{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "290d0b41-0b9a-4cc0-ad2b-55e88ac82f71",
   "metadata": {
    "originalKey": "290d0b41-0b9a-4cc0-ad2b-55e88ac82f71"
   },
   "source": [
    "# Concrete Strength Model Tutorial\n",
    "In this notebook, we show how to train and interact with the Gaussian process concrete strength model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ad164a9-3dc3-4f8f-99f4-e7e6b13e05d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:56.295922Z",
     "iopub.status.busy": "2026-03-18T21:34:56.295784Z",
     "iopub.status.idle": "2026-03-18T21:34:58.820982Z",
     "shell.execute_reply": "2026-03-18T21:34:58.820201Z"
    },
    "originalKey": "7ad164a9-3dc3-4f8f-99f4-e7e6b13e05d9"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import botorch\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "from boxcrete.units import STRENGTH_DISPLAY_SCALE, strength_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74dfddce-0947-493b-8442-a0fafa436906",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.823075Z",
     "iopub.status.busy": "2026-03-18T21:34:58.822825Z",
     "iopub.status.idle": "2026-03-18T21:34:58.830129Z",
     "shell.execute_reply": "2026-03-18T21:34:58.829450Z"
    },
    "originalKey": "74dfddce-0947-493b-8442-a0fafa436906"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1fd014-0886-4939-81ee-5073791d4167",
   "metadata": {
    "originalKey": "cf1fd014-0886-4939-81ee-5073791d4167"
   },
   "source": [
    "## System Setup\n",
    "\n",
    "This cell automatically configures the environment for both local development and CI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b75d27e5-29f2-498b-83ac-34d3e94a6ff8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.832567Z",
     "iopub.status.busy": "2026-03-18T21:34:58.832402Z",
     "iopub.status.idle": "2026-03-18T21:34:58.835484Z",
     "shell.execute_reply": "2026-03-18T21:34:58.834818Z"
    },
    "originalKey": "b75d27e5-29f2-498b-83ac-34d3e94a6ff8",
    "output": {
     "id": 36404349325830400,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization mode: concrete\n",
      "Include cost: False\n"
     ]
    }
   ],
   "source": [
    "# Configuration: optimization mode can be set via environment variables.\n",
    "# BOXCRETE_OPTIMIZATION_MODE: \"mortar\" or \"concrete\" (default).\n",
    "# BOXCRETE_INCLUDE_COST: \"true\" to add cost as an optimization objective.\n",
    "optimization_mode = os.environ.get(\"BOXCRETE_OPTIMIZATION_MODE\") or \"concrete\"\n",
    "include_cost = os.environ.get(\"BOXCRETE_INCLUDE_COST\", \"\").lower() == \"true\"\n",
    "print(f\"Optimization mode: {optimization_mode}\")\n",
    "print(f\"Include cost: {include_cost}\")\n",
    "# Smoke-test mode: CI sets BOXCRETE_SMOKE_TEST=1 to shrink expensive\n",
    "# acquisition-function parameters (num_candidates, raw_samples, ...).\n",
    "# See notebook cells using `SMOKE_TEST` ternary for the per-knob overrides.\n",
    "SMOKE_TEST = os.environ.get(\"BOXCRETE_SMOKE_TEST\", \"\").lower() in (\"1\", \"true\", \"yes\")\n",
    "print(f\"Smoke test mode: {SMOKE_TEST}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71d0da8-d13d-43ce-a849-dd1110678134",
   "metadata": {
    "originalKey": "a71d0da8-d13d-43ce-a849-dd1110678134"
   },
   "source": [
    "## Load Data\n",
    "Assumption on data format:\n",
    "- The \"Mix ID\" column contains entries of the format `<batch_name>_<mix_number>`, e.g. `Feb_2023_0` separates into a batch_name `Feb_2023` and a mix number `0`. Feel free to raise a GitHub issue if this format is restrictive for your use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e275b08-ede7-4d41-8534-628e40d91fbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.862991Z",
     "iopub.status.busy": "2026-03-18T21:34:58.862841Z",
     "iopub.status.idle": "2026-03-18T21:34:58.869582Z",
     "shell.execute_reply": "2026-03-18T21:34:58.868708Z"
    },
    "originalKey": "7e275b08-ede7-4d41-8534-628e40d91fbb"
   },
   "outputs": [],
   "source": [
    "from boxcrete.utils import DATA_PATH\n",
    "data_path = DATA_PATH\n",
    "df = pd.read_csv(data_path, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42c82f92-7769-4e0a-964c-9d3325645e4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.870970Z",
     "iopub.status.busy": "2026-03-18T21:34:58.870821Z",
     "iopub.status.idle": "2026-03-18T21:34:58.877897Z",
     "shell.execute_reply": "2026-03-18T21:34:58.877330Z"
    },
    "originalKey": "42c82f92-7769-4e0a-964c-9d3325645e4d"
   },
   "outputs": [],
   "source": [
    "from boxcrete import utils\n",
    "from boxcrete.utils import (\n",
    "    get_bounds,\n",
    "    get_constraints,\n",
    "    MORTAR_BOUNDS_DICT,\n",
    "    MORTAR_CONSTRAINTS,\n",
    ")\n",
    "\n",
    "data = utils.load_concrete_strength(data_path=data_path)\n",
    "\n",
    "# Always use default (concrete) bounds for training since the model\n",
    "# trains on all data (mortar + concrete). Mortar/concrete-specific\n",
    "# bounds are used for optimization constraints (below).\n",
    "data.bounds = get_bounds(data.X_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f85e6d88-8308-4597-98bb-62f2affbd425",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.879131Z",
     "iopub.status.busy": "2026-03-18T21:34:58.878998Z",
     "iopub.status.idle": "2026-03-18T21:34:58.882311Z",
     "shell.execute_reply": "2026-03-18T21:34:58.881907Z"
    },
    "originalKey": "f85e6d88-8308-4597-98bb-62f2affbd425",
    "output": {
     "id": 4275038529416104,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cement (kg/m3)',\n",
       " 'Fly Ash (kg/m3)',\n",
       " 'Slag (kg/m3)',\n",
       " 'Water (kg/m3)',\n",
       " 'HRWR (kg/m3)',\n",
       " 'MRWR (kg/m3)',\n",
       " 'Fine Aggregate (kg/m3)',\n",
       " 'Coarse Aggregates (kg/m3)',\n",
       " 'Material Source',\n",
       " 'Temp (C)',\n",
       " 'Time']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.X_columns  # model inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7ef7031-6cd0-4437-afd6-ea1b427fb8a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.883769Z",
     "iopub.status.busy": "2026-03-18T21:34:58.883642Z",
     "iopub.status.idle": "2026-03-18T21:34:58.885913Z",
     "shell.execute_reply": "2026-03-18T21:34:58.885360Z"
    },
    "originalKey": "b7ef7031-6cd0-4437-afd6-ea1b427fb8a4"
   },
   "outputs": [],
   "source": [
    "X, Y, Yvar, X_bounds = data.strength_data  # strength data contains all time-dependent data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75651f0-d5ce-4a2e-a222-74345e869a9b",
   "metadata": {
    "originalKey": "b75651f0-d5ce-4a2e-a222-74345e869a9b"
   },
   "source": [
    "## Load Search Space Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba14d279-3650-46c8-81b5-7b8a22885fec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.887088Z",
     "iopub.status.busy": "2026-03-18T21:34:58.886965Z",
     "iopub.status.idle": "2026-03-18T21:34:58.889488Z",
     "shell.execute_reply": "2026-03-18T21:34:58.888958Z"
    },
    "originalKey": "ba14d279-3650-46c8-81b5-7b8a22885fec"
   },
   "outputs": [],
   "source": [
    "if optimization_mode == \"mortar\":\n",
    "    bounds = get_bounds(data.X_columns[:-1], MORTAR_BOUNDS_DICT)\n",
    "else:\n",
    "    bounds = get_bounds(data.X_columns[:-1])  # excluding time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "011dfd2b-e870-4270-90c9-62c9756e5199",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.890602Z",
     "iopub.status.busy": "2026-03-18T21:34:58.890482Z",
     "iopub.status.idle": "2026-03-18T21:34:58.893640Z",
     "shell.execute_reply": "2026-03-18T21:34:58.893064Z"
    },
    "originalKey": "011dfd2b-e870-4270-90c9-62c9756e5199"
   },
   "outputs": [],
   "source": [
    "if optimization_mode == \"mortar\":\n",
    "    equality_constraints, inequality_constraints = get_constraints(\n",
    "        data.X_columns[:-1], **MORTAR_CONSTRAINTS\n",
    "    )\n",
    "else:\n",
    "    equality_constraints, inequality_constraints = get_constraints(\n",
    "        data.X_columns[:-1]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d96215-3acc-4d62-a4ad-1bca289e1f61",
   "metadata": {
    "originalKey": "09d96215-3acc-4d62-a4ad-1bca289e1f61"
   },
   "source": [
    "## Define and Fit Model\n",
    "1. fit strength model\n",
    "2. fit GWP model\n",
    "3. unify as composition-input-only with fixed-time outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cb3405d-cb07-44d8-819f-1567b7fe5bfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.895089Z",
     "iopub.status.busy": "2026-03-18T21:34:58.894896Z",
     "iopub.status.idle": "2026-03-18T21:34:58.897469Z",
     "shell.execute_reply": "2026-03-18T21:34:58.896668Z"
    },
    "originalKey": "0cb3405d-cb07-44d8-819f-1567b7fe5bfb"
   },
   "outputs": [],
   "source": [
    "from boxcrete.concrete_model import SustainableConcreteModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da2c7d05-9a77-4336-88b9-87025505940f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.898621Z",
     "iopub.status.busy": "2026-03-18T21:34:58.898497Z",
     "iopub.status.idle": "2026-03-18T21:34:58.900688Z",
     "shell.execute_reply": "2026-03-18T21:34:58.900169Z"
    },
    "originalKey": "da2c7d05-9a77-4336-88b9-87025505940f"
   },
   "outputs": [],
   "source": [
    "model_strength_days = [1, 28]  # the strength days that are converted to optimization objectives\n",
    "model = SustainableConcreteModel(strength_days=model_strength_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "817993fe-d857-4986-afea-55a7f7ba643f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:58.901792Z",
     "iopub.status.busy": "2026-03-18T21:34:58.901674Z",
     "iopub.status.idle": "2026-03-18T21:34:59.389912Z",
     "shell.execute_reply": "2026-03-18T21:34:59.388703Z"
    },
    "originalKey": "817993fe-d857-4986-afea-55a7f7ba643f",
    "output": {
     "id": 1677609463249588,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearModel()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_gwp_model(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f95e8dc-3b5c-4fb9-ac95-dee80d6b052d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:34:59.392017Z",
     "iopub.status.busy": "2026-03-18T21:34:59.391735Z",
     "iopub.status.idle": "2026-03-18T21:35:16.862839Z",
     "shell.execute_reply": "2026-03-18T21:35:16.862367Z"
    },
    "originalKey": "5f95e8dc-3b5c-4fb9-ac95-dee80d6b052d",
    "output": {
     "id": 1381603910371802,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SingleTaskGP(\n",
       "  (likelihood): GaussianLikelihood(\n",
       "    (noise_covar): HomoskedasticNoise(\n",
       "      (raw_noise_constraint): LogTransformedInterval(1.000E-06, 1.000E+00)\n",
       "    )\n",
       "  )\n",
       "  (mean_module): ConstantMean()\n",
       "  (covar_module): AdditiveKernel(\n",
       "    (kernels): ModuleList(\n",
       "      (0): ScaleKernel(\n",
       "        (base_kernel): MaternKernel(\n",
       "          (raw_lengthscale_constraint): LogTransformedInterval(1.000E-02, 1.000E+03)\n",
       "        )\n",
       "        (raw_outputscale_constraint): LogTransformedInterval(1.000E-02, 1.000E+02)\n",
       "      )\n",
       "      (1): ScaleKernel(\n",
       "        (base_kernel): RBFKernel(\n",
       "          (raw_lengthscale_constraint): LogTransformedInterval(1.000E-02, 1.000E+03)\n",
       "        )\n",
       "        (raw_outputscale_constraint): LogTransformedInterval(1.000E-02, 1.000E+02)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (outcome_transform): Standardize()\n",
       "  (input_transform): ChainedInputTransform(\n",
       "    (tf1): AffineInputTransform()\n",
       "    (tf2): Log10()\n",
       "    (tf3): Normalize()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use_fixed_noise=False because with only 3 measurements per sample,\n",
    "# we can't trust the empirical variance completely. Instead, we infer\n",
    "# the noise level using marginal likelihood optimization.\n",
    "model.fit_strength_model(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed36a6c5-5e6c-423a-a4ab-877ec0da8010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:16.865369Z",
     "iopub.status.busy": "2026-03-18T21:35:16.865096Z",
     "iopub.status.idle": "2026-03-18T21:35:16.871064Z",
     "shell.execute_reply": "2026-03-18T21:35:16.870416Z"
    },
    "originalKey": "ed36a6c5-5e6c-423a-a4ab-877ec0da8010",
    "output": {
     "id": 739888499211168,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full columns (no Time): ['Cement (kg/m3)', 'Fly Ash (kg/m3)', 'Slag (kg/m3)', 'Water (kg/m3)', 'HRWR (kg/m3)', 'MRWR (kg/m3)', 'Fine Aggregate (kg/m3)', 'Coarse Aggregates (kg/m3)', 'Material Source', 'Temp (C)']\n",
      "Optimization columns: ['Cement (kg/m3)', 'Fly Ash (kg/m3)', 'Slag (kg/m3)', 'Water (kg/m3)', 'HRWR (kg/m3)', 'MRWR (kg/m3)', 'Fine Aggregate (kg/m3)', 'Coarse Aggregates (kg/m3)']\n",
      "Fixed features: {8: 0.0, 9: 22.0}\n",
      "Model outputs: ['GWP', '1-day Strength', '28-day Strength']\n"
     ]
    }
   ],
   "source": [
    "# Build fixed_features based on optimization mode.\n",
    "# In mortar mode, Coarse Aggregates and Material Source are fixed.\n",
    "# In concrete mode, only Material Source is fixed (Coarse Aggregates is optimized).\n",
    "# Temperature is always fixed since it is an external condition, not a design variable.\n",
    "fixed_features = {}\n",
    "X_cols = data.X_columns[:-1]  # exclude Time (handled by get_model_list)\n",
    "\n",
    "if optimization_mode == \"mortar\":\n",
    "    coarse_idx = data.X_columns.index(\"Coarse Aggregates (kg/m3)\")\n",
    "    fixed_features[coarse_idx] = 0.0\n",
    "\n",
    "if \"Material Source\" in data.X_columns:\n",
    "    ms_idx = data.X_columns.index(\"Material Source\")\n",
    "    fixed_features[ms_idx] = 0.0  # default material source\n",
    "\n",
    "# Fix temperature: not a controllable design variable.\n",
    "temp_idx = data.X_columns.index(\"Temp (C)\")\n",
    "fixed_features[temp_idx] = 22.0  # most common curing temperature in training data\n",
    "\n",
    "# Optionally fit cost model (instant, no optimization required)\n",
    "if include_cost:\n",
    "    model.fit_cost_model(data)\n",
    "    print(f\"Cost model constructed with {len(data.X_columns) - 1} coefficients.\")\n",
    "\n",
    "model_list = model.get_model_list(fixed_features=fixed_features if fixed_features else None)\n",
    "\n",
    "# Compute optimization-space column names (excluding Time and fixed features)\n",
    "time_idx = data.X_columns.index(\"Time\")\n",
    "fixed_features_opt = {k: v for k, v in fixed_features.items() if k != time_idx}\n",
    "opt_columns = [\n",
    "    col for i, col in enumerate(data.X_columns[:-1])\n",
    "    if i not in fixed_features_opt\n",
    "]\n",
    "print(f\"Full columns (no Time): {data.X_columns[:-1]}\")\n",
    "print(f\"Optimization columns: {opt_columns}\")\n",
    "print(f\"Fixed features: {fixed_features}\")\n",
    "print(f\"Model outputs: {model.model_names}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac18bd52-4235-4cfe-9c15-4190f0734dec",
   "metadata": {
    "originalKey": "ac18bd52-4235-4cfe-9c15-4190f0734dec"
   },
   "source": [
    "## GWP Fit on Training Set\n",
    "\n",
    "The GWP \"observations\" come from a linear model in the default dataset, so \"predictions\" of GWP are perfect. However, note that the methods allow for arbitrary, even a-priori non-predictable quantifications of sustainability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f08a8378-cd38-4080-9160-b7ea43e15bd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:16.872495Z",
     "iopub.status.busy": "2026-03-18T21:35:16.872362Z",
     "iopub.status.idle": "2026-03-18T21:35:16.884413Z",
     "shell.execute_reply": "2026-03-18T21:35:16.881089Z"
    },
    "originalKey": "f08a8378-cd38-4080-9160-b7ea43e15bd4",
    "output": {
     "id": 1288893003336814,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bounds do not hold in training data: X_bounds[0, :], X.amin(dim=0) = (tensor([  0.,   0.,   0.,   0.,   0.,   0., 400.,   0.,   0.,   0.]), tensor([  0.,   0.,   0.,  43.,   0.,   0., 423.,   0.,   0., -20.],\n",
      "       dtype=torch.float64))X_bounds[1, :], X.amax(dim=0) = (tensor([1.0000e+03, 6.0000e+02, 1.3000e+03, 1.4500e+03, 2.9000e+02, 1.0000e+00,\n",
      "        2.6000e+03, 1.6000e+03, 1.0000e+00, 4.0000e+01]), tensor([8.9500e+02, 5.3300e+02, 1.1990e+03, 4.4300e+02, 1.4670e+01, 0.0000e+00,\n",
      "        2.3570e+03, 1.3560e+03, 1.0000e+00, 2.2000e+01], dtype=torch.float64))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GWP calibration: use raw gwp_model (model_list has FixedFeatureModel wrappers\n",
    "# that change the input dims for optimization, not suitable for calibration).\n",
    "from boxcrete.plotting import plot_calibration\n",
    "\n",
    "X, Y, Yvar, X_bounds = data.gwp_data\n",
    "gwp_post = model.gwp_model.posterior(X)\n",
    "pred = gwp_post.mean.squeeze(-1).detach()\n",
    "truth = Y.squeeze(-1).detach()\n",
    "\n",
    "# No error bars: GWP is computed deterministically from emission factors,\n",
    "# so the model uncertainty reflects coefficient estimation noise (rounding),\n",
    "# not genuine prediction uncertainty.\n",
    "fig = plot_calibration(\n",
    "    observed=truth,\n",
    "    predicted_mean=pred,\n",
    "    title=\"GWP Model Calibration\",\n",
    "    xlabel=\"Actual GWP (negated, kg CO\u2082/m\u00b3)\",\n",
    "    ylabel=\"Predicted GWP\",\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3648d0f4-4788-4b3a-b1e0-acd771bd058a",
   "metadata": {
    "originalKey": "3648d0f4-4788-4b3a-b1e0-acd771bd058a"
   },
   "source": [
    "## Strength Fit on Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f47381ab-6a48-4525-ae6b-8303e1a08461",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:17.003479Z",
     "iopub.status.busy": "2026-03-18T21:35:17.003242Z",
     "iopub.status.idle": "2026-03-18T21:35:17.429748Z",
     "shell.execute_reply": "2026-03-18T21:35:17.429006Z"
    },
    "originalKey": "f47381ab-6a48-4525-ae6b-8303e1a08461",
    "output": {
     "id": 912498821819604,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vHxf6WnfEWbV1W+eb8szRSDqnm20bNcj+o26xbcpx0mCmZdMys6U2L41XVN9+stntxZorkKfQCF9nAApVAAAAwEWvN/R1BqiAjieZGvKfVD17U6A61yueH8/OW0G6UemS5zsG3ZJiBcpmOWSbEu1yfI9ZQ/4y9XHACZfjfoap2+wbpL0bdOCNN5Upm6Jsx4onWSA/z3FqZWlAjyoAAAAA8IF18Zlq+WGyVhxwaNDcVB06Z7pxlecSrPAcK5i8KcUK1F6zRp6Fpa2OeqppJKiezXWR6lL1bCdU00jQVkc9L2cKoKygUAUAAAAAJWzjUYc6zUzR0fN/nOB3KsXSgK9SlJpRuBP98pMmf6/HlKT9ZqSOWlXUzH7A5fges4Ya2o4pxMMG7iFGmhrajmmPWcNLmQIoSyhUAQAAAEAJu76GTd0b5tzqdyrF0pHz3i9UBSrDq/F+ddTXdseVqmecyHMllWVJfjI9LlJlCTHSZJcpy/sfB4BSjh5VAAAAgCRN2OvrDFCB2CR9Nuac2nTurt/27NPNN92or2b9SxERVb1+r6DTp5X5cXuvbP/LsGz6S8ajmvXAzTKqheU577v/LVTPreOKdK/6thP6X7O3dOutvYsUB0DZQqEKAAAAEKc9oeRdFhqh+Qv+q88++0wvPPl/8vMrnh/PIiMiFGNrrS7WuiLHWma2VIqCC5wXvO3fRb6XJAVs+7dUXgpV/DcGcIthWSymhHfEx8erTp06kqTDhw+rdu3avk4JAAAA8Ln09HQFBATkP+m5ysWawzZHXV1jP1jkONsdV6qp/VC+c85bQQpWuldWcGVaNqUqQJWMC0WO5XOcKAcvKs8/f9OjCgAAAACKQVpamsaMGaO77rpLplk8p+65q6ntoA6YkUWKsd+MVBNb/kUqefmUQT/DVIJVySuxAJQNFKoAAAAAwMuOHDmim266Sf/61780f/58vfTSSz7NxzCkTNmUYgUW6voUK1AO2WQYBc/19imDaSpgNRqAcoVCFQAAAAB4kWma6tGjh9at+7Mn1KRJk7R48WKf5hVlO6a9Zg2Pi1UpVqD2mjXyPOHvUt4+ZTBQ6V6NB6B0o5k6AAAAAHiRzWbTW2+9pR49eji3/AUHBys1NTXvi0ro1MlmklauWqnqPz+jaCO+wPm7zdqalDFCv1l1lL3+NPeB9mqYx6l/3j1l0K6gUYukCBqRAxUFhSoAAAAA8LJbbrlFr776qiZMmKAGDRpo3rx5at68ed4XlOCJcJ16DNCuZjdr8LR/aZh9qXrYYnMUlTIsu5aYrTTb0U1rzasl5d7v5wiuKoW67h0VGRqh721t1dVaU+RcV9rbqmvdxkWOA6DsoFAFAAAAAMXgr3/9qyTpvvvuU5UqVXydTg42u11rzSZaazZRmFIUaZxVmFKVpGCdsC5XkkKKFD/1upHS5qIXqi5cd2+RYwAoW+hRBQAAAACFlJCQkOeYYRj629/+VuqKVJdKUoj2WrW0xYrSXqtWkYtUktTz9oGKs2oXKcZuq45u63VHkXMBULZQqAIAAAAAD1mWpXfffVf169fX5s2bfZ1OqWOz23Wk81tKLuQpg8lWoI52niqb3e713ACUbhSqAAAAAMADqampGjlypB599FElJiaqf//+On36tK/TKnW6dOmm76+d4nGxKtkK1PfXTlGXLt2KLTcApReFKgAAAADwwNtvv61PP/3U+fzgwYMaPHiwMjMzfZpXadRnwFCt7/y529sAd1t1tL7z5+ozYGix5wagdKJQBQAAAAAeGD9+vG644YYcr7Vv316Gkft0PPyxssocu1qD0ydqsaONMq2cP4ZmWHYtcrTV542n66pJW1hJBVRwnPoHAAAAAB4ICAjQ119/rVatWikxMVGffvqp+vfv7+u0SjV3Thlc2qUTPakAUKgCAAAAAE/VqFFD8+fPV1hYmK6++mpfp1OmJClESVbRTxYEUD5RqAIAAAAAFw4dOqQrrrhCQUFBLsdbt25d4jkBQHlHjyoAAAAAuMTy5cvVokULPfzww7Isy9fpAECFQaEKAAAAAC6yLEtvvvmmevTooTNnzmjGjBn68MMPfZ0WAFQYFKoAAAAA4KJDhw5p0qRJMk3T+dojjzyidevW+TQvAKgoKFQBAAAAwEV169bVJ598kuO1nj170jAdAEoIhSoAAAAAyGbQoEF64oknZBiGnn/+eX3zzTcKDw/3dVoAUCFw6h8AAAAAXGLy5Mnq3bu3OnTo4OtUAKBCYUUVAAAAgAonKSlJsbGxeY7b7XaKVADgAxSqAAAAAFQoe/bsUfv27dWtWzft3bvX1+kAALKhUAUAAACgwvjuu+/UunVrbdu2Tb///rv69eunpKQkX6cFALiIQhUAAACACmPWrFn6/fffnc+3bdumxx57zKc5AQD+RKEKAAAAQIXx0UcfqWnTps7n11xzjZ588kmf5gQA+BOFKgAAAAAVRlhYmObPn6/LLrtMgwYN0po1axQVFeXrtAAAF/n5OgEAAAAAKElRUVGKjY1VgwYNZBiGr9MBAGRDoQoAAAAow+JOnFf3qSvznbN0XCdFR1YqsZx8zTRNffHFFxoyZIjsdrvLOQ0bNizxvAAABWPrHwAAAIByIzExUQMGDNCwYcP0zDPP+DodAICHKFQBAAAAKBd2796ttm3basGCBZKkl19+WV9//bWv0wIAeIBCFQAAAIBy4dSpU9qzZ0+O10aNGqXff//dZzkBADxDoQoAAABAudCxY0e9/fbbzudVq1bVf/7zH1122WU+zQsA4D6aqQMAAAAoNx588EFt3LhRmzZt0rx581SvXj1fp1RmVbQm/ABKBwpVAAAAAMoNwzA0ffp0maapkJAQX6cDAPAQW/8AAAAAlCnz5s3T2rVr8xwPCgqiSAUAZRSFKgAAAABlgsPh0DPPPKMBAwbojjvu0PHjx32dEgDAyyhUAQAAACj1zp8/rz59+ujFF1+UJB09elQDBw5Uenq6r1MDAHgRhSoAAAAApV5wcLAuXLiQ47VVq1bpq6++8llOAADvo1AFAAAAoNTz8/PTV199pbp16zqfv/POOxo6dKivUwMAeBGn/gEAAAClSNyJ8+o+dWW+c5aO66ToyEolllNpERERoW+++UZ33HGHZs2apU6dOvk6JQCAl1GoAgAAAFBmtGjRQnFxcfL39/d1KgCAYsDWPwAAAAClxq+//qonnnhClmXlOYciFQCUX6yoAgAAAFAqfPXVV7rvvvuUkpKi6tWra9y4cb5OqdyKjqykA6/08nUaAJALK6oAAAAA+Nzzzz+vwYMHKyUlRZI0YcIE/fDDD75OCwBQwihUAQAAAPC566+/Psdzh8OhKVOm+CwfAIBvsPUPAAAAgM/17t1bzz33nJ577jlJ0ujRozVt2jRfpwUvYashAHdRqAIAAACQQ9yJ8+o+dWW+c5aO66ToyEpeve8zzzyjbdu2qVu3bhozZoxXYwMAygYKVQAAAABKBZvNpjlz5sgwDF+nAgDwEXpUAQAAACgRp06dUr9+/bRjx44851CkAoCKjRVVAAAAAIrdpk2b1L9/fx06dEg7d+7U+vXrVblyZV+nBQAoZVhRBQAAAKBYrVq1Sh06dNChQ4ckSXFxcRo6dKhM0/R1agCAUoZCFQAAAFBOhClFUUa8rjX2KMqIV5hSfJ2SJKlly5Zq0qRJjtc2bdqk+Ph4n+UEACid2PoHAAAAlGGmw6H2tu0aZl+m7rZY+Rl/rlLKtGxaYrbShh8SFDXobtnsdp/kGBwcrHnz5qlly5Y6ffq0OnbsqLlz56p69eo+yQcAUHpRqAIAAADcFHfivLpPXZnvnKXjOik6slKJ5BMTs0y1VjymLwNcr0zyM0z1sq+Xdq9X3POv6Ejnt9SlS7cSye1SV155pebMmaN58+bpjTfeUEBAgE/yAACUbmz9AwAAAMqghd/MVpsV9yjacG/7XLQRrzYr7tHCb2YXa1759Z3q0qWL3nnnHYpUAIA8UagCAAAAypgt61eo65bxCjXSPLou1EhT1y3jFROzzOs5ZWRk6JFHHtG4ceO8HhsAUHGw9Q8AAAAoUyxdG/ukx0WqLKFGmmquGCez0xav9aw6ceKEBg4cqJ9++km62Dx9+PDhXokNAKhYWFEFAAAAlCHtbTvc3u6Xl0bGYX23+D9eySc9PV0dO3Z0FqkkaezYsdq4caNX4gMAKhZWVAEAAACSlHy6wCn21CRVUWIBc85IyYVb7eTOPYbblxY6dnbBm2dIXW8uVA7K9j4DJD39t0d17wOP5hjfv3OLWjauW7QkQyOKdj0AoMwxLMuyfJ0Eyof4+HjVqVNHknT48GHVrl3b1ykBAAC477nKvs6gQOetIAUrXX5G3g3L3ZVp2ZSqAFUyLnglt0e+TdW0DRm6srKhbwaFqGVNL2wrfO6cN1IDgHKnPP/8zYoqAAAAoIxIsMJV13bSK7H8DFMJZiWvFaqm9AhSoJ+hJzoEqFooHUYAAIVDoQoAAAAoI9Lk7+V4AR7NT82wFOxvuBzztxt6o3uQlzIDAFRU/KoDAAAAKCMCleHleOluz/1wY7qipyXp0LmibzsEACAvrKgCAAAAJGnC3gKn7D2VpIHvr8l3ztwH2qthtbBCp5HfPUKUqhWBf/VKj6oMy66gUYukiNwNy7PnYGZmKH75TJ3ZEiNJavzvaoq++1n955GbivQ+AQBwhUIVAAAAIPdOmHMkBSpB4fnPCa4qhVYqdBr53SNB4VpitlIv+/pCx8+y0t5WXes2LjCHs6tnKvFikUqSUo/v156lnylzQn8pNP/PAgAAT7H1DwAAAChDZju6eSXOhevudWte5bZ3yu/ymjles4dUFoeHAwCKA4UqAAAAoAxZYzZRnFW0Y8h3W3V0W6873JprCwpTtQETZQQEy/ALVETvCbq8y32y2fhRAgDgfWz9AwAAAMoUQ1tavaJasaMUaqR5fHWyFaijnaeqkd3u9jUBEVeqWp/HZa9UVQFXNPD4ngAAuItCFQAAAFDGXNums77PmKKuW8Z7VKxKtgL1/bVT1KdL7u2DJ0+eVERERJ4rpYIbts7x/MDpZHWfujLf+y0d10nRkYXv1wUAqHhYrwsAAACUQX0GDNX6zp+7vQ1wt1VH6zt/rj4DhuYa++mnn9SsWTNNnjy5GDIFAMB9FKoAAACAMqpLl24yx67W4PSJWuxoo0wr57f3GZZdixxt9Xnj6bpq0hZ1uWQllWVZmj59um6++WadPHlSzz77rBYtWlTC7wIAgD+x9Q8AAAAow2x2u9aaTbTWbKIwpSjSOKswpSpJwTphXa4khWhpl06yuehJtWPHDj366KMyTVO6WLi65557NOe7GB+8EwAAWFEFAAAAlBtJCtFeq5a2WFHaa9VSkkLynd+0aVO9+uqrOV7r0KGDqlSNKOZMAQBwjUIVAAAAUIH99a9/1eDBgyVJf//73/Xf//5XlS+73NdpAQAqKLb+AQAAABWYYRj66KOPNHz4cN12222+TgcAUMGxogoAAAAohDClKMqI17XGHkUZ8QpTiq9TytOF1FTt2bMnz/HQ0FCKVACAUoEVVQAAAICbTIdD7W3bNcy+TN1tsfIzTOdYpmXTErOVNvyQoKhBd7tsXu4LmedOakif7kpO/F2xsbGKiKD/FACg9GJF1UWHDh3Ss88+q1atWqlatWoKCgpSnTp1dOONN2rSpEnatm1bvtd/99136t+/v2rXrq3AwEDVrl1b/fv313fffed2DpmZmXr//fd14403qlq1agoODlbDhg01duxYbd++3e04p0+f1qRJk9S8eXOFh4crPDxczZs316RJk3TmzBm34wAAAOBPMTHLZPugg74MmKye9vU5ilSS5GeY6mVfr3t2P6w9zzdXTMwyn+WaJfXgFh2b9Zh2bN2igwcPavDgwcrMzPR1WgAA5IkVVZLeffddPfXUU0pOTs7xenx8vOLj47Vq1SolJibqrbfeynWtaZoaM2aMZsyYkeP1I0eO6MiRI5o/f75Gjx6tDz74QDZb3nXB06dPq2fPntqwYUOO1/ft26cPP/xQs2bN0rRp0zR69Oh838u6devUr18/HT9+PMfrW7du1datW/XRRx9p/vz5atOmTb5xAAAA8KeF38xW1y3jFWqkuTU/2ohXrRX3aOHZKeozYGix5+eKZVk6t/pLmamJzte+//57Pfvss5o8ebJPcgIAoCAVvlD14osv6plnnpEkRUdH6/7771fr1q1VuXJlnTlzRps3b9a8efPyLDI9/fTTziLV9ddfr8cff1wNGzbU3r179dprr2nz5s366KOPVK1aNb300ksuYzgcDvXv399ZpBowYIDuv/9+ValSRevWrdOLL76okydPauzYsapVq1ae/QMOHz6s3r1769SpU/Lz89P48eN1++23S5IWLVqkKVOm6NixY+rdu7c2btyo2rVre+UzBAAAFVPcifPqPnVlvnOWjuuk6MhKJZZTcYiJWeZRkSpLqJGmrlvGK+bySHXp0q3Y8suLYRiK6PO4js96TI6kBElSo0aNNHSobwpnAAC4o0IXqr7//ntnkWr48OH66KOP5O/vn2NO165d9be//U3p6em5ro+Li9Mbb7whSWrVqpVWrlyp4OBgSVLr1q3Vp08f3XTTTYqNjdXrr7+u++67T1FRUbnizJo1S6tWrZIk/eUvf9H06dOdY23atNFtt92mli1bKjExUY8++qh27twpP7/cX7qnn35ap06dkiR98cUXGjhwoHPsxhtvVMuWLXXXXXfp5MmTmjhxombOnFnozw4AAKAiMB0O1VrxmMdFqiyhRppqrhgns9MWn/Ss8guromr9ntKZr/6u2267TZ9++qkqV65c4nkAAOCuCtujyjRNPfjgg5Kka6+9VjNmzMhVpMouICAg12tvvfWWc4//u+++6yxSZQkJCdG7774rXew/NXXqVJexs4pdVapU0euvv55rPCoqSk899ZQkac+ePZo3b16uOcePH9fnn38uSerRo0eOIlWWQYMGqUePHpKkzz77LNf2QAAAgNIg7sR51Xtycb5/4k6cL5Fcvl00V9FGfJFiNDIO67vF//FaTp4KrHW1/r1ouebNm0eRCgBQ6lXYFVVLly7Vb7/9Jkl64oknXK5Qyo9lWVqwYIEkqXHjxmrXrp3Lee3atVOjRo20e/duLViwQNOmTZNhGM7xuLg47dy5U7pYSAoJCXEZZ+TIkc5i1bx583IVohYuXCjT/KOh57333ptn3iNHjtSSJUtkmqYWLlyoMWPGePS+AQBABZN8Os8he2qSqigxz/E/5pyRkj1bjVRccQsj9JePvRInePMMqevNbs3N/v5DlKpqxjkFK02pCtQpq7JSFJzj/dtSzstv30+qVL95ju8zs7s2uqlsqQlu5+vO18A/LcG9r5PK9tZPAEDJqrCFqrlz50oX9+5n9XGSpISEBJ05c0ZVq1ZVlSpV8rx+//79Onr0qCTppptuyvdeN910k3bv3q0jR47owIEDql+/vnMsa8tfQXGqV6+u6OhoxcXFafXq1bnG3Y2TfWz16tUUqgAAQP5eb5jnUENJm4IKuL4QnQaKK66nzltBulHpkuvaj0c6met1/rWmqmRcKHBuA0v61L+u0g1/NTf25ThdMNOy6VergVJnZMiyHVRKhqWnF6Zq7/ZMvdcrSA+0yr0LQPL883Lra7DQza/Tc+c8uzkAoEKrsFv/1q5dK0mqV6+eKlWqpC+++ELNmjVT1apVFR0drapVq6pRo0Z64403lJaW+7d1O3bscD5u3LhxvvfKPp61eqoocQ4fPpzrhMKsOJUrV1b16tXzjFGjRg2Fh4e7zKUgWacg5vXn2LFjHsUDAAAozRKs8BxFoqLwM0wlWAWvLNpj1tBBK1LX2A+qhW1Prvv7GaZa2PboGvtBHbAiNWJFFc3Z/kcrike/u6DVhzK9ki8AAL5SIVdUmaapXbt2SZIiIiL0f//3f3rnnXdyzYuLi9OECRM0b948LV68WJdddplzLD7+z14FBZ2eV6dOHefjw4cP5xgrTBzLshQfH69GjRrliuPOSX516tTR9u3bc+XiznUAAAAVRZry7l9auHh5rHa6aKujnhrajinEzcbt9W0nNKtboFIVrW/XxCnDlAZ9narfHglTiL8XloEBAOADFXJF1blz55z9nLZu3ap33nlHNWrU0OzZs5WQkKCUlBT9+OOPzr5TP//8s+67774cMc6f/7OBZ1hYWL73Cw0NdT5OSkoq1jgFxcge59IYAAAA+FOgMrwcL/cp0ln2mDU8KlJlCTXSNKfbWbVpeqUqB0r/6h3k9SLVeStIB80rFGfW0kHzCp23CtrvBwBA4VXIFVXZt81duHBBISEhiomJybFCqVOnTvrhhx/Uvn17bdmyRfPmzdO6devUtm1b53VZXJ0ImF1gYKDzcWpqao4xb8cpKEb2OJfGKEhBK7COHTumNm3aeBQTAACUchP25jm091SSBr6/Jt/L5z7QXg2rFfyLtJKI66mg06eV+XF7r2z/y7DsChq1SIqIyDVmOhyy3rzZ4yJVllAjTTP7Xq4Bp97QxCo1NdFFGyxPP6/fjp/TyzM+1yD7j7rFtkl1bSedY5mWTd85WutI3f6avudyGfk08Zr7QHvl3eUMAIDcKmShKigo52+BRo8enaNIlSU4OFiTJ092Nlv/6quvnIWq7DHS0/P+7ZikHD2ugoOD88wlPT09V26exElJSSkwl+xxLo1REHe2FQIAgHImNHdhJYsjKVAJCs/3ckdwVSnUs1PfiiuupyJDI/S9ra26WvkXzdyx0t5WXeu67kf67YJ/63Yj3uWYu672P65rI0ytNV1/bp58XjExy1RrxWP6OMB1Tn6GqdvsG6T4DboxoLbGZzyo7VZ9l3MdwVU9eBcAAFTQQlWlSjn/J929e/c853bt2lV+fn7KzMzUhg0bXMYoaAtd9hVcl27NuzROfoWqguKkpKS4tZ0vK4472wQBAAAqsrRrBklbi16oymg6SEo+7XIs9JePixxfkobblyjOdP2LRXvqGSm54BVbixd/o87bJyrUzdVdjWzxmhPwvMZmjNcqs5nHOQMAcKkKWagKDAxUtWrVdOrUKamAJuFBQUGKiIjQ8ePHnfN1yeqi7A3RXcm+Ze7Se10aJ8LFcvBL4xiGkWt1U+3atXXixIkCc8keh+boAAAA+bvt13E6YEWqnu1EoWPsNyPV49fHpK2P5Ro7bwXpRqUrn91zbutu26gbA7eqkuFi79/Mgq/fY9ZQFyOhUH2yPvCfokHpk/JcWQUAgLsqZDN1SWratKnzscPhyHdu1rif3591vSZNmjgfZ50gmJfs41dffXWOscLEqVOnTo7G6tnjnDt3TsePH88zxrFjx5SYmOgyFwAAAORkGFKmbEqxAt2YnVuKFSiHbDLyKEQlWOFe6YGli1vyEqzCbYe0LMlPZpH6ZL3r/66uNfYoyohXmFIKFQcAgApbqOrUqZPz8b59+/Kcl5iYqNOn/1imXatWLefr9evXV82aNSVJP/74Y773WrlypfP6evXq5Rjr2LGj83F+cY4fP664uDhJUocOHXKNuxsn+5irOAAAAL4Ud+K8uk9d6es0coiyHdNes4bHxaoUK1B7zRqKsh3Lc06a/L2QYfZ4BR+s48p2s26RVo1JUgPbcS0InKTlgY/rl8Axmu7/ljb8MF9mAb8UBgAguwpbqLrjjjucj+fNm5fnvHnz5smyLEnSjTfe6HzdMAz17dtXurjSae3atS6vX7t2rXMlVN++fWVc8uu06Oho58qmOXPmKCXF9W+fZs78c712//79c4336dNHNtsfX85PPvkkz/eTFcdms6lPnz55zgMAAKgQkk/n/+eiZvYDOmpV0QEz0q2w+81IHbWqqJn9QL7zApVR5LeQM17BB+u4km54t2DmZ5jqZV+ve3Y/rL3PN9fKJd8U/FmXtz8AgEIxrKwqTAXUs2dPfffdd7LZbFq6dKm6du2aY/z48eNq3bq14uPjFRAQoH379uVYVRUXF6cmTZrI4XCoVatWWrlyZY6T9FJTU9WpUyfFxsbKz89PO3bs0FVXXZUrj48//lijRo2SJD300EOaNm1ajvG9e/eqRYsWSkxMVFRUlHbu3JljG2KW4cOH67PPPpMkzZ07V3feeWeO8blz52rQoEGSpBEjRuQofnlDfHy8s+/V4cOHOSUQAIByzp3VT0vHdVJ0ZN7b0Qq7gqqguG57rrJH0y1L2mFeqTQjQM2NfTm27WVYdm016ytQ6WpiO5Tndr/szltBCla6V7b/ZVh2XZC/6x5VJZRDXrJWlxVUuCtXnjvn6wwAlGPl+efvCruiSpLeeustXXbZZTJNU7fffrueeuop/fTTT4qNjdU///lPZ5FKkl544YUcRSpdXA01YcIESVJsbKw6dOigr776SrGxsfrqq6/UoUMHxcbGSpImTJjgskili0WjrG1406dP15133qklS5Zo/fr1mjZtmm644QYlJibKZrPpnXfecVmkkqTJkyerWrVqkqQhQ4boySef1KpVq7Rq1So9+eSTuvvuuyVJ1apV04svvui1zxEAAKCiMAypqf2QWtj2KFUBOmhWU5xZSwfNarogf7Ww71FTu3tFKkmqZFzQr1YDr+S21azvcZFKXu6TlZcQI00Nbce0x6xRrPcBAJR9FXpFlSStWrVKd955p06ccL0n3zAMPf3003rhhRdcjpumqfvvv18ff5z3scKjRo3Shx9+6Nya58rp06fVs2dPbdiwweV4YGCgpk2bptGjR+f7ftatW6d+/frl2VC9evXqmj9/vtq2bZtvnMIozxVdAACQW0VcUeVtu047FHP+Sj1Y/0iRY213XKmm9kMeXxdn1lK0rej3d8d+M1L1jBNuF/LKNFZUAShG5fnn7wq9okoXm5Bv375dzz77rK699lqFh4crKChI9evX17333quNGzfmWaTSxV5PM2bM0OLFi9W3b1/VrFlTAQEBqlmzpvr27atvv/1WH330Ub5FKkmKiIjQzz//rH/+85/q2LGjqlatqqCgIDVo0ED333+/Nm7cWGCRSpLatm2rrVu3auLEibrmmmsUFhamsLAwNWvWTBMnTtS2bduKpUgFAAAAz8zflaE2/0rWXz7dqb0O93pf5WW/GakmNs+LVCqGPln5qW87oR3mlSV2PwBA2VPhV1TBe8pzRRcAAORWLlZUedj0eu+pJA18f02+c+Y+0F4Nq4XlO+eFV97UpBdfcT7veP1V+l/v3xVqpHmUjyQlW4HaeMP76tSxk5RyxuPrT5w5q6pf3lbs2/+yxBht1OUv09yYWQghVYsnbmGERvg6AwDlWHn++dt1syMAAACgIvCwmOBIClSCwvOfE1xVCs2/iFa1Rp0cz1dt/k1PXdtPL1+52qNiVbIVqO+vnaI+PQb88cLrDd2+NkukpE1WlFoYezy+tjBuNGN1flqnQvXTKhDb7QCgzKvwW/8AAACAkvbggw/qvvvucz4fOnSoXv3nF1rf+XPFWe79Vny3VUfrO3+uPgOGFjmfAKvktv/5GaYSLC+shgMAlEusqAIAAECxyWtbn9e27pVRhmFo+vTp2rVrl+666y498sgjMgxDXbp0065GqzV42r80zL5UPWyxObbkZVh2LTFb6VzT4RoycIga2e1eyaep7aAOmJGqZ3N9wJC3pSmgRO4DACh7KFQBAACgyMKUoupGgkJ1QckK0nGripIU4uu0fMqd/lvfzV2sq2teluM1m92utWYTrTWbKEwpijTOKkypSlKwTliXK0khWtqlk2xeKlJJkmFImZZNKVagQgrRJ8tTgUov9nsAAMomClUAAAAoFNPhUHvbdg2zL1P3S1b+ZFo2LTFbabajm9aYTSQZPs3VF879flanF0/V5TeNlD3scpdz7AUUm5IUoiTLg4LfhL2epukUJWnx4m/UefvEQjV1d1eGZVfQqEVSBM3GAQC5UagCAACAx2JilqnWisf0ZUC8y3E/w1Qv+3r1sq/XbrO2xmc8qO1W/RLP01e2bdumO3v3UfKB/cr8/ZgiB0+WYfcv/hsX8aS5XoPGKCamvmqteEzRhuuvbVGttLdV17qNiyU2AKDso5k6AAAAPLLwm9lqs+IetwsZjWzxmhPwvDrathZ7bqXB2lUr1a5dOx06sF+SlBa/Qwnff+TrtNzWpUs3mWNXa3D6RC12tFGm5d0fGS5cd69X4wEAyhcKVQAAAHBbTMwydd0y3uOtYaFGmj7wn6Kmxv5iy620iL66qSIu2daWsnu1HMm/+ywnT2X1yXoo4zFdl/ahuqa9rr5p/9B+M7JIcXdbdXRbrzu8licAoPyhUAUAAAC3mA6Haq14rND9i0KNNL3p/74ky+u5lSZVqlbVN998o8CgIElSQI1o1RjxluyhlxV4bWmUpBDttWppi3WVHs54VMlWYKHiJFuBOtp5qlebwAMAyh8KVQAAAHDLt4vmFrlvUWPbYbWz7fRaTqVVixYt9OKb7yqseXdVv/sV+YWXj8bh2636Gpsx3uNiVbIVqO+vnaIuXboVW24AgPKBQhUAAAAKlnxaob987JVQQ+3LvBKntOtzx12qetujMvwCfJ2KV60ym2lQ+iTFmbXdmr/bqqP1nT9XnwFDiz03AEDZR6EKAAAABTr/WlPdaG7wSqxbbRuUcOaUV2L5imU6dPaHGUpcP8/XqfjEdqu+9g1cmmfD9QzLrkWOtvq88XRdNWkLK6kAAG7z83UCAAAAKP0SrHDVtZ30Siw/w9SJ+P1SkwZeiVfSHCnndHrha7pwcItk2OQf2UDBda/1dVolzmb7o+H6WrOJwpSiSOOswpSqJAXrhHW5khSipV060ZMKAOARClUAAAAoUJr8vRsvJdGr8UpKSnKyjn86XpnnTvzxgmXq9IJXVWPEW/KrfIWv0/OZJIUoyQrxdRoAgHKArX8AAAAoUKAyvBsvJNyr8UpKSGioQpvdkuM1Mz1F6cf3+CwnAADKEwpVAAAAyFfcifO6J/2pXH2ICivDsiuydn2vxPKFyjfcpeCotpIke1gVVR/yikIa3eDrtAAAKBcoVAEAAKBA8YrUErOVV2ItMVupStVqbs0NU4qijHhda+xRlBGvMKV4JYeiMAybIm7/q0Kv6aoaI95WYK3Gvk4JAIBygx5VAAAAPhZ34ry6T12Z75yl4zopOrJSieXkymxHN/Wyr/dKnOfznWGpvW2HhtmXqbstVn6G6RzJtGxaYrbShh8SFDXo7mJt1O1wOGTPI74tMEQRvcYV270BAKioKFQBAADALWvMJtpt1lYjW3yhY+wy62iteXWe402N/Zri/16e9/AzzD+KZbvXK+75V3Sk81vq0qVbofPJy6effqopU6boxx9/VOXKlZ2vR0dW0oFXenn9fgAA4A9s/QMAAICbDI3PeFDJVmChrk62AvXXjAckGS7HO9q2ak7A824XwqKNeLVZcY8WfjO7UPm4kpGRoUcffVQjRozQli1bNGzYMJmm6caVAADAGyhUAQAAwG3brfoamzHe42JVshWosRnjtd1y3US9qbFfH/hPUaiR5lHcUCNNXbeMV0zMMo+uy8u4ceP07rvvOp//97//1fPP579REQAAeA+FKgAAAHhkldlMg9InabdZ2635u8w6GpQ+SavMZnnMsDTF/z2Pi1RZQo001VwxTqbDUajrs5swYYIiIiJyvHb27FlZllXk2AAAoGAUqgAAAOCx7VZ99Uh/VYPTJ2qxo40yrZzfVmZYdi1ytNXg9Im6Nf2VPFdSSVJ7244i9b2SpEbGYX23+D9FiiFJdevW1Zw5c2S32xUYGKiZM2fq7bfflmG43q4IAAC8i2bqAAAAFU3yaY+m21OTVEWJLsfizNp6xrxPL2uIIoxEheiCUhSk01a4UhQsSaqi8y5inpGS02RPTdJw+9JCvpGcgjfPkLreXOQ4Xdo004fvvqnm1zRVqxbXefx5/alwvbwAAKjIKFQBAABUNK839Gh6Q0mbgrycw8w//nGFFaS6tnSvhOxkrtf515qqknGhwLnn0yxVCsx7ldR9knRC0vdFSOjBoq0SAwCgImLrHwAAAHwmwQqXn+GdU/X8DFMJVqUC562Nz1Tj6Un6dIt3CmQAAMB7KFQBAADAZ9Lk7+V4AfmOf7QpXTfNTNHR85bGLrqgjUeL3oAdAAB4D4UqAAAA+EygMrwcL+9VUmsOZ+r+/15Q+sXa1IVMacCcFJ1K9s6KLgAAUHT0qAIAAKhoJuz1aPreU0ka+P4ar6Yw94H2algtTKd2H1StBbd4ZftfhmVX0KhFUkSEy/H2kh72f1LTPpjhfO2q62+U8fCHUkTVIt8/lyTvhwQAoLyjUAUAAFDRhLou5OTFkRSoBIV7NYWu7293Pp7u30q97OuLHHOlva261m2c75wp776nLTvi9NNPP+lvf/ubXn75Zfn5FdO3xEm5TzsEAAD5o1AFAAAAn5rt6OaVQtWF6+4tcI6/v7/mzp2rn376SXfeeWeR7wkAALyLHlUAAADwqTVmE+02axcpxm6rjm7rdYckKS0tTSdOnMhzbmRkZIkXqcKUoigjXtcaexRlxCtMKSV6fwAAygpWVAEAAMDHDI3PeFBzAp5XqJHm8dXJVqCOdp6qRna7jhw5ojvuuEMOh0M//fSTgoKCiiVjd5gOh9rbtmuYfZm622Jz9OHKtGxaYrbShh8SFDXobtnsdp/lCQBAacKKKgAAAPjcdqu+xmaMV7IV6NF1yVagvr92irp06aaffvpJLVu21Lp16xQbG6sHH3xQlmUVW875iYlZJtsHHfRlwGT1tK/P1SzezzDVy75e9+x+WHueb66YmGU+yRMAgNKGQhUAAABKhVVmMw1Kn+T2NsDdVh2t7/y5+gwYKofDoQceeCDHlr+ZM2fqvffeK8aMXVv4zWy1WXGPoo14t+ZHG/Fqs+IeLfxmdrHnBgBAacfWPwAAAEiS4k6cV/epK312/zClKE3+ejxjjKKMI7rNvk6dbVvkZ/y5KirDsmuJ2Urnmg7XkIFD1Ojiljm73a65c+eqbdu2SkpKkiQ1aNBAN954Y4m+h5iYZeq6ZbzHWxhDjTR13TJeMZdHqlaTdsWWnzfViwjVgVd6+ToNAEA5Q6EKAAAAPmSpvW2Hyz5OliUZxp8zHZZNP5jX6XPHLXq20+25+jo1adJEs2bN0h133KEePXroiy++UJUqVUrsnZgOh2qteKxQfbZ0sVhVc8U4mY1WeT03AADKCgpVAAAA8Immxn5N8X9PjWyut8hlL1JJkt0w1cO+UT3sGxX3wSzFdH5LXbp0yzFnwIABWrJkibp27Sp7CTco/3bRXN3u5na/vDQyDuvzlYskXe61vAAAKEsoVAEAAKDEdbRt1Qf+Uwq9+ijaiFetFfdo4dkp6jNgaI6x7t27574g+XRhU3Vb6C8feyVO9d2fqoruzXeOPfWMlFy4z84pNKJo1wMAUAwoVAEAAMBtYUpRdSNBobqgZAXpuFVFSQrxKEZTY3+RilRZsvd1unRlVS6vNyzSvQpy3grSjUqXDDcmF+Amc4N+DNyiSsaFvCfNLPp99Nw5LwQBAMC7KFQBAACgAHn3kcq0bFpittJsRzetMZuo4EqNpSn+7xW5SJXF2dep05ZcPatKUoIVrrq2k16J5WeYSjAr5V+oAgCgnKJQBQAAgDwV1EfKzzDVy75evezrtdusrfEZD2q7VT/PeO1tO/KMVViNjMNavPg/6tVnkFfjeiJN/l6OF+DVeAAAlBUUqgAAAEqRKkp0+bpXehIVwJ6alOP+rWy7NdX/n26vfmpki9ecgOc1LuMvijUbuZwz3L7Ua/lmF7x5htT15mKJ7Y5AZXg5XrpX4wEAUFZQqAIAAChFNgU94HrAGz2JCtBQ0qagPx7vMWuoppGgEA+36IUaaXrL/586alVRlO1YjrHzVpCCi6kA08lcr/OvNfXZdrkqRqIyLVuObZGFlWHZVcU4rxYX3s9zztwH2qthtbAi3wsAgNKGQhUAAICH4k6cV/epK/Ods3RcJ0VHViqxnLzJsiQ/mR4XqbKEGGmyW6YsS0pSkBKscKXJXylWgK6z7/d6vioFfZ0qGRe0yYxSC2NPkWNtNeurhX2PEhSe5xxHcFUptGz+/QIAID8UqgAAAJDDdrOurrEfLFKM+rYT2mFeqWgj3mtNxgvi675OAZZ3tv+x7Q8AUJFRqAIAAEAO6YZ3GoM3sR3yShx3+brA09R2UAfMSNWznSh0jP1mpPNzO/BKLy9mBwBA2UChCgAAoBTpeGGqqhnnFKw0pSpQp6zKSlFw0XsSvd7QrWnnrSA1N/YV/j4+kmHZFTRqkRQR4bMcDEmHVq1UtZ8fcLsBfXbJVqAOd3hF9Tt2Kpb8PBEdWYlCGQDAJyhUAQAA+EDOPleW2tt2aJh9mbrbYnM05M60bFpittLaNZmqP+hu2ez2Ys0rwQovsa163rTS3lZd6zb2dRrq1GOAFianqOuW8R4Vq5KtQH1/7RT16TGgWPMDAKC0o1AFAADgQ02N/Zri/54a2eJdjvsZpnrZ10u71yvu+Vd0pPNb6tKlW7HlkybvbPsraReuu9fXKTj1GTBUMZdHqtaKxxRtuP66ZrfbqqOjnaeqTzF+XQEAKCtsvk4AAACgoupo26o5Ac/nWaS6VLQRrzYr7tHCb2YXW06B8k5D8JK026qj23rd4es0cujSpZvMsas1OH2iFjvaKNPK+W13hmXXIkdbfd54uq6atKVYi48AAJQlrKgCAADwgS3rV+gD/yke9zIKNdLUdct4rQwNUSdPehk9tL7AKfvPJOuhmau1MHBSju2HpVmyFagTNzyvRhfOFv/NQj3rf2Wz27XWbKK1ZhOFKUWRxlmFKVVJCtYJ63IlKURLu3Qq9u2cAACUJRSqAAAASpjpcOja2CcL1XBbF4tVdVY/KevnEzIM7+VVX9K3QdImM0otjD3eC1xMUqxA7TNrqNOae6U1JXDD584V+tIkhSjJCvFqOgAAlEds/QMAAChh3y6a61bvovzUt53QDvNKr+WUXYBV+rf/7TcjddSqomb2A75OBQAAeBErqgAAQPmVfLpYwtpTk1RFiQXMOSMlu14xFfrLx17JI00BXolzqaa2gzpgRqqe7USxxHeXZSnHirEMy66tZn0FKl1NbIe8upoMAACUDhSqAABA+fV6w2IJ21DSpqACJs10/fJ5K0g3Kl3yQpGluW2fzltBqmRcKHqwbAxDyrRsSrECFVLI7YmF9YujvkKMdAUqXUFK1wUrQGkKUKDSVcU4rxb20r8lEQAAFB6FKgAAgBKUYIWrru2kV2L5GaYSzEpeL1RJUpTtmLY66qmh7ViJFasyLLseznhUKQoucO7cB9qrYbWwEskLAACUHApVAAAAJShN/l6OVzzb/ySpmf2A9pg15GeZJbINcInZSvGKdGuuI7iqFFqp2HMCAAAli2bqAAAAJShQ3m1UHqh0r8a7VJTtmOoaJ7TdcaU2mVHKtHJ++5hh2bXd4Z2m7rMd3bwSBwAAlF2sqAIAAOXXhL3FEnbvqSQNfH9NvnNcbU3beypJI97/QSsC/yo/wyxyHhmWXUGjFkkREUWOJTffV4hSFWEkKkQXlKIgnbbClawg/dt4UY1sRwp9711mHa01ry709QAAoHygUAUAAMqvUO8UcC7lSApUgsLznxNcVXFJUvepKy8ZidQSs5V62dcXOY+V9rbqWrdxkeNkyf6+wpSi6kaCQnVByQrScauKkhSiBIUr3sq5PS/jzGHd+2uglt0aqNBC9LNKtgL114wH5JUO8wAAoEyjUAUAAFDCZju6eaVQdeG6e72STxbT4VB723YNsy9Td1tsjlVfmZZNS8xWmu3opjVmE2dRKfXALzo1b7KOpqdqkD1ac7qd9ahYlWwFamzGeG236nv1vQAAgLKJHlUAAAAlbI3ZRLvN2kWKsduqo9t63eG1nGJilsn2QQd9GTBZPe3rc21N9DNM9bKv15cBk7Uk4Ak1Nfb/8XrlK2QYf3xL+e2aON38n2DtynCvIfous44GpU/SKrOZ194HAAAo2yhUAQAAlDhD4zMeVLIVWKirk61AHe08VTa73SvZLPxmttqsuEfRRrxb8xvZ4jUn4Hl1tG2V/+U1FdF7gnOF1eZTfrol4QkNTp+oxY42LpuvL3K01eD0ibo1/RVWUgEAgBzY+gcAAOAD2636GpsxXh/4T/F4q9z3105Rny7eOSEvJmaZum4Z73FvqVAjTR/4T9Gg9Ena3rCVLus0TBmnD6nKrQ/L5h+ktWZ1rTWbKEwpijTOKkypSlKwTliXK0khXskdAACUPxSqAAAAfGSV2UyD0idpiv97amQreDXTbquOjnae6rUilelwqNaKxwrVAF0Xi1Vv+r+vW9NfUXi7gZIkw8jZED1JIUqyKEwBAAD3UKgCAADwoe1WffVIf1XtbDs1zL5UPS5pYp5h2bXEbKVzTYdryMAhauSl7X6S9O2iubrdze1+eWlsO6wuts06bF2R64RAAAAAT1GoAgAA8DlDa80m+W6VW9qlk2wXznr1rqG/fOyVODP835At20KqTMumZWZLzXV00mYzSoaM/C7PIUHhXskJAACUTRSqAAAASpF8t8q93tBr9zlvBelGpcuDGlKebJfE8DNM3WbfoNvsG3TAjFSmbIqyHXMrVr0LXxQ9IQAAUGZx6h8AAEAFlGCF59hiWFzq2U6oppGgrY56xX4vAABQ9lGoAgAAqIDS5F9i9wox0tTQdkx7zBpei9l96krVe3Kx4k6c91pMAADgexSqAAAAKqBAZZTo/UKMNNllyrJK9LYAAKCMoUcVAABAWTFhr9dCBZ0+rcyP25fI9r8s9W0ndG/637TFjCqxewIAgLKFQhUAAEUUd+K8uk9dme+cpeM6KTqyUonlhJJRRYl5jtlTzxQ4xxP21DNSWJhXYklSZESEYmyt1cVa57WY7rjX/j+9b/VWqgJ1yqqsFAXnGPf087KnnpGS07ycpbcE+joBAADKHApVAAAAhbQp6IG8B2dmzfHSzWZ6KU42VR1XSnbvx81PJ/s2dbJvkyRlWjb9ajVQgJWhpraDMgpzAmExfC5e82C8rzMAAKDMoUcVAABABfXWot3alRHps/v7GaZa2PboGvtBHbQivdpsHQAAlE0UqgAAACqoh9sEaPR/05Rs+X6LWj3bCdU0ErTVUc/XqQAAAB+iUAUAAFBBtahh1wNRJzVo2eWlolgVYqSpoe0YK6sAAKjA6FEFAABQSC0uvJ/n2NwH2kuSBr6/xiv3mvtAezWs5r1m6lmGStr/ypv6umaE2h/9WNGGb/sqhRhpclh2XX/hPRkquGlVcX0uXpHk6wQAACh7KFQBAAAUUoLC8xxzBFctcI4nHMFVpdDCnRyZnp6ugICAPMefeeFlSdKuo6M0eNq/NMy+VD1ssfIzTOcc0zJkM6xC3d9TjWzxamQ7orVmkwLnFuVzKW7RodKBV3r5Og0AAMoUClUAAADl2KZNmzTwzjv0r3en6ObON+Y71z89SXFmbT1j3qeXNUQRRqJCdEEpClIN45TeC5hWYnkPty9RnFm7wHn21DNSclrhbxQaUfhrAQCA11GoAgAAKKc+++wzjRkzRhcuXNCggXdo45hQ1b0s7xalDSVtCnI9ZlnSATNS9Wwnii/hbLrbNurGwK2qZFzIf+LMIt7ouXNFDAAAALyJZuoAAADl0P/+9z8NHz5cFy78Ueg5k2qp/1cpSsko3PY9w5AyZVNKCTVd9zNMJVilc0sfAAAoPhSqAAAAyqHu3burT58+OV4LDTAKXaiSpCjbMe01a5RYsSpNeffVAgAA5ROFKgAAgHLIZrPps88+U6NGjSRJD7f21/fDQxQRUrRv/5rZD+ioVUUHzEgvZZq3QKUX+z0AAEDpQo8qAACAIghTiqobCQrVBSUrSMetKkpSiK/TkiSFh4dr/vz5WrdyuUbcM7jA+XtPJWng+2vciv36ndfoH9/M1yD7j+pm25jjhEBvyLDsuif970pRcL7z5j7QXg2rhXn13gAAwHcoVAEAAJ+IO3Fe3aeuzHfO0nGdFB3peZ+i4owtSabDofa27RpmX6buttgcRZpMy6YlZitt+CFBLW/qXaj4nsjIyNCFCxdUqZLr99K4cWM1btzYrViOpEAlKNy9ucHVFGO2UIzZQmFKUaRxVmFK1Xi/r3WT/VeP3oMrS8xWilfBq7YcwVWlUHpZAQBQXlCoAgAAJcadAlJpFxOzTLVWPKYvA+JdjvsZpnrZ10u71ytu1ytqajyo7Vb9YsnlxIkTGjhwoC677DLNnz9fNptvujokKURJ1h+ryN539PZKoWq2o5sXMgMAAGUNhSoAACqQ4l5pVJbk9Tnk9/4XfjNbXbeMV6iR5tY9oo14zQl4XmMzxmuV2axI+V5q3bp1uuOOO3TkyBFJ0vPPP6/nnnvOq/cojDVmE+02a6uRzXUhzx27zDpaa17t1bwAAEDZQDN1AAAAN8TELPOoSJUl1EjTB/5T1NTY77Vc0tLSdOeddzqLVJL0j3/8QwsXLvTaPQrP0PiMB5VcyJMBk61A/TXjAUmG1zMDAAClH4UqAACAApgOh2qteMzjIlWWUCNNb/q/L8lyOR6mFEUZ8brW2KMoI15hSsk3XmBgoD799FPZ7Xbna1deeaVq1apVqPy8bbtVX2MzxntcrEq2AjU2Y3yxbZUEAAClH1v/AAAACvDtorm63Sj8VjZJamw7rHa2nVprNrn4iqX2th35NmSf7eimNWYTl6uLunTpojfeeEPjxo1Tly5d9NVXX6latWpFytGbVpnNNCh9kqb4v+fWNsBdZh39NeMBilQAAFRwFKoAACivkk/nesmemqQqSsz3MnvqGSm5cCuHCuLO/b2Ri6f3Keieob98XKhYlxpuX6I4s7auMg7rBf+ZirYdcTkvqyF7L/t6xZm19EzGSNlTm+bK6/9G36Nq4UG6645+8vMzXH7NPeHJ5+afllDg3GNWVQ1Of1rX2fZqkP1HdbNtzFGQy7DsWma20FzHTfrFbChDhlv3d/dkQgAAUPYYlmW5XoMOeCg+Pl516tSRJB0+fFi1a9f2dUoAUCJKbYPy5yqX7P3KqfNWkIKVnqPAUliZlk2/mvXU2HZEIR5sI0yxArXXrKFm9gNFzsGXzltBSrAqKU0BClS6qhjnVcm44HGcehe+cD6uKM3/AQDIrjz//M2KKgAAgHwkWOGqazvplVh+hqmmtkMKNDI9ui7ESFND2zHtMWsoynbMK7m464/iUrjS5K9AZaiKkVio4pIkVTIuFPpaAABQMVCoAgAAyEea/L0az9MiVZYQI012y5RlSUYxH4hnWdJ2s67SDX81N/blKNRlWjZtMqMUYGWoqe1gsecCAAAqFgpVAAB4UZhSVN1IUKguKFlBOm5VUZJCfJ0WiiBQGb5Owam+7YS2O65UU/uhYrvHHrOG/GTqGvtBl+N+hqkWxh5J0gEzUpmWrcRXeQEAgPKLQhUAAEVkOhxqb9ue7+ltG35IUNSgu2Wz20susQl7c72091SSBr6/Jt/L5j7QXg2rhRVLSu7c3xu5eHqf/O4ZdPq0Mj9u75UeVd5w0q+6mv7l67wnhFQtdOwvv/5Kffb9Q6Fu9s+qZzuhZCtQY9LHKdZsVOj75vV1zv51DFGqqhnnFKw0RRnxFIEBACinKFQBAFAEMTHLVGvFY/oyIN7leNbpbdq9XnHPv6Ijnd9Sly7dSia50IhcLzmSAgs8Mc0RXFUKLZ7m1O7c3xu5eHqf/O4ZGRqh721t1dUqXOHL2240Y3V+Wqe8ez09d65QcWNilnlUpMoSaqRpqv8/NSh9krZb9Qt177y+zhnn/NTIdrj0FYEBAECxsfk6AQAAyqqF38xWmxX3KNpwXaS6VLQRrzYr7tHCb2YXe27wrtTrRvo6BSc/w1SC5d1CoulwqNaKxzwuUmUJNdL0pv/7krx3mHRMzDLZPuigLwMmq6d9fa4VbVlF4Ht2P6w9zzdXTMwyr90bAAD4DoUqAAAKISZmmbpuGV+o1Sddt4znh+oypmeXLvrNKj3HPqcpIO/B5NN//PHAt4vmul1wzUtj22G1s+0sUowsFIEBAKi42PoHAICHvLH6pOaKcTI7bWG7UhlhmxItw6qhFAUqpJBfd28KVHreg683/OOfLnqU5SX0l4+9kJU03L5EcabnBT176hkp+Y/PdeWqlUUrAl8eWXLbawEAgNdRqAIAwEPfLpqr24u4+qSRcViLF/9HvfoM8lpeKF5RtmPa6qinhrZjPi1WZVh2VTHOFzwxq2BVgPNWkG5UumQUPbfuto26MXBr3v2z8jLzj39YlnSlFalQG0VgAAAqKgpVAICKwcOtUPnx1uqT4M0zpK43Fz2Qi6bpKB7N7Ae0x6whP8tUPdsJn+Sw1ayvFvY9XouXYIWrru2kV2L5GaYSzEqeF6ou2m7W1TX2g0XKgSIwAABlG4UqAEDF4ObqkoJ4c/VJJ3O9zr/WtNA/1DsV8pQ3FE6U7ZgsS9ruuFJpRoCaG/tyNfouTvlu+yuENPl7OV4+/bMKkG54J5ciF4Ep/gIA4DMUqgAA8EBpWn0C91RRokfzs/dLcnpofY6nB84k65GZsZKkIKXpciNJQUrTSL+lutG+vehJ52G/GakmtkNejRmoDC/HK1wh7bwVpObGPq/kUOQiMMVfAAB8hkIVAAAeKE2rT+CeTUEPeHbBzIKn1Jf0Q1DO185bQQr28mqn7FKsQDlkk+GF1XxOE/Yq6PRpZX7c3iurwtzun+UCRWAAACBJNl8nAABAWVJaVp+g9EmwwottC+AFy197zRqKsh3zbuDQCEXWbawfbW29Em6rWb/QxSGKwAAAQKyoAgBUGBP2eiWMt1efBI1aJEXQD6c88HahxRnX8tNhs6qa2Q8US3xJSr1upLR5TZHjFKXwShEYAACIQhUAoMLwUnPkyNAIfW9rq65W0X+oX2lvq651G3slL/ietwstkpRq+SverKqr7Me9Hju7nrcPVNymFxRtxBc6xm6rjq6esFyy23O8vvdUkga+n/+/L3MfaK8w4wJFYAAAQKEKAABPeWv1yYXr7vVKPshfiwvvezR/7gPt1bBaWL5zXBVfQpSqFYF/9dr2vxQrUAsaPKshd97llXj5sdntOtL5LdVacY9CjTQ3rsgp2QrU0c5T1Sg8MteYIylQCQrP93pHcFVFRlaiCAwAAChUAQDgKW+tPrmt1x1ezQuuFVQkuZQjuKoUWin/OS6KLwkK1xKzlXrZ1+d5nbsSrWANSZ+oqbcOLzAXb+nSpZsWnp2irlvGe1SsSrYC9f21U9SnS7ci50ARGAAA0EwdAAAPZa0+SbYCC3V91uoT2yVbpFD2zXYUvVgjSWMyxmu7Vd8rsTzRZ8BQre/8ueKs2m7N323V0frOn6vPgKFeuX/P2we6fe/8cqIIDABA2UWhCgCAQujSpZu+v3aKx8WqrNUnXbyw+gSlzxqziXabRSu07DLraK3ZxGs5eapLl24yx67W4PSJWuxoo0wr57eLGZZdixxt9Xnj6bpq0hav/l2mCAwAANj6BwBAIfUZMFQxl0eq1orH3NoGuNuqo6Odp3plixR8KyU5WefWzFF42ztk2LIXRQyNz3hQcwKeL3Svp79mPCDJ8Gq+nrLZ7VprNtFas4nClKJI46zClKokBeuEdbmSFKKlXToVS0GoNGxBBAAAvsOKKgAAisCXq0/gG3v37tVdt9+i31d+qt9/nJVrfLtVX2Mzxhdqtd1YH235y0+SQrTXqqUtVpT2WrWUpJBiv6evtyACAADf8emKqjNnzmjNmjXat2+fzp8/L4fDUeA1kyZNKpHcAABwly9Xn5QXYUpRdSNBobqgZAXpuFVFSQpR96krJUlLx3VSdGThmornFbswli9frkGDBuns2bOSpMT13yggsqFCm9yUY94qs5m+bTVD18Y+6dZqu11mHf0144FSV6TypS5dumlXo9UaPO1fGmZfqh622BwnKmZYdi0xW+lc0+EaMnCIGvHvFwAA5YJPClUnT57UuHHj9PXXXyszM9OjaylUAQBKsySFKMkq/hUn5YOl9rYdGmZfpu6XFCEyLZuWmK0029FNawrRr8l0ONTett3N2O5vswsNDVVSUlKO187GzFBIdHsZfgE5Xr+2TWeZLQsutMx2dNNa82qfb/crjSgCAwBQ8ZR4oers2bPq2LGj9u7dK8uySvr2AACgFGhq7NcU//fUyOZ6tZGfYaqXfb162ddrt1lbW9a/oujevd2KHROzTLVWPKYvA9yLPT7jQbdXMrVv317Tp0/XmDFjJEn+Va9UtQFP5ypSZXGn0AL3UAQGAKBiKPEeVa+88or27Nkjy7LUvXt3/e9//9OpU6fkcDhkmmaBfwAAQNnW0bZVcwKez7NIdalGtnj1jB2lhd/MLnDuwm9mq82Ke9zabpcVe07A8+po2+rWfEm6//77ddewexUSfYOqD3tD/lVquXWdL3o9AQAAlDUlXqhasGCBDMPQ7bffrv/973/q3r27qlatKsNguTsAAOXdlvUr9IH/FI9PxAs10tR1y3jFxCzLc05MzDKPT4rLiv2B/xQ1Nfa7fc0zL72hiH5PyRZIsQkAAMCbSrxQdejQIUnSQw89VNK3BgAAPmQ6HLo29kmPC0lZQo001VwxTqaLw1dMh0O1VjxWpNhv+r8vyZJpmpo8ebJWr16d53x/f39+yQYAAFAMSrxQFRYWJkmKjIws6VsDAAAfmvPhi25vyctLI+Owvlv8n1yvf7tobpFjN7YdVhvHFj0yaqgmTpyoO++8U0ePHi1STAAAAHimxJupN2vWTCtWrNDBgwd13XXXlfTtAQAVSfLpErmNPTVJVZRYwJwzUnLhVvt4k0e5ppzx2n2/W/Y/3Xn8La8cbBey+V9S+2tzvBb2y0dFDyxpaOY89f1uoyTp+PHjuqNPT62Y/5kCA3M2S/c/m6wGOpJvLP+zv0lSgfMKjGELdT0YUrXQcQvi9t8TVSq2HAAAQMVU4oWqsWPHKiYmRp999pn69u1b0rcHAFQkrzcskds0lLQpqIBJM0sklQJlz/W8FaQEK1xp8legMlTFSFQl44LXc91j1lAn44z8DO8cinKjGavz0zr9kevF99FR6V4pgvUM36vwsGAlJqVKktZv2qIfn2qn7g1zfstUX9IPBX3N//3HPwqc50aMkub23+nnzpVQRgAAoKIo8a1/gwYN0j333KN58+bplVdeKenbAwBQoVmWtM1RV5vMKAUrXXVtJxVtO6K6tpMKVro2mVHa5qgry/Le/fxkKtRI905ASX6GqQTrz5U8CVa414pgfoap1g0vlyRVCTa0ZGhIriIVAAAAik+Jf+e1cuVKjRo1Svv379fTTz+tb775RnfffbcaN26skJCCT87p1KlTieQJAEB5s8esIT+ZusZ+0OW4n2GqhbFHknTAjFSmZVOU7ViR7rndrJvn/YoiTQHZHvt7NfZTnSvpQoJdn/UPVv3LS/x3egAAABVaiReqOnfunOOUnI0bN2rjxo1uXWsYhjIzM4sxO+mJJ57Qa6+95nweExOjzp0753vNd999pw8//FAbNmzQqVOnVK1aNbVu3VpjxozRbbfd5tZ9MzMz9dFHH+nzzz/Xrl27lJSUpJo1a+qWW27Ro48+qqZNm7oV5/Tp03rnnXc0f/58HThwQJJUr1499evXT//3f/+nqlWLr58FAKD02uqop4a2Ywpx81S8erYTSrECtdVRT83sBwp933TDu0WkLIFKz/Y4w6uxo8Iz9dO9IZzqBwAA4AM+WctueWs/gZf98ssvmjJlitvzTdPUmDFjNGPGjByvHzlyREeOHNH8+fM1evRoffDBB7LZ8v6N7OnTp9WzZ09t2LAhx+v79u3Thx9+qFmzZmnatGkaPXp0vvmsW7dO/fr10/Hjx3O8vnXrVm3dulUfffSR5s+frzZt2rj9HgGgTJuwt0Rus/dUkga+vybfOXMfaK+G1cK8f3M3+nDtMWt4VKTKEmKkqaHtmPaYNQq1suq8FaTmxj6PrytIhmVXFeO883kVI1GZls0r2/9My1CCFaYrjVMeXXfzhddzvTZjZCtJ0qiZsYXOZ8bIVqpfteSbqbv9d7rYMgAAABVViReqYmJiSvqWbskqOmVmZuqKK67QyZMnC7zm6aefdhaprr/+ej3++ONq2LCh9u7dq9dee02bN2/WRx99pGrVqumll15yGcPhcKh///7OItWAAQN0//33q0qVKlq3bp1efPFFnTx5UmPHjlWtWrXyXKF1+PBh9e7dW6dOnZKfn5/Gjx+v22+/XZK0aNEiTZkyRceOHVPv3r21ceNG1a5duwifFgCUEaERJXIbR1KgEhSe/5zgqlJoyZ+QltUjytMiVZYQI012y5RlSZ4uMEqwwlXXVvD/Tz211ayvFvY9zueVjAvaZEY5ty0Whc2wdL19v8dbH/epVq7XMi6/6uJY4bdPZlx+lVSt5P/euP13GgAAwMtKvFB10003lfQt3fLOO+9ow4YNaty4sfr376+XX3453/lxcXF64403JEmtWrXSypUrFRwcLElq3bq1+vTpo5tuukmxsbF6/fXXdd999ykqKipXnFmzZmnVqlWSpL/85S+aPn26c6xNmza67bbb1LJlSyUmJurRRx/Vzp075eeX+8v29NNP69SpP377+8UXX2jgwIHOsRtvvFEtW7bUXXfdpZMnT2rixImaObOUHD8FAChW3ugRVd92QtsdV6qp/ZBH13m7d1SW7Nv+sgRY3t3+562tjwAAAPAMHUIlHTp0SM8884wk6f3331dAQECB17z11lvOflnvvvuus0iVJSQkRO+++650sf/U1KlTXcbJKnZVqVJFr7+ee9tAVFSUnnrqKUnSnj17NG/evFxzjh8/rs8//1yS1KNHjxxFqiyDBg1Sjx49JEmfffZZru2BAIBSJvm0e38K4K0eUdmbl7vL272jJGm/GakmttwFs6a2gzpgRnr1Xtm3PgIAAKBkcN6ypIceekhJSUkaMWKEbrrppgK3J1qWpQULFkiSGjdurHbt2rmc165dOzVq1Ei7d+/WggULNG3atByNWePi4rRz507pYiEpr1MPR44c6SxWzZs3L1chauHChTLNP/py3HvvvXnmPXLkSC1ZskSmaWrhwoUaM2ZMvu8TAOBDbvSeKog3e0Q1t+3TeStIlYwLbl/jzd5RkpRiBepwh1dUv2PuE4D3nUrS3z+Yo48D3lBoIbc5uhJipMmUXeb4ONnsdufr7vRwAgAAgOd8vqJq48aNevXVVzVs2DD16tVLvXr10rBhw/Tqq6+6fRpgUcyZM0eLFi1SlSpVnKubCrJ//34dPXpUcmMrY9b4kSNHnKfwZcna8ldQnOrVqys6OlqStHr16lzj7sbJPuYqDgCgfEmwwr1WJPIzTCVYnvVKqmRc0K9WA6/cP9Oyafm1U9Spx4A/eo9d8scRXFXrrKYamzFeyVagV+6ZJdqI13crfsx1vwSFO/8AAADAO3y2omrr1q0aM2aM1q9fn+ecv//972rbtq0++OADNWvWzOs5/P777/q///s/SdKrr76qiAj3mu7u2LHD+bhx48b5zs0+vnPnTtWvX7/QceLi4nT48GElJycrNPTPE4Cy4lSuXFnVq1fPM0aNGjUUHh6uxMRE50ouAED55e0eUYXZ/uet3lGzqz6qkQOG/vnCJdse7alJqqJE7TDr6t60v+nFgJmKth3xyr0lKXjzDKnrzbnudymKVgAAAEXjk0LV8uXL1bt3b6Wnp8uyLEmSv7+/qlb94/SYM2fOKCPjj29s165dqzZt2mjRokXq2rWrV/N4/PHHdfz4cXXo0EGjRo1y+7r4+Hjn44JOz6tTp47z8eHDh4scx7IsxcfHq1GjRrniuHOSX506dbR9+/Zcubgje76uHDtW+FONAADe5+0eUa6amBckq3dUPduJQt93l1lb7e4cl/PFS7ZGNpS0KUjaesKhYfNSlTYgWL9UjVJz237ZPDyt0JVO5nqdf62pc+tj1v0uVe/CF0W/GQAAQAVW4oWq06dPa+DAgUpLS5PNZtOoUaN0//336/rrr3eeZudwOLR582b961//0scff6y0tDQNHDhQv/32m7OYVVQ//fSTPvroI/n5+en999/P0TuqIOfPn3c+DgsLy3du9pVPSUlJxRqnoBjZ41wawx3Zi24AgGI2YW+RQwSdPq3Mj9t7ZftfhmVX0KhFkovVxwX1a7rKOFzo3lHJVqD+mvGgpmbrD5WXOdszdO+CVKVkSP3/naL5Y5Jkc1FMKgw/w1SCWcmjHl34Q3RkJR14pZev0wAAAGVEiReq3n77bZ07d04BAQFasGCB8yS67Ox2u1q1aqVWrVrpjjvuUO/evXXu3Dm9/fbbev7554ucQ3p6usaMGSPLsjRu3Dhdc801Hl1/4cKf36QWdEJgYOCffTJSU1OLNY47pxVmxbk0BgCglAl1bzt6fiJDI/S9ra26WkVv+r3EbKWH39sr6c8C2tJxnRQdWUmOpMB8t7yts5pqXvSr6h/3hEfFqmQrUGMzxmu7Vb/Auf/ZkaG7vv7z/217z1qavlF6r4PbtytQYbY+llUUlwAAgK+UeDP1xYsXyzAMPfzwwy6LVJfq3r27HnnkEVmWpcWLF3slh5deekm7du3SlVdeqWeffdbj64OC/vz1bHp6/tsg0tL+/IY8ODi4WOMUFCN7nEtjuOPw4cP5/smv3xgAwDdSrxvplTizHd2KdH2bW+7U+s6fK84qeJu6JO0y62hQ+iStMt3rUdkr2k9tauX8tubUee+d/qdCbn0EAACAZ0p8RdX+/fslSX369HH7mj59+mjKlCnat6/oR2zv2rVLL7/8siTp3XffzbGlzl2VKv156lFBW+iSk5Odjy/dmndpnOyFK0/jpKSkuLWdLyuOO9sEL+VODywAQOnS8/aBitv0gqKN/PsM5meXWUdrzauLnEuXLt20q9FqDZ72Lw2zL1UPW2yObYkZll1LzFaa7eh28X75bMu/ZGvkkVNJSk5YJL9ZTyszJVFVm3fW0Y5DlGk9USxbHwva7ggAAIDCKfFCVdY2NU8KRFlzs68qKqypU6cqPT1dDRo0UEpKiv7973/nmrNt2zbn4x9++EHHjx+XJPXu3VuhoaE5CjYFNRjP3rT80h5Pl8bJ79TBrDiGYeQqGNWuXVsnTpwoMJfsceg3BQDeU5q3Sdnsdh3p/JZqrbinCD2iHsi/aORhPmvNJlprNlGYUhRpnFWYUpWkYJ2wLleSQtwLdMnWSEdSoJLC66tqv78r4/QhhV53m44ahpaYrdTLXvQVvyvtbdW17p8n9Ba03REAAACFU+KFqurVq+vQoUPavHmzWrZs6dY1mzdvliRFRkYW+f5Zxa59+/ZpyJAhBc5/4YUXnI/379+v0NBQNWnSxPnarl278r0++/jVV+f8bfSlca677roC49SpUydXka9JkybauHGjzv0/e3ceHlV593/8c85kX6ggEBCiIBgwiCuiVERQAQXF7ZFqBdRaBfTxJ0td2vqglcdHaGuwLhX3jWoFtVZBG6iyiBIDShXDEkFQEIhgKJB1JnPu3x+QaUK2mWS2TN6v68p1Tc65z/f+zuRMlm/uZf9+7d69W126dKk3xq5du3TgwIF6cwEAxK5u2Wdr4pJpeio+J2RrRDVHiVJUYvwsTB1WVVUlY4zi4+PrPZ+UeZKSMv+z9uQ87/CgFKoqTr2xxTEAAADQtLCvUXXuuefKGKNZs2b5iiaNOXjwoGbPni3LsnTuueeGJcem9OzZU8ccc4wkafny5Y22XbFihSSpW7du6tGjR61zgwcP9j1uLM7u3btVWFgoSTrnnLqrwvobp+a5+uIAAGLXSqe/xrpnaJMTmjWiwmHv3r266KKLNGXKFL+vWeVk+/2cG7LJZOri0Ve1KAYAAAD8E/ZC1cSJE6XDo5OGDBmiNWvWNNh2zZo1Ou+887Rly5Za17bEiy++KGNMox81F1hfunSp73h1ocmyLF122WXS4ZFOeXl59faVl5fnGwl12WWXybJqT5vIysryjWyaP3++ysrKGsy52hVXXFHn/JgxY2Tbh76UL7zwQqPPXZJs2w5ojTAAQGwoMD010j1b17jv1SLvQFWZ2r8GeIxLC71n6Rr3vbrIPStkI6maY/26LzRgwAB98MEH+vOf/6znn3/ezystTfNMVqlJ9KNtXaUmUTuHzpHtcjXregAAAAQm7FP/zjnnHN16663685//rHXr1umss85Sv379dNZZZ6lz586yLEtFRUX69NNPVVBQ4Lvu1ltvjapRQFOmTNHTTz8tr9er22+/XStWrKi1k155ebluv/12SVJcXFyD//391a9+pZtuuknFxcW666679Pjjj9c6v2XLFt/i77179663UNWlSxddd911euWVV5Sbm6s33nhD//Vf/1WrzYIFC5SbmytJGj9+fIPTAwEAsc4KzhpRYeRUlunGseP17337fMcmT56sk046SQMHDmzy+gLTUxM9zZv6+MEpORozrGU7HqL5onn9NwAAEBphL1Tp8G57KSkpysnJkeM4+uqrr2oVpSTJGCMdHv3zq1/9SrNmzYpEqg3KysrSnXfeqVmzZmnNmjU655xzdPfdd6tXr17asmWLZs+e7Vtb684779QJJ5xQb5zrr79ezz//vD7++GM98cQT2r17t26++Wa1b99e+fn5mjlzpg4cOCDbtvXoo48qLq7+L9mDDz6of/zjH9qzZ4+uvfZarVmzRpdccokkaeHChXr44YclSZ06ddL//u//hux1AQCER2HRQY2Ys6LRNounDlFWRnqD55uzRlQk2Ikp+s3M2brrv2/xHTv66KN9vyv4o3rqY078k+pjN735yCaTqZ1D51CkAgAACLOIFKosy9Lvf/97TZgwQU8++aT++c9/6uuvv67V5oQTTtCFF17o+49pNHrwwQf1ww8/6Pnnn9fatWt1zTXX1Glz0003NVoYcrlcevvttzVq1CitXr1ab775pt58881abRITE/X444/r4osvbjBOZmam3n33XV1++eXavXu3Zs+erdmzZ9dq06VLF7399tt1dg0EACDaXfZf12jn5vV65JFHdM4552jBggXq2rVrQDGqpz6ebW/QeNdijbTXKM5yfOc9xqVcZ4D295uga6++Vn2Y7gcAABB2ESlUVTvppJP0xBNPSJLcbrf2HR7S3759eyUkJEQyNb/Ytq3nnntOV111lZ5++mmtXr1ae/fuVceOHXXmmWdq4sSJjRaXqnXs2FGffPKJnnnmGb366qvasGGDSktLdcwxx+iCCy7QHXfcoX79+jUZ56yzztK6dev0pz/9SW+//ba2bdsmHV78/bLLLtOUKVN09NFHB+W5AwAQbr///e913HHH6dZbb23B7wlNT31cPGxIk2tS+TslrbDoYDPzBAAAaJsiWqiqKSEhQRkZGZFOQ5J0//336/777/e7/ahRozRq1KgW9RkXF6fJkydr8uTJLYrTsWNHzZw5UzNnzmxRHAAAIsFbuk+u1Pb1nouPjw9ox7+mtJapjwAAAG1J2Hf9AwAAOJLxVql4yVztfO42Ve3/IdLpAAAAIEIoVAEAgIjylu5T0V9/q4OfL1Say6OMNY+rv7NBva0dSlNZpNMDAABAGIVs6t/5558vHV44/YMPPqhzvDmOjAUAAFq/f3/0is47xq3/vuo0XfyTrYqzNks6NIW9ytjKdQZo9YfF6j32502uHSVJaSpTF6tYqapQqZK023RQiaJ3il9TuzMCAAC0JSErVC1btkw6XFw68rhlWQFtKV3d/shYAACgdetnbVXOmB3qYxdJKqpzPs5yNNqVL23KV+EDs/T90Ec0bNjwOu0cr1eD7AKNdy3RiCN286suds3zDtcqJ1sSv08AAABEq5AVqoYMGVJvYamh4wAAoG0ZbK/TU/E5SrUq/WqfZe1Qt2XX6Z19ORpz5Tjf8aVLl6jbsil6LWFHvddVF7tGu/K1yemuaZ7JKjA9g/Y8AAAAEDwhH1Hl73EAANB29LO2BlSkqpZqVeqCL6ZpafsMDRs2XO+8NU8XfDHN7zh97B2an/CAJnqmaaXTv5nZAwAAIFRCVqgCAKCtKiw6qBFzVjTapi2uS7R+/XrZ7btJMsqJfzLgIlW1VKtSxyybqg+dPwZUpKp5/VPxORrrnsHIKgAAgChDoQoAAITcgr+8pAd+PV233D5NgxLaq49d/zQ9f/WxtitlxX+3qNj1cPxcXeSeFdI1q7Iy0rVt1uiQxQcAAIg1YS9U2bYt27b15ZdfKjs7269rtmzZohNOOEG2bauqqirkOQIA0KDSvU02cZWXqIMONNHmR6m0eUWWQPvwp23zcmg6vlPl0Y4PXta9/zq0a+/jD8/SwnvODUr/mdaeFl3f196uYfZafeH0rnW8pV+bViW1Y6QzAAAAqCUiI6oC2fEvGNcBABA0f+jVZJNekj5PaqLRiy1LI5A+/GrbHH7E/3RHlc75osz3ebu0ZI1MWBeCZJrndtfbOj1hc+2DLfzatCr37490BgAAALXYkU4gEOwWCABA63JW9zj9cUSi7/MeXdorznIimlNNJ9vf6KAJRRUPAAAAzdEqClV79x6aZpGamhrpVAAAQIDuOCtB1/WPV3KcNP3ctEinU0uc5ajYtK1F7QEAAKJZxBZT93d0VGlpqR577DFJUq9eTU+3AAAA0cWyLD19aZLuGZyg9I7RM5qqWqUSIp0CAAAADgt5oer444+v9/iIESMUHx/f6LWVlZX64Ycf5DiOLMvSpZdeGqIsAQDw051bmmyyZU+Jrp67qtE2CyYNUq9OzR9dFEgf/rRtjprxL/v9QnkrSpXc+dgG257UKU1rNn2rbn+/MKqm/93luUXfmQzf5y392gAAAKD5Ql6o2rZtW51jxhh9//33AcU5++yzdddddwUxMwAAmsGPXdK8JYkqVrvG2yQfLaU2f8pZIH3407ZZORyO/+k/P9OGl/5HVly8ul7/iFwpP2mwbbtuicp1Bmi0Kz/o+TSHx7i02XRTiVJ8x1r6tQEAAEDzhbxQdf3119f6/KWXXpJlWRozZoyOOuqoBq+zLEtJSUnq2rWrfvrTn+r8889nMXUAAKKIMUZPPPGEpkyZIqeqSpK05++zlfGzmbJsV4PXzfMOj5pCVa4zoFaRCgAAAJEV8kLVCy+8UOvzl156SZL04IMPKjs7O9TdAwCAEPF6vXr99ddVdbhIJUmV332p/XkLdNRPr2nwulVOtjY53dXH3tHsvr9zOulYe0+zr682zzu8xTEAAAAQPGHf9e++++7TjBkz1Llz53B3DQAADktTmXpbO3SKtVm9rR1KU1nAMeLi4rRgwQJldD3Gdyyp5xlKP/2SJq60NM0zWaUmsRmZS6UmUZuHPK5C071Z11fb6GQqzzmxRTEAAAAQXGHf9e++++4Ld5cAAESdEXNW1Dm2eOoQZWWEcm0ko0H2eo13LdEIe02tBc2rjK1cZ4DmeYdrlZMtyb/p9hkZGXr8uXm6+tKRajfwCh01+LpGp/1VKzA9NdEzTU/F5yjVqvT7GZSaRH1wSo7GXHiRlrpc6rbsuoCurxlnumeS388TAAAA4RH2QhUAAAi/ftZW5cQ/2eB0uzjL0WhXvka78rXJ6a5pnskqMD39in3y6QPU7ZanFdeuU0A5rXT6a6x7RqN51bTJZGrn0DkaM+zQdL1hw4brnX05uuCLaQEXuyZ6pvn9/AAAABA+YZ/698knn8jlcik5Odmvnf++//57JSUlKS4uTp999llYcgQAIJbk//MNzU94wO81ofrYOzQ/4QENttdJkhxPpQ5+sVjGmAavCbRIVa3A9NRI92xd475Xi7wDVWVq/2riMS4t9J6lv/R9QifM+ELDhtVeU2rMleOUP/Qvfk8D3GQyNdY9Qyud/s3KFwAAAKEV9hFVf/3rX2WM0SWXXKJu3bo12b5bt2669NJL9eabb+rVV1/VGWecEZY8AQCIBUuXLtEVhXcHPD0u1arUU/E5uurfU/TZu6+ou+uAMtoflHPcWdptOgR5pzxLeU628pxspalMGdY+palcJUpWkWmvEqVo8bAhsl31TykcNmy4Nvb5WNc8/ozGuxZr5BHTGj3GpVxngPb3m6Azhlyigkc/DmLuAAAACKawF6pWrlwpy7J08cUX+33N6NGj9eabb2rFirrreQAAgPo5Xq+6LZvSrDWcdLhYNf+oPyllvPtw4edNSW/61rNa/WGxeo/9eVBzLlGKSkzgRTDb5fKr2AUAAIDoFvZC1ZYtWyRJ2dnZfl/Tt29fSdLmzZtDlhcAALHms+Xv6jrLv+l+DWlnVdQ5Vr2elTblq/CBWfpiwKxIrCbQoOYWuwAAABB5YS9UVVQc+oU3KSnJ72sSEw9tX11aWhqyvAAAaDVK98pVXqIOOtBosy6bXg55KlnWDnVbc5OW2LdqsXNm0+0z0rVt1uhaxwqLDta7CyIAAADanrAXqjp06KAffvhB3333nU499VS/rtmx49B/g4866qgQZwcAQCvwh17qJenzRv7nc9AkKdm4JSv06aRalXok/s+62j2DnfQAAADQImEfp1895e+dd97x+5q3335bktSnT5+Q5QUAQCwpNu1qLSgeailWpR6On6s0laq3tUOnWJvV29qhNJWFLQcAAAC0fmEfUTVq1CgtXbpUL7/8sq6//nqde+65jbZfsWKFXnnlFVmWpUsuuSRseQIA0JpVKj7sffa1t+uLxFvksozv2JELrze0cx8AAACgSBSqJk6cqNmzZ+vHH3/UqFGj9NBDD+mXv/xlnTWrKioq9PTTT+u3v/2tqqqq1KFDB02ePDnc6QIAWhl/1jtaPHWIsjLSw5aTv47MuyV5ljjJEVnfvGaRSkcsvP71A7O066czNWTkleFPDAAAAK1C2AtVaWlpevXVVzVq1CiVlZXpjjvu0G9+8xudccYZ6tq1qyRp165dWrNmjcrKymSMUVxcnF577TW1a9cu3OkCANCoiCwEfucWbdlToqvnrqr3tJHRgoSZ4c3JDydYO9Ttk0l6p7RMY64cF+l0AAAAEIXCXqiSpAsvvFC5ubkaP368du7cqZKSEq1YUfuXfGMO/Ue2W7dueuWVVzR06NBIpAoAgE/U7E6X2lHekkQVq/5/4AyyC9Tb3hn2tPyRYlXqgi+maWn7DA0bNjzS6QAAACDKRKRQJUnDhg3Tli1b9PLLL2vhwoVau3at9u7dK0nq2LGjTj/9dF166aUaN26cEhMTI5UmAABB00EHAmp/zZyFWjBpkHp1SqtzzlVe0mC8Ca7Fzc4xHFKtSnVbNkXOGR8eXrOKn/MAAAA4JGKFKklKTEzUzTffrJtvvjmSaQBARLXmNZUQmM+TJgV+0Yv1H+4l6fOkuscPmiQlyx14P2GWZe1QwR8uVD/Xd9LkHWHpMypGwwEAAKBRES1UAQCAljtoklRs2qlS8SozCTrVtTXSKfmlUgmSJFf5j02ONnOV/yiVVjZyvuERZtUamioJAACA6EGhCgCAVsgYqcA5Tm4rXidb3+g4+4dIpxSwk+1vdNAkqdeLp9Q7OqyWBkaWVWtohFlNPSpeDThHAAAAhBeFKgAAWpnNTlfFydFJrm8jnUqLxFmOip10pVsVkU4FAAAAUSJkharjjz9ekmRZlrZs2VLneHMcGQsAgNbk9Iq5AV9z5GLqixa9paEF9yrVangaXGtSPf0PAAAAUCgLVdu2bZMOF5fqO94cR8YCAKA1ac4aSd7ko6XUQwvpL126JKaKVJKUGMGF39mkAAAAIPqErFB1/fXXB3QcAAA0zPF61W3ZlJgqUnmMS0k3LdQWk6Sr565qtO2RI8uOtGVPSZMxAAAAEP1CVqh64YUXAjoOAAAa9t7CBbrE2hHpNIJqhessXXBcX+0vOtjkaLOaI8vqPV+SyK5+AAAAMYDF1AEAaIYOOuBXuxSVq5O1X8mqVLkStcf8RGVK9rsfV/mPUmmlUv/1fAuyDa4qYyvOclocp+LUG4OSDwAAAGIHhSoAAJrh86RJDZ4zRipwjpPbitfJ1je1ijpVxtaX5nglGI/62d+qyeUXX5QOmiSdK7cUwaUay0y8Crw91Nn+t9rrgIrNUephFzU73iaTqYtHXxXUHAEAAND6UagCACCINjtdFSdHJ7m+rfd8nOXodGuzJGmbk6EqY6u3vavRmMWmnY6zfwhJvv4oM4na4nTVmXFf+4794NgqM4lKacaaWaUmUTuHzlEflyvImQIAAKC1C1mhasWKFSGJO2TIkJDEBQCgpdZ5e6iXvcvv4k0Pu0hlJlHrvD3U39XwrriVig9iloHZ6mTIK7tOfr3tXQE/Xx0uUn1wSo7GDBsegmwBAADQ2oWsUDV06FBZTc5nCIxlWaqqqgpqTAAAgmGz0zXgoo0kpViV6mXv0mana4MjqxLlCVKWh/zL21PJqtQBpcolr062t9WanugxLq1zeipRbmXb3zU4PbG/a9uhEWTG8Wsa4CaTqZ1D51CkAgAAQINCOvXPGBPK8ACAKFZYdFAj5jQ+unbx1CHKymh4J7dodnrFXN9jI6PXE/63WdPgdLhY5TUunVbxpKwjFqJaMGmQ0qwKVT0/KCgLmHuMS//t+X+1FnRPUbk6WgeUogqVKUl7TTu/FnxfMGmQendK09e79+vG517VWNdyDbc/q1P0ynUGaH+/Cbr26mtDNt0vKyNd22aN9n3uz/0HAACA6BOyQtXSpUsbPOd2u3Xvvfdq9erV6tSpk8aOHauBAwcqIyNDklRUVKTVq1dr/vz5+uGHH3TmmWfqwQcfVHx85KY+AABQU7Ha+R4PsguUZX/fonh97B3qY3+vPCe71nFv8tHKyEjXB/ZZusCsalEfkpTrDNAOZdQ6Vqx22mEyGrymId7ko6XUdJm0RC11TtdS53SlqUwZ1j6lqVwlSlaRaa8SpWjxsCGyWZMKAAAATQhZoeq8886r97gxRqNGjdKaNWt000036ZFHHlFqamqdduPHj9esWbM0ZcoUPfvss8rJydF7770XqnQBAGhY6V5Jkqu8RB10oM7pCa7FQelmgitXhU533+fGOLLL9kqllSo/9QZpbcsLVfO8oZ12V6IUlZiUkPYBAACA2BX2Xf+ee+455ebmavjw4XrmmWcabZuSkqKnn35a3377rXJzc/X000/rlltuCVuuAABIkv7QS5LUS9LnSbVPHTRJSpY7KN2MsD/TuYnrlG5V6N8VRtf9rVxvfe7SXeckatT/FKvw85nKsnY0O/5GJ1N5zolByRUAAAAIBTvcHb744ouyLEu33nqr39fcdtttMsbopZdeCmluAAAEqti0C8raUZIUZzkqNun66gevznymVO99XaVff1CpxVuqZLtc+n7oIyo1ic2KXWoSNd0zSVJwNzoBAAAAginshaqNGzdKko499li/r8nMzKx1LQAA0aJSwV0/ca87Xuc8X6rNxYeKX46RrnmjTN98842GDRuuD07JCbhYVWoSNdEzTQWmZ1BzBQAAAIIt7IWqiooKSdL27dv9vqa6bWVl83ZTAgAgVBLlCWq8jgke3X1O7UJUnG3phx9+kCSNuXKc8of+RYWmewMRattkMvXegOe00ukf1DwBAACAUAj7GlW9e/fWunXrNHfuXI0ZM8ava+bOPbQFeK9evUKcHQAA9bhziyRpy54SXT239oLmKSrXssTpQZn+5zEuXef+jUoHJOknOx7R/sLVOvnkk7Vw/svK7HOKr92wYcO1sc/HuubxZzTetVgj7TW1+vcYl3KdAdrfb4KuvfpaWXvLpI9XtDg/AAAAINTCXqgaO3asvvzyS+Xm5urWW29VTk6OkpKS6m1bWVmp6dOn6x//+Icsy9I111wT7nQBoE0rLDqoEXMaL3AsnjpEWRnpYcspIlI7SpK8JYkqVrtap4rVTrnOAI125be4m1xngHYoQ7KkdqPulI5+U/Nee0yZx3Wq09Z2uZTnZCvPyVaaypRh7VOaylWiZBWZ9ipRihYPGyLb5WpxXgAAAEC4hL1QNW3aNM2bN08bN27UU089pbfffltjx47VmWeeqc6dO8uyLBUVFWn16tVasGCBdu/eLUnq06ePpk2bFu50AQBo0jzv8KAUquZ5h/se24kpOmrIeCU28M+cmkqUohKT0uL+AQAAgEgLe6EqKSlJS5cu1ejRo/X5559r9+7deuyxx+pta4yRJJ122mlauHChEhObt9MRAAChtMrJ1ianu/rYO5odY6OTqTznxKDm1VpkZaRr26zRkU4DAAAAUSDsi6lLUkZGhj799FM99thjys7OljGm3o8TTzxRjz76qPLz89W1a9dIpAoACEBh0UH1uGeRetyzqMkpg7HF0jTP5IB346tWZhL1SNUVOsXaot7WDqWpLOgZAgAAAK1B2EdUVXO5XLrtttt02223affu3Vq3bp2Ki4slSe3bt1f//v0pTgFAK7Ntb2nQYrW29bEKTE9N9EzTU/E5SrX836XWa2wlyq25CY/6jlUZW7nOAM3zDpfjPSdEGQMAAADRJ2KFqpq6dOmiLl26RDoNAIh5ra3409qsdPprrHuGcuKf9HsaoKue3QLjLEejXfka7cpX4VMvaenQRzRs2PB6rwcAAABiSUSm/gEA2pY0lam3tUOnWJtjfmpbgempke7ZusZ9rxZ5B6rKtOxHbZa1QwOXXad33poXtBwBAACAaBXREVWO42jp0qVatWqVdu/erbKyMj344IO1pvy53W5VVVXJ5XKxmDoAtCpGg+z1Gu9aohH2GsXVGDlUPbVt9YfF6j3257JdrohmGnyW8pxs5TnZGm6v0Z/iH1OK5Wl2tFSrUhd8MU1L22cwsgoAAAAxLWKFqoULF+r//b//p2+//bbW8V/96le1ClXPPvusbr/9dqWlpWnnzp1KTU2NQLYAgED0s7Y2Ov2temqbNuWr8IFZ+j5GpralqUxdrGKlqkIHyip1dIqtR+KfaFGRqlqqValjlk2VM+SLGCzsAQAAAIdEpFD1zDPPaNKkSTLGSJI6duyovXv3yrKsOm1/+ctf6t5779X+/fv1t7/9TePGjYtAxgAQHjULHaVK0m7TQSVKiXRaARlsrwtoQfEsa4e6LbtO45ZM00qnf1DXyArP69nAyLFEyRipnh9tzdbH2q5Fi97U6DFjgxe0DYiF9xUAAEBbEfZC1ddff63bbrtNknT++efr8ccfV9++fWXb9a/hkZCQoKuuukrPPfecFi9eTKEKQMxxvF4NsgtiYorctq8+CXjXOx0eLfRUfI7GumdIGtKiHML5ejY1ciyYRapqR3/+J+mC8w9VwtCgWHpfAQAAtCVhX0x9zpw5qqqqUr9+/fTee++pb9++TV5z7rnnSpLWrl0bhgwBIHyWLl0i+6lz9FrCgxrlyq/1x7RqTJG7btN/a/MDJ2vp0iURy7UpjteroQX/E3CRqlqqVamH4+fK8XqbnUM4X8/B9jrNT3jA7939guUs85UOzM4Oa5+tTSy9rwAAANqasI+o+vDDD2VZlqZMmaKEhAS/rundu7ckafv27SHODgDC55235umCL6YFPEXunX05GnNl46NLC4sOasScFUHK1D/vLVygS1pYtOlrb9dfVixU32sCHz0bytfzSP2src0aORYMliVtco7VT8Lec+sQzvsAAAAAwRf2QtWOHYf+iDnllFP8vqZ6AfWystjdzhxA27J06ZKA/piuFszd3zroQL3HXeU/SqWVhx+XNNjuyPap/3q+RflU67LpZan0ooD6XrFyRRhfT6Oc+CcjUqSqliS3XOU/+v36uMpLwpZbJEXD+woAAAAtE/ZCVfWC6YEUnX788UdJ0k9+wv+PAbR+jterbsumtGiKXDB2f/s8aVL9J178z8Nekj5PaiLQi9JBk6Rz5ZaCsCbTec5qHfx9P/WyKvzq2xjpWJOhVDs8r+cge33Yp/sd6UT7O5W/cJY+T6povOHhr2UvSdKrYcgscqLlfQUAAICWCfsaVd26dZMkffPNN35fs3LlSknS8ccfH7K8ACBc3lu4QFlWywodfazten/Rm0HLqaWKTbs66wA1V5zlqNj4v+tfgXOcethFLeozkNdznCvy6xnFWSag16gtiMX3FQAAQFsU9hFVQ4cOVWFhoV566SVdf/31Tbbfv3+/5s6dK8uydP7554clRwCopXRvUMMFa4pc8trnDu/+Vpc/0+aCqVLxQY7n3xqGkuS2gtN3Y69nteIfD2ikvSYo/bVUIK9RcwUytdDftqESjvdV1EntGOkMAAAAgs4yxphwdrh27VoNGDBAkvTcc8/phhtukCTZti3LsrRu3TplZx/azejHH3/Uf/3Xf2n58uWKj49XYWGhjjvuuHCmiwDs2LFDmZmZ0uGF77t37x7plIDguD94044PmiQlyx2U0UdVxla5EpRuNTH9Kwy+dTrrOPuHIMbrpOPsPU22C/fr+T+6QzP1pxb3FQz+vkbVelQEPvVvW9LPA74mEmL1fdWk+/dHOgMAABAhsfz3d9hHVJ122mm644479Mgjj+imm27S+++/r6uuusp3/pNPPtG//vUvffzxx3r11Vd14MABWZal//mf/6FIBaDVKzbtglbQibMcFTvpQfuD+vSKuZKkBZMGqVenNEnSlj0lunruqkavWzBpkNKsClU9PygohQKPcSnppoXaYpLU68XGN94I5+tZUWW0dlWudG5QumsRj3Fpz+Wv64r5hY22q/5abtlTIs0tCFt+4RbN7ysAAAAEJuyFKkl6+OGHVVlZqSeffFJvvPGG3njjDd8i6xMnTvS1qx7sNWXKFN17772RSBUAgiqSU+SaUqx2kiRv8tFS6qH1j7wlib7jDfEmH62MjHR9YJ+lC0zjRS1/LLMHavhxfbW/6GCTbcP5eibFWRoz/lZp2/8Lap/NscJ1ljK7ZalYuxttV/219JYkhi23SIjm9xUAAAACE/bF1HV4578nnnhCubm5Gjp0qCzLkjGm1ockDRo0SIsWLVJOTk4k0gSAoEuUJ8jx3EGN1xLlp94QlDg/9Bnnd9twvp6bna46/5tZQe2vuSpOvTHSKUSVWH5fAQAAtDURGVFVbfjw4Ro+fLgOHjyotWvX6ocffpDX69XRRx+tU089VR07skgogChw55aghUrauzfoU+RUz/dKf6bsBduoS65W4WczlWU3f+e1jU6mzhhyie/z6umIDXl6bJa6/f3CkL+eixa9paEF9yrVCt1i4P7aZDJ18eirtHlvWcj72nLDF75poI2283OKqD+xmiNc7ysAAACEXtgLVb/4xS8kSRdffLGuvvpqSVJ6erqGDBkS7lQAwD9B3FkrI7Vj0KbIrXCdpQuO61vvOX+m7AWb7XJpWb+Z6rZ+UrMKOqUmUdM9kzTH5fIda+o5tOuWpeVBfj0rKyu1ZMkSXXLJoYLZ0qVLoqZIVWoStXPoHPWp8Rr5IysjXdtmjVZh0UGNmLPC7+tqTgNttJ2fU0T9idUc4XpfAQAAIPTCPvXvpZde0ksvvaR27cL7BxQARItgTZGLxulfx2afpT9VXakqE9iPl1KTqImeaSowPQPuM5iv586dOzV06FBdeumleuedd+R4veq2bErQi1TN2W+31CTqg1NyNGzY8KDmEiti+X0FAADQloR9RFWnTp20Z88eZWRkhLtrAIgKoy65WoWfz1SW1fwpctXTv6LJ0qVLdPzSKbooPrDntdHJ1HTPpGYVqRTE17PdUV10xhlnaPfuQwuUjxs3Tk888n8a34K49Sk1ibrBfZeOtg7o13Gv+bVb3SaTqZ1D52hMC4tU1SOrYlGsvq8AAADamrCPqMrOzpYkffvtt+HuGgCigu1y6fuhj6jUNG8nturpX3aA07+aY9veUr/avPPWPA1cdl1A61NVGVsPen6ui9yzmlWk6qADcpX/KLtin3b/dGaLXs+inz6gz/JW+IpUknTw4EG1L3i5WTEb62uq51ZtMd2U75yoy92/043uX+l975l1RqF5jEvvec/U673+oBOmf6BhA0+TSvf6PlzlP6qDDtT6aMta0/sKAAAADQv7iKpx48Zp2bJleumll3TZZZeFu3sAiArDhg3XO/tydMEX0wKaVlY9/aulI2uCadtXn+i69YE9D0mKsxxNiXtTnzj9mlWo+jxpkvTiocdDJK1zeqiXvUspAeRRZhL1jdNVQ1bdqHON0Zr+8frLukM7yJ10TIouSvs64Lwass3prCq59HTCnHrPHzRJKnbSVakEJcqtDtZBjXKtlraslnLurNO+l6TPk2of61HxatDybY1i6X0FAADQVoV9RNWNN96oCy64QH//+991//33yzRnoQ4AiAFjrhyn/KF/UaHp7lf7TSZT+UP/ojFXjgt5bv4zGlrwP81ewynVqtTD8XMltfxnQX/XNu00HbTN8W9q+VYnQztNB/V3bZMkWZalpy9N0qldbI3o5dJr47sGZRc5H+Oot72rwdPpVoWOs/coy/5ex9l7lG5VBK/vNiQ23lcAAABtV9hHVH300Uf61a9+pT179mjmzJl6/fXX9bOf/Uwnn3yy2rdvL1cTQ+7ZHRBALBk2bLg29vlY1zz+jMa7FmukvaZWccRjXMp1Bmh/vwm69uprA97tLZjSVKYuVrFSVaFSJWm36aD+9taApvvVp6+9XWfbG5TnZLc4x972LhkjFXiPVaWVoJOtb+q8nuucnkqUW9n2d7Ks2tenxFtaPC5FHZItbVFCi/OpqcRK1VfeVPWzv63TL4KrNb2vAAAAUFvYC1VDhw6VVeM39MLCQs2cOdOvay3LUlVVVQizA4Dws10u5TnZynOylaYyZVj7lKZylShZRaa9SpSixcOGRGTtHMfxapBdoPGuJRpxxB/7VcbWHv0kKP2Mcy0JSqFKkixL6uf6TqpnOl17HdDnX6zXZX3iZKXWP6i40+HjiY4nKPlUO8k+tDbjNidDVcZudHQVWi6a31cAAABoWNgLVZKY7gcADShRikpMSqTTkGrs4vdaQv0jpuIsR121Lyh9XWSvVprKVCL/n/vpFXO1YNIg9eqUJknasqdEV89d1WB7x+PW9sXPqfirj/Tc3kH66P23FBfX8I/BpL17VfX8oOBO/5PUwy5SqUnULe6pWuP0abJ9zedYU1PPF/8RTe8rAAAANC7shaqlS5eGu0sAQIDeeWveoQWp7eatPRWoOMtRhrUvoGJCsdrJm3y0lJouSfKWJKpY7eptW3Vgr/b87X/l3r1ZkpS3apXu+t3vlZOT02D8jNSO+sA+SxeY4BeDUq1KzYn/s8a6ZzS5kHzN51jreCPPFwAAAGitwl6oOu+888LdJQAgAF/kL9OoAHdNC4Y0lYcstuVyyVtSe/TXnDlzdMMNN+jkk09u8LryU2+Q1oZm1FL1QvIXuWdJYtEqAAAAQOHc9W/RokW6/fbbNWbMGI0ePVo333yz/vKXv8jjCe4aIACAljA6Zc09YS9SSVKJkkMW25XaXp2u+I3kOvT/mZTUNL311luNFqkkadQlV/u9e1xzVC8kDwAAAOCQkI+oKioq0uWXX678/Pw6555//nnNmDFDb7/9tvr37x/qVAAATRhkr1eW1bJd/JrDY1wqMu1D2kfiMX109Ihbtf/TN7Xgb3/TqCFnNnmN7XLp+6GPqNuy60JWvJvgylWh051pfAAAAECoR1R5vV6NGTNGn376qYwx9X5s3bpVI0eO1N69e0OZCgCghg46UO/HBNfiiOST6wwIaCH15ko7eYS63vCoevfp6/c1w4YN1wen5KjUJIYkpxH2Z1qeOCUksQEAAIDWJqQjqubPn6/Vq1fLsiz16tVLv/71rzVw4EDFx8dr3bp1evjhh5WXl6eioiI9/PDDeuihh0KZDgDgsM+TJtU5dtAkKVnuiOQzzzs8KHHKv/1C3v1FSjt5RINt7PjAC05jrhynpe0z1G3ZlKCPOIuzHBU7dRdLb02yMtK1bdboSKcBAACAGBDyQpUk9ejRQ/n5+TrqqKN857KysnT55Zfrwgsv1PLly7VgwQIKVQAQQcWmnY6zfwh7vxudTOU5J7YohjFGzz/5mH54/X8ky1Jc+2OUlHlS0HLU4ZFVG/t8rIInz5WRpZNc3wYtdqUSAr6G4hAAAABiUUin/q1du1aWZWn69Om1ilTVXC6Xfve730mStm7dqoMHD4YyHQBAIyoVH/Y+S02ipnsmtWjXO8dxNG7cOM3+3W8l40iOV3venqWqA8GfUm67XOrn+k7pVnB3KEyM0Eg2AAAAINqEdETVnj17JEkDBgxosE3Nc3v37lV6euue/gAArVWiwrsLa6lJ1ETPNBWYnk22TVOZuljFSlWFSpWk3aaDb00r27bVo0eP2u3tSnXf/p4yTvppnfYtdXrFXKWoXMsSpyvOclocz2Ncus79m6DkBgAAALR2IS1UlZeXy7IspaWlNdgmJeU/fzhUVFSEMh0AwGGnV8ytcyyYxZembHQyNd0zqdEileP1apBdoPGuJRphr6mVV5WxlesM0OoPi9V77M/1wAMPaMWqT5VS+p3+e1CaLv7JVsVZ/5D0j1rt53mHa5WT3aLci9VOxWqnXGeARrvq7mgbqFxngHYoo8VxAAAAgFgQ0kJVoIwxkU4BAFqlwqKDGjFnhd/ti9Wu3mPBKr7sNB3UWf+uVVzyGJevWHRoTaqGp/stXbpE3ZZN0WsJ9S9cHmc5h/LclK/CB2Zp7bG/0HODdynLLpJU1GD70a58bXK664v8Wcq69NIWPcd53uFBea2CtZA8AAAAEAuiqlAFAAitxqbQKYjFl2meW7XVyVA/e5s66KCKla4Cp4eKdHST1+b/8w1dUXi3Uq1Kv/rKsnbohO8ekOXnqot97B3qvuYmvePJ0Zgrx/l3UT1WOdna5HRXH7v5uwAGYyF5AAAAIJaEpVD15z//WZ07dw5KuxkzZgQxMwBoC4wG2esbnUJXPSUuGMWX75xOmuDK1Yj4zxrtq74RVf2srbqi8AG/i1TVrADXYk+1KnXBF9O0tH2Ghg1r7ogmS9M8kzU/IfB8FaSF5AEAAIBYE5ZC1ZNPPtnoeevwXxhNtROFKgAISD9rq3Lin2yw8HTklLhpnsktKr44xtKx9h4dqz1+9VV7jSqjnPgnm9Vvc6RalTpm2VQ5Q76Q7XI1K0aB6amJnml6Kj4noLwDWUgeAAAAaEv8nCjRfMaYoH0AAPw32F6n+QkP+D06qo+9Q/MTHlB7q0QTPdNUahID6s8Yybb8+15d3ddge53v2CB7fYtGcjVHH2u73l/0ZotirHT6a6x7hjY53f1qv9HJ1Fj3DK10+reoXwAAACAWhXRE1dKlS0MZHgDQgH7W1oBH+ejwKKOn4nM01j1DY90zGh2NVZNjLL+LVEf29Qv3r/S1ydQE1+KArg+WlLXPSBecX//J1I5+xSgwPTXSPVtn2xs03rVYI4+YYhnIQvIAAABAWxbSQtV5550XyvAAgHq1bApdqlWph+Pn6iL3LL+KLydbW3SsvbfZfc2Kf1ZH699KsTzNitFS5zprdPD3/ZRuVdQ516Pi1QAiWcpzspXnZCtNZcqw9ilN5SpRsopM+1qL1gMAAACoH7v+AUCMCcYUur72dp1tb/AVXhoqvvS3t+qShE9b1FdPu0j53hM00P66RXGaK85yVOyk11uoaq4SpajEUJgCAAAAAkWhCgBCrLDooEbMWRG2/sa5lgQlzgRXrgqPWHdpn0nXPqVLkhJUFbTpeu1UFpQ4zVWphIj2DwAAAOAQClUA0AqMmLNCi6cOUVZGeqPt0lSmkfaa4PRpf6ZzE9c1ONLooElSstxB6auv6/ugxGmuxCA9DwAAAAAtQ6EKAGJIF6u41jpSLdHUlLhi007H2T8Epa9I8hiXkm5aKHU8tHD6lj0lunruqkinBQAAALRJFKoAIIb81C4IarzGpsRVKj6ofUXKCtdZuuC4vr7PvSWJKla7iOYEAAAAtFV2pBMAAARHP2ur7okLZJe6pu1rZEHwREVml75gqzj1xkinAAAAAOAwRlQBQAxwvF7lxD+pFCu4xaOT7G/1s8rf6muTWedcisq1LHF60KYaRsImk6mLR18V6TR8/FmHDAAAAIhljKgCgBjw2fJ31cfeEfS4qZZbv4t/WcVKV7Ha1frYoQzlOgOC3me4lJpE7Rw6R7bLFelUAAAAABzGiCoACLMOOtCs61zlP0qllfWe67Lp5RZm1bC+9naNsldpo3NcnXPveQdqtCs/ZH37yxjJsvxvX2oS9cEpORozbHgo0wIAAAAQIApVABBmnydNat6FL9Z/+KBJ0nlySwEUagL1S9c/dEL8DhWbdqpUvBLlUQfrgNJUoW1OhnrYRaHrvAlbnQztddLVyT7oVx4bnUytO/MhXX3ppWHJDwAAAID/KFQBQCtXbNrpOPuHkPZxqr1Zjqxa/VQZW2tNb3mMS53MPqVa7pDmUJPHuLTO6alEuZVtf6eecUUyRirwHqtKK0EnW9/UWjurZvvRnoe0eOB5YcsVAAAAgP8oVAFAK1ep+JD3YVuSLVPrWJzl6HRrsyRpl9NetoySg7yYe02fVfVWul2uRLnVwTqo012ba523LKmf6zvp8CizYiddlUqo294TwqFniBpZGenaNmt0pNMAAABAgChUAUAIFRYd1Ig5K0LaR6JCVxzyV1d7n8pNvHY57dXV3heSPjra+3WcvcevtulWhdKtipDkAQAAACB0KFQBQJidXjG3zrGHx56i6fO/aFa8FJVrWeL0WlPdIuHQaKoS/a/nGp1pf60L7M8VZxk/rmyax7h0nfs3KlNyreMLJg1Sr05pkqQte0p09dxVQekPAAAAQGRQqAKARvgzImrx1CHKykj3O2ax2tU55knsUO9xf+PlOgOiYve9ZMujO+Pe0JXu+zXdTNYT8Y/qPNeXLY6b6wzQDmXUOe5NPlpKPfTae0sSm3wNA/1aBVuk+wcAAACinR3pBAAALTfPOzzSKfgkWlV6M+F3OtXeorne4OysF03PDwAAAEDoUKgCgBiwysnWJqd7pNPwSbI8eio+R14jFTrdWhRro5OpPOfEoOUGAAAAIHox9Q8AYoKlaZ7Jmp/wgFKtykgnI0lKtSo1O/5ZVRlLZSZRKc3Iq9QkarpnkqTI7NSXpjJ1sYqVqgqVKkm7TQeVKKXRa5jeBwAAADQfhSoALRaKdZxak+rnHunnWGB6aqJnmp6Kz4maYlVPu0gF3mO1xemqXvaugIpVpSZREz3TVGB6hjTHIzlerwbZBRrvWqIR9ppai9RXGVu5zgDN8w7XKic7YgU0AAAAIFYx9Q8AYshKp7/GumdE1TTASiWov2ubdpoO2ubUXRC9PhudTI11z9BKp3/I86tp6dIlsp86R68lPKhRrvw6OynGWY5Gu/L1WsKDyk24W/2srWHNDwAAAIh1FKoAIMYUmJ4a6Z6tezw3qcpE/tv8yfY3OmiS1NvepeOsQyOsPnd618nNY1xa6D1L17jv1UXuWWEfSfXOW/M0cNl1yrJ2+NW+j71D8xMe0GB7XchzAwAAANoKpv4BaBF/pv2FU1ufhljTJNe7dUYERUKc5ajYSVe6VSHLkvq5vpMknVTxrDKsfUpTuUqUrCLTXpLUxSrWKdYWv9eECoalS5fogi+mBTxlMtWq1FPxORrrnhH2whoAAAAQiyhUAUAMutm1SD3sHyKdhk+lEuocK1GKSkyKJKNB9npNj1sQkTWhHK9X3ZZNafa6XqlWpR6On6uL3LNYswoAAABoIQpVABBj+llbdXfcXyOdRi2Jctd7vJ+1VTnxT6qPXf90u+o1oUa78rXJ6a5pnslBH7n03sIFusTP6X4N6Wtv19n2BuU52UHLCwAAAGiLKFQBwBFCOZ0xTWXqYhUrVRUhmtpm9Ej8E1Ex5a+ax7h0nfs3KlNyreOD7XUB7VBYvSbURM+0Zi2y3kEH5Cr/USqt3V/qv54POFZ9JrhyVeh0r9tHasegxAcAAADaAgpVABBkdQtdh6a2jXctaXBq25bV10rq3OKpY4Ps9TrB3tmiGMGW6wzQDtXe7a+ftTWgIlW1lqwJ9XnSJOnF2scOmiSdK3dQZuyNsD/TuYnrlP5iRe0T9+9veXAAAACgjYj8dlARsmbNGj3wwAMaMWKEunfvrsTERKWlpSkrK0s33nijVq5cGVC8999/X1dccYUvVvfu3XXFFVfo/fff9ztGVVWV5s6dq3PPPVedOnVScnKyevXqpYkTJ6qgoMDvOHv37tWMGTN08sknq127dmrXrp1OPvlkzZgxQz/++GNAzwuA/0bMWaEe9yyqVaTqZ21VbsLdei3hQY1y5dcZ6VQ9tW3ytqnKTbhb/aytLcphnGtJi64PhXne4UccMcqJf7LFa0JJpsW5FZt2QRt9Fmc5Kjaxv0g/AAAAEEptckTVkCFD9NFHH9U57na79fXXX+vrr7/Wiy++qAkTJuiZZ55RQkLdRYCrOY6jW265Rc8991yt499//72+//57vf322/rlL3+pp556SrbdcF1w7969GjVqlFavXl3r+DfffKOnn35aL730kh5//HH98pe/bPS5ffrpp7r88su1e/fuWsfXrVundevW6dlnn9Xbb7+tgQMHNhoHQMuFd2qb0TB7rS6285uVa6h853RSnnNirWOD7PUNrknlr+o1oaTzWhSnUvEtur5uvIZ/XgAAAABoWpscUbVz56FpMcccc4zuuOMOvfHGG8rPz9eqVauUk5Ojbt26SZJefvll3XDDDY3G+u1vf+srUp122ml67bXXlJ+fr9dee02nnXaaJOnZZ5/Vvffe22AMr9erK664wlekuvLKK/X+++/r008/1aOPPqrOnTursrJSEydObHSE1vbt23XppZdq9+7diouL01133aUVK1ZoxYoVuuuuuxQXF6ddu3bp0ksv1Y4dLfsjEUDjWjq1LZCRVdWjtl5I+KPsKNt07jvTWR10UB10wPcxwbU4KLEnuHIPrwe1VyrdK1f5j7X68UeiPEHJ5T/x6l80HgAAAIB/LGNMy+dOtDKXXHKJJkyYoKuuukoul6vO+b179+qcc85RYWGhJGn58uUaMmRInXaFhYXq16+fqqqqNGDAAK1YsULJyf9ZLLisrEznnXee1qxZo7i4OG3YsEG9e/euE+f555/XTTfdJEm69dZb9cQTT9Q6v3nzZp1xxhk6cOCAevfurQ0bNiguru5guAkTJuiVV16RJM2fP19XX311rfPz58/Xz372M0nS9ddfrxdffLFOjJbYsWOHMjMzpcNFs+7duwc1PqKTvwuPL546RFkZoZ8W5U8+TeWyuGC3bnnlsxZkYZSbcHeLRg1tdDJ1kXtWk2tWBTpqK9yqjK1yJSjdOrRu00GTpGS5gzLd7sjYR+pR8WqtzzvogBZMGqRendJ8x4r27tXRzw8KSj4e49Kwyj/qpUnn1+qDxdQBAAAQbLH893ebHFG1cOFCjR07tt4ilSR17NhRDz/8sO/zN954o952jzzyiKqqqiRJjz32WK0ilSSlpKTosccekw6vPzVnzpx64/zxj3+UJHXo0EF/+MMf6pzv3bu3fv3rX0uHi1Z/+9vf6rTZvXu3/vKXv0iSRo4cWadIJUljx47VyJEjJUmvvPJKnemBQFtRvZZU9Udh0cGgxg/u1LaGNXfUVjgduW5TJNeEKlY7eZOPPlQ4OvyRcVxfLbfPCko+K1xnaeWsX6hXjx61+gAAAADgvzZZqPLHsGHDfI+3bNlS57wxRn//+98lSX379tXZZ59db5yzzz5bffr0kST9/e9/15ED2AoLC7Vhw6E/RseOHauUlPq3qa85BbG+QtU777wjxzn0x9+NN97Y4POqjuM4jt55550G2wFoWs1pZqGa2nZk7P9o2YLk4VRz3aZwrgm1bdboOh/1jaQrP7XxKd7+qji14e+9AAAAAPzTJhdT90dl5X/++Ktv5NXWrVt9a12dd17ji/med9552rRpk77//ntt27ZNPXv+Z0v1mrsLNhanS5cuysrKUmFhoT7++OM65/2NU/Pcxx9/rFtuuaXR3AE07POkSXWOVU9tC4aR9hr1iv9eR1mlqlCiKhWvWz13aLfpoP721haP2gqXmus2ReOaUKMuuVqFn89UltX813OTydTFo69qcS4AAABAW0ehqgHLly/3PT7xxBPrnF+/fr3vcd++fRuNVfP8hg0bahWqAo1TWFio7du3q7S0VKmpqXXi/OQnP1GXLl0ajNG1a1e1a9dOBw4c8I3kAhA8xaadjrN/CEosl2XUx7VTxkjW4aWq/pl4l6qMrT36SVD6CDWPcek6929UpkNTo1NUrmWJ04O2JtSey1/XcX2Oa1Ec2+XS90MfUbdl1zVrhFqpSdTOoXPUp4Hp5AAAAAD8R6GqHo7jaNasWb7Px44dW6dNzV3zmlq0rHqBMx1e5KylcYwx2rFjh29KYc04/iyglpmZqYKCgjq5NKWpnQJ37doVUDwgFgV7apv0nyJVtTjLUVftC3o/oZDrDNAOZfg+L1Y75ToDNNqVH5TYWd2ypNSWL9I/bNhwvbMvRxd8MS2gYlWpSdQHp+RozLDhLc4BAAAAAIWqes2ZM0f5+Yf+iLryyit1xhln1Glz8OB/Fl9OS0urc76mmiOfSkpKQhqnqRg14xwZoyk1C25ALPFn10J/BXtqW2s3z1u3gDPPOzwohap53uF6oMVR/mPMleO0tH2Gui2b4tc0wE0mUzuHzqFIBQAAAAQRhaojLF++XPfcc48kqXPnznryySfrbVdR8Z/t0BMSGl7MV5ISExN9j8vLy0Map6kYNeMcGQOINWkqUxerWKmqUKmStNt0UInq37CgOU6vmFvnWDCntrV2G51M5Tl1p06vcrK1yeneojW2GordUsOGDdfGPh/rmsef0XjXYo2019T6WnqMS7nOAO3vN0HXXn0t0/0AAACAIKNQVUNBQYGuuOIKVVVVKSkpSQsWLFDnzp3rbZuUlOR77HY3vphvzYXZk5OTG41T8/NA45SVlTWZS804R8ZoSlNTBXft2qWBAwcGFBMINsfr1SC7QONdSzTiiCJDlbGV6wzQPO9wrXKyJVmNxmpKsdrVeyxYU9tas1KTqOmeSQ28xpameSZrfsIDzV4TquHYLWe7XMpzspXnZCtNZcqw9ilN5SpRsopMe5UoRYuHDZFNkQoAAAAIOgpVh23dulUjRozQvn375HK59Ne//lVDhgxpsH16+n/WRGlqCl1paanv8ZFT846M01ihqqk4ZWVlfk3nq47jzzTBmvxZ/wqIpKVLl6jbsil6LaH+kTpxlqPRrnyNduVrk9Nd0zyTVWB61tu2WnNGZQVraltrVWoSNdEzrdHXtsD01ETPND0VnxPwmlBNxQ6mEqWoxARvFB4AAACAxlGokrRz505deOGF2rlzpyzL0vPPP6/LLrus0WtqFm2aWmS85kikI9d5OjJOx44dm4xjWVadolH37t1VVFTUZC4147DmFGLJO2/NC2gh7D72Ds1PeEATPdO00ul/xFmjQfb6gEZlddABX5uNTncVOt2UZX8fpGfXemxyMjXNM8mvQtJKp7/GumcoJ/5Jv6YBbnQyNd3P2AAAAABapzZfqNq7d6+GDx+ub775RpL02GOPacKECU1el52d7Xu8cePGRtvWPH/iibXXVDkyzqmnntpknMzMzFoLq1fH+eyzz7R//37t3r1bXbp0qTfGrl27dODAgXpzAVqrpUuXBLxbmySlWpV6Kj5HY90zfMWPftbWRgsnDY3K+jxpkq+NMVK+00dVxm6Va1UZI610TtJS51SdYW/SSPuzWs/DmNo7EVav2zTPO1w3XPtzFfzlX373VWB6aqR7ts62NzS6JtQ87/DDa1KFZrofAAAAgOjQpgtV+/fv18iRI7V+/XpJ0qxZs3Tbbbf5dW3Pnj11zDHHaOfOnVq+fHmjbVesOLSjWLdu3dSjR49a5wYPHux7vHz5cl1zzTX1xti9e7cKCwslSeecc06d84MHD9Yrr7zii/Ozn/2s3jg1c60vDtDaOF6vui2b0qy1jnS4WPVw/Fxd5J6lwfZXAU1Fqzkqq9pmp6vi5Ogs16Zm5RMNLEs63f5aT3kv1fOeUXXWaSoxSUqzKuqs2yRJv7Cbs26T1eSaUAAAAADaBjvSCURKWVmZRo8erc8//1yS9Nvf/lZ3332339dbluWbHrhx40bl5eXV2y4vL883Euqyyy6TZdUeDZCVleUb2TR//nyVlZXVG+fFF1/0Pb7iiivqnB8zZoxs+9CX84UXXmgw7+o4tm1rzJgxTT5PINq9t3CBsqzm7x4nSX3t7brG9WHA6yWpxqiszU5XrfP20DFWsXrYRS3KJxpUP69+1laVKEVbTDd9YXpri+mmIh1d6/NgFpKO7IsiFQAAANC2tMlCldvt1hVXXKGPP/5YknTHHXfof//3fwOOM2XKFLkO7/p0++23q7y8vNb58vJy3X777ZKkuLg4TZkypd44v/rVryRJxcXFuuuuu+qc37Jlix566CFJUu/evestVHXp0kXXXXedJCk3N1dvvPFGnTYLFixQbm6uJGn8+PENTg8EWovCooOy1jwXlFi/iXu1RaOykuTW8fZOpTQzRjSqHm0mmUinAgAAAKCNaJNT/6699lotXrxYknT++efrpptu0ldffdVg+4SEBGVlZdU5npWVpTvvvFOzZs3SmjVrdM455+juu+9Wr169tGXLFs2ePVtr166VJN1555064YQT6o1//fXX6/nnn9fHH3+sJ554Qrt379bNN9+s9u3bKz8/XzNnztSBAwdk27YeffRRxcXV/2V78MEH9Y9//EN79uzRtddeqzVr1uiSSy6RJC1cuFAPP/ywJKlTp07NKswB0ab4xz0aaa8JSqx2VrkfrRrW3f4xKHlEm772dp1tb1Cek+1HawAAAABomTZZqHrrrbd8jz/88EOdfPLJjbY/7rjjtG3btnrPPfjgg/rhhx/0/PPPa+3atfWuMXXTTTc1WhhyuVx6++23NWrUKK1evVpvvvmm3nzzzVptEhMT9fjjj+viiy9uME5mZqbeffddXX755dq9e7dmz56t2bNn12rTpUsXvf3223V2DQSCJU1l6mIVK1UVKlWSdpsOIZu+tXv7N61ysfLWZoIrV4XOoe8ZxWoX6XQAAAAAxLA2WagKJtu29dxzz+mqq67S008/rdWrV2vv3r3q2LGjzjzzTE2cOLHR4lK1jh076pNPPtEzzzyjV199VRs2bFBpaamOOeYYXXDBBbrjjjvUr1+/JuOcddZZWrdunf70pz/p7bff9hXYevbsqcsuu0xTpkzR0UcfHZTnDvyH0SB7vca7lmjEEbu2VRlbuc4Arf6wWL3H/ly2qzmLbdfPXX4waLHQsBH2Zzo3cZ3SrQr1qHg1IjksnjpEWRnpEekbAAAAQPi0yUKVMcFfb2XUqFEaNWpUi2LExcVp8uTJmjx5covidOzYUTNnztTMmTNbFAfwxxf5y5SbcI/62PUvaB5nORrtypc25avwgVn6fugjGjZseFD6TkimcBEOcZajYidd6VZFpFMBAAAAEOPa5GLqAILjnbfmadSamxosUh0py9qhgcuu0ztvzQtK/10yj1eV4dtYOFQqIdIpAAAAAGgD2uSIKgAtt3TpEl3wxbSAd8pLtSp1wRfTtLR9RsAjqwqLDmrEnBW1jj0RP+DQiC2EVKLckU4BAAAAQBvAUAQAAXO8XnVbNiXgIlW1VKtSxyybKsfrbXEu87zBmUaIhnmMS9e5f6PTK+ZGOhUAAAAAMY5CFYCAvbdwgbIs/6b7NaSPtV3vL3rTj5aNW+Vka5PDLpahlOsM0A5lsOMfAAAAgJBj6h+AgCX/68WgxEn61wvSmLH1nqtvml/9LE3zTNb8hAeaPcILjQv3qDV2+AMAAADaLkZUAQjI7qIfdJ7zaVBinefN0+6iH1ocp8D01ETPNJWaxKDkhf/Y6GQqzzkx0mkAAAAAaCMYUQUgIFu/KVQXywlKrDjL0dZtX6tLRucWx1rp9NdY9wzlxD/p9y6EaFypSdR0zyRJlt/X9OiYqm2zRjd43v+RcpGTlZHe6HMAAAAAEDqMqAIQkNKSA8GNd2B/0GIVmJ4a6Z6ta9z3apF3oKpM7W9xjglaV0F1ZJ7BVm7iVWHiA7qm1CRqomeaCkzPkOUFAAAAAEdiRBWAgKSmBXdB7dR2PwlqPMlSnpOtPCdbaSpThrVPaSqXI0t/S7hPtoIzGiyY4oI0Qq0+G53Mw6Oi5Pdos+prKFIBAAAACDcKVQAC0vP4LFUZOyjFFY9xqWePE4KSV31KlKISkyJJ6m3tCGlBKJp4jEu5zgDN8w4/vL7Uoal7I92zdba9QeNdizXSXlPr9WjoGgAAAAAIJwpVAALSJaOzPrDP0gVmVYtjrXCdpQsaWJ9q297SFsevKVUVQY0XrZZ5T9Z/e/6fSpRSz9n6R5uVKFlFpn0D1wAAAABA+FCoAhCw8lNvkNa2vFBVceqNQcnHH6VKCltfkTTXO8avglPN0WbBsnjqEGVlpAc1JgAAAIC2hcXUAQQsJe0oOaZlU8M2mUxdPPqqoOXUlN2mQ8gXLY+0jU7m4Wl7AAAAANA6MaIKQEAcr1fdV0yXbTV/Cz3HWJrmnqiC3/6j3vOLpw5pQYb1K1GKcp0BGu3KD3rsaFBqEg8vmh6ataWeHn+GbnnlsxbHycpI17ZZo4OSEwAAAIDYQ6EKQEDeW7hAl1hN7xzXGNsySrcqpObXupplnnd4TBaqSk2iJnqmBW2Xvvqm8BUWHQxKbAAAAABoDIUqAA0r3VvnUOq/ng9K6Btc/9APzk/qHP+30uUq/1HxlWXqoANB6avaRqe7Cp1uyrK/D2rcSNrkdNcMz/X62mQG7fVylf8olVYecazEF79Y7YLSDwAAAAAciUIVgIb9oVetTw+aJJ0rd1Bml11of65zEr86NLLqSC9KvSR9HoL1zzc7XVVmEpViVfrROjoYI1k1XnOPcWmd01OJcivb/k6vJz4Y3A5frHuo5tejR8Wrwe0PAAAAAA6L7ZWFAQRVsWmnOMsJSqw4y1GxCf8Ocb3tXdpyuFjVWliWVG4SlOftq2+dTqpQvE53bVY/13e1ClgAAAAA0NpRqALgt0rFBzleQlDj+au/a5t2mg7a5mREpP/mSLbcOtneKo/i6h+FBgAAAAAxgKl/APyWKE+Q47mDGi8Qve1dMkYq8B6rSitBJ1vfBG20WKikWJVyGafOVEAAAAAAiBUUqgA07M4ttT5N2rtXVc8PCkpBx2Ncmuy+QxWqPQXv30rXgkmD9F1xmabP/6LF/TTm86RJ6uf6Tjq8/tY2b4b6u74NaZ8t1dMu0o3uX+kLp3dI4i+YNEi9OqXVOb5lT4munrsqJH0CAAAAQDUKVQAaltqx1qcZqR31gX2WLjAtL1jkOgO0XsfXe86bfLQ8iUlB310uTWXqYhUrVRUqVZIOmiTfNLp0qyIYa8SHxVWuj7TUOT0ksb3JR0upddcO65XaUZ/P6hGSPgEAAACgGoUqAAEpP/UGaW3LC1XzvMODkk/TjAbZ6zXetUQj7DW1RoNVGVufO72VYDzqZ3+rfva32uP8RJ3s/WHKrXkuslcrTWUqUUqkUwEAAACAoGIxdQABGXXJ1So03VsUY6OTqTznxKDl1JB+1lblJtyt1xIe1ChXfp0pi3GWo9PtzTrJ9a2+Nt30QNV1SrNKQ55XS8VZjjKsfZFOAwAAAACCjkIVgIDYLpe+H/qISk2iH63rKjWJmu6ZJIV4ot1ge53mJzygPvYOv9pn2d9rRtxflGxVhTSvYElTeaRTAAAAAICgo1AFIGDDhg3XB6fkBFysKjWJmuiZpgLTM2S56fBIqqfic5RqVQZ0XWvaSa9EyZFOAQAAAACCjjWqgDagsOigRsxZ0WibxVOHKCuj7iLaDRlz5TgtbZ+hbsumKMtqetTSJidTX575kFZ+3HR9/Mhcj1wEfbfp0Mj6TEY58U8GXKRqTTzGpSLTPtJpAAAAAEDQUagC0GzDhg3Xxj4f65rHn9F412KNPGKxco9xKdcZoHne4brh2p/rlM7tpI8bL5j9R+OLoFfHXeVk15pGOMhe7/d0v9Yq1xnAQuoAAAAAYhKFKgAtYrtcynOyledkK01lyrD2KU3lKlGyikx7X0HlF7bL75j9rK3KiX+ywYJTnOVotCtfo1352uR01zTPZH1rMtTFKtZtrr8F7blFq5o7JgY22gwAAAAAohuFKgBBU6IUlZiWFUkG2+v0TPzDSrbcfrXvY+/QOwn/I8nIZZkW9d0aHNoxsa8G2QUBjzZrSKDTPgEAAAAgVChUAYga/aytejb+j0qyPAFd56pRqIllpSZRf666VLkJ9wQ02izUi9cDAAAAQLCw6x+AKGH0RPyfAi5StRWlJlGPVV2hh+Kf83sNrj72Ds1PeECD7XUhzw8AAAAAgoFCFdAGbNtbGpQ2oTTILlAP+4eI5hBO5SZB25zOfrXd6GTq156bdHvc3wLezTDVqtRT8TnqZ21tZqYAAAAAED4UqgBEhTvi3op0CmFTahJ1s2e6hrrn6Br3vVrkHagqU/vbsce4tNB7lq5x36uL3A/ptrh3Ai5SVUu1KvVw/FxJsb+GFwAAAIDWjTWqAERcmso00NoY6TTCYqOTqemeSb51o/zZMXGQXeD3dL+G9LW364mflmn0mLFBeR4AAAAAEAoUqgA0S2HRQY2YsyIosY63dspuenO6VssYKdcZoBe9FynPObHenfga2zFxnGtJUPJI+tcLEoUqAAAAAFGMQhUAvwWzOFVTphXba1NZlvSh9zTlOdkBX5umMo201wQlj/O8edpd9IO6ZPi3NhYAAAAAhBtrVAGIuBgeTOXzu/iXmrWgeRerWHGWE5Qc4ixHW7d9HZRYAAAAABAKjKgCQsifEUiLpw5RVkZ62HKKRt+Z2B/hk2y59XD8XF3knhVQaS5VFUHNo/TA/qDGAwAAAIBgYkQVgIj7xhwjpw1sSNfX3q6z7Q0BXVOqpKDmkNruJ0GNBwAAAADBxIgqAJKkW175rNHzi6cOCVnfJUpRvtNXZ7tif+e/ca4lAa1Vtdt0UJWxgzL9z2Nc6tnjhBbHAQAAAIBQYUQVgKjwJ++VkU4hLC6yVytNZX63L1GKcp0BQel7hessFlIHAAAAENUoVAGICqucftrmxH4RJc5ylGHtC+iaed7hQem74tQbgxIHAAAAAEKFQhWAKGHpNs8dKjfxkU4k5NJUHlD7VU62NjndW9TnJpOpi0df1aIYAAAAABBqFKqAGFVYdFA97lmkHvcsanL9qWhRYHrqZs+vYr5YVaLkAK+wNM0zWaUmsVn9lZpE7Rw6R7bL1azrAQAAACBcWEwdgF9GzFnRout7dEz1q91Kp7/+y32/Hol/XCfYu1rUZzTyGJeKTHu/2y+eOkRZGemSpHfeOkoXfDFNqVal39eXmkR9cEqOxgwLzvRBAAAAAAglRlQBiDoFpqeGu/+oVd6+kU4l6HKdASpRSrOuHXPlOOUP/YsKjX/TADeZTOUP/YvGXDmuWf0BAAAAQLhRqAIQMWkqU29rh06xNqu3teOI3fAsPeqNvTWVWrow+rBhw+VM/FjXuO/VIu9AVZna38Y9xqWF3rP0l75P6IQZX2gYI6kAAAAAtCJM/QMQZkaD7PUa71qiEfYaxVmO70yVsZXrDNA873CtcrK1zumhXc5R6mr/O6IZB8tGJ1N5zoktjmO7XMpzspXnZCtNZcqw9ilN5SpRsopMe5UoRYuHDWFNKgAAAACtDoUqAEGVpjJ1sYqVqgqVKkm7TQffVLcv8pcpN+Ee9bF31HttnOVotCtfo135KjWJSlKlXFaYn0ADbnPfLq9s5cTPVUoAa0RVKzWJmu6ZJCm4T6hEKSoxzZtKCAAAAADRhkIVgCBoepRU3isf6KqDryjVdvsVMZAFw0PNY2wtd045VBTypOip+JyAFzSf6JmmAtMzpHkCAAAAQGtHoQpohsKig03ugrd46pCw5RNJ/aytyol/sslRUirJD/ZgorBxydHs+Kc1zztcK52TNNY9o9HnXNNGJ1PTPZMoUgEAAACAHyhUAVHO36JYVkZ62HKqNtheF/DootbItuSbkrjJ6a5pnska6Z6ts+0NGu9arJFHjCLzGJdvra1Da1K10godAAAAAIQZhSogivhTlKrPkdeEYzRXP2trmyhSHamPvUPzEx7QRM80rXT6N7qgOQAAAAAgMLYfbQDgCEY58U+2uSJVtVSrUk/F56iftVU6vKD5FtNNX5je2mK6UaQCAAAAgGaiUAUgYIPs9X6tzxTLUq1KPRw/V5KJdCoAAAAAEDOY+geESHOm8LUW41xLIp1CVOhrb9fZ9gblOdmRTgUAAAAAYgKFKgABSVOZRtprIp1G1JjgylWh013Fahe2PrMy0rVt1uiw9QcAAAAA4cLUPwAB6WIV19rhrq0bYX+m5YlTIp0GAAAAAMQEClUAApKqikinEFXiLEfFJj3SaQAAAABATGDqHxBh4V7LKk1l6mIVK1UVKlWSdpsOAe1SV6qkkObXGlUqIdIpAAAAAEBMoFAFNMO2vaWRTqFJtXM0GmSv13jXEo2w19SauldlbOU6AzTPO1yrnGxJVqNxd5sOqjI20/9qSJQ70ikAAAAAQEygUAXEuH7WVuXEP6k+9o56z8dZjka78jXala9NTndN80xWgenZYLwSpSjXGaDRrvwQZt16eIxL17l/E+k0AAAAACAmsEYVEMMG2+s0P+GBBotUR+pj79D8hAc02F7XaLt53uFByjC0jAl9H7nOAO1QRug7AgAAAIA2gBFVQIza9tUneio+R6lWZUDXpVqVeio+R2PdMxocWbXKydbXzjE6wd4ZpGyDr9Qk6v88P9eEuCV+F+qaIxhFu8VThygrgwXZAQAAAIBCFRCDHK9XQwv+R6l2YEWqaqlWpR6On6uL3LMkWbUWYD/B2qERrs90vLU76HkHy0YnU9M9k1Rgeuov7gt1tr1B412LNfKI9bmMkazGl+Rqsp8858TgJA0AAAAAoFAFxKLPlr+r61o4iqivvV2/cL2vAXZhnQXYo5HHuHyLwh8qHlVXoCzlOdnKc7KVpjJlWPuUpnKVKFkddEAvJvw+4FFnOjxia7pnUpOLzwMAAAAA/EehCohBnTfNC0qcGfHBiRNqXmNrcOUjKtLRjbYrUYpKTIrv8y3qpomeaQFPkSw1iZromdboovMAAAAAgMBRqAJiTJrKNMzkt6mBPi7LUZpVoaJmLJ6+0umvse4Zje6MWFPNaYWBYi0qAAAAAGgcu/4BMaaLVRz10/RCIU3l9R6//9LsJq8tMD010j1b17jv1SLvQFWZ2t8aPcalhd6zdI37Xl3knsVIKgAAAAAIEUZUATEmVRWRTiEiSpTcwggNr2VVZNqrRCl+xAAAAAAAtASFKqAVq7kbX6mStNt0UKmSIp1W2HmMS0WmfdDiHbmWFQAAAAAgPChUAa2O0SB7vca7ltTZja/K2PrAOU1eY8llNWPBplYq1xnAiCcAAAAAiAEUqoBWpJ+1tdFFv+MsRyNdn4U9r0ib5x0e6RR8WDAdAAAAAJqPQhXQSgy21+mp+BylWpWRTqVFjJGsIO5IuNHJVJ5zYvACAgAAAAAihl3/gFagn7U1JopUUnCLVKUmUdM9kyQFMSgAAAAAIGIoVAFRzygn/smYKFIFU4WJ10TPNBWYnpFOBQAAAAAQJBSqgGbY+e/ysPU1yF7f4JpUrdVWJ6NF11eYeF3lvk8rnf5BywkAAAAAEHmsUQVEuXGuJZFOIag8xqU7PbfopYTfN2uUWLmJ13+571OBOT4k+fnr6fFnaES/LhHNAQAAAABiDSOqgCiWpjKNtNdEOo2gynUGaI05URM901RqEgO6ttQk6mbPryJepJKkHh1TI50CAAAAAMQcClVAFOtiFSvOciKdRlDN8w6XJK10+muse4Y2Od39um6jk6mx7hkBT/c75qjkJtssnjpEi6cOCSguAAAAACD4mPoHRLFUVUQ6haDaZDKV55zo+7zA9NRI92ydbW/QeNdijbTX1CrMeYxLuc4AzfMOP3xd07v7HTklr7DoYAieCQAAAAAgFChUAWGSpjJ1sYqVqgqVKkm7TQeVKKXRa0qVFLb8Qq3UJOrLAQ9JHx9ZbLKU52Qrz8lWmsqUYe1TmspVomQVmfZNvkYAAAAAgNhBoQoIKaNB9nqNdy3RiCNGC1UZ2zdaaJWTXe9ood2mg6qM3eqn/5WaRP0ta7YGDhwqfbyiwXYlSlGJoTAFAAAAAG0VhSogRPpZW5UT/6T62DvqPR9nORrtytdoV742Od01zTNZBaZnrTYlSlGuM0CjXflhyto/xkhVshXvRwFto5Op6Z5JmnPhf4Ult1BaPHWIsjLSI50GAAAAAMQsClXAEQqLDmrEnIZH/UjSrUMb33VusL1OT8XnKNWq9KvPPvYOzU94QBM90+osFj7POzzqClWWJcXrUJHqoElWsioUZxnf+easLQUAAAAAAIUqoIWOXHvqKB0MqEhVLdWq1FPxORrrnlFrZNUqJ1ubnO4NjsyKtHSrXGUmUb9xT9AmkxmWtaUY2QQAAAAAsYlCFdAMjuNokF1Q79pTxhwacdQcqValHo6fq4vcs2qMQrI0zTNZ8xMeCLj4FS4pVqXui39ZY90ztMV0i3Q6AAAAAIBWikIVEKB+1lZdkXeX7kmof4RTc4tU1fra23W2vUF5TrbvWIHpqYmeac0aqRUuqValcuKf1DXu38o6XGQrVrug9xOq0VRZGenaNmt00OMCAAAAAPxnRzoBoDUZbK/T/IQHQj4Nb5xrSZ1jK53+GuueoU1O95D23RJ97B2aF/+QPk+apM+TJkU6HQAAAABAK0OhCvBTP2tr2EY0XWTnK01ldY4XmJ4a6Z6ta9z3apF3oKpM9L2FK5UQ6RQAAAAAAK0UU/8AvxjlxD8Ztml3cZbRE/GPaq73Uq1yso/YNc9SnpOtPCdbaSpThrVPfa1v9UTC42HJrSkn29/ooElSulUR6VQAAAAAAK0MhSrAD4Ps9WHfde8815c6z/WlNjndNc0zudZOgNVKlKISk6I0lYc1t8bEWY6KnXQKVQAAAACAgFGoAvxQ35pR4dLH3qE3En6n/+e5TaucfupiFStVFSpVknabDipRikqVFLH86vNrzy+10RwX6TQAAAAAAK0MhSqgCWkq00h7TURzSLbcejp+jowku8YswCpjK9cZoDe8Q1RlLMVZJpJp+hSpQ0h2/AMAAAAAxDYKVUATuljFirOcSKchy6q9UpUOT7Mb7crXaFe+DppkpUfBFECPcanItI90GgAAAACAVij6tgwDokyqWsdaS+lW5ItUkpTrDFCJUiKdBgAAAACgFWJEFdCEaFv/KdrN8w4PeszFU4coKyM96HEBAAAAANGFQhXapMKigxoxZ4VfbXebDqoydlRM/4t2G51M5Tkn+j4/ssBUWHQwQpkBAAAAAFoDpv4BTShRinKdAZFOI+qVmkRN90xS3ZW0AAAAAADwD4UqwA+hmM4WS0pNoiZ6pqnA9Ix0KgAAAACAVoxCFeCHVU62NjndI51GVNroZGqse4ZWOv0jnQoAAAAAoJWjUAX4xdI0z2SVmsRIJxJW5SZeK70nqcrU/lbhMS4t9J6la9z36iL3LEZSAQAAAACCgsXUAT8VmJ56rOoK3R33V1ltYBmm6ul8K53+SlOZMqx9SlO5SpSsItNeJUqJdIoAAAAAgBhDoQrwUz9rq26Pe6tNFKm+cbrods/tvpFSJUpRiaEwBQAAAAAILQpVaJO27S0N8AqjnPgnlWq5Q5RR9Kgytsa4Z6pEqZFOBQAAAADQxrBGFeCHQfZ69bF3RDqNsPiHcyZFKgAAAABARDCiCvDDONeSSKcQNvO8wyOdQlBlZaRr26zRkU4DAAAAAOAHRlQBTUhTmUbaayKdRlhsdDKV55wY6TQAAAAAAG0UhSqgCV2sYsVZTqTTCLkKE6/pnomS2sBq8QAAAACAqMTUP6AJqaqIdAphkWR5lBM/V9M8k327/QVbfdPwCosOasScFSHpDwAAAADQujCiCmhCqZIinULY9LF3aH7CAxpsr4t0KgAAAACANohCFdCE3aaDqkzw3irGBC1USKRalXoqPkf9rK2RTgUAAAAA0MZQqAKaUKIU5ToDghbPsiTHRPc6UKlWpR6OnyspyqtqAAAAAICYQqEK8MM87/CgxrMtowoTH9SYwdbX3q6z7Q2RTgMAAAAA0IZQqAL8sMrJ1iane1BjHjSJUT8NcJxrSaRTAAAAAAC0Iez6B/jF0jTPZL2RcJ+SraqgROxklzR63hjpI+ck/dl7mSSjZ+LnKN0qD0rf/rrIXq00lalEKfWeXzx1iLIy0sOaEwAAAAAgdjGiCghAON8wliWdYX+tODnKc07SNe57VWYSw5iBFGc5yrD2hbVPAAAAAEDbxYgqwC9GOfFPKjFIo6n8Vb0D31j3DBWYnrrFM01Pxeco1aoMWw5pCu0orqyMdG2bNTqkfQAAAAAAWgdGVAF+GGSvVx97R0T6rrkD30qnv8a6ZwR9vazGlCg5bH0BAAAAANo2ClVAI9JUpt7WDt3m+ltE86i5A1+B6amR7tm6xn2vFnkHqsrUfht7jEtOkBZp9xiXikz74AQDAAAAAKAJTP0D6jAaZK/XeNcSjbDXKM5yIp2QdHgHvjwn+/BnlvKcbOU52UpTmTKsfUpTuUqUrCLTXrPjn9ZoV36L+8x1BjS4kDoAAAAAAMFGoQqooZ+1VTnxT0Zsml9jGtqBr0QpKjG1j83zDg9KoWqed3iLYwAAAAAA4C+m/gGHDbbXaX7CA1FZpFKAO/CtcrJV2MJ1rDY6mcpzTmxRDAAAAAAAAkGhCjg8kircu+k1h/878Fl69/gZKjWJzeqn1CRqumeSJKtZ1wMAAAAA0BwUqgAZ5cQ/GfVFKgW4A9/RJwzURM+0gItVpSZREz3TVGB6NiNDAAAAAACajzWq0OYNstdH7XS/mpqzA99Kp7/Gumf4ve7WRidT0z2T6i1SLZ46RFkZ6QH1DwAAAABAIChUoc0b51oS6RT80twd+ApMT410z9bZ9gaNdy3WyCN2MvQYl3KdAZrnHX54TSqm+wEAAAAAIoNCVYz69ttv9eijj2rRokXavn27EhMT1atXL40dO1a33XabUlICL3jEojSVaaS9JtJp+KVlO/BZynOyledkK01lyrD2KU3lKlGyikz7ZhXAAAAAAAAINgpVMejdd9/VuHHjdODAAd+xsrIyrVmzRmvWrNGzzz6rRYsWqXfv3hHNMxp0sYprjS4KFcdYsi3T7OuDuQNfiVJUYihMAQAAAACiD4upx5i1a9fqZz/7mQ4cOKC0tDQ9+OCD+uSTT/TBBx/o5ptvliQVFhZq9OjROnjwYKTTjbhUVYS8j1KTqBlVN7ADHwAAAAAATWBEVYy54447VF5erri4OC1evFiDBg3ynTv//PN1wgkn6K677lJhYaEefvhh3X///RHNN9JKlRTS+DUXJ99muuip+JyAdhdkBz4AAAAAQFvCiKoYkp+fr48++kiSdNNNN9UqUlWbPn26Tjzx0BSyP/3pT/J4PGHPM5rsNh1UZYL7NvAYlxZ6z9I17nt1kXuWr8hUvQPfJqe7X3E2Opka656hlU7/oOYHAAAAAEC0olAVQ95++23f4xtvvLHeNrZta8KECZKkf//731q6dGnY8otGJUpRrjMgKLGMkZZ7T9Ytnqn6b8//U56TXWe6XvUOfNe479Ui78A6RbKGilwAAAAAALQFTP2LIStXrpQkpaam6owzzmiw3Xnnned7/PHHH2vEiBFhyS9avVJ1oUa78lscx7Kk81xf6jzXl9rkdNc0z+QGCk3swAcAAAAAQH0oVMWQDRs2SJJ69+6tuLiGv7R9+/atc40/duzY0ej5Xbt2+R0rmvzjo39p43md1Tf+h6DF7GPv0PyEBzTRM63RqXvswAcAAAAAwH9QqIoRFRUV2rt3rySpe/fG10Bq3769UlNTVVpaqu3bt/vdR2ZmZovzjEZp/Yfrzxu26NGTg1eokqRUq1JPxedorHsGU/gAAAAAAPADa1TFiIMHD/oep6WlNdk+NTVVklRSUhLSvFqDuJ9kaESf0IxqSrUq9XD8XEkmJPEBAAAAAIgljKiKERUVFb7HCQkJTbZPTEyUJJWXl/vdR1Ojr3bt2qWBAwf6HS9aZOhHXZTwr5DF72tv19n2hsOLqwMAAAAAgIZQqIoRSUlJvsdut7vJ9pWVlZKk5ORkv/toakpha+I4Xg2yCzTetUQj7TVyWaEd8TTOtYRCFQAAAAAATaBQFSPS09N9j/2ZzldaWir5OU0w1ixdukTHL52i1xIaXxw+mC6yVytNZWHd0e+Yo/wvQgIAAAAAEA1YoypGJCUl6eijj5b82J1v3759vkJVrC6Q3pB33pqngcuuU5YdviKVJMVZjjKsfWHtEwAAAACA1oYRVTEkOztbH330kTZv3qyqqirFxdX/5d24caPv8YknnhjGDCNr6dIluuCLaUq1KiPSf5r8Xw8sEhZPHaKsjHQ/WgIAAAAAEBqMqIohgwcPlg5P6/vss88abLd8+XLf43POOScsuUWa4/Wq27IpEStSSVKJmIoHAAAAAEBjKFTFkMsvv9z3+IUXXqi3jeM4evnllyVJRx11lIYNGxa2/CLpvYULlGWFd7pfTR7jUpFpH7H+AQAAAABoDShUxZCBAwfq3HPPlSQ999xzWrVqVZ02Dz/8sDZs2CBJuuOOOxQfHx/2PCMh+V8vRrT/f5kT9NWsq7Vt1mgtnjokLH326JiqbbNGh7VPAAAAAABagkJVjPnTn/6k5ORkVVVVacSIEXrooYeUl5enpUuXauLEibrrrrskSVlZWZo+fXqk0w2L3UU/6Dzn04jmcJK1RUuXLoloDgAAAAAARDsWU48xp512ml5//XWNGzdOBw4c0G9+85s6bbKysrRo0SKlp7eNhbO3flOoLpYT0RySLY+OWTZVzpAvQhKfhdABAAAAALGAEVUx6NJLL9WXX36pqVOnKisrSykpKTrqqKM0YMAAzZ49W2v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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for time in model_strength_days:\n",
    "    Xt, truth, truth_var = data.strength_data_by_time(time)\n",
    "    truth = truth.squeeze(-1).detach() * STRENGTH_DISPLAY_SCALE\n",
    "    truth_std = truth_var.sqrt().squeeze(-1).detach() * STRENGTH_DISPLAY_SCALE\n",
    "\n",
    "    post_t = model.strength_model.posterior(Xt)\n",
    "    pred = post_t.mean.squeeze(-1).detach() * STRENGTH_DISPLAY_SCALE\n",
    "    pred_std = post_t.variance.sqrt().squeeze(-1).detach() * STRENGTH_DISPLAY_SCALE\n",
    "\n",
    "    fig = plt.figure(dpi=200)\n",
    "    plt.title(f\"Day-{time} Strength Fit\")\n",
    "    plt.ylabel(f\"Prediction ({strength_label()})\")\n",
    "    plt.xlabel(f\"Truth ({strength_label()})\")\n",
    "    plt.scatter(truth, pred)\n",
    "    lw = 3\n",
    "    plt.errorbar(truth, pred, yerr=2 * pred_std, fmt=\"o\", linewidth=lw)\n",
    "    plt.errorbar(truth, pred, xerr=2 * truth_std, fmt=\"o\", linewidth=lw / 2)\n",
    "    lo, hi = truth.min().item(), truth.max().item()\n",
    "    plt.plot([lo, hi], [lo, hi], color=\"black\", linestyle=\":\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe855804-5565-494c-90ac-4e2d7ca94a8f",
   "metadata": {
    "originalKey": "fe855804-5565-494c-90ac-4e2d7ca94a8f"
   },
   "source": [
    "## Strength Model LOO Cross-Validation\n",
    "\n",
    "Efficient O(n\u00b3) leave-one-out cross-validation using the closed-form GP identity. This evaluates the model's out-of-sample predictive accuracy without refitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da0c7cdb-c975-463a-8c90-befba13d91c1",
   "metadata": {
    "originalKey": "da0c7cdb-c975-463a-8c90-befba13d91c1",
    "output": {
     "id": 959499996994655,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strength LOO-CV R\u00b2:   0.954\n",
      "Strength LOO-CV RMSE: 598.8 psi\n"
     ]
    }
   ],
   "source": [
    "from boxcrete.plotting import compute_loo_cv, plot_calibration\n",
    "\n",
    "# The strength model has 128 synthetic day-zero points appended;\n",
    "# only compute LOO metrics for the real observations.\n",
    "X_strength, Y_strength, _, _ = data.strength_data\n",
    "n_real = X_strength.shape[0] - 128  # exclude synthetic day-zero conditioning points\n",
    "\n",
    "loo_obs, loo_pred, loo_std = compute_loo_cv(model.strength_model, n_real=n_real)\n",
    "\n",
    "res = loo_pred - loo_obs\n",
    "ss_res = (res**2).sum().item()\n",
    "ss_tot = ((loo_obs - loo_obs.mean())**2).sum().item()\n",
    "r2 = 1 - ss_res / ss_tot\n",
    "rmse = (res**2).mean().sqrt().item()\n",
    "\n",
    "print(f\"Strength LOO-CV R\\u00b2:   {r2:.3f}\")\n",
    "print(f\"Strength LOO-CV RMSE: {rmse * STRENGTH_DISPLAY_SCALE:.1f} {strength_label()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84fd5e3c-bce4-4bbe-8987-33f4e542792e",
   "metadata": {
    "originalKey": "84fd5e3c-bce4-4bbe-8987-33f4e542792e",
    "output": {
     "id": 27059836223652464,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bny+//BKAt99+mzFjxgS0OXDgQLnni4mJ4YYbbuC9995j6dKlbN68Ga/Xy+TJkwNSPoYNG1buiP2p7rvvPj7++GM6duzI0qVLiY2NDXlcenq6v4JJTExMuf2jdHTzTCUlJdGlSxc2bdpU6Yogp5OQkIDNZsNgMDBz5syQqUk1IT4+HrPZTGZmJg6HI+SLQ9lUpvpg9OjR7Nq1i48//pirrrqqwmN9n8z4KiH52j/44IMsXbqU7OzsCtOAPvnkE5RS9OvXL2Bk3RfAh2IwGBg1ahSfffYZc+bMqVawbjAY+OMf/8gLL7zA7Nmzad++PQsXLsRkMnHDDTdU+XxCiBJ1Y5hDCCFCmDp1KkVFRfTr14+BAwf6t/uC2VAjlKfL0fblkr/zzju8++67GI1Gbrvttmr17/HHH+ett96iZcuWfPfddxXmvDdq1MifOhJqdDQ/P59ff/0VgFatWp322qd7mfB6vaSmpgLQrFkz/3ZfH0KVsDwdk8nEsGHDyM/PZ9myZVVuX1lms5n+/fujlOKLL74I2r98+XKysrKCtp/JvZ1t99xzD1arlc8//5zVq1eXe9yHH37Izz//TEJCArfeeqt/e7du3RgxYgQOh4N//OMf5bbPyMhg+vTpUPr3pyqmTJmCpmm8/vrr5Zbf9CmvNv7NN98MpS8Mn376KU6nk5EjR1ZpPQAhRCAJ1oUQdc6BAwe4/vrreffdd4mIiODdd98N2O+bCPif//wnIGhds2YNL774YoXn7tevH3369OGrr74iNTWVMWPG0LRp0yr38ZVXXmHGjBkkJSXx/fffh8zxLstqtTJq1CgoDYrKTmJ0OBzcfffdFBcXM2jQIJKTk097/QULFnDdddeFDJqKioq46667yM7OpmnTpgEvOr579b0YVNVjjz2GwWDgtttuY+XKlUH7CwsLee+997Db7dU6v4/vpeqJJ57g6NGj/u3Z2dnlBqsJCQmYzWb2798fUGe/LmjdujXPP/88SinGjx/P119/HbBfKcXMmTP9n/L873//C5pn8M477xAbG8t7773HfffdR3FxccD+Xbt2cdlll3Hy5Emuvvpqrr/++ir18cILL+TBBx/EbrczfPhwf233svLy8njyySfLXY/gggsuoGfPnpw4ccKfty4pMEKcGUmDEULUKt/qkrquk5+fz2+//caePXtQStGhQwfmzJnDBRdcENDm/vvv5/333+ett95i5cqV9OjRg6NHj/LDDz8wderUCld4pDQQvOOOOwD8/62KrVu3+kct27Rpw4wZM0IeN3ny5IAFeV5++WU2bNjA1q1b6dSpEwMHDiQsLIyff/6ZY8eO0ahRI/79739Xqg+6rvP555/z+eefk5SURO/evYmLiyMjI4ONGzeSk5NDREQEH374YcBk0LFjxzJ9+nQeeOABvvvuO3+axPPPP1+p1VMHDx7Mm2++yb333ssll1xC9+7d6dixI2azmYMHD7J161acTicTJkwImbtfWX/84x/54osv+PLLL+ncuTOXXnopRqORFStW0K5dOwYMGMD69esD7s1isTBq1CgWLFhAz5496dOnDxaLhUGDBlX705PKOH78OAMGDCh3/7Rp0xgzZgx//etfcTgcPPbYY4wfP54OHTrQq1cvvF4vP//8M4cPH8Zms/HBBx8wfvz4oPO0bt2aFStWMHbsWN544w1mzZrF4MGDiY2N5cCBA/z0008opbjmmmv48MMPq3Uvzz77LCaTiWeffZYxY8bQqlUrevfuTVhYGEeOHGHDhg24XC46dOhQ7jluueUWtm3bRmZmJtHR0YwbN65afRFClKrt2pFCiIbp1NrgJpNJNWrUSHXv3l1NmjRJffHFFxUuzrJ79241duxY1bhxYxUeHq569+6t/vOf//jPXVHt63379vkXnanOAjArVqwI6n+or1D1uk+cOKH+/ve/q86dOyubzaasVqtq3769uueeeyq1YI6P3W5XixYtUvfff7/q16+fSk5OViaTSUVFRakePXqoKVOmqIMHD4Zs+9FHH6k+ffqosLCwoHrlvprop6s1vmXLFjVp0iTVqlUrZbFYVGxsrOrWrZu6/fbb1cKFCwNqtJ/unK1atQpZc9/lcqlnn31WdejQQVksFtWsWTN13333qfz8fNW+fXulaVrQwlgnT55Ut9xyi0pKSlJGo1EBatKkSf79Ff1szJw5UwHqySefrPDefXx11qv6c7Bz50515513qvbt26vw8HAVERGhunTpov7+97+rtLS00163qKhIvfjii2rgwIEqLi5OWSwW1bRpUzVx4kS1ePHiSvX9dH755Rd1zz33qC5duqioqChlNptV06ZN1dixY9Xs2bOVy+Uqt+2xY8f8z/62226rkf4I0ZBpqrKzqIQQ4jzx7LPP8uijj/Lkk0/y1FNP1XZ3RBUdOXKENm3a0L59+9PmVgshRH0nOetCiAYlPz+f//f//h8Wi6VaKTDi3Nm+fTtutztg28mTJ0lJScHj8fgnMwohxPlMctaFEA3CzJkzWbVqFatXr+b48eP87W9/q9bEUnHuPPjgg/z000/06tWLJk2acPz4cTZt2kRhYSH9+vWrcrUTIYSojyRYF0I0CKtWrWLWrFkkJiZyzz33hFyhVNQtKSkpKKXYsWMH69atw2g00rFjR6655hr+/ve/l7u6qRBCnE8kZ10IIYQQQog6ql7mrG/atInnnnuOCRMm0Lx5czRNq3AlPd/+ir6GDx8e0GblypUVHl9Rma4jR45w22230bRpU2w2Gx07duTJJ5/E4XCU28Zut/PEE0/QsWNHbDYbTZs25fbbbw+oLyyEEEIIIRqWepkG8/TTT/PVV19V+vhJkyaVu2/RokVkZmYyZMiQkPvbtWsXUCe57PZQ9u3bx8CBA8nMzKR79+4MGTKEjRs3Mn36dJYtW8ayZcuwWq0BbRwOB8OHD2f9+vUkJyczfvx4Dh48yMyZM1m4cCHr16+nbdu2lb5fIYQQQghxfqiXwfrAgQPp0aMH/fr1o1+/frRu3Rqn01nu8e+//37I7bm5uXzyySdQZonkUw0ePLjc9qGkpKSQmZnJ/fffz2uvvQalS19fd911fPnllzz77LNBpeKeeeYZ1q9fz8CBA/n222/9q9a9/PLLTJ06ldtvvz3kSoFCCCGEEOL8dl7krNtsNpxOJ1W9lf/+97/ccccdDBgwgB9//DFg38qVK7nkkkuYNGlSpYP1n376iQsvvJDGjRtz6NChgBH0kydP0qJFCyIjI0lPT8dkKnlPcrlcNG7cmLy8PDZv3kzv3r0DztmzZ0+2b9/Oxo0b6du3b5XuTwghhBBC1G/1Mme9psyePRtKl0auCYsWLYLS5bxPTXVp0qQJQ4YMIScnhx9++MG/fe3ateTl5dGuXbugQB3gmmuuAWDBggU10kchhBBCCFF/NNhg/dChQ6xZswaz2cz1119f7nF79+7lkUce4Y477uDRRx9l8eLF6Loe8tht27YB0KdPn5D7fdu3b99+Rm2EEEIIIUTDUC9z1mvCRx99hFKKK664gvj4+HKPW7duHevWrQvYdsEFFzBv3jw6dOgQsP3QoUMANG/ePOS5fNvT0tLOqE1FunXrFnL7r7/+SlhYGC1btqzUeYQQQgghzkeHDh0iIiKCEydO1HZXKqXBBuunS4GJiYnhH//4BxMnTvQH5Vu3buWxxx5j/fr1jBgxgq1btxITE+NvU1hYCEB4eHjIc0ZERABQUFBwRm2qQymFy+XC6/We0XnOlO/6RqOxVvtR38hzqzp5ZtUjz63q5JlVjzy3qpNnVj2nPjeXy1XLPaqaBhmsb968mV27dhEbG8vYsWNDHtO7d++gHPLhw4fzww8/cMkll7BmzRreeustHnnkkXPU68rZuXNnyO2+Effy9p8rmZmZACQkJNRqP+obeW5VJ8+seuS5VZ08s+qR51Z18syq59TnVl4WQl3VIHPWfaPq1157bdBE0NMxGo089NBDACxdujRgn6/kYnFxcci2RUVFAERFRZ1RGyGEEEII0TA0uGDd6/Wetrb66fjSYo4fPx6w3ZcPfuTIkZDtfNtbtWp1Rm2EEEIIIUTD0OCC9WXLlnH8+HFatWpV7qqlp5OTkwNl8sl9evbsCaVpNqH4tvfo0eOM2gghhBBCiIahwQXrvhSYm2++GU3TqnWOefPmQYhyi2PGjIHSmuinrqh68uRJ1qxZQ1xcHIMGDfJvHzRoEDExMezfv5+tW7cGXWvu3LlQWrtdCCGEEEI0LA0qWC8uLubLL7+ESiyE9Oqrr3L48OGAbUop3nnnHV555RU0TeOuu+4K2N+/f38GDRpEenq6P68dwOPxcPfdd+N2u7n//vsxm83+fRaLhXvvvReAe+65x5+jDvDyyy+zfft2Lr74Ylm9VAghhBCiAaqX1WAWLVrE008/7f/eV4JnwIAB/m3Tpk3zj3T7zJ8/n8LCQvr160enTp0qvMarr77KAw88QJ8+fWjTpg0Oh4MdO3aQmpqKwWDg9ddfDxlAz5w5k4EDB/Laa6+xfPlyunbtys8//8yBAwe46KKLQlaPefzxx/n+++9Zt24dHTp0YMiQIaSlpbFhwwYSExN57733qvWchBBCCCFE/VYvR9YzMjLYsGGD/0spBRCwLSMjI6hd2RSY05k6dSpXXHEFmZmZLFq0iCVLlqDrOjfffDPr16/3j4afqkOHDmzZsoWUlBQyMjL48ssvMRgMTJs2jWXLloWsPmOz2VixYgXTpk0jPDyc+fPnk5aWRkpKCps3b6Zt27bVeEpCCCGEEKK+05Qv0hXnNamzXr/Jc6s6eWbVI8+t6uSZVY88t6qTZ1Y95dVZr+2YqLLq5ci6EEIIIYQQDYEE60IIIYQQQtRREqwLIYQQQghRR0mwLoQQQgghRB0lwboQQgghhBB1lATrQgghhBBC1FESrAshhBBCCFFHSbAuhBBCCCFEHSXBuhBCCCGEEHWUBOtCCCGEEELUURKsCyGEEEKIWuV0OtF1vba7USdJsC6EEEIIIWqN3W5n1qxZfPXVVxKwh2Cq7Q4IIYQQQoiGKzc3l6ysLI4dOwbA+PHjMRhkPNlHnoQQQgghhKg1ycnJ3HLLLVitVmw2G5qm1XaX6hQZWRdCCCGEELWqefPm3HnnncTGxkqwfgoZWRdCCCGEEOeMw+HA7XYHbY+Li5NAPQQJ1oUQQgghxDlht9v54IMPmDNnTsiAXQSTYF0IIYQQQpx1drudDz/8kGPHjpGamsqcOXPweDy13a06T4J1IYQQQghxVpUN1H0aN26M0Wis1X7VBxKsCyGEEEKIsyZUoH7hhRcyatQoyVGvBAnWhRBCCCHEWSGB+pmTYF0IIYQQQtQ4CdRrhgTrQgghhBCiRkmgXnMkWBdCCCGEEDXG5XJJoF6DJFgXQgghhBA1xmw207x5c//3EqifGVNtd0AIIYQQQpw/NE3jiiuu8P+/BOpnRoJ1IYQQQghRo04N2EX1SRqMEEIIIYSoNrvdzq+//hq0XdM0CdRrgATrQgghhBCiWnxVXz755BO2bdtW2905L0mwLoQQQgghqqxseUalFPPnz+fo0aO13a3zjgTrQgghhBCiSkLVUe/fvz9Nmzat1X6djyRYF0IIIYQQlSYLHp1bEqwLIYQQQohKkUD93JNgXQghhBBCnJYE6rVD6qwLIYQQQtQxTo83aJvLo/v3WU3Gc9ofCdRrjwTrQgghhBB1zFNf7wra5nA4ALDZTvLshAvOaX8WLFgggXotkTQYIYQQQog6SleKoznFHM0p5ni+E6VUrfRjxIgRxMbGggTq55yMrAshhBBC1DFPjesKgMOlM2Px7pL/d2jcP7QFzZIbn/P+xMbGkpKSwrZt2xg6dKgE6ueQjKyLs+7gwYP+JYcTExNJTEzEarXStm1b/vznP3P48OGgNjfeeCM9evSgd+/edOvWjU8++aRW+l5WZmYmf/nLX0hOTsZms9G9e3feeuutKo1yuN1unnvuObp27YrVaiUhIYFrr72W3377LeTxr7/+OrfccgsDBw6kcePGaJrm/xi0PMXFxUyfPp3u3bsTHh5Oo0aNGDBgAG+//XaV71kIIUTtsJqMWE1GLCYDRoPm/7KYDOc8X90nNjaWiy++WAL1c0xG1sU5061bN0aPHg2Ay+Vi+fLl/O9//2PBggVs3bqVpKQk/7GTJk1i5MiRaJrGwoULueqqq+jRowddu3atlb7n5uYyePBgfv31V8aNG0fnzp1ZtmwZ99xzD/v37+ell1467Tl0Xeeqq65i8eLFdO7cmbvuuovc3Fw+++wzvvvuO9atWxd0f3/9618BaNGiBdHR0eTl5VV4jfT0dC699FJ27drFyJEjufLKKykuLmb37t0sWLCAu+666wyfhBBCiPOd3W5n8eLFjBgxgqioqNruToMnwbo4Z7p3786DDz4IQEJCArquM378eBYuXMibb77J008/7T921KhR/v/v378/Xq+Xffv21Vqw/n//93/8+uuvTJ8+nWnTpgHg8XgYPXo0r7zyCrfeeis9e/as8ByfffYZixcvZtiwYSxZsgSr1QrA3/72N/r3789dd93FqlWrAtosXryYfv36ATB+/HjWrVtX4TVuvfVWDhw4wIoVKxg6dGjAPo/HU617F0II0XCUrfpy/PhxJk2aJAF7LZM0GFFrDAYDt956KwCbN28u97gnnniC5ORkhg8ffg579ztd15k1axYxMTH84x//8G83mUw89dRTKKV47733TnueBQsWAPDQQw/5A3WAXr16cfXVV7N69eqgdJgrrriChISESvVzzZo1LF26lAcffDAoUPf1VwghhCjPqeUZMzMz2bZtW213q8GT396iTjCbzSG3v/vuu7z77rssWbKEyMjIc94vgN9++4309HRGjx6NzWYL2DdgwAAiIyNZs2bNac+Tnp4OQKtWrYL2tW7dGoBVq1bRsWPHavVz7ty5AEycOJFDhw6xcOFCCgoK6NSpE6NGjQrquxBCCOFTXh31QYMG1Wq/hATrohb5RqwBLrrooqD9X3zxBffccw8zZ87k0ksvrdQ5t27dyvz58yvdh6uuuopevXpVeMy+ffsAaNeuXdA+g8FA69at/cdUJD4+HoC0tDS6dOkSsO/gwYMA7N27t9J9P9WmTZugNOCfMmUKLpfLv69Vq1Z89dVXp03VEUII0fDIgkd1mwTr4pz55ZdfeOGFF6DMBNMdO3bQt2/foImPCxcu5JZbbuG9997jpptuqvQ1tm7dyj//+c9KH9+6devTBuv5+fkAREdHh9wfHR1NQUEBuq5jMJSfWTZixAg+/fRTXnzxRYYPH47FYgFg+/bt/heM000grUhmZiaU5sA//PDD3HXXXRgMBv773//y5JNPMnbsWH799VfCwsKqfQ0hhBDnFwnU6z4J1sU5s3PnTnbu3BmwrUePHqxYsSJo8srEiRMJDw/n5Zdf5uWXXwbgjjvu4I477qjwGikpKaSkpJyF3p+5m2++mf/9738sX76cXr16MXLkSH81mE6dOrFjx44Kg/3T0fWSZajHjh0bMFl32rRp7Nq1i08++YS5c+dyyy231Mj9CCGEqN8kUK8fZIKpOGeuv/56MjIySE9P5/Dhw9xzzz1s3749ZHDtdDrJyclh48aN/q/TBepni29E3TfCfqr8/HyioqJOG2hbLBa+++47HnzwQRwOB2+++SYrVqzgkUce4fHHHwcgMTGx2v2MiYmB0mD9VL5tvlQZIYQQDZsE6vWHjKyLc07TNJo3b84bb7xBWloaX3zxBZ999hnXXXfdGZ/7bOSst2/fHoD9+/cH7dN1nYMHD9KhQ4dKXS8iIoLnn3+e559/PmC7byS8d+/ele77qTp27MjGjRv9QXtZvm12u73a5xdCCHH+2L9/vwTq9YQE66JW/etf/+Kbb75h2rRpXHPNNWeUBsJZylnv2LEjjRs3Zu3atTgcjoCqKuvXr6ewsJAhQ4ZUu89KKT7//HOio6MZMWJEtc8zbNgw5syZw+7du4P2+ba1bNmy2ucXQghx/ujevTvFxcUsXrxYAvU6TtJgRK3q1KkT119/Pb/99huffPLJGZ8vJSUFpVSlvyqT324wGJg0aRJ5eXm8+OKL/u0ej4d//vOfaJrGbbfdFtDm0KFD7NmzB7fbHbD91FQapRRPPvkkO3bs4KGHHiIiIqLa937ttdcSFxfHG2+8wfHjx/3b09PTef3119E0jQkTJlT7/EIIIc4v/fv357bbbpNAvY6TkXVR6x577DE+/vhjZsyYwY033lgn/8F49NFH+eqrr3jiiSfYtGkTnTt35vvvv2fTpk1MmTIlaHT+1ltvZdWqVaSmpvprqFP6MWPr1q3p0qULBoOBZcuWsXXrViZOnOhf3bWs5557jj179uBwOPxlHf/85z9jNBqh9JMJ36JJsbGxvPXWW9x000306tWLq666CoPBwPz58zlx4gSPP/54UMlIIYQQDYPb7cZkMgX9jg219kdZTo+3wv1Wk7FG+ifKJ8G6qHVdu3Zl4sSJzJ07l7lz53LttdfWdpeCxMbGsnbtWh599FG+/vprlixZQrt27XjjjTe4++67K32e6667jnnz5vkXUerWrRv/+c9/mDx5csiXlCVLlrBq1aqAbbNnz/b//1NPPRWwwukNN9xAQkICM2bMYM6cOXi9Xrp3786LL77IzTffXM27F0IIUZ/5JpO2b9+eSy65pEqDYk99vavC/c9OuKAGeigqoimlVG13Qpx93bp1g9LyibXJVwu8bIApTk+eW9XJM6seeW5VJ8+seuS5VY7d5WX6wpKA2eFwMPWSljRPblL59qdUfRk6dGiVAvZHvtgBgK4Ux3N/L1KQHBuGQdPqRbB+6s9aXYmJKktG1oUQQgghzkOhyjM6nc4qneOpcV0BcLh0Ziz+vYDBo1d0wWaRqY/nggTrQgghhBDnmZqqo+7LSdd1MBp+b2cxGSRf/RyRVyIhhBBCiPOILHh0fpFgXQghhBDiPCGB+vlHgnUhhBBCiPOABOrnJwnWhRBCCCHqOQnUz18SrAshhBBC1HOapgUE5RKonz8kWBdn3cGDB/3/iCQmJpKYmIjVaqVt27b8+c9/5vDhwyHb+dokJyfj9YZeQe3NN9/0H/fUU08F7Z87dy6XXXYZjRo1wmKxkJSURP/+/Zk6dSq7dgUu9NC6dWv/uUJ9DRs2rIaeSNXde++9JCYmVti/U59jeno6f//73+nSpQvh4eE0bdqUyy+/nG+++abc6xw6dIi//OUvtGrVCqvVSlJSEiNHjmTFihWV7uu8efMYNGgQkZGRREVFMXToUJYsWRLy2GeeeYZLL72U5s2bY7PZSE5OZtiwYcybNw9ZAkIIISrPZrNxyy230KxZMwnUzzP1snTjpk2b+O677/jpp5/46aefOHr0KEC5v9yfeuop/vnPf5Z7voceeojnnnsu5L61a9cyY8YM1q9fj8vlomvXrtx7773ceuut5Z7vyJEjTJs2jaVLl5KdnU3Lli258cYbeeSRR7DZbCHb2O12nn32WT755BMOHTpEo0aNGDVqFE8//TTNmjU7zROpH7p168bo0aMBcLlcLF++nP/9738sWLCArVu3kpSUFNTGZDJx4sQJvv32W6644oqg/bNmzcJkMuHxeIL2PfLIIzz33HNER0czduxYWrRoQWFhIZs2beLVV18lOTmZrl27BrSJiIjggQceCNn/1q1bn8Hdn5nRo0fTsmVLwsPDA7afOHGCd955h44dO9KiRQv/9pMnT9KnTx+OHz/OqFGjGDduHFlZWcydO5fRo0fz+uuvc9999wWca/369YwcORJd1xk3bhytWrUiKyuLjRs3sm7dOi655JLT9vO5557jkUceIT4+nptuugmz2czXX3/NFVdcwYcffhi0iuo777xD8+bNGTVqFImJiWRnZ7Nw4UKuueYa7rvvPl5//fUzfnZCCNFQ2Gw2UlJSMJlMEqifT1Q9NH78eAUEfZXnySefVIAaNGiQmjRpUtDXZ599FrLd3LlzldFoVJqmqYsvvlhNnDhRxcbGKkBNnTo1ZJu9e/eqhIQEBaju3bur6667TrVt29Z/fYfDEdTGbrerAQMGKEAlJyer6667TvXv318BKjExUe3fv/8MnlaJrl27qq5du57xeaojNTVVAer6669XGRkZKiMjQymllNfrVVdeeaUC1OOPPx7UDlB9+/ZVjRs3VjfccEPQ/t27dytAjRkzRgHqySef9O/bv3+/MhgMqlWrVurkyZNBbY8ePaq2b98esK1Vq1aqSZMmNXTXNavscyvrueeeU4CaMWNGwHbfz/wLL7wQsP3AgQMqPDw86D6zs7NVcnKy6tSpkzpy5EjQddxu92n7eOjQIWU2m1VCQoI6evSof3tOTo7q0KGDio2NVdnZ2QFt7HZ70HkKCwtVt27dFKDS0tJOe93ylPfMRMXkuVWdPLPqkedWOcVOj3p43nb18Lzt6m8f/aQOHzuhVOm/n+np6bXSj4fnbVfFTs85u/aZOvVnrTZjouqol2kwAwcOZNq0aXz99dccP34cq9VaqXaTJ0/m/fffD/q69tprg47Nzs7m9ttvx+v1MnfuXFauXMncuXPZs2cP7du356WXXmLlypVB7VJSUsjMzOT+++9nx44dfPrpp/z6669cffXVrF27lmeffTaozTPPPMP69esZOHAgv/32G59++ikbNmzgpZdeIiMjg9tvv72aT6puMxgM/k8oNm/eHPIYk8nETTfdxPz588nPzw/YN2vWLMxmMzfeeGNQu40bN6LrOldffTWNGzcO2t+0aVMuuKDuL5F8OrNmzQp4jj4HDx4ECPo0ok2bNnTu3Jns7OyAT6LeeOMNjh8/zjvvvBPykxyT6fQfwi1ZsgS3282f/vQnmjZt6t8eGxvL3//+d3Jzc/n8888D2oT6pCkiIoIRI0YAkJqaetrrCiFEQ+NwOPjggw+YOXMm6enptd0dcZbVy2D9oYceYvr06YwdOzZk6kRN+N///kd+fj7jx49nwoQJ/u1NmjThhRdeAOCll14KaPPTTz+xdu1aGjdu7D+G0kDn7bffxmw28/rrrwekbLhcLt544w0ozb+OjIz075syZQo9evRg1apVbNq06azcZ11hNpvL3XfrrbficDj47LPP/Nt0XWf27NmMHj2a+Pj4oDaNGjUCYO/evWepx7Xv559/Zvfu3QwfPpzmzZsH7OvSpQtAUH56amoqe/bsYejQoQEfkc6dO5eEhAQuvvhifv75Z1566SVefvllVq9eXen++H5htGrVKmifL4Vo1apVpz2P0+lk5cqVmEwmOnXqVOnrCyFEQ+AL1I8dO0ZxcTHvv/9+0GCWOL/Uy5z1c2HRokUAXHPNNUH7xowZg81m4/vvv8fhcPhHB31txo4dGzTa36RJE4YMGcLy5cv54Ycf/JMV165dS15eHu3ataN3795B17rmmmvYvn07CxYsoG/fvmflXmuLruvMmjULgIsuuqjc43r37k337t2ZNWsWkydPBmD58uUcOXKE1157LWSbCy+8kGbNmrFo0SKuvPJKbrzxRoYMGULLli0r7FNhYWHIiaoAw4YNq9Qk01dffZXc3NzTHkfpqPPf/va3Sh17Kt+zS0lJCdp311138dFHH/HQQw+xYsUKevToQWZmJnPnzqVbt268++67/mOdTic7d+6kT58+3HHHHfz3v/8NONewYcP44osviIuLq7A/vpemtLS0oH2+kf7yXp7+7//+D6fTSWZmJt988w2pqak8//zzZ+1lXAgh6gOlFKmZhaRmFuL2KrwuB//79Ees9mx84y09evQgKiqqtrsqzqIGFawvX76crVu34nA4aN68OVdccUW5AfC2bdsA6NOnT9A+i8VC9+7d2bhxI7/99hs9evQ4bRvf9uXLl7N9+3Z/0FeZNgDbt2+vxh3XLb/88ov/EwffBNMdO3bQt29f7rrrrgrb3nrrrTz00EOkpqbSpk0bPvjgAxo1asSVV17J8uXLg46Pioriiy++4Oabb2bRokX+F6nk5GRGjhzJ3//+d/+fW1lFRUUVTkaubLAeKmANpVWrVtUK1l0uFx9//DHR0dFcffXVQfujo6NZs2YNN998MwsXLvSPsMfHx3PbbbcFvLTk5OTg9XrZvHkze/bsYfbs2Vx55ZWkp6fz8MMP88UXX3DHHXcEpbCc6rLLLkPTNN577z3++te/kpycDEB+fr7/pSovLy9k2//7v/+jqKgISj+J+te//sXUqVOr/FyEEOJ8YXd5mb0+jV+O5nEwsxiP10tRTjrZxnAaW810NmYweGB/Ro4cKZNJz3MNKlj/8MMPA76fNm0aEydO5P333w9IP8nPz/cHFaemF/g0b96cjRs3kpaW5g/6Dh06dNo2nDLyWJ02FenWrVvI7fv376d169ZkZmZW6jw1KTs7G4CdO3eyc+fOgH3dunVj7ty5OJ1OnE5nUFuPx0NmZiZXXHEFjzzyCP/+97+56667mDdvHtdff33An1VxcXHA/bVt25YffviBdevW8cMPP7B161Z+/PFH3n//fT766CPefffdgJxuXddJTEwMKulYVmWe38aNGyv5ZCp/zlOD3IULF5Kdnc0f//hHiouLKS4uDth/4sQJbrzxRpxOJ1988QW9e/cmNzeX//73v9x999389NNPvPjiiwHX93q9PPzww4wcORK3201cXBz/7//9P37++WfmzZvH1q1by/05pfRTgrvuuou33nqLCy64gDFjxmAymVi6dCkxMTFQOkoU6n4PHjyIruscP36c+fPn89hjj7F+/XrefvvtSj7F0z8zUTny3KpOnln1yHMrn1KKjzaeYMvRfA5lO3B73Nhz0vF4vGQbI/G4nTRp3J0+ffqQlZV1TvrkcHtxOBz+77OyMrGZjefk2mfq1J81r9eL0Vg/+k59zVmvqvbt2/Ovf/2LnTt3UlhYyOHDh/noo49o1qwZ8+bN45Zbbgk4vrCw0P//p5bK84mIiACgoKAgqN3ZblNfXXXVVezbt4+9e/eybds2br/9dnbu3Mm999572rZJSUkMGTKEzz77jIULF1JcXMx111132nYGg4HBgwfz8MMP88knn7B3714ef/xx3G43U6dODVnysT749NNPAbj++utD7n/iiSf45ZdfeO+99xgyZAiRkZE0b96cf/7zn1x++eXMmjWL/fv3Q+mnED4jR44MOI/FYmHYsGEopSr16c4///lPXnrpJZKTk/n000/58ssvGTp0KO+99x6USZUJxWAw0KxZM+655x4eeOAB5s6dy9dff13JJyKEEOePtBwHezOKOZzjpE0jK+6soyhHPkZXIVF6Ad6wOIhtyuHc4EEucf5pECPrp9Z2joiI4KabbuKSSy7hggsuYP78+axfv54BAwbUWh9ryqkj1z6+EfeEhIRz3KPfX0isVqt/hLV9+/a8++67pKens3DhQpYvXx4y+DaZTP4+T548mZtvvplnn32Wjh07MmrUKAD/OcPDwyt1f08//TSrV69m9erV5OTk+CdjGgwGDAbDGT+js5mznpCQQEZGBsuWLaNdu3aMHTs25HErV64kPj6ewYMHB+27/PLL+e677zh8+DAXXnghCQkJNG3alGPHjtGmTRv/5FyfJk2aQOkk4Mo8mylTpjBlypSAbWvWrAGgf//+lTrHuHHjmDFjhv+l7kzUxs/8+UCeW9XJM6seeW7BVqUdId8NiVFWMk8cwet2YtAM6AadZk0SMMUmku/WOFig0bfjuXl+dpcXm+33yjPx8QmEWerP6DRlftbq06g6DSVYL09ycjK33XYb//rXv1iyZIk/WC+bElNcXEx0dHRQW19+bdlRSV+7U1MSarrN+eRf//oX33zzDdOmTeOaa67BYCj/w56rr76aqKgojh07xtNPP31G1/U9d9/zrUlnO2d9zpw5uN3uChfmcrlcuFwu3G53UKUdXxqKxWLxbxs2bBhz5sxh9+7dDBo0KOD43bt3A5x2cm5FfJ8EhCqTGsrx48ehkiUjhRDifFPk8uB06+RmnEC3//57Kjy6EU2bNqXIa6DA7qHIVT8/HRZV0yDSYCrSoUMHKBMcUDo5zzdae+TIkZDtfNvLlqnzBTNnu835pFOnTlx//fX89ttvfPLJJxUeGx4ezjfffMOXX3552gmpP//8M3PmzMHlcoXct2LFCqKjo+nevfsZ38OpDh48iFKqUl++KilVMWvWLDRNqzBYHzhwIG63O2hl3qNHjzJz5kwsFgsDBw70b7/jjjugNI2l7DNbu3Yt33zzDc2bN6d///7+7ZmZmezZsycoDzBU+bAlS5bwn//8hxEjRgSM9B8+fDhk/npubq6/Io+v3roQQjQkERYTVrOBsOg4NK0kVIuIbURUfMknnUVOL1azgQiLDGg0BA3+TzknJwfK5Ib79OzZk9WrV7N58+agJendbje//PILNpuNjh07BrT56quvyl3gx7e9bBWSnj17BuyrTJvzzWOPPcbHH3/MjBkzuPHGGyuc1X7qqG95jh49yh//+Efuvfdehg0bRocOHfB6vezZs4clS5ag6zpvvfVW0KI8FZVutNlsPPzww1W8u5q1Y8cOtmzZwrBhw/y1y0OZMWMGP/zwA0888QTfffcdF154IVlZWXzxxRfk5eUxffp0EhMT/cdffPHF3HXXXbz99tv06dOHyy+/nIyMDObOnYvRaOQ///lPwAj9G2+8wT//+U9mzpwZUDpy6tSpbNu2jT/84Q/ExMSwbds2lixZQseOHf2lJn02bdrEDTfcwJAhQ2jXrh2xsbEcPnyYhQsXkp+fT0pKSlAOvRBCNAS9Wsaydn8mJ/MdNG3Rmjx1kqj4Jni9XuxuL9lFbro3i6F3y9ja7qo4Bxp0sK6U4ssvv4QQpRPHjBnD6tWrmTt3blDO+8KFC3E4HFx55ZUBwd6YMWOYPn06CxYswOl0BtRaP3nyJGvWrCEuLi4g4Bw0aBAxMTHs37+frVu30qtXr4BrzZ07F0prt5+vunbtysSJE5k7dy5z586tdKpERS699FI++OADvvnmG7Zs2cL333+P0+mkSZMmXHPNNdx///0ha7tXVLoxJiam1oP1imqrl9W/f382bNjAs88+y6pVq/jxxx8JCwujV69e3H333dxwww1Bbd588026d+/Ov//9b95++21sNhuXXnopTzzxBBdeeGGl+nf55Zezc+dOPv74Y+x2O61ateLRRx/loYceCkrl6tOnD3feeSerVq1i8+bN5OfnExsby4ABA7j99tvLnTwrhBDnu7YJEXRJiqbA4SE1swgi4rG7dFweL/sy7LRPiqFLUjRtEiIqcTZR32mq7Jrj9ZTNZsPpdBLqVjIyMvjss8+49dZbA4KFwsJCHnjgAd555x2SkpLYv39/QEWW7Oxs2rRpQ35+PvPmzfOvYpqens6gQYPYt28fK1asCKq7PXjwYNauXctf//pXXn31VSgtP3j99dfzxRdf8OSTTwaN3D7++OPMmDGDiy66iG+//dY/yv/yyy8zdepULr74YlauXHlGz8g3wbS8Cajnii/tQSYUVY08t6qTZ1Y98tyqTp5Z9chzC+RwOFi/fj1Dhw7FYDAE1FnfkJqNV1doKPo0j6Rv2ybcPKDVOZ3gaXd5mb7w99LGT1zZtd5MMD31Z62uxESVVS9H1hctWhQwwdCXY1u2msu0adMYM2YMRUVF3HvvvTz88MP069eP5ORkMjIy2Lx5M1lZWcTGxjJ37tyg0omNGjXivffe47rrruOaa65h2LBhxMfH8/3335Obm8uUKVNCLpAzc+ZMBg4cyGuvvcby5cvp2rUrP//8MwcOHOCiiy7ikUceCWrz+OOP8/3337Nu3To6dOjAkCFDSEtLY8OGDSQmJvrL3gkhhBDi/ONwOPjggw84duwYmZmZTJgwgTCLkclD2rD7eAEnCxy4vQp0D7f2b8rALi1lIaQGpF5OMM3IyGDDhg3+L9+IetltGRkZUFrX+aGHHqJv37789ttvzJs3j7Vr15KUlMTUqVP55Zdfys2DnjhxIqtXr2bkyJFs2bKFxYsX0759e95//31eeumlkG06dOjAli1bSElJISMjgy+//BKDwcC0adNYtmxZQGqMj81mY8WKFUybNo3w8HDmz59PWloaKSkpbN68mbZt29bo8xNCCCFE3VA2UKd0te8dO3YAoGkabRIiaJMQSccmUbSMC6NlnE0C9QamXo6sp6SknDZn1ycqKiqoIkZVDBo0yL9Ue2W1aNGCmTNnVqlNWFgY06dPZ/r06VXsoRBCCCHqo1MDdUqzBM7nohKi6urlyLoQQgghRH1WXqA+cuRIGTkXASRYF0IIIYQ4hyRQF1VRL9NghBBCCCHqo/oYqCulSM0sJDWzELdXYTZqpGYW0iU5us72+XwiwboQQgghxDlQHwP1siUkD2YW49UVRoPGO6sO0L1ZzDkvIdkQSbAuhBBCCHEOrFu3rl4F6kopZq9P46eD2aRmFqErhdlowKPr7DyeT7HbC8DkIW3q7D2cDyRnXQghhBDiHLj44ovp2rUr1INAHeBAZhG7T+STllVE56RIomxmwixGomxmOidFkpZVsj81s6i2u3pek5F1IYQQQohzwGg0MnHiRDp27EjPnj3rdKAOsPVQLhkFTuIjrIRbAkPGcIuJRhFWMgqcbDmUS9vEyFrr5/lORtaFEEIIIc4C36KNZRmNRnr16lXnA3WAIpcHp1sn3Bo6Jz3CasTp1ilyec553xoSCdaFEEIIIWqYw+Hg/fffZ9++fbXdlWqLsJiwmg0UO70h9xc5vVjNBiIskqhxNkmwLoQQQghRg3xVX9LS0vjkk0/qbcDeq2UsiVFWsoqcFJ8yel7s8pBd5CQxykrvlrG11seGQIJ1IYQQQogacmp5Ro/HQ2pqam13q1raJkTQJSmaVvER7DlRSIHDjd3lpcDhZs+JQlrFl+xvkxBR2109r8nnFkIIIYQQNaC8OuqXXXZZrfarujRN4+YBrQAINxvZkJqN26tjNGh0S47211mvD/n39ZkE60IIIYQQZ6g+LnhUGWEWI5OHtGH38QJOFjj8K5j+5eJ2dEmOqtf3Vl9IsC6EEEIIcQbO10DdR9M02iRE0Cbh9/KMbRIizot7qw8kZ10IIYQQoprO90Bd1D4J1oUQQgghqkECdXEuSLAuhBBCCFEN+fn55OTk+L+XQF2cDRKsCyGEEEJUQ+PGjbn11lsJCwuTQF2cNTLBVAghhBCimpKTk7nzzjuJjo6WQF2cFTKyLoQQQghRCXa7HafTGbQ9JiZGAnVx1kiwLoQQQghxGna7nQ8//JDZs2eHDNiFOFskWBdCCCGEqIAvUD927BiHDx9m9uzZuN3u2u6WaCAkWBdCCCGEKEfZQN2nadOmmEwy7U+cGxKsCyGEEEKEECpQv/DCCxk1apTkqItzRl4LhRBCCCFOUduButPjBcDl0fHqCgCvrnB5dJweL1aT8az3QdQNEqwLIYQQQpRR24E6wFNf7wLAq+tsSsst+X+vh1dXHSIiPJNnJ1xwTvohap8E60IIIYQQpepCoF6Wpmk0i7UB4HK7MUj6TYMjwboQQgghBOB2u+tMoP7UuK5B27IyswCIT4g/p30RtUsmmAohhBBCACaTiTZt2vi/r80RdavJGPRlMRmwmAySr97AyMi6EEIIIURpyslll10GgMfjkaovok6QYF0IIYQQolTZgF0CdVEXSBqMEEIIIRoku93Orl27grZrmiaBuqgzJFgXQgghRIPjq/ry2WefsXHjxtrujhDlkmBdCCGEEA3KqeUZFy5cyKFDh2q7W0KEJMG6EEIIIRqM8uqot2jRolb7JUR5ZIKpEEII0YD5lrUvz/lUJrCuLXgkRGVIsC6EEEI0YL5l7ctzvixrL4G6qK8kWBdCCCEEulIcz7X7v0+ODTtvlraXQF3UZxKsCyGEEA2Yb1l7h0tnxuLd/u2PXtEFm6X+T22TQF3UdxKsCyGEEA2YLydd18Fo+D14PV+WtV+0aJEE6mfAN6fB5dHx6sq/3eXRMRjOrzkNdZUE60IIIYQ4b40YMYJjx46RnZ0tgXo1+OY0eHWdTWm5/u0zFu/CaDCcN3Ma6jIJ1oUQQghx3oqOjiYlJYVt27YxePBgCdSrSdM0msXaAr4X54YE60IIIUQDE6pcY9k0h7LpMOeD6OhohgwZUtvdqJd8cxpE7ZFgXQghhGhgQpVrLJvm0L9No1ro1Zmz2+0sXLiQESNGEBMTU9vdOS9ITnrtk2BdCCGEaKDKlmv0KoVCoVE/R9XLVn05duwYKSkpErCL84IE60IIIUQDE6pco8erkxRtw2iof+UaTy3PmJOTw7Zt2xg6dGhtd02IMybBuhBCCNHAhCrXqJSGptW/fPXy6qhLjro4X9S/12chhBBCCFnwSDQQEqwLIYQQot6RQF00FBKsCyGEEKJekUBdNCQSrAshhBCi3pBAXTQ0EqwLIYQQot44ePCgBOqiQZFgXQghhBD1RpcuXRg3bhxIoC4aCCndKIQQQoh6pU+fPiQmJtK8eXMJ1MV5T0bWhRBCCFFnuVwulFJB21u0aCGBumgQJFgXQgghRJ1kt9t5//33+fbbb0MG7EI0BJIGI4QQQog6p2zVF9+E0hEjRshoumhwZGRdCCGEEHVKqPKMuq7Xap+EqC0SrAshhBCizpA66kIEkmBdCCGEEHWCBOpCBKuXwfqmTZt47rnnmDBhgr9sU3l/iXVdZ82aNTz44IP07duXqKgorFYr7dq148477yQ1NTVku5UrV/rPG+prwIAB5fbvyJEj3HbbbTRt2hSbzUbHjh158skncTgc5bax2+088cQTdOzYEZvNRtOmTbn99ts5evRoNZ6QEEIIUb9IoC5EaPVygunTTz/NV199ValjDxw4wNChQwFISkpi+PDhGI1GfvrpJ9555x3mzJnD4sWLGTx4cMj27dq1C7mvXbt2IY/ft28fAwcOJDMzk+7duzNkyBA2btzI9OnTWbZsGcuWLcNqtQa0cTgcDB8+nPXr15OcnMz48eM5ePAgM2fOZOHChaxfv562bdtW6n6FEEKI+kYCdSHKVy+D9YEDB9KjRw/69etHv379aN26NU6nM+SxmqZx+eWX8/DDD3PJJZf4/9I7nU7uvPNO3n//ff74xz+yb98+zGZzUPvBgwfz/vvvV7pvKSkpZGZmcv/99/Paa68B4PF4uO666/jyyy959tlneeqppwLaPPPMM6xfv56BAwfy7bffEhkZCcDLL7/M1KlTuf3221m5cmWVnpEQQghRHzgcDgnUhahAvUyDeeihh5g+fTpjx44lKSmpwmPbtWvHt99+y/DhwwP+0lutVt566y1iYmI4dOgQ69atO+N+/fTTT6xdu5bGjRvzwgsv+LebTCbefvttzGYzr7/+Oh6Px7/P5XLxxhtvAPDmm2/6A3WAKVOm0KNHD1atWsWmTZvOuH9CCCFEXWMwGAIGyyRQFyJQvQzWa0pYWBgdO3YECHijr65FixYBMHbs2KBUlyZNmjBkyBBycnL44Ycf/NvXrl1LXl4e7dq1o3fv3kHnvOaaawBYsGDBGfdPCCGEqCyXR8fp8Zb7VVMsFgt//OMfadWqlQTqQoRQL9Ngaoqu66SlpUFpPnsoe/fu5ZFHHiErK4uEhAQGDx7MqFGjMBiC33O2bdsGQJ8+fUKeq0+fPixfvpzt27czbNiwSrcB2L59e7XuUQghhKiOGYt3YQzxu87n2QkX1Ni1LBYLN998MyaTSQJ1IU7RoIP1jz/+mPT0dBITE7noootCHrNu3bqgFJkLLriAefPm0aFDh4Dthw4dAqB58+Yhz+Xb7ntBqG6binTr1i3k9v3799O6dWsyMzMrdZ6zJS8vr1avX1/Jc6s6eWbVI8+t6urzM3O4vTgcDpRS5Dk85Ba70RWYjBoxNiPRtpL0lBMFLn+bpCgLmqZV+/eJ3W6noKAg6BNocXr1+WetNp363LxeL0ajsdb6U1UNNg3m8OHD/O1vfwNg+vTpQf9oxMTE8I9//IP169eTlZVFVlYWy5YtY8CAAezYsYMRI0YE/eEXFhYCEB4eHvKaERERABQUFJxRGyGEEKKmeHTFvkw7ezOKOZLn5Eiuk8M5TkwGA81jbdwxsBmNIy3+r/uHtuAfw1tV61p2u53PP/+cjz/+mJMnT9b4vQhxPmqQI+tFRUVMmDCBzMxMrrrqKu68886gY3r37h2UQz58+HB++OEHLrnkEtasWcNbb73FI488cg57fno7d+4Mud034p6QkHCOexRaXelHfSPPrerkmVWPPLeqq4/PrNjp4XDePnLsXgocXqwmI1azAY+ucyDHhTKaCU+zEx5m86enJDdpTJil6qOSdrudL774gvz8fIxGI9988w0pKSn18rnVNnlm1eN7bvVpVJ2GOLLudru59tpr2bhxI4MHD2bOnDlVam80GnnooYcAWLp0acA+XyWX4uLikG2LiooAiIqKOqM2QgghRE04mFVEbrGLQqeH2DAzUTYTYRYjUTYznZMiScsq4tcTBRQ4PJU4W/lC1VHv1q0b0dHRNXAXQpzfGlSwrus6kyZN4ptvvqFXr14sWLCAsLCwKp/Hl6t+/PjxgO0tW7aE0hVMQ/Ftb9Wq1Rm1EUIIIWrCtiN5ONw6VpMBkzEwJAi3mGgUYSWz0ElWUei1TCqjvAWPLr30UplMKkQlNKhg/b777uPjjz+mY8eOLF26lNjY2GqdJycnB8rkk/v07NkTgM2bN4ds59veo0ePM2ojhBBC1IQipwevrjCVU/UlwmrE6dFxe1W1zi8rkwpx5hpMsP7444/z1ltv0bJlS7777jsaN25c7XPNmzcPQpRbHDNmDJTWRD91RdWTJ0+yZs0a4uLiGDRokH/7oEGDiImJYf/+/WzdujXoWnPnzoXS2u1CCCFETYqwmjAaNDy6HnJ/kdOL1WTAbKx6YC2BuhA1o0EE66+88gozZswgKSmJ77//3p96UpFXX32Vw4cPB2xTSvHOO+/wyiuvoGkad911V8D+/v37M2jQINLT0/157QAej4e7774bt9vN/fffH7BSm8Vi4d577wXgnnvu8eeoA7z88sts376diy++mL59+57RMxBCCCFO1bN5DDazAadHx+MNDNiLXR6yi5wkRFqJj6hamUUJ1IWoOfWyGsyiRYt4+umn/d+7XCX1XwcMGODfNm3aNMaMGcPWrVuZOnUqAG3atGHGjBkhzzl58mQGDx7s//7VV1/lgQceoE+fPrRp0waHw8GOHTtITU3FYDDw+uuvhwygZ86cycCBA3nttddYvnw5Xbt25eeff+bAgQNcdNFFIavHPP7443z//fesW7eODh06MGTIENLS0tiwYQOJiYm89957Z/jEhBBCiGCt4yOIDbfg9ipy7W6sJiNmY0k1mD0nCmmTEEGnpCgOZYcughCKUopPPvlEAnUhaki9DNYzMjLYsGFD0Pay2zIyMgDIzc1FqZJcux9//JEff/wx5DmHDRsWEKxPnTqVb7/9lp07d7Jr1y7cbjfJycncfPPN3H///fTr1y/keTp06MCWLVt44oknWLJkCV9++SUtW7Zk2rRpPProoyEXgbDZbKxYsYJnn32WOXPmMH/+fBo1akRKSgpPP/10uQsmCSGEEGdC0zQ6NC6pSmYyaDg8XtxeHaNBo1tyNN2bxTCxT3Ne/PbXKp3z4osvZs6cOXg8HgnUhThDmvJFsuK85quzXl4d9nPFt+Kd1IitGnluVSfPrHrkuVWe0+MFICszC4D4hPiA/VZT3a/lbHd5mb5wF0opcotdZBe58CowGzWeuLIbXZKjcLh1pi/c5W/zxJVdK1Vn/cCBA+zfv5/LLrssZKAuP2tVJ8+sek59bnUlJqqsejmyLoQQQtS2p74uCWAdDgcANlvgipzPTrjgnPfJ9wJRnrIvEEopUjMLSc0sxO1VGDSIj7QQG2ZB0zTaJESc0Wh427Ztadu2bbXbCyFKSLAuhBBCnAGlFCcKXFjsJRM0k2PDMNRSyofvBaI8vhcIu8vL7PVp/HI0j4OZxXh1hcEA6flO4iIs/tSYynA4HKxdu5Zhw4bVu5UhhagPJFgXQgghquGpcV0BOHo8nVdXHcZmswHw6BVdsFlqt9iarhTHc+3+78u+QCilmL0+jZ8OZpOaWYSuFGajAbdXJ6fYhUdX/uNOx+Fw8MEHH3Ds2DEyMjK49tprJWAXooZJsC6EEEJUgy+lxGIyYDRoGA2a//vaylf3vUA4XDozFu/2by/7AnEgs4jdJ/JJyyqic1Iku48XAmBTBsK8BvIcHsxGjYNZxXRtGl3utcoG6gB79uxh+/bt9O7d+yzfpRANS4Oosy6EEEI0BFaTEavJGPACYTRoAS8QWw/lklHgJD7CSrglcMzOZDRgNRlwuHW2Hckt9zqnBuqUlk/u1avXWbw7IRomGVkXQgghGpAilwenWycqLHQIYDKUpMQUOT0h9zscDj7/5KOgQH3kyJFSnlGIs0BG1oUQQogGJMJiwmo2UOwMXTnGo5fUWY+wBgfzHpeTj2Z/KIG6EOeQBOtCCCFEA9KrZSyJUVayipwUuwJHzz1eHadHx2Y20LN5bOA+l5NfVi7g+HEJ1IU4lyQNRgghhGhA2iZE0CUpmgKHhz0nCilwuP2pL06PlyibmdhwC63jw/1tfIF6QU46JCaCBOpCnDMSrAshhBANiKZp3DygFQDhZiMbUrNxe3UMBogLt/jrrJcNwo/t3VESqJeSQF2Ic0eCdSGEEKKBCbMYmTykDbuPF3CywOFfwTQh0kJM6QqmZbXo0gd7QS7pab/Rv/+FEqgLcQ5JsC6EEEI0QJqm0SYhgjYJJauVerw6mkbIIFwzGOjYfzhxSS25fIQE6kKcSzLBVAghhBABQq1eqhkMNG7dUQJ1Ic4xCdaFEEII4edxOdm+7Et+3bOntrsihJA0GCGEEEL4eFxOdq9eREFOOl98MRebxUirth1qu1tCNGhnZWS9sLCQtLQ00tPT0XX9bFxCCCGEEDXI43Kyc9UCf9UXr9fLoUOHartbQjR4NTKyfvToUebMmcOKFStYt24dBQUF/n2aptG5c2eGDh3K2LFjGTVqlOS7CSGEEHWIx+Vk5+oFFOVk+H9H9+9/IZdffjn5dg9e/fccdpenpMyjj9VkrI0uC9FgnFGw/tNPPzF9+nSWLl2K1+vFYrHQvXt3mjRpQlxcHHa7nZycHH777Tf+/e9/884779C0aVPuvvtu/va3vxEWFlZzdyKEEELUIU6Pt8L9dSXI9S14VJidgW8srVnHHv6qLzMW72JTWq7/+BmLd2EsE60/O+GC2ui2EA1GtYP1q6++mq+//pqWLVsyZcoUJkyYQJ8+fTCbzSGPP3bsGMuWLePjjz/mqaee4v/9v//HrFmzuPzyy8+k/0IIIUSQuhAoP/X1rgr314Ug1+Fw/L4yaalmHXvQptegoE/BFYpip5ejuXaMmkZybBgG+aRciLOu2sH64cOH+fzzz5kwYUKljm/atCm33HILt9xyC0ePHuXFF19k27ZtEqwLIYSocXUlUPbqCl0pTuTa/duSSoNcp8dbq6PrDoeDj2Z/GBCoN+0QHKg/NrorMxbvxqvrbDmUS5MoGyajgUev6ILNIkXlhDjbqh2sb9y4sdoXbdasGa+++mq12wshhBCVoSvF8TKB8rkcDX5qXFeemP8LSsGxPId/e5OYMDRN8dTXu2ptdN3hcPDBBx9w/Pgx/7amHXvQutdFQSPqFpMBo0EDNDRNw2go+bKYDHUmlUeI85mUbhRCCHHeeWpcVwAcLp0Zi3f7t5/L0WCryYjBYMBISVW0YldJas7JfAfN4mp3zlZhYSF5eXn+75t17EGLCwZKAQgh6iAJ1oUQQpx3fCO+uk7pqHCJcz0a/NS4rjhcOv9csJOth0smaTaJtvpfGmortz4hIYGUlBT++7/3aNYxmTa9BgVUfBFC1B3VDtaHDx+OpmnMmjWL5s2bM3z48Eq10zSNZcuWVfeyQgghRL1hNRn9Lwy+UeuyKSSPfLGjwvZnM00mMTGRyXf8hZdXHirtmwTrQtRF1Q7WDxw4gKZpuN3ugO+FEEKI2uYbsXZ59JA1ws/GiLXvOqfWIT+1D6Gc7dx6u90OXmNQyeSoqCj53S1EHVftYP3gwYMVfi+EEELUFl81GK+uh6wRfjZGrDel5QRcw8er62w9nBsyKK7J3PqyKTVlXxDcTgcffvghYRYjt9xyi6xxIkQ9IznrQgghzlu6DkVOT8D3xtPEwGeaR67rcCKv2P994yjbac8VKrf+dH05tR9ly1X6XlK8bicnNn1LvFbI5V2T+PDDD0lJScFisZQ5Vvn/W/I+EfoTAiFE7TjrwfrJkydZvXo1VquVUaNGBfwDIYQQQpwNvhHrnEI3mw/l+Lc/MKITcZGhF+/zt61mjfa+reIAmHJZR17+/jf/9imXdeSFpXvYdiQvZLua7oeuFEdz7RQUFpGx5TtUcTZ6TBgeXSepaVN0LXBiq+8TAaVKgnTfJwC+TwieuLJrlfothKhZNRasv/TSS8ycOZM1a9YQF1fyD9batWu58soryc/PB6Br166sWbOG2NjYmrqsEEIIEcQ36mwxeQPST6pSDaaqeeS+kfHf65IT8vuqqmw/yqbUTJu3iU07llGUm0G4peR+f9OT8Dhb8uOC39NtTg3Em8aG4euq5LILUTfUWLD+6aefkpCQ4A/UAaZMmYLL5WL69OmcPHmSN954g5dffpnp06fX1GWFEEKIGlUXarRXpx++l5CiIjt7fliEMz+L0qWMaNr+Aqyt+3CsNOgvG/D7PhEAeGx04LmVUhzIKCQ1sxCnR6fA4abA4SY2XD4lF+JcqbFgPTU1leuuu87//ZEjR/j555956KGHeOyxxwDYu3cv8+bNk2BdCCFEnXUmNdq9ugpZgcarK5RSVRqtrk4/7HY7cz6aTWF2BgDhFhO9+/6BNx68nVeW7fUf5wv4Kzq33eVl9vpD/HI0j4OZxXh0nSKnl93HC4iLsGB3eQmzyAqmQpxtNTZEUFhYGJDesnLlSjRNY/z48f5tvXr14tChQzV1SSGEEKJO2ZSWwwtL97ApLcf/9cLSPWw9nEtmofOsXttut/Phhx9y/Pgx/7aYll1p12cwVrMRo0Hzf53uxUMpxez1afx0MJudx/PRlcJsNACKnGIXGQVOPvn5kD/PXQhx9tTYyHrTpk3Zu/f3t/ZvvvmGyMhI+vXr59+Wn58vE0yFEKKBqK3VOc8Vp8eLUop9GcUczLKD0UW+3UVusRuv7sXhLglkj+c5qhTU+kbnfZVYKlOn3ePx8OGHH3LsWGCg3qhz/2rlnh/ILGL3iXzSsoronBTJ7uOFKKUoMhqIDTOT5/Dw64kCUjOLaJsYWeXzCyEqr8aC9UsvvZQPPviAN998k7CwMObOncvEiRMxGn//x/i3336jZcuWNXVJIYQQdVh1q5nUF49/+Qt70wvJLLCTWViyQKCmwd70AoqcHqLDzBg0jcZRFhKjLPxyNL9S592UlhNQq/3UWvGhmEwmOnbs6A/WkztcQE6jbtWeJLr1UC4ZBU7iI6yEWwJDBZPRgNVkILPQyZZDuRKsC3GW1Viw/sQTT7BkyRLuu+8+ABo3bsyMGTP8+7OyslizZg133nlnTV1SCCFEPXC2V+esDUop9qYXklHgJN/uKa1RrqGUKhlZVwrN4SEmzIzRoKGqUV3F99x0VVIrPtxirPAcw4YNAyA3vxCnozm5h6tWKrKsIpcHp1snKix0mGAyGHB6dIpcnpD7hRA1p8aC9ebNm7Nnzx6WLVsGwNChQ4mJifHvT09P5/nnn2fkyJE1dUkhhBB1WF2pqnI2HMgsolV8OEVODx0Soth4KB+tdCS8f+tG/HQwG6dHx+39fcEhXyrMqWkup+rbKo7HRncBYMbi3Xh1nWO5dnq1iMV0mhWdhg0bRrHTw9qvd57R/UVYTFjNBoqdXuIjgvd7dB2ryUCERdZWFOJsq9G/ZeHh4YwdOzbkvi5dutClS5eavJwQQog67EyqqtR1Ww/lkl3kIiHSSoSlJP/Fd4eRVhM2k5FCp8eft7/9SB7ZRS4AthzKDUhz4ZR6574JoPhrt2to2u+TQymdTLp371569OgR1LeaqI/eq2Usa/dn8svRPOJPWUTK49VxenQSIq30binrpghxttX4K7HH42HFihVs376d/Px8oqOjueCCCxg+fDgmk7yBCyGEqP8C00SCJ9KajCUpMfopE0sVUOzycCzXgUGrXkqQ3W5n7qdzOHbsGMXFxQwYMCDwGkr566HrSnEws4iDWUVVKh3ZNiGCLknRFDg87DlRSIHDjdGg4fLq5NrdRNnMdEqKok1CiGF3IUSNqtHo+csvv+Tee+/lxIkTATPfNU0jKSmJN954g6uvvromLymEEEKcc2XTRGy24P0eb0lg7AvEezSP4ZejJSUQNaBJtBWjwRBQ77wy3E4Hcz6aTWb6CQCWLFlCUlISrVu3htLa6O+vO8ju4wUUODwopUjLLubdH1I5mmunQ+PI06bSUPp7++YBrQAINxvZkJqN26ujoREXbiEuwsIN/VrKKqdCnAM1Fqx//fXXXHPNNYSFhXHnnXcycOBAGjduTHp6OuvWreODDz7g2muvZf78+Vx55ZU1dVkhhBDinCubJhJmDPxVand5cXq8GA2aP93HaChJZSlJaiGo3rndVXGZSwC3y8GvaxZjTfTVPIcLL7yQVq1KgmpfbfSNaTnkFLsAhcGgoeuw50Q+DnfJG0HnpKhK3WOYxcjkIW3YfbyAkwUOnB6dozl2uiRHERtukQWRhDhHaixY/+c//0lcXBwbNmygXbt2AftuvvlmpkyZwoUXXshTTz0lwboQQoh6rWyayG8n8nB7dYwGA7pS7M0oJNJmwunWMRtrZuTZ63aya/X3FOdmQmIilAbqo0aN8o9u+2qjH8ouJjbMTK69pJxklM1Eh8ZR/HggC7NRo8Dh8U9wDbXaqsHw+3wDTdNokxBBm4RIvLpOvt1DlM0sI+pCnEM1Fqzv2rWLyZMnBwXqPu3bt+eGG25g5syZNXVJIYQQolaUTRPB4+Zkvgu9tHxjpyZRHM+zk+/w1EhQ63Y6OLHpWxpRiKF0gumpgToBtdEtZBQErpYabjFiNRlwuHWyipz+Ca6n1nD3ba/vNfCFOJ/UWO2sxo0bn3Z1UqvVSmLpiIAQQghRn/nSRP74hyZEWo1E2kxE2UxMuqg1HZtE1kgtebfTwa7VC3HmZfq39evXPyhQp8yk1/By0lNMBgNeXfnLSVL60tEs1ub/khFzIeqeGhtZnzRpEjNnzuTxxx8nLi4uaH9WVhafffYZkydPrqlLCiGEELVK0zSax9qItBoxluaut2wUXiNBr91uZ+eqhRTmZPi3Ne1wASNGBgfqlJ30Wk7+u0fXMRo0zEaNx0Z3rfe17oVoKGo0WN+0aRM9e/bkr3/9KwMHDiQxMZGMjAzWrVvH66+/Tq9evbj11ls5dOhQQNuWLVvWVDeEEEKI88LSpUsoyEn3f5/c4QLa9h5c7ouAb9Lr9iN5eLyB5WWKXV6cHp24cDPxEdbzota9EA1FjQXrHTp08C+1/OCDDwbtV0px9OhROnToELBd0zQ8HlmuWAghxNnn8uj+hYpCqUsB7GWXXc7M77ZQlJdFTMuutOk1qMIRe9+k19xiNxsPZuPy6hi0kgmle9MLiLSaiA23EGWTNU+EqE9q7G/srbfeKrluQggh6rQXlu7Bai4/IK9LEysjIyO5YNg4jh/YzYmw1qf9Heub9Or26uxPLyS9wImuKwwG6JwU7a+zLr+rhahfaixYf//992vqVEIIIcRZpSvF8Vy7//vqrCRaGSWriXrILnKRZ3cBYDMZAxYOrIglLJzmXXpzskzFloqEWYykXNSa7Udysbu96ErRqlE4kwe34dONhyVQF6Ieks/ChBBCNBgPjuxMXKQZh0tnxuLd/u2+lURrkq4U+XY3v54swOH2UujwgAYOt85vJwvp2OT3xYnsdju71y6hTc+LsEVGn9F1NU0jymYmymYGoHVCBK3iI84oUFdKke9wU+Bwsy+9EKvZQGpmIV2So+UFQIizTIJ1IYQQDYZvYqWul6wieur2mqJKA3W724vLq2M1GUpWE1UKp8dLRqHTP8/Lbrcz56PZZB45QEF2Bj0uGV9j/agJdpeXPScKyClyUeDwcDinGJPBwDurDtC9WQw3D2glq5kKcRZVexihd+/ezJs3r1ptjx49yv3338+LL75Y3csLIYQQdVaB04PTo+P26kRbSxYk0pVCKdA0yCt2k1Pk5Nej2Xz44YccP34MAGdxAelpv9V29/2UUnzy8yEyCpzkFLsAhdlYci87j+fz08FsZq9Pq3RajxCi6qodrHfs2JHrrruOVq1a8eCDD/Ljjz/idDrLPf7w4cPMmjWLK664grZt2zJ//nz+8Ic/VPfyQgghRJ2VXejCqyuMGhS7dXKL3bi9JcG7V1d4dEVWoYu3P1/CsWPH/O2adriAFl371mrfyzqQWcSvJwoodHqIDTNjNhoIMxuJspnpnBRJWlYRu0/kk5pZVNtdFeK8Ve00mE8//ZRNmzbx9NNP88orr/DSSy9hMpno3LkzjRs3Ji4uDofDQXZ2Nvv27SMjIwOlFC1btmTGjBnce++92Gy2mr0bIYQQlVJe+UKXRw+5va6qqAxjbXLrCl3X0QGH24vHq6B0VB1VskCR2+Vml1sjuXTx76anqaNeG7YeyiWz0InVZMBkDBzfC7eYaBRhJaPAyZZDubRNjKy1fgpxPjujnPW+ffsyf/58jh8/zkcffcTKlSvZsGEDO3bs+P0CJhNdu3Zl4sSJjBs3jhEjRtSpf4iEEKIheurrXSG3OxwOAF65qXGVz1nRC4BXVwE54jWlvPvw+ceITpU6j8ujYzAEb/PqJekdVe272aCBpqF7dTwKzCYD7tKFihQK3e1Ex4BDmcjTrXTpfgH7cuPZm16I2aiRmllI6/iIKl3zbChylaTzmE59OKUirEYK7B6KXLJeihBnS41MME1OTuaBBx7ggQceAMDpdJKdnU1YWBixsbE1cQkhhBBnwaklDBvZtGoPqJQXOHt1nU1pufRv06ja/TydMy3FOGPxLoynBKS+fgOV7rtSirSsIuxuLx5d4VVgMIAv1ldK4XEUoRnNGJQXBRyJ7YHSm5KWle1/qXln1QE6JUXh8erU5vhWhMWE1WTAo+shM2eLnF6sZgMRFqlXIcTZclb+dlmtVpKTk8/GqYUQQtSAp8Z1BQgqYXjnhYnYKlg0qDJODZwbR529lMfy7uPRK7pgNWvsOlZAgcONrhQGTeNgVhFxEeZK9V1XJcF1ZV9efKUaP/gxjfQCp3/SpdurUEpHKYXX5QCDCYPuBl1HRURRaIxk5/F8dFUyedOj6+w8nk+Bs6Q+e4fGlR9hV0qRmlnIwawi8uwuDJpGvsNd7QmgvVrGsvq3DH45lo/NFJgiVezykF3kpHuzGHq3lIE5Ic4WeRUWQogGyFemMFQJQ4uperUHygucp1zWkZe/PzsVTsq7D6+u+PDHw2w9nEuBw+MPut/9IZVeLWKZ2Kd5wHkeG90Vm8Xg77tSijy7i7hwCzolAXBFdcWVUhSUlmr89WSBvz+6V6GVBvJK6SUdVV4MuDHbwjGareQUu+nXOo7dxwtLz2akc1Ike04U4vHqFDqtlXoWdpeX2evT+OVoHoeyiyl0eNA0jd3HCpi9IQ2PVw/KOz+dtgkRdEqKYmNaDrl2Ny6vjt3txasr9pwopE1CBF2SommTUPspO0KcryRYF0IIUSMqegE4G/nq5fGVG9xyOJcDGYWAwmAoqWm+50Q+rtKqLGVHzcvWX1dKsTe9kJwiFw6P138/FdUV9+jKX6qxQ2Ik+zIKKXZ5SkbU8ZVsNGA0m/DkZhHZKA6HORyA5Jgwwk9JIwm3mIiPMJOWZSer0FWpe569Po2fDmaTmlmEruO/55xiF5sP5ZJnd9M5KapKaU6apnFDv5Z8u+skJoNGeoETt7ckh71bcrT/echcNCHOHgnWhRBCnFcKHB5+PVFAWlYRHRpHkVHgKyus0aFxFAcyCwk3G/HoiuiwwJQYX6CeUeCkwOHGajIGpKYUu0sm0U4e0iYgQHV6FF5dYTJo/kDeoJUE+QZNw+UpqQxjMFuxxDfFa7Vi0TSsZiPxkZaQ9xFuMeHVFW799CksBzJLSiimZRXROSmSXccKSif8asSGmTmSU4xXVxQ4PEH3fDphFiOdk6LIs7uwp3lpFhuG1WzgLxe3o0ty1YJ/IUTVSbAuhBDivJJV5MSoacRHWAk/ZQQ83GKkUYSVzEInXhUcrB/MKiK32OWvK24yGkqD0ZLUlN9OFhJlM5GaWRRQqrAkzaVkBN9PKcxGDbPRgFIl1WV0BWaTmbhwCwpoGmuj2OklVOGXYpcHo0ErqSxzGlsP5ZJR4Cy958Bf7SajgbhwC0dy7GQVOascrFM6wh5lMxNlM9O+cSQmo4E2CRESqAtxDlR7USQhhBCiLnJ7S1JSwsylq4aWlDZHlQbV4WYDdrcXtzd4xHrbkTwcbr1SdcXLMmglVXR8Ezl13UtxxmFcuelE20yEWYwYjRpWk4FIq4muTaPo3jSaJlE2soqcFJ9S+rDY5SGryI3NbCh35L2sIpcHp1sn3Bp6cnC4xVgySh/inoUQdVu9DNY3bdrEc889x4QJE2jevDmaVrlSY++//z79+/cnMjKSRo0aMXr0aNatW1dhm7Vr1zJ69GgaNWpEZGQk/fv354MPPqiwzZEjR7jtttto2rQpNpuNjh078uSTT/rrF4dit9t54okn6NixIzabjaZNm3L77bdz9OjR096XEEKI35lLg+JfjuWz9XAuDrfX/7X1cC6/HMvnaK4dszH490aR01OazlJ+XXGnWw+qK241aRgNGh5dUWB3knX8CF6DGZdHJyMrG5dHx2oyEhdh8Y9QR4eZ6ZQURav4CPacKKTA4cbu8lLgcLPnRCGtGoUTG24h0nr6D8EjLCasZgPFztC17otd3pJR+hD3LISo2+plGszTTz/NV199VaU2f/vb33jttdcICwtjxIgROBwOvvvuO7799lvmzp3LVVddFdRm3rx5XH/99ei6ztChQ0lISGDZsmVMmjSJ7du3869//Suozb59+xg4cCCZmZl0796dIUOGsHHjRqZPn86yZctYtmwZVmvgzH6Hw8Hw4cNZv349ycnJjB8/noMHDzJz5kwWLlzI+vXradu2bTWelBBC1C6vrgIWF+KUBYh8k1JrUnyEFZNBY8fRPDQUuq7wFR3MLXbj1XUaRViJjwiushJhNZUG3VWrK24ylLwgeL1eftx7EocxEqyAwYBDM2JRCgO/11unzORNs9FAuNnIhtRs3F4do0GjW3I0nZKiSM0sqlSd9V4tY1m7P5NfjuYRHxmY5uLx6uQUu0pG6UPcsxCibqvRYN1ut/Pll1+yZcsWcnNz8XqD3/A1TePdd989o+sMHDiQHj160K9fP/r160fr1q1xOp3lHv/999/z2muvER8fz48//kiHDh0A+PHHHxk2bBi33XYbw4YNC1jAKTs7m9tvvx2v18u8efOYMGECACdPnmTw4MG89NJLXHnllQwbNizgWikpKWRmZnL//ffz2muvAeDxeLjuuuv48ssvefbZZ3nqqacC2jzzzDOsX7+egQMH8u233xIZWZIH+fLLLzN16lRuv/12Vq5ceUbPTAghasOmtBxeWLqHbUfy/NvKLkD07IQLavyaUTYTyTE2dh6zcSLfUVIvvXRfodNDq/hwRl+QXGbi6e96No/BZjaQU+zGZgosdVhRXXFN0wg3esjJy8AaHonSjGBQoGmYTWaUKlm5tNDhJSbs9+g7zGJk8pA27D5ewMkCB25vSZ77Xy5uR+v4cJ5etBuvHljfPJS2pSUUCxye0lF6Dx5doStFrt1Nn5Zx5NrdRNnq5RidEA1ajf2t/fXXXxkxYgRHjhypcPGFmgjWH3rooSod//LLLwPw+OOP+wN1SoP+O++8k9dff513332XqVOn+vf973//Iz8/n/Hjx/sDdYAmTZrwwgsvMGHCBF566aWAYP2nn35i7dq1NG7cmBdeeMG/3WQy8fbbb7Nw4UJef/11Hn/8cUymkkfvcrl44403AHjzzTf9gTrAlClTmDVrFqtWrWLTpk307du3ik9KCCHqBqUUxa6SAZxjuQ6axVVthdHq8I9ia/werZfh1VXQqHXr+Ahiwy24vSVBbtlqMBXVFfe6nZzc+SN6RBKG8HDMrmJ0oxVzRBTxERZQivRCV0nZyFOqu2iaRpuECNok/P7vf1Xrlmuaxs0DWgEQbjayPjULXS8pTRkXbqF3y1iO5NhlQqgQ9VCN5azfd999HDlyhOnTp5OWlobb7UbX9aCvUKPtZ5Pdbmf58uUAXHPNNUH7fdsWLFgQsH3RokXlthkzZgw2m43vv/8+IA/d12bs2LFBqS5NmjRhyJAh5OTk8MMPP/i3r127lry8PNq1a0fv3r0r3T8hhKgP+raK48GRnenVIpZwi5Fwi5Em0VYevaKLfxGlmlbg8HAgowg06NMyFmvp6LgGmIwaGQVOvt56jPUHstmUlsOmtBx/W03T6NA4ksQoK3HhFgwGcHt1DFpJakr/1o2C6oo7HHZObv4Ol24Ak40wXMSEmwmPjsFiNGA2GjCbjP70Gqf77Pwe9I3S/+XidrRsFE6kzUSUrWQy680XtqrygkhCiLqhxv7mrl27lmuvvZbHHnuMFi1aYDTWfB5idfz66684nU4SExNp3rx50P4+ffoAsH379oDt27ZtC9hflsVioXv37jgcDn777bdKtSnvWtVpI4QQ9YXRoPkXRfIVA/BtOxv56pSWbswsLCljmBBpxWwyYNBA0/BPHM2zu8m3u1EhhtxNRgOdk6Lo2jSKlo3CaRkfTuuEcP5ycTsmD2kTsCCS3W5n/hfzcOVngcEEBiPxsVFExzfh1DFsg6aBgkqUTa823yh96/gIYsJ+n8wqI+pC1F81lgYTHh5Os2bNaup0NebQoUMAIQN1gIiICGJjY8nJyaGgoICoqCjy8/PJy8ursF3z5s3ZuHEjaWlp9OjRo1LX8m1PS0urdP9CtalIt27dQm7fv38/rVu3JjMzs1LnOVt8z1VUjTy3qpNnVjkOtzfgE8K8/HycpsqN47g8oXOpHW4vRcV2/6ql2dmZOJxOvN6SCioOh4OsrExs5poL1sveh91RsuJndJgJp9OB0nV/SkyszYDLS+kEUuiSaMNoMPj7U/Y8ZnSSIk2YShtHa3aysn5fTdThcPDpp5+x+0gWqlErtPBGaLZIlCUSj9uLXpoSWvKJsoZX10sCeKVwOBzlXhcgKyvTfw2vruP1enA4nZgM2mmfncPtDXre2dmZQeev7PP39a2q/Tgd+TtadfLMqufU5+b1euvMoHJl1FiwPn78eFasWBGwfHNdUFhYCKUvE+WJiIggNzfXH6z72lTULiKiJJ+woKCg0teqqTZCCFEXvLg89CCCV9fZfqyQ3s2jz3mfKE11MWgadpdOtL/ueOnvJa0kaDYYSka5jQbN/1JRVW5dY4crkQOWCGhkxBAeA5YwjuSVBLNlfx96dIWug9mkYTVV/npKKQqdHgqdXlKz7FhNGodyHHRIDK9Tv2uFEGdPjQXrL730Epdffjm33HILL7zwAk2bNq2pU4sq2LlzZ8jtvhH3hISEc9yj0OpKP+obeW5VJ8+sYnaXF5st3f99THQ0NrOxUs/NZjsOpQsNHc+1+7c3jrJhNDqw2WwANGqUgM2agdFoL21nIz4+ISCdpCbvIynWiMmgsftEPgmaGc1gQNNK8sQVGm5dEWszl6wmarFiNBiIimmEzWLA7NIxW05iNJQE9Urhz/Uu22elFF/uSsWU3Aln4RFMuLCER+H26qA0il0lI/dmI9g9JYs0mU0GbCYjVrMJm61kRN93zlP/HOLjE7C7vBzM3UdOkYsil86JAhcmg4FPt2XTvZmXmwe0CvkM7S4vNusJ//O2Wq3kqzCOF3r81WbylY1m8dGVCvh9ffPqesmfq9WKyWiosT9D+TtadfLMqsf33OrTqDpnEqyHqvvtcrnYtGkTH3/8MXFxcURHB4+qaJrG/v37q3vZKvNVVykuLi73mKKiIgCioqIC2vjahbqPU9tU5lo11UYIIeoC3wRRh0tnxuLd/u1TLuvIy9+XzOdRSnEwq4iDWUXk2V0YNI18h7vCqmFnKspmomWjcIrdXvamF+D26iUpKQry7R6ibCYKHV7cXi9bDuWiaZq/nKRX19mUlkv/No0qvMaBzCJ2n8jnUI6dHq2bsPFwPgaDhsloJDnGRmpGEbpSePWSqjSx4WaKHB5sFgOFTg970wuxmgykZhbSJTn4d4yu67y5ch9pWUXYXV68SqFhxKvr7DyeT3HpJNXJQ9pUGHDrSvHbyULe/SGVg5nFeHWF0aDxzqoDdG8WU27AL4SoO6o9wVTXdZRSAV9ms5mWLVvSsmVLoqKigvYrpdArUS+2JrVs2RJKVxUNpaioiNzcXOLi4vwBcXR0NDExMRW2821v1apVpa9VU22EEKIusJqMWE1G/wRS35fve49XZ8+JAt79IZVD2cUUOjwUODzsPlbA++sOYnednaoovsWG+rduROek0kBYlWTCxISbSYiyEh1mQtM0VOmqpUdz7RzNKaaiX1F2u51jx44BsPVQLhkFJZNYI6xGfDUgNaBzUjRRNpP/WbSIC6d9YiSgkW/3UOj0cDi7mIOZxbyz6gD/W5Ma8Cw8Xp1Xvv+NpTtPUODwoEqDbrtbR1fQoXEEaVklLwupmUXl9lcpRb7dTUahkz0n8tGVwmw0oCvFzuP5/HQwm9nr087qi5MQ4sxVe2T94MGDNduTs6RTp05YrVYyMjI4evRo0CTYzZs3A/gnifr07NmT1atXs3nzZrp2DSwv5na7+eWXX7DZbHTs2DGgzVdffeU/56lCXatnz54B+yrTRggh6jqlFHvTC8kocFLgcKPrYCjN484pdrExLQez0RAwMuz0VBy8V6V6jK+M4dZDuWxIzaJQeUCDTk2iiAsvSYHZejiXC5rFsONoHk2ibJiMhoBPBcpyOx3M+Wg2BXk53HzzzRS5vNjdXqKtJnTlBaVQpfehK4XVbKDYpWEzG2nZKIy96UU4PF7/CqVmowGvrvyj5G6v7g+a96YX4vR4yS50omklzw1vyfPRleJYroO4cAsZBU62HMqlbWJkUH8B3N6S9Bu3V6dHs1j2pvvmYxnpnBTJbycLibKZSM0sKvccQojad94XXQ0LC2P48OEAfP7550H7586dC6W10csaM2ZMwP6yFi5ciMPh4LLLLvPnZJZts2DBgqAVVU+ePMmaNWuIi4tj0KBB/u2DBg0iJiaG/fv3s3Xr1kr3Twgh6rICh4fcYheFTg8dGkcRZSupqmI2GogNM3MouzhoZPipr3dV+FVVmqbRKj6CKJsZi8mAxWgg0mryl4889b9lPxUoy+10sHPVQo4fP4bT6WT27NngcXIk284vx/LZebwIh0eVVE1xe9lxJI+cYjeaVjLRtdDpJc/hxu3VsZhK6q6HmY1E2cx0TookLauIX08UUFD6yUNusYvMAidWc8mCTCaDhqG0b26vTm6xG4MGTrdOkctT7v07PV68usJqKqlvX1a4xUSjCKs/4BdC1F01Fqy3bduW119/vcJj3nzzzZC57mfblClTAHjmmWfYu3evf/uPP/7IO++8Q2xsLH/6058C2kyePJno6Gi++uorvvjiC//29PR0HnzwQYCAFU8B+vfvz6BBg0hPTw9YZdXj8XD33Xfjdru5//77MZvN/n0Wi4V7770XgHvuucefo07pyqvbt2/n4osvltVLhRD1SlaRE4dbx2oyBAWKJqOB+AhzuYGirhRHc4r9X3otpmm4XSWBekHO75M/e/XqxUWdmmIzG3B6dDwKTAYNk8GAqTTv3ZcbbjUZyS5y4XR7S4LuU/LLfUFzZqGTrCKn/7n5XjDK3ruh9KXC4fGSXuDCajYQYSn/A3K9NP3UZAyd0x5hNZ424BdC1L4aqwZz8OBBcnMrfjvPzc2tdL3wiixatIinn37a/73LVVL3dsCAAf5t06ZN8490X3bZZfz1r3/ltddeo1evXlx++eW4XC6+++47lFLMnDmT2NjYgGs0atSI9957j+uuu45rrrmGYcOGER8fz/fff09ubi5Tpkxh2LBhQX2bOXMmAwcO5LXXXmP58uV07dqVn3/+mQMHDnDRRRfxyCOPBLV5/PHH+f7771m3bh0dOnRgyJAhpKWlsWHDBhITE3nvvffO+JkJIcS55PYqvHpJjnQo4RYTRU5vQKBY3oTVR6/ogs1y7j8Idjsd7Fq9kOLc39enuPDCCxk1ahQA1/drwcaDOexPzyfCYsRiNuLxKiwmIwmRVlweHbNRw6OXTDQtbyJohNVIXrHbvzyTV1ckRlnx5ilydDdKCwzY3R6dAuUiMSqG3i1jQ57Td6ymaXi8oV92ipze0wb8Qojad07/9Tt+/HiF9c4rKyMjgw0bNvi/fHl+ZbdlZGQEtHn11VeZOXMmXbp04bvvvuPHH3/ksssuY/Xq1Vx11VUhrzNx4kRWr17NyJEj2bJlC4sXL6Z9+/a8//77vPTSSyHbdOjQgS1btpCSkkJGRgZffvklBoOBadOmsWzZMqxWa1Abm83GihUrmDZtGuHh4cyfP5+0tDRSUlLYvHlzrXwaIYQQZ8JsLBkF9pQzY7PY5QkKFCuasHq2Vjstjy9QL8z+/XdJv379GTVqlH8l1pSL2jCgbTydG4eXTqgtGU3vkhxN4ygr0WElK4eaDBpGA+VO5CxyerGaDJiNmv+5KVVSQcZk1HCWVrPx6gq3V8fu0UmMstElKZo2CRHl3oPVZMRo0HB6vBSfMpm32OUhu8hJYpS1woBfCFH7zuh1evr06QHfr1y5MuRxuq5z9OhRPv74Y3r16nUmlwQgJSWFlJSUc9Ju0KBBfPPNN1Vq06JFC2bOnFmlNmFhYUyfPj3omQohRH0UH2HlRJ6DnGJ3UKDo8epkFbnp0Ty8TgaKvhz1wpzfA/WmHS5gxMhRAaPjvkms3RKMZBSlgcGE2agxeXAb5vyUxuZDJasmNoqwkF7oJKfYjcGgAlJhfEFzl6RoPHpJMH8iz0F2sYv2iZEcybGXLPDk9uL1llSDiY+wMKR9AjcPaFVh2UazUcNqMmA1G9ibXpITbzIY8Og6e04U0iYhosKA3zfh1+UpSevx6iVpNV5doWlSQUaIc+WMgvWnnnrK//+aprFy5cpyA3aAJk2a8Pzzz5/JJYUQQtQDUTYTseEW3F7lDxQ9ukJXily7m35Nok47Mnw2lQ08ywageQVF7Fi5gKLcwEC9be/BIQNjTdNoGWejZVyYv+BAq/iIgGMjrUZibGayCl24PCXVYOzuksmfvqC5U1IUh7JL1tuIDbfQKMLCvoxCjAaNcLMBp8eLpmlEW42M6pbEg6M6YTBU/OG4pmlEh5mJtploHhfOprQcfzWabsnR/jrr5QX8vkm9vtrzSikyC51sPZwrq6cKcQ6dUbC+YsUKKC3RNXz4cFJSUpg0aVLQcQaDgUaNGtG5c+d6t2qUEEKIqtM0jQ6NS8oBNosNY2NaNrqu0DSNuHALfVvFnXZk+GxRSvFTajaZRU5y7S6KXV5/fvm2ZfP5LfUQzeLCAEiuIFCvLE3TaJMYzvE8BxrgVcofNHdJiqJr02iu7t2cl7/7DaOh5Lm1SYggympiQ2o2HqUwGQw0jrISF2Hh7mHtTxuo+xg0jY5NIrmpfyuyipz+FUz/cnE7uiRHVeq+NE2jWawNXRH0KYkQ4uw7o2D94osv9v//k08+ySWXXMLQoUNrol9CCCHqOZPRQOekKK7/QwvSCxyk6kUYNI2uTaNIuah1rayc6dF1UrPs5NpLSikWOcHt1cksdGI2GujTpS/70g5DaaDeuuegGnmhMBkMxISZcZSuPKpUyZdH10nLKubl7371r5xqMhpIuag1B7OKOVngwOnROZpjp0tyFLHhlgqfm9Pj9aet+HLkdQXJMWG0bBThL0vZJiHitPflm/Drc+rEXyHEuVFjU8CffPLJmjqVEEKI84Sv1nnr+Aiyi9wARNnMFQaKvmCz7PdlB5KrO9lUKcXBzGJyil1oGpiNBqJtZrIKnXh1hcGg6NqhHTbTRPJPHqZ5936la5LWDE0rqZduoCQ9xe7yku/wUOT00jjKFnRsm4QI2iRE4tV18u2e0z43SlNXvLrO1sO5ZBaWrPex5VAuLyzdw7YjefRv06jS/T31Oes6QTXohRBnX40F66tXrz7tMQaDgaioKNq3b09ERO3kKQohhCihlCI1s5DUzEJ/esShnGg6JJ551a4zMWPxLjal5QZ8bywTrT874YJqnVdXkOdwU+j0EBdmJtfuJsxs9C845PLopGYWYYppTOukZnh1nZou8R4fYUEDelRy5dQzEV5aaadpbFhQfXchRP1RY8H6sGHDKv1RoaZpXHLJJbzwwgv07t27proghBCikuwuL7PXp/HL0TwOZhb7F/H54KdjdG4SyV8ubVQraSplKRTFTi9Hc+0YNY3kMww6vUrhdHuxmoyYjAZ0Xacw+yQYovwLDmUXuTAYIDrMXIkznqb/pZG+V1cogks3nm7l1Op4alzXoHSVx0Z3AZAUFiHqqRoL1mfOnMnnn3/ON998w6hRoxg4cCCNGzcmPT2dH3/8kSVLlnDFFVcwfPhwtm3bxscff8zQoUP54Ycf6NmzZ011QwghxGkopZi9Po2fDmaTmlmErkoWL/LoOr+mF2P36Mxen8bkIW1OOwhTNkfap+z31QlAHxvdlRmLd+PVdbYcyvWPPp/J4khK+aq+gMUEXq8Xe8ZhPMqNkwKsjVtg0DScHr3cFT+rypeGsu1ISQnH7CKXf+GjbUdyAz4tqClWkzEoXcViKrmOpLAIUT/VWLAeFhbGihUr+OGHHxg4cGDQft8iRCkpKUyZMoU777yToUOH8swzz/D555/XVDeEEEKcxoHMInafyCctq4jOSZHsPl5YusdI20Zm9mc62H0in9TMItomRlZ4Ll+OdNm0FV9+NFClHGmf30eZtaDR5+rmqzs8OrquKHZ5KHIqPNlHcDrseAwaHm8RnuI8CIvGajJQ0zG0pkFStA2HWy/5tEAqqgghqqDG/kl69tlnufHGG0MG6gADBw7khhtuYMaMGf7vr7zyykrlugshhKg5Ww/lklHgJD7C6s9r9gkzG4kLN5NR4GTLodxyz3EqpRRFTg9FTg/H8xzlrtZZmzQNDCicRQW43G5/ProxIhZDeAxeXdEowkJ8RPBK09WREGklIdJK7xZxvHJdb4Z2TGRQuwQSI630bF73FoMSQtRNNRas//rrryQmJlZ4TOPGjfn111/933fs2JHc3Mr/MhBCCHHmilwenG6dcGvoUepwiwGnW6fI5TntuZ4a15XHRnelV4tYwi1Gwi1GGkdZ6dUilr6t4s5C76vHZjJgwYsl5wDY8zBGxKKFRRIV3wRzdCJur47ZaKB940iibDXzobOm/f7JgO/TAqPh9231hdPjDUh38n25PLp/lVMhxNlTY2kwCQkJfPPNN8yYMSPkYg26rrNkyRISEhL823Jzc4mJiampLgghhKiECIsJq9lAsdNLfIjCXMUunXCbiQjL6X9FlM2R9uW3+4LSuhSQKq+b/B3LSLK4IKwF6G5M4Y2wRMZhd7ixmoxYTQau7t2M//2QWnPXVb8Htr4gt+zKqfXBqSuZ+viq9FS3Oo8QonJqbGT91ltvZfv27YwaNYp169bh9Za8bXu9XtatW8cVV1zB9u3bufXWW/1t1q1bR/fu3WuqC0IIISqhV8tYEqOsZBU5KT5l9Nzu9pJT7CYxykrvlmcvVcPl0SlwuEN+FTo8ARNWz5TX7SJ32/e4C7LQlBfSfyNSc2OLiiE51obZaCA23ExMmBmbuWYr4GQWOtl6uKTO+aa0HH/9821Hfq+DXl/4VjL1fdXG6rNCNEQ1uijSb7/9xty5c1m2bBmaphETE0NeXp5/Fv6ECRP8iyedPHmSAQMGMHbs2JrqghBCiEpomxBBl6RoChwe9pwopMDhxmQoqQazL8NDy0Y2uiRF0ybh7K2HcWot9bKUUmiaRt9WZ/6y4HA4OLlpKe6CLP+2mJZdadShJ1lFLlrHR3A4247ZaDjj4NP3glF2BVEF6KX/bRZrw6tKJpj6ruXVFZqmQlbUqStOXclUCHFu1Viwbjab+eyzz1i6dClz5sxhx44dFBQU0KpVK3r06MGNN97IyJEj/cc3adKE//73vzV1eSGEEJWkaRo3D2gFQLjZyIbUbNxeHaNBo1PjcDo3ieTmAa3Oycipr5a6L/fZajISZjHW2LqhWzdvwpn/e6Ce1P4CChO7V3hvvqC5JOAGTfs9CDcYyl9BdVNaDpRWw/GvIKoUGmDQ4L+T/uCvge4rS7n1cC6apgVU0KH0ZQaoUnlH3zOs6cC/uhV4hBA1o8aCdZ+RI0cGBOVCCCFqxukm81UlqAqzGJk8pA27jxdwssDhX8H01v5N6ZAYftYXRHpsdFcMBnC4dJ5ZtIvtR0pG2Xu2iGXKZZ14Y+U+4MxTYS4ceBFRzdZhT91NePMutOgxkD0nCits4wu6fTnlvsC+sjnaBk2jaWyYv0Sjb0JpYA30khx/3zUMpSkmPmX3VVZFueVUMfAXQtQdNR6sCyGEODt8wVh5qjrRT9M02iRE0Cbh91rqLePOTS6yxWQgzGLEZPBiMRkCJqf6FvEpOxmzuoO7mqYR3/UinGEJWBOr+GmBBskxNoylbU7X1lf9JtSKob5tp+rVIhaT0cBjo4MXfHK4dJ5bsqfy/fV1uwYCfyFE3VHjwfq6devYsmULubm5/kmmZWmaxrRp02r6skII0WDoSnE81+7/Pjk2DEM9n+znm2y55VCuPyVEKeWfoHlh23h/GkoovlQPX757WZqmYWvc+rR9UEqRllVEfIQFt65jNhh4cERnOiVHVirI91W/CbViqG9bqDblLfikVzF7paLc8uoG/kKI2ldjwfqJEycYN24cmzZtqvANXoJ1IYSoHl8w5st79nn0iuBR2fpKAcfz7P+fvT+Pt6uq7//x51p77zOfO9+bhMxgQhKmMMoo0Q8q4IyiVq2ixVr7sNqv9qOtilIqah36Ez+1rdWKVbRWUYsDUAQUZAhDgEAgE2Se73zPuKe1fn/ss0/OnZLcmxMIYT0fj5Ocs6e1977Jue/13q/3603JDeqfY2IZykSESqEDn6fv/Q3HLT4VmFpRpNKaYjXgBw9uZdtAmVBpLCn43v2bOXl2K+85d/4RlwYdLgeSQU018DcYDEcPTQvWP/KRj/Doo4/yZ3/2Z7z3ve/luOOOw7aNysZgMBiaRRyM7dc9R0yUlT1cmqmPPxS6clHX0FNmt6LR7B1x0VqTdhIsnzvaFWaiJwuB57L23t9SGNxHYeVdPH3xCZx1+mmHNLbWmkIloBqErN9bQGmNY0XuOE/vHqHsR/fi6osWGrtCg8HwvNO0aPqOO+7g8ssvNw4vBoPB0AQOFCwfyN2jWUF2s/XxB6NRs67H6MPjiclnLl9GKiHHPVn4+CsX8pMf38TDg/sA0Fqxa+dOOMRgXWnwQoUfKhZ153iurxSPzJKZOTbsLZJP2WzuK3F8d+6Ax4obINHgKkODk4zBYDBMlaYF65ZlsXTpxAU0BoPBYJgaBwqWQ6UmlYMcLMgeq2sea/M3lqNJHx8/QWh8shB4Lj/7yY/Yt3d3fbvZi0/lFa+8BDcI8QJV7/WhtWa44jNS9dEavCBkX6GKG4QIBAgItK6Jb6LjZxI2HdkkvQWXx7cNHTRYX7V1sO6+MlG3z8+93niWGwyGqdG0YP2SSy5h5cqVzTqcwWAwGCYJlqe7nxRiXDA/1uaPC2bAi0QfH3gua/7waxLd+ycbsxefysLlF/DF29ZiSYnrh/SXPCp+iNLw2LYh3CDElhIv0KzZOUKgQKARAjbsLVDxQlrTTj27n01aFCoBpTHdXg/ERI4sBoPBMB2aFqx/4xvf4KKLLuLv/u7v+PznP08qlTqEvQwGg8EwEQcKloFJnT0OFmTHwXoczIdaU3IDMkkryi7XeD718dMhDtQLg/uguxsaAnUhBDT4l6cdScUTgMYPFFrv7ypa9WsuMrU/hit+/UlDW8YBoOSGJB1JNnHwX5lnzm+f0IYxxhR6GgyGqdK0YP1P//RPaW1t5Stf+Qr/8i//wqJFi2hpaRm3nRCCu+66q1nDGgwGwzHJgYLl6e6XtK1xwXwQKnYPVTl9btuLpmlOtVrdH6gTxeULTnk5zw51sHFfEccS/N2lSzlxVg7X1/zNzzwe3DSAG4R055MMlf26rjzpSKq+qk9TsgmbgZKHKxR+qCl7AQMll5Nnt3L6vLYDnFXEZDaMMRXvwDUFBoPBMJamBet/+MMf6u8LhQKPPfbYhNuZR4EGg8HwwjE2mNda1DtsNgb3RyvVapUf3fTDeqCuhKQ05+U8rY5ja//AOMvFK06fze6RSJeOBtdXBKFCaY0A7PiaI8k6JTdAExWJDpU91u0psrAry9KZLSzsyr6wF28wGF6SNC1YV+bZnsFgMBjGECqN64cMlT1GKh5Kw6beEhv2FDhxVq5eyHmolMtlSsUi1KQr9vHnUUj1sG33yDjLxYIbcP9zfewcrBCGGkQUjAdKo/V+D3dRO5iUgtaMg1tQqJoU5qRZLXWfdZNsMhgMLwTGCN1gMBgMR4xHtgzw5z98lM39ZSp+ABqe3hXw0Z88Tns2wffed/aUjtfR0cG7//S93PHMV8nOWcxI62y2DZRZMjPH2t3F2lYWJ87I8tDmQWwpKXshupY51zXZTByox038pARLwokz8gxXfAC680k+dPEJLJ2VN4G6wWB4wTgiwXqhUGDjxo2USiUuuuiiIzGEwWAwvKQ5Wjy8tdZs2FNgU2+JobKHFDBU9mlNR79ehss+VT/EDSI3FgEESjNY9giU5iePbDtg1+uJ6OzsZPmr38aOomJff5n5LSkyY4o/a32MGK54pGxJqDRKRxIZKSCsDekrXTNqFNhSYgmBFIL2jMOSmvTFBOoGg+GFpKnB+oYNG/jrv/5r7rjjDrTWCCEIgsjq6oEHHuDqq6/mW9/6Fq985SubOazBYDC85Fi1dbD+PvbwbqTRzztu1NO4SeyvfiCP9YNR8UJuWrmVm1ZuZV+hGhVtCnhocz9J2yKdsPBCRVvGoSefZN+IC0DKtjjv+E429ZVYv6dwwEZOoe/hVSvY2dF68UQ6QzBSIFSaTGJ8Mefj2wYZqQZopVE60qhrDVUvRLE/y+4FtesXECjFxt4iuaRNWyZBPmUePhsMhheepn0Tbdy4kXPPPZdSqcQVV1zBrl27ePDBB+vrX/7ylzM0NMQPf/hDE6wbDAbDYRA3+qEWcG4fKNeD9VmtKWxrdOAeN+ppDOhjf/U4sTJVtNbctHIrD28ZYLDsRbISEXUDrQYhvtK1bLqmI5ugr+jul6cLyCZtOnNJ+oouXjhxzVPoe+xd9b88synFqa9847j1jhUVxZYncFhRGrSKvNOTtkQKCMIoi64bJiiOFARaYwFSSE6ckWfPSJVFPTmTUTcYDEcFTXtw+pnPfIZqtcrKlSv56U9/yqtf/epR6y3L4sILLxwVwBsMBoNh6lx/6zP7u3Iqxe6hCjsHy+wcLOOHik9fvmTC/ZTW9e12DlXQTD+rvqmvxNo9I2ztL3HBy7qY2ZIi49jYQpC0JVppErYk5VgTZr6pNRtyA0UQ7j8PrTWFasBwqcLOZ9dQtXMMl6o89Ydf47ruqP07s0lSjqS/5FEe07Bodlsax5bk0w7nHd/JrNY0czsy9LQkSdoyksMAczrSJCxBLuWQT9m87/wFLJmZHzfhMRgMhheKpmXW7777bq688kpOP/30SbeZP38+t99+e7OGNBgMhpcs/aUomx3Us9IC2xI8uWOYL966bpQMJm7UA9QbJQWhYmZLCq1h9Y5haJTLCFGXpsRyGRjt2/7EtiF6Cy6d2ST5lI0UUQWnRoOWIDQVP0QAA2VvwmsouSFJW9Yz60prnt1XZLBUZaB3HyrVAYlWCsIn1ZYjZHTQn09FcpWObIJ1e4oUqj62jNxg/FDRmnYIQoVtiXqWPGFJLEsQao2U0JVLsnu4Wj/mVPTzY+9RfJ9iydHR0DzKYDC8+GlasF4qlejp6TnoNlMtJDIYDAbDaD5z+TIe3TKIRtNbcAlCjRCaTMLmtLnjG/fEjXpoCLi1FqhYSqOjQPPxbYP8U7mCJQWp1G6klHW5DMA5Czvqxyx5Aa6vyKf3/xrRWqM0eDUfc6Uiic2WvjJWLBKvxftlL2Sg5LJ0ZgtuENJf9Bip+FRcn/7BQVQYgApA2uiWGYQtLfz3o9v58IoT6uMJIVjUk2NhV5Z80uahzQP4ocKSguVzWil5IRpYv3d/IO+FIV6gsKXAC6k/jSi5AUIIfvDg1roM5mDZ9bgbbKlc5sldRSyrAg01BF+64pTp/5ANBoOhRtOC9eOPP55Vq1YdcJsHH3yQE088sVlDGgwGw0uShC05a0EHoVI8umWA/qKLEIKMY/Gp1y6hPeccUlv7x7dFQXhf0eXxbUP0FV2eCkOEgGpQYPGMXKT9nkDXnk3YJB1J2Q3pzEbb+GFUxBnqyPbFEpF3udKaih95l0sBfqjYuK/A8d05TpyZZ1NvET/UVD2fkUoJqzyA70bZ+ESune72FkaqIev3FNjcV2JWa7p+HrYluer8BWzpL7O3UMUPNY4l+IsVL2NBZ4YfPbRtVCAvBSQtGbnBaMVINbKTFFKA1qzfW6h3N10yM39IPw8pBDNbkiQcByZp/jdRFj6eJAlhklgGg2Fymhasv+Md7+Dv//7v+d73vscHPvCBceu//OUv8+STT/LVr361WUMaDAbDS5YoQx7JOxpfcav7qbS117VMd9qx6M459OQSrN1XoTufIlSKzmwC25KjXGWWz2vj/uf6WLNzmM6cgx/WAlCiIF0DScemLeOQTdg811uMijtryfXFM/KcOqeVt5w+h6/cvo5y1aUy1IclFEJH557ItZNs68GxLJK2rk8qZp2SHnX+QggWdmVZ2JWrL1vYlSWdsLj6ooWs3V2oB/JozY7BCkU3QAMtaQe3lmkXCBZ153hoywCOJShUg3H3qpFr3xhJjfr7+gHo7OqcfNtaFr6xsLev6PLE9iFTyGowGA5I04L1//t//y+//e1v+eAHP8h3v/tdwjD6sv3Qhz7EI488whNPPMF5553HX/3VXzVrSIPBYDAcBqfPa0PryOaQWtDbk0/ykYtm85GfP8sT24fq0sU4oIwlHl98y8ksndlCoRqwbk+R4YrfIKuJMurRxEGybFaefYUqQxUfKQS5pE0QKrb2l/mn361n1aZ97Nu0BjIdKB3g+yGJfDvJ1p66bMaWEjdQlLwDB9BjaQzktdb0F1021LLnuvbUoFGemU5YJG1J1Vf0l9wDHjvWpMcSo0PRqAshmN2WQmkmdLHBZOENBsMYmhasp9Np/vCHP/C5z32O7373uwwPRwVL3/nOd8jlcnzsYx/ji1/8Ik7tMaHBYDAYJqbRd7yxwDP+HAeHh4slBVrvD8SFiKwQE5asL4uy7gECEUldAg22xgsVV541h0ApUrZkz3CVOOaVMjpOPmnXu4UmLIkg6hKaS9pkk3bUi8MP2Pbw7fgyg51qA8vGqWXUGwmUImlLsonp/doKQsXGfUUGSi5VXxHUngIMV4JxUh9bSvxQRZn4JhFn4WOqnqoX+47b1mThDQZDA03t+JBOp/nqV7/KP/7jP7J+/XoGBwfJ5/MsXboU2zbNJQwGg+FQiIM1GgK2mOtvfYa/f+PJR/wczpzfDoDrh6zeMYQUgt6ii0Yj0PVz1FoTKIVAY9c8yxO2RArBQNlDAE9sH2Ko4qN15HXeV/LQOgpgB4s+v//jQsrPPoNunUGybQ6d7S0Uq0HdWDIIFW6g6MolOX3e+ALag6G1ZuO+Ir0Fl5GqD4CIJOr4tQy7H2pih8lARUWqjtW8wHhs1l2p0e46E3EoWXiDwXDs07QI2rIs3vWud/HDH/4QKSVLly5t1qENBoPhJYnSml1DVUpuJP2YzK/8SBAHkpZs0MTXiikRgp2D5fq2PfkUrekEGp+wJlNpTACHtcJSKeJs/uiMdeuCkxkqe+hEkvbWPIVqgB8qhIi2Har45FMOJ87Ms7ArS9U/hOrZBrb0lxgqexTdgNaUjVIaN4gcayxL4NekJkoKKl6IGyjaMw6d2eTh3cRpMpUsvMFgOPZpWrDe3t7OzJkzm3U4g8FgeMkSB2tVT3Hdb55m11BkCbh8bhufuXzZQfY+NMJad89Yt924fLKOol25JJ987RLSCWtU8PjxSxbzj7ev5Z4NfVSFwA8UyhK0pR1CFWm6z17QwZqdI3hhWAv+oycIrh/SX/Kwj1uKUprObBLH9ukrePUOpO2ZBO3ZBO88e960ZCCrdwxT9SMZjW1FWX9LCkKl6w2ZlI6ue2NvkVwy8m/Pp16YJ8JxFr5RDtXIVHzcJ/LLn+oxDAbDC0vTvolWrFjBAw880KzDGQwGw0uWOICKpRJxgNrol364PLF9v7SmrxTZJApgTRjyrT/uoCWXHbdP7DaTqHUAjUnUguCWtE3JC7Bl1HQoUBpbSk4+rpXju7NsH6zQO1ih2rcdlvTUPM511NipVpR6QneWsh9S9YZqDjOKTMIiaQv2jFRoz0697qnkBoRK48S+6QIcK9KlJ2xJ4IWR3l7DiTPydZ/1I6UP11qzua/I5r5i3Wpyc1+RpbNaRo05VrseMxUf92Ycw2AwvLA0rZ/y1772NbZu3crHPvaxenGpwWAwHCpuEB7w9VIndgPxAjXOJeRwm81lEhbZhEUmYXM48akUgoQlaMs45JI2c9szLOjK8KGLT+A9L5+PDjyC9fcQbH6E87K9zGxN05NPYklB0hak7KiwNZ2QKA2BigL+3qLL1v4K375nE9/94+Yp2VICZJM2lhQEStWD8kjSAy0pu1b4Kkg5kvedv4AlM/MHbYg0XSpeyHf/uJlv37OJLX1ltvWX2dJXPuC1xdr1+DWdSUQzjmEwGF4YmpZZ/8AHPkBnZyf//M//zHe+8x0WLlxIT0/PuC8EIQR33XVXs4Y1GAzHCI1FlRPxUs8Axm4g198a3afYJaS/5OMGmnRiasdbPrcNIcCSkiBU9ffVapW/uOA4fvj44LTOUwiBY0kStsXLal1AF3ZlGRgusmfVHbgjkSf5Q/fdQ3n+BWQ6jxvlRhNlnctU/LDejdSpebw/vXuEcm35RI2aJuO0Oa2kHMlg2afoRnp4AKVhpOrXC1mVhnkdmSOaUb9p5VYe3jLA5r4SSkfZ/kCp+rUBXH3RQoQQ47Tr06EZxzAYDC8sTQvW//CHP9TfV6tV1q5dy9q14wtizGzeYDAcCKU1u2sabYBZbemoqNEANWnMnpG46DRuMzR1InlNXPC5/31s3dhMKpUKP/2vH+EO99WXnX7m2Txtzal3C40puiHDVb8uUZFCkHYshBAsmZljw94iGcciUJqW9KFJYhZ0ZmnLJPBDzb6RauQHX2vcpPT+wteDmLMcNpv6SqzdM8LW/hJLZuZYu7tYW2PVry2fstncV+L47lxT9ORGk24wvPhpWrCuDqW3tcFgMExCY1FlY/Hipy9bSipxZCQJLya6ckmWz23jb15zIl+9Yx27hipoHemdB2ua86MR363y4x/dxJ49u+vLWuct45WXvIZn/vDcuO0Hyx6uH2JLMW6SlknYdGST9BVdQn3owboQgkU9udr76CkFOnp//gldPL5tiJa0fcQnhU9sG6K34NKZTZIZ4xcfX1tvIerSenx3btLjGAyGlxbG/NxgMBwVjC2qjIm6YJrsYL1hUa24s1E6cqSfWGqt6/l7L1B1J5WD4XtV1v/xVpLd+7dtnbeMjiXnTHrOgdKEavKnsNmkxXDZn/LzBNuSLJmZp7+Y4IntIbqmsf/T8+azua/0vDz1LXkBrq/Ipyf+1ZtNWhQqwZS7tBoMhmObpgXrr3rVq7jqqqt473vfO+k2N910E9/73ve4++67mzWswWAwHDEOVtj6UplE9Jei5kYAX/nfddiWGOUsMhGh7/LMvXdRHuqD7m44hEAdwJYCS8Z2kuO3K7khSVsSTqOoVghBPmWTTzn1c5jbfuQ06mPJJmySjqTshnSON9uJrs2ZfpdWg8FwbNJUzfqKFSsOuM3WrVu55557mjWkwWAwHFEmKnptlPxd9+bxnUSP5QBeA7uHq1hSUHIDMgmrXhRaqPoUqj5eoBgpVSisf5CuoLf+lOTMs87mkU0tBwyMda37aVBrWhRqjSNlvZi07AX0F6vMbc/SX3LZsLdQtz1cMFH0e5SxfF4b9z/Xx5qdw3TmRkt4yl7AQMnl5Nmt0+rSajAYjl2e1+n7wMAAyeQL0xHOYDAYpktj0euGvUUySQuB4Jr/eXrctv/w5pPq76cauI/N5McWjYdrzXi4dGYTSCFQWtOTT2Jbgl1DFZbPjYLKmx7aytrdhbrTynBphDA3k5LOkytu5eyzz+GiV76ab//7yknH0FrjK9g5WMGrdSj1A00gQvpKHilb8szuApYU7C1U6S+6hDXJ1Lfv2cSJM/MEoTpilovN4PiuLEtntlCoBqzbU6RQ9bFl5Aazbk+RhbX1C7uO/omHwWB4/jisYP0HP/jBqM9PPPHEuGXUMlE7d+7kP//zP1myZMnhDGkwGAzPGxMVve4YrHD63DaEENy7obe+bRzAN2bjp2o3OTaTHyrF49uGooLIGlprRmpZ7FBpNBqlNPIIxqixLj72I48181LAs70l+kseg2Uv0pELsDMthEg8P4nVM4tXv+a1uMHkEw6tddQAScNwxSfpSNwwnqiA64dopenOJwlCTdUPUZpRtocFN2Cg5LFkZv6odR0TQvCec+cDkHEsHto8ULenPGlWCyfPbuU9584/as/fYDC8MBxWsH7VVVeNKnK65ZZbuOWWWybcVmtNMpnk85///OEMaTAYDM8bExW9nrWgg89cvhSAt/zL/VCTh8zMp5BSsHOwfNh2k3EmX+lIHhGrtwOloiz2rgKFalDPuAdKYWkZWRI+jxTdgKGyR6Hq05Z2GKxEEwjHtrCyOXSYwmpvYetAhZktqUmPozWEWqM0tKRtHEvihdEkJPKAF9i2pCXtMFDyWDozP872cN2eIkGoKFSDQ3aJeSFIJyyuvmgha3cX2Fuo1juYfujiE1g66+idaBgMhheOwwrWb7zxRqgF4h/4wAd485vfzJve9KZx20kp6ejo4Nxzz6Wzs/NwhjQYDMcosQQkln7EeIFCyqNHCx47slAL3IF6U6G4IPJvXn0iqYQcJ2s52DWMzeR7QciOAUnSlgjg2b1F+oseA2UPjUZIgVKKSEavGakEhySZibufRuc8/v2h0l/yqPqKOe1pBsbYR1q2TSqVpOorVu8YYuaymRMeI5K/KGp9iqjWJDBojZSChLRI2RZeoOgrenTnJrY97Mw6bO2v0F9yj+pgnVpya2FXloVd++0ZF3ZlTaBuMBgm5LCC9fe973319/fccw9vectbeOMb39iM8zIYDC8xYglIqNQop5Hrb30GS8qjsoNpnG2PmwrF5/3VO9ZhTaBLOdg1jM3kr94+RF8tCNZK01f02NJfRmlNGGpSCYkWkjAMUVrjhYptA2U68weuDVq1dbAe1McFovH7MDx028Ag1ARhyHDfHgJrvM7atgRBqGsNnCbaX/HsviJBuN8asuQGVH1BoDSOFZ2TbQkqnsYLQjLJiSc8mYRNqCI5jcFgMBxLNK3ANM6yGwwGw+GgFKOCuyhwfUFPacooBXuGy/XP05XF9JU83EDVZSKiYYLQaF0ohEBoTagUT+0cZvm8Njb3FdncV6zLLCZyTNFoym4YZelrAe9UEDqgMtSLcASVUh+6ZRawP5gOQo0lBZmExZb+EoWqj9IaKQRb+0ps2Fugr+TR+DBA156mxIsca/9xErY1qe1h2QuwpMCxTHbaYDAcWxxWsB4EASMjI2Sz2XEuL4VCgeuuu4777ruPUqnEhRdeyGc/+1mOO+64wz1ng8FwDBJLQAaLPo9tG6wv/5vXnEh77uiWNcScOb8dgI9fsph/unNDfXnchXWqvu1duQR9BQ+tNSUvrGvXE7Y14bG0hv6ix7/d8xzP7Bphc1+JUOlxjilnzm8nCFUtuB+pZ9aXz20j8A+tG2rouww8+whaduI6nQShRpULkIkcYpTWuEFIW8ZhU2+J+5/tr+vshRD86z3PsW2gjB8qpAClo0A9LmCt+iEokCKSSOVTNvM7MmwfLE9oe9hf8kk5ks6scRwzGAzHFocVrH/jG9/gU5/6FPfccw8XXnhhfXmxWOTss89m48aN9V8Ca9as4bbbbuOxxx6jvb398M/cYDAcU8SBasIOR2l3X0wdTOOsd9xlNCa+hr/7xVMH3H+sTOaMee08tnWQQCnKfogAhICOrMNgefS+SoMfKn7+2HY0gkLVx68VZ2qtxzmmROc3uvupJQVaHjwz7btV9qy6g3aKOG0JsmQZaOlB2il8pfBDhdKarmyCINRs6iuxtb8ERDp0rTXr9oxQ9sL6fRIiSqv7tQ6p1AJ+L9C0ZxJ05ZKcMa8dKcWEtofzOzL0lzzyqUP7tRb/bvICNUrnH9dMWIdwHwwGg+H54LAeLv/hD39g3rx5owJ1gP/3//4fGzZsYMWKFWzatIl9+/bxiU98gq1bt/L1r3/9cM/ZYDAYXlR4gcINwloxqCIIFdsHyvXXZC4usUVibJsoRCSx8cIoDa115MGudKT5FgICpRkse7UiWFGThkiWzMyxbaBcc2+Zfjv7wKvyzL2/wR3uQwDZwhZOn9tGT0drPeAWIpp85dMOjiXZNlBmUU8ex5LYtfPpzCUJQoWqFbdKEe2XsGX0+KB2SyxL0pVPsnhGnj85Zx7nLOjgpFktSCFqWfnI9vDM+e0s6skdcpFmX9Glr+jyz79/tv6+r+jyjTs3sGrr4CEcwWAwGJ4fDiuz/swzz3DxxRePW/6zn/0MKSXf//73mTt3LgBf/epXue222/jtb3/LF77whcMZ1mAwGF5UxEWymjhjrNhVa7IE8K13nUEqceDcidJxAKvZO1ytL4/D/FgeM7stTThQIZu0cANFVy6BQIxzTMkkMlO+jtB3eebeu6gM7/eXn/Oyk/j7P301n71lDau8QQo6IJe0STkWrSmH7ZUKM1tTZBKjn46kExZSCkIdealLIbAEtGYcvEAxHPogIJuwWNyTw7bkAW0PF3Rm+Iffrp3yNUkxWqtvDFkMBsPRxmEF63v37q0H4zGlUoknn3yS5cuXj1t38cUX88Mf/vBwhjQYDIYXLULA7qEKodaUvZB0QiLYHx1OZl8Z76v1/mByrMOiEJCyJYGKmgXJOBvfIHU5FMcUrTXbB6v14lR0dD629tm76g46KNUbMM1adArHn34hUkryKYd8ysENFLmkjRCRo0uo9LhAHaA9k8CWY3T8AhwpkTYM17Lz2dqx9l/nxLaHU6UrF2nbP/7qE/nXe56rLx9bb2AwGAwvNIcVrGutGRwc/bjwoYceQinFueeeO2779vZ2fN8/nCENBoPhRcdnLl9GKiHr/ulBqNg9VCU2LIwz7zGxfeWZ89vqy6SApCNpS9sESrOvEH2X2gIsC2wZ6eSTdqTjnkjleDDHlEApnu2r8ONVe9nSV677rxerHt7ujQSFIchHxZ2t85axcPkFB5Sd2FYkwyl7Ie1jEvm5pI0lwbYkfqCI/V+KboAXKhxLkrAkzhHSjsfnPVF9gdGrGwyGo4nD0qwff/zx3HvvvaOW3X777QghOP/888dtv3fvXmbOnLgxxpHkD3/4w37d5wFe1113XX2fa6+99oDb/u3f/u2k491///1cfvnldHR0kMvlOOecc/jBD35wwHPcsWMH73//+znuuONIpVIsXryYz3/+81Sr1QPuZzAYjn7iAtM4EKxr0WtZb6Vg52C5/lJq/DGEEEgpsSyLpGNjW5H/uGNLbMtCymiMjmwCN4h08Y0czDFFa81zvSX6Sh4b+iIdvWNFXVFdPyCw09B9ArqWUe9Ycs5B9eGd2QQpR9Jf8ih7o91rql6IFIKMI6nHxjqalLRlHNKORUvaPmQN+sGIG0GFSte1/lrrCZ9iGAwGw9HEYWXW3/zmN/OlL32Jj370o3zwgx9k48aN/Nu//RvpdJrXve5147Z/4IEHOOGEEw5nyGkxc+bMUQ2cGgnDkJtuugmAiy66aNz6Cy64gJe97GXjlp955pkTHu/nP/8573jHO1BK8YpXvIKuri7uuusu3ve+9/Hkk0/yta99bdw+zz77LOeddx59fX2cfPLJXHTRRTz66KNcd9113HXXXdx1113jrDENBsOLn9PntWFJOU56sf/z1IJIxxK8rCfHUzuHGar4eKGi4oeESo9zTBkboPqhZrjsU3JDls3IsHUoKkJNKEHFT1BtnUEY+rTPWc7C5ecyuG34oOeTS9q0ZRJ0ZBNs3FfAqxWEKq3Z2Fskn3Jozzhs6i3WimwFc9ozdOYSbOotTenaD0ZcNBoqRX/Jq4uPvnHnBtbsGuGchR1NHc9gMBiaxWEF65/61Kf4+c9/zj//8z/zrW99C2rZmS9+8Yu0traO2nb16tWsXbt20qD5SLJkyRK+//3vT7jutttu46abbmLu3LmsWLFi3Pqrr76aq6666pDGGRgY4AMf+ABhGPLzn/+cK664AmpPFC688EK+/vWv8/rXv37cOFdddRV9fX189KMf5YYbboCah/3b3/52fvnLX/KlL32Ja6+9dhpXbjAYjmbiLPtkUoypZnyFELzl9Nnc92wfthTsK7j4ocKWkpNmtXDizDyb+0q1bPXoY7tBiKzJaNKOBQSAxg8VYaiRloPonIeY1Trp+BOdz6KeHAu7sqQdi11D1Zr7i+DEGXn2jFToyCR4trdYd7NpzyTIJa1RnVWbiRCQSVj1WgFTUGowGI52DksGk8/nWblyJZ/73Oe49NJLede73sUvfvGLCSUiDz/8MG9605t4y1vecjhDNp04q/7ud7/7sB+3fve732VkZIQ3velN9UAdYMaMGXzlK18BGGdd+fDDD3P//ffT09NT3wbAtm3+9V//Fcdx+OY3v0kQTN9qzWB4KeEGIW4Q4gWqbpnY+DrWSTkWS2bmWTorTz5lM7cjw4KuDB+6+ASuOn8B9iTtYJWOpCK2FIRhZDM5VPYZrvj4KpKKhEKye7jKhr3FSe0mx2JbkqvOX8DVFy4kn7LJpWzyKZt3njMXreHZ3iJ+qPDDaGKwYV+BZ/eVDvn4h8qZ89s5c347p89t58KXdfGKxd28YnE3f3vpUs6c316XyMSymMbPL4V/NwaD4ejlsDLrAK2trYeU9f3gBz/IBz/4wcMdrqmUSiVuueUWAP70T//0sI/329/+FoC3ve1t49a97nWvI5VKceedd1KtVkmlUqP2ecMb3jBO6jJjxgwuuugi7r77bu67774JM/8Gg2E01/7qGYB6vUcqtXfU+rGNhw5GHPg3Zrm9INKDH63Nc4QQdXeWRT05LCkP6pgiBUgp8IOAzZs3sc9No5M5/Fj7LiJ7yIof0lt0cX11yJlvIQTzO7PkU1FxqtaaR7cM0lf0GKn6kTc7UbJ/uOwTKI0fKFpSzdOsxz8nrQVCjG9g9fDmAQC+8r/rWL1jv8QnLv6d6r8bg8FgaBaHHay/mPnFL35BqVTi9NNPZ9myZRNuc/fdd/PEE09QrVaZM2cOl1122aR69dWrVwNwxhlnjFuXSCQ4+eSTefTRR9mwYQOnnnrqQfeJl9999908+eSTJlg3GKaA1po9BY9EJQo2Z7WlkdMI/K791TN1d5aY62+NJgSrtg4dUOscB/heoJCSUVnbIyHxOFS01hSqPr0Fl+FKpN+WQuBIzcDICLYIccMQjUcqmcANQnRtm9aUTbEaoHTk3NI+je6yfqh5dl+RohvQmrKp+mGkyhHQkrYZqQZoDb7SJCZxrjlSSCGY3Zaqf27WZMFgMBimy0s6WI8lMAfKqo/1hb/mmmt461vfyve//31yuf0+vyMjIwwPR9mYOXPmTHisOXPm8Oijj7J169Z6sL5t27aD7gOwdevWQ7qmk046acLlzz33HAsWLKCvr++QjnOkiO+RYWq8lO7bUNkjDPfLvgYG+lDVxCHv/1fnzwBg38Ag/7ayl0QiCrY+dE43Kcea8v+BarVKqBRBGFDxosB/y74RZuQThGFAtVrFDRQCXbdf3NO7j1K5wuqdIwBc84sKlpSESrF6ZwGlYaDsUyqnsKSsb08tAzww0FcfNwxDAqXQGnTtM4CqTQR8rRGhQEioej4D/dG+gdL187OkpL8/uu5iucLm/gpDFZ+qryi4QeRKoxWU+hG+y1C2nVBKJJogVIQKpIyy7wkJgRSUvJA9Q2XSlo7O13UJwhClNSoMQUDVdbGloL+/j6of1n+uFS9k71ARR4KkYeKiBQKNU7N7rLgBMmmNOk7Ksaj64SinrPjaxi5LOdFEonH7QOlRP6v4Xp80Iw3AX57XU9+vkQP9u5nO/8+JrmGicY9lXkrfa83C3LPpMfa+hWGIZb14/r+9ZIP13bt3c9ddd2FZFn/yJ38ybv3LXvYyvva1r3HZZZcxf/58BgcHuffee/nkJz/Jz3/+c8Iw5Je//GV9+2KxWH+fyUzcGTCbjR5DFwqFcftNZR+DwTA5CTsKwhxrv01ivDxeNxX+76vmU/VDvvb7bTy9O/p/2J1z+PCFs/m3+3cBsGZ3MWpAVEvCfuuPO3h6T4n+chScag27R1yU0uwt+NhS4AaaNbtLCLF/e4DT57TUx9Y60nGX/SgI9sMoKy1F5NCudfRCaEQIw9WAXz7VSzBJYarWms39FfpKHiU3xLEEUgjCUOFVijh+lGVPqiq+1YK07JpkJM68R5lmW4JGjxonPlc/VBTcMPJnr2XOxxKqaBs/VBSURsUymFrRqy0Fmuf36cPh/jsxGAyGI8VLNlj/r//6L8Iw5NJLL53Q+/0973nPqM/ZbJZ3vetdvPKVr+SUU07hf/7nf1i5cuWEzZ9eSJ5++ukJl8cZ966uruf5jCbmaDmPFxsvhfsmix6Wtf+rqaOji47coWfWY6p+SCIxVK8P6ezsIj1BJ81DoeKFZNP7sO0oE5rNpJnZ3UM2E9kB2lbNZlBA2Q0ZqEI1gO58CgF85g2n8U93biBUirX7ynRkEgyUPNww0n4MVEHKyAEllUrR0dGF7fSyaaDAiKsIQo3WEGhNwQ3rQXocysahpRdo1vd5FN2A47uyWJZNKhVl7zs7u9jcV6ToKSq+pj2TQEqBF1TxBndC4KNzHcjqEMu6U6ytJAmVxrElJS9AaAgR0UtrpJCkkw6pVIp0vp1tw5souIpAacq+QgjY1O/SntVk8u1kAMuyo0mH8hmshFR8VXN9oe4GI4REESKFxLIiD/lUMoltyfrPsOKFpFL76j+fzs7o/8XYZfHPu3H7IFS1SVUUnOdaOnAS+7Pm+dYOUonIs36qTOX/50TXMN1/ny92Xgrfa83G3LPpEd+3F1NWnZdysH4oEpiJmDVrFu9///v52te+xu23314P1hslMeVymZaWlnH7lkrRL/R8Pl9fFu9XLpcnHG+ifQwGw9HF8rlt9az649uG6Mkn2DtSZfncNmxLNlgzCrpzSU6e3cKTO4Zr2XhBTz7JrNZU3alFa83GfcWokDMI602DtAJPKVSD8aIGwlozIY3myR3DaKC/5I0LOFfvGKbqK5K2xLYkYRhQ6d1O6EWTEBl6OC2dnLT4eHZtHGLfiIuq+bTHqfzBkkeoo+x3ZzaB1pqbV22nrxCda9TYKMqMD5Y9AqX5ySPbeMdZc9FaM1KJCkgJQkIFsfJD1/4YrvgoHRXuJo+gLCT2XTcFpQaD4WjnJfmsb+3atTz++OPkcjne/OY3T3n/RYsWQU1KE9PS0lL3lt+xY8eE+8XL58+fX182b968Ke9jMBiOLmK5TdyZtPHvsW4xjevjV+P+AFsHygyVPYrVgIQtcWK7RVGLmSc4hzieRkQyE9cfbzdYcoOaPaNEqZDB3dvqgTpAOpMhmW0lZQuCMBqlGihitYvSUKm5wPihYs9wlbW7R3h82xBFNzpXWbsOx5K0pR2KbsD6PQW2DZTxQ41bK7I9ri1N0pGEDVIXDZS9kLRjkbQlzvPgtBMXlMYvU1BqMBiONl6SmfW4aPSKK66YVCt+IAYHo4xMrCePOe2007j33nt57LHHxrnL+L7PmjVrSKVSLF68eNQ+t9xyC4899tiEY8XL44JUg8Fw7LNmZ5wBt6gGkec5jJa+NCIaMtNpxyK0JMe1pSm6owP2bNLGkgI/DCjt20HgVurrErl2Erk2BFANNI4l0UDKkbiBqtsrWpaoFWnClv4ySmt22hVCpRENJ6K1wLYkSVvSV3R5aucwbhDWvdwX9eTpK3q1xkv7r0oKSFiShGPRzLA5duaJz3P53DYAPnXpElIJo1M3GAxHL9MO1u+9995pD/qKV7xi2vseLlprfvzjH8M0vdW11vXC0rF2i6973eu49957ufnmm8dp3n/zm99QrVZ5/etfX9fQxvtcd911/PrXv8Z13VFe63v37uWPf/wj7e3tXHDBBVM+V4PB0Dwa7RYbm+fEn+0mWgyWvHD/MYPRQfr43qMN68T+c0nYEsYE66fNaSXlSAZKAcJKAFGwnsi147R24waK1pSNFETv0w6OJegvemitCTVYQqDQKKDsRRaLRTeon5fWoH1VlwVZUlDxQ0Yqfv0eCin2Nz3SYty1hHp0cWkUYB9esWkse4mPG2fQv3rHOiN7MRgMRzXTDtZXrFgx7ceFsfXYC8Ef//hHtm7dyuzZs3nVq1414Ta9vb389Kc/5b3vfe8orXixWORv/uZveOihh5g5c+aoLqUAV199Nddffz233HILv/jFL+rr9+3bxyc/+UkAPvGJT4za55xzzuGCCy7g/vvv51Of+hTf+MY3AAiCgL/8y7/E930++tGP4jhO0++FwXC0obVmS3+JQjXSLUsh2NJfoj3rHHF5wmRdKrXWbNhT5I8beylUAxDgbVL84+1rebJmzai15qwF7U07l2zCQoqoSDZ2WImRItKoxzQG76GCqhfSmUvQmU0wUPJHHXdBZ5a2TAI/1IzoLsDGTmlktg0vVHSnk7Sm7UieoqLsumMJHFuilCasTVAcW+IHUXGolCC1wA8Vcd1m449qsOyhtOb363speQG+UhDCyk39FKpBVFQa32sgCDX9JS9yn7EimdAT24em9fOPf6ZesL+BkwZmtaSQUiCF8VE3GAxHP9MO1m+88cZxy376059y22238drXvpYLLriAnp4e9u3bx3333ccdd9zB5ZdfzpVXXnm453xYxIWl73rXu5By4seepVKJj3zkI/zt3/4tZ599NrNmzaK3t5fHHnuM/v5+2trauPnmm8dJaDo6Ovje977H29/+dt72trexYsUKOjs7ufPOOxkaGuLjH//4hI2NbrzxRs477zxuuOEG7r77bpYtW8YjjzzCpk2bOP/88/m7v/u7I3Q3DIbpcbD269Nx0qh4ITet3MoT24coVIMoAysE/3HfZpbPbeM9584/om4ZcefTRoJQsXFfkcFS5LDihwoE9BVdHts2RNUPySabryZc1JPDCxWFaoDSkRNMzFhXRj3mfag1vtKknPHfb0IIFvVERe22FJSdTlQtYE5aFl35JPPbEmQcC0sKAqVIEN3zUGt07E9es27Jp2xStoWUgn2FavQzQ9QlNJFHuyZlW3TlEuQSdv0+Vmt2lGnbIlRh/RqlBK00whKk7MgRZrrEP9NQ7Z/sCGBWW+SQ8w9vnrgvhcFgMBxNTPu3zPve975Rn3/605/y+9//nnvuuYeLLrpo3Pb33HMPl156KVddddV0hzxsXNfl5ptvhgmsGRvp7OzkU5/6FCtXrmTDhg088MADWJbFwoULueqqq/j//r//j9mzZ0+471vf+lbuvfdevvCFL7By5Uo8z2PZsmV85CMfGXfPYhYtWsTjjz/O5z73OW6//XZ++ctfMm/ePK655ho+/elPj5LGGAxHAxMFto1MVVKgteamlVt5eMsAm3qLgEZKgdaadXtG8GqZ5asvWnjEM6FKa3YPVdBas3OoQjVQFKtBFJSLEF3rQOoGIX6oOW1Oa1PPSWvNQ5sH6hKbsTp1PYkUpnG9Bjb1levZZN+tsmP7dubMnYttSZbMzDNU9ugtuGwdKEfe6o7F4p4cYeCzdEaWlCMZLPv1DqJaaZQCha4XhUaFpIKWtEPZCxgseVG2WkYylqGKT3c+yVvPnMN7z53P9beuZcPeAlv7y7hBGElqGmwoBZBPOlgSbEsytz3DvI5M3SVnugghmNOern+2LYkUYlqTSoPBYHi+aVpK6Etf+hJ/8id/MmGgDnDxxRfzzne+k+uvv563ve1tzRp2SiSTSQYGBg66XT6f58tf/vK0x7ngggu47bbbprTP3LlzJ3xaYTAczcSBbcystjRyGoHrpr4Sa/eMsLW/xKKePL0Ft7YmKkTc1Fckn7LZ3Ffi+O7cQY42Pa59Y1QUXvUU19+6lpGqT6gqpB2L0+a0sX5PIZJTSIGlNSUvygZX/JB8qnkytUI1oFCNmqXlklEmWqlII34wBNCacihVA4asKBCuVius/+Ot/GC9zZXviBrACSHIpxwyCYuhSjBOxz2nLVmXywxXfbwgJFBRIyZf6cgmUkfuMkGoOWVOC4Nlj2JVRk8Cal2O2jMJ2rMJ3vPy+SQdC9uSLJ6Rj2QuLhR1WNeux02l8imbQGmCMJoUTOSoc6jEP1ODwWB4MdO0YH39+vVceumlB9xmxowZrF+/vllDGgyGF4ixgW3Mpy9bSiox9SzoE9uG6C24dGaTZMZIXTIJi45skt6Cy+Pbho5YsB5nWZWKMsZDZQ83UMxszZAf04UzyspKSm5If8lrarDeX4omKinHIpe06pp1r0GofqCs+kDZA8AvKHQQcMcjv8Ud6ac4mOIP193AmZe+g3S+7YDn0CiXkUKzezisy28E+33U3Zqt48Z9JWwp6MgmqNaKS4WApbPytGUSo+RLtiWZ3Zai6is29ZVwLBE1KqodXIrosyXFYVs3msy5wWA4FmiaV1V3dze33npr3WJsLGEYctttt9Hd3d2sIQ0GwwtE0rZI2la92U/8StjT6/xY8gJcX5FJRvvqhpfSmowjqfghwxXvoHr5ZuGHkdxl7OQhxq7JdIJwstB5+uN6gcKWEhDI2n0VteLSA5nOCEDVHFf8QFHcsY7iYC9+GAXVrQtPYXfVZsPeQq2QNxjlujLq+mpymbntmZovfDR20pZRAamIJjVuELJjsMKMlhTd+SRtGYfWtEM+Fb0mkgh15VLMac8ghSDjRJr32HM+UAo3CEk5ks5pdK41GAyGY42mBetXXXUVTz31FJdeein3338/QRBAzdXkvvvu49JLL2XNmjW8//3vb9aQBoPhGCGbiBxIyjWrwaof1l9PbB9iza4RdgxUuOOZvQfVyzcLx4omIGVv4slBoKIC2GZaNsbjJmxJMCbxIUQUrEvJpP7jQkAqnlwELjpwsaXAth2WX3w5C04+m639Zbb1l9k2UGb93kK9Y+jExxO4QeRLnrQl6YRNJmHVJmSi3q00ZUvaMwlO6M4ekn4/n7JZPCNHLmkzXK3JfLRGKc1IJSCXtGnLJMgdQvFubKMZW2nGLy9Qz9vEzmAwGI4kTZPBXHPNNWzcuJGf/OQn3HXXXQghaG1tZXh4GF3zzP2TP/kTrrnmmmYNaTAYjhGWz2vj/uf6WLNzmNb0aElJoRpQ8UNySZuO7POXae3MJtkzXKW/5NGdHz2u0pEExJKCzimcU+wz3ujZTkPDHtuKxgVYu7tAyp7gSaUW2FLjN6waVXCqFSoMEXGwLwSpxedTzsxg554CSkeWjH6oGCz7BKEiCDWOLXh2XxFJyPbBat2NJ1C1hki17HpL2iFUmqCoyCZsQqWZ0ZLijHntbNhXOKT7IITgbWfO5ffre7Hkfr92IaA149CRTbCoJ4eoaeMPxKqtg1x/6zO190P15dff+ozxTzcYDMcETQvWbdvmxz/+Me9///v50Y9+xFNPPcXIyAgLFizg1FNP5d3vfjeXXHJJs4YzGAzHEMd3ZVk6s4VCNWDjvkJNdiFQtQxpW8ahO5fki28+hXTy+dEh51NRdrcjm2DdniKFakBQywCHStOVS+IF6pCyvzGPbxuir+jy5I5h+kuRtlwAq3cMIYXg5cd3kk/ZzGpN8cT2IYYqft2/XGtQQNIWaB0VesbE75TWVF0fEYaAAhWSW3gadMxhx2CFpbPyrN0dFa+mtMTxYc9IgB9qrECwfbCMBH68ai+9FcWinlwkxxFxM6Eoa+5YkoQlaU07FN2A1rRDNjW1Xydpx2LJzDz9xQT7Cm59cnDijDztGQfbkqMsFw+GEILZbalRnw0Gg+FYoOkGwa9+9at59atf3ezDGgyGYxghBO85dz4QtZrfNVSNvLYFtGcdOrJJFvXkSDrW81Y0GBdZLuzKkk/arNzcj1KxJMSiK5ugWJPIFKo+AyUPX2kKVf+AWnDqzX/iQHR0UNmYdbZr/uVxXB4XtgohqAZ+fW9NFM0rv4pFAEikXyLR0k1q7iJKbkh7JkEmsf8rX2tNpZaeD5VG1IJwPwjZ0FcmUNF5dWUT2FLiBiFSjr6mIFS4gaIrl+S0OW08vm1wyvc4n3JwLAm1YD2XtKcUaJ85v53PXD69wmaDwWB4MdD8bh41BgYGKJVKzJ0790gNYTAYjiHSCYurL1rIE9uGeGTLQF1HfeKMPJ255AuSKbUtyVXnL2BLf5ldwxU2q1Ldk/xlPVme2DHChn1Fhss+1SAkVJqSG7J+b4GSGxIoVfcID5Xm1DmtrN4+xOKZOfpGKiAEAs3SmS2jHFPirPNwxaO8JahZGSqStqxbG4oGz3UhwC8Po30Xy1KETpZcNo3XNjPyMNfjC2XjQlataw4vUpB2LJIWHN+ZYtWOyKmlK+uQsCJLRi9QFN0Ax5J4oWKo4pNPOZw4M8+Czsy4+zddQqURIpYLgRDRvwUvUIztZXc4hc0Gg8HwYqCpqYj+/n7+8i//kp6eHrq7u1m4cGF93SOPPMLll1/OqlWrmjmkwWA4hhBCML8zSz7l0JpOHNBR5Pk8p4VdWRZ0ZuvnlKgF4CMVn76Cy2DZQ6koMw2awbJPxQ95rrdUz7Cv2jpYl7+s3V3AC8ELNW6oeWb3yDgv8TjrnEva9W6kfqgYqQaU3WC/9KUmkbESaYQQUaCetDjuuOOQUkRNisT4Qlk3CKPmRgLEmKLVtGORtCVVXzFYDsin7doyCylETZojaM8k6M4neefZ85r6M3pi+xCrtg7y+Lah+vtVWwe57jdPc83/PP2iLiZ1gxA3CF/U12AwGJ5fmpZZ3717N+effz5bt27lrLPOoqenh7Vr9/svn3rqqaxcuZL//M//5Mwzz2zWsAaDwfCCUHRD3CDyQG9LO/UMesmStKRs9hZchsuRJKYRDVS8/c2ADtREKggVw5UgytprUKEmGHO8OnYSJ9tOq+UxZ/Ys5ndl2D5QIWlbVHzFYNmj7O3vhqx0ZPOIjoL5sZMFW0ZFqIFSSCFoTTt4oWJGPoVCs7OmgR/roz5d4klNY/GtRjOzNYVVs7B8fPsQAsH1tz7zoi0mjd2MQqVetNdgMBieX5qWWf/sZz/L9u3b+dWvfsXDDz/MlVdeOWp9MplkxYoV/P73v2/WkAaDwTApYzOXxWpQ05NHrzjDGb+mykDJI1SapG3VA/UY24r856tBSH/JxQsUy+e2ceqcVjoyTr1IFA0JS3DK7NZxx9das3Ffkaof4PrR+ek4yJ5ADi8AJ5ng9CULWTwjX/Npj6wgk7ZkTnumVijrU/FqmV0dafAtKcZNGgIVud3ExxFC4FiS+Z0ZFvXkmv7Uww001UDx1M7h+lMDgeC41gyz29N8531ncfb8ds6a31YvJo1fL8Zi0mPhGgwGw/ND0zLrt956K1dccQWvf/3rJ91mwYIFJlg3GAzPC9+4ZxtP7ipiWRUA3vHvD44KiM5Z2DFq+6lmNINaBngyn3UpBKHS+KHmK/+7jtU7IhvbgbIf2dnWtvNCPS6rTc2ycnNfmWqgxrUsFWhUGCAsG4mor7al3F+gWctOCyFoSTucMa+NtrTDQ5sH8ENFwhaE2qLqh9H4DadQ8aOnBu0Zh46sU++K+nwgBMxuT2PVfla2JWqFtRbXvfnk5+08jhRx91+DwWA4VJoWrA8ODo7SqE9EGIZ43vP3pW8wGAxa61F67UzCouyF7BwsAzCrLX1AKcpk2DV7ySDU4Ixfr3QUhDsNwbwmKhTVulYcOkGGXGvNlv4S6/aM4PphFIg3GqlrReiW0UIiwhCZiIpvtSbSoE+AFIL3vHw+ewsuewtV/FAj0AxXAnYOVWodTgUVP8QPQp7rr06pMdGBiPXYNPjJ0/DkIyZpR/fztDltXPemkyd0dzkWikiPhWswGAzPL00L1ufMmcMzzxy4s+CqVas4/vjjmzWkwWAwTMpfXzyPb9yzHdtJ8MT2IU6d04olJR971SJuuHtjPZv96cumZ/vXkU1gSYEbhKRDOUoKE4RRIJqyLTqzST752iX8050bcP2A3pEquha/x51BG/fbuK/If9y3OfIen6jhkVtG6HB/galXxUqm0Ry4gVBcKLuwK1cfyw0CBkpe7SlApL+XQrC4K1P3WQ8n0txMgcmaFsVPG06b01o/v1g7b9xdDAaDYT9N06y/6U1v4rbbbuN3v/vdhOt//OMf88ADD/DWt761WUMaDIaXABM5ZhyK1jxhR7rxuMFSwpYkbEkuZY9adyiBoda6rnUfrkR/a61JWJEv+FDFr3da9ULFcDWyN2zNOOTHjBcHpaIhQKVBo95bcFm3ZwRqdor1IFxrVBiiVXTNQkiQFjJRC9Rrwf9UsGXU2KgtE7nOzG3PMLc9ybvPmsGSmflxWvzDYaxGezpPMwwGg+GlSNMy65/97Gf51a9+xeWXX86VV15Jf38/AF/60pd46KGH+PWvf83ixYv5xCc+0awhDQbDS4DVO4awakWOsWNGI0faPaPihfzkkW2s3V2oNzsSAjbsKwKCjmwCx5JUg7DB0tCh5Iac0J0dFYwXqkEkOakF11rvdz4pugFDZY+iG3DK7E68QDNY9urNk5RWCCHRwkKhkLaDsGwU+1Pv1jQCYCEEiVpH0oVdWQLfoyefiIpYta67s0w3vz5Z06Kqp7j+1rUNzaEMBoPBMBFNC9bb29u57777+MhHPsJPf/pTVK1N9Gc+8xmEELzxjW/k29/+Nvl8vllDGgyGlxBaa3YNVYlrMaerNZ/qmD95ZBuPbh1ksOwBGiEFWkde6qHSSAFLZuYYLPv4KrI0PHFGnud6S3UnlYofsm5PgYGSix/uD3yVjpoTBUrRX/Ko+lHjo0zCwrGiILoCqNBHIxECZDId+aSM7Q40jcz6WJ7YPoRSId/64w7W9bpQsxjsL3kIoDOXPOgxxjLZ0wulonXGWtxgMBgOTFM7mM6YMYOf/exn9Pb28uijjzI4OEg+n+ess85i1qxZzRzKYDC8RDhtThtJxyIIVc1mMApSp6s1bySW1jTGvY2FjyU3YP2eAtsGyrSlHYYqfi3QFrTGXuqVgOPaBAu6sgCMVALyKXtURv3mVdvpLbiMVP1oEL3/Lz9UPLFtiHzaJggViVpQK4Qgk4CB4QpaWDW3lsi1RYj9JxxPXmK9eqNsqDFz7wVqQteZiZA1yQq1Tqf9pUMzBmgcUzd0Io3vsdGhGwwGw9RpWrC+bds22traaGlpobu7m8suu2zcNoVCgcHBQebNm9esYQ0GwzFOrPXWWtQ9walp0qcS/EUBuB4VjD+6ZYDrfvMMtgRZi9gbm9V05xNYQtCZTdBbcEcdb7SXukdLegJLmJoFY6FapOgGtKZsKm4QFZjWgmuloa/kUfICQrU/O67CgL7eflCRNh0UCGtUoA5gCfBVFMv7oebJHUPYlkTpyLe8r5YV/8r/rsO2xDgZUSPL57YR+B5/ffFcZs/qAWCw6POu764ExjlI1u9r7Pby2NZB+opufbvHtw0ipTQNfwwGg+EwaFr10MKFC7nhhhsOuM03v/nNg9o7GgwGw5Hg8W1R2/qv/O+6evv6vqLL49sGeWTrIDsHy+wcLLNrqFrPRvuhxg0UmUk6dMZe6kE4uaK7v+TSX3JJ1opM1QSuLUppKn7ULbRQDSi5AYWRYXwtwLIh9KJi1FqgHbtBiloRqmzI0o9Ug1rwrLGlJJuwyCTsQ5IMjS26TdoWCVuOKoQdS+z2cv2tz9BX8qgGCrc2ISr7oWn4YzAYDIdJ0zLrjY9bJ0MpZb60DQbDC4rSmpIbAJB2LE6b04aU++U1oVL05JPYlmTbQAlLiFE+7WOPZUkxaWOkUEXBvtZRIOyHuu6xHhMH3GlbUgkUUsDTu0dwrQwkXAhChJMCa/+EQQiBqElNGnsmKR01a5IS8kkHWgRnzm/HkpLPXL4UgC/fvq5Jd3I0QghOOa6VTX1FANIJmzPmtfPFK178jYwMBoPhhaSpmvWD8dRTT9HaOr6ttsFgMBxpTp/XhiUlH774BJ7c8Uh9+acuXUI6YTUEsaIeXHdmk9hS8PTukXGuJaO91BMTjvnE9iGKtYlBoeqj9MRSEgFUg0iTH4SashdQDRQKG6RASGvcfvUAXY12atFaM1T2CWpPBaTYX+R5JGh0e4kdXmI+c/nSA0qVGrX18eOGxjoCo3E3GAyGwwzWX/WqV436/P3vf58//OEP47ZTSrFz5042bdrEm970psMZ0mAwGA5I7L0ea9OFGv3UL5Z1NH6eLJDNp2zmdWQouAGPbhnAC1WtW6ge5aU+tstnHIAqrUnUvMq9UDFR/l0T6/A1GgES0o5NxVMoFaKkhRQwmdJmbJY+5VhkExZDlWhyUHQD2jJHLuhtlM3EDi8xB5sgrNo6SKgi2Uzc0nX1jiGjcTcYDIYGDitYbwzMhRBs2bKFLVu2jNtOCEFHRwdXXHHFQXXtBoPBcDhc+6uoW2apXObJXUWktGra9KEpy/CEELzz7Kgg/rl9xairqIp81ifyUo9ZvWOYgZJHZzaBYwlSjsWekUiyMm4MwJECNbwLke0kmc7Sk08ggKGyhxsoLAtCf39Y3jj5ELVi1XhJ0om6qSZti6Ib0F/0aMtM3XLx+SaSEglmtaaNXNJgMBgaOKxgPfZSh8hJ4dprr+Vzn/tcM87LYDAYDgutoewphNQ1b3ONmDC3fWDSCYurzl/AkzuGqPhh3Vt9rJf6/nE1fqjwQsVI1ScIYW67HUlrJsiQCzTh4E583ycQZVJS05VrZbgSkLAtQh2dfyOjPjYE6kKAUzsf24qeAPhquu2Mjjxnzm/H9UP6iy6duSRSCP5/b19Oe25iZx2DwWB4KdI0zfrvf/97FixY0KzDGQwGw7S49o3LAHhu+26e3FVASouyG3Dq7FaSzvS+8oQQ5FMO+ZQzruvos/uK2JagM5cg7UiGKz7phFWbFgi8MGCw4mFJQXvaYl8x0rBrAK3wC71UbYlOtmD7JRw/JJe0EELQkrajSYYICbzY4R2khEYJfewE2ej4EoQaIQTOIXqrvxDE7jOx24wQEzdQMhgMhpcyTQvWzznnHHp7e/E8j0RifLGV67rs3buXnp4eUqlUs4Y1GAwvcdyDtMCMg8A4MDxclNaMVALW7y1QqAZsHyxjScHe4Sq+0lT8EK8WSTs1l5iRmn7cCyO/XMsSuH6AKg7UtNoSoXzSuVZ65hxH0Q3RWiOFoCVlUw0EFc+LfOYFtGUchit+XVYjak4wcawehAo3CKMi2dzExa8vFGNrCsY2UjIYDAbDaJoWrH/+85/nW9/6Fjt37pwwWK9UKixbtoyPfvSjfPGLX2zWsAaD4SVOrFEfy0ixRH85oCffPNMrXQvUq0GIX468zB1LEoSa/qqH0pEDy3GtKUaqASnHouyF5FM2Ja+KF0Z6d6l8yusfQNpJnI7jwLFJZLJYmTw7hyv0lVxKbljvhOrUilSlAMeSpByLihcCUUDv1qJ2pTVFN8APFbmkjRuoccWvLzTxzytuPhUqRTVQ9NeaNxkMBoNhNE3z8rr11lt5zWteQ1tb24Tr29raeO1rX8tvfvObZg1pMBgMdZTW9cZGOwfLHAmpth9qvFDhh4rWlI1jSdKORT5lk7RlPaM+dmhbRmr5UGnCMGDkqbvw+7cjbAcEyEyeTK6FhG2jFAyWfSp+yEglqEtudC177oWawbKPG0ZuM6FuLDyNAvr2TIKufJKWtHPUFmsKIZjdlmJWa3qc7t9gMBgM+2laymXLli287nWvO+A2L3vZy7jzzjubNaTBYDDUNepjPb7/5NQ2ntlTbOpYbhASKlVrhDQ6wBRCIBBodD3TPXqDqCjf3bUBv9CPTOeRmTbsXBddbS0k7UinntKSZCDYW3CRQhAoydh4O+VYNc26jiQwtZmJbUnmtGeY0ZIkl7R5fPtwU6+/GcQ/r5jBos+7vrvyBTsfg8FgONppWrBu2zblcvmA25TL5VEOMgaDwXC4xMWIYz2+HUtOOau8X0MNQkTvi9WoIDTWWEfrBGMbNksRdSINAz1hVl8rjV8aICxHAbSV78LKdyKT4zPLtiWxpKh7kKccGRWWCkjagrPmd7Bhb6Hm9qIYLHl4ocaxBO2ZBP0lj70jLoWqz0jVpy199OjWxxaPJuzwqM3+GwwGw9FA04L1U045hV/96ld89atfnbCAtFqtcsstt3DKKabJhcFgODpZtXWwLjsRQtBbqPL4tkEAeosufqjwVdTAp7/k0VivmrQtBDWnlzGRfKAi60jtVQgLfZG3esccrEzruKx5jBQCpSKpCzVbxsZi2TjAjZdprfBDzYZ9Bfxad9WSG7J2V4H2bIKKF5JOGJcVg8FgeLHRNKHgX/zFX7B9+3Yuu+wynn766VHr1qxZw2WXXcbOnTv5i7/4i2YNaTAYDEcErTUlN8APNWUvci/JJCxyCRtHypp+fHRALmpKdSkFXhD5rFdrzjAjFR9LWrR0H4fQisz800h0zoWaDt0PFWO9UJSuFaMeQtY56pYaPRkYLvsoRa0oVTNY9ugtuPzkkW3jJhEGg8FgOPppWmb93e9+N/feey/f+c53OPXUU+nu7ua4445j165d9Pb2orXm6quv5r3vfW+zhjQYDIYDUrcE1LoucfECNSp73siZ89sJVVRAumbnCGlHctrcNj5zeaSz/sr/rmPdnhGe6y1hCYEXKip+SBBq3CCkPeswUgnoyCboK3r4YdSIqTXj4BU88rk0I2e8DiltVP0cob/kkknYtGciuUpQy4wnbYukPT6nMsruUEfZd60h1Jp82iZVk5qULElb2mG4GrB+T4Et/QeWKjbSaLEY3y+lFF6gUFrU76kQ0T01NaIGg8FwZGiqp9e3v/1tXvva1/Lv//7vrFq1ijVr1tDa2sprXvMa/vzP/5y3vOUtzRzOYDAYDkh/OUCIkGqgWL1jGEtKvnHnBvqKLgDd+dGSvUjzLlB6dJOeXCr6qrQtyaKeHLuGqmSTFr0FDz+MCk7b0g7phEUQarIJi0Gh6EhZSGGxeEaOodIApWoADYF6jBtogtAnCBUpx6IaKBxLkrAkthSjHF8Antk9wlDZq2f43SDKywtgpBKQyu+Xu9iWJGlL+oouq3cMHfK9iy0WXT+kt1AFoOKH9BUjv3cBPL5tECEE19/6DJaUfO71yw5yVIPBYDBMlaYb8F5xxRVcccUVzT6swWAwTB8NZS9ECsXukWo9sB23mdaMVH16R1yGKx5SCEaqPlrrehbetiStaYcTurNU/WFmt6URAspeQMULKbkhSlUpD/axe9jHaZtFyrFQGkre5A2cQg1lPwrS2zNO3We9dmKT7pdxLFxfjZLlaK3rNpPDFT+Sx1R8ilX/kG9ZbAaga5r7+L0bKIQAW0rKfohAmAJRg8FgOIIcXd0yDAbDUU8sj9Bas2FPkef2FfGVwpGSDXsKnDq3dVrB20Syi0b5ynTozNhIadGrqqQdiSUlPbkk+7KJcVrwIFRs3FdksORR8QOK1QAhBGt3Ffj+A1t459nz6tsKIcinbPKpKGjfsK/ISDXaRylFdbAfFQZU7Qzlvj1sak3hhapmtzgeW4pIUgJkkhYnzsjzXG8JJtDGAyyb1cKGvQUAlszK8dCmAYbKUSAedTcN6n7wJTdAac3ekSqPbx8im7DH2U5OhK5NZ6TQ9e0tKcgkbASwaEaeWa0ppIDPvm4pNDjmxMTymLEOMAaDwWA4dKYdrN97770AnHPOOaRSqfrnQ+EVr3jFdIc1GAwvMNf+6plRge2+glvPPH/0J4/znnPn855z50/ZeaRRdhHLVDSwescQlpScMW/ihmsH4mCThsZJwcZ9RXoLkd1hwpZIGemyB8sej24dHLV9I0U3YLjsU6wGtKQsdu/dioUPXoiNh5fppuBGgfpkUw5ds2WMOpLKeufSyQpCG91gLClIORZCBIRKM1LxCZTGrw0YNWOCqq8YKHnsGqqyZGb+oPcutsEUQnLa7Nb68pltaaQQ/MObT6ovG9uVNCaWx3zpCuMCZjAYDNNl2sH6ihUroqzT2rUsXry4/vlAxL/Qw3DyR8EGg+HoJg5s9xWqDJV8AqUQCKSIAtuHtwwAcPVFC6eVYZciyt4CaPRhSyz6Sh5uoOkv+UgBT+0cZqA8urX9lv4SQ2WPohvQlnbqji4QadG3DZTJJ20CpckmR09C+kse1SAkYQkKe7cRelWoTVRyLa0UnQxlX00aqFNraiRq9oyTh/STY8vIzlHryL1Ga41jS5TWBFqTdCRz2tP0FTwCpShUA9KOpFANKFQjmYwUUKj6pGQ0/tjmRWOZKFsedyVt/GwwGAyGw2Pawfr3vvc9hBDMmjULgBtvvLGZ52UwGI4yYpnKu14+j+/cu4mRik9HFoYrkbd4Zy7B0lktPNdbJJ+y2dxX4vju3CEf/9o3LkNrzfrdRbYNlPGVQiLozDm01pr6RBKLKJhsdCDxAkXVGy3B8MOanKYh9o2tGINQYVuyHrCv3jFM1VckbYltyVHSE9uSdGYd+oouodZkk5lR5x2EmiAIqQ7tQ7vV+vJEvp1c50zKBbcW+E+OEJGFoyUErWnnkO/Z/v0FCUsQqiho33+bRN1RZumsFnYOldnaX2FfoYobKIbKHoVqUEukwNrdBfJJSdUPpyRdOVhgPxETyZ7iz24wtfENBoPhWGbawfpVV1016vP73ve+ZpyPwWA4SomlDpv7imzpKxMqTTUIqdaCLlnTcXdkk/QWXB7fNjSlYF0puGnlNtbsHGbbQHR8KaGvaNGeTbCwK8Pq7cP1bG0ssQB4ePPAqCJQgJFiif5yNJFI2oKOjM1g2SftSCoedGUT9e1LbiQhcSbRcmcSdj2oHan6FKo+z/WW6pno8nAfYaDqX6iZ1g7cVEd0XVpPWMw66tprsaotBce1psZZM+qaNWP8fiKiZkmQdiw0GltKSl5Ae8YhYUtsK3piESjFrqEKSkOxGtSfXmitGSh7uIHkl0/18jdzZx1yZnw6gfVEsidq9phJxzLSGYPBYKhhCkwNBsOU8AJF2QuwpCAIxweO2aRFoRJQ8oJDPqbWmptWbuXhLQNs7iuhdBQ4+6FisOwRqNhLvBZYotk5VMGqBZNxQyGNpuxGk4e9xejYYzt9jn1F52xH16PUhL3iyl6AbQl2Dbn0FaNsdKgqlFyfUv8ufGWh7QQyrJLI58l1zsQrRbaOrq9GdTqdDEtAVy5JPuXw6NZBBkqRNWM9SK8F9W4wuUxGCIFjCSDSvfuhqk1ARP06CtWAQm0S4Vhy1NMKS0ddT5/rLU/5ych0aZQ9xZ8NBoPBsJ9pB+vbtm2b9qDz5s07hK0MBsPRRCx1+NkjO/h/v99IqDS2FNjSGuWDWHJDko4kmzj0r5dNfSXW7hlha3+JJTNzrN1dROtIRx0qRX/JpeqHLJmZ47i2TL0JUOxS8vUrlwPw5dvX8vi2qMCxK+PQl/axLWtU5nYiTpvTSsqRDJZ9UrZCNkTXQajoL/oknUgeUvVDQONIcEcGsCwHBAgNOt+DdFL1zqV9RQ8aMueTIWoThpOOaxmXzc4kLJTa30n1QFhCkHQsRio+6XD0pKPsBfSXfEQtNLekIJeMnhiAJlAaKQRJS9BfDqb8ZGSqxP+eqp7i+lvX1pd/5vKlpBKmw5LBYDDETDtYX7BgwbSLh0yBqcHw4iOWOpwxv420YzFQ8lBEAV78TVD2AgZKLifPbuX0Kbi3PLFtiN6CS2c2SSZho3TkauIGilApwlBTqPqs21Ok5CmO78pgSVl3LImbFjW6pEg5Ont+IBZ0ZmnLJPBDzVAlcoOJs/lDFZ9lx6UpVAKqfkhb2mGo4pNK2CQTDpSH8FMtJBNJ2lrzDJYD/DCaTMTnZ1vgh9QD5bHYMtKqt2UShEpz2pxWnto5AsAps1upBAF3PbMPiGpXY5kMjH4vBbSmHJSKzjvusBoqzbo9ReZ3ZCh7Aa4fkrAtXr6wgyd3DKPR9BU9OrMJql6AF6gpPRkxGAwGw5Fj2sH65z73uXG/BB944AF+97vfsWTJEs4//3x6enrYt28f999/P+vXr+c1r3kN5513XjPO22AwvEDEga0XKHqLLpYUSCEoVAPW7SmysCvL0pktLOzKHvIxS16A6yvyaTvShVd8Kn5Y7w4qpUApTckL6K1ZRS7qaU7WV2vNlv5STT4CTq3ANAwVUkraMwk6sgnQkHKsUR7liVwbmYxD0VNk8nm6ckkCBbNaU2zqi47pBSFSSCBqJjSR5NyxJR2ZWEOv60F+I3F23gs1a3YNM1KJfNXX7FQM1JxuhBAs6MpgDQocS7Kv4OKHCltKTprVwokz84xUfbYPVGoOMLXJTM3iUQpBoDQJe2pPRqaDsXs0GAyGQ2Pa38bXXnvtqM933303X/rSl/jBD37Ae97znnHb/+AHP+BDH/oQn/rUp6Y7pMFgOAoQQrCoJxdlv6sBoVKoWjHoSbNaOHl2K+85d/6UnrxlEzZJR1J2QxxL4AZRQ5+ELZFCRAGnJUg7FkU3wJaCohvQfpiOIUprbnpoK5t6S7UAthZQK7AsSTZhsey4PKfPbeN/n96LLcfLMzItHYh4YmHJeqOknUMVtKb+5EFM0jUVIONIEs7oY8fSndU7hgi1rmfkI8/0sK7zHnufbSlZ3JOj6AZU/JDZ7WmStuRDF5/Ags4MT+8aobfgMlj2x0lrglDhBorOjD2lJyOHg7F7NBgMhgPTtNTJZz/7Wd7ylrdMGKgDvPe97+XWW2/ls5/9LPfff3+zhjUYDC8AthUFhAMlD9cP0cC8jgwfuvgEls7KTzngWj6vjfuf62PNzmEqflDXw0sh0EqjFFG2N2njBopqENJf8mjPJic8nq4VShbdECGiwN8PFUrtfz9U9vBCxf3P9keSHq1xLCtaryKrRaUhl7TJpRxsCeVSgVRby7jxgjDKhjsNGXEpIiG7EJEcpjGh3iiHEUAqYdOZTUx4LUIIZuSS7Bys1I4LZ83voKVm8Vj1A1ZvH2Kw1sGUeodVh3zKYVFPDkvK+pOOfMquS3427itQqEbFwl6oGKr45JIWJ3RnpvRkZDpMx+7RYDAYXoo0rYpn9erVHH/88Qfc5vjjj2f16tXNGtJgMLyARN7eUSa5NZ1gQWeWhV3ZaWVGj69JZ+Z3ZtkxWCEIFUqDHyjcWlY9YUscS2DLyMFkIicaatny4YrPpv4KZS+k5EYa8qGyTyWItNx+qKPMsxfyzO4RHCsqtkwnLPIpm9aak4obKIYrPse3Jxjeu51iucxQf1/NoSV6eTX7yqQtacs4aK0JlSZhSVKONeo847eNZx7rzHPJ0bmTrlySzmyC0+a0cs3rlpFxJBlHkrIlH774BKhp9Bt1+odC/GSkO59kycyW+pMLgaA9k6Azm+Atp3Qf8Qx35P8++ctgMBgMEU3LrLe0tHDvvfcecJt7772XXO7IW4EZDIYXF0II3nPufAD6Ci4jlRGU0tiWIGlZJCxR77hZqVkh+kqN81Zv1Lu7flR4KS2JDqAaRBOAqhc9CUDp2oQgZLjq4/qKtkyUrbatqHg1VJpVm/vYcv+v2KPyBFYbA2EO4ZcIlI7cYYIQx5IUqyEb9xbpL3ms3jHMcMVjXnsGKUW9AdBEWDLSmY8NjoUQ9BVdntwxzL//cRNuQ9D/73/cxLo9Bc5Z2DGt+21bkiUz87zjrLn0l1zcQLFzsMLSWXlSUpFyTLBsMBgMRwtNy6xfeeWVPPDAA3zgAx8YZ+u4bds23v/+9/Pggw/yjne8o1lDGgyGY4h0wuLqixby4RUn0JK2sWoa9da0jRCC4UpQC9ZDvEAxUPRYt6dAEO7vDlpwg7rePZeycSxZk9Ps14urWmZbxc1NhWCg6NX22x8QSyFQSjG8bS2lwV6yI1tJVQfIOeAkU3UHlqRtkbItTpyZZ3Z7hkzCJo67dT3+Fg1/jsaWIIgmBuEkHo9SRFr0+HUovu0HQwjB/M4sC7tyLOrJ1WUzRjNuMBgMRxdNy6x/8Ytf5PHHH+f73/8+P/zhD5k1axbd3d309vaye/duwjDkwgsv5Itf/GKzhjQYDMcYQggufFkXc9oz9BZcRioeblkRKo1fC8ptS2JZUYGpKERFmHGGfaDoNfi/7w8664H5qMGiqD2oFYbGHuqxHCUMFf5IH3lV5qJF3diW4OyzT2bBaRdw/a3r8MKQHYMVTp/bRlsmwWdfF2mwr791LaFSPLRpgKIb2R925hL0FV1sKXEDNeo0vFDz1M7hupzljIbCzq5ckuVzW3nT8jms3NSP0lHg/rYz53LLEzvrAX5jZ9N42USOMgaDwWB48dG0YD2Xy3HPPffwH//xH/zoRz/iqaeeYvfu3bS0tHDhhRfy7ne/mw984APICdwUDAbD0clE8g0vUAfMAh8usaaaWhA+UOsE6lgC27JI2lF3TqU0w9UAxxJs6S+zsCuLXwtc5QSB6tiztUSkIRdCEIYKDfUJgRcEeOUCulog6Q1iySwXnHcel156KVVfcUJPjlApCtWQtkwC25Ik7Oi7LQqSo4JNwmjikKq52kRON2MKTIVgRmuKvsL4xk0aeK63xH89vI2iG9YnJf/18DYe3jxAPmWhNZGUxY/OffWOIaSAM+dPTyJjMBgMhqOLphrpSin54Ac/yAc/+MFmHtZgMLxAxF7YjcS+2Hoiw/BJAvxGDqV4MNZUh0pFVo01SUzKsUjYMrJEtARJW1L1Fat3DLGwK4tTy07Hri9RwBxJXyZCCGoBdNTEyA0UIxWP4eEhdHkYKsPYfomzz34ll1566ZQkInGX1XFadMCSEKgoS55P2fzjFafyr/c8B7UJCg36ezcIGal1GZVSoLVm/d4CI1WfoYqHFPtlPQC7h6uRA832ISNpMRgMhmOAI9v1wmAwHBMordk9VKm9Z9JAnUkC/EYOtdmNEJHzS8KWJCyLVN2HfH9e2pYSP1SUanKTjlwCKaASaIYrtU6ik4+AFJFrixeqmruLpjzUi1Up4JcGoPc5jjvrDF7z2qkF6jRYN469V2LMeykip5tYthK7x/ihruvvT17YSn/Rre+1qDvHjsEyEkHSkmRTdjQxARKWpOKZLtEGg8FwrNDUYN3zPL7xjW/ws5/9jPXr11MulwmC6Jfo6tWr+fa3v83HPvYxTjzxxGYOazAYjhCxF3bVU1x/61qoZdZ78pG/+RPbhybdtzHAB5jVlo4C2ClgW5E1YaDUhPXwgYo6nGZrOvNcwqoH59XgwDKdqIMnhFqTSdj4gSLtD6MHN2NVBqj2D9A2bxkLl18wrQx10rYQQjNY9gnGSIbi+F0IManzihuEhEqTrllKNpJOWLSlEwgR+dv/67vPJJWI7k/VU3z59nVorVm9Y3jK520wGAyGo4umBesjIyO86lWv4rHHHqOnp4eWlhZKpVJ9/fHHH88PfvADWlpa+PKXv9ysYQ0GwxEklqwoRUPBouAAifUJA3yAT1+2tB5QHiqd2QR7R6oMlX0SlsCxJWjQaPxAUfVDOrIJTpsTFWUWvbA+IZBMLn+hlpvXQEvKwQ0V7RmHZT0L2Dz0DMMjJdrmLaNjyTnTlpI4liCbsOkreoxUfFRcDNoQrEsxuhC2EVUrGrWtidc7VvRUQQO51P6A3pYhCVuOcskxGAwGw4uXplV7XnvttTz22GN885vfZPfu3Vx99dWj1ufzeVasWMEdd9zRrCENBsNRSNzUJpZ2xK+ELafc7CaXtClWQwKl2Vtw2T1cZc9Ild3DVfYWXAKlacskWNCZAai7wVhCYFuiJjOJjjU2JhZ1/bjgtNmtdGSTtOUznPSK17Ng+fmHFahTy5ov7M5E9pMZB8R+Xbklo7Eda/KGRlJE6yZr/hQ/VXAmCeYNBoPBcGzQtGD95z//OZdddhkf+chHEGLiX0DHH38827dvb9aQBoPhGEcIQUvaJu1YJC2JUpFji1KQtCRpx2JRT67+fRO7wYiGAN2SAjlBsA4gpeDUOW2ctaCjfhw7kWTWolObUpxpS0lr2uGE7qj4NeNIRM1bPQ7BGzXtsctO3AHVkoJqEFJ0g/qTAA0U3YCqH3VNbc8k8AJ1wMLeUGm8QI1y8gmVplgN6svizqvxtgcrFDYYDAbD80PTZDB79+7lne985wG3cRyHYrHYrCENBsNLgDPntyOFoL8UdfOMHFbg1DmtdGaT2JZEa82W/hLDZY9Qa1TNZ3y0XCcqTJW1DLclQQ3t5i1LF3P+SQv4h9+unfwkpoHWmkI1oOgGPLVjBD/USBHJVmKrdTdQ9Je8+j6rtg6itUZpzXDFww+j63h48wBlb7/n48ObBwi1pugGrN9T4Ppbn8GSctLi3VVbB7n+1mdq7/fXGbz3xocYKHl05ZL0FV2e2D6EUiHfuGcb2UzfIRcDGwwGg+HI0bRgvaenhy1bthxwmzVr1jB37txmDWkwGF4CRDKaKEPdUuuwqbWmNe1gW5E2+/sPbGH9ngLDFZ9Q6cixJtQoQDZYIVKza0Rr1NAe1PYnuO+3e3lZ91VNPecgVAxXfNbvLVCoBigdnZMXahBgCUGInrilae0cs0mHjNakExah0pT9IEqrC2jLODiWYFZrut5M6VAQQjC7LVX/vH2wVgAsIJOwmdWWIgyCKRcCGwwGg+HI0bRg/bWvfS0//OEPeeqppzjllPHZmHvuuYc777yTj33sY80a0mAwvIiZTGYRyzIOpQOn1pqN+4r0lzy2DZQRRPp4agE7DVlsoeN9QA3vg8I+cItUKhXWPvM0MLMp16W15rneEhU/rNkpalrTNv1Fr66hTzkWgQpI2ZLObKK+7xnz2hgqewyWfcKMxpGST1+2BDdQfOS/Hqt3MP36lcs5dW7LIQfpZ85v5zOXjy/wHSkHfPn2dfV7/ZnLl1IaGQSgs6uzKffDYDAYDIdH04L1z3/+89xyyy2cf/75/NVf/RXPPvssAD/5yU9YuXIl//Zv/8aMGTP427/922YNaTAYXsRM5sceN106Z+HBO3AWqgFDZY+iG7BkZo5ndhVwgxAv1Aj0BPaNClUaISj0k+h9DgGcedbZXLzildzbJBlMdE4+fqjoyCQZqQakHQspBY4lqfohvop09Y31PUGo2LivyGDJoxqEdQeeGx/YwvHdWXIpp57xfllPjpRz6F/fkxX45lLUO69C9N6vfZ5qMbDBYDAYjgxNKzCdM2cO99xzD0uWLOHLX/4yN998M1pr3v3ud/PNb36TZcuWcffdd9PT09OsIafEihUr6r8YJ3rdfvvtE+73/e9/n3POOYdcLkdHRweXX345DzzwwAHHuv/++7n88svp6Oggl8txzjnn8IMf/OCA++zYsYP3v//9HHfccaRSKRYvXsznP/95qtXqYV23wXC0o7Rm52C5/lJTcBzsL7lUfUVnNkEmEQWvQggSlqA1bWONcYPR1TI69BGJFHSfQMu8k/g/r35tUzt99pdcqkGILQW2NforVorIoSYq6Ny/PH5C0FtwGSx7KBVZMyqteXr3CI9tG2Kk4h+wGZXBYDAYjk2a2hRp6dKlPPLII6xatYqHH36YwcFB8vk8Z599Nueee24zh5o2b33rW8nlcuOWz549e9yyv/7rv+aGG24gnU7zmte8hmq1yu9+9zvuuOMObr75Zt785jeP2+fnP/8573jHO1BK8YpXvIKuri7uuusu3ve+9/Hkk0/yta99bdw+zz77LOeddx59fX2cfPLJXHTRRTz66KNcd9113HXXXdx1110kk8km3gWD4YVnMj/2j1+ymH+6c8MhHcMPI/eSzJimQaLmXy6kqAW4GlUaJKyWsWwHme3Abu3Eau9u8lXtP6dJJwBiv6tLzLaBcv0JQVtNix9p8yWLenKs31PADRReqKKOqzW3FpP9NhgMhmOfpgXrr3rVq7jooov4+7//e84880zOPPPMZh26qXzta19jwYIFB93uzjvv5IYbbqCzs5MHH3yQRYsWAfDggw+yYsUK3v/+97NixQra2trq+wwMDPCBD3yAMAz5+c9/zhVXXAE1p5wLL7yQr3/967z+9a9nxYoVo8a66qqr6Ovr46Mf/Sg33HADAEEQ8Pa3v51f/vKXfOlLX+Laa69t8p0wGF5YJm64RN2f/VBwah1Oy954/XugIlcYDajSEKI8GNnAhC5W2kaku/BCxbaBMktmtTTtuuJzmjQLrvd7vMc8s3uEqq9I2nJUNr6v6MIeGKn6VPyAoBgF61/533UkHcu4tRgMBsNLgKbJYB555BE8zzuELV8c/NM//RMAn/3sZ+uBOsB5553HX/zFXzA0NMR//Md/jNrnu9/9LiMjI7zpTW+qB+oAM2bM4Ctf+QoAX//610ft8/DDD3P//ffT09NT3wbAtm3+9V//Fcdx+OY3v0kQBEfsWg2GqRLZEvoUqj7DFb/+/vmWaXRmk6QcSX/Ji6wNG6j6CgXooIqqjNSXJ3Jt2C2dWJYgVJqndg43/5xsi0DpcV1ElY4mD5GDy/7lZS8kVBpbTvyV7FgSKSLNeyZhG7cWg8FgeAnRtGB9+fLlrFu3rlmHe0GpVCrcfffdALztbW8btz5e9utf/3rU8t/+9reT7vO6172OVCrFnXfeOUqHHu/zhje8YZzUZcaMGVx00UUMDg5y3333NeXaDC8N3CDEDcJ6ED32Fa+fDkGo2LCvyNrdBYpuQMkNKFQD1u4u8P0HtlCZIMt9pMinbNoyCeZ1ZFi3p0ihGhAojRcqKr5CaAW+iw59AOxsG8nWHgSibgFZGnO+sT96NBHx2NJXYnNf8ZAnItE5OTiWZLga4IWKqh+ilMYPVC3oHh1sZxIWlhQEYwT7Xbkkp89rZ05bmo5sgqUzW3jF4m6uef2yuozIYDAYDMc2TZPB/P3f/z2XXXYZv/jFL0ZllY82/uM//oP+/n6klCxevJg3v/nNzJs3b9Q269evx3Vduru7mTNnzrhjnHHGGQA8+eSTo5avXr161PpGEokEJ598Mo8++igbNmzg1FNPPeg+8fK7776bJ598cpx8xvDS5WCBduy08vDmgQnXx04rU5VRxLaEfQW33lVTSAFaM1j2eHTrII4lufqihVM67nQRQrCoJ8fCriz5pM3Kzf2oml484wiKWiNSaYIRgZPvRKfb8FXUdEiFCikF2Qa9exAqNuwtMlD2KFQDtNZsHSjz7Xs2cfLsVt5z7vxDOqcTurPsGKyQTVr0Fjx8FWlfErYkUJpc0h4l3Vk2q4WUIxks+6RsVZfCCCFwg4DBik/asejOJyd1djEYDAbDsUnTgvX77ruPiy66iCuvvJJzzjmHM844g56ennFFVkIIrrnmmmYNO2W+8IUvjPr8N3/zN1xzzTWjzmnbtm1Qc7iZiGw2S1tbG4ODgxQKBfL5PCMjIwwPDx9wvzlz5vDoo4+ydevWerB+sLHi5Vu3bj2k6zvppJMmXP7cc8+xYMEC+vr6Duk4R4r4Hhmmxtj7dv0dmw9pvzAMKHuR17cbaJJ2pJauVCoIIQ7530PVD6lWqxSqAf3F6O98ymakUss2S0EuabF53whpqXhspsOMfGLUU6T+/j5SzvgAMz52zMBAX/1zoCILRktKqoEiDENENDeg6noQRtKXN5w4g+UzHLb1jeAHIVIIZudsNvRX8Ulgd89H2zZKgR8qlAYVahxgRjKkv7+PSqXCs30V+ooeRS9E6SgLHgSKJ7cPMFQqUyqVeOtp3VSrVUKlCMOAqutiS0F/f3Qv43W5hGB+e5KyG9CdcyhWA7KOpFD1CcOAMNSEIsD3BZZXIOMIqo5gsOyRsCSOJQiU5ukdAXPaUgxWAhwCqtVw0nvZeD8DpaPzq1axpJxwn7H3vr+/D7dsukxPFfO9Nj3MfZs65p5Nj7H3LQxDLOvFk/BoWrDeWAD50EMP8dBDD0243QsVrL/iFa/g6quv5vzzz2fWrFls376dm2++mS984Qt87nOfo6Wlpd6wqViMflllMplJj5fNZhkaGqoH6/E+B9ovm80CUCgU6ssONtZE+xgMMVpr9hT214rMzCcQQvB/XxVlgEeqPv/v3h2ESvHMniJLZ+axpeCjr5g7abB3IAYrPm4QFTnaY4pAbSloy9j0lzye2l1kRv7gPunNQgjBvPYUx+UkAyWJkJKOjIUzJPAVIG3q8vEGNYvSsGrHMMtmZSm6IcOVgJIXkk/aFNxoIpBLWrysO81zfVVyiTLbh9xDPqdc0iKXtJjXnmJjb4WBSkCowC8FKKASwJ6Czyd+9RxSQFc2gS2je+yHUWOoE3synNCdYdug21SLSYPBYDC8OGhasP773/++WYc6Ilx33XWjPi9evJhPf/rTnHXWWbz2ta/l2muv5c///M9Jp9Mv2Dk2g6effnrC5XHGvaur63k+o4k5Ws7jxUZ837789naYwPYw7lIZSyTavZCW3AChUjiOR0s2jW1JZs3oIT3G7vBA0hrHU6RS+0D6aAQJR2JbFkJGAa0AbMuiLZum5IbIRJrOzq5onxqdnV3jxgSoeOGo7To6ukil+qEmSxECLCnBD+qZEC9Q7CsGUNN/j+gUMzKSZ1fewUDFpvuUV5BIJsgkIs9z0NTUOtQanOJYIvJ4L0KBDMUAfAUpxybpWHUtu2VZtOez9PiSER+2FgWpVIpQKSyrSiqZxLYknZ3RzyaV2jdqnWW5JJOJ+hMBIcCWEqUVtpRYMrp3lpScPKedobLHQMkjrJ3jR19zEgs6M/xDQ9Omye5l4/0MQoVlVUilUlhSTrjP2Hvf2dlFqTaJM/9Hp465Z9PD3LepY+7Z9Ijv24spq04zg/WLL764WYd6XnnNa17DWWedxaOPPspDDz3EihUr6j7s5XJ50v1KpRIA+XweYJR3e7lcpqVlvBXc2H0a95tsrIn2MRgOZHs4XS3zZB1FqXUVtaSs2xIGoQZn/HZlLyDpWGQTTW3hUEfVij8jz/EQraPz+dbdGylsX89AyaUQSLzn1pJxlkZFpkIja5aKfqixLUkYqrp+vL/ksnrHUN0f3bYmzl5nkxaFSlRQOx1StqQ94zBQ8rhwUTfr9xQ4dU4btiX52KsW8c9/eBYhBPmUQ0vaiSYowMKu7GHdM4PBYDC8uDlsN5j+/n6uvfZa3vCGN/DGN76Rf/iHf2BgYOKitqOV2Jpx9+7dAPWC0x07dky4falUYmhoiPb29noQ3dLSQmtr6wH3i5fPn7+/SO1gY020j8FwJBnXUbTBBSW2JXSDcJwtYRAq+ks+3fkkp89rm+DIh4fWmpFKQMWPnGyi7LgkDEMeXLed1cMJBrtOhfZ5BJkudo94lH1FqKJstmNF/u1OreGQEAIpBF6gKLnB6InIBJTckKQjySanNxERQiClREpJwt7/d8KW5FL2IXvLGwwGg+GlxWGlv/bu3cs555zDjh076rZmv/3tb7nxxht56KGH6O5ufnfAI8Hg4CA06MNPPPFEkskkvb297Ny5c1x308ceewygXiQac9ppp3Hvvffy2GOPsWzZaFs13/dZs2YNqVSKxYsXj9rnlltuqR9zLJONZTA0m8k6in76sqUAfPn2dXVbQj9UDFV8/FDVLRCHKj5nz8izdGYLC7uyVH016VjToeiGeKHCr2XFc0kboRWlfTuRyQwlUoSOjSWTJHL5qBlSrSmSHtsytIbSmoQdBeCd2SR7hqsMlLxaIe5+yl7AQMnl5NmtnDanjSd3mCIvg8FgMDw/HFZm/R/+4R/Yvn07l1xyCf/93//Nf//3f3PJJZewZcsWrr/++uad5RGkt7eXP/7xj9Bgn5hOp3nVq14FwM9+9rNx+9x8881Q80Zv5HWve92o9Y385je/oVqtcskll5BKpcbt8+tf/xrXHV24tnfvXv74xz/S3t7OBRdccNjXajAciKRtkbStegfR+BVnf2mwJezKJ2nPJBCAVhqBoD2T4Mz57bzn3PlHpBBysOQRKoUto4y4UiGDu7fh+T4BFhqBZUmcTA5HCjIJC8cSaB3F6YEe36AoVJrObJLT5rTVPdtzKbvujx6oSHazbk+R+Z1Zls5sYUHn5IXnkxEqjdb7X/HnUEUvg8FgMBgm47CC9dtvv52lS5dy2223ceWVV3LllVdy2223sWzZMm699dbmneVh8sADD/A///M/hOHoArotW7bwlre8hVKpxBvf+MZR9okf//jHoWb1uHHjxvryBx98kG9/+9u0tbXxZ3/2Z6OOd/XVV9PS0sItt9zCL37xi/ryffv28clPfhKAT3ziE6P2Oeecc7jgggvYt28fn/rUp+rLgyDgL//yL/F9n49+9KM4zgQCYYPhBcC2JIt7ciydlSeXtMkmbfIpm6Wz8lx1/oJJCx8Pl0DpWoGmQKuQod3b8N0KoZUkQJK0JLadGLWPJURdXuIFGlULjpXWeIHCsSSLZ+RY0Jmpe7Z355K0ZRwEAqU0UsJJs1o4Z0HHtCcia3aOUA0imVA1UKzZOUJf0eWxbYM8umUAL1D1wL3xNR0aj9U4IfACNe1GWAaDwWB44TgsGcyOHTv48Ic/jGxokS2l5NWvfjX/9m//1ozzawobNmzg/e9/PzNnzuSMM86gra2NrVu3smrVKqrVKieddBLf+c53Ru1zySWX8LGPfYwbbriB5cuX8+pXvxrP8/jd736H1pobb7yRtrbRutyOjg6+973v8fa3v523ve1trFixgs7OTu68806Ghob4+Mc/PmFjoxtvvJHzzjuPG264gbvvvptly5bxyCOPsGnTJs4//3z+7u/+7ojfI4NhKsSFkPmUM+rzkbQWtKVACAhDhTuwgwQ+UkS+8baTQNoOXqBGqV2EENhWlNm2pSDUuq7BT9oWCUvyptNm14tL4ycHnZUEVW8YDczvyPChi09g6aw8QojD6tAaO8HMaEnhBor+kocAvvK/61hdk9bEkkIhRL151VS4/tZnWLV1CK01fUWXx7cNIYTg+lufwZJyyo2wDAaDwfDCcljBuud5tLe3j1ve1taG53kT7vNC8PKXv5wPf/jDPPTQQzzyyCMMDg6SzWZZvnw5V155JR/+8IcntGz8xje+wfLly/nnf/5nfve735FIJLjkkku45pprOP/88ycc661vfSv33nsvX/jCF1i5ciWe57Fs2TI+8pGP8L73vW/CfRYtWsTjjz/O5z73OW6//XZ++ctfMm/ePK655ho+/elPk0wmm35PDIYXG+3ZBBJNtVxAB3792yuZzpBMJim5AZaU6AkS0gLIJCyqgSLjWJQ9TVvGYbjs8c27N2JbglVbh6AWLCutcQNFdz7Fgq4sC7uyhzUROXl2CwMlj65s5IP/j289lX+95zke3RIV40shmN0WyeNCrdk9XD3IEQ+NTMLmuLY0UmA82g0Gg+FFypHxVzvKWLp0Kf/yL/8yrX2vuuoqrrrqqintc8EFF3DbbbdNaZ+5c+dy4403TvHsDIaXDrlkpKmvKp8w006gPTKZLFa2BTeIuo3G2fOYWJcuZRSsKw35VFQgm6i5wlDLYsfBstKwc2hy29YDMZEERTecT+xCE9cFnLUgypzH/vhMUOA7VT5z+bIJvfcNBoPB8OLksIP1m2++mXXr1o1atmbNGgDe9a53jdteCMGPfvSjwx3WYDAcBl6gkJPEb17QXBeXZiGEoCWTANHNSO9uktksiVwbUghmtSRxQ8XekSp+GAXuJS/EDzWOHfmqj+24CtCVS/LJ1y6hPbe/JqTqKf7+10/zxPahKZ/jWAnK6h3DDJQ81uxUVCe4r7GevtEff6x3/lSJJwKNn6frvW8wGAyGF57DDtbXrFlTD87H8pOf/GTcMhOsGwwvPLF+eSLiBkhHI1II2jJJMsfNoac1hRBRo6ZPvnYJDz7Xz08f3c6+ghsVhiJwbEHKtvBqSvZGRxat9UGlIXF23AvU1ApnRSRBmdGSqmvc4zFpyMAbb3WDwWAwHIzDCtY3b97cvDMxGAzPO0prdg9V6p9ntY2v3ZiI2DfUG4UAAFRUSURBVKmkUeIRB7VS0rRMru9VqYwM0DFjv1OTEIKEYzO/M0vKib7Cls5qYcnMPGt2DVHZGqK0Zmbeob/kk3Js+oohA2UfN1C1vzV9DcWdSWf/+YZK8cT2IfqKkZXq49uGuP7WZ/jK20476PmOlaB4QUhvwY2KVxH1Mb9x5wbW7BqZVgGpwWAwGF5aHFawbrpqGgwvTj5z+TJSCXnABkgHIpaI9JWiQnJRW9ZMxxHfrfLMvb+hMjLISRdeRrrruANuP9ahZnZrgpFqOGH23LYiH3aBqDnKjD7OrLYU5dj1ZQrJ77ESFBl3SpUa25L1MU2tp8FgMBgOlZdEganBYBhNrGMeq4+Omx9NhTgAndWWaprjiO9Wefqe31Ac6EUIeOb+21n6yrccVmuIjozDYNmnIxtZTJ4xrx3bkhMWYI6dxHzm8qUHPHb8VIGGpw4Af/1/FnPD3RsBzePbhlg+tw3bknz8ksX8050bpn0tBoPBYHjpYIJ1g8EwZZbPjXoMPLF9iNPntWHJ0UGv1prNfUU29xWjIk9LsLmvyNJZLQcN6CuVCk/f8xsKg/vqy2Yev5R0vg2GR6Z9zrETS/xq7NA6VrYz1UnMqq2DXH/rM7X3+wtTv3HXBlbvGObM+W3jxjR6dYPBYDAcCiZYNxgMUyYONCcKeiteyHcf2MyancNs6SvXCym/fc8mTp7dynvOnT9psabvVvnpf/1oVKA+a9EpHH/6hUdN983J7BlDBVJQt4CkJoMxGAwGg+FwMMG6wWBoGlprblq5lYe3DLC5r4TSGseSBErx9O4Ryn4UcF990cJxGfZY+mJ17Lc4nLXoFBYuv+Coaugz1p5xv8WjRgjJd953Vn3b/XKaCTo1GQwGg8FwCJhg3WA4xhmbBRZiv2tLsz3VN/WVWLtnhK39JZbMzLF2d7G2xmLJzBwb9hbJp2w295U4vjtX3y8O1AuD+6CjC4DjFp3CvFPPR+nR10CDpeILan9Ys2ec1ZbCEgLbkkghRklqYjlNo479SBE/eYgz/zHNdugxGAwGw/OLCdYNhmOcVVsHR2WBhRB115Zme6o/sW2I3oJLZzZJJjH66yWTsOnIJuktuDy+bagerE+kUT+uJn0JleaxrYMIIQiVor9mfaiBJ3cMY0k5zv4wDuiV0uN81RutJqfL0doh9NpfRZr5UKlRuvlmOvQYDAaD4fnHBOsGw0sEDZS8AIFg51CFOe2Zpo9R8gJcX5FPT/zVkk1aFCoBJS+oL/ufX/5iVKB+5llno8L59Yy6RqOVxg9VPVCXUO9UOpbYH90LAobKAUKGuIGu+6xPl4Np5o+WzLUQYpRu/miSEBkMBoNh6phg3WA4xjlzfjuhUjy2dTAKdIVgRj51yJ7qUyGbsEk6krIb0pkdv77khiQdSbYh6/7KV/0ffnjX4/heleMWncL/efVr+fa/r4RallwpTaEa4gUhgdagoSXtYElBPmUThJMH4OmEREpZ2+bwgtajPXN97RuXvaDjGwwGg+HIYIJ1g+EYJ9J0i3qGNXZwmY6n+sFYPq+N+5/rY83OYTpzzqh1ZS9goORy8uxWTp8XWT9qranYOVrPuJzhgV7EnIVsHSijtUYIUf9biKjbKoCUAq1hqOwTKhCiWN8OoCuXBGBRVxJLSlLJJI9tG0QpxUBJ1aUwY/X7h5oZP1oz10dLZt9gMBgMzcUE6waDoWkc35Vl6cwWCtWAdXuKFKo+tozcYNbtKbKwtn5hV5aKF3LTyq2s2TnMnookTPZQ6S/zH/dtJp+yOaE7R9kPcaRg3Z4CL1vYwfo9hfpY6VAyXA1wLMGW/jILu6JUfhw8x5aSlowmKgPlADfU9Jd8hGCcfv9gmfFmZK4bJwimCNRgMBgMh4IJ1g0GwyGjtWak6tNf9AhCTaHqM1L1aUsnoBYov+fc+QBkHIuHNg/ghwpLCk6a1cKJPRk6B5/BdY/jpod3TWjxuG7PCFVfIUSJpC2wpaQrlySfsuuBuNYa25IkbUnVV6zeMVQP1g+GEFHX1djF5VAz480IpOMJwlf+dx2rdwzXlx8tUhqDwWAwHH2YYN1gMBwSQajYuK/IYMmjGoSESlNyQ9buKtCeTVDxQtIJi3TC4uqLFrJ2d4G9hWq9g+lVL5/Nw7/7H57cvYuNu/rY1XbKhBaPi3pyPLipH8cSZJM2aQdaM86E52RLiR8qSm4w4fpGOrMOAyVNVzbB6fPa+fwbTnrBXFzkUSqlMRgMBsPRhwnWDQbDQdFas3Ffkd6CS6Hqk7QtHEsCAYNlj0BpfvLINj684oSaxlywsCvLwq7IntF3q9x/+y/o27cHgKd2FRip9NOZa5nA4tGqZ8wtGdKWdiYtWA1UlLXPJif+Khvrzz6W51t2snxuG7Yljxq7R4PBYDAc/ZjfFgaD4aBs6S8xVPYougFtaYd8yibtRAF7W9qh6Aas31Ngc19p3L5xw6Pdu3fVl82efwKpTJ5McuJg2ZaSUGlSjkVXLkl/yaXsjc6eB6HCDRRJW7JsVktdBx77qYdK89SuIk9sH6K/5NFf8qkGir6SxxPbh7j+1meOwJ06MLGGPmFLkrY17jWWUEX69vja4pcXqINaSRoMBoPh2MBk1g2GY5xYZ16o+igdBYyFqj/KQeVgrN4xTNWPAmPbGj3Hj7XjfcXRzY4Y25m0uxuAl7/85ZR6TmbXE7sOmjHPJW1OnJmn7IejClb9MApW86loonDTyi0IIXh821DdZ90PAgbLAT0tURCcSViESpNNvHiKOFdtHaxPKo5Gu0iDwWAwHHlMsG4wHMNUvJB1ewoMlqKsuNaRPnrt7gLff2AL7zx73iEdp+QGhErXpC/jsaXEDdSoZkcTdSZ9+ctfzqWXXsqmvhIPPNc/icVjiBso2v//7d13fFRV+j/wz52emVSSQEIgIZRI7x0RsBCagIAoLC6ILquuomJdXIqo67qi39XVVVdXEAQsSBHECooIP0CKIoQuBAgJIY0k02fu+f2RzJDJTEL6zCSf9+s14pzbzj2cDM+cnPscvRoxoVrc2S8RaqXC44FVhQKI0msQZdDgitnunnrTMjIEJlvJiLMkCUTpVejZOhK/nC9At4Rw/JZRiF6JkVAqSqai1LeyI+Ku0f7GlC6SiIjqH4N1okZKiJJ55K555gKApJAAIZBvsmFfer57v2sFfwatCkqFBIcsQ6AkzzlKVxMVKFldVF06wo7SQH31qg89AvV+/fpj1KhRkCSp0hSPJ7OLEKpVIVKvKZlu4+OBVaUENDNoEKnXQBZwzwG32GS8sOUoAMBoMkHgaurGsn/WV5758l7Ykob96QUQQiCn2FrtdJF9kqI4v52IqIljsE7USP2eY8TxrCL3PPN8sx0oWR4JkSFqnMszIUyrgkMWCA/xnW3FpUerCOjUCuSb7DDaHFCUBvcWuxPZRVbYHDIuFpjx44kcjO8ai5UrV3rMUW/ZoRtGpo7yWJipohSPHePCkVFgRofmoR77l31g1SnL7t8SKCW454DLsmsRqKvKjmq7/iz5mlF/XPPJ3XPoIRCiVqJFhBZKVD1dZNn57URE1DQxWCdqpH4pnb9d0TzzaIMaOcVWOMW1g/U20QZE6jWwOwWyiyzuUWoAEDIQrlMj0lDy4Olvv/2Gixc9A/W2va73ClArSvF47/XJ+Hjf+VpP9TicaYQAoFKakVNsxa8XriDPaMPBcwX1Po1k8eeuh1dLv2ygJGNNywg9VApgyYQu9Xp9IiJqPBisEzVSxVY7rpjtsDlkXJHtJSPXkgTXwLNeo0KRxVGlMWZJktChecmotlIBWOwyZCGgVKjQv00zdG4Zjmn9ExGiUUKjVKCwsBDfb/+xwkC97HnLjpgDQFK0oU6DaSFKR7XDdTDbnBAQgGiYVUNVSgmtokI83iskiSPlRERUZQzWiRohs82JfWfzcanQAlPptBW7LMNROodbFgImmwNalQLOCnKQl6dSKtAxLgxXzFrkFNsgC0CtlHD/8PboFB/mEWDfdNNNaB6XgNXH7X57GLJrvAECEo5lm2HQqhAfocPlIqt7e31mVFk8vnOdn5OIiJomButE5Vwrf3Wgj4oKIfDh7nTkGm2w2GU4ZUCpLJmmLZf8BwUmG5QKCV1bRsAhV33+tiRJCNOpEapVu6fWJEbpvAJySZLQISUF0omGz2XuolRIEGXmh6sUCo9R7vr8EhHofYSIiIIHg3Wicq7ON/Yt0HNb/55jxNGsQuQUW9EqKgTpeSbYnXLJ9GlR8tBjsdWJTvFaXBcXhnN5phpfy2614IPly9C/b2/069evTu+jrvRJigIAZlUhIqKgxGCdqAKyEMgsMLvfx0eGuLOgBLJfzhXgcpEV0QYt4iN0yC6yQiFJMNsdJfG6BKgUEqL0GtzZLxEvf3O8RtdxLXikjRX44otMAKj3gN1Z+luAkiwrJbnUgavzz31xZYcJ9Kwqrt/ouHKzuzTE3HoiIgpcDNaJynHNNy6bsxsA5o8OjpFZo80Bq11GWIgKKqWESL0aNocMR5FckhddCIRolOidGIWQGq7mabdZcHzHFo+VSXNzc+v4TrztL5MbHmWmsrjmny8cF7xzxV2/0XHKcr2vVlrZFwObQ26QHPRERFQ1DNaJynGNYJbP2R3oI7MuBo0KWrUCJqsT0QYAkKBWKqAuDcDszpJgzKCr2Y+/3WpB2o+bYSrIcZcNHDgQqamptaq3K2isaGTZgwTER+igLJOHvbFoiNVKK/tiYLfZ8MzI5Dq/JhER1QyDdaJGpmdiJHaezsHhjCuIDvXMny6XLgqkUynRo1Vktc/tCtSL8y5DUfpFpn//AUhNTa11UOkaNf/n18fw64Ur7vKyo+au+ecoNwfd5pDdf3pMlSn9s/xCSRXx51QUf2SQaYgvBkREVDsM1okambYxBnSKC0eRxYFjWcUostihVEiwO0sCULVSgQi9Gm2i9dU6r2uOenH+ZXdZQkp33DKy9oF6WYpKAsiKftPha6TY4SjJIa9WWdA/uVmVrt2QU1HKa8jf2lT2xSA3p/6nMxERUdUxWCcKUkIInMkpxpmcYvfqn2dyitEpPhwzBiYBAPRqJfacyYPdKUMqDQi1KgXaxVZv4SGz2Ywj2zeXzFEvlZDSHck9h9RZoH6trC2yfO1zSJKE+IiSQN9itSG72AZR+tuE6oyON/YR58ragPPViYgCC4N1oiBktjnx4e50HM64grM5JvdUj3e2/46uCRGYMTAJ9w5NxtHMIlwqssDqkHEhzwStumSFUVUFqVOEEDiba/T4AnD0Qi52frnOI1BvWceBOqqQtcVsqzj/fdmR4gUbjgAAjCaBy8UliyDtT8+v0ug4FzMiIqJAw2CdKMi4Fj3aezYPZ3KMkEXJ1BaHLONIZiFM9pKg9t6hyUiOMSA5JhROWcYVkx2iklFih1PG8l1ncTyryOMLwHs/nUVhfghkSQGFkNGyQ3ck9RgcUKPNZYN7V9CvVioQH6GDRl0yb78q9Q2GB4iJiKhpYbBOFGRcix6l5xrRMS4URzOLS7co0TEuFCcuFSNMp8KZHCPiI0IqP1kpIQROZhcj12jDuTyTxxeAE5fNiGzWDoVxAs1ClEDrdii2OhCpVARUwO7iGh13zb2Ojon2c42IiIhqjsE6UZApu+iRXuP5I6zXqNDMoMXlIisOnitAfLeqBetFFgcKTDYUWx0eXwCEUECtlHD8khGO0BYo0ihhzTMhu9CKKIMGHZqH1ss91oZrdNw195qj5UREFMwYrBMFmbKLHvli0CpRZHbAaHNU+Zy5RissdhlJ4TpoFRKcDhuUKjWumO3QqhWwyzJkUbJqqCwD+SYbHKWpDV0LFBEREVHd42P/REGm7KJHvhitTmjVChg0Vf8ubneW5l9XSjh56iTyLqbDYrXB6pBRbHEgXKeCQpKgUkoI06kQGaJGsbVkNP5srqkO746IiIjKYrBOFGR6JkYiNkyLXKMVpnKj5yabA3lGK2LDtOiVWPVFj9RKCQoI/H4+AyaTEU67Dbk52XA4ZejUSgghQZIkKErnqKuUCmhVCljsMn69UHDN8xMREVHNcBoMUZDxteiRSlHyMOixrGIkl25PjjHAYi9JTi5ESZ5xS+kKna70jG2iDQCASI0Ec24GLFoDoqSS7/AqTQgcACQAFocTSoXkMf9bpVDA7pRhtFZ9ug0RERFVD4N1oiAjSZLPRY+UCgld4sPdedZdmVocThknLhWjwGyHQ5YBAZzLM+Gd7b/jurgwWCwWnN39JSRnKMJ0SuQLA5QRoVDowmC1OVBosSPKoIHVLkOtvJr9xSGXXNOg5ccIERFRfeG/skRBKESj9Fj0yLWA0Z+HtUOn+DB3oO5KyXi52Apr6ei4QiFBloEjmYW4YrIi7dBBqPOzYZBy0DzWgPiYZjhv1cLmKMmzrlIqEBGihkUlQ5IkCCHgcMqwOmRE6dXo0cpzuo21dPTe5pDhlK8+fFqdFUSJiIioBIN1oiAlSZJ70SOX5BiDR+7zs7nGkpSMFgc0KgUUkgQJQJhOhfYxeuw+kQETDNBGdIDKaUJq9yRcf/0QPLflKOwOGZeLbWgZqcPlIiuMVod76ovV4USYTo1IvQZtovUe9Vr8eRoAwCnL2J9+dT57VVYQJSIiIk8M1okasV8vXIHFLkOrUrrnqwOAw+nEr6fOw+KQYNeEwqnUwKBVIU20hPNkDlpH6aFSKtDWKSM5xoDjWUXu6TYKBRCl17jzrFe0MJIkSUiI1Hm8JyIiouphsE7UiBmtDjhlAZVSAkqfAxVCIDv7MpRqNRxQApCh0WihCw3DkcxCFFkdyDPa0DEuDCqlArMGt8HZXJN7uo1SApoZNIjUa3wG4K4VRImIiKj2GKwTNWIGrQpKhQSH8+rccVlIgFYPuyxDCRkKpQJhYXqE6dToGBeKY1nFcDhlFFkcCA9Re023ccoyhKh4pJxz0omIiOoO86wTNWI9WkVAp1bA6nBCLl1p1ClkSEo1QrQaKJQaaLQ6aFUl39v1GhWiDWpY7DJyjVY/156IiIgYrBM1Ym2iDYjUaxCqU8HmkGF3ypBlAYdTwC4UiArTQatSeKRk1GtUcMoC9jKj8UREROQfnAZDQcFa5uFIXyqbelGbY4OdJElIjtKgIPsitMowOIQoncIC6DVKxIZqUWhxeExpMdkcUCokjwC+PlQ1xSNTQRIRUVPGYJ2CgisdYEUqSwdYm2ODncViwbEfN8OWdwmI6ojQ1l3gcJbkT9drVGgVpcOxLKN7f5PNgVyjHTq1AtEGbb3WraopHpkKkoiImjIG6xRUZCGQWWB2v4+PDIGiiikBa3NsMLJYLFj14UoU5WdDAmA88wv0Ec3QrHkiQnUqxIRqcfySEUUWO1QKBRyyjGNZxUhqpkeu0YYwXcN8PFQ1xSNTQRIRUVPEYJ2CgisdoMUm44UtR93l80d3gk5T+aMXtTk2WFksFqxYsQKZmRfdZeGJnaGPbQ1JktAu1oAOzcMQplW586crFRK6xIfjurgwnMkx1nswXNUUj0wFSURETRmDdQoKrnnJsgwoFVeDSI1Kcc05y7U5Nhi5AvWLF68G6i07dEdOZKeSyeoAVArv/OlqpYQ/D2uHNtF6PPfF0UquUDeq2vaN8e+IiIioqhisEzUiFosFn360yiNQT0jpjsTug5CbXoCy+V3K508HgOQYQwPXmIiIiCrDYJ2okXDYrFj14UrkZGe5y3r36Q+bvTVkISCEgAAgADhlAZtDhkbVOKcBERERNRb8l5qoEXDYrDj8wyaPOeoDBw7EbnsrHDhXgIPnCpBjtCHXaENOsRW/nC/AC1sqz5JDRERE/sdgnagRSP9tL4rys93vBw4ciNTU1KsPiUqAQaOCXqOEXsNfqBEREQUL/qtN1Ai06T4Qxiu5AOwegfozYzq7M+A4ZRlCACplyXf0Z8Z08nOtiYiI6FoYrBM1Akq1Gl2GjkVqKyuGDh7oHlHXqBRlMuBIEOJqRhzOVyciIgp8TeJfa5PJhA0bNuCee+7BddddB51OB4PBgB49emDJkiUoLi72Ombx4sWQJKnC19NPP13h9Xbu3IkxY8agWbNmCA0NRf/+/bFixYpK63jhwgXcfffdaNmyJXQ6HVJSUrBo0SJYLJY6aQNq/JRqNfr168/FgoiIiBqRJjGyvnr1avzpT38CAHTq1Anjx49HYWEhdu3ahUWLFmHNmjXYvn07mjdv7nXskCFD0L59e6/yPn36+LzWZ599hjvuuAOyLOOGG25ATEwMtm7dipkzZ+LQoUNYunSp1zGnTp3CoEGDkJOTg65du2Lo0KHYt28flixZgq1bt2Lr1q3Qaut36XcKHmazGWvXrsVNN92EqJgW/q4OERER1aMmEayr1WrMmTMHjzzyCDp1ujpPNzMzE2PHjsXBgwfxyCOPYPXq1V7H3nvvvZg1a1aVrpOXl4fZs2fD6XTis88+w6RJkwAAly5dwvXXX49XXnkF48aNw/Dhwz2OmzVrFnJycjB37ly89tprAACHw4GpU6di/fr1ePHFF7F48eJatgI1BmazGStXrsTFixeRkZGB2+/8g7+rRERERPWoSUyDmTlzJt555x2PQB0A4uPj8eabbwIA1q1bB5vNVqvrvPfeeygsLMSECRPcgToAtGjRAv/85z8BAK+88orHMXv37sXOnTvRvHlz9z4AoFKp8NZbb0GtVuP111+Hw+GoVd0o+JUN1FG6ANLxY/W/0igRERH5T5MI1ivTo0cPAIDVakVubm6tzvXFF18AAKZMmeK1bezYsdDpdPjuu+885qG7jrn11lu9prq0aNECQ4cORX5+Pn766ada1Y2Cm9lsxqeffuqxMumAAQMwfMSNPve3OpywOpywOWQ4ZeH1IiIiouDQJKbBVOb3338HSqfKNGvWzGv7tm3b8Msvv8BisaBVq1YYPXp0hfPVf/31VwBA7969vbZpNBp07doV+/btw4kTJ9C9e/drHuMq37ZtGw4dOuQ1fYaaBlegnpWVBb1eD5QG6qNGjYLFLvs8ZvHnJQseOWUZ+9MLAABClATpkiShf7J3XyciIqLA0+SDddcc8VGjRvl8iHPlypUe7xcsWIDJkydj+fLlCA0NdZcXFhbiypUrAIBWrVr5vFarVq2wb98+pKenu4P1c+fOXfMYAEhPT6/S/XTp0sVn+enTp9GmTRvk5ORU6Tz1xdVGNWWxOz1+M5GbmwOdWgkAsDl8B64ushAVHlvdazUUV6B+9uxZd1nv3r3Rt29f5ObmVlhHV5kQArGGkjrLssClYhuk0ik0ubklfaH88dcqc8oyBCSoSlNA+qNdqqK2fa2pYrtVH9usZthu1cc2q5ny7eZ0OqFUBt6/WxVp0sH6li1b8L///Q9qtRrPPfecx7b27dtj6dKlGD16NJKSkpCfn48ff/wRTz75JD777DM4nU6sX7/evX/Z9I+u0c/yDAYDAKCoqMjruOocQ769vK3yLzSPjUhssLrUhbIj6i69e/fGTTfddM30jE/cmORVZrE78a/t5+ulrkRERFQ/mmywfuzYMcyYMQNCCLz88svuuesuM2bM8HhvMBgwffp0jBgxAt26dcOGDRuwe/duDBw4sIFrXrkjR474LHeNuMfExDRwjXyraT3MNid0umz3++joGIRoSr4d63SZQOkIemaB2b1PfGQIFJKE6OiYCo+t7rXqm9lsxrp161BYWOj+Ite7d2/ccccdHoF6depotjlh0Od47AvA6/hrlZVfCbUh26UmAqXPBxu2W/WxzWqG7VZ9bLOacbVbMI2qo6kG6xkZGRg1ahTy8/Mxb948PPzww1U+Nj4+HnfffTeWLl2Kr776yh2sl50SYzKZEB4e7nWs0WgEAISFhbnLXMeZTCaf1/N1DPm2eHxnAIDFJuOFLVezpMwf3Qk6jQJy5bNkAsrly5eRnX01YK7qiDoRERE1Lk0uG0xeXh5GjhyJ9PR0d9BdXR06dABK87S7hIeHIyIiAihdjdQXV3lS0tUpComJidU+hnzTqpTQqpTQqBRQKiT3S6NSQKvy/hZtc8jurCm+Xv6UmJiI6dOnQ6VSYcCAAQzUiYiImqgmNbJeXFyM0aNHIy0tDZMmTcK7775bowAoPz8fKDOf3KVHjx748ccfceDAAXTu3Nljm91ux+HDh6HT6ZCSkuJxzMaNG3HgwAGf13KVux5IpbrzwpY0KBUVf19dOK5zhdsaQtu2bXHfffchOjq61mlFiYiIKDg1mZF1q9WKCRMmYO/evUhNTcWaNWtqNGdJCOF+sLR8usWxY8cCANauXet13ObNm2GxWHDzzTdDp9N5HbNp0yZYrVaPYy5duoQdO3YgKioKQ4YMqXZdqWpkIZCRb3K/ZCEghMCZnGKcySnGiUtF7v93pT+saxaLBU6n92h+TEwMR9SJiIiasCYRrDudTkybNg3btm3D0KFDsW7dOmg0mgr3v3z5Mt58802vDCzFxcW4//77sWfPHsTFxXmsUgoA9957L8LDw7Fx40asW7fOXZ6dnY0nn3wSAPDYY495HNO/f38MGTIE2dnZeOqpp9zlDocDDzzwAOx2O+bOnQu1Wl3rdiBPz4zpjMXjO2P+6E6Iiwhxvx69OQVJ0Xq8s/13nM0x4VyuCWdzTHhn++94b8cZmG11O0XGbDZjxYoV+PTTT30G7IHC1+JKTlm4pxMRERFR3WsS02DeeOMN92h4TEwMHnjgAZ/7LV26FDExMTAajXjwwQfx9NNPo1+/foiPj8fly5dx4MAB5ObmIjIyEmvXrvVKt9isWTO8//77mDp1KqZMmYLhw4cjOjoa3333HQoKCjBv3jyfCxstW7YMgwYNwmuvvYZt27ahc+fO+Pnnn/H7779j8ODB+Otf/1pPLdO0ueayyzKgLM0ZLoTAugMXcPB8Ac7kGCELAbVSAYcs40hmIUz2kqD03qHJdTLibTabsXLlSly8eBEXL17Ep59+ittvvz0gn1Tfn14y/avs4kooM53oxUnd/Fo/IiKixqhJBOuuOeYAPHKjl7d48WLExMQgOjoaTz31FHbv3o0TJ05g165dUCqVSE5OxqxZs/Doo48iISHB5zkmT56MH3/8Ec8//zx2794Nm82Gzp0748EHH8TMmTN9HtOhQwccPHgQCxcuxFdffYX169cjMTERCxYswPz5830u1kT1o8jiwPGsIqTnGtExLhRHM13585XoGBeKE5eKEaZT4UyOEW1jQ69xtsqVDdRdIiIioKhkHn1AkID4CB2UpcE6p+kQERHVnyYRrC9evBiLFy+u8v5hYWH4xz/+UePrDRkyBF9++WW1jmndujWWLVtW42tS3cg1WqGUJEQbtNBrPH889BoVmhm0uFxkxcFzBbUK1n0F6gMGDMCoUaMCNvjtkxTl/v9nxpSkwyQiIqL61SSCdaKqsjsFHBCI0Pt+RsCgVaLI7IDR5qjxNYIxUAeuThVCmSlEREREVL8YrBOVoVZKUEoSTFYnog3e241WJ7RqBQyamv3o+CNQdz38aXPIcMpXs9nYHEG0ShQREVETxWCdqIxogxYqhYSjWYWIDvUcXTfZHMgzWtE1IQK9EiOrfW5/jagv/jwNAOCUZexPL3CXv7ClpLyyXPNERETkXwzWicoI06mQ2EwPk92JY1nFKLLYoVKUZIM5llWM5BgDOsWFIznGx7B7JaxWq9+nvkiShIRIncf7snnjXWkYXf/vwhF4IiIi/2GwTlSGJEm4s18i1EoF9Gol9pzJg90pQ6mQ0CU+HF0TIjBjYFK1A2y1Wo2YmBh3sF7bQL2yqS0KBTzmky8eX/FKrBabjH98dQwoTc3oGm3nCDwREVFgYLBOVE6IRol7hybjaGYRLhVZYHcKqJUS/jysHTrFh9UowFYoFJg4cWLJ+UNCaj2iXtnUlvI5zyt7EFT2MWh+rRF4IiIiajgM1ol8kCQJyTEGJMdcTc+YHGOoVYDtCtglSaqzqS++Auua6pMUVWFKxrIj8ERERNRwGKwT1QOz2YzMzEy0bdvWo7yuFjyqbGpLTSkVUoUpGX2NwBMREVH9Y7BOVMdcWV+ysrIwdepUdOzYsc6vwRznRERETQOfGCOqQ2XTM8qyjE8++QSXLl3yd7WIiIgoSDFYJ6ojvvKo9+vXD82bN/drvYiIiCh4MVgnqgP+WvCIiIiIGjcG60S1xECdiIiI6guDdaJaYKBORERE9YnBOlENMVAnIiKi+sZgnaiG1q9fz0CdiIiI6hWDdaIauuWWWxAaWrLCabAF6laHE1aHEzaHDKcs3C+bQ4bV4fR39YiIiKgUF0UiqqHY2FjMnDkThw8fxvDhw4MmUAeAxZ+nAQCcsoz96QXu8he2pEGpUODFSd38WDsiIiJyYbBOVAuxsbEYMWKEv6tRY5IkISFS5/GeiIiIAgeDdQoaQgicySnGmZxi2J0CaqWEMznF6BQfXu9Bpt1qweZNmzBuTCpCQkLq9VoNYfH4zv6uAhEREVUBg3UKCmabEx/uTsfhjCs4m2OCUxZQKiS8s/13dE2IwIyBSQjRKCs8vrJA/1rsVguObN8MbaxAXs4l/PGPfwz6gF2rqritiIiIKHAwWKeAJ4TAh7vTsfdsHs7kGCELAbVSAYcs40hmIUz2kgci7x2a7HOE/VqB/uTerSq8titQL8rPBmJjkZmZiV9++QWDBg2q13smIiIiAoN1Cga/5xhxNKsQ6blGdIwLxdHM4tItSnSMC8WJS8UI06lwJseItrGhHscKIfDRz+dw8HxBhYG+3SlDCOEV6JvN5quBeqkBAwZg4MCBDXLfREREREzdSAHvl3MFuFxkRbRBC73G8/ulXqNCM4MWl4usOHiuwOvYIosDx7OK3IF+mE6NEI0SYTo1OsaFIj3XiONZRSiyODyOM5vNWL3qQ49AvV+//kGVnpGIiIiCH0fWKeAZbQ5Y7TLCQnx3V4NWiSKzA0abw2tbrtEKpSRVGujnFFvhFALhIWqgNFBf+/FqZGZeXfCoZYduGJnKQJ2IiIgaFkfWKeAZNCpo1QqYrL4X6zFandCqFTBovIN5u1PA6pCh1/p+oNKgVcLqkGF3ipL9rRasXvWhx8qkLTt0Q9te1zNQJyIiogbHYJ0CXs/ESMSGaZFrtMJUbvTcZHMgz2hFbJgWvRIjvY5VKyVoVdcI9FUKqJWS+2HS8iPqDNSJiIjIXzgNhgJe2xgDOsWFo8jiwLGsYhRZ7FApSh4SPZZVjOTS7ckxBq8UjQ6njGiDFufzjYgOVXuc1xXod4oLh0MWyM046c76gtI56k57IgN1IiIi8hsG6xTwJEnCjIFJAAC9Wok9Z/Jgd8pQKiR0iQ9351m32GUfKRoBhywASUJaZhGKLQ6vQP+6uDCcyzMhvG1n2MwmwHYGAwYMwLAbb8GeL476+/b9yuoo+Y2EzSHDKQt3uc0hQ6FgvnYiIqL6xmCdgkKIRol7hybjaGYRLhVZ3Asb/XlYO3SKDwMAvLfjjM9c7Ba7E0pJQohKCSMcXoH+5N6t8PI3xwEAiV36YlrHwejSKQUWu+znu/a/xZ+nAQCcsoz96Vez7bywJQ1KhQIvTurmx9oRERE1fgzWKWhIkoTkGAOSY67mUk+OMUCSJJy+XFxhLvZO8SW52OMidFApJaiUJXPU/zS0DbokRHoF5e3at+fUl3IkSUJCpM7jPREREdU/BuvUKFQlF7vF7kSIRonkmFDYrRb88PnHKOrVAz379PdbvQPd4vGd/V0FIiKiJo3BOjUKVcnFfsVkhyhNz3hk+2ZoYwW+upSFs/k2nMnRuKfWnMkpRqf48Aa/h0DEOelERET+xWCdGoWyudijDd7bXSkabTYbjmz/GkX52XDEtMBxZwukHS3GeasWThlQKiS8s/1391x2IiIiIn9isE6NQs/ESOw8nYPDGVcqTNHYIdaAowe/hy0/GwLASbk5HFGJMCEUsrC7H0g9klkIk90Ju1OGEILzs4mIiMhvGKxTo3CtXOxJUToUXzgOa+45SAAcagMU0YmwKELRsYXnA6kd40oeSNWrlXDIAuEh6mtcnYiIiKh+MFinRqGyXOzXNTfAcvEE4opP4nTp/obEbtCExyBaoMIHUnOKrXAKButERETkPwzWqdHwlYtdITvQIvcXOI0X4JBKUjS27NANzpbtYXPIiND7DsTLPpBKRERE5C8Kf1eAqC6VzcWeHKlG8W9bYc05B9e085YduqFtr+uhVimgVZU8kOqL64FUtZLz1YmIiMh/OLJOjZYQMmTZAaAk/WC/fv3htCdCkiREG7RQKSQczSqs8IHUTnHhcMgcWyciIiL/4cg6NVoanR5dh49HbGxzDBgwACNTR7kzu4TpVLguLgxJ0Qb3A6lmmxNFFnvJA6nRBlwXF4YwHb/PEhERkf8wEqFGTaPTY9bdsxERGgKLXXaXS5KEO/slQq1UeD2Q2iU+3J1n/eVvjvu1/kRERNS0MVinRsNiscBsNkNn8Fx9VKvV+syV7uuBVLVSwp+HtUOn+DCP4J6IiIjIHxisU6NgsViwYsUKGI1GTJ02o8rHlX0g1SU5xsCFkIiIiCggcM46BT1XoH7x4kVcuXIFq1augM1i8ne1iIiIiGqNI+sU1CwWCz79aBUuXrzoLruuY0fk2UL8Wi8iIiKiusCRdQpaDpsVqz5c6RGoDxw4ELeMTOU0FiIiImoUOLJOQclhs+LwD5ugiRVQK0u+cw4cOBCpqam1fjDU5pChUJT86SyTZ91VrlUpa11/IiIioqpgsE5BxxWoF+VnA7GxQJlAvS5G1F/YkgalQgGnLGN/eoFX+YuTutX6GkRERERVwWCdgorFYrkaqJeqy0C9LEmSkBCp83hPRERE1JAYrFPQkGUZa1av8gjU+/cfUOeB+jNjOkOn4eMcRERE5H+MSChoKBQK9Ovf3x2YJ6R0r5eHSTUqBbQqZYUvIiIioobCkXUKKl27dkPKgJtQnHcZyT0Hc2oKERERNWoM1inoNE9KQfOkFH9Xg4iIiKjecRoMBSyLxYKdO3dCCFGFvYmIiIgaH46sU0CyWCxYsWIFLl68iPz8fIwdO5ZTXoiIiKjJ4cg6BZyygToA7Nu3D0ePHvV3tYiIiIgaHIN1CijlA3WU5lHv1KmTX+tFRERE5A8M1ilgVBSo18eCR0RERETBgME6BQQG6kRERETeGKyT3zFQJyIiIvKNwXoAMZvNWLhwIVJSUqDT6dCyZUvMnj0bGRkZ/q5avWGgTkRERFQxBusBwmKx4MYbb8Rzzz2H4uJiTJgwAa1bt8ayZcvQq1cv/P777/6uYr3Yvn07A3UiIiKiCjBYDxDPP/88du/ejUGDBuHEiRP4+OOPsWfPHrzyyiu4fPkyZs+e7e8q1osbb7wR7dq1AxioExEREXlhsB4AbDYb3njjDQDAm2++idDQUPe2efPmoXv37ti+fTv279/vx1rWD7VajTvvvBPjxo1joE5ERERUDoP1ALBz505cuXIF7dq1Q69evby2T5kyBQCwadMmP9SubgkhvMrUajX69u3LQJ2IiIioHAbrAeDXX38FAPTu3dvndlf5oUOHGrRedc1sNuPjjz9u1A/MEhEREdUllb8rQMC5c+cAAK1atfK53VWenp5+zXN16dLFZ/np06fRpk0b5OTk1KquNWU2m/Hpp5/i7NmzOHv2LAAgISGh2uex2J2wWCzu97m5OdCplVXap7rlgeTKlSv+rkLQYZvVDNut+thmNcN2qz62Wc2Ubzen0wmlMrD+na8MR9YDQHFxMQBAr9f73G4wGAAARUVFDVqvuuIK1LOysoDSOfqnTp3yd7WIiIiIAh5H1huZI0eO+Cx3jbjHxMQ0aH3MZjPWrVuHwsJC95eR3r17Y9KkSTWao262OaHTZbvfR0fHIESjrNI+1S0PRA3999cYsM1qhu1WfWyzmmG7VR/brGZc7RZMo+rgyHpgcGV/MZlMPrcbjUYAQFhYWIPWq7bMZjNWrlzpkUe9d+/euOmmm/gwKREREVEVcGQ9ACQmJgIALly44HO7qzwpKalB61UbvgL1AQMG1GvWF6vDCQCwOWQ45atZZ2wOGQp+LSUiIqIgxGA9APTo0QMAcODAAZ/bXeXdu3dv0HrVVEWB+qhRo5Cbm1ujc14rENeqlFj8eRoAwCnL2J9e4N7nhS1pUCoUWDiucy3uioiIiKjhMVgPAEOGDEFERAROnz6NX375BT179vTYvnbtWgDArbfe6qcaVl1lgXptRtSvFYi/OKmbu0ySJCRE6jzeExEREQUjBusBQKPR4MEHH8QLL7yAv/zlL/jmm2/cGWBeffVVHDp0CMOGDUOfPn38XdVrysvL80gPWReBelmVBeKLx/seObc5ZPefnB5DREREwYTBeoD429/+hu+++w67du1Chw4dMHToUKSnp2PPnj2IjY3F+++/7+8qVklCQgJmzJiBDz/8EL169aqzQL2iQLwsrcr3092cHkNERETBiuOKAUKn0+H777/HggULoNfrsWHDBqSnp2PWrFk4cOAA2rZt6+8qVlliYiLuu+++Oh1R16qUlb6qwjUq73pxegwREREFOo6sB5CQkBAsWbIES5Ys8XdVaq1Zs2b+roLbtUblZbnBqkJERERULQzWqdGraOS9KhlmiIiIiPyJwTo1WdXJMENERETkDwzWqcljqkciIiIKVAzWqcmqSoYZIiIiIn9isE5NFuekExERUaBj6kYiIiIiogDFYJ2IiIiIKEAxWCciIiIiClAM1omIiIiIAhSDdSIiIiKiAMVgnYiIiIgoQDFYJyIiIiIKUAzWiYiIiIgCFIN1IiIiIqIAxWCdiIiIiChAMVgnIiIiIgpQDNaJiIiIiAIUg3UiIiIiogDFYJ2IiIiIKEAxWCciIiIiClAM1omIiIiIAhSDdSIiIiKiAMVgnYiIiIgoQElCCOHvSlD9CwsLg91uR7t27fxaD6fTCQBQKpV+rUewYbtVH9usZthu1cc2qxm2W/WxzWqmfLudPn0aarUaRUVFfq5Z1XBkvYkwGAxQq9X+rgbOnj2Ls2fP+rsaQYftVn1ss5phu1Uf26xm2G7VxzarmfLtplarYTAY/Fqn6uDIOjWoLl26AACOHDni76oEFbZb9bHNaobtVn1ss5phu1Uf26xmgr3dOLJORERERBSgGKwTEREREQUoButERERERAGKwToRERERUYBisE5EREREFKCYDYaIiIiIKEBxZJ2IiIiIKEAxWCciIiIiClAM1omIiIiIAhSDdSIiIiKiAMVgnYiIiIgoQDFYJyIiIiIKUAzWiYiIiIgCFIN1IiIiIqIAxWCdGoTZbMbChQuRkpICnU6Hli1bYvbs2cjIyPB31eqEyWTChg0bcM899+C6666DTqeDwWBAjx49sGTJEhQXF3sds3jxYkiSVOHr6aefrvB6O3fuxJgxY9CsWTOEhoaif//+WLFiRaV1vHDhAu6++260bNkSOp0OKSkpWLRoESwWS520QU0MHz680jb46quvfB63fPly9O/fH6GhoWjWrBnGjBmDXbt2VXqtxtJmP/zwQ6Vt5notWbLEfUxT6Wv79+/HP/7xD0yaNAmtWrVy39+1BHJ/aojPzuq0myzL2LFjB5588kn06dMHYWFh0Gq1aNeuHe677z6cOXPG53HX6rcDBw6ssH6B2G7V7WvB8DMYaH0NQJU+62688UaPYxpbXwMACKJ6ZjabxcCBAwUAER8fL6ZOnSr69+8vAIjY2Fhx+vRpf1ex1t59910BQAAQnTp1ErfffrtITU0VYWFhAoDo2LGjuHTpkscxixYtEgDEkCFDxMyZM71en3zyic9rrV27ViiVSiFJkhg2bJiYPHmyiIyMFADEY4895vOYkydPipiYGAFAdO3aVUydOlW0bdvWfX2LxVIv7XItw4YNEwDE5MmTfbbBoUOHvI55+OGHBQAREhIiJkyYIFJTU4VKpRJKpVKsX7/e53UaU5sdPXrUZ1vNnDlTzJgxw90Pt23b5j6mqfS1CRMmuO+/7KsygdyfGuqzszrtdvLkSff2uLg4MX78eHHbbbeJhIQEAUCEhYWJHTt2eB33/fffCwCiXbt2PvvgkiVLKrxeILZbdftaoP8MBmJfE0JU+Fk3c+ZM9z0uXLjQ45jG1teEEILBOtW7Z555RgAQgwYNEkVFRe7yV155RQAQw4YN82v96sLy5cvFnDlzRFpamkf5xYsXRa9evQQAMW3aNI9trg/vZcuWVfk6ubm5Ijw8XAAQn332mbs8KytLtG/fXgAQ33//vddxQ4YMEQDE3Llz3WV2u13cdtttAoBYtGhRNe+4briC9TNnzlRp/2+//VYAENHR0eLEiRPu8l27dgmNRiMiIyNFfn6+xzGNrc0qs2XLFgFAtG7dWsiy7C5vKn3tH//4h1iwYIH4/PPPRWZmptBqtZUGAoHenxrqs7M67Xbq1Clxyy23iK1bt3r0MYvFImbNmiUAiMTERGGz2TyOcwVQM2fOrFbdArXdqtvXAv1nMBD7WmXy8/Pdx5b92RWNsK8JButU36xWq4iIiBAAxIEDB7y2d+/eXQAQ+/bt80v9GsKuXbsEAKHVaoXVanWX1+TD+6WXXhIAxIQJE7y2rVu3TgAQ48aN8yjfs2ePACCaN2/uNTKQlZUl1Gq1iIqKEna7vUb3VxvVDdZHjx4tAIj/+7//89o2d+5cAUAsXbrUo7yxtVllpk+fLgCIp59+2qO8qfa1awUCgdyf/PnZWdMAymQyuev8ww8/eGyrSQAVTO1WH8E6+1rF/vvf/woAYuDAgV7bGmNf45x1qlc7d+7ElStX0K5dO/Tq1ctr+5QpUwAAmzZt8kPtGkaPHj0AAFarFbm5ubU61xdffAGUabeyxo4dC51Oh++++85jfp3rmFtvvRVardbjmBYtWmDo0KHIz8/HTz/9VKu61Tez2Yxt27YBFdx/RX2pqbSZ0WjExo0bAQB33XVXrc/X2Nst0PtTMH52hoSEICUlBQBw8eLFWp+vqbRbRdjXKvbhhx8CdfRZhyBoNwbrVK9+/fVXAEDv3r19bneVHzp0qEHr1ZB+//13AIBarUazZs28tm/btg2PPPII7rvvPjz//PPYv39/heeqrD01Gg26du0Ki8WCEydOVOkYBMjfwf/+9z888MADePDBB/H666/j3LlzXvscP34cVqsVsbGxaNWqldf2iu6jsbZZeevWrYPRaESvXr3QuXNnn/uwr10V6P0pmNrSRZZlpKenAwDi4uJ87nPy5En89a9/xZw5czB//nxs2bIFsiz73Lcxtlsg/gwGepuVd+7cOezYsQNqtRp33HFHhfs1pr6mqvUZiCrhCrp8/WNYttz1Ad8YvfbaawCAUaNGeX1jB4CVK1d6vF+wYAEmT56M5cuXIzQ01F1eWFiIK1euANdoz3379iE9PR3du3cHguTv4Pnnn/d4//jjj2PBggVYsGCBu+xa92EwGBAZGYn8/HwUFRUhLCysUbdZeVUZaWJfuyrQ+1MwtaXLmjVrkJ2djdjYWAwePNjnPrt27fLKtNOtWzd89tln6NChg0d5Y2y3QPwZDPQ2K2/VqlUQQmD06NGIjo6ucL/G1Nc4sk71ypWyUK/X+9xuMBgAAEVFRQ1ar4ayZcsW/O9//4NarcZzzz3nsa19+/ZYunQpjhw5guLiYpw/fx6rVq1CQkICPvvsM6+gq2z6x+q0ZyD/Hdxwww1YuXIlTp8+DZPJhOPHj+OFF16ASqXCwoUL3V90UIX7gI97aYxt5ktmZia2bt0KpVKJadOmeW1nX/MW6P0pmNoSAM6fP49HHnkEALBkyRKvgYmIiAg88cQT2L17N3Jzc5Gbm4utW7di4MCB+O233zBy5Eh3cOrSmNotkH8GA7XNKnKtgYnG2Nc4sk5UT44dO4YZM2ZACIGXX37ZPXfdZcaMGR7vDQYDpk+fjhEjRqBbt27YsGEDdu/eXWlO2GBXNhc4AKSkpGD+/Pno27cvUlNTsXjxYsyZMwchISF+q2MwWLNmDZxOJ0aNGuVz+gH7GtUno9GISZMmIScnBxMnTsR9993ntU+vXr285vXeeOON+OmnnzBixAjs2LED//nPf/DXv/61AWvecPgzWDcOHDiAtLQ0REZG4tZbb/W5T2PsaxxZp3rl+rWeyWTyud1oNAIAwsLCGrRe9S0jIwOjRo1Cfn4+5s2bh4cffrjKx8bHx+Puu+8GAI9Fgcr+irQ67RmMfwcjR45E3759UVBQgD179gBVuA/4uJem0mY1fdiqKfe1QO9PwdKWdrsdt99+O/bt24frr78eq1evrtbxSqUSTz31FADg66+/9tjWmNvNJRB+BoOpzVyfdbfffrvPaaWVCea+xmCd6lViYiJQuiqYL67ypKSkBq1XfcrLy8PIkSORnp6Ou+++G0uXLq32OVzz6TIzM91l4eHhiIiIAKrZnsH6d1C+Da51H0ajEQUFBYiKinJ/ODaFNjt69CgOHjyI0NBQTJw4sdrHN9W+Fuj9KRjaUpZlzJw5E19++SV69uyJTZs21ei3YL76IBpxu5Xn75/BYGkzp9OJjz76CPDxm4qqCta+xmCd6pVr6seBAwd8bneVux6OCXbFxcUYPXo00tLSMGnSJLz77rtVWu68vPz8fKDMnDeXytrTbrfj8OHD7iWSq3IMAvjvoHwbXHfdddBqtbh8+bLPZZwruo/G3mauB9YmTZpU6fzrijTVvhbo/SkY2vKhhx7CmjVrkJKSgq+//hqRkZE1Ok9N+iCCuN3K8/fPYLC02datW5GZmYmkpCQMHTq0RucI2r5W60ztRJUou2jAwYMHvbY3pkWRLBaLuPHGGwUAkZqa6rEAUnXIsiwGDBggAIiVK1d6bKvPRTLKrzjoT9nZ2cJgMAgA4vz58+7yQFvExt9tJsuySEpKEgDEt99+W6PjG3NfC7ZFkcq2iz8/O6uyUI1r5cbExESRnp5eq+s98cQTAoC45557PMqDqd1qurhPIPwMBnpfc7nrrrsEAPHMM8/U+HrB2tcYrFO9c32oDx48WBQXF7vL63o5Xn9yOBzuJYmHDh0qjEZjpftnZ2eLN954QxQWFnqUFxUViT//+c8CgIiLi/M6T0XLT1+6dKlKy08//PDD7jK73S4mTZpU70vAV2Tnzp1i/fr1wuFweJSfOXPGXd/x48d7bKtseXitVlut5eGDsc3K2759uwAgEhIShNPp9LlPU+5r1woEAr0/+euz81rt9uqrr7r7Tfml3ivyf//3f+LcuXMeZbIsi7fffluoVCohSZLPoCZY2q2yNguGn8FA7WsuRqNRhIaGCgDi2LFjle7bGPsag3Wqd2az2T1yEB8fL6ZOnep+HxsbK06fPu3vKtbav/71LwFAABC33XabmDlzps/X5cuXhSgNSAGI0NBQMWLECDF9+nRxyy23iOjoaAFAREZGip9++snntdauXSsUCoWQJEmMGDFCTJkyRURGRgoAYt68eT6POXHihPvc3bp1E3fccYdo27at+0Om/EhCQ1i2bJn7H6kxY8aI6dOniyFDhgidTicAiC5duohLly55Hffwww8LAEKv14sJEyaI0aNHC5VKJZRKpVi/fr3PazWWNivvT3/6kwAgnnjiiQr3aUp9bfPmzWLAgAHulyRJAoBH2ebNmz2OCeT+1FCfndVpt4MHD7q3Dxo0qMLPuh07dnhcIykpSSiVStGvXz8xdepUMX78eJGcnCwACIVCIf79738HVbtVp82C4WcwEPtaWatWrRIARL9+/a55jcbW1wSDdWooJpNJLFiwQLRr105oNBoRFxcnZs2a5THFIZgtWrTIHaxX9jpz5owQQojCwkLx1FNPiWHDhomEhASh1WqFXq8XXbp0EY899pi4cOFCpdf76aefxKhRo0RkZKTQ6/Wib9++Yvny5ZUec+7cOTFr1iwRFxcnNBqNaN++vViwYIEwm8112hZVlZaWJu6//37Ru3dvERsbK1QqlYiIiBADBw4Ur7zyijCZTBUeu2zZMtGnTx+h1+tFZGSkGDVqlNi5c2el12sMbVaWxWIRUVFRAoD49ddfK9yvKfU11xfAyl7Lli3zeVyg9qeG+OysTrt9//33VfqsK9/Or7/+uhg3bpxITk4WBoNBaDQakZSUJGbMmCH27t1baf0Csd2q02bB8jMYaH2tLNeUtddee+2a12hsfU0IISQhhKj9zHciIiIiIqprzAZDRERERBSgGKwTEREREQUoButERERERAGKwToRERERUYBisE5EREREFKAYrBMRERERBSgG60REREREAYrBOhERERFRgGKwTkREREQUoBisExEREREFKAbrREREREQBisE6ETUps2bNgiRJOHv2rL+rElDOnj0LSZKwePFif1claNV133rrrbeg0+mQmZlZJ+erjKvuZc2bNw8JCQkwm831fn0iqhiDdSIKWk6nE++//z5GjBiB6OhoaLVaJCYm4q677sLBgwf9Xb1GTZZlvPvuuxgyZAgiIiKg1WqRkJCAoUOH4plnnsH58+fd+7q+CMyaNcuvda4LkiRh+PDh9X4dk8mEJUuWYPbs2YiPj6/36/ny+OOPIzc3F6+99ppfrk9EJRisE1FQKigowIgRI3DPPffgxIkTmDJlCubNm4e+ffvi008/RZ8+ffDKK6/4u5qN1h/+8AfMmTMHp06dwuTJk/HYY49h/PjxKC4uxt///nfs2bPH31UMav/973+RlZWFRx55pEGu9+KLL+Lo0aMeZS1btsTUqVPxz3/+ExaLpUHqQUTeVP6uABFRdQkhMH36dOzYsQPTp0/He++9h5CQEPf2Y8eOITU1FY8//jhat26NqVOn+rW+jc0PP/yAjz76CH379sX27duh1+s9tp86dQoKBceCauPdd99F7969kZKS0iDXi4+P9zmCf+edd2LlypVYt24dpk+f3iB1ISJP/DQloqDz5Zdf4ssvv0TXrl2xfPlyj0AdADp27IhPPvkEAPDYY4/Bbrd7ncPhcGDhwoVITEyETqdD7969sXHjRq/9zp8/jzlz5qBt27bQ6XSIiYlBv379sHTpUq9909LSMG3aNMTFxUGr1aJ9+/ZYvHgxrFarx37Lly+HJElYvnw5PvnkE/Tp0wchISGYNWsWFixYAEmS8N133/m89ylTpkChUODChQse9/L666+jV69e0Ov1iIiIwOjRo7F3716f51i9ejW6d+8OnU6HpKQkPPvss3A6nRW2d3mu886YMcMrUAeA9u3bo23btu57TU5OBgB88MEHkCTJ/XIZPnw4JEmC0WjEvHnz0KpVKyiVSvzwww/ufVauXInBgwcjLCwMoaGhGDp0KL7++muva7vOZbFY8MQTT6Bly5bQ6XQYNGgQ/t//+38+72fFihXo1q2bR3ucPn3aYw7/Dz/84K7z9u3bPe6j/Bx1WZbx4osvIjk5GVqtFt27d8fmzZur3L779u1DWloaJk2a5LWtbN9Zs2aNu97Jycl46aWXIMuyx/4mkwkvvPACunTpAoPBgIiICHTs2BH3338/TCaTez9fc9YBYOTIkQgNDcXKlSurXH8iqlscWSeioLNixQoAwKOPPgq1Wu1znwEDBmDo0KHYsWMHtm3bhtTUVI/tc+fOxa+//oqpU6fCZrNhzZo1uO222/Dpp59i8uTJAACj0YghQ4YgKysL48ePxx133IGCggIcOXIE77//Ph5//HH3+bZv344xY8ZAkiRMnDgRcXFx2L17N5599ln8/PPP2Lx5s1cwtGbNGmzfvh0TJ07EzTffjLi4OKSmpuL555/HRx99hJtvvtlj/6KiInzxxRcYOnQoWrVqBZQGhpMnT8bnn3+Ovn37Ys6cOSgqKsK6detwww034JtvvsENN9zgPsd///tf/PnPf0bz5s0xZ84cSJKEt99+u1rTVpo1awYAOHny5DX37dmzJx5++GG89tpr6NGjByZOnFjhvpMmTcKpU6cwYcIEAEB4eDgA4MEHH8Sbb76Jjh07YubMmXA6ndi4cSNGjx6NVatWYdq0aV7nuuOOO5CWlobJkyejqKgIa9aswciRI3Hs2DEkJCS49/vPf/6Dv/zlL2jRogXmzJkDhUKBd955x+uLTps2bbBo0SI8++yzSEpK8ph/HxkZ6bHvo48+in379mHcuHGQJAlr1qzBxIkTsXfvXvTu3fuabeb6kjJgwIAK9/n444+xfft2TJs2DaNHj8aGDRvw9NNPIyMjA6+//rp7vxkzZmD9+vW44YYbMHr0aMiyjFOnTuGDDz7AokWLfH7ZKkulUqFPnz7YsWMHHA4HVCqGDUQNThARBZmkpCQBQJw+fbrS/f72t78JAGLx4sXuspkzZwoAIiEhQVy+fNldfvLkSaHX60VcXJywWq1CCCE2btwoAIjXXnvN69w5OTnu/7dYLCIhIUHExcWJ9PR0j/0effRRAUCsWbPGXbZs2TIBQCiVSrFr1y6vc3fv3l1ERUW56+HywQcfCADirbfecpe98cYbAoB45plnhCzL7vKzZ8+KyMhI0alTJ3d5Xl6eCA0NFc2bNxeZmZnufbOyskRcXJwAIBYtWlRpmwohxLlz50RoaKhQKBRi+vTpYu3atR7nK+/MmTMCgJg5c6bP7cOGDRMAxIABA0RRUZHHts2bNwsA4o9//KOw2+3u8ry8PNGuXTvRrFkzYTQavc41bNgwj/KVK1cKAOK5555zl+Xm5gq9Xu/VHtnZ2SIhIcFne7jO7Yurb3Xs2NGjf+zYsUMAEPfcc0+FbVTW5MmTBQCPc7i4+o4kSWLnzp3ucpPJJHr06CEkSRK//PKLEEKI/Px8IUmSuO2227zOc+XKFWGz2bzq7su8efMEALF///4q1Z+I6hanwRBR0Ll06RIAeIyQ+uLa7tq/rLlz5yImJsb9vn379pg5cyaysrLw/fffe+zra/QxOjra/f+bNm1CRkYGFi1ahMTERI/9nn32WUiShE8//dTrHJMnT8agQYO8yqdPn478/Hx89dVXHuVr1qyBWq3G7bff7i5766230LJlS/d1XJKSknDvvffi6NGjOHz4MABg48aNKC4uxl/+8hfExcW5923RogUefvhhr3pUpHXr1vj444/RokULrF69GlOmTEF8fDySk5Px4IMP4syZM1U+V1mLFi1CaGioR9lbb70FtVqNf//73x6julFRUXj00UeRl5eHrVu3ep3rueee8/h7mzZtGlQqlUeWoI0bN8JkMuHBBx/0aI/Y2FjMnTu3RvcAAPPnz/foH9dffz2Sk5OrnKHowoULUCqV7t9g+DJq1CgMHjzY/T4kJATz58+HEAIfffQRAEChUEAI4bP/hoeHV/hbqfJatGjhrhcRNTz+PouImqSygY7LoEGD8NZbb+HQoUNITU3FDTfcgObNm+OBBx7Atm3bMHr0aAwfPhytW7f2OM41ZWL//v0+85SHhITg+PHjXuUVTYm488478de//hVr1qzB+PHjAQA5OTn47rvvMHLkSHcgaDQakZaWhnbt2uG5557zOk9aWhoA4Pjx4+jWrRsOHTpU4b0PGTKkgpbybcyYMTh79iy+/fZb/Pjjj/j555+xa9cuvPnmm/jggw+wdetW9O/fv1rn9NUee/fuRUREBF599VWvba5pOMePH8ett97qsa1Hjx4e75VKJVq0aIGCggJ3mas9fH1h8lVWVeWvjdLMKlXNl56Xl4fIyEifc8hdKuq/KHNf4eHhGDlyJFatWoWMjAzcdtttGDFiBLp27VrpucuLiooCAOTm5lb5GCKqOwzWiSjotGjRAunp6cjIyHA/yOjLxYsX3fuXFxsb61XWvHlzAEBhYSFQOhd5165deOaZZ7Bp0yasWbMGANCvXz+8+uqruP766wEA+fn5AID33nuvwroYjcYKr1deUlISBg8ejE2bNsFkMkGv12Pt2rVwOBweGTkKCgoghMCpU6fw7LPPXvParvuq7N6rQ6PRYOzYsRg7diwAoLi4GPPnz8e///1v3H///di/f3+1zuerDvn5+XA4HFW6v7Jc893LUqlUHg/SFhUVAXXYHtW5dmVCQkKumSqxKv0XANauXYslS5Zg9erV7t+etG7dGgsXLsS9995bpfq4FkUq/yA3ETUMToMhoqAzcOBAoMyDeBXZvn27x/5lXb582assOzsbKBdstWvXDh999BHy8vKwc+dOPPnkkzh8+DDGjh3rHikNCwsDAPz0008QQvh8+ZoaUtno5rRp02A0GvH5558DpVNg9Hq9++HLste9+eabK7yuEAIzZ870uK/K7r02QkND8dprr6FNmzY4ePCgzyC6Mr7aIywsDO3bt6/0/hYtWlSj+rrar77ao6ZiY2NhNBphs9kq3Keq/TcsLAwvv/wyLly4gCNHjrh/Q/GnP/0JW7ZsqVJ9XF9GfX1BIKL6x2CdiILOXXfdBQD417/+BYfD4XOfvXv3YseOHWjVqhVuvPFGr+27du3yKnOl9uvWrZvXNrVajcGDB+Oll17C/PnzUVhYiB9//BEoHWkHUKcLAU2dOhUqlQpr1qzBhQsXsGPHDtx6660ec7rDw8ORkpKCQ4cOVWnRmu7duwMV3PvOnTvrpN6SJEGv10MI4R6RdeVcL59WsCr69euHM2fO+AxOa8vVHrt37/ba5qsMpfdXk/uoji5dugDXyLZT3f4rSRI6d+6MRx99FKtXrwZKn7WoihMnTgAAunbtWsU7IKK6xGCdiILOmDFjMHLkSPz222+YPXu2V6B64sQJ3HHHHRBC4JVXXvH5IN3rr7+OnJwc93tXOrsWLVq4g/u0tDSPfVxcI5harRYAMHHiRMTHx+P55593P8xZ1uXLl71Wh7yW2NhY3HTTTfjqq6/w9ttvQwjhM0Xh/fffj+zsbDz22GNeX1yEEO4vFAAwYcIEGAwGvPnmm8jKyvK4n+osKf/1119j8+bNPoPWTZs24ejRo0hJSXE/wOua85yRkVHla5S9P6fTiTlz5njkBXf5+eeffZZXxfjx4xESEoI33njDYyQ9NzfXI/1hWc2aNavRfVSH6/mBffv2VbjPV1995RGwm81m/P3vf4ckSe5+cvnyZfdzC2WV77/XsnfvXqSkpPicTkZE9Y9z1oko6LhyV996661YuXIltm3bhnHjxqFZs2Y4ceIENm/eDJvNhpdffrnC1Uu7d++OHj16eORZN5vN+OCDD6DRaAAA33zzDZ566ikMHToUKSkpCA8Pxy+//IKvv/4anTp1wqhRo4DSubwfffQRxo4di169emHMmDG47rrrUFRUhFOnTmH79u1YsmQJOnXqVK37nD59Or7++mu89NJLiIyMxOjRo732mTt3LrZv347//Oc/+PbbbzFs2DBERUXh/Pnz2LNnDy5evOj+MhMVFYVXXnkF9913H3r27ImpU6dCkiR88skn6NWrF7788ssq1evo0aN49NFHERcXh2HDhiE5ORkWiwW//vorvv/+e2g0Grzxxhvu/cPCwtCnTx/88MMPmD17NpKTkyFJEv72t79d81oTJ07E3Llz8frrryMlJcWdjz4jIwMHDhxAWloaMjMzr5kv3Jfo6Gi89NJLmDt3rrsvKBQKfPLJJ+jZsycuXLjgtRLr8OHD8dlnn+H2229Ht27doFAo8NBDDyEiIqLa16/ILbfcAp1Oh23btrmnMJWXmpqKm2++GdOmTUN0dDQ2bNiAkydP4qGHHnI/4JqRkYFevXqhb9++6NatG1q0aIFz585h/fr1MBgMmDNnzjXrcu7cOZw6dQqPPfZYnd0fEVWTv3NHEhHVlN1uF++++64YNmyYiIqKEmq1WrRq1Ur84Q9/EPv27fN5jCuf9MmTJ8WCBQtEq1athFarFT179hQbNmzw2DctLU089NBDonv37iIyMlLo9XrRsWNHMX/+fJGbm+t17pMnT4p77rlHtG7dWqjVahEbGyv69esnFi1a5JF/3ZUre9myZZXeX2FhodDpdNfM0e10OsW7774rBg0aJMLCwoROpxNt27YVt99+u1i7dq3X/h9++KHo2rWr0Gg0IjExUSxatEicPHmyynnWs7OzxTvvvCMmTpwoOnToIPR6vdBqtaJt27bi7rvvFocPH/Y65vDhw+Kmm24SoaGhAoBHTm9XbvTKrF27Vtx0000iMjJSaLVakZSUJMaNGyeWLVvmkX+9snMlJSX5zJG+bNky0blzZ4/22L17twAgXn31VY99L1y4ICZMmCAiIyOFJEkCgDhz5owQZfqW631Zw4YNE0lJSZXeY1l/+MMfRHh4uLBYLF51dfWd1atXi65du7rb48UXXxROp9O9b35+vli4cKEYMmSIaN68udBqtaJNmzbij3/8ozh69KjHeSvKs/7SSy8JACItLa3KdSeiuiWJkkUeiIiIqNSyZcswe/ZsbN682Z3tpiHt3bsXAwYMwOrVqz2mPy1fvhx33303li1b5rGKan0QQqBbt26Ii4vDd999V6/XIqKKcc46ERE1Wbm5ubDb7R5lly5dwgsvvICwsDAMHz7cL/Xq378/brvtNrz44ovw15jahg0bcOTIEbz44ot+uT4RleCcdSIiarK+/PJLPP744xg5ciRatmyJjIwMfPHFF8jPz8cbb7wBg8Hgt7q9/PLLWLlyJS5duuSxwmpDsdvtePvtt93ZjojIPxisExFRk9WrVy8MHjwY3333HXJzc6HRaNCzZ0888sgjmDx5sl/r1q5dO58r4jaUih7OJqKGxTnrREREREQBinPWiYiIiIgCFIN1IiIiIqIAxWCdiIiIiChAMVgnIiIiIgpQDNaJiIiIiAIUg3UiIiIiogDFYJ2IiIiIKEAxWCciIiIiClAM1omIiIiIAhSDdSIiIiKiAMVgnYiIiIgoQDFYJyIiIiIKUAzWiYiIiIgC1P8HHIzHQOmD3BQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LOO CV by curing time\n",
    "for day in model_strength_days:\n",
    "    time_mask = X_strength[:n_real, -1] == day\n",
    "    fig = plot_calibration(\n",
    "        observed=loo_obs[time_mask] * STRENGTH_DISPLAY_SCALE,\n",
    "        predicted_mean=loo_pred[time_mask] * STRENGTH_DISPLAY_SCALE,\n",
    "        predicted_std=loo_std[time_mask] * STRENGTH_DISPLAY_SCALE,\n",
    "        title=f\"Day-{day} Strength LOO-CV\",\n",
    "        xlabel=f\"Observed Strength ({strength_label()})\",\n",
    "        ylabel=f\"Predicted Strength ({strength_label()})\",\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d579087a-037b-4ad7-a771-008459c1aecb",
   "metadata": {
    "originalKey": "d579087a-037b-4ad7-a771-008459c1aecb"
   },
   "source": [
    "# Compute Predicted Pareto Frontier\n",
    "This section uses a Polytope sampler to generate a random set of points satisfying the (adjusted) constraints and computing the Pareto frontier based on the predictive mean of the model. See the qLogNEHVI section below for gradient-based optimization of the predicted Pareto frontier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1e4d78b3-4c84-4dee-a16e-b83e48ed625c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:17.576060Z",
     "iopub.status.busy": "2026-03-18T21:35:17.575893Z",
     "iopub.status.idle": "2026-03-18T21:35:17.580801Z",
     "shell.execute_reply": "2026-03-18T21:35:17.580170Z"
    },
    "originalKey": "1e4d78b3-4c84-4dee-a16e-b83e48ed625c",
    "output": {
     "id": 1479336443571536,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference point (['GWP', '1-day Strength', '28-day Strength']): [-200.0, 1000.0, 5000.0]\n"
     ]
    }
   ],
   "source": [
    "# Reference point for the Pareto frontier, representing the lower bounds\n",
    "# on objectives below which solutions become irrelevant.\n",
    "ref_point = utils.get_reference_point(optimization_mode, include_cost=include_cost)\n",
    "print(f\"Reference point ({model.model_names}): {ref_point.tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4ca60ff1-0642-4ae3-9cde-5c3ce8ae38c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:17.582239Z",
     "iopub.status.busy": "2026-03-18T21:35:17.582095Z",
     "iopub.status.idle": "2026-03-18T21:35:17.586843Z",
     "shell.execute_reply": "2026-03-18T21:35:17.586191Z"
    },
    "originalKey": "4ca60ff1-0642-4ae3-9cde-5c3ce8ae38c0"
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tableau = list(mcolors.TABLEAU_COLORS.values())\n",
    "\n",
    "# Compute bounds in the optimization (reduced) space.\n",
    "# opt_columns excludes fixed-feature dimensions so bounds/constraints\n",
    "# match the input dimension expected by model_list.\n",
    "if optimization_mode == \"mortar\":\n",
    "    opt_bounds = get_bounds(opt_columns, MORTAR_BOUNDS_DICT)\n",
    "else:\n",
    "    opt_bounds = get_bounds(opt_columns)\n",
    "\n",
    "fly_ash_idx = opt_columns.index(\"Fly Ash (kg/m3)\")\n",
    "slag_idx = opt_columns.index(\"Slag (kg/m3)\")\n",
    "\n",
    "all_bounds = opt_bounds.clone()\n",
    "\n",
    "noash_bounds = opt_bounds.clone()\n",
    "noash_bounds[1, fly_ash_idx] = 0.0  # no fly ash\n",
    "noslag_bounds = opt_bounds.clone()\n",
    "noslag_bounds[1, slag_idx] = 0.0  # no slag\n",
    "\n",
    "# define a sequence of different constraints\n",
    "wb_ratios = [0.2, 0.35, 0.2, 0.35, 0.2, 0.35]\n",
    "bounds_list = [all_bounds, all_bounds, noash_bounds, noash_bounds, noslag_bounds, noslag_bounds]\n",
    "names = [\"all, w/b > 0.20\", \"all, w/b > 0.35\", \"no ash, w/b > 0.20\", \"no ash, w/b > 0.35\", \"no slag, w/b > 0.20\", \"no slag, w/b > 0.35\"]\n",
    "colors = [tableau[0], tableau[0], tableau[1], tableau[1], tableau[2], tableau[2]]\n",
    "linestyles = [\"-\", \":\", \"-\", \":\", \"-\", \":\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f78c6c9-35b9-4734-b67d-9a4e15668abf",
   "metadata": {
    "originalKey": "8f78c6c9-35b9-4734-b67d-9a4e15668abf"
   },
   "source": [
    "### Predicted GWP-1-day-strength trade-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d58caa4-f16b-44ab-b8e2-0ebd25b87575",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:17.588406Z",
     "iopub.status.busy": "2026-03-18T21:35:17.588180Z",
     "iopub.status.idle": "2026-03-18T21:35:17.592440Z",
     "shell.execute_reply": "2026-03-18T21:35:17.591846Z"
    },
    "originalKey": "5d58caa4-f16b-44ab-b8e2-0ebd25b87575"
   },
   "outputs": [],
   "source": [
    "def get_inferred_pareto_frontiers(pareto_dims: list[int], num_candidates: int):\n",
    "    X_all = []\n",
    "    Y_all = []\n",
    "    Ystd_all = []\n",
    "    for bnds, min_wb, name in zip(bounds_list, wb_ratios, names):\n",
    "        if optimization_mode == \"mortar\":\n",
    "            eq, ineq = get_constraints(\n",
    "                opt_columns,\n",
    "                **{**MORTAR_CONSTRAINTS, \"water_binder_bounds\": (min_wb, 0.5)},\n",
    "            )\n",
    "        else:\n",
    "            eq, ineq = get_constraints(\n",
    "                opt_columns, water_binder_bounds=(min_wb, 0.5)\n",
    "            )\n",
    "        X_pareto, Y_pareto, Ystd_pareto = utils.predict_pareto(\n",
    "            model_list,\n",
    "            pareto_dims=pareto_dims,\n",
    "            ref_point=ref_point,\n",
    "            bounds=bnds,\n",
    "            equality_constraints=eq,\n",
    "            inequality_constraints=ineq,\n",
    "            num_candidates=num_candidates,\n",
    "        )\n",
    "        X_all.append(X_pareto.detach().numpy())\n",
    "        Y_all.append(Y_pareto.detach().numpy())\n",
    "        Ystd_all.append(Ystd_pareto.detach().numpy())\n",
    "    return Y_all, Ystd_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf033003-7424-4d95-8167-9e4410df437c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:35:17.593742Z",
     "iopub.status.busy": "2026-03-18T21:35:17.593599Z",
     "iopub.status.idle": "2026-03-18T21:36:12.474556Z",
     "shell.execute_reply": "2026-03-18T21:36:12.473353Z"
    },
    "originalKey": "cf033003-7424-4d95-8167-9e4410df437c"
   },
   "outputs": [],
   "source": [
    "predicted_pareto_results = []\n",
    "gwp_dim = model.output_index(\"GWP\")\n",
    "for day in model_strength_days:\n",
    "    strength_dim = model.output_index(f\"{day}-day Strength\")\n",
    "    pareto_dims = [gwp_dim, strength_dim]\n",
    "    predicted_pareto_results.append(\n",
    "        get_inferred_pareto_frontiers(\n",
    "            pareto_dims,\n",
    "            num_candidates=100 if SMOKE_TEST else 10000,\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb82a31a-a8e0-4c39-8b57-4d9567a817a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:36:12.477698Z",
     "iopub.status.busy": "2026-03-18T21:36:12.477515Z",
     "iopub.status.idle": "2026-03-18T21:36:12.593185Z",
     "shell.execute_reply": "2026-03-18T21:36:12.592595Z"
    },
    "originalKey": "bb82a31a-a8e0-4c39-8b57-4d9567a817a8",
    "output": {
     "id": 938598445832488,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, day in enumerate(model_strength_days):\n",
    "    fig = plt.figure(dpi=200)\n",
    "    plt.title(\"Inferred Pareto Frontier\")\n",
    "    plt.ylabel(f\"{day}-day strength ({strength_label()})\")\n",
    "    plt.xlabel(\"-GWP (kg CO\\u2082/m\\u00b3)\")\n",
    "    Y_all, Ystd_all = predicted_pareto_results[i]\n",
    "    for Y, Ystd, name, ls, color in zip(Y_all, Ystd_all, names, linestyles, colors):\n",
    "        plt.plot(Y[:, 0], Y[:, 1] * STRENGTH_DISPLAY_SCALE, label=name, linestyle=ls, color=color, lw=4)\n",
    "    plt.xlim(-150, -10)\n",
    "    plt.ylim(1000 * STRENGTH_DISPLAY_SCALE, 15000 * STRENGTH_DISPLAY_SCALE)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3738c10a",
   "metadata": {
    "originalKey": "3738c10a"
   },
   "source": [
    "### Cost-Related Pareto Frontiers\n",
    "\n",
    "When `include_cost=True`, we visualize cost trade-offs:\n",
    "1. GWP vs Cost scatter on training data (shows correlation)\n",
    "2. Cost vs 1-day Strength Pareto frontier\n",
    "3. Cost vs 28-day Strength Pareto frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8c7f8d1",
   "metadata": {
    "originalKey": "c8c7f8d1"
   },
   "outputs": [],
   "source": [
    "if include_cost:\n",
    "    # GWP vs Cost scatter on training data\n",
    "    X_gwp_train, Y_gwp_train, _, _ = data.gwp_data\n",
    "    gwp_post = model.gwp_model.posterior(X_gwp_train)\n",
    "    gwp_vals = gwp_post.mean.squeeze(-1).detach()  # negated GWP\n",
    "    cost_post = model.cost_model.posterior(X_gwp_train)\n",
    "    cost_vals = cost_post.mean.squeeze(-1).detach()  # negated Cost\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(5, 4), dpi=150)\n",
    "    ax.scatter(-gwp_vals.numpy(), -cost_vals.numpy(), alpha=0.6, s=20)\n",
    "    # Correlation\n",
    "    corr = torch.corrcoef(torch.stack([gwp_vals, cost_vals]))[0, 1].item()\n",
    "    ax.set_xlabel(\"GWP (kg CO\\u2082/m\\u00b3)\")\n",
    "    ax.set_ylabel(\"Cost (USD/m\\u00b3)\")\n",
    "    ax.set_title(f\"GWP vs Cost (training data, r = {corr:.3f})\")\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"Skipping cost plots (include_cost=False).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6296819",
   "metadata": {
    "originalKey": "b6296819"
   },
   "outputs": [],
   "source": [
    "if include_cost:\n",
    "    # Cost vs Strength Pareto frontiers\n",
    "    cost_dim = model.output_index(\"Cost\")\n",
    "    cost_pareto_results = []\n",
    "    for day in model_strength_days:\n",
    "        strength_dim = model.output_index(f\"{day}-day Strength\")\n",
    "        pareto_dims = [cost_dim, strength_dim]\n",
    "        cost_pareto_results.append(\n",
    "            get_inferred_pareto_frontiers(\n",
    "                pareto_dims,\n",
    "                num_candidates=100 if SMOKE_TEST else 10000,\n",
    "            )\n",
    "        )\n",
    "\n",
    "    for i, day in enumerate(model_strength_days):\n",
    "        fig = plt.figure(dpi=200)\n",
    "        plt.title(f\"Inferred Pareto Frontier: Cost vs {day}-day Strength\")\n",
    "        plt.ylabel(f\"{day}-day strength ({strength_label()})\")\n",
    "        plt.xlabel(\"-Cost (USD/m\\u00b3)\")\n",
    "        Y_all, Ystd_all = cost_pareto_results[i]\n",
    "        for Y, Ystd, name, ls, color in zip(Y_all, Ystd_all, names, linestyles, colors):\n",
    "            plt.plot(Y[:, 0], Y[:, 1] * STRENGTH_DISPLAY_SCALE, label=name, linestyle=ls, color=color, lw=4)\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf59342",
   "metadata": {
    "originalKey": "ebf59342"
   },
   "source": [
    "### Per-Ingredient GWP Contribution Breakdown\n",
    "\n",
    "Shows the relative contribution of each ingredient to total GWP for a typical concrete mix, using the Material Source 0 emission factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "451ecbc9",
   "metadata": {
    "originalKey": "451ecbc9"
   },
   "outputs": [],
   "source": [
    "# GWP breakdown for a representative mix (median composition)\n",
    "from boxcrete.utils import DEFAULT_GWP_COEFFICIENTS\n",
    "\n",
    "# Use the median composition from training data\n",
    "X_gwp_data, _, _, _ = data.gwp_data\n",
    "median_comp = X_gwp_data.median(dim=0).values\n",
    "\n",
    "# Get column names (without Time)\n",
    "col_names = data.X_columns[:-1]\n",
    "\n",
    "# Source 0 coefficients\n",
    "gwp_coeffs = DEFAULT_GWP_COEFFICIENTS[0]\n",
    "\n",
    "# Compute per-ingredient GWP contributions\n",
    "ingredients = []\n",
    "contributions = []\n",
    "for i, col in enumerate(col_names):\n",
    "    if col in gwp_coeffs:\n",
    "        mean_coeff, _ = gwp_coeffs[col]\n",
    "        contrib = mean_coeff * median_comp[i].item()\n",
    "        ingredients.append(col.replace(\" (kg/m3)\", \"\").replace(\" (C)\", \"\"))\n",
    "        contributions.append(contrib)\n",
    "\n",
    "# Sort by contribution\n",
    "sorted_pairs = sorted(zip(contributions, ingredients), reverse=True)\n",
    "contributions, ingredients = zip(*sorted_pairs)\n",
    "total_gwp = sum(contributions)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4), dpi=150)\n",
    "bars = ax.barh(range(len(ingredients)), contributions, align=\"center\")\n",
    "ax.set_yticks(range(len(ingredients)))\n",
    "ax.set_yticklabels(ingredients, fontsize=9)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel(\"GWP Contribution (kg CO\\u2082/m\\u00b3)\")\n",
    "ax.set_title(f\"Per-Ingredient GWP Breakdown (Total: {total_gwp:.1f} kg CO\\u2082/m\\u00b3)\")\n",
    "\n",
    "# Add percentage labels\n",
    "for i, (contrib, bar) in enumerate(zip(contributions, bars)):\n",
    "    pct = 100 * contrib / total_gwp\n",
    "    ax.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height() / 2,\n",
    "            f\"{pct:.1f}%\", va=\"center\", fontsize=8)\n",
    "\n",
    "ax.grid(True, axis=\"x\", alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b35a9ff-9871-4cb2-a731-bb0d2d1d25a6",
   "metadata": {
    "originalKey": "9b35a9ff-9871-4cb2-a731-bb0d2d1d25a6"
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   "source": [
    "# Gradient-Based Experimental Design with qLogNEHVI\n",
    "\n",
    "The previous section used random sampling to approximate the Pareto frontier. Here, we use BoTorch's `qLogNoisyExpectedHypervolumeImprovement` acquisition function to find optimal next experiments via gradient-based optimization. This jointly proposes `q` candidate mixtures that maximize the expected improvement in the Pareto hypervolume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5d336ff4-2f54-4441-8a92-9f72eaa3690f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:36:12.702585Z",
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     "shell.execute_reply": "2026-03-18T21:37:57.043601Z"
    },
    "originalKey": "5d336ff4-2f54-4441-8a92-9f72eaa3690f"
   },
   "outputs": [],
   "source": [
    "from botorch.acquisition.multi_objective import qLogNoisyExpectedHypervolumeImprovement\n",
    "\n",
    "# Use the model_list for optimization (reduced space with fixed features)\n",
    "# X_baseline: the training data projected into the optimization space\n",
    "X_gwp = data.gwp_data[0]\n",
    "# Remove fixed-feature columns to get the optimization-space baseline\n",
    "fixed_indices_no_time = sorted(\n",
    "    k for k in (fixed_features or {}).keys()\n",
    "    if k != data.X_columns.index(\"Time\")\n",
    ")\n",
    "keep_mask = [i for i in range(X_gwp.shape[-1]) if i not in fixed_indices_no_time]\n",
    "X_baseline = X_gwp[:, keep_mask]\n",
    "\n",
    "acqf = qLogNoisyExpectedHypervolumeImprovement(\n",
    "    model=model_list,\n",
    "    ref_point=ref_point,\n",
    "    X_baseline=X_baseline,\n",
    "    prune_baseline=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "111d7984-8ae5-426f-9123-296982163e5c",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-03-18T21:45:29.402747Z"
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    "output": {
     "id": 26136393096039916,
     "loadingStatus": "loaded"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'acqf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[30]\u001b[39m\u001b[32m, line 17\u001b[39m\n",
      "\u001b[32m     11\u001b[39m     opt_equality_constraints, opt_inequality_constraints = get_constraints(\n",
      "\u001b[32m     12\u001b[39m         opt_columns\n",
      "\u001b[32m     13\u001b[39m     )\n",
      "\u001b[32m     15\u001b[39m q = \u001b[32m5\u001b[39m  \u001b[38;5;66;03m# number of candidate mixtures to propose jointly\u001b[39;00m\n",
      "\u001b[32m     16\u001b[39m X_next, acq_val = optimize_acqf(\n",
      "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     acq_function=acqf\u001b[49m,\n",
      "\u001b[32m     18\u001b[39m     q=q,\n",
      "\u001b[32m     19\u001b[39m     num_restarts=\u001b[32m5\u001b[39m,\n",
      "\u001b[32m     20\u001b[39m     raw_samples=\u001b[32m512\u001b[39m,\n",
      "\u001b[32m     21\u001b[39m     bounds=opt_bounds,\n",
      "\u001b[32m     22\u001b[39m     sequential=\u001b[38;5;28;01mTrue\u001b[39;00m,\n",
      "\u001b[32m     23\u001b[39m     equality_constraints=opt_equality_constraints,\n",
      "\u001b[32m     24\u001b[39m     inequality_constraints=opt_inequality_constraints,\n",
      "\u001b[32m     25\u001b[39m     options={\u001b[33m\"\u001b[39m\u001b[33mdisp\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mbatch_limit\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1\u001b[39m},\n",
      "\u001b[32m     26\u001b[39m )\n",
      "\u001b[32m     27\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAcquisition value: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00macq_val.item()\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m acq_val.dim() == \u001b[32m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAcquisition values: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00macq_val.tolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
      "\n",
      "\u001b[31mNameError\u001b[39m: name 'acqf' is not defined"
     ]
    },
    {
     "data": {
      "application/notebook-debug-button": "{\n\t\"notebookUri\": \"file:///data/users/sebastianament/github/SustainableConcrete/notebooks/prediction_and_optimization_tutorial.ipynb\"\n}"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from botorch.optim.optimize import optimize_acqf\n",
    "\n",
    "# Recompute constraints in the optimization (reduced) space to match opt_bounds.\n",
    "# Cell 12 computes constraints in the full (minus Time) space, which may not\n",
    "# align with the reduced space when features are fixed.\n",
    "if optimization_mode == \"mortar\":\n",
    "    opt_equality_constraints, opt_inequality_constraints = get_constraints(\n",
    "        opt_columns, **MORTAR_CONSTRAINTS\n",
    "    )\n",
    "else:\n",
    "    opt_equality_constraints, opt_inequality_constraints = get_constraints(\n",
    "        opt_columns\n",
    "    )\n",
    "\n",
    "q = 5  # number of candidate mixtures to propose jointly\n",
    "X_next, acq_val = optimize_acqf(\n",
    "    acq_function=acqf,\n",
    "    q=q,\n",
    "    num_restarts=5,\n",
    "    raw_samples=64 if SMOKE_TEST else 512,\n",
    "    bounds=opt_bounds,\n",
    "    sequential=True,\n",
    "    equality_constraints=opt_equality_constraints,\n",
    "    inequality_constraints=opt_inequality_constraints,\n",
    "    options={\"disp\": True, \"batch_limit\": 1},\n",
    ")\n",
    "print(f\"Acquisition value: {acq_val.item():.4f}\" if acq_val.dim() == 0 else f\"Acquisition values: {acq_val.tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2104a71-dbb9-421a-8895-fa5f7b457365",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-03-18T21:45:29.422096Z",
     "shell.execute_reply": "2026-03-18T21:45:29.421698Z"
    },
    "originalKey": "e2104a71-dbb9-421a-8895-fa5f7b457365"
   },
   "outputs": [],
   "source": [
    "# Show proposed candidates with predicted objectives\n",
    "X_next_df = pd.DataFrame(X_next.detach().numpy(), columns=opt_columns)\n",
    "\n",
    "# Add predicted objectives using name-based indexing\n",
    "preds = model_list.posterior(X_next).mean.detach()\n",
    "X_next_df[\"-GWP\"] = preds[:, model.output_index(\"GWP\")].numpy()\n",
    "for day in model_strength_days:\n",
    "    idx = model.output_index(f\"{day}-day Strength\")\n",
    "    X_next_df[f\"Day-{day} Strength ({strength_label()})\"] = (preds[:, idx] * STRENGTH_DISPLAY_SCALE).numpy()\n",
    "if include_cost:\n",
    "    X_next_df[\"-Cost ($/m\\u00b3)\"] = preds[:, model.output_index(\"Cost\")].numpy()\n",
    "\n",
    "# Add water/binder ratio\n",
    "binder_cols = [c for c in opt_columns if c in [\"Cement (kg/m3)\", \"Fly Ash (kg/m3)\", \"Slag (kg/m3)\"]]\n",
    "X_next_df[\"W/B Ratio\"] = X_next_df.apply(\n",
    "    lambda row: row[\"Water (kg/m3)\"] / max(sum(row[c] for c in binder_cols), 1e-6),\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "pd.options.display.float_format = '{:.1f}'.format\n",
    "X_next_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a2e23ad-b796-4853-91a9-6fa1abbe901b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-18T21:45:29.423288Z",
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     "iopub.status.idle": "2026-03-18T21:45:30.719154Z",
     "shell.execute_reply": "2026-03-18T21:45:30.718382Z"
    },
    "originalKey": "8a2e23ad-b796-4853-91a9-6fa1abbe901b"
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   "outputs": [],
   "source": [
    "# Plot predicted strength curves for each proposed candidate\n",
    "from boxcrete.plotting import plot_strength_curve\n",
    "\n",
    "for i in range(q):\n",
    "    # Map back to full composition space for plotting\n",
    "    full_comp = torch.zeros(1, len(data.X_columns) - 1)\n",
    "    free_idx = 0\n",
    "    for j in range(len(data.X_columns) - 1):  # exclude Time\n",
    "        if j in fixed_indices_no_time:\n",
    "            full_comp[0, j] = fixed_features[j]\n",
    "        else:\n",
    "            full_comp[0, j] = X_next[i, free_idx]\n",
    "            free_idx += 1\n",
    "    plot_strength_curve(model, full_comp, title=f\"Proposed Mix {i+1}\")\n",
    "    plt.show()"
   ]
  }
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