Ant_Colony_Optimization_5.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "***SET PROBLEMS***\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "PwGcAKliQUXg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Set Cover Problem`**"
      ],
      "metadata": {
        "id": "dQchRREuQZSK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mathematical Formulation**:\n",
        "\n",
        "Let $x_i$ be a binary decision variable for each subset $S_i$ in $S$, where $x_i = 1$ if $S_i$ is chosen in the cover, and $x_i = 0$ otherwise.\n",
        "\n",
        "Objective function $f(C)$ to be minimized is the sum of the decision variables $x_i$:\n",
        "\n",
        "$f(C) = \\sum_{i=1}^{m} x_i$\n",
        "\n",
        "Subject to the constraint that the union of the chosen sets must cover the entire universe $U$:\n",
        "\n",
        "$\\bigcup_{i=1}^{m} (x_i \\cdot S_i) = U$ where \"$\\cdot$\" represents the set intersection operation.\n",
        "\n",
        "Additionally, there is a constraint on the size of the set cover:\n",
        "\n",
        "$\\sum_{i=1}^{m} x_i \\leq k$"
      ],
      "metadata": {
        "id": "Sfue0vyoX6YI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def set_cover_ant_colony(U, S, num_ants, num_iterations, pheromone_decay, alpha=1.0, beta=2.0, max_stagnation=20):\n",
        "    num_elements = len(U)\n",
        "    num_subsets = len(S)\n",
        "    pheromones = np.ones(num_subsets)\n",
        "    best_solution = None\n",
        "    best_cost = float('inf')\n",
        "    stagnation_count = 0\n",
        "    for iteration in range(num_iterations):\n",
        "        solutions = []\n",
        "        costs = []\n",
        "        for ant in range(num_ants):\n",
        "            covered_elements = set()\n",
        "            solution = []\n",
        "            while len(covered_elements) < num_elements:\n",
        "                probabilities = pheromones * (1.0 / (np.arange(1, num_subsets + 1) ** beta))\n",
        "                probabilities /= probabilities.sum()\n",
        "                chosen_subset = np.random.choice(np.arange(num_subsets), p=probabilities)\n",
        "                solution.append(chosen_subset)\n",
        "                covered_elements.update(S[chosen_subset])\n",
        "            cost = len(solution)\n",
        "            if cost < best_cost:\n",
        "                best_solution = solution\n",
        "                best_cost = cost\n",
        "                stagnation_count = 0\n",
        "            else:\n",
        "                stagnation_count += 1\n",
        "            solutions.append(solution)\n",
        "            costs.append(cost)\n",
        "        pheromones *= (1.0 - pheromone_decay)\n",
        "        for i in range(num_ants):\n",
        "            for subset in solutions[i]:\n",
        "                pheromones[subset] += 1.0 / costs[i]\n",
        "        if stagnation_count >= max_stagnation:\n",
        "            break\n",
        "    return best_solution, best_cost\n",
        "U = set(range(1, 11))\n",
        "S = [{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {1, 4, 7}, {2, 5, 8}, {3, 6, 9}, {10}]\n",
        "num_ants = 10\n",
        "num_iterations = 200\n",
        "pheromone_decay = 0.1\n",
        "best_solution, best_cost = set_cover_ant_colony(U, S, num_ants, num_iterations, pheromone_decay)\n",
        "print(\"Best Solution =\", best_solution)\n",
        "print(\"Cost =\", best_cost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZsTwP6cRK9X",
        "outputId": "7526a2df-cb03-4a8a-d498-0a079d17f195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Solution = [2, 0, 0, 0, 1, 1, 0, 2, 0, 2, 1, 0, 0, 0, 2, 6]\n",
            "Cost = 16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "def set_cover_ant_colony(U, S, num_ants, num_iterations, pheromone_decay, alpha=1.0, beta=2.0, max_stagnation=20):\n",
        "    num_elements = len(U)\n",
        "    num_subsets = len(S)\n",
        "    pheromones = np.ones(num_subsets)\n",
        "    best_solution = None\n",
        "    best_cost = float('inf')\n",
        "    stagnation_count = 0\n",
        "    for iteration in range(num_iterations):\n",
        "        solutions = []\n",
        "        costs = []\n",
        "        for ant in range(num_ants):\n",
        "            covered_elements = set()\n",
        "            solution = []\n",
        "            while len(covered_elements) < num_elements:\n",
        "                probabilities = pheromones * (1.0 / (np.arange(1, num_subsets + 1) ** beta))\n",
        "                probabilities /= probabilities.sum()\n",
        "                chosen_subset = np.random.choice(np.arange(num_subsets), p=probabilities)\n",
        "                solution.append(chosen_subset)\n",
        "                covered_elements.update(S[chosen_subset])\n",
        "            cost = len(solution)\n",
        "            if cost < best_cost:\n",
        "                best_solution = solution\n",
        "                best_cost = cost\n",
        "                stagnation_count = 0\n",
        "            else:\n",
        "                stagnation_count += 1\n",
        "            solutions.append(solution)\n",
        "            costs.append(cost)\n",
        "        pheromones *= (1.0 - pheromone_decay)\n",
        "        for i in range(num_ants):\n",
        "            for subset in solutions[i]:\n",
        "                pheromones[subset] += 1.0 / costs[i]\n",
        "        if stagnation_count >= max_stagnation:\n",
        "            break\n",
        "    return best_solution, best_cost\n",
        "def visualize_set_cover(U, S, solution):\n",
        "    G = nx.Graph()\n",
        "    for i, subset in enumerate(S):\n",
        "        G.add_node(i, label=f\"S{i+1}\")\n",
        "    for i, subset in enumerate(S):\n",
        "        for element in subset:\n",
        "            G.add_edge(i, element - 1)\n",
        "    solution_nodes = [node for subset_index in solution for node in range(len(S[subset_index]))]\n",
        "    pos = nx.spring_layout(G)\n",
        "    nx.draw(G, pos, with_labels=True, font_weight='bold', node_color='darkblue', node_size=1200, font_color='white',font_family='serif', font_size='12')\n",
        "    nx.draw_networkx_nodes(G, pos, nodelist=solution_nodes, node_color='red', node_size=1200)\n",
        "    nx.draw_networkx_edges(G, pos, width=2, alpha=0.5)\n",
        "    labels = nx.get_edge_attributes(G, 'label')\n",
        "    nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)\n",
        "    plt.title(\"Set Cover Problem\")\n",
        "    plt.show()\n",
        "U = set(range(1, 11))\n",
        "S = [{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {1, 4, 7}, {2, 5, 8}, {3, 6, 9}, {10}]\n",
        "num_ants = 10\n",
        "num_iterations = 200\n",
        "pheromone_decay = 0.1\n",
        "best_solution, best_cost = set_cover_ant_colony(U, S, num_ants, num_iterations, pheromone_decay)\n",
        "visualize_set_cover(U, S, best_solution)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "u7ERMytkSTcD",
        "outputId": "3cc5f8d0-3853-4c81-e3b5-598cdc077cce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install libgraphviz-dev\n",
        "!pip install pygraphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crVQAMeHTljr",
        "outputId": "86de33bf-e8a0-4b91-fee2-2b5eff85236b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libgail-common libgail18 libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libgvc6-plugins-gtk\n",
            "  librsvg2-common libxdot4\n",
            "Suggested packages:\n",
            "  gvfs\n",
            "The following NEW packages will be installed:\n",
            "  libgail-common libgail18 libgraphviz-dev libgtk2.0-0 libgtk2.0-bin libgtk2.0-common\n",
            "  libgvc6-plugins-gtk librsvg2-common libxdot4\n",
            "0 upgraded, 9 newly installed, 0 to remove and 33 not upgraded.\n",
            "Need to get 2,433 kB of archives.\n",
            "After this operation, 7,694 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-common all 2.24.33-2ubuntu2 [125 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-0 amd64 2.24.33-2ubuntu2 [2,037 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail18 amd64 2.24.33-2ubuntu2 [15.9 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail-common amd64 2.24.33-2ubuntu2 [132 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxdot4 amd64 2.42.2-6 [16.4 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgvc6-plugins-gtk amd64 2.42.2-6 [22.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgraphviz-dev amd64 2.42.2-6 [58.5 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-bin amd64 2.24.33-2ubuntu2 [7,932 B]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 librsvg2-common amd64 2.52.5+dfsg-3ubuntu0.2 [17.7 kB]\n",
            "Fetched 2,433 kB in 2s (1,552 kB/s)\n",
            "Selecting previously unselected package libgtk2.0-common.\n",
            "(Reading database ... 121749 files and directories currently installed.)\n",
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            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
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            "Collecting pygraphviz\n",
            "  Downloading pygraphviz-1.12.tar.gz (104 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.9/104.9 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: pygraphviz\n",
            "  Building wheel for pygraphviz (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pygraphviz: filename=pygraphviz-1.12-cp310-cp310-linux_x86_64.whl size=168136 sha256=de231205aad8fe000eea0b32715b729c87630405e1e0f2acb776080b9cb48a3f\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/ee/b5/a2f54f9e9b3951599c05dcce270ca85e472f8e6cec470e397a\n",
            "Successfully built pygraphviz\n",
            "Installing collected packages: pygraphviz\n",
            "Successfully installed pygraphviz-1.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pygraphviz as pgv\n",
        "import matplotlib.pyplot as plt\n",
        "def visualize_set_cover(U, S, solution):\n",
        "    G = pgv.AGraph(strict=False, directed=True)\n",
        "    for i, subset in enumerate(S):\n",
        "        G.add_node(i, label=f\"S{i+1}\")\n",
        "    for i, subset in enumerate(S):\n",
        "        for element in subset:\n",
        "            G.add_edge(i, element - 1)\n",
        "    solution_nodes = [node for subset_index in solution for node in range(len(S[subset_index]))]\n",
        "    G.node_attr['style'] = 'filled'\n",
        "    G.node_attr['fillcolor'] = 'lightblue'\n",
        "    G.edge_attr['color'] = 'black'\n",
        "    for node in solution_nodes:\n",
        "        G.get_node(node).attr['fillcolor'] = 'salmon'\n",
        "    graph_file = \"set_cover_problem_aco_soln.png\"\n",
        "    G.draw(graph_file, format='png', prog='dot')\n",
        "    img = plt.imread(graph_file)\n",
        "    plt.imshow(img)\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Set Cover Problem\")\n",
        "    plt.show()\n",
        "U = set(range(1, 11))\n",
        "S = [{1, 2, 3}, {4, 5, 6}, {7, 8, 9}, {1, 4, 7}, {2, 5, 8}, {3, 6, 9}, {10}]\n",
        "num_ants = 10\n",
        "num_iterations = 200\n",
        "pheromone_decay = 0.1\n",
        "best_solution, best_cost = set_cover_ant_colony(U, S, num_ants, num_iterations, pheromone_decay)\n",
        "visualize_set_cover(U, S, best_solution)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "k2NF6Y64T5_n",
        "outputId": "3399d877-569a-4c92-fc63-ab2b694a0131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Partition Problem`**"
      ],
      "metadata": {
        "id": "4L7MfIz-ZqXK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "class AntColony:\n",
        "    def __init__(self, num_ants, set, max_iter, pheromone_decay, alpha=1, beta=1):\n",
        "        self.num_ants = num_ants\n",
        "        self.set = set\n",
        "        self.max_iter = max_iter\n",
        "        self.pheromone_decay = pheromone_decay\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.num_elements = len(set)\n",
        "    def initialize_pheromones(self):\n",
        "        return np.ones(self.num_elements)\n",
        "    def ant_solution(self, pheromones):\n",
        "        subset = []\n",
        "        for i in range(self.num_elements):\n",
        "            if np.random.rand() < pheromones[i]:\n",
        "                subset.append(self.set[i])\n",
        "        return subset\n",
        "    def update_pheromones(self, pheromones, solutions):\n",
        "        pheromones *= (1 - self.pheromone_decay)\n",
        "        for solution in solutions:\n",
        "            for element in solution:\n",
        "                index = self.set.index(element)\n",
        "                pheromones[index] += 1\n",
        "    def solve(self):\n",
        "        best_partition = None\n",
        "        best_score = float('-inf')\n",
        "        pheromones = self.initialize_pheromones()\n",
        "        for _ in range(self.max_iter):\n",
        "            solutions = [self.ant_solution(pheromones) for _ in range(self.num_ants)]\n",
        "            scores = [sum(solution) for solution in solutions]\n",
        "            local_best_score = max(scores)\n",
        "            local_best_partition = solutions[scores.index(local_best_score)]\n",
        "            if local_best_score > best_score:\n",
        "                best_score = local_best_score\n",
        "                best_partition = local_best_partition\n",
        "            self.update_pheromones(pheromones, solutions)\n",
        "        return best_partition\n",
        "if __name__ == \"__main__\":\n",
        "    input_set = [1, 5, 11, 5]\n",
        "    num_ants = 10\n",
        "    max_iterations = 100\n",
        "    pheromone_decay = 0.1\n",
        "    ant_colony = AntColony(num_ants, input_set, max_iterations, pheromone_decay)\n",
        "    best_partition = ant_colony.solve()\n",
        "    print(\"Best Partition =\", best_partition)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVvqkHTJnCoY",
        "outputId": "2b183372-05b4-4417-bd49-83d9054fb206"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Partition = [1, 5, 11, 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import pygraphviz as pgv\n",
        "import matplotlib.pyplot as plt\n",
        "class AntColony:\n",
        "    def __init__(self, num_ants, set, max_iter, pheromone_decay, alpha=1, beta=1):\n",
        "        self.num_ants = num_ants\n",
        "        self.set = set\n",
        "        self.max_iter = max_iter\n",
        "        self.pheromone_decay = pheromone_decay\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.num_elements = len(set)\n",
        "    def initialize_pheromones(self):\n",
        "        return np.ones(self.num_elements)\n",
        "    def ant_solution(self, pheromones):\n",
        "        subset = []\n",
        "        for i in range(self.num_elements):\n",
        "            if np.random.rand() < pheromones[i]:\n",
        "                subset.append(self.set[i])\n",
        "        return subset\n",
        "    def update_pheromones(self, pheromones, solutions):\n",
        "        pheromones *= (1 - self.pheromone_decay)\n",
        "        for solution in solutions:\n",
        "            for element in solution:\n",
        "                index = self.set.index(element)\n",
        "                pheromones[index] += 1\n",
        "    def solve(self):\n",
        "        best_partition = None\n",
        "        best_score = float('-inf')\n",
        "        pheromones = self.initialize_pheromones()\n",
        "        solutions = []\n",
        "        for _ in range(self.max_iter):\n",
        "            current_solution = self.ant_solution(pheromones)\n",
        "            current_score = sum(current_solution)\n",
        "            solutions.append(current_solution)\n",
        "            if current_score > best_score:\n",
        "                best_score = current_score\n",
        "                best_partition = current_solution\n",
        "            self.update_pheromones(pheromones, [current_solution])\n",
        "        return solutions, best_partition\n",
        "    def visualize(self, solutions, best_partition):\n",
        "        G = nx.Graph()\n",
        "        for i in range(self.num_elements):\n",
        "            G.add_node(i, label=str(self.set[i]))\n",
        "        for ant_solution in solutions:\n",
        "            for i in range(len(ant_solution) - 1):\n",
        "                G.add_edge(self.set.index(ant_solution[i]), self.set.index(ant_solution[i + 1]), color='gray', weight=1)\n",
        "        for i in range(len(best_partition) - 1):\n",
        "            G.add_edge(self.set.index(best_partition[i]), self.set.index(best_partition[i + 1]), color='black', weight=2)\n",
        "        pos = nx.spring_layout(G)\n",
        "        labels = nx.get_edge_attributes(G, 'weight')\n",
        "        edge_colors = nx.get_edge_attributes(G, 'color').values()\n",
        "        nx.draw_networkx_nodes(G, pos, node_size=700, node_color='yellow')\n",
        "        nx.draw_networkx_edges(G, pos, width=2, edge_color=edge_colors)\n",
        "        nx.draw_networkx_labels(G, pos)\n",
        "        nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)\n",
        "        plt.title(\"Partition Problem\")\n",
        "        plt.show()\n",
        "if __name__ == \"__main__\":\n",
        "    input_set = [1, 5, 11, 5]\n",
        "    num_ants = 10\n",
        "    max_iterations = 100\n",
        "    pheromone_decay = 0.1\n",
        "    ant_colony = AntColony(num_ants, input_set, max_iterations, pheromone_decay)\n",
        "    all_solutions, best_partition = ant_colony.solve()\n",
        "    print(\"Best partition =\", best_partition)\n",
        "    ant_colony.visualize(all_solutions, best_partition)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "i1JjDWF3n6p4",
        "outputId": "08d242e1-1646-48fe-917c-a8d3c06a6ff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best partition = [1, 5, 11, 5]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Weight Constrained Graph Tree Partition Problem`**"
      ],
      "metadata": {
        "id": "L2m65hlQpY5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "class AntColony:\n",
        "    def __init__(self, graph, num_ants, alpha=1, beta=2, evaporation=0.5, Q=100):\n",
        "        self.graph = graph\n",
        "        self.pheromone = np.ones_like(graph) / len(graph)\n",
        "        self.num_ants = num_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation = evaporation\n",
        "        self.Q = Q\n",
        "    def run(self, iterations):\n",
        "        best_path = None\n",
        "        best_cost = float('inf')\n",
        "        for _ in range(iterations):\n",
        "            paths = self.generate_paths()\n",
        "            self.update_pheromone(paths)\n",
        "            current_cost = self.get_path_cost(paths)\n",
        "            if current_cost < best_cost:\n",
        "                best_path = paths\n",
        "                best_cost = current_cost\n",
        "        return best_path, best_cost\n",
        "    def generate_paths(self):\n",
        "        paths = []\n",
        "        for _ in range(self.num_ants):\n",
        "            path = self.generate_path()\n",
        "            paths.append(path)\n",
        "        return paths\n",
        "    def generate_path(self):\n",
        "        path = []\n",
        "        visited = set()\n",
        "        current_node = np.random.choice(len(self.graph))\n",
        "        visited.add(current_node)\n",
        "        while len(visited) < len(self.graph):\n",
        "            next_node = self.choose_next_node(current_node, visited)\n",
        "            if next_node is not None:\n",
        "                path.append((current_node, next_node))\n",
        "                visited.add(next_node)\n",
        "                current_node = next_node\n",
        "            else:\n",
        "                break\n",
        "        return path\n",
        "    def choose_next_node(self, current_node, visited):\n",
        "        pheromone_values = self.pheromone[current_node, :]\n",
        "        unvisited_nodes = [node for node in range(len(self.graph)) if node not in visited]\n",
        "        if not unvisited_nodes:\n",
        "            return None\n",
        "        probabilities = pheromone_values[unvisited_nodes] ** self.alpha\n",
        "        probabilities /= probabilities.sum()\n",
        "        next_node = np.random.choice(unvisited_nodes, p=probabilities)\n",
        "        return next_node\n",
        "    def update_pheromone(self, paths):\n",
        "        self.pheromone *= (1 - self.evaporation)\n",
        "        for path in paths:\n",
        "            cost = self.get_path_cost([path])\n",
        "            for edge in path:\n",
        "                self.pheromone[edge] += self.Q / cost\n",
        "    def get_path_cost(self, paths):\n",
        "        costs = [self.graph[edge] for path in paths for edge in path]\n",
        "        return np.sum(costs)\n",
        "num_nodes = int(input(\"Enter the number of nodes: \"))\n",
        "graph = np.zeros((num_nodes, num_nodes))\n",
        "for i in range(num_nodes):\n",
        "    for j in range(i + 1, num_nodes):\n",
        "        weight = int(input(f\"Enter the weight for edge ({i}, {j}): \"))\n",
        "        graph[i, j] = weight\n",
        "        graph[j, i] = weight\n",
        "num_ants = int(input(\"Enter the number of ants: \"))\n",
        "aco = AntColony(graph, num_ants=num_ants)\n",
        "best_path, best_cost = aco.run(iterations=100)\n",
        "print(\"Best Path =\", best_path)\n",
        "print(\"Best Cost =\", best_cost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPCSXkynt2Kh",
        "outputId": "d33daf96-6048-4543-f0f3-0c233b84a472"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the number of nodes: 4\n",
            "Enter the weight for edge (0, 1): 2\n",
            "Enter the weight for edge (0, 2): 3\n",
            "Enter the weight for edge (0, 3): 4\n",
            "Enter the weight for edge (1, 2): 5\n",
            "Enter the weight for edge (1, 3): 6\n",
            "Enter the weight for edge (2, 3): 7\n",
            "Enter the number of ants: 4\n",
            "Best Path = [[(2, 1), (1, 0), (0, 3)], [(1, 0), (0, 2), (2, 3)], [(3, 1), (1, 0), (0, 2)], [(3, 0), (0, 1), (1, 2)]]\n",
            "Best Cost = 45.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "class AntColony:\n",
        "    def __init__(self, graph, num_ants, alpha=1, beta=2, evaporation=0.5, Q=100):\n",
        "        self.graph = graph\n",
        "        self.pheromone = np.ones_like(graph) / len(graph)\n",
        "        self.num_ants = num_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation = evaporation\n",
        "        self.Q = Q\n",
        "    def run(self, iterations):\n",
        "        best_path = None\n",
        "        best_cost = float('inf')\n",
        "        for _ in range(iterations):\n",
        "            paths = self.generate_paths()\n",
        "            self.update_pheromone(paths)\n",
        "            current_cost = self.get_path_cost(paths)\n",
        "            if current_cost < best_cost:\n",
        "                best_path = paths\n",
        "                best_cost = current_cost\n",
        "        return best_path, best_cost\n",
        "    def generate_paths(self):\n",
        "        paths = []\n",
        "        for _ in range(self.num_ants):\n",
        "            path = self.generate_path()\n",
        "            paths.append(path)\n",
        "        return paths\n",
        "    def generate_path(self):\n",
        "        path = []\n",
        "        visited = set()\n",
        "        current_node = np.random.choice(len(self.graph))\n",
        "        visited.add(current_node)\n",
        "        while len(visited) < len(self.graph):\n",
        "            next_node = self.choose_next_node(current_node, visited)\n",
        "            if next_node is not None:\n",
        "                path.append((current_node, next_node))\n",
        "                visited.add(next_node)\n",
        "                current_node = next_node\n",
        "            else:\n",
        "                break\n",
        "        return path\n",
        "    def choose_next_node(self, current_node, visited):\n",
        "        pheromone_values = self.pheromone[current_node, :]\n",
        "        unvisited_nodes = [node for node in range(len(self.graph)) if node not in visited]\n",
        "        if not unvisited_nodes:\n",
        "            return None\n",
        "        probabilities = pheromone_values[unvisited_nodes] ** self.alpha\n",
        "        probabilities /= probabilities.sum()\n",
        "        next_node = np.random.choice(unvisited_nodes, p=probabilities)\n",
        "        return next_node\n",
        "    def update_pheromone(self, paths):\n",
        "        self.pheromone *= (1 - self.evaporation)\n",
        "        for path in paths:\n",
        "            cost = self.get_path_cost([path])\n",
        "            for edge in path:\n",
        "                self.pheromone[edge] += self.Q / cost\n",
        "    def get_path_cost(self, paths):\n",
        "        costs = [self.graph[edge] for path in paths for edge in path]\n",
        "        return np.sum(costs)\n",
        "def visualize_graph_with_pheromones(graph, pheromones):\n",
        "    G = nx.Graph()\n",
        "    for i in range(len(graph)):\n",
        "        G.add_node(i)\n",
        "    for i in range(len(graph)):\n",
        "        for j in range(i + 1, len(graph)):\n",
        "            G.add_edge(i, j, weight=graph[i, j], pheromone=pheromones[i, j])\n",
        "    pos = nx.spring_layout(G)\n",
        "    edge_labels = {(i, j): f'P: {pheromones[i, j]:.2f}\\nW: {graph[i, j]}' for i, j in G.edges}\n",
        "    nx.draw(G, pos, with_labels=True, font_weight='bold', node_size=700, node_color='yellow', font_color='black', font_family='serif')\n",
        "    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='red')\n",
        "    plt.show()\n",
        "num_nodes = int(input(\"Enter the number of nodes: \"))\n",
        "graph = np.zeros((num_nodes, num_nodes))\n",
        "for i in range(num_nodes):\n",
        "    for j in range(i + 1, num_nodes):\n",
        "        weight = int(input(f\"Enter the weight for edge ({i}, {j}): \"))\n",
        "        graph[i, j] = weight\n",
        "        graph[j, i] = weight\n",
        "num_ants = int(input(\"Enter the number of ants: \"))\n",
        "aco = AntColony(graph, num_ants=num_ants)\n",
        "best_path, best_cost = aco.run(iterations=100)\n",
        "visualize_graph_with_pheromones(graph, aco.pheromone)\n",
        "print(\"Best Path =\", best_path)\n",
        "print(\"Best Cost =\", best_cost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "tMgTulqOvWgt",
        "outputId": "5dedac3b-2eb1-45b0-a8dd-e1eed5135a90"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter the number of nodes: 4\n",
            "Enter the weight for edge (0, 1): 12\n",
            "Enter the weight for edge (0, 2): 23\n",
            "Enter the weight for edge (0, 3): 43\n",
            "Enter the weight for edge (1, 2): 37\n",
            "Enter the weight for edge (1, 3): 75\n",
            "Enter the weight for edge (2, 3): 22\n",
            "Enter the number of ants: 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Path = [[(3, 2), (2, 1), (1, 0)], [(1, 0), (0, 3), (3, 2)], [(3, 2), (2, 0), (0, 1)], [(1, 0), (0, 2), (2, 3)], [(1, 0), (0, 3), (3, 2)], [(3, 2), (2, 1), (1, 0)], [(1, 0), (0, 3), (3, 2)]]\n",
            "Best Cost = 487.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install libgraphviz-dev\n",
        "!pip install pygraphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNfMH0DAwaS6",
        "outputId": "624b8e6d-0da8-4dd4-a377-b13a267ce01a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libgail-common libgail18 libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libgvc6-plugins-gtk\n",
            "  librsvg2-common libxdot4\n",
            "Suggested packages:\n",
            "  gvfs\n",
            "The following NEW packages will be installed:\n",
            "  libgail-common libgail18 libgraphviz-dev libgtk2.0-0 libgtk2.0-bin libgtk2.0-common\n",
            "  libgvc6-plugins-gtk librsvg2-common libxdot4\n",
            "0 upgraded, 9 newly installed, 0 to remove and 33 not upgraded.\n",
            "Need to get 2,433 kB of archives.\n",
            "After this operation, 7,694 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-common all 2.24.33-2ubuntu2 [125 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-0 amd64 2.24.33-2ubuntu2 [2,037 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail18 amd64 2.24.33-2ubuntu2 [15.9 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail-common amd64 2.24.33-2ubuntu2 [132 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxdot4 amd64 2.42.2-6 [16.4 kB]\n",
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            "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 librsvg2-common amd64 2.52.5+dfsg-3ubuntu0.2 [17.7 kB]\n",
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            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
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            "Collecting pygraphviz\n",
            "  Downloading pygraphviz-1.12.tar.gz (104 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.9/104.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pygraphviz\n",
            "  Building wheel for pygraphviz (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pygraphviz: filename=pygraphviz-1.12-cp310-cp310-linux_x86_64.whl size=168137 sha256=23582ccd896a8d4c3aa668d540a77dd0c3b6ecf537efff46edb21250fba94811\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/ee/b5/a2f54f9e9b3951599c05dcce270ca85e472f8e6cec470e397a\n",
            "Successfully built pygraphviz\n",
            "Installing collected packages: pygraphviz\n",
            "Successfully installed pygraphviz-1.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pygraphviz as pgv\n",
        "from IPython.display import Image\n",
        "class AntColony:\n",
        "    def __init__(self, graph, num_ants, alpha=1, beta=2, evaporation=0.5, Q=100):\n",
        "        self.graph = graph\n",
        "        self.pheromone = np.ones_like(graph) / len(graph)\n",
        "        self.num_ants = num_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation = evaporation\n",
        "        self.Q = Q\n",
        "    def run(self, iterations):\n",
        "        best_path = None\n",
        "        best_cost = float('inf')\n",
        "        for _ in range(iterations):\n",
        "            paths = self.generate_paths()\n",
        "            self.update_pheromone(paths)\n",
        "            current_cost = self.get_path_cost(paths)\n",
        "            if current_cost < best_cost:\n",
        "                best_path = paths\n",
        "                best_cost = current_cost\n",
        "        return best_path, best_cost\n",
        "    def generate_paths(self):\n",
        "        paths = []\n",
        "        for _ in range(self.num_ants):\n",
        "            path = self.generate_path()\n",
        "            paths.append(path)\n",
        "        return paths\n",
        "    def generate_path(self):\n",
        "        path = []\n",
        "        visited = set()\n",
        "        current_node = np.random.choice(len(self.graph))\n",
        "        visited.add(current_node)\n",
        "        while len(visited) < len(self.graph):\n",
        "            next_node = self.choose_next_node(current_node, visited)\n",
        "            if next_node is not None:\n",
        "                path.append((current_node, next_node))\n",
        "                visited.add(next_node)\n",
        "                current_node = next_node\n",
        "            else:\n",
        "                break\n",
        "        return path\n",
        "    def choose_next_node(self, current_node, visited):\n",
        "        pheromone_values = self.pheromone[current_node, :]\n",
        "        unvisited_nodes = [node for node in range(len(self.graph)) if node not in visited]\n",
        "        if not unvisited_nodes:\n",
        "            return None\n",
        "        probabilities = pheromone_values[unvisited_nodes] ** self.alpha\n",
        "        probabilities /= probabilities.sum()\n",
        "        next_node = np.random.choice(unvisited_nodes, p=probabilities)\n",
        "        return next_node\n",
        "    def update_pheromone(self, paths):\n",
        "        self.pheromone *= (1 - self.evaporation)\n",
        "        for path in paths:\n",
        "            cost = self.get_path_cost([path])\n",
        "            for edge in path:\n",
        "                self.pheromone[edge] += self.Q / cost\n",
        "    def get_path_cost(self, paths):\n",
        "        costs = [self.graph[edge] for path in paths for edge in path]\n",
        "        return np.sum(costs)\n",
        "def visualize_graph(graph, best_path):\n",
        "    G = pgv.AGraph(directed=True)\n",
        "    for i in range(len(graph)):\n",
        "        G.add_node(i)\n",
        "    for i in range(len(graph)):\n",
        "        for j in range(i + 1, len(graph)):\n",
        "            if graph[i, j] != 0:\n",
        "                G.add_edge(i, j, label=str(graph[i, j]))\n",
        "    if best_path:\n",
        "        for path in best_path:\n",
        "            for edge in path:\n",
        "                try:\n",
        "                    G.get_edge(edge[0], edge[1]).attr.update(color='red', penwidth=2)\n",
        "                except KeyError:\n",
        "                    pass\n",
        "    file_path = \"/content/graph.png\"\n",
        "    G.draw(file_path, format='png', prog='dot')\n",
        "    return file_path\n",
        "num_nodes = int(input(\"Enter the number of nodes:\"))\n",
        "graph = np.zeros((num_nodes, num_nodes))\n",
        "for i in range(num_nodes):\n",
        "    for j in range(i + 1, num_nodes):\n",
        "        weight = int(input(f\"Enter the weight for edge ({i}, {j}): \"))\n",
        "        graph[i, j] = weight\n",
        "        graph[j, i] = weight\n",
        "num_ants = int(input(\"Enter the number of ants: \"))\n",
        "aco = AntColony(graph, num_ants=num_ants)\n",
        "best_path, best_cost = aco.run(iterations=100)\n",
        "file_path = visualize_graph(graph, best_path)\n",
        "Image(file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "BmkO5aOPw9Du",
        "outputId": "a1929525-9bf8-4ae4-b4a6-1a9fc7d0bfd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter the number of nodes:4\n",
            "Enter the weight for edge (0, 1): 12\n",
            "Enter the weight for edge (0, 2): 23\n",
            "Enter the weight for edge (0, 3): 73\n",
            "Enter the weight for edge (1, 2): 29\n",
            "Enter the weight for edge (1, 3): 35\n",
            "Enter the weight for edge (2, 3): 22\n",
            "Enter the number of ants: 5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Arc-Weighted l-Cardinality Tree Problem`**"
      ],
      "metadata": {
        "id": "Tjx345KVxzLV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import math\n",
        "def initialize_graph(num_nodes):\n",
        "    G = nx.Graph()\n",
        "    for i in range(num_nodes):\n",
        "        G.add_node(i)\n",
        "    return G\n",
        "def add_random_edges(G, num_edges):\n",
        "    for _ in range(num_edges):\n",
        "        node1 = random.choice(list(G.nodes()))\n",
        "        node2 = random.choice(list(G.nodes()))\n",
        "        weight = random.randint(1, 10)\n",
        "        G.add_edge(node1, node2, weight=weight)\n",
        "    return G\n",
        "def visualize_graph(G):\n",
        "    pos = nx.spring_layout(G)\n",
        "    nx.draw(G, pos, with_labels=True)\n",
        "    labels = nx.get_edge_attributes(G, 'weight')\n",
        "    nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)\n",
        "    plt.show()\n",
        "def ant_colony_optimization(G, num_ants, num_iterations, evaporation_rate, alpha, beta):\n",
        "    pheromone = 1 / len(G.edges())\n",
        "    pheromone_matrix = {}\n",
        "    for edge in G.edges():\n",
        "        pheromone_matrix[edge] = pheromone\n",
        "        pheromone_matrix[edge[::-1]] = pheromone\n",
        "    best_solution = None\n",
        "    best_cost = float('inf')\n",
        "    for iteration in range(num_iterations):\n",
        "        solutions = []\n",
        "        costs = []\n",
        "        for ant in range(num_ants):\n",
        "            solution = []\n",
        "            current_node = random.choice(list(G.nodes()))\n",
        "            while len(solution) < len(G.nodes()):\n",
        "                neighbors = list(G.neighbors(current_node))\n",
        "                if not neighbors:\n",
        "                    break\n",
        "                probabilities = [\n",
        "                    (pheromone_matrix[(current_node, neighbor)]**alpha) *\n",
        "                    ((1 / G.get_edge_data(current_node, neighbor)['weight'])**beta)\n",
        "                    for neighbor in neighbors\n",
        "                ]\n",
        "                total_probability = sum(probabilities)\n",
        "                if total_probability == 0:\n",
        "                    break\n",
        "                probabilities = [prob / total_probability for prob in probabilities]\n",
        "                next_node = random.choices(neighbors, probabilities)[0]\n",
        "                solution.append((current_node, next_node))\n",
        "                current_node = next_node\n",
        "            cost = sum([G.get_edge_data(edge[0], edge[1])['weight'] for edge in solution])\n",
        "            solutions.append(solution)\n",
        "            costs.append(cost)\n",
        "            if cost < best_cost:\n",
        "                best_cost = cost\n",
        "                best_solution = solution\n",
        "        pheromone_matrix = {edge: (1 - evaporation_rate) * pheromone_matrix[edge] for edge in pheromone_matrix.keys()}\n",
        "        for solution, cost in zip(solutions, costs):\n",
        "            for edge in solution:\n",
        "                if edge in pheromone_matrix:\n",
        "                    pheromone_matrix[edge] += 1 / cost\n",
        "    return best_solution, best_cost\n",
        "def main():\n",
        "    num_nodes = 10\n",
        "    num_edges = 15\n",
        "    num_ants = 5\n",
        "    num_iterations = 50\n",
        "    evaporation_rate = 0.1\n",
        "    alpha = 1\n",
        "    beta = 2\n",
        "    G = initialize_graph(num_nodes)\n",
        "    G = add_random_edges(G, num_edges)\n",
        "    visualize_graph(G)\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "PGgpxadt3uK4",
        "outputId": "94729d96-b7f1-4ab7-b3dc-aee1fbfa47ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "44M-1FGM4ehA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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sbHJycmRyWTPnz+PiooKDQ3t37//zp07jRuY0bOXlJRMmzaNEPLuu+/m5ubWPtd27dpNmzat9vNgBhAAAdoRgHnTrmRGFyyRSGQy2YgRI/r27btv376uXbuGhIQsW7bs6tWrRo/N3AA3b95s3ry5nZ3drl27dD+bpj+DUaNG9ejRQ//+6AkCIMAYAjBvxpTSwIls3bqVw+E0a9Zs7ty5J0+erKiokAcwlPEYWC6Fp5NIJJs2bbK2tm7fvv3vv/9uQKXLly/38vIy4ISYCgRAgC4EYN50qZTpdN6/f3/kyJHBwcHNmze3t7eHbdcG/YsXL/r3708IWbhwYVlZWW2mUh978OBBQkhhYaH6KbSAAAgwmwDMm9n1rUl2N2/e7Ny58/r16y9duhQcHCwSifCqd004ymQCgcDd3d3Dw+PMmTM1m0H3qGvXrhFCrl+/rrsbzoIACDCPAMybeTU1QEb37983wCwWPMWbN2/mzJlDCBk0aJD8uT9jwCgsLCSEHDx40BiTY04QAAEqE4B5U7k6Ztam+HlbcWBmQTQJ/9tvv7Vp08bGxmbr1q3GRufp6bl8+XKagIFMEAABgxGAeRsMJSMnknvPzZs3GZmdwZOSSqXbt2+3tbVt1arVnTt3DD6/+oQ9evQYPXq0ejtaQAAEmE0A5s3s+hogux9++IEQcuHCBQPMxegpXr58OWTIEELIrFmzXr9+bZpcp02b1r59e9PEQhQQAAHqEIB5U6cWFFUilUq7du3apk0b+SbTFFVpbllnz5719PR0c3M7deqUKbV88cUX9vb28lfzTRkXsUAABMxLAOZtXv70iH79+nVCCNZG1Vit8vLyRYsWEUL69OmTlZWlsY/xGhMTEwkhmZmZxguBmUEABChIAOZNwaJQUdKHH37o5uaGrcZUanP//v3AwEArK6svvvjCLLe/6enphJBz586pCMNHEAABZhOAeTO7vgbL7sWLF46OjnPnzjXYjDSfSCqV7t69297ePiAg4MaNG+bKprKy0srKatu2beYSgLggAAJmIQDzNgt2Wgb9/PPPORzO3bt3aaneoKLz8vLee+89QsiUKVNKSkoMOne1J2vevDn+U1VtahgAAjQnAPOmeQFNKL+srKxx48Z9+/Y19rvLJsypJqEuXrzo4+Pj6up69OjRmow39Jhhw4b179/f0LNiPhAAAUoTgHlTujxUE5eQkEAIMfED1dSBIJVKo6OjWSxWjx49qPOM2OLFi/38/KhDCUpAAARMQADmbQLIzAkhlUr79u3bpEkTg++xQUFG6l8wVFZWXrp0ae3atZRa7D02NpbFYpnszXIKVgqSQMACCcC8LbDotUpZKBRyOJzo6OhazULVwS9fvty5c2dcXFx6erpGjeqOrrGbKRsvXbpECDHNgm6mzAuxQAAEdBCAeeuAg1OaCcyZM8fJyenFixeaT9O29cCBA+7u7l27dm3YsGG9evUuX75Mi1RevnxJCKHID/C0IAaRIMAAAjBvBhTR1Cnk5+e7ublNmjTJ1IGNGW/Xrl3+/v779u178+ZNRUVF7969Bw4caMyABptbKpVevHixuLjYYDNiIhAAAcoTgHlTvkSUFLhjxw4Wi8WYnaQlEkl4ePjq1asl//yRyWTx8fH+/v50WZSGgl/mU/KyhSgQYA4BmDdzamnKTCorK9u0adO1a1fG2EZWVtbjx48VDPfs2dOqVSvFRxyAAAiAAKUIwLwpVQ46iblw4QIh5NChQ3QSXZVWqVQqX+V0/fr1w4YNk8lklHqwvCr5OA8CIGApBGDellJpY+QZFhbm4+Nj9iXGDJuafPO0Dz74YOLEifKZnz17pu3hc8OGxmwgAAIgoCcBmLeeoNBNA4GMjAwbG5tPP/1UwzmaNwUHB3/55ZcymWzr1q0cDuf48eM0TwjyQQAEGEUA5s2ocpo+mYiICFtb2ydPnpg+tKEiqv9sX1RU1Llz53Pnzo0cOdLZ2fnIkSOGimWyeaRSaVFRUWFhockiIhAIgIApCcC8TUmbgbGKi4u9vLxGjRpF09yePXt2//79iooKZf0ZGRmsf/6EhITQxf/+/PNPgUAQHR3966+/ymSyhQsXdu7cOSQkJDExUf1/J8rJ4hgEQICOBGDedKwatTTv37+fEHLx4kVqydJDzYkTJ+rWrRsSEqLyVJpYLK5bty6N9tl88eJFSEhIYGBgeHh448aNlyxZ0r59+w0bNoSHh3fo0OHhw4d6wEAXEAABOhGAedOpWtTUKpFIOnfu3K5dOxULpKZauarS0tIZM2YQQoYPH56bm6suVeVeXL0DpVpWrVoVGBh46dIlsViclJRUv379HTt2yBUGBwfv3buXUmohBgRAoPYEYN61Z4gZZKmpqYSQXbt20YLFrVu3WrRoYWdnt3PnTmZ8pTx+/Pj58+cr4Pfu3TsiIkL+cdSoUUuXLlWcwgEIgAAzCMC8mVFH82fxwQcf1KtX79WrV+aXol2BRCL5z3/+Y21t3a5du7t372rvSLMzK1asGDRokFy0VCodPnx4SEjIjRs3jh071q5du507d9IsH8gFARCoigDMuypCOK8fgaysLAcHhwULFujX3Qy9srOzBwwYQAiZP3/+mzdvzKDAaCF/+umntm3bRkZG3r17d/Xq1U2bNv3ss888PT07dOgQFhZGl2fujIYHE4MAAwnAvBlYVHOltHbtWi6Xe+/ePXMJ0BFX/ktw/fr1T58+raMbTU+VlZVt2bLFz8+vRYsWTZs23b9/v0wmS0lJ2bVrl/KarzTNDrJBAATUCcC81ZmgpYYE3rx506hRI6ptxvXmzZt58+YRQgYOHJidnV3D3Ogw7NKlS0eOHElLS6PRk4N04AqNIEBFAjBvKlaFvpqOHz9OCElKSqJICkKhsG3bttbW1lu2bGHGs2nawDI7O21Zox0ELJYAzNtiS2+UxKVS6dtvvx0QEFBeXm6UAHpPKpVKd+zYYWtr27Jly9u3b+s9Dh1BAARAgAYEYN40KBK9JN65c4fNZm/cuNGMsv/888+hQ4cSQmbOnPn69WszKjFx6IqKCtyCm5g5woGAWQjAvM2CneFBZ86c6ezsnJOTY5Y8z58/7+Xl5ebmlpCQYBYB5gpaUlKyb9++p0+fmksA4oIACJiMAMzbZKgtKFBubq6rq+vUqVNNnHN5efnixYtZLFbv3r2zsrJMHN3s4YqKigghBw4cMLsSCAABEDA2AZi3sQlb6Pxbt25lsVhpaWkmy//BgwdBQUFWVlbR0dESicRkcSkVyMvLKzIyklKSIAYEQMAYBGDexqCKOWUVFRUtW7bs3r27CX6ClUqle/bssbe3b9q0qXxPLYstQM+ePem7w5vFVg2Jg0ANCMC8awANQ/QicPbsWUKIsTfDzs/PHzlyJCFk8uTJxcXFeiljbqfp06e3a9eOufkhMxAAgf8SgHnjUjAigaFDh/r6+paWlhopxo8//tiwYUMXF5f4+HgjhaDXtJs2bbKzs7PYXw3oVSyoBYHaEIB514YexlZB4OHDh1ZWVitXrqyiX/VPV1RUREZGstns0NBQkUhU/QmYOSIpKYkQAiDMrC6yAgElAjBvJRg4NAKBJUuW2NnZZWZmGnDujIyMLl26cDicNWvWYClQZbAPHz4khJw9e1a5EccgAALMIwDzZl5NqZVRUVGRp6fnmDFjDCXr4MGDTk5OjRo1unLliqHmZMw8lZWVVlZWMTExjMkIiYAACGgkAPPWiAWNhiSwb98+QsilS5eUJy0pqxRmFaSJ8oVZBSVllcqntB0XFhaGh4cTQsLDw7HNpTZKLVq0mDNnjrazaAcBEGAGAZg3M+pI6SwkEknHjh0DAwMlEkl6dlFUgjA0OsWPL+D9768fXxAanRKVIPzrrLZMrly50qhRIycnp4MHD2rrg3aZTDZ8+PB+/foBBQiAALMJwLyZXV+qZHf58mVuHY9eK4/x+AL/iCSFbSsfyNvDY1Mz8/71dLpYLF69ejWHw+nSpUtGRgZVUqKqjqVLl/J4PKqqgy4QAAHDEIB5G4YjZtFNIO6aqNGSk75LEpTdWuOxf0RSQGRy3LX/PkAuEom6d+/OZrOXL19eUVGhOwrOymSyvXv3slgsi9qOBXUHAQskAPO2wKKbOuWYlHSNPq27MSYlPT4+3sXFpWHDhj/++KOpRdM23s8//0wIwS6otC0ghIOAXgRg3nphQqcaE4i7JtJt0jrOOrbtO3LkyPz8/BpHt8CBf/75JyHku+++s8DckTIIWA4BmLfl1NoMmWbmlQZEJqvYs8eYtUTtj+f4L1S6+S5NbLwsUZRbYgbdNA9Zt27dNWvW0DwJyAcBENBFAOatiw7O1ZJAeGyq+uNpcvN2ChriNvhjxV+fud+qmLf80bbw2NRaarDA4SEhIePHj7fAxJEyCFgOAZi35dTa1JmmZxep+zGPL5Cbd73hfI1n1Rsf5mh9f8zUKdEk3sSJEzt16kQTsZAJAiBQEwIw75pQwxh9CEQlCNVvu5XNu+GC+CqfP/ePSIpKEOoTDn0UBNatW1enTh0TbMaqiIgDEAABExOAeZsYuAWFC41OUb+NVpg3y9ru7x++WWwb3zaeE/6jsae8MXRDigVRM0Sqx44dI4RkZ2cbYjLMAQIgQEUCMG8qVoUBmorLKpXXUFP2Zo/wDfbNuroNnOv+3nKXHhPYds4srrXXxC3KfZSP/fgCPddPZQA3g6QgFAoJIT/99JNBZsMkIAACFCQA86ZgUZggSZhVoGzAOo69p3/F4trYNgrU0UeYVcAEKKbKoaysjM1m796921QBEQcEQMDUBGDepiZuIfHSRPk6zFjllH2LUMLh6vj9O02EV72rd+H4+/svWrSoemPQGwRAgD4EYN70qRWtlOp/583jC5w7hxFCGi6IVzF1xUevlsHBwcFhYWHz58/fuHFjfHx8ampqVlaWRCIxPZXs7Ozk5GSKr7I+cODAQcPere6+baaHiYggAAI1IwDzrhk3jKqCQIn237wVlqw4sG/WlcW19l16StHy74PET6JWTZ48uW/fvs2bN3dwcFAs8cLlcv38/Lp37z527Fg+n799+/bExMRbt27l5eUZ41lr+Zzvv/8+i8UaMmRISQkVF5CR79vWckk8b2miAqM++7ZVUVGcBgEQoBIBmDeVqsEsLdqeNldZj8Xrw62EzbVr0lnhNCoHKk+bS6XSvLy8W7duJSYmbt++nc/njxs3rnv37n5+flwuV+HrDg4OzZs379u37+TJk1esWLFnz55z587dv3+/tPRfW5ZVF/m0adNGjhwZGhq6YMGC6o41dv/MvNLw2NQa7NtmbGGYHwRAwOAEYN4GR4oJ/0tA23veNr5t7Rp3dAkdX3fAbKeOw1hWNiwbB+8pX6p4tvxjtd7zFovFWVlZqamp8fHxGzdunD9/flhYWHBwsKenp8LUCSFubm7t27cfOnTorFmz1q9ff+jQoUuXLj158kT3rmWVlZWrVq3q3bt3Tk5O586do6KiKFXpuGuigMhkjS/Wq4BV2beNUllADAiAgJ4EYN56gkK3ahPQtsKaa59p1l4BbFsnwuZwHOs6tOrlPf0rFYNR/miQFdbKysoyMjIuXry4f//+zz77bMaMGYMGDWrTpk2dOnUUvv7XE9oNGjRYvHixeqpisXjbtm1t2rTJzs5+/Phx27Ztt2zZIpPJjPHlvHr0KltqvG9blTOjAwiAADUJwLypWReGqNK4trmyMes+9o9IMsHa5kVFRXfv3j19+vRXX321fPnyAwcOqD8Ht2vXriFDhjx//lwmk92+fTs4OPjYsWMUMe/a7Nt2+H/7pjPkgkMaIGAxBGDeFlNqcySqcVcx3YatfDYgMjkzr1Y/URsk6dLS0nfeeYfFYg0fPnzOnDnjx48PCAjYtm2binnPnTt39erV33zzTUpKyh9//FFWVmaQ6Lon0UbYe9ou+xbdOU5uLK4Nt65PnW7jGn58VJmt/JgihHXniLMgAALqBGDe6kzQYkgCDLgvlEgk169f3759+8KFC8eNG8fj8Z4LpfIAACAASURBVBwdHVks1n/+8x8FqTdv3gQGBtarV0/xJTwhpH79+h07dnz33Xfnzp37xRdfHDly5PLly8+ePROLxYqBtTzQ+N1Gg5n72DYOHOf6Lj0m1B0w26FNH0KIxkcCTfPdRi1zxHAQAAF1AjBvdSZoMTCBmv0iuy3loYF1GGg6Pp/fv3//q1evavzBu7S09MGDB+fPn9+3b9/KlSunTJnSv3//Fi1aODo6Knydy+X6+vp269ZtzJgxS5Ys2bZtW0JCws2bN3NzczXOqU24tqcKXEI/IIR4Td6uuNV2aP02IcRn/mFFi/KBQZ4q0CYS7SAAAsYgAPM2BlXMqUpA/ix0o2UCZc/QeCx/Fpqav8XKfwsPCwsbOHBgTk6OapI6P0ul0levXt2+fVsgEHz55ZfLli0LDw8PDQ1t1KiRlZWVwtft7OyaNWvWp0+fDz/8MCoqKjY29vvvv793757Gd8q1Pc/v3GXE31attEX63y0sdsOFGr45r9bz/DpTxEkQAAHTEYB5m461hUfKzCttM3cXA95C3rhx4/r16w34k7ZEInn+/PnVq1ePHj26adOmBQsWjBgxolOnTl5eXiwWS+Hrrq6u7dq1Gzx48MyZM9etW3fw4MHgVUka/wNUf9RK+ffkXh9ubTBzX71hS1g29k4dh2nszOMLVN6kt/ALFemDAC0IwLxpUSYmiHz27BmHw4natDMqQRi6IUV5z7G/1//akBKVIMT3tyqVLi8vf/To0Y8//njw4MG1a9d+9NFH77zzzl8vqrm6urKs7XyV1lBTMeY63cNZXBuF8Tt3Ha3SQfkj9m1TwY6PIEB9AjBv6teIIQo//fRTR0fHwsJCeT4lZZVYebs2pb3+8IWyAascuw3+2LZRYN0Bs93fjXBo25cQlmvf6Sp9lD9i37ba1AJjQcD0BGDepmduiRErKio8PT0/+ugjS0zeODnr2Let3tDFLK5Ng5lfK+zZoU0flpWNz7xDihaVA+zbZpwqYVYQMBYBmLexyGJeZQJHjhwhhPz222/KjTiuDQEd+7bZ+LSyadBC2Z7d3434+9W199coNyof4867NrXAWBAwPQGYt+mZW2LE0H/+WGLmRstZx75t3LoNrL2bKXtzvWFL/zbvUSuVGxXH+M3baFXCxCBgLAIwb2ORxbwKAnfu3CGEHDlyRNGCA4MQ0LZvm13jYMLhek/7+9l++V+7pl0Ii91g1jeKFuUDPG1ukHJgEhAwJQGYtylpW2isjz76yNPTU/eeXRaKpnZpa3vP22PsesJis+1d6nQfV7ffR7b+QYQQx3b9lA1bcYz3vGtXBIwGAfMQgHmbh7vlRC0sLHR0dPz0008tJ2WTZapthTUeX+D5wUZb/yCOgythc7l1G7iEfuC7JEFh2CoHeEPPZCVDIBAwFAGYt6FIYh7NBGJiYjgczrNnzzSfRmvtCGhc21zFm3V8xNrmtcOP0SBgNgIwb7Oht4TAUqm0RYsWI0aMsIRkzZKjtl3FdBi28insKmaWqiEoCNSeAMy79gwxg1YCKSkphJCUlBStPXCi1gQYsG9brRlgAhCwOAIwb4sruSkTfu+991q2bFmtnbJMKY8xsRi2bxtj6oJEQMB4BGDexmNr6TPLFzPftm2bpYMwSf7yfdv8IzRvVaL8VbnvkgS/JSfjrolMogtBQAAEjEIA5m0UrJhUJpOpLGYOJsYmkJlXGh6bWuW+bQPWC7h1PPbu3WtsPZgfBEDAeARg3sZja9Ezl5eXYzFzs1wB6dlFVe7bNmHChDp16mRlZZlFIYKCAAjUngDMu/YMMYMGAljMXAMU0zbp2LctLy/P09NzyJAheBzBtDVBNBAwGAGYt8FQYiJlAljMXJkGBY9PnDhBCDl06BAFtUESCIBAlQRg3lUiQodqE8Bi5tVGZo4Bo0ePdnNzy8nJMUdwxAQBEKgVAZh3rfBhsEYCM2bM8PLywmLmGuFQp/Hly5f16tUbNWoUdSRBCQiAgJ4EYN56gkI3fQkUFhY6ODhERUXpOwD9zEfg0KFDhJDjx4+bTwIigwAI1IQAzLsm1DBGBwEsZq4DDtVOSaXSoUOHenp65uXlUU0b9IAACOggAPPWAQenqk0Ai5lXG5m5B2RlZdWpU2fChAnmFoL4IAAC1SAA864GLHStkoB8MfMffvihyp7oQB0Ce/fuJYQkJydTRxKUgAAI6CYA89bNB2erRwCLmVePFzV6S6XSfv36+fj4FBYWUkMRVIAACFRBAOZdBSCc1p/A06dPORwOFjPXnxh1ej558sTR0XH69OnUkQQlIAACOgjAvHXAwanqEcBi5tXjRbHeO3bsIIRcuHCBYrogBwRAQAMBmLcGKGiqAQEsZl4DaJQaIpFIevTo0ahRo5KSEkoJgxgQAAF1AjBvdSZoqQmBw4cPE0KEQmFNBmMMNQg8fPjQzs5u3rx51JADFSAAAloJwLy1osGJahEIDQ3t0aNHtYagMwUJbNq0icVi/fzzzxTUBkkgAAIKAjBvBQoc1JyAfDHz+Pj4mk+BkdQgIBaLu3TpEhAQ8Pr1a2ooggoQAAENBGDeGqCgqboEsJh5dYlRuf/du3etra2XLl1KZZHQBgIWTgDmbeEXgAHSLygowGLmBuBIpSk+++wzDodz/fp1KomCFhAAgf8nAPP+fxY4qhmB8vLyzZs3Z2dn12w4RlGQQEVFRYcOHdq0aVNeXk5BeZAEAiAA88Y1YAACYrHYALNgCioRuHnzJpfLXbFiBZVEQQsIgMB/CcC8cSmAAAhoJrB8+XIul3v79m3Np9EKAiBgPgIwb/OxR2QQoDaBsrKyVq1aBQUFVVZWUlsp1IGAxRGAeVtcyZEwCOhP4OrVq2w2e/369foPQU8QAAETEIB5mwAyQoAAjQksXrzYxsbm3r17NM4B0kGAcQRg3owrqZETun//flpaWmJi4suXL40cCtNTgsDr16+bNm0aEhKCxxIpUQ+IAIF/CMC8cSFUg0BkZKSXlxePx2vatGmjRo0mT578yy+/VGM8utKTwKVLl1gs1ubNm+kpH6pBgIEEYN4MLKqRUtqxY0fTpk3PnTv39OnTVatWcbnczp07d+jQYe/evUaKiGmpQ2D27Nl2dnZ//PEHdSRBCQhYMgGYtyVXv3q5BwcH79y5UzEmPDx86dKlkyZNcnd3v3jxoqIdB4wkUFxczOPxevXqJZVKGZkgkgIBehGAedOrXmZTm5+f37t37y1btshkMvk/340bN05KSpLJZP37958+fbqi3WwSEdjIBM6dO0cI2bVrl5HjYHoQAIGqCcC8q2aEHnK3Xrp0aXBwsEgkevLkyc6dO21tbYuLi2Uy2datW3v06JGfnw9QjCcwZcoUJyenzMxMxmeKBEGA4gRg3hQvEIXkpaWltWnThsvl8ni8gICArVu3ysVt2bIlMDCQQkIhxWgECgoKGjRoMHDgQHx5bjTGmBgE9CIA89YLEzopCJw/f37Tpk2PHj2S//OdkZHRuHHj7du3KzrggNkEEhMTCSHffPMNs9NEdiBAcQIwb4oXiNLySktL9+/fP3r0aEqrhDhDEwgPD3dxcXn+/LmhJ8Z8IAAC+hKAeetLCv00EsjNzX3x4oXGU2hkKoHc3Nz69esPHz4cX54ztcTIi/oEYN7UrxG1FFZUVFBLENSYg8DRo0cJIUeOHDFHcMQEARCQwbxxEVSDwN69e0+ePIn7rWogY27XESNGuLu7Y5Vc5lYYmVGaAMyb0uWhlLjy8nIPD4+ZM2dSShXEmItAdnZ23bp1x4wZYy4BiAsClkwA5m3J1a9e7ocPHyaECIXC6g1Db+YSOHDgACHk5MmTzE0RmYEARQnAvClaGArK6t69e8+ePSkoDJLMRUAqlb7zzjteXl6vXr0ylwbEBQHLJADztsy6VzvrO3fuEELi4+OrPRIDGE3g6dOnzs7OkyZNYnSWSA4EKEcA5k25klBT0IwZM7y9vfGoOTWrY15Vu3fvJoR8//335pWB6CBgUQRg3hZV7homW1BQ4ODgsGLFihqOxzBGE5BKpb179/b19S0qKmJ0okgOBChEAOZNoWJQVsrWrVu5XG5WVhZlFUKYeQk8evTIwcEBbyKYtwqIblEEYN4WVe6aJCuVSps3bz5y5MiaDMYYiyEQExNDCMHO7hZTcCRqZgIwbzMXgPrhL1y4gH+UqV8msyuUSCTdunVr3LhxaWmp2cVAAAgwngDMm/Elrm2CYWFhrVq1wqpqteVoAeMfPHhga2u7cOFCC8gVKYKAmQnAvM1cAIqHf/r0KYfDwY6fFC8TdeRFR0ezWKwrV65QRxKUgAAjCcC8GVlWgyW1fPlyR0dHPEVsMKBMn6iysjI4OLhFixZv3rxheq7IDwTMSQDmbU76FI+NxcwpXiBqyvvtt9+srKwiIiKoKQ+qQIAZBGDezKijUbKIi4vDYuZGIcv0SVetWsXhcG7cuMH0RJEfCJiNAMzbbOipHxiLmVO/RtRUWFFR0e6fP1iSj5oFgioGEIB5M6CIRklBvpj5d999Z5TZMSnTCdy4cYPD4axevZrpiSI/EDAPAZi3ebhTP+r06dOxmDn1y0RlhREREVZWVthDlso1gjb6EoB507d2RlSOxcyNCNdipn7z5k2LFi2Cg4MrKystJmkkCgImIgDzNhFoeoXBYub0qhdl1V65coXFYm3YsIGyCiEMBGhKAOZN08IZUTYWMzciXMubeuHChba2tg8ePLC81JExCBiRAMzbiHBpOvX58+exmDlNa0dB2aWlpY0bN+7WrZtEIqGgPEgCAZoSgHnTtHBGlI3FzI0I1yKn/uGHHwghMTExFpk9kgYBoxCAeRsFK30nxWLm9K0dlZV/9NFHDg4Ojx49orJIaAMBGhGAedOoWKaQGhkZ6eTkhMXMTcHakmIUFRX5+vr26dMH29NZUtmRqxEJwLyNCJd2U8sXM581axbtlEMw9QmcOXOGEBIbG0t9qVAIAtQnAPOmfo1Mp1C+mPndu3dNFxKRLInAhx9+6Ozs/OzZM0tKGrmCgFEIwLyNgpWmk3br1q1nz540FQ/Z1CeQn5/v5eU1ePBgfHlO/WJBIcUJwLwpXiDTybt9+zYhBIuZm464RUZKSEgghMTFxVlk9kgaBAxGAOZtMJR0nwiLmdO9gnTRv3LlSpFIhJtvutQLOqlJAOZNzbqYWhUWMzc1ccuOh9XOLbv+yN4ABGDeBoDIgCnki5k/f/6cAbkgBRAAARBgPAGYN+NLrCHBkrJKYVZBmihfmFVQUlYpX8x81KhRGrqiCQRMSCA1NfXixYsmDIhQIEBXAjBvulauBrrTs4uiEoSh0Sl+fAHvf3/9+IKOKwWufaZ9K0ipwZwYAgKGIiASiQYNGhQUFHT58mVDzYl5QICpBGDeTK3sv/LKzCsNj03l8QX+EUkK2/7XwZIEHl8QHpuamVf6r5H4AAImISB/fi03N3fZsmX+/v4miYkgIEBjAjBvGhdPT+lx10QBkclabft/t+Byaw+ITI67JtJzZnQDgdoTkEgkYrFYMY9EIgkODt62bZuiBQcgAALqBGDe6kwY1RKTkv6vO2wlq9bRHpOSzigKSIZ6BKRSaW5uroousVgslUp79OgB81Yhg48goEIA5q0ChFEf466JdDi07lOHcf/NqGuBWslUVlY2bdrU3t5+wIABEyZM2LVrV2pqanZ2dmFh4alTp1gs1sGDB6mlGGpAgGIEYN4UK4jh5GTmlQZEJqs7tOfEzbaNAlnWdixrO1u/Dl4fblXvw+MLAiKT8fu34aqBmVQJrFq1isViffPNN+PHj2/dunVAQIC7uzuPx+NyuYsWLVLtjc8gAAL/JgDz/jcPBn0Kj01V/53bc+JmFtea6+rt2muSS68POXU8WDb23lN3qvu3f0RSeGwqg3ggFcoR8PX1XbVqlUwmKy8vLyoqunv37tGjRzMzM7GEC+VKBUHUIwDzpl5NDKEoPbtI3Y95fIFd445sW0efeYfkZxvM3s+ytrMP6KqxM48veJhTZAg5mAMENBBITU21srK6ceOGTCZTfmZNQ1c0gQAI/JsAzPvfPJjyKSpBqH7bzeML/rbq5t2UrdqucTDhcBsu/E65UX7sH5EUlSBkChLkQUUCkydPbtasGRWVQRMIUJsAzJva9amputDoFHUz5vEFhMN1aP228in75t0IIZ7jv1BuVByHbsDKLTWtAcbpR2DFihXY4Vs/VOgFAv9PAOb9/ywYc1RcVqm8hprCiXl8gZW7H7duA99/lmTh8QW+i09wnN0JIfWGL1Pupjj24wtKyioZQwaJUJBARUWFiipsOKYCBB9BQJ0AzFudCe1bhFkFCvdVOajbfyYhxKF1b68pO7wmb7Nv0Z2wuYQQt8Efq/RUfBRmFdCeCBKgCQG5bScmJpaVldFEMmSCgHkIwLzNw92oUdNE+QrrVT9wDhklN2xCiLVnU+euowkh7mGfqPeUt6SJ8o2qFpODgDKBR48ecbnc5cuXKzfiGARAQIUAzFsFCBM+6rjzlvuxz/zDHuM+95q0jccXOIeMJIR4Tdmhzbxx582Ea4JWOURFRXG53Fu3btFKNcSCgEkJwLxNits0wUq0/+at7tDWXk05TvV8l55SP8XjC/Cbt2lKhijKBMrLy1u3bt2hQwf1n8OVu+EYBCyZAMybmdXX9rS5ikPXG7aEEOLaa5JKu+IjnjZn5vVB+ayuX7/OZrPXrl1LeaUQCALmIQDzNg93o0aVSCTjNp3k/e+RcoUT8/gCj7HrbXntXHpOrDtwrmO7foTFtvUPUjx8rtxTvskY3vM2aqUwuQ4CS5cutba2/v3333X0wSkQsFgCMG9GlV4qlZ48ebJdu3ZWbg1VnFj+0Xv6bttGHdh2zoRjxXXzcekxwXfxCY095Y1YYY1R1wetknn9+nWzZs26dOmCxddoVTeINREBmLeJQBs7jFQqTUxMDAwMJIT06tXrp59+0ri2uQ6fVjmFtc2NXTLMXyWBn3/+mcVibdq0qcqe6AAClkYA5k37ikul0tOnTwcHBxNCunfvnpLy3zXRtO0qpmLS2j5iVzHaXxmMSGDu3Ll2dnYPHz5kRDZIAgQMRgDmbTCUpp9IKpWePXs2JCSEEBISEnLu3DmVpamwn7fpi4KIhiVQUlLSqFGjHj16SCQSw86M2UCA1gRg3nQtX0pKSrdufy9L3qlTpzNnzqjYtiKrmJR0bffWGtt9lyby+IJtKbjRUSDEgZkJnD9/nhCyY8cOM+tAeBCgEgGYN5WqoZ+WH3/8sWfPnoSQoKAggUCgzbYVk8VdEwVEJmvcZEzFv/0jkvyWnPQIGf748WPFcByAgNkJTJs2zdHR8cmTJ2ZXAgEgQBECMG+KFEIvGb/88kvv3r0JIe3atUtISKjSthWTZuaVhsemyt/+UjFs+Ue5tYfHpt7JyOLxeEFBQW/evFEMxwEImJdAQUGBj49Pv3799L/mzSsY0UHA2ARg3sYmbJj5U1NT+/fvTwhp3br1sWPHavb7X3p2UVSCMHRDivKeY358wV8rsUQlCBVvhf3666/W1tbTp083jHTMAgKGIJCUlEQI2bt3ryEmwxwgQHsCMG+ql/D69euDBg0ihLRs2TI+Pr5mtq2SZElZpTCrIE2UL8wq0Ljj51dffUUI+frrr1UG4iMImJHABx984OLikpWVZUYNCA0CFCEA86ZIITTISEtLGzp0KCGkWbNmhw4dMuVSFVKp9MMPP7S1tcXmEBoKgyYzEcjLy/Pw8Bg6dCi+PDdTBRCWQgRg3hQqhkLKnTt3wsLCCCFNmjQ5cOCAKW1boeH169ft27dv3Ljxq1evFI04AAHzEjh+/Phf/52Ni4szrwxEBwGzE4B5m70E/xIgFApHjvx7j85GjRrt27evsrLyX6dN+yEjI8PFxWXo0KEG+a7etNoRjbEERo0aVa9evZcvXzI2QyQGAnoQgHnrAckkXe7duzdmzBgWi8Xj8WJjYymyGeKpU6cIIevWrTMJAwQBgaoJ5OTkuLm5jR49uuqu6AECzCUA8zZ/bdPT08PDw9lsto+Pz86dO8vLy82vSUlBREQEm82+cOGCUhsOQcCcBL799ltCyPHjx80pArFBwKwEYN7mxJ+RkTFx4kQOh+Pt7b1t27aysjJzqtESWywW9+7d293d/dmzZ1q6oBkETEpAKpUOGTLE09MzLy/PpIERDAQoQwDmbaxS5OTkREVFXbp0SSaTqT8c+/jx48mTJ3M4HE9Pzy1btlB8RZSXL1/6+PiEhIRQ7VsBYxUP81KewLNnz+rUqTNhwgTKK4VAEDAKAZi3UbCWlZWNHj2axWItWrRI40Nnw4YNq1+//saNG0tLS42iwNCTXrlyxcrKau7cuYaeGPOBQA0J7Nmz56+1/ZOTk2s4HsNAgM4EYN7Gql63bt26d+/er18/+a/FyjffYrE4JyenpKTEWLGNM++2bdvwlo5x0GLWmhCQSqV9+/b18fEpLCysyXiMAQE6E4B5G6V69+7dGzx48K1btzp27Lho0SK5Tyv7t1GiGnlSqVQ6duxYBweHu3fvGjkUpgcBvQg8efLE0dERS/nqBQudmEUA5m2Uet67dy80NFQmky1evDg0NFQoFBoljMknLSkpadWqVbNmzYqKikweHAFBQAOB7du3E0JSUlI0nEMTCDCXAMzbKLXdtm3bkCFDZDJZfn5+ly5devbsaWtr++DBA6MEM+2k9+/fd3JyGjlyJN2/SDAtNkQzFgGJRBIaGurv70+736GMRQTzWgYBmLdR6hwTE7NlyxaZTLZ3714HBwdHR8dJkyZpfOzcKOGNPOnRo0cJIZs2bTJyHEwPAnoRePjwoZ2d3fz58/XqjU4gwAgCMG+jlHHo0KFBQUF+fn4NGzaMjIwMDg5esmQJxd8HqxaIjz/+mMPh/PTTT9Uahc4gYCQCX3zxBYvF+uWXX4w0P6YFAaoRgHkbpiIq3yFHR0e3aNFi06ZNubm5MpksIiKiWbNmp0+fNkwwCsxSWVkZGhrq5eX14sULCsiBBEsnIBaLO3Xq1KxZMyb9F9nSi4r8dRKAeevEo8fJwsLCEydOqHQsKCiQb8Yl39Lj0aNH69atY9hDXs+fP/f09OzRo4fGF9lVgOAjCBibgFAotLa25vP5xg6E+UGACgRg3jWvQnFx8dq1a+vWrWtnZ/fy5UuVm2/FvNraFR3oe/DTTz9xOJzFixfTNwUoZxKBNWvWcDic69evMykp5AICGgnAvDViqaKxpKQkOjq6Xr16VlZWM2fOfPr0aRUDmHt648aNhJBjx44xN0VkRhsCFRUV7du3b9OmDdbxpU3NILSmBGDe1SNXWlq6cePG+vXrc7nc6dOni0Si6o1nXG+pVPree+85OTkx40U4xtXH4hK6efMml8tdsWKFxWWOhC2MAMxb34K/efNmy5Ytnp6eHA5n8uTJjx8/1nck0/sVFhY2a9asdevWeNGW6aWmR36RkZFcLvf27dv0kAuVIFAjAjDvqrGVlZVt27bN29ubzWZPnDjxjz/+qHqMhfUQCoX29vbh4eEM/oHfwkpK43TLyspatmwZFBSERylpXEVIr4oAzFsXofLy8p07dzZs2JDNZoeHh+ObYR2wvv32W0LI9u3bdfTBKRAwDYHU1FQ2m/3555+bJhyigIDpCcC8NTOvqKiIjY3l8XgsFmvMmDH37t3T3A+tSgRmz55tZWWVmpqq1IZDEDAPgUWLFtnY2Ny/f9884REVBIxMAOatCriysnLfvn3+/v6EkJEjRzJmTxHVPI3wuby8vEuXLj4+Pi9fvjTC9JgSBKpBoLS0tEmTJl27dhWLxdUYhq4gQBMCMO//L5RYLD5w4ECTJk0IIWFhYXjg5f/R6H309OnTevXq9enTB/9i6s0MHY1F4McffySEyHcZMFYMzAsCZiJAe/MuKasUZhWkifKFWQUlZZU1wygWiw8dOtSsWTNCyNChQ9PS0mo2D0bJZLLz58+z2ezIyEjQAAGzE5g1a5a9vX1GRobZlUAACBiWAF3NOz27KCpBGBqd4scX8P73148vCI1OiUoQ/nVWT0wSiSQ+Pr5ly5aEkEGDBmFtJj256e62du1aQohAINDdDWdBwNgEioqKeDxer1698B6EsVFjfhMToJ95Z+aVhsem8vgC/4gkhW0rH8jbw2NTM/NKddCUSCTHjh1r06YNIaR///54zEoHq+qekkgkQ4YMcXFxwR1PddGhv8EJnD17lhCya9cug8+MCUHAjARoZt5x10QBkcnabFvFwgMik+OuaVgBTSqVJiQktG/fnhDSu3fvn3/+2YwFYGroV69e+fv7d+jQ4fXr10zNEXnRhcDkyZOdnJwyMzPpIhg6QaBKAnQy75iUdGV71vM4JiVdQUEqlQoEgqCgIEJIjx49fvzxR8UpHBicwM2bN21tbRctWmTwmTEhCFSLwKtXr7y9vQcOHIgvz6vFDZ2pTIA25h13TaSnW6t3O3xNJJVKz5w506lTJ0JIt27dUlJSqFwVxmg7c+ZMXl6efF9UxiSFROhIIDExkRDyzTff0FE8NIOAOgF6mHdmXmlAZLK6KytaXELHE0Ks6vkqWpQPmkQIOr09kBDSpUuXs2fP4n/f6teB8Vrg3MZji5mrRWDcuHGurq4vXryo1ih0BgFqEqCHeYfHpur4nbvBzK9ZVjYsK1tt5u27+GSTqVtOnz4N26bmVQhVIGACArm5ufXr13/33Xfx74AJaCOEsQnQwLzTs4uUb6PVj+1bdLfltbVp2FqbecuHPMzR9/0xY0PH/DKZTL5pBO7LcTGYkkB8fDwhJD4+3pRBEQsEjEGABuYdlSDUcdvtMXY9YbG9Jm3Tbd7+EUlRCUJjEMSc1SUgN2yFbZf886e6k6A/CNSAgFQqDQsLc3d3//PPP2swHENAgDoEaGDeodEp6nfb8hbfJQlW7n6O7Qfw+ALd5s3jC0I34CE1M1941USwyQAAIABJREFUz58/X7Vq1Xv//BkyZEhQUNDEiRPbtWvXvXv3V69emVkcwlsGgRcvXri6uo4dO9Yy0kWWjCVAdfMuLqtUXkNNxcXr9p3BsnHwmfutPubtxxfUeP1UxtbftInduHGDxWLNmTPn+++/v3DhwpV//nTu3NnV1TUmJsa0WhDNcgns37+fEJKQkGC5CJA5/QlQ3byFWQUqhq346DPvENvWybXXJHlLlXfePL5AmFVA/5LROAOpVMrn8729vd+8eSOTya5fvz5kyJCePXuuW7cOzwDTuK50ky6VSgcNGuTl5YXve+hWOuj9fwJUN+80Ub7CrVUOHDsM5Lp6+S4+ob95p4ny/z91HJmDQHFx8YABA0JDQ48cOdKlS5cBAwZ8++238i3I8AywOQpioTGfPn3q7Ow8adIkC80fadOfANXNW9udt/e0XYTFdu0zvcGMPfK/1t7NuHUbNJixx2denIrNKz7iztu8V6zcntPT052dnT08PGbPnn3x4kW5JDi3eUtjgdG/+uorQsj3339vgbkjZQYQoLp5l2j5zdtjzN/7Vmn849RxqMKtlQ/wmzcVrte7d++OGzeuRYsWLBZrw4YNVJAEDZZJQCqV9u7d29fXt6gIL5Fa5iVA76ypbt4ymUzj0+Y+c791D/tE+a9VPV+Os7t72Cdek7Ype7biGE+bm/1SLS8vHzRoUKdOnfbs2RMVFbV8+XKZTIZ7brPXxWIFPHr0yN7efubMmRZLAInTlwANzFv3e94Kb9b9wBre86bINfrbb7+dOnWKImIgAwS2bt1KCMEeRbgSaEeABuZd5Qprcv/Wbd48vgArrFHq6lTccCtWa6GUPIixEAISieStt95q0qRJaWmphaSMNJlBgAbmLZPJdK9trrj51nbgH5EUHpvKjIIxLAuJRHLjxo3Lly8zLC+kQyMC9+/ft7Gx+fjjj2mkGVJBgB7mXeWuYtpsW94eEJmcmYf/VlPxapdIJP369fPw8MjKyqKiPmiyDAKff/45m82+cuWKZaSLLJlAgB7mLZPJarmfNxNqxdAccnJyvL2933rrrYqKCoamiLSoTqCysrJjx44tWrQoKyujulboA4F/CNDGvGUyWUxKuu47bI1nt6U8RK0pTuCXX37hcrkLFiyguE7IYzCBO3fuWFlZffLJJwzOEakxiQCdzFt+/x0QmaxjkzGFf/tHJAVEJh++JmJStRicy5YtW7BXI4PrS4vUVq5cyeFw0tLSaKEWIi2cAM3MWyaTZeaVhsem8vgCbRYubw+PTcXv3DS6uKVS6fvvv+/o6Pj777/TSDakMolAeXl527Zt27Vrp/ILTklZpTCrIE2UL8wqwOZGTKo4rXOhn3nLcadnF0UlCEM3pCjvOeb3z76fUQlCvBVGx4uyuLi4xT9/iouL6agfmhlA4Ndff+VwOKtXr5bJZP/9RyZa7R+Z6JSoBOFfZxmQL1KgLwG6mreCOP5TrEDBgIN79+45Ojq+//77irfAGZAUUqAXgWXLltm6NRi+5Ty+3qNX4SxNLe3N29IKxvh84+PjCSFbtmxhfKZIkJoE9v/yB2/xCd6SBMUDNNoO5A/WxOHBGmoWkumqYN5MrzAN81uwYAGXy/3ll19oqB2S6U2gZq+0xKSk0zttqKchAZg3DYvGdMkVFRXdunXz9vbOyclheq7Ij0IEsJgEhYoBKVURgHlXRQjnzUEgKyvLw8OjV69elZWV5oiPmBZHQNsyjg6te2vcerjBrK+Vv07HMo4Wd8WYO2GYt7krgPhaCFy8eJHD4fD5fC3n0QwChiSgbQMFz/Eb3AZ/rPR3IcvKxqqer7Jzyx9twwYKhqwH5qqKAMy7KkI4bz4C0dHRhJCTJ0+aTwIiWwQBPbcu5PEFHuM+J4S4hH6gYt7yj3hJ1SIuF2okCfOmRh2gQhMBqVQaFhbm7Oz88CHWuNUECG0GIhCVINS26JOKSTt2GEQIq8GMPSrt8pvvqAShgRRhGhCoggDMuwpAOG1eAgUFBU2bNm3bti22WzZvIZgdPTQ6Rd2M1Vt8F59k2znb+LRUPyVvCd2QwmxQyI46BGDe1KkFlGgmcOfOHTs7uw8++AArt2gGhNbaESguq1ReqFGbMfP4AvcRnxJC6vabqa2PH1+A9VNrVw2M1pcAzFtfUuhnRgIHDx4khOzcudOMGhCaqQSEWQXazFil3b5lD8Lm+sw7pNKu/FGYVcBUUMiLUgRg3pQqB8RoJTBz5kxra+tr165p7YETIFAjAmmifGX31XbccOF3LCsbu8bB2jrI29NE+TVSgUEgUD0CMO/q8UJvcxEoKyvr1KmTr69vbm6uuTQgLpMIFBYW3rhxIz4+fuHqTbr9WH7WbfDHhJB6Qxfr7ow7byZdJFTOBeZN5epA278IZGZmurm59e/fXywW/+sEPoCAdgJSqfTPP/+8cuXKgQMHVqxYER4e3qVLF3d3d8XSKy71PHhLE3VbMo8vsPUPYlnbNfz4qI6e+M1bex1wxsAEYN4GBorpjErg7NmzLBYrKirKqFEwOU0JSCSSZ8+eXbx4cc+ePcuWLRs5cmRgYKCzs7PCpz08PN56660PPvhg1apVhw4dunr1al5enkwmq/Jpc5+53xI2x6FVLx3OzftnS2KaooNs2hGAedOuZJYuePXq1SwWKzk52dJBWHb+lZWVGRkZZ8+e3bFjx8KFC4cNG9aqVSs7Ozu5T7NYLF9f3169ek2dOvXzzz8/evTorVu3ioq07sBd5Xvern2nE0Lqj1qpw7z9I5LwnrdlX5UmzR7mbVLcCFZ7AhKJ5J133nF1dX38+HHtZ8MM1CdQVlZ27969xMTEzZs3z549e8CAAU2bNuVyuXKf5nK5TZo0GTBgwOzZs//zn/8kJibeu3fvzZs31cqryhXWrL2bse1dfKvaJxQrrFULOzrXhgDMuzb0MNY8BPLz8/38/IKCgqr7b7R55CKq3gSKi4tv3bp17Nixzz//fOrUqW+//bavry+LxZL7tK2tbatWrYYNG7Zw4cIdO3Z8//33GRkZhtq6Rtva5jputZVP+S5JeHv1cSxFoHep0bG2BGDetSWI8WYhcOPGDRsbm2nTppklOoLWnkBeXt61a9cOHTq0atWqCRMmvPXWWx4eHoofp52dnQMDA0eOHLls2bLY2NiLFy8+e/ZMIpHUPq62GbTtKqbs0DqOGy05ya3j0bdv34yMDG0h0A4CBiQA8zYgTExlUgJ79uwhhOzbt8+kURkXTCKRZGRknDhx4syZM8ZYg1Yqlb548eLSpUv79u2LjIx8//33O3bs6OrqqvDpevXqdenSJTw8PCoq6sCBA1euXHn58qVZbmFruZ93UlKSr6+vnZ3dhg0bDPV9AOMuNyRkMAIwb4OhxESmJzB58mRbW9ubN2+aPjQzIpaVlX3++edt2rRhsVjNmjV7+vRpbfISi8VPnjw5f/78rl27Fi9e/O6777Zt29bBwUHh0w0aNOjRo8ekSZPWrl0bHx9/48aNggJqrUcWk5Ku4/Za26ltKf/dOKe4uHjevHksFiswMDAtLa02MDEWBHQTgHnr5oOzlCbw+vXrDh06+Pv7v3r1itJCqSquoqJi9+7dx48f37Nnj4eHh/67t5WXlz948CA5OXnr1q3z5s175513mjVrZm1tLfdpNpvt7+/ft2/fjz76aOPGjSdPnhQKha9fv6Yqhn/pirsmCohM1meTMf+IpIDI5MPXRP8aL5Olpqa2adOGw+EsWbLEGF9mqITDR8skAPO2zLozJ+tHjx65uLgMGTLEqD+IMoeXlkweP37MZrOvXr2q8fyTJ09OnDjxxRdfzJgxo0+fPo0aNWKz2XKftra2bt68+eDBg+fPnx8TE3P69On09PTy8nKN89ClMTOvNDw2Vb7Lp8a7bbm1h8emZuaVakyqoqLis88+s7Gx8ff3P3/+vMY+aASB2hCAedeGHsZSgoBAICCErF27lhJq6CmioqKCxWIlJCSoyxeLxfPmzSOEODg4tGvX7r333luyZMlXX3114cIFkUjE4NXu0rOLohKEoRtSlPcc8/tnJZaoBKE+b4U9ePCgR48ehJCJEydiWV/1SwsttSEA864NPYylCoHIyEg2m41bnJrVQ/50mIuLS0xMjPqTYmKx+Pnz59nZ2eqnahaOdqNKyiqFWQVponxhVkF1d/yUSCS7d++uU6eOu7t7XFycxTKkXdGpLxjmTf0aQWHVBMRicd++fd3d3TU+clWbf3yrjk3tHhKJ5OnTpz/88MPu3btXrFihUaz80ejWrVvPnz+f7t94a0zQ7I3Pnz8fMWIEIWTQoEEikepv5GaXBwF0JADzpmPVoFkDgT///LNhw4ZdunRR2M9/v/aMVvvaMzolKkH411kNs9C5qbKy8o8//jhz5sz27dsXLFgwdOjQli1b2trayn+cZrFYPB6vuLhYPUW5eYeFhQ0ZMgQPWKnzMVTLyZMnGzRo4ODgsHnzZgb/3GAoXJhHNwGYt24+OEsnAqmpqVZWVnPmzKn9A0cUT/vNmzd37949derUpk2bZs2a1b9//8aNGytWDLWysgoICBg4cOCcOXO2bNkiEAju379fVlamLSn5s36rVq0KDAx8/vx5aWmpRo/XNhzt+hMoLCycOXPmX4vzd+rU6c6dO/oPRE8QUCEA81YBgo/0JrBjxw7Htv0aL0vU/1WfOLVXfSiFoKio6ObNm99999369eunTJnSs2fPhg0bKlYMtbOza9269fDhwxctWvTll1+eO3fu0aNH1V0hJD09fffu3X379mWxWN7e3g0bNoyJiaEUBIaJ+eWXX1q0aMHlcj/55BMs8cuw4posHZi3yVAjkCkIbL3w9yIbvnpsz6z8ClBMSropxOmMIZVKc3NzU1NTv/3225UrV44fP75r167169dXrHBSp06doKCgUaNGRURE7N2798cff8zKyjLIA1B79uxxdXUdMGDAypUr9+zZk5qaSpd3snUSpfTJsrKyFStWyL8juXjxIqW1QhwlCcC8KVkWiKoRgVoub1mjmDUZJJVKs7Kyfvrpp71790ZERIwePTooKKhOnToKn65fv35ISMj48eNXrlx58ODB1NTU3Nxcg/h0TeRijNEI3L17t2vXroSQqVOnYqEho2Fm5sQwb2bW1QKz0rixhNfk7fbN3uLW8WBxbdh2zjY+rdzfW658z604DohM1rbgRm1gisXix48fnzt37ssvv1y0aNHw4cPbtGljb2+v8GkfH5+ePXtOmTJl3bp133333c2bNwsLC2sTEWPpRUAikezYscPJycnT0/Po0aP4Lxq9ymdGtTBvM8JHaEMS0LilY/2RUbaNAut0G1t3wGzX3lNtfFoRQuoOmK3wbMWBf0RSeGxqbQSVlZXdv39fIBBs2bJlzpw5gwYNCggIsLKykvs0h8Np3Lhx//79Z86cuWnTplOnTt29exffTtcGOJPGPn36dNiwYYSQYcOGPXv2jEmpIRcjEYB5GwkspjUpgfTsIoUN6zjwXZJgVb8Rt66Ptj76LJslk8lKSkpu3759/Pjx6Ojo6dOn9+7dm8fjKVYMtbGxadmy5ZAhQxYsWLB9+/YzZ8788ccfFRUVJiWCYHQjIJVKjx496unpOXbsWI3axWJxZmbmgwcPNJ5Fo6URgHlbWsWZmW9UglCfx8t5fIFd42C2g4tG8/aPSIpKEKoAys/Pv379elxc3Jo1ayZOnNitWzcvLy/Fl96Ojo7t27cfMWLE0qVLd+/e/cMPP2RmZmKVdRWG+Kg/gVevXhUWFqq/Bf769esJEyb4+/s3b948LCysqIhpqxTojwg95QRg3rgSmEAgNDpFox/LGxsuPOoz91vv6btd355CWGz7lj20de60Kvmbb75Zvnz5mDFjOnXqVLduXYVPu7m5de7ceezYsZ9++un+/ft/+eWXnJwc/ELJhKuHDjns2LGje/fuMTExd+/eHTJkSJs2bX7//Xc6CIdGYxGAeRuLLOY1GYHiskrlrSPUjdmx/YD/ejCLbd/sLZ/5h9X7yFt8lyayrGy9vb27d+/+4YcffvbZZ4cPH/7111/xJLDJqolAGgns3LkzJCREfqq0tLRDhw5fffWVxp5otBACMG8LKTST0xRmFWgzY3m799Qv67+/xu2dBXaNg+0CQnzmHNDR/9eMbCbDQm50IyAWiy9evLh06dJWrVr9+uuvMpns8ePHHTp0WLlyJb74oVsxDakX5m1ImpjLLATSRPk6zFjllK1fB2uvpjpWcUkT5ZslCwQFAY0EPvroo5CQkLCwsDFjxnC53LCwsFatWnXr1u3YsWMa+6PRQgjAvC2k0ExOs8o7b2X/rjtgNiHEe+pO5UblY2FWAZNhITdaEXj06JG7u/vly5flT0GeOHGiffv2J0+ezMjIoFUeEGt4AjBvwzPFjCYmUFLVb97K3uzaeyohxPODjcqNimM/vqC6GzabOFmEsygCx48f9/DwkH9bLpPJRCKRl5fXhQsXLAoCktVIAOatEQsaaUZA29PmPnMOKoz57zXPF5+09mjM4to0XPidcrviOHRDCs0yh1xGE3jw4EGHDh327t0rzzItLa1du3Zff/01o5NGcnoRgHnrhQmdKE5A23vedk272PLa/r3C2sA5dbqHc918CCGub09WuLXygcb3vCmeOOQxnsC+ffucnJxGjx49a9asoKCg1q1bK++5LpFI8Nga468BjQnCvDViQSPNCGhbYa3e0MW2fu3ZDi6EzWHbOtr6tde2trncxfVcYY1mdCCX5gSeP38+ZcqU0aNHf/LJJzdu3FDJJiIi4urVqyqN+Mh4AjBvxpfYUhLUuLa58o217uPar21uKaCRp5kIaNz5u7CwMDAwkMVizZs3r7i42EzSENYMBGDeZoCOkMYgoHFXMd2GrXzWSLuKGSNTzGmZBLR9PV5ZWfnFF1/Y2dn5+vomJydbJhwLzBrmbYFFZ2zKdNnPm7EFQGLmI5CRkdG3b19CyNixY3NycswnBJFNRADmbSLQCGMaAjEp6cr303oeb0t5aBp5iAICxiMglUr379/v5uZWt27dr7/+WtuduvEEYGZTEoB5m5I2YpmCQNw1UUBksj6bjPlHJAVEJh++JjKFLMQAAZMQePny5bhx4wghffr0wVouJkFuniAwb/NwR1SjEsjMKw2PTeXxBdosXN4eHpuamVdqVCWYHATMQuD06dM8Hs/Ozi46OrqystIsGhDUqARg3kbFi8nNSSA9uygqQRi6IUV5zzE/vuCvlViiEoR4K8yctUFs4xMoLi5esGABm83u0KGD+gtmxo+PCMYlAPM2Ll/MTgUCJWWVwqyCNFG+MKsAq59SoSLQYDIC165da9u2LYfDWbRokfLqLiYTgEBGIgDzNhJYTAsCIAAClCBQUVGxbt06W1tbf3//c+fOUUITRNSaAMy71ggxAQiAAAhQnkB6enqvXr0IIRMmTMjNza2WXnx3VS1cpukM8zYNZ0QBARAAATMTkEqle/bscXFxcXd3//bbb6t8l+y/T41Eqz01Ev33UyN/nTVzPpYdHuZt2fVH9iAAAhZG4MWLF6NGjSKEDBw48MmTJxqzx/saGrFQqhHmTalyQAwIgAAImILAqVOnfHx8HBwcNm/eLBaLlUNWd6WEOKyUoIzPVMcwb1ORRhwQAAEQoBKBwsLC2bNns1isTp063b59Wy6tZmsUxqSkUykzi9AC87aIMiNJEAABENBI4PLly61ateJyucuWLfvm5z/0XFFYvRtWKtSI13iNMG/jscXMIAACIEADAuXl5atWrbKr58NbfFzdlX0XnXDu/B7HsS6La23tFVB/9Gr1Pjy+APvymbjSMG8TA0c4EAABEKAigXe3XOAtSVA3ZvsWoYTNce4UVnfAbJsGzQmb4zHuc/Vu/hFJ4bGpVEyMoZpg3gwtLNICARAAAb0JpGcXqfsxjy/w/GAjIcSl1yT5Wd9Fx7kuXjYNmmvszOMLsOqw3shr2xHmXVuCGA8CIAACdCcQlSDUuIuPc+cwwmI3XBCvcGuXHh8QQhrM3KdoURz4RyRFJQjpjoIu+mHedKkUdIIACICAsQiERqcoPFj5wNavvZVbQ+WW+u+vIYS4v7dcuVFx/NeuP8aSiHn/TQDm/W8e+AQCIAACFkaguKxSeec9hRPz+AKrer62vLbKLV5TdhBC6vafpdyoOPbjC7D3j2kuH5i3aTgjCgiAAAhQlIAwq0DhvioHXBdPW/8g5UbvGbGEENfeU5UblY+FWQUUzZNZsmDezKonsgEBEACBahJIE+Uru6/ycXXvvHl8QZoov5rxTd1dIpGYOqQR4sG8jQAVU4IACIAAfQjouPOu7m/ePL6AmnfeOTk5MTExISEh5H9/evTo8dVXX+Xl5dGnUP9SCvP+Fw58AAEQAAFLI1Ci/Tdv505qT5uHan3anMcXUPM379jYWFtbWy6XO3jw4E2bNk2cOHHjxo19+/Zls9lOTk7x8fF0rDjMm45Vg2YQAAEQMCQBbU+bq73nfYLr6mXt3Uz5q3XlY6o9bV5RUTFp0iRCyNSpU//8808VZM+fPx89ejQhZMGCBbT7Lh3mrVJNfAQBEAABiyOg7T1vHl9g37zb3yusdZavsNbi7xXWxq5XNmzFMQXf854/fz6Xy/3666+1VVQqlW7evJnFYq1Zs0ZbH2q2w7ypWReoAgEQAAHTEdC2whqPL/BddNy5UxjHwZVwrKy9mtYftVLh1uoHlFph7ciRI3+91bZ169YqOX766acsFuvs2bNV9qROB5g3dWoBJSAAAiBgNgLhsakaF1lTd2iNLb6LTwZMjxEKqbLCWl5enrOz8/vvvy+VSqtkKhaL+/Xr5+Xl9fr16yo7U6QDzJsihYAMEAABEDAngcy80oDIZI3GrE9jkwhB0/ZduFzukiVLSkpKzJnJP7EjIiLs7e1zcnL0VPLHH39wOJxNmzbp2d/s3WDeZi8BBIAACIAAJQjEXRPp49Ma+xy+JiorK1u9erWtra2vr+/JkyfNmFJubq6jo+OSJUuqpWHSpEkeHh6lpaXVGmWuzjBvc5FHXBAAARCgHIGYlHSN3qy7cVvKQ0UmGRkZAwcOJIQMGTLk8ePHinZTHmzcuNHGxubly5fVCpqRkcFisXQ83Vat2YzdGeZtbMKYHwRAAAToRCDumiggMlmf37/9I5ICIpMPXxOppCeVSv+vvXuNiSq94zj+IDNyWXZQdN2V4VbToG4i1mwElV1Iqm0RNKbNNho6qC9AqW4kKJaR0JDUUg2Y1F4WtuloYqPSKmJpRt6YoDFeCF4SZHwhm5AMEYNyEUECwuBUnTg7y2XOzHCZ56xfY+KZ5/zPOf/zeTC/DDPnnOrqar1eHxIScuTIkZcvX44pmOmXa9as2bJliw9HSU5O3rRpkw8bzv4mhPfsm3NEBBBAQGqBtu4Bg6kh1mieLMId4wZTQ1v3pL9k7uvrO3DgQGBg4PLly69cuTJrJ9zW1iaEOH36tA9HPH78+Ny5c3t7VXB7dsLbh/llEwQQQOCHL9DS0VdSa0kpr3d95lic0fz6TiwltRYPrwprampat26dECIrK8vzr49NBbeyslKj0fgWwI7gP3/+/FQamJ1tCe/ZceYoCCCAgFoFXgyNWNp771l7LO29Pjzxc3R01GQyRUREzJs3r7Ky0mazzSjErl27EhISfD5EZGRkUVGRz5vP2oaE96xRcyAEEEDg/RXo7Ox03Kk0MTHx7t27MweRlJRkMBh83n9aWpoqPvYmvH2eYjZEAAEEEPBO4Pr16ytWrAgKCnry5Imb26cMDg56t9931TabLTQ0tLy8/N2A1/8WFhZGR0d7vdmsb0B4zzo5B0QAAQTeY4Hh4eFr165N9iCQpqamgoKCrVu37tixw4crzVpaWoQQU7nR6dmzZ4UQ8j8qlPB+j/8PceoIIICATAI2m02v169du7awsDA7OzssLOzMmTNeNXjhwgUhREdHh1dbuRZbLBYhxNWrV10HJVwmvCWcFFpCAAEE3keBw4cPR0VFPXv2zHHyR48eNRgMbn67Pt7o1KlTQoipXFn++PFjIYTZbB6/c6lGCG+ppoNmEEAAgfdXIC8vLzU1dejtH7vdfvHiRZ1O19fX57kI4e25FZUIIIAAAghMg0BVVVVkZGRra6tjX/n5+evXr+/q6vJ814S351ZUIoAAAgggMD0Cu3fvDggI2Lt3b2Zmpkaj2b59u1f7Jby94qIYAQQQQAAB3wVu3brV2dnp2L6xsXHPnj3p6ek6ne7mzZte7ZTw9oqLYgQQQAABBHwUePr0aV5eXllZmd1ud1xCVlFRkZSUlJub6+0eCW9vxahHAAEEEEDAR4Hq6urw8PANGzbk5+enpaXpdLp9+/b19PQ4dvfq7R9Pdk14e6JEDQIIIIAAAtMjMDg4uH//foPBkJubW1dX57zJms1ma25uTk1NbW5uVjxSdXW1EGIqT0B58OAB13krOlOAAAIIIIDAdwLDw8PfvXi3dPv27aVLl2o0moMHD/b3978bnuDfhw8fCiEuX748wTrPhqqqqrjDmmdUVCGAAAIIIOBWYGhoqLS0NDg4ODo6uqamZrI7t9hstpCQkGPHjrndmbuVRqMxKirKXYUc67hJixzzQBcIIIAAAkoCra2tGRkZQoiMjAzn5eBjNkpMTMzKyhoz6HjpybNNN27cmJ6ePuHmUg0S3lJNB80ggAACCLgTePXqVU1NTVRUVEhISGlp6fg7oebk5KxcudJ1Fy0dfSW1lpSy+jijOfbd3zijOaWsvqTW8nqta7Ferz906JDriJzLhLec80JXCCCAAAKTCvT39xcUFAQGBi5btqy+vt61rqKiQqvVPn/+3G63t3UPGEwNsUbzkqJLzth2XXCMG0wNbd0Ddrv90aNHQohz58657lDOZcJbznmhKwQQQAABBYH79+8nJycLIV5/R935JDGr1ep4skhVozU6XLvUAAAGJElEQVS+uG6y2B4T4fHFdVWN1hMnTmi1WueTURQO79fVhLdf+Tk4AggggMAUBEZHR0+ePLlgwYLw8PCvv/7aZrPZ7fbExMTcv150jWcPl3/zp3+p4gNvu91OeE/hp4ZNEUAAAQQkEOjq6srOzhZCrF69+s6dOzsP/9PDtB5f9tWfz0pwQsotEN7KRlQggAACCMgvcOPGjYSEBO38xT8qrB2fyrFG8yc7j4f8OHFOcFiAJki7MGb+hl3jy+KL6xyff0t+voS35BNEewgggAACngqMjIx88fv/xPxugvBetPUPIlAzN3Lp/J9mR6R9pVvzpS7pV+PDe0nRJYOpwdPj+a+O8PafPUdGAAEEEJhWgZaOvvF5HGs0R+efm/PBvJD4tTGF/5uwYMzgt0++d/3YtPY4PTsjvKfHkb0ggAACCPhdoKTWMuHXyyN+sUcIEZld+SbI91e7j/AlRZdKai1+Pxf3DRDe7n1YiwACCCCgGoGUsvox76EdL0OXrgsICl207Y+aCL0QIkAbHLZqY0xBzYTFsUZzSvn3rh2X8PwJbwknhZYQQAABBLwW6B8acb2Hmmswaz+KC9AGBWiDPvxs80e/LPrws81CiNDlKa41rstxRvOLoRGvO5jFDQjvWcTmUAgggAACMyZgae91DWDXZc28T4QQYas2OgfDfpL25hfpu/7hHBmzYGnvnbFOp2HHhPc0ILILBBBAAAG/C9yz9owJYOdL7cIYIcTHmUedIx9nHhVCLMjId46MWbhn7fH7GblpgPB2g8MqBBBAAAHVCLh55x0ct+rN++ycb5wJHZlTKYSYvz7HOTJmgXfeqpl4GkUAAQQQUK/Ai8k/89at+VIIsWhbqTOhF20rFUIs3FzgHHFd4DNv9f4Y0DkCCCCAgMoEJvu2+eKdf3nzDbVPU50JHfppqpgTqN97yjniusC3zVU28bSLAAIIIKBegcmu8441mj9I+Nmb/F72RcTPfxu67HMhhG7tr10D27nMdd7q/QGgcwQQQAAB9QlMdoe1WKM55uB/wz/PDNQtEnM0mvmL3XzaHWs0c4c19c09HSOAAAIIqFfAYGqY8CZrzjfW7he4t7l6p57OEUAAAQTUKtDWPRBfXOc+od2s5aliap14+kYAAQQQULVAVaPVTTy7X/XvRqsqzp3rvFUxTTSJAAIIIOCFwN/qW9yH9IRr/17/rRfH8Gsp4e1Xfg6OAAIIIDAzAlWN1vjiOk8+/15SdCm+uE4t77kdWoT3zPzUsFcEEEAAAX8LtHUPGEwNsUbzZBHuGDeYGtq6B/zdrHfHJ7y986IaAQQQQEBdAi0dfSW1lpTyetdnjsW9fe5nSa1F/qvCJtQmvCdkYRABBBBA4Icm8GJoxNLee8/aY2nvlfyJn4r0hLciEQUIIIAAAgjIJUB4yzUfdIMAAggggICiAOGtSEQBAggggAACcgkQ3nLNB90ggAACCCCgKEB4KxJRgAACCCCAgFwChLdc80E3CCCAAAIIKAoQ3opEFCCAAAIIICCXAOEt13zQDQIIIIAAAooChLciEQUIIIAAAgjIJUB4yzUfdIMAAggggICiAOGtSEQBAggggAACcgkQ3nLNB90ggAACCCCgKEB4KxJRgAACCCCAgFwChLdc80E3CCCAAAIIKAoQ3opEFCCAAAIIICCXAOEt13zQDQIIIIAAAooChLciEQUIIIAAAgjIJUB4yzUfdIMAAggggICiAOGtSEQBAggggAACcgkQ3nLNB90ggAACCCCgKEB4KxJRgAACCCCAgFwChLdc80E3CCCAAAIIKAoQ3opEFCCAAAIIICCXAOEt13zQDQIIIIAAAooChLciEQUIIIAAAgjIJUB4yzUfdIMAAggggICiAOGtSEQBAggggAACcgkQ3nLNB90ggAACCCCgKEB4KxJRgAACCCCAgFwChLdc80E3CCCAAAIIKAoQ3opEFCCAAAIIICCXAOEt13zQDQIIIIAAAooChLciEQUIIIAAAgjIJUB4yzUfdIMAAggggICiAOGtSEQBAggggAACcgkQ3nLNB90ggAACCCCgKEB4KxJRgAACCCCAgFwChLdc80E3CCCAAAIIKAr8H+3IhZaFCbhwAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "mttI1nzY4WZb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "C9wIhtZ133_b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "i0bbLR_a4GtE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "kNf-KzBd4R_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pygraphviz as pgv\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "def initialize_graph(num_nodes):\n",
        "    G = pgv.AGraph(strict=False, directed=False)\n",
        "    for i in range(num_nodes):\n",
        "        G.add_node(i)\n",
        "    return G\n",
        "def add_random_edges(G, num_edges):\n",
        "    for _ in range(num_edges):\n",
        "        node1 = random.choice(list(G.nodes()))\n",
        "        node2 = random.choice(list(G.nodes()))\n",
        "        weight = random.randint(1, 10)\n",
        "        G.add_edge(node1, node2, weight=str(weight))\n",
        "    return G\n",
        "def visualize_graph(G):\n",
        "    G.layout(prog=\"neato\")\n",
        "    G.draw(\"AWlCTP_ACO_Solⁿ.png\")\n",
        "    img = plt.imread(\"AWlCTP_ACO_Solⁿ.png\")\n",
        "    plt.imshow(img)\n",
        "    plt.show()\n",
        "def ant_colony_optimization(G, num_ants, num_iterations, evaporation_rate, alpha, beta):\n",
        "    pheromone = 1 / len(G.edges())\n",
        "    pheromone_matrix = {}\n",
        "    for edge in G.edges():\n",
        "        pheromone_matrix[edge] = pheromone\n",
        "        pheromone_matrix[edge[::-1]] = pheromone\n",
        "    best_solution = None\n",
        "    best_cost = float('inf')\n",
        "    for iteration in range(num_iterations):\n",
        "        solutions = []\n",
        "        costs = []\n",
        "        for ant in range(num_ants):\n",
        "            solution = []\n",
        "            current_node = random.choice(list(G.nodes()))\n",
        "            while len(solution) < len(G.nodes()):\n",
        "                neighbors = list(G.neighbors(current_node))\n",
        "                if not neighbors:\n",
        "                    break\n",
        "                probabilities = [\n",
        "                    (pheromone_matrix[(current_node, neighbor)]**alpha) *\n",
        "                    ((1 / float(G.get_edge(current_node, neighbor).attr['weight']))**beta)\n",
        "                    for neighbor in neighbors\n",
        "                ]\n",
        "                total_probability = sum(probabilities)\n",
        "                if total_probability == 0:\n",
        "                    break\n",
        "                probabilities = [prob / total_probability for prob in probabilities]\n",
        "                next_node = random.choices(neighbors, probabilities)[0]\n",
        "                solution.append((current_node, next_node))\n",
        "                current_node = next_node\n",
        "            cost = sum([float(G.get_edge(edge[0], edge[1]).attr['weight']) for edge in solution])\n",
        "            solutions.append(solution)\n",
        "            costs.append(cost)\n",
        "            if cost < best_cost:\n",
        "                best_cost = cost\n",
        "                best_solution = solution\n",
        "        pheromone_matrix = {edge: (1 - evaporation_rate) * pheromone_matrix[edge] for edge in pheromone_matrix.keys()}\n",
        "        for solution, cost in zip(solutions, costs):\n",
        "            for edge in solution:\n",
        "                if edge in pheromone_matrix:\n",
        "                    pheromone_matrix[edge] += 1 / cost\n",
        "    return best_solution, best_cost\n",
        "def main():\n",
        "    num_nodes = 10\n",
        "    num_edges = 15\n",
        "    num_ants = 5\n",
        "    num_iterations = 50\n",
        "    evaporation_rate = 0.1\n",
        "    alpha = 1\n",
        "    beta = 2\n",
        "    G = initialize_graph(num_nodes)\n",
        "    G = add_random_edges(G, num_edges)\n",
        "    visualize_graph(G)\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "meKCWNBZ2j4y",
        "outputId": "66c4c08a-4367-4343-b6c8-43a53f885b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JUo3k4eFBjx490Ol0LFiwgJdeesm+P6V0PSEEycnJ7Nq1i+DgYLp163ZLz7X9+fvb2bNnWbRoEYsWLSIjI4PWrVvz3nvv0atXL6edJPR7MnlKklQj2dZ4DhkyhBkzZjBx4kSHl8BzVhaLhaNHj3Ls2DEGDBiAu7v7TRepN5vN5OXlce7cOQ4ePMiuXbvYuXMn165dIywsjGHDhjF8+HAaNmyIh4dHJb+TiiOTpyRJNZarqyvPPfcc33zzDdOnT2fSpEkOn5TjjPbs2cP69espLS1FpVLx9ddf2+vRenh4UFRUhF6vR6/X2/+enp7O5cuXuXbtGkVFRSgUCry9vUlKSuLJJ5+kQ4cOtGrVCjc3tyrZ2pfJU5KkGkuhUBAeHs6ECRP49NNP6d+/P3Xr1nXoptjOpqCggDVr1nD48GFcXV356KOPUCgUCCEwm82Ul5fj5eWFj48PXl5eeHl52SsEtWjRgoiICCIiIoiKiiIyMhIXFxdUKhUqlco+AaoqJk+FcPYdR/+EXq/Hx8eHwsJCvL29HR2OJElVmG1RfpcuXahTpw4zZswgNDS0Sl7QK5rVamXq1Kn88MMPJCYmMnHixBsqCwkh7K3Q3/9pS5AqlQq1Wm3/u7N/rjebX+TXK0mSarzAwEDee+89xo0bx6xZsxg7diwhISGODsvhfvrpJ1asWEHjxo0ZOXIkkZGRDl0u40zkpyBJUo1may117tyZRx55hB9//JGlS5eSnp7u6NAcatu2bcyYMYOgoCAGDBhAw4YNZeL8HdnylCRJAry9vXnkkUfIzMxk2bJlqFQq7rvvPsLDwx0d2l23b98+pk6dilKpZODAgbRp06ZKzYS9G+TXCEmSpP8TGxvL2LFjCQsLY/Xq1SxdupTz58/f9LKM6uDXX39l6tSp5Obm8sADD9ClSxen2/PUGcjkKUmS9DutW7dmzJgxBAYGsnbtWhYsWMDJkyedYleTymLbmm3nzp1MmzaN8+fPM3z4cPr27UtwcLCjw3NKsttWkiTpDzp16oSXlxdz5sxh/fr1ZGVlMWLECBo3boxWq3V0eBXKarWi1+s5dOgQH374IRkZGbzwwgv069cPLy8vR4fntGTylCRJ+hMJCQkEBASwePFifvjhB06dOsWECRNo27YtAQEBqFQqR4d4R2ytzczMTHbs2MF///tfvL29+eyzz2jbtq3Dtz9zdnKdpyRJ0t8oKyvj119/5eOPP+bIkSP07duXyZMnEx4ebp9E4+xrF3/PVtyguLiY06dP8/nnn7N+/Xq6d+/OtGnTCAoKqlLvp6LdbH6p8DHPN954A4VCcd2tQYMG9vvLy8sZP348/v7+eHl5MWjQIDIzMys6DEmSpArh4eFBt27dmDdvHq+//jobNmygQ4cOTJs2jby8PCwWi73YuTMTQmCxWCgrK+PgwYO8+uqrDBo0iGPHjvHll1+yePHiGp84b0WlTBhq3Lgx6enp9tvOnTvt9z3zzDOsWrWKJUuWsG3bNq5du8bAgQMrIwxJkqQKExAQwNixY9m/fz+jRo3i7bffplGjRrz33nukpqZiNpuv2zXEGfw+nuLiYlatWmWfCLRlyxaef/55fv31V4YMGWJv7Eg3p8K7bd944w1WrFjBkSNHbrivsLCQwMBAFixYwAMPPADAmTNnaNiwIbt3777p/dtkt60kSY7w+8tldnY2n376KTNmzKC8vJxu3boxZMgQunTpQmhoqFMUFBBCsH//fhYuXMiKFSvIzs6mdevWPPLII/Tr18++9ZdMmv+fQ8vzpaSkEBYWhru7O0lJSbz77rtERkZy8OBBTCYT3bt3tz+2QYMGREZG/m3yNBgMGAwG+7/1en1lhC1JkvS3bElGCEFgYCBlZWUoFAqeffZZ9u3bxzPPPINKpaJp06b06NGDLl26EB8ff0M92MoihMBgMLB3715WrVrF6tWrSU1NJSYmhmHDhtG/f38aNmyIRqOpEnVmnVmFJ8/ExES+/fZb6tevT3p6Om+++SYdOnTgxIkTZGRk4OrqesNGp8HBwWRkZPzlMd99913efPPNig5VkiTptlitVt5//33mzJnDW2+9xbBhw1Cr1Vy4cIG9e/eyfft2Zs2axfvvv49Op6Np06a0bt2aJk2a0KBBA2rVqnXHyUsIQWFhIampqaSkpHDy5En279/PsWPHKCoqIjo6mr59+9K9e3fi4+Px8fHBzc0NtVotk2YFqPTZtgUFBURFRfHJJ5/g4eHBo48+el0rEn5blNylSxfef//9Pz3Gn7U8IyIiZLetJEl3ncFgYM6cObz++us8+eST9gmQSqUSo9FISUkJxcXF5OTkcOHCBY4fP87Zs2dJSUkhJycHNzc3vL29iYiIICgoiICAAPz8/PD29kaj0eDm5oaLiwtubm72a195eTklJSXk5uaSnZ1NTk4O6enp5OTkUFJSgkKhICgoiLp169KgQQOaNWtG3bp18fb2RqvV4u7u7hTdyFWB0+yqotPpqFevHufOnaNHjx4YjUYKCgqua31mZmb+7Q4Gbm5ucoNaSZIcrqSkhFWrVvHxxx8zcOBAHn/8cXvihN8213Z1dcXX15ewsDDq1q1L69atKSwspKCggNzcXFJTU0lLS6OgoICioiIyMjIoLS2lvLwcs9mMyWTCYrFgsVjsW3m5uLjg6uqKRqPB09MTjUZD48aNCQwMJDw8nJCQEHx9ffHx8UGn06HT6fDw8JAtzEpU6cmzuLiY8+fPM3LkSBISEnBxcWHTpk0MGjQIgLNnz5KamkpSUlJlhyJJknTb9Ho9W7duZdq0abRo0YIJEyZQq1atv0xQKpUKrVaLVqslIiLCvr6yoKCA/Px8ioqKKCsrs9+MRiNms9meOC0Wy3X7Ybq4uODh4WG/eXp64u3tjbe3N15eXnID77uswj/t559/nn79+hEVFcW1a9f4z3/+g0qlYtiwYfj4+PD444/z7LPP2rspnnrqKZKSkm56pq0kSdLdptfr2b17N7Nnz7Zft+Li4m7pGAqFAhcXFwIDAwkMDKykSKW7pcKT55UrVxg2bBi5ubkEBgbSvn179uzZYz9ZPv30U5RKJYMGDcJgMNCrVy+++OKLig5DkiSpQuj1evbu3cv8+fPJzc3lpZdeon379o4OS3IwWZ5PkiTpL5SUlLBv3z6+++47Tp8+zZNPPsnIkSMdHZZUiRxWnk+SJKk6MBgMHD58mO+++46TJ0/yyCOP8NBDDzk6LMlJyBFmSZKkPzCbzRw/fpwvvviC8+fPM2rUKEaPHi1nr0p2MnlKkiT9jtVq5cyZM0yZMoWsrCyeeOIJRo0aVeW3IJMqlkyekiRJ/0cIQXJyMuPGjaO8vJznn3+egQMHysQp3UAmT0mSpP9z8eJFRowYAcB///tfunXrJtdPSn9KnhWSJEn8tsxu0KBBWK1Wpk2bRuvWrWUdWOkvyeQpSVKNJoQgJyeHBx98ELPZzMyZM2nWrBlubm4ycUp/SSZPSZJqLCEEubm5jBw5ktzcXGbOnEnTpk1xd3eXiVP6WzJ5SpJUI1mtVgoKCpg8eTJnz57l008/pWXLlrKgunRTZPKUJKnGsVqt5Obm8sknn7B9+3YmT55Mt27d0Gg0MnFKN0UmT0mSahQhBNnZ2cyfP5/Vq1czfPhwhgwZgpeXl0yc0k2TyVOSpBpDCEFGRgY//fQTixcvpm3btjz55JP4+fnJxCndEpk8JUmqEYQQZGVlsW7dOr777juioqJ44YUXCA0NdXRoUhUkC8NLklTtCSHIz89n3bp1zJs3D19fX15//XXq1Knj6NCkKkomT0mSqr3S0lJWrlzJnDlz0Gq1vPHGG7e8mbUk/Z7stpUkqVozmUwsXryYWbNmER4ezksvvURCQoKjw5KqOJk8JUmqtqxWK7NmzeKLL76gefPmTJw4USZOqULI5ClJUrX11Vdf8cEHH9CrVy/Gjx9PfHy8o0OSqgk55ilJUrX0zTffMGXKFO69914mTpxIfHy8XI4iVRjZ8pQkqdpZvHgxL7/8MkOHDuXpp58mJiZGJk6pQsnkKUlStSGE4Oeff+a5555j4MCBTJw4kaioKLmZtVThZPKUJKlCGY1G9Ho9+fn5FBYWYjKZKCsrw2KxIIRApVLh7u6Oi4sLWq0WnU6Hj48PGo3mjl7XYrGwa9cunn/+eZKSknjmmWeIjIyUm1lLlUKeVZIk3ZacnBzOnz/PlStXuHbtGteuXSM7Oxu9Xk9ZWRnl5eUYjUasVitms9nebWpLoEqlEldXV1xdXfHw8MDLywt/f3/Cw8MJCwsjIiKC2NhYgoKC/rHL1WQycfToUf7zn/8QFBTEiy++SJ06dWTilCqNPLMkSfpHVquV0tJSzpw5Q3JyMufPnyc1NZXs7GysVitqtRoXFxfUajWenp4EBQWh1Wrx9PRErVbj6uqKQqFAoVBgtVoxmUyYzWbKysooLi6mpKSEsrIyrl27xuXLlzGbzVitVoKCgoiMjCQmJoY6depQr149fH19r0uKRqORM2fO8PHHH1NUVMRbb71FixYt5BinVKlk8pQk6U9ZLBYKCwu5cOECFy5c4Pz585w9exa9Xk95eTkeHh6Eh4cTHBxMUFAQQUFB+Pv74+Pjg1arvS55urm5XXdso9F4XfIsKiqyd/VmZmaSmZlJRkYG165dY+/evRw9ehRPT097Eo2JiSEmJgY/Pz8uXbrE7NmzOXnyJJMmTaJXr172RC1JlUUmT0mSrlNaWkpmZiaXLl3i9OnTHD58mLS0NPR6PSEhIcTFxdG4cWMaNWpEnTp1bmvzaFt3rUajwd/f/4b7hRCYzWZSUlI4deoUZ86c4dSpU2zfvp1ff/2VkJAQGjRoQFRUFMnJyWzYsIFHH32UUaNGycQp3RUyeUqSBEBZWRnZ2dmcPn2anTt3smfPHjIzMwkNDaVt27a0a9eOxMREtFptpceiUChwcXGhUaNGNGrUyB7f8ePH2bNnD7t372blypUUFBRQUlJCvXr16NOnD+Xl5Xc88UiSboZCCCEcHcSt0uv1+Pj4UFhYiLe3t6PDkW6TEMLewjCbzVgslutuv38MYG9RKBQKlEolKpXKfrONt9keJ908i8VCfn4+J06cYMmSJWzbtg21Wk2bNm3o168f7du3x8fHx9FhXsdgMJCSksKaNWtYuXIlly9fJi4ujgkTJpCYmIifnx8qlUqeC9Itu9n8IpOndNdYrdYbEmRpaSmpqalcunSJrKwsMjIyyMnJIT8/HyEEJSUlmM1mANzc3HB3d0etVqPT6fD397ePtdWtW5fo6GhcXV2vS6pKpRKlUhbS+iPbr73RaCQtLY23336bNWvWEBERwYgRIxg4cCDR0dFOn3yEEOj1en7++WemTZvG4cOH6d27N2+++Sa1a9fGzc1NduNKt0QmT8mhfn9aWa1WhBCkp6dz9OhR9u3bx9GjRzl79iypqakYjUYUCgVarZbw8HCCgoIICAhAoVDg5eVlb1EaDAbKysowm83k5uaSlZVFeno6er0e+G0cLTY2loYNG5KQkEDr1q1p2LAh/v7+111Aa/qF1PZ/YzKZmDFjBm+88Qbe3t688sorDBw4EF9f3yr3Gdl6MNauXcsLL7xAZmYmzz33HE888YT9XKpq70lyDJk8JYexnVIWi4WdO3eyatUq1q9fz/nz57FarURHR9O0aVPi4uKoX78+9evXp27durc1liaEoLi4mOTkZE6ePMmZM2c4evQohw8fJisrC41GQ6NGjejZsye9e/emdevW9mozNfViarFYSEtL44knnmDLli1MnDiRl19+GZ1OV+Vb6UIIysvL+eCDD/j0009p0KAB77zzDm3atJFjodJNkclTcgiTycSZM2eYPXs2CxYsICcnhwYNGtCzZ086depEQkICQUFBqNXqG1qCt5PMfn/62v4uhMBkMnHx4kX279/P9u3b2b59OxcvXiQ4OJjBgwczevRoGjVqVOMSqF6vZ+PGjTz99NNoNBrmzp1LYmKiPWlW9c/j9+fAsWPHmDhxIufOnWPcuHGMHDmSqKgoB0coOTuZPKW7Kjs7mw0bNvDtt9+yd+9eQkNDGTJkCP379yc2Nva6sci70YUmhMBqtdqr25SUlNgnmPz444+kpaXRrl07xo8fT48ePXB3d6/yieOfXL16lcWLF/PRRx/RsWNHPvnkE4KDg1EqldXyvVssFvLy8vj4449ZsWIF7dq148knn5T7eUp/SyZP6a44f/48q1evZunSpaSlpZGQkMDAgQPp1KkTWq0WjUZzXSvTUWyt0bKyMvR6Pdu3b2fRokXs37+f+Ph4HnvsMXr06EFAQIBD46wsZ8+eZc6cOSxfvpx+/frx4osv4u/vX+W7af+J1WpFr9ezYMEC5s+fT0BAABMnTqRr166ODk1yUjJ5SpUqOzubZcuWsXz5cjIzM4mPj+e+++6jefPmBAYG2scvHZ00/8h2uhcVFZGVlcWhQ4dYtWoVx44do169eowYMYJOnTrh6+vr4EgrTkpKCrNnz2br1q307t2bMWPGEBoa6nT/N5VFCEFeXh7r1q1j8eLFKJVKXnzxRdq2bevo0CQnJJOnVCmsViubNm1i0aJFJCcnU7t2bTp37kxCQgLR0dFV7v9Dr9dz8eJFdu7cybp16yguLiYxMZH777+f1q1bV/mW2dWrV5k1axbbt28nMTGRJ554osaO++Xk5LBmzRoWL16MVqtlypQp1K9f39FhSU7mZvOLrDAk3bSioiIWLVrE8uXLUalU9OnTh86dO9OoUSN0Op2jw7st3t7eNG3alLCwMOrUqcOmTZs4duwYV65c4b777qNPnz5V7guBjcFgYOXKlezevZu4uDhGjBhRYxMnQEBAAD179qSsrIzvv/+eGTNm8NZbb+Hp6eno0KQqSCZP6R8JIcjKyuK7775j5cqV1KpVi379+tG5c2dCQkKqfOsMIDAwkJ49exITE8OGDRvYsmULc+fOJTc3l0GDBhEcHOzoEG/Z7t27Wbt2LSEhITzwwAP2Mnc1WUhICL179yYzM5OlS5eSlJTE4MGDHR2WVAXJ5Cn9LSEEmZmZzJ8/nzlz5tC2bVvGjh1LXFwcHh4ejg6vQimVSurXr09wcDB16tRhwYIFzJs3D6PRyJAhQwgNDXV0iDctPz+f77//HrPZTJ8+fWjZsmWNGeP8OwqFglq1ajFo0CCOHDnC9OnT6dixY5X8ciQ5VtVvMkiVxmq1kpuby6JFi/j888/p1q0b77zzDgkJCdUucf6eTqejZ8+evPzyyzRr1oyZM2eycOFC8vLyqCpTBHbs2MHBgwfp0qUL7du3d8j/l63qz6VLl9i/fz87d+5k7969nDt3jvLycod9lmq1mpiYGMaMGcPZs2dZu3btdTWUJelmyAlD0p+yWq0UFRWxfPlynn/+eUaNGsXbb79dI9ZD2tha3R999BHfffcd77zzDsOHD3f6z6C8vJzBgwcjhODVV1+lTZs2DonDZDJx+fJlPv30U/bt24der8fT05MmTZowYcIEGjdu7NAvYTk5OTz99NOcPHmS3bt339bWalL1c9P5RVRBhYWFAhCFhYWODqVaslqtoqioSKxcuVIEBweLMWPGCLPZLKxWq6NDu+usVqsoLCwU48ePF97e3mLt2rXCaDQ69Wexa9cuER4eLr755huRm5vrkBisVqtIS0sTI0aMEP7+/uLHH38Uer1e7Nq1S8TGxoo+ffqIQ4cOCYvF4pD4hBDCYDCI3bt3CxcXF7F+/XqHxiI5j5vNL7LbVrqB0Wjk8OHDPPXUU7Rr147p06dX2yo0/8RWsP6DDz6gW7duPPLII6SmptqL3TsbIQQ//PADoaGhtGzZEj8/P4fEUV5ezsmTJ1m+fDlDhw5l4MCBaLVa2rZty+jRozlw4AAHDx6ksLDQIfHBbxsJxMfH06JFC7777jvZdSvdkltOntu3b6dfv36EhYWhUChYsWLFdfcLIXj99dcJDQ3Fw8OD7t27k5KSct1j8vLyGDFiBN7e3uh0Oh5//HGKi4vv6I1IFUMIwcmTJ3nrrbfw8/Pj66+/xsXFpUYmThuFQoGHhwdz587F19eXf/3rXxQUFAD/f7/RW71VFqvVysqVK+nVq5dDqyXp9XpOnjyJSqWiZcuW193XuXNnvLy8OHjwINeuXXNQhL9xcXFh5MiRLF++HIPB4NBYpKrllmfblpSU0LRpUx577DEGDhx4w/0ffPABU6dOZe7cucTExPDaa6/Rq1cvTp06hbu7OwAjRowgPT2djRs3YjKZePTRRxk7diwLFiy483ck3ZFz586xePFiLl26xLx586pVpZ075eXlxdy5c+nevTsTJkyw188tLS3FaDQCoFKp8PLyQqvVotVq8fb2xtfXl8jISGJiYoiOjsbHx8e+zVpFO3PmDDk5ObRp08ahG1j/vn7xH8c1Y2JicHV1JTU11aEtT/ht8lCXLl0oLS3lyJEjtG7dutL+b6Tq5ZbPkj59+tCnT58/vU8IwWeffcarr75K//79AZg3bx7BwcGsWLGCoUOHcvr0adavX8/+/fvt30inTZvGPffcw0cffURYWNgdvB3pTphMJvbt28eWLVsYNmwYrVq1qtEtzt9TKBQIIWjZsiWjR49m8eLFxMXF2ZOjbdNli8VCcXExGRkZnDt3jpKSEvLy8sjMzEQIgVKpxNfXlzp16tCsWTNatmxJQkICsbGxuLi43HGc+/fvx93dnQYNGjh0Mo5GoyEmJgaLxUJycvJ19/n6+qJSqSgsLKS8vNxBEf5GoVBQt25d/P392bdvHwkJCTJ5SjelQs+SixcvkpGRQffu3e0/8/HxITExkd27dzN06FB2796NTqe7riune/fuKJVK9u7dy/3333/DcQ0Gw3VdKrbNj6WKdfjwYTZt2oSfnx+PPfaYfd9LR/jiiy+YNm3aDf/XXl5e9OjRg88///yux6RQKFCpVEyaNImVK1dSr149xowZQ2RkpP1Lxh+7Z207u5SXl5OWlkZaWhpXrlzh/PnzHDp0iBUrVmA0GgkODqZr16488MADJCUl2V/vVqWlpREaGurwGcEajYb4+HgSEhKYM2cOXbp0oWXLluj1eg4dOkRxcTHe3t5YrVaHxWijUqmIiYnhypUrThGPVDVUaPLMyMgAuGHBcXBwsP2+jIwMgoKCrg9CrcbPz8/+mD969913efPNNysyVOkPhBAcPnyYK1eu0K1bN8LDwx0aj631ZhtbtPH29r7hZ3dbeHg4/fv3Z+/evZSVlaHT6f4xUVmtVkJCQmjWrBlGo5Hy8nJKS0vJzc3l/PnzHD58mL1797Jq1SoaNGjAQw89xMCBA295R5rc3Fx7y86RyVOpVBIeHm7flHry5MmYzWZ8fX2JioqiqKgIjUaDq6urw2KE//8FJSAgoEqt45Ucr0r0T7z88ss8++yz9n/r9XoiIiIcGFH1c/XqVU6cOIGbmxtdunRxiq6rsWPH0qJFi+vWWqnV6hu+fN1tarWa+++/n3Xr1nH06FFiY2Px9/f/2+colUrc3d3t4/420dHRNGjQgDZt2nDfffdx/PhxduzYwTvvvMOWLVt49tlniYmJueku3dLSUqdZr+jm5kazZs149dVXyc7OxmQy4ebmhtVq5ZdffkGn093weTiKp6cnJSUljg5DqkIq9AoZEhICQGZm5nWlzDIzM2nWrJn9MVlZWdc9z2w2k5eXZ3/+H7m5ueHm5laRoUp/kJycTGZmJlFRUTRo0MDR4QDQoEEDunXr5pR7bNqKyZ8+fZqMjIx/TJ5/xcXFBZ1Oh06nIyYmhsaNG9OsWTO2b9/OihUreOONN3jmmWfs5RDNZrN9GMN2Kysro7y8HIPBQElJCWVlZU7RglIoFLi7uxMfH3/dzw8cOIBCoaBOnToOW0rzR2VlZU55nknOq0KTZ0xMDCEhIWzatMmeLPV6PXv37mXcuHEAJCUlUVBQwMGDB+07um/evBmr1UpiYmJFhiPdgpSUFEwmE3Xq1JFVm26Cj48PjRs3JjU1laysLBo3bnzDY8T/laczGo32rlqj0YjJZLL/aTAY7Pf//meenp6oVCrWrVsH/PZFQq1W/+ljbce1Wq1kZGSQl5fn1GN3W7duRaFQEB8fT2BgoKPDAX5bPhcbG+sULXaparjl5FlcXMy5c+fs/7548SJHjhzBz8+PyMhIJk2axNtvv01sbKx9qUpYWBgDBgwAoGHDhvYNeb/66itMJhMTJkxg6NChcqatA6WmpqJUKomMjHR0KHYnT57EYrHg4eGBWq1Gq9USEhJC7dq1nWILtEaNGnH48GF27tyJ0WjEYrFgNpsxmUz2my252cY4f5/szGaz/TkWi8V+M5vNmM1mAgICqF+/PmvWrGHr1q2Eh4ej1WpRqVSo1WpcXFxQq9W4urqiVqvx8PAgIiKC5ORkysrKsFqtDt3xpri4mN27d5OUlIRGo6G0tJSzZ8+yZs0amjdvTnx8vH3TdEcRQmCxWLh27RphYWHVYocg6e645eR54MABunTpYv+3bSxy1KhRfPvtt7zwwguUlJQwduxYCgoKaN++PevXr79ubGP+/PlMmDCBbt26oVQqGTRoEFOnTq2AtyPdDqvVSmZmJmq12ml2DgkJCWH//v2cPn0as9lsr3AUGhrKPffcQ58+fdBoNA6NsXbt2hQVFbFs2TJ2795tT5xGo9HeorTN0LUlPLVabf+7m5sbGo0GjUaDu7s7Hh4e9puXlxcajQYXFxcWLlxITk4Ow4YNIzExEQ8Pj+se7+npiYeHByqVip07d7JixQrS09OpVauWQ8cUc3JyeO+99/jXv/6FTqcjPz+fbdu2YbVaeeyxx6hTp47Dk5UQgpycHK5evUqjRo0cHo9Uddxy8uzcufPfjqcoFAqmTJnClClT/vIxfn5+siCCEykvL6esrAw3Nzen6bLt1asXrVu3Jjg4GI1GQ1ZWFitXrmTu3LkcOXKEyMhIWrVq5dAY/f39cXFxwc/Pj+bNm+Pr64uHhwcajQYvLy88PT3x9PS0J0itVmtPdG5ubje9FMjHx4c33niDkJAQWrdu/beTh+Lj49FoNBw9epSGDRs6NHm6ublRq1YtPvzwQ8rKytBoNDRr1owPP/yQJk2aOMVkIavVyv79+1GpVDRt2tShy7OkqsXxUyolhysuLsZoNNov/s4gODj4uiVPERERDB06FKPRyAcffMCKFSscnjy9vb1Rq9V07dqV0aNHV9qEk/r166NWq8nNzaWkpORvu6x1Oh1NmjRh586d9OrVC39/f4eN44WGhjJ37lyHvPbNEEJgMplYs2YNDRo0IDQ0VLY8pZsmzxTJvibQtqDfWfn7+xMREYHRaCQ1NdXR4WAymexVgyrzomt7HZVKdVOvM3jwYHbt2kVaWhomk6nS4qrqhBAUFxezfPlyRowYIROndEvk2SLh7e2Nm5sbZWVlTlOg/88KqNsq9gAOX1wPUFBQgNFotI9PVgYhBPv27bOP93p5ef3jcx544AE8PDxYvXo1ly9fdoplK85GCEFJSQkLFy6kpKSEhx56SM60lW6JTJ4SarUab29vjEYj+fn5jg4HgEGDBpGZmXndz2z1Yr28vGjevLmDIvv/bPFptdpKW4eck5PD119/TdOmTaldu/ZNXeB9fX15/PHHWbt2LQcOHJC7hfwJ27Kejz76iLFjxzrNRDmp6pDJU0KhUNiLeF+8eNHR4QBw7do1fvrpJy5fvkxZWRkXLlzgm2++4fvvv6dJkyYMHTrU0SFy9OhRgoODK22sMzc3lxEjRlBUVMQjjzxCw4YNbyp5KhQKxo4dS3h4OEuWLGHPnj2VEl9VlpGRwbRp0zCbzbz44ovA7dUSlmoumTwl4LdlFxaLhXPnzjlFN1+HDh345ptv6Nq1K7GxsfTs2ZOtW7fy8MMPM3XqVKeoTHP06FFCQ0MJCAio0AuvEILNmzfTo0cPzp8/z2effUbLli1vadcVHx8fJk2aREZGBkuXLuXMmTMVFl9Vl5eXxy+//MLy5ct59dVXCQ4OlolTumVytq0EQJMmTQgICODs2bOkpKRQr149h8YzefJk/vWvf103KcfNzQ2tVouPj4/DJ3ecOnWKs2fP8vjjj1dIEX3bGNy2bdtYunQpmzdvJi4ujv/97380b94cjUZzSxd4pVJJ165dOXbsGD///DMLFizgqaeecpqKPo5i+4y/+uorOnXqxPDhwx1+LklVk0yeEgC1atWiQYMG/PLLL+zYscPhyTMoKMjhBeD/zurVq3F3d7d/6bgdBoOBzMxMTp8+zdGjR/n111+5fPkyYWFhPP3003Tv3p369evb9wq9VZ6engwbNoz8/Hy2bt2Ku7s748ePd+gm2Y5UVlbGxo0b+frrr/H19eW5555zikpVUtUkk2cFEkJgMBjIy8tDr9fbq8xYLBaMRqO9woxarbbvsKHT6ezrBR3J3d2dpk2bcvDgQTZv3sx9991X41spfyU7O5uVK1eSkJBAdHT03878FUJgNBrJysoiOzub7OxscnJyuHLlCqmpqVy7do2ioiJcXFwICgrikUceIT4+nkaNGlVId2JUVBTDhg3DaDTy888/4+bmxuOPP17jkkZpaSnr169nwYIFKBQKxo0bR1xcnKPDkqowmTxvg9FoJCcnh8zMTHJycsjOziY3N5f8/HxKSkrQ6/WUlJTY65v+cQxRpVLh5uaGq6urvRKNj48P/v7+BAUFERgYSHBwMCEhIXd1e6n4+HiaN2/OqlWr2LBhAyNGjLgrr1vVrFq1ikuXLhEeHs6KFSvs9VltJQR/X9PWtvNJXl4ehYWFmM1m4LekatterXnz5kRGRlKvXj3i4uIqtMqTSqUiPj6eIUOGYDabWblyJUIIHn74Yadu2VekwsJC1q5dy9KlS7FYLIwYMYKuXbve0hiyJP2RTJ43wTat/erVq2RmZpKRkWHfTaO4uPi6LaFUKtV1Jdg8PT1RKpW4uLjYi35brVaMRiPFxcVkZWVRVlZmT6i28m4BAQFERkYSFhZGUFAQISEhhISEVGoh7ZCQEJKSkjh06BBLliyhffv2REVFVdrrVTVCCFJTU5kzZw516tShpKSEnTt3/unjfl/wXaVS4e3tjVarxc/PD39/f0JCQoiMjKR27dqEhoZWamUnV1dXEhISUCqVfPvttyxZsgSA+++/nzp16lTa6zqDK1eusHHjRn788UdcXV0ZPHgwffv2dXhdZKnqk8nzT9gW6Ofm5pKenk56ejpHjhwhOTmZq1evUlBQgFqtxt/fn7CwMBo2bEitWrUICQmxXxx9fX3tXbN/bDmaTCZKS0spKysjNzeXgoICsrOzSU1NJS0tjdTUVA4cOMD27dvx8vIiKCiI6OhoGjduTJ06dQgMDCQsLKzCLwAKhYJGjRrRs2dPpk2bxuLFi3nqqaecpmSfI9m65OfPn09aWhovvPDCdbVZbQnTtgvM72vaajQa+5cfV1dXh8zsdHd3p1WrVuh0OqZOncq8efPIy8tj2LBh9u3OqtOMU7PZzPnz51m5ciWrVq0iNDSUhx9+mG7dusnzWaoQCuEM6xJukV6vx8fHh8LCwgrt4rKNT+Xl5ZGdnc2OHTvYtm0bx44dQ6FQUL9+fZo2bUrTpk2Ji4sjIiKi0n4Rr169yunTpzl+/DhHjx7l9OnT6PV6AgMDad68OT179qRBgwb4+/vbJ4BU1MXv/PnzfP7556xdu5aZM2fSrl07ewm/msi2L+eePXt48MEHefTRR/n3v/99U9V+nI3VaiU3N5evvvqKb7/9lubNmzN58mRiY2PR6XRVfuapEAK9Xs/58+eZPn06u3btolu3bowePZr4+HiHzy2QnN/N5heZPPn/SVOv15OSksKPP/7ImjVryMvLIykpiT59+tCzZ08iIyMd8stn2zbJNpln48aNXLlyhebNmzN48GDuvfdefHx87OOjd5rkLBYLKSkpjB07luLiYpYsWUJMTEyFHLuqsbUoL168yODBg/H09GT16tXodLoq/VmYTCZ++eUXnnvuOcrLy5k4cSIDBw4kICAANze3KpdEbb/DRUVF/PTTT7z//vsIIXjppZfo378//v7+jg5RqiJuOr+IKqiwsFAAorCw8I6OY7VahclkErm5uWLdunVixIgRIjAwUMTExIhnnnlGnDhxQlit1gqKumJYrVZRXl4uNm7cKEaMGCG0Wq1o2LCh+PTTT0VaWpowGAzCYrHc8WuUlZWJvXv3irCwMNGmTRuRm5srLBaL030elcl2fly6dEl07NhRREdHiwsXLtzx5+ssrFaryMnJERMmTBDe3t4iKSlJzJ07V1y5ckUYjUan//+2Wq32/6PCwkKxbt060a1bN+Hu7i4ee+wxcfHiRWEymRwdplTF3Gx+qbHJ02q1CqPRKJYvXy569uwpdDqdSEpKEt98840oLi6uwGgrj9VqFWfOnBGPPvqo0Gq1om7duuLzzz8XV65csV9YbuVYtlteXp5YvXq16N+/v3BxcRFarVYkJSWJa9euOf0FtaLYzo+jR4+Ktm3biuDgYHHkyJFq+94PHDggBg4cKLy9vUV8fLz473//K06dOiXMZvMtn0uVzRaPxWIRmZmZ4rvvvhMdOnQQKpVKdOrUSezYsaPafMGR7j6ZPP/B/v37Rf/+/UVgYKDo3bu3WLFihSgtLXW6C8U/scV77tw5MWbMGBEUFCTatGkj5s6de0ufj8ViEadOnRLvvvuuaN68uXB3dxdt27YVM2fOFIcOHRLR0dGifv364uDBg8JgMFTiO3IORUVFYsWKFaJhw4aicePG4vjx41XqvLhVtvPo4MGD4sknnxQRERFCp9OJ7t27i1mzZon09HRHh2iXn58v1q5dKx577DERFhYmvLy8RN++fcWWLVuq3O+v5Hxk8vwLJpNJvPfeeyIkJEQkJSWJBQsWiOzs7CrforJ1X23fvl08+OCDIjIyUgwdOlQcOnTob59XXFwsVq5cKQYPHixCQkJEZGSkePTRR8XGjRuF0WgUZrNZWCwWcfnyZZGYmCgCAwPFZ599JjIzM+/SO7v7Tp8+LV544QVRq1Ytcc8994hLly7VmJaMxWIRJpNJpKamiu+//170799f+Pr6Cm9vb9G1a1fxySefiFOnTt3135Vz586JWbNmiQceeECEh4cLrVYrWrduLd59911x6tQpezezJN2pm80vNWbCkBCC7OxsRo8ezZ49exgzZgwPP/ww0dHRuLi4VLkJEn/FZDJRUFDAihUrmDNnDqWlpYwfP57Ro0dfN8ElLS2NhQsXsmjRIjIyMoiLi2PAgAF07dqVWrVq4ebmZl9ELv5v6U5OTg7vvvsuP/zwA23atOHJJ58kKSmp2qyZKy4uZtmyZXzzzTfk5uYydOhQnnjiCfz9/avN+XGzrFYrJpOJsrIyMjIy2LlzJ5s3b2b79u0UFxcTEhJC69atadmyJQ0aNCA2NtZe1ONOmM1mcnNzOXPmDGfOnGHfvn3s2bOHjIwMXF1dadGiBZ06daJDhw7Exsbi6emJq6srKpWqgt65VNPJ2ba/Y7VaSU1N5dFHHyUjI4MpU6bQoUMHAgICquXUdavVSnFxMUeOHGH+/Pls3bqV4cOH8/LLL5OcnMy8efNYuXIl8NvuJQMGDKBRo0YEBASg0Wj+8jMR/7f2dcOGDXzzzTdkZWXRtWtXhg8fTqtWrapsgrFarWzcuJFZs2Zx6tQpmjZtyrBhw2jfvr1TFKF3JPF/s41tG6UXFhZy4sQJjhw5wokTJ0hJSaGoqMheCMJWkzggIIDAwEB8fX1xcXG57ryyHc9kMqHX68nNzSUnJ4ecnBzS09PJzc3FZDLh4eFB/fr1ady4MU2aNCEuLg4/Pz+8vLzw8PBw2JpZqXqTyfP/2BZLT5gwgdzcXN555x2SkpLQarXV/qJYWlpKcnIyP/74I4sWLSI+Pp6LFy/i4+NDp06d6NKlCxEREQQHB6PRaG768ygoKOD8+fP8/PPPbN26lZKSElq3bk3//v3p0KFDlWgF2FrTO3bsYP78+Rw4cIDQ0FDuvfdeOnbsSFRUVKVWc6qKbJ9ZSUkJhYWF6PV6ioqK7LV6r127Rl5eHkVFRfYylWVlZVgsFgwGA1arFYDy8nLc3d3x8PDAw8MDLy8v+02n0xEaGkp0dDRBQUF4e3vbb15eXvYSiJJUWWTy5LfEefnyZV577TVOnz7Nm2++SY8ePf606k91ZTAYuHjxIgsXLuTzzz8nLCyMDz/8kAYNGlCrVq3bLn5gsVi4cuUKR44cYefOnRw9ehSLxULLli3p0qUL7dq1w8vLy+k+ZyEExcXF7Ny5k40bN3Lo0CE0Gg1JSUkkJSURFxdHYGBglfgC4CwMBgPFxcX2ms5lZWUYDAb7n1ar1V6WEuC5556jffv29OjRg9DQUHslLnd3dzQajT1ZykpAkiPcbH6pfn2W/8dqtXLt2jW++uorjh49yvPPP0/v3r1xcXFxugt6ZXJzc6Nu3bo8/PDDZGVlsXHjRsxmMxEREXeUIFQqFVFRUQQFBVG/fn0OHDjAgQMHOHHiBCdPnmTDhg20bNmSJk2aULdu3dveVqsi2LqxL1y4wMmTJzl06BCnT59GCEGrVq1ISkqiVatWhISEyGLht8HNzQ03N7ebLkTw3nvvERkZSefOnYmOjq7c4CSpklTb5Jmbm8v69etZs2YNDz74IEOHDq1xidNGrVYTFRXFhAkTuHjxIv/73/9o0KABMTExd9zC8vDwoEGDBtSuXZt27dqxY8cO9u3bx/Hjx0lJSWHbtm00btyY2rVrExYWRnBwMH5+fpXe+i8uLiYnJ4eMjAyuXbvGhQsXSE5OJiMjg/LycurVq0fbtm3p0qULQUFBsqV5F3l7e1NaWmrfYUaq+gwGAyUlJZSWltq3YrRarfYNEpRKJWq1GqVSiaurq33TDI1G49Av1neiWibPkpISDh48yA8//EB8fDxPP/10je8CUqvVNGjQgBdeeIFHHnmEhQsX8tRTT+Hj41MhJ66rqysxMTFER0czaNAgdu7cyZYtWzh69ChHjhxBp9PZdxGJiYkhODgYrVZrH+vy9PTE3d3d/ov0TzHZxt9sW38VFxdTXFxMUVERer2eq1evcuHCBVJTU8nIyECv1+Pv7098fDwdO3akQ4cOd3W7N+n/k8mzahK/K2NaWFho76q3TSTLy8uzb8to6663bZagUCiuS56enp74+fnh6+uLj4/PdePeWq0WHx8fPD09nfr3s9olTyEE58+fZ/Xq1RQWFvLOO+/g5+fn6LCcgkqlokuXLjz88MN89dVXdO7cmVatWl23O8idUigUeHp60qtXL3r16kVubi5btmzhk08+YcuWLWzfvh0XFxd8fHwICwuz7zZSq1YtgoKC7K1SpVJpH49VKpUIIbBarfY/LRYL5eXl5Ofnk5WVRWpqKunp6Vy7do2rV69SWlqKRqMhMjKSpKQk2rRpQ8uWLeW54AS8vb0pKSmRybMKsNULtt2ysrI4e/Ys58+fJzMzk2vXrpGRkYHZbMbFxcV+8/DwQK1W21uZFouF0tJS+4Qzo9Fo3+9YpVIREBBASEgIwcHBREREEBsbS3R0tH0rP51Oh5ubm6M/jutUu+RZWlrK7t272bNnD0OHDqV169YOiyU7O5vy8vK/vN/FxYWQkJC7GNFvXnrpJX766Se+/vproqKiqFWrVqXNPPb396dp06bo9Xr69etHnz59MBgMnDx5knPnznHy5EnWrl1rn5UphECn09m39VIqlXh4eGCxWOy/cGVlZZSVlVFYWIhCoUClUuHh4UHdunWpW7cuXbp0oUmTJjRq1IjAwMBqP6u6qvH29iYtLQ2TyeToUKQ/YbFY7BO/UlNT2bt3LwcOHODkyZNcu3YNlUpFcHAw9erVo0OHDtSvX5/w8HCCg4MJDAzEx8fnb5cACiEoLCwkKyuLzMxMrl69ysWLF+1zEjZs2EBxcTFeXl40atSIFi1a0LVrV+rWrWu/LjjDLk/VKnkKIdi/fz+bNm0iNDSURx55xKHxjBo1is2bN2MwGG64T6VSERcXx5EjR+5qTAqFAi8vL55//nmee+45hgwZgr+/P56enpXyehaLhffeew+FQsGDDz5Iq1atUKvV9OvXD/j/XUG2fVMzMzNJT0+nrKwMvV6P2WymtLQUtVqNm5sbrq6ueHl5odFoCA4Otrdcw8PDq92elNWVTqfj7NmzsuXpRGzreU0mE2lpaSxevJj169dz5swZXF1diYuLo3fv3rRq1YrmzZsTERFx26+lUCjQ6XTodDrq1at3w/35+fmcOXOG/fv3s2fPHr755hvef/996tevz7333suwYcOIjIzEzc3Nob/z1Sp5Wq1W9uzZQ1ZWFqNGjSIgIMDRIaFSqW6YjCKEwNvbmxYtWjgoKhg+fDiff/45y5YtIzY2ltjY2Ao9CW0roPbu3cvSpUt57733qFev3g3fSBUKBW5ubkRHR8uZlzWEj48PJSUlsuXpBGxzB8xmM1u3buXLL79k3bp1+Pj4cO+99/LMM8/Qvn17goKC7loPjq+vr33p2FNPPYXBYGDXrl2sXLmSWbNm8cEHH9CtWzcefvhhunbtSkBAgEO2S6xW/Vm2jaNDQkLo1auXo8PBzc2N48ePYzAY7P37JpOJvXv3Mm7cOMaMGeOw2FQqFcOGDWPHjh2kpaXZ1+BVJCEEzz//PAkJCdx3331yT0UJ+K3lWVxcLFueTqCoqIjly5fTqVMnevXqRV5eHrNmzeLcuXPMmjWLQYMGERwc7LDWne3LddeuXfnf//7HpUuXmDdvHkIIRo8eTZcuXfjggw84f/78XY+tWiXPffv2UVJSQosWLQgNDXV0OCxdupSYmBh7VRSFQkFxcTEXL14kNzeXNm3aOCw2WzeqQqHgwIEDZGRkVPhrzJkzh0OHDvHmm286RS+A5Bxky9Px8vPz+fHHH+nZsycPP/wwQUFB7Ny5k02bNjF8+HB7gZPf3xzl9zG4uroyePBgVq1axbZt2+jcuTNffvklvXr14pVXXiEtLe2uxVWtkmdKSgpqtZp69eo5xdiXbVD797EcPHiQM2fOcP/99zs8xtDQUOrUqcOFCxfIysqqsOPaJgS8/fbbjBo1imbNmuHq6lphx5eqNh8fH8xmM+Xl5bL1eZeZTCY2btzI2LFjmTRpEoGBgaxZs4YFCxaQmJiISqW67su+s7HNvler1TRr1owPP/yQlStX8uCDD7J06VL69u3L9OnT/3SeSUWrNmOehYWFXLlyBS8vL2JiYhwdzp/S6/WcOnWKwsJCOnTo4OhwUCqVxMXFcebMGXJycirsuEajkc8++4yioiKeeeYZNBqNU/4iSo5hK7ZfXFyM0WislpszOKNr167x9ddf89NPP+Hn58cbb7xBnz598Pf3d7plIDdDpVKh0Who3LgxkydPpmfPnixatIhPPvmE7du3895771VqLqg2Z21ubi7FxcXUrl2boKAgR4fzp/bs2UNeXh6JiYlOU7QhMjLS3t1dEWyF+GfPns24cePs3daSZOPj44NKpbInz+qypZ0z27NnD1OnTuXcuXN06dKFgQMH0rBhQ3Q6XZX/Yuvi4oKvry9t2rQhPDycVq1a8dVXX/Hwww8zZcoUOnfuXCnvsdokz8LCQgwGAxqNxil3wygqKuLAgQOYzWbatm3rNCesr6+vvaRWRSguLmbWrFm4ubnxyCOPyG2jpBt4e3ujUqnkuOddsnHjRqZOnYrJZOKhhx6iZ8+exMTEVMnW5l9RKBR4eHhQp04dfH19CQwMZObMmbzyyiu8+uqr9OrVq8JLcFab5GkbP3FxcXHK8bWDBw9SWFhI48aNHVIY4a9oNBr7LOA7VVZWxuHDh1m1ahWjRo1y2u5zybG8vb1RKpX2SjNS5dmwYQOfffYZKpWKESNG0K1bN0JCQqptb5CtWlGvXr3QaDRMmzaNjz76CCEE3bt3r9AvDNXmE7QNdNtKtzmTsrIyNm3ahFarJSEhwamKkFssFpRK5R3/MgkhyM7OZsmSJXh7ezN8+HCnnXQgOZZtI+vS0lKZPCuJEIIjR44wdepUzGYzo0aNom/fvoSFhVXbxGmjUChwd3enW7duTJw4EZVKxeeff87+/fspKyursNepNp+il5cXrq6ulJeXU1pa6uhwrnP69GlSU1OJiIigbt26jg7nOoWFhbi7u99xa72oqIiDBw+yZcsWHnroIWrXri0Tp/Sn1Go1Go2G8vJymTwrgRCC9PR0pk+fTlpaGuPHj6dPnz74+vo6OrS7SqlU0rVrVyZPnkxmZiazZ88mOTm5woaoqk3ytNVDLSoqorCw0NHh2JlMJpYtW0atWrWIi4tzunGG7OxsPD0972gCk8Vi4dy5c/z4448EBAQwcuTICoxQqo60Wq1MnpVACEFZWRk//PADq1atYtKkSfTq1avSym86O4VCQc+ePXnmmWfYvn07P/74I1evXq2Q3slqkzyDgoIICAigoKDgri6U/Sepqans2rWLRo0aUb9+fUeHcx0hBGfOnCEgIACdTnfbx8nPz2fv3r0cPHiQMWPGyIII0j/y9va27/soVRyz2czJkyd57733GDlyJA899FCF7ppUVY0cOZIRI0bwww8/sG3btgppYFWb5KlWq4mOjsZkMnHx4kV7bVVHW7ZsGY0aNaJRo0Z4eXk5OpzrmEwmTp06RXh4+G0nPKvVyv79+1mxYgWNGzdm6NChFRylVB1ptVqZPCuYEIL8/HxeeuklQkND+c9//iPX0P7O5MmTadCgAd9++y27d+++49ZntUmeAA0bNsRoNHLs2DGnmTQ0efJkpk+fTvPmzR0dyg1+/fVXsrKyiIuLu60ZwEIILl++zKZNmygsLGTixIlOOdNZcj7e3t6y27YCCSEoLi7m559/Zvfu3UyfPt2hm0nbJm6azWZMJhNms9m+5aCjeHl58dprr1FeXs4vv/zC+fPn7yieapU827VrR+3atTl16hQHDhxwdDhOTQjBwoULiYqKIi4uDh8fn9s6xsqVK9m5cyc9evRwiqpJUtVgS553o4xaTZGens5HH33EgAEDHLqWXAjBrl27ePLJJ6lfvz7BwcE0b96c559/nqysLIcm0ISEBPr06cO+ffvYuHHjHR3rlpPn9u3b6devH2FhYSgUClasWHHd/Y888sgNBYV79+593WPy8vIYMWIE3t7e6HQ6Hn/8cYqLi+/ojQAEBATQokUL+yQd6a9dvnyZVatW0bNnz9suor9582Y2bNhA3bp1GTt2bAVHKFVnPj4+MnlWoLy8PLZt28bVq1d59dVXHRrL5s2beeGFF7h27RpTp07l2LFjvPrqq6xevZrBgwc7vFfQVgh/7969nDlz5raPc8vJs6SkhKZNmzJ9+vS/fEzv3r3tmxunp6ezcOHC6+4fMWIEJ0+eZOPGjaxevZrt27dXyMVXoVDQuXNnWrZsydatW9m5c+cdH7O6+u9//4tOp6N///4EBwff8vOLior44YcfsFqt3HfffYSHh1dClFJ1JWfbVqxr166xceNGEhMTadCggUNjWbVqFfn5+fTu3ZuePXsSFhZG+/btGTp0KEeOHHF4r2B0dDQtWrQgNzeXX3/99baPc8vJs0+fPrz99tvcf//9f/kYNzc3QkJC7Lffry86ffo069evZ9asWSQmJtK+fXumTZvGokWLuHbt2u29i9+pVasWHTp0wMfHh+nTp1footjqQAjBnj17+PHHH3niiScIDw+/rUXTCxcu5MSJE7Rr147u3btX+4XXUsWSs20rjsViITMzk6NHj3Lfffc5/HcxIyMDV1dXvL29UavVKJVKPDw8CA8Px2w2k5qa6tD4lEolrVu3xtXVlcOHD9/2zj6V8ilv3bqVoKAg6tevz7hx48jNzbXft3v3bnQ6HS1btrT/zHbx3bt3758ez2AwoNfrr7v9FbVaTevWrRk4cCDHjh3jyy+/rLg3VsUJIdDr9bz44os0a9aMAQMG4O3tfctjI6mpqSxbtozY2Fi6du16R8tcpJrJNtvWYDA4zcz4qqqwsJALFy5gtVpJSkpydDj4+flhtVoxGo32a4vZbEav16NUKp2iPGm9evUIDg7m2rVrt720scKTZ+/evZk3bx6bNm3i/fffZ9u2bfTp08fez52RkXHDridqtRo/P7+/3JD53XffxcfHx36LiIj42xj8/Pzo3LkzPXr0YO7cuWzevLli3lwVJoTAYDDw/vvvc/HiRft09tspFTh37lzy8/Pp3Lkz8fHxDv+mK1U9ti9t5eXlsjj8HcrLy+PixYvodDrq1Knj6HDo3bs3Pj4+HDhwgKNHj1JQUMDp06fZvHkzLVq0ID4+3tEhEhoaSlhYGMXFxZw/f/62jlHhi4B+v84vPj6eJk2aUKdOHbZu3Uq3bt1u65gvv/wyzz77rP3fer3+bxOoWq0mKiqKQYMGcfbsWT766CMCAwOJi4urkSXjhBCUl5fz/fffs3TpUkaPHk3btm1xcXG55WMdOXKE1atX07ZtWxITE51yBxvJ+Xl5eaFSqSgrK8NgMMglTnegqKiI/Px8goODnWJ7t6SkJLp27cqvv/7Ka6+9hr+/Pzk5OWRlZfHCCy84RZlANzc3/Pz8UKlUf9lo+yeV3mSoXbs2AQEBnDt3DoCQkBCysrKue4zZbCYvL+8vm/Nubm54e3tfd/snGo2GZs2a8fjjj5OVlcXUqVPveF1PVWRb/7VmzRq+/vpr2rRpwyOPPHJba8BMJhPfffedveRVVFRUjfwyIt05rVaLi4sLpaWllJeXOzqcKs1gMFBWVoa/v79T/D5euXKF0tJSfH19CQ4OxsfHB19fX3Q6HVeuXPnbYbe7RaFQ4OnpiZub221XG6r05HnlyhVyc3PtyyGSkpIoKCjg4MGD9sds3rwZq9VKYmJihb62l5cXffr04bHHHmPXrl18/fXXpKSkOHyq9N1itVrJzc3l559/ZubMmeh0Op577jkiIiJu65fs+PHjrF69ml69etGkSZMaWy9TunNarRa1Wk1ZWZlMnnfItqWgM7Q6y8vLWbhwIYcPH6Z9+/b85z//4e233+all16iXbt2/O9///vLuS13m5ubGyqV6rY3Ernl5FlcXMyRI0c4cuQIABcvXuTIkSOkpqZSXFzM5MmT2bNnD5cuXWLTpk3079+funXr0qtXL+C3KkC9e/dmzJgx7Nu3j127djFhwgSGDh1KWFjYbb2Jv6JQKNBoNDz++OOMGjWKZcuWMXPmTE6cOEF5eXm1boWaTCbS09NZt24d06ZNw2Kx8MYbb9C0adNbTpy2bt8vv/wSjUbD/ffff8O4tSTdCi8vL9RqtWx5VjNZWVls2rSJ4OBg2rRpQ61atfDy8qJ+/foMHz6c/Px8lixZ4ugw7e4kB9zymOeBAwfo0qWL/d+2schRo0bx5ZdfcuzYMebOnUtBQQFhYWH07NmTt95667rdRObPn8+ECRPo1q0bSqWSQYMGMXXq1Nt+E39HoVDg5ubGCy+8gJ+fH1OmTCE5OZkXXniB+Ph4+9hLdWFLdBcvXuSnn37ihx9+ICQkhHfeeYdmzZrd9nFPnDjB8uXL+c9//kNMTMxtjZdKko1Go8HNzY3y8nKZPO+Qm5sb7u7uTtEdmp2djcFgsP//2tiWq3h4eHDlyhUHRvj/lZWVYTabb2oY8M/ccvLs3Lnz32brn3/++R+P4efnx4IFC271pe+IQqFgzJgxhISE8OKLLzJu3Diee+45unfvTlBQEC4uLk4xXnAnLBYLZWVl7Nixg2+++YZjx47Rq1cvXnzxRWrVqnVbxxRCYDQaefPNN4mJiWHYsGG3fbJJko1SqUSj0WAymWTyvEPu7u54eHhw7do1rFarQ2e/2yYt5eXlUVhYiMViQaFQYDKZyMrKoqioiKioKIfFZ2ObC2I0Gm+rNClUs9q2N6Nfv36sX7+ehg0b8sILLzBu3Dg2bNhASUkJVqu1SnblCiEwm80kJyfz/PPP88QTT5Cdnc2UKVP45JNP7ihxWq1Wtm7dytq1a3njjTdua12oJP0ZLy8vzGazTJ53yNfXl5CQEC5fvkxBQYFDYwkPDycpKYkzZ86wZs0aTp8+TU5ODrt37+btt99Go9Hw0EMPOTRG+G34MT09HavVSkxMzG0do8YlT4DIyEgWLVrEzJkzKS4uZvTo0YwYMYK1a9diMpkQQjh9ErXFKITgypUr/Pvf/6ZLly5s3bqVZ599lgULFjB06NA77l4tLS1l8uTJ9OvXj549e8olBVKF0Wq1mM1mWQXsDgUGBtKwYUPKyso4dOiQQ2NRKBRMmTKFSZMmcezYMbp37050dDRDhgzBbDYzb9482rVr59AYAS5cuMCVK1fsn93tqLGbvSkUCvr370+XLl1Yu3Yts2bN4qGHHqJevXr861//4oEHHnDq7kkhBMnJyUydOpXvvvsOX19fnnrqKR566KHbnk37R6WlpXz11VckJyezZMkSuTegVKG8vLzIzc2VLc875ObmRq1atQgPD2f16tV0797dofF4e3szduxYxowZc8N9ztJrdeDAAfLy8mjTps1tr1WvkS1PwL7ji7e3N4MHD2bJkiX88MMP1KlTh+eee4569erx5JNPsnfvXqfpzrVYLGRlZTFv3jy6d+9OixYt2LBhA1OmTGHfvn1MnjzZnjjv9CQ1m81cvnyZTz75hEmTJhEbG1tB70KSfiNbnhVDoVAQGhpKp06dWL16NUVFRQ6PR6FQoFQqb7g5Q/IsKSlhz549uLq60q5du9uOqcY3JRQKBWq1Gp1OR6dOnUhISCAtLY2ffvqJFStW8MMPPxAVFUWPHj3o1q0bLVq0QKfT3bUZukVFRaSkpLBr1y62bt3KwYMH7du3dejQgW+//RZvb288PDwqJGnaZGVl8fnnn+Ph4cGkSZMq9NiSBL+1POWEoYoRHBxMz549mT17NvPnz+df//qXo0NyWhs2bCAlJYWkpKTraqzfqhqfPG2USiVubm723QDCw8MZMmQIx44dY8eOHezYsYMff/wRjUZD7dq1ady4MQ0aNKBOnTpERUURHBx8RwlVCEFZWRkXL17k/PnznDlzhhMnTpCSkkJubi6enp7Ur1+f8ePHk5CQwKFDh3j77bfZuHEjw4cPr9AZdsXFxRw+fJh169bxzDPPEBgYWGHHliQbLy8vLBaLTJ4VwM3Njbp169K3b1+mT5/OkCFD0Ol08gvvH+j1en788Uf8/Pxo167dHZUXlcnzDxQKBS4uLgQGBhIQEEBoaCjNmzcnOzubS5cukZycTGpqKocOHWLTpk2YzWY0Gg06nY6QkBD8/Pzw8/PD19cXrVaLQqHAw8MDlUqFxWLBZDJhNBoxGo32mpR5eXlkZ2eTnp5OcXExZrMZHx8fQkJC6NixI3Xq1CEmJoaQkBACAgLw9fWlfv36nDlzhvfee4+IiAjatWtXIZN5hBBcunSJBQsWEBERweDBg6vVOljJeXh6esrZthVEoVAQEBDAww8/zNq1a5kxYwYvvviio8NyOosWLeLs2bMMGTKE1q1b31GjQybPv2EbE/X29qZu3bo0adKE7Oxs+y03N5ecnBwyMzPJzc21txyTk5MxmUxYLBaEENeVA/x9379arcbFxQVXV1dcXFyoX78+YWFhBAcH4+/vT2BgoP1m2xvPJjQ0lKeeeoqLFy/y4YcfotPpaNSo0XULk29HVlYW27dv58SJE0ycONEptg+SqieZPCuWm5sb8fHxjBgxgm+//ZYuXbrQqlUruevR/zl69CiLFy+mcePGtG/fHn9//zs6nkyet8DT0xNPT0+io6OB32rHlpaWkpubS25uLgUFBRQVFVFUVERJSQllZWX2IgNWq9WeMG03jUaDp6cnWq0WrVZLQEAAQUFB6HS6fxxcVyqVNGnShGeeeYbXXnuNGTNmMHHiROrWrXvby1PMZjMnTpxg06ZNxMbGcs8998huH6nSeHp6YrVaMRgMDl/cXx0oFAq0Wi2jRo1i27ZtTJs2jffff5/Q0NAa/9lmZmYyc+ZMTCYTffv2pUGDBnfcoyaT5x1QKpV4eXnh5eXlsKoZ99xzD+fPn2fGjBmEh4czYsQIoqKibuvEuHr1Kjt37iQzM5NJkybJVqdUqby8vFAqlZSXl2M0GnF3d3d0SFWeWq2mQYMGPPnkk7z11lvMnz+fRx99FH9//xqZQIUQFBUVMX/+fLZt28ajjz5K27Zt0el0d3zsmvdpViO26eBjxoyhR48eLFu2jHXr1pGZmXnLS2uMRiO7du1i3759xMfH2wv5S1Jl8fLywtXVlfLycrlcpQKpVCqGDx9Ov379mDFjBuvWrSMnJ6fG7CZlI4SgtLSUn3/+mS+//JKOHTvy4IMPVlijQCbPakCj0fDaa6/RqFEjvvvuO9auXUt+fv4tJdBLly6xbds2rFYrgwcPlptcS5XO09PTnjxvd1so6Ua2SY9vv/02CQkJvPfee6xfv56srCzMZrOjw7srhBCUlJSwfft2XnnlFeLi4njllVcIDw+vsBa4TJ7VREBAAG+//TZhYWHMmzeP1atXYzAYbuq5JpOJ5cuXc/r0adq1a0fHjh0rOVpJur7lKZNnxVIoFHh5efHVV18RFxfHf/7zHxYtWkRaWpq9BGl1ZbVaKSoqYvPmzTz22GPUrVuXL7/8ssLHfmXyrEZiYmJ45ZVX0Ol0zJ07lzVr1vxjV40Qwr7spm7dugwcOFCW4ZPuCk9PT/u2ZDJ5Vg4/Pz/mzp1L3759+fTTT3n99dfZu3cvZrO52iVQ28qG3Nxc5syZw8iRI2nfvj1LliwhODi4wsd8ZfKsZhISEpg8eTJarZbPPvuMTZs2/e0viclkYvr06QghuPfee2nUqNFdjFaqydzd3XF3d8dsNlNSUuLocKotDw8PPvvsM6ZMmcLJkyd54okn+N///mcf2qnqSdT2HsrKyti5cyfDhg3j9ddf58UXX2TRokVoNJpKWTUgk2c11L59e8aPH4+Pjw+vvvoqe/fu/cvHfv/99xw8eJB7772Xbt263cUoJem31icgW56VTKlUMmrUKBYsWEDXrl355JNP6NOnD4sWLcJqtTo6vDsihGD//v089dRTDBo0iNLSUtavX8/LL79cqfV0ZfKshhQKBV27duXZZ59Fp9MxYcIEzp49e8PjsrOz+eqrr0hMTKRjx463vSmsJN0ujUZjn9whVR5bbep69erx/vvvM3/+fGJiYhg/fjzdunXjp59+qnKtUKvVys6dOxk7diwPPvgghw8f5s0332T9+vUkJiZWej1umTyrKaVSSdu2bZk8eTJKpZKxY8eSnZ193WM++ugjioqKeOCBB2jUqJEsiCDddRqNBpAtz7tFqVTi4eFB+/bt+eyzz/j222/RarU88sgjdO7cmSVLllBaWurUSbS8vJyVK1fywAMPMGzYME6dOsVTTz3FwoULeeyxx9BqtXdlTaucGVJNKRQK3NzcSExM5PXXX2fy5MmMHz+eb7/9Fg8PD06dOsWSJUsYOXIk8fHxd1zWT5Juh208SibPu8e2lCUoKIhu3brRpEkTjhw5wrx585g8eTIff/wx9957L3369KFx48b2LziOJITg9OnT/PTTT/z0009kZGRQv359XnnlFdq2bUtERARarfauTnaUybMas01Xb9u2LS+99BL/+c9/mDJlCm+99RYfffQRAQEB9OvXj+DgYNnqlBzCdmGWRRLuPqVSiaenJx4eHgQEBBAbG0tycjK//PIL69atY9WqVURERNC8eXNatmxJ48aNCQ0Nve3yn7fCYrGg1+s5e/YsBw4c4NdffyUlJQWlUkmzZs14+umnqVevHpGRkfj4+NyVmP5IJs9qTqlU4uPjQ+/evbl48SJz587F19eXn3/+mZdeeok6depUyG4sknQ7ZLet49nKjDZq1IjatWsTGxvLuXPnOHXqFOfOnWP37t1s374dLy8vwsLCiImJISYmhlq1ahEWFkZoaCgqleq2v4CbzWZycnLIyMjg2rVrpKamcunSJS5fvkxubi5CCAIDA+nbty/x8fHUrVuXmJgYPD09HVpyUCbPGkClUtm3Kzp9+jQzZ87Ew8ODXr16yUpCkkN5eHjYy6hJjmXbPjEuLo7GjRuTlJTEhQsXOH/+PJcuXeLKlStkZWVx7do1du3ahUqlwtfXl6CgIHx8fPD29sbLywuNRoO7uzsqlcq+CYbVasVoNCKEoLy8nJKSEoqLi9Hr9RQWFpKXl0d+fj4GgwEhhD2ht2zZkujoaGJjY6lfvz6hoaH2WB1NJs8aQqVSERkZSevWrVm2bBkxMTGUlZVV+WnqUtUmu22dk0KhIDg4mODgYJKSkigtLSU9PZ2LFy9y6dIl0tLSyMjIIC8vj9TUVFQqFS4uLvZtFm0tUVv9bSGE/Vpj29fYbDZjNBqxWCx4enqi0+mIjIwkLCyMyMhIateuTVRUVIUUca8MMnnWELaSVb/++itNmjQhLy+PxYsXM2bMGCIiIhwyZiBJHh4eAPYZns7QopBupNFoqFOnDnXq1AF+m8BjNpu5evUqly9fJicnx956LCoqorS01J4cDQYDSqUSjUZj/9Pb2xtfX1/8/f3x9/cnPDyc0NBQh3fF3gqZPGsIs9nM7t272bhxI9988w3JycnMnDmT4OBgBg8eTEhIyB3vbydJt8o229bWCyLPwarBNmM3Ojravr9xTSOTZw1gtVrJzc3lww8/JCEhgfvvvx8hBJcuXeKLL75Ap9PRt29f/P395Td/6a7SaDS4urpiMBgoLy+3VxySJGdXNdrH0m2zbQa7Zs0a9u3bx1tvvWUfn/jkk09o2rQpH3/8MWvWrKGkpMSpF0dL1Y9tqYTRaKS4uNjR4UjSTZPJs5oTQnD16lU++eQThgwZQvv27e2tS61Wy+eff07dunWZNm0aS5YsqTH7/Ul3j63s2x9vVqsVDw8P3N3dMRgMFBUVYbVab7j92XMlydFkt201d/nyZb7++muKiop44403brg/KCiI999/n3//+9/MnDkThULBI488ctfjlKo3q9XK5cuXuXjxIunp6WRkZJCZmUlBQQFnzpwhLy+PiRMn4unpiUKhQKvVolKpCAwMJDg42L6eMCYmBn9/f7k2WXI4mTyrsdLSUg4dOsTKlSt55plniIiIuOExCoWC2NhY/v3vf/PRRx8xc+ZMNBoNDz74oAMilqqDoqIiUlJS2Lt3L/v37+fUqVOcPXvW3i3r6upqT4qenp5otVoCAgLspdUsFgtXrlzBYrGwd+9esrKyyMnJsbc4AwICiImJoUGDBjRr1ozWrVvTtGlT+8xdSbobFKIK9oHo9Xp8fHwoLCzE29vb0eE4rV27dvHxxx9TWFjIsmXL/nbXFLPZzJ49e/jqq69ISUlhypQp9OrV6y5GK1VVRqOR48ePs23bNrZt28axY8coKChAo9EQFxdH/fr1iY2NpWHDhkRFReHn54dKpbpuu6g/TlSzXZZs3bYGg4GsrCwuXLhgvyUnJ5OSkkJubi5eXl4kJCTQq1cv7r33XqKiouTkN+m23Gx+kcmzmkpPT2fOnDksW7aMl19+mfvvv/8f10+Vl5ezbds2pk+fTm5uLlOnTiUhIeEuRSxVNSdOnGDZsmX88ssvpKWl4e3tbW8NxsfHExsbi5eXF66urri6uuLm5oaLi8tt7bFoW1doMpkwmUwYjUaMRiNFRUWkpqZy+PBhjhw5wv79+zGbzTRp0oQBAwbQp08fgoKC5BIY6abJ5FmDCSFYunQpc+bMoVatWnzwwQc3XaVDr9ezZcsWPvnkE7RaLV9++eWfdvdKNY8QAovFwqZNm1iyZAmHDh3Czc2Npk2b0qpVK2JjYwkODsbHxwetVouHh0elL3i3Wq2Ul5dTVFREYWEh2dnZHDhwgD179nDy5El0Oh1t27alX79+NGnSRJajlP6RTJ412MmTJ/n8889JTU3lueeeo2vXrjf9XCEEeXl5rF+/no8++ohWrVrx4Ycf4u3tLbvBaihb7dlff/2Vn376iYMHDxIQEEBCQgItWrQgJiaGsLAwtFqtQytV2WbiZmdnk5aWRnJyMnv27OH06dMolUri4+Pp3r07iYmJ+Pr6OixOybndbH6RE4aqGYPBwC+//MKlS5do06YNLVu2vKXnKxQKfH196dGjB5mZmcyYMYOvvvqKZ555BhcXF5lAa5jS0lJSUlJYu3YtO3bsoKysjI4dO9K+fXuaNm1KWFiY03SJ2mqp2mqyxsfH06RJE44fP86BAwdISUnh/Pnz/Prrr3Tp0oV27drJWbvSbZPJ8y4zm82UlpZSWFhIcXExZWVllJSUUF5ejtlsxmKx2NdaqtVqlEolarUaV1dX3N3d0Wg0eHp64uXlhZ+fH2q1+rqEduzYMXbs2EFgYCC9e/e+rZa5UqnE39+fYcOGcenSJebMmUODBg3o27fvbY1XSVWPxWIhLS3Nvh3V6dOnqV27Nh07duSee+4hMDDQ6c8DNzc3+w4hbdq04ddff2X79u3s2rWLs2fPkpaWRteuXalVq5ajQ5WqIJk8K5HZbKagoID8/HwKCgrs4zK2vevy8/MpLy+ntLQUg8GAxWL50+Rpqwjk5uZmT54BAQFERETg6+uLVqvF19cXDw8Pli5dSmFhIT179qRZs2a3HbtKpSIoKIiJEyeSnJzMRx99RHR0NI0bN76ru7VLd19paSnJycmsX7+ezZs3I4TgnnvuYcSIEYSHhzs6vFumUCiIiYkhKiqKpKQk1q1bx9q1a/nqq6+4evUqAwYMoH79+vaWqyTdDDnmWYFsU+ptCTMnJ8e+xi01NZWMjAwKCwuxWCz2VqSvry+BgYHodDrc3NxQq9W4u7ujUCjsrVFb6bL8/Hzy8vIoKCiw1wL19fUlJCTEvgRg8eLFNG3alMcff5zWrVvj5eV1R91qVquVw4cP89BDD9GqVSvefPNNIiIiZAKtpvR6PQcOHOD777/n+PHjtGjRgkcffZTExMRqk1gMBgMpKSnMmDGD9evX07lzZyZNmkT9+vXvaFNnqXqQE4buEtsMRNvmrufPn+eXX35hx44dnD17FqVSSUREBI0bN6ZevXrUrl2b2rVrExkZeVuF2IUQmEwmrl69al/ndubMGQ4fPszVq1cxGo1ERkbSsmVL2rZtS/PmzQkICLDXEL2dC4MQgp9++onRo0czduxYnnzySbkLSzVUXFzM2rVrmTZtGhaLhZEjRzJkyBD8/PwcHVqlKCkp4YcffuDtt98mNDSUGTNmEBsbK8f2aziZPCuZbd1ZWVkZaWlprFixgmXLlnHmzBn8/f3p3bs33bp1o02bNoSFhd2VWYi29W47duxgx44dpKSkoNFoaN++PQ888ADdu3fHw8PDPkniVi8Q7733Hh988AFvvPEGQ4YMISgoSF5kqgmj0cjs2bP58MMPad68OZMmTSIpKanaf0GyWCycPHmSwYMH4+LiwnfffUejRo1wdXWV53YNddP5RVRBhYWFAhCFhYV3/bWtVquwWCwiOztbLF26VPTr1094e3uLoKAgMWbMGLFx40ZhNBqF1Wq967H9nsFgEGfOnBFTp04VnTt3Fq6uriIyMlK8+eabIjc3V5jNZmG1Wm8pTqvVKsaPHy/CwsLE7NmzRW5ursPfp3TnLBaL+Oyzz4Sfn5+YOHGiSElJcXRId5XVahWpqamiWbNmonbt2mLfvn2ivLxcnts11M3mF5k8b1FGRob49NNPRfPmzYWvr6/o1q2bmDdvnsjPz7cnI2f5pbPFYjAYxMmTJ8Xzzz8vdDqd8PX1Fa+//rrIyMgQZrP5lo5nNBrFww8/LOrUqSO+/vprh/wfSBXHarWK77//Xmg0GvHaa6+JjIwMR4fkEFarVeTk5IiWLVuKxo0bix07dgiDweDosCQHkMmzghmNRjFnzhwRFxcnwsLCxMiRI8WGDRtEWVmZsFgsTpMw/4wtiZrNZpGXlyfeeecdERQUJMLDw8VHH30krl27dtPxW61WUVBQIIYPHy7i4+PF9OnTRWlpaSW/A6kyWK1WsWfPHuHh4SEmTZpU43sSrFaruHTpkmjVqpXo0qWL2LlzZ43+PGqqSkme77zzjmjZsqXw8vISgYGBon///uLMmTPXPaasrEw8+eSTws/PT3h6eoqBAwfe8G328uXL4p577hEeHh4iMDBQPP/888JkMt10HHc7eR46dEh07NjR3jW7e/duUVRUJEwmU5X65bIl0fLycnHp0iXx5JNPipCQENG+fXuxdOlSodfrb/o4Fy5cECNGjBCJiYniyy+/vKUWrOR4ti9BTZs2Fd26davxidPGYrGIPXv2iGbNmol//etf4ujRo44OSbrLKiV59urVS8yZM0ecOHFCHDlyRNxzzz0iMjJSFBcX2x/zr3/9S0RERIhNmzaJAwcOiDZt2oi2bdva7zebzSIuLk50795dHD58WKxdu1YEBASIl19+ucLf3J2wjW3OnDlTREdHi44dO4qVK1eKq1evCoPBUOUvNGazWeTk5Ij169eLfv36ibi4ODF58mRx8uTJm3q+yWQSBw8eFA8//LBo3769+Pbbbys5YqkilZaWihdffFGEhISIo0eP2sfApd/mC/zvf/8TrVu3Fu+9957Izc11dEjSXXRXum2zsrIEILZt2yaEEKKgoEC4uLiIJUuW2B9z+vRpAYjdu3cLIYRYu3atUCqV17VGv/zyS+Ht7X3TYwyVnTytVqvQ6/Vi8uTJIiYmRowfP14cOnRIFBUVCYvFUimv6SglJSXi+PHj4rXXXhPt2rUTgwYNEmvXrr2p55aVlYnNmzeLhx9+WHTp0kWsWbOmkqOVKoLRaBTHjh0T4eHh4u2335aTY/5ERkaGePTRR8V9990nli9f7uhwpLvoZvPLHa10LywsBLCvAzt48CAmk4nu3bvbH9OgQQMiIyPZvXs3bdq0Yffu3cTHxxMcHGx/TK9evRg3bhwnT56kefPmN7yOwWDAYDDY/63X6+8k7L9lsVjIzMzk448/Zs2aNYwZM4aBAwdSp06darmAWqPR0LBhQx599FEiIiJYt24dU6dOpbCwkMGDB//tUgV3d3datmyJXq9n7ty5TJ8+neDgYLmNmZMrKipi3rx5eHp68tBDDzl0Wca+ffvYtm0bycnJFBUVARAUFETr1q0ZNGiQvWDI3RYUFES/fv2YNWsWW7duJTExkdDQ0Lseh+S8bnu/IKvVyqRJk2jXrh1xcXEAZGRk4OrqesP2V8HBwWRkZNgf8/vEabvfdt+feffdd/Hx8bHfKmuLLNsO9p9//jkbNmxg6NChPPzww9SrV++GGrLViUqlIiYmhvvuu49hw4bh7+/P7NmzWbp0KVar9W+fq9Vqadu2Lffffz9lZWVMmzaNy5cv36XIpVtlNptJT09nzZo1DBw4kMjISIfGc+TIEU6dOoVOp6NVq1Y0bdoUs9nM9OnTmTVrlsPiUigUtG3blnr16nHx4kUOHjzosFgk53TbyXP8+PGcOHGCRYsWVWQ8f+rll1+msLDQfktLS6vw17BarVy9epUlS5awZs0a7rnnHp544gnCwsKqbdL8o+DgYHr27MmIESPQ6XTMmDGDrVu3/uPzAgMD6d69OwMHDuTYsWN8/fXX5OfnI6pe/Y1qr7i4mIMHD5Kbm8uDDz7o8Hquer2eOnXq8NhjjzFu3DiefPJJhgwZQmFhIV988QVFRUUOO49svShCCH799VdMJpND4pCc020lzwkTJrB69Wq2bNly3Y4EISEhGI1GCgoKrnt8ZmYmISEh9sdkZmbecL/tvj/j5uaGt7f3dbeKlp2dzc8//8zy5ctJTEzkmWeeITQ0tMYkThsfHx/at2/P6NGjsVgsfPbZZ1y8ePEfL2AhISEMGjSIe+65hx9++IGVK1diNBplAnUy+fn5bN++nXr16hEfH+/ocGjYsCGdOnWifv36aDQafHx8aNq0Kc2aNePs2bPk5uY6NL6mTZsSGhrKiRMnyMrKcmgsknO5peQphGDChAksX76czZs3ExMTc939CQkJuLi4sGnTJvvPbEXRk5KSAEhKSuL48ePXnYgbN27E29ubRo0a3cl7uW2lpaXs2LGDpUuXEhoayr///e+/TOQ1ga0r9umnn+b48ePMnj2bsrKyv02Etn0Ux48fT+vWrXnvvfc4cOAAVqtVJlAnIYSgsLCQEydO0LlzZ6co7n/vvffSoUMHlMr/fylSKBS4ubld9zNHqVu3LnXr1iU/P5+TJ086OhzJidzS2Tl+/Hi+//57FixYgFarJSMjg4yMDMrKyoDfWi2PP/44zz77LFu2bOHgwYM8+uijJCUl0aZNGwB69uxJo0aNGDlyJEePHuXnn3/m1VdfZfz48bi5uVX8O/wHVquV48ePs3r1aoxGIxMnTiQ6Ovqux+FsvLy86NWrF6NHj+aLL75g//79iN9mZ//lc5RKJUFBQfz3v/9Fp9Px7LPPkpqaKhOokzCbzeTl5XH16lWnndQl/q9m9JUrVwgMDHT4FmgeHh6EhYXh4eHBmTNnHBqL5FxuKXl++eWXFBYW0rlzZ0JDQ+23xYsX2x/z6aef0rdvXwYNGkTHjh0JCQlh2bJl9vtVKhWrV69GpVKRlJTEQw89xMMPP8yUKVMq7l3dAr1ez7Jly0hOTmb48OF06NDBIXE4I09PTyZOnEiTJk2YPHkypaWl//gclUpFREQEc+bM4erVqzz33HPk5eXJ5OkEioqKuHjxIgqFgqZNmzo6nD9lsVjIzs7m0KFDjBo1yil2OAkKCsLPz49z5845NA7JudxSv83NXADd3d2ZPn0606dP/8vHREVFsXbt2lt56UohhGDVqlXs37+fxMREhg8f7tB4rFbrda00hUKBUql02KQOhUKBRqPh008/pVWrVqxYsYLBgwf/49IGpVJJgwYN+P777+nXrx8ffPABL7744m1twSZVHIPBQH5+PiqV6oYZ785ACEF2djYzZ84kMjKSV155xdEhAb99ifT09CQ/P9/RoUhOxPGDCg6Um5vLqlWr0Ol09O/fH09PT4fFYpvR16NHDwICAggPD2fUqFHs2rULi8XisLiUSiVNmjRh2LBhvPXWWxQXF9/U8xQKBZ07d2bq1Kl8+eWXzJ49m+zsbNkCdSCDwUBRUREeHh5oNBpHh3MdIQRZWVksX76cXbt2sWLFCrRarVN82XJ3d8fV1fWGiZBSzVajk+fPP/9Meno67dq1o127dg6LQwjBqVOn6NOnD4mJiZw6dYr169eTn5/PG2+8wYoVKxwWG/yWCF988UUuXrzItm3bbqr71uaRRx5h8uTJfPzxxyxevFjOWHQgW0/GP63ddYSLFy/y7bffsmXLFpYvX+7w9ae/Zxvrr+57m0q3psYmTyEEP//8MyEhIcTHxzt05qHFYuG9994jICCAF198kdDQUJo2bcqoUaMoKytj06ZNpKenOyw++G1JQWJiIkuXLr2lCk8KhYKXXnqJoUOHMnXqVJYsWUJOTk4lRir9UV5eHjt37mTevHmsXbsWg8FASUmJo8Oy279/P7Nnz6awsJAZM2YQEhLiFC1Om/LycoxGIz4+Po4ORXIijp+r7iBXrlwhJSWF7t27U69ePYf9sgohMJlMrFu3jh49euDh4WFvISQkJBAWFkZKSgpHjx51WHkw25hr//79mT59OsXFxQghbvozc3V15bXXXqO4uJh58+ahVqsZMWIEWq22kiOvmbKysjhw4AD79u1j//79pKSkUFRUhKenJ+7u7lgsFjIyMqhTp45D4xRCsHr1anbt2kWdOnV44IEH8PHxcYolKr9XUlJCaWmpU44TS45TY5PniRMnKC0tpXbt2g5d02mrbJSfn09UVNR1k4NCQ0Px8fEhJSWlUqoq3ao2bdrwzjvvcOnSJWrVqoWHh8dNPU+hUODv78/zzz/PO++8w6JFi1Cr1YwcOdIhy5OqE6vVil6v5+DBg+zfv5/9+/dz7tw5SktLCQkJoXbt2rRv355GjRoRERFBcnIyL7zwAsePH3d48kxPT2f58uWsXr0aDw8Pvvjiixu+kE2aNIkRI0Y4tMs0KyuLvLw8hw7tSM6nxibPM2fOoNPpCAwMdOgFXAhBZmYmVqsVb2/v6y4eHh4euLu7U1ZW5hSTFRo3boyrqyspKSk0b978ppMn/JZA69atyxNPPMGMGTNYunQpHh4eDBs2zOlaGs6upKSE1NRUDh8+zOHDhzl58iTZ2dm4u7sTHBxM7969adq0KeHh4fj7++Pr64uPjw8ajQalUklYWBgHDx5kwIABDn0fRqORvLw8srOzAUhNTb3hMZmZmQ6dZFZaWkp6ejrl5eU0aNDAYXFIzqfGJs+MjAx0Oh1eXl4OHV8RQtjHn1xdXa+7T6lU4uLigtVqpby83BHhXcfHxwedTkdWVtZ1u9zcLBcXF1q0aMGQIUOYP38+ixcvJigoiB49etzysYQQWK1WioqKKCwspKioCIPBgNVqxWAw2Cd4qNVqXFxccHV1xdPTEx8fH7RarVNU17lZJpOJK1eucP78ec6ePUtycjJXrlyhpKQEDw8PgoOD6dq1K5GRkYSEhBAWFkZYWBju7u43fDHx8fEhLi6OrVu3YjQabzjn7iZ/f3/GjRtHnz59/vIxbdq0ceiXq/Pnz3Pu3Dl8fX1p2LChw+KQnE/VuYJUsMLCQjQajUMvHja25P1n37CdaWmHUqnE19eXoqIizGbzbR1Do9HQvn179Ho9ixYt4ttvvyU4OJgmTZr85XOKi4vJzs4mOzub3Nxc8vLyyMvLQ6/XU1xcTFFREcXFxX9ZS1epVOLq6opGo0Gr1dqTaEBAAAEBAfj7+xMcHExgYKBDt+eysX2hSklJ4fz581y8eJFLly5RUFBAeXm5fZ1mdHQ0MTExxMTEUL9+/Zv6Iujr60uHDh1YtWrVX24BeLdotVp69erlsNf/J0IIjh49Snp6Oo0bN5ZjntJ1amzyNBgMqNVqh7dAFAoFXl5e9ph+z2KxYDKZUKlUt9RFWpk8PDwwGo13tNxBp9PRtWtXiouLmTt3LrNmzeKFF16wbzJgMBi4du0aGRkZZGdnc/XqVVJTU8nJyaG4uJiSkhLKysowmUy4ubnZu7fVajUqlQo3NzcUCoX98zObzfau79LSUsrLy1Gr1Xh5eeHp6YmXlxdhYWFERUURHBxMUFAQwcHBBAQE3LXP3WKxUFxczJUrV0hLS+PcuXMcO3aMzMxMCgsL8fHxISoqivr169O4cWPq1atHSEjILbfKvLy8aNmyJX5+fixZsoSmTZs6fGcVZ2WrdATQrl07XFxcHByR5ExqbPJ0cXHBYDA4tAAB/NYqCg4ORqVSUVBQcF3LyZYkNBqNfcNxRzMYDBXSOgsJCaFPnz7k5uYyd+5coqOjGTJkCDk5OaSlpbFv3z7OnTtHWloapaWleHh4EBQUREREhP0WFBREQEAAfn5++Pn5/eXGyRaLBYPBQGFhITk5OeTn55ORkcHly5e5dOkSly9f5tixYxiNRvz8/KhVq5Z915HIyEj8/f0JCgqq8F4Kk8lEcXEx+fn5ZGZmkpKSwr59+zh16hQZGRnUqlWLhIQEWrZsSYsWLQgLC7vj8Xm1Wk1oaCj33nsvP/zwA+PGjbtuZyTpN1arlV27dnHmzBliY2Np1aqVo0OSnEyNTZ62sTuj0ejQOBQKBWFhYQQGBnLx4kV7eT6FQsHVq1cpKCggKCjIKRaNW61W8vPz0Wq1FfItPDw8nEGDBnHy5Ek+/PBDCgoK2Lt3L6dPn0ar1dK8eXMGDBhAs2bNaNSoEUFBQbc161KlUqHRaNBoNH+63MdqtXL58mWOHz/OkSNHOHz4MAsWLMBoNBIdHU2bNm3o3bs3UVFR+Pr62pcT3Q6j0UhJSQnFxcVkZWVx8uRJ9u3bx+HDh7l69SohISEkJSXx9NNP06NHDzQaTYW3Cr29vXn00UeZP38+c+fO5YUXXnCKGrLOJDc3l59++gm1Wk2XLl1kl610gxqbPAMDA9m3bx8lJSW3tGaxoikUCtRqNX369OGXX35Br9fj6uqKEIKDBw+SkZFBQkICcXFxDonv98rKysjLy8PPz++OWmFWqxWj0UhOTg6nT5+21/SdPXs2vXr1YuzYsXTt2vWurflTKpX2scP77rsPi8VCeno6v/76K7/88gsLFizgyy+/pFOnTjz88MO0b98eT0/Pm2qB2yY2GY1GDAYDZ8+eZevWrWzdupVTp05hNBqJjY2lZ8+edO/endatW1d696BaraZ27do8+uijfPbZZ/Tv359GjRrZ6yjXdCaTie+++45jx47x4IMP0rlzZ0eHJDkjUQUVFhYKQBQWFt72MdauXSvi4uLE7NmzRXl5eQVGd+usVqs4efKk8Pb2FpMnTxaXLl0Se/fuFb179xbdu3cXP/74o0Pjs9mxY4fw8fERv/zyiygtLb3l51utVmEymcTly5fFjBkzRPv27YW3t7do06aNmD59usjJyRFWq7USIr99FotFFBQUiG+//VZ07NhRuLi4iPbt24sff/xR5OXlCbPZfEPMVqtVWK1WYTabhcFgEBcuXBCffPKJ6Nq1q9BqtSIwMFD07t1bfPTRR+LIkSPCZDLd9fdltVpFUVGRaN68uWjXrp3Iy8tzus/eEaxWq9ixY4eIi4sTEyZMECdOnHB0SNJddrP5pcYmz/T0dJGYmChefvllceHChQqM7vZYrVaxbds20b59e+Hl5SWCgoLE8OHDxfbt2x1ycf0z77//voiOjhbnzp27pefZkklubq74+OOPRVxcnAgMDBQPPvig2LRpk7BYLJUUccWyXVh79+4tXF1dRWJioli+fLnIz8+3v0db0szLyxMLFiwQ9957r1Cr1cLHx0cMGTJEzJ8/X2RmZjrFe7ZareLo0aNCo9GI0aNHi9zc3BqdQK1Wqzh79qxo0qSJ6NWrl9i9e7ejQ3Jqvz/nK+rmDG42v9TYbtvg4GDi4+M5ffo0p06dIiYmxqHxKBQKOnTowPbt26/bksyZLFy4kG7dutlnB9+KRYsW8cEHH5CRkcH999/P448/TvPmzZ3uPf4dhUJBu3btWLt2LXv37mXKlCk8/vjjtG/fnnHjxlGvXj127tzJsmXL2LRpE0ajkY4dOzJr1izuv/9+h68p/jPx8fHMnz+fkSNH4u3tzfPPP++wMpCOdu3aNQYOHIiHhwdvv/02zZo1c3RITk0IQVpaGqdPnyYtLY309HQyMjLsy8hMJhPl5eWUlZXZZ7crlUp8fHzw8/MjLCyM0NBQwsLCiI2NJSIiwul2+/k7CiGcaCHhTdLr9fj4+FBYWIi3t/dtH+fHH3/k008/5d5772XSpElOsxzE2Qgh2Lt3L506dWLlypV06tTppj+r3NxcJk2axLp16+jZsyf/+te/SEhIsE+6cbZkcrNsxRhWrVrFl19+yaVLl7BarZSUlNCsWTMGDBhA//797ZOcbBOdnPH9WiwWZs+ezSuvvMKAAQN45plnaNCggVPGWhmEEJw7d477778fq9XK999/T1xcnJxE9TulpaVcunTJXjP56NGjJCcno9frsVqtaLVaQkJC7BuH2wqR2JaSWSwWioqKEEJQVFREXl4emZmZZGRk2H/u6elJREQEjRs3pnnz5rRu3ZqmTZve9YL8N5tfanTyzM3NZeLEiRQVFTFu3Dh69+5dgVFWH1arlQceeIDLly/z888/39Sm1kIIDh06xJgxY9Dr9bzyyiv06NGDoKCganNREkJgMBhIS0tj/vz5zJ8/H61Wy7///W/uueeeP63w46zKyspYtGgRH374IfXq1ePpp5+mY8eODl8HXdksFgsHDhxg5MiR+Pj4MHPmTBo1auQUxTIc7dKlS+zYsYPNmzfbJy+6ublRu3ZtGjduTN26dalbty7169dHp9OhUqlQKpX2iWe2z+/3RWBsN9skQavVSmFhIRcuXLDfkpOTSU5O/n/t3XlclNX+B/DP7AzLzDBsww6ibIJLiIi4iwKaZlm5ZJKVmpmlLfemLS5107o3/WWWXq+lZSploS1imYLigoorCAKCbAoz7DMDw8As5/eHd54bpQUIzAyc9+s1L4V5mPmeeZ6Z7zznOed7UF1dDTs7OwwaNAhxcXGIj49HQEBAt+8XmjzbwWAwICkpCbt370ZoaCjefvttSCSSrgu0FzAYDDh79izi4+Oxbds2PProo38511Cv1yM9PR3Lli2DTCbD6tWrER4eDpFI1CvXRNTr9aitrcXx48fx1VdfQS6X49VXX8WsWbPMHVqHKJVKHDlyBNu2bYNWq8WsWbMwe/ZsuLq6mju0LkcIQWNjI/bu3YuNGzfC29sbGzduRHBwcK/5ctdRptfk0KFDOHjwIK5duwaDwYB+/fph8ODBGDRoEPr37w9HR0fY2tpCIBBAIBAwXxI7+5oZDAZmNHpLSwu0Wi3UajVu376NrKwsXL58GVeuXIFer0d4eDgefvhhxMfHw9HRsVs+T2jybCe5XI6NGzciMzMTTz75JBYsWNAn3zh3Y/pWOHfuXFRUVODIkSNwcXH507MprVaLtLQ0vPXWW3B3d8dbb72FQYMGMVV/eitCCJRKJc6fP4+vvvoKOTk5ePHFF5GYmGju0NqtqakJ6enp+Oyzz3Du3DnY2tpiwoQJmDt3LmJiYqzmLPqvaLVa5OXlYefOnTh27BhGjRqFxYsXIzw8HBwOp1cfp3djWqLu22+/xc8//wy5XA5/f39ERERg0KBBzAIDEokEdnZ2PdIbYbos0tDQgNraWigUCly5cgXnzp1DYWEhXFxcMHr0aDz44IMIDAzs0mulNHm2k9FoZD4wlEolVq5ciejo6C6K1Lo1NTVh+/btWL9+PdhsNmbPno0hQ4Zg0KBBGDBgAOzs7Np8oDY3NyMzMxNr1qyBUCjEqlWrmMLefeUDqbGxEZcuXcLOnTuRnZ2Nd999F3FxcRbZfkII9Ho9rl69yhRquHXrFthsNmQyGQghqKysBIfDwfjx4zFz5kz4+fmZO+xOMxgMKC4uxpEjR3D06FHU1NRg0qRJePjhhzFw4EAAlnlNuru0trbi1q1bOHLkCNLS0lBRUYH+/fsjKioKISEh6NevH1NZy9yLZxBCUFVVhaKiIuTm5uL8+fO4efMmRCIRIiIiMH78eAwePLhTgxl/jybPDlAqlfj++++xZ88e+Pn54fXXXzf76Ftza2pqYs4ghw0bBh6PB4VCAeBOcXdfX1/069cP/v7+6NevH5ycnFBQUICNGzfi5s2bWLNmDSZNmtSnPoxMmpubcenSJbz77rswGo3YuHEjQkJCLObMTa/Xo7KykqmolJWVhfr6etjY2CAwMBChoaFM92Vubi7S09NRXFyMwMBAxMTEYMKECXB3d7eafWswGFBSUoLMzEycPn0a+fn5EIlEiI+PR0JCAjw9Pc0dYo8yGo2Qy+XIyMjA8ePHkZeXB7FYjJiYGMTExCA8PPyepS4tgVarRU5ODjN4qaKiAk5OToiKisK4ceMQGhp6X925NHl2UElJCb755hskJydjypQpWLJkCVxcXLrksa1NU1MTLly4gE2bNkGpVGLLli2QSCTIyspCdnY2srOzoVAomFVW/P394eXlhfLycvz000949dVXMX/+fAB965v8b7W0tODYsWN47bXXEBsbi7Vr10IsFpvl9SCEQKPRQKFQ4NatWygtLWWWNqutrYWbmxsGDhyIyMhIREREMIM/gDsfVDdu3MD333+P48ePQ6/XY8yYMRg1ahSCgoLg6elpsV2dGo0GxcXFyM/PR2ZmJnJycmAwGJgBKKNGjer1A6J+T61W4/r160hLS8OZM2eg0WgwYsQIJCQkYPjw4Ra7L+/GYDAgNzcXx48fx+nTp1FTU4OQkBDEx8cjOjq60/XAafLsIEIIioqKsH37dqSkpGDZsmWYOXNmu0aW9iZqtRqXLl3C7t27mbOnKVOmtNmmuroaV65cwYULF5CVlYX8/HwoFAo4OTlh+vTpWLt2ba8cGNRROp0OW7duxXvvvYdPP/0Uw4cPB5fLbTPakMVigc1mM2uPmm58Ph9cLrfTxx4hBCqVCrW1taiurkZJSQlycnJw/fp13Lp1CzweD+Hh4ZgwYQLGjh37l8d5c3Mzzp8/j2+++QZnz56Fq6srRowYgREjRsDHxwcuLi4QiURmXeKPEAKDwYCamhrI5XIUFBTg1KlTuHr1KlgsFiIiIpCQkICoqCg4ODiYLU5zIIRAoVDg/PnzOHjwIK5fv46goCA8/vjjmDRpklWvGKPX65Gbm4uDBw/i2LFjYLPZmDdvHiZNmgRvb28AHfsST5NnJxiNRpSVleHdd9/FsWPHsGrVKjz66KOQSCS9PoGaRtqdPXsWX375JbKzs7F8+XI89dRTf/p3KpUK3333HT799FN4enpi+/btvXJ0ZmeYprLMnDkTlZWVeOyxx8Dn85nRhabkyeVyIRQKYW9vzyyTJpVK4ejoCD6fDz6fDx6PB6FQCKFQCB6P94cuYNPbWKPRQKVSoaGhAVlZWcy1zIqKCtja2iIsLAxjx47F+PHj0a9fvw63x2g04tSpU/jqq69w+vRpcDgcREZGIjo6GuHh4ZDJZLCzs4OtrS1sbGy69UuUKZ6WlhZoNBpm3deTJ08iPT0d165dg7OzMyZOnIgZM2Zg4MCBVjUJv6uYumm//fZbfPvtt+BwOHj88cfx2GOPwdnZ2dzhdRmNRoNLly7h448/xsWLFzF79mwsWrQIXl5eHZpTTpNnJxFCUFNTg7feegvffPMN3n77bSQmJkIkEvXagS+mtSRTUlKwc+dOKJVKvPzyy+2aalFWVoatW7ciLS0N77zzDiZNmtQDEd8bIQRarRZarZZZ59Pc8eTm5mL06NFobm6Gg4MDs8oLj8djisZrtVomGZj+dXBwgIeHBzw9PeHh4YHg4GCEh4fDz8+PKUxvSsamBHLhwgX8+uuvSEtLQ01NDdzd3REdHY3x48dj+PDh8PX17bK2FRcXY//+/UhJSUFhYSEEAgFCQkIQGRmJIUOGICwsDGKxGDwejzmj7mzBCKPRCIPBAIPBAL1ez9yUSiXy8/Nx8eJFZGZm4ty5c2Cz2YiJicFjjz2GyZMn99gCA5bIaDSiuroaa9aswdGjRzFmzBgsWrQIkZGRvfI1IYSgtrYW27dvx+bNmzFq1Chs2bKFmSXQnmOOJs/7YDpjeO+99/DBBx/g0Ucfxfr16yGTye6rK83SmD6kKysrsWXLFiQnJyMwMBB/+9vfMGbMmHb9/eeff46vvvoKQ4cOxb/+9S+zviFNZyFJSUn44YcfMG/ePMycOdNs8fzW888/j6NHj+LgwYMICgpqc0ZmGvWqUqmgUqnQ2NgIuVyO4uJilJSUoKSkBEVFRbh58yYaGxvh5OSE8PBwxMTEIDo6GiUlJfj1119x7NgxpsJRQkIC4uPjERIS0iUjEP8MIQT5+fk4ceIE0tLScOHCBVRUVIDP57dZvHvgwIHw9/eHTCaDra1tm2Pl9+8p08eS6V9TV2xRUREKCwtx48YN5OXloaioCBqNBlKpFCNHjsTUqVMxderUPne55fd++7o9++yzuHTpEl5//XXMnDkTMpnMzNF1v9bWVqSlpWHevHkYNGgQvvzyS2bx+L86Lmjy7AKEEBw6dAjPPvssxGIx3n33XUyePJl5Tmt+c5qGf//www/YsGEDqqqqkJiYiGeeeabdiyNXVFRgzZo1uH37NtauXYthw4Z1c9T3ZrpmvWLFCqSlpcHV1RXr16+3mEIF5eXliIiIwMqVK/HEE0+0u2v7t2/PhoYGnD59Gj/++CNOnjyJoqIitLa2gsPhIDg4GAsWLMDMmTPh4+Nj1i8xWq0Wt27dwvnz53H+/Hnk5OQgNzcXlZWVAO60SSwWw8XFBfb29m26eDkcDnQ6HZqbm9HS0oLq6mooFApm3V0ulwsvLy8MGDAAISEhGDp0KIYNG4bg4GB6nf2/ftuFP3XqVNy8eZNZUq+7v0hZEqPRiKtXr2LatGnw8/NDUlISPDw8/rILt735pW8NNesgFouFqVOn4uLFi1i+fDkWLFiA0aNHY8WKFYiJiYGdnZ25Q+y0nJwc/O1vf8PJkycRGxuL999/H9HR0R0aOJCeno7S0lIMHToUQ4cO7cZo/xwhBFlZWdiwYQOmT5+O1tZWZlqNpfDy8kJsbCx++eUXTJw4sd3JU6vV4uLFizh27Bh++eUX5OTkQKfTwdfXF48++ihcXV1RUFCAX3/9FTt37sSwYcPg5eVl1uQpEAgQEBCAgIAAzJkzh/l9XV0diouLIZfLIZfLoVAo0NTUBI1GA61Wy3TJ8vl82Nrags/nw8nJCe7u7szN29vbIgvsWxqDwYAnn3ySGUgTERFh1sFc5sBisTBkyBCkpqYiNjYWzz77LD755BP4+/t3zfHzp2uuWKiuWJKsI4xGI2ltbSWHDx8m48aNIy4uLmTy5Mnkq6++IrW1tT0SQ1cwGAwkJyeHLFiwgNjZ2ZHBgweT/fv3k5qaGmIwGDq8JNCKFSvIjBkzSHJystmXEzIYDKS1tZXo9Xry5JNPkqFDh5KkpCSzxvR7Bw8eJF5eXuTHH3+86xqyRqORaLVakpGRQTZs2EASEhKIVColNjY2JCwsjCxatIh89dVXpKCggGi1WtLa2kp0Oh3RarUkOzubJCQkEFtbW7Jx48Yee2+0l2nJKYPBQPR6PdHr9USn0zG31tbWNrff3qfX64nBYGCOUXMfa5autbWVrFy5kjg4OJDU1FTS0tLSZ18z0zF36tQp4u3tTZ577jly48aNP/0buiRZF2KxWODxeJgwYQIeeOABHD16FPv27cPKlSvx8ccf46GHHsKMGTMQEhJi7lD/gPx3FO3Jkyexd+9eHD58GD4+Pti0aRMefPBBSKXSu47e/Cu1tbUoKyuDVCpFYGCg2c8ETAWpAcvtTh85ciRsbGxw/fp1PPDAA3B3d0d9fT1ycnJw7tw5nDt3DpmZmWhoaICTkxOGDx+O1atXY9SoUfDy8mIGGXG53DbXbjgcDkJCQrBv3z58+OGHePfdd8Hn8zF79mw4OTmZudV3/L5IONU9Wltbcfr0aWzevBkbNmxgepP66utu6qIdPnw43nzzTbz//vsICgrC7Nmz7/vaL02eHcDn8+Hs7Izp06cjJiYGV65cQUpKCpKSkrBz50488MADmDp1KmJjYyGTycx6wNbW1iI7OxupqalIS0tDeXk5BgwYgDVr1mDq1KlwdnZmur86E+eNGzdQX1+P0NDQLh3B2ZtJpVL4+voiIyMDJSUlUCgUuHHjBtRqNezt7RESEoJnnnkGw4YNY+p1mm5/NlDNNFdUJBJh+fLlqKurw7Zt2zBw4EBER0f/ZSF/qncwGo2oqanBypUrMXbsWDz55JO9vqZ0e/F4PMyePRuZmZn49ttv4ePjg+nTp99XkQyaPDuIzWYzgxykUinCwsJQVlaGzMxMZGRk4F//+hc+/fRTDB48GJGRkQgPD0dgYCAzRaG7NDY2oqCgALm5ubh8+TJyc3NRU1PDzO177rnnEBYWBg8PD2Zu1/28qeRyOYA7CaEvzp3rDA6HAy8vL+Tl5UGlUsHDwwMPP/wwAgMD4e/vD7FYDIlEArFYDFtb2w7tH9O2UqkUL774ItLS0pCeng5/f3/65aaPUKlUSE5ORnFxMf71r39BJBLRxPkbIpEIzz//PF599VWkpqaif//+GDRoUKcfjybPTmKz2XBwcICDgwN8fHwQGBiIkSNHorS0FFevXkVxcTH27duHffv2wcnJCd7e3sx8PZlMBplMBolEAolE0u4zA4PBAI1Gg4aGBlRXV+PWrVu4desWysvLUVpaiurqagCAg4MDAgICEB8fz6zQ7uvr26Ujk5VKJXg83h+mHFB/TiqVQiQSYerUqRg5ciRcXFzg5OTUpYNgAgICEBYWhhs3bqC2tpYmzz7AYDCgoqICSUlJiIuLQ0REhNnjycnJwcmTJ5Gbm4umpiYIBAK4ublh2bJlZit9GhoainHjxuHkyZM4c+YMgoODOz2QiibPLsDj8ZjEOGLECERFRaGgoAClpaVMgistLUVxcTEIIWCxWLC1tYWDgwNTUcbOzo7pYjElU51OB71ez6x1p9Fo0NTUhKamJjQ0NECv1zPXvoRCISIiIuDp6Qlvb2/4+/vD29sbjo6O3fLtU6vVgsPhWHVZL3MwrYMYHh6OyMjIbnkO0xluYWEhmpubu+U5KMuiUqlw6dIllJSUYN26dRAKhWaN5+TJkzh8+DDq6urg7u4ODw8PqNVqnDt3DvPmzTNb8hQIBJgyZQouXLiAK1euoLS0FAMGDOjUY9Hk2YVMpdZMK6wDd+bmlZaWoqysjBmeX1VVhfr6eigUCiahmqqvAGDO5Mh/q2VUVVWBx+PBy8sLPB4PdnZ2EIvFcHNzYw5MPz8/+Pn5QSQS9Uixaw6HwxRZoNrPYDD0SKWq5ubmNtV8qN6tqqoKJ06cQL9+/TBixAizxnLr1i0kJSWhqqoK06ZNw5QpU+Do6Ijq6mqkpKSYfa5pWFgYQkNDkZubi0uXLtHkaalMXbODBw9mfqfT6aBUKlFXV4f6+nrU19ejsbERGo0GRqOROVvg8/nIzc3FmTNn4OnpiWXLlkEsFkMqlcLZ2dms1xrt7e2h0+mYsnL02kr7KJVKCIXCbp1z19DQgPz8fISEhHRrERHKMhiNRmax6GnTppl9DEJaWhouX76MadOm4aGHHmJWN/H09MTChQvNGhtw5+xzyJAhyM3NxZUrVzBz5sxOnXDQ5GkGPB4Pzs7O7SrKfOrUKVRWVsLW1hajRo2ymDMJqVTK1MTV6/W0+7YdCCGorq6GSCTqtpq7Op0OaWlpKC4uxhNPPAE3N7dueR7KcjQ3N+PWrVuoqanB+PHjzRqL0WhEamoqOBwOBgwY0OllwbpbWFgYHB0dUVxcjIaGhk4VyKcjPSycVCqFUCiEUqlEU1OTucNh+Pn5wcbGBnK5HFVVVWaNhfy3FnF1dTUqKyuh0WjQ2tqK+vp6yOVy1NfXM+XdzKm1tRWFhYVwc3ODRCLp0sc2vQbXr1/HmjVrMHjwYIwaNcpiP7yorlNTU4MbN27Azs4OYWFhZo2ltbUVBQUFEAgE4HA4UCgUqKiowO3bt6FQKNDY2Nim5KS5+Pn5wdPTE3V1dSgoKOjUY9DkaeGcnJwgkUjQ1NRkUSXn/P394ebmhsrKShQXF5s7HGRlZeGpp57C+PHjcfToUZSWlmLdunWYOnUq1q1bh/z8fHOHiLy8PNTX12PAgAFdVryA/HcNS61Wi9OnT2PmzJlgs9lYt25d15UhoyxaQ0MDFAoFfHx84OjoaNZYqqur0dzcjNLSUnz00UeYPn06RowYgYiICMyYMQO7du2CTqcza4wAYGdnBzc3N7DZ7E5/ftFuWwsnlUohlUqh0WhQWVnZ6YvbXY3H4yE4OBjp6enIyspCTEyM2T6oWSwWIiMjcejQIbM8f3sQQnD48GEIhUIMGDDgvq5Fkt+sONLa2opr167hk08+we7duzF58mRs27YNnp6eFtPFT3UvrVaL5uZmi6gmpdFoYDAY4ODggBkzZmDKlCmQyWSQy+XYsmUL/va3v8HR0RFPPPGEuUOFvb09BAIBlEplp/6ennlaOC6XC7FYDC6XyxQmsBRjxowBn8/H+fPnUVdXZ+5wLBohBElJSRg/fjyzskNnabVa5ObmYuPGjRg1ahSioqJw+vRpfPnll/jxxx/h7e1N5972IS0tLWhuboZYLDZ3KODz+WCz2fD29kZERASCg4MhkUgQFBSEFStWwMbGBp999pm5wwQApnKXSqXq1N/TM08Lx2KxIJFIIBQKmSWdLMUDDzyA8PBwHD9+HD/99BMSExPNHZLFSk5ORklJCTZs2NCh0o2NjY3Mep6XL19GZmYmsrOzoVAoIJVKkZCQgA0bNmDMmDFtSvjR7tq+g81mM0u5mZurqyt4PB7UajWam5vbHIdCoRCenp64efOmGSP8H4PBAEJIpwc70uRpBUzl2iztzJPP52PChAnIz89HcnIy4uPj6ejO3yGEQKfT4b333kO/fv1w9uxZ5OXltdnGVDmqqakJjY2NUKvVqKysRElJCWpqaqDX68FiseDj44OBAwfi+eefx9ChQxEWFgaJRAI+n9+rFmmnOobP58PGxqbTZ1BdydbWFj4+PkztaxPTsWk0GuHg4GCu8NrQaDTQ6/WdjqdDyXP9+vVITk5GXl4ehEIhRo4cyVSpNxk3bhxOnDjR5u8WL16Mbdu2MT+XlZVhyZIlSEtLg729PRITE7F+/foemdxvjUQiEYRCoUUNGAL+d60xLy8PO3bswPbt2/HWW2+ZOyyL8+9//xvFxcWQSqVITk5u06VqmiMrFApha2vL/Ovv74+YmBh4eHjAy8sLHh4ekEgksLGxgY2NDQQCAdNFRvVtpmPGEgbusVgsjB49Grm5uUwCdXR0hNFoZAY9jhs3ztxhghAClUqF5ubmTg+y6lC2OnHiBJYuXYrIyEjo9XqsWrUKkydPRm5ubpuFoRcuXIh169YxP/920q7BYMDUqVMhk8lw5swZVFZWYv78+eDxeHjvvfc61YjezjQvsLy83OIKEtja2mLs2LEoKirCwYMHERUVhcmTJ5s7LItgNBqRl5eHTZs2Yf78+Zg1a9Y96xibqgGZbnw+HwKBgEmWfD4fHA7HovY9ZRmkUim8vLyQmpoKuVx+30tt3a9p06bh9OnTOHHiBLy8vDB9+nQ0NDTgiy++gE6nw4IFC8waH3BnYXa5XA4ej4eAgIBOPUaHkufPP//c5uddu3bB1dUVFy9exJgxY5jf29ra3nMHHjlyBLm5uTh69Cjc3NwwZMgQvPPOO/j73/+ONWvW9LnVzttDLBZDLBYjJycHSqWyy+cI3g8WiwU/Pz8kJCQgPz8fmzdvRkBAQKcPyN7CaDSiqqoKa9euhVQqxaJFixAcHExHwFJdztHREf369YNWq0VWVpbZk6e3tzeWLFmClJQUpKSk4Oeff4Zer4fRaMQrr7yCmJgYs8YH3On9rKyshFQqhb+/f6ce4776fExDfH8/EXvPnj1wdnZGWFgYVq5cCY1Gw9yXkZGB8PDwNtfG4uLioFKpkJOTc9fnaWlpgUqlanPrSxwcHCCVSqHVas1ekOBuhEIhBg8ejNmzZ0OhUGDjxo2oqqqyiMnQ5mA0GlFbW4stW7bg0qVLWLFiBYKDg2kXK9UtbGxs4OnpCZlMhtTUVHOHAx6PhxEjRmDevHl47LHHMHbsWEyaNAnz589HYmKiRXz5z87OhkqlQv/+/Ts9SrnTFxmNRiOWL1+OmJiYNlUt5s6dC19fX3h4eCArKwt///vfmQElwJ11IH8/qMT0870GxKxfvx5r167tbKhWz8HBAU5OTtDpdKisrERgYKC5Q/oDqVSKiRMnory8HLt27YKPjw+effZZSKXSPtXVaDAYUFVVhe+++w7fffcdHnvsMTz00EM9Ugye6pvYbDZcXV0RERGB48ePo6GhwewJyvSF+rc1vS2FWq3G5cuXweFwMGTIkE5/qe108ly6dCmuXbuGU6dOtfn9okWLmP+Hh4fD3d0dEydORFFRUae78lauXImXX36Z+VmlUsHb27tzgVshgUDAzPW0tOkqJiwWCy4uLliwYAEqKyuxa9cuiEQizJw5E87Ozn3irEun06GiogKHDh3Crl27MHz4cKxYsQJ2dnY0cVLdysXFBaNHj0ZycjJOnTqFBx980NwhWaysrCzcuHED/v7+GDJkSKcfp1OfaC+88AJ++uknpKWlwcvL60+3jYqKAgAUFhYCAGQy2R9GjZp+vldfvUAggEgkanPrS367/qelJk/gzjdgZ2dnvPHGGxg1ahTef/99JCcno6ysDDqdrld342q1Wty8eRPfffcdtm3bhgEDBuDdd9+Fi4sLTZxUtxOJRIiIiEBQUBC2b9+OpqamXv1+6yyNRoODBw+ipaUFERER8PHx6fRjdSh5EkLwwgsv4MCBA0hNTW3XhdYrV64AANzd3QEA0dHRyM7ObnPt7tdff4VIJEJoaGhHwulT7OzsIJFIUFFRYe5Q/pQpgX788cd48MEHsXr1anzwwQcoLCxES0tLr1v/kxCCxsZGXLlyBVu3bsVnn32GyMhIfPTRR32qd4QyL1PPz7PPPosTJ07g5MmTIITQBPobRqMRFy5cwKlTpzB06FCMGjXqvnrEOtRtu3TpUuzduxfff/89HBwcmGuUYrEYQqEQRUVF2Lt3L6ZMmQInJydkZWVhxYoVGDNmDAYNGgQAmDx5MkJDQ/Hkk0/igw8+gFwux5tvvomlS5fecxg/9b/kefv2bXOH8pdYLBYEAgEz8vb9999HXl4eXn/9dURGRkIkEvWKa4B6vR4qlQr79+/HF198AY1Gg4ULF2Lx4sUQCoXmDo/qY+zt7REXF4cRI0bgtddeQ1RUlNmvfVoSlUqFDz74ADY2NoiNjUX//v3v7wFJBwC4623nzp2EEELKysrImDFjiFQqJQKBgPTv35+89tprRKlUtnmckpISkpCQQIRCIXF2diavvPIK0el07Y5DqVQSAH943N4sNzeXvPTSS2TMmDHEaDSaO5wOOXv2LImOjibOzs7k2WefJefOnSM6nY4YjUarawshhBiNRqLX68mRI0fI5MmTiZubG5k3bx45e/asuUOj+ji9Xk/y8vKIVColS5YsIS0tLVb5HutqBoOBvPrqq8Tf358kJSURtVp9z23bm186lDwtRV9MnqWlpWTt2rUkKCiIaLVac4fTYRqNhnzyySckPDyceHp6koULF5KMjAwmgVr6G/y3cZ45c4bMmDGD2Nvbk4kTJ5Iff/zxT9+MFNWTdDodSUpKIiwWi2zatIloNBpzh2RWRqORfPLJJ0QkEpFNmzYRuVz+p9vT5NnL1NbWku3btxM/Pz9SWFho7nA6zGg0EoPBQMrKysjGjRvJ0KFDiVQqJVOmTCEHDx606C8ERqOR6HQ6cuTIERIfH0+4XC6JiooiX3/9NampqbGK5E/1Hab32j/+8Q8iEAjIf/7znz71WflbRqOR7N27lwiFQvLGG28QhULxl+/V9uYXWkzWStjb28Pd3R16vR7l5eVWV8GHxWKBxWLB09MTS5cuxcyZM5Geno4vv/wSiYmJcHNzw6xZszB37lwEBQVZxPXQlpYWFBcX4+DBg9i3bx8KCwsRGRmJb775BuPGjYO9vT0tyE5ZHNPx+Morr6C6uhqvv/46ampqkJiYyAzc7AsMBgN27tyJFStWYMGCBVi+fHmXzjunydNK8Hg8iMViCAQClJWVmTucTmOz2eDz+fD09MQjjzyC8ePHIy8vD1u3bsW2bduwY8cOBAUFYfLkyRg7dizCwsJgb2/fI/NECSGoqqpCVlYWTp06hVOnTqGgoAA2NjaYOHEiPvzwQ4SHhzO1hmnSpCyVadDe22+/DXt7e/znP//B7du3sWjRIgwcOLBXz7smhKCpqQkfffQRNm7ciMWLF+Pvf/87pFJpl7abJk8rwWKxYGNjAxcXF9y6dcvc4dw3DocDW1tbcDgcODg4ICsrC4MGDcLjjz+Oc+fOITk5Gbt374ZYLEb//v0RGhqKAQMGwM/PDz4+PvddeIEQgurqahQXF+PmzZvIzc1FTk4OysvLmZUWQkND8fjjjyMiIgJubm5wdnamSZOyKo6Ojli6dCmkUimSkpJQUlKC+fPnIz4+3mKWButKra2tKCwsxD//+U+cOHECL774IhYuXAhnZ+cuf9/S5GlFbGxs4OzsbBXTVdrDYDCguLgYH3/8Mezs7LB69WqEhIRg5MiRkMvluHnzJgoLC1FZWYn09HQcOnQIwJ0ubEdHR7i4uMDR0RGOjo7Mcl08Ho9ZgUSn0zG35uZmqNVqKJVK1NbWoqamBjU1NVCr1WCz2ZBIJHB1dUV4eDj8/f3h4+MDNzc3uLm5dfk3VorqSTKZDLNmzYKrqyuSk5Oxbds25OTk4JFHHkF4eHiv+DJICIFCocDRo0exf/9+yOVyvPzyy5g+fTrc3d27pY00eVoRgUDQa848CSEoKSnB7t27kZWVhRdffBEjRowAj8djFhVQKpVQKBRQKBSoqqpCTU0NamtrUVVVhfr6etTV1UGhUECv1zOrwhNCYDQamaXbTPNJ2Ww22Gw2uFwuuFwuOBwOgoOD4e7uDhcXFzg7OzPJ0sXFhZbUo3oVDw8PTJ06FS4uLkhJScGZM2dw48YNTJo0CQkJCVa9iH1DQwMuXLiAX375BVevXoVIJMKyZcsQHx8PZ2fnbntemjytiCl5ZmVlmTuU+1ZRUYGUlBSkpqYiLi4Ojz76KHg8XpttTEuxmQrhGwwGNDY2oqamBtXV1VAqlVCpVFCr1VCr1dDpdNDr9dDpdDAYDOByueDxeOBwOBAIBEyJQ1OJR1OytLW1pWeWVK8nkUgwfvx4yGQyHD16FGfOnMHevXtRUFCAmJgYREVFwcnJyWq+NNbX1yM7Oxvnz5/HhQsX0NDQgPDwcMTFxWHs2LHdXnSHJk8rYkqeCoUCra2tVrv2aUNDA1JTU5GSkgJ/f38sWrSoXcsCcTgcJqFa22hjirIEPB4PgwYNgp+fHyIiIphC8llZWcjLy0NkZCQCAgIgk8ksrgqYqWdJoVCguLgYV65cwZkzZ1BRUQF3d3fMmjULcXFx8PDw6JF4aPK0IjY2NpDJZGhqakJtba1VDjvXarXIyMjAoUOHwOFwsGTJkvsvk0VRVIeIRCKMGTMGQ4cOxalTp5CUlISdO3ciNTUVo0ePxqhRoyCTyeDk5AR7e/s/9Ar1JPLf+tG1tbWQy+U4deoUTp8+jZKSEvj5+WH27NmIjY2Fv79/jyZ7mjytiEAggLu7O4xGI8rLy60ueRoMBuTk5GDPnj2ora3F4sWLMXr0aHOHRVF9EovFgkgkQkJCAiZPnoxjx45hz5492L59O7788kuMHDkSo0aNwuDBg+Hm5gYbGxvY2NhAIBCAw+F0W1xGoxE6nQ5arRZarRaNjY24evUqjh8/jtTUVDQ1NSEqKgqvv/46JkyY0C0jaduDJk8rIhAImO6U0tJSDB8+3NwhtRshBBUVFfi///s/3Lx5EwsWLMDDDz9s7rAoqs9jsVjgcrmIi4tDXFwcioqK8PXXX+PQoUP48ccfYWNjg4EDB2LYsGEYOnQowsPD4ezsDA6H0+ZmGphnesx7If9d6cU0uM9oNMJgMDA3tVqN4uJiXLp0CZcuXcKZM2dQXV0Nf39/PProo5gzZw4CAgLA5Zo3fbEIsb41a1QqFcRiMZRKZZ9b2/PmzZtISEjAokWL8Morr5g7nHbTaDR44YUXcOnSJSxevBhPPfUUXXmEoixcQUEB0tLScOLECVy8eBGlpaXgcDjw8/NDaGgoAgMDMXDgQHh7e8PDwwMuLi4QCARM8vxtEv1tqtHpdFAoFKioqMDt27dRUFCAGzduoKCgADdv3oRSqYRYLMbgwYMxZswYTJs27b4Wru6I9uYXmjytTHl5OZ566imEhobi448/Nnc47UIIwapVq/D1119j+fLleOKJJ+Dk5GTusCiK6oDW1lbI5XJkZmbi8uXLuH79OnJyclBUVAS9Xg/gTrK0t7eHWCwGn89nRrnr9Xqo1WoQQlBfXw+lUskkUw6HAx8fHwQGBiIoKAhhYWF44IEHEBISAltb2x5vZ3vzC+22tTJcLheenp4oLi42dyjt9vHHH+Pzzz/Hq6++ikcffRRSqdTcIVEU1UE8Hg/e3t7w9vbGI488wiS/lpYWyOVyVFZWQi6XM8mxtbUVWq0Wzc3N4HK5cHBwAJvNhlgshlQqhbu7O9zd3SGTydpU7rKkEb5/hiZPK8Pj8eDp6YnLly+bO5R2OXbsGN555x08/fTTmDVrFmQymdW8OSiK+p/fv29ZLBYIIbCxsYGvry98fHyY6SRA227a3//d7293e3xLR5OnlTElz/LychiNxjYHnyUhhKCoqAjPPfccRo8ejcWLF8PT05MWI6CoXsRaE19XoJ9kVobH48HLywvNzc1QKBTmDueuCCFQKpV4/vnn4eDggNWrV8Pb27tbh7dTFEX1JJo8rQyXy4WXlxcIIRZZ45YQAq1Wiw0bNiAvLw+rV69GcHCw2YeVUxRFdSWaPK0Ml8uFq6sreDyeRa7r2dzcjK+//hrffvstFi9ejAkTJoDP5/fJbh2KonovmjytDIvFgp2dHaRSKcrLy80dThsajQbnz5/H9u3bMWzYMCQmJsLe3p4mToqieh2aPK0Mi8UCh8OBh4eHRSXPlpYW3LhxA19++SUMBgOWL18OT09PmjgpiuqVaPK0Qmw2G97e3igrK7vncPCepNfrUVZWhuTkZGRkZGDZsmUYMWIETZwURfVaNHlaIQ6HAy8vL4u45kkIQXV1NQ4fPoz9+/fjkUcewdy5c80dFkVRVLeiydMK/f7M01xnn4QQNDU14fDhw9i9ezeioqLwxhtv0LmcFEX1evRTzgqx2Wz4+vpCoVBAo9GYLQ6DwYDk5GR8/vnn8PX1xbvvvmuWWpQURVE9jSZPK2Ra1YAQYrbrnoQQHDhwADt27ICrqytWr14NT0/PHo+DoijKHGjytEJsNht+fn4AgOLiYrMkz5MnT2LLli1wdnbG8uXLERYW1uMxUBRFmQtNnlaIxWLBwcEBTk5OKCkpgdFo7NHnz8vLw1tvvQV7e3ssWrQIo0aNoiNrKYrqU2jytEKmYvB+fn4oLS3t0TPPmpoavPzyy9Dr9Vi0aBHGjRtHBwhRFNXn0E89K8ViseDj49Oj1zx1Oh3eeOMNlJSUYOnSpRg9ejQEAkGPPDdFUZQlocnTSpkjeX7yySc4evQoFi1ahLFjx0IikdDuWoqi+iSaPK1UTyZPQgh+/vln/Oc//8G0adMwbdo0uLm50e5aiqL6LPrpZ8W8vb1RW1sLjUbTbQnUaDSisLAQ//d//wdvb2889dRT8Pb2pkuMURTVp9FPQCtACEFrayvUajVUKhU0Gg00Gg3kcjm4XC4OHToER0dHZtQtl8sFm80Gh8MBn8+HjY0NhEIhbG1t4eDgAEdHx3YtE2Y0GlFXV4fPPvsMxcXF2LhxI0JDQ8Hn83ui2RRFURaLJk8L1NzcDKVSiYaGBqjVaqjVatTX16OiogIKhQJqtRoajQa1tbXo168f9u7dCy6XC71eD+BO8uRwOOBwOBAIBBAKhRAKhbC3t4eLiwv8/Pzg5OQEe3t7iMViSCQSiEQiCAQCJqH+vvTesmXLEBcXR884KYqiQJOn2Zm6W5uamqBSqaBUKlFeXo5r164hPz8fpaWlkMvlaGxsBJfLha2tLRwdHeHs7AwPDw8EBwfDxsYGXC4XQqEQhBC0tLRAp9OhtbUVKpUK9fX1qKysRE1NDZqamqDVauHg4ABPT0/4+/sjODgYISEh8PDwgEgkgoODAzgcDi5fvoz33nsPI0eOxPLly8HhcMz8alEURVkGmjzNhBACvV4PjUaDxsZGnDt3DkePHkV6ejqqqqpgY2ODAQMGIDw8HA899BACAgIQEBAAb2/vTnebEkJQUVGBgoIC5OXlIS8vD5cuXcKPP/6IlpYWeHl5ISIiAqNGjYKbmxvef/998Hg8bNmypc1ZKUVRVF9Hk2cPMxqNMBgMaGpqwvXr17Fv3z589913qKurQ0hICGbMmIHx48fjgQcegFgs7tIRrSwWC56envD09MT48eOZ38vlcly9ehXp6ek4ceIE9u/fD0II+vfvj/nz58PW1hZ6vZ7psqVJlKKovo5FLGE15Q5SqVQQi8VQKpUQiUTmDqddTC/zrVu3cPjwYezatQsXL16El5cXEhMT8fjjjyMoKMjsiUmn0+H27dv45Zdf8PXXX+PkyZNwd3fHCy+8gEWLFkEkEjExmjtWiqKortbe/EKTZw+5fv06du/ejf3790OpVGLixIl4+umnMW7cOIs6o/vt4WAwGFBYWIgdO3bg3//+N4RCIZYuXYqFCxfCzc2NXgOlKKrXocnTQtTU1OCjjz7C3r17wePxMGPGDMyZMwcDBw4Em81m6tRaItOhYTAYUFVVhS1btuDzzz+HnZ0dli9fjkceeYQuQ0ZRVK9Ck6cFSE5OxjvvvAOlUonZs2fjscceQ1BQEPh8vlVN+SCEgBACrVaLsrIyfPTRR0hJSUFAQACWLFmCKVOmwM7OztxhUhRF3TeaPM3END/ynXfewb59+xATE4Nnn30W4eHhkEgkVl9gQK/Xo66uDmfPnsWuXbtQXFyM2NhYLFy4EIGBgeYOj6Io6r60N790aCjn1q1bMWjQIIhEIohEIkRHR+Pw4cPM/VqtFkuXLmUm4M+cORMKhaLNY5SVlWHq1KmwtbWFq6srXnvtNWZyv7UzGo2Qy+VYvHgxvv/+eyxevBirVq1CdHQ0XFxcrD5xAncKMLi6umL8+PF488038eCDD+Ls2bN48803kZaWZu7wKIqiekSH+g69vLywYcMGDBgwAIQQfPHFF3jooYdw+fJlDBw4ECtWrMChQ4ewf/9+iMVivPDCC3jkkUdw+vRpAHeunU2dOhUymQxnzpxBZWUl5s+fDx6Ph/fee69bGthT9Ho9iouL8Y9//ANZWVl4+eWXER8fDw8PD6vqom0vBwcHhIeHQyQSwcPDAykpKfjwww/R2NiIBx980GKv41IURXUJcp8cHR3Jjh07SENDA+HxeGT//v3MfdevXycASEZGBiGEkJSUFMJms4lcLme22bp1KxGJRKSlpaXdz6lUKgkAolQq7zf8LtHa2kry8/PJc889R4YMGUI++eQT0tDQQIxGo7lD6xGVlZXkq6++IjNmzCAJCQnkyJEj5g6JoiiqU9qbXzo9A99gMCApKQlNTU2Ijo7GxYsXodPpEBsby2wTHBwMHx8fZGRkAAAyMjIQHh4ONzc3Zpu4uDioVCrk5OTc87laWlqgUqna3CyFXq9HeXk5vvrqKxw/fhzz5s3DU0891WY+ZG8nk8kwZcoUzJ8/HwaDAVu2bMHVq1d7bJ1RiqKontbh5JmdnQ17e3sIBAI899xzOHDgAEJDQyGXy8Hn8yGRSNps7+bmBrlcDuBOJZvfJk7T/ab77mX9+vUQi8XMzdvbu6NhdwtCCBQKBX766SccOHAA06dPx9KlSyEUCvtM4jRxdHREbGwslixZgpKSEmzZsgU1NTU0gVIU1St1OHkGBQXhypUrOHfuHJYsWYLExETk5uZ2R2yMlStXQqlUMrfy8vJufb72UqvVSE9Px+7duzF8+HC8+eabsLGx6XOJ08TBwQGTJk3Ciy++iIMHD+LgwYNobW2lCZSiqF6nwyNZ+Hw++vfvDwCIiIhAZmYmPvroI8yaNQutra1oaGhoc/apUCggk8kA3OneO3/+fJvHM43GNW1zNwKBAAKBoKOhdiu9Xo/z589j//79kEgkeOutt+Dg4GDusMzOzs4OTzzxBC5evIi33noLUVFRcHNza7PUGSGEKQ5hKhRh+pfL5TJLqllS5SWKoqjfuu9hoEajES0tLYiIiACPx8OxY8cwc+ZMAEB+fj7KysoQHR0NAIiOjsY//vEPVFVVwdXVFQDw66+/QiQSITQ09H5D6VGmGrW3b9/G22+/DT8/P3OHZDEEAgHeffddHDt2DC+99BK4XC6TAJubm9Ha2goejwc7OzvmZm9vz4zc9fPzg6+vLzw9PSEUCpm1STkcDpNkKYqizKlDyXPlypVISEiAj48P1Go19u7di+PHj+OXX36BWCzGM888g5dffhlSqRQikQjLli1DdHQ0RowYAQCYPHkyQkND8eSTT+KDDz6AXC7Hm2++iaVLl1rcmeWfIYQgJSUFFy5cwIQJEzBlyhRzh8QwndmZzu66clWW9mKxWHB0dMT69esxe/ZsTJ48Ge7u7uBwOLCxsYFAIIBer0djYyMaGxuhVCpx69YtKJVKlJaWQqlUghACLpcLHx8fREREIDIyEsOHD0doaCgkEglz5koTKUVR5tCh5FlVVYX58+ejsrISYrEYgwYNwi+//IJJkyYBADZt2gQ2m42ZM2eipaUFcXFx+PTTT5m/53A4+Omnn7BkyRJER0fDzs4OiYmJWLduXde2qptlZWUhNTUVbm5uSExMNHc4beh0OqxevRp5eXmYN28e0wtgDjNmzMCIESOg1+uxdu1auLu7t+vv6urqUFRUhJKSEly7dg0XLlzAhx9+CIVCAbFYjJEjR2LOnDmYPn067SqnKMosaHm+Tti4cSN+/fVXJCQkYNmyZRZ19rNjxw5s3rwZEokEL730ktmSp+mw+v777zF//nz88MMPGDFiBGxsbNr9t789i9ZqtSgpKcHJkyfx448/4uTJk7C1tcWcOXPwxhtvwNnZ2aL2A0VR1qlbyvNRd17Ys2fPQiqVYtiwYRb1gZ2dnY2rV6+ivr7e7CNcTV2qcXFxkMlkSElJQU1NTYf+ls1mMwOH7OzsEBISgqeffhr79u3D2bNn8cILL+DgwYOIiIjAwYMHe02ZR4qiLB9Nnh2UlZUFhUKB4OBghIWFmTschlarxbZt2xAZGYkhQ4aYOxyGjY0NJk2ahMuXL6OhoaHTj8NiscDhcCAQCCASiRAUFIRly5YhJSUF48ePx5IlS7B9+3Y0NTV1XfAURVH3QJNnB+Xk5IDP58PLywv29vbmDofx5ZdfIiAgAGFhYRZ1HZDFYiEyMhI3b95EfX09DAZDlzwml8uFWCxGUFAQ1qxZg7lz52Ljxo1IS0tDY2NjF0ROURR1bzR5dlBRUREkEglcXV3NMpL1bvLz83HhwgWMGjUKnp6eFhOXSXh4OHQ6HW7fvt2lZ4ams1E/Pz88//zz8Pf3x+effw6FQmH2bmuKono3y/qUtQLV1dWws7OziLM7QghaW1uxa9cujBgxAgMGDGjXgJye5uPjAw6Hg+rqajQ3N3f547NYLPTv3x/z5s3DpUuXUFhYCK1W2+XPQ1EUZUKTZwepVCrw+XwIhUJzhwIAOHHiBGpqahAbG2txC4ObiMVi8Hg8qNVqtLa2dtvzTJo0CXq9HkVFRbTrlqKobkWTZwe1tLQw1W7MiRCC2tpaJCUlYebMmXBzczN7TPfC4/HA4XDQ2traJdc878XV1RVcLhcqlQo6na7bnoeiKIomzw7i8/kwGo3dmgTaw2g04sSJE8jKyoJQKMSlS5eQkZGBzMxM1NTUQK1WIz8/H9nZ2dBoNGaNVa/Xw2AwgM/nd2uCb2hogF6vh729fa9cgJyiKMtBP2E6SCQSoaWlpVuu3XUEIQTnzp1DXV0dnn76aeb3RqMRNTU10Ov12Lx5MzIzM/H+++8jMDDQbLE2NDRAp9NBJBJ1SxlG0+Cg1NRUAED//v0t4po0RVG9F02eHeTi4oLi4mKo1WqzxsHlcvHBBx/ggw8+aPN7pVKJpUuXorS0FMuXLzdreT6TW7duQa/XQyqVdvmAJlPirK+vx6ZNmzBkyBAMGDDAYq5JUxTVO9Fu2w4KCQlBXV0d5HI5nQ7RTpmZmeDxePD39+/SM0JT6b7GxkY888wzuHnzJlatWgUvL68uew6Koqi7ocmzg8LCwqDX61FaWnpfFXP6CkIIzp49i+DgYIjF4vsuZ/jbercGgwEZGRkYOXIk0tPTsXfvXkRERFjVCj0URVknmjw7aMiQIQgICMD169dx4cIFc4fzByKRCF9++SWOHz+ORx55xNzhQKVS4eeff8aoUaMglUrv+/EIIaipqcHevXvx4IMPYty4cXB1dcX58+cxbtw48Pn8Loiaoijqz9Frnh3E4/EQExOD/fv34/z585g4caJFVfSxtDUuv/76a+h0OsTHx8PJyalTj6FWq1FQUIALFy7g1KlTOHnyJNRqNUaOHIm9e/di6tSpsLGxsbi2UxTVe9Hk2UEsFgvjx4/HqVOncO7cOWRkZCAmJsbcYVkcQghaWlqwZcsWxMXFwcPD457TR0zbqtVq3Lp1C6WlpSgrK0NZWRkKCgpw48YNNDQ0wN7eHv3798czzzyDuLg4+Pv7w97eng4Ooiiqx9Hk2Qne3t4YPXo09u/fjwMHDmDYsGH0Ottd7NmzB2VlZTAajXj66afB5XLBYrGY16qpqQlNTU1obm5mCii0trbCaDTC0dERzs7OcHNzQ2RkJIKDg+Hr6wtHR0eIxWJIJBLaRUtRlNnQ5NkJfD4f48aNQ0FBAc6cOYPvv/8ejz/+uLnDshhGoxGlpaXYvHkzxo0bB5lMxnRtmwpMsFgsuLi4gMfjgc/nQyAQwNbWFi4uLnBzc4OdnR1sbW1hb2/PJEtbW1uLraJEUVTfQpNnJ3l7e2PChAkoKSnBnj17EB4ejpCQEHOHZXaEEGg0GmzevBlarRYvvvhim1G2hBDo9XqwWCzw+XzweDzmxufz4eDgAHt7e3r9kqIoi0aTZyfx+XxERESgoqICO3bswGeffYbXXnsNbm5u5g7NbEyJMzk5GQcPHsRzzz2H0aNHM921FEVRvYXlDBO1Qs7Ozhg3bhwmTZqEo0eP4ptvvoFKpTJ3WGZBCEFTUxPS09OxdetWDB06tM11ToqiqN6EnnneJ19fXyQmJkIul2PLli2QyWSIj49nuh77AkII1Go1zp8/j48//hhsNhvr1q2Ds7Nzn3kNKIrqW2jyvE9sNhu+vr5YtWoVqqqq8Nprr4HL5SI2NrZPJFCj0QiVSoVTp05h8+bNqK2txaZNmxAWFmbu0CiKoroN7bbtAmw2G15eXti+fTuGDx+OZ599FgcOHIBKpYLRaOy1NXANBgNqa2vx3XffYd26ddBoNNiyZQvGjBlj7tAoiqK6FU2eXYTNZsPJyQm7d+/GY489hoULF+L1119HRUUFDAZDr0qgprqy169fx9tvv4133nkHfn5++PzzzxEdHW3u8CiKorodTZ5dyFQAYNu2bfj888/x3XffISEhAQcOHEBDQwNT0NxameJvbm7Gv//9b8yZMwcnT57ESy+9hB07dph1zVCKoqieRJNnN5k7dy5Onz6NwMBALFy4EHPnzkVKSgo0Go25Q7svP/30E8aOHYvVq1dj5MiR+OKLL/DSSy/RxacpiupTaPLsJiwWCwEBAdizZw+++OIL6PV6PPPMM3j44YexZ88eKJVKc4fYLoQQGI1GpKSkYOzYsZgzZw48PT2xf/9+/POf/8TQoUNpQQOKovocFrHCfkSVSgWxWAylUgmRSGTucP6Uqeh5XV0dTpw4gW+//RaXLl2Cq6srpk+fjpkzZyI4ONjcYf6BwWBAfX09fvjhB+zZswdXrlxBdHQ0Fi9ejMjISEilUvB4PJo0KYrqVdqbX2jy7CFGoxFNTU2oqqpCdnY2fvnlF5w7dw7Nzc0YNmwY4uLiMHHiRLi5uZltiTODwQC5XI7MzEykp6cjPT0dSqUSDzzwAB599FEMGzYMrq6uEAqFFrUMG0VRVFehydNCmZJoZWUlysrKkJmZiczMTJSWlsLe3h7h4eEYMmQIQkJCEBgYCEdHx3su5dUVKisrkZ+fj7y8PGRnZ6OgoABqtRpSqRShoaEYPXo0AgIC4OPjAwcHB3qmSVFUr0aTp4UzTfeQy+W4ceMGiouLkZWVhdu3b6OxsREAIJVK4eHhAQ8PD7i5uUEmk8HFxQVSqRQODg7tarvRaERLSwuUSiVUKhUUCgUqKipw+/Zt3L59GxUVFaivrwebzYaDgwPc3d0xYMAA9OvXD/7+/vD396fLrVEU1WfQ5GlFTIn05s2bKCgoQGlpKcrLy3H79m00Nzcz01s4HA4EAgFEIhHs7OwgFoshEAjA5/PBZrPB5/OZVUtMN61WC41GA7VajcbGRqjVahiNRgB3EqtEIoGHhwe8vLzg6+uLfv36wdvbG3Z2duZ8SSiKosyivfmFluezACwWC1wuF4GBgcxcSZVKhbKyMpSVlaGyshKVlZWoqqpCbW0tKioqoNVq0dLSAi6XCw6HAxaLBQ6Hw4yONf1rMBjAZrMhFAohFAqZs1lPT094enrC398f3t7esLW1pV2yFEVR7USTp4USiUQICwtrUyPWYDCgubkZtbW1qK+vR01NDTQaDTQaDQwGA7RaLbNOpulmOkOVSqWQSqWQSCR0aglFUdR9osnTinA4HNjb28Pe3h6+vr7mDoeiKKrPovMNKIqiKKqDaPKkKIqiqA6yym5b0+hTlUpl5kgoiqKo3sSUV/5qIopVJk+1Wg0A8Pb2NnMkFEVRVG+kVqshFovveb9VzvM0Go3Iz89HaGgoysvLe8VcTxOVSgVvb2/aLitB22VdaLusiznaRQiBWq2Gh4fHn5YhtcozTzabDU9PTwB3pnT0poPFhLbLutB2WRfaLuvS0+36szNOEzpgiKIoiqI6iCZPiqIoiuogq02eAoEAq1ev7nVFy2m7rAttl3Wh7bIultwuqxwwRFEURVHmZLVnnhRFURRlLjR5UhRFUVQH0eRJURRFUR1EkydFURRFdZBVJs9PPvkEfn5+sLGxQVRUFM6fP2/ukDpkzZo1zJqapltwcDBzv1arxdKlS+Hk5AR7e3vMnDkTCoXCjBHfXXp6OqZNmwYPDw+wWCwcPHiwzf2EELz99ttwd3eHUChEbGwsbty40Waburo6PPHEExCJRJBIJHjmmWfQ2NjYg634o79q11NPPfWH/RcfH99mG0ts1/r16xEZGQkHBwe4urpixowZyM/Pb7NNe469srIyTJ06Fba2tnB1dcVrr70GvV7fk01poz3tGjdu3B/22XPPPddmG0tr19atWzFo0CCmQEB0dDQOHz7M3G+N+wr463ZZzb4iViYpKYnw+Xzy+eefk5ycHLJw4UIikUiIQqEwd2jttnr1ajJw4EBSWVnJ3Kqrq5n7n3vuOeLt7U2OHTtGLly4QEaMGEFGjhxpxojvLiUlhbzxxhskOTmZACAHDhxoc/+GDRuIWCwmBw8eJFevXiXTp08n/v7+pLm5mdkmPj6eDB48mJw9e5acPHmS9O/fn8yZM6eHW9LWX7UrMTGRxMfHt9l/dXV1bbaxxHbFxcWRnTt3kmvXrpErV66QKVOmEB8fH9LY2Mhs81fHnl6vJ2FhYSQ2NpZcvnyZpKSkEGdnZ7Jy5UpzNIkQ0r52jR07lixcuLDNPlMqlcz9ltiuH374gRw6dIgUFBSQ/Px8smrVKsLj8ci1a9cIIda5rwj563ZZy76yuuQ5fPhwsnTpUuZng8FAPDw8yPr1680YVcesXr2aDB48+K73NTQ0EB6PR/bv38/87vr16wQAycjI6KEIO+73ScZoNBKZTEb++c9/Mr9raGggAoGA7Nu3jxBCSG5uLgFAMjMzmW0OHz5MWCwWuX37do/F/mfulTwfeuihe/6NNbSLEEKqqqoIAHLixAlCSPuOvZSUFMJms4lcLme22bp1KxGJRKSlpaVnG3APv28XIXc+kF966aV7/o01tIsQQhwdHcmOHTt6zb4yMbWLEOvZV1bVbdva2oqLFy8iNjaW+R2bzUZsbCwyMjLMGFnH3bhxAx4eHujXrx+eeOIJlJWVAQAuXrwInU7Xpo3BwcHw8fGxqjYWFxdDLpe3aYdYLEZUVBTTjoyMDEgkEgwbNozZJjY2Fmw2G+fOnevxmDvi+PHjcHV1RVBQEJYsWYLa2lrmPmtpl1KpBABIpVIA7Tv2MjIyEB4eDjc3N2abuLg4qFQq5OTk9GD09/b7dpns2bMHzs7OCAsLw8qVK6HRaJj7LL1dBoMBSUlJaGpqQnR0dK/ZV79vl4k17CurKgxfU1MDg8HQ5kUDADc3N+Tl5Zkpqo6LiorCrl27EBQUhMrKSqxduxajR4/GtWvXIJfLwefzIZFI2vyNm5sb5HK5eQLuBFOsd9tXpvvkcjlcXV3b3M/lciGVSi26rfHx8XjkkUfg7++PoqIirFq1CgkJCcjIyACHw7GKdhmNRixfvhwxMTEICwsDgHYde3K5/K771HSfud2tXQAwd+5c+Pr6wsPDA1lZWfj73/+O/Px8JCcnA7DcdmVnZyM6OhparRb29vY4cOAAQkNDceXKFaveV/dqF2A9+8qqkmdvkZCQwPx/0KBBiIqKgq+vL7755hsIhUIzRka1x+zZs5n/h4eHY9CgQQgICMDx48cxceJEM0bWfkuXLsW1a9dw6tQpc4fSpe7VrkWLFjH/Dw8Ph7u7OyZOnIiioiIEBAT0dJjtFhQUhCtXrkCpVOLbb79FYmIiTpw4Ye6w7tu92hUaGmo1+8qqum2dnZ3B4XD+MKJMoVBAJpOZKar7J5FIEBgYiMLCQshkMrS2tqKhoaHNNtbWRlOsf7avZDIZqqqq2tyv1+tRV1dnVW3t168fnJ2dUVhYCMDy2/XCCy/gp59+QlpaGry8vJjft+fYk8lkd92npvvM6V7tupuoqCgAaLPPLLFdfD4f/fv3R0REBNavX4/Bgwfjo48+svp9da923Y2l7iurSp58Ph8RERE4duwY8zuj0Yhjx4616S+3No2NjSgqKoK7uzsiIiLA4/HatDE/Px9lZWVW1UZ/f3/IZLI27VCpVDh37hzTjujoaDQ0NODixYvMNqmpqTAajcwbxhrcunULtbW1cHd3B2C57SKE4IUXXsCBAweQmpoKf3//Nve359iLjo5GdnZ2my8Hv/76K0QiEdPt1tP+ql13c+XKFQBos88srV13YzQa0dLSYrX76l5M7bobi91XPTY0qYskJSURgUBAdu3aRXJzc8miRYuIRCJpM/LK0r3yyivk+PHjpLi4mJw+fZrExsYSZ2dnUlVVRQi5MwTdx8eHpKamkgsXLpDo6GgSHR1t5qj/SK1Wk8uXL5PLly8TAGTjxo3k8uXLpLS0lBByZ6qKRCIh33//PcnKyiIPPfTQXaeqDB06lJw7d46cOnWKDBgwwOxTOv6sXWq1mrz66qskIyODFBcXk6NHj5IHHniADBgwgGi1WuYxLLFdS5YsIWKxmBw/frzNNACNRsNs81fHnmmawOTJk8mVK1fIzz//TFxcXMw6/eGv2lVYWEjWrVtHLly4QIqLi8n3339P+vXrR8aMGcM8hiW26/XXXycnTpwgxcXFJCsri7z++uuExWKRI0eOEEKsc18R8uftsqZ9ZXXJkxBCPv74Y+Lj40P4fD4ZPnw4OXv2rLlD6pBZs2YRd3d3wufziaenJ5k1axYpLCxk7m9ubibPP/88cXR0JLa2tuThhx8mlZWVZoz47tLS0giAP9wSExMJIXemq7z11lvEzc2NCAQCMnHiRJKfn9/mMWpra8mcOXOIvb09EYlEZMGCBUStVpuhNf/zZ+3SaDRk8uTJxMXFhfB4POLr60sWLlz4hy9vltiuu7UJANm5cyezTXuOvZKSEpKQkECEQiFxdnYmr7zyCtHpdD3cmv/5q3aVlZWRMWPGEKlUSgQCAenfvz957bXX2swdJMTy2vX0008TX19fwufziYuLC5k4cSKTOAmxzn1FyJ+3y5r2FV2SjKIoiqI6yKqueVIURVGUJaDJk6IoiqI6iCZPiqIoiuogmjwpiqIoqoNo8qQoiqKoDqLJk6IoiqI6iCZPiqIoiuogmjwpiqIoqoNo8qQoiqKoDqLJk6IoiqI6iCZPiqIoiuogmjwpiqIoqoP+H4ybEFQzH/w7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Multiple Knapsack Problem`**"
      ],
      "metadata": {
        "id": "uLRzbdXK6zeb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def ant_colony_optimization(num_ants, num_iterations, alpha, beta, rho, q0, weights, values, capacities):\n",
        "    num_items = len(weights)\n",
        "    num_knapsacks = len(capacities)\n",
        "    pheromones = np.ones((num_items, num_knapsacks))\n",
        "    best_solution = None\n",
        "    best_value = float('-inf')\n",
        "    for iteration in range(num_iterations):\n",
        "        solutions = []\n",
        "        values_per_ant = []\n",
        "        for ant in range(num_ants):\n",
        "            solution = construct_solution(alpha, beta, q0, pheromones, weights, values, capacities)\n",
        "            value = evaluate_solution(solution, values)\n",
        "            solutions.append(solution)\n",
        "            values_per_ant.append(value)\n",
        "            if value > best_value:\n",
        "                best_solution = solution\n",
        "                best_value = value\n",
        "        update_pheromones(pheromones, solutions, values_per_ant, rho)\n",
        "    return best_solution, best_value\n",
        "def construct_solution(alpha, beta, q0, pheromones, weights, values, capacities):\n",
        "    num_items = len(weights)\n",
        "    num_knapsacks = len(capacities)\n",
        "    solution = np.zeros((num_items, num_knapsacks))\n",
        "    for item in range(num_items):\n",
        "        knapsack_probabilities = calculate_knapsack_probabilities(alpha, beta, q0, pheromones[item, :], weights[item], capacities)\n",
        "        if np.random.rand() < q0:\n",
        "            selected_knapsack = np.argmax(knapsack_probabilities)\n",
        "        else:\n",
        "            selected_knapsack = np.random.choice(num_knapsacks, p=knapsack_probabilities)\n",
        "        solution[item, selected_knapsack] = 1\n",
        "    return solution\n",
        "def calculate_knapsack_probabilities(alpha, beta, q0, pheromone_values, weight, capacities):\n",
        "    num_knapsacks = len(capacities)\n",
        "    visibility = np.array([1 / (1 + weight / capacity) for capacity in capacities])\n",
        "    probability_numerator = np.power(pheromone_values, alpha) * np.power(visibility, beta)\n",
        "    probability_denominator = np.sum(probability_numerator)\n",
        "    knapsack_probabilities = probability_numerator / probability_denominator\n",
        "    return knapsack_probabilities\n",
        "def evaluate_solution(solution, values):\n",
        "    return np.sum(solution * values.reshape(-1, 1))\n",
        "def update_pheromones(pheromones, solutions, values, rho):\n",
        "    num_items, num_knapsacks = pheromones.shape\n",
        "    pheromones *= (1 - rho)\n",
        "    best_ant_index = np.argmax(values)\n",
        "    best_solution = solutions[best_ant_index]\n",
        "    for item in range(num_items):\n",
        "        for knapsack in range(num_knapsacks):\n",
        "            if best_solution[item, knapsack] == 1:\n",
        "                pheromones[item, knapsack] += 1 / values[best_ant_index]\n",
        "num_items = int(input(\"Enter the number of items: \"))\n",
        "weights = np.array([float(input(f\"Enter the weight of item {i + 1}: \")) for i in range(num_items)])\n",
        "values = np.array([float(input(f\"Enter the value of item {i + 1}: \")) for i in range(num_items)])\n",
        "num_knapsacks = int(input(\"Enter the number of knapsacks: \"))\n",
        "capacities = np.array([float(input(f\"Enter the capacity of knapsack {j + 1}: \")) for j in range(num_knapsacks)])\n",
        "num_ants = int(input(\"Enter the number of ants: \"))\n",
        "num_iterations = int(input(\"Enter the number of iterations: \"))\n",
        "alpha = float(input(\"Enter the alpha parameter: \"))\n",
        "beta = float(input(\"Enter the beta parameter: \"))\n",
        "rho = float(input(\"Enter the rho parameter: \"))\n",
        "q0 = float(input(\"Enter the q0 parameter: \"))\n",
        "best_solution, best_value = ant_colony_optimization(num_ants, num_iterations, alpha, beta, rho, q0, weights, values, capacities)\n",
        "print(\"Best Value =\", best_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiLSX_f98Wvj",
        "outputId": "d3e12f31-8e15-471e-fb35-bc2f44f56cfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the number of items: 4\n",
            "Enter the weight of item 1: 2\n",
            "Enter the weight of item 2: 4\n",
            "Enter the weight of item 3: 3\n",
            "Enter the weight of item 4: 6\n",
            "Enter the value of item 1: 2\n",
            "Enter the value of item 2: 5\n",
            "Enter the value of item 3: 6\n",
            "Enter the value of item 4: 7\n",
            "Enter the number of knapsacks: 4\n",
            "Enter the capacity of knapsack 1: 3\n",
            "Enter the capacity of knapsack 2: 5\n",
            "Enter the capacity of knapsack 3: 7\n",
            "Enter the capacity of knapsack 4: 5\n",
            "Enter the number of ants: 4\n",
            "Enter the number of iterations: 10\n",
            "Enter the alpha parameter: 4\n",
            "Enter the beta parameter: 7\n",
            "Enter the rho parameter: 9\n",
            "Enter the q0 parameter: 3\n",
            "Best Value = 20.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "def visualize_solution(items, knapsacks, best_solution):\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    for i, knapsack in enumerate(knapsacks):\n",
        "        plt.scatter([knapsack], [i + 1], color='green', marker='s', s=200, label=f'Knapsack {i + 1}')\n",
        "    for j, item in enumerate(items):\n",
        "        selected_knapsack = np.argmax(best_solution[j, :])\n",
        "        plt.scatter([item], [selected_knapsack + 1], color='red', marker='o', s=50, label=f'Item {j + 1}')\n",
        "    plt.xlabel('Weight/Value of Items or Knapsacks')\n",
        "    plt.ylabel('Knapsack Index')\n",
        "    plt.title('Multiple Knapsack Problem')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "num_items = 4\n",
        "weights = np.array([2, 3, 4, 5])\n",
        "values = np.array([3, 4, 5, 6])\n",
        "num_knapsacks = 2\n",
        "capacities = np.array([8, 9])\n",
        "num_ants = 10\n",
        "num_iterations = 100\n",
        "alpha = 1.0\n",
        "beta = 2.0\n",
        "rho = 0.1\n",
        "q0 = 0.9\n",
        "best_solution, best_value = ant_colony_optimization(num_ants, num_iterations, alpha, beta, rho, q0, weights, values, capacities)\n",
        "visualize_solution(weights, capacities, best_solution)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "GNNMol2M_LPz",
        "outputId": "144e0aed-6360-44e4-b2de-73e366193f89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import pygraphviz as pgv\n",
        "num_items = 10\n",
        "num_knapsacks = 3\n",
        "weights = [random.randint(1, 10) for _ in range(num_items)]\n",
        "values = [random.randint(1, 10) for _ in range(num_items)]\n",
        "knapsack_capacities = [15, 20, 25]\n",
        "num_ants = 5\n",
        "pheromone_decay = 0.1\n",
        "alpha = 1.0\n",
        "beta = 2.0\n",
        "pheromones = [[1.0] * num_items for _ in range(num_knapsacks)]\n",
        "def ant_colony_optimization(num_iterations):\n",
        "    global pheromones\n",
        "    for iteration in range(num_iterations):\n",
        "        ant_solutions = []\n",
        "        for ant in range(num_ants):\n",
        "            solution = []\n",
        "            for item in range(num_items):\n",
        "                probabilities = [pheromones[k][item] ** alpha * (values[item] / weights[item]) ** beta for k in range(num_knapsacks)]\n",
        "                total_probability = sum(probabilities)\n",
        "                probabilities = [p / total_probability for p in probabilities]\n",
        "                knapsack_choice = random.choices(range(num_knapsacks), probabilities)[0]\n",
        "                solution.append(knapsack_choice)\n",
        "            ant_solutions.append(solution)\n",
        "        for k in range(num_knapsacks):\n",
        "            for item in range(num_items):\n",
        "                pheromones[k][item] *= (1.0 - pheromone_decay)\n",
        "                for ant_solution in ant_solutions:\n",
        "                    if ant_solution[item] == k:\n",
        "                        pheromones[k][item] += 1.0\n",
        "    return ant_solutions\n",
        "num_iterations = 20\n",
        "final_solutions = ant_colony_optimization(num_iterations)\n",
        "graph = pgv.AGraph(directed=True)\n",
        "for ant_solution in final_solutions:\n",
        "    for item, knapsack in enumerate(ant_solution):\n",
        "        graph.add_edge(f\"Item {item}\", f\"Knapsack {knapsack}\")\n",
        "graph.layout(prog='dot')\n",
        "graph.draw('MKP_ACO_Soln.png')"
      ],
      "metadata": {
        "id": "CayqN_4GBqhc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "w9JSgIgACCp7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Maximum Independent Set Problem`**"
      ],
      "metadata": {
        "id": "D-VLLFb7CDVE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import random\n",
        "import numpy as np\n",
        "class AntColony:\n",
        "    def __init__(self, graph, n_ants, alpha=1, beta=2, evaporation_rate=0.5, pheromone_deposit=1):\n",
        "        self.graph = graph\n",
        "        self.pheromone_matrix = nx.to_numpy_array(graph)\n",
        "        self.n_ants = n_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation_rate = evaporation_rate\n",
        "        self.pheromone_deposit = pheromone_deposit\n",
        "    def run(self, max_iter):\n",
        "        best_solution = None\n",
        "        best_score = float('-inf')\n",
        "        for _ in range(max_iter):\n",
        "            solutions = self.generate_solutions()\n",
        "            scores = [self.evaluate(solution) for solution in solutions]\n",
        "            if max(scores) > best_score:\n",
        "                best_score = max(scores)\n",
        "                best_solution = solutions[scores.index(max(scores))]\n",
        "            self.update_pheromones(solutions, scores)\n",
        "        return best_solution\n",
        "    def generate_solutions(self):\n",
        "        solutions = []\n",
        "        for _ in range(self.n_ants):\n",
        "            solution = self.construct_solution()\n",
        "            solutions.append(solution)\n",
        "        return solutions\n",
        "    def construct_solution(self):\n",
        "        solution = set()\n",
        "        for node in self.graph.nodes():\n",
        "            if random.uniform(0, 1) < 0.5:\n",
        "                solution.add(node)\n",
        "        return solution\n",
        "    def evaluate(self, solution):\n",
        "        return len(solution)\n",
        "    def update_pheromones(self, solutions, scores):\n",
        "        for i in range(self.graph.number_of_nodes()):\n",
        "            for j in range(self.graph.number_of_nodes()):\n",
        "                self.pheromone_matrix[i, j] *= (1 - self.evaporation_rate)\n",
        "                for k in range(self.n_ants):\n",
        "                    if i in solutions[k] and j in solutions[k]:\n",
        "                        self.pheromone_matrix[i, j] += self.pheromone_deposit / scores[k]\n",
        "def main():\n",
        "    G = nx.erdos_renyi_graph(10, 0.3)\n",
        "    ant_colony = AntColony(G, n_ants=5)\n",
        "    max_independent_set = ant_colony.run(max_iter=100)\n",
        "    print(\"Maximum Independent Set =\", max_independent_set)\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XA8YJjqdDszo",
        "outputId": "4cf9917e-90ac-4f38-a296-8b74e778184e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Independent Set = {0, 1, 2, 3, 5, 6, 7, 8, 9}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "class AntColony:\n",
        "    def __init__(self, graph, n_ants, alpha=1, beta=2, evaporation_rate=0.5, pheromone_deposit=1):\n",
        "        self.graph = graph\n",
        "        self.pheromone_matrix = nx.to_numpy_array(graph)\n",
        "        self.n_ants = n_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation_rate = evaporation_rate\n",
        "        self.pheromone_deposit = pheromone_deposit\n",
        "    def run(self, max_iter):\n",
        "        best_solution = None\n",
        "        best_score = float('-inf')\n",
        "        for _ in range(max_iter):\n",
        "            solutions = self.generate_solutions()\n",
        "            scores = [self.evaluate(solution) for solution in solutions]\n",
        "            if max(scores) > best_score:\n",
        "                best_score = max(scores)\n",
        "                best_solution = solutions[scores.index(max(scores))]\n",
        "            self.update_pheromones(solutions, scores)\n",
        "        return best_solution\n",
        "    def generate_solutions(self):\n",
        "        solutions = []\n",
        "        for _ in range(self.n_ants):\n",
        "            solution = self.construct_solution()\n",
        "            solutions.append(solution)\n",
        "        return solutions\n",
        "    def construct_solution(self):\n",
        "        solution = set()\n",
        "        for node in self.graph.nodes():\n",
        "            if random.uniform(0, 1) < 0.5:\n",
        "                solution.add(node)\n",
        "        return solution\n",
        "    def evaluate(self, solution):\n",
        "        return len(solution)\n",
        "    def update_pheromones(self, solutions, scores):\n",
        "        for i in range(self.graph.number_of_nodes()):\n",
        "            for j in range(self.graph.number_of_nodes()):\n",
        "                self.pheromone_matrix[i, j] *= (1 - self.evaporation_rate)\n",
        "                for k in range(self.n_ants):\n",
        "                    if i in solutions[k] and j in solutions[k]:\n",
        "                        self.pheromone_matrix[i, j] += self.pheromone_deposit / scores[k]\n",
        "def draw_graph(graph):\n",
        "    pos = nx.spring_layout(graph)\n",
        "    nx.draw(graph, pos, with_labels=True, font_weight='bold', node_size=1200, node_color='yellow', font_color='black', font_size=12, width=1, font_family='serif')\n",
        "    plt.show(block=False)\n",
        "def main():\n",
        "    edges = [(1, 2), (1, 3), (2, 4), (3, 4), (4, 5), (5, 6), (5, 7), (6, 8)]\n",
        "    G = nx.Graph(edges)\n",
        "    draw_graph(G)\n",
        "    n_ants = int(input(\"Enter the number of ants: \"))\n",
        "    max_iter = int(input(\"Enter the number of iterations: \"))\n",
        "    ant_colony = AntColony(G, n_ants=n_ants)\n",
        "    max_independent_set = ant_colony.run(max_iter=max_iter)\n",
        "    print(\"Maximum Independent Set:\", max_independent_set)\n",
        "    plt.pause(1)\n",
        "    plt.close()\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "f4uOC0K1Lw68",
        "outputId": "25438195-7124-402e-9313-35742a217ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the number of ants: 5\n",
            "Enter the number of iterations: 100\n",
            "Maximum Independent Set: {1, 2, 4, 5, 6, 7, 8}\n"
          ]
        }
      ]
    }
  ]
}