{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9rgN5_aJGc_"
      },
      "source": [
        "# Simple Linear Model\n",
        "\n",
        "ในมุมมองของผู้สร้างโมเดล เราอาจจะสร้างโมเดลที่มีความซับซ้อนมาก ๆ และทำนายชุดข้อมูลที่เรามีได้ดีมาก หรืออาจจะสร้างโมเดลที่ซับซ้อนน้อยลงมาหน่อยแต่ก็ยังทำนายชุดข้อมูลที่เรามีได้ดีรองลงมาก็ได้ ตัวเลือกในการสร้างโมเดลมีหลากหลายรูปแบบ แต่ว่าเราจะเลือกโมเดลที่เหมาะสมได้อย่างไร\n",
        "\n",
        "ในบทเรียนนี้เราจะลองสร้างโมเดลทำนายชุดข้อมูลในรูปแบบต่าง ๆ และดูกันว่าโมเดลแบบใดจะเหมาะสมกับชุดข้อมูลตัวอย่างของเรา"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "UwSov8-9Tivi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "from IPython.display import HTML # ใช้สำหรับโชว์ HTML element\n",
        "import ipywidgets as widgets  # ใช้สำหรับการทำ interactive display\n",
        "\n",
        "np.random.seed(42) # ตั้งค่า random seed เอาไว้ เพื่อให้การรันโค้ดนี้ได้ผลเหมือนเดิม"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "dkYA99qyIZa-",
        "outputId": "2bbfab63-26b5-4952-c0c4-afbb4e565cf5",
        "tags": [
          "remove-input"
        ]
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/DXAAvRMtSNY\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HTML(\"\"\"\n",
        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/DXAAvRMtSNY\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iwAc339cbU9"
      },
      "source": [
        "[Slides: Simple Linear Models 1](https://github.com/braincodecamp/brain-code-camp-2026-lectures/blob/main/IntroToModeling/modeling_part1a_linear1.pdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-v93fa3Tj74"
      },
      "source": [
        "## โมเดลเชิงเส้น (Linear Model)\n",
        "\n",
        "กำหนดให้สมการความสัมพันธ์ระหว่าง $x$ และ $y$ ที่แท้จริง เป็นสมการเชิงเส้น\n",
        "\n",
        "$$y = w_0 + w_1x$$\n",
        "\n",
        "โดยที่ $w_0$ คือค่าจุดตัดแกน $y$ และ $w_1$ คือค่าความชันของเส้นตรง\n",
        "\n",
        "ในการเก็บข้อมูลจริงมักมีสัญญาณรบกวน (noise) ที่มาจากหลายปัจจัย เช่น ความไม่เสถียรของเครื่องมือเก็บข้อมูล คลื่นไฟฟ้ากระแสสลับที่ใช้ในประเทศ ในกรณีที่เก็บข้อมูลผ่านอุปกรณ์อิเล็กทรอนิคส์ หรืออาจจะมี noise ที่เราไม่รู้ว่ามาจากกระบวนการไหนก็ตาม ปัจจัยเหล่านี้ส่งผลให้ข้อมูลที่เก็บมานั้นมีค่าที่แตกต่างไปจากความสัมพันธ์ที่แท้จริง ซึ่งสามารถเขียนอธิบายได้ด้วยสมการ\n",
        "\n",
        "$$y = w_0 + w_1x + noise$$\n",
        "\n",
        "แทน\n",
        "\n",
        "<br><br>\n",
        "ในส่วนนี้เราจะลองทดลองเลียนแบบกระบวนการเก็บข้อมูลผ่านการเรียกใช้ `generate_sample_linear`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7jKtIz8d-tDH",
        "outputId": "8e01bce7-ff77-4d82-dbe1-0ff13c9bd883"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def generate_sample_linear(x, w0=1, w1=2, include_noise=True):\n",
        "\n",
        "    # สร้างสมการเส้นตรงโดยที่จำลองการใส่สัญญาณรบกวนเข้าไป\n",
        "    # เลือก include_noise เป็น True เพื่อกำหนดให้มีค่า noise เพิ่มเข้าไปในสมการ\n",
        "    if include_noise:\n",
        "      # สร้าง Gaussian noise\n",
        "      noise = 0.20 * np.max(x) * np.random.standard_normal(x.shape)\n",
        "    else:\n",
        "      noise = 0\n",
        "    y = w0 + (w1 * x) + noise\n",
        "    return y\n",
        "\n",
        "# ทดลองสร้างข้อมูลโดยการเรียกใช้ generate_sample_linear\n",
        "num_samples = 100\n",
        "w0_true, w1_true = 1, 2 # กำหนดค่า w0 และ w1 ที่แท้จริง สำหรับสร้างข้อมูล\n",
        "x = 5 * np.random.rand(num_samples, 1) - 2.5 # สุ่มค่า x จากพิสัย -2.5 ถึง 2.5\n",
        "y = generate_sample_linear(x, w0_true, w1_true, include_noise=True)\n",
        "\n",
        "# สร้างข้อมูลที่ไม่มีสัญญาณรบกวนมาเปรียบเทียบ ซึ่งเป็นข้อมูลที่เรามักไม่มีโอกาสเข้าถึงในชีวิตจริง (จะใช้ในภายหลัง)\n",
        "y_true = generate_sample_linear(x, w0_true, w1_true, include_noise=False)\n",
        "\n",
        "# สร้างข้อมูลที่ไม่มีสัญญาณรบกวนมาแบบละเอียดสำหรับค่า x จำนวนมาก เพื่อใช้ในการวาดกราฟ (เส้นประสีดำ)\n",
        "x_whole_line = np.linspace(-2.5, 2.5, 100)\n",
        "y_true_whole_line = generate_sample_linear(x_whole_line, w0_true, w1_true, include_noise=False)\n",
        "\n",
        "# Plot ข้อมูล x, y ที่มีอยู่\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x, y, c='b', label='Observed')\n",
        "ax.plot(x_whole_line, y_true_whole_line, 'k--', label='True')\n",
        "ax.set(xlabel='x', ylabel='y')\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZua7f9ME1_r"
      },
      "source": [
        "\n",
        "กำหนดให้เรามีจำนวนจุดข้อมูลในรูปด้านบนทั้งหมด $n$ จุด $(x_1,y_1), (x_2,y_2), ..., (x_i,y_i),.., (x_n,y_n)$ แสดงด้วยสีน้ำเงิน หากเราพิจารณาภาพความสัมพันธ์ระหว่าง $x$ กับ $y$ จากการรันโค้ดด้านบน จะพบว่ามีลักษณะคล้ายกับเส้นตรง\n",
        "\n",
        "**หมายเหตุ** ถึงแม้ว่าในรูปด้านบน เราจะ plot เส้นประสีดำ ซึ่งแสดงความสัมพันธ์ระหว่าง $x$ และ $y$ ที่แท้จริง (ปราศจากสัญญาณรบกวน) ได้ แต่ในชีวิตจริง เรามักจะไม่มีโอกาสเข้าถึงข้อมูลตรงนี้ได้เลย\n",
        "\n",
        "<br><br>\n",
        "\n",
        "\n",
        "\n",
        "สมมติว่ามีคนเดินมาถามเราว่า ถ้า $x$ มีค่าเป็น $0.8$ แล้ว $y$ ควรจะมีค่าเป็นเท่าไหร่ เราจะตอบเค้าว่าอย่างไรดี\n",
        "\n",
        "\n",
        "ในการให้คำตอบตรงนี้ เราสามารถทำได้หลายวิธีมาก ๆ เช่น\n",
        "\n",
        "\n",
        "*   ถ้าเกิดว่าเราไปไล่ดูจุดข้อมูลทั้ง $n$ จุดในชุดข้อมูลของเรา พบว่ามีจุด $(0.8, 2.6)$ อยู่ เราก็อาจจะตอบได้ว่า $y$ น่าจะมีค่าเป็น $2.6$\n",
        "\n",
        "*   ถ้าเกิดว่าเราไปไล่ดูจุดข้อมูลทั้ง $n$ จุดในชุดข้อมูลของเรา แต่ไม่พบจุดที่มีค่า $x$ เป็น $0.8$ เลย แต่ดันมีจุด $(0.7, 2.4)$ และ $(0.9, 2.8)$ เราก็อาจจะตอบว่าค่า $y$ ที่สอดคล้องกับค่า $x=0.8$ ซึ่งเป็นจุดกึ่งกลางระหว่าง $x=0.7$ และ $x=0.9$ น่าจะมีค่า $y$ เป็นจุดตรงกลางระหว่าง $2.4$ และ $2.8$ หรือว่ามีค่าเท่ากับ $\\frac{2.4+2.8}{2}=2.6$ นั่นเอง\n",
        "\n",
        "หากสังเกตตัวอย่างด้านบนทั้ง 2 ตัวอย่าง เราจะเห็นว่าทุกครั้งที่เราจะตอบค่า $y$ เราจะต้องไปไล่ดูจุดข้อมูลในชุดข้อมูลของเรา สมมติว่า $n=1,000,000$ ล่ะ เราจะทำอย่างไรกันดี เราพอจะมีวิธีอะไรบางอย่างที่ช่วยให้เราสามารถตอบค่า $y$ ที่เหมาะสมจากค่า $x$ ใด ๆ ก็ตามได้อย่างรวดเร็วหรือไม่\n",
        "\n",
        "---\n",
        "\n",
        "เนื่องจากข้อมูลใน plot ด้านบน ดูมีลักษณะเป็นเส้นตรง เรามาทดลองใช้โมเดลที่เป็นสมการเชิงเส้น $\\hat{y}=\\hat{w_0} + \\hat{w_1} x$ กันดีกว่า ซึ่งสมการนี้มี\n",
        "\n",
        "* $\\hat{w_0}$ เป็นค่าจุดตัดแกน $y$\n",
        "\n",
        "* $\\hat{w_1}$ เป็นค่าความชันของเส้นตรง\n",
        "\n",
        "* $\\hat{y}$ เป็นค่า $y$ ที่ทำนายมาจากสมการเส้นตรงของเรา สำหรับค่า $x$ ใด ๆ\n",
        "\n",
        "ถ้าเกิดว่าเราสามารถหาค่า $\\hat{w_0}$ และ $\\hat{w_1}$ ที่เหมาะสมออกมาได้ (ซึ่งเราก็หวังว่ามันจะเป็นค่าเดียวกับ $w_0$ และ $w_1$ ของความสัมพันธ์จริง) เราจะสามารถทำนายค่า $y$ เป็น $\\hat{w_0} + \\hat{w_1} x$ จากค่า $x$ ใด ๆ ได้ทันที โดยที่ไม่ต้องไปนั่งไล่ดูจุดข้อมูลทั้ง $n$ จุดในชุดข้อมูลเลย\n",
        "\n",
        "**หมายเหตุ** ในบทเรียนนี้เราใส่สัญลักษณ์ hat เข้าไปบนตัวแปร (เช่น $\\hat{w_0}$ และ $\\hat{w_1}$) เพื่อทำให้เห็นชัดว่าเรากำลังพูดถึงตัวแปรที่เราประมาณค่าออกมา ไม่ได้กำลังพูดถึงค่าจริงของตัวแปรเหล่านั้น (เช่น $w_0$ และ $w_1$)\n",
        "\n",
        "<br><br>\n",
        "\n",
        "ใน code ด้านล่าง เรามาลองปรับค่า $\\hat{w_0}$ และ $\\hat{w_1}$ กัน เพื่อหาดูว่าค่าที่เหมาะสมมีค่าเป็นเท่าไหร่\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484,
          "referenced_widgets": [
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        },
        "id": "sIFdIr5m01fT",
        "outputId": "ed97d03b-c6a6-42eb-970c-6730f01c157d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a32c41eb0004b0992cd0c21bf93002e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w0_hat', max=4.0, min=-4.0), FloatSlider(value=0.0, …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ใส่แถบสำหรับปรับค่า w1_hat และ w2_hat รวมถึงช่องสำหรับให้เลือกว่าจะโขว์เส้นความสัมพันธ์ระหว่าง x และ y ที่แท้จริงหรือไม่\n",
        "@widgets.interact(w0_hat=widgets.FloatSlider(0.0, min=-4, max=4),\n",
        "                  w1_hat=widgets.FloatSlider(0.0, min=-4, max=4),\n",
        "                  show_true_line=widgets.Checkbox(False, description='Show true data'))\n",
        "def plot_linear_results(w0_hat, w1_hat, show_true_line):\n",
        "\n",
        "    # คำนวณค่า y จากค่า w0_hat และ w1_hat ที่เราเดามา โดยจะคำนวณมาเฉพาะอันที่มีค่า x ตรงกับที่มีในชุดข้อมูล\n",
        "    y_predicted_partial = w0_hat + w1_hat * x\n",
        "\n",
        "    # คำนวณค่า y จากค่า w0_hat และ w1_hat ที่เราเดามา สำหรับค่า x จำนวนมาก\n",
        "    y_predicted = w0_hat + w1_hat * x_whole_line\n",
        "\n",
        "    # สร้าง figure\n",
        "    fig, ax = plt.subplots(figsize=(4,4))\n",
        "\n",
        "    # Plot ข้อมูล x, y ที่มีอยู่ด้วยสีน้ำเงิน\n",
        "    ax.scatter(x, y, c='b', label='Observed')\n",
        "\n",
        "    # Plot ข้อมูลค่า y ที่เราทำนายมาที่ตำแหน่งค่า x ต่าง ๆ กัน ด้วยสีแดง\n",
        "    ax.plot(x_whole_line, y_predicted, c='r', label='Predicted')\n",
        "\n",
        "    # Plot ข้อมูลที่ไม่มี noise ด้วยสีดำ ซึ่งในความเป็นจริง เรามักไม่มีโอกาสเข้าถึงข้อมูลตรงนี้\n",
        "    if show_true_line:\n",
        "        ax.plot(x_whole_line, y_true_whole_line, 'k--', label='True')\n",
        "\n",
        "    ax.set(xlabel='x', ylabel='y')\n",
        "    ax.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSeTb-gj6aji"
      },
      "source": [
        "จะเห็นได้ว่าเราสามารถลองปรับค่าความชันและจุดตัดแกน $y$ ของเส้นตรงไปเรื่อย ๆ จนมีความสอดคล้องกับชุดข้อมูลที่เราเก็บมา (สีน้ำเงิน)\n",
        "\n",
        "หากเรากำหนดให้ค่า $\\hat{w_0}=1$ และ $\\hat{w_1}=2$ จะพบว่าเส้นตรงสีแดงมีความสอดคล้องกับจุดสีน้ำเงินค่อนข้างดี โดยเส้นตรงนั้นมีสมการคือ $y = 2x + 1$ ซึ่งเป็นเส้นตรงที่มีความชันเป็น $2$ และมีจุดตัดแกน $y$ คือ $(0,1)$\n",
        "\n",
        "\n",
        "\n",
        "อีกวิธีหนึ่งที่อาจจะช่วยให้เราเลือกค่า $\\hat{w_0}$ และ $\\hat{w_0}$ ด้วยตาได้ง่ายขึ้น คือการ plot โชว์ความแตกต่างระหว่างจุดที่ observed มา (สีน้ำเงิน) และสิ่งที่โมเดลทำนายออกมา (สีแดง) ดังตัวอย่างโค้ดด้านล่าง โดยเราจะพยายามเลือกเอาค่า $\\hat{w_0}$ และ $\\hat{w_0}$ ที่ทำให้เส้นสีส้ม ๆ ในรูปมีขนาดรวมกันสั้นที่สุด\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516,
          "referenced_widgets": [
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            "c2608704ee1e44518cbf422391d86af9",
            "03a5c6b0d0a6492991b6c9f56bdd68eb"
          ]
        },
        "id": "TlaTlUU_OBT4",
        "outputId": "1cddb538-ca96-40bf-debb-d70f1144548b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "269a483313b14d6bbf594437310241fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w0_hat', max=4.0, min=-4.0), FloatSlider(value=0.0, …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# เหมือน cell ก่อนหน้า แต่เพิ่มช่องสำหรับเลือกว่าจะโชว์ error หรือไม่\n",
        "@widgets.interact(w0_hat=widgets.FloatSlider(0.0, min=-4, max=4),\n",
        "                  w1_hat=widgets.FloatSlider(0.0, min=-4, max=4),\n",
        "                  show_true_line=widgets.Checkbox(False, description='Show true data'),\n",
        "                  show_error=widgets.Checkbox(True, description='Show error'))\n",
        "def plot_linear_results(w0_hat, w1_hat, show_true_line, show_error):\n",
        "\n",
        "    # คำนวณค่า y จากค่า w0_hat และ w1_hat ที่เราเดามา สำหรับค่า x จำนวนมาก\n",
        "    y_predicted = w0_hat + w1_hat * x_whole_line\n",
        "\n",
        "    # สร้าง figure\n",
        "    fig, ax = plt.subplots(figsize=(4,4))\n",
        "\n",
        "    # Plot ข้อมูล x, y ที่มีอยู่ด้วยสีน้ำเงิน\n",
        "    ax.scatter(x, y, c='b', label='Observed')\n",
        "\n",
        "    # Plot ข้อมูลค่า y ที่เราทำนายมาที่ตำแหน่งค่า x ต่าง ๆ กัน ด้วยสีแดง\n",
        "    ax.plot(x_whole_line, y_predicted, c='r', label='Predicted')\n",
        "\n",
        "    # Plot ข้อมูลที่ไม่มี noise ด้วยสีดำ ซึ่งในความเป็นจริง เรามักไม่มีโอกาสเข้าถึงข้อมูลตรงนี้\n",
        "    if show_true_line:\n",
        "        ax.plot(x_whole_line, y_true_whole_line, 'k--', label='True')\n",
        "\n",
        "    # Plot ความแตกต่างระหว่างค่า y ที่ observed มา และค่า y ที่ทำนายมาด้วยสีส้ม\n",
        "    if show_error:\n",
        "\n",
        "        # คำนวณค่า y จากค่า w0_hat และ w1_hat ที่เราเดามา โดยจะคำนวณมาเฉพาะอันที่มีค่า x ตรงกับที่มีในชุดข้อมูล\n",
        "        y_predicted_partial = w0_hat + w1_hat * x\n",
        "\n",
        "        y_min = np.minimum(y, y_predicted_partial)\n",
        "        y_max = np.maximum(y, y_predicted_partial)\n",
        "        ax.vlines(x, y_min, y_max, 'darkorange', alpha=0.3, label='errors')\n",
        "\n",
        "    ax.set(xlabel='x', ylabel='y')\n",
        "    ax.legend()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "tq5jS6fIIZfF",
        "outputId": "f6b0ad44-0caa-4a15-918d-bbb0a2546172",
        "tags": [
          "remove-input"
        ]
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ZWoUn5OH5no\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HTML(\"\"\"\n",
        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ZWoUn5OH5no\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3sMuqOuIZfp"
      },
      "source": [
        "[Slides: Simple Linear Models 2](https://github.com/braincodecamp/brain-code-camp-2026-lectures/blob/main/IntroToModeling/modeling_part1b_linear2.pdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Dwl8LDmNWyU"
      },
      "source": [
        "ที่ผ่านมาเราเลือกค่า $\\hat{w_0}$ และ $\\hat{w_1}$ โดยการดูด้วยตาว่าเส้นตรงสีแดงที่เกิดขึ้นมานั้น ดูสอดคล้องกับจุดข้อมูลสีน้ำเงินมากน้อยแค่ไหน ซึ่งนับว่าเป็นจากวัดความเหมือนหรือความแตกต่างเชิงคุณภาพ (qualitative)\n",
        "\n",
        "ในลำดับถัดไป เราจะมาลองดูวิธีการวัดความเหมือนหรือความแตกต่างเชิงปริมาณกันบ้าง (quantitative)\n",
        "\n",
        "เรามาลองใช้ฟังก์ชัน $L(y_{i},\\hat{y_i})$ สำหรับวัดความต่างระหว่าง $y_{i}$ และ $\\hat{y_i}$ ออกมาเป็นตัวเลข 1 ตัว โดยการนำเอาค่าทั้งสองมาลบกันแล้วยกกำลังสอง\n",
        "\n",
        "$$L(y_{i},\\hat{y_i}) = (y_{i} - \\hat{y_i})^2$$\n",
        "\n",
        "*   ถ้าหากเราพยายามนำเอาสมการนี้ไปตีความทางเรขาคณิต เราจะเห็นว่าค่า $y_{i} - \\hat{y_i}$ แสดงถึง\"ความยาว\"ของเส้นสีส้มแต่ละเส้นในโค้ดของ cell ก่อนหน้า (อาจะมีค่าเป็นลบหรือบวกก็ได้) ซึ่งแปลว่าค่า $L(y_{i},\\hat{y_i})$ นี้ ก็คือค่าความยาวของเส้นสีส้มยกกำลังสองนั่นเอง\n",
        "*   ถ้า $L(y_{i},\\hat{y_i})$ มีค่าน้อย แสดงว่า $y_{i}$ กับ $\\hat{y_i}$ มีความแตกต่างกันน้อย (เส้นสีส้มจะสั้น)\n",
        "*   ถ้า $L(y_{i},\\hat{y_i})$ มีค่ามาก แสดงว่า $y_{i}$ และ $\\hat{y_i}$ มีความแตกต่างกันมาก (เส้นสีส้มจะยาว)\n",
        "\n",
        "\n",
        "ชุดข้อมูลของเรามีทั้งหมด $n$ จุด ซึ่งหากเราต้องการวัดความแตกต่างในระดับชุดข้อมูล เราก็สามารถเอาค่า $L(y_{i},\\hat{y_i})$ จากแต่ละจุดข้อมูลมาเฉลี่ยกันได้ เกิดเป็นสมการ\n",
        "\n",
        "$$L(Y,\\hat{Y}) =\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-\\hat{y_i}\\right)^{2}$$\n",
        "\n",
        "ที่มีชื่อเรียกอีกว่า mean squared error (MSE) โดยในที่นี้เราใช้สัญลักษณ์\n",
        "\n",
        "* $Y$ เพื่ออ้างอิงถึง $y_1, y_2, ..., y_i, ..., y_n$\n",
        "\n",
        "* $\\hat{Y}$ เพื่ออ้างอิงถึง $\\hat{y_1}, \\hat{y_2}, ..., \\hat{y_i}, ..., \\hat{y_n}$\n",
        "\n",
        "หากเราย้อนกลับไปที่ตัวอย่างด้านบนที่เราพยายามปรับค่า $\\hat{w_0} และ \\hat{w_1}$ (จุดตัดแกน $y$ และค่าความชัน ตามลำดับ) แทนที่เราจะวัดผลด้วยตา เราสามารถลองใช้ $L$ สำหรับช่วยในการวัดผล โดยสุดท้ายแล้ว เราจะเลือกค่า $\\hat{w_0} และ \\hat{w_1}$ ที่ทำให้ $L$ มีค่าน้อยที่สุด\n",
        "\n",
        "อย่างไรก็ตาม การทดลองสุ่มค่าไปเรื่อย ๆ ในลักษณะนี้ เป็นวิธีการที่ใช้เวลานานมาก และไม่ค่อยมีประสิทธิภาพ\n",
        "\n",
        "<br><br>\n",
        "\n",
        "หากเรามองออกว่าโจทย์ที่เราพยายามแก้อยู่ เป็นโจทย์การหาค่าต่ำสุดของฟังก์ชัน $L$ ซึ่งสามารถเขียนในทางคณิตศาสตร์ได้ว่า\n",
        "\n",
        "$$\n",
        "\\min_{\\hat{w_{0}},\\hat{w_{1}}}L(Y,\\hat{Y})\n",
        "=\\min_{\\hat{w_{0}},\\hat{w_{1}}}\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-\\hat{y_i}\\right)^{2}\n",
        "= \\min_{\\hat{w_{0}},\\hat{w_{1}}}\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-(\\hat{w_{0}}+\\hat{w_{1}}x_{i})\\right)^{2}\n",
        "$$\n",
        "\n",
        "เราก็สามารถเอาเครื่องมือทางคณิตศาสตร์ (เช่น calculus, สถิติ และ linear algebra) มาแก้โจทย์ข้อนี้ได้ ซึ่งคำตอบที่ $\\hat{w_{0}}$ และ $\\hat{w_{1}}$ ที่ได้รับจากการแก้โจทย์ข้อนี้ ก็คือคำตอบที่ทำให้เส้นตรงของเรามีความแตกต่างจากข้อมูลที่เราเก็บมาน้อยที่สุดภายใต้มาตรวัดประเภทนี้ หรือพูดอีกอย่างว่ามีความเหมือนที่สุดนั่นเอง\n",
        "\n",
        "\n",
        "**หมายเหตุ** เนื่องจาก $L$ ในที่นี้แสดงถึงความแตกต่าง ซึ่งเราต้องการหาคำตอบที่ทำให้มันมีค่าน้อย (ความสูญเสียน้อย) เรามักจะเรียก $L$ ในบริบทนี้ว่า loss function\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "เนื่องจากมีผู้เรียนจำนวนหนึ่งยังไม่มีโอกาสได้เรียนเนื้อหาทางคณิตศาสตร์ที่จำเป็นต่อการแก้โจทย์ข้อนี้ (เช่น การใช้ calculus และ linear algebra) เราจะแก้โจทย์ข้อนี้ผ่านการเรียกใช้ `LinearRegression` จากไลบรารี่ `scikit-learn` เลย"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSuDsZG0CzjV",
        "outputId": "b4cc24c5-dbda-462b-fa00-20ac190297fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True slope 2.00\n",
            "Estimated slope 1.96\n",
            "\n",
            "True intercept 1.00\n",
            "Estimated intercept 0.99\n"
          ]
        }
      ],
      "source": [
        "model_linear = LinearRegression()\n",
        "\n",
        "# ให้โมเดลหาค่า w_0 and w_1 จากข้อมูล (x,y) ทั้งหมดที่มี\n",
        "model_linear.fit(x, y)\n",
        "w0_hat = model_linear.intercept_[0]\n",
        "w1_hat = model_linear.coef_[0][0]\n",
        "\n",
        "print(f\"True slope {w1_true:0.2f}\")\n",
        "print(f\"Estimated slope {w1_hat:0.2f}\\n\")\n",
        "print(f\"True intercept {w0_true:0.2f}\")\n",
        "print(f\"Estimated intercept {w0_hat:0.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZl_HD6IK_Wj"
      },
      "source": [
        "จะเห็นว่าความชันและจุดตัดแกน $y$ ที่ประมาณจากโมเดล linear regression มีค่าใกล้กับค่าที่เรากำหนดมาตอนสร้างชุดข้อมูล\n",
        "\n",
        "หลังจากที่เรา fit โมเดลแล้ว (โมเดลได้ทำการประมาณค่า $\\hat{w_0}$ และ $\\hat{w_1}$ เรียบร้อยแล้ว) เราสามารถทำนายค่า $y$ จาก $x$ ใดๆ ได้จากสมการ $ \\hat{y} = \\hat{w_0} + \\hat{w_1}x$ ได้โดยตรง หรือผ่านการเรียกใช้ฟังก์ชัน `predict` ได้เช่นกัน"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "s72s_4NWK_oo",
        "outputId": "47ce6763-eb56-45b4-ef44-b138f8360292"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ใช้โมเดลทำนายค่า y จากค่า x วิธีที่ 1\n",
        "y_hat = w0_hat + w1_hat*x\n",
        "\n",
        "# ใช้โมเดลทำนายค่า y จากค่า x วิธีที่ 2\n",
        "# y_hat = model_linear.predict(x)\n",
        "\n",
        "# วัด mean squared error จากการทำนาย\n",
        "def mse(y,y_hat):\n",
        "  return np.mean((y-y_hat)**2)/y.shape[0]\n",
        "\n",
        "mse_val = mse(y_true, y_hat)\n",
        "mse_val_noisy = mse(y_true, y)\n",
        "\n",
        "# แสดงผลการทำนาย\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x, y, c='b', label='Observed')\n",
        "ax.set(xlabel='x', ylabel='y')\n",
        "ax.scatter(x, y_hat, c='r', label='Predicted')\n",
        "ax.plot(x_whole_line, y_true_whole_line, 'k--', label='True')\n",
        "ax.legend()\n",
        "ax.set_title(f\"The error as measured by MSE = {mse_val:0.4f} (red) and {mse_val_noisy:0.4f} (blue) with respect to the black dash line\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed2kL65NZcOP"
      },
      "source": [
        "จากการสังเกตด้วยตา จะเห็นว่าค่า $y$ ที่โมเดลได้ทำนายออกมา (จุดสีแดง) มีความสอดคล้องกับความสัมพันธ์ระหว่าง $x$ และ $y$ ที่แท้จริง (เส้นประสีดำ) มากกว่าข้อมูลที่เราเก็บมาเสียอีก (จุดสีน้ำเงิน)\n",
        "\n",
        "นอกจากนั้น เราก็ได้ใช้ loss function ตัวเดิม\n",
        "\n",
        "$$MSE(Y,\\hat{Y}) = L(Y,\\hat{Y}) =\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-\\hat{y_i}\\right)^{2}$$\n",
        "\n",
        "มาเป็นมาตรวัดความเหมือน/ความคล้ายระหว่างชุดข้อมูลเชิงปริมาณ และพบว่า MSE ระหว่างค่า $y$ ของจุดสีแดงกับค่า $y$ ที่มาจากความสัมพันธ์ที่แท้จริง (สีดำ) มีค่าต่ำกว่า MSE ระหว่างค่า $y$ ที่เราเก็บมาซึ่งมีสัญญาณรบกวน​ (สีน้ำเงิน) กับ ค่า $y$ ที่มาจากความสัมพันธ์ที่แท้จริง (สีดำ)\n",
        "\n",
        "จะเห็นได้ว่าการเปรียบเทียบทั้งแบบเชิงคุณภาพและเชิงปริมาณมีความสอดคล้องกันในระดับหนึ่ง\n",
        "\n",
        "\n",
        "<br><br>\n",
        "MSE เป็นแค่หนึ่งในวิธีสำหรับวัดผล (evaluation metric) เท่านั้น ยังมีอีกหลายวิธีที่ใช้วัดผลออกมาเป็นตัวเลข เช่น mean absolution error (MAE), root mean squared error (RMSE), correlation coeficient, หรือแม้กระทั่งความเป็นไปได้(likelihood) ของแบบจำลองเทียบกับชุดข้อมูลที่เก็บรวบรวมได้<br><br> แต่ละวิธีในการวัดผลต่างมีสมมติฐานของข้อมูลที่แตกต่างกันจึงเหมาะสมกับข้อมูลที่ต่างกัน การเลือกใช้ evaluation metric ที่เหมาะสมจะส่งผลให้แบบจำลองที่เราสร้างขึ้นสามารถอธิบายข้อมูลได้ดีมากขึ้น <br><br>\n",
        "อย่างไรก็ตามวิธีการวัดผลข้างต้นนั้นหมาะสำหรับการสร้างแบบจำลองประเภท regression ส่วนปัญหาอื่นๆ เช่น classification ก็จำเป็นต้องมีการวัดความสามารถของแบบจำลองที่แตกต่างออกไป เช่น ความแม่นยำ (accuracy), F-score ฯลฯ<br>\n",
        "\n",
        "ใน module นี้ เราจะใช้ MSE เป็น evaluation metric ไปก่อน แต่ใน module ถัด ๆ ไป หรือการทำงานจริง เราจะได้เห็น evaluation metric ที่หลากหลายมากยิ่งขึ้น"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUAPcvutPUn8"
      },
      "source": [
        "\n",
        "---\n",
        "เราลองกลับมาดูตัวอย่างที่คล้าย ๆ กับตัวอย่างที่พูดไว้ในช่วง Introduction ของ module นี้ ที่เราให้แมวดูภาพที่มีลักษณะต่าง ๆ กัน ในขณะที่เราใช้ electrode บันทึกการส่งสัญญาณของ neuron ใน visual cortex ของแมวตัวนั้น\n",
        "\n",
        "หากเราลองเปลี่ยนแปลง contrast ของภาพที่ให้แมวดู (โดยใช้ orientation ที่ neuron ตัวนั้น sensitive ที่สุดเท่านั้น ไม่มีการเปลี่ยน orientation เลย) แล้วเก็บค่า average firing rate เอาไว้ เราสามารถลองศึกษาดูว่าการเปลี่ยน contrast ของภาพมันมีผลอย่างไรต่อ average firing rate ของ neuron ตัวนั้น\n",
        "\n",
        "สมมติว่าเราลองเอาข้อมูลมา plot ดู แล้วพบข้อมูลที่หน้าตาเหมือน code ใน cell ถัดไป"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "FrqWw1h1Lr1X",
        "outputId": "f36eda60-2334-48e9-cbff-0610aee9547b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ทดลองสร้างข้อมูลโดยการเรียกใช้ generate_sample_linear\n",
        "num_samples = 25\n",
        "w0_true, w1_true = 1, 2 # กำหนดค่า w0 และ w1 ที่แท้จริง สำหรับสร้างข้อมูล\n",
        "x_example = 3 * np.random.rand(num_samples, 1) + 3\n",
        "y_example = generate_sample_linear(x_example, w0_true, w1_true, include_noise=True)\n",
        "\n",
        "# Plot ข้อมูล x, y ที่มีอยู่\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x_example, y_example, c='b')\n",
        "ax.set(xlabel='contrast [a.u.]', ylabel='average firing rate [a.u]')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ajoqeR3S0HS"
      },
      "source": [
        "จากการดูหน้าตาของกราฟนี้ เราพบว่ามีลักษณะเป็นคล้ายเส้นตรง (อย่างน้อยก็ในช่วงค่า $x$ ระหว่าง $3$ กับ $6$)\n",
        "\n",
        "ถ้าเราต้องการทำนายค่า average firing rate ของ neuron ตัวนี้จากค่า contrast ค่าใดค่าหนึ่งในช่วง $x$ ระหว่าง $3$ กับ $6$ วิธีหนึ่งที่เราได้เรียนจากเนื้อหาในบทเรียนนี้ก็คือการประมาณค่าความสัมพันธ์ระหว่าง average firing rate ของ neuron (แกน $y$) กับค่า contrast (แกน $x$) ด้วยสมการเส้นตรง\n",
        "\n",
        "$$ \\hat{y} = \\hat{w_0} + \\hat{w_1} x$$\n",
        "\n",
        "โดยสามารถหาค่า $\\hat{w_0}$ และ $\\hat{w_1}$ การเรียกใช้ `sklearn.linear_model.LinearRegression` ได้อย่างง่ายดาย\n",
        "\n",
        "**ข้อควรระวัง**\n",
        "\n",
        "ข้อมูลแสดงความสัมพันธ์ที่แท้จริงระหว่าง average firing rate กับ contrast มีความซับซ้อนกว่าตัวอย่างที่เราสร้างไว้ตรงนี้มาก เช่น หากเราดูค่า contrast ที่นอกเหนือจากช่วง $3 - 6$ เราอาจพบว่า\n",
        "\n",
        "* ความสัมพันธ์เริ่มมีความโค้งงอ ไม่เป็นเส้นตรงอีกต่อไป\n",
        "* เมื่อมีค่า contrast สูงถึงระดับหนึ่ง ค่า average firing rate ก็ไม่เพิ่มขึ้นแล้ว\n",
        "\n",
        "ถ้าเราต้องการใช้โมเดลตัวเดียว เพื่ออธิบายความสัมพันธ์ทั้งหมดได้ เรามีความจำเป็นต้องพัฒนาโมเดลที่มีความซับซ้อนมากยิ่งขึ้น\n",
        "\n",
        "หรืออีกวิธีหนึ่งก็คือการแบ่งแกน $x$ ออกเป็นช่วงต่าง ๆ แล้วเราใช้สมการต่างชนิดกัน อธิบายความสัมพันธ์ในแต่ละช่วง"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71L6GultIZf3"
      },
      "source": [
        "## Multiple Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "m_9-vD3HIZgf",
        "outputId": "81434928-8ed4-4bc4-a753-befeaa5018cb",
        "tags": [
          "remove-input"
        ]
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/kWwz-K2sr7k\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HTML(\"\"\"\n",
        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/kWwz-K2sr7k\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePwC90AeJOtv"
      },
      "source": [
        "[Slides: Simple Linear Models 3](https://github.com/braincodecamp/brain-code-camp-2026-lectures/blob/main/IntroToModeling/modeling_part1c_linear3_multivariate1.pdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nm6kOPEOW5nl"
      },
      "source": [
        "\n",
        "\n",
        "ทีนี้เรามาลองดูการทดลองที่เปลี่ยนไปเล็กน้อย\n",
        "\n",
        "สมมติเราให้น้องแมวดูภาพที่มีการเปลี่ยนแปลง contrast แต่ไม่เปลี่ยน orientation เหมือนในตัวอย่างที่แล้ว **พร้อมกันกับ** การให้ฟังเสียงที่เรากำหนดความดังได้\n",
        "\n",
        "แล้วเราไปวัด ค่า average firing rate จาก neuron ประเภท multi-sensory neuron ในขณะที่เราเปลี่ยน contrast ของภาพ และ ความดังของเสียง ไปเรื่อย ๆ เราสามารถเอาข้อมูลนี้มาศึกษาดูว่าการเปลี่ยน contrast และความดัง มีผลต่อ average firing rate ของ neuron ตัวนั้นอย่างไร\n",
        "\n",
        "สมมติว่าเราลองเอาข้อมูล (contrast, loudness, average firing rate) มา plot ดู แล้วพบข้อมูลที่หน้าตาเหมือน code ใน cell ถัดไป"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "rQYob21hV1Md",
        "outputId": "629f0df1-8411-487c-f472-4107aeb7a84b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def generate_sample_multi_linear(x1, x2, w0=1, w1=2, w2=1, include_noise=True):\n",
        "\n",
        "    # สร้างสมการเส้นตรงโดยที่จำลองการใส่สัญญาณรบกวนเข้าไป\n",
        "    # เลือก include_noise เป็น True เพื่อกำหนดให้มีค่า noise เพิ่มเข้าไปในสมการ\n",
        "    if include_noise:\n",
        "      # สร้าง Gaussian noise\n",
        "      noise = 0.1*np.random.randn(*x1.shape)\n",
        "    else:\n",
        "      noise = 0\n",
        "\n",
        "    y = w0 + (w1 * x1) + (w2* x2) + noise\n",
        "\n",
        "    return y\n",
        "\n",
        "\n",
        "# ทดลองสร้างข้อมูลโดยการเรียกใช้ generate_sample_multi_linear\n",
        "num_samples = 200\n",
        "w0_true, w1_true, w2_true = 1, 2, 1\n",
        "x1 = 3 + 3*np.random.rand(num_samples, 1)\n",
        "x2 = 3 + 3*np.random.rand(num_samples, 1)\n",
        "y_multi = generate_sample_multi_linear(x1, x2, w0_true, w1_true, w2_true)\n",
        "\n",
        "# แสดงผลภาพ\n",
        "fig = plt.figure(figsize=(4, 4))\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "ax.scatter(x1, x2, y_multi, c='b', marker='o')\n",
        "ax.set_xlabel('contrast [a.u.]')\n",
        "ax.set_ylabel('loudness [a.u.]')\n",
        "ax.set_zlabel('average firing rate [a.u.]')\n",
        "ax.view_init(elev=20, azim=-70)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWlncYPDnJnX"
      },
      "source": [
        "หากเราสังเกตรูปด้านบน เราจะเห็นว่าข้อมูลมีลักษณะเป็นเหมือนแผ่นกระดาษสี่เหลี่ยมผืนผ้า\n",
        "\n",
        "ในทางคณิตศาสตร์เราสามารถเขียนสมการของแผ่นกระดาษสี่เหลี่ยมผืนผ้าได้\n",
        "\n",
        "$$ y = w_0 + w_1 x_1 + w_2 x_2 $$\n",
        "\n",
        "โดยที่ $w_0$, $w_1$ และ $w_2$ เป็นตัวแปรที่เราปรับค่าได้ ซึ่งการปรับค่าตัวแปรเหล่านี้ จะส่งผลให้แผ่นกระดาษนี้ (เราเรียกมันว่า **plane**) มันหมุนไปมา หรือเลื่อนขึ้นลงได้\n",
        "\n",
        "<br><br>\n",
        "เราสามารถลองใช้สมการนี้ มาเปรียบเทียบกับข้อมูลของเราดู โดยการลองปรับค่า $w_0$, $w_1$ และ $w_2$ ไปเรื่อย ๆ ตาม code ใน cell ถัดมา"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441,
          "referenced_widgets": [
            "3baee3a4256946a38916d7cc5b5a98d2",
            "932e11d576d74fa89a1c40f7339174f7",
            "e4fac01783ef475ca470d57a253d8640",
            "012d0fcdc2a941cba05d695d1d954f18",
            "cd53ec71640d479c928dce20b7b7353e",
            "172023a462ff4e4d92245234fa579265",
            "5aad7a8acd8d4191adfa6fbae1f5295d",
            "e09ca93f67a04afaaed8f53a9ac3b61f",
            "07be88f9b7f942b081671b53a00cd23c",
            "af6fc0b885784abbb461df495060c39a",
            "ded5f92f892e483cac5bce8591f52cfa",
            "9adaf318c1df410da2aa7d10a9bd27cb",
            "f9ef37855ead408ab02470c28276e24e"
          ]
        },
        "id": "65XSgJDSol-s",
        "outputId": "b551af69-83c0-4f57-b72f-d1307465efbb"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3baee3a4256946a38916d7cc5b5a98d2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.5, description='w0_hat', max=4.0, min=0.5), FloatSlider(value=0.0, d…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ใส่แถบสำหรับปรับค่า w1_hat และ w2_hat รวมถึงช่องสำหรับให้เลือกว่าจะโขว์เส้นความสัมพันธ์ระหว่าง x และ y ที่แท้จริงหรือไม่\n",
        "@widgets.interact(w0_hat=widgets.FloatSlider(0.0, min=0.5, max=4),\n",
        "                  w1_hat=widgets.FloatSlider(0.0, min=-1, max=4),\n",
        "                  w2_hat=widgets.FloatSlider(0.0, min=-1, max=4))\n",
        "def plot_plane_results(w0_hat, w1_hat, w2_hat):\n",
        "\n",
        "    # สร้างข้อมูลที่ไม่มีสัญญาณรบกวนมาแบบละเอียดสำหรับค่า x1 และ x2 จำนวนมาก เพื่อใช้ในการวาด plane\n",
        "    num_sample_per_axis = 100\n",
        "    X1_whole_plane, X2_whole_plane = np.meshgrid(3 + 3*np.random.rand(num_sample_per_axis),\n",
        "                                                 3 + 3*np.random.rand(num_sample_per_axis))\n",
        "\n",
        "    x1_whole_plane = np.reshape(X1_whole_plane, (-1, 1))\n",
        "    x2_whole_plane = np.reshape(X2_whole_plane, (-1, 1))\n",
        "\n",
        "    # คำนวณค่า y จากค่า w0_hat และ w1_hat ที่เราเดามา สำหรับค่า x จำนวนมาก\n",
        "    y_predicted_whole_plane = w0_hat + w1_hat * x1_whole_plane + w2_hat * x2_whole_plane\n",
        "\n",
        "    # Plot รูปออกมา\n",
        "    fig = plt.figure(figsize=(4, 4))\n",
        "    ax = fig.add_subplot(projection='3d')\n",
        "    ax.scatter(x1_whole_plane, x2_whole_plane, y_predicted_whole_plane, c='k', marker='.', label='Predicted', alpha=0.03)\n",
        "    ax.scatter(x1, x2, y_multi, c='b', marker='o')\n",
        "    ax.set_xlabel('contrast [a.u.]')\n",
        "    ax.set_ylabel('loudness [a.u.]')\n",
        "    ax.set_zlabel('average firing rate [a.u.]')\n",
        "    ax.view_init(elev=20, azim=-70)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc90mQG7uU7q"
      },
      "source": [
        "การลองปรับค่า $w_0$, $w_1$ และ $w_2$ ไปเรื่อย ๆ เราน่าจะพอเห็นภาพแล้วว่าตัวแปรแต่ละตัวส่งผลอย่างไรต่อ plane ของเรา เช่น การปรับ $w_0$ เพียงอย่างเดียวจะยก plane ขึ้นหรือลงโดยไม่เปลี่ยนความชันของ plane เลย\n",
        "\n",
        "อย่างที่เราได้เคยคุยกันแล้วว่าการลองปรับค่าตัวแปรไปเรื่อย ๆ จนกว่าจะเจอค่าที่อธิบายจุดข้อมูลสีน้ำเงินได้ดีที่สุด เป็นอะไรที่ค่อนข้างใช้เวลานาน เรามาลองใช้เทคนิคที่เราเรียนรู้มาในช่วงแรกของบทเรียนนี้กันดีกว่า\n",
        "\n",
        "<br><br>\n",
        "เราจะใช้ mean squared error (MSE) เป็นมาตรวัดเหมือนเดิม แล้วก็หาค่า $w_0$, $w_1$ และ $w_2$ ที่ทำให้มีค่า MSE น้อยที่สุด\n",
        "\n",
        "$$ MSE(Y, \\hat{Y})\n",
        "= L(Y,\\hat{Y})\n",
        "=\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-\\hat{y_i}\\right)^{2}\n",
        "=\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-(\\hat{w_0} + \\hat{w_1} x_{i1} + \\hat{w_2} x_{i2})\\right)^{2}  $$\n",
        "\n",
        "เราจะเห็นว่าสมการแทบจะมีหน้าตาเหมือนเดิมเลย ยกเว้นแค่เราเปลี่ยนจาก $\\hat{y} = \\hat{w_0} + \\hat{w_1} x_1$ ไปเป็น $\\hat{y} = \\hat{w_0} + \\hat{w_1} x_{i1} + \\hat{w_2} x_{i2}$\n",
        "\n",
        "โดยที่\n",
        "\n",
        "* $x_{i1}$ แสดงถึงค่า feature ที่ 1 ของจุดข้อมูลที่ $i$ (ค่า contrast ของจุดที่ $i$)\n",
        "* $x_{i2}$ แสดงถึงค่า feature ที่ 2 ของจุดข้อมูลที่ $i$ (ค่า loudness ของจุดที่ $i$)\n",
        "\n",
        "\n",
        "<br><br>\n",
        "การหาค่า $w_0$, $w_1$ และ $w_2$ ที่ทำให้มีค่า MSE น้อยที่สุด สามารถเขียนเป็นสมการทางคณิตศาสตร์ได้เป็น\n",
        "\n",
        "$$\n",
        "\\min_{\\hat{w_{0}},\\hat{w_{1}},\\hat{w_2}}L(Y,\\hat{Y})\n",
        "=\\min_{\\hat{w_{0}},\\hat{w_{1}},\\hat{w_2}}\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-\\hat{y_i}\\right)^{2}\n",
        "= \\min_{\\hat{w_{0}},\\hat{w_{1}},\\hat{w_2}}\\frac{1}{n}\\sum_{i=1}^{n}\\left(y_{i}-(\\hat{w_0} + \\hat{w_1} x_{i1} + \\hat{w_2} x_{i2})\\right)^{2}\n",
        "$$\n",
        "\n",
        "ซึ่งเราสามารถแก้สมการนี้ผ่านการเรียกใช้ `LinearRegression` จากไลบรารี่ `scikit-learn` เลย ได้เช่นกัน แต่ต้องลองอ่าน documentation ดูว่าเราจะต้องจัดเรียง dimension ของข้อมูลของเราอย่างไร เพื่อให้เรียกใช้ `LinearRegression` ได้อย่างถูกต้อง\n",
        "\n",
        "**ความท้าทาย** สำหรับคนที่มีโอกาสได้ศึกษา linear algebra มาแล้ว อยากให้ลองเขียนสมการนี้ให้อยู่ในรูป vector และ matrix ดู รวมถึงลองแก้สมการดูด้วยเทคนิคที่ผู้เรียนถนัด เช่น การคำนวณ gradient แล้วจับมาเท่ากับศูนย์ (สามารถมองได้ว่า คล้าย ๆ กับการคำนวน derivative แล้วจับมาเท่ากับ $0$ ที่เราเคยเรียนกันมาใน calculus ม. ปลาย)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VewjCxubhzOm",
        "outputId": "515e0905-d053-40eb-e4d3-2a304d7cbc85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True w0 1.00\n",
            "Estimated w0 1.07\n",
            "\n",
            "True w1 2.00\n",
            "Estimated w1 1.99\n",
            "\n",
            "True w2 1.00\n",
            "Estimated w2 1.00\n"
          ]
        }
      ],
      "source": [
        "# จัดเรียงข้อมูลให้อยู่ในรูปแบบที่เหมาะสม\n",
        "X = np.concatenate((x1, x2), axis=1)\n",
        "\n",
        "# ให้โมเดลหาค่า w_0, w_1 และ w_2 จากข้อมูลทั้งหมดที่มี\n",
        "model_multi_linear = LinearRegression()\n",
        "model_multi_linear.fit(X, y_multi)\n",
        "w0_hat = model_multi_linear.intercept_[0]\n",
        "w1_hat = model_multi_linear.coef_[0][0]\n",
        "w2_hat = model_multi_linear.coef_[0][1]\n",
        "\n",
        "print(f\"True w0 {w0_true:0.2f}\")\n",
        "print(f\"Estimated w0 {w0_hat:0.2f}\\n\")\n",
        "print(f\"True w1 {w1_true:0.2f}\")\n",
        "print(f\"Estimated w1 {w1_hat:0.2f}\\n\")\n",
        "print(f\"True w2 {w2_true:0.2f}\")\n",
        "print(f\"Estimated w2 {w2_hat:0.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sIuSmdP1Wl9"
      },
      "source": [
        "จะเห็นว่าค่า $w_0$, $w_1$ และ $w_2$ ที่ประมาณจากโมเดล linear regression มีค่าใกล้กับค่าที่เรากำหนดมาตอนสร้างชุดข้อมูล\n",
        "\n",
        "หลังจากที่เรา fit โมเดลแล้ว (โมเดลได้ทำการประมาณค่า $w_0$, $w_1$ และ $w_2$ เรียบร้อยแล้ว) เราสามารถทำนายค่า $y$ จาก $x_1$ และ $x_2$ ใดๆ ได้จากสมการ $ \\hat{y} = \\hat{w_0} + \\hat{w_1} x_1 + \\hat{w_2} x_2$ ได้โดยตรง หรือผ่านการเรียกใช้ฟังก์ชัน `predict` ได้เช่นกัน"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "c1LMJh-Z0NSX",
        "outputId": "5742a761-1a4b-4e9b-fbd7-fd2d805dad48"
      },
      "outputs": [
        {
          "data": {
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qSCSy6s++vqCs0WhQr9cplUpomsa3v/3tG86pV4usN/uc+vq1dzu2CN/Im286+T//TzfFooNo1OLsWSdnzrhJp1v82I/pHV/jdjoTXhmQiM2HLcJdwp04X3VCwDZLhE3T5NKlS0xPT3Pw4EEGBwc35eevdV33OivPla9ncXGRubk5Hn744RvOqUVkXSqV5L+JQpJbmZ6Ic+pO3fy7VeS2SyR8t+sFDAO+8AUXlYqD/ftNHA6wLJPFRYu//muFD37QIJX67ue0U5HwrQomux1bhLuAlcVXIu18MzqRjt6MM+F6vc7x48cxTZMjR47c8qzzXktHd+u6VnKn59SrFZRdf04tCtRuV1R2u5tvN2+qtosI3+31ZbMOZmYcpFImYGFZYBgasZjO5csqly87SKW+u6ZOFWZtl0j4ZtgivIWs1fmqE+noTp8JLy0tcfr0afr6+ti/f/9tC4BWE+FO3Cy6+ab9buBOjE9EX/TNjE9WCvXKc+rVUt/dXN27Hd5rW1GY5fFYOBxNyuU6Hk87EjYMjVbLhcNhoihO4LuC2Yk1tlotW4Rt1o5pmui6vibnq05Fwp0QYdM0uXDhAnNzc9x333309/ff8fevtpHYytarzaRb1wWdj+ScTic+nw+fz7fq11x/Ti1S39VqlVwuJ//dNE3efvvtVUVa/PtWDOiwI+FraTab1Go1dF1nz54aX/+6hdvtRlHqgMrsbJidO1scOHBtoaGdjrZF+K6zsvhKfEjWYjvZDYVZrVaLN954A4CjR4/esoK30z/fZvtzq3NqgWVZvPTSSxw4cECOtxS/xDm1pmlyQMetTE86fU59/WPpVu6GCBuGQalUYm5uThrR3H9/hampGBcuKBiGhscTYWgIfuqnggQC10rOvTzAYWZmhlqtZovw3eT61qO1CDB0x5lwqVSiWCwyMjLCvn371vwButcKs7r5Jt3NiM9GMBi85SZvtXPqlSMvxYAOcU59q57qO30/b5dIuNOFWdcfodXrdWZmZpiZmaHVasmZ4N/7vVUeeMBDq9XLwICf/fs19u/vueG4qhPp6O1k1gHtjYuiKPy7f/fveOmll2wRvlusLL7aiO/zVp0JG4bB+fPnmZ+fx+/3c+DAgXX9/HtNhKG709HdzJ08b3c6oONm/dQrz6kbjQbADQVlN4uuFUXZNiLcqfUJ33pxbGBZFuPjbr7whRlOn55GVZcYGamwa5fO8PAwLpfFAw+4GB0NoygWLlfwpkcUnSrM2k7paLER+ehHP8qBAwdsEd5sLMtC0zTy+TzRaHTdAgxbdyZcqVQ4ceIETqeTvXv3srCwsCk/v5tvaO9GtsPmoBPvCUVRbntOLTbJ14+8FOfU4t/FgA4x/evcuXM3ja634pz6Zo9po2swTRNN0yiVSpTLZdlGeeGCiz/8wxbz8xdwOJZpNJqMj1tYVoojR4ZQFIWBgQGi0SiWZREMBm8qtveaY9ZKfvAHfxCwz4Q3FVF8lc/nOXXqFE899dSGi4/u9pnw/Pw8Z86cYWRkhD179pBOpzfN7GMr5xxvFlt9I97O3M3Xc+W58q3WI6LBUqnEhQsX8Hg8aJp2TVQtsl1er3dVS1Gv13tXBnRs9Pr1ep3l5WWKxaJ8jODkL/8ySD6vMTLipdWKAyblspvLl3vRNB+joxEGBwdvK46diIRbrda2Skdfjy3Cm8D1xVcigt3oB6JTZh13IuS6rnPu3DnS6TQPPvggyWSyI2u41/qEbTZGN72uKwd0OJ1OnE4nY2NjN3zdynPqlZF1pVK54Zz6dpO0NjKgY6MCp+s6mUyGxcVFstkshUIBp9OJoiSZmzNJpcIEAh40LY1hOBgd3UE2uxNdb9HXd3tpsSxrw2u0LGvbzhOenp62BzhsBjcrvlIUZcMRLHQmHX0n1yiXy5w4cQKXy8UTTzxxzXlLJ0TUtq20uR3d/pzdalN9p+fUq/VTp9NpNE27ZkDHSoG+WXR9s/782238RZEVcMN8a9M0yefzLCwssLS0RKVSQVVVfD4f4XCUUKgHRfESCoHPF0FV/VhWgmLRTSjkRFHaZ7Wqqq4qsuKe2Il09HYSYfG6fPrTn2Z2dtYW4U6yWvFVJ9LInbrO7dLBc3NznDt3jtHRUXbt2nXDB6ibI+Fuv3HbrJ1uioSvZyNrW+uAjutHXq6s/hbn1NcLc7PZpFwuS9EWQi1EQETpTqdTfo2g1WpRq9WuGcsZCoWuirCP97zHy4svhonFmni9MSzLxfh4jaGhMsPDoOu+2xr3iM/rveaYJd43zzzzDLlczhbhTiB2lCvTzys/oJ2IYDt1ndXEStd1zpw5Qzab5aGHHiKRSKzp+zf68zdKt96su3Vd3U63b6juRg/uygEdt1qHOKdemf6u1+sYhsHs7CyTk5OyhxeQTmViIxAKhfD7/cTjcdlPLaxMg8GgrD7u6ekhEAjQarV4+uk8k5NVxsd1TBOczh56ezV+/Med+HwqmqbJQtTV6FQkvN2qowU/93M/B9hnwhvmTpyvnE4npmlu+IPbqUj4+muUSiWOHz+O1+vliSeeuGVqp1tFGLr3xt2t69oOdOsmplssNVeeU6+c0AOQy+XYu3cvjUaDdDpNNpuVPbWi0tnj8TA3Nyej4ZUFaqK1y+FwEIlEZNReKpVwuzV+8ifh+PEm5bKHSETjve/1sm9fr6wMv5OiLNh4JLzd0tEC8fhtEV4na3G+Ev++0Q9up8+ELctiZmaGCxcusHPnTnbt2nXb9W3GAIiV/7cZ17XZfnT7a9nN6xMBQbPZZH5+nsnJScrlMpqmEQwGCYfDqKpKKpWSf08mk9KZTNM0qtUq5XKZcrksC8ouXbokK6VN00RVVeLxOIcOjeB0OvH7e6hUKng8HsLh8G0/z6I9aSOfexEEbUcRFo/bFuF1sFbnK7HT22q3K3ENsf4zZ86Qz+d55JFHiMfjd/z9G/35m0E3RCXbkW5/3rp1fd0SCQvEvUXXdZl6FkVes7OzlEolWq0Wvb3tSHVgYIDe3l7q9Tp+v1/2PbtcLjRNw+l0EgwGCQaDtFotvF4/Z87UKJezDAzUsawSpmnicrnQdR2Hw0E2myWTycjnRqS9V6v+Ft+3EYTJynYUYYEtwmtEFEvcydQjgRBhMTh9vXSqRaler/Paa68RCAR44okn1lTU0IkpTJ0oUrsZ3Rid2BH6+uj256xbRFiIrmEYqKpKrVaTGbrTp09Tr9cpFotUKhUGBwdJJBJEIhF6enpusAU1DINarUa9XqdSqdBsNnE6nSwuqvzpn2pMTPhpNlXc7jxHjzr43u/1k0ol8Hg8UshN08Tn82EYxg3GJ/V6/Zqxl+I+8MYbb9xy5OWt+qk7JcIvv/wyv/Vbv8V3vvMdFhYW+OIXvyjNNKBtWPQv/sW/4C/+4i/IZrPs3LmTn//5n+dnfuZnNvRzwRbhO8ayLOkWc7Piq1uxMh29ETYqgJZlUS6XKRaL7Nmzh507d675RtKJdPR6/u9OrtvtN26bdw9bKcIiBSuc+Or1uixM0jQNj8fD4uIiuVwOv98vW6UGBgYYGRmhr6+PgYEBoB35ik14q9WiUChQqVSo1+tXj9zc/PEfJ7h0qUUqVUNR6mQyTb72tR52746SSrVkv6+u67IlSVGUW27uLcsin89z+vRp9uzZc01RmRjQ0Wg00HX9muptIcyZTIaLFy/Kc/CNBDcA1WqVBx54gE984hN87GMfu+H/f/EXf5FvfOMb/Mmf/Amjo6P8zd/8DT/7sz/LwMAA3//937+hn22L8B0gdpZCfNZqPbkyEt4IGxHAZrPJ6dOnKZfLpFKpm5oM3OkaurEwqxuikpvRrevaLnTz87cVaxOtQ+KstlAo0Gq1CAaDNBoNmR5uNpvEYjHi8TjVapWBgQHGxsbo6+tjcHDwhh5mXdel8ApvA5/Px/HjHsbHW/T3l3G7DRTFxcBAhIUFP9/4Bhw6lCccDuF0OolGo3c8rUr8DEVR6OnpWfXrRD/19VH1mTNn+K//9b+ytLQEQDwep7+/n6GhIQYHB/n4xz++JnF87rnneO6551b9/9dee42Pf/zjPP300wB86lOf4r/9t//GW2+9tW4RrtfrfOUrX7FF+FasLL5aS/r5esT3bNXwhXw+z4kTJwiHwwwODm5oDd0qwtD9Kcxuo5ufr25eG9y9SLjVakl3LkB6OGcyGTKZDJVKBUVRaDab+P1+EokEmqbR29tLKpUikUigqip79uyhr69PVi5fP8fXsiwqFQcvvhjmm990sLCg4vcHABelksrQUAuHQ6Nez9NqVVHVOHNzDhyOdhuTuN5aKp3vxDd6tX7qffv28dM//dNcvnyZRx99lIsXLzI/P8/c3Byzs7PEYrE7f5LvgKNHj/KlL32JT3ziEwwMDPDSSy9x8eJF/st/+S/rup5hGPz+7/8+v/Irv2KL8GpYlkWtVuPixYtyZN9WVzavNRK2LIuJiQnGx8fZs2cPO3bs4NKlS9KJZ71r2EwRXu+1uzkd3a3r2g50ayS82a+paZqUy2Wq1SqqqhIKhTBNk1wuRyaTIZ1Ok8vlcLlcxGIx3G430WhUmnb4fD5isRgPPPAA0WhUFl4JE4+VItwWYJ3f+R2L11+vkc3q5PN+LKsHy6rQbC5Tr2cYGlpAUTSCwSEqFdi928LrVSmXy/j9/jW3Gl2/EVgPjUYDr9fLjh072Llz54audSt+93d/l0996lMMDQ3JlPsf/uEf8uSTT67rerlcjt/5nd/hL/7iL2wRvhnCeKPVajE9Pc2+ffs2fDMQvcIbvcadfvibzSYnT56kWq3y6KOPEolEgK2PZDdzgIPNu4du37hsRiQs5vQKN6v5+XkURbmmZ7dQKFAsFnE4HNLBKhqN0tfXRzwep1QqUSgUWF5eJpvNAsgpSKIieWURk5gJ/OKLVb7zHZNw2MHcXIxo1MThKJBOXwJmWVpyYxgVBgc9VCpuLKvCk0+GCQYDGIYh257WQqfGGN5pCnwj/O7v/i5vvPEGX/rSl9ixYwcvv/wy//gf/2MGBgb40Ic+tObrVatVarUazz77rC3CK7m++Eq8qTZa1QybZ7RxM3K5HCdOnCAajXL06FG5C4bOVDeLQoz1pubvpXS0vTnYGN36/HVKhEW7YLPZlK1BlmUxPz9PoVDA5/PhdruJRCJYlkU0GqVWqxEKhaSgRqNRUqkU0E5Xi9GLmUyGbDZLPB6X0bGu67hcLnlvEwHH+LhKs9mgXi/TajXweBq0WiUcjiVCoQb1ukmlAqWSj2SywDPP+Hj6aT+qqhKLxW5pv7ka22WMYb1e5zOf+Qxf/OIX+Z7v+R4A7r//fo4fP85v//Zvr0uExcZlfn7eFmHBrYqvtsvwBcuyGB8fZ2Jign379jE8PHzDm3wzq5vvlHupMMtmfXTjhup61vueE5N/NE2T9xzDMCiVSgSDQfL5PDMzMzL6TSQShEIh6vU6TqeTWCxGMBiU7TtutxvLslhaWmJubo5CoUC1WsWyLKrVKsFgkFgsds1kJoGu6xSLRer1GvV6DmhhGAM4nQZQwzBAUWpEIhUGB2N86lNB9uyJMTSUIhqNyo3AeiLaTqSj74ZvtMiKXr/WjQzmCYVCPPvss5w8edIWYeH7rOv6qsVXnbghbHYk3Gg0OHnyJPV6nccee4xwOLzqNTqR9l1vJNApH+3rsc+E35106+ZqPe9/Xdep1WpUKhWWl5dRVRXDMK4JAAzDIJfLEQqFZE9/MpmUlcelUolEIiH7cpvNJi6Xi+XlZRYWFqjValSrVZrNJqFQiP7+foLBIF6vV1pJCjEpFossLCyQTqfp6ani8QRotVQURUfTmhhGAzAJBgOYZi/PPbeL97zHjc/nIxQKYVkWHo9nzWnojTyH1yMsKzd6nUqlwuXLl+XfJyYmOH78OPF4nJGREZ566il+6Zd+CZ/Px44dO/jmN7/JH//xH/Of//N/XtPPERqTSqX4jd/4DTRNu7dF+Hrnq+sFWDhhdUskvJrQZDIZTp48SU9PDw899NAtPxSdFOH1ci+lo23WR7e/lmtdn6ZpFAoFFhcXWVgocfx4lXJZJRCwGBkx6O93UywGyGQsHI4oY2MG4bAubSVFi46Y0NZoNKhWq0A7GhRtSqZp4vV60XWdpaUlaaTRtpX0y8+vpmksLy+Tz+fRNI2BgQZPPAHf+lYQpzNHLmcCQcJhDw6Hk4ceCvM93xPE4WjJiFpV1RtGIK6FThVmdSISfvvtt3nmmWfk33/xF38RgI9//ON87nOf40//9E/55V/+ZX78x3+cXC7Hjh07+I3f+I01m3WsfLyiNeueFWFxFnK71qNOFFR16jrXC7lpmly+fJmpqSkOHDjA4ODgbT8QG90MbNSCczP7hLvxxt2tkdx2oVufv7VEca1Wi1wux+LiIsePz/Dnf77MwoKJ2+3C7e4lHvfQ2xsikxmkXnegqhEikTIf/ajGM894Zeq62WxSqVTw+/2yQtfr9VKv12k2m+i6TqlUwuVykUgkqNfrJBIJ3G63zPQpioJlWWiaRrlclu5YhmHwzDMuHnnExdRUjCtXnBSLOn5/hf37LZ57LoTb3ZLRe61WIx6Pb+j16VRhVicsK59++ulb3j/6+vr4oz/6ow39jHQ6zYULF3j/+99/zb/fcyK8svgKbm+80SkR7nQ6WtM0Tpw4QavV4vHHH79hgspmrWOj5+S3EkvhAOT3+/F6vbKn8U7pRhGG7l0XdLfIdTM3E+HVIrtSqcT09DRXrlzh+efTzM4aDA+HcLmgry/JmTMRzp0zeO97FcbGAng8YWZnY3z9600OHcrT01Mnm81K4wqRBhajArPZrBzQ4Pf7GRsbo16vMz09LauthWjDdw0wRKZPFFcNDAygqiqPPdb+mvn5JcrlMqFQiFbLiWE4ZbvTSlFfL50qzNouvtEvvfQSn/70p5mbm6PRaMgswj0lwtcXX93JBI9ORsKdKsxaXl7m5MmTpFIpDhw4sKYzma1OR6/2fC8uLnLq1CnZxyjs6lZa1Yk/r/xdPPZujYRt1k+3bhDGxx28/HKATCaFpjk5dKhJo1GQvsnBYFCuvVqtsrCwQC6Xo1Bokcm46elxEI8P43C48PlieL0pWi0HilLHNJtYVp3hYS8nT2q8+WaZp55q0Gq1cDgcUgTFmXA+n5cVz8LOslqtUiwW0XWdRqOBz+fD5/PJDXi9XqdarcoUdSKRIJNx8/rrPhqNAvH4AsGgSa1WIxAIEAqFmJqawuPxUCwWSaVSDA0NbbhjpFOFWdtFhE3TZGFhgX/6T/8phmF8N62/1Qu7G9xJ8dVqdOrm3gkxF2mk48ePc99990n/17WuY6tFeOXzYJomFy9eZHZ2lkOHDhGNRlEURd5AxFmY+L1YLErrOmFa7/F45K56YmLiBrHe6M1iI3SrkHQ7d3NDZVlw9qyTkyedVKswNmbxyCMGV1vrr+Fv/1bhi19UWFjwUa83OX68xvDwIj/4gxX6+oLouo7b7ZbCUKvV8Hq9RCIRAoEmfn8Mt9vA7+8hFOonGEzSanlQlCoOR9txqtmsoaom4KDZdGGadSKRCK1WC5fLhdPplL8bhkG1WmVubk4WY4nPxEonq5UZLNM0CYVCVwXQyZe/HOBrX2sLeqVyhWhU59FHc6RSi3i9XuLxOJZlcfjwYTnCcLXCz7U97xsvzLob1dGdYmxsjPe85z289tprsniu2Wy++0X4dsVXt6Nb0tH1ep0LFy5gGAZPPPEEwWBw3evYahEWNBoNjh8/TqvV4siRI/h8PjkVRVVVVFW9weNWII4VhDjPzs7SaDSo1Wqy2ESk7lwu1w3CfP2fN7ojt9m+vPCCwvPPqzQa4HI5+Na3LL79bYVPfrLJSlvjyckmn/98E11vkkzOUa2WCIVSvPXWLNGom+/7Pge6rl8zFlS8v6LRKLt2WRw8GGZiIk406sXj8V/tuS9iWQVUVafZ9ON2+2g2HaiqxY4dLnn/Wl72U6162LnTTyxmce7cOc6ePUsmk6Fer7N7925ZOe1wOKjVajecu4rPX71ep1Ao8OabBs8/H8bpLBEMXsQ0TzM9XWN+PsuHPhRk375estksQ0NDGIYhe5U7sbnspsKsu8Gjjz7KW2+9dcO/v6tF+E6Lr25FN6Sjl5aWOH36NLFYTFZBrpdOnU1vtDArn8/LFoBHHnkEVVXXtC6Hoz2v1OVyEQqFZJHJwYMH5deIDZimaddE1OVymeXlZRlRA3JKy81S3qK3ciN9od1It65LcDeyCHNzDr76VZVgEHbtsgALXYdz55y8/LLKD/1Qu3akUqnwt387xZUrBYaGNHK5ErruBPI4HEW+850Y738/jIyMXCMKgUBA1p+Ew2F+5Ee8/I//EeT8+RKhkE6r5SEWKxEMupia0onHG7hcYcplB/v35xkeLlIqOfnCF+KcOeOh0YBAAPbvn2Rk5CT5/NI1A2Kq1aqcnOT3+6WlJbQFK5/Pk8vlqFaraJrGt78doNnUcLleIZs9TqtVBjRKJYV8fhhFUQgEAqRSKRwOB8lk8pYDF9ZCNxVm3Q1ERlYg3t/vShFea/HVrdjKSNg0TS5cuMDc3Bz33Xcffr+f73znOxtax2a2St0phmHw9ttvSz/r69vC1rumm/2b2+3G7Xavmj4T86GFQAuxFnNPhaGCw+G4JoJe7XzaTj93hru1QRgfd1IqOTh4sP3ZNAxoNCyCwRZvv23xAz9gYRj6VROcC1QqTZaWcrRaDcCPabpR1X4cjvZYwJ6eHnnPEG1E5XIZwzBwOBzE4zU+/nGdb3+7xfS0Rn+/l/37LZJJF1//usrZsw68XidPPlngySfbZ8yf/ayDb33LQFUz+HxearUWX/1qmj17Suzb17akTCaTOBwOfD4fiUSCZrMphRjaRY/pdJp0Oi2HQBiGwZUraSoVHYdj8apToEq7zCJAo2HRbDY5ePAgfX19uN1uhoeH190XfD33WmGWKIK7nnedCJumSTqdptVqkUgkNrzT2qo+4VqtxvHjx4H2BA+/30+pVNpyAd3INXRd5+LFi1iWxXve856OTzpZz5pE8Zc4t7sZopr0+jPqSqUi/75aIZl4rqrV6oaMDe5F7saGRvwI04SpKQeXLjUpl/M0Ghq7dwcolxWWlmb5zne+Q6UyR7lcR9MMUql+wIGqBjGMfg4fbspUcKvVolqtymORSqWCw+HA7/dfLZTS+YEfiOF0OtH1FqrqwTR1Pv5xFcty4vUaVCot0uk0J05ofPWrAYpFBUVxoyhBTHMer7fB5ctuBgcN3G43Q0ND7Nu3j8HBQaLRKEtLS/h8vmuMOaamppibm2NxcZGZmRKtVhyfL065vEQgUKbVqqFpRdxuP8HgLnbs6OHAgaR03guFQnJucScwTfMaS931sJ0i4dV419wRVhZfpdNpms2m9FPdCJ1yeFpLRL2wsMCZM2cYHByUE5xg61PJG7lGtVrl2LFj8rF0WoA3szp6tXFqK1mtkKxarWKaJt/+9rdlUctqZ9PdUEjWLdytSHhsTCcYNHjrLYXZWRPDmMeylimXFebmLD7/+RbR6HGmpqZwOssMD1uk0wMUi8P4/RFKpRh79vh59tl2i49lWdTrGm+8ofHNb1ZYXs4yPOzgwAGNwcGG7OVvf11dRoOiLa9SqbC4OM/i4iLZbJY33kiwtOSipwe8XheGsUS5PMvyco1Ewkso1MvRo22L2nA4TCgUwjDawtxseviLv1B5/fUi2ex5AoETeL1LvPWWxfS0n1bLiWnWKZVyVKvtvmXL6kHX93Lo0EHe/36TsbExvF4vbrd7w33B19OpwixbhLuA64uvREN6J7ib6WjDMDh//jwLCwscPnyY3t7eG9Zyt8chrnaNtawjnU5z8uRJBgcHGRkZ4ZVXXtnQz19tTVvJaoVk1WqVb3/72zz11FPXFJKJ3+1CstXZ7Ne0PSpwgd27m7z0Uop6vYWiTKLrdRIJN/398MorFh/8oMKuXbuYnp7mfe9z4PHs5coVL5YV5bnnDvP44w6SSeVqZXOT/+//q/E//2eBWs3AslpcuBDh4sUkP/mTbbcrVVVxu90YhiFFs16vU6/X5dAFTdNwOBwsLFg4nVUsK4jD4QdqBINxqtUwLleCw4crMuMnru3xeFhcbPJf/+s+ZmYUKpUFarUchuHA73eSyehEoy56e03q9QLNphfLUq8Oi/Dw8MMjfPjDJrt2DTA8PIxlWfh8vo5GwdC5wiyfz9ehFd19tn11tGVZ1/T+irPfjRhrX8/dKsyqVCqcOHECp9PJE088cdM3VicE9G4MkhBYlsWlS5eYmpri0KFD9Pf3o2ma/L9O32S7udDo+kKym2FZ1jXn07cqJLtV7/RGC8m6gc14LdtzcyvUajV0XZfDDpJJFz09JZrNHLpewO+HVMpPMpkgnU4AC7hcTZLJJP39/ezevZvl5WXc7haPPaZeUxg1Pd3i61/34vUq9Pe7MIweLMtiZsbFa695ec97AjJo0HWder1+zSSlVqtFvV6n1Wpd/Yw0CId9tFoRmk0nTmecet2LYcwwOFhneDghz35VVZUtUS+8EODSJR+7dlUol7MUCiWWl4vMzVUJBMKkUn0AKIoPVS2RzVZ45pkgTz01zL59u/D5fAwPD8sNX6cFWLwenSjM6kS71FZw6tQpvvCFL2xfEb5V8VWnznHFdTfbtnJ+fp4zZ84wMjLCnj17Vn1jrrSM3EgB092IppvNJidOnEDTNI4cOSIrujc6AOJWa+pGEV7LY1xZ/HW7QrLrI+r1FJJtxkaok3R6baVSiampKXlkNTU1haqqNJseFEUlFnMRiXgwTQO/P4bD0UcymeT++00sax6AZDIJtDfNosJ45THFhQuQzzvp79cxjDpOp4qqBhgc7OXy5TqtlkKhsEg+n8ftdhMMBkkkEtJgo1QqyY1WJBLh8OEEExMDeL1xJifn0bQMDkeaYLDEBz7Q7pbI5/OoqorX65VtRK+95sPlamKaaRqNZZrNMu2sbR+qOoDfP4xlNbCsFm53kErFZHg4ykMPDeDxeOSsYlHXsBnvk04VZm3GBmGzEJ+58+fP88lPfpJAILC9RVjcbK4XrU4JJ3RO0G92HV3XOXfuHOl0mgcffFB+wG91DegOEb7VNYrFIseOHSMSiXDkyJFripE6MQBitTV1K518rCsLyVbjTgvJxM312LFjq4r1VhWSdfr9oes6s7Oz0jLw9OnTVKtVQqEQoVCIfftGuHAhSSBQxe83cDr7mJ9XOXo0Rypl0GrFURRFbnYMw7gm4yaqoWu1IppWQ9dreL0xnE4Vy4JGo47T2SCTKbG0NIff75ff3+4VtqQdZDwev2ruESCZDHDlio9Tpy4Tix2jVstTq9UYG/PwwAMJlpaW8Pv9siZh9+7dV4/jwDAK1OtLVzdkPlwuFejBskK4XG5crl4sy6BYLBCJuHn00R56e9sbikAgICc8bdZ7YLuMMuwkwurzd3/3d9mxYwd/9md/tn1FWIjvzW6+nRThThZmrewRK5fLnDhxApfLxRNPPHFHu7mVaa/1vnk3+0x4ZmaG8+fPs3v3bkZHR1cVx81KN9rceSHZ9PQ02WyW3t7eGxzJNE27ZSHZyr93SyGZKM4UR1Ir//3YsWk+//lTXLli0GgskEw2OHiwB6cTent7efTR3XzlK17OnKmSTvvw+w127rzMwYMai4tueZzQbDaBdp1DJBKRbm1iEMLISJVk0sfSUnsykdNZo16vkE4n+chHnOh6VRpp1Go1EomEtJsULUAiTWtZFj09Fv/7/17jD/7gEi++eB7LyjE8XGV01M3Jk2WSySSjo6OYpkkgEJA1CQ8/XOTEiSqNhoZlGbhcLsrlCMlkPz5finzeQyzmplrVyGTgiSe8HDqkSjtLRVFwuVyb+pnq5CjD7YJ4PqempnjooYeAbVyYdasUSadFuJORsGVZzM3Nce7cOUZHR9m1a9cdC+pGhyfA5vUJG4bB2bNnWV5e5uGHH161oX+jU5hutaZupFvXJcTV7Xbf1P70ekey9RaSicra9Wwa1zq8Q5ylimyBaH+5cqXC7//+LBcvOvB4WhSLBpnMDvz+EA8/XGF0dJT9+4d58EEvk5MwNVWgVBpnYKAttgsLCrFYjFKpRDQapb+/n2q1SjgcxuVyYRjGCvEv8vDDU/zN3xicOaPi88XRdYMDB+CDHzRoNpvEYjE0TcPpdJJMJmVUnE6nZW+7rusoioKmaZw9+yqFwpdIJmfJ5/MEAr0kEvdLm8pms4miKESjUfl8PP10lhdfbDIzE8KyWphmDLc7ycc+pjI66uCv/1ojn2+gqi0+9KEqP/ETCtlsgUajQSQSQdd1ent7NzUT0qlIeDulo8V7enBwkMnJSWAbi/Ct6NbCLMMwOHnyJNlsloceeohEIrHma8DGBGwz0tGip9nhcHD06NE7+lDYkXB3s9WFZHfyWuq6TrPZlJ/RYrGIy+VCURQ57B7gtdcsFhZ8HDrUT6NRxLLimGacqSmDJ55wEo/H0TQNRVEYHLQolabI5aaYmGifr6ZSKfkc+Hw+DMOQrlSmaaJpGrOzsywtLTExMcHgoIu/9/ciXLmi4/Uq9PSoPPhgAdNs4nS2DV3C4TD9/f14PB7y+bz8FQqF0HWdWq1GNBplamqKl156iXQ6LetfxIzgWCxGf3+/3EittMuMxXR+7ueWuXJllOPHVfx+D08+GeDxx3VyuQx79xZIp5tUKktEIi4WFvzUajWSyaR8rTwez6ZuIjtVmLWd0tEiQ/Pxj3+cT3/603z2s599d4pwN0bCzWaTdDpNNBrliSeeWFcKpRPnqZ0WYTHRqb+/n/3799/2Q7WZZ8LdKsLduq6NshmFZEKUReYok8nc1JHMMAzK5bL8GcIBSlTzrvx8XboUJJnsQVHm8HhCxGKjhMMDnDpVo9XKo+sNWZVcq9VwOp2kUin5eQ2FQmiaRigUIhgMEo1GKZfLKIpCqVRiaWmJ8fFxeeauqioDAyajoyqBQJ5AICAHMMTjcdlOKeb/FgoFlpaWUBSFRqPB4uIihmGQyWQ4d+4cpVKJZrNJtVqVAxV8Ph8HDhwgkUjg8Xjo6+u7wSM6GDT4/u83eO45F6FQgGazydzcHOfPn5c/t9lsMjY2RrlcJhqN4vf70TSNRCKxYSON29GJwqztJsLQftxHjx7l13/91/nFX/zF7S3Cq914O12YtfIsd6202xNmmJmZwe/38573vGfD1owbnQcsCkE2sg7TNLl8+TITExMcPHiQwcHBO/5euLdE+F5mvYVkpVIJ0zQZHx+nWq1Sr9dRFIVwOCyj0Xq9PV1IFEr5/X5M0yQSiVxzYw6FvHi9/fj9DXS9icvlQ9erNBo5ms0i9Xr7PSmicdFv6/P55LlvOBwmHA6j67rcDDSbTbLZLIVCgWq1SqFQANqZITGjt7+/X6br4bvCY1kWxWJRRtDZbBZFUfB6vSiKwp49e5ienmZmZoZKpUKlUgFg3759PPDAAwwM7OHy5QGOH1fYsyfG4KALlwsZRYuzfdEKVa1WmZ6e5q233iKTyaBpGvV6ncHBQfL5PIqi0N/fj2VZhMPhjrgN3o5O9Qlvp3Q0tD8Ty8vLfPjDH+bUqVPbW4RXo1si4VarxenTpykUCgwPD8sG/I2uZyNi04k2J4ArV67QarV47LHH1tSn161npJtFNz/ebtm03KyQrFAoUKlUeM973sPi4iLVahXLsnC73ZimyczMDNVqlVwuR7lclkWalmUxMDBALBaTn9tYLEIm48SymoTDUKtlSadVfL4qg4MamtY+h63X61cNPNpj5np6eujt7aVer8v7gDDFEP4EDodDRvW6ruPz+RgaGmJgYAC32y2j6EqlIvt5W622JWUmk+HSpUtMTk7i8XjkyMCenh7y+TxXrlyhXC7Lmb/JZJIjR45gGEP8yZ/cz8KCG0Xx4fH08tWvOviVX9EIBtubgWw2SzqdxuVyyTnAZ86cYWpqinq9jqZp8qy+0WjQ19dHIpFAVVV6e3tXnV7WSe61UYaA3MT92q/9Gj/6oz/KE088YYvwnVxrPTerYrHI8ePHCQQCPPHEEywuLlKr1Ta8no1WN280Ei2VSvKGcvTo0TWnrDoRza923W4RlZvR7T253YZ4LYvFIul0WqakxZi+HTt2yJSuOLsWohgMBimVSszOzlKpVFCU8yiKxunTISCA09kW5iefbOJ01tG0diS1vLwsC82SySSGYTA1NYVhGCQSCYLBoJzXK1Ls4vPo8XjYu3evrFBOJpOoqkqpVJJiKIS6UChw5coVrly5gqZp6LpOOByW14nH46TTaer1Oh6PR4rk6OgokUiCz39+F+l0H2NjxlVRd3LihJPf+z2d/+V/mSOdTjM+Ps7y8jLQnt6Uy+WuzgtuR9WmacpUdiwW47777rvmrPlu0KlIeDtVR4tCtz/90z/le7/3e9v/tpUL2iy2MhK2LIupqSkuXbrErl272Llzp0xxdarVaaNnwmKda2Vubo6zZ8/idrsZHR1d95nRZoiRLXDbF9M05ZHPynPfer3OxMQEhUJBnlVGIhEpVtDONiWTSfkZFelc0zRlWtXvT/NjP1ZmetqiWFTw+30cOKASi5nUau2vXVhYoFKpkMvlWF5eJhqN0mq18Hq97NixQ44QFalPIai6rjMwMEAoFJIiJgZ2uN1ustks+Xye/v5+ms0my8vLFItFWflcr9dRVVUOABkcHCQUCpHJZAiHwzQaDUzTZGBggMcff5xSaYhcbjcjIyp+v4LT6cbrhVisxiuvzLB370VqtTlmZmbkKNd0Os3k5KRMQwOkUikOHjxIIBBgx44dpFIpms3mXTkLFmy0MEsUBm6ndPTXv/51wuEwTqeTS5cuMTIy8u4U4a1yzGo2m5w+fZpSqXTDlKCtHIl4/ffD2iJR0zSlp/WDDz7I5OTklheH3YxujITtzcGtsSyLWq1Gq9VCURQ511kUJlmWJc82d+3aRSgUwjRNCoUCuq4TiURu6Icul8tkMhmKxSL1ep1Go8HIyBDDw+02pnA4iN/vx+PxEAwGqdVqTExMyKKq3t5e+vr6WFxcRFVVWq0W2WwWr9fLwsKCHHmpKArz8/PyfSeK1ETx1rlz5+SZ9tzcnIyUi8UiCwsLMo3e399PNBplYGBApr/7+voIh8Mkk0ksy2LHjh309/dz6VIMw/Dg87lRlO9uqC1riXz+PNPT8zidOSqVCh6PRxabNRoNXC6XLOravXs30WiUWCzG8PAwwWBQps/vFp0qzNoOkbCI+v/Vv/pXXLp0iVwux2c+8xlcLtf2FuHbFWZ1IgV4p+KZz+c5ceIE4XCYo0eP3nBO0amNQSfPhO8ETdM4duwYlmVx5MgR/H4/U1NTXSfCdjp6eyCiF8MwUFUVy7JkpfFKkwrx+e3p6cE0TUqlEj09PfJztbI/ttVqyejNsiw0TcPn8zE4OMji4qJMYRuGQTAYJBgMyoEb8/PznDt3jsXFRer1Oj6fj56eHlwuF319ffKcdmRkRLpRNZtNXnnlFcrlMs1mU1aArzzrfeutt5idncXpdBKNRmV1dSAQYHl5mUqlQjAYJJlMkkwmicViDA0NXePTnMlkcDgc9Pb2kc0meO01PxAgEGiSyaj097fbXXS9zsLCAn5/AUUpkslkZeQuCsySyaRMoQ8ODrJjxw68Xi8DAwOEw2E5xeluDgbpVJ/wdhBh8Tj/8A//EFVV+Qf/4B/wC7/wC4yNjW1vEV4N0YvViRvf7cTTsiwmJiYYHx+/6ZB6QafS0XfzTDibzXLixAmSySQHDx6Uz+tGBW+zRNhm7dyN5800TfmeFZW6Ih1bqVRkcY0wEBHHNyLCtSyLwcFBvF6vNLlotVp4PB4p6ivT2O05vToLCwsUi0Xi8TjRaFQKsejtbTQaXLlyhYWFBUzTpFqt4vP5ZOQdi8WIx+P09PTQ39+P0+mkXC6ztLTE3NyctL2Mx+Pkcjm5UYjH45TLZXRdlxsKr9eLaZpks1kWFxcpFovXtG85HA75NeLxtA0zhvl//98Ar77apNFwXrW29GGaTup1B36/yeJilkZjlocfXqBUykuDlIGBAdm6lMvlSKfTJBIJ9u7dK9PnwWBQRqR3ezJXJ/qEG43GtirMOnToEAB/+Zd/yfDwMPAuPhOGzuy0biWezWaTkydPUq1WefTRR1cdCi+u06l0dCfOhO90Y7F//375ZunkGux09L2BSDcLowkxX9nj8VAul2k0GiiKQr1eJxaLySjQ4XCQSCQYGxvDMAxpdiOmDQmv5JVGH6IXv9lsSrOOnTt3YhgGuVyOQqGAYRiEw2F6enpkgZWoshZCGI/HGRgYkMVZIgKv1WpkMhlZ8yH6ff1+P6lUimq1SiAQoKenR54ri+h79+7d+Hw+isWiTPmKnmgRnReLRa5cuUKxWJSbk/n5/XzjG0nCYSfxeBSXq0qxOHL1nLhFPl9j794p9uypEA6bWFaPzBKEw2E5qMPtdtPb20t/fz/xeFxufBqNxg02n3frfdGp6ujtEAmvxLIshoeHSafT7SOPrV7QZrBShDtxrZtdJ5fLceLECaLR6B1VCXdLOlqsZbVr6LrOqVOnKBaLq24sulGEuz0dfa8goj/hP1yv1ymVSrhcLkqlEuVymWAwiK7rUhCFl7KIfMVsXafTSSQSodlsUiwWr2kNEme5QtDFOfHycpVz57ycOaPhdLa4//4GkUiBTGYZl8slLSJFa5FlWUQiERRFoa+vj8OHD2NZFsFgEJfLRTAYxOfzYZomi4uLnDhxgjNnzvDOO+/gcrno7++nVqvR399POBzGMAzm5+dRVZW+vj6azSY7d+6UX9doNNixY8c159/hcBhVVWUbVr1el+fdf/u3Bi5Xnp6eflRVx+lU6O01mJlpct99lxkbe5vl5XYldbHYbmdyOp3S5zqdTsv+afH8LS4uEgwGURRFbhLuVjGWQHxWN1qYtd2qo6F9rxofH+ef/JN/wltvvWWL8J1ca+V1LMtifHyciYkJ9u3bx/Dw8B3t5rolHX2rtVQqFd555x18Pt9Nz7VXrmGzRHijRibdyrt9gyDSuWIAgM/nQ1VVWRwlPKeFLaOiKDIiM01TRsHFYpFarUahUJBiJKwvS6USiqKyvOwmk1Hp7fUTCHikmGSzBp/7nI9jx2aoVM5jmg5ef32EJ59ssXdv+z1fKBSuMdtwuVzEYjF27Nghq6xDoZA8FxaiPTMzw7lz564xuxAiLdLj0WiUSCRCpVLB7XZjGIYsuBKOX6IATYilEMzTp0/z4osvcuXKFarVKqqqcvDgQSqVKm53Ap8vhGm2iEZ78fl6yOWcDAzs5cknfczMzDA3N0cmk5EFViJlXqlUpJmJKMTyer3s2rULQFabrzQhuVvvF9jY57bVagFsq+powS/90i+Rz+f58pe/vL1FeLUXUAx36LQINxoNOSN3PSYV3RwJLywscPr0aXbs2MGePXtu+eHYTBEW/76eD+e7Xei6CTHgQYxErNfrFArtAQA+n0+KXNvtSufKFZibC5LJJMhkGoRCQd7//ijvfW+dYLCdLm02m1cHFARkJCfm4waDQebn4S/+ws3kpB/TdJBKefne71U4cqTB5OQk//N/5nnjjSx+/wSJBHi9YSqVBm+84WPnTvD72323vb29eL1eKTyZTIaenh6q1SqAFCwxWrTRaDA/P0+lUpFOXeKs1zRNUqkUvb29jIyM0Gg0iMfjcvMwODhINBqVGxThyS02LaqqMj09zd/8zd9w/Phx6Rw2MjJCs9lkcLDB5ct+Wi2dYLAXtztKrQZOp8WOHe3NiaIo8vxapNDn5787/1hUcg8MDNBoNAgEAliWJVPeotJcFIXdyuO7UwMdxL1wI5GwaLfabpEwwCuvvMLLL7/MgQMHtrcI34pOtwRlMhlOnjxJT08PDz/88JrfjN0UCa+8hmmaXLhwgbm5OR544AFSqdQdff9GH8u9UpjVretaL5ZlSZ/lUqmEZVlSyGq1mqxWFg5Wphni+efhxIkWExMhqlUH4bDKvn1B/uzP3IyPq3zyky0sS6dYLMrh9ivdqZxOJ5lMnf/n/4GZGRfDwyZ+v5flZTd/8icmy8vnUdXzfPvbXhyOJSyriMMRQdMyeL01CoUdLC76ec97fKRSKYaGhshkMqTTaebm5nC73dKha//+/TgcDkqlEr29vTgcDjmQYmpqSkbB4XCYeDzOfffdR19fnzQMEVaXInpPJpMUi0Wazab0lrYsSz53iqJw7NgxJiYmqFQqFAoFPB5Pu3VFVXn22RFqtRiLi176+weoVl2Uyw4eeaTEyMgC6XS7ElqYjIiMQ7FYlP3Kos9YWH6Ojo7idDqJxWKkUikURZGbhpUe35qm3Xa05c2mZ93J+XIn0tEiEt5uIiwGj0gb1K1e0GbRSRFuNpscO3aMAwcOMDg4uK4bayenMW1UwMQ1Go0Gx48fp9VqceTIkTu2qrPPhNdON6/tThBnuGJurrg5ezwearUa6XRangP39vYSCoUwDIMvf1nn3DkHgUAYlyvO2FiFXM4glzN55JE6J074OXnS4PDhdtTb19dHPp+nWq3icrlk5fDFi15mZ+HAAS9OZxNVtRgdtTh1qs7f/u08R46Ur44ULOL1JnA6wTSdRKO7aTaHiUZb9PU5qdfrvPHGG1QqFVlYpSgKly5dkj7Qe/fuZffu3TgcDk6frvK1r6U5f76Irtdwuz1y5m9/f79sY4pEIvh8PtnHLM5hRY/xwsICpVKFCxdcXLiQp1ar8tBDScbGFrhw4QK5XE6uaXR0lKGhIcbGxjh8eCcDAwavvhphcjKAz2fxfd9X5ejRKdLpRQqFArVaA123iMWCcgDElSstTp+2yGYd9PTE+OAHY7zvfV76+vqIRqPU63UCgYAUTNGytdo9QGQ+rh/EUa1WyWazMoIXm4FbRdTCehQ2tkkVG53tlo42DIP777+fkydP8uyzz9oifCs0TePcuXNy6sVGGtk7lY7uVCRcKpU4duwY8XicRx55ZE2RfTeKMGx/obvb3O75EmeFQnzb565ZSqWSLGry+/00m015Q1cURU4bqlTg8uUm/f0GMzMBXC4TRdHp6XFRKilUq+0IbnLSyYMPKrKFSAhBsViUBUuZjIpluXA6mzgcThwO5eoNv0w+70JRHPh8F6nVHPT19RIMxvB6EzSbvYTDTjStxquvtoBpTDPNwsI8mqbh9/tlWnZsbExaRfb19fHFLzr5wz+sMz9fpV6HVsvLrl29vO99Kq2Wxs6dOxkZGcHn810T/YVCIVqtFsvLy1fnES9cTTk7OXeuF1134XKF+c53HITDZ9mxY1pGs0NDQzzwwAPs3LmTQ4cOEQqFePhh+J7vCaEoGk6nRTa7zMTEMrOzGZ5/vsKxYyq6HmDHDpXHH18ik0nzla/40DQDRfFSr9/H5z/vQtMUfvZng7L9ai3+0CKNvpHRlpqm0Ww2AWQh2OnTp+9otOXNaDQacuO3EV5++WV+67d+i+985zssLCzwxS9+kR/8wR+85mvOnTvHP//n/5xvfvOb6LrOwYMH+fM//3NGRkbW/PPcbjef+cxnqFQq7WEhG1p9F7NREU6n05w6dYpYLEatVtuwk0y32FZaloVhGJw/f559+/YxMjKy5t3oRp/beykS3q7paHFmKKbyCOcp0XsrBsqLKl4xACESiRCJRHA4HDQaJqap4Pc7URSNVquJZTUwzfrVXlc3hmFiWWVKpYosUhLVzu1rtMceejweDMNHqxXB7/fhdCo0GhWy2SVGR3UCgQBPP70Dj2eEuTkn9boT02yfpTocHv7qr/pYXPwWpdLL9PeX6OvLYZomu3btIpvNyoIlUSU9MeHic58zaLXKDA5WqdUc5HJ+Zmb6aTajjIwsMjo6Sl9fH4ZhUCqV8Hq9MhosFAosLCxw5coV5ufnuXSpyenTcTyePMmkH58vRrE4ycREjUAgwtCQgs/nY//+/ezcuZPh4WFSqRQ+nw+Px4PP5wNA0xrkcjnq9QZ//Mcmb7+t4/d7cLsVzp0zuXTJiaIE0bQmsZiGxxPB77eo1by8+OIufu7ngkQiLcLhcMfbkhyOOxtt2Wg0KBQKnD9/nlAodEejLVf+LvqoNU3rSCq6Wq3ywAMP8IlPfIKPfexjN/z/+Pg473vf+/jpn/5pfvVXf5VwOMyZM2fWHYEbhsHhw4cZGhoC3qV9wrB+oTBNk4sXLzIzM8N9991HJBLhW9/6VkfW04neuI1Ewrquc+bMGXRdZ9++fezYsWPd69iMSHijz4vN+rhZMZww0mg02jd9cabZHoigEAgESCQShEIhIpGI/LyJM8G2IYdOb6/ClSsBUqk6U1MKzWaIRqOOx6PQannxeusMDeUxzXaFsxD2YrGIpmnk83k0TeOBB4KMjlpcuuRgbExBUQwmJ0v4/S76+2N8/eug6wnuvz/C2Ngy8/MaTmeR8fEUHk8Cr/cdTPPLVCoXOX26QLHo48EHB6X3cDQaRdd19uzZw+DgIH/2Zw4KBZ2dO/1UKiEUZQCfr5+lJS9zc3He+952sZhlWeTzebLZLOFwmEAgQLlcZnFxkYWFBTKZDKVSiakplUajRSTixO3uRVGCBAJ+wuEdFItODh5cpL+/n0OHDjEyMsLOnTvx+XwEAgHZpSDOkiuVCm+9lefEiRLhsEE47MbrDdFo1Jid9VOrlfF4arjdPfj9B3G7/fh8YTKZECdOwPd8j/eu9wULROV8q9VCVdWb3oPEaMvrU9/iTP2LX/wiv//7v4/b7UbXdZ599lmGh4cZHh5mZGSED3zgA+zcufOO1/Tcc8/x3HPPrfr///Jf/ks++tGP8h//43+U/yaqy9fD7OwsH/jAB3jzzTfbgz7WfaUu4FY33vWIcK1W48SJE5imyZEjR67pRdwoK52qNio26xHAarXKsWPHZPHIRkaVdcowZK3/dzu6MRIWdOvaRKQLyOpXcY5ZqVQolUrSIENEOKKqNxgMkkqliMVi0qWqVqvh8XioVqtomsbjj6vMzemUSn4SCQfT0w4Mw8vAgBtVdfKBDzQYG2v/bF3XqVQq1Ot1uTbLsvD5fOh6hR/+YRevvBJjYsJBs+kgFrOwLDd/8zcN6vV2VfaZMypDQyof+lCVy5cVTp8u099vMTX1Jo1GgUAgSKORp1Lxy2hK9PHu3LmTRx999KqJhQOHw4mqBvF4YjidHhTFhaYFME3k2efc3ByTk5MoikKtViOZTKLrOvV6nUwmw+LiIg6HA7c7gGUFCIX6CQZH0PUyTmcIVTWADLt372bfvn309fXJHl5ReS4ol8vMzc1dPfOtYBjOq0MlUoCB0+kgFHJRq/lRVS+h0A5CoVHAwLLiKIoTt9uB1+vZ8k3rrdyybjbaciWPPfYY//yf/3P+6q/+is985jP8yI/8CLOzs8zMzPDaa6/R19e3JhG+FaZp8uUvf5l/9s/+Gc8++yzHjh1j586d/PIv//INKes7RbjBidT8thbhW7FWEV5aWuLUqVP09/ezf/9+uVPs1PzdTrl4rScdLR7b0NAQe/fu5fXXX9+w/7SYerMebNvKrcMwDGlRKNpk6vW6PKNzOp0UCgU5Yq/ZbMr+2bbAhpifj1GteigUnEQiDmkd2Gg0qNfrsrXI5/Px0ENu3O4ap06ZzM66efhhnURCYd8+i1RqkUAgR6HQkFXWhmHIs0NxDh2JRNA0jf37Qzz+uIu5OY1MpsylSwa//uvz1GoXCQab9PSYqGqGt946SalUJBgMU60OU6kkKZeXqdVy1GoZwMThSNLT00M0GpWf95GREXnjHxvTcTh0SqU8DoeGqvpoNpuUywZ79rSjsqmpqasFVyW5ERHPqRjQ4HA4iEaj3H9/iomJUVS1HfnV64s0Gk0aDY1HHw1w6NBuafahqiq1Wk1aeULbGSqbzUpDj0BAxeXy4XR6cTjA4QjhcnlQVQWPJ0Sr5cA0feh6FYfDSz7vobfXwRNPbNxFsBNsdHhDLBZj586dhEIhPvnJT27a5z+dTlOpVPjN3/xNfv3Xf53/8B/+Ay+88AIf+9jHePHFF3nqqafWfM1wOMz73/9+/v2///f823/7b20RFhOC5ufnue++++jv77/hOuLrNpLCWc/0otWuc6fXME2TS5cuMT09zeHDh+nr61vzNVZbQ7cVZtlnwqsj6gAqlQq1Wg1VVQmFQtRqNarVKq1WS94URe9rMBjE4/FIQwtVVTHNOM8/H2J8XEXXNSxL4+23Xfzoj+qI+pRQKCQdsoQt5OioxeCgycJCg0jEy549CoVClsuXL2MYLmkNKTJPgUCAhYUFAPx+P7quE41GCQQCtFpNyuVx3n57gd//fScTExNAjWIxzMLCZfr68qiqxuKiynvf68SynGSzVzDN6lVbyiia1sfDDz/C6ChEo1EURSGVStHf3y+7BnbvLnHwYJ3XXy/idpdQFD+lUotUaoKRkbb39OzsLPV6Hb/fT7lclt7WonUmHA4TiUQIhUIcPBihVEry+uth5ucnaTQW0bQK/f0a+/e30/eiP1gUGwWDQfn+abVashDJMAwOHrSIx0MUiyl8PgvLalGrNalWa3zkI03Onesjl4tTKBi4XD4ikRK/8AseYjHfFrwDb2S7DG8Q98kf+IEf4NOf/jQADz74IK+99hp/8Ad/sC4RrtVqHD9+nLfffpuvfe1r97YIV6tVTpw4AcDRo0dvmv7olAivdXrRra5zJ9doNpvSWESk1gVbLaJ2dXRnyeehVmunZ1e+hYWhhPA8XlhYwOv1yj7W5eVlGo0GtVoNr9crB9b7fD4qlQoAg4ODQPv99LWvKZw/bzI0tIzTqaPrJpcvt3j++RD/+B87AVOmUFVVxeVy4XSqvPiim1deCZLLtfB63ezeDY8+ukSrVSCVSl0zhUicFYqoMB6Po2maLJrKZDJcvHiZF14IsLj4beBNABqNPizLzeysk2QygGVp7NihMDFR4dKlLKoaQFFiNJtB9uw5wHve42V0tI+RkREsy2J0dFQObygUCmSzi7zvfZPoepnTp1vouskjj/g5fLhJOBzh0qU8gJxzHAqF6O/vJxgMSj9pMS7R4/EQiUT4pV+K89d/neX558+QTk8Ri+UYGmrh8eyiXq/LjQi0naxE4Y/o4RXPkd/vJ5lM8ulPx/jsZ5MsLOQxzQaK4uKRRyx++IedTEwcY3p6hEajj74+Dx/5iMpDDwW37Cz4ejrhGy1a5DZzo5tIJKR72UoOHDjAq6++uq5rGobBrl27OHLkCJVK5d4V4YWFBc6cOcPg4CD79u1bdVfWKQvMuxkJFwoFjh8/TiQS4ciRIze0H220wrobRbgbIs5bsRkbhEoFvv51ldOnnWiag2jU5PHHTY4c0Wm1GvKMtVqtcvnyZWq1GtFo9Go6MyCNF5rNJtFoVN70VzpemaZJuVwml3Nw4YJOMlnH4WhgWaCqDgYHTSYnVebmHPT3IwudhHHEt7/t4otfVAiFmoyNOdF1B8eONZiacvCDP+hhYWFBVleL9iYxCKFer8tpSaqqUigU+OY3v8mLL57k7bd1DOM04AE8WFYNCNJseqhUGuzb52XnzmG83gouV4NLl1ooSoiHH97Lxz62n8OHw0SjUYLBIIFAgEgkgq7rsmp3YmKCCxcuEI1afOQjfnw+H+Hwd89ohcuW6IfevXs3o6OjlMtloC3OgUBA2ki2N/gtduwY58knF0inyxQK7ccZjUZxuVzytfH7/YRCIfmzdF2XdpoiUo9EIkSjLXbtWuCVV8pksxWi0QIDAzVUNYXTWeF97ytx4MAgTmeFcDh81/2hb0WnIuHNfkxut5v3vve9XLhw4Zp/v3jx4roLW4eHh/mTP/kT+fdtLcLrKcwS7TmLi4scPnyY3t7eO/oZnRDPzfR9hvaNfmZmhgsXLsibws2eo60WUTsS3jiWBc8/r/L22wp9fRaxmEE+7+T55xUMo8rBg2U5zm96epqZmRmcTifNZlMOJRBnjoFA4Grxky4NJlqtFuVyGTEFqVAwqNVa+P1uPJ4wYAAOFMVFsQia1raXFIKpaRq5XI6XX45gmk4GBly43T5M02BwcJkLF0yy2R7uuy9GIpEgGo3KEYLi95UeywsL8Fu/9Qbf/OYpSiWNXO4SLlcVrzeCpjVp38oCQJixMS8/8iMD7NkzQDyeJRAwWVxcJh5PcfjwPkKhID09PSQSCZk2dzqdpNNpzp07x/Hjx5mZmaFSqVxtjTKkr7LwhBYDGrxeL4FAgOHhYZl1EBuHRCIhfaVN05Svg6ZpFAoFWq0WyWSScDjM4OAgiURCiq0QF8MwqFarlEoleXTgdDrlqMNcbore3gzhcJ1qtYphxOS5tKgeFlmObjgLFnRqjGEn0tGVSoXLly/Lv09MTHD8+HHi8TgjIyP80i/9En//7/99nnzySZ555hleeOEFnn/+eV566aV1/0xRmwHbXIRvxc1EuFKpcPz4cRRF4ejRo7L37lZ02oe6E77Pq20uzp49y/LyMo888gjxeHzN11jLGrpNhO+1M+G5OQfnzjkZGmrh9TYAi95eN42Gk1deqdLbW6RaLTIzM0M6nb56HtqOUJPJJP39/dIMQ0RgYnqRruu43W55Tul0OkkmXfT1eUmnFWIxAAWXy8vyskIyqZBI1Mhmc3LoQa1Wo9nUKRRUAgERQTpoNit4PO1e1kqlLAVY3JQrlQoOh0O6M7VT0E7+zb8p8cors1cj9AZQo9UKEomA1+vH6Ryl1Rqgv9/kx3/cx+HDo3K4gmVZ0q5yZc9tq9WSXQLlcoU33pjhW9+aZGlpEYcjS71eJxwOy82KSAN7vV4GBwfp6elBURR6enqwLItcLieLp5xOJ9lsFsuySCQS1Go1zp8/z9TUFOl0mkajwfDwMAMDA8TjcRKJhBQUkZEQc49XtuqI/tliscjc3BxTU1NomibHPIoqbWE4IqrYVx5HdQMbLcyCzonw22+/zTPPPCP//ou/+IsAfPzjH+dzn/scP/RDP8Qf/MEf8O///b/n53/+59m3bx9//ud/zvve9751/8yVxwL3jAjPzc1x9uxZRkZG2LNnz5p2YZ002uhERH39Wmq1GseOHZObi9s1kW+1iK72/YVCgQsXLqCq6jXN+eLXrVx07qV0tGEYLC8bFIsGiUSNZtPA6VSvWjDWSaeLLC6WMM0iXq8Xv98vb8TJZJJUKoXb7ZbiJ1qKRKQsWpDEcy7Olh99VOOFFwKMj7uIxz1UKiqK0uKxxypUKssyUhQThvx+L/F4gUuXgjidqnweGo22oI2MOGQ1tnhchUJBGj2YpollWXztaxZnzlwmHs9TKi1jGOM4nS5Mcxe1mp9Eoh+PZ4BQqMXTTzc4cGCHNNEol8v4fD7pib68vMzAwAAej0cWUNVqFr/5mxWef36ZdHqZej1NNKpx+LBKKuVlbGyMRCJBIpGQU5VisZgcmqAoCrlcjkwmI52hxAZA/D4zM8P8/Dy1Wk1WTB84cIB4PE48HsflcsnJUiIK1nWdcrlMtVqV7WKWZREKhSgUCiwuLpLP52U1eiqVkpkIUcgWiUSIxWJd9/noRDpaVOVvlKeffvq2n89PfOITfOITn9jwz7oZ73oR1nWdc+fOkU6nefDBB+VklPVca6N0Ilq7fkOwvLzMyZMnZWvVnbyxu/FMWGyShoeHcblcNBqNG+zuRDR3vTiLweWdeI26FdGyA1w10geHA9LpArFYEEWxME2DQqFOT48PrzeHZbXFx+VyISYRCZcekQ4TDkaivUiM9hOpaEAWWR05opBKqbzzjsX8vMboaJVHHnGwc2eNRqMlJxMJG8tAIMAzz8DCQoypKR89PU2qVQ+zswaHDze47z439XoNn89HuVxmcnISVVXJ5XI4HA6Zin711WmazdP4/S5U1YvXGyaV2kk2GyKV8vPAA4cZHY2yb1+FsTEPgUCAmZkZmQZWVZVKpcLS0pLseR4cHGRsbAyAP/ojjb/8yzRO5yyxWAZVtcjno8zO9vGRjyTYvXu3tGsUU4hUVSWZTBIMBsnlcuRyOZaXl6lWqzLSFtW7rVaLWq1GOByWYwXFMIVEon19RVFktA1tAS6VSszNzcnrWlZ79nG1WiWdTsvXSLiWievEYjE5FWllgVc30Yl0tEj7b3fe1SKsaRqvv/46brebJ554Yt1vxk4OX+hUYZZlfXeu8X333cfAwMCarrFREd3o4xA/37IsOcXpwQcflFHQ9Tv3lam5lek54awkxpq9+uqrNxVp8etuzky92eNYC6Zpytaic+cmcTgM/P52xBSNRuntLXLqlMnICIRCXmq1II2GySOPQDIZk61GyWRSCoNocRH2iuJ1EBNdRJ+umPkrhFCMLdyxQ2fvXkumjX0+H4VCS47Hq1arhMNhaS/42GNB/H4Xf/7n80xMZHG5XDz5ZJy/+3f9aNo8CwsLuN3uq+nr5jVpcq/XS71eR9OmaLUauFx+VDVAKnUfbncITfPywQ/u4X/9X4evfrba1rIi4szlctJOcmlp6aoQ7uLtt01isSjvfW+Y++/X+Ku/yuByLaGqTUzTSzTaRzicoFyOUKt9N3Mg2o9WemqvnDYk+oJFyjgejxMIBFhcXKTRaEgTj+HhYQ4dOkQqlZLnwC6X65qorlQqMTExwfLyMqVSSVo1ik2EOMs3TRO32y1nFnu9XlKpFOl0Gp/Ph9/v77ooGLorHX23EZPIRL/+thbh1V5EcTPIZrOMjY2xa9euDRtkdMvwBXETfeedd6hWqzz++ONr9rXe6jNhEYm3Wi1OnDhBvV7n8ccfv8ZF5mbfcysXnXq9zuuvv879999/jVgL28OVwnIzkV7pT9sNBSymaVKv1zl7tsiXvzzD5cvzeDx+hoYMHnvMz/CwxWOP1XC740xO6tRqPvr6Ihw96uTw4QK6/t0z4HA4jGmaFItFma5caaEqxESM1rMsS56DNhoNstksuq4TDocJhUJSYGu1GsViEYBAICCfy2AwKNOrXq+XBx+s43ROUK/7UJQ6/f06DodPCoWwxfR6veRyOdxuN8FgUDp37dtX5p13XJTLOYLBBNHoAQoFg1TKy9NPD+D3++VRxcmTJ3nzzTepVCqUy2UZGRaLRQqFXr7whSaNhk48nuJv/9bN0FCdQqFFMOhD18MEAk4sq4nLlSKbTeF2h+np6aFer1MsFtF1XVpRNptNms2mTEO3Wi2CwaCsbu7p6ZGi3dfXh8fjIRqNMjg4yK5du/D5fPJIwO12yzS0pmksLy+TyWSkc5nT6ZR1HoZhyPeo2+0mmUyyZ8+eqxXcYdne5Xa7u+K9fDM6EQk3m82OpKPvFuLz9tprr/H5z3+ewcHBdkveVi+s0wh/5Hw+T09PD3v27NnwNTtZmLXR6zQaDTKZDPF4nCNHjqyrRL9TIroRGo0Gr7/+OoFAQLZRiTfpeq4tNmShUGhV8/iVM1PFr5uNYrtepK//+3oGm6/1MdXrdU6fXub//r9nGR8/j8/XwOl0MzvrwTT3Eo836e0N8qM/6iedNvB43AwMtFAUk1yuHXGJjAkgRVGkSwOBgJx4I3p7xVhCEX2LYq1WqyVv6E6nk0ajIQt+LMuSblFOp1OO8hNVuQCnTp3iwoXz8lxV193SDlNVVcrlsjzDVBSFoaEhDMMgnU5jmiYf/OAAy8sRXn01R6ORoFBI4vPV+MAHWgwNtWRB0pkzZ3jhhRfIZDIymu7v77/qemTw9tteWi03e/eOMTAQodm0OHtWpdn0Ylmuqy5USSxLwbIGCYd9jIy0C8RENOtyuWQUV6/XqdVq7V7Pq5F7PB7H6/USa1evyeexWq2i6zoDAwNEo1E5T1ZRFNlXDe3NV6lUYnl5WbqPud1uwuHw1eeunaYWWYhQKCRHKwqjE/EeXm3D2g3ci5GwuL9NT0/ze7/3ezidTlKp1LtLhEulEsePH8fn8zEyMiLnTW6UbpmANDs7y/T0NH6/n4cffnjdb+KtPhNuNpssLy+zY8cO9u7d25F02Z14c99uZqowjbg+7S2M40UVqrjhrnY+vbKA7HaPTaQ1xQQh+O5G6xvfmOXixdMEg/M4HG2zin37HiWTGaJYbDAy4qNUcnD+vJvxcScOh8bevQ0GB/Poeomenh6ZORGPW1VVWWwlCpPE+WGxWGR+fl6aTSiKIk1qGo0GxWJRpjjFZjIej8szXeH1LKJip9PJmTNnOHPmjDQMOXjwIMlkkkKhgMPhIJ1O43K5pFtVJBIhlUoxPz8vzf59PhfPPdfg4MEgmtaP1+uipydHKFShVHJjWW3Rf+WVVxgfH5f+08lkUp6bNpsj1GoBxsZ2kEgcuBr1twiHi6TTOTStQL3uIx6Po+sK5bKD9763xPCwW0ajYlMhen9FFbeYnytGOcbjcQYHB+W0qUajIYVTmJ94PB65yRGuXdA+56zX67jdbjnSTxRYQfuzK4Tn+pawcDgsP5siC9KtdKowazuJsHi8Tz31FP/sn/0zEokE//Sf/tN3hwiv7I8dGxtjbGyMyclJWciyUbY6HW2aJufOnWNxcZHh4WHq9fqGhGurqqMty2JycpJCoUBvby/79u1b9xputqaN4nQ6pZDeDFEEc/259M0KyFaKM7T9u0UqV0T94vtFlCoehyjymZws4/O1CIf78XjawpdIDLK46AEiBAJB/sf/aHHxokkw2KJazXD8eIORkRof/GAD02xXAotzQRF5ejweGQHXajWy2Sy1Wo1yuSzbWwCZ0hS/9/b2yscAbfFuW0m20DQNn893TQFdo9GQLTS9vb0Ui8WrIqfj8/mk/7hYn8PhkNGhSHc7nc6rZ2cO9u514ffn5HnpwkKDeDxOb28v8/PzTE5OyrF4Ig2vqiq9vb1UKjtxu3uIxw/gdgfQdY1Go0CzOYPHs8B737vI6dMqlYqJ1+vhAx/w8z3f48AwnCwtLUnRiEQi9Pb2MjExIavJDcOQrUvBYJBIJCLnCdfrdXl+7vV6yWQyuN1ufD6fnDq1ckrSyj5p0zTx+XzEYjGSySTNZpNSqYSiKCwuLqLrOr29veRyOfl1Ivshns9u5V5MR7e7FxR++7d/m+XlZX7zN38TeBcUZrVaLU6fPk2hULimP7ZTwtnJa60nAq3X6xw/fhzLsjh69KjsQ9wIW3EmbBgGZ86cIZvNSqehzWAze4UdjtsPNr9Zbye0HZbm5ubkOEDDMK7aOjrp6ekhHG47OPl8PhkdJRJNFhZS+HwNvN4wicRuotEBcjmFaNTDsWMOzp/X2bmzjqqCplkEAjVmZwOUSlFGRkz8fr+8KQsnLE3TqNVqsuhH+D2LyE5RFPL5vDzfFOKTTCZxOByUy2WZ7nU6nVSrVTKZjFy/eB1EgRTAzMwMvb29RCIRWTXsdDpJJBKEw2HK5TLxeJxgMEiz2SSdTrO8vIzH42F8fJxWq0UkEmFycpL5+XnK5bJcX6lU4tKlS+TzeQqFAqqqMjY2xt69e3E6nYyMjFCpeHnzzUEqlX5CIYNGo0ChMMHCwjQjI3Ps2VNm/34PrZafoaEkiUSDaDRKvV6XleWi0E2cuTYaDdlt0Z5m5MXn88npSpcuXWJ+fp5CoYDP52Pfvn20Wi3pkCVabIRYig1EqVRC0zTZRhYOh6nX60xPT1MqleSGaXBwEL/fT7FYxO12s7i4eM1r0K3nwdCZdHSz2ZRp/+2EqCsQkfy2FmFN03jttdcIBAI88cQT1+yKulGE1yp+2WyW48eP09vby4EDB+T50Wb0Gm/m92uaxrFjx3A4HBw9epSLFy92vJ2oW3b9Nysgm5iY4ODBgzgcDnleuby8LKtpRQRdLpfRNI1sNnt1tq4Lh6OPVkuhpyeIwxHl8mWDZNJicLDFX/+1G68XHI4mmtbA6fRcPVeFQsFNKOSU82hFhLpyPGGpVJIRXqvVkmlrn6+dlhWRpIhGq9UqLpcLTdPk+e/s7CylUgloVyUPDQ2RSqXI5/NXhyu0I91UKsWhQ4ekLaWmaQQCAeLxuLR1FFaNmUxGCvibb77J1NQUqqpKa0kRWbYrpzUuXrzI0tISHk/78ff19XHo0CGi0SiRSISRkRHcbjf1+h6+9CUX58+XMc00uVwGj2eOffvy6Hp7nbGYQijkkNkJh8NBKpWS3RbX2lAiq+3FUUYgECCfz3P27FnOnTtHpVJlYsLEMBK8+ir09Cjs3+/nqac0du+OX2PMIVyxxNGEmM2saRrT09NMTU3JdQghNgyDYDBIb28v1WqVUCgkjTm65TNxM0QR4EbYbpGw2BR9+MMf5oUXXuBXfuVX+Omf/untLcJer5d9+/bR29t7wxuukyJ8tx2zLMtiYmKC8fFxDhw4IHs7xVru1hCI1VjLGgqFAseOHSORSHDffffJG3qnWXkmvNWItKLoURURZqVSIZ1OUygU5BmjqKIdGBiQN898Pk86nea++3R6ewMcO+Zibq6Jrpfp6SkzMJDm2LESMzPDpNMpfL4y9XoOMPF63UAfPT1h4vH26yzSvoZhyApfcROsVCrkcjk8Hg8ul0tGl0K8hU2iKMaanJyUUXOz2SSXy2FZljx/FUMgZmZmqFarxGIxXC4Xe/fuZWhoCF3XZQFYJBKRtpAiw7C8vMzbb78t2+/E2kKhkCzAAqS9o0hvx2Ixms0mPp+PgwcPkkqlZCXy0NAQkUiEBx7wsm9fjS99KcfUVIHR0Qz9/WWCwRaK0jY1iUQiBINBEomEfI2Ec1d/f78UWZHNEJsWwzDwer2USiXOnj3LyZMnWVrK8sILGtWqH/ABLmAn3/xmlL/4C4Pf+Z0Eg4NO+dqI445SqSTP3z0eD6VSicXFRRk9VatVBgYG0DRNOmOJorZoNCrfh90swp0qzOrGHujVEI/3+77v+1hYWOBXf/VX+aM/+qPtLcIOh0OO57ueTkfCd8sxq9VqcerUKUqlEo8++qgsyOjkWu5WOloYcOzZs4cdO3ZcU6y0WWLZDSKsaRr1el2OpGu1WhQKBZkuFeMCRfrV7/fL89RqtbriDBI++EEnzzwTZnKyBrgYHnYQDidQFAWfr8nUVItcro6qNnE4FGZnFTQtzcLCMb72tfbZayQSkVGtSJWLWb3BYJB4PC6jLtGiJN4f4XCY5eVlcrkcwWCQubk5ORloaWlJVgvncjk5uk9E9CLyDoVC9Pb24vF4ZO+zSEeLM2oRaX/rW9/i9OnTZDIZrly5In+WECCxVlVVOXz4sHxfNZtNKpUKg4ODjIyM4HK56OvrIxgMoqoq4+N+Xn3VSSZTYc+eJR59dJLFxRkKhSIeT4hQKMTg4KCsAhf2m319fbJtThxBiEhenOuLc2FxVjs+Pk6xWOS11+pUqybQB0SBJBCl0fBSLPr4jd+I8NhjGorSHhwhjDnK5TKmaRKNRtF1nbNnz7KwsEA2m5UtVwcPHpQjGEdGRjBNU05x0nUd6H4R3g4DHDYD0zT51Kc+xU/91E/x5ptvbm8RhtVv6N2Yjr7ddcrlMseOHcPv93P06NGbplo6lY7eTBFeacDx0EMPkUgk1vT9613TViCcpkQlr9PplJXCogpZnB8GAgGZghbzekXvbb1el05IooBJtBmFwxZjY22rQ8MwpLnDAw9Y/J2/o/LKK73k8x4UxUVPj5uHHirzxBN7ZOrY6/XSarXIZDIyCm82m3KNi4uLKIoi09KitScWi5HP52UL18zMDJZlEY1GZd91KpWSPb4i7VsqtSuzXS4XpVKJ0dFRGeWLzIAo/mo2m/Lcenx8nHPnzlGtVpmcnCSTyTAyMiJvtoODgwQCgate1m3/a+Ek5Xa72bNnDzt37qRarcoNgGVZvP76AH/6px7y+Tr1+gKatkAqVeSpp8yrZ+8JabkZj8evcQoThXMul0u2a2UyGZaWluTscdM0iUQi1Ot1FhYW0HUdVfWzsKABceAg0IfTOYqqetF1P8FgiLk5hTffdLBvX5apqSkWFxepVCoy+5DJZGTqX5wNq6pKKpWSmYx4PE4oFJIZCPjuRrSbRbhThVnbqTpaIB53o9Hg/vvv3/4ivBrdKMK3Ep/5+XnOnDnD6Ogou3fvXvUD1KlIeLMcs6434LhZO9CdiPh6byB3IxK2LIvFRYOJiTKFwgKJhE5Pz3fH4YkKVhFpitaolYPaU6kUgUAAj8cjq5TFWWilUqFQKNDT00Nvb6/83pU3V0VR0PUm+/bN43TmmJpqEY3GefDBXmIxl2xf8Xg89PT0YJomAwMDVKtVqtUqwWBQbpREOlX0pxqGwczMDKqqUqvVZEFXPp8nmUzKOcS9vb2y7UkUXQGy+lcMi1hp11iv12X1r8/nk+nlarXKmTNnKJVKZLNZ0um07EUOh8OMjo6ya9cumQqPx+PMzc1JEw3R3pTP56lWq/T29l49Bx/kT/80jmGYjI7mqVRq5PMNZmddnD3r58knv5sNiEajJJNJWVwnHKpEO1GpVGJ2dpZMJiPFX1STK4oiTU3a73kFVR3G7R6hVhsE2sYfoKAoQRwOBV2HQkFjYWGBdDrN0tIS+XwewzDo7++X5+bDw8NMTk5SqVSIx+McOHBAZh0ikYj8uus/A+/2wqzt1qIkaDabfOELX+CrX/0qy8vLtgjfzWvd7Dqmacqb4QMPPCCN5lejU65bm3EmXKlUeOedd64x4FjL92+Eu3UmbBjw0ksGb7xRZn5+hkplnoGBCO9/v8Xu3cY1hhaiUEi0y4iWkkAgIPtwW60WlUqFYrFILpeTDfxCKIX4iuk/QsjS6TRTU1NMT0+jqiqPPJIkGlUZGvLhdAZkZXY0GpWWi06n82qBUp3l5WXS6TS6rhOPx8nlciwsLKCqqkwfJ5NJstks5XJZFj6Jub6maXL58mWuXLlCMBiUaXSRXhbOZMKS0u/3UygUyGazsq1JFB8ZhsGpU6eYmppC13V5Htrb20s8Hmd4eJjDhw/LFqBgMEg6nebSpUu0Wi35XInIVVQIe71eZmaGKJddRKMNTp5sksuZgImqtpiY6OXv//32kdbIyIicRCRaG8W5uJgznM1myeVyFItFDMOQkZgYhyoqm0ulEoGAyvDwTpaXH8DpDGGaQ4CJYThxOBTqdQf1usF/+k9ZfL4r7NkzS29vGZ+v/RhFBkB4e4vq9H379hGJRPD7/XIWtLA/FHTDkcztuBcjYZGC/2//7b/x7/7dv+PgwYN2JHyndKow6/rraJrG8ePHMQyDo0eP3pHDTTecCd9sDcvLy5w4cYLh4eHbGnB06vm8/pqbgZjNKiKi8XEfL71UxTQv43BcxOPRmJtr8uKLSYaGQsRibWERLTRCaMUNQ1TcAldNJJoyUhYmGGJWrUjper1eGakahiHNI0RU6/f7ZRGRKF4SLkuBQEBaU+ZyOarVquzBXVxcpNlscunSJTk0weVySQETQwrEGalIN1uWRU9PD81mk2KxiN/vR1VVFhcXpTOUSN9ms1mZ6m2LU0BGceFwGMuyWF5e5vLlyzJTEIlEZK1HNBqVc3tjsRjBYJBiscj09DTFYpF8Pg+0W4SEc5XoZR4bGyOdjlCtGszM6NRqLZzOFpoWA3w4nUmmpz1kMjrBoMXwcJilpSVZoSxsKFceFYhKcLGBiEQi+Hw+5ubmZJQeDLbnFf/dv9vPH/9xDcMIXq3kDuNw+PD5FDIZUNUqk5MF6vUwx48neeYZL9///SF8Ph+1Wo1SqYTb7Zapfr/fTzQapVqtSlMY4e+9csMrosxuTkd3qkVpO4mwuGf+8R//MT/3cz/Hv/7X/xp4F/QJr0a3FmaJ6+RyOY4fPy6rhu+0XL/bWpSEAcfly5fXNEhiuxRmiQhIRHRvvlmmUlnCMC5TKmVwu1XC4RqNRgxNG8DrLcubt+i3Fb3BrVaLeDwuz3XF+0FUvOq6Lou0RFWx6CUW4+xEZbEYCu92u+X7IRaLEY1GpW2kOJMV7U9iQ3HixAmq1SrT09PX2FEKgw1RUCbak9xuN4lEQt7ohTWjEPpgMCiLsURE6fP5yOfzLCwsoCgKS0tL0lVKiFmj0cA0TZaWlpidnUXTNFng1dPTI8+cA4EAuq7LgjaRhhbFWP39/TL9LzYNIrV8+LBJqeSkVmsSDLpotUwUxU21qlAsNvi//q84Lhf8+Z87+KmfKtDbOyv7fcU5uTBkEU5UXq+XfD4vjTtEIVQkEmFpaUm+To895iAahZdf9nHmTItGw4XP56bRsGg0YGTEoF530WqFWV4u8dprCj/4gxH6+tppfXFPEFXT0HYF9Pv9cuTh9QIsvr6bBRg655i1Hauj+/r6pHMa2CJ8V68lIpLJyUkuXbrEvn37GB4eXtMHplvOhEXBkTDguFkl92p0alNz/ZpgYyIsLAZF4ZBlWaTTaZnubLfk+HE4GoBBT08f4MDrjeJyDaEofuJxlxQM0f8rImnR0iI8lzVNo1KpMDMzI12kRHpTDEcQhUiiD1UUgjUaDWq1GqFQSKZTY7HYNcYcYh6trutcvnxZRs4LCwvU6/VreoBF5Ov1etmxY4eMykX7j3Bs8nq9skAMkBHvlStXyGazUkDFbGCXyyUjy6GhIRRFIRAIsHPnTqLRKLVajStXruD3+7l48aI89wyFQlQqFaamppiampLpaUVRZBWxcIoSGYNkMsnw8DC1Wo1EIoHH4yEWa+HxaFjW8tW+2hKm6QNigEYgUCCV8jM97eI//IdFvvd7Gxw+7KSvr22qIZ5L0zTlbGYhfmK+8NTUFLlcjomJCdLptLTyNAyD973Pz/d9n45htBge1shk4KMf9ROJGFgWmKaBaVaJRDzk80MUi31EIlV57LC0tESj0WBgYIC+vj5ZhCXax27GdhDhe9ExS7wmP/zDP8xf/dVfceDAAe67777tL8J3qzp6tTf8WhGFL+9973uv6em7U7qlT9g0Td566y1pwLHWtNBmRMJ3euPJZBxMTjqoVBzE4xZjYyYuV4Pl5WXZa9pqtZifn2d2dlbe8NpndS6WllJ4PEuAjtcbJBQaQ9c9xGKGjF5KpRL5fJ7lZQcnT3oolx3s3RvlwIH2bF4xY7ZWq8nUpvi+dhEQLC5G0HUYGoKxsfaZ8NLSkjz37OnpQVFc1OtByuUwiYQbkVDRdZ3Z2VmZXhVDCETKVpyfikELqVRK9scODg5eU+Udi8VQVVVGL0KMRfQpznNFqrinp0duFhRFoVgs4vV6ZeFaX1+fdM5Kp9OyZSkQCDA2NkYqlWJ5eRm3283+/ftln67X6+XixYtMTU1dk/bOZrPybL3RaJBIJOQZda0GfX0+nM4y1WoBXVcolYqADrhxODQaDRflcplSqcJ//+8uEgmdRx+N8W/+jR9Nq8nrij5cv99PNpslHo8zMTHBW2+9xfLyMtlsFlV143INMzOjMDjYwOttv56Dg4NXDUDAslo0GsvAMpbVQFFiOJ19eDxOXK62S56oxBaGI2NjY7IKXBxxiD9fTycEbrPpVDp6O4mw4Ny5c3zlK1/h1Vdf5eGHH97+IrwaQoQ7sSvshKBXKhUWFhZwOp3rEi1BN7QoVSoVdF0nGAxKA461/vytSkePjzv4xjcUslkDp7OFaSoMDcFDD+VoNpel5Z+o0vV6vZTLZfx+/9W0p8nsrMnS0gPE4wqG4aJYDPLAA02SyRaW5ZPTmE6fdvK3f6ty8eIoi4t+Tp4Mcf/9Kh/+sJPjx3VOnTKpVrMMD1cYGSkTCHhJJBKcPt3iG9/wU6m4rkZgBocO6Tz7rHm12jjGxITG9LSL06dVCgUPqqqzY0eNH/7hEPfdp3DlyhUKhQLFYpHZ2Vn6+vqk8AOynUpVVQYGBuTgBeE1bZqmjPqEbaIYHmAYhrS3nJmZ4eLFi9I/uq+vT87NVVWVer1OKBSiv78fXdelwYZlWUxNTXHlyhXcbjezs7MychZ9zEJggsEgqVSKSqVyNdIeQddzjI3FGB0dIBAI0N/fL9PosVgMTdPIZDIsLi4Sj/u4cMEkFJogGHRTKrWnbDmdTbxehcuXoVSqAjGiUQNV1fnWt1J89rNe/sE/aLdnhcPha4YliCp4YaPZ9vuu8tZbHqrVNE5ngmj0QV580eCXf9ni/vvbwzR6ey0OHSrx6qsVEglxb2lSKFiEwy3GxqryeRIDOKAtOO3ecJ8UntWMb+6FSFgc42yXdPTK+5LL5eInfuInZG/8u1qEoTNvyI2K8OLiIqdPn5bGDBspJtjqdPTc3BxnzpzB4XBw6NChdT23myXCt7tuudzk5ZedFIs6w8O1q2t3cu5cleXlaR54oCqn0Yg2G1HY1NPTc9W8QeMHfsDJhQsJ5uZUDKPEwYMmDz6oALrc+BWLCq+8EqDZrDA6ajA6GkBVPbz9tou33qpTrWap1d5B0/K8/baPgwcdfPKTI1iWjxdftKjXA4yM1PB4fJimh9Ong/T0NBgeLvOVryhcuRLg0qUeWi2NXbs09u3zcv58k89+1uJnfqZCPp+Xz4doL2o2m7JoSUSLgJxpK1ymnE6nnGErXLWq1Sq1Wo1Wq4Xf779mQIO4GYpeaPF9Ilpe6ZWcTCbxeDxks1nOnz9PLpeTaedgMCjPy0WLULtPup3inp4u8vWvuzl7VkHX/fT1jfHhDyd45hkPo6Oj+Hw+gsGgTJfncjkUReHDH77E/HyeyUkdp1MFWoBFb2+VfN5HqaQBTsCLy9XC7w/gdHp44YUm739/Ga9Xk21hwoTEMAwuXLjAyy+/zOTkJMViiXPnIpjmIcCNaY5QKPh4+22T3/u9MM8847r6HoX/7X9rcPGik4UFA8OoAE6iUQ//6B8p7N7tkmM1xeAMYcAhKuVv5zq3HUT4XivMWnlc9mu/9mvX/N+7XoQ7UQCw3sjRNE0uXbrEzMwMhw4dkmdyW7GWlaxHyE3T5OLFi8zNzXHw4EHOnj277g/RamK5lorORgOWlhwYBiSTFsHgrdPR5XKZ06cLjI+rDA4a1OtOPB4frVYDp3OR6ek6u3cbQJ2+vj5SqRQOh4NSqST9jefm5uTZ4FNPtSfsNBp1DKOFaXpxOr2ydeXCBY3l5Rb79oWYmyuiql58PpV8vsTExCIPP3yWZnOCZDKKZbmYnnYzPW1RKBTJZKIcOuTB4wlfNXeoo6olXnppEcizsODH5UrgcOj4/XkmJoqEQg127Ypy5YrFq6+W2LfPlP7LXq9X9taK6t1IJHKN+IrZtMJuUwwXAGQaW1QKC2OIixcvMjs7i2EY1Go1BgcH2blzp5xPLKqhI5EITqdTVjeLiux8Pk+tVmN6elqmxUWKUaTMxRADw7D4y7/0cuqUg0AgSCi0F8PYzde+5mVw0GBwsEEoFJL9sqVSiZmZGaanp6nVlvjgB4ssLHhotVwYBhw/7qTVGsQ0/UAZiOP368RiPpzOBGBSLOZ5/fWT+Hw5mQXw+XykUinK5TLnzp2ToxhbLQ+m2Q88BPQA+zFNL6bp5qWXohw71uDBB01arRa7dzf5T/8pz1//dZPp6RADA2H+3t+Ls3dvQ76HRdW8MHURdQaVSkUW7K1GJwRus+mUY9Z2SUe/9dZbHDp0CL/fz7lz52THgKqq94YId+JaaxWtRqPBiRMnaDabPP744wSDQSYmJjoioLCx3e5aI9HrDTg6WV29HqanHbzxhkI67cA0IRq1eOghE8v6btpnpT+yYRhXJ++0qNWcZLN5otH2RCBNq+F2ewkE4vh8FeJxPz09PQQCAUZHRykUCui6ztLSEoZhEIlEpNCWSipXrkSZmWkQjTo4cACSyfLVyTveqwVPPlS1PfO21aqxvJwBKjSbFRTFTam0hMtVQNfvp1qNEYmo+P1RXK62DaVhtNC0Mpo2z/T0BA6Hh717I4yPV2g2s0SjQXTdz8yMxr59AVwujenpKnv3tp8H4ateLpelW1UqlSISiVCr1eQZqngvmabJ4uKibJUSDlFut5tyuUwsFsPn81EsFhkfH5fpX8uyuP/++4nH43KwgNvtJhKJyLYnYU+5tLTE8vKyLHzLZDIkEolrot5cLkej0SAWixEKhTh50mBubpiREQcej47H04dlVchkvLz55g6+//srsqK80Wif7wvzk2KxiMNhsnu3g3hcZWysl5mZAF//upuTJ3XARTTqYWDAg88XwjQdFIvLeL15duwIEArFiMVisr1LDL8QG4Tl5WXatpRjQABI4nC4cLligBddd/Anf9LgwIGGPOsdHQ3wsz8bkKMiA4EW7WgcOcpRCJVIg3s8Hmk3eisR3g5nwp0qzNou6eh/8k/+CZ/73OfYtWsXv/ALvyAnXzkcju0vwrdyloLOifBariOGFsRisfbB+9UWgk6kYVemNTYiwnf6eG5mwCHGu613DRvZpReL8PLLCtUq7NjR9lfOZBy89ppCJOKSFpFiFq2qqvKmDzqW1SSTCRCJ6Oh6i0AgxtycxtBQjljMI0fWiTM4YX/YLtwpycrbRqOH55/3MD9v4vW6aLVcnDjR4iMf8dDfXyIU0ggEvKTT7YI+p/O7M3JdrgUajRJOp5t4fCfBYA+FwgF8Pge7dvkIBlUymSqxWJNGo0Qut8j4+BWazZNoWohkcgeqOoZltW/EHo+FYfiwLJVSaZl4HDluUUwmikQiqKoqBVGkfsXUp0wmIyPaTCZDOByWlc2KouByueSUIJerXcgk+p6LxSJDQ0OEQiFZwCR6jh2O9hxjkdav1WpcvHhR2mEuLS3JNieXy3VNZbFw+2q1Wpw/n6ZcNujra6KqcRRFwTSd+Hx1ZmfztFoBma4tlUrkcjnZVuRyuaS7lDD7OHo0xpNPusjnG/wf/0eA1183KZd1DKNCtWpQKs3y9NN5qtUcEJAZkMOHD5PP55menmbv3r2cOnUKVVV54IF9XLz4DNCH253ENKtYVvPqZszi29+u8dJL35QbQ/HeFGfNwhtbtGEZhiEL3KBtodloNHC5XKua4AjulXT0dnLM+of/8B9K+94f+qEfks5ouq5vfxFeDZHavJsibFkWMzMzXLhw4YahBWu5zu3WAhtL59xpZC8MOEZGRtizZ498LBvdCGxkMzI76ySbdbBnj4nD0V5DKFRlerrM0lKd9743I20WvV4vi4uLTE9PU61WMU2TgwddLCwMsLjowuv14HIl2b1b4/3vD+L1FgFkxOPz+ahWq+RyOSkQosXmxRfdLC46OXjQgaI40HWDS5cKfOlLFZ59domenhAPPujk+HE3mUz7w1csNti9u0U63UBVU/h84PcnMIy99PV5OHTIyfCwh0ceafD1r2ssL5vU6+OMj7+Nrk+xY4ebuTk/1WqTvj4f6bSXbFbHNFWGhnqZmIB43GL//vZwgZGREYBr0peqqsqBA6K4ZWZmRrZQiX5eMfNUtC5pmkY8HpcWmC6XS04a8vl80hLT5/PJnmG32y3H67VtNnVOnz7NxMQErVZLRtZDQ0MUi0U5dtDr9WJZlhxvWCwWiUadGEaZZtOPotQpFpfweHqo18MMDSn09UXkYxOpXCGcLpcLt9tNf3+/rG5WFIVyuYRhNPjZnzUIBkO88ornqmtZmfe85xK7d2ep1dpGJcJj2ul0srCwQCaTkVaXjz32GCMjI7zwQoiLF720WhpOpwPLcl9tcYKhoRjvf//7yefzlMtluUEQ6fiJiQlpuOJ2u3G5XKRSKWnUIQqyxGbqVmwHEd5oJGwYhsy2bAc+8YlPyD+Pjo7yyU9+Ur6O71oRhrs3eAGuHVr/yCOPEI/HN2U9K9OGG7nG7bybxSjFmxlwrEyJd/rn3+7moWlcjcJNwEGxmCObnSafz9BsZpmb+27UY5om5XKZQCBALBZjYWGBhx9OASpTUya6rjI8rLF/v4LP52Zurimdq6rVqnRfEjaFwr1I131MTyv09uooiodms0a5vITLlWNuTmF6ukF/fw8f/rCLfftafPWreQIBBw8/XKe31+Kb3+xhcjJKsWhQqURIJHSeeKJGMOhF00wefzxNpTLPm28uUKtVSKVyhMPtsYaG4aZUitPXF+TgQTcnTxrAAOFwlMHBMs89F2JgoImuu6VnszCZEBW9YnBDrVaTBVzCWKTZbBKJRKhUKpimeU2h1fDwME6nk3w+L89wV9piCqtMISbi9RTGJcViUTqJiUhADG+IRqNyVKA4C3W5XMzPz19tB6rT01NmYaGPnp46qmrQaJg0GlU+8pE+AgGP7L0uFouUSiXK5TJOp1M6bsXjcdkLLUYDhsNhvF6LX/7lMD/yI3lOnZrE6VwGTJrN9rxj4eDV19fH0tKSfD+IamVFUajX63z84y5+7de8tFoqDocXVfUSDrff53/n77RHHYohDO3NgYfXXrM4e7Yf0+zh4MESLle78EuYcAizkOnpaflaivfhzX6JSVXdLsIbXaOmaQDbJh29Mmj66Ec/yqlTpzh48GD72GeL17apdNJk41bXqdVqHDt2DFVVOXLkyKpvjE4VVcHG+mxvJYKGYXD69Gny+fyqBhwbNcbYSCQcDOq0WgaVig40WVqaJJ2eIpvVCYcXWV5uV+XW63UZrS0vL9Nqtejr65P9lqlUGaezhsdj4HJ5KRTajlbFYlH24QpBAeSsWa/XS6XiuJpNgFarTqmURtebNJt1DMMlbQc9Hg+PPOKi0ZjhwIEgTqebbFbl+77Pw8WLy5TLYYLBJnv3tti7N06j0WBhYYF8PsfhwxaW1Z6otLBQp1o1UBSLp5+OUSz2srTkw+mED33Iw5EjAR54QKenx8DhsGg0AjLKFSnMer3O/Pw8mUwGv99POByWfcrJZFK6cCUSCelV3dPTI7MB4ixYpPZFwVA8HufCBY2vfc3B7KyLUCjPo486eOCBdio6FArh9/sxDEO6h2maRrVaZceOHQwNDUmxFxGwYHx8nGazKW06n3wyzuuvl5ifL+JwJInHnTz7rIMf+7GgbPcQDmeVSgW/3y8FS0TiwjVMbD6E41i1WqWvT0VVY8zOVvF647JNSsxZFjaVyWSSy5cvUyqV5Dm3z+fj7/29JBMTCs8/H6DVcuN0tquhjxyp8+STi8zMtJ9v0zQpFi1+8zcjvPOOhmH4MIw4kYjBz/98iOee62dgYED+7JXOWc1mU46jFL+y2azcBOm6Lu8RJ06ckMJ8vVBvpUiLo6yNRMJivOR2SUevfKw/8RM/wcWLF6VToi3Cd3id1UQjnU5z8uRJBgcH2bdv3y3fWJ1qL4KNi/DNnhdN03jnnXdwOp0cOXJk1Tf4Zorw7SL0eLxCb2+TixcVWq3LzM2dIpOpEomA253G4xmjr69PtsusNPaPRCIEAgGy2aw8mxPtMMLysF3E40DXdfL5vIw8QqGQPBf1eBzs2OHinXdUfL4quq6haVUWF1uEQg1GRnpIJBLSaN80Temq5HQ6CQaDPPbYAIZhMDs7iaa5WFxsXhX4irRvFN7Iwkxj586dPPjgg8RiMdLpOq2Wwt69ceLxtvOSpjmo1XQ5pahSqZDL5XC73eRyOdmyI6J60QbTaDSkDWMikUDXdXp6euSAgJWpZ5FKbrVahEIhTpyw+O3fNllc9KKqBSqVOt/5ToKf/En44Ae90nWqVCrJPuJ6vS5T2H6/X7aaeDwe6ZV9/PhxLl26JH24HQ4Hg4MqP/ADBQqFAD6fn+FhjSef3E08HsIwDGntKWw4Aenb3Ww2ZYvg5cuXmZ+fB9pV83v37pXe06KSHJDn3OFwmHg8TjabpV6vU6vVZOp9z5497Nq1i2AwSCAQ4F//6wbPPqvy8ssOzp9XyOU0FheX+N3fXeYjH6kQi7Xd2D77WYVvf9sgEnHj8fz/7P13mGRnda8N3ztUzqFznJwVRqM4QkiAEDLBJIPBpAO2sX1sfF74bGPgGPM6g/1iDD62gWOCwQSDhWUQJhgsCSWkyXmmp6dzV1fOaafvj93PQ49QmNG0pBnh57rmkqana9euql17PWut37p/QSwrTKEwzD/+Y4g3vxk0zZSbBr/fL8eSxPv0eHS6lZCWVColN5IrA7U4zsqg/OiM+lwxuk9lrYbLk+iVXypBWCzHcdixYwe//du/TbvdZseOHZd+EH6iHd3TWY52HIeJiQmmpqbOmZm8WqANuLBy9GNtBoSYTLCsn+gL8mxlwm5mV2XXLhvDKPLAA2dYWGjh9Sqk0wFisQg9PT0AsrcmsqxgMCiDQaVSkSxeMVcqABMiOxIrFAotz/1WZFYFsHt3iHxe4dQpG9MMUC6XicfD3H57jP5+v5yNXVpaYmlpifn5eUnGmp2dlSpXcUMsFouSe1ypVCgUCjIDE7CK9evXMzw8LI/jzpAGpMpZZG65XI7Z2VmpiBZq5pXAByGYEmzqZDJJb28vhmFIPKMwBxA35dOnT1Or1fB6vZTLZUKhCJ/7XJulpQ5r19p0Oi2GhhIsLQX41381ue22qFT/ip58IBCQYiTHcWRAF5sFMcJ05swZuWlptVqMjIzIsaVt20ZIJBIEg0HJ4BWCpna7zeTkpPws/X4//f39FItFKSgsl8v09/cvvwYX9OHz+VhaWpIl6p6enmUimcvQLpVKLCwsyPdnZGSE3bt3SwclUe73enVe8AKFb3xD4eGHNQzDxLYd9u8P8b3v2fzJnzTp71f50Y+i+HxegsEIqhpC0zr09HTJ5xW++12LW291SWqmaUrzkHNZQkXt9XoZHh7+qX8X42crM+lOpyP7+O12W/b8Hy9Aiz77U82mxb3rQrJxkQlfKj1hsVqtFn/xF39BNBqVfeJLPgg/0Xq6gnC32+XgwYM0m02uu+46IpHIOR9ntY0gnsp6dBCcn5/n6NGjjykme7zHwzMbhLvdLvl8frnPp5BMNohEeolENBynQrE4QKu1Bo9nBE1zZPlT9Nd8Pp9EHYobvQjEQkktUIorS5or3X7E/KurXDV59as7PPxwhWLRwe/vZWzMZuvWlCwjVioVcrkcBw8epNPpEA6HzxrxabVajI6OnsWGXkmJEqMoPp+PdDrNhg0bpJlBOByWfV3R683lcuRyRc6cKdBoZNE0i0QiIcesBAN5pUFCvV5H0zT6+voIBAJy9EYYOwhqVi7nspdFsNG0IN/4RpAf/KCFqoLjWPT2BgmH4/T1WWQyQaamgvT02BQKBfL5PJVKhWw2i/A2dvu3DY4da9LpaGzapJJIeFhcXKRYLNJut6WyeWRkRAq1xOzx0NCQzOhzuRzHjh0jl8sxNzcnjTCE+5Ft21SrVWzbJpVKycqGGDnLZDJSfGfbtoSUiN9rtVqS+S0AJCttBoUDmqIo/OAHOnfd5cHvd4hGFSzLg2l2mJ/38u1vB/nN34xi2z34fOA4JoZRwXHqOI6NZelMTpbJZtsEg8GnBPd5on6rqqoEAgFJhnv0EhujlT1+0WcXyFTDMKThx2MFafGzx9vIr0YmLKonF3vvWywhlvP7/fzbv/2b/JllWf8dhM/3OJVKhX379hGNRrn++uufcF7v0Wu11NoXehwRBFcCOK688kopoT+Xx8MzG4RF5uj3R6jVqkxMJFEUg82bTWw7RG/veo4e1ThwIMQtt5QJhYKSmWzbLiRBKGYVRZEzs+KmKoQewqdWBE0xU2vbtjSQFyXWeFzjec8LS/9boUL2er3U63UmJyfZu3cve/fulccYGhriec97HpZlScGScE8SZXDRHxUBMBQKMTY2JmduhZ2gKFfn83nK5RoPPGBzzz1LFIsVvN4umzbp7NhRQVXts4LXxo0biUajkm8diUQkZUq8LyshH+12W4I/3FKvl3/7t1HuuSdAvQ6a1qJW61Iux9ixw8Tj8eP1JvF4dNrtusR/CoymKIPPzJh87WsG+byObXtJJFRe/GKFa65xWwetVguv18uGDRtkQBoaGiIcDtPX1ycDqKg0TE1NUSwWqVar1Ot10um0LPFv2LBBBo6+vj4KhQI9PT0SxCIEUKJCIG6YAjQSj8fRdZ1CoSDJY+Jn4rsgNlD33KNh2xAIgOMEAAdV9eP36+zZE2Lt2gZDQ3WOHy+h6ybdbhMoYxgJwMvISA2/PyFhFOdzjxHnciHji2IT9nhLtDBWZtOtVotSqSSD9xMJyFZjhnklTOZSWOLzUFWV3bt3n/Vv/x2Ez2GJoDc3N8exY8dYt24da9asOe8L/WLKhC3LYu/evbTbba677jpJGjrXx8MzG4SLRY2HHvIyO6tRr8fI5ZJs22YSCPSjaTo+X4ClpTNMTVWZmSkRj1fOAu43m01Z/hVZr3A5UlVVevXOzc2RyWSkY49pmjJjDoVCMpA5jiNnh4XBgPDvLRaLHD9+nIMHD7Jnzx7q9TpDQ0M4jiPhGQADAwOEw2FJoBKZ2tDQkAyEIvhEIhEKBYNjxxS63SDBYIlweA7bdrPAe++1+M536th2AY+nTatl8PDDCRRF5fbbU7KXODAwQCQSkdeQ1+uVsA1x8xQq43DYFTydPn2aubk5OfubzQ6wZ08vg4NFOh2LUglisTitVj/ZbAOvN8DoqJ8NG+pkMpllUEpNbm4SiQTFYpOvfEUlm3WIx7t4PCa2PcQ3v6lQr9cIBJpomsbo6Cjr169HUVx0qKJo/OhHGouLCbxeL9u3l4jFZpiZOcOJEycoFAoEg0FUVWVgYIAdO3ZQLpcB5PsbCoXYvn070WiUcrksR9KEN3NfX58UlAUCAdkXF++LqIqIkmyz2ZSqevf6Zvn7AYqioyhhFMWPqjqoqkWzWedlL5vm6NEKc3MOqlrAtv1AlxtucFi/PiRnuIU6/XzW0z2iJNoUj+d//ngCMlHdaLVaAPzoRz96zAz6XARkIghf6Ou85557+MhHPsKePXtYXFzkjjvu4JWvfOVj/u6v/dqv8Q//8A989KMf5X/9r/91zs9RLpf5whe+wG/+5m/SbDb5+te/Tjwel9/J/w7C57hM0+TEiRPs3LmTVCr1rJ7PhUI/hBBJURSuu+66J507fDrO4XweOz8PX/xilGPHHPz+CppmMDXVwnE89PUFyGYDWBZUqwqDgxbJZC+GsSR34oZhyEAqsshcLieD50peseg1CV/b8fFxQqGQxDmKgC5U1JVKhf7+fum0JIhTYhZUZI+5XI7BwUFGR0elXWEsFpOzn6KELQKjruv09vbi8Xio1+ucPGnz5S/7mZ9XURSHVqvA0FCbW27JUyoVeOihEKpawu+vL8+19mCa/SwupohEVAKBrhyREZswYcpgmiaNRkOCLUQ2HgqFmJxs8s//3OLYsR40rcHOnTq6PoBtK6RScaBMu61Qr3ux7Q5TUz6uvtrD29/eJZud5ujRo9i2zcTEBHNzc0SjUUqlErOzcYrFNAMDDqpq4vFEl40cMvz4xw7Pe57NwMAA27Ztk1UbXQ/yiU9EeOihFIoSwbZbmGaWTZumuPrqKQn4WLduHcFgUGaqIyMjgPv9E2KldrstoSBiI9bX17c8suQ/y8c5n8/TbLpuSl6vF9M0ZRsgHA7LDZ5Yt9xi8ZnP2DSbFoFAF9vuYhhdTNPHjTfmOHz4FL29Z/i5n2twxx0qtq0C44AX0/QTjfbjmkv4HzfQPdl369kkZj2ZgMz15X6Iq6666qwg3Wg0HlNAtjI4Ly4uSs/mp3LfevRqNBpcfvnlvP3tb+fVr3714/7eHXfcwYMPPnjOfukrV7FYlEE4m83yjne8g1gsJitCl3wQfrqFWa1Wi0OHDuE4DjfccMPj9lLOZT2Tc8uPtwSAA2Dnzp3PCHXr0euJMvlHn0+97vD5z3u5/36IxyGfbwEdVNXDfffZ9PZ6SactvF4fExMJLKuLbZtyNjgUCklBjnjfRJAU2X8oFCISiciMLZFwMYUjIyP09/ejaZpUUosxmEwmI0VUmYxKsTiEaeoMD3fQtAonT57kyJEjMmPq6enhpptuYuPGjQBEo1FUVaWnp0cGWuE7LDJOYHnetc0//ZPF/LzG1q1xLKtELlfl2DET264yPm5SrVaIRHR0fRCPR8HnS6NpUarVXiwLRka0swKI8CTWdZ16vS4DmHjuWCxGJqPywQ+aHD5sEQx6MYwAZ870MjYWXN7EeYjFvFx1VYBsFhYWHMbHfXz4wybJZJF77z1CJpOh1WoxNTUliVYuLCSErqcJBDqoqg+PJ4VlVfD7GzQaKoFAmGQyKbPXYDDI977X5YEH+unvjxCJ2LTbVfL5NocO9TIyUmbr1jilUkmquQcHB6WvsPj8xYbMsiwMw8BxHLnhEMASFyPp/v3EiROcPHlSBtk1a9ZIsdlKitXKvu1117V52cuq/Ou/mlQqHWzbQNMM1q718uIXLzI/P0+j0eDgQRVQgBg+H1iWwSOP9PNnfxblE5+w5Qzy+a6LfU5YzMwK0eHj/c7Kkrf4//vuu4/Pf/7zZDIZDMMgnU4zOjrK2NgYo6Oj/Pqv/zpbtmw553O5/fbbuf3225/wd+bn5/mt3/otvvOd7/DSl770vF4rQE9PD3/+538OuNWvQ4cOSc3HcyIIP9G60KCXz+c5cOCAHM+40MHw1cBWiuOc7+taCeDYsGEDJ06cuKAv6jORCRuGwb59JgcP2kQiXer1DJ1OHdNs0+notFp+LCuCbbep11v09bXxenWOHDG56aaQFNfU63VZUhQKaUHEajabhEIh6vUG7XYIXe+lr89PX18Po6OjMjMEtxoiVMdihrVY7OPHP66jqi7wo9nMEI1W6esroKoq4+Pj+P1+du7cybZt2wD3iyiyTpFBC4VwoVCQ40FLS0vUajUWFkLMzOisW6ehKCaNRg5NaxMMLjE5qXHVVWkiEQWfz0Mo1EJRfPj9IZrNKKFQhJERnXTa7VMriiKzdGGY0Gg0ZBZuWRbJZJJQKMRnPtNhYsJi3boIllXH5+vFsnpZWLBxnC4LCw2SSVe529vrwzQ1fuVXbNassdi3b5KFhQUsy2JxcVGKnsTssK5HUVUF2w4TDPaiKGEsq0W1Cj09Ger1Cq1WhFwux+joKL29vXz1qyqqGiASERhYlUhEZ2EhwMmTClu3upSwtWvX4vP5ZN9WjGaJ1yjKzSJ4ioqA3++X36tut0sul2NpaeksoZbIesUctWBsi02Ty5Uu8653lVm3rsbdd3dpNm2uusrP85/fwLIayxtBh9OnHcCH378GjyeKxxPEMPr4wQ9MMhkPGzeeXy945XfrYg7C55KpP56AbPPmzfze7/0e//Ef/8Hv//7v89WvfpXp6WlmZmaYnp5elfvrymXbNm9+85v5nd/5Hfn9Pd918uRJ3ve+93H//fezuLjIl770Jf7wD/9Q/vt/B+HHWI7jMDk5yeTkJFu2bKGnp4fFxcULvrifrXL0owEcXq+X48ePX9DruZC+9Lmcv9gJT05qmGaDiYkClpVDUbrYtkq73cTnS7Jpkxddr+P3m3Q6ebzeHgqFOMmkj2w2K5WxYtQGXOu+crlMLpcjlUpRLse5884uCwtRNK2PgQGLF7zARyLhBqpIJCKFVocPH5biomZT59ChPlTVz+WXB7GsOsVilX376lSrsHZtQtoGCnMDn88nd8HgluYymQxLS0tMTk7K+V6XMe1yghuNLobRwufz0OmUcZnEGh6PD1UdZGCgzY4dJnv3BvH7E0QiKq2WTank4dprW/T3/0QNLlTjQozkit3c1yg2KkLtf//9HWIxnXh8nG63htebQFEsslkvmzZ1OXOmxcREAFVt4fNpPO95Hl76UksyoUXANwyDkZERwuGwnBXesCHCj3/sZ2IihWWp1Ov7KRYnsO0umzY1pMFErVaTVQjL2oKieJb71iaG0abdLqEoWQKBHrZsSaKqKiMjI9i2LTnY3W5X9uAEP7rb7cpyoECRCna2IK2ZpkksFpNthVQqxcDAAF6vV4I6Hn3NVioVpqenqVQqrFtXZ906998GBgZQFD/Vqjuv3OmogI7PN4jfH0FRguh6Ck3z02xaZLMKmzY9te/mxR6EVyNTF5aaO3fuZOfOnat0Zj+9/uIv/gJd13nXu971lI9RrVY5cuSI5PD/5V/+JR/84AdpNpv/Det4rGUYBgcPHqRer3PttdcSjUZln/BC7bdWk2V9rgHwsQAcYtD9QlWUTxe2UoiEGo0GnY6farWDZUE0mkDXGziOj9OnE3g8KdJpC7CJx31kszaFQgmvN0SjYVIsFiW5SmR8ApqeTCaXnXuSfPnLPubn4ySTbkY4Pe3hm99UGRz04TiuQlfMkAq1sDtmFKJcNrn88jiq6qHRyGHbLUIhm3q9j/HxXrxejU6nw+Dg4FmGCgIRWa1WOXXqFHv37pWORmIkSngT9/VZpNMxFheDNJt1SqUojmPS6aTYsSNAONzi9ts1PJ4Qx48HmZ52VbeJRI1i0eH97w+wfr3CjTdqRCJlaRpfqVSkyEbMw65ETCpKjVarQ6cTwuMJ4jgGptnGslps317k5S9vMTmpYlka27YpaJrKZz8LzWadoSEvqVSKfD5PPB6X89grhW2/+qsOd9xR5/77j9NuL+L3F9m4scbAQJBy2VXDr1+/Xm4Ubr55lOPHPbRaNRQlS6cztzzrHGXHDoNoNCrbBYIKJkrHwWBQXvfgXvvid1eOnwFy9CsScefOhWJ4dHSUnp4eqQJ+9BL0qkajQa3mummJGfVWqyU/T1VVWb8+hq73A6NoWhfwoigO7XYXj8fPmjXWTx3/XNez3RN+srVaNoZPN6hjz549fOxjH2Pv3r0XtGkYGxtj165dvOY1r5Fz3O95z3tk6+e/g/CKVavV2LdvH6FQiBtuuEGWmFYDFSmOI9SnF1oKPpfX9XgAjtWibl1IEH6iJRTJuq4TCrXodi0GBx3qdQ+1mo7jBInHo3Q6NvfcYxIOK2haGajS05NmfLyNaeqy/NxoNKTAI5VKSau9RCLBt75V5NChJdavD5JIDKPrXjQty6lTbR5+GG6+WaPZbFKtViXoQ9C3Eol+wuE1xOMDgIXXG8LnaxGJJLCsKBs2eOnrizM3NyezWsuypCtTNpvlzJkzTE9Pk8lkCIVCNJtNKRgTPrKjozFuuKGfv/s7D5VKEE2r0mzq+Hwhnv/8LrZtEAzqvO51DWZna8zPt5mY8LJvn0om4yUSMTh5UuWhhxx+6Zfg8st7CQaDLC4uypKfyBaFYKRWq/H854f4zGc8GIYfj0fFMKoUCj58vjpr11bYti3M9u01Wq0gH/94kCNHvLTbJVqtKj6fwQtekKe/32VOBwIBgsGg7LUCjI0lefWrZwiHD3L6dINSqUi12qHZXEun0yIWi7Fjxw6q1Srj4+PceGOYBx80ePjhFoqi0u16MM06Gzc6bNlSpVp1yWaC9yyU8K1WSwZNwzDkuJWYnxbuT6KsLMwnYrEYqVSKdevW4fP5ZNtACBtXLqEXEMxtocIOBAKkUinZjwYIBAIkEg7XX6/z4INt2m0Nvz9Oq6VgGB5e9zqd4eELC8IXcya8GpuEZ8JB6d577yWbzUojFHCriu95z3v467/+a6amps7pOGvXruVP/uRP+L//9/9y//33UyqVeOihh6S477+D8PJaWFjgyJEjrFmzhnXr1p11Ea+WLeLK4HehZe0nC4BPBOC4GILwE72X4riuoKlIJGJhmirhsLY8khSn0/FSr08SCNRoNh2qVQPHiXHttUHGx9uUy25mI27GyWRSov9Eb3B2dpbDh2dR1RiOYyxnsia2DapqceZMgRe+cEASrEQ209vby7Zt27CsGKdP91EshkinG+h6EFUN0O2u55prmoyOuqKv6elp2Tssl8ssLi7S6XTIZDLkcjmy2azEbAaDQXp6eqSKWbjptFoqkUiXUKhNs2kwMODHcXLs399k69YKsZh3uc/dZXQ0yLe+5SEYDDMwEEDXFUDn2LEa3/++n6uucuelvV4vwWCQRCIh7QYBCoUCxWKR66/vcORIhL17FSyrhmm6z/+iFxUZG1MktOEb30hx8KCPdLrG7OwpOp0GhUKAf//3GO9/f5xgUJd9aDEe1tPTQ71eZ//+g3z3u5MsLjYADzBKLred3bvzjI661pT9/f2Mjo4SicDHPtbmc5/r8F//1aLVqrFpU4UXvtBLT8+IVJ4L0ZRAPoqf1Wo1yY0Wc72iLO3xeEgk3PbByscLe0Hxe6Jv/OggLP7u8/lQVVWOfiUSCbkZEOSwTqdDNBrlQx/y8//9fxp33x2j3fbg9Zq84hUWf/ZnF3afudiD8GrZGD7dc8JvfvObedGLXnTWz2677Tbe/OY38z/+x/84r2Ndd911XHfddTz00EN8/OMf5wtf+IL8t0s+CF+oOtq2bY4fP87i4iJXXHGFxB4+1nNcDDaE4nwe71zOBcCxGq/n6ciEHcfh9OnTZ+EKdd1k2zY/+XwPgYCNz2dTr9scPFhmdHSW9etD1Go+2u0mmYyXUKiMYYSk163ANIrZWDGGVCqVOHToEJnMFIXCRnp704CCbZsEgwksyyVAWZZFtVqVo0NizjYcDpNOJ3jpS73ceafDkSMGhjFPs2kyNKRy661hBgYiLC0t0Wg0qFQq+P1+KQorlUqcPn2abDZLrVYjGAySTCbZtm0b69evl5Qmt1frZ8+eOmvWWAQCZSyrg2k2qddLzM21mJqCyy5z4R1uOV2hVvOwZUsCXfdh2wbg0NsLS0uDtNt1kklVcpsFFEJVVRYWFpibm+fIEY2TJ5tEo0FuvbVNt1sjHFbZsKHOunUa6XRqmbYV5oEHYvh8FocPV6lUOoADZMjlanzjG728+c3GWaVYsRkqFArs26ewuLgZmCIcTuLzraFcbvNf/zXEbbel2LVrJ+FwWAZHn6/Nq19d4+abizQa4PMN0tPTI0vOYuRMOB3Zti2tFAWBTLg+rbw2kskkkUhEMsMbjQatVgu/3y+50sLYotlsnnUNC5CLUHKLHrsoia+ElIgMWaji/+IvTKrVLpkMrF2rMTbmQVUvLEAJIdnFulYjExbX7IWuer3OxMSE/PuZM2fYv38/yWSS0dHRnxpH9Xg89Pf3s2nTpvN6HiF6vPrqq/mnf/onuWlTFOXSD8JPtMTu8/FWu91m//792LbN9ddf/7gzeWInezE4IInjPNYxDMNg//79dDqdJwRwPN1OTE/lsZZlsW/fPrLZLD09PRJF6LrhWBSLU5hmkFgsQSYziKoqpFImzeYCYNDXN4BhhFBVnXg8LFXHAuco5n7D4TDZbJZ7772XmZmZZWIRnDkTYNu2IWzb4PTpBvG4zhVX+GXmIrJS4akr/nvzzS0ikRL33rtAJlNi/fooa9aUUVU/J08uSmGSEP2IGeOlpSWazSaGYRCPx0kmk4yPj7N161ZpQShEXNlsnU7HwLZrKEoNx4FOp0S365azHEfHcRzJifb5dHQ9hKKElsurHRzHQFFieL0xQiEbn88hEolQLpclJ7hSqXDy5ARf/arJgw9GMQwvwWACRalz5ZVeXv7yMh6PTTjsjle52XoCx9FZXDSoVOrL4rFTwAnAw4MPernlFot2O0u73UbXdQzDkOYIp05FAYNgMIDfvxa/f4BUKs3CQi9791Z5xSuC0l1JuEJZlkU0GpWbCPHZaJomPYzz+TyAZEiHQiGphxCqaBHcBZWq0+kwMzPD1NQUrVaLWq1GJBJhaGgI27bp6+uTLOeVFCuxkRF99UAgIEWAnU5HKn0jkYisHgjhWDQaZXhYYd06Fb9/dcqrPyuZ8GqUox955BFuueUW+fd3v/vdALz1rW/ls5/97AUfXyxB6RNr5SbpOR+ERR/m0atQKHDgwAF6enrYunXrk+4cVzMIr0ZZ+9FBTCjvQqHQkwI4LoZy9MrHdrtd9uzZQ6vVYteuXXS7Xebn56V70ZVXKqxdq9JsDtBqhVhcLHHsmIHXq9FoGJhmF8cx6HRsIM/UVF5mPLlcDlVVGRoaIpPJ0O12yWQynD59GsdxGBzsIZEI0W73UCoFcBwfQ0MWr3hFmg0bfFSrbgYcjUZJJBKkUinJCnapWVXgJP39p0gmu8uK4AjBYJ8cf7Jtm1KpJLMxERiEi5AwahgeHiYcDsuSrcBf2naVtWsd7r+/xuBgB9tu0W6XyOdN4nEvGzYECIU0aRm4bZuPwcEICws+NmwI4PXadDptymUfL3xhhWDQoNu15QyzKM/PzMywZ0+d++6LEQpVGR9PEQjY1Goqe/ZE2bSpwctf7iMUCqEoCtFolEAgwOhogwcfPARM0+nUgFPLn2yATqfAgQMOmza55gJer5eenh4JuWg0Avj9GvH4VYRCV6CqJj5fGK83QKNRPes6ERmeyEZXmlGs/K43Gg332QMBWq0W3W6XaDQqx5KEYEtVVbrdLoZh0Ol0mJ2d5cc//jGZTIZKpU0yGZEmHOl0mm63K8VrzWZTnpdwOxJ4VFHOFkFZVGDK5TKappFOp6XYS7z3qwUVgp8NYdb5mFo80br55pvP6z52rn3g81nP+SD8WO5HU1NTTExMsHnzZoaHh89pV7YayubVCH7w069LADhGR0fZsGHDk76ei6EcLR5br9fZs2cP4XCYLVu2SBecU6dO0W635UjIli1pQqEgnU6bdttDNBrn5MkA6XQf4XAfxaJOMrmP/v4Gth3DNE2mpqY4fPgwjUaDgYEBwM24c7kc5XIZVXXN6rdtG+DKK32AjmkajI1FGBlJSIN7QcKKxWIyy7Esi4WFBSYnJ3nggQcoFApS9btt2zaJwVw5SypuzIJf3G63ZckyFArR19cnhXsCbOEqbR0ikSqNRp39+21CoTqOoxMM6qxfH+XgQY1EwssVV+iEwwrpdIK3vz3IP/6jxeHDDRyng67H2LpV5fbbFzGMnwiWRKAoFAqUSiVOnoROp87gYAKfL4mi2ASDJo5jcOBAgNe/3i8DoRsA4cYbj/PVr54ANGACOA4EgQiOkyASGWPNmgClUkmiMPfu3Uu9Xmft2ghHj15HPB7B4/HT7bZot0FVO2zZcrYxgigji8wS3O+CKA+LLHhlRiz63iITEcASAepoNpsEAgFyuRx79+7jzjunePDB5rKYL8DP/3yU170uwOjo6FkWkAIAI0RswqVKsL7D4TDz8ybf/Gader3Npk1d1q61icfj8hhCNS0es1rrYs+EV+P8nglh1jO1fqaCsGmaHDp0iEqlwtVXX008Hj+vY11o8FQUZdWCueixCQDH9u3bZaA512M825mw2xN01dt9fX0SWzc7OyuJRktLS/T29srRllarRTSq8PrXR7nnHpuZmQiBQITR0UVarXni8R7S6TSNRoNms8natWtlP7a3t5dTp07JrFS4EwWDQc6cOYmqTiwreIcBQ/YUVVWVPT4xYzo3N8fU1BQ/+tGPOHHiBH6/n1qtxvj4uHQjEtaEoVCIdDpNOByWfUNFcd2dBgfdnqZ4L4WSW9OCPPJIjZMnu/zHf3SZm2vjOBq2rdBoRNmwoUGno3P4cAhVjeP1htm3z8cv/7JNT4+foSEvY2M2d99do1ZzGBmpcMUVXWy7Sqfjl2hM0VerVqvLwSOCptUIBAZQFNenWFX9eL0auh7B63UzR5FJFgoFdH0v69bNcPp0FzgNRPB6VRQlSjy+neuuC+LxtEgmkwwMDEg7xHA4zFVXVThzJsrsbJlk0ott99Lp9LFpE9x22096r8I0QLQoBFs5lUpJyph4X8VMsDDGEEAO0ecFZJVBGHtMTU3x7W83+M//VHF72v10OsN89ateTLOfP/szRV4D2WwWgf5c2VsW4ye2bfPlL3f50z9t0mzWcTcnDrt3a7zvfe5YlEBjCtX4+Zo0PNG6VIhZF7KeiRGlp3uJDeUlH4TPVZhVr9fZt28ffr+fG2644byb+qttBnGhx7Asi4MHD0oAx+OZfD/eWg0TiKf6OsT57927l3Xr1kklqhDUKIoie2mqqsogLMq21WqVfH6OK690eOELQ0CJo0dPcODAAvm8IsUwpVJJCmAEe9YwDIaGhohEIqTTaa666ippqyeCs6qqkhdcLpel2EaUMpvNJrOzs9LQY6W3aTwep9VqkUql6OnpQdd1aV/oOA7VapVGo0EoFCIejxOPx9E0nVbLwONxy6NLS3E+8xmL06cbTE+bFItN1q2LsnFjg3a7xtKSjzNnggSDYdas8RGNBlFVL7OzHv7lX/xs2uQGqXi8y803V5ZFaQaVyk82AMJ0oFDw8cgjNRynhW2bbN7s55571mFZKRTFwLYtms0qrZbG5Zd3UdWf9FI7nQ779u1jenqanTvLLC5WaTbDhMPb6XQWCQSi3HqrxthYlHB4kL6+PsLhMHv27KFarZLJZBgZ8fDOd3r5wQ+SZDJ9hMNJdu9ucNttcwQCaWm3mMlkKJVKlEolfD6fnAkWZXuxKRUoRNEG6HQ6lEolWq0W5XKZZrMps3Hx+4VCgUqlzT33+HAV2jqBwACh0HYMI8l//dcgCwtVtm3zy/vGyvuO2CCIOeTZWQ9/8icmrVYVv7+AaXYxzQb33dfHv/6rj9e8xp1TF2XoC0HhPta6FDLh1RhRupRclFaufD7Pf/7nf3Lfffdxyy23XPpB+InWSlbwoUOHGBsbO6dy7RMdazXO6UIzatG/CwQCEsBxvuvZyoRFO8BxHK688kp8Ph+ZTEbShvL5/Flm9mvWrGHNmjWUSiWy2awUvAgFaqlUZHJykrm5OSkyEr7A4XBYjqPE43E5K+r1egkEAoyMjDA8PCznQwVIQ5QIW60WiUQCTdOIxWLouk6r1eLIkSPSJ3h+fh5wVZNr166VJeRYLEahUJBlxlqthuM4lEolyuWy7F+eOuXl4Yf9zM2FCIdNrr7az4EDbmbY16dy5oyDz9dhcTG8TN+yGB6O8cADQYaHfUSjXXTdZT4PDSlMTRmcPAnj41VJxhLCISHe6nQ6mGaUz3xmkL17HQwjTyzm4+abbV7+8gATEz386EcO+bwOtKnXK2zfnuDGG9toWlD2rcVc7NjYGMlkkmg0y/R0glzOIRBIcPXVfdx0U0giIU3TlOQ5v9/P4OAgY2NjjIyM8NrXdqlWDUKhAvG4j/l55KYln89L8plwMhIZrq7r0uhAfI6i4iTIY/l8nlwuJ00qVrpedbtdWq0W2axKtepD0+L4/Zvw+0fQ9Rh+f5xKxcOpU34uv/wntoXC8EO8B6ZpyqrHd75j0Wot4fEsYVlNLKuGrvswTbjrLpO3vS0iedbhcHhVjAge/R272HvCq1GOfjzu9MW8isUi7373u7n77ruZnZ1159Of7ZN6Opf4Ih4+fJjLLruMvr6+p3ysi8F8AVwARz6fJxwOc8011zzlL9uFZuRPZTNhWRaHDh2S1nICCeiaBWRot9tyTKS3t5dIJMLg4CCGYZDL5WQJu1gsSjC/a2SfW1YF+6TqVDgGCfN3XdeZm5tjeHgYcKHqmzdvlnQqx3HNzMUNu16vs7i4SG9vr5wxFvO0Auigqipr1qyRZe0bbrgBv98v+7+VSkWSk86cOSP9e8VrP3bM4r77BjBNSCS65PN+vvCFXtptjV27unS7xnL/M45hOOTzYYaH/bRaHkDBstpomg9Q0LQgpunC7mu1nwR9j8cjkZWJRGK5hGrw93/fxwMP+AkGzxCLdWg0dL7+9QB9fTF+7dfybNzo8OCDCu12k82bTV75Sge/35LVCUACLYSXbywW4q1vHZMVAF3XpSI8FApJyMVKcIfor8diUXp6XGs6v9/PwsKCzGS9Xq8UY/l8PhoNFduOsnZtD8lkAkCWm0WZW3yW2WyWZrNJsViUGEpVVSXQI5t1Vdt+v4mqelCUFNHoDhQlSKfjp1LRsawu3a4PTXNft2ma0j9X2CCK9zqbzTI7WwcyOE4d0zRQFA1dH8WyAtTrjkRkPpEd4IWsiz0TXq1y9KWUCQs86ic/+UlOnjzJ5OQkH/zgB1lcXHxuBOHHyso6nQ5nzpyh2+2ye/fuCxY+rBZy8kKOI8qf0WhUBoanulajHH0+j+90OhL/dsUVV3DffffJmV3RuwsGgxiGwfz8PI7jSJrT7Owsk5OTxGIxOW/p2uxNSoGM+HkymVwmbYWkynj9+vXU63U6nQ79/f34fD4SiQThcFjOgiqKQi6XY2pqisXFRdk7NAxDmj2IwCyOFY/HiUajaJrGrl27WLt2LZqmySBUrVZptVrcf//90jVJmEH4fH7uuSdKpaIyMLCEYUAwmEZRYhQKKsWiiddbJBCIUS4P4PWWl8ftFBoNL9Fok0ajRadj4PH4AI2lpQaxmEo6XaNebzA/H+fkSQVFgV27YsTjbsY+NRXnyJEIPT1lHKdNOBwjHO4wP+/wH/+hcM01NW6/PcJll81LUVqn4xAOp2QGl8/nWVpaIhgMEovFGB8fJ5FwA6LoN7fbbRn4ent7JalMkIK8Xi+tlkKppLF2rZ9I5OzvqKIoErQRCARYWvLzla+k2bOnB1X1cdllCu9+t82WLTVyuZxEVArBlaBbVatVqQIHpHBLiORisRi2bbNrl48f/1il03GoVFQsK4Hbz1X44AeD2HaXt73NVVILelqj0ZAVFhH8BwdrgINpegEDVQ3iOCaOY7JjR0xWEqLR6Kr2gsW62HvCq7FJMAzjgg11nskl7pUHDx5k9+7daJrGxMSEe694ls/taVmlUon9+/dLc+/VUB6uRhn5qR7Htm1OnDjBwsICO3fuJJPJrIpI7JkKwrVajb1790oMYbfblSMdon9rmuZP/DV1XToJHTp0iJMnT1IqlTh69CihUIgdO3bg8XgoFovSnCEQCODxeGTf0Z0vDkpamN/vZ2hoSN6MVwINDMOQ8IxcLke1WqXZbJJOp2WZ2ufzUSwWpfimp6eHSCSC4zgMDAzIAJxMJqXhgLDNK5fL6LqOaZoEAgH6+/uJxQb5wQ/GWbcuQizWwbYdAoF+HMdchm34icc9pNNVKhWHbNYgEmlx5AjoeokrryywsBDmxAmVRCIgb7wvfGELTWvyta8lefjhfhqNCrat8YMfJLjttja/8is6jUZ8eWb3JIZRxutN4Pf3Eo/rLC6WWVgo4zhuL1bAazqdjvxMisUiR48eldUA0RoA97sXDAZJpVKyFy6qFsJX2Q3AMT75SS//8R9tDMMhnQ7wtreFecc7HFQVqRCPRCLLiuY4H/5wmJkZnWjUAgwefFDhHe/o8Md/PE0slpfiPcH3brfb8vMWG7NwOEwqlSIUCknXqlKpRKFQ4M1vTlKvb+Do0QiQBFRU1SaZ9NDtwoc+5OXaa9s0GnU5i7ySAiYy/V272nz9623y+Q6gY1lDWJarrH/Xu6LL5LHQ0xKA4WcjE77UesLi8wgGg3KGXXDbn1NBWPRKT548yYYNGwgGg5w8eXJVjv1sCbNWAjgEUCSbza5KEH4mRpTy+Tz79+9nbGyM9evXy36dAKnkcjlOnz5NvV4nmUzKfp3gLE9MTFCpVEilUhSLRal8LRQKALKkKTxhBUijUCjQ39+PruuSwCVGgcrlsszShGFBJpOhXq+TzWalcCoQCBCLxejr65NlZtFTjMfjCD9gYfStaZo8rm27c7j5fF6aBAgDh0gkwpo1I4RCQRqNNvG4hs8XwePRSaVMIpEqpZJFMjlMPL5EOl0hEGgxMqLQ328zNjbH4GCFTCbA8eMtikWFZLLE5ZeX6etb4K67Ynz/+2lSqSrDwza67qHRMLnrLj833hggnYZOZwLHyeDz+bHtDqrqpV5vE4l08XhMKpUKsVhMlvjF+yuyTLefGeHIkTyFgsGaNSpeLwwODjI8PCyrC0KvIFjgIiD+zd94+d73coRCcfx+D9lsib/6q15A4Vd+xZDXmNi83HmnwtycSU9PFyjjODa9vSlmZjJ8/etlfu3XvJTL5Z8S8BmGa+zg8/kkFjQUCmGa7msURC0BYvnCFwLcdptGqaQSDisEAh40zYvfD+Uy3HFHhyuvzElTeVGGFr7VlUoFx2nzrncZfOMbXg4dGsBxYmzZYvFbv6Vzww1eLEuTCcLTsS72ILxaxKxLSR0tXu9rX/taPvShD3HXXXdRKBSIx+PPnSBsmiZHjhyhWCyya9cuEokEhUJh1Ybgnw1h1uMBOFZLYf10Z8Kzs7McP36crVu3MjQ0BECtZjE726JWs6U/78LCgix7ih7x7OysvAF3Oh1Onz6NruvSDEBg4DqdjsxGBbM3EAjQ19eHpmnMzMzIG3Cn06FYLFKv16WoQ4D3BVWr1XJdk/r7+xkYGKC/v59IJCJJV9PT0wCk02k0TWNoaEgStQKBgNxECECH6M2KjFCoont7Y7zwhWG+/OUuhuEnGAzQbjeZnfVw5ZUGa9aUOXzYptEoMjKS44YbdK68MrAMlwji8cQYHXV45Sv76e/vWRYJecjnFe6+ux9Ni+H31+l2VbpdG8hRLEa5444qL37xCcLhAlNTKqkUy71bFcOwednL4vT0uEp1oRoX7G3xugAWFz38/d9nmJlpAr309AzylrdEefWrPTiOLWd3m80mZ86coVarSRV8Ph/kvvs6hEIm4bBreB8IhKjXdb74RZU3vKErS76WZdHtdtm/37vce/VgWV0UxaTTqaEoVU6cMOh2FdlrFo5ZgoolNmrCU1jAOQTc5CdjYRqm2cGyUgSDKqGQgqquvNF3yOcrtFoteb2KoC/IarZt4/P5GBnx8J73eOnpSVIqFQgGFWIxqFZdtf/TmcVdCsKsC8VqCo73pbLE/fIlL3kJR48e5fd+7/fIZDJMTk4+N4Jwo9Fg7969eDwebrjhBrlDWq3AuZrHOtfjZLNZDh48+JgAjgsNoOI8nq4g7DgOJ06cYH5+nquuuopkMolpwr33OvzXf1UoFJrMz4c4dOghTPNBWi03UEWjUcbGXGFPoVCQJSeBGbz88stlMBdZc71el+5IYtY1FApRqVQ4c+YMU1NThMNhBgYGJOlIzIgmEgna7bYUhYlgE4/HGRwcJJ1OE4vFqFarFAoFjhw5wvz8vHTdEXjNVqt1FnNcwCCEylpQkzRNIx6Pywz9Va+CTMbPvn0hFhc7WFabdDrLG97QoK+vws6dearVFh5Pm2DQT7frBkGxgRBZnShr2ba9vElJ4ve3CYd1FEVH131oWg+1WhBVLVIoZLj22ilqNYVSaZB2+0piMYWdO+eIRqfYu7e7/JmZpNNpd7e+rKp2e8F1PvGJBGfOOPT0RPH7I1QqJp/6VILxcQ833dSV89QHDhyQpDJRWZiZ8dFqQW9vGq/Xh6J48Xp70TSVYtHi5Mk6+XxeZpvdbhdVjWKaGo1GE10P4PUOYVkVbDtCf78fRcnjwkEiBAIatVpNAjyEmlnXdTliJjLhfD6PaZr09fXJcadrrlG4+26blZd3twum2WbTppo0dBAtgEQiIeljK2Ew4XB4ebbcy+DgoKzuCAX307Uu9kzYtu0LLsUL/OiltMRn8u53v5vXv/71/PCHP2Rubu7SD8KO47Bnzx56enrYuHHjWTvA1QzCqynMeqLgdy4AjtUChzwdQVjML9dqtbP41Q88oPCNb7RQ1TLptJejRyf41rdO4fdPsWaNTjKZRFEU+vv75Xyu1+slmUzSarVYu3YtAwMDclxIiGv8fr+kJolsq1qt8sADDzA5OSnFOoJ2JcrhpmlKoIMomwp4S19fHz09PSSTSTqdDgsLC8zMzHDixAk5gpRKpfD7/dTrdQlqEPOq4n0Q4i6h8h4ZGWFgYIBEIrFM/FH43d8NsH9/mVOnqng8XdLpeXw+i1KpgW3X0bQWjuOWZcUNX5R1hQjt4EH41rd0Tp4MMDgYJhi0aTRqtNseAgEdyzJwnCQeT4DLLnMtC3/+55/P4OAj1Go2118/wvBwl1BoiGo1QqFQWPZLrlCtViXvWVgCHjo0QibTT3+/jd+v4TgN0mmVxcUaX/pSkJtv1gkGg+RyOUkdK5VKNJtNenp66O314/fH0bQ+AgFX3a2qOtWqg6rWsKwsMzMzklJmmiZr1uTx+9dTr8eIx01Ms0a1auLx6FxxRYCPfGQDBw+m0DSHXbs6/NIvdRkd7coZcUE6c+ewl8hmsxJZKWa6hU/0299e55FHVMrlKD4f2DZYlsEVV1S58so6R47UpFo+kUhQKnn4279t8sMf5lGUMjt2FHn5y103LMMwlk04utKp6unOUi/2ILwa53eplaPFEpW9wcFB3vSmNwHPAWKWoihcf/31jzlrt9qZ8GoJsx7vnCzL4vDhw08K4FhN6taFPP7R70e73Wbv3r1omsZ1110nS26tFtxzTwPbnsbjyVEqdWi3j+HxdKjVgjQaXRIJh7GxMen3K0aHGo0GyWSSTZs2oaqqxDyKIPqTkq+fU6c8tFpZEokzHDlymGq1SjKZlKVIkbl0u136+vpIJpNSSCRu1slkUmZ/tm2zsLAg+9ZiLMKyLCnaMk1TGgwEAgEJhxAjQaZpSkeWgYEBAoGAVA+7vXEYGMgTj7coFovL4h6VarUK8FNBRPgiRyIREokEDz6o8uEPm+RyS4RCOtmsjm2Dz2cwO+slErFQVR3T9HDNNRbPf76HiQmX7hUKBRkbi7N1a2uZa60xMDAgN0TiNYgeajabZXFxkfvv9+M4Jl5vHMvSMYwinU4W286wb1+c++/P09/fL0e/arUapmkyPj7Oli1buPbaGP/+73EOHfLi9Tr4fFCvQ7Xa5JZbpsnnJ8jlcrKv3t/fz5YtHt7ylg5f/WqIcrkf224QDoe49VabL31Jp1pN4vNpWFaJu++2OHw4wqc+ZTA25pNVCDGnLWbKhTjQ7/czPT1NOBwmEAhw+eVBPvc5jU99Ch54AIJBm9tvX+K1r82jKI4EvyQSCZaWVF7/+tPY9jRgAx4eecTHmTP9/M3f+AFXPyDEgb29vU97gLzY1dGrNaJ0KZWjxWv+m7/5GzKZjGTPezyeSz8IA2dlICvXpVSOFgFMVdUnBXBcLJnwytdRq9XYs2cPyWSS7du3n/Ulq1Rs5ucX8HhKeDx+Mplj1GpFAgF3DtU0vYyMjHDZZZfJ0nN/f78ELIyNjZFIJKjX63S7Xal8bjQazM3NcfRoh4cf7jI1ZWKaXSxrinC4zPr1PjmPKdxywPV87e3tBZA3etEHFsb2uq5Lp6OZmRlyuZyE7afTaQYGBlAUhZGREdnjEhlqrVaT5W3LsjBNUwqThLOTAEyUy2UajQamadJoNKSpgNhoCE6yUPmLvqff76fRaPG5zykUi11GR00CgQSGUSebbWMYbXbvrjA/nyYWS3DrrRa7d+fQtBYjIyOypyZGjnRdp16vy/cHkM8Hbt+4t7cXj8fDwMAStt3C640QCIRot1v4fGFaLYOBga7clADyPff7/bRaLQ4fPozf7+cd7+jl4x/fxJkzAUxTweu1uOaaPG96U41MpiodhsRGZ3BwkDe8IcGLXwzHj0OrpXPDDTr/8i8+KhUvqZSGomjYdgCfr0UuF+Kb32zwzne6I2GBgNtPz2azZLNZCoUC5XKZ3t5eObbW398vA9gNN3h43vPaOA50ux0KhRaVikWlYkjm9ve+V+eP/iiMS9nyAgpgADqFQoN/+zeFX//1AQYHByUZ65kooV4KmfDP2pywWHv37uWf//mfiUQiRKNRt03ybJ/U07lEwFuNi/LpVEeXy2X27dsnHZ2e7AJdjUx4NXvCwkBifHycdevW/dR7rWktNK1OpWLRbs+Tz8/iWuv1EAwmiURGmJ+/mh/+MMq2bR42bQJVRQqtBgcHqdVqsg8ksrIDBw6wuNjghz/sodWqkkjUaTbnmZtbIJMxiMeDjI/3sG3bNslnFobqYixJKGiF+lksYf4+OTnJxMSEhPMnk0l27NghoRViNyuIUOJaE+M76XSaQqEgz3vlRkAEXkGGEv1dl8fskqDi8bjcmIhji/nXej3M1FSLYLCMbQcxzQ6m2SCR0Mlkhtm+fYHf/V0/IyN+crkF5ufnZUa2bt06fD4ftVoNTdNkABaq3ZWbFjErLUaMBgYmiUYVpqc79Pf3oqoG5XIQVS1x660KY2Nj0nhDPNayLPL5vGRne71l/t//9wAHD4bIZhUikQzh8CTHj7ekIMuyLCKRiHStCoeTxOMKw8ONZQV6kwMHelDVIIri4DiuwxRUAQ8nTvjQtK7cDIk5+4WFBRqNBuFwWJa7hRexpmnyc3Cvc+SssQjeLoVtbDkA13Ez4CZQAxLAdiDJiRMqPT0+aRLxTImlLgVh1s+agYP4PP7n//yf6LrO29/+dm666SbgOVCOfqK10jf3YgnCjw5+4sawceNGRkdHz+k8L4ZMWJyD6JVu27ZNjuqsXK5qN8eGDSZf+UqHTCaDaXrodvtpNIIkEkMYxmXAICdPOiwu9mCaFtu2ZeX8aafTYW5ujnq9LjPIhYUFCoUCU1M6i4sZNmyIUi5XKRanCQZbmGYKy+pn165dbNq0CcMwZH9O13UWFxdpNBrSTUn07cQ5Ly4ukslkOHPmDIAUYfX390tnJNGbFhlwo9GgUqlIJfX8vM6JE16OHx9ifDzIrbfabNvWJBIJ4/F4qNfr0nIRkEHc6/VK95+VFn2iHy5mYFXVi2UVlj/LBp2Ohd/fs6zWNojHQxKg0Wg0pA1fuVymXq+fRbAKBAJYliX7pmK1Wi0mJyepVquSqR2Lqbz97SZf+1qJQiEJJAmH27z85SpvfGOIRCJOp9OR2EjTNOl2u0xPTxMIBGRZPp1Os2uXdxlZmmJxMcmxY8dwHIdutyv1BAcOBPnQh/zMzPjw+zvccEOVt72tSSQC0WgK0LDt+vKfyvL3PUQi4W5YAoEAMzMzHDt2jKWlJTqdDs1mk9HRUdLptNzsiKAvKFZi43D69GlZFYlEIgQCAb797TRuBmwAPmAYF+yRAgYBg0gkSCDgkZqDC1UEn+u62DPhCy1Hi+vjUixH/9Iv/RIf+9jHuOmmmyQv/2ciCK9GD2K1iVmPBnCkUqnzOsazPScMbgYsPIDFzX7lchyHYrG4TBUK0Ghkse0wtu1Qq9nYdozR0XXs2rUWj0eh02nRaDS5+26bdeuCDA8HaTabHD9+nIWFBWmGIEaN3PEVP47TQtcDQBWfz0sgMIiiePH5RlizZo1UKIfDYSzLYmlpiXK5LO3nRHlXBKNqtSq5wMLtSMwgC+ylwB4KlayYH56dnWVpaYmZGQ/f/W4/jYar/s1mvZw4ofP613f5hV/wy8AknH1isTgTE10yGYVg0Mf69e5ojktlcpieHqDRiJJOq2zZUsfjaWDbVbZsUdi3bx2JRBtNM9G0FPm8w9hYh1tuiRKNRpcpUh7277fpdqts3uyTWb9Q6orMfKU5veM4LCwssLCwgM/no91u02q1CIfD9PfXee97fXg8fny+OJs2WaRSLhVrenpaqpOFB2+5XJZ0NNM02bp1q3yOTqdDpVLBMAzpNez6FI8yM9PLP/xDP60Wy7hQD9/7np/FxQ7vf/9Rdu6c5p57AuTzRTyeOo7TotNxUNUOa9cuMDfnl8IwRVHktZBMJiVvWjyn2IyJYNlutyVYQSjchQXiwkIDiABxoIMbkM3ln9WBEC97mUduJlabD/1E62LvCa+WgcOllAmLJYS3gKymPSeC8ONdcCuD8IWulcjB1TjOnj17zgJwnO8xns1ytGmaFAoFTNN8wvNvt9s0m00yGZWDBw127Aji9a6lXM6TyShkMlcQCm0nGvVTLmdQFAiFOpw+neHEiQCBQEtmImLnKwKeKI8Gg23C4fUkEoOUy7Xl8nacZjPI1q3jckRFlHSz2azMhMR8qIB4iF6wyDQF/EGANgR/3DRNCeYQrGphMDE7O0utVufuu1MUi022bVNoNByCQZVcrsO3v53kZS/z0OlU5E7YNL187nN+HnxQpdGoo+stxse9vPa1DUxT4Z/+yc/Skmf5S2sxONjLO99ZZ2CgzFvfGqHRMDh1qo2iBPF42vT1+Xjve+P098Px4x3+z/9p8Z3vBKnXdUIhL4ODCX7pl1SuuWZ2GYWJnKEVWZSAqQgBnLjmRkZGJJqzr6+Pvr4Uuq6hKDqNRoNjx46Rz+fl5kJwn3O5HGNjY0SjUbxe77KzkyrL1WK8RzgciXnbL385QrPpIZ0OAw0cBzodhWPHQhSLV/LWt7ZpNMr83//bpl4vAiZeb5BXvzrPtm1NqtUup06dYm5ujmw2KwlXW7dulUFYlOWF2lwsYacpNkuiUjAwMMDISIRTp9q4Zegmrv1hCggBCs97XpDXvEbFcTT5/j1T61LIhFdDHX0p9YTF5/+Wt7yFj370o8RiMa655prnjjDr8dZqB+HVUEdblsXMzAyJROIsAMf5rGcT1iEEZLZt09/f/7gBWHituphDi2ZTZ9OmATRNJxTSsSwHyxqlVgtTrZap1TKYpsHCQpvpaYdDh/z4fBVMc1F+aYW8H5A9tuuv78Nx1nD4cA3T1NC0UZrN9Wze7OPGGx1Zcl1JSapWq7I3KTyC4/G4hG2IG28oFGLt2rWyTBmPxzEMQyqnxciO8EFeXFxkaWmJel0jl7NJp3U8niiq2kHXAwwNechkkpw82WDDBktuKL7xDR/f//4SkUiO3l6XWXz4cJdyeQmfT2d6WmNkRCORGMC2baan23z60xYf/KCXyy7r56//WuP731colWIkkwpXXVVh82YPDz3k4f/5f2yOH9ewLB+a5sVxUpTLOp/+tIPjpNi+fVqW6sW418prJJlMyg2LwHOKjYrI1MWI1qFDh3jwwQdlFqzrOsPDw6iqKudk2+22BK6Au6FrNpvU63VpYylsI0OhCEeOJAkEwHHq2HYVRQni82k0myGOHetwzTVlXvayJcbGiuzfb+HzaVx3ncbIiJ90ers0khDENFcVHqK3t5d2u02hUKDVakkeuQB7CFBMqVSSY2i9vb2Ew2Gmp6e55ZYcP/hBEFeM5QWCQA9g8KY3abz3vQaaphIOx5/xjO1i7wn/LBKzxPrABz7A0tIS7373uwkEAm7V6dk+qadziZnQi8H9CFwARy6XIxaLceWVVz7l3eBqlaPP9xjVapU9e/aQTqcfs/y8cq1k/0Yi7sgM6KiqQjw+TLUKpplibi7D6dMH8Xgc9u+vMTvbwutdwwMPVNizp8LmzRXWrHHFNb29vdLJSKATh4aS3HTTElDi8OEgfn+Sa67p49prm/T3m7KfKoRYgg1tWZZEXAYCAalGFq0C4WcsZoKTyaTM2ATjulqtSsVtJpORHr2BgA9F8eD3D+HxhIAGoKLrKVTVi6bVEY5Qi4sl/vM/dXS9hs/XxHG8qGqZ3t4W8/Mauh4mmVQJhdL4/VEA+vszzM8r5HIpRkcNEokAr3hFlFarSb0OqurDslQ++lEPCwuuuCgadXAcm07HolKBRELhe98LsnmzKYVTbhnay+HDKpmMwuioyvr17vsjgrTX66VWcw0TOp2OHKMrl8tMTExIUZrjOJLb3e12GR8fl+pqoR0QGxgx0uX3+wmHw0QiEebm5rAsk2jUIJvV8XorQAdN82HbCrbdpNEocdddZXS9S2+vwe23a0QiITRNkZm2gL6s5Edv2bJFiuni8TipVIp0Oo2wJ2y321KZLxjR4r+ui1ODUEjjV3/V4HOfC9LpOEAIXTf4jd9o8T/+R4pI5CdZ9jOdlf4sZMKXajna4/Hw93//97z//e/nyJEjz30rQ7g4LAhXAjhc39XoBV2EqyXMOp/XI9TIa9euZe3atZw8eVKWMh9rifKmy3suEIt1mJpKMzJioSg2zSaoapmRkUNMTs5RKmlMT9fp7R1i584AHs8C2WyHgweDDA7qjI0F2Lhxo1TyCkykaZosLR1j+3YPY2NtIpEO11+fXn6NuhTmiH6mYRjyyxuNRuWogKIo1Go1yuUymUwGXdelclaM1wDS6UnAF5rNpszOdV1fJnNFmJgY4OTJFAMDXjTNh6aFmZ/3MjiYI50uMjeXQ1VVKhWTSmURVa3RaFTR9RgeT4RAoHc54LQIBmOoagDLqgMaoVACrzeG36/Q6ZSWBWZ+PvOZGA884MNxggwOahw7pi2bWdQAC/Ch61XqdYVo1EMmY8l+cLvdZmHB5o/+yM++fSrttkIw6HDNNTbvelcVr7ctqx4zMzMUCoVlF6QWfX19ZLNZyuWydLWKx+NSgBWNRmV2K2hhwuBCQE5E773T6aBpGobhZpKveY2Pv/3bJu12F6/XwLYLVCoxQOELX/DQajmAwthYgN/8TYdQyJblbo/HQ6lUQtd1aYEpKiLRaJTx8XHZoxbXhBhlEte2aEVomiZdkk6dOkWr1WJ0NMtv/VaVxcUIwWCbgYEDRKM+9u5NEI1GGRgYkMIv8eeZyFAvhSB8ocKsld/jS3GNjIwwODjoVoye7ZN5utezHYQfDeAQPqkXsp7pEaWpqSlOnTrFjh076O/vl+fweMQskX1alrU8B1tg1642997b4vBhE58vQbXqcMUVpxkfX6BY1PnWtzL09Gjs2hUhGu3gOBFGR6NMTHQpFBpcdpnbrxVm7H6/n3K5zKlTpyRwo9VqkkiE5UyuYPsGAgE5iiP6ub29vQSDQQmjECreTCYjb9zhcFjiF4V1oSixi5Jas9nEsqxlpx9TBp9f/uU+PvWpMKdPO9TrAQKBEL29OV796gqtVoVsNovP56PVyqLrWep1D8FgjFBoHFX1YJpp4nGLQCBEtdpDKqUvewbXyGZ1EgmVwUEX6GGaPj70oSCHD+skkwG8XjhwwCKXc+jv96JpacBCUSooiomiuMFz+/afqMF13cMHPxjigQc0Eokm8XiXel3hrrsKVKsZ3vnOhlSqixEfMfcqxFwDAwMyiG7evFkGoNHRUTn7vFLRKkr6IuMUKvNwOIzX67KiX/e6Je67r8SePRbNZgRFCaAoESyrDRiEwwatVo0zZ0J87GMRPvtZnb6+BI7jcPDgQR5++GFKpRLlcpm1a9eyZcsWufGoVCroui5Jae576bpeVatVGo0GgOSRB4NB6bDl8XgkhW3btuSyD7bbshDaEcdxWFpaktm14zj4fL6zgrKAsKxWkBaAlIs5CF9oOVpsei8ldbRYtm1zzz33cPfdd8tK2nMiCD/RBfdsuR/BYwM4MpnME2aQ57KeqREl27Y5fvw4mUyGq6+++qyb1WM9XiiEBTv5kUem+OY391AuN+jr87Npk8PGjT0kEiFmZw+wZUuUdtvG5ysSi9nEYhvp7e3BcTS83gCm2UTXa/j9HinGEqMejuOQy+UolUqSQCUsDcVNUNC1xMbAMAz5s8HBQbxeL6FQiG63S6lUYnZ2lpMnT8r+s1BDi56geN8FgUmUWgXjuL+/n0QiQV9fH4FAgD/8Q4MHH9TYv7/OyIjDtm0FwuEmuVxFjkdFoyFe+MIxvvlNh1rNQdNMDCOCYUR58Yt93Hxzkr/6Kx8zM2VCoQbVapdAwMvrX68zNpbEMAx+8AMfx46pjIzYBAIOhlFjZKRAJqNSKKQIBiM0GhqaptLpaKiqTSzmcPvtHenqNDUVZO9eH7FYHU1bpNvtAHkc5zT33mvyghdESadL0sBe9FqFmnxxcVGOcCUSCYaHh2VW3Gg0CAaDZzkqtdttOaddqVTw+XwEg0Ep+hNz1s1mnve/v8mxY36OHi2TSnn50pcUyuUWfn8O2zYIhyNYVoy5OS+PPAIve5nC4uIihw4dktfHSoCIUEQrikI4HD6rtdLtdpfPG374Q53vfAe63RDXXmtz221FqtVZZmZmCAaDeL1eaZ1Yq9WkvaXAr6bT6bO+G+LYIii3Wi2pxBdBWgT8lYFZBOtzGXMSr/Fi7glfaDlaBOFLMRP+6le/yjvf+U7Wrl3LgQMH3CmBZ/uknu71bLgfgeurum/fPnp7e88CcKyc+3yq65kQZpmmyYEDB2i1Wlx33XU/JcB6rHNotVpy7vZ735vmk5+8h8XFRXQdPJ444+Mp3vKWNP39BmfOTFGtjkm7vCuuWMuxY5vpdNqEw0E0zUurpRGPRxkamscwahJiEQ6HqdVqcuYzFovR09MjzQ1EZitY0Y7jyPMXilgxn9vpdCgUCkxMTHDgwAE5C+r1euXY2MoRHuGb22g0qNVq0m9YqK9FX9FV0np55SvjjI5mpFBpaSmLbdts2LCBxcVFVFXl+c83CAR8PPRQjGrVQyQS4JZbHH7xFw36+pqEwyZ33qly+rTCtm1BXvnKADfc0MXnc0u8k5MOtq3g93uwbYN2ewHbbtDT46VYtIjF/Jiml3o9gKY5bNzY5Xd+x8tll9kYhjuLXS5rNBomuj5Js7mAqgaoVI7gOGXabZvJyQrRaJ90o1IURc7Mdruu4YMQwEUiEer1usx8vV4viURCvj8i+GSzWVqtFt1ul2azSaFQ5siRGMWiiuMEGBvr4PFohEIBrrtO59prHRRF5x//sY3Pp6CqfhxHAVwbRcepMD3to1w2qdVq+Hw+WZJWVVUaKKxbt47+/n7JchaBTZQ5HQfe+16Hu+8W13eII0csvvzlOX71V2ekE1c8HpfXWigUoq+vD5/Ph9frPWvDuvIaerzAIYK0CM4iMAv6mlDiiw3FY2XRKzPDizkTvtBytJhjv9SCsGmafOADH+CP/uiPeNe73kU8Huehhx762QjCF5p5iuOca+B7IgDHxUC7erLzaLVa7N27F6/Xy7XXXvuYqL1HB3F3FCmDYRjMzeX5/OcfYXFxknjcRNNUdB1yuTH27/fxohe5StSenh7JYb7mmu1YlsKxYzbhsIGu+3GcEJddVqG/v4mmaTQaDcmAPnjwoDQaCAaDjIyMyLlfRVEIhULL5d6WvNF3Oh36+voApN9tJpNhcXGR48ePs7S0RCQSoVKp0N/fL0vMwk9YbJ7EfJ+4eYtxGkFdEkFelB/F+zQ4OCjHXdrtNuD2zgcH07zpTWHe/GYvlYqG49QIh6v4/TEsy+Lqqx127nRQFO9yJm5gWbbMnHp7VYTlnuM0sW0DVQ2g6xZXXdXmec8zmJ3ViMVUdu/28IIXKIRCHiYnf/J5joxY6HqGYvEEHk+ZTqeEZeXodvvw+yEe12TWLHjLAwMDlMtlWcqv1x0OHgzR6SQYG+vycz+XwO/X5XwuINna+XyeQqFAJpMhEAiQzfr5xCdiZDIOqppAVfv5znfy/MmfWITDLXw+33KQCxAOO2QyZcLhAo7TQlEidDohPJ4Yl10Wp9mcodFoyA2Rpmns3r2bDRs2EIvFCIVC1Go1ORvsvm9uFadSqXDXXUXuvtvABXF4gBZQotms8Z3vdLn8clP2vYVJg7iWNU2TJhvn+30UQfqxmPErg/TKQP3oIC3e5+PHj8sgvTJYP1PAkCdaF1ouv1Qz4U6nQy6X421vexuVSgVVVd0k5dk+sad7PRPGC2KdC4DjYqBdPdF5VCoV9u7d+6QIzZWPtyyL+fl5crkcuq7z4INTzM3NE4sZdLttdN1HMjlIKrWe+fkQ0WiS/v5+SWdau3Yt69b18KpXZUinm0xNdQmHDS67zM/ISAnLcs+h23UdgIQ7TyKRkChJUfoT/VwRLDqdjmQya5omWcyaplEqlSgUCszNzbG4uLgcSOqk02k2b94sR1VERiUCqkvDmieTydBouL3SZDJJKBSiWCwCbsYtBD2uOYJFoVCQ1CthJh8KheRsLFikUg6m6SEej0vLQrGRFJmkmFkVz/OCF3j5whdgdlahv9+DZcVYWKhimg6/8AsR3vEOgLb0X3bfF/usz7+vr8UVV5zm+9838PsjWFaBbrcP0/Rx/fUGY2MJGbRESd/NXovs2VPk4Yd93H33EN3uIF6vH8fpcscdHj76UYc1a8LyNYgy/MyMm1G6c9Yt/s//iZLJDBCJNNF1k3q9y+nTJn/7tzE+/OGgJFmVSiVuv73Mpz89T6XSIhRKYNt+2m2NjRth27YiuZxboVi/fr3MwNevXy/Hn4Q9ptiIgds6mp+fZ2lpiW9/WwjZLKAXF75RAkyOHdPZvNmtvqTTaaLRqBR5iXbJ09GrPJcgbRgGtVqNAwcOSC2DGMFaGaQfL4t+NLr16VqrkQmLsbNLaZmmSSgUIpvN0t/fT19fH4cOHfrZCMLPhDBLeKc+GYBjtUAbT0c5emlpiYMHD7J+/XrGx8efdLcqHi9mbzVNY2LiNA8+eJiFhRKBgEkkEmBkZJSxsasxjDCdDni9AfmFHxkZYWhoiFarhapW2bXL4PLLm3i97WVOsms1KDKMcDgsHXZEFizmlf1+P9FolHg8jnDMMU2TYrFIIBCQ6lbx33w+T61Wo1Qq4fP5SCQSNJtNRkZGZDkxnU7j9XplBi6gHFNTU5RKJXp7e88CT4RCIRqNBn6/S2oSfVGRIYventfrpVgsYhgGlUpFjueEw2EJEBHjPiKbU1VVsqMdx5GK7aEhnfe9r8uHP+zl8GEf5fIglpUkElH413/V2bq1yzXXKPJmLDY/lmXJikilUuGNb2zT7bZ54IEi7bZJPL6OF7xA5xWvcBgfH5ajSuLaPnq0yQc+MMjkZAiXn5wCGsTjLXQ9xOHDYT796Qgf/aiFcFNaXFxkcXGRWq22TAqLMTsbYWFBwe9v4/PFcBwPXm8dVU2wdy/MzVmMj7eo1WrMzc3xohc55HIO3/1ugEYjgMfT5dprI/zBH+gYRknOHAs7zMHBQQzDIJ1O4/P5JCVN3MQNw2BpaYmpqSkKhQLVagvwAwHc8TIbKC//v3uNCZGiUHU3m018Pt+KDdUzuxTF9U4WG7exsbGzgpQI0o/OpEX/XbRQRJB+vJL3hQZpIRy70CB8qWXB4Fa9br/9do4ePcratWt58YtfzAc/+MHnRhB+toVZ9XqdvXv3Eg6HnxTA8WyCNh7vPBzHYWpqiomJibMU0OdyDmKm0i3lVfnGNyY5dszENCPU6wqOM0CjcRXh8BgnTuTYtk0lGHSFOUKdLPyDRVAQgifR+9F1nVQqRTAYpFAooKoq8XicaDQqLQKF2EfcbMrlsuwZ1+t1OWoiBDXCJ7fdbtPpdOQozfj4OJs3b8bj8chgIxTQ3W6X+fn5ZWb11FnK6J6eHjkaI9TXYpa4VqvJzUKj0ZDULmF/KPrYom8prh9xoxI9WJHNi/K3yG4Abr7ZIpMx+MM/9DEw4KG/X0dROszMGPzv/+3wt39bweutyk3Iyk2iqAD09UV4+9vbPP/5LQwjzciISm9vQD6XoBT5/X6OHtX4lV/pLCuIS7ikqCbQYWmpj7GxEOGwzXe/66NUahIM/qQML5Tquq7T6XRot3uAHnw+F3phmg6WpeDx+DFNjVZLw7bduVBd16nVSvz8z8N11zWp1x1GR3t44QtdVfKhQwUWF3N0u24bIp1Oy83aSj3Aypu4gK0Iwtu2bR327QsuvyaA4vJ/vVx2mS6zX6GUF25U8Xj8vOl3q73EfeHR90QRpL1eL9Fo9DEftzJIiz9PFqQfHaifLEg/3vmdz+p0OnKs7VJa4XCYP/7jP6ZYLKKqKu9///tZWlp6bgThJ1pPtzArm81y8OBBxsbGZMnrqRznfNZqBXIBpjh27BjZbPYJPYwfvQR+M5vN0mw2UVWVU6eazM0NMjgYIRAoUCz60LT1zM2t5777dNatM7jiCpN83pSP6Xa71Go1WWIV/F7TNGVJrV6vn+VxOzg4yJo1awgEAhK2IewEDcOgWCxKRGGtVsPv9xMKhVzbsGUSUq1WI5vNMj8/TzAYJB6P4/f72bx5sxyFEiKkVquFoihUKhVOnTolZ2LF+ff09LBmzRqJaozH40QiEarVKh6PB4/HI89LAD2CwSADAwMSaSg2NCLYixuecFkS41AuVMTHzIxJLKaSSrnkqW63y7e+peL324yNgW0bdDoF+voc5ucjfPvbVV7+clvCJsLhMJqmyT5VpVKhXq9jWRYbNw4xODhItVqVN25N02TAsSyLP//zMo1GBbdU6wHSQBe3hFuiVusSDA5hWQrVKuh6V/aPhSWlEG5FozGCwTSm6UdVS5jmNJbVpdNJkUqtZ+fOII3Gouz527ZNMplkzZo18rNttVr80z+V+Oxnu2SzriDvppv8vPOdbptCKOgFsEP0TkWJfCUp7YorPDzySIjjxyP8xKKwApR53vNcwVe1WpUbOzGudTGMzIh+6/kGqHMJ0mKju1I4ViqVWFxcpNVquWYEy+5fj6fwFutCR5QuxUy4VCrxox/9iNe85jUA9Pb28uUvf/m/g/BTPc5KAMf27dsZGBg4p+NcLMIs0aMTDOvrrrvurC/Jky1Fccub9XpdCpgajTgeT4Le3gzBoI6uD1Gp7KDdjpHNznPllXX8fpuZmaXloOJh374S3W6JZNJaRhWGiEajFItFOXMqArAIEkIA4/f7ZY9TjG+I/m+hUKBer8sSrhhH0TSNWq1GpeLO6qqqKkVcArFYr9cJhULyplwul6nVahJ1KUZQREbU29uLqqpSkOP1emVWW6lUJJdYlKFXZrwiMxO91mazKQP1ylK2u2FS+PznQ/zLv4QpFi1CIY2XvEThrW8toCh1ZmYCeL0xbDtAt1vBspooioVlVcnlDAzDBhQcJ4iuB2Rpu9vtkkql5Fy3oijkcjmZQYr3Qtd1ut0uJ05kOXhQwQ3A4CIbZ3DLtf1AAMcJ0mhEWb/eJpl01dDCNMPj8UgXJbeE6+d5zyvx3e8mabfraJoH0+zBcZr8wi8YxOM+TNMNfLFYnFwuzuJijHhcRdNckd0Xv+jw4Q97cR2NLByny913pzGMAH/wB+57KxTxK4WGrVaLSqVCp9ORFZFQKMRHP5rma1+z+eY3CzQaOdatW2Dz5jkCgYQU3QljhkqlIq+BZ3s9XTPCQlwnpgEe63lFkF5Z8i6VSvLvYkYf4OjRo9Iw5dHl7ic7f5EJX8i65557+MhHPsKePXtYXFzkjjvu4JWvfCXgtic+8IEPcNdddzE5OUksFuNFL3oRf/7nf/6YTnFPtsRnMjExwetf/3qazabk4D/n/YTh6QnCKwEc11577WPuHJ/oOBdDJmyaJrlcjng8/pQY1uIcXCJTnVKpRLdroqpBIpE+isVBut0henvDhEJLKEqeEyd8KEqO3bttJiZGeeCBANVqB48nwpo1FjffbJJKuV/Gvr4+crmc7NVms+5oTyKRkKYAgMxGhQGAUB0KLrMIkrFYjHjctdir1Wpy155Op6nVajiOg23b1Go1UqmUtBoUJUdB0Vq7dq2EfgSDQYaGhohGozJDE1m8yJ4FeSsWi0lRULPZBJA3plAoJJXVIgC7jkFuyd51QXL4+tfjfPrTfrxeh2TSodlU+Pznm5w6leV974N16wI8+GCTnh4Ny6qiKBqG4UNR6mzalOTAAZPPfQ5mZ3sIhyPceGOaW2/NoSgKnU6HfD4vDRZEiVyU7EVfPp/Ps7Awi9spqOO6oYos2Au4EI12O008rvFrv9ak1apJ44SVN3Fh+VgsKvT2RohGW1QqYFk2oVCZn/95g3e+syvL4JOT8OEP65w+rQI6waDOO98Z5C1vMfj4x+tABigAOWAIWM/995ssLfmJxxsycxWr3W6TzWbJ5/O0Wi00TaO/v395kwS7dk2SSJyW872djoHX6yWXywEu9WhlW+ViKI8+Ww5KTxakASkc279/v3RDE5S1lUH68bJosfEWPeELeZ2NRoPLL7+ct7/97bz61a8+69+azSZ79+7lf//v/83ll19OqVTit3/7t3nFK17BI488ct7PJc5zYGCAN77xjezZs4fdu3fLKYv/DsLneZzHAnA80+ezcqziqVyI5XKZ2dlZfD4fO3fuPO8dvOM4Z7nvuDfSIrGYic/noVDwUSqFCQZVdL1Mq1VjwwabcLjOyZMtEolhDhwI09+vMjrqoVaDw4dtHCfKr/yKW9JLJBLypt1sGkxMlIhENIaHUzIIChci0adaScoSfeNUKkUqlSIWi8kAWCqVOHz4sCQfCcqVCODhsEvdEllrMpmUSupOp8PAwADDw8P09fXJQAvI8xFjUuL8V46HiMxbzFSLLFcECEGKEiW3aDS6vDmwufPOMF6vQ3+/u4nz+bq029M8+OAi993X5aabWuzfv5bpaYNYzEenU6dSabFhgwdFsfnQhxK0Wh5isRDVqsOXvqRw6FCQP/szR3otp1Ipms2m3FSIPncoFKJer5PNZjHNIn19ZWZmHGAMN/DawAZcW784u3aF+PVf73DjjW0efFCl2XRQ1RyVSolWq0U0GmVkZITDhyv85V+myOUSQBaIAh0aDZtNm1IEAq7z1dJSnfe+16JUcoAwuu6h3Y7wsY+FKJVOUyyeWH58/ScXKlkgwqlTCTZu7PxUFix66n6/X7YCDMMNtIcPH+bw4cOyf9dut6XndK1WY+PGjcTjcRRFYXR09FnvBYt1MdOyVuosRkZGfuo8V2bSIjBXKhVJHfv85z/PnXfeKVsAv/mbv8n4+Lj8s23btnOu5t1+++3cfvvtj/lvsViM733ve2f97BOf+ATXXHMNMzMzjI6OPoVXj9xs/+qv/iof+chHGBkZcdskT+lol9BarTlhRVGwLIv777//pwAc53uc1ejnwlP7wmUyGQ4dOiRLvE/lNQjwvnDRmZ2dJZfL4ffrrFtnceBAgkIhia5b2PY86XSDVErDtluo6ginTg2i6wuk0z58vhAeTxOv18vsbJSFBUgmSxiGgaJofOtbBb7znQVqtQ4+n5frrrN46Utt/H5blqNFT1iwoUVgHhgYkO5Huq6Tz+eZnZ3loYceolQqSQzl0NCQ9NIVpvfDw8OkUinm5uaYnp6mWq2iKIrkTSeTSRmwRKYYjUbPmsMU/GzhFBQMBuVn32q1zvLTFeMyYscvZoBVVaXT6TA7a1EotIjHfbD8tTWMCl5vhlIpRT5f5HnPa/L+98NnP9tlYSGEqnrYvbvKu98d5g/+QKfZ1BgZCaCqCrZtYNtNjh9PMzHRZmysI52vRGlRVGxM06RarXLo0CGOHz+OYRjcdBPccUeKRiOFG3hVoM62bVE+/ekQW7e2+cIXFK67zmBxcQHHOUM8foLXvz7Ctm0BHMchlUrxta+p5HLu+A9owACuG5HOX/yFw003ldB1g7vvVimVxBy2Btj4fBqNRp4vfWk/cBio4W4IxnGzcgNIEgoZcjRMLPFa8/k8S0tL5PN5Wd0QZg2iBSFaAmJjmE6nWbNmjRRi9fb2nvd36OlaF7uDksjUH+u+tRIV+1jrsssu453vfCdf/epX+fd//3cCgQAPPfQQX/nKV5iamuLb3/42u3btelrOWzh8PRrCci5rZTn6C1/4Av39/bzqVa+SOovnRBB+JtTRS0tLAKxdu1a6wzyVtVr9XDi/ebuVPezLL7+cVqtFoVA47+fudrsUi0XZSxMjQI1GA0VR2LYNtm8f41//NUq9XiSZ7NLbG6TbLdNqBUgkBrFt33KWHCIUiuE4cUIhi1LJBMIkEm656P77Lb74xdN0u3OkUgkcJ8WPfhRC00K89rUN6ewj/GBbrRbNZlNyoSORCOFwWAbgXC7HgQMHOHPmDH6/XzoxCVeknp4e0um0HLcS/eVYLCbV2oFAAMuyZPAVLGsxSwxIYwOB7xR9McEpFgI09zmazM+7yt3LL1cJhx2ZWQvhVK1WIx4P4Pc7FAoWrZZOrWbQ7daXEZJ5EgnXZWrLFovdu7tMT5sEAip9fTqa5mFy0ks06kVVBWDEQtcdWq0q99+/QCjUlhxtwXMWQjZdd72C6/U6fr+fRqPB0JCX97zHz4MPLrG0lKC3d4zbblP4hV/oJZGI8N3vwu/+boFGYxJdP4Fp6pTLKp/5TJg//dMQHk+L6eksBw4M4QZfGzcQF4Aw4KPZNPn2txVuv91kdhbAwuVqG2iaF8tSgAU6nVmSyTLFYmf5OA7uxiBBKhXn1ls79PREZRZmmiaLi4tMTU0xPT1NsViUXsGCjV0sFiVQRVVV1q5dy4YNGyQWVbQsUqnUMzJbe67rYs6E4cJmhGOxGLt27eL06dPs37+fj3zkI6t8do+92u02v/d7v8cb3vCG82o9iiU+jy1btvDd735XVmSE6PPiuXqepnWhQVgAOObn5wF+ioD1TJ8PnJ0Jn8uybZujR4+Sy+WkAnpmZua8NwMCVCFUwIuLi8RiMcplt5enqgpjYwm2bk2iaQbf/Ka67DXro1is0e32c911XubmFI4d89No1CiVNEDHcVqoagPbblCvd6lWW9x5Z5Zud5ZYzMTjMQmHLTweH0eOBLntNp1ksiEtCEUPVsxtxuNxNE2TJej5+XnOnDnDI488QqVSAaC/v5/nPe95jI2NSYMGcTO2bVsSigQkQQT5dDot1dSiyuLxeM6yQex2u9IyUYwYiWOKHteJExZ/8ze9nD4dRVGCpNMKb3hDnZ//+RbiEqvX61SrVU6ebFGrqZw540dRNByntfyprCGdLrB9uyPRnfF4GNesARRFxbbby+YD3uUst4pp5jHNGt1uFU2r4/O5DO2lpSVqtZpUmyeTSYLBIJlMRv5c2EAOD6fZsMEimYwSCnVIJpMyi/nUp0y63RrRqJdOx8JxTGxbp9vNc999Qd75zjVABEUZwB0BauLO5rL8Xx/QplDoUCwWSSb9QA+WpaFpbRQFOp1pYAJosmtXk+9+tw2sBdYBKfz+Yf74j6vEYrGznMuy2SyHDh1iamqKarUqrQ5VVZUbTI/HQzQaJRwOk06n2b59u2xxlMtlfD4fPT09hEJijOniWM9WT/hc12psEp5JdbRhGLzuda/DcRz+7u/+7oKOFQ6HufHGGwGk8DEcDv9sBOGnWo5eCeC45ppruP/++y+Y9rJa6mjgnI5jGAb79u3DMAyuv/56OUbxVM5DmLsrikK5XGZhYZHDh7scP+7QaHiIx/twnE0MDSlcfXWbbtfHvfcqzM+XiUaHeelLN3DrrW0efrjDj38c40c/igAKrVYBx2mzc6dOPF6n2TRZXKyTyy2haVVs24/j2IRCQ8TjEU6csCkUFOl1K0ZeKpWKnGcVvV7btpmdnZVOUN1ul3Xr1lGr1RgeHmZwcJB2uy1t5wApIhK0rUqlgm3bDA4O0t/fL5XEYo7Z6/WSydT5r/+ymJgw8Pm63HijQjTaliVlUabWNG3ZaSfEX/2Vj9Onobe3g8fTJJu1+eu/ruPxmLzsZa5LVKfToVqN8qEP2RQKKoriwXHauPCIOl6viuP0cf/9AbZtc4O86JcLLrJlWbzqVTqf+pRGtdpC0+YwjC6FgkEotMCGDU1yuaZU/QrRmnAzyufzy25YDZrNJj09PaxduxaASCRCT08P7XabWCwmjzExYaModdzRng7g4PWuodOJUqsl6OtziWZXXlniwQdbuCV2C7eMrOGiIpfo68tSqVS48soW4XCQet3BDd4qjlMA2uzaZRKN6rzmNTEsawjb7mfdun7e/OYEul6Xojdwb+AzMzNyLCuTyaCqKqlUSrYPYrGYFMcpisL4+DjRaJRKpSIrJODSyi62gPdczoTFeqZgHSIAT09P84Mf/OApZcGPXhMTE3zlK1/hrrvuYufOnXz84x//2QjCT8Uw4dEADnFhXwzjReeaCTebTfbs2UMwGGTnzp1nlc3OF/gh+oIiGz558iRHj9aZnY3g93sZHk4TjW7g6NE+DKPCW9+q8MY39nD99SoLC3VGRpIMDXWoVmukUk3abRPD8OPztfH7F/D7FarVAPfe2yQSmSSXK9PpeDGMJJGIB6+3B12P0mppBAIq6TT4/R4571upVKQbjzCFCAaDzM/PS9FTLBYjmUzi8XgYHh5m586dRCIROToESKOGlRmRECZpmstPFhmwaZrL/GmH3/u9LseP6xiGC+H41rfivOlNdZ7/fNejNpVKMT4+Lu3/vvUtL1NTOoODZTTNRlE0Uqkcc3MW3/iGxTXXVOUG8t//vUupFCYSCdNqKahqA8vSMc0BEokKXm+Y739f55d/uSlVzCJAiJLyb/yGwalTKj/8YYlmM7fsUDXJjTdOoetjmKZPukqJz1uoXDOZjDTKEGYZpZLrYyzEb/39/bLn2ul0GBuzOXPGg+NoqOo4Ho+Ox5PGND0MD7fknPBv/VaXAweCtFoqbg9Xx91gVBkby9DTM0297n5+73tfjo99LMbSkkq3Owkc57LL5ti0qYqqBti5cyc9PT3EYjq9vSqaViMYDJ0l1hEiQkF5E8Kz4eFhef6NRmMZItKWM9LNZpNkMkkymeTUqVMAF1UZWqyLvSe8Gue3GiNKT7ZEAD516hQ//OEPHxNBfL4rk8nw+7//+5w8eZJEIsHhw4cBfjaC8PkGzscCcIhjXAxewOeyIRAuTgMDA2zevPmndsfnsxmwLEv2fcVYgapq5PNJPJ4Qg4MhotF+UqkkqVSHM2f8TEw0UdUMPp/Nrl2uGUKhUODQoSqf+YzC7KxGJFIDCoyOtunrC3L8eI7779f4jd9Yh65Ps3VrlQMHerCsIF5vD0tLbfL5MuvWVSkUlrj3XkfCHwR9SQichNWgEJ8J+Mf69euli9Ho6KgMsmI+V4wqCQSmAESI3bcgC3k8Hpldff7zBkeOqIyOunaBtu2wtGTypS/1c+utGn19ZWKxGKqqSqjB/LyOZXVwnAKmaeM4CqZZIxhMkMko2HZF2uTNzBioagxF8WPbXTSti6a1cZwA7XaQUMhPu92VJXHRuxafsztDrfDhD2f56lePcPKkgWkuYppHCAYV2esVo0qAdD7qdruSiS3aD4KitGbNGlmyTyaT8rvWbDZ51ata3HNPl1qtjs+nAya1Wp1Eoo+Xv9yWFYw1aww++1mdv/kbH/ffH1vO8ots3bqPN7yhi2F0sW03m1+zxuIrXwly/LjDqVNt/H4PmpakXHafV1ghCmCE2HiJYCkcs4TyXWgGBgcHCYfDMjPO5/Oy5y8gLGIDJsr0F2MWDJdGJrwa5egLDcL1ep2JiQn59zNnzrB//36SySQDAwO89rWvZe/evXzzm9/EsiwymQwAyWTyvJ9bZP933XUX09PTHDhwgC9+8Yt88pOfBJ4jQXi1hFmO4zA5Ocnk5ORP4RvPpwT8RGs1DSUe7ziLi4scPnyYjRs3MjY29pi/cz6bAcHiFb1XV9kbpNmsEY1aBAK99PVtJhRK0WqVUFUPnY5Ds1mUGWSlUuHYsQW+8AWT06dDqKqG16vTasHUlE6nM4uu9xAMDlCvz+D3+3n5yyMMDfUzNbWeVitIKKSzY0edV71Kx+/3cPr0aSqVCtPT07JX2Ww22b9/PwMDA1JkdPjwYarVKslkkrGxMbZv304wGJTUKwEFESVIQN7MxViBoHU1Gq4gTMzTttvwox8FiMe7eDwGjhMgEIgwOqozNaVy8KDJ4KBKo9GgWCzKL3A0WseyOliWB1U1MM3q8nvaYc0aU85Der1exscTPPBAhECgC1QwzQ6uIYMHrzdGq+Xh+uvbUrUujB4EmQtcccmZM6eJx2e58kqDQqFAPh+QVQMBC1FVFb/fT29vL7Ztc+bMGRYXF+V4lijxi/5osViU/VTBsy4Wi6xZs8Ab39jkq1+t024ngBiDg2He9z6D/n53FCiXy6FpOidO9NFohOnp8TI2luMVr6hTKk3g8fTi9fpl9cAd4dLYtKmGz9dgfr7F5OQchmGwbt06uVESQj1RHgf3uy20DGJURmy0gsEgS0tLciRJXAfCpKFarcpA7fV6GRgYOMuD+GJal0IQvhjK0Y888gi33HKL/Pu73/1uAN761rfyh3/4h9x5550AXHHFFWc97oc//CE333zzeT2XuE9nMhl53QhxKDxHgvATrXMNwpZlcejQIcrl8uMCOFZL2fx0saxXbiKuuOIKenp6nvDx5/JahNBIZIGCkBWN+unp6UPThunrW0MwGMM0W8sgDB8+X1eykQUvec+eFnNzMDzcpFwOoaom8XiSpSUP5XKHdHqMDRtc8ZOARbzhDaFlkESbWExn+/YIhgFnzizRbDblaFBvb6+cZb3hhhtoNpssLCywtLQkZz3L5TKZTIZSqSRJUD09PdKxSDCrxdyuyJ4DgQDtdlsKckRm7JLCwHE8+HwxdN1YvgHa2HYd03QolWxUNYNt23IGtaenhxtusBga6jI7axGPG3i9fkqlPny+Nq94RYNkMiZ7u698pYdvfUulVjMIhw3q9TiW5XKWLUtlfLzB619vSKiHuBbEH0VRqFarVCoV4vE4mUyG48ebHDkSBcJs2xbhxS+OydKr3+9ncXFRipeEX3B/fz+hUEjO12azWXp6euRmVWAMhXXkS15isnu3l/l5BY+nzlVX2fT1pVCUgBwNuuOOEO79LoiqWmSzDg8/3OTFL/by/OdbBINBUqkU4XCY0dFRGo0Ge/bs4cSJE2QyGarVKuPj48RiMTmG1Gw25YyzWKZpys2WZVk/oRUtk8nESFY4HKbT6chsutFokEwm2bBhA6FQiEQiQbFYvGgD3aUgzLoYgvDNN9/8hPe/1UiUxBKfRzqdltQ/v98vN8g/E0H4yYRZAsChadoTAjhWS9n8dLCsbduWO/lzoXidy4ZCwEnEDG2j0ZA2gZZlcdlleebmtrKwECWb7dDp5KhWHcbHZ1GUJplMLz/+sU212sTvb5LJ2GiaQyqlEg4rNBo67XYF2/bS7W5haMjP1q0F/P6IFDQZhkEi0aa/34eiWOi6SqlUl4QeYbwgesBer1fOQK9Zswafz0cgEJCmAclkkuHhYUzTlOXhbDbLwsKC9GYVM9DhcJiBgQHGxsZkqVfcrMWNJBSC7dvdOdZYzMGymhhGhXy+g9/vY2TEYGHBRUEmk0kJGonFAvzxHwf4y79sceyYAYTp7W3zi78IL3mJT44x6brOFVcofPCDXf7szxxMEwKBJpblMDRk83M/V+ENb1AZGOjSaDSXs3MXPCEoTsLSToBK/uVfFrn33n4gAQQ5eLCXQ4dUPvCBOomEJtXQAkwyPz8vISi5XI7R0VE2bNiA4zgMDQ0RDAZlmV5cK91ud7lsb7FzJ8t9dEOCTPL5PNWqnzvv9OLO/KrYdhPbtoA6P/zhKK9+tU5vby8+n0+WxPfu3ct3v/tdqtWq3Kx5vV4WFhYYGhqS8/zC8ENcx5VKhbm5OaampshkMjSbTYkZrdfrBAIBIpEIc3NzmKbJ8PAwO3bsoK+vj3g8Tm9vr8SgXszZ5sV8brB65ehLiR0t7hUvfvGL+fa3v8173/te5ufnqdVq7Nu377kThB8vs3uywCl6p+cC4LgYaFdirXy93W6Xffv2YVkW11133TmB5M8lExbiI5FdCHyfYRh0Oh2GhmqUy14eeMBLsTiN15tjYMDHy19eZ3IyxD33xCkW28vjNkkUxTV/d/1dqywuRqhWQ3i9fWza1OHmm0/QamUwTY+kEIkbuqIosmxqWZbkNotSYSqVwjRNKpUKuq5LhGWpVJLc6P7+fsbGxs7qG4uMSVgfzs/PMzk5KZWxi4uL8vlrtRqABICIGeXbbvOzb1+Eo0dreDwtOp08imLz4hdrqGpLBnTRaxZ0r7Ex+OhHYXY2iuNE6OurEI1qMnAKfJ9t2+zeneXzn++yb5+CrvvZtUslHG4sK3UVHnmkRbVqsnWrTSLhlRsSy7JYWFigUCjg8/mYnXW4995+dD1FMNjGNAtYVolDh1T++Z/j/M7v9EsMpzDAaDQarFu3jrVr12LbNqlUSropuecHP/gBfOtb0GzabNhQYmCgJOEl3a5bFRGfZ6lUQlVVDh+2cJXTaTodA1dJXQOSGIaXUskgnXY3PYlEgrm5OR555BEWFxcpl8tSGCdEXsLUIxwOSw44uDP+Bw4c4PTp01KMJSwlAdli0HWd0dFRent7ufrqq2XZOhQKSd6vUH9frIHuYj43uHjK0c/0sm2b8fFx/vRP/5Tf/u3f5sCBAwBcffXVz50g/HjriQLn3Nwcx44dY+PGjec0/7uaQVgIPC7kOLZty/JcOBzmqquuOmfF5pNl5KJ8KghUYtZV3MBM0+TkybUsLips2zZLozGPbXvIZpf42tcs8vkBOp0OfX0OAwMWPp/NyZN+IMTcXAtdtxkebtBq+QkEKtx00z46nRK6HpI3OzEGJEqC6XRamnkLfu/w8DDpdJqBgQGq1arMkASMJBaLSYOGlag84WwketzCltDv90scoYAxbNiwQQYQcWwxLz0zM0On0+Hnfs7m7rtNZmcdUqkuO3fC9dd7KBY98jnFsQXYo1qtArBuHXi9HdptpMmAuNFomkapVKJUKhEOh7jhBpNk0jXMKJfrzM97+ZM/sTl2TMe2k0Qibd70Jg//63+5wqxGo0Emk5HZ8eHDKtBHPK5Rr9dQVYNgUMMwQjz4oNv7bjTc+WsBoti5cyfDw8NYliUtF8XmzLZV/uf/1Pj617t0u3lgHiizc2eXt761RDjsQvqr1aoEpAinqWTSxs3GB3CJWw4wh8uhVnj4YZu+vqrEhh4+fJhcLiePIdjdqVSKLVu2yLJ4X1+fvEl3Oh054yzEeqLvLcw5/H4/iUSCoaEhyfcWlQjBHBdjW4JmdrGuS0EdvRqZ8OPxqS/WJap6AtiRz+c5ceIEfX19PxtB+NFfGgHgWFhYYOfOnecsP19t5OSFHqdSqXD69GmGhobYtGnTeV3cT5QJC1s94bBTLBaZmZmR5Ua35OdlYiJBKtXE7y8ABpbVQVW77N27HkXpJZHIMTPjIZv1sG2bh4EBP+6I5RK12gBQZWiozdVXL1Gvn6TT8UgilaIo8uaYz+eJxWJYlsXU1BSGYUg7upGREQzDkMKsR88Ji0C2EqwhskTTNGVmLsbYRFlSZM+Dg4NSabzS1k9RFMmjbrVahMN1duwwyOfzMnPudi0cx33OZrPJ9PS0NJoXqM1oNCpL6oFAQJZ1BdxdeLpWKhXpXSw2DtWqwXveA2fOqMTjXnw+jVrNzz/8Q4zxcR+vfa27iXJBHVUefvhhpqfLwAi2vQ5VDaPrCo5jo2l1TNNYPq4bMAcHB0mn0/T09EiFsKD9JBIJfD4fX/qSyte/3qLbnURVJ7HtGaDG3r1hNmxQuOkm95oUEA9BaatWq1xzTRxVHbNVi7UAAMyVSURBVMK2dWAS13jhNK514AA/+pGHV7/aHQU6ffo0mUxGKtOj0SgbN27kmmuuYWxsjM2bN0tx2Uo8pejvh0Ihstks7XabQCAgR6qE5aQw8QgEAvT29srrIB6Py7bAyu/HxZptXsznBj+bmbB4zXfeeSfZbJbbbruN0dFRdu/eDfyM9IRXBs6VAI7rr7/+vMDrq42cvJBlWRYnT55k8+bNTwko/kSvRZh7e71eqtUqp06dYmlpSXrsurZwSbpdC0VpoKo6gUA/lcoM5XISRRkgHHaIxQIoikOlojI5CanUIo1GgXe9yyGTKaPrXgYGWkxMHGNxcVFWBoaGhujt7ZVqVqHgFe5DnU6HcDgsvWCFBZp4T4UJvRBqRSIRgsEgrVZLgjOEZZrgNwtnJoGWFNeFEPKIm7DoqwqSla7r+Hw+8vk8uq7L7FFk35FIhMnJSRKJhHTdqdfrsrxfKpWk+EkE51AoJIVp1WpVWivWajX6+/ul4GrfvgQzM1V6elR8Pi+O0yWdjpDPh/jc5+Dqq09z4sRxqtUqc3NzdDodtm8f4NAhlXpdJRDwoChtFKWNYUS58soEtVqBubk5WRlIp9OyP59IJKQSWmxMvva1Dt3uAqp6Etue4yf8Zo29exO89KURWq2WRIkKPGipVMLv93HFFRX27q3hBl/hgpQAKmSzOqrqBslCoXCWrWU8HmfHjh2k02nprOX3+6VAS3xHms2mRH96PB42b97MyMgIo6OjUqglrhsxLyzAHY8XzC7mQPffwqyLb4n77IkTJ/jwhz/MF7/4RW699VZe8pKXsGHDhp+tIPxoAMf5DtuvJnLyqR7HpRFN0O125azrUz2PxwrChmHIILG4uMjRo0eZmppCVVW8Xi+KotDX14fXqxII1CiXo/j9YNtdLCtBp7OWdDoI2HS7EAgYhEImhUKBWm2O227rYc0aP93uieUxIReeIEQ1IyMjrFu3jnA4LA0PRF9UGCU0m02EG1EsFpPjJqJULTJlIcoSJgqq6noLt1otAoGADHqGYUg8o1tqVZmbs9D1BsmkLbNJVVWlDaLf7yefz8vMNRqNLnOcddlHXGnKILx5hYUb/GQjJBjV4tzEvO7MzAynTp2iUqng9XolMlGUS/fu7cFxvHg8fiCArmtoWoRQSOXMmSz33/8gjUZVznkHAgG2bPGyfn2JiYkGhrEOvz+AbQcYHAzxi784RaFQkJmfAHEIMphQDyeTSVlZKBQqQBHbNnEDsIHrL+yn0QgQjcaIx2Oyl68oCqVSSUJRYrFZ3OC7AJRwA7gfsBgYWEMymSSfz8sMt9FwLQnXrVsnXaxWWlAKRbTjOJTLZbLZrLQoHB0dlZs327bltZNOp2W15FwDxMUa6C7mDQJcPHPCz+QSLYz3ve99vOUtb+Gzn/0sX/rSl/jEJz7BDTfc8NwJwk8mzHosAMf5rmeD+7xy2bbNoUOH5IjN47mNnOt5PPq1iJt1vV5H0zRmZ2dZXFyk0WhIMdTw8DDDw8Nks1nWrZvlzJkxFhe7+P02nc4wlhUmnS7h99eYmXFot/3U6wq1WoXt26vs2hWiXG7LEmE4HCabzRIOh2V5WahZw+GwLEOLzEyoWnt6eqRSW2SzpmmysLAgs3UxBiDGU8T/C0tBYVsoQOr1eoP//M8oX/qSh7m5Grre4ZZbQvzyL5v4/bbsj4tysOBTl8tlGezFxkEEZxGYVzoiCc9gIaATgBCfz0cymSQUCp1VRrVtm2q1ysDAAIB8/lCojGluotEw0LQCqhpF133UairJ5DzZbIZUKimzdPf5W7ztbX2cOpXi3ntbGIbF7t0B3vCGJprWJp/XqVartFotenp6ZAY5Pj4umdwryVi7dtWXM9kyrnmCCsSB9SwsRPj1X3f4//3/UoyPn5Tgj0DAnU++884CP/xhHZcdXcQVabmjV5DmjW/sw+/30Gq5o28LCwtks1l6e3tlmXh8fFwCQlYawgtNgyj7i2telLIjkQiRSOQsa8NzXRdzoLvYe8KrUY4Wxi2X0hL32+HhYT7wgQ/wgQ98gEOHDvGJT3ziuROEH28pioJhGBw4cOCnABznu1YrCD+V4wgFtG3bXH/99ezdu/eCSuOPVY6u1+uUy2Ucx2F+fl6OqWiaJq3choeHCYfDFItF1q8vc9VVBnffrVGt9jM2NoBlZWi3M/T3g8/nZ3a2RqlUZdu2Eq94RZdOZ4ZweDuJREIGWiF8GhoaIhKJyOxUGK0LQZSu6yQSCWzbljdP0Qeu1WocPXqU06dPS6CEsDLsdrvLJXSPzOZEttloNCRF6aGHIvzt33owzTbRaINOx+aOO8pks0k+8QkVw2jj9/sJhUIywIqMWuzwRYAXmZWmaRQKBdmPFo5NkUhEqsxFSVQoohVFkeVuodrt6+ujr69PAjMMw+D1r7f4/vd9LC7Glk0uwtTrHmy7ye7dc5TLJU6cOE6j0WBsbEyOSEUiIa69dpHLLmsRiUS4/vo+2u0209NtOp0OlUqFRCKB3+9naWkJn89HLpdjaGhIlqGFEv2FL8zwla/kqVSauGXkPlw3pCAQZXGxw3veM8s73nGcrVv9cpOhqiqf/KQ7jgRV3OAbw7Ux7OHqq3Ve/nIve/fu4e6776ZYLEqHrMHBQcnGFpvvZDJ5VolSiK/ETLNAbopxtgsRRV7swqyLdYMAqyfMupQyYbFWbj6EaPR1r3vdczsIW5bFxMQElmVxww03XDCAe7VmfM/3OKKMHolEuOyyy2S/6kLO5dGVg5VIP8GHDoVCWJZFrVYjEomwdetW+vv7JS0pFIqiqmWGhxsEg6Ncd12Tqak8f/d3BR55xIdh1FHVOOvXO7zlLYNs3pwml8tJn16RAQubOK/XS6lUkmXLSCRyVm+u2WxKGpQQKYkxpGq1Si6Xw7Kss8ZiRP9I9AqFsKxer9NqtZiammJpaYlwOMK//quXTqfF8LBbfg2H/QSDJvv3B/jxj9ts3tyR8P7BwUFKpZIEhjiOI0viYgRJvM8iCxezs+IzbTabZ2EWRQlb+NkGg0FqtRo+n4+hoSGJyqxWqywuLv7/2Xvv+LrS6tz/e3qvOuqSJdly70UuMwPDXHoLoYTMMHRCCT8SAsnNMGRyCRAg1ECAkEsJhEsdQgllCAyQ6Z6xLVluslWsfiQdSaf3tvfvj+P35UiWZNmSbMnM+nz0sXwsb+1z9t7v8661nvU8mM1m7ruvwBe+UM/goJt02oDNluOuu6Z49asbCIWeS09PD+l0mrq6OpnFi88lm82i1Wr57W9/SyQSIRaLSQKWwWCgq6tLZsGCXSzGuWKx2CUP3ine/36FH/7QxrFjWkQpGabQaFRU1QYE+M1vFDZuzBMIBKiqqmJqKs3YmIYSI7qCkmmDk5J7kgunM83IyDCnT59mampKfsaid6/RaKisrMTpdErRldn3t9PpxGAwUFlZWTJPL8uUlxKrGehWe0/4D5GYJSIYDDIyMkJHRwePPvooDz30EJFI5OYF4XQ6zcmTJ+Xfl8MB43pITs6OYDBIZ2cnDQ0NbNq0ST5gSz2XchBWVVWCCZTmKoWKkBCZaGpqkqSgcDhMIBDi17+2EgzmUNVqwMQvfzlKIjFGKhWm5PzYiNO5gUIhyA9/OMoznpHmttsqUBSFYDCIw+HA6/WSz+eJRCLSHEGr1WIwGDCbzRK0xHiM6PWKHmMqlZI6xMFgEKfTSSQSoaKiQkoNin6rmHMuFArS9GFoaIh0Ok0gEGdgQI/TaUens6EoCVQ1hcPhJRzWMziosHevRWbl2WxWjpnpdLpLlo16WQaF39sbioxXGINbrVb5/x0Oh8yORe9bbCqErKLX6yWbzUqJSPGzVVVVGI0RvvWtLCMjBeLxIrW1QeLxMZLJouw1C7vHiooKKSOaSqWkg5DYkIiSvl6vJxwOS7u1YDAoy9CZTAaDwSBbFKX3McnLXqYwNVVgYABKfE8bqqpH9HpHRowkEnFJpMrnVcBCKWMu/XwJgONAEZ0uTCKRwe1243a7mZ6elp+zxWJh27Zt1NbWSgWyuRZ2wRtYzhDPzGoFutW8QYDlM3BYS+VosfH47Gc/y7e//W0KhQKHDh3iU5/6FK94xStuThAuF+BoaWnh0UcfXZabc7kkJxd7HL/fT1dXF1u3bqWhoWHGvy1WdnK+KM+kxSiSyWQiHA5L8pEYRfF4PKxbtw5VVenp6SGbzXLqlI4zZ7Js2qSlocFBKhXj4YczxONe3O6S76/d7kavz19iGztpb89yxx1gs5WIP5lMhmg0KokxYmxI6P+KaybkG4WmryhFC0BLpVL09PSwfv16Nm3aJM0HRH9WiHeEw2EymYwc+xkeHiYajV4aRzHh8dhJJk1oNAb0ei+qqlAsOtHrFRyOPIlERj5Q5SVlrVYr55mFMYAYZRKZZSgUkiVqQPZoRc9Y9CdFBu92u4nH43JmOJlMYjAY5PmKY5dIVFr27FFIJBL09w/PqAwIZyTRBsnlcuh0OkwmE06nk66uLgKBAOFwmFwuh9vtliVb0f8vFos0NDRQXV1NKpUiEonQ1dXFwMCA/AydTid6/TqggRKQGij1iacAB1VVWTwePTt2lFoR4XCYAwdGOXGiNN5W+nkdpZJ0NRs3HiWT8cqM3Ww2s3PnTp7xjGfQ2NhIdXW1zPquJ+isBRBe7T3hpbQCADlJsFZC3CtWq5X77ruPN73pTTPun5sGhMWbmi3AUc7sXC0gfCUAVVWV3t5ehoeH551jXuq5iM9CLPCqqkp1JIPBIEFSlCwFMzgQCOBwODh9OkaxGMNiyZJOh4jHHeRyoCgh0mkLFosZl0shm43h96vccUcDfX1VnDo1gNkclGM8LS0tVFVVSXKVeP/CuzabzUpZQTGmJMZkBBP6woULbNu2TQKwTqeTyl5QYidms1kKhQKZTIbx8XFUVZVgX8qYTDz/+U5+8AMLoVAKj8dEoWBjfFxh/fo0O3cmiMfTl8BGL6UtxX1V7rKVz5c0pGOxmAQRRVFwu90YDCX7RVGCLhaLciwpm83i9/uJRCLYbDY5pypGqywWC9FoVJbWFUXB6/VKxm8gEJAZo06nw+l0SpMFAdpibKqqqopAIMDY2JhkbxaLRdxuN0ajkXw+L92CBBgbDAYpHJJOp6WAi5CI3L49Tm9vglJPWE+p32sHnOzd24fBYJSqZIFAgLa2Kc6c0ZLNVlIqS2sBF+961yTJ5CjT0woej4cdO3bQ2NjILbfcIkecyseKbgQgrmYQXq3nBszgc1xrrLWesLge9957r3xNaAnodLqbB4QVReH8+fOXCXCUz+UudYd4PTJhYSQRjUY5fPjwvAzopfaExTmI3mR55mkwGIhEIhSLRUlmCQaDDAwMEAwGGR8fZ2pKi8HQgNVaC2SJx7UoigWLpRa9vgWtNo9Wm8FgcFEoTJJOR1CUHIlEFpvNJkvbgogEyKxQkJJEPzSbzcoMuFwHfHp6mvb2djZu3Mj69eulB6xQwLJardJ8QXghm0wmzGazBMKWlhZcLhfV1dUcPuxFVQ386lcORkYMmM0mtmyJcc89Cm63hXi8ILWlhci/1Woln89LG0Qx9iYYz6J0K1jO4uETn3PJDMOJRqPh3LlzTExMoKoqLpeL7du3zzCMECznclavKLeKTYbZbGZqakpmwaJ0HY1GJbgXCgVsNpsk4YnNl8vloqKiApPJxPr162U/vrKyEoPBQCqVYmBggJMnT9Ld3X1J/zmGw+FAq9WyaVMtf/qnKt//fh2lPm8BcPLiF8d49aur0eu1BAIBhoaGSCaTRCJhXvpShfHxAuFwkdraal70ohx2e5izZ+1y/leMjyUSCSoqKqioqJD3zPX29H06E15aLHWTIKYO1lI5ujy6urp46qmniEajZDIZ9Hr9zQPCZ8+eJRwOXybAsVziGLDyxKxsNiv72EeOHFlwt7dc5WhRdh4eHqanp4fp6WkJ/B6Ph7q6OoxGo1TMam1t5eLFi+za5WNoqJrBwTxjYwkKhSyqWoNO56OhwYPfH0FRAmQyQRwODRqNBb1eS3OzDpvNRHV1NeFwmJGREQqFgizrCl9gIStps9nQ6/USrIWt4MjICCdOnMDn86HRaOjv75f9YuGalM1mJTPaZDJJJrQAY71ej8vloqqqCpfLhaoW+Ku/SvHKV+oYHTXg8eTZtKlIPp8mny/IbFEArBilSqfTcpNnt9tlL1tobyuKIueMtVqtJJwJ+T2DwSClMH0+H1AioYlsV/AZhA2f6N+K+7xYLHLxYoivfS3Fo4/myOdD7NhR4EUvcgO/v/dFJUCUx4eGhuTIls/nY/fu3dJbWafTYbfbJfEpGNTw4Q/H+clPpohGQ9TVhWhqimKxaCV7u7W1ld27bfzJn1Ty8MMRcjkthw/rWb/ejtnsIxKJ4PV6JaO9traWdDrNrl1mXC4z69dXXNoUlIwzRkZGiMVi+Hw+Nm7cSKFQoFgsyl61YJ/fiHL0ao3lKPeuZCxHMrTWDBxEnDhxgje+8Y3E43FGRkakPO5NA8IbNmwo7Spm7YyXE4RXkpiVSCRob2/H5XKxc+fOKz5ISz0XsRCK7PHixYv4/X7y+bzsdYoZWCHRKHrFTqeT5z1vC7/4hZ2TJ/NoNDpMJiuJhIrJFMFiyWI265iYcGGzmfF4dIyNGTl4MM/+/aVSp8vloqamRmamIuONxWIEAgHi8TjBYFCK/U9MTMgZ00wmQyAQmCE1GY1GZfUjl8tRXV09IysQAC5kLYUqliAuCelKrVbLli1mNm36vRuQ6KMKP2Eh6SlUuMr72GJeWGT0wrHH6/WWSX6WLPrE/Sret5hzFqAGSEUwwVoXFQuTySSZyufPT/Ka10wyOKgDhoFhLl7UcuaMyl/8hQWdDik5KjLh6elpwuEwyWRSylOKUrmYA7bZbNTX1xOJaPhf/wsGBmKU+rxpRkZURkZcvO51XpqaatiwYQNerxeHw8Hk5CTV1X1YrVZ0Oh9W61Z8Ph/JZFJWEYQSmhDdqKiowO12zyDaHTlyRF4fce0ymYzUwhbiLcJIQgBz+Z/CiGO5Yi1kwqv13OAPVzFLo9Hwvve9j927d/Ptb3+blpYWfvCDH7B58+abB4TFOM3sEKW/60moutrjBINBTp48eVVCIkt5T8JQXTCKR0ZGOH/+PNFoVPY0m5ubaWpqIh6Po9FoaGhoIJPJMDY2RkNDA0NDbgwGCxs2RMnlLOh0Oex2mJqyoNXq2bLFTFNTBrvdRWOjjv37Y9x+exaXyyezOTHDKXqVQqpRqEqJcrUgVsXjcc6dO8f4+DjT09OMj4/Lkk5FRYUkj7ndbmKxGE6nc0YpVYBlKpWSGsJC7ENsesQCkUwmpTqX8KeNxWKEw2GZoafTaXneYmEQ0ozCWH5ycpLbbrsNvV7P5OSkFI8wmUxSBjIWi5HJZFi3bh1TU1OXzA28soQuMm2j0SjL4GLcJhgM8tGPnmRwMEqpr3qREsFJpb8/Smenldtvt0lwa25ulnKkNTU1soyu0Wi4cOGC1LG2Wq00NzdjsVj41381MDAQ4feSlCagEbAwPl7BLbeUWOuiQjA9PS1NOMTImchi4/E4Ho+H5uZm6eUsMtpMJiNtCysrK9mxY8eCi62iKBLQRRsikUgwPT0tOQGCDDcXQIuRsquJp0F4afGHaGUo4uzZs3zkIx8BShuJaDRaWn9u8Hldl1hO8LySN/FiohxAR0ZGJLGovr7+qs7lWjPhZDIpBeuHh0uzmP7STJFk9zY1NWE0GkkkEkAJuMXYj9ls5utfv4hGU8GuXSb0eiMajR2DwUFvr8rBg2ne8pYUJlMBuz0mAT+Xs6HX10ptZ1FKFFme+GyEmIL4Eq/39PTQ19eH1+tlcnKSmpoaLBYL6XRamq4XCgXsdvslF6UogcAkilJa/AWrUqiNCZUuvV4vyVTC5L3culEoiUWjUcksFpmZkMOcrTl97tw5urq62Ldvn6wsmM1maewtdLiFmhdAVVWVtGhMJBIyaygUCrInnsvlJHAkEglOnz7N0aNjlNSq/JSkHz0IxnFnZ462ttL7qq+vp7Kykv7+fnp7e7lw4QKpVIrKykophlFXV8f09DRVVVWy7/zrXxcoCWoolGZ545Tmga2cOaPnJS/JSkJfoVCQ7HBRPgfk5kGQJQVZTYxpCSa7IIkJwZMrPQNiZGmuEHPRAqAzmYxkyIvrKkbh5gPp2VnbWgDh1dwT/kOeEzYajVJnoLq6moGBgRKP5Aaf17LFQg/FcoGnYOMux3GEk9Po6Cj79+/H6/Ve1TGuNRNOJpOMjY0RCoUIh8MUCgUqKyvJ5XLy+927d9Pa2ko0GsVqtUoXHQHIJ06cQKerRlEK6PUuDAYTer0Zo9F2aZxHxWSKy/cYCASwWCxStEKUoIWLkTBmF9mSYB0L1SOtVsvx48fx+/2yJ51Op+VnuXHjRrZu3Uomk8FqtfLUU3Z+/nMbg4NavN4Uz31ukttuCxGJhCQxKpPJ0N/fT39/P6qq4nA4sNvtMuvW6XTSP1iMRpWXhQXA2Gw2uXERJK3R0VG6u7vZunWrdOkRfWBRXhegMz09TSQSkSAhhCXMZrMUTSl3XaqoqJCa1FNTU/j9flRVoVQmjlBSrDJTGvmpw+22YrOlZab60EMP0dvbK8eYhJym3++XDG2DwUBNTY2ce9bpcpTsBYOU3I5Sl45vxOHwUSxOSz/nSCSC0+kkFArh8/lYv349TqdTApuQkhRVCI/HIxXMRCzHMwbIvvZc5EYxE10O0ul0mqmpKfmaKPuXA7PYAAkgWG1gvNrFOpaDmLXWMmHxfp/3vOcxUBqk56UvfSn/8i//cnOB8EJxved7FxPDw8MUCgUOHz58TYIC15IJK4rC1NSUzKzGxzUMDrrJZFSKxRDV1SWyTE1NjZwPFubuiUQCvV7PyMgIdXV1/PEft3LqVIxYTMXjyaMoOoLBHHq9idtuc2E0lkaLEomE3P2K7EOAq8iSRKlX9F9TqZRkwGYyGbq6uohGo7S0tEjwFS44widYGBz8+tdWPvUplWxWh8ulZWhIz5e+ZCYcruKVr/y9Y5LBYJClT8EszuVy+P1+wuHwDMJVNBqVGbPNZpPaw2K8SWTP8XicwcFBJiYm2L59u5yxTSaT+Hw+3G63zBQdDgfxeJyJiQkJ8EKMRPRvBUiU94/F9/l8Xs7nbt+e4NFH/ZTYyCb0+koKBSel0aBSlm0wGGhvP8tDDz2EomRIJEr9/aqqqktAruJ0OtHpdGzYsIHm5mYA4vE4Bw7088QT54EuSnO/Pkp2g3qe/exK9PoIyWRSZvsejwev10tLS4sUTylpXdukkcViSFUrCSaCnS60pGeHWOzLAVpUSACeeOIJeR/Pl0kvl0LX1cRaKEcvJRMu50astfjbv/1bAoEAAH/913/NqVOneP/73/80CF9NLEdvWZBwDAbDFRnQVzqXawFhobn7+OMq//3flZTcd4poNLvYutXD3Xc343KpBINBzGazBAu9Xs/Y2BibN29m27ZtZDIFfvazIfr67Pj9KRQliNVq4YUv9NHWZiIaVaUAhyipVlRU4PV65UMoKgvJZFL2W3U6nWQWOxwOLl68iNFoZM+ePUxOTjIyMiJlFcX8qt1uv1TWhW9/W4Oi6GluLqLVanA6Ffz+MD/+cY5bby3S3GyXJu3CMcpsNuN2u6UP8bp16/D7/bI8Ojk5KctIFouFVCrF1NQUw8PDMtsS1ySXy0lpz6mpKWIxE319NjwePbfcUjKyLx8rEuSqTCaDy+WSymVCtUoQ6Kanp6murpaM6YGBAbq6usjlctx6a4bTp+uJRpuBGIWCDsizZ88E27bpcTpr+epXTfziF+OUerpx9PooGzaU+tkCeGtqarDZbOzfvx+AyclJzp8/j893hqqqCSYn05TA1wVoaWzMcuRICIulhvr6egm0omwurn1J4rQkGLKY+301MJDLCV9ut1u+nk6nefLJJ7n99ttnlLnFJkx4Fot+dDkozwbqlWAxr3YQXmq5XLRu1iIIb9q0iU2bNgElBcef/OQnwE0k1rFQLGcmvJQFIh6P097ejl6vl9nbtYZg2S42xMPpcrno6Bjk+98fJpOZZssWK253A2BidNTLr3+t5SUviaEoCpGIkQsXzEQiGYzGCEeO7JZKRWaznle8IkSh0EBHRwzQsXFjgrY2Ba22XpKIampqqKmpkV6v5fPIQsVqbGyMQqFAOBwGoKGhgWw2S19fHzU1NbS0tMhsymQy0draKrNlh8NBoVDAarUyMKAjEDBQUaFHVZNkMmEKhQg2W4RAwIDfb2fDBp3UcBbWjKKsmEqlSKfTstQoALCyslJ6zAqGuLDBEzOs7e3thEIhOX+rKCo//rGDX/+6kkzGjE6XxOMx8hd/keZVryqRoRKJhMzIxAjW1NSU1IgWlouCXCjOMxqNMj4+LvuuGk2aP/9zC729RU6ciJPPj7Frl8LBgy602no++1kvjz7aS8kysACkKRQ8qOputm2rkRwAs9nM+vXrqaysRFgBlrybzfzRH+Xo6THQ1ZXDYolx4EA1L35xDRs3tsh57EKhZFkp+APi/tZqtde0aK5GMBHPv2gpzOdHPrvULVTaxN8F0W6+LFpUWa7l/FZ7T3gp11WIL61FEJ4v/mBAeLlGi64VzKempjh16pQUw1+Oc1nseyoWi9KRx2AogVE2W0V1dRxFyZHNRjCbvfh8Zk6dMvOKV6g8+GCBX/3KyuhoiEzGTkvLrej1WQ4enJSSh3q9hp07CzQ3azAYdJcYxWY5DiT6uSLjFaVmMc4Tj8eJRqNSnEMoSEUiEfx+v5ShFKVZITwh5DRdLheKosgSp92eRa8vkMnk0enS5PNJCoU4mUwIrdZCoZAhlXLNsBoUDGWhGib0qEUPVczW6vX6Gecg+tpmsxm/349er+fAgQNlG50Kfve7LDpdlpoaPaqqEIlY+cxnvJjNT2Iy9UknJjGeI8rrokwrStMajYaqqiopUJBIJGQWPTIyQigUwu1209ZmZOvWPFNTpX58KJQjlXLw6KMBSqQtGyUFKxOwm56eBsxmlQ0bmqiqqsJut7Nu3TrZJrBYLFLqUlFy7Nxp4ZZbWtmyZQubN2+WjGtxjSsqKmbMc4vPTyy6QjpzLcdiM00hquJwOOY8hnAJKwdqYSFZnu3Nl0WLDeS1nt+NiuXKhNeqWMdccdOA8JWIWTeyJzw8PEx3dzfbt2+nrq6O06dPL3lTcDWl8XQ6LXWNE4kEpQS65GAjZmPt9mpU1YpWa2VkxMKPfhQjlRrDZCoAmzh5UuH06Wle+9okr3pVXi6w5WbzFosFj8cj+zblc7DCyF0waIVxgFjIY7GYFNzv6+tj+/bt7NixQ/ajBWFGZLDr1q3D4XBIhms+n8fnK7B7d5KHHzai0cQpFMZIpSaZmoING6zs2GGSWcbAgJ6urhy1tRaOHCmSzZZ6fWKHHY1G8fv9EuwE8Uj4D4tNw4ULF9BoNBw5ckT2zQF++MMcuZyGmhobUERVddTXW/H7oaPDwUteUgJesagmEglJlguFSgQyYc9nt9vlKFcymZTAe+rUKfr6+mRvU+hoK4oijRgCASslpnSRUhk5R0nNah0QZHBQx7p1pWvQ1NQkKwBigyQY4Rs3bmTbtm3y9wipSvF5CuKZxWJBVdUZX/PdpyLDL9d/Lt9crkYwWQ6QE2V5o9EoZ7LLQ4xelWfRQmhGVGpEP3o2QOfz+WXjraxELDUTFjK611spbSXj5nknC8SNAmFVVenu7sbv93PgwAE8Hs+ync/VZsKiLzs5OYnPl8fpNBCJVOLz1eLzrcNodDAwUOAFLyjS0aFnejqPxVJkdLSBYtGAwZBkelrLt79tx2jM8upX52UpVXi1CledYDAolaFsNhuxWExmu0IPubwcnEql8Hg8ZDIZhoeHOXDgwCUSVoYHHkjxwAMxJicVdu2q5/bbM9TWOqXln9CKFgpZb35zgsHBKF1dUQqFCVQ1TlWVjZe9LMNvftPExISFs2fz+P0aCgU3Op2DdevSvO99JhoapmQfVmQlAmDKy+hQUhObmJhAo9GwY8cOKbYhSsnj41oMBj0aDaiqBp3OjFZroFAYYWhoVGo9NzY2YjDUoNWm2L27gkgkLCUkRV9VANvQ0BC/+93vGBsbI5/P09/fL2eSk8nkjLK50WikubmZdLqZUh/YCGQpzRJ7KLkbaaitrWZ6elqWV6HEoA+FQmSzWTwejxxhMhgM2O12uXkQnsMLmSiI+7wcjK8E0iLbEVn0bJC+0bHSm4Py0SuxZpSHaJOUZ9HRaJSJiQkSiQTd3d1cvHhx3rGrlepHLyaWSsxaq+NJC8XTIHwVcTXZZ6FQ4PTp0yQSicsY0MtB8FrMMcqt6QAikQharZaGBgMvfKGT733PzORkNdmsjWg0TXV1DKNR5bvfLRIKadBoqslkCvh8GTQaPdmsBZ0uxv/8j46XvtQlvX0FSSwSiTAyMoJGo5EELKFPLVjIgPTETaVSmM1m+fr09LQcj8pkMnzpSxq++lU74XABjSZPV1eORx7R8uEP69ixIyfLdaKEnE6n8Xiy3HNPjEceCTI8nMLnc1NRYeab33QTjRpJp60kkyo6nYaNG51YLEkuXoxzzz0hPvGJKHp9ydDC6XRKxm80GkVVVWpqanA6nWi1Wi5cuIBer2fbtp10dxvIZrPs3m1DpyuZT2zZoufCBQOKUkSns6HTmcnlsuTzfqqrM+j1JiYmNHzlKy46OhQKhRxNTXr+/M8d3HqrFZ/PRzwex+l04vF4KBaL9Pb2SgOH0dFRAOx2u1TVEqxpAeLxeJxI5L8pZcJJSjPElZQAOUlTk4916xwkk8kZZVNRXRCkPECS6qqqqvD5fAh7yCtF+YI718I/G6RLEpwXL8mIqvNuNGeD8/Uqda+Gcq+YMZ9rquL48eM0NjZKMRkB0tPT01JlTIjFLNSPXqn3uBzl6OVWQbvR8QcDwss1J7yY7DOTydDR0YFer+fw4cOXEbCWo0e9mGMI7WRAZjmFQoF4PM4znxknmRwnn68FzBgME5w4McU//7PC2JiVQsELGNHr0+j14PFY0Wj0rFtnJ5vVMDamRa8vLZqiXDwyMkJfXx9ms5lYLEZTU5PsLwpbPpvNJmd1hfCEMLE/cOAAtbW1aLVaBgbgC18oEgxOURKHANCTyVTxne8ofPCDpVEl0cfNZrNks1mmp6cxGmH3bpX6+hxWa5Z//dcqIpEq1q0z0t2dR6/XotG4GRqK0do6iddbIBCY4uxZN89+to3p6elLZgtaHnvMy2OPWUinLdTVWdi0KU9FRYxDh0woylZe9SoDY2MpFMVATY2B9743y549KV70Ivjtb41MThrxeEyoqoZgMEZ1tcoddxiYmIjysY81MTnpw2bLodfb6ekJ8773mfjiF/O0tcWlsIhGo5FMZbGQjo6OSpnIqqoqNm7cKP2mhTPTsWPHaG9vp6EhyehoBVBLqSStQafTcNttwwwNxWhubp6hySzGsYSdYbmG9HIDXfnxNBoNXV1dFItF9u3bJ/2YZ2fOVyp1lwPzcmfRq4G5vVCINtBC/WghQlM+fiWIj8IZa67RK/H9UkBwOYhZT2fCazCuZzk6FovR0dFBRUUF27dvn/PBX65MeKEFQYCTKNUKTWihBFXK4nIcOpSjsjLP3/xNhLNnNeRydkymAlqt8ZKAh5FQyEI2q6WuzkxNDQwPh8hksqhqnDNnzqDRaBgbG8Pv98tMscSgNstZXGFHaLFYZHalKAr9/f1MTU3R1tZGbW2tnLn99rdzBIN+NJopTKYqtNoMiuIgm4WjR2MkkyaqqozyM9DpdOj1elKpFNFolGw2e8kkwkMgsIHqagc6nZ5CQRhFGMhkUqTTRpxOC4oSwO8PE4/rLilf2fnyl508+qgJgyFHMllgfFxPe7sZm62BX/xCJR4vkk5ncDozaLVVTExoue++OJ/6lIbt27P83d+N861vNTAwoEVRouzYcZG77orQ2urlZz+rZnp6M16vik5XpFjUoNGEicVcfPvbdm6/XS9FLrLZLL29vSQSCUwmE2NjY1RUVLB582Yp4LFu3TpJVqmpqSGRSEhhkKYmO01NRVTVSCYTp7lZx+23lzx5KysrcTgchMNhxsfHZU9Zq9VKhycx71ooFFYsU1JVla6uLlKpFPv375ebt4WAc3YWLe6FxYD0bKC+0u8qP8/VnIVd6fwEy34+IFMURW70BFBfjRSo8Lpe6PyWmgmvJRvDxcRNA8KrgZg1NTVFZ2cn69evZ/369fOe03Jk5lc6F42m5FUpJALFDKwoIatqaY43lUrR3x/kqac02O1aJiYKeL0e9PoK/H6FS4k0Op1KTU2Rzs4xqqun2bDBRS63jkgkQjgcZnJykuHhYSlD2NLSMkOEQtjSCaKVoij09PRQKBS4/fbbcTqdcuQmkUhw5kyIUt8yTT6vQ6fTYjCoqGqIWEylWMyRy+lkOVr0nQEpPFECfwuQpVjMoKoOrFYniYQZrbaIokChECUazaPXe1m/Po1er2K32zl92sjx4xo8niSxmI6SCpUC6NBqVfx+Dblchvr6MDpdEa02hdutY3Iyx89+ZmDHDoXDh7W84hVaJiZSDA0NoNPFSadzpNMaAoEmtFoFjSZ3KeseBKJotQbOnQO93icXSqGT7Xa7SSQSVFdXU19fLzdVmUyG7u5uPB4P69evp7q6mmAwKPWzp6amcDhM3HHHeqxWKxs2bJC64Fu2bJlR1izPlMq/hIewME0QC674EiStclWpxUbJhKKkXX7gwIFFL7JXW+qeK5OeKxYqda92EF5qprmY0atykJ59b4hWxnz96Kcz4cvjpgHhheJ6gPDQ0BA9PT3s2LGD2traFT+fxYh1GI1GaRcYCoXwer24XC4psyjKnMPDozid+0mnhSuNF51OS21tnPHxAlqtgWjUymOPhdHpxlCUKF/9aog3vMHHunXrJMGqoaFBGsHX1pY0ooVqViwWkz1WMXajKAr19fVyXlZRlDJ1rmEgjqrqUJRhtNp6CoUcoKGmpgq3O47w5xWleSHi4fF45KhPQ0OBmhqVsTEtVmuKqqoK0mkNqVQavd5FsRgnGjWwf7+FXbsSJJMlQYpz5ywUCiaMRhPptJ7fk5tUEokwFouVXC5NOp3DbnegqmE0GhsGg4nh4ZKzks/nw2o1U1OTIhRKYrVWoCieS8IcOQqFSVTVTKGgRVEy6PVmFCWPxxNHr69Bo9HMILqJDYfP56OqqgpVVaUrkSDe5XI5uru7eeqpp6irqyORSFBVVcWhQ4fYsWOHNKYQJLnZgFeeKZWLVIhQFOUygBakIOEoJRbhuYB69vyrqqr09PQQDAZpa2tb1gV2MSB9taXuQqEg/1xthDFY+U2CXq9fUAo0n8/PAGjh5S2AG6Cjo2NeEZP5Rq9EiDG+pbzHRx55hE9+8pO0t7czPj7Oj3/8Y/74j/94xvv4wAc+wFe+8hUikQi33norX/rSl9i4ceM1/86F4mkQvoqYC/jELn5iYmIGA/pKx1kpYpZwrNFqtaRSKcbHxxkfH5flaGGMLowDAoEAd9xxG0ePOjh2zInJpKdYVCgUpkinQzgcBlIpB3p9nPXrR/F4suRyVfzud3E2bTLyvOchxyI8Hg8+n4+amhq5uNfW1kq2rhhJeuqpp8hmszQ0NKDVapmenmZkZIRoNEosFmN8fJxiMQl4gWpUtYZ8vhHIotUaeclLkrL8XF4+Gx0dZWJiAofDQX39Rh57rJ7f/jZLJGIkmdQwPFzE4dBitWZJJnM4nQUMBiMvfGGSV796HFWFX/3KxIMPWhgZMZBKpTEaDZTGuUrjW4oSBYrodBEgg6qagSLFYh7IoihO1q834POB1+slk8kwOTlJPp9nfHxcssMPHhzjP/4Dpqby2GyxSyxxE5Dlj/6oSs4pRyIRKZvZ3d3NhQsXqKiowGQyUV9fT01NjVTUKhaLRKNRTp8+TXV1NXq9HrPZTHV1tdSerqurkz1ft9sty76LDTFGNp/Uavn8qyDLCaa5WITLF99UKkU8Hmfbtm1yM3W9Ms2rLXUrisLw8DB2u31FSt3LETdSrKN89GouKdBCocAjjzwiZ//FfRIKhaSt6mwpUAHQQo1NTAwsJZLJJLt37+bNb34zr3jFKy7790984hP8y7/8C//xH/9BS0sLf//3f8/zn/98urq6VmQ++WkQXsJxCoUCp06dIpVKcfjw4XlLOHMdZyWIWYVCgWg0Kv1vBevY4XAwNTVFPB7Hbrfj8Xg4d66fkRE3mzdvxOFw80d/lKC/X8vYmI7x8SwaTSkTtViMFIs5du824fNBJpPDYJgkna7g8cet7N8/gdVqlUYGQhVLSGSKjLRQKJBIJDh27BjFYpHbbrtNlr2Etd7o6CgjIyNMT0+zZ4+J0VEjPT0xtNpmtNosWq2GrVuL7Np1ntHRUr9UgNvIyAjpdPqSUbaBT31Kx7lzPnS6MJAAjOh0PmpqctxyS4JnPStCVVUMr9eM1aqSz5v5l38p8sADRTQaBZMpQSKhIRKxU3IOcqAoSSCH222iZGLgJJOxYzKFAR2JhBWzOckrX2nG6y0Z1J85c4bh4WFcLhcej4eGhgZyuRyJRIbdu00cPTpNSRGzDpMpy6te5eQv/7IenU7H8PAwjz76KJOTk7Ki4XQ6URRFWhDqdDrpT1xfXy9LzB6Ph0AgIGUjBctaGECsVCykx1xuPZhOpxkfHycSiWCz2Th//vwMqcfy8nb5Yny9RmtmZ9HiWiqKwt69e2eUp6+11L0SAL2ay+XiM/F4PHO2LMToVXk/OhqNEggE+PKXv8z3v/99WUl55StfSUtLi/y6/fbbF63B/8IXvpAXvvCF857jZz/7We677z5e9rKXAfDNb36T6upqfvKTn3DnnXde47ufP/5gQHi52NEChDOZDO3t7RiNRg4fPnxVGcVKZcJCkcpqtUonIjGqIMZLGhsb+cEPprn/fhfBoAuz2ch3vxvh9a+H973Pxve/H+fBByeJx9PY7QZMJjCboa6ugEZTgcHgIJeL43JVEYuZURQVne73fSRhMCD60eLPqakpjh07htPppLGxUb4Hcd7CajCVSl0ytffy9rfbOX++ioGBKnK5IrfdZuLZz86j03mIx+OMj48TDAZJJBIMDAyQz+epqqpidLSaEyc2UFOTxW73oChmamrsjI+bOHw4wF/8RYFcTks6bUKv1xEKRRkZUfnd77QYjWZstjS53BRms0Im46Pkz1vqCWu1egqFaVQ1z+23w/i4gfHxCjQaldpaD+98Z4jDh00YjUa6u7s5duyY7L1rtVqam5v5ylfifOITDorFLKVHsJK6Oicf/WiK//W/qnA6bUSjUX71q1/R1dWFyWQiGAxitVqpqqpiaGhIymf6fD5ZUSjpSN8qR8XcbjctLS1s3LjxioSZ6xHl86/Dw8PE43EOHTokWbzl2ZH4EoQgIfUoXI3m6kdfqZR5raGqKhcuXCAej9PW1nYZgFxNqVscb6VY3avZRalc8nOuWGj0qq2tjY9//ON8/vOf5xe/+AW33HILAwMDPPDAAwwMDPDf//3f12SEMzsGBgaYmJjgOc95jnzN5XJx6NAhjh49+jQILxRXImYtpwVhNBqlo6ODyspKWUa72uOsRCYsxDOEvKLIvtxutywT/exnA3zjGz4UJYXHk0Wni3H+vIbPftbG//k/Kd7zHoW7704wPp4lFiswPW3le9/TkkoVMZkSGAwObLYG/P4M69aNks2WfIFFpiUyMyEQkslk8Pv9nDx5EqPRiM/nw2g0UiwWURQFvV4vZRhjsRihUAir1Uo2m8Xns/P2tzdjsRgwmeyypJ1MlohewopPLMTCMjEUqkVVvWg0MVIp06USXQZVzfHggxme85xeSfAQjOozZyAed1JVlSWfT5DLZXG7jRQKCvG4hsOHE0xPawiFzHg8Wv74j+t4xSsK5PMhhoZKfd4dO8bxeEpVgVQqRW9vr1QEm56epqWlhe7uLJ/5jA5VdWGxRNFovEAFgUCG3/2ukle8wg0gR0Z8Pp8UzhBWifX19Wzbto0NGzag0+nIZDIEAgFuv/12GhsbyWQyNDc3YzQaL7MJXA3h9/u5ePGi9FoWcaXRmvIsuryUKUZrykF+rq9rVVkqZ/BfC2lsdlyJ1b0YhTHx99m/ZzVnwuJ9Xev5eb1eGhsbqaur46//+q+X89RkTExMACW/3/Korq6W/7bccdOA8EKx3HPCx44do7W1lebm5mu6oZaLmDX7GGIOV1jKicVMpyvpOnd0dHD06DpSqRA1NePEYkksFhMtLesYGrLx2GNxXv5yPV6vB4vFfElr2sbp0yYuXHDhdIYxGrVEIgas1iluv71ATU2JQOR0OuU5CbnKQqHA+Pg4Tz75JDabjcrKSmnN53a7sdvtl0qzCQKBAH6/n3w+L0ldjY2NUiyjsrISq9WKw+GQvUVxXePxOPX19WzduhW73c7ISA1arY1isYDTqcdodKGqEImksNs10rN4YGCAWCx2qRTuolDYSzyeQqMpotPp0WpLma/PZ+QDH8jgcuXJ5QrodEYqKsyXNg1BWltVqazlcrkwGAz09/dLqc7ShsJHc3MzX/6ylmLRhNEYQ1FUDAbzJQ9jL7/8pQOtVpGyk1arFb/fj9/vx+PxsGPHDhoaGqivr8dqtUrrxKmpKZ7znOfQ0NAwg7m8GmN8fJzu7m727t07p2TjfFE+u7qQipRgi6dSKWkOIiw0xYZtri+h/DU7RkZGGB4epq2tbdk+08WyuufLphcC6dk/s1oIY/D7LP1pdvTM+IMB4aWCnqqqUqVoMQzohWI5ytFzZcI6nU72Y3O5nNRjDgaDnD9//lL2qEOvn8RsdhIKjZHPuyg9pymmpn5PshJZiU6n4+1vT/Lzn+s4cUJHJmNi40Ydd9yhY+fOEggKwBXWf4IcMjQ0xOOPPy6JQaqqSjs+YeEnnHpUVZVzxeFwmJqaGlRVJZ1O43K5pOevIBUFAgEJ/tlsFqfTSUWFj/vvN/OrX+mIxVRiMT0Wi5WWFh0Qo1hUeeYzY7LkZTAYCIVCJBIJGhp0PPxwgslJI5WVoo9tIxyu4cCBIFNTfsbGShaLlZWVkvGdz+cl0DqdTmkGMT4+Lgk8AAcOHMBisZDNFlAUHcXiJDqdiqoaKRYN6PUuslkjuVySoaGLDA0N4XQ6qampwev1sm3bNlKpFC6Xi5qaGiwWi2Qjv/CFL1xwJG61xOTkJF1dXezZs2dRBMariYVKmYK1OxerO5VKzTAFEMYjohozOjrKrl275mQDr1SUW33OjoVkQCcnJ2V2LFjcIlZSwGSxsRykMcGOXqmoqakBIBAIzFjjA4EAe/bsWZHfeVOB8HxjO0sFYUVROH/+vDRkrqysvOZjifNZajm6/L1euKDhgQe0nD6dxWQKsX37GLffbsJiMTA5OUlvby/r169n3bp1VFZOkkwmyOfN6HQG7PZmjMYKFCVFU5MZux3p5yveu9cLb3tbjre8xUkkkkarnaBYzF76/3ZZAhfyiSaTib6+Ptrb22lsbJQALCztRPZRKBTQ6XQyC04mk5fckEojEMLNyOVySW1mQf6KxWJEIiUzeZER/+53Or71LS8ajUpFhUIopJBO6+nqCuPzhWlry/KsZ0XRarWMjo7Ksr3YRNxxx2keeWQn8Xg1uZwena6BQ4cc3HdfDp3Oy8DAAIqiEIvFCAQCjI2NSfEAIR/p8/mIxWIEg0H0ej0ej4eqqip0Oh0jIyNs2mS4BLwaTKZqVDWGomQpFs3cdptCOl3ypDWZTNTV1WG322V5v1gsSmJdMpkkHA7z3Oc+l6qqqiXdS9cjpqenOXv2LDt37qSiouK6/u7FGCaUj9WIXrTwt+7s7ESv18/bixZuYdcj5suiI5EIvb297NixYwZ7ezG9aLg+MqDL0a9e6Uy4paWFmpoafvvb30rQjcViPPXUU/z5n//5ivzOmwqE54ulgHChUKCzs5NMJsPBgwd57LHHliWLXY5jFItF2ttzfPKTWqanNej140xP9/Pkk2EuXrRyxx1p/P5R9u3bR0VFBel0muc9z8vx43oGBsZxOr3kcjYuXjTT3Kzh0KEMuVxp/lFkAg6HQ/bSzOYc4fA4+bwitYVtNhuZTEZKDAqTiDNnzrBx40Y5guT1euWCJZiz4XCYvr4+jh49Ks0SqqurOXz4MHq9Xmo2R6NRmTmHw2EpJhEKhdDpdNI0/je/8ZBMqmQyKtmsgZKtYg4IcvvtGe66a5RUKofB4JLVgkKhSHc3PPmkC6NxKy96kRmv10AuZ6a5OcbhwzGsVj2TkyWChtfrlazk6upqqVvd27uO++7bSk9PEafzIgcPati1qw+DQSfnJEstAhN79mzg5EkHiYQQs3fi8ei4556SZKDoU+dyOVwuF7W1tVKtqra2llAoxJkzZ9i1a9eaAOBQKMTp06fZtm3bqjzf2QIVgqm/bds26uvr5eyrGLlKp9PS1SudTssqznyz0SutdZxKpejs7GTjxo0zPt/lmo1erix6tWTCiUSCvr4++feBgQE6Ozvxer2sW7eOv/qrv+If//Ef2bhxoxxRqqurmzFLvJzxNAgvEOl0mvb2dsxmM4cPH5Y39fUwX1jMMTKZLPffnyMQ0NDQMEow2IXF4kejMfPgg3kgxItedIDq6mpZzq2qSvHWtxr46U9bGBiwkkqpbNuW5k/+JI3DkadY1Mi+phgrEqMjwkxeZHhiTEZkzWazmZGRSUZH/bS2tpLP52Wm6HA48Hg8OBwOFEXB7/czODjI6OgosViMTZs2EY/HZVaayWSorq6WNn5iN59IJMhkMnLhNBgM8sHu788TDlvQalWMRguqqieTCQJZcrk8er2KRmOUWbBer+fnPzfx85/XAzo0GivHjhVpbITPfCaH0RginbYQjeaIRCKYzWa5AfB6vXg8HtLpNMeP1/GpT20jlcoDp4hEQgwPV2G1buDeex1EIhGGhobQarUMDg7yutclqK9v5OjRatLpKvbu1fLmN5+lokIlEjFIi0an00lzczMVFRVy4RMbnJ07d65KQJsdkUiEzs5ONm/eLEt9qzlSqRQnT56kpaWF+vp64PdjV4shjKVSKamFLmwHxdjVfF9LYa3ncjlOnjxJXV2dnDpYKK6GMCa+Xy4Z0OXIhJcDhE+cOMEdd9wh//7e974XgDe84Q184xvf4G//9m9JJpO87W1vIxKJcNttt/Hf//3fKzba9zQIzxPRaJT29naqq6vZunWrvKFWqp97tZHL5ZiYiHH+fAyvV0M8Pko+n7oEWFFSqQrWrbudHTtaSCQSaLVaPB4PwWCQnTsL3Habm6eeGkenUzl0qA6t1kSh8HtLwHQ6LYEun88zOBjgF78IcPp0qYTc1lbgJS8p3T52u52engRf+UqakyfrMBor2bcvy4teFKKpyUB1dTVGo1GWoycmJuQIlVBw6u/vx+1209DQIEvWTqcTnU4nM5Hyzz6ZTFIsFnE4HNjtdvR6PaoaQVUVDIaSTrSi5DEaNeRyMDwcoFAoyJ9XFIWzZ6P8/Od1QAa7PYfRWKBYtDM6WsPXvx7izW8uKXtNTExItadCoYDH48HlcqHVatFqjfzHf7SQz+vQ6x9HUU6iqmZU1cP3v9/Ee9/rprraJI0uNm3axMaNG3nRi3RSDL9UCtVKIX2xkItZb5FRFYtFxsfH2bhxI16vd0n3z/WIeDzOyZMnaW1tlYC2miObzdLR0UFNTQ1NTU2L+j+LIYzN7kUHg0GZWSuKMoPhPxugF1KHKhaLnDp1CrvdvixqTistAypEhJZiZ1gii16dwMzseNaznrXg+qvRaPjQhz7Ehz70oSX9nsXGHwwIXw3oTUxMyHJqU1PTjIdgpZjNVxNCKxkKaDRJQqEgFksCVS1eUp5RcDprqK+vw2q1ylEhUUY1mUxYrQYaGoQtmEouVxrhEmQft9st3Y4mJ+P84z+O09k5gkZjxWCw0dVVycCAi/e+N0k4nOGeezT4/VX4fFpUNcGvf53l9Gkd739/lm3bNNjtdiwWixwXi0QiRCIRdDoddXV1FItF9u7dS2NjI8L8IZfLYbVaJZErFosxOTkpZ24rKytxu90SrKqrjfT0JMjlXKhqkUIhSLGYAooYjR5crpJRutPp5Ny5czz2mBGYxGiMXOrT6jGZKsjnFR55JMdb31r6nTqdjsbGRjl25fF4JHiGw16mppwYjQmy2QBQjU6Xp1CIkc0WeeSROM9/fh6Px4PH46FQKOB0OkmlUphMpjlFLeByechgMMjU1BRms5mLFy/S09NzGdu3fBFfSTu6xUQikaC9vZ3m5mbWrVt3w85jsZHP5+no6MDtdks3quUInU63oMzjtep0m81m+vv7UVWVHTt2XJdrvVQZ0Gg0KoV7RFxtqTubzV4Vq34txE0FwkslZqmqyuDgIH19fezateuyWbGFfsfVxFIzYbGL1uuLHDig8uMfFzGZnMTjU+j1VWg0G1i/fjOHDmnlbls87CaTiYqKCjmmEw6H0WhKIClk4ZLJpHxQtFot//VfU5w5E8XrdWGx6DEa9eh0Jp56ysTjj2c5d26MsbFttLYaKRanyeVieDzDjI0ZeeghK9u2lbJlrbaU7en1esmIbm5uluYNXq+XdDotZzrFg57P5wkGg0QiEfx+P5FIBI/Hg9Vqxev1Smb2jh1Z2tutqKqFfD4GKFgsOSDO+vVuFCXN2NgYg4ODlwzrDwBJNJoiipJDUSIUCi5U1YSqWjEYSjO+NTU12Gw2IpGI1HMWvWhFcaHXaykRbB1oNFNAFo2mDo3GiclUIpcJj99strQRMBqNC5a3yuUhJycnCQaD7N69W2pGC7ZveZ9SZNFzzczOzrJWUrgjlUpdslBsoKWlZcV+z3KFyChNJhPbtm27bpuXpeh0J5NJOcVw4sSJRel0r3Qs9LumpqYYHR1l7969l6pW1+Z4lclklkyMXW1xU4HwfLGYOWFFUejq6mJqaoqDBw/Ou9taLlLV1Rzj4kV44AEtFy5o8PmK3HKLwqZNdtLpNLt3++nsTHPuXBFFqcTlqqShoYH/7/+z4XRmSCQKslxbvouMxWKYzWZSqRQTExPk83kCgYDsRQIS6I4fT1IopLFaXej1TszmGkwmA9PTWX7zmxhQjcmkkM8PkMn40WqN6HRZjMYq/H4vWm1ejk2IUqtgNUejUQCampqk+YPYDAizh0wmw/j4uLRLNBgMUtxDMIhzuRzPfW6O3/7WRySivQS+OuJxHRpNls2bz3LhQpgLFy6QSCRoa2tDqw0BObLZIlZrAUUxAG4UxcDhw2CzmbFYjNIaMBQKYbPZJAhXVlZSUaFn69YAx44F0OsdgBa93kOxuInaWiOHD8fIZkvXXPgDi4x6MeIRc/WAr8T2LZ+ZnS1qUV4CLQfm8u+XojwleBQ1NTVs2LDhmo5xPUNVVc6ePXuZHOVqiPl0ukdHR+nt7eXAgQNoNJpF63TP/lppwpiIZDLJ2bNn2bp167yjaYspdauqyqOPPsr27dtX/JyvZ/zBgPBCoJfP5+ns7CSXy3HkyJErZijXE4TPnNHwoQ/pmJjQYLUqnDqV56GHivzpn1oxmQxUVpp59rMvsnOnB6NxA1VVDm691UxtbR6dzljWc0xLEY3fZ9ImotFmBgbMKEqaxkYnDkdUiqinUikGBgYIBo3kckYikTQORzU6XWnkKJMpoNEo1Nc7OH68lFGXjAzMZDI2wuE4k5N2AgE79fWl0Z6JiQmGhoaYnJyUO9q6ujop5i/cnTQajfQFDofDxONxWaYzGAyoqorb7ZbsZIvFQlubm09/2sBHPpLi4sUMhUI/TmeOl740gs02TmfnBSKRCK2traRSKSoq4OBBB8eOGUml7MAmCoUqKisV7rprgOnpIF6vVzo12Ww23G43oVBIij9ks1ne/e5h/vqvTUxMeNBoKlDVzfh8Br74xTQ1NaWyvhjdgrlLeXPFtZKwrjQzW14CFYt3KBSSjGzBBZhvJGehUqHw0l7Oku5KhaqWzFeSySQHDhy44bKei4lgMHiZ2MlidLqFRejU1BSpVOq66XSLtbWhoWFBbYXFlLo/+9nPkslk+OAHP7jk81pN8QcPwqlUSlprHTp06IrZyfWyIQRQVfjmN7UEAhq2blUpFrMoSp7RUQ3/+Z9ZXvAClfb2dioqKti5swqPR09NTclAIZfLyYwrkUigqiWP3XQ6TTweJxJJ87WvGTl+HHI5UJQ8bncNf/RHTm67LUFNTQ3JZJJMJsMznmFiYsKLy+XGbndTLOYJh3PodG62b89hNp+hWHTQ369gsUSJxdLEYi2And7eat7xDit/+qchXv3qkouSAFshxSg2CqIkLpjYUBoREwCRz+fx+XzY7XZqamouZaIVxONxAGw2Gxs2+PnUp2KcOpXgzJlJCgUD0aifWKyPZDImZTNVVcXn8/Ge9zh55BENTz5ZTyZj4cCBCK98ZYlZriiqVPTyeDxyHMtoNOJ2u+XCsWED/L//l+OXv1QZGTGxfXueu+4qUuIiXZvIw0qxoK9UAp1NJEqlUgSDwTn1m8sXb+Hy5HQ62bp166oHYICLFy8yPT3NwYMH14RRfDwel6NeVyLmlbcj5orrodMtTC+sViutra3X/L4Bfve73/Gxj32M3/zmN7S1tS3pWKst/qBBOBKJSDbkli1bFlWKup6ZcDAI3d0aqqtVNBpQ1SIajY7aWoWTJ+P092u5445KWltbMZvN+HwlI/hUKkUsFpMCEsAltaaSN20wGOShh3QcPQqVlSZcLh2pVJbp6Qw//rGOzZsdVFeXRi/cbje33AL9/VpOnlSJRlNkswUMBjMvepGDd75zC6qaJR4f4XvfyzE52UAm4wDWUVOjoaJCJRyO8+UvJ8nluqitHSOTyciHuqamRo5P6fV6/H4z//M/GiYmNNTVpdm6NSjPW1EUHA4HGzdupKGhQY5HCbGMaDTK4OAgmYzCt789wVNPaSgWSz1hrbaaP/5jOw0NXiorK/F4PLKf/Cd/YuFP/zRPKjWMXq+/RGTTU1lZSTQaJRQKSRDWarU4HA58Ph9QkgqtqqpCo5nizju11NRU4PVqgGvv+d/IMaQrEYlmZ1fJZFI6dIl/f/LJJ+fMrhbKoq93DA8PMzo6yoEDB1bUVWq5IpPJcPLkSZqampak1idiMTrd87U0FqvT3dvbK/UVlrIp6+/v541vfCOf+9znOHLkyFLe9qqMP1gQFgzoTZs2LXocAZaHmFWu8brQzanXg1YLhYJCoZCjWMyhKAVCoSlyuRxOp4WWlhZsNpvctSqKQjweJxaLyREgsaCK8q7ZbKajo0Tzt1rz5PNFTCYXlZUp/H4nfX0GqqsHGR8fx2AwUFNTw333ufnVr5I8+WQKj6eKQ4eKvOpVBTSaPIVCgde/3s7hw3E+/nEPXV0GWlrA4bCj0Riw2+NcvGigs1NHRUWOXC5HKBSisrKSs2fPks/nqayspKOjkh/9aBPZrIZiUQ8UqaqK8Za35HC73VJOsHwXLnySzWYzfr+fXC7H97+f5YknEuj1VkymFNmsDUWx86tfmXjWsyy4XHpJFPN6vbhcLiYmJnC73bJn7fGUTBnEWJPFYkGj0UjN6/JrKVjawkBjKREIBKSy1GqbA55rHKdQKMgS9Pbt2y8D6flELWaDtHDfuh4Z9MTEBH19fezbt++6ylFeawjBoIqKiutCdCu/zvNVS66k0y2MXHw+H4ODg4vS6Z4rEokEd911F3feeSdvectblvmdro64qUB4vgsr2MgCPPv7++nv75ds06uJ5cqE4cqOJ243tLWp/PznBWy2HEajgUgkwsBAkcZGEzU1MdkPFYuiMMfW6/UMDg7KPrDQdBbEh2RSwWy2oaoFVDWHVmtCr9dSLMLAQCcWSz+FQgGv10tTUxMGg0Jd3Xne9a46amqyl7SXFUKhqCxb22xJKivtOJ1W7HYTGo0eVc1QLEbI588TCPRhNltkD7i6uppQKARAKmXmv/7LQzZbxOOZIp9Pkc1mGBio4Pvfn+QFLwjh8/kkMGYyGbxeL9XV1TidTtkvVlV48MEEUIFer6LVanC5atBockQiRp54Isb+/ZMkEqWSe3V1tZTBLI0chbFarXg8HjnbXC4yMNf1EkSppcZqBuC5QrCKdTodu3btkqNkC2VX5ZlVIpFgcnKSdDpNPp+fIQ05G6SXSxoyGAxy7tw5du/ePSfArLZQFIUzZ85gMBhWTZn/SpyD6elpTp06RXNzM3q9flE63bMJY1B67+94xzvweDx89rOfXRXvfSXipgLh+UI8vMVikfPnzxMMBjl06NC8M5pXOtZy9ISBRQ2tv+ENRS5eLHLhgoZkMk4yGaauDu68UyGdzsmSkslkIhKJMD4+TjKZlAYFVVVV0lRAgL5Op+PAAQc//akbvT6PVlsgnZ4kkymi1WYxGofQ6XTSRGFsbAxFUbjtttvQ6/WYzSXnn1gsJhdW4JLTj5H2dg2KYkJREmQyo8RifqLRo7hcOSYm9NIWTkgxVlVV8dOfqmQydmprdRgMLjKZQYrFJBZLjP5+AzZbqRwsnJkmJiYoFAqsW7dOaimXZhETpFIOtFoHOl0crVZFo0miqg6gQCSSk5+5AHTRmy4Wi3g8HtatWyfZzAKgxczySpUu1xoAK4rC6dOnKRaL7Nu374oknvLsaq4QPcpykBYz4XMxfedbuBeKaDTKqVOn2LZtm2wnrOZQVZXu7m7S6fQlNv/qKOUvFNlslq6uLjZt2jTnfPhcOt1Ci11sxu655x4UpeQmFggEuO+++3jooYdoaWmhqalpTfTvryb+oEC4vb2dYrHI4cOHr3kxXe5MeKEouQ4V+NCHMnzjG7309YWoqzOzfXuGzZvdtLcb0elKykvRaJRIJIKqqhiNRiYmJmQ2VygUyOfzcizGarXy7GdnOHp0iO5u8HhqKRZ9pFJJ9u4tsn69QjgclVlziZz1DCorKykUCpI5LQwQRKVBr9fzspcZePhhK/39OSyWMdLpUQKBM3g8U2zebEdVdXg8HiwWC9FoFKfTeUk5yopOZwLCZDJhEgk/0WjnpfJ5HU1NLezY0crU1NQMM4OGhgbi8Tjj4+NEIhHi8Qher52xsTDJZB8ajYLR6AAsgIaWFjs6XUFuAMS1FIu50LcVxg56vf7SPHDJIWolduMCgHft2rUmZiBFdpbNZtm/f/81+/SWx0I9ynKmb/kozsTEhBSXMRgM85KIxP168uRJNmzYsCw91esRw8PDTE5OcvDgwSW3Oa5HFItFOjs7qaysnFdCc7ZO9+zI5/N86Utf4oc//CFf/vKX+aM/+iMeeugh/v3f/53BwUGampro6elZybdx3eMPAoTFTlqn0y150VhOEL7ScbLZLPF4nHPnOtizJ8+tt9qIxYJoNBpGRuKStSrKzOXOR2azWdoG6vV6jEYjudzv+7HJ5BRvfrOV//mfLMPDFjyeerZsmWLXrnGyWQ9er4tMJoPJZOIZz3gGJpNJZthCLAJK88aKolBZWYnJZKKhwc6XvqTns58N8sgjMfT6DHv2RNm1y4KiRMnn3TQ1NWGz2aSpQz6fZ/v2DAYDBIPjaLX9JBJ9ZDJDKIqLmpoo2WyUeDwumd9Go5Hm5mbcbrckU+XzeUKhEEeOZPjhD42AiqrqyOWsqGqRmposbvcQIyMlAQ273S6FQAyGkiOU1+u9bD6zJE+5MlnIWgNgVVXp6uqSYz3XAxzKSUBzsYLLbQrnm5cFJGlxcHBwRia9HJuI5Y5AIMDFixfZv3//vAzn1RSqqnLu3Dl0Oh1btmy55s2qwWCgoqKCb3/72/zf//t/edOb3iT/rVgsEg6Hl+uUV02svrtvmSMcDtPR0QHAtm3blvzALZf5AiwMwoVCgUAgwJkzZ7DZbDidTqampgiHw9jtdkwmE16vF4fDIW9MMVtbLBapqanB4XCQTCZJJBJAKdtwOp2oqkooFGLXrkp8vjEqK9M0NeUZGFCJx224XHWMjIzgcrnYtm0bVqtV9j11Oh2ZTEaqMwkHJbPZLAlidXVZ3v3uKC94wQTnzp0lGk3h9XoxGKplJqKqqhxPCoVCWCwBDh7U8MADo+RyYWACyKDRGNi/PyUXTYPBQDqdls5Jfr+fWCxGf38/Fy5cQKfT8exne/B49PzoR0VCIQWtNs3+/Qbe9jYfW7ZswWQyyXOYmJggHA6TTCbJZrNMTU1JkJ+r9CmUiJYjI16LAHzhQmnWuq2tbdWUBYXBwlztpWw2y4kTJzCbzVRVVc1QnSrPouebi74R8p+RSIRz586xY8eONSPRODg4SDQa5dChQ0vasMbjce666y7uvvtu3vjGN874N51OtybaCFcbNxUIz35YxsbGOHfuHJs3b+b8+fNLZjXD8nkBL8SyVhSFsbExnnrqKZnpdXd3S71h4eIjSneZTIZ8Po/ZbKO/30Nvrwer1U1DwxgNDeMUiyVGtHD+EUYEAwMD0kRgdHRU+vP29vZSUVHBrbfeil6vlxaFou8cDAaJx0uZuADfaDSKzWZDURTC4fAlhalJMplS9lwsFmltbWXDhg3E43FsNhsajYZ8Pk80GqVQKPDSl06Tzw9z4kSYcDhJVZWBAwds3H57K1u2bJH2im63G5/Px9DQkPR9PXnyJDt27JD+xDt3mmhpUdFoKmloqKClpRK9Xn9JN9sqF1ih5ywsBGHuzCoSicjMqjwzm0t1ajGL0FoE4J6eHqanpzlw4MCKerouVxSLRU6fPo3VamX37t2XXZdy+c/y611+rYV281xs7pWQ/xS2hK2trWuCGwClkbqBgYElb8wUReHtb387Pp+Pz3zmMzctEWt23FQgLEJVVS5evMjg4CB79uyhsrKS7u7uJWewsDzl6CsdZ2BggKNHj9La2orb7WZgYACj0Ug+n8dkMuFwOKiuriYQCKAoyqXxGT2f+MQUTz1lQqu1AUYURce+fVbe8Q4rWq1ySeUqg9vtZvPmzVI0I5FIcPHiRQD8fj/V1dU84xnPwGAwyN6o2Wwmm80yOjrK5OQkgBTJqKiowGQyYTAYiEajBINh/vM/Q/zkJ1kikVo2bKjm0KEA+/bZOHlymAsXTFRUuHj2s004HBmKxSLxeJxQaJI9e7Js2FAkl6vA4/FQX1/P/v37cTgcaLVadDqdzA7EpqS3txeHw4HVaiUUCskxop07a6ioqMBoNFIsFolEInLWWJRR5xorWiizEnq+5QSi2WIWcxGIyjP5tQbAUJoomJiYoK2tbU2URwVxDGDXrl1zboyuJP85l3bz7FGc+RyQZo/SLSby+TwnT56ktrZ2TZheQGmE6OzZs2zfvn3Ofv5iQ1VVPv3pT9PR0cHx48dXTZXlesRNB8Ili7qzhEIhDh06JG+M6wGeVxPzZcJ9fX0cO3aMTZs2YbfbCQQC0rdXZGzV1dVUVFSg1Wpln/axx/I89VQeu91CRUUao9FCKATHjxdobU1w220lglEikcBgMEj2tKqqpNNZzpzR8uSTKUymFp73vA1ksyomU4mgZDKZUFWVYDAoh/gFo1jI27ndblkq/s537Pzf/3uRfF6DRhOioyPBmTPrOH/eSEdHmmzWhVab4vOft/CWt2R4xjO00vReKFE5HA6cTieVlZVScMTpdEo960wmQy6X46mnnsLj8VBZWYnL5cLlcsnRIlEed7lcUubSYlmaw9B8er4wU8xCgHQ8Hp8xhiPmJ91uN9FolFwud0NLn4uJwcFBRkZGOHDgwLyEmtUUQo4yk8ksSY7yStc6n8/P2IwtJGgxV7m7/LwURaGzsxObzcamTZuu+b1fz8jlcnR2dtLU1DSn2c3VxK9+9Ss++clP8rvf/W7Jx1prcVOBcC6X4/jx4yiKwpEjR2aUzFYbCM8+jqqq9PX1cfbsWXbs2EFVVRX9/f1Eo1HS6TRms5nGxkbq6+upqakhHo8zNDRELpfDZrPxxBNWFKWe6upKcrlpisU4Tqed6WkdAwNZXv1qpDdvMBjE6XRiMBiIxVJ85jNRHn7YiEZTidns4vRpI08+meJDH1Jwu0vZWzweJ5PJkEgkKBaL0pKvoaFBAmYikaCrK8hXv3qCfL4bcKGqZqCafL6Khx6aQq93UlmZQaPRkkgU+bd/K5G53O5SibzECK+mqqoKq9UqP6NoNIrRaMRkMpFIJBgfH6e7u5t169axfft2CXBGo5FIJCJ/1uVySWGOlY4recuOjY3R1dU1Y35yucvcyx3Dw8MMDAxw4MCBNSFsAaWNbCgUoq2tbcWIY+VZ9JUELearmJTLQsbjcYrFIhs2bJCchNW4IRMhKg0Oh4P169cv6Vi9vb285S1v4Ytf/CIHDx5cpjNcO3FTgfDY2BhGo1EKB5THcmawyw3CwsHJ7/ezZ88eoKTqI8qnwoReELSsVivBYFDaEYZCIaana4E60ukgWm2eYrGAXm/CZLJhMhWBCZltAvLB/8lPRnj4YQW32051dR1gJJPJcPSolh/9KMob31hiI8diMfL5PNlsVs4fC3lDt9tNIBDA7/fzox+dIJN5BDABJTMH2AwEAZVCwY5GAzpdJR6Pg+npJL/4RZhnP3uC4eFh6d3r8/nweDySiCYYzKqqMjw8TF9fH1u3bpX9YaGPLTx6xez0ahHlDwQCnD9/nt27d89Zgl6OMvdyh9/vl8pSSyk1Xs8YGhrC7/fT1tZ2Q+UoF2uikUql8Pv9pFIp7HY7Z86cIZfLSXOF+TLpGz0z3NPTQz6fZ8+ePUvaLMRiMe666y5e97rX8frXv34Zz3DtxE0Fws3NzdTX1895UyxnBnslW8TFhChHi9m6WCzG5s2b5diMkE6MRCJkMhmp2iTKnTqdjubm5kvqV0m2b7fS1+ckHI6i1abQ6RIUiwbS6UoaGkokKqPRSDKZxOFwEIvFePTRR/npT4Oo6h58vgr0egfFYhKzWSGfV7n/fjOplJGWlihNTRNAUdoGrlu3DkVRsFqthMNhuru7GRwcZHi4E4hRMi5IUQJgIyVALpk6FApGDAYnqpommRylt/cs1dV9lzSlqwmFsng8RpqaXDJL9Hq9VFVVMTk5yfnz59m+fbvs2SqKIjNenU63aoBXxGJ6wEstc4v57+Vic09MTNDd3c2ePXvWhLIUlDbhYqxnrs9xtUS5iYaYXjh06JCsNJSbaIjrPdtcYbZF4WwzjZXMokdHR5mYmFiU4c1CIYhYVVVVfPrTn17Vmf9Kxk0FwuUG0LNjOVjN4jhiRnapx8lmsxw7dgyNRsOePXvI5XJyZzw4OEgqlUKr1eJ0OqmpqWHdunVSlMNoNNLQ0MDU1BS7d+/m5S/fQTBY5MknC+h0BlRVQz5vYts2lf37p5mampajSYIB6vf7UdUN5PMjZDJmtFo3RqOZiQmF6ekikUg1k5M6NBoTW7ZU8OY3D9DQUGJr5/N53G43Op0Ov9/P1NQUvb29qOooUKQEuh5gP2ADqoAKoEgmk8ZkypBKjQMhGhu1aDRazp6t4NQpI6DlBz8wsH+/lr/7Oy+bNllwOByEQiF6enrYv3+/BFyn0yk1h1fjQ7wcJKwrlbmvhs09VxY9O6uanJyU0o5XcutZLTE9Pc358+fZs2fPmhnrCQaDXLhwgb17984o9V+NiYbQOhcgncvlZsh/ziVcspQsWmy49+3btySCnqqqfPKTn6Szs5Pjx4+vCTGSlYqbCoQXiuXKYJcroxYzl263m23btkmLvN7eXjo7OwkEAlRVVcmRIqvVKh84g8FAXV0dNTU1uN1uXC4XWm2G++5T+fGPFY4f15HP59i3r8AddxSoq2tCVddJdvTx48c5d+7cJXENhQsX9MRiUChMUyiYGBqqAExUVJhwOCZJpwMcO5bGYgny53+up7a2Fp/Ph8PhIBgMEovFLpXEp3G7nbS1GTl+3AHcAmwECkAdpdstRLEYJ5UyEQxO43YbOHzYyAMP6Dh1SgN48Pm2o9NV0tnp5O/+Dv7rv6xMTU3R09PD3r178Xq9UvJzNQKviOvFgl5ONjfA+Pg4ra2tawbMIpEIp0+fZvv27VRUVNzo01lUJBIJTp8+zdatW69qo3OlDVm5RaG45nOZaMw3crUQGKbTaU6dOsXmzZvn/N1XEw888ACf/vSneeihh9bMKNZKxR8UCK8WYpYgW/l8PlpbWxkfHyeXywGlh1OICmSzWekD7HQ6pYKVoigMDg6iquqMERyrVeXlL1d56UsLqKrm0gxtBFV1SzZ1V1cXQ0NDl0wZDGzYYCcer6G/v4lMxkoymQCMWCwmrNZRkslhMplxMpk4jz+u59ZbzzE5OUk8HsdisRAOhwkGg0SjUdmP3bDBjMfTyq9/vQkYx+Wqwm5XiMVsFItpDAYzxaKGzZvTPPe5E2g0RXp6tgBN1NfvwGz2AQmMxiS9vTp++tMQ9fW97N27Vz78q63kPDsCgQDnzp274WNIV1PmDofDTExMYDabGRwcpLe3d9nL3MsdiURCztXW1NTc0HNZbGSzWU6ePMm6deuoq6tb1mNfyaJwtm7zXG2NuXrQRqORM2fOUFNTQ0NDw5LOsaenh7e+9a186Utf4sCBA0s61s0QT4PwVcZSrQynp6c5efIkJpOJqqoqgsGgZAULFapwOEw+n7+k3WzHZmvG6bTI2cQS+OU4fbqCkRE3FkuK/fszbNmiSPWqZDIpgTEUCqGqKrFYDL/fL5WqKisrqa2t5QUv2MCvf63j2DEzPT1FMhkjjY1+8vk+dLoptNoUVmsWo9HFunVePB4HLpeLqakp/H4/IyMj9Pb2SvGNQqHAc56Tx2AY5cknPZfGmmIcPuzmnnu0TEzEiEaDlxS4ashmXUAl1dVObLY6tFoTiqLHbM4TjRo4fz7IS1+6b81kZhMTE6sCgK8U5VmVVqulp6eHLVu2yEV2ucvcyx2ZTIaOjg4aGhrWzFxtsVjk5MmTeDyeJbOKrzaE8Mh8ZeS5hEuE/Gc6nQZKlZX29vZ5rSivFIKI9aY3vYnXvva1y/r+1mo8DcLX8Th+v5+uri62b9/OyMiINEAQO9RUKoXT6bwkfNHIL3/p4LvfbcJgsLFpU5GXvUzL1q06BgZi/PM/GxkedgMGMpko998f5jWvsfDqV9ulwb0gcOVyOXp7e0mn09hsNnK5HCaTCbfbTUNDA06niZe/PMvLXx6kp8fB29+eIBYbQacLk8tNoygpkkkbW7YYqa6uYOvWrZKpXSgUGB4epq6uTn42NTU1mM16br11hFtuSTEwUMBiUWhrO0cwaKRQSFNX5wJsl8rreVyuNIlENW63/pLTk5VEIodWq+GZz2zE5Vr9xuuwdgC4POLxOB0dHWzYsGFGlrOYMnc5QC+GzS2+X2oPMJfL0dHRgc/nY8OGDUs61vUKVVU5ffo0er2ebdu23fAqwuyY73pfvHiRsbExdu3aNaPcvZCJRvn1zmQyVFVVodFoeOtb30ptbS2f+MQnVt37v1FxU4HwQhf1RoKwqqr09/czMDDAvn37qKiokDO+YtwoHA5LBZ54XMM3vuFkZMSDx2NHp1Po6NDQ22vhvvtUfvnLBP39tTQ1GVDVMInENMFgmu9+t8CuXUU2b1ZlphsMBhkZGZE95MnJSWpra9FoNFJuslTKtuJyubjlFhNHjozyq1+lKBZTaLVZslkHPt867r67gvXrW7BYLIyNjXHhwgUefPBBhoeHqaiowGaz0dbWRmtrye1o/fr1OJ1ObrtNleNCxWKR4eFhMpkMyWSScDiMVqvlllsq+OUvawmFwGrNkc2qJJM69u5Vec5z1g4Ad3V1rSkATiaTtLe309zcfFXZ5NWyuefyDr7WMreYKLDZbKvGY3cxsdZsCaHUVhkaGuLgwYMLzomLLLqceyA8hP/yL/+S/v5+rFYr+XyeO++8k89//vOsX7+e9evXs2PHjjXzeaxE3FQgvFDcKBAW6j2BQICDBw/KXabIgt1utxxDEn3WY8eMDA2ZaWqyoddnKBaz2Gx6BgdT/PjH05w7Z8duN2EwFEgkQmg0BTwePdPTCp2dKk7nuMxE4vE4TU1NNDY24vf7peCG1+ulWCySSCTQaDTkcjmcTiepVIJ3vCOE3Z7l0Ud1JJOVtLX5eO1ra3nGMyqx2+1cvHiR3t5euru7mZiYwO12y3ELUf4uGTnU4fH83gc4Ho+j0WioqKjAarWi1Wrl7O8rXpGhtjbHD39oJBrVoNOp7No1zRvecIbHHlMuW7CF/vNqYVWKDHj37t1rRmQ+lUrR3t5OQ0MDLS0ty3bclWRzm0wmTp8+jVarZceOHWsGgIeHh+UasFru2StFycGtZCRxJaGWhaomR48e5d///d+57777eM973kOhUODxxx/nm9/8pvxcngbhP4C4ESAsBOTFHKDVapWD+vl8nkAgxcWL04RCk7jdOQwGA3q9nmDQhV7vw2y2kkoNk81OYzD40OmK9Pcb0GjcqGqEfN4AqGg0BrTaLIpiplBQZL/2xIkTtLa2YrPZ+O1vf0uxWJS6tB6Ph2AwKOeM3W43Go2GeDyO1arnla+08uxn5/B6vdTU1OB0OqmoqCCZTBKLxSR4OhwO2S8SO1uAmpoaVFXFYrFIYweRCZvN5ssMAKxWKx/5iMKLXnSWCxeK3HLLJrZtc5HPH5ILdSqVIpVKMT09TSqVmjejEn9eL+LQWgTgTCZDe3s71dXV172cu9Qyt0ajweVyceHChWUvc69ETE5O0tfXt2ZsCeH3kpQtLS1LZi8PDg7yj//4j3zta1/j7rvvnvFvqqqumY3USsXTIHwNx1kMMUv0rAAOHTokBcmz2SyRSJTf/a6C//kfJ9FoGlW10diY42Uvy1Jfn6eiwoJWaySXi5HNlkp4gcA0waCWurpGdu0q8JvfpLBYcuj1BXQ6C+l0BXa7k+3bJ8jlcgwPD+N0OtFqtTz00EOMj4/j8/lIJpMUCgWpQy3cg8SDVtKSLi2AFktpPtftdsuMNh6Po6oqgUCAUCiE0+mUhhBCcEAwl1VVld9rtdoFd9NCBk+vT/P61+/DZCp9XgtJAxYKhRkAPVdGNV8GbTKZlmX3LUrQawmAs9ks7e3tVFRUsGnTplW1CM5X5lZVle7ubqampti0aZPsTS5nmXslIhqNSinatUIsVBSFU6dO4XK5aG5uXtKxotEod911F295y1t4zWtec9m/L8f18Pv93HPPPfzyl78klUrR2trK17/+9QWZ1w899BDvfe97OXfuHI2Njdx3332XWSder3gahK8yFiNbmU6nOXHiBDabjd27d0sgUhSFbDbLr3+t8MMfuigUMtjtSfT6HF1dRaJRLffdZ+N5z3Pym98YGB4OoqpxxsedQBJw0NfnxGZL4fMVmZgoYjCoKIoWrdbIi16UxO0Ocfx4D9XV1ZjNZnp6ehgcHERRFBKJBIlEQpaB3W436XSa6upqrFYrIyMjTE5OMj4+LskUgMxc4/E4iUSCYDDI1NQULS0t+Hw+7HY7e/bsQafTyV5zucPMlaJYLHLq1Cny+fxVGcULf+S5Mqq5VIempqakBzIwL6t3sRZ15T3gtQLAYnPodDrXVD91aGiIQCBAW1vbvCYSq43NnU6n6ezsZMOGDWtmFlboFxSLRbZv376k+6NYLPJnf/ZnNDY28vGPf3xF7rVwOMytt97KHXfcwS9/+UsqKyvp7e1dcI55YGCAF7/4xbzjHe/g29/+Nr/97W/5sz/7M2pra3n+85+/7Od4pbipQPhKxKxCobDk33ElMI/FYrLMN3uRE0zo737XxshIjEJhFEhhNJqoq7MTDKr097u5/fY4b31rls9+tkB/vxEYBVRqazdTU+Ph/Hk9L3tZBr3eyNmzYLOlWL/+JC0tEc6cibJ9+3aqq6vp7+9neHiYVColM4v169fLeUqNRiMXh9OnTzMyMkIulyOZTOJyuaisrJQ6tplMhmg0KiUk161bh8vlQqPR4PV6ZZZhs9muquRWKBQ4efIkAPv371+SDF55LKQ6pCiKnJcUi7WwqEulUjOYvbMzaIvFgl6vX5MlaGGVZ7Val7zAXs/w+/0MDAywf//+BV2cllrmFqI4y8HmFp91dXX1mhmfAhgZGWFqaopDhw4taRZfVVU+9rGPcf78eY4dO7Zsz/Xs+PjHP05jYyNf//rX5WtX4jf827/9Gy0tLXz6058GYOvWrTz22GP88z//89MgvJJxPXrCwWCQkydP0tLSwvr162cscvl8nkwmw+BgnqNHkxQK54EhwEAuZ2dw0EllZRXxuJV8PsH27XFe+EIdX/+6F7c7g9NZh9NpQVVTGAw5jh1L8clPZrnrLjP9/SOcPt1FT0+c5uZmdDodiUSCQCAgXYm8Xi+bN2/m4MGDEiTz+Tw2m43R0VE53ytkMhVFQVEUvF4vdrudZDIpS8qpVEr2hysrK2ltbZVAdLX+qR0dHRgMhhkVg5UOUaa2Wq2XKSzNp9McCATkKIZer6dQKODxeIhGo+Tz+RmL9WoEN8EoNhgM7Ny5c80QYaampqS041zguthYLJtbXPellLlFOddisbB58+ZVeT/MFcFgkN7eXvbv379k84uf//znfP7zn+fRRx9d0U3qT3/6U57//OfzJ3/yJzz88MPU19fzzne+k7e+9a3z/p+jR4/ynOc8Z8Zrz3/+8/mrv/qrFTvPheJpEF6m44yNjXHu3Dm2bdtGfX39jH8rFArSEel//kdDJjNIKbsNAA5AC3hIJJw4HAnC4TCqqpJIZDCZdFRW1mGztVIoBEgkLqIoKsmkDavVgUaj4eLFi0xPT1NXVyct1orFIsViEa/Xi8PhoKKigqamJlKpFIVCAbvdjsfjkTt8QZSKxWLU1dVRWVlJU1MTXq9XHk+U9Hw+n1Tcstls1/TA5nI52tvbsVgs85qu34hYiNmrqip+v5/u7m6amprQ6/WkUimpx12u2ztXL/pGKUwJANZoNOzevXvVfNZXinA4zJkzZ9ixY8eKalhfC5tbjN/MVeYWG7OdO3dKQuJqj1QqxenTp9myZcuSDTvOnz/P2972Nr785S9LV7iViv7+fr70pS/x3ve+l/e///0cP36cv/zLv8RoNPKGN7xhzv8zMTFxmWdxdXW1FCa53uS5mw6E51O0Wkkf4MHBQfr6+tizZ8+c86GRSISpqSlUVaW3N4HBMEI+nwY0QAJwAnVYrT727x+nWDRRUVHBtm3T/PKXGXI5F0ZjmGRygHTaTySiY8cOJ6FQnsHBQYLBoPSobW5uprKyksHBQRwOBx6PR8rY5fN5YrEYer1emi8kk0lyuRyJRAK9Xs+OHTuoq6vD5XJRVVUlF2xRhi0WixgMhiUtLELpyG63r6kZwUAgQE9Pz7wlaEEWKieLTUxMXLZYzwXQSxXWny8E4a1YLLJv3741AQhQGo/p7Oxk06ZNN9zk/WrK3OPj4yQSCcxmMydOnFj2MvdKRKFQoLOzk7q6ussSiKuNSCTCnXfeydve9jbuvPPOZTrD+UNRFA4cOMBHP/pRAPbu3cvZs2f5t3/7t3lBeLXFTQfC88VKyFYKEsP4+DgHDx6ck/0oMiWtVksqleXChUnyeYVS9mun5DC0Ea3WzI4dg3R1nSWRSFwqgymsX7+TwUEH4XCMfD5KMpnD49Gwd2+Rrq5xkskkBw8eJJPJ4HQ6aW5upre3l97eXqxWqyyN5/N5CaCRSETOCMdiMVRVpampSZoyCFb17KxNLEZLiXQ6TXt7Ox6PZ1WqBs0XiyFhLaTbO9tIoXzUSox4lQvrz/7zWsBTVVXOnj1LNptd1n77Skc6nZbaykvVKV7pKC9zC06BmBK42jL3jaicqKrKmTNnMJvNbNy4cUnHKhaLvOUtb6GlpYWPfexj1+XZrq2tZdu2bTNe27p1Kz/84Q/n/T81NTUEAoEZrwUCAZxO5w0ZIVsbT+UyxHJnwsVikTNnzhCLxTh8+PCchBFFUWSZMpfL8YUvDHPs2CilDFgLVAMbcTq3UleX4AUvGMbr9eL1ehkaGsJu13P33VEefvgEJ05ANhuluTnI9u1xtFoniuKkqalJ+pNarVYeeeQRTp8+LZ2W9Ho9lZWV0u5MEKvEuJHD4SCTyVAsFnE6ndKP1+l0LvtDJNSZqqqq1lSvbHx8nPPnzy+JBX2lnqSQLRWLdSQSmUEUm51NzfaPneuY586dI5FIXBXj/EaHYG9XVVVdd23lpUQoFJJWioIMeC1l7rkqJ3NJQV7rxmx29PX1kUqlOHjw4JIqMaqq8pGPfISenp4VJWLNjltvvZXu7u4Zr/X09NDU1DTv/zly5AgPPPDAjNcefPBBjhw5siLneKV4GoSv4TiqqspS0+HDh+UM8OwQoxEul4uTJ/t5+OECTqcBs1nHxEQFimIFfBSLGV7ykl7gAsGgXbolCRGF17ymyF13qUQiLqJRCIcNeL1e3G43+Xye8fFxotHopXJ3L4VCgYqKCjlKFIlEsFgs+Hw+LBYLTqcTo9FILpcjnU5jMpkwm824XC6pZLXcpVGhT1xXV0dra+sfFABfKRYS1hfiLuUAPTubMhgMl2XOwumqra1t3vtztYVgyjscjjW1SUskEpw6dYotW7ZclZXijWZzj4+PMzo6uiwqXj/96U/513/9Vx599NHraif5nve8h1tuuYWPfvSjvPrVr+bYsWN8+ctf5stf/rL8mXvvvRe/3883v/lNAN7xjnfwhS98gb/927/lzW9+M7/73e+4//77+cUvfnHdzrs8ngbhqwxhOajT6Thw4MC8u9HyciPA8DBkMg6czgKKEmLduiQ6nRedzkoyWUCnGyOXyxGPx5mYmGDbtm0YDAZyuRwWi0WWtiYmJjAajbJ00tzczM6dO4lEIiQSCZLJJJOTk/L/CQ9QrVaLyWSS6lfiQRXgvFziFXNFNBqV5cW1lN1cDwC+Uogqh8lkmjebmq0o5vf7yWazADzxxBMLCpasFqATjGLBS1gt53WlELaEjY2NS+6nlsdysLkXIgjGYjF5b8/1O64murq6ePvb385XvvIVdu/evaRjXW20tbXx4x//mHvvvZcPfehDtLS08NnPfnaGMtf4+DjDw8Py7y0tLfziF7/gPe95D5/73OdoaGjgq1/96g0ZTwLQqEvx5VuFkc/n5wTbQCBAX18ft9566zUfOx6Pc+LECbLZLHfcccdl0ovlMT09TT6fJ5/P09vby2OPDfP5zycwm42YTCnMZh8OxwYSCTuZTJy77+7E6cwwPT2Nx+PhyJEjMkOCEpCdO3cOvV5PNptFp9Oh1WrZtGkTGzdu5Pz58wwNDTE5OYnZbJbC6A6HQzKbc7kcZrP5shnZ8rKXWKDFQ7tUwlA4HKazs5P169cvWCJabSEAePfu3WvGKB5Kjjejo6O0tbVhMpkuUxQrF9iffd1nZ1TXizAn+pKpVIoDBw6smd51sVjkxIkTWK3WVbVxmKvMLb7PZDLyPC0Wi9Rxv9Yydzgc5vbbb+eVr3wl//RP/7RqPoO1FGvjbl+GWGomHAqF6OjoYN26dfT39y94s4mZ23g8zvDwME888QTT01NUVXkZHa2kqsqDzVZHLqcyPT3Ojh1Ztm71EQhM4PP52L17N3a7nampKaanpykUCoyOjrJlyxZMJhP9/f14vV76+vokuaqk+VxaDCorK9m8eTMVFRVks1ni8Thmsxm73X7ZxmEuwtDU1NQMD9H5RCuutFAHg0FOnTrFpk2bVj3BpjzWKgAPDg4yMjLCgQMHJEfhSkSx2eXO0dFRWb1ZSLBkuVjWQo5SlM7XCgAL0ptWq111wicLlbnz+TwnTpzAYDBQVVW1pDK3IGK1trbykY98ZFV9Bmsp1sYdvwyxFBAeHx/n7NmzbN26lfr6evr7++c9ViwWk3O+fr+fxx9/nIsXL5JOp9m7t0TQikS2ks8bUdU069freN7zTAwOarHZPOzfv47m5mYuXrzIwMAAoVCIcDjMzp072bJlC+FwGIPBQCgUwuPxUFdXh8lkwmg0St1mnU43Y/5X9ATnekgWKnvNVpZKpVJzLtTli7P4PpFIcO7cObZu3Uptbe01fe43ItYqAI+MjEhVqSs53sDiiGKz52IFaahYLGIymeYtd15Nf3FgYEC6C62V3jWUyD+JRGLJhKbrGWLDo9Pp2Lt372UbqcWUuQuFAu9///tpbGwkGAwyNjbGV77yFcbHx6mvr18zn8VqiqdB+AoxODhIb28vu3fvlhKP8+lHlxSxBonFYmi1WoaGhmRfuFRCjnLnnTU4nXUkEjqMRiuPPWbl85/Pkk5fxGRy8spXVvGOd5TGiQqFAqFQiKqqKux2O36/H51OJyUjRWZbLBbxeDxks1nMZjMNDQ0zFuJr3aFeSVlqNqM3HA7j9/tJpVKoasnTeGxsTBLDViKTWs5YqwA8NjZGb28v+/btW5KqlIhyothskQxVVWUfWlx7wbwXzlaCazBXBm00GuX9ODo6ytDQEAcOHFgz7kJQsiUUY4lrhXUOpfMOhULzSlIuhs0dDof5y7/8S/77v/+bs2fPsm/fPt797nczPDyMwWDg2c9+9g0jOK3VeBqE5wmxa/T7/bS1tc1QkZnrWIqiEAgEGB8fx2QyMT4+zsWLF8nn85IU1dTUxKFDB2htrWd0dJz/83+K9PTksNvjuN0qhYKLH/5Qwe02s2dPgomJCaqqqqirq5OazHa7nWg0SlNTEw6Hg3g8TjabZfPmzXKRux4ZxXyM3rGxMc6fP8/WrVuxWCwzMqnx8fHLRm7myqJvBECvVQCemJjgwoUL7NmzZ8lKR4sJocg2n7PVbHN3MW71+42oTrYx4vE469atkyx9s9m86kuaU1NT9PX1sW/fvgV1rFdbTE9Pc/HiRfbv378gl2WhECXsgwcPcu+99/Kd73yHP/mTPwFKhNWhoSFisdhynvYfRNx0IDzfQ3w1IKwoCmfOnCESiXD48OHLSnZzHSuRSDA1NYVWqyUUCjE6OorRaMRsNlNXV4fP52P//v1s2bKF4eFhfvGLLnp6dJjNFvR6HVbrevR6O4FAgO99L4PDMcy+ffvweDw0NzeTz+clA1qj0UhiltPplJ6cN3oBGxkZobe3l71798oMaq5MSozciIU6FovNKHUajcYZAF0O0ivRM1yrADw1NcW5c+fYtWvXiso6Xk0s1I8UzlaTk5OS1yDKneXOVvMJltzoUmcsFuPMmTNs3779umx4liuSySRnzpxh69atS7ZTDIVC3HnnnbzrXe/iVa96lXzdaDQuWewD4B/+4R/44Ac/OOO1zZs3c+HChTl//hvf+AZvetObZrxmMpnk/bQW4qYD4flisSAs3E8KhQKHDx+ec9c421NYlI2FIpVgIYsREEVRaG5upra2FoPBQDabJRbTotFUYTD0o9U2oNOZKBYT6HQFEgmVzZsPs2GDA5/PR0VFhdQlzuVyeDyeGYIANxp8oVS2HxgYYN++fQsuUAuN3MwudZb3pITmtbBHnAugr6U0uFYBOBgMcvr0aUnEWwuh0+lQVZWhoSHJrxAh+AflGbTQ5BaEofkUxa5H9USoeK1fv/6Gy2heTeTzeTo7O2loaFgyN6NQKPDmN7+ZzZs38+EPf3jF1p3t27fzm9/8Rv79Shtvp9M5Q7BjNayHVxN/UCCsqqrMGueKTCZDe3s7JpOJgwcPznvxy3vCiqIQi8VIJpPY7XZZcnM4HPI1vV6Py+VCURQ0Gg0Oh4OaGhOqGiKfV3E66zAaq8lk0uRyXmpqNFRUJCkWi+j1eglcjY2NqKp6wzOC8lBVlf7+fkZGRti/f/+SepJXKnWKsmU5SM/uRc5enMXf53I3WqsAHA6HOXXqFNu2bVtTgJBKpejo6KC5ufmymdpy/sHsEIShcoAW1RPhbCWqJ3MB9FL7tmJjXlVVtabG7ERFz2q10trauqRjqarKBz/4QQYHB3nqqadWdNOj1+ul3epiQqPRXNXPr7b4gwJhYF5Xk0QiwYkTJ6ioqGD79u0LAp3IqsvZhFarlZGREc6ePUtvb6/06hV6zpWVlUxMTFAoFDCZTOzb52b9+jQDA1spFNaTTEI63YTVmuPlL5/C41GkA5KI1ZL1ihAKXePj4xw4cGBRrNylhADouUpqc4lWzHY3Kl+cc7kc4+PjbN++fdWUchcT0WhUGhusJdZ5Npulo6ODmpoampubr+r/lhOGZkd59aT82k9PT5NOp+e89uV/XkmwRBhgrDVbQoDe3l4ymQwHDx5c8nn/6Ec/4qtf/SqPP/74nKSt5Yze3l7q6uowm80cOXKEj33sYwt6MicSCZqamlAUhX379vHRj36U7du3r+g5Lmc8DcKUMgsxA7wYSUWtVkuhUCCfzxONRslms2QyGc6ePcupU6eIRCJUVVWRzWax2+04nU6y2az0FHY6ndTV1fLJT7r42td8PP54mkLBTnW1lVe/2sif/7kBg0GzqmcmhXnF9PQ0bW1tN5ykYjAYcLlccwJ0oVC4zDghEolgMBg4c+YMXV1dcxLEFrNIX8+Ix+OyJLqW5q6FHKXL5WLTpk3L+nleqXpS7mwl7oFIJCIlZUUGPlcGbTQauXDhAvl8ngMHDqyqCtSVYmxsjLGxMQ4dOrTkdeTs2bO8853v5Bvf+AY7duxYpjOcOw4dOsQ3vvENNm/ezPj4OB/84Ad5xjOewdmzZ+ecd9+8eTP//u//zq5du4hGo3zqU5/illtu4dy5c2vmGbnpFLMKhQLFYvGy14vFIg8++OBlSleBQIDTp0+zefPmBXdb5fH444/j8/kkZd9ms0kB98nJSYaGhrDZbFRUVHDbbbfR3NwsCVtCpzmRSOD1ejl37hzxuJd16/azZYuRtZCUKYpCV1cXkUiE/fv3r6nxktkl6GKxOGOBLv++fJGeC6SvJ0Ank0lOnDhBY2PjmpL+LBaLnDx5Eq1Wy549e1YVkAmi2HyKYqJ15Xa7sdvtl4lXrMYxOyjZCba3t7Nnz54lt1mCwSDPfOYzufvuu1e0DzxfRCIRmpqa+MxnPsNb3vKWK/58Pp9n69at3HXXXXz4wx++pt+ZyWSuySP9WmP1plrXGAuxo4EZ5KyhoSF6enrYtWvXonproqfsdrs5f/48Fy9elBdLsHxjsZjsUe3evZvW1lbsdrs0XPD7/SSTSSwWC4ODg6xbt45t27at2gd6diiKwtmzJbtFIY24VmJsbIwLFy7M6AGL/v1cu2yxSJcDcyAQmNMfeDZAL+e4jbB/rKuro6WlZVmOeT1CqEoVi8VVB8BQuvZ2u33ONoq4VwTjN51OEw6HZzhbLaQodqOqWJlMhlOnTrFx48YlA3ChUOCNb3wj27dv54Mf/OANqQi53W42bdpEX1/fon7eYDCwd+/eRf/8XPHSl74Uk8nEv//7v0ttiJWMmw6E5wvRTxW93N7eXinxt5geh6qqKIqCoihs2rSJqqoqfD4f2WxWWsblcjm0Wi319fXU1NTIWWOr1Up9fT1Wq5Xq6mqy2Sx9fX00NjauKUehYrHI6dOnyWazHDhwYE0pHM0FwFeKhRbp+eQ+BUAD87K4r0aPW5AFq6qq1tS9oqoq58+fJ5lMrik9aCi1p0S1ZC7jjnIuiLj+8Xh8Xhb/XIpiK3Edi8UinZ2dVFZW0tjYuKRjqarKBz7wAUZGRjh27NgNSxISiQQXL17kda973aJ+XljMvuhFL7qm3/f85z+fY8eOsWfPHl7xilfwqU99isOHD1/TsRYba+fJWIYQvdwzZ84QDoel+faVQlVVisWizISz2SyKouByuRgbG6OiooKqqiq0Wi01NTWYzWaqq6tJJpNks1m0Wq3sRSaTJdazTqcjEonM6Efe6F30QlEoFDh16hTFYpH9+/evKaWgawHgK8Vi5D7LM2jB4hZ63OVyn/PNw2azWdrb2/F4PGuOFNTf3y/5Amtps5ZMJuns7GTz5s3zOmctRllqdotjNklwIYeja7nOwj9ap9OxZcuWJd8r//mf/8nXv/51nnjiies6E/03f/M3vPSlL6WpqYmxsTE+8IEPoNPpuOuuuwB4/etfT319PR/72McA+NCHPsThw4dpbW0lEonwyU9+kqGhIf7sz/7sqn+3oig897nP5atf/Srd3d188Ytf5M/+7M/43//7f/OGN7xhWd9neay+1X4FQ6PR0NXVhaIoHDp0aFF1fwHAiqKg1WrJZDKEQiG0Wi12u536+npcLheJRIKxsTHi8TjV1dVUV1fLB0GAaiAQ4OzZs2zevBmn0ykf0rlmYWeLVdxINSkxoqHT6di3b9+q3CTMFysBwFeKhcZtFEWR4zazWdxC7lO4V8XjcSwWC5WVlbKFsRbaFiMjIwwPD9PW1ram+AK5XI6TJ0/S0NCwJFLPlUiCsysoQqhmdotjdol7oQrK4OAg0WiUQ4cOLbnsf/r0ad71rnfxzW9+k23bti3pWFcbo6Oj3HXXXQSDQSorK7ntttt48skn5Sz88PDwjPcXDod561vfysTEBB6Ph/379/PEE09c03lrtVr+5m/+BoDGxkacTiff+MY3+NjHPkZ3dzcf/ehHl+dNzoqbjpglNJdnRzab5aGHHsLhcCw4A1we5SVoUc6ORqMkk0msVivJZFJ6c4bDYTQaDfX19Xg8nst2/0JNaufOnfOKK8wlVlG+oxbjTXP1IFcKoHO5HB0dHZhMJnbt2rUmQEDEjQDgpYTQ404kEly4cAGNRoPdbpeL9VyCFatNj3tiYoKurq4rirastigWi7S3t2M2m9m5c+cNqTrM1eIo/xOYU7Akk8nQ29vLwYMH5+Q2XE1MT0/zzGc+k9e//vU3rA98o6NcS2JgYIBvfetb/OAHP2Dbtm184QtfWHZv8ZsOhBVFIZ/Pz3gtkUjQ3t5OLpdjz549i1IYmp0Bi4sitHBVVUWv1+PxeOTcsF6vv2wXqqoqAwMDDA0NsWfPnmuesVsIoMudbebKnq91gRblUJvNxs6dO1cdsWahEAC8Z8+eNTUHXCwW6ejoQKfTzSAzzSVYUf59+fWfC6CvR/UiGAzS2dnJrl271oyKF5Q+29OnT5PL5di3b9+q2MzMjrkMU0QVTTiazXX9BZN7Me2jfD7Py1/+cux2Oz/+8Y9X5edwIyISifCDH/yAb37zm6iqyqc//WkOHTq0bMe/6UFY0PUbGhoIBAJs3br1iguEqqoUCgWpTlW+GxQPg9A4XqjfJYhZgUCAffv2LXmXutDvKddjnr1QF4vFq86gBCPX7Xazbdu2pwH4OoQg1gjRgcUuguXXf7ai2GxFqbkW6eXo70ejUdrb29myZQt1dXVLPt71jJ6eHiYnJ9ecnWIul+PYsWPU1NTQ2Ng4bwY9l5qc+FNVVVwuFxqNhve///38+te/5ujRo2uqirGSIbJiRVH4xS9+wde+9jX6+vp43/vex2tf+9pl+R03NQhPTk5KU/mmpiYef/xxWltbFxxHEuXn2RnwtZzHuXPniEajN3SWdi6ALv8qL3GKL61WS39/P5WVlWzdunVNlaTWKgALZSaRjS1X5jqX9WD59+UL9FybtMUAdDKZ5Pjx4zQ3N1+1GtaNjpGRES5evEhbW9ucJLvVGoqi0NHRgcFgYNeuXQs+o7MVxcrvgXvuuYe+vj6cTifBYJA3vvGNHDx4kNbWVlpbW5/2CGZmefrxxx/nRz/6ET/60Y944xvfyAc+8IElH3/tMGyuMoaHh+nu7mbnzp1SV/RKJg4CfOfKgK8misUip06dIpfL3fDd9ZUME8pJQoLFGw6HAfD7/QSDwXlL3Kvt4VyrACzmadPp9LKP8yzGenB25hwMBuf0Bp5Lk1nIUdbV1a05AJ6amqKnp4f9+/evKQCGUvaez+fZs2fPFdephYhiv/71r/nZz37Gu971Lu6++270ej33338/Fy9eZGhoiIceeohnPOMZK/U2bniUA6zIR2d/nhqNhkKhgF6v59Zbb6WxsZEvf/nLZLPZZTmHmw6ExQzw8PDwZTPA84GwGD0SSltL0WjO5XJ0dnai1WpX/Xxk+aiF1+slHo/j9/tpaWmhpaXlsgx6tljBfIvzjQDotQzAXV1dJBIJDhw4cN1HvxZaoMu9gQVAl4/aCFcko9GIRqNhbGxM3gfitdUaa9WWEEoM4omJiWWRpMzlcvzjP/4j9957Lx/4wAdmXDOhe7CUuFprQoAf/OAH/P3f/z2Dg4Ns3LiRj3/849c893ul0Gg0nDt3TiqDAbIKWh7Cwc5oNPLTn/6UlpYW7rvvvmU5h9WLENcYwWAQv98/5wzwXCBczoAWP3Otkclk6OjowGazsWPHjjVFbIhEIpw8eZKWlhaZ0ZQDdHnMzqAFQPv9fin3t5g52OWKtQzA3d3dhMPhVSl+spA3cDab5eTJkwBUVVWRTqeJRqOkUinpJDafWMmN1uPOZDJ0dnbS0tKy5tx3wuEw3d3d7Nu3b8ktrnw+z+tf/3r27t3L3//93192TZbrfrwaa8InnniCu+66i4997GO85CUv4Tvf+Q5//Md/TEdHx4roViuKwhe/+EW++c1v8s///M+89a1vRavVSi2HY8eO8Q//8A/cf//9Ek/e9ra38brXvW7Z9PJvOhAWs2VzXejZIFwuwiH+/VojmUzS0dFBRUXFmuujhkIhOjs72bhx46KUdmZn0OVRzuKcaw4WLheqKJd6vNprsJYBuK+vj6mpKQ4cOHBdtWqXGoqicP78eTk3PnuzOVvuc65Z2PlIgssp9zlXCDMJn8+35srn6XSaU6dOsXnz5iU7Gamqyn333cfk5CT/9V//tWqsCT/3uc/xghe8gP/9v/83AB/+8Id58MEH+cIXvsC//du/Lfu5abVa3v72t+N0OvnQhz7EmTNn+Jd/+Rd0Op2c6T569Chf+9rXePe73w1wRULu1cZNB8Iw/06rHITnG0G6lohGo3LIf8OGDWsKgKenpzl9+vSysVo1Go0ci5g9mzsXQAeDQUZGRuQc5HzZk8Viuexz9fv9dHd3rzkAhtL84djYGAcOHFhTghZCjjKVStHW1jbn4r2Q3Ods04RUKiWFatLptBSrmH0fiAx6KRtlQX4zmUzLoip1PaNQKNDZ2UlNTc2yuAN973vf41vf+hZHjx6dsxWxnHE11oRHjx7lve9974zXnv/85/OTn/xkxc5v9+7dVFVVUVdXx5e+9CVe/OIX8x//8R/4fD6OHDnCsWPHpIb4SsRNCcLzRbkP8HIwoKFU/j516hQbNmxYU4bfUGKPi77Y9SjLXS1Az5Z6LF+Yc7kck5OTbNu2bcX9TZc7hoaGJGdhrRGC+vr6CIVCtLW1XVP/+lr0uAVoA5eN2Cy2iiKsN7PZLG1tbauOVLhQCElKg8HApk2blny8kydP8u53v5vvfve7bNmyZRnOcP64WmvCiYmJy6ZXqqurmZiYWNbzeuCBB3C5XNx6660A1NbW8qY3vYmKigr+/u//nhe/+MV84hOf4Pbbb5cAXE7iWs74gwTh5cqAJyYmOHfuHNu2bVtTBuvwe0u/1SKssBBAz9ZinpqakraQ586d49y5c3MuzMvtZrQcMTIyQn9/P/v371+UbvlqiuHhYfx+P21tbStSPl+sHrcA6GAwyOjo6JxtjtlEwaGhIaanpxetlreaor+/n3g8zsGDB5e8eZiamuI1r3kN99xzDy95yUuW6Qznjxe+8IXy+127dnHo0CGampq4//77F2VNuBIRjUb50pe+xPnz5/ngBz/I3XffDYDD4eDuu+/ml7/8Jffffz9ve9vbOHHihNwsrNQ6srbuxiWGVqslnU6TzWaXTA4RMpSrBcSuJkZHR+np6Vkzco7lWsx+v1/OXnu93ssAerabkQD3+dyMridAj42N0dvby759++YkO63mGB8fp6+v74aN8yykxz27iiJsBwVRULSgXC4XAwMDlwH0aiZQBgIBhoaGlmXUMZ/P87rXvY79+/fzd3/3d6vSmrCmpoZAIDDjtUAgsKyVOpfLxb333st3vvMdPvzhD3PhwoUZ3sOHDh1iw4YNvPnNb8bhcKxYBizipgPh+T4sVVWx2+309fUxNjY2Q6Bg9tdCO2VVVenv72d4eHjN6eNCqRTa39/P3r1711wZd64e8JXMEmaXN+fqP851Dyw3gzcQCEgd67V2z0xPT9PV1cWePXtWvH94LbFQFSUcDtPe3s769esxGo2SxT0+Pj6nL/DsLPpGAnQ8HufcuXPs2LFjyVUTVVV5//vfTzAY5Oc///kNK8dfyZrwyJEj/Pa3v+Wv/uqv5GsPPvggR44cWdbzuOWWW6ipqaGqqoof/vCHdHd385GPfIR0Os2//uu/8r73vU+2F1d6s3LTKWYBlw1Rl4twiMHr+RSkyiX+bDbbZaXNvr4+Jicn2bdv35orJ4rNw969e1flYrpQLDcJqxygZ4tVCICeK3u+FoCempri9OnTa7JqIuQot27duuZaLkLJa8OGDXOy/hcj92oymeatpKxkWTuXy/HUU0/R0NBAS0vLko/3rW99i/e97308+eSTy9JXXmzMZU3Y2dlJV1cXlZWVl1kTPvHEE9x+++380z/9Ey9+8Yv53ve+x0c/+tEVG1EKh8P8+Mc/5utf/7pUVNy8eTM/+9nPlv13zRc3NQhfrQjHfCYJyWRS7po1Gg0ulwu73b7qFaREiHGYsbGxFdWwXqm43izo+QB69ojNXCpiswFaEPe2b9++oFzqagwBYuvXr5+XzbpaQ+gqV1VVXRPozLcWiPuifLM+Vxa9FNEVRVFob2/HZDIti6NTR0cHL3jBC/j+97/Pi1/84iUd62rjzjvv5JFHHplhTfiRj3yEDRs2APCsZz2L5uZmvvGNb8j/84Mf/ID77rtPinV84hOfWBGxDpGUqarK1NQUnZ2dGI1GnvWsZwHIWeGVjpsShHO53AwNaFiaCpYwtM9ms7S2ts6pxQyXj9esBnKQEISYnJxck/J8q20Maa4ZWPH3TCYzQ6RCq9USCARoampi3bp1q15FqjwymQzHjx+ntraW1tbWG306VxXCllDYby73Zy4Aer77YCE9bmGYsVDb7Pz588RisXlHwK4mAoEAz3zmM3nHO97B+9///jVz/93omEs1a6XipgThbDYrXZBgaSIcwuhbr9eze/fuOUtQs4khInOeTQ4S5e3y71dyYRYPdCgUuqEmEtcaqw2ArxTlAB0KhRgdHcVisVAsFqWK1FzZ82qTeczn8xw/fhy3273mhGdUVeXMmTNkMhn2799/Q3q6sw0TykE6l8uh1+vnZfOPj48zODjIoUOHlsxAz+VyvPSlL6W6upr7779/1VbqViJEFlssFgmFQrJSKbgjAmSfeuopjh07xl/8xV/csHO96YhZ4XCYyclJqqur5/T3vZpIp9OcPHnyin66Vxqvmf0wBgKBGZnTfASxpbAhhYtTPB5fc4pM8HsAXksEMjEDC9DV1UVra6tUZZorg45EIjNkHue7DxbKnJY7isUiJ0+exGq1rjlBCyjNMcdiMQ4ePHjDSFUL6XELPkr5vRAOh6UeN4DVaqWnp+cykL6ajZqqqtx7772Ew2EeeOCBPygAVhRFXvu3ve1tdHR0kM1mefazn81rXvMajhw5glarJZvN8vDDD3PvvffS2to6Y5zqesZNlwl/97vf5bWvfS1Go5ENGzawYcMGacslviorK694UyYSCTo6OvD5fCuWDRSLxXkJYrlc7poZ3EIZKJ1Os3///lWnSXylWIsALCKZTHLixAmpnraYmO8+EON0er1+3lbHcgK0oiicOnWKQqGwas3tF4rR0VF6e3s5ePDgmmu7pFIpnnzySZqamrDb7ZcZZ8zFRViILPj//t//49577+Wpp55aUbWn1Ryve93rOHXqFJ/61Kfo7+/nQx/6EOvXr+etb30rb3jDG4ASWbWrq+u6zEzPFzcdCEMpg7148SI9PT309PTQ29tLb28vfX19jI+P43K5JDiLPzdu3EhraytOp5OHH36Yz33uc3z84x9n48aNNyQbKGdwJ5NJ+VAmk8nLTNrLmdxGo5GzZ8+Sz+fZt2/fdXflWWqsZQBOp9Mz+qjLcd9caaMmSptzlbmvZvMlVJlE5WSt3TfT09OcOnWKffv2rbn7plAocOzYMSoqKti8efOcPzO7klL+vQDoz33uc+Tzeex2O7/5zW/4h3/4B17zmtfQ0NCw5jZU1xLlfdwf/ehH/O3f/i2//e1vaWpq4p577uG73/0uO3fupLu7m7e//e28853vnLFZu5594PK4KUF4vlBVlVgsNgOYe3p66Ovro7e3l3A4jMvlIh6Ps2PHDl75yldKgF6/fv2qUV+aixhWPlah0Whwu92XjVitZgY3/F5EZC0CcCaT4cSJE/h8PjZv3nxd7hMhMH+1AD0Xe7enp4fJyUna2towmUwrfu7LGfF4nP+/vfMOi+rq3vYzMPSmIEV6GxELIoKdF2s0oqImalTUn7GkWLDFFhMsxBILryaKDcUYEaOxxMSa5BUsKE1UCG3ofZAmdYCZ/f3hd04YGBBkaLLv65orcgqzz2Q4z9lrP2utsLAw9O7du9OlURFCEBkZCUII7O3t3+nvk1nyunv3Lp48eQJfX18YGxtDLBYjJSUF8vLysLS0hJ+fH4YMGdIKV9G+CAQCKCsrSxTAOX/+PCIiInDgwAH4+flh06ZNePToEVRVVeHk5ASRSIRp06bhhx9+gLy8fLve17uUCDcGIQQnTpzA6tWrMX/+fGhra7OzZz6fj4qKCpiYmEiEuHk8HqysrGBubg4ul9uu/yOrq6sRHh4OLpcLc3PzelWkarcYrCvO7e3gBjq3AFdVVSEsLAxaWlro06dPh3hQaywXvq57VygUori4GH369IGOjk6nmgVXVlYiJCQExsbGsLS0bO/hNJuEhAQIBAIMHjy4xZ97VVUVJk+eDENDQwQEBEBOTg5VVVVITU0Fn8+Hk5MTevToIaORv2HPnj3YvHkzPDw88N///lfqMX5+fli0aJHENiUlJVRWVspkDJqamtiyZQs2bdrEph0JhUJkZ2fD0NAQU6ZMwcSJE7FmzRpUV1dj5syZ6NGjB1auXIkBAwbIZAwt4b0zZr0roaGhWL9+Pa5evYqJEyey25lcY2a9KS4uDvHx8Xjw4AFOnz6N5ORkAIC5uXm98LaVlRWMjIxalB7VFKqqqhAeHg4VFRXY2dlJfZpuqEFCWVlZg+UdW6t6VF06swAzDz/q6uodRoCBN53ENDU1pZbHrJ1ek5OTg8LCQqipqSE2NraeQNeNpHQkgWY6C+no6MikoEVbk52djYyMDJkIMCEEGzduxOvXr3Hr1i32HqCoqAgej9cq68KhoaE4fvw47Ozs3nqspqYm4uLi2J9l9XeybNkyiMVidk2XyftVUlKCubk5qqqqUFBQwJbCLC0tRWFhITZt2tQhBBigIszi5OSEyMjIemYaRkBNTU1hamqKsWPHsvuYfMGUlBR2/Tk+Ph43btwAn89HWloaVFRUYGVlBUtLS1aYGZHW0dFpcXi4srISERER0NDQQN++fTuVgxvo3ALM9KZVUVFBv379OowAvw0FBQUoKChAKBQiPz8fDg4ObAqYtAIVTDerxgS6tStI1YUxHyooKHS6NCrgTSUypoGKLExkP/30Ey5duoSQkJA2KcZTWlqKefPm4eTJk/Dy8nrr8RwOp1U6tY0cORJ37tzBzp07sXTpUowaNYr9HjJVEp2cnPDkyRPMnj0b8fHx0NfXx9ChQ2U+lneFhqNbCUIIaxBjZs9MeDshIQG5ubno1q2bxOy59ktDQ+OtN5aKigqEh4eje/furTYLq20IqZ3/XHfdUU1NTSL/uSk35c4swCKRCBEREZCTk4O9vX2nM74UFRUhIiKiWZW8pAm0tHKv0mbQshRopi1hYWHhO7dUbE+EQiGePn0KU1NTNoWtJYSEhMDV1RW//vqrRBSvNVm4cCG0tbXh7e2NUaNGwd7evtFw9JIlS2BkZASxWAwHBwfs2rULffv2lclYwsLCsHTpUigrK2PlypWYPHmyRAQoISEBhw4dQm5uLnR0dHDs2DEAbVcR621QEW4HCCEoLi6WmD0z4szn81FUVAQDAwN2/ZlxbltbW8PS0hJKSkp48eIFvLy84Onpib59+7a7g1vaumNDN2WmKEFnFWCxWIzIyEiIRKJOmcpTWlqK0NBQWFtbS62p/C5UVVU1aBJrTKBVVZvfJCElJYXtLNTZCtAw1bxUVVVl8nebk5MDZ2dnrFq1Chs2bGiT+0BAQAC+++47hIaGQllZ+a0iHBwczHacKy4uxv79+xEUFITo6GgYGxu/8ziYpUI5OTkUFRVh4cKFiImJwWeffYbZs2dL/O6amhpwuVzWAd1RBBigItzhEIvFEAgE7Mw5Li6OFejExEQIhUIYGBhAIBCAx+Ph//7v/1iRNjMza3enH8PbHNzAm/6dWlpancrBzYRBhUIhBg0a1Ol60zJpVEZGRk3OY24JDdVgZsyCzRXo3NxcREdHw9HRsdO1g2TSwMrLy2VSzUsoFMLV1RWmpqbw9/dvk7+b9PR0ODo64t69e+xa8NtEuC7V1dWwtbXFnDlzJFoIviu1BXXDhg3w9/fHxx9/jMWLF6N///4A/q0T3V5pSI1BRbiTwDSiuHnzJtzd3eHs7Axra2t29pycnAw5OTkJg1jtGXTPnj1b3SDWFNLT0xEfHw8ejwd5efl6udCMg1taDnR7O7gJIYiKikJpaWmnzKWtqqpCaGgotLW1O0Q1rMYEmnlYU1JSYr8HHA4HWVlZ4PF4MDIy6jAzmaaSkpKCtLQ0DBkypMVpYIQQrF69GiEhIXj06FGbdXS7du0apk+fLvHZM2mRTBWqpvx/mTlzJrhcLi5cuCCTcdUW4jNnzsDT0xODBw/G8uXLMXz48A6ddkdFuBORlZUFW1tbbN++XaLfJtOSLSUlhV1/5vP57H8zMjKgqqoKS0tLVpQZkebxeNDW1m6TG/Lb1oClObhrz5oANDhjam0HNyEE//zzD4qKiuDk5NTpqpDV1NQgPDwcysrKrdLUQNbUbjPI9ADOzMyEoqIiampq6gl03WhKRxPoV69e4cWLFxg0aJBM2oieOXMG3377LUJCQtokosFQUlKC1NRUiW2LFi1C7969sXHjxia1GxSJROjbty8mTZqEgwcPymxstWe5T58+xbJly6Cqqopt27ahe/fuOHHiBI4dO9bholdUhDsZz549w8CBA5t8PCEE5eXl4PP5iIuLY4uUMK+8vDxoa2tLmMNqz6LV1NRkcsNuqQnrbe0Fme5F0nKgW1rakelE9erVq05Zh5tZwyaEYODAgR0uHPc2qqurERISwhZCkdYHuPbDmkgkYqMp0tLu2vr6y8rKEBISIrNiIk+ePMGUKVNw9epVfPDBBzIYYcuoG46u2yN4x44dGDp0KKytrVFUVIR9+/bh2rVrCA8PR58+fZr1XkxYubKyUurfYe114oKCAixatAgxMTHIycnBRx99hDNnzrT4emVNx3okoLyV5ggw8CY1QE1NDQMGDKiXF0cIQVFRkYR7Oz4+Hn/88QdbCJ8pwcgIMyPWlpaWTS4ozwiwg4MDunXr1qzxM8jJyUFNTU1qOkdDzRHKysqaXTlKGnw+n60m1dkEmFmHrKqqgqOjY6cTYOYBQl1dne0LzOFwoKSkBCUlpXoPdNIEuri4GNnZ2Sgvf9MTvO5yR2v6EaqrqxEZGQljY2OZCHBOTg7c3d2xfft2jB8/XgYjlD1paWkSn2NhYSGWLl2KnJwcdO/eHYMGDcLjx4+bLcBMyPnp06e4f/8+XF1d6828mSU3kUgEbW1tXL9+HWvWrEFeXh58fHxkcn2yhs6EKfVgniZzc3PZ2XPtUp9JSUmoqqqCqamp1NmziYkJaxDz9vaGmpoaZs2a9c4C3BJa4uCWl5dHUlISa0bpbE0Bas/gO2M5SmYNvry8HI6Oji0OMRNCIBQKpdZfbg2BZh4gOBwO7O3tWxxREgqF+PDDD2FpaYmff/650z1QtQRGgLOysmBvb4+FCxfiiy++qFclrXZIuvY6cUVFRYd10lMRpjQLxiDGGKxqh7j5fD5SUlLA5XJhYWEBOTk5xMfHY+nSpZg6dSp4PB709fU7hEEMkMx7rZsDLRKJwOVyIRKJoKurCy0tLYl+0J3hBpicnIy0tLROmcoDgG24Mnjw4FZ/gKgr0A2VfK0rzI19H+Li4pCfn4/Bgwe3eB2SEIKVK1fi2bNnePjwYad7IJQVw4YNQ79+/XDy5EkAbx6ynzx5AnV1ddjb29c7viO6oetCRZgiM5hQYFJSEg4dOoSzZ89izJgxKCsrQ2JiIjIyMqCurt5gB6vu3bt3CHEmhCA1NRVJSUkwNTVl19Vrz5gaKvGpoqLSIa6BKbPq6OjYJhWUZE1mZibi4+Ph5OTUZs7fhmAMgw15EgDUE+iKigpkZmZiyJAhMhFMX19fbN++HaGhoZ2yRKcseP78Odzd3fHTTz9h4MCBCAgIwLlz5xAWFgYNDQ14enpi/vz57T3MZkPXhCkyg1mrCwsLw4ULF/DXX39h+PDhAN7cyMrKyiSKk8THxyMoKAh8Ph+vXr2Cjo6ORHi7dg1uJkWlLcjJyUFSUpJUE5k0B3d+fj7S09Pb3cHNwOSZDxw4sFMKcH5+PmJjYzFw4MB2F2BAsuQrU96TQdr3ITc3F0VFReBwOAgODmbPldY0pSmztODgYGzatAm//fZblxVgANDV1QWHw8HVq1dx8OBBZGZmwt7eHps3b8bu3bsRFRXV3kN8J+hMmCJzUlNTkZ2d3eT6rIQQFBQUSF1/TkxMRElJCYyMjKSW+LSwsJBpY/vc3FxERUXB3t6+Xo3ttyEWi6WmWDFNMuo2Za/t5JbVNRQWFuLZs2fo168f9PT0Wvz72hqmmpeNjQ0MDQ3bezjNprKyEk+fPoWFhQVMTEzemnInTZyZ+txcLhdZWVlwdnbGV199hTVr1nSIKEtbwazpZmdnQ05ODvr6+ti5cyf+/PNPFBcX49ChQxgwYAC6deuGL774AkKhECdPnuxw6Wlvg4owpcPCGMSYEpfMi1l/TkpKQk1NDczMzKTOnk1MTCAnJ9fkG1deXh5evHgBOzs76OrqyvRapDm4mZdQKGyxgxv4t68uj8drUTnA9qKyshKhoaEwNDRs09xXWSESiRAaGgpNTc23NpWo/cBW93sRGBiIvXv3wsDAAMXFxTAyMsKKFSvQq1cv8Hg81vj4PsOUmczMzMTUqVOxaNEiLFiwAJqamhAIBFBRUWGjPPfu3cP06dNx/fp1jB07lk1j6ixQEaZ0SgghqKmpQVpaWj2BTkxMREpKChQVFWFpaSm1Breenp6EQez27dsoKSmBs7Nzq3R7aYyampoGm2QwnYuk5T/XLutYXl6O0NBQmJqadsqQZU1NDcLCwqCurt5utdBbAiEEL1++ZMuZtsQMVFVVhbi4OGzYsAF8Ph9ubm5ITk5GQkICUlJSYGZmhsTERBmOvmNRW0T79u0LR0dHbNu2jf1eM2argoICHD58GAEBAZg/fz6+/vrrTifAAF0TpnRSOBwOFBQUWIH98MMP2X2M0zUxMVFCnC9evAg+n4+srCxoamqyM2d5eXlcuXIFK1aswPjx49v8D5nL5UJDQ0Pq+m3dso6lpaXIzc1l6y4rKSlBRUUFJSUlUFdXh5qaGkpLS9ulKMW7wggYl8vtUD2Zm0NKSgqKi4sxZMiQFn/uioqKePLkCf755x+EhoZKdFqqrq6GQCBo4Wils2fPHmzevBkeHh6N1oG+dOkSvvnmG6SkpIDH42Hv3r2YNGmSzMbB/P/38vKCgoICzp49C+CN2dDHxwcVFRWws7PDwoULoaCggM8++wxr1qyR2fu3NXQmTOlSEEJQUlLCCnNgYCBOnz4NAwMDlJWVoaCgALq6ulLXn62srDqM+5lxopeUlCAmJgby8vJQU1PrVA5u4N9c5oKCgk7ZlhB4Y4SLioqCk5OTTIxwjx49wrRp0/D7779j9OjRMhjh2wkNDcWsWbOgqamJ0aNHNyjCjx8/xn/+8x/s3r0bkydPhr+/P/bu3YuIiIgmlaxsDt9//z1CQkJw4cIFnD17Frdu3UJkZCT69euH1NRU3Lx5U8I30BnSkaRBRZjSZYmOjoaLiws2bdqE9evXgxCCV69eSV1/5vP5KC8vh7GxMVsxrPb6s7m5uUwNYk1BLBbj2bNnbDEI5gZUO+e1bnhbmiGodqi7rRzcDKmpqUhJSem0ucylpaUICQlpVl/mxsjMzMTIkSOxefNmifrwrUlpaSkcHBxw9OhReHl5NdoRafbs2SgrK8Pvv//Obhs6dCjs7e3ZPr2ywtfXF0uXLoWzszNiYmKwfv16LF26FPn5+Zg6dSpOnTrFZl90Zmg4mtJlEYlErAADb8Jgurq60NXVxYgRI9jjGINYZmYm214yPj4ejx49gp+fH5KSkkAIgbm5ucTsmRFoIyOjZhnEmgITwq2pqam3BsnhcKCsrAxlZeV6KTXSHNwCgYAV6LoO7tpCLeuHDIFAgMTERAwaNKhTCnBVVRUiIyNhZmYmEwGurKzE3LlzMWHCBKxatUoGI2way5cvh6urK8aNGwcvL69Gjw0ODsbatWsltk2YMAHXrl1r0RhqV7diTFmLFy+GmZkZwsPDceDAATg6OgJ4ky9cVlbWLhX4WgMqwpQui52dHdsTtTEYA5eJiQlMTEwwZswYdh9jEEtJSZGYQTP1t1NTU6GsrAwrKytYWlqy5jBGrHV1dZsdQiOEIDY2FqWlpXBycmpWNabaIlsXsVhcz7nN1FyWlYObobi4GFFRUejXr59Mugq1NUxfaQ0NjXqlE9/1961ZswaEEBw/frzNwqoBAQGIiIhAaGhok47Pycmp98Chr6+PnJycdx5DbQFet24doqKikJ+fj5kzZ2L27NkYN24cgDedqPh8PubNm4cvvvgCffr06ZRGrLq8dyLs4+MDHx8fpKSkAHjjrvv2228ljDt1aW2jAeX9hTGIMW0hXV1d2X1MIYfExESJJhn+/v5ISEhATk4OtLS0pK4/W1tbQ1NTU+oN5vHjxxCJRDJvqSgnJwd1dXWpBTJEIlE9gc7Pz5dwcEsLb9d2cDNUVFQgMjKSdal3RuLj41FdXS2TmtAAcPLkSdy6dQuhoaFtFhVIT0+Hh4cH7t27166NSZjvx/Tp05GRkYHVq1cjNjYWmzdvRo8ePbB48WIAwI0bN+Dt7Q03Nzds3bq13cYra947ETY2NsaePXvA4/FACMHZs2fh5uaGZ8+eoW/fvvWOf/z4MebMmSNhNJg2bVqrGA0oXQum0lK/fv3qfZcIIXj9+rXE2nNCQgLu3buHhIQEFBUVQV9fv1561YMHD3Dx4kVERES0aQhXXl6+WQ5uJsTNOLhrrzlnZmZCW1u7U+YyA29cujk5ORgyZIhMetM+ePAAW7duxR9//AEzMzMZjLBphIeHQyAQwMHBgd0mEokQFBSEH3/8EUKhsN4DlIGBAXJzcyW25ebmtjit7+rVq4iMjERoaCh69OiBdevWwd7eHjNnzgTwpgjNokWLYGFhgVGjRrFjfR/ypbuEMUtbWxv79u1jn6hq05ZGAwqlKYjFYuTl5UmEt/l8PsLCwpCWlgYAMDMzY81htV9mZmbgcrkdIkRHCGEFuqysDGVlZcjKyoJYLAYhhO1aJC0HuqklHduawsJCREREwMHB4Z36YtclPT0dzs7O2Lp1a5uuAwNvirukpqZKbFu0aBF69+6NjRs3Sp2EzJ49G+Xl5bhx4wa7bfjw4bCzs2vR/TIgIABnzpzBnTt3sHv3bhw6dAiBgYGwsbFBUFAQHj16hJUrV7JRms7qhJbGezcTro1IJMKlS5dQVlaGYcOGST2mtYwGFMq7wpTo09fXh7OzMwDg77//xpQpU/Dbb79hwIABEh2sAgMDcerUKSQnJ4PD4cDc3Lxee0lra2sYGhq2aQcrDocDRUVFKCoqQktLC9HR0VBWVoaTkxPk5OTqdS3Kz89HRkYG2xRBWoqVmppamzu4GSoqKvD8+XPY2NjIRIArKiowd+5cuLq6YsWKFTIYYfPQ0NCoJ7RqamrQ0dFhty9YsABGRkbYvXs3AMDDwwMuLi44cOAAXF1dERAQgLCwMJw4caJFYxGLxXj16hUePHiAvXv34uzZs7CxsQEAJCYm4ubNm1iwYAErwu+LAAPvqQi/fPkSw4YNQ2VlJdTV1XH16tUGG0i3htGAQpE1P/zwA44ePYopU6YAAExNTVnDCvDvrDMlJUVi/fn69evg8/lIT0+HqqoqW0GstkjzeDzo6Oi0qrAlJSWhsLAQgwcPZkOIzXFw5+XlvdXBraqqCkVFxVa5jpqaGkRGRsLAwEAmYXSxWAwPDw/Iy8vDx8enw4pKWlqaxNiGDx8Of39/bN26FVu2bAGPx8O1a9eatXQnzUz1ySef4OjRo3BxccHq1avh5ubGdjP7+uuvsWnTJhgZGcnsujoS72U4uqqqCmlpaSguLsbly5dx6tQpBAYGShViRUVFnD17FnPmzGG3HT16FNu3b6+39kGhtBctCb8RQlBRUQE+ny/R/5l5CQQCdO/eXcIcVnsWra6u3iJhy8rKQlxcnEzaEorF4gZLfDJrmA0ZxN61EAghBC9evEB1dTUcHBxkIphHjx7F/v37ERISAlNT0xb/vs5CbQGOj4+HgoIC1NXVoauri5CQEHzzzTeIiorC0qVLkZOTg7CwMFhbWyMgIKDe+e8L76UI12XcuHGwsrLC8ePH6+0zNTXF2rVrJRLjPT09ce3aNTx//rxVxtNcB7efnx8WLVoksU1JSQmVlZWtMj5K14EQguLiYonZM/Pi8/koLi6GgYGBVAe3paXlW0PDBQUFePbs2Tt1pWou0hzczKuqqkrCwV1bqFVUVBo1WCUmJiI7OxuDBw+WiRs9KCgIH330EW7evAkXF5cW/77OBCOiu3fvxs8//4zExERMnToVM2bMwCeffILo6GhcuHAB169fZ+tGM3n874sRqy5dQoTHjBkDU1NT+Pn51dvXWkaDxrhx4wbk5eUlHNz79u1r0MHt5+cHDw8PxMXFsds4HI5MCgRQKA0hFoshEAiktphMSkqCUCiEqalpvfC2tbU1TE1NERkZiZUrV8Lf37/dm0rUdXDXftXU1EBRUVGqQYwpCzp48GCZ9DZOS0uDs7MzPD0922UduD1hBDgkJATjx4/HyZMnUVVVhUuXLiEvLw+TJ0/GihUroKmpWS/y874KMPAeivDmzZvx4YcfwtTUFCUlJWxt0zt37mD8+PH1jAaPHz+Gi4sL9uzZwxoNdu3a1eYpSo05uP38/LB69WoUFRW12XgolIYghEAkEiEjI0Nqic+UlBRwOBwQQmBkZAQ3Nze2gpi1tTUMDAza1CD2tmuRJtBMqFssFkNRURHq6uoSM2c1NbVmO7jLy8vxwQcfYODAgTh16lSHuP62gPm+cLlclJaW4uTJkyguLsa2bdsAvPHl7N69G8HBwRg8eDCWLVvGFtF5H8PPdXnvjFkCgQALFixAdnY2tLS0YGdnxwow0DpGg5bQFAc38Ka+q5mZGcRiMRwcHLBr1y6ps2YKpbXhcDjgcrkwNzeHubk5PvjgA3YfIQSFhYVwcXGBjo4OXF1dwefzceXKFSQmJiI9PR3q6upselXtHtDW1tbQ1tZu05tubQd37TKIVVVVePLkCXr27AkdHR1WnAsKCth/A9Id3EyKVe3rEIvFWLVqFRQVFXHkyJH3XlgY8vLyoKury4b7V65cicDAQIl7nYGBAby9veHt7Y1ff/0V69evx5kzZ2BkZNQlPqf3bibcWajr4Pb392+wSldwcDASEhJgZ2eH4uJi7N+/H0FBQYiOju60BQ8o7y9LlixBQkIC7t69CyUlJXY7IQRlZWVsaJvP50vMoPPy8qCtrS1hDqtdg1tNTa1NbspisRjh4eFQUlJC//79pb4nUw2toSYZTL/bM2fOwMLCAq9evcKjR49w7949ODg4dAlx8fPzw5kzZ+Dl5cWm2h05cgS+vr4oKCjArl27MGfOHInP4ty5cygoKICHh0d7DbvNoSLcTjTHwV2X6upq2NraYs6cOdi5c2cbjJZCaTopKSnQ1NSsl3rUGMwMWtr6c2JiIl6/fg1DQ0PWEFY7/9nCwkJmqUmEEMTExOD169dwcnJ6p3VIxsGdkpKCy5cv48mTJ3j48CF69OgBgUAAdXV18Hg89OrVCydPnpRJ+8OOyNmzZ3H+/HmIRCK4u7uz5tLg4GB4enqiqqoKH330ERYuXAhNTc16579PBTkag4pwB6ExB7c0Zs6cCS6XiwsXLrTyyCiU9oPpYJWTk8MKMyPUfD4fiYmJqKmpgampqdTZs4mJCeTl5Zss0GlpaUhOTsaQIUNkUk85NTUVzs7O2LFjB7788kuUl5cjMTGRvZYNGzbIRGg6asZFSEgIjh49ipSUFEyYMAFr166FkpISUlNTsXXrVsTGxmLixIlYsGABeDyeTN+7s/DerQl3VsRiMYRCYZOOFYlEePnyZZs2mdizZw82b94MDw+PBnuNArQZBkW2MAYuQ0NDGBoasnWDgX8NP2lpaRItJu/evcsKEpfLZQuU1K0gpq+vL2EQu3XrFgoKCuDq6ioTAS4vL8fcuXMxffp0fPHFFwAAVVVV9O/fH/3792/x769Nc2vmA4Cmpma9jAtZIRQKoaSkBHt7e/Tq1QuPHj3C3r17kZGRgXXr1sHS0hJ+fn7w9PTEr7/+ivT0dPz4448ycaB3NqgItwPSHNz379/HnTt3ANQvFbdjxw4MHToU1tbWKCoqwr59+5CamoolS5a0yXhDQ0Nx/Pjxt7b9o80wKG0JYxCztLSEpaUlJkyYwO4jhEAoFCIpKUnCvX3p0iXw+XxkZmZCQ0ODFedu3brh/PnzWLJkCSZNmtRiV65YLMaKFSugrKyMH3/8sdXXgJlKagzfffcdfHx88OTJkwZFmMPhtLjxgjQIIawXoF+/fvjPf/4Dd3d3REdH4+HDh8jKysLatWvh7OwMLy8vtgpZVxRgAAChtDmffvopMTMzI4qKikRXV5eMHTuW3L17l93v4uJCFi5cyP68evVqYmpqShQVFYm+vj6ZNGkSiYiIaJOxlpSUEB6PR+7du0dcXFyIh4dHg8fOmjWLuLq6SmwbMmQI+eyzz1p5lBRK0xGLxeT169ckPDycXLhwgWzZsoVoaWkRPT09oqOjQwCQHj16kKFDh5J58+YRT09Pcu7cORIcHEwEAgEpLS0lZWVlDb5KS0vJ7t27Sc+ePUlGRkabX19NTQ25cOECUVRUJNHR0VKPOXPmDJGXlyempqbE2NiYTJ06lURFRcl0HNu3byf29vZEJBKx206fPk369+9PhgwZQi5evCjT9+us0DVhSqMsXLgQ2tra8Pb2xqhRo2Bvb99gOLo9qo9RKC1BJBLBzc0NNTU1+P333yEvL4+CggI2tM24uJk16NLSUhgbG7MGsdrrzxYWFlBQUMD9+/cxa9Ys3L17FyNGjGiza+loGRfffPMNbt26hQcPHki03Tx37hyWLVsGQ0NDfP311/j0009l8n6dFRqOpjRIQEAAIiIiEBoa2qTjaTMMSmejtLQUPXr0gLe3N5vLqqOjg+HDh2P48OHsceT/G8SysrIk1p+Dg4Px008/ISkpCSKRCEZGRsjOzsbhw4fbVIABwMbGBpGRkWzGxcKFCxvMuBg2bJhEru7w4cNha2uL48ePv3PGBakTwh8wYAAuX76M6OhoODo6studnJzg5OSEESNGYNasWe/0Xu8TVIQpUklPT4eHhwfu3bsnE5MKhdIR0dLSklrOti6MgcvY2BjGxsYYPXo0u48QgpqaGqSlpeHly5e4ceMGPvvss1YctXQUFRVhbW0NABg0aBBCQ0Nx6NChJmVcKCgoYODAgeDz+e/03owACwQCAICenh7GjRuHXbt2Yf78+Th9+jQcHBygpKQEPp8PJSUlrFq1Curq6l0mFakhuu6VUxolPDwcAoEADg4O4HK54HK5CAwMxOHDh8HlciESieqdY2BgUK/zVG5ubquYPxpiz5494HA4EiHxuvj5+bE3VeZFHzQo7wqHw4GCggKsrKwwbdo0+Pr6dohiHO+ScdGzZ893ei/mejdu3IgBAwYgMDAQ3bp1w5MnT2BiYoIPPvgAc+bMwaRJk+Du7o4PPviAfa+uLMAAnQlTGmDs2LF4+fKlxLZFixahd+/e2Lhxo9QiBsOGDcNff/0lIYD37t1rtBynLGmqixto3fQMCqWt6SgZF3PnzoVQKMT8+fOxadMmfPnll7h79y6OHTuGf/75ByKRCDNmzGDfp24IuytCRZgiFQ0NjXppRWpqatDR0WG31/3D9vDwgIuLCw4cOMA2wwgLC8OJEydafbylpaWYN28eTp48CS8vr7ce31rpGRRKe9DcmvmFhYVsz97u3btj0KBBePz4cZMq9jXG+PHjYWRkhNOnT2P37t2IiYnBDz/8gM8//xxVVVUSrSC7ehiagYow5Z3pSM0wli9fDldXV4wbN65JIkwbYlDeJ3x9fRvdf//+fYmfmYYJLYGZxUZGRsLIyAi6uroAgD59+mDNmjUwMDDA/v37kZeXh8OHD0NPT0/ifCrAb6ApSpROT0BAAL777juEhoZCWVn5ralUtCEGhSIbSktL4ebmhsLCQhw/fhxOTk7svoqKCnz00Ue4ffs2evXqhbCwsDZrwtGZoI8ilE4N4+I+f/58k81Vw4YNw4IFC2Bvbw8XFxdcuXIFurq6Ta7bTaFQ3qCqqoply5bBwsICixYtwvnz59l9KioqcHFxwVdffYVz585BXV2dCrAU6EyY0qm5du0apk+fLmEUE4lE4HA4kJOTg1AobFInHNoQg0J5Ow0ZqcLDw3H8+HEEBQVh5syZ2LlzJwQCAWbOnIn58+dTI1Yj0JkwpVPDuLgjIyPZl6OjI+bNm4fIyMgmCXBL0zOaw7Zt2+qlR/Xu3bvRcy5duoTevXtDWVkZ/fv3x82bN1t9nBSKNBgBffjwIc6cOYO9e/fi5cuXsLOzg5eXF9zd3eHv7w8NDQ2MHTsWpaWlVIDfAjVmUTo17+Libu+GGH379sWff/7J/sxUapIGbYpB6SiIRCLIy8sjICAAHh4eMDIyQk5ODnx8fDBv3jysWLECGzZsgJubG+7evQsdHR18/PHHEudS6kNFmPLe01bpGU2Fy+U2OT3q0KFDmDhxIr766isAwM6dO3Hv3j38+OOPOHbsWGsOk0IB8O8MVl5eHunp6Vi+fDm8vb0xbdo0aGpqwsvLCwEBAaioqMDBgwfrtWoUi8VUgBujzVtGUChdGE9PT6Kqqkp69uxJLCwsyNy5c0lqamqDx5uYmBBvb2+Jbd9++y2xs7Nr5ZFS2oOjR4+S/v37Ew0NDaKhoUGGDh1Kbt682eg5v/zyC7GxsSFKSkqkX79+5I8//pD5uJhOSNevXyc2NjYkKytLojvSuXPnCJfLJffu3ZP5e7/v0DVhCqUNGTJkCPz8/HD79m34+PggOTkZzs7OKCkpkXo8bYrRtTA2NsaePXsQHh6OsLAwjBkzBm5uboiOjpZ6PLNcsXjxYjx79gzTpk3DtGnTEBUV1eKx/Pzzz/D39wfwb05v9+7dUVxcjJKSEsjJyaGiogIA4O7ujv79+yMmJqbF79vlaO+nAErHQywWSzzlUlqPwsJCoqmpSU6dOiV1v4KCAvH395fYduTIEaKnp9cWw6N0ALp3797g96O1engLBAIyePBgMnLkSHLkyBHy+vVrQgghr1+/Jn369CEjR44kYrFY4hxHR0eyd+/eFr1vV4TOhCn1YNJ7GoLQrDaZ0a1bN/Tq1avB7jXt2RSjuU5u2hhDtohEIgQEBKCsrKzB+uvBwcEYN26cxLYJEyYgODi4Re+tq6uLX375BZaWlqwLms/nQ0NDAxcuXEB+fj6srKzwxx9/4Ndff8WGDRuQlJTULt2jOjvUmEWRoLKyEkFBQaisrISNjQ14PF49QW4szYDQNIRmUVpaisTERMyfP1/q/vZuitEcJzdAG2PIgpcvX2LYsGGorKyEuro6rl692qBpsDWXK8zMzODr6wtPT0/cvHkTubm5WLJkCYYMGYIrV67Ay8sL8+fPh7a2NgwMDHD79m1oaWmhpqbmrd8Tyr/QT4rCEhMTgx07diA2NhYVFRXIy8vD8OHD8f3338PW1hYAkJSUhPT0dJiZmcHc3ByApPDWvumKxWIQQiSckdXV1VBQUGi7i+pgrF+/HlOmTIGZmRmysrLg6ekJeXl5zJkzB0DHaooBNM/JDdDGGLLAxsYGkZGRKC4uxuXLl7Fw4UIEBga2mXu/NlwuF9999x2srKxw7Ngx7Nq1C0uWLMGUKVPw888/IzExEaqqqlBSUoK2tjbEYjEV4GZCw9EUlnXr1iEuLo4V4tjYWLi5ubEieubMGSxYsADu7u6ws7PDf/7zH8TExLDCm5ycjLi4OLaHqZycHHsus+3w4cMYPnw4a+AQi8VtfZntSkZGBubMmQMbGxvMmjULOjo6ePLkCVv8Pi0tDdnZ2ezxTFOMEydOYMCAAbh8+XKbNcUAgISEBBgaGsLS0hLz5s1DWlpao8czjTFMTEwaNRRRGkZRURHW1tYYNGgQdu/ejQEDBuDQoUNSj22r5YpPP/0U33//PcrKyrB//34cOXIExcXFsLKyQs+ePdG9e3cAtCnDu0DLVlIAvBEHCwsLXL16FZMnT663PyYmBiNGjMCcOXPwzTffgMvlYtq0aTAwMMCJEyegra2NnTt34vz58/j4448REhKC2NhYLF26FKtXr4aGhgaEQiGWL1+OrKwsXLlypcH1QpFIBDk5ORrKbGdu3bqF0tJS2NjYIDs7G9u3b0dmZiaioqKgoaFR73jaGKN1GDNmDExNTeHn51dv3+zZs1FeXo4bN26w24YPHw47O7tWySNPTU3Fli1bkJSUhMGDB2PLli31wuGUZtKerjBKxyE4OJgoKCiQvLw8qc7obdu2EVtbW4ltQUFBREVFhSQkJBBCCNm6dSvR0NAg8+bNI0FBQcTX15fo6emRy5cvE0II2bhxI1FQUCDy8vLE3NycDBgwgCQnJ5Py8nKSkJBQz21ZF7FY/NZjKK3H25zcdamqqiJWVlZk69atrTyy94dNmzaRwMBAkpycTF68eEE2bdpEOBwOuXv3LiGEkPnz55NNmzaxxz969IhwuVyyf/9+EhMTQzw9PYmCggJ5+fJlq42xurqarFq1ikycOJFUVla22vt0FWjwngLgzVoeIQRCobBeSKmiogKpqalsFRymGTePx0NlZSUbas7IyICJiQl+/vlnAICzszNOnz6Nv//+G1OnTsWePXtw/fp1jBo1CqNHj0ZERAR69OiB+/fvw9XVFadOncLt27cRFBSE27dvo3fv3oiPj4e6ujrMzc0bDXUx68+1nbmvX7+GpqZmo9dNqJGsybzNyV0XBQUFDBw4sMnHUwCBQIAFCxYgOzsbWlpasLOzw507dzB+/HgAHaOHN5fLxaFDh1BQUAAlJSX2fkB5N+gnRwEAaGtro3fv3vj+++/Zbcx6rVAohFAoRE1NDYB/U5SSkpKgpKQEFRUVFBcXIz8/H8OHD2fPr6qqAo/Hg0AggIKCAkpLS1FWVgYHBwfMmjULe/bsgbq6OvLz86GsrIxTp06x4W59fX2EhIRg8+bNGDlyJLp3745JkyYhPT1dYtxVVVUA/l1/lpOTY8ft6uqKzz77DEVFRVLTqmpqasDhcHDgwAGsWrUKR44cQWFhoQw/1fcLxsnd1EYXbdkYAwAyMzPh7u4OHR0dqKiooH///ggLC2v0nPv378PBwQFKSkqwtraWGvJtS3x9fZGSkgKhUAiBQIA///yTFWDgzXjrjnHmzJmsFyMqKgqTJk1qk7HSdWDZQD89CgCAx+Nh3bp1+PPPP7Fq1SqcO3cOp0+fRmBgILp16wZLS0tERESgqKgI8vLyKCsrw8WLF2FhYQFLS0vk5OSgpKQEhoaG7O989eoVioqK2NlocnIyxGIxevTowR5TU1ODpKQkKCoq4sqVK5g+fTqWL18OPT096OvrY9++fUhOTsajR49ACMHnn3/OnltUVIQNGzbAysoKhoaGmDRpEi5evAh5eXnEx8cjPz8fhoaG6Natm9TZLrNt165duHr1Knx8fFiTizTR7mqsX78egYGBSElJwePHj9mWkbWd3Js3b2aP37FjB+7evYukpCRERETA3d29zRpjFBYWYsSIEVBQUMCtW7fwzz//4MCBA6xQSCM5ORmurq4YPXo0IiMjsXr1aixZsgR37txp9fG+D9AIkmyg4WgKyyeffAJFRUVcuHABf/31F+zt7TFkyBAAwEcffYT//e9/WLZsGdzd3XHt2jX89ddf+OabbwC8CUXX1NTA1NSU/X2MCNvb2wMA4uPjoaqqyjqBgTd5ycnJyejbty8MDAzYbivy8vKwsbFBUlIS8vLy0K9fPxw4cADDhg1DYWEhVFVVcfDgQVy8eJEtaPD48WO8ePECs2fPxsqVKxEbGwsvLy/4+PjAxsYGgYGBEtfLOLcrKirg6+uLadOmsfs4HA4b4gbQJY1ijJM7Pz8furq6GDlyZD0nd0dpjLF3716YmJjgzJkz7DYLC4tGzzl27BgsLCxw4MABAICtrS0ePnwIb29vTJgwoVXHS6EwUBGmsCgrK2Pu3LmYO3duvX329vbYvXs3G7rt1asXfH19MWrUKABAYmIi8vPzJVywAoEAVVVV7M2Qz+dDR0cH2tra7DFlZWXIyMhg15tramogLy+PtLQ0+Pj44NatW3j16hUqKipACIGuri5SU1PRr18/CAQCODo6wsXFBQAwfvx4dv3x0KFDGDt2LLZu3QozMzOkpqYCqL8GnJubi8rKSmhpadW75q4eZgsICGh0//379yV+9vb2hre3dyuOqGF+++03TJgwATNnzkRgYCCMjIzw5ZdfYunSpQ2e01C1qdqFUSiU1oaKMEUCkUgk0bqsNs7OznB2dpZ6Xq9evfDhhx/C2tqa3ZaYmIjU1FQYGRkBeCPy5eXlEs0KiouLkZOTAzc3NwD/hoFPnTqFS5cuYdOmTRg3bhyUlJSwceNGxMbGQk5ODlwuF2PHjsXixYsxevRoLFy4EPPnz2eLisTExEBNTQ329vYS1aUYAWauMTY2FioqKtDT05O4noiICBw5cgRCoRB9+/bFxIkTMXDgwLd+fs01etE+q7IhKSkJPj4+WLt2LbZs2YLQ0FCsWrUKioqKWLhwodRzGqo29fr1a1RUVEBFRaUthk7p4nTtR31KPeTl5cHlcqUKg1gsRk1NjdQCG6NGjcKBAwdgZWXFbnN1dcXhw4cxaNAgAG/C3SYmJnB1dYW2tjZSU1NRUlKChIQEVjyZ2efz58/Ru3dvzJ07F+bm5ujZsydKSkqgoqICHR0dAG8MKVFRURg/fjwOHz6MqVOnIj4+HgAQGxsLbW1tdOvWjR173WsBgBcvXkBPT09i7bCiogLp6enQ0tKCnp4ebt68iRUrVuDRo0cNfm7Mw0NzQ9Z//vkn5OTk0Lt3bxw8eLBZ51L+RSwWw8HBAbt27cLAgQOxbNkyLF26lPZcpnR4qAhTmgwzA5UWphWLxfWEztjYGDNmzGCFUF9fHzdu3EBwcDCOHDkCAwMDqKqqYvjw4ey6IVPScsaMGYiNjcWRI0cQGhqKxYsX4+bNm7C1tYW2tjby8/NRUFAAU1NTfPXVVwgICMCzZ8/YOsdxcXHQ19dnw8x1xZERzRcvXsDMzAxqamrsdhUVFbi6uuLgwYM4ePAgHjx4gJ49e2LXrl0S59b+97NnzzB27FgcP36c3VdWVgY+n8+2e5PGhAkTEBgYCGVlZbx8+bKxj5/SCD179qy39mxra9toha+Gqk1pamrSWTClzaAiTJEJcnJy9cSZEFJPmDkcDng8HubMmQMlJSXY2tri77//ZtNYGLGcM2cO5s+fD19fX8ybNw+jRo2CsrIy1NTUoKKigtu3b2Pfvn0ICgpCdXU1cnJyWEMQAGRnZ8PAwICt7NTQDDUqKgo8Ho+t3sUc9/DhQ6xZswbLli2Dv78/NDU1IRKJkJaWJvG7GBEWCAQoLS2Fqqoqu+/58+dwc3PD4cOHAdSfjTPn6ujoQEdHB7169WrSZ02pz4gRIyQaRwBvjIBmZmYNnsM0x6hNWzbHoFAAKsKUVqSxloiMABFCIBKJ6u1XVFSEp6cnYmNjER8fD3d3dzx9+hTLly8H8KbIfWxsLKZPn46ePXtixYoVWLt2LetwNjY2ZlOipMGMKzk5GdbW1hJNJby9vbF48WLk5+ejpqYGvr6+uHDhQoNheuCNk5jD4Ug4cgsLC6GhoQEbG5tGP4P09HSUl5fT0o4tYM2aNXjy5Al27doFPp/P1ttmvi8AsHnzZixYsID9+fPPP0dSUhI2bNiA2NhYHD16FL/88gvWrFnTHpdA6aJQYxalXajddakhYWPEWV5evl4vW0dHR1y9ehXAm1loQUEBrK2t2Q4uK1euxLx582BkZARTU1P8888/Er+bEeFXr17ByspK4mEhICAAkydPxrZt29iZ9ZgxY9CtWzc2vF1X3LOysqCgoCAhpFlZWaiqqmK7TTU0G8/KygIhRCK9i9I8nJyccPXqVWzevBk7duyAhYUF/vvf/2LevHnsMdnZ2RLhaQsLC/zxxx9Ys2YNDh06BGNjY5w6dYqmJ1HaFCrClA5LXXGu7TwmhIAQAjk5Oejp6dVzN9vb2yM6OhpJSUnsjbeuczktLQ1isVjiXKFQCE1NTSQlJaF79+4QiUS4fv06nj59is8//xzq6uoA6qcv5eXlQU1NTSIHmqkUxghzXRFmfs7KygKXy2Vd5JR3Y/LkyVKbjzBIq4Y1atQoPHv2rBVHRaE0DhVhSqehtogx9aHfhqWlJSwtLSUEmKl1Gx0dDV1dXTg4OLDblZSU2LClnZ0dBgwYgPT0dKioqLACW15ejitXrsDQ0BBjxoxBdXU1W9yDMXgBbxza6urqbIUwZo2cebhgxpOTkwM1NbU2K+9IoVA6DnRNmNIlkGamiomJQV5eHpYtW4anT5+ys9sxY8bgf//7H9atW4fx48fj22+/xZQpU9iSnKmpqfDy8mKrM8nJycHCwgIhISHIzs4GIQS//PIL7ty5AxMTE/Y9pYXehUIhXr16BW1tbQkBp1AoXQM6E6Z0ORgh/PDDD1FdXY3nz58jKysLwL/FM3g8Hng8HnvOmDFj2H+bmpri0KFDrBNaXl4e06dPx++//84WB+FwOKiqqmJD3fn5+bh48SJ69uyJGTNmsKKcn5+PoqKiBs1bFArl/YaKMKXLYmtryxYJYWAEmllzZlo8Av+uA6upqdUz75ibm+PKlSsIDQ1FcXEx9PX10b9/fzZFJjMzEwcPHoSLiwtmzJiB6upqKCoqIicnB2VlZXQ9mELpolARplCkUHvNWdras7QeqlpaWhK1iEeOHMn+29bWFv7+/mzeMnNuZmYmqqqqGs1npVAo7y9UhCmUd+Bt+c8AJExYioqKbEcq4E3Ym8PhQCAQgMvlsmvHFAqla0FFmEKRIbVnzXVNWLVnz5mZmXB1dUVcXBx69+4t0YeZQqF0HTiEdi+nUNqFwsJCZGRkQCgUwsHBocu3TqRQuiJUhCkUCoVCaSfoozeFQqFQKO0EFWEKhUKhUNoJKsIUCoVCobQTVIQpFAqFQmknqAhTKBQKhdJOUBGmUCgUCqWdoCJMoVAoFEo7QUWYQqFQKJR2goowhUKhUCjtBBVhCoVCoVDaCSrCFAqFQqG0E1SEKRQKhUJpJ6gIUygUCoXSTlARplAoFAqlnaAiTKFQKBRKO0FFmEKhUCiUdoKKMIVCoVAo7QQVYQqFQqFQ2gkqwhQKhUKhtBNUhCkUCoVCaSeoCFMoFAqF0k5QEaZQKBQKpZ2gIkyhUCgUSjtBRZhCoVAolHaCijCFQqFQKO0EFWEKhUKhUNqJ/wfKd/hb6pnCdAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# สร้างข้อมูลที่ไม่มีสัญญาณรบกวนมาแบบละเอียดสำหรับค่า x1 และ x2 จำนวนมาก เพื่อใช้ในการวาด plane\n",
        "num_sample_per_axis = 100\n",
        "X1_whole_plane, X2_whole_plane = np.meshgrid(3 + 3*np.random.rand(num_sample_per_axis),\n",
        "                                              3 + 3*np.random.rand(num_sample_per_axis))\n",
        "x1_whole_plane = np.reshape(X1_whole_plane, (-1, 1))\n",
        "x2_whole_plane = np.reshape(X2_whole_plane, (-1, 1))\n",
        "\n",
        "# ปรับให้อยู่ในรูปแบบที่เหมาะสมกับ LinearRegression\n",
        "X_whole_plane = np.concatenate((x1_whole_plane, x2_whole_plane), axis=1)\n",
        "\n",
        "# ทำนายค่า y ออกมา\n",
        "y_predicted_whole_plane = model_multi_linear.predict(X_whole_plane)\n",
        "\n",
        "# Plot รูปออกมา\n",
        "fig = plt.figure(figsize=(6, 6))\n",
        "ax = fig.add_subplot(projection='3d')\n",
        "ax.scatter(x1_whole_plane, x2_whole_plane, y_predicted_whole_plane, c='k', marker='.', label='Predicted', alpha=0.03)\n",
        "ax.scatter(x1, x2, y_multi, c='b', marker='o')\n",
        "ax.set_xlabel('contrast [a.u.]')\n",
        "ax.set_ylabel('loudness [a.u.]')\n",
        "ax.set_zlabel('average firing rate [a.u.]')\n",
        "ax.view_init(elev=20, azim=-70)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tK00miuo28ov"
      },
      "source": [
        "จะเห็นได้ว่าเรา plane ที่เราหามาจากการแก้โจทย์ สามารถอธิบายข้อมูลที่เรามีอยู่ได้ดีพอสมควร\n",
        "\n",
        "---\n",
        "\n",
        "การเปลี่ยนจาก\n",
        "\n",
        "$$y=w_0 + w_1 x_1$$\n",
        "\n",
        "ไปเป็น\n",
        "\n",
        "$$y=w_0 + w_1 x_1 + w_2 x_2$$\n",
        "\n",
        "ส่งผลให้เรา\n",
        "\n",
        "* เปลี่ยนจากเส้นตรงใน 2 มิติ ไปเป็น plane ใน 3 มิติ\n",
        "\n",
        "* เปลี่ยนจากการใช้แค่ค่า $x_1$ ในการทำนายค่า $y$ ไปเป็นการมีโอกาสใช้ทั้ง $x_1$ และ $x_2$ ในการทำนายค่า $y$\n",
        "\n",
        "**ข้อสังเกต** สมการ $y=w_0 + w_1 x_1$ เป็นกรณีพิเศษ (special case) ของสมการ $y=w_0 + w_1 x_1 + w_2 x_2$ ที่กำหนดให้ $w_2=0$\n",
        "\n",
        "\n",
        "<br><br>\n",
        "เราสามารถเพิ่มจำนวนตัวแปรเข้าไปอีกจนเกิดเป็นสมการ\n",
        "\n",
        "$$ y=w_0 + w_1 x_1 + w_2 x_2 + ... + w_p x_p $$\n",
        "\n",
        "ที่มีตัวแปรทั้งหมด\n",
        "$p+1$\n",
        "ตัว ซึ่งประกอบด้วย $w_0, w_1, ..., w_p$\n",
        "\n",
        "ซึ่งสมการนี้จะถือเป็นสมการเชิงเส้นเหมือนเดิม แต่เราจะอยู่ในมิติที่สูงขึ้นเรื่อย ๆ เช่น\n",
        "\n",
        "* $p=1$: $ y=w_0 + w_1 x_1$ เป็นเส้นตรง\n",
        "* $p=2$: $ y=w_0 + w_1 x_1 + w_2 x_2$ เป็นแผ่นกระดาษสี่เหลี่ยม (plane)\n",
        "* $p>2$: $ y=w_0 + w_1 x_1 + ... + w_p x_p$ เป็นแผ่นกระดาษใน $p+1$ มิติ หรือมีชื่อเรียกทางเทคนิคว่า hyperplane\n",
        "\n",
        "\n",
        "ไม่ว่าเราจะใช้ $p$ มีค่าเป็นเท่าไหร่ก็ตาม เราก็ยังสามารถหาค่า $w_0, w_1, ..., w_p$ ได้โดยการเรียกใช้ `LinearRegression` จากไลบรารี่ `scikit-learn` ได้เหมือนเดิม!\n",
        "\n",
        "**หมายเหตุ** ในกรณีที่เราทำงานกับสมการเชิงเส้น (linear) ที่มีจำนวนมิติสูง เรามักจะใช้ linear algebra มาใช้ในการเขียนสมการออกมาให้อยู่ในรูปแบบที่กระชับในรูปของ vector และ matrix และสามารถแก้สามารถที่มีได้โดยใช้เทคนิคทาง optmization ดังแสดงเอาไว้ใน video ที่เป็น optional สำหรับผู้ที่สนใจด้านล่าง"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "KOh28-LzIZgl",
        "outputId": "675b977a-c59c-4085-c462-8bf51bd1cd51",
        "tags": [
          "remove-input"
        ]
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/BcGL0yM2cpc\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HTML(\"\"\"\n",
        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/BcGL0yM2cpc\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n",
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\n",
                    "text/plain": "<Figure size 400x400 with 1 Axes>"
                  },
                  "metadata": {},
                  "output_type": "display_data"
                }
              ]
            }
          },
          "9adaf318c1df410da2aa7d10a9bd27cb": {
            "model_module": "@jupyter-widgets/controls",
            "model_module_version": "1.5.0",
            "model_name": "SliderStyleModel",
            "state": {
              "_model_module": "@jupyter-widgets/controls",
              "_model_module_version": "1.5.0",
              "_model_name": "SliderStyleModel",
              "_view_count": null,
              "_view_module": "@jupyter-widgets/base",
              "_view_module_version": "1.2.0",
              "_view_name": "StyleView",
              "description_width": "",
              "handle_color": null
            }
          },
          "a008bf82e09a4242a779319ddf352b94": {
            "model_module": "@jupyter-widgets/output",
            "model_module_version": "1.0.0",
            "model_name": "OutputModel",
            "state": {
              "_dom_classes": [],
              "_model_module": "@jupyter-widgets/output",
              "_model_module_version": "1.0.0",
              "_model_name": "OutputModel",
              "_view_count": null,
              "_view_module": "@jupyter-widgets/output",
              "_view_module_version": "1.0.0",
              "_view_name": "OutputView",
              "layout": "IPY_MODEL_c57ca0fd9f364f71a00505b3ccaed4d0",
              "msg_id": "",
              "outputs": [
                {
                  "data": {
                    "image/png": 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              "grid_gap": null,
              "grid_row": null,
              "grid_template_areas": null,
              "grid_template_columns": null,
              "grid_template_rows": null,
              "height": null,
              "justify_content": null,
              "justify_items": null,
              "left": null,
              "margin": null,
              "max_height": null,
              "max_width": null,
              "min_height": null,
              "min_width": null,
              "object_fit": null,
              "object_position": null,
              "order": null,
              "overflow": null,
              "overflow_x": null,
              "overflow_y": null,
              "padding": null,
              "right": null,
              "top": null,
              "visibility": null,
              "width": null
            }
          },
          "cd53ec71640d479c928dce20b7b7353e": {
            "model_module": "@jupyter-widgets/output",
            "model_module_version": "1.0.0",
            "model_name": "OutputModel",
            "state": {
              "_dom_classes": [],
              "_model_module": "@jupyter-widgets/output",
              "_model_module_version": "1.0.0",
              "_model_name": "OutputModel",
              "_view_count": null,
              "_view_module": "@jupyter-widgets/output",
              "_view_module_version": "1.0.0",
              "_view_name": "OutputView",
              "layout": "IPY_MODEL_f9ef37855ead408ab02470c28276e24e",
              "msg_id": "",
              "outputs": [
                {
                  "data": {
                    "image/png": 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