data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKAAAAB4CAYAAAB1ovlvAAAQAElEQVR4AexdCYAUxdX+uufcOXZmZ+8DdmF3uUEQBEWUQ1SMimhQjngmKgF/TTQa8kcjmsQjarxCRCNeUbkiqIiCgIDcct/XLrDsfc7O7Ozc1/9e+zcBHBRYBXa2m31dVa+qq7pef7yq96q7RowqhyKBcygBEcqhSOAcSuA4ANJ/BCxfvhwvvfSSRC+++KIUtrb0xo0bz6FIlaZPRwLHAfDgwYNo164drr/+eolGjhwphWczvW/RuzAve0qixOVPS+GpplN2z5TuNykpCWvWrDkdOShlz5EEjgLwyy+/hN/vRyAQOKfhn7o0YGw3wxnRTSk1UKlU0v0zCOfNm3eOxKo0e6oSOArA9PR0BINBCYA9evTAFVdcgX79+uG+++7DHXfcgdtvvx07duzA9u3bkZycjO7du8PpdGLXrl1S2LdvX6kc87meTz75BH/4wx+k64YPHy7Vm5WVJV3H9TscDsjh7t27pXy+jm9cN/o5GP+4AeoLb4Lpb6XQjpgM09MHpTjnGx/fdpSnvep3zJLKciQUCh3tR0NDA7MUOo8lcBSADAjWgEwJCQkQRREpKSkoLi5GVVUVDAYDxo0bJwElHA5LWoaHay47Y8YMeL1e6HQ6CbBms1nq8ldffYVIJIK8vDyp/Pr16yWw7t+/H1qtFrW1tVLdXAe3yyRdSCf/omeh6Xszmie3h2BKRmDFa1JclT8Qvg8nHeVRUQmYoS3fajtZg3NdJSUlnB2bFO55IYGjAOQhmB8eayGOz549GwysmTNnYtasWXj66aexatUq5Ofn49ChQ9iwYQP4AfNQN2bMGKxevRrPPvssxo4di8rKSlx++eXgel599VU899xzklYymUySFq2pqYHP58O+ffvAmtdqtUr53D5Lxf/R78GA8r45jpPgdGDx36V4+OBaMMk85kdqiqQ8PvF/HK6H+3HjjTcyS6HzWAJHAfjMM8+ArUfWHPwAz1WofXQLTM8eOS0S0wqk8oHJWyXw8/3zFICnEOex7JVbIwkcBSDF8dBDD2HYsGHnlFJTUxGlmzkTSkqySffO89cXXniBalH+zncJHAdAvllBEKRh8uWXX5Z8gBzKxP5AOc7h+ZBesmQJBEE4jrgfCrUOCXwHgJ999hmMRqPkTzsXfsBfT5iA8Bd/kfx/3+cHTCRf4W233YaOHTtKhlLrELdylydK4DgA8hyQjYxzNf/jdresX4VfX2j6QT/gGPIV2mw2ySrn69hgOrFzSjqmBM4r5nEAnD9/vvRA2YJ84403kJGRgS5dumD8+PHgoU7227Ffj/19S5culfx67MLhPCY2AHjyzyGn2SLmcPHixZg0aZJUP6eZL1/Pfkb2L3K7saSju+mZWGyJx+3wdd26dZPSyql1SeA4ALKDmbUJU58+fSQ/3QMPPCC5VubOnQvZf8ea8pFHHsGWLVvA1ia7ajjctGkTevfujYqKCsnvx34+5rlcLnA9H3/8scRnZzH7GLkNXn+WfYzc7oniMzz0FULbPwP7/07M42v5Gqb6+voTs5V0K5DAcQAcPXr0UX9cQUEB+KGyX2/QoEGSH1D23x0+fBg8P7z77rulVRH2A/Jw6PF4JH8gr2ywVmI/H1+7detWrF27FjKf483NzZLhwP7Dp556SrqOtVkoHDpObJ4Xr5D8fuz7Oy6DEm63++j9sjYllvLXyiRwHABzcnKwefNmaZhkrcKAONthSNQj9L/bJb/e9/kDuQxrV74/XmFhJ3grk71yuySB4wBIafzmN7+R1oHPpT/QYrH+oC+Qy8j3yNMBvneFWp8EvgNAuQuCcLxvTRDO37R8z0rY+iRwUgD+FF1R6lQkcKIEFACeKBElfVYloADwrIpbaexECSgAPFEiSvqsSkAB4FkVt9LYiRJQAHiiRJT0WZXASQHIX5WxU7q0tBRlZWU4myG3u23btrMqiJ+2MaX2k0kgJgD37NmD7Oxs6ZsQXm/l7zpkOhtpXie2Wq1QQHiyxxY//JgA5DVd7iKD7bLLLsPAgQPBb5tMmzZNivP6Ln/zwenJkydLPF4z5nReXh54zZjr4K/omJjHHyjxmjHH+dWpDh06gOuXSW7v2DSvJzNfofiVQEwA8osFssbj4Ze/frvuuuvAa678kkJ5ebkkkXvvvRcrV66Ew+EAv+nCb8U0NTVh2bJl0mtaDEpeTy4qKgLXya978VDOFzO4+AMiuZ1YIb+dw2UVil8JxASgrIU45M8yP/zwQ/BHS2+99Rbefvtt6Q1k3sKD8/lLOdZyrPEYePwK1s033yyVYX7v3r2lt1569eoFfi2LAXnLLbfgyJEj0pssXMf3UfyKXukZSyAmAI/VRtnZWfA4G+B21P+XGuv+G5f5Mk8OZf5phK6GGuk74mPb55tUKH4lEBOAx2qk50dkocPs0Uj8aAK2Lp2Huu3LULdj+bfhdjlk3opveUfziMdlf4C2rfkKny9ago5zbkbB3LHgV8KObR/K0aol8EM3f1IAshZiIGSZVShW5wETPsGQsZOQP2wcDLl98c9p7xG9S8Qh07FxTr+H117/9zH5x6dnfrwEBVeMR+cBV2DY8Ctx6BcLpHvlXRK4Xbl9iamc4lYCMQEoP3wGAve8qdvPJYtV5nuevhnPZtb+ID2TUXNcmWPTvxe2gd9i5s2EeJeEhQsX4jPtMG5OGoa5bSaJoZziVgIxAcgPnsHGxD3Pvvg6fPDBB+BtOHhfGLUoMPuMydDrcularp/bkhJ0KjN3ozOOgp3zJYZyilsJnBSA3GMZHAwEdqlwmonzcv7yMQpml0qU98916DTfjsShY6SQAZb/QRGYuEz6/a9K5ZJ/8b986XEkCP8FM7fDmXIbcsg8heJTAjEByECQibstx9nFwnHmMRWPaQ+mkvsuwYGRNjQtny2Fnh0rcfDWQok4v+YfD0jlGj789vNKzgcdrE15OxD+TJOS0tArh9wOE6cVil8JxAQgax6ZuOscZzDIYb85xfD4/LA9OuuMKeuNbdJQy9sA8x6DsvEht8dtMXFaofiVQEwAMthk4q6zpuK0HP52SHccfPyWU6ZDU26BJ7MLiiLmo3TE7pI0ngwyNka4fm6P25KJ02dEykWtQgIxAcigkIl7wfE777wT8urHrWl+JOnEUyarVpT2mxEEAWlpaeAlNq6TSRC+nQNynEluj+NMnFYofiUQE4Cy9uGQu87h9OnTpR1QOc48NjpkA4ONECbmscHBxgfH2SjhNJc/EUys7ZjHoVqtlrShXDeHMvG1CsWvBGICkIEhE3edwcDbaEyZMkUCCujwbP+azoB50CgptH/0Mg7ckCzF2QBxLvkAGQ9OAxsgzOT6OJSJ01wvv6jAOy3Iac7nuEycVih+JXBSADI4GATcdY5v2rQJCxYskADIbsDyP93IDjvJumUrmC1gRKNHAcfAi3hcfLlEvB+MFPn/E9fJ9fP2H0wcZx5ny3EOOa1Q/ErgpADkLssA4JDBwSFTwbt7kP7+IbiCYSnkeCzi/JR3imB4bYe0YZFGo5Fe3eK3Yo6tj+uU6cR2Oa1Q/EogJgAZHDJx13mexmkOmRaP7oya2zqeEtXfVSj9dgeDjt9yZuI6uL6MjAxJo8px5nN7nJaJ0wqdlgRaVeGYAJS1EYfcGw5nz54tvRPI7/GpRBEl7shR8t796tE48x3D7j4uzdfwS6w812PiNNfJu5vym9ZMHGee3B7HmTitUPxKICYAZe3DIXedQ36JlH+qgTemvPBf65Dzj7VoN/XbUFvYV0ov7z9RCk1X3SmFcr7FYkHfvn2l70xYA5aUlEhvUnO9nMc81n6cltvjOBOnFYpfCcQEIGsembjrMhD4dXseSmWS03L48MMPS289nyyfyzHQ5LrZtcNvWcuh3I6czyG3r1D8SiAmABkIMnHXOd7Y2Ijnn39eopceexgvTb7/O3Tfz6/5Dk8q99JL0nX80wl79+6V5n28Efqd5NzmrX+ZOM7tyO1xnInTCsWvBGICkDWPTNx1BgLvRpqXl4f7778fEx/7KyY++Twm/fkFKeQ400nTEydK1/G1/DXcJZdcIv1OHLfx/vvvg4n3leY0t8ehTJxWKH4lEBOADDiZuOscZ+DwT3TZ7XZJgzGPh9OuXbuCQ06HQiEpj9M8X+SNyZkvE2/F26lTJ6kM87gch/3795e+uOO03B7zmTitUPxKICYAubusgThk4jgbC/wjhAwWTsvEu9vzrvnV1dVgAPHbLQcOHIBWqyW/dBRTp06VQi7PQy2X57hMF198MdatWwcOQwEfNycR50sR5RTXEogJQNY8MnHv134+G7xbwuOPPw7+tpfzGGwcDhw4UNJoV199tfT5JQPH6XTiiSeeABslTz75JAYMGCBpSdaIPXv2lMrz9TwU8yblcjjWuIubk/K5biaJoZziVgIxAcjWKveYwcThb9ULpZ9X4F/N5B+yYWBwHoe8hwwDi5fqOM0/x8Drxg0NDdKPH/LvgXAZuTzvlM/lCgsLpV/flEOdGEE/bSU3J2lMjjCQOVQofiUQE4D87QeDhElbOEjq/YiND2FYwydoWvDMKZFrwbNgOrb8senNL/5Syp/5t9+h15JJuHDxPVI78lvX3HZWVpbEU07xK4GYABwyZAjkddu0qydizbA3JWq6+q8wX/uHUyLTtZPBdGz5Y9McZ7pw1L3YPvyfUv3cDv+mCGs+r9crzQvjV/RKz1gCMQHIGUOHDgWvfvDc7mwTb+1xww038G0oFOcSOCkA47zfP3L3lOrOVAIKAM9Ucsp1P4oEFAD+KGJUKjlTCSgAPEZy4XAI0//1CkZdPQDXDOmGa4d2x/VX9MSVl/fA+NHDsfTLz4+6iI65TIm2QAIKAEl4O7Zuws+G9MT46y7A1lWzcEFXG8becCFu+/kluGvsQPzPXZeiZ6cUzJ/zCsbf0A/jf34VmpwOKEfLJdCmAchr1yOv7ounH7sHPQtTcOM1vTDookKkJRkQpGXBRocdDqcbXncYfbql4I+/G4fHfj8R995+Lf78+zF44tGHWv4E2ngNbRaAB4v244qBXVCQk4j77xmGXj3bY3dxHVZuKMHC5Yfw1er9WLpqD977zxq8OXMVXn9/JV585UOsX7sMeXlp+POUv2LoxR1w601DEAoF2yyMWtrxNgnA/Xt34+EJo/CrsZfi/juHYdHSzZgxbyPWfFOCigoHcnJSYLa0h16fjmRbJgTo0dgYwubtVfhowTZMffV1LPx8BvpeeBGmPPYAfjnuSvCe1y19GG3x+jYHQK/HjUm/ugG3jxmGKwZfgJemL8bOfQ1IsiZj4MBByGyXg91Fh7H6m41Yu2UXdu8rRlVVNdxuHwS1Gm6fCuu2VuPdWYvx+huvoF2HrvjLn5/AuFGXt0X8tLjPbQ6Ao64bhOuH9cPggT0wdfo8qDV69O/bB7akZKxfvxYr1m7HlL9Mxe4DNThc1oD9h2ux62A1fv3bySgpr0ezywWr1YbmJmDV2p2Y9sqTxsblWwAAEABJREFUaJffDb8YOwqP/PbeFj+QtlZBmwLgV4sXQAyRBnzwV3h3xnx4/SJSU9qhqKQay9duxEN/fAH7iysw/KoREAThKBYEQcDd90xAcUkFJjwwGYeOlCDRmggIOhq29+CLuVMxdOgwRJorUFP97Rs9UI5TkkCbAuBTf3oQT02ZhN0bVmL7vnLkZHZAZXU9NtBQO3/hSvzs+pH4oWPCxImY/t4cAmEFEhIs8Pu1mDNnAQSEMH7MTZj80IQfqkLJP0YCbQaA5aUlyEw1wGrWYva8JcjKzEJ1gwfrN+3Co48/gw4d848Ry/dHLx10GX5x+69QXVuPPj27wO704tO5s9Cz/yCEvE4EAoHvr0DJPSoB8WistUVO837//c409LugMwwmFY5UelCQ3wUHisoQDAPjb7vjNGsD/vDYFDQ0OHGwtBYFue1oPvgN1RGh+WRXzJ7xNsWVv1ORQJsB4OplC3DliEuwff1WWk4TYTQnw93swD2THjgVOcUsc8NNY0j7OZGWkgZnswdHDm5Hz84dsXzpZzHLK8zvSqDNADDgcyHdpsO2XUXk27PCoDfA4w3g5lvGfVcqp8gZf9ttqG9oIBdOEi3NeVC8bw+ys1JRUVp6ijUoxdoEAH1eD2xWI8ymBNTbm9G5oB3qCDiBiIi09PQzRkFh5y5QkfmRm21FeooJJUeqEAipoNVCOU5RAm0CgPX1tdDpRASjYTQ4fbCaTWh0OCGK0VMUU+xi/NmCiCgqa5th0iegvq4RKo0OOpWKhvmW1R27xfjjtgkAul1NUBMoIl4vfGShegMROF0eRCOqFj/RKEQcKquG3mCkVRI/io40Ikw8t7u5xXW3hQraBAD9fj98/jBCTgc0oghnUwDRkIDIf33NZ/ysI6Toml0+GnrV8PoCMGoEBMJRmhM6z7jOH7gwrrLbBAD5E89mTxD+MIEkEIbPFyQnMqEv0vJnyTtA1DW4oCJJBnw+MkKyCNwhclD7Wl55G6iBxBb/vRTIULBZjPB7XAgEw3A1e+F08BBJTsAWdl8U1MhKt6C81gtBFUVdvQNqnRY+rwLAUxFtmwBgOBpFeY0TzZ4QAv4oLu2fC4vJSObDqYjo+8uIImk7ArVAQHS7AzAYDfB6/AgGA99/oZIrSaBNAFAUBIRpzldCIEy0aGkJrREWC/NargF1OhVpuwAi0RBZ2hrotBFoNGqEwy2vW3pCcX5qEwCMkKXQ5PWjqsJJwAgiTPPAXftrIKqEFj9eH2nUtNRUdMtLhdcdkjQfe3cikR9hgon4P9oEAKP0HIP+CIEvAIGG49zcRFgTtQjR0ElZLfoTVGEYtSFEo34IhDy9VoVAgECuaMBTkutpAfCUajwPC4nkeiHcoY7cL0cqm7FoZRG5STyI0L+W3q6LHNt7D5aT5y+CUMgDV5MdvmAEgWCwpVW3ievjFoAB8v3Z6+xobKjD3t27yfXih05DBgLxr7u8I7rkJsMX8OJA0SHU1ztpXmhHLa1k1NE1NbUNqK6pQ3U1UVUtqohqKV5bW4e6unpK16CsohKlpRWwu5rh9QdQXOYkh7QHq9YdJPCFsWvHTjQ02MnadkAZjk/+fyluAXjT8C5Y+P5EWNQbMXIwsPzTP+P+B0bgm+VTMeRnw3DX3Zfig+dHY+283+HVx0bhrw+OwHOPXINnHx6BJx+4nNLD8dTvrsRzf7wG/3jiBvzr2fGYNfUuzHntbiyf/Qj2Ln0K5dvexKZ10zHn05fw7zl/x6rV7+GB39+ML+Y/h1+MyoElugkfvXkPfjakI5QjtgTE2OzWz21qDmPpqmKsmjsLb73+Oaa9tQj/8/AHeHDyu3hv2gy88/o8rFx/ADM+3Y8dRZUoPlKPTXsbcLCUyY+qWh+KDrpRVu4jXhPl12L77nIs+foAZn2+B9NnrMGChauweNZsvPX36XjtmTfwn+kf4uWnp+Hppz7C8s+WYsfyZSg+WIFoKG7F3GKgxK1kTHpALergD2ngofVftUaHAd1T0L9TEmqdIRyuDqP4sB85WVo0NUfgcAqIhENkSCRCm5CCakpbkxORmZ4Np0+LQ+VuhKIW5LVLhoEdzRED9h2JYEexCymJanh8VEdTED0LbUjUiYjQHNDucpJl7INKHW7xg4rXCsR47ViIrFCbRQ1BiMLeBBi0Ig6UuXG40gMxHEHH9hakpGjJMR2EwaBG98JUaDQqclDrcMOVF5A7JYhGhwd19npyLPswuHcuVCoNSqvcyM/PIbGpqLwe9c1qFJU1oWueEcmJKtTWB2GzaLB2mxMHj7ggQI1oVKTyyl8sCcStZPR6FYIRNawmA/QaAQcr3Kiq9+NgdQDeQBShsAADlUlKNCDdlgBBiECACnuO2PHCvz4lzZcGo8UMUZcIo96MOYt3YMfectgbw2i2N0BNYFVrBdJ8AVTbI7B7wmgK6Gkt2ITCXDO65NtQQKHH54dKiFsxo6VH3EomGgHMpgjctCxmNqrR0Bwg14sd016+G7uKa7BinQM7i2pQWuOHXgijtsEBXzCK7IxEjBzeCVk2ICFaB5PoQKf2AibcejmuHtIfBksCth20wxsKwO1sxJWDCkBVY+mK7bjznhuxYPlhhGnIv6xPIq2Q+KAmZ3cworhkTgZU8WQZrZ0fRRgp5iTYzFFywXigUaloGBYwduwfYbMKMOmbEIqQxkrW0ZzQTfM/EbZEDXSRepSX16LZaaf5oAZOjxfl1Q3YtnMvykq345ILOpOWy4SGtJrFGMVnX6xGfdleJCaa8I/npiHk92J/uR+JZg20Gg0gaBCmZUAoR0wJxC0AQQOqRhNGUqIOIy7NR36OGe1yslFqD9JqiAbm5CS4vQHsLnEReGi4tSYhySTAmmiEqDLh6y112FgcwM5DIWze44DLraahWgOPsxg5yWpc0rsnShsEZGZkoXtBFtLTk7D4m0p0KmyHgb2t1DrgIy3J80uRHOFQjpgSiFsAkp1B87wgaSGRHMU+BNxhQKPHpX3ySSvq0djohtcXJu0YQYNDRKccGwxGI7YciqDRGURhOxt65iYgLUmNboV5MJrTsXqPgLJ6F3p0zsTeXXtxce8C8LYdVXY/otCgT7dsdM0xoGthNqxpFgTJEFKLAvy0NBdT+gqTVpDiVQjRMK1IRODyBJGbpkJmGlBd7YTZkIDNu+toeFXhjtEXon+XBDQ4yzBz/jfYvN8Lky4ElSqIjtlGZGQkowe5bTq3F6FCE3p0sMIRzMHT//gUEW0EAXKz3HJNXwy+bABsFj1EtRE8LOtNCQR+ga4Jo8nlpnmgCOWILYG4lYwggHxxIThcPjS7I2iXqkP/bkaUNfrQr1ceuUmKIKhI61nbYeglF8Ganol0o5+G4SjyczMwc8kBvDN3C96euxNvzNsJq0VNQ7YLyYYI+vYfKLlcCjp1QVWNA2+88wnS0pIRcduRbEtE9ZEqhGl4z0w1QBBVEMnCji3+85/7U9+h+FM3cM7qjwI6nQh9gpos3GZkp0TRSI7iTIsOX2/ci855Kfh8yUoYtS4sXrUbOngBVRQpZEy8+/EGEHIgCCIEQYBAuuydj7YiySjA4/bCpA1j875aJKhd6JhlQI+e3bBs6SpodALe+2wbj/QI0DpzMBSGxaxT1oJx8kM8eVbrz4lEwtCpBQKbETO+rEe/zga4HHYkWkxon6LDqCt6orKmDlqDDrZELc3ZROS0S0cgROgl2AmCIAlBEARECIzJ1gTYLALSk0Ro9WYcKtqBDh2zcNtVeXAFVDAkUFs5VmzcVkVzQlF63ctPhohKrYJyxJZA3AJQIADpdWpygQRRVOqk4TOIbbsbSTsZcWGXVDIqwti6YycyUtPg8dISHK2YRCJB+MhJLVBcEKiG/ycWnUhxjVYlzecaHD4kJBhpfgcsW7UJH39VhAG92tGSnhqZWckorQ2Clj9AYy8QJhFHQ1yFQjEkQNKJwY0DVpjmXTqNQEMhUGsHUm2mbwEhRpBmNcDZJJBrxUvLaSIZHQLCERVZzGrsPVCOa4dehKjwLWgEQQDomv4X5KKixosmT4DAHEaQ/H280dHhUjt6FOhR19gMD5neew40wtUUxq69NVQmSGW9EAQVlCO2BOIWgJIGoj5v3GPHLlqr7Zihgo40mEmrxoY9FUi1ikgiIAb9fgi0LKfWRCCIIlzeMDmsm8k6zqcqohL1LshGrw5GuP2A3RVCQ5Mf7mY3rLTC0q1jBmq9WtQQANsnJyC3vQEhwYdlmxukFyFEUYNoJH7FjBYecSsZDVmfapp7FZU50OAIockXgD8oIKqOwEzrw7REi5r6EBkmPpgMGpqvRSEIAgRaMfEFIsjNtkiiFQmUOekJqG8GdJooWb8mWAxA94IUqktH68phZJhD8HpC8AUDNMf0oic5pqsafLROHAQvhkSiNCRLtSmnEyVw/gLwxDs9zTR/ikk2CAx6I+odXqza5oRWpYaj0YWNO8qQaFaBweUJ+NG/VwcCSxgJNGdkw4Wvq7E7pHwVAbLJH4ZaLaLSEQF/VxyKatGFNR3hKoIQ2melwRM1kHPbhW0HnNhDa8XtMxOh0yXA7yfQQ3Wad992iovx2tWgP4RwWAuVKkJrFAIuyjeCtaEo6MgZrUJqWhrN+aIEPAEGgwtev5aMiDD5AXVklHhRVm2XRBONRlFv98DpDiEQCKGZ/HsN9kY0OR3wUTrFoseGHZXIS9dAb7bAQ8PtBWTkZKVoCNAhqNRRGoLDUl3K6bsSiFsACqR06mieZtDp4ac5XiP55RLNAegJkFnpGfhkcRFZtCSQSAjlR+pw7fBL0NgchN/nIa0ZIee1nzIh+fAa7c2wGrTkSzSSvhNwzcBsmJNMIGjBZiS/4rZqpGdmwt7oQd8uBpRWVBD4gSi1qxKj0JD2lCpTTt+RQNwCkDVXgkYLnRbw+rwIRhNR5tDCERXJAm6GD3qa+5mhFsOAEEBl6TpkpmbjcHUAiKrJQvZJL6UGAgG4PG5yu2hR3RhGli2EpuZGFBW5kEBgXrmjhuaBIVRXVNKcMoD5K8pRaRcJzAHUEyDVNOzzy7FQjpgSiFsAqkQBeh2t6WYacPWgbNw7rjsG9EhB0b4q5Kbr0SnDjIXf1KBD+ywCipOGVh/y0hrRqX0HHKn2ETBFRMl/F41GUJibi9p6LyzaAFKMIo5UumCyiAiqTaiwC+iSbYXNpIHJJGDEpXkYM6onnI4GiCoBXr8fUGliCl9hAnELwDDNxfw0Xyuu8mHFN0ewdEUptu2uxaMT+8JH/rpAOIxOOSm0QlKClPQccr1oSIM5oEMpxoy8DHeMvQrPTh6FV54Yh26dOsBmixDIBKjIJ5hK4Gv0RPDJov1ol26BShvFobI6AnlvHK6ox6IvtlI5gSxrwOMBaVLSslCOWBIQYzHjgRcOk9EQUiErOQK1oEJzUx2qG1x499O9SKBeez0OpJAvMNkkYtnqUsxdUY3VW+00tzNizdoVKD20HXt27camDZvgcx9ByCaawNcAAATMSURBVOuDLcOCd+btwKzFFdhT1ISkNDN0qhBqK+thNeuxcdM+lJa6kJZtg82aCFosARsh+tNTgPEg/lPuAz2KUy7bqgoKgoAgGRimBIPk96usbkLXgjQMuKAHIlEBwy6+ENX1frTLsaBjthpXDewCvdGCL9bW4+st9TQsQzIyEhLMKKsLYdHaKsxadACpKVno3aM9NDTEmwwWpJiAywZfBJVGhag2BX16ZOHrNYfIAPFSmSi1FeCKWpXszubNimezsbPZVpTcJxGCkJrmYboEcrWYk1BUXI2la3Zj12EH3vt4JeocfkTID+glx/POvUWw6gX0694Fap0FtqQkuL0CAqEgjAk5ZJdo0bVdMlKtaridzejYzoKu7c1Ys6cJC5euRjWt/27YVCLtmpCdmURzSRt0ZAHxVMDti5zNrreqtsRWdbencbNRIUpzsCBCNBS7mgIIhkQkWay4qFca7rqpJ0Zf1Qdd84wg3zN8/jD0tBTX7Aui+FAJrr8si9JOVNYEsGZzJZLMlfjNrQNRWumAgcbvXoWJMGqD0Ahe9OmejIH9uuG6wR2RlZWEzh2zkW4zwUJA3VfiQDRCWjCizAFP9ujEk2W0ej4Ns420BFfVGEHXjsnkbBZhNAIbttagrMIOh7OGHMlu6NUqmPQi1CodQqEwbr4mF1qEadgVMGJwPoYPKoTbY8S+4v24c/TFcLgDKKXhPBgCGmlduL0VZJyoyU0jwJYQwuK1u6idMOzUtolWYZoI/DqNBsoRWwJxC0DuWJAs4SZyRjd5vKTtDBgxIBGDB6ShtNaNOmcE+TkmeEMRhMgiNtPSXG6GHm/N2Ylth90I+oHPVhZj6foSWrYzYucBH+Z8uhYJhCWNTkcGjBYDeiXCaNLDS2j0ur3khlHjussKoSbXDes8fq9QEFUEZ07FfgBtncvPKS5lwEtwwbCIg2VuHClz4kBpA3RaNYZemIq7rsvFrSPzEfS5gYgPGWkmJCdpkZoYRs8CnbSjViDsIWc0AyeM3UXF0GuDSLWJaJ8qkNYMkYHhpxVeLbLTtMTToabJB5VagImAvGlvPYoqGiGQyyY9TU/XquNSxj9Gp44D4I9R4flSR1RtJWdwPTrlieTHM8BoErHzUDN2HWxCnSuAGrsPyclWZKQaSJuJsBhUMBgTpHTvLolIMQeRm+5FYbYIgw40DxQIgBqo9Xpk0TVWWyJKaxqxv8ROdUeRn21EMrl1eOuPrIxEdOtgQ2F+MixmM9rnFUI5YktAjM1u/dwl6w5j5pdFmPDkV/jtU19g7MTX0WPYJAjZw7G3qQvWlVixqzYFVd5sVLtSUOdJhRtp8IRsiIgpSE7NRXJKHiy2LORkZSAlNZPWgS003CairEGLTft9OFyrwoEaPbaU2lAW7Ixo8uXoN/wu3PjLKRg89iUMHvdPPPfmOrw1c0XrF+hP1IO4BaAsL5VKBZPJjILCzrh+5E2Y8OsH8fiUv2Hqa//Gu+/Pw8yPFmHu52sw94u1mPnJWsyYvxEzF2zGvz/egHf+s16i9z/dhLfnrMH7H68nPoXzVuHDeSvx5ocL8cobc/DYX17Ffb95FONuuxdDh1+Lwk7dkGhJglqtlm9DCU8igbgH4En6rbDPEwkoADxPHkRbvY3/AwAA//80iN5qAAAABklEQVQDAPHcQOhSg/O3AAAAAElFTkSuQmCCi09i1timetimeTakenToCrack864008640086400604800604800Takes a text string as input, and reports a new text string containing all but the first character of the input.pt:_ sem o primeiro caractere
ca:_ sense la primera lletra
es:todos menos la primera letra de _
pl:_ bez pierwszej litery
All but first of empty word.Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)pt:_ sem a primeira palavra
ca:_ sense la primera paraula
es:todas menos la primera palabra de _
pl:_ bez pierwszego słowa
All but first of empty sentence.Takes a text string as input, and reports a new text string containing all but the last letter of the input.pt:_ sem o último caractere
ca:_ sense la darrera lletra
es:todos menos la última letra de _
pl:_ bez ostatniej litery
All but first of empty word.Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)pt:_ sem a última palavra
ca:_ sense la darrera paraula
es:todos menos la última palabra de _
pl:_ bez ostatniego słowa
All but last of empty sentence.Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing only the first word, with no spaces before or after it.pt:a primeira palavra de _
ca:primera paraula de _
es:primera palabra de _
pl:pierwsze słowo _
Takes a text string as input, and reports the last character in the string.pt:o último caractere de _
ca:primera lletra de _
es:última letra de _
pl:ostatnia litera _
0Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing only the last word of the input, with no spaces before or after it.pt:a última palavra de _
ca:darrera paraula de _
es:última palabra de _
pl:ostatnie słowo _
Last of empty sentence.Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.pt:a palavra _ está vazia
ca:paraula buida? _
es:¿palabra vacía? _
pl:puste słowo? _
0Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.pt:a frase _ está vazia
ca:frase buida? _
es:¿oración vacía? _
pl:puste zdanie? _
Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs.
Consider using SENTENCE (Lists palette) instead.de:füge Wörter zusammen _
ca:uneix les paraules _
es:unir las palabras _
fr:fusionne les mots _
pt:uma frase com as palavras _
pl:połącz słowa _
firstbffirst111bfTakes a sentence in list form and reports the sentence as a text string.de:Liste $arrowRight Satz _
ca:llista $arrowRight frase _
es:lista $arrowRight frase _
fr:liste $arrowRight phrase _
pt:uma frase com as palavras em _
pl:lista $arrowRight zdanie _
Takes a sentence in text string form and reports the sentence as a list of its words.de:Satz $arrowRight Liste _
ca:frase $arrowRight llista _
es:frase $arrowRight lista _
fr:phrase $arrowRight liste _
pt:uma lista com as palavras da frase _
pl:zdanie $arrowRight lista _
report a list in which each item is one letter from the input wordde:Wort $arrowRight Liste _
ca:paraula $arrowRight llista _
es:palabra $arrowRight lista _
fr:mot $arrowRight liste _
pt:uma lista com os caracteres da palavra _
pl:słowo $arrowRight lista _
join all the items of the input list
into a single word, and report itde:Liste $arrowRight Wort _
ca:llista $arrowRight paraula _
es:lista $arrowRight palabra _
fr:liste $arrowRight mot _
pt:uma palavra com os caracteres em _
pl:lista $arrowRight słowo _
SENTENCE is the main constructor for sentences, represented
as lists of words. It takes zero or more inputs, each of which can
be either a list or a text string. If a list, the input is assumed to be
a list of words. If a text string, it is converted to a list of words using
SENTENCE→LIST. Then all the lists of words are appended to form a new list of words.
If the inputs are lists of lists rather than lists of words, SENTENCE,
like APPEND, does only one level of flattening, reporting a list of
all the items of all the input lists.ca:frase _
es:oración _
pl:zdanie _
pt:lança o erro _
pl:błąd _
Takes a (possibly deep) list as input,
and reports a human-readable text form
of the list (namely, Lisp notation).ca:notació textual de _
es:imprimible _
pl:tekstowa _
()Helper function for word/sentence library.
Reports its first input, unless that input is empty, in which case it gives its second input as an error message.es:requerir no vacío _ _
Takes a text string as input, and reports the first character in the string.pt:a primeira palavra de _
ca:primera lletra de _
es:primera letra de _
pl:pierwsza litera _
2628000186400136001601fullTime years, months, days, hours, minutes, seconds,data:image/png;base64,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Crack speed is dependent on the quality of CPU, for this code I took the average which is ~25,000,000,000 guesses per secondFunction for determining length to crack: T=(C^L)/S
T is time, C is character set size, L is length, S is cracking speed!,@,#,$,%,&,.,_,-26065,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90password,qwerty,admin,12345618Instantly250000000000"37391565 years,","5 months,","11 days,","19 hours,","29 minutes,","16.75 seconds,"password