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http://www.numbersaplenty.com/32030024 | [
"Search a number\nBaseRepresentation\nbin111101000101…\n…1110101001000\n32020021021221012\n41322023311020\n531144430044\n63102303052\n7536151665\noct172136510\n966237835\n1032030024\n11170976a4\n12a887a88\n136835ca0\n14437aa6c\n152c2a59e\nhex1e8bd48\n\n32030024 has 32 divisors (see below), whose sum is σ = 65197440. Its totient is φ = 14664000.\n\nThe previous prime is 32030003. The next prime is 32030027. The reversal of 32030024 is 42003023.\n\nAdding to 32030024 its reverse (42003023), we get a palindrome (74033047).\n\nIt is a junction number, because it is equal to n+sod(n) for n = 32029990 and 32030008.\n\nIt is not an unprimeable number, because it can be changed into a prime (32030027) by changing a digit.\n\nIt is a pernicious number, because its binary representation contains a prime number (13) of ones.\n\nIt is a polite number, since it can be written in 7 ways as a sum of consecutive naturals, for example, 12449 + ... + 14799.\n\nIt is an arithmetic number, because the mean of its divisors is an integer number (2037420).\n\nAlmost surely, 232030024 is an apocalyptic number.\n\nIt is an amenable number.\n\nIt is a practical number, because each smaller number is the sum of distinct divisors of 32030024, and also a Zumkeller number, because its divisors can be partitioned in two sets with the same sum (32598720).\n\n32030024 is an abundant number, since it is smaller than the sum of its proper divisors (33167416).\n\nIt is a pseudoperfect number, because it is the sum of a subset of its proper divisors.\n\n32030024 is a wasteful number, since it uses less digits than its factorization.\n\n32030024 is an odious number, because the sum of its binary digits is odd.\n\nThe sum of its prime factors is 2501 (or 2497 counting only the distinct ones).\n\nThe product of its (nonzero) digits is 144, while the sum is 14.\n\nThe square root of 32030024 is about 5659.5073990587. The cubic root of 32030024 is about 317.5794712921.\n\nThe spelling of 32030024 in words is \"thirty-two million, thirty thousand, twenty-four\"."
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8681082,"math_prob":0.9866133,"size":2109,"snap":"2020-24-2020-29","text_gpt3_token_len":627,"char_repetition_ratio":0.17292161,"word_repetition_ratio":0.0056980057,"special_character_ratio":0.42200094,"punctuation_ratio":0.12871288,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9971646,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-06-05T20:18:27Z\",\"WARC-Record-ID\":\"<urn:uuid:fd538047-3f55-45ec-861b-92def32316c0>\",\"Content-Length\":\"9812\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0af58957-edaf-4e87-8535-e16a760f500c>\",\"WARC-Concurrent-To\":\"<urn:uuid:b142da36-d732-453c-8dfd-c4cc25197e7c>\",\"WARC-IP-Address\":\"62.149.142.170\",\"WARC-Target-URI\":\"http://www.numbersaplenty.com/32030024\",\"WARC-Payload-Digest\":\"sha1:LY5YH5TOFETKG4VDT2E7KUJPO6O2S26L\",\"WARC-Block-Digest\":\"sha1:FYA625JQMHVWJRZDB7AGPLSAZMNACXSL\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-24/CC-MAIN-2020-24_segments_1590348502204.93_warc_CC-MAIN-20200605174158-20200605204158-00271.warc.gz\"}"} |
https://www.mathdoubts.com/find-8p-power-p-if-9-power-p-plus-2-9-power-p-equals-to-240/ | [
"# If $9^{p+2} -9^p = 240$, find the value of ${(8p)}^p$\n\n$p$ is a literal and it involved in forming an expression in terms of number $9$ to represent the number $240$. The solution of the equation that contains exponential terms gives the value of $p$ and it is useful to find the value of ${(8p)}^p$ in this maths problem.\n\nFirstly, let us solve this equation to obtain the value of $p$.\n\n$9^{p+2} -9^p = 240$\n\n### Step: 1\n\nUse the product rule of exponents to express the exponential term $9^{p+2}$ as the product of two exponential terms having same base.\n\n$\\implies 9^p \\times 9^2 -9^p = 240$\n\n### Step: 2\n\nWrite the value of $9$ squared in number form.\n\n$\\implies 9^p \\times 81 -9^p = 240$\n\n### Step: 3\n\nSimplify the left hand side of the equation to obtain the value of $p$.\n\n$\\implies 81 \\times 9^p -9^p = 240$\n\n$\\implies 9^p(81-1) = 240$\n\n$\\implies 9^p \\times 80 = 240$\n\n$\\implies 9^p = \\dfrac{240}{80}$\n\n$\\implies 9^p = 3$\n\n$\\implies {(3^2)}^p = 3$\n\nApply the power law of exponent of an exponential term.\n\n$\\implies 3^{2p} = 3$\n\n$\\implies 3^{2p} = 3^1$\n\nThe bases of both sides of this equation is same. So, their exponents should also be equal mathematically.\n\n$\\implies 2p = 1$\n\n$\\therefore \\,\\,\\,\\,\\,\\, p = \\dfrac{1}{2}$\n\nTherefore, the given equation is solved and the value of $p$ is $\\dfrac{1}{2}$.\n\n### Step: 4\n\nSubstitute the value of $p$ in ${(8p)}^p$ to obtain its value.\n\n${(8p)}^p = {(8 \\times \\dfrac{1}{2})}^{\\frac{1}{2}}$\n\n$\\implies {(8p)}^p = \\sqrt{\\dfrac{8}{2}}$\n\n$\\implies {(8p)}^p = \\sqrt{4}$\n\n$\\therefore \\,\\,\\,\\,\\,\\, {(8p)}^p = 2$\n\nA best free mathematics education website that helps students, teachers and researchers.\n\n###### Maths Topics\n\nLearn each topic of the mathematics easily with understandable proofs and visual animation graphics.\n\n###### Maths Problems\n\nA math help place with list of solved problems with answers and worksheets on every concept for your practice.\n\nLearn solutions\n\n###### Subscribe us\n\nYou can get the latest updates from us by following to our official page of Math Doubts in one of your favourite social media sites."
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.85807216,"math_prob":0.9999896,"size":1785,"snap":"2023-14-2023-23","text_gpt3_token_len":578,"char_repetition_ratio":0.16563728,"word_repetition_ratio":0.020618556,"special_character_ratio":0.3602241,"punctuation_ratio":0.08882522,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":1.0000001,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-06-02T05:19:10Z\",\"WARC-Record-ID\":\"<urn:uuid:ba4295cd-a90a-4b02-94ae-1ea202534dbd>\",\"Content-Length\":\"14036\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c61a71c0-c683-4643-bc94-ea67dfba4f67>\",\"WARC-Concurrent-To\":\"<urn:uuid:18ddb05e-aef8-4bf4-a267-d12194bc0cb6>\",\"WARC-IP-Address\":\"172.67.210.18\",\"WARC-Target-URI\":\"https://www.mathdoubts.com/find-8p-power-p-if-9-power-p-plus-2-9-power-p-equals-to-240/\",\"WARC-Payload-Digest\":\"sha1:XINVXB4NHWRHQEEHHX4KSGRJF6FO76LL\",\"WARC-Block-Digest\":\"sha1:NXF6AZTADSKSZ6O2XW6D7ZALPDLIDVYW\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224648322.84_warc_CC-MAIN-20230602040003-20230602070003-00575.warc.gz\"}"} |
https://www.bkjia.com/gpkJ0XOIK/935742.html | [
"",
null,
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"云顶国际不能提款怎么办做得博客就是这个原因，开始不知道，就把程序卸了又装，折腾了一个晚上，连数据库都删除从\n\nSCP 将 LinuxPAServer.tar 云顶国际不能提款怎么办传到 Ubuntu Serve",
null,
"取出电池（图片引自IFIXIT）\n\nnclude <iostream>#include <stdio.h>#include <map>#include <string.h>#include <cmath>#include <algorithm>using namespace std;const int maxn=1000020;int w[maxn];//重量map<int,int>mp;//first 为求出的mode， 云顶国际不能提款怎么办second 为该mode出现的次数int output[maxn];//输出int n;int main(){ int t;cin>>t; int c=1; while(t--) { mp.clear(); scanf(\"%d\",&n); bool one=0;//只有一种mode for(int i=1;i<=n;i++) { scanf(\"%d\",&w[i]); int temp=10000-(100-w[i])*(100-w[i]); mp[temp]++; if(mp[temp]==n) one=1; } int maxn=-1;//找mode出现最多的次数 map<int,int>::iterator i; for(i=mp.begin();i!=mp.end();i++) { if((i->second)>maxn) maxn=i->second; } int len=0; i=mp.begin(); bool same=1;//判断mode出现的次数是否相同 int flag=i->second; for(i=mp.begin();i!=mp.end();i++) { if((i->second)==maxn) output[len++]=i->first; if((i->second)!=flag) same=0; } cout<<\"Case #\"<<c++<<\":\"<<endl; if(same&&!one)//当出现的mode的次数相同且mode的种类不唯一时 { cout<<\"Bad Mushroom\"<<endl; continue; } for(int i=0;i<len-1;i++) cout<<output[i]<<\" \"; cout<<output[len-1]<<endl; } return 0\n\ner 2005和Access中设置自动编号字段的相关知识就介绍到这里了，希望本次的介绍能够对您有所收\n\n2活动的商家，发货时间有调整",
null,
"终于掀开屏幕（图片引自IFIXIT）\n\nVirtualHost *:80> //配置apche的配置文件 &nbs\n\n，编辑你的手机型号和转账金额，为了增加真实性还得把时间调云顶国际不能提款怎么办好，好了长按保存就可以生成\n\nh 我列举的其中的\n\n（责任编辑：图门霞飞）"
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https://jieyanghu.com/tag/harris-corner-detection/ | [
"# UW CSE P576 notes – Harris corner detection\n\nThe following are my notes on the Harris corner detection algorithm for finding the features in an image. These slide screenshots were taken from the University of Washington course homepage here:\nhttp://courses.cs.washington.edu/courses/csep576/15sp/projects/project1/web/project1.htm\n\nThe idea is to consider a small window around each pixel p in an image. We want to identify all such pixel windows that are unique. Uniqueness can be measured by shifting each window by a small amount in a given direction and measuring the amount of change that occurs in the pixel values.",
null,
"More formally, we take the sum squared difference (SSD) of the pixel values before and after the shift, and identifying pixel windows where the SSD is large for shifts in all 8 directions. Let us define the change function E(u,v) as the sum of all the sum squared differences (SSD), where u,v are the x,y coordinates of every pixel in our 3 x 3 window and I is the intensity value of the pixel. The features in the image are all pixels that have large values of E(u,v), as defined by some threshold.",
null,
"After some fancy math that is best left explained by Wikipedia or the original slides which essentially involve taking the first order approximation of the Taylor series expansion for I(x + u, y + v), we are left with:",
null,
"where H is the Harris matrix and the I_x and I_y terms are the gradients in the x and y directions, respectively (the gradient values for each pixel can be done using the Sobel operator). Note that this is a sum of all the matrices in the window W. This is important later.\n\nRemember that we want the SSD to be large in shifts for all eight directions, or conversely, for the SSD to be small for none of the directions. By solving for the eigenvectors of H, we can obtain the directions for both the largest and smallest increases in SSD. The corresponding eigenvalues give us the actual value amount of these increases. Because H is a 2×2 matrix, solving for the eigenvalues can be done by taking the determinant and setting it to 0, and using the quadratic equation to find the two possible solutions.\n\nBecause solving the quadratic equation for every pixel is computationally expensive (it requires the square root operator), we can use a variant where instead of solving for the eigenvalues directly, we compute a corner strength function as defined by:\n\nc(H) = determinant(H) / trace(H) where the trace is the sum of the two elements in the main diagonal (upper left to lower right). This is the Harris operator.\n\nOne question that tripped me up as well as other students is why the determinant of the Harris matrix isn’t always equal to zero. The determinant of H at first glance is equal to\n\nI_x^2 * I_y^2 – I_x*I_y * I_x * I_y. This becomes I_x^2 * I_y^2 – I_x^2 * I_y^2 = 0. However, as previously noted, these individual terms represent the sums across all the pixel values in the window. So I_x^2 is summed up over all the pixels in the window W, as is I_x*I_y and such.\n\nHere then is the high level pseudocode:\n\n1. Take the grayscale of the original image\n\n2. Apply a Gaussian filter to smooth out any noise\n\n3. Apply sobel operator to find the x and y gradient values for every pixel in the grayscale image\n\n4. For each pixel p in the grayscale image, consider a 3×3 window around it and compute the corner strength function. Call this its Harris value.\n\n5. Find all pixels that exceed a certain threshold and are the local maxima within a certain window (to prevent redundant dupes of features)\n\n6. For each pixel that meets the criteria in 5, compute a feature descriptor.\n\nStep 5 is itself a topic of much discussion that is out of scope for these notes. The simplest approach is to use a 5 x 5 window. In terms of feature matching, such a feature descriptor is invariant to translation, but nothing else. Better feature descriptors would be invariant to rotation, illumination, and scaling."
] | [
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"http://jieyanghu.com/wp-content/uploads/2015/09/harriswindow.jpg",
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"http://jieyanghu.com/wp-content/uploads/2015/09/equation.jpg",
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"http://jieyanghu.com/wp-content/uploads/2015/09/equationsimplified.png",
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https://elegantrl.readthedocs.io/en/latest/algorithms/ppo.html | [
"# PPO¶\n\nProximal Policy Optimization (PPO) is an on-policy Actor-Critic algorithm for both discrete and continuous action spaces. It has two primary variants: PPO-Penalty and PPO-Clip, where both utilize surrogate objectives to avoid the new policy changing too far from the old policy. This implementation provides PPO-Clip and supports the following extensions:\n\n• Target network: ✔️\n\n• Reward clipping: ❌\n\n• Generalized Advantage Estimation (GAE): ✔️\n\n• Discrete version: ✔️\n\nNote\n\nThe surrogate objective is the key feature of PPO since it both regularizes the policy update and enables the reuse of training data.\n\nA clear explanation of PPO algorithm and implementation in ElegantRL is available here.\n\n## Code Snippet¶\n\n```import torch\nfrom elegantrl.run import train_and_evaluate\nfrom elegantrl.config import Arguments\nfrom elegantrl.train.config import build_env\nfrom elegantrl.agents.AgentPPO import AgentPPO\n\n# train and save\nargs = Arguments(env=build_env('BipedalWalker-v3'), agent=AgentPPO())\nargs.cwd = 'demo_BipedalWalker_PPO'\nargs.env.target_return = 300\nargs.reward_scale = 2 ** -2\ntrain_and_evaluate(args)\n\n# test\nagent = AgentPPO()\nagent.init(args.net_dim, args.state_dim, args.action_dim)\n\nenv = build_env('BipedalWalker-v3')\nstate = env.reset()\nepisode_reward = 0\nfor i in range(2 ** 10):\naction = agent.select_action(state)\nnext_state, reward, done, _ = env.step(action)\n\nepisode_reward += reward\nif done:\nprint(f'Step {i:>6}, Episode return {episode_reward:8.3f}')\nbreak\nelse:\nstate = next_state\nenv.render()\n```\n\n## Parameters¶\n\nclass elegantrl.agents.AgentPPO.AgentPPO(net_dims: [<class 'int'>], state_dim: int, action_dim: int, gpu_id: int = 0, args: ~elegantrl.train.config.Config = <elegantrl.train.config.Config object>)[source]\n\nPPO algorithm. “Proximal Policy Optimization Algorithms”. John Schulman. et al.. 2017.\n\nnet_dims: the middle layer dimension of MLP (MultiLayer Perceptron) state_dim: the dimension of state (the number of state vector) action_dim: the dimension of action (or the number of discrete action) gpu_id: the gpu_id of the training device. Use CPU when cuda is not available. args: the arguments for agent training. args = Config()\n\nexplore_one_env(env, horizon_len: int, if_random: bool = False) Tuple[torch.Tensor, ...][source]\n\nCollect trajectories through the actor-environment interaction for a single environment instance.\n\nenv: RL training environment. env.reset() env.step(). It should be a vector env. horizon_len: collect horizon_len step while exploring to update networks return: (states, actions, rewards, undones) for off-policy\n\nenv_num == 1 states.shape == (horizon_len, env_num, state_dim) actions.shape == (horizon_len, env_num, action_dim) logprobs.shape == (horizon_len, env_num, action_dim) rewards.shape == (horizon_len, env_num) undones.shape == (horizon_len, env_num)\n\nexplore_vec_env(env, horizon_len: int, if_random: bool = False) Tuple[torch.Tensor, ...][source]\n\nCollect trajectories through the actor-environment interaction for a vectorized environment instance.\n\nenv: RL training environment. env.reset() env.step(). It should be a vector env. horizon_len: collect horizon_len step while exploring to update networks return: (states, actions, rewards, undones) for off-policy\n\nstates.shape == (horizon_len, env_num, state_dim) actions.shape == (horizon_len, env_num, action_dim) logprobs.shape == (horizon_len, env_num, action_dim) rewards.shape == (horizon_len, env_num) undones.shape == (horizon_len, env_num)\n\nclass elegantrl.agents.AgentPPO.AgentDiscretePPO(net_dims: [<class 'int'>], state_dim: int, action_dim: int, gpu_id: int = 0, args: ~elegantrl.train.config.Config = <elegantrl.train.config.Config object>)[source]\nexplore_one_env(env, horizon_len: int, if_random: bool = False) Tuple[torch.Tensor, ...][source]\n\nCollect trajectories through the actor-environment interaction for a single environment instance.\n\nenv: RL training environment. env.reset() env.step(). It should be a vector env. horizon_len: collect horizon_len step while exploring to update networks return: (states, actions, rewards, undones) for off-policy\n\nenv_num == 1 states.shape == (horizon_len, env_num, state_dim) actions.shape == (horizon_len, env_num, action_dim) logprobs.shape == (horizon_len, env_num, action_dim) rewards.shape == (horizon_len, env_num) undones.shape == (horizon_len, env_num)\n\nexplore_vec_env(env, horizon_len: int, if_random: bool = False) Tuple[torch.Tensor, ...][source]\n\nCollect trajectories through the actor-environment interaction for a vectorized environment instance.\n\nenv: RL training environment. env.reset() env.step(). It should be a vector env. horizon_len: collect horizon_len step while exploring to update networks return: (states, actions, rewards, undones) for off-policy\n\nstates.shape == (horizon_len, env_num, state_dim) actions.shape == (horizon_len, env_num, action_dim) logprobs.shape == (horizon_len, env_num, action_dim) rewards.shape == (horizon_len, env_num) undones.shape == (horizon_len, env_num)\n\n## Networks¶\n\nclass elegantrl.agents.net.ActorPPO(*args: Any, **kwargs: Any)[source]\nclass elegantrl.agents.net.ActorDiscretePPO(*args: Any, **kwargs: Any)[source]\nclass elegantrl.agents.net.CriticPPO(*args: Any, **kwargs: Any)[source]"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.5902371,"math_prob":0.8516513,"size":5060,"snap":"2023-40-2023-50","text_gpt3_token_len":1281,"char_repetition_ratio":0.18156646,"word_repetition_ratio":0.53050846,"special_character_ratio":0.243083,"punctuation_ratio":0.26393628,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9802125,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-27T13:00:23Z\",\"WARC-Record-ID\":\"<urn:uuid:d52d72bd-0d64-4dfa-851f-95d71f0e6fa4>\",\"Content-Length\":\"37393\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:b03368a5-5185-4ee4-8da3-b94d55a72c82>\",\"WARC-Concurrent-To\":\"<urn:uuid:dce61f61-5c7e-43e0-b778-c0c2050e10b0>\",\"WARC-IP-Address\":\"104.17.32.82\",\"WARC-Target-URI\":\"https://elegantrl.readthedocs.io/en/latest/algorithms/ppo.html\",\"WARC-Payload-Digest\":\"sha1:5JJNQTE6KDCVORQDWDMXWUMC7EC644EI\",\"WARC-Block-Digest\":\"sha1:3ES647FRHFQLW6QO4UO3GW4CPG4BYFIE\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510297.25_warc_CC-MAIN-20230927103312-20230927133312-00388.warc.gz\"}"} |
https://www.sparknotes.com/physics/linearmomentum/conservationofmomentum/section1/ | [
"Up to this point in our study of classical mechanics, we have studied primarily the motion of a single particle or body. To further our comprehension of mechanics we must begin to examine the interactions of many particles at once. To begin this study, we define and examine a new concept, the center of mass, which will allow us to make mechanical calculations for a system of particles.\n\n### The Center of Mass of Two Particles\n\nWe start by defining and explaining the concept of the center of mass for the simplest possible system of particles, one containing only two particles. From our work in this section we will generalize for systems containing many particles.\n\nBefore quantifying our idea of a center of mass, we must explain it conceptually. The concept of the center of mass allows us to describe the movement of a system of particles by the movement of a single point. We will use the center of mass to calculate the kinematics and dynamics of the system as a whole, regardless of the motion of the individual particles.\n\n### Center of Mass for Two Particles in One Dimension\n\nIf a particle with mass m1 has a position of x1 and a particle with mass m2 has a position of x2, then the position of the center of mass of the two particles is given by:\n\n xcm =",
null,
"Thus the position of the center of mass is a point in space that is not necessarily part of either particle. This phenomenon makes intuitive sense: connect the two objects with a light but rigid pole. If you hold the pole at the position of the center of mass of the objects, they will balance. That balancing point will often not exist within either object.\n\n### Center of Mass for Two Particles beyond One Dimension\n\nNow that we have the position, we extend the concept of the center of mass to velocity and acceleration, and thus give ourselves the tools to describe the motion of a system of particles. Taking a simple time derivative of our expression for xcm we see that:\n\n vcm =",
null,
"Thus we have a very similar expression for the velocity of the center of mass. Differentiating again, we can generate an expression for acceleration:\n\n acm =",
null,
"With this set of three equations we have generated the necessary elements of the kinematics of a system of particles.\n\nFrom our last equation, however, we can also extend to the dynamics of the center of mass. Consider two mutually interacting particles in a system with no external forces. Let the force exerted on m2 by m1 be F21, and the force exerted on m1 by m2 by F12. By applying Newton's Second Law we can state that F12 = m1a1 and F21 = m2a2. We can now substitute this into our expression for the acceleration of the center of mass:\n\nacm =",
null,
"However, by Newton's Third Law F12 and F21 are reactive forces, and F12 = - F21. Thus acm = 0. Thus, if a system of particles experiences no net external force, the center of mass of the system will move at a constant velocity."
] | [
null,
"http://img.sparknotes.com/figures/E/ee7c361f874525e654c4b1f1da4ee08d/latex_img2.gif",
null,
"http://img.sparknotes.com/figures/E/ee7c361f874525e654c4b1f1da4ee08d/latex_img34.gif",
null,
"http://img.sparknotes.com/figures/E/ee7c361f874525e654c4b1f1da4ee08d/latex_img39.gif",
null,
"http://img.sparknotes.com/figures/E/ee7c361f874525e654c4b1f1da4ee08d/latex_img33.gif",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9272523,"math_prob":0.9662151,"size":2119,"snap":"2023-40-2023-50","text_gpt3_token_len":450,"char_repetition_ratio":0.15981087,"word_repetition_ratio":0.11780105,"special_character_ratio":0.20906088,"punctuation_ratio":0.07073171,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99240166,"pos_list":[0,1,2,3,4,5,6,7,8],"im_url_duplicate_count":[null,5,null,5,null,5,null,5,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-27T08:21:24Z\",\"WARC-Record-ID\":\"<urn:uuid:4d69700a-43ec-4ab1-b5f0-0f60aa3f3709>\",\"Content-Length\":\"241360\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:19394088-c783-4165-b774-81c3188134c9>\",\"WARC-Concurrent-To\":\"<urn:uuid:b954d49a-29db-471b-8759-5ad167008357>\",\"WARC-IP-Address\":\"23.0.29.162\",\"WARC-Target-URI\":\"https://www.sparknotes.com/physics/linearmomentum/conservationofmomentum/section1/\",\"WARC-Payload-Digest\":\"sha1:LZULMG4HYB3C5RVNDBLBKP7MOGF3PPY6\",\"WARC-Block-Digest\":\"sha1:QUFZO52ULOVNLWR7CCGUXN3IZCMJPU75\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510284.49_warc_CC-MAIN-20230927071345-20230927101345-00785.warc.gz\"}"} |
https://www.semanticscholar.org/paper/Gradient-and-GENERIC-Systems-in-the-Space-of-to-Renger/468ea72e710c0503fc842ae55bb9a60ff1557560 | [
"# Gradient and GENERIC Systems in the Space of Fluxes, Applied to Reacting Particle Systems\n\n@article{Renger2018GradientAG,\ntitle={Gradient and GENERIC Systems in the Space of Fluxes, Applied to Reacting Particle Systems},\nauthor={Michiel Renger},\njournal={Entropy},\nyear={2018},\nvolume={20}\n}\n• M. Renger\n• Published 27 June 2018\n• Computer Science\n• Entropy\nIn a previous work we devised a framework to derive generalised gradient systems for an evolution equation from the large deviations of an underlying microscopic system, in the spirit of the Onsager–Machlup relations. Of particular interest is the case where the microscopic system consists of random particles, and the macroscopic quantity is the empirical measure or concentration. In this work we take the particle flux as the macroscopic quantity, which is related to the concentration via a…\nVariational structures beyond gradient flows: a macroscopic fluctuation-theory perspective\n• Mathematics\n• 2021\nMacroscopic equations arising out of stochastic particle systems in detailed balance (called dissipative systems or gradient flows) have a natural variational structure, which can be derived from the\nEDP-convergence for a linear reaction-diffusion system with fast reversible reaction\n• Artur Stephan\n• Mathematics, Materials Science\nCalculus of Variations and Partial Differential Equations\n• 2021\nWe perform a fast-reaction limit for a linear reaction-diffusion system consisting of two diffusion equations coupled by a linear reaction. We understand the linear reaction-diffusion system as a\nFluctuating Multiscale Mass Action Law\n• Physics\n• 2021\nThe classical mass action law in chemical kinetics is put into the context of multiscale thermodynamics.Despite the purely dissipative character of the classical mass action law, inertial effects\nOn the role of geometry in statistical mechanics and thermodynamics I: Geometric perspective\n• Physics\n• 2022\nThis paper contains a fully geometric formulation of the General Equation for Non-Equilibrium Reversible-Irreversible Coupling (GENERIC). Although GENERIC, which is the sum of Hamiltonian mechanics\nM ay 2 02 2 O N THE ROLE OF GEOMETRY IN STATISTICAL MECHANICS AND THERMODYNAMICS I : G EOMETRIC PERSPECTIVE\nThis paper contains a fully geometric formulation of the General Equation for Non-Equilibrium Reversible-Irreversible Coupling (GENERIC). Although GENERIC, which is the sum of Hamiltonian mechanics\nAnisothermal Chemical Reactions: Onsager-Machlup and Macroscopic Fluctuation Theory\nWe study a micro and macroscopic model for chemical reactions with feedback between reactions and temperature of the solute. The first result concerns the quasipotential as the large-deviation rate\nGeometry of Nonequilibrium Chemical Reaction Networks and Generalized Entropy Production Decompositions\n• Physics\n• 2022\nWe derive the Hessian geometric structure of nonequilibrium chemical reaction networks (CRN) on the flux and force spaces induced by the Legendre duality of convex dissipation functions and\nOrthogonality of fluxes in general nonlinear reaction networks\n• Mathematics\nDiscrete & Continuous Dynamical Systems - S\n• 2021\nWe consider the chemical reaction networks and study currents in these systems. Reviewing recent decomposition of rate functionals from large deviation theory for Markov processes, we adapt these"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.81709146,"math_prob":0.75104725,"size":6706,"snap":"2022-27-2022-33","text_gpt3_token_len":1442,"char_repetition_ratio":0.14055505,"word_repetition_ratio":0.071503684,"special_character_ratio":0.18431255,"punctuation_ratio":0.04935834,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97434646,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-08-11T21:09:58Z\",\"WARC-Record-ID\":\"<urn:uuid:66ac1d23-292c-41e6-aeb6-2d11f723a744>\",\"Content-Length\":\"385092\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6db49676-d31f-4fb4-8920-9dd9cb7fa342>\",\"WARC-Concurrent-To\":\"<urn:uuid:f46361a1-804a-4bad-bc99-85fa4e0b88b2>\",\"WARC-IP-Address\":\"18.67.65.92\",\"WARC-Target-URI\":\"https://www.semanticscholar.org/paper/Gradient-and-GENERIC-Systems-in-the-Space-of-to-Renger/468ea72e710c0503fc842ae55bb9a60ff1557560\",\"WARC-Payload-Digest\":\"sha1:ZXKQ3CKXI2VVF62DI4AWVRF7AZI77SDJ\",\"WARC-Block-Digest\":\"sha1:WJMTYEQV6OWMOJ32S6Y6B4ZWS5FH3FGZ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-33/CC-MAIN-2022-33_segments_1659882571502.25_warc_CC-MAIN-20220811194507-20220811224507-00053.warc.gz\"}"} |
https://www.kcfefcu.com/pages/calculators-savings-worth.html | [
"# Saving Accounts Calculator\n\nAccording to payday loans Kankakee IL, for the temporary use of the depositor’s funds, the bank is obliged to charge a monthly fee. In fact, this is done in accordance with the terms of the agreement between the bank and the client. The calculation of interest is included in the account maintenance and is performed without the participation of the depositor. But the depositor can independently calculate the interest on his saving account. To do this, you will need to acquire some skills and knowledge.\n\n## What are the interest rates on bank deposits?\n\nPercentages are divided by default into simple and complex. And interest is accrued in two ways – according to a simple and complex formula. The second method includes several schemes that differ from each other in the variability of the calculations. What is the difference between simple interest and complex interest?\n\n## Simple rates on saving\n\nThe peculiarity of this type of saving rates is that the interest on the deposit is not added to the principal amount, they are sent to another account opened under the terms of the agreement. When concluding an agreement, the frequency of charges is also approved – once a month, quarterly, six months, a year, or at the end of the deposit term.\n\n## Complex rates\n\nThe second option is used for deposits with capitalization. Interest is automatically added to the body of the deposit, and each subsequent time the profit is accrued on a new, already increased amount. Thus, both the amount of the deposit and the amount of interest are regularly growing.\n\n## How to calculate deposit interest correctly?\n\nFirst you need to understand for yourself all the terms for the deposit, in particular, the frequency of interest accrual (monthly, quarterly, etc.), the type of interest accrual (simple or complex). After that, you can apply a calculator and proceed to calculations.\n\n## How to use calculators?\n\nThe simplest thing is to use the deposit yield calculator on the website of the bank where the client opened or is going to open a deposit. Having chosen one of the options, you can see an online calculator by which it is easy to calculate income for this type of deposit. It is enough to enter the amount, the annual rate (it is entered automatically in the calculator for each product), the term of the deposit and click “Calculate”.\n\nTo calculate your income manually, you need to type the amount of the deposit on the calculator, multiply it by the ready rate, then for the number of days during which the deposit is valid.\n\n## Simple formula calculation\n\nAccrual of interest on deposits without capitalization using a simple formula:\n\nS = (P * I * t: K): 100\n\nIndications:\n\n• S – accrued profit;\n• P – the amount of the deposit;\n• I – annual rate on the deposit;\n• t – term of the deposit (number of days);\n• K – the number of days in a year (when calculating rates, 365 days are always taken, even in a leap year).\n\n## Complex formula calculation\n\nAt the request of the client, interest on the deposit may not be charged to a separate account, but added to the body of the deposit. In this case, you need to calculate the profitability using a different, more complex formula:\n\nS = ((P * I * (t: K)): 100) + ((P 1 * I * (t 1: K)): 100)"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.93011385,"math_prob":0.96224284,"size":3211,"snap":"2022-40-2023-06","text_gpt3_token_len":682,"char_repetition_ratio":0.18553165,"word_repetition_ratio":0.010619469,"special_character_ratio":0.21800062,"punctuation_ratio":0.11308562,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99138296,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-09-25T06:02:05Z\",\"WARC-Record-ID\":\"<urn:uuid:90ddea51-bbfa-4057-b8e1-d1317e1442b9>\",\"Content-Length\":\"25608\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:f2f3c81e-6713-4457-80ad-2d0e58bdf657>\",\"WARC-Concurrent-To\":\"<urn:uuid:f2864720-0cc8-4df3-977d-b35cd678258c>\",\"WARC-IP-Address\":\"66.165.237.122\",\"WARC-Target-URI\":\"https://www.kcfefcu.com/pages/calculators-savings-worth.html\",\"WARC-Payload-Digest\":\"sha1:5UITOS2N5I3T6MTN7E67YNQDRR5RA54X\",\"WARC-Block-Digest\":\"sha1:KBCNOC5WP6SIVLGWIMZICG4IGFMTCTYA\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-40/CC-MAIN-2022-40_segments_1664030334514.38_warc_CC-MAIN-20220925035541-20220925065541-00744.warc.gz\"}"} |
https://www.officetooltips.com/word_365/tips/how_to_insert_an_equation_with_fractions__square_roots_and_exponents.html | [
"How to insert an equation with fractions, square roots and exponents\n\nWord\nThis tip displays how to add an equation with fractions, square roots and exponents, for example, the normal or Gaussian distribution.\n\nHow to add an equation in your document, see Working with Microsoft Equation.\n\nTo insert, for example, the normal, or Gaussian distribution, do the following:\n\nIn the Professional format:\n\n1. In your own equation, enter f(x)=.\n\n2. On the Equation tab, in the Structures group, click the Fraction button:",
null,
"In the Fraction list choose Stacked Fraction:",
null,
"3. Enter 1 at the top of your fraction.\n\n4. In the bottom of your fraction, do the following:\n\n4.1. On the Equation tab, in the Structures group, click the Radical button. In the Radicals list choose Square root:",
null,
"4.2. Enter 2.\n\n4.3. On the Equation tab, in the Symbols group, click the More button:",
null,
"In the list of symbols choose:",
null,
"4.4. On the Equation tab, in the Structures group, click the Script button. In the Scripts and Superscripts list choose Superscript:",
null,
"4.5. In the base box of the script, choose",
null,
".\n\n4.6. In the upper right box of the script, enter 2.\n\n5. In the left of your formula choose Script again to enter e in the base box, in the upper right box enter - and choose Fraction again, etc.:",
null,
"In the Linear format:\n\n1. In your own equation, enter f(x)=1/.\n\n2. On the Equation tab, in the Symbols group, choose",
null,
"or simply \\sqrt.\n\n3. In the brackets enter 2",
null,
"(or \\pi),",
null,
"(or \\sigma) and ^2:",
null,
"Then you enter a space key, this linear formula transformed to the professional format:",
null,
"4. Enter e^(-(x-",
null,
"(or \\mu), )^2/(2,",
null,
"(or \\sigma) and then ^2)):",
null,
"Then you enter a space key, the second part of your linear formula transformed to the professional format:",
null,
""
] | [
null,
"https://www.officetooltips.com/images/tips/equation365/fraction_button.png",
null,
"https://www.officetooltips.com/images/tips/equation365/fraction.png",
null,
"https://www.officetooltips.com/images/tips/equation365/square_root.png",
null,
"https://www.officetooltips.com/images/tips/equation365/symbols.png",
null,
"https://www.officetooltips.com/images/tips/equation365/pi.png",
null,
"https://www.officetooltips.com/images/tips/equation365/superscript.png",
null,
"https://www.officetooltips.com/images/tips/equation/sigma.png",
null,
"https://www.officetooltips.com/images/tips/232/1.png",
null,
"https://www.officetooltips.com/images/tips/equation/radical_sign.png",
null,
"https://www.officetooltips.com/images/tips/equation/pi.png",
null,
"https://www.officetooltips.com/images/tips/equation/sigma.png",
null,
"https://www.officetooltips.com/images/tips/232/3.png",
null,
"https://www.officetooltips.com/images/tips/232/4.png",
null,
"https://www.officetooltips.com/images/tips/equation/mu.png",
null,
"https://www.officetooltips.com/images/tips/equation/sigma.png",
null,
"https://www.officetooltips.com/images/tips/232/5.png",
null,
"https://www.officetooltips.com/images/tips/232/1.png",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.7016338,"math_prob":0.9854932,"size":1725,"snap":"2022-05-2022-21","text_gpt3_token_len":434,"char_repetition_ratio":0.16676351,"word_repetition_ratio":0.14569536,"special_character_ratio":0.25971013,"punctuation_ratio":0.20050761,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9919427,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"im_url_duplicate_count":[null,5,null,9,null,5,null,null,null,4,null,10,null,null,null,null,null,8,null,8,null,null,null,8,null,8,null,8,null,null,null,8,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-01-19T13:51:28Z\",\"WARC-Record-ID\":\"<urn:uuid:30f616c7-aeec-4ad1-8f74-37f198dbcdde>\",\"Content-Length\":\"27600\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:fae46b59-7fa4-41cf-96cf-6be0fefc8c6e>\",\"WARC-Concurrent-To\":\"<urn:uuid:e60ba4f0-5495-46e2-8481-0f623fe28822>\",\"WARC-IP-Address\":\"104.21.17.122\",\"WARC-Target-URI\":\"https://www.officetooltips.com/word_365/tips/how_to_insert_an_equation_with_fractions__square_roots_and_exponents.html\",\"WARC-Payload-Digest\":\"sha1:23AC4RAQLX52LDOJ76JUTNQGCDPLTKMM\",\"WARC-Block-Digest\":\"sha1:N5OUDDV5BVBO5CQ3AQF2M3D5ASD4LR4K\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-05/CC-MAIN-2022-05_segments_1642320301341.12_warc_CC-MAIN-20220119125003-20220119155003-00597.warc.gz\"}"} |
https://pdf.datasheet.live/b163be37/fairchildsemi.com/LM7810CT.html | [
"",
null,
"© 2005 Fairchild Semiconductor Corporation DS400018 www.fairchildsemi.com\nApril 1999\nRevised December 2005\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A 3-Terminal 1A Positive Voltage Regulator\nLM7805 • LM7806 • LM7808 • LM7809 •\nLM7810 • LM7812 • LM7815 • LM7818 • LM7824 •\nLM7805A • LM7806A • LM7808A •LM7809A •\nLM7810A • LM7812A • LM7815A • LM7818A • LM7824A\n3-Terminal 1A Positi ve Voltage Regulator\nGeneral Description\nThe LM78XX series of three terminal positive regulators are\navailable in the TO-220 package and with several fixed output\nvoltages, making them useful in a wide range of applications.\nEach type employs internal current limiting, thermal shut down\nand safe operating area protection, making it essentially inde-\nstructible. If adequate heat sinking is provided, they can deliver\nover 1A output current. Although designed primarily as fixed\nvoltage regulators, these devices can be used with external\ncomponents to obtain adjustable voltages and currents.\nFeatures\nO\nOutput Current up to 1A\nO\nOutput Voltages of 5, 6, 8, 9, 12, 15, 18, 24\nO\nThermal Overload Protection\nO\nShort Circuit Protection\nO\nOutput T ransistor Safe Operating Area Protection\nOrdering Code:\nProduct Number Output Voltage Tolerance Package Operating Temperature\nLM7805CT\nr\n4%\nTO-220\n40\nq\nC -\n125\nq\nC\nLM7806CT\nLM7808CT\nLM7809CT\nLM7810CT\nLM7812CT\nLM7815CT\nLM7818CT\nLM7824CT\nLM7805ACT\nr\n2% 0\nq\nC -\n125\nq\nC\nLM7806ACT\nLM7808ACT\nLM7809ACT\nLM7810ACT\nLM7812ACT\nLM7815ACT\nLM7818ACT\nLM7824ACT",
null,
"www.fairchildsemi.com 2\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nInternal Block Diagram",
null,
"3www.fairchildsemi.com\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nAbsolute Maximum Ratings(Note 1)\nNote 1: Absolute maximum ratings are those values beyond which damage to the device may occur. The datasheet specifications should be met, without exception, to ensure\nthat the system design is reliable over its power supply, temperature, and output/input loading variables. Fairchild does not recommend operation outside datasheet specifica-\ntions.\nElectrical Characteristics (LM7805)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 10V, CI = 0.1\nP\nF, unless otherwise specified)\nNote 2: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separately. Pulse testing with\nlow duty is used.\nNote 3: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Value Unit\nInput Voltage (for VO = 5V to 18V) VI35 V\n(for VO = 24V) VI40 V\nThermal Resistance Junction-Cases (TO-220) R\nT\nJC 5\nq\nC/W\nThermal Resistance Junction-Air (TO-220) R\nT\nJA 65\nq\nC/W\nOperating Temperature Range TOPR 0\na\n125\nq\nC\nLM78xx\n40\na\n125\nq\nC\nLM78xxA 0\na\n125\nq\nC\nStorage Temperature Range TSTG\n65\na\n150\nq\nC\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC4.85.05.2\nV\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 7V to 20V 4.75 5.0 5.25\nLine Regulation Regline TJ =\n25\nq\nCV\nO = 7V to 25V 4.0 100 mV\n(Note 2) VI = 8V to 12V 1.6 50.0\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 9.0 100 mV\nIO = 250mA to 750mA 4.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.08.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.03 0.5 mA\nVI = 7V to 25V 0.3 1.3\nOutput Voltage Drift (Note 3)\n'\nVO/\n'\nTI\nO = 5mA\n0.8 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 42.0\nP\nV/VO\nRipple Rejection (Note 3) RR f = 120Hz, VO = 8V to 18V 62.0 73.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 3) rO f = 1KHz 15.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–230mA\nPeak Current (Note 3) IPK TJ =\n25\nq\nC–2.2A",
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"www.fairchildsemi.com 4\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7806)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 11V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 4: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separately. Pulse testing with\nlow duty is used.\nNote 5: These parameters, although guaranteed, are not 100% tested in production.\nElectrical Characteristics (LM7808)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 14V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 6: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separately. Pulse testing with\nlow duty is used.\nNote 7: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 5.75 6.0 6.25 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 8.0V to 21V 5.7 6.0 6.3\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 8V to 25V 5.0 120 mV\n(Note 4) VI = 9V to 13V 1.5 60.0\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 9.0 120 mV\n(Note 4) IO = 250mA to 750mA 3.0 60.0\nQuiescent Current IQTJ =\n25\nq\nC–5.08.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5 mA\nVI = 8V to 25V 1.3\nOutput Voltage Drift (Note 5)\n'\nVO/\n'\nTI\nO = 5mA\n0.8 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 45.0\nP\nV/VO\nRipple Rejection (Note 5) RR f = 120Hz, VO = 8V to 18V 62.0 73.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 5) rO f = 1KHz 19.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 5) IPK TJ =\n25\nq\nC–2.2A\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC7.78.08.3\nV\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 10.5V to 23V 7.6 8.0 8.4\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 10.5V to 25V 5.0 160 mV\n(Note 6) VI = 11.5V to 17V 2.0 80.0\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 10.0 160 mV\n(Note 6) IO = 250mA to 750mA 5.0 80.0\nQuiescent Current IQTJ =\n25\nq\nC–5.08.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.05 0.5 mA\nVI = 10.5V to 25V 0.5 1.0\nOutput Voltage Drift (Note 7)\n'\nVO/\n'\nTI\nO = 5mA\n0.8 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 52.0\nP\nV/VO\nRipple Rejection (Note 7) RR f = 120Hz, VO = 11.5V to 21.5V 56.0 73.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 7) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–230mA\nPeak Current (Note 7) IPK TJ =\n25\nq\nC–2.2A",
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"5www.fairchildsemi.com\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7809)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 15V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 8: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separately. Pulse testing with\nlow duty is used.\nNote 9: These parameters, although guaranteed, are not 100% tested in production.\nElectrical Characteristics (LM7810)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 16V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 10: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 11: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 8.65 9.0 9.35 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 11.5V to 24V 8.6 9.0 9.4\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 11.5V to 25V 6.0 180 mV\n(Note 8) VI = 12V to 17V 2.0 90.0\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 12.0 180 mV\n(Note 8) IO = 250mA to 750mA 4.0 90.0\nQuiescent Current IQTJ =\n25\nq\nC–5.08.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5 mA\nVI = 11.5V to 26V 1.3\nOutput Voltage Drift (Note 9)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 58.0\nP\nV/VO\nRipple Rejection (Note 9) RR f = 120Hz, VO = 13V to 23V 56.0 71.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 9) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 9) IPK TJ =\n25\nq\nC–2.2A\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 9.6 10.0 10.4 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 12.5V to 25V 9.5 10.0 10.5\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 12.5V to 25V 10.0 200 mV\n(Note 10) VI = 13V to 25V 3.0 100\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 12.0 200 mV\n(Note 10) IO = 250mA to 750mA 4.0 400\nQuiescent Current IQTJ =\n25\nq\nC–5.18.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5 mA\nVI = 12.5V to 29V 1.0\nOutput Voltage Drift (Note 11)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 58.0\nP\nV/VO\nRipple Rejection (Note 11) RR f = 120Hz, VO = 13V to 23V 56.0 71.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 11) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 11) IPK TJ =\n25\nq\nC–2.2A",
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"www.fairchildsemi.com 6\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7812)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 19V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 12: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 13: These parameters, although guaranteed, are not 100% tested in production.\nElectrical Characteristics (LM7815)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 23V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 14: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 15: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 11.5 12.0 12.5 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 14.5V to 27V 11.4 12.0 12.6\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 14.5V to 30V 10.0 240 mV\n(Note 12) VI = 16V to 22V 3.0 120\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 11.0 240 mV\n(Note 12) IO = 250mA to 750mA 5.0 120\nQuiescent Current IQTJ =\n25\nq\nC–5.18.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.1 0.5 mA\nVI = 14.5V to 30V 0.5 1.0\nOutput Voltage Drift (Note 13)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 76.0\nP\nV/VO\nRipple Rejection (Note 13) RR f = 120Hz, VI = 15V to 25V 55.0 71.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 13) rO f = 1KHz 18.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–230mA\nPeak Current (Note 13) IPK TJ =\n25\nq\nC–2.2A\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 14.4 15.0 15.6 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 17.5V to 30V 14.25 15.0 15.75\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 17.5V to 30V 11.0 300 mV\n(Note 14) VI = 20V to 26V 3.0 150\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 12.0 300 mV\n(Note 14) IO = 250mA to 750mA 4.0 150\nQuiescent Current IQTJ =\n25\nq\nC–5.28.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5 mA\nVI = 17.5V to 30V 1.0\nOutput Voltage Drift (Note 15)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 90.0\nP\nV/VO\nRipple Rejection (Note 15) RR f = 120Hz, VI = 18.5V to 28.5V 54.0 70.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 15) rO f = 1KHz 19.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 15) IPK TJ =\n25\nq\nC–2.2A",
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"7www.fairchildsemi.com\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7818)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 27V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 16: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 17: These parameters, although guaranteed, are not 100% tested in production.\nElectrical Characteristics (LM7824)\n(Refer to the test circuits.\n40\nq\nC\nTJ\n125\nq\nC, IO = 500mA, VI = 33V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 18: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 19: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 17.3 18.0 18.7 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 21V to 33V 17.1 18.0 18.9\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 21V to 33V 15.0 360 mV\n(Note 12) VI = 24V to 30V 5.0 180\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 15.0 360 mV\n(Note 12) IO = 250mA to 750mA 5.0 180\nQuiescent Current IQTJ =\n25\nq\nC–5.28.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5 mA\nVI = 21V to 33V 1.0\nOutput Voltage Drift (Note 17)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC–110\nP\nV/VO\nRipple Rejection (Note 17) RR f = 120Hz, VI = 22V to 32V 53.0 69.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 17) rO f = 1KHz 22.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 17) IPK TJ =\n25\nq\nC–2.2A\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 23.0 24.0 25.0 V\n5mA\nd\nIO\nd\n1A, PO\nd\n15W, VI = 27V to 38V 22.8 24.0 25.25\nLine Regulation Regline TJ =\n25\nq\nCV\nI = 27V to 38V 17.0 480 mV\n(Note 18) VI = 30V to 36V 6.0 240\nLoad Regulation Regload TJ =\n25\nq\nCI\nO = 5mA to 1.5mA 15.0 480 mV\n(Note 18) IO = 250mA to 750mA 5.0 240\nQuiescent Current IQTJ =\n25\nq\nC–5.28.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.1 0.5 mA\nVI = 27V to 38V 0.5 1.0\nOutput Voltage Drift (Note 19)\n'\nVO/\n'\nTI\nO = 5mA\n1.5 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 60.0\nP\nV/VO\nRipple Rejection (Note 19) RR f = 120Hz, VI = 28V to 38V 50.0 67.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 19) rO f = 1KHz 28.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–230mA\nPeak Current (Note 19) IPK TJ =\n25\nq\nC–2.2A",
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"www.fairchildsemi.com 8\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7805A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 10V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 20: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 21: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC4.95.05.1\nV\nIO = 5mA to 1A, PO\nd\n15W, VI = 7.5V to 20V 4.8 5.0‘ 5.2\nLine Regulation Regline VI = 7.5V to 25V, IO = 500mA 5.0 50.0\nmV\n(Note 20) VI = 8V to 12V 3.0 50.0\nTJ =\n25\nq\nCV\nI = 7.3V to 20V 5.0 50.0\nVI = 8V to 12V 1.5 25.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.5mA 9.0 100\nmV(Note 20) IO = 5mA to 1mA 9.0 100\nIO = 250mA to 750mA 4.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.06.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 8V to 25V, IO = 500mA 0.8\nVI = 7.5V to 20V, TJ =\n25\nq\nC–0.8\nOutput Voltage Drift (Note 21)\n'\nVO/\n'\nTI\nO = 5mA\n0.8 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 21) RR f = 120Hz, IO = 500mA, VI = 8V to 18V 68.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 21) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 21) IPK TJ =\n25\nq\nC–2.2A",
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"9www.fairchildsemi.com\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7806A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 11V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 22: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 23: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 5.58 6.0 6.12 V\nIO = 5mA to 1A, PO\nd\n15W, VI = 8.6V to 21V 5.76 6.0 6.24\nLine Regulation Regline VI = 8.6V to 25V, IO = 500mA 5.0 60.0\nmV\n(Note 22) VI = 9V to 13V 3.0 60.0\nTJ =\n25\nq\nCV\nI = 8.3V to 21V 5.0 60.0\nVI = 9V to 13V 1.5 30.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.5mA 9.0 100\nmV(Note 22) IO = 5mA to 1mA 4.0 100\nIO = 250mA to 750mA 5.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–4.36.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 19V to 25V, IO = 500mA 0.8\nVI = 8.5V to 21V, TJ =\n25\nq\nC–0.8\nOutput Voltage Drift (Note 23)\n'\nVO/\n'\nTI\nO = 5mA\n0.8 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 23) RR f = 120Hz, IO = 500mA, VI = 9V to 19V 65.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 23) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 23) IPK TJ =\n25\nq\nC–2.2A",
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"www.fairchildsemi.com 10\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7808A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 14V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 24: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 25: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Unit\nOutput Voltage VOTJ =\n25\nq\nC 7.84 8.0 8.16 V\nIO = 5mA to 1A, PO\nd\n15W, VI = 10.6V to 23V 7.7 8.0 8.3\nLine Regulation Regline VI = 10.6V to 25V, IO = 500mA 6.0 80.0\nmV\n(Note 24) VI = 11V to 17V 3.0 80.0\nTJ =\n25\nq\nCV\nI = 10.4V to 23V 6.0 80.0\nVI = 11V to 17V 2.0 40.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.5mA 12.0 100\nmV(Note 24) IO = 5mA to 1mA 12.0 100\nIO = 250mA to 750mA 5.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.06.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 11V to 25V, IO = 500mA 0.8\nVI = 10.6V to 23V, TJ =\n25\nq\nC–0.8\nOutput Voltage Drift (Note 25)\n'\nVO/\n'\nTI\nO = 5mA\n0.8 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 25) RR f = 120Hz, IO = 500mA, VI = 11.5V to 21.5V 62.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 25) rO f = 1KHz 18.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 25) IPK TJ =\n25\nq\nC–2.2A",
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"11 www.fairchildsemi.com\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7809A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 15V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 26: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 27: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Units\nOutput Voltage VOTJ =\n25\nq\nC 8.82 9.0 9.16 V\nIO = 5mA to 1A, PO\nd\n15W, VI = 11.2V to 24V 8.65 9.0 9.35\nLine Regulation Regline VI = 11.7V to 25V, IO = 500mA 6.0 90.0\nmV\n(Note 26) VI = 12.5V to 19V 4.0 45.0\nTJ =\n25\nq\nCV\nI = 11.5V to 24V 6.0 90.0\nVI = 12.5V to 19V 2.0 45.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.0mA 12.0 100\nmV(Note 26) IO = 5mA to 1mA 12.0 100\nIO = 250mA to 750mA 5.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.06.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 12V to 25V, IO = 500mA 0.8\nVI = 11.7V to 25V, TJ =\n25\nq\nC–0.8\nOutput Voltage Drift (Note 27)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 27) RR f = 120Hz, IO = 500mA, VI = 12V to 22V 62.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 27) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 27) IPK TJ =\n25\nq\nC–2.2A",
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"www.fairchildsemi.com 12\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7810A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 16V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 28: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 29: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Units\nOutput Voltage VOTJ =\n25\nq\nC 9.8 10.0 10.2 V\nIO = 5mA to 1A, PO\nd\n15W, VI = 12.8V to 25V 9.6 10.0 10.4\nLine Regulation Regline VI = 12.8V to 26V, IO = 500mA 8.0 100\nmV\n(Note 28) VI = 13V to 20V 4.0 50.0\nTJ =\n25\nq\nCV\nI = 12.5V to 25V 8.0 100\nVI = 13V to 20V 3.0 50.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.5mA 12.0 100\nmV(Note 28) IO = 5mA to 1mA 12.0 100\nIO = 250mA to 750mA 5.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.06.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 12.8V to 25V, IO = 500mA 0.8\nVI = 13V to 26V, TJ =\n25\nq\nC–0.5\nOutput Voltage Drift (Note 29)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 29) RR f = 120Hz, IO = 500mA, VI = 14V to 24V 62.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 29) rO f = 1KHz 17.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 29) IPK TJ =\n25\nq\nC–2.2A",
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"13 www.fairchildsemi.com\nLM7805 • LM7806 • LM7808 • LM7809 • LM7810 • LM7812 • LM7815 • LM7818 • LM7824 • LM7805A • LM7806A • LM7808A\n•LM780 9A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7812A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 19V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 30: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 31: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Units\nOutput Voltage VOTJ =\n25\nq\nC 11.75 12.0 12.25 V\nIO = 5mA to 1A, PO\nd\n15W, VI = 14.8V to 27V 11.5 12.0 12.5\nLine Regulation Regline VI = 14.8V to 30V, IO = 500mA 10.0 120\nmV\n(Note 30) VI = 16V to 22V 4.0 120\nTJ =\n25\nq\nCV\nI = 14.5V to 27V 10.0 120\nVI = 16V to 22V 3.0 60.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.5mA 12.0 100\nmV(Note 30) IO = 5mA to 1mA 12.0 100\nIO = 250mA to 750mA 5.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.16.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 14V to 27V, IO = 500mA 0.8\nVI = 15V to 30V, TJ =\n25\nq\nC–0.8\nOutput Voltage Drift (Note 31)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 31) RR f = 120Hz, IO = 500mA, VI = 14V to 24V 60.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 31) rO f = 1KHz 18.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 31) IPK TJ =\n25\nq\nC–2.2A",
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"www.fairchildsemi.com 14\nLM7805 • LM78 06 • LM7808 • LM7809 • LM7810 • LM7812 LM7815 • LM7818 • LM7824 • LM7805A • LM780 6A • LM7808A\n•LM7809A • LM7810A • LM7812A • LM7815A • LM7818A • LM7824A\nElectrical Characteristics (LM7815A)\n(Refer to the test circuits. 0\nq\nC\nTJ\n125\nq\nC, IO = 1A, VI = 23V, CI = 0.33\nP\nF, CO = 0.1\nP\nF, unless otherwise specified)\nNote 32: Load and line regulation are specified at constant junction temperature. Changes in VO due to heating effects must be taken into account separate ly. Pulse testing with\nlow duty is used.\nNote 33: These parameters, although guaranteed, are not 100% tested in production.\nParameter Symbol Conditions Min Typ Max Units\nOutput Voltage VOTJ =\n25\nq\nC 14.75 15.0 15.3 V\nIO = 5mA to 1A, PO\nd\n15W, VI = 17.7V to 30V 14.4 15.0 15.6\nLine Regulation Regline VI = 17.4V to 30V, IO = 500mA 10.0 150\nmV\n(Note 32) VI = 20V to 26V 5.0 150\nTJ =\n25\nq\nCV\nI = 17.5V to 30V 11.0 150\nVI = 20V to 26V 3.0 75.0\nLoad Regulation Regload TJ =\n25\nq\nC, IO = 5mA to 1.5mA 12.0 100\nmV(Note 32) IO = 5mA to 1mA 12.0 100\nIO = 250mA to 750mA 5.0 50.0\nQuiescent Current IQTJ =\n25\nq\nC–5.26.0mA\nQuiescent Current Change\n'\nIQIO = 5mA to 1A 0.5\nmAVI = 17.5V to 30V, IO = 500mA 0.8\nVI = 17.5V to 30V, TJ =\n25\nq\nC–0.8\nOutput Voltage Drift (Note 33)\n'\nVO/\n'\nTI\nO = 5mA\n1.0 mV/\nq\nC\nOutput Noise Voltage VNf = 10Hz to 100KHz, TA =\n25\nq\nC 10.0\nP\nV/VO\nRipple Rejection (Note 33) RR f = 120Hz, IO = 500mA, VI = 18 .5V to 28.5V 58.0 dB\nDropout Voltage VDROP IO = 1A, TJ =\n25\nq\nC–2.0V\nOutput Resistance (Note 33) rO f = 1KHz 19.0 m\n:\nShort Circuit Current ISC VI = 35V, TA =\n25\nq\nC–250mA\nPeak Current (Note 33) IPK TJ =\n25\nq\nC–2.2A"
] | [
null,
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null,
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null,
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null,
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null,
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null,
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null,
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z/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBXP+If8AkOeE/wDsKyf+kV1R9j8Yf9B3Q/8AwTTf/JVYeu2vioax4YEus6MznU3ERXSZVCt9kuOWH2k7ht3DAxyQc8YIB3lFc/8AY/GH/Qd0P/wTTf8AyVUN2viuytZLibXdF8uMZYrokzED1wLqgDpqK5e9PiqwtWuJ9d0XYCBhNEmYkk4AAF1zUyW3i2SNXTXtCZWAII0aXkf+BVADvHf/ACTzxL/2Crr/ANFNXQVwfjS18VL4F8Qtcazo0kA0y5MiR6TKjMvlNkBjckA474OPQ1ufY/GH/Qd0P/wTTf8AyVQB0FFc/wDY/GH/AEHdD/8ABNN/8lUfY/GH/Qd0P/wTTf8AyVQB0FFc/wDY/GH/AEHdD/8ABNN/8lUfY/GH/Qd0P/wTTf8AyVQAeDf+QHc/9hXUv/S2augrg/Cdr4qbR7gwazoyJ/ad+CH0mVju+1zbjkXI4LZIHYEDJxk7n2Pxh/0HdD/8E03/AMlUAdBRXP8A2Pxh/wBB3Q//AATTf/JVH2Pxh/0HdD/8E03/AMlUAdBXP2f/ACUPWf8AsFWH/o27o+x+MP8AoO6H/wCCab/5KqnoMepReOtbXVLu0uZ/7MsSr2ts0ChfNu+CrSOSc55z3HHHIB1lc/4y/wCQHbf9hXTf/S2Gugrn/GX/ACA7b/sK6b/6Ww0AdBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz95/yUPRv+wVf/wDo20roK5+8/wCSh6N/2Cr/AP8ARtpXQUAc/wCO/wDknniX/sFXX/opq6Cuf8d/8k88S/8AYKuv/RTV0FABRRRQAUUUUAFFFFABRRRQAUUUUAc/4e/5Dniz/sKx/wDpFa10Fc/4e/5Dniz/ALCsf/pFa10FABRRRQAUUUUAc/47/wCSeeJf+wVdf+imroK5/wAd/wDJPPEv/YKuv/RTV0FADJoY7iF4ZkDxuMMp6EU5VCqFUAKBgAdqWigAooooAKKKKACuX8X3sWn6h4WupkneNNVfIggeZzmzuRwiAsevYcdeldRXP+If+Q54T/7Csn/pFdUAH/CZaX/z665/4Ir3/wCM0f8ACZaX/wA+uuf+CK9/+M10FFAHP/8ACZaX/wA+uuf+CK9/+M0f8Jlpf/Prrn/givf/AIzXQUUAc/8A8Jlpf/Prrn/givf/AIzR/wAJlpf/AD665/4Ir3/4zXQUUAc//wAJlpf/AD665/4Ir3/4zR/wmWl/8+uuf+CK9/8AjNdBRQBwdr4s05fHWrTm21nY+mWSADRbwtlZbonK+VkD5hgkYPOM4ONz/hMtL/59dc/8EV7/APGaLP8A5KHrP/YKsP8A0bd10FAHP/8ACZaX/wA+uuf+CK9/+M0f8Jlpf/Prrn/givf/AIzXQUUAc/8A8Jlpf/Prrn/givf/AIzWHrvizTpdY8MOttrIEWpu7btFvFJH2S4X5QYsscsOBk4yegJHeVz/AIh/5DnhP/sKyf8ApFdUAH/CZaX/AM+uuf8Agivf/jNQ3nifRr60ltbi018xSja4XRb9CR9ViBFdNRQBy934l0S+tTbXFnrzRHB+XRb9Twcj5hECOnrU6+MNJRFRLPXFVRgAaDe4A/7810NFAHB+NPFmnXPgXxDAltrIeTTLlFMmi3iKCYmHLNEAo9yQB3rc/wCEy0v/AJ9dc/8ABFe//GaPHf8AyTzxL/2Crr/0U1dBQBz/APwmWl/8+uuf+CK9/wDjNH/CZaX/AM+uuf8Agivf/jNdBRQBz/8AwmWl/wDPrrn/AIIr3/4zR/wmWl/8+uuf+CK9/wDjNdBRQBzfgaZbnw3JOgkCSanqDqJI2RgDeTHlWAKn2IBHeukrn/Bv/IDuf+wrqX/pbNXQUAFFFFABXP2f/JQ9Z/7BVh/6Nu66Cufs/wDkoes/9gqw/wDRt3QB0Fc/4y/5Adt/2FdN/wDS2Gugrn/GX/IDtv8AsK6b/wClsNAHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/ef8AJQ9G/wCwVf8A/o20roK5+8/5KHo3/YKv/wD0baV0FAHP+O/+SeeJf+wVdf8AopqP+Ee1T/oc9c/782X/AMj0eO/+SeeJf+wVdf8Aopq6CgDn/wDhHtU/6HPXP+/Nl/8AI9H/AAj2qf8AQ565/wB+bL/5HroKKAOf/wCEe1T/AKHPXP8AvzZf/I9H/CPap/0Oeuf9+bL/AOR66CigDn/+Ee1T/oc9c/782X/yPR/wj2qf9Dnrn/fmy/8AkeugooA5/wD4R7VP+hz1z/vzZf8AyPR/wj2qf9Dnrn/fmy/+R66CigDn/wDhHtU/6HPXP+/Nl/8AI9H/AAj2qf8AQ565/wB+bL/5HroKKAOD0LQtRfWPE6r4s1mMpqaKzLFZ5kP2S3O5swEZwQOMDCjjOSdz/hHtU/6HPXP+/Nl/8j0eHv8AkOeLP+wrH/6RWtdBQBz/APwj2qf9Dnrn/fmy/wDkej/hHtU/6HPXP+/Nl/8AI9dBRQBz/wDwj2qf9Dnrn/fmy/8Akej/AIR7VP8Aoc9c/wC/Nl/8j10FFAHB+NNC1GHwL4hlfxZrM6JplyzRSRWYVwIm+U7YAcHpwQfQitz/AIR7VP8Aoc9c/wC/Nl/8j0eO/wDknniX/sFXX/opq6CgDnJtD1C3heabxtraRoMsxissAf8AgPTl8P6kyhl8aa4VIyCIrLn/AMl66BlDKVYAqRgg96WgDmbXRdUubdJv+Et8Qxbs/JLb2SsOccj7PU3/AAj2qf8AQ565/wB+bL/5HroKKAOf/wCEe1T/AKHPXP8AvzZf/I9H/CPap/0Oeuf9+bL/AOR66CigDn/+Ee1T/oc9c/782X/yPWHruhaimseGFbxZrMhfU3VWaKzzGfslwdy4gAzgEc5GGPGcEd5XL+L72LT9Q8LXUyTvGmqvkQQPM5zZ3I4RAWPXsOOvSgCx/wAI9qn/AEOeuf8Afmy/+R6P+Ee1T/oc9c/782X/AMj0f8Jlpf8Az665/wCCK9/+M0f8Jlpf/Prrn/givf8A4zQAf8I9qn/Q565/35sv/kej/hHtU/6HPXP+/Nl/8j0f8Jlpf/Prrn/givf/AIzR/wAJlpf/AD665/4Ir3/4zQAf8I9qn/Q565/35sv/AJHo/wCEe1T/AKHPXP8AvzZf/I9H/CZaX/z665/4Ir3/AOM0f8Jlpf8Az665/wCCK9/+M0AH/CPap/0Oeuf9+bL/AOR6P+Ee1T/oc9c/782X/wAj0f8ACZaX/wA+uuf+CK9/+M0f8Jlpf/Prrn/givf/AIzQBh2uhaifHWrRDxZrIddMsmMois9zAy3WFP7jGBgkYAPzHJPGNz/hHtU/6HPXP+/Nl/8AI9Ydr4s05fHWrTm21nY+mWSADRbwtlZbonK+VkD5hgkYPOM4ONz/AITLS/8An11z/wAEV7/8ZoAP+Ee1T/oc9c/782X/AMj0f8I9qn/Q565/35sv/kej/hMtL/59dc/8EV7/APGaP+Ey0v8A59dc/wDBFe//ABmgA/4R7VP+hz1z/vzZf/I9Yeu6FqKax4YVvFmsyF9TdVZorPMZ+yXB3LiADOARzkYY8ZwRuf8ACZaX/wA+uuf+CK9/+M1h674s06XWPDDrbayBFqbu27RbxSR9kuF+UGLLHLDgZOMnoCQAbn/CPap/0Oeuf9+bL/5HqvfaRqdjYy3R8Xa9IsS7iqxWOcd+tv6c1Y/4TLS/+fXXP/BFe/8Axmo7jxTol3A8Fzp+sTQuMNHJ4fvWVvqDDg0AQ6jpepafZtcHxdr8pDKoRYrEEknHU2+Kni0LUpoUlTxnru11DDMNl0P/AG702fxTol1btb3GnaxLC2MxyeH71lOORwYal/4TLSx/y665/wCCG9/+M0AYfjTQtRh8C+IZX8WazOiaZcs0UkVmFcCJvlO2AHB6cEH0Irc/4R7VP+hz1z/vzZf/ACPWH408Wadc+BfEMCW2sh5NMuUUyaLeIoJiYcs0QCj3JAHetz/hMtL/AOfXXP8AwRXv/wAZoAP+Ee1T/oc9c/782X/yPR/wj2qf9Dnrn/fmy/8Akej/AITLS/8An11z/wAEV7/8Zo/4TLS/+fXXP/BFe/8AxmgA/wCEe1T/AKHPXP8AvzZf/I9H/CPap/0Oeuf9+bL/AOR6P+Ey0v8A59dc/wDBFe//ABmj/hMtL/59dc/8EV7/APGaAMPwnoWoy6PcMnizWYQNTv12pFZkEi7mBb5oCckjJ7ZJwAMAbn/CPap/0Oeuf9+bL/5HqPwNMtz4bknQSBJNT1B1EkbIwBvJjyrAFT7EAjvXSUAc/wD8I9qn/Q565/35sv8A5Ho/4R7VP+hz1z/vzZf/ACPXQUUAc/8A8I9qn/Q565/35sv/AJHqnoNlPY+OtbiuNTu9Qc6ZYsJbpYlYDzbv5R5aIMcZ6Z5PPTHWVz9n/wAlD1n/ALBVh/6Nu6AOgrn/ABl/yA7b/sK6b/6Ww10Fc/4y/wCQHbf9hXTf/S2GgDoKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOfvP+Sh6N/2Cr/8A9G2ldBXP3n/JQ9G/7BV//wCjbSugoA5/x3/yTzxL/wBgq6/9FNXQVz/jv/knniX/ALBV1/6Kaj/hDdL/AOfrXP8Awe3v/wAeoA6Ciuf/AOEN0v8A5+tc/wDB7e//AB6j/hDdL/5+tc/8Ht7/APHqAOgorn/+EN0v/n61z/we3v8A8eo/4Q3S/wDn61z/AMHt7/8AHqAOgorn/wDhDdL/AOfrXP8Awe3v/wAeo/4Q3S/+frXP/B7e/wDx6gDoKK5//hDdL/5+tc/8Ht7/APHqP+EN0v8A5+tc/wDB7e//AB6gDoKK5/8A4Q3S/wDn61z/AMHt7/8AHqP+EN0v/n61z/we3v8A8eoAPD3/ACHPFn/YVj/9IrWugrg9C8J6dLrHidGudZAi1NEXbrV4pI+yW7fMRLljljycnGB0AA3P+EN0v/n61z/we3v/AMeoA6Ciuf8A+EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//HqAOgorn/8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7/wDHqADx3/yTzxL/ANgq6/8ARTV0FcH408J6dbeBfEM6XOsl49MuXUSa1eOpIiY8q0pDD2IIPetz/hDdL/5+tc/8Ht7/APHqAOgornJvCej28LzTXutpGgyzHXr3AH/f6nL4P0plDLd64VIyCNevef8AyNQB0NFcza+FNLubdJvN8Qxbs/JLrd6rDnHI86pv+EN0v/n61z/we3v/AMeoA6Ciuf8A+EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//HqAOgrn/EP/ACHPCf8A2FZP/SK6o/4Q3S/+frXP/B7e/wDx6o5PA2izPC8susu8L74mbW70lG2lcr+94O1mGR2JHegDpKK5/wD4Q3S/+frXP/B7e/8Ax6j/AIQ3S/8An61z/wAHt7/8eoA6Ciuf/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeoA6Ciuf8A+EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//HqAOgorn/8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7/wDHqACz/wCSh6z/ANgqw/8ARt3XQVwdr4T05vHWrQG51nYmmWTgjWrwNlpboHLebkj5RgE4HOMZOdz/AIQ3S/8An61z/wAHt7/8eoA6Ciuf/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeoA6Cuf8Q/8hzwn/2FZP8A0iuqP+EN0v8A5+tc/wDB7e//AB6sPXfCenRax4YRbnWSJdTdG3a1eMQPslw3yky5U5UcjBxkdCQQDvKK5/8A4Q3S/wDn61z/AMHt7/8AHqP+EN0v/n61z/we3v8A8eoA6Ciuf/4Q3S/+frXP/B7e/wDx6j/hDdL/AOfrXP8Awe3v/wAeoAPHf/JPPEv/AGCrr/0U1dBXB+NPCenW3gXxDOlzrJePTLl1EmtXjqSImPKtKQw9iCD3rc/4Q3S/+frXP/B7e/8Ax6gDoKK5/wD4Q3S/+frXP/B7e/8Ax6j/AIQ3S/8An61z/wAHt7/8eoA6Ciuf/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeoAPBv/IDuf+wrqX/pbNXQVwfhPwnp0+j3Dvc6yCNTv0+TWrxBhbuZRwsoGcDk9Sck5JJrc/4Q3S/+frXP/B7e/wDx6gDoKK5//hDdL/5+tc/8Ht7/APHqP+EN0v8A5+tc/wDB7e//AB6gDoK5+z/5KHrP/YKsP/Rt3R/whul/8/Wuf+D29/8Aj1U9B0yDSvHWtwW8l26Npli5N1dy3DZ827HDSMxA46Zx19TQB1lc/wCMv+QHbf8AYV03/wBLYa6Cuf8AGX/IDtv+wrpv/pbDQB0FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHP3n/JQ9G/7BV/8A+jbSugrn7z/koejf9gq//wDRtpXQUAc/47/5J54l/wCwVdf+imo/4SHVP+hM1z/v9Zf/ACRR47/5J54l/wCwVdf+imroKAOf/wCEh1T/AKEzXP8Av9Zf/JFH/CQ6p/0Jmuf9/rL/AOSK6CigDn/+Eh1T/oTNc/7/AFl/8kUf8JDqn/Qma5/3+sv/AJIroKoy2Vw+rRXS3ZWBQN0G0/MQHHXOP4x2/hFAGb/wkOqf9CZrn/f6y/8Akij/AISHVP8AoTNc/wC/1l/8kVoW+nTQ6zd3zXskkU4UJAfux4AHHPsT/wACNaFAHP8A/CQ6p/0Jmuf9/rL/AOSKP+Eh1T/oTNc/7/WX/wAkV0FFAHP/APCQ6p/0Jmuf9/rL/wCSKP8AhIdU/wChM1z/AL/WX/yRXQUUAcHoWu6imseJ2XwnrMhfU0ZlWWzzGfsluNrZnAzgA8ZGGHOcgbn/AAkOqf8AQma5/wB/rL/5Io8Pf8hzxZ/2FY//AEita6CgDn/+Eh1T/oTNc/7/AFl/8kUf8JDqn/Qma5/3+sv/AJIroKp2lnLb3l3M9x5iTsGEe0jaRkZzk/w7Bxj7ue5oAy/+Eh1T/oTNc/7/AFl/8kUf8JDqn/Qma5/3+sv/AJIroKKAOD8aa7qM3gXxDE/hPWYEfTLlWlklsyqAxN8x2zk4HXgE+gNbn/CQ6p/0Jmuf9/rL/wCSKPHf/JPPEv8A2Crr/wBFNXQUAc83iDUmUq3gvXCpGCDLZc/+TFL/AMJDqn/Qma5/3+sv/kiugooA5/8A4SHVP+hM1z/v9Zf/ACRR/wAJDqn/AEJmuf8Af6y/+SK6CigDn/8AhIdU/wChM1z/AL/WX/yRR/wkOqf9CZrn/f6y/wDkiugooA5//hIdU/6EzXP+/wBZf/JFH/CQ6p/0Jmuf9/rL/wCSK6CigDn/APhIdU/6EzXP+/1l/wDJFH/CQ6p/0Jmuf9/rL/5IroKKAOf/AOEh1T/oTNc/7/WX/wAkUf8ACQ6p/wBCZrn/AH+sv/kiugooA5//AISHVP8AoTNc/wC/1l/8kUf8JDqn/Qma5/3+sv8A5IroKKAOf/4SHVP+hM1z/v8AWX/yRR/wkOqf9CZrn/f6y/8AkiugooA4O113UR461aUeE9ZLtplkpiEtnuUCW6wx/f4wckDBJ+U5A4zuf8JDqn/Qma5/3+sv/kiiz/5KHrP/AGCrD/0bd10FAHP/APCQ6p/0Jmuf9/rL/wCSKP8AhIdU/wChM1z/AL/WX/yRXQUUAc//AMJDqn/Qma5/3+sv/kisPXdd1F9Y8MM3hPWYympuyq0tnmQ/ZLgbVxORnBJ5wMKec4B7yuf8Q/8AIc8J/wDYVk/9IrqgA/4SHVP+hM1z/v8AWX/yRR/wkOqf9CZrn/f6y/8AkiugooA5/wD4SHVP+hM1z/v9Zf8AyRR/wkOqf9CZrn/f6y/+SK6CigDg/Gmu6jN4F8QxP4T1mBH0y5VpZJbMqgMTfMds5OB14BPoDW5/wkOqf9CZrn/f6y/+SKPHf/JPPEv/AGCrr/0U1dBQBz//AAkOqf8AQma5/wB/rL/5Io/4SHVP+hM1z/v9Zf8AyRXQUUAc/wD8JDqn/Qma5/3+sv8A5Io/4SHVP+hM1z/v9Zf/ACRXQUUAcH4T13UYtHuFTwnrMwOp37bklswATdzEr804OQTg9sg4JGCdz/hIdU/6EzXP+/1l/wDJFHg3/kB3P/YV1L/0tmroKAOf/wCEh1T/AKEzXP8Av9Zf/JFH/CQ6p/0Jmuf9/rL/AOSK6CigDn/+Eh1T/oTNc/7/AFl/8kVT0G9nvvHWty3GmXenuNMsVEV00TMR5t38w8t3GOcdc8HjpnrK5+z/AOSh6z/2CrD/ANG3dAHQVz/jL/kB23/YV03/ANLYa6Cuf8Zf8gO2/wCwrpv/AKWw0AdBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz95/wAlD0b/ALBV/wD+jbSugrn7z/koejf9gq//APRtpXQUAc/47/5J54l/7BV1/wCimroK5/x3/wAk88S/9gq6/wDRTUfbPGH/AEAtD/8ABzN/8i0AdBRXP/bPGH/QC0P/AMHM3/yLR9s8Yf8AQC0P/wAHM3/yLQB0FFc/9s8Yf9ALQ/8Awczf/ItH2zxh/wBALQ//AAczf/ItAHQUVz/2zxh/0AtD/wDBzN/8i0fbPGH/AEAtD/8ABzN/8i0AdBRXP/bPGH/QC0P/AMHM3/yLR9s8Yf8AQC0P/wAHM3/yLQB0FFc/9s8Yf9ALQ/8Awczf/ItH2zxh/wBALQ//AAczf/ItAB4e/wCQ54s/7Csf/pFa10FcHoV14qGseJzFo2jM51NDKG1aVQrfZLfhT9mO4bdpycckjHGTufbPGH/QC0P/AMHM3/yLQB0FFc/9s8Yf9ALQ/wDwczf/ACLR9s8Yf9ALQ/8Awczf/ItAHQUVz/2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0AHjv8A5J54l/7BV1/6Kaj/AIQ3S/8An61z/wAHt7/8erD8aXXipvAviFbjRtGjgOmXIkePVpXZV8pskKbYAnHbIz6itz7Z4w/6AWh/+Dmb/wCRaAD/AIQ3S/8An61z/wAHt7/8eo/4Q3S/+frXP/B7e/8Ax6j7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaAD/hDdL/5+tc/8Ht7/wDHqP8AhDdL/wCfrXP/AAe3v/x6j7Z4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFoAP8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7/wDHqPtnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5FoAP+EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//HqPtnjD/oBaH/4OZv8A5Fo+2eMP+gFof/g5m/8AkWgA/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeo+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWgA/4Q3S/+frXP/B7e/8Ax6j/AIQ3S/8An61z/wAHt7/8eo+2eMP+gFof/g5m/wDkWj7Z4w/6AWh/+Dmb/wCRaAD/AIQ3S/8An61z/wAHt7/8eo/4Q3S/+frXP/B7e/8Ax6j7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaAD/hDdL/5+tc/8Ht7/wDHqP8AhDdL/wCfrXP/AAe3v/x6j7Z4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFoAw7XwnpzeOtWgNzrOxNMsnBGtXgbLS3QOW83JHyjAJwOcYyc7n/CG6X/AM/Wuf8Ag9vf/j1YdrdeKv8AhOtWZdG0YznTLIOh1aUKF8262kN9mySTuyMDGByc8bn2zxh/0AtD/wDBzN/8i0AH/CG6X/z9a5/4Pb3/AOPUf8Ibpf8Az9a5/wCD29/+PUfbPGH/AEAtD/8ABzN/8i0fbPGH/QC0P/wczf8AyLQAf8Ibpf8Az9a5/wCD29/+PVh674T06LWPDCLc6yRLqbo27WrxiB9kuG+UmXKnKjkYOMjoSDufbPGH/QC0P/wczf8AyLWHrt14qOseGDLo2jK41NzEF1aVgzfZLjhj9mG0bdxyM8gDHOQAbn/CG6X/AM/Wuf8Ag9vf/j1H/CG6X/z9a5/4Pb3/AOPUfbPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLQAf8Ibpf/P1rn/g9vf8A49R/whul/wDP1rn/AIPb3/49R9s8Yf8AQC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItAGH408J6dbeBfEM6XOsl49MuXUSa1eOpIiY8q0pDD2IIPetz/hDdL/5+tc/8Ht7/wDHqw/Gl14qbwL4hW40bRo4DplyJHj1aV2VfKbJCm2AJx2yM+orc+2eMP8AoBaH/wCDmb/5FoAP+EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//HqPtnjD/oBaH/4OZv8A5Fo+2eMP+gFof/g5m/8AkWgA/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeo+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWgCPwNCtt4bkgQyFI9T1BFMkjOxAvJhyzElj7kknvXSVwfhO68VLo9wING0Z0/tO/JL6tKp3fa5twwLY8BsgHuADgZwNz7Z4w/wCgFof/AIOZv/kWgDoKK5/7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaAOgrn7P8A5KHrP/YKsP8A0bd0fbPGH/QC0P8A8HM3/wAi1T0GTUpfHWttqlpaW0/9mWIVLW5adSvm3fJZo0IOc8Y7DnngA6yuf8Zf8gO2/wCwrpv/AKWw10Fc/wCMv+QHbf8AYV03/wBLYaAOgooooAKKKKACiiigAooooAKKKKACiiigAooooA5+8/5KHo3/AGCr/wD9G2ldBXP3n/JQ9G/7BV//AOjbSugoA5/x3/yTzxL/ANgq6/8ARTV0Fc/47/5J54l/7BV1/wCimo/4QTwf/wBCpof/AILof/iaAOgorn/+EE8H/wDQqaH/AOC6H/4mqsfgrwudVngbwjoQt1hjeN/7Mj5YlgwztxxheOvPvQB1VFcxP4I8KJNaqnhHRGWSUq5GnRfKuxjk/L6gD8an/wCEE8H/APQqaH/4Lof/AImgDoKK5/8A4QTwf/0Kmh/+C6H/AOJo/wCEE8H/APQqaH/4Lof/AImgDoKK5/8A4QTwf/0Kmh/+C6H/AOJo/wCEE8H/APQqaH/4Lof/AImgDoKK5/8A4QTwf/0Kmh/+C6H/AOJo/wCEE8H/APQqaH/4Lof/AImgA8Pf8hzxZ/2FY/8A0ita6CuD0LwX4Vm1jxOkvhrRnSHU0SJWsIiEX7Jbthfl4G5mOB3JPetz/hBPB/8A0Kmh/wDguh/+JoA1L2zlupbV4rjyfIlEmNpO7sR1HVSw5z97PYVcrn/+EE8H/wDQqaH/AOC6H/4ms4eDfDv9um0/4Q7Q/snl+d5/9mRY/u+Xnb97OW+nHvQB0unWctlFKktx52+UyA7Su3OCRyT1bcfbdjtVyuf/AOEE8H/9Cpof/guh/wDiaP8AhBPB/wD0Kmh/+C6H/wCJoAPHf/JPPEv/AGCrr/0U1dBXB+NPBfhW18C+Ibi38NaNDPFply8ckdhErIwiYgghcgg85rc/4QTwf/0Kmh/+C6H/AOJoA6Ciuf8A+EE8H/8AQqaH/wCC6H/4msvTPB+gz315HeeC9AigjP7hxp0eXHmSLzlB/CqHjPXPegDtKK5//hBPB/8A0Kmh/wDguh/+Jo/4QTwf/wBCpof/AILof/iaAOgorn/+EE8H/wDQqaH/AOC6H/4mj/hBPB//AEKmh/8Aguh/+JoA6Ciuf/4QTwf/ANCpof8A4Lof/iaiuPBPhKGMMng7RpiWC7U0+HIBOM8r2oA6Wiucl8CeFPJfyfCmgebtOzfp0W3d2zhelEXgTwp5Ked4U0Dzdo37NOi2574yvSgDo6K5/wD4QTwf/wBCpof/AILof/iaP+EE8H/9Cpof/guh/wDiaAOgorn/APhBPB//AEKmh/8Aguh/+Jo/4QTwf/0Kmh/+C6H/AOJoA6Ciuf8A+EE8H/8AQqaH/wCC6H/4mj/hBPB//QqaH/4Lof8A4mgAs/8Akoes/wDYKsP/AEbd10FcHa+C/CreOtWt28NaMYE0yydIzYRbVZpboMQNuASFUE99o9K3P+EE8H/9Cpof/guh/wDiaAOgorn/APhBPB//AEKmh/8Aguh/+Jo/4QTwf/0Kmh/+C6H/AOJoA6Cuf8Q/8hzwn/2FZP8A0iuqP+EE8H/9Cpof/guh/wDiaw9d8F+FYdY8MJF4a0ZEm1N0lVbCIB1+yXDYb5eRuVTg9wD2oA7yqepWU99brFb6nd6e4cMZbVYmYjB+U+YjjHOemeBz1rL/AOEE8H/9Cpof/guh/wDiaP8AhBPB/wD0Kmh/+C6H/wCJoA6Ciuf/AOEE8H/9Cpof/guh/wDiaP8AhBPB/wD0Kmh/+C6H/wCJoAPHf/JPPEv/AGCrr/0U1dBXB+NPBfhW18C+Ibi38NaNDPFply8ckdhErIwiYgghcgg85rc/4QTwf/0Kmh/+C6H/AOJoA6Ciuf8A+EE8H/8AQqaH/wCC6H/4mj/hBPB//QqaH/4Lof8A4mgDoKK5/wD4QTwf/wBCpof/AILof/iaP+EE8H/9Cpof/guh/wDiaADwb/yA7n/sK6l/6WzV0FcH4T8F+FbnR7h5/DWjSuNTv0DPYRMQq3cyqOV6BQAB2AArUv8Awd4QstOubseENEk8iJ5Ng0+LLbQTj7vtQB1FQwWsNr5vkpt82QyPyTlj1PNc9aeDPCNzB5reD9Gi+d1CvpsQOFYgHBQHkDP41P8A8IJ4P/6FTQ//AAXQ/wDxNAHQVz9n/wAlD1n/ALBVh/6Nu6P+EE8H/wDQqaH/AOC6H/4mqeg6TpujeOtbt9L0+0sYG0yxdo7WFYlLebdjJCgDOABn2FAHWVz/AIy/5Adt/wBhXTf/AEthroK5/wAZf8gO2/7Cum/+lsNAHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/ef8lD0b/sFX/wD6NtK6CufvP+Sh6N/2Cr//ANG2ldBQBz/jv/knniX/ALBV1/6Kaj7Z4w/6AWh/+Dmb/wCRaPHf/JPPEv8A2Crr/wBFNR/wnfg//oa9D/8ABjD/APFUAH2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0f8J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VQAfbPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLR/wnfg//AKGvQ/8AwYw//FUf8J34P/6GvQ//AAYw/wDxVAB9s8Yf9ALQ/wDwczf/ACLUFvrHia6Z1t9K8PymMlXCa3KdpDMpB/0XsysPqDU//Cd+D/8Aoa9D/wDBjD/8VWdp/iHwNpl7e3UHi3Ry942+RW1KEgHczZHPH38fgO+SQDQF94uLFRomhEjqP7al4/8AJWl+2eMP+gFof/g5m/8AkWqFr4l8EWmp3t/H4t0cy3ZUuG1KEgYGOPm9qv8A/Cd+D/8Aoa9D/wDBjD/8VQAfbPGH/QC0P/wczf8AyLR9s8Yf9ALQ/wDwczf/ACLR/wAJ34P/AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FUAYehXXioax4nMWjaMznU0MobVpVCt9kt+FP2Y7ht2nJxySMcZO59s8Yf9ALQ//BzN/wDItYeheNPCsOseJ3l8S6MiTamjxM1/EA6/ZLdcr83I3KwyO4I7Vuf8J34P/wChr0P/AMGMP/xVAB9s8Yf9ALQ//BzN/wDItH2zxh/0AtD/APBzN/8AItH/AAnfg/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDBjD/8VQAfbPGH/QC0P/wczf8AyLR9s8Yf9ALQ/wDwczf/ACLR/wAJ34P/AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FUAYfjS68VN4F8QrcaNo0cB0y5Ejx6tK7KvlNkhTbAE47ZGfUVsJqfiqSaWFNG0JpIseYBrM3y5GQD/AKL1xz+I9ax/GnjTwrdeBfENvb+JdGmnl0y5SOOO/iZnYxMAAA2SSeMVrQ+MfBcE1zKnirQ99w4kcnUYeoVV/veij9aAJvtnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5Fo/4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof/gxh/wDiqAD7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaP+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16H/4MYf8A4qgA+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWj/hO/B//AENeh/8Agxh/+Ko/4Tvwf/0Neh/+DGH/AOKoAPtnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5Fo/4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof/gxh/wDiqAD7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaP+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16H/4MYf8A4qgA+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWj/hO/B//AENeh/8Agxh/+Ko/4Tvwf/0Neh/+DGH/AOKoAPtnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5Fo/4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof/gxh/wDiqAD7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaP+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16H/4MYf8A4qgDDtbrxV/wnWrMujaMZzplkHQ6tKFC+bdbSG+zZJJ3ZGBjA5OeNz7Z4w/6AWh/+Dmb/wCRaw7Xxp4VXx1q1w3iXRhA+mWSJIb+LazLLdFgDuwSAykjtuHrW5/wnfg//oa9D/8ABjD/APFUAH2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0f8J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VQAfbPGH/QC0P8A8HM3/wAi1h67deKjrHhgy6NoyuNTcxBdWlYM32S44Y/ZhtG3ccjPIAxzkbn/AAnfg/8A6GvQ/wDwYw//ABVYeu+NPCs2seGHi8S6M6Q6m7yst/EQi/ZLhct83A3Moye5A70Abn2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0f8J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VQAfbPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLR/wnfg//AKGvQ/8AwYw//FUf8J34P/6GvQ//AAYw/wDxVAGH40uvFTeBfEK3GjaNHAdMuRI8erSuyr5TZIU2wBOO2Rn1Fbn2zxh/0AtD/wDBzN/8i1h+NPGnhW68C+Ibe38S6NNPLplykccd/EzOxiYAABskk8Yrc/4Tvwf/ANDXof8A4MYf/iqAD7Z4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFo/wCE78H/APQ16H/4MYf/AIqj/hO/B/8A0Neh/wDgxh/+KoAPtnjD/oBaH/4OZv8A5Fo+2eMP+gFof/g5m/8AkWj/AITvwf8A9DXof/gxh/8AiqP+E78H/wDQ16H/AODGH/4qgDD8J3XipdHuBBo2jOn9p35JfVpVO77XNuGBbHgNkA9wAcDOBufbPGH/AEAtD/8ABzN/8i1h+E/GnhW20e4SfxLo0TnU79wr38Skq13MynluhUgg9wQa3P8AhO/B/wD0Neh/+DGH/wCKoAPtnjD/AKAWh/8Ag5m/+RaPtnjD/oBaH/4OZv8A5Fo/4Tvwf/0Neh/+DGH/AOKo/wCE78H/APQ16H/4MYf/AIqgA+2eMP8AoBaH/wCDmb/5FqnoMmpS+OtbbVLS0tp/7MsQqWty06lfNu+SzRoQc54x2HPPFz/hO/B//Q16H/4MYf8A4qqeg6tpus+OtbuNL1C0voF0yxRpLWZZVDebdnBKkjOCDj3FAHWVz/jL/kB23/YV03/0throK5/xl/yA7b/sK6b/AOlsNAHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/ef8lD0b/sFX//AKNtK6CufvP+Sh6N/wBgq/8A/RtpXQUAc/47/wCSeeJf+wVdf+imroK5/wAd/wDJPPEv/YKuv/RTUfY/GH/Qd0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Yf9B3Q/8AwTTf/JVAHQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP/Y/GH/Qd0P8A8E03/wAlUfY/GH/Qd0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVUMq+K4J7eF9d0XdOxRMaJMRkKW5P2rjgH8qAOmorn/sfjD/oO6H/4Jpv/AJKo+x+MP+g7of8A4Jpv/kqgA8Pf8hzxZ/2FY/8A0ita6CuD0K18VHWPE4i1nRlcamglLaTKwZvslvyo+0jaNu0YOeQTnnA3PsfjD/oO6H/4Jpv/AJKoA6Ciuf8AsfjD/oO6H/4Jpv8A5KqMR+Kjcm2HiHQTMEEhj/seXIUkgH/j66ZB/I0AdJRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE03/yVQAeO/8AknniX/sFXX/opq6CuD8aWvipfAviFrjWdGkgGmXJkSPSZUZl8psgMbkgHHfBx6Gtz7H4w/6Duh/+Cab/AOSqAOgorn/sfjD/AKDuh/8Agmm/+SqPsfjD/oO6H/4Jpv8A5KoA6CoriV4YwyQvMSwXamMgE4zz6VifY/GH/Qd0P/wTTf8AyVR9j8Yf9B3Q/wDwTTf/ACVQBtXX2j7O32TyvP42+bnb15zjnpmpq5/7H4w/6Duh/wDgmm/+SqPsfjD/AKDuh/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoKK5/7H4w/6Duh/wDgmm/+SqPsfjD/AKDuh/8Agmm/+SqAOgorn/sfjD/oO6H/AOCab/5Ko+x+MP8AoO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgAs/+Sh6z/2CrD/0bd10FcHa2vir/hOtWVdZ0YTjTLIu50mUqV8262gL9pyCDuycnORwMc7n2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/8lUAdBXP+If+Q54T/wCwrJ/6RXVH2Pxh/wBB3Q//AATTf/JVYeu2vioax4YEus6MznU3ERXSZVCt9kuOWH2k7ht3DAxyQc8YIB3lFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Yf9B3Q/8AwTTf/JVAHQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAHjv8A5J54l/7BV1/6Kaugrg/Glr4qXwL4ha41nRpIBplyZEj0mVGZfKbIDG5IBx3wcehrc+x+MP8AoO6H/wCCab/5KoA6Ciuf+x+MP+g7of8A4Jpv/kqj7H4w/wCg7of/AIJpv/kqgDoKK5/7H4w/6Duh/wDgmm/+SqPsfjD/AKDuh/8Agmm/+SqADwb/AMgO5/7Cupf+ls1dBXB+E7XxU2j3Bg1nRkT+078EPpMrHd9rm3HIuRwWyQOwIGTjJ3PsfjD/AKDuh/8Agmm/+SqAOgorm54vFdrby3E/iDQUiiQu7nRpsKoGSf8Aj69KZbf8JTdiTydf0QmN9jq2iTqVbAOCDcgjgg/jQB09c/Z/8lD1n/sFWH/o27o+x+MP+g7of/gmm/8Akqqegx6lF461tdUu7S5n/syxKva2zQKF8274KtI5JznnPccccgHWVz/jL/kB23/YV03/ANLYa6Cuf8Zf8gO2/wCwrpv/AKWw0AdBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz95/wAlD0b/ALBV/wD+jbSugrn7z/koejf9gq//APRtpXQUAc/47/5J54l/7BV1/wCimo/4Tvwf/wBDXof/AIMYf/iqPHf/ACTzxL/2Crr/ANFNXQUAc/8A8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVXQUUAc/8A8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVXQUUAc/8A8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVXQUUAc/8A8J34P/6GvQ//AAYw/wDxVQT+L/BNzNbyy+KtFL27mSPbqkagMQV5AfB4J656109FAHP/APCd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD/wDFV0FFAHB6F408Kw6x4neXxLoyJNqaPEzX8QDr9kt1yvzcjcrDI7gjtW5/wnfg/wD6GvQ//BjD/wDFUeHv+Q54s/7Csf8A6RWtdBQBz/8Awnfg/wD6GvQ//BjD/wDFVGPGvgxbh5x4p0PzHVUZv7Ri5CkkD73+0fzrpKKAOf8A+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16H/4MYf8A4qugooA4Pxp408K3XgXxDb2/iXRpp5dMuUjjjv4mZ2MTAAANkknjFbn/AAnfg/8A6GvQ/wDwYw//ABVHjv8A5J54l/7BV1/6KaugoA5//hO/B/8A0Neh/wDgxh/+Ko/4Tvwf/wBDXof/AIMYf/iq6CigDn/+E78H/wDQ16H/AODGH/4qj/hO/B//AENeh/8Agxh/+KravJpLezmliiaWVUJSNRyzdh+dLaytPaQzPE0TugZo26oSOQfpQBif8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVW3btO0ZNxGiPuIARtwxng9PSkjjmW4md598T7fLj2AeXgc8980AYv/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHB2vjTwqvjrVrhvEujCB9MskSQ38W1mWW6LAHdgkBlJHbcPWtz/hO/B//Q16H/4MYf8A4qiz/wCSh6z/ANgqw/8ARt3XQUAc/wD8J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VXQUUAc/wD8J34P/wChr0P/AMGMP/xVYeu+NPCs2seGHi8S6M6Q6m7yst/EQi/ZLhct83A3Moye5A713lc/4h/5DnhP/sKyf+kV1QAf8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVXQUUAc/8A8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVXQUUAcH408aeFbrwL4ht7fxLo008umXKRxx38TM7GJgAAGySTxitz/hO/B/8A0Neh/wDgxh/+Ko8d/wDJPPEv/YKuv/RTV0FAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHB+E/Gnh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null,
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null,
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null,
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null,
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null,
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null,
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null,
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null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.6765852,"math_prob":0.96484154,"size":2875,"snap":"2021-21-2021-25","text_gpt3_token_len":1246,"char_repetition_ratio":0.13862766,"word_repetition_ratio":0.5016077,"special_character_ratio":0.45669565,"punctuation_ratio":0.14447592,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97846335,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28],"im_url_duplicate_count":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-06-18T05:31:55Z\",\"WARC-Record-ID\":\"<urn:uuid:c391494f-71f3-4620-aeb5-703c82198ca1>\",\"Content-Length\":\"1049008\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:9395bb65-d27c-4ba8-a9c6-680e9703b47e>\",\"WARC-Concurrent-To\":\"<urn:uuid:c46e3815-ee96-4d6f-9f92-b68de9e942f8>\",\"WARC-IP-Address\":\"51.79.18.113\",\"WARC-Target-URI\":\"https://pdf.datasheet.live/b163be37/fairchildsemi.com/LM7810CT.html\",\"WARC-Payload-Digest\":\"sha1:VKCS5HCM6UAXBUH46FPEO6D2V53VHVY6\",\"WARC-Block-Digest\":\"sha1:NPHZPBS57YDY2ZMWUL42FY2KLBHRCGDK\",\"WARC-Truncated\":\"length\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-25/CC-MAIN-2021-25_segments_1623487635724.52_warc_CC-MAIN-20210618043356-20210618073356-00563.warc.gz\"}"} |
https://findthefactors.com/2018/08/05/1173-challenge-puzzle/ | [
"# 1173 Challenge Puzzle\n\nGetting started on this Challenge Puzzle will take some thinking, but solving it is worth all the effort. Remember use logic, not guess and check, and you will eventually be successful!",
null,
"Print the puzzles or type the solution in this excel file: 12 factors 1161-1173\n\nHere’s some information about the number 1173:\n\n• 1173 is a composite number.\n• Prime factorization: 1173 = 3 × 17 × 23\n• The exponents in the prime factorization are 1, 1, and 1. Adding one to each and multiplying we get (1 + 1)(1 + 1)(1 + 1) = 2 × 2 × 2 = 8. Therefore 1173 has exactly 8 factors.\n• Factors of 1173: 1, 3, 17, 23, 51, 69, 391, 1173\n• Factor pairs: 1173 = 1 × 1173, 3 × 391, 17 × 69, or 23 × 51\n• 1173 has no square factors that allow its square root to be simplified. √1173 ≈ 34.24909",
null,
"1173 is the hypotenuse of a Pythagorean triple:\n552-1035-1173 which is (8-15-17) times 69\n\n1173 is palindrome 3B3 in BASE 18 (B is 11 base 10)\nbecause 3(18²) + 11(18) + 3(1) = 1173"
] | [
null,
"https://i1.wp.com/findthefactors.com/wp-content/uploads/2018/08/1173-Puzzle.jpg",
null,
"https://i2.wp.com/findthefactors.com/wp-content/uploads/2018/08/1173-Factor-Pairs.jpg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.84650457,"math_prob":0.9940101,"size":962,"snap":"2019-51-2020-05","text_gpt3_token_len":336,"char_repetition_ratio":0.10542797,"word_repetition_ratio":0.0,"special_character_ratio":0.43347195,"punctuation_ratio":0.14009662,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9912364,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,6,null,8,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-01-25T06:09:09Z\",\"WARC-Record-ID\":\"<urn:uuid:32b0439b-507a-4eba-8fec-d8f4056298e0>\",\"Content-Length\":\"42898\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:5ab7e71a-7b67-48e1-b586-2d31efc3cc57>\",\"WARC-Concurrent-To\":\"<urn:uuid:05d48c36-f1bd-413f-9b0c-2b48653a08f9>\",\"WARC-IP-Address\":\"192.0.78.153\",\"WARC-Target-URI\":\"https://findthefactors.com/2018/08/05/1173-challenge-puzzle/\",\"WARC-Payload-Digest\":\"sha1:LFJ6DRCLUQRIJPD6KDIDCIWJ4XOGFY3V\",\"WARC-Block-Digest\":\"sha1:FLXOXXNFVECMHQVP3OIYK3MLVQQL45SM\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-05/CC-MAIN-2020-05_segments_1579251669967.70_warc_CC-MAIN-20200125041318-20200125070318-00499.warc.gz\"}"} |
https://patches.linaro.org/patch/124090/ | [
"# [v3,06/18] string: Improve generic memchr\n\nMessage ID 1515588482-15744-7-git-send-email-adhemerval.zanella@linaro.org New show Improve generic string routines show\n\n## Commit Message\n\nAdhemerval Zanella Jan. 10, 2018, 12:47 p.m.\n```From: Richard Henderson <rth@twiddle.net>\n\nNew algorithm have the following key differences:\n\nremove unwanted data. This strategy follow assemble optimized\nones for aarch64, powerpc and tile.\n\n- Use string-fz{b,i} and string-opthr functions.\n\nChecked on x86_64-linux-gnu, i686-linux-gnu, sparc64-linux-gnu,\nand sparcv9-linux-gnu by removing the arch-specific assembly\nimplementation and disabling multi-arch (it covers both LE and BE\nfor 64 and 32 bits).\n\n[BZ #5806]\n* string/memchr.c: Use string-fzb.h, string-fzi.h, string-opthr.h.\n---\nstring/memchr.c | 157 +++++++++++++++-----------------------------------------\n1 file changed, 40 insertions(+), 117 deletions(-)\n\n--\n2.7.4\n```\n\n## Patch\n\n```diff --git a/string/memchr.c b/string/memchr.c\nindex c4e21b8..ae3fd93 100644\n--- a/string/memchr.c\n+++ b/string/memchr.c\n@@ -20,24 +20,16 @@\nLicense along with the GNU C Library; if not, see\n\n-#ifndef _LIBC\n-# include <config.h>\n-#endif\n-\n#include <string.h>\n-\n#include <stddef.h>\n+#include <stdint.h>\n+#include <string-fza.h>\n+#include <string-fzb.h>\n+#include <string-fzi.h>\n+#include <string-opthr.h>\n\n-#include <limits.h>\n-\n-#undef __memchr\n-#ifdef _LIBC\n-# undef memchr\n-#endif\n-\n-#ifndef weak_alias\n-# define __memchr memchr\n-#endif\n+#undef memchr\n\n#ifndef MEMCHR\n# define MEMCHR __memchr\n@@ -47,116 +39,47 @@\nvoid *\nMEMCHR (void const *s, int c_in, size_t n)\n{\n- /* On 32-bit hardware, choosing longword to be a 32-bit unsigned\n- long instead of a 64-bit uintmax_t tends to give better\n- performance. On 64-bit hardware, unsigned long is generally 64\n- bits already. Change this typedef to experiment with\n- performance. */\n- typedef unsigned long int longword;\n-\n- const unsigned char *char_ptr;\n- const longword *longword_ptr;\n- longword repeated_one;\n- longword repeated_c;\n- unsigned char c;\n-\n- c = (unsigned char) c_in;\n-\n- /* Handle the first few bytes by reading one byte at a time.\n- Do this until CHAR_PTR is aligned on a longword boundary. */\n- for (char_ptr = (const unsigned char *) s;\n- n > 0 && (size_t) char_ptr % sizeof (longword) != 0;\n- --n, ++char_ptr)\n- if (*char_ptr == c)\n- return (void *) char_ptr;\n-\n- longword_ptr = (const longword *) char_ptr;\n-\n- /* All these elucidatory comments refer to 4-byte longwords,\n- but the theory applies equally well to any size longwords. */\n-\n- /* Compute auxiliary longword values:\n- repeated_one is a value which has a 1 in every byte.\n- repeated_c has c in every byte. */\n- repeated_one = 0x01010101;\n- repeated_c = c | (c << 8);\n- repeated_c |= repeated_c << 16;\n- if (0xffffffffU < (longword) -1)\n- {\n- repeated_one |= repeated_one << 31 << 1;\n- repeated_c |= repeated_c << 31 << 1;\n- if (8 < sizeof (longword))\n-\t{\n-\t size_t i;\n-\n-\t for (i = 64; i < sizeof (longword) * 8; i *= 2)\n-\t {\n-\t repeated_one |= repeated_one << i;\n-\t repeated_c |= repeated_c << i;\n-\t }\n-\t}\n- }\n+ const op_t *word_ptr, *lword;\n+ const char *lbyte;\n+ char *ret;\n+ uintptr_t s_int;\n\n- /* Instead of the traditional loop which tests each byte, we will test a\n- longword at a time. The tricky part is testing if *any of the four*\n- bytes in the longword in question are equal to c. We first use an xor\n- with repeated_c. This reduces the task to testing whether *any of the\n- four* bytes in longword1 is zero.\n-\n- We compute tmp =\n- ((longword1 - repeated_one) & ~longword1) & (repeated_one << 7).\n- That is, we perform the following operations:\n- 1. Subtract repeated_one.\n- 2. & ~longword1.\n- 3. & a mask consisting of 0x80 in every byte.\n- Consider what happens in each byte:\n- - If a byte of longword1 is zero, step 1 and 2 transform it into 0xff,\n-\t and step 3 transforms it into 0x80. A carry can also be propagated\n-\t to more significant bytes.\n- - If a byte of longword1 is nonzero, let its lowest 1 bit be at\n-\t position k (0 <= k <= 7); so the lowest k bits are 0. After step 1,\n-\t the byte ends in a single bit of value 0 and k bits of value 1.\n-\t After step 2, the result is just k bits of value 1: 2^k - 1. After\n-\t step 3, the result is 0. And no carry is produced.\n- So, if longword1 has only non-zero bytes, tmp is zero.\n- Whereas if longword1 has a zero byte, call j the position of the least\n- significant zero byte. Then the result has a zero at positions 0, ...,\n- j-1 and a 0x80 at position j. We cannot predict the result at the more\n- significant bytes (positions j+1..3), but it does not matter since we\n- already have a non-zero bit at position 8*j+7.\n-\n- So, the test whether any byte in longword1 is zero is equivalent to\n- testing whether tmp is nonzero. */\n-\n- while (n >= sizeof (longword))\n- {\n- longword longword1 = *longword_ptr ^ repeated_c;\n\n- if ((((longword1 - repeated_one) & ~longword1)\n-\t & (repeated_one << 7)) != 0)\n-\tbreak;\n- longword_ptr++;\n- n -= sizeof (longword);\n- }\n+ if (__glibc_unlikely (n == 0))\n+ return NULL;\n+\n+ s_int = (uintptr_t) s;\n+ word_ptr = (const op_t*) (s_int & -sizeof (op_t));\n\n- char_ptr = (const unsigned char *) longword_ptr;\n+ /* Set up a word, each of whose bytes is C. */\n+ repeated_c = repeat_bytes (c_in);\n\n- /* At this point, we know that either n < sizeof (longword), or one of the\n- sizeof (longword) bytes starting at char_ptr is == c. On little-endian\n- machines, we could determine the first such byte without any further\n- memory accesses, just by looking at the tmp result from the last loop\n- iteration. But this does not work on big-endian machines. Choose code\n- that works in both cases. */\n+ /* Compute the address of the last byte taking in consideration possible\n+ overflow. */\n+ uintptr_t lbyte_int = s_int + n - 1;\n+ lbyte_int |= -(lbyte_int < s_int);\n+ lbyte = (const char *) lbyte_int;\n\n- for (; n > 0; --n, ++char_ptr)\n+ /* Compute the address of the word containing the last byte. */\n+ lword = (const op_t *) ((uintptr_t) lbyte & -sizeof (op_t));\n+\n+ /* Read the first word, but munge it so that bytes before the array\n+ will not match goal. */\n+\n+ while (has_eq (word, repeated_c) == 0)\n{\n- if (*char_ptr == c)\n-\treturn (void *) char_ptr;\n+ if (word_ptr == lword)\n+\treturn NULL;\n+ word = *++word_ptr;\n}\n\n- return NULL;\n+ /* We found a match, but it might be in a byte past the end\n+ of the array. */\n+ ret = (char *) word_ptr + index_first_eq (word, repeated_c);\n+ return (ret <= lbyte) ? ret : NULL;\n}\n-#ifdef weak_alias\nweak_alias (__memchr, memchr)\n-#endif\nlibc_hidden_builtin_def (memchr)\n\n```"
] | [
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https://pdf4pro.com/tag/442c/mathematical.html | [
"Example: tourism industry\n\n# Search results with tag \"Mathematical\"\n\n### Some Remarks on Writing Mathematical Proofs\n\nsites.math.washington.edu\n\nSome Remarks on Writing Mathematical Proofs John M. Lee University of Washington Mathematics Department Writingmathematicalproofsis,inmanyways,unlikeanyotherkindofwriting.\n\n### Mathematics programmes of study: key stage 4\n\nassets.publishing.service.gov.uk\n\nstage 4 is organised into apparently distinct domains, but pupils should develop and consolidate connections across mathematical ideas. They should build on learning from key stage 3 to further develop fluency, mathematical reasoning and competence in solving increasingly sophisticated problems. They should also apply their mathematical\n\n### Geometric Constructions - Mathematical and Statistical ...\n\nwww-math.ucdenver.edu\n\npublished some notes that De Morgan had been preparing for a book, called A Budget of Paradoxes. A logician and teacher, De Morgan had been the first chair in mathematics of London University (from 1828). Besides his mathematical work, he wrote many reviews and expository articles and much on teaching mathematics.\n\n### 19th Bay Area Mathematical Olympiad BAMO-8 Exam\n\nwww.bamo.org\n\n19th Bay Area Mathematical Olympiad BAMO-8 Exam February 28, 2017 The time limit for this exam is 4 hours. Your solutions should be clearly written arguments.\n\n### ADVANCED MATHEMATICAL THINKING - Math League\n\nlhsmathleague.weebly.com\n\nADVANCED MATHEMATICAL THINKING Edited by DAVID TALL Science Education Department, University of Warwick KLUWER ACADEMIC PUBLISHERS NEW YORK / BOSTON / DORDRECHT / LONDON / MOSCOW\n\n### NYS K-8 Social Studies Framework - New York State ...\n\nwww.nysed.gov\n\neconomics, and/or government. Recognize forms of evidence used to make meaning in social studies. ... mathematical skills to measure time in years and centuries Employ mathematical skills to measure ... and models. Describe where places are in relation to each other and describe connections\n\n### Jesus Christ is Messiah Scientific Mathematical Proof\n\nwww.technicalindicatorindex.com\n\nJesus Christ is Messiah Scientific Mathematical Proof The following is Chapter 3 from the book, Science Speaks, a Scientific Proof of the Accuracy of Prophecy and the Bible, by Peter W. Stoner, Ph.D., Assisted by Robert C. Newman, Ph.D., Moody Press. Chapter 3 THE CHRIST OF PROPHECY\n\n### The Cattell-Horn-Carroll (CHC) Model of Intelligence v2.2 ...\n\nwww.iapsych.com\n\nGeneral Intelligence (g)Mathematical knowledge (KM) Mathematical achievement (A3) Reading decoding (RD) Reading comprehension (RC) Reading speed (RS)\n\n### Lecture Notes on Special Relativity - Macquarie University\n\nphysics.mq.edu.au\n\nThe laws of physics take the same mathematical form in all frames of reference moving with constant velocity with respect to one another. Explicitly recognized in this statement is the empirical fact that the laws of nature, almost without exception, can be expressed in the form of mathematical equations. Why this should be so is a\n\n### Euclid's Elements of Geometry\n\nfarside.ph.utexas.edu\n\nEuclid’s Elements is by far the most famous mathematical work of classical antiquity, and also has the distinction of being the world’s oldest continuously used mathematical textbook. Little is known about the author, beyond the fact that he lived in Alexandria around 300 BCE. The main subjects of the work are geometry, proportion, and\n\n### Foundations and Mathematical - U.S. Satellite\n\nwww.us-satellite.net\n\nFoundations and Mathematical . ... 2-Liter Bottle Rocket Launcher will help students to explore the science of rocketry. Students build &propel 2-liter bottle rockets to altitudes up to 400 feet with ... meter distance is sufficient for model rockets. Altitude. 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The discrete Fourier transform (DFT) is the family member used with digitized\n\nwww.georgiastandards.org\n\nGeorgia Department of Education Georgia Department of Education July 2018 • Page 4 of 75 All Rights Reserved STANDARDS FOR MATHEMATICAL PRACTICE\n\n### STEM Occupations: Past, Present, And Future - U.S. Bureau ...\n\nwww.bls.gov\n\nU.S. BUREAU OF LABOR STATISTICS Spotlight on Statistics Page 10 Projected growth rates for types of STEM occupations The STEM group that is projected to grow fastest from 2014 to 2024 is the mathematical science occupations group at 28.2 percent, compared with the average projected growth for all occupations of 6.5 percent. This group includes\n\nwww.ucm.es\n\n° Criminology ° Economics * ° Finance, Banking and Insurance ° Geography and Spatial Planning ... ° Mathematical Engineering, Statistics and Operational Research ** International Degree Programmes ... Computational Modelling ** {Health Sciences Biomedical Research ° Dental Sciences Health Care\n\n### The Ontario Curriculum, Grades 9 and 10: The Arts, 2010\n\nwww.edu.gov.on.ca\n\nin musical structure can be related to mathematical principles. Mathematics skills can be applied to drafting a stage set to scale, or to budgeting an arts performance. Students taking a history course can attempt to bring an event in the past to life by reinterpreting it in their work in drama.\n\n### Developing young children's mathematical thinking and ...\n\nwww.du.edu\n\nchildren explicate and fun:hcr develop these intuitive ideas by discussing thelll, giving language to their actions. Like Pratt, we bdicve that 'doing mathcmdtics' is natural and appropriate fix chil\n\n### A Brief Introduction to Stochastic Calculus\n\nwww.columbia.edu\n\nIEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh A Brief Introduction to Stochastic Calculus These notes provide a very brief introduction to stochastic calculus, the branch of mathematics that is most identi ed with nancial engineering and mathematical nance. We will ignore most of the technical details and\n\n### GCSE (9-1) Design and Technology - Edexcel\n\nqualifications.pearson.com\n\nguidance on delivering mathematical skills. Preparing for exams ... prototypes (together with evidence of modelling to develop and prove product concept and function) that solve real and relevant problems, c onsidering their own and others’ needs, wants and values. It gives students opportunities to apply knowledge from other disciplines,\n\n### Applications of Taylor Series\n\nsces.phys.utk.edu\n\nTaylor’s series is an essential theoretical tool in computational science and ... In the field of physics and chemistry, there is a great need for geometric optimization of physical systems. In chemistry, as an example, the quasi-newton method make ... Mathematical Methods for Physicists, ...\n\n### Integer Programming 9\n\nweb.mit.edu\n\nInteger-programming models arise in practically every area of application of mathematical programming. To develop a preliminary appreciation for the importance of these models, we introduce, in this section, three areas where integer programming has played an important role in supporting managerial decisions. We do\n\n### Physics 1011/2111 Mechanics\n\numsl.edu\n\nPhysics 1011/2111 Labs ~ General Guidelines ... The mathematical tools we will learn in this lab will answer some of these questions. They are some of the most basic methods of statistical analysis; they will allow us to give information about our measurements in a standard, concise way, …\n\n### The Measurement of Environmental - econdse.org\n\necondse.org\n\nthe book is the rigorous linkage between economic theory and the practice of ... or registered trademarks, and are used only for identification and explanation without intent to infringe. ... Mathematical Notation 17 2 Measuring Values, Benefits, and Costs: An Overview 20 ...\n\n### SS - AMSI\n\nwww.amsi.org.au\n\nProject 2009‑2011 was funded by the Australian Government . Department of Education, Employment and Workplace Relations. The views expressed here are those of the author and do not ... the education division of the Australian Mathematical Sciences Institute (AMSI), 2010 …\n\n### The Physics of Quantum Mechanics\n\nwww-thphys.physics.ox.ac.uk\n\ntum mechanics to second-year students of physics at Oxford University. We have tried to convey to students that it is the use of probability amplitudes rather than probabilities that makes quantum mechanics the extraordinary thing that it is, and to grasp that the theory’s mathematical structure follows\n\n### INFORMATION ON ARCHITECTURE AND ENGINEERING …\n\nwww.lapels.com\n\nJul 01, 2012 · ability to apply the mathematical, physical, and engineering sciences and the principles and methods of engineering analysis and design, acquired by an engineering education and engineering experience, is qualified to practice engineering, as evidenced by his licensure as such by the board. . . .\n\n### Introduction to Macroeconomics Lecture Notes\n\nhomepage.univie.ac.at\n\nThese assumptions are used in order to build macroeconomic models.Typi-cally, such models have three aspects: the ‘story’, the mathematical model, and a graphical representation. Macroeconomics is ‘non-experimental’: like, e.g., history, macro-economics cannot conduct controlled scienti fic experiments (people would\n\n### A Level Further Mathematics - Edexcel\n\nqualifications.pearson.com\n\nsolving, proof and mathematical modelling will be assessed in further mathematics in the context of the wider knowledge which students taking A level further mathematics will have studied. The Pearson Edexcel Level 3 Advanced GCE in Further Mathematics consists of four\n\n### COURSES YOU CAN DO WHEN YOU WANT TO GET A MATRIC ...\n\nwww.damtraining.co.za\n\nLevel 2 – 4 Fundamental subjects (compulsory) 1. English 2. Mathematics or Mathematical Literacy 3. Life Orientation . Level 2 Level 3 Level 4 . Electrical Principles & Practice Electrical Control & Workshop Practices . Construction OR\n\n### MATHEMATICS IN CONTEXT\n\nmcc.edc.org\n\nof initial mathematical concepts1; methods of evaluat-ing both students and programs2; and an integration of research on teaching, curricu-lum, and student thinking3. Thomas A. Romberg Developing Mathematics in Context(MiC) There were several influences on why and how we developed Mathematics in Context.\n\nnie.lk\n\n3.1 – Grade 12 Unit 1 - Measurement (30 periods) Competency Competency Level Content Learning outcomes No. of Periods 1. Uses experimental and mathematical frames in physics for systematic explorations. 1.1 Inquires the scope of physics and how to use the scientific methodology for explorations.\n\n### Probability Theory: STAT310/MATH230 April15,2021\n\nstatweb.stanford.edu\n\nThis chapter is devoted to the mathematical foundations of probability theory. Section 1.1 introduces the basic measure theory framework, namely, the probability space and the σ-algebras of events in it. The next building blocks are random variables, introduced in Section 1.2 as measurable functions ω→ X(ω) and their distribution.\n\n### Mathematical Modeling in Agricultural Economics\n\nwww.eolss.net\n\nUNESCO – EOLSS SAMPLE CHAPTERS MATHEMATICAL MODELS IN ECONOMICS – Vol. II - Mathematical Modeling in Agricultural Economics - Richard E. Just ©Encyclopedia of Life Support Systems (EOLSS) The discipline of agricultural economics has …\n\n### Mathematical and Drug Calculation Skills of Nursing ...\n\nwww.internationaljournalofcaringsciences.org\n\ndrug dose calculations difficult, and many studies indicate that nursing students have poor medication dose calculation and arithmetic skills. ... learning mathematical and drug calculation skills. In this study, the students were found to have insufficient mathematical skills, and\n\n### Mathematical Biology - Hong Kong University of Science and ...\n\nwww.math.ust.hk\n\nPreface What follows are my lecture notes for Math 4333: Mathematical Biology, taught at the Hong Kong University of Science and Technology. This applied mathematics\n\n### Mathematical Modeling of Plastic Injection Mould\n\nwww.ijste.org\n\nMathematical Modeling of Plastic Injection Mould (IJSTE/ Volume 2 / Issue 10 / 216) All rights reserved by www.ijste.org 1209 Determination of number of cavities by ...\n\n### Mathematical Formula Handbook\n\nhomepage.ntu.edu.tw\n\nThis Mathematical Formaulae handbook has been prepared in response to a request from the Physics Consultative Committee, with the hope that it will be useful to those studying physics. It is to some extent modelled on a similar document issued by the Department of Engineering, but obviously reects the particular interests of physicists.\n\n### Mathematical Statisticians at the Bureau of Labor Statistics\n\nwww.bls.gov\n\nA politically independent agency within the Department of Labor. Part of the Federal statistical system that includes the Bureau of Economic Analysis (BEA) and the Census Bureau."
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https://www.gradesaver.com/textbooks/math/precalculus/precalculus-6th-edition-blitzer/chapter-p-section-p-3-radicals-and-rational-exponents-exercise-set-page-46/97 | [
"## Precalculus (6th Edition) Blitzer\n\n$5x^{2}|y|^{3}$\n$(25x^{4}y^{6})^{1/2}=\\qquad$... apply $(ab)^{n}=a^{n}b^{n}$ $=25^{1/2}\\cdot(x^{4})^{1/2}\\cdot(y^{6})^{1/2}$ $a^{1/2}=\\sqrt{a}$, so we need absolute values, $\\sqrt{a^{2}}=|a|$ apply $(a^{m})^{n}=a^{mn}$ with this in mind. $=5\\cdot|x^{2}|\\cdot|y^{3}|$ a square is never negative, so we can drop the absolute value for $x^{2}$: $=5x^{2}|y|^{3}$"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.5066381,"math_prob":1.0000099,"size":383,"snap":"2020-10-2020-16","text_gpt3_token_len":181,"char_repetition_ratio":0.124010555,"word_repetition_ratio":0.0,"special_character_ratio":0.5430809,"punctuation_ratio":0.08510638,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":1.0000036,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-04-04T00:18:40Z\",\"WARC-Record-ID\":\"<urn:uuid:4a0280e7-2e53-4760-9f54-0ece16b093ba>\",\"Content-Length\":\"85149\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:547ac26b-666a-4720-ba41-e2c799aa8186>\",\"WARC-Concurrent-To\":\"<urn:uuid:3e4f2463-a925-4f2e-8302-b51f8101c304>\",\"WARC-IP-Address\":\"54.210.73.90\",\"WARC-Target-URI\":\"https://www.gradesaver.com/textbooks/math/precalculus/precalculus-6th-edition-blitzer/chapter-p-section-p-3-radicals-and-rational-exponents-exercise-set-page-46/97\",\"WARC-Payload-Digest\":\"sha1:ILDU33EHZ6BEPA7RH7ULKKHM5NCG277C\",\"WARC-Block-Digest\":\"sha1:NSLKAW3RGXWVCDP4XKXFWUT66MJOOFYY\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-16/CC-MAIN-2020-16_segments_1585370518767.60_warc_CC-MAIN-20200403220847-20200404010847-00132.warc.gz\"}"} |
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/223/ | [
"## Graduate Research Theses & Dissertations\n\n1991\n\n#### Document Type\n\nDissertation/Thesis\n\n#### Degree Name\n\nM.S. (Master of Science)\n\n#### Legacy Department\n\nDepartment of Mechanical Engineering\n\n#### LCSH\n\nElectronic apparatus and appliances--Cooling--Mathematical models\n\n#### Abstract\n\nA computational description of heat transfer dissipation in printed circuit boards is developed in this work. Accordingly, a mathematical model that describes forced convection cooling of a periodic array of heated modules attached to a wall of a parallel channel is presented. The model deals with a two-dimensional, laminar, newtonian, and incompressible flow which has constant thermodynamic properties; these conditions simplify the derivation of the governing physical laws. After all the conditions involved in the problem are reduced to a set of differential equations, a convenient method of numerical discretization is used to transform these differential equations into a set of linear algebraic equations. Because of the geometric characteristics, a finite difference scheme that employs a control volume approach is chosen. An algorithm is introduced to solve the continuity and the momentum equations linking pressure and velocity; then the velocity field, found previously, is used as the input parameter of the energy equation to determine the temperature distribution of the flow. The computational code is used to carry out a parametric study using different conditions and geometric parameters. Comparisons are made with other available predictions and experimental information.\n\nIncludes bibliographical references (pages -86)\n\nix, 86 pages\n\neng\n\n#### Publisher\n\nNorthern Illinois University"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.84846383,"math_prob":0.85630333,"size":2206,"snap":"2023-40-2023-50","text_gpt3_token_len":432,"char_repetition_ratio":0.09627611,"word_repetition_ratio":0.0,"special_character_ratio":0.17271079,"punctuation_ratio":0.10144927,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97559303,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-21T18:34:24Z\",\"WARC-Record-ID\":\"<urn:uuid:3618a93d-2f1d-4bf1-bf70-3cb7dc646ba6>\",\"Content-Length\":\"36479\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:1eeb22f2-e21d-4ab7-a1d7-1113da69b03b>\",\"WARC-Concurrent-To\":\"<urn:uuid:40cff4af-8f3c-464e-991a-c51f339c19d2>\",\"WARC-IP-Address\":\"50.18.241.247\",\"WARC-Target-URI\":\"https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/223/\",\"WARC-Payload-Digest\":\"sha1:VK424VTJU43245UNIIVDZB3NYN35IBE7\",\"WARC-Block-Digest\":\"sha1:O3OCD7PSE5I627HPMMY5GXQE27LUAPJX\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233506029.42_warc_CC-MAIN-20230921174008-20230921204008-00721.warc.gz\"}"} |
https://cemse.kaust.edu.sa/amcs/events/event/numerical-approximation-pdes-complex-geometries-fluid-structure-interaction-problems | [
"# Numerical approximation of PDEs on complex geometries: fluid-structure interaction problems and virtual element methods",
null,
"##### Location\nhttps://kaust.zoom.us/j/93585330887\n\n#### Abstract\n\nThe studies in numerical approximation of partial differential equations are characterized by the necessity of managing complex geometries and their discretization. We focus our attention on two different fields where complex geometries are very common: the mathematical modeling of fluid-structure interaction problems and the family of virtual element methods.\n\nWe first consider the finite element approximation of fluid-structure interaction problems described by a distributed Lagrange multiplier formulation. A crucial aspect of the method is the assembly of the finite element matrix describing the coupling between fluid and structure. This procedure consists in integrating over the solid domain both solid and fluid basis functions, which are defined on two different non-matching grids. This can be done in two ways: the exact approach is based on a composite quadrature rule defined on the intersection between the two involved meshes, whereas the approximate approach is carried out roughly integrating on each solid element. We discuss and compare these two approaches both from the theoretical and computational point of view. Moreover, we present a preliminary study on a parallel solver for our fictitious domain formulation. As a first step towards the design of an effective solver, we consider block preconditioners, which are tested on two simplified academic problems and compared in terms of optimality, weak and strong scalability.\n\nVirtual element methods are known to tackle complex geometries without limitations on the degree of the polynomial that partially contributes to the approximated solution. An important aspect of this method is that we are not required to explicitly compute the basis functions of the VEM space since these are solutions of PDEs. Due to this fact, several quantities are not computed exactly, such as the bilinear form, for which the action on the nonpolynomial part is handled by a stabilization term, and the error, where the contribution of the nonpolynomial part is neglected. In certain cases, such as when anisotropic problems are considered, the method may show poor performance if endowed with standard stabilization terms, which have an isotropic structure. We propose a model order reduction technique constructed by means of the reduced basis method for efficiently solving the equation associated with each virtual basis function. The idea is to replace the stabilization term with an actual approximation of the nonpolynomial contribution. We show that this operation produces good results even if done in a very rough way.\n\n#### Brief Biography\n\nFabio Credali is a Ph.D. candidate in the Applied Mathematics program at KAUST, in cotutelle with the joint Ph.D. program in Computational Mathematics at University of Pavia (Italy) and Universita' della Svizzera Italiana (Switzerland), under the supervision of Daniele Boffi and Silvia Bertoluzza. He obtained the Bachelor degree (2018) and the Master degree (2020) in Mathematics from the University of Pavia, under the supervision of Daniele Boffi.\n\nFabio's research concerns the finite element approximation of PDEs, with particular focus on the simulation of fluid-structure interaction problems and virtual element methods."
] | [
null,
"https://cemse.kaust.edu.sa/sites/default/files/styles/max_fullhd_scale/public/2023-11/KAUST-CEMSE-AMCS-PhD-Dissertation-Defense-Fabio%20Credali.jpg",
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https://www.allthescience.org/what-is-electromagnetic-theory.htm | [
"# What Is Electromagnetic Theory?\n\nDavid Isaac Rudel\nDavid Isaac Rudel",
null,
"Einstein realized that space and time had to be interdependent if Maxwell was correct.\n\nEinstein’s theory of special relativity describes magnetism as the byproduct of electric force. Hence, these two forces can be considered different facets of a more fundamental force, which physicists call electromagnetism. Electromagnetic theory describes a collection of interconnected scientific claims used to answer questions about this force.\n\nPhysicists use fields as abstractions to describe how a system affects its surroundings. The electric field of a charged object represents the force that it would exert on a charged particle. The field is stronger closer to the object because electrostatic force decreases as the distance between two charges increases. Magnetic fields are similarly defined, except they describe the force exerted on a moving charged particle.",
null,
"Physicist James Clerk Maxwell was noted for his work with electromagnetism.\n\nThe most basic ideas in electromagnetic theory are “a changing electric field generates a magnetic field” and “a changing magnetic field generates an electric field.” These principles are quantified by Maxwell’s equations, named for James Clerk Maxwell, the Scottish physicist and mathematician whose work in the 19th century established the discipline by revolutionizing how physicists conceived of light. Maxwell’s equations also cast previously known relationships — Coulomb’s law and the Biot-Savart law — into the language of fields.",
null,
"Visible light, X-rays, radar, are all inherently similar, and scientists call the continuum of all such waves the electromagnetic spectrum.\n\nA charged particle generates a magnetic field as it moves, but the magnetic field is perpendicular to the motion of the particle. Furthermore, the effect this magnetic field has on a second moving charge is perpendicular to both the field and the motion of the second charge. These two facts cause even basic problems in electromagnetism to require complex, three-dimensional reasoning. Historically, the development of vectors in mathematics and science owes much of its progress to the work of physicists trying to abstract and simplify the use of electromagnetic theory.\n\nIn the 19th century, electromagnetic theory changed how physicists understood light. Newton had described light in terms of particles called corpuscles, but Maxwell claimed it was the manifestation of electric and magnetic fields that pushed each other through space. According to this conception, visible light, X-rays, radar, and many other phenomena are all inherently similar, each a combination of electric and magnetic fields varying at a different frequency. Scientists call the continuum of all such waves the electromagnetic spectrum.\n\nThe success of electromagnetic theory led to the collapse of the rest of Newtonian physics in the 20th century. Einstein realized that Maxwell’s theory required space and time to interdependent, different coordinates of a four-dimensional space-time. Moreover, Einstein’s theory of relativity showed that space was curved and the passage of time measured by one observer differed from that measured by another. These discoveries were all thoroughly incompatible with Newton’s theory of motion. Thus, the study of electromagnetism has, directly or indirectly, altered how physicists understand electricity, magnetism, light, space, time, and gravity.",
null,
"",
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] | [
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"https://images.allthescience.org/slideshow-mobile-small/nobel-winning-physicist-albert-einstein.jpg",
null,
"https://images.allthescience.org/slideshow-mobile-small/james-c-maxwell.jpg",
null,
"https://images.allthescience.org/slideshow-mobile-small/electromagnetic-spectrum-with-gamma-rays.jpg",
null,
"https://www.allthescience.org/res/common/img/lightbox/lightbox-ico-loading.gif",
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"https://www.allthescience.org/res/common/img/lightbox/lightbox-ico-loading.gif",
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https://git.openpower.foundation/cores/microwatt/commit/88b28a7b178cf8c4d611725021862035629fbbcd | [
"### console: Improve putchar(), add puts()\n\n```Make putchar() match a standard prototype and add puts()\n\nAlso make puts() add carriage returns before linefeeds so the\nusers don't have to do it all over the place.\n\nSigned-off-by: Benjamin Herrenschmidt <benh@kernel.crashing.org>```\n `@ -98,12 +98,13 @@ int getchar(void)` ` return potato_uart_read();` `}` `void putchar(unsigned char c)` `int putchar(int c)` `{` ` while (potato_uart_tx_full())` ` /* Do Nothing */;` ` potato_uart_write(c);` ` return c;` `}` `void putstr(const char *str, unsigned long len)` `@ -113,6 +114,19 @@ void putstr(const char *str, unsigned long len)` ` }` `}` `int puts(const char *str)` `{` ` unsigned int i;` ` for (i = 0; *str; i++) {` ` char c = *(str++);` ` if (c == 10)` ` putchar(13);` ` putchar(c);` ` }` ` return 0;` `}` `size_t strlen(const char *s)` `{` ` size_t len = 0;`"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.64306456,"math_prob":0.96311224,"size":349,"snap":"2023-40-2023-50","text_gpt3_token_len":88,"char_repetition_ratio":0.11304348,"word_repetition_ratio":0.0,"special_character_ratio":0.24068768,"punctuation_ratio":0.09836066,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.96372426,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-23T23:22:51Z\",\"WARC-Record-ID\":\"<urn:uuid:46c38ed9-c979-4e9a-960d-83f13b9e5637>\",\"Content-Length\":\"53679\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:4f5bbf21-ac4a-4725-a3de-b4e0c206f070>\",\"WARC-Concurrent-To\":\"<urn:uuid:ac82118e-71a8-43de-bd71-cfb98f9f220a>\",\"WARC-IP-Address\":\"188.118.22.250\",\"WARC-Target-URI\":\"https://git.openpower.foundation/cores/microwatt/commit/88b28a7b178cf8c4d611725021862035629fbbcd\",\"WARC-Payload-Digest\":\"sha1:4ECFYAYTFRWI5WJAJUP5IJ45AKUPLNAL\",\"WARC-Block-Digest\":\"sha1:R3VUQONVRW46MF2GR6SSP2QLWHJNWJBD\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233506539.13_warc_CC-MAIN-20230923231031-20230924021031-00478.warc.gz\"}"} |
https://vivo.library.tamu.edu/vivo/display/n45353SE | [
"# Consistent asymptotic expansion of Mott's solution for oxide growth Academic Article",
null,
"•\n• Overview\n•\n• Research\n•\n• Identity\n•\n•\n• Other\n•\n• View All\n•\n\n### abstract\n\n• Many relatively thick metal oxide films grow according to what is called the parabolic law L = 2At + . . . . Mott explained this for monovalent carriers by assuming that monovalent ions and electrons are the bulk charge carriers, and that their number fluxes vary as t^{-1/2} at sufficiently long t. In this theory no charge is present in the bulk, and surface charges were not discussed. However, it can be analyzed in terms of a discharging capacitor, with the oxide surfaces as the plates. The theory is inconsistent because the field decreases, corresponding to discharge, but there is no net current to cause discharge. The present work, which also includes non-monovalent carriers, systematically extends the theory and obtains the discharge current. Because the Planck-Nernst equations are nonlinear (although Gauss's Law and the continuity equations are linear) this leads to a systematic order-by-order expansion in powers of t^{-1/2} for the number currents, concentrations, and electric field during oxide growth. At higher order the bulk develops a non-zero charge density, with a corresponding non-uniform net current, and there are corrections to the electric field and the ion currents. The second order correction to ion current implies a logarithmic term in the thickness of the oxide layer: L = (2At)^{1/2} + B ln t + . . . . It would be of interest to verify this result with high-precision measurements.\n\n### published proceedings\n\n• SOLID STATE IONICS\n\n### author list (cited authors)\n\n• Sears, M. R., & Saslow, W. M.\n\n• 0\n\n### complete list of authors\n\n• Sears, Matthew R||Saslow, Wayne M\n\n• August 2010"
] | [
null,
"https://vivo.library.tamu.edu/vivo/images/individual/uriIcon.gif",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9238667,"math_prob":0.8716102,"size":1593,"snap":"2022-40-2023-06","text_gpt3_token_len":336,"char_repetition_ratio":0.10950283,"word_repetition_ratio":0.008032128,"special_character_ratio":0.20150658,"punctuation_ratio":0.10283688,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.96633685,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-02-01T12:16:33Z\",\"WARC-Record-ID\":\"<urn:uuid:7926ce19-cb27-4242-8a1e-11827efc75a0>\",\"Content-Length\":\"23319\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:3ceb177f-a8ee-4d92-ae65-62467a90fb58>\",\"WARC-Concurrent-To\":\"<urn:uuid:07e72db5-a7cf-4118-914a-1d333c197f9c>\",\"WARC-IP-Address\":\"128.194.18.20\",\"WARC-Target-URI\":\"https://vivo.library.tamu.edu/vivo/display/n45353SE\",\"WARC-Payload-Digest\":\"sha1:SWXM5474VXCBPXVV4BZFALE63CMBJN6R\",\"WARC-Block-Digest\":\"sha1:F3JR444YK3B2X4Z5RWKONUWCM7DSZEC4\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764499934.48_warc_CC-MAIN-20230201112816-20230201142816-00663.warc.gz\"}"} |
https://mathoverflow.net/questions/284966/how-to-get-approximate-the-derivative-of-noisy-time-series | [
"# How to get/approximate the derivative of noisy time series?\n\nI have a set of Langevin equations given by\n\n$${\\mathbf{\\dot{x}}} = \\mathbf{-Q \\,x} + \\mathbf{\\eta} \\tag{1}$$\n\nwhere $\\eta$ is white Gaussian noise and $Q$ is not a function of $x$.\n\nUsing Euler's method for SDE, I generated a time series of $\\bf{x}$, which (as expected) is noisy due to $\\eta$. Using the time series of $\\bf{x}$, generating $\\langle(\\bf{-Qx} + \\bf{\\eta})(\\bf{-Qx} + \\bf{\\eta)^T}\\rangle$ gave me a finite matrix.\n\nHowever, for the case where I don't know $\\bf{Q}$ and $\\eta$, I need to recover $\\bf\\dot{x}$ from the noisy time series $\\bf x$. How do I get $\\langle\\bf\\dot{x}\\dot{x}^T\\rangle$ from the generated $\\bf x$? I hope this can be done and would give me the same value for $\\langle(\\bf{-Qx} + \\bf{\\eta})(\\bf{-Qx} + \\bf{\\eta)^T}\\rangle$.\n\nEquation 1 has the solution\n\n$${x} (t) = e^{-Qt}x(0) +\\int_{0}^{t}dt'e^{-(t-t)'Q}\\eta(t')$$\n\nbut now I want to recover $\\bf{\\dot{x}}$ from the time series of $\\bf{x}$. Any insight on this problem is highly appreciated. Thank you.\n\n• The stochastic process that solves (1) is called multivariate Ornstein-Uhlenbeck process. Its sample paths are in general not differentiable. For special choices of $Q$ and the correlation matrix of $\\eta$ you can make certain components of the process differentiable a certain number of times. – S.Surace Jan 1 '18 at 16:11\n\nFor the general question of estimating a derivative from a noisy time series, there exists a fairly large literature; the best tool is probably determined by which modeling assumptions you want to make about the data.\n\nFor example, a Savitzky-Golay filter is a technique for smoothing data that also provides an analytic, closed-form estimate for the derivative.\n\n• \"the best tool is probably determined by which modeling assumptions you want to make about the data.\" Right. In particular, for model (1) the derivative is not defined for all components of the process, and applying methods that assume its existence may lead to nonsensical results. – S.Surace Jan 1 '18 at 16:22\n\nto recover $\\dot{\\mathbf{x}}$ from $\\mathbf{x}$, maybe one way is to first get an estimation of $Q$ and $\\eta$ (treat $Q$ as a random variable, independent of all $\\eta$). Assume an estimation of $Q$ is $\\hat{Q}$, and by simple algebra, $<\\dot{\\mathbf{x}} \\dot{\\mathbf{x}}^T> = <(\\hat{Q}\\mathbf{x})(\\hat{Q}\\mathbf{x})^T> + var[\\eta]$.\n\nProbably the simplest method is to apply linear regression -- your output data is $\\dot{\\mathbf{x}} \\approx \\frac{\\mathbf{x}(t+\\Delta t) - \\mathbf{x}(t)}{\\Delta t}$, input data is $\\mathbf{x}$, and your error is $\\eta$.\n\n• I already tried $\\frac{x(t+\\delta t)-x(t)}{\\delta t}$ and get $<\\dot{x}\\dot{x}^T>$ but it's not giving me the right answer. – Bingkat Nov 2 '17 at 9:50\n• I think you cannot directly write $\\dot{\\mathbf{x}} = \\frac{\\mathbf{x}(t+\\Delta t) - \\mathbf{x}(t)}{\\Delta t}$. Instead you should do: 1. Applying linear (or nonlinear) regression of $\\frac{\\mathbf{x}(t+\\Delta t) - \\mathbf{x}(t)}{\\Delta t}$ over $\\mathbf{x}$; 2. Get an estimation of $\\hat{Q}$ by step 1, and calculate $var [\\eta]$ (square sum of residues); 3. Calculate $<\\dot{\\mathbf{x}} \\dot{\\mathbf{x}}^T>$ using information in step 2. – Xige Yang Nov 3 '17 at 15:30\n• Also here, one should first ask whether the difference quotient makes sense for the model in question. – S.Surace Jan 1 '18 at 16:30\n\nNote that $\\newcommand\\mean{\\left\\langle #1 \\right\\rangle} \\mean{(-Qx+\\eta)(-Qx+\\eta)^\\top}=\\mean{Qxx^\\top Q^\\top}+\\mean{\\eta \\eta^\\top}=\\mean{Qxx^\\top Q^\\top}+B\\, \\delta(0)$, where $B$ is the covariance matrix of the noise (identity matrix if they are normalized uncorrelated white noise).\n\nThe reason that you are having a hard time fitting with the time series is that the second term is proportional to the Dirac $\\delta$-function, while the first term is finite. As you take smaller and smaller $\\Delta t$s for numerically calculating the derivative the second term grows larger with a factor $(\\Delta t)^{-1}$.\n\nAs suggested by Xige Yang, you need to apply a linear regression on $\\frac{x(t+\\Delta t)-x(t)}{\\Delta t}$ to find $Q$. Then, $B$ is given by the covariance of the error multiplied by $\\Delta t$."
] | [
null
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https://www.r-bloggers.com/2018/07/hacking-our-way-through-upsetr/ | [
"Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.\n\nFor our club meeting today we were going to summarize the Demystifying Data Science conference but we forgot that the videos are not released yet.\n\nSo we adjusted plans and decided to continue our work on the UpSetR (Gehlenborg, 2016) package by Nils Gehlenborg.\n\n### What you can currently do\n\nFirst, let’s install the version we used for this post:\n\n`devtools::install_github('hms-dbmi/UpSetR[email protected]')`\n\nOur ultimate goal is to submit a pull request that enables `UpSetR` users to specify a color by row for the dots instead of the actual rows. We had already identified an example that we could work with.\n\n```library('UpSetR')\nmovies <- read.csv( system.file(\"extdata\", \"movies.csv\", package = \"UpSetR\"),\n\nrequire(ggplot2); require(plyr); require(gridExtra); require(grid);```\n\n`## Loading required package: ggplot2`\n\n`## Loading required package: plyr`\n\n`## Loading required package: gridExtra`\n\n`## Loading required package: grid`\n\n```upset(movies,\nsets = c(\"Action\", \"Comedy\", \"Drama\"),\norder.by=\"degree\", matrix.color=\"blue\", point.size=5,\nsets.bar.color=c(\"maroon\",\"blue\",\"orange\"))```",
null,
"We also explored the set metadata vignette that includes examples such as the following one.\n\n```set.seed(20180727)\n\n## Create the metadata object first\nsets <- names(movies[3:19])\navgRottenTomatoesScore <- round(runif(17, min = 0, max = 90))\nCities <- sample(c(\"Boston\", \"NYC\", \"LA\"), 17, replace = T)\n\"Romance\")), ]```\n\n```## sets avgRottenTomatoesScore Cities\n## 1 Action 68 Boston\n## 4 Comedy 40 NYC\n## 7 Drama 48 LA\n## 13 Romance 77 Boston\n## 15 Thriller 19 NYC```\n\n```accepted <- round(runif(17, min = 0, max = 1))\n\"Romance\")), ]```\n\n```## sets avgRottenTomatoesScore Cities accepted\n## 1 Action 68 Boston 0\n## 4 Comedy 40 NYC 1\n## 7 Drama 48 LA 0\n## 13 Romance 77 Boston 1\n## 15 Thriller 19 NYC 0```\n\n```## Now make the plot\ncolumn = \"avgRottenTomatoesScore\", assign = 20), list(type = \"matrix_rows\",\ncolumn = \"Cities\", colors = c(Boston = \"green\", NYC = \"navy\", LA = \"purple\"),\nalpha = 0.5))))```",
null,
"### Hacking our way\n\nUsing the `metadata` looked complicated to us and hopefully not necessary for what we are trying to accomplish. That is, we really wanted to change the colors of the circles in each row, not the rows themselves. So we found the GitHub repo with the code, plugged a laptop to a TV and started exploring as a group. We went the rabbit hole to see how the `matrix.color` argument got used. To actually hack our way through, we downloaded the latest version of the code using `git`.\n\n```git clone [email protected]:hms-dbmi/UpSetR.git\ncd UpSetR```\n\nNext, we created the objects that match the default arguments of `upset()` by finding and replacing commas by semi-colons. Well, not all of the commas. Also, for inputs that specified a vector (mostly 2 options), we chose the first one to match the default R behavior. This way we could execute them and have them in our session.\n\n```## Default upset() arguments\nnsets = 5; nintersects = 40; sets = NULL; keep.order = F; set.metadata = NULL; intersections = NULL;\nmatrix.color = \"gray23\"; main.bar.color = \"gray23\"; mainbar.y.label = \"Intersection Size\"; mainbar.y.max = NULL;\nsets.bar.color = \"gray23\"; sets.x.label = \"Set Size\"; point.size = 2.2; line.size = 0.7;\nmb.ratio = c(0.70,0.30); expression = NULL; att.pos = NULL; att.color = main.bar.color; order.by = 'freq';\ndecreasing = T; show.numbers = \"yes\"; number.angles = 0; group.by = \"degree\";cutoff = NULL;\nqueries = NULL; query.legend = \"none\"; shade.color = \"gray88\"; shade.alpha = 0.25; matrix.dot.alpha =0.5;\nempty.intersections = NULL; color.pal = 1; boxplot.summary = NULL; attribute.plots = NULL; scale.intersections = \"identity\";\nscale.sets = \"identity\"; text.scale = 1; set_size.angles = 0 ; set_size.show = FALSE```\n\nNext, we did the same (commas to semicolons) for the inputs of the first example.\n\n```## Initial inputs on the first example\nmovies <- read.csv( system.file(\"extdata\", \"movies.csv\", package = \"UpSetR\"),\n\n## comma -> semicolon\ndata = movies; sets = c(\"Action\", \"Comedy\", \"Drama\");\norder.by=\"degree\"; matrix.color=\"blue\"; point.size=5;\nsets.bar.color=c(\"maroon\",\"blue\",\"orange\")```\n\nNow we were ready to start modifying some of the internal UpSetR (Gehlenborg, 2016) code.\n\n### Hacking internals\n\nThe function `upset()` is pretty long and uses many un-exported functions from the package itself. In order to test thing quickly we added `UpSetR:::` calls before the un-exported functions. Here’s our modified version where we added a piece of code to modify the `Matrix_layout` object and add some colors.\n\n```## Piece of code we introduced\nfor(i in 1:3) {\nj <- which(Matrix_layout\\$y == i & Matrix_layout\\$value == 1)\nif(length(j) > 0) Matrix_layout\\$color[j] <- c(\"maroon\",\"blue\",\"orange\")[i]\n}```\n\nOk, here’s the full modified `upset()` function.\n\n```## Modified internal upset() code\n\nstartend <- UpSetR:::FindStartEnd(data)\nfirst.col <- startend\nlast.col <- startend\n\nif(color.pal == 1){\npalette <- c(\"#1F77B4\", \"#FF7F0E\", \"#2CA02C\", \"#D62728\", \"#9467BD\", \"#8C564B\", \"#E377C2\",\n\"#7F7F7F\", \"#BCBD22\", \"#17BECF\")\n} else{\npalette <- c(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\",\n\"#CC79A7\")\n}\n\nif(is.null(intersections) == F){\nSet_names <- unique((unlist(intersections)))\nSets_to_remove <- UpSetR:::Remove(data, first.col, last.col, Set_names)\nNew_data <- UpSetR:::Wanted(data, Sets_to_remove)\nNum_of_set <- UpSetR:::Number_of_sets(Set_names)\nif(keep.order == F){\nSet_names <- UpSetR:::order_sets(New_data, Set_names)\n}\nAll_Freqs <- UpSetR:::specific_intersections(data, first.col, last.col, intersections, order.by, group.by, decreasing,\ncutoff, main.bar.color, Set_names)\n} else if(is.null(intersections) == T){\nSet_names <- sets\nif(is.null(Set_names) == T || length(Set_names) == 0 ){\nSet_names <- UpSetR:::FindMostFreq(data, first.col, last.col, nsets)\n}\nSets_to_remove <- UpSetR:::Remove(data, first.col, last.col, Set_names)\nNew_data <- UpSetR:::Wanted(data, Sets_to_remove)\nNum_of_set <- UpSetR:::Number_of_sets(Set_names)\nif(keep.order == F){\nSet_names <- UpSetR:::order_sets(New_data, Set_names)\n}\nAll_Freqs <- UpSetR:::Counter(New_data, Num_of_set, first.col, Set_names, nintersects, main.bar.color,\norder.by, group.by, cutoff, empty.intersections, decreasing)\n}\nMatrix_setup <- UpSetR:::Create_matrix(All_Freqs)\nlabels <- UpSetR:::Make_labels(Matrix_setup)\n#Chose NA to represent NULL case as result of NA being inserted when at least one contained both x and y\n#i.e. if one custom plot had both x and y, and others had only x, the y's for the other plots were NA\n#if I decided to make the NULL case (all x and no y, or vice versa), there would have been alot more if/else statements\n#NA can be indexed so that we still get the non NA y aesthetics on correct plot. NULL cant be indexed.\natt.x <- c(); att.y <- c();\nif(is.null(attribute.plots) == F){\nfor(i in seq_along(attribute.plots\\$plots)){\nif(length(attribute.plots\\$plots[[i]]\\$x) != 0){\natt.x[i] <- attribute.plots\\$plots[[i]]\\$x\n}\nelse if(length(attribute.plots\\$plots[[i]]\\$x) == 0){\natt.x[i] <- NA\n}\nif(length(attribute.plots\\$plots[[i]]\\$y) != 0){\natt.y[i] <- attribute.plots\\$plots[[i]]\\$y\n}\nelse if(length(attribute.plots\\$plots[[i]]\\$y) == 0){\natt.y[i] <- NA\n}\n}\n}\n\nBoxPlots <- NULL\nif(is.null(boxplot.summary) == F){\nBoxData <- UpSetR:::IntersectionBoxPlot(All_Freqs, New_data, first.col, Set_names)\nBoxPlots <- list()\nfor(i in seq_along(boxplot.summary)){\nBoxPlots[[i]] <- UpSetR:::BoxPlotsPlot(BoxData, boxplot.summary[i], att.color)\n}\n}\n\ncustomAttDat <- NULL\ncustomQBar <- NULL\nIntersection <- NULL\nElement <- NULL\nlegend <- NULL\nEBar_data <- NULL\nif(is.null(queries) == F){\ncustom.queries <- UpSetR:::SeperateQueries(queries, 2, palette)\ncustomDat <- UpSetR:::customQueries(New_data, custom.queries, Set_names)\nlegend <- UpSetR:::GuideGenerator(queries, palette)\nlegend <- UpSetR:::Make_legend(legend)\nif(is.null(att.x) == F && is.null(customDat) == F){\ncustomAttDat <- UpSetR:::CustomAttData(customDat, Set_names)\n}\ncustomQBar <- UpSetR:::customQueriesBar(customDat, Set_names, All_Freqs, custom.queries)\n}\nif(is.null(queries) == F){\nIntersection <- UpSetR:::SeperateQueries(queries, 1, palette)\nMatrix_col <- UpSetR:::intersects(QuerieInterData, Intersection, New_data, first.col, Num_of_set,\nAll_Freqs, expression, Set_names, palette)\nElement <- UpSetR:::SeperateQueries(queries, 1, palette)\nEBar_data <-UpSetR:::ElemBarDat(Element, New_data, first.col, expression, Set_names,palette, All_Freqs)\n} else{\nMatrix_col <- NULL\n}\n\nMatrix_layout <- UpSetR:::Create_layout(Matrix_setup, matrix.color, Matrix_col, matrix.dot.alpha)```\n\nAs a little pause in `upset()`, let’s check what actually `Matrix_layout` looks.\n\n`Matrix_layout`\n\n```## y x value color alpha Intersection\n## 1 1 1 1 blue 1.0 1yes\n## 2 2 1 1 blue 1.0 1yes\n## 3 3 1 1 blue 1.0 1yes\n## 4 1 2 0 gray83 0.5 4No\n## 5 2 2 1 blue 1.0 2yes\n## 6 3 2 1 blue 1.0 2yes\n## 7 1 3 1 blue 1.0 3yes\n## 8 2 3 0 gray83 0.5 8No\n## 9 3 3 1 blue 1.0 3yes\n## 10 1 4 1 blue 1.0 4yes\n## 11 2 4 1 blue 1.0 4yes\n## 12 3 4 0 gray83 0.5 12No\n## 13 1 5 0 gray83 0.5 13No\n## 14 2 5 0 gray83 0.5 14No\n## 15 3 5 1 blue 1.0 5yes\n## 16 1 6 0 gray83 0.5 16No\n## 17 2 6 1 blue 1.0 6yes\n## 18 3 6 0 gray83 0.5 18No\n## 19 1 7 1 blue 1.0 7yes\n## 20 2 7 0 gray83 0.5 20No\n## 21 3 7 0 gray83 0.5 21No```\n\nWe figured out that we had to change the colors only the rows with `value = 1` and that `y` was the row grouping variable.\n\n```## our modification\nfor(i in 1:3) {\nj <- which(Matrix_layout\\$y == i & Matrix_layout\\$value == 1)\nif(length(j) > 0) Matrix_layout\\$color[j] <- c(\"maroon\",\"blue\",\"orange\")[i]\n}```\n\nHere’s our modified `Matrix_layout`:\n\n`Matrix_layout`\n\n```## y x value color alpha Intersection\n## 1 1 1 1 maroon 1.0 1yes\n## 2 2 1 1 blue 1.0 1yes\n## 3 3 1 1 orange 1.0 1yes\n## 4 1 2 0 gray83 0.5 4No\n## 5 2 2 1 blue 1.0 2yes\n## 6 3 2 1 orange 1.0 2yes\n## 7 1 3 1 maroon 1.0 3yes\n## 8 2 3 0 gray83 0.5 8No\n## 9 3 3 1 orange 1.0 3yes\n## 10 1 4 1 maroon 1.0 4yes\n## 11 2 4 1 blue 1.0 4yes\n## 12 3 4 0 gray83 0.5 12No\n## 13 1 5 0 gray83 0.5 13No\n## 14 2 5 0 gray83 0.5 14No\n## 15 3 5 1 orange 1.0 5yes\n## 16 1 6 0 gray83 0.5 16No\n## 17 2 6 1 blue 1.0 6yes\n## 18 3 6 0 gray83 0.5 18No\n## 19 1 7 1 maroon 1.0 7yes\n## 20 2 7 0 gray83 0.5 20No\n## 21 3 7 0 gray83 0.5 21No```\n\nOk, let’s continue with the rest of `upset()`.\n\n```## continuing with upset()\n\nSet_sizes <- UpSetR:::FindSetFreqs(New_data, first.col, Num_of_set, Set_names, keep.order)\nBar_Q <- NULL\nif(is.null(queries) == F){\nBar_Q <- UpSetR:::intersects(QuerieInterBar, Intersection, New_data, first.col, Num_of_set, All_Freqs, expression, Set_names, palette)\n}\nQInter_att_data <- NULL\nQElem_att_data <- NULL\nif((is.null(queries) == F) & (is.null(att.x) == F)){\nQInter_att_data <- UpSetR:::intersects(QuerieInterAtt, Intersection, New_data, first.col, Num_of_set, att.x, att.y,\nexpression, Set_names, palette)\nQElem_att_data <- UpSetR:::elements(QuerieElemAtt, Element, New_data, first.col, expression, Set_names, att.x, att.y,\npalette)\n}\nAllQueryData <- UpSetR:::combineQueriesData(QInter_att_data, QElem_att_data, customAttDat, att.x, att.y)\n\n}\n} else {\n}\n}\nMain_bar <- suppressMessages(UpSetR:::Make_main_bar(All_Freqs, Bar_Q, show.numbers, mb.ratio, customQBar, number.angles, EBar_data, mainbar.y.label,\nmainbar.y.max, scale.intersections, text.scale, attribute.plots))\nMatrix <- UpSetR:::Make_matrix_plot(Matrix_layout, Set_sizes, All_Freqs, point.size, line.size,\nSizes <- UpSetR:::Make_size_plot(Set_sizes, sets.bar.color, mb.ratio, sets.x.label, scale.sets, text.scale, set_size.angles,set_size.show)\n\n# Make_base_plot(Main_bar, Matrix, Sizes, labels, mb.ratio, att.x, att.y, New_data,\n# expression, att.pos, first.col, att.color, AllQueryData, attribute.plots,\n\nstructure(class = \"upset\",\n.Data=list(\nMain_bar = Main_bar,\nMatrix = Matrix,\nSizes = Sizes,\nlabels = labels,\nmb.ratio = mb.ratio,\natt.x = att.x,\natt.y = att.y,\nNew_data = New_data,\nexpression = expression,\natt.pos = att.pos,\nfirst.col = first.col,\natt.color = att.color,\nAllQueryData = AllQueryData,\nattribute.plots = attribute.plots,\nlegend = legend,\nquery.legend = query.legend,\nBoxPlots = BoxPlots,\nSet_names = Set_names,\n)```",
null,
"### Line colors\n\nOk, that’s great but we have a problem with the lines. The color is no longer black, so we went deeper into the rabbit hole and found that the internal `Make_matrix_plot()` function is where the lines are made. We made some edits but got a plot where the lines were on top of the circles as shown in this screenshot.",
null,
"Our club session was out of time, so we decided to continue our project another day and ask for help on twitter. And yay, we got help super fast!\n\nSo here’s our modified version of `Make_matrix_plot()` that keeps the lines black.\n\n```Make_matrix_plot <- function(Mat_data,Set_size_data, Main_bar_data, point_size, line_size, text_scale, labels,\n\nif(length(text_scale) == 1){\nname_size_scale <- text_scale\n}\nif(length(text_scale) > 1 && length(text_scale) <= 6){\nname_size_scale <- text_scale\n}\n\nMat_data\\$line_col <- 'black'\n\nMatrix_plot <- (ggplot()\n+ theme(panel.background = element_rect(fill = \"white\"),\nplot.margin=unit(c(-0.2,0.5,0.5,0.5), \"lines\"),\naxis.text.x = element_blank(),\naxis.ticks.x = element_blank(),\naxis.ticks.y = element_blank(),\naxis.text.y = element_text(colour = \"gray0\",\nsize = 7*name_size_scale, hjust = 0.4),\npanel.grid.major = element_blank(),\npanel.grid.minor = element_blank())\n+ xlab(NULL) + ylab(\" \")\n+ scale_y_continuous(breaks = c(1:nrow(Set_size_data)),\nlimits = c(0.5,(nrow(Set_size_data) +0.5)),\nlabels = labels, expand = c(0,0))\n+ scale_x_continuous(limits = c(0,(nrow(Main_bar_data)+1 )), expand = c(0,0))\n+ geom_rect(data = shading_data, aes_string(xmin = \"min\", xmax = \"max\",\nymin = \"y_min\", ymax = \"y_max\"),\n+ geom_line(data= Mat_data, aes_string(group = \"Intersection\", x=\"x\", y=\"y\",\ncolour = \"line_col\"), size = line_size)\n+ geom_point(data= Mat_data, aes_string(x= \"x\", y= \"y\"), colour = Mat_data\\$color,\nsize= point_size, alpha = Mat_data\\$alpha, shape=16)\n+ scale_color_identity())\nMatrix_plot <- ggplot_gtable(ggplot_build(Matrix_plot))\nreturn(Matrix_plot)\n}```\n\nUsing that modified version we can then run the code again (note that we are not using `UpSetR:::` before `Make_matrix_plot`) and get the plot we wanted.\n\n```Matrix <- Make_matrix_plot(Matrix_layout, Set_sizes, All_Freqs, point.size, line.size,\nSizes <- UpSetR:::Make_size_plot(Set_sizes, sets.bar.color, mb.ratio, sets.x.label, scale.sets, text.scale, set_size.angles,set_size.show)\n\n# Make_base_plot(Main_bar, Matrix, Sizes, labels, mb.ratio, att.x, att.y, New_data,\n# expression, att.pos, first.col, att.color, AllQueryData, attribute.plots,\n\nstructure(class = \"upset\",\n.Data=list(\nMain_bar = Main_bar,\nMatrix = Matrix,\nSizes = Sizes,\nlabels = labels,\nmb.ratio = mb.ratio,\natt.x = att.x,\natt.y = att.y,\nNew_data = New_data,\nexpression = expression,\natt.pos = att.pos,\nfirst.col = first.col,\natt.color = att.color,\nAllQueryData = AllQueryData,\nattribute.plots = attribute.plots,\nlegend = legend,\nquery.legend = query.legend,\nBoxPlots = BoxPlots,\nSet_names = Set_names,\n)```",
null,
"We have quite a bit more to do in order to complete our pull request. We are also curious if you would have used a different approach to hack your way through UpSetR (Gehlenborg, 2016). For example, maybe some functions from devtools (Wickham, Hester, and Chang, 2018) would have enabled to do this equally fast without having to introduce `UpSetR:::` calls.\n\n### Acknowledgements\n\nThis blog post was made possible thanks to:\n\n### Reproducibility\n\n`## Session info ----------------------------------------------------------------------------------------------------------`\n\n```## setting value\n## version R version 3.5.1 (2018-07-02)\n## system x86_64, darwin15.6.0\n## ui X11\n## language (EN)\n## collate en_US.UTF-8\n## tz America/New_York\n## date 2018-07-27```\n\n`## Packages --------------------------------------------------------------------------------------------------------------`\n\n```## package * version date source\n## assertthat 0.2.0 2017-04-11 cran (@0.2.0)\n## backports 1.1.2 2017-12-13 cran (@1.1.2)\n## base * 3.5.1 2018-07-05 local\n## bibtex 0.4.2 2017-06-30 CRAN (R 3.5.0)\n## bindr 0.1.1 2018-03-13 cran (@0.1.1)\n## bindrcpp 0.2.2 2018-03-29 cran (@0.2.2)\n## BiocStyle * 2.8.2 2018-05-30 Bioconductor\n## blogdown 0.8 2018-07-15 CRAN (R 3.5.0)\n## bookdown 0.7 2018-02-18 CRAN (R 3.5.0)\n## colorout * 1.2-0 2018-05-03 Github (jalvesaq/[email protected])\n## colorspace 1.3-2 2016-12-14 cran (@1.3-2)\n## compiler 3.5.1 2018-07-05 local\n## crayon 1.3.4 2017-09-16 cran (@1.3.4)\n## datasets * 3.5.1 2018-07-05 local\n## devtools * 1.13.6 2018-06-27 cran (@1.13.6)\n## digest 0.6.15 2018-01-28 CRAN (R 3.5.0)\n## dplyr 0.7.6 2018-06-29 CRAN (R 3.5.1)\n## evaluate 0.11 2018-07-17 CRAN (R 3.5.0)\n## ggplot2 * 3.0.0 2018-07-03 CRAN (R 3.5.0)\n## glue 1.3.0 2018-07-17 CRAN (R 3.5.0)\n## graphics * 3.5.1 2018-07-05 local\n## grDevices * 3.5.1 2018-07-05 local\n## grid * 3.5.1 2018-07-05 local\n## gridExtra * 2.3 2017-09-09 CRAN (R 3.5.0)\n## gtable 0.2.0 2016-02-26 CRAN (R 3.5.0)\n## htmltools 0.3.6 2017-04-28 cran (@0.3.6)\n## httr 1.3.1 2017-08-20 CRAN (R 3.5.0)\n## jsonlite 1.5 2017-06-01 CRAN (R 3.5.0)\n## knitcitations * 1.0.8 2017-07-04 CRAN (R 3.5.0)\n## knitr 1.20 2018-02-20 cran (@1.20)\n## labeling 0.3 2014-08-23 cran (@0.3)\n## lazyeval 0.2.1 2017-10-29 CRAN (R 3.5.0)\n## lubridate 1.7.4 2018-04-11 CRAN (R 3.5.0)\n## magrittr 1.5 2014-11-22 cran (@1.5)\n## memoise 1.1.0 2017-04-21 CRAN (R 3.5.0)\n## methods * 3.5.1 2018-07-05 local\n## munsell 0.5.0 2018-06-12 CRAN (R 3.5.0)\n## pillar 1.3.0 2018-07-14 CRAN (R 3.5.0)\n## pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)\n## plyr * 1.8.4 2016-06-08 cran (@1.8.4)\n## purrr 0.2.5 2018-05-29 cran (@0.2.5)\n## R6 2.2.2 2017-06-17 CRAN (R 3.5.0)\n## Rcpp 0.12.18 2018-07-23 CRAN (R 3.5.1)\n## RefManageR 1.2.0 2018-04-25 CRAN (R 3.5.0)\n## rlang 0.2.1 2018-05-30 cran (@0.2.1)\n## rmarkdown 1.10 2018-06-11 CRAN (R 3.5.0)\n## rprojroot 1.3-2 2018-01-03 cran (@1.3-2)\n## scales 0.5.0 2017-08-24 cran (@0.5.0)\n## stats * 3.5.1 2018-07-05 local\n## stringi 1.2.4 2018-07-20 CRAN (R 3.5.0)\n## stringr 1.3.1 2018-05-10 CRAN (R 3.5.0)\n## tibble 1.4.2 2018-01-22 cran (@1.4.2)\n## tidyselect 0.2.4 2018-02-26 cran (@0.2.4)\n## tools 3.5.1 2018-07-05 local\n## UpSetR * 1.4.0 2018-07-27 Github (hms-dbmi/[email protected])\n## utils * 3.5.1 2018-07-05 local\n## withr 2.1.2 2018-03-15 CRAN (R 3.5.0)\n## xfun 0.3 2018-07-06 CRAN (R 3.5.0)\n## xml2 1.2.0 2018-01-24 CRAN (R 3.5.0)\n## yaml 2.1.19 2018-05-01 CRAN (R 3.5.0)```",
null,
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"https://i1.wp.com/lieberinstitute.github.io/rstatsclub/post/2018-07-27-hacking-our-way-through-upsetr_files/figure-html/unnamed-chunk-11-1.png",
null,
"https://i2.wp.com/lieberinstitute.github.io/rstatsclub/post/2018-07-27-hacking-our-way-through-upsetr_files/Screen%20Shot%202018-07-27%20at%2012.17.58%20PM.png",
null,
"https://i2.wp.com/lieberinstitute.github.io/rstatsclub/post/2018-07-27-hacking-our-way-through-upsetr_files/figure-html/unnamed-chunk-13-1.png",
null,
"https://feeds.feedburner.com/~r/LIBDrstats/~4/8ZYTyu4yFIE",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.5226152,"math_prob":0.9295799,"size":21759,"snap":"2020-45-2020-50","text_gpt3_token_len":7226,"char_repetition_ratio":0.1401517,"word_repetition_ratio":0.2175357,"special_character_ratio":0.37170824,"punctuation_ratio":0.25516808,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9542678,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12],"im_url_duplicate_count":[null,4,null,6,null,4,null,4,null,6,null,5,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-11-25T20:34:24Z\",\"WARC-Record-ID\":\"<urn:uuid:6f58ffdc-f832-4111-b835-2bd2889cc3bc>\",\"Content-Length\":\"190461\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:e7fd5b78-e720-4ba1-9dd0-c11fd5962005>\",\"WARC-Concurrent-To\":\"<urn:uuid:6b71dce1-7516-4228-8df2-e035f07a0a7f>\",\"WARC-IP-Address\":\"104.28.8.205\",\"WARC-Target-URI\":\"https://www.r-bloggers.com/2018/07/hacking-our-way-through-upsetr/\",\"WARC-Payload-Digest\":\"sha1:DTNO7CEFDZRUB677RSYP6I4M4DFAHPZ6\",\"WARC-Block-Digest\":\"sha1:6QR52WQ7QD2WPVEYIBG2ECPK2HP7CQG7\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-50/CC-MAIN-2020-50_segments_1606141184123.9_warc_CC-MAIN-20201125183823-20201125213823-00634.warc.gz\"}"} |
https://www.flashcardmachine.com/prefix7.html | [
"# Shared Flashcard Set\n\n## Details\n\nPrefix\n11\nPhysics\n04/19/2012\n\nTerm\n Ton\nDefinition\n 1 000 kg\nTerm\n k=kilo\nDefinition\n 1 000=103\nTerm\n m=milli\nDefinition\n 1/1000=0,001=10-3\nTerm\n μ=mikro\nDefinition\n 1/1 000 000=0,000 001=10-6\nTerm\n n=nano\nDefinition\n 1/1 000 000 000=0,000 000 001=10-9\nTerm\n d=deci\nDefinition\n 1/10=0,1=10-1\nTerm\n c=centi\nDefinition\n 1/100=0,01=10-2\nTerm\n h=hekto\nDefinition\n 100=102\nTerm\n M=mega\nDefinition\n 1 000 000=106\nTerm\n G=giga\nDefinition\n 1 000 000 000=109\nTerm\n T=tera\nDefinition\n 1 000 000 000 000=1012\nSupporting users have an ad free experience!"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.51022106,"math_prob":0.9314777,"size":447,"snap":"2019-43-2019-47","text_gpt3_token_len":129,"char_repetition_ratio":0.37471783,"word_repetition_ratio":0.53333336,"special_character_ratio":0.27293065,"punctuation_ratio":0.04918033,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9936354,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-11-22T15:47:20Z\",\"WARC-Record-ID\":\"<urn:uuid:c10a17f7-fc50-4ac9-81ae-8710bbf00a98>\",\"Content-Length\":\"25930\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:94cc0f89-4f75-432d-9caf-4a462980c49e>\",\"WARC-Concurrent-To\":\"<urn:uuid:4c954142-2d58-4b18-85ea-fcbb96a1bbc8>\",\"WARC-IP-Address\":\"67.225.168.77\",\"WARC-Target-URI\":\"https://www.flashcardmachine.com/prefix7.html\",\"WARC-Payload-Digest\":\"sha1:CVLKCD3LU4I7ERPR53XCMHFWJ7EYDTI6\",\"WARC-Block-Digest\":\"sha1:LXVTGBHBIYIU3VHRI54E44D5ZEQLV6EW\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-47/CC-MAIN-2019-47_segments_1573496671363.79_warc_CC-MAIN-20191122143547-20191122172547-00450.warc.gz\"}"} |
https://www.ams.org/journals/tran/2002-354-07/S0002-9947-02-03003-9/home.html | [
"# Transactions of the American Mathematical Society\n\nPublished by the American Mathematical Society since 1900, Transactions of the American Mathematical Society is devoted to longer research articles in all areas of pure and applied mathematics.\n\nISSN 1088-6850 (online) ISSN 0002-9947 (print)\n\nThe 2020 MCQ for Transactions of the American Mathematical Society is 1.48.\n\nWhat is MCQ? The Mathematical Citation Quotient (MCQ) measures journal impact by looking at citations over a five-year period. Subscribers to MathSciNet may click through for more detailed information.\n\n## A Markov partition that reflects the geometry of a hyperbolic toral automorphismHTML articles powered by AMS MathViewer\n\nby Anthony Manning\nTrans. Amer. Math. Soc. 354 (2002), 2849-2863 Request permission\n\n## Abstract:\n\nWe show how to construct a Markov partition that reflects the geometrical action of a hyperbolic automorphism of the \\$n\\$-torus. The transition matrix is the transpose of the matrix induced by the automorphism in \\$u\\$-dimensional homology, provided this is non-negative. (Here \\$u\\$ denotes the expanding dimension.) That condition is satisfied, at least for some power of the original automorphism, under a certain non-degeneracy condition on the Galois group of the characteristic polynomial. The \\$(^n_u)\\$ rectangles are constructed by an iterated function system, and they resemble the product of the projection of a \\$u\\$-dimensional face of the unit cube onto the unstable subspace and the projection of minus the orthogonal \\$(n-u)\\$-dimensional face onto the stable subspace.\nSimilar Articles"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.64068395,"math_prob":0.6223017,"size":7044,"snap":"2023-40-2023-50","text_gpt3_token_len":2251,"char_repetition_ratio":0.12116478,"word_repetition_ratio":0.015120967,"special_character_ratio":0.3652754,"punctuation_ratio":0.27364185,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95212835,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-12-04T04:26:47Z\",\"WARC-Record-ID\":\"<urn:uuid:fa47bdea-893f-472e-ba0f-56a9a0bad05f>\",\"Content-Length\":\"78539\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:aed1369d-7ad2-4319-906d-24f89021589d>\",\"WARC-Concurrent-To\":\"<urn:uuid:f8e024fe-a271-4d6e-a946-a596f75bbba0>\",\"WARC-IP-Address\":\"130.44.204.100\",\"WARC-Target-URI\":\"https://www.ams.org/journals/tran/2002-354-07/S0002-9947-02-03003-9/home.html\",\"WARC-Payload-Digest\":\"sha1:MOQS763LQJKFQ22JRFLZREMVSUQ42XIX\",\"WARC-Block-Digest\":\"sha1:RVAKNKCULPVTW34OIZAG27GNTWZTHKIZ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100523.4_warc_CC-MAIN-20231204020432-20231204050432-00868.warc.gz\"}"} |
https://converter.ninja/volume/us-quarts-to-imperial-quarts/851-usquart-to-imperialquart/ | [
"# 851 US quarts in imperial quarts\n\n## Conversion\n\n851 US quarts is equivalent to 708.60573111927 imperial quarts.\n\n## Conversion formula How to convert 851 US quarts to imperial quarts?\n\nWe know (by definition) that: $1\\mathrm{usquart}\\approx 0.832674184628989\\mathrm{imperialquart}$\n\nWe can set up a proportion to solve for the number of imperial quarts.\n\n$1 usquart 851 usquart ≈ 0.832674184628989 imperialquart x imperialquart$\n\nNow, we cross multiply to solve for our unknown $x$:\n\n$x\\mathrm{imperialquart}\\approx \\frac{851\\mathrm{usquart}}{1\\mathrm{usquart}}*0.832674184628989\\mathrm{imperialquart}\\to x\\mathrm{imperialquart}\\approx 708.6057311192696\\mathrm{imperialquart}$\n\nConclusion: $851 usquart ≈ 708.6057311192696 imperialquart$",
null,
"## Conversion in the opposite direction\n\nThe inverse of the conversion factor is that 1 imperial quart is equal to 0.00141122200411851 times 851 US quarts.\n\nIt can also be expressed as: 851 US quarts is equal to $\\frac{1}{\\mathrm{0.00141122200411851}}$ imperial quarts.\n\n## Approximation\n\nAn approximate numerical result would be: eight hundred and fifty-one US quarts is about seven hundred and eight point six one imperial quarts, or alternatively, a imperial quart is about zero times eight hundred and fifty-one US quarts.\n\n## Footnotes\n\n The precision is 15 significant digits (fourteen digits to the right of the decimal point).\n\nResults may contain small errors due to the use of floating point arithmetic."
] | [
null,
"https://converter.ninja/images/851_usquart_in_imperialquart.jpg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8442978,"math_prob":0.99908936,"size":922,"snap":"2023-14-2023-23","text_gpt3_token_len":207,"char_repetition_ratio":0.17429194,"word_repetition_ratio":0.013422819,"special_character_ratio":0.22776572,"punctuation_ratio":0.093023255,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99659765,"pos_list":[0,1,2],"im_url_duplicate_count":[null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-06-01T02:41:36Z\",\"WARC-Record-ID\":\"<urn:uuid:2e8a0692-dd0e-4aeb-9305-1ca860cb0439>\",\"Content-Length\":\"18366\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:2a4ab0ad-d440-4a29-a172-f5a50e44a53f>\",\"WARC-Concurrent-To\":\"<urn:uuid:a883e78f-59f5-4277-9302-15ac1b2b30df>\",\"WARC-IP-Address\":\"172.67.167.118\",\"WARC-Target-URI\":\"https://converter.ninja/volume/us-quarts-to-imperial-quarts/851-usquart-to-imperialquart/\",\"WARC-Payload-Digest\":\"sha1:3TJELC46TTPVNZO73UCXOMGEE7AHFRYM\",\"WARC-Block-Digest\":\"sha1:O3OMBL3QAZIVTKG2KR7NR5BFWRMERL3D\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224647525.11_warc_CC-MAIN-20230601010402-20230601040402-00409.warc.gz\"}"} |
https://mathoverflow.net/questions/171957/homotopy-spheres-with-vanishing-and-non-vanishing-alpha-invariant | [
"# Homotopy spheres with vanishing and non-vanishing $\\alpha$-invariant\n\nI'm unsure whether this question is appropriate for mathoverflow, so feel free to criticize.\n\nAll manifolds are closed, smooth and have dimensions $n\\ge 5$.\n\nThe Atiyah-Shapiro-Bott-Orientation gives a ring homomorphism $$\\alpha\\colon\\Omega_*^{spin}\\rightarrow KO^{-*}(pt),$$ from the spin-bordism ring to real K-theory, whose vanishing is a necessary condition for a spin manifold admitting a metric of positive scalar curvature. Recall $$KO^{-n}(pt)\\cong \\begin{cases} \\mathbb{Z} &\\mbox{if } n \\equiv 0,4 (8)\\\\ \\mathbb{Z}/2\\mathbb{Z} & \\mbox{if } n \\equiv 1,2(8) \\\\ 0 &\\mbox{if } n \\equiv 3,5,6,7 (8). \\end{cases}.$$ In dimensions $n\\equiv0,4(8)$, $\\alpha$ is just the $\\hat{A}$-genus (respectively twice of it).\n\nConsidering the spin-cobordism class of homotopy spheres, the $\\alpha$-invariant induces homomorphisms $$\\beta_n\\colon\\Theta_n\\rightarrow KO^{-n}(pt),$$ ($\\Theta_n$ is the group of $n$-dimensional homotopy spheres), which are zero in dimensions $n\\equiv 3,5,6,7 (8)$ for trivial reasons.\n\nIn the other dimensions, we have\n\n1. $\\beta_n$ is zero in dimensions $n\\equiv0,4(8)$, what is equivalent to the vanishing of the $\\hat{A}$-genus for all homotopy spheres.\n2. $\\beta_n$ is surjective in dimensions $n\\equiv1,2(8)$.\n\nI am trying to better understand these two claims. Lawson and Michelson simply write in their book \"Spin Geometry\", that (2) follows from \"deep work of Adams and Milnor\".\n\nSince I am absolutely not an expert in this field, can someone elaborate a bit (more than \"It follows from the work of Adams and Milnor.\", what is not really helpful for me.) on what I really need to prove (2) and how one can place it in a wider context?\n\nHow can one prove (1) and is there a reference for it?\n\nI searched the literature without good results, so simply giving references might also answer my question.\n\nIf $M$ is a homotopy sphere of dimension $4k>0$, then the signature is clearly zero. By the Hirzebruch signature theorem, you get $0=\\langle L_k (TM); [M] \\rangle = b_k \\langle p_k (TM); [M] \\rangle$ for a certain number $b_k \\neq 0$. Therefore, the Pontrjagin classes of $TM$ are all trivial, and hence the $\\hat{A}$-genus is zero. This settles part (1).\n\nPart (2) is harder. Adams proved in his $J(X)$ papers (it is one of the main results stated in the introduction to part IV) that the unit map from the sphere spectrum to $KO$ is surjective in homotopy of degrees $8k+1$ and $8k+2$. The unit map $\\mathbb{S} \\to KO$ factors as\n\n$$\\mathbb{S} \\to MSpin \\to KO$$\n\nwith the first map the unit and the second the Atiyah-Bott-Shapiro orientation. All that is needed to show this claim is that the $\\alpha$-invariant of a point is $1$ (this is a statement about the homomorphism $\\pi_0 (MSpin) \\to \\pi_0 (KO)$).\n\nTherefore, there exists framed manifolds of dimension $8k+1$ and $8k+2$ whose $\\alpha$-invariants (as spin manifolds) are nonzero.\n\nNow use the work of Kervaire-Milnor. They prove in ''Groups of homotopy spheres'' that each odd-dimensional framed manifold is framed cobordant to a homotopy sphere, if the dimension is at least $5$. Take a framed manifold of dimension $8k+1$ with nonzero $\\alpha$ and replace it by a homotopy sphere cobordant to it. Therefore, $\\beta_n$ is surjective if $n = 8k+1$ (for $k=0$, this is also true).\n\nIn dimensions $8k+2$, a framed manifold is framded cobordant to a homotopy sphere if and only if its Kervaire invariant is zero. This is why I do not see a short argument in that case.\n\nEDIT: let me remark that $\\beta_2$ is not surjective: the unique spin structure on $S^2$ has $\\alpha=0$; to get a nonzero $\\alpha$, you need the torus.\n\nEDIT II: in dimensions 8k+2, the argument (due to Milnor, I believe) is as follows. Take a homotopy sphere $\\Sigma^{8k+1}$ with nonzero $\\alpha$-invariant, and take the product $M=\\Sigma \\times S^1$, where $S^1$ has the spin structure with nonzero $\\alpha$-invariant. Then $M$ has nonzero $\\alpha$-invariant as well. Then do surgery on the embedded $x \\times S^1$; the result is a homotopy sphere, and the $\\alpha$-invariant is unchanged.\n\n• Minor correction: \"[...]whose $\\alpha$-invariants (as spin manifolds) are NONzero.\" Jan 3, 2015 at 12:03\n• Thank you for your second editing, which was enlightening for me. Feb 17, 2015 at 22:48"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8155714,"math_prob":0.9988833,"size":1814,"snap":"2023-40-2023-50","text_gpt3_token_len":529,"char_repetition_ratio":0.097790055,"word_repetition_ratio":0.0,"special_character_ratio":0.28114665,"punctuation_ratio":0.11516854,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9996747,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-12-08T03:18:12Z\",\"WARC-Record-ID\":\"<urn:uuid:8664bcc7-c92b-4875-a91c-e8656690acfb>\",\"Content-Length\":\"111733\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:a6c5875e-50a8-4685-a777-2ac10063b12e>\",\"WARC-Concurrent-To\":\"<urn:uuid:ded38243-7b97-46cb-857d-4c32b6bb02ad>\",\"WARC-IP-Address\":\"172.64.150.182\",\"WARC-Target-URI\":\"https://mathoverflow.net/questions/171957/homotopy-spheres-with-vanishing-and-non-vanishing-alpha-invariant\",\"WARC-Payload-Digest\":\"sha1:EJFFJNWPG6TKVT3ON67ZVAX3WAIJRDJJ\",\"WARC-Block-Digest\":\"sha1:27CP4TF3GUJDEY6EAGVJHMHI7MU2IEP6\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100710.22_warc_CC-MAIN-20231208013411-20231208043411-00208.warc.gz\"}"} |
https://calculatorsonline.org/percentage-to-fraction/27 | [
"# 27 percent as a fraction\n\nHere you will see step by step solution to convert 27 percent into fraction. 27 percent as a fraction is 27/100. Please check the explanation that how to write 27% as a fraction.\n\n## Answer: 27% as a fraction is\n\n= 27/100\n\n### How to convert 27 percent to fraction?\n\nTo convert the 27% as a fraction form simply divide the number by 100 and simplify the fraction by finding GCF. If given number is in decimal form then multiply numerator and denominator by 10^n and simplify it as much as possible.\n\n#### How to write 27% as a fraction?\n\nFollow these easy steps to convert 27% into fraction-\n\nGiven number is => 27\n\n• Write down the 27 in a percentage form like this:\n• 27% = 27/100\n• Since, 27 is a whole number, we also need to check to simplify the fraction.\n• Greatest common factor [GCF] of 27 and 100 is 1, so this is the simplest form is 27/100.\n\nConclusion: 27% = 27/100\n\nTherefore, the 27 percent as a fraction, final answer is 27/100."
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.93652236,"math_prob":0.9879292,"size":945,"snap":"2023-14-2023-23","text_gpt3_token_len":254,"char_repetition_ratio":0.18916047,"word_repetition_ratio":0.022727273,"special_character_ratio":0.3100529,"punctuation_ratio":0.09090909,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9994987,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-03-29T12:38:33Z\",\"WARC-Record-ID\":\"<urn:uuid:1dd1c4f2-0126-4a1e-8990-06c447d2bc23>\",\"Content-Length\":\"16072\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:ff205fd5-59e1-45ba-94a3-3c592a6a3a7d>\",\"WARC-Concurrent-To\":\"<urn:uuid:3ab9975d-004e-4f64-93b5-741343d73238>\",\"WARC-IP-Address\":\"104.21.85.191\",\"WARC-Target-URI\":\"https://calculatorsonline.org/percentage-to-fraction/27\",\"WARC-Payload-Digest\":\"sha1:7VYVGE7PCZXSEMP6G46LCWDNR5UVHWXQ\",\"WARC-Block-Digest\":\"sha1:C7SMDNFGLPL7LJY5UBZIRBBHHL7EP7KI\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-14/CC-MAIN-2023-14_segments_1679296948976.45_warc_CC-MAIN-20230329120545-20230329150545-00750.warc.gz\"}"} |
https://xiith.com/r/r-program-to-swap-two-numbers-without-using-third-variable/ | [
"# R Program to swap two numbers without using third variable\n\nIn this program, You will learn how to swap two numbers without using third variable in R.\n\n``````Before swap : x = 10, y = 20\n\nx = x + y\n\ny = x - y\n\nx = x - y\n\nAfter swap : x = 20, y = 10``````\n\n## Example: How to swap two numbers without using third variable in R\n\n``````{\n\nx <- as.integer(readline(prompt = \"Enter x value :\"))\ny <- as.integer(readline(prompt = \"Enter y value :\"))\n\nx = x + y\ny = x - y\nx = x - y\n\nprint(paste(\"After swap x is :\", x))\nprint(paste(\"After swap y is :\", y))\n\n}``````\n\n#### Output:\n\n``````Enter x value :34\nEnter y value :30\n \"After swap x is : 30\"\n \"After swap y is : 34\"``````"
] | [
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https://ez.analog.com/studentzone/f/q-a/533543/input-capacitance-of-op-amp | [
"# Input capacitance of op amp\n\nHi, I have a question regarding to input capacitance of an op amp. The file attached is my simulation on op amp AD8034 using LTSpice. the roll-off frequency will give me the common mode input capacitance value: C=1/(2pi*f*R) which is equals to 1.14pF. That's the value obtained from my simulation. However, the value provided in datasheet is 2.3pF. So, I'm a bit confused, is it there's a mistake with my circuit or I read wrongly on the datasheet? The common mode input impedence value provided is 1000||2.3, unit Gohm||pF, the capacitance stated is actually included both inverting and non-inverting input pin or just non-inverting input pin? Thanks."
] | [
null
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https://coursesqchm.firebaseapp.com/groeber30045do/math-answers-and-work-mep.html | [
"# Groeber30045\n\nMath Worksheets | Dynamically Created Math Worksheets\n\nMATLAB Answers - MATLAB Central - MathWorks View questions and answers from the MATLAB Central community. Find detailed answers to questions about coding, structures, functions, applications and libraries. How Can You Find Answers for MathXL Questions? - Reference Answers to MathXL questions are not independently available because of the computer-based nature of the program. However, supplemental materials and tutoring support may be available through the publisher's website. MathXL includes tutorials and other helpful tools to assist students as they work on assigned problems. Answers about Math and Arithmetic Math is the study of abstractions. Math allows us to isolate one or a few features such as the number, shape or direction of some kind of object. Then we can study what can be learned about the ... Introduction to Algebra - Math is Fun - Maths Resources\n\n## 7,857 Questions for the topic Math Help\n\nAsk any math question and get an answer from our subject experts in as little as 2 hours. Explaining Answers in Math: How to Help Your Students Explain Their ... 3 Dec 2017 ... Explaining math answers can be tricky. This post shares practical tips, ideas, and strategies to get your students to explaining answers in math. \"Work\" Word Problems | Purplemath\n\n### Big Ideas Math - Login Page\n\nMathematics - SpringBoard - The College Board Mathematics. A blend of directed, guided, and investigative instruction. Real-world connections that get students engaged. A deeper focus on conceptual understanding, balanced with applications and procedural fluency. Mathfunbook.com Common Core Math Worksheets\n\n### Math lessons, videos, online tutoring, and more for free. All the geometry help you need right here, all free. Also math games, puzzles, articles, and other math help resources.\n\nMathematics - Quora My maths teacher threw me a puzzle when I was in 10th grade. This question was asked way back in 1994, where I cannot google the answer as computers were very expensive and I do not have access to ... Math Vocabulary Words - Math Spelling Lists VocabularySpellingCity has compiled comprehensive math vocabulary lists to make those tricky math words, a snap! Math vocabulary words help students understand the foundational principles taught in each math concept. Of course, students need to know the meaning of basic math terms before they can learn how to apply them to math principles. Mathematics - SpringBoard - The College Board Mathematics. A blend of directed, guided, and investigative instruction. Real-world connections that get students engaged. A deeper focus on conceptual understanding, balanced with applications and procedural fluency.\n\n## Problemoids Math Books for Children - Series by Royal Fireworks…\n\nMy Math Genius - Hire/Pay a math expert to do your math ...\n\nPDF SOLVING WORK-RATE PROBLEMS - Henry Ford College HFCC Math Lab Intermediate Algebra - 12 SOLVING WORK-RATE PROBLEMS Part I: Introduction To solve work-rate problems it is helpful to use a variant of distance equals rate times time. Specifically: Q rt In this formula Q is the quantity or amount of work done, r is the rate of work and t is the time worked. EX 1: If a machine can produce 1 2 Math - thoughtco.com Math. Struggling with scatterplots? Can't quite wrap your head around circumference? Here are resources and tutorials for all the major functions, formulas, equations, and theories you'll encounter in math class. Teachers can find useful math resources for the classroom. Where Is the Answer Key for the Big Ideas Math Program ... Where Is the Answer Key for the Big Ideas Math Program Located? Answers to questions from the Big Ideas Math program can be found in the Skills Review Handbook on the company's official website. Students should log in to access the curriculum that pertains to them."
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.90594023,"math_prob":0.5199691,"size":3792,"snap":"2021-31-2021-39","text_gpt3_token_len":755,"char_repetition_ratio":0.11457233,"word_repetition_ratio":0.12151067,"special_character_ratio":0.19857594,"punctuation_ratio":0.12809315,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9909555,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-07-26T12:15:02Z\",\"WARC-Record-ID\":\"<urn:uuid:a4aa379d-2bb2-4188-8b80-74219d0f35b8>\",\"Content-Length\":\"18576\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:24e2813d-d433-4785-81c5-9da43d176ff5>\",\"WARC-Concurrent-To\":\"<urn:uuid:1744f521-8280-4679-afe0-f3db8c5eec3a>\",\"WARC-IP-Address\":\"151.101.65.195\",\"WARC-Target-URI\":\"https://coursesqchm.firebaseapp.com/groeber30045do/math-answers-and-work-mep.html\",\"WARC-Payload-Digest\":\"sha1:ISLSGVUDSCLH5HNIEQOBMBJLPYXGS7HS\",\"WARC-Block-Digest\":\"sha1:ARHYQB64GBMHFTY63ZEPPQN7NU5ENKEQ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-31/CC-MAIN-2021-31_segments_1627046152129.33_warc_CC-MAIN-20210726120442-20210726150442-00551.warc.gz\"}"} |
https://segmentfault.com/a/1190000016672263 | [
"# 深拷贝的终极探索（99%的人都不知道）\n\n## 深拷贝 VS 浅拷贝\n\n``````// 基本类型\nvar a = 1;\nvar b = a;\na = 2;\nconsole.log(a, b); // 2, 1 ，a b指向不同的数据\n\n// 引用类型指向同一份数据\nvar a = {c: 1};\nvar b = a;\na.c = 2;\nconsole.log(a.c, b.c); // 2, 2 全是2，a b指向同一份数据``````\n\n``````var a1 = {b: {c: {}};\n\nvar a2 = shallowClone(a1); // 浅拷贝\na2.b.c === a1.b.c // true\n\nvar a3 = clone(a3); // 深拷贝\na3.b.c === a1.b.c // false``````\n\n``````function shallowClone(source) {\nvar target = {};\nfor(var i in source) {\nif (source.hasOwnProperty(i)) {\ntarget[i] = source[i];\n}\n}\n\nreturn target;\n}``````\n\n## 最简单的深拷贝\n\n``var a1 = {b: {c: {d: 1}};``\n\n``````function clone(source) {\nvar target = {};\nfor(var i in source) {\nif (source.hasOwnProperty(i)) {\nif (typeof source[i] === 'object') {\ntarget[i] = clone(source[i]); // 注意这里\n} else {\ntarget[i] = source[i];\n}\n}\n}\n\nreturn target;\n}``````\n\n• 没有对参数做检验\n• 判断是否对象的逻辑不够严谨\n• 没有考虑数组的兼容\n\n(⊙o⊙)，下面我们来看看各个问题的解决办法，首先我们需要抽象一个判断对象的方法，其实比较常用的判断对象的方法如下，其实下面的方法也有问题，但如果能够回答上来那就非常不错了，如果完美的解决办法感兴趣，不妨看看这里吧\n\n``````function isObject(x) {\nreturn Object.prototype.toString.call(x) === '[object Object]';\n}``````\n\n``````function clone(source) {\nif (!isObject(source)) return source;\n\n// xxx\n}``````\n\n``````function createData(deep, breadth) {\nvar data = {};\nvar temp = data;\n\nfor (var i = 0; i < deep; i++) {\ntemp = temp['data'] = {};\nfor (var j = 0; j < breadth; j++) {\ntemp[j] = j;\n}\n}\n\nreturn data;\n}\n\ncreateData(1, 3); // 1层深度，每层有3个数据 {data: {0: 0, 1: 1, 2: 2}}\ncreateData(3, 0); // 3层深度，每层有0个数据 {data: {data: {data: {}}}}``````\n\n``````clone(createData(1000)); // ok\nclone(createData(10000)); // Maximum call stack size exceeded\n\nclone(createData(10, 100000)); // ok 广度不会溢出``````\n\n``````var a = {};\na.a = a;\n\nclone(a) // Maximum call stack size exceeded 直接死循环了有没有，/(ㄒoㄒ)/~~``````\n\n## 一行代码的深拷贝\n\n``````function cloneJSON(source) {\nreturn JSON.parse(JSON.stringify(source));\n}``````\n\n``cloneJSON(createData(10000)); // Maximum call stack size exceeded``\n\n``````var a = {};\na.a = a;\n\ncloneJSON(a) // Uncaught TypeError: Converting circular structure to JSON``````\n\n## 破解递归爆栈\n\n``````var a = {\na1: 1,\na2: {\nb1: 1,\nb2: {\nc1: 1\n}\n}\n}``````\n\n`````` a\n/ \\\na1 a2\n| / \\\n1 b1 b2\n| |\n1 c1\n|\n1 ``````\n\n``````function cloneLoop(x) {\nconst root = {};\n\n// 栈\nconst loopList = [\n{\nparent: root,\nkey: undefined,\ndata: x,\n}\n];\n\nwhile(loopList.length) {\n// 深度优先\nconst node = loopList.pop();\nconst parent = node.parent;\nconst key = node.key;\nconst data = node.data;\n\n// 初始化赋值目标，key为undefined则拷贝到父元素，否则拷贝到子元素\nlet res = parent;\nif (typeof key !== 'undefined') {\nres = parent[key] = {};\n}\n\nfor(let k in data) {\nif (data.hasOwnProperty(k)) {\nif (typeof data[k] === 'object') {\n// 下一次循环\nloopList.push({\nparent: res,\nkey: k,\ndata: data[k],\n});\n} else {\nres[k] = data[k];\n}\n}\n}\n}\n\nreturn root;\n}``````\n\n## 破解循环引用\n\n``````var b = 1;\nvar a = {a1: b, a2: b};\n\na.a1 === a.a2 // true\n\nvar c = clone(a);\nc.a1 === c.a2 // false``````\n\n`find`是抽象的一个函数，其实就是遍历`uniqueList`\n\n``````// 保持引用关系\nfunction cloneForce(x) {\n// =============\nconst uniqueList = []; // 用来去重\n// =============\n\nlet root = {};\n\n// 循环数组\nconst loopList = [\n{\nparent: root,\nkey: undefined,\ndata: x,\n}\n];\n\nwhile(loopList.length) {\n// 深度优先\nconst node = loopList.pop();\nconst parent = node.parent;\nconst key = node.key;\nconst data = node.data;\n\n// 初始化赋值目标，key为undefined则拷贝到父元素，否则拷贝到子元素\nlet res = parent;\nif (typeof key !== 'undefined') {\nres = parent[key] = {};\n}\n\n// =============\n// 数据已经存在\nlet uniqueData = find(uniqueList, data);\nif (uniqueData) {\nparent[key] = uniqueData.target;\nbreak; // 中断本次循环\n}\n\n// 数据不存在\n// 保存源数据，在拷贝数据中对应的引用\nuniqueList.push({\nsource: data,\ntarget: res,\n});\n// =============\n\nfor(let k in data) {\nif (data.hasOwnProperty(k)) {\nif (typeof data[k] === 'object') {\n// 下一次循环\nloopList.push({\nparent: res,\nkey: k,\ndata: data[k],\n});\n} else {\nres[k] = data[k];\n}\n}\n}\n}\n\nreturn root;\n}\n\nfunction find(arr, item) {\nfor(let i = 0; i < arr.length; i++) {\nif (arr[i].source === item) {\nreturn arr[i];\n}\n}\n\nreturn null;\n}``````\n\n``````var b = 1;\nvar a = {a1: b, a2: b};\n\na.a1 === a.a2 // true\n\nvar c = cloneForce(a);\nc.a1 === c.a2 // true``````\n\n``````var a = {};\na.a = a;\n\ncloneForce(a)``````\n\n## 性能对比\n\n``````function runTime(fn, time) {\nvar stime = Date.now();\nvar count = 0;\nwhile(Date.now() - stime < time) {\nfn();\ncount++;\n}\n\nreturn count;\n}\n\nrunTime(function () { clone(createData(500, 1)) }, 2000);``````\n\n500 351 212 338 372\n1000 174 104 175 143\n1500 116 67 112 82\n2000 92 50 88 69\n\n• 随着深度变小，相互之间的差异在变小\n• clone和cloneLoop的差别并不大\n• cloneLoop > cloneForce > cloneJSON\n\n• clone时间 = 创建递归函数 + 每个对象处理时间\n• cloneJSON时间 = 循环检测 + 每个对象处理时间 * 2 （递归转字符串 + 递归解析）\n• cloneLoop时间 = 每个对象处理时间\n• cloneForce时间 = 判断对象是否缓存中 + 每个对象处理时间\n\ncloneJSON的速度只有clone的50%，很容易理解，因为其会多进行一次递归时间\n\ncloneForce由于要判断对象是否在缓存中，而导致速度变慢，我们来计算下判断逻辑的时间复杂度，假设对象的个数是n，则其时间复杂度为O(n2)，对象的个数越多，cloneForce的速度会越慢\n\n``1 + 2 + 3 ... + n = n^2/2 - 1``\n\n0 13400 3272 14292 989\n\n• 随着对象的增多，cloneForce的性能低下凸显\n• cloneJSON的性能也大打折扣，这是因为循环检测占用了很多时间\n• cloneLoop的性能高于clone，可以看出递归新建函数的时间和循环对象比起来可以忽略不计\n\n``````var data1 = createData(2000, 0);\nvar data2 = createData(4000, 0);\nvar data3 = createData(6000, 0);\nvar data4 = createData(8000, 0);\nvar data5 = createData(10000, 0);\n\ncloneForce(data1)\ncloneForce(data2)\ncloneForce(data3)\ncloneForce(data4)\ncloneForce(data5)``````\n\n## 总结\n\nclone cloneJSON cloneLoop cloneForce\n\n``````// npm install --save @jsmini/clone\nimport { clone, cloneJSON, cloneLoop, cloneForce } from '@jsmini/clone';``````\n\n@jsmini/clone孵化于jsmini，jsmini致力于为大家提供一组小而美，无依赖的高质量库\n\njsmini的诞生离不开jslib-base，感谢jslib-base为jsmini提供了底层技术\n\n5.7k 声望\n1.7k 粉丝\n##### 0 条评论",
null,
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] | [
null,
"https://image-static.segmentfault.com/317/931/3179314346-5f61e47221e07",
null
] | {"ft_lang_label":"__label__zh","ft_lang_prob":0.7409919,"math_prob":0.9955336,"size":8578,"snap":"2021-31-2021-39","text_gpt3_token_len":5596,"char_repetition_ratio":0.12223,"word_repetition_ratio":0.24813631,"special_character_ratio":0.3028678,"punctuation_ratio":0.18467432,"nsfw_num_words":1,"has_unicode_error":false,"math_prob_llama3":0.9865665,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-09-27T13:14:16Z\",\"WARC-Record-ID\":\"<urn:uuid:dc833f68-0902-4076-8ece-f424bfc923a9>\",\"Content-Length\":\"55544\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:1b92a635-5cae-4f5e-9081-003ae729a401>\",\"WARC-Concurrent-To\":\"<urn:uuid:b49b484c-25c7-4211-9976-7901e24b639f>\",\"WARC-IP-Address\":\"103.61.39.86\",\"WARC-Target-URI\":\"https://segmentfault.com/a/1190000016672263\",\"WARC-Payload-Digest\":\"sha1:F3TSXL2OIDUEHQNLK223I252IPKUGGBO\",\"WARC-Block-Digest\":\"sha1:CP2CWHL56RZ4EQOTEQRDTLVGGSF37I34\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780058450.44_warc_CC-MAIN-20210927120736-20210927150736-00101.warc.gz\"}"} |
https://rememberingsomer.com/principal-argument-of-a-complex-number/ | [
"The facility plane plays an essential role in Mathematics. The is additionally known as z-plane, which is created of two mutually perpendicular lines dubbed axes. The horizontal line represents actual numbers and is recognized as the real axis. On the other hand, the vertical line denotes imagine numbers and also is termed together an imagine axis. The complex plane is supplied to stand for a geometric interpretation of complicated numbers. This aircraft is comparable to the Cartesian aircraft having real and also imaginary parts of a facility number along with X and Y axes. There space two concepts related to complex numbers. They space magnitude and argument. In this article, permit us talk about in detail around the definition, properties, formulas because that the discussion of complex numbers.\n\nYou are watching: Principal argument of a complex number\n\n## What are complex Numbers?\n\nA complex number is a number the is created as a + ib, in i m sorry “a” is a real number, and also “b” is an imagine number. The facility number has a prize “i” which satisfies the problem i2 = −1. Complex numbers deserve to be described as the extension of the one-dimensional number line. In the complicated plane, a facility number denoted by a + bi is stood for in the kind of the allude (a, b). It is come be detailed that a complex number through zero actual part, such together – i, -5i, etc, is referred to as purely imaginary. Also, a complicated number with zero imaginary component is known as a actual number.\n\n## Argument of complex Numbers Definition\n\nThe dispute of a complicated number is defined as the edge inclined native the actual axis in the direction the the complex number stood for on the facility plane. That is denoted by “θ” or “φ”. The is measured in the conventional unit dubbed “radians”.",
null,
"In this diagram, the facility number is denoted by the allude P. The length OP is recognized as magnitude or the modulus the a number, if the angle at i m sorry OP is inclined native the positive real axis is stated to it is in the dispute of the suggest P.\n\n## Argument of complicated Numbers Formula\n\nIn polar form, a complicated number is stood for by the equation r(cos θ + ns sin θ), here, θ is the argument. The argument function is denoted by arg(z), whereby z denotes the complex number, i.e. Z = x + iy. The computation that the complicated argument can be excellent by using the adhering to formula:\n\narg (z) = arg (x+iy) = tan-1(y/x)\n\nTherefore, the dispute θ is stood for as:\n\nθ = tan-1 (y/x)\n\n## Properties of debate of complex Numbers\n\nLet us comment on a few properties mutual by the debates of complex numbers. Intend that z it is in a nonzero complex number and also n be part integer, then\n\narg(zn) = n arg(z)\n\nLet united state assume, z1 and z2 be the two complex numbers, the adhering to identities are:\n\narg (z1/ z2) = arg ( z1) – arg ( z2)arg ( z1 z2) = arg ( z1) + arg ( z2)\n\n### How to find the argument of facility Numbers?\n\nFind the real and imaginary parts from the given complicated number. Represent them as x and y respectively.Substitute the worths in the formula θ = tan-1 (y/x)Find the value of θ if the formula gives any standard value, otherwise compose it in the kind of tan-1 itself.This value complied with by the unit “radian” is the forced value of complicated argument because that the given facility number.\n\n### Example\n\nQuestion:\n\nFind the discussion of the facility number 2 + 2√3i.\n\nSolution:\n\nLet z = 2 + 2√3i.\n\nHere, the actual part, x = 2\n\nImaginary part, y = 2√3\n\nWe recognize that the formula to find the discussion of a facility number is\n\narg (z) = tan-1(y/x)\n\narg (z) = tan-1(2√3/2)\n\narg (z) = tan-1(√3)\n\narg (z) = tan-1(tan π/3)\n\narg (z) = π/3\n\nTherefore, the argument of the complex number is π/3 radian.\n\nSee more: 【Solved】 How To Delete Messages On Roblox 2020, How Do I Delete The Messages In My Inbox\n\nRegister through BYJU’S – The finding out App and download the app to clock the interaction videos."
] | [
null,
"https://rememberingsomer.com/principal-argument-of-a-complex-number/imager_1_5843_700.jpg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9110363,"math_prob":0.9937814,"size":3938,"snap":"2021-43-2021-49","text_gpt3_token_len":959,"char_repetition_ratio":0.1624301,"word_repetition_ratio":0.0028328612,"special_character_ratio":0.2412392,"punctuation_ratio":0.09457755,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9985397,"pos_list":[0,1,2],"im_url_duplicate_count":[null,2,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-12-04T11:19:32Z\",\"WARC-Record-ID\":\"<urn:uuid:45df8049-7a29-496f-9fd7-2629152a635b>\",\"Content-Length\":\"13375\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:36a93953-4302-47cf-8d9c-792d701200af>\",\"WARC-Concurrent-To\":\"<urn:uuid:af9c4df0-da7d-4647-96f9-4181cf452369>\",\"WARC-IP-Address\":\"104.21.71.80\",\"WARC-Target-URI\":\"https://rememberingsomer.com/principal-argument-of-a-complex-number/\",\"WARC-Payload-Digest\":\"sha1:PWXDYUBEHQPWPIEB7LBJ5PPHN3PCCHPJ\",\"WARC-Block-Digest\":\"sha1:QWAVVVACWIAMYYZBH5DZM5BWXDCNDD4W\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-49/CC-MAIN-2021-49_segments_1637964362969.51_warc_CC-MAIN-20211204094103-20211204124103-00501.warc.gz\"}"} |
http://www.numbersaplenty.com/28328 | [
"Search a number\nBaseRepresentation\nbin110111010101000\n31102212012\n412322220\n51401303\n6335052\n7145406\noct67250\n942765\n1028328\n111a313\n1214488\n13cb81\n14a476\n1585d8\nhex6ea8\n\n28328 has 8 divisors (see below), whose sum is σ = 53130. Its totient is φ = 14160.\n\nThe previous prime is 28319. The next prime is 28349. The reversal of 28328 is 82382.\n\nIt can be written as a sum of positive squares in only one way, i.e., 24964 + 3364 = 158^2 + 58^2 .\n\nIt is a tau number, because it is divible by the number of its divisors (8).\n\nIt is a plaindrome in base 12.\n\nIt is a nialpdrome in base 13.\n\nIt is an inconsummate number, since it does not exist a number n which divided by its sum of digits gives 28328.\n\nIt is an unprimeable number.\n\n28328 is an untouchable number, because it is not equal to the sum of proper divisors of any number.\n\nIt is a polite number, since it can be written as a sum of consecutive naturals, namely, 1763 + ... + 1778.\n\n228328 is an apocalyptic number.\n\nIt is an amenable number.\n\n28328 is a deficient number, since it is larger than the sum of its proper divisors (24802).\n\n28328 is a wasteful number, since it uses less digits than its factorization.\n\n28328 is an evil number, because the sum of its binary digits is even.\n\nThe sum of its prime factors is 3547 (or 3543 counting only the distinct ones).\n\nThe product of its digits is 768, while the sum is 23.\n\nThe square root of 28328 is about 168.3092391998. The cubic root of 28328 is about 30.4840012842.\n\nThe spelling of 28328 in words is \"twenty-eight thousand, three hundred twenty-eight\".\n\nDivisors: 1 2 4 8 3541 7082 14164 28328"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8900719,"math_prob":0.9966622,"size":1677,"snap":"2020-24-2020-29","text_gpt3_token_len":509,"char_repetition_ratio":0.15899582,"word_repetition_ratio":0.025889968,"special_character_ratio":0.4072749,"punctuation_ratio":0.13445379,"nsfw_num_words":1,"has_unicode_error":false,"math_prob_llama3":0.9967869,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-05-31T20:37:24Z\",\"WARC-Record-ID\":\"<urn:uuid:549c676a-4b92-4436-aaa9-3065eedbaa30>\",\"Content-Length\":\"8440\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:25acfd65-a126-4873-987a-65afbafaab15>\",\"WARC-Concurrent-To\":\"<urn:uuid:78fc1a3d-7a88-40fb-980d-3bb60ce3d2e2>\",\"WARC-IP-Address\":\"62.149.142.170\",\"WARC-Target-URI\":\"http://www.numbersaplenty.com/28328\",\"WARC-Payload-Digest\":\"sha1:IPHARQ2IP3YDRIGN3ZVJ46L5RC4VKVTK\",\"WARC-Block-Digest\":\"sha1:7OQ5RFAQ52Z6ZVLCQZPJTU23IE4EW6CC\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-24/CC-MAIN-2020-24_segments_1590347413624.48_warc_CC-MAIN-20200531182830-20200531212830-00072.warc.gz\"}"} |
https://www.tutorialspoint.com/momentjs/momentjs_date_of_month.htm | [
"MomentJS - Date of Month\n\nThis method will get/set the day of the month. It takes input from 1-31, if greater than the range provided it will add to the next month.\n\nSyntax\n\nmoment().date(Number);\nmoment().date();\nmoment().dates(Number);\nmoment().dates();\n\nExample\n\nvar m = moment().date(); // gets the current day of the month\nvar d = moment().date(2); // sets the day of month as shown below\nvar k = moment().date(40); //sets the day of month which is greater than the\nrange so the output shows the next month as shown in the output\n\nOutput",
null,
"momentjs_getter_setter.htm"
] | [
null,
"https://www.tutorialspoint.com/momentjs/images/date_of_month.jpg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.58813196,"math_prob":0.98265785,"size":489,"snap":"2019-43-2019-47","text_gpt3_token_len":118,"char_repetition_ratio":0.22680412,"word_repetition_ratio":0.0,"special_character_ratio":0.29447854,"punctuation_ratio":0.1574074,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9668368,"pos_list":[0,1,2],"im_url_duplicate_count":[null,3,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-10-24T02:07:38Z\",\"WARC-Record-ID\":\"<urn:uuid:a10a2c46-9f97-4806-a45e-7672084f7d11>\",\"Content-Length\":\"19198\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c329c83b-d73b-4b84-88a3-cf0a89676732>\",\"WARC-Concurrent-To\":\"<urn:uuid:9acfa801-583d-4bce-b25d-eca2ac852f24>\",\"WARC-IP-Address\":\"99.84.181.19\",\"WARC-Target-URI\":\"https://www.tutorialspoint.com/momentjs/momentjs_date_of_month.htm\",\"WARC-Payload-Digest\":\"sha1:WH2TRQ4VX36XT7KAZIR7DWGJJXYFZW2O\",\"WARC-Block-Digest\":\"sha1:ZUPCMBZZ4RW5MPE2A7TDOIHVOFZWZ6E3\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-43/CC-MAIN-2019-43_segments_1570987838289.72_warc_CC-MAIN-20191024012613-20191024040113-00450.warc.gz\"}"} |
https://www.gurufocus.com/term/mscore/AFL/Beneish-M-Score/Aflac | [
"GURUFOCUS.COM » STOCK LIST » USA » NYSE » Aflac Inc (NYSE:AFL) » Definitions » Beneish M-Score\nSwitch to:\n\n# Aflac (NYSE:AFL) Beneish M-Score\n\n: 0.00 (As of Today)\nView and export this data going back to 1974. Start your Free Trial\n\nNote: Financial institutions were excluded from the sample in Beneish paper when calculating Beneish M-Score. Thus, the prediction might not fit banks and insurance companies.\n\nThe zones of discrimination for M-Score is as such:\n\nAn M-Score of equal or less than -1.78 suggests that the company is unlikely to be a manipulator.\nAn M-Score of greater than -1.78 signals that the company is likely to be a manipulator.\n\nThe historical rank and industry rank for Aflac's Beneish M-Score or its related term are showing as below:\n\nDuring the past 13 years, the highest Beneish M-Score of Aflac was 0.00. The lowest was -3.17. And the median was -2.72.\n\n## Aflac Beneish M-Score Calculation\n\nThe M-score was created by Professor Messod Beneish. Instead of measuring the bankruptcy risk (Altman Z-Score) or business trend (Piotroski F-Score), M-score can be used to detect the risk of earnings manipulation. This is the original research paper on M-score.\n\nThe M-Score Variables:\n\nThe M-score of Aflac for today is based on a combination of the following eight different indices:\n\n M = -4.84 + 0.92 * DSRI + 0.528 * GMI + 0.404 * AQI + 0.892 * SGI + 0.115 * DEPI = -4.84 + 0.92 * + 0.528 * + 0.404 * + 0.892 * + 0.115 * - 0.172 * SGAI + 4.679 * TATA - 0.327 * LVGI - 0.172 * + 4.679 * - 0.327 * =\n\n* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.\n* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.\n\n This Year (Dec21) TTM: Last Year (Dec20) TTM: Total Receivables was \\$693 Mil. Revenue was \\$21,923 Mil. Gross Profit was \\$21,923 Mil. Total Current Assets was \\$104,440 Mil. Total Assets was \\$157,542 Mil. Property, Plant and Equipment(Net PPE) was \\$538 Mil. Depreciation, Depletion and Amortization(DDA) was \\$0 Mil. Selling, General, & Admin. Expense(SGA) was \\$0 Mil. Total Current Liabilities was \\$6,501 Mil. Long-Term Debt & Capital Lease Obligation was \\$7,956 Mil. Net Income was \\$4,325 Mil. Gross Profit was \\$0 Mil. Cash Flow from Operations was \\$5,051 Mil. Total Receivables was \\$796 Mil. Revenue was \\$22,104 Mil. Gross Profit was \\$22,104 Mil. Total Current Assets was \\$111,819 Mil. Total Assets was \\$165,086 Mil. Property, Plant and Equipment(Net PPE) was \\$601 Mil. Depreciation, Depletion and Amortization(DDA) was \\$0 Mil. Selling, General, & Admin. Expense(SGA) was \\$0 Mil. Total Current Liabilities was \\$5,625 Mil. Long-Term Debt & Capital Lease Obligation was \\$7,899 Mil.\n\n1. DSRI = Days Sales in Receivables Index\n\nMeasured as the ratio of Revenue in Total Receivables in year t to year t-1.\n\nA large increase in DSR could be indicative of revenue inflation.\n\n DSRI = (Receivables_t / Revenue_t) / (Receivables_t-1 / Revenue_t-1) = (693 / 21923) / (796 / 22104) = 0.03161064 / 0.03601158 =\n\n2. GMI = Gross Margin Index\n\nMeasured as the ratio of gross margin in year t-1 to gross margin in year t.\n\nGross margin has deteriorated when this index is above 1. A firm with poorer prospects is more likely to manipulate earnings.\n\n GMI = GrossMargin_t-1 / GrossMargin_t = (GrossProfit_t-1 / Revenue_t-1) / (GrossProfit_t / Revenue_t) = (22104 / 22104) / (21923 / 21923) = 1 / 1 =\n\n3. AQI = Asset Quality Index\n\nAQI is the ratio of asset quality in year t to year t-1.\n\nAsset quality is measured as the ratio of non-current assets other than Property, Plant and Equipment to Total Assets.\n\n AQI = (1 - (CurrentAssets_t + PPE_t) / TotalAssets_t) / (1 - (CurrentAssets_t-1 + PPE_t-1) / TotalAssets_t-1) = (1 - (104440 + 538) / 157542) / (1 - (111819 + 601) / 165086) = 0.33365071 / 0.3190216 =\n\n4. SGI = Sales Growth Index\n\nRatio of Revenue in year t to sales in year t-1.\n\nSales growth is not itself a measure of manipulation. However, growth companies are likely to find themselves under pressure to manipulate in order to keep up appearances.\n\n SGI = Sales_t / Sales_t-1 = Revenue_t / Revenue_t-1 = 21923 / 22104 =\n\n5. DEPI = Depreciation Index\n\nMeasured as the ratio of the rate of Depreciation, Depletion and Amortization in year t-1 to the corresponding rate in year t.\n\nDEPI greater than 1 indicates that assets are being depreciated at a slower rate. This suggests that the firm might be revising useful asset life assumptions upwards, or adopting a new method that is income friendly.\n\n DEPI = (Depreciation_t-1 / (Depreciaton_t-1 + PPE_t-1)) / (Depreciation_t / (Depreciaton_t + PPE_t)) = (0 / (0 + 601)) / (0 / (0 + 538)) = 0 / 0 =\n\nNote: If the Depreciation, Depletion and Amortization data is not available, we assume that the depreciation rate is constant and set the Depreciation Index to 1.\n\n6. SGAI = Sales, General and Administrative expenses Index\n\nThe ratio of Selling, General, & Admin. Expense(SGA) to Sales in year t relative to year t-1.\n\nSGA expenses index > 1 means that the company is becoming less efficient in generate sales.\n\n SGAI = (SGA_t / Sales_t) / (SGA_t-1 /Sales_t-1) = (0 / 21923) / (0 / 22104) = 0 / 0 =\n\n7. LVGI = Leverage Index\n\nThe ratio of total debt to Total Assets in year t relative to yeat t-1.\n\nAn LVGI > 1 indicates an increase in leverage\n\n LVGI = ((LTD_t + CurrentLiabilities_t) / TotalAssets_t) / ((LTD_t-1 + CurrentLiabilities_t-1) / TotalAssets_t-1) = ((7956 + 6501) / 157542) / ((7899 + 5625) / 165086) = 0.09176601 / 0.08192094 =\n\n8. TATA = Total Accruals to Total Assets\n\nTotal accruals calculated as the change in working capital accounts other than cash less depreciation.\n\n TATA = (IncomefromContinuingOperations_t - CashFlowsfromOperations_t) / TotalAssets_t = (NetIncome_t - NonOperatingIncome_t - CashFlowsfromOperations_t) / TotalAssets_t = (4325 - 0 - 5051) / 157542 = -0.0046\n\nAn M-Score of equal or less than -1.78 suggests that the company is unlikely to be a manipulator. An M-Score of greater than -1.78 signals that the company is likely to be a manipulator.\n\n## Aflac Beneish M-Score Related Terms\n\nThank you for viewing the detailed overview of Aflac's Beneish M-Score provided by GuruFocus.com. Please click on the following links to see related term pages.",
null,
""
] | [
null,
"https://static.gurufocus.com/logos/0C000009AH.png",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.883505,"math_prob":0.9252583,"size":9376,"snap":"2022-40-2023-06","text_gpt3_token_len":2733,"char_repetition_ratio":0.11011524,"word_repetition_ratio":0.13232389,"special_character_ratio":0.31015357,"punctuation_ratio":0.13692218,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9891412,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-02-02T17:59:44Z\",\"WARC-Record-ID\":\"<urn:uuid:4fed8aaa-000b-42d4-95cb-a8dcf7508643>\",\"Content-Length\":\"592191\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:85e7a5be-5a4e-47ab-8d4b-7124937d0100>\",\"WARC-Concurrent-To\":\"<urn:uuid:c1cf3558-5950-40ca-9f41-f834369628de>\",\"WARC-IP-Address\":\"172.66.43.2\",\"WARC-Target-URI\":\"https://www.gurufocus.com/term/mscore/AFL/Beneish-M-Score/Aflac\",\"WARC-Payload-Digest\":\"sha1:UTAF4AUPRQXWOVMJAPOJYQSE5EN2MNQO\",\"WARC-Block-Digest\":\"sha1:GZYEMNYCTHAQXYX6W3X7XJKWYVZXHYXN\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764500035.14_warc_CC-MAIN-20230202165041-20230202195041-00728.warc.gz\"}"} |
https://justaaa.com/economics/67932-apex-inc-is-a-us-based-firm-investing-in-a-two | [
"Question\n\n# Apex, Inc. is a U.S.-based firm investing in a two-year project in Mexico. The present exchange...\n\nApex, Inc. is a U.S.-based firm investing in a two-year project in Mexico. The present exchange rate is 15 Mexican pesos per U.S. dollar. It is estimated that Mexico’s currency will be devalued in the international market at an average 2.1 % per year relative to the U.S. dollar over the next several years. The estimated before-tax net cash flow (in pesos) of the project is a follows:\n\nEnd of Year Net Cash Flow (pesos)\n\n0 - 900,000\n\n1 480,000\n\n2 720,000\n\nIf the U.S. company’s MARR is 20 % (before taxes) based on the U.S. dollar, what is the present worth (PW) of the project in terms of U.S. dollars? (Enter your answer as a number without the dollar \\$ sign.)\n\nAns. Exchange rate at present, e = 15 Pesos/dollar\n\nDepriciation rate of currency each year, d = 2.1% or 0.021\n\n=> Exchange rate at end of year0, e0 = e*(1+d) = 15.315 pesos/dollar\n\n=> Exchange rate at the end of year 1, e1 = e0*(1+d) = 15.636 Pesos/dollar\n\n=> Exchange rate at the end of year 2, e2 = e1*(1+d) = 15.965 Pesos /dollar\n\n=> Dollar value of cashflow at end of year 0 = 900000/e0 = \\$58765.916\n\n=> Dollar value of cashflow at the end of year 1 = 480000/e1 = \\$30697.181\n\n=> Dollar value of cashflow at the end of year 2 = 720000/e2 = \\$45098.699\n\nPresent worth of these dollar cashflows i.e. present value at starting of year 0 at MARR of 20%\n\nPW = -58765.916/(1+ 0.20) + 30697.181/(1+0.20)^2 + 45098.699/(1+0.20)^3\n\n=> PW = -\\$1555.32"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8477983,"math_prob":0.97908425,"size":1537,"snap":"2023-40-2023-50","text_gpt3_token_len":492,"char_repetition_ratio":0.13568167,"word_repetition_ratio":0.07801419,"special_character_ratio":0.38191283,"punctuation_ratio":0.1424581,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9934727,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-26T06:17:54Z\",\"WARC-Record-ID\":\"<urn:uuid:8e361773-75a8-4109-a368-4cb2d5f8ae85>\",\"Content-Length\":\"46235\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:f4df653a-44cc-4951-adc5-e0dc43ad2bdc>\",\"WARC-Concurrent-To\":\"<urn:uuid:02f259d3-fb6c-4ea3-96fd-a94578360194>\",\"WARC-IP-Address\":\"172.67.222.198\",\"WARC-Target-URI\":\"https://justaaa.com/economics/67932-apex-inc-is-a-us-based-firm-investing-in-a-two\",\"WARC-Payload-Digest\":\"sha1:EZ56OEYZC76FIWDWAIIR4VPL26THVW74\",\"WARC-Block-Digest\":\"sha1:ZP4VQENH2KY67YZXY4SXAX4XYCRAEXWU\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510149.21_warc_CC-MAIN-20230926043538-20230926073538-00801.warc.gz\"}"} |
https://numberworld.info/117545144 | [
"# Number 117545144\n\n### Properties of number 117545144\n\nCross Sum:\nFactorization:\n2 * 2 * 2 * 43 * 341701\nDivisors:\nCount of divisors:\nSum of divisors:\nPrime number?\nNo\nFibonacci number?\nNo\nBell Number?\nNo\nCatalan Number?\nNo\nBase 2 (Binary):\nBase 3 (Ternary):\nBase 4 (Quaternary):\nBase 5 (Quintal):\nBase 8 (Octal):\n70198b8\nBase 32:\n3g365o\nsin(117545144)\n-0.95860885459726\ncos(117545144)\n-0.28472629644577\ntan(117545144)\n3.3667731662426\nln(117545144)\n18.582333022016\nlg(117545144)\n8.0702046923502\nsqrt(117545144)\n10841.82383181\nSquare(117545144)\n\n### Number Look Up\n\nLook Up\n\n117545144 which is pronounced (one hundred seventeen million five hundred forty-five thousand one hundred forty-four) is a amazing figure. The cross sum of 117545144 is 32. If you factorisate the number 117545144 you will get these result 2 * 2 * 2 * 43 * 341701. 117545144 has 16 divisors ( 1, 2, 4, 8, 43, 86, 172, 344, 341701, 683402, 1366804, 2733608, 14693143, 29386286, 58772572, 117545144 ) whith a sum of 225523320. The figure 117545144 is not a prime number. 117545144 is not a fibonacci number. 117545144 is not a Bell Number. The number 117545144 is not a Catalan Number. The convertion of 117545144 to base 2 (Binary) is 111000000011001100010111000. The convertion of 117545144 to base 3 (Ternary) is 22012011220121212. The convertion of 117545144 to base 4 (Quaternary) is 13000121202320. The convertion of 117545144 to base 5 (Quintal) is 220042421034. The convertion of 117545144 to base 8 (Octal) is 700314270. The convertion of 117545144 to base 16 (Hexadecimal) is 70198b8. The convertion of 117545144 to base 32 is 3g365o. The sine of the number 117545144 is -0.95860885459726. The cosine of 117545144 is -0.28472629644577. The tangent of the figure 117545144 is 3.3667731662426. The root of 117545144 is 10841.82383181.\nIf you square 117545144 you will get the following result 13816860877980736. The natural logarithm of 117545144 is 18.582333022016 and the decimal logarithm is 8.0702046923502. that 117545144 is great number!"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.6952082,"math_prob":0.91017693,"size":2283,"snap":"2020-24-2020-29","text_gpt3_token_len":778,"char_repetition_ratio":0.20710838,"word_repetition_ratio":0.23030303,"special_character_ratio":0.5300044,"punctuation_ratio":0.16428572,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.997452,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-07-11T16:30:05Z\",\"WARC-Record-ID\":\"<urn:uuid:19ec52a1-3b7a-44b7-9377-1de2d67952c2>\",\"Content-Length\":\"14425\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:5cd0f82d-5e9c-4907-a107-f7f11bcc01e8>\",\"WARC-Concurrent-To\":\"<urn:uuid:ae3b8d72-ac4f-4f8c-bfee-62a14d8a2389>\",\"WARC-IP-Address\":\"176.9.140.13\",\"WARC-Target-URI\":\"https://numberworld.info/117545144\",\"WARC-Payload-Digest\":\"sha1:4PL2MQ3EFBO2M5JI57JXDK2XBPEFZLFD\",\"WARC-Block-Digest\":\"sha1:OPV6HJD4LR5FW2WZDNAUFJNLKC3OHGHB\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-29/CC-MAIN-2020-29_segments_1593655934052.75_warc_CC-MAIN-20200711161442-20200711191442-00126.warc.gz\"}"} |
https://lhypercube.arep.fr/en/qualite-dair/qualite-dair-en-gares-souterraines/ | [
"# Air Quality in subterranean train stations\n\n## Introduction & Starting point\n\nAir quality in underground stations is a growing concern in cities with an urban transportation network. In France, continuous and open access measurement campaigns are carried out at the SNCF or at the RATP, giving access to data on the concentration of PM2.5 and PM10.\n\nBy using the available data, the correlation between rail traffic (continuous lines) and PM10 (dotted line). The figure below represents an average week in the St-Michel Notre-Dame underground station (Paris). It can then be assumed that the majority of the amount of aerosol in suspension comes from braking and resuspension.\n\nIn this figure, it can be seen that the maximum PM10 are concomitant with morning and evening traffic peaks, on the other hand, weekend concentrations decrease with rail traffic.\n\nBy observing the PM2.5/PM10 ration over one week at Magenta station (Paris) as shown in Figure 2 below from [Fortain 2007], we can see that it is between ~0.25 and 0.35 during the day, while it reaches 1 during the night. The PM2.5 being included in the PM10, this means that the larger particles gradually disappear from the aerosol during the night time: only the finer ones remain.\n\nThis can be explained by the deposition rate of the particles. Indeed, it is more important for large diameter particles. Figure 3 below shows the deposition rate of particles as a function of their diameter based on [Lai et Nazaroff 2000]. Not surprisingly, gravity deposition is predominant for On particles larger than 1[µm] in diameter. It should be noted that the finest particles, with a diameter of less than 0.01[µm] also settle more significantly: this is related to the Brownian motion which diffuses very small particles onto surfaces. In the intermediate diameter range, the particles are too large to be subjected to the Brownian motion of the gas in which they evolve (air) and do not have enough mass for gravity sedimentation: the natural convection movements of the air are sufficient to keep them in suspension.\n\nThese observations have led to the development of successive models for predicting air quality in underground stations. The following sections describe the method developed in [Walther et Bogdan 2017a] and [Walther et al. 2017b], available in the section Publications of this site.\n\n## Equation\n\nBased on the previous finding and the homogeneous mixing hypothesis, a differential system is established that considers the concentrations of two classes of particles $C_a$ for the $PM_{2.5}$ and $C_b$ for the $PM_{2.5-10}$, the sum of the two being equal to the concentration of the $PM_{10}$. This makes it possible to capture the slower and faster dynamics of the aerosol particles:\n\n$$\\frac{d C_a}{dt} = \\alpha_a N^2(t) + \\tau (C_a^\\text{ext}-C_a) - \\delta_a C_a$$\n\n$$\\frac{d C_b}{dt} = \\alpha_b N^2(t) + \\tau (C_b^\\text{ext}-C_b) - \\delta_b C_b$$\n\nWhere $N$ is the number of trains per unit of time, $\\alpha_a,\\alpha_b$ are the terms of emission of the PM2.5 and $PM_{2.5-10}$ and $\\delta_a,\\delta_b$ the deposition rates calculated according to the [Lai et Nazaroff 2000].\n\nThe air exchange rate $\\tau$ is broken down into a natural ventilation rate $\\tau_0$ and a piston effect ventilation $\\beta \\times N$, where $\\beta$ [-] is the volume of outside air caused by the departure and the arrival of the train, divided by the volume of the station.\n\nIn the literature review by[Nicholson 1988], the resuspension rate follows speed with a power law with an exponent ranging from 1 to 6. The apparent issue term $\\alpha$ is thus assumed to vary with the square of the number of trains $N^2$ : Indeed, if trains are discrete events, they are considered to lead to an increase in the average speed at the station. The experimental air velocity measurement campaign conducted at St-Michel Notre-Dame station in February 2017 corroborates this hypothesis (see Figure 4 below).",
null,
"Figure 4: Measured air speed (sliding average over 1 hour) and rail traffic at St-Michel Notre-Dame station\n\n## Identification of model parameters\n\nWith the differential system in place, it remains to identify the unknown parameters of the model. There are six of them:\n\n• the piston effect $\\beta$ whose order of magnitude can be determined by measurement or simulation (see our research on this subject)\n• the rate of air renewal by natural or mechanical ventilation $\\tau_0$\n• the apparent source terms $\\alpha_a,\\alpha_b$ which give the proportions of fine and larger particles emitted by braking and resuspension\n• the deposition constants $\\delta_a,\\delta_b$ which are actually identified from equivalent aerosol diameters $d_a,d_b$ : Indeed, the approach developed here considers that the total concentration of PM10 is that of an aerosol composed of only two types of particles, in different concentrations.\n\nThe detailed identification of these parameters makes it possible to find fairly accurately the concentrations measured as a function of rail traffic, as shown in the figure below Figure 5, where measurements and model are compared.\n\nThe PM2.5 are also simulated by the model (see green curve in Figure 5) but without experimental confrontation, as these measurements are not available for the Saint-Michel station at the time of preparing this document (this paragraph will have to be updated).\n\nThe PM2.5/PM10 ratio obtained, presented in Figure 6, is very similar to the one of the experimental campaign of [Fortain 2007] (see Figure 2).\n\nPending experimental results, this finding is considered to support the results obtained from the model. On this basis, it is possible to simulate the evolution of the PM10 concentration as a function of ventilation and filtration installation, by integrating these terms into the model with the identified parameters.\n\n## Further development\n\nThe model is being improved, depending on the availability of measured data. Two major axes are being explored: the addition of the quantity deposited on surfaces and the generalization of a model to $n$ classes.\n\n### Closing equation\n\nThe addition of the equation on the surfaces closes the differential system previously presented as in [Qian et al. 2008]. This results in:\n\n$$V \\times \\frac{\\partial C}{\\partial t} = a N + Q_{\\text{v}} (C^{\\text{ext}} - C) - \\delta V C + \\rho S_{\\text{r}} L$$\n\n$$S_{\\text{d}} \\times \\frac{dL}{dt} = \\delta V C - \\rho S_{\\text{r}} L$$\n\nIn the first equation, $V$ is the volume of the station, $Q_v$ is the external air flow rate, $\\delta$ is the deposition rate, $\\rho$ is the resuspension constant, $S_r$ the surface accessible for resuspension and $L$ the quantity of particles deposited per unit area $[\\mu g/m^2]$. In the second equation, $S_d$ is the surface accessible for deposition.\n\nIt should be noted that these two equations are coupled by the cross terms $\\pm \\delta V C$ and $\\pm \\rho S_r L$. These respectively represent the quantity of particles that passes from the air \"compartment\" to the surface \"compartment\" by deposition and the quantity resuspended from the surfaces to the air volume.\n\n### Model with n classes\n\nWith a reasoning similar to that used for two particle size classes, it is possible to extend the model to $n$ classes: this would represent the evolution of an aerosol by size class, as shown in the following figure.\n\nIt becomes possible to define sources from their « emission spectrum » by size class. An example is given below:\n\nAmong the scientific locks to be removed are the quantity of particles initially deposited, the differentiation of the resuspension term by size class (in other words: the \"resuspension spectrum\" for which there is not yet a unified theory) and finally the experimental comparison: external concentrations by size class are rarely available and the quantity of particles deposited on surfaces is difficult to evaluate.\n\n## References\n\n[Fortain 2007] Caractérisation des particules en gares souterraines. PhD thesis, University of La Rochelle\n\n[Lai et Nazaroff 2000] Lai, A. C. K. and Nazaroff, W. W. (2000). Modeling indoor particle deposition from turbulent flow onto smooth surfaces. Journal of Aerosol Science, 31(4):463–476.\n\n[Nicholson 1988] Nicholson, K. W. (1988). A review of particle resuspension. Atmospheric Environment, 22.\n\n[Qian et al. 2008] Qian, J., Ferro, A.R., Fowler, K.R., 2008. Estimating the resuspension rate and residence time of indoor particles. J. Air Waste Manage. Assoc. 58 (4).\n\n[Walther et Bogdan 2017a] Walther, E., & Bogdan, M. (2017). A novel approach for the modelling of air quality dynamics in underground railway stations. Transportation Research Part D: Transport and Environment, 56, 33-42.\n\n[Walther et al. 2017b] Walther, E., Bogdan, M., & Cohen, R. (2017). Modelling of airborne particulate matter concentration in underground stations using a two size-class conservation model. Science of The Total Environment, 607, 1313-1319."
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https://www.clutchprep.com/chemistry/practice-problems/65610/using-bond-enthalpies-table-8-4-estimate-160-916-h-160-for-the-followinggas-phas | [
"# Problem: Using bond enthalpies (Table 8.4), estimate ΔH for the following gas-phase reactions.\n\n###### FREE Expert Solution\n98% (354 ratings)\n###### FREE Expert Solution\n\nCalculation of ΔH using bond energies or bond enthalpies:\n\nΔHrxn = reactants - products\n\n98% (354 ratings)",
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"###### Problem Details\n\nUsing bond enthalpies (Table 8.4), estimate ΔH for the following gas-phase reactions.",
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"Frequently Asked Questions\n\nWhat scientific concept do you need to know in order to solve this problem?\n\nOur tutors have indicated that to solve this problem you will need to apply the Bond Energy concept. You can view video lessons to learn Bond Energy. Or if you need more Bond Energy practice, you can also practice Bond Energy practice problems.\n\nWhat is the difficulty of this problem?\n\nOur tutors rated the difficulty ofUsing bond enthalpies (Table 8.4), estimate ΔH for the follo...as medium difficulty.\n\nHow long does this problem take to solve?\n\nOur expert Chemistry tutor, Dasha took 8 minutes and 13 seconds to solve this problem. You can follow their steps in the video explanation above.\n\nWhat professor is this problem relevant for?\n\nBased on our data, we think this problem is relevant for Professor Caldwell's class at UCONN."
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http://www.faadooengineers.com/threads/49454-AC-Circuits-pdf?p=147808 | [
"##",
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"AC Circuits pdf\n\nDownload AC Circuits pdf by Chad Davis, This book covers alternating current (AC) circuit theory additionally us a brief introduction of electronics. It covers the fundamental theory of AC signals, sinusoidal waveforms, square waves, triangle waves, the mathematics background, resistors, inductors, and capacitors. Download the pdf from below to explore all topics and start learning.\n\nBOOK CONTENTS-\n\nModule 1 – AC Signal Overview\nSection 1.1 – AC Introduction\nSection 1.2 – Sinusoids\nSection 1.3– Defining AC Voltage Types for Common AC Waveforms\nSection 1.3.1 – Peak and Peak to Peak Voltage\nSection 1.3.2 – Average Voltage\nSection 1.3.3 – Root Mean Square (RMS) Voltage\nSection 1.4– Sine Wave, Square Wave, and Triangle Wave Example Problems\nSection 1.4.1 – Example Calculations of Vpp, Vpk, Vavg, and Vrms for Sine Waves\nSection 1.4.2 – Example Calculations of Vpp, Vpk, Vavg, and Vrms for Square Waves\nSection 1.4.3 – Example Calculations of Vpp, Vpk, Vavg, and Vrms for Triangle Waves\nModule 2 – AC Circuits Math Background\nModule 3 – RLC Circuit Analysis with AC Sources\nSection 3.1 – Introduction of Impedance and Admittance and the Laplace Transform\nSection 3.2 – Series RLC Circuits\nSection 3.3 – Parallel RLC Circuits\nSection 3.4 – AC Power Calculations\nSection 3.5 –AC Maximum Power Transfer\nModule 4 – Passive Filters\nSection 4.1 – Low Pass Filters (LPF)\nSection 4.2 – High Pass Filters (HPF)\nSection 4.3 –Band Pass Filters (BPF)\nSection 4.4 –Band Stop Filters (BSF)\nModule 5 – Transformers\nModule 6 – Diodes and AC to DC Conversion\nSection 6.1 – Diode Constant Drop Model\nSection 6.2 – Diode Logic Circuits\nSection 6.3 – Protection Diodes\nSection 6.4 – Rectifiers\nModule 7 –Operational Amplifiers (OpAmps)\nSection 7.1 – OpAmps Used as Comparators\nSection 7.2 – OpAmps used as Amplifiers\nSection 7.2.1 – Inverting OpAmp Configuration\nSection 7.2.2 – Non-Inverting OpAmp Configuration\nSection 7.3 – Superposition with OpAmps"
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https://books.google.gr/books?id=gyqPLHOWRy8C&pg=PA192&vq=%22centigram+(eg.)+%3B+10+centigrams%3D+1+decigram+(dg.)+%3B+10+decigrams%3D+1+gram+(G.)%3B+10%22&dq=editions:HARVARD32044096994090&lr=&hl=el&output=html_text | [
"Ĺéęüíĺň óĺëßäáň PDF Çëĺęôń. Ýęäďóç\n .flow { margin: 0; font-size: 1em; } .flow .pagebreak { page-break-before: always; } .flow p { text-align: left; text-indent: 0; margin-top: 0; margin-bottom: 0.5em; } .flow .gstxt_sup { font-size: 75%; position: relative; bottom: 0.5em; } .flow .gstxt_sub { font-size: 75%; position: relative; top: 0.3em; } .flow .gstxt_hlt { background-color: yellow; } .flow div.gtxt_inset_box { padding: 0.5em 0.5em 0.5em 0.5em; margin: 1em 1em 1em 1em; border: 1px black solid; } .flow div.gtxt_footnote { padding: 0 0.5em 0 0.5em; border: 1px black dotted; } .flow .gstxt_underline { text-decoration: underline; } .flow .gtxt_heading { text-align: center; margin-bottom: 1em; font-size: 150%; font-weight: bold; font-variant: small-caps; } .flow .gtxt_h1_heading { text-align: center; font-size: 120%; font-weight: bold; } .flow .gtxt_h2_heading { font-size: 110%; font-weight: bold; } .flow .gtxt_h3_heading { font-weight: bold; } .flow .gtxt_lineated { margin-left: 2em; margin-top: 1em; margin-bottom: 1em; white-space: pre-wrap; } .flow .gtxt_lineated_code { margin-left: 2em; margin-top: 1em; margin-bottom: 1em; white-space: pre-wrap; font-family: monospace; } .flow .gtxt_quote { margin-left: 2em; margin-right: 2em; margin-top: 1em; margin-bottom: 1em; } .flow .gtxt_list_entry { margin-left: 2ex; text-indent: -2ex; } .flow .gimg_graphic { margin-top: 1em; margin-bottom: 1em; } .flow .gimg_table { margin-top: 1em; margin-bottom: 1em; } .flow { font-family: serif; } .flow span,p { font-family: inherit; } .flow-top-div {font-size:83%;} meter (cm.3); 1000 cubic centimeters=1 cubic decimeter (dm. 3); 1000 cubic decimeters=1 cubic meter (Mo.). NOTE.—The higher denominations are not generally used. I indicate the cubic measures with an exponent, instead of writing cu. before the denominations. MEASURES OF CAPACITY. 422. The Liter is the unit of capacity. It equals a cubic decimeter; that is, a cubic vessel whose edge is onetenth of a meter. It is used for measuring liquids and dry substances. The liter is a cylinder, and holds 2.1135 pints wine measure, or 1.816 pints dry measure. TABLE.—10 milliliters (ml.)=1 centiliter (cl.); 10 centiliters=1 deciliter (di.); 10 deciliters=1 liter (L.); 10 liters =l decaliter (DL.); 10 decaliters=1 hectoliter (HL.); 10 hectoliters=1 kiloliter (KL.); 10 kiloliters=l myrialiter (ML.). Notes.-1. The liter is principally used in measuring liquids, and the hectoliter in measuring grains, etc. 2. The liter equals nearly 118 liquid quarts, or % of a dry quart, or nearly ste of a bushel measure. 3. The hectoliter is about 23 bushels or < of a barrel. 4 liters are a little more than a gallon ; 35 liters, very nearly a bushel. MEASURES OF WEIGHT. 423. The Gram is the unit of weight. It is the weight of a cubic centimeter of distilled water at the temperature of melting ice. The gram equals 15.432 Troy grains. TABLE.–10 milligrams (mg.)=1 centigram (eg.); 10 centigrams=1 decigram (dg.); 10 decigrams=1 gram (G.); 10 grams=1 decagram (DG.); 10 decagrams=1 hectogram (HG.); 10 hectograms=1kilogram (KG., or K.); 10 kilograms=1 myriagram (MG.). Notes.-1. The gram is used in weighing letters, in mixing and compounding medicines, and in weighing all very light articles. The five-cent coin adopted 1866 weighs 5 grams. 2. The kilogram is the ordinary unit of weight, and is generally abbreviated into kilo. It equals about 21 pounds avoirdupois. Meats, sugar, etc., are bought and sold by the kilogram. 3. In weighing heavy articles, two other weights, the quintal (100 kilograms) and the tonneau (1000 kilograms) are used. The tonneau is between our short ton and long ton. 4. The avoirdupois ounce is about 28 grams; the pound is a little less than } a kilo. The U. S. post offices receive 15 grams, though a little overweight, as equivalent to an ounce avoirdupois. 5. Some of the old weights and measures are still used in France; 1 livre=} a kilogram ; 1 marc=} a livre; 1 once=} a marc; 1 gros = an once ; 1 grain = 12 gros; 1 toise=2 métres ; 1 pied or foot=1 métre; 1 inch iz pied or foot; 1 aune=1} métres; 1 boisseau or bushel = 121 litres ; 1 litron 1.074 Paris pints. When these are employed, the word usuel is annexed to them, signifying customary. 424. Units of the common system may readily be changed to those of the Metric System by the following TABLE. 1 Inch=2.54 Centimeters. 1 Cu. Inch=16.39 Cu. Centim. 1 Foot= 30.48 Centimeters. 1 Cu. Foot=28320 Cu, Centim. 1 Yard=.9144 Meter. 1 Cu. Yard=.7646 Cu. Meters. 1 Rod=5.029 Meters. 1 Cord 3.625 Steres. 1 Mile=1.6093 Kilometers. 1 Fl. Ouncer 2.958 Centiliters. 1 Sq. Inch=6.4528 Sq. Centimeters. 1 Gallon=3.786 Liters. 1 Sq. Foot=929 Sq. Centimeters. 1 Bushel=.3524 Hectoliters. 1 Sq. Yard=.8361 Sq. Meters. 1 Troy Gr.= 64.8 Milligrams. 1 Sq. Rod=25.29 Centiares. 1 Troy lb.=.373 Kilo. 1 Acre=40.47 Ares. 1 Av. lb.=.4536 Kilo. 1 Sq. Mile=259 Hectares. 1 Ton=.907 Tonneau. NUMERATION AND NOTATION. 425. In the Metric System the decimal point is placed between the unit and its divisions, the whole quantity being regarded as an integer and a decimal. Thus, 3 decagrams, 5 grams, 6 decigrams, 8 centigrams, are written 35.368 grams. NOTE.-The initials of the denomination may be placed either before or after the quantity, though they are most frequently placed after it; thus, 27 grams may be written G27, or 27G. EXERCISES IN NUMERATION AND NOTATION. 1. Read 48.64 M., 85.87 A., 48.89 M2. 2. Read 854 17 S., 506.347 L., 4007.563 G. 3. Write 12 meters, 3 decimeters, 5 centimeters. 4. Write 8 hectares, 10 ares, 17 centiares. 5. 9 kilograms, 5 hectograms, 4 grams and 1 centigram. REDUCTION OF THE METRIC SYSTEM TO THE COMMON SYSTEM. 1. How many pounds Av. in 488.125 grams? Ans. 1 lb. 1 oz. 95.245 gr. 2. Grams in 24 pounds Troy? Ans. 8958.009 G. 3. Meters in 4 mi. 240 rd ? Ans. 7644.399 M. 4. Miles in 2000 meters? Ans. 1 mi. 77 rd. 11 ft. 2 in. 5. Acres in 1011.2 ares ? Ans. 24 A. 156.2624 P. 6. Ares in 11 A. 48 P.? Ans. 457.489 A. 7. Cu. ft. in 429.56 steres ? Ans. 15170.5987 cu. ft. 8. Steres in 32 cu. yd. 16 cu. ft.? Ans. 24.918 S. 9. Gallons in 90.1 liters ? Ans. 23 gal. 3 qt. 10. Liters in 73 gallons ? Ans. 276.319 L. 11. Bushels in 130.5 liters ? Ans. 3 bu. 2 pk. 6.49 qt. PRACTICAL PROBLEMS. 1. What cost 48.625 meters of cloth, if 9.725 meters cost \\$36.75 ? Ans. \\$183.75. 2. What must I pay for 75.25 steres of wood at the rate of \\$2.65 a stere ? Ans. \\$199.41. 3. Bought 15.25 liters of wine in Bordeaux, at 75.5 francs a liter ; what is the cost in U. S. money? Ans. \\$222.22. 4. How much must be paid for 12.5 grams of jewels, at \\$6.50 a gram? Ans. \\$81.25. 5. What is the cost of 672.25 grams of opium at 621¢ a gram? Ans. \\$420.16. 6. Mr. Brown imported for his house 35.429 meters of French carpet, at 19.75 francs a meter, including duty; required the whole cost. Ans. 699.72 +fr. 7. Mr. Winslow bought a valuable gem in Paris which weighed 245.25 grams, @ 10.25 francs, duty \\$4.75; how must be sell it a gram to clear \\$100 ? Ans. \\$2.41. 8. An importer bought 428.5 grams of drugs in France, at 12.5 francs a gram, paid 31į cents a gram duty and freight, and sold them for \\$2.25 a gram ; how much was gained or lost? Ans. Lost \\$204.61. 9. I bought 175.25 liters of French brandy at 7.50 francs a liter, paid 15 cents a liter duty and freight, and sold it in New York at \\$1.65 a liter; how much did I gain? Ans. \\$9.20. 10. Jordan, Marsh, & Co. bought 200 meters of silk in Lyons, at 16.25 francs a meter; after paying \\$2 a yard duty and freight, they sold it in Boston at \\$6.12 a yard; what was their profit? Ans. \\$274.98. REDUCTION OF COMPOUND NUMBERS. 426. Reduction is the process of changing a number from one denomination to another, without altering its value. 427. There are Two Cases: Reduction Descending and Reduction Ascending. These two cases have been considered in the examples under the tables, but we will present a few more problems under their proper heads. REDUCTION DESCENDING. 428. Reduction Descending is the process of reducing a number to a lower denomination. 1. Reduce £8 6 s. 4 d. to pence. OPERATION. £ S. d. 8 6 4 20 SOLUTION.-In 1 pound there are 20 shillings, and in £8 there are 8 times 20 shillings, plus 6 shillings are 166 shillings : in 1 shilling there are 12 pence, and in 166 shillings there are 166 times 12 166 s. pence, plus 4 pence equals 1996 pence. Therefore, 12 etc. 1996 d., Ans. Rule.—I. Multiply the number of the highest denomination given, by the number of units of the next lower denomination which equals one of this higher, and to the product add the number given, if any, of this lower denomination. II. Multiply this result as before, and proceed in the same manner until we arrive at the required denomination. 2. Reduce 8 lb. 4 oz. 6 pwt. 12 gr. to gr. Ans. 48156. 3. Reduce 9 lb. 11 3 3 3 2 2 4gr. to gr. Ans. 57344. 4. Reduce 124 A. 140 P. to sq. yd. Ans. 604395. 5. Reduce 120 cd. 6 cd. ft. to cubic feet. Ans. 15456. 6. Reduce 52 hhd. 24 gal. 3 qt. to pints. Ans. 26406. 7. 6 Circ. 10 S. 16° 20' 20\" to seconds. Ans. 8914820. 8. Cong. vij. 0.iv. fz vj. fz iij. to minims. Ans. 463860. 9. A farmer sold 16 A. 132 P. of land at \\$1.25 a square rod; how much did he receive ? Ans. \\$3365. 10. A man bought 6 bu. 3 pk. 5 qt. of berries for \\$10.25, and sold them at 10 cents a quart; how much did he gain? Ans. \\$11.85. OPERATION. REDUCTION ASCENDING. 429. Reduction Ascending is the process of reducing a number to a higher denomination. 1. In 246374 grains, how many pounds ? SOLUTION.—There are 24 gr. in 1 pwt., hence in 246374 gr. there are gr. as many pwt. as 24 is contained 24)246374 times in 246374, which is 10265 pwt. and 14 gr. remaining: there 20)10265+14 gr. are 20 pwt. in 1 oz., hence in 10265 12)513+5 pwt. pwt. there are as many ounces as 20 42 lb.+9 oz. is contained times in 10265, which Ans. 42 lb. 9 oz. 5 pwt. 14 gr. are 513 oz., and 5 pwt. remaining : there are 12 oz. in 1 pound, and in 513 oz. there are as many pounds as 12 is contained times in 513, which are 42 lb. and 9 oz. remaining. Therefore in 246374 grains there are 42 lb. 9 oz. 5 pwt. 14 gr. Rule.-1. Divide the given number by the number of units in that denomination which equals one of the next higher. II. Divide the quotient in the same way, and thus proceed until we arrive at the required denomination. III. The last quotient and the remainders, if any, will be the result required. 2. 346256 gr. to lb. Ans. 60 lb. 13 2 3 2 16 gr. 3. 4763254 li. to miles. Ans. 595 mi. 32 ch. 54 li. 4. 764325 cu. in. to cubic yards. Ans. 16 cu. yd. 10 cu. ft. 549 cu. in. 5.74625 m. to Cong. Ans. 1 Cong. 10. 11 fz 3 f3 45 in. 6. 25627542 sq. li. to acres. Ans. 256 A. 2 sq. ch. 7542 sq. li. 7. The side of a square field is 360 ft. long; how many rods of fence will enclose it? Ans. 87 rd. 1 yd. 1 ft. 6 in. 8. A dealer sold 1 ton of fish at \\$4.00 a quintal; what did it amount to? Ans. \\$80.00. 9. A miller sold 2560 lb. of flour at the rate of \\$9.00 a barrel ; what did it amount to? Ans. \\$117.55. 10. Bought 7420 square rods of land at \\$172 an acre, and sold it for \\$7000; how much did I lose ? Ans. \\$976.50. « ĐńďçăďýěĺíçÓőíÝ÷ĺéá »"
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https://www.futurestarr.com/blog/mathematics/find-relationship-between-numbers-calculator-or | [
"FutureStarr\n\nFind Relationship Between Numbers Calculator oR\n\n## Find Relationship Between Numbers Calculator oR",
null,
"# Find Relationship Between Numbers Calculator\n\nvia GIPHY\n\nA number calculator is a useful tool for analyzing mathematical relationships. This easy calculator is perfect for school projects, entering data for budgetary purposes, and more!\n\n### Calculator",
null,
"The correlation coefficient, or Pearson product-moment correlation coefficient (PMCC) is a numerical value between -1 and 1 that expresses the strength of the linear relationship between two variables.When r is closer to 1 it indicates a strong positive relationship. A value of 0 indicates that there is no relationship. Values close to -1 signal a strong negative relationship between the two variables. You may use the linear regression calculator to visualize this relationsh. is a basic mathematic function, represents the relative measure between 2 or more numbers. It's a a relationship between 2 or more numbers showing that how many times a first number is contained in the other numbers. The function GCD - Greatest Common Divisor is used to simplify the ratio to its lowest terms. The notation A : B : C represents the ratio between the numbers A, B and C. It's popularly used in investment, profit, loss, speed, distance, volume, density, etc. to measure the quantitative relationship between two or more quantities. Users may find the ratio between 2 or 3 numbers by using this numbers to ratio calculato.\n\nThe percentage difference calculator is here to help you compare two numbers. Here we will show you how to calculate the percentage difference between two numbers and, hopefully, to properly explain what the percentage difference is as well as some common mistakes. In the following article, we will also show you the percentage difference formula. On top of that, we will explain the differences between various percentage calculators, and how data can be presented in misleading, but still technically true, ways to prove various arguments. (Source: www.omnicalculator.com)\n\n### Number",
null,
"is a basic mathematic function, represents the relative measure between 2 or more numbers. It's a a relationship between 2 or more numbers showing that how many times a first number is contained in the other numbers. The function GCD - Greatest Common Divisor is used to simplify the ratio to its lowest terms. The notation A : B : C represents the ratio between the numbers A, B and C. It's popularly used in investment, profit, loss, speed, distance, volume, density, etc. to measure the quantitative relationship between two or more quantities. Users may find the ratio between 2 or 3 numbers by using this numbers to ratio calculator. (Source: getcalc.com)\n\n## Related Articles\n\n•",
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] | [
null,
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null,
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.92736256,"math_prob":0.9469976,"size":2677,"snap":"2022-27-2022-33","text_gpt3_token_len":536,"char_repetition_ratio":0.1597456,"word_repetition_ratio":0.4964539,"special_character_ratio":0.19462085,"punctuation_ratio":0.12830958,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99011314,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66],"im_url_duplicate_count":[null,null,null,null,null,1,null,null,null,7,null,null,null,6,null,null,null,5,null,null,null,7,null,null,null,3,null,null,null,1,null,null,null,2,null,null,null,5,null,null,null,2,null,null,null,5,null,null,null,6,null,null,null,8,null,null,null,5,null,null,null,4,null,null,null,7,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-06-29T01:19:36Z\",\"WARC-Record-ID\":\"<urn:uuid:aa9a36ad-617c-4eb1-ac44-f747b4a1a786>\",\"Content-Length\":\"123439\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:8d07d778-16f6-4605-bd36-48de4bf6100f>\",\"WARC-Concurrent-To\":\"<urn:uuid:2bc2a662-c408-490a-91cf-30d84717265f>\",\"WARC-IP-Address\":\"75.98.162.39\",\"WARC-Target-URI\":\"https://www.futurestarr.com/blog/mathematics/find-relationship-between-numbers-calculator-or\",\"WARC-Payload-Digest\":\"sha1:5NQA6B3GJKXJNHDLENKX2YZNO5TJY2TQ\",\"WARC-Block-Digest\":\"sha1:JUIUZZ2AN33WZKIX575YEBZ4EWBX2JAO\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-27/CC-MAIN-2022-27_segments_1656103619185.32_warc_CC-MAIN-20220628233925-20220629023925-00229.warc.gz\"}"} |
https://www.esc-history.com/semidetails.asp?key=5595 | [
"",
null,
"# Greece 2012",
null,
"",
null,
"Artist : Eleftheria Eleftheriou Title : Aphrodisiac Place : 4 Points : 116 Language : English Lyrics : Dimitris Stassos Mikaela Stenström Dajana LööfMusic : Dimitris Stassos Mikaela Stenström Dajana LööfStartposition : 3\n\nDetails and lyrics can be found here\n\nGreece gave points to :\nCountry Points",
null,
"Cyprus 12",
null,
"Albania 10",
null,
"Romania 8",
null,
"Russia 7",
null,
"Moldova 6",
null,
"Iceland 5",
null,
"Belgium 4",
null,
"Ireland 3",
null,
"San Marino 2",
null,
"Denmark 1\n\nCountry Points",
null,
"Romania 12",
null,
"Cyprus 12",
null,
"Ireland 10",
null,
"Azerbaijan 10",
null,
"Montenegro 10",
null,
"Moldova 10",
null,
"Belgium 8",
null,
"Albania 8",
null,
"San Marino 7",
null,
"Iceland 5",
null,
"Italy 5",
null,
"Russia 5",
null,
"Denmark 4",
null,
"Switzerland 3",
null,
"Spain 3",
null,
"Israel 3",
null,
"Austria 1\n\nTotal points : 116\n\n=1974= =1976= =1977= =1978= =1979= =1980= =1981= =1983= =1985= =1987=\n=1988= =1989= =1990= =1991= =1992= =1993= =1994= =1995= =1996= =1997=\n=1998= =2001= =2002= =2003= =2004= =2005= =2006= =2007= =2008= =2009=\n=2010= =2011= =2012= =2013= =2014= =2015= =2017= =2019= =2021= =2022=\n\nTotal Entries : 40\n\n=1996= =2004= =2008= =2009= =2010= =2011= =2012= =2013= =2014= =2015=\n=2016= =2017= =2018= =2019= =2020= =2021= =2022=\n\nTotal Entries : 17\n\n# Artist Title Points Place\n\nFinal | 12 March\nDora Katsikogianni\nΔώρα Κατσικογιάννη\nBaby I'm Yours",
null,
"Cassiopeia\nΚασσιόπη\nKiller Bee",
null,
"Velvet Fire No Parking",
null,
"Eleftheria Eleftheriou\nΕλευθερία Ελευθερίου\nAphrodisiac 1",
null,
"",
null,
"",
null,
"",
null,
"Date : Grand Final : 26-5-2012 Semi Final 1 : 22-5-2012 Semi Final 2 : 24-5-2012 Location : Baki Kristal ZaliBaku, Azerbaijan Host broadcaster : ITV Presentation : Leyla Aliyeva Narguiz Birk-Petersen Eldar Gasimov Participants : 36 Contest : 2 Semi-Finals & Final\n\n= Albania Final = = Austria = = Belgium = = Cyprus Final = = Denmark Final = = Finland = = Greece Final = = Hungary Final = = Iceland Final = = Ireland Final = = Israel = = Latvia = = Moldova Final = = Montenegro = = Romania Final = = Russia Final = = San Marino = = Switzerland =\n\n= Belarus = = Bosnia-Herzegovina Final = = Bulgaria = = Croatia = = Estonia Final = = FYR Macedonia Final = = Georgia = = Lithuania Final = = Malta Final = = Netherlands = = Norway Final = = Portugal = = Serbia Final = = Slovakia = = Slovenia = = Sweden Final = = Turkey Final = = Ukraine Final =\n\n= Albania = = Azerbaijan = = Bosnia-Herzegovina = = Cyprus = = Denmark = = Estonia = = France = = FYR Macedonia = = Germany = = Greece = = Hungary = = Iceland = = Ireland = = Italy = = Lithuania = = Malta = = Moldova = = Norway = = Romania = = Russia = = Serbia = = Spain = = Sweden = = Turkey = = Ukraine = = United Kingdom =",
null,
""
] | [
null,
"https://www.esc-history.com/images/logo/logo.svg",
null,
"https://www.esc-history.com/flags/GR.jpg",
null,
"https://www.esc-history.com/img_artists/5703.jpg",
null,
"https://www.esc-history.com/flags/CY.jpg",
null,
"https://www.esc-history.com/flags/AL.jpg",
null,
"https://www.esc-history.com/flags/RO.jpg",
null,
"https://www.esc-history.com/flags/RU.jpg",
null,
"https://www.esc-history.com/flags/MD.jpg",
null,
"https://www.esc-history.com/flags/IS.jpg",
null,
"https://www.esc-history.com/flags/BE.jpg",
null,
"https://www.esc-history.com/flags/IE.jpg",
null,
"https://www.esc-history.com/flags/SM.jpg",
null,
"https://www.esc-history.com/flags/DK.jpg",
null,
"https://www.esc-history.com/flags/RO.jpg",
null,
"https://www.esc-history.com/flags/CY.jpg",
null,
"https://www.esc-history.com/flags/IE.jpg",
null,
"https://www.esc-history.com/flags/AZ.jpg",
null,
"https://www.esc-history.com/flags/ME.jpg",
null,
"https://www.esc-history.com/flags/MD.jpg",
null,
"https://www.esc-history.com/flags/BE.jpg",
null,
"https://www.esc-history.com/flags/AL.jpg",
null,
"https://www.esc-history.com/flags/SM.jpg",
null,
"https://www.esc-history.com/flags/IS.jpg",
null,
"https://www.esc-history.com/flags/IT.jpg",
null,
"https://www.esc-history.com/flags/RU.jpg",
null,
"https://www.esc-history.com/flags/DK.jpg",
null,
"https://www.esc-history.com/flags/CH.jpg",
null,
"https://www.esc-history.com/flags/ES.jpg",
null,
"https://www.esc-history.com/flags/IL.jpg",
null,
"https://www.esc-history.com/flags/AT.jpg",
null,
"https://www.esc-history.com/images/x.gif",
null,
"https://www.esc-history.com/images/x.gif",
null,
"https://www.esc-history.com/images/x.gif",
null,
"https://www.esc-history.com/images/1x.gif",
null,
"https://www.esc-history.com/flags/az.jpg",
null,
"https://www.esc-history.com/logos/2012.jpg",
null,
"https://www.esc-history.com/locations/2012.jpg",
null,
"https://www.esc-history.com/images/icons/36-small.png",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.71920544,"math_prob":0.9998902,"size":2484,"snap":"2022-27-2022-33","text_gpt3_token_len":885,"char_repetition_ratio":0.18387097,"word_repetition_ratio":0.042462844,"special_character_ratio":0.4283414,"punctuation_ratio":0.065155804,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95747924,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76],"im_url_duplicate_count":[null,null,null,null,null,2,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,8,null,null,null,8,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-06-29T00:22:53Z\",\"WARC-Record-ID\":\"<urn:uuid:c4b34d7a-0d80-4ee1-9a60-6cf8ab722de3>\",\"Content-Length\":\"38222\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:97e74442-0400-487f-98f8-f6afd6e086e3>\",\"WARC-Concurrent-To\":\"<urn:uuid:f5332628-211e-447f-8ad3-babd0d23568a>\",\"WARC-IP-Address\":\"79.99.25.121\",\"WARC-Target-URI\":\"https://www.esc-history.com/semidetails.asp?key=5595\",\"WARC-Payload-Digest\":\"sha1:AIGAJBJ7B6PRJEB7ZELHF5SYKGCFSJHQ\",\"WARC-Block-Digest\":\"sha1:6QBSWOEUSEQZJJRFDZYI6DEHQCIM6CX4\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-27/CC-MAIN-2022-27_segments_1656103619185.32_warc_CC-MAIN-20220628233925-20220629023925-00796.warc.gz\"}"} |
https://www.tutorialspoint.com/cpp_standard_library/cpp_forward_list_unique_cmp.htm | [
"# C++ Forward_list Library - unique() Function\n\n## Description\n\nThe C++ function std::forward_list::unique() removes all consecutive duplicate elements from the forward_list. It uses binary predicate for comparison.\n\n## Declaration\n\nFollowing is the declaration for std::forward_list::unique() function form std::forward_list header.\n\n### C++11\n\n```template <class BinaryPredicate>\nvoid unique (BinaryPredicate binary_pred);\n```\n\n## Parameters\n\nbinary_pred − binary predicate which returns true if the elements should be treated as equal. It has following prototype.\n\n```bool pred(const Type1 &arg1, const Type2 &arg2);\n```\n\nNone\n\n## Exceptions\n\nThis member function never throws exception.\n\nLinear i.e. O(n)\n\n## Example\n\nThe following example shows the usage of std::forward_list::unique() function.\n\n```#include <iostream>\n#include <forward_list>\n\nusing namespace std;\n\nbool cmp_fun(int a, int b) {\nreturn (abs(a) == abs(b));\n}\n\nint main(void) {\n\nforward_list<int> fl = {1, -1, -1, -1, 2, 2, -2, -2, 3, -4, 4, -5, -5, 5};\n\ncout << \"List elements before unique operation\" << endl;\nfor (auto it = fl.begin(); it != fl.end(); ++it)\ncout << *it << endl;\n\nfl.unique(cmp_fun);\n\ncout << \"List elements after unique operation\" << endl;\nfor (auto it = fl.begin(); it != fl.end(); ++it)\ncout << *it << endl;\n\nreturn 0;\n}\n```\n\nLet us compile and run the above program, this will produce the following result −\n\n```List elements before unique operation\n1\n-1\n-1\n-1\n2\n2\n-2\n-2\n3\n-4\n4\n-5\n-5\n5\nList elements after unique operation\n1\n2\n3\n-4\n-5\n```\nforward_list.htm"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.5080686,"math_prob":0.8078413,"size":1351,"snap":"2019-43-2019-47","text_gpt3_token_len":364,"char_repetition_ratio":0.12991834,"word_repetition_ratio":0.13461539,"special_character_ratio":0.31828275,"punctuation_ratio":0.22262774,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.96817833,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-11-17T17:55:41Z\",\"WARC-Record-ID\":\"<urn:uuid:ae785407-9b90-48d3-945f-a5da00450c72>\",\"Content-Length\":\"23067\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0d11de29-3a3b-4713-9c31-e6e73db0d476>\",\"WARC-Concurrent-To\":\"<urn:uuid:6990ee33-7735-49e2-8e1b-7130fd6247c4>\",\"WARC-IP-Address\":\"72.21.91.42\",\"WARC-Target-URI\":\"https://www.tutorialspoint.com/cpp_standard_library/cpp_forward_list_unique_cmp.htm\",\"WARC-Payload-Digest\":\"sha1:JKZG4JDU2AADSFGFXSOS7W32PIC2JDW6\",\"WARC-Block-Digest\":\"sha1:NAYWZ4SJP2DKHSF6PXLT7LC4L577TUC5\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-47/CC-MAIN-2019-47_segments_1573496669225.56_warc_CC-MAIN-20191117165616-20191117193616-00006.warc.gz\"}"} |
http://umj.imath.kiev.ua/index.php/umj/article/view/3335 | [
"# Passive impedance systems with losses of scattering channels\n\n• D. Z. Arov\n• N. A. Rozhenko\n\n### Abstract\n\nA new model of the passive impedance system with minimal losses of scattering channels and with bilaterally stable evolution semigroup is studied. In the case of discrete time, the passive linear stationary bilaterally stable impedance system $\\Sigma$ is considered as a part of some minimal scattering-impedance lossless transmission system, that has a $(\\tilde{J}_1, \\tilde{J}_2)$-unitary system operator and a bilaterally $(J_1, J_2)$-inner (in certain weak sense) transmission function in the unit disk 22-block of which coincides with the impedance matrix of system $\\Sigma$, belongs to the Caratheodory class, and has a pseudocontinuation. If the external space of the system $\\Sigma$ is infinite-dimensional, then instead of the last mentioned property, we consider more complicated necessary and sufficient conditions on the impedance matrix of the system $\\Sigma$. Different kinds of passive bilaterally stable impedance realizations with minimal losses of scattering channels (minimal, optimal, *-optimal, minimal and optimal, minimal and *-optimal) are studied.\nPublished\n25.05.2007\nHow to Cite\nArovD. Z., and RozhenkoN. A. “Passive Impedance Systems With Losses of Scattering Channels”. Ukrains’kyi Matematychnyi Zhurnal, Vol. 59, no. 5, May 2007, pp. 618–649, http://umj.imath.kiev.ua/index.php/umj/article/view/3335.\nIssue\nSection\nResearch articles"
] | [
null
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https://www.chem.ucalgary.ca/courses/351/exams/3513/351f18/351mt18th.html | [
"Part 8: THERMODYNAMICS\n\nThis should have been a reasonably straight forward calculation, but care needs to be taken to get the right structures and the right molecular formula, then do the math correctly.\n\na. C5H10 + 15/2 O2 --> 5 CO2 + 5 H2O\n(2 marks)\n\nb. The calculation is shown on the figure below. (3 marks)\n\nc. The values are matched on the figure below. (2 marks)\n\nd.(3 marks)",
null,
"e. Stability will be affected by the degree of branching (more stronger primary C-H bonds will cause a more stable molecule) and the steric interactions between the groups (from the molecular models experiment). Based on branching, isomers i and ii are more stable than isomer iii since they have one more branch. The difference between i and ii is due to the cis and trans nature of the methyl groups. In isomer i where the methyl groups are cis, the two methyl groups are held in the unfavourable eclipsed position which destablises isomer i with respect to isomer ii. (2 marks)\n\nf. (1 mark)",
null,
"Common errors:\n\nIn part a:\n\n• did not show the right products from the combustion reaction (i.e. CO2 and H2O)\n• did not balance the coefficients\n• wrong molecular formula for hex-1-ene\n\nIn part d :\n\n• Incorrect math, e.g. using 10 H2 instead of 5.\n• getting the signs wrong"
] | [
null,
"https://www.chem.ucalgary.ca/courses/351/exams/3513/351f18/351mt18thd.gif",
null,
"https://www.chem.ucalgary.ca/courses/351/exams/3513/351f18/351mt18thf.gif",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9215206,"math_prob":0.97585595,"size":1009,"snap":"2021-21-2021-25","text_gpt3_token_len":250,"char_repetition_ratio":0.12039801,"word_repetition_ratio":0.0,"special_character_ratio":0.24182358,"punctuation_ratio":0.10344828,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95503163,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,1,null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-06-15T12:32:30Z\",\"WARC-Record-ID\":\"<urn:uuid:e1ecd348-f3b3-476e-b230-b717ce2c8590>\",\"Content-Length\":\"3554\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:fa5a91e8-4613-4579-b2bf-6309f253479a>\",\"WARC-Concurrent-To\":\"<urn:uuid:4dbe70eb-7378-4ae4-951d-f368b19d5cb9>\",\"WARC-IP-Address\":\"136.159.208.96\",\"WARC-Target-URI\":\"https://www.chem.ucalgary.ca/courses/351/exams/3513/351f18/351mt18th.html\",\"WARC-Payload-Digest\":\"sha1:ISGLVNJXLRQQHF3XIBMPKKLX3KYDHJXC\",\"WARC-Block-Digest\":\"sha1:CQP6DUWEMTLLIFVZKT6T5JLUXVM66DDL\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-25/CC-MAIN-2021-25_segments_1623487621273.31_warc_CC-MAIN-20210615114909-20210615144909-00567.warc.gz\"}"} |
http://archive.snoot.org/scribble/20.06/17.20.28.04-764.html | [
"``` 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5\nA - - - - - B O P - O - W A N T\nB - - - - - - - R - L - E - - -\nC - - - Q - E T A - O - D - - -\nD - - C U I F - Y - G - G - - -\nE - - - A - - - - - I - E D - -\nF - - H I - V O W - S - - E - -\nG - - O - - E V E C T I O N - -\nH P R E M I X E D - - - - T A J\nI L - - - - - R - - - - - - - O\nJ U - - - - - R - - Z I T I - B\nK T - - - - - A R - A - U - - S\nL O H - - - - N A - N - N - - -\nM - E - - - - - K E Y - I - - U\nN - R - - - - - E - - - N - A S\nO - D - F I L M S - - - G A L E\n```\n\nGame 6, Score: 764 tray:\n\n28. as by 24.187.21.74.\n27. use by 75.74.51.17.\n26. herd by 121.98.69.43 (Catherine/NZ).\n25. pluto by 24.187.21.74.\n24. gal by 121.98.69.43 (Catherine/NZ).\n23. tuning by 75.74.51.17.\n22. ziti by 24.187.21.74.\n21. vow by 121.98.69.43 (Catherine/NZ).\n20. bop by 24.187.21.74.\n19. ologist by 177.69.102.57 (Leandro.RPG).\n18. want by 24.187.21.74.\n17. ray by 121.98.69.43 (Catherine/NZ).\n16. wedge by 24.187.21.74.\n15. eta by 121.98.69.43 (Catherine/NZ).\n14. zany by 104.136.179.13.\n13. films by 24.187.21.74.\n12. cuif by 121.98.69.43 (Catherine/NZ).\n11. jobs by 24.187.21.74.\n10. a by 121.98.69.43 (Catherine/NZ).\n9. dent by 104.136.179.13.\n8. key by 75.74.51.17.\n7. evection by 104.136.179.13.\n6. quai by 121.98.69.43 (Catherine/NZ).\n5. hoe by 24.187.21.74.\n4. eve by 75.74.51.17.\n3. rake by 108.199.79.114 (loulin).\n2. overran by 104.136.179.13.\n1. premixed by 121.98.69.43 (Catherine/NZ)."
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.744182,"math_prob":0.9389462,"size":1557,"snap":"2021-04-2021-17","text_gpt3_token_len":793,"char_repetition_ratio":0.24146813,"word_repetition_ratio":0.093366094,"special_character_ratio":0.73410404,"punctuation_ratio":0.27586207,"nsfw_num_words":1,"has_unicode_error":false,"math_prob_llama3":0.9528733,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-04-15T07:40:04Z\",\"WARC-Record-ID\":\"<urn:uuid:5e1bad42-562e-4f32-ad4e-215f7233885c>\",\"Content-Length\":\"2461\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:8c5672d9-cc90-4796-ac5e-b60a15f6e3ce>\",\"WARC-Concurrent-To\":\"<urn:uuid:5e1977b6-4a44-40f6-962a-033f974b4cc3>\",\"WARC-IP-Address\":\"104.236.64.252\",\"WARC-Target-URI\":\"http://archive.snoot.org/scribble/20.06/17.20.28.04-764.html\",\"WARC-Payload-Digest\":\"sha1:KKO7MQQOUYI7244YUEQEHXLHPXADO7AM\",\"WARC-Block-Digest\":\"sha1:GKRGXGLJ5OPLIH2JXLT3EA5KHCUZX3H5\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-17/CC-MAIN-2021-17_segments_1618038084601.32_warc_CC-MAIN-20210415065312-20210415095312-00088.warc.gz\"}"} |
https://kazakhsteppe.com/ru/photo/159 | [
"# Photo solver\n\nApps can be a great way to help students with their algebra. Let's try the best Photo solver. Our website can help me with math work.\n\n## The Best Photo solver\n\nOne instrument that can be used is Photo solver. They all work in fundamentally the same way: they scan through all possible routes between points to find the shortest one. Here are some of the most popular ones: One great thing about solvers is that they're easy to use. You can run them on your desktop computer or tablet, so there's no need for expensive equipment. Plus, they don't have to be trained experts - anyone who has a basic understanding of mathematics can use them. So if you want to learn how to solve complex problems, get yourself a solver!\n\nThis means that it is easiest to solve a 3x3 if you can add or subtract the non-diagonal elements. You can also multiply or divide by the non-diagonal elements. It may seem more complicated than a regular matrix, but it is still very easy to solve. All you need to do is multiply or divide by one of the non-diagonal elements to get one side of your equation correct. One tip for solving 3x3: be sure to include all of the elements on each side of the equation when you are adding or subtracting. If you forget an element on one side, you will make a mistake on both sides! To solve 3x3, try dividing by all three elements on one side and then adding or subtracting them from each other. You may even have to simplify at some point so that you can get the right answer without making mistakes!\n\nTo find the answer, start with a whole number (e.g., 17) and a divisor (e.g., 5). Then, divide the divisor by the whole number (17 ÷ 5 = 4). Next, multiply the result by the dividend (4 × \\$5 = \\$20). Finally, add up all of your answers to find the total value of your item (20 + 4 + \\$5 = \\$25). The answer always works out to be one more than that original number because of rounding errors.\n\nAbsolute value equations are equations that have an expression with one or more variables whose values are all positive. Absolute value equations are often used to solve problems related to the measurement of length, area, or volume. In absolute value equations, the “absolute” part of the equation means that the answer is always positive, no matter what the value of the variable is. Because absolute value equations are so common, it can be helpful to learn how to solve them. Basic rules for solving absolute value equations Basic rule #1: Add negative numbers together and add positive numbers together The first step in solving any absolute value equation is to add all of the negative numbers together and then add all of the positive numbers together. For example, if you want to find the length of a rectangular room whose width is 12 feet and whose length is 16 feet, you would start by adding 12 plus (-16) and then adding 16 plus (+12). Because both of these numbers are negative, they will be added together to form a positive number.\n\nThe slope formula can also be used to find the distance between two points on a plane or map. For example, you could use the slope formula to measure the distance between two cities on a map. You can also use the slope formula to calculate the vertical change in elevation between two points on a map. For example, if you are hiking and find that your altitude has increased by 100 m (328 ft), then you know that you have ascended 100 m (328 ft) in elevation. The slope formula can also be used to estimate how tall an object is by comparing it with another object of known height. For example, if you are building a fence and want to estimate how long it will take to build it, you could compare the length of your fence with the height of some nearby trees to estimate how tall your fence will be when completed. The slope formula can also be used to find out how steeply a road or path rises as it gets closer to an uphill or downhill section. For example, if you are driving down a road and pass one house after another, then you would use the slope formula to calculate the distance between\n\nVery helpful app while doing school work! If I don't understand something completely and it explains it to me, sometimes in a better way that my teacher has. If there was one thing that I would have to critique and it would be how the app doesn't explain every step unless you have the pro version of the app however, I do understand that the app needs money and I understand that the pro version is how the app can stay operating. 10 out of 10 highly recommend downloading",
null,
"Yazmin Griffin\nThis app is really great! It has helped me pass test and quizzes. It not only solves and graphs the equation it lists the numerical steps to solve and a written literary explanation.",
null,
"Serenity Thompson\nApp that helps with math problems Take a picture of your homework and get answers online Algebra calculator solver Linear algebra system of equations solver Scatter plot solver Solve my geometry problems"
] | [
null,
"https://kazakhsteppe.com/LR56320a40ff0f65/25388788904_72d2f5ec6f_z-150x150.jpg",
null,
"https://kazakhsteppe.com/LR56320a40ff0f65/team_3-150x150.jpg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.94232595,"math_prob":0.9763189,"size":4055,"snap":"2022-40-2023-06","text_gpt3_token_len":881,"char_repetition_ratio":0.11305851,"word_repetition_ratio":0.03728362,"special_character_ratio":0.22564735,"punctuation_ratio":0.086269744,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9948653,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-01-29T06:57:56Z\",\"WARC-Record-ID\":\"<urn:uuid:a39f9539-9a75-40d3-9d4f-594738fa2e8b>\",\"Content-Length\":\"16303\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:eb96cc0b-1a6e-430b-83b4-1cea43ff00b7>\",\"WARC-Concurrent-To\":\"<urn:uuid:7fa51ac5-96d5-4db3-b489-e262e7bd6391>\",\"WARC-IP-Address\":\"107.167.10.244\",\"WARC-Target-URI\":\"https://kazakhsteppe.com/ru/photo/159\",\"WARC-Payload-Digest\":\"sha1:LBGKARS5CLNSN4OM53JH5HLFXBT3FPAV\",\"WARC-Block-Digest\":\"sha1:T74YIL7NVNC255IKKAS3GEFCB7QN44SD\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764499700.67_warc_CC-MAIN-20230129044527-20230129074527-00621.warc.gz\"}"} |
https://electronics.stackexchange.com/questions/295472/convert-vah-to-kwh | [
"# Convert VAh to kWh\n\nSorry, this might be a stupid question as I'm not an electrical person. I'm analyzing an electricity consumption dataset and the consumption is measured by apparent energy (VAh).\n\nHow do I convert this VAh to kWh as normally seen on a power bill? Or are they the same thing, just different ways to call?\n\nThanks.\n\n• See @Christian answer below. In most parts of the world, you pay only for actual energy but big industries pay penalties for apparent energy too. Assume a PF of 1 and it will get you very close to the real value. Then 1000 VAh = 1 kWh or 1 kVAh = 1 kWh. – winny Mar 29 '17 at 8:54\n• Related, but probably not a duplicate: What is the practical difference between watts and VA (volt-amps)? – a CVn Mar 29 '17 at 15:23\n\nYou need to know the power factor, which is highly depedant on your application. Then you simply use this formula: $$P = S * PF$$ where\n\nP = actual energy\nS = apparent energy\nPF= Power Factor\n\n\nThis answer tells us, that power factor in general public households can be expected to be greater than 0.9.\n\n• Thanks a lot. Also, I have 2 rows of data in chronical order (60 sec interval) like this: [PF=0.98; VAh=30925] and [PF=0.97; VAh=30926]. If convert to the actual energy kWh, the kWh values would be decreasing (30306 -> 29998), which doesn't make sense for me. So should I analyze the electricity consumption using apparent energy instead of actual energy? – Foo Mar 29 '17 at 12:14\n• 30306 kWh in 60 seconds is 1.8GW, are you working at a nuclear power plant? Why would you think that the power is constant? – Christian Mar 29 '17 at 12:19\n• The data is cumulative so it looks that big. I meant the power went from 30306 to 29998 kWh in 60 seconds. This is data of a household appliance. – Foo Mar 29 '17 at 12:23\n• My main concern is why the kWh can ever decrease. – Foo Mar 29 '17 at 12:50\n• Either the PF is a average since the beginning of the dataset, which would make sense, because your power consumption is a sum. Or it's the PF in the last 60 seconds, in that case you should apply the new PF value only to the delta since the last measurment, not to the whole set. – Christian Mar 29 '17 at 12:56\n\nYou can’t convert them.\n\nAll you know is that the actual power(’s absolute value) is ≤ the apparent power. The actual power might be 0 (purely inductive or capacitive load) or negative (if you are analyzing a generator). It might also be = the apparent power (purely resistive load).\n\nAs for the units, VA and W are equivalent.\n\nAs for the energy, give than the power is the derivative of the energy, you have the same result that actual energy(’s absolute value) is ≤ the apparent energy, and that VAh and Wh are equivalent units.\n\n• VA and W are only equivalent when the phase angle between voltage and current is zero. – Steve Mar 29 '17 at 15:00\n• @Steve Did you read the whole answer before commenting? I wrote that VA and W are equivalent units. I know that apparent power is different from actual power. I also know that VA is generally used for apparent power while W is generally used for actual power. – user2233709 Mar 29 '17 at 18:52\n• Yes I did, I just felt it was worth pointing out. – Steve Mar 29 '17 at 19:26\n\nPower: W=V*I\n\nEffective power: VA=VIcos(Φ) where Φ is the phase angle between the current and the power.\n\ncos(90)=0\n\nTherefore for a purely capacitive load, although the effective power would be 0, the actual power drawn is V*I\n\nThe same goes for a purely inductive load, where the current lags the voltage by 90° and cos(-90) = cos(270) = 0\n\nThis would mean that under those conditions, although the system draws V*I watts out of the system, the work done is 0, which is what the meter on the incoming mains, will also read.\n\nThis is not a good state for either the consumer or the supplier, therefore power factor correction is applied. As most loads are inductive, the method used to correct the power factor, is by using a capacitor of the appropriate size, for the load.\n\nDomestically, washing machines, drills, etc. have in built capacitors, and in anycase, their effect is minimal.\n\nIndustrially, where they have 3 phase power feeding large transformers, large motor driven machines, ones which require 10kVA or more, will also require the incoming mains to have power factor correction capacitor connected. These however tend to be dynamic, where the power factor is constantly monitored and capacitors are automatically switched in to keep the PF as close to 1 as possible.\n\nReferences:\n\nhttps://en.wikipedia.org/wiki/Power_factor\n\nhttp://www.eaton.com/ecm/groups/public/@pub/@electrical/documents/content/sa02607001e.pdf"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9246499,"math_prob":0.92820853,"size":1502,"snap":"2020-10-2020-16","text_gpt3_token_len":355,"char_repetition_ratio":0.120160215,"word_repetition_ratio":0.0,"special_character_ratio":0.22436751,"punctuation_ratio":0.13043478,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98774594,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-02-28T13:03:55Z\",\"WARC-Record-ID\":\"<urn:uuid:06d6ff1d-3b53-4583-9e85-38231f4e3719>\",\"Content-Length\":\"165609\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:a65a9153-30a9-43da-8e4f-ba17e6a842b5>\",\"WARC-Concurrent-To\":\"<urn:uuid:89d67440-52df-476f-b281-fb965ddb1423>\",\"WARC-IP-Address\":\"151.101.65.69\",\"WARC-Target-URI\":\"https://electronics.stackexchange.com/questions/295472/convert-vah-to-kwh\",\"WARC-Payload-Digest\":\"sha1:AJIHXNL5IQKGWDJUII5DUPK3MZH2X43W\",\"WARC-Block-Digest\":\"sha1:7PWOTU7NLBXLZH6NQVM4F5QMZ572Q3U6\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-10/CC-MAIN-2020-10_segments_1581875147154.70_warc_CC-MAIN-20200228104413-20200228134413-00280.warc.gz\"}"} |
https://books.halfrost.com/leetcode/ChapterFour/0200~0299/0236.Lowest-Common-Ancestor-of-a-Binary-Tree/ | [
"0236. Lowest Common Ancestor of a Binary Tree\n\n# 236. Lowest Common Ancestor of a Binary Tree#\n\n## 题目 #\n\nGiven a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree.\n\nAccording to the definition of LCA on Wikipedia: “The lowest common ancestor is defined between two nodes p and q as the lowest node in T that has both p and q as descendants (where we allow a node to be a descendant of itself).”\n\nGiven the following binary tree: root = [3,5,1,6,2,0,8,null,null,7,4]",
null,
"Example 1:\n\n``````Input: root = [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 1\nOutput: 3\nExplanation: The LCA of nodes 5 and 1 is 3.\n``````\n\nExample 2:\n\n``````Input: root = [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 4\nOutput: 5\nExplanation: The LCA of nodes 5 and 4 is 5, since a node can be a descendant of itself according to the LCA definition.\n``````\n\nNote:\n\n• All of the nodes’ values will be unique.\n• p and q are different and both values will exist in the binary tree.\n\n## 解题思路 #\n\n• 这是一套经典的题目，寻找任意一个二叉树中两个结点的 LCA 最近公共祖先，考察递归\n\n## 代码 #\n\n``````\npackage leetcode\n\n/**\n* Definition for TreeNode.\n* type TreeNode struct {\n* Val int\n* Left *ListNode\n* Right *ListNode\n* }\n*/\nfunc lowestCommonAncestor236(root, p, q *TreeNode) *TreeNode {\nif root == nil || root == q || root == p {\nreturn root\n}\nleft := lowestCommonAncestor236(root.Left, p, q)\nright := lowestCommonAncestor236(root.Right, p, q)\nif left != nil {\nif right != nil {\nreturn root\n}\nreturn left\n}\nreturn right\n}\n\n``````",
null,
"Apr 8, 2023",
null,
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] | [
null,
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null,
"https://books.halfrost.com/leetcode/svg/calendar.svg",
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https://byjus.com/question-answer/the-lcm-of-two-numbers-is-48-if-their-ratio-is-2-3-find-their/ | [
"",
null,
"",
null,
"Question\n\n# The Lcm of two numbers is 48. If their ratio is 2:3, find their sum\n\nSolution\n\n## Let 2x & 3x be the numbers their L.C.M will be 6x But their L.C.M is given = 48 ⇒6x = 48 ⇒x=8 So, the numbers are 2*8 i.e 16 & 3*8 i.e 24 Hence the sum of numbers = 16+24 =40",
null,
"",
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"Suggest corrections",
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"data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iNDQiIGhlaWdodD0iNDQiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgdmVyc2lvbj0iMS4xIi8+",
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"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7",
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https://learn.saylor.org/mod/book/view.php?id=29232&chapterid=4732 | [
"## BUS202 Study Guide\n\n### 3a. Summarize the rules in capital budgeting when using net present value calculations\n\n• How do firms use money from their capital budgets to invest?\n• What is a \"project\" to a firm?\n• How do firms decide which projects to invest in?\n\nFirms have finite financial resources and therefore have to make capital budgeting decisions about which capital investments to undertake. Capital investments are called projects. Firms have to make choices between which projects in which to invest by ranking projects based on the outcome of capital budgeting techniques. To do so, they use project evaluation criteria. The most common project evaluation criteria of capital budgeting are net present value (NPV) and internal rate of return (IRR). However, there are others such as the payback period, modified internal rate of return, or the average rate of returns. Each capital budgeting technique has its own decision criteria that must be considered after the return has been calculated. Net present valuation uses a technique that is called discounted cash flow valuation. Net present value is a variation on the present value calculation that allows for calculating both cash inflows and outflows and a changing discount rates and maturities. The decision criteria for an NPV calculation is as follows:\n\nIf the NPV > 0, accept the project;\n\nIf the NPV < 0, reject the project;\n\nIf the NPV = 0, accept or reject the project based on preference or other factors.\n\nIf more than one project has a positive NPV (an NPV > 0), as a rule, the project with the highest NPV should be accepted.\n\n### 3b. Use the incremental approach in finance to compare the net present value of a project with the net present value of another project\n\n• How do we compare the NPV of only 2 projects?\n\nThe incremental cost approach is optimal to use when only comparing two projects. It focuses on both cost increases and decreases. Only those costs and revenues that differ between the two projects being considered are the costs and revenues to which discounted cash flow analysis need to be applied. The final result of an incremental NPV analysis should be the same as the final result from using the total cost approach of NPV, which is the more common approach.\n\n### 3c. Calculate the depreciation expense of an asset and demonstrate how that expense factors into the income statement and cash flow statement\n\n• What is depreciation?\n• How does one record the depreciation of assets?\n• Which financial statements contain depreciation?\n\nOnce a firm acquires a capital asset, which is often done with the use of capital budgeting techniques, that firm is allowed to account in its financial statements for the deterioration in that asset's usefulness over time. This deterioration in the useful life of an asset is called \"depreciation\". Depreciation is classified as a \"non-cash expense\" for accounting purposes and provides a tax benefit for firms. Depreciation of an asset is an accounting convention that is used to reflect a significant loss of value or the loss of performance of an asset due to repeated use or expected maintenance over time. A firm decides based on the asset class how long it can use the asset before it will need to be replaced, re-sold, or otherwise disposed of. That length of time is called the life of the asset. Once the life of the asset is established, that becomes a very important input, almost like a target point, to calculating depreciation. Next, the firm must decide what method of depreciation to use for the asset. There are many calculation methods for depreciation. While straight-line depreciation is the most common, there are other depreciation methods, such as MACRS, modified depreciation, unit-of-service depreciation, hours-of-service depreciation, reducing balance, sum of year's digits, and double-declining balance.\n\nStraight-line depreciation assumes that the asset will be depreciated to a value of zero by the end of its useful life and that there is no remaining book value which can be recouped from the reselling of the asset. An equal amount of depreciation is expensed each year of the asset's life until zero remains as the value of the fixed asset in its final year of life. The value of depreciation expense is constant each year while the fixed asset balance declines and the balance in the accumulated depreciation account grows.\n\nThe effect of depreciation can be seen in all three of the major financial statements – the balance sheet, income statement, and the cash flow statement. All of the depreciation over time that a company claims is reflected in the accumulated depreciation account on the balance sheet. This account is called a contra-asset. It is listed on the asset side of the balance sheet, below the capital equipment account, which is usually called Property, Plant, and Equipment (PP&E) or a fixed asset. As a contra-asset, the accumulated depreciation account maintains a growing balance, but that a balance decreases the overall asset balance instead of increasing it like a typical asset would. The accumulated depreciation account is subtracted from the balance in the fixed asset account, which creates an account that is sometimes listed on the balance sheet as a Net Fixed Asset. Whenever you see a net fixed asset account, the effects of depreciation have already been figured into the value of assets on the balance sheet. The balance in the accumulated depreciation account should increase, which the balance in the fixed asset account (or net fixed asset account) should be declining. It is possible for that balance to decline to zero.\n\nOver time, the accumulated depreciation account will grow if assets are being actively depreciated, which means that the fixed asset account will decrease over time. The current value in the accumulated depreciation account reflects the sum of all the depreciation taken over the year or sometimes multiple years for the firm. The difference in the accumulated depreciation accounts on two consecutive years' balance sheets reflects the annual depreciation expense claimed by the firm. The annual depreciation expense is shown on the income statement of the most current year that corresponds to the most current year's balance sheet used to find the change in the accumulated depreciation accounts. When depreciation expense is on the income statement, it is known as a non-cash expense. This means that it is subtracted from sales revenue like all other expenses, but there is no actual cash outflow from the firm for having depreciation expense. Depreciation isn't paid to anyone. The \"expensing\" of it only happens on paper on the income statement. Expensing depreciation lowers the firm's taxable income, and in that way it provides a tax benefit. This is meant to account for anticipated future repurchases or costs associated with the asset.\n\nThe effects of depreciation are far-reaching. When trying to construct a cash flow statement for a firm, it is important to properly separate cash inflow versus outflows and to associate them with the appropriate activities. The first thing to do when assessing the cash inflows from operating activities is to add back the depreciation expense on the income statement so that the cash flow statement accurately shows that what has been depreciated is not actual cash that has flowed out of the firm. See the depreciation example below.\n\n### 3d. Calculate the net present value of an investment option\n\n• What are the components of the NPV formula?\n• How can the NPV formula be modified for changes in cash flows over time?\n\nThe net present value calculation is a modification of the present value calculation. The major differences are that the equation allows for 1) cash inflows can be subtracted and these do not have to be discounted; and 2) in each period the years left to maturity will change and any other inputs to the equation can also change such as discount rate, the frequency of compounding, and even the lump sum amount that is being discounted. The equation can be expanded or shortened as needed, as the structure of the project requires.\n\n$\\mathrm{NPV}=\\sum_{t=0}^{n}\\frac{CF_t}{\\left ( 1+r \\right )^t}$\n\n### Unit 3 Vocabulary\n\nThis vocabulary list includes terms that might help you with the review items above and some terms you should be familiar with to be successful in completing the final exam for the course.\n\nTry to think of the reason why each term is included.\n\n• Discounted cash flow valuation\n• Capital budgeting\n• Decision criteria/rule\n• Net present value\n• Internal rate of return\n• Depreciation\n• Accumulated depreciation\n• Depreciation expense\n• Capital asset\n• Straight-line depreciation\n• Replacement cost\n• Salvage value"
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https://fr.slideserve.com/yuli-davidson/photovoltaic-devices | [
"",
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"Download",
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"Download Presentation",
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"Photovoltaic Devices\n\n# Photovoltaic Devices\n\nDownload Presentation",
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"## Photovoltaic Devices\n\n- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -\n##### Presentation Transcript\n\n1. Photovoltaic Devices Fall 2008 University of South Alabama\n\n2. Outlines • Solar energy spectrum • Photovoltaic device principles • I-V Characteristics\n\n3. Solar Energy Spectrum • Photovoltaic devices (solar cells) convert the incident solar radiation energy into electrical energy. • Absorbed photons photogeneration current (photocurrent) in external cicuit. • Power range: from < mW (calculator) to few MW (photovoltaic power generation)\n\n4. Solar Energy Spectrum • Spectral intensity Iλ: Intensity per wavelength. • Iλδλ: intensity in a small interval δλ. • Total intensity I: integration of Iλ over the whole spectrum. • Solar constant or air-mass zero (AM0): the total intensity above earth’s atmosphere, approximately constant at 1.353 kW m-2.\n\n5. Solar Energy Spectrum • On sunny day, light intensity on earth’s surface is about 70% of the intensity above the atmosphere. • Absorption and scattering effects increase with the sun beam’s path through the atmosphere. • The shortest path through the atmosphere is when the sun is directly above that location and the received spectrum is called air mass one (AM1). • Air massm (AMm): the ratio of the actual radiation path h to the shortest path h0, m = h/h0. • Since h = h0secθ, AMm is AMsecθ.\n\n6. Solar Energy Spectrum • The spectral distribution AM1.5 has several sharp absorption peaks at certain wavelengths which are due to those wavelength being absorbed by various molecule in the atmosphere, such as ozone, air, and water vapor molecules. • Dust particles scatter the sun light reduces the intensity and gives rise to the sun’s rays arriving at random angle.\n\n7. Solar Energy Spectrum • Thus, the terrestrial light has a diffuse component in addition to direct component. • Cloud and sun position increase diffuse component spectrum shifted toward the blue light. • Scattering also increase with decreasing wavelength. • On a clear day, diffusion component can be about 20% of the total radiation. • The amount of incident radiation depends on the position of the sun. Photovoltaic device flat will receive less solar energy by factor cosθ However it can be tilted to directly face the sun to maximize the collection efficiency.\n\n8. Florida Solar Energy Center\n\n9. PV Cells have efficiencies approaching 21.5% Florida Solar Energy Center\n\n10. Silicon Solar Cell http://en.wikipedia.org/wiki/Image:Solar_cell.png\n\n11. PV Modules have efficiencies approaching 17% Florida Solar Energy Center\n\n12. Florida Solar Energy Center\n\n13. Solar Panel Solar panel by BP Solar at a German autobahn bridge http://en.wikipedia.org/wiki/Solar_panel\n\n14. Florida Solar Energy Center\n\n15. International Space Station Hubble Telescope Mars Rover Spacecraft\n\n16. Cross Section of PV Cell http://en.wikipedia.org/wiki/Solar_cells\n\n17. Photovoltaic Device Principles • Consider pn+ junction with very narrow n-region. • The illumination is through then thin n-side. • The SCL extend mainly in p-region with built-in field E0. • Electrode at n-side must allow illumination to enter the device and at the same time result in a small series resistance. • This electrode is formed from array of finger electrodes.\n\n18. Photovoltaic Device Principles • Photons are absorbed in SCL within the neutral p-side (lp) photogenerated EHP in this region. • The electron drifts and reaches the neutral n+ side whereupon it makes this region negative by an amount of charge –e. • Similarly, hole drifts and reaches the neutral p-side and thereby makes this side positive open circuit voltage between terminals of the device. • If there is external load, electron will travel through it and recombine with the excess holes in p-side.\n\n19. Photovoltaic Device Principles • Therefore the existence of built-in field E0 is important to create accumulated electrons in the n-side and holes in the p-side. • For long wavelength photons absorbed in the neutral p-side no E field diffusion. • Minority carrier diffusion length Le. • τe: recombination lifetime of electron. • De: diffusion coefficient in the p-side.\n\n20. Role of Diffusion Length • Only those EHPs photogenerated within the Le to the depletion layer can contribute to the photovoltaic effect. • Those photogenerated EHPs further away from SCL than Le are lost by recombination. • Thus, it is important to have the minority carrier diffusion length Le as long as possible. By choosing Si pn junction to be p-type which makes electrons to be minority carriers; the electron diffuse length in Si > the hole diffusion length.\n\n21. Role of hole diffusion length and short circuit current • For EHPs photogenerated by short-wavelength photons absorbed in the n-side, within diffusion length Lh, can reach SCL and swept across to the p-side. • The photogenerated of EHPs that contribute to the photovoltaic effect occurs in a volume of absorption coefficient Lh + W + Le. • If the terminals are shorted then the excess electrons in the n-side can flow through the external circuit to neutralize the excess holes in the p-side this current is called photocurrent.\n\n22. Photovoltaic Device Principles • Under steady state operation no net current through an open circuit solar cell Photocurrent inside the device due to photo generated carriers must be balanced by a flow of carriers in the opposite direction. • Those are minority carriers that become injected by the appearance of the photovoltaic voltage across the pn junction as in normal diode.\n\n23. Device optimization and carrier losses • For long wavelengths, 1 – 1.2 μm, α is small absorption depth 1/α is typically greater than 100 μm. Need a thick p-side and long minority carrier diffusion length Le. • Thus, p-side is 200 – 500 μm and Le is shorter than that. • Si has Eg = 1.1 eV correspond to a threshold wavelength of 1.1 μm The incident energy with wavelength > 1.1μm is then wasted (~ 25%). • Photons are absorbed and recombined near the crystal surface losses. absorption coefficient\n\n24. Sources of carrier losses • Photons are absorbed and recombined near the crystal surface losses severely reduce efficiency. • Crystal surface and interface contain high concentration of recombination-center. • Those facilitate the recombination of photogenerated EHP near the surface. • The losses due to this event as high as ~ 40%. • These combined effect bring the efficiency down to about 45%. • Antireflection coating is also contributing the reduction of photons collection due to imprefection with factor of 0.8 – 0.9. • And including the limitation of photovoltaic action the upper limit to a photovoltaic device that uses single crystal of Si is about 24 – 26% at room temperature.\n\n25. pn Junction Photovoltaic I-V Characteristics • Consider an ideal pn junction photovoltaic device connected to a resistive load R. • I and V define the convention for the direction of positive current and positive voltage. • If the load is short circuit the only current in the circuit is due to photogenerated (photocurrent),Iph.\n\n26. pn Junction Photovoltaic I-V Characteristics K is constant that depends on particular device • If I is the light intensity, then the short circuit current is • The photocurrent does not depend on the voltage across the pn jucntion, because it always some internal field to drift the photogenerated EHP. • If R is not short circuit the positive voltage V appears across the pn junction as a result of the current passing through.\n\n27. pn Junction Photovoltaic I-V Characteristics • The voltage across the load R (with opposite polarity) reduces the built in potential V0 of the pn junction and hence leads to minority carrier injection and diffusion. • Thus, in addition to Iph there is also a forward diode current Id in the circuit which arises from the voltage developed across R. • Since Id is due to the normal pn junction behavior diode characteristics, • where I0 is the reverse saturation current, n is the ideality factor which depends on semiconductor material and fabrication chaeacteristics (n = 1 – 2).\n\n28. pn Junction Photovoltaic I-V Characteristics • Thus, the total current (solar cell current), • The I-V characteristics of a typical Si solar cell (Fig.). • Normal dark characteristics being shifted down by photocurrent Iph (short circuit), which depend on light intensity, I. • The open circuit voltage, Voc, is given by the point where the I-V curve cuts the V-axis (I = 0).\n\n29. pn Junction Photovoltaic I-V Characteristics • The I-V curves for positive current requires an external bias voltage. • Although Voc depends on the light intensity, it value lies between 0.4 – 0.6 V. • Photovoltaic operation is always in the negative current region. • Thus the load line,\n\n30. pn Junction Photovoltaic I-V Characteristics • When a solar cell drives a load R, R has the same voltage as the solar cell but the current through it is in the opposite direction to the convention that current flows from high to low potential. • The current I’ and voltage V’ can be found by solving two previous equations simultaneously not trivial analytical procedure.\n\n31. Photovoltaic I-V Characteristics (load line analysis) • Or they can be found easily from load line construction. • The load line cuts the solar cell characteristics at P. Point P satisfies both equations represent the operating point of the circuit.\n\n32. Maximum power and fill factor • The power delivered to the load is Pout = I’V’ the area bound by I- and V-axes and the dashed lines. • Maximum power delivered by changing R max area when I’ = Im and V’ = Vm. • The fill factor FF, FF range is 70 – 80% FF is a measure of the closeness of the solar cell I-V curve to the rectangular shape\n\n33. Series Resistance and Equivalent Circuit • Practical devices ≠ ideal pn junction device. • Photogenerated electron has to transverse a surface semiconductor region to reach the nearest finger electrode create an effective series resistance Rs.\n\n34. Series Resistance and Equivalent Circuit • Equivalent circuit of an ideal pn junction solar is represented by a constant current generator Iph. • The flow of photogenerated carriers across the junction gives rise to a photovoltaic voltage V. This voltage leads to a normal diode current Id = I0[exp(eV/nkBT)-1] Id is represented by and ideal pn junction diode. • Iph and Id are in opposite directions open circuit the photovoltaic voltage is such that Iph and Id have the same magnitude and cancel each other.\n\n35. Series Resistance and Equivalent Circuit • Equivalent circuit for practical solar cell include series resistance Rs gives rise to a voltage drop. • Photogenerated carriers can also flow through the crystal surfaces or grain boundaries in polycrystalline devices, instead of flowing through the external load RL represented by an effective internal shunt or parallel resistance Rp. • Typically Rp is less important than Rs unless the device is highly polycrystalline.\n\n36. Series Resistance and Equivalent Circuit • Series resistance Rs can significantly deteriorate the cell performance. The max power decrease reduce cell efficiency.\n\n37. Temperature Effects • Temperature decreases Output voltage and efficiency increase. • Solar cell operate best at lower temperature. • The output voltage Voc, when Voc >> nkBT/e, • I0 is reverse saturation current and strongly depend on temperature, because it depends on square of ni. • If I is light intensity,\n\n38. Temperature Effects • Assuming n = 1, at two different temperatures T1 and T2 but the same illumination level, by subtraction, • Substitute, • Thus, • Rearrange for Voc2,\n\n39. Temperature Effects • Example, Si solar cell has Voc1 = 0.55 V at 20 oC (T1 = 293 K), at 60 oC (T2 = 333 K),\n\n40. Solar Cells Materials, Devices and Efficiencies • For a given solar spectrum, conversion efficiency depends on the semiconductor material properties and the device structure. • Si based solar cell efficiencies 18% for polycrystalline and 22 – 24% for single crystal devices. • About 25% solar energy is wasted not enough energy unable to generate EHPs. • Considering all losses, the maximum electrical output power is ~20% for a high efficiency Si solar cell.\n\n41. Solar Cells Materials, Devices and Efficiencies • Si homojunction solar cell efficiencies ~24%. Single crystal PERL (Passivated Emitter Rear Locally-diffused) cells.\n\n42. Solar Cells Materials, Devices and Efficiencies • Semiconductor alloy III-V different bandgap with the same lattice constant Heterojunction.\n\n43. Solar Cells Materials, Devices and Efficiencies • Example n-AlGaAs with p-GaAs.\n\n44. Solar Cells Materials, Devices and Efficiencies • To further increase the absorbed photons tandem or cascade cells (use two or more cells in tandem), such as GaAs – GaSb."
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http://scholarpedia.org/article/Bifurcation_theory | [
"# Bifurcation\n\n(Redirected from Bifurcation theory)\nPost-publication activity\n\nCurator: John Guckenheimer\n\nA bifurcation of a dynamical system is a qualitative change in its dynamics produced by varying parameters.\n\n## Definition\n\nConsider an autonomous system of ordinary differential equations (ODEs) $\\tag{1} \\dot{x}=f(x,\\lambda),\\ \\ \\ x \\in {\\mathbb R}^n, \\ \\ \\ \\lambda \\in {\\mathbb R}^p$\n\nwhere $$f$$ is smooth. A bifurcation occurs at parameter $$\\lambda = \\lambda_0$$ if there are parameter values $$\\lambda_1$$ arbitrarily close to $$\\lambda_0$$ with dynamics topologically inequivalent from those at $$\\lambda_0\\ .$$ For example, the number or stability of equilibria or periodic orbits of $$f$$ may change with perturbations of $$\\lambda$$ from $$\\lambda_0\\ .$$ One goal of bifurcation theory is to produce parameter space maps or bifurcation diagrams that divide the $$\\lambda$$ parameter space into regions of topologically equivalent systems. Bifurcations occur at points that do not lie in the interior of one of these regions.\n\n## Bifurcation theory\n\nBifurcation theory provides a strategy for investigating the bifurcations that occur within a family. It does so by identifying ubiquitous patterns of bifurcations. Each bifurcation type or singularity is given a name; for example, Andronov-Hopf bifurcation. No distinction has been made in the literature between \"bifurcation\" and \"bifurcation type,\" both being called \"bifurcations.\"\n\nAssociated with each bifurcation type are\n\n• defining equations that locate bifurcations of that type in a family $$\\dot{x} = f(x,\\lambda)$$\n• normal forms that give model systems exemplifying the bifurcation type\n\nInequalities called non-degeneracy conditions are part of the specification of a bifurcation type. The bifurcation types and their normal forms serve as templates that facilitate construction of parameter space maps. Bifurcation theory analyzes the bifurcations within the normal forms and investigates the similarity of the dynamics within systems having a given bifurcation type. The \"gold standard\" for similarity of systems used by the theory is topological equivalence. In some cases, bifurcation theory proves structural stability of a family. One of the principal objectives of bifurcation theory is to prove the structural stability of normal forms. Note, however, that there are bifurcation types for which structurally stable normal forms do not exist. An important aspect of the definition of structural stability in the context of bifurcation theory is the specification of which perturbations of a family are allowed. For example, bifurcation types of systems possessing specified symmetries have been studied extensively (Equivariant Bifurcation Theory).\n\n## Classification of bifurcations\n\nOne can view bifurcations as a failure of structural stability within a family. A starting point for classifying bifurcation types is the Kupka-Smale theorem that lists three generic properties of vector fields:\n\nDifferent ways that these Kupka-Smale conditions fail lead to different bifurcation types. Bifurcation theory constructs a layered graph of bifurcation types in which successive layers consist of types whose defining equations specify more failure modes. These layers can be organized by the codimension of the bifurcation types, defined as the minimal number of parameters of families in which that bifurcation type occurs. Equivalently, the codimension is the number of equality conditions that characterize a bifurcation.\n\nCodimension one bifurcations comprise the top level of bifurcation types. Single failures of the Kupka-Smale properties yield the following types of codimension one bifurcations:\n\nThis is not a comprehensive list of codimension one bifurcations. Additional types can be found in systems with quasiperiodic oscillations or chaotic dynamics. Moreover, there are subcases in the list above that deal with such issues as whether an Andronov-Hopf bifurcation is sub-critical or super-critical, and the implications of eigenvalue magnitudes for homoclinic bifurcation.\n\nThe classification of bifurcation types becomes more complex as their codimension increases. There are five types of \"local\" codimension two bifurcations of equilibria:\n\n## Numerical Methods\n\nOne of the principal uses of bifurcation theory is to analyze the bifurcations that occur in specific families of dynamical systems. Investigations commonly identify the types of bifurcations in parameter space maps either by comparison of simulation results with normal forms or by solving defining equations for those bifurcation types in the systems under investigation and computing coefficients of the normal forms. Several software packages (AUTO, CONTENT, MATCONT, XPPAUT, PyDSTool) give implementations of algorithms that perform the latter type of analysis. The numerical core of these packages consist of\n\n• Regular implementations of defining equations for the bifurcation types\n• equation solvers such as Newton's method\n• Numerical continuation methods for differential equations\n• Computation of normal forms\n• initial and\n• boundary value solvers for differential equations.\n\nThe continuation methods compute curves of solutions to regular systems of $$N$$ equations in $$N+1$$ variables. The bifurcation analysis of a system implemented to varying degrees in the packages listed above is based upon the following strategy:\n\n• An initial equilibrium or periodic orbit is located.\n• Numerical continuation is used to follow this special orbit as a single active parameter varies.\n• Defining equations for codimension one bifurcations detect and locate bifurcations that occur on this branch of solutions.\n• Starting at one of the located codimension one bifurcations,\n\ntwo parameters are designated to be active and the continuation methods are used to compute a curve of codimension one bifurcations.\n\n• Defining equations for codimension two bifurcations detect and locate bifurcations that occur on this branch of solutions.\n• Starting at one of the located codimension two bifurcations,\n\nthree parameters are designated to be active and the continuation methods are used to compute a curve of codimension two bifurcations.\n\nThis process can be continued as long as one has regular defining equations for bifurcations of increasing codimension, but these hardly exist beyond codimension three. Moreover, the dynamic behaviour near bifurcations with codimension higher than three is usually so poorly understood that the computation of such points is hardly worthwhile. In many cases, bifurcation analysis identifies additional curves of codimension k bifurcations that meet at a codimension k+1 bifurcation. Continuation methods can be started at one of these codimension k bifurcations to find curves of this type of bifurcation with k+1 active parameters. Switching to the continuation of a periodic orbit at an Andronov-Hopf bifurcation or to the continuation of a saddle homoclinic bifurcation curve from the Bogdanov-Takens bifurcation are examples of such starting techniques based on normal form computations.\n\n## Bifurcation Theory of Chaotic and Quasiperiodic Systems\n\nBifurcation theory has intensively investigated varied topics that bear on chaotic and quasiperiodic dynamics. Much of this theory has been developed in the context of discrete time dynamical systems defined by iteration of mappings. The bifurcation theory described above has analogous results for this setting. In some areas, bifurcation theory of discrete systems goes farther than that for continuous time systems. In particular, an extensive, deep theory describing the properties of iterations of one dimensional mappings was developed over the last quarter of the twentieth century. This theory characterizes universal sequences of bifurcations and the existence of chaotic attractors. Some of this theory carries over to the setting of invertible mappings in higher dimensions and to continuous time dynamical systems via Poincar\\'e maps. There are also results that are specific to continuous time systems, especially those that apply to homoclinic orbits of equilibrium points. Early results in this area include the theory of the Lorenz Attractor and Silnikov's analysis of systems with a homoclinic orbit of a saddle-focus in three dimensional systems. Methods originating in KAM (Kolmogorov-Arnold-Moser) theory describe how quasiperiodic invariant sets arise naturally in families of vector fields. Sophisticated numerical methods have been developed based upon this theory to compute invariant tori with (quasi)periodic motion in families of vector fields."
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.826408,"math_prob":0.97835094,"size":11460,"snap":"2022-27-2022-33","text_gpt3_token_len":2789,"char_repetition_ratio":0.19771299,"word_repetition_ratio":0.056839474,"special_character_ratio":0.2095986,"punctuation_ratio":0.118828,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9918089,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-06-25T23:31:01Z\",\"WARC-Record-ID\":\"<urn:uuid:b157c879-2e1f-4f9a-adf4-bbbe58d33f41>\",\"Content-Length\":\"47778\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:17325a93-163f-4520-a1cb-e62bc21c9a02>\",\"WARC-Concurrent-To\":\"<urn:uuid:51a7f315-b528-4e65-b4a5-ea5e7ad6b5d1>\",\"WARC-IP-Address\":\"173.255.237.117\",\"WARC-Target-URI\":\"http://scholarpedia.org/article/Bifurcation_theory\",\"WARC-Payload-Digest\":\"sha1:SPF55V4O3BHYZARVY6HFDAAYSPE7XD6R\",\"WARC-Block-Digest\":\"sha1:P2VYRQU4VZGPR7OOBQITO6X3SOKBPN6Z\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-27/CC-MAIN-2022-27_segments_1656103036176.7_warc_CC-MAIN-20220625220543-20220626010543-00054.warc.gz\"}"} |
https://curriculum.illustrativemathematics.org/MS/teachers/2/5/1/practice.html | [
"# Lesson 1\n\nInterpreting Negative Numbers\n\n### Problem 1\n\nIt was $$\\text- 5 ^\\circ \\text{C}$$ in Copenhagen and $$\\text- 12 ^\\circ \\text{C}$$ in Oslo. Which city was colder?\n\n### Solution\n\nFor access, consult one of our IM Certified Partners.\n\n### Problem 2\n\n1. A fish is 12 meters below the surface of the ocean. What is its elevation?\n2. A sea bird is 28 meters above the surface of the ocean. What is its elevation?\n3. If the bird is directly above the fish, how far apart are they?\n\n### Solution\n\nFor access, consult one of our IM Certified Partners.\n\n### Problem 3\n\nCompare using >, =, or <.\n\n1. 3 _____ -3\n2. 12 _____ 24\n3. -12 _____ -24\n4. 5 _____ -(-5)\n5. 7.2 _____ 7\n6. -7.2 _____ -7\n7. -1.5 _____ $$\\frac {\\text{-}3}{2}$$\n8. $$\\frac {\\text{-}4}{5}$$ _____ $$\\frac {\\text{-}5}{4}$$\n9. $$\\frac {\\text{-}3}{5}$$ _____ $$\\frac {\\text{-}6}{10}$$\n10. $$\\frac {\\text{-}2}{3}$$ _____ $$\\frac13$$\n\n### Solution\n\nFor access, consult one of our IM Certified Partners.\n\n### Problem 4\n\nHan wants to buy a \\$30 ticket to a game, but the pre-order tickets are sold out. He knows there will be more tickets sold the day of the game, with a markup of 200%. How much should Han expect to pay for the ticket if he buys it the day of the game?\n\n### Solution\n\nFor access, consult one of our IM Certified Partners.\n\n(From Unit 4, Lesson 7.)\n\n### Problem 5\n\nA type of green paint is made by mixing 2 cups of yellow with 3.5 cups of blue.\n\n1. Find a mixture that will make the same shade of green but a smaller amount.\n\n2. Find a mixture that will make the same shade of green but a larger amount.\n\n3. Find a mixture that will make a different shade of green that is bluer.\n\n4. Find a mixture that will make a different shade of green that is more yellow.\n\n### Solution\n\nFor access, consult one of our IM Certified Partners.\n\n(From Unit 2, Lesson 1.)"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8839336,"math_prob":0.98774815,"size":1556,"snap":"2022-05-2022-21","text_gpt3_token_len":471,"char_repetition_ratio":0.13144329,"word_repetition_ratio":0.23287672,"special_character_ratio":0.37275064,"punctuation_ratio":0.1,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9741264,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-05-27T11:45:13Z\",\"WARC-Record-ID\":\"<urn:uuid:645b7688-d04c-4865-89a5-da7975f4ffcb>\",\"Content-Length\":\"49311\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:eceae111-e043-4a32-be56-84962261a258>\",\"WARC-Concurrent-To\":\"<urn:uuid:920d26f1-3b7d-437f-b065-1d011f9df4f7>\",\"WARC-IP-Address\":\"54.196.16.164\",\"WARC-Target-URI\":\"https://curriculum.illustrativemathematics.org/MS/teachers/2/5/1/practice.html\",\"WARC-Payload-Digest\":\"sha1:ZQFTCDYDNY6XPQSAXVXI6WSMD4SAZQTS\",\"WARC-Block-Digest\":\"sha1:L5KTGARPGK3J3JXGYWY3FPP5IG4GQ4X7\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-21/CC-MAIN-2022-21_segments_1652662647086.91_warc_CC-MAIN-20220527112418-20220527142418-00750.warc.gz\"}"} |
https://dsp.stackexchange.com/questions/61064/coefficients-of-the-fourier-series-expansion-of-physical-signals-having-finite-p | [
"# Coefficients of the Fourier Series expansion of physical signals having finite power\n\nQuestion 2.7. Show that for all periodic physical signals that have finite power, the coefficients of the Fourier series expansion $$x_n$$ tends to 0 as $$n \\to \\infty$$\n\nI have computed $$|x_n|^2$$ by multiplying $$x_n$$ with $$x_n^*$$. However, as per my calculations, $$|x_n|^2 = P_x$$ i.e. power of $$x(t)$$, which is a constant and independent of $$n$$. I know that this is fundamentally incorrect because $$|x_n|$$ should vary with $$n$$. If anyone can guide me about where I went wrong that would be very helpful. Thanks and regards.\n\nMY SOLUTION:\n\nPlease refer to the solution from the half of the first page.",
null,
"",
null,
"• Hi, I have assumed $x(t) = x(t + nT_o)$. Since $x(t)$ is periodic, the power of the signal $x(t)$ can be approximated over one interval and is given by $P_x = \\frac{1}{T_o} \\int _ {T_0} |x(t)|^2dt$ – Soumee Oct 4 '19 at 18:50\n• The error in your calculations is in the step where you use the same variable $t$ in writing that $$x_nx_n^* = \\int x(t)\\exp(-j\\frac{2\\pi nt}{T_0})dt\\int x^*(t)\\exp(j\\frac{2\\pi nt}{T_0})dt.$$ You need to use different variables as in $$x_nx_n^* = \\int x(t)\\exp(-j\\frac{2\\pi nt}{T_0})dt\\int x^*(s)\\exp(j\\frac{2\\pi ns}{T_0})ds$$ and you will see that the next step where you cancel the exponential terms is no longer valid. – Dilip Sarwate Oct 5 '19 at 17:09\n• @DilipSarwate Alright, got it. I have seen this being done in many places(taking separate variables for each term). Can you please tell me why do we need to use different variables for each of the terms, for instance, $t$ and $s$ for $x_n$ and $x_n^*$. – Soumee Oct 5 '19 at 17:48\n• An integral is essentially a sum and when you multiply two sums, you need to account for crossproducts, not just the term-by-term products. What you have done is, in effect, equating $(a+b)(c+d)$ with $ac+bd$ and ignoring the $ad$ and $bc$ terms. When you take the cross-terms into account, you will see that $x_nx_n^*$ does not simplify to $P_x$. – Dilip Sarwate Oct 5 '19 at 20:00\n• @DilipSarwate Alright. Got it. Thanks. :)) – Soumee Oct 6 '19 at 4:27\n\nA finite power periodic signal will have the following property:\n\n$$P_x = \\frac{1}{T_0} \\int_{0}^{T_0} |x(t)|^2 dt < \\infty \\tag{1}$$\n\nwhere $$P_x$$ is the power averaged over a period of the signal. Then from Parseval's theorem, the total power can also be shown to be (for a real signal I'm assuming):\n\n$$P_x = |a_0|^2+2 \\sum_{n=1}^{\\infty} |a_n|^2 < \\infty \\tag{2}$$\n\nwhere the $$a_n$$'s are the continuous-time Fourier series coefficients for $$x(t)$$.\n\nFrom the theory of power series (calculus), it's known that in order for (2) to converge (sum being less than infinity) a necessary condition is\n\n$$\\lim_{n \\to \\infty} |a_n|^2 = 0 \\tag{3}$$\n\n(3) implies therefore that, for finite power periodic signal's CTFS coefficients $$a_n$$ also goes to zero as $$n$$ goes to infinity.\n\n• God forbid that DSP students should be expected to recall arcane results from an earlier course in a topic as mind-numbing and boring as calculus! – Dilip Sarwate Oct 4 '19 at 19:26\n• Thank you sir for the solution to the question. However, mathematically, I am not able to find any error in my solution as well! :'( – Soumee Oct 5 '19 at 6:51\n• @DilipSarwate So, does this mean $|x_n|^2$ is indeed equal to $P_x$ ? – Soumee Oct 5 '19 at 15:51\n• If $x_n \\to 0$ as $n \\to \\infty$ then why does $|x_n|^2$ not $\\to 0$ as $n \\to \\infty$ ? Moreover $|x_n|^2$ doesnot depend upon $n$. Rather it is a constant which is equal to $P_x$. – Soumee Oct 5 '19 at 15:57\n• @DilipSarwate ah $a_0$; thanks... – Fat32 Oct 5 '19 at 20:45"
] | [
null,
"https://i.stack.imgur.com/K1akM.jpg",
null,
"https://i.stack.imgur.com/3sTQO.jpg",
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9493956,"math_prob":0.9997084,"size":609,"snap":"2020-10-2020-16","text_gpt3_token_len":159,"char_repetition_ratio":0.097520664,"word_repetition_ratio":0.0,"special_character_ratio":0.29228243,"punctuation_ratio":0.12396694,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.999995,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,3,null,3,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-04-03T21:32:26Z\",\"WARC-Record-ID\":\"<urn:uuid:ca865e16-dfd3-4d78-ab82-26b12a396d12>\",\"Content-Length\":\"154348\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0e85845d-6787-4574-9a1b-c7da5606d0d5>\",\"WARC-Concurrent-To\":\"<urn:uuid:0f6a106c-cf30-477d-8f83-ba2815616285>\",\"WARC-IP-Address\":\"151.101.129.69\",\"WARC-Target-URI\":\"https://dsp.stackexchange.com/questions/61064/coefficients-of-the-fourier-series-expansion-of-physical-signals-having-finite-p\",\"WARC-Payload-Digest\":\"sha1:ESUYAD44Y7ESTQWCD43X4NXZN6VTBMVX\",\"WARC-Block-Digest\":\"sha1:OJKNVJHXQXJBJETVIDU7XW4ABKIYLBRD\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-16/CC-MAIN-2020-16_segments_1585370518622.65_warc_CC-MAIN-20200403190006-20200403220006-00208.warc.gz\"}"} |
https://brane-space.blogspot.com/2023/03/solving-differential-equations-using.html | [
"## Wednesday, March 15, 2023\n\n### Solving Differential Equations Using Variation Of Parameters\n\nThe method of undetermined coefficients to solve differential equations was examined in an earlier blog post, i.e.\n\nTricks to Solving Higher Order DEs (4): Undetermined Coefficients\n\nWhich I showed could be useful in solving many linear differential equations. However, a method with wider applicability is needed to solve more challenging differential equations. That method which I will focus on here is called \"the variation of parameters.\"\n\nI first saw the movie reference to this mathematical technique in the 1951 version of 'The Day The Earth Stood Still'. In the film the visiting alien, Klatu, had entered the office of a physics professor named Barnhardt. When the prof saw the changes he said he still didn't understand the celestial mechanics of how a space ship like Klatu's could travel between worlds. Klatu made a few more changes, reprimanding the prof (\"I thought you'd have solved it by now\") and adding, \"This is the answer using variation of parameters.\" The scene is shown below, from the movie:\n\nIn variation of parameters, the method entails replacing the constants in the complementary function with undetermined functions of the independent variable, x. Then determining these latter functions so that when the modified complementary function is substituted into the given differential equation, f(x) will be obtained. We use a detailed example and solution below:\n\nSolve:\n\nd2y/dx 2 + y = tan x\n\nWe write the complementary function:\n\nc = sin x + c 2 cos x\n\nAssume: yp = sin x + v 2 cos x\n\nWhere 1 , v 2 will be determined such that this is a particular integral of the original differential equation, e.g. y\" + y = tan x.\n\nThen: y'p = 1 cos x - v 2 sin x + v' sin x + v' 2 cos x\n\nImpose the condition that:\n\nv' sin x + v' 2 cos x = 0\n\nð\n\ny'p = cos x - v 2 sin x\n\nThen:\n\ny''p = - sin x - v 2 cos x + v' cos x - v' 2 sin x\n\nSubst. back into original DE: y\" + y = tan x\n\nð\n\nv' cos x - v' 2 sin x = tan x\n\nThen 2 equations are left from which to determine v' , v' 2:\n\nv' sin x + v' 2 cos x = 0\n\nv' cos x - v' 2 sin x = tan x\n\nSolving the above simultaneous eqns.:\n\nv' 1 = sin x\n\nv' 2 = cos x - sec x\n\nIntegrate: = - cos x + c 3\n\nv = sin x - ln |sec x + tan x | + c 4\n\nSubstitute into: yp = sin x + v 2 cos x\n\nð\n\nyp = c sin x + c 4 cos x - cos x (ln |sec x + tan x\n\nWe now write:\n\nyp = A sin x + B cos x - cos x (ln |sec x + tan x\n\nSince we may assign any particular values A, B to the constants: c 3 and c .\n\nThen write: y = c + yp\n\nð\n\ny = c sin x + c 2 cos x +A sin x + B cos x - cos x (ln |sec x + tan x |)\n\nOr:\n\ny = C sin x + C 2 cos x - cos x (ln |sec x + tan x |)\n\nWhere: C 1 = c + A, C 2 = c 2 + B\n\nThus we could as well have chosen the constants c 3 and c 4 = 0, for essentially the same result. I.e. The general solution to the differential equation being:\n\ny = c sin x + c 2 cos x - cos x (ln |sec x + tan x |)\n\nSuggested Problem:\n\nSolve using variation of parameters:\n\nd2y/dx 2 - dy/ dx - 2y = e 3x"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.82657236,"math_prob":0.9983573,"size":2972,"snap":"2023-14-2023-23","text_gpt3_token_len":907,"char_repetition_ratio":0.15330188,"word_repetition_ratio":0.21597633,"special_character_ratio":0.3152759,"punctuation_ratio":0.08885542,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99971104,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-06-06T09:52:50Z\",\"WARC-Record-ID\":\"<urn:uuid:eb0bf474-ddf5-4768-bb22-a8cde8ae4f1f>\",\"Content-Length\":\"138058\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:1546ff9b-1527-4d9b-baae-b9520ce2897b>\",\"WARC-Concurrent-To\":\"<urn:uuid:a420de0a-86e1-4894-be57-2917b8bcee78>\",\"WARC-IP-Address\":\"142.251.16.132\",\"WARC-Target-URI\":\"https://brane-space.blogspot.com/2023/03/solving-differential-equations-using.html\",\"WARC-Payload-Digest\":\"sha1:427KSQOFGHNONVM5C5LQHIZ2CVTBRMDJ\",\"WARC-Block-Digest\":\"sha1:YDDU4G7E4SHFL7PDBZZVUVIBH5HBOMGZ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224652494.25_warc_CC-MAIN-20230606082037-20230606112037-00421.warc.gz\"}"} |
https://blog.eviews.com/2018/11/principal-component-analysis-part-ii.html | [
"## Monday, November 26, 2018\n\n### Principal Component Analysis: Part II (Practice)\n\nIn Part I of our series on Principal Component Analysis (PCA), we covered a theoretical overview of fundamental concepts and disucssed several inferential procedures. Here, we aim to complement our theoretical exposition with a step-by-step practical implementation using EViews. In particular, we are motivated by a desire to apply PCA to some dataset in order to identify its most important features and draw any inferential conclusions that may exist. We will proceed in the following steps:\n1. Summarize and describe the dataset under consideration.\n2. Extract all principal (important) directions (features).\n3. Quantify how much variation (information) is explained by each principal direction.\n4. Determine how much variation each variable contributes in each principal direction.\n5. Reduce data dimensionality.\n6. Identify which variables are correlated and which correlations are more principal.\n7. Identify which observations are correlated with which variables.\nThe links to the workfile and program file can be found at the end.\n\n### Principal Component Analysis of US Crime Data\n\nWe will use PCA to study US crime data. In particular, our dataset summarizes the number of arrests per 100,000 residents in each of the 50 US states in 1973. The data contains four variables, three of which pertain to arrests associated with (and naturally named) MURDER, ASSAULT, and RAPE, whereas the last, named URBANPOP, contains the percentage of the population living in urban centers.\n\n#### Data Summary\n\nTo understand our data, we will first create a group object with the variables of interest. We can do this by selecting all four variables in the workfile by clicking on each while holding down the Ctrl button, right-clicking on any of the highlighted variables, moving the mouse pointer over Open in the context menu, and finally clicking on as Group. This will open a group object in a spreadsheet with the four variables placed in columns. The steps are reproduced in Figures 1a and 1b.",
null,
"",
null,
"Figure 1A: Open Group Figure 1B: Group Window\nFrom here, we can derive the usual summary statistics by clicking on View in the group window, moving the mouse over Descriptive Stats and clicking on Common Sample. This produces a spreadsheet with various statistics of interest. We reproduce the steps and output in Figures 2a and 2b.",
null,
"",
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"Figure 2A: Descriptive Stats Menu Figure 2B: Descriptive Stats Output\nWe can also plot each of the series to get a better visual sense for the data. In particular, from the group window, click on View and click on Graph. This brings up the Graph Options window. Here, from the Multiple Series dropdown menu, select Multiple Graphs and click on OK. We summarize the sequence in Figures 3a and 3b.",
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"",
null,
"Figure 3A: Graph Options Figure 3B: Multiple Graphs\nAt last, we can get a sense for information redundancy (see section Variance Decomposition in Part I of this series) by studying correlation patterns. In this regard, we can produce a correlation matrix by clicking on View in the group window and clicking on Covariance Analysis.... This opens a window with further options. Here, deselect (click) the checkbox next to Covariance and select (click) the box next to Correlation. This ensures that EViews will only produce the correlation matrix without any other statistics. Furthermore, in the Layout dropbox, select Single table, and finally click on OK. Figures 4a and 4b reproduce these steps.",
null,
"",
null,
"Figure 4A: Covariance Analysis Figure 4B: Correlation Table\nA quick interpretation of the correlation structure indicates that murder is highly correlated with assault, whereas the latter exhibits a strong positive correlation with rape. Moreover, whereas murder is nearly uncorrelated with larger urban centers, among the three causes for arrest, rape generally favours larger communities. Intuitively, this is in line with conventional wisdom. Murders are rarely observed on professional levels and typically involve assault as a precursor. Furthermore, due to higher costs of crime visibility and cleanup, murder generally does not favour larger population areas where police presence and witness visibility is generally more pronounced. On the other hand, rape favours larger urban centers due to the fact that there are simply more people and the cost to covering or denying the crime is notoriously very low. Furthermore, victims of rape in smaller communities are typically shamed into staying quiet since connection circles are naturally tighter in such surroundings.\n\n### Principal Component Analysis of Crime Data\n\nDoing PCA in EViews is trivial. From our group object window, click on View and click on Principal Components.... This opens the main PCA dialog. See Figure 5a and 5b below.",
null,
"",
null,
"Figure 5A: Initiating the PCA dialog Figure 5B: Main PCA Dialog\nFrom here, EViews offers users the ability to apply several tools and protocols readily encountered in the literature on PCA.\n\n#### Summary of Fundamentals\n\nAs a first step, we are interested in summarizing PCA fundamentals. In particular, we seek an overview of eigenvalues and eigenvectors that result from applying the principal component decomposition to the covariance or correlation matrix associated with our variables of interest. To do so, consider the Display group, and select Table. The latter produces three tables summarizing the covariance (correlation) matrix, and the associated eigenvectors and eigenvalues.\n\nAssociated to this output are several important options under the Component selection group. These include:\n• Maximum number: This defaults to the theoretical maximum number of eigenvalues possible, which is the total number of variables in the group under consideration. In our case, this number is 4.\n• Minimum eigenvalue: This defaults to 0. Nevertheless, selecting a positive value requests that all eigenvectors associated with eigenvalues less than this value are not displayed.\n• Cumulative proportion: This defaults to 1. Choosing a value $\\alpha < 1$ however, requests that only the most principal $k$ eigenvalues and eigenvectors associated with explaining $\\alpha*100 \\%$ of the variation are retained. Naturally, choosing $\\alpha=1$ requests that all eigenvalues are displayed. See section Dimension Reduction in Part I of this series for further details.\nSince we are interested in a global summary, we will leave the Component selection options at their default values.\n\nFurthermore, consider momentarily the Calculation tab. Here, the Type dropdown offers the choice to apply the principal component decomposition either to the correlation or covariance matrix. For details, see sections Variance Decomposition and Change of Basis in Part I of this series. The choice essentially reduces to whether or not the variables under consideration exhibit similar scales. In other words, if variances of the underlying variables of interest are similar, then conducting PCA on the covariance matrix is certainly justified. Nevertheless, if the variances are widely different, then selecting the correlation matrix is more appropriate if interpretability and comparability are desired. EViews errs on the side of caution and defaults to using the correlation matrix. Since the table of summary statistics we produced in figure 3b clearly shows a lack of uniformity in standard deviations across the four variables of interest, we will stick with the default and use the correlation matrix. Hit OK.",
null,
"Figure 6: PCA Table Output\n\nThe resulting output, which is summarized Figure 6 above, consists of three tables. The first table summarizes the information on eigenvalues. The latter are sorted in order of principality (importance), measured as the proportion of information explained by each principal direction. Refer to section Principal Directions in Part I of this series for more details. In particular, we see that the first principal direction explains roughly 62% of the information contained in the underlying correlation matrix, the second, roughly 25%, and so on. Furthermore, the cumulative proportion of information explained by the first two principal directions is roughly 87(62 + 25)%. In other words, if dimensionality reduction is desired, our analysis indicates that we can half the underlying dimensionality of the problem from 4 to 2, while retaining nearly 90% of the original information. This is evidently a profitable trade-off. For theoretical details, see section Dimension Reduction in Part I of this series. At last, observe that EViews reports that the average of the 4 eigenvalues is 1. This will in fact always be the case when extracting eigenvalues from a correlation matrix.\n\nThe second (middle) table summarizes the eigenvectors associated with each of the principal eigenvalues. Naturally, the eigenvectors are also arranged in order of principality. Furthermore, whereas the eigenvalues highlight how much of the overall information is extracted in each principal direction, the eigenvectors reveal how much weight each variable has in each direction.\n\nRecall from Part I of this series that all eigenvectors have length unity. Accordingly, the relative importance of any variable in a given principal direction is effectively the proportion of the eigenvector length (unity) attributed to that variable. For instance, in the case of the first eigenvector, $[0.535899, 0.583184, 0.543432, 0.278191]^{\\top}$, MURDER accounts for $0.535899^{2} \\times 100\\% = 0.287188\\%$ of the overall direction length. Similarly, ASSAULT accounts for 0.340103% of the direction, and RAPE contributes 0.295318%. Evidently, the least important variable in the first principal direction is URBANPOP, which accounts for only 0.077390% of the direction length.\n\nOn the other hand, in the second principal direction, it is URBANPOP that carries most weight, contributing $0.872806 \\times 100\\% = 0.761790\\%$ to the direction length. Accordingly, if feature extraction is the goal, it is clear (and rather obvious) that the first principal direction is roughly equally dominated by MURDER, ASSAULT, and RAPE, whereas the second principal direction is almost entirely governed by URBANPOP. For a theoretical exposition, see section Principal Components in Part I of this series.\n\nAt last, the third table is just the correlation matrix to which the eigen-decomposition is applied. The latter, while important, is provided only as a reference.\n\n#### Eigenvalue Plots and Dimensionality\n\nNow that we have a rough picture of PCA fundamentals associated with our dataset, it is natural to ask whether we can proceed with dimensionality reduction in a more formal manner. One such way (albeit arbitrary, but widely popular) is to look at several eigenvalue plots and visually identify how many eigenvalues to retain.\n\nFrom the previous PCA output, click again on View, then Principal Components..., and select Eigenvalue Plots under the Display group. This is summarized in Figure 7 below.",
null,
"Figure 7: PCA Dialog: Eigenvalue Plots\n\nHere, EViews offers several graphical representations for the underlying eigenvalues. The latter includes the scree plot, the differences between successive eigenvalues plot, as well as the cumulative proportion of information associated with the first $k$ eigenvalues plot. Go ahead and select all three. As before, we will leave the default values under the Component Selection group. Hit OK. Figure 8 summarizes the output.",
null,
"Figure 8: Eigenvalue Plots Output\n\nEViews now produces three graphs. The first is the scree plot - a line graph of eigenvalues arranged in order of principality. Superimposed on this graph is a red dotted horizontal line with a value equal to the average of the eigenvalues, which, as we mentioned earlier, in our case is 1. The idea here is to look for a kink point, or an elbow, and retain all eigenvalues, and by extension their associated eigenvectors, that form the first portion of the kink, and discard the rest. From the plot, it is evident that a kink occurs at the 2nd eigenvalue, indicating that we should retain the first two eigenvalues.\n\nA slightly more numeric approach discards all eigenvalues significantly below the eigenvalue average. Referring to the first table in Figure 6, we see that the average of the eigenvalues is 1, and the 2nd eigenvalue is in fact just below this cutoff. Since the 2nd value is so close to this average, while using the visual support we mentioned in the previous paragraph, it is safe to conclude that the scree plot analysis indicates that only the first two eigenvalues ought to be retained.\n\nThe second graph plots a line graph of the differences between successive eigenvalues. Superimposed on this graph is another horizontal line, this time with a value equal to the average of the differences of successive eigenvalues. Although EViews does not report this number, using the top table in Figure 6, it is not difficult to show that the average in question is $(1.490476+0.633202+0.183133)/3 = 0.768937$. The idea here is to retain all eigenvalues whose differences are above this threshold. Clearly, only the first two eigenvalues satisfy this criterion.\n\nThe final graph is a line graph of the cumulative proportion of information explained by successive principal eigenvalues. Superimposed on this graph is a line with a slope equal to the average of the eigenvalues, namely 1. The idea here is to retain those eigenvalues that form segments of the cumulative curve whose slopes are at least as steep as the line with slope 1. In our case, only two eigenvalues seem to form such a segment: eigenvalues 1 and 2.\n\nAll three graphical approaches indicate that one ought to retain the first two eigenvalues and their associated eigenvectors. There is however an entirely data driven methodology adapted from Bai and Ng (2002). We discussed this approach in section Dimension Reduction in Part I of this series. Nevertheless, EViews currently doesn't support its implementation via dialogs and it must be programmed manually. In this regard, we temporarily move away from our dialog-based exposition, and offer a code snippet which implements the aforementioned protocol.\n\n ' --- Bai and Ng (2002) Protocol ---\ngroup crime murder assault rape urbanpop ' create group with all 4 variables\n!obz = murder.@obs ' get number of observations\n!numvar = @columns(crime) ' get number of variables\nequation eqjr ' equation object to hold regression\nmatrix(!numvar, !numvar) SSRjr' matrix to store SSR from each regression eqjr\n\ncrime.makepcomp(cov=corr) s1 s2 s3 s4 ' get all score series\n\nfor !j = 1 to !numvar\nfor !r = 1 to !numvar\n%scrstr = \"\" ' holds score specification to extract\n\n' generate string to specify which scores to use in regression\nfor !r2 = 1 to !r\n%scrstr = %scrstr + \" s\" + @str(!r2)\nnext\n\neqjr.ls crime(!j) {%scrstr} ' estimate regression\n\nSSRjr(!j, !r) = (eqjr.@ssr)/!obz ' take average of SSR\nnext\nnext\n' get column means of SSRjr. namely, get r means, averaging across regressions j.\nvector SSRr = @cmean(SSRjr)\n\nvector(!numvar) IC ' stores critical values\nfor !r = 1 to !numvar\nIC(!r) = @log(SSRr(!r)) + !r*(!obz + !numvar)/(!obz*!numvar)*@log(!numvar)\nnext\n\n' take the index of the minimum value of IC as number of principal components to retain\nscalar numpc = @imin(IC)\n\nUnlike our graphical analysis, the protocol above suggests that the number of retained eigenvalues is 1. Nevertheless, for sake of greater analytical exposition below, we will stick with the original suggestion of retaining the first two principal directions instead.\n\n#### Principal Direction Analysis\n\nThe next step in our analysis is to look at what, if any, meaningful patterns emerge by studying the principal directions themselves. To do so, we again bring up the main principal component dialog and this time select Variable Loading Plots under the Display group. See Figure 9 below.",
null,
"Variable loading plots produce $XY$ ''-pair plots of loading vectors. See section Loading Plots in Part I of this series for further details. The user specifies which loading vectors to compare and selects one among the following loading (scaling) protocols:\n• Normalize Scores: Here, the scaling factor is the square root of the eigenvalue vector. In other words, the $k^{\\text{th}}$ element of the $i^{\\text{th}}$ loading vector is the $k^{\\text{th}}$ element of the $i^{\\text{th}}$ eigenvector, multiplied by the square root of the $k^{\\text{th}}$ eigenvalue.\n• Symmetric Weights: In this scenario, the scaling factor is the quartic (fourth) root of the eigenvalue vector. Namely, the $k^{\\text{th}}$ element of the $i^{\\text{th}}$ loading vector is the $k^{\\text{th}}$ element of the $i^{\\text{th}}$ eigenvector, multiplied by the fourth root of the $k^{\\text{th}}$ eigenvalue.\n• User Loading Weight: If $0 \\leq \\omega \\leq 1$ denotes the user defined scaling factor, then the loading vectors are formed by scaling the $k^{\\text{th}}$ element of the corresponding eigenvector by the $k^{\\text{th}}$ eigenvalue raised to the power $\\omega/2$ .\nFor the time being, stick with all default values. That is, we will look at the loading plots across the first two principal directions, and we will use the Normalize Loadings scaling protocol. In other words, we will plot the true eigenvectors since scaling is unity. Note that the choice of looking at only the first two principal directions is, among other things, motivated by our previous analysis on dimension reduction where we decided to retain only the first two principal eigenvalues and discard the rest. Go ahead and click on OK. Figure 10 summarizes the output.",
null,
"As discussed in section Loading Plots in Part I of this series, the angle between the vectors in a loading plot is related to the correlation between the original variables to which the loading vectors are associated. Accordingly, we see that MURDER and ASSAULT are moderately positively correlated, as are ASSAULT and RAPE, although the latter two less so than the former two. Moreover, it is clear that RAPE and URBANPOP are positively correlated, whereas MURDER and URBANPOP are nearly uncorrelated since they form a near 90 degree angle. In other words, we have a two-dimensional graphical representation of the four-dimensional correlation matrix in Figure 4b. This ability to represent higher dimensional information in a lower dimensional space is arguably the most useful feature of PCA.\n\nFurthermore, all three variables, MURDER, ASSAULT, and RAPE, are strongly correlated with the first principal direction, whereas URBANPOP is strongly correlated with the second principal direction. In fact, looking at vector lengths, we can also see that MURDER, ASSAULT, and RAPE are roughly equally dominant in the first direction, whereas URBANPOP is significantly more dominant than either of the former three, albeit in the second direction. Of course, this simply confirms our preliminary analysis of the middle table in Figure 6.\n\nAbove, we started with the basic loading vector with scale unity. We could have, of course, resorted to other scaling options such as normalizing to the score vectors, using symmetric weights, or using some other custom weighting. Since each of these would yield a different but similar perspective, we won't delve further into details. Nevertheless, as an exercise in exhibiting the steps involved, we provide below small snippets of code to manually generate loading vectors using only the eigenvalues and eigenvectors associated with the underlying correlation matrix. This is done for each of the four scaling protocols. These manually generated vectors are then compared to the loading vectors generated by EViews' internal code and shown to be identical.\n\n ' --- Verify Loading Plot Vectors ---\ngroup crime murder assault rape urbanpop ' create group with all 4 variables\n\n' make eigenvalues and eigenvectors based on the corr. matrix\ncrime.pcomp(eigval=eval, eigvec=evec, cov=corr)\n\nmatrix evaldiag = @makediagonal(eval) ' create diagonal matrix of eigenvalues\n\n'normalize scores\n\n'symmetric weights\n\n'user weights\n\n\n#### Score Analysis\n\nWhereas loading vectors reveal information on which variables dominate (and by how much) each principal direction, it is only when they are used to create the principal component vectors (score vectors) that they are truly useful in a data exploratory sense. In this regard, we again open the main principal component dialog and select Component scores plots in the Display group of options. We capture this in Figure 11 below.",
null,
"Figure 11: PCA Dialog: Component Scores Plots\n\nAnalogous to the loading vector plots, here, EViews produces $XY$ ''-pair plots of score vectors. As in the case of loading plots, the user specifies which score vectors to compare, and selects one among the following loading (scaling) protocols:\n• Normalize Loadings: Score vectors are scaled by unity. In other words, no scaling occurs.\n• Normalize Scores: The $k^{\\text{th}}$ score vector is scaled by the inverse of the square root of the $k^{\\text{th}}$ eigenvalue.\n• Symmetric Weights: The $k^{\\text{th}}$ score vector is scaled by the inverse of the quartic root of the $k^{\\text{th}}$ eigenvalue.\n• User Loading Weight: If $0 \\leq \\omega \\leq 1$ denotes the user defined scaling factor, the $k^{\\text{th}}$ score vector is scaled by the $k^{\\text{th}}$ eigenvalue raised to the power $-\\omega/2$.\nFurthermore, if outlier detection is desired, EViews allows users to specify a p-value as a detection threshold. See sections Score Plots and Outlier Detection in Part I of this series for further details. Since we are currently interested in interpretive exercises, we will forgo outlier detection and choose to display all observations. To do so, under the Graph options group of options, change the Obs. Labels to Label all obs. and hit OK. We replicate the output in Figure 12.",
null,
"Figure 12: Component Scores Plots Output\n\nThe output produced is a scatter plot of principal component 1 (score vector 1) vs. principal component 2 (score vector 2). There are several important observations to be made here.\n\nFirst, the further east of the zero vertical axis a state is located, the more positively correlated it is with the first principal direction. Since the latter is dominated positively (east of the zero vertical axis) by the three crime categories MURDER, ASSAULT, and RAPE (see Figure 11), we conclude that such states are positively correlated with said crimes. Naturally, converse conclusions hold as well. In particular, we see that CALIFORNIA, NEVADA, and FLORIDA are most positively correlated with the three crimes under consideration. If this is indeed the case, then it is little surprise that most Hollywood productions typically involve crime thrillers set in these three states. Conversely, NORTH DAKOTA and VERMONT are typically least associated with the crimes under consideration.\n\nSecond, the further north of the zero horizontal axis a state is located, the more positively correlated it is with the second principal direction. Since the latter is dominated positively (north of the zero horizontal axis) by the variable URBNAPOP (see Figure 11), we conclude that such states are positively correlated with urbanization. Again, the converse conclusions hold as well. In particular, HAWAII, CALIFORNIA, RHODE ISLAND, MASSACHUSETTS, UTAH, NEW JERSEY are states most positively associated with urbanization, whereas those least so are SOUTH CAROLINA, NORTH CAROLINA, and MISSISSIPPI.\n\nLastly, it is worth recalling that like loading vectors, score vectors can also be scaled. In this regard, we provide code snippets below to show how to manually compute scaled score vectors, exposing the algorithm that EViews uses to do same in its internal computations.\n\n ' --- Verify Score Vectors ---\n' make eigenvalues and eigenvectors based on the corr. matrix\ncrime.pcomp(eigval=eval, eigvec=evec, cov=corr)\n\nmatrix evaldiag = @makediagonal(eval) ' create diagonal matrix of eigenvalues\n\nstom(crime, crimemat) ' create matrix from crime group\nvector means = @cmean(crimemat) ' get column means\nvector popsds = @cstdevp(crimemat) ' get population standard deviations\n\n' initialize matrix for normalized crimemat\nmatrix(@rows(crimemat), @columns(crimemat)) crimematnorm\n\n' normalize (remove mean and divide by pop. s.d.) every column of crimemat\nfor !k = 1 to @columns(crimemat)\ncolplace(crimematnorm,(@columnextract(crimemat,!k) - means(!k))/popsds(!k),!k)\nnext\n\ncrime.makepcomp(cov=corr) s1 s2 s3 s4 ' get score series\ngroup scores s1 s2 s3 s4 ' put scores into group\nstom(scores, scoremat) ' put scores group into matrix\nmatrix scoreverify = crimematnorm*evec ' create custom score matrix\nmatrix scorediff = scoreverify - scoremat ' get difference between custom and eviews output\nshow scorediff\n\n'normalize scores\ncrime.makepcomp(scale=normscores, cov=corr) s1 s2 s3 s4\ngroup scores s1 s2 s3 s4\nstom(scores, scoremat)\nscoreverify = crimematnorm*evec*@inverse(@epow(evaldiag, 0.5))\nscorediff = scoreverify - scoremat\nshow scorediff\n\n'symmetric weights\ncrime.makepcomp(scale=symmetrics, cov=corr) s1 s2 s3 s4\ngroup scores s1 s2 s3 s4\nstom(scores, scoremat)\nscoreverify = crimematnorm*evec*@inverse(@epow(evaldiag, 0.25))\nscorediff = scoreverify - scoremat\nshow scorediff\n\n'user weights\ncrime.makepcomp(scale=0.36, cov=corr) s1 s2 s3 s4\ngroup scores s1 s2 s3 s4\nstom(scores, scoremat)\nscoreverify = crimematnorm*evec*@inverse(@epow(evaldiag, 0.18))\nscorediff = scoreverify - scoremat\nshow scorediff\n\nAbove, observe that we derived eigenvalues and eigenvectors of the correlation matrix. Accordingly, to derive the score vectors manually, we needed to standardize the original variables first. In this regard, when using the covariance matrix instead, one need only to demean the original variables and disregard scaling information. We leave this as an exercise to interested readers.\n\n#### Biplot Analysis\n\nAs a last exercise, we superimpose the loading vectors and score vectors onto a single graph called the biplot. To do this, again, bring up the main principal component dialog and under the Display group select Biplot (scores & loadings). As in the previous exercise, under the Graph options group, select Label all obs. from the Obs. labels dropdwon, and hit OK. We summarize these steps in Figure 13.",
null,
"From an inferential standpoint, there's little to contribute beyond what we laid out in each of the previous two sections. Nevertheless, having both the loading and score vectors appear on the same graph visually reinforces our previous analysis. Accordingly, we close this section with just the graphical output.",
null,
"### Concluding Remarks\n\nIn Part I of this series we laid out the theoretical foundations underlying PCA. Here, we used EViews to conduct a brief data exploratory implementation of PCA on serious crimes across 50 US states. Our aim was to illustrate the use of numerous PCA tools available in EViews with brief interpretations associated with each.\n\nIn closing, we would like to point out that apart from the main principal component dialog we used above, EViews also offers a Make Principal Components... proc function which provides a unified framework for producing vectors and matrices of the most important objects related to PCA. These include the vector of eigenvalues, the matrix of eigenvectors, the matrix of loading vectors, as well as the matrix of scores. To access this function, open the crime group from the workfile, click on Proc and click on Make Principal Components.... We summarize this in Figures 15a and 15b below.",
null,
"",
null,
"Figure 15a: Group Proc: Make Principal Components... Figure 15b: Make Principal Components Dialog\nFrom here, one can insert names for all objects one wishes to place in the workfile, select the scaling one wishes to use in the creation of the loading and score vectors, and hit OK.\n\n### Files\n\n1.",
null,
""
] | [
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https://mathematica.stackexchange.com/questions/25338/can-you-form-a-list-with-elements-which-are-terms-from-an-equation | [
"# Can you form a list with elements which are terms from an equation?\n\nIs there a command that takes an equation as an input and creates an output with a list of terms in the original equation? Something like:\n\nInput: Foo[ (1+a) x+ (2.5-b) y^2+ 3 c z^{1.3}]\n\nOutput: { (1+a) x , (2.5-b) y^2 , 3 c z^{1.3} }\n\n• You can expand your equation and then apply List. May 16, 2013 at 17:32\n• Variables, e.g. Variables[x^3 + 6 x^2 y + 3 x y z + x z^2 + 1] yields {x, y, z} May 16, 2013 at 17:32\n• If you want monomials there is e.g. MonomialList[a x + b y^2 + 3 c z^{1.3}] yielding {{1. a x, 1. b y^2, 3. c z^(13/10)}} May 16, 2013 at 17:38\n• Hi guys, thank you very much for your help so far. Unfortunately I have had to update the question as I try your suggestions and see what additional flexibility I else I need. May 16, 2013 at 17:41\n• So you need List @@ ((1 + a) x + (2.5 - b) y^2 + 3 c z^1.3) returning {(1 + a) x, (2.5 - b) y^2, 3 c z^1.3} May 16, 2013 at 17:44\n\nWhat you are asking to do, it seems, is to replace the Plus Head, with the List Head. The Apply function, shorthanded as @@, will do what you want:\n\nInput: expr = Foo[a + b + c];\n\n\nNow we can get just the a+b+c with First:\n\nInput: expr2 = First@expr;\n\n\nCheck out FullForm to get rid of shorthanded notation:\n\nInput: FullForm[expr2]\nOutput: Plus[a,b,c]\n\n\nAnd finally, we can turn Plus into List with Apply:\n\nInput: List@@expr2\nOutput: {a,b,c}\n\n\nAll in one line:\n\nInput: List@@First[Foo[a+b+c]]\n\n• I am fairly certain that Foo was given as the example of the function itself, meaning that Foo[a + b + c] should directly evaluate to {a, b, c}. Nevertheless +1. Oct 8, 2014 at 16:11\n\nExamining the structure of the expression with TreeForm\n\nTreeForm@Foo[(1 + a) x + (2.5 - b) y^2 + 3 c z^{1.3}]",
null,
"shows us another way:\n\nLevel[Foo[(1 + a) x + (2.5 - b) y^2 + 3 c z^{1.3}], {3}]\n\n(* {(1 + a) x, (2.5 - b) y^2, 3 c z^1.3} *)\n\n\n(Simply count the depth of the function arguments from the Head of the expression.)"
] | [
null,
"https://i.stack.imgur.com/TNBrW.png",
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http://www.bondeconomics.com/2014/07/if-r-g-dsge-model-assumptions-break-down.html?showComment=1493121848575&m=1 | [
"## Wednesday, July 2, 2014\n\n### If r < g, DSGE Model Assumptions Break Down",
null,
"The relationship between interest rates and the growth rate of the economy is critical for government fiscal dynamics. In the literature for Dynamic Stochastic General Equilibrium (DSGE) models, the discussion of the governmental budget constraint appears to have an embedded assumption that the real interest rate on government debt is greater than the economic growth rate (“r>g”). However, there is no reason that this has to be true, and the mathematics of the budget constraint fails if the condition does not hold. This poses a problem for the constraint, as a true mathematical constraint is something that is always true. Once this constraint is dropped, a good portion of the recent academic literature discussing fiscal policy becomes irrelevant. (Despite my opportunistic use of “r” and “g” in the title of this article – in order to capitalise on the popularity of a recent book – it has nothing to do with inequality.)\n\n### Introduction\n\nThis article discusses an “endowment” economy (economic output is fixed, without any labour input trade-offs) which was described in more detail in an earlier article. I will once again summarise the situation:\n1. The economy consists of a single “representative” household that receives a number of apples each year (without labour).\n2. The household starts off with an initial stock of government liabilities (money or 1-year Treasury Bills).\n3. The government taxes and spends via transfer payments, but does not purchase apples for government consumption.\n\nIn order to pin down the price level, I impose an assumption that velocity is constant (discussed further here, in my article on “monetary frictions”). For example, if velocity is fixed at 100, and the household produces 100 apples that year and it holds \\$1 in money, the size of the economy is \\$100 (implying the price of an apple is \\$1). I discuss the constant velocity assumption further in the Appendix, as it can be relaxed.\n\nAt the initial time point, the household calculates the economic trajectory that optimises its utility, which is based on consumption over time. The form of the utility function does not matter too much, other than the fact that future consumption is discounted at a certain rate.\n\n### Real Rates And Inflation\n\nFor simplicity, I will assume that prices are flexible (future prices can be set independently of the current price), but prices have to respect the velocity constraint. When prices are flexible, DSGE model assumptions force the real rate of return on Treasury Bills to equal the (real) discount rate in the utility function. This means that if the rate of nominal interest is set to any value, (expected) inflation will be equal to the nominal interest rate less the fixed real rate. For simplicity, I will assume that the authorities are targeting the price level, and so the expected inflation rate is zero. In this case, the expected nominal interest rate equals the real discount rate for all time.\n\nWe can see immediately that this particular model framework does not fit real world economic data. In recent years, central banks in a number of developed countries have kept real policy interest rates negative, which would be impossible in this framework (the real discount rate is assumed to be positive). The fact that DSGE models make no useful predictions about the real world should come as no surprise; most market practitioners figured that out years ago. I am only detailing these problems to explain why I will be ignoring the “state of the art” research that is being cranked out by academics in my future articles discussing fiscal policy.\n\n### Bringing In Economic Growth\n\nI will now assume that the number of apples produced grows each year by some factor. Since I need to look at the household sector in aggregate, this growth rate includes the population growth rate (typically denoted n), as well as per-household productivity growth (typically denoted g).\n\nIt should be noted that the representative household framework breaks down in the case of a growing population. If every single household shrinks its holdings of government liabilities by 0.5% per year, but the number of households grows by 1% per year, the aggregate growth rates of government liabilities is positive, not negative. Very simply, the representative household assumption makes very little mathematical or economic sense.\n\n### A Counterexample\n\nTake my model economy, and assume that we have price level stability. We then assume that the number of apples produces grows by 2% per year, but the real discount rate is only 1%.\n\nThe velocity relationship tells us that the money stock has to grow by 2% per year. However, the aggregate interest rates on government liabilities is less than or equal to 1%, since there is no interest paid on money. Even if interest were paid on “money” (it would have to be “reserves” at the central bank), the rate of growth of government liabilities due to interest compounding is 1%. The only way the desired economic trajectory could be achieved is that the government has to run a (growing) primary deficit every year, so that the money stock would grow in line with nominal GDP.\n\nThis appears unremarkable. But this solution violates the so-called governmental budget constraint, which states that the discounted trajectory of primary fiscal surpluses equals the stock of initial debt. Since the government never runs a primary surplus, this “constraint” is obviously not respected.\n\nIn particular, I will return to the Bond Valuation Formula (part of the Fiscal Theory of the Price Level) which I describe in this earlier post. The Bond Valuation Formula is a direct consequence of the governmental budget constraint; if you believe one holds, the other does as well. I look at this formula because it is one of the few references I have found where the mathematics of the budget constraint is actually fleshed out. Once again, formula is:\n(The formula, when translated into English, says that the real value of government debt is the discounted value of the path of real primary surpluses.) The formula is obviously violated by my example economy. How is this possible, when it was supposedly derived using actual mathematics? We need to look at the proof in the Appendix of the Cochrane working paper to see where the divergence occurs. After some algebra, the single period accounting identity for government finance was rearranged to:\nYou will need to read the paper for the description of the notation, but the problem lies is in the second term. Translating the mathematics into English, the term is the discounted value of the real value of government debt outstanding as time goes to infinity. John Cochrane then states “I impose the usual condition that the last term is zero.” (In the literature, this condition is known as the “transversality condition”.) This “usual condition” is what is violated in my example; the real value of government debt is growing by 2% per year, but the discount rate is only 1%. Therefore, the second term does not converge (loosely, “it goes to infinity”). (UPDATE) Note that this example is a worst-case error; if r=g, then the second term may converge to a non-zero value, creating an \"error\" in the price level predicted by the Fiscal Theory of the Price Level. (Previous sentence added to make this more general; thanks to \"srini\" for pointing this out.)\n\nJohn Cochrane argues that this condition must be imposed in order to meet the conditions of household optimality, which is a standard argument. In my view, this argument is incorrect. I have a discussion in another article – A Contradiction At The Heart Of DSGE Models – which (partially) explains my logic. (I will add more comments in a later article.) Instead, I will finish this post discussing the implications of this disagreement.\n\n### What If The Transversality Condition Holds?\n\nEven if the reader is unconvinced with my critique of the Transversality condition, the situation for DSGE modelling is still not very satisfying. If we impose the Transversality Condition on my model economy, we get the situation that is impossible (for some reason) for the government to target price level stability. It has to force the ratio of the money stock to GDP to shrink by (slightly more than) 1% per year in order for the term to converge to zero. This would imply a policy of deliberate deflation of (at least) 1% per year.\n\nIn the real world, we do not see any tendency for the ratio of government liabilities to GDP to go to zero, which is what the transversality condition says must happen.\n\n### What If r>g?\n\nIf the government does not pay interest on money, we can find a solution in which there are no surpluses, even if the real rate of interest is greater than the real growth rate. This is because the growth rate of government liabilities due to interest will be lower than the rate of interest if money holdings are non-zero. For example, if the nominal rate of interest on Treasury Bills is 4%, and half of government liabilities are in the form of money, the nominal stock of government liabilities is only growing at 2% per year.\n\nBy increasing the weighting of money in household portfolios, it is always possible to find a solution to the problem in which no primary fiscal surpluses are ever run. (If real economic output is shrinking, it is necessary to have an inflation rate that keeps nominal GDP growth positive.) Once again, this will violate the governmental budget constraint (and the Bond Valuation Formula).\n\n### The Case Of Pure Price Flexibility\n\nIf we do not impose monetary frictions of some sort, my logic breaks down. In this case, there is nothing to pin down the initial price level. One could use the Bond Valuation Formula to determine the initial price level (which implies that the governmental budget constraint holds). But since the price level is essentially arbitrary in this case, it could be set to anything and we can still find a solution to the household optimisation problem. Those solutions could violate the governmental budget constraint (and hence the bond valuation formula). There is no reason to prefer one solution over the other, other than ideological prejudices.\n\nThis is what happens if you work with mathematical models which are heavily under-determined: their solutions do not make a lot of sense.\n\n### Concluding Remarks\n\nIf we can find an example model economy where the “governmental budget constraint” is violated, it is not in fact a constraint. It is then a relationship which may or may not hold, which is an entirely useless piece of trivia. Dropping the government budget constraint from the DSGE framework is not easily done, as it is tied to a constraint for household consumption over time. The trajectory of the economy is supposed to be the optimal solution for all time going forward; but it we lack a constraint on future behaviour, we cannot solve backwards to get the solution in the present.\n\nAs seen above, the crux of my argument involves the transversality condition. I will return to transversality in a later article, adding to my earlier critique.\n\n(UPDATE) The paper \"Interest Rates and Fiscal Sustainability\" by Scott Fullwiler covers a lot of this ground, but in more historical depth. The Fullwiler paper notes that this growth rate condition goes back to the Domar 1944 paper \"The 'Burden of the Debt' and the National Income\". The reason why that earlier work is ignored in the modern DSGE literature appears to be the fact that the transversality condition is assumed to hold, and so what happens to the debt/GDP ratio is completely ignored. The earlier literature on sustainability was based on how the debt/GDP ratio evolved. (h/t Michael Sankowski at Mike Norman Economics.)\n\n### Appendix: The Constant Velocity Assumption\n\nIn my previous post, I discuss broader monetary frictions than just the constant velocity constraint than I use within this article. To be clear, I do not think velocity is constant in the real world. But some form of monetary friction has to be imposed in order for DSGE models to make even a little bit of sense.\n\nIf one uses a more complex monetary friction, such as velocity that varies based on other variables, or money in the utility function, you could derive similar results to my example. The only requirement is that velocity has an upper bound as well as a lower bound above zero. Such a constraint seems reasonable.\n\n• If velocity fell arbitrarily close to zero, the implication is that people would have money holdings that are arbitrarily large relative to their nominal incomes. We see very few people who earn \\$50,000 per year walking around with billions of dollars in their pockets.\n• If velocity became arbitrarily large, the economy would have to somehow function even though people would have almost no money in notes and coins or bank accounts (deposits at the central bank – reserves – would be an arbitrarily small portion of their balance sheets). Although this situation appears feasible, but I argue it would be untenable in practice, as the private sector would lack liquid government liabilities to back up private sector short-term liabilities. The relative lack of government liabilities would make paying taxes extremely difficult (since gross taxes paid would presumably rise in line with GDP).\n\nI believe that a constant velocity makes a lot of sense in a steady state growth model. The key is not adopting the logical fallacy that Monetarists fell for. If the economy is assumed to be growing at a steady rate, it makes sense that the central bank needs to grow the monetary base in line with that growth rate in order to avoid distortions. But the opposite direction of causality – attempting to drive steady GDP growth by growing the monetary base at a fixed rate – makes little sense. If we move away from the “steady state” condition, the balance of forces that led to a particular observed value of velocity would likely change.\n\n(c) Brian Romanchuk 2014\n\n1.",
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"Brian,\n\nYou have not discussed another condition--where the second term converges but not to zero. So, there is always some stock of government debt outstanding--that is, debt is never fully repaid. The strong conclusions of DSGE literature come from making the transversality assumption and the further assumption that the net present value of debt is zero.\n\n1.",
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"Thanks. I will modify my text to show that I am giving a worst-case error, but any non-zero value creates an error.\n\n2.",
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"Here you go:\n\nScott F's paper on this is great too.\n\n3.",
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"HI Brian\n\nNice job. FYI, Charles Goodhart wrote a number of good papers in the mid-to-late 2000s critiquing the transversality condition. He referred to it as the most dangerous idea in macro, or the worst idea, or something like that. I think several of the papers can still be found online.\n\nJamie Galbraith did a quick piece on r vs. g, too at Levy. http://www.levyinstitute.org/publications/is-the-federal-debt-unsustainable\n\nBest,\nScott Fullwiler\n\n1.",
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"Thanks, I will take a look.\n\n4.",
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"This comment has been removed by a blog administrator.\n\n5.",
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"Assume r > g what does it mean? It means that holders of goverment debt are getting bigger and bigger share of all the real wealth generated by the economy? That process has an obvious limit, the amount of real wealth. And the amount of real wealth has to be in some relation to growth variable g. Therefore I do not see how r > g can hold given infinite horizon?\n\nBtw. I think your coding project is really interesting! Thanks for all the effort!\n\n1.",
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"There is a big Piketty r > g debate, which I cheesily worked into the title.\n\nI am looking at this from the macro perspective, and all we can talk about is the household sector as a whole. How wealth is distributed is buried within the aggregation, and I don't have an opinion on that debate.\n\nBut in order for the debt/GDP ratio to remain finite, the government needs to run *primary* fiscal surpluses to keep debt levels from growing faster than GDP. That's a straight application of limit theorems. However, the budget constraint summation used by the mainstream does not converge in that case.\n\nIn other words, the only \"discount rate\" that can be used in these summations that makes any sense is the growth rate of GDP itself (or something slightly larger), not the interest rate.\n\nFurthermore, the overall budget would still be in deficit, with interest costs greater than the primary surplus.\n\n6.",
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"I liked a lot your post and there is nothing I would disagree with. I only thought that the whole r > g discussion looks moot to me because r ~ g needs to be true (w.r.t. “the only \"discount rate\" that can be used in these summations that makes any sense is the growth rate of GDP itself (or something slightly larger), not the interest rate.”).\n\nI brought in the wealth distribution because I think through that it can be seen that debt as an asset class cannot grow without limit (limit theorem) and also what are the possible limiting mechanisms. And also shows that the political choice is always between surpluses vs. a form of financial repression. All below assumes closed economy.\n\nThe government debt is just an indirect way for a group of people (not holding government papers) to be indebted towards another group of people (holding papers). I think this tells us how government debt being unsustainable actually means that wealth inequality is too high or unsustainable in the sense that non-holders of government papers are not able to pay papers back in real terms, meaning goods and services. This can be defused a) by wealth taxation (holders “pay it back to themselves”), b) by some form of financial repression ( “wealth tax”) and c) by defaulting (“wealth tax”). I think it is just that simple. Usually some form of financial repression is preferred (e.g. current real yield vs GDP growth).\n\nSo I would rather say that: “But in order for the debt/GDP ratio to remain finite, the asset prices will adjust [in a way that (expected) *primary* fiscal surpluses are enough] to keep debt levels from growing faster than GDP”. Or maybe that “asset prices will be made to adjust” by the central bank / government programs.\n\nThe main mechanism is usually the inflation/financial repression, which will reduce the value of government debt or overall indebtness, which always makes government debt sustainable. I think Cullen Roche (http://www.pragcap.com/hyperinflation-its-more-than-just-a-monetary-phenomenon/) is right that hyperinflation (unsustainability?) is “more than just a monetary phenomenon”. So inflation is an important adjustment mechanism while hyperinflation (unsustainability) ensues only if there are other factors in play.\n\nSo all in all I find it peculiar that in DSGE primary surpluses as flows/quantities are assumed to have the burden of adjustment in order to get things in equilibrium while usually prices, and esp. asset prices, are assumed to have that role (w.r.t, “DSGE model assumptions force the real rate of return on Treasury Bills to equal the (real) discount rate in the utility function”).\n\n1.",
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"My latest article gets back to this for the DSGE model case: http://www.bondeconomics.com/2017/04/does-governmental-budget-constraint.html\n\nFrom the SFC perspective, the way that fiscal policy is specified solves the problem. If the debt-GDP ratio gets \"high,\" activity rises (since consumption is explicitly a function of wealth), and the higher activity drives up the tax take automatically. http://www.bondeconomics.com/2017/04/sfc-models-and-introductory-mmt-style.html The real-world is far more complex than the model I use in that article, but the outcome is directionally correct.\n\n7.",
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"This comment has been removed by a blog administrator.\n\nNote: Posts are manually moderated, with a varying delay. Some disappear.\n\nThe comment section here is largely dead. My Substack or Twitter are better places to have a conversation.\n\nGiven that this is largely a backup way to reach me, I am going to reject posts that annoy me. Please post lengthy essays elsewhere."
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https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/demo_theoretical_background_generic_equilibrium_construction.html | [
":\n\nfrom lbmpy.session import *\n\nfrom pystencils.sympyextensions import prod\nfrom lbmpy.stencils import get_stencil\nfrom lbmpy.moments import get_default_moment_set_for_stencil, moments_up_to_order\nfrom lbmpy.creationfunctions import create_lb_method, create_generic_mrt\nfrom lbmpy.chapman_enskog import ChapmanEnskogAnalysis\nfrom lbmpy.moments import exponent_to_polynomial_representation\n\n\n# Demo: Theoretical Background - LB Equilibrium Construction using quadratic Ansatz¶\n\nAccording to book by Wolf-Gladrow “Lattice-Gas Cellular Automata and Lattice Boltzmann Methods” (2005)\n\nThrough the Chapman Enskog analysis the following necessary conditions can be found in order for a lattice Boltzmann Method to approximate the Navier Stokes mass and momentum conservation equations. In the Chapman Enskog analysis only the moments of the equilibrium distribution functions are used, thus all conditions are formulated with regard to the moments $$\\Pi$$ of the equilibrium distribution function $$f^{(eq)}$$\n\nThe conditions are: - zeroth moment is the density: $$\\Pi_0 = \\sum_q f^{(eq)}_q = \\rho$$ - first moment is the momentum density, or for incompressible models the velocity: - compressible: $$\\Pi_\\alpha = \\sum_q c_{q\\alpha} f^{(eq)}_q = \\rho u_\\alpha$$ - incompressible: $$\\Pi_\\alpha = \\sum_q c_{q\\alpha} f^{(eq)}_q = u_\\alpha$$ - second moment is related to the pressure tensor and has to be: $$\\Pi_{\\alpha\\beta} = \\sum_q c_{q\\alpha} c_{q\\beta} f^{(eq)}_q = \\rho u_\\alpha u_\\beta + p \\delta_{\\alpha\\beta}$$ - third order moments are also used in the Chapman Enskog expansion. The conditions on these moments are harder to formulate and are investigated later. A commonly used, but overly restrictive choice is $$\\Pi_{\\alpha\\beta\\gamma} = p ( \\delta_{\\alpha\\beta} u_\\gamma + \\delta_{\\alpha\\gamma} u_\\beta + \\delta_{\\beta\\gamma} u_\\alpha )$$. In Wolf-Gladrows book these conditions on the third order moment are not used but implicitly fulfilled by choosing fixed fractions of the coefficients $$\\frac{A_1}{A_2}$$ etc.\n\nNow the following generic quadratic ansatz is used for the equilibrium distribution.\n\n$f^{(eq)}_q = A_{|q|} + B_{|q|} (\\mathbf{c}_q \\cdot \\mathbf{u}^2 ) + D_{|q|} \\mathbf{u}^2$\n\nThe free parameters $$A_{|q|}, B_{|q|}, C_{|q|}$$ and $$D_{|q|}$$ are chosen such that above conditions are fulfilled. The subscript $$|q|$$ is an integer and defined as the sum of the absolute values of the corresponding stencil direction. For example: for center $$|q|=0$$, for direct neighbors like north, east, top $$|q|=1$$ for 2D diagonals like north-west its 2 and for 3D diagnoals like bottom-north-west its 3.\n\nlbmpy can create this quadratic ansatz for use for a given stencil:\n\n:\n\n# Create Stencil\nstencil_name = \"D2Q9\"\nstencil = get_stencil(stencil_name)\n\nansatz = generic_equilibrium_ansatz(stencil)\n\n# Show equilibrium for each stencil direction\nplt.figure(figsize=(12,8))\nps.stencil.plot(stencil, data=ansatz, textsize=9, slice=True)",
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"Next we define the restrictions obtained through the Chapman Enskog analysis, in the book listed as equations (5.4.2) and following:\n\n:\n\nmoment_restrictions = hydrodynamic_moment_values(dim=len(stencil), compressible=True, up_to_order=2)\nmoment_restrictions\n\n:\n\n$\\displaystyle \\left\\{ \\left( 0, \\ 0\\right) : \\rho, \\ \\left( 0, \\ 1\\right) : \\rho u_{1}, \\ \\left( 0, \\ 2\\right) : p + \\rho u_{1}^{2}, \\ \\left( 1, \\ 0\\right) : \\rho u_{0}, \\ \\left( 1, \\ 1\\right) : \\rho u_{0} u_{1}, \\ \\left( 2, \\ 0\\right) : p + \\rho u_{0}^{2}\\right\\}$\n\nThe parameter up_to_order can be modified to 3. Then the third order restrictions are included as well (see discussion above). Using these moment restrictions, the necessary conditions on the parameter $$A$$ to $$D$$ can be found.\n\n:\n\nequations = moment_constraint_equations(stencil, ansatz, moment_restrictions)\nsp.Matrix(equations)\n\n:\n\n$\\displaystyle \\left[\\begin{matrix}2 C_{1} + 4 C_{2} + D_{0} + 4 D_{1} + 4 D_{2}\\\\8 C_{2} - \\rho\\\\4 C_{2} + 2 D_{1} + 4 D_{2}\\\\A_{0} + 4 A_{1} + 4 A_{2} - \\rho\\\\2 A_{1} + 4 A_{2} - p\\\\2 C_{1} + 4 C_{2} + 2 D_{1} + 4 D_{2} - \\rho\\\\2 B_{1} + 4 B_{2} - \\rho\\end{matrix}\\right]$\n\nSince we have still more unknowns than equations, some additional restrictions have to be imposed.\n\n:\n\ndofs = generic_equilibrium_ansatz_parameters(stencil)\ndofs\n\n:\n\n$\\displaystyle \\left[ A_{0}, \\ A_{1}, \\ A_{2}, \\ B_{0}, \\ B_{1}, \\ B_{2}, \\ C_{0}, \\ C_{1}, \\ C_{2}, \\ D_{0}, \\ D_{1}, \\ D_{2}, \\ p\\right]$\n\nIn Wolf-Gladrows book the following arbitrary restrictions are added to the necessary constraints:\n\n$\\frac{A_0}{A_1} = \\frac{A_1}{A_2} = \\frac{B_1}{B_2} = \\frac{D_0}{D_1} =: r$\n:\n\nadditional_restrictions = [\n\"A_0 / A_1 - r\",\n\"A_1 / A_2 - r\",\n\"B_1 / B_2 - r\",\n\"D_0 / D_1 - r\",\n\"D_1 / D_2 - r\", # comment out this line to get solution dependent on r\n]\n\n:\n\n$\\displaystyle \\left[ \\frac{A_{0}}{A_{1}} - r, \\ \\frac{A_{1}}{A_{2}} - r, \\ \\frac{B_{1}}{B_{2}} - r, \\ \\frac{D_{0}}{D_{1}} - r, \\ \\frac{D_{1}}{D_{2}} - r\\right]$\n:\n\nsolveResult = sp.solve(equations + additional_restrictions, dofs + [sp.Symbol(\"r\")], dict=True)\nsolveResult\n\n:\n\n$\\displaystyle \\left[ \\left\\{ A_{0} : \\frac{4 \\rho}{9}, \\ A_{1} : \\frac{\\rho}{9}, \\ A_{2} : \\frac{\\rho}{36}, \\ B_{1} : \\frac{\\rho}{3}, \\ B_{2} : \\frac{\\rho}{12}, \\ C_{1} : \\frac{\\rho}{2}, \\ C_{2} : \\frac{\\rho}{8}, \\ D_{0} : - \\frac{2 \\rho}{3}, \\ D_{1} : - \\frac{\\rho}{6}, \\ D_{2} : - \\frac{\\rho}{24}, \\ p : \\frac{\\rho}{3}, \\ r : 4\\right\\}\\right]$\n\nThe equilibrium we found here is the same as obtained with the usual lbmpy construction technique:\n\n:\n\nconstructed_equilibrium = sp.Matrix(ansatz).subs(solveResult).expand()\nnormal_lbmpy_equilibrium = create_lb_method(stencil=stencil_name, compressible=True).get_equilibrium_terms()\nassert constructed_equilibrium == normal_lbmpy_equilibrium\n\n\n## Generalization of above technique¶\n\n:\n\ndef generic_polynomial(u, coeff_prefix, order=2):\ndim = len(u)\nresult = 0\nfor idx in moments_up_to_order(order, dim=dim):\nu_prod = prod(u[i] ** exp for i, exp in enumerate(idx))\ncoeff = sp.Symbol((\"%s_\" + (\"%d\" * dim)) % ((coeff_prefix,) + idx))\nresult += coeff * u_prod\nreturn result\n\ndef generic_polynomial_coeffs(dim, coeff_prefix, order=2):\nresult = []\nfor idx in moments_up_to_order(order, dim=dim):\nresult.append(sp.Symbol((\"%s_\" + (\"%d\" * dim)) % ((coeff_prefix,) + idx)))\nreturn result\n\n:\n\nallMoments = get_default_moment_set_for_stencil(stencil)\nallMoments\n\n:\n\n$\\displaystyle \\left[ 1, \\ x, \\ y, \\ x^{2}, \\ y^{2}, \\ x y, \\ x^{2} y, \\ x y^{2}, \\ x^{2} y^{2}\\right]$\n\nWe use, as before, all constraints for moments up order 2. This time we do not use a quadratic ansatz for the equilibrium distribution or the additional constraints (i.e. the ratios of coefficients being some constant $$r$$). Instead an arbitrary second order polynomial $$u$$ is used for the third order moments. The forth order moment does not appear at all in the Chapman Enskog expansion and is thus set to a constant.\n\nSo we end up with the following moments\n\n:\n\ndim = len(stencil)\nu = sp.symbols(\"u_:3\")[:len(stencil)]\nmoment_restrictions = hydrodynamic_moment_values(dim=len(stencil), compressible=True, up_to_order=2)\nmoment_restrictions[(2, 1)] = generic_polynomial(u, \"a\")\nmoment_restrictions[(1, 2)] = generic_polynomial(u, \"b\")\nmoment_restrictions[(2, 2)] = sp.Symbol(\"M_22\")\nmoment_restrictions = {m: v.subs(sp.Symbol(\"p\"), sp.Symbol(\"rho\") / 3) for m, v in moment_restrictions.items()}\nmoment_restrictions\n\n:\n\n$\\displaystyle \\left\\{ \\left( 0, \\ 0\\right) : \\rho, \\ \\left( 0, \\ 1\\right) : \\rho u_{1}, \\ \\left( 0, \\ 2\\right) : \\rho u_{1}^{2} + \\frac{\\rho}{3}, \\ \\left( 1, \\ 0\\right) : \\rho u_{0}, \\ \\left( 1, \\ 1\\right) : \\rho u_{0} u_{1}, \\ \\left( 1, \\ 2\\right) : b_{00} + b_{01} u_{1} + b_{02} u_{1}^{2} + b_{10} u_{0} + b_{11} u_{0} u_{1} + b_{20} u_{0}^{2}, \\ \\left( 2, \\ 0\\right) : \\rho u_{0}^{2} + \\frac{\\rho}{3}, \\ \\left( 2, \\ 1\\right) : a_{00} + a_{01} u_{1} + a_{02} u_{1}^{2} + a_{10} u_{0} + a_{11} u_{0} u_{1} + a_{20} u_{0}^{2}, \\ \\left( 2, \\ 2\\right) : M_{22}\\right\\}$\n\nNext all parameters are collected..\n\n:\n\nparameters = generic_polynomial_coeffs(dim, \"a\") + generic_polynomial_coeffs(dim, \"b\") + [sp.Symbol(\"p\")]\nparameters\n\n:\n\n$\\displaystyle \\left[ a_{00}, \\ a_{01}, \\ a_{10}, \\ a_{02}, \\ a_{11}, \\ a_{20}, \\ b_{00}, \\ b_{01}, \\ b_{10}, \\ b_{02}, \\ b_{11}, \\ b_{20}, \\ p\\right]$\n\n… and a lbmpy LB method is created. On this method an automatic Chapman Enskog analysis can be conducted to find constraints for the free parameters above.\n\n:\n\nrr = sp.symbols(\"omega\")\ncollision_table = [( exponent_to_polynomial_representation(m), v, rr) for m, v in moment_restrictions.items()]\ncollision_table\n\n:\n\n$\\displaystyle \\left[ \\left( 1, \\ \\rho, \\ \\omega\\right), \\ \\left( y, \\ \\rho u_{1}, \\ \\omega\\right), \\ \\left( x, \\ \\rho u_{0}, \\ \\omega\\right), \\ \\left( y^{2}, \\ \\rho u_{1}^{2} + \\frac{\\rho}{3}, \\ \\omega\\right), \\ \\left( x y, \\ \\rho u_{0} u_{1}, \\ \\omega\\right), \\ \\left( x^{2}, \\ \\rho u_{0}^{2} + \\frac{\\rho}{3}, \\ \\omega\\right), \\ \\left( x^{2} y, \\ a_{00} + a_{01} u_{1} + a_{02} u_{1}^{2} + a_{10} u_{0} + a_{11} u_{0} u_{1} + a_{20} u_{0}^{2}, \\ \\omega\\right), \\ \\left( x y^{2}, \\ b_{00} + b_{01} u_{1} + b_{02} u_{1}^{2} + b_{10} u_{0} + b_{11} u_{0} u_{1} + b_{20} u_{0}^{2}, \\ \\omega\\right), \\ \\left( x^{2} y^{2}, \\ M_{22}, \\ \\omega\\right)\\right]$\n:\n\nmethod = create_generic_mrt(stencil, collision_table, compressible=True)\nanalysis = ChapmanEnskogAnalysis(method, constants=set(parameters))\n\n:\n\nanalysis.does_approximate_navier_stokes()\n\n:\n\n$\\displaystyle \\left\\{0, - \\frac{b_{10}}{2} + \\frac{b_{10}}{\\omega}, - \\frac{a_{10}}{2} + \\frac{a_{10}}{\\omega} + \\frac{b_{01}}{2} - \\frac{b_{01}}{\\omega}, - \\frac{a_{01}}{2} + \\frac{a_{01}}{\\omega} + \\frac{b_{01}}{2} - \\frac{b_{01}}{\\omega} + \\frac{\\rho}{6} - \\frac{\\rho}{3 \\omega}, \\frac{b_{01}}{2} - \\frac{b_{01}}{\\omega} - \\frac{b_{10}}{2} + \\frac{b_{10}}{\\omega} + \\frac{\\rho}{6} - \\frac{\\rho}{3 \\omega}, \\frac{b_{01}}{2} - \\frac{b_{01}}{\\omega} + b_{10} - \\frac{2 b_{10}}{\\omega} - \\frac{\\rho}{3} + \\frac{2 \\rho}{3 \\omega}\\right\\}$\n\nThese constraints can be solved for the free parameters:\n\n:\n\nsolveRes = sp.solve(analysis.does_approximate_navier_stokes(), parameters)\nsolveRes\n\n:\n\n$\\displaystyle \\left[ \\right]$\n:\n\nnew_moment_restrictions = {a : b.subs(solveRes) for a, b in moment_restrictions.items()}\nnew_moment_restrictions\n\n:\n\n$\\displaystyle \\left\\{ \\left( 0, \\ 0\\right) : \\rho, \\ \\left( 0, \\ 1\\right) : \\rho u_{1}, \\ \\left( 0, \\ 2\\right) : \\rho u_{1}^{2} + \\frac{\\rho}{3}, \\ \\left( 1, \\ 0\\right) : \\rho u_{0}, \\ \\left( 1, \\ 1\\right) : \\rho u_{0} u_{1}, \\ \\left( 1, \\ 2\\right) : b_{00} + b_{01} u_{1} + b_{02} u_{1}^{2} + b_{10} u_{0} + b_{11} u_{0} u_{1} + b_{20} u_{0}^{2}, \\ \\left( 2, \\ 0\\right) : \\rho u_{0}^{2} + \\frac{\\rho}{3}, \\ \\left( 2, \\ 1\\right) : a_{00} + a_{01} u_{1} + a_{02} u_{1}^{2} + a_{10} u_{0} + a_{11} u_{0} u_{1} + a_{20} u_{0}^{2}, \\ \\left( 2, \\ 2\\right) : M_{22}\\right\\}$\n\nAll methods constructed with these constraints should theoretically approximate Navier Stokes."
] | [
null,
"https://pycodegen.pages.i10git.cs.fau.de/lbmpy/_images/notebooks_demo_theoretical_background_generic_equilibrium_construction_2_0.png",
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.66537523,"math_prob":0.9998485,"size":7164,"snap":"2020-45-2020-50","text_gpt3_token_len":1870,"char_repetition_ratio":0.14553073,"word_repetition_ratio":0.015837105,"special_character_ratio":0.2650754,"punctuation_ratio":0.15013169,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99996257,"pos_list":[0,1,2],"im_url_duplicate_count":[null,4,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-12-01T18:45:08Z\",\"WARC-Record-ID\":\"<urn:uuid:d9c88077-0aea-4cd9-9a5e-c903b25fbdf1>\",\"Content-Length\":\"50497\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6deca2d1-ea91-4ff5-9496-86ddd669346c>\",\"WARC-Concurrent-To\":\"<urn:uuid:4bcdca0f-95dc-43f8-8ae9-76ef249ee43f>\",\"WARC-IP-Address\":\"131.188.39.6\",\"WARC-Target-URI\":\"https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/demo_theoretical_background_generic_equilibrium_construction.html\",\"WARC-Payload-Digest\":\"sha1:IZJMKEYU2RUGDFNX5JKZQIECIMLJL67I\",\"WARC-Block-Digest\":\"sha1:3E3WXIF2BFAGMIFS4JIMR6KOKRMPADQC\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-50/CC-MAIN-2020-50_segments_1606141681209.60_warc_CC-MAIN-20201201170219-20201201200219-00079.warc.gz\"}"} |
https://eduzip.com/ask/question/in-the-given-figure-aob-is-a-straight-line-and-the-ray-oc-stands-521180 | [
"Mathematics\n\nIn the given figure, $AOB$ is a straight line and the ray $OC$ stands on it. If $\\angle BOC=132^{o}$, then $\\angle AOC=?$\n\n$48^{o}$\n\nSOLUTION\n$\\angle AOC$ = ?\n= $\\angle AOC + \\angle BOC$ = $180^{o}$....$[\\because Linear\\,pair]$\n= $\\angle AOC + 132^{o}$ = $180^{o}$\n= $\\angle AOC$ = $180^{o}-132^{o}$\n$\\angle AOC$ = $48^{o}$\n\nYou're just one step away\n\nSingle Correct Medium Published on 09th 09, 2020\nQuestions 120418\nSubjects 10\nChapters 88\nEnrolled Students 86\n\nRealted Questions\n\nQ1 Subjective Hard\nIf $PQ$ and $RS$ intersect at point $T$, such that $\\angle{PRT}={40}^{\\circ}$, $\\angle{RPT}={95}^{\\circ}$ and $\\angle{TSQ}={75}^{\\circ}$.Find $\\angle{SQT}$\n\nAsked in: Mathematics - Lines and Angles\n\n1 Verified Answer | Published on 09th 09, 2020\n\nQ2 Subjective Medium\nFind the value of x in the following figure.\n\nAsked in: Mathematics - Lines and Angles\n\n1 Verified Answer | Published on 09th 09, 2020\n\nQ3 Single Correct Medium\nSum of two obtuse angle results in:\n• A. Acute angle\n• B. Right angle\n• C. Obtuse angle\n• D. Reflex angle\n\nAsked in: Mathematics - Lines and Angles\n\n1 Verified Answer | Published on 09th 09, 2020\n\nQ4 Subjective Medium\nCan two angles be supplementary, if both of them be acute?\n\nAsked in: Mathematics - Lines and Angles\n\n1 Verified Answer | Published on 09th 09, 2020\n\nQ5 Subjective Medium\nFind five situations where obtuse angles are made.\n\nAsked in: Mathematics - Lines and Angles\n\n1 Verified Answer | Published on 09th 09, 2020"
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https://pykeen.readthedocs.io/en/stable/api/pykeen.nn.functional.triple_re_interaction.html | [
"# triple_re_interaction\n\ntriple_re_interaction(h, r_head, r_mid, r_tail, t, u=None, p=2, power_norm=False)[source]\n\nEvaluate the TripleRE interaction function.\n\n`pykeen.nn.modules.TripleREInteraction` for the stateful interaction module\n\nParameters:\n• h (`FloatTensor`) – shape: (*batch_dims, rank, dim) The head representations.\n\n• r_head (`FloatTensor`) – shape: (*batch_dims, rank, dim) The relation-specific head multiplicator representations.\n\n• r_mid (`FloatTensor`) – shape: (*batch_dims, rank, dim) The relation representations.\n\n• r_tail (`FloatTensor`) – shape: (*batch_dims, rank, dim) The relation-specific tail multiplicator representations.\n\n• t (`FloatTensor`) – shape: (*batch_dims, rank, dim) The tail representations.\n\n• u (`Optional`[`float`]) – the relation factor offset. If u is not None or 0, this corresponds to TripleREv2.\n\n• p (`int`) – The p for the norm. cf. `torch.linalg.vector_norm()`.\n\n• power_norm (`bool`) – Whether to return the powered norm.\n\nReturn type:\n\n`FloatTensor`\n\nReturns:\n\nshape: batch_dims The scores."
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http://docs.astropy.org/en/stable/api/astropy.convolution.convolve.html | [
"convolve¶\n\nastropy.convolution.convolve(array, kernel, boundary='fill', fill_value=0.0, nan_treatment='interpolate', normalize_kernel=True, mask=None, preserve_nan=False, normalization_zero_tol=1e-08)[source]\n\nConvolve an array with a kernel.\n\nThis routine differs from scipy.ndimage.convolve because it includes a special treatment for NaN values. Rather than including NaN values in the array in the convolution calculation, which causes large NaN holes in the convolved array, NaN values are replaced with interpolated values using the kernel as an interpolation function.\n\nParameters\narrayNDData or numpy.ndarray or array-like\n\nThe array to convolve. This should be a 1, 2, or 3-dimensional array or a list or a set of nested lists representing a 1, 2, or 3-dimensional array. If an NDData, the mask of the NDData will be used as the mask argument.\n\nkernel\n\nThe convolution kernel. The number of dimensions should match those for the array, and the dimensions should be odd in all directions. If a masked array, the masked values will be replaced by fill_value.\n\nboundarystr, optional\nA flag indicating how to handle boundaries:\n• None\n\nSet the result values to zero where the kernel extends beyond the edge of the array.\n\n• ‘fill’\n\nSet values outside the array boundary to fill_value (default).\n\n• ‘wrap’\n\nPeriodic boundary that wrap to the other side of array.\n\n• ‘extend’\n\nSet values outside the array to the nearest array value.\n\nfill_valuefloat, optional\n\nThe value to use outside the array when using boundary='fill'\n\nnormalize_kernelbool, optional\n\nWhether to normalize the kernel to have a sum of one.\n\nnan_treatment‘interpolate’, ‘fill’\n\ninterpolate will result in renormalization of the kernel at each position ignoring (pixels that are NaN in the image) in both the image and the kernel. ‘fill’ will replace the NaN pixels with a fixed numerical value (default zero, see fill_value) prior to convolution Note that if the kernel has a sum equal to zero, NaN interpolation is not possible and will raise an exception.\n\npreserve_nanbool\n\nAfter performing convolution, should pixels that were originally NaN again become NaN?\n\nA “mask” array. Shape must match array, and anything that is masked (i.e., not 0/False) will be set to NaN for the convolution. If None, no masking will be performed unless array is a masked array. If mask is not None and array is a masked array, a pixel is masked of it is masked in either mask or array.mask.\n\nnormalization_zero_tol: float, optional\n\nThe absolute tolerance on whether the kernel is different than zero. If the kernel sums to zero to within this precision, it cannot be normalized. Default is “1e-8”.\n\nReturns\nresultnumpy.ndarray\n\nAn array with the same dimensions and as the input array, convolved with kernel. The data type depends on the input array type. If array is a floating point type, then the return array keeps the same data type, otherwise the type is numpy.float.\n\nNotes\n\nFor masked arrays, masked values are treated as NaNs. The convolution is always done at numpy.float precision."
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.6867709,"math_prob":0.93171287,"size":3098,"snap":"2019-43-2019-47","text_gpt3_token_len":740,"char_repetition_ratio":0.14253394,"word_repetition_ratio":0.012658228,"special_character_ratio":0.21045835,"punctuation_ratio":0.1390845,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9819552,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-10-18T08:44:38Z\",\"WARC-Record-ID\":\"<urn:uuid:6aeb11b9-1a49-49d2-b9bf-955aba89e363>\",\"Content-Length\":\"15587\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:159bb8ef-eac0-42f4-b9e6-1477138576e6>\",\"WARC-Concurrent-To\":\"<urn:uuid:4d979010-a180-4091-9afd-15d96b60bc4b>\",\"WARC-IP-Address\":\"104.17.33.82\",\"WARC-Target-URI\":\"http://docs.astropy.org/en/stable/api/astropy.convolution.convolve.html\",\"WARC-Payload-Digest\":\"sha1:2SEKD7U65LCB3GUNPZ7CWH7WKEMX3XKI\",\"WARC-Block-Digest\":\"sha1:L5D4KD7TEKPO6NOQEYRNJBVUHEJZRX6N\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-43/CC-MAIN-2019-43_segments_1570986679439.48_warc_CC-MAIN-20191018081630-20191018105130-00039.warc.gz\"}"} |
https://mathoverflow.net/questions/255820/how-should-a-working-mathematician-think-about-sets-zfc-category-theory-ur | [
"# How should a \"working mathematician\" think about sets? (ZFC, category theory, urelements)\n\nNote that \"a working mathematician\" is probably not the best choice of words, it's supposed to mean \"someone who needs the theory for applications rather than for its own sake\". Think about it as a homage to Mac Lane's classic. I'm in no way implying that set theory is not \"real mathematics\" (whatever that expression might mean, though I've heard some people say it, and I don't respect this point of view that something abstract is \"not real mathematics\") and I have a great respect for that field of study.\n\nHowever, I'm personally not interested in set theory and its logic for their own sake (as of now). For a while I have treated them naively, and it was fine as I haven't needed anything beyond introductory chapters in compheresnive books on algebra, analysis or topology. But recently I decided to understand the foundations of category theory based on Grothendieck universes and inaccessible cardinals. So, I went to read some sources on set theory. And was really confused at first about such definitions as of a \"transitive set\", which implicitly assume that all elements of all sets are sets. Then I read more about it and discovered that in $\\mathrm{ZFC}$\n\neverything is a set!\n\nIt seemed absurd to me at first. After consulting several sources, I realized that ZFC was meant to be a (or even the) foundation for mathematics, rather than simply a theory which gives us a framework to work with sets, so at that time people thought that every mathematical object can be defined in term of sets. It didn't seem as unreasonable as before anymore, but still...\n\nIt still doesn't feel right for me. I understand that at the time when Zermelo and Fraenkel were developing axiomatic set theory, it was reasonable to think that every conceivable mathematical object is set. But it was a long time ago; is it still this way - especially concerning category theory?\n\nIf we work in $\\mathrm{ZFC}$ (+ $\\mathrm{UA}$) we have to assume that every object in any category is a actually as set. And the same should go for morphisms. Because, given a category $\\mathrm{C}$, $\\operatorname{ob} \\mathrm{C}$ and $\\operatorname{mor} \\mathrm{C}$ are sets, so their elements, namely, objects and morphisms of $\\mathrm{C}$, should also be sets.\n\nThe question is: is the assumption that there are no urelements, that is, that every conceivable mathematical object can be modeled in term of sets, reasonable, as of the second decade of the 21st century? Is there an area of mathematics where we need urelements? Can this way of thinking be a burden in some mathematical fields? (Actually, it's three questions, sorry. But they are related)\n\nP.S. I hope this question is not too \"elementary\" for this site. But as I understand there are quite a lot of working mathematicians who don't think much about foundations. So, even if this question is not useful for them, it can at least be interesting for them.\n\n• So once you relieve yourself of the Platonistic burden of a fixed mathematical universe, you can amend your boldface statement: Everything is a set in some universe of set theory. Nov 28 '16 at 18:14\n• What I wrote at mathoverflow.net/questions/90820/… might be relevant for you. Nov 28 '16 at 18:27\n• The main problem with the notion \"everything is a set\" is that it implies a semantics, and encourages people to pay attention to those semantics more than is wise. I once read a text that carefully checked that no real number was an ordered pair, and then defined the underlying set of the complex numbers to be $\\mathbb{C} = \\left( \\mathbb{R}^2 \\setminus (\\mathbb{R} \\times \\{ 0 \\}) \\right) \\cup \\mathbb{R}$ and addressed how to adjust the arithmetic operations defined on $\\mathbb{R}^2$ to be defined for $\\mathbb{C}$... just so that $\\mathbb{R} \\subseteq \\mathbb{C}$ was literally true of the sets\n– user13113\nNov 29 '16 at 0:12\n• @Hurkyl: I understand that. I just wanted to emphasize that this is not due to the nature of \"everything is a set\", but rather due to the failure of educators to teach \"how to use your foundations properly\". That's what happens when your department doesn't have a set theorist... And I'm not just saying that because I'm applying to postdoc positions these days! ;-) Nov 29 '16 at 3:45\n• The situation is very analogous to strongly-typed versus weakly-typed programming languages. ZFC is somewhat analogous to assembly language: when I read data from a memory address, I can treat the data as another address, or as a signed integer, or as a sequence of characters, etc. Other foundational systems are more like strongly-typed languages: each data type has its own operations, and I can only apply the appropriate operations to each type. It is well known that strongly typed programming languages are more convenient for some purpose, while weakly typed ones are convenient for others. Nov 29 '16 at 15:23\n\nSet theory provides a foundation for mathematics in roughly the same way that Turing machines provide a foundation for computer science. A computer program written in Java or assembly language isn't actually a Turing machine, and there are lots of good reasons not to do real programming in Turing machines - real languages have all sorts of useful higher order concepts. But Turing machines are a useful foundation because everything else can be encoded by Turing machines, and because it's much easier to study Turing machines than it is to study a more complicated higher order language.\n\nSimilarly, the point isn't that every mathematical object is a set, the point is that every mathematical object can be encoded by a set. It doesn't represent higher level ideas, like the fact that mathematical objects usually have types (as one of my colleagues likes to point out, the question \"is the integer 6 an abelian group\" is technically a reasonable one in set theory, but not in mathematics). But it's a (relatively) simple system to study, and just about everything we want to do can be encoded in set theory.\n\nTo answer your specific questions, yes, it's still true that every mathematical object can be encoded as a set. Because sets are very flexible, there's no reason to think this will not continue to be true. There is no current field of mathematics in which urelements are essential, and because things one would do with urelements can instead be encoded with sets, there is unlikely to be such a field.\n\nZFC does impose some limitations on category theory, because it doesn't allow objects on the same scale of the universe of sets. (For instance the category of categories is awkward to consider within ZFC, because the objects of this category cannot be a set.) These are reflected in the discussions of \"small\" and \"locally small\" categories. These issues can be worked around in mild extensions of ZFC by using things like Grothendieck universes. (Note that this is a feature of ZFC, not of set theoretic foundations in general. Quine's New Foundations allows certain self-containing sets.)\n\nThis way of thinking can't really be burden because ZFC doesn't impose a way of thinking. The fact that things can be encoded as sets doesn't, and shouldn't, mean that we always think of them that way. It's perfectly consistent with having a set theoretic foundation to work with things like urelements, or to think about groups and categories without thinking of them as sets. (Worrying about things like self-containing categories can be a burden, but it's a necessary one given the history of paradoxical objects containing themselves.)\n\n• One should add, perhaps, that the importance of being able to encode everything to sets is that it allows you to know that it is enough for set theory to be consistent in order for the rest of mathematics to be consistent. And also, it might be worth mentioning that there is a good reason for \"large\" objects to cause paradoxes, which can occur even if you ignore sets and just think about the category of all categories, or something like that. (Unless you worked in NF or something from the get go... but then... you're weird!) Nov 28 '16 at 18:18\n• @Heinrich: Do you also feel that a computing system which only operates on electrical pulses with two kinds of voltage does not model computation properly? Nov 28 '16 at 19:13\n• @HeinrichD: The proof in ZFC is straightforward. More seriously, I don't makes sense to talk about a proof in the mathematical sense; any such proof is probably just a statement about the system it's proven in. But as an empirical claim, it has survived well. I deny that this view is limiting; no one demands that all mathematics be presented in set theoretic terms, so there's nothing stopping people from producing new objects that would refute this hypothesis. Given the ease of encoding typed systems, I'm skeptical that the presence of multiple types is foundationally significant. Nov 28 '16 at 19:38\n• @HeinrichD: I think in order to say that the aforementioned point of view is \"limiting\", one should first be able to find a mathematical object which cannot be coded as a set (in ZFC or other set theories). No one forces mathematicians to only work with objects that can be encoded in set theories, they just happen to do so. Nov 28 '16 at 21:07\n• @coudy: We don't know how to define those notions rigorously, irrespective of how they're encoded; the obstacle to defining them rigorously happens at a higher level - at the level of choosing the right analytic framework - not at questions about how to encode the objects. Further, as Burak points out, the set theoretic perspective isn't limiting because it isn't stopping anyone from investigating these objects, and in the (spectacularly unlikely) event that they can be defined rigorously but can't be encoded in set theory, everyone will agree to move to a foundation that does include them. Nov 29 '16 at 14:58\n\nI prefer to think of ZFC as a proposed model of mathematics. I want to emphasize both words \"proposed\" and \"model\". For comparison, consider quantum mechanics. It can be modeled — as far as we know, perfectly — by the theory of Hilbert spaces. But the state right now of the electron in your retinal cell being excited by photon being emitted by the leftmost pixel in this word is not a vector in a Hilbert space: there is a Hilbert space $\\mathcal H$ with a vector $v \\in \\mathcal H$ that, so far as we know, perfectly models that physical interaction, but $(v,\\mathcal H)$ is only isomorphic to the physical system, it isn't itself a physical system.\n\nSo ZFC proposes not that mathematics \"is\" Sets, but that mathematics is \"isomorphic to\" Sets.\n\nOne reason to think that ZFC remains only a proposed model is that there is continued debate about the \"correct\" axioms for set theory. You can find, for example, recent papers by Woodin arguing that $\\mathfrak c = \\aleph_2$. Certainly such arguments are not \"proofs\" in ZFC — the value of $\\mathfrak c$ is independent of ZFC (beyond that ZFC proves $\\mathfrak c > \\aleph_0$) — but arguments about the \"actual mathematical world\" (specifically, the actual world of sets).\n\nI wonder how Asaf will interpret this answer in terms of Platonistic burdens.\n\n• Woodin has been a strong proponent of GCH in the past few years, actually. So his days of $\\mathfrak c=\\aleph_2$ are over... :-) Nov 29 '16 at 3:50\n• Also, Asaf interprets this answer as somewhat comparing mathematics and physics, which is not surprising if one bothers to read your profile. Not that it's bad. It's an interesting interpretation. (Asaf also finds that talking about Asaf in third-person is just weird. So he will stop doing that.) Nov 29 '16 at 4:15\n• @David: I guess you're going to have to do that now. Good luck! Nov 29 '16 at 6:42\n• So ZFC proposes not that mathematics \"is\" Sets, but that mathematics is \"isomorphic to\" Sets I like it. Similar to saying: Not that the real numbers \"are\" Dedekind cuts, but merely \"isomorphic\" to them. Nov 29 '16 at 13:20\n• @Stefan That was a sloppy sentence. I mean the following. As Gerald said, I do not accept that $\\mathbb R$ \"is\" the set of Dedekind cuts in $\\mathbb Q$, nor that it \"is\" the set of equivalence classes of Cauchy sequences, or the set of functions $\\mathbb Z \\to \\mathbb Z$ whose failure to be a homomorphism is absolutely bounded. $\\mathbb R$ \"is\" something unto itself, and probably not (just) a set at all. However, I accept that $\\mathbb R$ can be perfectly modeled by a complicated arrangement of sets. Similarly for all the mathematics I have ever come across. It can all be perfectly modeled... Nov 30 '16 at 1:09\n\nThere are already some excellent answers explaining in what senses ZFC can still be a foundation for most mathematics. But it also seems appropriate to mention some ways in which ZFC is insufficient as a foundation for modern mathematics. [Disclaimer: throughout this answer I will talk about \"ZFC\", but the remarks apply just as well to its variations including large cardinals and so on, and in some cases require variations such as removing choice or using constructive logic.]\n\nTo start with, by asking the question the way you did, as a dichotomy between sets and \"urelements\", you bias the answers you're likely to get. In fact, most real-world alternatives to ZFC are not simply obtained by \"adding urelements\" that have no members: instead they call into question the whole assumption of ZFC that there is a \"membership\" relation that can be meaningfully applied to any two mathematical objects. In such theories there are basic objects, sometimes still called \"sets\" but other times called something else like \"types\", and these objects have \"elements\"; but we cannot compare elements of two different sets/types or ask whether one set/type is an element or subset of another.\n\nOne such theory that calls its objects \"sets\" is Lawvere's ETCS. Those that call their objects \"types\" are generally called \"type theory\" of one sort or another; here is a blog post I wrote introducing type theory. In general, these alternative theories are inter-translatable with ZFC (or some minor variation of it), and in particular equiconsistent. Thus, any of them can serve equally well to encode most of mathematics and thereby guarantee its consistency.\n\nHowever, consistency is not the only purpose of having a foundation for mathematics. There are several other purposes that could be mentioned, but one that's particularly relevant is \"change of universe\" or \"internalization\". Any sufficiently powerful formal system like ZFC, ETCS, or type theory admits more than one model; even if we assume there is one \"real\" model (which is itself debatable), from that starting model we can always construct lots of other models. Moreover, it so happens that many of these other models are intrinsically interesting as mathematical objects even if we accept the original model as the only \"real\" one. For instance, if $X$ is any topological space, the sheaves on $X$ form a model of these theories (at least if we use constructive logic).\n\nNow if some formal system can be used as a foundation for (some fragment of) mathematics, that means that any theorem can be encoded into that formal system, and is therefore \"true internally\" in any model of that formal system. If this model is not the \"real\" one, then that \"internal\" truth will be different from \"real\" or \"external\" truth, but if the model is interesting then the internal truth is generally also interesting. For instance, the theory of local rings, when interpreted internally in the model of sheaves on $X$, becomes the theory of sheaves of rings on $X$ whose stalks are all local; while the theory of real numbers becomes the theory of continuous real-valued functions on $X$. In this way, using a formal system as a foundation for mathematics allows us to get much more bang for our buck: we prove one theorem, and we automatically deduce not only the \"real\" version of that theorem but also the \"internalized\" versions of that theorem in all other models of our formal system.\n\nThe reason I bring this up is that as compared with ETCS and type theory, ZFC is poorly-adapted to this sort of use. Even if our \"real\" model consists of ZFC-style sets with a global membership relation, most other interesting models do not come naturally with one: they generally present as categories of one sort or another, and in general there is no way to say that one object of a category is a member of another one. So it is much more straightforward to internalize ETCS or type theory into a category than to internalize ZFC.\n\nIt is possible to internalize ZFC (or related theories) into a category, such as by first internalizing ETCS or type theory and then passing across the above-mentioned translation to ZFC. However, in many cases this involves a loss of information. To construct of a model of ZFC from a model of ETCS or type theory, we explicitly build \"well-founded hereditary membership trees\" of some sort or other; see for instance here. The resulting model only \"sees\" those sets or types in the original model that can be equipped with such a structure, sometimes called the \"well-founded part\" of a category. In some cases this is the whole thing; in other cases it can be quite different. So if we want our internalized theorems to apply to all objects of a category, then ZFC-style theories aren't good enough.\n\nIn the case of 1-categories, we can to a certain extent fix this problem by... adding urelements! We consider the \"non-well-founded\" objects of our category to be \"sets of urelements\", thereby including them in the resulting model of ZFC+urelements (see for instance this paper). So this actually provides an answer even to your question as phrased, \"do we need urelements\"? The construction is still much more involved than modeling ETCS or type theory, but at least it is possible.\n\nMore radical still is the situation for higher categories, whose objects behave internally like higher groupoids (or even higher categories themselves). No ZFC-style theory is known whose basic objects behave in this way, even allowing urelements. But there is a version of type theory, called homotopy type theory, whose types do behave like higher groupoids. (The model theory of homotopy type theory is not completely developed, but indications so far are promising.) Thus, for the purpose of internalizing in higher categories, it seems that ZFC really is insufficient.\n\nA different way to put this last point is as follows. A central concept in homotopy theory and higher category theory is that of an $\\infty$-groupoid. Unsurprisingly, because sets are very flexible, the notion of $\\infty$-groupoid --- or at least A notion of $\\infty$-groupoid --- can be encoded using sets (for instance, as a Kan simplicial set). However, this encoding forces the thereby-encoded $\\infty$-groupoids to have certain properties, such as \"Whitehead's principle\" (a map inducing isomorphisms on all homotopy groups is an equivalence) or \"sets cover\" (every $\\infty$-groupoid admits a surjective map from a discrete one). But we might not necessarily want these properties to hold: for instance, when internalizing in a higher category, with $\\infty$-groupoids corresponding to objects of that category, they often turn out to be false.\n\nSo I would claim, contrary to what others have said, that ZFC does impose a way of thinking: namely, an assumption that everything should be encoded using sets. It's an observed fact that essentially all mathematical concepts can be encoded somehow as sets. But it's only an article of faith that every theorem we can prove about the encoding is necessarily true about the original concept.\n\n• I was waiting for you to show up. Nov 30 '16 at 11:23\n• @AsafKaragila: Is this the first line of a Wild West duel? ;) Nov 30 '16 at 11:54\n• @Heinrich: Not even a little bit! Mike is someone whose opinions on foundations of mathematics I respect and consider both interesting and well-motivated (in contrast to some people who will repeat buzzwords and \"junk theorems arguments\"). I was honestly waiting to see what he's going to say. (I know that you wrote your comment as a tongue in cheek kind of comment, but nevertheless, I felt obligated to explain.) Nov 30 '16 at 12:04\n\nThis complements the other answers. Let's take the natural numbers as an example for discussion.\n\nWhen we use mathematics, we typically want to use properties of the natural numbers: after every natural number there's a next one, adding two of them yields another one, same with multiplying, those two operations play nice together (distributive laws), the principle of induction, prime numbers and factorization, etc. etc.\n\nAt some point mathematicians wonder whether using these properties is a good idea. For example, could some of those properties contradict other properties in some subtle way? (That induction property, in particular, might hide some subtle pitfalls....) Does there even exist a set that (once $+$ and $\\times$ are defined on that set the way we want them to be defined) has all of the properties we want the natural numbers to have? In other words, are those properties \"consistent\", and do they have a \"model\"?\n\nSet theory provides a way to construct a set, and operations $+$ and $\\times$ on that set, for which it can be proved that all of the properties we want to hold for the natural numbers actually hold for that set. This provides mathematicians with formal reassurance that there isn't some subtle fatal flaw with our list of properties. Nevertheless, when we work with the natural numbers, we generally work with the properties themselves, without worrying about whether $6$ is a set or urelement or whatever.\n\nFormalizing \"the list of properties we want the natural numbers to have\" is something that itself required some thought; you can read more about the Peano axioms, for example. And formalizing what it means for a collection of mathematical statements to be \"consistent\" is also something that requires thought; this is what ZFC addresses.\n\n• I really like this answer. I've always sort of considered that even though technically math isn't founded on Peanos axioms, or the natural numbers; its sole purpose is to investigate using these tools. And if set theory is the foundation, it's only really the foundation because it meshes so well with natural numbers. Or intuition, and logic so addressed by them.\n– user78249\nNov 29 '16 at 0:45\n• \"This provides mathematicians with formal reassurance that there isn't some subtle fatal flaw with our list of properties\" ....Only if ZFC is itself consistent! But ZFC is clearly more \"problematic\", as for consistency, than PA. We would have (had) an actual \"formal reassurance\" about PA only if its consistency could be proved inside something even more \"elementary\", i.e. if Hilbert's program was possible. Sadly (or, rather, interestingly) Goedel's theorem tells us that this is not the case. Nov 29 '16 at 3:18\n• I agree with you. I used \"reassurance\" rather than \"proof\" for a reason :) Nov 29 '16 at 3:29\n• Greg, You mentioned that we generally work with the properties themselves without worrying about whether 6 is a set. Isn’t “being a set” a property too? We want to say that we care only about the structural properties. This requires giving an analysis of what it is for a property to be structural. To my knowledge, this is an open problem. Dec 6 '16 at 20:13\n\nIs there an area of mathematics where we need urelements?\n\nYes, in a certain sense. Indeed, we can get an example of such just within set theory.\n\nAdmissible set theory provides many pocwerful tools for reasoning about models of set theory. The downside is that they only directly apply to admissible sets (= transitive models of Kripke-Platek set theory, a weak fragment of $\\mathsf{ZF}$, possibly formulated to allow urelements). To get around this limitation, Barwise introduced the notion of the admissible cover of a (possibly ill-founded) model of set theory. Specifically, given $(M,E) \\models \\mathsf{KP}$ we can talk about admissible structures with $M$ as their urelements. The admissible cover of $M$ is the smallest such admissible structure satisfying certain reasonable properties. See the appendix of Barwise's book for details (conveniently available here). The admissible cover provides a way for some of the tools of admissible set theory to be applied to ill-founded models of set theory. To use the application Barwise gives in his appendix, one can prove that any countable model of $\\mathsf{ZF}$ has an end-extension to a model of $\\mathsf{ZF} + V = L$. Starting with a well-founded model of $\\mathsf{ZF}$, this is a straightforward argument using the Barwise compactness theorem. Proving the result for ill-founded models goes through looking at the admissible cover for the ill-founded model.\n\nThe notion of the admissible cover only makes sense if we allow urelements for our models of set theory, so this gives us something where we need urelements.\n\nBut, let me point out that there is a sense in which the use of urelements can be avoided here. Namely, we can always find copies of whatever structure we are interested in inside the pure sets, i.e. sets whose transitive closure contain no urelements. If $A$ is some set whose transitive closure contains urelements, via a simple inductive argument we can find a pure set $B$ and relation $E$ on $B$ so that $(\\mathrm{tc}(A),\\mathord\\in \\upharpoonright \\mathrm{tc}(A)) \\cong (B,E)$. (And from this we can identify the copy of $A$'s urelements in $B$.) To use the example from the above paragraph, $\\mathsf{ZFC}$ (formulated without urelements) proves that any model of $\\mathsf{ZF}$ can be end-extended to a model of $\\mathsf{ZF} + V = L$; the arguments involving the admissible cover can be simulated just using pure sets to get a proof that works in the no-urelements context.\n\nIf you have urelements, then you have objects that contain other objects (sets) or are empty, and objects that contain no other objects (urelements) and maybe you count $\\emptyset$. As long as you are not trying to find objects in them, they are no different. So the only thing urelements give you are objects in which you are not allowed to take elements.\n\nIf you want to limit the $\\in$-relation in this way for some set of urelements, you can do the following. You fix a set you do not care about. For example, you can think of all sets being generated in \"stages\" indexed by ordinals. In most of mathematics, you can limit how far you want to go, so you can take all sets generated at a certain sufficiently high stage, this will give you a set. Let $U$ be this set. You can then define $\\in^*$ by $(x\\in^* y)\\iff (x\\in y \\wedge y\\notin U)$. For the relation $\\in^*$, the set $U$ is a set of urelements and for most of your applications, you will be able to replace $\\epsilon$ by $\\epsilon^*$. There might be applications where you have to choose $U$ differently, but there should usually be some set you do not care about. Otherwise, you really are doing set theory. So urelements are not needed on a formal level. If you want to have large categories encoded in set theories, you can just assume that really high stages exist, the existence of inaccessible cardinals. It helps to have an understanding of set theory when doing such constructions, but it is usually clear that one can avoid any trouble.\n\nOn a more philosophical level, you might be worried by the fact that for certain ways of encoding real numbers as sets, you have $3\\subseteq \\pi$. You might think this answers a question that shouldn't be even askable, and you would not be alone in this. But foundations need to be somewhat artificial. A picture is not an arrangement of pixels, but the possibility to do so allows us to represent pictures in a way in which can talk about the concept. Mathematics is full of different things and a common foundation kind of requires us to break this diversity to a few things. Luckily, the working mathematician does not need to worry constantly about foundations, so nobody forces you to commit to a particular, somewhat artificial, representation of the things you work with.\n\nThat every object in set theory but the empty set contains other sets brings additional structure that is useless baggage for many applications. For perspectives on the general question of whether this is avoidable, see the question Set theories without “junk” theorems?.\n\n• I like the likening of junk theorems to pixels in an image! Nov 28 '16 at 18:15\n• I don't agree with \"But foundations need to be somewhat artificial\", in particular because there are non-artificial foundations. Nov 28 '16 at 19:08\n• @Heinrich: Do you mean category-based approaches, or HoTT? Those are not necessarily natural for every field of mathematics. The important things about foundational research is not \"my foundation is better than yours\", but rather how these different approaches interact and interpret one another, so when someone finds HoTT more natural to work with, they will have an easy way of translating what someone preferring set theory is saying from a foundational point of view. Nov 28 '16 at 19:12\n• @Qfwfq: My point is that if I want to think about sets in a concrete way, like we all naturally do, then $[0,\\pi]$ is a subset of $\\Bbb R$, but they are disjoint, and since elementhood is defined essentially by having a certain arrow from a singleton, it means that all the elements of all the sets are all isomorphic, so you stop being able to distinguish them. So the junk theorem are now due to the \"extra-structure\" imposed by these definitions, rather than \"the ability to ask if $3\\subseteq\\pi$\". (sorry, it's a very early morning for me.) Nov 29 '16 at 4:01\n• @Qfwfq: See? I don't understand the problem with having $3\\subseteq\\pi$ or not; these are just statements you can say. Exactly like \"My dog is a table\" or \"Desk wants to talk to you\". Sure, they can mean something in some context, but they don't really matter to us in an everyday communication, and they are certainly not a reason to worry that English is not a sufficient language for talking with other people. (Now I'm really signing off this comment thread.) Nov 29 '16 at 4:06\n\nA real number \"is\" an initial segment with no maximum in the linearly ordered set of all rational numbers. Or else a real number \"is\" an equivalence class of Cauchy sequences.\n\nIn the first case, the real number $1.4$ is a subset of the real number $\\sqrt 2$, and of course that is nonsense, as seen by the fact that in the second case, it's not.\n\nIt is only in that sense that everything \"is\" a set. It is more accurate to say that everything can be encoded as a set.\n\n• A real number is a function from $\\omega$ to $\\omega$. So no real number is a subset of another, nor it is an element of another. Now that everyone is happy about that, can we drop this tiring point already? Nov 29 '16 at 6:44\n• @AsafKaragila: I thought that a real number was simply an element of an uncountable standard Borel space. =P Nov 29 '16 at 12:10\n• Of course some will argue that the natural number 7 \"is not\" equal to the real number 7, merely canonically identified with it. Nov 29 '16 at 13:17\n• And here I thought a real number was a fixed point of any order-2 field automorphism of any algebraically closed field of cardinality $\\mathfrak c$. Nov 30 '16 at 1:33\n• @Theo: That is particularly false. Look at the hyperreal numbers, their algebraic closure is an extension of order two, and conjugation fixes exactly the hyperreal numbers. I might be a bit wrong, though. Nov 30 '16 at 5:17\n\nIf we follow the category-theoretic notion that we only care about things \"up to equivalence\", then we actually have\n\n\"Everything is a set!\" is equivalent to \"Sets have urelements!\"\n\nMore precisely, suppose we have some reasonable universe of sets with urelements in which the cardinality of every set is an ordinary cardinal number (e.g. the initial ordinal number of that cardinality). Define two categories:\n\n• SetU, the category of all sets (with urelements) and functions between them\n• Set, the full subcategory of SetU consisting only of ordinary sets\n\nThe premise implies that every object of SetU is isomorphic to an object of Set, and consequently we have an equivalence of categories SetU $\\equiv$ Set.\n\nOn the presumption that everything is \"built up\" from the category of sets, we conclude that, up to equivalence, doing math with urelements is the same thing as doing math without urelements.\n\nWithin traditional set theory (and without abandoning it for category theory) one can make a compelling case that not everything is a set, or more precisely that the assumption that everything is a set is both limiting and counterproductive when dealing with fruitful frameworks that are conservative extensions of the traditional set theory.\n\nThus, Edward Nelson's Internal Set Theory is a way of working with infinitesimals within the ordinary real line, modulo foundational adjustments that involve introducing a richer language into set theory. Namely, one works not merely with an $\\in$-language with with a $(\\in,\\mathbf{st})$-language. Here $\\mathbf{st}$ is a one-place predicate \"standard\", where $\\mathbf{st}(x)$ reads \"$x$ is standard\". To emphasize, Nelson's theory is a conservative extension of ZFC, unlike some of the other frameworks discussed in this space.\n\nThe point is that the collection of standard $x$'s is typically not a set, even when $x$ are ordinary (integer or real) numbers. Thus the new predicate violates the axiom of separation. A blanket assumption that \"everything is a set\" would make Nelson's approach incomprehensible.\n\nFor an alternative view have a look at Vladimir Voevodsky's Univalent Foundations of Mathematics and Homotopy Type Theory. I found the talk of Sept 5, 2011 reasonable.\n\nAs a working Engineer with a strong interest in maths, this was something that sparked my interest - I think it was one of these posts, especially as it covers mechanisms that allow computer proofs (I use the Mathcad CAS as a core part of my daily work)."
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.95413554,"math_prob":0.92566216,"size":7065,"snap":"2021-31-2021-39","text_gpt3_token_len":1506,"char_repetition_ratio":0.12052117,"word_repetition_ratio":0.0,"special_character_ratio":0.20240623,"punctuation_ratio":0.09056024,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97456264,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-09-21T04:47:54Z\",\"WARC-Record-ID\":\"<urn:uuid:60052e49-8fc7-44fb-8068-24a423cc0951>\",\"Content-Length\":\"289438\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:1a45ad94-ea53-4edf-b65b-c1637d8491c6>\",\"WARC-Concurrent-To\":\"<urn:uuid:38560f42-9f62-401d-92d0-f15922065d61>\",\"WARC-IP-Address\":\"151.101.1.69\",\"WARC-Target-URI\":\"https://mathoverflow.net/questions/255820/how-should-a-working-mathematician-think-about-sets-zfc-category-theory-ur\",\"WARC-Payload-Digest\":\"sha1:6XI4TULVXX5T4OZZZMKRFKMFTSIPMJET\",\"WARC-Block-Digest\":\"sha1:CB35ESYT6NR63232VLS2TYVNJRUEB34R\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780057158.19_warc_CC-MAIN-20210921041059-20210921071059-00455.warc.gz\"}"} |
https://www.tutorialspoint.com/python-program-to-count-upper-and-lower-case-characters-without-using-inbuilt-functions | [
"# Python program to count upper and lower case characters without using inbuilt functions\n\nIn this article, we will learn about the solution and approach to solve the given problem statement.\n\n## Problem statement\n\nGiven a string input, we need to find the number of uppercase & lowercase characters in the given strings.\n\nHere we will we checking ASCII value of each character by the help of built-in ord() function.\n\nHere we have assigned two counters to 0 and we are traversing the input string and checking their ASCII values and incrementing their counter respectively.\n\nNow let’s see the implementation below −\n\n## Example\n\nLive Demo\n\ndef upperlower(string):\nupper = 0\nlower = 0\nfor i in range(len(string)):\n# For lowercase\nif (ord(string[i]) >= 97 and\nord(string[i]) <= 122):\nlower += 1\n# For uppercase\nelif (ord(string[i]) >= 65 and\nord(string[i]) <= 90):\nupper += 1\nprint('Lower case characters = %s' %lower,\n'Upper case characters = %s' %upper)\n# Driver Code\nstring = 'Tutorialspoint'\nupperlower(string)\n\n## Output\n\nLower case characters = 13 Upper case characters = 1\n\nAll variables and functions are declared in global scope as shown in the figure below.",
null,
"## Conclusion\n\nIn this article, we learned about the approach to count upper and lower case characters without using inbuilt functions."
] | [
null,
"https://www.tutorialspoint.com/assets/questions/media/28847/51.jpg",
null
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https://www.brainkart.com/article/Highest-Common-Factor_44409/ | [
"Home | | Maths 8th Std | Highest Common Factor\n\n# Highest Common Factor\n\nWe know that, HCF is used in simplifying or reducing fractions. To understand how this concept applies in real life, imagine the following situation.\n\nHighest Common Factor\n\nWe have learnt in class VI that iteration is a process wherein a set of instructions or structures are repeated in a sequence for a specified number of times or until a condition is met. Here, we are going to learn to find HCF by listing all factors and find the biggest, then to find HCF by repeated subtraction and see to how much faster the iteration goes (and how in fewer steps you get the HCF) and then how to improve further by repeated division and remainder and that both lead to the same solution but one is faster than the other.\n\nWe know that, HCF is used in simplifying or reducing fractions. To understand how this concept applies in real life, imagine the following situation.\n\n1. Methods to find HCF (Highest Common Factor):\n\n1. Factorisation Method:\n\nSituation:\n\nLet us assume that you have 20 mangoes and 15 apples. You want to donate them together equally among the orphan children.\n\nHow many orphan children can you help at the maximum?",
null,
"Here, basically question demands finding HCF of two numbers. HCF is Highest Common Factor, also known as GCD (Greatest Common Divisor). HCF of two or more than two numbers is such that, it is the largest possible number which divides given numbers completely.\n\nHere, let us find the HCF of 20 mangoes and 15 apples.\n\nFactors of 20 = 1,2,4, 5 ,10,20\n\nFactors of 15 = 1,3, 5 ,15\n\nSo, the HCF of 20 and 15 is 5. That is, you can a help maximum of 5 orphan children.\n\nSo that for 5 children you can give 4 mangoes (20 ÷ 5 = 4)",
null,
"and 3 apples (15 ÷ 5 = 3)",
null,
"to each of them. In this way you can distribute equally the mangoes and the apples to each child.\n\n2. Prime Factorisation Method:\n\nSituation:\n\nSuppose there are 18 students in Class VII and 27 in Class VIII and each class is divided into teams to prepare for an upcoming sports tournament, with the winning teams from each class play each other in the final. What would be the biggest possible team size that you could divide both these classes such that each team has exactly the same number of students and that no one is left behind.\n\nThe problem here is to find the HCF of 18 and 27.\n\nPrime Factors of 18 = 2 x 3 x 3\n\nPrime Factors of 27 = 3 x 3 x 3",
null,
"Common prime factors of 18 and 27 = 3 x 3 = 9\n\nSo, the HCF of 18 and 27 is 9.\n\nNow, let us learn some more methods of finding the HCF. The largest team member each group is 9, Std VII has 2 teams and Std VIII has 3 teams.\n\n3. Repeated Division Method:\n\nThe above methods are easy to finding HCF, but for larger numbers these methods are tedious to find factors of the given numbers. In that case, alternatively we have some more methods to find HCF. Let us learn more about the other methods of finding the HCF.\n\nFor the above Situation, what if the Class VII had 396 students and Class VIII had 300 students? Then, what would be the biggest possible team size? Well, the above said two methods may not help us quickly. So, we can use continuous division method for finding the highest common factor.\n\nSTEP 1: Divide the larger number by the smaller number.\n\nHere, 360 is the larger number. So, we divide 360 (Dividend) by 300 (Divisor). We get the Remainder as 96.",
null,
"STEP 2: The remainder from Step 1 becomes the new divisor, and divisor of Step 1 becomes the new dividend.\n\nFrom the step 1, we got 96 as remainder. So, in the second step 96 becomes the new divisor and 300 becomes the new dividend.",
null,
"STEP 3: Repeat this division process till remainder becomes zero. The divisor of the last division (when remainder is zero) is the HCF.\n\nFrom step 2, we got 12 as the new remainder which will become the new divisor. In the third step 12 becomes the new divisor and 96 becomes the new dividend. Now, the remainder is zero when 12 is the last divisor of the division. Therefore,12 is the required HCF.",
null,
"Hence, the HCF of 396 and 300 is 12. So each team would be 12 students.\n\n4. Repeated Subtraction Method:\n\nTo find the HCF for the given two numbers say m and n we do the subtraction continuously until m and n are equal. For example,\n\nFind the HCF of 144 and 120\n\nSTEP 1: Check whether m = n\n\nHere, take m = 144 and n = 120\n\nCheck whether m = n or m > n or m < n Here m > n (144 > 120).\n\nSTEP 2: m > n perform m – n repeat the process till m = n or m < n perform n – m repeat the process till m = n\n\nIf m is greater than n, then we perform m – n and assign the result (the difference) as m. Again we check whether m and n are equal or not and repeat the process. If m is less than n, then we perform n – m and we assign the result (the difference) as n. Again we check whether m and n are equal or not and the process is repeated.\n\nSubtract 120(n) from 144(m) till m = n.\n\nFirst 144 – 120 = 24 Repeat 120 – 24 = 96 Repeat 96 – 24 = 72\n\nRepeat 72– 24=48 Repeat 48–24 = 24 Repeat 24 – 24 = 0\n\nSTEP 3: When m and n values are equal then that equal value will be the HCF (m, n).\n\nNow m = n, Hence, we conclude that the HCF of 144 and 120 is 24.\n\nComparing both the repeated division and repeated subtraction methods, in finding the HCF, we can conclude that the repeated subtraction, in one way is easier and gives the HCF faster that the repeated division and also that one would want to easily do subtraction rather than division. Isn’t it ?\n\nTags : Information Processing | Chapter 7 | 8th Maths , 8th Maths : Chapter 7 : Information Processing\nStudy Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail\n8th Maths : Chapter 7 : Information Processing : Highest Common Factor | Information Processing | Chapter 7 | 8th Maths"
] | [
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"https://img.brainkart.com/imagebk45/N5b7E2k.jpg",
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"https://img.brainkart.com/imagebk45/QfENUID.jpg",
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"https://img.brainkart.com/imagebk45/QlF1NXS.jpg",
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"https://img.brainkart.com/imagebk45/kx6naEb.jpg",
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"https://img.brainkart.com/imagebk45/UOC70PY.jpg",
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"https://img.brainkart.com/imagebk45/yLtFXBK.jpg",
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.91202873,"math_prob":0.971366,"size":5354,"snap":"2023-14-2023-23","text_gpt3_token_len":1466,"char_repetition_ratio":0.11906542,"word_repetition_ratio":0.051330797,"special_character_ratio":0.27344042,"punctuation_ratio":0.10909091,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9988632,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14],"im_url_duplicate_count":[null,2,null,2,null,2,null,2,null,2,null,2,null,2,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-06-10T04:25:21Z\",\"WARC-Record-ID\":\"<urn:uuid:250c2472-6e28-4cd1-9cf1-3a13fde13b22>\",\"Content-Length\":\"50159\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:f6502851-f745-4346-a884-3c31083fee21>\",\"WARC-Concurrent-To\":\"<urn:uuid:003a4585-9736-4434-800a-e7d2c8ca4207>\",\"WARC-IP-Address\":\"68.178.145.35\",\"WARC-Target-URI\":\"https://www.brainkart.com/article/Highest-Common-Factor_44409/\",\"WARC-Payload-Digest\":\"sha1:LGAALDMVQDCMXQSZJ4KIGQYOHEE7HGY3\",\"WARC-Block-Digest\":\"sha1:VHDWHESMXKKNXDM2FMK3AMVH7ZQ3KFWS\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-23/CC-MAIN-2023-23_segments_1685224656963.83_warc_CC-MAIN-20230610030340-20230610060340-00727.warc.gz\"}"} |
https://socratic.org/questions/how-do-you-describe-the-end-behavior-of-y-x-4-4x-2 | [
"# How do you describe the end behavior of y= x^4-4x^2?\n\nFeb 13, 2016\n\nWell, the end behavior can be found by the degree of the polynomial and the sign of the first term.\n\nThe degree for this polynomial is 4 because ${x}^{4}$ has the highest exponent\n\nThe sign of this term is positive.\n\nTo understand end behaviors, there are 4 possibilities\n\nOdd degree, positive\nOdd degree, negative\nEven degree, positive\nEven degree, negative\n\nThis equation is clearly even, positive.\n\nNow, the best way to think of their end behaviors is to think about parent functions\n\nodd, positive = $y = x$, which has an end behavior from quadrant 3 to quadrant 1\n\nodd, negative = $y = - x$, which has an end behavior from quadrant 2 to quadrant 4\n\neven, positive = $y = {x}^{2}$, which has an end behavior from quadrant 2 to quadrant 1\n\nEven, negative = $y = - {x}^{2}$, which has an end behavior from quadrant 3 to quadrant 4.\n\nTherefore, this equation has arrows pointing in quadrants 2 and 1.\n\nRemember: Just picture the graph for the parent functions above and you will understand end behaviors much more clearly."
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8987607,"math_prob":0.99728674,"size":812,"snap":"2020-24-2020-29","text_gpt3_token_len":182,"char_repetition_ratio":0.15717822,"word_repetition_ratio":0.0,"special_character_ratio":0.21551724,"punctuation_ratio":0.12738854,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9982081,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-07-15T12:42:21Z\",\"WARC-Record-ID\":\"<urn:uuid:14454142-e820-4c17-a46a-09653a6e1d5e>\",\"Content-Length\":\"34960\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:82c72f36-3dc1-40ae-956e-80ab772d6119>\",\"WARC-Concurrent-To\":\"<urn:uuid:8db4a1d8-1c91-44d9-874a-83314ba3a71e>\",\"WARC-IP-Address\":\"216.239.34.21\",\"WARC-Target-URI\":\"https://socratic.org/questions/how-do-you-describe-the-end-behavior-of-y-x-4-4x-2\",\"WARC-Payload-Digest\":\"sha1:FFTCKZVS4FUNYS5QE34S5Z4562AWRELA\",\"WARC-Block-Digest\":\"sha1:FV2MBRCNR5Y3BHA32L3OGHUJ2HGK2R7P\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-29/CC-MAIN-2020-29_segments_1593657167808.91_warc_CC-MAIN-20200715101742-20200715131742-00038.warc.gz\"}"} |
https://community.esri.com/blogs/dan_patterson/2018/10/08/generalization-tools-for-rasters | [
"# Generalization tools for rasters...\n\nBlog Post created by Dan_Patterson",
null,
"on Oct 8, 2018\n\nScipy ndimage morphology … I am betting that is the first thought that popped into your head. A new set of tools for the raster arsenal.\n\nBut wait! Their terminology is different than that used by ArcGIS Pro or even ArcMap.\n\nWhat tools are there? Lots of filtering tools, but some of the interesting ones are below\n\n``from scipy import ndimage as nddir(nd)[... snip ... 'affine_transform', ... , 'center_of_mass', 'convolve', 'convolve1d', 'correlate', 'correlate1d', ... 'distance_transform_edt', ... 'filters', ... 'generic_filter', ... 'geometric_transform', ... 'histogram', 'imread', 'interpolation', ... 'label', 'labeled_comprehension', ... 'measurements', ... 'morphology', ... 'rotate', 'shift', 'sobel',...]``\n\nI previously covered distance_transform_edt which performs the equivalent of Euclidean Distance and Euclidean Allocation in this post\n\nLet's begin with a raster constructed from 3x3 windows with a repeating pattern and repeats of the pattern.\n\nYou can construct your own rasters using numpy, then apply a NumPyArrayToRaster to it. I have covered this before, but here is the code to produce the raster\n\n``def _make_arr_(): \"\"\"Make an array with repeating patterns \"\"\" a = np.arange(1, 10).reshape(3, 3) aa = np.repeat(np.repeat(a, 3, axis=1), 3, axis=0) aa = np.tile(aa, 3) aa = np.vstack((aa, aa)) return aa``\n\n---- The raster (image) ----",
null,
"---- (1) Expand ----\n\nExpand the zone 1 values by 2 distance units",
null,
"---- (2) Shrink ----\n\nShrink the zone 1 values by 1 distance unit.",
null,
"---- (3) Regiongroup ----\n\nProduce unique regions from the zones. observe that the zone 1 values in the original are given unique values first. Since there were 6 zone 1s, they are numbered from 1 to 6. Zone 2 gets numbered from 7 to 12. and that pattern of renumbering repeats.",
null,
"---- (4) Aggregate ----\n\nAggregation of the input array using the mode produces a spatial aggregation retaining the original values. Our original 3x3 kernels are now reduced to a 1x1 size which has 3 times the width and height (9x area).",
null,
"---- (5) Some functions to perform the above ----\n\nIn these examples buff_dist is referring to 'cells'.\n\nFor the aggregation, there are a number of options as shown in the header.\n\nGenerally integer data should be restricted to the mode, min, max, range and central (value) since the median and mean upscale to floating point values. This of course can be accommodated by using the python statistics package median_low and median_high functions:\n\nSo think of a function that you want. Filtering is a snap since you can 'stride' an array using any kernel you want using plain numpy or using the builtin functions from scipy.\n\nEnough for now...\n\n``def expand_(a, val=1, mask_vals=0, buff_dist=1): \"\"\"Expand/buffer a raster by a distance \"\"\" if isinstance(val, (list, tuple)): m = np.isin(a, val, invert=True).astype('int') else: m = np.where(a==val, 0, 1) dist, idx = nd.distance_transform_edt(m, return_distances=True, return_indices=True) alloc = a[tuple(idx)] a0 = np.where(dist<=buff_dist, alloc, a) #0) return a0def shrink_(a, val=1, mask_vals=0, buff_dist=1): \"\"\"Expand/buffer a raster by a distance \"\"\" if isinstance(val, (list, tuple)): m = np.isin(a, val, invert=False).astype('int') else: m = np.where(a==val, 1, 0) dist, idx = nd.distance_transform_edt(m, return_distances=True, return_indices=True) alloc = a[tuple(idx)] m = np.logical_and(dist>0, dist<=buff_dist) a0 = np.where(m, alloc, a) #0) return a0def regions_(a, cross=True): \"\"\"currently testing regiongroup np.unique will return values in ascending order \"\"\" if (a.ndim != 2) or (a.dtype.kind != 'i'): msg = \"\\nA 2D array of integers is required, you provided\\n{}\" print(msg.format(a)) return a if cross: struct = np.array([[0,1,0], [1,1,1], [0,1,0]]) else: struct = np.array([[1,1,1], [1,1,1], [1,1,1]]) # u = np.unique(a) out = np.zeros_like(a, dtype=a.dtype) details = [] is_first = True for i in u: z = np.where(a==i, 1, 0) s, n = nd.label(z, structure=struct) details.append([i, n]) m = np.logical_and(out==0, s!=0) if is_first: out = np.where(m, s, out) is_first = False n_ = n else: out = np.where(m, s+n_, out) n_ += n details = np.array(details) details = np.c_[(details, np.cumsum(details[:,1]))] return out, detailsdef aggreg_(a, win=(3,3), agg_type='mode'): \"\"\"Aggregate an array using a specified window size and an aggregation type Parameters ---------- a : array 2D array to perform the aggregation win : tuple/list the shape of the window to construct the blocks agg_type : string aggregation type max, mean, median, min, mode, range, sum, central \"\"\" blocks = block(a, win=win) out = [] for bl in blocks: b_arr = [] for b in bl: if agg_type == 'mode': uni = np.unique(b).tolist() cnts = [len(np.nonzero(b==u)) for u in uni] idx = cnts.index(max(cnts)) b_arr.append(uni[idx]) elif agg_type == 'max': b_arr.append(np.nanmax(b)) elif agg_type == 'mean': b_arr.append(np.nanmean(b)) elif agg_type == 'median': b_arr.append(np.nanmedian(b)) elif agg_type == 'min': b_arr.append(np.nanmin(b)) elif agg_type == 'range': b_arr.append((np.nanmax(b) - np.nanmin(b))) elif agg_type == 'sum': b_arr.append(np.nansum(b)) elif agg_type == 'central': b_arr.append(b.shape//2, b.shape//2) else: tweet(\"\\naggregation type not found {}\".format(agg_type)) b_arr.append(b.shape//2, b.shape//2) out.append(b_arr) out = np.array(out) return out``"
] | [
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https://best-excel-tutorial.com/59-tips-and-tricks/226-calculate-yield-excel | [
"# How to Calculate Yield in Excel?\n\nPeople who invest in bonds are always anxious to know the current yield, the yield to maturity and the yield to call of the bonds they purchased. Using Excel, you can develop a bond yield calculator easily with the help of a number of formulas. You just need to enter the inputs like face value, coupon rate, years to maturity etc and Excel will calculate the bond yield and display it for you.\n\n## Prepare data set\n\nOpen Excel and save your file as yield.xlsx. Type \"Face Value\" in A1, \"Annual Coupon Rate\" in A2, \"Annual Required Return\" in A3, \"Years to Maturity\" in A4, \"Years to Call\" in A5, \"Call Premium\" in A6, \"Payment Frequency\" in A7, \"Value of Bond\" in A9, \"Current Yield\" in A11, \"Yield to Maturity\" in A12 and \"Yield to Call\" in A13. You can format these cells and make them bold. Now your screen will look like this:",
null,
"Select cells B1 and B9. Right click and go to Format Cells.",
null,
"You will get a window like this:",
null,
"From the Category: section on the left hand side, select Currency. Select \\$ English (U.S.) from the Symbol: drop down on the right hand side. Now your screen will look like this:",
null,
"Click OK.\n\nSelect cells B2, B3, B6, B11, B12 and B13. Right click and go to Format Cells. You will get a new window. From the Category: section on the left hand side, select Percentage. Now your screen will look like this:",
null,
"Click OK.\n\nEnter some reasonable values in the cells B1, B2, B3, B4 and B7.",
null,
"## Calculate Yield\n\nTo calculate the present value of the bond, click the cell B9. Go to Formulas (main menu) --> Financial (in the Function Library group) and select the PV function.",
null,
"You will get a window like this:",
null,
"In the Rate, Nper, Pmt and Fv textboxes, enter the values B3/B7, B4*B7, B2/B7*B1 and B1 respectively. Now your window like this:",
null,
"Click OK. Now you will get a negative value in the cell B9. Now go to the formula bar and add a - sign just after the = sign like this:",
null,
"To calculate the current yield, click inside the cell B11 and enter the formula \"=(B1*B2)/B9\" (without double quotes).\n\nTo calculate the yield to maturity, click inside the cell B12. Go to Formulas (main menu) --> Financial (in the Function Library group) and select the RATE function. You will get a window like this:",
null,
"In the Rate, Nper, Pmt and Fv textboxes, enter the values B4*B7, B2*B1/B7, -B9 and B1 respectively. Now your window will look like this:",
null,
"Click OK. As this value is for the half year, go to the formula bar and add *B7 at the end of the formula like this:",
null,
"Enter reasonable values in the cells B5 and B6 (say 1 and 3).\n\nTo calculate the yield to call, click inside the cell B13. Go to Formulas (main menu) --> Financial (in the Function Library group) and select the RATE function. You will get a new window. In the Rate, Nper, Pmt and Fv textboxes, enter the values B5*B7, B2*B1/B7, -B9 and B1*(1+B6) respectively. Now your window will look like this:",
null,
"Click OK. Go to the formula bar and add *B7 at the end of the formula like this:",
null,
"Now your bond yield calculator will look like this:",
null,
"By submitting the face value, coupon rate, required return, years to maturity, years to call, call premium and payment frequency, you get the current yield, yield to maturity and yield to call with this bond yield calculator. You can try changing the inputs and observe the difference in the output.\n\n```Further reading:\nYield to maturity calculator```"
] | [
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https://mathbb.com/2013/09/06/noethersproblem1intro/ | [
"# Noether’s Problem – 1. Introduction\n\nThis blog post is meant to be the introduction of a series of posts about Noether’s Problem, which, roughly put, asks to study the subfield of a rational function field invariant under some automorphisms (namely to determine its rationality). Since there are quite a number of variants of Noether’s Problem, and also different methods of studying them, it seems necessary to give an outline of what I hope to achieve in these posts.\n\nI studied Noether’s Problem as part of a project (for a course) in which my main goal was to try to understand the ideas behind results as Swan’s [Swa69] (counter)example and Lenstra’s [Len74] solution for (finite) abelian groups (in some special cases). Besides understanding these ideas and results, I also wanted to present them in the most straightforward way possible. This will also be the goal of this series of posts: giving an accessible introduction to studying Noether’s Problem for (finite) cyclic groups over",
null,
"${{\\mathbb Q}}$ where the results of Swan and Lenstra will be the main focus.\n\nThe prerequisites needed to be able to follow these posts should be relatively minimal. Basic group theory, Galois theory and some relatively basic facts about modules should essentially be all that is needed (which is almost all covered in the two algebra courses from my mathematics education up until now). Where I feel it might be useful and/or necessary (also for myself), I will expand on some of the more theoretic concepts or try to provide some references.\n\n1. The Problem\n\nLet",
null,
"${k}$ be an arbitrary field. For an arbitrary finite group",
null,
"${G}$ we can then define the rational function field",
null,
"${k(x_g\\>|\\> g\\in G)}$, where the",
null,
"${x_g}$ are just symbols or variables functioning as transcendent elements (over",
null,
"${k}$). Hence, there is an obvious action of",
null,
"${G}$ on",
null,
"${k(x_g\\>|\\> g\\in G)}$: each",
null,
"${h\\in G}$ permutes the elements",
null,
"${x_g}$ as",
null,
"${h(x_g)=x_{gh}}$. Noether’s Problem then asks the following question:\n\nNoether’s Problem\n\nWhen is the fixed field",
null,
"${k(G):=k(x_g\\>|\\> g\\in G)^G}$ rational (over k)?\n\nWhere by rational we mean purely transcendental, i.e., isomorphic to a rational function field over",
null,
"${k}$ with the same transcendency degree",
null,
"${|G|}$. Thus, there exists a generating set",
null,
"${\\mathcal{B}}$ of algebraically independent elements such that",
null,
"${k(\\mathcal{B})=k(x_g\\>|\\> g\\in G)}$.\n\nThe origin of the name of Noether’s Problem is more commonly traced back to a paper from Noether [Noe17] from 1917. We shall not go into the history of the problem (more can be found in [JLY02]) but we should probably remark on its connection with the still open inverse Galois problem. Namely, the rationality of",
null,
"${{\\mathbb Q}(G)}$ implies a positive answer to the inverse Galois problem for the group",
null,
"${G}$ (see [JLY02] again). Not surprisingly then, solving Noether’s Problem was once considered as a possible method for solving the inverse Galois problem. In order not to get lost in the many variants of Noether’s Problem (of which I am not very knowledgeable), I will already set the context for the series:\n\nAssumptions about",
null,
"${k}$ and",
null,
"${G}$\n\nIn all that follows we will assume that the field",
null,
"${k}$ is of characteristic 0 and",
null,
"${G}$ is a finite cyclic group (as such, we will mostly omit “finite”).\n\nLet us then first consider an important example as an introduction to the problem.\n\nExample 1 (",
null,
"${{\\mathbb Q}({\\mathbf C}_{2})}$ is rational)\n\nLet",
null,
"${k={\\mathbb Q}}$ and",
null,
"${G={\\mathbf C}_2}$ (the cyclic group of order two). Then the rational function field",
null,
"${k(x_g\\>|\\> g\\in G)}$ becomes",
null,
"${{\\mathbb Q}(x_1, x_2)}$, with the action of",
null,
"${G}$ corresponding to the transposition",
null,
"${x_1\\leftrightarrow x_2}$ (where",
null,
"${x_1}$ and",
null,
"${x_2}$ are transcendental over",
null,
"${{\\mathbb Q}}$). Noether’s Problem asks whether the fixed field",
null,
"${{\\mathbb Q}({\\mathbf C}_2)}$ of this rational function field under the action of",
null,
"${G}$ is rational.\n\nFor example,",
null,
"${x_1x_2}$ is clearly fixed under this transposition. As well as",
null,
"${\\frac{x_1x_2}{x_1+x_2}}$. However, since",
null,
"${G={\\mathbf C}_2=\\mathbf{S}_2}$, we can apply the fundamental theorem of symmetric polynomials (which has accessible proofs, mostly by induction) that says that",
null,
"${R[x_1,\\ldots,x_n]^{\\mathbf{S}_n}=R[e_1,\\ldots,e_n]}$, where",
null,
"${R}$ is a commutative ring,",
null,
"${\\mathbf{S}_n}$ is the symmetric group on",
null,
"${n}$ letters and",
null,
"${e_i}$ are the elementary symmetric polynomials (in the",
null,
"${n}$ variables",
null,
"${x_1,\\ldots,x_n}$). Thus,",
null,
"${{\\mathbb Q}({\\mathbf C}_2)={\\mathbb Q}(x_1x_2,x_1+x_2)}$ and the fixed field is rational.\n\nAfter seeing this example, there are two questions that naturally arise (at least for me):\n\n1. Can we exploit the fundamental theorem of symmetric polynomials further to give an answer to Noether’s Problem for other (classes of) cyclic groups?\n2. If not, how can we, in general, determine the (non-)rationality of certain fields?\n\nAs far as I know, the answer to question 1 seems to be “no”. This might make more sense if we just try another (seemingly) easy example. The next obvious group to look at is",
null,
"${G={\\mathbf C}_3}$, which seems like an easy step up from",
null,
"${G={\\mathbf C}_2}$ in Example 1. The corresponding problem is then to determine the rationality of",
null,
"${{\\mathbb Q}({\\mathbf C}_3)}$, the fixed field of",
null,
"${{\\mathbb Q}(x_1,x_2,x_3)}$ under the action of",
null,
"${G=\\langle \\sigma\\rangle}$, with",
null,
"${\\sigma:x_1\\mapsto x_2\\mapsto x_3\\mapsto x_1}$. Again we can see that",
null,
"${x_1+x_2+x_3}$,",
null,
"${x_1x_2x_3}$, etc., are elements of",
null,
"${{\\mathbb Q}({\\mathbf C}_3)}$. We could try to remember some results from Galois theory to help us out. For example, a basic property of a finite Galois extension",
null,
"${E/F}$ is that the roots of the minimal polynomial of an element",
null,
"${a}$ in",
null,
"${E}$ over",
null,
"${F}$, are the conjugates of",
null,
"${a}$ under the Galois group",
null,
"${\\mathrm{Gal}(E/F)}$ of the extension. Hence, constructing the minimal polynomial of an element in",
null,
"${E}$, will give us coefficients in",
null,
"${F}$. Here",
null,
"${x_1}$,",
null,
"${x_2}$ and",
null,
"${x_3}$ are all roots of the same minimal polynomial. Hence",
null,
"$(x-x_1)(x-x_2)(x-x_3) =x^3 - (x_1 + x_2 + x_3)x^2$",
null,
"$+ x(x_1x_2+x_1x_3+ x_2x_3) - x_1x_2x_3$ gives us",
null,
"${\\{x_1 + x_2 + x_3, x_1x_2+x_1x_3 + x_2x_3, x_1x_2x_3\\}}$ as elements of",
null,
"${{\\mathbb Q}({\\mathbf C}_3)}$. But these are just the elementary symmetric polynomials in three variables! Thus we are back to square one. Although this was a fairly weak attempt, we will not try to develop these ideas any further.\n\nQuestion 2 really asks a more difficult question than Noether’s Problem itself and it is one I struggled with myself initially. In particular, what is an “easy” (yet nontrivial) example of a non-rational subfield of a rational function field? (There is not really one, see for example this mathoverflow.net thread.) As far as I know, there is also not really something we could call a “general strategy” and thus we might feel kind of “lost”. Hopefully, the next post will address those feelings and make the problem feel somewhat more accessible.\n\nWhen we restricted ourselves to fields of characteristic zero and cyclic groups, one might have thought (or hoped) that this would reduce the problem to something relatively simple (where an elementary approach might yield some promising results). This, however, does not seem to be the case. We shall give a rough outline of the history of the problem (relevant to our case of the problem) on which this series will be loosely based.\n\nRelevant history of Noether’s Problem\n\nIn 1915 Fischer [Fi15] proved that",
null,
"${k(G)}$ is rational when",
null,
"${k}$ contains “enough” roots of unity (we will state his more general result in the following part). But it was not until 1955 that Masuda [Mas55] proved a theorem which allowed him to prove the rationality of",
null,
"${{\\mathbb Q}({\\mathbf C}_n)}$ for",
null,
"${n\\>{\\leqslant}\\> 7}$. In it he used Fischer’s theorem and introduced the idea of descent, central in our strategy for attacking the problem. Counterexamples seemed hard to find, Swan [Swa69] gave the first important example of a non-rational fixed field in 1969:",
null,
"${{\\mathbb Q}({\\mathbf C}_{47})}$ is not rational. Finally, in 1974 Lenstra [Len74] (other strong results had also been proved by Endo, Miyata and Voskresenskii) proved that for abelian",
null,
"${G}$ and arbitrary",
null,
"${k}$ the rationality is equivalent with two concrete conditions: 1. some well defined ideals have to be principal 2. some Galois extension, dependent only on the exponent of the group, has to be cyclic. The latter condition will be rather trivial to check; the former, however, certainly will not.\n\nTo make all of this slightly more concrete, the table below gives the results known until now (to the best of my knowledge) of Noether’s Problem for the case of",
null,
"${k={\\mathbb Q}}$ and",
null,
"${G}$ a cyclic group of prime order,",
null,
"${G={\\mathbf C}_{p}}$. The biggest prime for which we know that",
null,
"${{\\mathbb Q}({\\mathbf C}_{p})}$ is rational, is",
null,
"${p=71}$; for most of the primes",
null,
"${p}$ greater than 100, we do not know (in general, the higher the prime, the more difficult). In a way, the objective of these posts is to be able to color this table (at least as shown below).",
null,
"Green:",
null,
"${{\\mathbb Q}({\\mathbf C}_{p})}$ is rational. Red:",
null,
"${{\\mathbb Q}({\\mathbf C}_{p})}$ is not rational. Others: not prime or unknown. The squares with a black border are from own calculations, those without are from the tables of Endo and Miyata [EM71].\n\nIn the next post we will prove Fischer’s theorem since its proof will be of significant importance for our general solution.\n\n[EM71] Endo, S.; Miyata, T.; Invariants of Finite Abelian Groups, 1973.\n[Fi15] Fischer, E.; Die Isomorphie der Invariantenkorper der endlichen Abelschen Gruppen linearen transformationen, 1915.\n[JLY02] Jensen, Ledet, Yui; Generic Polynomials: Constructive Aspects of the Inverse Galois Problem, 2002.\n[Len74] Lenstra, H. W., Jr.; Rational Functions Invariant under a Finite Abelian Group, 1974.\n[Mas55] Masuda, K.; On a Problem of Chevally, 1955.\n[Noe17] Noether, E.; Gleichungen mit vorgeschriebener Gruppe, 1917.\n[Sw69] Swan, Richard G.; Invariant Rational Functions and a Problem of Steenrod, 1969.\n\n1 Comment\n\nFiled under Noether's Problem"
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9269791,"math_prob":0.9983737,"size":8359,"snap":"2020-45-2020-50","text_gpt3_token_len":1859,"char_repetition_ratio":0.1295033,"word_repetition_ratio":0.011347517,"special_character_ratio":0.21880607,"punctuation_ratio":0.11606023,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99967897,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170],"im_url_duplicate_count":[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,6,null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-10-30T04:07:33Z\",\"WARC-Record-ID\":\"<urn:uuid:5e4225b8-2093-4419-98a3-1b79ce754801>\",\"Content-Length\":\"99383\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:3d83bb07-6bcf-48ca-a260-fc1a7f4e6921>\",\"WARC-Concurrent-To\":\"<urn:uuid:b88c19cc-3684-4ea5-8735-641c1e202148>\",\"WARC-IP-Address\":\"192.0.78.25\",\"WARC-Target-URI\":\"https://mathbb.com/2013/09/06/noethersproblem1intro/\",\"WARC-Payload-Digest\":\"sha1:JOP6666RU34DVPK2V4WXGYO6BTRZQXJA\",\"WARC-Block-Digest\":\"sha1:M5JOJ6RHNLHIWFJ535SKRYQLEVLD3BWN\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-45/CC-MAIN-2020-45_segments_1603107907213.64_warc_CC-MAIN-20201030033658-20201030063658-00641.warc.gz\"}"} |
https://tathagat.mba/quant-question-of-the-day-90/ | [
"Numbers\n\nFor how many values of natural number n, both the numbers 48n + 4 and 27n + 19 are perfect square?\n(1) 0\n(2) 1\n(3) 2\n(4) 3\n(5) more than 3\n\nSee our previous ‘Questions of the Day’:\n\n1.",
null,
"What approach did you use ?\n\n1.",
null,
"48n+4=a^2\n27n+19=b^2.\nn=(a^2-4)/48 = (b^2-19)/27.\n16b^2-9a^2=268.\nonly one pair of (a,b) satisfies the given two equations.\n\n1.",
null,
"(2) 1\nonly possible value is n=10"
] | [
null,
"https://secure.gravatar.com/avatar/99cc1d4fb563af3aa67af9e0a6d67662",
null,
"https://secure.gravatar.com/avatar/9f3aa77353ddaa25d604dc9a0e046695",
null,
"https://tathagat.mba/wp-content/uploads/wpforo/avatars/aniketp_12847.jpg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.77552044,"math_prob":0.999511,"size":439,"snap":"2019-26-2019-30","text_gpt3_token_len":175,"char_repetition_ratio":0.101149425,"word_repetition_ratio":0.0,"special_character_ratio":0.42824602,"punctuation_ratio":0.10091743,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9983598,"pos_list":[0,1,2,3,4,5,6],"im_url_duplicate_count":[null,null,null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-06-18T02:40:27Z\",\"WARC-Record-ID\":\"<urn:uuid:1c5227c1-ee0d-4d3e-993f-a917e8f6cdeb>\",\"Content-Length\":\"59432\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:8ef35fca-8852-4ae4-ae53-d4cb0665f9a8>\",\"WARC-Concurrent-To\":\"<urn:uuid:6f25386a-ebfe-4c03-844c-fbdb045f58c5>\",\"WARC-IP-Address\":\"74.208.126.115\",\"WARC-Target-URI\":\"https://tathagat.mba/quant-question-of-the-day-90/\",\"WARC-Payload-Digest\":\"sha1:2WEXINO42ZAZZSXUQN6W7LCVGDKAUVRM\",\"WARC-Block-Digest\":\"sha1:C3CRGNTVQS22ZDYNPUVM6NTXFBHKI2BM\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-26/CC-MAIN-2019-26_segments_1560627998605.33_warc_CC-MAIN-20190618023245-20190618045245-00075.warc.gz\"}"} |
https://kr.mathworks.com/help/deeplearning/ref/imagelime.html | [
"# imageLIME\n\nExplain network predictions using LIME\n\n## Syntax\n\n``scoreMap = imageLIME(net,X,label)``\n``[scoreMap,featureMap,featureImportance] = imageLIME(net,X,label)``\n``___ = imageLIME(___,Name,Value)``\n\n## Description\n\nexample\n\n````scoreMap = imageLIME(net,X,label)` uses the locally-interpretable model-agnostic explanation (LIME) technique to compute a map of the importance of the features in the input image `X` when the network `net` evaluates the class score for the class given by `label`. Use this function to explain classification decisions and check that your network is focusing on the appropriate features of the image.The LIME technique approximates the classification behavior of the `net` using a simpler, more interpretable model. By generating synthetic data from input `X`, classifying the synthetic data using `net`, and then using the results to fit a simple regression model, the `imageLIME` function determines the importance of each feature of `X` to the network's classification score for class given by `label`.This function requires Statistics and Machine Learning Toolbox™.```\n\nexample\n\n````[scoreMap,featureMap,featureImportance] = imageLIME(net,X,label)` also returns a map of the features used to compute the LIME results and the calculated importance of each feature.```\n\nexample\n\n````___ = imageLIME(___,Name,Value)` specifies options using one or more name-value pair arguments in addition to the input arguments in previous syntaxes. For example, `'NumFeatures',100` sets the target number of features to 100. ```\n\n## Examples\n\ncollapse all\n\nUse `imageLIME` to visualize the parts of an image are important to a network for a classification decision.\n\nImport the pretrained network SqueezeNet.\n\n`net = squeezenet;`\n\nImport the image and resize to match the input size for the network.\n\n```X = imread(\"laika_grass.jpg\"); inputSize = net.Layers(1).InputSize(1:2); X = imresize(X,inputSize);```\n\nDisplay the image. The image is of a dog named Laika.\n\n`imshow(X)`",
null,
"Classify the image to get the class label.\n\n`label = classify(net,X)`\n```label = categorical toy poodle ```\n\nUse `imageLIME` to determine which parts of the image are important to the classification result.\n\n`scoreMap = imageLIME(net,X,label);`\n\nPlot the result over the original image with transparency to see which areas of the image affect the classification score.\n\n```figure imshow(X) hold on imagesc(scoreMap,'AlphaData',0.5) colormap jet```",
null,
"The network focuses predominantly on Laika's head and back to make the classification decision. Laika's eye and ear are also important to the classification result.\n\nUse `imageLIME` to determine the most important features in an image and isolate them from the unimportant features.\n\nImport the pretrained network SqueezeNet.\n\n`net = squeezenet;`\n\nImport the image and resize to match the input size for the network.\n\n```X = imread(\"sherlock.jpg\"); inputSize = net.Layers(1).InputSize(1:2); X = imresize(X,inputSize);```\n\nClassify the image to get the class label.\n\n`label = classify(net,X)`\n```label = categorical golden retriever ```\n\nCompute the map of the feature importance and also obtain the map of the features and the feature importance. Set the image segmentation method to `'grid'`, the number of features to `64`, and the number of synthetic images to `3072`.\n\n`[scoreMap,featureMap,featureImportance] = imageLIME(net,X,label,'Segmentation','grid','NumFeatures',64,'NumSamples',3072);`\n\nPlot the result over the original image with transparency to see which areas of the image affect the classification score.\n\n```figure imshow(X) hold on imagesc(scoreMap,'AlphaData',0.5) colormap jet colorbar```",
null,
"Use the feature importance to find the indices of the most important five features.\n\n```numTopFeatures = 5; [~,idx] = maxk(featureImportance,numTopFeatures);```\n\nUse the map of the features to mask out the image so only the most important five features are visible. Display the masked image.\n\n```mask = ismember(featureMap,idx); maskedImg = uint8(mask).*X; figure imshow(maskedImg);```",
null,
"Use `imageLIME` with a custom segmentation map to view the most important features for a classification decision.\n\n`net = googlenet;`\n\nImport the image and resize to match the input size for the network.\n\n```X = imread(\"sherlock.jpg\"); inputSize = net.Layers(1).InputSize(1:2); X = imresize(X,inputSize);```\n\nClassify the image to get the class label.\n\n`label = classify(net,X)`\n```label = categorical golden retriever ```\n\nCreate a matrix defining a custom segmentation map which divides the image into triangular segments. Each triangular segment represents a feature.\n\nStart by defining a matrix with size equal to the input size of the image.\n\n`segmentationMap = zeros(inputSize(1));`\n\nNext, create a smaller segmentation map which divides a 56-by-56 pixel region into two triangular features. Assign values 1 and 2 to the upper and lower segments, representing the first and second features, respectively.\n\n```blockSize = 56; segmentationSubset = ones(blockSize); segmentationSubset = tril(segmentationSubset) + segmentationSubset; % Set the diagonal elements to alternate values 1 and 2. segmentationSubset(1:(blockSize+1):end) = repmat([1 2],1,blockSize/2)';```\n\nTo create a custom segmentation map for the whole image, repeat the small segmentation map. Each time you repeat the smaller map, increase the feature index values so that the pixels in each triangular segment correspond to a unique feature. In the final matrix, value 1 indicates the first feature, value 2 the second feature, and so on for each segment in the image.\n\n```blocksPerSide = inputSize(1)/blockSize; subset = 0; for i=1:blocksPerSide for j=1:blocksPerSide xidx = (blockSize*(i-1))+1:(blockSize*i); yidx = (blockSize*(j-1))+1:(blockSize*j); segmentationMap(xidx,yidx) = segmentationSubset + 2*subset; subset = subset + 1; end end```\n\nView the segmentation map. This map divides the image into 32 triangular regions.\n\n```figure imshow(X) hold on imagesc(segmentationMap,'AlphaData',0.8); title('Custom Segmentation Map') colormap gray```",
null,
"Use `imageLIME` with the custom segmentation map to determine which parts of the image are most important to the classification result.\n\n```scoreMap = imageLIME(net,X,label, ... 'Segmentation',segmentationMap);```\n\nPlot the result of `imageLIME` over the original image to see which areas of the image affect the classification score.\n\n```figure; imshow(X) hold on title('Image LIME (Golden Retriever)') colormap jet; imagesc(scoreMap, \"AlphaData\", 0.5);```",
null,
"Red areas of the map have a higher importance — when these areas are removed, the score for the golden retriever class goes down. The most important feature for this classification is the ear.\n\n## Input Arguments\n\ncollapse all\n\nImage classification network, specified as a `SeriesNetwork` object or a `DAGNetwork` object. You can get a trained network by importing a pretrained network or by training your own network using the `trainNetwork` function. For more information about pretrained networks, see Pretrained Deep Neural Networks.\n\n`net` must contain a single input layer and a single output layer. The input layer must be an `imageInputLayer`. The output layer must be a `classificationLayer`.\n\nInput image, specified as a numeric array.\n\nThe image must be the same size as the image input size of the network `net`. The input size is specified by the `InputSize` property of the network's `imageInputLayer`.\n\nData Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`\n\nClass label used to calculate the feature importance map, specified as a categorical, a char vector, a string scalar or a vector of these values.\n\nIf you specify `label` as a vector, the software calculates the feature importance for each class label independently. In that case, `scoreMap(:,:,k)` and `featureImportance(idx,k)` correspond to the map of feature importance and the importance of feature `idx` for the `k`th element in `label`, respectively.\n\nExample: `[\"cat\" \"dog\"]`\n\nData Types: `char` | `string` | `categorical`\n\n### Name-Value Pair Arguments\n\nSpecify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.\n\nExample: ```'NumFeatures',100,'Segmentation','grid', 'OutputUpsampling','bicubic','ExecutionEnvironment','gpu'``` segments the input image into a grid of approximately 100 features, executes the calculation on the GPU, and upsamples the resulting map to the same size as the input image using bicubic interpolation.\n\nTarget number of features to divide the input image into, specified as the comma-separated pair consisting of `'NumFeatures'` and a positive integer.\n\nA larger value of `'NumFeatures'` divides the input image into more, smaller features. To get the best results when using a larger number of features, also increase the number of synthetic images using the `'NumSamples'` name-value pair.\n\nThe exact number of features depends on the input image and segmentation method specified using the `'Segmentation'` name-value pair and can be less than the target number of features.\n\n• When you specify `'Segmentation','superpixels'`, the actual number of features can be greater or less than the number specified using `'NumFeatures'`.\n\n• When you specify `'Segmentation','grid'`, the actual number of features can be less than the number specified using `'NumFeatures'`. If your input image is square, specify `'NumFeatures'` as a square number.\n\n• When you specify `'Segmentation',segmentation`, where `segmentation` is a two-dimensional matrix, `'NumFeatures'` is the same as the number of unique elements in the matrix.\n\nExample: `'NumFeatures',100`\n\nData Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`\n\nNumber of synthetic images to generate, specified as the comma-separated pair consisting of `'NumSamples'` and a positive integer.\n\nA larger number of synthetic images gives better results but takes more time to compute.\n\nExample: `'NumSamples',1024`\n\nData Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`\n\nSegmentation method to use to divide the input image into features, specified as the comma-separated pair consisting of `'Segmentation'` and `'superpixels'`, `'grid'`, or a two-dimensional segmentation matrix.\n\nThe `imageLIME` function segments the input image into features in the following ways depending on the segmentation method.\n\n• `'superpixels'` — Input image is divided into superpixel features, using the `superpixels` (Image Processing Toolbox) function. Features are irregularly shaped, based on the value of the pixels. This option requires Image Processing Toolbox™.\n\n• `'grid'` — Input image is divided into a regular grid of features. Features are approximately square, based on the aspect ratio of the input image and the specified value of `'NumFeatures'`. The number of grid cells can be smaller than the specified value of `'NumFeatures'`. If the input image is square, specify `'NumFeatures'` as a square number.\n\n• numeric matrix — Input image is divided into custom features, using the numeric matrix as a map, where the integer value of each pixel specifies the feature of the corresponding pixel. `'NumFeatures'` is the same as the number of unique elements in the matrix. The size of the matrix must match the size of the input image.\n\nFor photographic image data, the `'superpixels'` option usually gives better results. In this case, features are based on the contents of the image, by segmenting the image into regions of similar pixel value. For other types of images, such as spectrograms, the more regular `'grid'` option or a custom segmentation map can provide more useful results.\n\nExample: `'Segmentation','grid'`\n\nType of simple model to fit, specified as the specified as the comma-separated pair consisting of `'Model'` and `'tree'` or `'linear'`.\n\nThe `imageLIME` function classifies the synthetic images using the network `net` and then uses the results to fit a simple, interpretable model. The methods used to fit the results and determine the importance of each feature depend on the type of simple model used.\n\n• `'tree'` — Fit a regression tree using `fitrtree` (Statistics and Machine Learning Toolbox) then compute the importance of each feature using `predictorImportance` (Statistics and Machine Learning Toolbox)\n\n• `'linear'` — Fit a linear model with lasso regression using `fitrlinear` (Statistics and Machine Learning Toolbox) then compute the importance of each feature using the weights of the linear model.\n\nExample: `'Model','linear'`\n\nData Types: `char` | `string`\n\nOutput upsampling method to use when segmentation method is `'grid'`, specified as the comma-separated pair consisting of `'OutputUpsampling'` and one of the following.\n\n• `'nearest'` — Use nearest-neighbor interpolation expand the map to the same size as the input data. The map indicates the size of the each feature with respect to the size of the input data.\n\n• `'bicubic'` — Use bicubic interpolation to produce a smooth map the same size as the input data.\n\n• `'none'` — Use no upsampling. The map can be smaller than the input data.\n\nIf `'OutputUpsampling'` is `'nearest'` or `'bicubic'`, the computed map is upsampled to the size of the input data using the `imresize` function.\n\nExample: `'OutputUpsampling','bicubic'`\n\nSize of the mini-batch to use to compute the map feature importance, specified as the comma-separated pair consisting of `'MiniBatchSize'` and a positive integer.\n\nA mini-batch is a subset of the set of synthetic images. The mini-batch size specifies the number of synthetic images that are passed to the network at once. Larger mini-batch sizes lead to faster computation, at the cost of more memory.\n\nExample: `'MiniBatchSize',256`\n\nHardware resource for computing map, specified as the comma-separated pair consisting of `'ExecutionEnvironment'` and one of the following.\n\n• `'auto'` — Use a GPU if one is available. Otherwise, use the CPU.\n\n• `'cpu'` — Use the CPU.\n\n• `'gpu'` — Use the GPU.\n\nThe GPU option requires Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). If you choose the `'ExecutionEnvironment','gpu'` option and Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.\n\nExample: `'ExecutionEnvironment','gpu'`\n\n## Output Arguments\n\ncollapse all\n\nMap of feature importance, returned as a numeric matrix or a numeric array. Areas in the map with higher positive values correspond to regions of input data that contribute positively to the specified classification label.\n\nThe value of `scoreMap(i,j)` denotes the importance of the image pixel `(i,j)` to the simple model, except when you use the options `'Segmentation','grid'`, and `'OutputUpsampling','none'`. In that case, the `scoreMap` is smaller than the input image, and the value of `scoreMap(i,j)` denotes the importance of the feature at position `(i,j)` in the grid of features.\n\nIf `label` is specified as a vector, the change in classification score for each class label is calculated independently. In that case, `scoreMap(:,:,k)` corresponds to the occlusion map for the `k`th element in `label`.\n\nMap of features, returned as a numeric matrix.\n\nFor each pixel `(i,j)` in the input image, ```idx = featureMap(i,j)``` is an integer corresponding to the index of the feature containing that pixel.\n\nFeature importance, returned as a numeric vector or a numeric matrix.\n\nThe value of `featureImportance(idx)` is the calculated importance of the feature specified by `idx`. If you provide labels as a vector of categorical values, char vectors, or string scalars, then `featureImportance(idx,k)` corresponds to the importance of feature `idx` for `label(k)`.\n\ncollapse all\n\n### LIME\n\nThe locally interpretable model-agnostic explanations (LIME) technique is an explainability technique used to explain the classification decisions made by a deep neural network.\n\nGiven the classification decision of deep network for a piece of input data, the LIME technique calculates the importance of each feature of the input data to the classification result.\n\nThe LIME technique approximates the behavior of a deep neural network using a simpler, more interpretable model, such as a regression tree. To map the importance of different parts of the input image, the `imageLIME` function of performs the following steps.\n\n• Segment the image into features.\n\n• Generate synthetic image data by randomly including or excluding features. Each pixel in an excluded feature is replaced with the value of the average image pixel.\n\n• Classify the synthetic images using the deep network.\n\n• Fit a regression model using the presence or absence of image features for each synthetic image as binary regression predictors for the scores of the target class.\n\n• Compute the importance of each feature using the regression model.\n\nThe resulting map can be used to determine which features were most important to a particular classification decision. This can be especially useful for making sure your network is focusing on the appropriate features when classifying.\n\nIntroduced in R2020b"
] | [
null,
"https://kr.mathworks.com/help/examples/nnet/win64/VisualizePartsOfImageImportantClassificationExample_01.png",
null,
"https://kr.mathworks.com/help/examples/nnet/win64/VisualizePartsOfImageImportantClassificationExample_02.png",
null,
"https://kr.mathworks.com/help/examples/nnet/win64/VisualizeOnlyTheMostImportantFeaturesExample_01.png",
null,
"https://kr.mathworks.com/help/examples/nnet/win64/VisualizeOnlyTheMostImportantFeaturesExample_02.png",
null,
"https://kr.mathworks.com/help/examples/nnet/win64/ViewImportantFeaturesUsingCustomSegmentationMapExample_01.png",
null,
"https://kr.mathworks.com/help/examples/nnet/win64/ViewImportantFeaturesUsingCustomSegmentationMapExample_02.png",
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.71285236,"math_prob":0.9556548,"size":17821,"snap":"2021-21-2021-25","text_gpt3_token_len":4090,"char_repetition_ratio":0.17674132,"word_repetition_ratio":0.15413254,"special_character_ratio":0.21025756,"punctuation_ratio":0.12403846,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9815535,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12],"im_url_duplicate_count":[null,1,null,1,null,1,null,1,null,1,null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-05-07T13:37:29Z\",\"WARC-Record-ID\":\"<urn:uuid:ebc1fcdf-b2f9-4d50-adef-23524311af3b>\",\"Content-Length\":\"134173\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0da32618-b838-46a6-bdfd-7ad0ee7aab10>\",\"WARC-Concurrent-To\":\"<urn:uuid:2375c609-841e-4a5f-95f0-9730f1098601>\",\"WARC-IP-Address\":\"184.28.136.58\",\"WARC-Target-URI\":\"https://kr.mathworks.com/help/deeplearning/ref/imagelime.html\",\"WARC-Payload-Digest\":\"sha1:C6ZK4BJTMVN2MRJ7LGQQYHMTWRAZZCVQ\",\"WARC-Block-Digest\":\"sha1:4LBYQEYPC57WYCBOBL6DBDVT6FKDFANG\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-21/CC-MAIN-2021-21_segments_1620243988793.99_warc_CC-MAIN-20210507120655-20210507150655-00341.warc.gz\"}"} |
https://publicresources.lablearner.com/lablearner-discussion-energy-kinetic-and-potential-energy/ | [
"## LabLearner Discussion: Kinetic and Potential Energy\n\nIntermediate+",
null,
"LabLearner Discussions are designed for you to sit and discuss science topics with your child/student. Each slide below contains rich content to stimulate intelligent discussions with your student regardless of their age. Directed discussion is one of the most powerful ways we can interact with each other. Start with the first slide then move to the next slide below it as your discussion proceeds. Give it a try with your student.\n\n##### Slide 1",
null,
"The two main goals of this first LabLearner Discussion in the intermediate Energy Series is to develop a firm understanding of the Law of Conservation of Energy and to establish the meaning of both potential and kinetic energy.\n\nThe Law of Conservation of Energy states that energy can neither be created nor destroyed, however, it can change forms. Looking at the big picture, this means that all energy that exists in the Universe today came into existence at the moment of the Big Bang, almost 14 billion years ago, and has never and will never change. Never!\n\nNote: On a more practical level, the Law of Conservation of Energy helps us understand much more concrete phenomena that we encounter in everyday life.\n\n##### Slide 2",
null,
"Kinetic Energy\n\nKinetic energy is the energy of movement. If you see something moving, like an airplane or a baseball, for example, you easily can tell that the object has kinetic energy.\n\nHowever, many times kinetic energy refers to the motion of molecules that we cannot see. When the molecules of an object move more rapidly, they have greater kinetic energy. We can detect this molecular movement as heat. Therefore, heat is a form of kinetic energy.\n\nOn the left of this slide are images and words that should help you get the “feel” that kinetic energy implies movement, action, heat, light, and so on.\n\nNote: Another two examples of kinetic energy that might not be obvious to you are electrical and light energy. Both of these forms of energy are kinetic energy because of movement. In the case of electricity, it is the movement of electrons through a wire or a bolt of lightning. In the case of light, it is the movement of very small particles called photons that travel faster than anything else in the Universe!\n\nPotential Energy\n\nPotential energy is the energy that is stored in an object or substance. This is often more difficult to imagine than the concept of kinetic energy. Whereas it is easy to visualize the kinetic energy (the energy of movement) of a falling ball, the potential energy of the ball before it falls is much harder to visualize.\n\nSome examples of potential energy are shown on the right side of this slide. The runner is at the starting block, ready to go. He has stored chemical potential energy in his muscles which will be converted into kinetic energy when the gun sounds. In a similar manner, gasoline contains stored chemical potential energy that will be released as heat, sound, and mechanical kinetic energy when it is ignited in an engine.\n\nThe battery stores electrons that will begin moving (become kinetic) the moment it is inserted into a complete electrical circuit.\n\nFinally, the archer in the back has drawn his bow. At this point, all of the energy in the bow and arrow system is in a potential form, stored in the fibers of the bent bow. Upon release, the bow will spring back to its original shape and all of the stored potential energy will be transferred to the arrow, which will be propelled toward its target. We will discuss the potential/kinetic energy conversion of the drawn bow in greater detail in a later slide.\n\n##### Slide 3",
null,
"Gravitational potential energy is a form of potential energy that children will be most familiar with. It is the energy of position and is related to an object’s height. Before discussing this slide with your child, do a simple experiment to quickly demonstrate gravitational potential energy. Hold any small object in your outstretched hand, a pencil for example. At first, the pencil is not moving and doesn’t appear to have any energy at all. But now, release (don’t throw) it from your hand. It immediately begins to move toward the floor. You can see its kinetic energy easily now because of its movement. But that kinetic energy had to come from somewhere (remember, energy can not be created from nothing). The kinetic energy of the pencil’s downward movement came from the original gravitational potential energy that it contained while it still was held above the floor in your hand.\n\nNow let’s begin applying some math to the kinetic energy/potential energy conversion. The basic mathematical formula for potential energy (PE) is PE = mgh. The mass (m) of an object is expressed in kilograms (kg). The height (h) of an object above a surface (water in this case) is expressed in meters. The final term (g) is a number that never changes and is therefore called a “constant”. In this case, we are referring to the gravitational constant (g), which is 9.81 m/s2 (that is 9.81 meters per second squared).\n\nIf we multiply the units for m x g x h, the final unit for potential energy (PE) is kg x m2/s2. This is referred to as a unit of energy called a Joule, which itself is abbreviated as J. Let’s really understand this concept by further applying the PE (potential energy) equation on the next two slides.\n\nNote: While the concept of m2 is not difficult to think about, children are likely to have more of a problem with the concept of a second squared (s2). The s2 concept comes from a speed (which would be some distance per second, m/s, for example) that changes over time and thus is a property of something called acceleration. We will not discuss acceleration further at this time.\n\n##### Slide 4",
null,
"This slide provides practice on working through several gravitational potential energy problems.\n\nDiscuss the solution to the problem when the diver is at 25 m.\n\nNote: A second important aspect of this slide is that it provides us with a perfect platform to introduce the important concept of the conversion of potential to kinetic energy.\n\nAs the diver loses potential energy during the fall, he gains kinetic energy. At the moment he hits the water, all of the initial potential energy will have been converted to kinetic energy, the energy of motion. Thus, when he hits the surface of the water, he will have 19,865.25J of kinetic energy!\n\n##### Slide 5",
null,
"This slide provides the correct answers for the calculations on the previous slide. Note again that all of the gravitational potential energy at any position is converted to kinetic energy. Therefore, when gravitational potential energy reaches zero (at the water’s surface), all of the original gravitational potential energy has been converted to kinetic energy and the diver hits the water with 19,865.25 J of energy.\n\nYou may also discuss that, when hitting the surface, the diver will continue through the water for some distance before his buoyancy stops his further progress and he floats back up. To simplify the situation, we could have used an example of diving onto a solid concrete surface from a height of 25m. This tremendous amount of energy (19,865.25 J) hitting an immoveable surface and abruptly stopping would, of course, likely be lethal.\n\n##### Slide 6",
null,
"This slide shows various positions of a pendulum. Starting from the left, before the ball is released, 100% of the energy is in the form of potential energy. If we knew the height and mass of the ball we could easily calculate the potential energy and therefore the total energy.\n\nWhen the ball is released, the potential energy decreases as it is driven by gravity toward the perpendicular (straight down). At exactly the same time, the kinetic energy of the system increases. Halfway down, for example, 50% of the total energy is in the form of kinetic energy and 50% is still in the form of potential energy.\n\nAt the perpendicular, 100% of the total energy is in the form of kinetic energy. There is no more stored, potential energy at this point.\n\nUnlike the situation with the cliff diver, the pendulum ball continues on. As it does, it gains height due to the push of its kinetic energy, and therefore gains potential energy. As it gains more and more potential energy, it loses kinetic energy.\n\nFinally, when the pendulum ball reaches the top of its arc to the right, all of the energy in the system is once again 100% potential energy. There is momentarily a pause in movement as the pendulum changes direction from up and to the right to down and back to the left. At precisely that moment, there is no kinetic energy. All of the energy of the pendulum at that moment is in the form of gravitational potential energy.\n\nThis is similar to when you throw a ball straight up into the air and it momentarily pauses at its peak before falling back to Earth. After this momentary pause, the ball will be forced down again by gravity. Imagine what would happen if you threw the ball in outer space, where there is no gravity and therefore no gravitational potential energy!\n\n##### Slide 7",
null,
"This slide follows directly from the previous slide in which the pendulum was introduced. If possible, go outdoors with your child and let them call out how much potential and kinetic energy they have at various swing positions. In other words (see the illustration for numbers):\n\n1. “I’m all potential energy!”\n\n2. “I’m getting more kinetic energy.” or “I’m losing potential energy.”\n\n3. “I’m all kinetic energy!!”\n\n4. “Help! I’m losing kinetic energy.”\n\n5. “I’ve stopped. I’m all potential energy again.”\n\nOf course, in performing this demonstration, your child can’t “pump” to go faster. Pull them way up from behind and tell them to not pump their legs when you let go. As they swing to and fro, discuss how they are slowing down. Discuss how their height gets smaller and smaller with each cycle. Since energy can not be destroyed, where could this energy be going? [The answer complicates the demonstration but is nonetheless interesting. The energy is lost due to friction. Friction of your child’s body pushing against the air molecules and the rubbing of metal at the swings attachment point. We make up for this loss of gravitational potential energy to friction by pumping our legs, adding energy from our muscles.]\n\n##### Slide 8",
null,
"Note: This slide provides students with another opportunity to consider the potential/kinetic energy conversion.\n\nWhen a bow is drawn, energy is stored in the structure of the deformed bow. There is no movement; there is no kinetic energy. All of the energy is potential energy.\n\nNote: In the example in this slide, the amount of stored potential energy is shown to be 35 J. This is a reasonable amount of energy for draw on a bow.\n\nOnce the string is released and the bow snaps back to its original shape, the potential energy of the bow quickly collapses to zero. All of the stored potential energy is immediately converted to kinetic energy.\n\nNotice the important equation shown in this slide:\n\nPE + KE = Total Energy\n\nThis is a good time to return to the idea of the Law of Conservation of Energy (energy cannot be created or destroyed, but can change forms). In the systems we have discussed thus far (cliff-diver, pendulum, swing, and archer) the total energy is always the sum of the total potential energy and the total kinetic energy. The Law of Conservation of Energy is one of the most fundamental Laws of science.\n\n##### Slide 9",
null,
"This and the following slide should induce discussion regarding potential and kinetic energy. For example, before releasing his hand, the child on the slide is all gravitational potential energy. He will lose this potential energy as he goes down the slide and his kinetic energy increases.\n\nThe child climbing the ladder (assume he is going up the ladder) is increasing his gravitational potential energy with each step up. To climb the ladder and increase his height and potential energy, he must add kinetic energy from the muscles in his legs and arms.\n\nThe three swinging children are all in various combinations of kinetic and potential energy depending on their position. Notice that, without knowing if a child is swinging forward or backward, it is impossible to say whether they are increasing or decreasing in potential energy.\n\nFinally, the little girl sitting under the slide appears to be totally at rest. However, she has gravitational potential energy nonetheless. She would demonstrate this if she were to fall off her seat!\n\n##### Slide 10",
null,
"This final slide shows three positions of a car on a rollercoaster and asks three questions:\n\n-Which car has the most kinetic energy? [Answer: Car#2 has the most kinetic energy.]\n\n-Which car has the least potential energy? [Answer: A bit tricky. Even though it is moving and therefore has the most kinetic energy of the three cars, it also has the least height and therefore has the least potential energy.]\n\n-Which car has the most potential energy? [Answer: Car#3 has the most potential energy because it has the most height.]\n\nIn terms of potential energy, Car#3 > Car#1 > Car#2.\n\n### Kinetic & Potential Energy: Relevant LabLearner CELL Curriculum Units\n\nIntermediate+\n\nIntermediate +\n\n#### Intermediate\n\nForms of Energy\n\nEarth’s Forces\n\nKinetic & Potential Energy\n\nSimple Machines\n\nOpen Inquiry: The Pendulum\n\nHeat and Heat Transfer\n\nFriction"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.9419272,"math_prob":0.9572302,"size":13384,"snap":"2023-40-2023-50","text_gpt3_token_len":2783,"char_repetition_ratio":0.19790733,"word_repetition_ratio":0.034858387,"special_character_ratio":0.20614167,"punctuation_ratio":0.1,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9633772,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22],"im_url_duplicate_count":[null,2,null,2,null,2,null,2,null,2,null,2,null,2,null,2,null,2,null,2,null,2,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-30T22:28:01Z\",\"WARC-Record-ID\":\"<urn:uuid:e9f60190-baf8-4867-84aa-0759737a123b>\",\"Content-Length\":\"79073\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:3d6550cf-d0cb-45cf-9cfc-09fe0da39760>\",\"WARC-Concurrent-To\":\"<urn:uuid:a598e49f-4f2c-4e43-80dc-6448d251dfd8>\",\"WARC-IP-Address\":\"162.159.134.42\",\"WARC-Target-URI\":\"https://publicresources.lablearner.com/lablearner-discussion-energy-kinetic-and-potential-energy/\",\"WARC-Payload-Digest\":\"sha1:W6JHZDRHHL4GX2FU6AESRZABMHWMFYGH\",\"WARC-Block-Digest\":\"sha1:PEPMF5NSZDJZ33YNB7AQUIGK3ISYD2O6\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510730.6_warc_CC-MAIN-20230930213821-20231001003821-00079.warc.gz\"}"} |
https://eliteprofessionalwriters.com/2021/01/01/week-4-stastics/ | [
"# Week 4 stastics\n\nI don’t know how to handle this Health & Medical question and need guidance.\n\nOpen up the small Excel database that is linked below.",
null,
"With this database, you will:\n\n1. Identify and list categorical and continuous variables.\n2. Calculate frequencies and percentages for all of the categorical variables, and means, and standard deviations for the continuous variables.\n3. Run a one-sample z test of proportions for one categorical variable. Here is an example in terms of wording: “Is the proportion of English speaking patients greater than 50%?” Using .05 level of significant test the appropriate hypotheses.\n1. Write the null and alternate hypotheses\n2. What is the proportion of English speaking patients\n3. What is the standard error and z statistics\n4. Find critical z and p value. Is the p value significant? Conclusion?\n4. Run an independent samples t-test to compare two groups of people (specified by a dichotomous variable) on one continuous variable. Using .05 level of significant test the appropriate hypotheses. In order to run this test statistics you have to re-arrange your data.\n1. Write the null and alternate hypotheses.\n2. What is the calculated mean for each group?\n3. What is the confidence interval for each group?\n4. What is the critical t value?\n5. Is the p value significant? Conclusion?\n\nNeed Excel help? Click on the Excel Function Help Workbook under Course Materials.\n\n###### Place this order or similar order and get an amazing discount. USE Discount code “GET20” for 20% discount",
null,
""
] | [
null,
"https://learn.westcoastuniversity.edu/bbcswebdav/pid-4224184-dt-content-rid-7918970_1/xid-7918970_1",
null,
"https://homeworkcorp.com/wp-content/uploads/2020/08/paper.png",
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.84092987,"math_prob":0.7567138,"size":1398,"snap":"2021-04-2021-17","text_gpt3_token_len":281,"char_repetition_ratio":0.12123386,"word_repetition_ratio":0.13157895,"special_character_ratio":0.2067239,"punctuation_ratio":0.112,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9912983,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,2,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-01-17T06:47:32Z\",\"WARC-Record-ID\":\"<urn:uuid:7b844bae-b413-4606-9b8a-ec896dc9b59e>\",\"Content-Length\":\"51924\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:af5e0ed9-0bf4-4d47-a84a-38da6a803b09>\",\"WARC-Concurrent-To\":\"<urn:uuid:8356c623-03a1-430f-bb34-77bf23deed8a>\",\"WARC-IP-Address\":\"68.65.121.191\",\"WARC-Target-URI\":\"https://eliteprofessionalwriters.com/2021/01/01/week-4-stastics/\",\"WARC-Payload-Digest\":\"sha1:C66CRB2RE6G2RGIXRU6XJPOJIQHHAM7I\",\"WARC-Block-Digest\":\"sha1:SGGGNNQBNHDEC3H2326EAJYJYY7GHPQY\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-04/CC-MAIN-2021-04_segments_1610703509973.34_warc_CC-MAIN-20210117051021-20210117081021-00753.warc.gz\"}"} |
https://ri.itba.edu.ar/entities/art%C3%ADculo%20de%20publicaci%C3%B3n%20peri%C3%B3dica/c38dfe14-7e80-4535-8c24-18245ed20df4 | [
"## Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy",
null,
"2018-04\n##### Autores\nTraversaro Varela, Francisco\nRedelico, Francisco\n##### Resumen\n\"In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity is the Permutation Entropy. But there is still no known method to determine the accuracy of this measure. There has been little research on the statistical properties of this quantity that characterize time series. The literature describes some resampling methods of quantities used in nonlinear dynamics - as the largest Lyapunov exponent - but these seems to fail. In this contribution, we propose a parametric bootstrap methodology using a symbolic representation of the time series to obtain the distribution of the Permutation Entropy estimator. We perform several time series simulations given by well-known stochastic processes: the 1/f α noise family, and show in each case that the proposed accuracy measure is as efficient as the one obtained by the frequentist approach of repeating the experiment. The complexity of brain electrical activity, measured by the Permutation Entropy, has been extensively used in epilepsy research for detection in dynamical changes in electroencephalogram (EEG) signal with no consideration of the variability of this complexity measure. An application of the parametric bootstrap methodology is used to compare normal and pre-ictal EEG signals.\""
] | [
null,
"https://ri.itba.edu.ar/server/api/core/bitstreams/ae1b2336-7697-4606-8624-6b3021dbeef0/content",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8825994,"math_prob":0.6775055,"size":1650,"snap":"2022-40-2023-06","text_gpt3_token_len":326,"char_repetition_ratio":0.11725395,"word_repetition_ratio":0.082987554,"special_character_ratio":0.16848485,"punctuation_ratio":0.06439394,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9522977,"pos_list":[0,1,2],"im_url_duplicate_count":[null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-01-28T13:04:20Z\",\"WARC-Record-ID\":\"<urn:uuid:0e91c78f-3d94-4a94-936c-9f8206e45bc4>\",\"Content-Length\":\"383207\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:04180157-6aec-415f-b658-5c502853a4b2>\",\"WARC-Concurrent-To\":\"<urn:uuid:ef79e24c-259f-44fb-bd13-4b30f344e1b1>\",\"WARC-IP-Address\":\"54.232.170.194\",\"WARC-Target-URI\":\"https://ri.itba.edu.ar/entities/art%C3%ADculo%20de%20publicaci%C3%B3n%20peri%C3%B3dica/c38dfe14-7e80-4535-8c24-18245ed20df4\",\"WARC-Payload-Digest\":\"sha1:BIYY7RVVYGGP6A45KJIA23HDD77NUFBD\",\"WARC-Block-Digest\":\"sha1:FRGYTX7B3CPH2B3K3ZBJUD6TGW7A4YQF\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764499634.11_warc_CC-MAIN-20230128121809-20230128151809-00215.warc.gz\"}"} |
http://8degrees.co/examples-of-introductions-for-essays/ | [
"# Examples Of Introductions For Essays\n\n8 self introduction sample essay introduction letter .\n\nessay intro devlp concl .\n\n8 self introduction sample essay introduction letter .\n\n5 paragraph essay example on quotes quotesgram .\n\ncritical lens essay 3 how to write the introduction youtube .\n\nexamples of writing law school the university of .\n\npersuasive essay introduction .\n\n5 biographical introduction sample introduction letter .\n\nexamples of writing law school the university of .\n\nwriting an introduction persuasive essay .\n\n7 introduction essay about yourself introduction letter .\n\n7 introduction essay about yourself introduction letter .\n\nthe little blue writing book essays .\n\n8 self introductions examples introduction letter .\n\nexamples of writing law school the university of .\n\nfree 23 free essay examples in pdf doc examples .\n\nfree 7 college essay samples in ms word pdf .\n\n12 best images of academic plan worksheet student goal .\n\nfree 12 essay samples in pdf .\n\nintroduction writing for narrative essay youtube .\n\nwriting an effective introduction paragraph steps tips .\n\nenglish essay introduction example google search essay .\n\n8 self introduction sample essay introduction letter .\n\n7 introduction paragraph examples about yourself .\n\nhow to write argumentative essay we analysed 374 .\n\n003 argumentative persuasive essays sample of suiteblounge .\n\n6 how to write a letter of introduction for college .\n\nwriting introduction for research paper writing an ."
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.6450139,"math_prob":0.65690905,"size":1130,"snap":"2020-34-2020-40","text_gpt3_token_len":198,"char_repetition_ratio":0.26376554,"word_repetition_ratio":0.057142857,"special_character_ratio":0.19557522,"punctuation_ratio":0.12290503,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99820405,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-09-22T01:31:14Z\",\"WARC-Record-ID\":\"<urn:uuid:b137b197-a8b3-4377-a631-bfc293082b19>\",\"Content-Length\":\"38408\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:757b7b5c-2341-482b-9ea2-5a361b295e7e>\",\"WARC-Concurrent-To\":\"<urn:uuid:93b65c16-525e-4866-9173-8ecc86923e04>\",\"WARC-IP-Address\":\"172.67.212.152\",\"WARC-Target-URI\":\"http://8degrees.co/examples-of-introductions-for-essays/\",\"WARC-Payload-Digest\":\"sha1:G5LPYL5H2PUXJ5QSVK3LUSOE7HMYLBER\",\"WARC-Block-Digest\":\"sha1:2CH52STOOZG6JO4CK7QRM3PGTICTN6IE\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-40/CC-MAIN-2020-40_segments_1600400202686.56_warc_CC-MAIN-20200922000730-20200922030730-00474.warc.gz\"}"} |
https://search.r-project.org/CRAN/refmans/BALD/html/scaleParameter-comma-AnnualAggLossDevModelOutput-dash-method.html | [
"scaleParameter,AnnualAggLossDevModelOutput-method {BALD} R Documentation\n\n## A method to plot and/or return the posterior of the scale parameter for the Student-t measurement equation for models in BALD.\n\n### Description\n\nA method to plot and/or return the posterior of the scale parameter for the Student-t measurement equation for models in BALD.\n\n### Arguments\n\n `object` The object of type `AnnualAggLossDevModelOuput` from which to plot and/or return the scale parameter. `column` The scale parameter is allowed to vary with development time. Setting `column` results in the plotting and returning of the scale parameter corresponding to that column. Default value is `1`. `plotDensity` A logical value. If `TRUE`, then the density is plotted. If `plotTrace` is also `TRUE`, then two plots are generated. If they are both `FALSE`, then only the statistics are returned. `plotTrace` A logical value. If `TRUE`, then the trace is plotted. If `plotDensity` is also `TRUE`, then two plots are generated. If they are both `FALSE`, then only the statistics are returned.\n\n### Value\n\nMainly called for the side effect of plotting. Also returns a named array with select quantiles of the posterior for the scale parameter. Returned invisibly.\n\n`scaleParameter`"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.7016805,"math_prob":0.9592153,"size":1272,"snap":"2021-31-2021-39","text_gpt3_token_len":294,"char_repetition_ratio":0.14747635,"word_repetition_ratio":0.39901477,"special_character_ratio":0.2028302,"punctuation_ratio":0.114035085,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98752743,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-09-27T06:27:29Z\",\"WARC-Record-ID\":\"<urn:uuid:040caa4f-e0b9-428d-8695-9bde0b56fbdf>\",\"Content-Length\":\"2973\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:ac15347c-6c71-4f61-8867-5682f1e6b872>\",\"WARC-Concurrent-To\":\"<urn:uuid:233ed0a4-4d43-4f69-9b0d-0c367b3437e8>\",\"WARC-IP-Address\":\"137.208.57.46\",\"WARC-Target-URI\":\"https://search.r-project.org/CRAN/refmans/BALD/html/scaleParameter-comma-AnnualAggLossDevModelOutput-dash-method.html\",\"WARC-Payload-Digest\":\"sha1:HMGI3HN6DQUZNNQSJMB556GMCV5CSXUH\",\"WARC-Block-Digest\":\"sha1:7DZ3KZVI2TJHWAF3TUQJZY4JC7E77T2H\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780058373.45_warc_CC-MAIN-20210927060117-20210927090117-00245.warc.gz\"}"} |
https://www.crazy-numbers.com/en/3882 | [
"Discover a lot of information on the number 3882: properties, mathematical operations, how to write it, symbolism, numerology, representations and many other interesting things!\n\nMathematical properties of 3882\n\nIs 3882 a prime number? No\nIs 3882 a perfect number? No\nNumber of divisors 8\nList of dividers 1, 2, 3, 6, 647, 1294, 1941, 3882\nSum of divisors 7776\n\nHow to write / spell 3882 in letters?\n\nIn letters, the number 3882 is written as: Three thousand eight hundred and eighty-two. And in other languages? how does it spell?\n\n3882 in other languages\nWrite 3882 in english Three thousand eight hundred and eighty-two\nWrite 3882 in french Trois mille huit cent quatre-vingt-deux\nWrite 3882 in spanish Tres mil ochocientos ochenta y dos\nWrite 3882 in portuguese Três mil oitocentos oitenta e dois\n\nDecomposition of the number 3882\n\nThe number 3882 is composed of:\n\n1 iteration of the number 3 : The number 3 (three) is the symbol of the trinity. He also represents the union.... Find out more about the number 3\n\n2 iterations of the number 8 : The number 8 (eight) represents power, ambition. It symbolizes balance, realization.... Find out more about the number 8\n\n1 iteration of the number 2 : The number 2 (two) represents double, association, cooperation, union, complementarity. It is the symbol of duality.... Find out more about the number 2\n\nOther ways to write 3882\nIn letter Three thousand eight hundred and eighty-two\nIn roman numeral MMMDCCCLXXXII\nIn binary 111100101010\nIn octal 7452\nIn US dollars USD 3,882.00 (\\$)\nIn euros 3 882,00 EUR (€)\nSome related numbers\nPrevious number 3881\nNext number 3883\nNext prime number 3889\n\nMathematical operations\n\nOperations and solutions\n3882*2 = 7764 The double of 3882 is 7764\n3882*3 = 11646 The triple of 3882 is 11646\n3882/2 = 1941 The half of 3882 is 1941.000000\n3882/3 = 1294 The third of 3882 is 1294.000000\n38822 = 15069924 The square of 3882 is 15069924.000000\n38823 = 58501444968 The cube of 3882 is 58501444968.000000\n√3882 = 62.305697973781 The square root of 3882 is 62.305698\nlog(3882) = 8.264105763729 The natural (Neperian) logarithm of 3882 is 8.264106\nlog10(3882) = 3.5890555310523 The decimal logarithm (base 10) of 3882 is 3.589056\nsin(3882) = -0.84604367668188 The sine of 3882 is -0.846044\ncos(3882) = 0.53311358747138 The cosine of 3882 is 0.533114\ntan(3882) = -1.5869857691956 The tangent of 3882 is -1.586986"
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.73324066,"math_prob":0.99305505,"size":2295,"snap":"2019-26-2019-30","text_gpt3_token_len":733,"char_repetition_ratio":0.17110433,"word_repetition_ratio":0.036516853,"special_character_ratio":0.4204793,"punctuation_ratio":0.13194445,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99657726,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-06-19T05:16:36Z\",\"WARC-Record-ID\":\"<urn:uuid:1a06800c-d833-4fe6-b873-833c9c326ddd>\",\"Content-Length\":\"19606\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:19e1b8b7-5f91-4f90-90ed-4ce4bc9c10c6>\",\"WARC-Concurrent-To\":\"<urn:uuid:0c8200b6-1a6a-405f-8452-a72e10e34abb>\",\"WARC-IP-Address\":\"213.186.33.97\",\"WARC-Target-URI\":\"https://www.crazy-numbers.com/en/3882\",\"WARC-Payload-Digest\":\"sha1:2X2DWSRKUIBC65GDFQ77F3L3YJLNY3Q4\",\"WARC-Block-Digest\":\"sha1:CVQFPGZC2476SS2NSDG4NRFBSK2IGUSR\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-26/CC-MAIN-2019-26_segments_1560627998913.66_warc_CC-MAIN-20190619043625-20190619065625-00352.warc.gz\"}"} |
https://numbermatics.com/n/1886159080384489/ | [
"# 1886159080384489\n\n## 1,886,159,080,384,489 is an odd composite number. It is composed of a single prime number multiplied by itself.\n\nWhat does the number 1886159080384489 look like?\n\nThis visualization shows the relationship between its 1 prime factors (large circles) and 3 divisors.\n\n1886159080384489 is an odd composite number. It has a total of three divisors.\n\n## Prime factorization of 1886159080384489:\n\n### 434299332\n\n(43429933 × 43429933)\n\nSee below for interesting mathematical facts about the number 1886159080384489 from the Numbermatics database.\n\n### Names of 1886159080384489\n\n• Cardinal: 1886159080384489 can be written as One quadrillion, eight hundred eighty-six trillion, one hundred fifty-nine billion, eighty million, three hundred eighty-four thousand, four hundred eighty-nine.\n\n### Scientific notation\n\n• Scientific notation: 1.886159080384489 × 1015\n\n### Factors of 1886159080384489\n\n• Number of distinct prime factors ω(n): 1\n• Total number of prime factors Ω(n): 2\n• Sum of prime factors: 43429933\n\n### Divisors of 1886159080384489\n\n• Number of divisors d(n): 3\n• Complete list of divisors:\n• Sum of all divisors σ(n): 1886159123814423\n• Sum of proper divisors (its aliquot sum) s(n): 43429934\n• 1886159080384489 is a deficient number, because the sum of its proper divisors (43429934) is less than itself. Its deficiency is 1886159036954555\n\n### Bases of 1886159080384489\n\n• Binary: 1101011001101110011101000011111100111100111111010012\n• Base-36: IKL5IP9YI1\n\n### Squares and roots of 1886159080384489\n\n• 1886159080384489 squared (18861590803844892) is 3557596076516861237168071791121\n• 1886159080384489 cubed (18861590803844893) is 6710192144062509153448483555132252283576322169\n• 1886159080384489 is a perfect square number. Its square root is 43429933\n• The cube root of 1886159080384489 is 123554.7480907395\n\n### Scales and comparisons\n\nHow big is 1886159080384489?\n• 1,886,159,080,384,489 seconds is equal to 59,974,024 years, 25 weeks, 20 hours, 34 minutes, 49 seconds.\n• To count from 1 to 1,886,159,080,384,489 would take you about one hundred seventy-nine million, nine hundred twenty-two thousand and seventy-three years!\n\nThis is a very rough estimate, based on a speaking rate of half a second every third order of magnitude. If you speak quickly, you could probably say any randomly-chosen number between one and a thousand in around half a second. Very big numbers obviously take longer to say, so we add half a second for every extra x1000. (We do not count involuntary pauses, bathroom breaks or the necessity of sleep in our calculation!)\n\n• A cube with a volume of 1886159080384489 cubic inches would be around 10296.2 feet tall.\n\n### Recreational maths with 1886159080384489\n\n• 1886159080384489 backwards is 9844830809516881\n• The number of decimal digits it has is: 16\n• The sum of 1886159080384489's digits is 82\n• More coming soon!\n\nMLA style:\n\"Number 1886159080384489 - Facts about the integer\". Numbermatics.com. 2023. Web. 3 February 2023.\n\nAPA style:\nNumbermatics. (2023). Number 1886159080384489 - Facts about the integer. Retrieved 3 February 2023, from https://numbermatics.com/n/1886159080384489/\n\nChicago style:\nNumbermatics. 2023. \"Number 1886159080384489 - Facts about the integer\". https://numbermatics.com/n/1886159080384489/\n\nThe information we have on file for 1886159080384489 includes mathematical data and numerical statistics calculated using standard algorithms and methods. We are adding more all the time. If there are any features you would like to see, please contact us. Information provided for educational use, intellectual curiosity and fun!\n\nKeywords: Divisors of 1886159080384489, math, Factors of 1886159080384489, curriculum, school, college, exams, university, Prime factorization of 1886159080384489, STEM, science, technology, engineering, physics, economics, calculator, one quadrillion, eight hundred eighty-six trillion, one hundred fifty-nine billion, eighty million, three hundred eighty-four thousand, four hundred eighty-nine.\n\nOh no. Javascript is switched off in your browser.\nSome bits of this website may not work unless you switch it on."
] | [
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8178757,"math_prob":0.8370521,"size":3938,"snap":"2022-40-2023-06","text_gpt3_token_len":1106,"char_repetition_ratio":0.18988307,"word_repetition_ratio":0.07992565,"special_character_ratio":0.40883696,"punctuation_ratio":0.17474303,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9770932,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-02-03T10:04:32Z\",\"WARC-Record-ID\":\"<urn:uuid:9913e006-edd5-41b0-8229-d0cada71f95f>\",\"Content-Length\":\"18472\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:dab3e968-00e6-42ae-9da3-324093c48b8d>\",\"WARC-Concurrent-To\":\"<urn:uuid:84ab452b-395b-415c-9fa3-3c324a99a390>\",\"WARC-IP-Address\":\"72.44.94.106\",\"WARC-Target-URI\":\"https://numbermatics.com/n/1886159080384489/\",\"WARC-Payload-Digest\":\"sha1:MPRDHGNPHZMDWRPRIONQAKQVTSMLO4ZK\",\"WARC-Block-Digest\":\"sha1:PWWS6GICHPGCDLE5FRHMEQFJICGG2CZF\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764500044.66_warc_CC-MAIN-20230203091020-20230203121020-00774.warc.gz\"}"} |
https://physics.stackexchange.com/questions/119878/what-is-the-density-and-energy-of-a-photon | [
"# What is the density and energy of a photon?\n\nAs I understand, photons are considered mass-less, which is a necessary condition for moving at the speed of light. However, does that mean their density is 0, as they will occupy some volume. If their density is zero, that means there is no matter inside a photon. Thus, shouldn't a photon be able to pass through matter instead of colliding with it? As $E = mc^2$, shouldn't a photon have zero energy, as it has zero mass?\n\nThe complete energy relation is $$E^2 = m^2c^4 + \\lvert \\vec{p} \\rvert^2 c^2$$ The photon has $m=0$, so we are left with $$E = \\lvert \\vec{p} \\rvert c$$ (we don't care about the negative solution here). But according to De Broglie, it is $\\vec{p} = \\hbar \\vec{k}$, so we have $$E = \\hbar c \\lvert \\vec{k} \\rvert$$ but $k=\\frac{2\\pi}{\\lambda}$ and $\\hbar=\\frac{h}{2\\pi}$. Therefore $$E=h\\frac{c}{2\\lambda} = h \\nu$$ where $\\nu$ is the frequency of the photon."
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.91400546,"math_prob":0.99995744,"size":2465,"snap":"2019-51-2020-05","text_gpt3_token_len":688,"char_repetition_ratio":0.11255587,"word_repetition_ratio":0.014018691,"special_character_ratio":0.28397566,"punctuation_ratio":0.116959065,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9999975,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-12-08T02:42:18Z\",\"WARC-Record-ID\":\"<urn:uuid:f337b9eb-e402-4671-9d06-39ddca1a1001>\",\"Content-Length\":\"136174\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c3e7f870-4154-497b-84af-74830009291d>\",\"WARC-Concurrent-To\":\"<urn:uuid:23efd7c7-fe03-473e-8379-d076a238e63a>\",\"WARC-IP-Address\":\"151.101.1.69\",\"WARC-Target-URI\":\"https://physics.stackexchange.com/questions/119878/what-is-the-density-and-energy-of-a-photon\",\"WARC-Payload-Digest\":\"sha1:Z6IBSZD55W2C7COK6HR3WDNUQ33EGMLL\",\"WARC-Block-Digest\":\"sha1:TQBBVMFCAE5WNYHJJH5F25Y3I2RZANP3\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-51/CC-MAIN-2019-51_segments_1575540504338.31_warc_CC-MAIN-20191208021121-20191208045121-00095.warc.gz\"}"} |
https://catboost.ai/docs/concepts/loss-functions-ranking.html | [
"# Ranking: objectives and metrics\n\n## Pairwise metrics\n\nPairwise metrics use special labeled information — pairs of dataset objects where one object is considered the “winner” and the other is considered the “loser”. This information might be not exhaustive (not all possible pairs of objects are labeled in such a way). It is also possible to specify the weight for each pair.\n\nIf GroupId is specified, then all pairs must have both members from the same group if this dataset is used in pairwise modes.\n\nIf the labeled pairs data is not specified for the dataset, then pairs are generated automatically in each group using per-object label values (labels must be specified and must be numerical). The object with a greater label value in the pair is considered the “winner”.\n\n### Specific variables used\n\nThe following variables are used in formulas of the described pairwise metrics:\n•",
null,
"is the positive object in the pair.\n•",
null,
"is the negative object in the pair.\n\n### Objectives and metrics\n\nName Used for optimization User-defined parameters Formula and/or description\nPairLogit +\n\nCalculation principles\n\nNote.\n\nThe object weights are not used to calculate and optimize the value of this metric. The weights of object pairs are used instead.\n\nPairLogitPairwise +\n\nCalculation principles\n\nThis metric may give more accurate results on large datasets compared to PairLogit but it is calculated significantly slower.\n\nThis technique is described in the Winning The Transfer Learning Track of Yahoo!’s Learning To Rank Challenge with YetiRank paper.\n\nNote.\n\nThe object weights are not used to calculate and optimize the value of this metric. The weights of object pairs are used instead.\n\nPairAccuracy\n\nuse_weights\n\nDefault: true\n\nCalculation principles\n\nNote.\n\nThe object weights are not used to calculate the value of this metric. The weights of object pairs are used instead.\n\nName Used for optimization User-defined parameters Formula and/or description\nPairLogit +\n\nCalculation principles\n\nNote.\n\nThe object weights are not used to calculate and optimize the value of this metric. The weights of object pairs are used instead.\n\nPairLogitPairwise +\n\nCalculation principles\n\nThis metric may give more accurate results on large datasets compared to PairLogit but it is calculated significantly slower.\n\nThis technique is described in the Winning The Transfer Learning Track of Yahoo!’s Learning To Rank Challenge with YetiRank paper.\n\nNote.\n\nThe object weights are not used to calculate and optimize the value of this metric. The weights of object pairs are used instead.\n\nPairAccuracy\n\nuse_weights\n\nDefault: true\n\nCalculation principles\n\nNote.\n\nThe object weights are not used to calculate the value of this metric. The weights of object pairs are used instead.\n\n## Groupwise metrics\n\nName Used for optimization User-defined parameters Formula and/or description\nYetiRank * +\n\nAn approximation of ranking metrics (such as NDCG and PFound). Allows to use ranking metrics for optimization.\n\nThe value of this metric can not be calculated. The metric that is written to output data if YetiRank is optimized depends on the range of all N target values (",
null,
") of the dataset:\n•",
null,
"— PFound\n•",
null,
"— NDCG\nThis metric gives less accurate results on big datasets compared to YetiRankPairwise but it is significantly faster.\nNote.\n\nThe object weights are not used to optimize this metric. The group weights are used instead.\n\nThis objective is used to optimize PairLogit. Automatically generated object pairs are used for this purpose. These pairs are generated independently for each object group. Use the Group weights file or the GroupWeight column of the Columns description file to change the group importance. In this case, the weight of each generated pair is multiplied by the value of the corresponding group weight.\n\nYetiRankPairwise * +\n\nAn approximation of ranking metrics (such as NDCG and PFound). Allows to use ranking metrics for optimization.\n\nThe value of this metric can not be calculated. The metric that is written to output data if YetiRank is optimized depends on the range of all N target values (",
null,
") of the dataset:\n•",
null,
"— PFound\n•",
null,
"— NDCG\n\nThis metric gives more accurate results on big datasets compared to YetiRank but it is significantly slower.\n\nThis technique is described in the Winning The Transfer Learning Track of Yahoo!’s Learning To Rank Challenge with YetiRank paper.\nNote.\n\nThe object weights are not used to optimize this metric. The group weights are used instead.\n\nThis objective is used to optimize PairLogit. Automatically generated object pairs are used for this purpose. These pairs are generated independently for each object group. Use the Group weights file or the GroupWeight column of the Columns description file to change the group importance. In this case, the weight of each generated pair is multiplied by the value of the corresponding group weight."
] | [
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/kN9n7XdSnJoxdptBOL6eQZ7k_Hw.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/7QhqseAinO3vjNhUEVTwd6QhU6E.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/E9pD3C5DMhLzosdQa2wPy38UBCk.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/Slr0fhyMYm-xYKcgAJoJF1JA_Js.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/fOedYU5sqqdlntKKX4_jI771Aco.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/CEC_1gfyUN2Hi_-05d2fchakr0U.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/UlreWrHh5p6ggn5wHZiaLgTPX3w.svg",
null,
"https://yastatic.net/doccenter/images/tech2.yandex.com/en/catboost/doc/freeze/sgs3LOIkLVDEdSO2NThM_l9BL_4.svg",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.81500363,"math_prob":0.86470306,"size":4594,"snap":"2020-45-2020-50","text_gpt3_token_len":929,"char_repetition_ratio":0.1583878,"word_repetition_ratio":0.73994637,"special_character_ratio":0.19612539,"punctuation_ratio":0.08406524,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99096435,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"im_url_duplicate_count":[null,2,null,2,null,2,null,2,null,2,null,2,null,1,null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-10-26T01:15:04Z\",\"WARC-Record-ID\":\"<urn:uuid:e60dc5c9-aacb-44c5-a351-3d4f03a26a8d>\",\"Content-Length\":\"1051145\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:d2f2a144-f5f7-49fb-a47e-979e4e812324>\",\"WARC-Concurrent-To\":\"<urn:uuid:526fab03-f1d3-4950-bf70-37c7ad82ae73>\",\"WARC-IP-Address\":\"87.250.251.215\",\"WARC-Target-URI\":\"https://catboost.ai/docs/concepts/loss-functions-ranking.html\",\"WARC-Payload-Digest\":\"sha1:3QE4ZSZ2KZFB7G5EYMVR4U4NRTL6NSSR\",\"WARC-Block-Digest\":\"sha1:KFPG3INWW2I7WBHASPA3JAK63EFOQWHJ\",\"WARC-Truncated\":\"length\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-45/CC-MAIN-2020-45_segments_1603107890108.60_warc_CC-MAIN-20201026002022-20201026032022-00105.warc.gz\"}"} |
https://www.gushiciku.cn/pl/aSV0/zh-hk | [
"# 數據科學在量化金融中的應用：指數預測（下）",
null,
"## 特徵工程\n\n• 處理缺失值並提取所需變量\n• 數據標準化\n• 處理分類變量\n\n### 1. 處理缺失值並提取所需變量\n\n``````x_input = (df_model.dropna()[['Year','Month','Day','Weekday','seasonality','sign_t_1','t_1_PricePctDelta','t_2_PricePctDelta','t_1_VolumeDelta']].reset_index(drop=True))",
null,
"``y = df_model.dropna().reset_index(drop=True)['AdjPricePctDelta']``\n\n### 2. 數據標準化\n\n``````scaler = StandardScaler()\nx = x_input.copy()\nx[['t_1_PricePctDelta','t_2_PricePctDelta','t_1_VolumeDelta']]=scaler.fit_transform(x[['t_1_PricePctDelta','t_2_PricePctDelta','t_1_VolumeDelta']])``````\n\n### 3. 處理分類變量\n\n``````x_mod = pd.get_dummies(data=x, columns=['Year','Month','Day','Weekday','seasonality'])\nx_mod.columns``````",
null,
"``x_mod.shape``",
null,
"## 模型評估\n\n``trail_result = ensemble_method_reg_trails(x_train, y_train, x_test, y_test)``",
null,
"``pd.DataFrame(trail_result).sort_values('model_test_mape', ascending=True)``",
null,
"• 使用 RandomizedSearchCV 尋找最佳參數的大致範圍\n• 使用 GridSearchCV 尋找更精確的參數\n\n• n_estimators\n• base_estimator\n• learning_rate\n\nbase_estimator 是 ada boost 提升算法的基礎，我們需要提前建立一個 base_estimator 的列表。\n\n``````l_base_estimator = []\nfor i in range(1,16):\nbase = DecisionTreeRegressor(max_depth=i, random_state=42)\nl_base_estimator.append(base)\nl_base_estimator += [LinearSVR(random_state=42,epsilon=0.01,C=100)]``````\n\n### 1. 使用 RandomizedSearchCV 尋找最佳參數的大致範圍\n\n``````randomized_search_grid = {'n_estimators':[10, 50, 100, 500, 1000, 5000],\n'base_estimator':l_base_estimator,\n'learning_rate':np.linspace(0.01,1)}``````\n``````search = RandomizedSearchCV(AdaBoostRegressor(random_state=42),\nrandomized_search_grid,\nn_iter=500,\nscoring='neg_mean_absolute_error',\nn_jobs=-1,\ncv=5,\nrandom_state=42)\nresult = search.fit(x_train, y_train)``````\n\n``result.best_params_``",
null,
"``result.best_score_``",
null,
"### 2. 使用 GridSearchCV 尋找更精確的參數\n\n• n_estimators: 1-50\n• base_estimator: Decision Tree with max depth 9\n• learning_rate: 0.7左右\n``````search_grid = {'n_estimators':range(1,51),\n'learning_rate':np.linspace(0.6,0.8,num=20)}``````",
null,
"GridSearchCV 的結果如下：",
null,
"",
null,
"``best_reg.fit(x_mod, y)``",
null,
"``````m_forecast = best_reg.predict(x_mod)\nmean_absolute_percentage_error(y, m_forecast)``````",
null,
"## 模型預測\n\n``````def forecast_one_period(price_info_adj_data, ml_model, data_processor):\n# Source data: Data acquired straight from source\nnext_day = last_record['Date'] + relativedelta(days=1)\nnext_day_t_2_PricePctDelta = last_record['t_1_PricePctDelta']\nnext_day_t_1_VolumeDelta = last_record['Volume_in_M'] - last_record['t_1_VolumeDelta']\nif next_day_t_1_PricePctDelta > 0:\nnext_day_sign_t_1 = 1\nelse:\nnext_day_sign_t_1 = 0\n# Value -99999 is a placeholder which won't be used in the following modeling process\nnext_day_input = (pd.DataFrame({'Date':[next_day],\n'Volume_in_M':[-99999],\n't_1_PricePctDelta':[next_day_t_1_PricePctDelta],\n't_2_PricePctDelta':[next_day_t_2_PricePctDelta],\n't-1volume': last_record['Volume_in_M'],\n't-2volume': last_record['t-1volume'],\n't_1_VolumeDelta':[next_day_t_1_VolumeDelta],\n'sign_t_1':next_day_sign_t_1}).set_index('Date'))\n# If forecast period is post Feb 15, 2020, input data starts from 2020-02-16,\n# as our model is dedicated for market under Covid Impact.\n# Another model could be used for pre-Covid market forecast.\nif next_day > datetime.datetime(2020, 2, 15):\n# Add new record to original data for modeling preparation\n# Prep for modeling\nx,y = data_processor.data_modeling_prep(input_modified)\nnext_day_x = x.iloc[-1:]\nforecast_price_delta = ml_model.predict(next_day_x)\n# Consolidate prediction results\nforecast_df = {'Date':[next_day], 'price_pct_delta':[forecast_price_delta], 'actual_pct_delta':[np.nan]}\nreturn pd.DataFrame(forecast_df)``````",
null,
"## 分析預測結果",
null,
"“Stocks finished lower as data showing a solid US labor market bolstered speculation that Federal Reserve policy could remain aggressively tight even with the threat of a recession.”\n\n## 總結\n\n• 確定預測目標：反映北美股票市場的指數 — 標普500 ；\n• 數據收集：從公共金融網站下載歷史價格數據；\n• 探索性數據分析：初步瞭解數據的特性，數據可視化，將時間序列信息以圖像的形式呈現；\n• 數據預處理：將時間轉換為變量，更改價格數據，尋找週期和季節性，根據週期調整交易量數據；\n• 數據工程：處理缺失值並提取所需變量，數據標準化，處理分類變量；\n• 模型選擇和訓練：拆分訓練集和測試集，確定模型方向和評估指標，嘗試訓練各種模型；\n• 模型評估：根據指標選定最優模型，使用 RandomizedSearchCV 尋找最佳參數的大致範圍，再使用 GridSearchCV 尋找更精確的參數；\n• 模型預測：整合輸入數據，預測未來一個工作日的價格變動；\n• 分析預測結果：結合當日的實際情況，理解市場變動，發揮模型價值。"
] | [
null,
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null,
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null,
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https://www.blogdoacelio.com.br/problem-solving-square-feet | [
"# Problem Solving Square Feet\n\n## Solving Square Problem Feet",
null,
"Problem Solving Process. Solve the equation. Read and understand http://nasihaacademy.org/summary-of-when-in-disgrace-written-by-william-shakespeare the problem 70 Lesson 8 Solve Problems with Rational Numbers ©Curriculum Associates, LLC Copying is not permitted. Solution to Problem 2:. 3 And they tell us that the area is 24 square feet. Solution: Example A concrete company makes blocks for parking. To solve quadratic equations using completing the square method, the given quadratic equation must be in the form of ax 2 + bx + c = 0. The length is 14 feet Figure out what to add to the left side to make it a perfect square, and add that to both sides. Draw the figure and label it with the given information. The experiment is …. The formula for the perimeter of a rectangle relates all the information. _____ Area of the floor: 13 × 10 = 130 square feet Area of the rug: 8 × 6 = 48 square feet Subtract to find the area of the floor still showing: 130 − 48 = 82 square feet 2 Information recall - access the knowledge you've gained regarding the number of square inches in a square foot Problem solving - use acquired knowledge to solve conversion practice problems. Solve the equation. Repeat this procedure until the intended overall surface has been coated. Step 3. Thesis Of Food Safety And Sanitation\n\n### Restaurant Resume Server Examples\n\nFomula:A=L×W=Answer to Solving inches two Given:144 and 12 use for Divided the Answer of L×W. Solution: Fugitive Slave Act 1850 Essay Topics 3 What is the perimeter of the backyard? We can actually solve this quite easily using algebra but here I Author: patrickJMT Views: 580K Proportion Word Problems - Basic Mathematics https://www.basic-mathematics.com/proportion-word-problems.html Cross product is usually used to solve proportion word problems. Kozlow use? Problem Solving • Find the Area 82 square feet 1. Using a teacher created worksheet take students to different areas of the school and have them solve problems for square footage. Expert: Charlie Kasov. That's another way of saying that the width times the length is going to be 24. When deciding on methods or procedures to use to solve problems, the first thing you will do is look for clues, which is one of the most important skills in solving problems in mathematics. 2. Name. Nature of the roots of a quadratic equations. The circumference is the product of 2, p, and r, while the area is the product of r, p, and r.\n\n### Apa Format Psychology References Website\n\nResume Example Language Jul 08, 2020 · How to Calculate Square feet'Solving a Length and Width to find exact answer. Lesson 7 Problem-Solving Practice Surface Area of Pyramids 1. Solve the equation. Problem 3. Remainder when 2 power 256 is divided by 17. Since 4 × 6 = 24, x = 6 6 liters should be mixed with 8 lemons. Picture Sequences For Creative Writing Find its surface area. Solve the equation. How many feet of border did Mrs. Step 3: After the problem has been factored we will complete a step called the “T” chart. Theo threw his shoe up at the disc to dislodge it. Substitute in the given information. Well, areas are two-dimensional structures, so their units are squared.",
null,
"20 \" 16 2_ 3; try a longer width. This is an experiment in cooperation. The diagram shows the room and the rug. May 13, 2008 · 8x8 + 7x4 + 1/2x6x7 = 64+28+21 = 113 sq ft the area they cover. ft. Practice and Problem Solving: A/B Find the surface area of each net. rectangle. _____ Chapter 7 481 10 feet by 12 feet Guess: 6 ! Read the problem. Adam is 6 feet tall."
] | [
null,
"https://media1.shmoop.com/images/calculus/calc_ftoc_realworld_latek_8.png",
null,
"https://i.ytimg.com/vi/OhtFA4bw8W4/hqdefault.jpg",
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http://math.eretrandre.org/tetrationforum/showthread.php?tid=54&pid=667&mode=threaded | [
"• 1 Vote(s) - 3 Average\n• 1\n• 2\n• 3\n• 4\n• 5\n Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e))",
null,
"bo198214",
null,
"Administrator Posts: 1,389 Threads: 90 Joined: Aug 2007 11/04/2007, 10:53 PM Ivars Wrote:Logarithm itself has 2 branches such that log (-1) = +i *pi or - i*pi. The logarithm has a branch for each integer , that is for you get . This is because the logarithm is the inverse of the exponential and . So its not really about chosing the sign of but about chosing the . Quote:h(e^pi/2) = +-i +- 2pik. Again, this is not true. If we have a fixed point of then (or ) is usually not again a fixed point (and hence not a branch of ). For example then but also . PS: the name is sqrt and not sgrt",
null,
"« Next Oldest | Next Newest »\n\n Messages In This Thread Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 09/08/2007, 09:07 AM RE: The Complex Inverse h of x^(1/x) - by Gottfried - 09/10/2007, 01:59 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 09/11/2007, 12:18 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 09/19/2007, 05:17 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by GFR - 09/21/2007, 02:01 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 09/21/2007, 04:57 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by GFR - 09/21/2007, 05:11 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 09/22/2007, 07:36 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 10/20/2007, 01:49 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 10/30/2007, 06:00 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/02/2007, 08:43 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/03/2007, 12:10 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/03/2007, 12:54 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/03/2007, 03:17 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/03/2007, 08:33 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/03/2007, 09:09 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/07/2007, 12:30 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/03/2007, 10:12 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/04/2007, 12:10 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/04/2007, 12:42 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/04/2007, 02:17 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/04/2007, 02:38 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/04/2007, 04:31 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/04/2007, 05:00 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/04/2007, 10:15 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/04/2007, 10:53 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/05/2007, 05:50 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/06/2007, 01:34 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/06/2007, 10:14 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/07/2007, 12:12 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/07/2007, 10:12 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/07/2007, 11:59 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/07/2007, 12:58 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/07/2007, 01:48 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by andydude - 11/14/2007, 05:38 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/07/2007, 10:03 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/07/2007, 10:26 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/08/2007, 02:31 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/08/2007, 02:51 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/09/2007, 08:30 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/09/2007, 08:43 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/09/2007, 11:51 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by bo198214 - 11/12/2007, 08:23 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/13/2007, 11:01 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/14/2007, 05:04 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/14/2007, 02:10 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/14/2007, 02:14 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/14/2007, 02:49 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/14/2007, 04:52 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/14/2007, 08:57 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/14/2007, 10:10 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/15/2007, 06:16 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/15/2007, 09:40 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/15/2007, 04:15 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/15/2007, 10:45 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/16/2007, 09:09 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/16/2007, 12:06 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/16/2007, 02:02 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/16/2007, 03:17 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/16/2007, 05:28 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/17/2007, 11:01 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/20/2007, 10:11 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/22/2007, 01:01 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 11/22/2007, 06:29 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/29/2007, 10:37 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 12/09/2007, 03:18 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by andydude - 12/10/2007, 03:14 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 12/10/2007, 07:27 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 12/16/2007, 12:02 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 12/16/2007, 02:22 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 12/16/2007, 05:53 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by andydude - 04/14/2008, 01:01 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 04/14/2008, 10:35 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by andydude - 04/25/2008, 11:25 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 12/10/2007, 07:37 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by andydude - 12/10/2007, 04:11 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by jaydfox - 11/15/2007, 07:21 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/15/2007, 09:44 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 11/22/2007, 10:36 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 12/30/2007, 01:09 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 12/30/2007, 03:37 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 12/30/2007, 05:33 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by andydude - 01/05/2008, 08:00 PM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 01/06/2008, 02:55 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 02/07/2008, 10:49 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 04/28/2008, 08:11 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Ivars - 05/09/2008, 06:32 AM RE: Imaginary zeros of f(z)= z^(1/z) (real valued solutions f(z)>e^(1/e)) - by Gottfried - 03/03/2011, 03:16 PM RE: Tetration below 1 - by Gottfried - 09/09/2007, 07:04 AM RE: The Complex Lambert-W - by Gottfried - 09/09/2007, 04:54 PM RE: The Complex Lambert-W - by andydude - 09/10/2007, 06:58 AM\n\n Possibly Related Threads... Thread Author Replies Views Last Post Constructing real tetration solutions Daniel 4 1,836 12/24/2019, 12:10 AM Last Post: sheldonison b^b^x with base 0\n\nUsers browsing this thread: 1 Guest(s)",
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"https://math.eretrandre.org/tetrationforum/images/default_avatar.png",
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"https://math.eretrandre.org/tetrationforum/images/buddy_offline.png",
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"https://math.eretrandre.org/tetrationforum/images/smilies/wink.gif",
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"https://math.eretrandre.org/tetrationforum/task.php",
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https://edu.glogster.com/glog/polynomials-for-9th-graders/1wfmxipc7gs?=glogpedia-source | [
"In Glogpedia\n\nby Mikelin1\nLast updated 5 years ago\n\nDiscipline:\nMath\nSubject:\nAlgebra I",
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"",
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"",
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"Polynomials\n\nA polynomial is an expression consisting of variables (or indeterminates) and coefficients, that involves only the operations of addition, subtraction, multiplication, and non-negative integer exponents.\n\nPolynomials can be added,subtracted or divided. The general technique for solving bigger-than-quadratic polynomials is pretty straightforward, but the process. can be time consuming.The first step is to apply the Rational Roots Test to the polynomial to get a list of values that might possibly be solutions to the polynomial equation. You can follow this up with an application of Descartes' Rule of Signs. There are calculators and online calculators that can be used to solve polynomials. Also we can graph our polynomials with calculators and on desmos.com with desmos graphing calculator."
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https://revistatermotehnica.agir.ro/articol.php?id=1736 | [
"# FINITE-DIFFERENCE APPROACH OF THE PHASECHANGE HEAT CONDUCTION IN A SQUARE-SHAPED DOMAIN\n\nAutor/autori: Prof. Bogdan HORBANIUC, Prof. Gheorghe DUMITRASCU\n\nAbstract: The paper approaches the study of solidification within a square domain by means of an implicit finite difference method. To simplify the mathematical treatment of the problem, we use the decomposition technique. The imposed boundary condition is of the third kind (known convection heat transfer coefficient). As the decomposition technique applied “as is” to the two-dimension conduction heat transfer with phase-change led to a distorted shape of the solid/liquid interface, we have developed an original approach, referred to as the \"symmetry condition technique”. The numerical results show a good accuracy compared with other approaches.\n\nKeywords: unsteady conduction heat transfer, phase-change, implicit finite difference technique, decomposition method.",
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"DOWNLOAD PDF"
] | [
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"https://revistatermotehnica.agir.ro/images/icon_pdf.gif",
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https://www.radiomuseum.org/tubes/tube_bc113.html | [
"",
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"",
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"# BC113\n\nID = 38586\nCountry:\nItaly\n Brand: ATES Componenti Elettronici, Milano Tube type: Transistor Universal\nIdentical to BC113\nSimilar Tubes\nNormally replaceable-slightly different:\nBC108 ; DW6668\n\n Base Wires Filament Solid State Description NPN silicon planar transistor for universal purpose. - Text in other languages (may differ) Dimensions (WHD)incl. pins / tip 6 x 6 x 6 mm / 0.24 x 0.24 x 0.24 inch Weight 1 g / 0.04 oz Literature -- Collector info (Sammler) KTT Steidle 1981",
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"",
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"BC113: Sammlung PR Peter Roggenhofer † 25.4.18",
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"BC113: TTT Steidle 1981 Günther Stabe",
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"BC113: SGS-Ates Thomas Günzel\n\n Usage in Models 1= 1961? ; 1= 1964?? ; 1= 1964? ; 1= 1965?? ; 3= 1965? ; 3= 1966?? ; 6= 1966? ; 2= 1966 ; 3= 1967?? ; 1= 1967? ; 3= 1967 ; 11= 1968?? ; 4= 1968? ; 10= 1968 ; 2= 1969?? ; 4= 1969? ; 2= 1969 ; 12= 1970?? ; 2= 1970? ; 4= 1970 ; 3= 1971?? ; 1= 1971? ; 1= 1971 ; 1= 1972?? ; 1= 1972 ; 1= 1973? ; 1= 1985??\n\nQuantity of Models at Radiomuseum.org with this tube (valve, valves, valvola, valvole, válvula, lampe):85\n\nYou reach this tube or valve page from a search after clicking the \"tubes\" tab or by clicking a tube on a radio model page. You will find thousands of tubes or valves with interesting links. You even can look up radio models with a certain tube line up. [rmxtube-en]"
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https://wikieducator.org/Lens/Image_Hunt/Definitions | [
"# Definitions\n\n## Definitions\n\nDefinitions\n\n### Ray",
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"Ray\nIn optics, a ray is an idealized narrow beam of light. Rays are used to model the propagation of light through an optical system, by dividing the real light field up into discrete rays that can be computationally propagated through the system by the techniques of ray tracing.\n\n### lens",
null,
"lens\nAn object, usually made of glass, that focuses or defocuses the light that passes through it.\n\n### focal point",
null,
"focal point\n(optics) A focus; a point at which rays of light or other radiation converge.\n\n### focal length",
null,
"focal length\nThe focal length of an optical system is a measure of how strongly it converges (focuses) or diverges (diffuses) light. A system with a shorter focal length has greater optical power than one with a long focal length.\n\n### principle (optical) axis",
null,
"principle (optical) axis\nIn an optical system, the optical axis is an imaginary line that defines the path along which light propagates through the system. For a system composed of simple lenses and mirrors, the axis passes through the center of curvature of each surface, and coincides with the axis of rotational symmetry. The optical axis is often coincident with the system's mechanical axis, but not always, as in the case of off-axis optical systems.\n\n### focal plane",
null,
"focal plane\nThe front and rear (or back) focal planes are defined as the planes, perpendicular to the optic axis, which pass through the front and rear focal points. An object an infinite distance away from the optical system forms an image at the rear focal plane. For objects a finite distance away, the image is formed at a different location, but rays that leave the object parallel to one another cross at the rear focal plane.\n\n### converging lens",
null,
"converging lens\nIf the lens is biconvex or plano-convex, a collimated or parallel beam of light travelling parallel to the lens axis and passing through the lens will be converged (or focused) to a spot on the axis, at a certain distance behind the lens (known as the focal length). In this case, the lens is called a positive or converging lens.\n\n### diverging lens",
null,
"diverging lens\nIf the lens is biconcave or plano-concave, a collimated beam of light passing through the lens is diverged (spread); the lens is thus called a negative or diverging lens. The beam after passing through the lens appears to be emanating from a particular point on the axis in front of the lens; the distance from this point to the lens is also known as the focal length, although it is negative with respect to the focal length of a converging lens.\n\n### real image",
null,
"real image\nIn optics, a real image is a representation of an actual object (source) formed by rays of light passing through the image. If a screen is placed in the plane of a real image the image will generally become visible. The image seen on a cinema screen is an example of a real image.\n\n### virtual image",
null,
"virtual image\nIn optics, a virtual image is an image in which the outgoing rays from a point on the object never actually intersect at a point. A simple example is a flat mirror where the image of oneself is perceived at twice the distance from yourself to the mirror. That is, if you are half a meter in front of the mirror, your image will appear at a distance of half a meter inside or behind the mirror.\n\n### upright image",
null,
"upright image\nContent=My content goes here\n\n### inverted image",
null,
"inverted image\nContent=My content goes here"
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https://socratic.org/questions/how-do-you-solve-the-inequality-x-5-abs-x-5-and-write-your-answer-in-interval-no#380340 | [
"How do you solve the inequality x+5<abs(x+5) and write your answer in interval notation?\n\nFeb 20, 2017\n\n$\\left(- \\infty , - 5\\right)$\n\nExplanation:\n\nFor $x > = - 5$, we have $\\left\\mid x + 5 \\right\\mid = x + 5$ so there are no solutions greater than or equal to $- 5$.\n\nFor $x < - 5$, we have $x + 5 < 0 \\le \\left\\mid x + 5 \\right\\mid$\n\nSo the solution set is $\\left(- \\infty , - 5\\right)$\n\nAnother explanation\n\nIf $a$ is positive, then $a = \\left\\mid a \\right\\mid$\n\nIs $a$ is negative, then $a < \\left\\mid a \\right\\mid$\n\n$x + 5$ is negative for $x < - 5$"
] | [
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http://encyclopedia.kids.net.au/page/ch/Chain_rule | [
"",
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"## Encyclopedia > Chain rule\n\nArticle Content\n\n# Chain rule\n\nIn calculus, the Chain Rule states; if a variable, y, depends on a second variable, u, which in turn depends on a third variable, x; then, the rate of change of y, with respect to x, can be computed as the product of the rate of change of y, with respect to u; times, the rate of change of u, with respect to x. Suppose one is climbing a mountain, at a rate of 0.5 kilometers per hour. The temperature is lower at higher elevations; suppose the rate, by which it decreases, is 6° per kilometer. How fast does the temperature drop? Well, if one multiplies 6° per kilometer, by 0.5 kilometers per hour; one obtains 3° per hour. Such calculations are the \"heart\" of the Chain Rule.\n\nLeibniz would express the Chain Rule as:\n\ndy/dx = (dy/du) · (du/dx)\n\nThe Chain Rule is a formula for the derivative of the composition of two functions. Suppose the real-valued function g(x) is defined on some open subset, of the real numbers, containing the number x; and h[g(x)] is defined on some open subset of the reals containing g(x). If g is differentiable at x and h is differentiable at g(x), then the composition h o g is differentiable at x and the derivative can be computed as\n\nf '(x) = (h o g)'(x) = h '[g(x)] · g '(x)\n\nExample I Consider f(x) = (x2 + 1)3. f(x) is comparable to h[g(x)] where g(x) is (x2 + 1) and h(x) is x3; thus, f '(x) = 3(x2 + 1)2(2x) = 6x(x2 + 1)2.\n\nExample II In order to differentiate the trigonometric function:\n\nf(x) = sin(x2)\none can write f(x) = h(g(x)) with h[f(x)] = sin(x2) and g(x) = x2 and the chain rule then yields\nf '(x) = cos(x2) 2x\nsince h '[g(x)] = cos(x2) and g '(x) = 2x.\n\nThe General Power Rule The General Power Rule (GPR) is derivable, via the Chain Rule.\n\nThe Fundamental Chain Rule The chain rule is a fundamental property of all definitions of derivative and is therefore valid in much more general contexts. For instance, if E, F and G are Banach spaces (which includes Euclidean space) and f : E -> F and g : F -> G are functions, and if x is an element of E such that f is differentiable at x and g is differentiable at f(x), then the derivative of the composition g o f at the point x is given by\n\nDx(g o f) = Df(x)(g) o Dx(f)\nNote that the derivatives here are linear maps and not numbers. If the linear maps are represented as matrices, the composition on the right hand side turns into a matrix multiplication.\n\nA particularly nice formulation of the chain rule can be achieved in the most general setting: let M, N and P be Ck manifolds (or even Banach-manifolds) and let f : M -> N and g : N -> P be differentiable maps. The derivative of f, denoted by df, is then a map from the tangent bundle of M to the tangent bundle of N, and we may write\n\nd(g o f) = dg o df\nIn this way, the formation of derivatives and tangent bundles is seen as a functor on the category of C manifolds with C maps as morphisms.\n\nAll Wikipedia text is available under the terms of the GNU Free Documentation License\n\nSearch Encyclopedia\n Search over one million articles, find something about almost anything!\n\nFeatured Article\n Christiania ... Contents Christiania Christiania can refer to: Christiania - the name of Oslo, from 1624 to 1925. The Free State of Christiania - a partially ...",
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https://www.rdocumentation.org/packages/car/versions/2.1-6/topics/scatterplotMatrix | [
"car (version 2.1-6)\n\n# scatterplotMatrix: Scatterplot Matrices\n\n## Description\n\nEnhanced scatterplot matrices with univariate displays down the diagonal; `spm` is an abbreviation for `scatterplotMatrix`. This function just sets up a call to `pairs` with custom panel functions.\n\n## Usage\n\n```scatterplotMatrix(x, ...)# S3 method for formula\nscatterplotMatrix(formula, data=NULL, subset, labels, ...)# S3 method for default\nscatterplotMatrix(x, var.labels=colnames(x),\ndiagonal=c(\"density\", \"boxplot\", \"histogram\", \"oned\", \"qqplot\", \"none\"),\nplot.points=TRUE, smoother=loessLine, smoother.args=list(), smooth, span,\ntransform=FALSE, family=c(\"bcPower\", \"yjPower\"),\nellipse=FALSE, levels=c(.5, .95), robust=TRUE,\ngroups=NULL, by.groups=FALSE,\nuse=c(\"complete.obs\", \"pairwise.complete.obs\"),\nlabels, id.method=\"mahal\", id.n=0, id.cex=1, id.col=palette(), id.location=\"lr\",\ncol=if (n.groups == 1) palette()[3:1] else rep(palette(), length=n.groups),\npch=1:n.groups, lwd=1, lty=1,\ncex=par(\"cex\"), cex.axis=par(\"cex.axis\"), cex.labels=NULL,\ncex.main=par(\"cex.main\"),\nlegend.plot=length(levels(groups)) > 1, legend.pos=NULL, row1attop=TRUE, ...)spm(x, ...)```\n\n## Arguments\n\nx\n\na data matrix, numeric data frame.\n\nformula\n\na one-sided “model” formula, of the form ` ~ x1 + x2 + ... + xk` or ` ~ x1 + x2 + ... + xk | z` where `z` evaluates to a factor or other variable to divide the data into groups.\n\ndata\n\nfor `scatterplotMatrix.formula`, a data frame within which to evaluate the formula.\n\nsubset\n\nexpression defining a subset of observations.\n\nlabels,id.method,id.n,id.cex,id.col,id.location\n\nArguments for the labelling of points. The default is `id.n=0` for labeling no points. See `showLabels` for details of these arguments. If the plot uses different colors for groups, then the `id.col` argument is ignored and label colors are determined by the `col` argument.\n\nvar.labels\n\nvariable labels (for the diagonal of the plot).\n\ndiagonal\n\ncontents of the diagonal panels of the plot. If plotting by groups, a different univariate display (with the exception of `\"histogram\"`) will be drawn for each group.\n\nrelative bandwidth for density estimate, passed to `density` function.\n\nnclass\n\nnumber of bins for histogram, passed to `hist` function.\n\nplot.points\n\nif `TRUE` the points are plotted in each off-diagonal panel.\n\nsmoother\n\na function to draw a nonparametric-regression smooth; the default is `gamLine`, which uses the `gam` function in the mgcv package. For this and other smoothers, see `ScatterplotSmoothers`. Setting this argument to something other than a function, e.g., `FALSE` suppresses the smoother.\n\nsmoother.args\n\na list of named values to be passed to the smoother function; the specified elements of the list depend upon the smoother (see `ScatterplotSmoothers`).\n\nsmooth, span\n\nthese arguments are included for backwards compatility: if `smooth=TRUE` then `smoother` is set to `loessLine`, and if `span` is specified, it is added to `smoother.args`.\n\nif TRUE, estimate the (square root) of the variance function. For `loessLine` and for `gamLine`, this is done by separately smoothing the squares of the postive and negative residuals from the mean fit, and then adding the square root of the fitted values to the mean fit. For `quantregLine`, fit the .25 and .75 quantiles with a quantile regression additive model. The default is TRUE if `by.groups=FALSE` and FALSE is `by.groups=TRUE`.\n\nreg.line\n\nif not `FALSE` a line is plotted using the function given by this argument; e.g., using `rlm` in package `MASS` plots a robust-regression line.\n\ntransform\n\nif `TRUE`, multivariate normalizing power transformations are computed with `powerTransform`, rounding the estimated powers to `nice' values for plotting; if a vector of powers, one for each variable, these are applied prior to plotting. If there are `groups` and `by.groups` is `TRUE`, then the transformations are estimated conditional on the `groups` factor.\n\nfamily\n\nfamily of transformations to estimate: `\"bcPower\"` for the Box-Cox family or `\"yjPower\"` for the Yeo-Johnson family (see `powerTransform`).\n\nellipse\n\nif `TRUE` data-concentration ellipses are plotted in the off-diagonal panels.\n\nlevels\n\nlevels or levels at which concentration ellipses are plotted; the default is `c(.5, .9)`.\n\nrobust\n\nif `TRUE` use the `cov.trob` function in the `MASS` package to calculate the center and covariance matrix for the data ellipses.\n\ngroups\n\na factor or other variable dividing the data into groups; groups are plotted with different colors and plotting characters.\n\nby.groups\n\nif `TRUE`, regression lines are fit by groups.\n\nuse\n\nif `\"complete.obs\"` (the default), cases with missing data are omitted; if ```\"pairwise.complete.obs\"), all valid cases are used in each panel of the plot.```\n\npch\n\nplotting characters for points; default is the plotting characters in order (see `par`).\n\ncol\n\ncolors for lines and points; the default is taken from the color palette, with `palette()` for linear regression lines, `palette()` for nonparametric regression lines, and `palette()` for points if there are no groups, and successive colors for the groups if there are groups.\n\nlwd\n\nwidth of linear-regression lines (default `1`).\n\nlty\n\ntype of linear-regression lines (default `1`, solid line).\n\ncex, cex.axis, cex.labels, cex.main\n\nset sizes of various graphical elements (see `par`).\n\nlegend.plot\n\nif `TRUE` then a legend for the groups is plotted in the first diagonal cell.\n\nlegend.pos\n\nposition for the legend, specified as one of the keywords accepted by `legend`. If `NULL`, the default, the position will vary by the `diagonal` argument --- e.g., `\"topright\"` for `diagonal=\"density\"`.\n\nrow1attop\n\nIf `TRUE` (the default) the first row is at the top, as in a matrix, as opposed to at the bottom, as in graph (argument suggested by Richard Heiberger).\n\n...\n\narguments to pass down.\n\n## Value\n\n`NULL`. This function is used for its side effect: producing a plot.\n\n## References\n\nFox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.\n\n`pairs`, `scatterplot`, `dataEllipse`, `powerTransform`, `bcPower`, `yjPower`, `cov.trob`, `showLabels`, `ScatterplotSmoothers`.\n\n## Examples\n\nRun this code\n```# NOT RUN {\nscatterplotMatrix(~ income + education + prestige | type, data=Duncan)\nscatterplotMatrix(~ income + education + prestige,\ntransform=TRUE, data=Duncan, smoother=loessLine)\nscatterplotMatrix(~ income + education + prestige | type, smoother=FALSE,\nby.group=TRUE, transform=TRUE, data=Duncan)\n# }\n```\n\nRun the code above in your browser using DataCamp Workspace"
] | [
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http://www.reviewpe.com/strength-of-materials/ | [
"# Strength of Materials\n\n21.1. Example: Strength of Materials\nExample: Strength of Materials\n\n21.2. Exam Problems: Strength of Materials\nExam Problems: Strength of Materials\n\n21.3. Practice Exam: Strength of Materials\nPractice Exam: Strength of Materials\n\n21.4. Practice Problems: Strength of Materials\nPractice Problems: Strength of Materials\n\n21.5. Sample Exam: Strength of Materials\nSample Exam: Strength of Materials\n\n21.6. Sample Problems: Strength of Materials\nSample Problems: Strength of Materials\n\n21.7. Specification: Strength of Materials\nSpecification: Strength of Materials"
] | [
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] | {"ft_lang_label":"__label__en","ft_lang_prob":0.59066683,"math_prob":0.82023466,"size":518,"snap":"2023-40-2023-50","text_gpt3_token_len":115,"char_repetition_ratio":0.2782101,"word_repetition_ratio":0.15384616,"special_character_ratio":0.22393823,"punctuation_ratio":0.26213592,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9546606,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-12-02T12:55:31Z\",\"WARC-Record-ID\":\"<urn:uuid:63d72720-3b72-478f-a197-74148d176c26>\",\"Content-Length\":\"55613\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:8549d230-abed-44f1-907d-c32699a533cb>\",\"WARC-Concurrent-To\":\"<urn:uuid:13f991fe-a516-48e9-8c07-c897afc60c8c>\",\"WARC-IP-Address\":\"107.180.57.111\",\"WARC-Target-URI\":\"http://www.reviewpe.com/strength-of-materials/\",\"WARC-Payload-Digest\":\"sha1:3EN5UHPDY2KOGAPZRZ7YP44VUM3LPBCA\",\"WARC-Block-Digest\":\"sha1:6ABTOUNVE5J2WCSO5ECA5KFBWCPH6RWC\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100399.81_warc_CC-MAIN-20231202105028-20231202135028-00240.warc.gz\"}"} |
https://rdrr.io/cran/IAPWS95/man/JTcTD.html | [
"# JTcTD: Joule-Thomson Coefficient, Function of Temperature and... In IAPWS95: Thermophysical Properties of Water and Steam\n\n JTcTD R Documentation\n\n## Joule-Thomson Coefficient, Function of Temperature and Density\n\n### Description\n\nThe function `JTcTD(Temp,D,digits=9)` returns the Joule-Thomson coefficient for given Temp [K] and D [kg/m3].\n\n### Usage\n\n```JTcTD(Temp, D, digits = 9)\n```\n\n### Arguments\n\n `Temp` Temperature [ K ] `D` Density [ kg m-3 ] `digits` Digits of results (optional)\n\n### Details\n\nThis function calls a Fortran DLL that solves the Helmholtz Energy Equation. in accordance with the Revised Release on the IAPWS Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use (June 2014) developed by the International Association for the Properties of Water and Steam, http://www.iapws.org/relguide/IAPWS-95.html. It is valid from the triple point to the pressure of 1000 MPa and temperature of 1273. The temperature change produced during a Joule-Thomson expansion is quantified by the Joule-Thomson coefficient, which may be positive (cooling) or negative (heating).\n\n### Value\n\nThe Joule-Thomson coefficient and an Error Message (if an error occur: errorCodes)\n\n### Examples\n\n```Temp <- 500.\nD <- 838.025\nJT <- JTcTD(Temp,D)\nJT\n\n```\n\nIAPWS95 documentation built on June 24, 2022, 9:05 a.m."
] | [
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https://www.eastcoastcountrydancers.nl/5649/sodium/hydroxide/converted/ | [
"# sodium hydroxide converted\n\n## Sodium mEq/L mmol/L Unit Conversion -- EndMemo\n\nSodium Unit Conversion between mEq/L and mmol/L. Note: Fill in one box to get results in the other box by clicking \"Calculate\" button. Data should be separated in coma (,), space ( … The pH of sodium acetate will never be 7, because sodium acetate hydrolyzes at alkaline pH. It is true, the alkalinity of the formed sodium acetate is not harmful, so it had to be poured into ...",
null,
"## US3321268A - Conversion of caustic soda to soda ash ...\n\nUS3321268A US311411A US31141163A US3321268A US 3321268 A US3321268 A US 3321268A US 311411 A US311411 A US 311411A US 31141163 A US31141163 A US 31141163A US 3321268 A US3321268 A US 3321268A Authority US United States Prior art keywords sodium hydroxide moving bed bed soda ash sodium carbonate Prior art date … Calcium Hydroxide; Sodium Hydroxide. Instructions. Our goal is the conversion of insoluble limestone to soluble lime. To accomplish this, we need to heat the bejeesus out of crushed limestone. It might be possible to get a campfire hot enough to accomplish the conversion. But to secure the efficient conversion to lime, it is better to use a kiln.",
null,
"## What is the equation for the conversion of zinc nitrate to ...\n\nJun 16, 2017· When sodium hydroxide solution is added to a solution of zinc nitrate, a white precipitate of zinc hydroxide will be formed by double displacement. Zn(NO3)2 + 2NaOH = Zn(OH)2 + 2NaNO3 Sodium hydroxide should be added in limited quantity. In excess... Density of sodium hydroxide NaOH (M=40,01g/mol) Density ρ at 20°C (g/cm³) mass percent NaOH.",
null,
"## Sodium hydroxide - Essential Chemical Industry\n\nstorage, prior to being converted to chlorine and sodium hydroxide. By kind permission of AkzoNobel. Saturated brine, prior to the electrolysis, is purified to precipitate calcium, magnesium and other detrimental cations by addition of sodium carbonate, sodium hydroxide and … In a new study, they show that through a fairly simple process the waste material can be converted into useful chemicals — including ones that can make the desalination process itself more efficient. The approach can be used to produce sodium hydroxide, among other products.",
null,
"## Baume Hydrometer Correction table for Sodium Hydroxide ...\n\nRecently Viewed. The Journal of Organic Chemistry. Pd-Catalyzed Asymmetric Allylic Etherification Using Chiral Biphenol-Based Diphosphinite Ligands and Its Application for … About Sodium hydroxide; 2 130 kilograms [kg] of Sodium hydroxide fit into 1 cubic meter; 132.97156 pounds [lbs] of Sodium hydroxide fit into 1 cubic foot; Sodium hydroxide weighs 2.13 gram per cubic centimeter or 2 130 kilogram per cubic meter, i.e. density of sodium hydroxide is equal to 2 130 kg/m³; at 25°C (77°F or 298.15K) at standard atmospheric pressure.",
null,
"## Caustic Soda Handbook - Cheresources.com\n\nCaustic soda (sodium hydroxide or NaOH) is most commonly manu-factured by the electrolysis of a sodium chloride (NaCl) solution. OxyChem manufactures caustic soda using either membrane or diaphragm electrolytic cells. OxyChem does not use mercury based electrolytic cells to produce caustic soda. The co-products formed from the electrolytic produc- Beryllium hydroxide, Be(OH) 2, is an amphoteric hydroxide, dissolving in both acids and alkalis.Industrially it is produced as a by-product in the extraction of beryllium metal from the ores, \"beryl\" and \"bertrandite\" When alkali is added to beryllium …",
null,
"## Methods for Alkalinity Calculator - USGS\n\nTo convert moles per liter to milligrams per liter (mg/L), multiply the bicarbonate result by 61,017.1, the carbonate result by 60,009.2, and the hydroxide result by 17,007.3. The concentrations of hydroxide, carbonate, and bicarbonate are constrained to be non-negative. The density for HFC-236fa was obtained from this source, converted from 35.29 lbs/ft3 ... Sodium Hydroxide (caustic soda) Sorbaldehyde Stearic Acid Sulphuric Acid 95%onc. Sugar solution 68 brix Sunflower oil Styrene Terpinene Tetrahydrofuran Toluene Triethylamine Trifluoroacetic Acid",
null,
"## Sodium hydroxide volume to weight conversion\n\nAbout Sodium hydroxide; 1 cubic meter of Sodium hydroxide weighs 2 130 kilograms [kg] 1 cubic foot of Sodium hydroxide weighs 132.97156 pounds [lbs] Sodium hydroxide weighs 2.13 gram per cubic centimeter or 2 130 kilogram per cubic meter, i.e. density of sodium hydroxide is equal to 2 130 kg/m³; at 25°C (77°F or 298.15K) at standard atmospheric pressure. How can 100ml of sodium hydroxide solution with a ph of 13.00 be converted to a sodium hydroxide solution with a ph of 12.00? - 9806850",
null,
"## 2- (mg/L) = 0.6 *Carbonate Alkalinity as CaCO (mg/L) 3\n\nIn terms of consumption of protons, 2 moles of hydroxide ions (OH-) are equivalent to one mol (100 g) of CaCO 3. Since the molecular weight of OH-is 17 g/mol, the conversion is as follows: Hydroxide Alkalinity as OH-(mg/L) = 2*17/100 *Hydroxide Alkalinity as CaCO 3 (mg/L) or: Hydroxide Alkalinity as OH-(mg/L) = 0.34 *Hydroxide Alkalinity as CaCO 1 mole is equal to 1 moles Sodium Hydroxide, or 39.99711 grams. Note that rounding errors may occur, so always check the results. Use this page to learn how to convert between moles Sodium Hydroxide and gram. Type in your own numbers in the form to convert the units! Convert another chemical substance. Convert moles to grams.",
null,
"## How to convert Sodium Carbonate to Sodium Bicarbonate at ...\n\nA process commonly used to teach \\$ce{CO2}\\$ scrubbing to chemical engineering students involves not sodium bicarbonate, but sodium hydroxide (\\$ce{NaOH}\\$). The reaction is exothermic and is … The table below gives the density (kg/L) and the corresponding concentration (% weight) of Sodium Hydroxide in water at different temperatures in degrees centigrade (°C). The table was taken from \"Perry's Chemical Engineers' Handbook\" by Robert H. Perry, Don Green, Sixth Edition.",
null,
"## 5N sodium hydroxide | Sigma-Aldrich\n\nSearch results for 5N sodium hydroxide at Sigma-Aldrich. Compare Products: Select up to 4 products. *Please select more than one item to compare Sodium Hydroxide, 50 Percent (w/w), Solution, Reagent is commonly known as Caustic soda. This inorganic compound is water soluble, as well as in ethanol and methanol. The dissolution of solid Sodium Hydroxide in water creates a highly ex",
null,
"## liquid caustic soda conversion - OnlineConversion Forums\n\nNo announcement yet. I need to convert a price per dry ton of liquid caustic soda to a price per gallon of liquid caustic soda. How would i do this? For example: my customer is quoted a price for Liquid caustic soda of \\$390/dry ton. He wants the price as a price per gallon. I need to convert a price per dry ton of liquid caustic soda to a price ... Density of Common Liquids: Liquid. Density Kg/m^3",
null,
"## Alkalinity & pH Relationships - Veolia\n\nP-Alkalinity The P-alkalinity is a measure of the amount of acid required to drop the pH to approximately 8.3. As can be seen, this would measure the amount of any carbonate or hydroxide alkalinity present. Since the carbonate alkalinity is being converted to bicarbonate alkalinity, this test does not measure bicarbonate alkalinity. OH-Alkalinity Shipping Name: Sodium hydroxide, solid; Sodium hydroxide, solid. Hazard Class: 8, 8. UN Number: UN1823, UN1823. Packing Group: II, II. Section 15 - Regulatory Information US Federal Regulations TSCA Section 8(b): CAS# 1310-73-2 is listed on the TSCA inventory. CAS# 497-19-8 is listed on the TSCA inventory.",
null,
"## How to make 20% of sodium hydroxide from 50%w/w (NaOH) - …\n\n100 g of 50% contains 50 g of NaOH. Therefore 40 g of 50% contains 20 g. Add 80 ml (= 80g) of water and stir well. Note that this is twice the weight. (see later.) You now have 100 g of 20%. Practical notes: 50% NaOH is 100 times as viscous as wat... The pH of sodium acetate will never be 7, because sodium acetate hydrolyzes at alkaline pH. It is true, the alkalinity of the formed sodium acetate is not harmful, so it had to be poured into ...",
null,
"## Convert grams Sodium Hydroxide to moles - Conversion of ...\n\n1 grams Sodium Hydroxide is equal to 0.025001806380511 mole. Note that rounding errors may occur, so always check the results. Use this page to learn how to convert between grams Sodium Hydroxide … How to convert 1 US gallon of sodium hydroxide (caustic soda) to pounds. To convert a quantity of a substance or material expressed as a volume to mass we simply use the formula: mass = density × volume . We want to calculate the mass in pounds from a volume in US gallons. We have the density table at the bottom of this web page that shows us ...",
null,
"## sodium hydroxide turning into sodium carbonate - Inorganic ...\n\nJan 25, 2008· If there wre any exposed (CaOH)2 it would have been converted to CaCO3 by exposure to CO2 in the air. With very fresh lime mortar there might be enough Ca(OH)2 to convert sodium carbonate to the hydroxide but it wouldn't last anyway. Sodium hydroxide solution appears as a colorless liquid. More dense than water. Contact may severely irritate skin, eyes, and mucous membranes. Corrosive to metals and tissue. At room temperature, sodium hydroxide is a white crystalline odorless solid that absorbs moisture from the air. It is a manufactured substance.",
null,
"## Sodium Carbonate - an overview | ScienceDirect Topics\n\nLime, sodium carbonate (soda ash), and/or sodium hydroxide (caustic soda) are added to water to convert soluble calcium and magnesium hardness to insoluble calcium carbonate and magnesium hydroxide in a contact vessel for 60–90 min [10, 12, 14]. Lime is not a true coagulant but reacts with bicarbonate alkalinity to precipitate calcium carbonate. Sodium Hydroxide Sodium hypochlorite Consumption = 7 gal/day % by wt of sodium hydroxide=1% Concentration of sodium hydroxide in the solution = (7 gal/day)/(37,444,000 gal/day) = 0.1869 ppm Sodium hydroxide drift concentration in ppm = 0.00002 gal/gal drift x 0.1869 ppm x 1% =3.738 x 10-08 ppm Convert ppm to mg/m3",
null,
"## How do I prepare 1N sodium hydroxide to 0.1N? | Yahoo Answers\n\nJul 22, 2010· Dilute the 1N solution 10 times. For example if you want to make a 0.1N 100 mililiter solution. Add 10 mililiters of 1N sodium hydroxide solution to 90 mililiters of water. Sodium hydroxide is used to manufacture soaps and a variety of detergents used in homes and commercial applications. Chlorine bleach is produced by combining chlorine and sodium hydroxide. Drain cleaners that contain sodium hydroxide convert fats and grease that can clog pipes into soap, which dissolves in water.",
null,
"## Does anyone know how to convert chitin to chitosan using ...\n\ncan be highly facilitated by steeping in strong sodium hydroxide solution at room temperature before heating. source : Bioresource Technology 99 (2008) 1359–1367 Cite In a simple form: Convert the tons to pounds then divide by the caustic soda weight per pound, which is 12.7 lb. Example: density is 12.70 lb /gallon, 2 tons is 4,000 lb. 4,000 lb x 1 gallon/12.7 ...",
null,
"## Solved: How Can 100 ML Of Sodium Hydroxide Solution With A ...\n\nQuestion: How Can 100 ML Of Sodium Hydroxide Solution With A PH Of 13.00 Be Converted To A Sodium Hydroxide Solution With A PH Of 12.00? This problem has been solved! See the answer. How can 100 mL of sodium hydroxide solution with a pH of 13.00 be converted to a sodium hydroxide solution with a pH of 12.00? pH, hydrogen ion concentration Calculator. pH calculation formula: pH = -log(1/H +) Where: H +: Hydrogen ion concentration in the solution H + concentration of acid is depended on its pKa, for strong acid like HCl, its pKa=1, thus H + concentration of 1 M HCl is also 1 M; for weak acid such as acetic acid, its pKa=0.0000175, thus H + concentration of 1 M acetic acid is: 1 * 0.0000175 …",
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https://astronomy.stackexchange.com/tags/density/hot | [
"# Tag Info\n\n85\n\nSummary There's a 1 in 500 billion chance you're standing under a star outside the Milky Way, a 1 in 3.3 billion chance you're standing under a Milky Way star, and a 1 in 184 thousand chance you're standing under the Sun right now. Big, fat, stinking, Warning! I did my best to keep my math straight, but this is all stuff I just came up with. I make no ...\n\n45\n\nThe sun isn't the same density all the way through. According to MSFC's solar interior page, the core density at the centre of the sun is a whopping 150,000 kg/m$^3$. Surrounding it the radiative zone is around 20,000 - 200 kg/m$^3$ (already less dense than water). Eventually at the edge is the convective zone - the density at the part that we see is much ...\n\n37\n\nThe Pauli Exclusion Principle forbids two indistinguishable fermions occupying the same quantum state. It does not prevent them getting arbitrarily close together so long as they have very different momentum states. The big bang model relies on classical General Relativity. When we go back to scales where quantisation of space might become important (i.e. ...\n\n29\n\nFusion inside of a star affects the sun's density (which does not happen with a planet). It produces an outward pressure that balances against the attraction of gravity, thereby reducing the density as long as the star is burning. Once a star the mass of the sun is no longer able to sustain fusion, what is left is a white dwarf which is in fact much denser ...\n\n22\n\nI feel it's a cheap answer but heavy Jupiters can get much denser than Earth because planets with Jupiter's mass stop adding size as they add more mass. A planet with Jupiter's size and 10-12 times Jupiter's mass would be over twice Earth's density. As far as Earth-like planets, there's a cool property of terrestrial planets, more mass means more tightly ...\n\n21\n\nThe density of matter depends not only on its composition, but also on temperature and pressure. It's not meaningful to say that substance A is denser than substance B without specifying the conditions under which the comparison is being made. For a simple everyday example, at room temperature (and pressure) water is significantly denser than air. But ...\n\n19\n\nLet us define this as the largest observable density of a stable object, in order to exclude black holes which may have a very large (infinite) density at their centers or objects collapsing towards a black hole status. If we restrict the definition in this way, then the answer should be the core of the most massive neutron star that we know about. At ...\n\n15\n\nIn short: no one knows for sure, but currently it looks that the probability is 1. Longer: On our current understanding, the Universe is probably infinite in space. This depends on the recent WMAP satellite results, which have shown a zero curvature of the Universe below measurement precision. The other two options were a positive curvature (thus, we would ...\n\n12\n\nI'd say the most important answer is because the volume of stars is counted differently than for (inner) planets.For the former, most of the gas surrounding the dense core is counted. The latter don't have significant enough amounts of it. This is even more pronounced with larger stars. VY Canis Majoris: \"With an average density of 0.000005 to 0.000010 kg/...\n\n10\n\nStraightforwardly no. For a start there are almost no free protons inside a white dwarf. They are all safely locked away in the nuclei of carbon and oxygen nuclei (which are bosonic). There are a few protons near the surface, but not in sufficient numbers to be degenerate. Let us assume though that you were able to build a hydrogen white dwarf that had ...\n\n10\n\nI assume you're asking about central supermassive black holes (SMBHs, one per galaxy), not stellar-mass black holes. The answer is yes, but what actually happens is the two SMBHs have to merge first, and then the resulting combined SMBH can sometimes be ejected from the combined (merged) galaxy. [Edited to add: Since you've updated the question with a ...\n\n9\n\nThe vast majority of the particles in Saturn's rings are small, on the order of $\\sim10^{-1}$ m or lower. The columnar number density, according to data from Voyager 1 and Earth-based observations, can be approximated as a function of particle radius by a power law for all particle radii $a$ in meters such that $0<a<1$, as can be seen on this log-log ...\n\n8\n\nThe most-widely accepted hypothesis at the moment is that Mercury was struck by a large impactor that removed a significant fraction of its mantle (I believe this theory was originally proposed by Cameron & Benz in 1987, and the qualitative theory hasn't changed very much). For planets that are close to their parent stars (such as Mercury), the collision ...\n\n8\n\nAs the surface of the earth is solid the crust, Correct. underneath the crust the mantle which is liquid, No the mantle is also solid, although more plastic than the crust. There is a liquid core below the mantle the liquid density is smaller than the crust. No the core is made of iron and considerably denser than the mantle rocks above it. ...\n\n8\n\nFrom the wikipedia page on Chthonian planet https://en.wikipedia.org/wiki/Chthonian_planet \"Transit-timing variation measurements indicate for example that Kepler-52b, Kepler-52c and Kepler-57b have maximum-masses between 30 and 100 times the mass of Earth (although the actual masses could be much lower); with radii about 2 Earth radii, they might have ...\n\n8\n\nRed dwarfs, depending on your definition, can range from 2.5 to 150 times more dense than the Sun. What is the cause of this discrepancy? They give no calculations, so I can only guess. The article is from 1946 and we've gotten a lot better at science. It's 1946 and information exchange is limited. No internet, no TV, and long distance calls are expensive....\n\n7\n\nIcy objects, such as most in the Kuiper belt can reach an equilibrium if they are about 400km across, whereas the rocky asteroid Pallas, at 572km clearly has an irregular, non spherical shape. All rocky objects larger than Pallas (and there aren't many) are spherical. Rock tends to be stronger than ice. Rocky objects are able to withstand their own gravity ...\n\n7\n\nHere is a plot I generated in 5 minutes at the site exoplanets.org To construct this I took planets discovered by the transit method and which had a $M \\sin i$ measured using radial velocities. I divided the $M \\sin i$ by the sine of the measured inclination angle (this is required to avoid using masses that have been estimated using an assumed mass-radius ...\n\n7\n\nNo, the sun and all other stars do not have the same composition and density within them. The composition varies with depth. Most stars overall composition reflects the interstellar medium from which they form. Convection, settling and nuclear processes then lead to layers of different elements at differing depths. Fusion occurs at the core of our sun (...\n\n7\n\nProton degeneracy is not important, because its effect is much smaller -- much like nuclear particles in theory also are dictated by gravity, but the electromagnetic and nuclear forces are dominating, since they are much stronger. Proton degeneracy is weaker than electron degeneracy due to the far greater mass of the proton compared to the electron. The ...\n\n6\n\nThe test to see whether degeneracy pressure is going to be significant is to compare $kT$ with the Fermi energy $E_F$ The Fermi energy is the energy level up to which all energy states would be occupied in a completely degenerate fermion gas. It is given by (for non-relativistic conditions) $$E_F = \\frac{h^2}{2m}\\left(\\frac{3}{8\\pi}\\right)^{2/3} n^{2/3},$$...\n\n6\n\nThere is no 1:1 mapping between density and composition/structure. You have to look at detailed planetary models. For example, some hot Jupiters are extremely dense ($\\geq 10$ g/cm$^3$) but they are undoubtedly gas giants. The origins of this diversity are the source of much speculation and theory, but are certainly within the realms of known physics. An ...\n\n6\n\nDoes \"overhead\" mean over the center of your head, or over some part of your head? If we assume the latter, it changes the problem! I don't want to recapitulate all MichaelS's lovely work above, so I'll do a quick back-of-the-envelope calculation borrowing from his numbers. The area of a human head as viewed from above (or below) is, umm, let's see, ...\n\n5\n\nNo it does not have the same composition everywhere. In the core hydrogen is fused into helium, so the fraction of hydrogen (denoted by $X$, between 0 and 1) decreases while the fraction of helium ($Y$) increases as time goes by. There is not much exchange of matter between core and envelope so the envelope will essentially have the same constitution as when ...\n\n5\n\nThis question is more complicated than it seems like it should be! There is no threshold mass or density beyond which an object becomes perfectly spherical; even supermassive stars are slightly oblong. The only exception is black holes, which are perfectly round up until you reach the quantum level. If we want a simple answer, most guesses are somewhere ...\n\n5\n\nThe mean density of the star is really only defined by the formula $\\bar\\rho=M/V=3M/4\\pi R^3$. The radius of a star is a generally a very complicated function of a star's other properties. When we determine the radius in stellar models, it's only because we've solved equations that describe the structure of the whole star, and read off the value at what we ...\n\n5\n\nStarting from the index you mentioned, I clicked through the links for some individual planets, which in turn link to discovery papers or other relevant observations. For planets around Kepler-23, -24, -25, -26, -27, and -28, the relevant papers are Ford et al. (2012) and Steffen et al. (2012), two out of a series of papers. Both papers used transit timing ...\n\n5\n\nMost simply, the density is determined by the number of molecules, the surface area of the planet, the temperature, and the gravity. But it sounds like what you really care about would be called the \"column density\", which depends only on the number of molecules and the surface area, and you are wondering why there is not some direct correlation between the ...\n\n5\n\nWe can understand gravity as following a set of mathematical equations called \"General Relativity\" which were discovered by Einstein (and others) around the start of the 20th century. The same gravitational equations apply to black holes, stars, planets, people, apples etc. These equations are very hard to solve. Fortunately there is a very good ...\n\n5\n\nIt's simply because you can \"get closer\" to it, that's all. No special sauce. You know how gravity is pretty weak far away, and gets stronger close by? The closer you get to the Sun - more specifically to the center of the Sun, because that's how you measure the distance - the greater the pull. However, once you reach the Sun's surface, there's a problem. ...\n\nOnly top voted, non community-wiki answers of a minimum length are eligible"
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https://mysweetindulgence.com/easy-writing-tips/how-long-does-it-take-to-go-100-miles-at-85-miles-an-hour/ | [
"Easy tips\n\n# How long does it take to go 100 miles at 85 miles an hour?\n\n## How long does it take to go 100 miles at 85 miles an hour?\n\nHow Long Does it Take to Drive 100 Miles at 85 MPH? – 1 hour and 10 minutes is how long it takes to drive 100 miles at a speed of 85 mph.\n\nHow long would it take to drive 1000 miles at 85 mph?\n\nHow Long Does it Take to Drive 1000 Miles at 85 MPH? – 11 hours and 45 minutes is how long it takes to drive 1000 miles at a speed of 85 mph.\n\nHow to calculate your average miles per hour?\n\nMiles Per Hour Formula: MPH = Miles/(Hours + (Minutes/60)) Miles Per Hour Definition Our Miles Per Hour Calculator can tell you how many miles you drive in a single stretch. On long road trips, knowing how many miles you are averaging per hour can give you an idea of how long it will take to get to your destination.\n\n### How long does it take to walk a mile?\n\nHere are some figures for different walking paces: 1 Fast : 100 to 119 steps per minute / 11 minutes per mile. 2 Normal : 80 to 99 steps per minute / 15 minutes per mile. 3 Relaxed : 60 to 79 steps per minute / 20 minutes per mile.\n\nHow to calculate speed and distance in minutes?\n\nPlease enter the speed and distance values to calculate the travel time in hours, minutes and seconds. This online calculator tool can be a great help for calculating time basing on such physical concepts as speed and distance. Therefore, in order to calculate the time, both distance and speed parameters must be entered.\n\nHow long does it take to travel 200 miles in 4 hours?\n\nYou traveled 200 miles in 4 hours and 30 minutes. In the “Distance in miles” field, enter the 200. For the hours, enter “4.” For the minutes, enter “30.”\n\nMiles Per Hour Formula: MPH = Miles/(Hours + (Minutes/60)) Miles Per Hour Definition Our Miles Per Hour Calculator can tell you how many miles you drive in a single stretch. On long road trips, knowing how many miles you are averaging per hour can give you an idea of how long it will take to get to your destination.\n\nPlease enter the speed and distance values to calculate the travel time in hours, minutes and seconds. This online calculator tool can be a great help for calculating time basing on such physical concepts as speed and distance. Therefore, in order to calculate the time, both distance and speed parameters must be entered.\n\nYou traveled 200 miles in 4 hours and 30 minutes. In the “Distance in miles” field, enter the 200. For the hours, enter “4.” For the minutes, enter “30.”",
null,
"Ruth Doyle"
] | [
null,
"https://secure.gravatar.com/avatar/b8bb3fa0b87b2b6a8ad0c3a93eb2d50e",
null
] | {"ft_lang_label":"__label__en","ft_lang_prob":0.8776905,"math_prob":0.95395696,"size":2459,"snap":"2022-40-2023-06","text_gpt3_token_len":593,"char_repetition_ratio":0.14704685,"word_repetition_ratio":0.6581741,"special_character_ratio":0.26799512,"punctuation_ratio":0.10019268,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97861695,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-02-09T05:12:29Z\",\"WARC-Record-ID\":\"<urn:uuid:8835b98f-a92d-4c49-8fbb-96d75f03c97a>\",\"Content-Length\":\"128754\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:29cd8d1a-e602-481e-b156-ab98204a1021>\",\"WARC-Concurrent-To\":\"<urn:uuid:b9c5b1ec-bc30-4c56-8538-1b051c1a695b>\",\"WARC-IP-Address\":\"172.67.177.224\",\"WARC-Target-URI\":\"https://mysweetindulgence.com/easy-writing-tips/how-long-does-it-take-to-go-100-miles-at-85-miles-an-hour/\",\"WARC-Payload-Digest\":\"sha1:7CLIHTGGPYYWJP6VN63YUEIAKGQJJM4H\",\"WARC-Block-Digest\":\"sha1:6MZ3OETBCHSN2WG7CLCC235MT6VZJAVV\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-06/CC-MAIN-2023-06_segments_1674764501407.6_warc_CC-MAIN-20230209045525-20230209075525-00857.warc.gz\"}"} |
https://blog.paradisetechsoft.com/regex-in-python/ | [
"What's the first thing you need to know about RegEx? Hold on! 'Cause, that's what we are going to explain via this tutorial & that will surely help to make you understand thoroughly about whole regular expressions' concept.\n\n## What is RegEx?\n\nA regular expression (regex, regexp or re) is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern. Regular expression patterns are assembled into a set of byte codes which are then executed by a matching engine written in C. Regular expressions are widely used in the world of UNIX.\n\nNow let’s understand simple basic regular expression through the following image.\n\nThe caret sign (^) serves two purposes. Here, in this figure, it’s checking for the string that doesn’t contain upper case, lower case, digits, underscore and space in the strings. In short, we can say that it is simply matching for special characters in the given string. If we use caret outside the square brackets, it will simply check for the starting of the string.\n\nAn example of a \"proper\" email-matching regex (like the one in the exercise), see below:\n\n``````import re\nm = re.match( '(?=.*\\d)(?=.*[a-z])(?=.*\\W)',input_user)\nif m:\nprint(\"Email is valid:\")\nelse:\nprint(\"email is not valid:\") ``````\n\n## The most common usages of regular expressions are:\n\n• Search a string (search and match)\n• Finding a string (findall)\n• Break string into a sub strings (split)\n• Replace part of a string (sub)\n\n## 'Re' Module\n\nThe module 're' gives full assistance for Perl-like regular expressions in Python. The re module raises the exception re.error if an error occurs while compiling or using a regular expression.\n\nNow if we talk about the 're' module, the re module gives an interface to the regular expression engine, that permits you to arrange REs into objects and then perform with the matches. Regular expression is simply a sequence of characters that define a search pattern. Pythons’ built-in 're' module provides excellent support for the regular expressions with a modern and complete regex flavor.\n\nNow, let’s understand everything about regular expressions and how they can be implemented in python. The very first step would be to import 're' module which provides all the necessary functionalities to play with. It can be done by the following statement in any of the IDE’s.\n\n``````import re\n``````\n\n## Meta Characters\n\nMeta characters are characters or we can say its a sequence of such characters, that holds a unique meaning specifically in a computing application. These characters have special meaning just like a '*' in wild cards. Some set of characters might be used to represent other characters, like an unprintable character or any logical operation. They are also known as “operators” and are mostly used to give rise to an expression that can represent a required character in a string or a file.\n\nBelow is the list of the meta characters, and how to use such characters in the regular expression or regex like;\n\n``````. ^ \\$ * + ? { } [ ] \\ | ( )\n``````\n\nInitially, the meta characters we are going to explain is [ and ]. It’s used for specifying the class of the character which is a set of characters that you wish to match.\n\nCharacters can be listed individually here, or the range of characters can be indicated by giving two characters and separating them by a '-'. For instance, [abc] will match any of the characters a, b, or c; we can say in another way to express the same set of characters i.e. [a-c]. If you wanted to match only lowercase letters, your RE would be [a-z].\n\nLet’s understand what these characters illuminate:\n\nHere, [abc] will match if the string you are trying to match contains any of the a, b or c.\n\nYou can also specify a range of characters using - inside square brackets.\n\n• [a-e] is the same as [abcde].\n• [1-4] is the same as .\n• [0-9] is the same as [0123---9]\n\nYou can complement (invert) the character set by using caret ^ symbol at the start of a square-bracket.\n\n• [^abc] means any character except a or b or c.\n• [^0-9] means any non-digit character.\n\nThe basic usages of commonly used metacharacters are shown in the following table:\n\nFor example, \\\\$a match if a string contains \\$ followed by a. Here, \\$ is not interpreted by a RegEx engine in a special way.\n\n``````s = re.search('\\w+\\$','789Welcome67 to python')\n\nOutput:\n'python'``````\n\n\\ is used to match a character having special meaning. For example: '.' matches '.', '+'matches '+' etc.\n\nWe need to use '\\' to match . Regex recognizes common escape sequences such as \\n for newline, \\t for tab, \\r for carriage-return, \\nnn for a up to 3-digit octal number, \\xhh for a two-digit hex code, \\uhhhh for a 4-digit Unicode, \\uhhhhhhhh for a 8-digit Unicode.\n\nThe following code example will show you the regex '.' function:\n\n``````s = re.match('........[a-zA-Z0-9]','Welcome to python')\n\nOutput:\n'Welcome t'\n``````\n\n## Other Special Sequences\n\nThere are some of the Special sequences that make commonly used patterns easier to write. Below is a list of such special sequences:\n\n## Understanding special sequences with examples\n\n\\A - Matches if the specified characters are at the start of a string.\n\n``````s = re.search('\\A\\d','789Welcome67 to python')\n\nOutput:\n'7'\n``````\n\n\\b - Matches if the specified characters are at the beginning or end of a word.\n\n``````a = re.findall(r'\\baa\\b', \"bbb aa \\\\bash\\baaa\")\n\nOutput:\n['aa']\n``````\n\n\\B - Opposite of \\b. Matches if the specified characters are not at the beginning or end of a word.\n\n``````a = re.findall('[\\B]+', \"BBB \\\\Bash BaBe BasketBall\")\n\nOutput:\n['BBB', 'B', 'B', 'B', 'B', 'B']\n``````\n\n\\d - Matches any decimal digit. Equivalent to [0-9]\n\n``````a = re.match('\\d','1Welco+me to python11')\n\nOutput:\n'1'\n``````\n\n\\D - Matches any non-decimal digit. Equivalent to [^0-9]\n\n``````a = re.match('\\D','Wel12co+me to python11')\n\nOutput:\n'W'\n``````\n\n\\s - Matches where a string contains any white space character. Equivalent to [ \\t\\n\\r\\f\\v].\n\n``````a = re.match('\\s',' Wel12co+me to python11')\n\nOutput:\n' '\n``````\n\n\\S - Matches where a string contains any non-white space character. Equivalent to [^ \\t\\n\\r\\f\\v].\n\n``````a = re.match('/S','W el12co+me to python11')\n\nOutput:\n'W'\n``````\n\n\\w - Matches any alphanumeric character (digits and alphabets). Equivalent to [a-zA-Z0-9_]. By the way, underscore _ is also considered an alphanumeric character.\n\n``````a = re.match('[\\w]','1Welco+me to python11')\n\nOutput:\n'1'\n``````\n\n\\W - Matches any non-alphanumeric character. Equivalent to [^a-zA-Z0-9_]\n\n``````s = re.match('[\\W]','@@Welcome to python')\n\nOutput:\n'@'\n``````\n\n\\Z - Matches if the specified characters are at the end of a string.\n\n``````s = re.search('\\w\\Z','789Welcome67 to python')\n\nOutput:\n'n'\n``````\n\n## Module- Level Functions\n\n'Re' module provides so many top level functions & among them primarily used functions are: match(), search(), findall(), sub(), split(), compile().\n\nThese functions are responsible for taking arguments, primarily, regular expression pattern as the first argument and the string where regex has to be applied being the second. It returns either None or a match object instance. They store the compiled object in a cache for the purpose of making future calls using the same regular expressions and avoiding the need to parse the pattern again and again.\n\nWe will explain some of these function in the below section.\n\n1. re.match() - The match() function is used to match the beginning of the string. In the following example, the match() function will match the first letter of the given string whether it is a digit, lowercase or uppercase letter (underscores included).\n\n``````a = re.match('[0-9_a-zA-Z-]','Welcome to programming')\n\nOutput:\n'W'\n``````\n\nIf we add ‘+' outside the character set, it will check for the repeatability of the given characters in 'RE'. In the following example, '+' checks about one or more repetitions of uppercase, lowercase, and digits (underscore included, white spaces excluded).\n\n``````a = re.match('[_0-9A-Za-z-]+','Welcome to programming')\n\nOutput:\n'Welcome'\n``````\n\n'*' is a quantifier which is responsible for matching the regex preceding it 0 or more times. In short, we can say it matches any character zero or more times. Let's understand via the below given example. In the given string ('Welcome to programming'), '*' will match for characters given in the regex as long as possible.\n\n``````a = re.match('[_A-Z0-9a-z-]*','Welcome to programming')\n\nOutput:\n'Welcome'\n``````\n\nIf we add '*' inside the character set, the regex will check for the presence of '*' in the beginning of the string. Since, in the following example '*' is not present at the beginning of the string, so it will result in 'W'.\n\n``````a = re.match('[_A-Z0-9a-z-*]','Welcome to programming')\n\nOutput:\n'W'\n``````\n\nUsing quantifier '?' matches zero or one of whatever precedes it. In the following example '?' matches uppercase or lowercase characters including underscore as well in the beginning of the string.\n\n``````a = re.match('[_A-Za-z-]?','Welcome to programming')\n\nOutput:\n'W'``````\n\nThere's 're' module function that offer you the set of functions that mainly allows you to search a string for a match. Let’s understand what these functions perform for.\n\n2. re. search()- It is mainly used to search the pattern in a text. The function re. search() takes a regex pattern and a string and searches for that particular pattern within the string. In that case, if the search is successful, search() returns a match object or None otherwise. The syntax of re. search is as follows:\n\n``a = re.search(pattern, string)``\n\nYou can better understand with the following example.\n\n``````a = re.search('come', 'welcome to programming')\n\nOutput:\n<_sre.SRE_Match object; span=(3, 7), match='come'>\n``````\n\n3. re. findall()- Returns a list containing all matches. The function re. findall() is used when you want to iterate over the lines of file or string, it will return a list of all the matches in a single step. String is scanned left-to-right, and matches are returned in the order that found. The syntax of re. findall() is as follows:\n\n``a = re.findall(pattern, string)``\n\nBelow is an example of re. findall() function.\n\n``````a = re.findall('prog','welcome to programming')\n\nOutput:\n['prog']\n``````\n\n4. re. split () - Returns a list where the string has been split at each match. Split string by the occurrences of pattern. The syntax of re. split is given below:\n\n``a = re.split(pattern, string)``\n\nLook at the following example re. split() function:\n\n``````a = re.split('[\\W]+','welcome to programming')\n\nOutput:\n['welcome', 'to', 'programming\n``````\n``````a = re.split('\\s','Hello how are you')\n\nOutput:\n['Hello', 'how', 'are', 'you']\n``````\n``````b = re.split('\\d','hello1i am fine')\n\nOutput:\n'hello', 'i am fine']\n``````\n\n5. re. sub() - It replaces one or many matches with a string. It is used to replace sub strings and it will replace the matches in string with replacing value. The synatx of re. sub() is as follows:\n\n``a = re.sub(pattern, replacing value, string)``\n\nThe following example replaces all the digits in the given string by empty string.\n\n``````m = re.sub('[0-9]','','Welcome to python1234. Coding3456.')\n\nOutput:\n'Welcome to python. Coding.'\n``````\n\n6. re. compile() - We can compile pattern into the pattern objects all with the help of function re.compile(), and which contains various methods for operations such as searching for pattern matches or performing string substitutions.\n\nIn the following example, the compile function compiles the regex function mentioned and then the code asks user to enter a name. If user types/inputs any digit or other special characters, the compile results won't match and it will again ask user for input. It will continue doing this unless and until user inputs a name containing characters only.\n\n`````` name_check = re.compile(r\"[^A-Za-zs.]\")\n\nwhile name_check.search(name):\n``````\n\nThe output of the following code is as follows:\n\n``````Please, enter your name: 1234"
] | [
null
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http://dspace.nbuv.gov.ua/handle/123456789/3054/browse?type=dateissued | [
"Наукова електронна бібліотека\nперіодичних видань НАН України\n\n# Перегляд Theory of Stochastic Processes, 2007, № 4 за датою випуску\n\nСортувати за: Порядок: Результатів:\n\n• (2007)\nAn exponential estimate of ruin probability for an insurance company which invests all its capital in risk assets is found. The process which describes the risky assets is assumed to follow a geometrical Brownian motion. ...\n• (2007)\nThe asymptotic behavior of the general type third order autonomous oscillating system under the action of small non-linear random perturbations of ”white” and ”Poisson” types is investigated.\n• (2007)\nThe estimation for distribution of the norms of strictly sub-Gaussian random processes in the space L2(T) is obtained. The approximation of some classes of strictly sub-Gaussian random processes with given accuracy and ...\n• (2007)\nThe paper is devoted to modeling optimal exercise strategies of the behavior of investors and issuers working with convertible bonds. This implies solution of the problems of stock price modeling, payoff computation and ...\n• (2007)\nA review of different systems of financial analysts training which exist in European countries and the United States of America is proposed. MBA diploma and professional qualification such as Chartered Financial Analyst ...\n• (2007)\nNecessary and sufficient conditions for weak convergence of first-rareevent times for semi-Markov processes with finite set of states in series of schemes are obtained.\n• (2007)\nIn two papers: Dhaene et al. (2002). Insurance: Mathematics and Economics 31, pp.3-33 and pp. 133-161, the approximation for sums of random variables (rv’s) was derived for the case where the distribution of the components ...\n• (2007)\nAsymptotic expansions for the distribution of the surplus prior to and at the time of a ruin are given for nonlinearly perturbed risk processes.\n• (2007)\nWe consider a regression of y on x given by a pair of mean and variance functions with a parameter vector θ to be estimated that also appears in the distribution of the regressor variable x. The estimation of θ is based ...\n• (2007)\nAmerican options give us the possibility to exercise them at any moment of time up to maturity. An optimal stopping domain for American type options is a domain that, if the underlying price process enters we should exercise ...\n• (2007)\nThe paper is devoted to the problem of establishing the conditions on the stochastic process to belong it to the functional space Lq(R) with probability one. The corresponding results were obtained for the strictly Orlicz, ...\n• (2007)\nThe problem considered is the problem of optimal linear estimation of the functional Aξ = ∑↑∞↓j=0 ∫↓G a(g, j)ξ(g, j)dg which depends on the unknown values of a homogeneous random field ξ(g, j) on the group G × Z from ...\n• (2007)\nA theorem is proved that allows to use approximations for construction of the Karhunen-Loeve model of stochastic process with known correlation function.\n• (2007)\nWe describe the Analytical Finance Package, a set of Java applets which is developing at the Malardalen University.\n• (2007)\nRandom processes from the class V (φ, ψ) which is more general than the class of ψ-sub-Gaussian random process. The upper estimate of the probability that a random process from the class V (φ, ψ) exceeds some function is ...\n• (2007)\nThe strong invariance principle for renewal process and randomly stopped sums when summands belong to the domain of attraction of an α-stable law is presented\n• (2007)\nWe consider the behavior of integral functional of the solution of stochastic differential equation with coefficients contained small parameter. The dependence on the order of small parameter in every term of equation with ...\n• (2007)\nThe spectral representations for wide sense stationary multivariate random functions and for their covariance functions on two classes of additive vector groups are obtained under some assumptions about continuity of such ...\n• (2007)\nA general price process represented by a two-component Markov process is considered. Its first component is interpreted as a price process and the second one as an index process controlling the price component. American ...\n\nРозширений пошук"
] | [
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https://1st-in-babies.com/how-much-is-89-kg-new-update/ | [
"How Much Is 89 Kg? New Update\n\n# How Much Is 89 Kg? New Update\n\nLet’s discuss the question: how much is 89 kg. We summarize all relevant answers in section Q&A of website 1st-in-babies.com in category: Blog MMO. See more related questions in the comments below.\n\n## What is 90kg in stones and pounds?\n\nQuick reference weight charts\nKilograms Stone Stone and pounds\n90 kg 14.17 stone 14 stone, 2.4 lb\n92 kg 14.49 stone 14 stone, 6.8 lb\n94 kg 14.8 stone 14 stone, 11.2 lb\n96 kg 15.12 stone 15 stone, 1.6 lb\n\n89 kg to pounds\n89 kg to pounds\n\n## What does 80kg mean in weight?\n\n80 kg ≈ 176.37 lbs.\n\n## How much is 1 kg in English?\n\n1 kilogram is equal to 2.20462262 pounds, which is the conversion factor from kilograms to pounds. Go ahead and convert your own value of kg to lbs in the converter below. For other conversions in mass, use the mass conversion tool.\n\n## Is 90kg overweight?\n\nUsing a height of 1.73 m (5 ft 8 in) as a guide, as it squares to three and also lies conveniently between average adult male and female heights, overweight is defined as 75 kg and obese as 90 kg.\n\n## Is 90kg heavy?\n\nYes you are overweight. Your ideal weight should be between 65-70 kgs. Your weight is controlled by 4 major factors (1) refined sugar consumption (2) exercise and hydration (3) posture and sitting (4) how many times do you indulge yourself – alcohol, binge eating at parties, overeating etc.\n\n## What is the weight of a person whose mass is 75 kg?\n\nAnswer: The weight of a 75-kg person in lbs is 165.375 pounds. Pound (lbs) is an international term used to define the weight or mass of an object.\n\n## What is considered a healthy weight for my height?\n\nHeight and Weight Chart\nHeight Weight\nNormal Overweight\n4′ 10″ 91 to 118 lbs. 119 to 142 lbs.\n4′ 11″ 94 to 123 lbs. 124 to 147 lbs.\n5′ 97 to 127 lbs. 128 to 152 lbs.\n\n## What weighs the same as 34 kg?\n\nMammals ordered by their weight\nTufted deer 34\nFinless porpoise 33\nPampas deer 33\n\n## How do I convert my baby’s weight from kilograms to pounds?\n\nKg to Pounds conversion\n\n1 kilogram (kg) is equal to 2.20462262185 pounds (lbs).\n\n### Women try guessing each other’s weight | A social experiment\n\nWomen try guessing each other’s weight | A social experiment\nWomen try guessing each other’s weight | A social experiment\n\n## Is 1kg the same as 1 pound?\n\nA kilogram (kg) is stated to be 2.2 times heavier than a pound (represented as lbs). Thus, one kilo of mass is equal to 2.26lbs.\n\n## How do I read my weight in kg?\n\nThe numbers 0, 1, 2, 3, and 4 on this scale refer to the whole kilograms. In between each two numbers there are four little lines. They divide each kilogram into five parts. This means that each little line marks a 200-gram increment.\n\n## Is 100kg too heavy?\n\nAdults with a BMI greater than or equal to 40 are considered extremely obese. Anyone more than 100 pounds (45 kilograms) overweight is considered morbidly obese.\n\n## Is 90 kg overweight for 6ft?\n\nNot at all, 90kg at 6ft is not really overweight.\n\n## Is 95kg heavy?\n\nYup, you’re overweight.\n\n## What kg is considered obese?\n\nWeight, obesity, and health risks\nClassification BMI (kg/m2) Waist\nMen 40 inches or less Women 35 inches or less\nUnderweight 18.4 or less\nHealthful weight 18.5–24.9\nOverweight 25.0–29.9 Increased risk\n\n## How much protein do I need if I weigh 90kg?\n\nIf you weigh 90kg with 20 per cent body fat, you have 72kg of lean body mass. Multiply that number by 2.2, and you get a daily protein target of 158g per day. If you weigh 90 kilograms with 10 per cent body fat, you have 81 kilograms of lean body mass. Multiply that by 2.2, and you get 178 grams of protein per day.\n\n## What is the ideal weight for 158 cm male?\n\nHealthy weight range chart\nMetric Measurement\n154 cm 47-59 kg\n156 cm 49-61 kg\n158 cm 50-62 kg\n160 cm 51-64 kg\n\n## What is 75kg in earth?\n\nIt is given that the mass of a man is 75 Kg in earth.\n\nThe weight of the man in earth can be calculated by W=mg=75×9.8=735 N.\n\n### 2021 European Weightlifting M 89 kg A\n\n2021 European Weightlifting M 89 kg A\n2021 European Weightlifting M 89 kg A\n\n## Is 75 kg a normal weight?\n\nPercentage of women classified as underweight (<55 kg), normal weight (55–75 kg), overweight (>75–90 kg), and obese (>90 kg) by time period, 1988–2006.\n\n## Is 75 kg a good weight?\n\nUnderweight: Less than 65kg. Healthy Weight: 65kg to 75kg. Overweight: 75kg to 95kg. Obese: 95kg to 125kg.\n\nRelated searches\n\n• 89 kg to lbs and stone\n• 89 kg to pounds\n• 90 kg to lbs\n• how much is 89 kg in stones and pounds\n• how much is 89 kg in weight\n• how heavy is 89 kg\n• 85 kg to lbs\n• how much is 89 kg in stone\n• what is 89 kg in stones\n• 88 kg to lbs\n• 80 kg to lbs\n• how much is 89 kg in lbs\n• how much is 89 pounds\n• 86 kg to lbs\n• how much is 89 g in kg\n• how much is 89 kg in stone and lbs\n• how much is 89 kg in pounds\n• how much is 89 grams\n\n## Information related to the topic how much is 89 kg\n\nHere are the search results of the thread how much is 89 kg from Bing. You can read more if you want.\n\nYou have just come across an article on the topic how much is 89 kg. If you found this article useful, please share it. Thank you very much."
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