diff --git a/docs/user_guide/00_liquid-handling/_liquid-handling.rst b/docs/user_guide/00_liquid-handling/_liquid-handling.rst
index 32ea5d7d564..3de4229f65b 100644
--- a/docs/user_guide/00_liquid-handling/_liquid-handling.rst
+++ b/docs/user_guide/00_liquid-handling/_liquid-handling.rst
@@ -24,3 +24,4 @@ Examples:
    moving-channels-around
    tutorial_tip_inventory_consolidation
    mixing
+   container_no_go_zones
diff --git a/docs/user_guide/00_liquid-handling/container_no_go_zones.ipynb b/docs/user_guide/00_liquid-handling/container_no_go_zones.ipynb
new file mode 100644
index 00000000000..ec226e8d4da
--- /dev/null
+++ b/docs/user_guide/00_liquid-handling/container_no_go_zones.ipynb
@@ -0,0 +1,713 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "h4mxa2wu6t9",
+   "metadata": {},
+   "source": [
+    "# Container No-Go Zones\n",
+    "\n",
+    "> **Mode:** Simulation (`STARChatterboxBackend`) - no hardware required.\n",
+    ">\n",
+    "> **Topic:** Defining obstructed regions and automatic channel avoidance using `Container` no-go zones.\n",
+    ">\n",
+    "> **Extra dependencies** (not included in pylabrobot):\n",
+    ">   - `matplotlib` - used for cross-section visualizations in this tutorial only\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## What are no-go zones?\n",
+    "\n",
+    "Some containers have internal obstructions - divider walls, support beams, baffles - where pipette tips physically cannot go. When multiple channels target the same container (e.g. aspirating from a trough with 4 channels), `LiquidHandler` needs to know where these obstructions are to position tips safely.\n",
+    "\n",
+    "A **no-go zone** is a cuboid region inside a container, defined as a `Tuple[Coordinate, Coordinate]` - the front-left-bottom and back-right-top corners, relative to the container's origin.\n",
+    "\n",
+    "## How it works\n",
+    "\n",
+    "When `LiquidHandler.aspirate` or `.dispense` targets a single container with multiple channels:\n",
+    "\n",
+    "1. The container's Y axis is split into **compartments** (free space between no-go zones)\n",
+    "2. Each compartment is shrunk by `edge_clearance` (default 2mm) from each boundary\n",
+    "3. Channels are distributed across compartments **center-out, then back-first**\n",
+    "4. Each channel group is centered within its compartment\n",
+    "\n",
+    "```{note}\n",
+    "Single-channel operations are unaffected - the tip goes to the container center as usual.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "v15b2poml7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports and visualization helper (collapse this cell in Jupyter via View → Collapse)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "from pylabrobot.resources.container import Container\n",
+    "from pylabrobot.resources.coordinate import Coordinate\n",
+    "from pylabrobot.liquid_handling.utils import (\n",
+    "    _get_compartments,\n",
+    "    compute_channel_offsets,\n",
+    "    MIN_SPACING_EDGE,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "uan4wnlungq",
+   "metadata": {},
+   "outputs": [],
+   "source": "MIN_CHANNEL_SPACING = 9.0  # mm, minimum center-to-center distance between adjacent channels\nCHANNEL_DIAMETER = 9.0  # mm, minimum center-to-center spacing between adjacent channels\n\ndef plot_container_cross_section(container, num_channels_list):\n    \"\"\"Plot a Y-axis cross section of a container showing no-go zones, compartments, and channels.\n\n    Args:\n        container: Container with no_go_zones defined.\n        num_channels_list: List of channel counts to show (one subplot per count).\n    \"\"\"\n    n_plots = len(num_channels_list)\n    fig, axes = plt.subplots(1, n_plots, figsize=(2 * n_plots, 5), squeeze=False)\n    axes = axes[0]\n\n    size_y = container.get_absolute_size_y()\n    size_x = container.get_absolute_size_x()\n    center_y = size_y / 2\n    compartments = _get_compartments(container)\n    channel_radius = CHANNEL_DIAMETER / 2\n    label_bbox = dict(facecolor=\"white\", alpha=0.5, edgecolor=\"none\", pad=0.3)\n\n    for ax, n_ch in zip(axes, num_channels_list):\n        # Container outline (Y on vertical axis, X on horizontal)\n        ax.add_patch(plt.Rectangle((0, 0), size_x, size_y, fill=False,\n                                    edgecolor=\"black\", linewidth=2))\n\n        # No-go zones (red)\n        for flb, brt in container.no_go_zones:\n            ax.add_patch(plt.Rectangle((0, flb.y), size_x, brt.y - flb.y,\n                                        facecolor=\"red\", alpha=0.3, edgecolor=\"red\", linewidth=1))\n\n        # Free compartments (green)\n        for comp_lo, comp_hi in compartments:\n            ax.add_patch(plt.Rectangle((0, comp_lo), size_x, comp_hi - comp_lo,\n                                        facecolor=\"green\", alpha=0.1, edgecolor=\"green\",\n                                        linewidth=1, linestyle=\"--\"))\n\n        # Channel positions\n        try:\n            offsets = compute_channel_offsets(container, n_ch)\n            for i, o in enumerate(offsets):\n                tip_y = center_y + o.y\n                ax.add_patch(plt.Circle(\n                    (size_x / 2, tip_y), channel_radius,\n                    facecolor=\"none\", edgecolor=\"navy\", linewidth=1, linestyle=\":\"))\n                ax.plot(size_x / 2, tip_y, \"o\", color=\"navy\", markersize=4, zorder=5)\n                ax.text(size_x + 1.5, tip_y, f\"ch{i}\", ha=\"left\", va=\"center\",\n                        fontsize=6, color=\"navy\", bbox=label_bbox, zorder=6)\n        except ValueError:\n            ax.text(size_x / 2, size_y / 2, \"Cannot fit!\", ha=\"center\", va=\"center\",\n                    fontsize=12, color=\"red\", fontweight=\"bold\")\n\n        ax.set_xlim(-2, size_x + 8)\n        ax.set_ylim(-2, size_y + 2)\n        ax.set_xlabel(\"X (mm)\")\n        if ax == axes[0]:\n            ax.set_ylabel(\"Y (mm)\")\n        else:\n            ax.set_yticklabels([])\n            ax.set_ylabel(\"\")\n        ax.set_title(f\"{n_ch} channel{'s' if n_ch != 1 else ''}\")\n        ax.set_aspect(\"equal\")\n\n    # Legend\n    legend_handles = [\n        mpatches.Patch(facecolor=\"red\", alpha=0.3, edgecolor=\"red\", label=\"No-go zone\"),\n        mpatches.Patch(facecolor=\"green\", alpha=0.1, edgecolor=\"green\", label=\"Free compartment\"),\n        Line2D([0], [0], marker=\"o\", color=\"w\", markerfacecolor=\"none\", markeredgecolor=\"navy\",\n               markersize=10, linestyle=\"None\", label=\"Tip diameter\"),\n    ]\n    fig.legend(handles=legend_handles, loc=\"lower center\", ncol=3, fontsize=8)\n\n    name = container.name\n    model = container.model or \"\"\n    fig.suptitle(f\"{name} ({model})\\nsize_y={size_y}mm, {len(container.no_go_zones)} no-go zone(s)\",\n                 fontsize=11, fontweight=\"bold\")\n    fig.tight_layout(rect=[0, 0.06, 1, 0.92])\n    fig.subplots_adjust(wspace=-0.1)\n    plt.show()"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nhdap5csut",
+   "metadata": {},
+   "source": [
+    "## Real-world examples: Hamilton troughs\n",
+    "\n",
+    "The Hamilton trough family has internal support structures that create no-go zones. These were measured from physical troughs using calipers (top of beams) and visual inspection through the transparent walls (base of tapered beams).\n",
+    "\n",
+    "### 60 mL trough - 1 center divider\n",
+    "\n",
+    "The `hamilton_1_trough_60mL_Vb` has a single center support wall (~1.2mm wide), creating 2 compartments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72t56n0fkzm",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container: trough_60mL, size_y=90.0mm\n",
+      "No-go zones: [(Coordinate(x=0, y=44.4, z=5.0), Coordinate(x=19.0, y=45.6, z=60.25))]\n",
+      "Compartments: [(2.0, 42.4), (47.6, 88.0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_60mL_Vb\n",
+    "\n",
+    "trough_60 = hamilton_1_trough_60mL_Vb(name=\"trough_60mL\")\n",
+    "\n",
+    "print(f\"Container: {trough_60.name}, size_y={trough_60.get_absolute_size_y()}mm\")\n",
+    "print(f\"No-go zones: {trough_60.no_go_zones}\")\n",
+    "print(f\"Compartments: {_get_compartments(trough_60)}\")\n",
+    "\n",
+    "plot_container_cross_section(trough_60, [1, 2, 3, 4, 5, 6, 7, 8, 9, 16])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "shj0sw6dkcg",
+   "metadata": {},
+   "source": [
+    "### 120 mL trough - 3 tapered support beams\n",
+    "\n",
+    "The `hamilton_1_trough_120mL_Vb` has 3 internal support beams (~2.5mm wide at the base, ~0.8mm at the top), creating 4 compartments. The no-go zones use the base width since that is the worst case for pipette tip clearance.\n",
+    "\n",
+    "Dimensional breakdown (from outer y=0):\n",
+    "\n",
+    "| Element | Y range (mm) | Size (mm) | Modelled as |\n",
+    "|---|---|---|---|\n",
+    "| Back wall | 141.1 - 142.5 | 1.4 | Not modelled (included in `size_y`) |\n",
+    "| Compartment 3 | 109.8 - 141.1 | 31.3 | Free space |\n",
+    "| Beam 3 | 107.3 - 109.8 | 2.5 | `no_go_zones[2]` |\n",
+    "| Compartment 2 | 76.0 - 107.3 | 31.3 | Free space |\n",
+    "| Beam 2 | 73.5 - 76.0 | 2.5 | `no_go_zones[1]` |\n",
+    "| Compartment 1 | 42.2 - 73.5 | 31.3 | Free space |\n",
+    "| Beam 1 | 39.7 - 42.2 | 2.5 | `no_go_zones[0]` |\n",
+    "| Compartment 0 | 1.2 - 39.7 | 38.5 | Free space |\n",
+    "| Front wall | 0 - 1.2 | 1.2 | Not modelled (included in `size_y`) |\n",
+    "\n",
+    "```{note}\n",
+    "size_y=142.5` is the **outer** dimension of the trough.\n",
+    "The front and back walls are not modelled as no-go zones - they are accounted for by the `edge_clearance` parameter during channel positioning.\n",
+    "\n",
+    "The internal beams *are* modelled as no-go zones because they obstruct pipette access within the container's interior.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "20hbb649kmc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container: trough_120mL, size_y=142.5mm\n",
+      "No-go zones: [(Coordinate(x=0, y=39.7, z=12.0), Coordinate(x=19.0, y=42.2, z=70.0)), (Coordinate(x=0, y=73.5, z=12.0), Coordinate(x=19.0, y=76.0, z=70.0)), (Coordinate(x=0, y=107.3, z=12.0), Coordinate(x=19.0, y=109.8, z=70.0))]\n",
+      "Compartments: [(2.0, 37.7), (44.2, 71.5), (78.0, 105.3), (111.8, 140.5)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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vJdpMX9uZ5kRERERElPpYaCUiIiIiclIo9mmzr7CuIIqnetYtLFGihIwaNco6Y7Ns2bKqVSlmdwLa6mrrjKLoiIIAjBw5UrUtfeaZZ+LMusPMNq0IgkJJQECAWl9Su03tMo56HlBQePHFF62nvf766+o024IoCnNaC9Vt27apdRKxTdrPhg0b1Hl4bN9//706DWszolUq1i1FcQuz4lB8dYSvvvpKPd+2P9qalPY0b95cHeIxoQVwUnTo0MH6++DBg9XfGgUcPDdpBW1+y5Qpo35HUQktXPG30FrINm7cWJ03b948VSjH31grWqKgGLtFbErNPkSe0GZZg0EKOM1eG+n4IEeAAiBec3hcv/76a6LXe+WVV6yzHJFNbY1WvNbQFhenaYMnevbsab2P4sWLq+cDeY8NxTmtQIfsY+YjZlPavk4d8ZrdsmWL9XmzXScWxWKchvwBthlrKWvwnmX7etXaV2uPWVtT9eeff1YDJVBwhWrVqslLL70kjhD79Wq7rqse9tZiTajQir8RzsdrZPTo0db3q3fffTfG5bTnVXuPICIiIiKitMNCKxERERGRk8LMNRQVsA6n0XaXn376qZrhWbduXTULEAVEzIhDkUObuQkoWPzzzz+qAIP1GDE7aubMmVKnTp04tzlo0CBVfMJ24bIoBNmuO4jCa3rRt29fWbJkifj7+6vnBwVmzARGQUor9KU1tEPV/g5Y4zEpUGjDDGVkCH+zevXqyapVq9RaoGkJ63GiKBm7oI+cr1y5UhXRUHhEERbFYcxiRRtk22K7M8L2DRw40O5M6oTg74Hi2auvvqqKp2jfi2Jr69atZd26dTEKwE8//bSa9VqwYEH1/GDQxKJFi+zeLl6fyLq3t7faJqx/avscppfXLAqvmPmKGfTa84PHj8eCmb+2a8k6E8xotV1zF+/HtgXk2PD3njp1qpQqVUr9bWvWrKler5hlr0E7Ya2wPnToUAc/AiIiIiIiSoxLtN7+Y0REREREZGo3btyQCxcuWIt/aP35xhtvqNl2Wkth2xmUlHxYuxKFMhTEMfsWRWEivXbt2qUGRuTLl8/ahhptitFOHGsAo2iHGbOUfqDF8ltvvaUGYqAVtDbTmYiIiIiI0gY/kRMRERERkS4o0mBGFmbHYeYrZuFpRVbM3NLWI6SUg9ajaI2K9S2nTJmS1ptD6QzWY0ULY7TsxSFmNqPIiuIcZqezyJq+hIeHy7fffqt+/+STT1hkJSIiIiJyAvxUTkREREREumCNxa5du6qWplgzE+070f71m2++UW1dja73iDa9tmsyxv5BC02zw/Owb98+QSMirNNLxpw/fz7BjH3wwQe6byuh2xkyZIg4I7T3RrtorPN7+/Zt6zq0WFvZ3vqhicHjTOh5MIO0fA7QThizkPF+gHWFiYiIiIgo7Rlb6ImIiIiIiExdaMU6iUSUPvTq1Uv9EBERERERkWNwjVYiIiIiIiIiIiIiIiIiIoPYOpiIiIiIiIiIiIiIiIiIyCAWWomIiIiIyOk0adJErXdYrFgxyWhWr16t1scsXLiwdV1HrKOZkEePHknJkiWtl+/Tp0+M86dNmybdu3eXEiVKiLe3t1qLs1GjRmrt3OSuZYo1edOjqKgoGTp0qFSqVEk9BqwpnCdPHmnXrp1aH5icW2hoqBQoUEBlcMmSJYauW6tWLXU9rB9NRERERETkSCy0EhERERERpaKlS5fKnDlz5PLly7qvM378eDl79my853/66adq/dxz586pAtXt27dl8+bN0rVrV/niiy/EjFBonTp1qhw5ckSCgoIkIiJCbt26JcuXL5cWLVrI1q1b03oTKQHfffedXLt2TSpWrCjt27c3dN0333zT+roICQlx0BYSERERERGx0EpERERERE4IMw6jo6PVTMuMBrPtUDjduHGjrsufOXNGPvvsMzVTNT6+vr7ywQcfyOnTp1Vh6fvvv7ee98knn6gio14o1uK5137u3bsn6ZGrq6t6Tg4dOiQPHjyQS5cuSadOndR5kZGR8tdff6X1JlI8kFcUWgGzkjE71YguXbqo1wQK6zNmzHDQVhIREREREbHQSkREREREqWzPnj3SoUMHyZcvn3h6eqrDpk2byq+//ppg6+D4Wtvix7YV7Nq1a6V169aSI0cOdftly5aVjz/+WB4/fqxr+wYOHKhu08vLS4KDg62n792713p/EyZMSPLjHzBggIwaNUq19tXjxRdfVK2D33vvvXgvs2bNGhkzZoxqL4yC7IgRI1TLXMBsThScUgr+JngO8DeaNWuWlCpVSt0nZs9iJu2BAwekYcOG6rSqVavKunXrYlxfew6HDBmiWrsWKlRIsmbNKk899ZSajYvLV69eXXx8fMTf31/279+f5EIrnhM8D/hb4n6efvpp6/loJZwQzIbVthWzhYcNG6YyhfbDzz77rNpWWydOnJB+/fqpPGfKlEkKFiyornPx4kXdM3Dff/99dX089l69esmOHTus24Cise1lJ06cqJ4nPDY813Xq1FHbnBjttWXvx/Y+9Dwe25bT2HYU9dESO1u2bOo1GHugBF5PyH6ZMmXUaxPPZ8eOHdVry9bKlSvVbFbo0aNHjPMwG7xu3bqSM2dOyZw5sxQpUkQ6d+4smzZtsl4G24vbBT3PCRERERERUZJFExERERERpZKQkJBoPz+/aHwVif3Tvn176+UCAgLUaUWLFrWeZu862s+GDRvUZX7//fdoFxeXeG8/Kioq0W3ctm2b9To//vij9fTRo0er09zd3aOvXbuWIs+Hdj9169a1e/78+fPV+c2bN48+d+6c9fK9e/dO9LZLlSqlLps5c+bosLCwBC9re9u5c+dWjzF//vzRQ4YMib58+XKMy+JvgsvlyJEjznPdqFGj6Jw5c8Y4zcfHJ/rOnTtxHrO9HLRp0yba09Mzxmm4v/Dw8OjkwN/9woUL0R07dlS36eXlFX3gwIEEr4Msadvg6+sbZ1uRB83+/fvV47SXOzyfeH4T88EHH8S5boECBay/jxkzxnrZAQMGxPtaeOONNxK8H+21Ze8H22Dk8djmxt5z5O/vb73f+/fvR1euXNnubeJvvmnTJutlX331VXU6Mmhr69at8b6+v/jiixiXnTRpkjrdzc0tOigoKNHnn4iIiIiIKCk4o5WIiIiIiFLN8ePH5c6dO+r3efPmSXh4uFqr9J9//rHOQIuPbTvbVatWWWckYoZe/fr1VcvcV155RZ3ftm1b1SoWsw6xTqO2NuqyZcsS3cZ69epJjRo11O9Tpkyxno7tBczUwyy/2LMeE/pJSgtkbPurr76qZuf98MMPhq47ffp01UZYm0GL29ALs1/RuhUzCvH4MHvQ3ozYwMBA1aIYrYXx/ANmFRYtWlRdF7NVAX8XrIsa2927d2XhwoXqtosXL65OW7FihTRr1kxlZOTIkeq0CxcuyM6dOyWp/ve//6nZrdiuxYsXq1mUyEKVKlV03wZmTx4+fFi1cdb+9n///bf1fPydtLVA//zzTzWLWJv1jMf3zjvvJHj7tpfPnTu37N69W65fv65mfsaGltP4+wKed7x+Tp06JeXKlVOn4XYwGzWxttz4wWsEM1UBjwttepP6eB4+fCiLFi1Sf1esgQtYB/fKlSvqd+QBbZzd3NxkwYIFapb2yZMn1YzosLAwdZ8abYYr1me1hdvDdmMGNP4WuA3kHLPhK1euHOOy2nG0iU7qrGgiIiIiIqLEsNBKRERERESpBkUdFFoAxcOvv/5aFUEaNGig2rHqgda03bt3V62AUVxCW1cUElGEQUEIUNhDC1O0VX377bet143dxjY+L7zwgjpEge/IkSNy8OBBVRQCtLxNDWh3jCLja6+9ptof64UiotYiF8WmL7/8MtHroPXsuHHjVDERBd6jR4+qtr2AQpm9Qi9a8eJ5wlqYtm2Q0bYYRTsUuzX22ufi9tHyNVeuXFK7dm3r6a+//roqbKKgndD1kwoFYqzhiRbWemGbUPQrUaKENG7cOMY24fnS1tvF+rsobKN1LgrFeI60AnJCkC+tsIl81axZU/LmzSvvvvtunMvaFq1R8MRrCsVK5AS0gQiJwWsFfyP8ffH3X7JkiWrDm9THg78lBkugkN2tWzfr6drzpA1yQOETbabR9heFZG1AAIrLWqvumzdvqkM/P78Y94FiOeC5+vDDD+WXX35Rheb+/fvHyAsgV5obN24k+nwQERERERElBQutRERERESUalCA++6771RxDkVPrNeordc6duzYRK+PGXjt2rWT+/fvq7UyUbxBYQf0rEOK2XZ69O3b13q7v/32m8ydO1f9jgKg7cxbFMVsZ9rG92O71qweKHahQIrnCc8PitEofmowixSnYRahLcwURJELMwQrVKggq1evVkWyxGAW5VtvvaWKiVmyZJHy5cvHWId2165dca6jFb0A19GgWAe2s2ixPbHZPidJub5eP/74oyruITsvv/yytchoux5pYkqXLm39HQVCwGxsrXCL2wcU9zWYyawVJpE7XAb3aW+289WrV63X064T+3cN1sHV2N6f7e+JvRYwSAE5QWEdAx+w1i6Ku0YfT2LPke3fTs/rE/edEGwz1onFNv/xxx9q/WLMaM+fP78aYGAL69gSERERERE5GgutRERERESUqp5//nlVdMFs0RkzZkj79u1Vq1oUWjE7LbEZeChKYaYq2sBqLWe1YqEGszPtFTxtWwEnBIU/FHS01qlz5sxRv/fp00c8PT2tl3NU62AU8fCDx4zZvtWrV1fPk2blypXqNNsWsbNnz5ZevXqp6+G8f//9V82K1MNeUQrbbe93jbu7u93biu/0lL6+EWgdjCKhbTEf7Xb10tpU23suUJDXZmnb5hd5047bXsaeAgUKWH+3LbqiOByb7UxN2/uzvaztZexBtrXZ3Wjpazt4IKmPJ6HnyPb16ePjozIa+7WJDGrFewyiAK3NuO3fEQMfUGxGC+Sff/5ZDQrAwAO0Dbdle129rwMiIiIiIiKjWGglIiIiIqJUgxaemDmJNRjRhhUtgLUWtSi22M7Ws4XCDNqNoo0vii0o0NapUyfGZXA7mAEKX331laxfv17NpkMbUhRK0fIVrXj1eu6551TBCNukFTRTom0wZqviNm0fKwrN2mmYbWjUtGnTVItX3A7W7cRjj6/YhpmkeFyYCahBi9o33nhDraGJ5xpr6WqtaAHFXmdhb/vtmTlzpkycOFG1fMZanljz9KOPPrKej/ylBBT9tdbJmPmL+8WMaxQwtcJkmzZt1CFmtNqb7Yz1YrHuqPa3RCth5PaTTz6Jc3/abQHWH0br37Nnz6rMA56bVq1axbu9aDesrfGKdVHR6jmpj8cIrZU02v6i5TTeC/B32bdvn4wePTpGoVRbIxmvd1vINR4nHjNm4Pbs2dM6kzb2jFlkGVAQrlatmuHtJSIiIiIi0oOFViIiIiIiSjVodfvZZ59JvXr1VCEQLUZR+NHalKLdrT3btm1TRRbAzDcUXW1njGJ2G2bKYc1XHEfRpVmzZur2MZutd+/esmnTJkPbWrJkyRgFJWyb7VqiSW0d/Pnnn6vZfbYzcLFeqHbali1bJHv27HFu49y5c9bL4/HgNK2A9P7771tbueK5wvVjPz+JFX/RKhgFP8zYxSxBrHkLWAdXW7M2PUGBFa1lsb4tZiijvay2Xi1yYW/906RC8Q/rnALWC9XWNAXk3F7B1BYGCGAdWLh27ZpUrVpV5dZ2xrI2SzQgIEC1tgZkBTN1kdVjx45Zi6f4m8UHxVmN9nrRfrR2ysl9PPagkIo1gwFrq6JdOP4uKKqOHz/eur4yNG/e3Ppc2M4G19YsRotrFKYxs3bRokV2i79afvGa1dM+m4iIiIiIKClYaCUiIiIiolTj5+cnL730kmptiyIJ2o0WLFhQ+vXrJ2vWrImxLqctFBX1GDp0qFqXtHXr1ur2cXtY8xPruqK4Y9uiVW+bY83gwYMlo0LBGMVUFLBQ9MPzVqpUKVX8Q+HWWQpVKCajTSwkNksRM147d+6s/v4orOIxoeA9aNAg1bZam0mdEpBnzP5EARxtb9H+GIVdZAan27a4jg8Kv++99566PmaVYjDBTz/9ZD0f6wNrMCMVM0xRkMVjQ8GyVq1aqq2uVkxO68cTGwqjmzdvVusylylTRv09MCAAxX0Uh7VCrlY01V6r2vrIWtEUfz/MYsXACjxuLad4fWswk33JkiXW9wQiIiIiIiJHcYnWu8eCiIiIiIjIZH799VcZPny4KgphZh2KTZR2tm/frlojo3h6+PBha7vdjADtfzHjG8VuCA4OlqeeespaaEQ7YW1GqBlglitaCmMmOf7W9tZ9jc9ff/2l1lPGDHE8ryjKEhEREREROQJntBIREREREcWCtT3RjvWZZ55RxzGTj0XWtIfZyoCZnhmpyAqYZVupUiU1exhttNGiVyuyYma1mYqsgJnveM0dPXpUli5daui6X3zxhTpEW3IWWYmIiIiIyJE4o5WIiIiIiEwFbXL/+OOPeM/HVySsVTl27FjVRrdDhw6qsKetWUnkCJixira6+/btkzt37qi8oa0uZrVm5LbVRERERERE6RkLrUREREREZCp6Cq1ERERERERERIlhoZWIiIiIiIiIiIiIiIiIyCCu0UpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC61ERERERERERERERERERAax0EpEREREREREREREREREZBALrUREREREREREREREREREBrHQSkRERERERERERERERERkEAutREREREREREREREREREQGsdBKRERERERERERERERERGQQC63J8MEHH4iLi4vcvn1bMtLjIbKHeSczY/7JzIYMGSI+Pj6SkR5PsWLF0nozKJ1g/snMmH8yM+afzIz5JzNj/oniwj7EESNGSEZ6PB988EGK3maGLbSGhITImDFjpE2bNpIzZ0715E2dOjWtN4vIIXbt2qXe7CpWrCje3t5SpEgR6dWrl5w8eTKtN43I4Y4cOSI9e/aUEiVKiJeXl+TKlUsaN24sixcvTutNI0p1n3zyifrMU6lSpbTeFCKH27Bhg8q7vZ/t27en9eYRpYq9e/dKp06d1HdefA7C+/93332X1ptF5PCdxvG9/+PnypUrab2JRA516tQp6dOnjxQqVEi995crV04+/PBDCQ0NTetNI3K4PXv2qP392bJlk6xZs0qrVq1k//79ab1ZRGla24qKipLJkydLtWrVJEuWLOLn5yfNmjWTAwcOpOp2m5m7ZFCYdYQPGSg4Va1aVe2IIcqoPvvsM9myZYsqNlWpUkWuX78uEydOlBo1aqgdjdzhThnZhQsX5P79+zJ48GApUKCA+nI5b948tdPxp59+kmeeeSatN5EoVVy+fFk+/fRTNeCGyExeeuklqV27dozTSpUqlWbbQ5RaVq1aJR07dpTq1avLe++9p2ZfnDlzRv1/QJSRPfvss9KiRYsYp0VHR8v//vc/NWunYMGCabZtRI526dIlqVOnjvj6+qoB99gBv23bNrVDHgWof/75J603kcihA8waNmwohQsXVplHcWnSpEkSEBAgO3fulLJly6b1JhKlSW1r2LBhMmPGDBk0aJD6v+HBgweyb98+uXnzZqpts9ll2EJr/vz55dq1a5IvXz7ZvXt3nJ0vRBnJyJEjZebMmZIpUybrab1795bKlSvL+PHjZfr06Wm6fUSO1K5dO/VjCx8qatasKV999RULrWQar7/+utSrV08iIyMzTJtrIj0aNWokPXr0SOvNIEpVwcHBakdK+/btZe7cueLqmmGbVRHFUb9+ffVja/PmzWrAZf/+/dNsu4hSw59//in37t1TmUdXM8B3XhScpk2bJoGBgZIjR4603kwih8DAMszWw+ACzNiDAQMGSJkyZeTtt99Wg+6JzFbbmjNnjvzxxx8yf/586dq1a6puJ/0nw34b8/T0VEFMjuPHj6v2q7lz51Zv4hgV884778S5HD7goHVN9uzZ1YiyoUOHxmnX8fvvv6vp2nny5FHbVqFCBTWdOzaMvuzQoYP6wIQRapkzZ1btMPFhyRamimPKOGYxosiGbcQMFryYbt26Fed2ly9frnZC4TJoq4Av5Gi3SRmDv79/jCIrlC5dWn3oPnbsmK7bYN5FVq9erUbG4bFhRgCeA3xQo/THzc1NjXBEXvVg/pn/9G7jxo1qR/s333xj+Lo7duxQgxWwQwa5QWeEb7/9Ns7l0IavS5cuKh/IIQq7KOramjBhgvo/CV968VrCgAdsV3zreyxcuFB1XcBrBf9nrVixwu56wqdPn070tQcYWIT7xH1jdD9aqmHUf2Jmz56trofXDFpQYaCSveeAnBc6G0RERBi+HvPP/KdXGGR548YN1TIeRVaMWsdOdiOYf+Y/o70mkJl+/frpujzzz/yn54E2kDdv3jg75fH/Qex9Q/Yw/8x/erVp0ybV0UArsmrZx4zWJUuWqHariWH+mf+MVtvCJBPsV8R+QnwfwPcCI3Ad/P2RA+ybRN7RshgF3tgSy/CFCxfk+eefV/sUtRbG6MB5/vz5JO/r1Lv/VNtv+8orr6h9wthGdLpCJ9DEvidhfwKuh/vC9bBPt2XLlmoWvZi90JpcBw8elLp168q6detk+PDhKmx4c7W35h92zuOPMW7cOPU7gjJ27NgYl8FO9qJFi6qd1l9++aX6YyN0P/zwQ5zbw5spRuXjj4nL4o0fb672dpS/+OKLqtc22iU899xzavtiL0yM0W7Y0Y7/GBAsjP45evSo2qEeO+SUcaB1Ena+YL3KxDDvlnU+8aYdFhamWjNgW9B6Fm/4lD7ggwRm8aFl3tdff60Kjs2bN0/0esw/85/e4cse8vH000+rD8ZGoMCONY2Rk5dffln97Zs2baq+pMa+j9atW6sPyfgyiS+yuOzPP/8c43J4/aCFJXKENsbu7u7qQ/XSpUvj3Dc+JOO1gS+Dn3/+uTx69Ei6d+8ud+7cSdJrD8UGzO7CQCN80cCH5LVr16rHl9CgCzwHffv2Va8/vG7QCaJJkybMfzqCHQ/YQYAvXMivvS+E9jD/zH96tmbNGpV77ATEjgz834/j+IyAPCWG+Wf+M5LHjx+r2RzY2Y0dZIlh/pn/9Ax/J3jqqafUupQoqvz111/qeyiWU0hsGRHmn/lPz7DPAsWb2LBWcXh4uBw+fDjB6zP/zH9GHHyDttmY8Yr9kCjM43sBCpH4bKQH/j/RipPIxFtvvaW+W2M5QqMZ3rVrl2zdulVd5rvvvlPLOiCXyJi9wQJ69nXq3X+K28drFQMQ8NrA/Tdo0EBGjx6tirkJwXbi/1E8HrQjx8AKvNfoncCmRJvArl27ovFQf//9d93Xady4cXTWrFmjL1y4EOP0qKgo6+9jxoxRtzts2LAYl+natWu0n59fjNNCQ0Pj3Efr1q2jS5QoEeO0okWLqtvcuHGj9bSbN29Ge3p6Rr/22mvW0/BYcLkWLVrE2KZXX3012s3NLfrevXvq+P3796OzZ88ePXz48Bj3c/369WhfX98Yp2uPhzKGP//8U/09f/vtt0Qvy7xHR3/99dfq+K1btxJ8rsh5Pfvss+pviB9XV9foHj16RN+9ezfR6zH/zH96N3HiRPU3Rn4gICAgumLFioleLyIiIrp48eIqi4GBgTHOs83a4MGDVT4+/PDDGJepXr16dM2aNRPMf3h4eHSlSpWimzVrFuN03F6mTJmiT58+bT3twIED6vTvv//e8Gvv/Pnz6vXwySefxLjcoUOHot3d3WOcjseDx6x5+eWXo7Nly6aeD0pftmzZEt29e3f1Weeff/6JHjdunMpF5syZo/fu3ZvgdZl/C+Y//apSpUq0l5eX+nnxxRej582bpw6RmT59+iR4XebfgvnPOBYvXqzyMmnSpEQvy/xbMP/p20cffRSdJUsW6/df/LzzzjuJXo/5t2D+06/KlStHlylTJsbfLiwsLLpIkSIqN3Pnzo33usy/BfOfsWpb+N6L85CPvHnzqs9CM2bMiK5Tp060i4tL9PLlyxO87XXr1qnrv/TSS3HOs31d6M1wqJ19otu2bVOXmzZtmuF9nUb2n+L/Rm9v7+iTJ0/GuP+33npL3ebFixdjPB683jTYp/bCCy9EJwdntNqB6clowYdFhLHgsC1MabZX8baFlo2o5GvtPMB2tE1QUJCadYUK+9mzZ9VxW2gzidvQYOo0RinjsrFhHQbbbcL1MOoG07S1USoYxYKRKrhP7QdtNTGDa/369QafHUoP0Ab1hRdeUOvWDB48OMHLMu8WaMcB//zzj+G2a+QcMPoKGcC6BG3btlXZwIjGhDD/Fsx/+oX8vf/++2r2MvJjxL59++TcuXPqtaNlwGj+Y2fVNv9YHwqZx+XstVtBy6eSJUtaj6NlE2Zj2ct/Yq89rEWC7GK0r23+0WoHI3wTyz9mxOM1ROkLZi6hNRfewzELHyNvMeoW+cWo1YQw/xbMf/qF1ngYta2N1u7WrZs6fPbZZ1U7uFOnTsV7XebfgvnPWG2DPTw8VA4Sw/xbMP/pG2ZuY9YaZtdhTUp8FsJsuokTJyZ4PebfgvlPvzCb7uTJk2oGHmalYgYrPgthLUt4+PBhvNdl/i2Y/4xFa5eNfGCfHmaFYhkFzCLFjOyPP/44wevj/xDkHzNKY4v9utCT4Sw2rwl0HMF2oX0vcmfvdZHYvk4j+0///vtvdRnMdrV9XWC7cZvY/xsfbB/ail+9elWSyj3J18zAtD8Q+k3rEXvnvLboPN5gETbA9HsEFot1x54mjTdhTOuO7/a028TtGblv0L5gY71Ae7Tto4zj+vXrqnUoMoWdjyiyJIR5t+jdu7f8+uuvqvUmdtSi5Sx2WKEtAdY5IedXrlw59QP4oN2qVSvp2LGj+o/S3odmYP4tmP/0691331VrsaDdilFos603/9o6HYllFS2X8EEebczQ1klj7zWYUvlHtpF/DErEl0p7sPM1oS/raKmDARoFCxZU7x34woo1SSj9wZe4zp07q50P+DIV3+cg5t+C+U+/tJ0YGGBlCztWfvrpJ/U5JL5MMP8WzH/G2cGIHYtai8fEMP8WzH/6hcE02DGNYlOhQoXUafjuhqLLqFGj1P8L8b0WmH8L5j/9QgES7bK/+OILNcgeatWqJW+++aZqpYuWqfFh/i2Y/4z5naB48eJqkoUGrwXsE0Ub3YiICNXWOr7XRYECBdR+pcToyfDDhw9Vy+vff/9dLXFimTxqEXvyiZ59nUbuG68LLA8X3ySEmzdvxvvY0AoZk9XQPhnrF2MdZ+xbRgtmvVhoTQHx7cDRgoTAYqc1CgDomY4/GBanX7ZsmVpHMPbsocRuz8hltdvGun32FlCO70VG6RPesPAfJWa1YYF4vFGmtIyad/zHhJEtGPWFtRSwmDfWOUHRatWqVYkWrMn5oEiIWR34AopRTimB+Sdngg+RGMX+zTffxBh1h3UyMHIQ6/LiS5ieD8yJ0ZMB/L+DWYUYXY81LfLnz6++4OEDNmaa6L3NpOYfX2axNrO9yyb0hTtPnjzqi/HKlSvV9fGDbcaHau3LO6UveO9FRwOM1E6JQYXMPzkjfM7HmkR58+aN8zcFezvtkoL5J2e3cOFCNbixf//+KX7bzD85I+QMa0JqRVYNcoh1HDFrD7N3kov5J2eFgirWT8TnIAxkr1y5slqbEsqUKZMi98H8U3qh7fuP/Z1A+1tj3xC+F9tO+kgqPRl+8cUXVZ4wcxydNnG/yCrWbLXXQU/v60LP5XD7WMMVAy/sSej9AYMNMBt2wYIFaj8oBnNgvVoM4EatRQ9W2ezQKtWJLaCtFxbxxaiWRYsWxai+p0bbXm06N15YKfFBi5wXdqxjpAqKSmvWrFFT6vVg3v+DmXsokuEHRTK03nnnnXfUtvP1k/5oLWPsjZjSMP//Yf7TH4wOxAfJl156Sf3EhhGNL7/8sirEJpQZ5D8l/sZoOYORv/jC5unpaT0dH7IdDY8FH7DxmJPy5RoDIvB/KH7wnGKUL2aEoSUzZkhS+oJuBchiQjsYmP//MP/pE0Zao+Ub/i+wHVCmDbxJqJ088/8f5j/9mzFjhnq/x85uPZj//zD/6dONGzesM35sYWc6YOZSfJj//zD/6RteAw0bNrQex35QDD7QupzZw/z/h/nPWIVWTLbAd4LY8L0AGc2aNWuCWUKG7969myKD9OfOnatmhn755ZcxahaYFOZoeCzodJLU1zcGS+C1gB/Mfq1Ro4Ya2KG30Mp+gHbgSylGo0yZMkUuXryY6CiTxGgV99hTpVPjjRftczCSHzvMtQ9dsdcnpPQPrfHQ+hMtwtCPHCNG9GLeLfAfSmzVqlVTh7btP8j52Gv9gL//tGnT1EzNhAYdMP8WzH/6hJZHGG0X+6dixYqq0I/fsXZNfPChEV/MUIiN/aE3qfnHSEX8n6TBrFrMNHE0tEvD/Y8dOzbOtuM41gWJT+zzMOgAa40A8+/c7L2vHThwQA12QQushFqfM/8WzH/6pa1F+dtvv8U4HUsBoItFkyZN4r0u82/B/GeM/wewc71r167i5eWl6zrMvwXzn36hqIJZqxhkb2vWrFkx/o72MP8WzH/Ggm5cu3btUjPo+Pmf+Tcj1ATQUtt23V2sTYqlFdCpLqHXRffu3VVmkKXYkvq6iI51ve+//z7G68SR349QG0HhODa85uMbiIRtiz1JB5NYUMQ28prI0DNasQg8nkRtVC9mGl2+fNk6jTmhKdPfffedGhmDN2GsfYA3YrxZoqUiptcbgR092kgRtLFEZf2XX35RfzBtsW5HwU73yZMny8CBA9VjwTRtFBZQUMBjadCggXqeKH177bXX1E5FZAwFE/RftzVgwIAEr8+8i3z44YeqdSrWty1atKgq3qH1B0bE2Y6SI+eDnAUHB6uCKdaXwDrFGNl+/PhxNYIqoRlNwPwz/+lVrly5pEuXLnFO12aw2jvPFj5sIzPIKwrrQ4cOVSP48NpBGyZ7H04TgvxgNjTWdsEagcjRDz/8oEbEYp0MR49cxNo4o0ePVq9fPHaM2jx37pwqOOO1jfZS9mBtYvzfiS8gyPyFCxfUFwE8J+XLl3fodlPyv1BiQI2/v796nz169Khqp42d7ePHj0/wusy/BfOffqFt5LBhw9RgMew0CAgIkA0bNqhBl8hCQkuIMP8WzH/G2LmO/BtpG8z8WzD/6dcbb7yhWn2ixeGIESPUeqxYJxKn4e/K93/mPyPDfgvsv8C+F2R/+/btamA7MohuTglh/i2Y/4xX20IOsO4uiqYjR45Up//4449qEgYmYySkadOmaj8i9o1ieSrkGbOc0Rob5+H/GSM6dOigljTDNmDiCwqfGBQX39rhKf3/I+oj2IYhQ4aoDkBom3zo0CE10xavFexHi+3+/fvqtYAl6KpWrar2I2ObMYDDdmauqQutEyZMUG8WGvRUxo9WeEqo0IonFW/WmDKPN2FMccbOZ23ksBFo5YQ/5rvvvqve5DCd+7nnnlM7wPHl2NHwZo8PWtjhhP7SqMSjGIEPZfhPhdI/rRiEN1z8xJZYoZV5t6xngjdc7KzCqB+88WKHFUb0pEQfe3LsjnbM5kB2MTIPHy7xnyl66etpIcb8M/9mhpnQaA+NvzU+QOIDNb60DR8+3PBt4YsaXovIH0YTY9ACXofIlqO/aMJbb72lRvhjPWRtNCbW6sSX8ITeC/B/JIpzGFyALzF43eJ95YMPPkhw5CelPexQwMAa7ODAgBu812J095gxY3S1vGL+mf/0DjtQ0MEAOxixUw2fX5ABZDAxzD/znxHg/4CkLJvB/DP/6RkGGG/dulX9rfD3w3dg5A7tDeNbl84W88/8p2fYv4EZc9jfgeIIMoeCI4pL6OiRGOaf+c+ItS2sz7p582a1HxJ5QIEV3S4xEQv7PBOD7xKY1Yw8o1iJ261Vq5Ya0GzUt99+q16j+IyG/auY9IGiJV57joYB1//++68qLmPwKTodYlIKXicJ7d/E9dAuGGuz4vnF+wL2J+A1gn26erlEJ2UOMBERERERERERERERERGRiXGYAhERERERERERERERERGRQSy0EhEREREREREREREREREZxEIrEREREREREREREREREZFBLLQSERERERERERERERERERnEQisRERERERERERERERERkUEstBIRERERERERERERERERGeRu9AoZUVRUlFy9elWyZs0qLi4uktFER0fL/fv3pUCBAuLqyto6xcT8k5kx/2RmzD+ZGfNPZsb8k5kx/2RmzD+ZWUbPP/A1QPFh/h2PhVYRFbLChQtLRnfp0iUpVKhQWm8GORnmn8yM+SczY/7JzJh/MjPmn8yM+SczY/7JzMySf+BrgGJj/h2PhVYRVcnX/gjZsmWTjCY4OFi9kLTHSWSL+SczY/7JzJh/MjPmn8yM+SczY/7JzJh/MrOMnn/ga4BSK//du/8ljx9HyaJFfeXu3VAZOnSRfPllKylVKqesXn1GQkMfS6dOZVN19mxa55+FVhHrHxwhy6hvtJBRp4VT8jD/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mZpb8A18DlNL5R1vesWP/lfbtS0vt2gXlt996Sa5cXuLl5aFub/36Z6yXXbPmipw7d0/69q0l7u6upsk/C6121KpVS65fvy7OKF++fLJ79+603gzKwJh/MjPmn8yM+SczY/7JzJh/MjPmn8yM+SczY/7JzIzmPyrKQ+7caS3ffHNGfHyOJXr56GgXKVbsLYmM9BRX1zAxUvtMr/lnodUOhOzKlStpvRlEaYL5JzNj/snMmH8yM+afzIz5JzNj/snMmH8yM+afzCxp+f9ZwsOjJShI7+UzicgLIrJNRLZLRpemhdaNGzfKF198IXv27JFr167JggULpEuXLnYv+7///U9++ukn+frrr+WVV16xnn737l158cUXZfHixeLq6irdu3eXb7/9Vnx8fJK9fbi9/PnzizPA8xMVFZXWm0EmwvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mllj+o6NF7t1rJF5ep8TT0/gM8NDQ3ZIpU5C4uxd0eP49PT1lzJgx6tB0hdYHDx5I1apVZdiwYdKtW7d4L4cC7Pbt26VAgQJxzuvfv7/6I6xevVoeP34sQ4cOlWeeeUZmzpyZ7O1DyC5fvizOoFChQhxlQ6mK+SczY/7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn8wssfwHBj6Uzp1nyzvvfCStW5dK0/wPGbJQXn/dXypVyhPj9FGjVsvWrZelcOGsUrKkpJk0LbS2bdtW/SQETy5mrK5cuVLat28f47xjx47JihUrZNeuXaqvNHz//ffSrl07mTBhgt3CrG4uItEu0fI48rH1JDdXN3F1cZWo6CiJjIqMeXEXF3F3tTydttfR4DxcJiIqQi0ebEvv7WKbiFIF809mxvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5mZjvz7ZHOXtesHqPOQ69j5X7DguHz84WY5efKOlC3rJ2PGNJFOXUrHyP+ypadl7Zrz8t13bVM8/wcOoAXyfdm0aaiMHbteDh6UNOPUa7RiqvDAgQPljTfekIoVK8Y5f9u2bZI9e3ZrkRVatGihpjzv2LFDunbtavd2w8LC1I8mODg4xvnREi3iLRKRNULOBZ6znp49c3bJ4pFFHoQ/kOCwmNfJ5J5J/LL4qRBdD4k7jTqvT14V0rsP70pYxH/3DVk9s4pPJh95+Pih3Ht0L8Z5Hm4ekssrl0R6RIpks4SfKDmYfzIz5p/MjPknM2P+ycyYfzIz5p/MjPknM2P+ycxSKv+3bz6UrNkyiWdmtzj5X7Xkkrw4dLO4oGAbLXLo0E3p3n2O/DitqTRtm896m4fOnJNT527J/bD7hvKP+xkxYpkcPHhT3N1dxcvLQyZO3ClnzgSKt7eHLFjQW7ZuvSStWlmmsbZqVUKWLv1X0opTF1o/++wzcXd3l5deeineRXvz5Ik5VRiXz5kzpzovPuPGjZOxY8fGe74LSufhIi6RLuLp/l9P5/CocIkIj1CVd9vT1XVcXOR++H31e+zzICQ8RF0GP7HPj4yOVNdN6HajM0WLuMW7yUS6Mf9kZsw/mRnzT2bG/JOZMf9kZsw/mRnzT2bG/JOZpVT+u7f8R3r1Ly0j364eJ/8/TDhiLbICDnH8288PSJuORUUzcGhFGfKUm+H8L158UlxdXdRsVRg0aIH4+xeWH3/sIL17z1WF3cDAR5I/f1Z1vq+vpzx8KGnGaQute/bskW+//Vb27t2rnuyUNHr0aBk5cmSMin7hwoXjXM4l2kVV1GPDyBRXN9d4b9/edTTWadB2JHa7RCmB+SczY/7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn8wspfI/4fvGUqxEtjiXwfFzp4OtRVYNjp89FRTv68NI/o8duyUBAcX+u66ri1SvbpkpW7hwNrV+bPbsmSU42DJzNygoTLJkkTTjtIXWTZs2yc2bN6VIkSLW0yIjI+W1116Tb775Rs6fPy/58uVTl7EVEREhd+/eVefFx9PTU/3ER01P9uE0fcqYmH8yM+afzIz5JzNj/snMmH8yM+afzIz5JzNj/snMUir/TVvGLc5qSpT2leNH7sYotrq4iJQs4xvjcqNe2SSH99+RpRu6GHkIUr58blmz5qz06FFBHY+Kio4xIRP3ixmuX321TQYNqiqrV58VO7XkVOO0wyewNuvBgwdl//791p8CBQqo9VpXrlypLlO/fn25d++emv2qWbdunVrbtW7dumm49URERERERERERERERETpz907j+T9Udvk6pWQOOeNfKuGtV0waG2EcbqtHn1Ky/OvVhWjOnYsIxERUdKw4RRp2vQPuXMnbl/gatXySd683tKo0e9y9OhtqWCpyZpvRmtISIicPn3aevzcuXOqoIo1VjGT1c/PL8blPTw81EzVsmXLquPly5eXNm3ayPDhw+XHH3+Ux48fy4gRI6RPnz6qKJuhYGTAAxFJw+nPRGmG+SczY/7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn5yMh4errFl+QVq0KSIFCvrEOK9dp+Lyy/QW8vX4vXLmVJCULO0rI0fXkLYdi8e4XO168XeeTSj/mL06aVJ7uxedMKGV9fcvvrD8HhYWJuPGHRFTFlp3794tTZs2tR7X+kYPHjxYpk6dqus2ZsyYoYqrzZs3F1dXV+nevbt89913ktGoBYqjnxwSmQzzT2bG/JOZMf9kZsw/mRnzT2bG/JOZMf9kZsw/OZus2TLJ5v291fqo9qDYih97bt0MlY/e3SFPP1dJqlTPLRk9/2laaG3SpIlEx14xNwFYlzU2zH6dOXOmZHSqX7Yn+8aTOTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mxvyTM0KRNSwsUj7/aLcM+19FKVgo5szW+ITcfywXz9+XnH6ZUyX/mNE6duxYNZkzofVpTbdGa5rSpik723uaR1pvAJkC809mxvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5mZwfzfvB4qK5eel7OngxK8XERElKxYcl6uXA6R4iV9ZcHKjlKoSFZT5J+F1gw4TZkoOZh/MjPmn8yM+SczY/7JzJh/MrPk5B8d2nZsvSYnjweq48eP3pXBvVbKkUN3HLClRM6X/727bsrhg5a8XzwfLIN6rpDdO244YEuJnO/zz+EDt2Xf7pvq99u3HsrAHitk04YrKbyVRM6R/8JFs8r6nT2lUZOC6vjQPqtkzoyT6vdLF+7LXzNOSHBQuISHRcqrz/0rq5ZesNyPi3m+X7DQaoeanpyZ0/TJnJh/MjPmn8yM+SczY/7JzJh/MrPk5B8rYb36v39l5h/H1fHIyGjJlMlVSpb2Vcf/nHJMNqy5lOLbTOQs7/+jXtkkv/90WP0eEREt7u6uUqZcDnUcO+Ax+4koo+Z/zFvbZPJ3B9Xvjx9HqfyXq2DJ/z/zzqgfI0smEjl7/j08XK15v371gaxaZimmosg68rmNsm7VRfHy9pDlG7vKkGcqiNmw0OqUq9cSpTHmn8yM+SczY/7JzJh/MjPmn8zMQP5DHzyWkc//q2ZvYM2yv5d1kPc/qafOq1jZT36Z3lIyZ3ZXO9eXLDwr27ded9x2E6Vy/h89ipA3X94kp04EqllKf8xpLZ9/10idV6KUr0yZ1Uqy+WZSx1cvvyAb13F2H2Wc/KO49O7rW+TQ/tvq+A9TmsmkKc3U7/kLeMvvs1tJ7jxe6viGNZdl7cqLjtlmojT6/H/jeqhqDYyCa89+paVeg/zq9EHDKsjPfzaXtp2Kq+M/fXdQOrVYpC5rJvw6lZ6Ei4jl/ZrIfJh/MjPmn8yM+SczY/7JzJh/ckKBgWFy4migXLxwX7XRK1jIx+7lUISa/U876/GrV0KkQEH7l7WL+ScnFHL/scr/hXP3pXTZHAlmGoMOoqIsM6WYf8oIHoQ8luPHAuXc2SCpXC2X5MvvHe9lv54coPKP/wuuXX0g+fJ76W+hyvyTE7ofHC7tmy6UXv3KyJvv1ZJh/6tkPS9PPi9p37mE9XjHbiWkdfuiapa3Yek4/5zRmk64RLuooKlDIpNh/snMmH8yM+afzIz5JzNj/slZobC6ZH1nadC4QKKXxU51/GD2X6Pqc9QMPz2Yf3JWuXJnkYWrOkqLNkV0XR6zvq9cCpGAWn/LgjmndV2H+SdnlT2Hp/y9pL106lZSd/7v3H4oLerNk2m/HdN1HeafnFXWbJnki+8a6WoJ7N+ogDRpUVh195j0zQE5ezrIFPlnoTWdiMbqxG5PDolMhvknM2P+ycyYfzIz5p/MjPknZxMcFC7D+q6ytkw1olSZ7DJhYmOpUz+fw/Pv6ekpY8aMUYdEKQUtg5/qt0oOH7htOP8FC/uo/Ac0L6Tr8sw/OZvIyCgZPnCN7N5xw3D+/XJlkc+/byRtOxZLlc8/fA2QI5w7E6SKpk1bFpY8eb0MzQKfM+Ok7jba6T3/LLQmNE3ZmeB9PMuTQyJHY/7JzJh/MjPmn8yM+SczY/7JzHTk/8b1B3L71kPxyWpZf9II7Jjv2quU+Gb3TJH8DxmyUA4fvhnn9FGjVkuzZtNlwQKsJRhpeDvJpHTk/9aNh3L3ziPJ4pW0Feg6dy8pOf0y67sw809Olv87tx9J0L0wtS5lUrTvXFx/cSoZ+W/U6HcZOnSxRDL+lIL5R/Zb+M+TqT8fNXzzPlkzydrt3WX4C5Udlv+goEdSp84v4uPzqRw5ckvSEgutGXCaMlFyMP9kZsw/mRnzT2bG/JOZMf9kZnrzj/UoF63pLPkLxF2Tb9mic2onZIncU9Qhjtvz66TD8s3ne8URDhy4Lleu3Jd16waIn5/I/PknHHI/ZM78Yz3iBSs7ScnS2ZOc/xlTj8u4D3aKIzD/5Mj8o0g6Z3F7qVojd5LzP/+v0/L+m1vFkfnftGmolC3rJ0eN18PIhPTmHy2Df5/dWjp0LW73/MReA25urmpW+JlT98QRvLw8ZOnSftKjR+ItjR0taUORMji2KSIzY/7JzJh/MjPmn8yM+SczY/7JzPTkPyoqWs6fDZb8Bb0lS5aYu9GwQ3H4gDWCbpLR0SLHj9xVx3+Z3kLadYq5U/JeYJhEREYZ38boaBkxYpkcPHhT3N1d1U7FiRN3ypkzgeLt7SELFvSWrVsvSatWlnUDS5US2bbtsgwaVN3wfZG56H3/R9vIvPm8xMvbI8n5x6yoR4+MT7Vj/imt84/3/1y5M8fpaGAk/8FBYfLwYYTD89+qVQlZuvRfw/dD5qM3/1hruHHTgnbP0/saGD92lyxdeE62HuyT4vn38HCT3LnjDoJLC5zRag/bFJGZMf9kZsnMf+DdR2pHDBzaf1v+mn5CHoYa/zBNlB7zj7XLHj+27Dw8fvSuzP7zhDqNyAz5x/ozWL8Mzp4OklnTjqsWe0RmyD92HIY+eKx+v3zxvsz847jcuB6asttIlIb5vx8cLo1qzJE1K+KuMfbV+L3WHYyAQxz/enzcmauvv1NT3nq/tuFNXLz4pNrRidlK69cPFj+/LOLvX1hWrx4onp7ucujQTQkMfCTZsllaE2fOLHL3Lv8PopTJP77fNqw+RxbOPZOs/D//SlUZO76+4U1k/imtP/+0bjRfpk89nqz8D3mmonzxfWOH59/X11MePjR8N2RGOvM//sNd8a6xqvc10GdgWflqcoBD8u9MWGhNTzjAmMyM+Scnd+FcsNQqN1M2rb+ijq9ddVEmf3tQMmdxU8d377ghERHGR7ArzD85uVs3Q6VmuRmyYsl5dXzLv1fly3F7xMvbMutj766bEh6exMVimH9yciH3w6VOhVmqJZj2fv/J+zut65jt33PLWoQ1jPknJxcWFikNqv4l06YcU8cPH7wjY97aJpkzu1kHnmlFWMOYf3IS3j4eMmthW2nQuECc886eCrLuYNTg+JlTQXEui9Z5utnc5rFjtyQgoJj1OHY6Vq+eT/1euHA2CQx8KNmzZ5bg4DB12qNHIjlz6lwPkygRyBvy37JtkTjnMf9kBr/NbKnWGU4P+Q8KCpMsKJ4RpRDsy7l+zf4ASr2vgZKls0u9Bvkdkn9nwkJrOqH6ZT/gujlkTsw/OSvM3lu+2LL+QJFiWWXMuHpSqaqfOv7KmzXUou8uLi6qCNWtzWL5dfJhw/fB/JOzwpfFpf+cU+1ccufxUiPUa9fLq8576rlKsv1QH9XeBbNAerZfIt9+vs/wfTD/5KyQ+yULz6pDtBHD+39As0LqvF79y8iBMwNUe0kUofp1XSafvm98TTLmn5wVco/3f/w/4OnpJu9+VFfatLfsBGnToZgcvzxYfLN7qllQQ/qskndeN74mGfNPzgSfZxo3KyQ5/eIWb0qU9lWzN2zheMkyvnEu27HZPzLiqXWG81++fG7ZuPGC9Xy8tvAdw3anJmZ4rFlzVh0/c0akfn3L/0lEKQH5x+f95OS/b5flMqjnikTvi/knZ9MwoKDd9bmN5P/ZQWula+tFDs//6tVnpXBhY4+PKCFYn7j/kHJ2z9P7Gti2+Zrq+OeI/DsTFlqJiIiSaNG8M/LckHWqTST+sx/0VAXxy5UlxqLvgC+lKzd3k2deqGzdQUmU3q1ddUmeG7JWjhy8o473G1xO8uX3jpP/rNkyybJ/u8gro2qo48w/ZQT4soj3/x1br1uLqwUL+8TJP4pQi9Z2lrc/rKOOM/+UEWCW6vND18q6VZfU8W69S0mxEtni5B+jzuct6yAffW5pFcn8U3p28nigvPzsBjWAxtbIt2pYW+WB1kIPp9tC/t8aU1uGPlvR8H137FhGdcZp2HCKNG36h9y5E3cGR7Vq+SRvXm9p1my63Lol0rVrWcP3Q5TQGpXIP7p4JCX/2mWfe7mq4ftm/imtXb0SIq8+96/cuf0wyfl/7uUqajC+I/PfqNHvcvTobalQwfDdECUI2bc3K1vva2DJgrOyZIFlkkpK5x/atZshq1adkeefXy7790uasfSzoriS2N3RUaJdokW8nxwSORrzT2ZmIP/d+5SWuv75pFCRrIletnzFnOpw57br8vnHu+XPv9tY20omhPknZ81/q7ZFZeuB3rryX7a8Jf8H991SLSX/mNNGsvlmSvR6zD85a/79GxWQbQf15b9U6ezWnfRvvLhJtR/LlTvxnl7MPzlr/qtUzy1b9uvLv1aAxRILLz2zQSZNaRZjUEJ8mH9KVTryj4EDGFx29XKIFC/530yNdp2Kyy/TW6j1yNAqr2RpXxk5uoa07Vg8xvUxKBOzAvWInX9cd9Kk9nYvO2FCK+vvX3zRSsLCwmTcuHGSKZOlfTdRSuQ/k6er+hx/6WKI9XutkfyD3raRzD85W/4zZ3aX/XtuyoVz92MMrDeS/5p1LJ2fHJl/sLwGjui6LyI9+b94PlgCav0tP//ZQlq2LRrjPL2vgU++bKBrKZ2k5n/Zsv7qUPs/IK2w0GqHmp4c6oRtipxscyhjYv7JzPTm/8rlEDmw95a0aldU105GWz5ZPSRfPi8JDg7XVWi1bJgkiaenp4wZM0YdEqVU/jGacfOGq9K2UzHD+cfsVsx6DQ4K01VotWyYJAnzT47IP2ZyLF98Xjp2K5G0/BfwkqB7YboKrZYNkyRh/skR+cdMvoV/n5ZO3UomPf9BYboKrZYNkyRh/skR+S9VJrus3totRss62x2N+IkP1ur+6fuDakZr0eLZdG6YvosRpUb+CxT0kXU7eiQp/8eP3pWvxu+VN96pKaXL5tC5YfouRpQa+Ufb+KTmH7PBP3l/h7z8Zg2pVMVP54bpuxhRauS/cNGsMv7bRlKrrv3BAom9Bi5duK9uAwMW9G2YpFtsHUxERGTQqqUX5LUXNkp4rNZhelSo5CcTf2smefPFXeMmKYYMWSiHD9+Mc/qoUatV66QFC7CWrPHtJIrP+tWXZeTz/0rI/ceGr4sZIJOnNje8gz4+zD+lti0br8rrIzbK3TuPDF8Xazv99EcLKflkhmtyMf+U2nbvuCGvj9ikBpwZhZ2UyD8+B6UE5p/SAnayX7/2QN58aZPcCwzTfT2023v4MELypNDn/4RgNsfYsWPVIVFK5x+ff5D/27fst2+0JzIyWh4+iIixxIijMP/kyPwHB4XLqFc2qVbCekVERknogwgpUNDx+Qe+BsgR2e/dv4zkyJnZ0Hs/7NpxQ+pVni3LFhlvG5we889Cqx1sU0RmxvyTmenNP9ZW2rCrp3h5e8Q5Dx8gWvjPkxK5p6hDex8osID7mhUX1eheRzhw4LpcuXJf1q0bIH5+IvPnJ77oPJHe/PfoW1q2HuyjdponJf9Yo2zjusuq/ZgjMP/kyPy3bl9Mdh7tp2Z2JCX/2vquKFg5AvNPjsx/g8YFZPfxfmpmX1Lzv2fnDdm66ao4AvNPqfH99/rVUNm1/brqzpHQ2sP3g8Nl6s9HVIEVLSOn/tVasmRhUzlK3/m/eSNULYUTePdRgvkPffBY/pxyTHXxqFjZT/6c10Z1NiBKz/lHZ6edW6/L7ZsPE8w/WqT+Nf2E3LgeqpYQmbGgrd3vzkTpKf9nTt2T+lVmyz/zziR+29HR6qdWnTwyc0FbaduxmJgBC60ZcJoyUbIx/2RmOvNvb0YqdioOH7BGjh+5q9rr4RDHY+9sxAeOl5/ZoNpPGoXrvvDCUmnU6He1GPytW6EyceJOadnyT+nSZbY6f+vWS9KqVUl1+VKlRLZtu2z4fsikUiH/8PqLm2Th3MQ/oMfG/FNGyP97b2xVO1+MYv4pI+T/4/d2yLTfjhnePOafnOX7b7WauWXNtu5SpFg2eRgaIS3qz5ON66+o8w4fuC07tl5Tv2PWx9i3t8veXXFnXhOl1/yXq5BT1m7vrloAP34cJW0aL5BVyy6o8zCIePO/ltfC/fuP5YO3tsmOrdcdtdVEqZ5/dGfC+z/Wqsfg+U4tFsmCOafVeadP3pN/11o+d4Q9ipT33twmW/51zMAyorTIf4lSvvLxF/7S6sk6rRg8jME09gaatWm8QFYsOa9mwwY0L2S37XZGxEJreoHBBRgww0mGZEbMPzkRfJjADhV7LTOw9gw+P2gDG3GI41gY3pabm6vsOtZXXnmzuuH8L158UlxdXWTTpqGyfv1g8fPLIv7+hWX16oHi6ekuhw7dlMDAR5Itm2VdssyZRe4+GXFMlFyH9t+W5vXmyoVzwUnOPz5kb9jZQ979qG7id8j8kxM5ezpImtWdK0cP30ly/mHRmk7y2beNEr9D5p+cCGYxNa3zt93Z2EbyP3NBO5k0pVnid8j8kxPDZ3kIDX2sZqsWLWZZEuHH7w+q4mpERJTaIb/v1AA1E9wwfv+l9JD/B4+ldt28UqiIpcvHtF+Pytsjt6gCLAbl7DnRX1q1s+yQN4T5p3SQfxRTsWYl1p6EubNOyavPb5Dw8Ejxze4pO4/0lW69Sxm/A+afnLmF8ICyksXLXb3PD+u7Sn3mgTnTT0j1MjNk/epLqntBoyYFJW8+b9Pln4XWdMIFQwwinxwSmQzzT84kew5PaRBQQDw93eKcd/ZUkHUnowbHz5wKinNZtB3WM6ordv6PHbslAQH/td3ATsfq1fOp3wsXziaBgQ8le/bMEhxsGVn26JFIzpxsU0MpwyerhzQIKCjePh7Jzj+ymxjmn5wJvlQ2DCggvr6WQo4t5p8yOg8PV2kYUNBu6zsj+cfriPmnjPS9ADsbd223DEDo1K2kHNh7W44cvGP93JQU/P5L6UE230wSFSX/5b97SfW+v32LZVY3808ZGT7PuLu7WAegdepWQm5ceyhrV15Ux5l/yujfCypV8ZNzZywD8DNldlOJLfxk4BkKrbduhsbbXjuj5p+F1nRC9cvOxHUzyZyYf3Im5SvmlLHj69tdY6ZEaV81g8MWjpcs4xvjtMjIKHlm0BrVSsNo/suXzy0bN1raMwFa1tgWbPE5BjM81qw5q46fOSNSv34h4w+UyA7Mzvjws/qSK3eWJOcfH7ZfHL7e2mYpIcw/OZP8Bbzlw8/9pWBhnyTnH958aZPM/ON4ovfH/JMzyZEzs3z0hb9qG5ac/L8/apv8NvlwovfH/JMzu3vnkXVmUyZPV+vgAcxu+nNuG6lSPZc6PqjHCjXDzyh+/6X0kH+8B3tmdhM3Nxfr9+Spf7WyzuJ+bug6eemZ9YZvn/knZ8+/Vjzy8HATd3dLaaVo8Wzy+1+tpEWbotbP+5jxZ7TQxPyTs0KWv/l8rxzcd0sd/2pSgEyf10b93qVHKdl7sr9akxiW/nNOfv/5qERGmiv/LLTag79lqBNOU+a68ZQamH8yM535R9tgbf0NWyPfqmFtlwdaGz2cbivoXrh1p73R/HfsWEa1I2vYcIpao+zOnbgtjKtVyyd583pLs2bT5dYtka5dy+q7HzI3nfkPvPvIOlI3KfkPfRAh0VHRksfOOn92Mf/kRPkPDgqXVcv/K/YYzT/aiWH2U648cQcr2MX8kxPl/0HIY7uDxPTmH8VRvAb8cumcacr8kxN+/z196p40rP6XdQ3iz75pZG0PiRnfzVoVVgUo7JBs17m4dO+ThNaRwO+/5IT5v3IpRAJq/W1da37Mp/VkwNDy6ne0S23Ztqh14EHrdkVVm8kkYf7JCfN/5/ZDadlgvvw2+Yg6/uZ7teTp5yup39HxCWtXYqYf4P+C/kPKJW1tSuafnDD/+B6wbtUl2bPTsvY82mbb63QGn3/XSA08w0AE7D8yNOAgHeffPa03wBmp6clR6XeaMlFyMP9kZnrzP+XHIzJj6nHZfbyf9YM0tOtUXH6Z3kKtSYa2SSVL+8rI0TWkbcfiMa6PnTA/T2uRtG10cZFJk9rbPW/ChFbW37/4opWEhYXJuHHjJFOmuG2OiZKa/9l/npBvPt8n+072Vy1QNXrzjw/jE39rlrRtZP4pjfO/aP4Zee+NrWrdMdsWqnrzjzx+PTkgadvI/FMa53/Nyosy4qn1sv1Qnxgzu/XmHzvfx3/dMGnbyPyTk3z/LVnKV94eW0e1xUvwdl1crAUoDBL4eeIhGTK8QozPTkTpLf8FCnnLux/VUQXVxPToW9o6yObnHw5J34FlVTGWKL3m3y9XFnn7g9rSqGnC7//QoUsJdYgC0+8/HZGO3UpI7jw6BxoTOWH+fbJmkoWrOulaAgSwrzTkfri0brRAnh1RWZ56zjIoISNjoTUDTlMmSg7mn8xMb/6Hv1BJjVy0LbLa7mzET3yOHbkrt24+lEZNCiRtdCNRGue//5Dy0rNfGbs7ChPL/5lT99Q6Hk1bFlLt9ojSW/679y4tLdoUsbtOZWL5xyyQg/tvqZ2TWpsxovSU/7Ydi8m2g73tts9OLP9Yp2nb5mvSpkMxFkAp3X7/vXkjVPLk9bIWUPW6cC5YJn61X4qXzBZnAEJK8/T0lDFjxqhDIkfk3+gs1WtXH8gPXx2QXLmyWIuvjsL8k6Pz371PacOthid+dUBc3VzVYBtH42uAUjr/GCwweuQWlf3adfMaug+frJlk1Hu1pGrN3JIa0jr//IafAacpEyUb809mlknfOmXYyY4WkthpaMQfvx6VT8fslEePIsXRMKNj7Nix6pAopfKfzTeTWqP1YWiEbNpwxdDN/zX9pHz83g7VPtjRmH9yRP6zeLlLvvzeEhYWKRvWXDJ08/PnnJaxb29X/3c4GvNPjsg/CqSFimRVs/PQOsyIpf+clw/e2i53blvW9nMk5p8ckf/jR+9KzbIzZfsWY5/9oWTp7GrtspQqsg4ZslAOH7a07tMEBT2SOnV+ET+/L+VmzLOIkp3/SxfuS61yM2W1neUTElOwkI/sOtY3xYqszD+ldv7RMrhepdmyYM5pwzePWbBbD/ZOsSJrQvn38flUjhyxrJ9JlFL5x9JnRw/dkZDgpH2H7d6ntHXtVkfkf+fOK1K//m/SuPHvMnDgPxLp+F2t8WKhNT15nNYbQJSGmH9yQhiZ/tyQtRL64HGiI8Dw5RQ+meAvs/9pJ1myGGgqwfyTE/p18mF5ZuAateZGYrT8j/6gtsxf0VGyZjMwoof5Jyc0a9oJear/ajVLQ2/+R4ysKss2dLU7GzZezD85oUXzzsrQPivl7Okg3fnHDsY127rrX58emH9yIkWKZpUvvm8kNWrniXMe1mtt4T9PSuSeog619VttZc7sLpGRUWpWlCPy7+XlIUuX9uPaxOQQWFt+wsTG0qBxgSTnHy2Eb1xn/in9wXfXryY1loDmhZKcf+wTun4t8e8Nycl/jx6OnzFL5pM9h6csWtNZmrSIm3+9r4F1qy6pAceOyH/hwtlk3bpBsnHjUCla1FdOWJYQTxMstKYTLtEuImFPDolMhvknZzXq/Voyb3kH1UIVXxq/+Hi33Au0zJ7AB2nM+IA/fj0mTevOlaB7YapdKj6o6MX8k7N64dWqsnhdZzXDG9keN3aX3L710Jr/x48t+Z//12lpWP0vtWMR7bKNFJmYf3JWg58uLys3dVNFo4cPI1SnAm3niW3+MfOjfpXZcv5sMPNPGUa33qVU0bREKV/1WQf51wqqtvnHzL96lWfL4QO31XHmn9IzfN7vM7BsnNbX2KE4fMAaOX7krup2gEMct7ejEae/89oWw/nH6+qFF5ZKo0a/S9Omf8itW6EyceJOadnyT+nSZbY638PDTXLnNjCQgcgADBLu1T/u0iFG8v/qc//KS8+sT/S+mH9yNnjf79KzVJzPMUby//6b22RYn1WJ3hfzT84G7a/DwyPtLn2m9zVw+dJ9XQM0k5L//PmzSpYsHtbXalqu0JamhdaNGzdKx44dpUAByzp1CxcutJ73+PFjGTVqlFSuXFm8vb3VZQYNGiRXr16NcRt3796V/v37S7Zs2SR79uzy1FNPSUhIiGQ00RKN1Ykth0Qmw/yTs0LRFK3AADsRZ0w9bv1PvUHVv+TLT/eo39t1Kia//NnC2Cy+J5h/clauri7WFjBYe3jWH8clMtKS0zaNF8jY0dvU781bF5af/2xhbBbfE8w/OSt8dylVxpL/U8cDZfafJ+TRQ0ufop4dlsrrIzaq3xs2KSiTpza3u6ZlYph/cmaly+ZQhxhEMGfGSQl60hJ7WN/V8uygNer3WnXzyqQpzaR0OctljWD+yZlgByMGFKB9cGxfjd+rPv9HP4kqDnH86/F741z2fy9VkeEvVDac/8WLT6rPXZs2DZX16weLn18W8fcvLKtXDxRPT3c5dIi9UslxsCN73Ac75eC+W8nK/+CnK8iLr1VL/P6Yf3IyX43bI7t23EhW/jFQ4c33aiV6X8w/OZtWDefLt5/vs3ue3tfAoKcqyB9zWjs0/xcu3JO1a89JmTKSZgz0LUx5Dx48kKpVq8qwYcOkW7duMc4LDQ2VvXv3ynvvvacuExgYKC+//LJ06tRJdu/ebb0ciqzXrl2T1atXq+Ls0KFD5ZlnnpGZM2cmb+OcrU0RdtxjcAoH9FJqYP7JzJKY/+ati8iuY/3Ew8MyhumtD2pL0WLZ1O958npJnpZeSbth5p/SQf7rNcgve070t+b/lTerS+48lsz7ZveU1u2LJe2GmX9KB/mvUj13jPz/78Uq4u3jYZ0B0rFriaTdMPNP6SD/GHBg+/ln6DP/ta1zd3eVzj1KJu2GmX9yovxjIM3CuWdU29RyFXLGOO/sqSDrDkYNjp85FXfmRp36+ZKU/2PHbklAwH+fpbDTsXr1fNaWeYGBlo4iRI7IP/K84O8z6vMOfpKaf3ttt+1i/snJPv8smn9WChbJKrXr5k1y/itXy6Vve5h/crL8j/uqgepiY4+R14Aj8x8cHCYDBy6QX35pL/Pm/SymLLS2bdtW/djj6+uriqe2Jk6cKHXq1JGLFy9KkSJF5NixY7JixQrZtWuX1KplGRXy/fffS7t27WTChAlqFmxSsE0RmRnzT2aW3PxrOxmhU7ck7lgkygD5b9uxeApuGVH6yn+LNkVScMuI0lf+Gzezv34TUXrOfzbfTLLzSF+755Uo7ata5dnuaMRsjpJl4u6UXL/6knhkcpWGAQUNbWP58rllzZqz1vX3sNalbQu/2Ds5iVIy/9ixnRL53/zvFXkcHiVNWxY2tI3MP6X1558Nu3omO/87t11Xy0y1alfU0DYy/5TW+W/ZNv7M6n0NvD1yixw6cFsWr+2c4vnHMiZ9+syVMWMCpEwZP0lLaVpoNSooKEg9mWgRDNu2bVO/a0VWaNGihbi6usqOHTuka9eudm8nLCxM/WiCg4NjnK+mJ7uyTRFlTMw/mRnzT2bG/JOZMf9kZsw/mVlK5v9+cHicpUBGvlVDrUemtc7TDnF6jPuJjpYJn+yRWvXyGi60duxYRlasOC0NG05Ra/F5ecVcJ1PTrt0M2b//uri5iVSvflCGD69t6H4o43GW/MPErw5IgYLehgutzD85S/59snrEKPIYyf8vkw6rM40WWo3m//jxW5JPZ/MEythSKv9Yo3XiV/vV0gf5C8RcC1jva6BT9xJSr6HxYOrJ/6xZh2THjivy0UcbJSoqSvLobJ5g6kLro0eP1Jqtffv2VeuxwvXr1yVPrGfP3d1dcubMqc6Lz7hx42Ts2LHx3xneM9HtjhP6KANi/snMmH8yM+afzIz5JzNj/snMUir//669LMP6rpJN+3pJgYL/rbvdrlNx+WV6C7UeGVrllSztKyNH14jT3QM75xet7SQPQyMMPwZcd9Kk9nbPmzChlfX3Zcv6q52qeMyDBlUxfD+U8aRU/nfvuCG9OiyVlZu7WtfoNpJ/mLmgrTwIMd6nnvmntM4/1udu33ShzF3aQarXypOk/P88rbmE3Hds/kF7DRClVP6xFMiSheckoFmhOIVWva+Beg3yJ+kx6M3/wIFVnSL///X4cWJYe7VXr15qBODkyZOTfXujR49Ws2O1n0uXLkm6wAHGlAKYfzIz5p/MjPknM2P+ycyYfzKzlMp/zTp5ZPQHdSRr1pgz+rQdjau3dpezt4apw9g7GLEvCzNC3NxcxcfO9e1i/smJ8o/1JUeNqSW5cmdJcv7Rgjj2jNh4Mf/kRPkvXTa7vPleLSlcNKvh/APyj4IR80/pMf9YPmH7oT4S0Nz+8iAJvQbu3H4oI5//Vw4fuK3/DtNx/l3TS5H1woULas1WbTYr5MuXT27evBnj8hEREXL37l11Xnw8PT3V7dj+ODvVL/sB182k5GP+ycyYfzIz5p/MjPknM2P+ycxSKv8okD79fCW1oxwtJI2YNe2E1Co3U04eD3R4/jGbAzNYbNsFknmlVP49Pd3k2RFVJEfOzIbzv3jBWaleerrs2x1z/218mH9ytvxjkAzyj4EGRvO/Yc0lqVpyumzacCVVPv/wNUCO+PyPgTLh4ZEy7oOdcvVKiO7r3QsMk5PH76lirRny75oeiqynTp2SNWvWiJ9fzAVt69evL/fu3ZM9e/ZYT1u3bp3qx1y3bt002GIiIiIiIiIiIsqINq67LHUqzpIjh+7ovk633qVk8tRmUqbcfy1XidKjXTtuqPzjUC/Mbpo8tXmMlqtE6RHe9+tWmqVayevVqGlB9f7fMKCAQ7eNyNFuXAuVxfPPyukT9xK8XGRklKxefkEVZEuWzi6L13aSIsWcf5Bnui+0hoSEyP79+9UPnDt3Tv1+8eJFVWTt0aOH7N69W2bMmCGRkZFq3VX8hIdbRo+UL19e2rRpI8OHD5edO3fKli1bZMSIEdKnTx8pUCCZb2BONk052iVa9c1Wh0SO5mQxY/4pVTlZzJh/SlVOFjPmn1KVk8WM+adU5WQxY/4pVRmIWe36+eTlN6tLuQo5rGtXYqdibKdP3pNWDefLxfPBkjmzu7RuX0z/5jD/lJoMxKxq9Vzy8hvV1SHs3XVTIiLi5v/yxfvSLmCBHD18Rzw8XFVrSd2bw/xTajIQM7zvv/RGdanjb+miiVnamOUX2+1bD6Vzy0WyY+s1NRu2Q5cSqnWwrs1h/ik1GYgZWmdv2N1TGjeztBAePmC1zJ11Sv1++dJ9WfD3KQm5Hy6PHkbKi8PXy4rF59V5erOfEfKfpoVWFFGrV6+ufmDkyJHq9/fff1+uXLkiixYtksuXL0u1atUkf/781p+tW7dabwNF2HLlyknz5s2lXbt20rBhQ/n5558zZpsip55/TBkF809mxvyTmTH/ZGbMP5kZ809mZjT/WbK4y/9erKJ2nl+5FKJ2pi+ad1ad9/PEQ/LpmJ3q94KFfKR0mewSHh63CKUL809OmP9Mmdzkfy9VUYdYd7Jr60UyY+pxdd6fU47Je29Y9tfmze+tZjJFJ3VfOfNPTph/vO/j/R//D4Q+eCy9Oi5V7/swb/YpeX3ERvV7Tr/MUqKUr3h4uCVtw5h/ctLP/3jvh8ePo+TShfuycqmlmDr7zxMy4qkNsnr5RfH28ZDlG7vK0Gcrmi7/7ml5502aNFGLoscnofM0OXPmlJkzZ6bwlhEREREREREREdlXsLCPLFnXWcpVzKmOY9KGNnEji5e7/DClWdpuIJEDoZi0dENXKVI0a5z8Yxbr9782TdsNJHIgL28PWbq+i/jlymydtaflH+tZfj05IG03kMgBbt0MVe/9eI/v3qe0RD2p3Q0YUl5Klc4ubTpaunf8MvGw6mgwd1kHcXdPx5XT9FRodVbpfZoyUXIw/2RmzD+ZGfNPZsb8k5kx/2Rmyc2/7bqTw1+onIJbRuT8+a9Uxc/6+4Ch5VNwy4icP/+2625jLW78EGXU/N8PDpf2Tf+Rnn1Lyxvv1orxmSdfAW/p0vO//LftVEyaty5sqiIrsNAaH3PlgCgm5p/MjPknM2P+ycyYfzIz5p/MjPknM2P+ycyYfzIzA/nPmi2TfPplA6lU9b8BNvFp1KSgtVvtzz8cktbtikmxEtkko+PbSXqBwQWPjC1STJRhMP9kZsw/mRnzT2bG/JOZMf9kZsw/mRnzT2bG/JMTwnqsKJq2aFNE8uX31n29ByGPZfqU47JymWUtV0fn39PTU8aMGaMO0wILremEi7iIRDw5JDIZ5p/MjPknM2P+ycyYfzIz5p/MjPknM2P+ycyYf3I2wUHh0rTuXPnjl6OGr+uTNZOs39lDnh1RJUXyP2TIQjl8+GaM027cCBF//98kIGCqtG49U+7flzTDQms6EY1SvseTQyKTYf7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn8yM+Sdn45PVQ375s4W061zc7vnLFp2TFv7zpETuKeoQx21hndaoqGg5fzbYIfnPlctLNm8eJv/+O0T6968k+/ZJmuEarfbgb/nQyabpo5CPWc8c0EKOxvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mpjP/rq4u0rRlYbvnoag6fMAacXHBeqwix4/cVcd/md5C2nX6rzA7fuwuWbrwnGw50NtQ/tGueMSIZXLw4E1VsPXy8pCJE3fKmTOB4u3tIQsW9BY3t//mkYaEhEvu3JJmOKPVDjU9OZLT9MmcmH8yM+afzIz5JzNj/snMmH8yM+afzIz5JzNj/snM9OZ/wid7ZP3qS3bP+2r8XmuRFXCI41+P3yu2eg0oI59911CMWrz4pCr0bto0VNavHyx+flnE37+wrF49UDw93eXQIUsb4f37r0vdur/K5Ml7JX9+STMstNrBafpkZsw/mRnzT2bG/JOZMf9kZsw/mRnzT2bG/JOZMf9kZnrzv33LNbl8KcTueWdPBVmLrBocP3MqSGyVKp1dGgYUFKOOHbslAQHFrMdRdK1ePZ/6vXDhbBIYiCm5ItWq5ZMdO56WMWMayebNkmbYOjihacpuIo8jH1tPdnVxFTdXNzVtOSIqIs7VPNw81CHOw2Vs4Xq4flR0lERGRca8OxcXcXe1/Cls70+jnUeUKph/MjPmn8yM+SczY/7JzJh/MjPmn8yM+SczY/7JzHTmf9bi1up07TK2+S9eKpucOBoYo9jq4iJSsrRvjNvcte2GXL4YIr36lYtxW4nlv3z53LJmzVnp0aOCOo61XvE60uB+w8MjJVMmN3Xc19dTPCyblyb4Co5PpEi0S7SERYRZT8rskVmyZsqqgnTrwa04V8mZJac6vBN6R8Ijw2Oclz1zdsnikUUehD+Q0IjQGOdlcs+kbhcBvh52Pc7tZvfJLi7hLiLYFK8UfIxE8WH+ycyYfzIz5p/MjPknM2P+ycyYfzIz5p/MjPknM9OZ/8C7YZLN10Oth2qb/xderygvDt1sbR/s8uTwtdG1Ytzm/L9PydWLD+WpIbUN5b9jxzKyYsVpadhwinh4uKk1WmND2+DXX1+lti1TJlfx95c0w0KrHS7RLiLBIu5Z3aV4juJ2K/o+mXziXE+r6OMNNb4RLd6ZvMXPyy/eES24bmw4z+2xm0ioiEt29o0nx2L+ycyYfzIz5p/MjPknM2P+ycyYfzIz5p/MjPknM9Ob/7NnAyWgys8y5+/u0rZd8Rj5f3ZgQcnjk0c++WiznDhxR8qWyyUfjAmQzl3KxpjR/ccvxeXRowjJ6pnFUP7xmpk0qb3d7Z8woZX1940bh6rDsLAwGTdunKQVFlrjgyp8tIs1PLbwR7Z3up6p/girq1v8S+MmdLtsGU+phvknM2P+ycyYfzIz5p/MjPknM2P+ycyYfzIz5p/MTEf+y5TKLRMnthP/+kVjXE7Lf68eldVPbFr+L18OlkKFsomHt0eGzz8LrfHILCJZo6JEgmIu3ptWsC3YJqLUwPyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mpif/mFv6dM+SWFlV7py7Ln45LbNRg4LC5ObtUCldMoearfr2h1ukc7uSEtCwkPW6W3dclQat58i8aR2kW6dSDs8/ZrSOHTtWRo4cKZ6eWIA2dbHQakfm6GjpJCLFQ0NFZs4UZ9A5NFTOicieWC0JiFIa809mxvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5mZ0fyfvBEhtT4LlF/7Z5VeNTNLr+/vSWRUtKx5OYfcvx8ly+YFyiDf8yIXPWT7ucdqsdb6xT1k6fO+0vb+DpGZOzN8/llotcMjOlpyiMgjrOCbA7+lPWxLjifbRuRIzD+ZGfNPZsb8k5kx/2RmzD+ZGfNPZsb8k5kx/2RmRvNfOnu0dK0dIWVL5RDJkVn6BbhIvVJZRHJ4Su4cIse/saxJHBwaKe0nnxU/Hzc5OqG4tGvkYpr8s9CagDD84+0tTrMtRKmI+SczY/7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn8xMb/7vh0bKznNhsuzoY6lQNqe8NuO0PNM6l3w6KKf8temuzPw3UP55t6Rk8xYZ1MxP2tfKJu7ZfCTscZR4erjq35Z0jIVWIiIiIiIiIiIiIiIiIoohm5eb7P26vGTxtBRN939bXjK5W2aremd2k9y+/5UZv366sLU46z/qhLzRNa8qvmZ0LLQmQC2Z++CBOMu2PErrjSBTYf7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn8yM+Scz05P/D+fdli61fKRK0cwiEZbTCmV5cuaDcOlQwV06VMgV53Z8oqNlcMOsUtYvWtdrLLn59/T0lDFjxqjDtMBCqx2PXVwkEIsBox90IH5zjgWKrz3ZNiJHYv7JzJh/MjPmn8yM+SczY/7JzJh/MjPmn8yM+Scz05v/wNAombo+UCrmeCxVsmU2dB8uIvJ6A/z7SCTwUbLzP2TIQnn9dX+pVClPnPNmzTokL720XF54QdIMC63xLLy7SESKeXnJ+H79xBn889FHcj4oSPz4RksOxvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mpjf/OUTk9LBoiUZB1i3uWqvzF52WsZ9tl5OnA6VMqRwyZlQ96dapVIzLbN1xVTZtuyKjXqntkPxHRkbJ338flUKFsolIqKQVFlrjgRr7fVdXEV9fcQbYFrYOoNTC/JOZMf9kZsw/mRnzT2bG/JOZMf9kZsw/mRnzT2amJ/8osLrGU/icP/+YdB+0RHB2dLTIoaO31fF583pJt27lrZfbtO+QzF92XkaNaWEo/7jvESOWycGDN8Xd3VW8vDxk4sSdcuZMoHh7e8iCBb3FxcVFZs06LD17VpAJE7ZKWmKhNT4IiEu0REVHiauLqzqMjIqMeREXF3F3tTyFjyMfx7kJnIfLRERFWKr+Ntxc3QzdLrZFzbcmSg3MP5kZ809mxvyTmTH/ZGbMP5kZ809mxvyTmSUj/6tWnhV3dxdp1bK0XL8eIh06zpQPPgiQNm1LWi/D/FN6z3+ZUj/IU0/VkHffbRzn/f+DDzZYi6yAQxwfO3aDdOxcypr/N970l5Gv141xfT35X7z4pLi6usimTUPV8UGDFoi/f2H58ccO0rv3XDl06KZUrJhb5sw5IgsX9mGh1RmpP2o2kYisEXL7wW3J4pFFHoQ/kOCw4BiXy+SeSfyy+KkPEddDrse5nbw+eVWY7j68K2ERYTHOy+qZVXwy+cjDxw/l3qN7Mc7zcPOQXF651O+4Xdw+tgXbpLaNyIGYfzIz5p/MjPknM2P+ycyYfzIz5p/MjPknM0tu/j/78l/xy+Up9RrnlQfhD6VspWySt+xjORd4Tmb/cVrCwyLl+RE11WuA+af0mv/nXqso9WoWkOBHwXHe/0+cvG0tsmpw/PiJ2+p1kNz3/2PHbklAQDHrdVB0rV49n/q9cOFsEhj4UKZPPyi9elVU56U1Flrj4ybiEu0i4VHhEhEeoSr6nu6eMS6Cyvv98Pvq99jnQUh4iLoMfmKfHxkdqa6b2O1mcstkOS3aRW0TUapg/snMmH8yM+afzIz5JzNj/snMmH8yM+afzMxA/h+GRsjI/22TgU+Xk3oN8smk35uKT1YPlX+vnC4y/tsG1hndF848UIWfKIli/ild57/PwLJq5mnI45A455Uo5SsnjgbGKLa6uIiULO2rLqu9/3/+8S45uO+2/D6nhaH8ly+fW9asOSs9elRQx6OiotX1NLjfo0dvyb5911XB9fTpQFm+XGT0aEkTLLQmJErUiCzAoaudBX9tq/Dx0aZB26P7djFr+4GIZNG36UTJxvyTmTH/ZGbMP5kZ809mxvyTmTH/ZGbMP5mZzvyjiOrm6iqYtI285szhEW/+P/qsgfX3k8cDVeEpodcO80/Omv/7weEy44+D0qtfGcnplznGea+NrinDB6yxtg92eXKI023zXrlqLsmX3zve10B8+e/YsYysWHFaGjacIh4ebmqN1tg++6yl9feaNX+Stm2vSVphoTU+MdtRpzkX1TT7ySGRozH/ZGbMP5kZ809mxvyTmTH/ZGbMP5kZ809mZiD/Xt4eMnlqc0M3f+f2Q2nfdKG89nZN+d+LVRK9PPNPzpb/x4+j5JvP9krJUr7Ssm3RGOe161RcfpneQr4ev1fOnApSAwpGjq4hbTsWj3G52Mf15h+zVydNam/3shMmtIpz2tatQ2TcuHGSVlhotUNNU3745NBJqN7UnuzRTo7H/JOZMf9kZsw/mRnzT2bG/JOZMf9kZsw/mZne/IeHR0rvjktVsbRhQEFD9+GXK4tM+7uNVK1uWYvS0fn39PSUMWPGqEOilMg/ZrHuPdFfDTSwB8VW/NgTcj9cJn59QM2GRZvhjP7+H3/PBnI+8XcYIMr4mH8yM+afzIz5JzNj/snMmH8yM+afzIz5JyeC1qkFCvpI7jxJ6+dbv2H+eItUdiVw0SFDFsrhwzdjnLZz5xWpX/83adz4dxk48B+JdLJZ6pT+Ib9YH/W3yYflXmCY7utdvHBfFs09I2Fhkany/h8WFiZjx45Vh6YrtG7cuFE6duwoBQoUUFOBFy5cGKf3+fvvvy/58+eXLFmySIsWLeTUqVMxLnP37l3p37+/ZMuWTbJnzy5PPfWUhISEJGu7VNXcJ/1Wz4mSg/knM2P+ycyYfzIz5p/MjPknM2P+ycyYfzIzvfnHrNQfpjSTsuVzxjlv2aJz0sJ/npTIPUUd4rg9f/x6VF58er04QuHC2WTdukGyceNQKVrUV06ccMjdkMnf/8+fDZavP9sr27ckvgbqwX235O6dR1Khkp/8u6eXlK8Y97WTEaVpofXBgwdStWpV+eGHH+ye//nnn8t3330nP/74o+zYsUO8vb2ldevW8ujRI+tlUGQ9cuSIrF69WpYsWaKKt88880wqPgoiIiIiIqL/Rr1funBfrWdDZDYPQh7LxfPBxkauE2UQD0Mj5MK5YHn0KCKtN4Uo1eF9Hzvi8Togykj27ropt26GxjkdRdXhA9bI8SN3Vf5xiOP2iq3e3h6Sv6C34fvGJLQXXlgqjRr9Lk2b/iG3boXKxIk7pWXLP6VLl9nq/Pz5s0qWLJZpgJkyuYmL83QCpwwErX+37O8tbToUU8ffHrlFViw5r36/F/hIDh24rfKI78Jd2yyWv2edVOd5eJinoW6aPtK2bdvKxx9/LF27do1zHv4w33zzjbz77rvSuXNnqVKlikybNk2uXr1qnfl67NgxWbFihfz6669St25dadiwoXz//fcye/ZsdTkiIiIiIiJHwtoz/bosk/WrL6njyxefl0Y15qgvnMAd7pSRYcfikN4rZdH8M+r4pg1XxL/qX3L96gN1nPmnjCwyMkr+N2StzJp2XB3fu/umyv+ZU0HqOPNPGRn22458/l/5eeIhdfzksUBpUO0vObj/ljrO/FNG0afzMpn31+k4p381fq8qakY/mRCIQxz/evzeOJft0be0vD22juH7Xrz4pLi6usimTUNl/frB4ueXRfz9C8vq1QPF09NdDh36r43whQv3ZO3ac1KmjOG7IdLFN7tl7V8MKN6x9Zosnn9WHf9l0mFp02iBzP7zhGTNlkn+Wd1Jnn6ukpiN05aUz507J9evX1ftgjW+vr6qoLpt2zZ1HIdoF1yrVi3rZXB5V1dXNQM2PujTHBwcHOMnXQhP6w2gjID5JzNj/snMmH8yM0fk//o1SyHJ28dD8ub3Fvcno3WbtCgk81d0lNx5vNRaNi3qz5MvP92TtDth/snJ8+/p6Sb5CnhLlizu6nid+vlkwcqOUrR4NrUTvnPLRTL27e1JuxPmn5w8/25urpIvv5f4ZM2kjles7CfzlneQCpUsLfL6d10ub7y4MWl3wvyTE+cf7+9YAi5vPi/JkdOy471YiWwyZ0l79f8A/G/wWnl28Jqk3QnzT06U/0VrOkmfAWXjnH72VJC1yKrBcW2wja2ge2Fqpp8uNhc7duyWBARYZhACiq7Vq+eztgwODHyofg8ODpOBAxfIL7+0Fzc3vY+MMjJHvP+jcw3e/zFLtXX7YlK4WFZ1ereepeS10TWkU7eS6viieWfV7O6IiChTvf87baEVRVbImzdvjNNxXDsPh3ny5Ilxvru7u+TMmdN6GXvGjRunirbaT+HChcXZuUS7qKCpQ6JkYP7JzJh/MjPmn8wspfOPtWn8q/wlu7ZfVzsav54cII2aFFTn5cnrJTVqW76jREZGy/AXKkuv/saHljP/5Kz5x7pLDar+ZZ3FPf7rhtKybVH1e06/zFK7Xj7rzsYhwytI/yHlDN8H80/Omv/TJ++prgXaLO4PxtWXjl1LqN+z5/CUeg3yq/8XsCOy3+ByMvjpCobvg/knZ83/lUsh0qzuXJn5h2URyFHv15ae/SyfcTCLqUHjAir/gNOffr6y4ftg/snZ8l+uQk71/h5bidK+cdr04njJMr5xLjvi6fUysMcKw/kvXz63bNx4wXo+BnFqrzHtsxaKWX36zJUxYwKkTBk/ow+PMqiUfv/HQIEOzRbKpG8OqONvvldL3nq/tvq9ZJnsMnJ0TTUAGSpW8ZO2HYuJu7urqd7/nbbQ6kijR4+WoKAg68+lS5YviFYYjYIBik60Fnw0NsbtySFRMjD/ZGbMP5kZ809mltL5r10vr3z2bUOpXivmoM/YMNoXO9kLF82qWgx/8fFuCQ/Xt3Yl80/Omv/K1XLJZ982Ev/GBRK8HGZd9B1UTkqVyS4PH0ao/Otdu4/5J2fNf8nSvvLZN42kResiCV4OO8K79yktlarmUu/7Ez7Zo3s2E/NPzpr/goV9ZPw3Da2DCxLSvnNxqV03r2qxjVaqd+9YllRIDPNPzpZ/rD0/sPsKufZkWQTNyLdqWNsFg9ZGGKfH9uFn9eX9T+oZzn/HjmVUIbVhwylqjdY7dywzWG3NmnVIduy4Ih99tFFatpwhhw8nejdkAin9/o/BNM+MqCytn6zRmpDO3UtaB+Es/Pu03TWOM+L7v6W/jxPKl88yCvbGjRuSP39+6+k4Xq1aNetlbt78rxc5REREyN27d63Xt8fT01P9xMdF8M745NBZYFOyPDkkSgbmn8yM+SczY/7JzFIq/9hZeP1aqBQs5GP98qjX8aOBMv3349KiTZFEC7TJzT8e65gxYxJ8zGQeKZV/zNDDbKZCRbKqtcaMOHcmSOW/fqP80jDAMvs7Qcw/OeHnn0sX7quBM916lzK0DXjdzJh6TKrUyCWtnsz+ThDzT06cf601pF43rofK9KnHpXjJbNKlp47XDvNPTpb/HDkzy6OwCLlz66HkL+BtPb1dp+Lyy/QWaiAB2gVjIM7I0TWkbcficW6jeElfKa7npRMr/xi0M2lSe7sXnTChlfX3gQOrWtvFYiYjUUq+/wcHhUs230xqAKURIffD1RIig56uIK+OijsAIaX3/6T1/wFOO6O1ePHiqli6du1a62noJY21V+vXr6+O4/DevXuyZ89/ax6tW7dOoqKi1FquSRXtEi2S+ckhkckw/2RmzD+ZGfNPZqY3/2tXXZJ6lWaropFRtermlT0n+ukrsuowZMhCOXw45qDToKBHUqfOL+Ln96XEGo9KlOz879h6XepVni2HD9w2fB8VKvmp/OsqsurA/FNq5//wwTsq/9s2XzN8H9jBvutYP31F1iTmf+fOK1K//m/SvPl0mTdP5PFjfd0TyNz05v/82WCpX2W2rFr2XwtTvQoU9JEdh/voK7LqwPxTaucfM/n+XtJBdSiIDcXW1Vu7y9lbw9Rh7CLrkUN3pEurRbJn540U336i1Mg/utHg/X/WtOOG78Mnayb5d3cvfUXWJL7/nz9/T3Ln/kKaNJmqZnQ/iDnx3DyF1pCQENm/f7/6gXPnzqnfL168qEZsvPLKK/Lxxx/LokWL5NChQzJo0CApUKCAdOnSRV2+fPny0qZNGxk+fLjs3LlTtmzZIiNGjJA+ffqoy2XMub5EqYD5JzNj/snMmH8yMx35b9CogPwwpanaaR7bskXnpIX/PCmRe4o6xPE4d+HuKg9CHqvZHY7g5eUhS5f2k65dyzrk9snc+a9aI7f88Fszte5SUvOPFsJXr4SIIzD/5Mj8ly6bXSZNaaZaxyc1/2FhkWp2qyMULpxN1q0bJGvXDhBfX5HFi0855H7InPlHy+Af/2huXY8+Kfl//DhKzYp1BOafUuP7780bofJUv1Vy6kSg7utg7fqixbOpdV5TA2a0jh07Vh0SpUT+Xd1c5P1P6tp9/9fzf0A230zq8F6g4zIZEFBUNmwYIqtX9xfv/yadm2t32u7du6Vp06bW4yNHjlSHgwcPlqlTp8qbb74pDx48kGeeeUbNXG3YsKGsWLFCMmfObL3OjBkzVHG1efPm4urqKt27d5fvvvsuTR4PERERERFlXN4+HnZb5uEL5fABa6xrMx0/clcdRzsxjHS31bnlIvFvlF8+/Nzf0H2jbeuIEcvk4MGbaoclikoTJ+6UM2cCxdvbQxYs6C0eHm6SO3cafrukDC1LFnfp3CN5+e/XZZna4fjNj00M3TfzT2nN09Mt2fl/uv9qyZTJVX6b+V+7x5TKf/78WdVlsXPdzc2yRjJRSsGa8x26lEhW/l95doPcvBmqZgYawfyTs4iMjJage+HWfGHtVGTSXrvUqb8clS49SqrlFr79ydhnHiJn+/zTe4D9QYx6/w/4+rO98s/cM7JhV88Uf/+HLVsuSaNGv0v9+gUlC1oPm3FGa5MmTdQTFvsHRVbArNYPP/xQrl+/Lo8ePZI1a9ZImTIx10LKmTOnzJw5U+7fv68W9p0yZYr4+PhIWsIoLc2xI3dly8ar6nElGzv5UTpgm3+M8tq04Yr68JFszD+lk/xr7/doK7lx/RV59Cgi+TfM/FM6gPf6qChLWDFafeO6yxL64HHyb5j5JyeBWajvv7nV7myMr8bvtX7BBBziONZsiu3jCf7Sb0g5w/lfvPik2rGzadNQWb9+sPj5ZRF//8KyevVA8fR0l0OH2CuVHCfoXpi898ZWOX3qXrLy/86HdeXp5yvru1Pmn5wEZqK++/oW1QIyOfl/bXRNeen16g7N/4ULQXL2rEj79inTppUI32/x+WfvrpvJyv9zL1eRUe/V1nmn//3K/JOzwPqsc5d1kJKls0tkZJQ0rTNXpv9+VJ23c9t1+fjdHXLlcojK62+TD8v+vbeSdkf8/ktOZOrPR2TX9ut2z9P7f0CzloXllTcd8/knf34fOX36Rdm4cYjcuvVAjh2TNOO0a7SmV/jgUb30dOsOGLyxfvbhLlU0hqS2yXCJdhF58OSQyEkdP3pXapadoQYYwMw/TqgP5G5uzD9lfBfPB0vdirNk907LF72Ff5+R10dsVKN/gfmnjOzWzVC1bseGNZfV8RVLzssLT61XbWa0/Cdl0BnzT87kfnC4bP73qoSGxh1AcPZUkPULpgbHz5yKu5ZrvQb5dbUPi53/Y8duSUBAMev5+NJZvXo+a8u8wMCHSXlYRLqEhkbI5n+vSPC98GTlH2sVV7LTejg25p+cSXhYpHr/v3vnUbLyX61mbtWC21H5Dw4Ok2HDFkvnzpiB6Gb8gRLZgTxjADE+7ycn/1jfEv8HJIb5p/QyyB77erZutKzbjdl6k787KJcv3hcvbw9ZtKaztEzCutz8/kvOBrOz99gZaGPk/4CqNXLrWqc7Ke//KLh6e2dStbcuXcrKjTRcDpmF1vjE/f6Y4AyOA09GqZSrkEP6Dy0vnpkt/6mP+bSe/DGntfr9xLG7Uq/ybFm84KxjtpkoDfKP2Uv7dlvecDGqq++gstb+66+/XVPmLe+o3uywFo1/1b+StHg2kbPmH4UjbWQvWsL0GlBGcue29Kl49sXKsvzfLuLm5qp2yjSuOUd+mnjQUVtNlKb5z5U7i/QZUFYKF7V0FRkwtLys29FdMmd2V+tRYq2Or8bFHdlOlJ7yX6pMdlm3o4eULR+3SFqitK8avWsLx0uWibuW67zZp2THVstOGSPKl88tGzdeiPEZTBvMCSnRQIdMLDzxWRzrd/aUGrXzJCv/+C6MHfZGMf+UlvnPmi2Tandnb30yI/lfufS8rF150SH5x36pPn3myjvvNJRcuQzfBZlZIvnHjm3kv3X7/3Z2JyX/61ZdUoMxjWL+yVm+/8LhA7dVlwN8z8Us7W69S6vT3/+0ruw82lfq1LcUgd59Y6t0a704ZTpcEqVh/vH+/78Xq9g9T+//AUcP35ENay455P3//v3/1n7dvPmS5Eyd5ZDtYqHVDlU1D9c/emTiV/ulV8elEnj3kRq1MnpMbcmT18v6gTxHTsuassVK+MrUv1pJ+84x1ynQI9olWsT7ySGRE+UfLQS6t10i164+UKO5Rn9QRwoW8rGuY4aF3yFPPi+ZMqul9OwXs/23Hsw/OWv+/555Sq21d/Z0kPoC+tb7taVYiWzqPPx/4JfLUnT1zZ5Jfp3RUoYMr2h4m5h/ctb8r1x6QTo2/0d92cSH3dferimly+ZQ52Xxcpfcebysv//0R3P530v2P5wnhPknZ8z/ndtxZ86NfKuGtVWSur0nLZRwuq3w8EjVYunIwbjtJxPLf8eOZdSOxIYNp0jTpn/InTv2Z/C1azdD1q49L4sXi0ybxgE+5Dz5x86R7ybsk727Eh9qzvxTRss/drb/+N1B2brpmkPyP2vWIdmx44qMG7dFsBrX33+nYe88SjdSK/8w9ZcjqtiaGOafnPX7L14DPdovkZ++t3y+wP7NFm2KqN8xqw77QrUi0Dsf1pGvJjeOURTSg99/yRnzj8EF9gYN6P0/YPK3B2XiVwcc8v6/efNFqVnzZ7VG69Wr96WyzhVKHME97e7aeUWjGbTbk0Mdnn+lquo1rRVUE1o8WGsbgFHs/8w9K5986a//TZddA8gJ8z94eAWpXiuPGuWeEBRhtfxjBvi0347KF983ti4inyjmn5ww/z36llYznEqUijti1xZmtTZvXcTa3QAfML6aFGBtK5wo5p+cMP+t2xeVRWs6qTZgCcH7fJMWha1rF0/4ZI9M+KGxZMmi82Mo809OlP+ZfxyXD0Zvlz3H+6kBlZp2nYrLL9NbqPVo0CqpZGlfGTm6hrTtGHOAZaZMbrJxT0+JiIg2nH98Z5g0qb3di02Y0Mr6+7Jl/SUsLEzGjRsngwYZH+BA5qM3/4vmn5FXnv1XzdhAJwOj+cf/Bys3dVMDDnRh/smJ8r9+9SUZ0nulbN7XWwoXzWo4/8jw/BUd5dEjx+V/4MCq1vz37Fle3/2QqenNP9bnwwD7lZu7SfmKOQ3nH9Dtj/mn9Pz9FwPp/17SQcqUtwwuTkiZcpbLPHwYIe+8tkWeGVFZ19IhCr//khPlH8uktW/6j/w0rbn4NyoQ4zy9/wd8+1MTu8svpNT7f9u2lpnllv8DDktaYaE1vj9olsTf2LDe2KNHEWr2RpXqia+zYevKpQdy6eJ9Cbn/OMZOGkfw9PSUMWPGqEOilMo/ZrDiTbJiZT9VaDXixo1QuXL5gZoFrs34cxTmnxyRf4xkvHwxRK0zYK+FXkLu3glTrbRv3gi1zv52FOafHJF/rFOJNbmxxlLNOomvsxT7upcvhci1Kw8SHaCQXMw/OSL/TVsWFm9vD+syIbG/aOInPvjegFba+OzjxqXDKB3mH21Tx3/TUHyyehjOP0aj37n9SPLm81Lt9ojSW/7r+ueTcV83FL9cmQ3nH7O5b1wPVYOTdQ80I3Ki/FermUc++bKBFCrsYzj/mAWF/UcFCvow/5Qu8w+bNlxRbYErVzPWmzoyIkp9/z114p7+Qmsy8DswpXT+Mbhs2P8qSuEi/w0yM/J/QOiDx6rjn+0gzYyKrYOT4dfJh6VP5+USGRll+LrdepeSP+e2SbEi65AhC+Xw4bgLE48atVqaNZsuCxZgoW6dI8eIdJjx+3Hp2X6J2mloVKu2RWX2P+1SrMjK/FNqmzv7lHRrs1iCgwwu6CEi9RvmV6PZU6rIai//QUGPpE6dX8TP70u5aX/NeqIkW7LwrBrRfuum/baNCcHAtH9Wd0qxIivzT6kNO8k79yipZqbqHpX7xLef75OG1efIrZuh4mgYzTt27Fh1SJRS0MGpV/8yqlBqNP+//HBIGlT7Sw1WdjTmnxwBOwn7DS6nDo3mH90Q6leeLSePB4qjMf/kCOjENHBYebUP02j+F80/K3UrzpZ9ux3/wZz5J0fAQIG+nZfJonlnDF/XJ2smmbO4vXTsWsJh339v3AgRf//fJCBgqrRuPVPuO/6jFpkIZpW+OqqGKrgarQHs2XlDKhf/U61RnxrS+v8AFlqT4e2xdWTG/DaqJWRsyxadkxb+86RE7inqEMftjerateOG4Q8peh04cF2uXLkv69YNED8/kfnzTzjkfsicXn6zusxd1sHuiHQ9+ddaCF+/9sAh28f8kyM9/VwlWbS2s2TzzZTk/B85dMdhOxu9vDxk6dJ+0rVrWYfcPplb7wFlZdmGLtb16JOS/1MnAtXaxo7A/FNqmDXtuCoaYcdLYrT1bEaMrCa/z25lXb+YKL1avOCs1Ks8W9f7uJb/Ic9UlKl/tYrRcpUoPUIL4ToVZ8lhPWttP8k/BihM+7u1tZUkUXq1c9t1qV1hpjrUm/8OXYrLn3NbG+6ERuRMAy3X7eghHewUS/V+/8W+fz2vm6TIlctLNm8eJv/+O0T6968k+/Y55G7I5M6fDZYG1ebIqmUXdF+nWs3c8vEEf+taxhkdC63JgDVX7U37x5vq8AFr5PiRu2qxYBzieOw328C7YdKl5SL1QT1R+HyCiSPR/31geeGFpWqhXywGfOtWqEycuFNatvxTunSZrc7fuvWStGpVUl2+VCmRbdsup9AjJ7KMaKxQyS/J+cc6BZ1a/CMrFusY1cL8k5PBABu0zU5q/tE+r3vbxbLg79MOyb+Hh5vkzp3wuslESYU19uyty6o3/8hon07LVKEqUcw/OakOXUrImE/rqR0vaAm5cd1lu11udmy9Jo1qzJGge2Hi7eMh9Rrk138nsfJP5CxatC4i739SV4qXzKaOI//4bBPbwX231CxuDKxEu8iGAQX13wnzT06qQUABlf8KlXJa20naW3cYg8rw/o8BCeiC0LhZIf13wvyTk8KyOe9/Us+6fM6WjVftznDCgOKmdeaq/wfw3blJi8L674T5JyeEgTKxW1/r/f4Ln47ZKaNf3eyQ7794jeE7OoSEhEtuY6sbEulSqIiPDBhSTi0hpX3Osff5/35wuAzts0o2rr+istl3UDm7kxQz4vs/C63xSaQb8OrlF2RI75XWEVq2vhq/V1xc8GZoOY5DHMfCwLZy+mWWDbt6SJsOxRLdHBc0zI58cohRxItPqjfRTZuGyvr1g8XPL4v4+xeW1asHiqenuxw6dFMCAx9JtmyWnuyZM4vcveuYmbNkvvzjw/SgnitUsTSp+ccHlLU7ekj3PpYFqxPC/JMz5R8tjwb2WGG3G4He/Lu7u8qyDV1l8NMVHJJ/Ikfl/8SxuzKw+wq5cjkkyflH65l5yzvK869UTXRzmH9KVQZWA0HrvD4Dy1pbIvXtsly2bbqqjr/87HoZ3GulWs8bbbKxUx47X4yKnX8iZ8l/Fi93GTC0vHo/x5rdyP+KJZbBk++8tkX6dlmmZnsXK+ErDRoXkMgI43tLmH9y1vyjaDroqQrq88iVSyFq8NjcWSfVeeM+2KmWV7h4PljN3saSIfgsZBTzT86af3yPxXdYHOL7cN/OS2Xqz0fUed9/uU86Nv9HTp+6J/kLeksd/3x217RPDPNPzpb/p/uvtn7OScr3X3jzvVoybW4bh33/3b//utSt+6tMnrxX8hsY10kmZ/D9/9W3aqh61uPHUdKtzRJ59/Ut6ryl/5xVM7oxyNgnq4fa5x8dZb7P/1yF3A6XaBeR0CeH8cCbHFrT4ctlbGdPBVnfZDU4fuZU3NZKpcvqax0T7RItkunJoYgcO3ZLAgKKxdie6tXzqd8LF84mgYEPJXv2zBIcbOlJ/eiRSM6cmXXdF5mbnvwj9pmzuFtHTCU1/6VKZ9e1Tcw/OVP+Afl3c0te/vWuUZmU/BM5Kv/Ic+YsbsnOf7ESlllQiWH+ydne/+0pVyGHeHm7y9nTwdKwiUjogwjZsfW6WpMJHXC69y4t3t4ehm83dv6JnDH/xUv6im92Tzl3xvJeH/owQnZtuy4+Ph5qQAJapuLQKOaf0kP+8xXwknz5vdT7Pzx6FCl7d91Q67hiiZ3+Q8pL9hyWwb9GMP+UHvKfI6enFC3ua/2sHx4eJQf33ZZMHq5qh/ygYeXFL1cWw7fL/JOz5R8d/fCTnO+/9pbdScnvv9Wq5ZMdO56WGTP2y9dfL9R1X2RuyXn/x+uheIlscuKoZe35wDthqrWwVisb/kIlu/uMMvr7P2e0JlHz1kVk0u/N7J5XorRvnFGLOF6yTMyd6g9DI1TbALTS0MXm+2n58rll48b/emKjZZlt0Rdv7BjhsmbNWXX8zBmR+vUNtKohSoB/owLy87QWaudhUvOP9gIY8b5ru841Cph/chJYW+aXP1uonYpJzT+6IXwweptqNeaI/BM5CpZM+GV6S8mX3zvJ+YdxY3fJmhUX9d0p809OCNn7e+ZJdZg1m6dMmNhYWj/pUoPXyLFLg9TnJMxkRReczz/enbQ7Ml6fInI4fI6ZN/uUGs2OnH/xfSPp1tvSpebrSQFy6vpQVVxFO+1nB6+VsW9vS9odMf/kpPlf+Pdp1S4VrfDGfd1QBg4rr84bO76+nLv9lPjlyqwu9/KzG+TNl3W0irSH+ScnXqf7Qchj9RkcmX/2pSrq9NdG15QLd5+yrsX91qtbZMTT65N2J8w/OZHJU5urOkByvv/ic9Okbw445Puvbft6X19P8TA+vpNIlzkzTqqCKoz7pqFMnGKpjQ0YVl5OXx8qlatZlpj6eeIhGfXKZruthTPy+z8LrXaoqrl34tXzkPvhqoVebCPfqmFtFwBaGwGcbuvKlRC1EHZSRvh27FhGhbVhwymqR/udO3FncGA0S9683tKs2XS5dUuka1dLezOilMg/BgocO5L0/N+4Fiq7dtxQI32NYv4prfOPHeeHD95Jcv7RZmn3jptJGuGlJ//Qrt0MWbv2vCxeLDJt2kHD90Pmozf/2LF+aP/tJOcfn58w28neepaJYf4prfOvwYzVN17cZB0w1rl7Scmb77+R6toOEBShVm7qJu98WMdyPxwNQBkg//g/4PURG9XarNC+c3EpWMgnTv5RhFq8tpN8/EUDy/0w/5QB8o+ZSq+N2Cgrl1h2fLdqV1SKFo/ZqQOvAfzMXdZevpzY2HI/zD9lgPyjXTbe/+fPOa2ON2tVOE6nMu3/gGl/t5ZJT3bCM/+UnvOP/B7YeyvO91e933/hyKE7cu3KA4d8/0Xb4MaNLWu4fv/9bvH3N3w3ZEJG3/+x9ur3X+6Xf+adUccrVvaL8fnf1sTfmsrsf9qp7gb21rHPqNg6OD469n2PenmznDwRKKu3dI9xertOxeWX6S1UT3Z8CC9Z2ldGjq4hbTsWj3E5fBhZs6273fbDiW6ei4tMmtTe7nkTJrSy/v7FF60kLCxMxo0bp9YSIdJFRyTHvrNd/l17WbYe6B0jw3rzX7Cwj6zc1JX5J+ejI5JffrpHZv95Qvac6B+jhYze/KOF0pJ1nZP0hVNv/pct62/N/6BBllHGRCmR/8nfHpCJXx2Q/af6q9Z4RvOPdqrzV3Rk/sn5GPhIgnX3dhzpG6O4Gh9tZsfRw3dk5PMb5Y85rXVdj8hZ81+lem7Zflhf/gsUtOyAOXs6SJ4ftk5+ntZcihTT1z6eyBnzX6pMdtl6oI+u/OfO42UtTj09YLV8+1MTKVNO3/JRRM6Yf+zH2binl+TJm3hLYKzjB7duhsrQPqvl4y/8pVrN3MnZUqI0yf/Rw3elXZOFMmN+G2nSorDh77/w/if1HPr9d+PGoepQ+w5MlNLv/5goiP34WbwSLye6ubmqLoAYZN+l9WJ5/uWq0q13KcnoWGhNBrx5xjcbD2+2+InP6ZP31MLA+JBClB49/3IV+d+LVewWShPLv9ZmQO8afUTOZsgzFdSaY/bW6Ugs/9jR8vBhhNpJk5SBBkRpre+gstKybdEYRVa9+b9xPVTN6C5fMSfzT+lScFC4GsXbvXcpw8XS3HmySNnyOST0wWNxNE9PTxkzZow6JEopaJU6+8+TSco/drgj//gM5GjMPzkCZhRNn3JMuvQ0nn+s04rlF8IeOX5WB/NPjoAC0R+/HpOOXYsbzj92zmM9+yS1kDSI+SdHqFApp8xZ0l7qNbCsjWrk+y9mwW7bfE0N0kTxiSg9+m7CPtXBA59ljPD28ZAuPUpK4WKWgccZ/f8AvsKToWTp7KpQiqnTF85ZCkd6vf3aFnlu6Fpjo1mSuE8Go1nGjh2rDolSCkaio1CKFsJnTt0zdN2P3t0uT/dfrdpP6sb8kxPB7AwUStEC49QJy+Lven3xyW4Z3HOleu3oxvyTE8HsDBRKsbPk+NG4LeQT8sNX+2VA9xUSdM9AJpl/ciLbt16TMaO2SXBweJJeO5jNVLxk3HWbkpL/IUMWyuHDN2OctnPnFalf/zdp3ny6zJuHVt/madVEjrd310354K1tcuum/bbtiRWakP+y5Q3soGH+yYkcPXRHxr69XS5duG/4utjR+PXkAOvaZY7I/40bIeLv/5u0aDFD/vhD5Nq1EMPbSRSfM6eD5KN3tsvxo8a++wImqGAt+1p18+q/EvNPTgQDhBs0LqAKpWgBbGRf/qb1V6Rv5+WyfYtluRFdkjEmk9+BKaXdCwyT6b8fV5MGk/LaGTGymtR24Ps/bNhwXpo3nyatWs2UY8cMb2aKYaE1BaCF8JDeK9WC1InBgvHw87QWMmVWK92zOVyiXUTCnhwSOZEP3t4m/bou19VzXcs/drL8Oa+N3dmA9jD/5Ky++Hi3dG+7RNfsJC3/475uKH8tbqer3QYw/+SsJn1zQDq3XCSBdx/pzv+7H9eV+cs7qDYyejD/5GxatS0qe070k3z5veOct2zROWnhP09K5J6iDnHcng1rLsn2Ldcckv/ChbPJunWDZO3aAeLrK7J48Snd1yVKjH+jArLvVH8pUco3yfnfuumqdW3XhDD/5GzQMnvvyf52i6V68797xw1ZvdyytmtK5z9XLi/ZvHmYrFnTX6pWFZk69YDu6xIlBkuf7T7eT/wb5U9y/g/uuxXvebaYf3JWWKO+VYP5snr5xUQviw4eGJiMVsPrd/VQhVo9+P2XnA0GS2472Fvadixm93w9/wfs2XlDfvzuoEPy//DhY/nyy22yfHl/WbWqn5QvL2mGrYPtQb009MmhDh9P8JerVx6Iq6uLmtm6b/dNad+lRJwi0jef75V5s0/Lv7t7qpAa2ySsrv3kkMiJ8v/uh3Wl78Byag3U69ceyMZ1V6Rzj5Li6RlzTdTfJh+Wyd8dlG0H+6j1+fCjf5OYf3LO/I98q6a0bl9MtVDFKK+l/5yTbr1KxSmi/jX9hHz07g7ZdbSfOq9QEf1tM5h/ctb8P/tiFanrn09y5MysZmjPmWlpJxn7/X3JwrPy+oiNsv1QX/X5p2hx/W3jmX9yxvwj87HhC+XwAWsEYygxyP34kbvqONZtit1O7JMxO6VmnbxSr0F+Q/nH6PkRI5bJwYM3xd3dVby8PGTixJ1y5kygeHt7yIIFvSV/fsv/LxjF7uYm6vsJkTPlH+vcY6BC42aFEtkk5p8yXv6//3K/GqCPJRhSOv+2LSkxkalCBQOzZ8m8UjH/v/xwWC5dvJ9gm1XLJjH/5JzffzHQBoPmtaLpnBknVTvh2GvPY13iJrXmyoQfGqn1WjFQQf8m8fsvOV/+42t7rff/gK2brqnL/u+lKin+/r9t22W1PGfHjrMkc2ZXKV1a0gxntNrhgr9o1JNDnevNVKrip35ft+qSvPfmNtWDHR+g61WeJX/PPKnOa96qiLw2ukaSFr9Wm4KB8/yuSE6Wf6y3Ua1mbvU7iqzvvblVwsMss1tb1J8nU38+on5v3KygvPluraRuFPNPTpl/FE21FkiYofH+m1slJMTSThIz/SZ+tV/9jvU43hpTW1yS8r8u809Omn8MqKnrbykU7dpxQ7VTvXPbMru1f9flMm7sLvU7irGj3qstmTIl4QXA/JMT5f/0qXviX2W2HD54J855X43fa/2CCTjE8a/H741z2eX/dpXxXzc0nP/Fi0+qwtGmTUNl/frB4ueXRfz9C8vq1QPF09NdDh36r43ShQtBcvasSPv2pfQ9AWRqevKPAZX1q8yWXduvJyv/fy1uLz9MaaZno5h/cpr8ozMH9u2sX30pWfn/bWZL+XNuG4flf//+69Ko0R+ya5dItWpx1xIkSkr+sW8Tn38weDI5+f/6xwBZsLKjno1i/skpv/9Cw4CCqjslupq9P2qr/Dr5sDr9s492Sen8v6v/J7BcyKtv1ZDKVZNQ8Of3X3Ky/Deo+pdao9Uevf8HvPhaNfUd2BHv/2gdf/r0XVm8uK8MG1ZNNmyQNMNCqx3RLtEimZ4cGjT02YqycU9PtQYBgtB7QFkpXTaHdeRLl56luPg1Zdj89+pfRrYf6qOKr9CtdykpX8myDhNeBzhfb7tgovSWf4zWQjslfKiGjl1LSNUalkEIGOE4YGh59X8DUUbMf+OmBVU7SW22ausORa3rcOA1gc9HmPlNlJ7z7+PjIR26lpCcfnE705w9FWT9gmm9zWiRM6eC4lwWo3GT4tixWxIQ8F/LJnzXqF49n7VlamCgZd3M4OAwGTZssXTuLOLhEbPDCFFS84/PMB26FLd+zrHF/FNGz7+rm4t06FJCChb2cer8o7i0adNgadpU5IsvtiXpvshc9OQfO83bdS4uRWPN2ktK/vUun2aL+Sdn/P6L77bY33P7lmWgccXKflKmXA7rPiAvb3dZs/JS0iZbETlR/p9/tao0bmq/E42R/wOSQs/7f/bsmaVBg8Kq02bTpkXl1i1JM6x4xEd/V9ME22m8OqqGdbYfkdny//wrVa2znYjMlv+nn68kjZoUTJltIkotKZT/QU9VkBZtiqTMNhE5Sf7R7vTtsXWkQMG4O9pLlPZVOyJt4XjJMnHXsnxx+HqZ9ttRw5tXvnxu2bjxQowZJrY7LPGlFmtB9ekzV955p6HkYtc8SsH8o/X7Ox/WlWIlsiUr/6Ne2SQ/fp/4Gk2xMf+UlvlHS7p3P6qrdqInJ/8fjN5md6ZfSuQ/PNzSVQo8PUW11yNKifwja8i/vfWJjeT/8492yyfv7zC8ecw/Ocv3X6y7+sKwdbJj6zV1/OdpLeSH35qq3zEYZ+n6LqrrJRw7fFfOnrrnmG0mSsX89x9SLt7alt7/A74at0eG9F7pkPf/2rULyrFjt9WghgMHbkqOuB/VUg0LrURERERERDrcvfNIFi+I2zpv5FtYHsTyxRK0Fko43RZ2BGJmbKHC+tfq1nTsWEYVkho2nCJNm/4hd+5YZnDYmjXrkOzYcUXGjdsiU6eK/P33McP3QxSfoHthsvDv00nOP3aOYGZsITuzAhPD/FNaC7kfLvNmn4ozO0lv/nE9z8zuUsBB+Ufb1MaNf5dWrWbKjh0ir75a1/D9EMXnYWiEyj+WSUtK/sEzs5sULMT8U/oVGREl4Y+j5H7wY3XcN7tnvLO0P/rCXz783F+dj+VH8BmIKL1+/vl10mH1PTg2vf8HlCmfQ5o0tz8rNrnv/7lyeUnXruUkIGCqvP32emncWNIMeximJ3xPJjNj/snMmH8yM+afnMiqZRfknde3SIPGBawj1rX28b9Mb6FmKqFVUsnSvjJydA1p27F4jOujpdE4Peuz2sk/dtRMmtTe7sUmTGhl/X3gwKoSFhYm48aNk549yxt6fEQJ2fzvVXnlf/9K7Xr5YrRQ1Zt/tPsaO76+/jtk/smJ7N19S+Ufs/psZ7bqzT8yPHpMbYfmf+PGodb8589vvKBFFJ8TxwLl5Wc3SPGSvlKjdh7D+YeX36iu/w6Zf3JCPlkzyS9/tjB0nTu3H0q7gIXy2ts15NkRVfRdid9/yYk8ehSp1iAuUiyrtGpXNMZ5ev8P6NClhEPf/194oY760f4PSCsstKYTLtEuIg9EXLy4GjaZD/NPZsb8k5kx/+RsOvcoqb5g2hZZbb9o4ic+588Gy77dN6V9l+Kq4JoY5p+cDbK/82hfyZPXy3D+r119IJvWX5GO3UqoNqyJYf7J2TQMKKDyn7+At+H8YxbIyqXnpXP3krrWrGf+ydmgbSTyb2/5hMTyfz84XP6Zd0blP2u2xPtUMv/kbNCRpl/X5WqWnn+jAoau65cri0yZ1VKqPVm71dH59/T0lDFjxqhDopSQK3cW2XeyvxpoYE9C/wc8CHksk789KD36lra7/EhGe/9n6+D4WLoAEJkT809mxvyTmTH/ZGY68o8CEYqsWKMJO82NWL74nHw5bo+EPogQR8No3rFjx6pDopTKv4eHqyqyYofjskXn4rRQTciaFRfli092qx3ujsb8kyPyjxnZKLKihd2ShWcN5X/jusvy2Ye75c7tuG33UhrzT476/I8iK9qfYgkFI21Qt2+5JuPH7pIb10PF0Zh/ckT+g4PCxS9XZsmRM+5ASz0aBhSMt0hl1JAhC+Xw4Zt2z8MSCoUKfZsi90MmofP9H/nF+/7UX46q14Ne588Fy9+zTkpISOrsaErr/wNYaI2veh725NBJRLtEi3g9OSRyIOafzIz5JzNj/snMjOb/75kn5YWn1qtZev9n7z7AmyrfNoDf3ZsWStkFyt57yN57bxQU1L84ABWcuBD0ExXFhThQhjJVtiDK3nvvvWeBUmgLnfmu5y0JLU0haZM26Xv/riuenMxTcyck5znv8z6KzMskXnylKpas7oag3JYdYc78k6Pmf8mCUxj8zEqcOnHL4vw/+Ux5rNjU0+xoWHOYf3LU/K9efl7l/8C+GxblXwqyXXuVwtodvRFazLL5uZl/ctT8b910WeV/2+bLj7ztqROR6sCEVu2KYeOevihVJsii52D+ydHyLyP6fpraEuUr5klznRx41rL+HJQImaSWsm7OtMmH8OoLq+2Wf5k/+c8/D6JIkUePGiTKyOe/dGca+9F2bFh74ZG3PbDvuurmUbFyMNbt7I1KVYK1+PxnodUMgzSDdr23dCR8tSgLMP+kM+afdMb8k86szX9y0aiHGt10JyYBH7y5ERfOR6nrZKeiXGac07VJzT9x4l6xKVeglUezM//kgPmXotHqbb1QolQg4uOTVP5l54txR19MdPJR65vWX1L537srXK0z/5QT8t+ybVFVNJWdhjK648MRm3DsSESa/O/fc03lf8Pai2qd+aeckP/HGhRUO83r1i+oDiL4vw+2YP/e6+o6eT9Im0gh/yY0rP4Hlv1zRq0z/+TM+ZfvMTLX6oOkqPpc/+U4fOAGYmMT1VLWzRVbZdoQaSNsbf7lfTZ48GI0ajQZzZpNRXh4DMaP34pWrX5H166zTN0VZs7cj169KqjuC0T2+PyX7/3rd/c2zb8q3///u/cZfysyVs3lLaR7TecWC9RIVmHJlDk55fPf6k2Xobdr167F77//jp9++glz587FqVPmj9ZwWvKZJAfa8rOJdMT8k86Yf9IZ8086szL/Li4uKBaWfMS4FFEXLzyNpMTkH6m9Oy7GW6+uU+cbNy+MKbNbWzQnDZEzff4b83/m9C0sXXxGtdMWT/f9T432FnXq5VfzkpWvZNlR7ETOln/pavDf4jO4GZHcou6VQavxVK9/1fmKVYJV/uvUK2Cf7SbK5vxH3IjFv4vP4Mql5O4e7762Ad3aLlLn5XvP5Nmt0bRlqH22mygL89+zw2L8OfNYmsvHfboTLi5SDE1el6Wsf/XpzjS37d2vDN7/uK7Vm7ho0VFVPF237mmsWjUAwcE+qF8/FMuWPQkvL3fs23dVHeTzxx8H0KdPJasfnzSWgc9/Y/tsOdBy/ZqLWPDnCbX+0/h9aF73L8z6/Yiai3vu0o549gX98uhu6Q03bNiAb775BosWLUJ8fDwCAwPh4+ODGzfkqI1YlChRAoMGDcILL7yAgADL2qEQERERERE5s0pV82LbwcdNR5C/+mZ1+Pp5qPPe3u6qZR5RTlWqdBA27+tryr+0yJadjMLNzRVtOhTP3g0ksqPCRfyxfncfU+YHDKpo6mggB+Qw/5STyZz1a7b3MhWZ+vQvm+o7T2t+/6EcYu7STihcxC/N5SePRZrybyTrJ45Fprlt1O049b3Ix9fiUoxy6FA4mjS5/2+JfN+qXj35AJ7Q0FyIiLiDadP2onfvihzNSnYnXTskwx4ermjVvhgM9+br7tazpDrouGPX5NGuSxaexvhxe/DDlBZwd3fiIapWsugv7dy5M/r06YPixYvjv//+w+3bt3H9+nWcP38eMTExOHbsGN577z2sWLECZcqUwbJly2yycYmJiXj//fcRFhamirolS5bERx99ZBoWL+T8Bx98gIIFC6rbtGzZUm0PERERERFRVki5Y6Nx8yKoVTd/tm4PUXblv17Dgqq1JJFO+ZeiqqhdNz8aNyuc3ZtElGUk+8Z/A6rVDEHz1hzBSjmPtIo3juRLqUTpQNOBNkayXrJMYJrbvvTMSvTtssTq5y5fPgRr1ya3ZzW26Db+myOkRHLwYDh++20P2radhuPHI/DPP1Y/DdEjSUvgTi0W4Mdv96r1ESNr451RddT5UmVz460PasM/ILlNfLkKedCiTVGtiqzCosMoOnTogDlz5sDDI/nI7AfJaFY5DRgwAAcPHsSlS5dssnGfffYZfvjhB0ydOhUVK1bE9u3b8fTTT6vRtC+//LK6zeeff45vv/1W3UYKslKYbdOmjdoOb++0H4JOS2rLdwFY2M6dKEdh/klnzD/pjPknnTH/pDPmn3TG/JPOmH9yMGdP38IHb23CmK8aomCh+yNbh79dQ83JamwfbFzK5Q/64JPHVLtta/PfqVMZLF16HA0bToKHhxt8fdPWZj77rJXpfM2aP6FdO9vUZYhSkpbAAwdVxGMNHj0lQrfepUznF849gfqNCiFviE+O//y3qND6/PPPW/yAFSpUUCdb2LhxI7p06aIKvUJG1M6cORNbt241jWb9+uuv1WhauZ347bffkD9/fsyfPx99+/bN+JNbNg9wlnGRhtkJ95ZE9sb8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6QzC/IflNsbkTdjce3qnVSF1vadwzBxWks1J6u0Cy5ZOhDDR9RAu07J7VMfnGohI/mX0asTJiTXRR70xRet01y2ceNAjBkzxqLnIrL0819aX8to1SefKW/Vw0fdjsP7b2zCgP+Vx/ARNe3++e/l5YWRI0eqZXawrjH4A6KiopCUlJTqsly5kidFt4X69evj559/xtGjR1VL4j179mD9+vUYN26cuv7UqVO4fPmyahdsJKNd69ati02bNqVbaJU5ZeVkdOvWrVTXuxhcgGjAJa/j/KNukOR73FsSZQLzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPObJX/XIGemPdvZ7PXSbFVTuk5dOAG3n9jo2qxWqN2vkduM/NPjpZ/mXu+XpXZeHd0HfR9sqxV2+Af4Knm8g7KbVnh81H5HzhwPl5/vT4qVbr/Xtq06RxGjFihzl+4cAvBwcCIEcgWVjdKluKmjDD18/NTRc3cuXOrU1BQkFra0ttvv62KpeXKlVNti6tXr45XX30V/fr1U9dLkVXICNaUZN14nTlyZIdsu/EUGuoEcwhI5iWTjvPdh5wU8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzmyd/2vhdzDoqeU4fuymxffJlcsTBQr6oky5oCzJvxTWRo0alarARnqyVf5dXIG3R9ZG/UYFzV6/ZOEptKw/ByVCJqmlrKdkLLLeioyzS/7r1QvF6tUD1emxxwqjXDlkG6sLrf3790dERAQmTZqEFStWYOXKleq0atUqtbSlP/74A9OnT8eMGTOwc+dONQ/rF198oZaZMWLECERGRppO586dS3W9wcUA+N5bEuUwzD/pjPknnTH/pDPmn3TG/JPOmH/SGfNPOrN1/uPiEhF+JQaGpOTbJyam7vJpFB0Vjx+/3YsL56JQONQf439trkb2ETlj/r293dFvYDkULZ62i60UVWWe4sMHbiA2NlEtZf3BYus3Y3ehS6sFVv8NMm3o4MGL0ajRZDRrNhXh4TEYP34rWrX6HV27zlLXp3x/bt9+CcWKIdtY3TpY2vfu2LEDZctaN1Q4I9544w3TqFZRuXJlnDlzRlXkBwwYgAIFkiffvXLlCgoWvF9Vl/Vq1aql+7jSp/mRvZqtLkETOQfmn3TG/JPOmH/SGfNPOmP+SWfMP+mM+Sed2Tr/hQr7Y+7STmreVCmyNq87By++UkW1U923+xpOnYxE5+4l1W1/+GYvChXxU4VWImfO/2+/HkSFSsGoVTd1R1kx7tOdcHGRgmjyuixlXeYtTtlSu0nzIiiSgffCokVH4erqgnXrnlbrTz01D/Xrh+LHHzuiT5+/sG/fVVSpkrxdy5efRLNmxeDicg1OU2itXbu2qoBnRaE1JiYGrq6pX3E3NzfTvLBhYWGq2Coja42FVek3vWXLFrz44ouZ3wBXID4x/v6qiyvcXN1UtTwhKSHNzT3cPNRSrktZUVfb7eqm7p9kSEJiUmKq6+QD2t01+aVI+XxGxuuIshTzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOMpD/uLgkdOtTAmUrBar7Lvn7JObMOI6O3cLg5++BTft7w8PTJdXjMv/kjPn/efw+9HmyNKrWypPm8//ksZumIquRrJ84FpnqMStWC0Kl6venHLU0/4cOhaNJk+KmdSm6Vq+ePPAyNDQXIiLumK7788+D6NevAtav34HsYvU7+JdffsELL7yACxcuoFKlSmru1JSqVKlis43r1KkT/u///g9FixZFxYoVsWvXLowbNw7PPPOM6QNK5mz9+OOPUbp0aVV4ff/991GoUCF07do1c0+emDx0Ojbhfk9zbw9vBHgGqCCFR4enuUsen+TAXY+5jrjE1H2ng7yD4OPhg+i4aMQkxKS6ztPdUz2uBPhybNq5ZYP8g+AS5wLIpvhm7s8isgjzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOMph/6QT80rDKKv9y30GvlsNLr5eHp6unyr8ronEr9laq+zH/5Iz5X7q5g7rceJuUn//FS+bC0UOpi60uLkDJMkGpHlNuc/N6Arq0q2xV/suXD1EjVXv2rKDWk5IMqh5oZHze+PhEbNt2ARMmtMH69XCeQmt4eDhOnDiBp59OHrIr5A+U/0nJQ+dTH62UGd99950qnL700ku4evWqKqA+//zz+OCDD0y3efPNNxEdHY1Bgwbh5s2baNiwIZYuXQpvb+8MP6+LwQW4BbgHuCMsd5jZir6/Z9rhzsaKvnygpndEl5+nH4J9g1M/X4ojWuS+D5Lr3OLdgBjAJYizwZN9Mf+kM+afdMb8k86Yf9IZ8086Y/5JZ8w/6Yz5J51Zk3+Z/9TDw1VlOGX+R49qjj695praB7vcW47+sHmqxxz5wwJcuHAb/bvWN93Xkvx36lQGS5ceR8OGk+Dh4QZf39QDPo2kGNu8eZga8ZqdrC60ymjS6tWrY+bMmcifP3+qKrKtBQQE4Ouvv1an9Mjzjx49Wp1sSsJhuB+eB5/T3OWWDPWXsLq6pd8A+2GPK9tElCWYf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzizI/6lTEahX71f88UcvNG5cLFX+e/esDPc57hg9eg2OHLmOsmWDMXJkE3TrVj7VY037vTvCw2Oszr9sw4QJySNqH/TFF61N59u1K61OsbH3R9E6RaH1zJkzWLhwIUqVKoWcTMbDBshcsJGRcASyLRkfo0tkHeafdMb8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6QzS/JfLMgFg56qhCKBLqlut3rdOcTcSUD31mFoVacHmnb8CyOG10K35oVS3e7OnQT4+LijgAxifcT7zNnzb3WhtXnz5tizZ0+OLrR6GwzoDCAsJgaYMQOOoEtMDE4B2PHgDMNENsb8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90Zmn+ZVz2aOkCvPEUYtcY4OWR3N32x18jEZtgQPtrQUiMSUKtgCjUu7AZmLENv2+5g8u3ktCghAdafXcTM54ORJeqXnbPv4xoHTVqFIYPHw4vr0c/X7YXWjt16oRhw4Zh3759qFy5Mjw8Ug/17dxZXiLn5mEwIDeAu9IWObecy36yLbnvbRuRPTH/pDPmn3TG/JPOmH/SGfNPOmP+SWfMP+mM+SedWZv/gxdiUfudM3itQ26M7h2CiYMD4e/toiZmDcoN/DT4/pzEx24n4uqtBLxaLQTjnvRCx8aB0k87x+ff6kLrCy+8oJbm5kSVvsmJiYnIKVRXZz8/OILs7TBNOmL+SWfMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086szT/RUO9UaqQF8JCA9TtL928i5K5veDmlrqAeismEQcuJWJw+3zwCAxAk5oeSPLxgpu7S47Pv9WF1iTp26wJNcA4OhqOsi13s3sjSCvMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5JZ5bm3x/AnjHF1Pn4yCg0eusEnm0WiE/6hmDJrij8vTMaE57NjwCDAW5JiYiPvoPb14CGb57Ei62C8FHvvDk+/1YXWnUQ7+KCCOlRLcOUI+ScY/TNvnRv24jsifknnTH/pDPmn3TG/JPOmH/SGfNPOmP+SWfMP+nM0vwnJRnUPKvDmvuiY+XkeU9lItG/X8iFvP4u6r6R4Xdx6uJddV6S+8cAHwBxwN04LHg+AFUKJ9/O3vmXeVlHjhyZLfOzZrjQum3bNqxatQpXr15NM8J13LhxcHbSD3ohgOK+vvj0iSfgCBZ89BFOR0YimB+0ZGfMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5JZ5bm/3ZkLIpuXIOQHpWBWgVNl9dOcZvHnwAeT+f+DWyY/4ED5+P11+ujUqV8qQrBzzyzACdORCAxMQk1aiDbWF1o/eSTT/Dee++hbNmyyJ8/v5qX1SjleWcnw5Rvu7oCgYFwBLItzjx0mpwL8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0pkl+ZerJk/vle71c+cewqhRa3D06DWUKZMXI0c2Qffu5VPd5rff9qjbzZ/f1+b53737MmJjE7Fu3dNYseI43nxzGpym0PrNN99g0qRJGDhwIHI0F8DgYkCSIQmuLq5qmZiUmPomLi5wd03+XxifGJ/mIeQ6uU1CUgIMMgw7BTdXN6seV7ZFjb0mygrMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5JZxbkf/v2SyhaJAiFC+dKk/958w6jT6+5kLGXBgOwb98V9OjxB2b/2R3dupUz5d/H1x1Fi6W+vyX5l/fTkCFLsHfvVbi7u8LX1wPjx29Vo1f9/Dwwb14fFCmSS91OTjdv3oWvL5yn0Orq6ooGDawZ9Ot81IuaC0gISMC16Gvw8fBBdFw0bsXeSnU7T3dPBPsEqxfyctTlNI+T3z+/CtONOzcQmxCb6roArwD4e/rjTvwd3Lx7M9V1Hm4eyOubPEGwPK48vmyLbJPaNiI7Yv5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0hnzTzqzNP/duy/AUwMq490P6qfJ/wcjV5qKrEKWsj7yw5Wo1tTLlP8mbUNQtakHTkWcgjX5X7ToKFxdXdRoVfHUU/NQv34ofvyxI/r0+Qv79l1VbYQ9PNxQrtz3uHMnHj17wnkKrcOGDcP333+Pr7/+GjmaG+BicEFcUhwS4hJURd/LPfVEulJ5vx13W51/8DoRFRelbiOnB69PNCSq+z7qcT3dPJMvM7iobSLKEsw/6Yz5J50x/6Qz5p90xvyTzph/0hnzTzpj/klnFuR/2rw2yJ/PH1HxUWmuO33ilqnIaiTrp47fVrc15j8y8i4SEl2QKzA555bm/9ChcDRpUty0LkXX6tULqPOhobkQEXEH//13Qo12PXJkCDZuPI1Bg6ZgzBg4R6H19ddfR4cOHVCyZElUqFABHh4eqa6fO3cucowkqCNShCxd3ZLPmyNV+PQYh0GbY/HjyqjtaAA+lm06UaYx/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pjPknnT0i/+XLJ486NZf/EqWDcPjAjVTFVhcXoGSZwFS3fWfYRhw+eAMrNve0Kv/ly4dg+fKT6NmzQvKmJhlUgdZInldGwgYHJ99BlnezcZLj9N/h6Xj55ZexatUqlClTBsHBwQgMDEx1yjFSt6POdi6qafa9JZG9Mf+kM+afdMb8k86Yf9IZ8086Y/5JZ8w/6Yz5J51ZkP9LF6MxoPe/OH40detrMfztGqZ2wcLYRlguT2nEh7Ux5quGsDb/nTqVQUJCEho2nIRmzabi+vU7ae7TqlVJnDt3C02aTMFTTy1EkybINlaPaJ06dSrmzJmjRrXmVGqY8p17SwehelN7sUc72R/zTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOmH/SmaX5z53HC3fvJiAiIvX8w6J95zBMnNYSX326EyeORaJk6UAMH1ED7TqFpbpdkaIB6mRt/mX06oQJ5muQX3zR2nR+9uzkkbKxsbEYk119gzNSaM2TJ49qG0zZIP3uBEQ5H/NPOmP+SWfMP+mM+SedMf+kM+afdMb8k86Yf3Ig3t7umL0w/QGXUmyVkzknjt3Ea4PX4o33aqFB40LI6fm3unXwhx9+iJEjRyImJgY5laqa+/PoEdIT8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0pm1+Y+4cRf/67cM+/dcs/g5gnJ7oVhYLlSsHIysICNaR40apZZOMaL122+/xYkTJ5A/f34UL14cHh6py8w7d+605fYRERERERERERERERERURaTuVdv346Di2tyq2GZO9XdPe0Yzjt3EjD1l4No0ToUpcvmxjc/NYUurC60du3a1T5bQkREREREREROJTY2EUmJBvj4uuPCuSisX3sRzVoWQb78vtm9aUR2Fx+fhLjYRPj5e+DqlRisWnYODZsWRuEi/tm9aUR2Jzva78QkICCXpxrt9O/iM3isQUEUL5EruzeNyO6SkgyIuh2PXIGeiI6Kx8K5J1D7sQIoVSYouzeNyObyBHubWggbDAa0bzof3XuXxAsvV8XeXeGYM/s4nn2hIgoU8sPknw4gdx5vVWjVidWFVmkbTNkkDgB/q5KumH/SGfNPOmP+SWfMPzlBkbV+1dkYNKQSnh9SBXt2hePd1zZgx+En1PX7915HydKB8PGxetcD809OsZO9ed2/0KVHSbz+bk0cORSBN19ehy0HHlfXHzpwA0WLBagirNWYf3JwsqO9U4sFqrA0+rN6OHv6Nl4fshZrd/RW1x89HIECBf1UEcpqzD85gb5dlqBIqD/GTWiCK5dj8PqQdVi6tptpbkopTEmxyWrMPzm4hAQDPDxcsW7VRVVo/W/pWfwyYT8aNyuMosVzYe7STir/GeLE+Xe39B9PF5fkYcGUPVwMLipoakmkGeafdMb8k86Yf9IZ80+OSvYPyKilVu2KwsvLDSNG1kaN2vnUde07h6FVu2Jq50tiYhIG9P5X7XT56ocmVj0H80+O7L9/zqBpiyLw9HTD6+/URLmKySM2GjUtjONXnoG7u4t6n7wwYAVKlw3CL9NbWfX4zD85shX/nkX9xoXUATRDX6umdqqLqjVCcPr6s3BzS87t8JfWwtfPHX8sSh4BZSnmnxzZ2pXnUb1WPjWK+7mXKiFviI+6vESpQJy5cT//772+EZE3Y7F4dVerairMPzkyOYCsWPEA+Pp54IWXq8D1Xhvhl1+riieeKov8BZIrpGNGblUHWy7f1MNse+Gcmn+L/tKKFSti1qxZiIuTknL6jh07hhdffBGffvopnJrMARx9b+kgDLIxbveWRPbE/JPOmH/SGfNPOmP+SWdW5n/vrmsY9ORyrPzvnFrv+XhptYPRSIqsws3NFX/+3UGNdFJPI5M7WbxJzD85Zv6PH72JQf2X4+95p9R6l54lUbZ8nlT5l53qcpo2py0+/7ZR8tMw/5QD8i/t4Z8fsAKzpx01HVxTqUqw6XrZoW4sKk2c1hLfTWyW/DTMP+WA/F+/dgcvDFyh5p8UcmCZFF3N5f+7X5ph4vRWap35p5yQ/9u34tC38xJ89dlOtd6pWwl06BKmznt6uqNQYX/13V+89k5NjP+1uVVF1pyQf4tGtH733Xd466238NJLL6FVq1aoVasWChUqBG9vb0RERODgwYNYv349Dhw4gCFDhqhiqzNzgYsKmVo6CtkUOUjGgTaJcibmn3TG/JPOmH/SGfNPOrM2/zJqad3O3igW9ug5+IwF2LOnb+HVF9aoHY8WzV3J/JOD5l/m3luzvReKFg945G1DiyXf5tLFaAx5diXGftc41UEJD9ko5p8cMv+FQ/2xcnMPU7YfpmAhv1TFqQ/H1EPFysGWbBTzTw6Z/+C8Pvh3XXf1PngU40jXW5FxeHHgCgx7uwZq1c1vyUYx/+SQ+ZdR3DMXtENxC77/h5VM/q4TF5eI997YiMefLJvqoIScmn+LysotWrTA9u3bsXDhQuTLlw/Tp09XBdV+/frhww8/VCNZn3rqKZw/fx6fffYZAgMt+OLowAwuBsD73pJIM8w/6Yz5J50x/6Qz5p90Zmn+ZWfJXzOP4c6dBIuKrCnJHJXBeb1xMyIW9ubl5YWRI0eqJZGt8i+tsP+YfhTRUfEq/9a0gvTz80BwiA/zT06bfxmRJ5//UjSSVsHW5N/X1wN58/qo+9ob80/2+v4/74/jiLhxVx1kYGyXaglvHzfkyeutRgNmBb4HyB7537juovr+X6FSsGobbM1c9pfOR6tuIDrk36rxuw0bNlSjW3fv3q1Gst69e1cVVxctWqQKr7lzJ89LkSNYNNaXKIdi/klnzD/pjPknnTH/pDML8r9102W8+sJqnD972+qHl1EgE6e1smw0kwUGDpyP/fuvprrsypUo1K//K1q2nI6pU4FLl6Js8lykAQvyv2/3dbw2eC2OHIqw+uFzBXri599amuYytkf+jWbPPoixY23yNKQLC/J/4ngkhr+0Bru2m8/dw/j4uuOHKS1Qr2FB2ALzT1mdf+lKIPlfv+ai1Q8vc3lLC+1mrUJhr/yfPn0TISFj0bTpFLRqNR3R0g6WyEb5l4PE+nX7B7N+O2L1w3t7u+P3OW3R64kysFf+5UC4/v3nolmzqRg0aDGSkpBtrGuUTERERERERETaadikMLYdegKly6Y9wHrJwlNoWX8OSoRMUktZN2f3jnBs3nDJLtuXN68v1q9/BsuX90PVqsCUKXvs8jykp2o1Q7D14OOoXiskw/k/sO861q66YLdtlJ2Nc+ceRi7rBpwTPVKp0kEq/42aFc5w/o8ejsCKf8/abRuZf7IXaYMt33/adiye4fyfOhGJfxeftts2NmlSDKtXD8SyZf3gl9y1m8gmgnJ7YeWWnuj5eGmz11vyHoi8GatGxdrDvHmHERYWhFWrBqBs2WAcOoRsw+O2nQk7mZHOmH/SGfNPOmP+SWfMPzkY45x7KckOlef6L4d0kjQYgMMHbqj1idNaon3nsFS3/XDEJuQv6IvHGhS0Kv/StnLIkCXYu/cq3N1dVSvK8eO34sSJCNWWdd68PnBzu38ceWwsUKFC3kz+tUS2zf+no7YhMSEJjc0UqzKbf2nlOnPmfnTvXg5bt1o/6oToUQoUzFz+v/l8F86cvo0WbYo++smYf3IwxjlXM5r/n8bvw6Z1l9CmQ9pibWbzLzZsOIdGjSajXr3C8Em7qUSZYpxzNaPvga8/34W/55/EtoNP2Dz/J07cQLVqBdTtZTl/PrINR7Q6CReDCxB9b0nkwA7uv656sAs5WvHLT3YgPj5z4/aZf3IWcpSuMe/r11zA5x9tx52YhEw9JvNPzuLEsZuIiY5X57dtuYLPRm/L9FxkzD85i7Onb6kjdcX+Pdfw6ehtuHolJlOPyfyTI5E8t6w3Bzu2Xklz3bhPd5p2sAhZyvpXn+5Mc9vpc9vh+1+bW53/RYuOqjnR1q17Wh2xHhzsg/r1Q7Fs2ZPw8nLHvn3JbcR2776MRo2mYtu25J0tRLYg329aPPaX2dGo1uT/xyktMPXPtnbJv5pD9o8D6NWrvC3+ZKJUO7pllNLSv09nKv9jv2uEuUs7PvL5mH9yNJ1bLsDc2cczlf+R//cY/tvQ3S75L1jQH8ePD8XatQMRHh6drSP6KOcZ+r9VZvNvzXtgyPCqmP9vZ7vkv0KFEKxcmTyKdtWq07h7F45faL140T7Dex1W1sxRTZSj8i87FlvVn4v1q5N/gB46cANHj0TAwyP5o2bOrGNZNgE8UYZlMKJnTt1Cszp/4b8lZ9T6scM3sW/3NTUnjVjw1wlE3MjGf/GJ7Jj/8KsxKv8L555U66eOR2LLxssIDPJU63L04rXwO7bcUiKHyX/U7Tg0q/sXZk87qtZltMaaFeeRJ9hbrcuOycuXOFkSOXf+5Sjy2o/lV+3DHnTyWKRpB4uRrJ84Fpnmtn7+HuqxrHXoUDiaNLk/CkR2ulSvnlxIDQ3NhYiI5H9jpLi6bt0ANGsGjB27yernIU09Iv8yWq52vQLIG5L8uZ6Z/Bt/G9s6/9Om7UXv3hXVdUS2/v5Tq25+5Mvvm6n8+/p5qPkqrcX8U3bnv1rNfChY2C9T+Zf9Ql5e9sm/FJz8/DzVv1Vdu5bFlbTHxBFlOP+eXq7qZI6l74HgvD4oHOpvl/x37FhGzQXbvPlUREfHZWvrbIu/4VWsWBEzZsxAVrtw4QL69++P4OBg+Pj4oHLlyti+fXuqI6s++OADFCxYUF3fsmVLHDt2LFPPqarmcY519LjBxQD43VsS2ZG1+b9x/S4mfr9PvRcrVgnG9LltUb9xIXXdkOHV8NPUlqYilEwev/K/c1ZvE/NPjpp/OXDgx2/3qvwXC8uFmfPboVW75FZITz9fUU36bixCDXtxDf6eb36+jodh/slR8y+jtSd8vUcdQR6SzxfT57VD114l1XW9+5XB3KWd1I89eZ8Mf2kt/pieXISyBvNPjpr/2NhEfP/VbsTFJcI/wBO//dkWTz6TPIqiQ5cw/LOmmyomye3eGLoOUyYetHqbmH9ypPzLgQNjvmqIkqWD0lxXonSgOno91WO6ACXLpG0zNvrdzZjy8wGr81++fAjWrk0+mE1IBx35N8Z0ewPU+9HIywuqvRiRLfIvO8g//aohKlQKzlT+x368HT98s8cu+T94MBy//bYHnTrNxo0bwPDhyx75PESW5F+yJvmvUTtfpvI/ftxu1fHsUZh/crTv/6M/q4d6DQtmKv+//rAf//fBFrvk//bt+12k1q8/hzx5Hvk0RBbn/8vvm6Bj1xJmr7P0PbB4wSmzI71tkX9Z//LLNli5cgDy5PFBuXJw/ELr//3f/+H5559Hr169cEP+1coCERERaNCgATw8PPDPP//g4MGD+PLLL5E7d27TbT7//HN8++23+PHHH7Flyxb4+fmhTZs2uJuJccIGaQbtdm/pSByn7ks5mLX5X770LMaP24PLl2LUh1vTlqFmj1KXItTu4/3RpWfyTnjZ8WgV5p8cMP/rVl9QPxhPn7yl1hs3L2L2KF0pQu048oRpJzzzTzkh/1s3Xcb34/bgyKEItd6oaWF1JOGDAnJ5YuuBx/HiK1XUOvNPOSH/e3eFY8JXe7F31zW13qBxIVMHg5TkyPX1u3rjzfdqqXXmn5w5/3KAzX//3N/ZYTT87RqmVmHC2EJMLk9Jdo7IwTfy74K1+e/UqQwSEpLQsOEkNGs2Fdevp+2SIG2DGzeejNatZ2DLFmDYsLqWPQ9pzdL8372bgH8Xn1YHWGYk/3I/yb+MarVH/j/7rBX+++9JLFrUR+1kHzeulWXPQ1qzNP9yIIt06DBOE2Vt/kVUVDzzT06Zf8mfdC57cFo0a/IfHR0PH5+0vxVskf/168+iZs2f1RytFy/eRuXKlj0N6c2a37/SwdLc71hL3wNHDt7AubO37ZL/y5ej1HUtWvym9scWK4ZsY+E7HHjppZfQrl07PPvss6hQoQImTpyITp062XXjPvvsM4SGhmLy5Mmmy8LCwlJ9Uf3666/x3nvvoUuXLuqy3377Dfnz58f8+fPRt2/fjD2xvKAycTR3bJCOrMy/jFrq2DVMtYF5lNx5klstzf/zOL76bBeWru1mdqckkbPkXyZ3b9qiiFX5lx2UH761CUvWdDPbfo/IWfLfpEURbDv0uEX5N2Zd5i5+bfBaLFzeBfkLpG0/RuQs+a/9WAGL8x8YlJz/nduu4vkByzFnSUcULZ4rs1tMlOX5l52Mg59diS37H0/V/ku+D02c1lIdqS6twkqWDsTwETXQrtP93+7Gdl9jv2ucsU10ccGECR3MXvfFF61N59eufRqxsbEYM2aMmrOMyFb537j2Ep55fBlWbe2JMuVyW51/yfDoz+vbNf9GgwZl6GlIRxbmf8+ua3j2iWVYuLwzatbJb3X+xdsf1M7YJjL/lM35P3HsJp7u+5/q4CeDSzKS/5dfr27X/LdrV1otk78D7c/Qc5FmLMy/dKhs02geJkxuji49kgdPWfseGD6iZpoD1WyZf5m/9X7+1yC7WFXhkCLnypUrMX78eHTv3h3ly5eHu3vqh9i589HDgC21cOFCNTpVRtGuWbMGhQsXVgXf5557Tl1/6tQpXL58WbULNgoMDETdunWxadOmdAut8j9dTka3biWPRCLSga3yLyM5tm+5gv7PlLdoJ2NKlarmRfc+pVS7SXvz8vLCyJEj1ZLIVvk/diQCK/49hwHPVbA6/xUrBaNb71LICsw/2SP/58/extw/juOZ5yuqlqnWKF8xj8q/m5v9j2Zj/ske+b9+7Q6m/HwQz75YyeqDZUqXDUL33qXgkYH5yazF/JM98t+uc3Fs2N3H7BxLsqNFTumRebqlE0Lr9sUyNEcrUXbnv2nLIli7o5fZ9tmPyn/kzVisWXkebTsWz9AclUTZnf9adfJhzY5eKJWB/MdEx+PfxWfQtlNxy0f0ETlQ/suWz5Ph/MsowMXzT6Ftx2JW7zsicoT8S4fKv5Z0RO3H7h9kY+l7wGAwYMPai3isQUEtvv9b/ReeOXMGc+fOVe17ZRTpgydbOnnyJH744QeULl0a//77L1588UW8/PLLmDp1qrpeiqxCRrCmJOvG68yRo1ulIGs8yahZIl3YKv87tl7F1F8OZeiDslSZILzyRnWrd9CnZ+DA+di//2qqyyIj76JOnYkIDv4SV1NfRRqzVf53bQ/H5J8PZKhYJDsn33ivls1Gs5rL/9atF1Cv3q9o0WIa5swB4uOtbFVJOZKt8i9HtEuhSUYmWSs4r486mj1viBw6mXnMP2V1/vftvqbmW32wdZ4lpF3qiA/roGAhP9gr/1euRKF+/V/RsuV0yE+mS5eibPJc5NxslX/Vjissl2rhtWbFeavuu2juSbwzfAOuXonJ0HNbQ3YqjRo1KtXOJdKXrfIv33ukyCqf/6uWnbNqZMayf85ixKsbcP6s/T+TmX+yR/5lVJEUmST31uZ/7aoLeHvYepw6EQl7Y/7JXvv/jUXW1cvPWTVoRA4ye+vVdTi4P2umYeR7gOyRf5mjWPb/H9h33arP/+1br+LxLv9g5X/noEP+raqQSLvgypUrIygoCAcOHMBHH32kjpROebKlpKQk1KhRA5988gmqV6+OQYMGqdGsMh9rZowYMQKRkZGm07lzWfNiZ4pkWNpQO9i0seR8bJX/p5+viGUbu5vd0b5k4Sm0rD8HJUImqaWsmzuq68dv96odlvbIv6+vBxYvfgLdupW1/E6U49kq/9Iye93O3maPSLck/7KD8tcf9mPb5vQPCspM/kNDc2HlyqewYkV/BAYCixYds/zOlGPZKv8duoRh8/6+Zo/ItST/8sX8t18PYu1KC3bSM//kYPmXdmEy53ae4OR28NbmX/wx/ajZeS5tkf+8eX2xfv0zWL68H6pWBaZM2WP5nSnHsvXv34VzTuKpXktx8vijd5pfOB9l+u2wfFMPFCpsYTtf/v4lB83/in/P4smeS3Fg73WL8i/fe3o+XlqNhipRKtCyJ2H+yUHzv3nDZZX/LRsf/Tv20sVoVZCSkdzSDaFCpWDLnoT5JwfNv+y/fLLnvxYVjcKvxqj9no2aFsamvX1Rq6750YBpMP/koPmXFtrtm8zDnFnHH3nb2NhENbd37br5sWpbT9XRRof8W9yzoW3btti6datqG/zUU08hKxQsWFDNB5uStCueI8MTABQoUEAtr1y5om5rJOvVqlVL93GlhdYj22jZv6OpVVykYXbivSVRJtgi//JjUY5oTK/I9Fz/5aYJsA8fuKHWpWd7ylYC7u4umPTTAeTO44XK1fJalX95/iFDlmDv3qvqiBopqo4fvxUnTkTAz88D8+b1gYeHG0JCbDNihHIOR8m/jISdNvkQ+qO8mufP1vkvWDBA3VaO4nJzSz4Cn8hR8i/3/2P6MbRsG4rGzYs89PmYf8pp+RfSerty1WC0blfM5vl3c7t/HK0cyFuhwsO/Y5EebP37t1vvkqhQOY8qGsnBY19+sgP9ny5vaiksI/7ks3fLxkvo3vZv/LehOypWDraqmwF//5Kj5r9Vu2LqoIFyFfKoz+UvP9mJHn1LIaxkYKr8799zDW0bz8Ocfzqibv2CzD/liPzLqCZj/sXXn+9U33OM8xYb83/65C00rvkHfp3RSr1nmH/KCfmXfZcy4MSY/wlf71G/ZytVCU6Vfymy1qkwE19+30RNm8b8U07Iv3T1mLWwA+rUSz5oYN4fx1Glet40UyrcvhWHhtX/wLuj66hBKuZabufU/Fs8ojUxMRF79+7NsiKraNCgAY4cOZLqsqNHj6JYsWKmOWOl2LpixYpU/aa3bNmCevXqZfh5XQwuQMy9pYMwuBgAz3tLIjuyJP9yFHvpgpPVXBsPGvfpTtNORiFLWZeJsVOSHYFbDzyOPv3LWp3/RYuOqi8v69Y9rSa8Dg72Qf36oVi27El4eblj3z72Cib75V+OXixVYDKuXI7JcP5lR/2qrb3UHH/2zP+ZM5E4eRLo0CFr5oSlnJ9/mZu7ZP5JOH7sZobzL/5e2QWvvlnjkdvE/JMj5f/IoQiUzDcJu3eEZyr/sxa0x7uj69ot/7t3X0ajRlOxbRtQrdrDD+YhysjvX/keY9zJKK0gpZ32wX3Jo/t6tl+EWuVmqNGuNevkx3cTm1k+ii8F/v6lrJKR/T/G/F84F4UpEw+o70fimcf/RdWS01RrvQqVg/HNT01RqYr1B7ww/+QM+b9x/a6aTsfYpWbYC6tRsdhv2LH1CoqXyKXyL/PyWYv5J2fIv+wPlfz/+/dptT7qnU0oW2gK1q+5gJB8vvj6p6Zo1urhBxWbw/yTI+dfDraR/fnx8UkYOWITxo9L7p708/i96nfy4gWn1HQ5w0fUSHdO15ycf4sLrcuWLUORItZ/QGTGsGHDsHnzZtU6+Pjx45gxYwZ+/vlnDB482PQD79VXX8XHH3+MhQsXYt++faoQXKhQIXTt2hU5jm2msyTKNDmC/d1RdeBhZkTHyWORpp2MRrJ+4likzfJ/6FA4mjQpblqXnY7VqxcwtYyMiJA+A0T2IXMMS/4DAjwcOv+3bsXimWcWQaZPlxHeRLYgo5Uk/3nzpj0ql/mnnC4knw/eGVUHRYr6O3T+pbi6bt0ANGsGjB27KXPPT/QIRYvngoeHK/btSZ4OREYuFS0WgKLFA9TI61u34rDfgharZvH3Lzm4QkX84efvoQqronnroggt5q8OLpDPaNkRuWtHBg8CZv7JwUl3MvlNYJx7snGLIuqzX34vG78HbducfBCC1Zh/cnAyjY78Nj50IDn/DRoXVgcYlC6b29TFbPOGS1bNZ2nC/JODk+/+desVwLXw5N+fpcvlRtnyuVG9VohaDwjwVPPTWzOfcU7Iv1VztGa12rVrY968eZg5cyYqVaqk5oT9+uuv0a9fP9Nt3nzzTQwdOlTN3yq3j4qKwtKlS+HtnXbeJEupqrmf81bPiTLDkvzLF4eBgyqqD9YHlSgdqEZwpCTrJcsEpmkl0OKxv/DPIvPzlz1M+fIhWLv2/txm0p5DDrww/Q1865Id818sLJeaZ8zc/JSW5l/mK5BWYn/NPGaX/Esbv759/8K77zZEXnaNJBvmX+bWe+aFSgjK7ZXh/MuX7c4tF2DqLwet3kbmn7Iz/8F5fVQnAnPtvyzNv+xs6dN5sZqn3h75l7lwjKRTlLQXJrL171/5Hv/6kLWqNZ6Xl5vqUjDs7eQuBc8PrYL5yzqrIqtkdPa0I9i49qKd/wKirMv/nZgEvPnKOjWaVYqpfy3uiA/+7zF1nbTQXrK6G3x83NXn/V+zjmH1cgvmpCdykvzL79h3hm9Qc/XJd5Cpf7TB5982Utd161UK/67rjsCg5N8Jf88/iaWLk0f7EeWE/MvvzA9HbMLB/ckH13z/a3P8MKWFOt+ybVEs29gD+Qv4qvX/lpzBornW7+8kctT8y+/MYS+uMc1RLK2xf/uzjTrfrGUolqzppvYXiWNHInD0cIR20zhZPEdrdunYsaM6pUf+YR89erQ62ZReOSCyOv97d4Xj2JGb6NG3dKrLh79dI9UcZcalXJ6SHN3boElhVK2RfLSLNTp1KoOlS4+jYcNJaqRSejsR27efrtrnyRx91avvxXPP1bb6uUhDFuRfjlrcszMcfZ8sm6H8x95NRKOmhVDt3tFets7/zJn7sGXLBURFxeLsWaBUqUPo3z/9ucuJrMn/8aM3sXHdRTz1bIUM5T8uNgkNmxRGjVr5rN485p+yO/9nTt3Cin/PqgMOMvr9R9ro1ahtn/zL957XX/9PPf/Fi9KV6NEtioms/f179UoMdu8Mx5lTt1V7PBnVao7sXFm8qqvpIAS5X778yTsgiZw1/9IuVX4HnDwRqUYzhRYLMP+QLi748+8OpoOAmX/KCfmPvBmr8i8dO2ReviJFzedfyPyscsCNYP4pJ+RfDjSTKUSq1QhBhUrBKFwkbZcbo29/bqYOMJZ/CyT/0hkn5QGSRA7BikjK53l8XCKi700jmCsw/aGnb31wf/+7TCci35XMDdbKaRy+0EpEjumfRaex/N+zamL3lF8W2ncOw8RpLdWcZMlfvgNVb/Z2ncJS3T9PsDdGf5axuZTl+SZM6GD2ui++aG06v2RJP8TGxmLMmDF46qkqGXouInNWLTuH6VMOqwMNUn5ZsDT/8oXEkvn5MpP/J5+sasp/r17lM/RcROZsWn8J34zdjZ59S6ca2W1p/n183fHm+7Uy9NzMP2U32bny1We70LVXKfVdxtr8e3q6Ydhbj56fODP5X7v2aVP+CxZMfwcQUUbJzvVlG7pbtMPQeCS77Jjv3nYRZi5ojzr1OHcwOS8pri5d282i/Mtt5GYysqNdk/n4+beWaN46NEu2k8gepFj696ouFudf2qeeP3sbLerNwdjvGqFz95JZsp1E9pA7jzfm/dvJ4oKpzGV5/dodNKvzl5p+pN/AcnbfRiJ78fZ2x/hfm1t1n6jbcaqb2cDnKuL1d2sip2Oh1ZkkHzBA5BBefr26+pA09wVDdjbKKT3L/jmDGzdi0aNPKdVWzCLMPzkQaR08aEhls/l9VP7Xrb6gRkT17ldG7XC3CPNPDkSy26e/+fw+Kv/btlzBvl3heHxAOdVWzyLMPzmQ9l3C0KZjMfVD09r8yzyV69dcQP+B5eAfYOHkM8w/ORCZh+m9NzaqubrTG8WXnsrV8uKTcQ3U/E0Wy2D+vby8MHLkSLUkshXZWfjakLV4471aKFU6eQ5KS8mclZ982QDValrRzYb5JwciLYNfeX41hr5WDRUrB1t9cILkv17DgpbfifknByIjU19+bjX+91IlVLeyK5NMPSL5r1PfioPMMvH9n+8BsjUZydq/+z9qCp0WbYpadV//AE/8Mr2VGgGuQ/5z/pjdHMLF4ALE3lsSOQAZkSRHZ0kLgAVzTlh13zUrzqsRgXJ0o73zLyM6Ro0apZZEtiIFIimyytxMf844atV9ZZ6yf/8+Y/FcBcw/ORqZj0+KrNICacbUw2oOMktt33wZixecYv7JaUkXAymyRty4q+YZtib/u7ZfxaI5J5l/clqXLkSrg8X8/K2f+1dy36d/WdPcfZnN/8CB87F//1Wz182efRBjx1q9iUQPdfFCNM6dvg1fX+vHK8jByXKgWspOCLbO/+nTNxESMhatWk3HlClAeHiM1dtJlJ4rl6Jx/myU+h2QkfxLJyhpNW/R7Zl/cjDXwu/i0sXoDN+/S8+SKFjIz67ff1avPo0WLX5D69YzcOhQhjeVKI2o2/EIDvGx+Dv8g2TanIe1Gc5s/iMj76JOnYnw9/8EBw6Emy7/888DqF//V/W+OH/+FrICC63myP4S+TfZ8v0mdmeQjZG5nhxpoyhnsjL/spP9m893qUmxH+ZOTAK2bb6szn80tj6+m9jM4nYbzD85av7/mnUM4z7diZh7cxQ87AjgLRsvmeYqmDSrtcWjuZl/ctT8/z3vJL78ZAduRjy8kCNzUkq7YfHiK1Uxa2EHi3fSMP/kqPlf9s9ZfPF/O3Dl8sN35CUkJGHD2ovq/JPPlMf8ZZ1Ttdx++CYx/+RY+ZdRqf+s6Wa2WLRk4Sm0rD8HJUImqaWsmzPh6z349Yf9dsu/jDqZO/cwcpmfNpYow/kvUy43lqzphkKF/TOc/8k/HcB3X+62W/6bNCmGZcv6YeBAICSE82GS7fIvc3H/vbKLGp2d0fzP+v0IPv9ouwWbxPyTY+U/fwFfzF3ayexoVkvzv+CvExj1zma75P/OnXh8+eUm/PNPP/z33xMoz5lzyIb5lyKp7MOvVTe/2esteQ/MmXUMzz253C759/X1wOLFT6BnzwqpfoOPG7cZq1cPxOjRTfHRR2uQFVhoNcNFXtGke0tHIZsiB7840CZRzmRt/t8dXUd94ZDRTdISb/hLa0w73WUujhvX76rzP3+/D/26L1UFVymwWjUJNvNPDpr/l1+vhiWru6qd5qdORGLo/1apUX7iwvkoNR+HmDHlMPp0WqJGQAnmn3JC/p95oRKWb+qh5qqRYtOLA1eoUd7i8qVohF+NMf2o7NXhb9NRwMw/5YT8y8iktTt6oUBBP0TejMXzA5arLh9C/h0wFmBX/ncOvTsuxvGjN9U680/Omn/ZYSGf8eYOrpQdKs/1X47DB26og8tkKevmdrTIe+PWrTir8y+jxwcPXoxGjSajWbOparTS+PFb0arV7+jadZZpdPnMmfvRvXs5NS8mka3yL/mS37aS78zkX/59uG3H/G/YcA7Nm0/DihXJ9yGy1fcfyf/duwmZyn/41Tu4FWlBpw3mnxws/7JvR/ZlZib/Mv3Cow5Qzmj+N206r7qudeo0E717z0FU8k9yIpvkf/+ea+keXGzpe8DTyw358vnYJf8eHm4ICUk9Yvz48RsoXz6vqlU0aFAUe/ea74Jjayy0mmFwMQCe95ZEmrE2/1I0Dcqd3D7g6uUYtZPRPyB5pEabhvPUiFfjKI7/1ndXLYeJclL+pcgkLl+KUcXWgFzJLTF6tFuEn79PHrEhrZKMBSminPT9x5hpaSd5+tQt0+f/kz2X4qtPd6nzHbqG4b8N3S1ul0TkdPm/GI2zp2+b2qnKQQcfv7dFnW/eOhT/rOlqdgQIkTPl//LFaNSpOBOb1iV3KEhJuntIYdO4X1uWsv7VpzvT3HbUp/Uw7K0aVm/jokVHVfvhdeuexqpVAxAc7IP69UOxbNmT8PJyx759V9Vo1j/+OIBevTiUg2yb/1uRcahbaRb+W3ImU/mXzjbvjKpjl/wXLOiP48eHYsWKfoiOBubPP2L185B+LMm/zM8n+Z87+3im8i/zu378RQOrt5H5p+z+/t+87l+Y+uvBTOVf5rf86ocmdsn/lStRqrC0aNHjeOaZali92uqnIQ1Zmv8Bff7Db7+kzb8174FO3Urg/760z+e/ORERd5Er1/1Wx/IbISuw4pEey1pHE+VMGcy/7EyUk9GkWa1QpGiAOi8txiydk4bIGfNfr2FB/L2qq2l9/C/NVYsZY6sNS+ckIMpWGYxptZohqp2k0effNDIdhCNH11aoFGyrLSRyuPyXq5AnVf4/HFPPdGCZtImvUj3EVltIlG35l7mZfv+rLapUz5vmupPHIk07WIxk/cSx5FHeqS83WDx9SEqHDoWjSZPipnXZ6VK9egF1PjQ0FyIi7mDatL3o3buixfMgE1mafzmQ5rc/26BqjRCHzb/scPTySp6jW9pGyuiNvn2tfirS0SPyL5GV/Feskvb7PPNPOnz//2lqC5Qum9th8x8U5I0GDULV6L1mzYrh5ZetfhrSlQX5nzanLfLlNz8a1dL3QHRUvPq3xNIpdKzJvznynrh16/4Icje3rBlryhGtRGQ3desXROEiaeewIdKBzF8QWiz5QAMi3cj8NWElA7N7M4iyhcxjyRGslNPIQTNyQKW57hwlSgemadUr6yXLBKbZydiszl9497UNVj9/+fIhWLv2TKoRVil3WMpOnYMHw/Hbb3vQqdNs3LgBDB++zOrnITJHDppp0aYo8ob4ZDj/olOLhRj63Cq75P/27fs7FM+cAUqWTFsUIMoIyZrkX6ZLyEz+n+j6D57u+5/Vz8/8U3Zr2jIUhUP9M5X/5wesQM/2f9sl/7VrF8ahQ9fU96w9e64iN+NPNlS+Yh4E5zVfaLX0PfDWK+vQqcUCu+TfnFKlcqv3hEx5snHjOVSpknZ+ZXvgiFZnwk7GpDPmn3TG/JPOmH/SGfNPDuTAvuuY+stBfPpVw1SjRoe/XUPNx2RsHWZcyuUpyWVvfVDL8gMxU+S/U6cyWLr0OBo2nKTmYvL1TXtE/GeftVJLGdEUFjYG48YlrxPZgsy1/eO3e/HJuAZq1JD1+Tfg1TerIzDI0y75X7/+LN57bxW8vd3U/Hx9+1bMxF9LlNrZ07fw7Re7Vft341QJ1uRfvPhqVbhaOtyH+ScHItOESCvUt0fWTtWpz5r8P/tCRURHpZ3n1Rb5z5vXF926lUOTJlPUvzWNG2fwDyUy4/KlaHWQpEx9ULJ06oOJLX0PvPFeLVy8EG2X/Iv27adj9+7LOHw4HAUKQN321VfromnTKfD2dsfUqfe7D9oTC61OwsXgAkQDLr5sg0T6Yf5JZ8w/6Yz5J50x/+Ro7sQkYMeWK2qHS6HC94ul7TuHYeK0lmonpLQKK1k6EMNH1EC7TmGp7i/F2QcvszT/cvT6hAkdzN72iy9ap7ls0CAr/ziiR5D5vbZuuozzZ6NQolSg1fmXDLdsW9Su+W/XrrQ60GDMmDFqFC6Rrcjn96b1l3D61C1UrBxsdf5F42aFLXou5p8cjZeXGzauu4iTxyNTFVqtyb90/LNn/gcPrqNOxvcAka3IlFCRN+Nw4/pdlCyd+jpL3wPFwnKpk73yv2RJP7VMmf8+fSqpU1ZioTU98dm9AUTZiPknnTH/pDPmn3TG/JPOLMh/zTr5sHxTD7NzjMmOFjmlZ8vGS5g97SjefL+W2faTRI6e/7Ll82DN9l4Zyv++3dcwccI+vPFuLU4tQk6Z/yJFA7B+V+8M5f/YkQh8/dkuDBtRA6UeGA1F5Az5l+Lqup0Zy7+MBv901HYMea0qKlRKO88xkaPnX0aE/rWkY7rXP+w9cOpEJN4Yug6vvVMT9RpadrCBM+MhPulVz2PvLR2EwcUA+N5bEtkR8086Y/5JZ8w/6Yz5J51Zmn/ZwSinC+ej8P4bGxETbfnRCbGxSbgZEZtqJIi98i9Hs48aNUotiWz5+S/5D78ao1ro3YqMs/g57t5NQMT1WLNzvJrD/JOj5j/ixl28M3yDGtlkqbt3EnH9+l3ky+dr0e2Zf3LU/EfdjsN7r29QnT0sFRubiGvX7iBffvvnP/n5+B4g+/z+le/xzw9YrqYSsZR/gAcKFPRF2fK5tcg/C61mGKQZtOu9pSPhq0VZgPknnTH/pDPmn3TG/JPOrM2/HJ2+dtUFRD6i0CSFqN8nHVItV6Vl5KSZrVPNbflIzD85YP7PnYlS+X9UoSk6Kh7TJh9SRdbajxXA73PawsfXiqZyzD85YP5lrsq1q86rAw4e5s6dBMz6/Yj6d6BytbyYtaA9cgVaOD+xYP7JAfN/5XIM1qy8gMsXYx5ZXJ0z65h6n5Qumxt/LOpg8YE2CvNPDpj/hIQkXA+/C0NS8u3l+316n/+/TNiv5rYPyeeL8b82t/hAS2fPvxNvuh1JIV8ONHGcA9qJsg7zTzpj/klnzD/pjPknnVmZ/4ZNCmPF5h4oWMgPkTdj0arBHOzYesXUInX3jnB1/vTJSDXyY99uy498J3L0/NeonQ+rtvZE8RK51M7Eto3nYf2aC+q6QwduYPuW5PfCpUvRauSfcZ0oJ+RfWp+u3tZLtdKWne4dmy/Asn/OmFoES5t448int19dj03rL9pz64myNP8lSwepz/9qNUNgMBjQs/3fWDDnhLpO5m/dsPaiaU77t15djzUrkv9tIMoJ+ZeDBf5c3AGVquZV+e/QbAF+Hr9PXbd/zzV8/P4WnD97G25uLvhp/F4tv/9wjlYiIiIiIiIispi7e/Ix29HR8ShXIY9p3smvPtuJ+Pgk/P5XW1SpHoKdR/shOK8VoziInCn/UfEoXzEPioQm5//Hb/fizKlbmP9fZzUX5a5jzD/l7PxXrJIHBQv7q/XffjmEjesuYsXmnupAnB1HnmD+KcfmX1piy+e/ZF38NfMY/px5FNsOPoGg3F7Ysr8v8085jnGe4oQEgyqoSoeDQUMqY+mSM/jhm72oW7+AmtN73r+d1GhW3bDQSkRERERERERWK1TYH99NbGZaH/tdI3h43G8PzJ2MlJPJ6I6vfmhiWh/5yWNwdb0/NIT5p5wsMMgLn33dyLQ+fEQNDHu7hmmd+aecTFrBfzS2vmn9+aGV8b+XKpnWmX/KiY4cuoGixXKp/A8aXBku93rlDh1eFX2eKG068Oaz0dvVKNdlG3uYDk7QAQutzkLaX8sUIPycJh0x/6Qz5p90xvyTzph/ckI227HI/JMTsmoOsodh/skJ5c7D/JPeBx7YBPNPDur2rTj07rgEvfuVxruj66JLz5Km67y83BFaLJdp/ZU3q+NOdIL1RVYnzz8LremxbB7gLOMiDbMT7i2J7I35J50x/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pzIr8B+TyxPS5bRFWMvCRty1VOkgtZTqRkW9tQp/+ZVC1RkiOz78+Y3et4GJwAaLvLR2EQZLvcW9JZEfMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5JZ9bkf8vGS7h7NwGVquaFn7+Hxc+RkJCEUycicfjgjSzJv5eXF0aOHKmW2YGFVmchmZeMOM5nP1HWYf5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0hnzTw7mZkQs+nZeghlTDlt9Xx8fd8xc0B59+pe1Sf4HDpyP/fuvprosMvIu6tSZCH//T3DgQLjp8qeemoeQkLEYP34rsgoLrWYYXAyA770lkWaYf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzizNf1BuLyzb1AM9Hy9j9volC0+hZf05KBEySS1l3dz8rjIq1h58fT2wePET6NmzQqrLP/20JcaObYWsxDla08MSNOmM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5JZ8w/6czC/BvnXH2QFFWf678cLi6AwQAcPnBDrU+c1hLtO4eZbjfu0534e/5JbDv4hFWbZzAYMGTIEuzdexXu7q6qqCojVE+ciICfnwfmzesDDw83hIT4pblvoUIByGostD6MKxCfGH9/1cUVbq5u6kVOSEpIc3MPt+Qe1XKd3CYluZ/cP8mQhMSkxFTXubi4wN01+aVI+XxGxuuIshTzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOmH/S2SPyP/zFdWjSojC69CyRJv9fjtlhKrIKWcr6V5/uRKsORWD03NAKePJ/99sHW5r/RYuOwtXVBevWPW1qB1y/fih+/LEj+vT5C/v2XUWVKvnhKPgOTk9i8tDp2IRY00XeHt4I8AxQQQqPvt/z2SiPTx61vB5zHXGJcamuC/IOgo+HD6LjohGTEJPqOk93T/W4EuDLsZfTPG6QfxBc4lwA2RRfG/6NROlh/klnzD/pjPknnTH/pDPmn3TG/JPOmH/SGfNPOrMg/0lJiUhCouk2KfN/8nikqchqJOsnjkWmesxceVyR193b6vwfOhSOJk2Km24jRdfq1Quo86GhuRARcQeOhIVWM1wMLsAtwD3AHWG5w8xW9P09/dPcz1jRlw/U9I5o8fP0Q7BvcLpHtMh9HyTXucW7ATGASxBnwyb7Yv5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0hnzTzqzNP9/zLh/3YP5L1smL/bvv5qq2OriApQrlzfVY86ffwSHD13E++8Vtyr/5cuHYPnyk6b5V5OSDOp9ZPRgkTe7sdCaHhnqbHAxhScleUHNXW7JUH8Jq6tb+g2wH/a4sk1EWYL5J50x/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pzML879t3BWXL5oWnp1uq/H/4YVP06PGHqX2wy73lyJFNUj3mvj3hOH36ptX579SpDJYuPY6GDSepuVhljlZz2refjt27L+Pw4XAUSB7wihEjlmPhwqNITEzCiRM38NVXbWFvLLSmwxtAQFISEBkJRyDbIttElBWYf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzizJ/6nTkahSbTJmT26P3t3KpLque4tCmPNbR4z+fDOOHItA2dK5MfKtx9CteaFUjzn6tRrJo78f8T57MP9S7J0woYPZ237xRWvT+SVL+qllbGwsxowZo86PGdNSnbISC61meBsM6AwgLCYGmDEDjqBLTAxOAdjhaGOiKcdh/klnzD/pjPknnTH/pDPmn3TG/JPOmH/SGfNPOrM0/9IAeMUrQWgUtQ2YsT3N9d3l9JKM3r7XKvv2VmDGVnVWiqvrjsejQUkPuLm65Pj8O1Wh9dNPP8WIESPwyiuv4Ouvv1aX3b17F6+99hpmzZqlqtZt2rTBhAkTkD9//gw/j4fBgNzy2DLeObecy36yLbnvbRuRPTH/pDPmn3TG/JPOmH/SGfNPOmP+SWfMP+mM+SedWZP/5o8lL/efi0XFIp6p5kl9mE1H76Dp1+GYN7wQutQKsHv+pTY4atQoDB8+HF5eXshqTlNo3bZtG3766SdUqVIl1eXDhg3D4sWL8eeffyIwMBBDhgxB9+7dsWHDhkw/Z6z8x88PjkBtC1EWYv5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0hnzTzqzNP9HL9xF9RGnMfnlYujf7N7o1XTExSepYmz96n7Y/50fKhT1sXxbnFj6szI7kKioKPTr1w8TJ05E7hQV9sjISPz6668YN24cmjdvjpo1a2Ly5MnYuHEjNm/enK3bTEREREREREREREREROSsyhT2xtIPS6Fv4zxqffa6GzhxKW1p9FZMIoo/dwCz1t1Q65YWWXMCpxjROnjwYHTo0AEtW7bExx9/bLp8x44diI+PV5cblStXDkWLFsWmTZvw2GP3xjWbGUYsJ6Nbt26ZvZ0aYBwdDUcg23I3uzeCcgTmn3TG/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kM3vkv0UpN+BuDOITDHjt1/N4qVUQ3ukajJ+W38T3/0Vg7+dhyAVgRKfcqFPE1er3lbPn3+ELrTL36s6dO1Xr4AddvnwZnp6eCAoKSnW5zM8q16VnzJgxql9zeuJdXBAhk/1KP+gIOecYExRfurdtRJnB/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdGbP/HsAOPZBbiQmJd+3YmAc+lTzMD3O0Lry32ggIlqr/LsYDI47u/K5c+dQq1YtLFu2zDQ3a9OmTVGtWjV8/fXXmDFjBp5++ulU1XlRp04dNGvWDJ999pnFFf3Q0FDVijhXrlwoUqQIrl+4gOIFC+LQoUNwBOXLl8fpS5cQXLgwzp8/b9V95e+T+WuNfx/pjfknnTH/pDPmn3TG/JPOmH/SGfNPOmP+SWe65V/wPUBGzH8uZDWHHtEqrYGvXr2KGjVqmC5LTEzE2rVrMX78ePz777+Ii4vDzZs3U41qvXLlCgoUKJDu43p5eanTw8gw5duurkBgIByBbIszD50mx8H8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvxnPYcutLZo0QL79u1LdZmMYJV5WN966y1Vhffw8MCKFSvQo0cPdf2RI0dw9uxZ1KtXL3NP7gIYXAyIT4yHq4sr3FzdIIN/E5IS0tzUw00GTENd9+AAYbmf3D/JkITEpMTUT+HiAnfX5JdAnudBcp3cRj2ui0FtE1GWYP5JZ8w/6Yz5J50x/6Qz5p90xvyTzph/0hnzTzrLRP7nzz+CuLhE9O5dAVG341Gt6s/4ZExz9O5TXtv8O3ShNSAgAJUqVUp1mZ+fH4KDg02XP/vssxg+fDjy5MmjhgQPHTpUFVkfe+yxDD+velFzAQkBCTgVcQreHt7I7Z1bveDh0eFpbl8woKBaXo+5jrjEuFTXBXkHwcfDB9Fx0bgVm3rSYU93TwT7BKtwXo5KO6dsfv/8KuQ37txQ2yLbpLaNyI6Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzjKSf8mw5FTyP/PPnWqO1tqtfOAW54vefcuiar0A9VjTfz2KixdiMOydKvD19tYm/w5daLXEV199BVdXVzWiVfpOt2nTBhMmTMj8A7vJBLYu8HL3ggtccDvutgqErD9IrhOyHV4uqa+PS4pDQlyCOqLlwftKtd54X3OPGxUXpW6jTgYXtU1EWYL5J50x/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pzIr8X4m4geefWoHOvcLQtVcJfP5dI3h4uCY/jCcw7L2q90Z0uyI+1gXenh7w8/bRKv9OV2hdvXp1qnVvb298//336mRTcmBK0v1h0UJe8JTrDzIOgzZHKvOubsnhM+eRj5tk0VYT2QbzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOmH/SmRX5d/VOQqmyQShQwF9d7+GWfv6HDKtuumzX9qsoVyEPfHzdc3z+03/na0xVz+PuLR2FjJiOvbcksiPmn3TG/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kM2vyL6Nc3dxcMfKTx9CoaWGLnyPqdhz6d1+Kr8fu0iL/LLSaYZBX0+3e0kHI8G3E31sS2RHzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOmH/SmaX5T0hIQuuGc/H3/JNWP4d/gCfm/tsRr79T0yb5HzhwPvbvv5rqssjIu6hTZyL8/T/B8eO3MHLkSMTFAc2bT0XjxpPV8syZm8gKLLSaI6+lz72lg1Chd3esD3/KoZh/0hnzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOLMz/3TsJaNysCMJKBmboacqWz6PmcpVRsfbIv6+vBxYvfgI9e4C4sSEAAFEUSURBVFYwXebh4YZp07pj7dqn8dZbDTB27EZkBaebo1VbEnpvx/rwJ8oyzD/pjPknnTH/pDPmn3TG/JPOmH/SGfNPOmP+ycHIqNT3P66b7vVLFp7CuE934uSxSJQoHYjhb9dA+85hqW7z268HsXDOSfy1pKNV+Zfi7JAhS7B371W4u7uqour48Vtx4kQE/Pw8MG9eH1VUDQnxS/Uw3t7uCAz0Uuc9Pd3g6po1byiOaCUiIiIiIiIiIiIiIiIiZc2K8zh5PDLdIutz/Zfj8IEbiI1NVEtZl8tTCi0agPqNClr93IsWHVVF0nXrnsaqVQMQHOyD+vVDsWzZk/Dycse+fanbCD8oLi4RH364BkOH1kFWYKGViIiIiIiIiIiIiIiIiJThg9di7uzjZq+TkawuLjLyNHldlrL+1ac7U92uWatQDB9h2TytKR06FI4mTYqb1qXoWr16AXU+NDQXIiLuPPT+gwYtwksv1ULp0sHICiy0picpuzeAKBsx/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pjPknnVmQ/3/WdMWQ16qavU7aBT849arBAJw4lnoE7NUrMTh7+pbVm1e+fAjWrj1zf3OTDHCRSm6K50rPqFGrUaJEbvTpUwlZhXO0muFicAFi7i0dCT/8KQsw/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pjPknnVma/3z5fdO9TuZklXbBKQueLi5AyTKBqW73/psbcebUbSxd282q/HfqVAZLlx5Hw4aT1FysMkerOe3bT8fu3Zdx+HA4ChQAzp27hY8+WouGDYti5cpTqFevCMaMaQl7Y6HV2cLv52Af/kRZgPknnTH/pDPmn3TG/JPOmH/SGfNPOmP+SWfMPzmaC+ej8MKAFfj064aoWDl1C97hb9dQc7Ia2we73FvK5Sl9+lVDXLwQbXX+ZfTqhAkdzN72iy9am84vWdJPLWNjYzFmzBjVVjgh4QNkNbYONsPgYgD87i2JNMP8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90Zmn+ZURrkVB/JCWmvV37zmGYOK0lylfMAy8vN7X8ZXpLtOsUdv95DAbkzuOdpkibE7HQmh4HO3CEH/6UpZh/0hnzTzpj/klnzD/pjPknnTH/pLNM5l92oos7dxJwYN913IqMy9zjMf/khPmPi0vE/r3XcTMiNnOPx/yTg+Xfw8MVP0xpgcrV8pry/mCxddnGHjgZ/oxapiyyHtx/HU1q/YnVy89lSf5lROuoUaPUMjuw0OpMHOzLP1GWYv5JZ8w/6Yz5J50x/6Qz5p90xvyTg5Md7i8OXIHx4/ao9WOHI9C6wVwcOxKh1m/fijO7U94izD85gTeGrsVnH21X5y+ci0KbhnOxe8dVtR51m/mnnOXypWj0aPc3tm66bPF9ihYLQIs2RVGnXgEt8s9CKxERERERERERERE91JlTt1QBSebOq1A5GMVL5FKXlyoThCWru6JG7XxqXYqwzzy+LJu3lsi2zp25jcTEJHW+TLncKFkqUJ0vWNgPi1d1RePmRdT660PXoVfHxRkvthI5GH9/D+Qv4IuQfD5qPToq3uztYqLjMebDrdi/5xr8Azwx8pPH4OvnAR2w0EpERERERERERERE6ZJRe60azMXvkw6p9aGvVUOnbiXUedmRXrVGiCrAiv+9VAnD366RrdtLZEvXr91B28bz8NN3+9T6c4Mro9cTZdR5b293VKsZAlfX5Pw/9Wx5vPVBbdP7gcjZSdFUWgiHlQxEUpIBrerPwRef7FDXbdl4CS89swJHD0fAy9sNK5edw4ljkdANC63myMEmMfeWRLph/klnzD/pjPknnTH/pDPmn3TG/JPOrMx/4VB//DC5OXr2Lf3I2zZtGarm9JO5Kz96bwuuXJYnInLe/Afn9cGESc3R/+nyj7xt/UaFULtufjX69dPR23D29K3Mby+Rg3z/kUJr7mBvbN98Ra1v3XgZC/46iatXYuDm5opfp7dCo2aFoRsWWs1wkWbQSfeWjkJCH80v/2R/zD/pjPknnTH/pDPmn3TG/JPOmH/SmTX5P3zwhmqDKvPtWdMGMuJGLP5dfNryef0ykX8vLy+MHDlSLYlsmf8jh5Lz36RFEeQK9LT4OW5FxuHfv09j0wb751/wPUD2/P4jn+M3I2Lh7u6K19+piaGvV1OXD329Ok6GP6MOMBD/N3IrurZeiISE5Dbbunz/YaHVDIOLAfC8t3QQKvQGB/vyTzkS8086Y/5JZ8w/6Yz5J50x/6Qz5p90Zmn+Txy7iRaPzcF/S85Y/Rwyp9+6nb1NLYYzm/+BA+dj//6raS5fvfo02radialTgQULjli9naQfS/N/+VI0WtWfi79mHrP6OXLn8cbKLT3Rp19yi2F75H/TpnNo2nSKOlWq9BOWLrV6M0lD1n7/uX0rDoOeXI4JX+9R681ahaJB4+TCqvDycjO1zv7kywaY+kcbVZDV6fsPC63psfzglCyhQu/lWF/+KQdj/klnzD/pjPknnTH/pDPmn3TG/JPOLMi/zMk3fW5b1Q74QUsWnkLL+nNQImSSWsr6g2Seyjt3EnD86E275P/OnXh8+eUmLFzYGwMGAF26lLX4vqQ5T8sOFpi5oD3adiye4fzHxiaq+Svtkf969UKxevVAdXrsscIoV87iu5LurPj+E5DLEwuXd8Yb79WyqM12WMlA3IlJwHNPLsf6NRe0+P7jnt0bQFawvDMHUc7D/JPOmH/SGfNPOmP+SWfMP+mM+ScHIqOU0iuyPtd/OVxkEJIBOHzghlqfOK0l2ncOS3XbYS+uwbWrd/DXko5W5V/atQ4ZsgR7915Vo6N8fT0wfvxWnDgRAT8/D8yb1webNp2Hj487unf/C+fPA08/HYVixdg6lWxDCqUpR+5lJP/vv7ERu3eG47/13W2ef9k+IfMhb99+Cb172+CPJnog6481KIiixXNZdT8PT1f4+Lgh8macFXdChmV362yOaCUiIiIiIiIiIiKiVKTQM/ylNWbnWB336U5TkSn5tlKUAr76dGea2742ogZGf17P6udftOioKvSuW/c0Vq0agOBgH9SvH4ply56El5c79u27iitXonD8+A3MndsTNWoAH3+8PmN/LJEZI4atx9qV5zOV/xderoIvxze2S/6Nli8/iWbNiqltILIVmZN16HOrzI7WfhR3d1d8+3MzdOiS+sCDjDLXOjsy8i7q1JkIf/9PcOBAuOnyHj3+QJMmU1C37i9Yu9b6tvcZwUIrEREREREREREREaUixaOjhyLU/HwPOnks0lRkSnn7E8ci09y2dNncqFAp2OrnP3QoHE2a3G/ZKkWn6tULqPOhobkQEXEHQUHeaNAgFJ6ebggLAw4evGb18xCl59iRm4iMzFz+S5QKROVqee2Sf6M//zyI7t3ZN5hsKyi3F7YeeBw9+pQ2e70l7bMvXojC0r9P22X7ZJT34sVPoGfPCqkunzmzB9asGYjZs3ti9Og1yAostKYnPrs3gCgbMf+kM+afdMb8k86Yf9IZ8086Y/5JZ4/IvxR2/l7VFS3aFE1zXYnSgWlGz8l6yTKBaW4rO9/n/XHc6s0rXz4k1WikpCSDqVWqsbBVu3ZhHDp0TY2+vXwZCAsLsvp5SFMWfP5Lu+tO3UpkKv/Ll57F7GlH7JJ/ER+fiG3bLqgDDohs/f1H5lz18XVPt322tM2OjU00tc9+sNg6+acDePe1DVZvnnymDx68GI0aTUazZlMRHh6jWme3avU7unadpa738HBDSIhfmvvKgTfi9u1YVKqUD1mBhVYzXAwuQOy9pSOxop01UUYx/6Qz5p90lpn8yxfcHVuvmI50X7/mAt56dR0ibtzN/IYx/+QEn//7dl/D9WvJR5Rv33IFb76yDpcvRWd+w5h/coL8Hz54w5T3A/uu482X1+H0yVuZ3zDmn5wg/8eP3cS5M7fV+RPHbuKNoWtx5NCNzG8Y808Olv/zZ5NzntLwt2uY2qWqx7vXRlUuf9C8P09g2+YrVue/U6cySEhIQsOGk9SO9uvX74/gM8qb1xfdupVDy5bTsXw58M47DSx7HtKaNfm/cC5K/ebNaP7/WXQK69dctEv+jW2DmzcPUwdGENky/0/2WIrpUw6bvc7S9tmvvlkDa3f0tjr/1rTONqdx48mqKNu+vfnRuLbGQqsZBhjU/xm1dBAq9HEOuPOfchzmn3TG/JPOMpP/69fuonvbRVg076Rp/Xr4XdVmRkybfAhnT1u/0535J2fIf9TtOHRtsxB/zjym1uUAgwtno5Avv69al6PXZUe8tZh/cob8y9HrPdr+jd9/PaTWI2/G4uTxSBQqknxk+dzZx1Uh1lrMPzlD/mVkUd/OSzDx+31q/fateBw9fBNFQgPUunwvkgNxrMX8k6PlX+ZnrVtpVpo8t+8chonTWqJ8xTzw8nJTy1+mt0S7Tmnn45v4e0uM+qye1fmX0XsTJnTA+vXPqB3t0iLSODrpiy9ao2nT5LaqgwfXwYoV/TFwIFCyZG6r/j+QnizN/8H911G30kxsXHcpw/n/8vsmGDehid3y365daXz7bTur/n7Sm6X5L1M+NwoUTP5dm9H22X7+Hupkbf6taZ1tztq1T2Pr1ufw1lvLkRVYaDVHXkvJjwN9p1Whd3Osnf+UQzH/pDPmn3RmZf5vRsTi4/e3qJ3seUN8sHhVV/R9sqy6rkuPkvhleiv1w1BGuX7+0XasXHbe6k1i/slR8x8dFY+P3tuCmOh4+Ad4YsGyLvjfi5XUda3aFcP0ee3UD0F5f3zxyQ78s9D6OWmYf3LU/N+9m6A+/6WoKjsW/1zSAa++lTx6o36jQqrFnrTrSkxMwjdjd2WoVSTzT46a//j4JIz5cCuuhd9Rn/PT5rTFux/VVddVqxmCBcs6q52JMvLpx2/2YsZU86NAHob5J0fLf43a+TD+l2YoXS5tS14pNi3b2AMnw59RyweLTHJAgvGAMw+PR++GZv7J0fIvBdTvJjZD9ZohVudf/i04fpT5J+fN//sf1zXbOt6a9tmzpx3ByLc3WZ1/S1tnp3kcg0G10xb+/p7qlBVYaHUWkiEfx9r5T5RlmH/SGfNPDkqO7F049yTOnEoeqVqpal6zrYoCcnli68HHMfC5Cmrd2F7YIsw/OahjR26qkUqyFJWqBMPdPe1PKylCrd/VB0OGV1XrzD/lBNIWWPJ/cH/ySNUKlYLN7jx0c3PFso3d8dYHtdU68085gbSPXDT3JPbsDFfr5Sokj2R6kOwIXLC8Mz4aW1+tM//kzOQ7TrfepeDt7Y5LF62bGkHeL01r/Yld2x/e4tEW+Y+NjcWoUaPUkshW5PNc8u/r52F1/lctO48mtf7EutUXsuTzn+8BsjUpbq5deR53YhLSXGdp++ybN2PTFGQtyb+lrbPbt5+O//47gZde+ge7dyd33JGWwU2bTkGXLrPwySfNkRXSzmJLRERERPQIMmpp454+ZotLD5KdMmLVsnMY+twqLF3bDUWKJrfVI3JGMmrJ0vwbd8Bv2XgJz/RdhvnLOqF0Wba0I+clhaUNuy3Lv4xsFfv3XEOfTkswc0E7VKmedkQIkbMoXiIX1u7sbVH+jbeR0UxdWi1U3T7qNSyYBVtJZB+bN1xS7bJnL2qPuvUfnmXpaiAHYXbqXgIFC/uheq3kdqdEzkq+y3RqsQATp7VCy7bmR/ilLE6JZq2KYO7Sjo98vxA58gFmT3T7B99Paq46l5lrny1zsp44FomSpQMxfESNNCO7nx9SJUPPbWydbY60zjZasqSfWsoBBmPGjFH7n1avHois5tAjWuV/TO3atREQEIB8+fKha9euOHLkSKrb3L17F4MHD0ZwcDD8/f3Ro0cPXLli4eTqRERERGQVmXvvwxGbVOtgS3YyplSzTn68/Hp1NcrV3ry8vDBy5Ei1JLKVy5ei8e5rG1TLSGvzL8WloW9UQ3BeOUzXvph/sgf53H972Ho1msPa/JetkEflv0Ch5Llb7Yn5J3uQkRxvvbpOzTlvbf7DSubCK29UR7Ew+x9kxvyTPdWpVwBfft8YtR9LniNv2T9n1Fz1D5L3Sb0qs7Fre7gqtsr9iJxdxSrBaq7Vpi2LqPWV/51T340eFH41Bo2q/4F1qy6oQhGLrOTMQosFYPmmHujcvYTZ6x/WPjsxMQkL555Q0+7owKELrWvWrFFF1M2bN2PZsmWIj49H69atER19f5j+sGHDsGjRIvz555/q9hcvXkT37t0z/+RshU46Y/5JZ8w/6cyC/J84dlO1QPL2Sdsm71FyBXpi0JDKCAyyzc6/gQPnY//+1G3IrlyJQv36v6Jly+mYOhW4dCnKJs9FGrAo/5FYveK82TaRj+Lj444XhlZBnmBv2Cv/RrNnH8TYsTZ5GtKFwbIDbdauOA93d+v72UlrYcl/vvwyGZR98n/69E2EhIxFq1bTMWUKEB4eY5PnIg1YkP/Tp25h/aqL6c4H9jDSRlu+/xQq7A97fv6vXn0abdvOVN9/FixIPUiBKF1WZFqKpj36llbLG9fv4rn+y7Fgzkl13Q/f7MHgZ1aq84VD/dGhSxgCg7JmXjyirMi/FE279ymlDraJiY7HCwNXYNbvyZ+1U385iKf7/qfO5w3xQYeuYchf0DbfeYiyO//SzUbyv3HdRdXK11K7d4Rj2AtrsH1L1gyKzO7W2Q5daF26dCkGDhyIihUromrVqpgyZQrOnj2LHTt2qOsjIyPx66+/Yty4cWjevDlq1qyJyZMnY+PGjao4m1EuBhcg+t7SkXDnP2UB5p90xvyTzizNf6t2xbB6W09TO+CUliw8hZb156BEyCS1lPUHGQwGfDN2F/5dfNou+c+b1xfr1z+D5cv7oWpVYMqUPdY9AGnJ0vw3aFwI63f1Njsq25L8i5/H78P8P4/b7fNfjhyeO/cwcuWy/r6kJ0vzX6N2PmzY0wch+XwznP/fJx0y7ZS0R/6bNCmGZcv6YeBAICSEOzjJdvkvXzEP1u/ujWJhuTKc/z9nHMXknw7YJf937sTjyy83YeHC3hgwAOjSpax1D0BayszvXzlwbOPePujeu5RalwMJSpUJMh1cMPKTx1CydPK61fj7lxw8/zJfq/wmeGJAObVeoKAvypRPnhpEClLvjKqjilMZwvyTA+ZfDrh/vMsSLPjrxCNvG3kzVnUCkY5mG/f2RZMWyaPAc3r+nWqOVimsijx5kj+opOAqo1xbtmxpuk25cuVQtGhRbNq0CY899pjZx5GqdsrK9q1bt+A04fd1sJ3/5HSYf9IZ8086s0X+5ejF+Lgks6NZZaeiHNXu4iLFVODwgRtqXebskHYyRvLDU45olNF91uZfirRDhizB3r1X1ZHEvr4eGD9+K06ciICfnwfmzeujduzc/5uBChXyWv13Us5ji/xLATP2biJ8fN0znH+xc/tVlL23I8bW+Zf318yZ+9G9ezls3crRTGS7/MtcY7LDxNcvc/nfveOqRaO6M5J/sWHDOTRvPg2ursDbbzvxnhpyqPxL/qTtnZ+/R6byv3fXNSQmGuyS/02bzqvvVt27/4Xz54Gnn45CsWJsH6w7e//+TTlKu0vP1HP3ZRR//5Kz5D9ll442HYqrU2Yx/+So+ZcDZ5as6YYKlZLrclN+PqAOwpTpcYzfVVxcXHD7VhzqVJyJ9z6qiyefKY/8BXy1yb9Dj2hNKSkpCa+++ioaNGiASpUqqcsuX74MT09PBAWlPkIqf/786rqHzf0aGBhoOoWGhqa63uBiAHzvLYlyGOafdMb8k85skf+d266iVIHJOHbkZprrxn2607STUT2eQYqqwFef7kxz29//aqta6Flr0aKjqlXZunVPY9WqAQgO9kH9+qFYtuxJeHm5Y9++5DZ6u3dfRqNGU7FtG1CtGueEItvk/+jhmyhdcAp2bL2aqfz/OKUFhr1Vwy75l2LwH38cQK9e5a1+fMq5bJH/SxeiUabQFKxZcT5T+Ze5zd4dXdcu+S9Y0B/Hjw/FihX9ILMNzZ/Pgw3INvm/fSseZQtPxaJ5yS1SM5r/j8bWxyfjGtgl/zJ1wvHjNzB3bk/UqAF8/PF6q5+Hch7+/iWdMf+kM3vkv2LlYFVMjY9Pwviv9mDSvS4d33yxE0Vz/6pGu0rnp8+/bYQ2HYpBN05TaJW5Wvfv349Zs2Zl+rFGjBihRscaT+fOnXP4/zMq9H788KfMY/5JZ8w/6cwW+Q8rmQvf/9oMhQr7pbnu5LHINPOWybrMaWmr/B86FI4mTe4fKSw7HatXTy6khobmQkTEHXVeiqvr1g1As2bA2LGbMvz8lHPYIv8FC/lh/C/NULJ0oMPmf9q0vejdu6K6jsiW+c+dxwvfTWyGilWCHTb/UnDy8/NUO4DKl4ca/Udki/xLJ4Nvf26KWnXzO2z+g4K80aBBKDw93RAWBhw8eC3Dz085B3//ks6Yf9KZPfIvo1aFh4crGjQqhLjYRLVet15BNG5WGA2aFDK1DpYirDXzueaE/DvYx4l5Q4YMwd9//41Vq1ahSJH7PZ0LFCiAuLg43LyZelTFlStX1HXp8fLyQq5cuVKdnAL3l5ANMP+kM+afdGaL/Mu8fF17lYJ/QNr5KUuUDlQjOFKS9ZJlUhel5OjHhtVnq3kqLZLiMcuXD8HatWdStbKUHepG8r0/Li75y77w8oJqr0dki/wH5fZCt96lkDuPd4bzL5lt1WCO2ZFOtsj/wYPh+O23PejUaTZu3ACGD19m+R9IOZYt8i9zkXXvU8rs/KyW5l92znRtvRAfvbfFLvm/fft+e7QzZ4CSJR/doptyPlvkX3Yo9uhbOlWbVGvzL/p3/wdvvbrOLvmvXbswDh26pt5n0uAtLCyDc2NSjpKVv3+lq8adOwm2eTD+/iUny79xigVjISpTmH9ywPxLvgf2+Q8L5iTP0Tp2fCNMmNxCnX+sQUFMn9cOeUN81Lq0D05MSFLTHeiUf4cutCbPQzEE8+bNw8qVKxEmh+WlULNmTXh4eGDFihWmy44cOYKzZ8+iXr162bDFRERERDnb3l3h+PaLXWkuH/52DVO7PGFsoyeXP7gTZuBzFdG05f2D5yzVqVMZdVRkw4aT0KzZVFy/njyCNSVpG9y48WS0bj0DW7YAw4ZZ36KSKD2HDtzAF/+3IxP5N6D/0+XRok3q1k22yv9nn7XCf/89iUWL+iBPHmDcuFZWPw9Rek4cu4kxH25NsxPR0vzLZX36l8lQKzFL8r9+/VnUrPmzmqP19m2gb9+KVj8PUXrOnr6F//tgS5rRGZbn36AOVuvwwLyttsp/3ry+6NatHFq2nI7ly4F33rG+RTGRNSTTn4zcitnTj6r1QwciUCr/ZFOLeZsUnIgc2Fef7cSUiQfV+fNnb6spdhYvOKXWmX/KaTw8XVGseAACA5MPupcOGukZPKyaaaoQ2X9ks4NwHJw7HLxd8IwZM7BgwQIEBASY5l2VvtI+Pj5q+eyzz2L48OHIkyePqswPHTpUFVkfe+yx7N58IiIiohznyKEIzJ19HP97sZIa4WTUvnMYJk5rqUbqSbs8aa86fEQNtOuUeoeit7c7/vdSpQw9t4zemDChg9nrvviiten82rVPIzY2Vs1LInP2EdlyR/vc2cfwzAsVkSfY2+r8y6ioAf+rYNf8Gw0alKGnIUrXlcsxmPvHcTz1vwooXMTf6vxLu9PHnypn1/y3a1fa9PmfoaPoidIReTMOc2YdR58ny6JU6SCr8y8Z7vl4abvmf/DgOvjf/6qq/HNEN9lL1O041d1GcnkzIhZRt+LU5TK1iLSYb9SssFp/5fnVqu32Z183yuYtJrKd6Kh4+Pl7mNqjGr9rSMcbaTHfun3ywWTvvrYRtyJj8d0vzVJ1ICByRnLggGR99Of1rf734vEu/6D/s+UxYmRt5HQOXWj94Ycf1LJp06apLp88eTIGDhyozn/11VdwdXVFjx491A+qNm3aYMKECZl7YjnoRA4Q5MEnpCPmn3TG/JPOLMy/tI6UHYXmfjDKzkY5pWf18nPYvuUqXnyliukHKpEz5b9Vu2LqZG4O1Eflf+umy1jx71m8+EpV1YaYyNnyX69hQWza29dsAfNR+d+3+xrm/XkcL71a1dRWjMiZ8l+5Wl5sPfh4hvJ//OhN/PbrIfX9R+b7JnLW378Xzkehc8uFeG90HTWdwuff3i+iygFo8jvBqGad/KkOyiFy9vxfv3YHnZovwAuvVMFTz1bAh2Pud9MMyOWpWswbVa0ZAl9fdxZZyenzLx01OrVYgKGvVXvodx1z5KCcP5d0QMkUB6hlxsCB8/H66/VRqVI+02WRkXfRqtXvagqdtWufwsiRI1XbZHHmzE2UKTMeO3YMSnUfe3F19Gq5uZOxyCq8vb3x/fff48aNG4iOjsbcuXMfOj+rJVykGXTivaWj4M5/yiLMP+mM+SedWZp/NzdX9YNRRvaNH7dbzUdjqbNnbuPAvuvw8k6/zYyt8i8H4I0aNUotiWyVfymwyunypWjVLuzBFpIPc/5sFPbuuqZGd1iE+ScHy7989kuRSXY0fvnJjlRzYluyc373jnC109EizD854Pd/yb+MYBr78Xar2uBdPB+FnduuMv/k9L9/ZdTqC0Mro37jQo+8rXTwaNm2qNqPO2PqYTUS0CL8/UsOmn85mGDQ0Cpo1vLRU4D06VcGnbqVUOf/mH5Ujf62CPNPDpb/u3cSUKdeARQtnrE5XitUCoaXl5tl7bQzkH9fXw8sXvwEevZM2zXq8883oEED66fsyZGF1uxikFfT497SQTjkzn/KkZh/0hnzTzqzNv8b1l7ErN+P4FZkcruwh7WL+WdR8lw1cuTvpJmtLG7nyPyTo+ZfRqdK/m9cv/vQ292JScDf80+qH5YyymPG/Hbqh6YlmH9y1PxLwXTGb0dw9XLMQ293924CFs07qQ7IaduxOOb80zFVy/mHYf7JUfN/cP8NzPztCC6ci3ro7eRABPn8j49PQuPmRbBoRWcEBlnWzYD5J0fM/+1bceqAm+cGV0b+Ar4WP8fF89EY9c5m9X6wd/5lFFPK0UxEts7/wOcqILRYgMXPIQenjRqxGX/NPJYln/98D5Ct8y+jUkd9Wg+VqgSbvX7JwlNoWX8OSoRMUktZf9C0yYfQp/MSq/Mvv6EHD16MRo0mqznqw8NjMH78VjWCtWvXWep6Dw83hISk7RZy6lSEes8WLRqIrMJCqznyWsrnkQN9pzW4GADPe0sie2L+SWfMP+nMyvzLPHvLNvZQLVBlZ2Ofzotx5tQtdd2RQzdM51f8ew7PP7UCly5GJz+NFe2TmH9y1Px37l4Sq7f1Qr78vqrY2qvj3zh6OMLUIvLEsZvq/Kb1l/DCgBVq3j71NMw/5YD8t2hTFOt39UaRogFqhJLkf++ucHXdyeOROHYk+b2wb891lf89O5OvY/4pJ+RfWmhv2NMHpcoEqWJq3y5LsG3zZXWddPs4fPCG6b0g33/k3wH1NMw/OXH+5Xt8tdLTsPK/c1Y/ReFQf2w7+AT69C9rk/xL68j9+6+mumzTpnNo2nQKWrWaju++A954Y7nV20kasjD/Mhq1ZrkZWDj3hNVPEZzXB5v29cX/Xqpkt/zLAW1yeXIxahquXbN6M0lHFuZ/3eoLOH0yed/Og6So+lz/5Th84AZiYxPVUtYfLLbKtAm16uS3Ov+LFh1V3aTWrXsaq1YNQHCwD+rXD8WyZU/Cy8sd+/alfi+k9NlnG1Sb4azEQqsz8czuDSDKRsw/6Yz5Jwfl45PcAi8iIhbubq6mefeGPrcaP367V51v17m4+nGZ4TnJmH9y8PzfuHEXnh5upvy//ep6fPXpTnW+acsi2Hhvh3yGMP/k6Pm/fhfe3u4Ivpf/j97bok6iVp182LC7D6rXyuCcSMw/OXj+Zee7nDeOVP3ikx0YMWy9Ol+uQh6V/8bNCmfsSZh/ciABAR5458M6qP1Y/gyNZsoV6KlGHlncPtXK/NerF4rVqwdi2bJ+CA0FOnUqY90DED2EdKN576M66kCbjOZf2Cv/u3dfVkUuKUZ99FETbNpk3f2JHubVF9bgzxlHzV437tOdkOPIjF2BDQY5sAym38IpD9J88/1aVuf/0KFwNGlS3LQuRdfq1ZOnDA0NzYWICOkznNaJE8kHfRYvbpu5YS1l4QQRRERERETmSRuZ6fPamdZ/mNwcAbmSvyF7erqhcBH/bNw6IvsqVTooVf7HftcI3vd2wsuPwYzOZ0PkDKR93u9/tTWtj/r0Mbjdaw8vI/iKl2D+KeeSrgaTZ7U2rb/5Xi3TzkbB/FNOIa0jn32xUrqjmYw72o2jmSZOa4n2ncNS3fbNl9epLjgz5re36rmlQDtkyBLs3XtVTT8i8/FJ60jZke7n54F58/qYRozLKPMLF4CGDbNuTj7K+Xx83dUUOJnJ/+h3N2Prpiv4e2UXm+e/SJFc6nbqYIabd+FreWdvokeSqQ+C0pn64OSxyFTfe4SsG7s5GV0Lv6MOBrB2v1D58iFYvvykaf5VGb2dskNIetO+ykjXAwfC0bbtNHX++PEbWL78KXVwqD1xRCsRERER2VTJ0kFq5yORjsJKBmZ8BDeRk5MDC3hwDelK2mlbM3cfkbMYP243Dh1IboudkdFMolvvUhj4fEWrn9ua1pErV55GWFjygW5EtvLT+L2maRAymv92ncPw4itV7JL/vHl91TyV5cp9j2HDlqF27Yz9nUTmFCrsD18/D7PXlSgdqDKfkosLULJM6nlR3319A555/D+rn1u6EyQkJKFhw0lqjtbr182PYG3ffjr+++8EXnrpH+zeDXTtWla9Z5Yu7Y9WrUrgxx872r3IKjiiNT0J2b0BRNmI+SedMf+kM+afdMb8k86Yf9IZ8086e0T+ZQTR91/tQZFQf5SvmCdDo5lE/UaFMrR51rSOnDPnMCpaX8slnVnw+f/z+P1q9GjVGiEZzn/tuo+enzKj+ZcCk4x2PXJkCDZuPI1Bg6ZgzJgMPR3pxoL8XzgfhSHPrsQn4xqm+Tdg+Ns1Uo3qdrm3lMtTGjOuoepoYC0ZvTphQgez133xxf2OIkuW9FPL2NhYjHkg/FOmdEVW4YhWM1wMLsDde0tHEp/dG0A6YP5JZ8w/6Yz5J50x/6Qz5p90xvyTzizJvxR2Dp0bgK69SmV4NJPYsfUKtmy8ZHX+pXXk2rVnTOvptY6Mj0/Ejh2XULSoZU9BZOnn/47DT6D/0+Uzlf+9u8KxYe1Fu+RfWgbLSFchy7t3LXsa0pul+ZdOZXnz+SIuNjHNddIiW1plSwHWy8tNLX+Z3hLtOt1vnS35zBPsjcrV8ub47z8stJphgJTg7y0dhAp9rAN++acch/knnTH/pDPmn3TG/JPOmH/SGfNPOrMm/zK/3oNk1JJxFJNIbzST+GTkVkybfNjq/FvaOlLm8WvatFiawheRI+Rf2gxP/H6fXfLfqlVJnDt3C02aTMFTTy1EkyaPfBoii/Pv4eGKib+3TDOiO2WxddnGHjgZ/oxapiyyHj54Ay0em4M1K85nyfcfGdE6atQotcwObB1sjryWMq2SA/3j7Ihf/imHYv5JZ8w/6Yz5J50x/6Qz5p90xvyTzizM/85tV9Gv+z/4Z003FC+RK81oJpmTUtqlliwdiOEjaqTa0W40a2EH3L4VZ3X+LW0d2a5daTRvXhRjxmx/5HMQWZP/I4duoHubvzF7UXtUqpo3Q/n/dUYrRNyItVv+Z8/umW7rVCJbfP+5eiUGg59diTffr21xK+xChf3RsEkh1LLw9s7+/YeFVmfhgF/+ibIM8086Y/5JZ8w/6Yz5J50x/6Qz5p8cTPlKefC/FyvBP8AjzXVSbJLTw8REx8PXz0O1j3wk5p8cTMnSQXjmhYoIDkluz5vR/Oc1c/80mH9yUN7e7gjK7Y3cebzU+p2YBPj4upvN+/hxe9ChaxgqVg7G6M/rW/4kTp5/tg4mIiIiIiIiIiIiojR8fNzx2js1VaHo7t0Eq+67ZOEpVCk5Dbu2X4W9ZXfbSMqZ3N1dVf4LFvKzOv9rV55H5bDfsW71BWQFvgfIXnIFeqoWwqVKB6m5glvW/wtffbpDXbd102W8/NwqHDsSAU8vNyz9+zSOHoqAblhoJSIiIiIiIiIiIqJ0SbG0XuVZqpWwpZq1CsX/fdkA1Wqan9+PyFnIfJP1q8zG+jWWF03rNSqk8i/tU4lyCim0BgV5Y+vmK2p90/qLmDP7OC5djFEHJkyZ3RrNW4dCNyy0EhEREREREREREVG6ylXIg8efKqdaCRvnrjQY0s6ld+F8FHp3WqxGN8lo2D79yqi5JomcWYlSgXj8qbKoViP5oAHJtxScHnT92h307/6POiDBw8MVfZ8sy/xTjrB9yxVE3oxVxdRhb9fAiy9XUZe//Hp1HLs00HRAwScfbkOnFguQkJAEnbDQaoaLwQWIurd0JM45DzA5GeafdMb8k86Yf9IZ8086Y/5JZ8w/6cza/Mt8fG++X0sVT29cv4u2jebh90mH1HV/zjiKMaO2qfPSYlhuEx1lXZtVE+afHDD/np5ueOO9WvAP8FTzUHZsvgA/fLNHXbdo3kmMfHuTOh+U2wvePu6Ii0vM2IYx/+SA+b99Kw7/67cM33+VnPmWbYuicfMiyY/l4qLmIXZ1TX6s0Z/Vw+RZrVVBVqf8p52xlhySCn004OLrYF/+ibIA8086Y/5JZ8w/6Yz5J50x/6Qz5p+cQe48Xpi1sD3KlMut1m9FxuHa1Rh13svLDVP/aJOhx2X+yRnIQQcz57dH4VB/tR51Ow5XLyfn383NFb9Mb5Whx2X+yVEF5PLE/P86o1ARv0feNl9+X3W6cycBw19cg6f+VwH1GhbM8flnodUMg4sB8Lm3JNIM8086Y/5JZ8w/6Yz5J50x/6Qz5p90lpn8ywimuvXv7zh/9sVKNt46IsfOf43a+Uzr0lJbTkQ5Mf//LTmDuvULoHiJXFY9h7u7qxrlGn7vIBx78/LywsiRI9UyO7B1cHrc4FBU6H355Z+yCPNPOmP+SWfMP+mM+SedMf+kM+afdMb8k86Yf9KZBfm/GRGLFweuwII5J6x+eA8PV3w/qTk6dy9pk/wPHDgf+/dfTXVZZORd1KkzEf7+n+DAgXDT5aVLf4emTaeo07Jl1m97RnBEqzNhWZx0xvyTzph/0hnzTzpj/klnzD/pjPknnTH/pDPmnxyIzDm8aV9f+Pt7mL1+ycJTGPfpTpw8FokSpQMx/O0aaN85LNVtrlyOwZ5d4WjdrpjN8+/r64HFi5/AG28sS3V5YKAXVq8eiKzEQisRERERERERERERERERmch8q+kVWZ/rvxwuLoDBABw+cEOtT5zWMlWxdeL3+zDvzxOWFVpTMBgMGDJkCfbuvaraEEtRdfz4rThxIgJ+fh6YN68PPDzcEBKSdt7YqKg4NGkyBYULB2D8+PbIk8cH9sZjJIiIiIiIiIiIiIiIiIhIGdD7X8z87bDZ62Qkq7HIKmQp6199uhMpvfpmdazc3APWWrToqJrjdd26p7Fq1QAEB/ugfv1QLFv2JLy83LFvX+o2wilt2PAM1qwZiLZtS2HkyFXICiy0miPhuHtvSaQb5p90xvyTzph/0hnzTzpj/klnzD/pjPknnTH/pDML818sLBfyhpgfDSrtgo1FViNZP3EsEin5B3giMMgL1jp0KBxNmhQ3rUvRtXr1Aup8aGguRETcSfe+wcHJo3B79qyAPXuuICuw0GqGC1yAhHtLR8EPf8oizD/pjPknnTH/pDPmn3TG/JPOmH/SGfNPOmP+SWeW5n/0Z/XQKp2WvzInq4xgTfW4LkDJMoGpLvtzxlGMfnez1fkvXz4Ea9eeMV2dlGSAS4onfLDIaxQXl4jY2AR1ft26MyhVKg+yAudoNcMgr6b7vaWDcMgPf8qRmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdMb8k84szb/Mk7px3SXUqJ0PPj6pS4nD366Rao5Wl3tLuTyla+F3kBCfZHX+O3Uqg6VLj6Nhw0lqLlaZo9Wc9u2nY/fuyzh8OBwFCgAREXfRrds0NY+rtBieNKkzsgILrebIa+l9b+kgVOg9HOvDn3Io5p90xvyTzph/0hnzTzpj/klnzD/pjPknnTH/pDML83/uzG307rgYEyY3R5ceJVNd175zGCZOa6nmZD1xLBIlSwdi+IgaaNcpLNXtXnylaobyL6NXJ0zoYPa2X3zR2nR+yZJ+ahkbG4sxY8Ygf34/7NgxCFmNhVZnIaH3cqwPf6Isw/yTzph/0hnzTzpj/klnzD/pjPknnTH/pDPmnxxM0eK58O/67qhY2Xz7XSm2ysmcxMQk/Lv4DJq1DIWPr3uOzz/naCUiIiIiIiIiIiIiIiIik0pVgtXo0i0bL6niqaV27wjH4GdWYvOGS8gKMqJ11KhRapkdWGglIiIiIiIiIiIiIiIiolROHLupWgjP++PEI297+1Yc7txJQM06+bFxb180axUKHbB1cHqkOO8KxCfGmy5ydXGFm6ubmgQ4ISkhzV083JIn5JXr5DYpyf3k/kmGJCQmJaa6To4IcHdNfilSPp+R8TqiLMP8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90ZkX+i5bww/zlHVGten61PvXX/ahaMy8qVg423cbN1Q3RtxNQp+JMvP1hTTwxsCzy5vdUj69D/p3/L7jn+++/x9ixY3H58mVUrVoV3333HerUqZOhx3IxuACxgMHFgNiE+0ONvT28EeAZoEIWHh2e5n55fJJ7VV+PuY64xLhU1wV5B8HHwwfRcdGISYhJdZ2nu6d6XAnw5djLaR43yD8ILnHJ2wTfDP1JRBZj/klnzD/pjPknnTH/pDPmn3TG/JPOmH/SGfNPOstI/ktX9Ecur1yIj0/ExO8PoO+AkihV3h9TfjyMSRMOY+/RgQjMG4hPvmiIKnUDUj2uDvnPEYXW2bNnY/jw4fjxxx9Rt25dfP3112jTpg2OHDmCfPnyZexB7wLut90RljvMbEXf39M/3SNa5AM1vSNa/Dz9EOx7v9IvUlb05b4Pkuvc4t2AGMAlyElnAybnwvyTzph/0hnzTzpj/klnzD/pjPknnTH/pDPmn3SW0fx7AYcODlYFVy8vd3Rp7Y1CufMhr19edf8Xn23w0BHdOTX/OaLQOm7cODz33HN4+umn1boUXBcvXoxJkybh7bffztiDGpIr+8YPzweDYe5yS4Y6S9hc3dKfGvdhjyvbRJQlmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdMb8k84ykX8vD0943bu6bp2i6qR7/p2+0BoXF4cdO3ZgxIgRpstcXV3RsmVLbNq0yex9YmNj1cno1q1bZm936dIlFClSBI5AtoXIFph/0hnzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPOmP+s5/SF1mvXriExMRH58ydPxGsk64cPHzZ7nzFjxmDUqFGPfOykpCRcuHDBZttK5AiYf9IZ8086Y/5JZ8w/6Yz5J50x/6Qz5p90xvyTzpj/rOf0hdaMkNGvMqdryop+aGioab1AgQJwVI68beQcmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdMb8k86Y/6zn9IXWvHnzws3NDVeuXEl1uayn96J4eXmpU3q2b99u8+0kchTMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5JZ8x/1nP6Qqunpydq1qyJFStWoGvXrqYhz7I+ZMgQix7DYDA8tFe1szP+Xca/kygl5p90xvyTzph/0hnzTzpj/klnzD/pjPknneX0/Au+Byg9zL/9OX2hVcgw6AEDBqBWrVqoU6cOvv76a0RHR+Ppp5+26P63b99Wy5TDp3Mi+TsDAwOzezPIwTD/pDPmn3TG/JPOmH/SGfNPOmP+SWfMP+lMl/wLvgfoQcy//bkYcsghDuPHj8fYsWNx+fJlVKtWDd9++y3q1q1r0X1lBOzFixcREBAAFxcXU8/qc+fOIVeuXHA2D26/vMQSsEKFCsHV1TW7N48cDPNPOmP+SWfMP+mM+SedMf+ks5yWf5Hyb5C/i/mn9DD/pLOcnn9+B6KHYf7tL8cUWm39IknVOzIy0imD5uzbT9nL2fPj7NtP2cvZ8+Ps20/Zy9nz4+zbT9nL2fPj7NtP2cvZ8+Ps20/ZKyfkJyf8DZQ9ckJ2csLfQNkjJ2QnJ/wNlD1yQnZuOdjfwEMbiIiIiIiIiIiIiIiIiIisxEIrEREREREREREREREREZGVWGg1w8vLCyNHjlRLZ+Ts20/Zy9nz4+zbT9nL2fPj7NtP2cvZ8+Ps20/Zy9nz4+zbT9nL2fPj7NtP2Ssn5Ccn/A2UPXJCdnLC30DZIydkJyf8DZQ9ckJ2vBzsb+AcrUREREREREREREREREREVuKIViIiIiIiIiIiIiIiIiIiK7HQSkRERERERERERERERERkJRZaiYiIiIiIiIiIiIiIiIisxEIrEREREREREREREREREZGVWGh9wPfff4/ixYvD29sbdevWxdatW+GIxowZg9q1ayMgIAD58uVD165dceTIkVS3adq0KVxcXFKdXnjhhWzbZnJ8zD/pzFnyL/geIFtj/klnzD/pjPknnTH/pDPmn3TnLO8B5p/sgfm3PRZaU5g9ezaGDx+OkSNHYufOnahatSratGmDq1evwtGsWbMGgwcPxubNm7Fs2TLEx8ejdevWiI6OTnW75557DpcuXTKdPv/882zbZnJszD/pzJnyL/geIFti/klnzD/pjPknnTH/pDPmn3TnTO8B5p9sjfm3EwOZ1KlTxzB48GDTemJioqFQoUKGMWPGGBzd1atXDfJyrlmzxnRZkyZNDK+88kq2bhc5D+afdObM+Rd8D1BmMP+kM+afdMb8k86Yf9IZ80+6c+b3APNPmcX82wdHtN4TFxeHHTt2oGXLlqbLXF1d1fqmTZvg6CIjI9UyT548qS6fPn068ubNi0qVKmHEiBGIiYnJpi0kR8b8k86cPf+C7wHKKOafdMb8k86Yf9IZ8086Y/5Jd87+HmD+KTOYf/txt+NjO5Vr164hMTER+fPnT3W5rB8+fBiOLCkpCa+++ioaNGigwmT0xBNPoFixYihUqBD27t2Lt956S/Wwnjt3brZuLzke5p905sz5F3wPUGYw/6Qz5p90xvyTzph/0hnzT7pz5vcA80+ZxfzbDwutOYD0qd6/fz/Wr1+f6vJBgwaZzleuXBkFCxZEixYtcOLECZQsWTIbtpTI9ph/0h3fA6Qz5p90xvyTzph/0hnzTzpj/klnzD/pbLCD55+tg++RocVubm64cuVKqstlvUCBAnBUQ4YMwd9//41Vq1ahSJEiD71t3bp11fL48eNZtHXkLJh/0pmz5l/wPUCZxfyTzph/0hnzTzpj/klnzD/pzlnfA8w/2QLzbz8stN7j6emJmjVrYsWKFamGI8t6vXr14GgMBoMK2Lx587By5UqEhYU98j67d+9WS6nqE6XE/JPOnC3/gu8BshXmn3TG/JPOmH/SGfNPOmP+SXfO9h5g/smWmH87MpDJrFmzDF5eXoYpU6YYDh48aBg0aJAhKCjIcPnyZYOjefHFFw2BgYGG1atXGy5dumQ6xcTEqOuPHz9uGD16tGH79u2GU6dOGRYsWGAoUaKEoXHjxtm96eSgmH/SmTPlX/A9QLbE/JPOmH/SGfNPOmP+SWfMP+nOmd4DzD/ZGvNvHyy0PuC7774zFC1a1ODp6WmoU6eOYfPmzQZHJDVyc6fJkyer68+ePasClSdPHvXGKVWqlOGNN94wREZGZvemkwNj/klnzpJ/wfcA2RrzTzpj/klnzD/pjPknnTH/pDtneQ8w/2QPzL/tuch/7DliloiIiIiIiIiIiIiIiIgop+EcrUREREREREREREREREREVmKhlYiIiIiIiIiIiIiIiIjISiy0EhERERERERERERERERFZiYVWIiIiIiIiIiIiIiIiIiIrsdBKRERERERERERERERERGQlFlqJiIiIiIiIiIiIiIiIiKzEQisRERERERERERERERERkZVYaCUiIiIiIiIiIiIiIiIishILrUREREREREREREREREREVmKh1caOHDmCAgUK4Pbt21n+3EuXLkW1atWQlJSU5c9NJJh/0hnzTzpj/klnzD/pjPknnTH/pDPmn3TG/JPu+B5Ii4XWByQmJqJ+/fro3r17qssjIyMRGhqKd99996H3HzFiBIYOHYqAgABktbZt28LDwwPTp0/P8uemnIH5J50x/6Qz5p90xvyTzph/0hnzTzpj/klnzD/pju8BOzBQGkeOHDH4+PgYpk2bZrrsySefNFSpUsUQGxub7v3OnDlj8PDwMJw/f96QXcaPH2+oVatWtj0/OT/mn3TG/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kO74HbIuF1nR88803hty5cxsuXrxomD9/vgrP7t27H3qfsWPHpnmBJ0+ebAgMDDQsWrTIUKZMGRXeHj16GKKjow1TpkwxFCtWzBAUFGQYOnSoISEhwXQ/ufyjjz5S4fbz8zMULVrUsGDBAsPVq1cNnTt3VpdVrlzZsG3btjRBl/r58ePHbfx/hHTC/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdMf3gO2w0JqOpKQkQ9OmTQ0tWrQw5MuXT73gjyIv/gsvvJAmZBLQVq1aGXbu3GlYs2aNITg42NC6dWtD7969DQcOHFAB9PT0NMyaNStVyPLkyWP48ccfDUePHjW8+OKLhly5chnatm1r+OOPP9QRB127djWUL19ebWtK+fPnV89LlFHMP+mM+SedMf+kM+afdMb8k86Yf9IZ8086Y/5Jd3wP2A4LrQ9x6NAhVRmXqnl8fPwjb1+1alXD6NGjU10mL/aD1fXnn3/e4Ovra7h9+7bpsjZt2qjLU4asf//+pvVLly6px3n//fdNl23atEldJtelVL16dcOHH36Ygb+Y6D7mn3TG/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kO74HbMPVHvO+5hSTJk2Cr68vTp06hfPnzz/y9nfu3IG3t3eay+UxSpYsaVrPnz8/ihcvDn9//1SXXb16NdX9qlSpkup6Ubly5TSXPXg/Hx8fxMTEWPhXEpnH/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdMf3gG2w0JqOjRs34quvvsLff/+NOnXq4Nlnn5XRvw+9T968eREREZHmcg8Pj1TrLi4uZi9LSkpK935yfXqXPXi/GzduICQkxIK/ksg85p90xvyTzph/0hnzTzpj/klnzD/pjPknnTH/pDu+B2yHhVYzpBI+cOBAvPjii2jWrBl+/fVXbN26FT/++OND71e9enUcPHgQ2enu3bs4ceKE2haijGD+SWfMP+mM+SedMf+kM+afdMb8k86Yf9IZ80+643vAtlhoNWPEiBGqcv/pp5+qdRni/MUXX+DNN9/E6dOn071fmzZtsGnTJiQmJiK7bN68GV5eXqhXr162bQM5N+afdMb8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Q7vgdsi4XWB6xZswbff/89Jk+erPpKGz3//POoX7/+Q4dPt2vXDu7u7li+fDmyy8yZM9GvX79U205kKeafdMb8k86Yf9IZ8086Y/5JZ8w/6Yz5J50x/6Q7vgdsz8XwqKbLZBUJ6MKFC/Hvv/9m+XNfu3YNZcuWxfbt2xEWFpblz0/E/JPOmH/SGfNPOmP+SWfMP+mM+SedMf+kM+afdMf3QFruZi6jTJCq/82bN3H79m0EBARk6XPLkO4JEyY4VMBIL8w/6Yz5J50x/6Qz5p90xvyTzph/0hnzTzpj/kl3fA+kxRGtRERERERERERERERERERW4hytRERERERERERERERERERWYqGViIiIiIiIiIiIiIiIiMhKLLQSEREREREREREREREREVmJhVYiIiIiIiIiIiIiIiIiIiux0EpEREREREREREREREREZCUWWomIiIiIiIiIiIiIiIiIrMRCKxERERERERERERERERGRlVhoJSIiIiIiIiIiIiIiIiKyEgutRERERERERERERERERERWYqGViIiIiIiIiIiIiIiIiMhKLLQSEREREREREREREREREVmJhVYiIiIiIiIiIiIiIiIiIiux0EpEREREREREREREREREZCUWWomIiIiIiIiIiIiIiIiIrMRCKxERERERERERERERERGRlVhoJSIiIiIiIiIiIiIiIiKyEgutRERERERERERERERERERWYqGViIiIiIiIiIiIiIiIiMhKLLQSEREREREREREREREREVmJhVYiIiIiIiIiIiIiIiIiIiux0EpEREREREREREREREREZCUWWomIiIiIiIiIiIiIiIiIrMRCKxERERERERERERERERGRldytvQMREREREWVcYmIi4uPjs3sziIgoB/Pw8ICbm1t2bwYRERERUY7HQisRERERURaJiorC+fPnYTAYsntTiEgT8nlz82YcYmIS4OvrjqAgT7i4uGT3ZpGdyWtcpEgR+Pv7Z/emEBERERHlaC4G7uUhIiIiIsqSkazHjh2Dr68vQkJCWOggIru6efMupk3bj4kTd+Ho0Rumy8uUyYPnnquO/v0rISjIO1u3kexDdvOEh4cjJiYGpUuX5shWIiIiIiI7YqGViIiIiCgL3L17F6dOnULx4sXh4+OT3ZtDRDnYv/8eR+/efyEmJh49epRXp9y5fRARcQdz5hxSJ19fD/zxR0+0aVMquzeX7ODOnTs4ffo0wsLC4O3NgjoRERERkb2wdTARERERURZKNZL1zh0gLs72T+LpCbCYmy0SkxKRZEiy+eO6urjCzZWj0siyImvHjjPRpk1J/PJLZxQokLp1bK9eFXH5chT+97+F6nZ///04i605ELsmEBERERFlDY5oJSIiIiLKwhGtptFFUmRdsACIiLD9k+XODXTp8shiq4yulW3Zv38/3N2Tj8GsVasWvvjiCzRt2tT226VBkfVc5DnEJdq+eO7p5onQwNBHFlvlNfXy8jKNmpbX85dffoHOVq9erd5/bdu2zbLnlJGES5cuxQsvvICsbhdcrNjXaNSoKObP7wt3d9d0b5uQkISuXWdh3bqzOHPm1Qy1EU5ISMD//d//YebMmeozRE516tTB559/jt27d+PVV19Vy+wmr0e1atVw8+bNR952/Pjx2L59O6ZMmYKFCxdi1apV+Oqrr7JkO+fPn48CBQrgscces/2/OUREREREZBcc0UpERERElB1kJKsUWaUgZsud4HfvJj+uPL4Fo1pjY2Px66+/4vnnn7fdNmhKRrJKkVWKoe6utvuplZCUoB5XHt8Njx7VOnv2bFVUeth8wbrM2SiFQCm0SoEtqwutP/74Y5YXWqdO3a3aBctI1ocVWYVcP3FiJxQt+jV++20PXn65rtXP9+yzz+LGjRvYtGkTcufOreYG/euvv9RlOUHnzp3VKatIoVXeuxkptOr0viYiIiIiciQP/+VFRERERET2JUVWPz/bnaws2n744Yf46KOPEBMTk+a6q1evonv37qhcuTIqVaqEn376Kd3HuXTpElq3bo0KFSqoZd++fdVji6ioKDzzzDPqMeQ0atQos48xevRoVWSQk9xOWl+eOXNGFRDeeOMN0/2HDh2KuHstlwcOHKiKxC1atECZMmXU9hqvi4+Px9tvv61G2Mlj9u7dGxH2GEH8ACmyerh52OyU2aKtjMxr1qwZevTooV7LrVu3Ytu2bWjevLka8Vq9enX8+eefptv/+++/aNiwIWrWrKn+38mIPnMuXLiAnj17qsesUqUK3n///UfmRkbcvvfee6hfvz5CQ0NVMXLy5MmoV6+eum7WrFmm28rrL7eV7ZPXdvr06abr+vXrp7ZdnrdDhw64fPmyqcAZFBSEt956CzVq1FCjE+U55L6SAcmY8TayvXKb0qVLY8OGDRg2bJgpezLK2+j3339H3bp11W0bN26MPXv2mP6/tmzZEo8//rj6W2V7Tp48qa6TAuuRI0fU42VVoU6KnD/8sF3Nx/pgu+D0FCwYgO7dy2PChG3q/tY4fvy4yo28flJkNb5mvXr1QokSJUyF7pdeeglVq1ZFxYoV1UhR4+Vt2rRR/8/k8ieeeALR0dHqOimMy2tg7n7G127kyJEqn6VKlcKSJUtM2/SwXKfn9u3b6NOnD8qWLatyv2/fPtN18hp37dpVnZeMyftInle2aciQIUhKSkqTBfkMlHwfPHgQ3bp1Q/ny5dVnonwOPuxzSf4OGUE7duxYdblxJPrD8vfg+5qIiIiIiLIeC61ERERERBqTQobsrDfXGlMKmlJ8kMLDypUr8fHHH2Pz5s1mH+fll19WxTIpLvz222+qWGIkhVwZObt3715s2bJFjdqSUZcP+uCDD1SbUTlJoUKKs8WKFcPPP/+sCig7duxQ1504cSLV9splixYtwqFDh3DlyhXMmTNHXS4FCz8/P1WAkNtIMUIKdzmdFI2MBet58+apy+T/+yeffKJeSyn8DBo0SBUfpYC1bNkyvPbaa6pwKoVCKZBL0Uf+f8+YMUMVweT1e1D//v1V0UkeU15byYAluZGC2saNG1UBV4qb8rwyIlKKYnLflKRwt2vXLtWGV66TQpv4+uuv1bbL8zZq1MhU1BeRkZGqELZz507VulaKnlKYlQxIxoy3kW2X20jRS4p+UhCV2wwYMMB0MIAUYKUt7tq1a9VtpU2u/P8wklwa/79Koe2zzz5Tl0txV/4fyONJ8SwrXL9+B0eOXFeFVmvI7eV+N27csep+8v9DitR58+ZN9zaHDx9W/z+lOCiv37vvvqsul5GXki15DaWoHRgYiO++++6R9zO+dlJgl3xKIV0yJGTUcnq5fhgpvku7bXnOxYsXq9faHCnwyueMPK/kTrL4xx9/pMqCvP7yGViyZEl06tRJ5UA+lzw9PTF16tSHfi61b99eZVAOKpHL//e//z0yfynf1/L5S0REREREWY+tg4mIiIiINCeFUBld9WCb0+XLl6uigsiXL58apSiXmWtruWLFCjW3q5A5Bjt27Jjqcb788ku4urqqAsNTTz2liiBSEDRHCnNnz57F33//bbq/jFyVYoh47rnn8P3336tRi0JGjfn6+qrz8ndIIVZIQVeKMsbCq4x0lVGTOd2DrYNl5JsUrqXwJ6TIKQXVdu3apbqfjMCUYpOMVJSRc0byusnrIUU1Ixmdt379ejX61SgkJMSi3BhfdxmNKHNHyqhYIaMQpeWsFMykqCWk2CRkhKRskxSc5DWUIp2M9JN5KOWUstjn4eGhisAPI89rHKkoz+vv768OODBmyDh6dsGCBarYJyMKjWQb78gcy4AqbskcmMbzKYuFWS0qKnkkd+7cj24ZnlLu3Mmj4G/fjkNwcPL7yFbkNTb+v5P/P8bPCBk9KwdLSGFTRrfK+1Qy+qj7GV87yZTxOuP7/WG5No6wNUc+u2RbpKgvBV8pZBofMyUZvSqfOZJ72X4ZuS0jb2X0vnFbihYtasqUjFzNnz+/Wq9duzaOHTtm9efSo/KX8n1NRERERETZg4VWIiIiIiLNyU5+KS5IgfNhpBAhpBDWtGlTdV6KTMZRk+Zu+7DHMUdGw86dO1cV1Nzd3S26vxRejGSknBRuhBRDpPAlbTt1J4VEI/n/IiM+pTD1IBl916pVK1XItJVHvV7GdbmdnIyvX3qPJYWub7/9Vo2ClUKujBg1jlQVUnSX4vDDGIv2D26DuQzJyEoZNWhOevfLDv7+nmoZEWHdyNSIiLtqGRCQfH9LSStbKR5ev34dwcHBVv3/kXzJaOc1a9YgV65c6vWU9Ufdz/jaGTMl10lr8Ufl2jgS2hLpfT6NGzdOFVdlFKls3/Dhw1WRP71ttsXn0qPyl/J9TURERERE2YOtg4mIiIiISLWunDZtGi5evGi6TFqhTpw4UZ0PDw9XBVApwsloQ2OLX2ORVeZFlJGTQtr3GkejGh/n119/VUUDaRsrIxHNFRlk1KOMrpVRbikLCHJ/KcDKyC8pVsjchZYUKWTEooxUM84/K8sDBw5AdzIK7tSpU+r/t5G8lvL/V1royuXSGtXI3NyP8vrICFMZqWz0/+3dz0vTcRzH8XdbjkyHbeIhsBIKNC8h1E2v6qEo+jEikA4S0i72D/gPCEFeuggeuiSakUQHve7YqUN0KbASFgz6ofbD5ixen1hJ6Xd9v/n9jrbnA3aZ+3733b6f7Uu99n6/C4WC57oJQvM/y0FZLpdzbYI1zzKZTLpwT8fsNTtYFOSpgjAItXLV50IVveWqxvK80LCeM6jW1kbr7Gy1ublnvrbT47VdOu2vElZVp5oPOjw87H58IfqMq1KzPKt2JzqHqkLW+6QZqeXvjrDWtRetV60zHfvKyopr1bvTMataXwGq5rX+zfxXv99Lv6+boOsPAAAAQHQIWgEAAIBqUkXUx4+7d9tSYeWHQg/N2Mzn8z/vU5WZKhw1Q1BtVTUncWsLy60mJiZcENbd3e3mYepx5favY2Njrp2r9qP7FR5kMpk/9qH5gwod1PqzPGNUwa/mLqp6Tjfdpwpczd6sRG0+1bJTz6mZjmpdq+AlbBubG1YsFXftpv3tplQq5cJsVclpRq/OmeaUKsRReKZqw5GREfc3zXPVPNTtKDBX6KMqQp0Xzcv0u24qUbViT0+PC9a1X537wcFB1y5VNwWvW9skb0etpXXe9TjN4/RD+x8fH3f70Puh1zo9PV1xO603PVatZbXeo6BKzOvXT7rg9M2btb/aJp9ftfv3n1k2e8qz0nwnU1NT7n3R+dXr1VpaXFy0dDrtuZ3ah+uzrnOoz7ve5zDXtRd9P6kVb1dXl5uT2tvbu+3jRkdHXTWrXufQ0JALaIPw+l7SfjX3VWtePygJuv4AAAAARGfPN/1sEwAAAECo1GJS1VZqtetaSmrG3vy8yqR2/8lSKbOzZ80a/VWo/QsFFQpT1e5XrUQVHqgSK2jA9j8qbZbs9YfX9rXkXUEXRCKesEMthywei1u9UPCnKsJyYI/K3r//YkeO3LK+vsP24MFl27t3599Wb2xs2rlz05bLvbKXL2/YgQO/Wt2iBq85AAAAAELBjFYAAACgGhSCKgyt0NYykEQi0pBVNKtRVWr6HadadWaz2boKWUUhqMLQzW/eFXRBxPbE6ipkRTAKS2dmLtrp03ddiDo5ecYOHkxuW8l67dpDW1h4YY8eXSFkBQAAAICAqGgFAAAAIkB1EYCoLCw8t0zmnn36VLTz54/bhQvHLZXaZ+/efXGthdUueP/+BpudvWT9/UerfbgIAdccAAAAIBpUtAIAAAAAUEMGBo65dsB37jyx27cf28zM059/6+xstZs3++3q1RPW0kIABwAAAAD/gopWAAAAIMLqoo6ODmuMuK0vgPqlf/K/ffvZVle/WjKZsHS60c2/RW3T3OylpSUqWgEAAICQUdEKAAAARKChocGFG4VCwdra2gg6AESmqSlmTU0/wrb19fVqHw4iCNd1rdF1RtceAAAAAOGhohUAAACIyNrami0vL7v/BAcAICwKWdvb2625ubnahwIAAADUNIJWAAAAIEKlUsmKxWK1DwMAUMNUyRqPx6t9GAAAAEDNI2gFAAAAAAAAAAAAAJ9ifjcAAAAAAAAAAAAAgHpH0AoAAAAAAAAAAAAAPhG0AgAAAAAAAAAAAID58x3iLOK5gnRGwgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x500 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_120mL_Vb\n",
+    "\n",
+    "trough_120 = hamilton_1_trough_120mL_Vb(name=\"trough_120mL\")\n",
+    "\n",
+    "print(f\"Container: {trough_120.name}, size_y={trough_120.get_absolute_size_y()}mm\")\n",
+    "print(f\"No-go zones: {trough_120.no_go_zones}\")\n",
+    "print(f\"Compartments: {_get_compartments(trough_120)}\")\n",
+    "\n",
+    "plot_container_cross_section(trough_120, [1, 2, 3, 4, 5, 6, 7, 8, 9, 16])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2prx1963mj",
+   "metadata": {},
+   "source": [
+    "## Defining no-go zones on a new container\n",
+    "\n",
+    "Any `Container` (or subclass: `Trough`, `Well`, `Tube`, etc.) accepts a `no_go_zones` parameter. Each zone is a pair of `Coordinate`s - the front-left-bottom and back-right-top corners of the obstructed cuboid, relative to the container's outer origin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "zzzwi3r4vaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container: custom_trough, size_y=100.0mm\n",
+      "Compartments: [(2.0, 28.0), (34.0, 63.0), (69.0, 98.0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylabrobot.resources.trough import Trough, TroughBottomType\n",
+    "\n",
+    "# Define a custom trough with two dividers\n",
+    "custom_trough = Trough(\n",
+    "    name=\"custom_trough\",\n",
+    "    size_x=19.0,\n",
+    "    size_y=100.0,\n",
+    "    size_z=50.0,\n",
+    "    max_volume=80_000,\n",
+    "    bottom_type=TroughBottomType.V,\n",
+    "    no_go_zones=[\n",
+    "        (Coordinate(0, 30.0, 0), Coordinate(19.0, 32.0, 50.0)),  # divider 1\n",
+    "        (Coordinate(0, 65.0, 0), Coordinate(19.0, 67.0, 50.0)),  # divider 2\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "print(f\"Container: {custom_trough.name}, size_y={custom_trough.get_absolute_size_y()}mm\")\n",
+    "print(f\"Compartments: {_get_compartments(custom_trough)}\")\n",
+    "\n",
+    "plot_container_cross_section(custom_trough, [1, 2, 3, 6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ydm1lbbn8m",
+   "metadata": {},
+   "source": [
+    "## Edge clearance and tip size\n",
+    "\n",
+    "`edge_clearance` (default: `MIN_SPACING_EDGE = 2mm`) controls how close the automatic positioning places tip **centers** to compartment boundaries. It is a positioning safety margin, not a physical gate.\n",
+    "\n",
+    "It does **not** prevent a pipette from entering a container. Whether a tip physically fits is determined by tip diameter vs. container opening - e.g. a 1000 µL tip won't fit into a 1536-wellplate well. This is the **user's responsibility** to verify when choosing tips and containers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bee6v46lawm",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Container size_y: 20.0mm\n",
+      "No-go zone: Y=8-12mm -> two 8mm raw compartments\n",
+      "After 2mm edge clearance: compartments = [(2.0, 6.0), (14.0, 18.0)]\n",
+      "1 channel: [Coordinate(x=0, y=6.0, z=0)]\n",
+      "2 channels (1 per compartment): [Coordinate(x=0, y=6.0, z=0), Coordinate(x=0, y=-6.0, z=0)]\n",
+      "3 channels (needs 2 in one 4mm compartment): ValueError - Cannot fit 3 channels into the compartments of 'tiny_container' while respecting its no-go zones. Use fewer channels or spread='custom' with manual offsets.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RalRUlFjOR6951UbxZfz48YrKuXDhQvVzzp8/r3WUzM1XSo7wNS9hYWE6X+e3335T14x16NBB1AZkb4bjy40bN9TLP/74Y/VyXU0luprh+P3Idu7cmaPZTvN9yM2k3KQmLytbtqz6+QMHDszRlKhZ08TrZxcvXlQva9Wqlfqx3Owi156YsqapcePG6mXcZCYvlzvIDxgwQCsGdG3/TZs25flaFSpUEI/jJmhunpKVKVNGK765qUleZ79+/dSP4yZQufapQYMGit7flClT1OuaOHGienlB3o+8Xfm7KjdRa8bDqFGjxDKuLcwv9n/88UfxWP6u6/qsuJlWbp5u1KiRaErn2tAHDx7ofH/cVMiP7datm6LtAaBPqGkCo+Iamh9//FF0TN6zZ484uu3Ro4dY/umnn+b7fK494tP14+PjqXjx4qLmiGeJZ9mP3nXhI2YlBg4cqF4v17DIM83zKf+aNWLZj/Bzu+gaOmHOnDk0evRocX+bNm1EzQ538pYVK1ZMfZ1rKGT83nU9JrvCPl8uKw9xICtTpoz6urOzs/jLtYS66Ho+dz7P/ny5BsZUKleurL7OtTsyuVw8ybGu9695nWOOa1uy1yxOmzZN3P/gwQPxl4eV4OERZPKEvLLcXos7PwcEBKhfKz8cq3LNz8svv0wzZszI9zWyvx9NXAsl10zq2kb6+M45ODjQ4sWLxWudOHGCpkyZIobV4G3EQ0tkxzVdAKaCpAmMbuzYsWJny80gf/75J3Xv3l1MSspJEzfV5YZ/+LnZjX+I+MeEm4zkqvrsCQD/WOhKXjSb2/LCP/jDhw8X15ctW0arV68W1wcMGEAuLi7qxxW2eY6baPjsNS4TN/fxWXHc9KBJbiJkV65cUV+XmzL4R7hu3bq5vof8np/9MTLNxC2vZbkp6vONRW7eZfwZZceTG8s045ITd12P0YWTJfbw4UOtsYWyx3lur8XNY3Jzdn6vxU16fLYk47G4eD5BzfdVmPeT3zbS/M6tWLEix/eNExz5DFA+yJHJ70nGSRJ/r8+cOSO+a0OGDBHPX7BgAR06dEjrsfJz5WZFAGNC0gRG9ejRI/roo49E/4YKFSrQiy++SM2bNxf38U5S82hYE9dqcD+MCxcuiGSBk63sp5vzergGS+4bwf03+IiY+1nwjrh169aiz4pSY8aMET8UXCY56ZD7vhTF559/Th988IG4zrPXb9q0SatWRsanV8u1Mpxk8Q8v/zByDR3jZEv+kdNM3ni4Bjkhql69uri+cuVKcRTPQw7MmzdPLOMajC5dupA50FX+3HC/H/5M+MLXNX9MeRn3Y9MHzW3zySefiPXzjzrX5jBO3Fu1aqWe30/zItc0tWzZUvzlPjo8OCPX8v3www85kqYqVaqoDwD4YGDXrl2ib9DkyZPV7zGvz+r06dMiXvjgo1KlSqKPoGZyX5D3UxA8aCrXFDFOjjjG5H6KfIBSv359nQm63M9JxgcQ//77r+hP17t3b7FemWZtFvdblA9AeBgOAKPTa2MfQD64b09ufR+4n0dqaqrOPk18dpySIQcWLVqU69lzefUtyk3Xrl3Vz+XT5/WhIP2flA5uqdknSXP4Bd5uSga3zO358jL+PGR8qr68XFefJrn8mp81Pyev/mq5vb4ufH9e24/LomT7a5ZJc51cFsb9y1q3bp1vX53CnD3Hp9RrntnG/v77b3GfrtfiYRHkvnu6DBs2LNdyytu5IO8ntz6FurZdXmfPacYI9+nivl26PqPcnstnOGqeqarZHw9DDoApoKYJjIprN95++21xBMp9hrj6n/suvPLKK+LoWq5ZyU7ptAl8Bs/OnTtFLQyvn9fH/Wm4HxSfKSU3lxSkKVGmeUaSsXCtHNfC8PbiPiX8nrjGLTQ0lKpVq5bv87mv1P79+6ljx47irDWuTeAauS1btoh+W+ZCbnLhaV64psQccJPijh07RA0Kb2uOJW5C5dqYDRs2iNqR/HANEje9cjMqP5/PGuO+a/LZl9xHTsYxylPpcB8//pz59Tl2x40bJz5vuY9dYb8f+ng/uvD6uCaX18MxxnEq1yJzjbCMa4jl75DcR1DGfRubNm0qak55n8Dbp1evXmKfIG8rzefx2bJVq1YtVHkBisQkqRqAhZDHOuLamtzO5oGi69y5s9jOP/zwg2RtePwluQaV/fnnn+ra0Pfff1+y1WlUeCDPguAzCf39/XVONwRgLHb8X9HSLgDrw2e2zZ49m8LCwsRRPJ/F89tvv5m6WFaJ+51xLQrXpnG/Fs2zzKwB17xwh2juuMz9lPjCuC/U0aNHtTpI2wIee4r7dXFNVPYap7xwnzC5lprHaNLVMR3A0JA0gc3hztx8ZlFu5I68fDYfdyzn5hIe2I+bjgAKioeV4M77PHcgJ0+cLPEZozx/njycAABYBiRNYHOUJE0AAADZIWkCAAAAUMC6Og8AAAAAGAiSJgAAAAAFkDQBAAAAKICkCQAAAEABJE0AAAAACiBpAgAAAFAASRMAAACAAkiaAAAAABRA0gQAAACgAJImAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKICkCQAAAEABJE0AAAAACiBpAgAAAFAASRMAAACAAkiaAAAAABRA0gQAAACgAJImAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKICkCQAAAEABJE0AAAAACiBpAgAAAFAASRMAAACAAkiaAAAAABRA0gQAAACgAJImAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKICkCQAAAEABJE0AAAAACiBpAgAAAFAASRMAAACAAkiaAAAAABRA0gQAAACgAJImAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKICkCQAAAEABJE2Qp2nTppGdnR09efKErOn9gOVBLII5Q3zaBiRNFiwhIYGmTp1KXbp0IX9/fxHgS5YsMXWxwAYdP36cxo0bRzVr1iQPDw8qW7YsvfTSS3T16lVTFw2ALly4QP3796cKFSqQu7s7BQYGUuvWrWnz5s2mLhpYGEdTFwAKj49oPvvsM/EDVbduXdq3b5+piwQ26quvvqJDhw6JH6Y6depQeHg4zZkzhxo0aEBHjhyhWrVqmbqIYMNu375N8fHxNHToUCpZsiQlJSXRX3/9Rb169aJff/2VRo0aZeoigoVA0mTBSpQoQQ8fPqTg4GA6ceIENW7c2NRFAhs1fvx4Wr58OTk7O6uXvfzyy1S7dm2aOXMm/fHHHyYtH9i2bt26iYsmrhlt2LAhfffdd0iaQDE0z1kwFxcXkTAVxeXLl0UzSrFixcjNzY2qVq1KH3/8cY7HxcTE0LBhw8jX15d8fHzotddeE0drmhYvXkzPP/88FS9eXJStRo0aNHfu3BzrKl++PPXo0YMOHjxITZo0IVdXV1Ft/vvvv2s9jpsaucmRazD4R5nLyE0/ffv2pYiIiBzr3bZtG7Vq1Uo8xsvLi7p37y6q5cHwmjdvrpUwscqVK4vmukuXLilaB2KRaOfOndSyZUvx3jw9PcU2+N///qdo+0HBODg4UJkyZUQ8KYH4RHwyJE027OzZs9S0aVPas2cPjRw5kn744Qfq06ePznZ+3llw9faMGTPEdf6Sfvrpp1qP4S99uXLlxE7+22+/FTuksWPH0s8//5xjfdevX6d+/fpRx44dxWP9/PzEjkbXF/ett96i//77T/TfGjNmjCgfHyVqWrZsmfji8w8NNxVNmTKFLl68KH6Abt26pZftBQUjSRI9evRI9B/JD2JR1e+GfyBTU1NFszuXhZuP+IcQ9CMxMVF0a7hx4wbNnj1bJA/t27fP93mIT8SnmgRW4fjx4xJ/nIsXL1b8nNatW0teXl7S7du3tZZnZWWpr0+dOlWsd/jw4VqP6du3rxQQEKC1LCkpKcdrdO7cWapQoYLWsnLlyol1HjhwQL3s8ePHkouLi/T++++rl/F74cd16NBBq0zvvfee5ODgIMXExIjb8fHxkq+vrzRy5Eit1wkPD5d8fHy0lsvvBwxv2bJlYlsvXLgw38ciFiVp9uzZ4nZERESe2woKb/To0WIb88Xe3l7q16+fFBUVle/zEJ+ITxlqmmwUV9keOHCAhg8fLjqSa9J1mukbb7yhdZurdiMjIykuLk69jKusZbGxseKIrk2bNnTz5k1xWxNXR/M6ZFydzNXd/NjsuL+BZpn4eZmZmaJzp9ykwVXiAwcOFK8pX7j6nY8O9+7dW8CtA0XFTRlvvvkmNWvWTHS+zQtiUYWbc9jGjRspKysr18dB4b377rviM1q6dCl17dpVfHZpaWl5PgfxqYL4VEFHcBslf+GUntWUfWfBVcQsOjqavL29xXWupuVq4dDQ0Bxt+Lwj4Pb93NYnr5PXV5DXZteuXRN/uY+ALnL5wDj4zDmu/ufPe+3atWKHnBfE4rOO8wsWLKDXX3+dPvroI9Fs9MILL4imGXt7HN/qQ7Vq1cSFvfrqq9SpUyfq2bMnHT16NNcxiRCfKohPFSRNoEhuP3zcb4VxHwH+EvEOic9G4TZ67hi8detW0Xcg+5FJfusryGPldXNbva6O8Y6OCHNj4R0+H8Hz0ey///4rTu/WN2uNRa594BoNPtr/+++/afv27bRq1SrxA/fPP//km3xCwfEP/ujRo8V4Ylx7ow+IT+uGXxMbxWdgsPPnz+tlfdzhkDsIbtq0SetoxxhNYxUrVhR/+UyUDh06GPz1QLeUlBRx1M4/QLt27RLNCkogFp/hI3b+QeUL/6B++eWX4gwtLjtiW/+Sk5PF3+xNYpoQn8/YIz5x9pyt4nZxHhF30aJFdOfOnXyPYPIjH2VoPpd3RHxqraF17txZVCvzFzg9PT3H/bpOuQX94n4TXH3PzQ1r1qwRfZmUQiyqREVF5VhWr1498Zd/ZKHwHj9+nGMZfz586j7XoOSV4CM+VRCfKqhpsnA86jI3hTx48EB9FHPv3j316aeabePZ/fjjj+I0Ux61mTsQhoSEiFNOuer1zJkzBSoH9w3gKmauaeDqbp7iZf78+eKIhgfgNCTeCfApvEOGDBHvZcCAAWJHxzs4fi8tWrQQ2wkM5/333xdHzvz58841+2CWgwcPzvP5iEUSp3Fz8wf3B+PT0fmH/pdffqHSpUuLbQOFx3HAHbE5+SlVqpTod/fnn3+KExb41Hk+/T4viE/EpwxJk4X75ptv1GdGsHXr1omL/EOVV9LEU6/wFBc8Tgd/kbh5hb8MPLZIQXF/AO70O3nyZJowYYJoL+dxQvgLyWedGNorr7wi+s/w6NOzZs0SRz68c+SzR3hwOTAs+YeDk3ZdY9fklzQhFkmMecM/xFyjwWc08fhWfEYVj/GT1/cY8se1oAsXLhSxxWey8YCOPBo4j1PE2z0/iE/Ep8yOxx1Q3wIAAAAAndCnCQAAAEABJE0AAAAACiBpAgAAAFAASRMAAACAAkiaAAAAABRA0gQAAACggNWP08Rz7fDAjzwuR24TMgKYMj55QDseXK+o8cmjh/BAeSVKlLCpCTTBcBCfYCskSaL4+HgxhlVe8Wn1SRMnTDwhIgAAAEBe7t69K0Y5t9mkiWuY5A3BQ8gDmBOec4on7UR8gjlCfIKtiIuLExUscs5gs0mTXKXMX3h86cFcIT7BnCE+wVbY5dMMjYZlAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApMnKJCamUWjoXUpOThe3//nnBv3001H1/b/9dpIOHbojrqenZ1JkZJLJygq2Jy0tk44du09RUcni9okTD+jTT/ep71+16jxt3XpNPev4o0cJ4i8AgDlA0mQFJk7cSStXnhfXz59/TM2bL6IrVyLFbf6BWrv2kvqx8+adoJ07b4rr5849psDAWXT06D1xm/9u337dJO8BrNfs2aH0/fdHxPX4+FRq2nQB7d0bJm5fvvyEFi8+o37smjUX1bEcH59GwcHf0ooVqtuXLkXQ+vWXRLIPAGBzSdOMGTOocePGYlbh4sWLU58+fejKlStaj0lJSaE333yTAgICyNPTk1588UV69OgR2bJNm65Qp07L1EfgDx4k0JMnqhqjOnWC6OzZN6h69UBxe/Lk1rR//zD1c0+dGk1Tp7YR10NCfGnVqn5Uu3aQuL1gwSn69NP96scuX36OHj9ONOp7A8vHyXfz5gspISFN3H70KFEdR/7+bnTq1Cjq3LmSuD14cB26detd9XPXrn2Jli7tI647OzvQ+vUv0/PPh6jj/o03/iYHB9Vua8OGy3TrVozR3x8A2DDJhDp37iwtXrxYOn/+vHTmzBmpW7duUtmyZaWEhAT1Y9544w2pTJky0u7du6UTJ05Izz33nNS8eXPFrxEbG8uZhfhrqTIyMqWZM/+V9uy5KW7v339LGjZsg5SQkKrX18nKypJiYpLF9QcP4iQHh0+lv/66KG7zci4H6Jc1xCdbsOCktGLFOXH9xo0oqX//1dKdOzF6fx05PtPSMiQ/v5nS118fFLf5u5CSkq7317N11hKfAPqKdTv+j8xERESEqHHav38/tW7dmmJjY6lYsWK0fPly6tevn3jM5cuXqXr16hQaGkrPPfdcvuuMi4sjHx8fsS5vb2+yJA8exFPJkl7ieqtWi6lfv+r0zjv5v2d94X4n7u5O5OrqSK+/vomuXYvSqrWCorP0+CxRwpPs7Oxo0KB1FBTkQd9919mo/fcyMyXy9nahmTMPiqbna9feIicnB6OVwdpZcnwCGCLWHcmMcGGZv7+/+Hvy5ElKT0+nDh06qB9TrVo1Klu2bK5JU2pqqrhobgh9adSoEYWHh5MxpKSUpMjIrlS8+DpycoomTm1nzVJdTCEtLZAyM92pdOnJlJXlTMnJIeTufpXs7IyTcwcHB9OJEyfI0llLfGZkeNOjR/3I338XubndEfFpZ0e0ejWZRHq6D6WnB1JIyDckSXaUmFhdxKe9fYZRXh/xaV7xCdYZn+bAbJKmrKwsevfdd6lFixZUq1YtsYy/YM7OzuTr66v12KCgoFy/fNxP6tNPPzVIGfk179+/T4bjTERliYg7Y/P7y6DHj7kTtzl0fNV83zWJqCnFxBwjogQTlsnyWHZ8cl+iKlzf+zQeNlNU1EVOqcn0VO87Opr/Ly3iMzaWO5A/NHXBLIplxyeADSVN3Nn7/PnzdPDgwSKtZ9KkSTR+/HitI6UyZcqQPtnb21OJEiVI3+LiGlBiYg0KDl5OdnZZRPSYjxHI/MRQZuYqcnDwIUnyE+X28jpL9vb6//F8+PChSKithSXHZ1JSRYqObkvBwSvIwYFPPIggomJkfiTKzFwuOoxLUimKj29A7u5XyNFR/yc1ID7NJz7B+uPTHJhF0jRu3DjasmULHThwgEqX5qPEZ1WKaWlpFBMTo1XbxGfP8X26uLi4iIsh8Rf+3j3Vafr66JfBwwQ0bVqakpLSKTw8gSpUmEmW4tSph9Slyx+0ffuv4sw9feN4sKajU0uLz4yMLDp8+C61bl2OsrIkMURAjRqGqYkwhPv348QQB7/++g117861ZPqF+DRtfIJtxSfZ+pAD3AedE6b169fTnj17KCREdWqxrGHDhuTk5ES7d+9WL+MhCe7cuUPNmjUjazBlyl7q12+NGPSPO11XqOBHlqRBgxJ0+/a7ImHiH9U//jgr/oJ14MFQO3f+QwwZYG9vRzVqmGPNUu5KlfIWncPlhInHgEpJMU4/JwCwPo6mbpLjM+M2btwoxmqS+ylxD3Y3Nzfxd8SIEaK6mDuHc4/2t956SyRMSs6cM2epqRnk4uJI06a1pREj6osxaSyVm5uT+Hvw4B167bWNVLmyv6g5A8uPz1GjGlKTJqWoeHEPsvT4DAuLFvHJBgxQ9ZsEALCYmqa5c+eKM+batm0rqmzly6pVq9SPmT17NvXo0UMMasnDEHCz3Lp168iSrV17kWrU+EUcvfPp0jVrFidrwE04V6+OUydM3PQIloeb4ypW/FGMwO3oaE+NGpUkaxAS4kcXL46ll1/mExkQnwBggc1zui7Dhj0bC8jV1ZV+/vlnioqKosTERJEw5dafyVI0a1aaXnmlFvn5uZK14R8mtnDhKapdey5FR6vmGAPLUatWcRo0qDaVLu1tlfHJ40rt2nWTKlT4ka5dU003BABgMR3BbQXPm9WxY0XRz2L69OfJmvH7jI5OIV9f60sMrdXOnTfElDrBwZ701VcdyZpx7dnYsY3UST4AgBKYsNdIIiISadiwjbRkybPJSa1Z2bI+NGFCc3FUv3//LcxhZ+b4RASe1+2rr4o25Iel4GR+6tS2ovmRz17lpkgAgPygpslIihXzoNOnR4tJcm0Jz0jPyeILL1Sjb7813hQbUDB8IsK+fUMpKMiTbM24cVtFErVhwwBTFwUAzBxqmgzszp1Ymjx5jxjvhocT4JoXW8LzgO3YMZhmznw2FQ6Yj7i4VJow4R9KSEijMmV8LPoszsL6/fe+4gIAkB8kTQZ29Og9WrXqAsXGppCtqlIlQCRPV648oeXLz5m6OKDh3LlHIj558l1bxU3JfBYrDyz7yy/HTV0cADBjSJoMrH//mnT+/BgKCHAnW7dw4Wn6+utDotYNzEOLFmXp+vW3RGJr6zZtukKff36AoqJwxicA6IakyUAOHbpD3357mDIzs8QggUD02WftKDR0hOh8C6bFAz1OmrSLkpPTEZ9PjRzZgC5efJP8/d1MXRQAMFP49TLgAIHr1/Ns8CBzdXUUozNzU9C6dZdMXRybdvz4AxGfqPV7hvsbcodwbkpftuw/UxcHAMwQkiYD+eCDFrR371Ax0zpo434j7767HXOAmdBLL9Wkc+fGkJeXYSdntUSc0L/55lYx2S8AgCb8ousZ16KsWHFOTFrLnZ8hp48/bkUXLowVNU9gXNwcx5Pw8rhMiE/dhg6tRzdvviMGoQUA0ISkSc82brxM48Zto/j4VFMXxWxxEx3XcHAzyL17OJo3pn37btHbb2+j27djTF0Us2Vvb0eBge5i0uLr16NMXRwAMCM41C8oOyLJTqLMrExysHcQc+VlZD1rZnp9VD3q2bsS+fiopg/h+/gxmvh59nb2lCVlifVord7OjhztVR9LemZ6jpfn+/gx/Dx+vjHWy+vU9V5lTg5Oub7XvNbbucsfVKqkN61d2z/P9fI2B/3EZ4dO5en6zTepdEnV9CGIz9zXO3z4JrpwIYJOnRpFmVJmrutFfOovPpV8ZojPgq0X8alfSJoKgL/s5E2U4ZVB9+Pvk5+rnwjmx4mPxf2PHiZRUAl3ImeiuBQHEeyRSZGUlqk9m7qPqw+5O7lTUnoSxabEat3n7OBMAe4B4osQnhCeowzFPYqLL0p0SjSlpGuP/eTl4kWezp6UkpFC0cnROb58xTyKieu83uxfzkD3QPEl4/JwuTR5OHuQt4u3eB/8frJ/cYM8g8T1iMSIHF9efzd/cnF0ofjUeEpIS9C6b9Ln9al6hZIUkxIjnptdCa8SlOmUSeTxdNtD0eIzPImKB7mRnbMd4lNBfL72dmXyc2tEsamxiE8jxKemYM9gxGc+8enq5Cq2IT8H8Wk8SJoKyoHITrIj/hefFi++PCKo49KoS7O/6ePpjWnQa1UpIV0V4Pb29uRip93ZNj0rXTyXjxz4uZp4R8H3sez3scT0RPEY/pf9fj4aVrJe3rFkl5yRTCmZKSSR6v1o4vVpvtfs5PU6OjiKHZKm1MxUSstKE2XL/twGjdzIwV4SO5nc1is5SyIJhaLFJ/exG9xzC3XpUZb+N70x4lNBfFatxvHpgPg0Qnxmh/jMPz7z24aIT8NA0lQYWaqqXPnLxEcY3l4ONGdBO6rXsPizatGnRyi54aMM+zzOrtNcT3b8+vzPmOuV32tu8nqvua33xNFHNHViKK3a3I08vfDtNlR8SvYSfTOnNQUWc0V8FmC9t27G0bgRe+jH+e2oQiWfXJ8PRYvP3CA+i75e0C8kTQXBNZzcv1tHTScP2Ni5e3lTlMqilSztQWXKe1FcXBqSJgPGJ+9YW7QuaYpSWTRubi9R2pNSU3P2aQL9xSeApcDZcwXA1aGU/vSvhmtXomn82P30JALTLxRUyVKeNG9Je/EXDBOfcbFpNPa1PXT9Gs6YKyg3N0eav6wDVa/pb+qiWG18AlgSJE0FwO3VXEMq/mp4/CiZLl2IIk8vVJEWBo8ZtH/3PfHjngNvau6viaPTQsfno/BEunUzllxdMC5TYXB/sONHwun+Xe2OuALis8jxCQaE+NQ7JE0FwQdIPC1VtgMlbvbYtr8vBmsspMgnKfRK3220f8+9HPeJo9IMHJ0WJT4rV/Wjrfv7UumyXqYqmUXjqWaG9NtO69dcz3Ef4rPo8QmGg/jUP/zKF1FiQjolJ2dQYDFM8llYJUp60IGT/XV2tBVHpU44Oi0snjD64f1EJExF4OzsQFv39aXyFXKOEI74BHOG+NQ/1DQV0b/77lO9Sn+gP1MRVazsKzor58CL+GxaHCgVyrUrMdS01ko6eeyRqYti0Tih55HCc0B8gjlDfOodkqYiatQ0iOYtbU8BgaoRwKFwrlyKolf6bKWIx9oDw0HRlCrtSQv+7EDVaqAjc1HwQdGwl3fQ2dM5BxEEANuBpKmgtEeyF81yPfpU0F1LAop5eDiRs4sDJcTnnKIACh+fXt7O1LVnCHl44iSFovD2cabMDIlS07JtYCgYbD6wcEiaCoBHsqWkp3+fmv3VKVFLAkXDfW6WrOpMIRUxgKA+43PRvPN0/Cia5vTRr2nZX12ocVPVlBegn/gEsDRImoqAO4AvnX+RLl/UnqcICocHEIyO0p4PSsC4goW2YtkVOnLwoamLYTVn0UVFIj7BwiA+9QpnzxWAmPRQY/JDHvjuzPXBpi6W1Rj3+l5KiEujFRu7qZeJo9JkIjtPHJ0WND7ZzkMvmrRM1uTr6Sdo8/qbFHp2gHoZ4rNo8QmGhfjUP9Q0FRRiz2DeeKs2jZ/UwKCvMWzYBjp/Pues6hMn7qRWrRbTkCHrKT3dgg/NEJ8G8+KASjTjuxYGfQ3EJ4B5Q9JUBP9svU3d2qynlJQMMldbN4VRh+Z/UYVii8Rfvm2uGjYJosbPBWstE0elnoY9Ov3vv3C6fz+e/v33NapWLYDWrr1I1uD82Ujq1HId3Q6LI3NlSfFZtbo/te1QRmsZ4hPMmTHi09YgaSoCXz8Xqt+ouNmOBM4/QCMH76LLF6JEfyH+y7fN9YeJ5/BbtuiSXtcpSRK9+ebf4ii9XbulFBGRRHPmHKOOHZdRnz4rxf2HD9+lTp0qisd36VKJDh26S9bA1dWBGjUJEmfQmSNLi8/wh4kiPuPjdEz3U0i2HJ8AlghJUxE0aRZMX3xr2Or6ovhu5inikRCkpwcZ/Jdvz555iszRiaOP6OP3D4m5vvRl8+arYlBCPkrfu3coBQS4UfPmZWjnziHk4uJI5849pujoFPL25hHgiHx8XCkqyjoGKq1UxZe+/K4F+QeY5xhilhafYTfiRHzqcyBbW45PAEuEpKkgeOfOYy8+3cnzEeedW+bb9HHzWqz6B0nGt29ciyVzNPDVanQn+nXdIy8X0qVLEdSmTXn1bV53/fqqJsAyZbwpOjqZfH1dKS4uVSyLjU0hf383q4hPPruTm+b4rC9zZGnx2axlCRGf+hwWw5bjE8ASIWkqADHpYdazyQ/XrrhGbRuvJXNVobKPOHLXxLcrVrGdsZCqVy9GBw7cVt/mWizNgUj5R5qP7Hftuilu79hxg1q00O63YqnxyUMNNK+7iiIem2fNBOLTtuMTwBIhaSoA0ZnO5Vmnuk7dy4kB78zV+I8aqJs8mNwUwsvN0d8bw2jEK/9oL+RNnVD4o9OePauImpaWLReJPiORkTkTiHr1gikoyEP0K7lwIYJefLEGWUN81m1QjFZt7kZ+/qqmHXNjafHJzceD+m6jmGhVrY+A+Cx0fIIRFDE+ISfz7MFszpy05/Xii7nq1iuE5v/RQfQR4SaPipV9xCn9PK2GObK3J/Lz1+5/Ix+VFvbolI/af/mlu877vvmmk/r6rFnPrltLfHJfppZtSpG5srT45E7Zrm4O5On1bCMjPgsIs/kYVVHjE3JC0lQEfMS5fOll6vVCBTENiLn+MPHFEvCPZfYfTHFU6oqj08LIzMyiBXPPU4tWJalW3UAyR5YUnzwchs4hMRCfYKYQn1bWPHfgwAHq2bMnlSxZUhxxbdiwQev+YcOGieWaly5dzKc5jAeZ++nbMxR203w7g1sSnkJFZ6dlpPaFwp2K535/ls7998TURbEKsTGplJamY2BJxCeYM8Sn9WzOxMREqlu3Lg0fPpxeeOEFnY/hJGnx4sXq2y4u5tM/o1hxd7p0d6ipi2E1erbfSO07l6VPZzYzdVGsAh9kYJof/Rk3Yi/ZO9jR0tWdTV0UALDFpKlr167ikhdOkoKDtavETUp/49pBNl9+15ICi5nnmEIWA/FpMO9OrK/XMcRsEuITLJzZnz23b98+Kl68OFWtWpXGjBlDkZGRpp38MO3p36cWzTtPL/X822Rlsiat25WiGrUCTF0Mi6UrPrdtDqPWDVeL/k2g/2l+oGjxCWBpzLq1k5vmuNkuJCSEbty4Qf/73/9EzVRoaCg5ODjofE5qaqq4yOLi9NffSOLzNu2f/n2qQiUfMegdn1mjOb4KFMyBvffpcXgS9RtYWfsO3tT8cVroeH6mjs8yZb2oS4/ylJqSSe4eZn+MZLbOnXkihhwYMqI6OTpqbEfEZ5HiEwzMyuLTHJj1XnTAgAHUq1cvql27NvXp04e2bNlCx48fF7VPuZkxYwb5+PioL2XK6HEgOM6J3LVn6uYJPN+b2AAJUxEdOvBAnImYnThVNt16Tpk1dnzyWXP/+7QJuXvgXO+i+O90BP3yw3/k4KAdh4jPosUnGJa1xac5MOukKbsKFSpQYGAgXb9+PdfHTJo0iWJjY9WXu3cNP7nl3dvxdCw03OCvY80mTW1MKzd1y7FcHJU6Ws/RqSniM+JxEh3Yc8/gr2PNBr9WnQ6deTnHwRHiE8yZtcWnOTDr5rns7t27J/o0lShRIs+O48Y+w46HHeCq+z1H+xn1da1FenoWOTnZk7OzjiZX/o3ivuFWcqBkivhc/edV+vGbM3QubIjubQz5xqejox3iEyyPlcUn2XpNU0JCAp05c0ZcWFhYmLh+584dcd8HH3xAR44coVu3btHu3bupd+/eVKlSJerc2bxO+X3/fw1p485epi6GxZo6MZSGvLjd1MWwWq8MrUahZwcgYSqkxb9eEHNMojM9AJi0punEiRPUrl079e3x48eLv0OHDqW5c+fS2bNnaenSpRQTEyMGwOzUqRNNnz7dtEdCOmo5g4Ld1dMsoG9TwfXoG0J37/AESWCI+JSnpkF8Fk7z1iXFXwcHi+rNYJ7QSgQWzqRJU9u2bcWOPDc7duwgcyJOlU0ksgvM+cOzfcst+nbGSdq6r69oagLlmrdS/SiB4eLz9InH9M7ofbR+R08KCMSpNAVRq06AuIDh4hPAUuDXXU/KhXhTsxYlKDkpw9RFsSg8WSufzp0ntIroJT4bPxdEKSk6pgGBXC2ce57+3Xc/7wchPsGcIT71CklTAYhJD911T35YvaY/ffZ1c/L2cTZJ2SxRQnwarV9znW7eiM376DQJA+IVNT79A1zp25/bUKnSniYpmyXiWvBtm2/RqeOPc30M4lM/8QmGgfi08bPnzCbNtCdKz0x/tsjOnhzsHURH0dUrrlDFyj5Uv1Ex9f1ODqoxcjKyMnI0R/Lz+PlZUhZlZmnXAnD/E0d71Uek+Xoyvo8fY6j18vP4+Vpv/+l75dfj181Ofq9K1uvibke7jvYRU1PwMl3rxQ5Wf/HJ23bH1jCxrH2XZ+PvID5zX++KzZ3FJNKIT+PEZ16fGeKz4OtFfOofkqaCylQFYmrGs1FzXZ1cycvZi9Iy0mnR3AvUvmspqlHPW32/v5u/+BuZFElpmdqTL/m6+pKbkxslpiVSUkaS1n3Ojs5ivfxFCE/NOQ6Ur6ev+LJEJUdplYd5uXiRp7MnJacnU0xaTI4vpp+rn7genhCeY4fh7eEtvqAxKTE51uvh7CHKxMu5zJr4Syu/18eJj3PsbDzcPMjF0YXiUuPo0uXH5OxiTyVLe6jKRI5ivfyFj0iMUD9HcpDEaLb48hc9PnnbrvrjCrl7OFLLDsXV9yM+c8bn3ftRFBWZSlWq+z69F/FpjPjU3LYyxGfO+ORyaeJtgPg0DiRNBSCqOOOIHL0cKcQvJGeW7yzR4UOvk7u7k86jBw7s3I5o+MsU4B6Q6xENPze3I4/81uvn5pfrel0dXXNdr7uTe65HNLxD8XH1yfFc+b3mt973vjtEV65E0vETI8QyzSMlXrf6OfGOYpvbeaJ6ucjxKUm0esVAcnNTfQ4yxGfO9f607CItmH+awm6/Ra6ujohPI8Wn5raVIT6VrxfxaXhImgpKUn355eDWxAHt46UafmDnzhvUpk15rbFx5C+aLhz09nmc0qzr9Qy9Xv4S8j9d+L3m9dz81rtwQW+6cydWHA3mtV6xo8VBkt7j88iRe1S5sj8FBKhuM8Tns/V+9unz9PJLtcjLQ/vHFvFp2Pg0RRxZYnwqXS/iU//QEdwAbt+OoW7dltOqVedNXRSzEx+fShERieTl5UI1az5rIgLjSUpKp549V9CPPx41dVHMDvdL5O8vH+zUr5/7zAMAYJuQNBlAuXK+dPLkKBo8uI6pi2J2PvlkLz333EJKT8ep76bCzcf79g2lKVPamLooZmfevBNUu/ZcevJEu38MAABD85yB1KkTJP6eOPGAatcuTi4u2NTso49aUpculcjJCVN6mJJcy3flyhPRRBcY+KyZzpYNHVqPSpb0wvYAAJ1Q02RAjx4lUOvWi2nu3BNk6+7fj6PY2BQKCvKkzp0rmbo4QESpqRnUocMymjZtH9k6js179+LI09OZ+vatburiAICZQvWHAXGCsG/fMGrQAH0jhgxZL85C2rp1kKmLAk9x7eeWLQOpUiXVac62bMKEf+jgwbt0/vwYzDEHALlC0mRgTZqUEn/PnXtEkZHJ1LZtebJF8+b1oLQ09GMyN3XrBou/XMty/Ph9m61l+fzz5+n69SgkTACQJ+whjOSzzw7Q9OkH8pyg2Nrwe128+LRIlqpUCaBatXC2nLmaM+cYTZy4y+YSWz7DVW42btGirKmLAwBmDkmTkSxe3JvWrXtJjKNhK4nTxYsRNGbM37Rr101TFwUU1LQcOPCaONXeVuIzMjJJxOeyZWdNXRQAsBBImoyEO5j6+LiKU5lbtVosmkKsFc8lxz+8fIbWtWtvUbdulU1dJMiHo6M9BQd7UnJyOnXvvpw2brxM1j4eE581+N9/b9CbbzY2dXEAwEIgaTIyPpLn05n9/HIO628tCVP//mvoiy/+FbfLlMk5VQCYd/LE8WnNp9xzp+8RIzaJxJ7jU3NKGQCAvCBpMjJvbxfasGGAOGOJT/nmwfQ40bAW9vZ29NxzpahePVUHY7AsPH7W77/3Ff17OKng+ExJyTkbuyXjs1mfe640kiUAMPzZc6mpqXT06FG6ffs2JSUlUbFixah+/foUEvJsAkZQZu/eWzR+/A5q1aqsxU8p8vffVykuLpUGDqxNH3zQwtTFAT24dOmJiM/y5X3FgKSW7OjRe3Ts2H16662m9MortU1dHACw9qTp0KFD9MMPP9DmzZspPT2dfHx8yM3NjaKiokQiVaFCBRo1ahS98cYb5OXlZdhSWwn+Ibp58x3Rl4Rrm3iS306dKlrkEfDmzVfp8eNEGjCglkWWH3KqUaOYOj7Zjh3XqWPHiqI20dIcOHCb1qy5SCNHNhTjhQEAGKx5rlevXvTyyy9T+fLl6Z9//qH4+HiKjIyke/fuidqma9eu0eTJk2n37t1UpUoV2rlzZ6EKY4vkH6StW69Rly5/0oULEWQpNm26ImqY2I8/dqW//lKdHQjWF58XLjwW8Sl/3pbg8OG7tHTpGXF9/PhmdPjwCCRMAFAkivYg3bt3p7/++oucnJx03s+1THwZOnQoXbx4kR4+fFi0UtmgHj2q0OnTo8VYRtyXhKdeGTiwlll3GP/99//Iw8OZunevIjq4g/Xi5mOOz7p1VXMqcjLSvn0FKl3am8wVnwEYGnqPhgypi0ErAcB4SdPo0aMVr7BGjRriYq1cicgrK4snq9L7uuuFuIn13gyLoQ8/3EnlAp2oe+cQkUSZQw1OcnIGDRi+lcaNqksd25Wj3396ntzcHA2yLWS8rXmbg5nEZ1wcJSSk0UcTd9Kk9xrT22/UN5v45HKMGb+HGtYrTiOH1qZp7zUgl48ak318nMFeE/FpPvEJOSE+9a9IddUJCQmUxV8ADd7e5nvkWVSukkS9iCgkKYlo+XKDvU5FIro11YcCnhwmWh5KQ5fEUYVAe5rWQ9VUYkxJaRLtv5ZGXWu6iPfv8iiOsnZHEj10IWOclN47KYnCiOikjQy4aAnxyVF45UN3cnW6SLT8En2yOYHux2TRoiHG/+6nZ0q07UIa9ajlLPpaOd2MJ+fMW0RO58gYdbSIT/OLT3gG8WkGSVNYWBiNGzeO9u3bRykpKerl8tFmZqb1TsPgJEnkR0QpfFTtx9cMJ1Bj9XUqS1TC15HIz5tuPEqjpQfiaHw3P/L1MEyTWFxSJiWnSRTk60h//RtLr82LpbtzKlIJf0da/YFxJ3flbe33dNuD+cSnt8bqq4bYk19cpnjNhJQsmrb2Cb3dxY/KBupuzi+qlLQsehKfSaUDnOj41WTqPS+CDn9alppVcaOf3jDs+85RFsSnWcYnqCA+zSBpGjx4sEiQFi1aREFBQWZRLW9sqfyfh4fRXm/Cy89e61JkBi3aH0eTBpYhcrGnn/+OoJL+TtS3mW+h1x8Rm05nbyVT+7qqmoIG712gHo296fuRZaj/827Uol4AlQh2IZNtazDr+Bzc5dlrXQtPouWhCTSmV0kiDxdaeSBKJFKvdwos9PpjEzMp9HICdarvLWqTen99jZwc7GjLJ5WoWT13ujDHg2qUNU3fP8Sn+cenLUN86l+Be0f+999/tHjxYnE2Xdu2balNmzZaFzCsHo196M7CWuTmovro9pyNp+PXEsX1uxFpVGnUBTp5PUnc3n8+nn7Y9FhcX3MwmtaHxojr8UmZ1OOz63TgfLy4vfZQDHWZdl0cwbP548rSe71V40a5OttTBRMlTGB56ld0p3uLalHFEqqYOXo1ScQoy8iUqNqYC7TxiCoOz91Kps9WPhTLd52Jo8W7ItXreeWbMJFwqdaRSF0/vUHXH6p+AqYPKkmzXislrvNBm6kSJgCwPQVOmho3bkx37941TGlAEc1xcv6aVIG+fFX1A5IlSfRCMx/RrMaOXU2iZftUPzxrD0fThqc/Vu4u9uSgsY4Brf0obH4tcnFSLWtXx4vKFUeiBEWPz9mvl6blE1QD3yalZlHf53ypXHFncfvSvRSau001xMau/+K1kibNGG1Zw5Ou/1qTKpdUxWSTKh5UvQwSJQCwgOa5BQsWiAEs79+/T7Vq1coxDEGdOnX0WT4oAE50vn6ttPr2By8EiQtb9WEF9XIHBzvaOJm7m6v4eTqSn/H7mION8XZ3oBlDVQk+e6mln7iwmRrL2YK3ymklUHLNFQCARSVNERERdOPGDXrttdfUy7iK3BY6ggMAAIDtKnDSNHz4cDHX3IoVK2y2IzgAAADYngInTTxR76ZNm6hSJcuewBMAAADAoB3Bn3/+eXEGHQAAAIAtKXBNU8+ePem9996jc+fOUe3atXN0BOfJfQEAAEAhHiX999+Jjh8niowk4pk2evYkeu45oo8/Vj3m+eeJ3n3X1CW1eQVOmvjMOfbZZ5/luA8dwQEAQBe3rCxqz3MYxscTDRpExDNK8MjgZcsStWpF1LIlUS6TwluMjRuJElXj5tErryh/3pIlRNu3K3/8o0dEu3errleooEqusvv+e5oboxpm5tVUDHNpsqQp+1xzAAAAebp4kTY8eUJl+TofWHPixB4/Vl1OnCAqV06VAFiyzZtV76egSRPXMDFHR6Lx44n8/VUXLy+imTNV9/lqzPrAr7Fy5bMaKF1JE5jfhL0AAAB5iooi6tqVyj5thYixsyPf4cOJypcnSk4mOn/+Wa2JLW8jxjVvXOOmqUYNkxQJ9Jg0HT9+nPbu3UuPHz/OUfP03XffKV7PgQMHaNasWXTy5El6+PAhrV+/nvr06aO+n8d+mjp1Ks2fP59iYmKoRYsWNHfuXKpcuXJhig0AAMb2zTdEd+6IqzzB00wvL5rZu/ez+7mWpF8/HnVXdZv79PzxB9GNG6rr3N/HzY0oJORZPx/ZuXPafX5at1Y99/ZtIh8for59Vc+RLV/+rIbm7bdVTWl//0305AlR6dJEI0YQ1a2rXf4HD4jWrOE5xIi4uYvLUqUKEf9WyY/lpO+HH7Sfp9m/d9Mm3dtGszwsIuLZ8955h6h48Zx9mv73P1WiKduzR3XRfAyYT9L05Zdf0uTJk6lq1ao5xmkq6JhNiYmJVLduXTH20wsvvJDj/q+//pp+/PFHWrp0KYWEhNCUKVOoc+fOdPHiRXJ1dS1o0QEAwNhWrVJf5fqkWHsdJ21rNj1x4pC95ikhQZUg8YWTAk4OsuNEYt8+VSdqxonQ/PlEZcoQ1auX8/GrVxOFhz+7fesW/8ARLVxI5Pl0ioSrV4mmTFHViMm4afHkSaJTp4hGjybq1k35tgDbS5p++OEHWrRoEQ0bNqzIL961a1dx0YVrmb7//nuRoPV+elTy+++/i0Rtw4YNNGDAgCK/PgAAGBAnOzdvqm9eV/IcbqIaOpSoRAkiDw+ezFCVSC1eTBQbq0p2dCVN3M+naVOiTp1UydO//6qWcwdrXUkTJ0wvvkhUvTrRn38ShYWpkqP9+4m6d+cfIVXtkZwwNW9O1KED0ZUrqponTs4WLOAJWYkaNlT1PfrqK6LoaNXj5b5IeenYUVW2jz569t4nTlRdL1mSSNc8r6NGqRLE335T3ebX7t8/Z/LprJrjkaViEGrTJU329vaimczQwsLCKDw8nDpwkD7l4+NDTZs2pdDQ0FyTptTUVHGRxcXFGbysAEohPsGm4pOTHA2qc7nyERSk+vHnJi1uZuPmOU5gNJvLeJm7u/bzuDnuww9VZ+Dx4Mty0vTwoe7X4QSLkzPG73nWLO3Hc7InJy2czEyYoOqo3aiRavnhw0QZGaq/fGDPZdY8+09JX6RixVQXGT9f83m6kibuCyZ3pJfft67X4o7kTz2Smz7B+INb8hhNP//8MxkaJ0yMa5Y08W35Pl1mzJghkiv5UoarZgHMBOITbCo++Qddg0Y9SN6n7XMNz8WLqj5HmgmTTD6tX1PVqs+SFm/vvB/LatV6dp3PUsv+eE7OZBUrqhImmWa/Ws3HmZPAQPGHu99H6GoSBePUNE2YMIG6d+9OFStWpBo1auQY3HLdunVkSpMmTaLxfMqmxpESfpjAXCA+wabik/sG8TACT5voKnI3ofyes2XLs+vc17V+fVUyNHeuquaJ6Rr6Ru6HxJTUrOT2eF1JWnaW0NwVECD+cJ1UliWU11qTprffflucOdeuXTsKCAgw2IS9wcHB4u+jR4+oBLdtP8W36+lqn37KxcVFXADMEeITbC4+X36Zq7DEVR7c8rCuhIfPSuPEhWt8+Iw5xtflvrM8EKZ8Wr6xcJ8iGSd9PGSCnFxxB3Fdj9P8PeT3aagaHs3XyS3Jq1+fxvj6kvR0gEswUdLEZ7L99ddforbJkPhsOU6cdu/erU6S+Kjn6NGjNGbMGIO+NgAA6An3BeKO1nfukAcRfcj9cbi/Eg9mqTlO0xdfqBIlPs2em7z4cWvXqvrw8KCRmv14jIFryLiWjfsVccL27bdE7durOoIfOaJ6DDfZcQdxzdorHq1brjHjvlXc94rfgz5p1pJxMyafzcdDIXACJ3cGx4jg5pE0+fv7i6Y5fUhISKDr169rdf4+c+aMeI2yZcvSu+++S59//rkYl0kecqBkyZJaYzkBAIAZ4w7J27bRnTp1xACX/lwzwmed5aZzZ9WZcoznY5P7KJUqRXT/PhkN1+bwWEnykAMHD6oumve//rp2R+7atVXjSzH5PXLfKR7KQJ84mePO6XymHidpn36qWs7l5cQODKbAdYfTpk0TA04m8dkLRXTixAmqX7++uDBuS+frn3zyibj94Ycf0ltvvUWjRo2ixo0biyRr+/btGKMJAMCS1KhBfQIDaQ0POyA3w3EtDXdW5v0/j70k953iwR0HD1bVOHFTIScin3+uShKMjQexnD1bNcQB9xHisnMtT4MGqkQl+xhNfFY3J32cKBqyHxGXY/Jk1VlzXMMERmMn8YBIBcBJzY0bN8Q4SuXLl8/REfwUD/hlRrhJj88CiY2NJW/NMyoKoXrJktTm4UOy8/WlufIREBjUmFdfFW3y+0uUoEvmepZKESA+LRviUznEp/FZe3yaItYL3DyHpjEAAACwRQVOmrhpDgAAAMDWKOrTVMAWPAAAAADbTJpq1qxJK1eupLS0tDwfd+3aNTEcwEwlc+4AAAAAWFvz3E8//UQTJ06ksWPHUseOHalRo0bi1H8+iy06OpouXrxIBw8epAsXLtC4ceMwjhIAAADYZtLUvn17MTwAJ0arVq2iP//8k27fvk3JyckUGBgozqh79dVXadCgQeRnitNCAQAAAMypI3jLli3FBQCgMDIzJboflU7+ng7k6eZAF+4k078XEuiNrqoBAv85HUeervbUvLpqxOOMTIkcHTBvFgCYB0x9DAAGtWR3JC3drZpPLCohg8qNOE+7/lNNiXHoYiK9u+Ce+rHfrH9EP22JENfjkzLJ5cXTtPZQtLh961EqHb6UIBIvAABTQNIEAHp15mYSvfDlDUpJU03MyonO0auJ4nqAlyNtnVqRWlTnWciIRnUJpJS/VDMCsG1TK9H8cWXFdQcHO/p5dBlqUkX12D/3R1GP6TdITpkOXkyg6IQMI787ALBlBR6nCQAgu01HY8jJ0Y66NvQhFyc7iozPpPDodCof5EK/jSunfpy9veoxueFEiZvtmLuLvbrZjn3QN4heae0vmuvSMyTq/tl1sWzyyyXEbUcHnrkCTXkAYAY1TQ8wBDsAaEhIzlRfX7AzktYeUs2oXr2MG+2fUUUkTPrk7GRPIcGqdXKCdvHnGjSqc6C4PXdbBNV757JIngAATF7TxGM1/fzzz/TKK6+QrRO77URVcwMYflunmLoQFsYY8XntYRo1mXKb1o8vRW1ruNOqN4uTm7O9Ub8XpeR5uxPTqGlZe5Jae5FTahJlJUu0/ngC9W7kafBO5IjPgsP+03gQnyZMmr744gsaPXo0rV+/nn799Vfy51mcbUy6nR1xl9QQHiE9WtU5FQzLVZLo4dNtD6aNT54Z4PDNdGpR0ZkquUg0sYMbVXNPIopOJTHPugl/B5sGqC78vo/cSKd+30fT4Ql+1KyC9oTi+ob4VA77T+NDfOqfnVSAOVLCwsJoxIgRYjDL+fPnU8+ePcmWZukuXbo0Rd6/T+V5xuhLl/RWRshd9erV6dbDhxRQqhTdu/fsLCtrYUnx+ceqSzRs7D90+9wIKlVSNSSAubp6PZqqVFKNGffr4rP0Ut8q5OcrV03pD+JTOew/jc/a49MUsV6gjuAhISG0Z88emjNnDr3wwgviA3F01F7FqVOnyJpxVWe8vT2RT+6dWUF/eFujetl08cnHVBcuRFCtWsVpwPAmVKZqMJWqXorMXZWnnc3v3YujidMOkU+wHw0YUEvvr4P4LBjsP40L8WkGZ8/xSODr1q0TI3/37t07R9IEANZj3rwT9P77/9CtW+9S8eIe1KZNebIkpUt70/Xrb1NAgGhApD17wqhNm3Lk4IDRVgCg4AqU8XCT3Pvvv08dOnQQ88wVK/bsdGAAsB5ZWZIYHmDYsHoUEuInEiZLFRjoLv6GhUVT585/0JIlvWnQoDqmLhYAWHPS1KVLFzp27JhomuN55gDAOp0+/ZCGDt1AmzcPpHLlfKlLl0pkDTj5O3JkBNWvX0LczsjIIkdH1DgBgHKK9xiZmZl09uxZJEwAVq5UKW9q0KAEubkZ9swzU2jYsKSoQTt8+C7VrPkL3bqlGlsKAECvNU07d+5U+lAAsEAnTjygypX9RVPckiV9yJqFhPjS88+Xp+Bg8z4LEADMC+qmAYDS0jKpX7/VNGXKXrIFJUp40dy5PcjV1VHUNt25E2vqIgGABcCpbwBAzs4OtHXrICpb1vZOBef+Wz4+LrRp00BTFwUAzBySJgAblpKSQT/9dJTeffc5qlHDNs+GnTu3u3pIAgCAvKB5DsCGhYbepc8//5euXIkkW8XJYlCQJ8XFpdKaNRdMXRwAMGNImgBsWLt2IXTr1jtixG9bt2TJGRo1agtFRSWbuigAYKaQNAHYoPDwBPrhhyOUmZlFfn5ommJjxzamc+fGkL8/tgcA6IakCcAG7dhxnWbOPEQxMZiZSsYDXfK0K6mpGbR9+3VTFwcAzBCSJgAbNHRoPbp8+U0KCFBNMQLPLF36H/XuvZLu348zdVEAwMwgaQKwIenpmbRx42WSJIl8fFxNXRyzNHx4fbpwYawYGR0AQBOSJgAbws1OffuuokuXnpi6KGbdTFepkr+YtPjRowRTFwcAzAjGaSooOyLJTqLMrExysHcQR+wZWRk5HubkoJq3i+/jx2ji59nb2VOWlCXWo7V6OztytFd9LOmZ6TnWy/fxY/h5/HxjrJfXWdj3qo/18jYH/cRnl24V6L9zo9RjMiE+c1/vqNf/posXn1Bo6HDKlDJzXS/iswCw/yzQe8X+0/wgaSoA/rKTN1GGVwbdj79Pfq5+IpgfJz7O8dhgz2AR7JFJkZSWmaZ1n4+rD7k7uVNSehLFpmhP3+Ds4EwB7gHiixCeEJ5jvcU9iosvSnRKNKWka3fi9XLxIk9nT0rJSKHo5OgcX75iHqofSl5v9i9noHug+JJxebhcmjycPcjbxVu8D34/2b+4QZ5B4npEYkSOL6+/mz+5OLpQfGo8JaRpH7W7OrmKbcjP4edmV8KrBGU6ZRJ5PN32UKT4TExIJw9PJ3IJIopL8UB85hOfL7xahgZK1Sg2NRbxqQfYf2L/aQ2QNBWUA5GdZEf8Lz4tXnx5OKizS0hXBbi9vT252Gnfn56VLp7LRw7Zn8s7Cr6P6VpvYnqieAz/y34/Hw0rWS/vWLJLzkimlMwUkijn++H15fVe5fU6OjiKHZKm1MxUSstKE2XLUaZ8tqG4z1kiyllcKGB88t9e3bZR557laPyk+ohPBfFZr4GbeDz/CCI+9QT7zxzPxf7TsiBpKowsVVWu/GVSV4PqIFfp6sJHGfYOuXcry2u9/Pr8z5jrLcp7Lcp6oejxyf1z3p5Qn8qFeGtta8Rn3uu9dyeepnwYSp9/05xKlfbM9flQANh/6oT9p2VA0lQQXMOZ+vQvgAXFp729HfXpX8kUpbJovn4uFBOdQhGPkpE0FRX2n2AFcPZcAXB1KKU//QtgIfGZlJhOn0wMpbu3Vc0AoJynlzOt39GL6jW0zcmM9Qn7T7AGZp00TZs2TdX+rHGpVq2aycrD7dVcQyr+gnHwpub+mtjkhY7P27fiaeumMEpP1z77BpQLuxFLTyJ0zEmH+FQM+08TQHzaVtLEatasSQ8fPlRfDh48aLrC8AEST0uFAyWjEUelGTg6LUp8Vq/pT8cvDqQKlXxMVTKLlpqaSR1brKNVf1zJcR/iswCw/zQ6xKcN9mlydHSk4OBgUxcDTEQclTrh6LSwuAM4N89xMxMUjouLA63a1E0kn9khPsGcIT5tsKbp2rVrVLJkSapQoQINGjSI7ty5k+fjU1NTKS4uTusCFowPkPhsWis5UDJ2fN68EUu1Q5bRf6dyjuMCyjVsEkTuHjrOUEJ8gjmzsvg0B2adNDVt2pSWLFlC27dvp7lz51JYWBi1atWK4uNz79A6Y8YM8vHxUV/KlClj1DID5MXY8enn50LTZjajkIpomiuKqMgU+vDtf+nyxSiyZth/Alhw0tS1a1fq378/1alThzp37kxbt26lmJgYWr16da7PmTRpEsXGxqovd+/e1W+h0JcWisDY8RkQ6EZDX69B3j5onisKdw9H+u90BEU+0R5F2tpg/wlg4X2aNPn6+lKVKlXo+vXruT7GxcVFXAyBR7KlpKd/AQrB2PG5fvV1qlbTX2d/HFDO1dWRdvz7Alk77D8BLLimKbuEhAS6ceMGlShRwtRFAWPKOVcqKPTF1GO0dWOYqYthNTIzdVSVID7BnCE+bSdpmjBhAu3fv59u3bpFhw8fpr59+5KDgwMNHDjQJOURkx5i8kOjEkelyTg6LWx8HrswkMa9X8+k5bIW33xxkto0WqO1DPGpHPafxof4tLGk6d69eyJBqlq1Kr300ksUEBBAR44coWLFTDg6L2IPzJldzulT+JR5KLrnO5Wh9yY2MOhrDBu2gc6ff6y1LDY2hZo0mU+enl/muM/iYP8JFs6s+zStXLnS1EUAExNHpZ44Oi2Mq5ej6bP/HaGvfmhFpcqY57xpPFL5dzNP0c1rsVShsg+N/6gBdesVQuaoQePi4mLs+HR3d6K//36FPvhgp8FeA6wT9p82ljQBQOFlZGSRm4cTubg6mG3CNHLwLrKzI5IkossXosTt+X90MMvEiYcdOBYaTq3bldI9ZlMhSJJE48ZtpbNnH5Ojo71IkObMOUY3bkSTh4cTrV//Mjk5OVCxYh56eT0AsOLmOQAovBq1Amj+sg4UWIznrjA/XMMkJ0yM//Lt2TNPkTm6cimaRryykx6FJ+ltnZs3XxVNqP/++xrt3TuUAgLcqHnzMrRz5xBycXGkc+csvDkOwMqgpqkgeOfO+0vUdIIFxCef6ZWWmiVqmviH2dxwk5ycMMn49o1rsWSOGjYpTufChpCPr/7GvLp0KYLatCmvvs2fU/36qmmjypTxpuhoHZMEWyrsP8EKoKapAMSkh1mY/BAsIz4P7LlPlYIX67VmRJ+4DxPXLGni2xWrmOfo5c7ODuQf4EoODvrbbVavXowOHLitNVegncZGyZ5UWjLsP8EaIGkqANGZzgWd6oyKN3UCjk4LE581agfQnAXtyMdMRwPnTt9ykxyTm+p4uTk6/98Temf0PoqLTdNbfPbsWUX0PWvZchG1a7eUIiN11yx16/Yn/fPPDRo5cjMtWXKGLBH2nyaA/afeoXmuoPTT/xMUko9KcXRa8PgMCnanvi9VInPFnb250zf3YeImuYp89tykBtS1p/l1AmfR0al083osubo56C0+uVbpl1+667zvm286qa9v3TqIrAL2n0aF/af+IWkCsyaOSl1xdFoY3NSzce0Nql0/kCpV9iVzTZzM8Uw5XVq1LSUumhCfYM4Qn/qH5jkwf0jtC4WbuyaMO0D/7rlv6qJYTRKqE+ITzBniU6+wOQGsFDf9nL/1Krm542uuD8MH/iPORPx1aQdTFwUATAR704LS6AMKYO7xiYRJf4YMr651ZhsUAvafYOHQPFcAYtLDNEx+CJYTn3v+uUv9um3JvWkJFGvfuayYfw4KB/tPsAZImgpA4vM27Z/+BePgTZ2KU2YLG59ePs5UuqwnJSdlmLRslu7alWjavP6mGB5AC+JTMew/TQDxqXeouy8IPkByx0zdxiROlU3HKbOFjc/GTYPEBYpm36579P3Xp6lHH+0z/RCfBYD9p9EhPvUPNU1g1sRRqSOOTosiOTmDzp15YupiWLSRb9amf0+9lKNPE+ITzBniU/+QNIF5498oVxydFsW8H89S/x5bKC0t09RFsUjS07lMeAqVHBCfYM4Qn3qHpAnAyg18tSpt2dOHnJzwdS+MZYsuUd/Om8QEyABg29CnqaBQywkWFp/BJTyISpiiMNahanU/atqihF4n6rVZ2H+ChcNeoADEqbKJOGUWLC8+z5+NFEMPaE02C4o0bV6CPvqksamLYfGw/wRrgKQJzB9aRYqsWHE3cvdwpOioFFMXxaKsW3U9/070iE8wZ4hPvULSVABi0kN3TH5oTOKoNAlHp0WNz6Bgd/p9TRcqF+JtkrJZagfwX374j/7eFJbrYxCfymH/aXyIT/1Dn6bCpJn2ROmZ6c8W2dmTg72D2MlmZOUcRNDJwUn85fvkM3Fk/Dx+fpaURZlZ2mc38enNjvaqj0jz9WR8Hz/GUOvl5/Hztd6+wveqr/ViB6vf+Dxx/CGlpmRS0xbB6vsRn7mvd8u+HmJ78TLEpx5g/4n9p4VD0lRQmapATM3gYVZVXJ1cycvZSwRrRGJEjqf4u/mLv5FJkZSWqd2nxNfVl9yc3CgxLZGSMpK07nN2dBbr5S9CeGp4jvX6evqKL0tUcpRWeZiXixd5OntScnoyxaTF5Phi+rn6ievhCeE5dhjeHt7iCxqTEpNjvR7OHqJMvJzLrIm/tPJ7fZz4OMfOxsPNg1wcXSguNU6USxNvA13bUHKQiNzw5ddXfH7zxUlyd3ekek1Vnz9DfOaMz0dPYsUo6kEl3MnB5WmZEZ9Fh/0n9p8WDklTAYgqzjgiRy9HCvEL0Znl8xctt6MHDuzcjmj4yxTgHpDrEQ0/N7cjj/zW6+fml+t6XR1dc12vu5N7rkc0/D59XH1yfa+FXW/2begY7yi2uZ0nqpf1EZ+rVwwgPz9XrTPBEJ851/vzV5do/m+n6Oatt8jV1RHxqQfYf2L/aQ2QNBWUpPryy8GtiQNa13KZ/EXThYPePo9Tmk2xXv4S8j9d8nuv+lqv2NHiIElv8VkiSLWjvnLlCZUq5U2ens7q+xGfz9Y78cOW1P75CuTlof1ji/gsIuw/Bew/LRc6ggPYmKSkdGrRYhHNmnXI1EUxO3ykHhWVTD4+rtS+fQVTFwcAzAySJgAb4+7uRBs2DKCJE1uauihmZ9Gi01St2hx68kS7fwwAAEPzHIANatmyrPj78GG8aKLz8nra29nG9ehRhTIysigw0N3URQEAM4SaJgAblZqaQc89t5A+/ngP2brk5HSKjk6moCBPGj26kamLAwBmCjVNADbKxcWRfv+9D9WqVZxs3YQJ/9DBg3fp1KlRmGMOAHKFpAnAhrVpU1785VqWs2cfqW/bmvffb05du0YgYQKAPGEPAQA0ffoBGjp0A6WlaQ+oZ+327AmjlJQMqlDBT/RnAgDIC5ImAKAvvniedu4cQs7Ousd/sUaRkUnUp89Kmjv3uKmLAgAWAkkTAJCbmxNVrhwgzhx77bWNtG/fLbJ2AQHudOjQcHr77aamLgoAWAgkTQCgxs1zDx7EU2Ki9hxf1mTmzIP0wQf/iOu1awehHxMAKIa9BQBoDXy5bdsg6t5d1b9nw4bLlJmpPc+VpfPwcCIPD+cc840BAOQHSRMAaLG3V03ueeHCY3rxxdW0des1snTXrkXSypXnxfW33mpK06a1FfN0AQBYXdL0888/U/ny5cnV1ZWaNm1Kx44dM3WRAKxezZrF6fz5MdSzZ1Vx+/TphxZbO7Ns2VmaNm2fOFMOAMBqk6ZVq1bR+PHjaerUqXTq1CmqW7cude7cmR4/fmzqogFYverVi6lrnRo0+I02b75KluLKlSe0efMVcf3jj1vRyZOjyNUVQ9MBgBUnTd999x2NHDmSXnvtNapRowbNmzeP3N3dadGiRaYuGoBN1Trt2DFYPZYRN9nFxqaQOfvxx6M0deo+ysqSxOjn3I8JAKAozPqwKy0tjU6ePEmTJk1SL7O3t6cOHTpQaGiozuekpqaKiywuLk7v5Xr48CGVLl1a7+sF3dvamlhDfGZlOVJ4+Cvk5fWfuJgLbjmMj29Ejo5R5O5+k7KynMnOLpPKlp1usNdEfBYc9p/GY23xaQ7MOml68uQJZWZmUlBQkNZyvn358mWdz5kxYwZ9+umnBi1XVlYW3b9/36CvAdbJeuJzDsXFpVBcXDoRNSMiTyLaSaZRiojk98tlSaPoaHw/bTs+AWwwaSoMrpXiPlCaR0plypTRy7qDg4P1sh6w3W1vXfHpLf5PSPCirCw38vYuRZLkQAkJNcjd/To5OCQbrEZJkpzI3j6dUlOD6cmTnhQYuJFcXB6TJIWS6qQ4TqSMB/FpO9vIEmHb20jSFBgYSA4ODvTo0SOt5Xw7tyBwcXERF0M4ceKEQdYLtsPa4/P48fvUps0S2rPnd9GJfNu2a+KMtb59qxdpwM2LFyOobt0gMUxAr14rxHQva9e+JPor7d9/i9q0+UQ9VAIUnrXHJ4BVdwR3dnamhg0b0u7du7Wqdvl2s2ZcDQ8A5qRx41IUGfkhVasWKG6vWXORFi48La7zIJmcUG3ffl09dtJvv50Uy48evUebNqnOdGPvvLNNfebbzp03qH79X+nWrRhxm6c9eecd1dQnnCi1axeChAkAjMKsa5oYVxUPHTqUGjVqRE2aNKHvv/+eEhMTxdl0AGCe89jJFi3qLeazY/HxaVSxoh/5+rqK24cP36XRo7fQ8OH16c8/z9H+/bepVy/VmFA3b8ZQZKSqea9Vq3JijriSJb3E7Q4dKpjgXQEAENlJFjBa3Zw5c2jWrFkUHh5O9erVox9//FEMcqkEt8n7+PhQbGwseXur+mAAmAtbj09595OZKYm+SJgHzrzYenyC7YhTGOtmX9PExo0bJy4AYF3kqUwcHdG8BgDmD4d1AAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKICkCQAAAEABJE0AAAAACiBpAgAAAFDAIqZR0cfcVjyvDIC54XmOGOITzBHiE2xF3NMYz286XouYsLco7t27R2XKlDF1MQAAAMDM3b17l0qXLm27SVNWVhY9ePCAvLy81JODFiUT5QSMN6olzfhtqeW2hbJzfD58+JA8PT31Ep81a9ak27dvk6+vL1kKa/+MzRXi0/o/Z0sttynKzqlQfHw8lSxZkuzt7W23eY7ffF5ZY2HwB2hpAWjJ5bb2suvrB0SuXub1WeK2subP2JwhPq3/c7bUchu77D4+Pvk+Bh3BAQAAABRA0gQAAACgAJKmAnBxcaGpU6eKv5bEUsvNUHbzfT1bLzdD2c339fTJUstuqeU257JbfUdwAAAAAH1ATRMAAACAAkiaAAAAABRA0gQAAACgAJImAAAAAAWQNAEAAAAogKRJoZ9//pnKly9Prq6u1LRpUzp27BiZu2nTpompDzQv1apVI3N04MAB6tmzpxjCnsu5YcMGrfv5JM9PPvmESpQoQW5ubtShQwe6du0amXu5hw0bluMz6NKli97Lgfg0LEuNT3OJUcSnYSE+jQdJkwKrVq2i8ePHizEjTp06RXXr1qXOnTvT48ePydzxXE88d5R8OXjwIJmjxMREsV1556rL119/TT/++CPNmzePjh49Sh4eHuIzSElJIXMuN+MvuOZnsGLFCr2WAfFpeJYan+YQo4hPw0N8GhGP0wR5a9KkifTmm2+qb2dmZkolS5aUZsyYIZmzqVOnSnXr1pUsDYfl+vXr1bezsrKk4OBgadasWeplMTExkouLi7RixQrJXMvNhg4dKvXu3dugr4v4NC5LjU9TxSji07gQn4aFmqZ8pKWl0cmTJ0V1puYkwHw7NDSUzB1XwXK1Z4UKFWjQoEF0584dsjRhYWEUHh6u9RnwxIpczW8Jn8G+ffuoePHiVLVqVRozZgxFRkbqbd2IT9Oz9Pg0ZIwiPk0P8alfSJry8eTJE8rMzKSgoCCt5XybA9Gc8ZdiyZIltH37dpo7d6748rRq1Yri4+PJksjb2RI/A65W/v3332n37t301Vdf0f79+6lr164ipvQB8Wl6lhyfho5RxKfpIT71y1HP6wMzwoElq1OnjtgJlCtXjlavXk0jRowwadlsxYABA9TXa9euLT6HihUriiOn9u3bky1DfJoHxKhuiE/zMMDM4hM1TfkIDAwkBwcHevTokdZyvh0cHEyWxNfXl6pUqULXr18nSyJvZ2v4DLian2NKX58B4tP0rCk+9R2jiE/TQ3zqF5KmfDg7O1PDhg1F1aAsKytL3G7WrBlZkoSEBLpx44Y47dSShISEiC+35mcQFxcnzgKxtM/g3r17oj1eX58B4tP0rCk+9R2jiE/TQ3zqmYE7mluFlStXijMNlixZIl28eFEaNWqU5OvrK4WHh0vm7P3335f27dsnhYWFSYcOHZI6dOggBQYGSo8fP5bMTXx8vHT69Glx4bD87rvvxPXbt2+L+2fOnCm2+caNG6WzZ8+KsylCQkKk5ORksy033zdhwgQpNDRUfAa7du2SGjRoIFWuXFlKSUnRWxkQn4ZnqfFpDjGK+DQ8xKfxIGlS6KeffpLKli0rOTs7i1Nojxw5Ipm7l19+WSpRooQoc6lSpcTt69evS+Zo79694guT/cKnm8qnzU6ZMkUKCgoSO+D27dtLV65cMetyJyUlSZ06dZKKFSsmOTk5SeXKlZNGjhxpkB8LxKdhWWp8mkuMIj4NC/FpPHb8n75rrwAAAACsDfo0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDRBgVy5ckXMYxQfH2/0196+fTvVq1dPzF0FkBvEKJgzxKdlQ9JkYzIzM6l58+b0wgsvaC2PjY2lMmXK0Mcff5zn8ydNmkRvvfUWeXl5kbF16dKFnJyc6M8//zT6a4PxIEbBnCE+bZwRp2wBM8FzDrm5uUl//PGHetmQIUOkOnXqSKmpqbk+jydQ5Pl/7t27J5nKnDlzpEaNGpns9cE4EKNgzhCftgtJk4364YcfJD8/P+nBgwfShg0bxBf5zJkzeT5n1qxZOb5sixcvlnx8fKTNmzdLVapUETuSF198UUpMTBSzmvMEizy79ltvvSVlZGSon8fLp0+fLnY0Hh4eYjJPnoGbZxDv1auXWFa7dm3p+PHjOXY6nOub68SZoD+IUTBniE/bhKTJRvGs123bthWzXRcvXlx8+fLDX8Q33ngjxxeedxYdO3aUTp06Je3fv18KCAgQM1O/9NJL0oULF8TOgGcKX7lypdYX3t/fX5o3b5509epVacyYMZK3t7fUpUsXafXq1eJIrk+fPlL16tVFWTXxTN38umDdEKNgzhCftglJkw27dOmSOOLgo5H09PR8H1+3bl3ps88+01rGX7zsRy2jR4+W3N3dpfj4ePWyzp07i+WaX/jBgwerbz98+FCsZ8qUKeploaGhYhnfp6l+/frStGnTCvGOwdIgRsGcIT5tDzqC27BFixaRu7s7hYWF0b179/J9fHJyMrm6uuZYzuuoWLGi+nZQUBCVL1+ePD09tZY9fvxY63l16tTRup/Vrl07x7Lsz3Nzc6OkpCSF7xIsGWIUzBni0/YgabJRhw8fptmzZ9OWLVuoSZMmNGLECK51zPM5gYGBFB0dnWM5n42hyc7OTuey7Ke5aj6G789tWfbnRUVFUbFixRS8S7BkiFEwZ4hP24SkyQbxEcawYcNozJgx1K5dO1q4cCEdO3aM5s2bl+fz6tevTxcvXiRTSklJoRs3boiygPVCjII5Q3zaLiRNNojHCeEjopkzZ4rbXA38zTff0Icffki3bt3K9XmdO3em0NBQMU6JqRw5coRcXFyoWbNmJisDGB5iFMwZ4tN2IWmyMfv376eff/6ZFi9eLNrRZaNHjxYDtuVVxdy1a1dydHSkXbt2kamsWLGCBg0apFV2sC6IUTBniE/bZse9wU1dCLAcvLPYtGkT7dixw+iv/eTJE6patSqdOHGCQkJCjP76YBkQo2DOEJ+WzdHUBQDLwkdTMTExYt4kY08DwNXev/zyC77skCfEKJgzxKdlQ00TAAAAgALo0wQAAACgAJImAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKICkCQAAAEABJE0AAAAACiBpAgAAAFAASRMAAACAAkiaAAAAABRA0gQAAACgAJImAAAAAAWQNAEAAAAogKQJAAAAQAEkTQAAAAAKIGkCAAAAUABJEwAAAIACSJoAAAAAFEDSBAAAAKAAkiYAAAAABZA0AQAAACiApAkAAABAASRNAAAAAAogaQIAAABQAEkTAAAAgAJImgAAAAAUQNIEAAAAoACSJgAAAAAFkDQBAAAAKOCo5EFQNJIkUUZGBmVmZpq6KAAAYKUcHBzI0dGR7OzsTF0Uq4WkycDS0tLo4cOHlJSUZOqiAICNHazFxKRRUlIGubs7kq+vM35MbYC7uzuVKFGCnJ2dTV0Uq2Qn8TcLDCIrK4uuXbsmsv9ixYqJIMZOCwAMKSYmhf744zzNn3+arl6NUi+vUsWfRo6sT4MH1yJfX1eTlhH0j3/K+SA9IiJCtGpUrlyZ7O3RA0ffkDQZUEpKCoWFhVG5cuVE9g8AYEg7dlynl15aS0lJ6fTii9XFxc/PjaKjk+mvvy6Ji7u7E61e3Y86d65k6uKCAXCrxu3btykkJIRcXZEc6xua54wgR7afnMztdvp/Ia6OdXPT/3ohT5lZmZQlZRlk3fZ29uRg72CQdYP1JUw9eqygzp0r0oIFvSg42FPr/v79a1J4eAK9/vom8bgtWwYicbJCqF0yLNQ0GaGmSSvj54Rp40ai6Gj9v6CfH1Hv3vkmTuXLlxflOX/+vOg0yBo1akTffPMNtW3bVv/lsvKE6W7sXUrLNEASzHmwgzOV8SmTb+LEn6mLiwu5Pf3s+fNcsGAB2bJ9+/aJ72CXLl2M9pq3bt2i7du30xtvvEHGbpIrV+57atWqLG3YMIAcHXP/4czIyKI+fVbSv//eodu33y1UUx2f2PLFF1/QihUrxD6EL02aNKGvv/6azpw5Q++++674a2r8edSrV49iYmLyfeycOXPoxIkTtGTJEtq0aRPt3buXZs+ebZRybtiwgYKDg+m5554zzO8O6A1qmoyNa5g4YeIfN30GdEqKar28fgW1TampqbRw4UIaPXq0/spgg7iGiRMmTmoc7fX7dcrIyhDr5tdwoPxrm1atWiV+IHLD/Ry4f50t4B91Tpr4x9LYSdO8efOMnjQtXXpGNMlxDVNeCRPj++fP70lly35Pv//+H739dtMCv96IESMoKiqKQkNDyc/PT/SnWbt2rVhmDXr16iUuxsJJE393C5M02dL32hygHs9UOGHy8NDfpYAJ2LRp02j69Ok6z+p7/PgxvfDCC1S7dm2qVasW/frrr7muh88M7NSpE9WoUUP8HTBggFg3S0hIoOHDh4t18OXTTz/VuY7PPvtM7DD4wo/jzvLcJs87gw8++ED9/Lfeekt0dGTDhg0TCV/79u2pSpUqorzyfenp6fTRRx+JI19e50svvUTRhqjZ08AJk5ODk14vRU3C+Ii5Xbt29OKLL4rP8tixY3T8+HF6/vnnRU1U/fr1ac2aNerH79ixg1q2bEkNGzYU246PtHW5f/8+9evXT6yzTp06NGXKlHzjhmvCJk+eTM2bN6cyZcqIxGLx4sXUrFkzcd/KlSvVj+XPnx/L5ePP9s8//1TfN2jQIFF2ft3u3btTeHi4Olnx9fWliRMnUoMGDUStAb8GP5djgGNMfgyXlx/DHWUPHTpE7733njr2uPZVtmzZMmratKl4bOvWrem///5Tb9cOHTrQwIEDxXvl8ty8eVPcx8nSlStXxPqM9aPLCcvcuSdE/6XsTXK5KVHCi154oTr98stx8fyCuH79uogb/vw4YZI/s/79+1OFChXUSevYsWOpbt26VLNmTVGDIy/v3Lmz2Ga8/JVXXqHExERxHye5/Bnoep782U2dOlXEZ6VKlWjr1q3qMuUV17mJj4+nl19+mapWrSri/ty5c+r7+DPu06ePuM4xxt8jfl0u07hx48RJPtljgfeBHN8XL16kvn37UvXq1cU+kfeDee2X+H1wzdasWbPEcrmGOK/4y/69BiPi5jkwjOTkZOnixYvir1pMjCT98oskrVghSZs26e/C6+P18vrzUa5cOen06dPS4MGDpc8//1wsa9iwobR3715x/aWXXpI++ugjcf3Ro0dS6dKlpdDQUJ3r6tevn/TJJ5+I6w8fPpSCgoKkqVOnitsffvih9Morr0iZmZlSQkKCVK9ePWnlypV5lm3kyJHS8OHDxfVffvlFatOmjZSSkiKlp6dLXbt2lWbOnCnuGzp0qNSkSRMpMTFRysjIkJo3by4tX75c3PfFF19In332mXqdfH3s2LGSIaRlpElXIq5It6JvSffj7uv1wuvkdfNrKPlMq1SpItWtW1dc1q1bJy1evFhyc3OTLl++LB4THR0tPoMHDx6I2xEREVKZMmWke/fuSTdu3JCee+45KTY2Vtx37do1KTg4WGz77Nq2bSt9+eWX6tuPHz/ON264fO+++6563a6urtL06dPF7WPHjkmBgYHq9fFuafLkyeI6l8vPz08KCwvTei02Y8YMafTo0eI638/PW7p0qfp+jsN33nlHfVt+zPr168XtBQsWSB4eHtKePXvE7a+//lrEMzt48KCIN/n9HzhwQKpRo4a4ztvV29tbunnzprg9ceJEadSoUeI6f4d4+xtTRESiRDRNWr36fIGet2rVefG8J08SC/i8VVKdOnVyvZ+3gYODg3TkyBFxe+7cuVKnTp3E9aysLOnJkyfq62+88Yb4HPN7nvzZrV27Vtzetm2biPf84pqf5+Pjo7OcEyZMkIYMGSLKERMTI1WrVk3sV+TPuHfv3uI677/j4+PFdd7XdO/eXVrB+1uNWLh9+7a4zfvUChUqSOHh4eI2P3bOnDn57pf4dWfPnq2+L7/40/xeK/rdAb1B85wN45omPurJ3pSwa9cuOnnypLhevHhxUXvAy3RVHe/evVv0hWLcJt+jRw+t9Xz77beiY6KHhwe9+uqrtHPnTnF0p8vnn39Od+7coS1btqifzzVK3FeHjRw5kn7++WdRm8D4aE4+K5Hfx40bN9RV3bGxsfTXX3+J21wDxbUZ1i578xwfkfKRLx9Js8OHD4saka5du2o9j2tGLl++LGoQ+IhWxp8bfx5cIyPjo+aDBw+KWikZD6ehJG7kz51rCbivBddWMa4d4GYdbkrj2gT2+uuvi79cc8FlOnDggPgMly9fLo7Aud8GXwIDA9XlcHJyosGDB+e5jfh15RoEfl1PT09x1C7HkFyrtXHjRnFkz0f6Mi5jMvdJJBI1ZNxnRL7+008/kakkJKhqWPksuYLw81PVTsfHp1FAgH7P7uXPWN52vH3kfQTnxNxP6O+//xa1Tvw95RjN73nyZ8cxJd8nf9/zimu55ksX3ndxWbiWzMfHR9R6yevUxLVKvM/huOfyc40q14hxrbpclrJly6pjimuUgoKCxO3GjRuLYWcKul/KL/40v9dgXEiabBh/YXlHwclKXuSxpfhHTe4ozj8Y69evz/Wxea1Hl99//53WrVsnfhzlzun5PV+zkyO36fNOmPGOjX/EuGrc1nFSIOPtws0L/COT3aVLl6hjx44iKdGX/D4v+TY/ji/y55fbuvhH68cffxT9aDgp4yaNTz75RP0YTqDzO3NITsCzl0FXDA0dOpS+/PJLnevJ7Xmm4OmpGsSQhxUoiOjoFPHXy6tggyBycxEnApGRkRQQEFCg7cPxtWfPHtq/fz95e3uLz5Nv5/c8+bOTY4rvk2dYyCuuuVlPqdz2T999951IlI4ePSrKN378eJGw51ZmfeyX8os/ze81GBf6NNk47jvyxx9/0IMHD9TLuI1+/vz54joPlMbJDP+gci0AnxHDFzlh4n4EXKPBHj16pK4lktfDnc15B8D9FriGQNcOg2sjuNaLjz41dwb8fE6m+IiMdzzc1q9kh8M1CXwEKffX4r8XLlwgW8dHp3xWDW9vGX+WvH25nwkvP3v2rPo+XX0l+PPhmh+uQZRxjOQVN4XB/WXkH71///2XWrVqJfp/eHl5iR9qLnNefe0Y/yjzkX1hcH8k/l5wTZtc2yD3rzHUaxZWQIAbVa0aIMZgKgh+PD/P379gNVRcG8T9abgzuHxWGn/HuQZF7tuVG/4MuXaQtxP3KZL3HYaK67xwvHKccdnj4uLEmYC5lZlr0TkZ4v5NSvpLFXS/lD1uCht/YHhImkyFj1S4A6S+LhpHPgXBO7C3335bdOiW8dEf1zxwJ0Nuuvj444+1qok1/fDDD+JHjTtBciddfpzcxMIdbrnJhNfDy3lHwJ0fs+NTl3kHwtXrcodwTuJGjRoljmr5wsu4ZoxPZc4PV6VztTi/JncY5uYhQ5/+zGe6pWem6/XC69Qn7rTLiSkfvXJHW/7MuGMq75D5h5BrAbhzPd/HnVi///57nevh5Jd34Hx0z58Ld7ouaNzkh2sRuEMvJ8m8Xv7s+Sw4bpLgCydReZ0pKDff8ucudwQvCF4/nz7P65A7JWt2Vs8Nxxs/lptvjNURnGtIxoxpJJIgHodJiYcP42nduks0dmzjQs1SsGjRIrFd+PPl98ux9M8//5C/v3+ez+Mmev6u82fI33fezoaM67zw/ombu6pVq0bdunUTncF1eeedd0QtE7/PIUOGiGSrMPLaL/F6V69eLWKeDw4LG39geBinyQbHadIn3ulwYsRNalxdzzsCPkIq7I+lpTGXcZqsCf+I89G9nHyD+Y3TBOYL4zQZFvo0GRsnNJzYWMmI4Ny3gY8e5XmP+HRhW0mYGCcznNRgRHAwJU58eGoUHumbEyIeh4mHFdBVwzRy5GbaseMG/f33K0iYAAoINU0GhIwfAEw19xyPw6Sae85VdPrm5jtukuO559as6U+dOlU0dXHBAPC7Y1ioaQIAsBI8lxw3ufFI3zxw5erVz06A4E7f337biYYOrUs+PvgxBSgM1DQZIeMvV66cejwhAABj4F17VFSyGIeJhxXgs+QK0+kbLAt3tOcZFVDTZBioaTIgZ2dnMW4MnwnGAwDybey0AMBYPDx4YFlX9XyTYL3kfqU83Af/7vDvDegfapoMjIOYT+fXNccbAACAPnGrRokSJZA0GQiSJiPgTcyDM8oj2AIAAOgbj0DOw7+gRcNwkDQBAAAAKIARwQEAAAAUQNIEAAAAoACSJgAAAADK3/8BR7tm7QclVC4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A narrow container where compartments are smaller than the default 9mm channel spacing.\n",
+    "# 1 channel per compartment still works - the channel is simply centered.\n",
+    "# 2 channels in one compartment would fail (needs 9mm, only 4mm available).\n",
+    "tiny = Container(\n",
+    "    name=\"tiny_container\", size_x=10, size_y=20, size_z=10,\n",
+    "    no_go_zones=[(Coordinate(0, 8, 0), Coordinate(10, 12, 10))],\n",
+    ")\n",
+    "print(f\"Container size_y: {tiny.get_absolute_size_y()}mm\")\n",
+    "print(f\"No-go zone: Y={8}-{12}mm -> two 8mm raw compartments\")\n",
+    "print(f\"After 2mm edge clearance: compartments = {_get_compartments(tiny)}\")\n",
+    "print(f\"1 channel: {compute_channel_offsets(tiny, 1)}\")\n",
+    "print(f\"2 channels (1 per compartment): {compute_channel_offsets(tiny, 2)}\")\n",
+    "try:\n",
+    "    print(f\"3 channels: {compute_channel_offsets(tiny, 3)}\")\n",
+    "except ValueError as e:\n",
+    "    print(f\"3 channels (needs 2 in one 4mm compartment): ValueError - {e}\")\n",
+    "plot_container_cross_section(tiny, [1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "snfkeopew6",
+   "metadata": {},
+   "source": [
+    "## Spread modes with no-go zones\n",
+    "\n",
+    "The `spread` parameter on `aspirate` and `dispense` controls how channels are positioned within each compartment:\n",
+    "\n",
+    "- `spread=\"wide\"` (default) - channels are spread as far apart as possible within each compartment\n",
+    "- `spread=\"tight\"` - channels are packed at minimum spacing (9mm), centered in each compartment\n",
+    "- `spread=\"custom\"` - no-go zones are ignored entirely; the user controls all positioning via offsets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bbjg3lf6qa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compartments: [(2.0, 58.0), (63.7, 116.0)]\n",
+      "spread='wide'   -> offsets (from center): ['+43.9', '+30.9', '+17.8', '-15.0', '-29.0', '-43.0']\n",
+      "spread='tight'  -> offsets (from center): ['+39.9', '+30.9', '+21.9', '-20.0', '-29.0', '-38.0']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare wide vs tight spread on the 200mL trough (2 large compartments, 6 channels)\n",
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_200mL_Vb\n",
+    "\n",
+    "trough_200 = hamilton_1_trough_200mL_Vb(name=\"trough_200mL\")\n",
+    "print(f\"Compartments: {_get_compartments(trough_200)}\")\n",
+    "\n",
+    "for mode in [\"wide\", \"tight\"]:\n",
+    "    offsets = compute_channel_offsets(trough_200, 6, spread=mode)\n",
+    "    positions = [f\"{o.y:+.1f}\" for o in offsets]\n",
+    "    print(f\"spread={mode!r:8s} -> offsets (from center): {positions}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "yayka43i62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visual comparison: 6 channels on the 200mL trough with wide vs tight\n",
+    "# The 200mL trough has 2 large compartments (~55mm each) where the difference is clearly visible\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(6, 5), squeeze=False)\n",
+    "axes = axes[0]\n",
+    "\n",
+    "size_y = trough_200.get_absolute_size_y()\n",
+    "size_x = trough_200.get_absolute_size_x()\n",
+    "center_y = size_y / 2\n",
+    "compartments = _get_compartments(trough_200)\n",
+    "channel_radius = CHANNEL_DIAMETER / 2\n",
+    "label_bbox = dict(facecolor=\"white\", alpha=0.5, edgecolor=\"none\", pad=0.3)\n",
+    "\n",
+    "for ax, mode in zip(axes, [\"wide\", \"tight\"]):\n",
+    "    ax.add_patch(plt.Rectangle((0, 0), size_x, size_y, fill=False, edgecolor=\"black\", linewidth=2))\n",
+    "    for flb, brt in trough_200.no_go_zones:\n",
+    "        ax.add_patch(plt.Rectangle((0, flb.y), size_x, brt.y - flb.y,\n",
+    "                                    facecolor=\"red\", alpha=0.3, edgecolor=\"red\", linewidth=1))\n",
+    "    for comp_lo, comp_hi in compartments:\n",
+    "        ax.add_patch(plt.Rectangle((0, comp_lo), size_x, comp_hi - comp_lo,\n",
+    "                                    facecolor=\"green\", alpha=0.1, edgecolor=\"green\",\n",
+    "                                    linewidth=1, linestyle=\"--\"))\n",
+    "\n",
+    "    offsets = compute_channel_offsets(trough_200, 6, spread=mode)\n",
+    "    for i, o in enumerate(offsets):\n",
+    "        tip_y = center_y + o.y\n",
+    "        ax.add_patch(plt.Circle((size_x / 2, tip_y), channel_radius,\n",
+    "                                 facecolor=\"none\", edgecolor=\"navy\", linewidth=1, linestyle=\":\"))\n",
+    "        ax.plot(size_x / 2, tip_y, \"o\", color=\"navy\", markersize=4, zorder=5)\n",
+    "        ax.text(size_x + 1.5, tip_y, f\"ch{i}\", ha=\"left\", va=\"center\",\n",
+    "                fontsize=6, color=\"navy\", bbox=label_bbox, zorder=6)\n",
+    "\n",
+    "    ax.set_xlim(-2, size_x + 8)\n",
+    "    ax.set_ylim(-2, size_y + 2)\n",
+    "    ax.set_xlabel(\"X (mm)\")\n",
+    "    ax.set_title(f'spread=\"{mode}\"')\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "\n",
+    "axes[0].set_ylabel(\"Y (mm)\")\n",
+    "axes[1].set_yticklabels([])\n",
+    "fig.suptitle(\"6 channels on 200mL trough: wide vs tight\", fontsize=11, fontweight=\"bold\")\n",
+    "fig.tight_layout(rect=[0, 0, 1, 0.93])\n",
+    "fig.subplots_adjust(wspace=-0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98rmf1z9e0c",
+   "metadata": {},
+   "source": [
+    "### Container without no-go zones\n",
+    "\n",
+    "`compute_channel_offsets` also works on plain containers. Without no-go zones, it falls through to standard wide/tight spread across the full Y axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "rp2rrixry2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "spread='wide'   -> offsets: ['+27.0', '+9.0', '-9.0', '-27.0']\n",
+      "spread='tight'  -> offsets: ['+13.5', '+4.5', '-4.5', '-13.5']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ImUtqnVeuXNmoZ8TPRTrWV199pV+fkfuRvj/+VqUuJe5SkbZj2WLYGpOe7E+bNk1sy9+6tC4iIkJ/fu6W8fLyEuu5+4W7vtauXSt8HVKCTfu8bevWrY16HsA2QEtcJXALlodXcZzqvXv3ito/J5ng9d9//326+3Prmlt6b968oRw5coiWtZSnOmnrJiW4RWEMnKhCOu68efP0mad4SJOhxSBpCyi1SRoqx+k3Oc81x8nmFhAPj+IEHD179tQfU8qJ7efnp1/HLTgJvncJw22SYur+0rUapu80jOnNOcmT5iqXc/+0MPeZGGKYg51bvxLSdT1//ly/zjBPueE8yxy3RpNaXr777jvx+5MnT/QpRg3TxXIOeENSOxcnSeGEKdK50oNlVWoZc6z2SZMmpXuOpPdjCMuoZOVI6RnJ8c3Z29vTggULxLnYWsM53Xn4ID+jgQMHJtueLQFAeUCJq4ihQ4eKj5/NlkuXLhWJJmJjY4USZ9N6anChzWZyLhi5cOMsU5JpLWnhzYVXSsrU0DyeFqyABgwYIOY5defKlSvFfI8ePUSMbYmMmtMZrsCwmZFNhmzu59jdbEaVfmNzNiOZ9KU82hKS6TFr1qyJ7j8p6e2fdBuJlNKEZiR1qLn7pwU/G0kZZuSeUkLqjmFSipOePXt2/byhXHJFMqVtUoKVN/P06dNEY5uTynlq52JzttS9kt652AT/8ccfi3mOC8Cx7A3vy5T7Se8ZGX5zy5YtS/a9scIdP368+J0r3RLSPUmw0ubv+ty5c+Jb69Onj9h/7ty5dPjw4UTbSvtK3QBAGUCJqyj945gxY0T/WOHChalLly5Uu3Zt8Rt/tIatBUO475z78S5fviwKcVb+SXNE83FYCUp9a9z/xy0G7qfjgqF+/fqiz9NYuLXMBRdfk6Qw0srdbSx87efPnxcKnAt3LuSk/mGu4EiVBE7EIfHjjz+Kig+3WLh/VLIWSAqNW35JKwxc4ZFacbNmzaJbt26Jlo6Uf5sDyFSqVIlsgZSuPyVYybBPAsOWHFZc/Ax///13fSv/vffek+WaWrZsqZ//9ttvhfJgJcOtXYYrkpyXnS0PSZWX1BKvW7eu+M8VNg5WwhaDP//8M5kSL168uL5CxpVTzhvPfcvjxo3TV/AMrycpnFiF75srw0WLFhU+JoaVzYzcT0bgwEHckmZYjlm+JD8XrjAbypdh5UrqJ5cYMWIEHTx4kHLnzk0dOnQQx5UwbO2z34skH1WqVMnQtQIrY217PpAH7htOre+M+wmjoqJS7BNn73NjhpjxOOrUvNMN+6YzOiZZznHV0r0lnbjf2bCvMCPBXgz7tA3v0dhgLyntb/iuuM87LX8Fwz5xCX4n0jo+vgTvx+v4OOldvyWCvaR0T4bXyn4Okjd3/fr10+3rNcU7nYdQGXqOM1u2bBG/pXQuHgYn+X6kRL9+/VK9Tuk9ZeR+UvNJSenZpeWdbigP7BPAvgFJ5cHwuEknHkFgOBLEcHQChpgpC7TEVQK3DD/55BNRQ+c+ZzbXcd8X9wlz60PqL02KsWEW2UN2165dorXGx+fjcX8s96OzJ7Jk3jQWbhlLGHr8mgN3H3DIVfby5n5G9tRlD3r2xjXsd2TY7M5WhVKlSol7YfMle+weOXIkkXkyNbi1vnnzZmGl4JYWn7NZs2YiMxpbJmwFyUTK7ye9Pu2SJUvS0aNHhWWG3zE/M5YnNh+zj4VccBfAjh07RAuTz8nPn72jubW6fv160XpMD25hsw8Ej0rg/fldr1u3Tj+6gX0sJFhGDxw4IHxE+L74/Cy7w4cPF/cr+WiY+n3IcT8pwcdjSxcfR5JpycrGVicJthpJ35DkYyLB741zu7OlhcsEfj7t27cXZYL0rAz349EoJUqUMOl6gZWwdi0CaBPJk5vHyqbmLQvMh4OI8HPesGGDTm3w+G/JwsQsXbpUbx357LPPdFoNu8qBbTICe+r7+PiIfVesWGGxawSWAUocZCrTp08Xw4OkwpbjnQPL8ODBA/GMeQiUGmFzOg8Ty5cvn34olWRKNyb+vdrgID18/126dMnQfmzu5/0qVapkVNAmYFsgnziQDXZOY9NrakiOSewtz45ybN7kQBecBQyAjDJ48GDhhMeZ59hbmx3huEvl//7v//SOhwCoHShxkKlKHAAAgHxAiQMAAAAKBd7pAAAAgEKBEgcAAAAUCpQ4AAAAoFCgxAEAAACFAiUOAAAAKBQocQAAAEChQIkDAAAACgVKHAAAAFAoUOIAAACAQoESBwAAABQKlDgAAACgUKDEAQAAAIUCJQ4AAAAoFChxAAAAQKFAiQMAAAAKBUocAAAAUChQ4gAAAIBCgRIHAAAAFAqUOAAAAKBQoMQBAAAAhQIlDgAAACgUKHEAAABAoUCJAwAAAAoFShwAAABQKFDiAAAAgEKBEgcAAAAUCpQ4AAAAoFCgxAEAAACFAiUOAAAAKBQocQAAAEChQIkDAAAACgVKHAAAAFAoUOIAAACAQoESBwAAABQKlDgAAACgUKDEAQAAAIUCJQ4AAAAoFChxAAAAQKFAiQMAAAAKBUocAAAAUChQ4gAAAIBCgRIHAAAAFAqUOAAAAKBQoMQBAAAAhQIlDgAAACgUKHEAAABAoUCJAwAAAAoFShwAAABQKFDiAAAAgEKBEgcAAAAUCpQ4AAAAoFCgxAEAAACFAiUOAAAAKBQocQAAAEChQIkDAAAACgVKHAAAAFAoUOIAAACAQoESBwAAABQKlDgAAACgUKDEZeK7774jOzs7ev78OanpfoD66NevH3l4eJCa7qdgwYLWvgxgASCr6aMJJX7y5EkaPnw4lSlThtzd3Sl//vzUrVs3unHjhrUvDYA0+fHHH0VlqmzZsta+FABS5MyZM9S+fXvy8fEhNzc3IavTpk2z9mVpBgfSAL/88gsdPnyY3nvvPSpfvjwFBATQjBkzqHLlynTs2DEUkMAmefToEf3000+i4gmALbJz505q164dVapUib755hvRar59+7aQXZA5aEKJjx49mv79919ycnLSr+vevTuVK1eOfv75Z1qyZIlVrw+AlPj888+pZs2aFBcXp5puGqAeXr9+TR988AG1adOGVq9eTVmyaMKwa3No4qnXrl07kQJnihUrJszrV69eNeoY165dEyZ4Pz8/cnV1pRIlStDXX3+dbLuQkBDR75E1a1by9vam/v37U3h4eKJtFixYQI0bN6YcOXKQs7MzlS5dmmbNmpXsWNx30rZtWzp06BBVr16dXFxcqHDhwrR48eJE2y1cuFCYXNnawBUWvkZuvXXq1ImCgoKSHXfbtm1Ur149sY2np6f4CC9fvpzuM9i1axfVrVtX3BvXuPkZ/N///Z9Rzw9kjAMHDoiC8Y8//sjwvsePH6fWrVtTtmzZxDtm69Off/6ZbLvHjx9Tx44dxbtkmeFKA1cYDJk8ebL4fnx9fYXcV6lSRVxXUlj+uMtq/fr1wrLFcs3f1/bt21P0tbh161a63wnDFWw+J5+bzbU9evSghw8fpvsMli9fLvZj+fby8hIV9pSeATAdbhg9e/ZMdPmwAg8LC6P4+PgMHQOySmbLqiaUeErodDohgNmzZ0932wsXLlCNGjVo7969NHDgQPGAWaA2bdqUbFtW9G/evKFJkyaJeVaw33//faJtWGEXKFBAKMDff/+d/P39aejQofTXX38lOx4LUNeuXalZs2ZiWxZ2FqiUlO6IESPo/PnzNH78eBoyZIi4PhZWQ/755x+htPlj4G4GNoFduXJFKOd79+6l+gz4fFyhiIqKogkTJohr4X4wrjgAeeHCid/lRx99JD7ojMAVrfr164t3OnLkSPGeGjVqRJs3b052jhYtWogCjwu/Bg0aiG3/97//JdqOZZ1NpfzO2bTv4OAguqW2bNmS7Nxc2WQ55sLr119/pcjISOrSpQu9ePHCpO+ElQO39LjCPWXKFBo1ahTt2bNH3B9XltN6Bu+//774VljG2drWsGFDyKrM7N69WygdVrBcoecyhZe57OF3nx6QVZJHVnUa5Z9//tHx7c+bNy/dbevXr6/z9PTU3b9/P9H6+Ph4/fz48ePF8QYMGJBom06dOul8fX0TrQsPD092jhYtWugKFy6caF2BAgXEMQ8cOKBfFxgYqHN2dtZ99tln+nULFiwQ2zVt2jTRNX366ac6e3t7XUhIiFh+8+aNLmvWrLqBAwcmOk9AQIDO29s70XrpfiSmTp0qloOCgtJ8VsB8ZsyYId4Hv2umQYMGujJlyqS7X2xsrK5QoUJCboKDgxP9ZigXffv2Fe9ywoQJibapVKmSrkqVKmnKanR0tK5s2bK6xo0bJ1rPx3NyctLdunVLv+78+fNi/fTp0zP8ndy7d0/I7o8//phou4sXL+ocHBwSref74XuWGDlypM7Ly0s8D2A5ypcvr3NzcxPTiBEjdGvWrBH/+f326NEjzX0hq/LJqiZb4mwaHzZsGNWqVYv69u2b5rZsjmbT5oABA4RXuyEpDcH6+OOPEy2z2Zprd9x/JMHmFolXr16J/k6uXd65c0csG8Kmdj6GBJuSuNbL2yZl0KBBia6J9+Na7P379/W1Pq4Vcs2PzylN9vb2wtKwb9++VJ8Dm5KYDRs2ZNhkBoyHZeXbb78VFhJ+1xnh7NmzdPfuXdEKkN5XRmU1qVwZympwcLCQT96OPZKT0rRpUypSpIh+mU2j3DJLSVbT+07Wrl0r5IxbPoaymitXLtHaSU9W2bTL8g4sR2hoqDArcwuUvdE7d+4s/g8ePFiYiG/evJnqvpBV+WRVc0qcPdPZnMx9G9xfwgosLaSXaqwHe1JFz2YSSagk2FTCQsR9QPwSubCW+paTKvGkx5OOaXg8Y88tfVTcH8/nNJzYyzQwMDDV+2JHwDp16ggTb86cOYUZauXKlVDoMjNu3DjRn8bm9IzCXsHGyir7VyStJKQkV2zaZOc63p6vi/fh7qCkcmoJWeVGExeCSWWV/VjSklU2kxYvXpxatWpF+fLlExXwpP2dwHwkpcmNAkN69uwp/h89ejTVfSGr8smqJrzTJfhl8sPi1ujBgwcpT548sp8jtUpBghUnQXibNGlCJUuWFH0n3B/OTndbt26lqVOnJlOK6R0vI9tKx+Z+ca4lJoX7kNL6YNkiwbVK7mNiQVuxYoWoEHAFIL3KEEgfLgy4n4+d2Z48eaJfz/11MTExwmeBWwtcQJmLMe+LvxH2e+B+vZkzZ1Lu3LnJ0dFROGayU5OxxzRVVrlFxk6YKW2bVgAQdhg9d+4c7dixQ+zPE18ztxgXLVqU5j0D4+Hyk31luFKf9PkzKSlEU4Cspo1mlDgXhDyekQO8sEMGm6mNgb3BmUuXLslyHexsxs5hGzduTFTDS8vkIheS+YgFhy0BGYU9ULkCwhNXQNh5hD30+dpNOR5IDDsIcYHwySefiCkphQoVEg5AqXmsS++XZVWO97FmzRrRquEChj14JbiQsTR8L1xI8j1zSyWjcMWYv3ee+Jlyi+fvv/8W3RRFixa1yDVrDfaoZjOw5NgmIVVA0+oOgqzKJ6uaMKdzvzCbg9m8s2rVKtEXbiwsiFy7mz9/Pj148CDdWlt6SDU1w33ZQpAZwsYentySY+XLLbukpDQcTeLly5fJ1lWsWFH850oJMB82La5bty7ZxMNfuMLH8x9++GGq+3PwIi5IWMkn9Yg1VVa5hWE4lIetATw0x9Jw/yqfn72Ak147L6fkRSyR9DeufHKfJwNZlQ/uA2bmzZuXaP3cuXOFVY+9rFMDsiqfrGqiJf7ZZ5+Jli/XdFgZJQ3u0rt37zT3Z2cNHoLFgsfOYyx8LCBsVmZTSEZo3ry5vubFDiDsHDJnzhzROn769ClZElbg3EfUp08fcS/cr82VFK6c8L1wnzdHsksJHrbB5nT2J+DhcdzPw2Yr7sfhZwPMh4c78tDFpEgt75R+S1oA8Ptl2eIKFo9nZbMiO3Ky2ZNbKRmB3zVbXFq2bCn6Ofmd8zBIbh3wsEtLt25++OEHGjt2rPjW+N55HC07Q3Flhr9DHiucEuy3wd85d/WwfLJj5/Tp08UzKVWqlEWvW0vwcC7uw+UGTmxsrHDO/e+//0RDid9bWt2VkFX5ZFUTSlxStGzKTmlsd3pKvEKFCiI8K5s3WPDYNM+KTKqJZgQ2O7FDHTsw8YvlvmkeV8nKlD8IS8MCzh8Xj0f87bffRG0vb968wuOSP6TU4P4mFlD+YNnzkhUOf7Rc+2QnQWAbsLWFuzf4vfBYWjbPcSHD8Q0yChcs3MpiWWEvYq688lhWlgNLF4zMmDFjhHmSfUWkcbnsQ8IVYZbH1ODvmX0LuJLJrTz+xtgSx8E7EFVMXmbPni2sRGxJZIXF5SK/L5aX9ICskiyyasfjzGS7EwAAAABkGqiWAgAAAAoFShwAAABQKFDiAAAAgEKBEgcAAAAUCpQ4AAAAoFCgxAEAAACFovpx4jz2kMMA8uD7lLLjAPPgEYqca5fHnmMMrnlAVi0LZFU+IKu2I6uqV+IsaDzoHliWhw8fiohDwHQgq5kDZNV8IKu2I6uqV+JcU5QeBocdBfLCOXX5Y5aeMzAdyKplgazKB2TVdmRV9UpcMvWwoEHYLAdMauYDWc0cIKvmA1m1HVlFxxAAAACgUKDEAQAAAIWienN6alStWpUCAgJIK3B2nFOnTln7MoAJQFaBUoCsZj6aVeIsaI8fP7b2ZQCQLpBVoBQgq5mPZpW4BI/B42T0auXp06diTCdQPpBVoBQgq5mH5pU4C9qjR49IrfAYQ9SM1QFkFSgFyGrmAcc2AAAAQKFAiQMAAAAKRdvmdDsinZ1OxKnlQfWx8bFi3hD7LPaUxS4LxeviKS4+LvHudnbkkCXhEcbExSQ7PP/G2/B+vL8hfEw+Np+Pz5sUR3tHWY7L98f3CRQOZBUoBchqpqJZJS5eghdRrGcsvY56LV7Sy4iXFBUblWg7T2dP8nDyoIiYCAqJDEkmENndsov5gNCAZILq5+4nBIP34/0NcXdyJy9nL3E+Pq8hLCw53HOI+cCwwGRC7uPqQ84OzuK6w6LDEv3m6uhKWV2yCkELCgsS98f3Ke4XKBLIKlAKkNXMR7NKXGBPZKezo9DoUFED44lfoiFxujh6E/1G1MyS/sbb82+Mk71TssOHx4Trw+Yl3ZePx/uygCb9jZGOy8Iq1UolouKiKDo+OsVrkvaVjsv3x/cJFA5kFSgFyGqmom0lzsS/i0+b9KUawjXKLPapuxBIZpqU4Bog/6UEnzutfc0+LluFrF9ZBHIAWQVKAbKaacCxTeWIGmPY2/8A2DCQVaAU7GxIVjWrxMXDD7WNlwBAWkBWgVKArGY+mlXiWkE4XrjZhgMGAGkBWQVKQWdDsqpZJS4evqttvASLo9m3rA4gq0ApQFY1exlWwka8CwFIF8gqUAqQ1UxF20ocAAAAUDBQ4gAAAIBCgRJXO9w1FWk7YxoBSBXIKlAKOtuRVe0qcX74UbbxEiyJHQf4jX37HygTyCpQCpDVTEezSlw8/BjbeAmWRMdfk+Pb/0CRQFaBUoCsZj6aVeLi4TvYxkuwKPwtcRhgdX9TqgayCpQCZDXz0awSFw/fxTZeAgBpAlkFSgGymuloV4kDAAAACgdKHAAAAFAoUOJaINbaFwCAkUBWgVKIJZtA20o8jlSPyCYUiaxCigeyCpQCZDVT0awSFw8/wjZegiWRvERV7y2qYiCrQClYWlZ1uney0bvzNvrj1zNi/tqVl1Sx6BK6cDZILEdFxWlGVjWrxDUDf0se8BYFCgCyCtJg59b71KzOWoqJiRfLNerkpirVc4r57H6u1KtfSfIv4CmWv/niCH3Yc6cmZNWqSjwuLo6++eYbKlSoELm6ulKRIkVo4sSJiWpbPP/tt99S7ty5xTZNmzalmzdvmn1ukSrPQyMp84CigawCLctqWGiM+J+/oCfVqpObwsMSlkd8VpHqNcyrV+JfjKtK2Xx4fBtRq3YFqX3nImI+IjyWHj14Q2rFqkr8l19+oVmzZtGMGTPo6tWrYvnXX3+l6dOn67fh5WnTptHs2bPp+PHj5O7uTi1atKDISA5cCwAAQK1M/eUMdWi+UbS+S5b2oYm/1SbvrBxlJW0aNfOnDl0TlPhfU89Ty/rr6PWraFIjDtY8+ZEjR6hDhw7Upk0bsVywYEFatmwZnThxQt8K/+OPP2jcuHFiO2bx4sWUM2dOWr9+PfXo0cOalw8AAMCCtOlQiHyzu5CDg+l260HDy1GdBnnIy9uJ1IhVW+K1a9emPXv20I0bN8Ty+fPn6dChQ9SqVSuxfPfuXQoICBAmdAlvb2+qUaMGHT16NMVjRkVF0evXrxNNANgikFWgFDJbVk8cDaDo6DgqXjIbffBhabKzM12Je3k7Ua26uUWjcNaf5+nWzRBSE1ZV4mPGjBGt6ZIlS5KjoyNVqlSJRo0aRb169RK/swJnuOVtCC9LvyVl0qRJQtFLk7+/P2kZ4SUaqn7PZiUCWU0MZNV2yUxZDQmOot5dttP82ZdlPW5MTDwtW3ydDu17rCpZtaoSX7lyJS1dupT+/fdfOnPmDC1atIgmT54s/pvK2LFj6dWrV/rp4cOHKW/Ifhdh6k+ZB2wXyCpQCpkpq1mzOdP6ne2o36DSJCdOTva042Bn6jeoDKkJq/aJf/HFF/rWOFOuXDm6f/++qPX17duXcuXKJdY/e/ZMeKdL8HLFihVTPKazs7OY0kOkytNpIGUee4m6wrPZFoGsJgayartklqyGvokmdw9HKl3WlyyBq5sDxcbG075dD6lxc3+yt8+ieFm1aks8PDycsmRJfAn29vYUH58wDpCHnrEi535zCe6LYS/1WrVqmXVu8fBdbOMlWBx7a18AMAfIKtCKrI4ZdYj697Dg+G4iunzhBfXrvpOOH0m5S1ZpsmrVlni7du3oxx9/pPz581OZMmXo7NmzNGXKFBowYID4nZ0ZuI/8hx9+oGLFigmlzuPK8+TJQx07dlT43QOQASCrQAOy2qlbUXJ2sax2rFDZj/Ye70IlSvmQpenXbz19/nltKls2R6L1X321i44ceUQFC2al+fPbk6OjvTKLBh4Pzkp56NChFBgYKJTz4MGDRXAXiS+//JLCwsJo0KBBFBISQnXr1qXt27eTi0vCoH4AAADqoEmL/BnafuvGuzTl5zN05+YrKlzMm0aPqUyt2xdKd7/MUOCpcf58AD1+/IYOHuxPP/54gFavvkLvv19OmeZ0T09PMQ6c+8EjIiLo9u3botXt5PRuPB+3xidMmCC80TnAy+7du6l48eLWvGwAAAAyc+ZkIG1YfTtDCnxg79107fJLESud//Myr0+P82eCqHv7LfpocHLBw9iGDdtC9eotoEaNFlFQUDjNmHGCmjX7hzp2XC5+P3LkITVvnhCIpmXLonT4cCpOgkaC2Olqh7umouDZDBQAZFXTHNr/mMaPSTn+R0pwC9yO/ejeygv/5+WpPyckRUkLB8cs5OnpROHhMbLK6qZNNyhLFjvRyt63ry/5+rpS7dr+tGtXH3J2dqCLFwMpODiSvLwSnAS9vV3o5csIMgdtK3F1RuFLhPASjVG/Z7PqgawClcvqJ59XorM3E2KEGAOb0HVJlCgv3775Kt19y5TzpblLm5FfDjdZZfXq1SBq0KCgfpkVeqVKCaOs/P29KDg4grJmdaHXr7kGQPTqVST5+LiadA36c5BGEYP0o21jsL4lEanyHGwjZR6wfVm9eO45Xb/6Usy/fBFJ2zffozevM6cGAVlVPubKakYis3EfuF2SzXm5SHHvdPdlM3pkZCzJLaulSvnRgQP39cvx8bpE98SVDG6Z7959Ryzv2HGb6tQxL3COZpW4ePj2GigwWH7YB1DddRVVY0lZ5b5EDnEpMaDnTtq0NqFP8e7tV/Rhz1309EmYPhTmpQsvyGJAVjUtq1cuvaCOzTdSwNMEeUsPdmLTvTWhM5Jpndenxz8LrlKlYkuFkpVTVtu1Ky7GodetO1/0ib94kdxUXrFiLsqZ0130m1++HERdupgX1Ea7A1f44bMVAwUG0LCsLpl/lX745jidu9VbZIdatqE15c3LiZKJKlfLQaeu9aQcORPMfdN/PycKysWrWsp/IYC0LqscqS13HneKjIgzanv2Qp+zpKnoA2cTehH2Th9bmVq1S987vXmrAuSf31OYu+WEW90zZyYk9ErK5MnN9fO//fZu3ly0q8QB0Cjcsr50/jk1a1WAur5fjBo0yadP71i0WNZEBRIXqhILljen4JcJKYDPnQ6iY4efigxRcheEQJvkyetBsxY2ydA+rdsXMmpIWVIKF/UWkxrQrDkdAK0y64/zNHHccWH2Y+VdtPg7xZ0WDg5Z9I5AJ48F0Ob1dykq0rhWEwDGwObtA3sfifCrlmL39gcimxkP91IDUOJaAOUsMGDcDzVozba2QimbysBh5USSCo5FzY5vshWIkFVN8ywgXGQw27/X/ExjqfHw/htxfHPSm9qSrGpbiSeEaFc1wks0Qv1e+Kon3vwWzsjB/9HpE89ENidTh9YYwpWAiIhYatdkA61dccvs40FWVYIZssrdN1v2daTW7d8N05Kb/oPL0MIVzVUjq5pV4uLhh9vGSwDA0rLKOZof3HtN0dHy1lxdXR2oQ9ciVLaCZbJOAe3JarmK2UUr+eypQDF6Qi6uXn5JK5ZcF1YjFxf1uINpVolrBZFNyEMjGbBAqvj4utDa7e2oVt13KX3l4tOvKssSixqyCiQePwylrm0209IFV2U75raNd2nurEsUKYMfhy3JqmaVuHj47rbxEgCwpKzu3/OITh1/Zn4fYBrcvB5MfbpuFy1+oF3kKlfz+nvQhl0dqN+gMmLZnMAsjx68Ef8/HVOZ1m1vJ6xHakKzSlwASzrQgKxOm3xWtEAsCXu5s0dx4LNwi54HaKdcLVveVwxfvHUjhGqWXS4SpGSULRvuUr3KK+nendeiEuvh+S65llrQthIHQAOs3tqWpv2voUXPkSOnG63b0Z6Kl8xGmZGj+dKl5AU652jmKFh9+qyjmBgbcR0GZpPdz5Xe61mMSpVN6LLZt+sh3b/7OtXtL5wNonUrExwtm7bMT1NmNqAChTxJrUCJA6ByuAXCHunGwqkcm9ZeQ4X95ov/xqR2lDzgOVSrtXM0lyzpK3I0A3XAkdy+nlBDmMHj4uLp8+EHaPWym+K3SxdeUNfWm0X/OfejMxvX3qE5f10UDmzOzvbUqVtRi3YlWRsocbXDXVMcihhd/5pk7sxL1L/HzkzJ0bx5/R2qW2mlPqqbXLJqjRzNwDaxt89CB053owEfJ/SVOzgkRBXMlcuNQkIS/DFGfVmJNu/raFnFbUPlqnaVOD/8cNt4CZZEpMrjJAFwANCkrLIZsWTpbJmSo7lO/Ty0bH0rEQBGTlm1Ro5mYLvlqruHI2Xz4ewjRCVL+9D0uY1oxrzGIr0ow/3elg4FbEvlqrrc9DKAePjxtvESLInwEnWBF75WZZXjo/OUGTmafbO7Uv3G+TJ8jenJqjVyNAPTQLma+Wi2JS4evpNtvASLo9mqmjowR1Y5PjoHuTA25aI5OZo5GtzieVfMC8GagqxaI0czMA2Uq5mPZpW4QH2jDYBaMVFWjxx8Sk1rraE7RjqcmZOj+fiRAJo367LsfZHWyNEMzADlqhbrEgAAS1ClWg6R7CSff0KOcEvmaB46qgINHlGO5MYaOZoBUApQ4gCoGHYCqlknt8VzNEeEx5Kzi73wHgYAZB744rSA5VLzAgXw9EmYGGZ255blxnBP/uk0dWi20fyUpJBVoBSiySbQdks8hlSPyCYUjWxtWpbVbD7OFBQYQeFhlhP4ru8Xo8JF2SnOdDmDrKoElKuZimaVuHj4UbbxEiyJjgcz2r/9DzQpq5x2cdOe9hYJfiF5vZcq4yMmc4CsKh+Uq5mPZs3p4uFnsY2XYFH4W+Ihs+r+plSNHLLKCvzli0j6dMh+unblpWzX9udvZ+m9NpvlyfsMWVU8KFczH80qcfHw3WzjJQCQGbJqb28nEkcEv5QvXWjdhnmF5zrHqAbA0uWqoc9Fjw5bacqk02KeYyGUKbBYJD9hIiJizffPUAjaVeIAaAxOF8rDzWrVzS3M4BtW3zY6CIwhAU/DROHJhWS1Gjnpo6FlLXK9ABiyffM9alxjNUVHJ1h9GjTOSzXejrzIkdNVyGH+gl5i+ZsvjmQoZ4CSgRIHQENI/eIcmGX4R/vowtnnRu8rtWyuXHxJSxZcoyePOAMEAJbl9asEN3B2nGzY1J8iIxKU+JCRFUS8fink76dfVRYZz5j2XYoIZ0uGHTrTSl2qdKDEtUC8tS8A2BrcGj9w+j2qWMVPLA/tv5c2rLmtL/R4ONqtmyEi9SPz19RzNLjvHjHfuLk/HT7XnfIaGUAmQ0BWgQFs8enYfKNofXOu+vE/1SQv7/RDwtVvlJfadiws5mf+cYFaN1yvrwyoTVY1650u0ECXifASDSeyc0fnv6KxgKwWKpIQD50LSA9PR3J0TKjTHzscQH26bhfzOw93FtmhcuZyp2o13vV7m5qpLC0gqypBRllt37UI5c7rrpdNU/j4k3LUoEleo5S/EmVVs0pcvIQwIrvs1n8JAFhTVp2c7OnXafX0y5Wq+om+c7a8582X0NqWTJMAZIasHjn4hKpUz0lFi2UVkzl4eDpRtZq5RHfQtMnnqFW7gqJVrxZgTlc5IpuQu0ayCgFZ4FzNHKq1Ru3c+j7GzACyCpiQ4Cjq130nLfj7sqzHjYmJp/WrbtHRQ09VJauabYmLh+9mGy/B4sDYoGggq0BLssoVRw5OVLBw+ulvM2px2nagkwh+pCZZ1XZLXNt3D5QEZBVoQFbZ+YyHPZYo5WOR2AMuLg6iRb5t012906bSQdEAAADAJhgz6hD17bbDoue4dvklfdRrN504+ozUAJQ4AAAAm6B77+IiL70lKVcxO+0//Z4YZmlp+vVbT5cuBSZa9+pVJFWvPoc8PH5K9pspQImrHe6aCtfGcDqgcCCrmqdBk3wZUq5bN96lprXXUGG/+eI/LxuDuR7v5siqm5sjbdnSk7p2LU1yoF0lzg8/Qv0Fhh17X8S//Q+UCWQVaEBWTx1/RquX3TR6e1bYA3vvFuZxTsDD/3nZGEV+9lQgdWm1iULfRMsqqzyMbdiwLVSv3gJq1GgRBQWF04wZJ6hZs3+oY8fl4ndHR3vy83MnudCsEhcPP85yBYYUopL/t2m4nv6ecUEsnzgaQGULLhbRsJiI8FiLnF9/Hewl6qQRz2aVYmlZtRUgq9qW1WOHn9Kk708avf2Un8+IWAZSnhP+z8tTfz6T7r4urg7kl9ONIiPjZJXVTZtuUJYsdnTwYH/at68v+fq6Uu3a/rRrVx9ydnagixfNN58nRbNKXKTKc7RMyrw1y29Sy/rrhJclx6pu3qYAVa2eU/yWv6AnffhxWcr3NmTlJ4P/oxEf7SOLIl+gIqAyWbU5IKualdXhoyvS6Ws9jd7+zs1XegWuP7+O6PbNV+nuW6qMD81e2ISy+3E+Uflk9erVIGrQoKB+mRV6pUq5xLy/vxcFB7OZQl40q8RFRZHjWMjUuOEW96uQhBSPRYtnpUbN/CnqbS1v5BeVRPQhJldud/p0TGX9WMUevYtT+y6F9UEOHj8KleeCgHqQWVYBUIOsFi7mLVreiU5vR1SkePrjy9+8jraIFbRUKT86cOC+fllqyElYIjuqdpW4zHw39hj16rRNvLQKlf1ozLfVjIov3aRFfmrWqoA+2H/7phtFLlwAANASly++oLaN1tPTJ8Zlxxs9prLehM5IpnVenx5LF16jSsWXmJSKNy3atStOsbHxVLfufNEn/uJFyi3v1q2X0s6dt2ngwE20cOE5s86p2YhtctOtV3GqXDWHMJ+Yymf/V4U6dy9Grq54LQAAbeHj60KFinpTzNt84enRun0hmrOkqegDZxN6kWLeNHpsZWrVrlC6+7ZsW1CkNjWnvE4JbnXPnNkmxd8mT26un9+6tZds54S2MJMDex9RrXp5RKYnnszBO6uzSA3JtcNfJp6iXn1L6JPcm0WM+YcAIFOArGqW3HncafqcRhnap3X7QmLKKAULe4lJDbKqbXO6mVbrgKdhIrrQqn9vkJxERsTS5nV36OSxZ/JkFYp6+x8oFw30sEBWVYIZssqhUPfseCD6rC3Fzm33adrks/oRREqXVc0qcfHwI817CeyktnV/JxFlSE7c3B1p7/Gu1KWH+ekfhZdoFo14NqsUOWRVCUBWlY+5shoUGEH9e+ykg/89Jkvx9HEYHT8ckMjhTMmyqlklLh6+nekvgT3RuSbHQxXs7eV/jBz8PzIyVgQuMKfGKLxE3eDZrGVZVQyQVdK6rHLDaPvBTiaZyI2l70elaf7yd/3TSpdVzSpx8fDdTX8JwwbspeEfWnZ89+kTgSIC0eWLLy16HqBuWQVASbJaumyCb9HJ489EQ0YuLl14IbzSuVFkiQxpmlXijx8/pt69e5Ovry+5urpSuXLl6NSpU/rf+YF/++23lDt3bvF706ZN6eZN40PzWYr+g8vQgI/LWPQctevlpkNnu1HZ8uY5zJkaqJ/56qtdIoRgnz7rKCbGtOhGAACQER4/DKUe7bfQskXXZTvm7u336Z/5V0WIVjVhVSUeHBxMderUIUdHR9q2bRtduXKFfv/9d8qWLZt+m19//ZWmTZtGs2fPpuPHj5O7uzu1aNGCIiMjrXnpYny3FMDFUoH6uc+mUJH0AxdYivPnA+jx4zcihGDJkr60evUVq10LAEA75PX3oM17O1LfgQlJQswJzHLvzmt90K2129rqA22pBasq8V9++YX8/f1pwYIFVL16dSpUqBA1b96cihQpom+F//HHHzRu3Djq0KEDlS9fnhYvXkxPnjyh9evXW+26Dx94QhvW3M6UQP1HDj6hzi03iUT2JpNC95QxgfqPHHlIzZsnvIuWLYvS4cMPTb8GAIxB5d3+wHjY34jHcV+/+pKql1lGp09kfLTO5vV3qGG1VXT39ivRKGKnYbXJqlWV+MaNG6lq1ar03nvvUY4cOahSpUo0Z84c/e93796lgIAAYUKX8Pb2pho1atDRo0dTPGZUVBS9fv060SQ37Dn5569nMydQv4uDcPaIMrFvSHiJhiX3FjUmUH9wcCR5eTm/fe4u9PKl/HF/tUxmyKqSSE1WgbZlNXceD+rdvySVedutuGvbfbpzK/X46GdOBuqH/XI0zOlzG5k/JtyGZdWqSvzOnTs0a9YsKlasGO3YsYOGDBlCn3zyCS1atEj8zgqcyZkzsdmal6XfkjJp0iSh6KWJW/opIR5+qGkvgUOq8hCwzAjUX7laDpq5oDF5eMqbGcKYQP1Zs7rQ69dR+kT2Pj5mJAsAVpFVAJQuq17eTvTVt9VEg4bHkY8dfZjWr7olfrt0/jl1aLaROjbfSI8evBHrtm26S4vmXtU7sLXrVNis4WS2jlWVeHx8PFWuXJl++ukn0QofNGgQDRw4UPR/m8rYsWPp1atX+unhQ+ubgM0J1M9JUczpDxKp8tySp8wzJlA/t8x3774jlnfsuE116qT84QL1yKo1SU1WgfWxFVnl4bwHz3SjD4eUFcuOTllE+FRuab9+GyDm068q06Y97S2quG1JVq2qxNnjvHTpBMcFiVKlStGDBw/EfK5cCS3DZ88S94XwsvRbUpydncnLyyvRlBLi4bua9hJ46EOrBuuMjipkTqD+v6dfoLqVV5Lcb9mYQP0VK+ainDndRb/55ctB1KVL4ncFzCMzZFVxWH28DLB1WeXcEhyimilRyoemzmpAf8xuqB+axv3emdLythFZtaqbHnumX7+eeAjBjRs3qECBhKxe7OjGynrPnj1UsWJFsY77YthLnU3vZmPiUEEfH2fhdBEeHkueXk4WDdTf9f1iVK1myhWWzAjU/9tvZgZFAPKgnmGtQO1AVrWjxD/99FOqXbu2MKd369aNTpw4Qf/73//EJCmaUaNG0Q8//CD6zVmpf/PNN5QnTx7q2LGj1a67SLGsNGVmg0wJ1M/n4gmAzODLkQfF0MnuvYrTjWvBNO6LIzRpSh3IIAA2ilUNAtWqVaN169bRsmXLqGzZsjRx4kQxpKxXr3dp2r788ksaMWKE6C/n7UNDQ2n79u3k4uJizUsXpmh2oJAzolBS2Hnj7xkXLHZ8AHgI44iB+/ShfTn5jptbQt2enYK8vZ0om0/CtzbpuxM0cdxxq14vACAxVh/13rZtWzGlBrfGJ0yYICZb4sG9N2Ks97INralew7wWOceTx2F0/myQeQfhspm7u+FYDgxgpc3fVlycjl4ERdDrV9Gin3Ha/96lgixQyIvmLGmmX86R041i4+L1lViOXcD9k/JdFGQVKASd7ciq1ZW4VV8CB30z0f+CPSJ3H+1CJUv7kKUYOqoCRUebFyLQjoMYx739DzQpq0lZPO8KHdz3mP73T1NRATW2Eip5BDMz/zhPa5bfom37O8oWQAOyqgJkllVbxc6GZNVG/OsyH/HwY817CZICP3roqXkR1ZJw4mgArV52U7SWnJzM8xIR2YQcNZABS8XIIauG5PP3pHz5PUUr3FTYv2PYpxVkjYAFWVU+csuqraKzIVnVrBIXD9/B/Jdw60YIvd9hK21YbXwY1vTYufU+LVt8zaxCVg9/SzwaQ93flKqRS1YfPwoV/xs396fxP9UkBwfTP/+ixbNSt17FxfyWDXfp5QsZchlAVhWPXLJq89jZjqxqVomLh+9i/kvgwozz33bpUVQsmxqYhVvdHN+X+XpCdVqytpVZhSxQETLI6ovnEdSk5mqRxUlOIiJi6auRB2nl0oQwl0DjyFSuAuOBlpABNquzk9DZU4EiUP/VyxnP/738n+siu1nA0zBxLFkdhoDm8fF1oV+n1aP2nQvLelyW0y37OtLgEeVkPS4AGW0E6ZLGttYIUOIywt68H3xYioqVSBhTy1nKHtxLPVEA96VLZvgOXYrQzPmNRbITACzhid6+cxF9pCu55Z6Pf/+uthO4AMvD4aElurbZTJN/PC3mueFUMt8iOn8mYTRPeFiMZpQ6lLjMrZ0vxlUVZnA2M34x4iDt2JIQn/z4kafUrskGat90g77/kNOZrliSELGOHYRatHmXkERWLDeUHSiA/xt9mMaPSTnrn1ywfNeusEJfiJoMZBWkAjeK6ldZKdI5My1aF6C6DfOIeW78jPisoj5bGQcp+uC9HZa9IBuRVW3bbM0bvZWumfHU1Z5iPC3DGXi4hc6VQ05qwgr/2x9qkOvbwBqWQmQTiiSy80QnlVZltVARb/LLYdkBrVVr5KTZi5pQyTI+FpfVfv3W0+ef16ayZXPo13GWvWbN/qErV4Lo2LGPEv0GlFuucmv6VUg0Zc3mTMVLZhOjIqIi40QgooHD3nXhcHk6fHRCaG4pXPWrkIQMjGGhMfQsIFwMC1ZjuapZJS5eQoRl0zsaKugKlf2ShWqVNUF9KggvUTsNeIuqGHNlddDwchlu8Uz5+YxIocsZ+DhRT3ohgzm7FKd8tJasurk50pYtPemLL3aZdQ3AtspVNpfz6IedhzsLJ+L/+766UfvVrpfQQpdiGiycc4WOXugh0pqqrVzVrBLXDPwtcTe79SuMwArcvhkizI9ShidjFDhHIpQy7V27/FIscwKf9BT59asvacn8azR+konD11KRVW6NDR++lS5cCBTHZYU9Y8YJun07mNzdHWnduu7k6GhPfn7wJ1EbXXoUEyZyc+JlDPu0AjVpkV82BW5r5apm+8RFqjwPjaR3BJqV1bUrbmWob5Bb4JICF+d+m0KXM/ClR1BgBO3f+0h0F8nJpk03KEsWOzp4sD/t29eXfH1dRa77Xbv6kLOzA128GCjr+YD1y9UD+x4LvyI2gb/XMyEegam4uTtS5Wo5RGXw959O07UrGR89ZMtoVokDoAU4VOrKTSmnnE0JNqEnderlZU6hmx51G+SlA6e7UXY/efvfr14NogYN3jl9skKvVCkhPa+/vxcFB3MQa6AWuBI4sPcuWjT3iqzHjYnhpFX36OSxZ6QmYE4HQMWwww9PxsJ94GxCN1Tk3BIvUtzb6KFsclOqlB/t3n2HunYtrR9mZHgejYwk0gzsxLblv46Uv4CnrMd1crKnrfs7mh3K2tZASxwAFXPrZgh9MeKA8NA1BnZik0zojGRa5/XpMW/WZWrVYJ3s43PbtSsuRnnUrTufGjVaRC9epNzybt16Ke3ceZsGDtxECxeek/UaQOa1wrmSVrRYVosoWycne+EjsnHtbf3IIaWDlrjKEV6ioUR2rjbggQEynZjoeDp3JsjoITbsvMZObNwHzib0IuydPrYytWqXtlMbU6a8L8XExJncGk9NVvl4M2em3CUweXJz/fzWrb1MOi+wHcZ8eohCX0eLsNOW4ua1YBrSby+t3tqWatXNrfhyVbtKnBsLYURknNMuAIqU1VJlfGjX4S4Z2ocVeXqe6CnBBaKphSJQCWaWq737lxRjwC1J2QrZ6cj57iLSoKVJKabBiROPaeTI7eTomIXy5vWixYs7itEVmabEo6Ki6Pjx43T//n0KDw8nPz8/qlSpEhUqlPGP3pqIVHliqJ/1a1KWRHiJusILX8nIIasP77+h3HndLZZU59TxZ/T6VTQ1apbP5JY4ZFX5mCur7Bxp6ZgGjLkK3BxZZWfMvXs/IFdXRxo7djdt2HBd7+9hCkYr8cOHD9Off/5JmzZtopiYGPL29iZXV1d6+fKlUOyFCxemQYMG0ccff0yenvI6JFgC8fBdNFJgqMuPQ3OYK6sc07xOxRU0f1lzat66AFmC7Zvv0bHDASLNqVlAVjUrqyeOBohMjt17l7B4TIMzJwPpu7FHaenaVuTp5SSbrBoT0yB3bs9EffQ82sIcjKqWt2/fnrp3704FCxaknTt30ps3b+jFixf06NEj0Rq/efMmjRs3jvbs2UPFixenXbsUEjVJu50JQGmYIavc6pg+pxE1amamgk0DTp/79+ImFjs+UL+snj7xjKb+kn48AjliGri5O1CBgl76OOzWiGlw/34I7dx5RzhuWvxxt2nThtasWUOOjimHCeVWOE99+/alK1eu0NOnT826KACAvHTqlpDvPvBZuIijLtdQsCePQykwIIIqVvGjvPk8ZDkm0CZDRlYQU2bENChZ2oemz21EcmNsTIPXr6OoT591tHBhB7P6w8U5jNlo8ODBqSrwpJQuXZqaNEGNHABbg02VnGlsxZIbsh1zxu/naUj/PbK3aABID+4Dt0tSFzU2pgEnR3nzOtoiMQ0OHEjIXJlaTAMe2tajx2oaP74BlSiR3exzmuXlEhoaSq9fv040ARuDa6qc+VQDXf8g/WxmU2c1ELnrzSUyMiEPI8dJX7W5jTwexZBVTXPpwgtqUW+tsO5YOqbBv4uuUdVS/ybKTy6HrBoT02DZsot0/PhjmjjxADVsuJBWrLhEmdp7cffuXRo+fDj9999/FBmZkBfbMFpTXJxCauT88KPUX2AIL9FY9XvhqxoZZVXKNMYFJY+V/X1mfRFYIyPs3Hqfxo4+TNsPdCS/HG6UL788jqyQVW3LanY/FypTzpfiYo3b2ZyYBm07FhapTU11KktNVo2NadCnj/HdBrIr8d69ewuFPX/+fMqZM6dFwixmBuLhx6i/wBCp8hxsI2UesB1ZjYyIE+Ets2dPiHN+41qwcIBLrUUd8DSMHj0IFXnDq1TPQT16FycPTxmzQkFWSeuymiu3e7J0zZaKaeBfwFNMapDVDCvx8+fP0+nTp6lECeOGAdgq4uHb28ZLsCj8LXHobHXXVVSNJWSVo7ctWtlCzEdHx1GXVpup70el6fOvq9DN68E0f/ZlUUH/8puqQtmzJ/CZE4G0+2gX8s3uSl+Mq0qyA1klrcsqm6L37HhAtermkTd1aJLhkFcuvqBPx1Q2vRFqQ7KaYSVerVo1evjwoeKVuHj4rrbxEgCwpqzyWNWVm9uQu3tCcfDyRSSdPhkoLG4cJIaV+LBRFcR/ACwpqy+eR9KgPrtp9qImRpnFTeF5UIQIRaxUK7LZSnzu3LkioMvjx4+pbNmyybzWy5cvL+f1AQAyAQ7PKlGjdm7aeahzot8zI0QlADlzudGuI11Ef7Wl6N2/lNEBZVSpxIOCguj27dvUv39//Tqu0SjOsQ0AAIDNISnwo4eeivgDrq7yROW6cDZIRGr74KPSIm65WsjwnQwYMEDESj969CjduXNHeKsb/gc2COpVQClAVgERPX4YSn26bqfli6/Ldsz9ex/Tqn9vCh8QNclqhqs4nPhk48aNVLRoQgQoRaOOdLLpp8yLILLzUEf/j2aBrAINyWpefw/atr8jFS2eMPwxLDSG3D2MCziWlFs3Q8QwyhGfVaSBw8qSi4uDqmQ1wy3xxo0bCw91pSNeQvjb/wDYMJBVoEVZLVYim+iivXr5JVUvvUxkyssom9ffoSY1VotohYwcCtzWyPAdtWvXjj799FO6ePEilStXLpljGydLAbaDyCbkoZFsbUDRQFZBSvjn96CPhpalchWz64eIcQtdaqUn5eTxZ3TnZohwXuOsfX8vbkoFC3upVlYzrMTZM52ZMGFCst+U5NgmHr67bbwEANICsgq0LKscVIjHdDNxcfH0zZdHqOcHJcW6i+ee05hRh0TI1VkLm4gALru23qfjRwKoW6/iYvhky7bvEpKokQwr8fh4FXXOwToJlAJkFSgFC8qqvX0WOnS2O0W/Tbjj4mpPZcr7itFRYWExYh0rdxcXe9WMA08P9XUQAAAAUC0cGlgKD1ysRDb6dVq9RL/LNSRNKZh0tydPnqR9+/ZRYGBgspb5lClT5Lo2AAAAAMipxH/66ScaN26cCLuaNAGKVswXioK7psLehkIEwJaBrAKloLMdWc2wEv/zzz9FBrN+/fqR4l9CuEZSkXLOXXSqKhfIKlAKkFXbHyeeJUsWqlOnDikd8fDjbeMlWBLhJeoCz2YlA1kFSgGyqgAlzmPE//rrL1I64uE728ZLsDja8vNQHZBVoBQgqwq4jM8//5zatGlDRYoUodKlSycL9rJ27VpSDKZF8QMg84GsAqUAWbVtJf7JJ58Iz/RGjRqRr68vnNkAAAAApSjxRYsW0Zo1a0RrHAAAAAAK6hP38fERpnSgELhrKkr93qJABUBWQQbR6RKEJT5eJyYtymqGlfh3331H48ePp/BwHkegcKJJ9Qgv0Rj1e4uqHsgqUAoWlFWOnS7RsflG+u2H02L+2pWXVCz3Ajp3Okgsh76J1it4tctqhs3p06ZNo9u3b4tALwULFkzm2HbmzBlSAiJVXrT60zvquKpo//Y/UCSQVaAULCmrnFb0x29P0L4TXUVK0Q5dilDZCr7it1y53emLcVWpUJGEbGXjvjhCQc8iaOm6VqR2Wc2wEu/YsSOpAfHws9jGS7Aodm+jCqm7/Fc1kFWgVVnl1nTwyyjy8XWhUmV8qNN7RSkmOp5cXIj6Dy6j345//3hEef1yz74lKSSE7d1Eb15H07OA8FRTlypdVjOsxNmUrgr44bvZxksAIE0gq0CjsvrrxFO0ZcNd2n20CxUplpW+/KaqUftVr5VLPz/rzwu0aO4VOnqhB3l5O5HacDC2NoShZAAAADKTHn1KUIlS2URecFMZ8XlFatGmgCoVuNGObWXKlKHly5dTdHTaHgs3b96kIUOG0M8//yzX9QEAANAYe3c+pPCwGCpQyIs6vlfUrGO5ujpQhcp+ojH6y4STdPniC1ITRinx6dOn0+TJkylXrlzUvXt3+u2332jp0qVivPjcuXNp9OjRVL16dapYsSJ5eXkJRZ5RWPFza3/UqFH6dZGRkTRs2DARVMbDw4O6dOlCz549y/CxNU/ibLEA2C6QVc0TEhxFQwfsoX8WXJX1uDEx8bR310M6fybBg10tsmqUOb1JkyZ06tQpOnToEK1YsUIo8Pv371NERARlz56dKlWqRB988AH16tWLsmXLZlJ+8r///pvKl3/nmCDFad+yZQutWrWKvL29afjw4dS5c2c6fPgwyYLK/YQY4SUaTmTnju4QRZNJsvr6VTRFRsZSjpxuFBYaQ48evhEOQfb2GR6NmmEgqyrBTFnNms2Ztu3vRHn9PUhOnJzsafPejuTomEVVspqhu6lbt65olZ87d46Cg4NFS/nRo0e0adMmoWBNUeChoaFC+c+ZMyfR/q9evaJ58+bRlClTqHHjxlSlShVasGABHTlyhI4dO0ayvIQw9Q/bAcrHkrLKJsbHj0L1y/WrrKR/5iW0gK5fDabGNdbQ1UsvxfKtGyH08kWk7NcA1IO5ssryxWPBCxXxNqsfPDVYgUdExNLaFbdEy1wNWL56nQ5sLucQrk2bNk20/vTp0xQTE5NofcmSJSl//vx09OjRVI8XFRVFr1+/TjRpGZFNyF0jWYUUhi3I6qI5V6hR9dViGA4zbU4j6vFBCTHPQ3pWbWlDxUslVK6//fIIDe6722LXAlm1XTJLVr8adYj6dNlOluTOrVc0YuA+On3imSpk1arJ1NhZjoPDsDk9KQEBAeTk5ERZsyYe28dBZvi31Jg0aRJ9//336Z5bPHw323gJFgfGBpvEWrLKZnIuyMpVzE4t2xUUZks394SioH6jvPrtXN0cqHa9PPrl6XMb0YvnCS3xu7df0Y1rwdSiTUGSFciqpmX1oyFlyMHBsm3LMuV86cTl98031xshq/36rafPP69NZcvm0K979iyUOnVaQY6O9mRvb0dLl3am3Lk9ldcSf/jwIY0cOVL0r7vwyH2ZGDt2rDDFSxOfx3btEEDLWEtWJ35znAZ9sJtiY+NFpKtmrQoY1eftm92VipdMaJWvXnaTvht7THgQA/WTWbJao3ZuqlI9p9Hbb914l5rWXkOF/eaL/7xsDHL3t2eE7Nnd6NChAbR/fz/64IMKNG/e2cxpiT958oTy5HlXKzcXNpcHBgZS5cqV9evi4uLowIEDNGPGDNqxY4cY0hYSEpKoNc7e6ewlnxrOzs5iAsDWsZas/t931an/oNJmtXg+/7oKfTS0LLm5O4o+zMxwfAPqltVjh58Kv4ve/UsZtT0r7IG9dxOHMOEw6dcuvxTLc5Y0pdbtC6W5L5vSv/niCK3Y1IY8vZxk9TEZPnwrXbgQKL4vNzdHmjHjBN2+HUzu7o60bl33RN/KmzdRVKaMn1nnNPrL47Hi//77L8kFe7xfvHhROMlJU9WqVYWTmzTPcdn37Nmj3+f69ev04MEDqlWrlmzXAYAW4MLlj1/P0IN7r0XQixKlfMw6Hg8HzebjIjzZe3baRgf2PZbtWoE2OXcmiGZPu2D09lN+PqNX4Az/5+WpP6efv8PD05FKlPah6Og4kpNNm25Qlix2dPBgf9q3ry/5+rpS7dr+tGtXH3J2dqCLFwPFdufOBVCNGnNpxoyTVLly7sxpif/44480ePBgWrdunRgOxilJzcHT05PKli2baJ27u7sYEy6t//DDD8UYdD4Xjz8fMWKEUOA1a9Y069yaggU8/G2cX6BZ2Ot32eLrVLioN+UvmJAkQg7YgzhnLjdReJoNZFXTcOxzw/jn6XHn5iu9Apfg5ds3X6W7L1dip85qYMplpimrV68GUYMG7/xEWKFXqpRgOfb396Lg4AgxX7FiLjp+/CNaufIyTZp0iGbPbmv5lvjQoUPpwoUL9OLFCypdurQYVmZppk6dSm3bthVBXurXry/M6GvXrpXn4PwS+Hmq3K9NpMqLt42UecB6ssr92f+dfI/ady4i55WJQmra/xpRvYbvHOJMBbKqAjKxXC1czDtZ5ZGXixT3NiqgTPDLSNlltVQpPzpw4L5+mXOcG4Ys50qGYevf29tZmNwzzTu9UKFCtHfvXtFnzUFXSpUqRQ4ODrKlIv3vv/8SLbPD219//SUmuREPP079BYbwEnXWiBe+SjFXVi9deEHZ/VyEE5ulePwwlP6ecZHGfldNhLk0BciqtmX10vnnNHLwf7R4dUvKmy99x7PRYyon6hOX/vP69Ph30TX649ezdO1RX1ERlUtW27UrTtu336K6decL7/OUFDSb0j//fKfoG+eUqvPntydzyPDXxpHauDXMgVk6dOiQTIkrBfHwnTRSYJhX0QMKl9Uxow5RtmzO9M+almQpOHDGlvV3qFvPYlS2QnbTDwRZ1ays+uV0oyo1cpIu3rh92XltzpKmog+cTehFinnT6LGVqVW7tJ3amA5di4ihZqYo8LRklVvdM2e2SXHzyZOb6+cPHOhPcpEhDcxR1T777DMRgOXy5cvk52eeV53VUWdSG6BGzJDVpWtbUlSUvA48SSlY2ItOXHk/U7zUUxp7e+LEYxo5cruIyJU3rxctXszhNeWP+AUsJ6vsW/Hrn/UytE/r9oXS9URPCW7pG9PaVwJGf3EtW7akr776SpjSuSWueAUOgEbwzuosYqFbeuwtK3C5vX2NhZ2G9u79QLRwChb0pg0brlvlOoD5Fp3N6++IPmtLsWXDXZGnnEdsaEqJ8xhudmzjRCcAAGWwZvlN+var1MMUpzb2lsfccutdGntrjCLfue0+Fc+9UB/CVS64sB02bAvVq7eAGjVaREFB4WLsbbNm/1DHjsvF7xzxytXVUe8xb5aZFFgNdjYb/uE+MWbcUrx+FUVXL79M5HCmCSW+a9cuypcvn2WvBlgGectUoCDi4nQUnIGkJeaMvS1V2oe++7mWCCUpp6waO/aWuX8/hHbuvCMcjIDyYIvRnmNdqGVbmcP5GvD+ByVFX7paylVth1mKJdUjsglFI1ub4jFRVrv1Ki5inmfG2Fv/Ap7Ub2BpEcVNTlk1duzt69dR1KfPOlq4sAP6wxVcrhYplhCh8+B/jykiXL5C+uypQJo785IY9mVufHZbKlc1q8TFw4+0jZdgSXQ8YDPL2/9Ak7LK8c2N7f8zZ+wth8zkyG2m9jWmJqvGjL3lOPA9eqym8eMbUIkSZnjHA5soV3nI4oD3d9KKJfL5Nhw7/JQ2rr0tSwpSWypXNavExcO3s42XYFH4W2KfJnXXVVSNObJ6+MATKpF3kSgUjYHH2EomdCYjY29XL79Jo4fuN72vMRVZZdM4K2kee8t94i9eJLS8DVm27CIdP/6YJk48QA0bLqQVKy6Zdg3AJspVTlCy42Bn6juwtFg2x8/i+tWX4v+QkRVo1Za25Oxsr6pyVZmDvOWAH7575r0Ebj2wGZCTRTBIGAEyQ1Y5J/iv0+qSh6eTxcfefjmuqjCny42xY2/79Kkg+7mB9cpVDhHMXLn0gjq33ERL1rSiqjWMz3DGbFp3h4YN2Ev7T71HhYp4y6PAbQxoEgshKWs2LTaqvkof2P/U8WdUPM9CYXpkXr+KVs1QB2B7+Pi6CEeerNmMz0DFinzXkS50J2iA+G+MAmcZ5kqqJaPCAW1SsJAXDfu0IpWvlNBNwkPQpNZ1Shw/8pSWLrwm5tlBbv6y5kKBqxUocQvA/TiNqq8WipxbEVyI1m2QR1+7/GJcVeEExHCYwcF932VqA8ASyU8mfH2MAp6GWewc038/J3KUo0IK5IYdJUd8VlEMHeQydeK447R14z3x2/kzQdS87lpqUW8t3b/7Wqzbu/MhrV1xU8giB/9p2jI/qRntmtMtYC7nMY6caILDTr7XszhFR8eTq2sWGjS8nH47vxxuiTL18G+So8WL5xGiZS57rRHlqqZhT9wNq29TizYFLNZSLlchO0VFxpk/9hayCtKAuyEPne1O0W8jELp7OFKVagmR+zgtLjN6bBVycspi+XHgNiKrUOIyMe7zI3T65DPatr+TiMnLkzHUqvsul+wfv5ylrZvu0aGz3UxOIpEU4SUaRmTnZgMeGMAqcP7ww+e7i2QLlqJRM38xmQNkFRgDt655YooWz0qTptZN9Htm9Hvbkqxq1pwuXkKofEPMBnxchj77vypmRYriDFBL1rSUTYEDdSCHrLIC5whs0yafldWsvvyf6/TpkP1WC7cK1F2ugvTRrBKXix1b7lFERKyoETZvVcDsvh/2JuZ+n2+/PEK3byY4v5mDyCbkppFsbSBNngdFiBSMZ06+i3BmLmzOdHd30LeMzAGyCpSCzoZkVbNKXDx8V/NeArdohvbfK/ob5SQ6Kl6M7+U80LKg2besDuSQVYazNu078Z4+69OFs0EmHYejaK1beUvMt+tUmH6YXEe+/kfIqqKRS1YVQRayCWzkMqyEmV0n7CS0+2gXEdpSTlzdHGjHoc7UoUsRWY8LFIxM3XxSV82Rg0+oVYP1JrXK2fr0+fAD9OjBG3kuCqgL9Q3Ftmm0rcTNNE2y2Zs9yS2RMYk9ikPfRNOqf28Iz3cA5IQdKpetb0WVqiakFP5lwkkRMpXhoTk8sdxJQ8Z42CQP7WE6dC1CB053o3z5E4ZJAgCsB5S4iQz7cC8N6b/Xoue4cukljR56QPwHQE7Y/F2/cT7xn53STp8MpIf3E1rW+/c8onzec8k/61y6fSsh8UlISBTFvHVe4304LCYAwPrADdpERn1ZmTw9TcvWZCzVauakE1fep9x5zBjbyw0pDjXtmvZm/fqtp88/r01lyyaMuWRevYoUOZuvXAmiY8c+SvQbUA8cRGPlpndhTYuVzEa//1VfzLu/zUg2ePi72AYWw0hZBcDq6GxHVh00/RI4zbKJlmrD8d3GsHXjXZGrmVM9cqYoTighORilBrd4zFLgIoSxHVHc2/8ZxM3NkbZs6UlffLHLrGsA1pVVUxzgevQpQZmNObIKtCmr1sKWZFWz5nTx8GNNewn/7X6YoRR5rMAH9t5N1y6/FGN1+T8v8/r04H5KDiloavo84SXqlNxblPs6hw3bQvXqLRCZoYKCwmnGjBOi5d2x4/K3IQvtyc8PsbCVLKtKIjVZBcohs2U1/q2/EPsncaa7zMKWZFWzSlykynM0LWXe2VNB9M+8q0Zvzy1wKaWjOPfbVI+cKSo9smZ1ovIVs+vDDJpECgmsNm26IRzyDh7sT/v29SVfX1eqXdufdu3qQ87ODnTxonxjiYH1ZFVxGJdsDWhUVg0VddvGG+i3H06J+etXg6lYrgV09lRCufUqJMryDsE2IquaVeKiosiJnUyoMH46pjJt3tfR6O3ZhJ40LwQvc6rH9ChfyY9+m15fBNWQk6tXg6hBg4L6ZVbolSrlEvP+/l4UHJw8ZzNQnqwCoBZZ3bj2NtWusEIE12K69SpGTVokJDfhbsdxE2tQkWJZxfI3Xx6hnp22kRbQrhLPRLgPPGksDF4uUtzbqKFsnFhFbkqV8qMDB+7rl7nWahiwA8moAADWhrv1ggLDxXzZ8tnp/T4lKO5ta/yDD0vr84tn83GhD4eUFXkCmH4Dy9DAoWXFPCeVunZFvSN8oMRNgPPV1q+yUphsjIGd2CQTOiOZ1nl9esyZeYma1FxDctOuXXFhmqpbd77oE3/xIuWWd+vWS2nnzts0cOAmWrjwnOzXAQAAqfHz9yepU4tNYhgkp3FmK6iHZ/p27MrVcuhb6bOnXaAuLTcLZa5GtOudbmaktgaN8xmd9IG90OcsaSr6wNmEXoS908dWplbt0vZOZ/r0L0kNGuc174ITrE+J4Fb3zJnvhhUZMnlyc/381q29zDs3AGbKKtAufQaUEl2KPAzSVEZ+WYladyikb6WrTVa1rcRN9BUrUMiLJv5WO0P7sCJPb0hZSnBULHMiY4lsQpFEdp7oUFU0GkgSBllVCTLI6s5t96lOvTxml39SatKy5X2Faf7Hb09Qx65FqGyF7KQWWdWsOV28hAjTU+ZxAvrVy26KZBCWYsXSG/THr+l7sKeF8BK104hns0oxV1aVAmRV+cghqyHBUfTJwH20dOE1Wa8tNlZHRw8+lSWxlC3JqrZb4mYQGBBOo4fup9x53alO/TwWOUfo62i68zbspcnwt8RDvdVd/gM1AFkFPKw2mzPtPNSZcueVN7Svo2MW2rC7vchLoSZZ1awSF4P0PUwfrJ+/oJdIAlGwsBdZCva25CAGQNuYK6sAKEVWeTSOd1ZnUb5aAgeHLBQeFkOb1t+lzt2KCsWudJR/B1aEFTj3s+ze/kBEYpOLg/89pkVzr4hhX/b2eEUAAG0wZtQh6tN1u0XPcf/eG/ps6H46c/IZqQFoCDNhb/PBH+ymLevTD6FqLMcOPaUdm+/p00ACAIAW+PiT8jTm22oWPUepMj506lpPqlE7Y/kvTIETS126lHL0y2XLLpKf329mn0Oz5nS5KFo8K+0+2kXkFWd47DibgzIKK+yrl19S6bK+9PnXVUSsdLTCAQBaQgreYsnEUtIwYWvC3aSrVl0R0THNBVpCBiQFfvJYAFUvs4wuX8y49+O/i65T20YbKOBpmBjDbc64SEOEl2io+j2bgfKBrGqbIwef0MI5VzIlsdTJ48+oWZ01JgeASU1WjUksxSxbdonee6+0CHdtLtpV4vwsQ+VNmVeytA+N/KISlSydTSyvWX6Tbt0MSTsb2tIbYr5Lj6K0cGULq9cQgTZkFQBbk9XLF17QojmXMy2xVKUqOWTPfGZMYiluha9ceZm6d08IC2sumlXiUqo8OVPmeXo50dBRFYQZnIP0Txx3nA7seSR+O3zgCTWttYaa1l4jPDCZnVsfiL5vxsXFgeo3MjMyWwoIL1FXeDYrGUvIqi0CWdW2rA4cVo72nXgvUxJLFSuRjX6dVo98fF0yfJ1pyaoxiaWWLLlA3bqVkaUVruk+cfHwXSxXYLi6OtDJqz0p9m0ecO4nr1kn4WWGhcZQdj9X+v6XWpkzxEEeyzxQqazaFJBVRZOZssp94Ncuv0ykyI1NLPXyRaRoEfvlcJNVVjmx1O7dd6hr19KpJpa6ciWIzp4NEMr85s2X9Mkn22jatFYmX4ZmW+KZUYVhBe3qlnASDvv3w+Q6YuKwrdLvABiFZqvbQCuyevHcc5FY6vFDtsdbNrHUssXXqE7FlbLnHDcmsdQvvzSjnTv70PbtvalYMR+zFDiDogEAAIDVyZnbLUNdiuYklurSoxhVqOwnm0k7o4mlJE6dGmT2OaHEAQAAWJ0cOd2EpTIzEkvlyu2uGidi2HPVDluLIuHZDBQAZFXzcHrn9atuUfBLFgTLsHHtbfpp/AnzgmnZkKxqV4nzw4+yjZdgSYSXaKz6PZtVDWQVaERWedz2p0P204mjAWQpIiLiRGIpQ4czJcuqZs3p4uHH2MZLsCQiVZ6jbaTMA6YBWQVakVUetfPfyff0zr+WoHuv4tS1R1HVyKpmW+Li4TvYxkuwKPwtcRRYdZf/qgayCrQkq5IC37froRiOKxenjj+jWX+eF0PLzA5pbUOyqlklLh6+i228BABsRVb/mnqOdm69L+bv331NX406aPSQHwDkklWWuY/77aFVy27KdWV0+uQz2rXtAcXFqasyrF0lDgCgK5de0NRf3oWp5ELuWUC4mOd41KePB1IW+4QSecHfl2nerEtWu1agHfL6e9DOQ52p70elxHJIMHe0ZxydTkeXLiTkshg8vDyt2NRGtrwUtgKUOAAahh18Vv97U2TfY9bvbE99BiQUnMVLZhMZ+nLnSRiK8+RxKD188EZfOCJVLrAkbFZn5zNWwpxYihNMZZRN6+5Qq/rr6O7tV6oNsKW+OwLJibP2BQBbYvf2B/TDN8fFfKt2Beng2W5Gpc/9ekIN+m5SLTG/YskNer/jNgoPk6/PUgBZBUkQQVzGVKaKVXKI5Q2rbwsLUlrZ0BbNTciGxmPIl6xpqc80qUZZ1bYSlzeBjU0iUuVFIL2j4pFRVp8+CRMRrjg8JDv4mBK1is2dlavm0IcVlgPIqkqIlz8PxceflBetaHZK+3nCSb3fxrnTQdS4xmpqUnM13bvzWqzbv/cxbV5/R1iKHByyUIMm+eS9IBuTVasq8UmTJlG1atXI09OTcuTIQR07dqTr168n2iYyMpKGDRtGvr6+5OHhQV26dKFnz56ZfW7x8MNt4yUAkBmyypn1GDaXz1/WTBRwplKvYV768puqwtx59lSg/thA21i6XOVK56Gz3UT/NuPh5Uh1G+ShOvXziEAxzOf/V4VWbW5r1jhwJWFVJb5//36hoI8dO0a7du2imJgYat68OYWFhem3+fTTT2nTpk20atUqsf2TJ0+oc+fO1rxsRSGyCXloJAMWSBXu825YbRWtW3lLLMtVwLHy7t1luywOb5BVYKwilyxARYtlpQm/1hYT+3BkVr+3LcmqVYO9bN++PdHywoULRYv89OnTVL9+fXr16hXNmzeP/v33X2rcuLHYZsGCBVSqVCmh+GvWrGnyucXDd7eNlwCApWWVC72efUtSjbfpcOU0dXKrXuqvBNoG5arGI7ax0mZ8fHzEf1bm3Dpv2rSpfpuSJUtS/vz56ejRoykq8aioKDFJvH6d0E+SItqwtgAbJTNllYfVjPyiElmCGrVzi/8R4bGy9pED2wHlqu1iM45t8fHxNGrUKKpTpw6VLVtWrAsICCAnJyfKmjVrom1z5swpfkutn93b21s/+fv7Z8r1A5BRMktWp/9+jmZPu0CW5PSJZ1Sh6BK6fTPEoucB1gHlqu1iM0qc+8YvXbpEy5cvN+s4Y8eOFS16aXr48KFs1wiAnGSWrL54HmHxMd1lyvvS8NEVKJsPh+sCagPlqu1iE7av4cOH0+bNm+nAgQOUL9+74QC5cuWi6OhoCgkJSdQaZ+90/i0lnJ2dxQTewmU3R810tfaFAGvJqjS225K4uDjQJ59XyhRZ7ddvPX3+eW0qW/ZdP/yzZ6HUqdMKcnS0J3t7O1q6tDPlzu1p3vUAPShXbbdctWpLnFsHrMDXrVtHe/fupUKFEid3r1KlCjk6OtKePXv063gI2oMHD6hWrVrmvwR2gle5/4WUTUjtGbBUjRmy+vJFJIW+ic7QPls33qWmtddQYb/54j8vGxvvev7sSxQfr8t0Wc2e3Y0OHRpA+/f3ow8+qEDz5p016RqAmaBc1VZLnE3o7Hm+YcMGMVZc6ufmPhdXV1fx/8MPP6TRo0cLZzcvLy8aMWKEUODmeKbrH77ONl6CJRFeoi7wFlUy5sgqxzv/d/F1On2tp1Hbs8Ie2Hs38Qg0tsBfu/xSLM9Z0lREv0qLyxdf0Pgxx6htp8KUI6ebbLKaUNnfShcuBIqx7W5ujjRjxgm6fTuY3N0dad267omyUr15E0Vlyvhl+PzAfFCuaqwlPmvWLNG/0rBhQ8qdO7d+WrFihX6bqVOnUtu2bUWQFx52xmb0tWvXmn1u8fCdbeMlaKPTBFhDVju8V4Smzqxv9PZTfj6jV+Di3Fwg2xFN/fldkpTUaNYqPz0I/tAkBZ6WrG7adENElTt4sD/t29eXfH1dqXZtf9q1qw85OzvQxYuBYrtz5wKoRo25NGPGSapcOcFjHmQuKFc1dhnGONu4uLjQX3/9JSbZcZT/kABYBBNllYNh8GQsd26+0itwCV7mMK3pYakIWVevBlGDBgX1y6zQK1VK8Inx9/ei4OAIMV+xYi46fvwjWrnyMk2adIhmz25rkesBtlOuxsbGC+sMh2PlFKNqy1CmKO90AID8cOaxmX+cp8hI48KiFi7mLVrehvBykeLpJ5BYs/wmDeyzW3ZP+FKl/OjAgYRY2Qz3uRtWGPh0UshNxtvbWZjcgfqIiXkXmL1l/XX024+nxfz1q8FULNcCOnMywSoT/DJSKHYtACUOgIp59jSc/vj1LD28n5BCND04W5RkQmck0zqvTw8XVwfy83OVvUXerl1x0eKqW3c+NWq0iF68SGh5G8Km9Pr1F4jf//jjOH3xRW1ZrwFYnw1rblPNssv0cfo/+LAUtWxTQMznyeshQq8WK5FgdfrmiyMiy54WsBGrPrAY3CiKso2hECDzqVDZj64+/CCR41dasPMaO7FxHzib0EUayLGVqVW7tJ3amDYdColJblnlSsHMmW1S3GXy5Ob6+QMH+pt+bmCTsFXnWUA45crtThUq+VG/QWUoPi7B0sNhhCWyZnOmvh+V1i8PGl5OjMxgQoKj6NHDUCpb3pfUWK5qW4lnbOSNIhFeojHq9xZVPSbKakKaUTsREtXF1d6oVjIr8vQ80ZPCkdr42IWLmp63GbKqEmQsVyd9d5K2rL9L+052pYKFvWjEZxWN2q98pXejE/4346LIL370Qg/y8naS5bpsSVY1a04XqfKi1Z+KVMdVRoe3/4EmZfX+3ddUpeRSOvjfE7IU82dfpgHv7zSrPxyyqnzkLlf7DixN3/5UwyyHtVFfVaI129rKpsBtTVY1q8TFw7e3jZdgUfhb4kiY6q6rqBpzZTV/QU/6aEhZKldBRnNiEr75sQZN+19D8/rDIauKR65yleMVvHkdTXnzeVCLNu9GJpiCk5M9lSztIxwix485SufPBJHZ2JCsalaJi4fvahsvAQBLyior1tFjq4i45oZe3HLABS17wHPYVUMTJtAoMpSr3If9+fADtPyf63JeGfEQtLOnAoUnu5rQrhIHQGPcu/OaaldYQZvW3ZHtmD+OP0Edm2+iqCh5KwdAu7CT2q7DXaj/4DKyHtfRMQut29GOuvUqTmoCShwAjVCgkKcYllOzTsrJgzKC1Pf95biq9L/FTcnZWXtBNoD8BD4LF9aivP4eIoiL3NjbZ6Gw0BhauvCa7FYpawElrgW0EfMAGGFW50xjfjnc6FVIFI36+D8KeMrZKjLGkYNPqH3TjaIw9PF1oYpVZDSjQ1Y1zZhRh6hP1+0WPcejh29o7KeHhGldDbKqbSWucp82RniJhqvfC1/1yCyrD+69oXOngyguNuHArNTTglstD+691gfW8M/vKbsJHbKqEsyQVa5kjptQgyxJiVI+dOZGL6pRO7fFZZXT5l66lLiycO9eCPn5/UYNGy4UU1BQxivShmh2nLh4+GFEdtlRYADtyWq5itlp7/GuYhw5h6dsXnct9RlQioaPrkiPHryhbZvuie169StJbu6ONO7zI3TuTBDtPNRZjNeduaCxbNcC1IO5sppRq87WjXdF0h6O+c8hgzmyoDExDrL7WTdKS4MGBWj16m6yHEuzSlwriGxC7hrJKgRMCASTwPifalL+gl5iniO1/TLxlJivUj0nVa6WQyjzD4fI62iUFMiqtjn432O6fiWYPhpa1uJpc08eC6AvPzlIG3Z1MGn8eGqyakzaXObw4YdUr94CqlcvP/34Y2OzhmZq1pyuqQIDxgZFY2lZZWcfLvSksJQNmuSjWwH9xcQKXArfymZIiwNZ1ays3rgaTMuXXM+UtLnZfFyoVt08Iia/nLJqTNrc3Lk96NatEXTgQD8KDAyjtWuvmn4NWlbiAhQYQClAVoHKZfXDIWVp95EumZI2t2jxrPTTlDrCMTOz0+ayMnd3dxKt786dS9H588/MOqe2lTgAAABFYk7a3BfPI0SQImukzX3z5p0T6cGD96loUfMsXFDiAAAArM6Fs0FUu/xy4Vhp6bS5yxZfp0bVVydryWdG2txDhx5QlSr/E33ijx+/oZ49y5l1Tji2qR0W0jDbSJkHQJpAVjVN7rzu1LxNAbIzcLi0VNrc7r2LU7WaORM5d8ohq8amzW3VqhjJhXaVOL8EriSp3K9NpMrj2io6VZULZBVoQFY5CNF3k2plaJ/WJqTNlc7FkxpkVbPmdPHw42zjJVgS4SXqrBEvfJUCWQVakdXIyFha9e8NevkikizF+lW3aMLXx8xLm2tDsqpZJS4evpNtvASL42jtCwDmAFkFWpFVDuXLoVdPnTDPYzstYmN19PhRqHlpc21IVrVrTmfkyxEPgGWBrAINyKpvdlc6cLqbSIBiKbq+X4w6dy9KakGzLXEAAAC2h6TAd267T6FvomU77omjATRt8lnhPW6yQ5sNAiUOAADApnj8MJQ+GbiP1qy4JdsxL5x9Tgf2PZZ9WJm1gRLXAvJVZgGwLJBV8LY1ztHbPviwlFg21dFNp9PR+TNBYp5jsi/f0JocHbOoSla1rcRjSBtZhaKR3lHxQFaBxmQ1X35P4Xx28dxzql5mmUhaklE2rbtDbRtvoLu3E0KxclIStcmqZpW4ePhRtvESLImOBzPav/0PFAlkFSgFS8hqsZJZacy31ahS1YRkPGuW36RL55+nmQ1t/uxLYr5Nh0K0fGNrKlQk/VCsSpVVzSpx8fDtbOMlWBS7t1GF1F3+qxrIKtCyrLq4OAhTOLei4+LiRfayPTsfit/OngqkepVXUv0qK/Wt7cMHntCubQ+EKZ0z9NWpn4fULKvaHWLGD9/dNl4CAGkCWQVKwcKyykr54JluFB0dJ5a9vJ2oeesCQmFLaUW/+LqK2E4raFeJAwAAUBw8PIxb50yRYlnpmx9qJPpdSwqc0dbdAgAAACoCSlwLqLwrFagIyCpQCjqyCbStxG3kJVgS4SUapn7PZtUDWQVKAbKaqWhWidvSSwAgLSCrQClAVjMfzSpxrSCyCblrJAMWUDSQVaAUdDYkq5pV4uLhu9nGS7A4qBQrGsgqUAqQ1cxHs0pcoO27B0oCsgqUAmQ1U8HjBgAAABQKlDgAAACgUKDE1Q53TUVoY9gHUDiQVaAUdLYjq9pV4vzwI23jJVgSO/a+iHv7HygTyCpQCpDVTEezSlw8/FjbeAmWRHiJOmnEW1SlQFaBUoCsZj6aVeIiVZ6jBtI7Mk7WvgBgDpBVoBQgq5mPZpW4qCg6285YPwBSBbIKlAJkNdPRrhIHAAAAFA6UOAAAAKBQEjKra5ksRDFxMe8W7bKQfRZ70ul0FBsfm2xzR3tH8Z9/420M4f14/3hdPMXFxyX6zc7OjhyyJDxuw/NJ8G+8jdzHTfghvYcAFAFkFSgFyGqmoW0lHpfgXRgVG6Vf5eLoQp5OnuKlB4UFJdvFx9VH/H8R/oKi46IT/ZbVJSu5OrpSWHQYhceGJ/rNycFJHJcFKSAqINlxs3pkFQL1MuJlouthPJ09ycPJgyJiIigkOiSZ8GdzySbmA0IDkgmqXYwdUTiRnRc6qRQNZBUoBchqpqJZJS5S5b0mcvB0oELZCqVYY+QXnFqNkYUqtZqdu5M7+br5plqz431TqzGmd9xsrtlSPa6Lg0uy42aJziI+KqBcIKtAKUBWMx9FKPG//vqLfvvtNwoICKAKFSrQ9OnTqXr16uYfWJcgdJIAJX2JKa2XkF5wSrBgZLFP3d0gM4+r9vGamgGyCpQCZDVTsXnHthUrVtDo0aNp/PjxdObMGaHEW7RoQYGBgda+NAAAAMCq2LwSnzJlCg0cOJD69+9PpUuXptmzZ5ObmxvNnz/f2pcGAAAAWBWbNqdHR0fT6dOnaezYsfp1WbJkoaZNm9LRo0dT3CcqKkpMEq9fv07zHE+fPqV8+fKRWuH7A7YJZDUxkFXbBbJqu7Jq00r8+fPnFBcXRzlz5ky0npevXbuW4j6TJk2i77//3uhzxMfH0+PHj82+VgAyCmQVKAXIqu1i00rcFLjVzn3ohjVGf3//ZNvlypWLtITW7lcJQFZTRmv3qwQgqyljC/dr00o8e/bsZG9vT8+ePUu0npdTe3jOzs5iSo9Tp07Jdp0AmAJkFSgFyKrtYtOObU5OTlSlShXas2dPIjMNL9eqVcuq1wYAAABYG5tuiTNswunbty9VrVpVjA3/448/KCwsTHirAwAAAFrG5pV49+7dKSgoiL799lsR7KVixYq0ffv2ZM5uqSFF6UnPmxKYhvRck0ZDAhkHsmpZIKvyAVm1HVm106lcoh89epSiAwaQl4cPH6p6SElmAFnNHCCr5gNZtR1ZVb0S5z70J0+ekKenpwj5l9S7kh+Sl5cXaQFL3DOLz5s3byhPnjxiDD8wHcjqOyCrtg1k1XZk1ebN6ebCDyCtmgw/dK0Im6Xu2dvbW7ZjaRnIanIgq7YJZNV2ZBXVUQAAAEChQIkDAAAACkWzSpwDF3BmNGMCGKgFLd6zGtDie9PiPasBLb43Zyvfs+od2wAAAAC1otmWOAAAAKB0oMQBAAAAhQIlDgAAACgUKHEAAABAoWhSif/1119UsGBBcnFxoRo1atCJEydILUyaNImqVasmIinlyJGDOnbsSNevX0+0TWRkJA0bNox8fX3Jw8ODunTpkizdK7ANIKuQVSWhVnmdZMuyqtMYy5cv1zk5Oenmz5+vu3z5sm7gwIG6rFmz6p49e6ZTAy1atNAtWLBAd+nSJd25c+d0rVu31uXPn18XGhqq3+bjjz/W+fv76/bs2aM7deqUrmbNmrratWtb9bpBciCrkFUloWZ5bWHDsqo5JV69enXdsGHD9MtxcXG6PHny6CZNmqRTI4GBgTyEULd//36xHBISonN0dNStWrVKv83Vq1fFNkePHrXilYKkQFYhq0pCS/IaaEOyqilzenR0NJ0+fZqaNm2aKAYwLx89epTUyKtXr8R/Hx8f8Z/vPyYmJtEzKFmyJOXPn1+1z0CJQFYhq0pCa/L6yoZkVVNK/Pnz5xQXF5csFzkvc65yNWYaGjVqFNWpU4fKli0r1vF9Ojk5UdasWTXxDJQKZBWyqiS0JK/xNiarqs9ipmXYyeLSpUt06NAha18KAGkCWQVKYZiNyaqmWuLZs2cne3v7ZB6DvJwrVy5SE8OHD6fNmzfTvn37EqUM5Ptk01dISIjqn4GSgaxCVpWEVuR1uA3KqqaUOJs7qlSpQnv27ElkGuHlWrVqkRpgZ0UWtHXr1tHevXupUKFCiX7n+3d0dEz0DHioxIMHD1TzDNQAZBWyqiTULq86W5ZVnQaHQTg7O+sWLlyou3Llim7QoEFiGERAQIBODQwZMkTn7e2t+++//3RPnz7VT+Hh4YmGQvDwiL1794qhELVq1RITsC0gq5BVJaFmeR1iw7KqOSXOTJ8+XTxsHtPIwyKOHTumUwtcL0tp4jGOEhEREbqhQ4fqsmXLpnNzc9N16tRJCCSwPSCrkFUloVZ5JRuWVaQiBQAAABSKpvrEAQAAADUBJQ4AAAAoFChxAAAAQKFAiQMAAAAKBUocAAAAUChQ4gAAAIBCgRIHAAAAFAqUOAAAAKBQoMQBAAAAhQIlnolwQHzOaPPmzZtMP/f27dupYsWKIikBAOkBWQVK4brGZRVKPANw0vvatWtT586dE61/9eoV+fv709dff53m/mPHjqURI0aQp6cnZTYtW7YUWXaWLl2a6ecGmQ9kFSgFyKqZWDw6u8q4fv26ztXVVbdkyRL9uj59+ujKly+vi4qKSnW/+/fv6xwdHXWPHj3SWYsZM2boqlatarXzg8wFsgqUAmTVdKDETeDPP/8UmWqePHmiW79+vRCic+fOpbnPb7/9luxFcwYcTm+3adMmXfHixYUQd+nSRRcWFibS+RUoUECk8hsxYoQuNjZWvx+vnzhxohByd3d3kTVow4YNusDAQF379u3FunLlyulOnjyZTOC53nbr1i2ZnwiwVSCrQClAVk0DStwE4uPjdQ0bNtQ1adJElyNHDvHi04OFgPPNJhU2FtRmzZrpzpw5o9u/f7/O19dX17x5c123bt10ly9fFoLIaf04V6+hsPn4+Ohmz56tu3Hjhsh16+XlpWvZsqVu5cqVolbbsWNHXalSpcS1GpIzZ85E6fOAuoGsAqUAWTUNKHETuXr1qqh9cc0sJiYm3e0rVKigmzBhQqJ1/NKT1uAGDx4sctG+efNGv65FixZivaGw9e7dW7/MOWv5ON98841+3dGjR8W6pPlsK1WqpPvuu+9MuGOgVCCrQClAVjMOHNtMZP78+eTm5kZ3796lR48epbt9REQEubi4JFvPxyhSpIh+OWfOnFSwYEHy8PBItC4wMDDRfuXLl0/0O1OuXLlk65Lu5+rqSuHh4UbeJVADkFWgFCCrGQdK3ASOHDlCU6dOpc2bN1P16tXpww8/ZItGmvtkz56dgoODk61nz0ZD7OzsUlyXdAiD4Tb8e2rrku738uVL8vPzM+IugRqArAKlAFk1DSjxDMK1rX79+tGQIUOoUaNGNG/ePDpx4gTNnj07zf0qVapEV65cIWsSGRlJt2/fFtcC1A9kFSgFyKrpQIlnEB6TyLXDn3/+WSyziWby5Mn05Zdf0r1791Ldr0WLFnT06FExJtJaHDt2jJydnalWrVpWuwaQeUBWgVKArJoOlHgG2L9/P/3111+0YMEC0eciMXjwYBGsIC3zT6tWrcjBwYF2795N1mLZsmXUq1evRNcO1AlkFSgFyKp52LF3m5nHAEbCgrpx40basWNHpp/7+fPnVKJECTp16hQVKlQo088PlAVkFSiFvzQuqw5WOatG4ZplSEiIiPGb2SEC2SQ1c+ZMFIrAKCCrQCkM1risoiUOAAAAKBT0iQMAAAAKBUocAAAAUChQ4gAAAIBCgRIHAAAAFAqUOAAAAKBQoMQBAAAAhQIlDgAAACgUKHEAAABAoUCJAwAAAAoFShwAAABQKFDiAAAAgEKBEgcAAAAUCpQ4AAAAoFCgxAEAAACFAiUOAAAAKBQocQAAAEChQIkDAAAACgVKHAAAAFAoUOIAAACAQoESBwAAABQKlDgAAACgUKDEAQAAAIUCJQ4AAAAoFAdSEDqdjmJjYykuLs7alwIAAECl2Nvbk4ODA9nZ2ZGtoxglHh0dTU+fPqXw8HBrXwoAQENw4yEkJJrCw2PJzc2BsmZ1UkThDszDzc2NcufOTU5OTmTL2OlYQm2c+Ph4unnzpqgd+fn5iYeKjwgAYElCQiJpyZJLNGfOWbpx46V+ffHiPjRwYCXq3bssZc3qYtVrBPLDKpEbjUFBQcLqW6xYMcqSxXZ7nhWhxCMjI+nu3btUoEABUTsCAABLsmPHLerWbTWFh8dQly6lxJQtmysFB0fQmjVXxeTm5kgrV3alFi2KWvtygQVgq+/9+/epUKFC5OJiu5U1xZjTmWS1oYgItrPLfyI2n7i6yn9ckCZx8XEUr4u3yLGz2GUh+yz2Fjk2UJ8Cb9t2GbVoUYTmzm1PuXJ5JPr9vffKUEBAKH300Uax3ebN70ORq5AsNtz6VmxLPFGNiBX4hg1EwcHynzBbNqIOHdJV5AULFhTXc+nSJeEEwVStWpUmT55MDRs2lP+6VK7AH756SNFxFqiUcb3M3on8vf3TVeT8Tp2dncn17bvn9zl37lzSMv/995/4Blu2bJlp57x37x5t376dPv74Y8psE3qBAn9QvXr5af36HuTgkHpBHhsbTx07LqeDBx/Q/fujTDKts6Pujz/+SMuWLRNlCE/Vq1enX3/9lc6dO0ejRo0S/60Nv4+KFStSSEhIutvOmDGDTp06RQsXLqSNGzfSvn37aOrUqZlynevXr6dcuXJRzZo1LaN3bBBFtcQTwS1wVuBc2Mr5gCMjE47LxzeiNR4VFUXz5s2jwYMHy3cNGoRb4KzAWck6ZJFXLGPjY8Wx+Rz2lH5rfMWKFaLASg3uJ2P/DC3ASoaVOBfema3EZ8+enelKfNGic8KEzi3wtBQ4w7/PmdOO8uf/gxYvPk+ffFIjw+f78MMP6eXLl3T06FHKli2b6I9dvXq1WKcG2rdvL6bMYv369eLbNUWJK/W7Voa9IC1Ygbu7yzdlsELw3Xff0cSJE1P0mg8MDKTOnTtTuXLlqGzZsvT333+nehz2vG/evDmVLl1a/O/Ro4c4NhMaGkoDBgwQx+Dp+++/T/EYEyZMEALME2/Hzn/cp8PC+cUXX+j3HzFihHDcYPr16ycqIE2aNKHixYuL65V+i4mJoTFjxoiWAR+zW7duFGwJy4cBrMAd7R1lncytFHCLolGjRtSlSxfxLk+cOEEnT56kxo0bi5Z6pUqVaNWqVfrtd+zYQXXr1qUqVaqIZ8ctkZR4/Pgxde3aVRyzfPny9M0336QrN2wpGDduHNWuXZv8/f2FoluwYAHVqlVL/LZ8+XL9tvz+eVu+Pn63S5cu1f/Wq1cvce183jZt2lBAQIBeeWbNmpW++uorqly5smhV8Tl4X5YBljFpG75e3oYdfw4fPkyffvqpXvbYOiXxzz//UI0aNcS29evXp/Pnz+ufa9OmTen9998X98rXc+fOHfEbK+/r16+L42WWEmAFOmvWKdH/ndSEnhq5c3tS586laObMk2L/jHDr1i0hN/z+WIFL7+y9996jwoUL6ytRQ4cOpQoVKlCZMmVEC1da36JFC/HMeH3Pnj0pLCxM/MaVLn4HKe0nvbvx48cL+SxatCht3bpVf01pyXVqvHnzhrp3704lSpQQcn/x4kX9b/yOO3bsKOZZxvg74vPyNQ0fPlw4LSeVBS4DWb6vXLlCnTp1olKlSokykcvBtMqlrVu3ipb/b7/9JtZLFrS05C/pd61IdAogIiJCd+XKFfFfT0iITjdzpk63bJlOt3GjfBMfj4/Lx0+HAgUK6M6ePavr3bu37ocffhDrqlSpotu3b5+Y79atm27MmDFi/tmzZ7p8+fLpjh49muKxunbtqvv222/F/NOnT3U5c+bUjR8/Xix/+eWXup49e+ri4uJ0oaGhuooVK+qWL1+e5rUNHDhQN2DAADE/c+ZMXYMGDXSRkZG6mJgYXatWrXQ///yz+K1v37666tWr68LCwnSxsbG62rVr6/7991/x248//qibMGGC/pg8P3ToUJ0liI6N1l0Puq67F3xP9/j1Y1knPiYfm89hzDstXry4rkKFCmJau3atbsGCBTpXV1fdtWvXxDbBwcHiHTx58kQsBwUF6fz9/XWPHj3S3b59W1ezZk3dq1evxG83b97U5cqVSzz7pDRs2FD3008/6ZcDAwPTlRu+vlGjRumP7eLiops4caJYPnHihC579uz64/HnPW7cODHP15UtWzbd3bt3E52LmTRpkm7w4MFinn/n/RYtWqT/neVw5MiR+mVpm3Xr1onluXPn6tzd3XV79+4Vy7/++quQZ+bQoUNC3qT7P3DggK506dJinp+rl5eX7s6dO2L5q6++0g0aNEjM8zfEzz8zCQoK0xF9p1u58lKG9lux4pLY7/nzsAzut0JXvnz5VH/nZ2Bvb687duyYWJ41a5auefPmYj4+Pl73/Plz/fzHH38s3mN6+0nvbvXq1WJ527ZtQt7Tk2vez9vbO8Xr/Pzzz3V9+vQR1xESEqIrWbKkKFekd9yhQwcxz+X3mzdvxDyXNW3atNEt4/LWQBbu378vlrlMLVy4sC4gIEAs87YzZsxIt1zq27evburUqfrf0pM/w+/aKL1jgyjXnG5DcEuca4VJTX+7d++m06dPi/kcOXKI1hWvS8nUs2fPHtGXznCfTtu2bRMd5/fffxeOFu7u7vTBBx/Qrl27RO03JX744Qd68OABbd68Wb8/t7i5r5cZOHAg/fXXX6K1xXBtV/L65/u4ffu23jT16tUrWrNmjVjmFjq39tROUnM619i5ZcAtDebIkSOixdiqVatE+3HL8dq1a6KFxTV+CX5v/D64xSrBrYpDhw6JVrsED580Rm6k986tKO6r49Y8w60nNsOy6ZtbW8xHH30k/nPLjq/pwIED4h3++++/ooXC/X48Zc+eXX8djo6O1Lt37zSfEZ9XamHxeT08PESrRpIhqdW/YcMG0fLhlpAEX2ME+7QQCQsC9zlK89OnTydrERqaYIFiL/SMkC1bgvXuzZto8vWVd/QMv2Pp2fHzkcoIrqNxP/OWLVtEq5y/U5bR9PaT3h3LlPSb9L2nJdeSZSAluOzia2Ergre3t7AKSMc0hFvdXOaw3PP1s8WJLQZsdZSuJX/+/HqZ4hZ3zpw5xXK1atXEMOOMlksb0pE/w+9aqUCJywALEAsuK8+0kMa2cyErOb5xAbZu3bpUt03rOCmxePFiWrt2rSisJWe79PY3dNrgPiEuFBj+0LhQZVOW1mElJcHPhc2BXOgl5erVq9SsWTOhJOUivfclLfN2PEnvL7VjcSE6bdo00Q/LlQQ2QX777bf6bbhCl55nrlQhTHoNKclQ37596aeffkrxOKntZw08PBKCevAwsowQHBwp/nt6ZiwoCJt3WTG9ePGCfH19M/R8WL727t1L+/fvJy8vL/E+eTm9/aR3J8kU/yZFwExLrtkMbyyplU9TpkwRivv48ePi+kaPHi0qkKldsxzlki4d+TP8rpWK8vvEbQTue1yyZAk9efJEv477eObMmSPmOXAAK1cu4LmVxB6nPEkKnPuhuMXHPHv2TN+Klo7DznMskNzvxS2olASYW2tsFeDauaFw8v6s3LnGyh8C9xUZ8wFwS4tr2FJ/P/+/fPkyaR2uvbPXKj9vCX6X/Hy5n5LXX7hwQf9bSn1t/H64ZcwWFgmWkbTkxhS4v1UqhA8ePEj16tUT/Yeenp5CcfA1p+WrwbCS4JaPKXB/Nn8XbImQWmNS/6ylzmkqvr6uVKKErxgDnhF4e97PxydjLXhuLXN/LDu3SV7f/I1zC1PyDUgNfodsPeHnxH3SUtlhKblOC5ZXljO+9tevXwtP+9Suma2MrJy5f9yY/vaMlkteSeTGVPlTEspX4lyTY4cOuSaDmmFG4A/qk08+EQ5qElw75pYZO02wqfHrr79OZNYx5M8//xSFLDt1sNMRbyeZRNmBiE2cfBxez4LJzhxJ4aEqLNBsDpMc3LhSMWjQIFHr54nXseWAh66kB5u+2IzF52QHKDbnWnq4C3uSx8TFyDrxMeWEnZC4osS1e3Yc4nfGjjZcQHDBzK0kdhbk39gp548//kjxOFwZ4wKFWz/8XtiJLKNykx7cymIHJa608XH53bOXOZsQeWKlnpYnvtTdwu9dcmzLCHx8Hi7Fx5CcrAyd71KD5Y23ZXNrZjm2cQtyyJCqQinzOHBjePr0Da1de5WGDq1mUhTJ+fPni+fC75fvl2Vp586d5OPjk+Z+3KXG3zq/Q/7e+TlbUq7TgssnNk+XLFmSWrduLZzbUmLkyJGiFc732adPH6H8TSGtcqlPnz60cuVKIfPcWDFV/pQExombMU5cTvgjYEXNJnA2r7Fgcg3S1MJbadjKOHE1wUqFWz9SZRDY3jhxYLtEYpy4hWEFy4pWJRHbuG+Ma9dS3F4eHqIVBc6wcmUli4htwJqwIuZQqhyJjRU0jwPnYWQptcAHDtxEO3bcpi1bekKBA6uh3JY4AABkQux0HgeeEDvdRTixsbmdTegcO33VqveoefMi1r5coGG9o9yWOAAAWAiOhc4mco7ExoFcVq5859DJTmy//96c+vatQN7etlu4A22gqJY4spgBADIbLiJfvowQ48B5GBl7oSMVsvoJRxYz+eD84TxulT2tkU8cAJDZuLtzoCUXfb4EoI184lmyZBH6xpZRREuc4YfKw7dSilEOAAAAyAlbfXPnzg0lLid8qRysRIowBAAAAMgNR4jj4b5KsPgqSokDAAAAQE0R2wAAAACNAiUOAAAAKBQocQAAAICUyf8DXumqqN86bgAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A plain trough without no-go zones\n",
+    "plain_trough = Trough(\n",
+    "    name=\"plain_trough\",\n",
+    "    size_x=19.0,\n",
+    "    size_y=90.0,\n",
+    "    size_z=65.0,\n",
+    "    max_volume=60_000,\n",
+    ")\n",
+    "\n",
+    "for mode in [\"wide\", \"tight\"]:\n",
+    "    offsets = compute_channel_offsets(plain_trough, 4, spread=mode)\n",
+    "    positions = [f\"{o.y:+.1f}\" for o in offsets]\n",
+    "    print(f\"spread={mode!r:8s} -> offsets: {positions}\")\n",
+    "\n",
+    "plot_container_cross_section(plain_trough, [2, 4, 6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qpv4b5xxlcq",
+   "metadata": {},
+   "source": [
+    "### Custom per-pair channel spacings\n",
+    "\n",
+    "When using non-adjacent channels on a Hamilton STAR, the physical spacing between channel pairs varies. `compute_channel_offsets` accepts `channel_spacings` - a list of per-pair minimum distances - so each gap respects its own constraint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9wtrqbm2upq",
+   "metadata": {},
+   "outputs": [],
+   "source": "# 6 channels on the 120mL trough with mixed spacings:\n# 4 pairs at 9mm, 2 pairs at 18mm (e.g. non-adjacent channels on STAR)\nmixed_spacings = [9.0, 18.0, 9.0, 18.0, 9.0]  # 5 gaps for 6 channels\n\nfor mode in [\"wide\", \"tight\"]:\n    offsets = compute_channel_offsets(trough_120, 6, spread=mode, channel_spacings=mixed_spacings)\n    centers = sorted([trough_120.get_size_y() / 2 + o.y for o in offsets])\n    gaps = [round(centers[i + 1] - centers[i], 1) for i in range(len(centers) - 1)]\n    print(f\"spread={mode!r:8s}  centers={[f'{c:.1f}' for c in centers]}  gaps={gaps}\")\n\n# Visual comparison - circle diameter matches each channel's spacing\nfig, axes = plt.subplots(1, 2, figsize=(6, 5), squeeze=False)\naxes = axes[0]\n\nsize_y = trough_120.get_absolute_size_y()\nsize_x = trough_120.get_absolute_size_x()\ncenter_y = size_y / 2\ncompartments = _get_compartments(trough_120)\nlabel_bbox = dict(facecolor=\"white\", alpha=0.5, edgecolor=\"none\", pad=0.3)\n\n# Each channel's diameter = its spacing value\nchannel_diameters = mixed_spacings + [mixed_spacings[-1]]  # 6 values for 6 channels\n\nfor ax, mode in zip(axes, [\"wide\", \"tight\"]):\n    ax.add_patch(plt.Rectangle((0, 0), size_x, size_y, fill=False, edgecolor=\"black\", linewidth=2))\n    for flb, brt in trough_120.no_go_zones:\n        ax.add_patch(plt.Rectangle((0, flb.y), size_x, brt.y - flb.y,\n                                    facecolor=\"red\", alpha=0.3, edgecolor=\"red\", linewidth=1))\n    for comp_lo, comp_hi in compartments:\n        ax.add_patch(plt.Rectangle((0, comp_lo), size_x, comp_hi - comp_lo,\n                                    facecolor=\"green\", alpha=0.1, edgecolor=\"green\",\n                                    linewidth=1, linestyle=\"--\"))\n\n    offsets = compute_channel_offsets(trough_120, 6, spread=mode, channel_spacings=mixed_spacings)\n    for i, o in enumerate(offsets):\n        tip_y = center_y + o.y\n        ax.add_patch(plt.Circle((size_x / 2, tip_y), channel_diameters[i] / 2,\n                                 facecolor=\"none\", edgecolor=\"navy\", linewidth=1, linestyle=\":\"))\n        ax.plot(size_x / 2, tip_y, \"o\", color=\"navy\", markersize=4, zorder=5)\n        ax.text(size_x + 1.5, tip_y, f\"ch{i}\", ha=\"left\", va=\"center\",\n                fontsize=6, color=\"navy\", bbox=label_bbox, zorder=6)\n\n    ax.set_xlim(-2, size_x + 8)\n    ax.set_ylim(-2, size_y + 2)\n    ax.set_xlabel(\"X (mm)\")\n    ax.set_title(f'spread=\"{mode}\"')\n    ax.set_aspect(\"equal\")\n\naxes[0].set_ylabel(\"Y (mm)\")\naxes[1].set_yticklabels([])\nfig.suptitle(\"6 channels, mixed spacings [9, 18, 9, 18, 9] mm\", fontsize=11, fontweight=\"bold\")\nfig.tight_layout(rect=[0, 0, 1, 0.93])\nfig.subplots_adjust(wspace=-0.1)\nplt.show()"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "t7qstevvf8b",
+   "metadata": {},
+   "source": [
+    "## Try it yourself\n",
+    "\n",
+    "Edit the parameters below to experiment with any container geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cqrxs83dgvi",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compartments: [(2.0, 28.0), (34.0, 63.0), (69.0, 98.0)]\n",
+      "  1 ch: ['y=-1.5']\n",
+      "  2 ch: ['y=+33.5', 'y=-1.5']\n",
+      "  3 ch: ['y=+33.5', 'y=-1.5', 'y=-35.0']\n",
+      "  6 ch: ['y=+38.3', 'y=+28.7', 'y=+3.3', 'y=-6.3', 'y=-30.5', 'y=-39.5']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# --- Edit these ---\n",
+    "CONTAINER_SIZE_X = 19.0   # mm\n",
+    "CONTAINER_SIZE_Y = 100.0  # mm\n",
+    "CONTAINER_SIZE_Z = 50.0   # mm\n",
+    "\n",
+    "# List of (y_start, y_end) pairs for no-go zones\n",
+    "NO_GO_Y_RANGES = [\n",
+    "    (30, 32),   # divider 1\n",
+    "    (65, 67),   # divider 2\n",
+    "]\n",
+    "\n",
+    "NUM_CHANNELS = [1, 2, 3, 6]  # channel counts to plot\n",
+    "# ------------------\n",
+    "\n",
+    "no_go_zones = [\n",
+    "    (Coordinate(0, y_lo, 0), Coordinate(CONTAINER_SIZE_X, y_hi, CONTAINER_SIZE_Z))\n",
+    "    for y_lo, y_hi in NO_GO_Y_RANGES\n",
+    "]\n",
+    "\n",
+    "custom = Container(\n",
+    "    name=\"custom_container\",\n",
+    "    size_x=CONTAINER_SIZE_X,\n",
+    "    size_y=CONTAINER_SIZE_Y,\n",
+    "    size_z=CONTAINER_SIZE_Z,\n",
+    "    no_go_zones=no_go_zones,\n",
+    ")\n",
+    "\n",
+    "print(f\"Compartments: {_get_compartments(custom)}\")\n",
+    "for n in NUM_CHANNELS:\n",
+    "    try:\n",
+    "        result = compute_channel_offsets(custom, n)\n",
+    "        status = [f\"y={o.y:+.1f}\" for o in result]\n",
+    "    except ValueError:\n",
+    "        status = \"Cannot fit\"\n",
+    "    print(f\"  {n} ch: {status}\")\n",
+    "\n",
+    "plot_container_cross_section(custom, NUM_CHANNELS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gfshxjfrbkm",
+   "metadata": {},
+   "source": [
+    "## End-to-end simulation\n",
+    "\n",
+    "Below we set up a full `LiquidHandler` with `STARChatterboxBackend` (simulation - no hardware needed) to verify that `aspirate` correctly distributes channels around no-go zones.\n",
+    "\n",
+    "The Hamilton-specific trough definitions (`hamilton_1_trough_60mL_Vb`, `hamilton_1_trough_120mL_Vb`) already have `no_go_zones` pre-configured - you don't need to define them manually when using these resources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "wnozu8h6wt8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trough: trough_60\n",
+      "Pre-configured no-go zones: [(Coordinate(x=0, y=44.4, z=5.0), Coordinate(x=19.0, y=45.6, z=60.25))]\n",
+      "Compartments: [(2.0, 42.4), (47.6, 88.0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pylabrobot.liquid_handling import LiquidHandler\n",
+    "from pylabrobot.liquid_handling.backends.hamilton.STAR_chatterbox import STARChatterboxBackend\n",
+    "from pylabrobot.resources.hamilton import STARLetDeck\n",
+    "from pylabrobot.resources.hamilton.tip_racks import hamilton_96_tiprack_1000uL_filter\n",
+    "from pylabrobot.resources.hamilton.trough_carriers import Trough_CAR_5R60_A00\n",
+    "from pylabrobot.resources.hamilton.troughs import hamilton_1_trough_60mL_Vb\n",
+    "from pylabrobot.resources import TIP_CAR_480_A00, set_tip_tracking, set_volume_tracking\n",
+    "\n",
+    "set_tip_tracking(True)\n",
+    "set_volume_tracking(True)\n",
+    "\n",
+    "backend = STARChatterboxBackend(num_channels=8)\n",
+    "deck = STARLetDeck()\n",
+    "lh = LiquidHandler(backend=backend, deck=deck)\n",
+    "await lh.setup()\n",
+    "\n",
+    "tip_car = TIP_CAR_480_A00(name=\"tip_carrier\")\n",
+    "tip_car[0] = hamilton_96_tiprack_1000uL_filter(name=\"tips_1000\")\n",
+    "deck.assign_child_resource(tip_car, rails=1)\n",
+    "\n",
+    "trough_car = Trough_CAR_5R60_A00(name=\"trough_carrier\")\n",
+    "trough_car[0] = hamilton_1_trough_60mL_Vb(name=\"trough_60\")\n",
+    "deck.assign_child_resource(trough_car, rails=10)\n",
+    "\n",
+    "trough = deck.get_resource(\"trough_60\")\n",
+    "print(f\"Trough: {trough.name}\")\n",
+    "print(f\"Pre-configured no-go zones: {trough.no_go_zones}\")\n",
+    "print(f\"Compartments: {_get_compartments(trough)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "w71fiy95wq",
+   "metadata": {},
+   "source": [
+    "### Aspirate with 2 and 4 channels\n",
+    "\n",
+    "Pick up tips, fill the trough, then aspirate. The chatterbox backend prints raw firmware commands - the `yp` values show absolute Y positions in 0.1mm units. Verify that channels land in separate compartments and avoid the divider at Y=44-46mm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bc6s5vhj5wh",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C0TTid0001tt01tf1tl0871tv10650tg3tu0\n",
+      "C0TPid0002xp01179 01179 01179 01179 00000&yp1458 1368 1278 1188 0000&tm1 1 1 1 0&tt01tp2266tz2166th2450td0\n",
+      "2 channels -> offsets: ['+22.8mm', '-22.8mm']\n",
+      "4 channels -> offsets: ['+29.5mm', '+16.1mm', '-16.1mm', '-29.5mm']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tip_rack = deck.get_resource(\"tips_1000\")\n",
+    "await lh.pick_up_tips(tip_rack[\"A1:D1\"])\n",
+    "trough.tracker.set_volume(50_000)\n",
+    "\n",
+    "# Show expected offsets before aspirating\n",
+    "for n in [2, 4]:\n",
+    "    offsets = compute_channel_offsets(trough, n)\n",
+    "    print(f\"{n} channels -> offsets: {[f'{o.y:+.1f}mm' for o in offsets]}\")\n",
+    "\n",
+    "plot_container_cross_section(trough, [2, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "mvpra250mkk",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation stopped.\n"
+     ]
+    }
+   ],
+   "source": [
+    "await lh.stop()\n",
+    "print(\"Simulation stopped.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "plr",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/pylabrobot/liquid_handling/backends/backend.py b/pylabrobot/liquid_handling/backends/backend.py
index dc3253986e3..65fa23ea153 100644
--- a/pylabrobot/liquid_handling/backends/backend.py
+++ b/pylabrobot/liquid_handling/backends/backend.py
@@ -18,6 +18,7 @@
   SingleChannelAspiration,
   SingleChannelDispense,
 )
+from pylabrobot.liquid_handling.utils import GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS
 from pylabrobot.machines.backend import MachineBackend
 from pylabrobot.resources import Deck, Tip
 from pylabrobot.resources.tip_tracker import TipTracker
@@ -158,6 +159,19 @@ async def request_tip_presence(self) -> List[Optional[bool]]:
 
     raise NotImplementedError()
 
+  def get_channel_spacings(self, use_channels: List[int]) -> List[float]:
+    """Get the minimum spacing between each adjacent pair of channels.
+
+    Args:
+      use_channels: The channels being used, in order.
+
+    Returns:
+      List of minimum spacings (mm) between each adjacent pair. Length is
+      ``len(use_channels) - 1``. Defaults to ``GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS`` (9mm)
+      for all pairs. Backends with variable channel spacing should override this.
+    """
+    return [GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS] * max(len(use_channels) - 1, 0)
+
   @abstractmethod
   def can_pick_up_tip(self, channel_idx: int, tip: Tip) -> bool:
     """Check if the tip can be picked up by the specified channel. Does not consider
diff --git a/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py b/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py
index 553a27608fb..5d969a7995a 100644
--- a/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py
+++ b/pylabrobot/liquid_handling/backends/hamilton/STAR_backend.py
@@ -2193,10 +2193,6 @@ def _compute_channels_in_resource_locations(
           )
           min_ch = min(use_channels)
           offsets = [all_offsets[ch - min_ch] for ch in use_channels]
-
-          if num_channels_in_span % 2 != 0:
-            y_offset = 5.5
-            offsets = [offset + Coordinate(0, y_offset, 0) for offset in offsets]
         # else: container too small to fit all channels — fall back to center offsets.
         # Y sub-batching will serialize channels that can't coexist.
 
@@ -2307,8 +2303,8 @@ async def probe_liquid_heights(
     Notes:
       - All specified channels must have tips attached
       - Containers at different X positions are probed in sequential groups (single X carriage)
-      - For single containers with odd channel counts, Y-offsets are applied to avoid
-        center dividers (Hamilton 1000 uL spacing: 9mm, offset: 5.5mm)
+      - For single containers with no-go zones, Y-offsets are computed to avoid
+        obstructed regions (e.g. center dividers in troughs)
     """
 
     if move_to_z_safety_after is not None:
@@ -4524,6 +4520,12 @@ async def move_channel_z_relative(self, channel: int, distance: float):
     current_z = await self.request_z_pos_channel_n(channel)
     await self.move_channel_z(channel, current_z + distance)
 
+  def get_channel_spacings(self, use_channels: List[int]) -> List[float]:
+    sorted_channels = sorted(use_channels)
+    return [
+      self._min_spacing_between(lo, hi) for lo, hi in zip(sorted_channels[:-1], sorted_channels[1:])
+    ]
+
   def can_pick_up_tip(self, channel_idx: int, tip: Tip) -> bool:
     if not isinstance(tip, HamiltonTip):
       return False
diff --git a/pylabrobot/liquid_handling/liquid_handler.py b/pylabrobot/liquid_handling/liquid_handler.py
index 60eb2e8f11a..acd94dc3bf1 100644
--- a/pylabrobot/liquid_handling/liquid_handler.py
+++ b/pylabrobot/liquid_handling/liquid_handler.py
@@ -30,8 +30,7 @@
   get_strictness,
 )
 from pylabrobot.liquid_handling.utils import (
-  get_tight_single_resource_liquid_op_offsets,
-  get_wide_single_resource_liquid_op_offsets,
+  compute_channel_offsets,
 )
 from pylabrobot.machines.machine import Machine, need_setup_finished
 from pylabrobot.plate_reading import PlateReader
@@ -348,6 +347,20 @@ def _check_args(
 
     return extra
 
+  def _compute_spread_offsets(
+    self,
+    resource: Resource,
+    use_channels: List[int],
+    spread: str,
+  ) -> List[Coordinate]:
+    """Compute channel spread offsets for a single-resource multi-channel operation."""
+    return compute_channel_offsets(
+      resource=resource,
+      num_channels=len(use_channels),
+      spread=spread,
+      channel_spacings=self.backend.get_channel_spacings(use_channels),
+    )
+
   def _make_sure_channels_exist(self, channels: List[int]):
     """Checks that the channels exist."""
     invalid_channels = [c for c in channels if c not in self.head]
@@ -768,10 +781,7 @@ async def discard_tips(
       raise RuntimeError("No tips have been picked up and no channels were specified.")
 
     trash = self.deck.get_trash_area()
-    trash_offsets = get_tight_single_resource_liquid_op_offsets(
-      trash,
-      num_channels=n,
-    )
+    trash_offsets = compute_channel_offsets(trash, num_channels=n, spread="tight")
     # add trash_offsets to offsets if defined, otherwise use trash_offsets
     # too advanced for mypy
     offsets = [
@@ -947,18 +957,8 @@ async def aspirate(
     if len(set(resources)) == 1:
       resource = resources[0]
       resources = [resource] * len(use_channels)
-      if spread == "tight":
-        center_offsets = get_tight_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "wide":
-        center_offsets = get_wide_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "custom":
-        center_offsets = [Coordinate.zero()] * len(use_channels)
-      else:
-        raise ValueError("Invalid value for 'spread'. Must be 'tight', 'wide', or 'custom'.")
+
+      center_offsets = self._compute_spread_offsets(resource, use_channels, spread)
 
       # add user defined offsets to the computed centers
       offsets = [c + o for c, o in zip(center_offsets, offsets)]
@@ -1130,18 +1130,8 @@ async def dispense(
     if len(set(resources)) == 1:
       resource = resources[0]
       resources = [resource] * len(use_channels)
-      if spread == "tight":
-        center_offsets = get_tight_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "wide":
-        center_offsets = get_wide_single_resource_liquid_op_offsets(
-          resource=resource, num_channels=len(use_channels)
-        )
-      elif spread == "custom":
-        center_offsets = [Coordinate.zero()] * len(use_channels)
-      else:
-        raise ValueError("Invalid value for 'spread'. Must be 'tight', 'wide', or 'custom'.")
+
+      center_offsets = self._compute_spread_offsets(resource, use_channels, spread)
 
       # add user defined offsets to the computed centers
       offsets = [c + o for c, o in zip(center_offsets, offsets)]
diff --git a/pylabrobot/liquid_handling/liquid_handler_tests.py b/pylabrobot/liquid_handling/liquid_handler_tests.py
index d31cfed45d0..8bc827ee26b 100644
--- a/pylabrobot/liquid_handling/liquid_handler_tests.py
+++ b/pylabrobot/liquid_handling/liquid_handler_tests.py
@@ -1207,3 +1207,121 @@ async def test_load_state_backward_compatible(self):
     # Old state format without head96_state or arm_state
     old_state = {"head_state": {c: self.lh.head[c].serialize() for c in self.lh.head}}
     self.lh.load_state(old_state)  # should not raise
+
+
+class TestNoGoZoneIntegration(unittest.IsolatedAsyncioTestCase):
+  """Integration tests for no-go zone channel distribution through LiquidHandler."""
+
+  async def asyncSetUp(self):
+    self.backend = _create_mock_backend(num_channels=8)
+    self.backend.get_channel_spacings.return_value = [9.0]
+    self.deck = STARLetDeck()
+    self.lh = LiquidHandler(backend=self.backend, deck=self.deck)
+
+    # A trough-like container with a center divider
+    from pylabrobot.resources.trough import Trough
+
+    self.trough = Trough(
+      name="trough",
+      size_x=19.0,
+      size_y=90.0,
+      size_z=65.0,
+      max_volume=60_000,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(19, 46, 65))],
+    )
+    self.deck.assign_child_resource(self.trough, location=Coordinate(100, 100, 0))
+
+    self.tip_rack = hamilton_96_tiprack_300uL_filter(name="tip_rack")
+    self.deck.assign_child_resource(self.tip_rack, location=Coordinate(0, 0, 0))
+    await self.lh.setup()
+
+  async def test_aspirate_2_channels_avoids_no_go_zone(self):
+    """2 channels on a trough with a center divider should be placed in separate compartments."""
+    t0 = self.tip_rack.get_item("A1").get_tip()
+    t1 = self.tip_rack.get_item("B1").get_tip()
+    self.lh.update_head_state({0: t0, 1: t1})
+    self.trough.tracker.set_volume(50_000)
+
+    await self.lh.aspirate([self.trough] * 2, vols=[100, 100], use_channels=[0, 1])
+
+    ops = self.backend.aspirate.call_args.kwargs["ops"]
+    y_offsets = [op.offset.y for op in ops]
+    # Both offsets should be non-zero (not centered) and in opposite compartments
+    self.assertEqual(len(y_offsets), 2)
+    # One positive (back compartment), one negative (front compartment)
+    self.assertTrue(y_offsets[0] > 0 and y_offsets[1] < 0, f"offsets: {y_offsets}")
+    # Neither should be near the divider (Y=44-46, container center=45, so offset ~0 is bad)
+    for y in y_offsets:
+      self.assertGreater(abs(y), 5, f"offset {y} too close to divider")
+
+  async def test_single_channel_respects_no_go_zone(self):
+    """Single channel on a container with no-go zones should be placed in a safe compartment."""
+    t = self.tip_rack.get_item("A1").get_tip()
+    self.lh.update_head_state({0: t})
+    self.trough.tracker.set_volume(50_000)
+
+    await self.lh.aspirate([self.trough], vols=[100], use_channels=[0])
+
+    ops = self.backend.aspirate.call_args.kwargs["ops"]
+    # Single channel should be placed in a compartment, not at container center
+    # (container center Y=45 is inside the no-go zone at Y=44-46)
+    self.assertNotAlmostEqual(ops[0].offset.y, 0.0)
+    # Should be in the back compartment center: (48+88)/2 = 68, offset = 68-45 = 23
+    self.assertAlmostEqual(ops[0].offset.y, 23.0)
+
+  async def test_dispense_uses_no_go_zones(self):
+    """Dispense should also use no-go zone logic."""
+    t0 = self.tip_rack.get_item("A1").get_tip()
+    t1 = self.tip_rack.get_item("B1").get_tip()
+    self.lh.update_head_state({0: t0, 1: t1})
+    t0.tracker.add_liquid(volume=100)
+    t1.tracker.add_liquid(volume=100)
+
+    await self.lh.dispense([self.trough] * 2, vols=[100, 100], use_channels=[0, 1])
+
+    ops = self.backend.dispense.call_args.kwargs["ops"]
+    y_offsets = [op.offset.y for op in ops]
+    self.assertTrue(y_offsets[0] > 0 and y_offsets[1] < 0, f"offsets: {y_offsets}")
+
+  async def test_no_go_zones_skipped_for_custom_spread(self):
+    """spread='custom' should bypass no-go zone logic."""
+    t0 = self.tip_rack.get_item("A1").get_tip()
+    t1 = self.tip_rack.get_item("B1").get_tip()
+    self.lh.update_head_state({0: t0, 1: t1})
+    self.trough.tracker.set_volume(50_000)
+
+    await self.lh.aspirate([self.trough] * 2, vols=[100, 100], use_channels=[0, 1], spread="custom")
+
+    ops = self.backend.aspirate.call_args.kwargs["ops"]
+    # Custom spread: offsets should be zero (user controls positioning)
+    for op in ops:
+      self.assertAlmostEqual(op.offset.y, 0.0)
+
+  async def test_no_go_zones_tight_vs_wide(self):
+    """spread='tight' should pack channels closer than spread='wide' within compartments."""
+    tips = [self.tip_rack.get_item(f"{chr(65 + i)}1").get_tip() for i in range(4)]
+    self.lh.update_head_state({i: t for i, t in enumerate(tips)})
+    self.trough.tracker.set_volume(50_000)
+    self.backend.get_channel_spacings.return_value = [9.0, 9.0, 9.0]
+
+    # wide (default): channels spread far apart within each compartment
+    await self.lh.aspirate(
+      [self.trough] * 4, vols=[100] * 4, use_channels=[0, 1, 2, 3], spread="wide"
+    )
+    wide_ops = self.backend.aspirate.call_args.kwargs["ops"]
+    wide_offsets = sorted([op.offset.y for op in wide_ops])
+
+    self.lh.update_head_state({i: t for i, t in enumerate(tips)})
+    self.trough.tracker.set_volume(50_000)
+
+    # tight: channels packed at minimum spacing within each compartment
+    await self.lh.aspirate(
+      [self.trough] * 4, vols=[100] * 4, use_channels=[0, 1, 2, 3], spread="tight"
+    )
+    tight_ops = self.backend.aspirate.call_args.kwargs["ops"]
+    tight_offsets = sorted([op.offset.y for op in tight_ops])
+
+    # within each compartment, wide channels should be further apart than tight
+    wide_gap_lower = abs(wide_offsets[1] - wide_offsets[0])
+    tight_gap_lower = abs(tight_offsets[1] - tight_offsets[0])
+    self.assertGreater(wide_gap_lower, tight_gap_lower)
diff --git a/pylabrobot/liquid_handling/utils.py b/pylabrobot/liquid_handling/utils.py
index a59ed6e4e2d..7799a499dc7 100644
--- a/pylabrobot/liquid_handling/utils.py
+++ b/pylabrobot/liquid_handling/utils.py
@@ -1,75 +1,276 @@
-from typing import List
+import warnings
+from typing import List, Optional, Tuple
 
+from pylabrobot.resources.container import Container
 from pylabrobot.resources.coordinate import Coordinate
 from pylabrobot.resources.resource import Resource
 
 GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS = 9
 MIN_SPACING_BETWEEN_CHANNELS = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS
-# minimum spacing between the edge of the container and the center of channel
-MIN_SPACING_EDGE = 1.0
+# minimum spacing between the edge of the container and the border of a pipette
+MIN_SPACING_EDGE = 2.0
 
 
-def _get_centers_with_margin(dim_size: float, n: int, margin: float, min_spacing: float):
-  """Get the centers of the channels with a minimum margin on the edges."""
-  if dim_size < margin * 2 + (n - 1) * min_spacing:
+def _get_compartments(
+  container: Container,
+  edge_clearance: float = MIN_SPACING_EDGE,
+) -> List[Tuple[float, float]]:
+  """Compute the usable Y compartments within a container created by no-go zones.
+
+  Each compartment is the free-space region between no-go zones and/or container walls,
+  shrunk by ``edge_clearance`` on each side.
+
+  Args:
+    container: The container whose no-go zones define the compartments.
+    edge_clearance: Minimum clearance between the pipette border and a compartment
+      boundary (container wall or no-go zone) in mm.
+
+  Returns:
+    List of (y_min, y_max) tuples representing usable Y ranges for channel centers.
+  """
+  container_y = container.get_size_y()
+  zones = sorted(container.no_go_zones, key=lambda z: z[0].y)
+
+  raw_compartments = []
+  prev_end = 0.0
+  for flb, brt in zones:
+    if flb.y > prev_end:
+      raw_compartments.append((prev_end, flb.y))
+    prev_end = max(prev_end, brt.y)
+  if prev_end < container_y:
+    raw_compartments.append((prev_end, container_y))
+
+  usable = []
+  for lo, hi in raw_compartments:
+    raw_width = hi - lo
+    usable_lo = lo + edge_clearance
+    usable_hi = hi - edge_clearance
+    if usable_hi > usable_lo:
+      usable.append((usable_lo, usable_hi))
+    elif raw_width > 0:
+      warnings.warn(
+        f"Compartment Y=[{lo:.1f}, {hi:.1f}] (width={raw_width:.1f}mm) is smaller than "
+        f"2 * edge_clearance ({2 * edge_clearance:.1f}mm). Automatic channel positioning will "
+        f"skip this compartment. Ensure the attached tip physically fits in the container.",
+        stacklevel=3,
+      )
+  return usable
+
+
+def _resolve_channel_spacings(
+  num_channels: int,
+  channel_spacings: Optional[List[float]] = None,
+) -> List[float]:
+  """Resolve channel_spacings to a validated list of per-pair spacings."""
+  expected = max(num_channels - 1, 0)
+  if channel_spacings is None:
+    return [GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS] * expected
+  if expected == 0:
+    return []
+  if len(channel_spacings) != expected:
+    raise ValueError(
+      f"channel_spacings has {len(channel_spacings)} entries, "
+      f"expected {expected} (num_channels - 1)."
+    )
+  return channel_spacings
+
+
+def _position_channels_wide(
+  resource_size: float,
+  num_channels: int,
+  spacings: List[float],
+) -> List[float]:
+  """Compute channel Y centers spread wide across a single region.
+
+  Distributes channels as far apart as possible while respecting minimum spacings.
+  Surplus space is shared equally across all gaps (n+1 slots: edges + between channels).
+  Returns centers in front-to-back order (ascending Y).
+  """
+  if num_channels == 1:
+    return [resource_size / 2]
+  min_spacing = min(spacings)
+  needed = sum(spacings)
+  if resource_size < MIN_SPACING_EDGE * 2 + needed:
     raise ValueError("Resource is too small to space channels.")
-  if dim_size - (n - 1) * min_spacing <= min_spacing * 2:
-    remaining_space = dim_size - (n - 1) * min_spacing - margin * 2
-    return [margin + remaining_space / 2 + i * min_spacing for i in range(n)]
-  return [(i + 1) * dim_size / (n + 1) for i in range(n)]
+  # If there's enough room for equal distribution (margins + gaps), use it
+  if resource_size - needed > min_spacing * 2:
+    # Evenly distribute across n+1 slots (same as old _get_centers_with_margin)
+    return [(i + 1) * resource_size / (num_channels + 1) for i in range(num_channels)]
+  # Otherwise, tight distribution with edge margins
+  remaining_space = resource_size - needed - MIN_SPACING_EDGE * 2
+  return [MIN_SPACING_EDGE + remaining_space / 2 + sum(spacings[:i]) for i in range(num_channels)]
 
 
-def get_wide_single_resource_liquid_op_offsets(
+def _position_channels_tight(
+  resource_size: float,
+  num_channels: int,
+  spacings: List[float],
+) -> List[float]:
+  """Compute channel Y centers packed tight in the center of a single region.
+
+  Channels are placed at minimum spacings, centered in the region.
+  Returns centers in front-to-back order (ascending Y).
+  """
+  if num_channels == 1:
+    return [resource_size / 2]
+  needed = sum(spacings)
+  start = (resource_size - needed) / 2
+  if start < MIN_SPACING_EDGE:
+    raise ValueError("Resource is too small to space channels.")
+  centers = [start]
+  for s in spacings:
+    centers.append(centers[-1] + s)
+  return centers
+
+
+def _centers_to_offsets(centers: List[float], resource: Resource) -> List[Coordinate]:
+  """Convert absolute Y centers to offsets relative to the resource center, sorted back-to-front."""
+  center_y = resource.center().rotated(resource.get_absolute_rotation()).y
+  offsets = [Coordinate(x=0, y=c - center_y, z=0) for c in centers]
+  offsets.sort(key=lambda o: o.y, reverse=True)
+  return offsets
+
+
+def compute_channel_offsets(
   resource: Resource,
   num_channels: int,
-  min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
+  spread: str = "wide",
+  channel_spacings: Optional[List[float]] = None,
 ) -> List[Coordinate]:
-  resource_size = resource.get_absolute_size_y()
-  centers = list(
-    reversed(
-      _get_centers_with_margin(
-        dim_size=resource_size,
-        n=num_channels,
-        margin=MIN_SPACING_EDGE,
-        min_spacing=min_spacing,
-      )
-    )
-  )  # reverse because channels are from back to front
-
-  # offsets are relative to the center of the resource, but above we computed them wrt lfb
-  # so we need to subtract the center of the resource
-  # also, offsets are in absolute space, so we need to rotate the center
-  return [
-    Coordinate(
-      x=0,
-      y=c - resource.center().rotated(resource.get_absolute_rotation()).y,
-      z=0,
+  """Compute Y offsets for positioning pipette channels in a resource.
+
+  Single entry point for all channel positioning logic. Handles containers with no-go zones
+  (distributing channels across compartments) and plain resources (wide/tight spread).
+
+  Args:
+    resource: The target resource (Container, Trough, Well, etc.).
+    num_channels: Number of channels to position.
+    spread: Positioning strategy:
+      - "wide": spread channels as far apart as possible (respects no-go zones if present)
+      - "tight": pack channels at minimum spacing (respects no-go zones if present)
+      - "custom": return zero offsets (caller controls positioning)
+    channel_spacings: Per-adjacent-pair minimum spacings in mm (length = num_channels - 1).
+      If None, defaults to 9mm for all pairs.
+
+  Returns:
+    List of Y offsets relative to the resource center, sorted back-to-front (descending Y).
+
+  Raises:
+    ValueError: If channels cannot fit, or if spread is not "wide", "tight", or "custom".
+  """
+  if spread == "custom":
+    return [Coordinate.zero()] * num_channels
+  if spread not in ("wide", "tight"):
+    raise ValueError(
+      f"Invalid value for 'spread': {spread!r}. Must be 'wide', 'tight', or 'custom'."
     )
-    for c in centers
-  ]
 
+  spacings = _resolve_channel_spacings(num_channels, channel_spacings)
 
-def get_tight_single_resource_liquid_op_offsets(
+  # Container with no-go zones: distribute across compartments
+  if isinstance(resource, Container) and resource.no_go_zones:
+    compartments = _get_compartments(resource)
+    if not compartments:
+      raise ValueError(
+        f"Cannot fit {num_channels} channels into the compartments of "
+        f"'{resource.name}' while respecting its no-go zones. "
+        f"Use fewer channels or spread='custom' with manual offsets."
+      )
+
+    n_comp = len(compartments)
+    base = num_channels // n_comp
+    remainder = num_channels % n_comp
+    center_idx = (n_comp - 1) / 2
+    priority = sorted(range(n_comp), key=lambda i: (abs(i - center_idx), -i))
+    distribution = [base] * n_comp
+    for i in priority[:remainder]:
+      distribution[i] += 1
+
+    container_center_y = resource.get_size_y() / 2
+    offsets = []
+    spacing_idx = 0
+
+    for (comp_lo, comp_hi), n_ch in zip(compartments, distribution):
+      if n_ch == 0:
+        continue
+      comp_width = comp_hi - comp_lo
+      group_spacings = spacings[spacing_idx : spacing_idx + n_ch - 1]
+      spacing_idx += max(n_ch - 1, 0)
+      needed = sum(group_spacings)
+      if comp_width < needed:
+        raise ValueError(
+          f"Cannot fit {num_channels} channels into the compartments of "
+          f"'{resource.name}' while respecting its no-go zones. "
+          f"Use fewer channels or spread='custom' with manual offsets."
+        )
+
+      if n_ch == 1:
+        centers = [(comp_lo + comp_hi) / 2]
+      elif spread == "wide":
+        min_gap = min(group_spacings) if group_spacings else GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS
+        even_gap = comp_width / (n_ch + 1)
+        if even_gap >= min_gap:
+          # Even distribution with equal edge margins
+          centers = [comp_lo + (i + 1) * comp_width / (n_ch + 1) for i in range(n_ch)]
+        else:
+          # Compartment too small for even distribution; use tight (centered) instead
+          start = (comp_lo + comp_hi) / 2 - needed / 2
+          centers = [start]
+          for s in group_spacings:
+            centers.append(centers[-1] + s)
+      else:
+        start = (comp_lo + comp_hi) / 2 - needed / 2
+        centers = [start]
+        for s in group_spacings:
+          centers.append(centers[-1] + s)
+
+      for c in centers:
+        offsets.append(Coordinate(0, c - container_center_y, 0))
+
+    offsets.sort(key=lambda o: o.y, reverse=True)
+    return offsets
+
+  # Plain resource (no no-go zones): wide or tight across full Y
+  resource_size = resource.get_absolute_size_y()
+  if spread == "wide":
+    centers = _position_channels_wide(resource_size, num_channels, spacings)
+  else:
+    centers = _position_channels_tight(resource_size, num_channels, spacings)
+  return _centers_to_offsets(centers, resource)
+
+
+# ---------------------------------------------------------------------------
+# Deprecated wrappers (remove after 2026-09)
+# ---------------------------------------------------------------------------
+
+
+def get_wide_single_resource_liquid_op_offsets(
   resource: Resource,
   num_channels: int,
   min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
 ) -> List[Coordinate]:
-  channel_space = (num_channels - 1) * min_spacing
-
-  min_y = (resource.get_absolute_size_y() - channel_space) / 2
-  if min_y < MIN_SPACING_EDGE:
-    raise ValueError("Resource is too small to space channels.")
+  """Deprecated. Use ``compute_channel_offsets(resource, num_channels, spread='wide')`` instead."""
+  warnings.warn(
+    "get_wide_single_resource_liquid_op_offsets() is deprecated and will be removed after 2026-09. "
+    "Use compute_channel_offsets(resource, num_channels, spread='wide') instead.",
+    DeprecationWarning,
+    stacklevel=2,
+  )
+  spacings = [min_spacing] * max(num_channels - 1, 0)
+  return compute_channel_offsets(resource, num_channels, spread="wide", channel_spacings=spacings)
 
-  centers = [min_y + i * min_spacing for i in range(num_channels)][::-1]
 
-  # offsets are relative to the center of the resource, but above we computed them wrt lfb
-  # so we need to subtract the center of the resource
-  # also, offsets are in absolute space, so we need to rotate the center
-  return [
-    Coordinate(
-      x=0,
-      y=c - resource.center().rotated(resource.get_absolute_rotation()).y,
-      z=0,
-    )
-    for c in centers
-  ]
+def get_tight_single_resource_liquid_op_offsets(
+  resource: Resource,
+  num_channels: int,
+  min_spacing: float = GENERIC_LH_MIN_SPACING_BETWEEN_CHANNELS,
+) -> List[Coordinate]:
+  """Deprecated. Use ``compute_channel_offsets(resource, num_channels, spread='tight')`` instead."""
+  warnings.warn(
+    "get_tight_single_resource_liquid_op_offsets() is deprecated and will be removed after 2026-09. "
+    "Use compute_channel_offsets(resource, num_channels, spread='tight') instead.",
+    DeprecationWarning,
+    stacklevel=2,
+  )
+  spacings = [min_spacing] * max(num_channels - 1, 0)
+  return compute_channel_offsets(resource, num_channels, spread="tight", channel_spacings=spacings)
diff --git a/pylabrobot/resources/container.py b/pylabrobot/resources/container.py
index 633cc6c9653..26106be6045 100644
--- a/pylabrobot/resources/container.py
+++ b/pylabrobot/resources/container.py
@@ -1,4 +1,4 @@
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 from pylabrobot.serializer import serialize
 from pylabrobot.utils.interpolation import interpolate_1d
@@ -24,6 +24,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones: Optional[List[Tuple[Coordinate, Coordinate]]] = None,
   ):
     """Create a new container.
 
@@ -35,6 +36,9 @@ def __init__(
         ``compute_volume_from_height`` and ``compute_height_from_volume`` are auto-generated
         via piecewise-linear interpolation if not explicitly passed. The data is also available
         for direct use (e.g. building firmware segments from calibration knots).
+      no_go_zones: List of cuboid regions within the container where tips must not be positioned.
+        Each zone is a tuple of two Coordinates: (front_left_bottom, back_right_top), relative to
+        the container's front-left-bottom origin.
     """
 
     super().__init__(
@@ -77,6 +81,46 @@ def compute_height_from_volume(v: float) -> float:
     self.tracker = VolumeTracker(thing=f"{self.name}_volume_tracker", max_volume=self.max_volume)
     self._compute_volume_from_height = compute_volume_from_height
     self._compute_height_from_volume = compute_height_from_volume
+    self.no_go_zones: List[Tuple[Coordinate, Coordinate]] = self._validate_no_go_zones(
+      no_go_zones or []
+    )
+
+  def _validate_no_go_zones(
+    self, zones: List[Tuple[Coordinate, Coordinate]]
+  ) -> List[Tuple[Coordinate, Coordinate]]:
+    """Validate no-go zones to ensure they are inside the container and well-formed.
+
+    Each zone is defined as (front_left_bottom, back_right_top).
+    """
+    validated: List[Tuple[Coordinate, Coordinate]] = []
+    for idx, (flb, brt) in enumerate(zones):
+      if not isinstance(flb, Coordinate) or not isinstance(brt, Coordinate):
+        raise TypeError(
+          f"no_go_zones[{idx}] must be a tuple of Coordinate instances, got {type(flb)!r}, {type(brt)!r}."
+        )
+
+      # Ensure front-left-bottom is not beyond back-right-top on any axis.
+      if flb.x > brt.x or flb.y > brt.y or flb.z > brt.z:
+        raise ValueError(
+          f"no_go_zones[{idx}] has invalid ordering: front_left_bottom must not exceed "
+          f"back_right_top on any axis (flb={flb}, brt={brt})."
+        )
+
+      # Ensure all coordinates lie within the container bounds.
+      for coord_label, coord in (("flb", flb), ("brt", brt)):
+        if coord.x < 0 or coord.y < 0 or coord.z < 0:
+          raise ValueError(f"no_go_zones[{idx}].{coord_label} has negative coordinates: {coord}.")
+        if (
+          coord.x > self.get_size_x() or coord.y > self.get_size_y() or coord.z > self.get_size_z()
+        ):
+          raise ValueError(
+            f"no_go_zones[{idx}].{coord_label}={coord} is outside the container bounds "
+            f"(size_x={self.get_size_x()}, size_y={self.get_size_y()}, size_z={self.get_size_z()})."
+          )
+
+      validated.append((flb, brt))
+
+    return validated
 
   @property
   def material_z_thickness(self) -> float:
@@ -99,6 +143,7 @@ def serialize(self) -> dict:
       if self.height_volume_data is not None
       else serialize(self._compute_height_from_volume),
       "height_volume_data": self.height_volume_data,
+      "no_go_zones": [(flb.serialize(), brt.serialize()) for flb, brt in self.no_go_zones],
     }
 
   def serialize_state(self) -> Dict[str, Any]:
diff --git a/pylabrobot/resources/container_tests.py b/pylabrobot/resources/container_tests.py
index 7d737092c2c..b45b3d63008 100644
--- a/pylabrobot/resources/container_tests.py
+++ b/pylabrobot/resources/container_tests.py
@@ -4,6 +4,7 @@
 from pylabrobot.serializer import serialize
 
 from .container import Container
+from .coordinate import Coordinate
 
 
 class TestContainer(unittest.TestCase):
@@ -41,6 +42,7 @@ def compute_height_from_volume(volume):
         "compute_volume_from_height": serialize(compute_volume_from_height),
         "compute_height_from_volume": serialize(compute_height_from_volume),
         "height_volume_data": None,
+        "no_go_zones": [],
         "parent_name": None,
         "rotation": {"type": "Rotation", "x": 0, "y": 0, "z": 0},
         "type": "Container",
@@ -165,3 +167,115 @@ def test_height_volume_data_validates_minimum_points(self):
       Container(
         name="c", size_x=10, size_y=10, size_z=10, height_volume_data={0: 0}
       )  # only 1 point
+
+  def test_no_go_zones_default_empty(self):
+    c = Container(name="c", size_x=10, size_y=10, size_z=10)
+    self.assertEqual(c.no_go_zones, [])
+
+  def test_no_go_zones_stored(self):
+    zones = [(Coordinate(0, 44, 0), Coordinate(10, 46, 10))]
+    c = Container(name="c", size_x=10, size_y=90, size_z=10, no_go_zones=zones)
+    self.assertEqual(len(c.no_go_zones), 1)
+    self.assertEqual(c.no_go_zones[0][0], Coordinate(0, 44, 0))
+    self.assertEqual(c.no_go_zones[0][1], Coordinate(10, 46, 10))
+
+  def test_no_go_zones_serialize(self):
+    zones = [(Coordinate(0, 44, 0), Coordinate(10, 46, 10))]
+    c = Container(name="c", size_x=10, size_y=90, size_z=10, no_go_zones=zones)
+    serialized = c.serialize()
+    self.assertEqual(
+      serialized["no_go_zones"],
+      [
+        (
+          {"type": "Coordinate", "x": 0, "y": 44, "z": 0},
+          {"type": "Coordinate", "x": 10, "y": 46, "z": 10},
+        )
+      ],
+    )
+
+  def test_no_go_zones_multiple(self):
+    zones = [
+      (Coordinate(0, 34.6, 0), Coordinate(10, 36.6, 10)),
+      (Coordinate(0, 70.2, 0), Coordinate(10, 72.2, 10)),
+      (Coordinate(0, 105.9, 0), Coordinate(10, 107.9, 10)),
+    ]
+    c = Container(name="c", size_x=10, size_y=142, size_z=10, no_go_zones=zones)
+    self.assertEqual(len(c.no_go_zones), 3)
+
+
+class TestNoGoZoneCollision(unittest.TestCase):
+  def _make_container(self, size_y, no_go_zones=None):
+    return Container(name="c", size_x=10, size_y=size_y, size_z=10, no_go_zones=no_go_zones)
+
+  def test_no_zones_uses_standard_spread(self):
+    from pylabrobot.liquid_handling.utils import compute_channel_offsets
+
+    c = self._make_container(90)
+    result = compute_channel_offsets(c, num_channels=1)
+    self.assertEqual(len(result), 1)
+    # No no-go zones: single channel goes to center (offset 0)
+    self.assertAlmostEqual(result[0].y, 0.0)
+
+  def test_1_channel_in_2_compartments(self):
+    from pylabrobot.liquid_handling.utils import compute_channel_offsets
+
+    # 90mm container, divider at Y=44-46 -> 2 compartments [0,44] and [46,90]
+    # edge_clearance = 2.0
+    # Usable: [2.0, 42.0] and [48.0, 88.0]
+    # 1 channel -> center-out back-first -> goes to back compartment (index 1)
+    # Center of back usable = (48.0 + 88.0) / 2 = 68.0
+    # Container center = 45.0, offset = 68.0 - 45.0 = 23.0
+    c = self._make_container(
+      90,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(10, 46, 10))],
+    )
+    result = compute_channel_offsets(c, num_channels=1)
+    self.assertEqual(len(result), 1)
+    self.assertAlmostEqual(result[0].y, 23.0)
+
+  def test_2_channels_across_2_compartments(self):
+    from pylabrobot.liquid_handling.utils import compute_channel_offsets
+
+    c = self._make_container(
+      90,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(10, 46, 10))],
+    )
+    result = compute_channel_offsets(c, num_channels=2)
+    self.assertEqual(len(result), 2)
+    # Sorted descending by Y (back-to-front)
+    self.assertGreater(result[0].y, result[1].y)
+
+  def test_4_channels_across_2_compartments(self):
+    from pylabrobot.liquid_handling.utils import compute_channel_offsets
+
+    c = self._make_container(
+      90,
+      no_go_zones=[(Coordinate(0, 44, 0), Coordinate(10, 46, 10))],
+    )
+    result = compute_channel_offsets(c, num_channels=4)
+    self.assertEqual(len(result), 4)
+
+  def test_raises_when_impossible(self):
+    from pylabrobot.liquid_handling.utils import compute_channel_offsets
+
+    # Entire container is no-go
+    c = self._make_container(
+      12,
+      no_go_zones=[(Coordinate(0, 0, 0), Coordinate(10, 12, 10))],
+    )
+    with self.assertRaises(ValueError):
+      compute_channel_offsets(c, num_channels=1)
+
+  def test_3_compartments_6_channels(self):
+    from pylabrobot.liquid_handling.utils import compute_channel_offsets
+
+    # 150mm container, 2 dividers -> 3 compartments, 6 channels -> 2 per compartment
+    c = self._make_container(
+      150,
+      no_go_zones=[
+        (Coordinate(0, 49, 0), Coordinate(10, 51, 10)),
+        (Coordinate(0, 99, 0), Coordinate(10, 101, 10)),
+      ],
+    )
+    result = compute_channel_offsets(c, num_channels=6)
+    self.assertEqual(len(result), 6)
diff --git a/pylabrobot/resources/hamilton/troughs.py b/pylabrobot/resources/hamilton/troughs.py
index 71c3b80b369..49026ba0bcd 100644
--- a/pylabrobot/resources/hamilton/troughs.py
+++ b/pylabrobot/resources/hamilton/troughs.py
@@ -2,6 +2,7 @@
 
 import warnings
 
+from pylabrobot.resources.coordinate import Coordinate
 from pylabrobot.resources.trough import Trough, TroughBottomType
 
 # --------------------------------------------------------------------------- #
@@ -43,11 +44,9 @@ def hamilton_1_trough_60mL_Vb(name: str) -> Trough:
   Trough 60 mL, w lid, self standing (V-bottom).
   True maximal volume capacity ~80 mL.
   Compatible with Trough_CAR_?? (194057 <- not yet integrated into PLR!).
+  Has a center support wall (~1.2mm wide at Y=44-46mm) but is still open
+  at the bottom.
   """
-  warnings.warn(
-    "hamilton_1_trough_60mL_Vb has a center support that can interfere with pipetting.\
-     If using an odd number of channels, use spread='custom' and define offsets for each channel to avoid collision."
-  )
 
   return Trough(
     name=name,
@@ -60,6 +59,9 @@ def hamilton_1_trough_60mL_Vb(name: str) -> Trough:
     model=hamilton_1_trough_60mL_Vb.__name__,
     bottom_type=TroughBottomType.V,
     height_volume_data=_hamilton_1_trough_60mL_Vb_height_volume_data,
+    no_go_zones=[
+      (Coordinate(0, 44.4, 5.0), Coordinate(19.0, 45.6, 60.25)),  # center divider
+    ],
   )
 
 
@@ -92,12 +94,9 @@ def hamilton_1_trough_120mL_Vb(name: str) -> Trough:
   Trough 120 mL, without lid, self standing (V-bottom).
   True maximal volume capacity ~120 mL.
   Compatible with Trough_CAR_?? (194058 <- not yet integrated into PLR!).
+  Has 3 in-container support beams (~2.5mm wide at base, ~0.8mm at top, tapered)
+  but is still open at the bottom.
   """
-  warnings.warn(
-    "hamilton_1_trough_120mL_Vb has 3 (!) in-container support beams that can interfere with "
-    "pipetting. If using an odd number of channels, use spread='custom' and define offsets "
-    "for each channel to avoid collision."
-  )
 
   return Trough(
     name=name,
@@ -110,6 +109,11 @@ def hamilton_1_trough_120mL_Vb(name: str) -> Trough:
     model=hamilton_1_trough_120mL_Vb.__name__,
     bottom_type=TroughBottomType.V,
     height_volume_data=_hamilton_1_trough_120mL_Vb_height_volume_data,
+    no_go_zones=[
+      (Coordinate(0, 39.7, 12.0), Coordinate(19.0, 42.2, 70.0)),  # beam 1
+      (Coordinate(0, 73.5, 12.0), Coordinate(19.0, 76.0, 70.0)),  # beam 2
+      (Coordinate(0, 107.3, 12.0), Coordinate(19.0, 109.8, 70.0)),  # beam 3
+    ],
   )
 
 
@@ -136,6 +140,7 @@ def hamilton_1_trough_200mL_Vb(name: str) -> Trough:
   Trough 200 mL, w lid, self standing (V-bottom).
   True maximal volume capacity ~300 mL.
   Compatible with Trough_CAR_4R200_A00 (185436).
+  Has a center support wall (~1.2mm wide at Y=59-61mm) which is open at the bottom.
   """
   return Trough(
     name=name,
@@ -148,6 +153,9 @@ def hamilton_1_trough_200mL_Vb(name: str) -> Trough:
     model=hamilton_1_trough_200mL_Vb.__name__,
     bottom_type=TroughBottomType.V,
     height_volume_data=_hamilton_1_trough_200mL_Vb_height_volume_data,
+    no_go_zones=[
+      (Coordinate(0, 60, 8.0), Coordinate(37.0, 61.7, 60.0))  # center divider
+    ],
   )
 
 
diff --git a/pylabrobot/resources/petri_dish.py b/pylabrobot/resources/petri_dish.py
index 410c105d1ee..2d9cbfe5dbf 100644
--- a/pylabrobot/resources/petri_dish.py
+++ b/pylabrobot/resources/petri_dish.py
@@ -20,6 +20,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     super().__init__(
       name=name,
@@ -33,6 +34,7 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.diameter = diameter
     self.height = height
diff --git a/pylabrobot/resources/petri_dish_tests.py b/pylabrobot/resources/petri_dish_tests.py
index 956f191a964..a9e916c4b3e 100644
--- a/pylabrobot/resources/petri_dish_tests.py
+++ b/pylabrobot/resources/petri_dish_tests.py
@@ -25,6 +25,7 @@ def test_petri_dish_serialization(self):
         "compute_volume_from_height": None,
         "compute_height_from_volume": None,
         "height_volume_data": None,
+        "no_go_zones": [],
         "parent_name": None,
         "type": "PetriDish",
         "children": [],
diff --git a/pylabrobot/resources/trash.py b/pylabrobot/resources/trash.py
index 98ecd3e7f52..40c633d5072 100644
--- a/pylabrobot/resources/trash.py
+++ b/pylabrobot/resources/trash.py
@@ -17,6 +17,7 @@ def __init__(
     compute_volume_from_height=None,
     compute_height_from_volume=None,
     height_volume_data=None,
+    no_go_zones=None,
   ):
     super().__init__(
       name=name,
@@ -30,4 +31,5 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
diff --git a/pylabrobot/resources/trough.py b/pylabrobot/resources/trough.py
index 1bdab42aec8..55aca488c44 100644
--- a/pylabrobot/resources/trough.py
+++ b/pylabrobot/resources/trough.py
@@ -31,6 +31,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     if isinstance(bottom_type, str):
       bottom_type = TroughBottomType(bottom_type)
@@ -47,6 +48,7 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.through_base_to_container_base = through_base_to_container_base
     self.bottom_type = bottom_type
diff --git a/pylabrobot/resources/tube.py b/pylabrobot/resources/tube.py
index b684f5bb854..85e48d740a7 100644
--- a/pylabrobot/resources/tube.py
+++ b/pylabrobot/resources/tube.py
@@ -38,6 +38,7 @@ def __init__(
     compute_volume_from_height: Optional[Callable[[float], float]] = None,
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     """Create a new tube.
 
@@ -67,6 +68,7 @@ def __init__(
       compute_volume_from_height=compute_volume_from_height,
       compute_height_from_volume=compute_height_from_volume,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.tracker.register_callback(self._state_updated)
 
diff --git a/pylabrobot/resources/well.py b/pylabrobot/resources/well.py
index 3cb6f5a4003..47983ca31c9 100644
--- a/pylabrobot/resources/well.py
+++ b/pylabrobot/resources/well.py
@@ -50,6 +50,7 @@ def __init__(
     compute_height_from_volume: Optional[Callable[[float], float]] = None,
     cross_section_type: Union[CrossSectionType, str] = CrossSectionType.CIRCLE,
     height_volume_data: Optional[Dict[float, float]] = None,
+    no_go_zones=None,
   ):
     """Create a new well.
 
@@ -99,6 +100,7 @@ def __init__(
       compute_height_from_volume=compute_height_from_volume,
       material_z_thickness=material_z_thickness,
       height_volume_data=height_volume_data,
+      no_go_zones=no_go_zones,
     )
     self.bottom_type = bottom_type
     self.cross_section_type = cross_section_type
diff --git a/pylabrobot/resources/well_tests.py b/pylabrobot/resources/well_tests.py
index f322d88521b..284c7cca82a 100644
--- a/pylabrobot/resources/well_tests.py
+++ b/pylabrobot/resources/well_tests.py
@@ -39,6 +39,7 @@ def test_serialize(self):
         "compute_volume_from_height": None,
         "compute_height_from_volume": None,
         "height_volume_data": None,
+        "no_go_zones": [],
       },
     )