diff --git a/doc/examples/derivative.ipynb b/doc/examples/derivative.ipynb
index 13e0edb..f0ee5de 100644
--- a/doc/examples/derivative.ipynb
+++ b/doc/examples/derivative.ipynb
@@ -29,10 +29,10 @@
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    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:28.471197Z",
-     "shell.execute_reply": "2026-09-09T05:33:28.470283Z"
+     "iopub.execute_input": "2026-09-09T06:36:28.225510Z",
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+     "shell.execute_reply": "2026-09-09T06:36:28.653464Z"
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@@ -88,10 +88,10 @@
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-     "iopub.status.idle": "2026-09-09T05:33:28.477982Z",
-     "shell.execute_reply": "2026-09-09T05:33:28.477278Z"
+     "iopub.execute_input": "2026-09-09T06:36:28.655469Z",
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+     "iopub.status.idle": "2026-09-09T06:36:28.659165Z",
+     "shell.execute_reply": "2026-09-09T06:36:28.658795Z"
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    "outputs": [
@@ -166,10 +166,10 @@
    "id": "18be37c7",
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-     "iopub.status.idle": "2026-09-09T05:33:28.481799Z",
-     "shell.execute_reply": "2026-09-09T05:33:28.481412Z"
+     "iopub.execute_input": "2026-09-09T06:36:28.660314Z",
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+     "shell.execute_reply": "2026-09-09T06:36:28.662541Z"
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@@ -217,10 +217,10 @@
    "id": "74d96b49",
    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:28.485963Z",
-     "shell.execute_reply": "2026-09-09T05:33:28.485607Z"
+     "iopub.execute_input": "2026-09-09T06:36:28.664088Z",
+     "iopub.status.busy": "2026-09-09T06:36:28.663998Z",
+     "iopub.status.idle": "2026-09-09T06:36:28.667271Z",
+     "shell.execute_reply": "2026-09-09T06:36:28.666540Z"
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    "outputs": [
@@ -290,10 +290,10 @@
    "id": "7267dc15",
    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.175417Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.174984Z"
+     "iopub.execute_input": "2026-09-09T06:36:28.668388Z",
+     "iopub.status.busy": "2026-09-09T06:36:28.668299Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.245428Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.244887Z"
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@@ -355,10 +355,10 @@
    "id": "d1d3c440",
    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.361823Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.361248Z"
+     "iopub.execute_input": "2026-09-09T06:36:29.247464Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.247367Z",
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+     "shell.execute_reply": "2026-09-09T06:36:29.417825Z"
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@@ -431,10 +431,10 @@
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    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.367834Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.367409Z"
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+     "shell.execute_reply": "2026-09-09T06:36:29.424008Z"
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@@ -527,10 +527,10 @@
    "id": "24d24f2b",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.501473Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.500927Z"
+     "iopub.execute_input": "2026-09-09T06:36:29.425628Z",
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+     "iopub.status.idle": "2026-09-09T06:36:29.547249Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.546441Z"
     }
    },
    "outputs": [
@@ -588,8 +588,8 @@
     "with no single adjacent pair of rungs disagreeing badly enough on its\n",
     "own to make this obvious.\n",
     "\n",
-    "Confirmed via 13 distinct real AMICI/SBML models -- captured directly\n",
-    "from case 00026 of the SBML semantic test suite (`dS1/dt = -k1*S1`, an\n",
+    "Confirmed via 13 distinct real [AMICI](https://github.com/AMICI-dev/AMICI)/SBML models -- captured directly\n",
+    "from case [00026](https://github.com/sbmlteam/sbml-test-suite/tree/release/cases/semantic/00026) of the [SBML test suite](https://github.com/sbmlteam/sbml-test-suite) (`dS1/dt = -k1*S1`, an\n",
     "event `S1 < 0.1 -> S1 := 1`; the direction below is `d(S2)/d(k1)` at a\n",
     "timepoint just before the first event fires):"
    ]
@@ -600,10 +600,10 @@
    "id": "648beb74",
    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.507197Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.506792Z"
+     "iopub.execute_input": "2026-09-09T06:36:29.549426Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.549267Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.552354Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.551988Z"
     }
    },
    "outputs": [
@@ -667,7 +667,15 @@
     "(`fiddy.discontinuity.check_cross_regime_disagreement`) -- not because\n",
     "it always finds the *right* answer, but because disagreement between\n",
     "the two, more than either side's own uncertainty can explain, is itself\n",
-    "the signal a hidden discontinuity is nearby."
+    "the signal a hidden discontinuity is nearby.\n",
+    "\n",
+    "\"Far\" describes reach in *step-size* space, not distance from the\n",
+    "point: it means the ladder reaches much farther down toward `h=0`\n",
+    "than the main ladder ever does, not that its evaluation points sit\n",
+    "farther from `k1_nom`. In fact the opposite is true in absolute\n",
+    "terms -- because it's anchored so close to machine epsilon, the far\n",
+    "ladder's points land *closer* to `k1_nom` than any main-ladder point\n",
+    "(see A.8 below for exactly how much closer, on a real case).\n"
    ]
   },
   {
@@ -676,10 +684,10 @@
    "id": "db7c445e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-09T05:33:29.508335Z",
-     "iopub.status.busy": "2026-09-09T05:33:29.508253Z",
-     "iopub.status.idle": "2026-09-09T05:33:29.511174Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.510763Z"
+     "iopub.execute_input": "2026-09-09T06:36:29.553323Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.553231Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.555909Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.555486Z"
     }
    },
    "outputs": [
@@ -733,7 +741,7 @@
    "id": "556e2bb4",
    "metadata": {},
    "source": [
-    "The far ladder lands on AMICI's own analytically-computed value\n",
+    "The far ladder lands on [AMICI](https://github.com/AMICI-dev/AMICI)'s own analytically-computed value\n",
     "(`0.230595`) and stays there, while the disagreement between the two\n",
     "(`0.971`) is far larger than either side's own uncertainty could\n",
     "explain -- so `estimate_directional_derivative` reports\n",
@@ -745,8 +753,15 @@
     "this extends the same \"disagreement between independent estimates is\n",
     "itself the signal\" idea the two-corroborating-chains mechanism above\n",
     "already uses (see Solution 1/2), just applied across two disjoint\n",
-    "dynamic-range regimes instead of within one ladder.\n",
-    "\n",
+    "dynamic-range regimes instead of within one ladder. See A.8 for a\n",
+    "live, closed-form version of this exact case.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1322ef18",
+   "metadata": {},
+   "source": [
     "### Solution 4: near-zero-gradient directions are flagged, not guessed\n",
     "\n",
     "Revisiting Problem 3: fiddy's relative-error check compares its error\n",
@@ -761,10 +776,10 @@
    "id": "4af56935",
    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.515953Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.515540Z"
+     "iopub.execute_input": "2026-09-09T06:36:29.557807Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.557553Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.564165Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.563757Z"
     }
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    "outputs": [
@@ -809,10 +824,10 @@
    "id": "67141599",
    "metadata": {
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-     "iopub.status.idle": "2026-09-09T05:33:29.522646Z",
-     "shell.execute_reply": "2026-09-09T05:33:29.522302Z"
+     "iopub.execute_input": "2026-09-09T06:36:29.565092Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.564996Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.571194Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.570799Z"
     }
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    "outputs": [
@@ -880,7 +895,8 @@
     "full citations.\n",
     "\n",
     "Throughout, $f$ is the blackbox function being differentiated, and $x$ the\n",
-    "point (or one component of it) the derivative is estimated at.\n"
+    "point (or one component of it) the derivative is estimated at. A.1-A.7\n",
+    "cover the formulas; A.8 works a full closed-form example end to end.\n"
    ]
   },
   {
@@ -1070,7 +1086,8 @@
     "optimal-step theory) found no existing published technique for this exact\n",
     "scenario -- this is fiddy's own extension of \"disagreement between\n",
     "independent estimates is the signal,\" applied across two disjoint\n",
-    "dynamic-range regimes instead of within one ladder.\n"
+    "dynamic-range regimes instead of within one ladder. See A.8 for a\n",
+    "worked closed-form example.\n"
    ]
   },
   {
@@ -1096,6 +1113,402 @@
     "tiny compared to the real achievable agreement with an independently-\n",
     "computed `expected`.\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d36b1702",
+   "metadata": {},
+   "source": [
+    "### A.8 The far ladder illustrated\n",
+    "\n",
+    "Solution 3 and A.6 above showed the far-ladder mechanism on a real,\n",
+    "[AMICI](https://github.com/AMICI-dev/AMICI)-captured case ([SBML test suite](https://github.com/sbmlteam/sbml-test-suite) case [00026](https://github.com/sbmlteam/sbml-test-suite/tree/release/cases/semantic/00026)) via hardcoded\n",
+    "arrays.\n",
+    "That same case has an exact closed form, so it's worth working through\n",
+    "directly -- both as independent validation of A.6's story, and because\n",
+    "having a live function (rather than 8+4 fixed points) lets us plot the\n",
+    "whole picture continuously.\n",
+    "\n",
+    "The model itself is a single mass-action reaction `S1 -> S2` with rate\n",
+    "`k1*S1` (so `dS1/dt = -k1*S1`, `dS2/dt = +k1*S1`), `S1(0)=1`, `S2(0)=0`,\n",
+    "and an event `S1 < 0.1 -> S1 := 1`.\n",
+    "\n",
+    "Before the event fires, `S1(t) = exp(-k1*t)` and (mass conservation)\n",
+    "`S2(t) = 1 - S1(t)`. The event fires at `t_event(k1) = ln(10)/k1`\n",
+    "(*decreasing* in `k1`: faster decay crosses the `0.1` threshold sooner).\n",
+    "After it fires, `S1` resets to `1` (`S2` is unaffected by the reset\n",
+    "itself) and decays again: `S1(t) = exp(-k1*(t - t_event(k1)))`,\n",
+    "`S2(t) = 1.9 - S1(t)` for `t >= t_event(k1)`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e2f316f",
+   "metadata": {},
+   "source": [
+    "We evaluate at `k1_nom = 1.0` (case 00026's own nominal value) and\n",
+    "choose `t*` so the crossing distance is `delta_rel = 0.1%` relative\n",
+    "(`k1_crit = k1_nom * 1.001`), matching the real captured example's\n",
+    "approximate scale above.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b20c1f3f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-09T06:36:29.572237Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.572144Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.575945Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.575351Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t* = 2.300285\n",
+      "true derivative at k1_nom: 0.230558  (real captured value above: 0.230595)\n",
+      "max relative difference vs. the real captured values -- main ladder: 3.1%, far ladder: 0.02%\n"
+     ]
+    }
+   ],
+   "source": [
+    "def t_event(k1):\n",
+    "    return np.log(10.0) / k1\n",
+    "\n",
+    "\n",
+    "def S2_closed_form(t, k1):\n",
+    "    # Closed-form S2(t; k1) for SBML case 00026 (S1 -> S2, rate k1*S1,\n",
+    "    # event S1 < 0.1 -> S1 := 1), vectorized over k1.\n",
+    "    te = t_event(k1)\n",
+    "    s2_pre = 1 - np.exp(-k1 * t)\n",
+    "    s2_post = 1.9 - np.exp(-k1 * (t - te))\n",
+    "    return np.where(t < te, s2_pre, s2_post)\n",
+    "\n",
+    "\n",
+    "k1_nom = 1.0\n",
+    "delta_rel = 0.001  # crossing distance, matching the real case's ~0.1%\n",
+    "k1_crit = k1_nom * (1 + delta_rel)\n",
+    "t_star = t_event(k1_crit)  # a timepoint just before k1_nom's own event fires\n",
+    "true_derivative = t_star * np.exp(-k1_nom * t_star)\n",
+    "\n",
+    "print(f\"t* = {t_star:.6f}\")\n",
+    "print(\n",
+    "    f\"true derivative at k1_nom: {true_derivative:.6f}  (real captured value above: 0.230595)\"\n",
+    ")\n",
+    "\n",
+    "# Sanity check: recompute the *same* ladders captured above, from this\n",
+    "# closed form instead of a real solver run.\n",
+    "main_central_values_cf = (\n",
+    "    S2_closed_form(t_star, k1_nom + main_ladder)\n",
+    "    - S2_closed_form(t_star, k1_nom - main_ladder)\n",
+    ") / (2 * main_ladder)\n",
+    "far_central_values_cf = (\n",
+    "    S2_closed_form(t_star, k1_nom + far_ladder)\n",
+    "    - S2_closed_form(t_star, k1_nom - far_ladder)\n",
+    ") / (2 * far_ladder)\n",
+    "print(\n",
+    "    \"max relative difference vs. the real captured values -- \"\n",
+    "    f\"main ladder: {np.max(np.abs(main_central_values_cf - main_central_values) / np.abs(main_central_values)):.1%}, \"\n",
+    "    f\"far ladder: {np.max(np.abs(far_central_values_cf - far_central_values) / np.abs(far_central_values)):.2%}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "461e1b63",
+   "metadata": {},
+   "source": [
+    "A few percent agreement on the main ladder (real solver tolerances vs.\n",
+    "an exact formula) and near-exact agreement on the far ladder confirms\n",
+    "this closed form really is the same mechanism. Having it in closed form\n",
+    "means we can now plot the whole picture directly, not just the 8+4\n",
+    "points captured above.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f58d16d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-09T06:36:29.577102Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.576914Z",
+     "iopub.status.idle": "2026-09-09T06:36:29.875212Z",
+     "shell.execute_reply": "2026-09-09T06:36:29.874459Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1NVV2796thIaGKr6+vsq+ffuUQYMG6fqo1WqlRYsWiq2treLi4qL4+fkpXl5eytq1a5VZs2al6fwkdW5T+95E2kihUKTalClTFEC5d++eoiiKMnfuXKVIkSKKovzvH238xcIff/yhAMr+/ft12ydV8Bk1apRiamqqvHr1KsGxRo4cmegiY+rUqYqhoaFy9+7dBH09PDwUQNm2bVuysa9atUoBlMuXL+vaNBqNUrJkSaVjx44pxpja4/7888+KkZGREhQUpCiKosycOVOpWrWq0rVr1wQXMSVKlFD69++fbKyK8r9EmZo/X3/9dYr7ii9WjRw5MkH7tGnTFFNTU2XUqFEJ2r///nvF0NBQCQkJSXG/iqIon3/+uVKlShXdzykVCt+9uN2/f78CKKdPn07xGKkpFCYXT1p+v7LidzH+vb97ESyEyB/ivxwDyjfffJNkn+7duyuWlpZKTExMgnZvb28FUCZMmKAoiqL89NNPCpDoc0ej0ShFixZNcAFUrVo1pUKFComOVaJECaVMmTKJ2kuXLq3LSxEREYqpqWmSn1sXL15UAOXIkSOKoihKgwYNlJo1aybqt3379jQVCt/9fD9+/HiC7xQHDhxQAGXHjh2J9jF9+nTFzMwswfm7cuWKMm3aNKVfv35Kjx49lM8++0wpWLCg0qdPH12f+ELh3r17U4wxnr+/v+4GaKdOnRRLS0tl8+bNqdpWCCEU5X8FN0AZOHBgkn02bdqkAMrOnTsTtLu6uiYooqW2X/w1Wu3atRP0i/9cbdSoke5miaIoyuHDhxN9P4//zv7uZ15oaKhiZ2enuxb5888/FSDBzShFUZSNGzcqQIYKhal9v8lJTaEw3rp16xRAd00Xfw4rVaqU4FwlJzWFwngLFixQjI2NdTfBFi9erADKxYsXE/RbtGhRouJfWvrG/90cPHgwQV9/f3/F0tJSmTlzpqIo/ysU1q9f/72xK4qidO3aNdHv1rviz+dff/2Vqn2+7d3zk9S5Te17E2kjjx6LVGvVqhXwv7kHz5w5Q/PmzQEoWbIk5cqVS/CaoaEhzZo1S3Gfp0+fpkGDBhQtWjRBe9euXRP1PXjwIDVq1KBKlSoJ2mvWrImdnR1///13ssfp06cPFhYWbNiwQdd2/PhxvLy8Esy5mJTUHrdNmzbExsbqzsHJkydp06YNbdq04fTp02i1Wu7evcvLly9p06ZNisfMCu3bt0/wc5UqVYiOjqZ169YJ2qtWrYpGo+H58+cJ2jds2EDDhg11j1OrVCrWrVvHo0ePUnX8Tz/9NMHP8ZMJP3nyJK1vJdXxpOX3Kyt/Fzt37pzGdyeEyAsmTJiAq6srLVu2ZMGCBUn2efbsGWXKlEk0L16xYsWwtLTk6dOnun4qlYry5csn6GdgYECFChV0/eK9vfBYPFtb22TbX79+DcDz58+Jjo7G3d0dZ2dnevfuTe/evenVqxc///wzgG46jadPnyaKB6BChQpJvtfklCtXLsHP8Y+fxT+SdP/+fSDucz8+pl69etGrVy+OHDlCVFQUvr6+QNziAB9++CEvX76kWbNm9OzZE2dnZ6ysrJKcpiSp+JMSPz/hsmXLcHV1xc3Njf79+6fpfQoh8reff/6ZvXv3Uq9ePVavXp1kn9OnT2NgYECXLl0StDdv3hxbW1tOnz6dpn7x2rVrl+Dn+O+wjRs3RqVS6dqrVq0KJPx+fvDgQQwNDenWrVuCfVhaWtKwYUPd997Tp09jampK27ZtE/R7d7v0SOv7TS1XV1fatWuHnZ0dhoaGCR53fvcap1OnTgnOVcmSJRM8LjtmzJgUj3XkyBFatWpF4cKFMTAwQKVSMXHiRNRqte666/Tp09jb29OwYcME2yZ37ZLavgcPHqRAgQKJrgdtbW2pVatWuq9dnJyccHd3Z9KkSbr5g9915MgRLCwskozr3X7vOz9JSet7E6kjhUKRao0bN8bExARXV1fUajX//vsvLVq00L3evHlz3RyGrq6u1KlTRzePT3LevHlDkSJFErUn1ebj44OHhwdGRkYYGRlhaGio+xDx9/dPcX4KKysrevbsybZt23TzHKxfv54iRYokmgQ+vceNn2foxIkThISEcOXKFV2h8M2bN/z333+cPHkSIFFx7l12dnYocSN+dX/mz5+PtbV1ovak5l5MSrFixRL8HD/5e3LtQUFBurZly5YxbNgwunXrxu3bt1Gr1SiKwjfffENsbGy6jh8/qfzbx0mt1MaTlt+vrPxdLFGiRJrfoxAid9u0aRNLliyhTJky7Ny5E0NDwyT7mZiYJDn/jlarJTo6GlNTU10/RVGIjo5O1Dc8PFzX7+39vit+3p+k2uO/3McXLKtXr06PHj3o2bMnPXv2pFevXgwZMoRdu3bpcpiJiQkRERGJ9pdUW0rejSn+QuzdmNq0aaOLKb5QOG3aNHbt2oWNjQ3h4eH88MMPDB8+nI0bN/LFF1/Qq1cvevTokeycTW8vhJKS+ELh6tWriYyMZN26dUn2O3fuHIcOHeLQoUPpvhEmhMh7Dhw4wA8//ICDgwN79+7FzMwsyX5v3ryhcOHCSX5WFy1aVDePeGr7xcvIdYCPjw8ajQZra+sE33sNDAw4dOiQ7nvvmzdvsLe3T1BMg7jv/O/mqLRK6/tNjUuXLtGmTRuKFi3Kv//+S0REBIqisH37diBusa63ZeT7/MmTJ+nYsSNVq1blypUrREVFoSgKq1atSnCsrLx2if+u8O61yz///JPua5epU6cyffp0duzYQe3atbG1taV3797cuHFD18fX15dixYol+r14W2rPT1LS+t5E6siqxyLVzM3NadiwIWfPnuXSpUuEh4cnKhS6uLjg4eGBl5dXqu60vz2K4W1JtdnZ2VGlShXOnz+frviHDRvGn3/+ye7du2nfvj0HDx7k66+/xsgo5X8GqT2uoaEhLVu25MSJE7Rr1w6VSkXz5s2xtLSkTJkynDhxgosXL1KtWjWKFy+erveQEcl9OKf0oR1v06ZNNGrUiClTpiRof3cES3qOnx6pjSctv19Z+buY3AqaQoi86erVq4wYMQILCwv27duX4kqLtWvXZu3atbx+/TrBl/tr166hVqupXbu2rh/A5cuXdSP8Ie5i7sGDB4lGWaRX2bJlcXBwIDw8nB49eqTYt3bt2ro43/6cu3r1aqbEEi9+tISVlVWKMcWPhqxRo0aC9v/++4/g4OAMxXD9+nVKlizJ559/rhtxUqRIESZOnJig344dO3j+/DlXrlxh8uTJiV4XQuQ/9+7dY8CAARgZGbFnzx5KliyZbN/ChQsTEBCQ6HMV4r6T1qtXL0394mXkOsDOzg5zc3NCQkJSvG6ytbXFz88PRVES7DckJCTJm1xpkdb3mxpbt27F0NCQtWvXJthnctc37x43LQtlbN68GRsbG5YuXZrg3CR17fLgwYNE2yd37ZLavnZ2dtjb2+tG379Paq9dTE1N+fnnn/n555958uQJrq6uzJkzhyZNmvDgwQOKFCmCvb09//33X6Lfi7el9vwkJa3vTaSOjCgUadKqVSv8/PxYsmQJxYsXp1KlSrrXWrRoQUxMDLNmzQKgZcuW791fy5YtuXr1aqIPtP379yfq26lTJ65evZqm4tTbmjRpQuXKlVm/fj2bN28mJiaGoUOHvne7tBw3fmXIVatW0ahRIywtLXXtR44c4ezZs3p57DgzvHsn8PXr15w4cUJP0aQunrT8fmXn76IQIu96/fo13bt3Jyoqig0bNuDk5JRi/9GjR2NgYMC4ceN0d8xDQ0OZOHEihQsXZvDgwQA4OztTtGhRpk6dqltBUqPRMHHiRKKiovj6668zJX4DAwN++OEHDhw4wJo1axKsphwVFcXq1at1q0F+9dVXvHr1KsHqzE+ePEnyczMjPvzwQzp06MB3333H9evXE7z28uVL3bQijo6O2NracujQId3qxUFBQUyfPj3JERZp8d9//1G3bl0g7sbjrFmzmDx5Mps2bUrQb/ny5Rw6dIg6depk6HhCiLwhODiYLl26EBISwuLFi2ncuHGK/Vu1aoVWq+XAgQMJ2s+dO4e/v7/uRlFq+2WGTp06ERkZmehY72rZsiXR0dGJvo/v27cvVccpUKAAQJJFxax6v/Ej0OIpioKLi0u69vU+JiYmCYpgarVaN3oxXsuWLfHz8+PKlSsJ2pO7dklt306dOuHn56ebIisrlCtXjmHDhjF37lxCQkJ0q2d37NiRiIiI9/4epOb8JCU73lt+JIVCkSbxxb89e/YkGE0I/5uncM+ePZiYmLw3EQJMmTIFMzMzevXqxd27dwkMDGTlypVJDh+fNm0a5cuX59NPP+Xo0aMEBgby5s0b/vnnH4YOHcrBgwffe7yhQ4dy7tw5Fi5cyMcff5xojrmkpOW48UXAY8eO8cknn+jaP/nkE/7991/CwsJyZaGwc+fOnDt3DhcXF8LDw7l+/To9evR47yPU+o4nLb9f2f27KITIm3bv3o2XlxeFChVi165d9OjRI8k/8+bNA+Ie8d25cyenT5+mfPnytG3blgoVKuDl5cWRI0d08/VZWVlx5MgRQkJCqFixIp988gkVKlRg3759uLi40KBBg0x7D6NGjWLZsmVMnTqV8uXL06FDBxo2bEjZsmXx8PDQ3aj55JNP+O233/jll1+oVq0arVq1olevXkyfPj3TYom3Y8cOOnbsSMOGDalbty6ffvopNWrUoFmzZrpHlA0MDPjjjz+4ePEiVapUoW3bttSuXZsvv/wSOzu7dB87LCyMR48e6QqFANOnT+fLL79k2LBhHD58OMPvTwiRNw0ZMoQHDx7w+eefM2rUqPf2d3Z2pl69eowZM4bjx48TGhrKuXPnGDx4MJUrV2bkyJFp6pcZunXrRvfu3fniiy/YsGEDr169IiwsDHd3d7777jvdzSJnZ2ecnJwYMWIEf//9N6GhoRw9epTDhw+/dzoqiHt6rVy5cpw9ezbR1ERZ8X47d+5MREQE3377LUFBQTx58oT+/fvr5mnMTJ07d8bHx4eff/6Z0NBQHjx4QM+ePRONhBw2bBgVKlRg4MCBXLp0iZCQEP76669EN8nS2nfgwIG0adOGfv36sWXLFnx9fQkJCeHatWtMnDiRJUuWpOt99e3blxUrVvDgwQOio6N5+vQpmzdvxtraWvckxIABA2jevDlffPEFW7Zswd/fH29vb9atW8fs2bPTdH6SklXvLd/L7tVTRO4WExOjWFpaKoCydu3aRK8PGzZMAZSmTZsmei251Wtv3LihtG7dWjE3N1fs7e2VyZMnK7du3UpyxcTg4GBl6tSpSuXKlRUTExPF3t5eadasmbJx40YlOjr6vfH7+PgoxsbGCqBs2LAh1TGm5bhly5ZNtMJyQECAYmBgoBgbGythYWHvjTMp8+fPV6ytrdO8XfzKu+8ua793794kV8o6ePCgAijnz5/XtcXExChTp05VSpYsqZibmysffvihcvbsWd1K2PFSWvX43eOntLLZ25LaZ2rjUZS0/X5l9u9icu9dCJF3PXr0SNm1a9d7/1y4cCHBdlFRUco///yj7Nu3T7ly5YoSGxub5P41Go1y7do1Zf/+/crff/+tREREJOpz4sQJ5Z9//knUfvLkyQSf7fFOnz6tnDt3LlF7VFSUcvHiRWX//v3KlStXlNDQ0CRjev36tXL48GHl77//VtRqtRISEqLs2rVLefbsWZL94z18+FDZtWuXEhUVlaA9KChI2bVrl+Lp6Zlomzdv3iinTp1SDh06pNy+fTvJ8xQYGKicPn1aOXnypBISEqIoSuJz8vjxY2XXrl1KeHh4ijEqStzn/a5du5Tnz58naNdoNMq+fft0q0C/rW3btu/Nb0KIvK9EiRIK8N4/b38fDw4OVr7++mvF0dFRMTIyUooVK6YMHz5c8fX1TbDv1PSLXyV26dKlCbZ99eqVAii///57gvbkvp9rNBpl2bJlSr169RQLCwvFyspKqVu3rjJnzhwlICBA18/X11fp37+/Ym1trRQsWFDp2bOn8ubNG8Xa2vq9qx4rSlw+qlmzpmJiYqIAyrBhw9J8XpKS3KrHGzZsUCpXrqyYmZkplStXVtavX5/oGim5c5ic5FY9Xrp0qVK+fHnFzMxMqVatmrJt2zbljz/+UADl7t27un5eXl5Kz549lYIFCyrW1tbKwIEDlZCQkEQrGae1b0xMjLJgwQKlVq1aipmZmVKoUCGlQYMGyu+//67LlfGrHr+7wnVyHj58qIwdO1Z3DkuUKKE4OzsrN2/eTNAvKipK+fHHH3XXTY6Ojsrw4cOVV69epen8JHduU/PeRNqoFOWtZ0qEEEIIIYQQGdKuXTtat24tcxQKIYQQIteRR4+FEEIIIYQQQgghhBBSKBRCCCGEEEIIIYQQQkDy65sLIYQQQgghUu369eu8fPkSPz8/7t69y6FDh3BycsLR0VHfoQkhhBBCpIrMUSiEEEIIIUQmmDdvHufOnUvQNnbsWD755BM9RSSEEEIIkTZSKBRCCCGEEEIIIYQQQsgchUIIIYQQQgghhBBCiHw+R6FWq8Xb25uCBQuiUqn0HY4QQoj/pygKoaGhFC9eHAMDuacVEBBARESE5CshhMhhJF8lJNdXQgiRM6UlX+XrQqG3t7dMLi2EEDmYp6cnJUuW1HcYeqPRaNixYwcXL15k9erVqNVqfYckhBAiCfk9XymKQnBwMGFhYXJ9JYQQOVhq8lW+LhQWLFgQiDtRVlZWeo5GiIyJvHuXF/0HUMplM+ZVq+o7HCEyJCQkBEdHR93ndH7k4+MDQFhYGOPGjWPZsmWSr4Teubi4ANC/f/9070PylchLJF9BTEwMmzZtwtfXly+//BKQ6yuRN2RGzhMip0hLvsrXhcL44fBWVlaSyESuZ2xpiaWhIVaWlpjL77PII/LjY0tqtZr9+/fz999/M3nyZIYPH05ISAgg+UroX1RUFECGfg8lX4m8KD/mK4DAwEBWrFiBsbExY8aM0bVLvhJ5QWbkPCFymtTkq3xdKBRCCCFykmfPnrFx40ZMTU2ZNm0axYoV03dIQiQwfvx4fYcghMghAgICmDNnDjVq1KBv374YGRnpbmwJkRdIzhP5lRQKk6DRaGQeqGxmamqab+/EZhYjGxts+vbByMZG36EIIdIhJiaG33//nbZt29K2bVsMDQ3fu43kK/2QnJUxkq+EyP1u3ryJjY0Nffr0oXbt2u/9TJR8pR+Sr4QQ6SGFwneEhYXh5eWFoij6DiVfsbKyokSJEvoOI1czLl6cojNm6DsMIUQavXz5ks2bN9O6dWt+++03jIxSl5olX+lPfs5ZMTExAJiYmKR7H5KvhMi9NBoNe/bs4eLFi0yaNIk6deq8dxvJV/qTn/NVZsiMnCdEbiSFwrdoNBq8vLywsLDA3t5e7r5kk5CQEN68eUORIkVSfYEsEtNGRhL95Amm5cphYG6u73CEEO+h1Wo5ceIEhw4dolWrVjg5OaX6M1Dylf7k95y1ZMkSACZOnJjufUi+EiJ3ioiIYO3atQQGBjJ16lQcHBzeu43kK/3J7/kqM2RGzhMiN5JPjLeo1WoURcHe3h5z+eKabRRF4c2bN8TGxkoSy4DoJ0949lkPyuzZjXm1avoORwiRAkVRWLx4MYGBgYwfP55y5cqlaXvJV/qT33NWZoxMkXwlRO60du1aDA0NmTJlSqpzj+Qr/cnv+SozyGhMkV/JJ0YSctudrkuXLhEdHU2zZs30HUq65LbzLYQQ6aXVavnnn3+wsrKiTZs2VKpUKUOPs+S2z8/cnq8g953zzNanTx99hyCEyGa3bt3i6dOnDB48mIIFC2JgYJDmfej7s3PXrl04OTlRqVKlVG+jKAorV65k9OjRWRbXsmXLGDhwYLpW1Z09ezbTp0/n/v37eHp60rp16wSv6/uc5wWS80R+lfZPeZHjlC1bFicnJ3bt2sWjR4/0HY4QQogkvHnzhkWLFnHs2DFsbGyoXr16vpvzRvKVEELkHoqicPLkSdasWUPx4sWxtrZOV5EwJ1i8eHGa50jcunUrwcHBWRRRnIIFC6a7oLd06VIgLrd+9913REdHZ2ZoQoh8TEYU5kDnz5+nTJkyODo6JmhftmwZhw8fpkmTJtSsWRMvLy8uX76Mk5MTXbp0Yfz48ZQqVYrFixdTr149PUUvhMjvXgZFsv78U1pUsadJRXt9h5MjXL16FRcXF+rVq8fo0aMxMzPTd0iZQvJV/vP8+XMASpcuredIhMi4sOhYJux0Z3K7KpS3t9R3ODmKWq3GxcWFe/fuMX78eMqUKaPvkNJNURQePHhAhQoV0rTdsmXL2LNnTxZFFWfQoEEZ3oeJiQnNmjVj7969ODs7Z0JUIp7kPJFTBEXE8NPBOwxtXJbqJayz/Hi585ZQHjdz5kyCgoIStG3evJnr168zfvx4evXqxcmTJ7l+/TqjRo3C3d0dBwcHqlWrxuzZs9M0pF7kHSoDAwwKFECVS+/0itzvvk8o43e402yeKxv+fcqyMzJiLDg4mJcvX/Lq1Su++OILBgwYkGeKhCD5Kj/atWsXu3btytA+JF+JnECrVRi3w53jt18zYvM1tFpZkfdtt27dwtvbmxEjRuTqIiHA06dPKVasGIaGhsTGxrJ06VJu3LiR4jZv3rwhMDCQ4sWLA3D58mVOnTrF3r17mTNnDg8ePND1vXfvHvPmzWPx4sX4+vrq2v766y+OHj3KrFmzePDgAb6+vsybN4/169ej1WqBuGJkSEgIgYGBrFixgv/++485c+Zw8uRJ3f5//fVXpk6dyoIFC3SFq3e1aNGCQ4cOZeg8icQyI+cJkVFn7/vSdtE5/rr+kkm7b2RLvpIRhTnQnTt3qFy5coK2o0eP8ueff2JsbAyAp6cnW7duxczMjFWrVmFkZESZMmVo3ry5HiIWOYFZ1apUvuam7zBEPnT1WQArzz7mzD1fXdtH5W0Z0aw8iqLk2zlyrl69yrZt22jevDmdO3fWdzhZQvJV/pMZI0AlX4mcYNGpB5y88xoTIwMW9HTCwCB/5qp3PXv2jLVr1/LixQtcXFzw8/Nj0aJF+g4rQzw8PKhevTp3795l/PjxDB8+nJo1axIUFISBgUGS8wM+fvyYkiVL6n6+fv068+fPp127dhgZGdGyZUuePHnC3bt3ad26NYMGDSI4OJi6dety48YNHj16xOjRo+nUqRMFChSgTZs2VK9enbp167J7926Cg4MZP348GzdupGvXrsTGxjJz5kxatGhBlSpVGDhwIPv376dBgwZYWVmhKAo+Pj60atUKNzc3ChUqlCDeMmXK8PDhw6w+lfmOPPUg9Ck8OpbZR+6y9fILAMrZF2BO9xrZkq+kUJjDeHl5YWNjo5u36ujRo5QqVYqyZctSq1Yt7O3tOXToEBYWFpiZmfH06VNKly5NdHQ0hw8fZubMmfz44496fhdCiLxOURQuPn7DkjMPufQkAACVCtpXL8qIpuVxciyk3wD1KCwsjK1bt3L//n369etH3bp19R1SlpB8lT9JgVfkBUduvmLJ/496n9OtBrXycc5625UrV3BxcaFo0aI8fvyY6tWrZ8lxFixYgIODAwMHDgTA29ubrVu34uTkRJs2bQC4du0arq6utGvXThfH4cOHuXv3LkOGDMHW1haAdevW8fnnn6d4PA8PD27dusWUKVPYuHEjRYoUQaPRUKZMGSwtLfHy8kq0jUqlSjSnYfv27Vm2bJlun8+fP2fr1q18++23TJgwAYD+/ftz/PhxLC0tadSoEWvXrgXiFvOaNm0aH3/8MS1btmTlypWJjlmoUCF27twJgIWFBZcvX6ZBgwY0atSI48ePY2xsjJWVFZcuXaJdu3YJttVqtfn2xmxWkpwn9OXK0wAm7HLHMyASgCEfl2Fy2yqYmxhmy/GlUJjDeHh4UK1aNfz9/fn666+pVasWbdu2Zfbs2fTo0YPg4GDMzc354YcfAChcuDAjRozAysqKs2fPEhsbq983IPQm+tEjvL75hpKLFmGaxjlYhEgtRVE4+8CPZWcece15IADGhip61C3J8KblKWtXQM8R6peiKOzduxeNRsPMmTPTtYphbiH5SqSX5CuhT3dfhTBhpwcAnzcuy2d1S75ni/zh4MGDnD59mg4dOjB06FD++ecfunfvru+wMoWHhwefffYZR48e1Y12NzQ0JCgoiK5duya5TcWKFfH09EzQZmdnp/t/MzMzoqOjMTQ0RK1W69pjYmIwNIy7kI8vZsb3j/85ftt3vds/OjqaR48e0aVLF/r27YuVlRXGxsaJpvyAuMerq1Sp8p4zIYTI6aLUGhaefMDa809QFChRyJz5PWvyUXm792+ciaRQ+B5qX19i/fwStBlaW2NSsiTa6Giik1i10bxaNQCinzxFGxmR4DWTEiUwfGeo+Ns8PDzw8/Ojb9++LFiwgJo1a+peq127tu7/4yfjtba2xto6bjLL8uXLp+3NiTxFGx1NzKPHaGXFM5EZ1JEQ9hosi4CxedzKh3des8z1ETe84lYANDEyoE99R0Y0K0/xQuZ6Dli/IiMj2bFjB4GBgYwbN04vMUi+Etnh0qVLADRs2DDd+5B8JfQlIDyGLza5EanW0KSiHd+2l8JKVFQUERERxMbGMn78eLp06cKcOXMoV65clh1z4sSJCX4uXrx4ora6desmGpH/6aef8umnnyZoe99oQoCbN2/y559/0qhRI7p06cLhw4ffeyOvUKFCFC1alBcvXlCqVKlk+w0YMICmTZvy+PFjgoKCuHbtGuvWrePcuXPvjSs1tFotsbGxhIWF4ePjQ0BAQJL9zpw5Q6dOnTLlmOJ/MiPnCZFat14GM36nOw9ehwHQq15Jvu/4AQXNjLM9FikUvkfQjp34L1+eoM2qUydKzJ9HrI8Pzz7rkWibqvfuAvBq6lQiPTwSvFZ83q9YpzBXlYeHBxUrVuTly5e6eZ8UReH06dOYmprSpEmTjL4lIYRInlYDrr/ApRWgjkAxNudJhUGMfdWe2z7hAJgbG9Lvw1IMb1oOB6u8szBHet29e5c///yTYsWKMXjwYL3FIflKZId//vkHkIsmkfuoNVpGb7mGV2AkpW0tWNqnNkaG+XtBHT8/P5YvX07VqlXp3bs3X331FRUrVkzTSrzR0dEJRseFhIRkRajppigK48ePx8rKijZt2mBsbIybmxstW7YkJCQEtVpNUFBQojn/AMaOHcv69ev58ccfadiwIWFhYbrXhg4dStGiRbGzs+PatWscPXoUU1NT1q1bh5WVFVWrVtWNLAQYPnw4Dg4OAJQqVUr32PVXX32FtbU1Wq2WL7/8Ute/cePGaLVaKlWqxL59+7h06RJOTk4MHDiQokWLAvDdd98BccXeixcvMnfu3Ew/f/md5DyRHdQaLStcH7P0zENitQp2lqbM7V6D1h8U0VtMKuXdyRf0IH4kxqpVq7h37x6HDh2icePG793O09OTmTNncubMGSwsLBg+fDhjx47FIJWr6IWEhGBtbU1wcDBWVlZERUXx9OlTypYtq1uVMrtHaFStWpXz589z8OBB9u/fz65duzA0NOSTTz7B09OT+/fvp+q95SZJnXeRdpG3b/Pssx6U2bNb9zsoRJqd/hnO/wb8LzUoCizTdGW1YV8GNirNsMZlsbU0zdIw3v18zqnOnz/Prl276NGjB02aNMmy+YEkX+Uc+T1nxf+9vruITVpIvhL6MHP/Lf68+JwCJobs+/JjKhYpmCn7zS356l13795l7dq1NGrUiO7du+Pq6krPnj05ceIENjY2AHTu3JkPP/yQ6dOn60aHv+uHH35Icr7ZlPJVTlG5cmVev34NwOvXrzE1TfzdZtOmTbqiXk716NEjfH19+eijjxK05+Rzn1tkRs4TIiWPfEMZv9ND98RWhxpFmdW1BoULmGT6sdKSr3LEiMIffvgBHx8fJk2aRI8ePVI1b5G3tzcffvghjRs35ujRo1hYWLBs2TL+/fffTB3FYOzggPH/3/15l4GpaYpfcE3LlU3TsRRFoVOnTtjZ2TFkyBAMDQ05fvw4HTt25NSpU8nOoSGEEJlCHRk3kpCE949UKhhhcpwBXy+h0P8/OprfPXr0iIsXL9K+fXt++OEHChcurO+QJF+JbCEXSyI32n7lBX9efA7AIufamVYkzK1u3rzJmjVrcHZ25uOPPwbAx8cHW1tb+vTpo+vn6emJt7c358+f5/79+wlGyMWbOnUq48eP1/0cEhKCo6Nj1r+JTJCaG1o5vUgIcVN8JFfIFRkjOU9kFa1W4Y8Lz5h37B7RsVqszIz4uWt1OjsVzxELE+WIQuHcuXNRqVRJrjiVnOnTp2NlZcXWrVsxMop7G/Pnz8+qELOFSqVi3rx5up9zQ2ISOYeJoyMlVyzHJJd8ORM5z71Hj6iijkjyNRNtJCbaQCB/FwrVajUHDhzg7NmzdO/ePcHE5vmJ5CuREZKvRHZyexbA9/tvATChTSXa6PFRLn2LjY3l/PnzVKpUicmTJyco6PXv35/+/fsn6F+rVi2aN2/OokWLkt2nqalpkiPxhBBCJM0zIIKJuzy4/DRuztGmleyZ91lNilrnnJG/OaJQmNaKqVarZe/evYwfP15XJMzrzp8/j5+fH6dOnaJly5apfrxa5B+GVlYUbNlS32GIXOipfzjzjt3jzK0XXDc1wUIVk7iTsUXcwib5mJ+fHytWrMDY2Jhp06ZRrFgxfYeUI0m+ytv27dsHkKFRo5KvRHbxDopkpMt/qDUKHWoUZUzL/DvqKiQkhNWrVxMTE0Pjxo11q/8KIZKXGTlPiHiKorDTzZOfDt4hPEaDhYkh0z+tSt8GpXLEKMK35coqm5+fH8HBwdjY2NCxY0euXbtG8eLFGTRoEGPGjEn2oiSnT7abkqVLl2Jubs7cuXNp1qyZXHiJRGL9/Aj6ay+FunfDyN5e3+GIXCAgPIYlpx/icuk5sVoFlcqEf+x60+aNC6oEjx+roOFoMM6fKxtrNBpu3LhBwYIF+eijj2jZsmWSj1+JOJKv8rZHScx1mVaSr0R2iFJrGLH5Gv5h0VQpWpAFPZ1y3IVYdvH09GTFihWUK1eOQYMGpbpIWKpUKezl36jIxzIj5wkB4BsSxbd/3eTMPV8A6pexYUFPJ0rbFtBzZEnLlYVCjUYDxD1+vG7dOtasWcPly5cZMGAAUVFRTJ48Ocnt5syZk+Rku7nBzp079R2CyOHUvr74/f47BRp/LBdeIkVRag0bLzxjuesjQqPi5oRtXtmeb9tXoYpDO3Atolv1GGOLuCJhi2l6jlo/Xr58ycaNG9FqtXz77bcyB1AqSL7K27744osM70PylchqiqLw7Z4b3HwZjI2FMWsH1sPCJFde9mSYRqPh999/p3Xr1rRv3z5NxdIDBw5kYWRC5HyZkfOEOHTDm+/23SIoQo2JoQET21ZiWONyGBrk3JtXuTJj2traYmhoiLOzM7169QKgW7duDB06FBcXl2QLhbl5sl0hhMgorVbh4A1v5h27z8ugSAA+KGbFtA5VaVzxrbn2Wn0PTSdC2Ou4x43z4UhCrVbLyZMnOXjwIK1ataJjx47ymJYQgLUsaCRygTXnnrDP3RtDAxUr+tXFsbCFvkPKdlqtlmPHjmFlZcXs2bMxN89/uVyIjJKcJzIiKCKG7/ff5qCHNwDViluxsFctKhfN+Qtq5crngUxNTalTp06iiXNNTU1TXDHZ1NQUKyurBH+EECI/cHsWQNcV//L1dndeBkVS1MqM33o6ceirxgmLhPGMzcGmTL4sEsbExPD8+XOuXLnCuHHj6NatmxQJhRAilzh735e5x+4BMLPTBzQqb6vniLJfVFQUa9as4d9//6V8+fJSJATWrFlDp06duHjxYrq237lzp26+utQaOnQoarU61e1CiLzD9b4vn/x+joMecTetxraswN7RH+eKIiHkohGFY8eOxc3NjQsXLgAwYcIERo0axcCBA6lfvz7Xr1/nzz//ZOTIkXqOVAghcg7fkCjmHL3H3usvAShgYsjoFhUY+nFZzE1knr23abVa/v77b/bu3cv333/P999/r++QhMhxVq5cCcCoUaP0HIkQiT3xC+OrbddRFOjTwJEBDUvrO6Rs9+bNG1asWEGBAgWYOnUqlpaW+g5J7548ecLChQtZsGAB5cuXT/c+zMzStiLpuXPn0Gg0iW42Jtcuch7JeSKtwqJjmX34DtuueAJQ3r4AC3vVwsmxkH4DS6McUSjcunUro0ePRlHiJs/v2LEjRkZGfPvtt3z77bcAREREJFh8pHfv3vj6+tKxY0eCgoKwtrbm888/Z8aMGXp5D0Lom6GVFQXbtsVQRsoKICZWyx//PmXJ6YeEx2hQqaB3PUcmfFIZ+4Km799BPhMQEMCff/6Jn58fo0ePlsnbhchCkq9EVgiJUvP5JjdCo2KpV9qGHztXz3eLl2i1Wq5fv07lypX57LPPcvXCW1GxUfhH+mNnboeZUdoKdO+6f/8+TZo0oWPHjpkUnRBCJHb5yRsm7vbAMyBuiqehH5dlcrvKmBnnvs/iHFEo7NGjBx06dEjU/vZdm6VLlyZ6rPirr77iq6++IioqKs13ePKq+FWd330sO7P6A4SGhlKwYOIhs8m1i+xh4uhIycWL9B2GyAHO3vflp4N3eOIfDkAtx0L82LlarruTlV1CQkL48ccfqVevHiNHjpRHtNJgyZIlNG/enJo1a6Z6m5iYGGbNmsVPP/2UZXFNmTKFKVOmULhw4TRv+8UXX7B27Vpu377Nf//9x4ABA7IgwtwrM0ZVSL4SmU2jVfhmuztP/MIpZm3Gyv51MTHKlTMspdu5c+c4ePAgc+fOzdUFQo1Ww3L35Wy+s5koTRRmhmYM+GAAX9b6EkODtL+v58+fM336dPz9/WnXrh179uzh77//ZsmSJZiYmFCnTh0mT56MhYUFLi4uGBkZ8d9//1GgQAFmzpyZ5D6PHDmS5PYxMTHMmzePW7du6ebRB5Jt12q1rF+/nlOnTlGoUCEmTJhApUqVUh2HyHoyklCkRpRaw4Lj91n/71MUBUoUMmdBT6dcPfVFjsigJiYmFCpUKNGft4t/5ubmyRahpEj4PytXrmTDhg2p7v/777/rhlSnVu3atYmKikp1u8geSkwMah8flJgYfYci9MQzIILP/3Rj8B9XeeIfjp2lCfN71OSvUR9JkTAJwcHBbNiwAT8/P6ZMmcKAAQOkSJhGmzdvTvMNotWrV1O0aNEsiihO27Zt0/13efDgQQA++OADVqxYQWhoaGaGJpB8JTLfbyfuc+aeL6ZGBqwZUC9fjZzXaDRs27aN/fv388UXX+TqIiHAcvflrL25lihN3DVFlCaKtTfXstx9ebr2Z2trS6dOnahRowbffPMNpqamuv8fPnw4r1+/1hXiHj16xIwZM6hWrRqdOnVKdp/Jbf/9999z/vx5evXqxenTp/Hy8kqxfc6cORw6dAhnZ2ecnJzo2rUr0dHRqY5DCKF/N72C6bT0H9b9E1ck7F3PkWPfNMnVRULIISMKcz11ZI5ZHfSbb77R6/GF/kQ9fMizz3pQZs9uzKtV03c4IhupNVrWnX/K4tMPiFJrMTJQMfijMoxtXRErM5n/JilXr15l27ZtVKtWjRIlSsgNp3TQaDQ8e/aMMmXKpGm7tWvX8vfff2dNUP+vZcuWGd6HSqWiffv2bN++nS+++CITosobgoODgYytBCn5SmSmgx7erDj7GIB5PWpSo2T+WaU0NjaWJUuWEB4eztSpU7GzS2JxslwkKjaKzXc2J/may10XhtccnubHkC0tLalduzavX7+mXbt2QFz+OnbsGE+ePCEsLEz3hBXEjSofNGhQivtMbvsTJ06we/duypcvT7du3di1a1eK7Xv37sXc3JzVq1cD4Ovry/3791Mdh8h6mZHzRN6k1mhZ7vqIZWceEatVsLM05dfPatCqahF9h5YppFCYEVoNuP4Cl1aAOgKMLaDhaGgxDdIxNB7iVikzMDDAxMQEtVqtm+Q2JiYGExMTXb+YmBgiIiIwMTHBwsJC1/72o8Rv7ys2NhYjo5T/upPbZ7zk9pFc+9vxv/veIiMjZeSOEJng2vNApu+9yT2fuFFPDcsVZlbX6lRwkGkAkqIoChs2bOD27dv069ePunXr6jukXOvBgweULVsWlUpFUFAQs2bNYuDAgSk+hvzq1SsURcHGxgaAkydP4u/vj5eXF/fu3WPAgAE0b95c99quXbswNTVl+PDh1KhRg2vXrnHp0iW0Wi3Xrl1j6NCh2NrasnjxYmxtbfnuu+8oUKCA7tFjjUbD77//ToMGDTh48CD169fXLXo2evRo/Pz8sLW1ZdCgQTRq1ChRvI0bN2blypVSKHzL2rVrAZg4caKeIxECbr0MZtJuDwBGNCtHl1ol9BxR9vH390er1VKxYkXatGmTJ254+Uf660YSvisyNhL/SH9KFiyZ4eOMGDGChg0b8sUXX/DixQvd5xqQqlHyyW1fuHBhHj58SPny5fHx8dHNr59cu62tLc2bN6d27dq6fZcuXTrVcYisJzlPJOXh61Am7PLghldcIfnTGsX4uWt1Chcwec+WuUeOePQ413L9Bc7/FlckhLj/nv8trj2d5s6dy8CBAylTpgxWVlasXr2afv36YWVlRfXq1QkICABgy5YtlClTBgcHB6pUqcKtW7eAhI8Sz507l5EjR1K2bFksLS358ccfUzx2cvt88OAB1atXx9raGmdnZ91ckcm1v3z5klatWlGoUCFsbGx0d8nmzp3LiBEjKFu2LEOGDEn3ORJCQHCkmu/23aTHqgvc8wnFxsKYBT2d2PZFQykSJuPo0aOMHTuWc+fOUahQISpWrPjebfbv38/o0aPp27cvS5YsSTDqAOJugKxfv55hw4YxYsQINm/ejEajSbSf27dv8/XXX+Ps7Mzs2bMTLM6VW3l4eFCtWjVOnjxJr1696Nu3LzVr1sTNzQ0PD48kt3n+/DnFihXT/fz48WOmTJmCkZERtWrVwtnZmcjISM6ePcvgwYOpU6cOJUuWpHXr1rx8+ZJXr14xY8YMIiIiqFevHr179+a7776jcePG3L59mwULFgBw+vRpIiIiCA8PZ+nSpZw7d45mzZqxePFiTp8+DUCnTp3o3bs3tWrVYsiQIfj4+CSKt2TJkjx9+jQLzl7uVaFCBSpUqKDvMITAPyya4ZvciFJraVbJnsltq+g7pGzj4eHBzz//jKenJ506dcoTRUIgbuESw6Tfi7mROXbmmTNiskqVKmzZsoWFCxdy6NChTNt+2rRp9OvXj2bNmtGzZ08KFSqUYvvs2bNZs2YNs2bNYtGiRSxatEhGruUwkvPE27RahXXnn/Dp0n+44RWMtbkxi51rsaxv7TxVJAQZUZh+6si4kYQo77ygxLU3nZjux5DDw8O5d+8enp6eVKlShcOHD7NlyxY+//xzdu3axYgRIxgyZAhDhgwhIiKCQ4cO8dNPP7Fz585E+3r9+jW3b98mLCyM6tWrM3369GRHFia3z6lTpzJ06FDGjx/P2bNndcPlk2ufPHky7dq1Y/fu3bx584YOHTrQo0cPIK6IeOvWLQoUKJCucyNEfqcoCodvvuLHg3fwC40rWvWoW5JpHarmuQSVWSIjI1mxYgW3bt3C0NCQpk2bsmbNGnbv3s2VK1eSvcD65ptvcHFx0T3OtWbNGvbu3cvp06cxMDBAq9XywQcf0KpVKz7++GMiIiKYPn06O3fu5MCBA7rVNt3c3GjatCl9+/alTZs2rFmzhh07dqR47PRYsGABDg4ODBw4EABvb2+2bt2Kk5MTbdq0AeDatWu4urrSrl07qlevDsDhw4e5e/cuQ4YMwdY2bj6VdevW8fnnn6d4PA8PD86ePUtUVBQHDhzAzMwMjUbDyJEjCQoK4tGjR4m2iR8t/7bevXszbtw4APbt28fz5885ePAg06dP143+e/z4Ma6urhQqVIhPPvmEKVOmALB9+3a+++476tatS7ly5Vi0aFGiY5YpU4aFCxcCcTnx5s2btGrVipiYGI4fP05QUBAajQY3N7dEK2LGxMSkabGv/KBr1676DkEIYmK1jHb5D+/gKMrZFWBJn9oYGuT9FY4VReHo0aMcO3aMQYMG5blR8WZGcQuXrL25NtFr/av2T/fqxx999BGVKlXS/bx48WIGDhyIsbExlSpV4r///os7Rv/+yX7m9+7dGwMDgxS3b9WqFXfu3OHJkyfUqlULNzc3TExMkm2vV68ed+7c4ebNm7rBIO+LQ2QvyXkinmdABBN2eXDlady/1WaV7JnXoyZFrPLGjZp3SaEwvcJe/28k4bvUEXGv25RJ1647dOiAmZkZFStWxM7OTjefRq1atfD39wfiLo6++eYbQkNDMTIySnZkTMeOHbGwsMDCwoLixYsTHBysuxB8V3L7fPjwIb///jsAzZs3x8HBIcX227dvc+jQIWbPnq3b9/PnzwHo0qWLFAmFSCef4Cim773J6Xu+AJSzK8DsbjVy/WS5Wen+/fts3LgRLy8vQkJC2L9/PwDdu3enZMmSbNiwgdGjRyfazt/fn8WLF7N161b69OkDQLdu3ShRogS7d++mV69eqFQqLly4kGBhjnr16tGoUSPc3NyoX78+EHdTpVWrVqxbty5Vx84tPDw8mDFjBsuWLcPb25ty5cphaGiIm5tbsl+sq1SpossH8d7OCcbGxsTGxmJlZYWnp6eu/fnz57rHsN7ub2RkpPvZyMhIN7L9ffu/ceMGkydPZvLkyVhZWfH69WvCwsISbXv//v00regshMgePx68zZVnARQ0NWLNwHpYm+eP+Xj37dvHlStXmDRpEo6OjvoOJ0t8WetLIG5OwsjYSMyNzOlftb+uPT0cHBx01ynx3i6yfvTRRwApjhwrW7bse7cHKFKkCEWKxM1R1qRJk/e2m5ub06BBgwT7lhFsQuQciqKw/aonsw7dITxGg4WJId99+gF9GjjqBgXkRVIoTC/LInFzEiZVLDS2iHs9nd7+hXv3l09R4kYwrlixgnnz5tG2bVv27NnDmjVr3ruvt7dPSnL7rF27NkuXLmX8+PGcPHmS169fp9j+4Ycf0q1bN4YNG4alpSUAhQoV4sCBA7l+JbaczKxqVSrf8ED1nrkoRe6jKAq7r3nx06E7hEbFYmJowKjm5RndojymRvJvKilqtZrY2FhOnDhBq1ataNeuna5QB2BjY0OrVq04evRoksW6ly9fAiQYgWBlZUWRIkU4dOiQrlD47uq9JUrEzY8VP/l1VFQUrq6uaTp2er07f07x4sUTtdWtWzfRCJRPP/2UTz/9NEHb+0YTAty5cwdnZ2fq1atHr1692LdvHyVLpjx3lIWFBVWrVuXu3btUrVo12X5ffPEFDRs25NKlSwQFBWFmZkb79u05ceLEe+NKDRsbG4KCgti9ezeRkZG6v693nThxQjciXsSJn2i/cuXK6d6H5CuRES6XnrPl8gtUKljcpxYVHCz1HVKWCwgI4Nq1azRo0IA2bdrovl/nRYYGhoytM5bhNYfjH+kf9zhyOkcSCpFRmZHzRO7lGxLFlD03cL3vB0CDMoVZ0NOJUraJ13PIa+QbWnoZm8ctXHL+NxI+fqyKa0/nY8dmZmYJhpq/PU+Fqamp7jG18ePHM2nSJObOncugQYN0Iy3e3v7dfRUsWFA3ZD6p4yW3z19//ZVhw4bRsGFDnJ2dcXJyQqVSJds+f/58Jk2aRNOmTXXD6OMv9GQYfdZRGRigMpFHT/OaV8GRTP3rJmf/P0E5lbRmfk8nKhWReQiT8/jxYzZu3Ej9+vX56quvePjwIRqNJtHoC0dHx2RX361cuTLW1tZs27ZNV1i7fPkyT58+TVQcfNuiRYuwsbHRjQ7w9PRM87EhbmGqt+dDzGlzGiqKwsqVKzE3N6dGjRps2bIFHx8fSpYsiYeHB0FBQbi5uVG3bt1EN6wmTpzIqlWrWLx4MZ988glRUf+buP67776jVKlSWFlZcffuXS5duoSpqSmNGjXC0NCQevXqJZjj8Mcff9QVZytVqqR7JHnevHnY2tqiKApz5szR9e/YsSNarRZHR0fc3d1xd3enWrVqBAcH6xZYiS/qBgUFcf/+fVq3bp01JzGXOnjwIJCxiybJVyK9Lj95ww8HbgMwqW1lWlbJG6tLpuTx48esWrWKunXr6j7v8gMzI7NMWbhEiIzIjJwncqeDHt58v/8WQRFqTIwMmPRJZYY2LpsvprkAUCkpDTHL40JCQrC2tiY4OBgrKyuioqJ4+vQpZcuWTd28UVmw6nF+lObzLpIU/fQpPjNmUvSnHzF95/EIkfsoisJON09mHbpLaHTcKMJxbSrxRZOyGBnm/XWo3v18Tg21Ws2BAwc4e/YsnTt3plWrVhgYGHDr1i1q1KjBhQsXEqxsO2XKFPbs2ZPkXHoABw4cYMiQIZQoUQI7OztevHhBsWLFiI6O5sqVK4n6b9u2jf79+7Nz504+++wzgHQf+4cffkhyAap056ts1K5dO900GRcuXMAkiYLQmTNnaNmyZXaHliZeXl5ERkYmmtojJ5/77HDp0iUAGjZsmO59SL4S6eEVGEGXZf/yJjyGTk7FWeJcK0c89pWefJVaFy5cYNu2bXTr1o0WLVrkiPf7Phm+vhKZRs59xmVGzhO5S2B4DN/vv8WhG68AqF7CioW9auWJQRppyVcyojAjDAyh1fdxC5eEvf7/x5HTN5JQiIzSRkQQcfUq2ohk5s4Uucar4Eim7LnJuQdxowhrORZifo+aVMwDCSorbdiwgTdv3jB16lSKFy+ua48fmR0YGJig/5s3b3SjyJLSuXNnnj9/joeHB5GRkTRq1Iju3bvrVit8219//cXgwYNZvXq1rkiYkWNPnTqV8ePH634OCQnJNfNRHTt27L19cnqREHjvY9T5VWZcLEm+EmkVGaNh+KZrvAmPoVpxK+Z9VjNXFM0ywtvbm127djF69OgUp2rILfLx2BS9kXOecVIgzF9c7/kyec8N/EKjMTRQ8WWLCnzVsgLG+WCQxrukUJgZjM3TvXCJEEK87YCHN9/tvUlIVCwmRgZM/KQSwxqXyzfD3NNKo9Fw9OhRIiMjcXZ2xtLSMtFcqCVLlqRw4cJ4eHjQoUMHXbu7uztOTk4p7t/S0pKPP/4YgLCwMC5cuMAPP/yQoM++ffvo06cPS5cuTTS3X3qPbWpqKlM1CCEEccWOSbs9uPMqBNsCJqwZWA9zk7z75E54eDhbtmyhWbNmLFiwINfP721sbIxKpcLPzw97e/s8X+DNSUJDQ4G4xb6EEMkLi45l1qE7bL8at5BeefsCLOxVCyfHQvoNTI/kU0MIIXKA4Eg1M/ffYp+7NwBOjoVY2MuJ8vZ5d8LyjPL29uaPP/4gNjaWoUOHJpjT9W0qlYr+/fuzfv16Ro4ciY2NDWfOnOHatWssWLBA12/58uU8ePCAxYsXA3ELWTRq1IiCBQui1WqZMGECVlZWDBs2TLfNgQMHcHZ2ZunSpQwfPjzdxxYitzh79iwAzZs312scIv9YcfYxh268wthQxcr+dSlRKO8+vePj48OKFStwcHCgdOnSub5ICGBoaEjJkiXx8vLi2bNn+g4n37GyspJCYQZIzsv7Lj15w8RdHngFRqJSwdCPyzKpbWXMjHP/529GyKdGEmSYdvaS8y3yu0tP3jBhpwcvgyIxUMGYlhXz7TD31Lpy5QqbN2+mZcuWdOzYEWNj4xT7z5o1C3d3dypXrkyVKlVwc3NjxowZCb74Xb9+XTcXDcTNeVirVi3Kly/Po0ePMDEx4dixY7pHj0NCQujZsyfW1tYcOXKEI0eO6LYdM2aMbhGM1BxbiNzCzc0NkIsmkT1O333NghNxq47+2Lk6DcoW1nNEWefOnTusWbOGZs2a0aVLl0QLEOZmlpaWVKxYEbVare9Q8hWVSpXkPMEi9STn5V1Rag0Ljt9n/b9PURQoaWPOgp5ONCxnq+/QcgQpFL5FhsbrhwyLzxzGxYtT9OefMH5rbjaRs8XEall48gGrzz1GUaBUYQt+712LuqWTn7suv/P19eX+/fvY2dkxbtw4ypUrl6rtChYsyNmzZ7l+/TqvX7+mevXqieb8GzNmDH379tX9/Omnn9K4cWPc3NwoVKgQtWvXTnDhZmZmxo4dO5I83tsLYKTm2ELkFj179szwPiRfidR45BvK19vdURTo37AUfT8spe+QsoSiKKjVam7evEnfvn1p0KCBvkPKEoaGhnlihKTIXzIj54mc54ZXEON3evDINwwA5/qOfNfxAyxNpR4RT1Y9fmfVl7CwMLy8vGSUWzazsrKiRIkS+g5DiGzzxC+Mr7Zd57Z3CAC96znyfSdJUPHe/XzWarWcO3eOv/76i5YtW9K1a1d9h5itZBXJnEPOvRBZLzhCTdcV//LUP5wGZQvjMuxDTIxy5gi7jKx6rFarcXFxISgoiHHjxmVRhNkrK1eBFkKIjFBrtCw784hlro/QaBXsC5ry62c1aFmliL5Dyxay6nEGyND47CfD4jNHbGAgoadOUbB1a4xSWE1V6N/e615M33uLiBgNNhbGzOlek3bVi+o7rBwrKCiIjRs38vr1a0aPHk2VKlX0HZLIBKtXr8bU1JTBgwdnSX+AVq1acfTo0UQ5Jrl2kT0kX4mUaLQKX22/zlP/cEoUMmdFvzo5tkiYEcHBwaxcuRKAUaNG6TkaIYTI2x6+DmX8Tg9uvgwGoGPNYvzcpTo2BeS7YFKkUJgEGRovciO1tzc+38/A7IMP5MIrh4qIieWHA7fZ6eYFQKNytixyrkURKxmVlBx/f398fX2xt7dnxIgRmJvn3Uns85vevXunaYqPwMDANI/ge/78OVqtNtXt4v22bdsGQJ8+fdK9D8lXIiXzjt3j3AM/zIwNWDOwLnaWeW8FeD8/PxYsWECVKlXo37//e+fZFULoR2bkPKFfGq3Chn+eMv/EfWJitVibG/Nz1+p0dpLpT1IihUIhhMgGD16H8uWW/3joG4aBCr5uVYkxLStgaCBzoSYlJCSEtm3bsmbNGn755RcaNmyo75DyBnUkhL0GyyJgrN+ia/yiMCJ3efnypb5DEHnYvusvWX3uCQALejpRrXjSq9nnZg8fPsTMzIyuXbvSsGFDmRNdiBxMcl7u9uJNBBN3eXDlWQAAzSvb8+tnNWWQRipIoVAIIbKQoijscvNixoFbRKm1OBQ0ZbFzbRqVlxW1kuPm5saWLVtQq9V8/fXXeWrlR73RasD1F7i0AtQRYGwBDUdDi2lgkL4R9EuWLCEkJAQ3NzdevnzJ/PnzuX79Ohs3bqR27dq6R4W3bdvG999/j4mJCXXq1GHhwoU4ODgkeJR4yZIlqNVq/vnnHzw9PZkyZUqKE4gnt8/AwEDGjRvHrVu3cHZ21vVPrj0iIoIff/yRU6dOUahQIWbMmEGzZs1YsmQJMTExXLhwAQcHB1atWpWuc5QXjR07Vt8hiDzqhlcQU/bcAODLFuXpWDNvjfbQarXs27ePc+fOMW3aNBo1aqTvkIQQ7yE5L3dSFIVtVzyZdfgOETEaCpgY8l3HD3Cu7yg3Z1JJrr6EECKLRMTEMn6nB5P33CBKraVpJXuOfN1EioQpuHHjBlu3bqV79+6cOXOGAgUK6DukvMH1Fzj/W1yREOL+e/63uPZ0CggIYN++fXz77bdMnDiRrl27EhkZiYuLCz4+PuzZsweIWz362LFj/PXXX1SvXp3vv/8eiCveBQUF6fa1detWJk2axOzZs/nqq69SXFQsuX1+++23GBoasmHDBgCePn2aYvt3331HeHg4GzduZMqUKQwfPpywsDACAgLYtGkTEyZMYObMmek+R3mRiYmJzO0oMp1faDQjNl8jOlZLqyoOTGhTWd8hZarIyEhWrFiBu7s73377LQ4ODvoOSQiRCpLzcp/XIVEM2XiVaXtvEhGjoUHZwhz7pil9GpSSImEayIhCIfIIAwsLLOrXx8DCQt+hCOCZfzgjXa5xzycUQwMVEz6pxMim5TGQR42TdOPGDfbu3cu4ceOYO3cuUVFR+g4p71BHxo0k5N3CmxLX3nRiuh9DHjRoEA0bNqRhw4aMHj2aqVOnolKp+PTTT3n27BkA9+/f58cff+TJkydERUVhb2+f5L4GDx7MRx99BKAbHVi4cOEk+ya3z0uXLnHgwAFKly5NzZo1+e2331Jsd3V1JTAwkGPHjgHw6tUrHjx4AMDQoUP5+OOP03VeRMokX4m3xcRqGeVyjVfBUZSzL8DvzrXyXK5cunQppqamTJkyRW6ACSFEFjng4c33+24RHKnGxMiAyW0rM/Tjsnkup2QHKRQKkUeYli1L6c2b9B2GAE7ffc03O9wJjYrFztKUFf3q0KBs0gWP/C4yMpJdu3bx33//0bt3b6ysrACkUJiZwl7/byThu9QRca/blEnXrt+egN/Y2Fh3p1alUukWC5k8eTLOzs40bdoUNzc3XZEupX0ZGBikuNhIcvssVaoUZ8+eZdCgQdy4cQN/f/8U28uWLcvAgQPp1KmTbt+Ojo4cOHAgzQun5BcLFy4EYPz48eneh+Qr8bYfDt7G7XkgBU2NWDuwHlZmeWdhj3v37uHv70+/fv0oWrSoLJYoRC6TGTlPZL3A8Bi+23+LwzdeAVC9hBW/96pFxSIF9RxZ7iWFQiHyCEWrRYmNRWVkhErmdNMLrVZh0emHLDn9EIC6pW1Y0a+OTJibjICAAObPn0+RIkWYMWNGsqPHRAZZFombkzCpYqGxRdzrWah9+/ZMmDABOzs73YjBrNrnrFmz6NChA9OmTaNGjRoULVo0xfYFCxbQs2dPfv31VywtLQF49OhRpsSYV9lkwirFkq9EvC2Xn7P18gtUKljSpzbl7S31HVKmUBQFV1dX9u7dy+DBgylRooS+QxJCpENm5DyRtc7ce82UPTfxC43G0EDFmBYVGNOyAsaG8v0iI1RKSpMA5XEhISFYW1sTHBysG8UiRG4Vefs2zz7rQZk9uzGvVk3f4eQ7QRExfLPDnbP3/QAY1Kg00z/9ABMjSVLvio6O5tSpU9SqVYvXr19Tu3btRHOGyOdzQu+ej6ioKJ4+fUrZsmVTN/Lt9M9xcxImePxYBU0mQKvv0xVTQEAARkZGur+fZ8+eUaZMGQCCg4PRarW6L9iBgYEYGRlRoEABXr58iaOjI0FBQahUKqytrRPty9PTkxIlSiRYyObt/sntE0Cj0RAYGIidnR0vXrzA0TFu4urk2uPfS0BA3Ip4FSpUSBTP29J87kUikq8EwJWnAfRde4lYrcLkdpUZ3byCvkNKl3c/n2NjY9m2bRs3btxg5MiRlC9fXt8hZivJ30KI7BAapWbWobvscPMEoIKDJQt7OVGzZCH9BpaDpeXzWUYUCiFEBt19FcLwzW54BkRiamTAnO416F6npL7DypEeP37Mxo0bsbKyonnz5jLKIru0mBb336RWPU6nd0eAxhcJAV0xL97bd+TjC3qFChVKdl/xfd72dv/k9glgaGiInZ0dEPfI8fva44//dgwyulWIrOUdFMnoLdeI1Sp0rFmMUc3yTjHtypUreHl5MW3aNBmNJIQQWeDi4zdM3OXBy6BIVCoY9nFZJratjJmxTO+QWaRQKIQQGXD8tg/jdrgTEaPBsbA5q/rXpVpx6/dvmM8oisLevXtxdXWlc+fOtGrVKsFoMZHFDAzjRg42nRg3J6FlkXQvYCLyNx8fHwDd49tCpFWUWsPwzW74h8VQtZgV83rUzBMrUb58+ZKlS5fy1VdfZdo0C0II/ZKcl7NEqTXMO3afDf8+BaCkjTkLejrRsJytniPLe6RQKIQQ6aAoCivOPmb+8fsANK5gx7K+tSlkYaLnyHIeX19fDA0N8ff3Z+rUqRQvXlzfIeVfxubpXrhECAAXFxcAJk6cqOdIRG6kKArf7rnBrZchFC5gwpoBdbEwyf2XI2XLlmXlypW0a9eOggVl8nwh8grJeTnHDa8gxu1w57FfOAB9Gjgy/dMPsDTN/TkkJ5KzKoQQaRSl1jBlzw32u3sDcfMRft/xA4xk0twENBoNR48e5dixY4wZM4bhw4frOyQhRAbVqFFD3yGIXGzd+afsc/fG0EDF8r51cCxsoe+QMuzkyZM0a9aMPn360KhRI32HI4TIRJLz9E+t0bL0zCOWuz5Co1VwKGjKr5/VpEUVB32HlqdJoVCIPMKsYkUqnHXFSObWylK+IVF8sfkaHp5BGBqo+LFzNfo3LK3vsHIcb29vNm7ciFqtZvLkyYnmhBNC5E5t27bN8D4kX+VP5x74MefoXQBmdPyARuVz96NiarUatVqNn58f+/fvZ/HixfoOSQiRyTIj54n0e/A6lPE73bn1MgSAjjWL8XOX6tgUkCe4spoUCoXII1QmJhjL/BlZ6tbLYL7Y5Mar4CiszY1Z2a8OH1Ww03dYOY5Wq2Xx4sU0aNCAzp07Y2xsrO+QhBA5iOSr/OeZfzhjtv6HVoFe9UoysFHuvsHm7+/PypUrqVmzJn379mXUqFH6DkkIIfIMjVZh/T9PWHDiATGxWgpZGPNzl+p0cpLpi7KLPCcnRB4R4+mJ19ffEOPpqe9Q8qTTd1/Tc9VFXgVHUd6+APu//FiKhO/w8/Pjt99+4+zZs/zyyy989tlnUiTM43bs2MGAAQNwc3NL1/YHDhzg6NGjadrmm2++Qa1Wp7pdZC53d3fc3d0ztA/JV/lLWHQsX2xyIyQqltqlCvFz1+q5evGSBw8eMGfOHCpUqEDHjh31HY4QIgtlRs4TafPiTQR91lzilyP3iInV0qKyPSe+aSpFwmyWowqFMTEx+Pj4EBMTk6btYmNj8fHxITQ0NIsiEyLn04SEEHr8OJqQEH2HkudsvvScLza5EanW0KSiHXu//JgydgX0HVaOoSgKf//9Nz///DPFixfn448/xtDQUN9hiSz24sULpk2bRtu2bSlWrFi69nHnzh3u37+fpm0OHTqERqNJdbvIXKdOneLUqVMZ2ofkq3xCHYn2zVOmbLvMQ98wHAqasqp/XUyNcm9+cHd3Z+nSpXTp0oU+ffpIrhMij8uMnCdSR1EUtlx+TrvF57jyLIACJobM7V6DDYPr42Blpu/w8p0c8ejx8+fPWblyJRs3buT169e4urrSvHnzVG8/cuRI1q9fz9dff82iRYuyLE4hRP6i1Sr8evweq/9+AsQ9LjW7Ww2MZdGSBNavX8+jR48YNWoUVatW1Xc4IgVRsVH4R/pjZ26HmVHGvnTduXOHli1b0r9//0yKTuQGbdq00XcIIqfTasD1F7i0AgN1BPMVE6oZf0qjfgsokksv9jQaDe7u7hQqVIjx48dTtmxZfYckhMgGkvOyh09wFFP23ODvB34ANChbmN96OuWJBa9yqxxRKNy5cyfW1tYcOXKEunXrpnlbd3d3PvjggyyKTgiRH0XHapi46wYHPeJWNh7fphJftayQqx+XykyKonD58mW0Wi1NmjShX79+mJub6zsskQyNVsNy9+VsvrOZKE0UZoZmDPhgAF/W+hJDg7SPiPHy8mL27Nm8evUKZ2dnNmzYwIULF1i3bh0mJibUqVOH0aNHY2Jiwu7duzEzM+PGjRuYmJgwceLEJPd56tSpJLfXaDSsXLmSW7du8dlnn/3vPSXTDrBr1y5OnTpFoUKFGDNmDI6OjqmOQ6TMyclJ3yGInM71Fzj/G6AAYKGKYZThXlSPy0OZ7/UbWzqEhYWxZs0awsPDmTZtWpaPItRqtURERGBpaZmlxxFCvJ/kvKylKAoHPLyZsf82wZFqTIwMmNy2MkM/LouBgVxz6VOOGBYzadIkpk6dioND2pa4fvr0Kd988w1btmyRebCEEJkmKCKGAeuvcNDDGyMDFQt6OjG2VUUpEv6/kJAQVq5cyZ49e7C1taVy5cpSJMzhlrsvZ+3NtURpogCI0kSx9uZalrsvT9f+ChYsyIcffkjZsmXp2rUrxsbGuv//5JNPuHr1Kj/99BMAt27d4ptvvqFAgQJ8+OGHye4zue1//vlntm/fToMGDXBxccHb2zvF9oULF7Jy5UoaNmyItbU17du3R61WpzoOIUQGqCPh0grii4TxVBDXro7UR1Tp9vLlS+bMmYOFhQWTJk3KkiLhDz/8wOPHjwG4du0axYsXp1ixYkyfPj3TjyWEEDlFQHgMY7Ze5+vt7gRHqqlZ0pojYxvzeZNyUiTMAXLEiML0UKvVODs7M3PmTCpXrqzvcITQO2MHB+zHjcM4jQV3kZBXYASDNlzhsV84BU2NWDWgLh/LoiU6t27dYsOGDVStWpWZM2fKiIdcICo2is13Nif5mstdF4bXHJ7mx5Ctra1p3LgxYWFhODs7A1CgQAHu3bvHkydPCAsL4+zZs7r+w4YN4+uvv05xn8ltf/DgQbZt20alSpUYPHgwhw8fTrF9+/bt2Nracvz4cSBuddL4eRBTE4dIWfziM+3bt0/3PiRf5WFhr0EdkfRr6oi4123KZGtI6RUZGcm8efNo3bo1n376KQYGWTO+4vjx47Rr147y5cszb948vvvuOwYMGECFChUYO3YsRYoUyZLjCiHeLzNynkjs9N3XTNlzE/+waIwMVIxpWYEvW1SQ6Z1ykFxbKJw2bRpFihRhxIgRqd4mOjqa6Oho3c8hMom2yEOM7O2xGzFc32Hkao98Qxmw/gqvgqMoamXGxqH1qVLUSt9h5QhhYWH4+/vj4+NDnz59qF+/vr5DEqnkH+mvG0n4rsjYSPwj/SlZsGSGj/P5559TqVIl2rRpw+vXr3FxcdG9ZmNjk+7tLS0t8fb2plKlSgQGBhIREZFie/xoxypVqgDQtWtX3WIrqYlDpOz27dtAxi6aJF/lXbHm9mhUppgq0YlfNLYAy5xf9FIUhdOnT1OkSBG+//577Oyy9maho6Mjd+7coWHDhgQEBFC9enWsra0pUaIEAQEBUigUQo8yI+eJ/wmNUvPzoTvsdPMCoKKDJQt71aJGSWs9RybelSsLhX///Tfr16/n77//xsfHB4hb+TgiIgIfHx+KFi2a5HZz5szhxx9/zM5Qhcg2mpAQItzcsKhXD0MrKW6llbtnEEP+uEJghJoKDpZsHtaAYtbyOC3AzZs32bRpE7Vq1aJfv376DkekkZ25HWaGZkkWC82NzLEzz5yL4GLFiuHq6srTp091RbvM2H7ixIn07NmTjz76iICAAAoUKJBi+w8//ED//v2pUaOGbsRr/KhHkXEDBw7M8D4kX+Vd8868oKC6PWMM95Fwtg4VNBwNxjk7r8bExPDnn3/y5MkTxo4dm+VFQogb6dynTx/CwsLo3r07GzZs4OXLl0RFRVGpUqUsP/7bYmNj2bNnDxcuXMDIyIjGjRvTtWtXmXpF5FuZkfNEnIuP3zBxlwcvgyJRqeDzxmWZ8EllzIxl9ficKFcWCp88eYKJiUmCVYjevHnD06dPOXDgAC9fvkxyDpGpU6cyfvx43c8hISE4OjpmS8xCZLUYT0+8Rn9JmT27Ma9WTd/h5Cr/PvLni01uRMRocCppzR9DGlC4gIm+w9K7yMhIdu3axbVr1+jduzeNGjXSd0giHcyM4hYuWXtzbaLX+lftn+7Vj+vXr0/Jkv8bibhq1SrOnj2LsbExtWvX5sKFCwD06NEj2Tksu3TposvXyW3fqVMnLl++zP3792nUqBH//vsvJiYmybY3adIEd3d3rl69SkBAgO5YKcUhUi+t80knRfJV3rTv+kvWnHuCAT1oV60oFZ9sinvc2NgirkjYYpq+Q0xRQEAAK1euxMTEhKlTp2KVTUXstm3bsnXrVn777TcePHhAgQIFMDY25tixY1m+cMrbtFotH3zwAXXr1qVRo0ZEREQwatQotm/fzo4dO7ItDiFykszIefldlFrDr8fu8ce/zwBwLGzOgh5OfFjOVr+BiRSpFEVR3t8te3h5eeHo6IirqyvNmzdP8FpISAhqtRpb26R/oWrVqkXz5s1ZtGhRqo8XEhKCtbU1wcHB2fZlQIisEnn7Ns8+6yEXXml07NYrxm5zJ0aj5eMKtqweUA9L01x5DyVTKYrCgQMHePr0KQMHDqRw4cLZenz5fE7o3fMRFRXF06dPKVu2LGZm7y/0xa967HLXhcjYSMyNzOlftX+6Vz3Oz9J67kVikq/ynptewfRYdYHoWC1ftijPpLZV4hYuCXsd97hxDh9JCHGrpcfExNC7d2+MjFL/PSCv5CtFUXj+/DllypTRtZ0/f56mTZty7do16tSpk6r95JXzIYTIOHfPIMbvdOeJXzgAfRqUYvqnVeVaS0/S8vmcI/6GIiMjCQ4Oxs/PD4i7o+fj44OlpaXusaHx48dz6dIlbt26pc9QhRB5yPYrL5i29yZaBdpXL8oi51qYGuXvoklMTAx//fUX9+/fZ+bMmfoOR2QSQwNDxtYZy/Caw/GP9I97HDmdIwlF/rZhwwYAhg4dqudIRE7hFxrN8M1uRMdqaVnFgQlt/n+RQWPzXLFwyb///svZs2eZNm1ajnnE9s2bN7i5ueHr64tKpaJYsWLUr18/SwtvKpUqQZEQoFy5ckDcolBC5EeS89InJlbLsjMPWX72MRqtgkNBU37tUZMWlWWEZm6RIwqFBw4c0K1CWKRIEUaPHg3EzT80ceJEIG6FxZTmCbGzs5O7VkKI9/v/EQ4b3CP46fhTAJzrOzK7Ww0MDXLGBYK+PH78mI0bN1KwYEFGjhyp73BEFjAzMsuUhUtE/iULwYm3xcRqGb3lGq+CoyhnX4BFzrUwyCW5VKPRsGfPHi5evMjw4cNzRJEwIiKCL7/8EhcXF2JjYxO8ZmpqyqhRo5g/f36aRjxmxIoVKyhYsCANGjRIto8sFinyMvl9Trv7PqGM3+nObe+4c9fZqTg/dalGIQuZ1ik3yRGFwt69e9O7d+8U+/z2228pvn7q1KnMDEmIXMfA1BSTCuUxMDXVdyg5k1YDrr/ApRWgjsBZMSHcqAMRH01mcvsPcsQFgj5dv36dDRs20KlTJ1q3bo2BgYG+QxJC5EDffPNNhvch+Srv+PHgba4+C6SgqRFrB9bDysxY3yGlSkxMDCtXriQwMJBp06Zhb2+v75AA+PLLLzl//jzr1q2jRYsW2Nvbo9Vq8fX15eTJk8ycORNzc3N++eWXLI9l3759/Prrr/z5558UKlQo2X6yWKTIyzIj5+UXGq3CuvNP+O3EA2I0WgpZGDOra3U61iyu79BEOuSoOQqzm8yhIUQ+cvpnlPO/oeJ/H3kKoGoyEVp9r7+49OzFixecOnWKrl27otFocszFknw+J5TcHIVlypSRBTqyWWRkJM+ePZM5CkW+t+Xyc6bvvYVKBesH1aNllSL6DilVQkJCiIyM5MyZM3Tt2jXDn6GZla8URcHCwoIbN25QsWLFJPucP3+egQMH8vTp03QfJzWOHTtG165d+fXXX3VPfSUnqRGFjo6Okr+FyEeevwln4i4Prj4LBKBlFQfmdq+Bg5V8T8pJct0chUIIkaXUkSiXlicoEgKoIG6EYdOJuWKi9cyk0Wg4duwYR48epX379tm+WInIGGNjY1QqFX5+ftjb2+f7EbHZKTQ0FCDbHv3LaeILAqYyGjBfu/osgJn7bwMw8ZPKuaZIeOvWLdatW8fnn39Onz599B1OAhqNBrVaneIqq0WKFCE8PDxL4zh+/DjdunVjzpw57y0SQtxngXweiLxKcl7KFEVhy+UX/HLkLhExGgqYGDKj0wf0quco301zufz5LVeIPCjq7l2e9x9AaZfNmFWtqu9wchQl1AeVOjLpF9URcasy5oIJ1zNLcHAwy5cvR61WM2nSJEqXLq3vkEQaGRoaUrJkSby8vHj27Jm+w8l3rKys8m2hcOnSpQC6OaTTQ/JV7uYdFMkol2vEahU+rVmM0c3L6zuk91IUhZMnT3Lo0CH69+9P9erV9R1SIkZGRjRq1IiRI0eyePHiRAVDLy8vvvrqK5o3b55lMZw8eZKuXbvyyy+/MG7cuCw7jhC5RWbkvLzKJziKyXtucO5B3IK0H5YtzIKeTjgWttBzZCIz5M9vuULkQYpWizY8HEWr1XcoOYpWq/Dz2QAmKSZYqGISdzC2AMvcMRIio7RaLe7u7tjZ2VG7dm1at26NsXHumE9KJGZpaUnFihVRq9X6DiVfUalUmJjk3wm5S5bM+GI4kq9yryi1hhGbr+EfFkPVYlbM71EzV4wa2bNnD1evXmX8+PGJVvbNSdauXUv79u0pVqwYVapUSTBH4YMHD6hWrZpuFdbMFhoaSpcuXbCysuLatWv0799f99rnn3+epQVKIXKqzMh5eY2iKOx392bG/luERMViamTA5HZVGPJRmVyzmJV4PykUCiHyLK1WYfq+W2y74oOtUQe+NNpHwvSlgoaj88Vjx35+fmzcuJHg4GC+/fZb2rdvr++QRCYwNDTE0NBQ32GIfMTZ2VnfIQg9URSFqX/d5ObLYGwsjFkzoC4WJjn7UiI4OJiHDx9SvXp12rRpg7W1tb5DSlGVKlW4e/cu+/fv5+LFi/j6+gLQoEEDGjduTMeOHbNsNLOJiQlr1qxJ8jVHR8csOaYQOZ3kvIQCwmP4bt9Njtz0AcCppDW/9XKigkNBPUcmMlvOzu5CCJFOiqIw88Bttl15gYEKinX9CVVwBd2qxxhbxBUJW0zTd6hZSlEUzp07x549e2jUqBFjx47Nt/OsLF26lEWLFvH69Wtq1KjBwoULadSoUbL9/fz8mDZtGsePHycoKIiGDRuyePFiqr71qGRwcDCbN29m1apV3Lt3j9OnT9OsWbME+/nyyy9ZvXp1grbq1avj7u6eqe9PCCGy0vp/nrL3+ksMDVQs71cnxz9e9uzZM1auXEnt2rVz1cW+mZkZvXv3pnfv3tl6XFNT0wSjCIUQ4m2n7rzm279u4h8WjZGBirGtKjK6eXmMDA30HZrIAlIoFELkOYqi8POhu2y+9ByVCub3cOKzuiWB7+MWLgl7Hfe4cR4fSRgTE0NQUBCurq6MGjUqQYErv/njjz+YMmUKW7dupVGjRsybN49PPvmEO3fuJDlSQlEUunTpopvXys7Ojt9++40WLVpw9+5dbGxsAJg/fz6BgYEsWLCA9u3boyhKon1pNBq6dOnCjh07dG254VE9IZISPydmTn58U2S+cw/8+OXIXQC+/7QqH5W303NEKbt8+TJbtmyhU6dOtG7dWt/hZBqtVsvt27epUaOGvkMRIl+QnAehUWp+OniHXde8AKjoYMnvvWtRvUTOHqEtMkbKv0LkEablylFmz25My5XTdyh6pSgKc4/dY8O/TwGY273G/xcJ/5+xedzCJXm4SKgoChcvXmTy5MmEhYXxww8/5OsiIcQV9IYOHUrXrl0pUqQICxYswMrKipUrVybZ/8mTJ1y8eJG5c+dSuXJlbG1tmT17NiqVKsH8ULNmzWL58uXvnRhfpVJhZGSk+yOPC4vcavfu3ezevTtD+5B8lbs8fxPOV9uuo1WgZ92SDPqojL5DStGjR4/Ytm0bw4cPp02bNnnqxkxERESKI+GFEJkrM3JebnbhsT/tFp1n1zUvVCoY3rQcB79qLEXCfEBGFAqRRxiYm2NerZq+w9C7308+YPXfTwD4uWt1etcvpeeIsldISAhbtmzhyZMnDBkyhHJyIU5gYCB3797l559/1rWpVCpatGjBhQsXktxG+/+LLBgYGCTYxsDAgH/++YcJEyakKYbjx49jZWWFtbU1TZo0Ye7cuZQqlb9+N0XeUL9+/QzvQ/JV7hEWHcsXm9wIjlRTy7EQs7pVz7GFt8jISHbv3k2LFi345ZdfsLDI2Y9GCyFyvszIeblRZIyGX4/dY+OFZwCUKmzBgp5ONChbWL+BiWwjhUIh8gi1tzdv1q3D9vPPMS5eXN/h6MWS0w9ZcuYRADM6fsCAhqX1HFH2iomJ4aeffqJSpUrMnDkTS0tLfYeUI7x69QoABweHBO329va4ubkluU358uWpUaMG33//PZs2bcLOzo6FCxfy6tUr3f5Sq2zZsmzatIlmzZrx8uVLxo0bR5MmTbh58yZWVlZJbhMdHU10dLTu55CQkDQdU4is8u4cnOkh+Sp30GoVJux058HrMBwKmrJ6QF1MjXLmaGhfX19WrFiBjY0NdnZ2mJmZ6TukNAsPD6dEiRL6DkMI8ZbMyHm5zfUXgUzY5cETv3AA+n5YiukdqlLAVEpH+Yn8bQuRR8QGBhK4dRvWn32WLy+8Vv/9mIUnHwAwtX0VhjYuq+eIsk94eDh//fUXtWrVYsKECRQrVkzfIeUKBgYGSc4pGP/agQMHGD9+PLVq1SIyMpKePXvSo0cPPD0903ScKVOm6P6/cOHC7Nq1i2LFirF9+3aGDx+e5DZz5szhxx9/TNNxhMgt8nu+yi2WnnnE8duvMTE0YNWAuhSxypnFt7t377JmzRo++ugjunfvnmundjA2NkatVjNz5sxkR0PGxMQwY8aMbI5MCJEfxMRqWXL6ISvOPkKrQBErU379rCbNKzu8f2OR50ihUAiR6229/II5R+8BMPGTSoxoVl7PEWWfmzdvsnnzZkqVKkXZsmVlFGESihYtCsStYvw2X19fihQpkux2ZcqU4a+//krQ9uGHH1KhQoUMxWNjY0OpUqV4+PBhsn2mTp3K+PHjdT+HhIQkueiKENnt4sWLADJPWh534rYPv5+Ku/k2q1t16pSy0XNESVMUBVdXV3r27MlHH32k73AyxMTEhN69e2Nra8uwYcOS7BMWFiaFQiGyUV7NeVGxUfhH+mNnboeZkRn3fUIZt8OdO6/inmDp7FScn7pUo5CFiZ4jFfoihUIhRK526IY30/fdBGBU8/KMaVlRzxFln+3bt3Px4kV69erFRx99lGPnjdK3woULU6lSJf7++2+6d+8OxF1cnj17ln79+qV6P48fP8bNzS3N8xO+KyQkBE9PzxRHfpqammJqapqh4wiRFf79918g7100if958DrughFg8Edl6FUv592kUKvVbN++nejoaEaPHq3vcDLN6NGjmT17drKFQiMjIzp16pTNUQmRf+W1nKfRaljuvpzNdzYTpYnCzNCMapaduHC1LjEasLEwZlbXGnxaU55Oyu+kUCiEyLX+fuDHuB3uKErc/BmT21bWd0jZ4tGjRxgaGmJra8uMGTOwtbXVd0g53oQJExg/fjwdOnSgYcOGzJs3j4CAAEaOHKnrM2LECC5fvoy7uzsACxcupFq1ajRv3pwHDx4waNAgWrZsSY8ePVJ93OjoaPr378/UqVOpVq0aL1684Ouvv6ZAgQJpKlIKkVN07txZ3yGILBQcoWb4JjfCYzQ0KmfL9E+r6jukREJCQli1ahVqtTpPFQkB6tWrx969e5N93czMjG3btmVjRELkb3kt5y13X87am2t1P0dponAL2gWF/WllO4A5n9XAoWDOnGZCZC+D93cRQuQGRra2FB40CKN8UjS69jyQkZuvodYofFqzGD93ybkrMWaWmJgYduzYwZIlSwgJCaFNmzZSJEyl4cOHM2PGDIYMGYKtrS2HDx/myJEjlClTRtdHo9EQGxur+/mzzz5j+fLl2NjY8Mknn9C8eXP279+fYCXkTZs2YWRkpNtPq1atMDIy4qeffgLiRgb279+fMWPGYGNjQ7NmzShYsCCXL19O8bFnIXKqSpUqUalSpQztI7/lq9xCo1UYs+0/nr2JoEQhc5b3q4OxYc66VHj9+jW//PILhQsXZtKkSdjY5MxHooUQeUNm5LycIio2is13NidqV6nA0v4iy/pVlyKh0FEpyc3kng+EhIRgbW1NcHBwsitPCiFynns+IfRefYngSDVNKtqxflB9TIxy1sVMZnv69Cl//PEHBQoUYPDgwXm+yKSPz2etVouiKGmaCF9RFDQaTaJ2AwODBAXFjJJ8JYTIanOO3GX1uSeYGRuwZ9RHVCture+QEnj58iWRkZF4enrSvHnzHHNzUD6fE5LzIUTO5BXqRfu/2if7+tHuRylZsGQ2RiSyW1o+n+XRYyHyCG14OFEPHmBWqRIGBQroO5ws8+JNBAPWXyE4Uk2dUoVYPaBuni4SxsbGEh0dzdmzZ/n4449p06ZNphagxP+k57yqVCqMjCSVivwjfoGf+Pk+0yO/5KvcZL/7S1afewLAgp5OOapIqNVqOXToEKdPn+bHH3/M8IJSQgiRWpmR83ICRVG48CAatMZgoE70urmROXbmdnqITORUcnUjRB4R/ewZz/v0pcye3ZhXq6bvcLKEf1g0AzZcxi80mspFCrJhcH0sTPLux9iLFy/4448/qFChAkOGDNF3OEIIwZMnTzK8j/yQr3KTm17BTN59A4DRzcvTsWZxPUf0P1FRUfzxxx94eXkxZcoUChUqpO+QhBD5SGbkPH17ExbN9L23OHbbBxP7xpjausI7A7L7V+2PmZE8diz+J+9eYQsh8pSImFiG/enG8zcRlLQxZ/OwBhSyMNF3WFlCo9Fw7Ngxjh49Stu2benQoYO+QxJCCCBu0R+Rd/iHRTNisxvRsVpaVLZnwic5Z1EwRVFYuHAhZmZmTJ06FUtLS32HJITIZ3J7zjt55zVT/7qBf1gMRgYqvnQaDTYV2HLPhcjYSMyNzOlftT9f1vpS36GKHEYKhUKIHC9Wo2Xstut4eAZRyMKYP4c2wMEq79712rFjBw8ePGDSpEmULl1a3+EIIYROwYIF9R2CyCQxsVpGu/yHd3AU5ewKsLhPbQwNcsa8f48ePSIyMpJu3bpRqVKlNM0dmxd07NiRfv360adPnwTtixcvxt3dnT/++ENPkQmRv+TWnBcSpeang3fYfc0LgEpFLFnYqxbVS1gDVRjhNBz/SH/szO1kJKFIkhQKhRA5mqIo/HDwNqfu+mJiZMC6gfUob5/3RhVotVrOnDmDp6cnPXv2xNTUFGNjY32HJYQQIo/66dBtrjwLoKCpEWsG1sPKLGfknHPnzrFr1y4GDx5MjRo19B2OXgwZMoT+/fuj0Wjo378/AAsXLmTGjBkcPHhQz9EJIXKyC4/8mbT7Bi+DIlGpYHjTcoxvUwlTo//dcDEzMpOFS0SKpFAoRB6hMjLC0MYGVR5bWGHV309wufQClQoW965FvTKF9R1SpvPz8+PPP/8kKCiIIUOGyONVQogca/ny5QB8+WX6H1PKq/kqN9l6+YUuty5yrkUFB/3nHY1Gw44dO7h27RpfffUVlSpV0ndIevPZZ59hYGBA37590Wg0vH79mp9++onDhw/TrFkzfYcnRL6RGTkvu0TGaPj12D02XngGQKnCFvzWy4n6efDaSWS9dH9D02g0XL58mX/++YeXL19iYGBAqVKlaNasGXXq1MnMGIUQqWBWuTKVLl7QdxiZar/7S349dg+AGR0/oH2NYnqOKPPdvHmTtWvX0rBhQ7766itMTU31HZIQQiQrMx4BzYv5KjdxexbAzAO3AJj4SWVaVS2i54jinDlzhsePHzN16lTs7GT1zW7durF9+3Z69uyJsbExx44do0mTJvoOS4h8JbdMe3D9RSATdnrwxD8cgH4flmJah6oUMJUbciJ90vybExMTw7Jly1iyZAkvXrygfPnyODg4oNVq2bdvH+PHj6dKlSqMHz+eoUOH5pp/XEKInOXCY38m7vIA4PPGZRnycVk9R5S5AgMDuXbtGuXKlePLL7+kcuWcM4G8EEIkZ+TIkfoOQWTAq+BIRrr8h1qj8GmNYoxuXl7fIfHy5Uv27NnDF198QatWrTAwMNB3SDnG7du3MTU1JTY2lgcPHkihUIhsltNzXkyslsWnH7Dy7GO0ChSxMmVeDyeaVbLXd2gil0tToTAmJoZq1arh6OjIL7/8Qvv27bGxsUnQx9fXlwMHDrBu3TrWrl3LlStXMjVgIUTSoh8+xPPLMTguX4ZpxYr6DidDHvuFMXLztbgLmZrFmNahqr5DyjSKonD58mW2b99OgwYNaN26tb5DEkKIbJWX8lVuEqXWMGLzNfzDoqlStCDze9ZEpdLv4iXxC3O0bt0ac3NzvcaS0/z000/89ttvHD9+nMDAQHr06EFsbGyuX4VVCJE57r4KYfxOD+6+CgGga63i/Ni5OtYWOWO+WZG7pXlE4fr162natGmyrzs4OPD555/z+eefc+rUqQwFJ4RIPW1MDOoXL9DGxOg7lAwJiojh8z/dCImKpW5pG37r6YRBDlmFMaPCwsLYvHkzT548YciQITg5Oek7JCGESJOgoCAAChUqlO595JV8lZsoisK0v25ywysYGwtj1g6sh4WJfh9JO3LkCMeOHWPw4MEybdE7Zs6cyeLFizlx4gQffvghAPv27aNbt25oNBpGjx6t5wiFyB8yI+dlNo1WYc25Jyw8eR+1RsHGwpjZ3WrQIQ9O0ST0J03fEExMTFIsEr5LRsoIIdJCrdEyyuU/nvqHU6KQOasH1MXMOG9MX/D69Wt8fX0xNzdn5syZsmCJECJXWrduHQATJ07UcyQiLdb/85S/rr/E0EDF8r51cCxsobdYtFot0dHR3Lt3j0mTJuHo6Ki3WHKq2NhYTp8+Td26dXVtbdu25cCBA1y/fl2PkQmRv+S0nPfMP5wJuzy49jwQgNZVHfilew0cCprpOTKR12T5rcQrV65Qv359vT/aIITI2RRFYcb+W1x88oYCJoasH1wPO8vcv7BHeHg427dv5/bt28yZM4caNWroOyQhhEi3ivKocK5z/qEfvxy5C8B3n1blowr6WygkICCAlStX0qBBA8aPH6+3OHK62bNnJ9neunVrGYghRDbKKTlPURRcLj3nlyP3iFRrsDQ1YmanD+hRt6TUWUSWyPJCYdOmTQkKCsLMTKrcQojkbfj3GduueKJSwdK+talS1ErfIWXYrVu32Lx5M46OjsycOVNWNBZC5HpdunTRdwgiDZ6/CWfM1utoFehRtySDPyqjt1geP37MqlWrqFmzJi1atNBbHEIIkVo5Iee9Co5k8u4bnH/oD0CjcrbM71mTkjb6Gxku8r5MKxSOGjUKtVqdqD02NjazDiGESIFJ6dI4rl2LSenS+g4lzVzv+TL78B0ApneoSssqRfQcUcY9ffqUtWvX0qtXLz766CO52yeEEP8vN+er3CQ8Opbhm64RHKnGybEQs7pW11suun79Ohs2bKB79+40b95ccqIQQryHoijsvf6SmQduExoVi6mRAd+2r8KgRmXyzPztIufKtELhH3/8wcqVKzE2TrjKzqZNmzLrEEKIFBhaWmLZpLG+w0iz+z6hfLUtbrSDc31HhjUuq++QMuT+/fvs3LmT4cOHM2/ePBlFKITIU+7ejXuEtWrV9K9Gn1vzVW6i1SpM2OnB/deh2Bc0ZY2e5vzVaDQ8ffoUlUrFmDFjqFy5crbHIIQQ6ZUZOS893oRFM33vLY7d9gHAybEQC3s5Ud5e5jgX2SNTHz3u06dPokeMR44cmeq7hs+fP+fhw4fUrVsXGxub9/aPjo7mzp07GBkZUalSJbkgF/ma2teXoB07KdS7F8YODvoOJ1WCImL4fNNVwqJj+bBsYX7qor/RDhkVExPD3r17+ffff+nWrRsODg659r0IIURyDh8+DGTsoik35qvcZrnrI47d9sHE0IBV/etSxCr7pwAKDw9n3bp1hIaGMn36dMmJQohcJzNyXlqduO3DtL038Q+LwchAxTetKzKyWXmMDA2yLQYh0l0o1Gg0aLVa3QjC+/fvJ1moCwsLe+++Ll26xKxZs7h27Ro+Pj64urrSvHnzZPsrisKMGTNYs2YNJUqUICIigsDAQFauXEn37t3T+5aEyNVi/fzwX74cy5YtcsWFl0ar8NW263gGROJY2JxV/etiYpQ7E2B4eDi//vorBQoUYPr06RQpkvsfnRZCiKQ0bdo0w/vIbfkqtzl15zULTz0A4Kcu1ahb+v033zObj48Py5cvp2jRokyYMEGKhGkUHR3NtWvXePnyJQYGBpQqVYratWtjZJTl08sLId6SGTkvtUKi1Px44A57/vMCoHKRgvzWy4nqJayzLQYh4qU720RGRjJ58mRWrFgBQOkMzDPz+PFjRowYgZOTU6r2oygKpqamPHz4ECuruAUP5syZQ79+/Xj8+DHFixdPdyxCiOyx4MR9zj/0x9zYkDUD6mFTwETfIaWZWq3mzJkzVK1alc6dO1OnTh0MDHJnsVMIIVKjQYMG+g5BpOCRbxjjdrijKDCgYWmcG5TK9hgCAwOZO3cuzZo1o0uXLpIX0+Dy5cssWrSI/fv3ExkZiYGBAYqioCgKBQsWpHfv3nz99ddUr15d36EKkS9kV87795E/k3Z54B0chUoFw5uWY3ybSpgaZf+UEUIApDtzm5mZcfLkSf74449Er6nVasaOHZvqffXr149OnTql+ouEgYEB3333na5ICDB06FCioqJwd3dP9XGFEPpx+MYrVp59DMCvPWpStVjuW+HY09OTuXPncvXqVQoWLEi9evXkYkgIIYTehESpGb7ZjdDoWBqUKcz3HT/I1uMrisL58+cJDAzkm2++oVu3bpIXUyk2Npa+ffvSoUMHbGxs2LlzJz4+PqjVamJiYvDy8uLPP/9ErVbTuHFjvvrqK32HLITIBJExGmbuv0W/dZfxDo6itK0Fu0Y0Ymr7qlIkFHqV7hGFRkZG/PXXXzRv3pzq1atTv359AAICAujevTsBAQGZFmRqXLp0CYCKFSsm2yc6Opro6GjdzyEhIVkelxAioXs+IUzc5QHE3S3r7JT7RgAfO3aMQ4cO8cknn9ChQwd5FEgIkW+cOXMGgJYtW+o5EvE2rVZh/A53nviFU8zajOX96mTrdB5qtRoXFxfu3bvHuHHjKFq0aLYdOy9Qq9VUq1aNtWvXUqBAgQSvGRgYUKJECbp160a3bt0IDAxk2bJleopUiPwlK3Pefy8CmbDTg6f+4QD0b1iKqe2rUsBUriuE/mXot7BGjRosWbKEzz77jGvXrhEQEEDHjh0pW7Ys586dy6wY38vHx4cxY8bQv3//FAuFc+bM4ccff8y2uITITobW1lh16oShdc6dxyI4Qs2IzdeIVGtoXMGOyW1z1+qH/v7+GBoa4uXlxcSJEylTpoy+QxJCiGz133//ARm7aMoN+Sq3WXT6Iafu+mJiZMDqAXWxL5h9C/wFBQWxcuVKVCoV06ZNw1r+XtPM3Nyc6dOnp6qvjY0N33//fRZHJISAzMl574qJ1bLo1ANW/f0YrQJFrcyY16MmTSvZZ9oxhMgolaIoSmo7x8TEsHjxYmrXrk2tWrWws7MDYMyYMVy6dInHjx/Tt29fFi9enK4RNl5eXjg6Or53MZO3BQQE0LJlS6ysrDh27BgWFhbJ9k1qRKGjoyPBwcEJHmMWQmQ+jVZh2J9XOXvfj5I25hwc0zjXzEuo1Wo5c+YM+/fvZ/DgwdStW1ffIeV5ISEhWFtby+fz/5PzIXIKT09PABwdHfUciYh37JYPI12uAfBbTyc+q1syW4+/Zs0aTExM6Nevn26Rw/xEPp8TkvMh8pLMznl3/4+9+w6PovoaOP7dTTa9F0J6aAkthQ7SuzRBBCwo4s8KKAqCgK+IWEAUFEWwogIiSBMRRQRBQJrUhN5LAqRAetlkszvvH5GVmBCSsMlukvN5njy6s1POLMncnTP33nMtnfErojlxrWBk4/3N/HljQBNcHWretVNUvrJcn8uUzTMYDHz//fe89tpr5OXlERAQQFRUFE2aNOH06dOMGjWKWbNm3VXwZZGSkkKPHj1wcnLi119/LTFJCGBra1tsZWYhqgNDbi758fFY166N2gJ/z+duPs2fp5Kw0xT0dqgqScKkpCQWLVpESkoKY8eOLbHXshBCVHemuFmy9PaqKjmdkMHLKw4D8ET7kEpNEu7du5fo6GieeuopmYvQhHbu3MkPP/zAX3/9VajqcefOnRk+fDiRkZHmDlGIGsNUCcJ8vYEvdpznw02n0ekVPBxteGdQU/qE+5pk/0KYWpladTs7Ow4dOkRmZiaHDx/m7bffpn79+uzduxdra2vee+89PDw86Nq1KxMnTjRpoCdOnDDOQwj/JgkdHBzYsGEDTk5OJj2eEFVN7tmznOt9L7lnz5o7lCK2nkpk3paCuGY9EEETv6ozLOnTTz/Fz8+PqVOnSpJQCCFMwJLbq6okLVvHM4v3k5Wnp11dT17t26hSjmswGFi9ejXLli2jffv2kiQ0kZ07d9K6dWt69erF6dOn6devH6+99hqTJ0+mW7duHDhwgNatW9O9e3diYmLMHa4QopQuXM9i2Oe7ee+3U+j0Cj0b+7DxpU6SJBQWrVxzFGo0GiIjI4mMjOTxxx83Lr906RKHDx/m8OHDnD59utT7u3btGseOHeP69esAHDhwgPz8fOrWrUvdunUBmDNnDnv27OHo0aPk5eXRu3dv4uLi+Pzzz9m7d69xX40bN8bPr+oVRxCiWtLlkHD1ElOWnwHUPNY2mIFR/uaO6o5SUlL47rvvCAoK4rXXXpObICGE+MfSpUsBGD58uJkjqdn0BoWxyw9x8UY2/m72fPJIMzRWFd9W5ebm8uWXX5KYmMjkyZOlaImJZGVl8eijjzJlyhQeeeSR23aASE5OZvHixQwZMqRM91pCiPK5mzbPYFD4bu8lZv56khydHmdba14f0JghLQJQqVSmDlUIkzJpSZ3g4GCCg4MZOHBgmbY7cuQI7733HgDdu3dnw4YNbNiwgREjRhgThY0bNzb+QWm1WlxcXAgPDy9S9evll1+WRKEQ5mbQw9YZKHvm46PLYYtiw8/u9zOor2VX6VMUhb179/LDDz8QGRlJz549JUkohBC3uHbtmrlDEMDs30+x7fS/03l4OlX8EG6tVktSUhKOjo5Mnjz5jlP+iNKzt7fn1KlT2NiUPC2Lh4cHL730Es8991wlRSZEzVbeNu9qag6vrIrhr7MFHaHuqefJ+0Mj8XezN2V4QlQYi6i93atXL3r16lXiOuPHjzf+v4uLC5s3b67osIQQ5bV1BuyYg4qCWkkOqjyG5axAtSMQultupb4ffviBAwcOMHLkSJkDSAghivHSSy+ZO4Qab33MVT798xxQMJ1HU/+Kn87jxIkTfPHFF4wZM4Ynnniiwo9X06jV6jsmCW9lZ2dXgdEIIW4qa5unKAprDl7hjZ+PkaHNx06jZvK9DRnRLgS1WnoRiqrDIhKFQohqRJcDexYAhQuqq1AKlneaABrLepp26NAhbty4QZs2bejfv7/MeSqEELdhbS1fHc3pxLV0Jq4smJ/u2U51K3w6D0VR2Lp1Kz/++COPPPII9evXr9DjidKbOHEiM2fOlL9JISpQWf6+rmfm8uqaI/x+PAGAqEA3PhgWSV1vua8QVY+0LEJUE/ZNmtDo5AlzhwGZCaDLLv49XXbB++4hlRrS7WRlZfHDDz9w5MgRRo4cSZ06dcwdkhBCVHsW015VMSlZeTyzZD85Oj0dG3jxyr0NK/yYq1at4u+//2bcuHHG6YCEZZg3bx5vvfWWJAqFsAC/HY3n/348wo2sPDRWKl7qEcqznepiXQlzxwpREUzesmi1WoYMGUKfPn147rnnsLKyMvUhhBAWTGvrBdhiR27RNzUO4ORT6TEV5+zZs3z55ZcEBAQwbdo03NzczB2SEEJYvNmzZwMwYcIEM0dSs+TrDbyw7BCxyTkEeTgw7+FmWFXgMLb09HQSExMJCAigR48euLu7V9ixRMm8vLzQarVFlufmFvM9SwhhUsW1edp8LddzruNl70WuzorpPx9jzcErADSs7cycYZE08av4KSGEqEgV8ghKq9Xy/vvvs3DhQg4ePFgRhxBC/Efu+QtcmzIF35kzsa1rvp5xMzddxCu/D89br6XwLYwK2o42+7BjrVZLbGwsiYmJ3Hfffdxzzz1SeUwIIUrJ29v7rvdhKe1VVTLrt5P8dfY6DjZWfDGiBW4OpZ/PrqwuX77MggULaN68OcOGDauw44jSyczM5MaNG9jaFi5YI9OkCFHxbm3z9AY98w/PZ8nxJWj1WmzUtihpnUiO64papebZzvV4qUcDbK2lo5So+kyeKLSzszMWGjl16pSpdy+EuA1DTjY50dEYcm4z7LcSbD6ewKLdl1AzhAERvoScWVQw3FjjUJAk7Pqq2WIDOH36NN9++y3BwcE8++yzZo1FCCGqoscff/yu92EJ7VVVsvbQFb7ccQGA2UMjaVjbpcKOdeDAARYtWkTfvn3p3bt3hR1HlF6bNm3QaDRFhhg/8sgjFTZyKysri5SUFHx9fWV0mKjRbm3z5h+ez5dHvjS+zjPkojhtwjtIxWf9/o8WwR7mCFGIClGhk1qEhYVV5O6FEBYkIV3LxFXRADzRoR4h/QeAblrBnIROPmbtSZiXl8fatWv566+/GDRoEF26dDFbLEIIIURpHb2SxqTVBcVLxnStR99w34o71tGjLF68mCeffJLIyMgKO44oWW5uLleuXDHOCblt27Zi1/v6669Nfmy9Xs/YsWP56quvcHR0xMrKinnz5vHQQw+Z/FhCVAU3hxk7aZxYcnxJkfdVKsBlB038HSo/OCEqkMx+K4S4a3qDwrgfDpOSraOJnwuv3PvPQwKNvdkLlxgMBnbu3MmlS5f4v//7P3x8LGOORGEeR44cISEhgcaNG+Pn53fH9XNzczl27BipqalERUXh4VH80+LTp09z+vRp2rVrh6enp0mOLYQlunbtGgC+vhWXsBIFrmfm8szi/eTmG+ga5s34nhXzAF6r1bJ+/Xo6duzIG2+8IfMRmll+fj73338/u3fvxsGhaPIhOTn5tm3R3XrvvfdYsWIFhw8fplGjRnzxxRc8+uijNG3alKZNm1bIMYWwRHqDno8PfsyS40vQKTps1DbkGfKKXVerz+F6znUCnAMqOUohKk6py/Ckp6cX+/9CCPH59nPsOncDe40VHz/czCLm5sjPz2ft2rVMnjyZzp07M3HiREkS1mCZmZl0796drl27MnXqVOrVq8fbb79d4jZbt26lfv36DB8+nGnTphESEsKXX35ZaJ0dO3bQo0cPunfvzoABAzhy5IhJji2EpVq6dClLly41dxjVnk5vYMzSg1xN01LHy5G5D1VM8ZLr16/z3nvvcfnyZdzd3SVJaAEcHR1p2rQpTz75ZJH3jh49SufOnSvs2J9++ilPPfUUjRo1AuCZZ56hbt26fPHFFxV2TCEsjd6gZ8SGEXx97Gt0ig6APEMeilL8+vbW9njZe1VihEJUvFInChcvXsynn35KUlISDz30EDk5ORUZlxCijGz8/fF7bxY2/v6VetxDl1P44PfTAEy/rwn1vM0/uXZsbCwzZ87k6NGjvPjii6jVpb7UiWpq6tSpXLhwgVOnTrF7927WrVvH1KlT2b59e7Hr5+bmMmzYMAYPHsyJEyfYsWMHv/zyC2PGjOHYsWPG9a5evcqkSZPYtWuXyY4thCWLiIggIiLirvZhrvaqKnnnlxPsvZCMk601X45ogau9xuTHOH36NDNnziQ0NJQXX3wRG5uKK5AiyubLL7/k6NGjzJkzx7js119/5Z577qFnz54VcsyEhARiY2Np165doeXt27dn//79FXJMISzRRwc/IuZ6TJHlt6t9+GijR7GztqvgqISoXKUeetymTRsmTpzId999x5NPPolOp8Pe3rzVS4UQ/7Jyc8P1vvsq9ZgZWh1jlx8i36DQL8KXoS3N3+X+9OnTfPzxx/Tq1Yu+ffsWmfxb1DyKorBkyRLGjx9vHBbcs2dPmjdvzuLFi+nUqVORbU6dOsX169cZMWKEcVnHjh0JCgpi0aJFvPfeewA8+OCDAMTFxZns2EJYsl69et31PszRXlUlK/fH8u2uiwB8MCyS+rWcTX4MRVFYvnw5gwYNomPHjibfv7g7Dg4OrFmzhrZt29KsWTOOHj3KpEmTmDt3boUVY7t+/TpAkekzvLy82Llz5223y83NJTc31/haRp6JqujWuQi/O/ZdieveHIZsb23Po40eZUzUmEqKUojKU+o76Ly8PAwGAzqdDp1Oh8FgqMi4hBBllJ+cTPqGDbj06YN1Bc1d819T1x4lNjkHfzd7Ztwfjup2j9oqQXx8POvXr2fQoEG89tpr1K5d22yxCMty5coVbty4UWRy/qioKKKjo4vd5uYw9XPnztGiRQugYAhxYmIihw8frtBjg9x4ierNHO1VVXE4NpX/W3sUgJd6NKBXE9O2ZXq9nhUrVmBra8vrr79u0n2L8svOzqZjx440a9aMqKgomjVrRmRkJAsXLqRfv344ODjwyy+/0K1btwqL4eboC51OV2h5Xl5eiZWPZ86cyfTp0yssLiEqkt6gZ/7h+Sw5vgStXouN2gYduhK3mdNlDvXd6uNl7yU9CUW1VerxeEePHmXIkCGsW7eOdevWYWtrW5FxCSHKSHftGglvvY3un4nmK9q66KusPXwVtQo+eiiqQoZFlYbBYGDz5s288847uLq64uHhIUlCUUhqaipAkcnfPT09je/9l4+PDyNHjuSFF17g448/5vvvv6d///7Y29uTkpJSoceGghsvV1dX409gYGCpjylERTp48CAHDx68q31UdntVVSRmaHluyQHy8g30bOzD2G4NTLr/zMxM5s6dy9mzZyt0njtRdjY2NgwbNoysrCzmz59P586dcXFxYeLEiVhbWzN48GB0Oh0JCQkVFkNAQMGokPj4+ELL4+Pjje8VZ8qUKaSlpRl/YmNjKyxGIUxt/uH5fHnkS7R6LVDyXIQ3RXlHEeAcIElCUa2Vukfh0KFDjTc6ixcvlmHHQtRg8WlaXvuxoGjD890a0DLEPD1CsrOz+fTTT0lOTmbs2LE0aGDamypRPdycdys7O7vQ8uzs7BLn5Fq4cCHfffcdW7duJScnh7Fjx/LDDz+U6UatvMeeMmUK48ePN75OT0+XZKGwCFu2bAGgefPmZo6kesnLNzD6u4PEp2up5+3IB8MiUZuweEl8fDwff/wxwcHBjBkzBjs7ucG1JNbW1kyaNMn4Oicnh5iYGA4fPmz8WbZsGVlZWYSEhHDhwgWTx+Ds7EyLFi3YuHEjDz30EFDQm3Dz5s2F2qP/srW1lQ4kokrS5mtZcnxJkeUlDZCK9I7Ezc6t4oISwkKUOlF4a2+I/85dIYSoORRFYeKqaNK1+UQEuPJCt/pmiSE6Ohpvb29CQ0Pp2bOn3PSI2woKCsLKyqrIPIKxsbHUqVPnttup1WpGjBhRaJ7CCRMm0L9//wo/ttx4CUvVu3dvc4dQLU3/+Rj7L6XgbGvNlyNa4mxnul76qampxMfH07lzZ3r27CkFvqoAe3t72rRpQ5s2bYzLDAYDZ86c4fjx4xV23GnTpjF48GCioqJo164dH3zwATY2Njz33HMVdkwhzOV6znVjT8LiqFCh8G/3wijvKBb2WlgZoQlhduX+pqAoCllZWaaMRQhRBXy35xI7zlzH1lrNB8Mi0VhV7g1HSkoK8+bNY9myZdja2jJgwABJEooS2dnZ0blzZ9asWWNclpqayh9//MG9995rXBYTE1OoEnFaWlqh/axbt45Lly7xxBNPmPzYQlQV4eHhhIeHmzuMauX7vZdZuvcyKhV89HAUdb2dTLJfRVH49ddfef3112nYsCG9e/eWJGEVplarCQsL4/7776+wYwwYMICVK1fy008/Gdu67du3SycRUS0dupAPhuIfythb27P9we380P8HPu/xOTse3MGSvkuwsZbq8KJmKHc50KysLPz8/GSCdSEshJWjI47t22Pl6Fhhx7hwPYt3fj0BwKR7G1ZIJcaS7N27l+XLlxMREcG0adNwcHCo1OOLqmvmzJl06tSJUaNG0a5dOz777DNCQkJ48sknjet8/PHH7Nmzh6NHCwoJfPjhhyQkJNC1a1dOnTrFe++9x9tvv02zZs2M28TFxXH48GFu3LgBwO7du8nMzCQ0NJTQ0NBSH1uImqQy2quq4sClZKatK7jmTOgVRreGPibZb15eHosWLeL8+fNMnDhRHqhZuKysLB588EHeeuutQm1McbZt28asWbP49ddfKySWQYMGMWjQoArZtxCWIC1Hx/R1x1hz6Ao23h2w9dwK/xlu/GijR3Gzc5NhxqLGKneiELht5ePs7Gx69erFX3/9dTe7F0KUgU1ICEELv6qw/efrDYz74TBanYF76nky8p6QCjvWf+l0OnJzc9mwYQMjRoy445doIf6rdevW/P3333z22Wf89NNP9OrVi5deeqnQfLuRkZGFhvtOmzaNb775hh9//BE3Nzd++eUXOnXqVGi/p06d4rPPPgOgX79+7Ny5k507d/LII48YE4WlObYQVcUvv/wCFPy+l1dFt1dVRUK6lue+O4hOr9A3vDaju9QzyX71ej2zZ89Go9EwZcoUXFxcTLJfUXEcHBzo06cPPXr0oG7duvTr14/mzZtTq1YtDAYD165d4++//+ann34iPT2d999/39whC1El7TiTxCurYriWpkWtgicaP4uddz2WnVpKTn4O9tb2PNroUcZEjQFM0+YJURWpFOVOdX0Ky8zMJDg4mGbNmrFjxw6+++47IiIiaNCggXE4w40bN6hXr16JFR0tQXp6Oq6urqSlpcmXKFHlKXo9hpwc1Pb2qKysTL7/T7acYfbvp3G2tea3cZ3wd6ucJMehQ4dYunQpjz76KFFRUZVyTGF+cn0uTD4PYSlmz54NFMzXWV4V3V5VBbn5eh78fA+HY1MJ83Fmzeh7cLS9q+f3AFy6dAmDwUBCQgItW7bE2vru9ylKZsrrc3p6Ot9++y3Lly9n//796HQ6oGAai3bt2jF8+HAeeeQRi37QJO2VsETZefnM/PUkS/ZcAiDE04E5wyJpEVxQh0Gbr+V6znW87L0KVTM2RZsnhKUoy/W5zN8eHBwc+PTTT9m1axdbt25lzJgxJCUl4eDgQNOmTQkPD+fKlSuEhYWV+wSEEGWnPXmSiw8MIWT1KuybNDHpvo9eSWPu5jMATB/YpFKShFlZWfzwww8cOXKEhx9+mMjIyAo/phBCiJKNHDnyrvdRke1VVaAoClPXHuVwbCqu9hq+GNHCJEnCnTt3snz5cp544gnatm1rgkhFZXNxcWHs2LGMHTsWnU5HUlISarUab29vrGpoUl2Iu3XgUjIvr4jm4o1sAEa0C2Zyn4Y42Px73bWztiPAOaDItqZo84Soisr8rUStVjNs2DDuu+8+Dh06xLZt27h27RoxMTFER0cTExODm5sb7777bkXEK4SoZLn5esavOEy+QeHeJrW5v5l/hR/TYDDw7rvvUqtWLaZNm4abm1uFH1MIIcSdeXl5mTuEKu+7PZdYsT8OtQrmPdyMYM+7m6tRr9ezevVq9uzZw5gxY2jYsKGJIhXmpNFo8PPzM3cYQlRZufl6Ptx0hi+2n8OggK+rHe8PiaRDg9K3Y9LmiZqq3I8v7ezs2LZtGwC+vr74+vrSu3dvkwUmhLAM87ec5XRCJp6ONrxzf1NUKtWdNyonrVbL2rVrCQkJ4fnnn6dWrVoVejwhhBCiMu09f4PpPx8HCoqCdQr1vut9rl+/nuPHjzNlyhS8ve9+f8KyGAwG1q5di5OTE7169TIunzt3Li+88IL0NBSiGMeupvHyimhOxmcAMLi5P9MGNMHVvvgqx0KIwsqUKFQUBYPBUOoGKT8/X+ZGEaIKO3Y1jQV/ngPgrUFN8XSyvcMW5Xf69GkWLVqEh4cHvXv3xt3dvcKOJYQQony++qqgCMlTTz1l5kiqnqupOYxeepB8g8KASD+e6VT3rvYXHx/Pxo0beeCBB+jTpw82NjYmilRYkgkTJvDbb7/RoEEDvvnmG7777jusrKyYPHkyzz33nCQKhbhFvt7AZ9vO8dEfZ9DpFTwdbZgxOJzeTWqXa3/S5omaSl2WlbOzs4mIiGDlypXGyXVvt97ChQulMqkQVZhOb2DiyhjyDQp9mtamb7hvhR3r559/5pNPPqF79+6MGzdOkoRCCGGhsrKyyMrKMncYVY5Wp+fZJQe4kZVHY18X3nsg4q56zB89epR3330Xd3d3nJycJElYjf3www/8/PPP/PTTT/Tp04ennnqKMtaiFKJGOJ+UyZDPdjP799Po9Aq9m/iwcVyncicJQdo8UXOVqbufo6MjH374IS+99BLPP/88PXv2pHnz5tSqVQuDwcC1a9f4+++/2bRpEw0bNuSLL76oqLiFEP9hFxpKg107sXJ2vrsd6XIgM4Gv9mdw/Fo6bg4a3hzY1DRB/sfFixfJycnB1dWV//u//8PHx6dCjiOEEMI0Xnzxxbveh8naqypCURReXXOEI1fScHfQ8PljLbC3KV8vMEVR2LRpE+vXr+exxx6jVatWJo5WWBpHR0cMBgMAI0aMICkpySR/h0JUFwaDwuLdF3n3t5NodQacba15474mDG7uf9dTGMnfmqipyjwuuFevXhw7dowNGzawfPlyPv30U65cuYJarSYwMJCOHTvy888/07lz54qIVwhxGyqNBmsPj/LvwKCHrTNgzwLQZfO4YkO+dV8C+7+Ft7Nphxzn5+fzyy+/sGnTJoYPH06nTp1Mun8hhBCW667bqyrmm50XWXPoClZqFfMfaU6gh0O595Wens6ePXt4+eWXCQ4ONmGUwlK98sorvP/++8YOGC+//DIvvvgiubm5Zo5MCPO7kprDxJXR7Dp3A4AO9b14b0gEfm72Zo5MiKpNpdTgvuvp6em4urqSlpaGi4uLucMR4q7kXb5Mwsx38ZkyGZugoLLv4I+3YMcc4N9LggLQcQKq7lNNFSZXrlzh66+/BuCJJ54gICDAZPsW1YdcnwuTz0NYipycHADs7ct/E3bX7VUVsuvsdR77+m/0BoXX+zfmfx3qlGs/qampfPnll3Ts2JG2bduaOEpxNyrj+nzp0qVCiWGDwcCJEydo3LixxRV9k/ZKVAZFUVh1II43fz5ORm4+dho1r/ZtxKNtglGrTfc3YYo2TwhLUZbrc5nmKBRCWC59RgaZW7eiz8go+8a6nIKehBR+bqACVHsWFLx/t/Hp9aSnp7Nr1y4iIiKYMmWKJAmFEKKKmT9/PvPnz7+rfdxVe1WFxCZnM+b7g+gNCoOb+/NE+5By7efChQvMmDEDb29vWrRoYdoghUU5ceIE77zzTpHlQf9JqKvVapo0aWJxSUIhKkNSRi5PLz7AxFUxZOTm0zzIjQ0vdmJEuxCTJgnBNG2eEFWRyRKFWVlZvPfeeyxYsACtVmtc/sILL5Cfn2+qwwghKkJmAuiyi39Pl13w/l2Ij4/n/fffZ+nSpQwdOpSBAwdKRXQhhKiCgoKCiiQtRFE5eXqeWXKAlGwd4f6uzLg/vFxJnYMHD/LBBx/Qs2dPHn/8cTQaTQVEKyzFG2+8gaOjY6Flo0ePxs7ODn9/f7777jszRSaEZdhw5Bq9525n84kENFYqXrk3jJXP3UMdL8c7b1wO0uaJmspkd+rPPfccV69eJSoqin79+vHTTz/h5OTEl19+yfvvvy9JASEsmZMPisYeVXE9BzUO4FS+IiMGg4GtW7fy008/0bFjRwYOHHiXgQohhDCnYcOGmTsEi6coCq+sjuHEtXQ8HW34/LEW2GnKVrzEYDCQlJRERkYGo0aNonHjxhUUrbAkmzdv5qOPPjK+3rZtG59++ikjRoxArVbzxBNP0KhRI+lZKmqctGwd09YdZe3hqwA0rO3Mhw9G0ci3Yoe3S5snaiqTZe82btzI4cOH8fPzY+vWrTz00EOsXr3aVLsXQlQkjT0ngh+j0ZkvKNzhQQVtR4OmfPNy/PLLL+zZs4fnn3+e0NBQk4QqhBBCWLIvtp/n5+irWKtVLBjevMyT6ufk5LBw4UKysrKYNGlSBUUpLI1erycrKwsfn38fzv7444+0adOGRYsWARAYGMjs2bNZtmyZucIUotJtP53EK6tiiE/XolbBqC71eLF7KDbWMouaEBXFZIlCPz8/kpKS8PPzo2vXrly/fp2HH36YGlwrRYhKpfHxodakSWh8yt77Lzkrj8fOdWOkPpHnbDaiMeQU9CRsOxq6vlqmfSmKws6dOzl8+DBPPvkkvXv3xsbGpswxCSGEsDznz58HoG7duuXex920V5Zu2+kkZv12EoBpAxrTpq5nmbZPTExkwYIFuLu788ILL1REiMJCWVlZ4e/vz7Fjx2jatCkAW7ZsYciQIcZ1Hn30URmdIWqM7Lx8Zvx6gu/2XAagjpcjs4dG0iLYvdJiMEWbJ0RVZLJE4dtvv8306dNZs2YNAEOHDuXKlSv8+OOPpdreYDDw+++/c/LkSYYMGVKqIgc5OTn89ttvJCQkEB4eTvv27e/qHISoyqy9vPB8YmS5tn37l+PcyDHwS+0nee65BZCTVDDcuIw9CVNTU1myZAlxcXE8/vjjUiFMCCGqmZvf8yZMmFDufdxNe2WxdDnExV5kwvdnMShqHmwZyKNtg++83S2SkpKYOXMm7du3Z/DgwajV0lumpnn00UcZNWoUX3zxBefOnePIkSN89dVXxvddXV1JTk42Y4RCVI79F5N5eWU0l24UzKH+eLtgJvVpiINN5U5nZoo2T4iqyGR/af3796d///6Flr300kuMGjUKW1vbErdds2YNEydOpHbt2uzatYuoqKg7Jgrj4+Pp3LkzNjY2REVF8dprr9G/f3++/fbbuz0VIaokfVoaWbt349iuHVaurqXebufZ66w5eAWVCmYODkdj5wh2ZZ8Q+OzZs8yfP5+IiAhef/31IpNxCyGEqPratGlz1/sob3tlkQx62DoDZc98AnQ5bFNs+MXjfu6775NSFy9RFIV9+/bh6+vLM888Q6NGjSo4aGGpJkyYwG+//Wack7JNmza0atXK+P6pU6ekZ5Oo1nLz9Xyw6TRfbD+PooCfqx3vD42kfX0vs8RjijZPiKqoXInCnTt3snTpUhYsWGBcpigK58+fx93dHQ8PD+PyOyUJAdzc3Ni0aRM2NjYEBgaWKobJkydjb2/Pnj17sLOzIyYmhmbNmnH//fdLl3xRI+XFxXHlpXGErF6FfSlvvLQ6Pf/34xEAHmsbTLOgsnflz8jIYOfOnTRu3Jgnn3zSOFxGCCFE9dOxY8e73kd52iuLtXUGyo45qCiYasdBlceQ7BWodgRC96l33Fyn07Fs2TKOHDnCxIkTS/09WFRPzs7O7Ny5k02bNpGcnMyAAQMKJZwXLVrEgAEDzBihEBXn2NU0xv8QzamEDAAeaB7AtPsa42JnvmrvpmjzhKiKyjWm4c0336Rdu3bG1waDgZ49e1K/fn18fHx4/fXXy7S/bt26lenpmMFgYPXq1YwcORI7OzsAIiIiaN++PStWrCjTsYWoyeZvPcvFG9n4uNgyoXdYmbc/fPgw06dP5/LlywQEBEiSUAghRM2hy4E9C4xJwptUKLBnQcH7JUhPT+fDDz8kNjaWV199lVq1alVktKKKsLa2pk+fPgwfPhwXl8IVXf39/Rk1apSZIhOiYuTrDcz74wwDP9nJqYQMY7X4OcMizZokFKImK3OPQkVR2L17N8uXLzcuW79+PVu2bGHGjBmo1WrefPNNOnToQK9evUwa7E2XL18mMzOThg0bFlresGFD9u3bd9vtcnNzyc3NNb5OT0+vkPiEqArOJGTw2bZzALwxoEmZGmKtVsuyZcuIiYnh4YcfplWrVqUeYiWEEKLq2rlzJ4DMCw2QmQC67OLf02UXvO8ectvNFy5ciIeHByNGjJCiX6JU3nzzTXOHIIRJnUvKZPyKaKJjUwHo3cSHd+4Px8vpzqMSK4O0eaKmKleiMD8/v9ATrvXr19OzZ0+mTJkCgJOTE/PmzauwRGFGRkF3ZDc3t0LL3d3dje8VZ+bMmUyfPr1CYhKiKjEYFF798Qg6vUKPRrW4t2ntUm8bHx9PdnY2er2e119/HXf3yqs8JoQQwrx2794NyE0TwHmtI76KLfaq3KJvahwKioIV48CBA1y4cIHnnnsOOzs7edAmhKhxDAaFRbsv8u6Gk+TmG3C2s+bNgU0YFOVvUddEafNETVXmocdqtZo6deqwd+9e47LNmzfTs2dP4+t+/fpx4sQJ00RYDAcHB4AiScH09HTje8WZMmUKaWlpxp/Y2NgKi1GIyqa2s8O2cSPU/wzHL8maQ1fYdzEFe40V0wc2LVWDrNVqWbp0KTNmzMDDw4OnnnpKkoRCCFHDDBo0iEGDBt3VPsrSXlmqDK2Op78/xlf6Pv8ZeAyggrajQWNfaKnBYGDdunUsXryYhg0bYm9vb1E3xEIIURniUrIZ/tVepv98nNx8Ax0beLHxpU7c3yzA4q6JpmjzhKiKylXMZNSoUYwcOZJZs2Zx7tw5Lly4QL9+/YzvazQasrKyTBbkfwUFBWFra8u5c+cKJSjPnTtHgwYNbrudra1tqYqrCFEV2darR901a0peSZdD+vUrfPjrSUDF2O4N8HezL3kb4PTp0yxatAgPDw9ef/31Ir15hRBC1Az169e/632Uqr2yYAaDwrgfojmXlMUy5+H8r3kIjge/KBhurHEoSBJ2fbXQNlqtlm+++Ya4uDgmTZqEn5+fmaIXVYnBYODUqVNSCVtUC4qisPJAHG/+fJzM3HzsNVa82rchj7YNtrgE4U2maPOEqIrKlSh89tln2b17N4MHDwZgyJAhhRqwo0ePEhZW9sIIJdm8eTPx8fE8+uijaDQa+vbty/fff88zzzyDWq3m8uXL/PnnnyxcuNCkxxWiWjDoYesM2LMAF102mxQb1rgMZNg9n95x0+vXrzNv3jwGDRpE165dUavLVQNJCCGEqBY++uMMm08kYGOt5tMRrXEM7AU9JhfMSejkU2xPwgsXLgAFo1ucnJzMEbaogrKzs2nVqhWZmZnmDkWIu5KYoeXVNUfYfCIRgBbB7sweGkkdL0czRyaEKE657vg1Gg1Lly7l/Pnz7N+/v1BhE4AFCxbw0EMPlXp/x48fZ+7cucYk36pVq5g7dy579uwxrrN8+XLeffdd4+tZs2Zx4sQJ+vTpw+uvv063bt3o1KkTjzzySHlOSYgqT3v8OCfDI9AeP170za0zYMcc46TrDqo8huetxGbHu0XX/cfFixd55513SE9PZ/bs2XTv3l2ShEIIUcOtWrWKVatW3dU+SmyvLNzGY/F89McZAN4Z1JTIQLeCNzT2BYVL/pMkPHXqFK+88gpubm6MGjVKkoRCiBrn1yPX6P3hdjafSMTGSs2kexuy4tl2VSJJaIo2T4iqqFw9Cm+qU6cOderUKbL8gQceYOjQoaXeT2ZmJhcvXgTgxRdfBAqSFLdWNe7Zs2ehYcUNGjTg6NGjLF26lISEBN544w0eeughrKysynk2QlRtiqKg6HQoyn9mS9LlwJ4F8J9ZlFRQsLzThEI3Nvn5+fzyyy9s2rSJfv36ERwcLH9XQgghAIzf1+7GbdsrC3cmIYPxPxwGYOQ9IQxtGVji+tu2bWPVqlUMGzYMX1/fSohQCCEsR1q2jmnrjrL28FUAGvm68OGDkTSs7XKHLS2HKdo8Iaqiu0oU3s6IESPKtH7r1q1p3bp1ies8+OCDRZb5+Pgwfvz4Mh1LiBonM8HYk7AIXXbB++4hBS91Ot577z0MBgOTJ08mICCg8uIUQghh8UaNGmXuEMwiLUfHM0sOkJWnp21dD/6vX8lzxq1cuZK9e/cyduzYEufPFkKI6mjb6SReWRVNQnouahWM7lKfsd0bYGNdtUYn1dQ2T4gKSRQKISyIkw+KtT2q/Jyi72kcwMkHvV7Ptm3bCAoK4t577yUyMhJra7k8CCGEKMzR0fKHipma3qDw0vJDXLiehb+bPfMfaY7Gqvib3aysLDIzM3F0dGTKlCl4enpWcrRCCFG5tPlarudcx8veC73emhm/nmDp3ssA1PVyZM6wSJoFuZs5yvKpiW2eECCJQiGqP409f3k9SIdr31K4oJgK2o4mITmdb775huzsbMaMGSPVvYQQQohbfLjpNFtPJWFrrebzx1rg6WRb7HpXrlxh/vz5tGjRggceeKCSoxRCiMqlN+iZf3g+S44vQavXYqO2Q53eiaTYLoCakfeEMOnehtjbyBRGQlQ1kigUopqwrVePuj+vQxNYeM6kk/Hp/O9yT8aqUxlttxGr/JyCnoRtR7PduiOr3nmHDh06MGjQIGxsbMwUvRBCiKrgk08+AeD5558v9z5u115Zot+OXuOTrWcBmPVABE39XYtd7/Dhw3zzzTf07NmTvn37VmaIQghhFvMPz+fLI18aX+cZtCiOv+MZoPDxva/Svr6XGaMzDVO0eUJURZIoFKKaUNvZYXvrPEi6HJSMeGb8dBmdQcXxxmOxevAzyEwgJd8WxcqWi+vX8/zzzxMaGmq+wIUQQlQZpnigVKS9slCnEzIYvyIagCc71GFQM/9i1zt48CDffvstI0eOpHnz5pUZohBCmIU2X8uS40uKLFepQO32Fy1CqkeFd+lEIWoqSRQKUU3orlwh6dNP8X72GTSnF8GeBah02Xym2PCtTT/u6zMPxdqOncfiWLlyJYMHDy5z4SEhhBA12zPPPHPX+zC2V6NGofEvPvlmbmk5Op5dcoDsPD3t6noypU/DIuvk5eWxefNmmjdvzmuvvUatWrXMEKmozjQaDU899ZS5wxCiiISsRLR6bbHvafU5XM+5ToBz1S+KaIo2T4iqSBKFQlQT+amppK1ajXd4OpxYDCgAOKjyGKX6kbTdIXxywZO4uDiefvppmjZtat6AhRBC1Eg32yv3hx+2yESh4T/FSz55pBnW/ylekpyczIIFC7C1taVXr15SAExUCFtbW+bOnWvuMIQo5GxiJuNWnEVx1KBS64q8b29tj5d91R92LERNJt9qhKhGVFYK1qd/4GaS0Lgc+HZTDC7hfXj99delgpcQQohySU5OBsDDw8PMkVScDzeXXLzk3LlzfPrpp0RGRvLwww9LklAIUSMYDArf7rrIrN9OkptvwNm3E7j9UWS9Rxs9ip21nRkiNL2a0OYJURz5ZiNENWJtp0d1yzCATIMN36e1xF6lY6zbH6gf+AAkSSiEEKKcvv76awAmTJhg5kgqxm9HrzFvS0HxkncfCC9SvESv17NgwQL69+9Ply5dUKlU5ghTCCEqVWxyNhNXRbPnfEHirGMDL2Y+8DZrztfluxPfkZOfg721PY82epQxUWPMHK3pVPc2T4jbkUShENWFosetXjYKBT0ID2v9+S6tFfU0SQx0iUFt4wBOPuaOUgizuXLlCosXLyYhIYHw8HAee+yxO05SvWfPHn7//XdSU1Np27YtQ4cOLZIYuNN+v//+e7Zv315oG39/f6ZOnWq6kxOikoSFhZk7hApzJiGDl/8pXvK/9nW4v9m/82vp9XrWrl2Lh4cH7733HlZWVuYKUwghKo2iKKzcH8eb64+TmZuPvcaKV/s14tE2QahUKsY2H8szEc9wPec6XvZe1aYn4U3Vuc0ToiTqO68ihLBkmZmZXL9+nfTob1Dq5nBDa8XKlKZ8m9qGoS6HeM59Jy5WedB2NGjszR2uEGZx8uRJwsPD2b17N97e3rz//vv06NGD/Pz8227zzjvv0KNHDzIyMvDx8eGNN95g6NChZd7v9u3b+fvvv4mKijL+yBdPUVUNGDCAAQMG3NU+rL288Hz6aay9LGcOq7QcHc8sOUBWnp62dT2Y0vff4iVZWVnMmzePo0ePEh4eLklCYRH0er25QxDVXGKGlqcW7eeV1TFk5ubTItidDS925LG2wYUemtpZ2xHgHFDtkoRgmjZPiKpIpSiKcufVqqf09HRcXV1JS0vDxcXF3OEIUWaZmZksWbKErIw0OLIKFAMAuSp7nMnkf0GXcdIY4J4Xocc0UMvNjagaTH19HjhwIOnp6WzZsgWVSsXVq1epU6cOn3/+OSNHjiyyfkpKCl5eXnzxxRc8+eSTACQlJREcHMyKFSvo379/qff73HPPcf36dVatWlXu+KW9EqLiGAwKTy3ez5aTifi72bPu+fbGeQkTEhL45JNP8PX15X//+x92dtXvRljcnYq+Pv/555+MGDECJycnPvnkE7p16waAnZ0dqampFvc7Ke1V9fBLzDVeW3uElGwdNlZqxvcK5emOdbFSy3QLQlRVZbk+S49CIaowbUYKWSkJ1Le5TrOwAJo1DKJZwyCaBHuSp1ih1f/TmLf6nyQJRY2l0+n47bffePjhh41PwP38/OjatSvr1q0rdptLly5hMBho1qyZcZm3tzf+/v78+OOPZd7v6dOnGT9+PNOnT2fLli0VcZpCVIpjx45x7Nixu9qHPjOLrL1/o8/MMlFUd2fu5tNsOZlYpHhJXl4eJ0+epHXr1jz33HMWl5ARNcPLL7/MtGnT+OWXX5g1axZbt241d0iiGkvNzmPsskOM+f4gKdk6Gvu6sO6F9jzXuV6NTBKaos0ToiqSOQqFqAp0OZCZUDDHoMYecjNh8xuweylc9MW5YRCu9rf+Od8ynNLaXuYmFDXapUuXyMvLo06dOoWW16lTh507dxa7TYMGDbC3t2fDhg00b94cKBhmfPHiRXx9fcu0X5VKRWBgILVr1+bKlSsMGDCARx55hC+//PK2Mefm5pKbm2t8nZ6eXraTFqKCbNiwAYAmTZqUex95ly5y+fHHCVm9Cvu72I8pbDwWz8f/FC+ZObigeImiKPz+++9s3LiR999/X4YaC7O6fPkyAwcOxMvLizVr1jBgwAAcpTCdqAB/nkpk0uoYEtJzUatgTNf6vNCtATbWNbdvkSnaPCGqIkkUCmHJDHrYOgP2LABddkHSzzcSruwHQz7kl+LmpdlwmZtQ1Gg5OTkAODs7F1ru4uJCdnZ2sds4Ojry6aefMmrUKLZt24aXlxd79uwhKirKuL/S7ve1117D39/f+Hro0KF07NiRIUOG0Lt372KPP3PmTKZPn17GMxWi4nXp0sXcIZjM2cQMxv9wGIAn2ocwuHkAOp2OJUuWcOrUKcaOHStJQmF2vXr1YtOmTTz88MM4OjqyfPly+vXrV+Icu0KURVZuPu/8eoLv914GoK6XI3OGRdIsyN3MkZlfdWrzhCiLmvt4QAhLoMuBlIsF/y3O1hmwY05BkhAgPwcldk9BkvAWGVoDaTn5xp9rmWpyVXYoKg30kGSDqNluzsGRmppaaHlKSkqJ83M8/vjjnD17lmeffZa+ffuya9cuatWqhdc/BRhKu99bk4QAHTp0ICgoiF27dt322FOmTCEtLc34Exsbe8fzFKIytGzZkpYtW5o7jLuWrtXxzOKC4iVt6njwat9G6HQ6Zs+eTWJiIq+++iohISHmDlMIPvjgAy5dumR8XatWLb7//nueeOIJrK2lz4e4O39fSObej7Ybk4Qj7wnhl7EdJUn4j+rS5glRVtK6CGEO/+0pqHEoqErc9VXjXIKG3GzYPR81hesN3To7iJ2VgiM5nL0YV2idZE1tku0b86E2iqiffiUyMpLQ0NBCFcqEqCkCAwNxdnbm+PHj3Hvvvcblx48fv+NQEj8/Px544AGgYDjwrl27mDBhwl3vV6vVllix0tbWFltb2zuemxCi7AwGhXHLD3P+ehZ+rnbMH96cpIR4VCoVrVq1onPnzmg0GnOHKWqg3NxcEhISCAoKMi7z8fFh8uTJhdZr0KBBidNXlFd+fj6rV69m165dWFtb06FDBwYNGiTfH6shrU7PB5tO8+WO8ygK+LvZ8/6QCO6pbznV6IUQ5iM9CoUwh//2FNRlo+yYw9kVr/LuhpM88uUe+s5cjTr/Nj0N/+GkMfBYcDwjAy4W+hnvs4cZfb158PGnyc3NZfHixVy7do3Vq1ezb98+45BJIWoCtVrNsGHD+Oabb4xDgg8cOMCuXbt46KGHjOt99913vPXWW8bXe/bsQafTGV9Pnz4dtVrN008/Xer95ufns3HjxkLxfPPNNyQmJhZKLgpRVWzevJnNmzff1T5U1hqsfXxQWZsnGTf3jzP8cTIRG2s1nz3WgrNHD/Huu++SmJhIjx49JEkozOaTTz5h3LhxhZYdOHCAiRMn8v777xfpwW5KBoOBxo0bs3btWurVq4e3tzejRo0q1E6K6uHolTTu++QvvthekCQc2iKADS91lCRhMUzR5glRFakURVHuvFr1VJby0EKYjC4HZVYdVMUkAbMUW5rnfk4uNtiSxyHbZ3BQ5ZX9GPe8CL3eLLTIYDDw66+/cuDAARISEggNDeWee+6hdevW5T0TISqMqa/PSUlJdO3aFZ1OR2RkJJs2bWLYsGF8/vnnxnWeeuop9uzZw9GjRwFYtmwZb731Fi1btuTUqVNcvnyZFStW0LFjx1LvV6/Xc//99xMXF0eTJk24fPky+/fvZ/r06caeieb4PIQor9mzZwOU6ffXkvx+LJ5nlhwAYPYD4agu7+Ovv/7iqaeeksnqRbmY8vocFhbG0qVLjUMd4+PjCQ0NJScnB0VRqFevHvv37y8yN64pKIrCpUuXCg2537FjB506deLAgQPGwl53Iu2V5dLpDSzYeo55W86Qb1DwcrJh5uAIejaWooe3U9XbPCFuVZbrsyQKpSETFUxvUDgVn8GByykcvJTC1Ysn+SHn2duu/37DFQTWbUREgBthx+di9dcHQCn/TIsZwlycpKQkYmJiyM7OJigoiPXr1xMeHk5ERARBQUGo1dLZWJhXRVyf8/Ly2Lx5MwkJCURERNCiRYtC72/bto2EhASGDRtmXHb58mV27NiBm5sbXbt2xcHBocz7hYLhyIcOHcLd3Z0WLVrg41O2L+XSXglLERdXMNVFQECAmSMpu7OJmQyav5PM3HxG3hNCY+0xjh49ypgxY8r8NynETaa6Puv1ehwcHMjKyjLOPTh37lzeeecdjh8/jpWVFX379mXQoEFFhiJXlCtXrhAQEMDGjRvp1atXqbaR9soynU3MYPyKaGLi0gDoG16btweF4+FoY+bILFtVbvOE+C9JFJaSNGSi3HQ5kJkATj5FKgqnZes4GFuQFDx4OYXDl1PJyvt3LrISewpqHOCV8//u83ZVj+Nj/n3dbDh0nkzuqaPETphGwGdfYRcWWvpT0ek4dOgQMTExHDlyBDs7O4YPH05ISAj29vYyBEuYhVyfC5PPQ1Qn2lOniX3mGQK/+KJM7dXdSNfqGDR/J+eTsmjna82DIVra33MPTk5O2Nvb33kHQtyGKa/Pnp6eXLhwwbif/v374+fnxxdffAHAli1bmDJlCnv37r3ruEvj//7v/5g3bx6XL1/Gzc2t2HVyc3PJzc01vk5PTycwMFDaKwthMCh8s+si7/12ktx8Ay521rw1qCn3RfrJ3JNC1DBlaa+kmIkQZVFMEZLMZs/wh++T7LmQxr6LyZxNzCyymZOtNc2C3Gge5E6LYHesLj4Puz6kcE9BVUFvwFsTj2or6D4VOk0onJgsJlFpsKuF7loSSr6OstBoNLRu3ZrWrVuTn5/P2bNn8fb2Zvny5Rw5coTGjRsTGRlJeHh4hQx1EUIIUbMo+TryExLK3F6Vl8GgMP6Hw5xPyqKBTSq1LuyD4C54e3tXyvGFKK22bdvyxRdfMGHCBNLT09m6dSsLFy40vt+0adNCFZDv5IsvvmD79u0lrjNnzpxie9SuXbuWWbNmsWjRotsmCQFmzpzJ9OnTSx2TqDyxydlMWBnN3gvJAHQK9ea9ByKo7Wpn5siEEJZOEoVClMXWGSg75qC6meDTZeO4dy6X9RdZlv/vcMU6Xo40C3KjRXBBYrBBLWes1Lc8tav/Glipi696XByNPbiH3P61iVhbW9OwYUMAnn76aa5evUp0dDTbtm1j6dKlvP322xw9epQGDRpQu3Ztkx9fCCGEZVuyZAkAjz32mJkjKb2P/jjD5uMJ1NFdJDTzJA8NH067du3MHZYQRbzxxht06NCBTZs2kZSUhFqtpm/fvsb3k5KSSkza/Vd4eHixU2bcytHRsciy3377jYceeog5c+YwfPjwErefMmUK48ePN76+2aNQmI+iKKzYH8ubPx8nK0+Pg40Vr/ZtxPA2QdKLsIyqYpsnhClIolCIO4hNzmb3+RscOHuNN07Ow/4/8wWqVPCU9W9ktn6R5vX8aBnsjqeTbck7vV1PQQuiUqnw9/fH39+fvn37otPpyM/P58iRIyxfvhwPDw8iIyNp164d/v7+5g5XCCFEJUhISDB3CGWy6XgCH/1xBkdDJuFc5KXx46lbt665wxKiWK1atWLDhg3MmjULKysrvv3220LDw7Zv317qoiIA7dq1K3NSfOPGjdx///3MnDmTF1988Y7r29raYmt7h++9otIkpmuZvOYIW04mAtAy2J05wyIJ9iyaEBZ3VtXaPCFMRRKFQvxHWraOXeeus+PsdXaevc6lG9kABKgSsbfNLXYbe7RM6egB7mXsZVdBPQMrgkajQaPRMHr0aHJzczlx4gTR0dHs3r2bunXrEhMTQ0REBI0bN8bOToY0CCFEdTRu3Dhzh1BqZxMzeeX7PbTI2EeD5u2Z+b/3pTeNsHjdunWjW7duxb736aef8vnnn1fYsTdt2sSgQYOYMWNGlfpbFwXWx1zltbVHSc3WYWOl5uVeoTzVsW7hUU2iTOTvQNRUkigUNV5uvp6Dl1L562wSf529wZG4VAy3dBq0UquIDHClQ4g/+gP2WOlziu5E41DQK9CMbIJDCFq0CJvgkAo/lq2tLVFRUURFRQGQnJzMxYsXWbduHQsXLiQ0NJShQ4fi6+srN2VCCFGNWFlZ3fU+KqO9ytDqeOHL3wm/vg21mx9TH+kq7ZGo8g4ePGisiGxqGRkZDBw4EBcXFw4cOMCjjz5qfO+pp56iS5cuFXJccfdSs/OY+tMxfo6+CkATPxc+GBZFWG2ZW/xumaLNE6IqkkShqJHOJ2Wy9VQSO84ksfd8Mjk6faH369dyokN9LzrU96JNXQ+c7f6p/Gs7BnbM4Y5FSMzAyskRxzatzXJsDw8PBg8ezODBg0lISODYsWNYWVnx7rvvYjAYiIiIIDIyksDAQLlRE0KIKsxgMACgVqvLvY+Kbq8MBoVxC9bid3krSe6N+erVp3C0l57uwnLl5OTw0Ucf8eKLL5ZYhdva2prU1FTmzZvH1KlTTRqDjY2Nsbryf8mcg5Zr66lEJq2KITEjFyu1ijFd6vF8twbYWJf/Gi3+ZYo2T4iqSBKFokbIzdez93wyW08lsvVkIhf/GU58k5eTLR3qe9K+vhcdGnjh63qbL2k3i42UtghJJdIlJJDy3VLcHx2OppjqdZXFx8fHWD1v7NixHDlyhJiYGDZt2oSbmxuvvvoqcXFxBAcHo9FozBanEEKIsvvggw8AmDBhQrn3UZHtlcFg4MMNR7l06SKZLi35bMxgarlIklBYNo1GQ3R0NMHBwQwfPpx+/frRvHlzPDw8UBSFxMRE/v77b3766SdWrVpVqLefqdja2lbIfkXFyMzN551fjrPs71gA6no78sGwKKIC3cwbWDVjijZPiKpIEoWi2rqWlsPWk0lsPZXIzrPXyc77t9egxkpFmzqedA71pkMDLxrWdi5dTzcLLkKSf/06N778Eud7e5s1UXgrR0dH2rZtS9u2bdHpdCQlJZGRkcFXX31FdnY2TZo0ITIyksjIyBKfoAshhLAMPiZoXyqqvdJqtcz6+DNiLl3njHM73h8SQaTcNIsqwNrammXLlrFnzx4++OAD+vfvT25uLtbW1iiKgl6vx9HRkaFDh7Jjxw7Cw8PNHbIwo73nbzBhVTSxyQXTIT3RPoRJ9zbETiPDZE3NFG2eEFWRJApFtaEoCseuprPxWDybTyRy4lp6ofd9XGzpGlaLLmG16NDACyfbu/j1r0JFSCyFRqPBz88PgJkzZxIbG0t0dDR//PEHcXFxNGjQgMTERCIiIqRRFkIIC/XYY4+ZO4RiXb9+nbkff8KZlHxiHFswol0wQ1vKcElRtbRt25YVK1ag1Wo5cOAAV65cQa1WExgYSPPmzWUkRg2n1emZvfEUC3deQFHA382e94dGcE89L3OHVm1ZapsnREWTRKGwfLqc2/bey9cb2H8phY3H4vn9WAJXUv8tNKJSQbNAN7qG1aJrw1o08XOR+fEshEqlIigoiKCgIAYMGADAqVOnOHnyJGvXrsXLy4uIiAj69OmDg4ODmaMVQghhyeLj45n13ntc1fjzt0Morep4MbV/Y3OHJUS52dnZ0b59e3OHISzIkbg0xq84zJnETACGtQxgav/G/86jLoQQJiSJQmG5DHrYOqPIfIDaDpP461xBcvCPk4kkZ+UZN7HXWNE51JteTXzoElYLD0cbM56AKIuwsDDCwsLQarUcP36cEydOkJ6ezieffEKtWrWIioqiUaNG2NramjtUIYSosa5cuQKAv7+/mSMpcOTIEVxd3cgMvIe9CfbUdrFj/vDmaKxk4nkhRNWn0xuYv/Usn2w5S75BwcvJlncHh9OjsYy+qQyW1uYJUVksJlF45coVFi9eTEJCAuHh4Tz22GPY2JSc5Nm3bx8bNmwgJSWFoKAgHnnkERmyWJ1snVG4wrAuG2XHbL7dfo53c4caV3Nz0NC9oQ+9m/jQsYE39jY1c34Oazc3XIc8gLWbm7lDuSt2dnY0b96c5s2bAzB06FAOHDjA6tWrSU5OpmXLlowYMYKcnBycnJzMHK0QQtQsy5YtA+5uYndTtFd6vZ4ffviBgwcPUrvTQ2xKsMfGWs1nj7XA21keKAkhqr4zCRm8vDKamLg0APqG1+btQeHSEaISmaLNE6IqUimKopg7iJMnT3LPPffQoUMH2rRpw5IlS6hVqxZbtmzB2rr4XOa8efOYMGECzz33HCEhIWzatImdO3eya9cumjRpUqrjpqen4+rqSlpaGi4uLqY8JXG3dDko79VBpcsp8laWYks/22/p3CSI3k1q07qOB9bSc6BGiI+P5/r162g0GubOnUtQUBARERFERkbi7+8vQ8urEbk+Fyafh7AUmzdvBqBHjx5miyEzM5PPP/+c7OxsInsN48W1Z1EUeG9IBMNkXkJRyeT6XJh8HnfPYFD4eucF3tt4irx8Ay521rw1qCn3RfrJd91KZgltnhCmUpbrs0UkCgcOHEh6ejpbtmxBpVJx9epV6tSpw+eff87IkSOL3SYsLIx+/foZS5YbDAYaNGjAkCFDmDVrVqmOKw2Z5cnXG9h17gZ/7dvPq2ceuu16ytjDqDzqVGJkls+g1aKLjUUTGIjazs7c4VS4jIwMjhw5QnR0NMePH6dv377Uq1cPvV5PgwYNbvuQQVQNcn0uTD4PUZ3cbXs1Y8YMPD096dx/CEO/2E9Gbj6PtQ3mrUFNKyBaIUom1+fC5PO4O7HJ2by8Mpq/LyQD0DnUm1kPRFDbtfp/txdCVKyyXJ/Nfiet0+n47bffmDdvnvEJiZ+fH127dmXdunW3TRT6+fmRnJxsfK3VasnOziYgIKAywhYmpCgKBy+n8uOhOH49Ek9yVh625PGSrQ0OqryiG2gcUDnXrvxALVzuuXNcfGAIIatXYV/KXrVVmbOzM/fccw/33HMPOp0OtVrN9u3b+e2339BqtTRt2pTIyEhatmyJWi09ToUQwlKUt72Kjo7mxo0bPPPMM2gcnHngsz1k5ObTKsRdipcIIao0RVH4YV8sb60/TlaeHgcbK/6vXyMeaR0kvQiFEJXO7InCS5cukZeXR506hXuH1alTh507d952u2+++YbnnnuOrl27EhwczL59+xg5ciTPPffcbbfJzc0lNzfX+Do9Pf3uT0CUW2xyNj8eusKag3FcvJFtXO7haEOfpkEkG57G/sgCVNza6VUFbUcXqX4sajaNpqDiW9euXenSpQuXL18mOjqabdu2ERQUxM8//0ydOnWIjIzE29vbzNEKIUTVtX//fgBatmxZacdUFIVff/2VjRs38vTTT+Ph4clz3x3gbGKmsXiJjbU8EBJCVE2J6VomrznClpOJALQKcWf20EiCPR3NHJkwR5snhCUwe6IwJ6dgDjpnZ+dCy11cXMjOzi5uEwAOHz5MdHQ0gwcPpl69eiQkJLBx40ZGjx5NYGDx89PMnDmT6dOnmy54UWYZWh0bjsSz+mAcey/82yPUXmNFn6a1GdTMn3vqeRbMOWhoDG72Raoe0/VVM56BsHQqlYrg4GCCg4OBghvMsLAwDh48yJo1a/Dx8aFTp0507doVRVHkKa0QQpTBn3/+CVTeTVNeXh7ffvstFy9eZOLEiQQGBjLvjzP8fjwBGys1nz7anFrOMiRPCFE1/Rx9lak/HSU1W4eNlZoJvUN5skNdrNTy/dQSVHabJ4SlMHui8ObY6NTU1ELLU1JSbjtuOjc3l5EjRzJlyhQmTZoEwPjx42nXrh2TJk3i+++/L3a7KVOmMH78eOPr9PT02yYVhekYDAo7z11n5f44Nh6LJzffAIBKBffU82RwswDubVobR9v//DqqraD7VOg0ATITwMlHehKKMlOpVHTq1IlOnTqRk5PDsWPHyM3NZc+ePaxevZrw8HAiIyNp1KjRHSutCyFETdenT59KPd7BgwfJzs5mypQpODs7s+VkAh9sPg3A24Oa0izIvVLjEUIIU0jJymPqT0dZH3MNgKb+LnwwLIpQH+c7bCkqU2W3eUJYCrMnCgMDA3F2dub48ePce++9xuXHjx+/bfXipKQk0tLSaNasWaHlUVFR7Nu377bHsrW1xdbW1jSBiztKSNeycn8sP+yPJTb53+rF9bwdGdw8gPub+ePnVorEn8Ye3EMqLtBqQqVSodJopIdcCezt7Y1PBA0GAx4eHkRHR7Ny5UpSU1N58skn8fHxwcnJSSbgFkKIYtzuu1lZlKa9Onv2LIsWLWLixIm0bdsWgPNJmby47DCKAo+2DWJYK3nYK4SoeraeTOSV1TEkZeRipVYxpmt9XuhWH42VTKFgaUzR5glRFZk9UahWqxk2bJhxzkEHBwcOHDjArl27mDJlinG97777jgsXLjB16lT8/f3x8PDgl19+oVevXkDBEOYtW7bQvn17c52KAPQGhW2nE1n2dyxbTiaiNxTML+hsZ82gKH+GtAggIsBVklkVwK5xYxoeiTF3GFWGWq0mNDSU0NBQhgwZQkJCAs7OzqxatYo9e/YQHBxMZGQkUVFR+Pr6mjtcIYSoNu7UXu3cuZPly5czZMgQ40ObzNx8nl1ygIzcfFoGu/N6f7l5E0JULZm5+by9/jjL98UCBZ0nPhgWRWSgm3kDE0KI/1ApiqLcebWKlZSURNeuXdHpdERGRrJp0yaGDRvG559/blznqaeeYs+ePRw9ehSAtWvXMmLECCIiIqhbty47duzAzs6OP/74Az8/v1IdtyzloUXJrqTmsGJfLCv3x3I1TWtc3jLYnYdbB9E33Bd7GyszRihE6aWnp3PkyBGio6O5ePEikydPZufOnYSGhlK/fn2srOR3uaLJ9bkw+TyEpVi/fj0A/fv3r5D9r1q1il27dvHss88SFhYGFExhMmrpATYeS8DHxZafX+gg8xIKiyHX58Lk8yjenvM3mLAymriUglFW/2tfh1fuDcNOI98pLVlFt3lCVKayXJ/N3qMQwNvbm4MHD7J582YSEhKYNGkSLVq0KLTOY489Zuw9CDBo0CAuXLjA7t27uXHjBv/73//o2LGj3MBXIkVR2HXuBt/uusgfJxL4p/Mg7g4aBjcP4KFWgTSQeTYqTe65c1yZOBH/99/Htl49c4dTpbm4uNC+fXtjD2WtVktaWhpfffUV+fn5NG3alE6dOtGgQQMzRyqEEJXr5MmTwN3dNBXXXmm1WvLy8sjNzWXKlCmFKtQv+PMsG48VFC/57NEWkiQUQlQZWp2e2RtPsXDnBRQF/N3smT00knb1PM0dmigFU7R5QlRFFpEoBLCxsaFv3763fb9z585Flnl6esofrRlk5eaz5mAci3Zf4mxipnF5u7qePNwmiN5NfLC1loRtZTNoteQeP4FBq73zyqJM7OzsePTRR3nkkUe4dOkS0dHRXLp0iWvXrnHw4EEiIiKIiIjAy8vL3KEKIUSFeuKJJ+56H/9tr65du8aCBQto3bo1w4cPL7Tu1pOJzNlUULzkrUFNpHiJEKLKiIlLZfyKaOP90oMtA3mtfyOc7TRmjkyUlinaPCGqIotJFArLdz4pk8W7L7H6QBwZufkAONpY8UCLAEa0C6Z+Lek9KKo3tVpNnTp1qFOnDgCZmZnk5+cbC6L4+fkxcuRIPDw8sLe3R62WSamFENWLp6dpe8EcOXKEhQsX0qVLF/r161fovQvXsxi7/BCKAsPbBPFgqyCTHlsIISqCTm/gky1n+WTrWfQGBS8nW2Y9EE73Rj7mDk2UkanbPCGqCkkUihIpisL2M9dZ+NcFtp9OMi6v6+XIiHbBPNAiQJ6KiRrLycmJbt260a1bN7Kzszl58iQuLi588MEHZGRkEBERQWRkJA0bNkSjkb8TIYS41cHTp/lh61Yee+wxWrVqVei9zNx8nlm8nwxtPi2C3Zk2QIqXCCEs35mEDMaviObIlTQA+kX48vbAprg72pg5MiGEKD1JFIpi5eUb+Dn6Kl/uOM/J+AwAVCroFlaLEfeE0LG+F2q1VC4W4iYHBweaN28OwJQpUzhz5gwxMTEsW7YMvV7P1KlTOXHiBGFhYTK5txCiyvryyy8BePrpp8u9j7z8fI7Wrk2kmxuTJk3C39+/0PuKojBhRTRnEjPxcbHl0+HNsbGWHtpCCMulNyh8/dcF3v/9FHn5BlztNbw1qCn3RZauyKawTKZo84SoiiRRKApJ1+r4fu9lvtl5gYT0XAAcbKwY1jKQJ9qHEOzpaOYIxe3YBATgP/dDbAICzB1KjWdtbU2jRo1o1KgRw4YNIycnh6ysLDZv3szXX39NnTp1iIiIoG3btri5uZk7XCGEKDXtXc6Dm5KSwqc//wzt2jIoKgqNe9E5Bxf8eY7fjsWjsVLx6aMtqOUixUuEEJbr8o1sJqyM5u+LyQB0DvXmvSER+Mi1q8q72zZPiKpKpSiKYu4gzKUs5aGru6upOXz91wWW74sl85/5B72dbXmifQjDWwfj6iDDJoUwhbS0NGJiYoiOjqZBgwY4ODiQkJBAREQE9erVk8rt/5Drc2HyeYjq4MKFC3z66ac0btyY4cOHFzslw9ZTifzv230oCswcHM7DrWVeQmHZ5PpcWE36PBRFYdnfsbz9y3Gy8/Q42FjxWr/GPNw6EJVKRl4JISxLWa7P0qOwhjuflMn8ref46fAV8g0FOeMGtZx4ulNdBkb5SfXiKiT/+nXSfl6P64D+WEv1XYvl6upKx44d6dixIwCXLl3iwoULfPHFFxgMBsLDwxkyZAjOzlIcSAhRfeTk5PDhhx9y33330TkykvTvlhZpry5ez+LFZQXFSx5pEyRJQiGExUpI1zJpdQx/niqYw711iAezh0YS5Olg5siEEOLuSaKwhjoZn878redYH3OVm31K29X15JlOdekc6i3zD1ZBuoQEEmfNwqF1K0kUViHBwcGMGDECg8HAhQsXOHnyJPn5+bz66qv4+PgQGRlJREQEHh4e5g5VCCHIzs4GCuZlLQ2DwcAvv/yCv78/77zzDs7OzuQcO1akvcrKzeeZJftJ/6d4yRtSvEQIYaHWRV9l6tqjpOXosLFWM7FXGP/rUAcruX+qdsra5glRXUiisIY5EpfGvC1n+P14gnFZj0a1eL5bA6IC3cwXmBA1nFqtpl69etSrVw+AcePGcfjwYQ4cOMAPP/xAeHg4TzzxBDdu3MDf31+GtAghzGLBggUATJgw4Y7r5uTk8NVXX5GUlETbtm1v21NaURQmrormdEImtZyleIkQwjKlZOXx2k9H+SXmGgBN/V34YFgUoT4yCqS6KkubJ0R1IonCGuLApRQ+2XKGrf90j1epoG9TX0Z3rUcTP1czRyeE+C9vb2969uxJz549yczMJDU1lcuXL7NgwQLs7e2JiIggIiKChg0bYm0tl3IhROUICQkp1XqJiYnMnz8fT09PJk+eXGJvjAV/nuPXI1K8RAhhubacTGDS6iMkZeRipVbxfNf6PN+tPhoreahRnZW2zROiupG7y2ruSFwaczadMs6fYaVWMTDSj9Fd61G/ljz9EqIqcHJywsnJCYA5c+Zw+vRpoqOjWbp0KT179sTJyQm9Xk94eLhxPSGEqAhDhgy54zqKorBr1y4iIiK4//77UatvfyP956lEZv9+CoDp9zWlRXDRKshCCGEuGVodb68/wQ/7YwGoX8uJD4ZFEhHgZt7ARKUoTZsnRHUkicJq6lR8Bh9uOs1vx+IBsFareKB5AKO71iPY09HM0YmKYOXsjFPXrlhJEYxqzdramsaNG9O4cWMefvhhAHbv3s2WLVtYvHgx9erVIyoqiq5du0oF5f9YsWIFH330EQkJCYSHhzNjxgwaNWp02/UzMzOZNWsWGzduJDU1lbZt2zJjxgwCAgLKvN+yHluIqkhRFLZs2cL27dt54403bjtFws326lq+NWNXFhQvebh1EI+0keIlQgjLsfvcDSasjOZKag4qFfyvfR0m9g7DTiPfr4QQ1ZskCquZi9ez+HDzadZFFxQpUang/ih/XuzRQBKE1ZxNUBCBny4wdxjCDNq1a0e7du1ISUkhJiaGM2fOcP36dRYtWkS9evWIjIykbt26Jfbqqe5+/PFHhg8fzrx582jXrh1z5syhc+fOHDt2DG9v72K3GTp0KLGxscyfPx8vLy8+/PBDOnfuTExMDI6OjqXeb3mOLYSlOnfuHIBxPtWbdDody5Yt48iRI4waNarEeVRtgoLwmPsxgxfsIl2bT/MgN964r3GFxi2EEKWl1el577dTfL3zAgAB7vbMHhpJ27qeZo5MVLbbtXlCVHcqRblZ87bmSU9Px9XVlbS0NFxcXMwdzl25mprDx3+cYeWBOPSGgn/SvuG1GdcjlAYywW6NoOh06DMysHJ2RqXRmDscYWaKohATE8OhQ4c4cuQIAN26dePee+9Fr9djY2Nj5ghLZurrc/PmzWnevDlfffUVAPn5+fj6+vLCCy/w+uuvF1n/0qVLhISEsHHjRnr16gWAXq+ndu3avPHGG4wZM6bU+y3rsYtTndorUbXNnj0bKDyxu1ar5eOPPyY/P59Ro0bh7l7y8GFDXh4Tvt3JT2cz8HB1YP0LHfCReQlFFSXX58Kq+ucRE5fKuB8Ocy4pC4CHWgXyWv/GONlK/5qaqLg2T4iqqizXZ7niVXHpWh0Ltp7j650XyMs3ANCtYS3G9wylqb8UKalJtKdPc/GBIYSsXoV9kybmDkeYmUqlIjIyksjISAwGA+fPnycvL49du3axfPlyGjZsSGRkJBEREbi5uZk73AqVnp7OoUOHmDRpknGZtbU13bt3Z/v27cVuk52dDVCoSquVlRUODg78+eefjBkzplT7Lc+xhbBknuGe7MjYwW/nfqNLcBcyUzPJz88nNDSUvn37lvgQQqvTczU1h00/bePpDyZwrNs4ZowZIklCIYTZ6fQG5m05y/ytZ9EbFLydbZn1QDjdGvqYOzRhRm3btjV3CEKYhSQKq6i8fANL917i4z/OkJKtA6BNHQ9euTeMFsEeZo5OCGFJ1Go19evXN75u0KABMTEx7N27l2XLlvH0009jb2+Pk5MTAQEBJQ4ZrIquXLkCQO3atQst9/HxITo6uthtGjRoQN26dXn33Xf5/vvvcXR05KuvvuLy5cv4+fmVer/lOTZAbm4uubm5xtfp6el3PE8hKlJSZhLdVnczvt731z4813rS8HxDnh/9PIMGDSJfbyA2OZvYlGziknOITckmNjmbuJSC/09IL/idrpcaRyfg2c515TuLEMLsTidkMH7FYY5eKWhr+0f48tbAprg7WvboC1HxOnToYO4QhDALSRRWMYqisOFoPO/9dpKLNwp6vNSv5cSUPg3p1rBWtbvBF0KYXu3atalduza9evUiKysLOzs7Vq1axc6dO3F0dCQiIoIWLVoQGhpq7lBNwmAo6G1tbV24ydNoNOj1+mK3sba2Zu3atTz77LN4enpia2tLmzZtuO+++0hISCj1fstzbICZM2cyffr00p6iEBXu1iQhCgTGBuJ7zZfjDY7zwp4l6Nb35lqa1jj9ye042FgR4lEwx2efpr4VGbIQQpRIb1BY+Nd5Zv9+mrx8A24OGt4a2JQBkX7mDk0IIcxKEoVVyIFLKbzzy3EOXk4FwMvJlnE9G/Bgy0CsrWpukQIhRPndLMrx4IMPMnjwYE6dOkV0dDSrVq3i6aefZsOGDYSFhdG0aVPjulXNzYIh169fL7T8+vXrJRYTCQ8PZ9euXWi1WrRaLW5ubnTs2JGgoKBS77e8x54yZQrjx483vk5PTycwMLDE8xTibimKQnJWHrEpOf/2DEzJ4VjyYbAC/nkW2eRIExxyHIj3iUelqMjJ3kF8SlvABhtrNQHu9gS4OxDobk+ghwMB7vYEujsQ6OGAu4MG7fHjXFxjxhMVQtR4l29kM2FlNH9fTAaga5g3sx6IoJZMhSBusWPHDgA6duxo5kiEqFySKKwC4tO0vLvhBGsPXwXAXmPF053q8kynujKxrhDCZDQaDU2bNqVp06YA5OXl4e7uzqZNm/j222+pX78+PXr0IDIy0syRlk2tWrUIDg5m586dDBw40Lj8r7/+YsCAAXfc3s7ODjs7O65cucKePXv48ssvS73f8h7b1tYWW1vbMp+rEHeSlqMjLiWb2OScf/7779DguJQcsvOK9nS18dmE7S0jhFOsUtDaabHX2uOW5oZGp2HsYFfO7tyDNjsTd4M7HooH3lbe9G7bm8uXL2Or5GOjWANSbEsIYT6KovD935d555cTZOfpcbSx4rX+jXmoVaCMzBJF7N27F5BEoah5JMtkwXLz9Sz86wKfbDlLdp4elQqGtgjg5V5hMvG3KMKuYUNC9+9DbW9v7lBENWFjY8OAAQMYMGAAycnJxMTEoNPp+PXXX9m3bx8RERFERkYSEhKCWm3ZvZrHjBnDu+++y/DhwwkPD+eTTz7h8uXLPPPMM8Z1XnnlFQ4ePMjmzZsB+Oqrr2jVqhWRkZHEx8fz2GOPERUVxfDhw8u039KsI4SpZOflE5eSY0wG3tozMDY5m3Rtfonbq1Tg42xHoMe/vQL11g+zOPaQcZ2rDQoeXPLP9F0aNHzW8R5ym7cgPj6elJQUkpOTyc7OJi8vj9WrV5OQkEB2djYajQZ/f3+e27qF1Zs343z2LO7u7nh4eODv73/HislCCFFe8WlaJq2OYdvpJABa1/FgztBIAj0czByZsFT333+/uUMQwiwkUWiBFEVhy8lE3lx/nEv/zEPYPMiN6fc1JTxAKhmL4qmsrLBycjJ3GKKa8vDwoEuXLkBBoY2bxTjmzZuHtbU1L7zwAtbW1nh5eZVY9dRcXn75Za5du0bbtm2xsrLCxcWF5cuX06hRI+M6ycnJxMfHG1+3adOGJ598krNnz6LVahk0aBArV65Eo9GUab+lWUeI0srN13M1VVskARibksOVlGyuZ+bdcR+ejjYEeBQkAQPcHQj0+HdosJ+bHbbWVv/ZIozFi1759+V//sRHho/EztoOO1c7XF2Lfk+ZPHkyAFqtltTUVHQ6Hfbu7rh7eHDjxg0uXrxISkoKXl5edO/enS+++AJXV1djAjEiIoKQkBCuXr1qXGaJ1xkhhGVSFIV10Vd5/adjpOXosLFW80rvMP7Xvg5qtfQiFLdXr149c4cghFmoFEUpedbpaiw9PR1XV1fS0tJwcXExdzgAnEvK5K31x/nzVMGTrlrOtkzp25BBUf7SHV6UKO/iReLfepvaU1/DJiTE3OGIGkKv13Pp0iX8/Pz48MMPuXLlCg0bNiQyMpKIiIhikwalUVHX57y8PNLS0vDy8ipyTU1JSSEvLw8fH59Cy1NTU3FwcCgxMVHSfsuyzu1YYnslKka+3sC1NO2/w4FvGRocm5xDQoaWO31zc7GzLpIADLhlvkAHm7I/J/5v1eObHgl7hFdav4KV+r/Jxdsrqb0yGAxcu3aN5ORkY89Ef/+C70Br164lJSWF/Px8HB0dGTRoELa2tpw7dw53d3djErFu3bpFCggJUVHk+lyYpX0eyVl5TF17lF+OXAMg3N+VD4ZF0sDH2cyRCSFE5SrL9Vm+RZmTLgcyE8DJBy02zNtyhi+2n0enV9BYqXiyQ12e71Zf5iEUpaLPyiJr5070WVnmDkXUIFZWVtStWxco6DUUHx9PdHQ0u3fv5vfff2fcuHEcOHCAxo0b4+fnZ/YHHjY2NrctInK7IY9ubm53td+yrCOqP4NBITEjt2BocKG5AguSgaWpHGyvsSo0NPhmAjDgn6Sgq73p5wH0dvLmyONHmP3dbPbr9zOyw0i6BHfBzrrsU6GU1F6p1Wr8/f3x9/cv8l7Lli1RFIWMjAxSU1Px8PDg2rVrxMXFceXKFY4ePUpKSgoPPfQQO3bs4OLFi8YEoqenJ3369CEhIQGVSoWHhweurq4WP22CEKL8/jiRwKTVR7iemYuVWsUL3eozpmt9NFIEUpTSypUrARg6dKiZIxGickkGyhwMetg6A/YsAF02eit7Vqj682nmQAyo6RrmzesDmlDHq2pWGBVC1EwqlQpfX198fX259957CyqoJidz5swZ1q1bh4uLCxEREXTp0qVIrz0hLNItD/TQlG7+V0VRuJGVd8uQ4H+HB8el5HAlJYc8vaHEfdhYqfF3t78l+VfQM/Bmr0BPRxvzJd3joSUtubfevWY5vEqlwsXFxfgkvEGDBjRo0KDIevXr1y80X2J6ejoGg4Hff/+dixcvkp6ejkqlwsvLiwkTJrBu3Trs7e3x8PDA3d0dPz8/uU4JUUVlaHW8tf44K/bHAVC/lhMfDIskIsDNvIGJKufSpUvmDkEIs5BEoTlsnQE75gAFPQas9Dk8pqxE76DgO/gdejfxMXuvGyGEuFsqlQpPT09Gjx5NXl4ep06d4vDhw5w/f559+/YRHx9PZGQkTZo0wcFBJhIXFuQ/D/TQOEDb0dD1VVBbkZat+ycBeEuPwFuSgTm6opWDb2WlVuHravfPsOB/k4EFPQQdqOVsa7HzZo0ePdrcIZSKg4ODsbfzrW7Gn5+fT1paGtnZ2Tg6OuLn50diYiInT54kOTkZa2trHnvsMWbNmoWzszMeHh64ubnRsGFDoqKiOH/+PG5ubnh4eODg4CDf24SwELvP3WDCymiupOagUsGT7eswoXcYdprST48gxE1Vpc0TwtQkUVjZdDkoe+ajovCwIpUKRlr9iirsk4IXQghRjdjY2BAeHk54eDgACQkJ7N69mw0bNvD1118TFhbGE088gb29vRQpEGaXt/ltNLs+wNga67JRdsxm+b5YZmgfIKMMlYNv9gQMuDlPoLsDvq52WFfRoW/VJalvbW2Np6cnnp6eAHTv3r3Y9aZOncqNGzeMPRMVReHKlSusWbOGlJQUcnJysLGxoUePHoSEhHD48GHjXInu7u7Uq1cPW1tbSSQKUcG0Oj2zfjvJNzsvAhDgbs+coZG0qetp3sBElVZd2jwhykoShZXs/MXz1NXlFPueSpddMMTJPaRygxLVgsbXF5+pr6Hx9TV3KELckY+PD4MGDWLQoEFcv36dU6dOoVarmThxIp6enjRs2BBvb28MhpKHaApRHlqdnriUf3sCxqVkE/fPHIFJyan8of8Em//kdVTAfTlreSO3D2CDl5ONcU7AmwnAm0ODi68cLG6qSu2Vt7d3sXOLTps2DSio5JycnIyTkxMZGRm4u7uTkpLC+fPnSU5OplevXly4cIEDBw4YE4geHh706tWLjIwM8vLyjPMoykMSYUm0Wi1xcXG4ubnh5eVl7nBKdDg2lfErDnM+qWDe04dbB/J//RrLPO9CCFFOUvW4kqpy6fQGPt92js//OM5e66dwUOUVXUnjAK+cL/U8SEIIUd1kZ2dz7Ngx9u/fz969e2nSpAlPP/00cXFxhIaG1tgbaUurImnpdHoDV1NzCs0NWFA4pOD/EzNyb7ttgCqRv2xfuu37F4bvonZwQ+xtamYi8OOPPwZg7NixZo6k6sjLyyMhIYGUlBTjzz333MPmzZs5duwYKSkp6PV6XFxceOWVV/j9999Rq9XGxKKPjw/BwcEoiiI9Ey1Qdb0+P/rooyxdupQXX3yRuXPnlnq7yvw88vINfLLlDPP/PIfeoFDL2ZZZD0TQtWGtCj2uqDmkzRPViVQ9tjDHr6YzcVU0x66mA9b84TWE/mnL/jP8WFUw/5EkCUU56VNTydy+HadOnbAqRZVWISyRg4MDrVq1IiwsjDFjxhAXF0diYiLff/89GRkZNG7cmIiICFq0aIGdXdmrrYrqQW9QiE/XEpf8b4/Am1WDr6TkcC0thzsUDsbRxuqf3oD/9gQMdLcn0FmFsngKqvxiev9rHKgTUhdq8FxXphiGVdPaKxsbGwIDAwkMDCy0/JFHHgHAYDCQmZlJRkYGnp6e1K1blytXrhAbG0tMTAxarZbRo0fz5ptvYm9vb5wbMSQkhM6dO3Py5ElcXFxwd3eXSs7CJL799lvOnz9P06ZNzR3KbZ2Kz2D8isP/3F/BgEg/3hrYBDeHmvlAUVQMGXosaipJFFagvHwD87eeZf7Ws+QbFFztNbxxX2P6R/RG9adf8ZOkC1FOeVeucPWVSYSsXoV9DbjxEtWfoig4OTnh5+fHO++8w9WrV4mOjmbHjh1YWVmRlpaGoihERkZSu3Zt6WlTjSiKQlJG7i1JwH97Bcal5HA1NQedvuRMoK212pgAvDk0+Nb/d3PQ3P53pt2YQkXHCsgDPYCnnnrqrvch7VVharW6UCXndu3aFbveW2+9xfXr143zJVpbW5OSksKGDRuMlZ3VajVt2rShffv2bNu2rch8iU5OTgByvRS3derUKV599VX++usvBg8ebO5witAbFL7acZ45v58mT2/AzUHD24Oa0j/Cz9yhiWrIFG2eEFWRJAoryNEraUxYGc3J+AwAejfx4a1BTanl/E8PmO5TodOEgjkJnXxq/I2HEEKURKVS4e/vj7+/P3379gUgJiaGHTt2sH79etzd3YmKiuK+++5Do9GYOVpxJ4qikJKtK9QT8NYKwnEpOeTmlzw/pcZKhZ9b4bkBA9z/rSDs7XQXBSRuPriTB3rCgri6uuLq6lpk+ZQpU4CCSs6pqaloNBr0ej3+/v6kpKRw7NgxUlNTadmyJTqdjo0bN+Lu7m7smdi1a1dUKhXp6enGpKK9vb0kE2ug3NxcHnroIWbOnFls1XBzu3Qjiwkro9l3MQWArmHezHogglouMsJACCFMSRKFJpavNzB/6zk+3nIGvUHBw9GG6fc1oX+Eb9EvXBp7KVwihBDlFBERQUREBHl5eZw4cYKzZ8+SmprKhx9+SP369YmMjKRx48bY28uDmFLR5Zj04VW6VmcsEHKzR+DNJGBscjZZefoSt1erwNfVvlDy79akoI+LHVbqCkpkqK3kgd5t3LhxA8BYLVhYDmtr60JFJ/r06VNkHb1eT6tWrYy9ElNSUrCysiImJoZ9+/aRkpKCVqvFzs6OsWPHcujQIbKzs409E728vAgNDUVRFBniXAUkJiaSnp5e4jrBwcHGB2wTJkygQYMGPP7446U+Rm5uLrm5/877eqfjlYeiKCzde5kZv54gO0+Po40VU/s35sFWgZLQFhVK2jxRU1lMonDXrl3Mnz+fhIQEwsPDmTx5Mj4+PiVuk5OTw6effsqWLVtwcHDg6aefpmfPnpUUcVEXrmcx7ofDHI5NBaBfuC9vDmyCp5Ot2WISQojqzsbGhsjISCIjIwF4+umnOXz4MD///DMLFy6ke/fu9OnTh9zcXNzd3c0crQUy6GHrjOJ7z6lvPxdfdl4+V4xFQv6dJzAuteC/aTm6Ox66lrPtf4YG/5MUdHfA180OjZWZExHyQK+Ib775BihIKIiqx8rKitq1a1O7du1CywMDA+nXrx9Q8P06NTWVWrVqkZmZycWLF7lx4wZnzpwhKyuLUaNGMWPGDBRFMSYQfX19GTBgAEePHsXR0REPDw/c3Nykh7eZzZkzh9WrV5e4zpYtWwgKCmLz5s189913/P7775w9exYoKMSTlpbG2bNnqV+/frHbz5w5k+nTp5s89pvi07S8sjqG7aeTAGhTx4PZQyMJ9JC540TFkzZP1FQWkSjcvn07PXr0YPz48QwbNoxPPvmE9u3bc/jwYeNcKv+VmZlJp06dsLKy4pVXXsHBwYG5c+fi6upK69atKzV+RVFY9ncsb60/To5Oj7OdNW8PasrAKP9KjUPUbGp7B+wjI1HbyxcnUbPVqVOHOnXqcP/995OYmEheXh4HDx5k6dKl+Pv7ExERQWRkJEFBQdITAQqShLfOx6fLhh1zyDcoxDZ7mdjkbOPcgLf2DLyemXfHXXs42hD4T4/AgP/0CPR3s8euBhcFqaoaNWp01/uQ9sqy2dvbG3ti3/oQ5lZvvfUWN27cMPZMzMvLQ6vV8ueffxrnUTQYDISFhfHggw+yfv16XF1djYnFOnXq4OnpicFgwMpKrgMVZdasWcyaNatU68bHx+Pp6cnDDz9sXBYbG8vVq1fZsWMHp06dKvbfasqUKYwfP974Oj09vUjhnvJQFIWfDl/l9Z+Okq7Nx8ZazSu9w/hf+zqoK6o3uRD/YYo2T4iqSKUoyh3qAla8jh074uvry4oVKwDIysrC19eXN954o1DDc6tXXnmFJUuWcOrUqUKlnXNycko9zKws5aFv53pmLpNXx7D5RCIA7ep6MntYJP5uMkRJCCHKyxTX5//Kysri6NGjREdHc+zYMf73v/+RmZmJq6srYWFhFt3zpSI+j3y9gfgbKfh+3gQrfdEKv1mKLc1zPyeX21eQdLazLpT8CzQOEy5Y5mhrEc8jhRCVzGAwkJGRgaIoaDQa9uzZYxzqnJycTL169fD19eW7774rlEBs3749bm5uJCQkGJc5Oztb9DDnirg+W4qoqCi6dOnC3LlzS72NKT6P5Kw8Xlt7hF+PxAMQEeDKB8MiqV/LuVz7E0IIUbbrs9m/wWdnZ7Nr1y4WLVpkXObo6EiPHj3YtGnTbROFS5Ys4bHHHitygpU5F9Xm4wlMWh3Djaw8bKzUTOwdxpMd5CmXEEJYIkdHR9q0aUObNm0wGAyo1Wp+/vln1q9fT1ZWFk2aNKFly5a0aNHC3KGaVFZuPhuPxRt7BN7sHXgtTYuvksBftkWThACOqlwCNRmoPYL/GQ78n4Ih7g64OlhuclUIYT5qtbpQ4ZXu3bsXWcdgMNC4ceNCCURbW1suXLjAtm3bSElJISMjAysrK5566imuXbtGfHw87u7uuLu74+npSZMmTYzHkx7i1cPm4wlMXnOE65m5WKtVvNCtAaO71jP/VBRCCFGDmD1RGBsbi8FgwN+/8DBdf39/tmzZUuw2N27cID4+nsaNGzN58mQOHDiAn58fjz/+ON26dbvtsUw52e6BSyk8tXg/AA1rOzP3oSga1q5eTxFF1ZJz7BgXHxhCyOpV2P/zxVkIUbybvVMGDBhA//79iYuLM07mX7t2bVatWkWjRo2IjIy843y5li4v38D4FdHFvpdm5YEWW+zILfKeonFg06tDUNnI8FDxr6NHjwLQtGnTcu9D2isBBddhDw8PPDw8Ci2vX78+HTp0AECn05GammrsWahSqUhJSeHq1aukpaXh7u7OwoULuX79ujGBWKtWLR544AGOHz+Ora1toUrOomyCgoLw9vautOPden/VoJYTHwyLIjygaKVvISqLKdo8IaoisycKdbqCyc7t7AqXtbe3tycvr/j5j3JyCno/TJgwgdGjRzNx4kT27t1Lr169WLJkSaG5NW5lysl2mwe50S/CF383e17uFYqttcyvIoQQVZFKpSIwMNA4p5Jer6dZs2ZER0fz008/4enpSf/+/WnZsiWARQ+BK46bg4auYd54O9sWDBP2uFk4xAFvJ1vUW2MKz1EIgApV29EgSULxH7/99hsgN02icmg0GmOiqkGDBjRo0KDIOq+++mqh+RKzs7MxGAzs3buXxMREkpOTyc3NxcvLi5dffpkffvgBJycnYwIxODgYf39/dDqdRU9BYQ7r1q2r1OM1D3KjX7gvfm52vNwrTOaxFWYnbZ6oqcyeKLz5FPFm6fGbbty4UeQJ463bqFQqevTowZtvvglAr169OHfuHB9++OFtE4WmnGxXpVIx76FmMsxYCCGqGSsrKzp16kSnTp3QarUcP34cJycn1qxZw65du4iIiCAiIoLGjRsXechliVQqFd88UUKRr66vFvy3uKrHQvxH165dzR2CEIXY2Njg6+uLr69voeWjR48GCopiZGdnk5+fj729PU2bNuXGjRskJiZy8uRJTp8+TcuWLZk3b56xYrO7uzstWrSgbt26XLhwwThfopubG9bWZr99qrZUKhXzHpb7K2E5pM0TNZXZWzo/Pz98fHw4cOAA/fv3Ny7/+++/ad++fbHbODg4EBYWVuxw5b/++uu2x7K1tcXW1tY0gYM0YkIIUc3Z2dnRvHlzoGA4XFRUlLGn4cKFC5k8eTIZGRn4+vri7u5u5mjLSW0F3adCpwmQmQBOPqCRIXqieNVtDk9R/alUKhwdHY2vO3bsWOx677//PikpKcYfFxcX4uPjjfMlpqamoigKQ4cORaVScfr0adzd3XFwcKBu3brk5uai0+mwsrKqcj3PLYncXwlLIm2eqKnMnigEeOKJJ/jqq6945pln8PX1Ze3atRw9epSvvvrKuM6sWbM4duwYixcvBuCZZ57h448/ZtKkSdSqVYsbN26wcuXKYidLFkIIIe6WWq2mfv361K9fnwceeIDk5GRcXFz48ccfOXHiBIGBgURERNCiRYsiPVuqBI09uIeYOwohhDALFxcXXFxcCA4OLrQ8IiICKCi+kpaWhpOTEwkJCej1elJSUrh06RLh4eHExsaydOlSTp06haurKx4eHnh6ejJkyBDOnTuHSqUy9kx0cnKSZKIQQgiLpVIURbnzahVLq9XyyCOPsHHjRoKDg7l48SLvvvsuY8eONa7z1FNPsWfPHuOEogaDgVGjRrF8+XLq1avH2bNn6dKlC0uWLClUZa0kZSkPLYSlM+Tmkh8fj3Xt2qhN2HNWCHOoatfnzMxMjhw5QkxMDFlZWQwbNozdu3fTpEkTQkND73qoWlX7PET1tWnTJgB69uxZ7n1IeyWqk1uvz46OjoV6Jaanp9OuXTtWr17N5cuXSUlJITMzEwcHB15//XW+//577O3tjQnEgIAA6tWrh1arxdbWtkpWcpb2SlQnpmjzhLAUZbk+W0Si8KbLly+TkJBAaGhokWTf2bNnycjIoFmzZoWWJyQkEBsbS1BQELVq1SrT8aQhE0IIy1TVr8+pqan8+uuvREdHo9VqadKkCffeey9BQUHl2l9V/zxE9TF79mygoKCcEKLs1+e8vDxycnJwdnbmwIEDJCYmGhOLVlZW3Hfffbz99ttoNBpjArFJkya0bNmS48eP4+bmZlxuifPkSnslqhNp80R1UmUThZVNGjJRneTFxZH00cd4vzgWm4AAc4cjxF2pLtdnRVGIjY0lOjqaOnXqEB0dTXx8PJGRkURGRhqred5Jdfk8RNV39epVoGCO6fKS9kpUJxVxfc7JySnUM9HZ2RkXFxd++uknkpOTSUlJIS8vjx49ehAYGMi+ffuMxVZuJhbt7e1RFAUbGxuTxFRa0l6J6sQUbZ4QlqIs12eLmKNQCHH39GlppP/8Mx4jHwe58RLCIqhUKoKCgow9CYOCgjh48CDR0dGsWbMGHx8fxo0bh1arxcvLS+asEhbPFDdL0l4JUTJ7e3vs7e2L/L299NJLwL+VnDUaDenp6eTm5pKcnEx8fDwnTpxAo9Fw7Ngxdu3ahZOTk7EH4qBBg0hKSiI3N9dY3dnNzQ0rKysznKUQlk8ShKKmkkShEEIIUUlcXFzo0qULXbp0IScnhwsXLqBWq3n//fdRFIWIiAgiIiJo1KgRtjJ3mxBCiGLcWsnZy8uLzp07F1mnefPmDBo0iJSUFJKTk0lNTcXe3p4LFy5w6tQpYyVnKysr3njjDVasWIG1tTXu7u64u7tTu3ZtmjZtSk5ODnZ2dvIgSwghahBJFAohhBBmYG9vT+PGjQF49913OXfuHNHR0axatQovLy+GDRvG+fPnCQ8Pr5IT2ovqafHixQCMGDHCzJEIIUqiVqtxdXXF1dWVkJAQ4/JBgwYZ/1+v15OdnY2TkxOdO3fm6tWrpKSkcPbsWY4dO4avry/Tpk3DYDAYE4h169ald+/eHDhwwFjd2d3dHScnJ2mrRLUjbZ6oqWp0ovDm9Izp6elmjkSIu5eTmUmmXk96ZiY6+Z0WVdzN63JNmUbXysqK0NBQQkNDGTJkCAaDgUuXLrFr1y6WLl1KUFAQ1tbW0l4Js7t8+TJwd9+dpL0S1UlVbq+srKxwdnYGoGnTpjRt2rTIOnPnziU1NdXYM9Ha2prs7GyOHj1qnC8xKyuLFi1a0K5dOzZv3iztlag2TNHmCWEpytJe1ehiJnFxcQQGBpo7DCGEELcRGxtLQA2fwyw9PZ39+/fTvXt3c4cihBDiNmpye5Wbm4tKpSIvL4/du3fTu3fvKpk4FUKImqA07VWNThQaDAauXr2Ks7NzubrKp6enExgYSGxsbLWr6lVdz626nhdU33OrrucFcjW2MIcAACvbSURBVG4lURSFjIwM/Pz8ZF4kpL2yFPI5moZ8jqYhn6NpSHtlWnfbXpma/J2UTD6fksnnUzL5fEpmaZ9PWdqrGj30WK1Wm+TJn4uLi0X8w1eE6npu1fW8oPqeW3U9L5Bzux1XV1cTR1N1SXtlWeRzNA35HE1DPkfTkPbKNEzVXpma/J2UTD6fksnnUzL5fEpmSZ9PadsreewlhBBCCCGEEEIIIYSQRKEQQgghhBBCCCGEEEIShXfF1taWadOmYWtra+5QTK66nlt1PS+ovudWXc8L5NxE5ZF/D9OQz9E05HM0DfkcTUM+x+pN/n1LJp9PyeTzKZl8PiWryp9PjS5mIoQQQgghhBBCCCGEKCA9CoUQQgghhBBCCCGEEJIoFEIIIYQQQgghhBBCSKJQCCGEEEIIIYQQQgiBJApLtGnTJrp160a9evW499572bt3b6m3XbZsGQEBATz77LMVGGH55OXlMXXqVMLDw2nUqBHjxo0jMzPzjtvt3buXwYMHExoaSp8+fdi3b18lRFs2J06c4P7776d+/fq0b9+eFStW3HGblStX0qNHDxo0aEDr1q2ZNm0aOTk5lRBt6eXm5vLdd9/RsWNHAgICOHXqVKm2W7hwIW3atKFBgwY8/PDDXLx4sWIDLYcLFy4wZcoU6tevz+DBg++4fn5+Pt9++y29e/cmLCyMXr16sWbNmkqItGwURWHjxo0MGjSIgIAAVq5cWabtV65cSUBAAE899VQFRVh+KSkpzJ07l8jISEJCQkq93YEDBxgyZAihoaH07t2bXbt2VVyQAoPBwIYNG7jvvvsICAjgxx9/LNV2d9P2VVerVq2iQ4cO1K9fn0GDBnHs2LES1586dSoBAQGFfrp06VI5wVqA8rQ9VaG9qkyKovDBBx/QvHlzQkNDefLJJ0lMTCxxm8GDBxf5vRszZkwlRWyZytNe6XQ6pk+fTkREBA0bNmTs2LGkpaVVbKDCpBISEpgxYwaNGzemVatWpd5u27ZtDBgwgAYNGnDfffdx5MiRCozSfPR6Pe+++y5RUVGEhYXx3HPPkZycXOI2cXFxPPfcc0RFRdGoUSMGDx5cLb4fSHtVsvXr19O5c2fq1atH//79OXToUInr63Q6Zs6cSdu2balfvz5dunRh0aJFlRRt5du6dSvDhg0jMDCQhQsXlmqbnJwc3nrrLVq0aEF4eDjTp08nLy+vgiMtB0UUa9euXYpGo1HeeecdJSYmRhk/frzi4OCgnDx58o7bnj17VgkICFDCw8OVBx54oBKiLZsnn3xSCQoKUjZv3qz89ddfSuPGjZV+/fqVuM2GDRsUGxsbZdq0acrJkyeV7du3K717966kiEsnMTFR8fLyUh5//HElOjpaWbBggWJtba2sWbPmttusXbtWUavVyieffKKcPn1a2bhxoxIYGKg88cQTlRj5nT355JPK8OHDlQULFiiAcuTIkTtu8+WXXyp2dnbKkiVLlIMHDyoDBw5UgoODlczMzEqIuHTS0tKUunXrKu+8847ywAMPKO3bt7/jNjNnzlRGjhypbNy4UTl16pTx3/mbb76p+IDL4KOPPlJ69uyprF69WrGysipTfBcuXFACAgKUyMhIZeDAgRUWY3l16NBBGTt2rPLqq68qVlZWpdrmjz/+UGxtbZVXX31VOXHihPLXX38p3bt3r+BIa7Y5c+YovXv3Vn788UcFUJYsWXLHbe6m7auu1q1bp1hbWyvz589XoqOjlZEjRyqenp5KfHz8bbcZM2aM0rt3byU2Ntb4U9L61Ul52p6q0F5Vtrfeektxc3NTfvzxR+Xvv/9WOnfurERGRio6ne6227Rv314ZN25cod+7GzduVGLUlqc87dWYMWMUPz8/ZePGjcquXbuUyMhIaa+qmMaNGyuTJ09WRo8erfj7+5dqmxUrVig2NjbKrFmzlNOnTyubN29W7rvvvgqO1DwmTZqkeHt7K+vXr1f27NmjtGnTRmnXrp1iMBhuu02jRo2Uzp07K3///bdy/PhxZdSoUYqDg4Ny/vz5SozctKS9KtnmzZsVa2trZc6cOUpMTIwyatQoxcXFRbl48eJttxk3bpzi4+OjrF+/Xjlz5ozy1VdfKRqNRlm0aFElRl45vv32W6Vz587KsmXLFHd3d+XDDz+84zY6nU7p2LGjEhkZqfzxxx/K6dOnlXfeeUf5/PPPKz7gMpJE4W0MGDCgSCKsSZMmytNPP13idnl5eUqrVq2UxYsXK/369bO4RGFsbKyiUqmUH3/80bhsx44dCqAcOHCg2G3y8/OVwMBAZdSoUYWW6/X6igy1zKZPn67UqlVLyc/PNy57/PHHlWbNmt12m5deekmJjIwstOz1119XQkJCKirMcrl5TocOHSpVotBgMChBQUHKpEmTjMvS09MVOzs7Zf78+RUaa1kYDAbj79Gzzz5bqkThrf++N40YMaJU21amW+MsS6JQp9Mpbdu2Vb7++mtl4MCBFpkovHlu33zzTaluvPR6vVK/fv0iCXhLu4ZUN7f+DpY2UVjetq86a9WqlfL4448bX+fn5ys+Pj7K1KlTb7vNmDFjLPJvt6KVp+2pKu1VZcrJyVGcnZ0L3XRcvnxZUalUyurVq2+7Xfv27ZVp06ZVfIBVSFnbq4SEBMXKykpZtmyZcdm+ffsUQNm5c2eFxSlM6+a/+8yZM0uVKMzOzlY8PT2VKVOmFFpeHb+npKWlKba2tsqXX35pXHbixAkFUDZu3FjsNvHx8QqgbNiwwbgsOztbAZTly5dXeMwVQdqrO+vatWuhXIbBYFBCQkKUcePG3XabqKgo5cUXXyy0rFOnTsrIkSMrKkyzufV7tqenZ6kShfPnz1fs7OyUuLi4Qsst8VojQ49vY9u2bfTs2bPQsnvvvZft27eXuN3kyZOpW7cujz32WEWGV247duxAUZRC59a+fXucnZ1ve267d+8mNjaWJ554otBytdqyfn22bdtGt27dsLKyMi679957OXToEBkZGcVu06NHD86ePWvsRp2SksLvv/9O7969KyXm0rr1nErj4sWLXL58udC/s7OzM+3bt7/j73BlUqlUZf49Ku6z0Ol0aDQaU4VlEmX9N7vp//7v//D39y/y92ZJynpuBw8e5OzZsxZ/DaluyvM7WN62r7rKyspi//79hT4TKysrevToccfP5K+//iI0NJRWrVoxbty4Ow7rqg7K0/ZUlfaqMt383nLrZxIYGEjjxo3v+Jl8/fXX1KtXjw4dOjBz5kxyc3MrOlyLVtbr4M6dO9Hr9YU++5YtW+Lp6Vljfx+rorL+u//xxx/cuHGjRnxP2bt3L7m5uYV+xxs2bEhwcPBtf8e9vb2JjIzkxx9/RKfTAbB8+XKcnZ255557KiVuU5P2qmT5+fns3Lmz0LmqVCp69+5d4rn27NmTrVu3cv36dQCOHz/OkSNHLO7e2hTK8z17xYoV9OrVC39//0LLLfFaY3kRWYCMjAzS09OpXbt2oeU+Pj5cuXLltttt2LCBVatW8dlnn1V0iOUWFxeHk5MTjo6OxmUqlQpvb+/bntvp06cBSEtLo1OnToSFhTFo0CD+/vvvSom5tOLi4or9NwO4evVqsdv069ePefPm0a5dO7y8vKhVqxZ16tRh/vz5FR5vRYqLiwMo8+9wVXTw4EFWrVrFI488Yu5Q7tqmTZv4/vvv+eKLL8wdikndvIbk5OTQpUsXwsLCGDBgADt37jRzZOJW5W37qrOrV6+iKEqZPxMvLy+mT5/O2rVrmTlzJjt37qRdu3YWN/+tqZWn7alJ7VVplfczCQsLY9asWfzyyy+89NJLfPbZZwwdOrRCY61u4uLisLa2xtPTs9Dymvz7WBOcPn0aBwcHzp8/zz333EPDhg0ZMmQI0dHR5g7N5MpzfVGr1WzcuJEDBw7g6uqKm5sbkydP5vfffycwMLDCY64I0l6VLCkpiby8vDKf66xZs2jbti21a9fG29ubqKgopk+fzkMPPVTRIVcJp0+fJiwsjJdeeonQ0FDatm3L22+/jVarNXdoRVibOwBLZDAYALC2LvzxaDQa9Hp9sdvEx8fzxBNPsHz5ctzc3Co6xHIzGAxFzgtKPjedTodKpeKll17i448/xtfXl88//5zOnTuzf/9+mjRpUtFhl0px53azl9ntzm3Tpk08//zzzJ49m169enHu3DlGjx7N5MmTef/99ys85opSnt/hqujSpUsMHDiQwYMH8/TTT5s7nLuSmJjI448/zuLFi/Hw8DB3OCZ18+nzSy+9xEcffURAQADffPMNXbt2Ze/evTRr1szMEVYN77//Ph999FGJ66xcuZJ27dqVa/815brxyiuv8P3335e4zrZt26hXr165P5Np06ahUqkAaNy4MeHh4QQHB7N06VKLLFBkKuX5vGrK711ZlPcz+eqrr4y/dw0bNsTb25suXbqwf/9+WrZsWXEBVyPl+Z4sKl5ISAj5+fm3fb958+asW7eu3PvX6XTk5eUxZcoUPvroIzw8PPjwww/p2LEjMTExZSrcZg5dunTh7Nmzt33fw8ODmJgY4N/ry397Q5X0O67X6xkyZMj/t3fvUVGV6x/Av1yEGRhgBgWCoRRvYAaCIaSGQIipdbyllZfQUg/lQZJDuNT0oJJ201MUkUc7CmWmFamh1UFTDFCk1PECUmC4vCAICCMgDgzz/P5gsX9Mw0Wuw+jzWcu13Lf3ffdmZr97P/NeIJFIcPToUUgkEnz66ad47rnnkJmZaZDBQq6vWtfRc42JiUFycjKSkpIwbNgwpKWl4fXXX8fDDz+MadOmdWeRDUJdXR1iY2OxYsUKfP/99/jzzz8RGhqKS5cuYceOHfounhYOFDbDysoK5ubmQpPZRqWlpbCzs2v2mMzMTJSVlWHevHnCurKyMhgZGcHZ2RmZmZlwdnbu1nLfCzs7OyiVSqjVaq0vfmvnZm9vDyJCTEwMnnrqKQDABx98gL1792L37t2IiYnpkbK3xc7Ortm/WeO25rz99tt45plnEBYWBgAYOnQo3nrrLYSEhGD16tWwsbHp3kJ3k8bzLS0thaurq7C+tb+zobly5QoCAwMxatQofPHFF/ouTqdlZWXh5s2bWLBggbCurKwMAODs7Iz09PRe/6DaEnt7ewANM8E2dmF47733sH//fuzatYsDhfcoNDQUs2fPbnWfzny/O1L3GaKVK1ciPDy81X0af0Fvei9tqq1r0hisaeTg4AAXFxdcvHixI0U2GB2pex6E+qq9ml6Tps8hpaWlePzxx1s87q+fuyeeeAIAcPHiRQ4U3iM7OzvcvXsX1dXVWr1vHuTPY2+QkZEBImpxu7m5eafSt7e3h1qtxubNm+Hn5wcA2LJlC7777jskJSUhMjKyU+l3tz179gg/yjanaVCw8XNcVlYm9LwCGj7jvr6+zR7/888/Iz09HZcuXcLAgQMBAB9//DGSk5OxZcsWbNiwoStOo0dxfdW6vn37wtjYuF3PP3V1ddiwYQM++OADTJ06FUDDu3VmZiZiYmI4UIiGe80jjzyCdevWAWj4UW/NmjUICwvD1q1be9VQWtz1uBnGxsbw9vbW6RaXlpYGHx+fZo+ZOHEiCgoKkJmZKfwbO3YsAgMDkZmZCUdHx54oept8fHxARDh+/LiwLjc3F2VlZS2em4+PD4yMjCCRSIR1jcu9qRuVj4+P1nkBDX+zAQMGtHhDU6lUsLKy0lpnbW0NjUbTO6cpv0dDhw6FVCrV+gyr1WpkZma2+Hc2JFevXkVgYCA8PDywZ8+eXnVT7ajx48fj8uXLWvcQf39/+Pv7G+yvtY28vb1hYmKidQ8B0OvuIb2dtbU1nJ2dW/3XmZeljtR9hkgmk7V5HRt/SLO1tcWgQYN0rkl6enq7rolKpUJhYeF911r4rzpS99zv9VVHeHl5wdTUVOuaKJVKnD9/vl3X5M8//wSA+/5z15Uar2/Ta19QUIDr168/sJ/H3kAul7d6z+5skKYxqN70OcXU1BRisdggnlMcHBxavT5N30O9vb1hZGSk9RkvLi5GXl5ei5/xxrFOm74zNb4L9sYuk/eC66vWiUQiuLu7t+uZsL6+Hmq1utl3a0P9nHS1J554Qud9yMrKCvX19a0G+/VCr1Op9GK7du0ikUhEP//8MxERff3112RsbExHjhwR9tm8eTM9+uijLabRG2c9JmqYecjPz48qKiqourqapkyZQo8++qjWzD1eXl60ceNGYXnWrFkUHBxMSqWSiBquj7GxMaWmpvZ4+VuSnZ1NpqamFBcXRxqNhs6cOUNSqZTef/99YZ/Dhw+TXC6ngoICImqYKdna2pp+/fVXIiIqLS0lPz8/GjlypD5OoU2tzXq8cOFCmjlzprC8fPlycnJyory8PFKr1RQdHU0WFhY6syz1Fi3Nenzz5k2Sy+W0f/9+IiK6du0aDRo0iKZNm0a1tbU9XcwOaWnW49jYWBo6dGiLx/XWWY8btTaL5BNPPKE1++ZLL71E/v7+dOvWLSIiSkpKImNj4xZn2GNdCy3MevzXeuxe6r4Hzb///W+SSqV06tQp0mg09Mknn5CpqSmdO3dO2GfFihXk7+9PRA2z4C1ZskS41yqVSgoJCSELCwu6dOmSPk6hR91L3WPo9VVPCAkJITc3NyosLCSVSkWvvfYa2dnZCc9hRA2zlC9ZsoSIiC5cuEAbN26k8vJyIiK6evUq+fn5kYuLC9XU1OjjFHqV9tRXEyZMIF9fX7p16xbduXOHZs6cSYMHDzaYZw72/1qb9XjQoEFaM9UGBwfT9OnTqbq6moiI4uPjydTUlE6fPt0jZe1JM2bMoBEjRtDNmzfp7t27FBISQs7OznTnzh1hn6CgIIqKiiKihmdxGxsbevXVV0mlUhERUWJiYqszJRsCrq9at3XrVpJIJJSRkUEajYYSEhLI2NiYMjMzhX1iYmLI29tbWA4MDKSRI0fSjRs3iIjo/PnzZGdnR//85z97vPw9qaVZj3fs2EFyuVyIs5w6dYrMzc3phx9+ICKikpIS8vX1pQkTJvRkce8JBwpbsXHjRrKysiJLS0uytbWl//znP1rbo6OjycbGpsXje2ugsLCwkIKCgqhPnz5kbm5Ovr6+lJeXp7WPg4OD1tTvSqWSZs6cSSKRiKytrUkul1NiYmJPF71NSUlJ5OjoSJaWliQSiSgiIkJruvHk5GQCIJxvbW0tLVu2jCQSCclkMjI3N6cJEyZQfn6+vk6hWVu2bCG5XE729vYEgBwcHEgul2sFn6ZOnSq8qBIRqVQqWrhwIZmZmZGFhQUNGDCADh061POFb8OTTz5JcrmcLC0tyczMjORyudYN9caNGwSAvvrqKyIiioiI0LoGjf+8vLz0eRo6jh8/LpQNAMlkMpLL5RQRESHsExMTQ5aWli2m0VsDhUuXLiW5XE4ymYwACOeZlZUl7NO/f396/fXXheXKykqaPXs2iUQisrGxIUdHR9q2bZseSv/gSEtLa/Yz2PjgT9R8PdZW3feg0Wg09MYbb5BYLCZLS0t66KGH6JtvvtHaJzQ0lFxdXYXlbdu20cCBA0kqlZK5uTn5+flpfT/uZ/dS9xhqfdWTKisradasWdSnTx8Si8U0bNgwOnnypNY+vr6+9MILLxARUU1NDa1du5bs7e1JJpORWCym5557Tvhh9EHVkfqquLiYnn76aeE52dvbmy5evKiH0rOOevHFF0kul5O1tTWZmJgIf/em3wdzc3N6++23heWSkhJ65plnyNzcnKysrGjAgAE69/r7xa1bt2jKlClkampKIpGIPDw8SKFQaO0zfPhwWrhwobB89OhR8vDwIJFIRFZWVuTo6Ejx8fE9XfQuxfVV21avXk2WlpZkaWlJdnZ29Pnnn2ttj4yM1ArGX716laZMmULm5uZka2tLFhYW9Pe//10rCH2/yM7OFu4txsbGZGNjQ3K5XOt78/HHHxMAqqurE9Z9/fXX5OTkRFKplEQiET333HNUVFSkj1NolRFRKwM+MKjVapSXl8PW1lZn0Nfbt2+jqqoKTk5OzR7bOEZhb+3yUVlZifr6+mYnX7lx4wYsLS1hbW2ttb6mpgYqlapXT9hCRMK4PmZmZlrb7t69i9LSUjg6Our8PcvKymBjY9PsINb6VlVVhYqKCp31MplMGEOnrKwMGo1Gp/vF3bt3UVVVhb59++qMX9QbFBUVNTtAdeOYnhqNBoWFhejbty/EYjGUSiUqKyt19jcxMek1XfyBhm4aJSUlOustLS0hk8kANHwHKysrW72HANCZfVHfysvLUV1drbPe3t5e+M4VFRVBLBbrjPNpCPeQ+8W9fAZbqsdaq/seVLW1tVAqlcK4PU1VVFRApVJpjfcENHQXlUgkD+Q1bK3uMdT6Sh/u3LmDmpqaZuuBkpISmJiY6DxnlpeXQyqV8jVE5+qryspKqNVq4X7JDEdpaWmzXR2bPv9fv34d1tbWOt0kq6urUV9fr/MOdD+qrq6GSqVq9l21uLgYZmZmOp//u3fvora29r66Plxfta6urg4VFRXNPv8olUrU1NTozI5cX1+PiooK2Nra3rfXp66uDsXFxTrrxWKxUGdXV1ejvLxcZ64KIkJZWRlkMlmvfUbkQCFjjDHGGGOMMcYYY4wnM2GMMcYYY4wxxhhjjHGgkDHGGGOMMcYYY4wxBg4UMsYYY4wxxhhjjDHGwIFCxhhjjDHGGGOMMcYYOFDIGGOMMcYYY4wxxhgDBwoZY4wxxhhjjDHGGGPgQCFjBqmsrAybNm1CdXW1vovCGGOM9SorVqxATU2NvovBuklaWhrmzJkDNzc3JCcn90ieJSUliI6ORkBAAMaOHYtly5bhxo0bPZI3Y71ZcnIyDh06pJe8iQibN2/G1atXuyS9CxcuICkpqUvS6qy4uDj88ccfXZrm7t27cfz4cQCARqNBbGwslEpll+bBtOmjvtqyZQvc3Ny0/vn4+LQ7HQ4UMmaAMjIysG7dOojFYn0XhTHGGOtVvLy8IBaLUVpaipiYGH0Xh3WhDz/8ECtXrsTkyZPx+++/99hL7qRJk2Bqaoq1a9finXfeQXZ2NsaMGYPy8vIeyZ+x3io6OhoKhUIveefl5eGNN97okrQ0Gg3mz58PjUbTJel11urVq3Hu3LkuTTMuLg4pKSkAAGNjY+Tk5GDdunVdmgf7f/qqr0pLSyESibBv3z7h35dfftnudDhQyJgBUigU8PDwgLExf4UZY4yxpl544QUAQHZ2Ni5cuKDn0rCu9NprryE9PR3z5s1rdb+DBw/i2WefhaenJ2bMmIFffvmlU/keP34ca9asQUBAAPz8/PDtt9/iypUr+OmnnzqVLmOGTK1WIycnB56ennrJX6FQwNbWFg8//HCn0/ruu+9QUVGBmTNndkHJOm/p0qVwdXXt1jyioqLwySefoKioqFvzeVDpq74CAJFIpNWicMiQIe1Ow7TTpWCM9TiFQgEvLy9h+c6dO/jvf/+LYcOGYfz48XosGWOMMUNVXl6OPXv2ICQkBBYWFj2e/2+//YbCwkJMmTKl2e1EhG+++QYZGRlQKpV49dVXIZVKkZqaColEguPHjyM8PBxpaWmYPHky/vWvf6G0tBQLFixAZGQk3N3de/iMWFczNzdvc58dO3Zg1apVePfdd/HYY4/h119/xeTJk3Hw4EH4+/t3KF8zMzOtZSMjIxgZGXUoLcbuFzk5OVCpVFqBwl9++QWnT5/GK6+8Amtr627N/6/vQxqNBjt27ICVlRWef/75dqUVHx+Pl156Set7nZCQAA8PD1haWuLEiRPo06cPJk+eDJlMpnXsH3/8gaNHjwIAAgMDMXToUGFbRkYGrl27hsDAQKSlpaG4uBhBQUFwdXVFcXExfvzxR9TX12PixImQy+XCcQ4ODsL97vbt29i6dSsWLVqEc+fO4eLFi3B2dsakSZOERiMlJSVITEwE0HCfHDx4MIKDg2Fq2nK4Z/DgwfDy8sL27duxatWqdl0v1jZ91VdAw2dy1KhREIlE8PHxwapVq9C3b992pcHNkRgzQAqFQqiU8/Ly4Ovri5MnT2Ls2LH6LRhjjDGDlZGRgcjISIhEIr3k/+GHH2Lv3r3NbiMizJgxA9u3b4erqysCAgLQv39/pKSkYOPGjcjOzsZTTz2FlJQUlJeXw9TUFKamphg/fjwCAgLg6OjYw2fD9EGtVmP58uWIi4tDSEgIRo4cidDQUCxZsgSbNm3qsnzWr18PiUSC4ODgLkuTMUOjUCggl8thZ2cHIsLGjRvx/PPPw8PDo9uDhI35N74PlZaWYsKECdi5cycCAwPblU5VVRXS0tJ0AjObNm3C4sWLMXPmTJw8eRIffvgh3N3dcevWLWGfTz75BB4eHjhy5AiOHDkCd3d3xMfHC9t//PFHREZGwsfHBz/88AOSk5Ph7u6ODRs2YPTo0UhPT8fu3bvh7u6Oy5cvC8c17Xp869YtREVF4emnn0ZMTAzOnDmDxYsXC63nAaCurg5FRUUoKirC77//jqioKHh7e7c5nn1AQAB++OGHdl0v1jW6q74SiURYunQpPvroI6xatQqZmZnw8vJqd9dnblHImIG5ffs2Ll++DC8vL+zbtw+LFi3C6tWrsWzZMn0XjTHGmAHT97AWCoUCixcvbnbbgQMHUFVVhZSUFK0WH1lZWVizZo1w3Lx587Bo0SI4ODigsrISK1as4CDhAyQ3NxelpaVYuXIloqOjQUQgIpSXl2sFLmJjY/Hpp5+2mtbnn3/e7ADwO3bsQGxsLL799lv069evy8+BMUPR2KJPqVQiJCQE169fR1ZWFh555JEey3/u3Lk4efIkZs2ahRkzZmDTpk2ttqJrzsWLF6FWq7VaAjbSaDQ4deoUzMzMoFarMXjwYCQmJiIiIgLXr19HVFQUtm7dipCQEADA9u3bERYWhqlTpwotBG/evImzZ89i2LBhABpaHb711lu4cOECBg0aBAAYNWoUtm3bhg0bNrRYzoCAALz77rsAgNDQUIwcORJ5eXkYMmQInJyctIJL9fX18PX1RXx8PKKiolpM083NDXFxce26XqxrdFd9FRERARMTE2HbmDFjMHDgQMTHx2PlypX3XD4OFDJmYBQKBYyMjLBz50589dVXSEpK0voFLCsrCzk5OQCAadOmQSqV6qmkjDHGDEnT1hkAoFKpsGvXLri4uCAgIKBb81apVPj999+18s/Pz8fhw4fx7LPPIicnB2PGjNHp7vnrr7/io48+EpZPnTqFLVu24Pbt27hz5w4HCR8wjbNdx8bGwsXFRWtbnz59hP/PnTsXTz/9dKtpNRfs+PLLLxEaGorExERMnTq1C0rMmOFSKBSwtrbG448/jrFjx2LPnj1Ci/Ta2lrs2rULADBgwIAW65Dz58/jf//7X6v5TJo0CcOHD9daV1JSghs3buDkyZMICwtDXFwc5s6dK2zPy8tDRkYGAMDf31/nftBU46RENjY2OtumT58uDD1gamqKESNGoKCgAABw+PBhmJmZaY1BN3/+fERERODnn38Wgofu7u5CkBAAPDw8UFtbKwQJG9c1ptuSpi0IPT09YWpqioKCAmH8uerqahw6dAhXr16FSqWCWCxuc6IZGxsbVFdXo7a2VmeIBda9uqu+ahokBBr+xh4eHsjOzm5X+ThQyJiBUSgUsLKyErpo/bWZfH5+PlJTU3HgwAF4e3tzoJAxxtg9USgUWL58OQDgypUrmDlzJuRyOT7//PNuz/v8+fNQq9VCoHDfvn1YvHgx1q1bB2dnZzz11FMIDg7GhQsXYGVlhcmTJwvjL9na2gJoeNnr06cPJBIJysvLUVhYiLlz52Lx4sXdHuhkvcPgwYNhYmKCoqIiTJo0qcX9+vXr1+7WgLt27cIrr7yC7du3Y86cOZ0tKmMG7+zZs9BoNBgwYAASEhK0fshRq9VITU3FtWvXIJVKW7wH19TUtDmZRmNApakzZ85AJBJh27ZtiI6O1goSAsCNGzeQmpqK9PR0iESiVgOFja23KisrIZFImt3WqE+fPqitrQUAXLt2DY6Ojlqt8E1MTODk5IRr164J66ysrHTSaG5dW11Dm5bFyMgIJiYmQlkuXLiAgIAAuLq64rHHHoOVlRXu3r2r1U26OZWVlRCLxRwk1IPurK+aIiJcvXoVjz76aLuO40AhYwZGoVBg+vTpeOSRR7BkyRJ4e3vD2dlZ2D5nzhzMmTMH3t7eeiwlY4wxQ1JZWYk///wTnp6eSElJwbx58xAeHo4333yzRyZtUCgUcHFxgUQiwcqVK5GYmIh9+/YJY++OGjUKZ8+eRWZmJmpqajB8+HDU19drdZmqr69HbGwsAEAmkyEtLQ2nT59u9QWR3V9kMhkWLFiANWvWwNPTE15eXtBoNDh27Bjy8/Nb7Nrelj179uDll1/G9u3bdQISjD2Irly5glu3biEjIwOzZs1CdHQ01q9fL2y3sLBAQkICDhw4gISEhBbT8fHxabaLf1saW8CHh4dj0aJFGD9+vNa7z7hx4zBu3DgsWLCgzbTc3NxgbGyMvLy8drVCd3R0xM2bN3XWFxcX93hr9vfffx+BgYH45ptvhHXz5s1DaWlpq8fl5+frtNZkPaO76qtVq1bh9ddfh4ODA+rr67F27VoUFBS0OfvyX3GgkDEDo1AoMH/+fISHhyM3NxdTpkxBWloaLC0t9V00xhhjBurs2bMwMjLC999/jy1btuCLL77Q+oX7/PnzuHTpEgAgKChIpzVEo507d6KqqqrFfKysrJoNtDQOij9hwgTU1NTgt99+g5OTk9Y+/fv3R//+/bXWNR20vl+/flrLw4cP5xeg+8yJEyfw8ssvC8vLly/HW2+9hVmzZiEmJgYAEBcXh+XLl2Ps2LGQSqWoqqqCr68vNm/e3OF8Fy5cCBMTE8TExAj5AEBYWBjCwsI6fkKMGSiFQgGpVIoxY8Zg//798Pf3h5ubW4+1tm0MFM6ePRs5OTmYOnUqsrKytGYOvldSqRQ+Pj5IS0vDuHHj7vm4gIAAVFZW4sCBA3j22WcBAPv370dVVVWnZqztiNu3b8Pe3l5YvnnzJg4ePAhfX99Wj/vll18wceLE7i7eA0lf9dXQoUPh6+uLuro6KJVKODs7Y//+/Rg9enS70uFAIWMGRK1WIycnByNGjICRkRESEhIwbtw4zJs3D0lJSXobgJ4xxphhUygUsLCwwIYNG7B3716dbjBZWVlITk7GsWPHkJaWhscee6zZdLKzs4XxnprTt2/fFvM/e/YsRCIRCgoKdLp/MQYAI0aMwL59+3TWNx1mRSQS4aOPPsL777+PwsJCPPTQQxCLxZ3K9/Tp09BoNDrreTIT9qBSKBQYMWIEAMDb2xsJCQkICQmBi4tLuwMSHc0/PDwcQMMs5Lm5ufjb3/7W4cYToaGheO+99/Dmm2/e8zEDBw7Em2++iRdffBGLFi0CAGzbtg2rV6/GwIED212GzggJCcGLL74IExMTSKVSfPnll23Wo9euXcPx48dbbfHJOk5f9dWCBQuwYMECFBUVwcLCosMzkHOgkDEDolQqERYWBi8vLwCAWCzG/v378cEHH+DcuXNag8Azxhhj90qhUGDGjBmwt7fHsmXLMHr0aDg4OAjbFy5ciIULF7Y5rMXbb7/d7ryJCOfOncNnn32GmJgYLF26FDt27Gh3Ouz+Z2FhATc3t3va19zcvMu6nTc3GypjD7IhQ4ZoTdAxa9Ys3LlzB+np6T0SKJwyZQqCgoIANIzXl5iYiA0bNiAjIwMTJkxod3pz587FO++8gx9//FH4oezll1/G448/rpNv00BkdHQ0goKCcPjwYRgZGSElJUUYMgMAnnzySZ1WjgEBATr3seDgYFRXVwvLS5cuhaurK4CGySgiIyMhk8m0jomIiBAmRJk+fTqOHTuGlJQUGBkZYffu3SgrK9Ma/3H27NnCxCdAwyQaL730EgYMGHDP14ndO33VV40eeuihTh1vRETURWVhjPUCubm5yMzMxLp167Bo0SJ4enrimWee0XexGGOM9WLe3t6YM2cOli1bhmnTpqGkpARHjx4VZrBsul9CQkKLLQo7Ij8/H0OGDMH169ehUqng4+ODyMhIrFixQtjHw8MDhYWFGDJkCE6cONFleTPGGOseu3fvxsmTJ5Geno5//OMfCA4O7lDX4I4oKirCTz/9hM8++wzu7u548skn2xxf9MSJE8jNzdXqLnq/0mg0WLt2LcLCwrS6LDPWiPspMnafuX79OlJTU+Hv74+8vDycPn1a30VijDHWi6nVamRnZ2PEiBEwNjbGrl27UF1djVdeeaVH8lcoFLCzs4OTkxNcXFzw3XffYd26ddi7d6+wz7Fjx3Do0KFWuzUzxhjrPTIyMlBeXo7hw4cjNTW1zRl4u1JFRQVSU1MxePBg1NTUIC0trc1jRo8e/UAECQHA2NgY69ev5yAhaxF3PWbsPhMUFCQ0xWeMMcbaUlFRgfnz52PkyJEAAIlEguTkZLzzzjs4c+aMMNxFd5FIJIiKihKW/fz8sHPnTqSnp2PSpEkQiUSQyWRQKpXdWg7GGGNd5+OPP9Zb3m5ubjz2HmOdwF2PGWOMMcZYqwoKCnD27Fm88cYbCA8Ph6enZ7tmh+wKly9fxsSJE5Gbm9uj+TLGGGOMPUi46zFjjDHGGGvVxYsXhbEJjxw5goMHD+q7SIwxxhhjrBtwi0LGGGOMMdbrcYtCxhhjjLHuxy0KGWOMMcZYrzZ16lSMHDkS+fn56NevH+Lj4/VdJMYYY4yx+xK3KGSMMcYYY72aUqlEXV2dsGxpaQmxWKzHEjHGGGOM3Z84UMgYY4wxxhhjjDHGGOOux4wxxhhjjDHGGGOMMQ4UMsYYY4wxxhhjjDHGwIFCxhhjjDHGGGOMMcYYOFDIGGOMMcYYY4wxxhgDBwoZY4wxxhhjjDHGGGPgQCFjjDHGGGOMMcYYYwwcKGSMMcYYY4wxxhhjjIEDhYwxxhhjjDHGGGOMMXCgkDHGGGOMMcYYY4wxBuD/AM+QZ7MoKTVFAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1300x350 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "s2_nom = S2_closed_form(t_star, k1_nom)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(13, 3.5))\n",
+    "\n",
+    "# --- Panel 0: wide view ---\n",
+    "k1_wide = np.linspace(0.4, 1.6, 2000)\n",
+    "axes[0].plot(k1_wide, S2_closed_form(t_star, k1_wide))\n",
+    "axes[0].axvline(k1_crit, color=\"tab:red\", ls=\"--\", lw=1, label=\"$k_1^{crit}$\")\n",
+    "main_k1_points = np.concatenate([k1_nom - main_ladder, k1_nom + main_ladder])\n",
+    "axes[0].scatter(\n",
+    "    main_k1_points,\n",
+    "    S2_closed_form(t_star, main_k1_points),\n",
+    "    s=18,\n",
+    "    color=\"tab:orange\",\n",
+    "    zorder=3,\n",
+    "    label=\"main ladder\",\n",
+    ")\n",
+    "axes[0].set_xlabel(\"$k_1$\")\n",
+    "axes[0].set_ylabel(\"$S_2(t^*, k_1)$\")\n",
+    "axes[0].set_title(\"Wide view + main ladder\")\n",
+    "axes[0].legend(fontsize=7)\n",
+    "\n",
+    "# --- Panel 1: zoomed near k1_crit ---\n",
+    "k1_zoom_lo, k1_zoom_hi = k1_crit - 0.01, k1_crit + 0.01\n",
+    "k1_zoom = np.linspace(k1_zoom_lo, k1_zoom_hi, 2000)\n",
+    "y_zoom = S2_closed_form(t_star, k1_zoom)\n",
+    "axes[1].plot(k1_zoom - k1_crit, y_zoom)\n",
+    "axes[1].axvline(0, color=\"tab:red\", ls=\"--\", lw=1, label=\"$k_1^{crit}$\")\n",
+    "axes[1].axvline(\n",
+    "    k1_nom - k1_crit,\n",
+    "    color=\"tab:gray\",\n",
+    "    ls=\":\",\n",
+    "    lw=1.3,\n",
+    "    label=\"$k_1$ (nominal)\",\n",
+    ")\n",
+    "far_k1_points = np.concatenate([k1_nom - far_ladder, k1_nom + far_ladder])\n",
+    "for pts, color, label in [\n",
+    "    (main_k1_points, \"tab:orange\", \"main ladder\"),\n",
+    "    (far_k1_points, \"tab:green\", \"far ladder\"),\n",
+    "]:\n",
+    "    mask = (pts >= k1_zoom_lo) & (pts <= k1_zoom_hi)\n",
+    "    axes[1].scatter(\n",
+    "        pts[mask] - k1_crit,\n",
+    "        S2_closed_form(t_star, pts[mask]),\n",
+    "        s=18,\n",
+    "        color=color,\n",
+    "        zorder=3,\n",
+    "        label=label,\n",
+    "    )\n",
+    "axes[1].set_xlabel(\"$k_1 - k_1^{crit}$\")\n",
+    "axes[1].set_title(\"Zoomed near $k_1^{crit}$\")\n",
+    "axes[1].xaxis.set_major_locator(plt.MaxNLocator(5))\n",
+    "axes[1].ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0, 0))\n",
+    "axes[1].legend(fontsize=7)\n",
+    "\n",
+    "# --- Panel 2: zoomed to far-ladder scale ---\n",
+    "far_zoom_half_range = far_ladder[0] * 3\n",
+    "k1_far_lo, k1_far_hi = (\n",
+    "    k1_nom - far_zoom_half_range,\n",
+    "    k1_nom + far_zoom_half_range,\n",
+    ")\n",
+    "k1_far_zoom = np.linspace(k1_far_lo, k1_far_hi, 2000)\n",
+    "y_far_zoom_raw = S2_closed_form(t_star, k1_far_zoom)\n",
+    "y_far = (y_far_zoom_raw - s2_nom) * 1e6\n",
+    "axes[2].plot(k1_far_zoom - k1_nom, y_far)\n",
+    "axes[2].axvline(0, color=\"tab:gray\", ls=\":\", lw=1.3, label=\"$k_1$ (nominal)\")\n",
+    "yf = (S2_closed_form(t_star, far_k1_points) - s2_nom) * 1e6\n",
+    "axes[2].scatter(\n",
+    "    far_k1_points - k1_nom,\n",
+    "    yf,\n",
+    "    s=18,\n",
+    "    color=\"tab:green\",\n",
+    "    zorder=3,\n",
+    "    label=\"far ladder\",\n",
+    ")\n",
+    "axes[2].set_xlabel(\"$k_1 - k_1$ (nominal)\")\n",
+    "axes[2].set_ylabel(r\"$(S_2 - S_2(k_1)) \\times 10^6$\")\n",
+    "axes[2].set_title(\"Zoomed to far-ladder scale\")\n",
+    "axes[2].xaxis.set_major_locator(plt.MaxNLocator(5))\n",
+    "axes[2].ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0, 0))\n",
+    "axes[2].legend(fontsize=7)\n",
+    "\n",
+    "\n",
+    "def mark_zoom_region(ax_from, ax_to, xlo, xhi, ylo, yhi):\n",
+    "    # Highlight [xlo, xhi] x [ylo, yhi] on ax_from (in its own data\n",
+    "    # coordinates) and connect it to ax_to's left edge, to make clear\n",
+    "    # ax_to zooms into exactly that region.\n",
+    "    ax_from.add_patch(\n",
+    "        mpatches.Rectangle(\n",
+    "            (xlo, ylo),\n",
+    "            xhi - xlo,\n",
+    "            yhi - ylo,\n",
+    "            facecolor=\"0.6\",\n",
+    "            edgecolor=\"0.2\",\n",
+    "            lw=1,\n",
+    "            alpha=0.5,\n",
+    "            zorder=5,\n",
+    "        )\n",
+    "    )\n",
+    "    for corner_from, corner_to in [((xhi, yhi), (0, 1)), ((xhi, ylo), (0, 0))]:\n",
+    "        fig.add_artist(\n",
+    "            mpatches.ConnectionPatch(\n",
+    "                xyA=corner_from,\n",
+    "                coordsA=ax_from.transData,\n",
+    "                xyB=corner_to,\n",
+    "                coordsB=ax_to.transAxes,\n",
+    "                color=\"0.4\",\n",
+    "                lw=0.8,\n",
+    "                ls=\"--\",\n",
+    "                zorder=1,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "mark_zoom_region(\n",
+    "    axes[0], axes[1], k1_zoom_lo, k1_zoom_hi, y_zoom.min(), y_zoom.max()\n",
+    ")\n",
+    "mark_zoom_region(\n",
+    "    axes[1],\n",
+    "    axes[2],\n",
+    "    k1_far_lo - k1_crit,\n",
+    "    k1_far_hi - k1_crit,\n",
+    "    y_far_zoom_raw.min(),\n",
+    "    y_far_zoom_raw.max(),\n",
+    ")\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6983dc9a",
+   "metadata": {},
+   "source": [
+    "`S2` itself is *continuous* at `k1_crit` (an integrated quantity, so the\n",
+    "`S1` reset doesn't make it jump) but has a genuine corner there: the\n",
+    "reset makes `S1` (and hence `dS2/dk1`, which equals `t* * S1(t*)` on\n",
+    "either side, exactly) jump by a factor of 10 (from `0.1` to `1`) the\n",
+    "instant `k1` crosses `k1_crit`. That corner is `fiddy`'s target -- and\n",
+    "it sits at `k1_crit`, `0.1%` away from `k1_nom`, well inside the range\n",
+    "any of the main ladder's rungs above would step past.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3ae09f69",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-09T06:36:29.877569Z",
+     "iopub.status.busy": "2026-09-09T06:36:29.877473Z",
+     "iopub.status.idle": "2026-09-09T06:36:30.166380Z",
+     "shell.execute_reply": "2026-09-09T06:36:30.165642Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h_rel = np.logspace(0, -8, 400) * 0.6  # ~0.6 down to ~6e-9, relative to k1_nom\n",
+    "central = (\n",
+    "    S2_closed_form(t_star, k1_nom + h_rel)\n",
+    "    - S2_closed_form(t_star, k1_nom - h_rel)\n",
+    ") / (2 * h_rel)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 4))\n",
+    "ax.plot(\n",
+    "    h_rel,\n",
+    "    central,\n",
+    "    lw=0.8,\n",
+    "    color=\"tab:blue\",\n",
+    "    alpha=0.6,\n",
+    "    label=\"continuous sweep\",\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    main_ladder,\n",
+    "    main_central_values_cf,\n",
+    "    s=28,\n",
+    "    color=\"tab:orange\",\n",
+    "    zorder=3,\n",
+    "    label=\"main ladder rungs\",\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    far_ladder,\n",
+    "    far_central_values_cf,\n",
+    "    s=28,\n",
+    "    color=\"tab:green\",\n",
+    "    zorder=3,\n",
+    "    label=\"far ladder rungs\",\n",
+    ")\n",
+    "ax.axhline(\n",
+    "    true_derivative,\n",
+    "    color=\"tab:green\",\n",
+    "    ls=\"--\",\n",
+    "    lw=1,\n",
+    "    label=f\"true value ({true_derivative:.4f})\",\n",
+    ")\n",
+    "ax.axvline(\n",
+    "    delta_rel,\n",
+    "    color=\"tab:red\",\n",
+    "    ls=\"--\",\n",
+    "    lw=1,\n",
+    "    label=f\"crossing distance $\\\\delta$ ({delta_rel:.1%})\",\n",
+    ")\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.invert_xaxis()\n",
+    "ax.set_xlabel(\"relative step size $h$\")\n",
+    "ax.set_ylabel(\"central-difference estimate\")\n",
+    "ax.legend(fontsize=9)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd2fb2e7",
+   "metadata": {},
+   "source": [
+    "The plot above shows why the main ladder alone gets this wrong with\n",
+    "high apparent confidence. For every `h > delta`, the forward\n",
+    "evaluation (`k1_nom + h`) has already crossed `k1_crit` while the\n",
+    "backward evaluation (`k1_nom - h`) never does (decreasing `k1` only\n",
+    "delays the event further) -- so the central difference mixes one\n",
+    "post-event and one pre-event point at every single main-ladder rung.\n",
+    "As `h` shrinks through that range, what changes smoothly isn't the\n",
+    "estimate converging to the truth -- it's *how far past* the event the\n",
+    "forward point lands, which happens to vary smoothly, producing the\n",
+    "same dome shape genuine curvature would. Extrapolation and the\n",
+    "adjacent-rung gap check are both built to see through ordinary\n",
+    "curvature, which is exactly why neither one flags anything wrong\n",
+    "here: without the far ladder, fiddy would extrapolate this dome to a\n",
+    "smooth, confident, and wrong `\"converged\"` result.\n",
+    "\n",
+    "Only once `h` drops below `delta` do *both* evaluations finally land\n",
+    "on the correct (post-nominal-event) branch, and the estimate snaps to\n",
+    "the true value -- visible as the sharp corner at the red dashed line.\n",
+    "The main ladder's finest rung never gets that far, but the far\n",
+    "ladder, anchored near machine epsilon, sits entirely inside that\n",
+    "correct region. That's what lets `check_cross_regime_disagreement`\n",
+    "catch the problem: it compares the main ladder's converged-but-wrong\n",
+    "value against the far ladder's value and flags the two for disagreeing\n",
+    "far beyond either one's own error estimate.\n"
+   ]
   }
  ],
  "metadata": {