From 034472de15a3e2e26c5bfd3f29b37783528238eb Mon Sep 17 00:00:00 2001 From: Mohamad Ibrahim <150104488+mofermino@users.noreply.github.com> Date: Sun, 31 Aug 2025 01:28:25 -0500 Subject: [PATCH] Created using Colab --- ...Inductance_Scatterplot_for_SC_Metals.ipynb | 250 +++++++++++++++--- 1 file changed, 208 insertions(+), 42 deletions(-) diff --git a/Jupyter Notebook Scripts/Kinetic_Inductance_Scatterplot_for_SC_Metals.ipynb b/Jupyter Notebook Scripts/Kinetic_Inductance_Scatterplot_for_SC_Metals.ipynb index ab78d4e..373d2f3 100644 --- a/Jupyter Notebook Scripts/Kinetic_Inductance_Scatterplot_for_SC_Metals.ipynb +++ b/Jupyter Notebook Scripts/Kinetic_Inductance_Scatterplot_for_SC_Metals.ipynb @@ -4,7 +4,6 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyNz38lxa+I7HUphvQyXtU53", "include_colab_link": true }, "kernelspec": { @@ -23,7 +22,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -37,36 +36,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "qUcxIGmprV9u", - "outputId": "f851225c-d44e-419a-c111-32ec29eeda42" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], "source": [ "import math\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", + "# ---------------- Plot style ----------------\n", "plt.rcParams['figure.dpi'] = 250\n", "\n", + "# ---------------- Physics constants ----------------\n", + "hbar = 1.054571817e-34 # J*s\n", + "kB = 1.380649e-23 # J/K\n", + "pi = math.pi\n", + "\n", + "# ---------------- Controls ----------------\n", + "freq_GHz = 21.0 # Frequency at which to evaluate Ls(T, f) [GHz]\n", + "thickness_nm = 20.0 # Film thickness for scaling [nm]\n", + "use_two_gap_MgB2 = True # Toggle two-gap model for MgB2\n", + "\n", + "# ---------------- BCS helpers ----------------\n", "def delta_ratio_BCS(T, Tc):\n", + " \"\"\"\n", + " Returns Δ(T)/Δ0 using a common BCS approximation.\n", + " \"\"\"\n", " if T <= 0:\n", " return 1.0\n", " if T >= Tc:\n", @@ -75,9 +68,14 @@ " return math.tanh(x)\n", "\n", "def Ls_T_from_Ls0(Ls0, T, Tc):\n", + " \"\"\"\n", + " Your original temperature dependence (per band if needed).\n", + " Keeps the visual behavior near Tc and low-T limit.\n", + " Returns NaN above Tc.\n", + " \"\"\"\n", " if T >= Tc:\n", " return np.nan\n", - " delta0_over_kBT = 1.764 * Tc / T\n", + " delta0_over_kBT = 1.764 * Tc / T # (Δ0/kB)/T = 1.764 Tc / T\n", " ratio = delta_ratio_BCS(T, Tc)\n", " if ratio <= 0 or np.isnan(ratio):\n", " return np.nan\n", @@ -87,57 +85,225 @@ " return np.nan\n", " return Ls0 * (1.0 / (ratio * tanh_term))\n", "\n", + "def bcs_gap_J(T, Tc, delta0_J=None):\n", + " \"\"\"\n", + " Δ(T) in Joules. If delta0_J is None, uses weak-coupling BCS Δ0 = 1.764 kB Tc.\n", + " \"\"\"\n", + " if delta0_J is None:\n", + " delta0_J = 1.764 * kB * Tc\n", + " r = delta_ratio_BCS(T, Tc)\n", + " if np.isnan(r):\n", + " return np.nan\n", + " return delta0_J * r\n", + "\n", + "def f_MB(omega, Delta_J, eps=1e-12):\n", + " \"\"\"\n", + " Mattis–Bardeen–inspired frequency factor:\n", + " f_MB = 1 / sqrt(max(1 - (ħω / 2Δ)^2, ε))\n", + " Clipped to avoid divergence; only valid for ħω < 2Δ in this simplified model.\n", + " \"\"\"\n", + " if np.isnan(Delta_J) or Delta_J <= 0.0:\n", + " return np.nan\n", + " x2 = (hbar * omega / (2.0 * Delta_J))**2\n", + " denom = max(1.0 - x2, eps)\n", + " return 1.0 / math.sqrt(denom)\n", + "\n", + "# ---------------- Materials ----------------\n", + "# Ls0_pHsq is the *baseline* sheet kinetic inductance at low T & low f for a reference thickness t_ref_nm.\n", + "# For thickness scaling we use simple thin-film limit Ls ∝ 1/t (if t_ref_nm present).\n", + "# For two-gap materials, provide \"weights\" (sum to 1) and optional \"deltas_eV\".\n", "materials = {\n", - " \"NbTiN\": {\"Ls0_pHsq\": 36.0, \"Tc_K\": 15.0},\n", - " \"Nb\": {\"Ls0_pHsq\": 0.051, \"Tc_K\": 9.2},\n", - " \"Mo79Ge21\": {\"Ls0_pHsq\": 61.0, \"Tc_K\": 4.4},\n", - " \"grAl\": {\"Ls0_pHsq\": 100.0, \"Tc_K\": 2.2},\n", - " \"MgB2\": {\"Ls0_pHsq\": 5.5, \"Tc_K\": 39.0},\n", + " \"NbTiN\": {\n", + " \"Ls0_pHsq\": 36.0, \"Tc_K\": 15.0, \"t_ref_nm\": 20.0\n", + " # single-gap default (BCS from Tc)\n", + " },\n", + " \"Nb\": {\n", + " \"Ls0_pHsq\": 0.051, \"Tc_K\": 9.2, \"t_ref_nm\": 100.0\n", + " # single-gap default\n", + " },\n", + " \"Mo79Ge21\": {\n", + " \"Ls0_pHsq\": 61.0, \"Tc_K\": 4.4, \"t_ref_nm\": 20.0\n", + " # single-gap default\n", + " },\n", + " \"grAl\": {\n", + " \"Ls0_pHsq\": 100.0, \"Tc_K\": 2.2, \"t_ref_nm\": 20.0\n", + " # single-gap default\n", + " },\n", + " \"MgB2\": {\n", + " \"Ls0_pHsq\": 5.5, \"Tc_K\": 39.0, \"t_ref_nm\": 20.0,\n", + " # Two-gap (illustrative):\n", + " # If you set use_two_gap_MgB2=False above, this entry behaves as single-gap using Tc.\n", + " \"weights\": [0.6, 0.4], # conductance split (sum to 1)\n", + " \"deltas_eV\": [7.0e-3, 2.5e-3], # Δ0 values for the two bands (at ~0 K), in eV\n", + " },\n", "}\n", - "temperatures_K = [0.020, 0.045, 10]\n", "\n", + "# ---------------- Temperatures to sweep (x-axis) ----------------\n", + "temperatures_K = np.linspace(0.020, 50.0, 100)\n", + "\n", + "# ---------------- Frequency / thickness setup ----------------\n", + "omega = 2 * math.pi * freq_GHz * 1e9 # rad/s\n", + "thickness_m = thickness_nm * 1e-9\n", + "\n", + "def thickness_scale_factor(mat, t_target_nm):\n", + " \"\"\"\n", + " Simple thin-film scaling: Ls ∝ 1/t.\n", + " Uses material's t_ref_nm if available; otherwise returns 1.0.\n", + " \"\"\"\n", + " t_ref = materials[mat].get(\"t_ref_nm\", None)\n", + " if t_ref is None or t_ref <= 0:\n", + " return 1.0\n", + " if t_target_nm <= 0:\n", + " return np.nan\n", + " return float(t_ref) / float(t_target_nm)\n", + "\n", + "def Ls_sheet_singlegap_Tf(Ls0_pHsq, Tc_K, T, omega):\n", + " \"\"\"\n", + " Single-gap sheet Ls at (T, f):\n", + " 1) Temperature dependence from your original Ls_T_from_Ls0\n", + " 2) Frequency factor f_MB(ω, Δ(T))\n", + " \"\"\"\n", + " # temp dependence:\n", + " Ls_T = Ls_T_from_Ls0(Ls0_pHsq, T, Tc_K)\n", + " if np.isnan(Ls_T):\n", + " return np.nan\n", + " # Δ(T) using BCS:\n", + " Delta_T_J = bcs_gap_J(T, Tc_K, delta0_J= kB * Tc_K) # None => 1.764 kB Tc\n", + " if np.isnan(Delta_T_J) or Delta_T_J <= 0:\n", + " return np.nan\n", + " # frequency factor:\n", + " f = f_MB(omega, Delta_T_J)\n", + " if np.isnan(f):\n", + " return np.nan\n", + " return Ls_T * f\n", + "\n", + "def Ls_sheet_multigap_Tf(Ls0_pHsq, Tc_K, T, omega, weights, deltas_eV=None):\n", + " \"\"\"\n", + " Two-gap (or multi-gap) support. We split Ls0 across channels by conductance weights:\n", + " Ls0_i = Ls0 / w_i\n", + " Then compute each band's Ls_i(T,f) and combine in parallel:\n", + " 1/Ls_total = Σ_i 1/Ls_i\n", + " If deltas_eV is None, each band uses BCS Δ0 from Tc.\n", + " \"\"\"\n", + " if np.isnan(T) or T >= Tc_K:\n", + " return np.nan\n", + "\n", + " inv_sum = 0.0\n", + " for i, w in enumerate(weights):\n", + " w = max(float(w), 1e-12)\n", + " Ls0_i = Ls0_pHsq / w\n", + "\n", + " # Δ0 for this band\n", + " if deltas_eV is not None:\n", + " delta0_J = deltas_eV[i] * 1.602176634e-19\n", + " else:\n", + " delta0_J = 1.764 * kB * Tc_K\n", + "\n", + " # Temperature scaling for this band's Δ(T)\n", + " ratio = delta_ratio_BCS(T, Tc_K)\n", + " if np.isnan(ratio) or ratio <= 0:\n", + " continue\n", + " Delta_T_J = delta0_J * ratio\n", + "\n", + " # Use your original temperature scaling for Ls(T) per band:\n", + " Ls_T = Ls_T_from_Ls0(Ls0_i, T, Tc_K)\n", + " if np.isnan(Ls_T):\n", + " continue\n", + "\n", + " # Frequency factor for the band:\n", + " f = f_MB(omega, Delta_T_J)\n", + " if np.isnan(f):\n", + " continue\n", + "\n", + " Ls_i = Ls_T * f # pH/sq\n", + " inv_sum += 1.0 / max(Ls_i, 1e-30)\n", + "\n", + " if inv_sum <= 0.0:\n", + " return np.nan\n", + " return 1.0 / inv_sum\n", + "\n", + "# ---------------- Compute rows ----------------\n", "rows = []\n", + "\n", "for mat, params in materials.items():\n", " Ls0 = params[\"Ls0_pHsq\"]\n", - " Tc = params[\"Tc_K\"]\n", + " Tc = params[\"Tc_K\"]\n", + "\n", + " # thickness scaling: map from material's reference thickness to chosen thickness\n", + " t_scaler = thickness_scale_factor(mat, thickness_nm)\n", + "\n", + " # multigap config if present\n", + " weights = params.get(\"weights\", None)\n", + " deltas_ev = params.get(\"deltas_eV\", None)\n", + "\n", " for T in temperatures_K:\n", - " Ls_T = Ls_T_from_Ls0(Ls0, T, Tc)\n", + " if weights is not None and use_two_gap_MgB2 and mat == \"MgB2\":\n", + " Ls_Tf = Ls_sheet_multigap_Tf(Ls0, Tc, T, omega, weights, deltas_eV=deltas_ev)\n", + " else:\n", + " Ls_Tf = Ls_sheet_singlegap_Tf(Ls0, Tc, T, omega)\n", + "\n", + " if not np.isnan(Ls_Tf) and not np.isnan(t_scaler):\n", + " Ls_Tf *= t_scaler # apply thickness scaling (thin-film ~ 1/t)\n", + "\n", " rows.append({\n", " \"Material\": mat,\n", " \"Temperature_K\": T,\n", - " \"Sheet_Kinetic_Inductance_pH_per_sq\": None if np.isnan(Ls_T) else Ls_T,\n", + " \"Sheet_Kinetic_Inductance_pH_per_sq\": None if (np.isnan(Ls_Tf) or np.isnan(t_scaler)) else Ls_Tf,\n", " \"Assumed_Tc_K\": Tc,\n", - " \"State\": \"superconducting\" if (not np.isnan(Ls_T)) else \"normal (not superconducting at this T)\"\n", + " \"State\": \"superconducting\" if (not np.isnan(Ls_Tf) and not np.isnan(t_scaler)) else \"normal (not superconducting at this T)\"\n", " })\n", "\n", "df = pd.DataFrame(rows)\n", "\n", + "# ---------------- Plot ----------------\n", "plt.figure(figsize=(8, 6))\n", "for mat in materials.keys():\n", " sub = df[(df[\"Material\"] == mat) & (df[\"State\"].str.startswith(\"superconducting\"))]\n", " if len(sub) == 0:\n", " continue\n", " plt.scatter(sub[\"Temperature_K\"], sub[\"Sheet_Kinetic_Inductance_pH_per_sq\"], label=mat)\n", - " for _, r in sub.iterrows():\n", - " plt.annotate(mat, (r[\"Temperature_K\"], r[\"Sheet_Kinetic_Inductance_pH_per_sq\"]), textcoords=\"offset points\", xytext=(5, 5))\n", "\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")\n", "\n", - "xticks = [0.020, 0.045, 10]\n", + "xticks = [0.020, 50, 50]\n", "xticklabels = [f\"{x:.3f}\" for x in xticks]\n", "plt.xticks(xticks, xticklabels, rotation=45, ha=\"right\")\n", "\n", "plt.xlabel(\"Temperature (K)\")\n", "plt.ylabel(\"Sheet Kinetic Inductance (pH/sq)\")\n", - "plt.title(\"Sheet Kinetic Inductance vs. Temperature \\nNbTiN, Nb, Mo79Ge21, grAl, MgB2\")\n", - "plt.grid('true', linestyle = ':')\n", + "plt.title(\n", + " \"Sheet Kinetic Inductance vs. Temperature\\n\"\n", + " f\"(f = {freq_GHz:.3g} GHz, t = {thickness_nm:.0f} nm)\"\n", + ")\n", + "plt.grid(True, linestyle=':')\n", "plt.legend()\n", "plt.tight_layout()\n", "\n", - "# out_path = \"/content/kinetic_inductance_scatter_decimalK.png\"\n", - "# plt.savefig(out_path, dpi=180)\n", - "# print(out_path)" + "out_path = \"/content/kinetic_inductance_scatter_decimalK.png\"\n", + "plt.savefig(out_path, dpi=180)\n", + "# print(out_path)\n" + ], + "metadata": { + "id": "6_xP3qoUwot3", + "outputId": "67639459-b207-449f-9d2b-ab94d0dccce8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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RrVq1yr/ev3//AGYDAAAAAACqIis/S69sfsXQmC9velmDWgxSQlSCoXEB1A+JcRF6clSSHrnhSm09eEY7juZqf/Y5Od1ehdus6hAfre4tYnVVm4ayh4YEOl0AQS4+Pl5ut7tOTXFbd94JUAtZLBZ1HdRCrbs21rdrD2v3l8fkLHCX2T480qYu/Zurx3WtSsyxfTE7w7xVW2UeDnC5XLr77rvlcp1/grJXr17q0aNHTaQHAAAAAAAMMDdtrgrcBYbGLHAXaF7aPE2/erqhcQHUL/bQEPXv2ET9O1ZvGkQAVeMu8uhYeq6yD55VzvECuV1e2UKtimsWqfg2DdS8faxsYXXnwZK62GGP4jZQAxo0smvAhCvU78ftlZWeqxOHzionq9hFMyFSTVs3UEIdu2jWZgMGDNDYsWM1fvx4dezYscQ+n8+n9evX6/HHH9fXX3/t3/7MM8/UdJoAAAAAAOAyOdwOLdm3xJTYi/ct1v297pfdRgcEAACCwdnTDm1fe1h7KtEJsXP/5kquoBMiAofiNlCDbGEhatm5kVp2bhToVOq99PR0zZgxQzNmzFB8fLw6dOig6OhonT17Vt99951ycnJKtL/33ns1duzYgOQKAAAAAACqLjU7VXlFeabEzivK0/bs7erXvJ8p8QEAgDF8Pp92fZ6pL9+r3PSxzgK3tn98WGmfZ6r/LR3VdVDp08cicChuA6iXrFarfzk7O1vZ2dmltgsPD9fjjz9Or20AAAAAAIJM2qk00+NT3AYAoPbyeLz6ZPZufb/peJWPdTk9Wv/Wdzq2L0fXTeuikBBrxQehRlDcBoLQ2bNn5fV6ZbVa1aBBg0CnE5S2bdumxYsXa/369dq5c6eOHDmic+fOyWazqXHjxurUqZOuu+463XXXXWrRokWg0wUAAAAAAFV0IPeAqfEzcjNMjQ8AAC6fz+e77MJ2cXs3nj9++F1JQdmD+4MPPpDH41FISIhuvvnmQKdjCIrbQBDyer3y+Xzyer2BTiVoNWvWTPfee6/uvffeQKcCAAAAAABM4PQ4gzo+AAC4fLs+z6x2YfuCvRuPK/GKOHUdFHwd4TweT4k/6wL60ANByGq1ymKxlBhaGwAAAAAAAD8IDwkP6vgAAODynD3t0Jfv7TM05heL9unsaYehMWtCSEhIiT/rAnpuA0GIocgBAAAAAADK1za2ranx28W2MzU+AAC4PNvXHpbLaWxPZZfTo2/XHtaACVcYGtdsdWUo8uLo9gkAAAAAAACgzklqnBTU8QEAQNW5izza8+UxU2Lv/vKY3EV1Z3jvYEVxGwAAAAAAAECd0zO+p2LCYkyJHRMWo+T4ZFNiAwCAy3csPVfOArcpsZ0FbmWl55oSG5VHcRsAAAAAAABAnWO32TW241hTYo/rOE52m92U2AAA4PJlHzxravwTh8yNj4pR3AaCkMPhUGFhoRwOR6BTAQAAAAAAqLWmJE1RpC3S0JhRoVGanDTZ0JgAAMAYOccLzI2fZW58o3355Zf67LPP9OWXXwY6FcNQ3AaCkNPp9L8AAAAAAABQuoSoBD3S5xFDYz7S5xElRCUYGhMAABjD7fIGdXyjZWdn68yZM8rOzg50KoaxBToBoKqs1pLPZHg8ngBlAgDGu/iaFhISEqBMAAAAAKBuSOmUoq0ntmpF+opqxxrVfpQmXDHBgKwA1HUOl0dbDp7RjqO5Ss8+J6fbq3CbVe3jo9W9Rax6t2koeyj3fQCj2ULN7ddrdnxUjOI2go7NVvLHtqCgQA0aNAhQNoERFRUln88ni8US6FQAGKygoOSwNhS3AQAAAKB6LBaLZg6YKUnVKnCPaj9KMwfM5H4MgHJl5hTqjQ0ZWrjliHILXWW2i40IVUrvlrp7YDslxkXUYIZA3RbXzNjpSC6Jn2BufKMlJyfL4/HUqfvMFLcRdGw2myIiIlRYWChJysvLU9OmTevVfywuLvADqBt8Pp/y8vL86xEREfx7BwAAAAADhFpD9fuBv1fvZr318qaXVeCu/HyZkbZITb96uiZcMaFe3X8CUDU+n09vbTykF1bsVn5RxaON5ha69PqGDL298ZCeGNVFt/dtzTUGMEB8G3M7QzZtHVydLdu2bRvoFAxH33kEpZiYGP+y2+1WZmamfD5fADMCgOrx+XzKzMyU2+32byt+rQMAAAAAVI/FYlFKpxQtHbtU05KmKSas/P9zxYTFaFrSNC0du1QpnVIoOgEok8vj1UPvpurJxTsrVdguLr/IoycX79Sv302VyxNcc/kCtVHz9rEKjzSnw1B4pE0J7WNNiY3KozsYglKDBg104sQJf0E7Ly9PBQUFiomJUWRkZJ0aXgFA3ebxeFRQUKC8vLwShW2LxVLvplwAAAAAgJqQEJWg6VdP1/297tf27O1KO5WmjNwMOT1OhYeEq11sOyU1TlJyfLLsNnug0wVQy/l8Pk1fuF1LUzOrFWfJ/45/bVJPHqYBqsEWFqLO/Ztr+8eHDY/dpX9z2cKoPwUaxW0EpdDQULVs2VJHjhzxF7jdbrdOnz6t06dPBzg78xXvpc4XHaDusVgsatmypUJDQwOdCgAAAADUWXabXf2a91O/5v0CnQqAIPbWxkPVLmxfsCQ1U33bNdbt/VobEg+or5Kva6W0zzPlclZtJIXyhNpD1OO6VobFqylZWVny+XyyWCxKSEgIdDqGYFhyBK3o6Gi1bNmyXhZ3HQ6HCgsL5XA4Ap0KAINdKGxHR0cHOhUAAAAAAACUIzOnUC+s2G1ozOdXpCkzp9DQmEB906CRXf1v6WhozAG3dFSDRsE3oss333yjjRs36ptvvgl0Koah5zaCWnR0tDp06KCzZ88qLy9PhYX145d+UVGR/0mbiIiIQKcDwAARERGKiYlRgwYN6LENAABQTV6HQ4Xbtsmxa5ecGRnyOYtkCQ9TeLt2snftqohevWS1B9+NKQAAULu8sSGjynNsVyS/yKP/fpGhJ0clGRoXqG+6DkrUsX052rvxeLVjderbTEkDEw3ICkaguI2gFxoaqkaNGqlRo0Zyu93yeDzyeIz9QlHb7N+/Xy6XS6GhoWrTpk2g0wFQDSEhIQoJCZHNxq9kAACA6nIdO6bTb85RzuLF8ubmltnOGhuruHHj1GjaVIU2b16DGQIAgLrC4fJo4ZYjpsResPmIHrnhStlDmdsXuFwWi0XXTesiSdUqcHfq20zXTesStKMIx8XF+etJdUWtvJPucrl08uRJ5efnS5KioqLUpEmTOvXBwxw2m61eFIi6d+8e6BQAAAAAoNbw+XzKeXeBTrz0krwFBRW29+bm6vTs2cpZsEBNH31UcZMmBu3NKgAAEBhbDp5RbqHLlNi5hS5tPXhG/Ts2MSU+UF+EhFg1/K4kJV4Rpy8W7avSHNyh4SEaMKGjkgYmBvX/Fa699tpAp2C4gFcBHQ6H1q9fr08//VSbN29WWlqajh8/Lp/PV6KdxWJRs2bNlJSUpN69e+vaa6/VkCFDGJIZAAAAAIB6zOdyKfPxJ5T3wQdVPtZbUKCsZ59VwZYtSnzheVl4qB4AAFTSjqNljxJjVHyK20D1WSwWdR3UQq27Nta3aw9r95fH5Cxwl9k+PNKmLv2bq8d1rYJyju36ICDFbY/Ho+XLl2vevHlauXKlnE6nf9/FRe3i248dO6asrCytXbtWf/jDHxQWFqaRI0dq8uTJGjNmTL3osQsAAAAAAM7z+XyXXdguLm/5cklS4kuzgrpXBgAAqDnp2edMjb/f5PhAfdOgkV0DJlyhfj9ur6z0XJ04dFY5WQVyu7yyhVoVlxCppq0bKKF9rGxhTAlQm9VoNTg3N1d//etf9Y9//EOZmZmSyi5ml6V4e6fTqWXLlmnZsmVq1qyZ7rvvPt1///1q2LChoXkDAAAAAIDaJ+fdBdUubF+Qt3y5Ivv0UcNJEw2JBwAA6jan2xvU8YH6yhYWopadG6ll50aBTgWXqUaK2+fOndPLL7+sP/3pT8rLy5NUskjdrl07XX311erevbs6dOigFi1aqGHDhoqIiJDP51NhYaHOnDmjo0ePKj09XTt27NDmzZuVnp7uj3P8+HE9++yzeuWVV/TAAw/oN7/5jRo0aFATbw+ocV999ZWcTqfCw8P1ox/9KNDpAAAAAECNcx07phMvvWRozBOzZil68CCFNm9uaFwAAFD3hNusQR0fQP3w4Ycfyu12y2az6cYbbwx0OoYwvbj9n//8R0899ZROnDjhL0SHhoZq+PDhGjdunEaOHKmWLVteVuyjR49q1apVWrx4sdasWaOioiLl5eXp+eef17///W89//zzuvvuu418O0CtkJeXJ4fDIbud+R4AAAAA1E+n35wjb0GBoTG9BQU6PWeums141NC4AACg7mkfH21q/A4mxwdQPxQVFZX4sy4w/dGfe+65x1/Y7tChg15++WUdOXJEK1as0E9/+tPLLmxLUosWLXT33Xdr+fLlOnr0qF555RVdccUV8vl8On78uH72s58Z+E6A2sNqtfpfAAAAAFDfeB0O5SxebErsnPffl9fhMCU2AACoO7q3iA3q+ADqB4vFUuLPuqBGhiXv3r27nnrqKY0fP960Ylzjxo3161//Wg899JAWL16smTNn6ttvvzXlXECgXX/99YFOAQAAAAACpnDbNnlzc02J7c3NVWFqqqKuucaU+AAAoG7o3aahYiNClVvoMjx2bESormrT0PC4AOqfH//4x4FOwXCmd/t86623lJqaqgkTJtRIL1OLxaLx48dr27Zteuutt0w/HwAAAAAAqFmOXbuCOj4AAAh+9tAQpfS+/JFpyzOxT0vZQ0NMiQ0Awc70avOtt95q9inKNGnSpICdGwAAAAAAmMOZkWFu/PR0U+MDAIC64e6B7RQVZmwROjrcprsGtDM0JgDUJUzYWw8cPnxYf/rTnzRmzBi1bdtW4eHhio6OVteuXfXggw8qnf+0AwAAAACCiM9ZFNTxAQBA3ZAYF6EnRnUxNOYTN3VRYlyEoTEBoC6pkTm3ETiHDx9WmzZt5PP5/NtiYmJUWFiotLQ0paWl6d///rdmz56tiRMnBjBTVMWBAwfkdrtls9nUtm3bQKcDAAAAGMrrcKhw2zY5du2SMyNDPmeRLOFhCm/XTvauXRXRq5esdnug00QAWcLDgjo+AACoO27v21qbMk5rSWpmtWON7Zmo2/q2MiArADhvy5YtcrlcCg0NVe/evQOdjiEobtdxHo9HkjRy5EhNmzZNw4cPV5MmTeR2u/Xll1/qV7/6lb799ltNnjxZXbp0Uffu3QOcMcpS/AZfWmysXGFhCi0qUoOPP+YGHwAAAOoE17FjOv3mHOUsXixvbm6Z7ayxsYobN06Npk1VaPPmNZghaovwduYO1Rnevr2p8QEAQN1hsVj0h5RkSapWgXtsz0T9ISVZFovFqNQAQEeOHPEvU9xGUGjYsKG2bt2qnj17lthus9k0ePBgrV69Wt27d1d2drZee+01vfHGG4FJFGUq7Qaf74nHpbAw+QoLdeLlVyRxgw8AAADBy+fzKefdBTrx0kvyFhRU2N6bm6vTs2crZ8ECNX30UcVNmshNwHrG3rVrUMcHAAB1S2iIVa9N6qm+7Rrr+RVpyi/yVPrYqLAQPTkqSbf1bcV3WgCoBIuv+HjVNey5556rsXM988wzNXauYHPnnXfqzTffVI8ePbR9+3bTz9eyZUsdPXpULVq0KPHECEoq7wbfuc6d5QsNlcXlUvSePSX2WSMjucEHAACAoOFzuZT5+BPK++CDy44RM3q0El94XpbQUAMzQ23mdTj0/bVDyu3hf7mssbG6Yv2njIwFAAAuS2ZOof77RYYWbD6i3EJXme1iI0I1sU9L3TWgHXNsAzDN7t27/dPcdunSxdRz1VT9L6A9t5999tkaK75R3C5bkyZNJElutzvAmeCCim7wXVzQLs5bUKCsZ59VwZYt3OADAABArebz+apd2JakvOXLJUmJL83iAc96wmq3K27cOJ2ePdvw2HHjx1PYBgAAly0xLkJPjkrSIzdcqa0Hz2jH0Vztzz4np9urcJtVHeKj1b1FrK5q01D20JBApwugjjO7oB0IAR+WvLSO4xaLpdTtF/aVdVxZzLy5kZ2drc2bN2vTpk3+P7Oysvz7//vf/+rOO++87PhffvmlZs+erQ0bNvifcmjZsqUGDhyoO++8U/3796/uW9D69eslSd26dat2LFQfN/gAAABQX+S8u6Da33svyFu+XJF9+qjhpImGxEPt12jaVOUsWFCpoewryxoVpUZTpxgWDwAA1F/20BD179hE/Ts2CXQqAFCnBLS4PW3aNP/yihUrdOrUKfl8Pvl8PjVt2lRdu3ZV48aNFR4errNnzyojI0N79uyRy3V+KA+LxaLhw4crMTGxxnPPysrSNddco4MHD5oSPz8/Xw888ECpc2Dv3r1bu3fv1r///W/dfffd+vOf/6yoqKjLOs+SJUu0efNmSdJdd91VrZxhDG7wAQAAoD5wHTumEy+9ZGjME7NmKXrwIIU2b25oXNROoc2bq+mjjyrr2WcNi9n00Uf5+QEAAACAWiygxe3//ve/Kigo0N13362TJ0/KarXqzjvv1P33369evXqVesy5c+e0aNEi/e53v1N6erq2bt2qhx56SDfeeGON5u5wOEwrbHs8Ho0fP16rV6/2b4uIiFDXrl1ls9mUlpamvLw8SdIbb7yho0ePasWKFQoJqdoQJocOHdLPfvYzSdKYMWM0cuRI494ELktlb/B5IiPls1hk8fkUUkEvBW7wAQAAoDY6/eYcQ3vcSuen6Dk9Z66azXjU0LioveImTVTBli3+kauqI2b0aMVNTDEgKwAAAACoHc6dOyefzyeLxaLo6OhAp2MIa6ATuOeee7Rw4UI1aNBAH330kf7zn/+UWdiWpOjoaN15553atWuXxo0bp1OnTmnChAnatm1bDWZdUnx8vEaOHKmnnnpKS5curXa8p59+ukRh+5577tGRI0e0adMmffXVV8rMzNRTTz3l379q1aoqzymek5Oj0aNHKzs7W+3bty+1hzhqXmVv8B186EGlP/O0Dj70YIVtL9zgAwAAAGoLr8OhnMWLTYmd8/778jocpsRG7WOxWJT4wvOKGT26WnFiRo9W4gvPM6UTAACoNIfLoy/2ndQ/1u/Xo4u268F3tunRRdv1j/X79cW+k3K4PIFOEQD0ySefaO3atfrkk08CnYphAtpz+6OPPtLbb78ti8Wi1157TcOHD6/0seHh4XrrrbeUnJysvXv36uc//7k2btxoYrYlNWrUSAsXLtTVV1+tNm3aGBb36NGjeu211/zrU6ZM0b/+9a8SbaKiojRz5kxJ0u9+9ztJ0muvvaZf/vKXlRqi/dy5c7rxxhv17bffKjExUWvWrFGjRo0Mew+4PGbf4It/8AFZ7XZT4gMAAABVUbhtm7y5uabE9ubmqjA1VVHXXGNKfNQ+ltBQJb40S5F9+ujErFlVGhHAGhmppjNmKG5iCoVtoIY53A6lZqcq7VSaDuQekNPjVHhIuNrGtlVS4yT1jO8pu437GABqn8ycQr2xIUMLtxxRbqGrzHaxEaFK6d1Sdw9sp8S4iBrMEADqtoAWt//zn/9IOl8oLj7/dmWFh4fr3nvv1cMPP6wtW7Zo586d6tatm9FpliomJkYTJkwwPO6f//xnOf7XyyAyMlJ//OMfy2z79NNP680339Thw4dVWFioP/3pT5o1a1a58QsKCjRq1Ch9/fXXio+P18cff6z27dsb+RZwmapygy/y++/liYyscEjyC7jBBwAAgNrEsWuX6fH57lu/WCwWNZw0UdGDB+n0nLnne/CX8/8ra2ys4saPV6OpU5jCCahhWflZmps2V0v2LVFeUV6Z7WLCYjS241hNSZqihKiEGswQAErn8/n01sZDemHFbuUXVdwrO7fQpdc3ZOjtjYf0xKguur1vax6mA1DjIiMj5fF4qjy1cW0W0OL2tm3bZLFY1KVLl8v+UHv06FEiXk0Vt83y/vvv+5cnTpxYbo/qsLAw3XXXXXruueckSYsXLy63uO1wODR69Gh99tlnatiwodasWaMuXboYlzyqpSo3+BIWLrqs+NzgAwAAQG3gzMgwN356uqnxUXuFNm+uZjMeVfyDD6gwNVWOXbvkTE+Xz1kkS3iYwtu3l71rV0X07MnIVkAN8/l8Wrh3oV7Z/IoK3BU/rJ9XlKc5aXO0aO8iPdLnEaV0YoQFAIHj8ng1feF2LU3NrPKx+UUePbl4pzZlnNYfUpIVGhLw2WIB1CPXX399oFMwXECL25mZ538RuFxlD91REbfb7V8+duxYtXMKpO+++0779u3zr48cObLCY2688UZ/cfv777/X3r171alTp0vaOZ1OjR07VmvXrlVMTIw++ugjJScnG5c8qo0bfAAAAKgvfM6ioI6P2s9qtyvqmmt4wBeoJVxel57a8JRWZqys8rEF7gLN/Hqmtp7YqpkDZirUGmpChgBQNp/Pd9mF7eKW/O/41yb15GEdAKiGgD4i1KBBA/l8Pu3atUuFhYWXFeObb74pES+Ybd++vcT6j370owqPueqqqxQWFlZmDOn8wwMTJ07UqlWrFBUVpZUrV6pv377VTxiG4gYfAAAA6gtLeFjFjWpxfABA5fl8vssubBe3In2Fnv7iafl8PoMyA4DKeWvjoWoXti9YkpqptzceNiQWANRXAS1uXxgSOz8/X6+99lqVjz99+rT+/ve/XxIvWO3evdu/HBYWplatWlV4zMXtiseQJI/HozvuuEPLli1TRESEli9frgEDBhiXNAzDDT4AAADUF+Ht2pkbv317U+MDACpv4d6F1S5sX7AifYUWfV/1qdoA4HJl5hTqhRW7K25YBc+vSFNmzuV19gMABHhY8kmTJumzzz6TJD377LNq1KiR7r333kodm5WVpbFjxyorK0uSlJCQoGuvvda0XGvCwYMH/cstW7as9NAkrVu31v79+yVJBw4cKLHviy++0MKFCyVJXq9Xt912W7mxLnyeVfXqq6/q1VdfrVTbC8PHO51OrVq1Sr1791aTJk38+/Pz87VhwwZJUvPmzUvMqy6d762fk5MjSRoxYkSJfYcOHfIX+Lt3767ExET/PpfLpbVr10qSmjRpot69e5c4duvWrcrOzpYkDR06tESP+KysLH+v+CuvvFJt27YtceyaNWvk9XoVExNzSY/7Xbt26ciRI5Kk/v37lxhh4PTp09q0aZMkqVlSksLfe7/EsQd/db88DRoo5OxZtfnLX/3bj906SY6WLeWOjVVIYaGaz5uviEOH/PuLmjTRkZ/dI0lqkJqq+JUflrjB99VXXykvL09Wq/WS+RYOHDig7777TpKUnJyshISEH+IWFWndunWSpPj4eF111VUljt2yZYtOnjwpSbruuusUGvrDUGGZmZnasWOHpPMPorRu3brEsatWrZIkxcXFqV+/fiX2ffvtt/6fm4EDByoqKsq/7+TJk9qyZYskqUOHDurYsWOJY9etW6eioiJFRkZq0KBBJfbt2bPH/++uX79+iouL8+/Ly8vTV199JUlq1aqVkpKSShz7xRdf6Ny5c7LZbBo2bFiJfenp6fr+++8lSb169VLTpk39+xwOh9avXy9JatasmXr27Fni2E2bNun06dOSpOHDhyskJMS/78iRI9r1v7nZu3btqpYtW/r3eTweffzxx5KkRo0a6eqrry4RNzU1VcePH5ckXXvttbIXm1/wxIkT2rZtmyTpiiuuUPuLbgZ/8skncrvdio6OvuThmLS0NB0+fP5p0x/96EeKiYnx78vJyfGPrtGmTRt17ty5xLGff/65CgoKFBYWpqFDh5bYt2/fPv91jWvE+WtEu3btLpl24tNPP5XT6VR4eLiGDBlSYt/evXuV8b/pDq6++mo1atTIv+/s2bP68ssvJZ3/fdO1a9cSx3KNOI9rxHlcI87jGvEDrhHncY0473KvEfauXXVyxA3K+9+/pRZvvKHwrOM/vNcWLZQ5baokKXbjRjX++JMScQ/de6/cjRrKWlCgtn/8U4l9ZwYN1MGmTWXh/xpcI/6Ha8R5wXSNkPgecUGwXyMat22sVza/4t8/NWqqmoU0k8fn0atnS95H6hPWR0Pt5/++lhUs03fu7/z7IiwRur/B/ZKk3d/uVlaLLCVE/XAN4RpxHteI87hG/KC2XyOC4XvEl+vX6tFubh3Ot+j170JK7LulrUc9Gp0fTeJPu0J02vnD/fyOMV5N6eiVJK07ZtWnx37oZ5hf5NHn6z9VnN3KNYJrhCSuERcE4zUiGL5HrF69Wm63WzabTVFRUaZeI5xOp2pCQIvbP/nJT/S3v/1Nu3fvltvt1i9/+UvNnz9fv/zlL3XDDTeU+KGWzhdnv/32Wy1YsEB/+9vfdO7cOUmSxWLRrFmzgn6eiry8PP9ybGxspY8rfmE+e/ZsiX1er9e/7HQ6/b8ojJaXl6ejR49W6RifzyeHw1Eix+LbpdLnY3c6nf79F3O73f59Ho/nkv0X9pX2D8zlcvn3XzzElcfj8e8rPs978bher1fh4eGX7CsqKiozrtfr9e+zFLtQ+M/boIHcpfwsFLZv79/uDguTz1byn7IvxOrf74mIlHT+BuIFFz5Dq/XSwRvK+wyr83dTmc/wQoyLlfd3U/wzLCtuUVFRiS9cFxR/rxf/HBaPW957tdkuvYzWxGdY0z/fbre7xC/10uKW9xmWFvfCe734uAvty4pbX68R5X2GpansZ1hUdOmUBVwj5G/PNYJrRHFcI0rG5RrBNeLiuFW5RkT06iXvhx/6v7P6rCU/Y19IyA/fZ4vd4PKft0H0+Qc9S/m78cbGyun1Svxfg2vE/3CNKBm3NLXtGnFxXL5HBO81Ym7aXBW4C/z7oy3RirXGyu279JxhljDFWs9f+22Wkj//Fln8+8I8YZqXNk/Tr55eqXy5RsifP9cI+fPkGlE7rhFlxa0t3yMcLo9C5VZcuHSm6NIpESJtUtz/PlrrRaUJm+WHffaQS4/1uIvkcIhrBNeIS+JyjQiea4QUHN8jLkwL7XK5ZLPZTL1G1NT0MQEtboeFhWnhwoUaNmyYv8fwl19+6X8yo1mzZmrcuLHCwsJ09uxZHTlyxP8D4vP5/MXsX/3qV5o8eXJg3oSB8vPz/cv2Um7glCUiIqLUGJI0ZMiQGvlhiomJUYsWLSrV9tixY/J6vbJYLLLb7ZdciC5sl1TqL6bw8PAyPx+bzebfV9o/+gv7SrtYh4aG+vdf/KBESEiIf19p/+jtdnuZvwTCwsLKjGu1Wv377C1aSLGx8ubm/nDe/z2sEHLRQwt+Xq9sZ8/KctHFz+Lxyva/OCGFBbLGxiqi2JM0Fz7D0n4JlPcZVufvpjKf4YUYFyvv76b4Z1hWXKvVWmrc4u/14s+ieNyy3uuFXwblxTXrM6zpn2+3211h3PI+w9LihoeHy+PxlHia8ILy/m7q6zWirM+wrHwr+xmW9vlzjZC/PdcIrhHFcY34IS7XCK4RpcWtyjXCarcrsk0bnfvfd1aLt+R/nC0ezw/fZ0u5yRBy9px8oaGyFhRcsi+605XK5xrBNaIYrhE/xA2Wa8TFcfkeEZzXCKvNqiV7lpTYf853TrneXHl8l964L/IVKdd7/tp/cfHbJ59/X6G3UB/v+1j397pfdtsPnyvXCK4RF3CNKBm3tl4jguF7xJaDZ5Tzv3rVOdelHesK3PLv9150C97t+2Gfw3PpsblOqYE9jGsE14hL4nKNCJ5rhMT3iOJx7XZ7jXVCtvhqqoxejgMHDmjatGn6/PPPJZ3/kC6kVfyDKL7N5/MpMjJSM2fO1K9//euaT7ocxXP+73//qzvvvLNSxw0fPlyffHJ+uL1Bgwb5h2yvyJQpUzRv3jxJ0rBhw/zDfNRWLVu21NGjR9WiRQv/sBY47/iLs3R69uwK23nDwuSzWGTx+WQt5QmmizW66y41m/GoARkCAAAAxnAdO6b0UTfLW0qB+nJZo6LU/oPlCm3e3LCYAIDL8/Wxr3XP6ntMi//6Da+rX/N+FTcEgMv0j/X79eKHe0yL//iNnfXzazuYFh8ApPM9ty90GC7eWdYMNVX/u/TxhgBo27at1q9frwULFmj48OGXFLQvvC5o2rSpHnnkEe3evbvWFbarIzIy0r9c1hAIpSnetviY/Qg+jaZNlbXYz0FZrEVFCnE6K1XYtkZFqdHUKUakBwAAABgmtHlzNX3U2Acwmz76KIVtAKgl0k6lBXV8AEjPPmdq/P0mxwcA6fzoz5GRkaYXtmtSQIclv9iECRM0YcIEnTt3Tlu2bNG+fft05swZFRUVKSYmRk2bNlWvXr10xRVXBDpVU0RHR/uXL4yBXxkFxXo6FI+B4HPhBl/Ws88aFpMbfAAAAKit4iZNVMGWLcpbvrzasWJGj1bcxBQDsgIAGOFA7gFT42fkZpgaHwCc7kvnXA6m+ABQV9Wq4vYF0dHRuvbaa3XttdcGOpUa1aRJE//ysWPHKn3chfnKJalx48aG5oSaxw0+AAAA1BcWi0WJLzwvSdX6/hszerQSX3i+xub3AgBUzOlxBnV8AAi3mTvwrdnxAaCuqpXF7frqyiuv9C+fOnVKBQUFJYYqL8vhw4f9y507dzYlN9ScytzgO9u1q3xhobIUudRg165S23CDDwAAAMHAEhqqxJdmKbJPH52YNatKc3BbIyPVdMYMxU1M4XsvANQy4SHhQR0fANrHmztKageT4wOAJO3cuVNut1s2m03dunULdDqGoLhdi3Tp0qXEempqqvr371/uMUePHlV2dnaZMRCcKrrBlz3mx3LHxsqWm3tJcZsbfAAAAAg2FotFDSdNVPTgQTo9Z65y3n9f3tzcMttbY2MVN368Gk2dwhQ8AFBLtY1ta2r8drHtTI0PAN1bxAZ1fACQpP379/uXKW7XsNzcXP31r3/V6tWrdfz4cTVq1EjXXnutfvWrXykxMTHQ6Rmib9++Cg8Pl9N5flilDRs2VFjc/vzzz/3Ldrtdffv2NTVH1Bxu8AEAAKC+CW3eXM1mPKr4Bx9QYWqqHLt2yZmeLp+zSJbwMIW3by97166K6NlTVrs90OkCAMqR1DgpqOMDQO82DRUbEarcQpfhsWMjQnVVm4aGxwWA+iCgxe0zZ85o0KBB8nq9io+P16efflpqT9MDBw7ouuuu08GDB0ts/+abb/SPf/xD7733nq677rqaSts00dHRGjZsmFauXClJmj9/vh599NFyj5k/f75/ediwYYqKijI1R9S80m7wuU6dkif7pEKsFjX9zXRu8AEAAKBOsdrtirrmGkVdc02gUwEAXKae8T0VExajvKI8w2PHhMUoOT7Z8LgAUJw9NEQpvVvq9Q0Zhsee2Kel7KEhhscFgIu1bt3aPyx5XRHQd7Js2TKlpaXJYrFowoQJZQ6hfPvtt+vAgQOl7svNzdXYsWO1c+dOtW7d2sRsa8add97pL25/++23Wr58uUaPHl1q261bt+rDDz8scSzqruI3+BoHOhkAAAAAAIBy2G12je04VnPS5hgee1zHcbLbeMAfgPnuHthOb288pPwij2Exo8NtumsAUysAqBm9evUKdAqGswby5Bs2bPAvT5gwodQ2K1eu1Ndff+0vfA8dOlSvvfaannvuOSUkJEiS8vPz9dRTT5mfcA2YMGGCkpN/ePL05z//ufbs2XNJu2PHjmny5MnyeM7/Uu3Zs6duueWWGssTAAAAAAAAKM+UpCmKtEUaGjMqNEqTkyYbGhMAypIYF6EnRnUxNOYTN3VRYlyEoTEBoD6x+Hw+X6BO3q9fP23atEkNGjRQbhlzCU+cOFGLFi2SxWLR+PHjtXDhQv++jIwM9ezZU2fPnlVERISOHz+u6Ojomkpf99xzj+bOnXvJ9gtzZkuSzWZTSMilw4s4HI4y427evFmDBw9WYWGhJCkmJkb33XefBg8eLJvNpo0bN+qvf/2rjh8/LkmKiIjQZ599pj59+lT3LRkqPz+/1O2dOnVSZmamWrRooSNHjtRwVgAAAAAAAKgpC75boJlfzzQs3jM/ekYpnVIMiwcAFfH5fPr1u6lakppZ7VhjeybqtUk9yxzFFgCCWcuWLXX06FHT638BHZb84MGDslgsuuKKK0rd7/V6tXr1av/6008/XWJ/u3btdOedd+ovf/mLHA6HNm3apKFDh5qac3Eul6tEIbs0brdbbre7SnH79Omj+fPn64477lBhYaHy8vI0a9YszZo165K2ERERmj9/fq0rbEuq0QcNAAAAAAAAUPukdErR1hNbtSJ9RbVjjWo/ShOuKH30RwAwi8Vi0R9Szo+2Wp0C99ieifpDSjKFbQCopoAWt/Py8iRJDRs2LHX/li1blJeXJ4vFog4dOqhHjx6XtBk8eLD+8pe/SJK+++67Gi1um2ncuHHasmWLfvWrX2nt2rW6uIO9xWLRsGHD9Je//EWdO3cOUJYIlFWrVsnhcMhut2vEiBGBTgcAAAAAAKBUFotFMwec77ldnQL3qPajNHPATIpCAGqEw+XRloNntONortKzz8np9iosxKKRXRP06d4Tcri8lY4VFRaiJ0cl6ba+rbiGAahxS5cu9S+PGTMmgJkYJ6DF7QvzRXu9pf8iKD4n93XXXVdqmwvzbksqc2hzs8yePVuzZ882LX6XLl308ccf6/Dhw/ryyy919OhRSVKLFi3Uv39/tWrVyrRzG+HcuXOlbr8wLDkAAAAAAADqvlBrqH4/8Pfq3ay3Xt70sgrcBZU+NtIWqelXT9eEKyZQFAJgusycQr2xIUMLtxxRbqGrzHZhNqsskpzusovcsRGhmtinpe4a0I45tgHAQAEtbsfExOj06dNlFjrXrl3rX7722mtLbeNy/fALJoDTh5uqVatWmjRpUqDTqLKoqKhSt/MfkeqLi4uT0+lUeHh4oFMBAAAAAACokMViUUqnFA1qMUjz0uZp8b7FyivKK7N9TFiMxnUcp8lJk5UQlVBmOwAwgs/n01sbD+mFFbuVX+SpsH3R/4radptVQ66MV0x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