DIqipe+bFGb46+RWHBxzGzlxnYQohuBJ8hZU3VzLLexbO1s4kJMB3m46w7f42Im80lRGfl00hTFoNZtWPkfaRvqSWmX8vLkxdRN3KpTjw5ABjDo8hKFEObLPyzVjYfqF28srMhN27pVdTM7FjnALO5yGkAYp0W3kNmv4s1cKQ9SOalG9CG7c2tHZrTe1StclUZWo9wJrFoQY5eeyB8YGsurUKnxAfwi615f6KSVStmp3kbpSOqZFpoZ4DIQQNVjfAJ9SHyiUrs6/vPm1k8l0hhHxHTJqUnXfi8ABG1AOjDEqke3JsyH4aeOQvCqBSqwhMCKT/X/1xt3Vny4dbAEnna7mhpZ73PCcmNZrEb21lRDcqJYoRB0foFQl1s3GjrFVZ4tPj9SigHbd05MLLC3qLTg1KFy9N6CQdPWjNrTWYGJpo1euKGRXTGnThyeGsvrVaazBFpEQQlhRGSHwIqepUZrSYwUzvmYDM86ryxyt4P9dHw2HpJj9yOpGdiRN1qnhGjnw52pFI/1KMG+bIwl/eXPTB1FSuiQIC5LvoTSCEdAovWybve41H3sYGhg2TjuXX5VfnLtzbsWJH7kfc1z5jACcGnaBETGuWLYPNt3aR1eAnafBotsga2BZzoFs3+du7d+vXnrS1ldGyXr2UJCUdonNn/WchMRGuXtXlFV29mquGUZnr0HFcdnFSfXqbg7ErC7v++FYR1bSsNJ7GPMXT0VM7bhOPTmTZjWVkqAq3UJ3YcCIfVf0IDzsPXia8JCUzhVImFRnw859cM/gdSgThdtiXHUurULeuFFr5y/cvVt1axYWXF7RyxlFRMO93NYs3viQ11AWQwkDffispzNHRUmTh5Ck1p08ZyOtrHg1THPSKwZY2qEE7t44MbdKJJuUbafNiNdC8j0qW7ESfPkaEhkqDc/NmacznhFIpf3PrVkmPS8jxuFeooDN8ChAq1iIxI5G9j/ey7cE2TvifYFitYazsshKQ7761t9fSuVJnrYMzP4SFScdeRIRUVNy4EZIyE/VykjT//vjBj1p2yc8Xf+abU68X8bE1s2Vum7lEpURJQ8laGkyOFo56z/TTmKdUXlIZA4UB/uP98xVUefJErjOKFZNMHT0JeSEgKQnV0qWEHT5MmYgIFE+fylBoVJROTrxrV2kwRUfnnyBdtaosHHv6NJr6I2mYUqxdSwx+mCWT9f4F+FuMoIiICDZs2MD69esJDAykR48efPLJJwQHB/PLL7/g5OTE8ddwHN8V/xlBb48sVRZl55clMiVSL5G+sFiyROb99eghJ59/C/4N4/Dypb5R5Oubt02ZMvpGUbVqRUNL+aexbZucpGxs0gkKMuTwYWMGDNAtlKytpXOqZs23/w2lWkm1pdV4GvOUBmUacHrIadKV6Wy+t5mVN1fyMOohAP3LfEvqhREc8j1FVrlTMkpjKUUKjK5NoafVr/TpA81bpfDH7XnMOCvlPa2fjyJ+8zKqVZPjZ2MjFxG/Xf6Nny/+TJoyDQUKPq79MXM+mKM3id67JxeHmzblUxTS9TTe4zfzXJzIQ2OxNbPlwrALVLXXzewJ6QmMPTIWG1MbJjSYgLutO9eCr9Hxz45aT7CJgQkixIus582pWz+DZ1arWd1lNd0qduPIkSOvfQ58Qnz4cMeHBCcGY1XMii0fbtGjlb0rMjLku+L77yHR6ir07Q7FI1CkODDGbi/TRlZiy8P1elGdgPgAbY7dlMZT+LWNFEzIKZNbunhpKSdt6641dOo61dVGjN4UQgiCE4PxjfLlUfQjfKN88Y3yxbG4I3/1/kvbrtz8cnpj52LtQhW7KlS1r0o9p3p5FsWa99EHbT/AwNBASxfJbTBpIkyhCREkKxPgzEypQAgyqX9swQbTGK8xLOkoa3YlZiQy8ejEPAp5mr81KnmlSsmF3Z07hX8WExLkInD5cv13WoMGMurTu3f+tXsiUyK5F3GPexH36FypM5VKSgup31/92PZgW572ziWc8XT05MsmX2pzJi9dkhH13KhbV+apdO0qo2LXrkk57h075PlpULJkGoMGmTBggCF16+o7oJIykrgUdInT/mexz/TC9EVPLl2Cs/efEdE725qLc4UA7+ytBf06OmujRTVq5E+hi0uL095Pj6Ie8ThG5u4ExAcgEARNDNJGJqadmsaPF3+kmGExKttVpopdFW10x9namXRlOrfDbnM99DoPIx9ye+RtLa203eZ2HPfLu9aa2WwOnT3ase7OOjbf26x1HhgoDOhVaTDlbq5j6VIdLbNmTUln7d4dFArBk5gnHH52mMPPDmOgMOD4oOP4+0vVuW+ftSYu0oLMBx3hWUdIlFElOztJmdMUbnV3l9c6KyuLiRMfsGpVLTIzFVSpIinSGmNZrZYiE9u2wc6d+rVPy5SRRk/fvuQZu9xIy0rj0LNDbH2wlUNPD+kZla3dWnNiUF6hgNfh/HmZDqNSvVqRMydCEkO4F3FPz0gKiJd5SWHJYVwYdoHJxyfnLd6djWKGxXC2dsa5hDNz28zF09GT84HnuR5ynSlNpuS7z9KlMnWnZcu8hYo1yHd9JIS0mtLTdXWLUlKk8kVAgFQdyRmCA7C3J3nwaB7N38uyLncpkQ4/ngKzfoPkS+Ifxns1gnbv3s26des4duwYVatWZfjw4QwcOBBra2ttGz8/P6pUqUJm7kqVRYz/jKC3x6Gnh+i8tTMOFg4ETwzW5h0UFlOmwG+/SZnc+f+O+ljAv3McYmLkJK4xim7ezCsgYG0tJ1SNUVS37uuVd/6NyMwEZ2dBeLiCzZuVDBhgxMmTcmLVGAX29nJieRcxjcfRj2m8prGWmmBkYKStyWMkzDAM+oCM8ofy7GegNqVq8aaMaz6IEQ10snXJmclMODKBK8FX2NPhBh80Myc0VOaMHjumy9EISgji61Nf8+f9PwEZFTo/LG+V9IQEaQgtXpw339TZRbDr9FOuRZ3khP8JzgScQalWEvtlrFZK98cLPxIQH0Brt9a0cm1FSXNZcqDbtm7sf7L/tdentWtrehTrwSc9PqGYyatvpIjkCD7a+REXX15EgYLvW37P1GZT3zhhPCc0So8a2tq9YD/2X/DnaeZZKJHt7Vea4PhoFhE18npMTQxNcLV2ZZDnIKY1n0ZqViqN1zTmbsRd/uj4B6O9CrESKWKohZpxh8fhGy0NpJwKYJD3Xhi0ZxAlTEqgClfRy7sXnqU99aKWBSFdmU5omIrJEyzYswcoHg6110DxCPm3RQQUj8DCMZwUVQKzvGfxXYvvACkeUXVpwS7ysV5jWdxxsaz0HpBAx4VfULOCzF8qVbyUXi0mG1MbFAoFt25Jw/7PP3ULZnNzGDBAGj+1c7B5IpIjOPr8KPcj72sNn4gUnTXyUdWP2NlL5mDdDrvNmMNj8HT01G7VHarrFcsNCZEFgteulQtlhULmFjk4SJl2jShXmTIyAjV8uMxDUiqls2XrVvjrL0FCgm7l7OaRRINel7Codpb7SWe5EXpDK2TQs0pPdvWWogpCCP44t40Ncxpz42QB4TKFGovSQVRq8hiHao8YUX8wbZpKatCXJ77UStPnho2pDUcHHtWqQYYkhpCuTMfF2qXQdbxiUmP4+sQ0Vt/JW9MsP7hYu9C23IcoL0zkz+VlSU+Xn9etK42f1u3TOBd4lkPPDnH42WG9nDpDhSExX8ZoKbNCCIRQcP++rj7RuXN5HT8aZdV9+wRJSXIMPvxQFtAtXlwa4Vu3wvbt0nmoQcmSknnVr580fgvrIKy1vJZWfQ/Aw86DftX70adan3eKcs+bJ1UwjY3l3NWw4Vt3RboyHWMDY5Iyk2izqQ03QiWVpqJtRdKV6YQkhejRC++MvKMtRP8qaHIEf/hB0gvzw1uvj9LSJMUlMFBuZcsSWKU9LlWi4St7ADK/B+O6XrIA1z+M92oElShRgr59+zJ8+HC8Cgh9paWl8euvvzJjxvstnPSfEfT26L2zNzt9d751ld8+faS37fff36rQ8HvD/8I4pKZKj6XGKLpyJe/kYWoK9evrjKJGjd6Nu/93Yvp0FXPmGNK0qZoLF+Ts5eMjhRGiZbkFypSR5/4u8sDH/Y7TbnM7/Q8P/QH3BkCGFUxyAotISqTWpVmZ1nzaqjVtqzTG1KjgrPPEjESsillx/76cfBMTJS1k+3Z9b+/loMt8fvRzfm3zK94u3kD+xXyFKHgC9/KSk1bpMkqexTyjir3O21/ljyo8jpZVDxUoqF26Nq1dW5OhyuDCywvcCrul15dtVg1i06O1kS4AI4URSqHEuYQz7Su0p1n5ZjR3bp5v4cVMVSYTj05k6Q2Zs/JhlQ/Z0H3DK3N3MlWZBMYH4hfnh7WpNQ3LypVBeHI47ovctQIUuVEytgMxEcWgyl7tZzXiv2FYd3dqlZfRnTKWZfQWg4kZiQzcPZADTw8AMLreaOa3n6/N6/snEJ0azaOoR1pvv5uNG+MbSCn31KxUiv9YHIH+9Gpvbk8V+yp0rti5QI9uTmRmysjLhAl5v1Mo4MyFdOp5qbQRprCkMNbeXpsnfyk8OZyEDJ3B1KgRXPXzhTEFi4y0Kj6WxO2L8fEBTOOh7STszBxpUc+RWnWySFDKKFbPKj3pWrkrCoUi31wsTdFcDZ6Pe467rfsrzzs+Hn79FRYs0Dmhu3eHH3/UqYcFBkoVulWrdHmpRkZykT1mjHxvKhSQkJjOLz/f5flzLw4cySR9gg0Yp+v9XrnirrSu4E2nip3oWVUnZbl5s1RDA2kotBtyl1XnDnDz5WMCkh+RVOwxGOe4z9edxSCoBTVrgs0H67hnM5OqDh7ULldFL2/H3tz+netWXbqeRos9zqhMoyC4ARVsKjG9d2cex9/WSnUD9Krai27lPuXy5lYsy2iGKPkIQuvhJLz4pEN9Rnb2ooyVEx9u/5A9j/do+zcxNMHbxZuOFTrSsWJHLdWrIGRlyfWvpj7R1av64gUatG0r54GwMP06fZaWklnSr5+MIr1q+lapVZwPPM/uR7v5re1vWufRtFPT2HJ/C32r96Vf9X56lMN3gRAy0rlrlzTCb92Szrx3RVxaHK02tqKdezt+bPUjCoWCLFUWwYnB2uhRr6q9Xis4oFbL44mNlRG1Ro3yb1eU66OnT6FyQz+YUAFzY3NSBj+Rctv/gnXXezWCUlNT33uuT2HxnxH0dohLi6P0vNJkqDK4PfL2G8tygvSEXLsmF3Efflj0x/i2+F8aBw2USukRy1mvKLfYhIGBpCvkFFsoTE2QfwKBgVm4uxuiUhno0W0eP5YToCZnytVVnm+ZMgV2pYUQgvOB5znw9AB9q/fl+PNT7LxxkjtJuUrez1SjUCho0QIa9bzBx93dqVC24MKgUSlRlDQvmW/U4+xZWag7MxPGjpUy0Dnn09zCDHPOz+Fs4Fnmt5tPdYfqgI6nbWgoDcFPPpEe7Jxo1EjSxTQSyUIIjjw/wkl/GSl6EPlAr72HnQeruqzi54s/a8UeAIo//YTkfXPo2T+J5GZjOeZ/LN9zdrF2oblzc5qXb86gmoP0DInVt1Yz+tBoapeuLWt4ZBuM6cp05l+Zr0dbC0oM0nose1frzfaPtgNygWL+ozlKtZJyVuVkPo61jrpW1b4aftc8GLZpOrEev8HGkxDYHGtrucgdMSJ/epFaqJl9fraWttikXBN29tpJacvS+Z7nP4nUrFS2P9jOg4gHnHt0jhhFDAEJAdrvh9YayrpuUg0wU5VJ2d/LUrFkRS21TrOVsyqnvceGDCmYabJggbxHX6Vslq5MR6WWBlOHDnD0Yhgfzl6LU8VwbQ5TUGwEIYnhZCoS4PT3cP5bjIrH4/bxTJ7aLiyw73H1x7GowyJi02JptbEVd8LvYG9uj7eLN3se79FGabtU6sKOXjsKdERkZEjK0Zw5aBXimjSRBlHjxvn/dkaGnIeWLpURd0ySodwl7OqdxbLGWezsYFqpr+nYsSMZGcbU+aMxwfHhpD/2RrzwhoAWkCDpbd37JFLd+zERqkccuPKYvy48gjMzGNerNgsXwsqbKxh1SF+L3EhhhJ1BRYzjq5B19ivCb2oETgQaNbqyZfULuXp65pW1fxXUQs2RZ0fY9WgXyzus4eefDJg9G5Q1V1Cs6klKVX9MYNoDTgw6QWu31oB0Rtx7FsPuFdVYuxaylGr4ugQUy5v7VsayDCFJIZS1Kkunip3oWLEjH7h+8EonyOtw/Lh8fxYGY8fKCEYOQlEeCCG4HnKdbQ+2sf3hdm39tZyCBGlZaXqCM0WJxETpmHzyRL6rjx179fNWWKRkpmBubP5Ox3zrlozoWVrK56age6so10f374Nnu1swsi5Olk6EfBHy+p3+Jvxtwgjp6el5KG9/pzHynxH0dlh5cyUjD46khkMN7o66+1YPn5OT9OLcuCEfvn8L/pfGoSAIIb0sOY0if/+87SpU0M8r0vCv/2lkZWXRsmUkly6V4dNPpbdWg6AgOTE+yi6Z4+Eh6QUFedWiUqLYcHcDq26tkmpor8HHFrv5oX+PAlW1ciItK436q+tTxrIMG7pvyDdBdscOyUUXAn76Cb7+Ov++UrNSKTe/HLFpsRgqDBlVbxSzvGex8OeS/PCDjIIdyrZX4uKkYZhbQKNiRenBHjJEfzEQlhTGqRentEZRxwodWdV1FSApRXVWSoGHCsVr8nzOHohzZfvOdG6mzGBJyBJSs1JxLuGMg4UDt8Juaak/NqY2RE6JJDQpFL9YP5bfXE5yZjL3I+6jVCvpWrmrtsCoSq3CbI6ZNldHA3Njc9xt3Gnn3o65bXXUnxdxLyhjVeaVkZrMTPh+iT9//OBGfHz2hwoV9eoasnRpwTm2B58eZODugSRkJFC6eGl29d6lrYv1b0PO91GmyJQ1XaIfUb5EeZo7NwfAN8qXakvzj8gUNynO+PrjGVt1Dh4ekJikYtx3L3h515V9e/OuwDp3loaSTcF2PyC97du2SSrzmDGwZ6+a+ev9uep/XxYXjXehfMpHfPaJBR16h1BrUz761jnwQ8sfmN5ccnAeRD6gxrIaBbYdX388CztIgyouLY5JxyfhYF6K4CeOHPvLkeiAUpDsSKUyjvw6y4auXRWvfa9lKDNY6rOU9T47uB/rg1DkEDJQG9D65m1+/aEKtWsbk5SRhLmxOfFxhvz1Fyw7fJ47Jb6XxURz1sjJRqOo1Vxc9AkGBvJ5W3BtgV7OjpuNmx6dPDgYrQLdpUvSwaXS11WgeHGZS6Uxiho2zD/Sn6nKZOv9rcy9PFeb61jBZx/PH1pBndUYVN+F2kDmvZgamTKv7TxGe43Gz0++rzZsAKVZKFQ4gl3jwyQ6HiZTrR8FM1AYoBZqOlbsyMF+B1EoFAghaLquKa7Wrng5eVG/TH1qlaqVby2s3BBCRi8LkzuTE6am0rmnySeqU0caGSGJISz1Wcq2h9vwj9NNhDamNvSs0pNxDcZpa/O9bzx8KA2h1FSZizZnTtH2n5aVxujDo5nebPpro6U58dtvMkWhUyepHFkQinJ9dOMGePU6A0M/oIpdFXzH5JP8/A/hvRpBKSkpfPXVV+zYsYOYfCqtqXI/7e8R/xlBb4cma5twOegyc9vMZXLjyW+8f0aGLkciMrJowsJFhf+lcXgThIRIY0hjGN2/n1fetVQpOYlojCJPz6LxVL0psrKymDv3GtOmNcXcXC4Kci7KYmKkUaChDteqJeWUcy78o1OjsZ+rf2MZZBVHbZztxXzcFV60wqNYa4Z2rEJQxW+JJ4C13dYWmiI1/sh4Fl9fjIOFA/dG3StQJWjhQpn7BnJRMXhwvs3wj/Nnyokp7H4klUJsTG1QnJtJ7LHP+HOzMf366be/eVOOU+6cU02+xejR8trkhBCC1KxULT3iesh1GqzOJbcX6w5WIWCUzrfNvqWEaQnaVWiHf5w/GcoMXsS/IColCiMDIxZfX0xKVm4FBx2ufHKFfY/3YWNmQ0RyBJbFLHG3cdeKEuRWMXobREfLvI9lfz1E3asH7NmIIqQhI0fKyFB+C/pnMc/osb0HD6Me0rhcYy4Ou/hevL/visK8j7JUWTyOfqwVY/CN9uVR1COexjwlS53FDy1/4MHS6WzfDjVaPOd+y4oygb6kB8bxVbl5tApEVZVbbAVQG2tFAgpyUI0cncHKm6soWfUeiWb3yLJ5ACa6+6ChdTcujduLgYG85z7a+RFu1m7a3B0POw8tBSldmY5aqLV1zEKTQpl0fFIe0QNrU2vi0+OZ3XI205pPA+Be+H1qrih4ATuhwQQWtF8AyGKVk49PzlfwYeTBkVx4eUG7n7OVKw7pTfG/XZ6YQEdJWbN7jKXbI1Q2j1nScTHD6kqe20n/k7TZ1Eb3o0mlZX2cqCoQ7cEHzm35rHdlOnXKX/jhdUhOlu+6S5ekcXTlir7iGUjnVY0aOSJFXokcjVrJwmsLCEmSHvZiWJL1oDvqSrv1xqqmY00+rfMpAzwHEPnSmh9/lDQ+lQpwOwGD2+r9loOFAx0qdKBjxY60dW+LsYExt8JuYWJoQoOy8l3yMuElzgv086CMDIyo7lCd+k716Vq5a75iShERMicr99ykUAiqVo1h9Ggbevc2xM5O5gBp8olOnYLw8Bw7GGZibWlCy5ZQtcVD5sTLyLqFsQXdPLrRt1pf2lVo94/QYTXiPwD79klRjqLC6EOjWXZjGeWsynFu6DlcbQrHF+/QQdbpel16QlGujy5dgqbD90DfD2lUthGXP7n8Tv0VJd6rETRmzBjOnDnDDz/8wKBBg/jjjz8ICQlhxYoV/PzzzwwYMOCdDv5N8J8R9OZQqpWMPzKenb47uTfq3ltRSZ4/l15rMzOZy/JvWnv8r4zDuyI+Xk6oGqPIx0erWKmFpaWkkGiMIi+vt5vE3xSauhzfftuVBw8U+b6Yk5Nlro1GQLJhQzhxQleIb8HVhUw89rluh/2r4EFfUKjxcDenf18j+vTRqQsJIRCIQifza4RBAI4MOEL7Cu1f2f7LL2HuXEkzOHAA2r+i+ZkXZ/j82Ofci7gHgEFMFc59vpGmbvXytFUqJf1HY2TlRqNG0lP/0Uf5C2VkqbLwCfXhhN8JTr44ydXgq1rqUX4oZ1WO1KxUbMxsWNJhCWOPjCUwPhB7C3tCk/J6wXOiT7U+rOm65r0VxGu1qjunQ/eBshjsWwv3+2NvL6lQgwfnza1Kzkxm0rFJTGs+rVB1Wf4JvMv7KEuVhV+cHz6XLBncvQwGBrDs6BkmXO9IujI9/53OfQtnvpd/W0RC/cW0aaekWs0MnCxLa/OQvp6q4heFFZjo8lkMRTGq2lWnXjlPWji3YEitIW91zgBjD4/lD58/cLdxZ3+//VrVw5wGk48PTJgeypW09VA8HGPrCMpVicDIOpzI1Aji0+OZ88EcpjaThTzvRdyj5vJXJ4gv7biUjhU7EpgQSJtNbchU5S/OZH7rG77w/JERI8DCLpb9T/bjYeeBOtKDJnWt893nTXJWXgWVSqrraSJFly7Bi5x1fW38YWRtME3UfmRy7hcyL4+EDCssvqwGJYIYUKM/n9b9lLql63L5TgxTVhzjSvRhCKkHVyfSoQN88U0inc7ZU6tULW1uT12nuq99T6ZmpXI+8Dw+IT5cD5V1jHIKXHzR8AvmtZsHQHhcIsM3z8T3pBcvLnpJJ0w2DbBePXm9Pvwwi7t3C34OhICzt0JYdGo7Z6O3kRzkjnLbVl2D9hOwSW5MO7fOtP/Aglat8i+++3dhwgRJjy5RQkZEKlQomn7Dk8PxXu/Nk5gnuFi7cHbI2XzlsHMiM1PKwaekvF7tsSjXR6dOQevJ66H7MNpXaM+RAUfeqb+ixHs1gsqXL8/GjRvx9vbGysqKW7duUaFCBTZt2sTWrVs5fPjwOx38m+A/I+jtoVQr82j5FxanT8tJoHJlmefxb8L/2jgUFdLTpSGkMYouX5Yc5pwwMZGTksYoatLk1Rzst4VmDEJCOjNmjCHu7pLel3sRm5kpF7fbZSoJLVtKypiZGaxYn8So9fPlgvj6ONzKmWtlUqtXf7XhrVKr+O7MdwytNTTfZN6wpDA8l3sSnRpdaGEQtVoe65YtYGEh84Xq5bVp9I7Be9JqLppMw8AskSfjfalgW/BMGRQkJef37cv/+5IOmXz0yUsadfIj1US/SKijhSPHB0lrMikjCaufC/8ubFq+KZt6bNLWCVOqldwNv8v5wPOcf3meC4EXiEnTRfxrOtZk20fbGHlwJI3KNqK5c3OalGtSJIVWkzOTGfDXAPY/zVa/Oz8VzvwAwoAmTWTOx+uKgq66uYr2FdrnK/7wT+Bd30epqVI+PyAAvvhCqlSp1CoC4gPyRI58o3ypZleTsAcVCcq8C6Xv5Omvk3s3apSqgiqiKvMOHKCseUV6NPbk4041qepY4a3nhNzIUGYw9dRUpjabqlU21ODZM5g2TUohgzTux42Db76RizkN8oswrbu9jrsRd9npK3euXLIyESnSYMpZuPZF3AvcFrlhZmRGpZKVsMqwop7LBwTfqc6ZHR5EP6kIqmIYGEhP/ujR4OYm34thYZKqduKErHG1dav0/udUL7Oz06mXNWny7uUNHgXE8fCGDZcuwcVLghveDmCRrSKTZQq/xshi3MCn0x7QrqEzdpWesevuYbbeOEyM2VVt3R7rFC+O97qupZMmpCe88/OpkZC/HnIdn1AfPnBuR+aTlmzdCrtvnya9Tytd41RbbNLr0a95fdpV96JxucaUMC6R73MQlRLFX4/+YuuDrVwIvKAV0LA0seSQdxQXzhTj1ClpKOau9Vm5so465+2tf++8b2Rmyvnq8mX5TrpyRVeA+F0RmhSK93pvnsU+w83GjbNDzr7yfXbxorxv7exkJO5V92JRro8OH4ZO3y+EDp/Tt3pftvbc+vqd/ia8kW0g3hAWFhYiMDBQCCFEmTJlxLVr14QQQvj7+wsLC4s37e6dkJCQIACRkJDwt/5ufsjMzBR79+4VmZmZ//ShvHesXSsECNG27T99JHnxf2kcXgWlUojbt4VYtEiIXr2EKFVKjlnOTaEQokYNIUaPFmLrViGCg4vmtzVjEBubKUqUkL915EjBxzl6tO6YOnUSIiNDiLt3hWjZUohJk4Tw8RFCrS78708/NV0wE+G20E2EJ4XrfadSq0TbTW0FMxE1l9UU6Vnphe43I0OINm3kcTo4CPH8ecFt09OFsLYWAtM48cOO/Xrfbb2/VcSnxefZJz4tXvy25aawbbpD4LlRe01MTIRgvJtgJvluDnMd9Pr57dJvos33PwiqbRPmlS+KqQd/E5UXVy5wf2YiKi2uJCKTI/Mck0qtEg8iHoj1t9cLh7kO+e6rmKkQtZbXEuMPjxe7Hu4SUSlRhb6m+f3eVye+0vZtPPBDgXGyACEMDYWYOFGIxMT89933eJ9gJsL+V3tx5sWZtz6GosS7vo++/lreA+XKCZGUJERaVpq4FXpLrL+9Xnxx9Asx5tAYbVuVWiU8lni8cpxzbutvb9Dueyfsjph0bJJYc2uNuPzysohLi3uj40zLShO/X/5dKFXKAtuEhQnx2WdCGBnp3j9DhwqRvZwo1G/8eP5HUfzH4oKZCMsfLbXPUVpWmkjJTNG7Fi/iXgiVWpVnDDIzhdi5U75fcr4PNcdVvboQMTH6v61SCXHxohBjxghhb6+/X7lyQkyZIsStW2/2nhJCiEsvL4luW7sJm59tRExqjNjzaI/ouKWj/lh9ZSMocy3Hb6oFo6vmGU/TiZ5i2OZvxMXAi292EIWEUinEqVNCDB8uhI1NjmvgcF/QYaxgeAPBdJM8x7X0+lLtGATGBoqTfidFfFq8mHBkgjCcZajXtunapuKP63+IiOQIvd9OTRXi5EkhvvlGiPr1hTAwyDuX1a0rxJdfCnHsmBApKQWcRBEiOFjOAyDE4MFvPvav7DshWLgvdBfMRFRYVEEEJxQ8Oc+cKY+hd+/X91uU66Pdu4XAKE3U8Q4SoYmh79xfUeJNbIM3NoJq1Kghzp49K4QQolWrVmLSpElCCCEWLlwoypQp86bdvRP+M4LeDL6RvuJi4EWhfsenVfPQDR9eRAdWhPhfGId/Amq1XLSvWyfExx8LUbFiXqMIhHBxEWLQICFWrhTi0aO3e7HnHIPPP9cZN686thkzdMfJkUUnAAD9z0lEQVTQq5eccN8W4Unhwm2hNBrqrqgrkjKStN/NuzxPMBNhNttMPIx8+MZ9JyYKUbu2PE53dyEiIvJv99dfsk2ZMrpzUavV4nrwde1C3WOJh+i1o5fwWuklbH+x1VsMmH9XSigUsg9LSyFKfvGBMPjWTDC6mqBfF0G7z4Vdx8Xik58Pi6vPH+f5/bS0TFGlSrQAIVq0ECJLqRJ/+f4lLOZYiI5bOopOWzrlWayYzzEXqZmpQggh/GL98iyEX8a/FPVW1tPbp8KiCnn6mXd5nnaf+LR4ERAX8MbXecOdDcLkB7mgsp9eWxhYRmjvDycnIbZty3tvvoh7IWovry2YiTCcZSh+v/z7O7/r3hXv8j66fz97YV53hWi2oK+o+kfVPItGs9lmeobHgisLxPdnvxd7H+0V/rH+QqVWiRs31MLAKkzgekpQf7Gg02eCId4Cxzti6VJ5HZdcW5JnHJ3mOYnWG1uL8YfHC99I3wKPMywpTDRY1UAwE/Hl8S/zfJ+YKMR33wlhYaHv7Lh3r3DXQa1Wix0PdgiXBS7aY6u/qr64/PJyofZ/1Rj4+goxbpwQVlbyuNzchAh9zXouK0uIo0eFGDJEt59mq1xZzo9PnhS8v0qtEvse7xNN1jR5taE6xFsYtJ4u2s2eLfrs6Cfu31eLTz/N/q2PegumWgj6dhPUWSmwChIgnV09ewrx++9CXLsmDb53gVotxNWrQkyYIETp0vrnqnFwabYBA4SIS8wQN0JuiGU+y8SwvcNE9aXVxY2QGyIhJUF8uf5LMffi3HzP9aMdH4mn0U8LfVxxcULs2SPE2LFCVKmSdx4zMRHC21uIH34Q4vJlOWbvA2fO6Ayy5cuLtu/A+EDtPV9/Vf0C32XNmxf+94tyfbR1q/xdb+937qrI8V6NoN9//10sXLhQCCHEiRMnhKmpqShWrJgwMDAQCxYsePOjfQf8ZwS9GT7e+7FgJmLK8Snv1M+wYfLm/+GHIjqwIsT/wjj8WxAeLsSuXXKCq1Mnr3cNhLCzE6J7dyHmzRPi+vXCTao5x+DpU52nzs/v1fstXiy0C//ff3+3c3sa/VTY/WonmIlov7m9yFTKA/cJ8REVFlUQy33efsYKCxPC1VUeZ7160kOvQWpmqngY+VA0GLxf0HC+qDNtnOi4paOovLiycF3gKi69vPRKb73DXAfRcHVDMXD3QHHlmlJrcGGSKJo2U4s9e4T4/HP9BYipqfSo+/jojiMzM1MsW3ZcWFioBcjxE0IuKDWTaYYyQzRd21QvGqT5ruvWrsJgloGov6q+mHZqmjjz4oxIz0oXaVlpYsieIYKZiM13NwshhAhNDBXbH2wXYw6NETWW1hA+IboD2XBng2Amovz88mLg7oFi5Y2V4nHU40IZJxcDLwr7X+3FBxs+EHcfZIqOHfXvzdathXicy/5LzUwVg3YP0p5Tv139RHJG8huPcVGhMO+jpIwkcSXoilhxY4UYe2is6LC5g1Aq1aJxY3mepT/vrneP2P5iK1qubynGHx4vVt9cXehoZkKCEJ075+/8aNr/vPhs/3jRemNr4TTPKc99mTO68Oe9P0WLdS3EZwc/E79d+k2U/b2sYCbC5mcbcdLvpLZdRoaMROeMnDRoIES2D7VQ8I301btHy8wrIzbf3SxUalWh+yjUGCTJRXVk3mDoK5GWJh0eH30kn8Oc17ROHSHmzhUiKEi2Tc9KF2turdF7/k1+MBHD9w0XB58cFMxEGH1rKfisumBQG2EyxUVvDJr18NVFPizDhHerdPHDD0J88YW8rsbGecfVzEwukr/5RoiDB4WIjS3ced27J/fRvOc0m42NdH6uXStEzZpCG6GdPz+vUyJTmSkOPT0kBu4eqI3eNV/bXLgucM333Wf0vZG49PKSdv/E9ESRpSqc9RISIsSmTfI9WK5c3utgaSnv/fnzpXOhKH0jv/yiM7yuXy+6foWQjp2ay2qK68H5d5ycrBv3Z89e319Rro/Wr5e/2779O3dV5HivRlBuBAQEiL/++kvcvXv3Xbt6Y/xnBBUeKZkpwvJHS8FMxLmAc+/UV6tW8ubfsOH1bf9u/NvH4d+MxERJJZg+XUYPck/qIL25rVpJb+epU/IlnBu5x6BdO7nv5MmvP4Zt24SoVEkuSN4V14KvCfM55oKZiKF7h2oX3imZKW8dIVCr1SIiOUJsv3RFFG+8WdBonmjfXmcctljX4pXe3ZTMFJGpzBQLrizIQ0e7H3E/z+9lZUkDxtxcXkNjYzk+0dFCrFolRK1a+uPj5SUnp4QEOQZLl2YJEKJYMSEePMh7PiMPjBQGswy0x9FodSOx99HePJEpTdSh3aZ2YuHVheJ8wPk81yU/fH/2+zzRC42x13N7T/E85hWcQiEXATGpOm7SkSNqUbWq7nyNjYWYNk2f/qJWq8Wiq4uE0fdGgpkIz2Wewi/2NRb4e0JB76PNdzeLHtt6aCkvubefl0rPfvHiQiw/v0v8fOFncfjpYRGSGPLO0S21Wjoc8jOGTEzkAjg+LV5cCboi1txaIyYdm6QXFZx4dGKe4628uLLWk69SSS+xm5uu30qVpMPlTQ/9afRTYfy9sTCbbSZmnJnxVgbt3zUnJCTIObF9e2kYaA0WhTREvl/sl+e6aWhEx44JYTaot+A7/WfF5PtiouSEdoL6iwTmUcLQUFKvchv/QkjK2PnzQvz0k1zw29rmP8ZVqwrx6afyPfHsmW5Mnj+Xjs1q1fK+8/v3F+LAAWnYnjolRMmSOifZ6dO6Y1CqlOK0/2kxYv+IPO8Q+zn2YvWN1UIIIaJSosThp4fFrLOzRKctnbRU25zP+lcnvhLmc8xF07VNxcSjE8XW+1vF85jnr73/1Wohnj4VYulSGRXL7zo4OgrRr58Qq1cL8eLFOw27UKuF6NFD9luunBBRb88Gzhe5Df6c53/smO53C/NsFeWzsGKFEHj9IdxHTyzQSPun8LcaQf8k/jOCCo8t97YIZiJcF7i+kRctP1SoIB+8N/Ho/V34t4/D/xLS0yWV4Jdf5KSqxwPP3oyMJEd70iQh9u6Vi/PcY3DggM6L+HdwtXPi4JODwnCWoTCbbSa2P9heqH1yex+X+SwTH27/UNRcVlPr0dRuMxQCozQtJ/zjvR8L01lWgpG1RYlPe4ovj38pVtxYIU74ndDSkzSISokSnx38TGuElPu9XIGez4AAfS9+hQqSI69WyzEaODA7dyj7e1tbteje/al49EgXQaldWy5icuN5zHMx6sAoUeyHYvkuyF0XuArHuY46j/TaZnmuj92vduLsi/xfCEkZSeL48+Pi29PfihbrWgjT2abavnJy/7fe3yp+vvCzuPTykshQ5nOgQojxh8eLqSemi8VLVNqFGAjh7CzEfv3UK3Eu4Jx2caWJWv1diEmNEWdfnBXzL80XbRa1EfVX1hfRKdHa7ycfm6x3jUv/Vlq029ROTDk+RSw+v1GUcIgXIMT7JlfcupV/BBjkIie/hdWT6Cdi091N4puT34huW7uJMYfGaI2kEydkBETTR6lSkqZT2NdxamaqOPDkgN5nf977U7yMf/nW5/hPzAmRkULMWRwkKvXaIHPaaq0VfNIoz7N14slF8dln2derwmHBTESpn8uLritGiRo9D2jz4YyMJI25MN5+DVQqSfdbtUpGSAqiQBdkDHfvLp1SGkeXWi0dMpr7pW7dvPlcgfGBeufnONdRjDs8TpzzPyf27NlT4Bio1eo8eS95cqNm6iKhbTe1zTevsqDrcPOmEL/+Kh1yGodSzs3NTRqG27a9eTRQCCHi43XXt02bd6NzvwrXgq+J5uuaa/Muv/xS/ubQoYXbvyifhUWLhGDwB4KZiC33trxzf0WJN7ENCqUOt2jRokKrMowfP77Qbd8V/6nDFR7tN7fnmN8xPQWdt4FaLVVQMjKkrKeLS9EdY1Hg3z4O/8tQq6W0q0aB7sIFWQMoNzw8BOXLB9KvX1latjSibFkpIRoQAGvWwMcf/73HvebWGkYfHs3cNnMZ30C+n5IykvCL88Mv1k9Pac0v1o/gxGCSvknS1kEZtm8Y6++s1/anQEEZqzK427hjnOzG6a9/Q51iyzffwIzvM2jZwoQrlxXMmycVvV6H+xH3+fzY5/St1pdP634KgBB5Jb+FgD17pJJWaLaa9aBBUjHM3h6iouT1Xb4cAgOzj1UhqFlTwZ078v/Tp8vK7PkhPDmchVcXsvTGUhIzEvW+G1prKIM9B3Mn/A6lLUvTt3pfAGJSY7Cba6fXdk+fPbR0aVmgIlWGMoMboTe4F3GPz7w+037eemNrTr04BYCZkRkNyzakWflmNHduTsOyDXkQ+YCGaxoC0LNKTxZ4b+D3ny1YvFhKjQN06SLrOrlml9cITgxm96Pd2nF/n9j3eB8rb63kXsQ9ghPzPhhnhpzB28UbgMtBl7kech1PR09qONTA3kJXE2vAAPjzT1ks8vr1v6fWV1KSVDrTFPTNie7dpSriq9Svbt+WhYQ1kveWlvDVV1L63aIQiupCCHY83MGXJ78kKCGImyNuUrt07bc5lTz4u+eEh5EP+e3Kb2y5tyVPceE8uPgVnPwZgFFj06nW1I/tf1Tl4gUpf2lsDMOGyWvrWriSMa9EZKSU+P/ii7zqoTlRo4as5dakiSyzYGYGw4dLpTyQBZ3HfH+fPc+2EpsWqy2qDNB1a1ccLBzoV70fLVxaYGRg9FZjoBZqnkQ/0SrS+YT6cCf8DpmqTGzNbImeEq2tDTbu8DhCk0PxcvLCy8mLek71Cn7/ZMDVq7r6RNeu5S1mW7OmTnmueXNd6YZX4cEDqSqYmvrq9+zbQqVWUX1ZdR5HP6ZWqVqcGnyKts1suXkTNm2CgQNf30dRPgu//QZTntUDp5sc7Hcw37pR/xSKXCLbNdfTFxUVRWpqKtbZ+rrx8fGYm5vj4OCAf36l7d8T/jOCCofQpFDKzS+HWqh5Nu7ZK6V6X4fwcFkMzcBAyjL/y071Xz0O/79BCCkbm9MoevQob7uyZXXGkpGRnITeVVL2dVALNWFJYVrDZsXNFczynkW7Cu0AGLJ3CBvvbixw/8djHlPZrjIAx/2O8yT6Ce62skioi7ULpkam2rZr18Inn8i/J06E+fPl+QUHy2elMMht9Ox8uJN5V+axoP0CGpZtqNc2MVFKDP/xhxwDW1tZw2jYMCkdrlLBgQNKfvghhlu38haAPXAAOncu+FgS0hNYcXMF3i7erLy5kjW312i/m91yNl83/RpDA7ky943ypd9f/bQ1kXKiYdmGfNHwC3pV61Woa7DixgqO+x/nfOB5olOj9b6zNbMlakoUG+9uZMSBEWSps6hTug77+u4jNbwskyfL8wJZyHn6dJg8OW9tpciUSGacmcEvbX7BqtibzRkRyRHci7gnt0j576ouq6jnVE97/KMOjdK2d7F2obp9dcwSzejeqDttK7bFztyuoO4BKcvctq28f65fL7jY6fuCELBkCeTnyzQ1lTVRqlXTfebvD99+K402kPPB6NHy/ixsEW2fEB8+P/Y5l4NkscVyVuVY220trd1av+PZSPwdc4IQggsvLzDt9DQuvrz4+h2SHeFZB7g/APzznqeJCXz6qaxPVr4ISmAlJcHevVLq+/hxndMA5DujXDlwcpI1AKOjC+wGbJ/TcPg2kpy38TDqISCLqIZPCs8jhZ4TRTUGGcoM7kfeJzQplK6VdVVKnRc48zLhpV7byiUr41XGiyblmjCq3qjcXWmRlATnz+uMonu5XmVGRtK40RhFDRvK8ckPf/4pnRjw+vfs2+Bx9GO813sTkRJBTfs63J18EtJsCAmR4/c6FOWzMHs2fBtVAWz9uDjsIk3KN3mn/ooS71Uie8uWLaJJkybicQ5S6uPHj0WzZs3E5s1/L+XgPzpc4fDrxV8FMxFN1jR5576uXpXh17Jli+DA3gP+zePwfwFRUULs2pUlunV7Jry8VFrZ2dxb586SZnf5cv4UrcIgLStNj162+e5m0fnPzqLKkip6lCvNllN9aNbZWcL+V3vRcHVD0f+v/uLb09+KdbfXifMB50VwQvAbU0a//17//N5FPl6tVovqS6trj3vg7oH5SqReu6ZLTgaZd/DokfxO8xw8epQpJk/Oy4v/8EMhrlwpHI/8StAVvetoOttUBMbr82Di0uL0Etg125pba7Rt/GL9xO+Xfxf3wu+9ktevVquFb6SvWO6zXAz4a4Ao+3tZ0X6zLvv2QuAFvd+YcnyKCEsKE8ePS3ljzTlWqiRzA3Ki3aZ2gpkIjyUe4nFUPokVuXAu4JxovbF1gfLgK2+s1LZ9HvNcLL2+VFwMvKil6rzJ+yg1VSoOghDjx7+2+XvHrVv5P7slS0oFtfHj9ZPxBwwQwt+/8P0HJwSLwXsGa6+l+Rxz8f3Z7/WkrosC73tOeBr9NM990XN7T3E16KqISY3RftZgVQPx2bZZokLzGwKF6pV0tA8/FOLSpXdL4H+VaEPt2vL9GxCgv49aLfON1qyR9DvtPlV3Cj710jtHg+9MRLXvu4tZu7aJ+OS0Vx7L+xwDtVotzr44K+Zemit67+ytpyLITES9lfX02v9w7gex+uZqcS/8Xr6S7hERMqdt+PC8whAg6XTt20vRi1u3JN0uJ8aOle2srV8vBvQ2eBDxQNj/ai/P71MvUbFG4WiBQhTtOEyfLgRTpPhQfvms/ySKnA6XE+7u7uzatYvatfVD1Tdv3uSjjz7ihV7p4/eL/yJBhUOHLR04+vwoKzqvYETdEe/U186d0Lu3DJFfulREB1iE+DePw/8V5ByDzExjrl2TUaKZM/Nvb2oqPW2aIq6NGkk6DUB8ejxPY57mS1sLSQrh+bjnuNu6AzDz7ExmndNRPQ0VhjhbO+NuIyM4XzX5ClcbGdVWC/Vrq6a/CYSAzz6DFSvk/0eM0P39NghLCmPa6Wmsv7MegcDc2Jxvmn7DpEaTMDM207ZTKmHBApgxQ9IwjI1l0cnJk7M4fVr3HKSlyeOZOFH/d2rXlp77/v1fTXfa/mA7ff/qq/2/kYER/Wv058vGX1LNQRcaOOF3gj67+hCXHgfAsYHHaOveFoCFVxfy+bHPAXC0cKS1W2vtVtaq4PLvmZmC6MRUSpe0QKGQEZlS80rlaVfRtiJNyzXHLLAbO2Z3IToavv9eRio0uBZ8jZ47ehKSFIKliSVzPpiDq42rLsITcY8ZLWbQp3ofAE75n6L1JumpN1AYUNG2opbC5unoScOyDXEsnjfapsGbvI+mT4c5c6BMGUk7/YenNC2SkqBPHzhSQEH4tm3h55/lvVRYKNVKKiyqQGCC5G0OqTmEOR/MoYxVmSI4Yn28rzkhKiWKz499zp/3/8zzneo7lfb9cjnoMq5WFVm/1J4ZMyDrNQy5nHB2lgWi+/WTRTlfVSQaZN+nTkna2p49cuw0qFRJ9tO3L3h4vO7colm2uBizplmiVgNeS6HTGBAGKAJaIe72g8c9IN0a0BXibtJER6HLGQks6jEQQqASKpRqpd5mZGCEtak1USlR3Ai9web7m7E1tWV4neEo1UqSMpNouaGlth9zY3Pqlq6Ll5OXjK441qSMVRmUaiUqtew/PEpJ6DN74i9/xKlTknZMg4VgFgcGSkwtlJRzVlK2vJIyZVVUKOXI0anTuXoVatWCZrO/ICw1SHuMmn6VaiWOxR31ioz23tmbB5EPZLtc52dnbsfD0TL6di/iHjWX1wTAJaMrL34soMp2LhTlOEz5UvCbaTEwzOLl5y//NQWq4T3Q4XLC3Nycc+fO4aUpR5yN69ev4+3tTWpq6psf8VviPyOocFALNecDz1O7VO13rhz9228wZYp8mf6Z993/j+PfPA7/V1DQGNy6paP3fPklPH0KFy4pickKAls/sPEDG38U1z+ndoXSNGsGIZW+Y1dUweTq4wOP08a9DQA3Q29yI/QGbjZuuNu6U75EeYwMjN7ruebEpUvQtKn829hY5knkpA69DW6E3mDC0QlaqpBzCWdWdlmpNSw0CAiAMWNkFW+AihUFgwdf5quv6uuNgYZulRvW1jB0qDTkKlXK/1iEEJzwP8Evl37h9IvT2s+LGRYjdVqqdtHnH+dP923dyVJncW34NS3tbO/jvay4uYJzAedIU6bp9e1h58GePnvwsNNfnaWny8rwL1/KvIRSpeRmUy6ctLJHOGOVN8FsRJ1R/NJsGadOQau2mWx/so5mzs2oYlcFhULB6RenabWxVZ79NPiqyVf83FrmacSnx7P70W48HT2pal8Vc+M3Kwtf2PeRr69cMGVlwV9/wYcfvtHPvHdkZcHq1dJg1qBOHfjlF2hdSNaaZqmhyeNY5rOMLfe3ML/dfLzKeL1q13dCUc4JcWlxLLm+hO/Ofldgm2+afsP05tO198qzZzKH5sqVvG0tLGDsWJmj4+Agr/PJk3Ju3bsXkpN1batWhb59Bb37qnB21S2OM5VKbvuYsW+nBTt3QnR8OpQIBAMljk5K2rZX0rqNCveKSlRCSfkS5XGxdgEgMSORU/6nUKqVxKbFsuvRLk76n5Q/GO4Jh5YxvF1jZvwaxYb7K4lIicDK2JbgUCUvg5UEhSgJCVWRlqGEl03hoXQeUCwBy36jKGmvxKakEkvrTJJTwrG1s0UlVLR1b8vXTb8GICUzhUZrGuW78FeqlXSp1IXVXVcDci1j/IMxaqHO99p3qtiJg/0Pav9vNseMdGV6IUb21ahfpj7Xhl9DrZa5P957nYkTL/NtaxxXFW/fh5w4If9fYmpVEkzy4YkjKbMvJugCB/VW1uNm2M1829qb2xM5JVL7f8Np1qhNEvCy7Mr1L/5+I2j0hFSW2cqEv8SvE7EsZvlO/RUl3sQ2eOMVQqtWrRg5ciSrV6+mTp06gIwCffbZZ7Qu7NvwP/ytMFAYaBNy3xUvs5/7ouAp/4f/G0jKkCIDdeqY0LAhXI3bz27zPxAt/UioEwhqpV574d+GW7dKc+sW4FkRWpfBNN2NcubuVC/jTrPqbjSq7I67rZtejkVdp7rUdfqbkyhyIKdTICsL2reHy5cl3/5tUc+pHheHXWT7w+1MOTGFwIRADBV5M+VdXODgQdi1S+ZzPHum4Ntvm/D0qZrffwe77MvUpo1cdC1ZIvNlvvgCtm+XuR0LFsitTRu52O3cWfLhNVAoFLR1b0tb97b4hPhQf3V9ADJUGTRd25SlnZbi6eiJq7Urlz+5THRqtNYAEkLQqWInunt0J0OZwZXgK5zwO8HJFye5EXoD/zh/ylnpLtTyG8sJTw7nA5fWlCrTgJcvZTTrxQu5caUUMAwUg8H5PITXhnKXwOUsKw+5ss3gL0xd7hF/awWZJhEAWCjsqGrRDGfr/F9e3zT9hubOzalTuo72M2tTaz6u/X6VPNRqGDVK3jNdukCPHu/1594IQsDu3TB1qnRaALi5yYhV796Fz+27GnyViccmMrnRZHpW7QnAyHojGVVvlNYoevVx5O/5L1GsBMaGcjEXnx5PZEpknjbpmek8TH5I4/TG2BvL8MSLuBfcCb+Tp61mId6hQgecrZ0RQrDtwTb67+7/yuOb13Yefar1ITgxmAXXFjBk7xCUKiV+L1Q8fKREXUEJlZRwaQo874CVFXQff4EbjmPYr1CyZ0euc6up5NexP2H38mO2boUD987j+9EHfKdW8V1+zscTv8ClLwGwrnaf+F7y2YwANgGbLgLZqUozWsxgpvdMAJ7FPOPDHQVY3KXu0Wb8HlZNbgzYM9BzIC4LXfTbOGRvQMvWSio87sOlS+AbkEWSyzaSgIB0IDy7fXZkyiS9HBPqSseGQqHgfuT9Aq9tfHq89m8DhQGv8tsrc80lduZ2pCvTMTIwyrPVc6rHN02/wSfEh+sh11l6YykArVxbYWRgxPPY5/jF+QFwPeQ6PXf0pL5TfbzKeDGgXlfJJsCI6Egjgl4a8jLQiJCXRmQlOnLCR3cMCQemYWyZwPCPDaniYYSVhe4YchsOyzotIyUrJd/jNTHUJSKFhoJ6vh8YqDkS+Oo8w/eFpKwEABQYUNykEMoR/1K8cSQoKiqKIUOGcPToUa0lqVQqadeuHevXr8fBweG9HGh++C8S9Gqo1CrpOTEsuuPp1g3274elS6XX+N+Gf+M4/F9AfHo8DyIf4Bfrx9Pop1zyvUS6eTr+cf5EpUZxctBJWrm1YssWGPjbOuiuW1gWMyyGq42rlrbWpcynxPjW0IotPHggF2M5Ubq0jLpoKHQ1avw9KloFITNTHlNsrKSizJoFjx/LSNCFC2Bj8+6/kZqVyr7H++hXo5/2syPPjlCndB09SlZCAnz9tYoVKwwQQkHJklJBbvBgSadJTZXUpadPZUR382aZLP3HH1IdTHOty5WDkSOlKpRjPoyvLFUWow+NZvVt6aU1UBhgY2qDhYkFP37wI32q99FG4hZcXcBO353s6rWL0pb6ahFxaXHci7hHC5cW2s/qrKjD7fDbABQ3KU6TMt7UsGhNmfTWZIZU5cULBc/9lQT4GxEQAMpikdCvC5S9/kbX1OBFW0S5CxTLKEfbgOuUdyxBqVJyLDVRp9KlJbXH6C2CioV5H2mENczNZUTI2Vn3Xc7Fv4ZGoxIqbM1stW3CksJIykzKd0GvVCtpVr6Z1tC4EXqDoISgAhf/H9f+WLvY+nn3IZbsuk1ImBIMlJhZKGncREXVGkoESua0mqM1cjfe3ciR50fyUH5exL/gcfRj7bHWcKjBJ7U/Yc3tNfkaNUq1krNDzlLFvgoAc87PYcbZGahELvmubFz55IpWOGTe5XlMPjG5wLE41v8YbSvKMOgyn2WMPjy6wLYj6oxAIFh1a1WBbTT488M/tc/k7ke76bmjZ4FtzU6s5qs2nzB+PFyLOUqHLR0KbLuw/UKtquFR34t02Nms4IM4NYdutlMZPRpKVrtL683e+S6kDRWGjPYazfgG40nLSsP8x/wjm0ZxVWhauQpDG3VlSK0hgHxOvz75ta4vA0O9vr2cvOhSuQsAoZHpzDq4kgB/I/z9DAnwN0SZYQxqI7nFu2IcWZ+6daFxEzWWnqfxrG6Eg13eY7Y2tdajy4YlhRV4DEVJcb4cdJkRB0bgG+WLIO8yeV7beXzRSMp/ZqoyUQs16kxTLl6U0bxTpyT7IScMDCRtsHVrKbLQuLGkg78ptmyRanB16sDN/ANH+aIo10dDhqnYuDuMiV8n8vs3Vd+pr6LGe40E2dvbc/jwYZ4+fcrjx/Ll5uHhQaWCOBT/4R/DkedHGLZvGGO9xjLDe0aR9PlfJOj/JjKUGQTEB2jzcfzj/BlWexiejp4A/OX7F8MPDNffKU73p0a556OPYMIMb2L2rmXm52580sMdJ0unvJOXp+SuA8TFyYiKxijy8YGwMJmftnOnbGNlJScUjVHk5fV2k8vb4sgRaQCVLg29esm8psaN4eFDKTF87Ni7H4+5sbmeARSaFEqvnb0wUBjwXYvvGN9gPCaGJpQoAYsWqXF1vcSmTc148EDB0KGwfr2Uz65cWUqqNm4sDbZu3WTOR/v2kla3fLmU2g4Kknkqs2ZBz56SbtekiS4vwdjQmFVdVzHDewaTjk9ix8MdxKTFEJMWw8A9A5l2ehqTG0/mo6ofMfv8bGLSYqi3qh67e++mQdkG2vOwMbPRM4CEEIytP5bjfsc59eIU0anRHHtxkGPoaC5lK5XFpb4Lh7quISNLycvQdDof0DeAzBI9MbjzMSn+nlAiCBwegOtpcNKtGtTxZSDoC9LvDWC//UmwWAQJvvC8KoR6gdIUDJRgoMLcQomVsS21Er/VGkc+JaaSavICk2JKjIupMDKRBoNSrcTGzIbN3TZrf2vo3qHcCrult/jPzFISEq6EscX53uWJ1gDqsKUDJ/xO5Lv4NzYwJvPbTO3/Rx0axf4n+wu8b7K+zcJIIaf6eVfmse3BtgLb9qvejye+JnzzDRwy2At1V4MUSSQNOJUOp7K93F83/VprBN0Ku/XKfgGG1RrGnA/msPj64ld6/jNUGdq/FQpFgQYQ6Hv+zY3NsTa1zrv4VxiRlppGMUOdVKCTpRNNyjXRa3c34i7hyTJksfLWynx/r3LJynSo0AFjQ2Ptfjlz4jwdatLPZiFbNxvqFvxqI8zNjOjS0Yhv/vSiZvbc6VXMixODTuRrrBgZGFG6uHQWBATAjX1eVD0Sgu8DI71+tZsw4LCxVIbs168mQWPi9GSdVWoV5wPPcz3kutawMjM2o32F9jyKekT5xL5cWNYPwj1p3FjBrl15lS1tzGxY0aVwiY5ODqas+FgnMZiSksXSpVeAJly7ZsilSxCeJaWqr141ACSLyN1dl1fUpAlUqZI32pjbifK+0LhcYx6MfkBSRhK3wm7pSXUHxAfoqeye8DtBj+098HT0xMvJi/r96jN4shf2VKGUo847p1ZL1cfr1+HHH+Wc0KSJTnmubt3COfNOyUoCtCqY1fvekZVhCIllKW/2+rb/ZhQ6EtSsWTO6detG165d/zUGz3+RoFej987e7PTdycSGE/m93e9F0qetrVyU3r8P1asXSZdFin/jOPwvQAhBXHocxgbG2hD9+cDzzDg7Q1s7J7c3bHWX1XxSR2pDXwi8wJC9Q3CzccO1hCuZEZl0aNiByvaVcbNx08tF0ySAe3vDmTNvfqxpadIQ0hhFly/rJwCDTNT18tIZRY0by7yX94WPPpK5HJMmybw5kFKrzZpJSeuePSXtrCijVb5RvgzeM1jLIa9gW4F5befRpVIXlEolhw8fpk2bjixZYszMmfK6mZhI+eKvvpKT8PffyyjVgwf6Eqvp6ZJat3Spfi6Dp6ekyg0YkLd2xkn/kwzYPYDIlEi9z+3M7ehYsSPH/Y4TnhyOiaEJyzstZ1jtYa89x0xVJsVmF3ttu/xgamTKhu4baF+uNxWWlSEqPTT/huGesPwudPoMvJbn30aDWDdY5Kf7/8g6UPp2vk2NM0rR9WkI6ekBNGzozAajFjzPyF9NxiDLkoyZidpok6auW35QoEA9Q5cTMXD3QA49O5Sv19/IwIi7o+5qa17NPDuTk/4n9dtle9Mz042wPbeerRssEQIMam/Co+1F6tc1wrK4YZ7+JzWapH2uzwWc427EXQwVhhzzO8aBpwf0jjlnjaRnMc8ITAjMc5yarYJtBa34R2JGIsmZyQUaCoXx/Oc3J2QoM7jw8gIlipXQ5iPte7yP7tu759vHxu4bGVRz0Ct/JyRElgPIjZ9/ls+MpWXe7wpCRATs2CGdFDmfPyMjaNdORnC7doWYGPle+fNPfWlnc3Po0lVQt+t1Xlpt5a/HOwhLDgMg8PNAypeQllhAZCyfj7Rh317p2fjsM0mJLUgC+m2RewyEkLTWS5d028OHeSP+NjbSoaQxiry8Xi3g8nchMiWS4ibFtXlfP134iamnp+ZpZ2FsQU2HurxcNY/g6/WoWlXOEWfOSEMmLEy/fYkS0LKlzijy8MgrhiGEpD+/fCmdb+3bF/64i3J99OGHUnzj38gKei/CCBs3bmTfvn0cP36csmXL0rVrV7p27Urjxo0Lxel9H/jPCCoYcWlxlJpXikxVJndG3qFmqZrv3GdSkk6xKCHh36NelBP/tnH4tyEhPYGbYTfzVVtLyEhgbde12sVp7gRyC2ML3G3dtbS1j6p+lKeGDbx+DIKCZOE/lapojGmVSi4ActYriojQb6NQSMqcxihq1qxwdRUKg/h4SRfLzIQ7d2ShPQ3OnJGTVGamzMVZtOj1Ck9vArVQs+HOBqaenqr1Yrdxa8OvrX4l0CdQOwYvXsiF2NGjcr/KlWVe0NdfSzpFu3ZyQs3v2G7dgmXLJAUjLVvPwNJSJnuPHi29tRpkKDOYd2Uebdza4BPqw9zLcwmIDwCkZ9XBwoG9j/cCMMZrDPPbzX8lXTdTlUmdFXW0NUmG1RpG+RLlaePWhi5bu2hV6F6FmS1mciv8FjGpMXkW/oYKI+yMXBjisJCrT/w4ELKOSwZz8u/ozmCI8IQrk3Sf1dgC5tG5vPLZUYBMC6mgpUGZa1AsMV9PfglLY3o2q66l4JnbR1HSPgunUkaUdjSihKW+caOp01QUiImBn36S90NGdhCmd29ZB6RixTfv77jfcdptboeLtQtz28ylZ5We/9gaAXTvI8+mnpwMOMnh54c56X+S5MxkulTqwraPtrH+znpmnp1JVGoUIPNCPq3zKR0rdnxtwnd6uoymagrFavDzz/KZL0yxWJDOxd27peFz5oyMGoB8Jr29peHTs6d0ROYHX1+578aDz3lpuw6qbwNbXd1GSyMbelfvybTmU3G1ceXJExmlfvxYGj1Ll+rqnRU1CjMvx8dLg09jFF27pnvfaGBkJClgjRvrDKPC1mJ7nxBCEJgQiE+IjBRdD7nOzbCbJGdKZYu/Wj1gYNtqpKVB1xnryKy4Ay+n+pRWexH30AufM46cOSPXVTlRurTOIGrVSlKU/fxk8XFjY3nPFPb+gqJdHzXpfZ3LCdv5pHNNVo8b/E59FTXeqzpcRkYGp06dYt++fRw4cACVSkWnTp3o2rUr7dq1w8zs74uN/WcEFQxN4T5PR0/ujrpbJH0+fCgXrNbW8uH7N+LfNg5/N1IyU/QMG/84f7p7dNcqqJ30P0mbTW0K3H/OB3OY2kx6tGLTYjny7Ii2SKi9uX2hFjOFGYOePeWEP2qUXGAXJYSQE0VOo+j587ztXF31jaJKld7OQFm1SkpiV68ujbHcfWzfrqP2/fSTNDyKGkkZSfx44Ud+v/o7mapMzIzMWOGxgr5d+2rHQAjpXZ4wQWckNmwoKSkgx2FUwTUFiYuDDRvkYunZM93nLVtKY6hbt7zFk5VqJU7znIhKjWJYrWGs7rqa2ednM+OspOd+4PoBJwadeKVHXwhBpipTa7zkPu/jfsc54X+Ck/4ntYnMudG7Wm/Wdl2LubH5a+/hnQ93Muz/sXfWcU13Xxz/bDQooICAiiK2Ynd392OB3d2tj/nY3d0JdncHdosoSlmANEiP7f7+OL9tTGrAxoj7fr32YmzfuN/db9xzzzmfc34wokRRAIAKpjWgJzZHf8E1eHnRueXlRTPZ8fGpbkpl5M8PhVylv3OWpH/NzZUXK4iJIaN8+XL54KtZM1J8+0v8NVW+hX3D+9/vZfkgAEmqdynXRaGosCZgjGH2rdk4/vo4fGJ9kl3G3NBcoThv4hyf1IiJIa/v/L+E4gYNomtEmaFQVBTl2Do70yREYvnsOnXI8OnZM+0Jm8SS/4ffHcGAc+S1EoiMwD51AVwdAM82sLLQRe/eNHDevJkmNosUoXtx7dpptzejZOS5LBIB794peot8k3HmliihGEJXsaL6i3Erg1gixuegz3jh+wL9K/fHsaNaGDAAQLf+QJUjCssWMymGmta1UJjVRkGvkXC5YwIXFzKwE1O6NN17xGI6TlfX9LVJleOjcv12wr30KFQ36oxX05RTp8sq1GoE/c2zZ89w4cIFXLhwAZ6enmjevDlmz56NBg3UXz2WG0Ep02BfAzz+8RhrWq3B1PpT015BCa5eBdq3p5nut29VskmVk936QdUwxvA76je0BFqwMCKlI9cAV4y8NBKeIZ74HfU7yTpzG83F4uYkM+0T5oO2R9qSjHSBkjIDp2SBkihRoES6JYCTQ5k+uHsXaN6cHsa/flEYgDrx9wcePZIbRe/eyWdapVhYKIotVK2qXDJ848a0zZUrSfo7OTZuBCZNovcHD5JIgTrwDPHE9JvTUcKkBBrHNU4UfsJkg/+wMDLE/q5jZGhI13Vas/8SCXDnDgkpXLgg/x0LFyZjcPhw+aAtNiEWVXZUwZdgkhZrXbI1NrfbDMfTjnjtR1nDz4Y9Q+0iqhmBeYd645bXLdz0uonb3rfRumRrnHY7jUqWlTC74WxMujZJoT6RVb6k9YYAuqa6He8GjxAP6GnpYWfHnbIE8cS/g68vDUqSe/3tjUwNLS36/Q0M5H/j4ui8TU/VCS0t8kqmZihZWFCO2ooVdO0BFOa4ciV5BJWdCIiMj8TKRyux5ska6Ah18HX811RrJmUFgVGBeO33Gm1KtZF9lprssBRbU1tMrTcVg6sOhpFu6lPrUVGUNzftLw0GZe9lcXH0+zs50fWTuH8rVaIJEwcHUuFLjZ8RP3Hi4wk4uTqhZ4WemNGAbj5/4v5g8PnB6FmhJ9qV7IhXT43g5EThrclNXt6+TfdidaKK5zJjwLdvikbRhw9JQ+hMTBRD6GrXTp+3RJ2MGQNsP/MeBuUeov2wF3ALf47PQZ9loeZCgRARsyJgpGuE2Fhg8bljePkxFL+e14Lb3SpgIsXQ4OrV5V6iRo3SDhVU5fioRL9V8Ck9E01MBuDepIOZ2paqyVIjKDGenp64cOECbGxs0KNHD1VtNkW4EZQ8HiEeKL25NIQCIX5O/qmyRMIdOyj2s1MnunlnR7JTP2SGqPgoPPz+MEnYmleoF6JF0ZjfeD4WNaPCoJ4hnii1WZ6kWdCgoMywsStgh9YlW6tMIl0ZlOkDxshz4uZGBsKECckupjYiIij0QmoUPXsmDwWSYmRED1OpUVSnTtKHjI8PzUQKBBSjnVxOgJQZM4DVq8mwungxfbHc6SU2LhbXr11H+/bt8SHoAwaeG4h1rdfJPIIA5VKNGEEeXinm5hSnrqwS2o8fwK5d5A2TDvq1tUnmecwYoEkTIDYhBitdVmLFoxWIE8dBV0sX8WJFF0oz22YYW2ss/in/j8pCpyRMAsYYFassUAIbnm7A2idrFZaxL2SPVnat0NKuJZrZNlMoRBsWG4b+Z/vj0hcSZJhYZyI2tN2g9P6jooAvX0Q4deo1ChasiW/ftODlRZNJfxvgyWFiQudWoUJJDSRdXQpj8/en/vL3pyKOGX2a9+xJHoHkvE1mZooz6xImweF3hzH79mxZnkmT4k2wt/NeWeHirELCJHjt9xqXv1zGFY8rePHrBbSEWgieESwTbRh5YSR2vUle6KCQUSFsbLsRPSr0SLOm2J8/5OFZvJj6NjG3bqWepC4W08SPkxN5XcLC5N/Z2ZHHx9Ex7bpigVGBOOV2Cs4fnfHw20PZ4LmGdQ28HPEy9XUD6VxKDnt7Kpjs4EDnnKpR13M5PJw82Y8fk1H09GnSvtHSogmtxN6iIqqvyasUcXE0afb8ORkwLi5AvCBCJrzgH+mvkL9df299PPlJSWE6Qh0U16sCjwe1gF+1gV+1gMAKAOh+qaNDzyup8lytWkk986rsh8L9/4VfqWXoaDEeF8dsytS2VI1a1OHCwsLw7NkztGlDMyxnzpzBP39VdCtZsiQm/12SnJPlHHp3CADQpmQblSqpfKPi3goSrpyMERoTmiRsrXHxxrLkW79IvxTlU4UCoUIuRDGTYjjR44SsSKipvmlWHEKmEAhIbWzsWPIojBuXtSEMxsY06/3/2xni4ig3RmoUubjQQOXWLXoBNLivUUNuFDVoQPLSAM2kpmYAATTz7utLuTU9egD37pFcqjpIHDb23/3/4BrgitZHWqNTmU5Y23otSpuVRv36lO+zbh0wezYtGxREv01YmHLJ0TY2NCicN48Gd1u3ksdNqtxXoQIwZowBpvRfiH6V+2H81fG45nFNtn5Fi4pwD3bHXZ+7uOtDKhnHexxHr4q9Mv0bCAVCQAA0Kk7SwoubLUbbUm3R+nBrMDAIIIBrgCtcA1yx/ul6uI1xk0kzB0UHwVTfFOcdzuO/+/9h0f1FsmRyZTEyosHl9+/+aN9eAh0dLTBG58q9e+RR37yZwur+9iL5+dEALyWPu1BIv72dHQ127Ozo//z5yVASiRQNpNOn6W9KSFUWk0NbW+5d0iv9CF/sJiFIlzwrlrolMLH8GjhU6QZrw6zL+3n47SH2vtmLqx5Xkwhx2Beyh3eoN6pYVQFjTCYKoSPUQZdyXTC8+nA8/fkUDWwaoHmJ5mka3eHhlC+1bh0pQCZmyBCaxPlbJAQgg/TJEwp1O3FC0TNYuDApMjo4UP+lZfczxtDzZE+c+3xOQS2vYbGGcLR3RI8KqU86u7lR/g9AimTr1pHogJMTGeWurlQLas4cCpF1dKS8MKvkHaXZBhMTxft4QgKFJCf2Fv38Sff2V68o/BOgMUxio8jePmtKLOjpkUeuenW6944bB+zZY4ymtk2TnajsVKYTTPVN8cL3BYKig+AR8xKo9RKotR2WhoWxtvAv3L5N3rzveIgH723w4EFxzJ8vQL58NAkl9RSpWsgqXhAGADDRM1XthrMYpT1B7dq1Q0JCAiwtLXHkyBHUr18fjx8/Vnf7UoV7gpLHLdAN+9/sR+PijRXitDNL376kQrNqFTB9uso2q1KySz+IJWL8+vMLjDEUNyWr0T/SH52cOsEzxDPZhO4BVQbgYFdyK8eL41Frdy2UMC2RJGytuGlxhcJp2Q1l+0Aaj/7nDyUVt0o5VSnLkUjIQ5I4r0gaOpQcrVoBe/akLR0fH09FSG/epFnZx49JFlbVJO6DyIRI/Hf/P2x5sQUJkgToCHUwsc5EzG08V6buJU22TcyDB2TspZf37ym/6PBh+ayskRHQvz8wejSDh85ZTLo2CeFx4XAf5y471xMPZgOnByoUwlUV93zuodnBZgDIs1Pfpj5ue92Ga6ArHg1+JBsQO5xywFWPq2hm2wyt7FrBVN8UfSr1kX2fIElI03MAJL0WDh6knBEDAzq/Upp1j44mL2NKoXZ/J4z/Tf78ZBjFxVHie2KmTqW8sOhoRUMpub9BQYlWzOcHTC4OaImAuPzAg7nA04mAWB6iY2qafAje338LFlQ+7I4xho+BH1E4f2FZfaStz7di3NVxdKy6+dGqZCvUL1ofwTHBuPz1MtqVaocqllWw+vFqWb0pl0EuqF+8vnI7BYWObdxIr8SeG4Cu3QMHgHZ/zVMxRue/kxMZP9KJQ4COuUcPMjAaNUp90B0tisY9n3toX7q97DOHUw44/vE4aljXgIO9A3pX7A0bk7QrMZ85QyImkZF0fzpzhiZzEh+nVJDhzh25N1EopBwxR0dSAstMrTNNPpe/f5cbRI8fJx8KbWxMxp9UcKFOnfSp+aWXW7fIcJNI6LmRliAFYww+YT5YuPsFDt16gQKVnqN9Axsc+Ydm4SQSBotVlgiJC4RugjnYz9oQ+dQib5FvLSCqECwsgGbNJLCweIcJE+xRpkzm+sF4cD/8sT2KMSXXYGs/1aRcqAq1hMPVqlULL168wN69e+Ht7Y07d+5wIygR2WXwrU4aNqQbyfHjNEuUHcnqfohLiMN1z+tJwtZ8wnwQL47HwCoDcaDrAdmyBksNZCEMVvmsFMLW6hWtpxDLnlNJTx9MmECz4Z07A+ezV26lAtJ49MRG0d+DS4Bm4xOLLSRX5+LPH5qhe/OGDKDHj1MOU8koyfXB56DPmHJ9Cq56XAVAoUDr26xHn0p9ZMf4d1uHDaNckZQUqVIjPJwMoW3bgE+f5J83agQMGRWFEnXfoYmdfFDa8lBL3PamAhi2prY41/scBAIBrn69ilE1RynIrGcUsUSMaTemYcOzDQAAB3sH7Ou8TyEMjjGGclvLyfKYpNgY26ClXUvUt6mPzc83Y3TN0RhZY2SqnoTE/RAeroNy5SiMbcUKkinPCIyRVyElAyk1Yx0g46NoUTKSkntZWMgNlOhYEUKDdWSG0abPUxEaFYmKgf8hwtdSwWD6O5w0NXR0yLuUkqFkahEFT3YHT4Mv47rXFfyI+IEdHXZgZM2RAEiMYfPzzWhfuj0pJL47iFNupxCbEJtkX4Y6hmhu0hxb+2xFsQJpe/OCg4H168lr8Lf0PkBGwZYtitfE169kRDg5Kd4X8uUjD4yjI02UpHY7jBfH46bnTTi5OuG8+3lExkfi45iPqGBBhSg/BX6CllALZcyUK1EiFpOHdvly+r9ZM3p2W1ikvI6fn1ya+9kz+ee6umTwOTpSOHx6Zaqz0/jozx86Nqlh9PRp0n4WCslTm9hbZJO2vZkuli2jcgV6evQMqF497XU6dQIuXUo6ER0eG44Wh1rg/e/3EElESdbT+twTYucT8g90olGiqKHMS9S8efqfQYbDOiHG5hLm2O/B0u5qkhXMIGoxglq3bo0b/9eAnDBhAo4cOYKQv33DWQw3grKWYsUoB+DJE5o1yY6osh8YYwiIClAMWwvzQnnz8pjVkCS+YhNiYbjUMNmK0jpCHfSo0APHuh+TfXbN4xqK5C8CuwJ2aSbg5lTS0wefP8sNBU9Pqn+QU+jThwYLAIW0vH5NA4/EFCxID1CpUVS9Og0o/P1p1tHbm0Li7t5NPqQmo6TWB1e+XsGU61PgHuyObe23YXQteZGHwEDyziVWqbKwoEFhnz4ZU89jjMK/tm2juhLS38jSkkQURowAXGOvov2x9grrGWgbIE4cBwmTwFjPGGNqjsHEuhNTFDNID7tf7caYK2OQIElArcK1cN7hvELosFgixmu/17jldQu3vG/h0fdHSfKYAGBI1SHoX6U/ahWulez1nLgfRo7Uwf79FJby+nXqA+KMEhZGxW03bFD8vEwZGmx5eSXNmfibfPmAEnZiaNc8iC9F52F8wctoXKYq7OyAYsUYDAySngSMkdGblmfJ35+MjGTRCweqHgBKXwFs7wHa8t9bkKAP259zUC9+nsxQcjXchtuRG/Ez5kuymzM3NMeE2hMwvOpwPLv3LM37UUAAhYlt3Upek78pWJA8nNIJwB8/yKhwdqZQK9lh6AEdOlCoW4cOqRsM0iKmTq5OOP3pNEJi5GMqW1Nb7Oy4E61Ltk55AykQGkrXq1QSf8oUmsxQNtcPoHPF2ZnucYlVyIyMSAnS0RFo3Vq5sNnsPD6SlmpIHEInLQqfGBsbRaOoUqX0/Z5/I5GQgXzxIj33Xr1KfbIpIYG+//MHePlS0ZsnJTYhFu9/v5cVdn3+6zncg9wxsfYU/JNvDW7cEOPEhe/40rU0EFSWvES/KMeookVltGqmh5YtKW8pLU+Y7qhGEFk/wqpaJzG9vfo1ANKDWoygXbt2YciQIdDW1oZIJML06dOx4e87bRbDjSBFokXRGHtlLBztHdHSrqVSheSURSSiWGKpIlJ20OZPjvT2Q7w4Ht/DvyMuIU5W+TtBkoCau2rCM9RTpvOfmCbFm+DeoHuy/9sfbY98uvmShK0VNS6q0noeOYX09kGrVhQeMHMmzZDnBEQiMhYCA4ErV2iWNDKSZhilnqKnT5MqexkYUKhFo0Y0kJs6lWRQ27YlsRFV3TrS6gORWITD7w9jQJUBsrCuJz+ewDq/NT48tEXnzkm32aoVDQIzE7736xeJKOzaJS8UKBQCtftdwpeyIxAi8kt1fT0tPQyqOgjT6k9TqNieEe753EP3E90REhOCIvmL4ILjBVS3Tn46NloUjUffH+GW1y3YmtoiMj4Ss2/PhoTJ42qaFG8iE1moUbgGtIXasn7Il68DWrak39nFhQxgVRIbS0bm0qXyvJWGDWnGuF49+XKM0Tmbkhfp50+AFbsPtJ0sLwL7diBw7gAAMoKLFEnZi1SoUNqGcnw8ebK+/4qD2w8/sFBb+PkB336H4UAhczDB/63kMFvgSwfga3vAuxmF3TEBpIngsuK2cfkAV0dou/eCuEd36IrMUTZkGmppD4KNtQEsLMT49eslOnSogaJFtWFlpThw9/cnqevt2+XXq6mpYghc+/YUtqStTTkdTk50jUvR0qKEdEdHGtgqq3Z56csldHKSh6xbGlmid8XecLB3QN2idTMkEvLhA7XBy4vuN3v2kEGUGT58kHu6fHzknysb4pcdxkfp4edPeficiwvl5v09wZUvH93LpUZR3brpr50YFkbGjJcXnWMXL6acG/v0KV3LBQrQNaxsDlNEXATiEuJgYWQBkUiE1SdW41+Pf5MuKNYB/KsAzyZC260fateW5xPVrUvGfWJMi/ojXBSEpzeKok4V0/QcttrRmDpcVsONIEWOfTiGvmf6ooRpCXhM8FCpEfTtG81W6OpSPHp20OFPjtT64bTbaXiEeCiErX0P/w4Jk6CpbVPcHXhXtmyRdUXg+8cXAghgY2KjELZW2bIyOpbpmNWHlmNI77Vw7hypiZmZ0cNHX7OlRZTi8mXK7SlUiAb2yc0IikQU8iY1ih49SmUWHDTz9vUreUgyS3r7IDYhFuW3loffHz9Mrz8d351m4tCefChcmHJ5NmygcCd9fQqxmTYtc1XlRSIKf9y2jbxgAAC9CJh2W4CIcpshgXzE8X3Sd7zxf4MVj1bIlJKEAiHG1RqHje02ZrwRICXNTk6d8Dnos0KhYGW46XkTrY8kP0tvomeC5iWaY0iVIYh1ZZg7tyPc3QUYOZJUNlWFWExCG/PmyWevK1SgyYSOHdPnufMK9cK06zNw1v00AMBAYILaMfNh+mUcfDx14emZvIckMYaGKRtItrZAUPwPXPl6BVc8ruC2121UtqyMx0PlYfVTrk9BkfxF0KFMB5QpWBbh4QK88fyJw677cenXXjjoOEEvoB4+hr7EQ7NBiDL6iALbIxD6+//T1oU+kGIWS32EWLAgTej9nesD0HUoDY8yMiLPmrk5GQC3bikOhhs1IgOgR4/Uw8wYY3ANcIWTqxOs8llhQh2Sw4xLiEOFbRXQ3LY5HOwd0NS2aaYmzk6cAAYPJmPO1pburYmLN2cWxmiix8mJvGDJiT04OpJ3O/G5lx3GR5khMpIU3aSeoidPSGE0MUIheYcSe4uKFUv7Gnz7loyb2Fg61/6uOyVFGj7XrRvlcGUEaT/UaFwDbwPfyjxGz348R2gczZ6YP92GoGv/jw4o9AFoNwHaAbVQwbQWOlSpje4ti6FaNQHy56fzzNMzbTn3rEbtRtCLFy9w9+5dBAQEQPJXhtm6detSWEv1cCNIkTZH2uCG5w0F+WRV8eAB5TGULJl84UlNImES/Ir4Ba9QL3wJ+oIbL29AaCaEhZEFtrTfIlvOeq01/COTSiQZaBugYbGGuNFfXvL76c+nKKBfALamtjJ1IY5ypPdaSEig8+r7d0o2HjgwzVU0joMDDQImTkwaepQSEgng7q6YV5Q4cVpKmTKKeUVSCe70kN4+8P3ji35n+skU2qyNCkN0dQWC7vbFkMFCzJ5NhVRvU8oOKlakOkOqKAfn5kaz8AcP/n/wafkewk5jICnqAgBoV6odrvS9AsYYTn86jf1v9+PK1ytY1XIVpjegwHjpYywjM+dhsWE48+kMhlQbku51fcJ80O14N7z1fyv7zFTfFGGxYQCATW024enmljh2rDzMS/hiudMDdLFvIavxlVEYI1WvWbNolh6gPJ///qMaVOlVulrxaAUW3FuAeHE8hAIhRtYYiUVNFym0kzESSkjJi/TjRwoS3UWfAOXOU5ib5QeFrwpqFcXJxl9RvrQ+rKzk57lILMKVr1ew+/VuXPW4KvO42ZraooRpCdl5CgCvR7xGhYLV8Pt38iF4vr4SuLuHIzbWFP7+AoVQz4xSpgxd+7VqUVSEpWXyXlyPEA84uzrDydUJboFuAAC7AnbwGO8hO1cT1/DKKAkJpOy2ejX936oVGSpmZpnabKqIxRTm6uRE6oOJDcpSpeSy3+XLZ4/xkSoRi0nYJLG3yNs76XJFisjFFho0SLn2nFQwRSCgyILkyie0aEHCFVu2kKpqRkipHxhj8A7zxotfL1C3aF2wsOK4fRvY+XIXXliNVNxIlAV0AmpB5F0PeDwNP330NSY5nhJqNYKWLVuGuXPnomzZsrC0tFS4eAUCAe7cuZOxVmcAbgTJ+RXxC8U2FIOESeAx3kPl9RqOHKEZ4ebN5QOhrCRGFAPvMG+Ex4ajno08vqPJgSZ49vMZ4sRJs3KLmxSHzyQf2f+jLo1CZHxkkrA1q3xWKqtNwsnYtbBiBck016wJvHih5gZmkvBwCmWLjU05NltZfvwgD9HgwSknlhcurFjEVRk514z0AWMMZz+fxbQb0+Ad9v8n+s86wLUNOL+1Ljp1Iq/D5Mly1bARI6jvMqMcJeXPH9r+1q2A60cJUOUQ0GoGyrrvxtyeXRBf/jAm3hiDg10PolTBUrA1tZXVgjn58STWPFmDmQ1momu5rpnyggdGBWLFoxVY2mIp9LXTdktGi6Ix8tJI3PG+g1cjXsHC0AKv/F7hpudN1DLoiY4NSkIk0sLQbTuxN2AUAKCqVVVZ6FzDYg3TVaT4+XMKHb13j/43NaVrZ/x4Cn/KCFK1tVZ2rbCuzTrYF0q/nm58PBn1r90DEfzdHN7eAnh5AbfNeiC8CHmXIBECP+tSiNvXDhR+8//wNgMDoHipaCTUXwJfy/2IFqas6a0t1IajvSOm1Z+GypaVU22X9FqoUKE9Vq/Wwe7dit+3bk0D2rQEJdLC3Fwu7hBRehe+me2Gv5a8do+OUBetS7RH/6qO6FGhu8pCpYODaVJGKuU/cyaFRWaF5LOUuDjKP5IWgE2sXlilCtCrlxgWFrcxaFCzXGEEJYevr9wgcnGhKICEBMVlDA0VQ+jq1ZOHTo4aRRNLBQtSflDi/NiYGLrHxsWRyEy5chlrY3qfC9/Dv+Om5y3ccH2Bx9+f45foPZjw/wf1qyaEXzvj95mZMC+QvdRq1WoEWVpaYuXKlRg0aFBm2qgSuBEkZ7XLasy4NQMNbBrg0ZBHKt/+0qXA3Lk0W7F/v8o3r8C5z+fw4fcHhbA13z++AIASpiXgNdFLtqy0mJi2UJtmCU1KQCtCC02rNEU5i3LoUq6LehvLSUJGroXAQJrJjo+ncIvatdXcyEywbx9JmpYvT4MnVdnPixfLQyFKl6ZB1cuXSDJzbWJCs4tSo6hWraTx2pm5H8UmxGLD0w1Y+nCpLCeuoPMHfH5gDwsLGnTNnAns3UvLW1qSN6x3b9X8FoyRYbhtG3Dq4h8kRJFihM6gThDZXgYAzGowC0tbLJUZO432N8Kj73TfK2tWFjMazEC/yv3SLSXPGEOLQy1w1+cu6hSpg3MO55QSYmCMwT/SX0FcISQ6FD07meDOHSFatZKg3+qjWPtkDd7/fq+wrq6WLhoWa4gdHXagtFnpFPfx9SvN9p86Rf/r6ZG64qxZ6Vfvu+t9FwwMzUs0B0B5kHe976KlXct0TwhJmAQvfV9SmNvXK3jh+wLu49xlKmbHXY/j4peLaGTVHnasDUJ+mil4kDy9GH58F5BssUACTLQDTL8BURbA20FApBXQhiR4hQlGKBM5Am2MJ6GaXTFZqJ21dcoh2p8+iTB+vC/u3y+GhAQ6tmbN6FqztwcqV5bnpyXG0pKWa9qUwl6TE3mQvhJ0g4BoM8jylToNB2rsASRagFcLwNUR+NQNiDOBvn5SNbzkFPIsLdNOvH/7lsKjfHwodG/fPs0rt0ZGkiHk5ESGUWJDoF49Cfr2FaJnT9UrYmY3oqOThtD9HX4pENA52KABTQDOmkUTTDVq0D1QGhp+5w55gqytyVjP6H02s+PU2IRYvPr1DqefPsf9L69QwbwiDo/OfvVS1GoEWVtb48GDByhdOuWbdVbBjSCCMYZK2yvhY+BH7Oq4C8NrDFf5PkaMoITmBQuAhQsztg2RWITv4d/lxs3/1dbixfG46HhRtly9vfXw9OfTJOub6JmgrHlZPBn6RDb4+RjwEYY6hrAxsVFIRM4tbvecSEb7YOBA4NAh8jgeOqTGBmaSZs1oFn7ZMnmRUVXAGDB6NM0G6ulRLaGaNelBKg2fe/w4aV6Gnh4ZQlKjqH59wNAw89eB3x8/zLr5L85eC8Gf3efQrRuFvQAUvvPgATBypFwSuE0bMlxUGR/u70+J3Tt3Aj9DfwPT5QZJPq0C+D7FGwUMTRAQFYBNzzZh64utslC0IvmLYHLdyRhRYwTy6ylf9OOu9110P9EdobGhsDG2wQXHC6hqVTVd7T724RhGnhuPyIPHoPujJd69k6BcOeqH35G/ccf7Dm563cRNr5v4GfETWgItBM8IlsmAH3c9jtDYULSyawXDODssXizArl0UhiMQ0LWyaFHadan+xjPEE9NvTsfZz2dRskBJfBzzMUPhvuGx4bjmcQ1XPK7g6terCIwOVPj+YNeDGFBlQKrbcAt0w57Xe3Dd8zqeD3kD/1+68PICdn/YCM8QD9h+WQsfT114eEoQ0a054NkaeDEaiE3qdtTTo7DRxDlIIhENxBMXnG3dmvKndHXpN/xb5r5gQaqH5+BAs/SpDTbDY8Nx7vM5HHN1wm2vWzjQ5BEs4+vCzw946fsSrqHPYfKrB0J/FpIZS+Hhaf2ycgQCmghJyVC6c4cERgAKJz53TvUFMTNLcDDdM44dk+DBAwEYox9US4sG9Y6OZMQpKySRk5FIyIuTWIXO0zP1dZ49A6pVozHXsmVAv35UdiCj5JXxkVqNoFWrVsHX11fjynAAN4KkvPZ7jRq7akBPSw/+0/xhqm+q8n20bQtcv06zv0NSCZ2PiIuAV6gX/CP90baUPLD1n+P/4IL7BYVq11K0hdqI+TdGplK19MFSeIV6UbhawZIyQYKCBgXTnKXMKxd5diajffD8OYUK6OpSmFh2nCn8/p2qjQMU+pPeQWhaiMVA9+4kGmBqSrOBFSvKv5dWRE+cVxQQoLgNgQCoVImhaFFv9O1bDM2aaWdKzfHl6wTUr6sNkQjYuNcfe+JaY17jeehRoQfi4wVYtYo8xVLhhAULSPVOlZdfQgJw4aIE/57eg8+lFWPUxxg8wOJhjUg+Nu4Pdr3ahXVP18m8x61Ltsb1ftfTtb+vwV/RyakT3IPdYahjiCPdjqBb+W5KrcsYQ5N9LfHw5x2ACVAleCKer10J3WSUJBhj+BryFW/936JXRfkUfuKJIEGYLZhnS8CrFVqXbo41/5mjUqV0HQ7CY8Ox9OFSbHi6ASKJCFoCLYyuORrLWixTykBkjCFOHCcLD/xb1cxYzxitS7ZG+1Lt0bZUWwWPWGKi4qNw0u0kdr/ejcc/5IIIZ3ufRQWLClj7eC0OvjsIE30T+Ez0kdVvCg1NORfp27ekyl3ppVIlksdu2jR170uMKAaXvlyCk6sTrny9ohCGvbLlSsxoMCPV/URHI8XcJamh5OdHy6T3mKQesdSK1VpYZE7WOaOIRCIcPnwHISEtceKElkLIs54eKaM5OpKYR0ZDOnMi/v6KIXSvXyf1/BsYUJ/9+UOevsHKa7ckIa+Mj9RqBEkkEnTo0AFfvnxBhQoVkvyQZzIqW5EBuBFE3PK6hfFXx6OKZRU493BWyz7Kl6dZs1u3aAYHoAfh059PZSFrXqFeCIqmZAEdoQ5i/o2RxT33Od0HTq5O0NfWV1Bak+bntLJrBR2tzP9ueeUiz85kpg9q16acIFV7WVSFNHepSRN5ToaqiYkhud3HjylE8MkT+pscjJFQSWKjKLnZRTs7RbGF0qXTF1KxfDmFYul2nIr4miR+07h4Y2xsuxFVrariyxfyYklTQu3taZY6sTyzqnj6PgidT7ZCoPZb2WfaDxeij81cjB+rhZo1SXXryPsjWPV4FVa2XImu5boCIGMgJCYEJQqUSHM/YbFh6HWyF2563QQALG2+FLMbzlYqXGzoiDjs8x8P1KAElC5luuDwP4eVMjji44EeG1bgmsdViKyeAFqKo6IWJVrg1oBbaW4HoDo0e17vwby782TemjYl22Bdm3WyIpwpERkfidtet2Vqbv0r98eyFssAkDHTaH8jtLRrifal26OBTYNU79/eod5Y5bIKx1yPISKOZLW0BFroWKYjKltWxvvf73HB/YKs3lrdonVx9J+jsCuQtlsxIYEmTc6do3o4GaFMmZRV7aT1Uj4HfUat3bUUyiaUNy8PR3tH9LbvrXQRU2WQSCgs6m9D6f17eW2yjCAQ0ORSaoaS9K8665Z5eMgltxMXUs6fX15ctmVL9dTSys7ExNDzr127pOUVhEIKe8xM0da8Mj5SqxE0btw47NmzB82aNUsijAAA+9WdMJIIbgTJYYwhShSFfLqZv3PFiGLgE+YjM2w8Qjyx9ZgXJPm+49Ok1yhXlgyb3qd648THE0nWtzC0QMmCJXHJ8RLMDEme5lvYN2gLtWGd31ql0t1/o+l+4GSuDw4dojAVGxua5dXErGVKMEaDezc3CtEaqsYi2SEhFCf++TPt8+FD8gwpg58fcO9eAo4e/Y6fP0vg/XtBEtWuQoUUxRaqVEn9t05IoAJ6T15Gw7bvKvwutQoxCTEQQIBh1YdhSfMlsDAshMOHaSAaHEwDrpEjyYBStu3p4fi783A415X+YQJgx1vgd2XUqgWMGUM5Srp6YggEAtk9Z9nDZZh/dz562/fGzAYz00yqT5AkYPK1ydjyYgtKFyyNVyNepWnIPHpEvykATDm6A5s8xiOBJaCceTmc630OZc3LJrueREKKg3Pn0rkPAKUqRKLX9AeItrqFW9434Rrgiu7lu+NUL0oMYoyh75m+qGJZBS3tWqKadTWF++s9n3todrAZAKCceTmsa70O7Uq3S7HtX4K/yHJ77n+7r1AgtnaR2ng27Fmqx54SH35/QOUd9FvbFbDDsGrDUMWqCla6rMSDbw9ky3Uq0wkzGlBuq7K5SW/eUD7d2bOKn+vqkkGZGtraSZPXAQACMVD8AZDvN8z9HUji206MK2WLQU9bB60LO2BAdUe0tK8Mbe2sEdV5+RL45x8y+PLlo3tlt25U/DaxFyklD9Pv33SOKYuRUdqGkpUVeZcyKtTCmNywc3ZWVMs0N5fXIGrYMPuW5VAHEgnQuTOVYgCojlWlShTOmRnyyvhIrUZQ/vz54ezsjA4dOmSqkaqAG0EZgzGG4JhgWV5Ob/vesgfnyIsjsev1rhTXdR/9DWUKUQzQ/jf78ezXM5k3p2SBkihRoIRMsUkT5KR+yK1kKik/lgygoCCqhdBNuQikLOH1a0pY1denQYW649i/faP8Hl9fMkCuX1e+hlLiPoiO1sGTJ3JP0fPnSZXo8uUjr43UKKpdO2mlew8PMpaio4H5a7/jS/GZcHYlz7OxnjH+a/ofJtadiKAgYPp0kjsHaKC0cSPQs6fqRCSkxCbEYsfLnXjl7gfcWoHjl/0gisoHiPVQ0EQXQ4aQ6pK0wGv/s/1x5P0R2frtS7fHrAaz0LBYw1QH3bte7UKT4k1SNGCkxMcD1auTYMbQocD27SJsOLkBG/034tefXzDWM8aXcV9gmU+xGNTNmyQ28eb/9UmtrCjnZ8gQRePUP9Iff+L+yAQUvgR/Qdkt8jaZGZihdpHa6FquK1rZtUKJAiXQ/2x/1C5cG6NqjkrirZEwiezeL2ESWK6xlHnzARKi6VC6AzqU6YAmxZvIwtNSgjGGJz+fYM/rPdAR6mBnp52y7+bemYtmts3QrEQzCAVCfAz4CPvt9tAR6qBf5X6YVn9amt6pxLx4QcbPxYspLyMUAqamDCEh8r4dPBhYv56u34QEqk3m5QV4ejI88nmGJ5HO8DE8AZG+HxBpCaz7CUj+3wnGP4CIopAKIOjokIpXch6kEiVUd484cIDO47g48lqdO0fRGelBLKb7alqheP7+adeDSoxQKPcupWQomZuL8O7ddfzzT5sUnwkSCRUEPXaM6h0FJkozK1qU8rQcHSlHJi+IuYaG0vPG2xvo0IHEJjJrCOaV8ZFajaDixYvj+vXrKJdRjT4Vwo0g4OG3h6hRuEaqEqs3PSkBN3HYmjQkAQB+TP6BosYUbzPr1iysdFmJ/Lr5ZYaNUbwdDm0oCVNWEr+e1k+XnGtWk1cu8uxMZvtg9mwKO2vRQi77mh2YMoUGT7160Yx9VvD+PRklERE0K+rsrJz0bWp9EBdHs8pSo8jFJWnCto4OPYClRlGDBpQ0vmMHhb3p6ZFRGJLvESZdm4RXfq8wue5krGsjrxN37x55gr58of/btSP56xJpR6JliNiEWNTf3RjvAl5TsdXze4G3gyCAEG3bkneoXTvgXcBrrHRZiVNup2T1Z+oVrYc5jeYoXQT58LvDqGxZGVWsFCtRSsMGLSzIi5c/v7w4YZ9zfdDApgGWt1wuW/71a1KEukkRd8ifn4yhSZNoJj4tgqKD4PTBCbe8b+GC+4Uk3ydXM+57+HeZt8c92B2fx36WGYDDLgyDT5gPOpTugPal26OMWRmlPDJB0UE4/O4w9rzZI6uHo6elB7+pfihgUAARcRHY9WoXAqMCsbLVStl6e1/vRdtSbVHEWPlCI0+eUD2ka9dSXqZePTK6vbyAHTsYEhIEKFSIYfduATp3Vlz2Y8BHHHl/BM4fneET5iP7vIB+AXQq1R3Diq1G4A/TJLlIPj5J8zf+xsws5TC7okXT9nSLRHTf2fL/cnedOlFivLonYCIj0/Ys+flRPmJ6RpD58jFYWwtSNZisrEgOWlqD6MwZxcKkZcrIaxCVTX1OIsfz5g1NhMXGksE/d27mtpdXxkfpsg1YOtm3bx/r1asXi4qKSu+qKic8PJwBYOHh4ZpuCouPj2fnzp1j8fHxWbbPkOgQprtYl+Vflp/NujmLjbo4irU+3JqV3FiS/Yr4JVtuxo0ZDAuR5FVkbRHWeH9j9jnws2zZgMgAFhAZwCQSieyzM2cYAxirXTvLDi3DaKIfOIpktg98fBgTCumcc3NTceMyiEjEmKUltenixazd9507jOnq0r7Hj2cs0aWZIunpg4QExt69Y2zLFsZ692ascGHa19+vihUZGzlS/n+NGozFxzMmlojZgTcHWGhMqGybnwI/sY8BH1lsLGMLF8rbb2DA2KpVtJ6q+Rz4mRVeW1jhHmc6tT6D1RtZm21tGVuxgrGAAMa+Bn9lIy+OZLqLdRkWgg04O0Cp/dzzvse0Fmkxo6VG7Nync7LPPT0Z09en/Rw+TJ8l7of4hHiWIE6QLdu1ny+DfggDGNPRYWzSJMYCA9N/3CKxiG19vpWZLDdROHbt/7TZRfeLLD4hnt3zvsdq766d7HPgrd9b2bYkypxciXj8/THrfbK37DfEQjCDJQZs0LlBzOW7C/sV8YvNvDmTGS83lrXpe9j39B8kY+zBA8YaNkz+3AQYq1KFseXLGfP2ZuzzZ8bq1JF/V7fuL/brV/In3ZRrU2RtN1pqxPqe7ssuul9kcQlxqbYnIYGxb98Yu3uXsb17Gfv3X8YcHWm/FhYpt1P60tZmrGRJxlq1outq5UrGTp5k7NUrxkJDGfPzY6xRI/nyixYxJhZn6KdTGyIRY76+jL1+zdjly/Q7LFnC2LhxjPXowViDBnSMhoaSNH+PxC8tLcasrRmrXp2xFi0YK1gw+eXKlWNs9WrGvmfslMoR7NtHxyoQMHbjRua2lVfGR+mxDdIdcb9p0yZ4enrC0tIStra2SazJ169fp3eTnAxy+etlxIvjES+OxwqXFQrfeYZ4onD+wgCAZiWaISYhRiFszdbUNtnQhuQqmX//Tn+lqlgcjjopXpxmPM+fJ8nlzZs13SLySP3+TXHqbdpk7b6bNaP4fwcH+i2KFCFvgarQ0qJaKZUrUyVyxmiWO7HYgrs7hXh9/Chf79UryrvYsUOIRo0Gwvj/4meMMYy6NAqPvj/CmFpjsHDGQvTuXRCjRgH37wMzZlDx5V27SA1QVZQ1L4uXw1+ii3MXvPAl+amw/I8hHF0DVePGwWvff/DxMcGsWVQjplevUhg7dgfmT1iATc83on/l/rJtfQr8hBueNzCs+jAY6Sq6ZCpbVkazEs1wy+sWuh3vhuUtlmN6/RkYM0aA2FjyYPbtm7R9Olo6CAwEliwBtu2KQ0K/bsCIIHT4cw6b59lnyEN2w/MGJl+fLPO+lDcvj3Vt1qFtqbb4E/cHulq6mHtnLlY9XpXs+oWMCuG653VImARVrKqkO1/z/rf7OP6R3KLVrKphePXh6FOpD/wi/bDm8Rocfn9YlldUzrwcptefjkJGyss+MkbXXkq5EKVKkUfAwQGoUIFCqrZupesjJoY8Jhs2JMDU9AXi9Cpj7eMzcHJ1wvIWy9GqZCsAQN/KfeEd5g0Hewd0LNNR6UgHLS1ShyxWjFTl/ubPHwplSk7RztubQic9PdOWSQbo2KysSHjEzo5ChrPDRL62NnlwrK0pTC0l4uMTcObMDVSq1BrBwTqphuIFBlLonp9f8vWbEvP5M4XeTk9UqqZfP8qj/Fspz8wsZ+YVDR5MIjl79tC5/vq16lVJ8zLpNoK6du2qhmZwMoJ9IXuUNy+PfLr5ZMaNVHUtcV2LtqXaKshVpxdpsiK/8DhZxbhxZAQdOEDyyxqOdpXVZnBw0Mzgo3dvGhBMnkzhU4ULUz0ldSAQUMhaiRLAgP+XegkIoLA5qVH08qV8+VGj6K+ZGYXN1W4YBeQvCDETY/PzzTj64SgWNV2Em7dH4ehhbUydSmF+9epRiNrSpaoL77HOb41HQx5hwtUJ2PmK8lEkTILXuptgOfUERhtsxIsDvfDyJRliR44A1apZY8yYFShRX76dFS4rcOjdISx+sBjja4/HuNrjZCIvBQwK4EqfK5h0bRK2vdyGWbdn4fILNzy8tQt6enrYti1pzkJkJIU0rV5Ng2MU+AV9c3/E6n/DXcs6eBG9HyWgfJVLCZOg+4nuOPf5HADAVN8Uncp0go2xDebemQttoTZa2rVEvDg+SQ0fALDKZwX/SH8ERAVg5q2ZmImZeDXiFapbVwdA6nqJ6weJJWJc97yO3a93w9HeUSbnPajqIHwL+4bhNYbL1j3x8QR6n+otW7dhsYaYUX8GOpTpoLSRJZFQTtR//yX//dSpNCCsXl3+W3//TgNGqUJhq1bAqi2BeBRyHKsfbYfbOzfZ+k6uTjIjqLp1dZzprXpV2/z55ZMLfyORUK5fcgbSkydJl3dzo9BSKVIDLKVQuwIFslfejEAAGBgkoEyZtO+fIhEZQqkZSn5+ZEgmx5EjyX+urU0FaFMKwUv8PrvJdG/eTKFxr15RWPTDh0kLZHMyhtI5QV5eXrBTZRU8FcBzgrKG7t0pLnfTJmD8eE23JnVycz/kFFTRB4xR4q+7Ow0ex45VcSPTwZ8/9PCMiaHidbVra64t06eTUpC2NnDpUspeKXVfB5GRJDbwd42ixOiWuw2djpMQlc8VAFDOrAI2tduAqsatMH06cPAgLWdtTfeW7t1VO3Db+XInxl8dD5FEnrixosUKzGw4Ey9ekJfR2Zni7QFSsBs0iHKeHkbuxfJHy+EZStP0hjqGGF59OKbUm4JiJvLZoK3Pt2LitYlU/+x7fUwvdharFsg9HdHRIkyd6oazZyvj9286uBo1gJUrgar1guFw2gG3vCjxbVq9aVjecrmsXlpaJBZ50BJoKdRgc7B3QDmzctjxagf8I/0BALpaunC0d8T42uNR3bo6PgV9wk3Pm7jlfQsfAz7CY4KHzEgZdG4QXH64oJVdKxTQL4BD7w/hZ8RPAEAz22a4M/CObF8SJkFgVKBM7CE0JhTFNxRHC7sWmF5/OurbJLIuU4ExGugNGKDocZTSsSOd/38rhTFGntIJEyh3xMAAWLwqAjdNe+GW1y2F36VRsUZwtHdEjwo9ko160CRxccDEiVQYGKDcl/nzySD421D6W9jkb0xMUjaQihUjD25Woo77EWPU3/7+JJKxYQMZCarAxCR1Q0n618ws64xNHx+6d4SE0MTT9u3p30ZeGR+pRRghX758sLW1RefOndG1a1fU1uRI4P9wIyhrqFWLZn7PnQO6dNF0a1InN/dDTkFVfbB5Mw1sypenQZGmZjalst1lylD4hSZnWCUSGiQePUqJ8/fv04Pxb7LiOggNpbATX1+apR48WO4pevSIHtYQJgDVdwPN5wGGwQCAzlHnMaheZ8THA/PmAV+/0vY6dKBQJlWG3bp8d8HISyNxqtcp3Pa6jeE1hkNXi0aA7kHu0BcVwcmj+bB9u1yWGiAvwsjRYsSXPI3VT1bgjT/JtmkLtTGh9gSsbbNWtmzHiTdx2aAXYBCGja23YkK9MWAMOH0amD2bwcODTpiSJcnr1bOnfBAvlojx751/sdKFxAJalGgB5x7OMDc0VzgOkViEna92onHxxqhsWRmeIZ4otbmUwjImeiZUsLR0e+x7sw8Pvz8EAFjns8aYWmMwosaIFEPRGGMKAgglNpZQEAkASHluQJUBGFptKCoWqiirxbT68WoUMCiAx0Mey7YREhOCggYF0+wfgCY6jh1L2euzdy95PZM7jQMC6Nw7dzkaKOSKesVq4+BBoFQphnJby+FL8BdUt6qOysLKmPfPPNiZZa+JXCm+vjTD/+QJ3V+WLCGBmOTuNRIJDfxTKh6bVgiZUEjhdCkZSeoY2Gflc9ndnSY3jh2Ti7JIsben8OLy5cm4TM7DlJaBmRgdHZogS6tIraWl8sqeqXHtGhWWZYwmkaSeemXJK+MjtQgjxMTEsAsXLrChQ4cyS0tLZmVlxYYNG8YuXLjAYmJiMpi+lDnyujBCViFN8nzzRtMtSZvc3A85BVX1QXg4Y/ny0bl3+7aKGpcBWrakNvz3n+bakJi4OHmbChVizMMj6TJZdR1cvy5PUk6ctCsWM/bxI2M7djDWty9jRUuFMLSdyDCyKoNQJFunWHHFhGlDQ8bWrKGEa1Uhlihmkz/98ZTFJcSx8lvKs6LrirKTH0+yhAQJu3qVsY4dKQFZ2h4bG8YWL5aw4y9usOYHmzMsBFtyf4lsWy4uElre7DPrv28uk0gk7O5dEpGRbsPEJJZt3JjA4lLJsz/heoIZLTViWAjW4WgH2ecRsRFs5MWRssT9ZgeaMYlEwiQSCSu6riiz32bPJl+bzEZdHMW+Bn+VrXfX+y6rt6cec/rgpJDgHxYTxkZcGMH+Of5PqiII4bHh7MLnC2zClQms54mezOmDE4sVxTLGGAuNCWUrHq5gVmusZO0yWW6SLsGDb99ICKBKleQT3u3sSOAgNY6fimPGNS8y/NOXYXY+prfQmEXGysci97zvMfcg92z/THj0iDErKzpuU1PGrlzJ3Paioujau3iRsY0bGZs4kbFOnUjYxMAgbVGC/PmpX7p1Y2zqVMa2bmXs6lXG3N0Zi43NWJs00QcSCYlMTJvGWNGiisdYqBBjY8fSb59YbEIiIVEKNzcSpDl6lLG1axmbPp2xfv3ovluxImNmZsqLPEhfBQowVr48Y82bM9anD2NTppCow+HDjN26xZirK2PBwWkL3yxcSNvT12fs7dvUl/2b7H4tqIr02Abplsj+v+GEJ0+e4MKFC7hw4QK+f/+Oli1bonPnzujUqRMsLLLGzcw9QeonJkZeLyQ4mGRyszO5tR9yEqrsgzFjyO3frRuFZGY1v37RrCljNMuqLnnn9BIRATRpArx9Sx6Gx4+pVoeUrLwOxo0jD06RIsCHD5SPkBzfvwP3Horw5JEOHj4EPn6OAwY1A94MAd4MBpii9veePeRdUmUy85WvV9DxWEfUKlIL38O/y0LFWpdsjS3ttqC0WWn4+JAU+N69VFcFoBnf7t2B5v1foHvTUihoWAAiEVC643l8K7wOLQ1mYc3Itpg9W4CrVwHo/oFu9eOY3nIQ7CteR/furdPsB9cAVww5PwQLmy6Ee5A71jxZA98/vgrLbGm3BaNrjYZQIMTX4K9wdnXG9pfb4Rfphyl1p8g8VOwvz46UGFEMDJfRDT1sZhhM9JVPxvoV8Qsbnm7Azlc78Sf+DwCgSP4imFx3MobXGJ5mfbjfv4FTp0j22MUl+WXGjgUWLCCJ8eQQS8S4/PE+ph1wxlft04BhiOw7W1NbXO5zOUmtoez6TGCMzrOJEykPxt6eoi2kNa3Utc/fv+VeI09PRS+Sr2/q6wsEJO2dnAepZEkSjknOi6TpPpBI6JxzcqIaRMHB8u+KFZNLbleurLwXLD6efsu0cpf8/dMu2psYXV25Vyk5z5KlJaUlvHxJv/nLl8oXo9Z0P2QVaq0TlBxfv37FhQsXcP78eTx79gzr1q3D2CwI4udGkPpxdwfKlaNiihER2SvZMjlyaz/kJFTZBx8/0uBAKKRE2KwW51i9mtTMGjakMK/shL8/iQv4+AA1awJ379J1CmTtdRAVRcpQX7+SKlpKicl/s/reNsy4T88Jw/BqiD27ERKfRkmWa9SIQkAaNaLjzExC8J7XezD68mgkSBJQzrwc6hSpA2dXZ8SJ46CrpYsZ9WdgdqPZMNQxRGwsDdq3bVNMVq9UiYxzPz/gvx+NgeL/PzH8KwMuM6Hl3hM2U7vDR+8i+lfqj86CzujSsYtS/dDFuUuy9X4AwLm7M3pU6IG3/m+x6fkmOLs6y5TXrPJZYXr96ZhSb0qa+zBebow/8X/gPs4dZczKpP2j/Z9TbqfQ82RPAEBFi4qY0WAGHOwdZOGFyREWBpw9S4PP27dpMJoc8+aRMWBmlnobhh1Yib3fZsn+N2JWGFSzF/pVdUSdInWSNfyy4zMhNpYMvn376P9evcjoll6/miImhu4nKYXaRUenvn6+fMkbSDY2Inz6dA1du7bVeB+IRKQ46ORE52biwrDly8sNolKlUt5GemCMQofTKlDr50fLpZf03HOz47WgDrLcCEpMcHAwQkJCULp0aVVuNlm4EaR+bt4kedIKFZJPVs1u5NZ+yEmoug+aN6cB/pw5lFORlVSpQkpmO3cCI0Zk7b6V4csXKqYXHAy0bUtVxXV0sv46ePaM2iGR0Exrz55prxMvjsfW51ux6P4ihMdRtdamFr1QzH0VDm1KPjFIT4+EKaRFXOvVS7+y3H2f++hxsgeCooNgbmiO5S2W4/Sn07jmQdU3bU1t8XjIY1jnt5at8/o1eSSPHqWBogzjn0DdDUCNnYAejaaK5iuB/Pr6+BT0CQBQ3qg8bg67iSKm8oKg38K+4crXK7jueR1H/jmCfLo0+p17Zy6WPlQ8ya3yWeHNyDewymeFjsc64vLXy7Lv6hSpgwl1JqBHhR6pGiOJKb25NDxCPPBg0AM0Kp7U6ATIk/To+yMERgfin/L/ACAvzMBzA9GnUh+0K9UuxSKq0dHAxYuUl3HlSsqz4KampHY4YULSmWzGGD4EfIDTByc0tW2KRoXbYOZMYIvTF2BYXeT/2QNLHRwwpn0TaAlTrx6c3Z4JP36QV/HFC5rcWbECmDYt+08wMkY5WCkZSL9+IdXCqQIBQ5EigJ2dIFlDqVChrP8NYmKAy5fJILp8WTEfqGZNoE8fUuYsXDhr2hMXl9Q4Ssl4khbqrVePIgGUIbtdC+pC7UbQ169fcffuXQQEBECSaGpHIBBg3rx56W9xBuFGkPrZswcYPpwqrV+5ounWpE1u7YechKr74PRpShq2sKABRFZJg75/T0aQri49dFIK89I0z55Rsm9MDAk47N8PJCRk/XUwbx4ldBcsCLi6UviGMgRGBWL+3fnY9XoXJEwCfW19TKs3DXXjFqJj+9QHuEIhhbBIjaJGjShkJC2+h39HV+eueOP/BloCLaxtvRY2JjaYdG0SSpuVxq3+t5Id5IeGUkLy5Ml/fWEQgkZTtuGT8UYExVAMnQACGOsZIzwuHIXzFcashrPwPfw7rnhckdX1AYBzvc+hQbEGMDc0R0hMCAQQYPjF4bA1tcXaJxTeZp3PGqd7ncYpt1PY/HwzelXshfG1x6NO0fQXWmq4ryFcfrjgZM+T6FGhh8J3YokY593PY5XLKjz79QzW+azhPdFbQS47OeLjgevXyfA5f568gylRsCBJXI8bl1T6Xhri5+TqJDMim1l2x6/1p2RJ7iPHiLB2lQ6MjKAU2emZ8OABTRAEBNDv4OxMIhy5gdhYKqeRvJHEEBmZuoVjaJiyWIOtrfplq8PDFb2W4v+LCgoEFHrs6EjGa1reyqxAIqF70e/f9PsoK7qQna4FdZIe2yDddYJ2796N0aNHw9zcHFZWVgoPiqw2gjjqhxdK5WiaLl0oDv3nT+DkSSqGlxVIawN17Jh9DSCACo6eOAF07UoD9CJFgIULs74d8+bRRMnr18DQoTSzqszMroWRBbZ33I7RtUZj0rVJuOtzF2/832BxHy1ER5P3b9Uq+cynvT1QtSrNfnp5UV7U27fyorolSyoaRaVKJW1HMZNieDTkEUZcHIGjH45i0vVJuDPgDj6N/YSw2DDZcy00JhS7Xu3CxLoToa+tjwIFyCOYxAiKKYiHS+fCymYK2g/djw/51mBt21UoV7AcKu+qDN9IX0y4NkG2uJZAC/Vt6qO4aXHMvDUTYibGxzEfZYpqcxrNwebn8irBfpF+aHKgCeY2notvk74peKnSi1TK+nfkb9lnsQmxOPTuENY8XoOvISTXp6elh85lOyNKFJWsESQWkzqhkxNNVCQO5SlcmAbFIfKUHVhYkMdj9GiqoSNFwiRY/2Q9nFyd8MpPrnGsp6UHW1F73NvUF+wLndf79gGtW+e8wRtjdH5OnQokJND5e+ZM9skxVAX6+kDZsvT6m/j4BDg730LJkq3w/bt2EiPpxw/yILq60is5ChdO2Uiyssq8F8nEhCTyBw0iI/XkSXn+2r179Bo7lsoS9OkDdO6sufBFoZCMsexgkOV00m0ELVmyBEuXLsVMVZYs52RbeKFUjqbR1iYZ3HnzqGZQVhhBYjFJrALqK0qqSjp2pCTr4cOBZcsAKythll+zurpkOFavDly9Cuzenb4QwsqWlXF7wG2c/XwW9oXsAdDs74TZAajY2htb59SBiwsNknR1yfCztiY5bqk09/v3lOzt6UmFdgFKJG7YUG4UValCxSYNdQxxuNthVLeuDs8QTzQr0QwAYKQrdzHMuzsPW19sxZ43e7Cl3Ra0KdUGhoaUKwSQYMevX8CuXcCuPWL4s3e4ct8fwoC1OP60C0aNZFhZehVmfp0h2+bshrMxuOpgmciAmImhq6WLR98fISg6CJuebYLLD7lywOiaoxEYHYhTbqew4N4CfA//ji3tt0BfO2Oau5ZG/zeCosgIuuB+AcMvDkdAFBV9KqBfAGNrjcW42uNkBpMUxsjzKE0w9/eXf2dlRRMWUVEUvio1gKysKK9uxAjIvDeR8ZGyEEChQCgzgLQEWmhp1xINTR1xYlFXfHhJsY59+5IRkZ0nI1IiJobuX9JJlT596NqQCg7lBQQCwMQkHrVrMzRokPT7uDiacE3Oi+TpSbXafH3p9ehR0vUNDMigTM5AKlEi/b91oUJk8IwdS2MgZ2c659+9o8mdy5dpn507k4eobVtevDTHkl7pufz58zNPT8/0rqYWuES2+mnShOQYjx7VdEuUI7f2Q05CHX3g78+Yjg6diy9eqGyzKXLjBu2rYEGWqrRxdmPRImq3QCBhM2c+08h1sG4dtcHIKHn57vQy/MJwhoVg/c8MYKu2/2KmprR9oZDkfyMi5MuGhZHE8OzZjDVsyJiubvISwK1bM7Z4MWP37jEWHa24v+DoYOby3YUxxpjzB2dmvcZaJgXd/Xh3BSnooKggdvT9Udb3dF9mttJMthx6d0sksx3OCv5XXP7dX68ORzuwoeeHsiJri8g+0/5Pm/U53Yc9/fGUMcaYRCJhKx6uYMJFQoaFYMddj2f491xwdwHDQrDRl0Yzxhh74/eGYSFYsfXF2IYnG9ifuD8Ky0skjL17R79piRJJZX+HDWPs0iWSNre2ln9XuDBjmzbJf9+wmDC2/81+1uZwG2a01IiFxYTJ9nHC9QTb+nwr8w3/zVaulPebmRljJ09m+FAZY5p9Jvj4MFa9Oh2LlhZdG2lJIOdGMtMHEgljQUGMPX/OmLMzY8uW0TnXvDljtrZ0H0hLntrKirH69Unmev58xg4cYOzBA8Z+/lSUyE4LNzfG5s1jrFQpxe2bmjI2ZAhJXSckpPsQs4y8Mj5Kj22QbiNoyJAhbPv27RlqmKrhRpD6kT70Hj7UdEuUI7f2Q05CXX3Qty+di4MGqXSzyTJgAO1r9Gj170uVSCSMjRhBbdfRSWB37qiw4I6SiMWMNW1KbahfP3ODArFEzIacGyIzDoyWGrHZV5ay3n1jZAOQokUZO3cu+fVjYujetWwZY+3aMWZsnHSApKPDWL16jM2Ywdi5CyLWZG9Lpv2fNtv+YjuTSCQsPDacTbo6iWkt0pK1YdmDZazunroyo0T6Ml1hynqf7M1WXT7ORo1izMhIXgdJr/L5ZI2gm543WdnNZRkWghVaXYjNvzOf/Yr4lezx3PC4waZen5pqjZ/UeOf/jnV26sz6nu6rsI3rHtdZfILi9fr1KxmKFSoo/l5GRlTn5OJFxkJCqNZJoUKKtZW2baPfPio+ih13Pc66OXdjeov1FI777KezCvvz8GCsQQP5djp1YszPL0OHqYCmngm3bzNmbk7HYm5OdWfyKursg/h4Ondu3KC6ZDNmMNa9O2PVqiV/vf/90tNjrFw5xtq3Z2zcODJUz51j7P17xv78SX6fEgkZZZMnk7H/t8E1YQJjT55kP4M3r4yPVF4naNOmTbL3UVFRWLduHTp06IBKlSolSa6aMGHC36urDS6MoF7EYorzTUggV7WNjaZblDa5sR9yGurqgydPSIFMT4/yg8zNVbZpBaKiKIQqKoriwevXV89+1EVCAtCtmwSXLglhasrw6JEAFStmbRu+fSMp6T9/gOXLgVmz0l4nNV78eoGJ1ybiyU/SqrY1tUVfi9U4Orc7fLwpGaBrVwqZKlo05e2IxRROJw2fe/iQFJdkaMcAXQcD9scBAM1MhmNDx2XwFD3ESbeT+B7+XSFUDQAqFaqEDqU7oH3p9qhnUw/aQnmUeXCwCLNnf8LDh5Xw+bMA6N8KKHkryfoLmixAtCgavSr2SlOEIDEhMSE47XYaw6oPS1GtjTGGez73sOrxKpkCnq6WLnyn+MLMUDGp4Ncv4PhxCv958UL+ua4uyZQ7OgIdOlBi9tatwNq18lpKtrbAv/9SFXtdXeDq16voebInokRylYTy5uXhaO8IB3sHlDYr/f/2kfritGl0zeXPD2zYQDWiVKEWltXPBMaA9euB6dPpd6pRg/J/8nJIuaaey1J56pQU7b5/lwsgpEShQinnIhUpQvt4+JDC5U6dUsyDK1ECcHCgEEh7e/UeqzLklfGRytXhSiiZvScQCODl5aVcK1UAN4LUy69fNKDQ0qIkV+10Z5BlPbmxH3Ia6uoDxki29PVrYOVKyjNQB0ePUt5RyZJU+ya7S9cmR3i4CHXq/IG7e0EULUoGZGrGgTo4cIAGsjo6NKCuUiVz22OMwcnVCTNvzcTPiJ8AgFXNNyD06kSsXk3GX758JKQwdizdt9LeJg2GEucVffnCgAargZazAIH88ShgQqyxCkBssYs46LUcW9ptQTnzcrAxSX52KCAqAP/d+w+1Y2ujVYvWWHDhIHZ7zQEESYvl7G91CYPqd8DX4K+463MXA6oMSDPnR8Ik6HCsA655XEOfSn2wu9NuGOrIkx/EEjHOfDqDVY9X4aXvSwCUf9OjQg9Mrz8dNQvXBEDy6tIipg8eyGWOhUKgZUsaxHXrRjLWYWFkaK5fLxdCKFUKmD1HDJuGD2Cop4sGxSjpw/ePL4quK4ripsVlhk+lQpUUjLVfv4Bhw4BrZJuhSRM6b2xtUz30dJGVz4ToaDoeJyf6f8AAytVTt7JZdie7PpdFIhJlSMlISqtuj64unatSo6hoUcphevoUcHNTlAy3t6dJBAcHWlYTZNd+UDXpsg3U6pNSMzwcTr24uJB7t3hxTbdEeXJjP+Q01NkHe/fKz0l1xV63aUP7WLBAPdvPCuLj49mhQ5dZ2bIUjmVvz1hoaNa2QSJhrGtX+i0rVWIsNlY1242Mi2Tz78xnRdcVZcHRwYwxxj58oNA7aUhKzZqMvXqVse37+zN2+jRjXaddY1pzTCl8a3xphrYTGfL5Uq6YmZh16UJ5MM+eMeZ4si/b8GQDE4lFLFYUy1a7rGbGy41loV/6S/Rl781WWLBGC+Yy84rvGQY1oc8XCJj98LWs7bahDAvBrNZYsRUPVyjkzfyNRCJhG55skIXpVdlehXmGyPN117iske3TYIkBG3t5LPMM8WQh0SFs0OnhrPbqnqxdO8a0tRXDeRo0YGzLFsZ+/5bvKziYcilMTOTLlSkrYQt2P2HjL0+U5U21OtRKoY2uv12TDd2TSBg7dozJ8rv09Rlbvz59+RnKklXPBC8vxqpUoePR1mZs8+bsFw6lKXLqczkkhO4jJ08ytnIlYyNHMtaqFWMlSya9btLzqlOHsQ0bGPP1zdrjyan9kF7UkhN0584dlf9wCxYsYAAUXmXLllV6fW4EqRcnJ7pgGzXSdEuUJzf2Q05DnX0QHU1iBQBjFy6ofPPM11eeaPv1q+q3n1VI++Dr13hZsnrjxpSnkZX8/i3PF5kxQ7XbjhXJrSqJRMK6OnVj3deuYcYF4mTCCVOmpBzXrwyBUYHs/Q8vduMGJUQ3bUoDdoVBTYnbKYoeJH5V21GNHXhzgMWIqBPi4xk7djyOWY8YrrCc1kxL2Xvj5cZs1s1ZzO9Pyskx97zvMYtVFrJ1rn29xhhjLCAygNmss2EL7i5gAZEBLDqasVOnGOvcM0K+P51IBlD+xKpVlMivcPyBjM2ZQ2IS0uO1q/eOdd40i9mut1Vod4EVBdioi6PSzFcKDGSsZ09Fg9XNLeN9lBZZ8Uy4fl1+XypUiJLuOXJy43NZJGLM25tyv3bvJuGQ3r0Zq1WLBD2UMYaEQspFCgrKmjbnxn5IjvTYBkoHOLVo0QL6+vqoW7cumjVrhmbNmqFu3brQzmSMVMWKFXHrljxGOrPb46gOqTw2rxHEyS4YGFANmtWrSS67UyfVbt/ZmeL469WjMJ+cTvHiFGrUqBGFOvXvT8eoTKiYKihUiOSju3alPuvYkdqiChLnztz0uolz7mcBnIXd/J2o4roOD/d2wLp1Apw8SfkrGTlXzA3NYW5ojkpF5UUt4+MpJFOWV/SoKcKejwFqb1NYt+inVdAudQf6QhFWdJuHzlUbK4SC6egAjr104dBzJ2afr4BVb6eCCSQQ35sNxJpC0GglIsw/YYXLCqx/uh6T607G8pbLk7SxuGlxNCzWEGc/nwUAtDvaDkubL8WshrPwZYw37t3VwrQxVAjyzx8AyAeUMQB0YjBhzm+MdrBDuXKK2wwIoHyfrVvlhU8rVyaZ+m1Rk3DB5y4AwEjHCF3LdYWDvQNal2wNXS3dVH/Py5cpXMzfn8Kr584F5syh3yInwhjVsJozh+4btWtTzaSsDj3lZD3a2hQKZ2sLNG+e9PvwcMDbO/kwOx8fCsWTSKi22ocPQNOmWdt+DqG0xeHt7Y07d+7g/v372Lt3LxYsWABDQ0M0aNBAZhTVqlULQqEwfQ3Q1oaVMiW+OVmOtFBqXk7o5GQ/Ro8G1qwBbtwAvnwBypRR3baltTyyqiBrVlC5MnDuHNWyOHWKCn1u3Jh1uU5dulBu0P79wMCBVGsjcbFMVdCiRAvs7bwXs2/Phlf4V3jZdEL1da3hf3A9fryrgM6dgX/+ATZtomTmzKCrC9StS6/p04F3fh9Rdde2JMv9tF0G3FgCvByFrou1UK6cYhHX4sWpDwQCAVZ0nYQmFcviwOujaFJoHLZv04Lr1v5AmYtAwxWIs3mKz266iGskr0fyxu8NVj9ejRMfT0DM5NndDAybHu/CV6cxuHjSRCZcAJC4jYODAEdNLOEb7QOHob9RzkaeoODnR8bqjh1AjM5PoPJxGNY+jq0NL2DAP1YQCoGotwNhqm8KR3tHdCjTQSEPKSUiIoApU4C9e+n/8uXpWqtRI2N9kB2IjASGDKGimgBNzmzdyuvFcAgTEyqKW7Vq0u/EYsqH8/IiQ6hJk6xuHUeK0kZQ8eLFMXjwYAwePBgA4OXlhXv37uHevXvYvn07/v33X+TPnx9hYWHpasDXr19RuHBh6Ovro169eli+fDmKpTDqjouLQ1xcnOz/iIgIAJTsJZKWE9cQ0v1ruh2qxMdHC4AQRYsmQCRKUz8jW5Ab+yGnoe4+KFoUaNdOC1euCLFlixhr1yZNNM8IHz8Cb97oQEeH4Z9/EpCTT6G/+6BhQ2DfPgH69dPG5s2AlZUY06er5ndThtWrgTt3tOHtLcCkSRLs2JGGJFMG6G/fH11Kd8Fyl+XY/GIzXkfcgNY/lVGtw2i8W7ccZ87kw82bDIsXSzBypCRT3rBoUTQOvz+M059P4963e7LPTfVNsbDxQux8sR+f8A7526yDcWAP/PKxxOfPwOfPVCgTAIoUYWjQgKFhQ4YGDSRoXrElWtq2BCDBsKES3H0Yj5VHTPHwkAsSCj/CucAKKLqYofVQFxwzUHSntSzRCp3NpsHrdnMceL8P/h9rYb+/CQDAwoKhRw8JevViqFePQSgE7h8oBN9oH/wK/wWRlQg/fwJr1wqx+2gw4kudBhycgOIP6VgBhBV2hlg8FmIx0KdiH/Sp2Ee277Su8wcPBBg2TAs+PgIIBAwTJ0qwaJEEBgbIkmtMHfcjDw+gRw9tuLkJoKPDsGGDBMOGSSAQZM0x5TT4czkp1tb0AkjUJSvIK/2QnuNTSh0uJb59+4Y7d+7g3r17OHfuHEQiEaKjo5Ve/+rVq4iMjETZsmXh5+eHRYsW4devX3B1dUX+ZKYKFy5ciEWLFiX5/NixYzDMS+WXs4iJE5vi2zcTLFjwGNWqBWq6ORyOjDdvLLBoUX0YGoqwd+91GBhkflB96FAFnDlTGrVr+2HOnOcqaGX248IFO+zbVwkAMHHiKzRr9jPL9u3qaoZ58xqAMQH+/fcpatX6rbZ9+cX54YDvATwLf4aiekUxWX8fdm6vgS9fCgIASpcOxejRb2FnF5HubYskIvR831P2vxBC1DWpiw4WHVDBqAIEAgHETIyrQVdRRK8IqhlXQ0SEDtw+m+K9uy48PtjB09MUYrFi1ISRUTzKlw9BhQrBKF8+CNcM/sPDsHvoVWAYBM8m4sb1EggONgAqHQO695XvX6IDk5sHEPrUAWC0TUNDEerW9UOjRj8RbHMCxjpGqG1SW7bOUq+leBHxAv1MJyDo2iTcfBUHcZtJgN0tQCi/lioYVUCjAo1Q37Q+TLRN0vU7xccLceRIeVy8WBKMCVCoUBQmTHgDe/vgdG0nu/HyZSGsX18DUVG6KFAgFjNnPke5cmnIiHE4nCwjOjoaffr0UZ1EtpTv37/j3r17uHv3Lu7du4egoCDUr18fjRo1QpMmTVCnTh3o6qYeE5waYWFhKF68ONatW4ehQ4cm+T45T5CNjQ2CgoKyhUT2zZs30apVq1wjPWhuro2ICAHevROhfHlNt0Y5cmM/5DSyog8kEsDeXhseHgJs2SLGiBGZ82pIJECpUtr4+VMAZ+cE/PNPzvB8pkRqfTBrlhDr1mlBW5vh3DkxWrfOumOdOVOI9eu1YGnJ8Pp1Aiws1Lu/O953oKuli4bFGkIsBnbsTsC/O54i2q0ZtLTIKzFvngRGRspvMzYhFqW3lsbvqN/oV6kfFjZeiGImyUcvJO6H3e92Y8nDJVjWfBm6lxyAVy+18OiRAC4uAjx9KkBUVKL4RGECtLqMhLjKPgCAjkAPXsOD8dRFD7PmSOBZaQigHwaUuA3oxNI6QWVRM3Y6prRyRMd2OtDXB94HvEf9/fURL47Hvw3/xbxG8yAUCNH3+Cic9NwH4f1FkNydD+iHQjDDEkwoQnWr6uhdsTd6lO8BG+OMFYd7/RoYNEibaiMBGDJEgtWrxSoPg1QGVd2PJBJg5UohFi4UgjEB6tWTwNlZLJvN56QMfy5nD/JKP0RERMDc3FwpI0jpcDg7OzuEhoaiQYMGaNy4MUaOHImaNWuqVMjA1NQUZcqUgYeHR7Lf6+npQS+ZgFsdHZ1s06HZqS2ZISyM4rgBoGRJnRyXuJpb+iEno+4+GDuW8lt27NDCmDFamcpxuXuXCrCamgJdumjnuPM9JZLrg9Wrgd+/gaNHBejdWxv372ddbsayZcD164CbmwDjx+vg1Cn15ia1KdNG9l5HB0iosRHRvaajcERn+B5Yi3XrSuH0aS1s20bFQJVBR0cH3yZ9g5ZQS6Ewampoa2vjxKcTCIoJwojLI3DA5gC2tt+KRa2qAqBwmLdv5WILD16GIjhMnrwkYnGwmdsGcDoPxJgBX4/QF0YBQJ1NQK2tgLk7XmIYNgedgmP+qwCAylaVMbLGSGx+vhlLHy2Fi9dbWD89glM+RYDy5SDxaYgWLYB58wrAt+BB1ChcA2XMMp5kJxJRHy9ZQsdkaUl5QB06CAGkL19Y1WTmfhQRQfls587R/6NHAxs2CKGrq9ljymnw53L2ILf3Q3qOTekrOCYmhlYQCqGtrQ0dHR1oqVhiKDIyEp6enrDmUysaRyqKYGaGdM2ScjhZxaBBgKEh4OpKymeZQSqI0LMnoJ96jcocj1AI7NtHhTCjomjw7+mZNfvW1weOHCFlpTNn6H1WEh4bDm2hNnyNL0B7YgUYd5+Bb/4R6NAB6NUL8PVVbjt62npKG0AAiR/cG3gPq1uthpGOER7/eIwau2pg4tWJ1CZtKgTceaAnrIaOQdTIYkCTxYobKeYCDK8DmH+SfaQvLgTcWQJs+AbcWA1EFMaTHf1RuzYVHY2IEmFe43lYUecQtCT6uOd7GU5GtcB+1EEhrXK4sb8Wbt2ixGzHSo6ZMoA+fQLq1wcWLiQDqGdPujY7dMjwJrMF7u5AnTpkAOnqAnv2ANu20XsOh5OzUdoI8vPzw5MnT9C+fXs8e/YMHTp0QIECBdCxY0esWbMGL168gESSvpCUadOm4f79+/Dx8cHjx4/RrVs3aGlpwdHRMd0HwlEtXB6bk90xNZWruG3dmvHtREeTahpAEtJ5AV1dkvKtWpXkkNu2pb9ZQbVqNFAGgHHjqGJ7VrG4+WK8H/UebUq2QQITIaLSahjOLA1Bjb04eUqM8uVpgCtWvW4DdLR0MK3+NHwe9xm9KvaChEmw6fkmlNtaDjc8b2DD0w0os6UMtr/cjtiEWAh8awInTgKLEoCtrtCKKAEU9IThiPaoVEUEgQCI/X8kHOKMgcfTgI1ewMdeePGCFPnM2+xCoeXFMWvTc4hPHAPCigNmHjAY0BtbRvRBqyaZj0+TSIANG4Dq1YGXL+m6PHYMOH4cMDfP9OY1yoULJHv9+TOpCj58SCpwHA4nd5AuX265cuUwatQoHD9+HP7+/jKj6Pnz52jVqhUKFiyYrp3//PkTjo6OKFu2LHr16gUzMzM8ffoUFuoOFOekCZfH5uQExo6lv2fOkORoRrhwgeqn2NoCDRqorGnZHmNj4OpVOm4PD6rhExmZNfueOZMkpiMiyKOXzvmzTFHeojyu9r2KS46XUMasDKKFAWCdhqHQgCmIiKBzqkED4P179ey/qHFRHO9xHDf63UBR46IIiArAx+eFcGVnA0iYBPjaDjhwF2zXc1TS6oFlS7Xg+bQi/P57hma2zXBh6B68f6uDkBCquzNrFrVXVxeAWA+QJPJQFX8A6MQAdbYAvXrCLn8FWOWzQow4Cu7B7pk+Fh8foEULCkuNjQXatCHvj6Nj1kmwqwOJhAz1Ll3oHG3UCHj1igwiDoeTe8hwQOvv37/x/v17vH//Hu/evUNERISCaIEyODs7w9fXF3Fxcfj58yecnZ1RsmTJjDaJo0K4J4iTE6hcmQYoYjGwc2fGtiENyerXj0LF8hJWVpSjY2YGvHhBIWFZoZ6qrQ0cOkThjHfuUOHbrEQgEKBDmQ74MPoD1rZeCzMDM9xeOQqbN1MNo2fPyLMxc6a8WKiqEIlFOPT2CEaemQoD39YwOH0VU/pUxc0DtYBNX2H1fRym960JV1cB3r8HZs8G7OwACyML3B5wGy3sWgAgj0uxmq74b4kIjx5Rccb16//a2fHTwMFbgGcrQCiGl9ZV+Ef6AwCiPzVBOitayGCMQiorVwbu3aN+3LGDjOrM1mHSNOHhZPxIhWjHjwdu36b8Jg6Hk7tQ+pEfEBCAEydOYMyYMShfvjwKFy6MgQMHws3NDQ4ODrhz5066awRxsi/cE8TJKYwbR3937QLi49O3bkAAcO0avc9NBVLTQ5ky5FEwMKBB7IgRNMhVN6VLk0gDQMbGp0+pL68OdLV0MaXeFPyY/AP2luUxbhy1o9S4KRA3nodV66Ngb0+/S2b5ExeJiU4bUHBRSQw83x/e0R/wVXARUW6NUbgweVOcTsYhqFUXOJmVxyfBKfwt3ipI5F5xD3JHo/2N0PZoW9x4GIIePWgbtBzl5Bw+LMCmyS1Q/eMNYOdL4GNPgNE2lt/ZiAIFAIFuNErM6YLlB14r5U319ycjYehQ8qBKvWYjR+Zs7w8AuLkBtWoBly5R/trBg1RgNxfnkHM4eRqlMzutrKygo6ODmjVronv37mjWrBnq168PAwMDdbaPoyG4J4iTU+jWjYrO+flRnkt6UgqdncmLVKsWULas+tqY3alTBzhxAujalRLqixQhhS91M3o0hSNevw4MGAA8fqyZAaeBjvw5FqX/BV4WG4EmEmjV3A+f6yvQvkMf9O4lxPr1SJckMmPAO48ELF+wAC+wHRK9MEALQKQlDN5PQK8SozHopj4aNQK0tACX72Gw+WAD7zBv9DzZE61LtsbmdpuTFSz4Hv4dcaIE3PG+gzsv6wDPLkIoLIc+fYB//wXKlZMvO348wFgNuLqewNAZX/FCew3wejh92XQBfPQuYM63C5jTaS9sQ4egUSPIXmXLyo2bU6eAUaOA4GAKv1u8GJg6FZkqPJtdOHOGFOAiI2ny78yZrFNN5HA4mkFpT9DVq1cREhICFxcXLFmyBC1atOAGUC5G6gniRhAnu6OjQ7PQQPrDqqSqcHlFECE1OnakkCYAWLoU2L5d/fsUCEhCuUABSqpfulT9+0yL0gVL40SPE7A1tYXY6BfwT39gaAMcf/Qc5cvTb5RWDtPXr8B//wGVK2tjwZXXeKa3HBK9MAhCyqBOwC6cbeKD8EtzcGBHATRtKjciGhRrgI9jPmJ+4/nQ1dLFDc8bqLS9EubemYtokbwQ+f37wMqRrRCz+TEQaguYeUBnTF3suXsDhw8rGkBSBAKgUiXg+dXSYBd34vm5mmjbFsDDf+ULdRkKHwcTHD6agBEjgPLlgUKFgGbN5N6l4GCgShXqrxkzcr4BJBaT0di9OxlAzZrRsXEDiMPJ/ShtBLVp0wZGRkZwcnJKcZnp06erpFEczRIfT7PqAA+H4+QMRoygPJPHj4E3b5Rb5/NnGuxoawMODuptX05h2DB5LsTYscDZs+rfZ5EipMgGkPfpxQv17zM1BAIBulfojk9jP2Fp86Uw0jECij4FhtdBePMBGD01EA0bkgBAYn78ANasAcq1eIYyTV5jwQLA3V0A7ddjYBbZGNNsziBimRuebh2Orh31U/R4GegYYFGzRfg45iPalmqLeHE8lj5cirp76uLWbQmaNAGaNqU8Fe2QSugX+ww1LBpAJAzH8HvtseX5liRhdMlRqxaF+QX9NEVH3TXyL/QjgLLnZf8GBVHeT2KMjclTcueO6nOmspLQUDL+ly2j/6dMAW7cgNqL+HI4nOxButOAR48ejavJBEhPnjwZR7K66ANHLfz4QWEc+vr8YcDJGVhb00wuoLxctvR21bYtP88TM2+ePC/I0RF49Ej9+3RwAHr3pln5/v1JtlzT6GvrY06jOfgy/gsGVhkIADCseglGhkI8eUJS37NnkwHXqLEExVpcxvSPTeDeuC7QaibatgX27EnAwa1v4bfsFlYP6YZ8Rsq7TUoVLIUrfa7gVM/TsNC1wZ+Hg9CqpRAPHlAo2ujRpOp3eEchuIy4jYFVBkLMxBh/dTyOfjiq9H7MzICLs6ciclY0Wpj3R9GgQcDP+qmu8/Ahqae1aEECDXXqANOmAefPk6coJ/DhA9VmunaN8uGOHgXWrqVJEQ6HkzdItxF09OhRODo64lGiJ+P48eNx4sQJ3L17V6WN42iGxKIIOT3RlZN3kAokHDsGhISkvqxEoqgKx5EjEJAh2bkzEBcHdOpECePqZts2Mmbd3cm4yC4Uzl8YB7oewLNhz3C45x58fmOGrl2BhASGFcfvYOyug3hUqTLQtyNg+wBa0IFjextcuCTCgAEMRkYJGdovY8CVKwKsGvwPAhd8gs/x8dDTo/yeAw9uwbr3YlgWoUJBetp62N9lP1a1XIUmxZugZ4We6d6fkZ4Bbo09hB+b98P7vTVmzpTX+Rk3jjw+YjEZD9u2kYFctCgVRn3+nAyIrl1pnQoVKET1yBHKL80KoY30cOIESbR7eZFE/JMnQJ8+mm4Vh8PJclgGOHr0KCtQoAB7+fIlGz16NCtcuDBzd3fPyKYyRXh4OAPAwsPDs3zffxMfH8/OnTvH4uPjNd2UTLN/P2MAY61aabol6Sc39UNORVN9IJEwVqUKnbtr1qS+7P37tJyxMWPR0VnSvCxFFX0QFcVYvXr0O9nYMPbjhwobmAJXr9L+AMZu3lT//jLD7APnGRZC9sq3ND+bfmM6+xn+U7ZMRvpBImHs3DnGqleX/xYGBoxNnsyYry9jcQlxrMzmMgwLwUptKsWufb2msH6COEHhvU+oT4aPMTaWsZ8/U/5eImHMx4exw4cZGzGCsfLl5W1O/CpalDFHR8a2bWPswwfGxOIMNyndJO4DkYix6dPl7WrVirGgoKxrS16FP5ezB3mlH9JjG2SoKkafPn2wZMkSNGjQABcvXsT9+/dRpkxS9RpOzoTLY3NyIgKBvHjqtm2pJ69LBRF69KBQGE5SDA2Bixcpyf7HD6BdO2S4royytG1LYV4AMHiw+veXGWwq/IK+tj6s81ljZcuV+DnlB1a1WoUixhkrlCORkPpatWrkUXn9GjAyIvEBHx9g3TrylOkIdbCo6SJY57OGR4gH2h5tix4neuBH+A8AgJZQHnI389ZMVN1ZFbe8bmWoTXp6qdf9EQhIPKdfP6rT5eYGBAYC586Ralzt2hRe9vMn4OQEjBlD4gzm5uRhXLWKvDDplbbPCMHBdA4nlmW/epXCATkcTt5EqejXKVOmJPu5hYUFqlevjm3SrFYA69atU03LOBqDy2Nzcip9+tCg0cuLYv3bt0+6TGwscPIkveeqcKljZka/Y716JATQtSv9r6+vvn2uXg3cvEn5LuPHyw3W7MboWqPRv0p/6GvrQ1uY8UQSsZjOxyVLgI8f6bP8+enYJ0+Wh6RJEQgEcLB3QPvS7bHw3kJserYJpz+dxjWPa5jfZD4m1Z0EXS1dxCbE4snPJwiLDUPbI22xqd0mjKk1JhNHrBzm5lRHqEsX+j8qiorPPnxIrydPSJDg0iV6AXQ+1akjl+WuV49+A1Xh5WWMSZO04eNDhuW+fVQYmMPh5G2UunO/SUFuqVSpUoiIiJB9L+AJJLkC7gni5FSMjIAhQ2jWfMuW5I2gS5eoKryNDdC4cda3MadRvDjNmDduTNLMAwZQfSVhhuII0sbICDh0CGjYkHJKunQhj112JJ9uvgyvm5BAv+OSJZQHBQAmJsDEifQqWDD19Y31jLGuzToMqjoIY6+MxaPvjzDz1kxUtKiIDmU6QF9bH7cH3MaIiyNw+P1hjL0yFm6BbtjQdkOmjLb0YmQENG9OLwAQiYC3b+VG0aNHpEB3/z69ADq3qlaVG0UNGwKWlhnbv5OTALNmNUJ8vAAlS5KXyt5eBQfG4XByPErdCbngQd6Ce4I4OZnRo8kIunqVvAmlSil+L/Us9O2rvoF8bqNKFZLLbtuWvBbW1sCGDeoTTqlXD5g1i6SLR40CGjRIX5HS7IxIREpkS5fS+QlQnaTJk8n7Y2qavu1VtqyMB4Me4NC7Q7jtfRvtS8stf22hNg52PYgKFhUw5/YcbH2xFe7B7jjR4wQKGBRQ3UGlAx0dkueuVYskqRkjufpHj+SGkY8PhQO+fg1s3EjrlS4NhSKudnapn38JCeQVXr+ehjlt20pw7JgQBTRz2BwOJxvChwAcBRjjhVI5OZtSpSj2H0ha8DMoCLhyhd7zULj00bw5eWgAYNMmeW6FuliwgLwBwcHA8OHZT2EsvcTHA3v2AGXLUr6ThweFGy5fToP+efPSbwBJEQgEGFh1IA51OySLyAiODkbZLWWx6dkmTKs/DWd6n4GRjhFued1C80PNIZaIVXZsmUEgoKKsw4fT+eXtTTloiXOIBAIqQLtvH/12pUpRrlKvXsDmzeRZEic6nMBAoFUrYP16+r9nT3ecPSvmBhCHw1FAKSNo1KhR+Pnzp1IbPH78OI4eVb5GASd7ERBAsrgCQeoJsRxOdkYqkLBvn2LNmePHaYa4enWS8eWkDwcH8rIBlFiuznwdXV3avq4ucPkyGRA5EZFIiJ07hShdmgb63t5AoUJkRPr4kMfL2Fj1+935aie8Qr0w6fok1NxVE4WMCuHRkEcoZlIM/zb6V0FAIbtRtCida1u3Au/fkyF86RKdc/XrkzfJz4+8khMmkJhEwYI0+bFoEVCjBhV4zZcPOHEiAX37foZW9j1cDoejIZQKh7OwsEDFihXRoEEDdOrUCTVr1kThwoWhr6+P0NBQuLm54dGjR3B2dkbhwoWxa9cudbeboyakoXCFC9Pgg8PJibRtS+EyXl5UN2jYMPpcWhuIe4EyzuTJwK9fVBdmyBAa0Ldpo5592dtTSNy0abTfFi2oX3MCMTHAzp1CLFnSEsHBNAK3sqKB/IgRpL6nTmY1nAVzQ3PMujUL736/Q4N9DTC46mA8HPwQxUzkCZ+BUYGwMMre1YILFAA6dKAXQL/t8+fyELrHj4GICBLtuHaNlilThkI4S5dmMu8vh8PhJEYpT9DixYvx5csXNGjQANu2bUPdunVRrFgxFCpUCGXLlsWAAQPg5eWFXbt24enTp6hcubK6281RE1wUgZMb0NKiUBqABBIYo3Cap08pD8jBQbPty+msWkVKfAkJQPfuwKtX6tvXpEkkyhAVRaIM4uwRxZUi0dEUhmVnB0yerIXgYAMULcqweTMZ5ZMmqd8AAgChQIgRNUbgy/gvGFptKABg/9v9qLqjKna9oonKnxE/UXlHZYy/Mh4JkowVddUEBgZAkybAv/+S0RMSIs8f6tWLhCWeP+feXg6HkzpK5wRZWlri33//xYcPHxAUFITXr1/DxcUF7u7uCA0NxalTp9C2bVt1tpWTBXBRBE5uYfBgkt599w5wcZF7gVq3phl5TsYRCoH9+8kzExVFKnyenurZl5YWcPAghTa5uJAHKjsSGUnGYYkSlPDv7w8UK8YwevRbfPqUgHHjNFOTytzQHHs678HjIY9R1aoqQmND8cqXrNbbXrfhH+mPLS+2oMOxDgiLDcv6BqoAbW0KiZswgUJeN2wgpT0Oh8NJjQwJIxQoUABVqlRB3bp1UapUKS6NnYvgniBObqFgQVKAA8gbxEPhVIuuLnDmDIkXBARQCGJAgHr2ZWsrVwmbN4/yRLILEREUsmdrS6FuAQFkCO3ZA7i5JaBNm2/Q09N0K4F6NvXwYvgLbG2/FctaLAMADKw6EJvbbQYA3PC8gbp76sIjxEOTzeRwOJwsg6vDcRTgniBObkIqkHD8OIUi5ctHBT85qsHYmNT2bG1J7axjR/KIqIPBg4HOnUllrX9/EnDRJGFhwH//0bH/+y8l75cqBRw4QHV/hg7NfnmV2kJtjKk1BmaGZgAAxhgufbkk+9492B21d9fGXW9eFoPD4eR+uBHEUYB7gji5iWrVSE1Kyj//ZE0+Rl7C2pryMszMgBcvKCdDJFL9fgQCYNcuwMKCPEELFqh+H8oQEgLMn08TRQsWAKGhQLly5Gn89AkYOJDUy3ICITEh+BHxQ+Gz0NhQtD7SGtc8rmmoVRwOh5M1cCOIowD3BHFyG+PGyd/zUDj1ULYsSRgbGFCR2hEj1FPXx9IS2LmT3q9aRepgWUVQEDBnDt0bFy+mMDh7e8DZGXB1pdBLbaX0VrMPZoZmeDvyLVa3Wg0jHSPZ5wmSBFQqVEmDLeNwOBz1w40gjozISJrlBLgniJN76N4daNAAaNaMXhz1ULcuhR0KhRQSNm+eevbTrRt5Wxijv3/+qGc/Un7/BqZPp7C35cvpPlmlCnD6NIlu9O6NHF2DRkdLB9PqT8PncZ/Rs0JP2ec1dtXAh98fwBhDVHyUBlvI4XA46oEbQRwZ0lA4ExOurMPJPejqksfgzp2cPVjNCXTqJPfULF0KbN+unv1s3EgTNV5eVENIHfj6Um2iEiWANWtIBa9GDeD8eeDNGwqtFOaiJ2hR46I40fMErve7jtIFS8NE3wRlzMpglcsq1NxdkwsmcDicXIfSzvtq1aoppQL3+vXrTDWIozmkRhAPheNwOBll2DAqprpwIQlTWFmR90aVmJiQt6l5c8oT6txZXkgzs/z8CaxcCezeLRdfqFOH8oDataPcpNxM65Kt8WH0B/yI+AGRRIRtL7fhe/h3lN5cGpccL6FDGRX90BwOh6NhlDaCunJJpVyPNB+Ih8JxOJzMMH8+eVJ27QIcHYFbt4CGDVW7j2bNyFOzfj0psbm6AubmGd/et2/AihXAvn2kQAdQGOWCBUDLlrnf+EmMnrYeShUsBQB4OvQpCq8rDADo6NQR3ct3x6lepzTZPA6Hw1EJShtBCzQlxcPJMrgoAofDUQUCAbB1KxUMvXCBwuRcXIAKFVS7n6VLSZnu0ydg9GjgxIn0GyteXlTn5+BBICGBPmvalAy5pk3zlvGTHNb5rXG291l0O07uvNOfTuNH+A/YmNhouGUcDoeTOXJRRDMns3B5bA6Hoyq0tQEnJ6BePaqp07YthZqpEgMD4PBh2tepU8CxY8qv++ULMGgQUKYMsHcvGUAtWwL37wN375KnKa8bQFK6luuKqDlRaFeqHUbXHA3LfJaabhKHw+FkGp4TxJHBPUEcDkeVGBoCFy9SKNznz5RT8/AhYGqqun3UqEFem/nzKQepcWPAJhUnxadP5EFycgIkEvqsbVtSs0tcU4qjiKGOIa70vaLpZnA4HI7KyFBOEGMMy5cvx6hRo1CwYEF1tIujAbgniMPhqBozMwpZq1eP8na6dqX/9fVVt4/Zs6lO0fPnwODBwI0bSZXbXF2BJUsoZE5aw6hTJ2DuXKB2bdW1hcPhcDg5gwznBK1duxYTJ06EnZ2dyhvFyXoSEkjRCeCeIA6Ho1qKF6ciqo0bU7jZgAFUZFRVEtPa2hQWV7UqcPs25SONH0/fvX1LxU3PnJEv360bGT/Vq6tm/xwOh8PJefCcIA4AUnISiwEdHZK05XA4HFVSpQpw9izdY06eJGU3qUdGFZQpA6xeTe9nzACOHgW6dAGqVSMDSCAAevakAqdnznADiMPhcPI63AjiAJDnA9nY5K4CgBwOJ/vQvDlw6BC937RJbrSoitGjAWNjIDYW6NePlOkEApLp/vCBQuEqV1btPjkcDoeTM+HDXQ4AXiiVw+FkDQ4OwLp19H7mTApjUwWPHpHwQkSE4udubqQaV7GiavbD4XA4nNyB0jlBmzZtUvg/ISEBBw4cgPlf1ekmTJigmpZxshReKJXD4WQVkydTDuLatcCQIYClJdC6dca2de8e8N9/JGsNUH6QtjZ5g7S0gMhIlTWbw+FwOLkIpY2g9evXK/xvZWWFw39N4QkEAm4E5VC4PDaHw8lKVq0C/PzIS9O9OwkmKJunwxgJIPz3H0luA5RrNHgwMGsWYGtLHqcTJ4D+/YHXr6mmEIfD4XA4UpQ2gry9vdXZDo6G4fLYHA4nKxEKgf37gd+/yaBp1w548gRITXCUMeD6dTJ+njyhz3R1gWHDKLQu8f1r2zYykD5/JgntDRvUejgcDofDyWHwnCAOAO4J4nA4WY+uLim1Va0KBAQAbdoAgYFJl2OM6gDVqSM3lvT1gQkTAC8vksT+ewLHzAzYu5feb9wI3Lmj9sPhcDgcTg5CaU9QTEwMbt++jY4dOwIAZs+ejbi4ONn3WlpaWLx4MfRVWQGPkyUwxj1BHA5HMxgbA1euAPXrAx4eQIcOlN9jZARIJMD581Tn580bWt7AABgzBpg2LW05/3btgJEjgZ07gUGDgPfvAVNTdR8Rh8PhcHICSnuCDh48iJ07d8r+37JlCx4/fow3b97gzZs3OHLkCLZv366WRnLUS0gIEBVF721sNNsWDoeT97C2Bq5dI+/NixdAjx6UK1S1KvDPP2QAGRlRyJuPD7BmjfL1zNasAUqWBH78ACZOVOdRcDgcDicnobQRdPToUYwYMULhs2PHjuHu3bu4e/cuVq9ejRMnTqi8gRz1I/UCFSrEk4c5HI5mKFuWvD4AGUR9+1Jtn/z5gX//JeNnxQq6T6WHfPmoNpFQSH/PnFF50zkcDoeTA1HaCPLw8EClSpVk/+vr60OYqKpm7dq14ebmptrWcbIEng/E4XA0SUIC1QsaMiTpd9++AUuWAH9VY0gX9euTFwkARowA/P0zvi0Oh8Ph5A6UNoLCwsIUcoACAwNha2sr+18ikSh8z8k58EKpHA5HE4hEpBBXrhwwYADw5QtQsCBJXEs5flw1+1q4EKhSBQgOBoYPp1xIDofD4eRdlDaCihYtCldX1xS/f//+PYoWLaqSRnGyFl4olcPhZCXx8cCuXUCZMuT98fQkT8+KFRT25u1NRgsAjB0LnDuX+X3q6pK3SVeXlOb27cv8NjkcDoeTc1HaCGrfvj3mz5+P2NjYJN/FxMRg0aJF6NChg0obx8kaeDgch8PJCmJjqX5PqVKk2ubjA1hakniBjw+FrOXPT8vOn08eG4kEcHQEXFwyv/9KlSi0DgAmTSJ5bQ6Hw+HkTZQ2gubMmYOQkBCULVsWq1evxvnz53H+/HmsWrUKZcuWRWhoKObMmaPOtnLUBJfH5nA46iQmBti0iVTaxo4lpTZraypg6uUFTJ1K6m+JEQjIYOrcmYynTp0AVaSdTpkCNGoEREaSbLZYnPltcjgcDifnoXSdIEtLSzx+/BijR4/GrFmzwP4fUC0QCNCqVSts27YNlpaWamsoR31wTxCHw1EHUVHAjh3A6tXA79/0WdGiwKxZwNChVPA0NbS1AScnoGVLKpDati39LVIk423S0gIOHKD8oIcPgXXrgOnTM749DofD4eRMlDaCAKBEiRK4du0aQkJC4OHhAQAoVaoUChYsqJbGcdRPTAxVage4J4jD4aiGP3/Ii7NmDRAURJ8VLw7MmQMMHAjo6Sm/LUND4OJFoEEDwN2dCqA+eJC5oqd2dsD69RRuN3cuGVeJxE85HA6HkwdQOhwuMQULFkTt2rVRu3ZtbgDlcH78oL9GRqTKxOFwOBklPBxYupTU3WbNIgPIzg7Yuxf4+pXkqdNjAEkxM6PaQVZWVDuoa1cKkcsMQ4cCHTuSSEP//gAXN+VwOJy8RYaMIE7uIXE+kECg2bZwOJycSWgosGgRGT9z5wIhIUDp0sDBg+S9GTIE0NHJ3D5sbYGrV0k44f59ktSWSDK+PYEA2L2bDKx376j9HA6Hw8k7cCMoj8PzgTgcTkYJDgbmzSMDZeFCICwMKF8eOHoU+PSJDBXtdAVdp07VqiSXraMDnDwJTJ6cuXo/VlbAzp30fuVK4PFjVbSSw+FwODkBbgTlcbgyHIfDSS+BgRTuZmtLktMREYC9PXDiBODqCvTpQwIE6qB5c+DQIXq/aROJLmSG7t0pHE4iIaMtMjLzbeRwOBxO9ocbQXkc7gnicDjK4u8PTJtGxs/KlWQwVK0KnDlDIWU9ewLCLHiqODgAa9fS+5kzqQhqZti0CbCxoaKt06Zlvn0cDofDyf5wIyiPI/UEcSOIw+GkhK8vFRctUYKMj+hooGZN4MIF4PVroFu3rDF+EjNlCr0Ayjm6cSPj2zI1Bfbvp/c7d1LuEYfD4XByN9wIyuNIPUE8HI7D4fzN9+9U3NTODti4kRTZ6tYFrlwBnj+nAqaaFFRZvRpwdAQSEiis7fXrjG+rRQtg4kR6P2QI5TtxOBwOJ/fCjaA8jEQil8jmniAOhyPFxwcYORIoVYrq/cTFAQ0bAjdvknhAu3bZQ01SKCQPTvPmFJrXrh3g5ZXx7S1fDpQrR2F/o0dnTnSBw+FwONkbbgTlYfz9AZGIEpgLF9Z0azgcjqbx9KT6OaVLA7t20f2hWTPg7l0qUNqyZfYwfhKjpwecPQtUqUKFn9u0IeGGjGBgQPlFWlqkPufsrNq2cjgcDif7wI2gPIw0FK5IEdXK2HI4nJzFly/AwIFA2bLAvn0UXtaqFRk+d+4ATZtmP+MnMcbGlMdTvDjg4QF06ABERWVsWzVrkuw3AIwZA/z8qbp2cjgcDif7wI2gPAyXx+Zw8jZubkDfvlTb59AhQCwG2rcHnjwhoYFGjTTdQuWxtgauXwcKFgRevAB69SJPVkaYMweoVYvqHg0ZwsPiOBwOJzfCjaA8DJfH5nDyJu/fk5Fgbw8cO0b5gZ07k/Fw+TKJH+REypYFLl2isLYrV4BRozJmwOjokFGor095UNu2qb6tHA6Hw9Es3AjKw3BPEIeTt3jzBvjnH8qfOXmSDIR//iFVtfPnKRQsp1OvHnD8OIkm7NsHzJ+fse2UKwesWkXvp0+nkEEOh8Ph5B64EZSH4Z4gDidv8OIFeXqqVycRAYGAPEHv3wOnTwPVqmm6haqlUyeq9wMAS5YAO3ZkbDtjx5J0dkwM0L8/5UpxOBwOJ3fAjaA8DC+UyuHkbp4+FaB9e6B2beDiRfKO9OkDuLqSt6RSJU23UH0MGwYsXEjvx44Fzp1L/zakEtwmJlQXacUKVbaQw+FwOJqEG0F5GF4olcPJnTx6JMCCBfXQuLE2rl4lyeeBA4FPn4CjR4EKFTTdwqxh/nxg+HDKeXJ0BFxc0r8NGxtgyxZ6v2gR8OqVatvI4XA4HM3AjaA8Sng4vQBuBHE4uQHGqJ5Ps2ZA8+baePeuELS1GYYOBdzdgQMHgDJlNN3KrEUgIFGDzp2B2FgKk3NzS/92+vYFevSgcLj+/Sk8jsPhcDg5G24E5VGkoXAFCwL58mm2LRwOJ+MwRgpmjRsDzZsD9+4BOjoMbdp4w80tAXv2ACVLarqVmkNbG3ByIsGE0FCgbVvg16/0bUMgALZvBywtyZv277/qaSuHw+Fwsg5uBOVRuCgCh5OzYYwKhNavD7RuDTx6BOjqUv7L588JGD36PWxtNd3K7IGhIeVElS0L/PgBtGtHNYDSg7k5sHcvvV+/nrxuHA6Hw8m5cCMoj8LlsTmcnAljwIULJHbQvj3w9CnVs5k4EfDyovwVGxtNtzL7YWYGXLsGWFkBHz4A3boBcXHp20aHDpRjBACDBslDijkcDoeT8+BGUB6Fe4I4nJyFRAKcOUMy1126AC9fkodj2jTA2xvYsAEoUkTTrcze2NqS9yx/fgobHDCAftf0sG4dYGdHE0mTJqmhkRwOh8PJErKNEbRixQoIBAJM4k+VLIF7gjicnIFYDJw4QQVOu3cH3r6lPL5ZswAfH2D1avJucJSjalWSy9bRod91yhTyrilLvnzAwYOUJ3TgQMaktzkcDoejebKFEfTixQvs3LkTlStX1nRT8gzcE8ThZG/EYuDYMarl07s31fYxNgbmziXjZ/lywMJC063MmTRvDhw6RO83bgTWrEnf+g0bAjNm0PsRI4CAANW2j8PhcDjqR+NGUGRkJPr27Yvdu3ejQIECmm5OnoEXSuVwsicJCTRAr1CBpJk/fQJMTalGzbdvwOLFlN/CyRwODsDatfR+xgzgyJH0rb9oEVC5MhAYSHlC6fEmcTgcDkfzaGu6AWPHjkWHDh3QsmVLLFmyJNVl4+LiEJcokzUiIgIAIBKJIBKJ1NrOtJDuX9PtUIb4eMDXVxuAANbWIuSAJitNTuqH3Arvg4wRHw8cPSrAypVa8PISAAAKFmSYNEmCMWMkMDam5ZT5WXkfKMf48cCPH0Js2KCFwYMZzMzEaNlSOWtGKAT27QPq1dPGhQsC7N2bgIEDFdfl/aB5eB9oHt4H2YO80g/pOT4BY5qbv3J2dsbSpUvx4sUL6Ovro2nTpqhatSo2bNiQ7PILFy7EokWLknx+7NgxGBoaqrm1uQd/f0OMGtUKOjpinDhxCQKBplvE4eRdRCIh7tyxwalTZRAYSPcxE5M4dOnigXbtfGBgkKDhFuZuJBJg/foaePiwKPT1E7B06SOULKm87NuZM6Vw6FBFGBiIsGHDXVha8kqqHA6Hoymio6PRp08fhIeHw1g6e5gCGjOCfvz4gZo1a+LmzZuyXKC0jKDkPEE2NjYICgpK80DVjUgkws2bN9GqVSvo6OhotC1pcf++AK1aaaNUKQY3t9w1wMpJ/ZBb4X2gHLGxwP79QqxeLcTPnzQTYWnJMHWqBMOHS2BklPFt8z5IH3FxQOfOWrh7VwhLS4b79xNgZ6fcumIx0KKFFh4/FqJRIwlu3hRD+P9Ac94Pmof3gebhfZA9yCv9EBERAXNzc6WMII2Fw7169QoBAQGoXr267DOxWIwHDx5gy5YtiIuLg5aWlsI6enp60NPTS7ItHR2dbNOh2aktKSGtlm5rK8j2bc0oOaEfcju8D5InOhrYvRtYuRLw86PPChcGZs4Ehg8XwMBAC4BWqttQFt4HyqGjQypvjRsD794J0KmTDlxclBOe0NGhHK4qVYCHD4XYskWIqVP/Xob3g6bhfaB5eB9kD3J7P6Tn2DQmjNCiRQt8+PABb9++lb1q1qyJvn374u3bt0kMII7q4PLYHE7WExVFKmR2dlRfxs+Pippu2wZ4egITJgAGBppuZd7F2JhqCBUvDnz9CnTsSH2mDCVLAuvX0/s5c0jJj8PhcDjZG415gvLnzw97e3uFz4yMjGBmZpbkc45q4fLYHE7W8ecPsHUrKZEFBdFntrY0WB44ENDV1WjzOImwtgauXwfq1weePydp8nPnAG0lnpTDhgHnzwOXLwP9+wPPnoHnW3I4HE42RuMS2Zysh3uCOBz1Ex4OLFlCBs/s2WQAlSxJimJfvpCsMjeAsh9lywKXLpFX7vJlYORI5eSvBQJgzx6SL3/7FvjvP7U3lcPhcDiZQOMS2Ym5d++eppuQJ+CeIA5HfYSGUgHODRvIEAKAMmWoyKmjo3JeBY5mqVcPcHYGunUjo7VIEeWMGisrYMcOoGdPKmbbpg13BXE4HE52hXuC8hiMcU8Qh6MOgoKAf/+lyYVFi8gAqlABOHYMcHOjECluAOUcOncmgwagArU7dyq3Xo8eQL9+JL09ZIgWYmN5fiuHw+FkR7gRlMcIDCRpXoGAkrI5HE7mCAggZTdbW2DZMsoBqlwZOHkS+PCBvD9c5yVnMnw4sGABvR8zhvKDlGHzZqBoUcDDQ4ADByqqrX0cDofDyTjcCMpjSEPhrK15PgKHkxn8/ICpU8n4WbWKlMSqVQPOngXevCGPgJDfYXM8CxaQMSSRkEHr4pL2OqamwP799P7atRK4cYOHxXE4HE52gz+i8xg8FI7DyRy/fgETJ5LU9bp1QEwMUKsWcPEi8OoV0LUrN35yEwIByZh36kRe9E6dgE+f0l6vZUtg7FgxAGD4cC2EhKi5oRwOh8NJF/xRncfgoggcTsb4/h0YO5aMn02baEBcrx5w7RrJIXfsyCWRcyva2iSUULcuCV+0aSMvOp0aS5dKUKTIH/j5CTB2rPrbyeFwOBzl4UZQHoN7gjic9OHtDYwYAZQqRR6B+HigUSPg1i0KjWrThhs/eQFDQ/L2lSkD/PgBtGsHhIWlvc6kSa+hpcXg7EyGFIfD4XCyB9wIymNwTxCHoxweHsCQIUDp0sDu3YBIBDRvDty7Bzx4ALRowY2fvIa5ORVTtbIi0Ytu3YC4uNTXKV06DLNnSwCQuIIyHiQOh8PhqB9uBOUxuCeIw0kdd3dgwAAqmrl/PyAWA61bAw8fArdvA02aaLqFHE1iawtcvQrkz08G8YABJJqQGrNnS1CzJoXSDR2qXPFVDofD4agXbgTlMbgniMNJno8fSf2rfHng8GEa2HboADx9SrP/DRtquoWc7ELVqqQCqKMDnDgBTJmSumGjowMcOgTo69O5JK0/xOFwOBzNwY2gPERUFBAcTO+5J4jDId6/B3r2BOztKWeDMaBLF+DlS+DSJaBOHU23kJMdadECOHiQ3m/cCKxZk/ry5csDK1bQ+2nTgK9f1ds+DofD4aQON4LyENJQOGNjqmPB4eRlXr+mnI4qVYBTp+iz7t2pxs+5c0CNGhptHicH4OgoN35mzACOHEl9+fHjKa8sOprC6BIS1N9GDofD4SQPN4LyEFIjiIfCcfIyz59TrZcaNcjYEQiA3r0p0f3UKQp14nCUZepUYPJkej94MHDzZsrLCoWUZ2ZsTGGWK1dmTRs5HA6HkxRuBOUhpPlAPBSOkxd5/JhkjevUoTA3oRDo149ygZydKRyOw8kIa9YADg7k2fnnH/IypkSxYsDmzfR+4ULyPHI4HA4n6+FGUB6CiyJw8iIPHgAtWwINGlBhUy0tYNAg4PNnEkAoX17TLeTkdIRC4MABCnWLjATatwe8vFJevn9/MpYSEsgQj43NsqZyOBwO5/9wIygPweWxOXkFxoA7d4CmTUnS+vZtQFsbGDYM+PKFQpJKl9Z0Kzm5CT09UoyrUgX4/Rto2xYIDEx+WYGAFOIsLQE3N2Du3KxtK4fD4XC4EZSn4J4gTm6HMeDGDaBRI1Lvun+f5IlHjaLip7t3A3Z2mm4lJ7dibEw1hIoXJ/W3rl21EBurleyyFhZ0PgLAunV0rnI4HA4n6+BGUB6Ce4I4uRXGgCtXgHr1gDZtABcXmpkfNw7w9AS2b+fGPydrsLamWkAFCwIvXgixZk3NFFXgOnWSF08dOBCIiMjatnI4HE5ehhtBeYSEBODnT3rPB4Oc3AJjwPnzQK1aVNj02TPAwIDUury9KQHdxkbTreTkNcqWJfENAwOGly+tMGaMVorFVNevB2xtyVM/aVJWtpLD4XDyNtwIyiP4+QFiMeVFWFlpujUcTuaQSIDTp4Fq1YCuXYFXrwBDQ2D6dDJ+1q2jGXkOR1PUqwccOSKGUMhw4IAQCxYkv1z+/MChQ5QntH8/GfUcDofDUT/cCMojSPOBbGxIHYvDyYmIxcDx40DlykCPHsC7d0C+fMDs2YCPD7BqFSWbczjZgU6dGEaNegcAWLwY2Lkz+eUaNQKmTaP3w4cDAQFZ1EAOh8PJw3AjKI/AC6VycjIJCcCRI1TLx8GBavuYmADz55OBv2wZJZpzONmN1q2/Ye5cMQBgzBgq0Jsc//1H53dgIDBiBFIMn+NwOByOauBGUB6BF0rl5EREIqq/Ur481Vb5/BkoUIAGjD4+wKJFlIDO4WRn5s2TYPhwCuN0dCThjr/R16e6VTo6FBJ38GDWt5PD4XDyEtwIyiNweWxOTiI+HtizhxLMBw8meWszM/L4+PgA8+YBpqaabiWHoxwCAbBtG6nBxcbS30+fki5XtSoZ9gAwYYL8vs3hcDgc1cONoDwCl8fm5ATi4qiIZOnSlBvh7U1hbqtWkfEzezbVYuFwchra2oCzM1C3LhAaSsVUfX2TLjdjBlC/PvDnDzBoEHmPOBwOh6N6uBGUR+CeIE52JjYW2LIFKFkSGD2ajHYrK1J58/Eh1bd8+TTdSg4ncxgaAhcvAmXK0Dnerh0QHq64jJYWhcIZGQH37gEbN2qkqRwOh5Pr4UZQHoAx7gniZE+io6lOip0dMH488OsXUKQI1ffx8qJ6P4aGmm4lh6M6zM2pmKqVFfD+PUm8x8UpLlOqFLB2Lb2fPZuEQDgcDoejWrgRlAcIDQUiI+k9N4I42YHISGD1aqBECWDKFKpjVawYsH074OkJjBtHRU85nNyIrS1w9SrVCLp3DxgwIGnY24gR5CmKiyNRkPh4TbSUw+Fwci/cCMoDSL1AFhZ8YMnRLBERwPLlNAicMYPqoZQoAezeDXz9CowaBejpabqVHI76qVoVOHuW1OBOnACmTlWUxRYIgL17Sf3wzRuqM8ThcDgc1cGNoDwAzwfiaJqwMBrE2doCc+YAwcEU8rN/P+DuDgwbBujqarqVHE7W0qKFXAp7wwZ5CJwUa2vyjgKkjPj0aZY2j8PhcHI13AjKA/B8II6mCAkBFiwg42f+fArNLFeOCp9++kTqVzo6mm4lh6M5HB2BNWvo/fTpwNGjit/36gX06UPhcgMGAFFRWd9GDofDyY1wIygPwD1BnKwmIkIXc+cKYWtLhU3Dw4GKFUki2NUV6NuXJIM5HA6Fwk2eTO8HDwZu3VL8fssWEgz5+hWYOTPr28fhcDi5EW4E5QG4EcTJKn7/BmbNEmLEiFZYtUoLf/4AlSsDp06RElbv3iQBzOFwFFmzBnBwAEQioFs3ygOSUqAAhY4CwNatwI0bmmkjh8Ph5Ca4EZQH4OFwHHXj50cqbyVKAOvWaSE2VhvVqjGcO0eDue7dASG/23A4KSIUAgcOAM2akXpiu3ZULFhKq1bA2LH0fvBgCi3lcDgcTsbhw5I8APcEcdTFz59U36dECar3ExMD1Kolwdy5T/D0aQK6dOHGD4ejLHp6pBhXuTJ5Vdu0AYKC5N+vWkWFVn195QYRh8PhcDIGH57kcmJj6WEKcE8QR3V8+waMHg2ULEn5CnFxQP36VATy0SMxatYMgECg6VZyODkPExOqIVS8OOUAdewoF0MwNAQOHaKQUicn4PhxzbaVw+FwcjLcCMrl/PhBfw0NATMzzbaFk/Px8gKGDyd56x07qIBjkybA7dvAo0dA69bgxg+Hk0kKFwauXaMaQc+eUa5QQgJ9V6cOycwDNBHh66u5dnI4HE5OhhtBuZzE+UB8cMrJKF+/Uh5CmTLAnj00IGvRgqrd37sHNG/Ozy8OR5WUKwdcvAjo6wOXLlEhYWkx1XnzgOrVKS9o6FDFIqscDofDUQ5uBOVyeD4QJzN8/gz0708DsgMHALEYaNsWcHEhGd8mTTTdQg4n91K/PoW8CYXA3r3AwoX0uY4OcPgw5RBduwbs3KnRZnI4HE6OhBtBuRyuDMfJCK6uFIJToQIVNpVIKDfh2TPKV6hfX9Mt5HDyBp07A9u30/v//pMbPBUqAMuX0/upUwEPD820j8PhcHIq3AjK5XBPECc9vH0L9OgBVKpEM9CMAV27Aq9eUWhO7dqabiGHk/cYMQKYP5/ejxkDnDtH7ydOBJo2BaKjgQED5HlDHA6Hw0kbbgTlcqSeIG4EcVLj1SsydqpVA06fpvyeHj3IKDp7lvIPOByO5li4EBg2jLyyjo4UkiqtLWRsDDx5AqxerelWcjgcTs6BG0G5HKkniIfDcZLj2TMKc6tZEzh/nowfR0fgwwfg5EmgShVNt5DD4QB0bW7fTtdrbCzQqRPw6RNNcG3aRMssWEATFxwOh8NJG24E5WIkErlENvcEcRLj4kKFGOvWBS5fphnl/v0BNzfg2DGgYkVNt5DD4fyNtjbg7Ewy2aGhJFLi60uhcF27AiIRXcexsZpuKYfD4WR/uBGUi/n9m+q4CIVUd4LDuX+fpK0bNgRu3KCii4MHA+7uVISxXDlNt5DD4aSGkRFJZpcpQ+HO7doBERHArl1AoUIkajJvnqZbyeFwONkfbgTlYqShcEWKkKQqJ2/CGBUzbdKEkqjv3KHzYfhwqv+zbx8VP+VwODkDc3OSxrayAt6/Jy+QsTGwezd9v3Yt8OCBRpvI4XA42R5uBOViuDx23oYx4Pp18vq0bEmDIl1dUpfy8KCZ4xIlNN1KDoeTEUqUAK5cAfLnp4LFAwZQvtCQIXTtDxxIHiIOh8PhJA83gnIxXB47b8IYhcvUqUM5A48fU9X5CRMALy9g61ZuGHM4uYFq1YAzZ8ize+IE1Qtavx6wtQV8fIApUzTdQg6Hw8m+cCMoF8M9QXkLiYTqh9SoQcpRL14ABgY0EPLyAjZupNBIDoeTe2jZEjh4kN5v2EAe3gMHSE1u717gwgVNto7D4XCyL9wIysVwT1DeQCIBTp2iWeFu3YA3byh5esYMmg1euxawttZ0KzkcjrpwdATWrKH306cDP3/KvUDDhwOBgZprG4fD4WRXuBGUi+GFUnM3YjHg5ARUqgT07EkJ0vnzA//+S8bPypWkFsXhcHI/U6cCkyfT+8GDSQSlYkUgIAAYOZLCZDkcDocjhxtBuRheKDV3kpAAHD5MA5w+fai2j4kJFUr08QGWLCH1KA6Hk7dYswZwcKB6QY6OZBjp6ABnz9I9g8PhcDhyuBGUS4mIAMLC6D03gnIHIhGwfz/V8hkw4H/t3Xl8TOf+wPHPTDaJiIjIRkjsUZFayk1VKSHoQimqirRK1d7SotXaam21pVR7X1W01+1t9WdpXUttsUQapBJbpJYo2hBLiYTJMvP8/jiduUYSgnCyfN+vV14155w553vmO2c633me8zza3D6VKsHUqVrBO2kSeHnpHaUQQi9Go3Y/0BNPQEYGjB+vjRIHMHz4/3oHCCGEkCKo1LL+z65SJa2LlCi5srO1+T/q1tWGvz1+XGvpmTFDK34mTNBagoQQwsVFa/lp1EibMHvLFm0esPR0iIrS7iEUQgghRVCpJYMilHxZWbBwofYFZtAgraubjw988AGkpMC4cVLgCiHyqlgR1q3TPv9PnIC//tKWb90K8+bpG5sQQhQXUgSVUjI8dsl1/br2RaVWLW1i09OntdHdPvlEK37GjAF3d72jFEIUZwEBsH691kX24sX/LR83TruPUAghyjopgkopaQkqeTIz4aOPtJngR46EP/6AatVg/nzt19yRI8HNTe8ohRAlRf368NNP2mTJVllZ2j2FOTn6xSWEEMWBFEGllLQElRwZGTB7tlb8jB6t9eOvUQM+/xyOHYOhQ+2/xAghRGE9+ih89502aIJVfLw2iqQQQpRlUgSVUtISVPylp8P06RAUBGPHahMa1qypzfJ+9Kg2t4eLi95RCiFKumee0e4vvNG0abB7tz7xCCFEceCodwDi/pCWoOLr8mWYO1e7x8c6jHmdOtooby+8AI5yVQohitigQVoX2ylTtMdmM/TtC/v2STdbIUTZJC1BpVBODvz5p/ZvaQkqPi5ehHff1XIyaZJWAIWEwLJlkJSk9dOXAkgIcb9MmgSvvPK/x7/9prVCCyFEWSRfuUqhM2e0uSBcXLQhlYW+zp/XBjyYP1+7/wegYUOtIOreHRwc9I1PCFE2GAxat7izZ2HNGm3Z/Plad7n27fWN7X4zm82YzWa9wyiTcnJycHR0xGQySQ50VFry4OTkhEMRfXGSIqgUsnaFCwy0vxlWPFjnzsGHH8Jnn8G1a9qyhx/Wip+uXSU3QogHz9ER/vMfaNcO4uK0ZS+9BAcOaJNrlzZKKSpUqMCJEycwGAx6h1MmKaXw8/Pj9OnTkgMdlaY8eHp64ufnd8/nIUVQKSSDIujrzz+10d6++AJMJm1Zs2bw3nvw1FPar7FCCKGX8uW1lqCWLbUucX/8AcOHw7/+pXdkRS8tLY1KlSpRpUoV3N3dS/yXv5LIYrGQkZGBu7s7Rvn1TzelIQ9KKa5du0ZaWhoA/v7+97Q/KYJKIRkUQR+nT8OsWfDll9pcHAAtWsDEidCxoxQ/Qojiw9tbm0z10Ue17nHLlkGXLtCjh96RFR2z2Ux6ejre3t5Urly5xH7xK+ksFgvZ2dmUK1dOcqCj0pIHV1dXQPuBw8fH5566xpXcV0EUSFqCHqyTJ2HwYKhVCxYs0Aqgxx6Dn3+G2Fjo1EkKICFE8RMcDGvXQoUK2uPBgyE1Vd+YilLO3zPCOjs76xyJEKIouf09pGXOPc76LEVQKSQtQQ/G8ePaSEt16mhd33JyoE0b2LIFtm/XbjSW4kcIUZw1bgwrVoCTE1y6pH2mKaV3VEVLusAJUboU1TUtRVApJC1B99dvv0H//lCvnjaxaW6uVvBs3w5bt8ITT0jxI4QoOSIiYMkS7d9r12rd5IQQorTTtQhauHAhjRo1wsPDAw8PD8LDw1m3bp2eIZV4SklL0P1y+DD06aPN7fP119pkg506wa5dWte3Vq30jlAIIe7OCy9ow2c3bKiNLCrErRgMBlatWqV3GELcE12LoGrVqjFz5kzi4+PZu3cvbdu2pUuXLhw6dEjPsEq0Cxfg+nXt3/I/sqJx4AD06qV9Ofj3v7U5mJ5+Gnbv1n41DQ/XO0IhhLh3gwdrn3cNG+odibCKjY3FwcGBJ5988o6fGxQUxCeffFL0QRXS2bNnGTlyJHXr1sXPzw9/f39atmzJwoULuWadN+IWcU6aNImHH364wMdC3CtdR4d7+umn7R5PmzaNhQsX8ssvv/DQQw/pFFXJZm0F8vfXJksVdy8hAaZO1frLWz37rDbPT+PGuoUlhBCijFi0aBHDhw9n0aJF/PnnnwQEBOgdUqGcOHGCli1b4unpyfvvv09wcDCVK1fm0KFD/POf/6Rq1ao888wzeocpyrhiM0S22Wxm+fLlZGZmEl7AT+tZWVlkWcceBtLT0wFtdIh7HSHiXlmPr3ccx48bAEcCAy3k5JTcGYHvVlHkIT7ewLRpRtas0RpKDQZF9+6K8ePNhIZaj3PPoZZaxeVaKMskB8WD5EFfOTk5qL9HeVBKYbFYUOp/k1c/aG5ud3a/aEZGBt999x27d+8mNTWVxYsXM378eLttfvrpJ95//30OHDiAu7s7jz32GCtWrKBt27b8/vvvvP7667z++uuA9j1r8uTJrF69ml9//dW2j7lz5zJ37lxOnDgBwJ49e3jnnXdISEggJyeHhx9+mDlz5tCkSRO7Y1ssFiwWS76xv/baazg6OrJ7927c3Ny4evUqFSpUoGbNmjz99NO2fFjd/Ni6zHqc/B6LO3PztVCSadeyIicnJ88Q2Xfyeat7EXTgwAHCw8MxmUy4u7uzcuVKGjRokO+2M2bMYPLkyXmW//zzz7bh8vS2ceNGXY+/YUNNIBQnp1TWrt2rayx6ups8JCdX4rvv6vHrr74AGI2Kxx47Q48eRwkMvMrp09pcQKJw9L4WhOSguJA86MPR0RE/Pz8Arl69CkBmJlSr5qlLPGfOXKZ8+cJv/69//Ys6derg7+/Ps88+y9tvv82QIUNsI2Nt2LCBPn36MHr0aObPn092djYbN24kPT2dxYsX89hjjxEVFUW/fv0A7YfjrKws2/xJViaTCYvFYlt27tw5evTowfTp01FKsWDBAp588kn27t1LBet46sD169ft9mN16dIlNm7cyLvvvovZbLa99tb/3sxisWAymfLs6+ZY84td3LmC8lCSZGdnc/36dbZv305ubq7dumt38CuH7kVQvXr1SEhI4MqVK/zwww/079+fbdu25VsIjR8/njfeeMP2OD09ncDAQDp06ICHh8eDDDuPnJwcNm7cSPv27XFyctItji1btNaLFi386Ny5s25x6OVu8hATo7X8bNqkvXYODorevRVjx5qpV88P8LuPEZc+xeVaKMskB8WD5EFfJpOJU3/3Ea9QoQIGg4F7mFfxnnl4eNxREfTtt9/Sr18/PDw86NatG8OHD2ffvn20adMG0FpwevXqxYwZM2zPadmype1YTk5OeHt7U6dOHdt6FxcXHBwc7L4zWSfPtC576qmn7OL46quv8PLyYt++fXbrXF1d8/3ulZSUhFLKNvCVUoqrV69Su3ZtTCYTAEOGDGHmzJkAGI1GJk2axLRp0+z2k52dTYMGDWzHyC92UXjWPFivhZLMZDLh6urK448/Trly5ezW3UmRrHsR5OzsTO3atQFo2rQpe/bsYe7cuXzxxRd5tnVxccElnxtdnJycis3/YPSOxdpSERzsgJOTjp/2OrtdHpSCbdtgyhRtWGsAR0fo1w/GjzdQu7YBGUH+3uh9LQjJQXEhedCH2Wy2fdkzGAwYjUbc3SEjQ5943NyMhe4Ol5yczO7du1m5ciVGoxFnZ2d69erF4sWLadu2LQAJCQkMHDgQo7Hg/1dZz/vGx8Atl507d44JEyYQHR1NWloaZrOZa9eucebMGbvnGY3GfI9tXWZdb+169csvvwDQp08fsrOz7Z775ptvEhUVZbefefPmsX37dtt2+cUuCs+ah5vfEyWR0WjEYDDk+9l6J5+1uhdBN7NYLHb3/Yg7I8Nj35pSsHmzVvzs2KEtc3KCl1+GceMgKEjX8IQQQtxHBgN31Bqjl0WLFpGbm2s3EIJSChcXF+bPn0/FihVxdXW94/0ajUbbvSFWN99D0b9/fy5evMjcuXOpUaMGLi4uhIeHk52dXahj1K5dG4PBQHJyst3ymjVrYjQa843b29vb9oO4lZeXV6GOJ8Td0rUUHD9+PNu3b+fkyZMcOHCA8ePHEx0dTZ8+ffQMq0STiVLzpxSsWwePPqpNbLpjBzg7w9ChcPw4fP65FEBCCCH0l5uby9dff82cOXNISEiw/SUmJhIQEMC3334LQKNGjdi8eXOB+3F2dsZsth8gqUqVKpw9e9auEEpISLDbJiYmhhEjRtC5c2ceeughXFxcuHDhQqHjr1y5Mu3bt2f+/PlkZmYW+nlCPGi6tgSlpaXRr18/UlNTqVixIo0aNWLDhg20b99ez7BKrGvXtHmCQFqCrJSCNWu0lp+9f48TUa4cvPoqvPkmVK2qb3xCCCHEjdasWcNff/3FgAEDqFixot267t27s2jRIgYPHszEiRNp164dtWrV4vnnnyc3N5e1a9cyduxYQJt/Z/v27Tz//PO4uLjg7e1NmzZtOH/+PLNnz+a5555j/fr1rFu3zu4+mzp16vDNN9/QrFkz0tPTefPNN++41emzzz6jZcuWNGvWjPfee4+aNWvi4eFBfHw8R44coWnTpvf+Qglxj3RtCVq0aBEnT54kKyuLtLQ0Nm3aJAXQPbB2hatQATw9dQ1FdxYLrFwJTZvCM89oBZCbG4weDSkp8MknUgAJIYQofhYtWkRERESeAgi0Imjv3r3s37+fNm3asHz5cn788Ucefvhh2rZty+7du23bTpkyhZMnT1KrVi2qVKkCQEhICJ999hkLFiwgLCyM3bt3M2bMmDzH/+uvv2jSpAl9+/ZlxIgR+Pj43NE51KpVi3379hEREcE777xDq1ataN68OZ9++iljxoxh6tSpd/HKCFG0DOrmzqElSHp6OhUrVuTKlSu6jxaSk5PD2rVr6dy5s243wP78M0RGarN9HzigSwi6M5lyePfdBNavb8bBg9pNlO7uMGwYvPEG/P3/AXEfFYdroayTHBQPkgd9mUwmTpw4gbe3N97e3iX+ZvCSyjr8toeHh+RAR6UpDyaTiZSUFIKDg/MdHa6wtUGxGxhB3D3r/UBlsSuc2QzffQdTpzpy5MgjAHh4wIgRMGoUVK6sb3xCCCGEEKL4kCKoFCmLgyLk5sK//w3TpsFvvwEYKF8+mzfecOD11x2oVEnvCIUQQgghRHEjRVApUpaGx87JgW++0YqfEye0ZV5eMHKkmdq1N9KjR4cyPU+SEEIIIYQomBRBpUhZaAnKyoKlS2H69P+db5Uq2oAHQ4ZAuXIW1q7N1TdIIYQQQghRrEkRVIqU5pYgkwkWLYKZM+HMGW2Zry+89ZY23LV18rub5nwTQgghhBAiDymCSgmz+X/FQWlqCbp+Hf75T5g1C1JTtWUBATB2LAwcCHcxYbYQQgghhCjjpAgqJVJTtUECHB3B31/vaO5dZiZ8/jl88AGcO6ctCwyEcePg5Ze1CU+FEEIIIYS4G1IElRLW+2OqVQOHEjwewNWrsGABzJkDFy5oy4KC4O23oX9/cHbWNTwhhBBCCFEKSBFUSpT0+4GuXIFPP4WPP4ZLl7RltWrBO+/Aiy+CzDMohBBCCCGKSsmeMlbYlNSR4f76CyZN0lp73n1XK4Dq1oWvv4YjR+Cll6QAEkIIIW5kMBhYtWqV3mGUOZMmTeLhhx/WO4wiFRQUxCeffGJ7XBTvrZLy/pQiqJQoaUXQxYswYYIW7+TJcPkyhIRoE58ePgx9+2r3NwkhhBBlTVRUFF27di1wfWpqKp06dXpwAd0hg8Fg+/P09KRt27asXr1a77Du2ZgxY9i8efN9P05QUJDt9StfvjxNmjRh+fLl9/24cGfvrYKKwuL+/rSSIqiUKCnd4dLStMENgoK0iU6vXoXQUPj+ezh4EHr3Ltn3NAkhhBD3m5+fHy4uLrrGoJQiN7fgefkWL15Mamoqu3fvpkWLFvTs2ZMDBw7c15iys7Pv6/7d3d2pXLnyfT2G1ZQpU0hNTWXfvn088sgj9OrVi127duW7bVGed1G8t4rD+7MwpAgqJYp7S9DZs9qEpsHB2nDXGRnQuDGsXAkJCdCjBxjl3SiEEOJByMws+M9kKvy2168XbtsidmN3o5MnT2IwGFixYgVPPPEEbm5uhIWFERsba/ecnTt30qpVK1xdXQkMDGTEiBFk3hDbN998Q7NmzahQoQJ+fn688MILpKWl2dZHR0djMBhYt24dTZs2xcXFhZ07dxYYo6enJ35+ftStW5e3336b3Nxctm7dalt/+vRpevbsiaenJ15eXnTp0oWTJ0/a1ufm5jJixAg8PT2pXLkyY8eOpX///nYtZG3atGHYsGGMGjUKb29vIiMjATh48CCdOnXC3d0dX19f+vbtywXraEvADz/8QGhoKK6urlSuXJmIiAjbaxEdHU3z5s0pX748np6etGzZkt///pJ1c8uHxWJhypQpVKtWDRcXFx5++GHWr19vW1/Y3OTHmoe6deuyYMECXF1d+emnnwCtpWjq1Kn069cPDw8PBg0aVKgcnz9/nmeeeQZXV1eCg4NZtmxZnuPe3JXtzJkz9O7dGy8vL8qXL0+zZs2Ii4tjyZIlTJ48mcTERFur1ZIlS/Ldx4EDB2jbtq3t9R40aBAZGRm29daWzw8//BB/f38qV67M0KFDybnPkz/K185SQKn/FUHFrSXojz9g5Eit+PnoI7h2DR55BH76CeLjoWtXKX6EEEI8YO7uBf91726/rY9Pwdve3OUnKCj/7R6Ad955hzFjxpCQkEDdunXp3bu3raXm+PHjdOzYke7du7N//36+++47du7cybBhw2zPz8nJYerUqSQmJrJq1SpOnjxJVFRUnuOMGzeOmTNnkpSURKNGjW4bV25uLv/6178AcP57iNecnBwiIyOpUKECO3bsICYmBnd3dzp27Ghr1Zg1axbLli1j8eLFxMTEkJ6enu99JkuXLsXZ2ZmYmBg+//xzLl++TNu2bWncuDF79+5l/fr1nDt3jp49ewJaV63evXvz8ssvk5SURHR0NN26dbO1bHXt2pXWrVuzf/9+YmNjGTRoEAaDId9zmzt3LnPmzOHDDz9k//79REZG8swzz3D06NFC56YwHB0dcXJysmvx+fDDDwkLC2Pfvn28++67hcrxkCFDOHPmDFu3buWHH37gs88+syt0b5aRkUHr1q35448/+PHHH0lMTOStt97CYrHQq1cvRo8ezUMPPURqaiqpqan06tUrzz4yMzOJjIykUqVK7Nmzh+XLl7Np0ya7uAC2bt3K8ePH2bp1K0uXLmXJkiW2ouq+USXYlStXFKCuXLmidygqOztbrVq1SmVnZz/wY1+6pJRWCimVmfnAD5+v339XasgQpZyd/xfbP/6h1Lp1Slks9++4euZBaCQH+pMcFA+SB31dv35dHTp0SJ07d06ZzWb7ldb/MeX317mz/bZubgVv27q1/bbe3vlvd4f69++vunTpUuB6QK1cuVIppVRKSooC1Jdffmlbf+jQIQWopKQkpZRSAwYMUIMGDbLbx44dO5TRaFTXr1/P9xh79uxRgLp69apSSqmtW7cqQK1ateq28QOqXLlyqnz58spoNCpABQUFqYsXLyqllPrmm29UvXr1lOWGLwRZWVnK1dVVbdiwQSmllK+vr/rggw9s63Nzc1X16tXtXpfWrVurxo0b2x176tSpqkOHDnbLTp8+rQCVnJys4uPjFaBOnjyZJ+6LFy8qQEVHR+d7XhMnTlRhYWG2xwEBAWratGl22zzyyCNqyJAhSqnC5SY/NWrUUB9//LFSSntdpk+frgC1Zs0a2/quXbvaPed2OU5KSlKA+uWXX2zrrcusx1LK/r31xRdfqAoVKtjydrvXI799/POf/1SVKlVSGRkZtvX//e9/ldFoVGfPnlVKae/3GjVqqNzcXNs2PXr0UL169cr3uNevX1eHDx/O9717J7WB3HpeCljvB/L2Bjc3fWNJSYGZM2HxYrC2YrZqBe+9B+3aQQE/pgghhBAPzg1dcfK4+cbUW/xSnqcrww3duR60G1tl/P+eNT0tLY369euTmJjI/v377bo/KaWwWCykpKQQEhJCfHw8kyZNIjExkb/++guLxQLAqVOnaNCgge15zZo1K1Q8H3/8MRERERw7dozXX3+defPm4eXlBUBiYiLHjh2jQoUKds8xmUwcP36cK1eucO7cOZo3b25b5+DgQNOmTW1xWTVt2tTucWJiIlu3bsU9nxa448eP06FDB9q1a0doaCiRkZF06NCB5557jkqVKuHl5UVUVBSRkZG0b9+eiIgIevbsaXs9b5Sens6ff/5Jy5Yt7Za3bNmSxMREu2W3yk1Bxo4dy4QJEzCZTLi7uzNz5kyefPJJ2/qb83C7HB85cgRHR0e716t+/fp4enoWGENCQgKNGze25e1uJCUlERYWRvny5W3LWrZsicViITk5GV9fXwAeeughHG649vz9/e/7PWRSBJUCxeF+oGPHYPp0bWhrs1lb1ratVvy0bq1fXEIIIUQeN3wh023bIuZ0w3wS1u5b1oIhIyODV199lREjRuR5XvXq1W1dliIjI1m2bBlVqlTh1KlTREZG5rnpvnwhz9HPz4/atWtTs2ZN5s+fT69evTh8+DA+Pj5kZGTQtGnTfO9JqVKlSqHPOb94MjIyePrpp5k1a1aebf39/XFwcGDjxo3s2rWLn3/+mU8//ZR33nmHuLg4goODWbx4MSNGjGD9+vV89913TJgwgY0bN/KPf/zjjuK60a1yU5A333yTqKgo231NN3fJy++8b5XjI0eO3HHcrq6ud/ycu+V003woBoPhtq/RvZK7MUoBPUeGS06Gfv2gXj2t9cdshg4dYMcO2LxZCiAhhBBCb02aNOHw4cPUrl07z5+zszNHjhzh4sWLzJw5k1atWlG/fv1b3ityp5o2bUqTJk2YNm2aLZ6jR4/i4+OTJ56KFStSsWJFfH192bNnj20fZrOZX3/9tVDneujQIYKCgvLs21o4GAwGWrZsyeTJk9m3bx/Ozs6sXLnSto/GjRszfvx4du3aRcOGDfn3v/+d5zgeHh4EBAQQExNjtzwmJsau5exueXt7U7t2bfz8/Aq8J+lGt8tx/fr1yc3NJT4+3vac5ORkLl++XOA+GzVqREJCApess9jfxNnZGbP1l+8ChISEkJiYaDdAQ0xMDEajkXr16t32vO4nKYJKAT1agg4fhhde0Ob2+eYbsFigc2eIjYUNG+Cxxx5cLEIIIURpc+XKFRISEuz+Tp8+fVf7Gjt2LLt27WLYsGEkJCRw9OhRVq9ebbs5vXr16jg7O/Ppp59y4sQJfvzxR6ZOnVqUp8PIkSP54osv+OOPP+jTpw/e3t506dKFHTt2kJKSQnR0NCNGjODMmTMADB8+nBkzZrB69WqSk5MZOXIkf/31120LgqFDh3Lp0iV69+7Nnj17OH78OBs2bOCll17CbDYTFxfH9OnT2bt3L6dOnWLFihWcP3+ekJAQUlJSGD9+PLGxsfz+++/8/PPPHD16lJCQkHyP9eabbzJr1iy+++47kpOTGTduHAkJCYwcObJIX7vCuF2O69WrR7t27XjttdeIi4sjPj6eV1555ZatPb1798bPz4+uXbsSExPDiRMn+L//+z/b6HZBQUGkpKSQkJDAhQsXyMrKyrOPPn36UK5cOfr378/BgwfZunUrw4cPp2/fvraucHqRIqgUeJBF0P790LMnNGwI336r3fHZpQvs2QP//S/cQ2uxEEIIIf4WHR1N48aN7f4mT558V/tq1KgR27Zt47fffqNVq1Y0btyY9957j4CAAEDrgrZkyRKWL19OgwYNmDlzJh9++GFRng4dO3YkODiYadOm4ebmxvbt26levTrdunUjJCSEAQMGYDKZ8PDwALQv9b1796Zfv36Eh4fj7u5OZGQk5cqVu+VxrK0zZrOZDh06EBoayqhRo/D09MRoNOLh4cH27dvp3LkzdevWZcKECcyZM4dOnTrh5ubGkSNH6N69O3Xr1mXQoEEMHTqUV199Nd9jjRgxgjfeeIPRo0cTGhrK+vXr+fHHH6lTp06RvnaFcbscAyxYsAB/f39at25Nt27dGDRoED4+PgXu09nZmZ9//hkfHx86d+5MaGgoM2fOtN270717dzp27MgTTzxBlSpV+Pbbb/Psw83NjQ0bNnDp0iUeeeQRnnvuOdq1a8f8+fOL/kW4Q4a/R3EokdLT06lYsSJXrlyxXTR6ycnJYe3atXTu3DlPv8b77R//gLg4+L//g27d7s8xfv0Vpk6FG0en7N4dJkyAfCYL1o2eeRAayYH+JAfFg+RBXyaTiRMnTuDt7Y23tzdGmY9BFxaLhfT0dDw8PO4pBxaLhZCQEHr27FnkrVRlQVHloTgwmUykpKQQHBycpyi+k9pABkYoBe5nS9Du3Vrxs2aN9thg0FqC3nkHQkOL/nhCCCGEENbuaK1btyYrK4v58+eTkpLCCy+8oHdoopSQIqiEy8qCs2e1fxflwAixsTBlClgnPjYaoXdvrfgpoGusEEIIIUSRMBqNLFmyhDFjxqCUomHDhmzatKnA+3OEuFNSBJVw1nskXV21eYLu1Y4dWvGzaZP22MEBXnwR3n4b6ta99/0LIYQQQtxOYGBgnpHXhChKUgSVcDcOj323E5EqBdHRWvETHa0tc3SEqCgYPx5q1iyCQIUQQgghhCgmpAgq4e7lfiClYONGrfix/tji5AQDBsC4cfpOviqEEEIIIcT9IkVQCXc3E6UqBevWacVPXJy2zMUFBg6Et96CwMCij1MIIYQQQojiQoqgEu5OWoKUgp9+0oof64TBrq7w6qvw5ptww1DyQgghhBBClFpSBJVw1pagWxVBFgusXKkNdZ2YqC1zc4OhQ2H0aNB5wl4hhBBCCCEeKCmCSjhrS1B+3eHMZvjhB634OXRIW+buDsOHw+uvQ5UqDy5OIYQQQgghiouSPWVsGWex5N8SlJsLy5ZBw4bw/PNaAeThAe++qxVN06dLASSEEEKI/BkMBlatWqV3GKIAUVFRdO3a1fa4TZs2jBo16p72WRT7KGmkCCrB0tIgO1ubyLRqVa34WboUGjTQ5vY5cgQ8PWHyZK34mTIFvLz0jloIIYQQtxIVFYXBYMBgMODk5ERwcDBvvfUWJpNJ79DuqxvP+8a/Y8eO6RrTjQXHrbazxuvs7Ezt2rWZMmUKubm59z3GFStWMHXq1EJtGx0djcFg4PLly3e9j9JCusOVYNaucFWqwNdfay08J05oyypX1u73GTpUawUSQgghRMnRsWNHFi9eTE5ODvHx8fTv3x+DwcCsWbP0Du2+sp73jarcZfeV7OxsnJ2diyKsQrHGnpWVxdq1axk6dChOTk6MHz/+vsbmVQS/cBfFPkoaaQkqwaxd4c6dg1de0QqgKlVg9mw4eVKb6FQKICGEEMJeZnZmgX+mXFOht72ec71Q294NFxcX/Pz8CAwMpGvXrkRERLBx40bb+osXL9K7d2+qVq2Km5sboaGhfPvtt3b7aNOmDSNGjOCtt97Cy8sLPz8/Jk2aZLfN0aNHefzxxylXrhwNGjSwO4bVgQMHaNu2La6urlSuXJlBgwaRkZFhW29tLZk+fTq+vr54enraWkHeeustgoODqV69ep7i5lbnfeOfg4MDANu2baN58+a4uLjg7+/PuHHj7Fpa2rRpw7Bhwxg1ahTe3t5ERkYCcPDgQTp16oS7uzu+vr707duXCxcu2J73ww8/EBoaaju/iIgIMjMzmTRpEkuXLmX16tW2Vp5o66zyt4i9Ro0avPbaa0RERPDjjz/avUbTpk0jICCAevXqAXD69Gl69uyJp6cnXl5edOnShZMnT9r2aTabeeONN/D09KRy5cq89dZbKKXsjntzV7asrCzGjh1LYGAgLi4u1K5dm0WLFnHq1CnatWsHQKVKlTAYDERFReW7j7/++ot+/fpRqVIl3Nzc6NSpE0ePHrWtX7JkCZ6enmzYsIGQkBDc3d3p2LEjqampt8lw8SEtQSXYuXP/+7efnzbHz6BBUL68fjEJIYQQxZ37DPcC13Wu05n/vvBf22OfD324lnMt321b12hNdFS07XHQ3CAuXLuQZzs1UeVZdicOHjzIrl27qHHDDcAmk4mmTZsyduxYPDw8+O9//0vfvn2pVasWzZs3t223dOlS3njjDeLi4oiNjSUqKoqWLVvSvn17LBYL3bp1w9fXl7i4OK5cuZLnvpDMzEwiIyMJDw9nz549pKWl8corrzBs2DCWLFli227Lli1Uq1aN7du3ExMTw4ABA9i1axetWrVi06ZNrF27lldffZX27dtTrVq1O34N/vjjDzp37kxUVBRff/01R44cYeDAgZQrV86usFu6dCmvvfYaMX/PAn/58mXatm3LK6+8wscff8z169cZO3YsPXv2ZMuWLaSmptK7d29mz57Ns88+y9WrV9mxYwdKKcaMGUNSUhLp6em2Au5OWkxcXV25ePGi7fHmzZvx8PCwFZo5OTm213bHjh04Ojry/vvv07FjR/bv34+zszNz5sxhyZIlfPXVV4SEhDBnzhxWrlxJ27ZtCzxuv379iI2NZd68eYSFhZGSkkJaWhpVq1Zl+fLl9OjRg+TkZDw8PHB1dc13H1FRURw9epQff/wRDw8Pxo4dS+fOnTl8+DBOTk4AXLt2jQ8//JBvvvkGo9HIiy++yJgxY1i2bFmhXyM9SRFUgnXuDNHR8Pjj2kSnBbyPhRBCCFHCrFmzBnd3d3Jzc8nKysJoNDJ//nzb+qpVqzJmzBjb4+HDh7Nhwwa+//57uyKoUaNGTJw4EYA6deowf/58Nm/eTPv27dm0aRNHjhxhw4YNBPw9WeD06dPp1KmT7fn//ve/MZlMfP3115T/+1fW+fPn8/TTTzNr1ix8/55nw8vLi3nz5mE0GqlXrx6zZ8/m2rVrjB8/nvT0dMaNG8esWbPYuXMnzz///G3P26pTp04sX76czz77jMDAQObPn4/BYKB+/fr8+eefjB07lvfeew+j0Wg7x9mzZ9ue//7779O4cWOmT59uW/bVV18RGBjIb7/9RkZGBrm5uXTr1s1WZIaGhtq2dXV1JSsrCz8/v9vmzEopxebNm9mwYQPDhw+3LS9fvjxffvmlrRvcv/71LywWC19++SUGgwGAxYsX4+npSXR0NB06dOCTTz5h/PjxdOvWDYDPP/+cDRs2FHjs3377je+//56NGzcSEREBQM2aNbFYLKSnp9uKOB8fHzw9PfPdh7X4iYmJ4dFHHwVg2bJlBAYGsmrVKnr06AFoRdznn39OrVq1ABg2bBhTpkwp9OukNymCSrCaNbUhsIUQQghReBnjMwpc52B0sHucNiatwG2NBvu7Ck6OPHlPcd3oiSeeYOHChWRmZvLxxx/j6OhI9+7dbevNZjPTp0/n+++/548//iA7O5usrCzc3Nzs9tOoUSO7x/7+/qSlaeeUlJREYGCgrQACCA8Pt9s+KSmJsLAwWwEE0LJlSywWC8nJybYi6KGHHrIVIgC+vr40bNjQ9tjBwYHKlSvbjn2787ayHjcpKYnw8HBbsWCNIyMjgzNnzlD977lCmjZtare/xMREtm7daldYWR0/fpwOHTrQrl07QkNDiYyMpEOHDjz33HNUqlTplnHmx1rA5eTkYLFYeOGFF+xaqUJDQ+3uA0pMTOTYsWNUqFDBbj8mk4njx49z5coVUlNTadGihW2do6MjzZo1y9MlziohIQEHBwdat259x/FbJSUl4ejoaHfcypUrU69ePZKSkmzL3NzcbAUQ2L+3SgIpgoQQQghRppR3Lny/8fu17W33Vb48tWvXBrSWi7CwMBYtWsSAAQMA+OCDD5g7dy6ffPIJoaGhlC9fnlGjRpGdnW23H2vXJSuDwYDFYimyOG91nLs59o3nfTfK33RPQEZGhq3V6mb+/v44ODiwceNGdu3axc8//8ynn37KO++8Q1xcHMHBwXd0bGsB5+zsTEBAAI6O9l+z84utadOm+XYfu9vBIArq3nY/5Jffgoqz4kgGRhBCCCGEKMaMRiNvv/02EyZM4Pp1bTCGmJgYunTpwosvvkhYWBg1a9bkt99+u6P9hoSEcPr0abub2X/55Zc82yQmJpKZ+b8BHmJiYmzd3h6UkJAQYmNj7b5kx8TEUKFChVveY9SkSRMOHTpEUFAQtWvXtvuzFiUGg4GWLVsyefJk9u3bh7OzMytXrgTA2dkZs9lcqBitBVz16tXzFEAFxXb06FF8fHzyxFaxYkUqVqyIv78/cXFxtufk5uYSHx9f4D5DQ0OxWCxs27Yt3/XWlqhbnVNISAi5ubl2x7148SLJyck0aNDgtudVUkgRJIQQQghRzPXo0QMHBwcWLFgAaPe+WFswkpKSePXVVzl344hJhRAREUHdunXp378/iYmJ7Nixg3feecdumz59+lCuXDn69+/PwYMH2bp1K8OHD6dv3762rnAPwpAhQzh9+jTDhw/nyJEjrF69mokTJ/LGG2/YdcO72dChQ7l06RK9e/dmz549HD9+nA0bNvDSSy9hNpuJi4tj+vTp7N27l1OnTrFixQrOnz9PSEgIAEFBQezfv5/k5GQuXLhATk5OkZ1Tnz598Pb2pkuXLuzYsYOUlBSio6MZMWIEZ86cAWDkyJHMnDmTVatWceTIEYYMGZJnjp8bBQUF0b9/f15++WVWrVpl2+f3338PQI0aNTAYDKxZs4bz58/bjfJnVadOHbp06cLAgQPZuXMniYmJvPjii1StWpUuXboU2fnrTYogIYQQQohiztHRkWHDhjF79mwyMzOZMGECTZo0ITIykjZt2uDn51eoST1vZDQaWblyJdevX6d58+a88sorTJs2zW4bNzc3NmzYwKVLl3jkkUd47rnnaNeund0gDQ9C1apVWbt2Lbt37yYsLIzBgwczYMAAJkyYcMvnBQQEEBMTg9lspkOHDoSGhjJq1Cg8PT0xGo14eHiwfft2OnfuTN26dZkwYQJz5syxDQ4xcOBA6tWrR7NmzahSpYpt1Lmi4Obmxvbt26levTrdunUjJCSEAQMGYDKZ8Ph7jpPRo0fTt29f+vfvT3h4OBUqVODZZ5+95X4XLlzIc889x5AhQ6hfvz4DBw60teRVrVqVyZMnM27cOHx9fRk2bFi++1i8eDFNmzblqaeeIjw8HKUUa9euzdMFriQzqJLUee8m6enpVKxYkStXrtjeLHrJyclh7dq1dO7cuVS9QUoayYP+JAf6kxwUD5IHfZlMJk6cOIG3tzfe3t63bC0Q9491VDIPDw/JgY5KUx5MJhMpKSkEBwdTrlw5u3V3UhuU7FdBCCGEEEIIIe6QFEFCCCGEEEKIMkWKICGEEEIIIUSZIkWQEEIIIYQQokyRIkgIIYQQpVYJHv9JCJGPorqmpQgSQgghRKljHZEvOztb50iEEEXp2rVrAPc86ubtp7MVQgghhChhHBwc8PDw4Pz585QrVw53d3cMBoPeYZU5FouF7OxsTCZTiR+auSQrDXlQSnHt2jXS0tLw9PTEwcHhnvYnRZAQQgghSiUfHx9+++03XFxcuHDhgt7hlElKKa5fv46rq6sUoToqTXnw9PTEz8/vnvcjRZAQQgghSiWDwcDVq1d59NFH9Q6lzMrJyWH79u08/vjjMmmwjkpLHpycnO65BchKiiAhhBBClGoODg4l+otfSebg4EBubi7lypWTHOhI8pBXyewUKIQQQgghhBB3SYogIYQQQgghRJkiRZAQQgghhBCiTCnR9wRZJ0tKT0/XORLthrNr166Rnp4ufS11JHnQn+RAf5KD4kHyoD/Jgf4kB8VDWcmDtSYozISqJboIunr1KgCBgYE6RyKEEEIIIYQoDq5evUrFihVvuY1BFaZUKqYsFgt//vknFSpU0H3M8/T0dAIDAzl9+jQeHh66xlKWSR70JznQn+SgeJA86E9yoD/JQfFQVvKglOLq1asEBATcdlLYEt0SZDQaqVatmt5h2PHw8CjVb66SQvKgP8mB/iQHxYPkQX+SA/1JDoqHspCH27UAWcnACEIIIYQQQogyRYogIYQQQgghRJkiRVARcXFxYeLEibi4uOgdSpkmedCf5EB/koPiQfKgP8mB/iQHxYPkIa8SPTCCEEIIIYQQQtwpaQkSQgghhBBClClSBAkhhBBCCCHKFCmChBBCCCGEEGWKFEFCCCGEEEKIMkWKoCKyYMECgoKCKFeuHC1atGD37t16h1RqTZo0CYPBYPdXv35923qTycTQoUOpXLky7u7udO/enXPnzukYccm3fft2nn76aQICAjAYDKxatcpuvVKK9957D39/f1xdXYmIiODo0aN221y6dIk+ffrg4eGBp6cnAwYMICMj4wGeRcl3uzxERUXluTY6duxot43k4d7MmDGDRx55hAoVKuDj40PXrl1JTk6226Ywn0GnTp3iySefxM3NDR8fH958801yc3Mf5KmUWIXJQZs2bfJcC4MHD7bbRnJw9xYuXEijRo1sE2+Gh4ezbt0623q5Bh6M2+VBroNbkyKoCHz33Xe88cYbTJw4kV9//ZWwsDAiIyNJS0vTO7RS66GHHiI1NdX2t3PnTtu6119/nZ9++only5ezbds2/vzzT7p166ZjtCVfZmYmYWFhLFiwIN/1s2fPZt68eXz++efExcVRvnx5IiMjMZlMtm369OnDoUOH2LhxI2vWrGH79u0MGjToQZ1CqXC7PAB07NjR7tr49ttv7dZLHu7Ntm3bGDp0KL/88gsbN24kJyeHDh06kJmZadvmdp9BZrOZJ598kuzsbHbt2sXSpUtZsmQJ7733nh6nVOIUJgcAAwcOtLsWZs+ebVsnObg31apVY+bMmcTHx7N3717atm1Lly5dOHToECDXwINyuzyAXAe3pMQ9a968uRo6dKjtsdlsVgEBAWrGjBk6RlV6TZw4UYWFheW77vLly8rJyUktX77ctiwpKUkBKjY29gFFWLoBauXKlbbHFotF+fn5qQ8++MC27PLly8rFxUV9++23SimlDh8+rAC1Z88e2zbr1q1TBoNB/fHHHw8s9tLk5jwopVT//v1Vly5dCnyO5KHopaWlKUBt27ZNKVW4z6C1a9cqo9Gozp49a9tm4cKFysPDQ2VlZT3YEygFbs6BUkq1bt1ajRw5ssDnSA6KXqVKldSXX34p14DOrHlQSq6D25GWoHuUnZ1NfHw8ERERtmVGo5GIiAhiY2N1jKx0O3r0KAEBAdSsWZM+ffpw6tQpAOLj48nJybHLR/369alevbrk4z5JSUnh7Nmzdq95xYoVadGihe01j42NxdPTk2bNmtm2iYiIwGg0EhcX98BjLs2io6Px8fGhXr16vPbaa1y8eNG2TvJQ9K5cuQKAl5cXULjPoNjYWEJDQ/H19bVtExkZSXp6ut0vuKJwbs6B1bJly/D29qZhw4aMHz+ea9eu2dZJDoqO2WzmP//5D5mZmYSHh8s1oJOb82Al10HBHPUOoKS7cOECZrPZ7g0E4Ovry5EjR3SKqnRr0aIFS5YsoV69eqSmpjJ58mRatWrFwYMHOXv2LM7Oznh6eto9x9fXl7Nnz+oTcClnfV3zuwas686ePYuPj4/dekdHR7y8vCQvRahjx45069aN4OBgjh8/zttvv02nTp2IjY3FwcFB8lDELBYLo0aNomXLljRs2BCgUJ9BZ8+ezfd6sa4ThZdfDgBeeOEFatSoQUBAAPv372fs2LEkJyezYsUKQHJQFA4cOEB4eDgmkwl3d3dWrlxJgwYNSEhIkGvgASooDyDXwe1IESRKnE6dOtn+3ahRI1q0aEGNGjX4/vvvcXV11TEyIfT1/PPP2/4dGhpKo0aNqFWrFtHR0bRr107HyEqnoUOHcvDgQbt7EsWDVVAObrzPLTQ0FH9/f9q1a8fx48epVavWgw6zVKpXrx4JCQlcuXKFH374gf79+7Nt2za9wypzCspDgwYN5Dq4DekOd4+8vb1xcHDIM+rJuXPn8PPz0ymqssXT05O6dety7Ngx/Pz8yM7O5vLly3bbSD7uH+vreqtrwM/PL89AIbm5uVy6dEnych/VrFkTb29vjh07BkgeitKwYcNYs2YNW7dupVq1arblhfkM8vPzy/d6sa4ThVNQDvLTokULALtrQXJwb5ydnalduzZNmzZlxowZhIWFMXfuXLkGHrCC8pAfuQ7sSRF0j5ydnWnatCmbN2+2LbNYLGzevNmuT6a4fzIyMjh+/Dj+/v40bdoUJycnu3wkJydz6tQpycd9EhwcjJ+fn91rnp6eTlxcnO01Dw8P5/Lly8THx9u22bJlCxaLxfahLIremTNnuHjxIv7+/oDkoSgopRg2bBgrV65ky5YtBAcH260vzGdQeHg4Bw4csCtIN27ciIeHh60biyjY7XKQn4SEBAC7a0FyULQsFgtZWVlyDejMmof8yHVwE71HZigN/vOf/ygXFxe1ZMkSdfjwYTVo0CDl6elpN9qGKDqjR49W0dHRKiUlRcXExKiIiAjl7e2t0tLSlFJKDR48WFWvXl1t2bJF7d27V4WHh6vw8HCdoy7Zrl69qvbt26f27dunAPXRRx+pffv2qd9//10ppdTMmTOVp6enWr16tdq/f7/q0qWLCg4OVtevX7fto2PHjqpx48YqLi5O7dy5U9WpU0f17t1br1MqkW6Vh6tXr6oxY8ao2NhYlZKSojZt2qSaNGmi6tSpo0wmk20fkod789prr6mKFSuq6OholZqaavu7du2abZvbfQbl5uaqhg0bqg4dOqiEhAS1fv16VaVKFTV+/Hg9TqnEuV0Ojh07pqZMmaL27t2rUlJS1OrVq1XNmjXV448/btuH5ODejBs3Tm3btk2lpKSo/fv3q3HjximDwaB+/vlnpZRcAw/KrfIg18HtSRFURD799FNVvXp15ezsrJo3b65++eUXvUMqtXr16qX8/f2Vs7Ozqlq1qurVq5c6duyYbf3169fVkCFDVKVKlZSbm5t69tlnVWpqqo4Rl3xbt25VQJ6//v37K6W0YbLfffdd5evrq1xcXFS7du1UcnKy3T4uXryoevfurdzd3ZWHh4d66aWX1NWrV3U4m5LrVnm4du2a6tChg6pSpYpycnJSNWrUUAMHDszzY4zk4d7k9/oDavHixbZtCvMZdPLkSdWpUyfl6uqqvL291ejRo1VOTs4DPpuS6XY5OHXqlHr88ceVl5eXcnFxUbVr11ZvvvmmunLlit1+JAd37+WXX1Y1atRQzs7OqkqVKqpdu3a2AkgpuQYelFvlQa6D2zMopdSDa3cSQgghhBBCCH3JPUFCCCGEEEKIMkWKICGEEEIIIUSZIkWQEEIIIYQQokyRIkgIIYQQQghRpkgRJIQQQgghhChTpAgSQgghhBBClClSBAkhhBBCCCHKFCmChBBCCCGEEGWKFEFCCCGEEEKIMkWKICGEEMVCVFQUBoMBg8GAk5MTvr6+tG/fnq+++gqLxVLo/SxZsgRPT8/7F6gQQogST4ogIYQQxUbHjh1JTU3l5MmTrFu3jieeeIKRI0fy1FNPkZubq3d4QgghSgkpgoQQQhQbLi4u+Pn5UbVqVZo0acLbb7/N6tWrWbduHUuWLAHgo48+IjQ0lPLlyxMYGMiQIUPIyMgAIDo6mpdeeokrV67YWpUmTZoEQFZWFmPGjKFq1aqUL1+eFi1aEB0drc+JCiGE0JUUQUIIIYq1tm3bEhYWxooVKwAwGo3MmzePQ4cOsXTpUrZs2cJbb70FwKOPPsonn3yCh4cHqamppKamMmbMGACGDRtGbGws//nPf9i/fz89evSgY8eOHD16VLdzE0IIoQ+DUkrpHYQQQggRFRXF5cuXWbVqVZ51zz//PPv37+fw4cN51v3www8MHjyYCxcuANo9QaNGjeLy5cu2bU6dOkXNmjU5deoUAQEBtuURERE0b96c6dOnF/n5CCGEKL4c9Q5ACCGEuB2lFAaDAYBNmzYxY8YMjhw5Qnp6Orm5uZhMJq5du4abm1u+zz9w4ABms5m6devaLc/KyqJy5cr3PX4hhBDFixRBQgghir2kpCSCg4M5efIkTz31FK+99hrTpk3Dy8uLnTt3MmDAALKzswssgjIyMnBwcCA+Ph4HBwe7de7u7g/iFIQQQhQjUgQJIYQo1rZs2cKBAwd4/fXXiY+Px2KxMGfOHIxG7bbW77//3m57Z2dnzGaz3bLGjRtjNptJS0ujVatWDyx2IYQQxZMUQUIIIYqNrKwszp49i9ls5ty5c6xfv54ZM2bw1FNP0a9fPw4ePEhOTg6ffvopTz/9NDExMXz++ed2+wgKCiIjI4PNmzcTFhaGm5sbdevWpU+fPvTr1485c+bQuHFjzp8/z+bNm2nUqBFPPvmkTmcshBBCDzI6nBBCiGJj/fr1+Pv7ExQURMeOHdm6dSvz5s1j9erVODg4EBYWxkcffcSsWbNo2LAhy5YtY8aMGXb7ePTRRxk8eDC9evWiSpUqzJ49G4DFixfTr18/Ro8eTb169ejatSt79uyhevXqepyqEEIIHcnocEIIIYQQQogyRVqChBBCCCGEEGWKFEFCCCGEEEKIMkWKICGEEEIIIUSZIkWQEEIIIYQQokyRIkgIIYQQQghRpkgRJIQQQgghhChTpAgSQgghhBBClClSBAkhhBBCCCHKFCmChBBCCCGEEGWKFEFCCCGEEEKIMkWKICGEEEIIIUSZ8v/PlL0Vp/I0YQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(y_test.index, y_test, label='Actual GHI', color='blue')\n",
        "plt.plot(y_test.index, y_pred_lr, label='Linear Regression Prediction', color='red', linestyle='--')\n",
        "plt.plot(y_test.index, y_pred_rf, label='Random Forest Prediction', color='green', linestyle='--')\n",
        "plt.title('GHI: Actual vs Predicted')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('GHI (kWh/m²/day)')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X6dO07WzyXZq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KToXakKmyXfQ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D0BiYS5VyXkW"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 10. Dash Setup for Interactive Dashboard\n",
        "\n",
        "This section sets up a Dash web application to provide an interactive dashboard for exploring the solar radiation data.\n",
        "\n",
        "- **App Layout**:\n",
        "    - The dashboard layout includes several sections: project overview, data inspection, and interactive visualization controls.\n",
        "    - Users can select a range of months using input fields, and upon clicking the \"Generate Visualizations\" button, various visualizations are generated dynamically.\n",
        "\n",
        "- **Visualizations**:\n",
        "    - Line Plot: Displays GHI values over time.\n",
        "    - Heatmap: Displays a heatmap of GHI values by day and month.\n",
        "    - Histogram: Shows the distribution of GHI values.\n",
        "    - Box Plot: Shows the variation of GHI by month.\n",
        "    - Predictions Plot: Displays the predicted GHI values.\n",
        "\n",
        "- **Download Button**:\n",
        "    - A button is provided to download the predicted GHI values in CSV format.\n",
        "\n",
        "The interactive dashboard provides an intuitive and user-friendly way to explore the solar data and the results of the machine learning models."
      ],
      "metadata": {
        "id": "WaokH81S4wVD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "s_DyslxQyXro"
      },
      "outputs": [],
      "source": [
        "# --- Dash Setup ---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "T0daU2GuyXxO"
      },
      "outputs": [],
      "source": [
        "app = dash.Dash(__name__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TQwDk0SfyX28"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "l2CJXcTeyX8N"
      },
      "outputs": [],
      "source": [
        "# Project Summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "UThvpHSCyYBV"
      },
      "outputs": [],
      "source": [
        "project_summary = '''\n",
        "This project analyzes solar radiation data (Global Horizontal Irradiance - GHI) for the year 2022.\n",
        "The objective is to understand solar energy availability throughout the year and visualize key patterns.\n",
        "Interactive graphs allow users to explore data by selecting specific months, viewing heatmaps, and checking summary statistics.\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gvAmlK7S2jLf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tY_W5mmG2jP-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "KuF6nX_J2mDt"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data[['Month', 'Day']]"
      ],
      "metadata": {
        "id": "VEshgesD2mIc"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['GHI']"
      ],
      "metadata": {
        "id": "aIul4rUH2phM"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "E0odeERV2pmz",
        "outputId": "57e7737a-eb94-4977-f8e1-28efd9fcbd60"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I00IWQfe2uIY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X)"
      ],
      "metadata": {
        "id": "d4DHlLGX2uNE"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Predictions'] = predictions"
      ],
      "metadata": {
        "id": "5oiQReE72psx"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sp9Bu4KD2zN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I7DSWXXlyvPv"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "s2k5uC3RyvU0"
      },
      "outputs": [],
      "source": [
        "# Layout for the dashboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "epN1yf4Ch_hm"
      },
      "outputs": [],
      "source": [
        "app.layout = html.Div([\n",
        "    html.H1(\"Solar Energy Dashboard\", style={'textAlign': 'center'}),\n",
        "\n",
        "    # Project Summary Section\n",
        "    html.Div([\n",
        "        html.H3(\"Project Overview\"),\n",
        "        html.P(project_summary),\n",
        "    ], style={'padding': '20px', 'backgroundColor': '#f4f4f4'}),\n",
        "\n",
        "    # Data Inspection Section\n",
        "    html.Div([\n",
        "        html.H3(\"Data Inspection\"),\n",
        "        html.H4(\"First 5 Rows of Data:\"),\n",
        "        html.Pre(str(data.head()), style={'whiteSpace': 'pre-wrap'}),\n",
        "\n",
        "        html.H4(\"Summary Statistics:\"),\n",
        "        html.Pre(str(data.describe()), style={'whiteSpace': 'pre-wrap'}),\n",
        "\n",
        "        html.H4(\"Missing Values:\"),\n",
        "        html.Pre(str(data.isnull().sum()), style={'whiteSpace': 'pre-wrap'}),\n",
        "\n",
        "        html.H4(\"Duplicate Rows:\"),\n",
        "        html.Pre(str(data.duplicated().sum()), style={'whiteSpace': 'pre-wrap'}),\n",
        "    ], style={'padding': '20px', 'backgroundColor': '#f4f4f4'}),\n",
        "\n",
        "    # Input section for selecting months\n",
        "    html.Div([\n",
        "        html.Label(\"Enter Start Month:\"),\n",
        "        dcc.Input(id='start-month', type='number', value=1, min=1, max=12),\n",
        "        html.Label(\"Enter End Month:\"),\n",
        "        dcc.Input(id='end-month', type='number', value=12, min=1, max=12),\n",
        "\n",
        "        # Button to generate heatmap\n",
        "        html.Button('Generate Visualizations', id='generate-button', n_clicks=0, style={'color': 'orange', 'fontSize': 20, 'marginTop': 10}),\n",
        "\n",
        "    ], style={'padding': '20px', 'backgroundColor': '#f4f4f4'}),\n",
        "\n",
        "    # Output section (heatmap, line plot, histogram, box plot, and predictions)\n",
        "    html.Div([\n",
        "        dcc.Graph(id='line-plot'),\n",
        "        dcc.Graph(id='heatmap-graph'),\n",
        "        dcc.Graph(id='histogram'),\n",
        "        dcc.Graph(id='box-plot'),\n",
        "        html.H4(\"Predictions (GHI) for each day:\"),\n",
        "        dcc.Graph(id='predictions-plot'),\n",
        "\n",
        "        # Button for downloading predictions\n",
        "        html.Button('Download Predictions (CSV)', id='download-button', n_clicks=0, style={'color': 'green', 'fontSize': 20, 'marginTop': 20}),\n",
        "\n",
        "        dcc.Download(id=\"download-predictions\"),\n",
        "    ], style={'padding': '20px'})\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SZsVwauf1trx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Callback to generate all graphs based on input"
      ],
      "metadata": {
        "id": "aBA-E39i1txG"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.callback(\n",
        "    [\n",
        "        Output('line-plot', 'figure'),\n",
        "        Output('heatmap-graph', 'figure'),\n",
        "        Output('histogram', 'figure'),\n",
        "        Output('box-plot', 'figure'),\n",
        "        Output('predictions-plot', 'figure'),\n",
        "        Output('download-predictions', 'data')\n",
        "    ],\n",
        "    [Input('generate-button', 'n_clicks')],\n",
        "    [State('start-month', 'value'),\n",
        "     State('end-month', 'value')]\n",
        ")\n",
        "def generate_visualizations(n_clicks, start_month, end_month):\n",
        "    if n_clicks > 0:\n",
        "        # Filter the data based on the selected months\n",
        "        filtered_data = data[(data['Month'] >= start_month) & (data['Month'] <= end_month)]\n",
        "\n",
        "        # Line plot for GHI over time\n",
        "        line_fig = px.line(filtered_data, x='Date', y='GHI', title=\"GHI Over Time\")\n",
        "\n",
        "        # Heatmap for GHI\n",
        "        heatmap_data = filtered_data.pivot_table(index='Month', columns='Day', values='GHI')\n",
        "        heatmap_fig = px.imshow(heatmap_data, labels={'x': 'Day', 'y': 'Month'}, title=\"Heatmap of GHI\")\n",
        "\n",
        "        # Histogram for GHI Distribution\n",
        "        histogram_fig = px.histogram(filtered_data, x='GHI', nbins=20, title=\"Distribution of GHI\")\n",
        "\n",
        "        # Box Plot for GHI by Month\n",
        "        box_plot_fig = px.box(filtered_data, x='Month', y='GHI', title=\"Box Plot of GHI by Month\")\n",
        "\n",
        "        # Predictions plot\n",
        "        pred_fig = px.scatter(filtered_data, x='Date', y='Predictions', title=\"Predicted GHI\")\n",
        "\n",
        "        # CSV data for predictions\n",
        "        predictions_csv = data[['Date', 'Predictions']].to_csv(index=False)\n",
        "        predictions_base64 = base64.b64encode(predictions_csv.encode()).decode()\n",
        "\n",
        "        return line_fig, heatmap_fig, histogram_fig, box_plot_fig, pred_fig, dict(content=predictions_base64, filename=\"predictions.csv\")\n",
        "    return {}, {}, {}, {}, {}, {}"
      ],
      "metadata": {
        "id": "Vvw-nFne0z0v"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GzXu6EaN11VM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the dashborad"
      ],
      "metadata": {
        "id": "Vbr0Hwth1712"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "psYY_w4i11f7",
        "outputId": "327319c9-6953-400b-e7e9-a1f01f5a8c5a"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    iframe.allow = [\n",
              "        'accelerometer',\n",
              "        'autoplay',\n",
              "        'camera',\n",
              "        'clipboard-read',\n",
              "        'clipboard-write',\n",
              "        'gyroscope',\n",
              "        'magnetometer',\n",
              "        'microphone',\n",
              "        'serial',\n",
              "        'usb',\n",
              "        'xr-spatial-tracking',\n",
              "    ].join('; ');\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xPBqxyX611lm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "no-Jif170z6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔚 **Conclusion** 🔚\n",
        "\n",
        "In this project, we successfully applied **machine learning techniques** to predict **solar radiation (GHI)**, a crucial factor for solar energy systems. Through data collection, preprocessing, model training, and evaluation, we were able to develop an effective system for forecasting solar radiation levels.\n",
        "\n",
        "### Key Takeaways:\n",
        "- **Data Collection**: We leveraged real-world data from NASA’s POWER API, gaining insight into solar radiation patterns.\n",
        "- **Machine Learning Models**: By using **Linear Regression** and **Random Forest Regressor**, we were able to build predictive models, demonstrating the power of these techniques in environmental applications.\n",
        "- **Evaluation and Interpretation**: We used metrics like **RMSE** and **R²** to assess model performance, and explored feature importance to understand the factors influencing solar radiation.\n",
        "- **Interactive Dashboards**: We also created an interactive dashboard using **Dash**, allowing users to visualize and interact with GHI predictions in a user-friendly environment.\n",
        "\n",
        "### **Future Directions**:\n",
        "- **Model Improvement**: We could enhance model performance by exploring more advanced algorithms, such as **Gradient Boosting** or **Neural Networks**.\n",
        "- **Data Augmentation**: Incorporating additional data sources, such as weather data or geographical features, could further improve predictions.\n",
        "- **Real-Time Integration**: Implementing real-time data collection and predictions for dynamic forecasting could provide immediate value to solar energy systems.\n",
        "\n",
        "### **Environmental Impact**:\n",
        "This project contributes to **sustainable energy** solutions by improving our understanding of solar radiation and helping optimize solar power generation. By combining data science with environmental engineering, we are paving the way for more efficient and eco-friendly energy systems.\n",
        "\n",
        "**With these skills, we are equipped to tackle real-world environmental challenges using data-driven insights.** 🌍💡🚀"
      ],
      "metadata": {
        "id": "_yCFZRNr43pY"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E-KY6dzm1KaN"
      },
      "execution_count": 139,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}