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      "source": [
        "---\n",
        "title: \"Surrogate Model-Based Multi-Objective Optimization Using Desirability Functions\"\n",
        "author: \"Thomas Bartz-Beielstein\"\n",
        "date: \"15 July 2025\"\n",
        "format:\n",
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        "    runninghead: \"MO Optimization and HPT With Desirability Functions\"\n",
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        "            \\gothamset{numbering=pagenumber}\n",
        "            \\gothamset{sectiontocframe default=off}\n",
        "bibliography: references.bib\n",
        "---\n"
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      "source": [
        "import os\n",
        "from math import inf\n",
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.optimize import minimize\n",
        "from spotpython.hyperparameters.values import set_hyperparameter\n",
        "from spotpython.data.diabetes import Diabetes\n",
        "from spotpython.fun.mohyperlight import MoHyperLight\n",
        "from spotpython.hyperdict.light_hyper_dict import LightHyperDict\n",
        "from spotpython.hyperparameters.values import set_hyperparameter\n",
        "from spotpython.mo.functions import fun_myer16a\n",
        "from spotpython.mo.plot import plot_mo\n",
        "from spotpython.plot.contour import (mo_generate_plot_grid, contour_plot,\n",
        "                                     contourf_plot)\n",
        "from spotpython.utils.eda import print_exp_table, print_res_table\n",
        "from spotpython.utils.file import get_experiment_filename\n",
        "from spotpython.spot import Spot\n",
        "from spotpython.utils.init import (fun_control_init, surrogate_control_init,\n",
        "                                   design_control_init)\n",
        "from spotdesirability.utils.desirability import (DOverall, DMax, DCategorical, DMin,\n",
        "                                                 DTarget, DArb, DBox)\n",
        "from spotdesirability.plot.ccd import plotCCD\n",
        "from spotdesirability.functions.rsm import rsm_opt, conversion_pred, activity_pred\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
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      "source": [
        "# Introduction & Motivation\n",
        "\n",
        "## Motivation from Industry\n",
        "\n",
        "- **The Dichotomy:** Significant gap between industrial adoption and academic use\n",
        "  - Desirability functions (Harrington, 1965): Established in industrial optimization\n",
        "  - Seldom used in academic multi-objective optimization (MOO)\n",
        "  - (Never?) used in ML/hyperparameter tuning (HPT)\n",
        "\n",
        "- **The Problem in ML/DL HPT:** Manual, irreproducible trial-and-error processes\n",
        "  - Balancing model accuracy, training time, complexity\n",
        "  - Lack of systematic multi-objective approaches\n",
        "\n",
        "- **Background:** This work is motivated by requests from industrial partners:\n",
        "  - \"Confused\" by the Pareto-front concepts\n",
        "\n",
        "- **Our Aim:** Providing easy to use tools\n",
        "\n",
        "\n",
        "\n",
        "## Core Research Questions\n",
        "\n",
        "1. **Application:** How can desirability functions be methodically used for:\n",
        "   - Classical multi-objective optimization\n",
        "   - Contemporary hyperparameter tuning\n",
        "\n",
        "2. **Long-term Goal:** What are the concrete advantages and disadvantages compared to other MOO methods?\n",
        "\n",
        "3. **Enhancement:** How can the desirability framework be improved to overcome known limitations?\n",
        "\n",
        "## Materials\n",
        "\n",
        "### Jupyter Notebook\n",
        "\n",
        "* Updates and Jupyter Notebook of this Presentation:\n",
        "    * Research Cluster \"Technische Hochschule Köln - Artificial Intelligence\"\n",
        "    * [https://thk-ai.de](https://thk-ai.de)\n",
        "\n",
        "# Theoretical Foundation\n",
        "\n",
        "## The Desirability Function: Fundamentals\n",
        "\n",
        ":::: {.columns}\n",
        "\n",
        "::: {.column width=\"50%\"}\n",
        "### Core Concept\n",
        "- Transform multiple **incommensurable objectives** $f_r(x)$\n",
        "- Single **dimensionless scale** $d_r \\in [0,1]$\n",
        "- 0 = completely unacceptable\n",
        "- 1 = perfectly desirable\n",
        ":::\n",
        "\n",
        "::: {.column width=\"50%\"}\n",
        "### Three Function Types\n",
        "\n",
        "- **Larger-is-Better ($d_{\\max}$):** Maximization (accuracy, yield)\n",
        "- **Smaller-is-Better ($d_{\\min}$):** Minimization (error, cost)  \n",
        "- **Target-is-Best ($d_{\\text{target}}$):** Specific target value\n",
        ":::\n",
        "\n",
        "::::\n",
        "\n",
        "## Maximization Formula\n",
        "\n",
        "\n",
        "### Maximization\n",
        "\n",
        "* For maximization of $f_r(\\vec{x})$ (\"larger-is-better\"), the following function is used:\n",
        "\n",
        "$$\n",
        "d_r^{\\text{max}} =\n",
        "\\begin{cases}\n",
        "    0 & \\text{if } f_r(\\vec{x}) < A \\\\\n",
        "    \\left(\\frac{f_r(\\vec{x}) - A}{B - A}\\right)^s & \\text{if } A \\leq f_r(\\vec{x}) \\leq B \\\\\n",
        "    1 & \\text{if } f_r(\\vec{x}) > B\n",
        "\\end{cases}\n",
        "$$\n",
        "\n",
        "* Parameters $A$ (\"acceptable\"), $B$ (\"ideal\"), and $s$ (\"scale\") are chosen by the investigator.\n",
        "* Similar in the minimization case (\"smaller-is-better\").\n",
        "* Scale parameter $s$ can be adjusted to make the desirability criterion easier or harder to satisfy.\n",
        "\n",
        "## Target\n",
        "\n",
        "### Target Optimization\n",
        "\n",
        "* Target value: $t_0$.\n",
        "* In \"target-is-best\" situations, the following function is used:\n",
        "\n",
        "$$\n",
        "d_r^{\\text{target}} =\n",
        "\\begin{cases}\n",
        "    \\left(\\frac{f_r(\\vec{x}) - A}{t_0 - A}\\right)^{s_1} & \\text{if } A \\leq f_r(\\vec{x}) \\leq t_0 \\\\\n",
        "    \\left(\\frac{f_r(\\vec{x}) - B}{t_0 - B}\\right)^{s_2} & \\text{if } t_0 \\leq f_r(\\vec{x}) \\leq B \\\\\n",
        "    0 & \\text{otherwise.}\n",
        "\\end{cases}\n",
        "$$\n",
        "\n",
        "\n",
        "\n",
        "## Visualization of Desirability Functions"
      ]
    },
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",
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "#| label: fig-kuhn16a-1\n",
        "#| fig-cap: \"Examples of the three primary desirability functions. Panel (a) Larger--is--better function, panel (b) Smaller--is--better desirability function and panel (c) target value.\"\n",
        "\n",
        "# Create a 1x3 grid of subplots\n",
        "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
        "\n",
        "# (a) Plot for DMax\n",
        "axes[0].set_title(\"(a)\")\n",
        "dmax1 = DMax(1, 3)\n",
        "dmax2 = DMax(1, 3, scale=5)\n",
        "dmax3 = DMax(1, 3, scale=1/5)\n",
        "\n",
        "# Plot DMax with scale=1\n",
        "x = np.linspace(dmax1.low, dmax1.high, 100)\n",
        "y = dmax1.predict(x)\n",
        "axes[0].plot(x, y, label=\"scale = 1\", color=\"black\")\n",
        "\n",
        "# Plot DMax with scale=5\n",
        "y2 = dmax2.predict(x)\n",
        "axes[0].plot(x, y2, label=\"scale = 5\", color=\"red\")\n",
        "axes[0].text(2.73, 0.3, \"scale = 5\", color=\"red\")\n",
        "\n",
        "# Plot DMax with scale=0.2\n",
        "y3 = dmax3.predict(x)\n",
        "axes[0].plot(x, y3, label=\"scale = 0.2\", color=\"blue\")\n",
        "axes[0].text(1.3, 0.8, \"scale = 0.2\", color=\"blue\")\n",
        "\n",
        "axes[0].set_xlabel(\"Input\")\n",
        "axes[0].set_ylabel(\"Desirability\")\n",
        "axes[0].legend()\n",
        "\n",
        "# (b) Plot for DMin\n",
        "axes[1].set_title(\"(b)\")\n",
        "dmin1 = DMin(1, 3)\n",
        "dmin2 = DMin(1, 3, scale=5)\n",
        "dmin3 = DMin(1, 3, scale=1/5)\n",
        "\n",
        "# Plot DMin with scale=1\n",
        "y = dmin1.predict(x)\n",
        "axes[1].plot(x, y, label=\"scale = 1\", color=\"black\")\n",
        "\n",
        "# Plot DMin with scale=5\n",
        "y2 = dmin2.predict(x)\n",
        "axes[1].plot(x, y2, label=\"scale = 5\", color=\"red\")\n",
        "axes[1].text(1.5, 0.1, \"scale = 5\", color=\"red\")\n",
        "\n",
        "# Plot DMin with scale=0.2\n",
        "y3 = dmin3.predict(x)\n",
        "axes[1].plot(x, y3, label=\"scale = 0.2\", color=\"blue\")\n",
        "axes[1].text(1.5, 1, \"scale = 0.2\", color=\"blue\")\n",
        "\n",
        "axes[1].set_xlabel(\"Input\")\n",
        "axes[1].set_ylabel(\"Desirability\")\n",
        "axes[1].legend()\n",
        "\n",
        "# (c) Plot for DTarget\n",
        "axes[2].set_title(\"(c)\")\n",
        "dtarget1 = DTarget(1, 2, 3)\n",
        "dtarget2 = DTarget(1, 2, 3, low_scale=5)\n",
        "dtarget3 = DTarget(1, 2, 3, low_scale=1/5)\n",
        "\n",
        "# Plot DTarget with low_scale=1\n",
        "y = dtarget1.predict(x)\n",
        "axes[2].plot(x, y, label=\"lowScale = 1\", color=\"black\")\n",
        "\n",
        "# Plot DTarget with low_scale=5\n",
        "y2 = dtarget2.predict(x)\n",
        "axes[2].plot(x, y2, label=\"lowScale = 5\", color=\"red\")\n",
        "axes[2].text(1.9, 0.1, \"lowScale = 5\", color=\"red\")\n",
        "\n",
        "# Plot DTarget with low_scale=0.2\n",
        "y3 = dtarget3.predict(x)\n",
        "axes[2].plot(x, y3, label=\"lowScale = 0.2\", color=\"blue\")\n",
        "axes[2].text(1.3, 0.9, \"lowScale = 0.2\", color=\"blue\")\n",
        "\n",
        "axes[2].set_xlabel(\"Input\")\n",
        "axes[2].set_ylabel(\"Desirability\")\n",
        "axes[2].legend()\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Parameters\n",
        "\n",
        "### Larger is Harder\n",
        "\n",
        "The values of $s$, $s_1$, or $s_2$ can be chosen so that the desirability criterion is easier or more difficult to satisfy (examples on the next slides):\n",
        "\n",
        "\n",
        "* Values of $s$ **greater than 1** will make $d_r^{\\text{max}}$ **harder to satisfy** in terms of desirability.\n",
        "    * If $s$ is chosen to be less than 1 in $d_r^{\\text{max}}$, $d_r^{\\text{max}}$ is near 1 even if  $f_r(\\vec{x})$ is not low.\n",
        "    * As values of $s$ move closer to 0, the desirability reflected by $d_r^{\\text{max}}$ becomes higher.\n",
        "\n",
        "\n",
        "* Scaling factors are useful when one equation holds more importance than others.\n",
        "* Any function can reflect model desirability.\n",
        "\n",
        "\n",
        "\n",
        "## Examples: Parametrization\n",
        "\n",
        "### Maximization for s=0.5 and s=5\n",
        "\n",
        ":::: {.columns}\n",
        "\n",
        "::: {.column width=\"50%\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "dMax_obj = DMax(low=0, high=10, missing=0.5, scale=0.5)\n",
        "dMax_obj.plot(figsize=(2, 2), xlabel=\"Objective Value\", ylabel=\"Desirability. s=0.5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ":::\n",
        "\n",
        "::: {.column width=\"50%\"}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "dMax_obj = DMax(low=0, high=10, missing=0.5, scale=10, tol=None)\n",
        "dMax_obj.plot(figsize=(2, 2), xlabel=\"Objective Value\", ylabel=\"Desirability. s=10\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ":::\n",
        "\n",
        "::::\n",
        "\n",
        "* Dotted blue lines: desirability where objective value cannot be computed (`NA`s)\n",
        "\n",
        "## Target Desirability\n",
        "\n",
        "### Target-is-Best\n",
        "\n",
        ":::: {.columns}\n",
        "\n",
        "::: {.column width=\"50%\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "dTarget_obj = DTarget(low=0, high=10, target=5, missing=0.5, low_scale=1, high_scale=1)\n",
        "dTarget_obj.plot(figsize=(2, 2), xlabel=\"Objective Value\", ylabel=\"Desirability (1, 1)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ":::\n",
        "\n",
        "::: {.column width=\"50%\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "dTarget_obj = DTarget(low=0, high=10, target=5, missing=0.5, low_scale=.1, high_scale=2)\n",
        "dTarget_obj.plot(figsize=(2, 2), xlabel=\"Objective Value\", ylabel=\"Desirability (0.1, 2)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ":::\n",
        "\n",
        "::::\n",
        "\n",
        "\n",
        "\n",
        "## Zero-Desirability Tolerances\n",
        "\n",
        "### Zero-Desirability Problem\n",
        "\n",
        "* In high-dimensional MOO outcomes, finding feasible solutions where every desirability value is acceptable can be challenging.\n",
        "* Each desirability R function has a `tol` argument, which can be set between [0, 1] (default is `NULL`).\n",
        "* If not null, zero desirability values are replaced by `tol`.\n",
        "* Research Question:\n",
        "    * Using a default, small value for `tol` can be usefull (similar to the $\\lambda$  nugget in Kriging).\n",
        "\n",
        "\n",
        "\n",
        "## Custom or Arbitary Desirability Functions \n",
        "\n",
        "The `dArb` function (`Arb` stands for \"Arbitary\") can be used to create a custom desirability function.\n",
        "\n",
        "### Example: Logistic Desirability Function\n",
        "\n",
        "* The logistic function defined as \n",
        "$$\n",
        "d(\\vec{x}) = \\frac{1}{1+\\exp(-\\vec{x})}.\n",
        "$$\n",
        "* Inputs in-between these grid points are linearly interpolated. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: kuhn16a-logistic\n",
        "#| echo: true\n",
        "def logistic(u):\n",
        "    return 1 / (1 + np.exp(-u))\n",
        "x = np.linspace(-5, 5, 20)\n",
        "logistic_d = DArb(x, logistic(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plotting the Logistic Desirability Function\n",
        "\n",
        "*  @fig-kuhn16a-7 displays a `plot` of the `logisticD` object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| label: fig-kuhn16a-7\n",
        "#| fig-cap: \"Using the `DArb` function. The desirability function is a logistic curve.\"\n",
        "logistic_d.plot(figsize=(2, 2), xlabel=\"Input\", ylabel=\"Desirability\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Overall Desirability and \"Veto\" Property\n",
        "\n",
        "### Aggregation Formula\n",
        "$$D = \\left(\\prod_{r=1}^{R} d_r\\right)^{1/R}$$\n",
        "\n",
        "- **Geometric mean** ensures the \"veto\" property:\n",
        "    - **If any $d_r = 0$, then $D = 0$**\n",
        "- All objectives must meet minimal acceptability\n",
        "- Translates specification limits into **hard constraints**\n",
        "\n",
        "\n",
        "# Case Study 1: Validation and Verification\n",
        "\n",
        "## How to Validate and Verify the Software Package?\n",
        "\n",
        "### Testing the Package\n",
        "* `pytest`\n",
        "\n",
        "### Comparison with Existing R Packages\n",
        "\n",
        "* Validate `spotdesirability` on classic RSM problem (quadratic response surface model) [@Myers2016].\n",
        "* R package `desirability` is used for comparison.\n",
        "\n",
        "\n",
        "### Methods Compared\n",
        "\n",
        "- Direct Search (Nelder-Mead):\n",
        "    - R: `desirability` with `optim` with `method=\"Nelder-Mead\"`\n",
        "    - Python: `spotdesirability` with `scipy.optimize.minimize` with `method=\"Nelder-Mead\"`\n",
        "\n",
        "In addition: **Surrogate-Model Based Optimization (SMBO)**\n",
        "\n",
        "## Validating and Verifying by Comparing to R Package\n",
        "\n",
        "### Chemical Reaction Optimization\n",
        "\n",
        "* Well-studied optimization problem from @Myers2016.\n",
        "* Study based on the work of @kuhn16a.\n",
        "* Three input variables normalized to $[-1, 1]$:\n",
        "    * $x_1$: Time (hours), \n",
        "    * $x_2$: Temperature (°C), and\n",
        "    * $x_3$: Catalyst concentration (g/L)\n",
        "\n",
        "## Chemical Reaction Optimization\n",
        "\n",
        "### Two Objectives\n",
        "- **Maximize Percent Conversion:** $d_{\\max}$ (80% min, 97% target)\n",
        "- **Target Thermal Activity:** $d_{\\text{target}}$ (55-60 range, 57.5 ideal)\n",
        "\n",
        "\\begin{align*}\n",
        "f_{\\text{con}}(x) =\n",
        "&\n",
        " 81.09\n",
        "+\n",
        "1.0284 \\cdot x_1\n",
        "+\n",
        "4.043 \\cdot x_2\n",
        "+\n",
        "6.2037 \\cdot x_3\n",
        "+\n",
        "1.8366 \\cdot x_1^2\n",
        "+\n",
        "2.9382 \\cdot x_2^2 \\\\\n",
        "&\n",
        "+\n",
        "5.1915 \\cdot x_3^2\n",
        "+\n",
        "2.2150 \\cdot x_1 \\cdot x_2\n",
        "+\n",
        "11.375 \\cdot x_1 \\cdot x_3\n",
        "+\n",
        "3.875 \\cdot x_2 \\cdot x_3\\\\\n",
        "f_{\\text{act}}(x) = \n",
        " & \n",
        " 59.85\n",
        "+ 3.583 \\cdot x_1\n",
        "+ 0.2546 \\cdot x_2\n",
        "+ 2.2298 \\cdot x_3\n",
        "+ 0.83479 \\cdot x_1^2\n",
        "+ 0.07484 \\cdot x_2^2\n",
        "\\\\\n",
        "&\n",
        "+ 0.05716 \\cdot x_3^2\n",
        "+ 0.3875 \\cdot x_1 \\cdot x_2\n",
        "+ 0.375 \\cdot x_1 \\cdot x_3\n",
        "+ 0.3125 \\cdot x_2 \\cdot x_3. \n",
        "\\end{align*}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: kuhn16a-conversion\n",
        "#| echo: false\n",
        "\n",
        "def conversion_pred(x):\n",
        "    return (\n",
        "        81.09\n",
        "        + 1.0284 * x[0]\n",
        "        + 4.043 * x[1]\n",
        "        + 6.2037 * x[2]\n",
        "        - 1.8366 * x[0]**2\n",
        "        + 2.9382 * x[1]**2\n",
        "        - 5.1915 * x[2]**2\n",
        "        + 2.2150 * x[0] * x[1]\n",
        "        + 11.375 * x[0] * x[2]\n",
        "        - 3.875 * x[1] * x[2]\n",
        "    )\n",
        "\n",
        "def activity_pred(x):\n",
        "    return (\n",
        "        59.85\n",
        "        + 3.583 * x[0]\n",
        "        + 0.2546 * x[1]\n",
        "        + 2.2298 * x[2]\n",
        "        + 0.83479 * x[0]**2\n",
        "        + 0.07484 * x[1]**2\n",
        "        + 0.05716 * x[2]**2\n",
        "        - 0.3875 * x[0] * x[1]\n",
        "        - 0.375 * x[0] * x[2]\n",
        "        + 0.3125 * x[1] * x[2]\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: false\n",
        "variables = {\n",
        "    \"time\": (-1.7, 1.7),\n",
        "    \"temperature\": (-1.7, 1.7),\n",
        "    \"catalyst\": (-1.7, 1.7)\n",
        "}\n",
        "resolutions = {\n",
        "    \"time\": 50,\n",
        "    \"temperature\": 2,\n",
        "    \"catalyst\": 50\n",
        "}\n",
        "functions = {\n",
        "    \"conversionPred\": conversion_pred,\n",
        "    \"activityPred\": activity_pred\n",
        "}\n",
        "plot_grid = mo_generate_plot_grid(variables, resolutions, functions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 1: The response surface for the percent conversion model\n",
        "\n",
        "### Objective 1: Maximize Percent Conversion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "#| label: fig-kuhn16a-2\n",
        "\n",
        "contourf_plot(\n",
        "    plot_grid,\n",
        "    x_col=\"time\",\n",
        "    y_col=\"catalyst\",\n",
        "    z_col=\"conversionPred\",\n",
        "    facet_col=\"temperature\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 1: The response surface for the thermal activity model\n",
        "\n",
        "### Objective 2: Target Thermal Activity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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RvCkKRVFUZ0P7MopyDyaTCRqNBqezfKBSMbd9fG2tA8nx5TAajVCr1RxEyL3bvwsURVEURVEURfGKJu0URVEURVEU5eZo0k5RFEVRFEVRbo7OaacoiqIoiqLcyvU57Z8f7wOZ8vbFDhvrmvByj0N0TjtFURRFURRFUfyhSTtFURRFURRFuTm6udJtOBwOFBcXQ6VSdWh7Z4qi+OF0OlFbWwu9Xg+G6Z7jE7T/oqjOifZfVGto0n4bxcXFCAoK4jsMiqI6qKCgAIGBgXyHwQvaf1FU59ad+y/qRjRpvw2VSgUAiHjxTQgkUp6jcQ27xYziHcug7NsH8pgovsNxCXP1JTQcPQePB8fyHUoLpu8WI/ylERCquP3sOB1OFL6/DGM+Gcz6CGt9ZQMK5h/AY2+HsXL8uhobdv73Iv71kaZ9z6tzoHfPyua/4e7o+mvX/3sWGFnX6L9ux2m3w3q1CCjIQuOlEgh1Cqh7RkGZHApGIuI7PLfkdDhhWboOmiA1oibF8h3ObRV/tw3xA3SI76flLQZLgx2/vJ6JOXO1EAhc38fW1TnQr1f37r+oG9Gk/TauJzwCibTLJO0CiRTBE55AwdbFIAAUKUl8h3TH5Pok1Dedga2sCpKwAL7DaeY7LgVVey8iYEovztv2jNKhvrwBHhFaVtsRKzXIqrFDpmRYOUGQqwSor0ObdsRrTXeeFnL9tTMyabdJ2gFAlhANJERDNgZoqjbAfuUECr/8FUQshKZPNDS9Y8BIaQL/e4Inp6Dx57W4tPYiEqa793dC4POjseuNtYjtpYFCy8/vUaYU4q5xUuzYbsXk+2SstdOd+y/qRnSiVDfFCEUIHv0wGs6cRd3R43yH4xK6keNRs2I73KmKqTkiHTVHc+F0cB+Ts1c6ruzI56Qtn2AJyvMtrB1foyEw1DhYOz7VdQk9tBD0HAbtjCehnHwfHGYbrn60CvmfrkXtiStw2unnCriWHAqn3Q2ryYIzP53iO5xbEkqFmPz/QrH0gxxe4/CZlIRVvzTCanWf7xyqa6NJezdGBAIEDp8Gc04uTPsP8h3OHROq1ZDGhqH+0Bm+Q2lGGAba9FDUHM3lvG11vB5lpyo5aSuxrxrnDppYO35KqgiZmTbWjk91DwKlAkzKIGifeQqyMePRmFOGnLd/RvEP22EurOI7PN4RQkAmT0CTuQlnF7pPP9qawuAe8NRLcfTXCt5iEIoZTJ4iw89LGniLgepeaNLezRGGQcDgKbCVlcO4ew/f4dwxTc+hqN1xFA6r+yR4zp4DUL75NOftEgEDpb8CpsJa1tsyxqXi/BH22iHxQTRpp1xK6KGFaMBo6J5/BkxCT1SsPoTcd5ah5rdzcNia+A6PN4QQkHvHw1zTiPPLzvEdzi1ppw/Dvl/KYKyw8haDclQitm2xoKGBXrGh2EeTdgqEEOj7TYLdaIRxzz6+w7kjRCiEemRvmDYd4DuUZgK1AoxMDHOJgfO2w4YEIve3QtbbkWolqDM2sTY1KThOjqxzNGmnXI8QAkloIJRTpkL9yEOw15uR+89lKJm/E7Yq9k943REhBMKpd6O2sBbZqy7wHc5NCUQCRDw/BEvfz+FtWiQjIHj4ETkW/ERH2yn20aSdAnCtk/bvPRFNFZWdfqqMPCwd5ot5aDK4zxeusH9/lP7K/Wh7TWQaio6UctJWQKQMRZfNrBxbLGFgtcKt1itQXQ8jk0KQPgS655+BqkcEiuZuRf5n69CYw83fkDshhED80D2ozq7ClY2X+A7npnSRHvCPlOPYJm6mAraGGZSAA/utMBnpaDvFLpq0U80IIdD3vwfWomLUHjrCdzgdRgiB7r7hMPyyg+9QmkmiglGXXQpHk53TdhmxECKFCI017CTTvxffW4WsQ+zNaw8MEqCwkNv3j+qeCCGweSVC89hjkA4bjaqtmcj957JrC1e70YkjYQikj9+H0hOlyN3K76LPW1E/MBR7l5eitpqfq3GEEDw5Q4Hv59bz0j7VfdCknWqBEIKAQZNhzs1D3bETfIfTYRJVBJx2OyxXS/gOBcC199WjbySqD17mvO3QQYHI21PEejuG2DRcOMre1Y2UVBFOnqRTZChuiXy9oJg0BaoHH0T9+ULkvvUzTEcudZvknTAEihlTULDnKooOsj/VriMEIgHufS0UKz7ifsH/ddae8Th7polWuaJY1amS9j179mDChAnQ6/UghGDNmjW3fPzu3buvLar5w620tPtd6mwPQhgEDrkfDecvoP6Ue1cQuBXdsHEw/LLDbb5c7Sl9UbEji/N2a+PTkb+/mPV2xEoRzA12OFgqb9kUE4zTpzpv0k77r85NoJRDMnw8NI8/isa8MuS8uQTGgxfdpn9hExEwUD33AC7+ch4VZ8v5DqdVBUE9oPYU4fTual7aJ4Tgiafk+OF7OtpOsadTJe319fVISUnBV1991a7nXbx4ESUlJc03Hx8fliLsOgjDIGj4NNQdO4HGy1f4DqdDhFoNxMF+aMzM5jsUAIBAKYNALoG51Mhpu0KlFI4mB2yN7FfECIqWo+BiIyvH1odLcfly550eQ/uvroGRSSEeNBbaJx+HuaASuW/9jLrTeXyHxTpGLIT25anI/PYEDLkGvsNplecjw7H1xyI01vFT/acxPR5nz9joaDvFmk6VtI8dOxbvvfce7rnnnnY9z8fHB35+fs03hulUL5s3RCBA0KgHYdy2E9Yi9kdq2aDpMxzGTfvhdLhHJ+o7Jgllm7m/ehHY2w+Fh9mfKhTXW4Xzh9mZ184ICAgB7PbOObJJ+6+uhZFKIB40BupHHobp2BXkvreiyy9YFcgl0L10P4785yDqy+r4DucGQqkQE14IwupPr/LS/vW57XS0nWJLt+j9U1NT4e/vj5EjR2L//v23fKzFYoHJZGpx684YsQSBY6ajavU62Kr4uex4JxipFPIecajb7x47/NX5JaI2q4jzXRjNKT04mddeHZ2Ki8fZm9ceGSnE5Uvdq4Y27b/cm0Aph+yuSVDeey8qNxxD/idrYa3g9moal0Q6JbTP3Yv9/9wLs5H9Be7tVR7TC/YmJy4d4+d3QEfbKTZ16aTd398fc+bMwcqVK7Fy5UoEBQVhyJAhOHHi5gssP/zwQ2g0muZbUFAQhxG7J6FcicBRU1H583LY6zrfCII6eQDq9pyA0w02TCGEQNczjPMdUiU+atSXN8DB8smCWCGCpdHB2rz27rQzKu2/OhehhxbK+6dBMmQEiuZsQdnPe+GwdM3PqsRPh4w/9cL+t/bAbuG/X/0j/YzhWPdFPpqs3CfOdLSdYlOXTtpjYmLwzDPPoEePHujXrx9+/PFH9OvXD5999tlNnzNr1iwYjcbmW0FBAYcRuy+RxgMe90xExaIlcFj5232uI4hQCOWgdJh2HuU7FACAI60fyrdzv9Ogb5IXys+yv1V7ULQchdnszGu3xoTg9OmumQj9Ee2/OidxgB80Tz0BaYg3ct9ZCuOBC11ysWqBLgmx98fh4If73e71iVUSDJjih20/sX91sTWN6fHw8OjS6RXFk273qerVqxcuX7552T2JRAK1Wt3iRl2jEQZCM3QIKpcsd5s54m2ljO6FhmPn4TBb+A4FAo0ShBBYa7gdibGlpiGPg91RY3uqWCv96BUgRlFh5/rsuRLtvzoHQggc4T2hfXYGzFcrkPf+ClhKOt/0wtupjOoN7yQfZH7rfuWBLf374crJWlQUcD+FhxCCx59UcN4u1fV1u6Q9MzMT/v7+fIfRaem0MZBGR6Jm0xa+Q2kXwjBQj+kL0+YDfIcCAPAekYDybdyOtisifVBxgf3EoTo6hbV57YQQCEWAxeJeI3tcof1X58KIRRAPvQvKuyeh+IftKP/lAJwcb7DGNvOg4bBb7Li8wb12TSWEIOy5ofjl37m8XAnY3hB528dUVnatzwLFvk6VtNfV1SEzMxOZmZkAgNzcXGRmZiI/Px/AtUvDjzzySPPjZ8+ejbVr1+Ly5cs4e/YsXnnlFezcuRMvvPACH+F3GT4R/eC02lB39DjfobSLPCgF5ov5sNc18B0KGkNTYTjG7ZcJIQTqQBWMBewtFAUAiUqMBpOdtdcWFyvEhQvuN4/2dmj/1X0JvT2hefIJiDxVyHl7KRou8jNtgy3CqXej+GAhSo65V5UxdbAGATEKnNzG/rTA9rJanXj0oRrU13ffK4dU+3WqpP3YsWNIS0tDWloaAGDmzJlIS0vDm2++CQAoKSlp/gIEAKvVitdeew1JSUkYPHgwTp06he3bt2P48OG8xN+V6PtMRP2ZszDn5vEdSpsRQqCZMBDG9Xv4DgWEYaCK06P2HLdf3qGDAziZIqMPl6Ikh53L0kkpImRnd76knfZf3RshBIjrB81jD6Nq8wkUzd3WZRaqEoZA9ez9OLvgDIxX3atyjnrKEOxaXAIzD7XbtzdEtliUf/3/bTYnxGKC6BghNm10vwo8lPsiTndbQeJmTCYTNBoNol/7AAKJlO9w3IrdYkb+uh/hPX0qhDot3+G0WdmC7+H5xN0Q6vid72srrYJt+3ZEvjqaszYd1iYUf7Qcd302hNV2xAcOo87YhGEPuH4jIKfTiUHy2y+wrK11ICGuHEajsdvO7b7efwV+8Q4YGe2/3Img4CQqVh+C/2PDII/W8x2OS9iqa1H9+XIM+dcwiFUSvsNp5nH2MLL2GzDlz2Gctz1CfvM1KPv2WjDvxwb8ME93w89qax1Ijqf9l0ajwefH+0CmFN728Y11TXi5x6Eu/Z51qpF2yr0IJFIEjLgfFT8v71QVZTQTBsO4YS/fYUDk5wlrZR3sHI62MWIhBCIG1jp22zTEJCObxXntFNXZ2YPSoHniUVSuP4LSRbu7xFx3kYcK6c9n4MB7+1gvL9se1Ym9YSi3ovAi92UYl5wPwBuvGbFxgxlffVGHH7+vx9bNZsz7sR5VlQ6cOmlDYUHnu3JI8YMm7dQdEeu8oB0xFFXLV7pd2a+bkeqi0FRRg6Ya/jee8egfier9Nx+JYUNgb38UHmF3d1S5pwzGKvpFRFG3IlDKoZr+MCTB3sh5ZynM+RV8h3THinxSEDQ4BCe/ca81T0FPD8Pqz/I4/57y0EugVhOsWNaAgwesyM1pwn9n12HbFgv27bXC6XQiK4v2lVTb0KSdumM6TQxE/n4w7b31bo3uRDNhkFuMtjcl9EblngucttmQkIar+9hfMObhJ0ZVCfclNvfvt3SaE0iKAgBE94F6+oMomb8LVZtPdPrPb13PQXDancjZcoXvUJopfBWISFPj2KZKTtsVihikv5SCnxZ6YNHPHnj9Lyps3OyFJcs88J9PNTh+yhejRtOpa3wpKirCQw89BE9PT8hkMiQlJeHYsWPNP6+rq8OLL76IwMBAyGQyxMfHY86cObzFS5N2yiV8E4bBkpvXaRamSrVRaKo08D7azsilEEhFsFTWcdbm9d1R2U4MYjPYq9d+ndPpbF7cdf31vPNWLS5f7vxTDajuRahVQ/Pk42iqbUT+J2thr+/cCxSFD0zE1e25qDrPbZJ8K/J7hmDPslJYGrntH8RSQfP/q9UMGhudWLKoAY8+VI3nn63BqpWNqCinfRbXampq0L9/f4hEImzatAlZWVn45JNPoNP9b43BzJkzsXnzZixatAjnz5/HK6+8ghdffBHr1q3jJWaatFMuQQhBwND7UbNxE+y17CZqrqIZNxDGjfv4DgPew+JRuSuL0za9onWovFjDahu1sUnIPs7Oyciqy764fLkJhBAwzLU57vb/+86LixciP59ebqY6H8IwEA8cA69xPZD3/go0XHKvEortQQQM1M9PwfEvjsJcw84Oye0lEAsw4lE9Ns9lv4LWH21viERlpR1/n2VEenIZ5v1Yj4yeYtw1ToptW8346kvu59t3dx999BGCgoIwb9489OrVC2FhYRg1ahQiIiKaH3PgwAE8+uijGDJkCEJDQ/H0008jJSUFR44c4SVmmrRTLiOQSOF57yRULv2lU+yYKtVFoamsCnYjd6PcrWkIToHh+FVO27QlJyN/P7sJgUqvQHkhO9Njdi+vwN//ZsLMVw14600T9u2zNG+45OcnQM4VOmpFdV5WXTw0TzyG8lWHULH+aKedLiNUytDz1d448P5+t1mYakzrg8KL9agq4u5KxvXf39bNFlRWOrBmvSe27fTGS39SYvwEGf71kQbbtnbuKyvuxGQytbhZLK1/D61btw4ZGRmYMmUKfHx8kJaWhrlz57Z4TL9+/bBu3ToUFRXB6XRi165dyM7OxqhRo7h4KTegSXsblW1bjcaS25eZ6+7UggDIU5Jg2LSV71DaRDNuIIy/8jvaTgQCyEM9UX+lnLM2VfF6lJ5id9EbIQQKtQD1RtePeo96xBdTp8qg0zFw2IHPZ9ehd88KPP+cAacybTh2rPNUM6Ko1giUcqgfeQROiw0Fs9fDYe6cNd3ztYkIGRaCU3NP8h0KgGv9UvBTQ7DqU+4GSgghMFZYMXeJAy++rERMrKjFzy9caELPXmLU1rrHiY27OWCIxF5D9G1vBwzXdqENCgqCRqNpvn344YetHjcnJwfffPMNoqKisGXLFjz33HN4+eWXMX/+/ObHfPHFF4iPj0dgYCDEYjHGjBmDr776CoMGDeLktf/R7QtfUgAA39D+qDx3AhW7f4UqOhGapAwwYvepQ+tOvEN6oTB3ORrOX4A8LpbvcG5J6hkDQ+Ee2E31EKgV/AXSow/Ktx1GWITr65q3hhEKIFaKYDZaINWw9zmO7qHCxeO1SB92Yx3iO6HxFGHgPTJMukcGc6MTFf+3HXhmpg0b1pnh6yO4zREoyv0RQiAaMBqy0lPIfXcZAl8aB4mfa/+WuFDXczAsZ35B4b4CBA4I4jscaMO0aPAU4cIhA2L7aDlpU+MtBgBcv2hiszlx5LAV27ZasGWTGS+/qoRKRcdRXaGgoKBFnXaJpPXvOIfDgYyMDHzwwQcAgLS0NJw9exZz5szBo48+CuBa0n7o0CGsW7cOISEh2LNnD1544QXo9XqMGDGC/RfzBzRpbyOZ1gehPnfDYW9CefUZFK6aD6mvHrqMARCptHyH53b0/Sfh6tofIAkIgECt4jucW9LcNQDGX/fDYyo/l7sAQBziD8MvFXDaHSACbjruoD7+KDhUgqjRoay10RCbgOxdJ12etAPXLjkTQiCVEQQFXevKfHwEGDVKComE0FErqsto8kuB+sFgFH61DD739IEqPeL2T3Izkocm4fxHC6EN10Kp5/87wevhofj1HxsQ3VMDRsDN3g+9x3vjH58YIamvgkhMoNUwCAgU4OtvtUhLF3MSQ3egVqvbtLmSv78/4uPjW9wXFxeHlStXAgAaGxvx17/+FatXr8a4ceMAAMnJycjMzMTHH3/MS9JOT+vaiREI4eedhuihM6CMTEDZ9nUo3vAzzOWdd8EQGxiRGP6D7kblipVuP79d6h0L69US2Ov5WyxFCIEmLQSGk9xdsq2NTUPBAXbrtXtEaFCQ3cDKsfeZQ7BnjwXPzKhBvz7liI0uQ3JiOZ6ZYcDGDWY0NXXOecAU1Rqhpw7aGU+iZk8WKlYf6nTz3BmxEJoZk3DwXwdgt/K/5kSskiB9tBf2ryrjrM1hj+hxz8wQTJgow1MzFHjsCTmeeFKOtHRxp/t9dgX9+/fHxYsXW9yXnZ2NkJAQAIDNZoPNZgPDtEyVBQIBHDzlNTRp7yBCCDwlYYjq/wgCYkag5vgBFK5eQJP335H66CGLjoJpD/8VWm6FEAL1qN6o3c7PavDrHKm9UbHzPGftSbyUaKhqhNPB3pcFI2AgEBJYLa7v4PLO1WPFskYEBQvwr39rsGefF85m+eDxJ+T48os6XMqmFWSoroURi6B8YBocTXYUfbWp0+2iKvHTIfa+OBz7nN++9joyciCObqyAuZ6b95FhCAKiFPC7LxlDhkqQ0VMMX79rU/noTs/ce/XVV3Ho0CF88MEHuHz5MpYsWYLvvvsOL7zwAoBrI/aDBw/G66+/jt27dyM3Nxc//fQTFixYgHvuuYeXmGnS7gJSjTfCe9yH4OTxqDm2D0VrFsJSUcp3WG7BO2oAzLl5sBQW8R3KLckCktF47gocVv4Wewk9tWiqNcPeyN0iSq8Y9ks/hiUqkHvW9eXMNv1UirBwAf7+DzUGDZLAx0cAhgEGD5EgOlqIM2dp0k51PYQQiAeOgTI9HHkfrkRTnXuUU2yr6rg+EIgFuLozj+9QwAgZjHw8AFu+57YE5PVRdTq6zq+ePXti9erV+Pnnn5GYmIh3330Xs2fPxvTp05sfs3TpUvTs2RPTp09HfHw8/vWvf+H999/Hs88+y0vMNGl3IYnKA+EZ9yMocSyqDu9G8bolsBqq+Q6LV4QQBAy5D9Vr1sNxk7JL7oAQAtWgdNT9xu/W2x59I1F98DJn7dkSk5F/gN2rQ9bYWGQfc33tfqlCAKm05eiUQEBw8YINTXbAx5t2b1TX5QzvCd+pA3D1g5WwlHSu7xnBfROQveYi6kr4LbcLADXJvVFwoR7Vpdx9PxFCcCrThm/n0NrsfBs/fjzOnDkDs9mM8+fPY8aMGS1+7ufnh3nz5qGoqAiNjY24cOECZs6cyduVEfqtxgKJ2gsRvaZCHzMM5dvXonz3r3BY3TdhZZtQroR25DBUr93Adyi3pIjMQP2RLDjt/F1ytsX1RNW+S5y1p0rQo+w0u6UffRK9cOW067+c0oZocfKEDS+/aMA/3zHhH38zYcaTNXh6hgF6fwZDhtLqTlTXZlHFQP3QNBR+tQn1F7jfMKijGJEAmqfuxuGPDsDRxO+aJ0IIgh4fiPVf5HPabllkLPbtscJqpaPtVNvRpJ1FMp0fogY9DnlQGApW/AjD6SNwOt17USZbdNpYEJEIDee43fmzPYhAAHlGHOqPnOMtBoFSBgBoquVmow1GKIBQKoSllr0pOSKZEFaLw+WXglOHaNH/6QjoAwRQKAj89QxGjZFi/kId/vaP21cOoFrndDrhtNvhtNngbLK7/ULy7k7ooYX2ycdQvnw/TEe5u0p3pyT+HggbHY7TP2byHQo8oj3hdDhReIG7kW+GIbhrvBQb1tNNlai2oyUfOeCtioPX8GgUFxxAwbK58BkyDlK/QL7D4px/z7twdc1cSEJDIVDI+Q6nVaqEvihbOA+KPkm8Xf7yHBCFqn3Z8B2bzEl7Ab38UHS0FOHDgllrwzdYirKrFviFSl163IhkJR7rrYLZfO2E4Pp0GQeLi2s7o7q9R+G0WGCvrYfDVAenreXaDacTaPFxZ5hrN6cTcDj+V1z6OkL+dx8hIDIpBCoFBColGJUCArUKAk8thJ46MFJ6xYNtjEwKzeOPoWbpEjSZGuAxnJu+407VZgxC/dGlKDtZCt80P15j8XtsCNZ+vh0vfBV/+we7iHZsAn5+8TjuuVdKF6JSbUKTdo4QRoCAkIGweaci/8h6CJQqeA8Y1a02aGJEYujG34Xq1Wvh/dA0vsNpFSORQBoVBPO5HMgS+amFbInqgdq5SzhL2utjU1C5dherSXt0uhLZJ2pdnrQD10aG/zi3nWEIAJq4XyclHhD5e0AQqYRAoQQjdl1NaKfTCYe5Efb6Otjr6mBvqIO5shqW7Fw0VdfAYbYCcIKIRBD5ekHk7wOhnzdEft5gZK7/PHRXRCSEavpDaFy/CuWGenjf26dTJIKyR+/BqY8WYfC/hkGi5u/7UOGrhF+YHFn7axDfn5sNrMRSAdLSRTh00Iq+/bpPLkB1HE3aOSaSqxDR50FU1J5HwYof4Nl3GJTh7r1rqCtpZWFo0JxD/akzUKQk8R1Oq9Q9BqNy1TLeknZGKgEjFcFaUw+xjv1dWqX+WpQX1TdvVsQGQ1QSihftx6B7vV1+7C2bLcjoKYKXF90F9WbkUbEQSNlJkAkhEMjkEMjkgNe1HX2VrTzOYbXCVlWBelsJzGcvonbbPjgazSBiIcSB/hAHB0AUEgChlk5t6ijCMJBNnAzbns0o+WE7/J8c4faJu0AuQfoLGTj87wMY+O4QXuPVTh2Czf/YgNi+2v878WdfyJQELPjgFE3aqTahc9p54q2KQ9SQJ1Gfm43iDUthN3eusl13wi99DEz7DsBe554r5wVKJQQ6FSxX2d146Fa8Bkajas/F2z/QRdRBSpgK2avkoPSTo6qEnXnzZWV2nD5Fyzu6O0YshsQ/AB7BGfDpORYBEx9B0ANPQz/uIaj84tBUbYBxzVaUz/4Bld/9jNqdB2DNL6Zz6tuJEALx4LGQBHhcq+XeCaaKFfmkQBuhQ84mfufki5ViJA3W4eivlZy1qfEWQ61mcOUK7cOo26NJO48EIglCkyZCm9IbhSvnoSH/Ct8hcYIRCuEx4S5UrV7Ldyg3pek9FKZN+3lrvzE8DTVHczlrL6i3PwoOsXeSQgiBVC5AY53rK/PYIgORlcVffX3qzjBiMWRBofCKGwT9mGkImvos/EZMhtBTh4ajp1DxxXxUfLMQtTsPwFZaQWtbtxGTNgTyuAAUfL4eTrv7n/g4xo5B7pYc1JXyWwZSNHYg9i4vRZONu/csZmoMFvzEzs7RVNdCk3Y34CkJQ9TgJ2A8ewzlO9fD0dT1z7g1khAItVrUnzrDdyitEvl4w2ltQlO1kZf2GbEIQpUUlkrX1zdvTU1kKoqPsbudd0SyAldOu/4LOThGjqysrv83050I5ApofZPh23/itdH4ux6EwEOL2p0HUDH7B1QtWInGU+dvWFBLtUQSBkCVHoH8T9e6/e6pRMBA/fjdOPLxQV6vDggkQvSZ6I39K9ntD38vOE6JwgI7amvd/+SK4hdN2t2EQCxFeMYDkOpDULj8+26xo6pf+hiY9u6Hvd49RxjUo/rAtO0wb+17DYrhbIqMUCGBrbGJ1ZrJDdGxuHTS9Um7TClAQz0dfe3KGIkUOr8U+A+7D0HTnoN3xkjYKqpQ8c1iVM5ZjPqDJ+Bw036Ed9F9oO0fh6sfr4HD5t4nt5IAD+j7BOLCCn5LA9uHDMCxTZWwNHJ3onPf/TL8sqL7TJOlOoYm7W7G1yMZ4f0eRPnujTCePcZ3OKxihELoxo1BzfqNfIfSKolHNKw5RXBY2KthfiuNIakwHMvjrD3veE+Un2dvZ0XvWA/knWMnsZIrCOrq6ChVdyHy8IR34lAE3T8DfiPvg9PhRNWClaj4ZiHqj5zi7W/WXTnCe8JjeAry/70aDqt7J+7mQcNQeqwEhlwDbzEwQgZDpvlj1yLu1jUxAxKwY5uFlqulbokm7W5IJFcjauDjsFSWo2TzL116uoxWHg4IBGi87H7z+QkhUPRPRd3+U/y0LxJC5KGApdzESXuWuEQUHWbvS0ooFcJmdf0mSwAQGyvEhQtd9++EujmBQgnPiD4IvOcJ+I9+AM5GMyq/XYyqH5ej8Vw2Xcj6f+xBafAYk478j9fAYXPfqTKEECifmISjnx3mdbfU+t59cf6QAQ213PQrAiFBv/5i7PmNnnBSN9epkvY9e/ZgwoQJ0Ov1IIRgzZo1t33O7t27kZ6eDolEgsjISPz000+sx+kKhGEQEn8XlBFxKFzxA6wG9kZA+ebfaxwMm7fB6YYnJ8qoDNQfOsPb4jevQTGo5GiKjCrWH2Vnq1htwzdYirJ8i8uPGxsnxPnz7vf5+b3u1H/xRSBXwDNmAILufxre/e6CNbcQ5Z9+D+PGnbAbuDn5dWf2gFR4jEhBgZvPcRd7qRExNgJnF5zmLQZCCEY+GoBt84o4a9N/UgKWLqHTvKib61RJe319PVJSUvDVV1+16fG5ubkYN24chg4diszMTLzyyit46qmnsGXLFpYjdR0fTQJCe92H0k3LUZfDXQlALgmkMqj694Fhx26+Q7kBEQohjQ6BOSuHl/YbgpJhOHGVk7YYkQCMkIGtkb3kNzJVgcuZrp/XXhceggvn3XtRYnfsv/gk0urgkzEaQQ8+B3FoIGqWb0DlnMXXRt+7cQUae3A6NAPjkT/bvavKmHoMRM2VGhhyaniLoTqpF66erUNtNTd9i8pDBLWaQV6uew9AUPzpVEn72LFj8d577+Gee+5p0+PnzJmDsLAwfPLJJ4iLi8OLL76I++67D5999hnLkbqWROWJqMFPwnTuOKqP7u2SXzie/mmwFhbBVl7Bdyg3UKUNQO32I7y0TYRCiD2VMJdxU8XGP80bJSfLWTu+MTKRlaTdSy9GUaH7JiBA9+2/+EYYBlrPBARMfBS+IybDmluAik+/R92ew3BY3ftEjy3O8J5Q94xCwRcb3DZxJ4RA8fBEHJ19hLdpMoQQjHk6EJvnFnLWZswDMVg4n462U63rVEl7ex08eBAjRoxocd/o0aNx8ODBmz7HYrHAZDK1uLkDRihCeK9pcFjNKN2yEk67+17a7AhCCPwH3I3qtevd7qREqFaDSESwlbE7deRmPAdEoWrfJU7aaohNRuFh9ioXqQKUKC9w/fQYQghA0KUWcXWl/stdCJUq+GSMQcADTwNCISq/nA/Dmi2wG7kprepWYvpAmRSKwm82uV2fe53IU4WwkWE4v/QcbzGURfZE+dVGGCu4mWsekqhETk4TGhrc82SK4leXTtpLS0vh6+vb4j5fX1+YTCY0NrZeWunDDz+ERqNpvgUFBXERapsQQhAUNRKKkCgUrvqpy+2iKtZ6QBIWivrjJ/kO5QbqjIG8lX9sDEqBkaMpMvJQL1RfMbB2fEIIRGIGVrPrv5CCAgUoLOw6J7Ndrf9yJ4xQCM/wPgic9iyksZGoXrQa1T+vQ1MNP/sy8IUk9Ics1Bcl83fxHcpN1fUejLLMMpgK+DsBHf1UIDZ/z91o+6R7ZNi43vWDG1Tn16WT9o6YNWsWjEZj862goIDvkG7g65kCr/4jUbjyJ1gN/Iz+ssU3fihqDx2B4yZJCV8kIcGwFZXDYeZ+ZT8RCSFUSWCtZn+nQEIIpBoJzAb2vjBCE+TIPVfv8uPGxglxwc0Xo7KtM/Rf7oQQAo02FoGTn4Sidxpqlq5H9cLVaKrsugv//0iQMRSMSIDyVYf4DqVVhBAoH52AY7MP83ZFoCQ8A5UFZhjKuen/xUMTsHWrmZO2qM6lSyftfn5+KCtruatZWVkZ1Go1ZDJZq8+RSCRQq9Utbu7IQxiM8L5TUbJxOczlxXyH4zJEIIB21HDUbN7Gdyg3UPZPRd3+TF7a9ugfhar9lzlpS5/hiyIWd0e1RMfgyinXn4BYwgO6VNnHrtx/uSO1PByB9zwOj8T+MKzajOqFq7rNyLt42DhYywyo3sFftZZbkfjp4Jehx6W12bzFMPqpQGzhaLRdJGYw8Z/JnLRFdS5dOmnv27cvduzY0eK+bdu2oW/fvjxF5FpipQ6RAx9G+c71aMh3vzrnHaXTxMBuqoW11L12hVVE9ED94bO8jPaYQ9NgOJrLSVt10ckoPMLee+8d74mc064faQ+MkneppL2r91/uSuIfgICJj0IX3w81i9fA8MumLr/bKiEE8rsno/bEFZiOcLN+pr2sQ4cjf1ceGspd33e0RUl4BioLuRttV3mIOGmH6lw6VdJeV1eHzMxMZGZmArhWEi0zMxP5+fkArl0afuSRR5of/+yzzyInJwdvvPEGLly4gK+//hrLly/Hq6++ykf4rBBKlYgc+Biqj+xBbfZZvsNxGf++41Gz3r0WSBGhEJLIIJgv5HHeNiMVg4gEsJnYnzYk9deitpi9qTgSlRiNda6fey5TCtBQ7z6flz+i/VfnIg0IQuB9T0GaEIXK75bAtGl3l642QxgGqqkPomrLSdRnud+0KiJgoHp4PI79l59KXgAwZgZ3o+0U1ZpOlbQfO3YMaWlpSEtLAwDMnDkTaWlpePPNNwEAJSUlzV+AABAWFoaNGzdi27ZtSElJwSeffILvv/8eo0eP5iV+tghEEkQOeAS1F0/DcJq/Ds2VRBodxEEBaDjLX9WA1qhT+qNu1zFe2vboE46aw9xcUVH4yFFXyt6IltpTBGOl6xMgiRQwN7pn4k77r85Jo4tD4NRnIfT2QMXn89BwnJ+rbVwgIiHUDz+E0iV7YClyv3n90hBvqAJVKNibf/sHs6A4LAMVBWbOKslQ1B8J+Q6gPYYMGXLLzrK13QKHDBmCkyfdrxqJqxFGgPBeU5GXuRrVVis8MgbwHdId800chqtr5kIWGwNG5B6XCoUeOjisNthN9RCoFZy2bYlMR/2iX+AzMpH1tgJ6XpvXHjM+nJXjRyQrcOV0HdKH6Vx63KgoIS5dbkJSknt8Xn6P9l+dFyEEusAe0DyQhMpTO1H/5TFo7hkNcaA/36G5HCOVQD19Ggq/WoyQP0+GUCPnO6SWxo/F+Q8XwC/DHyIZ93/no58MwNYfizDlz2Gct01RnWqknbo1QhiEpt4LW00lqo/u5TucO8aIxFD17wvTbvd6LarB6aj97Tjn7QqUcjisTbCb2b9Eb4xIQfFx9haj1kbG4cop14/kx8QK3X5nVKrzYkRi+GSMge/I+2DasgfVS9bC0eBela5cQajTQD9jFPI/XQuH1b3WiTBSERIeTsLpHzJ5ab80sifKchthqqKj7RT3aNLexRBCEJJ8N2zGalQf+Y3vcO6Yp38azDm5aDK6TxUHmT4JjacvwengfvMLXUYoDMfYX5Aq8VKivpK9ZMQjUov8i65f3FcfGtylFqNS7kmk1SFg3EOQZySh4uuFqD96qstNmTHLIuE1sRcKPt8Ap5ttWlYZ2QsN5fWouczPFJ6RT1wbbacortGkvQsihCAkaSJsdSZUHd7Ndzh3hBACv753oWbjZr5DaUYYBrKEcDSe5b5ijy22B6oOcFP6UeWnQG0JOwtSBSIBHE1Olyc6fqFS5OZ2nQ2WKPemUccgcOozaCqtQOU3i9BUVcN3SC5lD0iFMjEYpQvdb/Ml2YMTcOLLY7ycUJRH90Tx5QbUG+hVPS6crfLHqQr9bW9nq7redLU/okl7F0UIQUjCeDTVmVDVyUfcpb4BACGwFLrPyIYysR/qfjvBebtCDw1shgY47eyP8ut7+KL4eDlrx/cJlqA837WbOAmEBA6as1McYoRC+PQcC5+B41G9eA1M2/fxchWOLUzqYDjtTlRvP8V3KC2IPFXQ9wnA5Q38lKi8++UQWFjY2ZmiboUm7V0YIQQhiRPQZDKg5sQBvsO5I34ZY2DYvJXvMJoJNWrA6USToZbzttWJgTCeYb/smCE8mdV57WGJCuScdf28dq2OoKaGfplS3BJ7+yDwvqdAREJUfDkfTRXuV32lo6RjJsJ05BLqz7tXuUPLkOHI3XoFZiP3u4fmB6TDw0/CebtU90aT9i7u+lQZc2khjOe4Hxl2FZFaC5GfHxrOX+Q7lGbKwT14GW23x6Wjej/7o0sSLyXqK9ib124IT0DOGRYWo8YIcZHOa6d4QBgGXjED4TdiMqp/Xou63w53ibnuhGGgmjYNpQt3w1pp4jucZkTAIO2ZHsj8hvvCABTFB5q0dwOEEISl34e6S+dQezmL73A6zDd5OIw7d7vNpWeZXzwaz17mPB5RgDca86s4SQaULM5r14aoUHzF9ScFTeF6XLhA55pS/BF5eCLwvqfgsFhR+fVCNNW4z0L6jmJkUqgemIKC/26Aw+I+f1/FfqlwNDlQdb6S87b3GqJb/LvJ5kB1qQWnd1fj1E5u+miqe6FJezdBGAbhvafBkHkIDfk5fIfTIQKJFIqkBNQdc49RlesLUs3nuH0/CSGQh3ujIaeC9bb06T6szWtnBNe6H4eLF5IFRctoBRmKd4Rh4J087Npc93kr0HDSvTaK6wiRrxd87u2Dwq9+dauEVHz/OGR+e4LXmM7tq8FXz5/H7CfOYul7OTi8vgI//jkbFw4ZeIuJ6npo0t6NMAIhIvpOR+WBbTCXuc+izvbwjuyHuiPH4LC5x0iPMrEvLzXbHbEpqD7EfvUaY0Qyik+wtxjVP1yG0lzXzkf18BOjtNQ9rsZQlNjHFwH3z4DlYg6qf14Hp61zn1A2+adAFuaLitWH+Q6lmUinhF9Pf+Ru4X5Aaq8hGlt/LMRPf70EOIHQZBUefT8ST38Wi+ieGmz8poDzmKiuiybt3YxAJEFEv4dQtmMdrIbOt1CKCIRQ9euL2r37+Q4FACDUaOBsssNucv3c7FuRRofAdI79Ey+Jtwr1Fa6vp35dOAuLUQkhAOBWI4FU98YIhfAbci+kcZGo+HwebKXsXyVjk6DPCJivlqM2032u2lqHjsDl9dmwNXA7oFNXXIv8rHq8tTYNf/o+AXc9HYgVH+UBAAZO8UO9sQm11e4xyER1fjRp74aEUgXCej+Akl+XwW7ufLv5eepT0XAhG3Y32YlQ2T8VdQe4LYdGhAKI1FJYa9g/WVB4yVhL3KtD45HLwmJUPz8G5WV0tJ1yLzq/FPiNfxA1yzag/mDnLQxACIHi3ikoX3EA1gr3mK/PiARImJ6Is/O57YslOikuZjVBqhQAAPzC5VDohCi+fK3P7DHaC9Ulri1tS3VfNGnvpiQqD/gMHY/idYvhtHeuy7WEMNAOHwrjDvfY8EMelIyGkxc5H9nV9YpAzWH2R7r8031ZmyKjCVKhNM/15dqio4W4fIkWbKfcj0ijReB9T8JWUo7qJWvhbOpc/e91jEQM1f1TUPjFRjhs7vG3VhnVG8arJtQWclfhRiQTQalXYefCYmTtr8FXz2dBIhNAHykHAIx7LgghCUrO4qG6Npq0d2MegiBoU/ugZPMvnW4qgU4bA1tpKewm7uuk/xERCCAO8oU1r5jTds1hqag5mst6O6bwRNYWoxKGAARw2F37+bOGBeDS5c6ZDFFdHxEI4NtvwrXpMl8ugN3gPmUU20Pk6wXPu3qg5MftfIfSTD71LpzkuARkjxczcK5AgZWf5EHrK8HD/4yEw+HEj3/JxssZB7Fm9lUYK6ycxkR1TTRp7+Z8tImQePuj6sAOvkNpN82IYTBs38l3GAAAVVIf1O05yWmbAqUMDosNDiu7yanET8Na2UcA0EfIUJzj2qlOgVEyXKZJO+XmdP6p8B0+CZXfL4Xlch7f4XSIIzQDRCRAzR73qI4jCfCA3FeBkqPcDaJoQrXI+FNvPPWfGFQWmvH1C+exc0ExrA12vL4oCQ2mJuxcxO2gDtU10aSdQkDIIDTVGWHK4jbpvFNaeTiaamrQZOB/TqVY7w9bSSXnl7rVyUEwsbw7KiEEUo0EZgM78zLDEhTIPevaOfNqTyGqKumcdsr9ib19EXDfk6jdsR+1vx3iO5wOkY6aiJqdZ2AurOI7FAAAM2EMzi44DYeduz7AXNOIL/9aBO8gKR58KxzGShtqyqwIilVi6HR/nPmthrNYqK6LJu0UCCEITZkE47kTaCx1r22qb0c7cjgM293jKoE8LQYNJ7jdsbUpOhXVXMxrT/NBSSY7U2Sqw+KRy0IFGa2OuPSYFMUWgVQK/YRHYDeYULN0HZx295gj3lZEKIBq6gMo+mYT7Gb+p4EIlFIEDQpGzib2y+JeV1/eAIfNjgffjEBQrBKTXglByZUGVBWZIRARaH3EtIoMdcdo0k4BAAgjQHjvqSjfsQ5N9fzPE28rjSQEjrp62Kr5L1+piOnJeRUZkZ6b3VFrwxPZW4waqEJZvusXo054K9Hlx6QothCGgW/vcRCHBqFyzmI4Gl3/N8EmoVYNnyn9UPz9Nr5DAQCYBwxFzqbLsDVykyh7xngCACoKrv3eGkxNiEhTY+0X+fj7qOMIS1ZBqRNyEgvVddGknWomlCoQ0mMSSjYu61QjPT7pI2Dcxv/cdoFCASIQwG5kb/73HxFCIAvyQGMhu5deZcEeMOSxs1iOMAQZI3WsHJuiOhuP0F5QjxmMiq8Xoqmqc02paPJLgVAtd4v57UQoQOz98Ti/hLtY/DL0+PbDSsz50wX8a9opBMUqMPWv4fjiRF9MeDG4eQ8JiuoomrRTLcg89NAkZqB81wa+Q2kzqW8AHGYzbFX8j7Yr+yWjbj+3o+26XuGoOcLuFBlCCIRSAWsblwy934eV41JUZ6RWRMJ/7AOomrcCltzOtaOmZNg41Gw/BUsp/ycc1XF9UHG2HI1V3OzpkfxEKsJGhcNTL8Hk10Jx72uhkKuFkMgFcDg6V4U2yj3RpJ26ga9XKgjDwHiO27JZd8InfQSMblBJRhaQhMZT2Zy2Wa9PhOkU+1/svkneKD1dyXo7FEUBIg9PBEx+Asb129F4ltu1MneCCAVQTZmCom82w9nE7xVbQghSnkrDqR+4KbIglAoRPCQUfs+MRvoorxY/Yxg6yk7dOZq0U60Kjr8LpqxMmMuK+A6lTaQ++v8bbee3egERCCAK9IHlaglnbTJiEYiAoKmB3QVg9eFxKD3Fzvbr+0xRzf/fZHWg3tgEh8MJq5lWgHEFa3kJzEUFaMzPRUPOZTTkXEZj7hU0Xs1FY34ezIVXYSkthq26Ck11tXBYLZ1u74auSCCTIeCex1F/4ESn2kFV6O0B3bAklP68h+9QUOSTAlu9DcY8A2dtXv/bsVlp/0W5Fl0VQbXq+sLUy9vnI3Dy4xBIZXyHdFs+6SNQuX0XvB64j9c4lIm9UbfnCCQP+3PWpiY9BMYTefAcEM1aG4pIX5SsZO+S94Wjtdi9ogJ5WfUQighK8yxI7K/G3c/pERInpyNVd6A+6yyIRAIiEIAIrm23DqcTTocDcDjhdDrgtNmuJetWKxw2K5w2G/D7xF0ggECugFCjhUijg1CrhVCjhVCjAyOkXyVsYUQi6Mc/hNLdq2A31UI9ejDfIbVNTF80ZS5B3ek8KJNDeQ1Fct8YZH63EYM/GMZJe4QQ7CoNx9m31+OlOQmctEl1D7SnpW5KKFXAe/BdKN2yEvqJ091+EY3URw/HsQY0GQwQarW8xSEO0MO6uQxOhwOE4eZiljUiFfVbtrKatDMiAZx2BxxNDjBC174uw1UTslZWQKZkEJWmhKXBgWc+CkfxFTOWfVyAe14MQEwPlUvb7E4C4kdCIJbe0TEc9ibYG+tgrTOgTmiApaQI9efPwmY0wGm3gxACkc4DIi8fSHz8IPbXd4qT/c6AMAz8hk5GxfEtqFmxEdrJYznrW+6E/O7JKPvuB0jDfCFU8fdZkPjpQHwUKMsshW+qHydtCqVCeAfJkHemFqFJtO+iXIMm7dQteYpD0eiTA8PJg9Cl9+M7nNvSDBsC487f4Hnv3bzFQAiBLD4c5nM5kCVFctKmyFuHujITnE4nqydXHlE6VF02wDvWw6XHPbs8G9EBEtz7UgAAYPmnhbh0og7Dpvrgyqk6ZB0y0aSdZ4xACEaphUiphQIAWk7ZhdNhh9VUDUtNOUwFeTAcOQCHxQxGLIbEVw9JYBCkQSE0ke8gQgh8Msag8uJeVC9aDY+H7nH7xJ2RSuD/6DAUf7cVwa/x1ycDADN+FM5+tQI+Kb6cDUCpJ/XDzoV78MS/aN9FuYZ7/8W34quvvkJoaCikUil69+6NI0eO3PSxP/30EwghLW5S6Z2NNnVHAeFDUJ9zsVPMb9dIQ9FUXQ17HXdlF1ujiM3gvIqMPNQTjVfZndPvn+LNyrx2hbesxfxPuUqAkrxr9Y59giQwVHSNTUm6cv9FGAEkWm+owxIQGD0cEYMfRtSoGQjtNxU6bSSsFWUoX7sCRfO/Q/m6X2A6eQw2A/8VRjobr5iBkEaFoXreik5RmteiiYXYT8t7GUihRgHPeC8UHeBuA0FVgBrWBjuMFfxvOEV1DZ1qpH3ZsmWYOXMm5syZg969e2P27NkYPXo0Ll68CB+f1kvGqdVqXLz4v5X37j7Fwx0RwiC05xRc2b4AQVOeBCOW8B3SLakHD4Rx9154jB/LWwwiDw/YTXVwWG1gxCJO2tRmhKHmaC7koV63f3AHVQYlonrTeiQ9EOPS4/okeiFzQTmYzwpRW9OEnDN1eOajcABAdIYKMqXApe3xobv2XwKxBAr/MCgQBoRfW6Rnq61BdUMuqndtRZOhBiIvb8gjYyALi6Aj8W3gEdYbNSIhqn5YBs8n7gdx8zUF4sFjUf3tXCjjgyDyUvMWh2PUSJz/eDEC+gaCcLRGZtBUf+xZVooJLwZz0h7Fv9OnT7f5scnJye06tnv/pf/Bp59+ihkzZuDxxx8HAMyZMwcbN27Ejz/+iL/85S+tPocQAj8/buawdWUimRJeA0ahdOsq6MdP4zucW9KqomAs3A1HYyMYGX8JgDw9Fg0nLkDZJ4mT9ur9E1G7bhECpvRkrQ2RWgZLretHjfTpPiAEMO08BYGQ4Kn3wxAULYfV4oBEyqD/3eydiHCF9l/XEEIgVnvAT+0B+PWA0+mE1ViJKmM2ylcvh6PJBnl4FJRxiRB5ePIdrtvSBfYAEYlQ+d3P8JoxFUTEzeBARxChAKp77kHhnI0I/dt9vJ18CuQSBPQLRN62HISNjuCkzcq4nrg0dzWarA4IxZ1ucgPVAampqSCEtGm6qr2dV8s6zSfIarXi+PHjGDFiRPN9DMNgxIgROHjw4E2fV1dXh5CQEAQFBeHuu+/GuXO3vkRnsVhgMpla3KhrvGSREGk8YDhzlO9QbokQAvWAfjDtu/nngguKyB5oOHyWs/YYsQhgALuZ3akkSh856soaXHpMwhD4JHjCL1SK4hwzdi2rQN65erwy5BTempKFlZ8Xwt7UeUsQ0v7r5gghkGi9oQ/pj4ihjyJi6KMQe3mjes8OFP30Laq2b4KlrJTvMN2S1jcZysG9UfntEjgs7j0FQ6T3hTIpGNWb+S1daRk0DJfWZcPRxE05RkII0kd64sQ2fssRU9zJzc1FTk4OcnNzsXLlSoSFheHrr7/GyZMncfLkSXz99deIiIjAypUr233sTpO0V1ZWwm63w9fXt8X9vr6+KC1tvUOPiYnBjz/+iLVr12LRokVwOBzo168fCgtvPqftww8/hEajab4FBQUBAJxOWm8VAIKiRsB07iSsBv53H70VnVc8GrMvwWHlby60QKGA0+GEvZ6b3fgAQJ0UBNMZduds+qV4o+y06+e1Z626jJO7DOg7zgPhyQp8NfMKpv05CHOOpOPUb0YUXeHufXQ1vvuvzoQRiuCtiUdYn/sROXoGPDxjYTx6AEXz5qB6zw7Yaty77+Ga1jMBquH9UTX352tlOt2YoOcwGI9cgqWEv98hIxYidGQ4rmy8xFmbjiH9cGhtOWftUW1XVFSEhx56CJ6enpDJZEhKSsKxY8dafeyzzz4LQghmz559y2OGhIQ03z744AN8/vnneOaZZ5CcnIzk5GQ888wzmD17Nt599912x9tpkvaO6Nu3Lx555BGkpqZi8ODBWLVqFby9vfHtt9/e9DmzZs2C0WhsvhUUXNtpMmf7T6gt5u6P3F0RRoDQjHtRtnXVtRrPbooQBqpeGag7wu9VAUXPeDQc5W4Bli08GYbjuay2YQhJYGVn1Kv7ijDpOT2G3u+DwZO94akXQyy51kX5hkhRdLnzJu0d4cr+q7MihIHCPwyhGfcicvQMSPwDUbVzC4oWfg/j8SNwWC18h+gWNLo4KIf2ReXcpXDamvgO56YIw0B172QUfbsVTgd/V84aeg9C7pYc2C3cvFciuQg+ITJcPcdvgQSqpZqaGvTv3x8ikQibNm1CVlYWPvnkE+h0uhseu3r1ahw6dAh6vb5dbZw5cwZhYWE33B8WFoasrKx2x9xpknYvLy8IBAKUlZW1uL+srKzNcz5FIhHS0tJw+fLlmz5GIpFArVa3uAFAXNx9MBVexJVtP6Khyv2rqLBJovaCMjoR1Ud+4zuUW/IMTEd95mleKyzIQ1PRcPw8Z+2JArzRwHIFGVmgDoarrp92oQ1RY9+F/y1SE0sZnNpjQHm+udPvzsl3/9UVEEYAb1UMwvtPQ/iQh0EIULJ0AcrX/QJzMXcVQdyV1jMBygEZqPphKZxN7pu4C709oO4VhcqNrY9mcoEIBYi8OxrZqy/e/sEuoprQF7uXcLdTNnV7H330EYKCgjBv3jz06tULYWFhGDVqFCIiWq53KCoqwksvvYTFixdD1M61I3Fxcfjwww9htf5v+prVasWHH36IuLi4dsfcaZJ2sViMHj16YMeOHc33ORwO7NixA3379m3TMex2O86cOQN///bvVCkSyRETPAaxkRNRcW4fru5djiZz9z1r9vfvjcbifJjLi/kO5aYII4AiJQn1mW1fye1qjEQCIpWgqYabucWEEIi9VLBU1LLahkAsgK3RtYmBf5o38vcXY/4/r+Ljp7MhFDEIS1Rg5ojTEAgJUgZpXdoel/juv7oagUgCP30vRI2aAb/IAajNPI6i+d/CdPKYWyesbNP6JEPeOw1VP7p3OUgmdRDqTubAUspfyU9T6gAU7MuHrZGbKUXqYA3qjU2orXbvKUzdybp165CRkYEpU6bAx8cHaWlpmDt3bovHOBwOPPzww3j99deRkND+3W3nzJmDLVu2IDAwECNGjMCIESMQGBiILVu2YM6cOe0+XqdJ2gFg5syZmDt3LubPn4/z58/jueeeQ319fXM1hkceeQSzZs1qfvw///lPbN26FTk5OThx4gQeeughXL16FU899VSHY5DItEiIngyvmF7I3bUIFecPuPU0EbYQQhCafi/Kt6+D0+6+X5Jeob1Qd+QoryO1it6JqD/C3RQZbY9QGI7nsdqGd5wHKi64dl5q+LBgxE4Mh6XBjohkBe6fGYiR032xKLsXnvtPBOSqzl320R36r65I6uGHkPS7ETHscTibbChaMBfVu7ahqb57Dqro/FMhT09A9fyVbvvdRBgGirsnofj7bbz1zYQhiJkch+yVFzhrc8B9vti/suz2D6TuyB8X41ssrU+jy8nJwTfffIOoqChs2bIFzz33HF5++WXMnz+/+TEfffQRhEIhXn755Q7F0qtXL+Tk5OC9995rntP+/vvvIycnB7169Wr38TpVyccHHngAFRUVePPNN1FaWorU1FRs3ry5eXFXfn4+mN/tEFdTU4MZM2agtLQUOp0OPXr0wIEDBxAfH3/Hsfg6AuGT+gTyao7i8pa50GfcBYV351v0dSdEchW06X1RsXcrfIbcxXc4rWLEEoiDgmC+kgNZJDclvv5IHpCEsp0/QjO6bSOqd8oclISG5avhO4a9UpP1oTEoO50NfVrr9cU7ghEyCB8WjAGTLC3KozmdTjidAMNRXWW2uFP/1RUxIjH8g/rCL7APKuuyUb52BQQKJTwGDut2pSN1gT3gsFhRs3Q9dNMmumV9f5GvFxTxwajZfgoeI1N5iaEmvi8qVs1D9ORYiGTsl8ysSeqFc/NWY9QTAWAE7vc7cVeGMiUY2e03lnM0Xvsd/nEB/ltvvYW33377xsc7HMjIyMAHH3wAAEhLS8PZs2cxZ84cPProozh+/Dj++9//4sSJE3f0N6RQKPD00093+Pm/R5ydfbIoy0wmEzQaDfqN/ieEotY/NFZLHbLzNkEokUOfMRaMUMxxlPy6cvhn6NL7QhYQyncorbLVmVCyZxV8Hn+EtxgqVi2B9t5hEHnfuMCFDTWffY/49yaztoGIvdGK8v+uxuh/D3T5sQeoXbPgu6HWjhnpx2E0GrvU3O72uN5/JT79AQRi991NlS2NlcUoOb8LYBh4DBwGsY/v7Z/UhVSc2g6nxQrNxJF8h9Iqp92OmjnfI/jVCRB5qHiJQXv2AGoLa5H4MDf7aTSs3Am/MBlSht36RLKxrgkv9zhE+y+NBoFfvNPGpN2MwpfeQkFBQYv3TCKRQCK5cVPIkJAQjBw5Et9//33zfd988w3ee+89FBUVYfbs2Zg5c2aLwRS73Q6GYRAUFIS8vLw2vY6FCxfi22+/RU5ODg4ePIiQkBB89tlnCA8Px913392mY1zXqabHuCuxRInEmClQ6aNwZesPqCtjt3qHuwlJmYjy3ZvgcNO5pCKlGkQiha3c9WUK20rRJxn1h85w1p4ywgf1V9grMSaQiWFrtLF2aZuOJVCuIPPSI3zgdOjjh6Nm3y6ULF8IS6n7rsNxNe+UEXBam1C78wDfobSKCARQ3j0RxXO38RZDTXxflBwp4mxuOzO8Hw6spuUf2fTHxfitJewA0L9//xY7TgNAdnY2QkJCAAAPP/wwTp8+jczMzOabXq/H66+/ji1btrQplm+++QYzZ87E2LFjUVNT07yZkk6nu23pyNbQpN2FAsTRSEp6CFUXj6Dw8Do4mtx7swtXEUoV0PXoh8r9/HW8t+OdNAjG3Xt4a1/mGwfzuRzO2muKSIThGLsnj2q9ErXF9S4/7uZsPRa9n+/y41Ldl1Tng7B+UxGYehcMB/ei9JclsFa5vmypO/LpPwHWwhLUH8nkO5RWiQP9IQn0hGEfd1W2fu/a3PZYXPyFm/alWikkcgEqC82ctEfd3KuvvopDhw7hgw8+wOXLl7FkyRJ89913eOGFFwAAnp6eSExMbHETiUTw8/NDTExMm9r44osvMHfuXPztb3+DUPi/GekZGRk4c6b9A3k0aXcxoUiGhOjJUPlH4vKW77tNeUhfjxTYqivctpqM1EcPu8kEe71rd/JsKyIQQOCpga2cm01FpNHBMGWx+7vwTfJC2RnXJz4KHznK8mn9bcr1JGpPhPV9APqE4ajavgnl61eiqdb9d429E4QQ+A+fgsYTZ2HOcs+9RsSDxqDq1+Ow1/GTyFbH9UXJkWLORtsHTvHF3hV0l1++9ezZE6tXr8bPP/+MxMREvPvuu5g9ezamT5/usjZyc3ORlpZ2w/0SiQT19e0f9OpQ0j5s2DAYDIYb7jeZTBg2bFhHDtnlBIijkZj4IEpObEVF1r5ucbk/OPVulO9Y77YVC1R9e6P2wCHe2lf0SkT9YW6qyBCREIQhsJvZ+xIyBCewkrQThvzf4lN2/mZo/0VJdb6IGPww1GkZKF+zHFU7NnfpjZqIQAD/cdNh2roX1gL3G1ghIiF8HxyIkkW7+WmfIYieFINLHNVtL4nIQO7pOjTZ3PO7sjsZP348zpw5A7PZjPPnz2PGjBm3fHxeXh5eeeWVNh8/LCwMmZmZN9y/efNm7uq07969u0Wh+OvMZjP27t3bkUN2SWKJEsmJ0+GwNyF350I0mV0/lcCdiBUaqGKTUXPMPT8DOs94NGZf4q1+scw3DuasK5y1p04MhOkse5vOSAO0MBawUw/eSy9BZTE708to/0Vd5yEMQcSIJyEJCELxwh9Qe+pElx1gYURi6Cc+hJplG9BUbeA7nBvYvBLhtDah4SI/V6drEvuh8EABmszsr80ihCBlmAdO7eDmyivFn5kzZ+KFF17AsmXL4HQ6ceTIEbz//vuYNWsW3njjjXYfr10lH0+f/t8mNVlZWSgt/d/lHbvdjs2bNyMgIKDdQXRlhDCI8B6AMr9i5OyYD33GWCh9b9zStqvw1/fBpd9+gDI6CWKtB9/htEAIc22zpZOnoMxI5759gQACnRq2ihpOqsjYwpNgOH4Iugx2Pm+EEAhEDJosdgglrq2hHhwrR/6FBngHtL6AqCNo/0W1hhACH20CvMbEoCRnD4oXzoXn8LGQBnS9Er4CuQJ+Y+9H2bwV8Hr+4TZV5OCSbOxElMxfiPB3poEIuJ29SxiCqLtjcGnNRcRNbf8mOu02pC8OfbgJPcZ4sd8WxZunnnoKMpkMf//739HQ0IAHH3wQer0e//3vfzF16tR2H69dSXtqaioIISCEtHoZWSaT4Ysvvmh3EN2Br10Pz+RHcP7cajRWFcMrrp9b1s69U4QQBKfdjfztaxA4+XG3e41eIT2Rv+EnKHqk8RKbolcCGo6cg2bcANbbEgX5wrCS3cV2XrEeqLxYDb9kb5cetyYoGqbMc+gx3HUnN7T/om6FEQgREDUMNn0GCo9sgkkohOeIuyCQyfgOzaXEnt7QTBqFqh+Xw+vZ6SAC99m0TKBSQDsoAZUbjsH77vZvPHOnjEn9UP7uPETfEwOBhN1tbMQqCZQ6EcqvNsInpGt9xqhrmpqasGTJEowePRrTp09HQ0MD6urq4OPT8f1N2nUqm5ubiytXrjQP8efm5jbfioqKYDKZ8MQTT3Q4mK5OKJIiMW4qmiwNKNi/Ag433kn0Tkg13pDpg2HKyuQ7lBswYgnEen9Y8q7y0r7MNx6NZ7mZIkMIgVAphc3UyFob9SHRKD9b5fLjekRoUXDJtXHT/otqC5FCjbC+D0CVmIqSn39C7dlTXW7KjFoRCXmvFNQsXe92r40k9kftyRzYqtiZenfLtgUMIu6KxJWNlzlpTzW2J/auoDukdlVCoRDPPvsszOZrC6zlcvkdJexAO5P2kJAQhIaGNu8iFRIS0nzz9/eHwI3O2N0VIQRR/kOhDkpAzrYfYWvompULAsKGwJB5CHaL+5W18oodgNr9B3lpmwiFEKgVaKo2ctKeJiUIptMFrB1fGe2H8nOuT9rFChEs9a5de0D7L6o9PGURiBw9A9byUpQuXQCboYbvkFzKIygDAq0atdv38R1KC4RhoBg/HiU/7eCl/dr0AcjbkQtHE/uLRL0SvJGfVYcmK12Q2lX16tULJ0+edNnxOjRpbP78+di4cWPzv9944w1otVr069cPV6/yM4LZ2QRKYhAdOR65uxahvrzrvWeMQAivfsNRubdtGxBwSaz1gMNmg93E/UgOAMh7xqPhaBYnbVmCE2HMZK/muUgtg6WWnQWjIikDS6PrFw3T/otqK0YgRFD8aASkjkH5+pUwHN7vttWxOsI7fRRshaVoPMtN1ZS2Egf6Q6hVovYkd3tbXEeEAgQPCUHeDvY3SSSEIHW4B07tpAtSu6rnn38er732Gr788kscPHgQp0+fbnFrrw4l7R988AFk/zfP7+DBg/jyyy/x73//G15eXnj11Vc7cshuSa70QXLywyg9tQM1ue3/5bk7L0U0mhrq3LJ2u6pvb9QeOsxL2zL/RDSc5qZestDXA43FBlbbkGrEaKxx/RWVwCgZii67fmoP7b+o9pLqfBE54gnA4UDJknldZtSdEAK/EVNQu20fbKX87RjdGsnwu1C+4gAcNu6rfTX2HYwrGy7B6WB/6pBjUF8cWkd3SO2qpk6ditzcXLz88svo378/UlNTkZaW1vzf9upQ0l5QUIDIyEgAwJo1a3Dffffh6aefxocffkhLprWTUCRDUsJ0mArPo/wsfzt2siU4aTwqdv/qdvMmddoYNGZf5qX8IyMWgZFJYDfWsd4WIQRirRzWKvba8knwYmWKTFC0HPkXXJ+00/6L6ghCGOjDBiIoYyLK166A6cQRt+vXOoIRieA/bhqqF62Go4G99S/txUgl8Bidisq13A+uMBIR/DL8UbifvamF14lVEii0IpTnu897T7nO79dOXb/l5OQ0/7e9OpS0K5VKVFVd+5LeunUrRo4cCQCQSqVobKQfvPZiGAHiI++F3dqIwsPr4HR2ncuvYoUG8uAImLJO8B1KC4QwkCfEo+EcP1tny3vEoeEEN21rUoNhPMXel48pKAblWa5P2isC41CQ7fodbGn/Rd0JidYbkSOfQlNtLUqXLUBTPfsn32wTqjXQ3jsGVT/94lbTf5zRfVB3Nh+2au6nMtoGD0X2qguctKUc1QP7V9LR9q7GZDIhOzsbZ8+ehVwub7GO6vqtvTqUtI8cORJPPfUUnnrqKWRnZ+Ouu+4CAJw7dw6hoaEdOWS3RwhBlH44pFofXP3t5y5VWUYfMhCGzCNutyjVK6wX6o4c46VteWASGjKzOWnLHJQI4yn25rUrInxQddH10wXUgUqU57t+l0raf1F3ijAMAmOGw2PwCJQuW4iGHG6mu7FJLY+ALCUOxjVb+Q6lGSEEfg8PQcmC3Zy3LVBIoY3UoSyz9PYPvkM+Kb7IPWWCw975r9xQ12RmZiI2NhZjxozBhAkTEBkZiS1b7nyNX4eS9q+++gp9+/ZFRUUFVq5cCU9PTwDA8ePHMW3atDsOqjsL1WRAF5GO3B3zYbd1jW21GYEQnv2GoXL/Nr5DaUEgk4NRyGEr534uJyOVAoTA0cD+iYzQSwtLOXtVihixEHabw+VTBRgBw8qXGO2/KFfRkQBEjHgCtZnHUbV9E2+7LbuKZ0RfOK1W1B89xXcozSyKKDBCAeovsLe78804RwzD+aXsFw0ghCCunw7n9nWNtRIU8Oc//xlhYWHYt28fjh8/juHDh+PFF1+84+N2aPcArVaLL7/88ob733nnnTsOiAICRFEQJEuQu2M+Qoc+DKGk82+84K2IgdF0BJbKMki8fPkOp5lXXH9U7z8Iz3smct62PDUGDScvQtk/hfW2xF4qWMpNkPioWTm+0k+ButJ6qPyVLj2uxksEY6UNGi+Ry45J+y/KlQRiCcL6TUVp6XEUL/4BPhOnQKRlf8djtvgOnITClT9AHOAHkd49+mrp6HEoXbAI4e88CMJwtymeyEMFqU6KmkvV0EWxu8O3YHgfHPxqO5IGu9dO4lTHHD9+HFu3bkV6+rXd13/88Ud4eHjAZDJBre7493CHRtpDQ0Pxz3/+EwUF7C/S6K78EAx9xljk7pyPJnPnnzMJAEFJd6Hit018h9GCzD8IttIyOKw2ztuWh6Sg4SQ3cyavzWtnb4qMT4InK5ssBcXIUXDRtfPaaf9FscHPrweCe92D8tXLUH+Jm79rNhCBAP5jp6J6yVo4zO5xtVegUkLdMwo1O7mvssaMGYZzi8+w3o7MUw5HkxO11dx/F1GuV11djcDAwOZ/a7VaKBSK5vVUHdWhpP2VV17BqlWrEBYWhpEjR2Lp0qWwWNzjj7sr8WnyR2DvicjdubBLbMIkUXlCrPNEfS43c7nbSpGWgvrMTM7bFSgVcDRa4LSxv37BHJAA42n2Li8bAmNRft71tYaNgZEoyHbt4lDaf1FskWi9ETHyCdSdPYXq3dvcalFnewhVamgmjkD14jVuUyFHkD4ENTvPwN7A7d+qRO8Bh92JulL2B896T/TB4fV0QWpXkZWV1aImu9PpxPnz57mv0/7KK68gMzMTR44cQVxcHF566SX4+/vjxRdfxIkT7lUlpLPztvogJvpu5O5aBGu9ge9w7lhg9AhUHdzpVl9mnoHpqD+RyUvbsvhwNGaxv4mH0FMDayV7FRjkwZ6oyXX9Lq+6cC0KL7k+aaf9F8UWRihGaJ/7wcgVKF2+EHZz56xIpFHHQBzgh7pd/Owe/UdEKIDPfX1Rtnw/520LRw/GhWXsz203pfbGmd9q3OZEibozw4cPR2pqavOtoaEB48eP575O+3Xp6en4/PPPUVxcjLfeegvff/89evbsidTUVPz444/0g+cicqUP4mLvRd7uJZ1+xF0glkEVkwTj6SN8h9KMEYkh8vaGpYj7TaDkYSloOM7N7qgSHzVrC1KJgAEcTpdvRiL3ksJQzs6Oq7T/othCCIE+uB+0/QajZPE8WMrZr0DCBq+U4bBk58CS4x5TyZr0qbAWVcFaZuC0XUVMAIx5Bljr2OmLrmOEDAKi5cjPqme1HYp9v6/Hznud9utsNhuWL1+OiRMn4rXXXkNGRga+//57TJ48GX/9618xffr0Ozk89TsyhRdiYyYhb/di2Bq5r1nrSv4BfWA8d9KtSkB6RvdB7cFDnLcr8vZCU1k1J1ceNClBrM5r14SoYbjq2pMCQghAAAcLOxPS/otim6c4FGFDHkLl5nWou3CO73DajRACv9FTYVi1CfY690gkZWPuQumi3zhvN2pSDC6tuch6O9KRvbF/VRnr7VDsaq0mO2912k+cONHiknJCQgLOnj2Lffv24fHHH8c//vEPbN++HatXr+7I4ambkCt9EB01EXm7FqPJ7B4daEcQRgDP3oNRfXg336E0k/ro0VRZxcuCVHGYHta8EtbbaQxMZHVeu3ecBypYmNfupZegqth1I1y0/6K4JFKoETH8CdRnnUHN/t863RUcgUwG3f3jUL1wlVvELg7wAyMVoSGb2yuj1TG9UXSoEI4mdgdYdBE6lOc1wmZ1nymkVMddL3yQn++aAbMOJe09e/bEpUuX8M0336CoqAgff/wxYmNjWzwmLCwMU6dOdUmQ1P8oVL4I6DUeebsXwW51n5Hq9vJSxcFcVgRbrevnQXeUPDkJDafZrxJwQ7vhyZzsjirUqWGtYm8xVU1AHCs7owZEyVw6r532XxTXGIEQoX0fgNNmQ8X6lZ2unrtKHApJVBjqdhzgOxQAgHTEXSj9eQ+nJxFEwCB4cAiu7sxjva2UYZ44t4fWbO8Krhc+CA8Pd0nhgw4l7Tk5Odi8eTOmTJkCkaj1+skKhQLz5s3rcGDUzfk0+cEvbTRydy2Eo4ndOXZsIYQgMGE0Kvfe+Q5hruIZmI76k5mctysJCYblMjcbh4g9lbBUsDO9SqrXwZjv+mMbAqJcmrTT/oviAyEEgbEjIAuPQsnPP8He2LkWqHolDIH5Ug6s+dyv/fkjgUYFebQepiPc7kTb0GcQLm+4xPrJgn1AHxzfUslqGxQ3XF34oENJe0fm4bjKV199hdDQUEilUvTu3RtHjtx6QeOKFSsQGxsLqVSKpKQk/PrrrxxFyi4/ZyB8k4Ygb/cSOB2da9TmOrlXIJwOOywV7rFISyCRglEoYavktrMkDAOBToWmKgPrbWmSg2A8zc6iMsIQEIa4/PKxLlyDosuuq9VO+y+KT75eKfAYMhIlP/8Em6HzjKYSQuA36n7ULN8Ah4X/wSJRv5GoXHsEzibuvv8EUjE8YzxRfordOecSjQQpw+gmS12JqwoftDlp1+l08PDwaNONLcuWLcPMmTPx1ltv4cSJE0hJScHo0aNRXt56XdMDBw5g2rRpePLJJ3Hy5ElMmjQJkyZNwtmzZ1mLkUv+TBh04anI37/SLeYadkRg3ChU7NvKdxjNPGN6o/Yg95Vt5OmxaDjB/oYs5sAEmFhK2gFAF6Z2eelHmU6K2uo7q2VP+y/KnXgIgxHS/36UrfwZljL3GLRoC6FCCc24YahZuo7vUMBIxNAOSUDV1kxO23UMG4KLK9ifzmjt25f1NijuuKrwgbCtDc6ePbujsbrMp59+ihkzZuDxxx8HAMyZMwcbN27Ejz/+iL/85S83PP6///0vxowZg9dffx0A8O6772Lbtm348ssvMWfOHE5jZ0uQPBE5OhNKT26Ff/povsNpN4nKE0K5Eo1FVyEL4G8E9DpZQCjKDm2G0+EAYe6ouFL72vVPQMW+xVCP7MNqO0IvLWpZmh4D/G8xqmeUa7dxJwyBw+EE08EtzLt7/9VYVQKRXAVGKAYjunYjhLvPN3UjicYTYUMfRt6mJfAYOgqykDC+Q2oTjS4OZtVl1B89BUXPFF5jIYkDYPjqW+iGJEIgl3DSpshLDYFEgNpCE1SBHd+OnuoeTpw4gXnz5uHnn38GwzB45JFH8Nlnn7VYR3XPPfegZ8+ebTpem5P2Rx99tP3RupDVasXx48cxa9as5vsYhsGIESNw8GDrmz8cPHgQM2fObHHf6NGjsWbNmpu2Y7FYWiwSMJncvy56uFc/XLj6KyovHoJXDLtJHxsCY0Yg98AKBE15ku9QQAiBLDYajRcuQh4fx1m7jEQCOJ1wmK1gpGJW2xKpZbAZGiDSyl1+7Cp9HKy/7kHsxAiXHtc7UIzKQgt8gqUden5377+MV84ATgccTVY4bFY4bBbgdxfnCMNAqFBDpNBArNJCpNRCpNRBrNKCMIIOvGKqLURyFcKHPYrcvUtgb2yAMjaB75DaxKf3XShc/h0k4cEQerr2BL09CMPA++7eqFh9CH7TB3PWLjNyMM4vO4her3W+71uKWz179sTIkSPxzTffYNKkSa2uo2pP4YM2J+03YzabYbW2nN+mVrv+7LOyshJ2ux2+vr4t7vf19cWFC61PKygtLW318aWlN78c+eGHH+Kdd96584A5FhM8BucuroBIroEmiLtk0xVEcjUkXr6oz82GIiya73DgFdoTxfvWcJq0A4AsMRKNZy9DkRHPajvqxACYzhbCc4Dr32upvxaVha6vUBMQIUPh5cYOJ+030136r4ig4RCKbv7eORx22MwmWBoNMDK1aCgrgO3KaVhra+B0OMEIBBBrPCHR+kDmpYfU0w+MkN2Ty+5CIJYifMgjyDu4HI6GBqjT2zbixiciEMB31H0oW7wG3i8+wulVyT9qCkpDw8ZjaDLWQ6hRcNKmPNIflctNsNZZIVbSvwO2ictEEEhbLxrwe3az+63vy8nJue06qvYUPujQX1p9fT1efPFF+Pj4QKFQQKfTtbh1ZrNmzYLRaGy+FRS4x05wt0MIg/ioyajI2o+GSm4qkbhSQMRQVB3e5RZz84VKNZwOB+x17JVHbI0sJAkNJ9nfvMMcEA/TWXY+I4Swsxi1OiAKRS6qIEP7rxsxjAASuQ5qzzAEKZIR7jcIMZF3IyntMST3eBzxSQ8i0DMDAqkcprws5G9djJz1c3F16yKUn9yN+pJcOJq43+Ogq2AEQoT1nwpzUT4Mh/fzHU6biD29IUuORe12fuMlhMDn/v4oW85tOcqI8VG48utlTtukOp+hQ4eiqurGUsgGgwHh4eHtPl6HkvY33ngDO3fuxDfffAOJRILvv/8e77zzDvR6PRYsWNCRQ96Wl5cXBAIByspartouKyuDn59fq8/x8/Nr1+MBQCKRQK1Wt7h1FoxAiIS4KSg8vA62Bvef1vN7QqkC8qBw1F1yj10DlT3SUXes/eWY7oTIwwP2KiPru6OKArzRmO/6TZCu04WpUZPn2s+fLkyDwsuuSdpp/9V+jEAIudoXgbIERAaNRGLqI0ju8QRiou+GB/FDXeFlXN26EDnr56J431oYr5yG3dK5ShryjRAGIRn3wlZdiZq9O/kOp008YwbAcjkP1iJ+F9PaPBNgqzDCWs7dvh+G+D4o+O0qnCzs1kx1HXl5ebC3si+DxWJBUVFRu4/XoaR9/fr1+PrrrzF58mQIhUIMHDgQf//73/HBBx9g8eLFHTnkbYnFYvTo0QM7duxovs/hcGDHjh3oe5NV1n379m3xeADYtm3bTR/fFYjECsRG342re5bCYb+zihtc04cOQs2xfXA6+d8JzsMrAQ1Z7Fdz+SNxqB7WPHbrIBNCwEhFsDeyU7bNK9YDlRdde1Ig00lRZ3DN55n2X64jFMuh9YlCRMAwJKU+iqT0xxDo1RO2hloU7FqBnPXfoeTQr6grvNzp+iM+EEIQnDoRDosFVbu2usWVx1shhMBvxGQYlm3gtPRia2QjRqNs2T7O2iNCAfwy/FF8pP2JF9X1rVu3DuvWXauytGXLluZ/r1u3DqtXr8a7776L0NDQdh+3Q3Paq6urm4f11Wo1qquvfUEPGDAAzz33XEcO2SYzZ87Eo48+ioyMDPTq1QuzZ89GfX19czWGRx55BAEBAfjwww8BAH/6058wePBgfPLJJxg3bhyWLl2KY8eO4bvvvmMtRncgV/nCO2EACvb/guCBD4CQjlXc4JpAJIEiIha1509DHZ/KayxEIIDYzxeWomJIAvSctSsPTUBD5kVIwgNZbUcV54/a8yXQpru+Yk+VXyzs2/cjZpxrj0vInVWQuY72X+whhIFc7YtQ+AKefeB0OlBnKEJ56WWUn9wNgVgCdWg8VCGxEEq5mX/c2RBCEJQ4FoXnt6Fq+yZ4jhjr1n24UK2BYkAGjBt3Qnv3SN7iEAfrYf7NCktRNSQB3NQ4tw4YjEvfr0JAH3b7a6rzmTRpEoBrf89/LIQgEokQGhqKTz75pN3H7dBIe3h4OHJzcwEAsbGxWL58OYBrI1harbYjh2yTBx54AB9//DHefPNNpKamIjMzE5s3b25erJWfn4+SkpLmx/fr1w9LlizBd999h5SUFPzyyy9Ys2YNEhMTWYvRXQSIoiHV+qD87G98h9Iu+qB+qDl5kPUpIm3hGdETdUeOcdqmJDQElkvsr6OwBMTCdI6dee2yAB2MBa4vK+mpF6Oq5M6vDtD+izuEMFDpghChH4rkHo8jOnoinA4HCnevRO6G71F5eh9sDeyVIO3MAuNGQiCToWrrBrcfcdcFZaCpvBLWPH7XU8lGjkXZ0j2ctSfSKiBSiFBb2Lmmo1LsczgccDgcCA4ORnl5efO/HQ4HLBYLLl68iPHjx7f7uMTZgd7gs88+g0AgwMsvv4zt27djwoQJcDqdsNls+PTTT/GnP/2p3YG4K5PJBI1Gg36j/3nL6gvuyOl04lz2L9CFp3aqijJFeXsgUKigSUjjNQ6n04m8Vd/C75knQQTclb0rX7oAno9NgEDl+pKM1zmbmmD6ZiHi/nkPK8cveHsx7po9BOQOR8V/r2HdPvgESZA6WNu2x9faMSP9OIxGY4u53d2x/+o94V2367/sdhtKrNkwXj4FR5MNmvBEaMKTIJDI+A7NrRRe2gmH2ez2I+72+joUrZ4Pnz89DtJKWTuu1C3/GV4TekIW5nv7B7tAQ3YxRMcOI+NPvV16XGudFUuHL7qh/+pOrvdf4X//AALp7fsvu9mMnPf+2qXfsw5Nj3n11Veb/3/EiBG4cOECjh8/jsjISCQnJ7ssOOrOEEIQHzkJp88sgETtBanGm++Q2sQ/sA+yd/8AdXwKrxvAEEIgi4lCY/YlyONib/8EF5ElRaLxzCUo+7G3cQkRCuF0OuG0O0AErn+P1YFKmIrqoAlSueyYNf6RsF3ObnPSfjO0/3IPAoEIgbIEBCYloMlmRnFjFvJ3/AxGKIZHfG8oAyLoBlAAAqOGofDCNlTv2grPYe67gZ5AoYRqxAAY126H9r6xvMUhHT4GZcvWIvQvkzlpTx6tR8UKA2yNNohk/J2sUO7j888/x9NPPw2pVIrPP//8lo99+eWX23XsDvWICxYsaLGBR0hICO69917ExsayVn2B6hhGIEJszGQU7F8JRxM7Cw9djRGKoYyIQ+2FM3yHAs/gHqg/fpLTNmWBiWg8dYn1dhTh3qi/Us7Ksb1jPVB5wbWLUXVhGhRfMd/xcWj/5X6EIimC1elISn0UkeFj0VCSi5y136Ls2HbY6unUg8DYkYDTieo9O27/YB7p/FJgN5p4nSYj9NRBqFWg4SJ3C0TDRkUgd0sOZ+1R7u2zzz5DfX198//f7NaRnbo7lLQ//vjjMBpvLK1UW1vbvKiKch9SmRa+yUNRcHAN36G0mX9gXxhOHuR9LqdIrYWj0QzH75I8tgk1atiNdazP61cnBMB0jp0vtmq/aFRcrHHpMWWeUhgq7vzEk/Zf7k0i1yIiYDiSez4FuU8QivetxdWti1Bfksd7f8CnwPjRcDQ2oOYAd3O2O8JnyCQYVm7itZqMZMholP/CXd12U0o/XN2R260/n9T/5ObmwtPTs/n/b3bLyWn/iV6Hknan09nq3LrCwkJoNJqOHJJimV4YAbFCg6rsI3yH0iYCkQTy0Ei3qNsuT05E/emznLYpDvWHNa/k9g+8A3U+cajNYqe8pDzECzU5rq2ZfL3PudMvRtp/dQ6EMPAXRiIh6UFEho+BMfcMctfPRc3F492yfCQhBEHJ49FUUwXjsUN8h3NTQoUSioE9Ydq8m78YtGqIvNSo52i0nREL4RnnhYrT7Fy5pDqvfftcW4a0XXPa09LSru14SAiGDx8OofB/T7fb7cjNzcWYMWNcGiDlOpH+Q3H67CLIPAMg9wzgO5zb0gcPwOW986GMSuB1AZaHfwoKty6GqmcPztqUhSSg4VQ2JOHs/Z4EChma6i03TWLvBCMSwGl3/ZUCrbcYxkobtN7t3zqc9l+dl0SuQ3ToXbAHWlFgOonc9XOhDomDR2JfCEQSvsPjDCEEwel3I+/gcjAyGVQJ7K17uRO6oAwUHf8JtuIyiPTcLAj9I8ngUahYuRKKv97HSXv2wYORvfxX+KTw83op9zRs2DAEBARg2rRpmD59OhISEu7oeO1K2q/XnczMzMTo0aOhVCqbfyYWixEaGorJk7lZ/EG1HyEM4mMm48yhxYgY+QQEYveqJvFHArEUssBQ1OdcgDKCv+o3AokURCxBk9EEoYabFenSsFAY97I/f1Xio4al3ASpr+tHmGWeMjRUNkLu5bpqIPoIKYqumDuUtNP+q/MTCMUI9egNZ8+eKGrMQt6v86DUR8AreUC3qTpDCIPQ3lOQs2cRBFI55BFRfId0A0II/Ibfg5LlS+H98uMgDPcLigW/G21XxLA/SCX20aDeYkdjdSNkHt3js0jdXnFxMZYuXYqff/4Z//rXv5CcnIzp06dj2rRpCAxsf33/diXtb731FgAgNDQUDzzwAKRtKMFDuReRWAF9xljk7/8FoUOmu3UJMQDQhw7ElQOLeU3aAUCZnoL6E5nQDB3ESXtEIAAjEcNe3wiBgr0vAHVCAGrPFbGStHtF61CZXYNgFybtBv8IFF/JRUKf9p880f6r6yCEQaA8EQEZCSixZePqloWQ+wbBO3VIt0jeiUCAsAHTcGXXT2CkUkgDgvgO6QZCtRbytETU7ToI1fD+vMQgHjSS09H2yPFRuLLxEhIfplWoqGu8vLzw4osv4sUXX0Rubi6WLFmC+fPnY9asWRg0aBB27tzZruN16PT30UcfpV94nZifMwgyD39UXTzMdyi3JZTIIfXxR0P+FV7j0HnEofHCRU7blCZGwHyO3dfd6B8L0zl25rUb/KNQ6eLFqLpQNYqvNN7RMWj/1XUQQqAXxyCpxxNQ+Icjb/N8lJ/YCYetc1TKuhOMSIywQQ+hcssGWCsr+A6nVR5R/dB4NhtNVa7tB9pKqFVD6KFEQzY7fdwfVUb2RPGhIjgddEEqdaOwsDD85S9/wb/+9S8kJSXht9/av/llh5J2u92Ojz/+GL169YKfnx88PDxa3Cj3F+EzEIarZ2E2uP/CGX34YFQf4bdiAhEIIfT0gLWMu/dLFpDAeulHoY8HzCUGVo6tCPdG1SXXflkr/RWoLLqzhIz2X10PIQT+wkgkZzwFsdoTuRt/QNXZA3Da+atgwgWhVI6wQQ+ifO0KNNW6X2lMwjDwHXY3DCs28haDZNBIzirJEAEDvx7+KDnGzUkC1Xns378fzz//PPz9/fHggw8iMTERGze2/++iQ0n7O++8g08//RQPPPAAjEYjZs6ciXvvvRcMw+Dtt9/uyCEpjhHCIC56EgoOrnL7SgwimQpCpRrmMu7q7rbGI5zbmu0iTw80VRpYLSNGCIFAKkJTg+tHJoUKCax1NpcekxEwcNjv7P2g/VfXRQhBoDwRyRlPAgyDnHXfwpR7rkuX4hMpNQjuNxllvyyBw8pdadq2Env7QujnjYaT/FQCE3por9Vtv8xuNa7rrP0G4vK6bE7aotzfrFmzEBYWhmHDhiE/Px///e9/UVpaioULF3ao8EGHkvbFixdj7ty5eO211yAUCjFt2jR8//33ePPNN3HokPuWoqJaksi08Irth5Ljm/gO5bb0kYNRfbj9l5JcSRYYCnMut7WiRXpv2IorWW1DFeePugvsjAyJZELYGlybuMuUAjTUdnwElfZfXR9hBAjR9kRij8dRX5aPvE3zYK4u4zss1kh1vtANGYGylUtZ39+hI7x7jELtjv1wmPk5qZAM5m60XeSpApxAY1UDJ+1R7m3Pnj14/fXXUVRUhA0bNmDatGmQy+UdPl6HkvbS0lIkJSUBAJRKZfNGJePHj+/QcD/FnyBZPJosDagtvsx3KLckUXvB6bDDZnTtLpvtQQgDSaAe1kLuRvxlyVFoPMPuFBmLPoa1eu0ekVpUXXFtvXb/CBlKcjo+r532X92HQChGVPAoREVNQNmxbSjauwZ2y52tiXBXXrJIKOISULl5Hd+h3IARiaEeMxjG9fzs6Cr01EGglKIxl5sTt4gJUbi8gf1drSn3d31ajJeXl0uO16GkPTAwECUl1y41RUREYOvWrQCAo0ePQiLpPjVzu4qYsHEoObkVTRb3HhnwjxzE+9x2XUg66k9mctae1DsG5vN5rLYhDtWj7jI7c/W9YnSouujaEy19uBTFOeYOP5/2X92PVOGBhMRp0IQlIm/TT6jJPtklp8z4+WWAkcpgOLSX71BuoPVKhN1ghLWolJf2JYNGomLVQU7aqgjriZKjxXRBaje1bt062Gy25v+/1a292lXy8bp77rkHO3bsQO/evfHSSy/hoYcewg8//ID8/Hy8+uqrHTkkxSOhUIqAnneh8OAahA55kO9wbkrhE4KirG2wNzZAIOv45aU7IfULhGX/BlY2JGoNI5EAdjuctiYQUYf+XG/fhlgEp83Oymuq8IlFw772lbS6nTKfaFj3Znb4+bT/6r78mDD4ZDyB3NK9uLrpJ/gPmAiJ2pPvsFwqMG4Ucg8sRf3FLChi4vkOpwXfwRNR8styeL/8GOflhkU+njADsBRXQ6Jnd8E5Yci1BalHi6Hv7f4bGVKuNWnSJJSWlsLHx6d5f5DWEEJgb+di+Q5lAf/617+a//+BBx5ASEgIDhw4gKioKEyYMKEjh6R45usIhEGhRU3uKejC3HOXPQDQpvdDzckD8Oo3gpf2CSGQhATDcjUf0tAQTtqURAfDfCkfsvhw1tqQ6rUwFxkgC9S59LhibxUqyupdekxNsBpn8jo+N5b2X90bwwgRoR+KRnUlLu9dA4V/GLxTBoMIBHyH5hKEEIT2mYIrO+ZBqNFC4qfnO6RmQrUW0rhI1B84AWV/7naYvk4yaATKV/2GoBfHsd6Wte9AXF60jibt3ZDjd+tKHC5eY9Kh6TEffvghfvzxx+Z/9+nTBzNnzkRFRQU++ugjlwVHcStKPwyV5w+gyVzHdyg35a2KRX3eJTia+Kt44xGShvoTmZy1JwuIQ+MZdtccqOL0qD3v+nnthBCAEJdeJhbJhLA0dnwhKu2/KACQKb2QmPooRAo1cjbMRWMVN9VFuMAIhAgbOA0VG9fAXu9e/blXwmA0HDoBRz330zHFQf6w15phq6plvS2RpwpOJ9BY1TXXUFBts2DBAlgsNw4yWa1WLFiwoN3H61DS/u233yI2NvaG+xMSEjBnzpyOHJJyA4xACH3P8Sg85H4Lma4jhIE6Pg2mcyd4i0Hi7Q9rSSlnVRrEgQGw5rGbUDT4xqD2AjttqAOVMBa69kuSEZAOl36k/Rd1HSEEQao0xCU9gLIjW1F+YqdbVl/pCKFMCe9xk1C2eplb1asnAgHUE4bDuH47L+17T+qFirVHOGmrz5/7QaqjG7l1Z48//nhzsYPfq62txeOPP97u43W4eoy/v/8N93t7ezcv8KI6J1+7P0QKNQxXz/Idyk35+fSA8dxx3haSEUIgDQ2B5Wo+N+0xDBiZBPZ69kZshN46WFjaZMkrWoeqS649tpdejMrijtWWp/0X9UdiqRoJydMhlCmRu/F7WAzuucNoe+mghyq1h9tVlNGoY2Cva+BlUapVFw9zfgWa6tgfAb9gCQNhuJ27T7mXm60VKywshEajaffxOpS0BwUFYf/+/Tfcv3//fuj17jN/juqYKP1wVJzbiyaza+ciuwojFEEeFI76XP42sNAFp6L+5CnO2pPGhcGclcva8QkhIGIh7BbX1lQHgGqfKFRlu3ZnVL9QKUpyO/alS/svqjWEEARreiAm7l4U71+HyrMHukSFGV+vVDASKYzHD/MdSgs+A8bDuHoLL++x17gMVP16nPN2ATS/Xlu96/tayn2kpaUhPT0dhBAMHz4c6enpzbeUlBQMHDgQI0a0f21ehxaizpgxA6+88gpsNhuGDRsGANixYwfeeOMNvPbaax05pNvLPrMK0Un3QCiS8R0K6xiBCPqMsSg6sh4hg6byHU6r9CH9kXNkOZThMby0L/H2h3XPtSkyhOnQuW+7yPRxMB39DYqe7FWDUEb5ov5SGdSJgS49rjzUC6UrXVurvdo3Es6cy0gZ1P7ndsf+i2o7iVyHxNRHkFu2D/lbFyFg8GQIpfxUq3KVwITRyPltIcTevpAFh/IdDgBApNVBHBqExhNnIe+RxGnbTYGpqFv7Lbwn9gYjFbHa1qkKPVK8i2E2mHFl4yXUZFej+EgxvBO9ETE+Cn49/CFWilmNgeLe9aoxmZmZGD16NJRKZfPPxGIxQkNDMXny5HYft0NJ++uvv46qqio8//zzsFqvXaKWSqX485//jFmzZnXkkG7PP6gnzh1bgJDokdB6slfFw134OgJRI5HDWJAFTZB7lQ0DAKFUCaFcAUtVOSSePpy3TwiBNCwElryrkIaHsd6e0NsLttIqVtuw+keh9nyuy5N2RiyE3eLaObXaUBVKDnSsVnt37L+o9iGEQbjfIJQHFOHqpp/g12csFP7s/52zhRAGof0ewJXt8+A3ZTqEKjXfIQEAvFKGoXDZd5AmxYIRs5s8/x4hBB4jUlC94xS8xmWw3t6FFVk4+fVxgCHQhmnR5y/9IPOQ4dyiMzDmGpD0mPtWbOvqioqK8Oc//xmbNm1CQ0MDIiMjMW/ePGRkXPtcOJ1OvPXWW5g7dy4MBgP69++Pb775BlFRUbc87ltvvQUACA0NxdSpU122B0iHhggJIfjoo49QUVGBQ4cO4dSpU6iursabb77pkqDckU+9Dhn+k1FeeBy5FzbB4XCfhT1siQocgbLTu2G38bP19O34hQ1AzbF9vLWvC0pFfeZpTtoihECgVaGpxsRaG5KIQNRlszPHVKwQwVrnusvBSl8Fqko6Nqe9O/ZfVMf42AOQkP4oqs4eQNmx7XA6O+8iVYFYiuA+96BszXK3WZjKiERQDe2H2q3cbwbljOoF4/7zcNrZ/Z1aiqpRc6ka4xfejWk7HkLslDiUHS+Bd5IPUp9JR/5vV1ltn7q5mpoa9O/fHyKRCJs2bUJWVhY++eQT6HT/K33873//G59//jnmzJmDw4cPQ6FQYPTo0TCb2zZoFB8fj8zMzBvuP3z4MI4dO9bumO/our5SqUTPnj2RmJjYLXYSFAokSPEYA6Vaj6xj82Exu/aSv7sRCMTwTR6KkhNb+Q6lVXLPANiM1bCb+SmpxXUVGVliBMznclg7PiOXwl7PzgmaR6QW1VcMLjseYcgdz4Xtbv0X1TFCkRRx8Q9AKFPg6qb5aDK7987RtyL18IM6OR1V2zfxHUozrT4Vlpx8NNVw+31KBAKo+8TAeOACq+3YzVYUXaiHKvDa1Y2gwSEoOXqtvK46RANGyMBs7PgOz1THffTRRwgKCsK8efPQq1cvhIWFYdSoUYiIiABwbZR99uzZ+Pvf/467774bycnJWLBgAYqLi7FmzZo2tfHCCy+goKDghvuLiorwwgsvtDtm9ifjdkHB9nDE64biYuYyGCqv8B0OqwJEUbA1GNFQVcR3KK3SJPeE8fRRXtomhEASGgxL/o1/kGyQ+sWi8Ry7nzeRVg5rjesXIHtEalF12eDSY0pkApgb3GPEkOraCCEI0fWCT48RuLrpJzRWuGd/2Ba+vulw2KyoO+8eFcIIIfAZeBeMq7dw3jaTPADV2zJZXQwrj/CDvd4MU4EJZqMZp3/MhL5PAOxWOxgBg4yXe0Eg7hobe7kLk8nU4tZanXQAWLduHTIyMjBlyhT4+PggLS0Nc+fObf55bm4uSktLWywY1Wg06N27Nw4ePNimWLKyspCenn7D/WlpacjKymrnK+tESXt1dTWmT58OtVoNrVaLJ598EnV1t940YsiQIdeqYvzu9uyzz7okHoXUCz38JqO08BgKLu/qElUGbiY67C4UH93glpeGfbSJqL10jrfYPDicIiPUaGCvqWX1s6aM80ftedeXPazwikbVJVdXkJGgNK9zjFC5W/9FdYyPXY/41IdQemQzqi+0/9K2uwhOmwjjkf2wVlXyHQoAQOIfACISwpLLzQDIdYxUAlm4H+rPsduuz5R+2PbWYWx6cgMMV2oQ0C8ItkYbqrOroNSrIJJxN5+/M5KXA/LSNtzKrz0+KCgIGo2m+fbhhx+2etycnJzm+elbtmzBc889h5dffhnz588HcK08MAD4+vq2eJ6vr2/zz25HIpGgrKzshvtLSkogFLZ/WWmnSdqnT5+Oc+fOYdu2bdiwYQP27NmDp59++rbPmzFjBkpKSppv//73v10Wk1AgRqrHWAiEElw4uQRNTZ0jgWgviVQNTXACqi66V8kwACCMAIqwKNTnXOSlfYmPHtbiEs5O2oS+Hmgqr2bt+GbfaNRddH3SLvHToK7EtSP4fqFSlF11z/UWf+SO/RfVMSKJAompj8BSXYqiPavdZn54ezACIUL63Y+Kdb/AYXOP0oPefe+Ccf12zgfAhH2GonIjuydgHsNTEDLrPgz5aDh0kTpkr76AzU9twK7Xt+Pop4dwceV5VtvvbgoKCmA0GptvNysw4HA4kJ6ejg8++ABpaWl4+umnMWPGDJdusjdq1CjMmjWrxQZLBoMBf/3rXzFy5Mh2H69TJO3nz5/H5s2b8f3336N3794YMGAAvvjiCyxduhTFxbfeel0ul8PPz6/5pla7dtU8IQQRgiToQ/sh69hCmBsNLj2+uwjV9UJN7inYGt1rS2wA8AvoA0PmIV7aJoRArPeHtZibTXlkCeFoZHFeuzjIDw25rh99I4TA6YRLv5DLfaJQkuv+J8ru3H9RHUMIg6jQsVDow5C3eT7sls63Vb1YpYO2/2BUblrLdygAAKFSBUlYEP5/e/cd3dZ1J/r+ew56BwmABMHeiyhRVLVkWXKRLffuJB5PHGcySSbvJpkkTuba8+bGiWcynpXJ3Jsy8+6smRTHSdzi2I57kWTJsqxiFapTEsXeO0GwoL8/aNGWrUbpHACk9mctrGWC4N4/wNDGD/vs/dsT+xKbwGqddmS9lmCnepMhABqzgd0/30nHtg5CoyEya73c8ad7mP9ADU1vNtK5c/YuuUo1drv9lNuZ9ixlZWVRVXVqdbzKykpaW6cOTvR6vQCfminv6emZ/t25/OQnP6GtrY38/HyuuuoqrrrqKgoLC+nu7ubf/u3fZvrUZkfSvm3bNpxO53QJHoC1a9ciyzI7dpx99vcPf/gDbreb6upqHn74YcbHz76JKBgMfmo91PnwjrmZ776Oo3XPMDqc2Et8iSBJMr4lN9L5wavJDuVTdCYrssFIaCg5l3rTchYwvv9AQvoyZlSoe8iSVkM8GlNltsviMTHer1xy48i1zYrlMbNh/BIuTI55PplL1tL8+uMER1JjqclMeOyVyAYDowfrkh0KMFUCcnT9e8Qjib16Ybj8Kvr+vFPVPvpf2UW0uJibn7iNa39xPXq7gcNPHSKtNJ3c1Xm0b5l7eUOqu/zyyzl69NSr9MeOHSM/Px+AwsJCvF4vGzZsmP693+9nx44drFix4rz6yM7OZv/+/fz4xz+mqqqKxYsX87Of/YwDBw6Qm5s745hnRdLe3d1NRsaptbi1Wi3p6elnXVf0F3/xF/z+97/nnXfe4eGHH+Z3v/sdf/mXf3nWvh577LFT1kLN5EU1G9JZnHUnrcc30NeVmCQukTKjPiSNlkC3ejO9F8pbeDlDe95PSt+mnHyCza0J6UtjsRAbG1f1ErLR52SyY1jxdtNLnAwcV65do9PA6FBqXNo/m9kyfgkXJiOaTUX1Z2jf9ByBztQbG88lp/oG/Lt2EB5Sd6b5fMh6A5bLaglsUTeB/iR9no9w3wiRUTWvmMSJ+j9q31XhYqR5GICMmkyCooJMwn37299m+/bt/PM//zMNDQ08+eST/Nd//dd0VRdJkvjWt77FP/3TP/HSSy9x4MAB7r//fnw+3/ThSefDYrGwatUqbrnlFlavXo3T6eT111/npZdemnHMSU3aH3rooU9ttPrkrb7+wssxfeUrX2HdunXMnz+f++67jyeeeIIXXniBEyfOXIHj5Nqjk7fTleo5G53GyKLM2xnub6B1Dm5QLc29lq49byWszOH5snjyCPZ1EQtfWO3uiyFJMtr0NML9iZlp02VnEO7oVa19a0UWoyqsax/OKGZQwQoyJ8eIZJmL45dwYQxmJ9W199O/fwuD9cmpZnWhZI2WvBV30/vScymxPj+96DLG9xwkNpHYJNZ1wyIGXt+jWvv2paUEDrXSd6CX438+yr5f1lF0w1RpwfRyF8u/d34zt4Jyli5dygsvvMBTTz1FdXU1//iP/8hPf/pT7rvvvunH/N3f/R3f+MY3+MpXvsLSpUsJBAK88cYbGI3G8+qjsbGRmpoaqquruemmm7j99tu54447pm8zldSk/cEHH+TIkSNnvRUVFeH1euntPTVJiUQiDA4Onve6IoDly5cD0NDQcMbHGAyGT62HmilZ0jDfcQ0S0Hj45ZSsunKhdHozzsIFDBxLvU2p9qpF+A+pN+iejXlBNeMHDiWkL1NVkapLZCY8ZQSOKn/IkqXQw2CjsrWYzXYNYyMRRds8X3N1/BIujEZrYN78v2Cir5OeXW/Pqgkbg8OFbeFiBt9J/pkckixjX7ca/5ubE9pvJHshgbom1ZbmGHPdZH5mFe/8005aN7dSdns5mbVT//41eg16mzgrIhluvvlmDhw4wOTkJEeOHOHLX/7yKb+XJIlHH32U7u5uJicnWb9+PWVlZefd/t/+7d9SWFhIb28vZrOZgwcPsnnzZpYsWcKmTZtmHO/M680oyOPx4PF4zvm4FStWMDw8zO7du1m8eDEAGzduJBaLTX+QnY+Tp1JlZWVdULwzIUkSZYYlNOoPc2zfc5QuuAtZnhu1WAucS6jb+2uchQvRGkzJDmdapruG4+/+BufCyxLet9NeRuu7W3FctUb1vozuMvrfexb7deo8T21mOmMdypZnhKka8JNDys6eefONdDdPUlxjVbTd8zGXxy/hwkiSTFnRTTT1vEvnlhfwrbodSZ4Vq1DxZi6mseEpJppOYCosTmosDtc8OjZtJzLsR+tMzBdPSZZxrKpk+L0jpF1ZrUofzlWVOFdVUmVtEWUeLxHbtm1j48aNuN1uZFlGo9GwatUqHnvsMb75zW+yd+/eGbU3K0aTyspKrr/+er785S+zc+dOtm7dyte//nU+97nP4fP5gKnTpSoqKti5c2ot3IkTJ/jHf/xHdu/eTXNzMy+99BL3338/q1evZsGCBQmLvUiqIj2zkvq9TxKNpv762/MhSTLehWvp3pv8WZmPk7V69K4MJrvbk9C3FtloJHqO2tuK9GUyEZsIqrZESZJlkCViasw4SRIxBY8N9xYYU34z6mwev4SZkySJIu8aTBm5tK5/klg0OVeCLkT+0jsY2Pgm0YnknvoqSRLuldfjf+ntxPY7byVDG/erfpVEJOyXjmg0is1mA8Dtdk9XDMvPz//UJtjzMSuSdpiqolBRUcE111zDjTfeyKpVq/iv//qv6d+Hw2GOHj06XV1Br9ezfv16rrvuOioqKnjwwQe56667ePnllxMee16kgKy8yziy+3dEwrOvNNjpZMkFhCf8TA6rt7b6QnjzVjC0NznlH83VVYwfnPkJZxdCl5NBuKNPtfYtBW7Gm5Vfo2/PtjLaqVy99m5PKV0pnrRD8sevvvZ9jPSdYCLQT2yOTB6kujz7ItLKF9Py+uOzpiSkRm/Eve4m+l55PunLe4y+HGKhMOFu9ca5T5INeoyFmYzXq1t+cV+fj/BEOOmvsaC+6upq9u3bB0wtcfzxj3/M1q1befTRRykqKppxe0ldHjMT6enpPPnkk2f8fUFBwSn/AHJzc9m8ObFr4s7GN+lFU7qWw7t/T+Wiv0CntyQ7pItWkr+O47tepWjtF5MdyjRTmpewf4hocBKN4fw2iiglzV1F+4ansV22TPW+TJWFTB5uRJ+bee4HXwBruZfAsW6sJcq2n17sYOD4MI5cmyLtOXJtHHkm9ZP2ZI9fkiQTGGoj2HmQ0MQIsdjU7K/eaMNo9WCyujHbvRgt6UjSrJnLSXk+XRmaJWaa3/gt+df9JVpT4pdxzVS6Np8xVz2j+3ZjX7jk3H+gIs+K6+l7+XXcX743YX3qlq1h4LVXsFTmqNrPwd/uJ2dVLp7qjHM/WJi1/uEf/oGxsamJqkcffZSbb76ZK664ApfLxTPPPDPj9mZN0j4XZAbSkCtv4sieJ6lcdB86vTnZIV0UkzkdgyMDf3s99pyKZIczzVFVi/9IHWkJXtuuMZogGiMWCiHr9ar2ZXCVEXjvWezr1Kk4MOYqI7TrTbixRtF2Bz0lmI4ehKuVKUVosOmZCCS/4kWqy48XoTUawQikTd0Xj8cJhkcZDw4yFAzQdeI9JscGIR7HYE7D4vRhS8vD7PCKRP4iZMZy0Ky6nZY3f0fetfehs6T+5uCcirU0vPVLTIUl6BzOpMWhd3mQjQZCLR3o87MT0qfWlUY8EiM8OIouXZnJhdMJLb2Mpje3iKR9jlu3bt30f5eUlFBfX8/g4CBpaWkXVP1MjMQJ5vHbKKq8kSN7/kA4lNx1g0oozlpDz/53iMdSJ3HKcC1gtH5/Uvo2lZcycfSY6v1ozCZi45OqXV7VuByEekcVb9ec71K8ggwoe9LqpUKSJIx6O+m2Aoo11cxPu5alOZ9lSc5nKbUuQ6e30Ne+lyPbHufw+7+m5dAbDHXXE40Ekx36rOMOeiirvIPWt35PaFT5Td5Kk2QNuctvT4llMu5l6xh5dWNC+1S7/COAMc+Dv3WEWGTuVJcTzk96evoFlysWSXsSePz2OZO4a7VG0opq6T+aOiUgZa0efbqbyd6zHxGvhjTvfMYPJGhde5abcJc6teElSULSaoiFlf0yprUaCQWUXVNtS9MRGJo9m/1SnSRJmAxp5EWLmGe/kqU5n2Wx726y5SImAn0c3/UMh7f+itbDbzHS3zi91EY4O5PNQ3n13bStf4qgfyDZ4ZyTMS0TU2EJIzuTc2jdSTqHE60rjcnj6pW5/aSQp5qxI+2Kj3+flLXMR+cOddfPC3OLSNqTxOO3U1h5A0f2/GHWb07Nt9cy3LSPaDh1ZuC8eZcxnIQNqTpHGtGRkYQcPmWsLGSyvlm19s2F6mxGlbUy0bByr09mvoHultR5781FsqzBYcmmVL+Ixb67WJxzD1nk4e9vpH77Exzd+Qd6W3cTDiq3yXguMlpcVMz/LO0bn2VyKLU28Z+Or3AV48eOEBpI3GbQ03EvuRb/65sSNusvSRKOlRWMbD2iaj8Ti1fS9NbsO0VXSB6RtCdRht9BYcVU4j6bLzlLsgbPvNX0HtiU7FCmmdJ9hIb7k3JCqqEgj2Bzi+r9GD1lqibt1tJMAseUP2TJkWPF367c0htvgZHultTfjDqXyJIGpzWXCvMKluZ8lmrXtQA07nuBw+//ms6GLQQnlF8GNRcYzE4q53+Ojs1/Ijic3GT4XCRJJm/5nfS9+kJST8HWWqzo87OZPKT+0sOTpKoVDL1zQNU+dC4b4bEw4TFR0Uk4PyJpT7KMUQd5JddQv/fpWX2ZOVtfRnjcn9SB/ZPs5QsYPZr4te1O33zGD6p/OqrGaiXt7mtUa3/MVUbgeI/i7aYVOxVd197tLqVHJO1JpddZKKSCRd47WOy7C6PFRcuh1zj8/q/pbtwmZuA/QW+yT824b3ou5ZfK6O3pWKsWMPz+u0mNw73gakbf3pKw2XbZaMCQ7WK8oUvVfvKuzKd1s/qTPMLcIJL2FOAdd+PNW8qxfX8kHk+dpHcmJEkib9U9KXX6X4anFv/huoT3a8zMJtSRmPX0uox01dqe2ozqV7zdQXcxQyeUS9rtOTZ6xPKYlCHLWnLD+dRm3Ept1h1o9CZO1D1P/Y7fMdh1OKU2rSeTweSgovoztG94JuU3p3qzlzPR0kioP3lXBjQmE4byYibqErNnCEC3bDUDr+1WtQ9/9XJaNzWr2ocwd6ROhnWJywnlkuYp48TBl5K+W/9CWbvPfaUgkYcxaXQGtFYbocHEftBIkoQ2LY3wwGBC+/2ki30fTW9GVfhkVFOei6Em5ZJ2g03P+OjsvUo1l2k1egpipSzOupP57nVMBPo4tPWXtBx8nYmAOpuoZxOD2Ul59T20rX+KcGA42eGckSRJ5C29nb7X/5zUq6nueWsIvLMtYTHovB4i/nEio+rtO9NYjGj0GiYGZvfeNiExRNKeQgriZRgt6bQcS+zRzUo6XeIengjQtfdtjjz/rxx54SeEJwIJiycjdznD+z9IWH8nmeZVMnFY3U1MAPQZpv8zGhgn1N5LsLGDUGv3BZeU+jhzvouJFmUv32stBkJiDeclR6+zUGZYwrK8+/DJBbQf3ciRbY8z2HVk1l5hVILRkk7ZvLtoXf8U4THlr2wpRW9Px1JazsgH25IWg2wwYKwqZWJv4mbb09fWMPj2PlX7KFhbSMvGxFXHEWYvkbSnmFJdLfFYlM7m5JbZuhjxeJxYZGoDaDQcpPHtXzPcvJ/clXeRVriQweOJS6ItGflMdLYkfHYozVnOxLHjCelrfG89Xf/0S9q//b/p+dcn6PnJEww+9Saj7+wiNn5xa70tZZmqrGtXuoKMxaElMCxm22cDSZJwWvNY6LmJBZk3Me7v5tB7/03nifeIhC/NvQkmq5vSyttpffsPRIOpO+OalX85Y0cPEx5O3nIeV9UVBDYlbrY9mreI0V0NxGPqXQEfKF1Gx/vtqrUvzB0iaU9BlZbL8Q+3MdiTgJlaFYzufI+hpqkNoBqdAVfZUozOTKzeIjKqVzN4Ym/CYpEkCUtBKWMtiUmgT5L1BojHiYXUrV4T6ullfHc95toK0u5Zi7m2nKwf/g3uv76NsV2HGdt5cRtix9JLVUnala4gk5lnoKf10kz4ZjO91ky5aRlL8+5Fb7BxdOfvaT74GqHJxF2NSxVmWwZZK26m5a3fTU96pBpJksldeit9r72YtGWcsv7D2fYErW2XZBnrgnwC+9SbCZf1WoxpJgLdl977XpgZkbSnIEmSWJB2HR3N7xPwq7tzXQ06nYXBho8276SXLGZyuJdoOIjZnYPZlc3kSOLWmWdmLcF/QN3NRKdjLClh8niDqn2M1e1H0mlx3LQK+3WXofW68L+2Fa3LiWVJFZPHLq4qgdaTRrBH+dJ9aUUORde1Z+Yb6W0Vm1FnK1nSkB8rYWnO50jLLOfE3udo3PdnguPDyQ4toTKiPjwLr6T17adSdsOuMS0Toy+HwP7ETb58kqtq9dTa9kRVklm4isG361Tto2BtAS3rxRIZ4exE0p6iNLKWhRk3ceLQS4QmlT9OXk0e3wLigQATQ1MztIHuJkzpXmIfHr6UvexmdCZrwuLRW5xEQ5NEg4mdiU3LrGL8cL2qfegzPUR7PzpVVzabiEemlolo3U5igYs7cVeSJJAk4lFlL0UPuksYUrLso6uEHpG0z3qSJOGb8LIk+x7ydBU0HXiFhj3PMTme2tVVlJSlKcZZupD2d/6YskUJfKVXMbJ7B9Gx5MwMywYDhsqShM22a512iEN4UL3P4r7CJXR9kPhTvIXZRSTtKUyvtVDtvo6j+54hGk3Ny6Vn4s6aT+euV2ne/BQtW57B6MxEZ7YDEI9F0ehNCY3HXlHD6JG6hPapT/cQ7utX9YPXUJBPLBSi9xfP0Pvzpxl6bj32dSumflecQ9pnr7voPkw5aUy0K5s0mfJditZqt+fY6BXLY+YUhzWHxVl3UGSupfnAKzTu+/Osm8C4UDnmakyZeXS9/3KyQzktWaPFfd2N9L/1atJicM9L7Gx7+rXqbkiVtBosXiv+ttTdjCwkn0jaU5zV6Can+EqO7XtuVlVYyClcTXH+Wgy2dAqv+ktsvlI6PniFA089StPG39G1923G+loTFk9G+nxGjx1MWH8n6b2ZhHuUXxN+ktbpxH3PXeh8bvT5WWQ++JfoczKBqWoywWMtRMcubmObrdLHZLeyS2S0FoOipwCa0gyMDIiKNHORzexlcdadeHIX0rDnj7QceoNIOHU3ayqlIH05ss5A395NyQ7ltNK1+Ug6HeNN6i4BPBPZYMRQVsTkfnWvZp4U9i4gsK9Z8auOH1dwbSHN6xtVa1+Y/UTSPgv4JjJxuIpoPb4h2aGcN1mjxWr3kVG9mv767Rz9888YOrEXT9XlFK19AID27X9OXDxaPRqLldBwYk8fNFVWMKHyEpnJxiaMFYU4b1uDIT9ruqqCbDYR2LqPyfrmi2o/VLmc9OVFCkT6CbKkWEUGJcpbCqnNG3CzJPsz2N2F1O/4HV0ntqbsum+lFGdfw+RQDyMnEn+y8/nInX8Dg++8Pb0kL9Fc1asZ3fh+QmbbJVnGWlvIaJ166857chbRs6dbtfaF2U8k7bNEsTyP0KSfgZ7E1adVwnjdHrRGC/P/4gcUXHkf4/3tyFo9WbXXQjzGWG/ijm92VC9h5GBiN6Q67SVMnlB35iSwew/d//xrhl/cRHQkMH0qrcZqwlRTRqgpNddJ2rwWRruUO95eZ5AJBWfP1Shh5iRJIieYw7Lce5E1Og5t/SXDvceSHZZqJEmivOwOBus/YLwn9Y661xhM2BcvY2jr5uT0bzShL8pj8nBiqoNpFlzO4HoVl8hoZOx5DoabhlXrQ5jdRNI+i1Q7rqaz+X0mAsk7Snqmetp2k16yCJ3ZhtVbBPE4Q011ADgL5ic0aXcZixhvPZHQzV2yVgeyhtikeuutrbULMZWXgSzR82+/Z+JAwykzX5GBYdX6vhhphXZFK8h4cgz0t4vNqJcCSZIpkuexOOduhnuOUb/jd3P2hFVZ1lBZ/Vm6tr1GyJ/cU5ZPJzNjEZNtzYSHkhObe/4aRjdsTUhfGocNSVJ3Q2pkyVJaNogqMsLpiaR9FpFlLQvcN3D8wAtEI7MjObE5c5g8MnV1IOjvJx6LYkrLAsBdsZKM6tUJi0WSZEy+fCY6EjtjZSorYULF0o9aVzqy0YDz1jU4br6Cgd+/Rv9/v8jgs28TPNaC7aolivYXi0SJjE29/2LhC1+eMOgqYqhJuU1XGbkGUUHmEqPVGJnnuIqKtDW0HHyV1iNvEYvOvUO2tDoT5dX30LbxmZQ7fEmSJHIX30L/G8nZNKsxW9BlZRBsaE5If+nXLlR1tt1SlUvvvh7ipGblICG5RNI+yxj1dvLK1nJs/59SthzYx2XlLcc/3MqR539C08bfIesMGJ0ZAGj0xoTHk+lbzMjBXQnt0+mpUnVduz7LS7CtAwDLsnn4fvg36LI9RHoHMS+dh7EsX5F+/AfbOf5vb7D/G7/n0EN/ZPvtP+fYv7zKWFPfBb0XTbkuRWfa+1xF9LaJCjKXIovRxaKsuzDbvRx+/1eM9J1IdkiKM5rTyFpxM61vP0k8mlpr+Q1pGeg9GQSOJH6zP4B74dX4396SkL7CmfOnNqSqdEKqJEs4C52MNA6r0r4wu2mTHYAwc1njHgLOHDqatpBTlLiZ6gthtmVSNv9u/EPNRLPTMbtzkGRN0uIxOjMJDw8Qj0aQNIl5++vTXEQGB4nH46psmJRNJhxXXkE8HEHSaZGNepy3rlG0j5b9EUJvHUDWa7GUZBILRij59nWMN/fT/F+byLv/cmyVvhm1qXdZmBhQbtbQnmOlb4+Yab9USZJEfrQYX3Y29R1b6G3dQ0H1jegMlmSHppiMqI9Q5VI63nuRnDV3JTucU2RXXEPDm7/EXFyGrNcntG+tzY7GaiHU3oU+J0vVvqZOSC0gsK8JW60KG/SB8JKltG3eo0rbs5GlK4pWd+4vqpGLuPI7W4iZ9lmqRLuQwEgHI4Opv/ZNb7ThzpqPJSM/qQn7SdaiSgInElMm7CRdRgbhXnX2IsTjceyrVyENn5qcxGOx6UoyF8v/1naMWU6Kv3ktpQ9ejzHTTuBYD56rqzDluRipm3n5TqW/wNiyLPS1z67zDATl6bRG5qdfi7dwGUd3/p6BzuTM/qolxzQPrdHCwMH3kx3KKWStHuflaxjasjEp/bsWXc3om+8mpC+5Rt0NqebyHPqPzJ69a0LiiKR9lpIkiflp19J89C3CIeUqcKjJ2h1RLIm8GBkZC/En+KAlU5V6pR8lSTrt8hRJlqcryVwsjcN6yvp1jVnPeOtU+UyTz0lo8MLegwaHgYkhZZa0aHQaopHUXzImJIY34GZJ7mcZHWzh+O5niYRSay34xSjOuYZARwOBztSq6e1xVBHs6SI8mNjSugB6l4d4LEakT/0NsVqnnXg4SmTk4k6cPhNJllj2nctUaVuY3UTSPotpNQaKKm/i+IHnZ8X6doCOD15hYii5dWh1ZjuxUJBYKHFLKZy2EiYb1fuAPTlrPVnfTDSg/AeJoTiHsRO9tP3+fU78/G0Gt58g49p5AFiKM7CWZl5Qu2kFdoZblD0BcLb8WxDUp5F1zLNfSWbBMup3/JahnqPJDkkRkiRTUXk3PTvfTKmKMpIkkVN7E/1vJ+ekVNfiq/G/lZjZ9rSr5jO0Sb2rOE26UtXaFmavWZO0/+hHP2LlypWYzWacTud5/U08Huf73/8+WVlZmEwm1q5dy/HjiannmigZow4caYV0NqfWpdIzyXMtpWf/O8kOA1tZdUJPSJV1eojFVT+EJNTWzeRR5avjGOcVkXXLQgLHp053zf7MMiyFHgDs1TlkXFd9Qe36FmciaZQbhuzpWvyDqVc9RIxfyZU15mFJzmcZ6q7nRN0LRKOz//RcjdZA+by7aXvnWWLh1FkWZkzLQOtwMt6Q+Pr5Rl8O0WE/UX9A9b6iubX4dx0XkwRCQs2apD0UCnHPPffwta997bz/5sc//jE///nP+c///E927NiBxWJh3bp1TKpYMzsZirULGBloJOBPzUN0Ps5s9RCPRggFhpIahyd9PqNHDyS0T0NRAZON6u5BMLhLCB5vU7xdSZaZKFxE5Q/vIDw8Tt/GI/S8vv+iKyj05tbgne9WKErw5Broa0u9zahi/Eo+jUbP/LS1pHsrOfL+rxn39yQ7pItmtKSTsehq2jc9l1LJY07ltQy+uyEpVW5sV69kdKP6ddslrQZTQSYTJ8QJpkLizJqk/Yc//CHf/va3mT9//nk9Ph6P89Of/pR/+Id/4LbbbmPBggU88cQTdHZ28uKLL6obbIJJksR817U0HnqFaCR1ZlzOpMC3ip4Dm5Iag9ZgBkkiOpG4/QBOdzkT9erOPumzvITa1PkQiUemPoBH67twry4j7J/g4INPMfRBE5HR1EgkM3IM9KZg0i7Gr9SRE8plYdZttBx6je6mHSmV7F6ILE0xRreP/v2JKXl4PjQGE7YFtYx8sC3hfdudFYSa2ohNqj8OaBauYPBt9TakCsInzZqkfaaampro7u5m7dq10/c5HA6WL1/Otm2JH0jUptdaKJl/u2IbD9Vkc+YSGh0kMpncDbS28sTOthsyfYQ61b0aImmmqvOoMcMlaafaNmWnYSnJJOezyyn//m10v1LH4X/4E4GGi5u5jMfjRMMXt1G5y1VC3xw4FfVSG78SzaC3sdh3N5HQGMd3PUUkPLs3qRZmrGK8u4WxrtSpJub1LWPsyEGi44kd5yVJwnrFMgJbdqrelz7bS6h7iFgo9ZbkCXNT6md4F6i7e2q2MTPz1A1ymZmZ0787nWAwiN/vP+U2W7iGTMjyuWuPp8Jpqp6qVfQdfi+5MTjnMXr8UML6kyQZ2WwmGlB3vaU+10uoTflL/+HeQYZ2NRELRWj4yesc+5dXOfbPr0Acgn2jBI5dWJ/9RwfZ+f/t4/XvbOat/7mFJ+98ia3/tpvg6MyvGtl91pScaZ+pS3H8SjRJkik3LcdbtJL67U8wNtKV7JAumCRJlFfcSff21wmPjyY7HGBqSV36VdcxsPHNhPftzFrIxL4jCVmeY19exsj2ubHBWUh9SU3aH3roISRJOuutvj6x9bQfe+wxHA7H9C03Nzeh/V8sU9uZB+zASCcHd/6GAzt+mcCITi9LU0Sgp4lYEpfzaHQGZL2BSCBxH3KmslImjjWo2oehNJdgg/Lr2oMNbXT+aRexcJRIIIhzaSGZN9Xgu2sxlT+8A/ea8hm3OdE+RN3vjjDQMExkMkosEuOyr9eiM2l597Gd+Dtn9gXH5DLiH0jMJkMxfs0NWWMean130Hr4Dfra9iY7nAum1RkpqbiV9neeTYnSugAuYxGxiQmCvYld9y1pNJgXzmNir/qTMlLFcobfPax6P4IASU7aH3zwQY4cOXLWW1HRhZ045vV6AejpOXX2r6enZ/p3p/Pwww8zMjIyfWtrUz75SbRoNMTRumc4tOu3WOxZxONx+rsSuwnzkyRJwlW6lIFjHyQ1Dlv5Avz1iVuT6HSVM3FU3XXtRmexKkm7qaaM0u/dQP4Dq3CtKiXjmircq8tx1ORhLc1EazHMuM32J7eRMc/FDf97Dbf+32tw5Nkwu4ws+39qiEVidO+b2QEjUzXrZxzGBRHj19yh11lY5LuLseEOmva/RCw2O09WtDiycBTNp+eDt5IdyrTsmusZ3PBGwvtNL72MwHsfqL5nQTabkI06wv3iqpagvsSc434GHo8Hj8ejStuFhYV4vV42bNjAwoULAfD7/ezYseOsFRwMBgMGw8yTj1RiahtlLNtELBpGq5taMhOLRXBlVFJYcT2DfcdoPfY27qzz2xSnllxzNXXHfo27YkXS1uJ77BU07H+C9CWrEtKfzp5GdHiYeDyu+ImgJ2nsNqIjyq8j1VhM9Edz8C2zYi2bShwl+eKeg2zS0x11seDkHTHo2tuLt8aDPcfGeN/M1xrLGhJyyJIYv+YWWdIwz3EVLXID9dt/S8miz6A3WpMd1ozlORZzpOs5RluPYsub+dUvpRnsLrRp6YyfOI65OHG1x2WDEV1OFqETLRhKClTt62TN9oy7V6rajyDMmjXtra2t1NXV0draSjQapa6ujrq6OgIfWx9cUVHBCy+8AEzNuH3rW9/in/7pn3jppZc4cOAA999/Pz6fj9tvvz1JzyJxBvZtofX41HHSkiRTPO82RoaaiMUipHvKMFrcjA4ndxZOkjXYc6sYbkneMeOyVo/GaCY8OpywPrUeN5F+dU8MlC0mVQ5ZOknnNCvSjrUkg5G9rdS/dILtv9iLvzNA7kofAM48G1rTzOcV0r16BntSq4qSGL9mj/xYCRWuKzn2we9n7Tr3stLb6N2zkXBgONmhAJBdsZahdzckfNmOa/4aRjeqf4ZJJGsBo3VNs74SkZD6Zk3S/v3vf5/a2loeeeQRAoEAtbW11NbWsmvXrunHHD16lJGRkemf/+7v/o5vfOMbfOUrX2Hp0qUEAgHeeOMNjEZjMp5CQnnsJYx0frSeVqszYjA6phP14qqbMZrTkxXetHznYgaO7UhqDLbKGkaPJG6JjKm8TPUlMoaSHFWWyAB0Pr/r3A86T55rqnDU5NK0qZ3weISyGwtIL3IAUHl7CdX3lM34g9CTY0i5CjJi/JpdbKZMan130nLwNQa7jiQ7nBnTaHSUVt4+Vb89Bda3a41mzOVVjO7bk9B+dQ4naDRE+tSdJJFkGXNpFuNHO1TtRxBmTdL++OOPE4/HP3W78sorpx8Tj8d54IEHpn+WJIlHH32U7u5uJicnWb9+PWVlZYkPPglMeidOSy4NB/9McGLkwzXsEva0fOLxODq9BZ3ekuww0eqMGB0exvqSN+vvtpYTaErc6X1Oeyna8zwV80IZXEUEG9pVaTtwrIewX5kSebJei/fmhZTdWMhY/wRNm9p56WsbePKOlzj47NELWkbkyU69A5bE+DX76HUWFmffzUDnATobtsy6WVSzLQN70Xx692xMdigAZOWvxL9nJ7FwYk+jdS1cw+gG9WfbNQtWMLRhv+r9CJe2WZO0CzNXnLkazegk9XVP03DwRRzpBcSiEeIptskq37OCvsPJOxhE1mjRWqyE/Yk5pVVjNGGurlK1D0NONqEWdS7tmwvdjDfNbIPo2QxsOcax15ow2vWMD0zizLNxzaMr6TsyyHv/OvNZ/Y60Uvo6UitpF2YnWdZS476RaCRE474XU27sPJc8x2ImB7sZ625OdijIGi2OZSsZ2ZHYUr/G7FzCPf3EJtQ9AE6XlUGod4RYWNRsF9QjkvY5zKCzUJVzIzUZN1K76htMjPVzePfvaKp/jYM7f01P+256O/YyPprc47xNFhexSJjwePJ239vKFyT0oKWzUWJGT9JqiUeiqswOTroKGWvqV6y9tie3sfLbi7jyf13GDf97DZPDQTLnu7nqkRW0be8iPDGzD0Gbz5Jyy2OE2UuSJCrMl2F3F3L0gydnxanTJ0mSRHnZ7XRve41oMPkHSGW4axg/cSzhBy5ZVy0lsEX9SmW2JSWMfqBuSV/h0iaS9kuASe/E9OHGU5e3iqKqmymsvJHQpJ/jB15gfwrUbfdUXk7fka1J699tKSPQmLgDMow9H/3TiwyPMNnUzNiBQ0wcb1CsqozO6yLSrfxaTn1upqIz7cTB6JyqeKK36Aj0jDPy4XkDJqeB8f6ZJRsGu56xkdk1IyqkvoJYKd6iFdTveIJwMLmnOc+EVm/Ce9kNdLz7QtKX+EiSRNrqaxjcvCGh/TqzapjYf0T19f1SxTKGtoia7YJ6klryUUiMWDyKqW2U/PLr6Gx+n8ycxQz21jPYW09W/mVYbJnEomFkjS5pMXqlfLp71xOLhJG1iY9D1urQGE1EAn60VntC+hzbd4Dh9RsJd3UhW6zIeh1aj5tgcwvWZUvROi4uDkNJLsET7eiy3ApFPEWTZifUr9yprpbSTN74RSMl5RratnfhrfFgSpvabJk530M0NLMEXK1SmoKQPZGFbt6NHN35e0oWfwajOS3ZIZ2XzHgeo45jDB3dRXrF0qTG4jaVMDL8HuHhIXTOxLx+kixjWlDJxN7DmBdXq9aPxmaFaIxoYBKNVWwYF5QnZtovAbKkASDdU87E2AB7t/yCibF+svIvIytvORm+hUlN2GEq0UovXsRQY/JOJHQtv5J4PDGVFoIDvUzUH8W2Yjnuez+LZeECMr/8V2T+1RcIdXYxtufiXwdDepEqFWQkSQIJ4jFlZu0KvryGeDRO69ZOLG4T8+4uRW+dej+u/PYi0oudM25To5WIhpNfNUOYezL8DhZ4b6Jh9zOM+xN70ufFKPZdxfDxvQT96lZSOR+++dfi37MzoX2ml65g7H3lKl+dSe53bhUJu6AakbRfIvpHG9m36RcYTU6i0RAF5evwZC3AZHEhyZpkhwdArmU+gyf2Ju0Sbpqcg87mTEhfgYapS6j2lZdhXVyLPsvL0JtvI2m1WGoXEmxuueg+tC4X4Z7Bi27ndAyZDoI9I+d+4HnQ2U0U/Y9ruO5fVnHF/1xKWqHjott0ZekZSrFa7cLcYTKksdB3B037X2J0sDXZ4ZwXSdZQWn4bHZtfSNjkxJmY3NmkX3VdQvvUmExo0hxE+tQZE0/yD7tUbV+4tImk/RKRZsmlwHMZtd7byS5YCUgpk6yfJGt0mN25jPU0JTuU01KycoQhI4tY9/D0z5Jeh6zXA6B12IlHL74vSZKQtDLxiPLVDCyFbsYUXNcej8fR6JV7P3pyDPR3iaT9pIPNf6axawt9w8eYDI0o9sV4IjhM33DiyqWmEoPOSq3vLtqOvM1If2OywzkvJqsbR+E8+uveTXYoWHrOvYxN6Qkc71V3ofUk/3wSQbhQYk37JUIj6/A6q5AlDdk5VyBJqfl9rcC9jGNHXsfqLUpaDPF4bPr1GW9rZPTYQSY6Wwn2dVH0199DZ3dedB+mrDxknY7u//wlyBLBljay/sdXATDk55F+520X3QeALtdLqLUHQ1G2Iu2dNJ5WiK7pMK6VyhxLrvQ69L60AjQdF3+1Yq6oiC9kcjSAXzNM3/BRJkMjyBodDnM2DmsOdrMPrUY/43aPtr1Bv/8ETksuOZ7FeNPnqRB96tJpjdT67mDvsReJRyM4M1O/jn5e2lIO7fsdtvwKjOneZIczLTo5Qai7i7Gjh9CYLVjn16K1O0DBsUHSaIAzX2W4kHMhziYWjjDy3hEmmnoY3dOIqcSL++alGPMzkHWpNWkmzA4iab+EnFzbbm4fY8xnJBIJojdYkxzVqQwmJ8QhNDaC3nLxyyRmanLHPqSiTAweL/FYlN5NrwHgvnwtgzs2Md52Ase8xRfdj8ZoIvO6OxjuOwKShPOaq4gMDTNRf5RYMIjtsuUX3QeAoSibYGO74km7PieTsU3KztYdebEBR64N3+LMi27LlmWh/6go+3iSTtJj0nlJGwPIBh1E4mH8o/0M00Zrzw5isTA2SxYuWxFOay6yfPaPh8BEL+PBQVZVf53ARC9N3VuZCA6Rl7kcjZzcPTKJpNUYWOS7k7qmPxOLRUjPUvcMhoslSRKl5bdRv+VZim7+8oeJbHKYu2HcCyMfbGPo/c3EQ2EMWT70mVn0vvQc1spqHEtXqNZ/PB6HSJR4JEJ0dAxdhjJLW4a6bcT2bqLr8XeIToYw5rpxXb8IjdVI9+8347y8gvRrFyrSl3BpEUn7JUpq6qRh+F2qlnw+2aF8iqt8OQNHt5O1aF3C+x5pPYw2PoDB40WSNdjL5xMeHcFaVEF4ZIix5gZFknYAnc2JNu5i4E8vEpucROtwIJtMaGxWBp7/M5ZFNVhrF15UHwZnEUN7XlMk3o+TzUaiE8ouP+nR+Yg1dyiTtPsstHeJpP1stJKOdF0W6ZOApoi4HMM/PsCA3E5L73YkJBYU3X3G5L2lZ9vU6cpaM25HKW5HKcFw4JJK2E/SyDpqvbezt/Vl4rEoruz5yQ7prAwmB+mVy+jZvR7vssSPsx839N47+Pd+gKWkHEmjxZCdg21+LZMdbfQ8/7TiSbuhW0fQGybc009g0zbCHT0EG1sxlORjrCzBvGQBWtfFVbUJd/cROtFD8b98HkO2i6EN+4iOh3DfvASt00zPM1tF0i5cEJG0X6LMhnRMVg8DPYdxZabWzFCWXEhd9wbisWjC193bcyoY6v+oXrvWkcZ429Qae3NeMaP1yh1THRrsY3zLTuyrVmJdthRZ/1Gy49+6jfH9By86adc6HESHRi8y0tOTtDKxcFSxy7zG7DRG3qlXpC1Rq33mJEnGofXgmPSApohYPIp0ltn2LFcNEjI7j/yS/MwVZLlq0GvN078PRcbpHTqCJGnIdi9MwDNILlnWUpt5K3UdL4Mk4fKpV1pQCTmWBTSObEzKOHtSKDBM+HgrOV/6H2jMFmLBSVr/708xl1RgzM5FYzYz2d6CMSdf0X79699j5M9vo7Ga0Wa48XzjAXS+TEZefBP/a++Q/vk7L6r96LCfQOMwOYVTExD25WU0/q8nybhrBeaybGKTIeKxOJIsytMKM5OaC5tTUDQ2944mLjMupb1xC7FoONmhnEKSJOy5VYy0Jv6QCltWMaGODsZbG4lOjDFavx9rSRXxaBSDKwPXiqsVO6AjNDxAeGQI+6qVpyTsAFqng8iwMtVZJL2OWFD5TZmmnHQm2pWrxGD0OvB3KFP/XdRqv3iypEFq6jjlBkxXHkm3FVBVcAslOWsZGG0E4tN7QZq736ehYyNH296ko38PwbBydf1TmSxrWJhxC/3tdQx2pfYhO5IkUZx9zTkTdjUrzeitToIj/cQjU59BssGIwesjMjw1rqRftQ7ZZD5bEzMWGugndqyHrH98EN9j/xPbdVcwumErGpsF553XE2y4+L0whtICosMjRCdDxMJRhjcfwrqwiFg4gmzQ4b55KbEJcSUw2X7wgx9MFWz42K2iouKUx2zbto2rr74ai8WC3W5n9erVTEwk73RhMdN+nva3vkhN/p3otaZkh6IYrUaPL38F7Y1byCu9OtnhnCLPUcvho8/hLEjsZWad2U5G9Wr6t77FRGcr5rxibBULptd9mnOLkGRlvusaM3xExvxE/H60djuxYJBwdw8TR48xUX+MtOuVKYmmL8gi1NKFsUzZ2SpzgZvxpn4shR5F2pP1WmJh5WbHyxfbOPCeX7H2BJCaOghEBtHLRvTFU5uQ0635nOjYyFCglWg0yIC/iVg8Qo5nMRPBIbLdi9DNoXHzXE4m7ntb/owka0jLLE92SBckMjFGz663CfkHKLzpS6r1Y80uwv/q28hFPgIH60DWoHNnAKBLS0eXpmy1F0mWCXZ14HROHV5nmlfGyPNvEA2MT828u9MId/eh8174uCZpNFjXXEbj//sHomOTSFoNOd+4CVk3lXKlX7NAkeciXLx58+axfv366Z+12o/S4m3btnH99dfz8MMP84tf/AKtVsu+ffuQFcoBLoRI2s9TccYq9rU8R3XurZj0id8gqZbcSAG7hnczOTGM0eRMdjjTdHozWoOZyZE+jA5lksLz5Spdin5hJeHRYTR6A/FoBH/9PjqefwJbWTXpy9Zgzrv46jZaq520hSvoe+JJIgMDyGYzstGIbDJhu3wFxtJiBZ4NGFwFBE90KJ60jzkKkQ/sVrRNZIlYNIasufhB0XHn5fCzZxUISvi4oUg3zRP7yT1cSYa+gH77KOPBQcYn+2nq3kptyV9gNXlo7n4fncaI3Zw1vQn+UiHLWhZ6b2Vv44tIkgZnRkmyQzoja2eUgO+j/z/xaJSuHa8zcrwOszef4FAvo61HseWp8+Ujc+k6hhvqGDp2GOu8BRh8OQy+8xaj+/cgabWY8ouwlFdirVIm0T35JSC6tYlYqZ3RN99FX5CLbDQAkP7Fe5DNF/8l03bdFUTml2PW9jJ2qI2JE93IOg2motSp2CNMJele7+n/n3z729/mm9/8Jg899ND0feXlyf0SLpbHnCf7IMyTl3Ow7SVGJ3qTHY5iJEmiwrGa5vo3kh3Kp+R6ljJwdHvC+43H44T2Hqb1yf9L+/OP07v+JfwH95Bz9xdx1CyjZ+NLivXlWnE1Watvw/OFv8T9uXtwfeYuMr74eayLaxWb0Tfk5hL6cGmDknQ+DxNtyp6uaM0wM9abvEuPwrnlGatYar+JiegoBwObmOhto9p0Ba4RC7Kk4Vj723QO7KN/5DhZrhqMc2iSYyamNqfeRmfDu/j7U/PsiZPM7SGiocnpn4NDPdjyyslf93m8l91IX90m1frWGs24q1dSuOpzjB09TNdTvyUaGMV7933kf/17mAqKGHp3o6J9uq69keHt79H7b/9N1D+K9YqlSFoN8UiU0IlWBp/400X3IUkSOl8mhjwPPc9sIRqYYOC1PZz4X3/A/0EDwa7BpB0kKHzk+PHj+Hw+ioqKuO+++2htnTosrbe3lx07dpCRkcHKlSvJzMxkzZo1vPfee0mNV8y0z4BZY6NGupz9nW9T4l2D05KT7JAUYTNloA2ZGBlowuEqTHY402zOPE40v00sGkHWJO6tGg1NMtRYR+49X8KcW0RosI/m3/6MnLu/CEDf5teITk6gMSpzyd+YmQ2Z6q0b1VgtRAPjircr63XEQspu9nTk2hhpG8WWZVG0XUFZJo2NKusqwrEgOtkwff8qyx30hpo50vIWYcJEokHkFDvELZE0Gj21Wbezp/5P5FffiNWpbOlVpfQ07WCsYwzv8uuRNBp8q26j5Y0nAHCW1DBwaDsT/Z2Y3D7VYhg6thetzUHWvQ8g6z96T9lrlzL07kZCfT3oPRdfWQrAUlaJMTefoCeMpNVMz7JLWg2GknyG//wWobZO9LkX93zjkSg6pwVDtouMey5HkiTCA6O0/exlJhp7yPkfN+JYMTuXT6Uyv//UZZEGgwGDwfCpxy1fvpzHH3+c8vJyurq6+OEPf8gVV1zBwYMHaWycOjDtBz/4AT/5yU9YuHAhTzzxBNdccw0HDx6ktFSZM0pmSsy0z5BeNrFQu4rGnvcYDDQnOxzFlFtW0HJ8fdKPt/44SZJw5s9nuPlAQvvVGkxTdeLTp9ZV6tM9SDoDo8cPTf3eYiM8ou5R2EqTDXpik8pvfJINWqKTym1kHnTm429Xp9qNoLyPJ+yxD8eODH0BJtmOT1fCZE8r4cjkmf78kqDVGKj13U7zgVeYCPQnO5zTcniKCRw7NP2z3paG1mRhrKsZgJwr70JjVHZD6MfF43GG6j8gs3jFKQk7wPC2LRjzCtCluxXts/P3v8b/m1eI9J76/0S2mDHVVDJRd/EbiSWthqFuG8aCDFp/8iLdf9hM289eRtLrMBV7iU2KU5vPh6V9DEvredzaxwDIzc3F4XBM3x577LHTtnvDDTdwzz33sGDBAtatW8drr73G8PAwzz77LLEPC0589atf5Ytf/CK1tbX8n//zfygvL+fXv/51wp77J4mZ9guglfTUaC5nX9/7yJJ2Tsy467UmXBmV9HbsJTNHmTrkSsi1LeDQkWdJL65NaL9Gh5vgnkNor1hO4MQRTL48AscP0f7HX+OoWYo+Xdl19tFAgHg0itahzlICfYGPUHMXxooCRds15bmYaBvEWqrMDJgp28nIlqPnfqCQcuQPK8f0h9owyGaKzYswyCb4cAlVvHBqljkUGUcj6y6peu46rZmarFvZt/c5ypb8BXqTPdkhncJsz8Rsz2Rw/es4Vl/FaOtRJI0Oc2YuAAaHm8ik8lfrTpIkCVNGDsMN+3HW5BMZ9TO6fy+BQ/uJTY7jXneL4odAGXPz8O/eQfTNKMRi2K5bjS7bi6zXIWm0RP0XP3kQau1k+I+vwoSfUO8IntuW4VhViTHHjTbdis5lU+CZCJ/U1taG3f7Rv7HTzbKfjtPppKysjIaGBq6+eqo4R1XVqSWxKysrp5fQJINI2i+QRtJSI68kbsw452NDkTH02tS/3F+kqWZn2zO4sxag0aTGB6pWZ0JrsjI50ovRce7XWimeeavpPbiZjp++itZqI23xKuyVC/GsuQGtRfmBVqofwD9wjLR11yreNoAhvYBgY7viSbs538V4c79iSbsxy0mPmGmf1dz6XNJ0WUifuJB7smTkuCdGQ8dGXPZicjOWotWc3wfqbGfU26nOvIH+wWbc2alXPSS3ci1t9esZePm/CQ5247viDiRZw1hXMyde+A8s2SWYM3PJuuxGVfr31KymZ9d62n/2vwlN+jEVFGFftBRr1Xw0ZuU/P23VCwn1dJN9y/307X2bwcefQ5vhQjYZCXf24Prrz110H1p3GsaaStLKzPT8cSvuW5ehsRhFfXaV2e32U5L28xUIBDhx4gSf//znKSgowOfzcfToqZNIx44d44YbblAq1BkTSftF0Eha6BiAvKwzPqZjcB9HOt4gx7WICp86CZlSZFmDr2AlHU1byCtJnRKQue6ldNZvJ2f5rQnr05ZVjNGRwZhlDGQZg+ujLwwnNw8pWQvcmJVL394NirX3SfrcbEZf26l4u2P2QqQ65dqV9VqiQXEo0mynkc780ZLWJ7NEdw09xlHqGp7G4ywjx7Pkkph5txjdkH32Kk7h0DiNdc9jS8/HV3JFgiIDk9VD2ZJ7mQj0MeaG8e5Wune8gaTRkLlsHe75l3Pixf+Lo3D+9Ay8kozpXvKuvY+JvnaipS4kWUbS6pA0GuLxuOJnLxhz8ohOjBMeHMB+w5VYrlhK8GgjobZOrGuWX1TJx5NkswnbmsuI6rRk/7UZjUkvEvYU8t3vfpdbbrmF/Px8Ojs7eeSRR9BoNNx7771IksT3vvc9HnnkEWpqali4cCG//e1vqa+v57nnnktazGJNuxJauz51VyweZV/L87T276Lcdy3DY20fHkCS2nLD+YwMNBEKps5hKDZnLhODncRjiU3mdGYbBo/3lIQdmD6EQUmyVks8HiceVec5aiwWYipsRtV6XYoesHSSqKowt0mShHfYzhLd1ei1FvYc/wMd/XuIxef+FzZL69gZf9fT/AEHNv9/RMKTdDdtZ3JM2epM50OSNDS+9F8Mn9iH3uFiqH4XGr0BjcGEa95l9O5Rb3JBkiTMGbnYRszIBuP0khi1DkszZGYx2T611EFjtWBePB/n7eswFCtXHlf6sDa7uTwbSXvpbspORe3t7dx7772Ul5fzmc98BpfLxfbt2/F4pr6wfetb3+Lhhx/m29/+NjU1NWzYsIG3336b4mJlyjFfCJG0K+XDxP1kshEMj+If78KXVk2uaxHFmaup73w75U9WlSSJvNJraD2u3sA8U5Ik4UjSCammBFb3NOTmEGxrV619NTajynqdogciAZhcJsb7L+3Ni5cKSZLx+V0s0V1DLBZlz7Hf0ztcP+e/tH08cT/5XCfHBmk59DouXzXzLv9rvIWX0Vj3YsJjGx1qJc1VStEtXya9YinZa+6id/dGIhMBQoFhDA63YqdCJ5vn5juxLajF0P3RVZ54PK7a+2+uv69nm6effprOzk6CwSDt7e08/fTTn0rIH3roIdra2hgbG+P9999n1apVSYp2ikjaFTTZ3MB4aGrW0aR3siD/DjqG9hOOTuKxl1DgWc5YMDWrB3ycd8xFOBRgYix1Ys211zDYoPBBPudprOkYsbD6u/zt6SVMnlDvaow+P4tQ86evCl0sWachFlLuy6gj1yYqyFxiZEkmb8xHrW4N/rEu6k48zehET7LDUpWldYxwcIzQxAgARks6hTW3MjrYAoCv5Ao0ehOjg4nd9GYwORkf6cLaOfVl3OjKwujOpmPLiwwe3oG9aL5iZ0icjbEjQs/zT6vah6z79JIsNa6kAgy0GGj72SuKtytcWkTSrqChSDcHjv9x+meH2YfFkM7g6NTBGtnpC7EaPIwFU79cYLn9cpqPvpXsMKbp9BYkWUN4PPHH0gf7uhlrPq56P6bsfILNLaq1r08vINjUqXi7ppx0JtqUe087cqyMtImk/VKklXSUhsqoZBHN3Vs50vIqoYh6VUuSbfxQHQ17PvrM8OQsRJJkBrumrioWL7wTgzk9oTHZXQVoDRbaj26k/8BWGv/8nxhdXnKv+Rzz/uqHWLzKnqx8JrJGi6TVEuxS/mC409F3aQl1dKvWvmzQEx2dIDohyjwKF27WJO0/+tGPWLlyJWazGafTeV5/88ADD0x/az55u/7661WLMUtfjEm203j0dQAi0RChyDhmw9SgG4tF6R45zL7m5xgPDqkWhxIsBhdanQn/UPJKG31SeuliBht2JbxfV1olgYYjqvcj6/TEwxHVLj0bcrMJtaiQtOe7GG9Vbu1tj70If3vq7KlQwmwYv1KJSWNlAcvxps/nQONztPftTqkzJJTicy9Enogw0DF1FkU0GkbW6jFap2qSa3XGhB4sd1LhgluJRkKM7N9JetVyfCtvno4jkUs8ssqvYui9dxLSlyRJDD/7CvGwcudOfJJ9aQn+D9SfABLmrlmTtIdCIe655x6+9rWvzejvrr/+erq6uqZvTz31lEoRTqkyr6Qv3MqBI0+z7dh/E4/HMenTgA+rs6TNJ8e1iCMdr6sahxLKLZfR2pA6a9t9mmL87ccSvi7Q6PAQHu5PSL/6LC/hLnVmezRWK9FR5Wctx2wFjLcol7QbfU5G5tjymNkyfqWa9H4Ni7VXEY2F2dvwFIGJBG4ySZCynLV07H+LlkNvcOT9XxEcH4Z4nP6O/exd/2807PkjJ+peYKgncecXmG0Z5FWto+aqb1LgXnnK79TaFHo6BocLJJnQQF9C+jMtqmZ8z6FzP/ACxYuX4N8mzqEQLtysKfn4wx/+EIDHH398Rn9nMBjwer0qRHR6OtnAfMtVhOITZMYmyCi5bPp3sXgUWdKQ515Cz8gRuocP43VWnaW15DLorFisXob6G0hzlyQ7HCRZg9mdw3hfK5aMxFyiPcngySLY14UxQ71jvAEMRYVMNjWjz1anH0mnJRYKI+uVK6+n83kIvKXc8hitxUB4TL3ZrmSYLeNXKpIkmYLxXLxSGkc7N2M2uijMumLOlIi0W3yU513P+OQA2swKMvKX0HLodYa6j5KRt5isklUM9dTTuO/P1Fz1TbQ6Y0LiOpmcJzJJP52siivp2bqZjFvvVr2vtPyldL38OyzLF6rSvsZmIRaKEJ0IoTHpVelDmNtmzUz7hdq0aRMZGRmUl5fzta99jYEB9UtomTU2nNoMYsQYDDTjn+gmFBlHljTEYlFCkXH0Wsv0splUVmJcTPuJzSmz6z03fREDx5SvN34uLncVgQb1q9c4rIVMNjar1r4+z0uoVdmZfNmoJzY5t5LsVJGM8StVGTVWauSV2M0+9hz/A0Oj6u3/SLR0WwE5nsVkFi7n0NZfMTrYSkb+EiKRSeLxGJn5S0n3VtB25O2kxGftjNK7ewOxaOKrn5ncPiKBUSKj6u9n0hiNyDYr4V71/p3Zl5bg3ymWyAgXZtbMtF+I66+/njvvvJPCwkJOnDjB3//933PDDTewbds2NGc4EjkYDBIMflQWz++/8IHiyPhWwieCZKVV45/oxqCzI0sykWiIWDyCQZv6RxjrtSbsaXkM9tbjyqxMdjiYrRmEAkPEIiFkbeJmKqzeIjo3b1K9H63ZSmz8zHWcL5YhP4tQUwfGEmUPR5E0MrFIFFmhOsQ6i47gaAiD7dKdjUr2+JWqModtpGvXcHRgH73D9ZRkXz1nZt2jxxpxZpRSUD116mj70Y00H3iZ8mV/SXbZVfgHmpIWm87qZGD/e3hqr0x432kr1zD8/ru4192sfl+Vl+F/fxfO29ep0n68eDH+F/5E2pp5qrQvzG1JnWl/6KGHPrXR6pO3+vr6C27/c5/7HLfeeivz58/n9ttv55VXXuGDDz5g06ZNZ/ybxx57DIfDMX3Lzb3w5GaJ7UasmnQq47UsL/krCtzLyXcvJ9e1iMtK/wqDTvmjmdVQoq+lo+m9lJltd+RXM9yi3rrD05FkDbLeQHRC/UoWGoeDyPCIKm3r7YUEVSj7aPQ5mOxSLmZHjo3RztTejDrXx69UppP1VMcXk24rZO/xJxkZS0yFEbWNBNqJfexqijUtF73RQTweR2+04c5ekLTYcqw1jLYfIzye+P0m6YZCgj2dRCcnVO/LmFdA8ESragUBNDYrscmwuDopXJCkJu0PPvggR44cOeutqKhIsf6Kiopwu900NDSc8TEPP/wwIyMj07e2trYL7s+mScerL+LA2LtoOwZw24tx2QrJSqsGZs9BC1qNAae7hIGexCbKZ5Jrnc9Q496E92stKmesSf1NRMbCAiab1JlR0zjsRAeVn3015bmYULCCjD3HykiKV5CZ6+PXbJAxZKFGu4rW3h00dGwkluBTk5WWn3kZo+M9jO3bRU/zTk7UPY/OYEn6unL48BTbZTfQvSPxRRQkScKxdCUjH2xLSF/GyhImj5z539nFsi0qZnRv6p+QLqSepC6P8Xg808fFJkJ7ezsDAwNkZWWd8TEGgwGDwaBYn0XGGnaNvk5fuA1PK5D3Ud+pMBCfr2LtAnY3P48rswpJSu5WCK3OhKzVExobQW9xJKxft6OS1gOvYq+qVbUfu72I/iPvYa1dqHjbkiQhaWTi0ej0EeFKGLflo2+th8tLFWmvz16AtTW1qyxcCuPXbKCXjSxgOZ2mQfY2PEVV/k2YDGnJDuuCyLKW8tx19A0fZbCzmezy1XiLViQ7rGkZUR8D8TjjvW2YMxJ7FcfjrOL4ti04V6xG1qqbuqSXLKNn88uY5pWp0n68ZDEjr72EY0W5Ku0Lc9es2Yja2tpKXV0dra2tRKNR6urqqKurIxD4aDauoqKCF154AYBAIMD3vvc9tm/fTnNzMxs2bOC2226jpKSEdevUWat2JrXWa/HoPhzgWpVfmpAIWo2e9Ixy+rsOJDsUANJLFif8hFSd2U5kbFT1etH6dA/hPvVOo9X5PIQ7lW1f5/MoWqvdmOWcU6eizubxa7bwjaRTJS/mcMsrdA8eTHY4F8xlL6I8dx0r530tpRL2k4oLrqN7x+sJv1IsSTK2mkWM1ql/VofW7iQeChEbU2c5pNZpJ+KfIBZO/MZeYXabNUn797//fWpra3nkkUcIBALU1tZSW1vLrl0f/QM+evQoIyNT62o1Gg379+/n1ltvpaysjC996UssXryYLVu2JHwmSmZqRvPkIBdv6eRg28tEY7NrTVuRppqu1h0pcciJT1PEaGfid+AbvdlMdqu7flaSJCS9jtjHNhQqSZ+fRahF2S+PGruFiH9Ssfb0LitjfeqvX02U2Tx+zSZmjZ1F2ivxj3dxuOVlotHZefrkyauZ5papL3WpMOaepDfasWaXMHy8LuF9Z3oXM7p/r2rrzT/OsnwhYzv3qda+raaAwP65UwFJSIxZUz3m8ccfP2eN449/8zeZTLz55psqR3V+PlnvVpIkspzVHG5/jercW2fNMhmNrMOVUUVf5z4ystVdInIukqzB6MxgYrATU7q6tdM/Lj2tnOHGo5iy1L00bMjPI9jahqlU+fr4ensegf3bYdVCxduOx+OKvJ8lWYLY7NjzcT5m8/g128iSTHm4ir70iQ+Xy9yM2ehKdlgXRJIkLK1jHBzZiMs3H7urINkhAVDguZwDu36Fo3Aesi5xFZ5kjRZzcSnjx+uxlKt7xokjcwEd7/0K21UqXe0oWczIprewLy5Wp31hTpo1M+1zjWvIiM2USUvf9mSHMiOFmiq62z5IiZmfnLSFDDbsSWiflowCJjqaVe/H7igi2KROPzqPh3CX8stv9G4roQHlylVKGplYNPnvM2F28gyaqNZcxpHW1+gbTu39EedSbllJ6+E3CAfVKwc7E7JGi3vBKvrqNie8b2/+ioRsSJV1OrQeF+HOHlXa13rSCfeNEBdjnDADImlPovzxPEYne+kfnT27yDWyjjRPOQPd6h80dC5WRw7j/W0J/QIha7RIGi3RoHJLQU7HmJVDsLVdlbYljYZ4LKb4mlRTbjoTbcqta7dmmhnrVb/EpjB3mTRWFmnX0DdynIaOd1JisuFC6LRGCubfQsOeP6bMc/AZqxjrbk54CUit0YLW4STYpX6Zz7SKywhsVW/vlKUyl7Ejc7vCk6AskbQnkSRJVMUX0dz7PhMhdepyq2Fqbfv2pJeslCQJq7eIQFdiv/RYCkoZaz6mah+yVkc8GlFt7aY2TfnSjxP2PGXLPmZbGWlL7bKPQuqTJQ3zYrWYDA72nfgj4cjs3CuR6XeSnlVFW/2GZIcCnCwBeR09OxO/jCurdDVD77+rej8GXw7h1g7iUZVKiZYtYmTrhZ/lIFx6RNKeZBpJS6W8hMPtrxKLz44aw1qNHntaPkN96iau5yPHUcNQY2KXyLgcFYw1qv/cdRkZqlWR0ednEVK4kpHO52GibVCx9gbtuSl/wJIwe+SMZlKYtYp9J55lbFK9Y+rVVCTPIzg+xHCfejXEZyIjmkMsHGRyUJ0lJGdicHqIh0JERtU98VeSJIzzypg8rE7RA50vk8m2/qRPgAmzh0jaU4BF4yA7fSHHu95JdijnrUhXQ2fz1mSHgcniJhQYIhZNXOksg91FeFj9D31jQb5q69r19jxCLd2Ktqn1OJnsUe5D1JjlxC+SdkFBzl6Yr1lBfeurDPrVOcBMbdXpa2mvX58y69uLCq6le+cbCe/XsfxyRnao/xmUVryMse3qHOYnSRKmYi8TJ5Qdi4W5SyTtKcLrTyMWi9DrT/7s9fnQa02YrZmMDCb/g8+eU4G/PbEbzXRpLkJD6tVSB7BbC5lsVqckmD47i1Crsh8UkiyDgjNGBq8Df4qfiirMPkaNhYXa1bT37aK9L7FX6ZSg1RgomH8LJ+qeT4kZWqPFhc7iJNCZ2GWKLlMxk20txMLqlvXU2uzEQ2Fi4+osq5LLahnZNrs3SguJI5L2FFIeraa174NZs769xFhLR+OWZIdBtnU+Q43qzIScibWogrFGdQdand1JdHhYlbZlo5HYpPJ14GWDjuiEMh+iWouB8Lg4fERQnlbSsUBawURwkGNtb6XM5s7zlel3YncV0N34frJDAaAo5yp6d72d0C8RkiRhq1nMaJ36h+yZF89nfLc6BwvqC3MZP9apStvC3DNr6rQny8lBKBJPzCEdZVINbf27Kcq8PCH9XQyNrMdk8TAxPohOZ05eHBo9WqOVSHAiYTXvndocusbaVa8io8/2ER0bQ9JolG87N5NoYHxqhlwh9upsgj1+9B6bIu2lFdoJBS7sELLQ2NTfpcJsZLKcMn7Nrrw0IYqCxXTY+mjt3Um2e2Gyw5mRArmKQ0PrCU0GkDXJ/SiXZA3WnFLG+9owOjMS1q8rrZLOI+uJTqo7Dttd5Qzs30xsQp1+zBXZhPpG0FiM0/dFx6cmVS7l8Uv4NCku3hFn1d7eTm6uugfpCIKgnra2NnJycpIdRlKI8UsQZrdLefzy+/04HA6urn0IrcZ4zsdHopNs3PsvjIyMYLfbExBh4omZ9nPw+Xy0tbVhs9lmzcmln+T3+8nNzaWtrW3OvpEvhHhdzmwuvDbxeJzR0VF8vsSdmJtqxPg1d4nX5czmwmsjxi/hdETSfg6yLM+Zb7l2u33WDmBqEq/Lmc3218bhcCQ7hKQS49fcJ16XM5vtr82lPn4JnyY2ogqCIAiCIAhCihMz7YIgCIIgCEJKkpq7kGT9uR8XS0zBkGQSM+2XAIPBwCOPPILBYEh2KClFvC5nJl4bIVWI9+LpidflzMRrI8xVonqMIAiCIAiCkFJOVo+5Ju0LaM9jpj0SC7Fh6LdzunqMmGkXBEEQBEEQhBQnknZBEARBEARBSHEiaRcEQRAEQRCEFCeSdkEQBEEQBEFIcSJpv8T86Ec/YuXKlZjNZpxOZ7LDSar/+I//oKCgAKPRyPLly9m5c2eyQ0q6d999l1tuuQWfz4ckSbz44ovJDkkQponx6yNi/Po0MX4Jc51I2i8xoVCIe+65h6997WvJDiWpnnnmGb7zne/wyCOPsGfPHmpqali3bh29vb3JDi2pxsbGqKmp4T/+4z+SHYogfIoYv6aI8ev0xPglzHWi5OMl6vHHH+db3/oWw8PDyQ4lKZYvX87SpUv593//dwBisRi5ubl84xvf4KGHHkpydKlBkiReeOEFbr/99mSHIginEOOXGL/ORYxfs58o+fhpYqZduOSEQiF2797N2rVrp++TZZm1a9eybdu2JEYmCIJwdmL8EoRLl0jahUtOf38/0WiUzMzMU+7PzMyku7s7SVEJgiCcmxi/BOHSJZL2OeChhx5CkqSz3urr65MdpiAIwqeI8UsQBOH8aJMdgHDxHnzwQR544IGzPqaoqCgxwcwCbrcbjUZDT0/PKff39PTg9XqTFJUgXJrE+DUzYvwShEuXSNrnAI/Hg8fjSXYYs4Zer2fx4sVs2LBhepNSLBZjw4YNfP3rX09ucIJwiRHj18yI8UsQLl0iab/EtLa2Mjg4SGtrK9FolLq6OgBKSkqwWq3JDS6BvvOd7/CFL3yBJUuWsGzZMn76058yNjbGF7/4xWSHllSBQICGhobpn5uamqirqyM9PZ28vLwkRiYIYvw6SYxfpyfGL2GuEyUfLzEPPPAAv/3tbz91/zvvvMOVV16Z+ICS6N///d/513/9V7q7u1m4cCE///nPWb58ebLDSqpNmzZx1VVXfer+L3zhCzz++OOJD0gQPkaMXx8R49enifFrbhElHz9NJO2CIAiCIAhCShFJ+6eJ6jGCIAiCIAiCkOJE0i4IgiAIgiAIKU4k7YIgCIIgCIKQ4kTSLgiCIAiCIAgpTiTtgiAIgiAIgpDiRNIuCIIgCIIgCClOJO2CIAiCIAiCkOJE0i7MSZs2bUKSJIaHh5MdiiAIwoyI8UsQhNMRSbswJ1x55ZV861vfmv555cqVdHV14XA4kheUIAjCeRDjlyAk3g9+8AMkSTrlVlFRAcDg4CDf+MY3KC8vx2QykZeXxze/+U1GRkaSGrM2qb0Lgkr0ej1erzfZYQiCIMyYGL8EITHmzZvH+vXrp3/WaqfS4s7OTjo7O/nJT35CVVUVLS0t/M3f/A2dnZ0899xzyQpXzLQLs98DDzzA5s2b+dnPfjb9bfnxxx8/5fLy448/jtPp5JVXXqG8vByz2czdd9/N+Pg4v/3tbykoKCAtLY1vfvObRKPR6baDwSDf/e53yc7OxmKxsHz5cjZt2pScJyoIwpwjxi9BSB6tVovX652+ud1uAKqrq/nTn/7ELbfcQnFxMVdffTU/+tGPePnll4lEIsmLN2k9C4JCfvazn3Hs2DGqq6t59NFHATh06NCnHjc+Ps7Pf/5znn76aUZHR7nzzju54447cDqdvPbaazQ2NnLXXXdx+eWX89nPfhaAr3/96xw+fJinn34an8/HCy+8wPXXX8+BAwcoLS1N6PMUBGHuEeOXIJxdJB6C2Hk+DvD7/afcbzAYMBgMp/2b48eP4/P5MBqNrFixgscee4y8vLzTPnZkZAS73T49G58UcUGYA9asWRP/27/92+mf33nnnTgQHxoaisfj8fhvfvObOBBvaGiYfsxXv/rVuNlsjo+Ojk7ft27duvhXv/rVeDwej7e0tMQ1Gk28o6PjlL6uueaa+MMPP6zekxEE4ZIixi9B+LSJiYm41+uNA+d9s1qtn7rvkUceOW37r732WvzZZ5+N79u3L/7GG2/EV6xYEc/Ly4v7/f5PPbavry+el5cX//u//3uVn/XZiZl24ZJhNpspLi6e/jkzM5OCggKsVusp9/X29gJw4MABotEoZWVlp7QTDAZxuVyJCVoQBAExfgmXHqPRSFNTE6FQ6Lz/Jh6PI0nSKfedaZb9hhtumP7vBQsWsHz5cvLz83n22Wf50pe+NP07v9/PTTfdRFVVFT/4wQ9m9iQUJpJ24ZKh0+lO+VmSpNPeF4tNXYcLBAJoNBp2796NRqM55XEf/6AUBEFQmxi/hEuR0WjEaDQmpC+n00lZWRkNDQ3T942OjnL99ddjs9l44YUXPvVvLtFE0i7MCXq9/pQNWEqora0lGo3S29vLFVdcoWjbgiAIJ4nxSxCSLxAIcOLECT7/+c8DUzPs69atw2Aw8NJLLyXsy8PZiOoxwpxQUFDAjh07aG5upr+/f3q26WKUlZVx3333cf/99/P888/T1NTEzp07eeyxx3j11VcViFoQBEGMX4KQDN/97nfZvHkzzc3NvP/++9xxxx1oNBruvfde/H4/1113HWNjY/zqV7/C7/fT3d1Nd3e34l+wZ0Ik7cKc8N3vfheNRkNVVRUej4fW1lZF2v3Nb37D/fffz4MPPkh5eTm33347H3zwwRl3lwuCIMyUGL8EIfHa29u59957KS8v5zOf+Qwul4vt27fj8XjYs2cPO3bs4MCBA5SUlJCVlTV9a2trS1rMUjwejyetd0EQBEEQBEEQzknMtAuCIAiCIAhCihNJuyAIgiAIgiCkOJG0C4IgCIIgCEKKE0m7IAiCIAiCIKQ4kbQLgiAIgiAIQooTSbsgCIIgCIIgpDiRtAuCIAiCIAhCihNJuyAIgiAIgiCkOJG0C4IgCIIgCEKKE0m7IAiCIAiCIKQ4kbQLgiAIgiAIQooTSbsgCIIgCIIgpLj/H1yXzubZj/PgAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| label: fig-kuhn16a-3\n",
        "contourf_plot(\n",
        "    plot_grid,\n",
        "    x_col=\"time\",\n",
        "    y_col=\"catalyst\",\n",
        "    z_col=\"activityPred\",\n",
        "    facet_col=\"temperature\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Package Installation\n",
        "\n",
        "### `spotdesirability` Python Package\n",
        "\n",
        "* Available on GitHub [https://github.com/sequential-parameter-optimization/spotdesirability](https://github.com/sequential-parameter-optimization/spotdesirability) and \n",
        "* on PyPi [https://pypi.org/project/spotdesirability](https://pypi.org/project/spotdesirability) \n",
        "* It can be installed via `pip install spotdesirability`\n",
        "\n",
        "\n",
        "## Defining the Desirability Functions \n",
        "\n",
        "### Larger-is-Better and Target Desirability\n",
        "\n",
        "* A larger-is-better function ($d_r^{\\text{max}}$) is used for percent conversion with values $A = 80$ and $B = 97$.\n",
        "* A target-oriented desirability function ($d_r^{\\text{target}}$) was used for thermal activity with $t_0 = 57.5$, $A = 55$, and $B = 60$.\n",
        "\n",
        "### Creating Desirability Objects\n",
        "\n",
        "* The two desirability objects can be created as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: kuhn16a-desirability-obj\n",
        "#| echo: true\n",
        "conversionD = DMax(80, 97)\n",
        "activityD = DTarget(55, 57.5, 60)\n",
        "overallD = DOverall(conversionD, activityD)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Predicting Desirability Values\n",
        "\n",
        "#### Computing Desirability at the Center Point\n",
        "\n",
        "* Predict the desirability for the center point of the experimental design.\n",
        "* Overall desirability computed using the `DOverall` class.\n",
        "* Based on these desirability predictions, contour plots can be generated to visualize the desirability surfaces."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Conversion Desirability: [0.06411765]\n",
            "Activity Desirability: [0.06]\n",
            "Overall Desirability (geom. mean): [0.06202466]\n"
          ]
        }
      ],
      "source": [
        "#| label: kuhn16a-desirability-predict-tree\n",
        "# Objective values at the center point\n",
        "pred_outcomes = [\n",
        "    conversion_pred([0, 0, 0]),\n",
        "    activity_pred([0, 0, 0])\n",
        "]\n",
        "overall_desirability = overallD.predict(pred_outcomes, all=True)\n",
        "print(\"Conversion Desirability:\", overall_desirability[0][0])\n",
        "print(\"Activity Desirability:\", overall_desirability[0][1])\n",
        "print(\"Overall Desirability (geom. mean):\", overall_desirability[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: kuhn16a-desirability-all-true\n",
        "#| echo: false\n",
        "d_values = overallD.predict(plot_grid.iloc[:, [3, 4]].values, all=True)\n",
        "individual_desirabilities = d_values[0]\n",
        "overall_desirability = d_values[1]\n",
        "d_values_df = pd.DataFrame(individual_desirabilities).T  \n",
        "d_values_df.columns = [\"D1\", \"D2\"]\n",
        "d_values_df[\"Overall\"] = overall_desirability\n",
        "plot_grid = pd.concat([plot_grid, d_values_df], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## First Objective: Individual Desirability Surface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x300 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| echo: false\n",
        "#| label: fig-kuhn16a-4\n",
        "contourf_plot(\n",
        "    data=plot_grid,\n",
        "    x_col='time',\n",
        "    y_col='catalyst',\n",
        "    z_col='D1',\n",
        "    facet_col='temperature',\n",
        "    aspect=1,\n",
        "    as_table=True,\n",
        "    figsize=(3,3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Second Objective: Individual Desirability Surface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x300 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| label: fig-kuhn16a-5\n",
        "contourf_plot(\n",
        "    data=plot_grid,\n",
        "    x_col='time',\n",
        "    y_col='catalyst',\n",
        "    z_col='D2',\n",
        "    facet_col='temperature',\n",
        "    aspect=1,\n",
        "    as_table=True,\n",
        "    figsize=(3,3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Overall Desirability Surface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x300 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#| label: fig-kuhn16a-6\n",
        "contourf_plot(\n",
        "    data=plot_grid,\n",
        "    x_col='time',\n",
        "    y_col='catalyst',\n",
        "    z_col='Overall',\n",
        "    facet_col='temperature',\n",
        "    aspect=1,\n",
        "    as_table=True,\n",
        "    figsize=(3,3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Optimizing the (Overall) Desirability Function\n",
        "\n",
        "### Optimization Function and `minimize` Call"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: kuhn16a-optimization-rsm\n",
        "#| eval: false\n",
        "#| echo: true\n",
        "def rsm_opt(x, d_object, prediction_funcs) -> float:\n",
        "    predictions = [func(x) for func in prediction_funcs]\n",
        "    desirability = d_object.predict(np.array([predictions]))\n",
        "    return -desirability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "ename": "IndentationError",
          "evalue": "unexpected indent (979975796.py, line 3)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;36m  Cell \u001b[0;32mIn[20], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m    result = minimize(\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
          ]
        }
      ],
      "source": [
        "#| eval: false\n",
        "#| echo: true\n",
        "# result = minimize(\n",
        "#     rsm_opt,\n",
        "#     initial_guess,\n",
        "#     args=(overallD, prediction_funcs), \n",
        "#     method=\"Nelder-Mead\",\n",
        "#     options={\"maxiter\": 1000, \"disp\": False}\n",
        "# )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results of the Optimization\n",
        "\n",
        "### Input Parameters\n",
        "\n",
        "* The optimization is performed over a grid of input parameters, and the best result is selected based on the overall desirability:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: kuhn16a-optimization\n",
        "#| echo: false\n",
        "# Define the search grid\n",
        "time = np.linspace(-1.5, 1.5, 5)\n",
        "temperature = np.linspace(-1.5, 1.5, 5)\n",
        "catalyst = np.linspace(-1.5, 1.5, 5)\n",
        "\n",
        "search_grid = pd.DataFrame(\n",
        "    np.array(np.meshgrid(time, temperature, catalyst)).T.reshape(-1, 3),\n",
        "    columns=[\"time\", \"temperature\", \"catalyst\"]\n",
        ")\n",
        "\n",
        "# List of prediction functions\n",
        "prediction_funcs = [conversion_pred, activity_pred]\n",
        "\n",
        "# Individual desirability objects\n",
        "conversionD = DMax(80, 97)\n",
        "activityD = DTarget(55, 57.5, 60)\n",
        "\n",
        "# Desirability object (DOverall)\n",
        "overallD = DOverall(conversionD, activityD)\n",
        "\n",
        "# Initialize the best result\n",
        "best = None\n",
        "\n",
        "# Perform optimization for each point in the search grid\n",
        "for i, row in search_grid.iterrows():\n",
        "    initial_guess = row.values  # Initial guess for optimization\n",
        "\n",
        "    # Perform optimization using scipy's minimize function\n",
        "    result = minimize(\n",
        "        rsm_opt,\n",
        "        initial_guess,\n",
        "        args=(overallD, prediction_funcs), \n",
        "        method=\"Nelder-Mead\",\n",
        "        options={\"maxiter\": 1000, \"disp\": False}\n",
        "    )\n",
        "\n",
        "    # Update the best result if necessary\n",
        "    # Compare based on the negative desirability\n",
        "    if best is None or result.fun < best.fun:\n",
        "        best = result\n",
        "print(\"Best Input Parameters:\", best.x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Output Parameters and Desirability\n",
        "\n",
        "* Using these best parameters, the overall desirability and the predicted values for conversion and activity can be calculated as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"Best Overall Desirability:\", -best.fun)\n",
        "print(f\"Conversion pred(x): {conversion_pred(best.x)}\")\n",
        "print(f\"Activity pred(x): {activity_pred(best.x)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_temperature = best.x[1]\n",
        "best_point = np.delete(best.x, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "plot_grid_best = plot_grid.copy()\n",
        "plot_grid_best[\"temperature\"] = best_temperature\n",
        "plot_grid_best[\"conversionPred\"] = conversion_pred(plot_grid_best[[\"time\",\n",
        "     \"temperature\", \"catalyst\"]].values.T)\n",
        "plot_grid_best[\"activityPred\"] = activity_pred(plot_grid_best[[\"time\",\n",
        "    \"temperature\", \"catalyst\"]].values.T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Response Surface for the Percent Conversion Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-kuhn16a-best-conversion\n",
        "#| fig-cap: \"Response surface for percent conversion. Temperature fixed at the best value.\"\n",
        "contourf_plot(\n",
        "    plot_grid_best,\n",
        "    x_col=\"time\",\n",
        "    y_col=\"catalyst\",\n",
        "    z_col=\"conversionPred\",\n",
        "    facet_col=\"temperature\",\n",
        "    highlight_point=best_point,\n",
        "    figsize=(3, 3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Response Surface for the Thermal Activity Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-kuhn16a-best-activity\n",
        "#| fig-cap: \"Response surface for thermal activity. Temperature fixed at the best value.\"\n",
        "contourf_plot(\n",
        "    plot_grid_best,\n",
        "    x_col=\"time\",\n",
        "    y_col=\"catalyst\",\n",
        "    z_col=\"activityPred\",\n",
        "    facet_col=\"temperature\",\n",
        "    highlight_point=best_point,\n",
        "    figsize=(3, 3)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Analysis of the Nelder-Mead Optimization Results\n",
        "\n",
        "### Analysing the Best Values From the Nelder-Mead Optimizer\n",
        "\n",
        "* Objective function values for the best parameters found by the optimizer are:\n",
        "  * `conversion` = 95.1\n",
        "  * `activity` = 57.5\n",
        "* Percent conversion should be maximized (`conversionD = DMax(80, 97)`).\n",
        "    * Obtained a value of 95.1, close to the maximum value of 97.\n",
        "* `thermal activity` not maximized, but close to target (`activityD = DTarget(55, 57.5, 60)`).\n",
        "    * Obtained a value of 57.5, exactly the target value.\n",
        "\n",
        "\n",
        "## Surrogate-Model Based Optimization Using Desirability \n",
        "\n",
        "### Using spotpython for Surrogate-Model Based Optimization\n",
        "\n",
        "* Define the desirability objects (identical to the previous step)\n",
        "* Setting up the spotpython optimization function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: true\n",
        "def fun_desirability(X, **kwargs):\n",
        "    y = fun_myer16a(X)\n",
        "    conversionD = DMax(80, 97)\n",
        "    activityD = DTarget(55, 57.5, 60)\n",
        "    overallD = DOverall(conversionD, activityD)\n",
        "    overall_desirability = overallD.predict(y, all=False)\n",
        "    return 1.0 - overall_desirability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "* `spotpython` uses minimization, but desirability should be maximized,  `fun_desirability` is returns `1 - overall_desirability`.\n",
        "\n",
        "## Testing the Surrogate-Model Based Optimization\n",
        "\n",
        "### Simple test of the Desirability Function\n",
        "\n",
        "We can test the function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: true\n",
        "X = np.array([[0, 0, 0], best.x])\n",
        "print(f\"Objective function values: {fun_desirability(X)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Running the Surrogate-Model Based Optimization\n",
        "\n",
        "### `spotpython`: Swiss Army Knife for Surrogate-Model Based Optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: true\n",
        "#| label: kuhn16a-spot\n",
        "fun_control = fun_control_init(\n",
        "              lower = np.array( [-1.7] * 3),\n",
        "              upper = np.array([1.7] * 3),\n",
        "              var_name = [\"time\", \"temperature\", \"catalyst\"],\n",
        "              fun_evals= 50,\n",
        "              show_progress=False\n",
        ")\n",
        "S = Spot(fun=fun_desirability,\n",
        "         fun_control=fun_control)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## The Run\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: true\n",
        "S.run()\n",
        "print(f\"Best Desirability: {1.0 - S.min_y}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Importance of the Parameters\n",
        "\n",
        "### Importance Plot Considering the Overall Desirability"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: true\n",
        "S.plot_importance(figsize=(4,2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Surface Plots for the Surrogate-Model Based Optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: false\n",
        "#| label: fig-spot-importance-contour\n",
        "#| fig-cap: \"Contour plot of the overall desirability for the important parameters\"\n",
        "P = S.plot_important_hyperparameter_contour(max_imp=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 1: Results\n",
        "\n",
        "| Language | Algorithm | Overall Desirability | Conversion | Activity |\n",
        "|----------|-----------|---------------------|------------|----------|\n",
        "| R | Nelder-Mead | 0.9425 | 95.10 | **57.50** |\n",
        "| Python | Nelder-Mead | 0.9425 | 95.10 | **57.50** |\n",
        "| Python | **SMBO** | **0.9449** | **95.37** | **57.50** |\n",
        "\n",
        "\n",
        "- Python implementation matches R package exactly\n",
        "- All methods achieve perfect target thermal activity\n",
        "- **SMBO finds superior solution** with slightly higher desirability\n",
        "\n",
        "# Case Study 2: ML Application\n",
        "\n",
        "## Neural Network HPO Problem\n",
        "\n",
        "### Application Context\n",
        "\n",
        "* PyTorch neural network on Diabetes regression dataset\n",
        "* Feasibility study of MOO in hyperparameter tuning\n",
        "\n",
        ":::: {.columns}\n",
        "\n",
        "::: {.column width=\"50%\"}\n",
        "### Competing Objectives\n",
        "- **Validation Loss** (minimize)\n",
        "- **Number of Epochs** (minimize)\n",
        "\n",
        "### The Trade-off\n",
        "- Few epochs → undertrained models\n",
        "- Many epochs → computational waste\n",
        ":::\n",
        "\n",
        "::: {.column width=\"50%\"}\n",
        "### Search Space\n",
        "11 mixed-type hyperparameters:\n",
        "\n",
        "- Hidden layer sizes\n",
        "- Activation functions\n",
        "- Optimizers, dropout\n",
        "- Learning rates\n",
        "- ...\n",
        ":::\n",
        "\n",
        "::::\n",
        "\n",
        "## Experimental Comparison\n",
        "\n",
        "### 1. Single-Objective (Baseline)\n",
        "\n",
        "- Optimize validation loss only\n",
        "- Ignore number of epochs (computational cost)\n",
        "\n",
        "### 2. Weighted-Sum (Common Practice)\n",
        "\n",
        "- Linear combination: $w_1 \\times \\text{loss} + w_2 \\times \\text{epochs}$\n",
        "- Problems: Weight sensitivity, scaling issues\n",
        "\n",
        "### 3. Desirability Function (Proposed)\n",
        "\n",
        "- `lossD = DMin(low=10, high=6000)`\n",
        "- `epochsD = DMin(low=32, high=64)`\n",
        "- Concrete, interpretable specifications\n",
        "\n",
        "\n",
        "## 1. Single-Objective Approach\n",
        "\n",
        "### `spotpython` for Hyperparameter Tuning (Single-Objective)\n",
        "\n",
        "* `Spot` object is created.\n",
        "    * Calling the method `run()` starts the hyperparameter tuning process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: des_spotpython_init\n",
        "#| echo: false\n",
        "PREFIX=\"0000_no_mo\"\n",
        "data_set = Diabetes()\n",
        "fun_control = fun_control_init(\n",
        "    # do not run, if a result file exists\n",
        "    force_run=False,    \n",
        "    PREFIX=PREFIX,\n",
        "    fun_evals=inf,\n",
        "    max_time=10,\n",
        "    data_set = data_set,\n",
        "    core_model_name=\"light.regression.NNLinearRegressor\",\n",
        "    hyperdict=LightHyperDict,\n",
        "    _L_in=10,\n",
        "    _L_out=1)\n",
        "fun = MoHyperLight().fun\n",
        "set_hyperparameter(fun_control, \"optimizer\", [ \"Adadelta\", \"Adam\", \"Adamax\"])\n",
        "set_hyperparameter(fun_control, \"l1\", [3,4])\n",
        "set_hyperparameter(fun_control, \"epochs\", [3,10])\n",
        "set_hyperparameter(fun_control, \"batch_size\", [4,11])\n",
        "set_hyperparameter(fun_control, \"dropout_prob\", [0.0, 0.025])\n",
        "set_hyperparameter(fun_control, \"patience\", [2, 7])\n",
        "design_control = design_control_init(init_size=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: des_run\n",
        "S = Spot(fun=fun,fun_control=fun_control, design_control=design_control)\n",
        "S.run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Single-Objective Approach: Optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-plain_results\n",
        "#| fig-cap: \"Results of the hyperparameter tuning process. Loss and epochs are plotted versus the function evaluations.\"\n",
        "loss = S.y_mo[:, 0]\n",
        "epochs = S.y_mo[:, 1]\n",
        "iterations = np.arange(1, len(loss) + 1)  # Iterations (x-axis)\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.plot(iterations, loss, label=\"Loss\", color=\"blue\", marker=\"o\")\n",
        "plt.plot(iterations, epochs, label=\"Epochs\", color=\"red\", marker=\"x\")\n",
        "plt.xlabel(\"Iterations\")\n",
        "plt.ylabel(\"Values\")\n",
        "plt.title(\"Loss and Epochs vs. Iterations\")\n",
        "plt.yscale(\"log\")  # Use log scale for better visualization\n",
        "plt.grid(True, which=\"both\", linestyle=\"--\", linewidth=0.5)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Weighted-Sum Approach\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: des-mohyperlight-0001-agg\n",
        "#| echo: false\n",
        "PREFIX=\"0001_aggregate\"\n",
        "\n",
        "# Weight first objective with 2, second with 1/10\n",
        "def aggregate(y):\n",
        "    import numpy as np\n",
        "    return np.sum(y*np.array([2, 0.1]), axis=1)\n",
        "fun_control = fun_control_init(\n",
        "    # do not run, if a result file exists\n",
        "    force_run=False,\n",
        "    fun_mo2so=aggregate,\n",
        "    PREFIX=PREFIX,\n",
        "    fun_evals=inf,\n",
        "    max_time=10,\n",
        "    data_set = data_set,\n",
        "    core_model_name=\"light.regression.NNLinearRegressor\",\n",
        "    hyperdict=LightHyperDict,\n",
        "    _L_in=10,\n",
        "    _L_out=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The remaining code is identical to the single-objective approach. The only difference is that the `fun_mo2so` argument is set to the `aggregate` function. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "set_hyperparameter(fun_control, \"optimizer\", [ \"Adadelta\", \"Adam\", \"Adamax\"])\n",
        "set_hyperparameter(fun_control, \"l1\", [3,4])\n",
        "set_hyperparameter(fun_control, \"epochs\", [3,10])\n",
        "set_hyperparameter(fun_control, \"batch_size\", [4,11])\n",
        "set_hyperparameter(fun_control, \"dropout_prob\", [0.0, 0.025])\n",
        "set_hyperparameter(fun_control, \"patience\", [2, 7])\n",
        "\n",
        "design_control = design_control_init(init_size=20)\n",
        "\n",
        "S = Spot(fun=fun,fun_control=fun_control, design_control=design_control)\n",
        "S.run()    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Results of the Weighted-Sum Approach"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "_ = S.print_results()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Weighted Multi-Objective Function Approach\n",
        "\n",
        "* Weighted MOO approach results in a validation loss of `5824` and `64` ($=2^{6}$) epochs.\n",
        "* Although the number of epochs is smaller than in the single-objective approach, the validation loss is larger.\n",
        "* Inherent problem of weighted multi-objective approaches, because the deteriination of **\"good\"** weights is non-trivial.\n",
        "\n",
        "\n",
        "## 3. Multi-Objective Hyperparameter Tuning With Desirability\n",
        "\n",
        "\n",
        "### Setting Up the Desirability Function\n",
        "\n",
        "* Desirability function is defined as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "PREFIX=\"0002\"\n",
        "data_set = Diabetes()\n",
        "fun = MoHyperLight().fun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: des-mohyperlight-0002-desirability\n",
        "#| echo: true\n",
        "def desirability(y):\n",
        "    from spotdesirability.utils.desirability import DOverall, DMin\n",
        "    lossD = DMin(10, 6000)\n",
        "    epochsD = DMin(32, 64)\n",
        "    overallD = DOverall(lossD, epochsD)\n",
        "    overall_desirability = overallD.predict(y, all=False)\n",
        "    return 1.0 - overall_desirability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plotting the Desirability Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-des-mohyperlight-0002-lossD\n",
        "#| fig-cap: \"The desirability function for the loss outcome.\"\n",
        "lossD = DMin(10, 6000)\n",
        "lossD.plot(xlabel=\"loss\", ylabel=\"desirability\", figsize=(4, 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plotting the Desirability Functions for Epochs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-des-mohyperlight-0002-epochsD\n",
        "#| fig-cap: \"The desirability function for the epochs outcome.\"\n",
        "epochsD = DMin(32, 64)\n",
        "epochsD.plot(xlabel=\"epochs\", ylabel=\"desirability\", figsize=(4, 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Performing the Hyperparameter Tuning Run\n",
        "\n",
        "### Calling `pyspot`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fun_control = fun_control_init(\n",
        "    # do not run, if a result file exists\n",
        "    force_run=False,\n",
        "    fun_mo2so=desirability,\n",
        "    device=\"cpu\",\n",
        "    PREFIX=PREFIX,\n",
        "    fun_evals=inf,\n",
        "    max_time=10,\n",
        "    data_set = data_set,\n",
        "    core_model_name=\"light.regression.NNLinearRegressor\",\n",
        "    hyperdict=LightHyperDict,\n",
        "    _L_in=10,\n",
        "    _L_out=1)\n",
        "\n",
        "set_hyperparameter(fun_control, \"optimizer\", [ \"Adadelta\", \"Adam\", \"Adamax\"])\n",
        "set_hyperparameter(fun_control, \"l1\", [3,4])\n",
        "set_hyperparameter(fun_control, \"epochs\", [3,10])\n",
        "set_hyperparameter(fun_control, \"batch_size\", [4,11])\n",
        "set_hyperparameter(fun_control, \"dropout_prob\", [0.0, 0.025])\n",
        "set_hyperparameter(fun_control, \"patience\", [2, 7])\n",
        "\n",
        "design_control = design_control_init(init_size=20)\n",
        "\n",
        "S = Spot(fun=fun,fun_control=fun_control, design_control=design_control)\n",
        "S.run()    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: des-mohyperlight-0002-results\n",
        "#| echo: false\n",
        "#| eval: false\n",
        "_ = S.print_results()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# | echo: false\n",
        "# | eval: false\n",
        "print(f\"S.y_mo.shape: {S.y_mo.shape}\")\n",
        "print(f\"min loss: {np.nanmin(S.y_mo[:,0]):.2f}\")\n",
        "print(f\"min epochs: {np.nanmin(S.y_mo[:,1])}\")\n",
        "# print unique values of S.y_mo[:,1]\n",
        "print(f\"unique epochs values: {np.unique(S.y_mo[:,1])}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| echo: false\n",
        "#| eval: false\n",
        "# generate a dataframe with S.y and S.y_mo\n",
        "df = pd.DataFrame(S.y_mo, columns=[\"loss\", \"epochs\"])\n",
        "df[\"y\"] = S.y\n",
        "# get the row where y is min and show the corresponding loss and epochs\n",
        "df_min = df.loc[df[\"y\"].idxmin()]\n",
        "print(f\"min y: {df_min['y']}\")\n",
        "print(f\"loss: {df_min['loss']}\")\n",
        "print(f\"epochs: {df_min['epochs']}\")\n",
        "# put df_min loss and epochs into a 1-d numpy array\n",
        "best_point = np.array([df_min[\"loss\"], df_min[\"epochs\"]])\n",
        "print(f\"best_point: {best_point}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 2: Pareto Front Visualization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-des-mohyperlight-0002-pareto-2\n",
        "y_orig = S.y_mo\n",
        "df_z = pd.DataFrame(y_orig, columns=[\"loss\", \"epochs\"])\n",
        "df_z_sel = df_z.dropna()\n",
        "target_names = [\"loss (log-log)\", \"epochs\"]\n",
        "combinations=[(0,1)]\n",
        "plot_mo(y_orig=df_z_sel, target_names=target_names, combinations=combinations, pareto=\"min\", pareto_front_orig=True, title=\"Pareto front (minimization)\", pareto_label=True, x_axis_transformation=\"loglog\", figsize=(6, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 2: Pareto Front Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#| label: fig-mohyperlight-0002-pareto-3\n",
        "# remove loss values larger than 1e5 from the y_orig array\n",
        "y_orig = S.y_mo\n",
        "df_z = pd.DataFrame(y_orig, columns=[\"loss\", \"epochs\"])\n",
        "# remove rows with loss > 1e5\n",
        "df_z = df_z[df_z[\"loss\"] < 1e5]\n",
        "df_z_sel = df_z.dropna()\n",
        "target_names = [\"loss\", \"epochs (log)\"]\n",
        "combinations=[(0,1)]\n",
        "plot_mo(y_orig=df_z_sel, target_names=target_names, combinations=combinations, pareto=\"min\", pareto_front_orig=True, title=\"Pareto front (min). Points with loss > 1e5 removed\", pareto_label=True, y_axis_transformation=\"log\", figsize=(6, 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 2: Results\n",
        "\n",
        "| Algorithm | Validation Loss (min!) | Epochs (min!)| Performance |\n",
        "|-----------|----------------|--------|-------------|\n",
        "| Single-Objective | **2890** | 1024 | Best accuracy, huge cost |\n",
        "| Weighted-Sum | 5824 | 64 | Poor solution |\n",
        "| **Desirability** | **2960**  | **32** | **Superior trade-off** |\n",
        "\n",
        "- Desirability achieves **nearly identical accuracy**\n",
        "- **Reduction** in computational cost\n",
        "- Acts as intelligent, budget-aware search space pruner\n",
        "\n",
        "# Analysis & Discussion\n",
        "\n",
        "## Advantages of Desirability Functions\n",
        "\n",
        ":::: {.columns}\n",
        "\n",
        "::: {.column width=\"50%\"}\n",
        "### Intuitive & Practitioner-Focused {.example}\n",
        "- Direct translation of engineering specs\n",
        "- \"Error rate < 5%\"\n",
        "- \"Latency < 50ms\" \n",
        "- \"Cost < $10/unit\"\n",
        "- No abstract weight assignment\n",
        ":::\n",
        "\n",
        "::: {.column width=\"50%\"}\n",
        "### Single, Actionable Solution {.example}\n",
        "\n",
        "- Avoids decision paralysis\n",
        "- Industry prefers definitive solutions\n",
        "- Ready for deployment\n",
        "- Best combined preference satisfaction\n",
        ":::\n",
        "\n",
        "::::\n",
        "\n",
        "## Limitations & Caveats\n",
        "\n",
        "### Critical Limitations {.alert}\n",
        "\n",
        "**Scalarization Blind Spots:**\n",
        "\n",
        "- Cannot find solutions in non-convex Pareto regions\n",
        "- MOEAs superior for complete front mapping\n",
        "\n",
        "**Sensitivity to Outliers:**\n",
        "\n",
        "- Geometric mean sensitive to extreme values\n",
        "- Single poor objective → overall desirability collapse\n",
        "\n",
        "**The Plateau Problem:**\n",
        "\n",
        "- Zero desirability creates flat landscapes\n",
        "- No gradient information for optimizers\n",
        "- Navigation challenges in unacceptable regions\n",
        "\n",
        "\n",
        "## Future Work: Enhancements\n",
        "\n",
        "### 1. Synergy with MOEAs\n",
        "- Step 1: MOEAs generate complete Pareto front\n",
        "- Step 2: Desirability selects best solution\n",
        "- Combines exploration power + preference articulation\n",
        "\n",
        "### 2. Bio-Inspired Modifications\n",
        "- \"Leaky\" desirability functions (inspired by Leaky ReLU)\n",
        "- Small non-zero scores in unacceptable regions\n",
        "- Provides optimization signals on plateaus\n",
        "\n",
        "## Future Work: Continued\n",
        "\n",
        "### 3. Rigorous Benchmarking\n",
        "- Compare against advanced scalarization techniques\n",
        "- Augmented Tchebycheff functions\n",
        "- Test on non-convex Pareto fronts\n",
        "\n",
        "### 4. Broader Model Exploration\n",
        "- Beyond Kriging: Random Forests, Neural Networks\n",
        "- Identify optimal surrogate-desirability pairings\n",
        "- Leverage `spotpython` framework\n",
        "\n",
        "# Conclusion\n",
        "\n",
        "## Summary & Impact\n",
        "\n",
        "- Desirability functions: **underappreciated** in academic ML\n",
        "- **Practical, powerful method** for multi-objective HPO\n",
        "- **Key contributions:**\n",
        "  - `spotdesirability` Python package\n",
        "  - Easier, more intuitive than weighted-sum methods\n",
        "  - Superior trade-offs in practical applications\n",
        "  - 32-fold computational savings demonstrated\n",
        "\n",
        "**Impact:** Valuable addition to modern multi-objective optimization toolkit\n",
        "\n",
        "## Thank You! Questions?\n",
        "\n",
        "### Contact Information\n",
        "\n",
        "* **Updates and Jupyter Notebook of this Presentation:** [https://thk-ai.de](https://thk-ai.de)\n",
        "\n",
        "### Key References\n",
        "- @hari65a - Original desirability functions\n",
        "- @derr80a - Geometric mean formulation  \n",
        "- @kuhn16a - Reference implementation in R\n",
        "- @bart25a - ArXiv Paper\n",
        "\n",
        "### Software\n",
        "\n",
        "* **Code:** `spotdesirability` Python package available on GitHub and PyPi:\n",
        "    * [GitHub](https://github.com/sequential-parameter-optimization/spotdesirability)[https://github.com/sequential-parameter-optimization/spotdesirability]\n",
        "\n",
        "## References {.unnumbered}\n",
        "\n",
        "::: {#refs}\n",
        ":::"
      ]
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