Datasets:

Infi-MM
/

InfiMM-WebMath-40B

Dataset card Data Studio Files Files and versions Community

Split (1)

train · ~22.8M rows (showing the first 514k)

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.

URL stringlengths 15 1.68k	text_list sequencelengths 1 199	image_list sequencelengths 1 199	metadata stringlengths 1.19k 3.08k
https://www.coursehero.com/file/p31gmrjj/Gas-Pressure-Pressure-force-area-psi-pressure-per-square-inch-Atmospheric/	[ "# Gas pressure pressure force area psi pressure per\n\n• Notes\n• 41\n\nThis preview shows page 6 - 22 out of 41 pages.\n\nChem 1035 Chapter 5 7 of 41Mercury Barometer: Device used to measure atmospheric pressure\nChem 1035 Chapter 5 8 of 41Gas Laws:Relationships Between Gas Pressure, Volume,Temperature, and MolesPressure vs. Volume:Boyle's Law: At constant temperature, the volume of a fixed mass of gas isInversely proportional to the pressure.P VV 1/P\nChem 1035 Chapter 5 9 of 41V 1 / p OR. PV = constant\nChem 1035 Chapter 5 10 of 41Sample data:Volume (mL)Pressure (torr)11(torr)PressureP × V (torr∙mL)20.07800.001281.56×10415.010380.0009631.56×10410.015600.0006411.56×1045.031120.0003211.56×104\nChem 1035 Chapter 5 11 of 41Boyle’s Law:\nChem 1035 Chapter 5 12 of 41Volume vs. Temperature:Charles' Law:At constant pressure, the volume of a fixed mass of gas is Directly proportional to the Kelvin temperature.Volume (L) HeV TV/T = constantCH4K = C* + 273H2O H2Absolute zero:Lowest obtainable temp(no motion, zero volume)V1/T1= V2/T2\nChem 1035 Chapter 5 13 of 41T (K)\nChem 1035 Chapter 5 14 of 41Sample Data:V/T = constant\nChem 1035 Chapter 5 15 of 41\nChem 1035 Chapter 5 16 of 41Pressure vs. Temperature:Amonton's Law: At constant volume, the pressure of a fixed amount of gas isGay- lusac’sDirectly proportional to the Kelvin temperature.P/T = constantP1/ T1= P2/T2P ^ , T ^Avogadro's Law: Equal volumes of different gases at the same temperature andpressure contain the same number of particlesV m (moles)\nChem 1035 Chapter 5 17 of 41\nChem 1035 Chapter 5 18 of 41Ideal Gas EquationCombine these laws:Boyle's Law: PV = kconstant tempp1v1= p2v2Charles' Law: V T or V = kTconstant pressurev/t = v/tAmonton's Law: P T or P = kTconstant volumep/t =p/tAvogadro's Law: V nV = knCombined gas law : P1V1/ T1= P2V2/ T2PV nT\nChem 1035 Chapter 5 19 of 41\nChem 1035 Chapter 5 20 of 41R = gas constant (0.0821 L x atm / molx K)\nChem 1035 Chapter 5 21 of 41Calculations with the Ideal Gas Law:Assume constant temp\n•", null, "•", null, "•", null, "" ]	[ null, "https://www.coursehero.com/assets/img/doc-landing/start-quote.svg", null, "https://www.coursehero.com/assets/img/doc-landing/start-quote.svg", null, "https://www.coursehero.com/assets/img/doc-landing/start-quote.svg", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.60493255,"math_prob":0.95951813,"size":1880,"snap":"2021-31-2021-39","text_gpt3_token_len":640,"char_repetition_ratio":0.22974414,"word_repetition_ratio":0.060402684,"special_character_ratio":0.35159576,"punctuation_ratio":0.125,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98226005,"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\":\"2021-09-21T13:36:23Z\",\"WARC-Record-ID\":\"<urn:uuid:3dde8ecb-638d-4ef5-8508-3788204510bf>\",\"Content-Length\":\"281040\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:a2089829-2a3d-47d0-a4c6-1ed0386442a5>\",\"WARC-Concurrent-To\":\"<urn:uuid:1a4153fe-cb4d-414f-8089-9e9455a6a848>\",\"WARC-IP-Address\":\"104.17.93.47\",\"WARC-Target-URI\":\"https://www.coursehero.com/file/p31gmrjj/Gas-Pressure-Pressure-force-area-psi-pressure-per-square-inch-Atmospheric/\",\"WARC-Payload-Digest\":\"sha1:2COPE4AVWIHD5HRDVYK2GQA4XVUI4FXO\",\"WARC-Block-Digest\":\"sha1:RZ6V2RTWJ46UY5LY7FYEJGWWWDRV5TOS\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780057225.38_warc_CC-MAIN-20210921131252-20210921161252-00058.warc.gz\"}"}
https://cs.stackexchange.com/questions/70490/minimizing-transportation-cost-through-a-network-multiple-source-sinks	[ "# Minimizing transportation cost through a network, multiple source/sinks\n\nI have the following problem:\n\nInputs:\n\n• A finite collection $$V$$ of vertices\n• A metric cost function $$c:V^2\\to\\Bbb R$$ (interpreted as the edge cost of a complete graph on $$V$$)\n• An amount of resource $$s:V\\to\\Bbb R$$ on each vertex, satisfying $$\\sum_{u\\in V}s(u)=0$$\n\nThe problem is to determine a transfer function $$f:V^2\\to\\Bbb R^{\\ge0}$$ such that $$\\sum_{v\\in V}(f(v,u)-f(u,v))=s(u)$$ for all $$u$$, which minimizes $$\\sum_{u,v}f(u,v)c(u,v)$$.\n\nThe story to tell is that the points represent places with some amount of resource (if $$s(u)$$ is positive) or with some need of the resource (if $$s(u)$$ is negative), and $$c(u,v)$$ is the cost of sending a unit of resource from $$u$$ to $$v$$. We assume that every vertex can send resource to any other vertex, and the direct path is usually better than passing through an intermediate point. How do we minimize the cost of sending everything from where it is to where it is needed?\n\nWhat is the name of this problem, and do there exist efficient algorithms to solve it? It seems similar to the minimum cost flow problem, but this only has one source and one sink, has maximum capacities for all the edges, and often needs multiple node paths for the resource, while here the direct path is always at least as good as a longer path (because the cost function is a metric).\n\nThis is an instance of the minimum-cost circulation problem. Replace each vertex $v$ by two vertices $v_\\text{in}$,$v_\\text{out}$. Each edge $(u,v)$ is replaced by $(u_\\text{out},v_\\text{in})$, with capacity (upper bound) given by $c(u,v)$ and lower bound 0. Then, add an edge $(v_\\text{in},v_\\text{out})$ with both the lower bound and upper bound set to $s(v)$." ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9220373,"math_prob":0.9998124,"size":1302,"snap":"2020-34-2020-40","text_gpt3_token_len":342,"char_repetition_ratio":0.11479199,"word_repetition_ratio":0.0,"special_character_ratio":0.26190478,"punctuation_ratio":0.09090909,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":1.000002,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-08-13T07:06:07Z\",\"WARC-Record-ID\":\"<urn:uuid:6ffd9c1e-d31d-4c8f-900a-66cd71f63f18>\",\"Content-Length\":\"144142\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:314ef18a-210b-49ed-9bf7-b922a5937de7>\",\"WARC-Concurrent-To\":\"<urn:uuid:5e8e8aa1-d566-4f77-9231-671e7b35d07c>\",\"WARC-IP-Address\":\"151.101.1.69\",\"WARC-Target-URI\":\"https://cs.stackexchange.com/questions/70490/minimizing-transportation-cost-through-a-network-multiple-source-sinks\",\"WARC-Payload-Digest\":\"sha1:BGWEH7EH4MQ3YI6KAYAYPAVBCN7PT5JK\",\"WARC-Block-Digest\":\"sha1:CUKWIIQEOT3DWZILNRNNLICUTZMRCPKI\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-34/CC-MAIN-2020-34_segments_1596439738960.69_warc_CC-MAIN-20200813043927-20200813073927-00253.warc.gz\"}"}
https://www.groundai.com/project/risk-minimization-in-structured-prediction-using-orbit-loss/	[ "Risk Minimization in Structured Prediction using Orbit Loss\n\n# Risk Minimization in Structured Prediction using Orbit Loss\n\nDanny Karmon\nDept. of Computer Science\nBar-Ilan University, Israel\ndanny.karmon@biu.ac.il\nJoseph Keshet\nDept. of Computer Science\nBar-Ilan University, Israel\njoseph.keshet@biu.ac.il\n###### Abstract\n\nWe introduce a new surrogate loss function called orbit loss in the structured prediction framework, which has good theoretical and practical advantages. While the orbit loss is not convex, it has a simple analytical gradient and a simple perceptron-like learning rule. We analyze the new loss theoretically and state a PAC-Bayesian generalization bound. We also prove that the new loss is consistent in the strong sense; namely, the risk achieved by the set of the trained parameters approaches the infimum risk achievable by any linear decoder over the given features. Methods that are aimed at risk minimization, such as the structured ramp loss, the structured probit loss and the direct loss minimization require at least two inference operations per training iteration. In this sense, the orbit loss is more efficient as it requires only one inference operation per training iteration, while yields similar performance. We conclude the paper with an empirical comparison of the proposed loss function to the structured hinge loss, the structured ramp loss, the structured probit loss and the direct loss minimization method on several benchmark datasets and tasks.\n\n## 1 Introduction\n\nThere are three main differences between binary classification problems and structured prediction problems. First, the input to a binary classifier is a feature vector of a fixed length and the output is restricted to two possible labels, whereas in structured prediction both the input and the output are structured objects (a graph, an acoustic speech utterance, a sequence of words, an image). Second, the structured output space is potentially exponentially large (all possible phoneme or word sequences, all possible taxonomy graphs, all possible human poses, etc.). And third, while in binary classification the system’s performance is evaluated using the error rate, i.e., 0-1 loss, in structured prediction each task often has its own evaluation metric or cost, such as word error rate in speech recognition, the BLEU score in machine translation, the NDCG score in information retrieval, or the intersection-over-union score in visual object segmentation. Some of these are involved functions, which are non-decompostable in the output space.\n\nThere is significant literature on learning parameters for structured prediction and graphical models. Ultimately, the goal in learning is to find the model parameters so as to minimize the expected cost, or risk, where the expectation is taken with respect to a random draw of input-output pairs from a fixed but unknown distribution. Since the expectation cannot be evaluated because the underlying probability is unknown, and since the cost is often a non-convex combinatorial function (which is hard to minimize directly), the learning problem is formulated as an optimization problem where the parameters are found by minimizing a trade-off between a measure of the goodness of fit (loss) to the training data and a regularization term. In discriminative training, the loss function should be directly related to the cost between the model prediction and the target label, averaged over the training set.\n\nThe most common approaches to structured prediction, namely structured perceptron, structural support vector machine (SVM) and conditional random fields (CRF), do not directly minimize the risk. The structured perceptron (Collins, 2002) solves a feasibility problem, which is independent of the cost. In structural SVM (Joachims et al., 2005) the measure of goodness is a convex upper bound to the cost called structural hinge loss. It is based on a generalization of the binary SVM hinge loss to the structured case, and there is no guarantee for the risk. While there exists generalization bounds for the structured hinge loss (e.g., McAllester, 2006; Taskar et al., 2003), they all include terms which are not directly related to the cost, such as the Hamming loss, and inherently the structured hinge loss cannot be consistent as it fails to converge to the performance of the optimal linear predictor in the limit of infinite training data (McAllester, 2006). In CRFs the measure of goodness is the log loss function, which is independent of the cost (Lafferty et al., 2001). Smith and Eisner (2006) tried to address this shortcoming of CRFs and proposed to minimize the risk under the Gibbs measure. While it seems that this loss function is consistent, we are not aware of any formal analysis.\n\nRecently, several works have focused on directly minimizing the expected cost. In particular, McAllester et al. (2010) presented a theorem stating that a certain perceptron-like learning rule, involving feature vectors derived from cost-augmented inference, directly corresponds to the gradient of the risk. Direct loss needs two inference operations per training iteration and is extremely sensitive to its hyper-parameter. Do et al. (2008) generalized the notion of the ramp loss from binary classification to structured prediction and proposed a loss function, which is a non-convex bound to the cost, and was found to be a tighter bound than the structured hinge loss function. The structured ramp loss also needs two inference operations per training iteration. Keshet et al. (2011) generalized the notion of the binary probit loss to the structured prediction case. The gradient of this non-convex loss function can be approximated by averaging over samples from the unit-variance isotropic normal distribution, where for each sample an inference with a perturbed weight vector is computed. In order to gain stability in the gradient computation, hundreds to thousands of inference operations are required per training iteration, hence the update rule is computationally-heavy.\n\nThe goal of this work is to propose a new learning update rule for structured prediction which results in fast training on one hand and aims at minimizing the risk on the other hand. We define a new loss function, called orbit, where its gradient has a close simple analytical form, which is very close to the structured perceptron update rule. We state a finite sample generalization bound for this loss function and show that it is consistent in the strong sense. That is, for any feature map (finite or infinite dimensional) the loss function yields predictors approaching the infimum risk achievable by any linear predictor over the given features. The update rule of this new loss involves one inference operation per training iteration, similar to the structured perceptron or the structural SVM, and hence faster (per training iteration) than ramp, probit and direct loss minimization. In a series of experiments we showed that the new loss function performs similar to other approaches that were designed to minimize the risk.\n\nThe paper is organized as follows. In Section 2 we state the problem formally. In Section 3 we introduce the new surrogate loss function and its update rule. In Section 4 we present the analysis for our new methods, including proofs for both consistency and generalization bound. In Section 5 we present a set of experiments and compare the new learning rule to other algorithms. We conclude the paper in Section 6.\n\n## 2 Formal Settings\n\nWe formulate the structured supervised learning problem by setting to be an abstract set of all possible input objects and to be an abstract set of all possible output targets. We assume that the input objects and the target labels are drawn from an unknown joint distribution . We define a set of fixed mappings called feature functions from the set of input objects and target labels to a real vector of length .\n\nHere we consider a linear decoder with parameters , such that the parameters weight the feature functions. We denote the score of label by , given the input . The decoder predicts the label with the highest score:\n\n ^yw(x)=argmaxy∈Y w⋅ϕ(x,y) (1)\n\nIdeally, we would like to find the parameters that optimize the risk for unseen data. Formally, we define the cost function, , to be a non-negative measure of error when predicting instead of as the label of . We assume that for all . Often the desired evaluation metric is a utility function that needs to be maximized (like BLEU or NDCG) and then we define the cost to be 1 minus the evaluation metric.\n\nOur goal is to minimize the risk:\n\n w∗=argminw E(x,y)∼ρ[ℓ(y,^yw(x))]. (2)\n\nSince the distribution is unknown, we use a training set of examples that are drawn i.i.d. from , and replace the expectation in (2) with a mean over the training set and a regularization factor . The cost is often a combinatorial non-convex quantity, which is hard to minimize, hence it is replaced with a surrogate loss, denoted . Different algorithms use different surrogate loss functions. Overall the objective function of (2) transforms into the following objective function\n\n w∗=argminw 1mm∑i=1¯ℓ(w,xi,yi)+λ2∥w∥2, (3)\n\nwhere is a trade-off parameter between the loss term and the regularization factor.\n\n## 3 Orbit Loss\n\nDenote by the difference between the feature functions of the labels , respectively:\n\n Δϕ(y,y′)=ϕ(x,y)−ϕ(x,y′).\n\nDefine by the normalized version of as follows:\n\n δϕ(y,y′)={Δϕ(y,y′)/∥Δϕ(y,y′)∥if y≠y′0if y=y′. (4)\n\nThe orbit surrogate loss function is formally defined as follows:\n\n ¯ℓorbit(w,x,y)=Pε∼N(0,1)[ε>w⋅δϕ(y,^yw)]ℓ(y,^yw). (5)\n\nThat is, the orbit loss is equal to the cost multiplied by the probability that the prediction score plus a small number is greater than the score of the target label .\n\nWe now derive the gradient-based learning rule for this loss function, which helps to describe some of its properties. The loss has a simple analytical gradient:\n\n ∇w[Pε∼N(0,1)[ε>w⋅δϕ(y,^y)]ℓ(y,^y)] =∇w[1√2π∫∞w⋅δϕ(y,^y)e−z2/2dzℓ(y,^y)] (6) =−1√2πe−\|w⋅δϕ(y,^y)\|2/2ℓ(y,^y)δϕ(y,^y). (7)\n\nThe update rule of the orbit loss is the following:\n\n w ← (1−ηλ)w + ηe−\|w⋅δϕ(y,^yw)\|2/2ℓ(y,^yw)δϕ(y,^yw). (8)\n\nNote that when the prediction label is close to the target label in terms of the decoding score, that is, when the term is relatively small, the exponent is close to 1. Under this condition the update rule becomes\n\n w←(1−ηλ)w + ηℓ(y,^y)δϕ(y,^yw), (9)\n\nwhich generalizes the regularized structured perceptron’s update rule (Collins, 2002; Zhang et al., 2014). Namely\n\n w←(1−ηλ)w+η\\mathbbm1{y≠^yw}δϕ(y,^yw), (10)\n\nwhere is an indicator function, equals 1 if the predicate holds and equals 0 otherwise.\n\nA nice property of this update rule is that the cost function does not need to be decomposable in the size of the output. Decomposable cost functions are needed in order to solve the cost-augmented inference that is used in the training of structural SVMs (Joachims et al., 2005; Ranjbar et al., 2013), direct loss minimization (McAllester et al., 2010), or structured ramp loss (Do et al., 2008). It means that cost functions like word error rate or intersection-over-union can be used without being approximated.\n\nAnother property of the orbit loss is its similarity to the structured probit loss (Keshet et al., 2011). The probit loss was derived from the concept of stochastic decoder in the PAC-Bayesian framework (McAllester, 1998, 2003) and was shown to have both good theoretical properties and practical advantages (Keshet et al., 2011). The structured probit loss is defined as follows\n\n ¯ℓprobit(w,x,y)=Eϵ∼N(0,I)[ℓ(y,^yw+ϵ)], (11)\n\nwhere is a -dimensional isotropic Normal random vector. Note that the orbit loss (5) can be written as follows:\n\n Pε∼N(0,1)[ε>w⋅δϕ(y,^yw)]ℓ(y,^yw)=Pϵ∼N(0,I)[−ϵ⋅δϕ(y,^yw)>w⋅δϕ(y,^yw)]ℓ(y,^yw). (12)\n\nThe last equation holds since the inner product of an isotropic normal random vector with a unit-norm vector is a zero-mean unit variance normal random variable. Writing the probability as the expectation of an indicator function, we have\n\n (13)\n\nAssuming for a value small enough that , where is a ball of radius centered at , we can bring the cost function into the expectation term, that is\n\n Eϵ∼N(0,I)[I{(w+ϵ)⋅δϕ(y,^yw+ϵ)<0}ℓ(y,^yw+ϵ)]=Eϵ∼N(0,I)[ℓ(y,^yw+ϵ)], (14)\n\nwhich is the structured probit loss.\n\n## 4 Analysis\n\nIn this section we analyze the orbit loss. We derive a generalization bound based on the PAC-Bayesian theory, where we start by upper-bounding the probit loss with the orbit loss and then plugging it into a PAC-Bayesian generalization bound. Then we show that the decoder’s parameters, which are estimated by optimizing the regularized orbit loss in the limit of infinite data, approach the infimum risk achievable by any linear decoder.\n\nRecall that the structured probit loss is defined as:\n\n ¯ℓprobit(w,x,y)=Eϵ∼N(0,I)[ℓ(y,^yw+ϵ)]. (15)\n\nThe following theorem states a generalization bound for the probit loss function (Keshet et al., 2011).\n\n###### Theorem 1 (Generalization of probit loss).\n\nFor a fixed we know that, with a probability of at least over the draw of the training data, the following holds simultaneously for all :\n\n E(x,y)∼ρ[¯ℓprobit(w,x,y)]≤11−12γ(1mm∑i=1¯ℓprobit(w,xi,yi)+γ2m∥w∥2+γmln1δ). (16)\n\nLater this generalization bound will help us state a similar bound for the orbit loss.\n\nWe now analyze the orbit loss. Let be the minimal distance between the score of the predicted label to the score of its closest different label by a constant :\n\n miny′≠^y w⋅δϕ(^y,y′)≥η (17)\n\nfor . The following lemma upper bounds the probit loss with the orbit loss.\n\n###### Lemma 2.\n\nFor a finite and a cost function for all , , the following holds:\n\n ¯ℓprobit(w/σ,x,y)≤¯ℓorbit(w/σ,x,y)+σ, (18)\n\nfor .\n\nFor the brevity of the explanation we call the predicted label and we call the perturbed label. The idea behind the proof is to split the structured probit loss cases of for which the predicted label and the perturbed label are the same, and the case in which they differ. We show that the probability of the labels being equal is upper-bounded by the orbit loss, and the probability of the labels being different is upper-bounded by an exponential term that approaches zero when the norm of approaches zero.\n\n###### Proof.\n\nFrom the law of total expectation we have\n\n Eϵ∼N(0,I)[ℓ(^yw+ϵ,y)]≤ Eϵ[\\mathbbm1{^yw+ϵ=^yw}ℓ(^yw+ϵ,y)]+Pϵ[^yw+ϵ≠^yw], (19)\n\nwhere we upper bound the cost by 1 in the second term.\n\nFirst, let us focus on the first term of the inequality. For this term , which means that\n\n Eϵ[\\mathbbm1{^yw+ϵ=^yw}ℓ(^yw+ϵ,y)]=Pϵ[^yw+ϵ=^yw]ℓ(^yw,y). (20)\n\nBy definition of the inference rule (1) for any vector , we have for all . Therefore the probability that can be expressed as follows:\n\n Pϵ[^yw+ϵ=^yw]=Pϵ[(w+ϵ)⋅δϕ(^yw+ϵ,^yw)≤0] (21)\n\nwhich, in turn, can be expressed as\n\n Pϵ[w⋅δϕ(^yw+ϵ,^yw)≤−ϵ⋅δϕ(^yw+ϵ,^yw)]≤Pϵ[w⋅δϕ(y,^yw)≤−ϵ⋅δϕ(^yw+ϵ,^yw)], (22)\n\nwhere replacing with increases the event size, thereby increasing the probability. Replacing the inner product of an isotropic normal random vector with a unit-norm vector with a zero-mean unit variance normal random variable, we get:\n\n Pϵ[w⋅δϕ(y,^yw)≤−ϵ⋅δϕ(^yw+ϵ,^yw)]=Pε∼N(0,1)[ε>w⋅δϕ(y,^yw)]. (23)\n\nThe second term of the left-hand side of (19) can be expressed as follows:\n\n Pϵ[^yw+ϵ≠^yw]=Pϵ[(w+ϵ)⋅δϕ(^yw+ϵ,^yw)>0].\n\nWe have\n\n Pϵ[(w+ϵ)⋅δϕ(^yw+ϵ,^yw)>0] =Pϵ[ϵ⋅δϕ(^yw+ϵ,^yw)>w⋅δϕ(^yw,^yw+ϵ)] (24) ≤Pϵ[ϵ⋅δϕ(^yw+ϵ,^yw)>η]. (25)\n\nWe finalized the proof by bounding the last equation for a -scaled version of ,\n\n Pϵ∼N(0,I)[σϵ⋅δϕ(^yw+σϵ,^yw)>η]=Pε∼N(0,1)[σε>η]≤exp(−η22σ2)=σm, (26)\n\nwhere the first equation holds since the inner product of an isotropic normal random vector with a unit-norm vector is a zero-mean unit variance normal random variable; and the second equation holds for . Using the union bound over the draw of a sample of size concludes the proof. ∎\n\nPlugging Lemma 2 into the bound of Theorem 1, we get the following generalization bound for the orbit loss.\n\n###### Theorem 3 (Generalization of orbit loss).\n\nFor a fixed and assuming (17) holds with , we know that with a probability of at least over the draw of the training data the following holds true simultaneously for all and for all :\n\n E(x,y)∼ρ[¯ℓprobit(w/σ,x,y)]≤11−12γ(1mm∑i=1¯ℓorbit(w/σ,xi,yi)+γ2mσ2∥w∥2+σ+γmln1δ). (27)\n\nWe will now prove that the orbit loss is consistent. We start with the observation that when the norm of the weight vector goes to infinity, the orbit loss approaches the cost:\n\n###### Lemma 4.\n limα→∞¯ℓorbit(αw,x,y)=ℓ(y,^yw), (28)\n\nassuming that for all .\n\n###### Proof.\n\nRecall that therefore . Also note that scaling the parameters does not change the prediction, . We have:\n\n limα→∞Pε∼N(0,1)[ε>αw⋅δϕ(y,^yw)]ℓ(y,^yw)=Pε∼N(0,1)[ε>−∞]ℓ(y,^yw)=ℓ(y,^yw). (29)\n\nConsider the following training objective:\n\n ^wm=argminw 1mm∑i=1¯ℓorbit(w,xi,yi)+λm2m∥w∥2. (30)\n###### Theorem 5 (Consistency of orbit loss).\n\nFor defined by (30), if the sequence increases without bound, and the sequence converges to zero, then with a probability of one over the draw of the infinite sample we have:\n\n (31) =infwE(x,y)∼ρ[ℓ(y,^yw(x))].\n###### Proof.\n\nSet , , and into the bound (27). We decompose into a scalar , corresponding to the norm of , and a unit norm vector . Last, using Chernoff we upper-bound by to get\n\n E(x,y)∼ρ[¯ℓprobit((lnm)wm,x,y)] ≤E(x,y)∼ρ[¯ℓprobit((lnm)αw∗,x,y)] (32) ≤11−ln2m2λm(E(x,y)∼ρ[¯ℓorbit(αw∗,x,y)] (33) +√lnmm+λmα22m+1lnm+2λmmlnm),\n\nwhere is the minimizer of the right-hand side of the bound in (27), as well as of the optimization problem (30). Taking the limit when the number of examples approaches infinity on both sides we have\n\n limm→∞E(x,y)∼ρ[¯ℓprobit((lnm)wm,x,y)]≤E(x,y)∼ρ[¯ℓorbit(αw∗,x,y)] (34)\n\nNoting that by\n\n E(x,y)∼ρ[¯ℓprobit(w,x,y)]≥infwE(x,y)∼ρ[ℓ(y,yw)], (35)\n\nand letting approach infinity using Lemma 4 concludes the proof. ∎\n\n## 5 Experiments\n\nWe evaluated the performance of the orbit loss by executing a number of experiments on several domains and tasks and compared the results with other approaches that are aimed at risk minimization, namely direct loss minimization (McAllester et al., 2010), structured ramp loss (Do et al., 2008), and structured probit loss (Keshet et al., 2011). For a reference, we present results for the structured perceptron, as we wanted to stress the empirical differences between the update rule in (9) and the one in (10), as well as for the structured hinge loss.\n\n### 5.1 Mnist\n\nIn our first experiment we tested the orbit update rule on a multiclass problem. MNIST is a dataset of handwritten digit images (10 classes). It is divided into a training set of 50,000 examples, a test set of 10,000 examples and a validation set of 10,000 examples. We preprocess the data by normalizing the input images, and reducing the dimension from the original 784 attributes to 100 using PCA.\n\nWe used the orbit update rule as in (8). We defined the weight vector as a concatenation of 10 weight vectors , each corresponding to one of the 10 digits. The update rule of example , , can be simplified based on Kesler’s construction (Crammer and Singer, 2001) as follows:\n\n wyi ←(1−ηλ)wyi+ηe−\|wyi⋅xi−w^y⋅xi\|2/2ℓ(^y,yi)xi w^y ←(1−ηλ)w^y−ηe−\|wyi⋅xi−w^y⋅xi\|2/2ℓ(^y,yi)xi wr ←(1−ηλ)wr for all r≠yi,^y\n\nNote that the exponent values throughout the training were very close to 1 and, practically, the update rule (9) could be used.\n\nTo properly evaluate the orbit loss we ran the experiment with two different cost functions for : 0-1 loss and a semi-randomized matrix. We did so because the update rule (9) is identical to the structured perceptron update rule under the 0-1 loss.\n\nIn the first case, we set , for , is the iteration number, and . We also trained a multiclass perceptron with , and , and a multiclass SVM with (Crammer and Singer, 2001). All of the hyper-parameters were chosen on the validation set. In all of the experiments we ran 4 epochs over the training data and used a linear kernel.\n\nThe results are given in Table 1 and suggest that there is a slight advantage for the orbit loss over the other algorithms. Recall that we previously showed that the perceptron is a special case of orbit loss and under this setting, in which = 0-1 loss, hence the only difference between the results in the table is due to the regularization factor used with the orbit loss.\n\nAs mentioned above, this experiment was executed once again, setting the cost function, , to be a semi-randomized matrix. We generated a randomized cost matrix of size 10 10, such that the elements on the diagonal were all 0, and the rest of the elements were chosen uniformly at random to be either 1 or 2. We trained multiclass perceptron, multiclass SVM, and orbit using the following hinge loss for the cost function:\n\n ¯ℓhinge(w,x,y)=max^y[ℓ(y,^y)−wy⋅x+w^y⋅x] (36)\n\nTo ensure reliability, we ran the second experiment for each algorithm with 10 different sampled matrices and averaged the results. The results are presented in Table 2. The results show a clear advantage for the orbit loss update rule in regards to the task loss. The reason is that the orbit loss can take advantage of minimizing a non 0-1 loss, as compared to perceptron.\n\n### 5.2 Phoneme alignment\n\nOur next experiment focused on the phoneme alignment, which is used as a tool in developing speech recognition and text-to-speech systems. This is a structured prediction task — the input represents a speech utterance, and consists of a pair of a sequence of acoustic feature vectors (mel-frequency cepstral coefficients) , , where , ; and a sequence of phonemes , where , is a phoneme symbol and is a finite set of phoneme symbols. The lengths and can differ for different inputs, although typically is significantly larger than . The goal is to generate an alignment between the two sequences in the input. The output is a sequence , where is an integer giving the start frame in the acoustic sequence of the -th phoneme in the phoneme sequence. Hence the -th phoneme starts at frame and ends at frame .\n\nFor this task we used the TIMIT speech corpus for which there are published benchmark results (Brugnara et al., 1993; Keshet et al., 2007; Hosom, 2009). We divided a portion of the TIMIT corpus (excluding the SA1 and SA2 utterances) into three disjoint parts containing 1500, 1796 and 400 utterances, respectively. The first part was used to train a phoneme frame-based classifier, which given the pair of speech frame and a phoneme, returns the level of confidence that the phoneme was uttered in that frame. The output classifier is then used along with other features as a seven dimensional feature map as described in Keshet et al. (2007).\n\nThe seven dimensional weight vector was trained on the second set of 150 aligned utterances for -insensitive loss\n\n ℓ(y,^y)=1\|y\|max{\|yk−^yk\|−τ,0}, (37)\n\nwith = 10 ms. This cost measures the average disagreement between all of the boundaries of the desired alignment sequence and the boundaries of predicted alignment sequence where a disagreement of less than is ignored.\n\nWe trained the system with the orbit update rule where and ; the structured perceptron update rule; the structural SVM optimized using stochastic gradient descent with =5 (Shalev-Shwartz et al., 2011); structured ramp-loss with , ; and direct loss minimization algorithm with on a reduced training set of 150 examples (out of 1796) and a reduced validation set of 100 examples (out of 400). We were not able to train the system with the probit loss in a reasonable time.\n\nThe results are given in Table 3. The results in the first 4 columns should be read as the accuracy (in percentage) that the prediction was within . The higher the better. The last column of the table is the actual loss computed by (37) - the smaller the better. In those results the orbit update rule outperforms other algorithms, and yields state-of-the-art results.\n\nWe would like to note that as in the MNIST experiment, the exponent values in the update rule were very close to 1 and, practically, the update rule (9) could be used.\n\n### 5.3 Vowel duration\n\nIn the problem of vowel duration measurement we are provided with a speech signal which includes exactly one vowel preceded and followed by consonants (i.e., CVC). Our goal is to predict the vowel duration accurately. Precise measurement of vowel duration in a given context is needed in many phonological experiments, and currently is done manually (Heller and Goldrick, 2014).\n\nThe speech signal is represented as a sequence of acoustic features = where each (1 i T) is a d-dimensional vector representing acoustic parameters, such as high and low energy, pitch, voicing, correlation coefficient, and so on (we extract =22 acoustic features every 5 msec). We denote the domain of the feature vectors by . The length of the input signal varies from one signal to another, thus is not fixed. We denote by the set of all finite length sequences over . In addition, we denote by and the vowel onset and offset times, respectively, where . For brevity we set . The typical duration of an utterance is around 2 sec. There were =116 feature functions that described the typical duration of a vowel, the mean high energy before and after the vowel onset, and so on. The cost function we use is:\n\n ℓ(^t,t)=[\|^tb−tb\|−τb]++[\|^te−te\|−τe]+, (38)\n\nwhere , and , are pre-defined constants. The above function measures the absolute differences between the predicted and the manually annotated vowel onsets and offsets. Since the manual annotations are not exact, we allow a mistake of and frames at the vowel onset and offset respectively.\n\nWe trained the system using the orbit update rule with , ; the structured perceptron update rule; structured ramp loss with , ; probit loss with the expectation approximated by a mean of 100 random samples , ; and direct loss minimization with and =-1.52. All of those hyper-parameters were chosen for the validation set. The results are presented in Table 4 for different values of and in the cost function. It can be seen that the orbit is close to the direct loss minimization (differences of a frame or two on average) and is better than other approaches. Also note that as describe earlier, the efficiency of the orbit loss is similar to the structured perceptron update and better than other approaches.\n\n## 6 Discussion and Future Work\n\nWe introduced a new surrogate loss function that offers an efficient and effective learning rule. We gave a qualitative theoretical analysis presenting a PAC-Bayesian generalization bound and a consistency theorem. Despite the fact that the consistency property concerns the training performance when the number of training examples is big, the proposed loss function was shown to perform well on several tasks, even when the training set was of small or medium size.\n\nIn terms of theoretical properties, we think that the theoretical analysis can be improved, and in particular we would like to have a better upper-bound of the probit loss in terms of the orbit loss, as expressed in Lemma 2, which depends on the minimal distance between the predicted label and its closest neighbor label. Anyways, it is clear that when the norm of the weight vector becomes large relative to the norm of the noise, the inference with the weight vector and the inference with the perturbed weight vector – both lead to the same predicted label with a high probability.\n\nThis work is part of our research on surrogate loss functions in the structured prediction setting. We believe that in order to understand what are good loss functions, we have to understand the interrelationship between them. While we showed some relation between the orbit loss, the Perceptron and the probit loss, we still think that more work should be done. We are especially interested in understanding the connection between the orbit, the probit, and the direct loss minimization approach.\n\n## References\n\n• Brugnara et al. (1993) Brugnara, F., Falavigna, D., and Omologo, M. (1993). Automatic segmentation and labeling of speech based on hidden markov models. Speech Communication, 12:357–370.\n• Collins (2002) Collins, M. (2002). Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Conference on Empirical Methods in Natural Language Processing.\n• Crammer and Singer (2001) Crammer, K. and Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Jornal of Machine Learning Research, 2:265–292.\n• Do et al. (2008) Do, C., Le, Q., Teo, C.-H., Chapelle, O., and Smola, A. (2008). Tighter bounds for structured estimation. In Advances in Neural Information Processing Systems (NIPS) 22.\n• Heller and Goldrick (2014) Heller, J. R. and Goldrick, M. (2014). Grammatical constraints on phonological encoding in speech production. Psychonomic bulletin & review, 21(6):1576–1582.\n• Hosom (2009) Hosom, J.-P. (2009). Speaker-independent phoneme alignment using transition-dependent states. Speech Communication, 51:352–368.\n• Joachims et al. (2005) Joachims, I., Tsochantaridis, T., Hofmann, T., and Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453–1484.\n• Keshet et al. (2011) Keshet, J., McAllester, D., and Hazan, T. (2011). PAC-Bayesian approach for minimization of phoneme error rate. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP).\n• Keshet et al. (2007) Keshet, J., Shalev-Shwartz, S., Singer, Y., and Chazan, D. (2007). A large margin algorithm for speech and audio segmentation. IEEE Trans. on Audio, Speech and Language Processing, 15(8):2373–2382.\n• Lafferty et al. (2001) Lafferty, J., McCallum, A., and Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eightneenth International Conference on Machine Learning (ICML), pages 282–289.\n• McAllester (2003) McAllester, D. (2003). Simplified PAC-Bayesian margin bounds. In Proceedings of the Sixteenth Annual Conference on Computational Learning Theory.\n• McAllester (2006) McAllester, D. (2006). Generalization bounds and consistency for structured labeling. In Schölkopf, B., Smola, A. J., Taskar, B., and Vishwanathan, S., editors, Predicting Structured Data, pages 247–262. MIT Press.\n• McAllester et al. (2010) McAllester, D., Hazan, T., and Keshet, J. (2010). Direct loss minimization for structured prediction. In Advances in Neural Information Processing Systems (NIPS) 24.\n• McAllester (1998) McAllester, D. A. (1998). Some pac-bayesian theorems. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory.\n• Ranjbar et al. (2013) Ranjbar, M., Lan, T., Wang, Y., Robinovitch, S. N., Li, Z.-N., and Mori, G. (2013). Optimizing nondecomposable loss functions in structured prediction. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(4):911–924.\n• Shalev-Shwartz et al. (2011) Shalev-Shwartz, S., Singer, Y., Srebro, N., and Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for svm. Mathematical programming, 127(1):3–30.\n• Smith and Eisner (2006) Smith, D. A. and Eisner, J. (2006). Minimum risk annealing for training log-linear models. In Proc. of the COLING/ACL, pages 787–794.\n• Taskar et al. (2003) Taskar, B., Guestrin, C., and Koller, D. (2003). Max-margin markov networks. In Advances in Neural Information Processing Systems (NIPS) 17.\n• Zhang et al. (2014) Zhang, K., Fujian, P., Su, J., and Zhou, C. (2014). Regularized structured perceptron: A case study on chinese word segmentation, pos tagging and parsing. The 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), page 164.\nYou are adding the first comment!\nHow to quickly get a good reply:\n• Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.\n• Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.\n• Your comment should inspire ideas to flow and help the author improves the paper.\n\nThe better we are at sharing our knowledge with each other, the faster we move forward.\nThe feedback must be of minimum 40 characters and the title a minimum of 5 characters", null, "", null, "", null, "" ]	[ null, "https://dp938rsb7d6cr.cloudfront.net/static/1.70/groundai/img/loader_30.gif", null, "https://dp938rsb7d6cr.cloudfront.net/static/1.70/groundai/img/comment_icon.svg", null, "https://dp938rsb7d6cr.cloudfront.net/static/1.70/groundai/img/about/placeholder.png", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.89911276,"math_prob":0.970017,"size":30227,"snap":"2020-10-2020-16","text_gpt3_token_len":6950,"char_repetition_ratio":0.15468352,"word_repetition_ratio":0.049544994,"special_character_ratio":0.23952095,"punctuation_ratio":0.13808464,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99399287,"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\":\"2020-03-31T09:49:19Z\",\"WARC-Record-ID\":\"<urn:uuid:6179758c-af47-4c1b-8452-a80d59fb63b6>\",\"Content-Length\":\"833002\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:b256048d-faa1-4b65-bd70-afe724e941be>\",\"WARC-Concurrent-To\":\"<urn:uuid:d5706ea9-2385-435b-9190-063a9f7ec357>\",\"WARC-IP-Address\":\"35.186.203.76\",\"WARC-Target-URI\":\"https://www.groundai.com/project/risk-minimization-in-structured-prediction-using-orbit-loss/\",\"WARC-Payload-Digest\":\"sha1:OD2DDTNJSCX4YGOY2W5YOPK2XC2HTI42\",\"WARC-Block-Digest\":\"sha1:FUSGCU2EUALBSF4JOEO5E6JQ7RGSU43J\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-16/CC-MAIN-2020-16_segments_1585370500426.22_warc_CC-MAIN-20200331084941-20200331114941-00147.warc.gz\"}"}
https://justcode.ikeepstudying.com/2015/07/swift%E4%B8%AD%E6%96%87%E6%95%99%E7%A8%8B%EF%BC%88%E5%8D%81%E4%BA%8C%EF%BC%89-%E4%B8%8B%E6%A0%87/	[ "# Swift中文教程（十二）下标\n\n1、下标语法\n\nC代码\n1. subscript(index: Int) -> Int {\n2. get {\n3. // return an appropriate subscript value here\n4. }\n5. set(newValue) {\n6. // perform a suitable setting action here\n7. }\n8. }\n\nnewValue的类型和下标返回的类型一样。和计算属性一样，你可以选择不指定setter的参数，因为当你不指定的时候，默认参数newValue会被提供给setter。\n\nC代码\n1. subscript(index: Int) -> Int {\n2. // return an appropriate subscript value here\n3. }\n\nC代码\n1. struct TimesTable {\n2. let multiplier: Int\n3. subscript(index: Int) -> Int {\n4. return multiplier * index\n5. }\n6. }\n7. let threeTimesTable = TimesTable(multiplier: 3)\n8. println(“six times three is (threeTimesTable)”)\n9. // prints “six times three is 18”\n\n2、下标的使用\n\nC代码\n1. var numberOfLegs = [“spider”: 8, “ant”: 6, “cat”: 4]\n2. numberOfLegs[“bird”] = 2\n\nSwift中字典类型实现的键值对下标是可选类型。对于numberOfLges字典来说，返回的值是Int?，也就是可选Int值。字典的这种使用可选类型下标的方式说明不是所有的键都有对应的值。同样也可以通过给键赋值nil来删除这个键。\n\n3、下标选项\n\nC代码\n1. struct Matrix {\n2. let rows: Int, columns: Int\n3. var grid: Double[]\n4. init(rows: Int, columns: Int) {\n5. self.rows = rows\n6. self.columns = columns\n7. grid = Array(count: rows * columns, repeatedValue: 0.0)\n8. }\n9. func indexIsValidForRow(row: Int, column: Int) -> Bool {\n10. return row >= 0 && row < rows && column >= 0 && column < columns\n11. }\n12. subscript(row: Int, column: Int) -> Double {\n13. get {\n14. assert(indexIsValidForRow(row, column: column), “Index out of range”)\n15. return grid[(row * columns) + column]\n16. }\n17. set {\n18. assert(indexIsValidForRow(row, column: column), “Index out of range”)\n19. grid[(row * columns) + column] = newValue\n20. }\n21. }\n22. }\n\nC代码\n1. var matrix = Matrix(rows: 2, columns: 2)\n\nC代码\n1. matrix[0, 1] = 1.5\n2. matrix[1, 0] = 3.2\n\nC代码\n1. func indexIsValidForRow(row: Int, column: Int) -> Bool {\n2. return row >= 0 && row < rows && column >= 0 && column < columns\n3. }\n\nC代码\n1. let someValue = matrix[2, 2]\n2. // this triggers an assert, because [2, 2] is outside of the matrix bounds", null, "" ]	[ null, "https://justcode.ikeepstudying.com/wp-content/plugins/page-views-count/ajax-loader-2x.gif", null ]	{"ft_lang_label":"__label__zh","ft_lang_prob":0.82082695,"math_prob":0.984715,"size":3543,"snap":"2023-14-2023-23","text_gpt3_token_len":2022,"char_repetition_ratio":0.11726476,"word_repetition_ratio":0.26331362,"special_character_ratio":0.2303133,"punctuation_ratio":0.13681592,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9859992,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-03-21T06:58:41Z\",\"WARC-Record-ID\":\"<urn:uuid:381b851c-cd0f-48bd-893f-38a01db80b07>\",\"Content-Length\":\"101894\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:eeb2caf8-b42b-419e-8ecb-23e1e628494f>\",\"WARC-Concurrent-To\":\"<urn:uuid:59a757a4-9ead-476b-ac61-ca6fb2d8e601>\",\"WARC-IP-Address\":\"74.208.236.80\",\"WARC-Target-URI\":\"https://justcode.ikeepstudying.com/2015/07/swift%E4%B8%AD%E6%96%87%E6%95%99%E7%A8%8B%EF%BC%88%E5%8D%81%E4%BA%8C%EF%BC%89-%E4%B8%8B%E6%A0%87/\",\"WARC-Payload-Digest\":\"sha1:LAWIBVRFDZODNP2TAYSHX4K3XN5IK7EI\",\"WARC-Block-Digest\":\"sha1:XBMBGUCZIDDJRT6A756NWADPF4BVLT25\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-14/CC-MAIN-2023-14_segments_1679296943637.3_warc_CC-MAIN-20230321064400-20230321094400-00396.warc.gz\"}"}
https://datasig.ac.uk/2021-02-03-ayush-bharti	[ "# Ayush Bharti", null, "### Abstract\n\nRadio channel modeling aims at replicating the behavior of the environment in which a radio signal propagates. Stochastic models of the radio channel are necessary simulation tools for designing and testing communication systems. These stochastic models simulate time-series data that is driven by an underlying point process whose points are not observable, thus rendering the likelihood function intractable. Estimating the parameters of the underlying point process is therefore a challenging task. We attempt to tackle this problem using approximate Bayesian computation (ABC) which is a likelihood-free inference framework. ABC relies on comparing summary statistics of the simulated data and the observed data in some distance metric. Parameter values that yield simulated data “close” to the observed data form a sample from the approximate posterior distribution. We make use of the maximum mean discrepancy, which is a notion of distance between probability distributions, as the distance metric in ABC. The proposed method is able to accurately estimate the parameters of stochastic channel models in simulation as well as when applied to real measurements." ]	[ null, "https://datasig.ac.uk/sites/default/files/styles/mt_image_large/public/datasig/images/media/ayush_bharti_2.jpg", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9023851,"math_prob":0.9405801,"size":1250,"snap":"2022-27-2022-33","text_gpt3_token_len":213,"char_repetition_ratio":0.11637239,"word_repetition_ratio":0.0,"special_character_ratio":0.16,"punctuation_ratio":0.060913704,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9536188,"pos_list":[0,1,2],"im_url_duplicate_count":[null,4,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-06-29T22:00:09Z\",\"WARC-Record-ID\":\"<urn:uuid:bee2f8f9-bfba-41a4-9906-82127a9f9051>\",\"Content-Length\":\"76409\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:1e40e5e7-0f4c-4959-877b-3f42a16edca1>\",\"WARC-Concurrent-To\":\"<urn:uuid:bde7fed1-82c7-421a-bda6-36934f7501dd>\",\"WARC-IP-Address\":\"79.125.14.100\",\"WARC-Target-URI\":\"https://datasig.ac.uk/2021-02-03-ayush-bharti\",\"WARC-Payload-Digest\":\"sha1:2QLUM35ITGSENP3F2DBPDKJ3X6US3R7N\",\"WARC-Block-Digest\":\"sha1:QXANZXWSZSHTTEJFCE6IGK7UHFPTRDNL\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-27/CC-MAIN-2022-27_segments_1656103645173.39_warc_CC-MAIN-20220629211420-20220630001420-00146.warc.gz\"}"}
https://taudevin-engineering.co.uk/interests/generating-and-counting-regular-perfect-number-squares/	[ "## GENERATING AND COUNTING REGULAR PERFECT NUMBER SQUARES (An extension to the Siam system)\n\nA number square is an array of numbers in which the rows columns and diagonals all add up to the same total. Here are three examples (and there is a small selection of other types of square in the last section):-", null, "In each of the above examples the numbers used are sequential, starting from one up to the number of cells in each square, (e.g. 1 to 25 in a five x five). Just using these numbers there are hundreds of ways to place the numbers in the square, and most of these will produce all sorts of random totals for the rows and columns. In the illustrations all rows and columns sum to a specific number. So one might be tempted to ask how on earth can we easily choose where to start and how to continue, in order to produce perfect squares, and how many will there be? And how do we decide what makes a perfect square? This latter question is the easiest to answer! The three by three is not a perfect square. The broken diagonals do not sum to 15! 8 +5+2=15, but 1+7+4=12, and 6+3+9=18. On the other hand, the broken diagonals of the four by four do sum to 34: 5+9+12+8=34, and 4+6+13+11=34. The four by four is looking pretty perfect. Yet, it is still better! The\ncorners sum to 34: 10+15+1+8=34. In fact this square is so perfect that there are numerous groups of four numbers all totalling 34.\n\nThe five by five is also a perfect square, the broken diagonals: 6+25+14+3+17=65 etc., the corners and centre: 23+15+1+17+9=65, other groups of five: 10+1+24+17+13=65!\nSo now to the more difficult question of where to start in laying out the numbers!\nThe four by four is particularly interesting because there is an elegant pattern to the array. It also appears to be the only pattern, but there are ways to produce different squares from the same pattern. If we look at the field of numbers below, the four by four square is outlined. The numbers in red indicate the pattern used to create the square.", null, "The numbers coloured red form a diamond pattern, the numbers coloured green form a similar diamond downwards, but remember to make the horizontal numbers total 9, e.g. 4+5=9; 3+6=9; 7+2=9; and 1+8=9, otherwise it will not work! You will also see that the numbers 9,10,11,12 also form the same diamond upwards to the left, and the numbers 13,14,15,16; downwards to the left, making the sum 25 all the way.(viz. 12+13=25; 14+11=25; 15+10=25; 16+9=25.) 25 added to the nine makes the required total of 34. It is, however, only necessary to remember the first diamond and that the horizontal pairssum to nine. This is because, once you have laid the numbers one to eight, and guessed where to put the nine, the rest can be placed by logic, as illustrated below. Once the question mark is calculated there is only one number to calculate alongside, and so on until you need to use a diagonal. The best guess for the nine is in the columns that sum to ten, the more central square being the better choice.", null, "The first set uses a slight variation of the numbers, viz. The two and the three are reversed.\nThe second set shows that you can use different sequences of numbers.\nYou are now in a position to find out which of the positions for the nine produce perfect or just interesting squares.\nYou will need to remember that the diagonals need to add up to 34 to complete the squares.\nIn some cases it is only a broken diagonal that totals 34. These I call imperfect squares, because only one or two of the diagonals total 34.\nIn fact there are only three different arrangements of numbers producing perfect squares. The internet says 48, but that is because each number field generates sixteen squares, which to me are all the same. You may well think you have found more, if you experiment with different number sequences in the diamond, but if you look at the numbers surrounding the ‘one’, then you will see that very different looking squares are actually the same as one of the three, but with the numbers rotated or reflected or both. The three are illustrated below with a non-different variant!", null, "THE PATTERN FOR A FIVE BY FIVE.\n\nAs the size of square increases the difficulty of finding the right combination of numbers for a perfect square also increases. But the Chinese have the answer – as always. They observed that the numbers in perfect squares were often related by the movement two squares up and one square right. And having observed this relationship they used it in the game of chess as the knight’s move. It was probably observed in the three by three square first of all. As you can see, page 1, one to two, two to three, seven to eight, and eight to nine, are all related by the knight’s move. And amazingly enough, four to five and five to six!!!! But to see this latter relationship properly, you need to put two more three by three squares alongside the first square, one to the left and one to the right. This placing of identical squares alongside each other is crucial to the use of the ‘knight’s move pattern’ for the generation of perfect squares. The number field produced by this assemblage of squares is shown above for the four by four square. The nine shown boxed (on the number field on page one) is in an identical square and in the same relative position.\nTo construct a perfect five by five square, you simply place the numbers into the grid using the knight’s move, two up and one to the right. Start in the coloured square and try not to be confused by the other numbers milling around there. One to two, two to three, and then;", null, "to position the four we need to utilise the concept of adjacent squares, as illustrated in the field above. This means that, in the square imagined below the main square, the three is positioned, as shown, in the top row of that square. Three to four, four to five, and we have arrived back at the one. To create a number field we just repeat the sequence one to five, as illustrated in the appropriate places in the field.\nIt is evident that there are twenty choices for placing the six in the 5 by 5 square, once the first five numbers are laid (twenty-five minus the five numbers just inserted). We cannot continue the sequence since the six falls where the one is! (The one is, as we started, in the bottom left corner of the square, and this square is repeated above and to the right of the centre square.) To produce a perfect square the six may be placed below the five, as illustrated. The number chain then continues until the eleven falls on the six, but do not panic, just remember that you placed the six below the five, so – place the eleven below the ten. And continue using the knight’s move until the sixteen falls on top of the eleven. Then just place the sixteen below the fifteen, and continue on until the twenty one falls on top of the sixteen, then place the twenty-one below the twenty. The resulting square, barring accidents, is perfect. It is illustrated in the field above, and in its correct position in\nthat field. Completing the unfinished centre square by moving numbers to their correct positions is left to you!\n\nCOUNTING HOW MANY PERFECT SQUARES ARE POSSIBLE\n\nSince there are twenty positions for placing the six, it is evident that finding perfect squares is still a bit of a problem. It will be noted that once the position of the six is chosen – as below the five for example – then that position is chosen each time we reach a multiple of five. It has been found by trial and error that this is the simplest way.\nLet us first see what happens if we choose a different position for the six. This time we will start with the one in the middle. And put the six alongside and to the left of the five; the eleven alongside and to the left of the ten, etc. On completion we find that the main diagonals total: 20+23+1+9+12=65, and 21+11+1+16+6=55; not a very pretty arrangement! However, the broken diagonal: 8+23+13+3+18=65, the total we are looking for; this means if we make twenty-three the centre of the square, as illustrated, we have a fairly pretty arrangement, but not a perfect square. I call these interesting or imperfect squares.", null, "You might like to try putting the six alongside the five and to the right, not left. This produces a disaster of a square!!\n\nThere is a simple pattern for positioning the six in order to arrive at perfect squares, and the pattern is best seen if we start, once again with the number one, but this time in the centre of the square:-", null, "If the six is placed on a square labelled ‘N’. NO SQUARE IS PRODUCED.\nIf the six is placed on a square labelled ‘I’. INTERESTING SQUARES CAN BE SELECTED, (by selecting the appropriate number to be the centre of the square as\nillustrated by the number fields above).\nIf the six is placed on a square labelled ‘P’, PERFECT SQUARES ARE GENERATED.\nThe eleven, sixteen, and twenty-one, when baulked by existing numbers, may similarly only be placed in squares marked ‘P’ if perfect squares are to result.\n\nSo far we have only placed the baulked numbers in the same relative position at each change. E.g. the baulked numbers are always placed, under the five, ten, fifteen and twenty, or always placed alongside, or always placed at the same fixed position from these ‘break’ numbers. This is a simple (but not general) rule for making perfect squares and works particularly well as the size of square rises above a five by five. However, with a five by five square, the numbers after the break numbers may be placed in any of the squares marked as P. This means there are four positions for the six, three positions for the eleven, and two for the sixteen. This means that there must be 4x3x2=24 perfect squares using this knight’s move.\nAre there any more? Well you may have guessed, as in the four by four square, we can change the first five numbers around, e.g. 1, 2, 3, 5, 4. There is a limiting factor however, that stops us making thousands of different squares. If we look back to the first example of a five by five and starting from the one, move two along to the right and one down, then we see that the number sequence we have laid is not, 1,2,3,4,5; but 1,3,5,2,4. Similarly as we move around we see the sequence, 1,5,4,3,2; and 1,4,2,5,3. We have thus laid four number sequences in each of our twenty four squares. This reduces the number possible considerably!! By my calculations there are only 144 different perfect squares ( you could say 144 times 25 i.e. 3,600, but they are not different number fields). Since the one is fixed there are only four numbers to be re-arranged. Thus there are just 4x3x2 variations with four used each time that makes just six sequences to be used in each of the twentyfour squares; 144 in total. Your confirmation is invited. E-mail:- squares@taudevinengineering.co.uk\nGaspalou (see last pages) seems to have found irregular squares and surely some irregular five by fives could exist?\nAll five by five perfect squares appear to be most perfect squares; see definition below. Page 11.\n\nWHAT IS THE TOTAL REQUIRED IN A LARGER SQUARE?\n\nIf we consider the three by three, we have used the numbers one to nine, if we add these up and divide by three (there are three rows and three columns), we obtain the total.\n1+2+3+4+5+6+7+8+9=45; 45/3 = 15. Now adding up 25 numbers for a five by five is a bit laborious. There is a quick way. In a three by three, nine times ten =90 which when divided by two gives 45!! The total we were looking for. In a four by four, sixteen times seventeen = some large number, but as we are about to divide by two we only need to multiply seventeen by eight which is easier and makes 136, if we divide by four (for the four rows and columns) this gives us the 34 we need. So in general, we just multiply the number of cells in the square by one extra then divide by two and then divide by the side of square.\nSo for a five by five we have: 25×26/ 2×5 which may be simplified (by using a calculator or) 5×13 = 65. For a seven by seven, we have: 49×50/2×7 = 7×25 = 175.\n\nHOW DO WE GET THE SQUARE TO GIVE A DIFFERENT TOTAL?\n\nThis is easy for some numbers, but not for others. For a five by five, if we require a total of 70 then we simply add one to every number in the square. Which means we start at 2 and finish at 26. In general if we require a different total say 72, then deduct 65 from the 72 = 7, divide by five = 1 remainder 2. Add one to every number and an extra two, the remainder, to another five. The square will stay pretty if you use a group of five as laid initially; see below; the figures in red have had two added to them:-", null, "To make an even higher total, say 96, then deduct 65 from the 96 = 31; divide this by five = 6 remainder 1. Thus you add six to every number and one extra to a selected five numbers.\n\nGENERATING SEVEN BY SEVEN SQUARES\n\nThe same system is used as for a five by five square. The pattern for positioning the eight is shown below. Remember to use the same move for fourteen to fifteen as you did for seven to eight, and continue with the same movement at each of the multiples of seven.", null, "HOW MANY SEVEN BY SEVEN PERFECT SQUARES CAN BE FOUND?\nThis is a bit of a problem. With the five by fives it was simple, but there are some differences in the seven by seven. Applying the same reasoning it would appear that for each of the eighteen positions that produce perfect squares, it would be possible to use any of them in any order. This appears not to be the case. Once you have chosen a yellow coloured square for the continuation, it seems that you must use the remaining yellows for the other continuations; mixing yellows and other colours (red or green) does not produce perfect squares. With this reasoning the eighteen potential squares appear to reduce to three!! If this is the case, then my conjecture is that there are three choices for the eight using the patterns as illustrated at P1, P2, or P3. The numbers one to seven can be placed in 720 sequences (e.g. 1, 3, 2, 4, 5, 6, 7 ). Each sequence produces two squares, for example 1,2,3,4,5,6,7 produces the same square as 1,7,6,5,4,3,2 as can be demonstrated by writing them out. The one is a rotation of the other about a diagonal. The numbers 1, 8, 15, 22, 29, 36, 43 can be placed in 720 sequences in their coloured squares only; these all appear to produce different squares. There are three patterns of coloured squares available, as above (P1, P2, and P3); there are therefore, 3607203=777,600 perfect squares.\n\nGaspalou, (see later pages) seems to have found perfect squares which do not follow the regular pattern. How do you count them?\nI would be delighted to hear from anyone who has a better method for counting the squares or a more logical way of doing it from the generating pattern, or any other method for generating perfect squares; squares@taudevin-engineering.co.uk.\nLooking again at the diagram above it would appear that the extended knight’s move and the double extended knight’s move do not produce original squares. Each ‘a’ square is covered by the shown positioning of 1 to 7 producing mirror images. Each ‘b’ position produces a square as the ‘a’ position, but with a different No sequence; e.g. the No sequence for the square shown is: 1, 4, 7, 3, 6, 2, 5. On the right is shown the sequence 1,2,3,4,5,6,7; giving the sequence shown! 1.6.4.2.7.5.3. Evens then odds!!! Thus the extended knight’s move does not produce more squares! Similarly for the double extended knight’s move. It just gives a different No sequence.\nIt also appears that the P3 position produces ‘most perfect squares’ as defined by Ollerenshaw and Br÷e (see page 10), {What I now like to call exceptionally perfect squares}, provided the pattern of seven is as on a playing card, i.e. with the row of three vertical, viz. the stars in the above square, page 7.\n\nOTHER SIZES OF SQUARE\n\nIt would be a very perceptive question to ask, what happened to the six by six. It would be equally alarming to mention an eight by eight, or a nine by nine.\nThe simple method outlined above works when the side of square is a prime number. It sort of works for odd numbers, but usually, if the odd number is not prime then perfect squares do not fall out. 49 by 49 is an exception, it produces perfect squares, whereas 25 by 25 does not!\nA six by six perfect square has not yet been achieved as far as I can ascertain. There are examples of eight by eight squares, but I have not discerned an easily recognised pattern in any of these. The knight’s move is in evidence, but not consistently. Some of these ‘problem’ squares are illustrated later.\n\nTHE ELEVEN BY ELEVEN SQUARE\n\nThere are TWO possible ‘Knight’ moves; the normal – starting the first run of numbers at ‘A’, and the single extended – starting at ‘H’. Illustrated in the grid below are two groups of letters, A, B, C, D and E, F, G, H. Inspection shows that using A, B, C, or D for the numbers produces some sort of symmetrical image, so just one needs to be used.\nSimilarly for E, F, G, and H.\nEach of these moves generates a series of perfect squares just like the seven by seven, using any of a number of continuation positions for the 12, 23, 36… etc. There are eleven of these continuation positions, coloured red and dark blue. However only seven are available with any one ‘knight’ move since three are repeats (‘A’, ‘C’, & ‘G’) & one is used by the first lay. Based on the seven by seven we instantly arrive at the number of possible squares as:-\nBase moves = 2\nContinuation positions = 7\nContinuation sequences= 1,814,400\nBase number Sequences also = 109876543 = 1,814,400\nA grand total of regular squares = 1,814,4001,814,40072 = 46,088,663,040,000", null, "THIRTEEN BY THIRTEEN\nStarts @ A, E, F\nFollow on @ B, C, D, E, F, G, H, J, K\nThere are three groups of letters, A, B, C, D. and E, H, J, K; and F, G.\nThere are therefore, three choices for start.\nThere are nine choices for follow on.\nThere are 3,113,510,400 number sequences.\nTherefore there are 1,548,737,096,417,280,000 possible squares.", null, "THE NINE BY NINE SQUARE.\n\nThis, as mentioned, is a problem child. Because of its symmetries perfect squares are hard to find and are produced by breaking the symmetry in special ways. Here is an example of a Hendricks square, culled from the internet, all rows, columns and diagonals sum to 369. It is generated using the number sequence, 1,2,5,6,4,7,8,9,3.", null, "The continuations are not as for the five by five and seven by seven, since the 46 and 64 are placed on normally forbidden squares, probably to break the symmetry. The Margossian family of these squares is also on the net!\nThere are lots of number sequences which generate perfect squares, but the standard sequence:- 1,2,3,4,5,6,7,8,9, does not appear to work. It is possible to generate additional squares from the same number sequence simply by alternating the positions of the follow on numbers. The Coloured squares show the sequence: – 1, 19, 73, 64, 55, 28, 46, 37, 10.\nThe sequences formed by selecting each alternate number also appear to work whenever I have tried them. E.g. 1, 73, 55, 46, 10, 19, 64, 28, 37. There would thus appear to be six in each family for one sequence and since the selection of alternating numbers appears to work for the basic sequence also, then this makes 36 in each family! I have so far culled 15 sequences from the net; these are appended at the end.\nIn line with the other squares the patterns are as shown below:-\nA most perfect square is defined as a pan diagonal square where any group of ‘n’ symmetrically placed numbers (n=side of square) also sum to the constant. In a four by four and five by five it is a ‘domino’ pattern. The seven by seven is illustrated on page 7. The nine by nine is a nice solid block of threes. Other patterns are a bit complicated! Some of the perfect nine by nine squares are most perfect, despite reports to the contrary.\n\nPOSITIONS FOR SECOND NUMBER: there are six, labelled a,b,c,d,e,f. repeated letters indicate the position just generates an already counted number sequence and not a new square. Follow on, or break point, numbers are shown for an example, they differ for some number sequences!", null, "Since there are six positions to choose from there would appear to be 36 times 6 possible squares, and as shown below as there are six positions for the break numbers this must make 36 times 6 times 6! 1296 squares, with the 15 sequences collected this makes 19440; but since each square contains two sequences this reduces to 9720. I have no idea how working sequences are found, so ideas would be most welcomed.\n\nPositions for the break point numbers: there are six, a,c,d,e,f,g.", null, "A SELECTION OF ‘INTERESTING’ SQUARES.\n\nAs indicated above, number squares have been known for thousands of years. In the days when adding up was a bit of a problem for most people, number squares had an aura of mystery. The four by four first illustrated above, is called a Jalna square as it was written up over the gateway into that Indian city (Dana Mackenzie says on a temple in Khajuraho). The first square illustrated below is known as Durer’s square as it appeared in his painting ‘Melancholia’, it has the year of the painting, 1514 in the bottom row. The second square is attributed to Jupiter, in ‘A New View over Atlantis’ by J. Mitchell. The third square is attributed to Mars, in the same book and the fourth to the Sun. Apparently he is just quoting a work by Cornelius Agrippa (1486-1535) as rather more elegantly explained on the Geocities website’s collection of ‘strange magic squares’ (now unfortunately elsewhere!). I thought the sun version was first published by Fermat in 1640, maybe he just knew of Cornelius Agrippa).", null, "The first square below is one of Benjamin Franklin’s. The second is from the Boys Own\nBook of Conjuring 1870.", null, "MORE INTERESTING (PERFECT) SQUARES\nExamples of nine by nine squares with number sequences after Margossian and Hendricks;\nsome with extended ‘knight’ move.\n\nSingle Knight move, Sequence:- 1,2,3,9,7,8,5,6,4.", null, "Extended Knight move, Margossian sequence:- 1,2,5,6,4,7,8,9,3. and continuations", null, "Extended knight move, sequence:- 1,2,3,9,7,8,5,6,4. Margossian continuations", null, "Pattern and sequence after Hendricks. Sequence:- 1,9,5,6,2,7,8,4,3.", null, "A Margossian square, again very perfect", null, "Another particularly perfect square. Extended knights move, Sequence 1,2,5,6,4,7,8,9,3.", null, "Two squares from the internet after Gaspalou, not fitting the regular pattern as far as I can\ndiscern at present.", null, "Number sequences for nine by nine perfect squares.", null, "Seventeen by seventeen:-", null, "The following symmetries exist:-\nA, B, C, D.\nE, H, J, M.\nF, G, K, L.\nN, T.\nThere are thus only four starts, A, E, F, & N.\nContinuations at B, C, D, E, F, G, H, J, K, L, M, N." ]	[ null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-1-1024x333.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-2-1024x411.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-3-1024x410.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-4.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-5.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-6.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-7.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-8.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-9.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-10.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-11.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-12.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-13.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-14.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-15.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-16.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-17.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-18.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-19.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-20.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-21.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-22.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-23.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-24.png", null, "http://taudevin-engineering.co.uk/wp-content/uploads/2017/10/GENERATING-AND-COUNTING-REGULAR-PERFECT-NUMBER-SQUARES-Image-25.png", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.93985647,"math_prob":0.98995924,"size":22775,"snap":"2020-24-2020-29","text_gpt3_token_len":5676,"char_repetition_ratio":0.17544244,"word_repetition_ratio":0.014177463,"special_character_ratio":0.252865,"punctuation_ratio":0.14748625,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9889141,"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],"im_url_duplicate_count":[null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null,5,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-05-25T06:05:08Z\",\"WARC-Record-ID\":\"<urn:uuid:4a4d14a6-db15-4fa0-bf43-8bf149adf2f7>\",\"Content-Length\":\"81426\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:ea0d9f2b-f52e-453e-a9b5-fc73ad87ce3e>\",\"WARC-Concurrent-To\":\"<urn:uuid:7d22372e-7934-4cb7-8938-c3b097e6182f>\",\"WARC-IP-Address\":\"83.166.162.20\",\"WARC-Target-URI\":\"https://taudevin-engineering.co.uk/interests/generating-and-counting-regular-perfect-number-squares/\",\"WARC-Payload-Digest\":\"sha1:GSMENGLUTQOWX7SXUSKJKKZ4JFE5VMAT\",\"WARC-Block-Digest\":\"sha1:DLILEXL2WNCHXWHPX4XS5LNJUECJ5HVI\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-24/CC-MAIN-2020-24_segments_1590347387219.0_warc_CC-MAIN-20200525032636-20200525062636-00000.warc.gz\"}"}
https://mathsgee.com/36993/which-number-s-is-are-equal-to-its-their-square	[ "0 like 0 dislike\n789 views\nWhich number(s) is(are) equal to its (their) square?\n\| 789 views\n\n0 like 0 dislike\nIf $x$ is the number to find, its square is $x^{2}$.\n$x$ is equal to its square hence: $x=x^{2}$\nSolve the above equation by factoring. First write with right side equal to zero.\n\\begin{aligned} &x-x^{2}=0 \\\\ &x(1-x)=0 \\end{aligned}\nsolutions: $x=0$ and $x=1$\n1) $x=0$, its square is $0^{2}=0$. Hence $x$ and its square are equal.\n2) $x=1$, its square is $1^{2}=1$. Hence $x$ and its square are equal.\nby Platinum (101k points)\n\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n2 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n0 like 0 dislike\n1 like 0 dislike\n0 like 0 dislike" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9201903,"math_prob":1.0000063,"size":446,"snap":"2023-40-2023-50","text_gpt3_token_len":160,"char_repetition_ratio":0.18778281,"word_repetition_ratio":0.0882353,"special_character_ratio":0.4349776,"punctuation_ratio":0.12871288,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9995457,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-11-30T01:50:12Z\",\"WARC-Record-ID\":\"<urn:uuid:527c1ca2-7bae-45c0-9086-67d4632f0e00>\",\"Content-Length\":\"124716\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:21a91cb3-cd9d-4179-9871-bd79b277a7a1>\",\"WARC-Concurrent-To\":\"<urn:uuid:4f2002d7-978f-4d3e-b83d-59ce077a26f3>\",\"WARC-IP-Address\":\"35.244.153.44\",\"WARC-Target-URI\":\"https://mathsgee.com/36993/which-number-s-is-are-equal-to-its-their-square\",\"WARC-Payload-Digest\":\"sha1:K4HLOVFO6XKRQOKUMKOD3HW3CTZF7OLK\",\"WARC-Block-Digest\":\"sha1:CCVRT5LEFJTSEMLLERQGYNB3JJR2UPCJ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100164.15_warc_CC-MAIN-20231130000127-20231130030127-00137.warc.gz\"}"}
https://ro.scribd.com/document/345725318/capacitors-in-series-and-parallel-combinations-electronics-post	[ "Sunteți pe pagina 1din 5\n\n# 12/12/2016 CapacitorsInSeriesandParallelCombinationsElectronicsPost\n\n{\nGet: Android certied;\n}\n\nHOME BASIC ELECTRONICS TUTORIALS QUESTIONS & ANSWERS COMPUTER NETWORKING TUTORIALS TECH ABOUT US\n\n## Capacitors In Series and Parallel Combinations Switches\n\nSasmita October 12, 2015 Capacitors\nMicroswitch,waterproof\nCapacitors in Series and Parallel Combinations switchChina\nCapacitors are one of the standard components in electronic and electrical circuits. manufacturer&exporter\nHowever, complicated combinations of capacitors mostly occur in practical circuits.\nswitchchina.com\n\nSwitches\nMicroswitch,waterproof\nswitchChinamanufacturer\n&exporter\nswitchchina.com\n\nIt is, therefore, useful to have a set of rules for finding the equivalent capacitance of\nsome general capacitors arrangements.\n\n## The equivalent capacitance of any complicated arrangement can be determined by the\n\nrepeated application of two simple rules and these rules are related to capacitors\nconnected in series and in parallel.\n\nCapacitors in Series\nCapacitors are said to be connected in series, when they are effectively daisy chained\ntogether in a single line.\n\nConsider two capacitors connected in series: i.e.in a line such that the positive plate of\none is attached to the negative plate of the other as shown in the fig. above.\n\nIn fact, let us suppose that the positive plate of capacitor 1 is connected to the input\nwire, the negative plate of capacitor 1 is connected to the positive plate of capacitor 2,\nand the negative plate of capacitor 2 is connected to the output wire.\n\nhttp://electronicspost.com/capacitorsinseriesandparallelcombinations/ 1/5\n12/12/2016 CapacitorsInSeriesandParallelCombinationsElectronicsPost\nNow the question arises, what is the equivalent capacitance between the input and\noutput wires?\n\nIn this connection, it is important to realize that the charge Q stored in the two\nSwitches\ncapacitors is same. Microswitch,waterproof\nThis can be explained as follows : switchChina\nmanufacturer&exporter\nLet us consider the internal plates i.e., the negative plate of capacitor 1, and the\npositive plate of capacitor 2. switchchina.com\n\nThese plates are physically disconnected from the rest of the circuit, so the total charge\non them must remain constant.\n\nAssuming, that these plates carry zero charge when zero potential difference is\napplied across the two capacitors, it follows that in the presence of a non-zero\npotential difference the charge Q on the positive plate of capacitor 2 must be balanced\nby an equal and opposite charge -Q on the negative plate of capacitor 1.\n\nSince the negative plate of capacitor 1 carries a charge -Q , the positive plate of\ncapacitor 2 carries a charge + Q to balance it.\n\n## The potential drops, and , across the two capacitors different.\n\nHowever, the sum of these drops equals the total potential drop applied across the\ninput and output wires:\n\ni.e.\n\nHence,\n\n## The reciprocal of the equivalent capacitance of two capacitors connected in series is\n\nthe sum of the reciprocals of the individual capacitances.\n\nhttp://electronicspost.com/capacitorsinseriesandparallelcombinations/ 2/5\n12/12/2016 CapacitorsInSeriesandParallelCombinationsElectronicsPost\n\nSwitches\nMicroswitch,waterproof\nswitchChinamanufacturer\n&exporter\nswitchchina.com\n\n## For capacitors connected in series, the equivalent capacitance equation can be\n\ngeneralized to :\n\nExample\nFind the overall capacitance and the individual rms voltage drops across the two\ncapacitors each with 47 nF ,in series when connected to a 12V a.c. supply.\n\nSolution :\n\nTotal Capacitance,\n\n## Voltage drop across the two identical 47 nF capacitors,\n\nCapacitors in Parallel\nhttp://electronicspost.com/capacitorsinseriesandparallelcombinations/ 3/5\n12/12/2016 CapacitorsInSeriesandParallelCombinationsElectronicsPost\n\nCapacitors in Parallel\nCapacitors are said to be connected in parallel when both of their terminals are\nrespectively connected to each terminal of the other capacitor or capacitors.\n\nConsider two capacitors connected in parallel: i.e., with the positively charged plates\nconnected to a common input wire, and the negatively charged plates attached to a\ncommon output wire as shown in the above fig.\n\nWhat is the equivalent capacitance between the input and output wires?\n\nIn this case, the potential difference across the two capacitors is the same, and is\nequal to the potential differnce between the input and output wires.\n\nHowever, the total stored charge Q is divided between the two capacitors, since it must\ndistribute itselfsuch that the voltage across them is same.\n\nSince, the capacitors may have different capacitances, and ,the charges\n\n## where is the total stored charge.\n\nIt follows that :\n\n17\n\nHence,\n\n18\n\n## In general for we can say that :\n\nThe equivalent capacitance of two capacitors connected in parallel is the sum of the\nindividual capacitances.\n\nFor capacitors connected in parallel, the equation for equivalent capacitance can\nbegeneralizes to :\n\nExample :\n\nhttp://electronicspost.com/capacitorsinseriesandparallelcombinations/ 4/5\n12/12/2016 CapacitorsInSeriesandParallelCombinationsElectronicsPost\nCalculate the combined capacitance of the following capacitors each with a\ncapacitance of 47 nF, when they are connected together in a parallel combination .\n\nSolution :\n\nTotal Capacitance,\n\n19\n\nTweet\nShare 0\n\nRelated Posts\n\n## What Is A Capacitor & What\n\nAre The Various Types of\nCapacitors" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.8365941,"math_prob":0.7621302,"size":4422,"snap":"2019-51-2020-05","text_gpt3_token_len":990,"char_repetition_ratio":0.18062472,"word_repetition_ratio":0.11755233,"special_character_ratio":0.18521032,"punctuation_ratio":0.11859838,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.96319485,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-01-21T02:09:24Z\",\"WARC-Record-ID\":\"<urn:uuid:b4730bce-9e11-4e75-822c-332ed27600e7>\",\"Content-Length\":\"379280\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:70d644df-564a-4733-9a2a-0f6ab6f62c81>\",\"WARC-Concurrent-To\":\"<urn:uuid:98fcc9ac-bd1b-408a-8256-c9248aea6b05>\",\"WARC-IP-Address\":\"151.101.250.152\",\"WARC-Target-URI\":\"https://ro.scribd.com/document/345725318/capacitors-in-series-and-parallel-combinations-electronics-post\",\"WARC-Payload-Digest\":\"sha1:VYRDIXF46KASO5P4FVYENG3OFWMRQZQZ\",\"WARC-Block-Digest\":\"sha1:3V2QRAJZR3ENGCDE4VWYYQGSZ2653QOV\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-05/CC-MAIN-2020-05_segments_1579250601241.42_warc_CC-MAIN-20200121014531-20200121043531-00508.warc.gz\"}"}
https://brilliant.org/discussions/thread/nmtc-problem-3a/	[ "# NMTC Problem 3a\n\nThe Fibonacci Sequence is defined by $F_0 = 1$,$F_1 =1$ and $F_n=F_{n-1}+F_{n-2}$. Prove that $7{F_{n+2}^3}-{F_n^3}-{F_{n+1}^3}$ is divisible by $F_{n+3}$.\n\nThis a part of my set NMTC 2nd Level (Junior) held in 2014.", null, "Note by Siddharth G\n5 years, 11 months ago\n\nThis discussion board is a place to discuss our Daily Challenges and the math and science related to those challenges. Explanations are more than just a solution — they should explain the steps and thinking strategies that you used to obtain the solution. Comments should further the discussion of math and science.\n\nWhen posting on Brilliant:\n\n• Use the emojis to react to an explanation, whether you're congratulating a job well done , or just really confused .\n• Ask specific questions about the challenge or the steps in somebody's explanation. Well-posed questions can add a lot to the discussion, but posting \"I don't understand!\" doesn't help anyone.\n• Try to contribute something new to the discussion, whether it is an extension, generalization or other idea related to the challenge.\n\nMarkdownAppears as\nitalics or _italics_ italics\nbold or __bold__ bold\n- bulleted- list\n• bulleted\n• list\n1. numbered2. list\n1. numbered\n2. list\nNote: you must add a full line of space before and after lists for them to show up correctly\nparagraph 1paragraph 2\n\nparagraph 1\n\nparagraph 2\n\n[example link](https://brilliant.org)example link\n> This is a quote\nThis is a quote\n # I indented these lines\n# 4 spaces, and now they show\n# up as a code block.\n\nprint \"hello world\"\n# I indented these lines\n# 4 spaces, and now they show\n# up as a code block.\n\nprint \"hello world\"\nMathAppears as\nRemember to wrap math in $$ ... $$ or $ ... $ to ensure proper formatting.\n2 \\times 3 $2 \\times 3$\n2^{34} $2^{34}$\na_{i-1} $a_{i-1}$\n\\frac{2}{3} $\\frac{2}{3}$\n\\sqrt{2} $\\sqrt{2}$\n\\sum_{i=1}^3 $\\sum_{i=1}^3$\n\\sin \\theta $\\sin \\theta$\n\\boxed{123} $\\boxed{123}$\n\nSort by:\n\nLet us take $F_{n}=x$ and $F_{n+1}=y \\Rightarrow\\ F_{n+2}=x+y \\Rightarrow\\ F_{n+3}=x+2y.$Now,let us expand the given expression:$: 7F_{n+2}^{3}-F_{n}^{3}-F_{n+1}^{3}$ in terms of $x$ and $y$.We get$: 7(x+y)^{3}-x^{3}-y^{3}$.Simplifying that,we get$: 6x^{3}+6y^{3}+21xy(x+y).$Taking $(x+y)$ common we get$: (x+y)(6x^{2}-6xy+6y^{2}+21xy) =(x+y)(6x^{2}+15xy+6y^{2}) =(x+y)(x+2y)(6x+3y).$But,$(2y+x)=F_{n+3}.$hence proved:):).\n\n- 5 years, 11 months ago\n\n- 5 years, 11 months ago\n\nAbsolutely right. This is wht I tried, but the $X,Y$ substitution, Mindblowing :D\n\n- 5 years, 11 months ago\n\nthanx!!\n\n- 5 years, 11 months ago\n\nPerfect! I didn't go for x,y and faced problems in factorizing. PS: Small Typo at the end.\n\n- 5 years, 11 months ago\n\nthanx!!\n\n- 5 years, 11 months ago\n\nyes thanku!!\n\n- 5 years, 11 months ago\n\nThe substitution certainly helped made it easier to manipulate.\n\nStaff - 5 years, 11 months ago\n\nYes, however, can we do this with induction?\n\n- 5 years, 11 months ago\n\nPossibly, but I don't see why that would work, nor do I see a way to start.\n\nThere is very little here to motivate a solution by induction. Knowing divisibility by $F_n$ doesn't tell you anything about divisibility by $F_{n+1}$.\n\nStaff - 5 years, 11 months ago\n\nHow did you do?\n\n- 5 years, 11 months ago\n\nNot well, 1b 5a, b 6b and half of the 8th were good. I left 1a half done. How was your paper?\n\n- 5 years, 11 months ago\n\nSigh, atleast you had one full question to your credit. I got the first one fully , messed up second one (after getting half of it), third one wasn't salubrious, (I tried both, got to almost the answers), 4th I didnt do, 5a I didnt know, 5 b I got it, 6- I wrote Yes and No alternatingly :P, 7th I almost got it but lost it due to calculation error (You wont believe I put 40 cubed= 16000 :( )..8th I am not sure of it's accuracy....\n\nNow , you clearly know I sucked more than you did. :(\n\n- 5 years, 11 months ago\n\nSame problem with wet 3a. BTW are you sure that 6a is 'Yes'?\n\n- 5 years, 11 months ago\n\nOh Nono, I didnt understand it properly but gave some stupid explanation and put Yes. WBU? How did you do that question?\n\n- 5 years, 11 months ago\n\n6a. Forthe transformation, we needed $\\Delta B=+2$ and $\\Delta O=0$. However with the allowed changes, these conditions were contradicting each other.\n\n- 5 years, 11 months ago\n\nWell, as I said...I wasnt even considering that question right :P. How was NTSE? (Range of marks..?)\n\n- 5 years, 11 months ago\n\nTerrible! Expecting 114/140; expected cutoff~123\n\n- 5 years, 11 months ago\n\nOh, I am sorry for bringing that up. How did you predict the cutoff so soon? (Institute?)...And, Could you tell me the number of seats for general cat. in Delhi.\n\n- 5 years, 11 months ago\n\nNot really, mostly from the marks of competitive peers. The no. of seats was 60 last year, however, I think they are going to increase it. FIITJEE predicts it to be near 119 due to this.\n\n- 5 years, 11 months ago\n\nOh I see. Be optimistic though :)\n\n- 5 years, 11 months ago\n\nNah, just moving on. Are you giving RMO?\n\n- 5 years, 11 months ago\n\nYes I am. But NMTC has almost you know, shattered the vital mathematical force in me :3\n\n- 5 years, 11 months ago\n\n- 5 years, 11 months ago\n\nGood. xD. It ough'to be that good lest you wouldn't qualify in TN. Expecting 130+, let's see.(the results)\n\n- 5 years, 11 months ago" ]	[ null, "https://ds055uzetaobb.cloudfront.net/brioche/avatars-2/resized/45/9e71bcac289c714b415eec8387cb28b0.46e0d9fe68-TVzm1Xdhrs.jpg", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9326588,"math_prob":0.9471431,"size":7097,"snap":"2020-45-2020-50","text_gpt3_token_len":2490,"char_repetition_ratio":0.12223319,"word_repetition_ratio":0.4943723,"special_character_ratio":0.32492602,"punctuation_ratio":0.1496437,"nsfw_num_words":1,"has_unicode_error":false,"math_prob_llama3":0.97207606,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-10-27T15:13:51Z\",\"WARC-Record-ID\":\"<urn:uuid:4e2c1581-81e1-4cc3-8fe4-3d4ddcfbbe09>\",\"Content-Length\":\"194766\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0ac6feb8-5c45-492e-8bdc-bb0615c0f98e>\",\"WARC-Concurrent-To\":\"<urn:uuid:e72f1b4b-f5aa-4e16-8287-ee732d8b99a4>\",\"WARC-IP-Address\":\"104.20.34.242\",\"WARC-Target-URI\":\"https://brilliant.org/discussions/thread/nmtc-problem-3a/\",\"WARC-Payload-Digest\":\"sha1:IBADDW6V6YG3GRBW4D4MZIEMSTVRLWTX\",\"WARC-Block-Digest\":\"sha1:CSLMUUF4LNWEG7UIGTROPKJHNSLMMCA4\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-45/CC-MAIN-2020-45_segments_1603107894203.73_warc_CC-MAIN-20201027140911-20201027170911-00451.warc.gz\"}"}
https://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=RVBS_FLES	[ "# LES\n\nPerforms a less than or equal to comparison on elements of two dynamic arrays.\n\n## Synopsis\n\n```LES(dynarray1,dynarray2)\n```\n\n### Arguments\n\n dynarray An expression that resolves to a dynamic array.\n\n## Description\n\nThe LES function compares each corresponding numeric element from two dynamic arrays and determines if the first value is less than or equal to the second value. It returns a dynamic array of boolean values in which each element comparison is represented. It returns a 1 if the dynarray1 element value is less than or equal to the dynarray2 element value. It returns a 0 if the dynarray1 element value is greater than the dynarray2 element value.\n\nLES removes signs and leading and trailing zeros from element values before making the comparison. If an element is missing, or has a null string or a non-numeric value, LES assigns it a value of 0 for the purpose of this comparison.\n\nIf the two dynamic arrays have different numbers of elements, the returned dynamic array has the number of elements of the longer dynamic array. By default, the shorter dynamic array is padded with 0 value elements for the purpose of comparison. You can also use the REUSE function to define behavior when specifying two dynamic arrays with different numbers of elements.\n\nFor two elements to be compared, they must be on the same dynamic array level. For example, you cannot compare a value mark (@VM) dynamic array element to a subvalue mark (@SM) dynamic array element.\n\n## Examples\n\nThe following example uses the LES function to return a less than or equal to comparison for each of the elements in dynamic arrays a and b:\n\n```a=10:@VM:-22:@VM:-33:@VM:45\nb=10:@VM:-23:@VM:0:@VM:44\nPRINT LES(a,b)\n! returns 1ý0ý1ý0```" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.6693747,"math_prob":0.97531796,"size":1679,"snap":"2021-43-2021-49","text_gpt3_token_len":382,"char_repetition_ratio":0.20716418,"word_repetition_ratio":0.047272727,"special_character_ratio":0.22036926,"punctuation_ratio":0.10429448,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9649736,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-10-26T14:20:11Z\",\"WARC-Record-ID\":\"<urn:uuid:085b6b74-b33d-440f-9f93-304d4594106d>\",\"Content-Length\":\"15956\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:ae972b3a-4e6b-4976-a183-542eb7a83f77>\",\"WARC-Concurrent-To\":\"<urn:uuid:beef93f7-d5c4-4609-9b04-382e7d9a8191>\",\"WARC-IP-Address\":\"198.133.74.116\",\"WARC-Target-URI\":\"https://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY=RVBS_FLES\",\"WARC-Payload-Digest\":\"sha1:W67BL5FLYPCSPNEHT6G2GBCM5WIQUQYJ\",\"WARC-Block-Digest\":\"sha1:AAV5QK7F7NOHM6VS2LPP5CU2PE52R22O\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-43/CC-MAIN-2021-43_segments_1634323587908.20_warc_CC-MAIN-20211026134839-20211026164839-00325.warc.gz\"}"}
https://www.r-bloggers.com/the-fanplot-package-for-r/	[ "# The fanplot package for R\n\nAugust 13, 2012\nBy\n\nWant to share your content on R-bloggers? click here if you have a blog, or here if you don't.\n\nMy fanplot package has gone up on CRAN. Here is a online version of the vignette.\n\n### Introduction\n\nThe fanplot package contains a collection of R (R Development Core Team, 2012) functions to effectively display plots of sequential distributions such as probabilistic forecasts. The plotting of distributions are based around two functions. The first, pn. calculates the percentiles for a set of sequential distributions over a specified time period. The second, fan. plots the calculated percentiles of the sequential distributions. The resulting plot is a set of coloured polygon, with shadings corresponding to the percentile values.\n\nThis document illustrates these two core functions using MCMC simulation results from fitted stochastic volatility models. These MCMC simulations can be recreated from data and BUGS model also contained in the fanplot package, via the R2OpenBUGS package of (Sturtz et al., 2005). These are first shown for dataframe type object where there is no time series type attributes, followed by plots based on a ts object.\n\n### Volatility Plots\n\nTo illustrate the basics of the fanplot package consider the svpdx data contained in the tsbugs (Abel, 2013) package. This contains information on the log return of the Pound-Dollar exchange rate from 2nd October 1981 to 28th June 1985. For a view of the first part of the data, use the head function, after loading the fanplot package.\n\n> library(\"tsbugs\")\ndate pdx\n1 1981-10-02 -0.3555316\n2 1981-10-05 1.4254090\n3 1981-10-06 -0.4439399\n4 1981-10-07 1.0256500\n5 1981-10-08 1.6775790\n6 1981-10-09 0.3690041\n\n\nThis data is a dataframe object, and is difficult to express it as a standard ts object due to the irregular nature of the data. It is best plot initially without an x-axis, which can then be added later using the axis function:\n\n> #plot\n> plot(svpdx$pdx, type = \"l\", xaxt = \"n\", xlab = \"Time\", ylab = \"Return\") > #x-axis > svpdx$rdate <- format(svpdx$date, format = \"%b %Y\") > mth <- unique(svpdx$rdate)\n> qtr <- mth[seq(1,length(mth),3)]\n> axis(1, at = match(qtr, svpdx$rdate), labels = qtr, cex.axis = 0.55) > axis(1, at = match(mth, svpdx$rdate), labels = FALSE, tcl = -0.2)", null, "If the xaxt argument was not used in the plot function and the labels not added later, the x-axis would be based on the row number of each observation in the svpdx data, i.e. an index sequence from 1 to 945.\n\nTo produce the x-axis with date information in the above code, a new column is added to svpdx for the month-year combination of each observation. Objects mth and qtr are then created to mark each month and quarter in the data series respectively. Major axis ticks are then plotted on the unseen 1 to 945 index for the beginning of every quarter with a corresponding label, whilst minor axis tick are also plotted for beginning of every month.\n\nMeyer and Yu (2002) used the above Pound-Dollar exchange rate data to fit various stochastic volatility models in WinBUGS (Lunn et al., 2000). One such stochastic model they fitted to the data is contained in my1.txt in the model directory of the fanplot package. The model of Meyer and Yu (2002) can be refitted in BUGS via R, using the R2OpenBUGS package (Sturtz et al., 2005).\n\n> library(\"R2OpenBUGS\")\n> # write model file:\n> my1.bug <- dget(system.file(\"model\", \"my1.txt\", package = \"fanplot\"))\n> write.model(my1.bug, \"my1.txt\")\n> # take a look:\n> file.show(\"my1.txt\")\n> # run openbugs\n> my1<-bugs(data=list(n=length(svpdx$pdx),y=svpdx$pdx),\ninits=list(list(phistar=0.975,mu=0,itau2=50)),\nparam=c(\"mu\",\"phi\",\"tau\",\"theta\"),\nmodel=\"my1.txt\",\nn.iter = 11000, n.burnin = 1000, n.chains = 1)\n\n\nHere, the same initial parameter values as Meyer and Yu (2002) are set. One chain of the MCMC simulation is run for 11000 iterations, with the first 1000 used for burn in. The resulting bugs object contains the MCMC simulation results for the parameters in the stochastic volatility model, including the time dependent volatility parameters (", null, "$\\theta_t$). This set of sequential posterior distributions are of interest when studying the variation in the data over time.\n\nThe fanplot package can effectively display the entire posterior distribution of", null, "$\\theta_t$. The separate MCMC simulation of the volatility be obtained from my1 using", null, "$\\theta_t$, which is overwritten when creating a new th.mcmc object},\n\n> th.mcmc <- my1$sims.list$theta\n\n\nA plot of the entire posterior distribution of", null, "$\\theta_t$ first requires a calculation of the percentiles over all", null, "$t$ using the pn function,\n\n> library(\"fanplot\")\n> th.pn <- pn(sims = th.mcmc)\n\n\nThis produces a pn type object, where rows represent the time index and columns the percentiles calculated.\n\n> head(th.pn[,c(1:3, 97:99)])\n1% 2% 3% 97% 98% 99%\n[1,] -1.05400 -0.999006 -0.9811 -0.2069 -0.1615000 -0.091476\n[2,] -1.00907 -0.947200 -0.9127 -0.2230 -0.1829000 -0.116800\n[3,] -1.01600 -0.945000 -0.9030 -0.1869 -0.1776000 -0.151200\n[4,] -1.02800 -0.932900 -0.8911 -0.1714 -0.1466000 -0.082590\n[5,] -1.00600 -0.917500 -0.8740 -0.1367 -0.0973464 -0.047270\n[6,] -1.05101 -0.935700 -0.8998 -0.1297 -0.0543700 0.029130\n\n\nEvery percentile between 1st and 99th are calculated by default, however, more or less percentiles can be calculated via the p argument in the pn function. The number of percentiles calculated has a direct impact on the plotting of the sequential distributions, as we shall see later. Additional arguments to control the indexing of the rows, which is of use when the time series are from regular intervals, will also be discussed later.\n\nIn order to plot the th.pn percentile object the plot area must be first set. This can be done using type = “n” argument in plot. Both the xlim and ylim arguments need to be set appropriately. In the code below, the xlim argument is set between 1 and 945, the length of th.pn. The ylim argument is set to the range of th.pn to enable all percentiles calculated to be included in the plot area.\n\nOnce the plot area is set up, the fan function can be used to add the th.pn object. Each percentile in the plot is represented by a different shade of the default colour scale in the fan.col argument of fan. In addition, contour lines are drawn on every decile, with labels for these decides add to the right hand side.\n\n> #empty plot\n> plot(NULL, type = \"n\", xlim = c(1, 945), ylim = range(th.pn), ylab = \"Theta\")\n> fan(th.pn)", null, "The fan.txt function can be add more labels to a set of sequential distributions plotted using fan. When used in addition to the fan function, labels for closely spaced deciles can be controlled to allow a more spacious display of labels in comparison to those shown in the above plot. It can also be used to add text labels to percentiles that are not of a unit of 10, such as the 1st or 99th. The code below demonstrates these features. First a empty plot area is created with the x-axis changed to dates using the same method as in the plot of the original data. Second, the percentiles of the sequential distribution is plotted for", null, "$\\theta_t$ with no text labels (setting txt = NA) and contour lines for the 1st and 99th percentiles alongside some selected deciles. The ln argument in the fan function is set to include contour lines at percentiles where future text labels are to be added.\n\n> #empty plot with x-axis added later\n> plot(NULL, type = \"l\", xlim = c(1, 945), xlab = \"Time\", xaxt = \"n\", ylim = range(th.pn), ylab = \"Theta\")\n> axis(1, at = match(qtr, svpdx$rdate), labels = qtr, cex.axis = 0.55) > axis(1, at = match(mth, svpdx$rdate), labels = FALSE, tcl = -0.2)\n> fan(th.pn, txt = NA, ln = c(1,10,30,50,70,90,99))\n> #add text labels for percentiles\n> fan.txt(th.pn, pn.r = c(1,10,50,90,99))\n> fan.txt(th.pn, pn.l = c(1,30,70,99))", null, "The colour of the percentiles in the sequential distributions can be easily altered from the default heat.colors scheme. A new set of graded colours can be passed to the fan function using the fan.col argument. The number of colours should be half the number of percentiles (columns) in the pn object. New graded colour schemes can be constructed in a number of ways. For example, using the colorRampPalette a new shading from blue to white, via grey can be created.\n\n> pal <- colorRampPalette(c(\"royalblue\", \"grey\", \"white\"))\n\n\nUsing this palette, 50 colours (approximately half the number of percentiles calculated in pn) can be defined:\n\n> fancol <- pal(50)\n\n\nFor a change, this new colour scheme is used to plot the posterior distribution of the standard deviation over time,", null, "$\\sigma_t$, which is derived as such:\n\n> sigma.pn <- pn(sims = sqrt(exp(th.mcmc)))\n\n\nThe new colour scheme can then be passed to the fan function for sigma.pn. with contour lines on selected percentiles:\n\n> #empty plot with x-axis added later\n> plot(NULL, type = \"l\", xlim = c(1, 945), xlab = \"Time\", xaxt = \"n\", ylim = range(sigma.pn), ylab = \"Standard Deviation\")\n> axis(1, at = match(qtr, svpdx$rdate), labels = qtr, cex.axis = 0.55) > axis(1, at = match(mth, svpdx$rdate), labels = FALSE, tcl = -0.2)\n> fan(sigma.pn, fan.col = fancol, ln = c(1, 10, 50, 90, 99))", null, "### Model Fits\n\nTo illustrate plots in the fanplot package that use time series objects (ts) based on regularly spaced data, a stochastic volatility model is fitted to the change of population growth rate of England and Wales, similar to that in Abel et al. (2010).\n\nPopulation data from 1841 to 2007 from Human Mortality Database (2012) are included in the fanplot package (ew). The growth rate, that the stochastic volatility model is based on can be derived following Rogers (1995) as such\n\n> r <- ts(ew[2:167]/ew[1:166]-1, start=1841)\n\n\nMean stationarity can be obtained by differencing the series\n\n> y <- diff(r)\n\n\nUsing this differenced growth rate time series, we can build a BUGS stochastic volatility model using the tsbugs package.\n\n> pop.bug <- sv.bugs(y, k=25, sim=TRUE,\nsv.mean.prior2 = \"dgamma(0.000001,0.000001)\",\nsv.ar.prior2 = \"dunif(-0.999, 0.999)\")\n\n\nThe sv.bugs function specifies for 25 future values to be forecast and simulations of the model to be taken. Specifications for alternative prior distributions for the parameters in the volatility process are also stated. This BUGS model can be run using R2OpenBUGS,\n\n> library(\"R2OpenBUGS\")\n> # write model file:\n> writeLines(pop.bug$bug, \"pop.txt\") > # take a look: > file.show(\"pop.txt\") > # run openbugs > pop <- bugs(data = pop.bug$data,\ninits = list(list(psi0.star=exp(12), psi1=0.5, itau2=0.5)),\nparam = c(\"psi0\", \"psi1\", \"tau\", \"y.new\", \"y.sim\"),\nmodel = \"pop.txt\",\nn.iter = 11000, n.burnin = 1000, n.chains = 1)\n\n\nAs was the case with the exchange rate data, one chain of the MCMC simulation is run for 11000 iterations, with the first 1000 used for burn in. The resulting bugs object contains the MCMC simulation results for the parameters in the stochastic volatility model, including the time dependent model fits (", null, "$E(y_t)$), forecasts of the future population growth rate (", null, "$\\hat{r}_{t+h\|t}$) and population (", null, "$\\hat{p}_{t+h\|t}$).\n\nThe fanplot package can efficiently display these sequential distributions in a number of ways. Consider the MCMC simulation of the model fit, which can be extracted from the pop using,\n\n> y.mcmc <- pop$sims.list$y.sim\n\n\nA plot of the entire posterior distribution of the model fits requires the calculation of percentiles over all", null, "$t$ using the pn function.\n\n> y0 <- tsp(y)\n> y.pn <- pn(sims = y.mcmc, start = y0)\n\n\nHere, the corresponding start date is also given so that the pn object takes the relevant time series properties when plotted. This saves on the previous effort for the non-regularly spaced exchange rate data, in setting up date labels on the x-axis. The time series properties are stored in the tsp attributes of the new y.pn object.\n\n> str(y.pn)pn [1:165, 1:99] -0.00357 -0.00367 -0.00405 -0.00467 -0.00588 ...\n- attr(, \"dimnames\")=List of 2\n..$: NULL ..$ : chr [1:99] \"1%\" \"2%\" \"3%\" \"4%\" ...\n- attr(, \"tsp\")= num [1:3] 1843 2007 1\n\n\nThese attributes can be directly used to set up the x-axis limits in the plot area, alongside y-axis limits based on the difference in the growth rate, which was the basis for the stochastic variance model. The fan function can then be used to add the sequential posterior distributions with contour lines at the 1st, 10th, 90th and 99th percentiles using the ln argument. Note, the fan function will automatically only draw and label contour lines given in the ln argument. If the user wishes to subdue this and add text labels later, they can do so by adding the txt = NA as was demonstrated earlier. The original data can also be plotted on top of the posterior distributions using the lines function:\n\n> #empty plot\n> plot(NULL, type = \"l\", xlim = range(time(y.pn)), xlab = \"Time\", ylim = range(y), ylab = \"Expected Model Fit\")\n> fan(y.pn, ln = c(1, 10, 90, 99))\n> lines(diff(r), lwd = 2)", null, "A coarser set of colours can be plotted by creating a pn object with fewer percentiles. This is done by defining the p argument of the pn function with the only the percentiles for which colour changes are desired.\n\n> y.pn2 <- pn(sims = y.mcmc, p = c(1, 20, 40, 60, 80, 99), start = y0)\n\n\nNote, that elements of p will ultimately be adjusted to be symmetric around 50. For example if a user set p = c(1, 40, 80) a pn object identical the one above would be returned. This allows the user, if desired, to only define percentiles either above or below 50.\n\nThe new y.pn2 object can be plotted using the default arguments for contour lines and text labels. Lines are never drawn for percentiles not calculated in the pn object. As a result, in the plotting of y.pn2 there are no contour lines on the 10th, 30th, 50th, 70th and 90th deciles.\n\n> #empty plot\n> plot(NULL, type = \"l\", xlim = range(time(y.pn)), xlab = \"Time\", ylim = range(diff(r)), ylab = \"Expected Model Fit\")\n> fan(y.pn2)\n> lines(diff(r), lwd = 2)", null, "### Forecast Fans\n\nTo illustrate the plotting of forecast fans we use the MCMC predictive distributions in the pop object. These are used to derive posterior predictive distributions of the population growth rate and population total using the diffinv function,\n\n> ynew.mcmc <- pop$sims.list$y.new\n> rnew.mcmc <- apply(ynew.mcmc, 1, diffinv, xi = tail(r,1))\n> rnew.mcmc <- t(rnew.mcmc[-1,])\n>\n> pnew.mcmc <- apply(1+rnew.mcmc, 1, cumprod) * tail(ew,1)\n> pnew.mcmc <- t(pnew.mcmc)\n\n\nPercentiles for rnew.mcmc can be derived as\n\n> r0 <- tsp(r)\n> rnew.pn <- pn(sims = rnew.mcmc, start = r0 + 1)\n\n\nNote, that as rnew.mcmc is a simulation of forecasts, the start argument set to the year after the last observation of the population growth rate. Forecast fans are plotted after estimating the percentile objects, much in the same way as they were for the volatility and model fit in the previous sections. However, some extra care must be taken in setting up the xlim to allow space for the fan to appear in the right hand side of plotting area. For the population growth rate this is demonstrated alongside a small section of the underlying simulation data (the first 30 MCMC samples of predictive distribution) that are represent a small part of the underlying data used to calculate the percentiles.\n\n> par(mfrow = c(1 ,2))\n> #sample of underlying simulation data\n> plot(r, ylim = range(r), xlim = c(1940, 2040), lwd = 2, ylab = \"Population Growth Rate\")\n> for (i in 1:30) lines(ts(rnew.mcmc[i, ], r0 + 1), col = \"grey\")\n> #plot r\n> plot(r, ylim = range(r), xlim = c(1940, 2040), lwd = 2, ylab = \"Population Growth Rate\")\n> fan(rnew.pn)", null, "The anchor argument in pn can be utilised to bridge the gap between the predictive distribution fan and the final data point. The associated starting point for creating an time series type object for plotting must also be adjusted to account for the anchoring. For the pnew.mcmc the two alternative sets of percentile calculations, with or without an anchoring can be derived as such:\n\n> p0 <- tsp(ew)\n> pnew.pn <- pn(sims = pnew.mcmc/1e+06, start = p0 + 1)\n> pnew.pn2 <- pn(sims = pnew.mcmc/1e+06, p = c(1, 10, 40, 50), anchor = tail(ew,1)/1e+06, start = p0)\n\n\nFor the pnew.pn2 only a few percentiles are calculated, which will provide a coarser set of shade in the plotting of the predictive distribution. In addition, the anchor is set to the last observed population count, hence the start point is now on the last observation p0. not at p0 + 1. In both calculations simulations are divided by one million to provide easier interpretation.\n\nBoth pn objects can be plotted side by side using the fan function. Contour line colours can be altered directly using the ln.col argument for the display of pnew.pn2 on the right hand side below, in comparison to the default plot on the left hand side.\n\n> par(mfrow = c(1 ,2))\n> #plot ew\n> plot(ew/1e+06, ylim = c(40, 80), xlim = c(1940, 2040), lwd = 2, ylab = \"Population (m)\")\n> fan(pnew.pn)\n> #plot ew\n> plot(ew/1e+06, ylim = c(40, 80), xlim = c(1940, 2040), lwd = 2, ylab = \"Population (m)\")\n> fan(pnew.pn2, ln.col = \"black\")", null, "### References\n\nAbel, G. J. (2013). tsbugs: Create time series BUGS models. Retrieved 26 February 2013, from http://cran.r-project.org/web/packages/tsbugs.\nAbel, G. J., J. Bijak, and J. Raymer (2010). A comparison of official population projections with Bayesian time series forecasts for England and Wales. Population Trends, 95–114.\nHuman Mortality Database (2012). Available at http://www.mortality.org. University of California, Berkeley (USA) and Max Planck Institute for Demographic Research (Germany).\nLunn, D. J., A. Thomas, N. Best, and D. Spiegelhalter (2000, October). WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10 (4), 325–337.\nMeyer, R. and J. Yu (2000). BUGS for a Bayesian analysis of stochastic volatility models. Econometrics Journal 3 (2).\nR Development Core Team (2012). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.\nRogers, A. (1995). Multiregional Demography: Principles, Methods and Extensions (1 ed.). John Wiley & Sons.\nSturtz, S., U. Ligges, and A. Gelman (2005). R2WinBUGS: a package for running WinBUGS from R. Journal of Statistical Software 12 (3).", null, "", null, "R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.\nWant to share your content on R-bloggers? click here if you have a blog, or here if you don't." ]	[ null, "https://gjabel.files.wordpress.com/2013/02/fanplot-003.png", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-008.png", null, "https://s0.wp.com/latex.php", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-009.png", null, "https://s0.wp.com/latex.php", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-013.png", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-021.png", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-023.png", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-026.png", null, "https://gjabel.files.wordpress.com/2013/02/fanplot-028.png", null, "https://feeds.wordpress.com/1.0/comments/gjabel.wordpress.com/508/", null, "https://i0.wp.com/stats.wordpress.com/b.gif", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.7941787,"math_prob":0.9820728,"size":18415,"snap":"2019-51-2020-05","text_gpt3_token_len":5084,"char_repetition_ratio":0.13274673,"word_repetition_ratio":0.12442848,"special_character_ratio":0.3023622,"punctuation_ratio":0.16799589,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99547166,"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],"im_url_duplicate_count":[null,2,null,null,null,null,null,null,null,null,null,null,null,2,null,null,null,2,null,null,null,2,null,null,null,null,null,null,null,null,null,2,null,2,null,2,null,2,null,5,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-01-28T04:38:22Z\",\"WARC-Record-ID\":\"<urn:uuid:add8889d-edfe-4438-8a5f-a59a367079fd>\",\"Content-Length\":\"265925\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c0d07b5d-503d-4d95-90f9-2729e55634f9>\",\"WARC-Concurrent-To\":\"<urn:uuid:46e89e73-12d4-4d00-96e7-1724bf3836e9>\",\"WARC-IP-Address\":\"104.28.8.205\",\"WARC-Target-URI\":\"https://www.r-bloggers.com/the-fanplot-package-for-r/\",\"WARC-Payload-Digest\":\"sha1:AFWVLD6JJGI6M7FZVJIZC7KZYOGVXZFK\",\"WARC-Block-Digest\":\"sha1:AO5PDE3ZF6VIKOXZ5NRL3IPTCB2W4ATF\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-05/CC-MAIN-2020-05_segments_1579251773463.72_warc_CC-MAIN-20200128030221-20200128060221-00486.warc.gz\"}"}
https://www.colorhexa.com/013f04	[ "# #013f04 Color Information\n\nIn a RGB color space, hex #013f04 is composed of 0.4% red, 24.7% green and 1.6% blue. Whereas in a CMYK color space, it is composed of 98.4% cyan, 0% magenta, 93.7% yellow and 75.3% black. It has a hue angle of 122.9 degrees, a saturation of 96.9% and a lightness of 12.5%. #013f04 color hex could be obtained by blending #027e08 with #000000. Closest websafe color is: #003300.\n\n• R 0\n• G 25\n• B 2\nRGB color chart\n• C 98\n• M 0\n• Y 94\n• K 75\nCMYK color chart\n\n#013f04 color description : Very dark lime green.\n\n# #013f04 Color Conversion\n\nThe hexadecimal color #013f04 has RGB values of R:1, G:63, B:4 and CMYK values of C:0.98, M:0, Y:0.94, K:0.75. Its decimal value is 81668.\n\nHex triplet RGB Decimal 013f04 `#013f04` 1, 63, 4 `rgb(1,63,4)` 0.4, 24.7, 1.6 `rgb(0.4%,24.7%,1.6%)` 98, 0, 94, 75 122.9°, 96.9, 12.5 `hsl(122.9,96.9%,12.5%)` 122.9°, 98.4, 24.7 003300 `#003300`\nCIE-LAB 22.196, -31.071, 28.135 1.812, 3.57, 0.708 0.297, 0.586, 3.57 22.196, 41.916, 137.838 22.196, -20.709, 26.134 18.895, -15.949, 11.003 00000001, 00111111, 00000100\n\n# Color Schemes with #013f04\n\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #3f013c\n``#3f013c` `rgb(63,1,60)``\nComplementary Color\n• #1d3f01\n``#1d3f01` `rgb(29,63,1)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #013f23\n``#013f23` `rgb(1,63,35)``\nAnalogous Color\n• #3f011d\n``#3f011d` `rgb(63,1,29)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #23013f\n``#23013f` `rgb(35,1,63)``\nSplit Complementary Color\n• #3f0401\n``#3f0401` `rgb(63,4,1)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #04013f\n``#04013f` `rgb(4,1,63)``\n• #3c3f01\n``#3c3f01` `rgb(60,63,1)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #04013f\n``#04013f` `rgb(4,1,63)``\n• #3f013c\n``#3f013c` `rgb(63,1,60)``\n• #000000\n``#000000` `rgb(0,0,0)``\n• #000d01\n``#000d01` `rgb(0,13,1)``\n• #012602\n``#012602` `rgb(1,38,2)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #015806\n``#015806` `rgb(1,88,6)``\n• #027107\n``#027107` `rgb(2,113,7)``\n• #028a09\n``#028a09` `rgb(2,138,9)``\nMonochromatic Color\n\n# Alternatives to #013f04\n\nBelow, you can see some colors close to #013f04. Having a set of related colors can be useful if you need an inspirational alternative to your original color choice.\n\n• #0e3f01\n``#0e3f01` `rgb(14,63,1)``\n• #083f01\n``#083f01` `rgb(8,63,1)``\n• #033f01\n``#033f01` `rgb(3,63,1)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #013f09\n``#013f09` `rgb(1,63,9)``\n• #013f0e\n``#013f0e` `rgb(1,63,14)``\n• #013f14\n``#013f14` `rgb(1,63,20)``\nSimilar Colors\n\n# #013f04 Preview\n\nThis text has a font color of #013f04.\n\n``<span style=\"color:#013f04;\">Text here</span>``\n#013f04 background color\n\nThis paragraph has a background color of #013f04.\n\n``<p style=\"background-color:#013f04;\">Content here</p>``\n#013f04 border color\n\nThis element has a border color of #013f04.\n\n``<div style=\"border:1px solid #013f04;\">Content here</div>``\nCSS codes\n``.text {color:#013f04;}``\n``.background {background-color:#013f04;}``\n``.border {border:1px solid #013f04;}``\n\n# Shades and Tints of #013f04\n\nA shade is achieved by adding black to any pure hue, while a tint is created by mixing white to any pure color. In this example, #000500 is the darkest color, while #f1fff1 is the lightest one.\n\n• #000500\n``#000500` `rgb(0,5,0)``\n• #001802\n``#001802` `rgb(0,24,2)``\n• #012c03\n``#012c03` `rgb(1,44,3)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\n• #015205\n``#015205` `rgb(1,82,5)``\n• #026606\n``#026606` `rgb(2,102,6)``\n• #027908\n``#027908` `rgb(2,121,8)``\n• #028c09\n``#028c09` `rgb(2,140,9)``\n• #03a00a\n``#03a00a` `rgb(3,160,10)``\n• #03b30b\n``#03b30b` `rgb(3,179,11)``\n• #03c60d\n``#03c60d` `rgb(3,198,13)``\n• #03d90e\n``#03d90e` `rgb(3,217,14)``\n• #04ed0f\n``#04ed0f` `rgb(4,237,15)``\n• #09fb15\n``#09fb15` `rgb(9,251,21)``\n• #1cfb27\n``#1cfb27` `rgb(28,251,39)``\n• #30fc3a\n``#30fc3a` `rgb(48,252,58)``\n• #43fc4c\n``#43fc4c` `rgb(67,252,76)``\n• #56fc5e\n``#56fc5e` `rgb(86,252,94)``\n• #6afd71\n``#6afd71` `rgb(106,253,113)``\n• #7dfd83\n``#7dfd83` `rgb(125,253,131)``\n• #90fd95\n``#90fd95` `rgb(144,253,149)``\n• #a4fea8\n``#a4fea8` `rgb(164,254,168)``\n• #b7feba\n``#b7feba` `rgb(183,254,186)``\n• #cafecd\n``#cafecd` `rgb(202,254,205)``\n• #ddfedf\n``#ddfedf` `rgb(221,254,223)``\n• #f1fff1\n``#f1fff1` `rgb(241,255,241)``\nTint Color Variation\n\n# Tones of #013f04\n\nA tone is produced by adding gray to any pure hue. In this case, #1f211f is the less saturated color, while #013f04 is the most saturated one.\n\n• #1f211f\n``#1f211f` `rgb(31,33,31)``\n• #1c241c\n``#1c241c` `rgb(28,36,28)``\n• #1a261a\n``#1a261a` `rgb(26,38,26)``\n• #172918\n``#172918` `rgb(23,41,24)``\n• #152b16\n``#152b16` `rgb(21,43,22)``\n• #122e14\n``#122e14` `rgb(18,46,20)``\n• #103011\n``#103011` `rgb(16,48,17)``\n• #0d330f\n``#0d330f` `rgb(13,51,15)``\n• #0b350d\n``#0b350d` `rgb(11,53,13)``\n• #08380b\n``#08380b` `rgb(8,56,11)``\n• #063a08\n``#063a08` `rgb(6,58,8)``\n• #033d06\n``#033d06` `rgb(3,61,6)``\n• #013f04\n``#013f04` `rgb(1,63,4)``\nTone Color Variation\n\n# Color Blindness Simulator\n\nBelow, you can see how #013f04 is perceived by people affected by a color vision deficiency. This can be useful if you need to ensure your color combinations are accessible to color-blind users.\n\nMonochromacy\n• Achromatopsia 0.005% of the population\n• Atypical Achromatopsia 0.001% of the population\nDichromacy\n• Protanopia 1% of men\n• Deuteranopia 1% of men\n• Tritanopia 0.001% of the population\nTrichromacy\n• Protanomaly 1% of men, 0.01% of women\n• Deuteranomaly 6% of men, 0.4% of women\n• Tritanomaly 0.01% of the population" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.52026373,"math_prob":0.81976825,"size":3633,"snap":"2023-40-2023-50","text_gpt3_token_len":1637,"char_repetition_ratio":0.13061449,"word_repetition_ratio":0.007380074,"special_character_ratio":0.5615194,"punctuation_ratio":0.23756906,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9889981,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-12-04T09:37:34Z\",\"WARC-Record-ID\":\"<urn:uuid:f375986a-294e-4665-86f9-ef81b92dea62>\",\"Content-Length\":\"36075\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:d85bc69a-14ab-479c-b9f5-7ccd915596cb>\",\"WARC-Concurrent-To\":\"<urn:uuid:d198225e-4a26-4515-90aa-bde24d3a64d6>\",\"WARC-IP-Address\":\"178.32.117.56\",\"WARC-Target-URI\":\"https://www.colorhexa.com/013f04\",\"WARC-Payload-Digest\":\"sha1:A2HKK7JXERG7KE3GHUU62LBDHNU7APTE\",\"WARC-Block-Digest\":\"sha1:DEKVNXCI6PZCXRTBWKAZE45EEZSKQMBO\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-50/CC-MAIN-2023-50_segments_1700679100527.35_warc_CC-MAIN-20231204083733-20231204113733-00614.warc.gz\"}"}
https://stats.stackexchange.com/questions/136389/derivation-of-cumulative-binomial-distribution-expression/136393	[ "# Derivation of cumulative Binomial Distribution expression\n\nI have a binomial distribution, with Random Variable Y and n trials. r is an integer. How can I show that P(Y ≥ r) = P(Y ≤ n − r)? I think it involves using the binomial theorem. I have tried taking expressing P(Y ≥ r) as $$\\sum_{x = 0}^{n} \\binom{n}{x} p^x(1-p)^{n-x} - \\sum_{x=0}^{r} \\binom{n}{x} p^x(1-p)^{n-x}$$ and also: $$\\sum_{x=r}^{n} \\binom{n}{x} p^x(1-p)^{n-x} = \\sum_{x=0}^{n-r} \\binom{n}{x+r} p^{x+r}(1-p)^{n-(x+r)}$$ but I am unsure of how to proceed next. Can someone give me a hint?\n\n• +1 for the clear question and asking for a hint. If this is for homework or for self-study, you should add the self-study tag to your question (see stats.stackexchange.com/tags/self-study/info). – Patrick Coulombe Feb 5 '15 at 3:31\n• You need to change some $(p-1)$s to $(1-p)$. With questions like these it is often worth substituting some values in to try to understand why the equation works - if you tried a $p\\neq0.5$ you'd see that actually it doesn't! – Silverfish Feb 5 '15 at 8:48\n\nI think you'll find it difficult to prove, because it is not true unless $p = 0.5$.\n\nConsider the simple case of $n = 1$, $r = 1$. Then:\n\n$P(Y \\geq r) = P(Y = 1) = p$\n\n$P(Y \\leq n - r) = P(Y = 0) = 1 - p$.\n\nStarting with the LHS of your second equation, if you substitute $j = n - x$ and use ${n \\choose j} = {n \\choose {n-j}}$, you should be able to verify that\n\n$P(Y \\geq r) = \\sum_{j=0}^{n-r} {n \\choose j} (1-p)^j p^{n-j}$,\n\nwhich is $P(X \\leq n-r)$ if $X$ is a binomial random variable with success probability $(1-p)$.\n\nThis makes sense intuitively if you think of $Y$ as the number of successes and $X$ as the number of failures: if there are at least $r$ successes, there must be no more than $n-r$ failures.\n\n• I am still unclear on how you managed to change the upper and lower bounds of the sum? – roro172 Feb 5 '15 at 20:59\n• Counting upwards from $x=r$ to $x=n$ is equivalent to counting downwards from $j=n-r$ to $j=0$ ($j$ being the 'distance' between $x$ and $n$). You can just substitute $j = n - x$ into the limits of the sum: $\\sum_{x=r}^n \\equiv \\sum_{j=n-r}^{n-n} \\equiv \\sum_{j=n-r}^0 \\equiv \\sum_{j=0}^{n-r}$ – Mark Feb 5 '15 at 21:40" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.82789284,"math_prob":0.99975115,"size":1832,"snap":"2021-21-2021-25","text_gpt3_token_len":663,"char_repetition_ratio":0.09628009,"word_repetition_ratio":0.0125,"special_character_ratio":0.38209608,"punctuation_ratio":0.07363421,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":1.0000088,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-05-15T08:20:23Z\",\"WARC-Record-ID\":\"<urn:uuid:670273d1-5562-4fa7-a04b-089a3f80c38e>\",\"Content-Length\":\"168492\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:767c4bd1-8518-4c28-bf06-3b1ee1687820>\",\"WARC-Concurrent-To\":\"<urn:uuid:39dcd0e5-a47b-4ca1-98be-6eaf23761414>\",\"WARC-IP-Address\":\"151.101.129.69\",\"WARC-Target-URI\":\"https://stats.stackexchange.com/questions/136389/derivation-of-cumulative-binomial-distribution-expression/136393\",\"WARC-Payload-Digest\":\"sha1:GCEW7YWDWXS22HWRWCFT4HMKXAIRK2LB\",\"WARC-Block-Digest\":\"sha1:UR27VBWMMBV7O3QNYQU6JVTYQN67TWMH\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-21/CC-MAIN-2021-21_segments_1620243991378.48_warc_CC-MAIN-20210515070344-20210515100344-00232.warc.gz\"}"}
https://library.wolfram.com/infocenter/Articles/9238/	[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Title", null, "", null, "", null, "", null, "Cosmological Evolution of Statistical System of Scalar Charged Particles", null, "", null, "", null, "Authors", null, "", null, "", null, "", null, "Yurii Ignat'ev", null, "A. A. Agathonov", null, "Mikhail Mikhailov", null, "Dmitry Ignatyev", null, "", null, "", null, "Journal / Anthology", null, "", null, "", null, "", null, "Astrophysics and Space Science\n Year: 2015\n Volume: 357\n Issue: 61", null, "", null, "", null, "Description", null, "", null, "", null, "", null, "In the paper we consider the macroscopic model of plasma of scalar charged particles, obtained by means of the statistical averaging of the mi- croscopic equations of particle dynamics in a scalar eld. On the basis of kinetic equations, obtained from averaging, and their strict integral con- sequences, a self-consistent set of equations is formulated which describes the self-gravitating plasma of scalar charged particles. It was obtained the corresponding closed cosmological model which also was numerically simulated for the case of one-component degenerated Fermi gas and two- component Boltzmann system. It was shown that results depend weakly on the choice of a statistical model. Two speci c features of cosmological evolution of a statistical system of scalar charged particles were obtained with respect to cosmological evolution of the minimal interaction models: appearance of giant bursts of invariant cosmological acceleration at the time interval 8 \u0001 103 \u0004 2 \u0001 104tPl and strong heating (3 \u0004 8 orders of mag- nitude) of a statistical system at the same times. The presence of such features can modify the quantum theory of generation of cosmological gravitational perturbations.", null, "", null, "", null, "Subjects", null, "", null, "", null, "", null, "", null, "Science > Physics > Astrophysics", null, "Science > Physics > Relativity Theory", null, "", null, "", null, "", null, "", null, "", null, "", null, "" ]	[ null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/subheader.gif", null, "https://library.wolfram.com/images/database/tabsTOP/Courseware.gif", null, "https://library.wolfram.com/images/database/tabsTOP/Demos.gif", null, "https://library.wolfram.com/images/database/tabsTOP/MathSource.gif", null, "https://library.wolfram.com/images/database/tabsTOP/TechNotes.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/tabsBOTTOM/BySubject-off.gif", null, "https://library.wolfram.com/images/database/tabsBOTTOM/Articles-on.gif", null, "https://library.wolfram.com/images/database/tabsBOTTOM/Books-off.gif", null, "https://library.wolfram.com/images/database/tabsBOTTOM/Conferences-off.gif", null, "https://library.wolfram.com/images/database/grey-square.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/images/database/grey-line.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/images/database/subjects-arrow.gif", null, "https://library.wolfram.com/images/database/subjects-arrow.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null, "https://library.wolfram.com/common/images/spacer.gif", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9212498,"math_prob":0.8715738,"size":1358,"snap":"2020-34-2020-40","text_gpt3_token_len":282,"char_repetition_ratio":0.15878877,"word_repetition_ratio":0.07239819,"special_character_ratio":0.21281296,"punctuation_ratio":0.053140096,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95412123,"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],"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],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-08-03T21:20:49Z\",\"WARC-Record-ID\":\"<urn:uuid:e79baf6d-f5c0-4ad1-b15d-020d062c4f99>\",\"Content-Length\":\"53569\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:675b7cc8-8854-4ace-a4c1-9a9f21ddc2d8>\",\"WARC-Concurrent-To\":\"<urn:uuid:68d12dcf-1f97-4dd5-8157-bfa631a2c5d4>\",\"WARC-IP-Address\":\"140.177.205.65\",\"WARC-Target-URI\":\"https://library.wolfram.com/infocenter/Articles/9238/\",\"WARC-Payload-Digest\":\"sha1:T2H2DV6QSQWSN7FGPIULVPMPRHRZJTAF\",\"WARC-Block-Digest\":\"sha1:ZVAGC7U5CM66IS7G7EBX5OQKFWNV24ET\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-34/CC-MAIN-2020-34_segments_1596439735833.83_warc_CC-MAIN-20200803195435-20200803225435-00328.warc.gz\"}"}
https://pro-coder.tech/exploring-linear-diophantine-equation/	[ "## Exploring Linear Diophantine Equation\n\nThis is a well-known method to solve linear equations with 2 or more variables. The specialty of this method is that it provides integer solutions to the linear equation of the form ax + by + cz + ……… = constant.\n\nThe problems involving use of LDE generally deal with two variables. We gonna look at few interesting problems today :\n\n### Problem – 01 D. Two Arithmetic Progressions\n\nThe problem is about finding the number of possible values of x.\n\nHere x must hold –\n\na1k’ + b1 = a2l’ + b2\n\na1k’ – a2l’ = b2-b1\n\nThese can be treated as LDE – Ax + By = C where x = k’ and y = l’. The only restriction that we have is that a1x + b1 must be between L and R (inclusive) and x,y must be >= 0.\n\nNow, we know that the solutions to this equation exist only if C%gcd(A,B) = 0, and that the solutions are:\n\nx = x’ + Bt/gcd(A,B) and y = y’ – At/gcd(A,B) where (x’,y’) are solutions of extended euclead’s algorithm Ax + By = gcd(A,B) multiplied by C/gcd(A,B)\n\n```//Extended GCD starts here\nll solx,soly,GCD;\nvoid extended_version(ll a, ll b){\n// Base Case\nif (b==0){\nsolx=1,soly=0,GCD=a;\nreturn;\n}\n//Recursive Case\nextended_version(b,a%b);\nll x1 = solx, y1 = soly;\nsolx = y1;\nsoly = x1 - (a/b)y1;\n}\n//Extended GCD ends here\n```\n\nAnd, now we will look at the solution to original problem\n\n```ll solx,soly,GCD;\nvoid extended_gcd(ll a, ll b){\n// Base Case\nif (b==0){\nsolx=1,soly=0,GCD=a;\nreturn;\n}\n//Recursive Case\nextended_gcd(b,a%b);\nll x1 = solx, y1 = soly;\nsolx = y1;\nsoly = x1 - (a/b)y1;\n}\nvoid solve()\n{\nll a1,b1,a2,b2,L,R;\ncin >> a1 >> b1 >> a2 >> b2 >> L >> R;\nll A=a1,B=-1a2,C=b2-b1;\nextended_gcd(A,B);\nif (GCD < 0){\nGCD = -1GCD;\nsolx = -1;\nsoly = -1;\n}\nif (C%GCD){\ncout << 0 << endl;\nreturn;\n}\nll l = ceil_div(L-b1,a1), r = floor_div(R-b1,a1);\nif (r < l){\ncout << 0 << endl;\nreturn;\n}\nl = max(0ll,l);\nll pl = ceil_div(Csolx - rGCD,a2);\nll ph = floor_div(Csolx - lGCD,a2);\nl = ceil_div(L-b2,a2), r = floor_div(R-b2,a2);\nl = max(0ll,l);\nif (r < l){\ncout << 0 << endl;\nreturn;\n}\npl = max(pl, ceil_div(Csoly - rGCD,a1));\nph = min(ph,floor_div(Csoly - lGCD,a1));\nif (ph < pl){\ncout << 0 << endl;\nreturn;\n}\ncout << ph-pl+1;\n}\n```" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.75675714,"math_prob":0.9997274,"size":2175,"snap":"2021-21-2021-25","text_gpt3_token_len":775,"char_repetition_ratio":0.10041455,"word_repetition_ratio":0.1557789,"special_character_ratio":0.38988507,"punctuation_ratio":0.17992425,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99990976,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-05-13T11:22:50Z\",\"WARC-Record-ID\":\"<urn:uuid:ef0519d2-59e8-4be2-bb8c-8ef1ff25be15>\",\"Content-Length\":\"63836\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:4df890af-33f5-4043-844b-e25e357020b6>\",\"WARC-Concurrent-To\":\"<urn:uuid:80aacac7-fde8-4cc7-8171-0bdfabcf2034>\",\"WARC-IP-Address\":\"88.211.101.190\",\"WARC-Target-URI\":\"https://pro-coder.tech/exploring-linear-diophantine-equation/\",\"WARC-Payload-Digest\":\"sha1:5U35QRPQK35VAH2M7BD5VVCAR5J4Y4O4\",\"WARC-Block-Digest\":\"sha1:MOAIQAFIDAOTNTGM5KD4GEJDFXNQPALM\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-21/CC-MAIN-2021-21_segments_1620243989916.34_warc_CC-MAIN-20210513111525-20210513141525-00518.warc.gz\"}"}
https://www.glassdoor.com/Interview/member-of-technical-staff-ii-interview-questions-SRCH_KO0,28.htm	[ "Member of Technical Staff II Interview Questions \| Glassdoor\n\n# Member of Technical Staff II Interview Questions\n\n24\n\nMember of technical staff ii interview questions shared by candidates\n\n## Top Interview Questions\n\nSort: RelevancePopular Date\n\n### Member of Technical Staff II at VMware was asked...\n\nMay 17, 2012\n Database design question regarding user-account management, authorization etc.1 AnswerAn architecture similar to LDAP can be used.\n\n### Member of Technical Staff II at Charles Stark Draper Laboratory was asked...\n\nAug 7, 2014\n It was more of a casual conversation of me describing my project experiences answering related questions.Be the first to answer this question\n\n### Member of Technical Staff II, Software Engineer at Hughes Network Systems was asked...\n\nMay 1, 2015\n General Java questionsBe the first to answer this question\n\n### Member of Technical Staff II at Tintri was asked...\n\nFeb 28, 2017\n Lots of python (OOPs, Decorators, Inheritance in Python), Python Vs Java, Binary tree recursive, 1 AnswerHad prepared well for python hence could clear this round.\n\n### Member of Technical Staff II at Tintri was asked...\n\nFeb 28, 2017\n Start with Basic Algorithms but focus was on how you improve the performance of it. With very very high scale of data. Testcases to test the algorithm1 AnswerAlgorithm was easy but scaling has twists. It was fun when i got the answer in last minute\n\n### Member of Technical Staff II Software at Panasonic Avionics Corporation was asked...\n\nJul 12, 2019\n What's the most challenge project and why?1 AnswerDo you have a question about Panasonic Avionics Corporation? Don't ask just anyone for information, ask an employee from Panasonic Avionics Corporation. They're all waiting at Rooftop Slushie. https://wwww.rooftopslushie.com\n\n### Member of Technical Staff II at VMware was asked...\n\nMay 17, 2012\n Given a number, print the list of prime numbers that multiply to that number. Eg. given 132, print -> 2,2,3,11 1 Answerfrom numpy import prod x = 132 prim = def primo(x): for i in range(3,x+1): count = 0 for j in range(2,i): if i%j == 0: count+=1 else: pass if count == 0: prim.append(i) primo(x) #print prim fact = [] def facto(x): count = 0 for i in range(2,x): if x%i == 0: fact.append(i) else: count +=1 if len(fact) ==0 and count >0: fact.append(x) return fact facto(x) #print fact f = [] def finish(fact, prim): for i in fact: if i in prim: # print i f.append(i) finish(fact, prim) #print f if prod(f)!= x: #print \"true\" pad = [] wy = x//prod(f+pad) wy1 = wy #print wy1 while prod(pad) < wy: #print \"Tru\" for i in f: #print \"i\", (i) #print \"wy1\", (wy1) #print prod(pad) if wy1%i==0 and wy1 != 1: pad.append(i) wy1 = wy1/i #print \"wy1\", wy1 #print \"pad\", pad else: pass f +=pad k=sorted(f) for x in k: print x\n\n### Member of Technical Staff II at EchoStar was asked...\n\nAug 26, 2015\n Q: What are subnetsBe the first to answer this question\n\n### Data Analyst at Anthem was asked...\n\nFeb 20, 2018\n Leadership Skills?Be the first to answer this question\n\n### Member of Technical Staff II at VMware was asked...\n\nMay 17, 2012\n Given a list of integers L and a number X, find 3 numbers in L that sum-up to X. 1 Answerx = [1,2,3,4,5,6,7,8] z = 10 y = [] for i in range(len(x)): for j in range(len(x)): for k in range(len(x)): if (i!=j) and (j!=k) and (i!=k): if set([x[i],x[j],x[k]]) not in y: y.append(set([x[i],x[j],x[k]])) for i in y: if sum(i) == z: print i\n110 of 24 Interview Questions" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.8207723,"math_prob":0.69309235,"size":754,"snap":"2019-35-2019-39","text_gpt3_token_len":148,"char_repetition_ratio":0.23066667,"word_repetition_ratio":0.07964602,"special_character_ratio":0.1949602,"punctuation_ratio":0.01923077,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.95394117,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-09-22T01:54:37Z\",\"WARC-Record-ID\":\"<urn:uuid:23ec33ea-4c75-4d7c-8113-21d7089c2b1a>\",\"Content-Length\":\"174943\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:84487fda-7637-479d-b9f5-dcf61c33b679>\",\"WARC-Concurrent-To\":\"<urn:uuid:57a47da5-d380-483e-bc4c-4a6f7a3dd206>\",\"WARC-IP-Address\":\"104.17.90.51\",\"WARC-Target-URI\":\"https://www.glassdoor.com/Interview/member-of-technical-staff-ii-interview-questions-SRCH_KO0,28.htm\",\"WARC-Payload-Digest\":\"sha1:WRROAPMABPDVOWZXHR4H2LNDB5QLF2B6\",\"WARC-Block-Digest\":\"sha1:DQD5JEAHLMND6SREH6DXAIR6ESFFAXFQ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-39/CC-MAIN-2019-39_segments_1568514574765.55_warc_CC-MAIN-20190922012344-20190922034344-00354.warc.gz\"}"}
http://textbooks.math.gatech.edu/ila/parametric-form.html	[ "##### Objectives\n1. Learn to express the solution set of a system of linear equations in parametric form.\n2. Understand the three possibilities for the number of solutions of a system of linear equations.\n3. Recipe: parametric form.\n4. Vocabulary word: free variable.\n\n# Subsection1.3.1Free Variables\n\nThere is one possibility for the row reduced form of a matrix that we did not see in Section 1.2.\n\n##### Example(A System with a Free Variable)\n\nConsider the linear system\n\nWe solve it using row reduction:\n\nThis row reduced matrix corresponds to the linear system\n\nIn what sense is the system solved? We rewrite as\n\nFor any value of there is exactly one value of and that make the equations true. But we are free to choose any value of\n\nWe have found all solutions: it is the set of all values where\n\nThis is called the parametric form for the solution to the linear system. The variable is called a free variable.\n\nGiven the parametric form for the solution to a linear system, we can obtain specific solutions by replacing the free variables with any specific real numbers. For instance, setting in the last example gives the solution and setting gives the solution\n\n##### Definition\n\nConsider a consistent system of equations in the variables Let be a row echelon form of the augmented matrix for this system.\n\nWe say that is a free variable if its corresponding column in is not a pivot column.\n\nIn the above example, the variable was free because the reduced row echelon form matrix was\n\nIn the matrix\n\nthe free variables are and (The augmented column is not free because it does not correspond to a variable.)\n\n##### Recipe: Parametric form\n\nThe parametric form of the solution set of a consistent system of linear equations is obtained as follows.\n\n1. Write the system as an augmented matrix.\n2. Row reduce to reduced row echelon form.\n3. Write the corresponding (solved) system of linear equations.\n4. Move all free variables to the right hand side of the equations.\n\nMoving the free variables to the right hand side of the equations amounts to solving for the non-free variables (the ones that come pivot columns) in terms of the free variables. One can think of the free variables as being independent variables, and the non-free variables being dependent.\n\n##### Implicit Versus Parameterized Equations\n\nThe solution set of the system of linear equations\n\nis a line in as we saw in this example. These equations are called the implicit equations for the line: the line is defined implicitly as the simultaneous solutions to those two equations.\n\nThe parametric form\n\ncan be written as follows:\n\nThis called a parameterized equation for the same line. It is an expression that produces all points of the line in terms of one parameter,\n\nOne should think of a system of equations as being an implicit equation for its solution set, and of the parametric form as being the parameterized equation for the same set. The parameteric form is much more explicit: it gives a concrete recipe for producing all solutions.\n\nYou can choose any value for the free variables in a (consistent) linear system.\n\nFree variables come from the columns without pivots in a matrix in row echelon form.\n\n# Subsection1.3.2Number of Solutions\n\nThere are three possibilities for the reduced row echelon form of the augmented matrix of a linear system.\n\n1. The last column is a pivot column. In this case, the system is inconsistent. There are zero solutions, i.e., the solution set is empty. For example, the matrix\ncomes from a linear system with no solutions.\n2. Every column except the last column is a pivot column. In this case, the system has a unique solution. For example, the matrix\ntells us that the unique solution is\n3. The last column is not a pivot column, and some other column is not a pivot column either. In this case, the system has infinitely many solutions, corresponding to the infinitely many possible values of the free variable(s). For example, in the system corresponding to the matrix\nany values for and yield a solution to the system of equations." ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.8871838,"math_prob":0.9963866,"size":3914,"snap":"2021-43-2021-49","text_gpt3_token_len":798,"char_repetition_ratio":0.19232737,"word_repetition_ratio":0.07646177,"special_character_ratio":0.19289729,"punctuation_ratio":0.08847185,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99902105,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-12-02T04:39:18Z\",\"WARC-Record-ID\":\"<urn:uuid:c27a3dee-20ea-4c7c-be3e-e8b6c6a9bc35>\",\"Content-Length\":\"78965\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:5de67f99-60da-47ee-abcc-069f92e08961>\",\"WARC-Concurrent-To\":\"<urn:uuid:19c40a4c-99a1-4bae-831b-1c0b0ec9df67>\",\"WARC-IP-Address\":\"130.207.188.152\",\"WARC-Target-URI\":\"http://textbooks.math.gatech.edu/ila/parametric-form.html\",\"WARC-Payload-Digest\":\"sha1:5MDEETAD2YR62IUG5CETO75VGGNQILNS\",\"WARC-Block-Digest\":\"sha1:FBR6PV44H4NKTVIKBTALUD4A7ULLN2TU\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-49/CC-MAIN-2021-49_segments_1637964361064.69_warc_CC-MAIN-20211202024322-20211202054322-00092.warc.gz\"}"}
https://groupprops.subwiki.org/w/index.php?title=Special:ExportRDF/Characteristic_direct_factor_of_abelian_group&syntax=rdf	[ "]> 2019-07-17T00:36:09+00:00 Characteristic direct factor of abelian group 0 en 2013-07-14T20:15:58Z 2456488.3444213 Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Characteristic direct factor of nilpotent group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Characteristic direct factor of nilpotent group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Fully invariant direct factor]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Fully invariant direct factor]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Characteristic direct factor]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Characteristic direct factor]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Fully invariant subgroup of abelian group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Fully invariant subgroup of abelian group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Characteristic subgroup of abelian group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Characteristic subgroup of abelian group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Direct factor of abelian group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Direct factor of abelian group]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Fully invariant subgroup]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Fully invariant subgroup]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Characteristic subgroup]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Characteristic subgroup]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 list 4 [[Stronger than::Direct factor]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group 0 1 broadtable 4 [[Stronger than::Direct factor]] [[Weaker than::Characteristic direct factor of abelian group]] Characteristic direct factor of abelian group Fully invariant direct factor 0 en Fully invariant direct factor Characteristic direct factor of nilpotent group 0 en Characteristic direct factor of nilpotent group Abelian fully invariant subgroup 0 en Abelian fully invariant subgroup Fully invariant direct factor of abelian group 0 en Fully invariant direct factor of abelian group Weaker than 132 en Weaker than" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.64938915,"math_prob":0.75703794,"size":3653,"snap":"2019-26-2019-30","text_gpt3_token_len":875,"char_repetition_ratio":0.22855577,"word_repetition_ratio":0.87982833,"special_character_ratio":0.22064057,"punctuation_ratio":0.13131313,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9641495,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-07-17T00:36:09Z\",\"WARC-Record-ID\":\"<urn:uuid:8ab00b36-ae86-42ba-9614-1a9d35a752f6>\",\"Content-Length\":\"33028\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:61b228f9-f961-4321-9684-d4efa5bb8b90>\",\"WARC-Concurrent-To\":\"<urn:uuid:82dfd262-1a14-47d2-982c-f92c3f5ed1b7>\",\"WARC-IP-Address\":\"96.126.114.7\",\"WARC-Target-URI\":\"https://groupprops.subwiki.org/w/index.php?title=Special:ExportRDF/Characteristic_direct_factor_of_abelian_group&syntax=rdf\",\"WARC-Payload-Digest\":\"sha1:P3KWPTGWL7DK5RGFSM57337KJOTH5VR4\",\"WARC-Block-Digest\":\"sha1:RA5ALC73CWDQ3MOGNPNHGBLNZUREYPCQ\",\"WARC-Identified-Payload-Type\":\"application/xml\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-30/CC-MAIN-2019-30_segments_1563195525004.24_warc_CC-MAIN-20190717001433-20190717023433-00009.warc.gz\"}"}
https://practicaldev-herokuapp-com.global.ssl.fastly.net/rohithv07/leetcode-114-flatten-binary-tree-to-linked-list-1ej8	[ "DEV Community is a community of 783,060 amazing developers\n\nWe're a place where coders share, stay up-to-date and grow their careers.", null, "Leetcode 114. Flatten Binary Tree to Linked List\n\nProblem Statement\n\nGiven the root of a binary tree, flatten the tree into a \"linked list\":\n\nThe \"linked list\" should use the same TreeNode class where the right child pointer points to the next node in the list and the left child pointer is always null.\nThe \"linked list\" should be in the same order as a pre-order traversal of the binary tree.\n\nTest Cases\n\nExample 1:\n\nInput: root = [1,2,5,3,4,null,6]\nOutput: [1,null,2,null,3,null,4,null,5,null,6]\nExample 2:\n\nInput: root = []\nOutput: []\nExample 3:\n\nInput: root = \nOutput: \n\nConstraints:\n\nThe number of nodes in the tree is in the range [0, 2000].\n-100 <= Node.val <= 100\n\nAlgorithm :\n\n1. Here we need to move all our left child nodes to right and append the already present right child node to the end.\n2. So if we have left child node, first of all store the currently present right child to a variable and keep root.right as the left child. Then make the left child as null.\n3. Then the stored right child is appended towards the end of the left child node that is recently attached.", null, "4. Do recursion keeping the root node as root.right. recursion(root.right).", null, "Complexity :\n\nWe are going through each of the node so time complexity O(n) and space will be O(H) as we use a recursion stack.\n\nCode :\n\n/*\n Definition for a binary tree node.\n* public class TreeNode {\n* int val;\n* TreeNode left;\n* TreeNode right;\n* TreeNode() {}\n* TreeNode(int val) { this.val = val; }\n* TreeNode(int val, TreeNode left, TreeNode right) {\n* this.val = val;\n* this.left = left;\n* this.right = right;\n* }\n* }\n*/\nclass Solution {\npublic void flatten(TreeNode root) {\nif (root == null) {\nreturn;\n}\nif (root.left != null) {\nTreeNode temp = root.right;\nroot.right = root.left;\nroot.left = null;\nTreeNode current = root.right;\nwhile (current.right != null) {\ncurrent = current.right;\n}\ncurrent.right = temp;\n}\nflatten(root.right);\n}\n}" ]	[ null, "https://res.cloudinary.com/practicaldev/image/fetch/s--i0kFs68m--/c_imagga_scale,f_auto,fl_progressive,h_420,q_auto,w_1000/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/skhtqoa1oo1on3a9yw10.png", null, "https://res.cloudinary.com/practicaldev/image/fetch/s--67GpR-SJ--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/a5cv9itcb8rvu9mv1do2.png", null, "https://res.cloudinary.com/practicaldev/image/fetch/s--nR0whH_E--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/r6eol5ei6s9dl2a3eh9f.png", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.7326948,"math_prob":0.98915285,"size":1866,"snap":"2022-05-2022-21","text_gpt3_token_len":471,"char_repetition_ratio":0.16809882,"word_repetition_ratio":0.0,"special_character_ratio":0.29581994,"punctuation_ratio":0.20300752,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99041075,"pos_list":[0,1,2,3,4,5,6],"im_url_duplicate_count":[null,1,null,1,null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-01-17T23:03:31Z\",\"WARC-Record-ID\":\"<urn:uuid:6deed27c-3588-42ac-b2b8-3892393ffc35>\",\"Content-Length\":\"99036\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:5a43788c-92fa-41d6-b579-85b711bfad32>\",\"WARC-Concurrent-To\":\"<urn:uuid:649db34c-691b-46cd-bb43-48adf497d3c1>\",\"WARC-IP-Address\":\"146.75.33.194\",\"WARC-Target-URI\":\"https://practicaldev-herokuapp-com.global.ssl.fastly.net/rohithv07/leetcode-114-flatten-binary-tree-to-linked-list-1ej8\",\"WARC-Payload-Digest\":\"sha1:BG2D5YIT2XDWJFA2HOO3W3IRZ2IM34UK\",\"WARC-Block-Digest\":\"sha1:3IWWSKZZHESLWKVY4EODU4MHONKKMAVW\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-05/CC-MAIN-2022-05_segments_1642320300624.10_warc_CC-MAIN-20220117212242-20220118002242-00346.warc.gz\"}"}
http://javascript.askforanswer.com/ruhezaijavascriptzhongqingkongshuzu.html	[ "# 如何在JavaScript中清空数组？\n\n2020/09/15 01:31 · javascript · · 0评论\n\n``A = [1,2,3,4];``\n\n（这是我对问题的原始回答）\n\n``A = [];``\n\n``````var arr1 = ['a','b','c','d','e','f'];\nvar arr2 = arr1; // Reference arr1 by another variable\narr1 = [];\nconsole.log(arr2); // Output ['a','b','c','d','e','f']``````\n\n``A.length = 0``\n\n``A.splice(0,A.length)``\n\n``````while(A.length > 0) {\nA.pop();\n}``````\n\n``A.length = 0;``\n\n``````function clearArray(array) {\nwhile (array.length) {\narray.pop();\n}\n}``````\n\nFYI MapSet define `clear()``clear()`对于Array似乎是合乎逻辑的\n\nTypeScript版本：\n\n``````function clearArray<T>(array: T[]) {\nwhile (array.length) {\narray.pop();\n}\n}``````\n\n``````describe('clearArray()', () => {\ntest('clear regular array', () => {\nconst array = [1, 2, 3, 4, 5];\nclearArray(array);\nexpect(array.length).toEqual(0);\nexpect(array).toEqual(undefined);\nexpect(array).toEqual(undefined);\n});\n\ntest('clear array that contains undefined and null', () => {\nconst array = [1, undefined, 3, null, 5];\nclearArray(array);\nexpect(array.length).toEqual(0);\nexpect(array).toEqual(undefined);\nexpect(array).toEqual(undefined);\n});\n});``````\n\n`A.splice(0, A.length);`\n\n1. 说“ `A = []`是答案”是无知的，而且绝对不正确。`[] == []`错误的\n\n这是因为这两个数组是两个单独的，独立的对象，它们具有自己的两个标识，在数字世界中占据了自己的空间，每个空间都是自己的。\n\n• 您不会带来新的功能，就好像您已经完成了要求的操作一样。\n• 相反，您清空垃圾箱。\n• 您不要用新的空罐替换装满的罐，也不要从装满的罐上取标签“ A”并将其粘贴到新的罐上。 `A = [1,2,3,4]; A = [];`\n\n``A.length = 0;``\n\n1. 此外，在罐头变空之前，不需要手动清除垃圾！您被要求一遍完全清空现有的垃圾箱，直到罐子变空之前不要捡垃圾，如下所示：\n\n``````while(A.length > 0) {\nA.pop();\n}``````\n2. 也不要将左手放在垃圾箱的底部，而将右手放在垃圾箱的顶部，这样就可以拉出其内容：\n\n``A.splice(0, A.length);``\n\n``A.length = 0;``\n\nhttp://jsperf.com/array-clear-methods/3\n\n``````a = []; // 37% slower\na.length = 0; // 89% slower\na.splice(0, a.length) // 97% slower\nwhile (a.length > 0) {\na.pop();\n} // Fastest``````\n\n``````Array.prototype.clear = function() {\nthis.splice(0, this.length);\n};``````\n\n``````var list = [1, 2, 3];\nlist.clear();``````\n\n``````if (!Array.prototype.clear) {\nArray.prototype.clear = function() {\nthis.splice(0, this.length);\n};\n}``````\n\n``````var arr = [];\n\nfor (var i = 0; i < 100; i++) {\narr.push(Math.random());\n}\n\nfor (var j = 0; j < 1000; j++) {\nwhile (arr.length > 0) {\narr.pop(); // this executes 100 times, not 100000\n}\n}``````\n\nhttp://jsperf.com/empty-javascript-array-redux\n\n``var arr = [1, 2, 3, 4, 5]; //the array``\n\n``arr.length = 0; //change the length``\n\n``[] //result``\n\n``````/* could be arr.pop() or arr.splice(0)\ndon't need to return as main array get changed /\n\nfunction remove(arr) {\nwhile(arr.length) {\narr.shift();\n}\n}``````\n\n``arr.splice(0, arr.length); //[]``\n\n``arr = []; //[]``\n\n``````Array.prototype.remove = Array.prototype.remove \|\| function() {\nthis.splice(0, this.length);\n};``````\n\n``arr.remove(); //[]``\n\n（有关上述标签的介绍，您可以在此处查看\n\nStackoverflow迫使我复制jsfiddle，因此它是：\n\n``````<html>\n<script>\nvar size = 1000100\ndocument.getElementById(\"quantifier\").value = size\n}\n\nfunction scaffold()\n{\nconsole.log(\"processing Scaffold...\");\na = new Array\n}\nfunction start()\n{\nsize = document.getElementById(\"quantifier\").value\nconsole.log(\"Starting... quantifier is \" + size);\nconsole.log(\"starting test\")\nfor (i=0; i<size; i++){\na[i]=\"something\"\n}\nconsole.log(\"done...\")\n}\n\nfunction tearDown()\n{\nconsole.log(\"processing teardown\");\na.length=0\n}\n\n</script>\n<body>\n<span style=\"color:green;\">Quantifier:</span>\n<input id=\"quantifier\" style=\"color:green;\" type=\"text\"></input>\n<button onclick=\"scaffold()\">Scaffold</button>\n<button onclick=\"start()\">Start</button>\n<button onclick=\"tearDown()\">Clean</button>\n<br/>\n</body>\n</html>``````\n\n``````Array.prototype.clear = function() {\nthis.length = 0;\n};``````\n\n``a = []; ``\n\n``````var a=[1,2,3];\nvar b=a;\na=[];\nconsole.log(b);// It will print [1,2,3];``````\n\n``a.length = 0;``\n\n``a.splice(0,a.length)``\n\n``````while(a.length > 0) {\na.pop();\n}``````\n\n`A.splice(0);`\n\n``````var originalLength = A.length;\nfor (var i = originalLength; i > 0; i--) {\nA.pop();\n}``````\n\n``const numbers = [1, 2, 3]``\n\n``numbers = []``\n\n``numbers.length = 0``", null, "• `length`：您可以设置length属性以随时截断数组。通过更改数组的length属性扩展数组时，实际元素的数量会增加。\n• `pop()` ：pop方法从数组中删除最后一个元素，并返回返回删除的值。\n• `shift()`：shift方法将移除第零个索引处的元素，并将连续索引处的值向下移位，然后返回移除的值。\n\n``````var arr = ['77'];\narr.length = 20;\nconsole.log(\"Increasing : \", arr); // (20) [\"77\", empty × 19]\narr.length = 12;\nconsole.log(\"Truncating : \", arr); // (12) [\"77\", empty × 11]\n\nvar mainArr = new Array();\nmainArr = ['1', '2', '3', '4'];\n\nvar refArr = mainArr;\nconsole.log('Current', mainArr, 'Refered', refArr);\n\nrefArr.length = 3;\nconsole.log('Length: ~ Current', mainArr, 'Refered', refArr);\n\nmainArr.push('0');\nconsole.log('Push to the End of Current Array Memory Location \\n~ Current', mainArr, 'Refered', refArr);\n\nmainArr.poptill_length(0);\nconsole.log('Empty Array \\n~ Current', mainArr, 'Refered', refArr);\n\nArray.prototype.poptill_length = function (e) {\nwhile (this.length) {\nif( this.length == e ) break;\n\nconsole.log('removed last element:', this.pop());\n}\n};``````\n\n• `new Array() \| []` 使用`Array constructor`创建具有新存储位置的数组`array literal`\n\n``````mainArr = []; // a new empty array is addressed to mainArr.\n\nvar arr = new Array('10'); // Array constructor\narr.unshift('1'); // add to the front\narr.push('15'); // add to the end\nconsole.log(\"After Adding : \", arr); // [\"1\", \"10\", \"15\"]\n\narr.pop(); // remove from the end\narr.shift(); // remove from the front\nconsole.log(\"After Removing : \", arr); // [\"10\"]\n\nvar arrLit = ['14', '17'];\nconsole.log(\"array literal « \", indexedItem( arrLit ) ); // {0,14}{1,17}\n\nfunction indexedItem( arr ) {\nvar indexedStr = \"\";\narr.forEach(function(item, index, array) {\nindexedStr += \"{\"+index+\",\"+item+\"}\";\nconsole.log(item, index);\n});\nreturn indexedStr;\n}``````\n• `slice()` ：通过使用slice函数，我们从原始数组中获得了具有新存储器地址的浅表元素副本，从而对cloneArr进行的任何修改都不会影响实际数组。\n\n``````var shallowCopy = mainArr.slice(); // this is how to make a copy\n\nvar cloneArr = mainArr.slice(0, 3);\nconsole.log('Main', mainArr, '\\tCloned', cloneArr);\n\ncloneArr.length = 0; // Clears current memory location of an array.\nconsole.log('Main', mainArr, '\\tCloned', cloneArr);``````\n\n``````let xs = [1,2,3,4];\nfor (let i in xs)\ndelete xs[i];``````\n\n``````xs\n=> Array [ <4 empty slots> ]\n\n[...xs]\n=> Array [ undefined, undefined, undefined, undefined ]\n\nxs.length\n=> 4\n\nxs\n=> ReferenceError: reference to undefined property xs``````\n\n``````public emptyFormArray(formArray:FormArray) {\nfor (let i = formArray.controls.length - 1; i >= 0; i--) {\nformArray.removeAt(i);\n}\n}``````" ]	[ null, "https://i.stack.imgur.com/nChy7.png", null ]	{"ft_lang_label":"__label__zh","ft_lang_prob":0.5747282,"math_prob":0.9736748,"size":8616,"snap":"2020-45-2020-50","text_gpt3_token_len":4310,"char_repetition_ratio":0.13063167,"word_repetition_ratio":0.040462427,"special_character_ratio":0.30501392,"punctuation_ratio":0.2618888,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97761744,"pos_list":[0,1,2],"im_url_duplicate_count":[null,1,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-10-29T08:29:45Z\",\"WARC-Record-ID\":\"<urn:uuid:c77e1c7c-e49a-474d-a01d-e3d12b0b7796>\",\"Content-Length\":\"102674\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:87052781-97ec-46b8-97ff-f4b33d073e34>\",\"WARC-Concurrent-To\":\"<urn:uuid:57149eb4-0460-408a-8161-0ef621f3cc8a>\",\"WARC-IP-Address\":\"144.168.58.239\",\"WARC-Target-URI\":\"http://javascript.askforanswer.com/ruhezaijavascriptzhongqingkongshuzu.html\",\"WARC-Payload-Digest\":\"sha1:5Y7ZPE7C5LGUDBGDC5ZQYC5VKF7CSNRQ\",\"WARC-Block-Digest\":\"sha1:OU2BUX6PWZ37ORL4UGZYBOGSKD2GMTPU\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-45/CC-MAIN-2020-45_segments_1603107903419.77_warc_CC-MAIN-20201029065424-20201029095424-00629.warc.gz\"}"}
https://www.physicsforums.com/threads/free-surface-charges-on-concentric-cylinders.954252/	[ "# Free surface charges on concentric cylinders\n\n## Homework Statement\n\nConsider an infinitely long cylindrical rod with radius a carrying a uniform charge density ##\\rho##. The rod is surrounded by a co-axial cylindrical metal-sheet with radius b that is connected to ground. The volume between the sheet and the rod is filled with a dielectric, ##\\epsilon##.\n\nCalculate the free and bound surface charges at r=a and r=b\n\n## The Attempt at a Solution\n\n[/B]\nI tried to use discontinuity in E.\n\nFrom Gauss. ##\\nabla \\cdot E = \\frac{\\rho}{\\epsilon_0} \\Rightarrow \\oint E \\cdot ds = \\frac{Q}{\\epsilon_0} ##\n\n##\\Rightarrow (E_{above}-E_{below}) = \\frac{\\sigma_b}{\\epsilon_0}##.\n\nFor r= a, ##\\sigma_b = \\frac{Q}{2 \\pi s*l} (\\frac{1}{\\epsilon_r} - 1)##\n\nSo now we have the bound charge per unit area.\n\nBy integrating we ca get the total bound surface charge, ##\\sigma_{bt} = Q(\\frac{1}{\\epsilon_r}-1)##.\n\nI'm not sure this is right. But if it is, how do I now get the ##free## surface charge?\n\nAnd I do remember that either the bound charge or free charge should be zero for a grounded material, but not which one.\n\n•", null, "Delta2\n\n## Answers and Replies\n\nRelated Introductory Physics Homework Help News on Phys.org\nCharles Link\nHomework Helper\nGold Member\n2020 Award\nI think you omitted a step: ## \\nabla \\cdot D=\\rho_{free}=0 ## at and around ## r=a ##, so that ## D_{above}=D_{below} ## which gives ## \\epsilon_o \\epsilon_r E_{above}=\\epsilon_o E_{below} ##. (And then ## E_{below} ## is computed from knowing ## \\rho ## which was given). ## \\\\ ## To get the polarization surface charge density at ## r=b ##, the polarization charge per unit length at ## r=b ## must be equal and opposite that at ## r=a ##. (Recommend don't use ## \\sigma_b ## at ## r=a ##. Better to call it ## \\sigma_{pa} ##.(## p ## for polarization). ## \\\\ ## The free charge at ## r=b ## is the easiest. If the outside is grounded, by symmetry, the net charge enclosed must be zero to have ## E =0 ## outside of the coaxial metal layer, as well as inside this metallic layer. The inside surface of the metallic layer at ## r=b ## will thereby have some charge.## \\\\ ## Additional item: For charge per unit length and surface charge per unit length, suggest using ## \\lambda ##. Do not use ## \\sigma ## for that. ## \\sigma ## is a surface charge density per unit area. e.g. For the core, you would have ## \\lambda_{core}=\\rho \\, \\pi \\, a^2 ##. Also ## \\lambda_{pa}=\\sigma_{pa} \\, 2 \\pi \\, a ##, etc.\n\nLast edited:\n•", null, "Philip Land and Delta2\nDelta2\nHomework Helper\nGold Member\nI am a bit lost here, the central cylindrical rod is conducting/metal or non conducting? And the charge density given as ##\\rho## is equal to ##\\rho_{free}## or not?\n\nCharles Link\nHomework Helper\nGold Member\n2020 Award\n@Delta² The central rod is ## \\rho_{free} ## and non-conducting. It's free charge, (as opposed to polarization type charge), but is not free to move. It is embedded in the rod. ## \\\\ ## And it's not a polarization type charge that forms as the result of dipoles in the material.\n\n•", null, "Delta2\nDelta2\nHomework Helper\nGold Member\n@Delta² The central rod is ## \\rho_{free} ## and non-conducting. It's free charge, (as opposed to polarization type charge), but is not free to move. It is embedded in the rod. ## \\\\ ## And it's not a polarization type charge that forms as the result of dipoles in the material.\nEhm, I don't understand then why in your first equation you say ##\\rho_{free}=0## at ##r=a##, ok its not free to move, but still it should be ##\\rho_{free}=\\rho## at ##r=a## , right or wrong?\n\nI think you omitted a step: ## \\nabla \\cdot D=\\rho_{free}=0 ## at and around ## r=a ##, so that ## D_{above}=D_{below} ## which gives ## \\epsilon_o \\epsilon_r E_{above}=\\epsilon_o E_{below} ##. (And then ## E_{below} ## is computed from knowing ## \\rho ## which was given). ## \\\\ ## To get the polarization surface charge density at ## r=b ##, the polarization charge per unit length at ## r=b ## must be equal and opposite that at ## r=a ##. (Recommend don't use ## \\sigma_b ## at ## r=a ##. Better to call it ## \\sigma_{pa} ##.(## p ## for polarization). ## \\\\ ## The free charge at ## r=b ## is the easiest. If the outside is grounded, by symmetry, the net charge enclosed must be zero to have ## E =0 ## outside of the coaxial metal layer, as well as inside this metallic layer. The inside surface of the metallic layer at ## r=b ## will thereby have some charge.## \\\\ ## Additional item: For charge per unit length, suggest using ## \\lambda ##. Do not use ## \\sigma ## for that. ## \\sigma ## is a surface charge density per unit area. e.g. For the core, you would have ## \\lambda_{core}=\\rho \\, \\pi \\, a^2 ##. Also ## \\lambda_{pa}=\\sigma_{pa} \\, 2 \\pi \\, a ##, etc.\nBy surface charge ##density## do you mean surface charge, because it's only the surface charge I want.\n\nIs polarization charge same as bound charge? I failed to understand how to use the ##\\lambda## concept.\n\nHowever, I did a new calculation, I did a little wrong above.\n\nSee picture.\n\nSo now I think I have the total surface charge for both the cylinders. However, there's still bound charge present. How do I differentiate bound and free charge, so I eventually can extract only the free surface charge from the total surface charge?", null, "#### Attachments\n\nCharles Link\nHomework Helper\nGold Member\n2020 Award\nYour calculations are leaving off an important step or two in the solution. I recommend you use the equation ## \\nabla \\cdot D=\\rho_{free} ## where ## D=\\epsilon_o \\epsilon_r E ##. You can do the calculation just using ## E ## and deriving everything else from ## -\\nabla \\cdot P=\\rho_p ##, but it's easier to use ## D ##. ## \\\\ ## With this equation, Gauss' law reads ## \\int D \\cdot dA=Q_{free} ##. You seem to get the correct answer for ## \\sigma_pa ##, but you aren't showing the steps to get there. ## \\\\ ## And yes, your equation ## E_{above}-E_{below}=\\frac{\\sigma_{pa}}{\\epsilon_o} ## is correct. ## \\\\ ## And, yes, it is bound polarization charge, usually called just simply polarization charge. ## \\\\ ## Using the ## D ## form of Gauss' law with a pillbox around ## r=a ## gives: ## \\\\ ## ## \\epsilon_o \\epsilon_r E_{above}-\\epsilon_o E_{below}=0 ##, so that ## E_{above}=\\frac{E_{below}}{\\epsilon_r} ##. ## \\\\ ## This gives ## E_{below}(\\frac{1}{\\epsilon_r}-1)=\\frac{\\sigma_{pa}}{\\epsilon_o} ##. ## \\\\ ## ## E_{below} ## is readily found: (Edit) ## E_{below} 2 \\pi \\, a L=\\frac{\\rho \\pi a^2 \\, L }{\\epsilon_o} ##. ## \\\\ ## Now we can simply solve for ## \\sigma_{pa} ##. (I think you got that part correct). ## \\\\ ## Because this is polarization of the dielectric between ## r=a ## and ## r=b ##, ## \\lambda_{pa} L=-\\lambda_{pb} L ##. ## \\\\ ## (It can be shown that ## -\\nabla \\cdot P=\\rho_p=0 ## inside the dielectric, so that the only polarization charge is surface polarization charges. The net polarization charge must be zero. Alternatively, ## \\int D \\cdot dA =Q_{free} ## so that ##\\epsilon_o \\epsilon_r E(r) 2 \\pi r=\\rho \\, \\pi a^2 ##. We can compute ## \\nabla \\cdot E(r)=\\frac{\\rho_{total}}{\\epsilon_o}=0 ## in the dielectric, (google \"divergence in cylindrical coordinates\"), so that ## \\rho_p =0 ## in the dielectric. ) ## \\\\ ## This is why you need the surface charge per unit length.## \\\\ ## Now ## \\lambda_{pa}=\\sigma_{pa} 2 \\pi a ## and ## \\lambda_{pb}=\\sigma_{pb} 2 \\pi b ##. You need these last two relations to solve for ## \\sigma_{pb} ## from ## \\sigma_{pa} ##.\n\nLast edited:\n•", null, "Delta2 and Philip Land" ]	[ null, "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7", null, "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7", null, "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7", null, "data:image/svg+xml;charset=utf-8,%3Csvg xmlns%3D'http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg' width='450' height='600' viewBox%3D'0 0 450 600'%2F%3E", null, "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.75574356,"math_prob":0.58564544,"size":1077,"snap":"2021-04-2021-17","text_gpt3_token_len":309,"char_repetition_ratio":0.12581547,"word_repetition_ratio":0.0,"special_character_ratio":0.30083567,"punctuation_ratio":0.078431375,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9932206,"pos_list":[0,1,2,3,4,5,6,7,8,9,10],"im_url_duplicate_count":[null,null,null,null,null,null,null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-04-10T12:29:45Z\",\"WARC-Record-ID\":\"<urn:uuid:096d03eb-675d-4c46-93dd-8707399d88ad>\",\"Content-Length\":\"85511\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:55af733c-48c0-4002-94b1-5e2abf334815>\",\"WARC-Concurrent-To\":\"<urn:uuid:da5e2474-9db3-4098-992e-0f47fdf59339>\",\"WARC-IP-Address\":\"104.26.14.132\",\"WARC-Target-URI\":\"https://www.physicsforums.com/threads/free-surface-charges-on-concentric-cylinders.954252/\",\"WARC-Payload-Digest\":\"sha1:Q7K3KDS4DCTKEROBHF6LVAT5JXREUYRB\",\"WARC-Block-Digest\":\"sha1:4JT5PVK3LRO6MWMKOII6EMIUG774SMBC\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-17/CC-MAIN-2021-17_segments_1618038056869.3_warc_CC-MAIN-20210410105831-20210410135831-00075.warc.gz\"}"}
https://mathoverflow.net/questions/84622/is-the-maximum-tree-path-length-distributed-lognormally-in-the-limit	[ "# Is the maximum tree-path length distributed lognormally (in the limit) ?\n\nConsider a full binary tree with $k>10$ levels. Let the lengths of individual edges in this tree be i.i.d. random variables with finite moments. Then total lengths of the $2^{k-1}$ source-to-sink paths in this tree are approximately Gaussian by the CLT, regardless of the edge-length distribution. We are interested in the limit distribution ($k\\rightarrow\\infty$) of the maximum path length in the tree.\n\nOur numerical simulation built 100K independent trees ($k=15$) with $(a)$ uniform and $(b)$ Gaussian edge lengths. The resulting distributions for $(a)$ and $(b)$ did not look qualitatively different and were somewhat skewed to the right. Lognormal distributions provided very close fits --- better than Gumbel and Airy. If lognormal is indeed the limit distribution, we would appreciate references or suggestions on proving this analytically.\n\nUnless I misunderstood your question, this can be entirely rephrased in terms of branching random walks. This goes as follows: at time 0 there is 1 individual at position 0. Each individual gives birth to two descendants, whose position is the position of the parent plus a jump, where all jumps are i.i.d. random variable. You are asking about the maximum position at time $k$, $M_k$.\nThis is a much studied problem, with deep links to traveling wave partial differential equations such as the Fisher-KPP equation. (Eg, in the space-time continuous case where branching random walk is replaced by branching Brownian motion, the function $u(t,x) = \\mathbb{P}(M_t >x)$ solves the KPP equation with initial condition $u(0,x) = 1_{x<0}$.)\nSee this recent paper http://front.math.ucdavis.edu/1101.1810 by Elie Aidekon, which provides complete answers to your question under minimal assumptions on the jump distribution. The main result is then that $M_k - ck + (3/2) \\log k$ converges to a random variable, where $c$ is a constant that is easy to compute. The distribution of the limiting random variable doesn't have to be either Gumbel or lognormal." ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9015541,"math_prob":0.9956521,"size":848,"snap":"2019-35-2019-39","text_gpt3_token_len":186,"char_repetition_ratio":0.13270143,"word_repetition_ratio":0.0,"special_character_ratio":0.22287735,"punctuation_ratio":0.087248325,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9989907,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-09-17T23:33:32Z\",\"WARC-Record-ID\":\"<urn:uuid:17954d15-90cc-4d5c-85ba-922a8a46d7e7>\",\"Content-Length\":\"117582\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:ef40db93-003f-4647-b930-a6f9d0bdaa32>\",\"WARC-Concurrent-To\":\"<urn:uuid:2f407d61-7df4-499b-9e17-2f7ea5fe708c>\",\"WARC-IP-Address\":\"151.101.1.69\",\"WARC-Target-URI\":\"https://mathoverflow.net/questions/84622/is-the-maximum-tree-path-length-distributed-lognormally-in-the-limit\",\"WARC-Payload-Digest\":\"sha1:L3NQO2VZ65VUMATN5G5E65DIJS73A62Q\",\"WARC-Block-Digest\":\"sha1:PRTVZBSZWMP5PHU6W4NTFMJ7QKRSPTMS\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-39/CC-MAIN-2019-39_segments_1568514573124.40_warc_CC-MAIN-20190917223332-20190918005332-00229.warc.gz\"}"}
https://americanring.com/(X(1)S(gf1iyr45vlffzs555e1ote45))/products/snap-ring.aspx?item=US-950	[ "", null, "", null, "", null, "### US-950\n\nDisplay Units: inches \| metric\nRequest Quick Quote for this part.\n Ring Specs (D) Free Diameter: 9.170 +0.000 / -0.070 in. (t) Thickness: 0.076 +0.002 / -0.002 in. (b) Radial Wall: 0.345 +0.004 / -0.008 in. Groove Specs: (B) Application Diameter: 9.500 in. (G) Groove Diameter: 9.263 +0.008 / -0.008 in. (W) Groove Width: 0.082 +0.005 / -0.000 in. Groove Depth: 0.119 in. Other Specs Approximate Weight per 1000: (m) Material Thickness: 0.075 in. (N) Ring Number of Turns: 1.000 (Pg) Theoretical Thrust Load Capacity - Groove Yield:Notes: Yield Strength of Groove Material (Ys): 45,000 psi. Calculated using a safety factor (K) of 2Equation:Pg = [ B * d * Ys * pi ] / K = [ (9.500 in.) * (0.119 in.) * (45,000 psi) * 3.14 ] / 2 79,869 lbs. (Pr) Theoretical Thrust Load Capacity - Ring Shear:Notes: Ring Material: Carbon Spring Steel (SAE 1070-1090). Shear Strength of Ring Material* (Ss): 120,000 psi. Calculated using a safety factor (K) of 3Equation:Pr = [ B * t * Ss * pi ] / K = [ (9.500 in.) * (0.076 in.) * (120,000 psi) * 3.14 ] / 3*Shear Strength of Material value is for Carbon Spring Steel material only. 90,683 lbs. Industry Equivalent Part Number(s):US-950, CL-950, VS-950" ]	[ null, "https://americanring.com/images/sub_banners/AR_1020x200_5.jpg", null, "https://americanring.com/images/product_spec_us.jpg", null, "https://americanring.com/images/product_app_shaft_spiral.jpg", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.5393927,"math_prob":0.98503536,"size":793,"snap":"2022-27-2022-33","text_gpt3_token_len":284,"char_repetition_ratio":0.12040558,"word_repetition_ratio":0.16438356,"special_character_ratio":0.443884,"punctuation_ratio":0.22033899,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9575002,"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\":\"2022-06-26T17:54:22Z\",\"WARC-Record-ID\":\"<urn:uuid:a4393374-22c8-4532-a31e-63c1ff1ffbc6>\",\"Content-Length\":\"18111\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6eee000c-97d0-4dbe-9e67-ff78b2711cae>\",\"WARC-Concurrent-To\":\"<urn:uuid:0f8dc179-c3bf-4372-b181-11184e400761>\",\"WARC-IP-Address\":\"172.67.134.163\",\"WARC-Target-URI\":\"https://americanring.com/(X(1)S(gf1iyr45vlffzs555e1ote45))/products/snap-ring.aspx?item=US-950\",\"WARC-Payload-Digest\":\"sha1:ZUH64QPFI4JE25UM5RK6IPNPNLPTUSDR\",\"WARC-Block-Digest\":\"sha1:YL2EOBMJVE6TIZTSK6IUE6GI76N5NIZ6\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-27/CC-MAIN-2022-27_segments_1656103271763.15_warc_CC-MAIN-20220626161834-20220626191834-00362.warc.gz\"}"}
https://cloudstack.ninja/muhammad-alif/overlapping-pyplot-imshow-plot-in-single-grid/	[ "# Overlapping pyplot.imshow() plot in single grid\n\nI want to plot this figures. For the first figure:\n\nFigure1.png\n\nFor the code snippet:\n\n``````import matplotlib.pyplot as plt\nrow = 30\ncol = row\nareas_pher = [[-0.01 for i in range(col)] for j in range(row)]\n\nfor i in range(1, row-1):\nfor j in range(1, col-1):\nareas_pher[i][j] = 0\nmax_pher = 20\nfor pher in range(1, col-1):\nareas_pher[pher] = max_pher * pher/max_pher\nplt.imshow(areas_pher, cmap=\"Blues_r\", vmin=0, vmax=max_pher)\n``````\n\nAnd the second figure:\n\nFigure2.png\n\nFor the code snippets:\n\n``````import matplotlib.pyplot as plt\nimport numpy as np\n\nareas_ant = [[6 for i in range(col)] for j in range(row)]\nprob_ant = 0.05\n\nfor i in range(1, row-1):\nfor j in range(1, col-1):\nrand = np.random.rand()\nif rand < prob_ant:\nareas_ant[i][j] = 5\nplt.imshow(areas_ant, cmap=\"Blues\")\n``````\n\nMy question, what the best way to make its figures overlapping, for illustrate like this image below?\n\nOverlapping Figures Illustration.png" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.8022455,"math_prob":0.9875322,"size":1180,"snap":"2020-45-2020-50","text_gpt3_token_len":339,"char_repetition_ratio":0.13860545,"word_repetition_ratio":0.1183432,"special_character_ratio":0.28898305,"punctuation_ratio":0.15510204,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99931586,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-12-05T00:00:41Z\",\"WARC-Record-ID\":\"<urn:uuid:0c7b2888-b7bb-4f80-ab2d-4cec06a64d0e>\",\"Content-Length\":\"62528\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:e49385ca-7755-4031-b709-bb27192f12dd>\",\"WARC-Concurrent-To\":\"<urn:uuid:d3e91526-ee24-4c75-8d66-1344e1fed6ab>\",\"WARC-IP-Address\":\"192.0.78.178\",\"WARC-Target-URI\":\"https://cloudstack.ninja/muhammad-alif/overlapping-pyplot-imshow-plot-in-single-grid/\",\"WARC-Payload-Digest\":\"sha1:NG3FYQPES3EO4TDYJ3OQIK26KEND7X66\",\"WARC-Block-Digest\":\"sha1:4KAQJSL7WM74AVC3MOYCUUXUZIZBKJKD\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-50/CC-MAIN-2020-50_segments_1606141745780.85_warc_CC-MAIN-20201204223450-20201205013450-00267.warc.gz\"}"}
https://www.bsynchro.com/m7b2uyfd/decision-making-under-risk-questions-and-answers-54cf56	[ "8.5. In such cases, the problem is classified as decision making under risk . 150) + 0.3 (Rs. 450) (8.7). ‘Do not Invest’, i.e., E(U2) = 0. If the original payoff table is stated in terms of losses or costs, the decision-maker will then select the smallest loss for each event and subtract this value from each row entry. The optimal decision would still be the same, viz., ordering 200 units; thus the manager’s decision is not very much sensitive to changes in the probability assignments. Risk is objective but uncertainty is subjective; risk can be measured or quantified but uncertainty cannot be. Thus, a situation of complete uncertainty prevails. Let us consider a simple competitive market where the demand (average revenue curve) faced by a seller is a horizontal straight line. Exam 10 October 2014, questions. Therefore, marginal utility measures the satisfaction the individual receives from a small increase in his stock of wealth. 300 (CE = Rs. All other trademarks and copyrights are the property of their respective owners. Mr. X’s EMV from playing this gamble is Rs. If Mylo adopts a maximin approach to decision-making, which daily supply level will he choose? We illustrate the concept in table 8.6 below: If we adopt the simple EMV criterion, a cursory glance would make project B apparently seem to be the best possible choice. It is not possible for you to wait for some time to study the nature (or determine the level) of demand, nor can you place more than one order. Concept of Decision-Making Environment 2. To illustrate, a discount rate of 10% becomes a discount factor of 1.46 [= (1.10)4] by the end of four years, and the 13% rate becomes 1.63 [=(1.13)4]. In case of two or more projects (alternatives) having unequal costs or benefits (payoffs) the CV is undoubtedly a preferable measure of relative risk. 300 and if demand were 200 units, he would order 200 and the payoff would be Rs. 167.50, Rs. The conversion of a payoff matrix to a regret matrix is very easy. Recall that the word ‘margin’ always refers to anything extra. It is gratifying to note that the expected utility approach to decision problems under risk accommodates both factors and provides a logical way to arrive at decisions. The decision maker is able to assign probabilities based on the occurrence of the states of nature. The minimax regret has been proposed by Savage. all of the unknown outcomes of a decision, a complex thought process in decision-making, a decision that will definitely have a negative outcome, the possible negative outcomes of a decision. Therefore they would decide not to participate in this type of gamble characterized by highly uncertain outcome against an unlimited payment (that has to be made if the gamble is accepted). It is because the total cost is Rs. Fig. Therefore, by using the maximization of expected value criterion, the inventory manager would choose A2, i.e., order 200 units. If the decision-maker analyses the expected values of each of the actions, he arrives at the decision to select the option which is having the highest expected value, i.e., option 2 in this example. Thus the lottery is equivalent to tossing an unbiased coin. If a head appears in the first toss Mr. X owes Mr. Y Rs. Four major criteria that are based entirely on the payoff matrix approach are: In those situations where the decision-maker is willing to assign subjective probabilities to the possible outcomes, the two other criteria are. a. It is zero for the alternative action. Since the first decision (A1) has the highest expected value it will be taken. A risk neutral decision maker will always prefer C to A or B. c. A risk seeking decision maker will always prefer C to A or B. d. All of the above are correct. The two competitors may not have the same approximate utilities (with a negative sign). Now, in the context of our NPV model we may assert that risk aversion is reflected in the fact that any decision that a firm makes will surely change its risk level — the degree of risk to which it is exposed. 150) + 0.2 (Rs. So the maximization of EMV criterion is not a reliable guide in predicting the strategic action or strategic choice of an individual in a given decision environment. -4000) x .80 = Re. However, in real life most people prefer to play safe and avoid risk. He is considering whether or not to make long-term investment for introducing the product in the market. The Society for Education in India (in short SEI) had been engaged in running primary schools in different parts of the country since 1950s. If this happens, such a value is called a saddle point. Based on this estimation of probabilities, the expected payoff can be computed as follows: A1 (100) = 0.5 (Rs. 300 (Rs. Positive payoff implies profit and negative pay-off implies loss. In this article we will discuss about Managerial Decision-Making Environment:- 1. In the context of decision problems whose uncertain possible outcomes constitute rupee payments with known probabilities of occurrence, it has been observed by many that a simple preference for higher rupee amounts is not sufficient to explain the choices (that is, decisions) made by various individuals. 200; if demand were going to be 150 units, he would place order for 200 units with a payoff of Rs. Choose an answer and hit 'next'. Decision-Making under Uncertainty (70985) Akademisches Jahr. The maximum regret values for each of the action or actions are presented below: The smallest possible regret (or minimum opportunity loss) would be incurred by ordering 200 units. 10 per shirt, if 200 or more are ordered, the cost is Rs. The player is supposed to receive or win 2n rupees as soon as the first head appears on the n-th toss. Share Your Word File There will also be a cost saving of Rs. Thus, the criterion is conservative in nature and is well-suited to firms whose very survival is at stake because of losses. In other words, even if the returns from project B are higher on average than that of A, the former exhibits greater variability. The most obvious defect of the CE approach, outlined above, is that it requires the specification of a utility function so that risk premium can be numerically measured or quantified. For example, we know that if we toss an unbiased coin, one of two equally likely outcomes (i.e., either head or tail) occur, and the probability of each outcome is predetermined. So B chooses the minimax criterion. Under uncertain conditions the profits in the numerator, Rt – Ct = Pt, are really the expected value of the profits each year. This minimises A’s payoff and therefore maximises his own. ACCA BT F1 MA F2 FA F3 LW F4 Eng PM F5 TX F6 UK FR F7 AA F8 FM F9 SBL SBR INT SBR UK AFM P4 APM P5 ATX P6 UK AAA P7 INT AAA P7 UK. In the final analysis, the inventory manager can easily toss out the A3 option, but he must still bear the burden of choosing A1 or A2 in the face of uncertain demand. Recall that risk is characterized as a state in which the decision-maker has only imperfect information about the decision environment, i.e., the impact of all of the available alternatives. Thus, the prediction is that actual monetary values of the possible outcomes of the gamble fail to reflect the true preference of a representative individual for these outcomes. In our T-shirt example the minimum payoffs associated with each of the actions are presented below: If the decision-maker is a pessimist and assumes that nature will always be niggardly and uncharitable the optimal decision would be to order 100 T- shirts because this action maximizes the minimum payoff. Uncertainty refers to a state in which the decision-maker lacks even the information to assign subjective probabilities. It is just a retail store selling readymade garments. of only Rs. (8.1) assuming an alpha value of 0.25 are presented below: Thus, the decision-maker would choose A1, i.e., order 100 T-shirts. Since different shareholders are involved and they have different utility functions, which are not directly comparable, it is virtually impossible to arrive at a group utility function. Question 1 1.5 Pts â¢ Decision Making Under Risk Means That: The Decision Maker Does Not Know The Alternatives Available. Bernoulli observed that gamblers did not respond to the expected rupee prices in games of chances. 9 per shirt; and if 300 or more shirts are ordered the cost is Rs. 65 lessons This reveals the increasing marginal utility hypothesis The implication of this hypothesis is simple enough: as the individual’s wealth increases, he receives more extra utility from each extra rupee that he receives. (b) By reference to a theoretical probability distribution (such as the binomial distribution, Poisson distribution or normal distribution). 300), then his risk premium (RP) can be defined as: In such a situation Mr. Hari is willing to pay Rs. For simplicity, we assume that the product is perishable. For this reason it is necessary to look at the probability distribution of the random variable, which is a listing- of the possible outcomes with the associated probabilities of those outcomes. Before publishing your Articles on this site, please read the following pages: 1. 125. 8.9 illustrates the relationship between K* and project risk. Under a state of risk, the decision maker has incomplete information about available alternatives but has a good idea of the probability of outcomes for each alternative. When these probabilities are known or can be estimated, the choice of an optimal action, based on these probabilities, is termed as decision making under risk. MC Question 16 - September 2016. 600? The implication is that the price that the firm faces is not stable. Pulmonary aspiration is defined by the inhalation of oro-pharyngeal or gastric contents into the larynx and the respiratory tract. Thus the optimal decision would be to accept the project, i.e., invest in the product. We can now compare the figures in brackets — (Rs. Table 8.2 depicts the regret matrix for the T-shirt inventory problem. Mainstream economics and finance is dominated by models of decision- making under risk under the rationality axioms, where modern macroeconomics has its analytical roots in the general equilibrium framework of Kenneth Arrow and Gerard Debreu (Arrow and Debreu, 1954). Decision theory involving 2 or more decision makers is known as game theory. They calculate expected utility in the same way expected value is calculated by multiplying the utility of each outcome by its probability of occurrence, and then summing up the whole thing, thus: This criterion apparently appears to be very effective. Risk Analysis 4. It is based on the belief that nature is unkind and that the decision-maker therefore should determine the worst possible outcome for each of the actions and select the one yielding the best of the worst (maximin) results. Chapter 4 Decision Analysis 97 includes risk analysis. You will receive your score and answers at the end. This criterion is, however, criticized on the ground that the assumption of equally likely events may be incorrect and the user of this criterion must consider the basic validity of the assumption. It may also be that the opponent’s utilities are not known at all: The decision problem would then have to be treated under uncertainty. The Decision Maker Is Generally Ignorant About The Whole Problem He Is Trying To Solve. The states of nature occur passively and independently of the strategies chosen. A decision tree is used for sequential decision-making. By assigning subjective probabilities, the decision maker is, in essence, converting an uncertain situation into a situation of risk. The price of tea next week may also be random owing to unforeseen shifts in supply and demand. Step 2: Developing a set of potential responses or viable solutions. So the manager has to sell all the output rather than store some of it for future sales. Dealing with Risk and Uncertainty in Decision Making. Since there are constant changes in market conditions and in the number (range) of competitive (rival) products, it is not possible to repeat the experiment under the same conditions hundreds of times. 8.8 presents the decision tree associated both the problem faced by Mr. Ram. The results of such computations are presented in Table 8.10 below: It is clear that construction of the prototype using conventional materials (A1) is the least risky alternative. Acowtancy. 150) + 0.2(Rs. 6,000). The specific consequence or outcome depends not only on the decision (A1, A2, or A3) that is made but also on the event (D1, D2, or D3) that occurs. 6,000. Suppose Mr. Hari has purchased a lottery ticket that has a 50-50 chance of paying Rs. Laplace criteria. If profit maximization does not appear to be a sensible goal, one has to search out or identify another objective function for the firm. True, expected value is a mathematical average the mean of a probability distribution that neatly summarizes an entire distribution of outcomes. If the firm has to choose between alternative methods of operation, one with high expected profits and high risk and another with smaller expected profits and lower risk, will the higher expected profits be sufficient to neutralize the high degree of risk involved in it? Since NPV analysis uses a compounding factor in the denominator (1+r)t the incorporation of a risk adjustment factor in the denominator to deflate future values, heightens this compounding. However, in order to measure the riskiness of the three alternatives, Mr. Ram computes the standard deviation of each of the alternatives. After finishing this lesson, you should be ready to: 9 chapters \| Decision Making: Solved 67 Decision Making Questions and answers section with explanation for various online exam preparation, various interviews, Logical Reasoning Category online test. Hence, it involves more risk. When decisions are based on the EMV criterion, it is implicitly based on the assumption that a decision-maker is able to withstand the short-run fluctuations and is a continuous participant in comparable EMV decision problems. However, the real commercial world is characterized by uncertainty. 175) + 0.2 (Rs. 200. Thus, the inventory manager knows that the maximum amount that he would pay for a perfect prediction of demand would be Rs. Such objective probability is couched in terms of relative frequency. Thus even if the two alternative have the same EMV, the decision maker would choose the option having the least dispersion (or maximum concentration). Uncertainty does not seem to suggest that the decision-maker does not have any knowledge. Read our guide, together with our How to handle competency-based interview questions tips, and double your chance of interview success. Firstly, in a large organization, whose utility function has to be used remains an open question. Our mission is to provide an online platform to help students to discuss anything and everything about Economics. flashcard set{{course.flashcardSetCoun > 1 ? Certainty Equivalents. For the T-shirt example, the probability assigned to each of the three events would be 0.33, and the expected monetary value (EMV) would be. Since the events are mutually exclusive, the sum of their probabilities is equal to 1. The Decision Maker Knows The Payoffs As Well As â¦ 4,000, i.e., the cost of production and marketing. 600) (8.6), A3 (100) = 0.5 (Rs. Suppose that you have the following payoff matrix: Select the optimal action by applying maximin, maximax, Hurwicz (= 0.3 ), minimax regret and the. If the future event that will occur could be predicted with certainty, the decision-maker would merely look down the column and select the optimal decision. For project A it is 0.183 and for project B, 0.297. We may now illustrate the concept. With external economies, such games could arise. The utility function is characterized by diminishing marginal utility of money. As another example, let us consider the following discrete probability distribution of prices. Alternatively, he may be a risk-lover, in which case he would not exit the game (part with the lottery ticket) unless he received more than Rs. Since profit is a random variable, the concept of maximum profit becomes meaningless. We simply calculate the standard deviation for project A and B as the square root of the variances σA2 and σB2. 500 per ticket. It means to choose one risk over another. Thus we can say that a payoff matrix provides the decision-maker with quantitative measures of the payoff for each possible consequence and for each alternative under consideration. It is interesting to note that this is the same decision (that is, indifference) as was obtained in the first part with the EMV criterion. If the conflict of interest is not complete, the game is called a non-zero sum game. How can you give the answer an employer is looking for unless you know the questions theyâll ask? 300, Rs. To a rational decision-maker, the value of information can be treated as the difference between what the payoff would be with the information currently available and the payoff that would be earned if he were to know with certainty the outcome prior to arriving at a decision. An important characteristic of a random variable is its expected value or mean. Whatever strategy B chooses, A will try to maximise his own pay-offs. Decision-Making Environment under Uncertainty 3. Managers are required to examine the risk associated with each project before making a decision. - Process, Methods & Examples, Quiz & Worksheet - Risk & Uncertainty in Decision Making, Dealing with Risk & Uncertainty During Decision Making, {{courseNav.course.mDynamicIntFields.lessonCount}}, Types of Problems & Problem Solving Strategies, Availability Heuristic: Examples & Definition, Ways to Manage Risk: Insurable and Uninsurable Risk, UK Clinical Aptitude Test (UKCAT) Flashcards, Working Scholars® Bringing Tuition-Free College to the Community, What's involved with assessing the risks involved in a decision, Action to take after making a decision that involves risk, Definition of uncertainty in decision making, Question to ask yourself when making a decision that involves uncertainty, Outline important characteristics of the risk evaluation process, Discuss the goal in the decision-making process, Explain why it can be helpful to involve others when making decisions involving uncertainty. 100,000 if the newly designed chip is used. Thus we get σA = Rs. The manufacturer of these has imposed a condition on you: You have to order in batches of 100. All we have to do is to subtract each entry in the payoff matrix from the largest entry in its column. When you take this quiz, you'll be asked about how to assess the risks involved in a decision and the definition of uncertainty as it applies to decision making. This is the average price which is arrived at by multiplying each possible price by the probability of its occurrence and adding up the results. There will be interaction, the basis of which is conflict of interest. The focus is on an index which is based on the derivation of a coefficient known as the coefficient of optimism. It differs from the EMV in the sense that it involves the use of the regret matrix. For example, if the inventory manager knew, before arriving at the decision, that actual demand were going to be 100 units, the optimal decision would be to order 100 units with a payoff of Rs. Thus Mr. Hari’s average or expected payoff in this game is Rs. The decision-maker thus attaches his best estimate of the ‘true’ probability to each possible outcome. Find out his optimal strategy considering that (a) he is a partial optimist (Hurwicz criterion, with the coefficient of optimism 60%), (b) he is an extreme pessimist (Savage criterion) and (c) he is a subjectivist (Laplace criterion). By putting the values of cash flow (X), expected value (EMV), and assigned probability from Table 8.6 into equation (8.13) we are in a position to quantify this risk. It is known as the criterion of optimism because it is based on the assumption that nature is benevolent (kind). The payoffs are measured in terms of profit. Here, in Fig. A decision tree is used for sequential decision-making. This website includes study notes, research papers, essays, articles and other allied information submitted by visitors like YOU. This corroborates the diminishing marginal utility hypothesis. 8.1 illustrates this observation. where the Xs refer to the payoffs from each event and to the probabilities associated with each of the payoffs. Secondly, in case of large private firms characterized by separation of ownership from management whose utility function — the managers’ or shareholders’ — has to be used is another question. In terms of actual conditions a large number of problems is involved with states of nature. Therefore, by using the maximization of expected utility criterion, the rational entrepreneur would decide against the project. However, a closer scrutiny of the cash flows also reveals that project A has a small expected value, but, at the same time, it shows less variation and according to our yardstick, appears to be less risky. In some cases, however, a relative frequency (also known as the classical) interpretation of probability does not work because repeated trials are not possible. ACCA CIMA CAT DipIFR Search. Or the role of ambiguity in decision-making. Secondly, complex problems arise in measuring the utility function of an individual. The regret value in Table 8.2 represent the difference in value between what one obtains for a given action and a given event and what one could obtain if one knew beforehand that the given event was, in fact, the actual event. He estimates that the probabilities associated with each of these outcomes are 0.25, 0.50 and 0.25, respectively. One may, for instance, ask what is the probability of successfully introducing a new breakfast food (like Maggie). 500 or Rs. While attending a conference on employee selection, Mr. J Mehta, a senior member of the society learned that a leading school had recently employed a psychologist to perform employment functions, i.e. Here, for the sake of simplicity, we consider only two probability distributions. (Try to guess why.) With complete conflict of interest the game is a zero-sum game. You have to decide how many men’s T-shirts to order for the summer season. 8.2 makes one thing clear at least: when demand is random, the actual price is subject to a probability distribution. 500) and (Re. \| {{course.flashcardSetCount}} But its major defect is that it can obscure the presence of abnormally high potential losses or exceptionally attractive potential gains. 0) — in the upper tree with the expected utility figures — (-0.25) and (0) — in the lower tree. Decision-Making Environment under Uncertainty: Decision-Making Environment under Risk Analysis: Decision-Making Environment under Certainty Equivalents. If head appears, Mr. Hari will get Rs. 400,000, Mr. Ram has the option of simultaneously pursing the development of both prototypes. Expected Value of Perfect Information (EVPI): So long our stress was on selection of an alternative on the basis of information currently possessed by the decision-maker. Therefore, the entrepreneur with a linear utility function would show indifference to the two alternative actions when attempting to maximise expected utility. If so, the riskier alternative will surely be preferred; otherwise the low-risk project or method of operation should be accepted. Finally, let us consider a situation in which the entrepreneur has a linear utility function, as shown in Fig. Looking at the worst case scenario and what can possibly go wrong with each decision is a good way to understand the pros and cons of different choices. Management Science 29:1066 1076. It is a nice way of summarizing the interactions of various alternative action and events. The first one is deductive and it goes by the name a priori measurement; the second one is based on statistical analysis of data and is called a posteriori. By contrast, the RADR method focuses on the denominator. Equation (8.1) indicates that the more optimistic the decision maker, the larger will be the Hi value, and vice versa. Hence Mr. Ram is faced with a perplexing dilemma — a trade-off between risk and profitability. 200) + 0.3 (Rs. The slope of the utility function at any point measures marginal utility. The R&D engineers have succeeded in identifying two approaches, one utilizing conventional materials and another using a newly developed chip. Of probability as the coefficient of variation to make a comparison of the decision be. His maximum regret CE is less risky than project a it is also based on the concept of random.! Frank Knight who noted that risk is weighed during a manager second of. Of production and marketing a batch of the product ) are risk averters occur passively and of. The worth of a future event. ) for risk youâre likely to say I. Decide to use the three actions ( order 100, 200 ( A2 ) or 300 ( A3 =... Analysis the decision tree associated both the prototypes are developed, an ’. ProJect B is characterized by greater degree of risk suggested that they responded to the alternative levels of or... Appears, Mr. Ram U ( Rs s gain is B ’ s and! Section with detailed description, explanation will help you determine the qualifications of candidate! Men ’ s attitude toward risk is objective but uncertainty can easily be converted risk... A cost saving of Rs used by investors to determine the worth of a functional prototype s and. Help other people take more risks in their decisions and maximin principles are the same approximate utilities ( with payoff... Maker would again choose A4, investors ) are risk averters still able to derive probability estimates without carrying any! That they responded to the maximum amount that he receives the same and equal to 1 ) be! Employer is looking for unless you know the alternatives Available of gathering additional information before arriving at a.. Problem, decision making under risk questions and answers RADR approach is very easy by F. H. Knight who noted that once subjective probabilities to problems... Minimax criterion is followed, the cost of Rs.107,000 has to determine the worth of a decision problem facing players... Associated costs with the number that comes up is a price-maker to adjusting our basic valuation model of the chosen! Show indifference to the alternative levels of demand or sales in advance the price. Lover to be eager and willing to accept — Rs prices are possible but unlikely presented above number difficulties! – ( payoff to a ) by an analysis of historical patterns or. B are, respectively, 0.001 and 0.002 interactions of various alternative and. Type of games given day by the nature of decision-making assumes strategic significance both in reducing the anxiety the! For introducing the product a retail store selling readymade garments falls as the coefficient of optimism because it a! Frequency, we calculate the expected payoff can be computed as follows: A1 100! A crop planted in July have the following principle of decision or action and event. ) management involves decision! Tree associated both the prototypes are developed, an additional labour cost production... + 0.2 ( 0 ) + 0.3 ( Rs knows that the price... Probability distribution ( such as the unfavorable consequences that may occur the strategies chosen be computed as follows A1... Is called a non-zero sum decision making under risk questions and answers, player a has an extra option of simultaneously pursing the development both... Under risk [ 27 ] and double your chance of losing Rs conventional materials these characteristics... “ I feel the probability distribution have to order 200 units with a function... ‘ take bet ’ with ‘ decline bet ’, shows that the firm ’ s EMV from playing gamble! Of which is produced can be measured by the height of the errors decision. Is neither an optimist nor a pessimist alternatives Available before making a decision problem in product... To Rs of interest game will exist sales of the decision maker is, minimise a s... In Table 8.6, a solution: 2 s T-shirts to order 200 units because is. Observed that gamblers did not respond to the EMV implies risk indifference measure of risk involving objective of. To illustrate these common characteristics of the decision problem in the market is.. Adjust our basic valuation model of the bell-shaped curve sell decision making under risk questions and answers much it! Many men ’ s average or expected payoff in this article we will discuss about Managerial decision-making subjective... Or sales at the end real-life situations, the less dispersed the probability occurrence... By using equation ( 8.19 ), EOL ( A2 ) = 0.5 (.... 0.50 and 0.25, 0.50 and 0.25, 0.50 and 0.25, respectively assigned a probability distribution of possible,. In particular, managers are required to construct a payoff of Rs below the matrix we show decision making under risk questions and answers! Does not have any knowledge, such a value is subject to probabilistic variation thus attaches his estimate! Easily be converted into risk analysis: decision-making Environment under certainty Equivalents and. Happening is the distinction among three different states of nature this is equivalent to assuming with optimism! Competitive market where the Xs refer to the possible outcomes are equally likely i.e.... Depicts the regret matrix for the decision maker is able to assign probability estimates to the payoff! Prefer to play safe and avoid risk in a random variable is expected! Yet to be reluctant to undertake investments having negative EMVs be computed as follows: A1 ( )! The favorable as well as the coefficient of variation to make long-term investment for introducing the product following ten! A will try to implement it 1 1.5 Pts â¢ decision making, accountability and flexibility always to. Conservative in nature and effectiveness of various alternative action and event. ) is equivalent to tossing an coin... A1, B will chose B1 the word ‘ margin ’ always refers to taking number... Of losses for the sake of simplicity and reliability when compared with the number of decisions although... 200 ( A2 ) = 0.5 ( Rs because the expected value is a calculus decision-making... A lottery ticket that has a linear utility function has to be put into the market characteristic a... Use of conventional materials by uncertainty essential tool of decision-making maximise his own payoff that. Also be random owing to unforeseen shifts in supply and demand this article we will about. In the first toss Mr. X owes Mr. Y Rs decision-maker thus attaches his best estimate the. The information to assign subjective probabilities ) we can compute CV for a... Established and built in norms, see e.g is virtually impossible, the. In real life example decision-maker should attempt to minimize his maximum regret manufacture the product 8.16 ) a be. Calculate expected utility criterion, alternative a would be Rs risk level this... By passing quizzes and exams between them and taking in our expertsâ advice the... Are possible but unlikely measurement of probability is based on the concept let us consider a competitive! Has developed a new technique of decision outcomes EMV is the assignment of probabilities to possible! To investigate the nature and effectiveness of various states of nature occur passively independently... A is less than the increase in utility from winning Rs in an Environment! OrDered, the decision maker is, whose value is a risk averse decision maker would choose. The present government, arranging a coup invasion information about the Whole problem he is whether! Disclaimer Copyright, Share your PPT File, Steps involved in Managerial decision-making extreme! Us consider the following discrete probability distribution when demand is 150 units he. Lottery ticket ) say “ I feel the probability distribution with the implementation of each option before making a problem... Variation for projects a and B as the coefficient of variation to make long-term investment introducing... RiSky than project B term of EMV criterion is also possible for the optimal decision would be equal to.! From declining the bet of which is yet to be reluctant to undertake investments having negative.. From them under certainty Equivalents matrix ( Table 8.4 ) do is to provide an online platform help! Treated as less risky than alternative B computes the standard deviation for project a the. This gamble is Rs than the budgetary limit of the product decision trees payoff and therefore his. Twin advantages of simplicity, we may now summarize the basic characteristics a. 2: Developing a set of potential responses or viable solutions by decision making under risk questions and answers H. Knight who first drew a between! Condition on you: you have to decide how many men ’ average... Word File Share your PPT File, Steps involved in Managerial decision-making the chosen. Now summarize the basic characteristics of a decision if 100 T-shirts are ordered, the problem classified., although A2 is dominant associated expected values average or expected payoff in game... InventOry problem: risk-averter, risk-indifferent and risk- lover between risk and uncertainty, invest in valuation. Variable can assume may not be Laplace criterion, the decision maker will always occur satisfaction! Such dissimilar utilities that cause non-zero-sum type of games be Available, it is decision making under risk questions and answers! Whose very survival is at stake because of the degree of risk involving objective of... Of optimism and pessimism following pages: 1 yet is ; how much would Mr. has. Implies selection of the three actions ( order 100, 150, or units. Ce exactly equalled the EMV under conditions of uncertainty upsets the profit- maximization objective or normal distribution ) s York... ) could be developed to specifications forth the probabilities or the index of relative risk, decision-makers are classified three. Manager 's decision making process is benevolent ( kind ) new computer chip offers the twin advantages simplicity!, by assigning subjective probabilities are introduced, the cost is Rs considering problems having reasonably few of! ProbAbilities of various alternative action and events events ) its EMV is the assignment of probabilities decision...\n2020 decision making under risk questions and answers" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9400803,"math_prob":0.9306096,"size":34637,"snap":"2021-04-2021-17","text_gpt3_token_len":7129,"char_repetition_ratio":0.14812462,"word_repetition_ratio":0.04803801,"special_character_ratio":0.21009326,"punctuation_ratio":0.12133252,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.97358507,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-04-11T14:56:25Z\",\"WARC-Record-ID\":\"<urn:uuid:149deedd-9a89-48d6-8505-ef0b01d02e56>\",\"Content-Length\":\"46176\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:d65f4604-8073-4904-bc98-2b68be91d317>\",\"WARC-Concurrent-To\":\"<urn:uuid:9e69de21-6603-4537-8ad7-73d17dc3cf21>\",\"WARC-IP-Address\":\"23.97.216.47\",\"WARC-Target-URI\":\"https://www.bsynchro.com/m7b2uyfd/decision-making-under-risk-questions-and-answers-54cf56\",\"WARC-Payload-Digest\":\"sha1:BDWPIU5US54YWR64TKDVNMLYJFTTH2U6\",\"WARC-Block-Digest\":\"sha1:6KHAZOMFBRGLXGTI5XMGRJAEIPCZZDN5\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-17/CC-MAIN-2021-17_segments_1618038064520.8_warc_CC-MAIN-20210411144457-20210411174457-00195.warc.gz\"}"}
https://stacks.math.columbia.edu/tag/00RZ	[ "Lemma 10.131.16. Suppose $R \\to S$ is of finite type. Then $\\Omega _{S/R}$ is finitely generated $S$-module.\n\nProof. This is very similar to, but easier than the proof of Lemma 10.131.15. $\\square$\n\nThere are also:\n\n• 12 comment(s) on Section 10.131: Differentials\n\nIn your comment you can use Markdown and LaTeX style mathematics (enclose it like $\\pi$). A preview option is available if you wish to see how it works out (just click on the eye in the toolbar)." ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.8018053,"math_prob":0.95338595,"size":579,"snap":"2022-40-2023-06","text_gpt3_token_len":145,"char_repetition_ratio":0.09043478,"word_repetition_ratio":0.0,"special_character_ratio":0.2642487,"punctuation_ratio":0.1322314,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9666904,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-10-02T13:13:17Z\",\"WARC-Record-ID\":\"<urn:uuid:39b90ccd-22ea-4421-8fea-911a8e4502bb>\",\"Content-Length\":\"14028\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:86b15ed0-aed7-4ccd-ba68-f8090b21a90c>\",\"WARC-Concurrent-To\":\"<urn:uuid:6f59c936-ccc8-43bd-8284-f1fca392d163>\",\"WARC-IP-Address\":\"128.59.222.85\",\"WARC-Target-URI\":\"https://stacks.math.columbia.edu/tag/00RZ\",\"WARC-Payload-Digest\":\"sha1:ZVLEQDTSXS5VF6SVCVF5PM7TZCUDWQAG\",\"WARC-Block-Digest\":\"sha1:D636E4IPNJCQUMDKHU5X6XK7FSGVYLPS\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-40/CC-MAIN-2022-40_segments_1664030337322.29_warc_CC-MAIN-20221002115028-20221002145028-00783.warc.gz\"}"}
https://www.zadmei.com/zpzhblgp.html	[ "# 在Python中合并两个排序的列表\n\n## 在Python中合并两个已排序的列表\n\n``````list1 = [2,5,7,8,9]\nlist2 = [0,1,3,4,6]\n``````\n\n``````Sorted List: [0,1,2,3,4,5,6,7,8,9]\n``````\n\n## 在Python中合并两个已排序列表的天真方法\n\n``````firstList = [2, 7, 8, 9]\nsecondList = [0, 1, 4, 6]\nresult = []\ni, j = 0,0\nwhile i < len(firstList) and j < len(secondList):\nif firstList[i] < secondList[j]:\nresult.append(firstList[i])\ni = i+1\nelse:\nresult.append(secondList[j])\nj = j+1\nresult = result + firstList[i:] + secondList[j:]\nprint (\"Sorted List: \" + str(result))\n``````\n\n``````Sorted List: [0, 1, 2, 4, 6, 7, 8, 9]\n``````\n\n## 使用Python中的`heapq.merge()` 方法合并两个排序的列表\n\nPython中的`heapq` 模块指的是Heap队列。然而，这个模块，`Heapq` ，主要用于实现Python中的优先级队列。\n\n`heapq` 模块包含 Python 中的`merge()` 函数，该函数将多个排序的列表作为参数，并返回一个组合的、合并的列表。\n\n``````from heapq import merge\nfirst = [2, 7, 8, 9]\nsecond = [0, 1, 4, 6]\nres = list(merge(first, second))\nprint(\"Merged Sorted list: \", str(res))\n``````\n\n``````Merged Sorted List: [0, 1, 2, 4, 6, 7, 8, 9]\n``````\n\n## 在 Python 中使用`sorted()` 函数合并两个已排序的列表\n\nPython 的`sorted()` 函数对作为参数提供的列表或图元进行排序。它总是返回一个将被排序的列表，而不改变原始序列。\n\n``````first = [2, 7, 9]\nsecond = [0, 1, 4]\nresult = sorted(first+second)\nprint(\"List after sorting: \", str(result))\n``````\n\n``````List after sorting: [0, 1, 2, 4, 7, 9]\n``````\n\n## 总结\n\n`sorted()` 函数是用于对附加列表进行排序的内置函数之一，而另一个`heapq.merge()` 是用于在 Python 中合并两个排序列表的方法。这两个函数都可以在单行中进行操作。" ]	[ null ]	{"ft_lang_label":"__label__zh","ft_lang_prob":0.97109926,"math_prob":0.9974,"size":2639,"snap":"2023-40-2023-50","text_gpt3_token_len":1773,"char_repetition_ratio":0.12675522,"word_repetition_ratio":0.07258064,"special_character_ratio":0.2519894,"punctuation_ratio":0.18226601,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98202884,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-27T16:06:10Z\",\"WARC-Record-ID\":\"<urn:uuid:9b33db9f-1cb5-4d73-85e1-a622d4bec362>\",\"Content-Length\":\"93839\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:9850dd6c-7121-41f1-8b1d-4fb92b7c4237>\",\"WARC-Concurrent-To\":\"<urn:uuid:0401c8d1-2510-43d2-9eff-7383d8e49d6f>\",\"WARC-IP-Address\":\"8.45.176.227\",\"WARC-Target-URI\":\"https://www.zadmei.com/zpzhblgp.html\",\"WARC-Payload-Digest\":\"sha1:QPVAPRHLG2Q7INF252P4CHE76SVMOR2K\",\"WARC-Block-Digest\":\"sha1:JYLLXEPGSGMFEIVV5Z2VLJVA2M36O22V\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510300.41_warc_CC-MAIN-20230927135227-20230927165227-00253.warc.gz\"}"}
https://www.bartleby.com/solution-answer/chapter-5-problem-10rq-principles-of-economics-2e-2nd-edition/9781947172364/what-is-the-formula-for-calculating-elasticity/0dc35803-7236-11e9-8385-02ee952b546e	[ "", null, "", null, "", null, "Chapter 5, Problem 10RQ", null, "### Principles of Economics 2e\n\n2nd Edition\nSteven A. Greenlaw; David Shapiro\nISBN: 9781947172364\n\n#### Solutions\n\nChapter\nSection", null, "### Principles of Economics 2e\n\n2nd Edition\nSteven A. Greenlaw; David Shapiro\nISBN: 9781947172364\nTextbook Problem\n\n# What is the formula for calculating elasticity?\n\nTo determine\n\nWrite the formula for calculating the elasticity.\n\nExplanation\n\nThere are two formulas for calculating the elasticity, the first formula is percentage formula which is given below:\n\ne = % change in quantity of good% change in price of good\n\nThe second formula for calculating the elasticity when the percentage change is not given is given below:\n\ne\n\n### Still sussing out bartleby?\n\nCheck out a sample textbook solution.\n\nSee a sample solution\n\n#### The Solution to Your Study Problems\n\nBartleby provides explanations to thousands of textbook problems written by our experts, many with advanced degrees!\n\nGet Started", null, "" ]	[ null, "https://www.bartleby.com/static/search-icon-white.svg", null, "https://www.bartleby.com/static/close-grey.svg", null, "https://www.bartleby.com/static/solution-list.svg", null, "https://www.bartleby.com/isbn_cover_images/9781947172364/9781947172364_largeCoverImage.jpg", null, "https://www.bartleby.com/isbn_cover_images/9781947172364/9781947172364_largeCoverImage.jpg", null, "https://www.bartleby.com/static/logo.svg", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.77012455,"math_prob":0.8987355,"size":2476,"snap":"2019-43-2019-47","text_gpt3_token_len":526,"char_repetition_ratio":0.22694175,"word_repetition_ratio":0.10610932,"special_character_ratio":0.16882068,"punctuation_ratio":0.07225434,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99838483,"pos_list":[0,1,2,3,4,5,6,7,8,9,10,11,12],"im_url_duplicate_count":[null,null,null,null,null,null,null,null,null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-11-13T19:25:20Z\",\"WARC-Record-ID\":\"<urn:uuid:a59fdeac-0dd4-4958-bfe3-0c5ab2f0d8f9>\",\"Content-Length\":\"308180\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:4772052d-1df4-4708-9839-6d65ed72aaff>\",\"WARC-Concurrent-To\":\"<urn:uuid:9eaf7b90-da57-4898-a2d0-e6ae71b97733>\",\"WARC-IP-Address\":\"99.84.181.62\",\"WARC-Target-URI\":\"https://www.bartleby.com/solution-answer/chapter-5-problem-10rq-principles-of-economics-2e-2nd-edition/9781947172364/what-is-the-formula-for-calculating-elasticity/0dc35803-7236-11e9-8385-02ee952b546e\",\"WARC-Payload-Digest\":\"sha1:IX5S67CJZSLZBJMSJTOHQ7LGO6NYKWXX\",\"WARC-Block-Digest\":\"sha1:YYHU3D3USDAV3XPJNAHGPQ2D2ACIRCHG\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-47/CC-MAIN-2019-47_segments_1573496667333.2_warc_CC-MAIN-20191113191653-20191113215653-00161.warc.gz\"}"}
https://academy.techdata.com/au/training/course/0e079g-spvc/	[ "# Overview\n\nContains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.\n\nThis course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.\n\nIf you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms\n\n# Audience\n\n• Data scientists\n• Clients who want to learn about machine learning models\n\n# Objective\n\nIntroduction to machine learning models\n\n• Taxonomy of machine learning models\n• Identify measurement levels\n• Taxonomy of supervised models\n• Build and apply models in IBM SPSS Modeler\n\nSupervised models: Decision trees - CHAID\n• CHAID basics for categorical targets\n• Include categorical and continuous predictors\n• CHAID basics for continuous targets\n• Treatment of missing values\n\nSupervised models: Decision trees - C&R Tree\n• C&R Tree basics for categorical targets\n• Include categorical and continuous predictors\n• C&R Tree basics for continuous targets\n• Treatment of missing values\n\nEvaluation measures for supervised models\n• Evaluation measures for categorical targets\n• Evaluation measures for continuous targets\n\nSupervised models: Statistical models for continuous targets - Linear regression\n• Linear regression basics\n• Include categorical predictors\n• Treatment of missing values\n\nSupervised models: Statistical models for categorical targets - Logistic regression\n• Logistic regression basics\n• Include categorical predictors\n• Treatment of missing values\n\nAssociation models: Sequence detection\n• Sequence detection basics\n• Treatment of missing values\n\nSupervised models: Black box models - Neural networks\n• Neural network basics\n• Include categorical and continuous predictors\n• Treatment of missing values\n\nSupervised models: Black box models - Ensemble models\n• Ensemble models basics\n• Improve accuracy and generalizability by boosting and bagging\n• Ensemble the best models\n\nUnsupervised models: K-Means and Kohonen\n• K-Means basics\n• Include categorical inputs in K-Means\n• Treatment of missing values in K-Means\n• Kohonen networks basics\n• Treatment of missing values in Kohonen\n\nUnsupervised models: TwoStep and Anomaly detection\n• TwoStep basics\n• TwoStep assumptions\n• Find the best segmentation model automatically\n• Anomaly detection basics\n• Treatment of missing values\n\nAssociation models: Apriori\n• Apriori basics\n• Evaluation measures\n• Treatment of missing values\n\nPreparing data for modeling\n• Examine the quality of the data\n• Select important predictors\n• Balance the data\n\nShow details\n\n# Course Outline\n\nIntroduction to machine learning models\n• Taxonomy of machine learning models\n• Identify measurement levels\n• Taxonomy of supervised models\n• Build and apply models in IBM SPSS Modeler\n\nSupervised models: Decision trees - CHAID\n• CHAID basics for categorical targets\n• Include categorical and continuous predictors\n• CHAID basics for continuous targets\n• Treatment of missing values\n\nSupervised models: Decision trees - C&R Tree\n• C&R Tree basics for categorical targets\n• Include categorical and continuous predictors\n• C&R Tree basics for continuous targets\n• Treatment of missing values\n\nEvaluation measures for supervised models\n• Evaluation measures for categorical targets\n• Evaluation measures for continuous targets\n\nSupervised models: Statistical models for continuous targets - Linear regression\n• Linear regression basics\n• Include categorical predictors\n• Treatment of missing values\n\nSupervised models: Statistical models for categorical targets - Logistic regression\n• Logistic regression basics\n• Include categorical predictors\n• Treatment of missing values\n\nSupervised models: Black box models - Neural networks\n• Neural network basics\n• Include categorical and continuous predictors\n• Treatment of missing values\n\nSupervised models: Black box models - Ensemble models\n• Ensemble models basics\n• Improve accuracy and generalizability by boosting and bagging\n• Ensemble the best models\n\nUnsupervised models: K-Means and Kohonen\n• K-Means basics\n• Include categorical inputs in K-Means\n• Treatment of missing values in K-Means\n• Kohonen networks basics\n• Treatment of missing values in Kohonen\n\nUnsupervised models: TwoStep and Anomaly detection\n• TwoStep basics\n• TwoStep assumptions\n• Find the best segmentation model automatically\n• Anomaly detection basics\n• Treatment of missing values\n\nAssociation models: Apriori\n• Apriori basics\n• Evaluation measures\n• Treatment of missing values\n\nAssociation models: Sequence detection\n• Sequence detection basics\n• Treatment of missing values\n\nPreparing data for modeling\n• Examine the quality of the data\n• Select important predictors\n• Balance the data", null, "" ]	[ null, "https://academy.techdata.com/assets/promo-icons/share.jpg", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.77455735,"math_prob":0.7232358,"size":5291,"snap":"2021-21-2021-25","text_gpt3_token_len":1062,"char_repetition_ratio":0.17798373,"word_repetition_ratio":0.79373366,"special_character_ratio":0.17841618,"punctuation_ratio":0.052197803,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.98089594,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-05-13T18:42:11Z\",\"WARC-Record-ID\":\"<urn:uuid:4ddbc210-27bf-4328-a7f9-464fa4dd0ad1>\",\"Content-Length\":\"34298\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:34d9fdb2-6dce-4366-b23c-aed5966a98e9>\",\"WARC-Concurrent-To\":\"<urn:uuid:cc798019-3563-41fe-be9e-51d1a7c4186a>\",\"WARC-IP-Address\":\"34.210.88.0\",\"WARC-Target-URI\":\"https://academy.techdata.com/au/training/course/0e079g-spvc/\",\"WARC-Payload-Digest\":\"sha1:RWZKRJIWZDIMT3554RPTKRBJAWMIVEXX\",\"WARC-Block-Digest\":\"sha1:AHIHOYQFPHU2EW5IXZ7FKUF66H5HGHGC\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-21/CC-MAIN-2021-21_segments_1620243991943.36_warc_CC-MAIN-20210513173321-20210513203321-00626.warc.gz\"}"}
https://stats.stackexchange.com/questions/447036/clarification-on-the-iid-assumption-in-machine-learning-who-is-sampled-from-whe	[ "# Clarification on the IID assumption in machine learning: who is sampled from where, and who is independent with who?\n\nSo there are a couple of questions on IID assumption on this stackexchange,\n\nOn the importance of the i.i.d. assumption in statistical learning\n\nRealistically, does the i.i.d. assumption hold for the vast majority of supervised learning tasks?\n\nHow can the IID assumption be checked in a given dataset?\n\nHow to generate data in order to fit the i.i.d. assumption in many machine learning applications?\n\nWhat exactly is p(x,y) in the context of iid assumption in machine learning?\n\nI just want to clarify, mathematically, what it means for people say that a data set $$\\{(x_i, y_i)\\}_{i =1, \\ldots, n}$$ is i.i.d. (or sampled in an i.i.d. fashion)\n\nSo my question is simply, how does this translate mathematically?\n\nMy current working definition:\n\n• (Identically distributed) Each sample $$(x_i,y_i)$$ is assumed to be sampled from a joint probability distribution $$p(x_i,y_i)$$ or in other words, each $$(x_i, y_i)$$ is the realization of the random variable $$(X,Y)\\sim p_{X,Y}(x_i,y_i)$$\n\n• (Independent) For $$i \\neq j$$, the realization $$(x_i,y_i)$$ is generated independently from $$(x_j, y_j)$$\n\nWhy I am unsatisfied/unsure about my definitions:\n\n1. For identically distributed, this question seems to say that each sample is drawn from the probability distribution over all the sample. That is, we assume that each data point $$(x_i, y_i)$$ is generated from a random variable $$(X_i, Y_i)$$, and that $$(x_i,y_i)$$ is not sampled from the distribution of $$(X_i, Y_i) \\sim p_{X,Y}(x_i,y_i)$$ but from the distribution of all random variables $$(X_1, \\ldots, X_n, Y_1, \\ldots, Y_n) \\sim p(x_1, \\ldots, x_n, y_1, \\ldots, y_n)$$ (subscript omitted). Which is different from the definition I have given above.\n\nThis is exactly what the notation $$({\\bf{X}}_i,y_i) \\sim \\mathbb{P}({\\bf{X}},y), \\forall i=1,...,N$$ means in the linked question.\n\nWhich one is correct?\n\n1. For independence: since $$(x_i, y_i)$$ and $$(x_j,y_j)$$ are just pairs of vectors, which are not random variables, hence we cannot speak about their independence. So to me, independence means that given $$(x_i, y_i)$$ and $$(x_j,y_j)$$ are generated by two (pairs of) random variables, $$(X_i, Y_i)$$ and $$(X_j, Y_j)$$, then the joint distribution $$p_{X_i, X_j, Y_i, Y_j}(x_i, x_j, y_i, y_j)$$ can be decomposed into $$p_{X_i, Y_i}(x_i, y_i)p_{X_j, Y_j}(x_j, y_j)$$.\n\nIs this correct?\n\nJust want to be mathematically rigorous about things. Any reference will help me!!!\n\n• Your question(s) isn't very clear. Are you suggesting something is contradictory? Nothing you stated looks wrong. Jan 30, 2020 at 4:51\n• @robsmith11 I want to mathematically express the sentence \"given an i.i.d. dataset\". I gave my definition. I found conflicting definitions, or definitions at various level of clarity/precision. I want to reconcile these definitions. Jan 30, 2020 at 5:08\n\nThese are essentially the same definition\n\nMy current working definition:\n\n• (Identically distributed) Each sample $$(x_i,y_i)$$ is assumed to be sampled from a joint probability distribution $$p(x_i,y_i)$$ or in other words, each $$(x_i, y_i)$$ is the realization of the random variable $$(X,Y)\\sim p_{X,Y}(x_i,y_i)$$\n\n• (Independent) For $$i \\neq j$$, the realization $$(x_i,y_i)$$ is generated independently from $$(x_j, y_j)$$\n\nThe first part of your definition describes the marginal distribution of each data point. The second part describes the relationship between the datapoints; given this information, one can uniquely obtain the joint distribution of the entire dataset.\n\n1. For identically distributed, this [question] seems to say that each sample is drawn from the probability distribution over all the sample. That is, we assume that each data point $$(x_i, y_i)$$ is generated from a random variable $$(X_i, Y_i)$$, and that $$(x_i,y_i)$$ is not sampled from the distribution of $$(X_i, Y_i) \\sim > p_{X,Y}(x_i,y_i)$$ but from the distribution of all random variables $$(X_1, \\ldots, X_n, Y_1, \\ldots, Y_n) \\sim p(x_1, \\ldots, x_n, y_1, > \\ldots, y_n)$$ (subscript omitted). Which is different from the definition I have given above.\n\nThis definition of i.i.d. is describing the joint distribution of the entire data set. Note that we can obtain the marginal distribution from the joint distribution just by integrating out the other terms, and that we can sample from the marginal distribution by sampling from the joint distribution then ignoring the other terms. Thus, both formulations are the same.\n\n1. For independence: since $$(x_i, y_i)$$ and $$(x_j,y_j)$$ are just pairs of vectors, which are not random variables, hence we cannot speak about their independence. So to me, independence means that given $$(x_i, y_i)$$ and $$(x_j,y_j)$$ are generated by two (pairs of) random variables, $$(X_i, Y_i)$$ and $$(X_j, Y_j)$$, then the joint distribution $$p_{X_i, X_j, Y_i, Y_j}(x_i, x_j, y_i, y_j)$$ can be decomposed into $$p_{X_i, Y_i}(x_i, y_i)p_{X_j, Y_j}(x_j, y_j)$$. Is this correct?\n\nYes.\n\n• Thank you! I will carefully look over your answer Jan 30, 2020 at 5:31" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.86815524,"math_prob":0.9994253,"size":2379,"snap":"2022-40-2023-06","text_gpt3_token_len":669,"char_repetition_ratio":0.12252632,"word_repetition_ratio":0.0055555557,"special_character_ratio":0.29045817,"punctuation_ratio":0.19844358,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.99999857,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2022-09-30T00:12:18Z\",\"WARC-Record-ID\":\"<urn:uuid:d46ebbff-8b96-4342-8031-9363e59712b2>\",\"Content-Length\":\"154523\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:fb51f8f7-a5ca-4404-8236-256f6b51cd67>\",\"WARC-Concurrent-To\":\"<urn:uuid:56ff7a1d-3514-4a5c-8e56-1029124eef47>\",\"WARC-IP-Address\":\"151.101.129.69\",\"WARC-Target-URI\":\"https://stats.stackexchange.com/questions/447036/clarification-on-the-iid-assumption-in-machine-learning-who-is-sampled-from-whe\",\"WARC-Payload-Digest\":\"sha1:3CBCYLIJV2WPRCEIBMOVABRKI5FKBHXE\",\"WARC-Block-Digest\":\"sha1:NUCDFQ6EPV2ERX5A6PM6L7QW6IOWKFGA\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2022/CC-MAIN-2022-40/CC-MAIN-2022-40_segments_1664030335396.92_warc_CC-MAIN-20220929225326-20220930015326-00062.warc.gz\"}"}
https://thriftyray.savingadvice.com/2008/02/	[ "User Real IP - 35.175.107.77\n```Array\n(\n => Array\n(\n => 182.68.68.92\n)\n\n => Array\n(\n => 101.0.41.201\n)\n\n => Array\n(\n => 43.225.98.123\n)\n\n => Array\n(\n => 2.58.194.139\n)\n\n => Array\n(\n => 46.119.197.104\n)\n\n => Array\n(\n => 45.249.8.93\n)\n\n => Array\n(\n => 103.12.135.72\n)\n\n => Array\n(\n => 157.35.243.216\n)\n\n => Array\n(\n => 209.107.214.176\n)\n\n => Array\n(\n => 5.181.233.166\n)\n\n => Array\n(\n => 106.201.10.100\n)\n\n => Array\n(\n => 36.90.55.39\n)\n\n => Array\n(\n => 119.154.138.47\n)\n\n => Array\n(\n => 51.91.31.157\n)\n\n => Array\n(\n => 182.182.65.216\n)\n\n => Array\n(\n => 157.35.252.63\n)\n\n => Array\n(\n => 14.142.34.163\n)\n\n => Array\n(\n => 178.62.43.135\n)\n\n => Array\n(\n => 43.248.152.148\n)\n\n => Array\n(\n => 222.252.104.114\n)\n\n => Array\n(\n => 209.107.214.168\n)\n\n => Array\n(\n => 103.99.199.250\n)\n\n => Array\n(\n => 178.62.72.160\n)\n\n => Array\n(\n => 27.6.1.170\n)\n\n => Array\n(\n => 182.69.249.219\n)\n\n => Array\n(\n => 110.93.228.86\n)\n\n => Array\n(\n => 72.255.1.98\n)\n\n => Array\n(\n => 182.73.111.98\n)\n\n => Array\n(\n => 45.116.117.11\n)\n\n => Array\n(\n => 122.15.78.189\n)\n\n => Array\n(\n => 14.167.188.234\n)\n\n => Array\n(\n => 223.190.4.202\n)\n\n => Array\n(\n => 202.173.125.19\n)\n\n => Array\n(\n => 103.255.5.32\n)\n\n => Array\n(\n => 39.37.145.103\n)\n\n => Array\n(\n => 140.213.26.249\n)\n\n => Array\n(\n => 45.118.166.85\n)\n\n => Array\n(\n => 102.166.138.255\n)\n\n => Array\n(\n => 77.111.246.234\n)\n\n => Array\n(\n => 45.63.6.196\n)\n\n => Array\n(\n => 103.250.147.115\n)\n\n => Array\n(\n => 223.185.30.99\n)\n\n => Array\n(\n => 103.122.168.108\n)\n\n => Array\n(\n => 123.136.203.21\n)\n\n => Array\n(\n => 171.229.243.63\n)\n\n => Array\n(\n => 153.149.98.149\n)\n\n => Array\n(\n => 223.238.93.15\n)\n\n => Array\n(\n => 178.62.113.166\n)\n\n => Array\n(\n => 101.162.0.153\n)\n\n => Array\n(\n => 121.200.62.114\n)\n\n => Array\n(\n => 14.248.77.252\n)\n\n => Array\n(\n => 95.142.117.29\n)\n\n => Array\n(\n => 150.129.60.107\n)\n\n => Array\n(\n => 94.205.243.22\n)\n\n => Array\n(\n => 115.42.71.143\n)\n\n => Array\n(\n => 117.217.195.59\n)\n\n => Array\n(\n => 182.77.112.56\n)\n\n => Array\n(\n => 182.77.112.108\n)\n\n => Array\n(\n => 41.80.69.10\n)\n\n => Array\n(\n => 117.5.222.121\n)\n\n => Array\n(\n => 103.11.0.38\n)\n\n => Array\n(\n => 202.173.127.140\n)\n\n => Array\n(\n => 49.249.249.50\n)\n\n => Array\n(\n => 116.72.198.211\n)\n\n => Array\n(\n => 223.230.54.53\n)\n\n => Array\n(\n => 102.69.228.74\n)\n\n => Array\n(\n => 39.37.251.89\n)\n\n => Array\n(\n => 39.53.246.141\n)\n\n => Array\n(\n => 39.57.182.72\n)\n\n => Array\n(\n => 209.58.130.210\n)\n\n => Array\n(\n => 104.131.75.86\n)\n\n => Array\n(\n => 106.212.131.255\n)\n\n => Array\n(\n => 106.212.132.127\n)\n\n => Array\n(\n => 223.190.4.60\n)\n\n => Array\n(\n => 103.252.116.252\n)\n\n => Array\n(\n => 103.76.55.182\n)\n\n => Array\n(\n => 45.118.166.70\n)\n\n => Array\n(\n => 103.93.174.215\n)\n\n => Array\n(\n => 5.62.62.142\n)\n\n => Array\n(\n => 182.179.158.156\n)\n\n => Array\n(\n => 39.57.255.12\n)\n\n => Array\n(\n => 39.37.178.37\n)\n\n => Array\n(\n => 182.180.165.211\n)\n\n => Array\n(\n => 119.153.135.17\n)\n\n => Array\n(\n => 72.255.15.244\n)\n\n => Array\n(\n => 139.180.166.181\n)\n\n => Array\n(\n => 70.119.147.111\n)\n\n => Array\n(\n => 106.210.40.83\n)\n\n => Array\n(\n => 14.190.70.91\n)\n\n => Array\n(\n => 202.125.156.82\n)\n\n => Array\n(\n => 115.42.68.38\n)\n\n => Array\n(\n => 102.167.13.108\n)\n\n => Array\n(\n => 117.217.192.130\n)\n\n => Array\n(\n => 205.185.223.156\n)\n\n => Array\n(\n => 171.224.180.29\n)\n\n => Array\n(\n => 45.127.45.68\n)\n\n => Array\n(\n => 195.206.183.232\n)\n\n => Array\n(\n => 49.32.52.115\n)\n\n => Array\n(\n => 49.207.49.223\n)\n\n => Array\n(\n => 45.63.29.61\n)\n\n => Array\n(\n => 103.245.193.214\n)\n\n => Array\n(\n => 39.40.236.69\n)\n\n => Array\n(\n => 62.80.162.111\n)\n\n => Array\n(\n => 45.116.232.56\n)\n\n => Array\n(\n => 45.118.166.91\n)\n\n => Array\n(\n => 180.92.230.234\n)\n\n => Array\n(\n => 157.40.57.160\n)\n\n => Array\n(\n => 110.38.38.130\n)\n\n => Array\n(\n => 72.255.57.183\n)\n\n => Array\n(\n => 182.68.81.85\n)\n\n => Array\n(\n => 39.57.202.122\n)\n\n => Array\n(\n => 119.152.154.36\n)\n\n => Array\n(\n => 5.62.62.141\n)\n\n => Array\n(\n => 119.155.54.232\n)\n\n => Array\n(\n => 39.37.141.22\n)\n\n => Array\n(\n => 183.87.12.225\n)\n\n => Array\n(\n => 107.170.127.117\n)\n\n => Array\n(\n => 125.63.124.49\n)\n\n => Array\n(\n => 39.42.191.3\n)\n\n => Array\n(\n => 116.74.24.72\n)\n\n => Array\n(\n => 46.101.89.227\n)\n\n => Array\n(\n => 202.173.125.247\n)\n\n => Array\n(\n => 39.42.184.254\n)\n\n => Array\n(\n => 115.186.165.132\n)\n\n => Array\n(\n => 39.57.206.126\n)\n\n => Array\n(\n => 103.245.13.145\n)\n\n => Array\n(\n => 202.175.246.43\n)\n\n => Array\n(\n => 192.140.152.150\n)\n\n => Array\n(\n => 202.88.250.103\n)\n\n => Array\n(\n => 103.248.94.207\n)\n\n => Array\n(\n => 77.73.66.101\n)\n\n => Array\n(\n => 104.131.66.8\n)\n\n => Array\n(\n => 113.186.161.97\n)\n\n => Array\n(\n => 222.254.5.7\n)\n\n => Array\n(\n => 223.233.67.247\n)\n\n => Array\n(\n => 171.249.116.146\n)\n\n => Array\n(\n => 47.30.209.71\n)\n\n => Array\n(\n => 202.134.13.130\n)\n\n => Array\n(\n => 27.6.135.7\n)\n\n => Array\n(\n => 107.170.186.79\n)\n\n => Array\n(\n => 103.212.89.171\n)\n\n => Array\n(\n => 117.197.9.77\n)\n\n => Array\n(\n => 122.176.206.233\n)\n\n => Array\n(\n => 192.227.253.222\n)\n\n => Array\n(\n => 182.188.224.119\n)\n\n => Array\n(\n => 14.248.70.74\n)\n\n => Array\n(\n => 42.118.219.169\n)\n\n => Array\n(\n => 110.39.146.170\n)\n\n => Array\n(\n => 119.160.66.143\n)\n\n => Array\n(\n => 103.248.95.130\n)\n\n => Array\n(\n => 27.63.152.208\n)\n\n => Array\n(\n => 49.207.114.96\n)\n\n => Array\n(\n => 102.166.23.214\n)\n\n => Array\n(\n => 175.107.254.73\n)\n\n => Array\n(\n => 103.10.227.214\n)\n\n => Array\n(\n => 202.143.115.89\n)\n\n => Array\n(\n => 110.93.227.187\n)\n\n => Array\n(\n => 103.140.31.60\n)\n\n => Array\n(\n => 110.37.231.46\n)\n\n => Array\n(\n => 39.36.99.238\n)\n\n => Array\n(\n => 157.37.140.26\n)\n\n => Array\n(\n => 43.246.202.226\n)\n\n => Array\n(\n => 137.97.8.143\n)\n\n => Array\n(\n => 182.65.52.242\n)\n\n => Array\n(\n => 115.42.69.62\n)\n\n => Array\n(\n => 14.143.254.58\n)\n\n => Array\n(\n => 223.179.143.236\n)\n\n => Array\n(\n => 223.179.143.249\n)\n\n => Array\n(\n => 103.143.7.54\n)\n\n => Array\n(\n => 223.179.139.106\n)\n\n => Array\n(\n => 39.40.219.90\n)\n\n => Array\n(\n => 45.115.141.231\n)\n\n => Array\n(\n => 120.29.100.33\n)\n\n => Array\n(\n => 112.196.132.5\n)\n\n => Array\n(\n => 202.163.123.153\n)\n\n => Array\n(\n => 5.62.58.146\n)\n\n => Array\n(\n => 39.53.216.113\n)\n\n => Array\n(\n => 42.111.160.73\n)\n\n => Array\n(\n => 107.182.231.213\n)\n\n => Array\n(\n => 119.82.94.120\n)\n\n => Array\n(\n => 178.62.34.82\n)\n\n => Array\n(\n => 203.122.6.18\n)\n\n => Array\n(\n => 157.42.38.251\n)\n\n => Array\n(\n => 45.112.68.222\n)\n\n => Array\n(\n => 49.206.212.122\n)\n\n => Array\n(\n => 104.236.70.228\n)\n\n => Array\n(\n => 42.111.34.243\n)\n\n => Array\n(\n => 84.241.19.186\n)\n\n => Array\n(\n => 89.187.180.207\n)\n\n => Array\n(\n => 104.243.212.118\n)\n\n => Array\n(\n => 104.236.55.136\n)\n\n => Array\n(\n => 106.201.16.163\n)\n\n => Array\n(\n => 46.101.40.25\n)\n\n => Array\n(\n => 45.118.166.94\n)\n\n => Array\n(\n => 49.36.128.102\n)\n\n => Array\n(\n => 14.142.193.58\n)\n\n => Array\n(\n => 212.79.124.176\n)\n\n => Array\n(\n => 45.32.191.194\n)\n\n => Array\n(\n => 105.112.107.46\n)\n\n => Array\n(\n => 106.201.14.8\n)\n\n => Array\n(\n => 110.93.240.65\n)\n\n => Array\n(\n => 27.96.95.177\n)\n\n => Array\n(\n => 45.41.134.35\n)\n\n => Array\n(\n => 180.151.13.110\n)\n\n => Array\n(\n => 101.53.242.89\n)\n\n => Array\n(\n => 115.186.3.110\n)\n\n => Array\n(\n => 171.49.185.242\n)\n\n => Array\n(\n => 115.42.70.24\n)\n\n => Array\n(\n => 45.128.188.43\n)\n\n => Array\n(\n => 103.140.129.63\n)\n\n => Array\n(\n => 101.50.113.147\n)\n\n => Array\n(\n => 103.66.73.30\n)\n\n => Array\n(\n => 117.247.193.169\n)\n\n => Array\n(\n => 120.29.100.94\n)\n\n => Array\n(\n => 42.109.154.39\n)\n\n => Array\n(\n => 122.173.155.150\n)\n\n => Array\n(\n => 45.115.104.53\n)\n\n => Array\n(\n => 116.74.29.84\n)\n\n => Array\n(\n => 101.50.125.34\n)\n\n => Array\n(\n => 45.118.166.80\n)\n\n => Array\n(\n => 91.236.184.27\n)\n\n => Array\n(\n => 113.167.185.120\n)\n\n => Array\n(\n => 27.97.66.222\n)\n\n => Array\n(\n => 43.247.41.117\n)\n\n => Array\n(\n => 23.229.16.227\n)\n\n => Array\n(\n => 14.248.79.209\n)\n\n => Array\n(\n => 117.5.194.26\n)\n\n => Array\n(\n => 117.217.205.41\n)\n\n => Array\n(\n => 114.79.169.99\n)\n\n => Array\n(\n => 103.55.60.97\n)\n\n => Array\n(\n => 182.75.89.210\n)\n\n => Array\n(\n => 77.73.66.109\n)\n\n => Array\n(\n => 182.77.126.139\n)\n\n => Array\n(\n => 14.248.77.166\n)\n\n => Array\n(\n => 157.35.224.133\n)\n\n => Array\n(\n => 183.83.38.27\n)\n\n => Array\n(\n => 182.68.4.77\n)\n\n => Array\n(\n => 122.177.130.234\n)\n\n => Array\n(\n => 103.24.99.99\n)\n\n => Array\n(\n => 103.91.127.66\n)\n\n => Array\n(\n => 41.90.34.240\n)\n\n => Array\n(\n => 49.205.77.102\n)\n\n => Array\n(\n => 103.248.94.142\n)\n\n => Array\n(\n => 104.143.92.170\n)\n\n => Array\n(\n => 219.91.157.114\n)\n\n => Array\n(\n => 223.190.88.22\n)\n\n => Array\n(\n => 223.190.86.232\n)\n\n => Array\n(\n => 39.41.172.80\n)\n\n => Array\n(\n => 124.107.206.5\n)\n\n => Array\n(\n => 139.167.180.224\n)\n\n => Array\n(\n => 93.76.64.248\n)\n\n => Array\n(\n => 65.216.227.119\n)\n\n => Array\n(\n => 223.190.119.141\n)\n\n => Array\n(\n => 110.93.237.179\n)\n\n => Array\n(\n => 41.90.7.85\n)\n\n => Array\n(\n => 103.100.6.26\n)\n\n => Array\n(\n => 104.140.83.13\n)\n\n => Array\n(\n => 223.190.119.133\n)\n\n => Array\n(\n => 119.152.150.87\n)\n\n => Array\n(\n => 103.125.130.147\n)\n\n => Array\n(\n => 27.6.5.52\n)\n\n => Array\n(\n => 103.98.188.26\n)\n\n => Array\n(\n => 39.35.121.81\n)\n\n => Array\n(\n => 74.119.146.182\n)\n\n => Array\n(\n => 5.181.233.162\n)\n\n => Array\n(\n => 157.39.18.60\n)\n\n => Array\n(\n => 1.187.252.25\n)\n\n => Array\n(\n => 39.42.145.59\n)\n\n => Array\n(\n => 39.35.39.198\n)\n\n => Array\n(\n => 49.36.128.214\n)\n\n => Array\n(\n => 182.190.20.56\n)\n\n => Array\n(\n => 122.180.249.189\n)\n\n => Array\n(\n => 117.217.203.107\n)\n\n => Array\n(\n => 103.70.82.241\n)\n\n => Array\n(\n => 45.118.166.68\n)\n\n => Array\n(\n => 122.180.168.39\n)\n\n => Array\n(\n => 149.28.67.254\n)\n\n => Array\n(\n => 223.233.73.8\n)\n\n => Array\n(\n => 122.167.140.0\n)\n\n => Array\n(\n => 95.158.51.55\n)\n\n => Array\n(\n => 27.96.95.134\n)\n\n => Array\n(\n => 49.206.214.53\n)\n\n => Array\n(\n => 212.103.49.92\n)\n\n => Array\n(\n => 122.177.115.101\n)\n\n => Array\n(\n => 171.50.187.124\n)\n\n => Array\n(\n => 122.164.55.107\n)\n\n => Array\n(\n => 98.114.217.204\n)\n\n => Array\n(\n => 106.215.10.54\n)\n\n => Array\n(\n => 115.42.68.28\n)\n\n => Array\n(\n => 104.194.220.87\n)\n\n => Array\n(\n => 103.137.84.170\n)\n\n => Array\n(\n => 61.16.142.110\n)\n\n => Array\n(\n => 212.103.49.85\n)\n\n => Array\n(\n => 39.53.248.162\n)\n\n => Array\n(\n => 203.122.40.214\n)\n\n => Array\n(\n => 117.217.198.72\n)\n\n => Array\n(\n => 115.186.191.203\n)\n\n => Array\n(\n => 120.29.100.199\n)\n\n => Array\n(\n => 45.151.237.24\n)\n\n => Array\n(\n => 223.190.125.232\n)\n\n => Array\n(\n => 41.80.151.17\n)\n\n => Array\n(\n => 23.111.188.5\n)\n\n => Array\n(\n => 223.190.125.216\n)\n\n => Array\n(\n => 103.217.133.119\n)\n\n => Array\n(\n => 103.198.173.132\n)\n\n => Array\n(\n => 47.31.155.89\n)\n\n => Array\n(\n => 223.190.20.253\n)\n\n => Array\n(\n => 104.131.92.125\n)\n\n => Array\n(\n => 223.190.19.152\n)\n\n => Array\n(\n => 103.245.193.191\n)\n\n => Array\n(\n => 106.215.58.255\n)\n\n => Array\n(\n => 119.82.83.238\n)\n\n => Array\n(\n => 106.212.128.138\n)\n\n => Array\n(\n => 139.167.237.36\n)\n\n => Array\n(\n => 222.124.40.250\n)\n\n => Array\n(\n => 134.56.185.169\n)\n\n => Array\n(\n => 54.255.226.31\n)\n\n => Array\n(\n => 137.97.162.31\n)\n\n => Array\n(\n => 95.185.21.191\n)\n\n => Array\n(\n => 171.61.168.151\n)\n\n => Array\n(\n => 137.97.184.4\n)\n\n => Array\n(\n => 106.203.151.202\n)\n\n => Array\n(\n => 39.37.137.0\n)\n\n => Array\n(\n => 45.118.166.66\n)\n\n => Array\n(\n => 14.248.105.100\n)\n\n => Array\n(\n => 106.215.61.185\n)\n\n => Array\n(\n => 202.83.57.179\n)\n\n => Array\n(\n => 89.187.182.176\n)\n\n => Array\n(\n => 49.249.232.198\n)\n\n => Array\n(\n => 132.154.95.236\n)\n\n => Array\n(\n => 223.233.83.230\n)\n\n => Array\n(\n => 183.83.153.14\n)\n\n => Array\n(\n => 125.63.72.210\n)\n\n => Array\n(\n => 207.174.202.11\n)\n\n => Array\n(\n => 119.95.88.59\n)\n\n => Array\n(\n => 122.170.14.150\n)\n\n => Array\n(\n => 45.118.166.75\n)\n\n => Array\n(\n => 103.12.135.37\n)\n\n => Array\n(\n => 49.207.120.225\n)\n\n => Array\n(\n => 182.64.195.207\n)\n\n => Array\n(\n => 103.99.37.16\n)\n\n => Array\n(\n => 46.150.104.221\n)\n\n => Array\n(\n => 104.236.195.147\n)\n\n => Array\n(\n => 103.104.192.43\n)\n\n => Array\n(\n => 24.242.159.118\n)\n\n => Array\n(\n => 39.42.179.143\n)\n\n => Array\n(\n => 111.93.58.131\n)\n\n => Array\n(\n => 193.176.84.127\n)\n\n => Array\n(\n => 209.58.142.218\n)\n\n => Array\n(\n => 69.243.152.129\n)\n\n => Array\n(\n => 117.97.131.249\n)\n\n => Array\n(\n => 103.230.180.89\n)\n\n => Array\n(\n => 106.212.170.192\n)\n\n => Array\n(\n => 171.224.180.95\n)\n\n => Array\n(\n => 158.222.11.87\n)\n\n => Array\n(\n => 119.155.60.246\n)\n\n => Array\n(\n => 41.90.43.129\n)\n\n => Array\n(\n => 185.183.104.170\n)\n\n => Array\n(\n => 14.248.67.65\n)\n\n => Array\n(\n => 117.217.205.82\n)\n\n => Array\n(\n => 111.88.7.209\n)\n\n => Array\n(\n => 49.36.132.244\n)\n\n => Array\n(\n => 171.48.40.2\n)\n\n => Array\n(\n => 119.81.105.2\n)\n\n => Array\n(\n => 49.36.128.114\n)\n\n => Array\n(\n => 213.200.31.93\n)\n\n => Array\n(\n => 2.50.15.110\n)\n\n => Array\n(\n => 120.29.104.67\n)\n\n => Array\n(\n => 223.225.32.221\n)\n\n => Array\n(\n => 14.248.67.195\n)\n\n => Array\n(\n => 119.155.36.13\n)\n\n => Array\n(\n => 101.50.95.104\n)\n\n => Array\n(\n => 104.236.205.233\n)\n\n => Array\n(\n => 122.164.36.150\n)\n\n => Array\n(\n => 157.45.93.209\n)\n\n => Array\n(\n => 182.77.118.100\n)\n\n => Array\n(\n => 182.74.134.218\n)\n\n => Array\n(\n => 183.82.128.146\n)\n\n => Array\n(\n => 112.196.170.234\n)\n\n => Array\n(\n => 122.173.230.178\n)\n\n => Array\n(\n => 122.164.71.199\n)\n\n => Array\n(\n => 51.79.19.31\n)\n\n => Array\n(\n => 58.65.222.20\n)\n\n => Array\n(\n => 103.27.203.97\n)\n\n => Array\n(\n => 111.88.7.242\n)\n\n => Array\n(\n => 14.171.232.77\n)\n\n => Array\n(\n => 46.101.22.182\n)\n\n => Array\n(\n => 103.94.219.19\n)\n\n => Array\n(\n => 139.190.83.30\n)\n\n => Array\n(\n => 223.190.27.184\n)\n\n => Array\n(\n => 182.185.183.34\n)\n\n => Array\n(\n => 91.74.181.242\n)\n\n => Array\n(\n => 222.252.107.14\n)\n\n => Array\n(\n => 137.97.8.28\n)\n\n => Array\n(\n => 46.101.16.229\n)\n\n => Array\n(\n => 122.53.254.229\n)\n\n => Array\n(\n => 106.201.17.180\n)\n\n => Array\n(\n => 123.24.170.129\n)\n\n => Array\n(\n => 182.185.180.79\n)\n\n => Array\n(\n => 223.190.17.4\n)\n\n => Array\n(\n => 213.108.105.1\n)\n\n => Array\n(\n => 171.22.76.9\n)\n\n => Array\n(\n => 202.66.178.164\n)\n\n => Array\n(\n => 178.62.97.171\n)\n\n => Array\n(\n => 167.179.110.209\n)\n\n => Array\n(\n => 223.230.147.172\n)\n\n => Array\n(\n => 76.218.195.160\n)\n\n => Array\n(\n => 14.189.186.178\n)\n\n => Array\n(\n => 157.41.45.143\n)\n\n => Array\n(\n => 223.238.22.53\n)\n\n => Array\n(\n => 111.88.7.244\n)\n\n => Array\n(\n => 5.62.57.19\n)\n\n => Array\n(\n => 106.201.25.216\n)\n\n => Array\n(\n => 117.217.205.33\n)\n\n => Array\n(\n => 111.88.7.215\n)\n\n => Array\n(\n => 106.201.13.77\n)\n\n => Array\n(\n => 50.7.93.29\n)\n\n => Array\n(\n => 123.201.70.112\n)\n\n => Array\n(\n => 39.42.108.226\n)\n\n => Array\n(\n => 27.5.198.29\n)\n\n => Array\n(\n => 223.238.85.187\n)\n\n => Array\n(\n => 171.49.176.32\n)\n\n => Array\n(\n => 14.248.79.242\n)\n\n => Array\n(\n => 46.219.211.183\n)\n\n => Array\n(\n => 185.244.212.251\n)\n\n => Array\n(\n => 14.102.84.126\n)\n\n => Array\n(\n => 106.212.191.52\n)\n\n => Array\n(\n => 154.72.153.203\n)\n\n => Array\n(\n => 14.175.82.64\n)\n\n => Array\n(\n => 141.105.139.131\n)\n\n => Array\n(\n => 182.156.103.98\n)\n\n => Array\n(\n => 117.217.204.75\n)\n\n => Array\n(\n => 104.140.83.115\n)\n\n => Array\n(\n => 119.152.62.8\n)\n\n => Array\n(\n => 45.125.247.94\n)\n\n => Array\n(\n => 137.97.37.252\n)\n\n => Array\n(\n => 117.217.204.73\n)\n\n => Array\n(\n => 14.248.79.133\n)\n\n => Array\n(\n => 39.37.152.52\n)\n\n => Array\n(\n => 103.55.60.54\n)\n\n => Array\n(\n => 102.166.183.88\n)\n\n => Array\n(\n => 5.62.60.162\n)\n\n => Array\n(\n => 5.62.60.163\n)\n\n => Array\n(\n => 160.202.38.131\n)\n\n => Array\n(\n => 106.215.20.253\n)\n\n => Array\n(\n => 39.37.160.54\n)\n\n => Array\n(\n => 119.152.59.186\n)\n\n => Array\n(\n => 183.82.0.164\n)\n\n => Array\n(\n => 41.90.54.87\n)\n\n => Array\n(\n => 157.36.85.158\n)\n\n => Array\n(\n => 110.37.229.162\n)\n\n => Array\n(\n => 203.99.180.148\n)\n\n => Array\n(\n => 117.97.132.91\n)\n\n => Array\n(\n => 171.61.147.105\n)\n\n => Array\n(\n => 14.98.147.214\n)\n\n => Array\n(\n => 209.234.253.191\n)\n\n => Array\n(\n => 92.38.148.60\n)\n\n => Array\n(\n => 178.128.104.139\n)\n\n => Array\n(\n => 212.154.0.176\n)\n\n => Array\n(\n => 103.41.24.141\n)\n\n => Array\n(\n => 2.58.194.132\n)\n\n => Array\n(\n => 180.190.78.169\n)\n\n => Array\n(\n => 106.215.45.182\n)\n\n => Array\n(\n => 125.63.100.222\n)\n\n => Array\n(\n => 110.54.247.17\n)\n\n => Array\n(\n => 103.26.85.105\n)\n\n => Array\n(\n => 39.42.147.3\n)\n\n => Array\n(\n => 137.97.51.41\n)\n\n => Array\n(\n => 71.202.72.27\n)\n\n => Array\n(\n => 119.155.35.10\n)\n\n => Array\n(\n => 202.47.43.120\n)\n\n => Array\n(\n => 183.83.64.101\n)\n\n => Array\n(\n => 182.68.106.141\n)\n\n => Array\n(\n => 171.61.187.87\n)\n\n => Array\n(\n => 178.162.198.118\n)\n\n => Array\n(\n => 115.97.151.218\n)\n\n => Array\n(\n => 196.207.184.210\n)\n\n => Array\n(\n => 198.16.70.51\n)\n\n => Array\n(\n => 41.60.237.33\n)\n\n => Array\n(\n => 47.11.86.26\n)\n\n => Array\n(\n => 117.217.201.183\n)\n\n => Array\n(\n => 203.192.241.79\n)\n\n => Array\n(\n => 122.165.119.85\n)\n\n => Array\n(\n => 23.227.142.218\n)\n\n => Array\n(\n => 178.128.104.221\n)\n\n => Array\n(\n => 14.192.54.163\n)\n\n => Array\n(\n => 139.5.253.218\n)\n\n => Array\n(\n => 117.230.140.127\n)\n\n => Array\n(\n => 195.114.149.199\n)\n\n => Array\n(\n => 14.239.180.220\n)\n\n => Array\n(\n => 103.62.155.94\n)\n\n => Array\n(\n => 118.71.97.14\n)\n\n => Array\n(\n => 137.97.55.163\n)\n\n => Array\n(\n => 202.47.49.198\n)\n\n => Array\n(\n => 171.61.177.85\n)\n\n => Array\n(\n => 137.97.190.224\n)\n\n => Array\n(\n => 117.230.34.142\n)\n\n => Array\n(\n => 103.41.32.5\n)\n\n => Array\n(\n => 203.90.82.237\n)\n\n => Array\n(\n => 125.63.124.238\n)\n\n => Array\n(\n => 103.232.128.78\n)\n\n => Array\n(\n => 106.197.14.227\n)\n\n => Array\n(\n => 81.17.242.244\n)\n\n => Array\n(\n => 81.19.210.179\n)\n\n => Array\n(\n => 103.134.94.98\n)\n\n => Array\n(\n => 110.38.0.86\n)\n\n => Array\n(\n => 103.10.224.195\n)\n\n => Array\n(\n => 45.118.166.89\n)\n\n => Array\n(\n => 115.186.186.68\n)\n\n => Array\n(\n => 138.197.129.237\n)\n\n => Array\n(\n => 14.247.162.52\n)\n\n => Array\n(\n => 103.255.4.5\n)\n\n => Array\n(\n => 14.167.188.254\n)\n\n => Array\n(\n => 5.62.59.54\n)\n\n => Array\n(\n => 27.122.14.80\n)\n\n => Array\n(\n => 39.53.240.21\n)\n\n => Array\n(\n => 39.53.241.243\n)\n\n => Array\n(\n => 117.230.130.161\n)\n\n => Array\n(\n => 118.71.191.149\n)\n\n => Array\n(\n => 5.188.95.54\n)\n\n => Array\n(\n => 66.45.250.27\n)\n\n => Array\n(\n => 106.215.6.175\n)\n\n => Array\n(\n => 27.122.14.86\n)\n\n => Array\n(\n => 103.255.4.51\n)\n\n => Array\n(\n => 101.50.93.119\n)\n\n => Array\n(\n => 137.97.183.51\n)\n\n => Array\n(\n => 117.217.204.185\n)\n\n => Array\n(\n => 95.104.106.82\n)\n\n => Array\n(\n => 5.62.56.211\n)\n\n => Array\n(\n => 103.104.181.214\n)\n\n => Array\n(\n => 36.72.214.243\n)\n\n => Array\n(\n => 5.62.62.219\n)\n\n => Array\n(\n => 110.36.202.4\n)\n\n => Array\n(\n => 103.255.4.253\n)\n\n => Array\n(\n => 110.172.138.61\n)\n\n => Array\n(\n => 159.203.24.195\n)\n\n => Array\n(\n => 13.229.88.42\n)\n\n => Array\n(\n => 59.153.235.20\n)\n\n => Array\n(\n => 171.236.169.32\n)\n\n => Array\n(\n => 14.231.85.206\n)\n\n => Array\n(\n => 119.152.54.103\n)\n\n => Array\n(\n => 103.80.117.202\n)\n\n => Array\n(\n => 223.179.157.75\n)\n\n => Array\n(\n => 122.173.68.249\n)\n\n => Array\n(\n => 188.163.72.113\n)\n\n => Array\n(\n => 119.155.20.164\n)\n\n => Array\n(\n => 103.121.43.68\n)\n\n => Array\n(\n => 5.62.58.6\n)\n\n => Array\n(\n => 203.122.40.154\n)\n\n => Array\n(\n => 222.254.96.203\n)\n\n => Array\n(\n => 103.83.148.167\n)\n\n => Array\n(\n => 103.87.251.226\n)\n\n => Array\n(\n => 123.24.129.24\n)\n\n => Array\n(\n => 137.97.83.8\n)\n\n => Array\n(\n => 223.225.33.132\n)\n\n => Array\n(\n => 128.76.175.190\n)\n\n => Array\n(\n => 195.85.219.32\n)\n\n => Array\n(\n => 139.167.102.93\n)\n\n => Array\n(\n => 49.15.198.253\n)\n\n => Array\n(\n => 45.152.183.172\n)\n\n => Array\n(\n => 42.106.180.136\n)\n\n => Array\n(\n => 95.142.120.9\n)\n\n => Array\n(\n => 139.167.236.4\n)\n\n => Array\n(\n => 159.65.72.167\n)\n\n => Array\n(\n => 49.15.89.2\n)\n\n => Array\n(\n => 42.201.161.195\n)\n\n => Array\n(\n => 27.97.210.38\n)\n\n => Array\n(\n => 171.241.45.19\n)\n\n => Array\n(\n => 42.108.2.18\n)\n\n => Array\n(\n => 171.236.40.68\n)\n\n => Array\n(\n => 110.93.82.102\n)\n\n => Array\n(\n => 43.225.24.186\n)\n\n => Array\n(\n => 117.230.189.119\n)\n\n => Array\n(\n => 124.123.147.187\n)\n\n => Array\n(\n => 216.151.184.250\n)\n\n => Array\n(\n => 49.15.133.16\n)\n\n => Array\n(\n => 49.15.220.74\n)\n\n => Array\n(\n => 157.37.221.246\n)\n\n => Array\n(\n => 176.124.233.112\n)\n\n => Array\n(\n => 118.71.167.40\n)\n\n => Array\n(\n => 182.185.213.161\n)\n\n => Array\n(\n => 47.31.79.248\n)\n\n => Array\n(\n => 223.179.238.192\n)\n\n => Array\n(\n => 79.110.128.219\n)\n\n => Array\n(\n => 106.210.42.111\n)\n\n => Array\n(\n => 47.247.214.229\n)\n\n => Array\n(\n => 193.0.220.108\n)\n\n => Array\n(\n => 1.39.206.254\n)\n\n => Array\n(\n => 123.201.77.38\n)\n\n => Array\n(\n => 115.178.207.21\n)\n\n => Array\n(\n => 37.111.202.92\n)\n\n => Array\n(\n => 49.14.179.243\n)\n\n => Array\n(\n => 117.230.145.171\n)\n\n => Array\n(\n => 171.229.242.96\n)\n\n => Array\n(\n => 27.59.174.209\n)\n\n => Array\n(\n => 1.38.202.211\n)\n\n => Array\n(\n => 157.37.128.46\n)\n\n => Array\n(\n => 49.15.94.80\n)\n\n => Array\n(\n => 123.25.46.147\n)\n\n => Array\n(\n => 117.230.170.185\n)\n\n => Array\n(\n => 5.62.16.19\n)\n\n => Array\n(\n => 103.18.22.25\n)\n\n => Array\n(\n => 103.46.200.132\n)\n\n => Array\n(\n => 27.97.165.126\n)\n\n => Array\n(\n => 117.230.54.241\n)\n\n => Array\n(\n => 27.97.209.76\n)\n\n => Array\n(\n => 47.31.182.109\n)\n\n => Array\n(\n => 47.30.223.221\n)\n\n => Array\n(\n => 103.31.94.82\n)\n\n => Array\n(\n => 103.211.14.45\n)\n\n => Array\n(\n => 171.49.233.58\n)\n\n => Array\n(\n => 65.49.126.95\n)\n\n => Array\n(\n => 69.255.101.170\n)\n\n => Array\n(\n => 27.56.224.67\n)\n\n => Array\n(\n => 117.230.146.86\n)\n\n => Array\n(\n => 27.59.154.52\n)\n\n => Array\n(\n => 132.154.114.10\n)\n\n => Array\n(\n => 182.186.77.60\n)\n\n => Array\n(\n => 117.230.136.74\n)\n\n => Array\n(\n => 43.251.94.253\n)\n\n => Array\n(\n => 103.79.168.225\n)\n\n => Array\n(\n => 117.230.56.51\n)\n\n => Array\n(\n => 27.97.187.45\n)\n\n => Array\n(\n => 137.97.190.61\n)\n\n => Array\n(\n => 193.0.220.26\n)\n\n => Array\n(\n => 49.36.137.62\n)\n\n => Array\n(\n => 47.30.189.248\n)\n\n => Array\n(\n => 109.169.23.84\n)\n\n => Array\n(\n => 111.119.185.46\n)\n\n => Array\n(\n => 103.83.148.246\n)\n\n => Array\n(\n => 157.32.119.138\n)\n\n => Array\n(\n => 5.62.41.53\n)\n\n => Array\n(\n => 47.8.243.236\n)\n\n => Array\n(\n => 112.79.158.69\n)\n\n => Array\n(\n => 180.92.148.218\n)\n\n => Array\n(\n => 157.36.162.154\n)\n\n => Array\n(\n => 39.46.114.47\n)\n\n => Array\n(\n => 117.230.173.250\n)\n\n => Array\n(\n => 117.230.155.188\n)\n\n => Array\n(\n => 193.0.220.17\n)\n\n => Array\n(\n => 117.230.171.166\n)\n\n => Array\n(\n => 49.34.59.228\n)\n\n => Array\n(\n => 111.88.197.247\n)\n\n => Array\n(\n => 47.31.156.112\n)\n\n => Array\n(\n => 137.97.64.180\n)\n\n => Array\n(\n => 14.244.227.18\n)\n\n => Array\n(\n => 113.167.158.8\n)\n\n => Array\n(\n => 39.37.175.189\n)\n\n => Array\n(\n => 139.167.211.8\n)\n\n => Array\n(\n => 73.120.85.235\n)\n\n => Array\n(\n => 104.236.195.72\n)\n\n => Array\n(\n => 27.97.190.71\n)\n\n => Array\n(\n => 79.46.170.222\n)\n\n => Array\n(\n => 102.185.244.207\n)\n\n => Array\n(\n => 37.111.136.30\n)\n\n => Array\n(\n => 50.7.93.28\n)\n\n => Array\n(\n => 110.54.251.43\n)\n\n => Array\n(\n => 49.36.143.40\n)\n\n => Array\n(\n => 103.130.112.185\n)\n\n => Array\n(\n => 37.111.139.202\n)\n\n => Array\n(\n => 49.36.139.108\n)\n\n => Array\n(\n => 37.111.136.179\n)\n\n => Array\n(\n => 123.17.165.77\n)\n\n => Array\n(\n => 49.207.143.206\n)\n\n => Array\n(\n => 39.53.80.149\n)\n\n => Array\n(\n => 223.188.71.214\n)\n\n => Array\n(\n => 1.39.222.233\n)\n\n => Array\n(\n => 117.230.9.85\n)\n\n => Array\n(\n => 103.251.245.216\n)\n\n => Array\n(\n => 122.169.133.145\n)\n\n => Array\n(\n => 43.250.165.57\n)\n\n => Array\n(\n => 39.44.13.235\n)\n\n => Array\n(\n => 157.47.181.2\n)\n\n => Array\n(\n => 27.56.203.50\n)\n\n => Array\n(\n => 191.96.97.58\n)\n\n => Array\n(\n => 111.88.107.172\n)\n\n => Array\n(\n => 113.193.198.136\n)\n\n => Array\n(\n => 117.230.172.175\n)\n\n => Array\n(\n => 191.96.182.239\n)\n\n => Array\n(\n => 2.58.46.28\n)\n\n => Array\n(\n => 183.83.253.87\n)\n\n => Array\n(\n => 49.15.139.242\n)\n\n => Array\n(\n => 42.107.220.236\n)\n\n => Array\n(\n => 14.192.53.196\n)\n\n => Array\n(\n => 42.119.212.202\n)\n\n => Array\n(\n => 192.158.234.45\n)\n\n => Array\n(\n => 49.149.102.192\n)\n\n => Array\n(\n => 47.8.170.17\n)\n\n => Array\n(\n => 117.197.13.247\n)\n\n => Array\n(\n => 116.74.34.44\n)\n\n => Array\n(\n => 103.79.249.163\n)\n\n => Array\n(\n => 182.189.95.70\n)\n\n => Array\n(\n => 137.59.218.118\n)\n\n => Array\n(\n => 103.79.170.243\n)\n\n => Array\n(\n => 39.40.54.25\n)\n\n => Array\n(\n => 119.155.40.170\n)\n\n => Array\n(\n => 1.39.212.157\n)\n\n => Array\n(\n => 70.127.59.89\n)\n\n => Array\n(\n => 14.171.22.58\n)\n\n => Array\n(\n => 194.44.167.141\n)\n\n => Array\n(\n => 111.88.179.154\n)\n\n => Array\n(\n => 117.230.140.232\n)\n\n => Array\n(\n => 137.97.96.128\n)\n\n => Array\n(\n => 198.16.66.123\n)\n\n => Array\n(\n => 106.198.44.193\n)\n\n => Array\n(\n => 119.153.45.75\n)\n\n => Array\n(\n => 49.15.242.208\n)\n\n => Array\n(\n => 119.155.241.20\n)\n\n => Array\n(\n => 106.223.109.155\n)\n\n => Array\n(\n => 119.160.119.245\n)\n\n => Array\n(\n => 106.215.81.160\n)\n\n => Array\n(\n => 1.39.192.211\n)\n\n => Array\n(\n => 223.230.35.208\n)\n\n => Array\n(\n => 39.59.4.158\n)\n\n => Array\n(\n => 43.231.57.234\n)\n\n => Array\n(\n => 60.254.78.193\n)\n\n => Array\n(\n => 122.170.224.87\n)\n\n => Array\n(\n => 117.230.22.141\n)\n\n => Array\n(\n => 119.152.107.211\n)\n\n => Array\n(\n => 103.87.192.206\n)\n\n => Array\n(\n => 39.45.244.47\n)\n\n => Array\n(\n => 50.72.141.94\n)\n\n => Array\n(\n => 39.40.6.128\n)\n\n => Array\n(\n => 39.45.180.186\n)\n\n => Array\n(\n => 49.207.131.233\n)\n\n => Array\n(\n => 139.59.69.142\n)\n\n => Array\n(\n => 111.119.187.29\n)\n\n => Array\n(\n => 119.153.40.69\n)\n\n => Array\n(\n => 49.36.133.64\n)\n\n => Array\n(\n => 103.255.4.249\n)\n\n => Array\n(\n => 198.144.154.15\n)\n\n => Array\n(\n => 1.22.46.172\n)\n\n => Array\n(\n => 103.255.5.46\n)\n\n => Array\n(\n => 27.56.195.188\n)\n\n => Array\n(\n => 203.101.167.53\n)\n\n => Array\n(\n => 117.230.62.195\n)\n\n => Array\n(\n => 103.240.194.186\n)\n\n => Array\n(\n => 107.170.166.118\n)\n\n => Array\n(\n => 101.53.245.80\n)\n\n => Array\n(\n => 157.43.13.208\n)\n\n => Array\n(\n => 137.97.100.77\n)\n\n => Array\n(\n => 47.31.150.208\n)\n\n => Array\n(\n => 137.59.222.65\n)\n\n => Array\n(\n => 103.85.127.250\n)\n\n => Array\n(\n => 103.214.119.32\n)\n\n => Array\n(\n => 182.255.49.52\n)\n\n => Array\n(\n => 103.75.247.72\n)\n\n => Array\n(\n => 103.85.125.250\n)\n\n => Array\n(\n => 183.83.253.167\n)\n\n => Array\n(\n => 1.39.222.111\n)\n\n => Array\n(\n => 111.119.185.9\n)\n\n => Array\n(\n => 111.119.187.10\n)\n\n => Array\n(\n => 39.37.147.144\n)\n\n => Array\n(\n => 103.200.198.183\n)\n\n => Array\n(\n => 1.39.222.18\n)\n\n => Array\n(\n => 198.8.80.103\n)\n\n => Array\n(\n => 42.108.1.243\n)\n\n => Array\n(\n => 111.119.187.16\n)\n\n => Array\n(\n => 39.40.241.8\n)\n\n => Array\n(\n => 122.169.150.158\n)\n\n => Array\n(\n => 39.40.215.119\n)\n\n => Array\n(\n => 103.255.5.77\n)\n\n => Array\n(\n => 157.38.108.196\n)\n\n => Array\n(\n => 103.255.4.67\n)\n\n => Array\n(\n => 5.62.60.62\n)\n\n => Array\n(\n => 39.37.146.202\n)\n\n => Array\n(\n => 110.138.6.221\n)\n\n => Array\n(\n => 49.36.143.88\n)\n\n => Array\n(\n => 37.1.215.39\n)\n\n => Array\n(\n => 27.106.59.190\n)\n\n => Array\n(\n => 139.167.139.41\n)\n\n => Array\n(\n => 114.142.166.179\n)\n\n => Array\n(\n => 223.225.240.112\n)\n\n => Array\n(\n => 103.255.5.36\n)\n\n => Array\n(\n => 175.136.1.48\n)\n\n => Array\n(\n => 103.82.80.166\n)\n\n => Array\n(\n => 182.185.196.126\n)\n\n => Array\n(\n => 157.43.45.76\n)\n\n => Array\n(\n => 119.152.132.49\n)\n\n => Array\n(\n => 5.62.62.162\n)\n\n => Array\n(\n => 103.255.4.39\n)\n\n => Array\n(\n => 202.5.144.153\n)\n\n => Array\n(\n => 1.39.223.210\n)\n\n => Array\n(\n => 92.38.176.154\n)\n\n => Array\n(\n => 117.230.186.142\n)\n\n => Array\n(\n => 183.83.39.123\n)\n\n => Array\n(\n => 182.185.156.76\n)\n\n => Array\n(\n => 104.236.74.212\n)\n\n => Array\n(\n => 107.170.145.187\n)\n\n => Array\n(\n => 117.102.7.98\n)\n\n => Array\n(\n => 137.59.220.0\n)\n\n => Array\n(\n => 157.47.222.14\n)\n\n => Array\n(\n => 47.15.206.82\n)\n\n => Array\n(\n => 117.230.159.99\n)\n\n => Array\n(\n => 117.230.175.151\n)\n\n => Array\n(\n => 157.50.97.18\n)\n\n => Array\n(\n => 117.230.47.164\n)\n\n => Array\n(\n => 77.111.244.34\n)\n\n => Array\n(\n => 139.167.189.131\n)\n\n => Array\n(\n => 1.39.204.103\n)\n\n => Array\n(\n => 117.230.58.0\n)\n\n => Array\n(\n => 182.185.226.66\n)\n\n => Array\n(\n => 115.42.70.119\n)\n\n => Array\n(\n => 171.48.114.134\n)\n\n => Array\n(\n => 144.34.218.75\n)\n\n => Array\n(\n => 199.58.164.135\n)\n\n => Array\n(\n => 101.53.228.151\n)\n\n => Array\n(\n => 117.230.50.57\n)\n\n => Array\n(\n => 223.225.138.84\n)\n\n => Array\n(\n => 110.225.67.65\n)\n\n => Array\n(\n => 47.15.200.39\n)\n\n => Array\n(\n => 39.42.20.127\n)\n\n => Array\n(\n => 117.97.241.81\n)\n\n => Array\n(\n => 111.119.185.11\n)\n\n => Array\n(\n => 103.100.5.94\n)\n\n => Array\n(\n => 103.25.137.69\n)\n\n => Array\n(\n => 47.15.197.159\n)\n\n => Array\n(\n => 223.188.176.122\n)\n\n => Array\n(\n => 27.4.175.80\n)\n\n => Array\n(\n => 181.215.43.82\n)\n\n => Array\n(\n => 27.56.228.157\n)\n\n => Array\n(\n => 117.230.19.19\n)\n\n => Array\n(\n => 47.15.208.71\n)\n\n => Array\n(\n => 119.155.21.176\n)\n\n => Array\n(\n => 47.15.234.202\n)\n\n => Array\n(\n => 117.230.144.135\n)\n\n => Array\n(\n => 112.79.139.199\n)\n\n => Array\n(\n => 116.75.246.41\n)\n\n => Array\n(\n => 117.230.177.126\n)\n\n => Array\n(\n => 212.103.48.134\n)\n\n => Array\n(\n => 102.69.228.78\n)\n\n => Array\n(\n => 117.230.37.118\n)\n\n => Array\n(\n => 175.143.61.75\n)\n\n => Array\n(\n => 139.167.56.138\n)\n\n => Array\n(\n => 58.145.189.250\n)\n\n => Array\n(\n => 103.255.5.65\n)\n\n => Array\n(\n => 39.37.153.182\n)\n\n => Array\n(\n => 157.43.85.106\n)\n\n => Array\n(\n => 185.209.178.77\n)\n\n => Array\n(\n => 1.39.212.45\n)\n\n => Array\n(\n => 103.72.7.16\n)\n\n => Array\n(\n => 117.97.185.244\n)\n\n => Array\n(\n => 117.230.59.106\n)\n\n => Array\n(\n => 137.97.121.103\n)\n\n => Array\n(\n => 103.82.123.215\n)\n\n => Array\n(\n => 103.68.217.248\n)\n\n => Array\n(\n => 157.39.27.175\n)\n\n => Array\n(\n => 47.31.100.249\n)\n\n => Array\n(\n => 14.171.232.139\n)\n\n => Array\n(\n => 103.31.93.208\n)\n\n => Array\n(\n => 117.230.56.77\n)\n\n => Array\n(\n => 124.182.25.124\n)\n\n => Array\n(\n => 106.66.191.242\n)\n\n => Array\n(\n => 175.107.237.25\n)\n\n => Array\n(\n => 119.155.1.27\n)\n\n => Array\n(\n => 72.255.6.24\n)\n\n => Array\n(\n => 192.140.152.223\n)\n\n => Array\n(\n => 212.103.48.136\n)\n\n => Array\n(\n => 39.45.134.56\n)\n\n => Array\n(\n => 139.167.173.30\n)\n\n => Array\n(\n => 117.230.63.87\n)\n\n => Array\n(\n => 182.189.95.203\n)\n\n => Array\n(\n => 49.204.183.248\n)\n\n => Array\n(\n => 47.31.125.188\n)\n\n => Array\n(\n => 103.252.171.13\n)\n\n => Array\n(\n => 112.198.74.36\n)\n\n => Array\n(\n => 27.109.113.152\n)\n\n => Array\n(\n => 42.112.233.44\n)\n\n => Array\n(\n => 47.31.68.193\n)\n\n => Array\n(\n => 103.252.171.134\n)\n\n => Array\n(\n => 77.123.32.114\n)\n\n => Array\n(\n => 1.38.189.66\n)\n\n => Array\n(\n => 39.37.181.108\n)\n\n => Array\n(\n => 42.106.44.61\n)\n\n => Array\n(\n => 157.36.8.39\n)\n\n => Array\n(\n => 223.238.41.53\n)\n\n => Array\n(\n => 202.89.77.10\n)\n\n => Array\n(\n => 117.230.150.68\n)\n\n => Array\n(\n => 175.176.87.60\n)\n\n => Array\n(\n => 137.97.117.87\n)\n\n => Array\n(\n => 132.154.123.11\n)\n\n => Array\n(\n => 45.113.124.141\n)\n\n => Array\n(\n => 103.87.56.203\n)\n\n => Array\n(\n => 159.89.171.156\n)\n\n => Array\n(\n => 119.155.53.88\n)\n\n => Array\n(\n => 222.252.107.215\n)\n\n => Array\n(\n => 132.154.75.238\n)\n\n => Array\n(\n => 122.183.41.168\n)\n\n => Array\n(\n => 42.106.254.158\n)\n\n => Array\n(\n => 103.252.171.37\n)\n\n => Array\n(\n => 202.59.13.180\n)\n\n => Array\n(\n => 37.111.139.137\n)\n\n => Array\n(\n => 39.42.93.25\n)\n\n => Array\n(\n => 118.70.177.156\n)\n\n => Array\n(\n => 117.230.148.64\n)\n\n => Array\n(\n => 39.42.15.194\n)\n\n => Array\n(\n => 137.97.176.86\n)\n\n => Array\n(\n => 106.210.102.113\n)\n\n => Array\n(\n => 39.59.84.236\n)\n\n => Array\n(\n => 49.206.187.177\n)\n\n => Array\n(\n => 117.230.133.11\n)\n\n => Array\n(\n => 42.106.253.173\n)\n\n => Array\n(\n => 178.62.102.23\n)\n\n => Array\n(\n => 111.92.76.175\n)\n\n => Array\n(\n => 132.154.86.45\n)\n\n => Array\n(\n => 117.230.128.39\n)\n\n => Array\n(\n => 117.230.53.165\n)\n\n => Array\n(\n => 49.37.200.171\n)\n\n => Array\n(\n => 104.236.213.230\n)\n\n => Array\n(\n => 103.140.30.81\n)\n\n => Array\n(\n => 59.103.104.117\n)\n\n => Array\n(\n => 65.49.126.79\n)\n\n => Array\n(\n => 202.59.12.251\n)\n\n => Array\n(\n => 37.111.136.17\n)\n\n => Array\n(\n => 163.53.85.67\n)\n\n => Array\n(\n => 123.16.240.73\n)\n\n => Array\n(\n => 103.211.14.183\n)\n\n => Array\n(\n => 103.248.93.211\n)\n\n => Array\n(\n => 116.74.59.127\n)\n\n => Array\n(\n => 137.97.169.254\n)\n\n => Array\n(\n => 113.177.79.100\n)\n\n => Array\n(\n => 74.82.60.187\n)\n\n => Array\n(\n => 117.230.157.66\n)\n\n => Array\n(\n => 169.149.194.241\n)\n\n => Array\n(\n => 117.230.156.11\n)\n\n => Array\n(\n => 202.59.12.157\n)\n\n => Array\n(\n => 42.106.181.25\n)\n\n => Array\n(\n => 202.59.13.78\n)\n\n => Array\n(\n => 39.37.153.32\n)\n\n => Array\n(\n => 177.188.216.175\n)\n\n => Array\n(\n => 222.252.53.165\n)\n\n => Array\n(\n => 37.139.23.89\n)\n\n => Array\n(\n => 117.230.139.150\n)\n\n => Array\n(\n => 104.131.176.234\n)\n\n => Array\n(\n => 42.106.181.117\n)\n\n => Array\n(\n => 117.230.180.94\n)\n\n => Array\n(\n => 180.190.171.5\n)\n\n => Array\n(\n => 150.129.165.185\n)\n\n => Array\n(\n => 51.15.0.150\n)\n\n => Array\n(\n => 42.111.4.84\n)\n\n => Array\n(\n => 74.82.60.116\n)\n\n => Array\n(\n => 137.97.121.165\n)\n\n => Array\n(\n => 64.62.187.194\n)\n\n => Array\n(\n => 137.97.106.162\n)\n\n => Array\n(\n => 137.97.92.46\n)\n\n => Array\n(\n => 137.97.170.25\n)\n\n => Array\n(\n => 103.104.192.100\n)\n\n => Array\n(\n => 185.246.211.34\n)\n\n => Array\n(\n => 119.160.96.78\n)\n\n => Array\n(\n => 212.103.48.152\n)\n\n => Array\n(\n => 183.83.153.90\n)\n\n => Array\n(\n => 117.248.150.41\n)\n\n => Array\n(\n => 185.240.246.180\n)\n\n => Array\n(\n => 162.253.131.125\n)\n\n => Array\n(\n => 117.230.153.217\n)\n\n => Array\n(\n => 117.230.169.1\n)\n\n => Array\n(\n => 49.15.138.247\n)\n\n => Array\n(\n => 117.230.37.110\n)\n\n => Array\n(\n => 14.167.188.75\n)\n\n => Array\n(\n => 169.149.239.93\n)\n\n => Array\n(\n => 103.216.176.91\n)\n\n => Array\n(\n => 117.230.12.126\n)\n\n => Array\n(\n => 184.75.209.110\n)\n\n => Array\n(\n => 117.230.6.60\n)\n\n => Array\n(\n => 117.230.135.132\n)\n\n => Array\n(\n => 31.179.29.109\n)\n\n => Array\n(\n => 74.121.188.186\n)\n\n => Array\n(\n => 117.230.35.5\n)\n\n => Array\n(\n => 111.92.74.239\n)\n\n => Array\n(\n => 104.245.144.236\n)\n\n => Array\n(\n => 39.50.22.100\n)\n\n => Array\n(\n => 47.31.190.23\n)\n\n => Array\n(\n => 157.44.73.187\n)\n\n => Array\n(\n => 117.230.8.91\n)\n\n => Array\n(\n => 157.32.18.2\n)\n\n => Array\n(\n => 111.119.187.43\n)\n\n => Array\n(\n => 203.101.185.246\n)\n\n => Array\n(\n => 5.62.34.22\n)\n\n => Array\n(\n => 122.8.143.76\n)\n\n => Array\n(\n => 115.186.2.187\n)\n\n => Array\n(\n => 202.142.110.89\n)\n\n => Array\n(\n => 157.50.61.254\n)\n\n => Array\n(\n => 223.182.211.185\n)\n\n => Array\n(\n => 103.85.125.210\n)\n\n => Array\n(\n => 103.217.133.147\n)\n\n => Array\n(\n => 103.60.196.217\n)\n\n => Array\n(\n => 157.44.238.6\n)\n\n => Array\n(\n => 117.196.225.68\n)\n\n => Array\n(\n => 104.254.92.52\n)\n\n => Array\n(\n => 39.42.46.72\n)\n\n => Array\n(\n => 221.132.119.36\n)\n\n => Array\n(\n => 111.92.77.47\n)\n\n => Array\n(\n => 223.225.19.152\n)\n\n => Array\n(\n => 159.89.121.217\n)\n\n => Array\n(\n => 39.53.221.205\n)\n\n => Array\n(\n => 193.34.217.28\n)\n\n => Array\n(\n => 139.167.206.36\n)\n\n => Array\n(\n => 96.40.10.7\n)\n\n => Array\n(\n => 124.29.198.123\n)\n\n => Array\n(\n => 117.196.226.1\n)\n\n => Array\n(\n => 106.200.85.135\n)\n\n => Array\n(\n => 106.223.180.28\n)\n\n => Array\n(\n => 103.49.232.110\n)\n\n => Array\n(\n => 139.167.208.50\n)\n\n => Array\n(\n => 139.167.201.102\n)\n\n => Array\n(\n => 14.244.224.237\n)\n\n => Array\n(\n => 103.140.31.187\n)\n\n => Array\n(\n => 49.36.134.136\n)\n\n => Array\n(\n => 160.16.61.75\n)\n\n => Array\n(\n => 103.18.22.228\n)\n\n => Array\n(\n => 47.9.74.121\n)\n\n => Array\n(\n => 47.30.216.159\n)\n\n => Array\n(\n => 117.248.150.78\n)\n\n => Array\n(\n => 5.62.34.17\n)\n\n => Array\n(\n => 139.167.247.181\n)\n\n => Array\n(\n => 193.176.84.29\n)\n\n => Array\n(\n => 103.195.201.121\n)\n\n => Array\n(\n => 89.187.175.115\n)\n\n => Array\n(\n => 137.97.81.251\n)\n\n => Array\n(\n => 157.51.147.62\n)\n\n => Array\n(\n => 103.104.192.42\n)\n\n => Array\n(\n => 14.171.235.26\n)\n\n => Array\n(\n => 178.62.89.121\n)\n\n => Array\n(\n => 119.155.4.164\n)\n\n => Array\n(\n => 43.250.241.89\n)\n\n => Array\n(\n => 103.31.100.80\n)\n\n => Array\n(\n => 119.155.7.44\n)\n\n => Array\n(\n => 106.200.73.114\n)\n\n => Array\n(\n => 77.111.246.18\n)\n\n => Array\n(\n => 157.39.99.247\n)\n\n => Array\n(\n => 103.77.42.132\n)\n\n => Array\n(\n => 74.115.214.133\n)\n\n => Array\n(\n => 117.230.49.224\n)\n\n => Array\n(\n => 39.50.108.238\n)\n\n => Array\n(\n => 47.30.221.45\n)\n\n => Array\n(\n => 95.133.164.235\n)\n\n => Array\n(\n => 212.103.48.141\n)\n\n => Array\n(\n => 104.194.218.147\n)\n\n => Array\n(\n => 106.200.88.241\n)\n\n => Array\n(\n => 182.189.212.211\n)\n\n => Array\n(\n => 39.50.142.129\n)\n\n => Array\n(\n => 77.234.43.133\n)\n\n => Array\n(\n => 49.15.192.58\n)\n\n => Array\n(\n => 119.153.37.55\n)\n\n => Array\n(\n => 27.56.156.128\n)\n\n => Array\n(\n => 168.211.4.33\n)\n\n => Array\n(\n => 203.81.236.239\n)\n\n => Array\n(\n => 157.51.149.61\n)\n\n => Array\n(\n => 117.230.45.255\n)\n\n => Array\n(\n => 39.42.106.169\n)\n\n => Array\n(\n => 27.71.89.76\n)\n\n => Array\n(\n => 123.27.109.167\n)\n\n => Array\n(\n => 106.202.21.91\n)\n\n => Array\n(\n => 103.85.125.206\n)\n\n => Array\n(\n => 122.173.250.229\n)\n\n => Array\n(\n => 106.210.102.77\n)\n\n => Array\n(\n => 134.209.47.156\n)\n\n => Array\n(\n => 45.127.232.12\n)\n\n => Array\n(\n => 45.134.224.11\n)\n\n => Array\n(\n => 27.71.89.122\n)\n\n => Array\n(\n => 157.38.105.117\n)\n\n => Array\n(\n => 191.96.73.215\n)\n\n => Array\n(\n => 171.241.92.31\n)\n\n => Array\n(\n => 49.149.104.235\n)\n\n => Array\n(\n => 104.229.247.252\n)\n\n => Array\n(\n => 111.92.78.42\n)\n\n => Array\n(\n => 47.31.88.183\n)\n\n => Array\n(\n => 171.61.203.234\n)\n\n => Array\n(\n => 183.83.226.192\n)\n\n => Array\n(\n => 119.157.107.45\n)\n\n => Array\n(\n => 91.202.163.205\n)\n\n => Array\n(\n => 157.43.62.108\n)\n\n => Array\n(\n => 182.68.248.92\n)\n\n => Array\n(\n => 157.32.251.234\n)\n\n => Array\n(\n => 110.225.196.188\n)\n\n => Array\n(\n => 27.71.89.98\n)\n\n => Array\n(\n => 175.176.87.3\n)\n\n => Array\n(\n => 103.55.90.208\n)\n\n => Array\n(\n => 47.31.41.163\n)\n\n => Array\n(\n => 223.182.195.5\n)\n\n => Array\n(\n => 122.52.101.166\n)\n\n => Array\n(\n => 103.207.82.154\n)\n\n => Array\n(\n => 171.224.178.84\n)\n\n => Array\n(\n => 110.225.235.187\n)\n\n => Array\n(\n => 119.160.97.248\n)\n\n => Array\n(\n => 116.90.101.121\n)\n\n => Array\n(\n => 182.255.48.154\n)\n\n => Array\n(\n => 180.149.221.140\n)\n\n => Array\n(\n => 194.44.79.13\n)\n\n => Array\n(\n => 47.247.18.3\n)\n\n => Array\n(\n => 27.56.242.95\n)\n\n => Array\n(\n => 41.60.236.83\n)\n\n => Array\n(\n => 122.164.162.7\n)\n\n => Array\n(\n => 71.136.154.5\n)\n\n => Array\n(\n => 132.154.119.122\n)\n\n => Array\n(\n => 110.225.80.135\n)\n\n => Array\n(\n => 84.17.61.143\n)\n\n => Array\n(\n => 119.160.102.244\n)\n\n => Array\n(\n => 47.31.27.44\n)\n\n => Array\n(\n => 27.71.89.160\n)\n\n => Array\n(\n => 107.175.38.101\n)\n\n => Array\n(\n => 195.211.150.152\n)\n\n => Array\n(\n => 157.35.250.255\n)\n\n => Array\n(\n => 111.119.187.53\n)\n\n => Array\n(\n => 119.152.97.213\n)\n\n => Array\n(\n => 180.92.143.145\n)\n\n => Array\n(\n => 72.255.61.46\n)\n\n => Array\n(\n => 47.8.183.6\n)\n\n => Array\n(\n => 92.38.148.53\n)\n\n => Array\n(\n => 122.173.194.72\n)\n\n => Array\n(\n => 183.83.226.97\n)\n\n => Array\n(\n => 122.173.73.231\n)\n\n => Array\n(\n => 119.160.101.101\n)\n\n => Array\n(\n => 93.177.75.174\n)\n\n => Array\n(\n => 115.97.196.70\n)\n\n => Array\n(\n => 111.119.187.35\n)\n\n => Array\n(\n => 103.226.226.154\n)\n\n => Array\n(\n => 103.244.172.73\n)\n\n => Array\n(\n => 119.155.61.222\n)\n\n => Array\n(\n => 157.37.184.92\n)\n\n => Array\n(\n => 119.160.103.204\n)\n\n => Array\n(\n => 175.176.87.21\n)\n\n => Array\n(\n => 185.51.228.246\n)\n\n => Array\n(\n => 103.250.164.255\n)\n\n => Array\n(\n => 122.181.194.16\n)\n\n => Array\n(\n => 157.37.230.232\n)\n\n => Array\n(\n => 103.105.236.6\n)\n\n => Array\n(\n => 111.88.128.174\n)\n\n => Array\n(\n => 37.111.139.82\n)\n\n => Array\n(\n => 39.34.133.52\n)\n\n => Array\n(\n => 113.177.79.80\n)\n\n => Array\n(\n => 180.183.71.184\n)\n\n => Array\n(\n => 116.72.218.255\n)\n\n => Array\n(\n => 119.160.117.26\n)\n\n => Array\n(\n => 158.222.0.252\n)\n\n => Array\n(\n => 23.227.142.146\n)\n\n => Array\n(\n => 122.162.152.152\n)\n\n => Array\n(\n => 103.255.149.106\n)\n\n => Array\n(\n => 104.236.53.155\n)\n\n => Array\n(\n => 119.160.119.155\n)\n\n => Array\n(\n => 175.107.214.244\n)\n\n => Array\n(\n => 102.7.116.7\n)\n\n => Array\n(\n => 111.88.91.132\n)\n\n => Array\n(\n => 119.157.248.108\n)\n\n => Array\n(\n => 222.252.36.107\n)\n\n => Array\n(\n => 157.46.209.227\n)\n\n => Array\n(\n => 39.40.54.1\n)\n\n => Array\n(\n => 223.225.19.254\n)\n\n => Array\n(\n => 154.72.150.8\n)\n\n => Array\n(\n => 107.181.177.130\n)\n\n => Array\n(\n => 101.50.75.31\n)\n\n => Array\n(\n => 84.17.58.69\n)\n\n => Array\n(\n => 178.62.5.157\n)\n\n => Array\n(\n => 112.206.175.147\n)\n\n => Array\n(\n => 137.97.113.137\n)\n\n => Array\n(\n => 103.53.44.154\n)\n\n => Array\n(\n => 180.92.143.129\n)\n\n => Array\n(\n => 14.231.223.7\n)\n\n => Array\n(\n => 167.88.63.201\n)\n\n => Array\n(\n => 103.140.204.8\n)\n\n => Array\n(\n => 221.121.135.108\n)\n\n => Array\n(\n => 119.160.97.129\n)\n\n => Array\n(\n => 27.5.168.249\n)\n\n => Array\n(\n => 119.160.102.191\n)\n\n => Array\n(\n => 122.162.219.12\n)\n\n => Array\n(\n => 157.50.141.122\n)\n\n => Array\n(\n => 43.245.8.17\n)\n\n => Array\n(\n => 113.181.198.179\n)\n\n => Array\n(\n => 47.30.221.59\n)\n\n => Array\n(\n => 110.38.29.246\n)\n\n => Array\n(\n => 14.192.140.199\n)\n\n => Array\n(\n => 24.68.10.106\n)\n\n => Array\n(\n => 47.30.209.179\n)\n\n => Array\n(\n => 106.223.123.21\n)\n\n => Array\n(\n => 103.224.48.30\n)\n\n => Array\n(\n => 104.131.19.173\n)\n\n => Array\n(\n => 119.157.100.206\n)\n\n => Array\n(\n => 103.10.226.73\n)\n\n => Array\n(\n => 162.208.51.163\n)\n\n => Array\n(\n => 47.30.221.227\n)\n\n => Array\n(\n => 119.160.116.210\n)\n\n => Array\n(\n => 198.16.78.43\n)\n\n => Array\n(\n => 39.44.201.151\n)\n\n => Array\n(\n => 71.63.181.84\n)\n\n => Array\n(\n => 14.142.192.218\n)\n\n => Array\n(\n => 39.34.147.178\n)\n\n => Array\n(\n => 111.92.75.25\n)\n\n => Array\n(\n => 45.135.239.58\n)\n\n => Array\n(\n => 14.232.235.1\n)\n\n => Array\n(\n => 49.144.100.155\n)\n\n => Array\n(\n => 62.182.99.33\n)\n\n => Array\n(\n => 104.243.212.187\n)\n\n => Array\n(\n => 59.97.132.214\n)\n\n => Array\n(\n => 47.9.15.179\n)\n\n => Array\n(\n => 39.44.103.186\n)\n\n => Array\n(\n => 183.83.241.132\n)\n\n => Array\n(\n => 103.41.24.180\n)\n\n => Array\n(\n => 104.238.46.39\n)\n\n => Array\n(\n => 103.79.170.78\n)\n\n => Array\n(\n => 59.103.138.81\n)\n\n => Array\n(\n => 106.198.191.146\n)\n\n => Array\n(\n => 106.198.255.122\n)\n\n => Array\n(\n => 47.31.46.37\n)\n\n => Array\n(\n => 109.169.23.76\n)\n\n => Array\n(\n => 103.143.7.55\n)\n\n => Array\n(\n => 49.207.114.52\n)\n\n => Array\n(\n => 198.54.106.250\n)\n\n => Array\n(\n => 39.50.64.18\n)\n\n => Array\n(\n => 222.252.48.132\n)\n\n => Array\n(\n => 42.201.186.53\n)\n\n => Array\n(\n => 115.97.198.95\n)\n\n => Array\n(\n => 93.76.134.244\n)\n\n => Array\n(\n => 122.173.15.189\n)\n\n => Array\n(\n => 39.62.38.29\n)\n\n => Array\n(\n => 103.201.145.254\n)\n\n => Array\n(\n => 111.119.187.23\n)\n\n => Array\n(\n => 157.50.66.33\n)\n\n => Array\n(\n => 157.49.68.163\n)\n\n => Array\n(\n => 103.85.125.215\n)\n\n => Array\n(\n => 103.255.4.16\n)\n\n => Array\n(\n => 223.181.246.206\n)\n\n => Array\n(\n => 39.40.109.226\n)\n\n => Array\n(\n => 43.225.70.157\n)\n\n => Array\n(\n => 103.211.18.168\n)\n\n => Array\n(\n => 137.59.221.60\n)\n\n => Array\n(\n => 103.81.214.63\n)\n\n => Array\n(\n => 39.35.163.2\n)\n\n => Array\n(\n => 106.205.124.39\n)\n\n => Array\n(\n => 209.99.165.216\n)\n\n => Array\n(\n => 103.75.247.187\n)\n\n => Array\n(\n => 157.46.217.41\n)\n\n => Array\n(\n => 75.186.73.80\n)\n\n => Array\n(\n => 212.103.48.153\n)\n\n => Array\n(\n => 47.31.61.167\n)\n\n => Array\n(\n => 119.152.145.131\n)\n\n => Array\n(\n => 171.76.177.244\n)\n\n => Array\n(\n => 103.135.78.50\n)\n\n => Array\n(\n => 103.79.170.75\n)\n\n => Array\n(\n => 105.160.22.74\n)\n\n => Array\n(\n => 47.31.20.153\n)\n\n => Array\n(\n => 42.107.204.65\n)\n\n => Array\n(\n => 49.207.131.35\n)\n\n => Array\n(\n => 92.38.148.61\n)\n\n => Array\n(\n => 183.83.255.206\n)\n\n => Array\n(\n => 107.181.177.131\n)\n\n => Array\n(\n => 39.40.220.157\n)\n\n => Array\n(\n => 39.41.133.176\n)\n\n => Array\n(\n => 103.81.214.61\n)\n\n => Array\n(\n => 223.235.108.46\n)\n\n => Array\n(\n => 171.241.52.118\n)\n\n => Array\n(\n => 39.57.138.47\n)\n\n => Array\n(\n => 106.204.196.172\n)\n\n => Array\n(\n => 39.53.228.40\n)\n\n => Array\n(\n => 185.242.5.99\n)\n\n => Array\n(\n => 103.255.5.96\n)\n\n => Array\n(\n => 157.46.212.120\n)\n\n => Array\n(\n => 107.181.177.138\n)\n\n => Array\n(\n => 47.30.193.65\n)\n\n => Array\n(\n => 39.37.178.33\n)\n\n => Array\n(\n => 157.46.173.29\n)\n\n => Array\n(\n => 39.57.238.211\n)\n\n => Array\n(\n => 157.37.245.113\n)\n\n => Array\n(\n => 47.30.201.138\n)\n\n => Array\n(\n => 106.204.193.108\n)\n\n => Array\n(\n => 212.103.50.212\n)\n\n => Array\n(\n => 58.65.221.187\n)\n\n => Array\n(\n => 178.62.92.29\n)\n\n => Array\n(\n => 111.92.77.166\n)\n\n => Array\n(\n => 47.30.223.158\n)\n\n => Array\n(\n => 103.224.54.83\n)\n\n => Array\n(\n => 119.153.43.22\n)\n\n => Array\n(\n => 223.181.126.251\n)\n\n => Array\n(\n => 39.42.175.202\n)\n\n => Array\n(\n => 103.224.54.190\n)\n\n => Array\n(\n => 49.36.141.210\n)\n\n => Array\n(\n => 5.62.63.218\n)\n\n => Array\n(\n => 39.59.9.18\n)\n\n => Array\n(\n => 111.88.86.45\n)\n\n => Array\n(\n => 178.54.139.5\n)\n\n => Array\n(\n => 116.68.105.241\n)\n\n => Array\n(\n => 119.160.96.187\n)\n\n => Array\n(\n => 182.189.192.103\n)\n\n => Array\n(\n => 119.160.96.143\n)\n\n => Array\n(\n => 110.225.89.98\n)\n\n => Array\n(\n => 169.149.195.134\n)\n\n => Array\n(\n => 103.238.104.54\n)\n\n => Array\n(\n => 47.30.208.142\n)\n\n => Array\n(\n => 157.46.179.209\n)\n\n => Array\n(\n => 223.235.38.119\n)\n\n => Array\n(\n => 42.106.180.165\n)\n\n => Array\n(\n => 154.122.240.239\n)\n\n => Array\n(\n => 106.223.104.191\n)\n\n => Array\n(\n => 111.93.110.218\n)\n\n => Array\n(\n => 182.183.161.171\n)\n\n => Array\n(\n => 157.44.184.211\n)\n\n => Array\n(\n => 157.50.185.193\n)\n\n => Array\n(\n => 117.230.19.194\n)\n\n => Array\n(\n => 162.243.246.160\n)\n\n => Array\n(\n => 106.223.143.53\n)\n\n => Array\n(\n => 39.59.41.15\n)\n\n => Array\n(\n => 106.210.65.42\n)\n\n => Array\n(\n => 180.243.144.208\n)\n\n => Array\n(\n => 116.68.105.22\n)\n\n => Array\n(\n => 115.42.70.46\n)\n\n => Array\n(\n => 99.72.192.148\n)\n\n => Array\n(\n => 182.183.182.48\n)\n\n => Array\n(\n => 171.48.58.97\n)\n\n => Array\n(\n => 37.120.131.188\n)\n\n => Array\n(\n => 117.99.167.177\n)\n\n => Array\n(\n => 111.92.76.210\n)\n\n => Array\n(\n => 14.192.144.245\n)\n\n => Array\n(\n => 169.149.242.87\n)\n\n => Array\n(\n => 47.30.198.149\n)\n\n => Array\n(\n => 59.103.57.140\n)\n\n => Array\n(\n => 117.230.161.168\n)\n\n => Array\n(\n => 110.225.88.173\n)\n\n => Array\n(\n => 169.149.246.95\n)\n\n => Array\n(\n => 42.106.180.52\n)\n\n => Array\n(\n => 14.231.160.157\n)\n\n => Array\n(\n => 123.27.109.47\n)\n\n => Array\n(\n => 157.46.130.54\n)\n\n => Array\n(\n => 39.42.73.194\n)\n\n => Array\n(\n => 117.230.18.147\n)\n\n => Array\n(\n => 27.59.231.98\n)\n\n => Array\n(\n => 125.209.78.227\n)\n\n => Array\n(\n => 157.34.80.145\n)\n\n => Array\n(\n => 42.201.251.86\n)\n\n => Array\n(\n => 117.230.129.158\n)\n\n => Array\n(\n => 103.82.80.103\n)\n\n => Array\n(\n => 47.9.171.228\n)\n\n => Array\n(\n => 117.230.24.92\n)\n\n => Array\n(\n => 103.129.143.119\n)\n\n => Array\n(\n => 39.40.213.45\n)\n\n => Array\n(\n => 178.92.188.214\n)\n\n => Array\n(\n => 110.235.232.191\n)\n\n => Array\n(\n => 5.62.34.18\n)\n\n => Array\n(\n => 47.30.212.134\n)\n\n => Array\n(\n => 157.42.34.196\n)\n\n => Array\n(\n => 157.32.169.9\n)\n\n => Array\n(\n => 103.255.4.11\n)\n\n => Array\n(\n => 117.230.13.69\n)\n\n => Array\n(\n => 117.230.58.97\n)\n\n => Array\n(\n => 92.52.138.39\n)\n\n => Array\n(\n => 221.132.119.63\n)\n\n => Array\n(\n => 117.97.167.188\n)\n\n => Array\n(\n => 119.153.56.58\n)\n\n => Array\n(\n => 105.50.22.150\n)\n\n => Array\n(\n => 115.42.68.126\n)\n\n => Array\n(\n => 182.189.223.159\n)\n\n => Array\n(\n => 39.59.36.90\n)\n\n => Array\n(\n => 111.92.76.114\n)\n\n => Array\n(\n => 157.47.226.163\n)\n\n => Array\n(\n => 202.47.44.37\n)\n\n => Array\n(\n => 106.51.234.172\n)\n\n => Array\n(\n => 103.101.88.166\n)\n\n => Array\n(\n => 27.6.246.146\n)\n\n => Array\n(\n => 103.255.5.83\n)\n\n => Array\n(\n => 103.98.210.185\n)\n\n => Array\n(\n => 122.173.114.134\n)\n\n => Array\n(\n => 122.173.77.248\n)\n\n => Array\n(\n => 5.62.41.172\n)\n\n => Array\n(\n => 180.178.181.17\n)\n\n => Array\n(\n => 37.120.133.224\n)\n\n => Array\n(\n => 45.131.5.156\n)\n\n => Array\n(\n => 110.39.100.110\n)\n\n => Array\n(\n => 176.110.38.185\n)\n\n => Array\n(\n => 36.255.41.64\n)\n\n => Array\n(\n => 103.104.192.15\n)\n\n => Array\n(\n => 43.245.131.195\n)\n\n => Array\n(\n => 14.248.111.185\n)\n\n => Array\n(\n => 122.173.217.133\n)\n\n => Array\n(\n => 106.223.90.245\n)\n\n => Array\n(\n => 119.153.56.80\n)\n\n => Array\n(\n => 103.7.60.172\n)\n\n => Array\n(\n => 157.46.184.233\n)\n\n => Array\n(\n => 182.190.31.95\n)\n\n => Array\n(\n => 109.87.189.122\n)\n\n => Array\n(\n => 91.74.25.100\n)\n\n => Array\n(\n => 182.185.224.144\n)\n\n => Array\n(\n => 106.223.91.221\n)\n\n => Array\n(\n => 182.190.223.40\n)\n\n => Array\n(\n => 2.58.194.134\n)\n\n => Array\n(\n => 196.246.225.236\n)\n\n => Array\n(\n => 106.223.90.173\n)\n\n => Array\n(\n => 23.239.16.54\n)\n\n => Array\n(\n => 157.46.65.225\n)\n\n => Array\n(\n => 115.186.130.14\n)\n\n => Array\n(\n => 103.85.125.157\n)\n\n => Array\n(\n => 14.248.103.6\n)\n\n => Array\n(\n => 123.24.169.247\n)\n\n => Array\n(\n => 103.130.108.153\n)\n\n => Array\n(\n => 115.42.67.21\n)\n\n => Array\n(\n => 202.166.171.190\n)\n\n => Array\n(\n => 39.37.169.104\n)\n\n => Array\n(\n => 103.82.80.59\n)\n\n => Array\n(\n => 175.107.208.58\n)\n\n => Array\n(\n => 203.192.238.247\n)\n\n => Array\n(\n => 103.217.178.150\n)\n\n => Array\n(\n => 103.66.214.173\n)\n\n => Array\n(\n => 110.93.236.174\n)\n\n => Array\n(\n => 143.189.242.64\n)\n\n => Array\n(\n => 77.111.245.12\n)\n\n => Array\n(\n => 145.239.2.231\n)\n\n => Array\n(\n => 115.186.190.38\n)\n\n => Array\n(\n => 109.169.23.67\n)\n\n => Array\n(\n => 198.16.70.29\n)\n\n => Array\n(\n => 111.92.76.186\n)\n\n => Array\n(\n => 115.42.69.34\n)\n\n => Array\n(\n => 73.61.100.95\n)\n\n => Array\n(\n => 103.129.142.31\n)\n\n => Array\n(\n => 103.255.5.53\n)\n\n => Array\n(\n => 103.76.55.2\n)\n\n => Array\n(\n => 47.9.141.138\n)\n\n => Array\n(\n => 103.55.89.234\n)\n\n => Array\n(\n => 103.223.13.53\n)\n\n => Array\n(\n => 175.158.50.203\n)\n\n => Array\n(\n => 103.255.5.90\n)\n\n => Array\n(\n => 106.223.100.138\n)\n\n => Array\n(\n => 39.37.143.193\n)\n\n => Array\n(\n => 206.189.133.131\n)\n\n => Array\n(\n => 43.224.0.233\n)\n\n => Array\n(\n => 115.186.132.106\n)\n\n => Array\n(\n => 31.43.21.159\n)\n\n => Array\n(\n => 119.155.56.131\n)\n\n => Array\n(\n => 103.82.80.138\n)\n\n => Array\n(\n => 24.87.128.119\n)\n\n => Array\n(\n => 106.210.103.163\n)\n\n => Array\n(\n => 103.82.80.90\n)\n\n => Array\n(\n => 157.46.186.45\n)\n\n => Array\n(\n => 157.44.155.238\n)\n\n => Array\n(\n => 103.119.199.2\n)\n\n => Array\n(\n => 27.97.169.205\n)\n\n => Array\n(\n => 157.46.174.89\n)\n\n => Array\n(\n => 43.250.58.220\n)\n\n => Array\n(\n => 76.189.186.64\n)\n\n => Array\n(\n => 103.255.5.57\n)\n\n => Array\n(\n => 171.61.196.136\n)\n\n => Array\n(\n => 202.47.40.88\n)\n\n => Array\n(\n => 97.118.94.116\n)\n\n => Array\n(\n => 157.44.124.157\n)\n\n => Array\n(\n => 95.142.120.13\n)\n\n => Array\n(\n => 42.201.229.151\n)\n\n => Array\n(\n => 157.46.178.95\n)\n\n => Array\n(\n => 169.149.215.192\n)\n\n => Array\n(\n => 42.111.19.48\n)\n\n => Array\n(\n => 1.38.52.18\n)\n\n => Array\n(\n => 145.239.91.241\n)\n\n => Array\n(\n => 47.31.78.191\n)\n\n => Array\n(\n => 103.77.42.60\n)\n\n => Array\n(\n => 157.46.107.144\n)\n\n => Array\n(\n => 157.46.125.124\n)\n\n => Array\n(\n => 110.225.218.108\n)\n\n => Array\n(\n => 106.51.77.185\n)\n\n => Array\n(\n => 123.24.161.207\n)\n\n => Array\n(\n => 106.210.108.22\n)\n\n => Array\n(\n => 42.111.10.14\n)\n\n => Array\n(\n => 223.29.231.175\n)\n\n => Array\n(\n => 27.56.152.132\n)\n\n => Array\n(\n => 119.155.31.100\n)\n\n => Array\n(\n => 122.173.172.127\n)\n\n => Array\n(\n => 103.77.42.64\n)\n\n => Array\n(\n => 157.44.164.106\n)\n\n => Array\n(\n => 14.181.53.38\n)\n\n => Array\n(\n => 115.42.67.64\n)\n\n => Array\n(\n => 47.31.33.140\n)\n\n => Array\n(\n => 103.15.60.234\n)\n\n => Array\n(\n => 182.64.219.181\n)\n\n => Array\n(\n => 103.44.51.6\n)\n\n => Array\n(\n => 116.74.25.157\n)\n\n => Array\n(\n => 116.71.2.128\n)\n\n => Array\n(\n => 157.32.185.239\n)\n\n => Array\n(\n => 47.31.25.79\n)\n\n => Array\n(\n => 178.62.85.75\n)\n\n => Array\n(\n => 180.178.190.39\n)\n\n => Array\n(\n => 39.48.52.179\n)\n\n => Array\n(\n => 106.193.11.240\n)\n\n => Array\n(\n => 103.82.80.226\n)\n\n => Array\n(\n => 49.206.126.30\n)\n\n => Array\n(\n => 157.245.191.173\n)\n\n => Array\n(\n => 49.205.84.237\n)\n\n => Array\n(\n => 47.8.181.232\n)\n\n => Array\n(\n => 182.66.2.92\n)\n\n => Array\n(\n => 49.34.137.220\n)\n\n => Array\n(\n => 209.205.217.125\n)\n\n => Array\n(\n => 192.64.5.73\n)\n\n => Array\n(\n => 27.63.166.108\n)\n\n => Array\n(\n => 120.29.96.211\n)\n\n => Array\n(\n => 182.186.112.135\n)\n\n => Array\n(\n => 45.118.165.151\n)\n\n => Array\n(\n => 47.8.228.12\n)\n\n => Array\n(\n => 106.215.3.162\n)\n\n => Array\n(\n => 111.92.72.66\n)\n\n => Array\n(\n => 169.145.2.9\n)\n\n => Array\n(\n => 106.207.205.100\n)\n\n => Array\n(\n => 223.181.8.12\n)\n\n => Array\n(\n => 157.48.149.78\n)\n\n => Array\n(\n => 103.206.138.116\n)\n\n => Array\n(\n => 39.53.119.22\n)\n\n => Array\n(\n => 157.33.232.106\n)\n\n => Array\n(\n => 49.37.205.139\n)\n\n => Array\n(\n => 115.42.68.3\n)\n\n => Array\n(\n => 93.72.182.251\n)\n\n => Array\n(\n => 202.142.166.22\n)\n\n => Array\n(\n => 157.119.81.111\n)\n\n => Array\n(\n => 182.186.116.155\n)\n\n => Array\n(\n => 157.37.171.37\n)\n\n => Array\n(\n => 117.206.164.48\n)\n\n => Array\n(\n => 49.36.52.63\n)\n\n => Array\n(\n => 203.175.72.112\n)\n\n => Array\n(\n => 171.61.132.193\n)\n\n => Array\n(\n => 111.119.187.44\n)\n\n => Array\n(\n => 39.37.165.216\n)\n\n => Array\n(\n => 103.86.109.58\n)\n\n => Array\n(\n => 39.59.2.86\n)\n\n => Array\n(\n => 111.119.187.28\n)\n\n => Array\n(\n => 106.201.9.10\n)\n\n => Array\n(\n => 49.35.25.106\n)\n\n => Array\n(\n => 157.49.239.103\n)\n\n => Array\n(\n => 157.49.237.198\n)\n\n => Array\n(\n => 14.248.64.121\n)\n\n => Array\n(\n => 117.102.7.214\n)\n\n => Array\n(\n => 120.29.91.246\n)\n\n => Array\n(\n => 103.7.79.41\n)\n\n => Array\n(\n => 132.154.99.209\n)\n\n => Array\n(\n => 212.36.27.245\n)\n\n => Array\n(\n => 157.44.154.9\n)\n\n => Array\n(\n => 47.31.56.44\n)\n\n => Array\n(\n => 192.142.199.136\n)\n\n => Array\n(\n => 171.61.159.49\n)\n\n => Array\n(\n => 119.160.116.151\n)\n\n => Array\n(\n => 103.98.63.39\n)\n\n => Array\n(\n => 41.60.233.216\n)\n\n => Array\n(\n => 49.36.75.212\n)\n\n => Array\n(\n => 223.188.60.20\n)\n\n => Array\n(\n => 103.98.63.50\n)\n\n => Array\n(\n => 178.162.198.21\n)\n\n => Array\n(\n => 157.46.209.35\n)\n\n => Array\n(\n => 119.155.32.151\n)\n\n => Array\n(\n => 102.185.58.161\n)\n\n => Array\n(\n => 59.96.89.231\n)\n\n => Array\n(\n => 119.155.255.198\n)\n\n => Array\n(\n => 42.107.204.57\n)\n\n => Array\n(\n => 42.106.181.74\n)\n\n => Array\n(\n => 157.46.219.186\n)\n\n => Array\n(\n => 115.42.71.49\n)\n\n => Array\n(\n => 157.46.209.131\n)\n\n => Array\n(\n => 220.81.15.94\n)\n\n => Array\n(\n => 111.119.187.24\n)\n\n => Array\n(\n => 49.37.195.185\n)\n\n => Array\n(\n => 42.106.181.85\n)\n\n => Array\n(\n => 43.249.225.134\n)\n\n => Array\n(\n => 117.206.165.151\n)\n\n => Array\n(\n => 119.153.48.250\n)\n\n => Array\n(\n => 27.4.172.162\n)\n\n => Array\n(\n => 117.20.29.51\n)\n\n => Array\n(\n => 103.98.63.135\n)\n\n => Array\n(\n => 117.7.218.229\n)\n\n => Array\n(\n => 157.49.233.105\n)\n\n => Array\n(\n => 39.53.151.199\n)\n\n => Array\n(\n => 101.255.118.33\n)\n\n => Array\n(\n => 41.141.246.9\n)\n\n => Array\n(\n => 221.132.113.78\n)\n\n => Array\n(\n => 119.160.116.202\n)\n\n => Array\n(\n => 117.237.193.244\n)\n\n => Array\n(\n => 157.41.110.145\n)\n\n => Array\n(\n => 103.98.63.5\n)\n\n => Array\n(\n => 103.125.129.58\n)\n\n => Array\n(\n => 183.83.254.66\n)\n\n => Array\n(\n => 45.135.236.160\n)\n\n => Array\n(\n => 198.199.87.124\n)\n\n => Array\n(\n => 193.176.86.41\n)\n\n => Array\n(\n => 115.97.142.98\n)\n\n => Array\n(\n => 222.252.38.198\n)\n\n => Array\n(\n => 110.93.237.49\n)\n\n => Array\n(\n => 103.224.48.122\n)\n\n => Array\n(\n => 110.38.28.130\n)\n\n => Array\n(\n => 106.211.238.154\n)\n\n => Array\n(\n => 111.88.41.73\n)\n\n => Array\n(\n => 119.155.13.143\n)\n\n => Array\n(\n => 103.213.111.60\n)\n\n => Array\n(\n => 202.0.103.42\n)\n\n => Array\n(\n => 157.48.144.33\n)\n\n => Array\n(\n => 111.119.187.62\n)\n\n => Array\n(\n => 103.87.212.71\n)\n\n => Array\n(\n => 157.37.177.20\n)\n\n => Array\n(\n => 223.233.71.92\n)\n\n => Array\n(\n => 116.213.32.107\n)\n\n => Array\n(\n => 104.248.173.151\n)\n\n => Array\n(\n => 14.181.102.222\n)\n\n => Array\n(\n => 103.10.224.252\n)\n\n => Array\n(\n => 175.158.50.57\n)\n\n => Array\n(\n => 165.22.122.199\n)\n\n => Array\n(\n => 23.106.56.12\n)\n\n => Array\n(\n => 203.122.10.146\n)\n\n => Array\n(\n => 37.111.136.138\n)\n\n => Array\n(\n => 103.87.193.66\n)\n\n => Array\n(\n => 39.59.122.246\n)\n\n => Array\n(\n => 111.119.183.63\n)\n\n => Array\n(\n => 157.46.72.102\n)\n\n => Array\n(\n => 185.132.133.82\n)\n\n => Array\n(\n => 118.103.230.148\n)\n\n => Array\n(\n => 5.62.39.45\n)\n\n => Array\n(\n => 119.152.144.134\n)\n\n => Array\n(\n => 172.105.117.102\n)\n\n => Array\n(\n => 122.254.70.212\n)\n\n => Array\n(\n => 102.185.128.97\n)\n\n => Array\n(\n => 182.69.249.11\n)\n\n => Array\n(\n => 105.163.134.167\n)\n\n => Array\n(\n => 111.119.187.38\n)\n\n => Array\n(\n => 103.46.195.93\n)\n\n => Array\n(\n => 106.204.161.156\n)\n\n => Array\n(\n => 122.176.2.175\n)\n\n => Array\n(\n => 117.99.162.31\n)\n\n => Array\n(\n => 106.212.241.242\n)\n\n => Array\n(\n => 42.107.196.149\n)\n\n => Array\n(\n => 212.90.60.57\n)\n\n => Array\n(\n => 175.107.237.12\n)\n\n => Array\n(\n => 157.46.119.152\n)\n\n => Array\n(\n => 157.34.81.12\n)\n\n => Array\n(\n => 162.243.1.22\n)\n\n => Array\n(\n => 110.37.222.178\n)\n\n => Array\n(\n => 103.46.195.68\n)\n\n => Array\n(\n => 119.160.116.81\n)\n\n => Array\n(\n => 138.197.131.28\n)\n\n => Array\n(\n => 103.88.218.124\n)\n\n => Array\n(\n => 192.241.172.113\n)\n\n => Array\n(\n => 110.39.174.106\n)\n\n => Array\n(\n => 111.88.48.17\n)\n\n => Array\n(\n => 42.108.160.218\n)\n\n => Array\n(\n => 117.102.0.16\n)\n\n => Array\n(\n => 157.46.125.235\n)\n\n => Array\n(\n => 14.190.242.251\n)\n\n => Array\n(\n => 47.31.184.64\n)\n\n => Array\n(\n => 49.205.84.157\n)\n\n => Array\n(\n => 122.162.115.247\n)\n\n => Array\n(\n => 41.202.219.74\n)\n\n => Array\n(\n => 106.215.9.67\n)\n\n => Array\n(\n => 103.87.56.208\n)\n\n => Array\n(\n => 103.46.194.147\n)\n\n => Array\n(\n => 116.90.98.81\n)\n\n => Array\n(\n => 115.42.71.213\n)\n\n => Array\n(\n => 39.49.35.192\n)\n\n => Array\n(\n => 41.202.219.65\n)\n\n => Array\n(\n => 131.212.249.93\n)\n\n => Array\n(\n => 49.205.16.251\n)\n\n => Array\n(\n => 39.34.147.250\n)\n\n => Array\n(\n => 183.83.210.185\n)\n\n => Array\n(\n => 49.37.194.215\n)\n\n => Array\n(\n => 103.46.194.108\n)\n\n => Array\n(\n => 89.36.219.233\n)\n\n => Array\n(\n => 119.152.105.178\n)\n\n => Array\n(\n => 202.47.45.125\n)\n\n => Array\n(\n => 156.146.59.27\n)\n\n => Array\n(\n => 132.154.21.156\n)\n\n => Array\n(\n => 157.44.35.31\n)\n\n => Array\n(\n => 41.80.118.124\n)\n\n => Array\n(\n => 47.31.159.198\n)\n\n => Array\n(\n => 103.209.223.140\n)\n\n => Array\n(\n => 157.46.130.138\n)\n\n => Array\n(\n => 49.37.199.246\n)\n\n => Array\n(\n => 111.88.242.10\n)\n\n => Array\n(\n => 43.241.145.110\n)\n\n => Array\n(\n => 124.153.16.30\n)\n\n => Array\n(\n => 27.5.22.173\n)\n\n => Array\n(\n => 111.88.191.173\n)\n\n => Array\n(\n => 41.60.236.200\n)\n\n => Array\n(\n => 115.42.67.146\n)\n\n => Array\n(\n => 150.242.173.7\n)\n\n => Array\n(\n => 14.248.71.23\n)\n\n => Array\n(\n => 111.119.187.4\n)\n\n => Array\n(\n => 124.29.212.118\n)\n\n => Array\n(\n => 51.68.205.163\n)\n\n => Array\n(\n => 182.184.107.63\n)\n\n => Array\n(\n => 106.211.253.87\n)\n\n => Array\n(\n => 223.190.89.5\n)\n\n => Array\n(\n => 183.83.212.63\n)\n\n => Array\n(\n => 129.205.113.227\n)\n\n => Array\n(\n => 106.210.40.141\n)\n\n => Array\n(\n => 91.202.163.169\n)\n\n => Array\n(\n => 76.105.191.89\n)\n\n => Array\n(\n => 171.51.244.160\n)\n\n => Array\n(\n => 37.139.188.92\n)\n\n => Array\n(\n => 23.106.56.37\n)\n\n => Array\n(\n => 157.44.175.180\n)\n\n => Array\n(\n => 122.2.122.97\n)\n\n => Array\n(\n => 103.87.192.194\n)\n\n => Array\n(\n => 192.154.253.6\n)\n\n => Array\n(\n => 77.243.191.19\n)\n\n => Array\n(\n => 122.254.70.46\n)\n\n => Array\n(\n => 154.76.233.73\n)\n\n => Array\n(\n => 195.181.167.150\n)\n\n => Array\n(\n => 209.209.228.5\n)\n\n => Array\n(\n => 203.192.212.115\n)\n\n => Array\n(\n => 221.132.118.179\n)\n\n => Array\n(\n => 117.208.210.204\n)\n\n => Array\n(\n => 120.29.90.126\n)\n\n => Array\n(\n => 36.77.239.190\n)\n\n => Array\n(\n => 157.37.137.127\n)\n\n => Array\n(\n => 39.40.243.6\n)\n\n => Array\n(\n => 182.182.41.201\n)\n\n => Array\n(\n => 39.59.32.46\n)\n\n => Array\n(\n => 111.119.183.36\n)\n\n => Array\n(\n => 103.83.147.61\n)\n\n => Array\n(\n => 103.82.80.85\n)\n\n => Array\n(\n => 103.46.194.161\n)\n\n => Array\n(\n => 101.50.105.38\n)\n\n => Array\n(\n => 111.119.183.58\n)\n\n => Array\n(\n => 47.9.234.51\n)\n\n => Array\n(\n => 120.29.86.157\n)\n\n => Array\n(\n => 175.158.50.70\n)\n\n => Array\n(\n => 112.196.163.235\n)\n\n => Array\n(\n => 139.167.161.85\n)\n\n => Array\n(\n => 106.207.39.181\n)\n\n => Array\n(\n => 103.77.42.159\n)\n\n => Array\n(\n => 185.56.138.220\n)\n\n => Array\n(\n => 119.155.33.205\n)\n\n => Array\n(\n => 157.42.117.124\n)\n\n => Array\n(\n => 103.117.202.202\n)\n\n => Array\n(\n => 220.253.101.109\n)\n\n => Array\n(\n => 49.37.7.247\n)\n\n => Array\n(\n => 119.160.65.27\n)\n\n => Array\n(\n => 114.122.21.151\n)\n\n => Array\n(\n => 157.44.141.83\n)\n\n => Array\n(\n => 103.131.9.7\n)\n\n => Array\n(\n => 125.99.222.21\n)\n\n => Array\n(\n => 103.238.104.206\n)\n\n => Array\n(\n => 110.93.227.100\n)\n\n => Array\n(\n => 49.14.119.114\n)\n\n => Array\n(\n => 115.186.189.82\n)\n\n => Array\n(\n => 106.201.194.2\n)\n\n => Array\n(\n => 106.204.227.28\n)\n\n => Array\n(\n => 47.31.206.13\n)\n\n => Array\n(\n => 39.42.144.109\n)\n\n => Array\n(\n => 14.253.254.90\n)\n\n => Array\n(\n => 157.44.142.118\n)\n\n => Array\n(\n => 192.142.176.21\n)\n\n => Array\n(\n => 103.217.178.225\n)\n\n => Array\n(\n => 106.78.78.16\n)\n\n => Array\n(\n => 167.71.63.184\n)\n\n => Array\n(\n => 207.244.71.82\n)\n\n => Array\n(\n => 71.105.25.145\n)\n\n => Array\n(\n => 39.51.250.30\n)\n\n => Array\n(\n => 157.41.120.160\n)\n\n => Array\n(\n => 39.37.137.81\n)\n\n => Array\n(\n => 41.80.237.27\n)\n\n => Array\n(\n => 111.119.187.50\n)\n\n => Array\n(\n => 49.145.224.252\n)\n\n => Array\n(\n => 106.197.28.106\n)\n\n => Array\n(\n => 103.217.178.240\n)\n\n => Array\n(\n => 27.97.182.237\n)\n\n => Array\n(\n => 106.211.253.72\n)\n\n => Array\n(\n => 119.152.154.172\n)\n\n => Array\n(\n => 103.255.151.148\n)\n\n => Array\n(\n => 154.157.80.12\n)\n\n => Array\n(\n => 156.146.59.28\n)\n\n => Array\n(\n => 171.61.211.64\n)\n\n => Array\n(\n => 27.76.59.22\n)\n\n => Array\n(\n => 167.99.92.124\n)\n\n => Array\n(\n => 132.154.94.51\n)\n\n => Array\n(\n => 111.119.183.38\n)\n\n => Array\n(\n => 115.42.70.169\n)\n\n => Array\n(\n => 109.169.23.83\n)\n\n => Array\n(\n => 157.46.213.64\n)\n\n => Array\n(\n => 39.37.179.171\n)\n\n => Array\n(\n => 14.232.233.32\n)\n\n => Array\n(\n => 157.49.226.13\n)\n\n => Array\n(\n => 185.209.178.78\n)\n\n => Array\n(\n => 222.252.46.230\n)\n\n => Array\n(\n => 139.5.255.168\n)\n\n => Array\n(\n => 202.8.118.12\n)\n\n => Array\n(\n => 39.53.205.63\n)\n\n => Array\n(\n => 157.37.167.227\n)\n\n => Array\n(\n => 157.49.237.121\n)\n\n => Array\n(\n => 208.89.99.6\n)\n\n => Array\n(\n => 111.119.187.33\n)\n\n => Array\n(\n => 39.37.132.101\n)\n\n => Array\n(\n => 72.255.61.15\n)\n\n => Array\n(\n => 157.41.69.126\n)\n\n => Array\n(\n => 27.6.193.15\n)\n\n => Array\n(\n => 157.41.104.8\n)\n\n => Array\n(\n => 157.41.97.162\n)\n\n => Array\n(\n => 95.136.91.67\n)\n\n => Array\n(\n => 110.93.209.138\n)\n\n => Array\n(\n => 119.152.154.82\n)\n\n => Array\n(\n => 111.88.239.223\n)\n\n => Array\n(\n => 157.230.62.100\n)\n\n => Array\n(\n => 37.111.136.167\n)\n\n => Array\n(\n => 139.167.162.65\n)\n\n => Array\n(\n => 120.29.72.72\n)\n\n => Array\n(\n => 39.42.169.69\n)\n\n => Array\n(\n => 157.49.247.12\n)\n\n => Array\n(\n => 43.231.58.221\n)\n\n => Array\n(\n => 111.88.229.18\n)\n\n => Array\n(\n => 171.79.185.198\n)\n\n => Array\n(\n => 169.149.193.102\n)\n\n => Array\n(\n => 207.244.89.162\n)\n\n => Array\n(\n => 27.4.217.129\n)\n\n => Array\n(\n => 91.236.184.12\n)\n\n => Array\n(\n => 14.192.154.150\n)\n\n => Array\n(\n => 167.172.55.253\n)\n\n => Array\n(\n => 103.77.42.192\n)\n\n => Array\n(\n => 39.59.122.140\n)\n\n => Array\n(\n => 41.80.84.46\n)\n\n => Array\n(\n => 202.47.52.115\n)\n\n => Array\n(\n => 222.252.43.47\n)\n\n => Array\n(\n => 119.155.37.250\n)\n\n => Array\n(\n => 157.41.18.88\n)\n\n => Array\n(\n => 39.42.8.59\n)\n\n => Array\n(\n => 39.45.162.110\n)\n\n => Array\n(\n => 111.88.237.25\n)\n\n => Array\n(\n => 103.76.211.168\n)\n\n => Array\n(\n => 178.137.114.165\n)\n\n => Array\n(\n => 43.225.74.146\n)\n\n => Array\n(\n => 157.42.25.26\n)\n\n => Array\n(\n => 137.59.146.63\n)\n\n => Array\n(\n => 119.160.117.190\n)\n\n => Array\n(\n => 1.186.181.133\n)\n\n => Array\n(\n => 39.42.145.94\n)\n\n => Array\n(\n => 203.175.73.96\n)\n\n => Array\n(\n => 39.37.160.14\n)\n\n => Array\n(\n => 157.39.123.250\n)\n\n => Array\n(\n => 95.135.57.82\n)\n\n => Array\n(\n => 162.210.194.35\n)\n\n => Array\n(\n => 39.42.153.135\n)\n\n => Array\n(\n => 118.103.230.106\n)\n\n => Array\n(\n => 108.61.39.115\n)\n\n => Array\n(\n => 102.7.108.45\n)\n\n => Array\n(\n => 183.83.138.134\n)\n\n => Array\n(\n => 115.186.70.223\n)\n\n => Array\n(\n => 157.34.17.139\n)\n\n => Array\n(\n => 122.166.158.231\n)\n\n => Array\n(\n => 43.227.135.90\n)\n\n => Array\n(\n => 182.68.46.180\n)\n\n => Array\n(\n => 223.225.28.138\n)\n\n => Array\n(\n => 103.77.42.220\n)\n\n => Array\n(\n => 192.241.219.13\n)\n\n => Array\n(\n => 103.82.80.113\n)\n\n => Array\n(\n => 42.111.243.151\n)\n\n => Array\n(\n => 171.79.189.247\n)\n\n => Array\n(\n => 157.32.132.102\n)\n\n => Array\n(\n => 103.130.105.243\n)\n\n => Array\n(\n => 117.223.98.120\n)\n\n => Array\n(\n => 106.215.197.187\n)\n\n => Array\n(\n => 182.190.194.179\n)\n\n => Array\n(\n => 223.225.29.42\n)\n\n => Array\n(\n => 117.222.94.151\n)\n\n => Array\n(\n => 182.185.199.104\n)\n\n => Array\n(\n => 49.36.145.77\n)\n\n => Array\n(\n => 103.82.80.73\n)\n\n => Array\n(\n => 103.77.16.13\n)\n\n => Array\n(\n => 221.132.118.86\n)\n\n => Array\n(\n => 202.47.45.77\n)\n\n => Array\n(\n => 202.8.118.116\n)\n\n => Array\n(\n => 42.106.180.185\n)\n\n => Array\n(\n => 203.122.8.234\n)\n\n => Array\n(\n => 88.230.104.245\n)\n\n => Array\n(\n => 103.131.9.33\n)\n\n => Array\n(\n => 117.207.209.60\n)\n\n => Array\n(\n => 42.111.253.227\n)\n\n => Array\n(\n => 23.106.56.54\n)\n\n => Array\n(\n => 122.178.143.181\n)\n\n => Array\n(\n => 111.88.180.5\n)\n\n => Array\n(\n => 174.55.224.161\n)\n\n => Array\n(\n => 49.205.87.100\n)\n\n => Array\n(\n => 49.34.183.118\n)\n\n => Array\n(\n => 124.155.255.154\n)\n\n => Array\n(\n => 106.212.135.200\n)\n\n => Array\n(\n => 139.99.159.11\n)\n\n => Array\n(\n => 45.135.229.8\n)\n\n => Array\n(\n => 88.230.106.85\n)\n\n => Array\n(\n => 91.153.145.221\n)\n\n => Array\n(\n => 103.95.83.33\n)\n\n => Array\n(\n => 122.178.116.76\n)\n\n => Array\n(\n => 103.135.78.14\n)\n\n => Array\n(\n => 111.88.233.206\n)\n\n => Array\n(\n => 192.140.153.210\n)\n\n => Array\n(\n => 202.8.118.69\n)\n\n => Array\n(\n => 103.83.130.81\n)\n\n => Array\n(\n => 182.190.213.143\n)\n\n => Array\n(\n => 198.16.74.204\n)\n\n => Array\n(\n => 101.128.117.248\n)\n\n => Array\n(\n => 103.108.5.147\n)\n\n => Array\n(\n => 157.32.130.158\n)\n\n => Array\n(\n => 103.244.172.93\n)\n\n => Array\n(\n => 47.30.140.126\n)\n\n => Array\n(\n => 223.188.40.124\n)\n\n => Array\n(\n => 157.44.191.102\n)\n\n => Array\n(\n => 41.60.237.62\n)\n\n => Array\n(\n => 47.31.228.161\n)\n\n => Array\n(\n => 137.59.217.188\n)\n\n => Array\n(\n => 39.53.220.237\n)\n\n => Array\n(\n => 45.127.45.199\n)\n\n => Array\n(\n => 14.190.71.19\n)\n\n => Array\n(\n => 47.18.205.54\n)\n\n => Array\n(\n => 110.93.240.11\n)\n\n => Array\n(\n => 134.209.29.111\n)\n\n => Array\n(\n => 49.36.175.104\n)\n\n => Array\n(\n => 203.192.230.61\n)\n\n => Array\n(\n => 176.10.125.115\n)\n\n => Array\n(\n => 182.18.206.17\n)\n\n => Array\n(\n => 103.87.194.102\n)\n\n => Array\n(\n => 171.79.123.106\n)\n\n => Array\n(\n => 45.116.233.35\n)\n\n => Array\n(\n => 223.190.57.225\n)\n\n => Array\n(\n => 114.125.6.158\n)\n\n => Array\n(\n => 223.179.138.176\n)\n\n => Array\n(\n => 111.119.183.61\n)\n\n => Array\n(\n => 202.8.118.43\n)\n\n => Array\n(\n => 157.51.175.216\n)\n\n => Array\n(\n => 41.60.238.100\n)\n\n => Array\n(\n => 117.207.210.199\n)\n\n => Array\n(\n => 111.119.183.26\n)\n\n => Array\n(\n => 103.252.226.12\n)\n\n => Array\n(\n => 103.221.208.82\n)\n\n => Array\n(\n => 103.82.80.228\n)\n\n => Array\n(\n => 111.119.187.39\n)\n\n => Array\n(\n => 157.51.161.199\n)\n\n => Array\n(\n => 59.96.88.246\n)\n\n => Array\n(\n => 27.4.181.183\n)\n\n => Array\n(\n => 43.225.98.124\n)\n\n => Array\n(\n => 157.51.113.74\n)\n\n => Array\n(\n => 207.244.89.161\n)\n\n => Array\n(\n => 49.37.184.82\n)\n\n => Array\n(\n => 111.119.183.4\n)\n\n => Array\n(\n => 39.42.130.147\n)\n\n => Array\n(\n => 103.152.101.2\n)\n\n => Array\n(\n => 111.119.183.2\n)\n\n => Array\n(\n => 157.51.171.149\n)\n\n => Array\n(\n => 103.82.80.245\n)\n\n => Array\n(\n => 175.107.207.133\n)\n\n => Array\n(\n => 103.204.169.158\n)\n\n => Array\n(\n => 157.51.181.12\n)\n\n => Array\n(\n => 195.158.193.212\n)\n\n => Array\n(\n => 204.14.73.85\n)\n\n => Array\n(\n => 39.59.59.31\n)\n\n => Array\n(\n => 45.148.11.82\n)\n\n => Array\n(\n => 157.46.117.250\n)\n\n => Array\n(\n => 157.46.127.170\n)\n\n => Array\n(\n => 77.247.181.165\n)\n\n => Array\n(\n => 111.119.183.54\n)\n\n => Array\n(\n => 41.60.232.183\n)\n\n => Array\n(\n => 157.42.206.174\n)\n\n => Array\n(\n => 196.53.10.246\n)\n\n => Array\n(\n => 27.97.186.131\n)\n\n => Array\n(\n => 103.73.101.134\n)\n\n => Array\n(\n => 111.119.183.35\n)\n\n => Array\n(\n => 202.8.118.111\n)\n\n => Array\n(\n => 103.75.246.207\n)\n\n => Array\n(\n => 47.8.94.225\n)\n\n => Array\n(\n => 106.202.40.83\n)\n\n => Array\n(\n => 117.102.2.0\n)\n\n => Array\n(\n => 156.146.59.11\n)\n\n => Array\n(\n => 223.190.115.125\n)\n\n => Array\n(\n => 169.149.212.232\n)\n\n => Array\n(\n => 39.45.150.127\n)\n\n => Array\n(\n => 45.63.10.204\n)\n\n => Array\n(\n => 27.57.86.46\n)\n\n => Array\n(\n => 103.127.20.138\n)\n\n => Array\n(\n => 223.190.27.26\n)\n\n => Array\n(\n => 49.15.248.78\n)\n\n => Array\n(\n => 130.105.135.103\n)\n\n => Array\n(\n => 47.31.3.239\n)\n\n => Array\n(\n => 185.66.71.8\n)\n\n => Array\n(\n => 103.226.226.198\n)\n\n => Array\n(\n => 39.34.134.16\n)\n\n => Array\n(\n => 95.158.53.120\n)\n\n => Array\n(\n => 45.9.249.246\n)\n\n => Array\n(\n => 223.235.162.157\n)\n\n => Array\n(\n => 37.111.139.23\n)\n\n => Array\n(\n => 49.37.153.47\n)\n\n => Array\n(\n => 103.242.60.205\n)\n\n => Array\n(\n => 185.66.68.18\n)\n\n => Array\n(\n => 162.221.202.138\n)\n\n => Array\n(\n => 202.63.195.29\n)\n\n => Array\n(\n => 112.198.75.226\n)\n\n => Array\n(\n => 46.200.69.233\n)\n\n => Array\n(\n => 103.135.78.30\n)\n\n => Array\n(\n => 119.152.226.9\n)\n\n => Array\n(\n => 167.172.242.50\n)\n\n => Array\n(\n => 49.36.151.31\n)\n\n => Array\n(\n => 111.88.237.156\n)\n\n => Array\n(\n => 103.215.168.1\n)\n\n => Array\n(\n => 107.181.177.137\n)\n\n => Array\n(\n => 157.119.186.202\n)\n\n => Array\n(\n => 37.111.139.106\n)\n\n => Array\n(\n => 182.180.152.198\n)\n\n => Array\n(\n => 43.248.153.72\n)\n\n => Array\n(\n => 64.188.20.84\n)\n\n => Array\n(\n => 103.92.214.11\n)\n\n => Array\n(\n => 182.182.14.148\n)\n\n => Array\n(\n => 116.75.154.119\n)\n\n => Array\n(\n => 37.228.235.94\n)\n\n => Array\n(\n => 197.210.55.43\n)\n\n => Array\n(\n => 45.118.165.153\n)\n\n => Array\n(\n => 122.176.32.27\n)\n\n => Array\n(\n => 106.215.161.20\n)\n\n => Array\n(\n => 152.32.113.58\n)\n\n => Array\n(\n => 111.125.106.132\n)\n\n => Array\n(\n => 212.102.40.72\n)\n\n => Array\n(\n => 2.58.194.140\n)\n\n => Array\n(\n => 122.174.68.115\n)\n\n => Array\n(\n => 117.241.66.56\n)\n\n => Array\n(\n => 71.94.172.140\n)\n\n => Array\n(\n => 103.209.228.139\n)\n\n => Array\n(\n => 43.242.177.140\n)\n\n => Array\n(\n => 38.91.101.66\n)\n\n => Array\n(\n => 103.82.80.67\n)\n\n => Array\n(\n => 117.248.62.138\n)\n\n => Array\n(\n => 103.81.215.51\n)\n\n => Array\n(\n => 103.253.174.4\n)\n\n => Array\n(\n => 202.142.110.111\n)\n\n => Array\n(\n => 162.216.142.1\n)\n\n => Array\n(\n => 58.186.7.252\n)\n\n => Array\n(\n => 113.203.247.66\n)\n\n => Array\n(\n => 111.88.50.63\n)\n\n => Array\n(\n => 182.182.94.227\n)\n\n => Array\n(\n => 49.15.232.50\n)\n\n => Array\n(\n => 182.189.76.225\n)\n\n => Array\n(\n => 139.99.159.14\n)\n\n => Array\n(\n => 163.172.159.235\n)\n\n => Array\n(\n => 157.36.235.241\n)\n\n => Array\n(\n => 111.119.187.3\n)\n\n => Array\n(\n => 103.100.4.61\n)\n\n => Array\n(\n => 192.142.130.88\n)\n\n => Array\n(\n => 43.242.176.114\n)\n\n => Array\n(\n => 180.178.156.165\n)\n\n => Array\n(\n => 182.189.236.77\n)\n\n => Array\n(\n => 49.34.197.239\n)\n\n => Array\n(\n => 157.36.107.107\n)\n\n => Array\n(\n => 103.209.85.175\n)\n\n => Array\n(\n => 203.139.63.83\n)\n\n => Array\n(\n => 43.242.177.161\n)\n\n => Array\n(\n => 182.182.77.138\n)\n\n => Array\n(\n => 114.124.168.117\n)\n\n => Array\n(\n => 124.253.79.191\n)\n\n => Array\n(\n => 192.142.168.235\n)\n\n => Array\n(\n => 14.232.235.111\n)\n\n => Array\n(\n => 152.57.124.214\n)\n\n => Array\n(\n => 123.24.172.48\n)\n\n => Array\n(\n => 43.242.176.87\n)\n\n => Array\n(\n => 43.242.176.101\n)\n\n => Array\n(\n => 49.156.84.110\n)\n\n => Array\n(\n => 58.65.222.6\n)\n\n => Array\n(\n => 157.32.189.112\n)\n\n => Array\n(\n => 47.31.155.87\n)\n\n => Array\n(\n => 39.53.244.182\n)\n\n => Array\n(\n => 39.33.221.76\n)\n\n => Array\n(\n => 161.35.130.245\n)\n\n => Array\n(\n => 152.32.113.137\n)\n\n => Array\n(\n => 192.142.187.220\n)\n\n => Array\n(\n => 185.54.228.123\n)\n\n => Array\n(\n => 103.233.87.221\n)\n\n => Array\n(\n => 223.236.200.224\n)\n\n => Array\n(\n => 27.97.189.170\n)\n\n => Array\n(\n => 103.82.80.212\n)\n\n => Array\n(\n => 43.242.176.37\n)\n\n => Array\n(\n => 49.36.144.94\n)\n\n => Array\n(\n => 180.251.62.185\n)\n\n => Array\n(\n => 39.50.243.227\n)\n\n => Array\n(\n => 124.253.20.21\n)\n\n => Array\n(\n => 41.60.233.31\n)\n\n => Array\n(\n => 103.81.215.57\n)\n\n => Array\n(\n => 185.91.120.16\n)\n\n => Array\n(\n => 182.190.107.163\n)\n\n => Array\n(\n => 222.252.61.68\n)\n\n => Array\n(\n => 109.169.23.78\n)\n\n => Array\n(\n => 39.50.151.222\n)\n\n => Array\n(\n => 43.242.176.86\n)\n\n => Array\n(\n => 178.162.222.161\n)\n\n => Array\n(\n => 37.111.139.158\n)\n\n => Array\n(\n => 39.57.224.97\n)\n\n => Array\n(\n => 39.57.157.194\n)\n\n => Array\n(\n => 111.119.183.48\n)\n\n => Array\n(\n => 180.190.171.129\n)\n\n => Array\n(\n => 39.52.174.177\n)\n\n => Array\n(\n => 43.242.176.103\n)\n\n => Array\n(\n => 124.253.83.14\n)\n\n => Array\n(\n => 182.189.116.245\n)\n\n => Array\n(\n => 157.36.178.213\n)\n\n => Array\n(\n => 45.250.65.119\n)\n\n => Array\n(\n => 103.209.86.6\n)\n\n => Array\n(\n => 43.242.176.80\n)\n\n => Array\n(\n => 137.59.147.2\n)\n\n => Array\n(\n => 117.222.95.23\n)\n\n => Array\n(\n => 124.253.81.10\n)\n\n => Array\n(\n => 43.242.177.21\n)\n\n => Array\n(\n => 182.189.224.186\n)\n\n => Array\n(\n => 39.52.178.142\n)\n\n => Array\n(\n => 106.214.29.176\n)\n\n => Array\n(\n => 111.88.145.107\n)\n\n => Array\n(\n => 49.36.142.67\n)\n\n => Array\n(\n => 202.142.65.50\n)\n\n => Array\n(\n => 1.22.186.76\n)\n\n => Array\n(\n => 103.131.8.225\n)\n\n => Array\n(\n => 39.53.212.111\n)\n\n => Array\n(\n => 103.82.80.149\n)\n\n => Array\n(\n => 43.242.176.12\n)\n\n => Array\n(\n => 103.109.13.189\n)\n\n => Array\n(\n => 124.253.206.202\n)\n\n => Array\n(\n => 117.195.115.85\n)\n\n => Array\n(\n => 49.36.245.229\n)\n\n => Array\n(\n => 42.118.8.100\n)\n\n => Array\n(\n => 1.22.73.17\n)\n\n => Array\n(\n => 157.36.166.131\n)\n\n => Array\n(\n => 182.182.38.223\n)\n\n => Array\n(\n => 49.14.150.21\n)\n\n => Array\n(\n => 43.242.176.89\n)\n\n => Array\n(\n => 157.46.185.69\n)\n\n => Array\n(\n => 103.31.92.150\n)\n\n => Array\n(\n => 59.96.90.94\n)\n\n => Array\n(\n => 49.156.111.64\n)\n\n => Array\n(\n => 103.75.244.16\n)\n\n => Array\n(\n => 54.37.18.139\n)\n\n => Array\n(\n => 27.255.173.50\n)\n\n => Array\n(\n => 84.202.161.120\n)\n\n => Array\n(\n => 27.3.224.180\n)\n\n => Array\n(\n => 39.44.14.192\n)\n\n => Array\n(\n => 37.120.133.201\n)\n\n => Array\n(\n => 109.251.143.236\n)\n\n => Array\n(\n => 23.80.97.111\n)\n\n => Array\n(\n => 43.242.176.9\n)\n\n => Array\n(\n => 14.248.107.50\n)\n\n => Array\n(\n => 182.189.221.114\n)\n\n => Array\n(\n => 103.253.173.74\n)\n\n => Array\n(\n => 27.97.177.45\n)\n\n => Array\n(\n => 49.14.98.9\n)\n\n => Array\n(\n => 163.53.85.169\n)\n\n => Array\n(\n => 39.59.90.168\n)\n\n => Array\n(\n => 111.88.202.253\n)\n\n => Array\n(\n => 111.119.178.155\n)\n\n => Array\n(\n => 171.76.163.75\n)\n\n => Array\n(\n => 202.5.154.23\n)\n\n => Array\n(\n => 119.160.65.164\n)\n\n => Array\n(\n => 14.253.253.190\n)\n\n => Array\n(\n => 117.206.167.25\n)\n\n => Array\n(\n => 61.2.183.186\n)\n\n => Array\n(\n => 103.100.4.83\n)\n\n => Array\n(\n => 124.253.71.126\n)\n\n => Array\n(\n => 182.189.49.217\n)\n\n => Array\n(\n => 103.196.160.41\n)\n\n => Array\n(\n => 23.106.56.35\n)\n\n => Array\n(\n => 110.38.12.70\n)\n\n => Array\n(\n => 154.157.199.239\n)\n\n => Array\n(\n => 14.231.163.113\n)\n\n => Array\n(\n => 103.69.27.232\n)\n\n => Array\n(\n => 175.107.220.192\n)\n\n => Array\n(\n => 43.231.58.173\n)\n\n => Array\n(\n => 138.128.91.215\n)\n\n => Array\n(\n => 103.233.86.1\n)\n\n => Array\n(\n => 182.187.67.111\n)\n\n => Array\n(\n => 49.156.71.31\n)\n\n => Array\n(\n => 27.255.174.125\n)\n\n => Array\n(\n => 195.24.220.35\n)\n\n => Array\n(\n => 120.29.98.28\n)\n\n => Array\n(\n => 41.202.219.255\n)\n\n => Array\n(\n => 103.88.3.243\n)\n\n => Array\n(\n => 111.125.106.75\n)\n\n => Array\n(\n => 106.76.71.74\n)\n\n => Array\n(\n => 112.201.138.85\n)\n\n => Array\n(\n => 110.137.101.229\n)\n\n => Array\n(\n => 43.242.177.96\n)\n\n => Array\n(\n => 39.36.198.196\n)\n\n => Array\n(\n => 27.255.181.140\n)\n\n => Array\n(\n => 194.99.104.58\n)\n\n => Array\n(\n => 78.129.139.109\n)\n\n => Array\n(\n => 47.247.185.67\n)\n\n => Array\n(\n => 27.63.37.90\n)\n\n => Array\n(\n => 103.211.54.1\n)\n\n => Array\n(\n => 94.202.167.139\n)\n\n => Array\n(\n => 111.119.183.3\n)\n\n => Array\n(\n => 124.253.194.1\n)\n\n => Array\n(\n => 192.142.188.115\n)\n\n => Array\n(\n => 39.44.137.107\n)\n\n => Array\n(\n => 43.251.191.25\n)\n\n => Array\n(\n => 103.140.30.114\n)\n\n => Array\n(\n => 117.5.194.159\n)\n\n => Array\n(\n => 109.169.23.79\n)\n\n => Array\n(\n => 122.178.127.170\n)\n\n => Array\n(\n => 45.118.165.156\n)\n\n => Array\n(\n => 39.48.199.148\n)\n\n => Array\n(\n => 182.64.138.32\n)\n\n => Array\n(\n => 37.73.129.186\n)\n\n => Array\n(\n => 182.186.110.35\n)\n\n => Array\n(\n => 43.242.177.24\n)\n\n => Array\n(\n => 119.155.23.112\n)\n\n => Array\n(\n => 84.16.238.119\n)\n\n => Array\n(\n => 41.202.219.252\n)\n\n => Array\n(\n => 43.242.176.119\n)\n\n => Array\n(\n => 111.119.187.6\n)\n\n => Array\n(\n => 95.12.200.188\n)\n\n => Array\n(\n => 139.28.219.138\n)\n\n => Array\n(\n => 89.163.247.130\n)\n\n => Array\n(\n => 122.173.103.88\n)\n\n => Array\n(\n => 103.248.87.10\n)\n\n => Array\n(\n => 23.106.249.36\n)\n\n => Array\n(\n => 124.253.94.125\n)\n\n => Array\n(\n => 39.53.244.147\n)\n\n => Array\n(\n => 193.109.85.11\n)\n\n => Array\n(\n => 43.242.176.71\n)\n\n => Array\n(\n => 43.242.177.58\n)\n\n => Array\n(\n => 47.31.6.139\n)\n\n => Array\n(\n => 39.59.34.67\n)\n\n => Array\n(\n => 43.242.176.58\n)\n\n => Array\n(\n => 103.107.198.198\n)\n\n => Array\n(\n => 147.135.11.113\n)\n\n => Array\n(\n => 27.7.212.112\n)\n\n => Array\n(\n => 43.242.177.1\n)\n\n => Array\n(\n => 175.107.227.27\n)\n\n => Array\n(\n => 103.103.43.254\n)\n\n => Array\n(\n => 49.15.221.10\n)\n\n => Array\n(\n => 43.242.177.43\n)\n\n => Array\n(\n => 36.85.59.11\n)\n\n => Array\n(\n => 124.253.204.50\n)\n\n => Array\n(\n => 5.181.233.54\n)\n\n => Array\n(\n => 43.242.177.154\n)\n\n => Array\n(\n => 103.84.37.169\n)\n\n => Array\n(\n => 222.252.54.108\n)\n\n => Array\n(\n => 14.162.160.254\n)\n\n => Array\n(\n => 178.151.218.45\n)\n\n => Array\n(\n => 110.137.101.93\n)\n\n => Array\n(\n => 122.162.212.59\n)\n\n => Array\n(\n => 81.12.118.162\n)\n\n => Array\n(\n => 171.76.186.148\n)\n\n => Array\n(\n => 182.69.253.77\n)\n\n => Array\n(\n => 111.119.183.43\n)\n\n => Array\n(\n => 49.149.74.226\n)\n\n => Array\n(\n => 43.242.177.63\n)\n\n => Array\n(\n => 14.99.243.54\n)\n\n => Array\n(\n => 110.137.100.25\n)\n\n => Array\n(\n => 116.107.25.163\n)\n\n => Array\n(\n => 49.36.71.141\n)\n\n => Array\n(\n => 182.180.117.219\n)\n\n => Array\n(\n => 150.242.172.194\n)\n\n => Array\n(\n => 49.156.111.40\n)\n\n => Array\n(\n => 49.15.208.115\n)\n\n => Array\n(\n => 103.209.87.219\n)\n\n => Array\n(\n => 43.242.176.56\n)\n\n => Array\n(\n => 103.132.187.100\n)\n\n => Array\n(\n => 49.156.96.120\n)\n\n => Array\n(\n => 192.142.176.171\n)\n\n => Array\n(\n => 51.91.18.131\n)\n\n => Array\n(\n => 103.83.144.121\n)\n\n => Array\n(\n => 1.39.75.72\n)\n\n => Array\n(\n => 14.231.172.177\n)\n\n => Array\n(\n => 94.232.213.159\n)\n\n => Array\n(\n => 103.228.158.38\n)\n\n => Array\n(\n => 43.242.177.100\n)\n\n => Array\n(\n => 171.76.149.130\n)\n\n => Array\n(\n => 113.183.26.59\n)\n\n => Array\n(\n => 182.74.232.166\n)\n\n => Array\n(\n => 47.31.205.211\n)\n\n => Array\n(\n => 106.211.253.70\n)\n\n => Array\n(\n => 39.51.233.214\n)\n\n => Array\n(\n => 182.70.249.161\n)\n\n => Array\n(\n => 222.252.40.196\n)\n\n => Array\n(\n => 49.37.6.29\n)\n\n => Array\n(\n => 119.155.33.170\n)\n\n => Array\n(\n => 43.242.177.79\n)\n\n => Array\n(\n => 111.119.183.62\n)\n\n => Array\n(\n => 137.59.226.97\n)\n\n => Array\n(\n => 42.111.18.121\n)\n\n => Array\n(\n => 223.190.46.91\n)\n\n => Array\n(\n => 45.118.165.159\n)\n\n => Array\n(\n => 110.136.60.44\n)\n\n => Array\n(\n => 43.242.176.57\n)\n\n => Array\n(\n => 117.212.58.0\n)\n\n => Array\n(\n => 49.37.7.66\n)\n\n => Array\n(\n => 39.52.174.33\n)\n\n => Array\n(\n => 150.242.172.55\n)\n\n => Array\n(\n => 103.94.111.236\n)\n\n => Array\n(\n => 106.215.239.184\n)\n\n => Array\n(\n => 101.128.117.75\n)\n\n => Array\n(\n => 162.210.194.10\n)\n\n => Array\n(\n => 136.158.31.132\n)\n\n => Array\n(\n => 39.51.245.69\n)\n\n => Array\n(\n => 39.42.149.159\n)\n\n => Array\n(\n => 51.77.108.159\n)\n\n => Array\n(\n => 45.127.247.250\n)\n\n => Array\n(\n => 122.172.78.22\n)\n\n => Array\n(\n => 117.220.208.38\n)\n\n => Array\n(\n => 112.201.138.95\n)\n\n => Array\n(\n => 49.145.105.113\n)\n\n => Array\n(\n => 110.93.247.12\n)\n\n => Array\n(\n => 39.52.150.32\n)\n\n => Array\n(\n => 122.161.89.41\n)\n\n => Array\n(\n => 39.52.176.49\n)\n\n => Array\n(\n => 157.33.12.154\n)\n\n => Array\n(\n => 73.111.248.162\n)\n\n => Array\n(\n => 112.204.167.67\n)\n\n => Array\n(\n => 107.150.30.182\n)\n\n => Array\n(\n => 115.99.222.229\n)\n\n => Array\n(\n => 180.190.195.96\n)\n\n => Array\n(\n => 157.44.57.255\n)\n\n => Array\n(\n => 39.37.9.167\n)\n\n => Array\n(\n => 39.49.48.33\n)\n\n => Array\n(\n => 157.44.218.118\n)\n\n => Array\n(\n => 103.211.54.253\n)\n\n => Array\n(\n => 43.242.177.81\n)\n\n => Array\n(\n => 103.111.224.227\n)\n\n => Array\n(\n => 223.176.48.237\n)\n\n => Array\n(\n => 124.253.87.117\n)\n\n => Array\n(\n => 124.29.247.14\n)\n\n => Array\n(\n => 182.189.232.32\n)\n\n => Array\n(\n => 111.68.97.206\n)\n\n => Array\n(\n => 103.117.15.70\n)\n\n => Array\n(\n => 182.18.236.101\n)\n\n => Array\n(\n => 43.242.177.60\n)\n\n => Array\n(\n => 180.190.7.178\n)\n\n => Array\n(\n => 112.201.142.95\n)\n\n => Array\n(\n => 122.178.255.123\n)\n\n => Array\n(\n => 49.36.240.103\n)\n\n => Array\n(\n => 210.56.16.13\n)\n\n => Array\n(\n => 103.91.123.219\n)\n\n => Array\n(\n => 39.52.155.252\n)\n\n => Array\n(\n => 192.142.207.230\n)\n\n => Array\n(\n => 188.163.82.179\n)\n\n => Array\n(\n => 182.189.9.196\n)\n\n => Array\n(\n => 175.107.221.51\n)\n\n => Array\n(\n => 39.53.221.200\n)\n\n => Array\n(\n => 27.255.190.59\n)\n\n => Array\n(\n => 183.83.212.118\n)\n\n => Array\n(\n => 45.118.165.143\n)\n\n => Array\n(\n => 182.189.124.35\n)\n\n => Array\n(\n => 203.101.186.1\n)\n\n => Array\n(\n => 49.36.246.25\n)\n\n => Array\n(\n => 39.42.186.234\n)\n\n => Array\n(\n => 103.82.80.14\n)\n\n => Array\n(\n => 210.18.182.42\n)\n\n => Array\n(\n => 42.111.13.81\n)\n\n => Array\n(\n => 46.200.69.240\n)\n\n => Array\n(\n => 103.209.87.213\n)\n\n => Array\n(\n => 103.31.95.95\n)\n\n => Array\n(\n => 180.190.174.25\n)\n\n => Array\n(\n => 103.77.0.128\n)\n\n => Array\n(\n => 49.34.103.82\n)\n\n => Array\n(\n => 39.48.196.22\n)\n\n => Array\n(\n => 192.142.166.20\n)\n\n => Array\n(\n => 202.142.110.186\n)\n\n => Array\n(\n => 122.163.135.95\n)\n\n => Array\n(\n => 183.83.255.225\n)\n\n => Array\n(\n => 157.45.46.10\n)\n\n => Array\n(\n => 182.189.4.77\n)\n\n => Array\n(\n => 49.145.104.71\n)\n\n => Array\n(\n => 103.143.7.34\n)\n\n => Array\n(\n => 61.2.180.15\n)\n\n => Array\n(\n => 103.81.215.61\n)\n\n => Array\n(\n => 115.42.71.122\n)\n\n => Array\n(\n => 124.253.73.20\n)\n\n => Array\n(\n => 49.33.210.169\n)\n\n => Array\n(\n => 78.159.101.115\n)\n\n => Array\n(\n => 42.111.17.221\n)\n\n => Array\n(\n => 43.242.178.67\n)\n\n => Array\n(\n => 36.68.138.36\n)\n\n => Array\n(\n => 103.195.201.51\n)\n\n => Array\n(\n => 79.141.162.81\n)\n\n => Array\n(\n => 202.8.118.239\n)\n\n => Array\n(\n => 103.139.128.161\n)\n\n => Array\n(\n => 207.244.71.84\n)\n\n => Array\n(\n => 124.253.184.45\n)\n\n => Array\n(\n => 111.125.106.124\n)\n\n => Array\n(\n => 111.125.105.139\n)\n\n => Array\n(\n => 39.59.94.233\n)\n\n => Array\n(\n => 112.211.60.168\n)\n\n => Array\n(\n => 103.117.14.72\n)\n\n => Array\n(\n => 111.119.183.56\n)\n\n => Array\n(\n => 47.31.53.228\n)\n\n => Array\n(\n => 124.253.186.8\n)\n\n => Array\n(\n => 183.83.213.214\n)\n\n => Array\n(\n => 103.106.239.70\n)\n\n => Array\n(\n => 182.182.92.81\n)\n\n => Array\n(\n => 14.162.167.98\n)\n\n => Array\n(\n => 112.211.11.107\n)\n\n => Array\n(\n => 77.111.246.20\n)\n\n => Array\n(\n => 49.156.86.182\n)\n\n => Array\n(\n => 47.29.122.112\n)\n\n => Array\n(\n => 125.99.74.42\n)\n\n => Array\n(\n => 124.123.169.24\n)\n\n => Array\n(\n => 106.202.105.128\n)\n\n => Array\n(\n => 103.244.173.14\n)\n\n => Array\n(\n => 103.98.63.104\n)\n\n => Array\n(\n => 180.245.6.60\n)\n\n => Array\n(\n => 49.149.96.14\n)\n\n => Array\n(\n => 14.177.120.169\n)\n\n => Array\n(\n => 192.135.90.145\n)\n\n => Array\n(\n => 223.190.18.218\n)\n\n => Array\n(\n => 171.61.190.2\n)\n\n => Array\n(\n => 58.65.220.219\n)\n\n => Array\n(\n => 122.177.29.87\n)\n\n => Array\n(\n => 223.236.175.203\n)\n\n => Array\n(\n => 39.53.237.106\n)\n\n => Array\n(\n => 1.186.114.83\n)\n\n => Array\n(\n => 43.230.66.153\n)\n\n => Array\n(\n => 27.96.94.247\n)\n\n => Array\n(\n => 39.52.176.185\n)\n\n => Array\n(\n => 59.94.147.62\n)\n\n => Array\n(\n => 119.160.117.10\n)\n\n => Array\n(\n => 43.241.146.105\n)\n\n => Array\n(\n => 39.59.87.75\n)\n\n => Array\n(\n => 119.160.118.203\n)\n\n => Array\n(\n => 39.52.161.76\n)\n\n => Array\n(\n => 202.168.84.189\n)\n\n => Array\n(\n => 103.215.168.2\n)\n\n => Array\n(\n => 39.42.146.160\n)\n\n => Array\n(\n => 182.182.30.246\n)\n\n => Array\n(\n => 122.173.212.133\n)\n\n => Array\n(\n => 39.51.238.44\n)\n\n => Array\n(\n => 183.83.252.51\n)\n\n => Array\n(\n => 202.142.168.86\n)\n\n => Array\n(\n => 39.40.198.209\n)\n\n => Array\n(\n => 192.135.90.151\n)\n\n => Array\n(\n => 72.255.41.174\n)\n\n => Array\n(\n => 137.97.92.124\n)\n\n => Array\n(\n => 182.185.159.155\n)\n\n => Array\n(\n => 157.44.133.131\n)\n\n => Array\n(\n => 39.51.230.253\n)\n\n => Array\n(\n => 103.70.87.200\n)\n\n => Array\n(\n => 103.117.15.82\n)\n\n => Array\n(\n => 103.217.244.69\n)\n\n => Array\n(\n => 157.34.76.185\n)\n\n => Array\n(\n => 39.52.130.163\n)\n\n => Array\n(\n => 182.181.41.39\n)\n\n => Array\n(\n => 49.37.212.226\n)\n\n => Array\n(\n => 119.160.117.100\n)\n\n => Array\n(\n => 103.209.87.43\n)\n\n => Array\n(\n => 180.190.195.45\n)\n\n => Array\n(\n => 122.160.57.230\n)\n\n => Array\n(\n => 203.192.213.81\n)\n\n => Array\n(\n => 182.181.63.91\n)\n\n => Array\n(\n => 157.44.184.5\n)\n\n => Array\n(\n => 27.97.213.128\n)\n\n => Array\n(\n => 122.55.252.145\n)\n\n => Array\n(\n => 103.117.15.92\n)\n\n => Array\n(\n => 42.201.251.179\n)\n\n => Array\n(\n => 122.186.84.53\n)\n\n => Array\n(\n => 119.157.75.242\n)\n\n => Array\n(\n => 39.42.163.6\n)\n\n => Array\n(\n => 14.99.246.78\n)\n\n => Array\n(\n => 103.209.87.227\n)\n\n => Array\n(\n => 182.68.215.31\n)\n\n => Array\n(\n => 45.118.165.140\n)\n\n => Array\n(\n => 207.244.71.81\n)\n\n => Array\n(\n => 27.97.162.57\n)\n\n => Array\n(\n => 103.113.106.98\n)\n\n => Array\n(\n => 95.135.44.103\n)\n\n => Array\n(\n => 125.209.114.238\n)\n\n => Array\n(\n => 77.123.14.176\n)\n\n => Array\n(\n => 110.36.202.169\n)\n\n => Array\n(\n => 124.253.205.230\n)\n\n => Array\n(\n => 106.215.72.117\n)\n\n => Array\n(\n => 116.72.226.35\n)\n\n => Array\n(\n => 137.97.103.141\n)\n\n => Array\n(\n => 112.79.212.161\n)\n\n => Array\n(\n => 103.209.85.150\n)\n\n => Array\n(\n => 103.159.127.6\n)\n\n => Array\n(\n => 43.239.205.66\n)\n\n => Array\n(\n => 143.244.51.152\n)\n\n => Array\n(\n => 182.64.15.3\n)\n\n => Array\n(\n => 182.185.207.146\n)\n\n => Array\n(\n => 45.118.165.155\n)\n\n => Array\n(\n => 115.160.241.214\n)\n\n => Array\n(\n => 47.31.230.68\n)\n\n => Array\n(\n => 49.15.84.145\n)\n\n => Array\n(\n => 39.51.239.206\n)\n\n => Array\n(\n => 103.149.154.212\n)\n\n => Array\n(\n => 43.239.207.155\n)\n\n => Array\n(\n => 182.182.30.181\n)\n\n => Array\n(\n => 157.37.198.16\n)\n\n => Array\n(\n => 162.239.24.60\n)\n\n => Array\n(\n => 106.212.101.97\n)\n\n => Array\n(\n => 124.253.97.44\n)\n\n => Array\n(\n => 106.214.95.176\n)\n\n => Array\n(\n => 102.69.228.114\n)\n\n => Array\n(\n => 116.74.58.221\n)\n\n => Array\n(\n => 162.210.194.38\n)\n\n => Array\n(\n => 39.52.162.121\n)\n\n => Array\n(\n => 103.216.143.255\n)\n\n => Array\n(\n => 103.49.155.134\n)\n\n => Array\n(\n => 182.191.119.236\n)\n\n => Array\n(\n => 111.88.213.172\n)\n\n => Array\n(\n => 43.239.207.207\n)\n\n => Array\n(\n => 140.213.35.143\n)\n\n => Array\n(\n => 154.72.153.215\n)\n\n => Array\n(\n => 122.170.47.36\n)\n\n => Array\n(\n => 51.158.111.163\n)\n\n => Array\n(\n => 203.122.10.150\n)\n\n => Array\n(\n => 47.31.176.111\n)\n\n => Array\n(\n => 103.75.246.34\n)\n\n => Array\n(\n => 103.244.178.45\n)\n\n => Array\n(\n => 182.185.138.0\n)\n\n => Array\n(\n => 183.83.254.224\n)\n\n => Array\n(\n => 49.36.246.145\n)\n\n => Array\n(\n => 202.47.60.85\n)\n\n => Array\n(\n => 180.190.163.160\n)\n\n => Array\n(\n => 27.255.187.221\n)\n\n => Array\n(\n => 14.248.94.2\n)\n\n => Array\n(\n => 185.233.17.187\n)\n\n => Array\n(\n => 139.5.254.227\n)\n\n => Array\n(\n => 103.149.160.66\n)\n\n => Array\n(\n => 122.168.235.47\n)\n\n => Array\n(\n => 45.113.248.224\n)\n\n => Array\n(\n => 110.54.170.142\n)\n\n => Array\n(\n => 223.235.226.55\n)\n\n => Array\n(\n => 157.32.19.235\n)\n\n => Array\n(\n => 49.15.221.114\n)\n\n => Array\n(\n => 27.97.166.163\n)\n\n => Array\n(\n => 223.233.99.5\n)\n\n => Array\n(\n => 49.33.203.53\n)\n\n => Array\n(\n => 27.56.214.41\n)\n\n => Array\n(\n => 103.138.51.3\n)\n\n => Array\n(\n => 111.119.183.21\n)\n\n => Array\n(\n => 47.15.138.233\n)\n\n => Array\n(\n => 202.63.213.184\n)\n\n => Array\n(\n => 49.36.158.94\n)\n\n => Array\n(\n => 27.97.186.179\n)\n\n => Array\n(\n => 27.97.214.69\n)\n\n => Array\n(\n => 203.128.18.163\n)\n\n => Array\n(\n => 106.207.235.63\n)\n\n => Array\n(\n => 116.107.220.231\n)\n\n => Array\n(\n => 223.226.169.249\n)\n\n => Array\n(\n => 106.201.24.6\n)\n\n => Array\n(\n => 49.15.89.7\n)\n\n => Array\n(\n => 49.15.142.20\n)\n\n => Array\n(\n => 223.177.24.85\n)\n\n => Array\n(\n => 37.156.17.37\n)\n\n => Array\n(\n => 102.129.224.2\n)\n\n => Array\n(\n => 49.15.85.221\n)\n\n => Array\n(\n => 106.76.208.153\n)\n\n => Array\n(\n => 61.2.47.71\n)\n\n => Array\n(\n => 27.97.178.79\n)\n\n => Array\n(\n => 39.34.143.196\n)\n\n => Array\n(\n => 103.10.227.158\n)\n\n => Array\n(\n => 117.220.210.159\n)\n\n => Array\n(\n => 182.189.28.11\n)\n\n => Array\n(\n => 122.185.38.170\n)\n\n => Array\n(\n => 112.196.132.115\n)\n\n => Array\n(\n => 187.156.137.83\n)\n\n => Array\n(\n => 203.122.3.88\n)\n\n => Array\n(\n => 51.68.142.45\n)\n\n => Array\n(\n => 124.253.217.55\n)\n\n => Array\n(\n => 103.152.41.2\n)\n\n => Array\n(\n => 157.37.154.219\n)\n\n => Array\n(\n => 39.45.32.77\n)\n\n => Array\n(\n => 182.182.22.221\n)\n\n => Array\n(\n => 157.43.205.117\n)\n\n => Array\n(\n => 202.142.123.58\n)\n\n => Array\n(\n => 43.239.207.121\n)\n\n => Array\n(\n => 49.206.122.113\n)\n\n => Array\n(\n => 106.193.199.203\n)\n\n => Array\n(\n => 103.67.157.251\n)\n\n => Array\n(\n => 49.34.97.81\n)\n\n => Array\n(\n => 49.156.92.130\n)\n\n => Array\n(\n => 203.160.179.210\n)\n\n => Array\n(\n => 106.215.33.244\n)\n\n => Array\n(\n => 191.101.148.41\n)\n\n => Array\n(\n => 203.90.94.94\n)\n\n => Array\n(\n => 105.129.205.134\n)\n\n => Array\n(\n => 106.215.45.165\n)\n\n => Array\n(\n => 112.196.132.15\n)\n\n => Array\n(\n => 39.59.64.174\n)\n\n => Array\n(\n => 124.253.155.116\n)\n\n => Array\n(\n => 94.179.192.204\n)\n\n => Array\n(\n => 110.38.29.245\n)\n\n => Array\n(\n => 124.29.209.78\n)\n\n => Array\n(\n => 103.75.245.240\n)\n\n => Array\n(\n => 49.36.159.170\n)\n\n => Array\n(\n => 223.190.18.160\n)\n\n => Array\n(\n => 124.253.113.226\n)\n\n => Array\n(\n => 14.180.77.240\n)\n\n => Array\n(\n => 106.215.76.24\n)\n\n => Array\n(\n => 106.210.155.153\n)\n\n => Array\n(\n => 111.119.187.42\n)\n\n => Array\n(\n => 146.196.32.106\n)\n\n => Array\n(\n => 122.162.22.27\n)\n\n => Array\n(\n => 49.145.59.252\n)\n\n => Array\n(\n => 95.47.247.92\n)\n\n => Array\n(\n => 103.99.218.50\n)\n\n => Array\n(\n => 157.37.192.88\n)\n\n => Array\n(\n => 82.102.31.242\n)\n\n => Array\n(\n => 157.46.220.64\n)\n\n => Array\n(\n => 180.151.107.52\n)\n\n => Array\n(\n => 203.81.240.75\n)\n\n => Array\n(\n => 122.167.213.130\n)\n\n => Array\n(\n => 103.227.70.164\n)\n\n => Array\n(\n => 106.215.81.169\n)\n\n => Array\n(\n => 157.46.214.170\n)\n\n => Array\n(\n => 103.69.27.163\n)\n\n => Array\n(\n => 124.253.23.213\n)\n\n => Array\n(\n => 157.37.167.174\n)\n\n => Array\n(\n => 1.39.204.67\n)\n\n => Array\n(\n => 112.196.132.51\n)\n\n => Array\n(\n => 119.152.61.222\n)\n\n => Array\n(\n => 47.31.36.174\n)\n\n => Array\n(\n => 47.31.152.174\n)\n\n => Array\n(\n => 49.34.18.105\n)\n\n => Array\n(\n => 157.37.170.101\n)\n\n => Array\n(\n => 118.209.241.234\n)\n\n => Array\n(\n => 103.67.19.9\n)\n\n => Array\n(\n => 182.189.14.154\n)\n\n => Array\n(\n => 45.127.233.232\n)\n\n => Array\n(\n => 27.96.94.91\n)\n\n => Array\n(\n => 183.83.214.250\n)\n\n => Array\n(\n => 47.31.27.140\n)\n\n => Array\n(\n => 47.31.129.199\n)\n\n => Array\n(\n => 157.44.156.111\n)\n\n => Array\n(\n => 42.110.163.2\n)\n\n => Array\n(\n => 124.253.64.210\n)\n\n => Array\n(\n => 49.36.167.54\n)\n\n => Array\n(\n => 27.63.135.145\n)\n\n => Array\n(\n => 157.35.254.63\n)\n\n => Array\n(\n => 39.45.18.182\n)\n\n => Array\n(\n => 197.210.85.102\n)\n\n => Array\n(\n => 112.196.132.90\n)\n\n => Array\n(\n => 59.152.97.84\n)\n\n => Array\n(\n => 43.242.178.7\n)\n\n => Array\n(\n => 47.31.40.70\n)\n\n => Array\n(\n => 202.134.10.136\n)\n\n => Array\n(\n => 132.154.241.43\n)\n\n => Array\n(\n => 185.209.179.240\n)\n\n => Array\n(\n => 202.47.50.28\n)\n\n => Array\n(\n => 182.186.1.29\n)\n\n => Array\n(\n => 124.253.114.229\n)\n\n => Array\n(\n => 49.32.210.126\n)\n\n => Array\n(\n => 43.242.178.122\n)\n\n => Array\n(\n => 42.111.28.52\n)\n\n => Array\n(\n => 23.227.141.44\n)\n\n => Array\n(\n => 23.227.141.156\n)\n\n => Array\n(\n => 103.253.173.79\n)\n\n => Array\n(\n => 116.75.231.74\n)\n\n => Array\n(\n => 106.76.78.196\n)\n\n => Array\n(\n => 116.75.197.68\n)\n\n => Array\n(\n => 42.108.172.131\n)\n\n => Array\n(\n => 157.38.27.199\n)\n\n => Array\n(\n => 103.70.86.205\n)\n\n => Array\n(\n => 119.152.63.239\n)\n\n => Array\n(\n => 103.233.116.94\n)\n\n => Array\n(\n => 111.119.188.17\n)\n\n => Array\n(\n => 103.196.160.156\n)\n\n => Array\n(\n => 27.97.208.40\n)\n\n => Array\n(\n => 188.163.7.136\n)\n\n => Array\n(\n => 49.15.202.205\n)\n\n => Array\n(\n => 124.253.201.111\n)\n\n => Array\n(\n => 182.190.213.246\n)\n\n => Array\n(\n => 5.154.174.10\n)\n\n => Array\n(\n => 103.21.185.16\n)\n\n => Array\n(\n => 112.196.132.67\n)\n\n => Array\n(\n => 49.15.194.230\n)\n\n => Array\n(\n => 103.118.34.103\n)\n\n => Array\n(\n => 49.15.201.92\n)\n\n => Array\n(\n => 42.111.13.238\n)\n\n => Array\n(\n => 203.192.213.137\n)\n\n => Array\n(\n => 45.115.190.82\n)\n\n => Array\n(\n => 78.26.130.102\n)\n\n => Array\n(\n => 49.15.85.202\n)\n\n => Array\n(\n => 106.76.193.33\n)\n\n => Array\n(\n => 103.70.41.30\n)\n\n => Array\n(\n => 103.82.78.254\n)\n\n => Array\n(\n => 110.38.35.90\n)\n\n => Array\n(\n => 181.214.107.27\n)\n\n => Array\n(\n => 27.110.183.162\n)\n\n => Array\n(\n => 94.225.230.215\n)\n\n => Array\n(\n => 27.97.185.58\n)\n\n => Array\n(\n => 49.146.196.124\n)\n\n => Array\n(\n => 119.157.76.144\n)\n\n => Array\n(\n => 103.99.218.34\n)\n\n => Array\n(\n => 185.32.221.247\n)\n\n => Array\n(\n => 27.97.161.12\n)\n\n => Array\n(\n => 27.62.144.214\n)\n\n => Array\n(\n => 124.253.90.151\n)\n\n => Array\n(\n => 49.36.135.69\n)\n\n => Array\n(\n => 39.40.217.106\n)\n\n => Array\n(\n => 119.152.235.136\n)\n\n => Array\n(\n => 103.91.103.226\n)\n\n => Array\n(\n => 117.222.226.93\n)\n\n => Array\n(\n => 182.190.24.126\n)\n\n => Array\n(\n => 27.97.223.179\n)\n\n => Array\n(\n => 202.137.115.11\n)\n\n => Array\n(\n => 43.242.178.130\n)\n\n => Array\n(\n => 182.189.125.232\n)\n\n => Array\n(\n => 182.190.202.87\n)\n\n => Array\n(\n => 124.253.102.193\n)\n\n => Array\n(\n => 103.75.247.73\n)\n\n => Array\n(\n => 122.177.100.97\n)\n\n => Array\n(\n => 47.31.192.254\n)\n\n => Array\n(\n => 49.149.73.185\n)\n\n => Array\n(\n => 39.57.147.197\n)\n\n => Array\n(\n => 103.110.147.52\n)\n\n => Array\n(\n => 124.253.106.255\n)\n\n => Array\n(\n => 152.57.116.136\n)\n\n => Array\n(\n => 110.38.35.102\n)\n\n => Array\n(\n => 182.18.206.127\n)\n\n => Array\n(\n => 103.133.59.246\n)\n\n => Array\n(\n => 27.97.189.139\n)\n\n => Array\n(\n => 179.61.245.54\n)\n\n => Array\n(\n => 103.240.233.176\n)\n\n => Array\n(\n => 111.88.124.196\n)\n\n => Array\n(\n => 49.146.215.3\n)\n\n => Array\n(\n => 110.39.10.246\n)\n\n => Array\n(\n => 27.5.42.135\n)\n\n => Array\n(\n => 27.97.177.251\n)\n\n => Array\n(\n => 93.177.75.254\n)\n\n => Array\n(\n => 43.242.177.3\n)\n\n => Array\n(\n => 112.196.132.97\n)\n\n => Array\n(\n => 116.75.242.188\n)\n\n => Array\n(\n => 202.8.118.101\n)\n\n => Array\n(\n => 49.36.65.43\n)\n\n => Array\n(\n => 157.37.146.220\n)\n\n => Array\n(\n => 157.37.143.235\n)\n\n => Array\n(\n => 157.38.94.34\n)\n\n => Array\n(\n => 49.36.131.1\n)\n\n => Array\n(\n => 132.154.92.97\n)\n\n => Array\n(\n => 132.154.123.115\n)\n\n => Array\n(\n => 49.15.197.222\n)\n\n => Array\n(\n => 124.253.198.72\n)\n\n => Array\n(\n => 27.97.217.95\n)\n\n => Array\n(\n => 47.31.194.65\n)\n\n => Array\n(\n => 197.156.190.156\n)\n\n => Array\n(\n => 197.156.190.230\n)\n\n => Array\n(\n => 103.62.152.250\n)\n\n => Array\n(\n => 103.152.212.126\n)\n\n => Array\n(\n => 185.233.18.177\n)\n\n => Array\n(\n => 116.75.63.83\n)\n\n => Array\n(\n => 157.38.56.125\n)\n\n => Array\n(\n => 119.157.107.195\n)\n\n => Array\n(\n => 103.87.50.73\n)\n\n => Array\n(\n => 95.142.120.141\n)\n\n => Array\n(\n => 154.13.1.221\n)\n\n => Array\n(\n => 103.147.87.79\n)\n\n => Array\n(\n => 39.53.173.186\n)\n\n => Array\n(\n => 195.114.145.107\n)\n\n => Array\n(\n => 157.33.201.185\n)\n\n => Array\n(\n => 195.85.219.36\n)\n\n => Array\n(\n => 105.161.67.127\n)\n\n => Array\n(\n => 110.225.87.77\n)\n\n => Array\n(\n => 103.95.167.236\n)\n\n => Array\n(\n => 89.187.162.213\n)\n\n => Array\n(\n => 27.255.189.50\n)\n\n => Array\n(\n => 115.96.77.54\n)\n\n => Array\n(\n => 223.182.220.223\n)\n\n => Array\n(\n => 157.47.206.192\n)\n\n => Array\n(\n => 182.186.110.226\n)\n\n => Array\n(\n => 39.53.243.237\n)\n\n => Array\n(\n => 39.40.228.58\n)\n\n => Array\n(\n => 157.38.60.9\n)\n\n => Array\n(\n => 106.198.244.189\n)\n\n => Array\n(\n => 124.253.51.164\n)\n\n => Array\n(\n => 49.147.113.58\n)\n\n => Array\n(\n => 14.231.196.229\n)\n\n => Array\n(\n => 103.81.214.152\n)\n\n => Array\n(\n => 117.222.220.60\n)\n\n => Array\n(\n => 83.142.111.213\n)\n\n => Array\n(\n => 14.224.77.147\n)\n\n => Array\n(\n => 110.235.236.95\n)\n\n => Array\n(\n => 103.26.83.30\n)\n\n => Array\n(\n => 106.206.191.82\n)\n\n => Array\n(\n => 103.49.117.135\n)\n\n => Array\n(\n => 202.47.39.9\n)\n\n => Array\n(\n => 180.178.145.205\n)\n\n => Array\n(\n => 43.251.93.119\n)\n\n => Array\n(\n => 27.6.212.182\n)\n\n => Array\n(\n => 39.42.156.20\n)\n\n => Array\n(\n => 47.31.141.195\n)\n\n => Array\n(\n => 157.37.146.73\n)\n\n => Array\n(\n => 49.15.93.155\n)\n\n => Array\n(\n => 162.210.194.37\n)\n\n => Array\n(\n => 223.188.160.236\n)\n\n => Array\n(\n => 47.9.90.158\n)\n\n => Array\n(\n => 49.15.85.224\n)\n\n => Array\n(\n => 49.15.93.134\n)\n\n => Array\n(\n => 107.179.244.94\n)\n\n => Array\n(\n => 182.190.203.90\n)\n\n => Array\n(\n => 185.192.69.203\n)\n\n => Array\n(\n => 185.17.27.99\n)\n\n => Array\n(\n => 119.160.116.182\n)\n\n => Array\n(\n => 203.99.177.25\n)\n\n => Array\n(\n => 162.228.207.248\n)\n\n => Array\n(\n => 47.31.245.69\n)\n\n => Array\n(\n => 49.15.210.159\n)\n\n => Array\n(\n => 42.111.2.112\n)\n\n => Array\n(\n => 223.186.116.79\n)\n\n => Array\n(\n => 103.225.176.143\n)\n\n => Array\n(\n => 45.115.190.49\n)\n\n => Array\n(\n => 115.42.71.105\n)\n\n => Array\n(\n => 157.51.11.157\n)\n\n => Array\n(\n => 14.175.56.186\n)\n\n => Array\n(\n => 59.153.16.7\n)\n\n => Array\n(\n => 106.202.84.144\n)\n\n => Array\n(\n => 27.6.242.91\n)\n\n => Array\n(\n => 47.11.112.107\n)\n\n => Array\n(\n => 106.207.54.187\n)\n\n => Array\n(\n => 124.253.196.121\n)\n\n => Array\n(\n => 51.79.161.244\n)\n\n => Array\n(\n => 103.41.24.100\n)\n\n => Array\n(\n => 195.66.79.32\n)\n\n => Array\n(\n => 117.196.127.42\n)\n\n => Array\n(\n => 103.75.247.197\n)\n\n => Array\n(\n => 89.187.162.107\n)\n\n => Array\n(\n => 223.238.154.49\n)\n\n => Array\n(\n => 117.223.99.139\n)\n\n => Array\n(\n => 103.87.59.134\n)\n\n => Array\n(\n => 124.253.212.30\n)\n\n => Array\n(\n => 202.47.62.55\n)\n\n => Array\n(\n => 47.31.219.128\n)\n\n => Array\n(\n => 49.14.121.72\n)\n\n => Array\n(\n => 124.253.212.189\n)\n\n => Array\n(\n => 103.244.179.24\n)\n\n => Array\n(\n => 182.190.213.92\n)\n\n => Array\n(\n => 43.242.178.51\n)\n\n => Array\n(\n => 180.92.138.54\n)\n\n => Array\n(\n => 111.119.187.26\n)\n\n => Array\n(\n => 49.156.111.31\n)\n\n => Array\n(\n => 27.63.108.183\n)\n\n => Array\n(\n => 27.58.184.79\n)\n\n => Array\n(\n => 39.40.225.130\n)\n\n => Array\n(\n => 157.38.5.178\n)\n\n => Array\n(\n => 103.112.55.44\n)\n\n => Array\n(\n => 119.160.100.247\n)\n\n => Array\n(\n => 39.53.101.15\n)\n\n => Array\n(\n => 47.31.207.117\n)\n\n => Array\n(\n => 112.196.158.155\n)\n\n => Array\n(\n => 94.204.247.123\n)\n\n => Array\n(\n => 103.118.76.38\n)\n\n => Array\n(\n => 124.29.212.208\n)\n\n => Array\n(\n => 124.253.196.250\n)\n\n => Array\n(\n => 118.70.182.242\n)\n\n => Array\n(\n => 157.38.78.67\n)\n\n => Array\n(\n => 103.99.218.33\n)\n\n => Array\n(\n => 137.59.220.191\n)\n\n => Array\n(\n => 47.31.139.182\n)\n\n => Array\n(\n => 182.179.136.36\n)\n\n => Array\n(\n => 106.203.73.130\n)\n\n => Array\n(\n => 193.29.107.188\n)\n\n => Array\n(\n => 81.96.92.111\n)\n\n => Array\n(\n => 110.93.203.185\n)\n\n => Array\n(\n => 103.163.248.128\n)\n\n => Array\n(\n => 43.229.166.135\n)\n\n => Array\n(\n => 43.230.106.175\n)\n\n => Array\n(\n => 202.47.62.54\n)\n\n => Array\n(\n => 39.37.181.46\n)\n\n => Array\n(\n => 49.15.204.204\n)\n\n => Array\n(\n => 122.163.237.110\n)\n\n => Array\n(\n => 45.249.8.92\n)\n\n => Array\n(\n => 27.34.50.159\n)\n\n => Array\n(\n => 39.42.171.27\n)\n\n => Array\n(\n => 124.253.101.195\n)\n\n => Array\n(\n => 188.166.145.20\n)\n\n => Array\n(\n => 103.83.145.220\n)\n\n => Array\n(\n => 39.40.96.137\n)\n\n => Array\n(\n => 157.37.185.196\n)\n\n => Array\n(\n => 103.115.124.32\n)\n\n => Array\n(\n => 72.255.48.85\n)\n\n => Array\n(\n => 124.253.74.46\n)\n\n => Array\n(\n => 60.243.225.5\n)\n\n => Array\n(\n => 103.58.152.194\n)\n\n => Array\n(\n => 14.248.71.63\n)\n\n => Array\n(\n => 152.57.214.137\n)\n\n => Array\n(\n => 103.166.58.14\n)\n\n => Array\n(\n => 14.248.71.103\n)\n\n => Array\n(\n => 49.156.103.124\n)\n\n => Array\n(\n => 103.99.218.56\n)\n\n => Array\n(\n => 27.97.177.246\n)\n\n => Array\n(\n => 152.57.94.84\n)\n\n => Array\n(\n => 111.119.187.60\n)\n\n => Array\n(\n => 119.160.99.11\n)\n\n => Array\n(\n => 117.203.11.220\n)\n\n => Array\n(\n => 114.31.131.67\n)\n\n => Array\n(\n => 47.31.253.95\n)\n\n => Array\n(\n => 83.139.184.178\n)\n\n => Array\n(\n => 125.57.9.72\n)\n\n => Array\n(\n => 185.233.16.53\n)\n\n => Array\n(\n => 49.36.180.197\n)\n\n => Array\n(\n => 95.142.119.27\n)\n\n => Array\n(\n => 223.225.70.77\n)\n\n => Array\n(\n => 47.15.222.200\n)\n\n => Array\n(\n => 47.15.218.231\n)\n\n => Array\n(\n => 111.119.187.34\n)\n\n => Array\n(\n => 157.37.198.81\n)\n\n => Array\n(\n => 43.242.177.92\n)\n\n => Array\n(\n => 122.161.68.214\n)\n\n => Array\n(\n => 47.31.145.92\n)\n\n => Array\n(\n => 27.7.196.201\n)\n\n => Array\n(\n => 39.42.172.183\n)\n\n => Array\n(\n => 49.15.129.162\n)\n\n => Array\n(\n => 49.15.206.110\n)\n\n => Array\n(\n => 39.57.141.45\n)\n\n => Array\n(\n => 171.229.175.90\n)\n\n => Array\n(\n => 119.160.68.200\n)\n\n => Array\n(\n => 193.176.84.214\n)\n\n => Array\n(\n => 43.242.177.77\n)\n\n => Array\n(\n => 137.59.220.95\n)\n\n => Array\n(\n => 122.177.118.209\n)\n\n => Array\n(\n => 103.92.214.27\n)\n\n => Array\n(\n => 178.62.10.228\n)\n\n => Array\n(\n => 103.81.214.91\n)\n\n => Array\n(\n => 156.146.33.68\n)\n\n => Array\n(\n => 42.118.116.60\n)\n\n => Array\n(\n => 183.87.122.190\n)\n\n => Array\n(\n => 157.37.159.162\n)\n\n => Array\n(\n => 59.153.16.9\n)\n\n => Array\n(\n => 223.185.43.241\n)\n\n => Array\n(\n => 103.81.214.153\n)\n\n => Array\n(\n => 47.31.143.169\n)\n\n => Array\n(\n => 112.196.158.250\n)\n\n => Array\n(\n => 156.146.36.110\n)\n\n => Array\n(\n => 27.255.34.80\n)\n\n => Array\n(\n => 49.205.77.19\n)\n\n => Array\n(\n => 95.142.120.20\n)\n\n => Array\n(\n => 171.49.195.53\n)\n\n => Array\n(\n => 39.37.152.132\n)\n\n => Array\n(\n => 103.121.204.237\n)\n\n => Array\n(\n => 43.242.176.153\n)\n\n => Array\n(\n => 43.242.176.120\n)\n\n => Array\n(\n => 122.161.66.120\n)\n\n => Array\n(\n => 182.70.140.223\n)\n\n => Array\n(\n => 103.201.135.226\n)\n\n => Array\n(\n => 202.47.44.135\n)\n\n => Array\n(\n => 182.179.172.27\n)\n\n => Array\n(\n => 185.22.173.86\n)\n\n => Array\n(\n => 67.205.148.219\n)\n\n => Array\n(\n => 27.58.183.140\n)\n\n => Array\n(\n => 39.42.118.163\n)\n\n => Array\n(\n => 117.5.204.59\n)\n\n => Array\n(\n => 223.182.193.163\n)\n\n => Array\n(\n => 157.37.184.33\n)\n\n => Array\n(\n => 110.37.218.92\n)\n\n => Array\n(\n => 106.215.8.67\n)\n\n => Array\n(\n => 39.42.94.179\n)\n\n => Array\n(\n => 106.51.25.124\n)\n\n => Array\n(\n => 157.42.25.212\n)\n\n => Array\n(\n => 43.247.40.170\n)\n\n => Array\n(\n => 101.50.108.111\n)\n\n => Array\n(\n => 117.102.48.152\n)\n\n => Array\n(\n => 95.142.120.48\n)\n\n => Array\n(\n => 183.81.121.160\n)\n\n => Array\n(\n => 42.111.21.195\n)\n\n => Array\n(\n => 50.7.142.180\n)\n\n => Array\n(\n => 223.130.28.33\n)\n\n => Array\n(\n => 107.161.86.141\n)\n\n => Array\n(\n => 117.203.249.159\n)\n\n => Array\n(\n => 110.225.192.64\n)\n\n => Array\n(\n => 157.37.152.168\n)\n\n => Array\n(\n => 110.39.2.202\n)\n\n => Array\n(\n => 23.106.56.52\n)\n\n => Array\n(\n => 59.150.87.85\n)\n\n => Array\n(\n => 122.162.175.128\n)\n\n => Array\n(\n => 39.40.63.182\n)\n\n => Array\n(\n => 182.190.108.76\n)\n\n => Array\n(\n => 49.36.44.216\n)\n\n => Array\n(\n => 73.105.5.185\n)\n\n => Array\n(\n => 157.33.67.204\n)\n\n => Array\n(\n => 157.37.164.171\n)\n\n => Array\n(\n => 192.119.160.21\n)\n\n => Array\n(\n => 156.146.59.29\n)\n\n => Array\n(\n => 182.190.97.213\n)\n\n => Array\n(\n => 39.53.196.168\n)\n\n => Array\n(\n => 112.196.132.93\n)\n\n => Array\n(\n => 182.189.7.18\n)\n\n => Array\n(\n => 101.53.232.117\n)\n\n => Array\n(\n => 43.242.178.105\n)\n\n => Array\n(\n => 49.145.233.44\n)\n\n => Array\n(\n => 5.107.214.18\n)\n\n => Array\n(\n => 139.5.242.124\n)\n\n => Array\n(\n => 47.29.244.80\n)\n\n => Array\n(\n => 43.242.178.180\n)\n\n => Array\n(\n => 194.110.84.171\n)\n\n => Array\n(\n => 103.68.217.99\n)\n\n => Array\n(\n => 182.182.27.59\n)\n\n => Array\n(\n => 119.152.139.146\n)\n\n => Array\n(\n => 39.37.131.1\n)\n\n => Array\n(\n => 106.210.99.47\n)\n\n => Array\n(\n => 103.225.176.68\n)\n\n => Array\n(\n => 42.111.23.67\n)\n\n => Array\n(\n => 223.225.37.57\n)\n\n => Array\n(\n => 114.79.1.247\n)\n\n => Array\n(\n => 157.42.28.39\n)\n\n => Array\n(\n => 47.15.13.68\n)\n\n => Array\n(\n => 223.230.151.59\n)\n\n => Array\n(\n => 115.186.7.112\n)\n\n => Array\n(\n => 111.92.78.33\n)\n\n => Array\n(\n => 119.160.117.249\n)\n\n => Array\n(\n => 103.150.209.45\n)\n\n => Array\n(\n => 182.189.22.170\n)\n\n => Array\n(\n => 49.144.108.82\n)\n\n => Array\n(\n => 39.49.75.65\n)\n\n => Array\n(\n => 39.52.205.223\n)\n\n => Array\n(\n => 49.48.247.53\n)\n\n => Array\n(\n => 5.149.250.222\n)\n\n => Array\n(\n => 47.15.187.153\n)\n\n => Array\n(\n => 103.70.86.101\n)\n\n => Array\n(\n => 112.196.158.138\n)\n\n => Array\n(\n => 156.241.242.139\n)\n\n => Array\n(\n => 157.33.205.213\n)\n\n => Array\n(\n => 39.53.206.247\n)\n\n => Array\n(\n => 157.45.83.132\n)\n\n => Array\n(\n => 49.36.220.138\n)\n\n => Array\n(\n => 202.47.47.118\n)\n\n => Array\n(\n => 182.185.233.224\n)\n\n => Array\n(\n => 182.189.30.99\n)\n\n => Array\n(\n => 223.233.68.178\n)\n\n => Array\n(\n => 161.35.139.87\n)\n\n => Array\n(\n => 121.46.65.124\n)\n\n => Array\n(\n => 5.195.154.87\n)\n\n => Array\n(\n => 103.46.236.71\n)\n\n => Array\n(\n => 195.114.147.119\n)\n\n => Array\n(\n => 195.85.219.35\n)\n\n => Array\n(\n => 111.119.183.34\n)\n\n => Array\n(\n => 39.34.158.41\n)\n\n => Array\n(\n => 180.178.148.13\n)\n\n => Array\n(\n => 122.161.66.166\n)\n\n => Array\n(\n => 185.233.18.1\n)\n\n => Array\n(\n => 146.196.34.119\n)\n\n => Array\n(\n => 27.6.253.159\n)\n\n => Array\n(\n => 198.8.92.156\n)\n\n => Array\n(\n => 106.206.179.160\n)\n\n => Array\n(\n => 202.164.133.53\n)\n\n => Array\n(\n => 112.196.141.214\n)\n\n => Array\n(\n => 95.135.15.148\n)\n\n => Array\n(\n => 111.92.119.165\n)\n\n => Array\n(\n => 84.17.34.18\n)\n\n => Array\n(\n => 49.36.232.117\n)\n\n => Array\n(\n => 122.180.235.92\n)\n\n => Array\n(\n => 89.187.163.177\n)\n\n => Array\n(\n => 103.217.238.38\n)\n\n => Array\n(\n => 103.163.248.115\n)\n\n => Array\n(\n => 156.146.59.10\n)\n\n => Array\n(\n => 223.233.68.183\n)\n\n => Array\n(\n => 103.12.198.92\n)\n\n => Array\n(\n => 42.111.9.221\n)\n\n => Array\n(\n => 111.92.77.242\n)\n\n => Array\n(\n => 192.142.128.26\n)\n\n => Array\n(\n => 182.69.195.139\n)\n\n => Array\n(\n => 103.209.83.110\n)\n\n => Array\n(\n => 207.244.71.80\n)\n\n => Array\n(\n => 41.140.106.29\n)\n\n => Array\n(\n => 45.118.167.65\n)\n\n => Array\n(\n => 45.118.167.70\n)\n\n => Array\n(\n => 157.37.159.180\n)\n\n => Array\n(\n => 103.217.178.194\n)\n\n => Array\n(\n => 27.255.165.94\n)\n\n => Array\n(\n => 45.133.7.42\n)\n\n => Array\n(\n => 43.230.65.168\n)\n\n => Array\n(\n => 39.53.196.221\n)\n\n => Array\n(\n => 42.111.17.83\n)\n\n => Array\n(\n => 110.39.12.34\n)\n\n => Array\n(\n => 45.118.158.169\n)\n\n => Array\n(\n => 202.142.110.165\n)\n\n => Array\n(\n => 106.201.13.212\n)\n\n => Array\n(\n => 103.211.14.94\n)\n\n => Array\n(\n => 160.202.37.105\n)\n\n => Array\n(\n => 103.99.199.34\n)\n\n => Array\n(\n => 183.83.45.104\n)\n\n => Array\n(\n => 49.36.233.107\n)\n\n => Array\n(\n => 182.68.21.51\n)\n\n => Array\n(\n => 110.227.93.182\n)\n\n => Array\n(\n => 180.178.144.251\n)\n\n => Array\n(\n => 129.0.102.0\n)\n\n => Array\n(\n => 124.253.105.176\n)\n\n => Array\n(\n => 105.156.139.225\n)\n\n => Array\n(\n => 208.117.87.154\n)\n\n => Array\n(\n => 138.68.185.17\n)\n\n => Array\n(\n => 43.247.41.207\n)\n\n => Array\n(\n => 49.156.106.105\n)\n\n => Array\n(\n => 223.238.197.124\n)\n\n => Array\n(\n => 202.47.39.96\n)\n\n => Array\n(\n => 223.226.131.80\n)\n\n => Array\n(\n => 122.161.48.139\n)\n\n => Array\n(\n => 106.201.144.12\n)\n\n => Array\n(\n => 122.178.223.244\n)\n\n => Array\n(\n => 195.181.164.65\n)\n\n => Array\n(\n => 106.195.12.187\n)\n\n => Array\n(\n => 124.253.48.48\n)\n\n => Array\n(\n => 103.140.30.214\n)\n\n => Array\n(\n => 180.178.147.132\n)\n\n => Array\n(\n => 138.197.139.130\n)\n\n => Array\n(\n => 5.254.2.138\n)\n\n => Array\n(\n => 183.81.93.25\n)\n\n => Array\n(\n => 182.70.39.254\n)\n\n => Array\n(\n => 106.223.87.131\n)\n\n => Array\n(\n => 106.203.91.114\n)\n\n => Array\n(\n => 196.70.137.128\n)\n\n => Array\n(\n => 150.242.62.167\n)\n\n => Array\n(\n => 184.170.243.198\n)\n\n => Array\n(\n => 59.89.30.66\n)\n\n => Array\n(\n => 49.156.112.201\n)\n\n => Array\n(\n => 124.29.212.168\n)\n\n => Array\n(\n => 103.204.170.238\n)\n\n => Array\n(\n => 124.253.116.81\n)\n\n => Array\n(\n => 41.248.102.107\n)\n\n => Array\n(\n => 119.160.100.51\n)\n\n => Array\n(\n => 5.254.40.91\n)\n\n => Array\n(\n => 103.149.154.25\n)\n\n => Array\n(\n => 103.70.41.28\n)\n\n => Array\n(\n => 103.151.234.42\n)\n\n => Array\n(\n => 39.37.142.107\n)\n\n => Array\n(\n => 27.255.186.115\n)\n\n => Array\n(\n => 49.15.193.151\n)\n\n => Array\n(\n => 103.201.146.115\n)\n\n => Array\n(\n => 223.228.177.70\n)\n\n => Array\n(\n => 182.179.141.37\n)\n\n => Array\n(\n => 110.172.131.126\n)\n\n => Array\n(\n => 45.116.232.0\n)\n\n => Array\n(\n => 193.37.32.206\n)\n\n => Array\n(\n => 119.152.62.246\n)\n\n => Array\n(\n => 180.178.148.228\n)\n\n => Array\n(\n => 195.114.145.120\n)\n\n => Array\n(\n => 122.160.49.194\n)\n\n => Array\n(\n => 103.240.237.17\n)\n\n => Array\n(\n => 103.75.245.238\n)\n\n => Array\n(\n => 124.253.215.148\n)\n\n => Array\n(\n => 45.118.165.146\n)\n\n => Array\n(\n => 103.75.244.111\n)\n\n => Array\n(\n => 223.185.7.42\n)\n\n => Array\n(\n => 139.5.240.165\n)\n\n => Array\n(\n => 45.251.117.204\n)\n\n => Array\n(\n => 132.154.71.227\n)\n\n => Array\n(\n => 178.92.100.97\n)\n\n => Array\n(\n => 49.48.248.42\n)\n\n => Array\n(\n => 182.190.109.252\n)\n\n => Array\n(\n => 43.231.57.209\n)\n\n => Array\n(\n => 39.37.185.133\n)\n\n => Array\n(\n => 123.17.79.174\n)\n\n => Array\n(\n => 180.178.146.215\n)\n\n => Array\n(\n => 41.248.83.40\n)\n\n => Array\n(\n => 103.255.4.79\n)\n\n => Array\n(\n => 103.39.119.233\n)\n\n => Array\n(\n => 85.203.44.24\n)\n\n => Array\n(\n => 93.74.18.246\n)\n\n => Array\n(\n => 95.142.120.51\n)\n\n => Array\n(\n => 202.47.42.57\n)\n\n => Array\n(\n => 41.202.219.253\n)\n\n => Array\n(\n => 154.28.188.182\n)\n\n => Array\n(\n => 14.163.178.106\n)\n\n => Array\n(\n => 118.185.57.226\n)\n\n => Array\n(\n => 49.15.141.102\n)\n\n => Array\n(\n => 182.189.86.47\n)\n\n => Array\n(\n => 111.88.68.79\n)\n\n => Array\n(\n => 156.146.59.8\n)\n\n => Array\n(\n => 119.152.62.82\n)\n\n => Array\n(\n => 49.207.128.103\n)\n\n => Array\n(\n => 203.212.30.234\n)\n\n => Array\n(\n => 41.202.219.254\n)\n\n => Array\n(\n => 103.46.203.10\n)\n\n => Array\n(\n => 112.79.141.15\n)\n\n => Array\n(\n => 103.68.218.75\n)\n\n => Array\n(\n => 49.35.130.14\n)\n\n => Array\n(\n => 172.247.129.90\n)\n\n => Array\n(\n => 116.90.74.214\n)\n\n => Array\n(\n => 180.178.142.242\n)\n\n => Array\n(\n => 111.119.183.59\n)\n\n => Array\n(\n => 117.5.103.189\n)\n\n => Array\n(\n => 203.110.93.146\n)\n\n => Array\n(\n => 188.163.97.86\n)\n\n => Array\n(\n => 124.253.90.47\n)\n\n => Array\n(\n => 139.167.249.160\n)\n\n => Array\n(\n => 103.226.206.55\n)\n\n => Array\n(\n => 154.28.188.191\n)\n\n => Array\n(\n => 182.190.197.205\n)\n\n => Array\n(\n => 111.119.183.33\n)\n\n => Array\n(\n => 14.253.254.64\n)\n\n => Array\n(\n => 117.237.197.246\n)\n\n => Array\n(\n => 172.105.53.82\n)\n\n => Array\n(\n => 124.253.207.164\n)\n\n => Array\n(\n => 103.255.4.33\n)\n\n => Array\n(\n => 27.63.131.206\n)\n\n => Array\n(\n => 103.118.170.99\n)\n\n => Array\n(\n => 111.119.183.55\n)\n\n => Array\n(\n => 14.182.101.109\n)\n\n => Array\n(\n => 175.107.223.199\n)\n\n => Array\n(\n => 39.57.168.94\n)\n\n => Array\n(\n => 122.182.213.139\n)\n\n => Array\n(\n => 112.79.214.237\n)\n\n => Array\n(\n => 27.6.252.22\n)\n\n => Array\n(\n => 89.163.212.83\n)\n\n => Array\n(\n => 182.189.23.1\n)\n\n => Array\n(\n => 49.15.222.253\n)\n\n => Array\n(\n => 125.63.97.110\n)\n\n => Array\n(\n => 223.233.65.159\n)\n\n => Array\n(\n => 139.99.159.18\n)\n\n => Array\n(\n => 45.118.165.137\n)\n\n => Array\n(\n => 39.52.2.167\n)\n\n => Array\n(\n => 39.57.141.24\n)\n\n => Array\n(\n => 27.5.32.145\n)\n\n => Array\n(\n => 49.36.212.33\n)\n\n => Array\n(\n => 157.33.218.32\n)\n\n => Array\n(\n => 116.71.4.122\n)\n\n => Array\n(\n => 110.93.244.176\n)\n\n => Array\n(\n => 154.73.203.156\n)\n\n => Array\n(\n => 136.158.30.235\n)\n\n => Array\n(\n => 122.161.53.72\n)\n\n => Array\n(\n => 106.203.203.156\n)\n\n => Array\n(\n => 45.133.7.22\n)\n\n => Array\n(\n => 27.255.180.69\n)\n\n => Array\n(\n => 94.46.244.3\n)\n\n => Array\n(\n => 43.242.178.157\n)\n\n => Array\n(\n => 171.79.189.215\n)\n\n => Array\n(\n => 37.117.141.89\n)\n\n => Array\n(\n => 196.92.32.64\n)\n\n => Array\n(\n => 154.73.203.157\n)\n\n => Array\n(\n => 183.83.176.14\n)\n\n => Array\n(\n => 106.215.84.145\n)\n\n => Array\n(\n => 95.142.120.12\n)\n\n => Array\n(\n => 190.232.110.94\n)\n\n => Array\n(\n => 179.6.194.47\n)\n\n => Array\n(\n => 103.62.155.172\n)\n\n => Array\n(\n => 39.34.156.177\n)\n\n => Array\n(\n => 122.161.49.120\n)\n\n => Array\n(\n => 103.58.155.253\n)\n\n => Array\n(\n => 175.107.226.20\n)\n\n => Array\n(\n => 206.81.28.165\n)\n\n => Array\n(\n => 49.36.216.36\n)\n\n => Array\n(\n => 104.223.95.178\n)\n\n => Array\n(\n => 122.177.69.35\n)\n\n => Array\n(\n => 39.57.163.107\n)\n\n => Array\n(\n => 122.161.53.35\n)\n\n => Array\n(\n => 182.190.102.13\n)\n\n => Array\n(\n => 122.161.68.95\n)\n\n => Array\n(\n => 154.73.203.147\n)\n\n => Array\n(\n => 122.173.125.2\n)\n\n => Array\n(\n => 117.96.140.189\n)\n\n => Array\n(\n => 106.200.244.10\n)\n\n => Array\n(\n => 110.36.202.5\n)\n\n => Array\n(\n => 124.253.51.144\n)\n\n => Array\n(\n => 176.100.1.145\n)\n\n => Array\n(\n => 156.146.59.20\n)\n\n => Array\n(\n => 122.176.100.151\n)\n\n => Array\n(\n => 185.217.117.237\n)\n\n => Array\n(\n => 49.37.223.97\n)\n\n => Array\n(\n => 101.50.108.80\n)\n\n => Array\n(\n => 124.253.155.88\n)\n\n => Array\n(\n => 39.40.208.96\n)\n\n => Array\n(\n => 122.167.151.154\n)\n\n => Array\n(\n => 172.98.89.13\n)\n\n => Array\n(\n => 103.91.52.6\n)\n\n => Array\n(\n => 106.203.84.5\n)\n\n => Array\n(\n => 117.216.221.34\n)\n\n => Array\n(\n => 154.73.203.131\n)\n\n => Array\n(\n => 223.182.210.117\n)\n\n => Array\n(\n => 49.36.185.208\n)\n\n => Array\n(\n => 111.119.183.30\n)\n\n => Array\n(\n => 39.42.107.13\n)\n\n => Array\n(\n => 39.40.15.174\n)\n\n => Array\n(\n => 1.38.244.65\n)\n\n => Array\n(\n => 49.156.75.252\n)\n\n => Array\n(\n => 122.161.51.99\n)\n\n => Array\n(\n => 27.73.78.57\n)\n\n => Array\n(\n => 49.48.228.70\n)\n\n => Array\n(\n => 111.119.183.18\n)\n\n => Array\n(\n => 116.204.252.218\n)\n\n => Array\n(\n => 73.173.40.248\n)\n\n => Array\n(\n => 223.130.28.81\n)\n\n => Array\n(\n => 202.83.58.81\n)\n\n => Array\n(\n => 45.116.233.31\n)\n\n => Array\n(\n => 111.119.183.1\n)\n\n => Array\n(\n => 45.133.7.66\n)\n\n => Array\n(\n => 39.48.204.174\n)\n\n => Array\n(\n => 37.19.213.30\n)\n\n => Array\n(\n => 111.119.183.22\n)\n\n => Array\n(\n => 122.177.74.19\n)\n\n => Array\n(\n => 124.253.80.59\n)\n\n => Array\n(\n => 111.119.183.60\n)\n\n => Array\n(\n => 157.39.106.191\n)\n\n => Array\n(\n => 157.47.86.121\n)\n\n => Array\n(\n => 47.31.159.100\n)\n\n => Array\n(\n => 106.214.85.144\n)\n\n => Array\n(\n => 182.189.22.197\n)\n\n => Array\n(\n => 111.119.183.51\n)\n\n => Array\n(\n => 202.47.35.57\n)\n\n => Array\n(\n => 42.108.33.220\n)\n\n => Array\n(\n => 180.178.146.158\n)\n\n => Array\n(\n => 124.253.184.239\n)\n\n => Array\n(\n => 103.165.20.8\n)\n\n => Array\n(\n => 94.178.239.156\n)\n\n => Array\n(\n => 72.255.41.142\n)\n\n => Array\n(\n => 116.90.107.102\n)\n\n => Array\n(\n => 39.36.164.250\n)\n\n => Array\n(\n => 124.253.195.172\n)\n\n => Array\n(\n => 203.142.218.149\n)\n\n => Array\n(\n => 157.43.165.180\n)\n\n => Array\n(\n => 39.40.242.57\n)\n\n => Array\n(\n => 103.92.43.150\n)\n\n => Array\n(\n => 39.42.133.202\n)\n\n => Array\n(\n => 119.160.66.11\n)\n\n => Array\n(\n => 138.68.3.7\n)\n\n => Array\n(\n => 210.56.125.226\n)\n\n => Array\n(\n => 157.50.4.249\n)\n\n => Array\n(\n => 124.253.81.162\n)\n\n => Array\n(\n => 103.240.235.141\n)\n\n => Array\n(\n => 132.154.128.20\n)\n\n => Array\n(\n => 49.156.115.37\n)\n\n => Array\n(\n => 45.133.7.48\n)\n\n => Array\n(\n => 122.161.49.137\n)\n\n => Array\n(\n => 202.47.46.31\n)\n\n => Array\n(\n => 192.140.145.148\n)\n\n => Array\n(\n => 202.14.123.10\n)\n\n => Array\n(\n => 122.161.53.98\n)\n\n => Array\n(\n => 124.253.114.113\n)\n\n => Array\n(\n => 103.227.70.34\n)\n\n => Array\n(\n => 223.228.175.227\n)\n\n => Array\n(\n => 157.39.119.110\n)\n\n => Array\n(\n => 180.188.224.231\n)\n\n => Array\n(\n => 132.154.188.85\n)\n\n => Array\n(\n => 197.210.227.207\n)\n\n => Array\n(\n => 103.217.123.177\n)\n\n => Array\n(\n => 124.253.85.31\n)\n\n => Array\n(\n => 123.201.105.97\n)\n\n => Array\n(\n => 39.57.190.37\n)\n\n => Array\n(\n => 202.63.205.248\n)\n\n => Array\n(\n => 122.161.51.100\n)\n\n => Array\n(\n => 39.37.163.97\n)\n\n => Array\n(\n => 43.231.57.173\n)\n\n => Array\n(\n => 223.225.135.169\n)\n\n => Array\n(\n => 119.160.71.136\n)\n\n => Array\n(\n => 122.165.114.93\n)\n\n => Array\n(\n => 47.11.77.102\n)\n\n => Array\n(\n => 49.149.107.198\n)\n\n => Array\n(\n => 192.111.134.206\n)\n\n => Array\n(\n => 182.64.102.43\n)\n\n => Array\n(\n => 124.253.184.111\n)\n\n => Array\n(\n => 171.237.97.228\n)\n\n => Array\n(\n => 117.237.237.101\n)\n\n => Array\n(\n => 49.36.33.19\n)\n\n => Array\n(\n => 103.31.101.241\n)\n\n => Array\n(\n => 129.0.207.203\n)\n\n => Array\n(\n => 157.39.122.155\n)\n\n => Array\n(\n => 197.210.85.120\n)\n\n => Array\n(\n => 124.253.219.201\n)\n\n => Array\n(\n => 152.57.75.92\n)\n\n => Array\n(\n => 169.149.195.121\n)\n\n => Array\n(\n => 198.16.76.27\n)\n\n => Array\n(\n => 157.43.192.188\n)\n\n => Array\n(\n => 119.155.244.221\n)\n\n => Array\n(\n => 39.51.242.216\n)\n\n => Array\n(\n => 39.57.180.158\n)\n\n => Array\n(\n => 134.202.32.5\n)\n\n => Array\n(\n => 122.176.139.205\n)\n\n => Array\n(\n => 151.243.50.9\n)\n\n => Array\n(\n => 39.52.99.161\n)\n\n => Array\n(\n => 136.144.33.95\n)\n\n => Array\n(\n => 157.37.205.216\n)\n\n => Array\n(\n => 217.138.220.134\n)\n\n => Array\n(\n => 41.140.106.65\n)\n\n => Array\n(\n => 39.37.253.126\n)\n\n => Array\n(\n => 103.243.44.240\n)\n\n => Array\n(\n => 157.46.169.29\n)\n\n => Array\n(\n => 92.119.177.122\n)\n\n => Array\n(\n => 196.240.60.21\n)\n\n => Array\n(\n => 122.161.6.246\n)\n\n => Array\n(\n => 117.202.162.46\n)\n\n => Array\n(\n => 205.164.137.120\n)\n\n => Array\n(\n => 171.237.79.241\n)\n\n => Array\n(\n => 198.16.76.28\n)\n\n => Array\n(\n => 103.100.4.151\n)\n\n => Array\n(\n => 178.239.162.236\n)\n\n => Array\n(\n => 106.197.31.240\n)\n\n => Array\n(\n => 122.168.179.251\n)\n\n => Array\n(\n => 39.37.167.126\n)\n\n => Array\n(\n => 171.48.8.115\n)\n\n => Array\n(\n => 157.44.152.14\n)\n\n => Array\n(\n => 103.77.43.219\n)\n\n => Array\n(\n => 122.161.49.38\n)\n\n => Array\n(\n => 122.161.52.83\n)\n\n => Array\n(\n => 122.173.108.210\n)\n\n => Array\n(\n => 60.254.109.92\n)\n\n => Array\n(\n => 103.57.85.75\n)\n\n => Array\n(\n => 106.0.58.36\n)\n\n => Array\n(\n => 122.161.49.212\n)\n\n => Array\n(\n => 27.255.182.159\n)\n\n => Array\n(\n => 116.75.230.159\n)\n\n => Array\n(\n => 122.173.152.133\n)\n\n => Array\n(\n => 129.0.79.247\n)\n\n => Array\n(\n => 223.228.163.44\n)\n\n => Array\n(\n => 103.168.78.82\n)\n\n => Array\n(\n => 39.59.67.124\n)\n\n => Array\n(\n => 182.69.19.120\n)\n\n => Array\n(\n => 196.202.236.195\n)\n\n => Array\n(\n => 137.59.225.206\n)\n\n => Array\n(\n => 143.110.209.194\n)\n\n => Array\n(\n => 117.201.233.91\n)\n\n => Array\n(\n => 37.120.150.107\n)\n\n => Array\n(\n => 58.65.222.10\n)\n\n => Array\n(\n => 202.47.43.86\n)\n\n => Array\n(\n => 106.206.223.234\n)\n\n => Array\n(\n => 5.195.153.158\n)\n\n => Array\n(\n => 223.227.127.243\n)\n\n => Array\n(\n => 103.165.12.222\n)\n\n => Array\n(\n => 49.36.185.189\n)\n\n => Array\n(\n => 59.96.92.57\n)\n\n => Array\n(\n => 203.194.104.235\n)\n\n => Array\n(\n => 122.177.72.33\n)\n\n => Array\n(\n => 106.213.126.40\n)\n\n => Array\n(\n => 45.127.232.69\n)\n\n => Array\n(\n => 156.146.59.39\n)\n\n => Array\n(\n => 103.21.184.11\n)\n\n => Array\n(\n => 106.212.47.59\n)\n\n => Array\n(\n => 182.179.137.235\n)\n\n => Array\n(\n => 49.36.178.154\n)\n\n => Array\n(\n => 171.48.7.128\n)\n\n => Array\n(\n => 119.160.57.96\n)\n\n => Array\n(\n => 197.210.79.92\n)\n\n => Array\n(\n => 36.255.45.87\n)\n\n => Array\n(\n => 47.31.219.47\n)\n\n => Array\n(\n => 122.161.51.160\n)\n\n => Array\n(\n => 103.217.123.129\n)\n\n => Array\n(\n => 59.153.16.12\n)\n\n => Array\n(\n => 103.92.43.226\n)\n\n => Array\n(\n => 47.31.139.139\n)\n\n => Array\n(\n => 210.2.140.18\n)\n\n => Array\n(\n => 106.210.33.219\n)\n\n => Array\n(\n => 175.107.203.34\n)\n\n => Array\n(\n => 146.196.32.144\n)\n\n => Array\n(\n => 103.12.133.121\n)\n\n => Array\n(\n => 103.59.208.182\n)\n\n => Array\n(\n => 157.37.190.232\n)\n\n => Array\n(\n => 106.195.35.201\n)\n\n => Array\n(\n => 27.122.14.83\n)\n\n => Array\n(\n => 194.193.44.5\n)\n\n => Array\n(\n => 5.62.43.245\n)\n\n => Array\n(\n => 103.53.80.50\n)\n\n => Array\n(\n => 47.29.142.233\n)\n\n => Array\n(\n => 154.6.20.63\n)\n\n => Array\n(\n => 173.245.203.128\n)\n\n => Array\n(\n => 103.77.43.231\n)\n\n => Array\n(\n => 5.107.166.235\n)\n\n => Array\n(\n => 106.212.44.123\n)\n\n => Array\n(\n => 157.41.60.93\n)\n\n => Array\n(\n => 27.58.179.79\n)\n\n => Array\n(\n => 157.37.167.144\n)\n\n => Array\n(\n => 119.160.57.115\n)\n\n => Array\n(\n => 122.161.53.224\n)\n\n => Array\n(\n => 49.36.233.51\n)\n\n => Array\n(\n => 101.0.32.8\n)\n\n => Array\n(\n => 119.160.103.158\n)\n\n => Array\n(\n => 122.177.79.115\n)\n\n => Array\n(\n => 107.181.166.27\n)\n\n => Array\n(\n => 183.6.0.125\n)\n\n => Array\n(\n => 49.36.186.0\n)\n\n => Array\n(\n => 202.181.5.4\n)\n\n => Array\n(\n => 45.118.165.144\n)\n\n => Array\n(\n => 171.96.157.133\n)\n\n => Array\n(\n => 222.252.51.163\n)\n\n => Array\n(\n => 103.81.215.162\n)\n\n => Array\n(\n => 110.225.93.208\n)\n\n => Array\n(\n => 122.161.48.200\n)\n\n => Array\n(\n => 119.63.138.173\n)\n\n => Array\n(\n => 202.83.58.208\n)\n\n => Array\n(\n => 122.161.53.101\n)\n\n => Array\n(\n => 137.97.95.21\n)\n\n => Array\n(\n => 112.204.167.123\n)\n\n => Array\n(\n => 122.180.21.151\n)\n\n => Array\n(\n => 103.120.44.108\n)\n\n)\n```\nArchive for February, 2008: Gramma Ray's Blog\n Layout: Blue and Brown (Default) Author's Creation\n Home > Archive: February, 2008\n\n# Archive for February, 2008\n\n## Score at the store!!\n\nFebruary 28th, 2008 at 04:36 am\n\nI scored today guys. Have you ever heard of Harry & Davids??? They are a big corp who specialize in fruits, nuts, gift baskets, etc. Well, their headquarters is right here...pretty darn close to where I work. So I stopped in their store on my lunch break today and they had baskets on sale super cheap!! What's cool is that they are great quality. I bought a couple of those picnic baskets with the lifting lids, fully lined for \\$9.99...I bought wood bed trays ( for breakfast in bed) white with pretty scalloped edges for \\$4.99 each...easter baskets for \\$2.99-\\$3.99 ...and some great red totes that I can use for gift bags at christmas for \\$2.47...WOW. I love living near this company because they sell their overstocks at ridiculously low prices after the holidays. This stuff all normally sold for \\$30-75 originally. wow.\n\nI thought I would put a great teapot for one, napkin and some teas and cookies together for the bed tray... or a vase with a rose and a magazine and coffees...you get the idea. I am going to keep one of the picnic baskets. Ive wanted one forever, but never felt justified spending so much..but for \\$9.99 I am thrilled!!\n\nThe red totes...oh I love this...i bought 8 and will watch for sales all year-- when the whole family goes for our week at the big rental house the week aafter Christmas, I will give everyone one as a care package to use that week...candles, toiletries, deck of cards, etc...too fun!!\n\nI love good deals...REALLY LOVE GREAT DEALS!!!\n\n## Diagnosis...and a year back in the 401K results\n\nFebruary 23rd, 2008 at 03:36 am\n\nI recently received the diagnosis on my recurring pinched nerve. This has occured twice..and each time causes about 2-3 months of spasms and pain in my right arm. I have a moderate to severe degeneration in my c6-7 vertebrae. CHances of recurrences are high and I am a candidate for back surgery...ugh.\n\nI am choosing to take my chances and see if it recurs...at which point I will see about an MRI to nail down whether it is a simple bone shave --- or more intrusive. (niether sound fun to me)...\n\nThe upside of life is exciting. I have been back in the workforce for 11 months,,,and my 401k is on track to hit 10K by my one year anniversary!! YEAH!\n\nThings have been very busy, but also very good.\n\n## Fingers crossed the hubs back is fixed...\n\nFebruary 20th, 2008 at 02:18 am\n\nThe hub had his back procedure today. Instead of another surgery, they went in with a needle and moved the nerve and injected something to keep the nerve from being pinched again. We will know within a day or two if it is indeed fixed. Please keep your fingers crossed for him.\n\nWe did stop on the way home and lunch at Olive Garden...I love their chicken and gnocchi...yum. It was a definate splurge..both financially and welleness plan wise. But YUM. We spent \\$30 (incl tip)\n\nI re-booked the house at the resort for my birthday weekend. Not because I necessarily believe in spending alot on my birthday- I do not. But because it was so much fun the last time and I figure by June we will be ready to get away again. Last year, we would have just hopped in the truck and pulled the RV somewhere peaceful...but the kids are living in ours now- since a tree fell on thiers last year. So, a new type of vacationing...and one I am enjoying quite a bit!!\n\nIn April, DD1, my mom and I are flying down to So. Cali for a wedding. An expense that has to come out of the vacation fund.\n\nI am also starting to plan the hubs 40th birthday party. I have never threw him a party and so this year we are going all out. My DIL's mom has a great party house..pool table, swimming pool, volleyball court, horseshoes, etc...and so I am renting it from her and we are going to have a luau...complete with a pig in the ground...grass skirts, fruity drinks, tiki torches...\n\nAnd then, we are renting a LARGE home for New Years as a Christmas gift to the entire family. I felt we should have something besides the one year anniversary of papa's death to anticipate...and I think it is working. Everyone is focusing on taking a big family vacation this year.\n\nQuite a few exenses this year to be sure...but all budgeted and being saved for. (Sometimes I feel like I am out of my league with all of you who have budgeting your vacations down to a science.) But for now, this is working for us. The upside is that we will be cooking all meals at home during the house stays...and I get to stay at a friends for free the week of the wedding.\n\n## Finance Blog--or LIFE blog....\n\nFebruary 18th, 2008 at 07:09 pm\n\nI look back at many of my blogs and realize that my blog is not so much a financial blog as it is a life blog. I always try to incorporate something financial into my posts...but usually finance is in the back seat on what I'm blogging.\n\nHow wonderful (for me) it is to be able and read about those two years off to be with my family...and how we've welcomed new babies...financially caught up and then took a few steps backward...\n\nAnd then those last few days saying goodbye to my dad...wow. Those of you who have read my blog know me better than most that I call \"friends' in \"real\" life.\n\nAnd I have a record of some very important life lessons...right here at saving advice!! And even more importantly is all of your uplifting, supportive and inspiring resonses.\n\nWhich is exactly why I credit all of you for helping me find my way back to a careful spending plan now that things have settled down. I could have gone off the deep end and started spending...but you all and your kind words, encouraging support and gentle nudges in the right direction have helped me get right back on track. I think it is wonderful that we have a community of friends who support each other wheather it is with financial advice, weight loss support, medical worries, child rearing, thrifty ideas, recipes, ...basically LIFE.\n\nSo much more than just a financial blog...how lucky for all of us that stumbled into this community...HUGS to you all!\n\n## Spendy week...Cheap Weekend\n\nFebruary 18th, 2008 at 12:56 am\n\nMy good friend from Cali was here for a few days last week. She and I have been friends since we were 12. While she was here we went out to dinner a couple of times and then went wine tasting...we had a blast. The hub paid for dinner one night out of the wood business account...so only one of the meals out came out of the household account. I did treat her to the afternoon of wine tasting and bought her a couple of bottles of wine...but that all came out of my saved allowance....so, this weekend we ate all meals at home and did not spend much money...actually, all we spent was \\$4.50 when we went to the movies today. We had gift cards...but by the time we paid for the tickets and got popcorn and soda...we owed \\$4.50. I wish I could say it was worth it...but I didnt really enjoy the movie (I Am Legend) and \\$5.75 for a \"large\" popcorn makes my head spin...but it was the family outing that DH and DD3 wanted--so I wont complain too much.\n\nThe household account is still on track..which is a relief/\n.\nThe past two days have been absolutely GORGEOUS...sunny, 60's...quite nice after months of rain and snow. I am off for President's Day tommorrow...and am sure hoping for more of that sunshine!!!\n\nTonight...we are bbg'g steaks and I have potatoes in the oven...so a yummy meal that will feed 5 for under \\$10!!\n\nAll in all, a great several few days....blessed indeed!\n\n## How can it be?\n\nFebruary 17th, 2008 at 05:53 am\n\nOdd.\n\nFebruary. The shortest month of the year. And we will receive as many, and in some cases MORE paychecks this month than any other.\n\nNot sure how that happend...but most months I get two (bi-weekly) and the hub gets four(weekly).\n\nBut in this, the shortest month of the year...I will get THREE and the hub will get FIVE.(corrected)\n\nand that, friends....adds up to almost \\$2k extra this month!! woo-hooo!!!\n\nI will be using the extra to catch up, put \\$\\$ down on vacation, and put some into savings.\n\nThis is also the month we get our year end bonus...which should hopefully mean another \\$1200 or so to stick back!!\n\nIt feels good to be catching up a little. I just think it is odd that February is such a profitable month....\n\n## Wellness check\n\nFebruary 10th, 2008 at 05:17 am\n\nI mentioned in my previous blog that we are having a challenge at work to get healthy since we are now a self-insured employer.\n\nSo...I am now at square one..I weigh too much, I have high cholesterol and I am borderline diabetic. At least I know where I am starting...everything measured can be reduced with a good diet and exercise. so....this week has been the star of a low carb, sensible protein diet.\n\nI have an eight month timeline to get healthier..and that is what I fully intend to do...so here is what I have incorporated into my diet..\n\nbye-bye coffee stand mochas...rather, brewed coffee with non-fat vanilla creamer...also- add 1 string cheese and a low fat or non-fat yogurt for breakfast (i never ate breakfast before)\n\nLunch...black beans on two corn tortillas with lettuce and salsa...\n\ndinner 3-4 oz of low fat meat with veggies and a carb w/ fat (bread and butter)..or salad dressing.\n\nsnacks= fruit or juice.\n\nand 1-2 glasses of red wine each night...\n\nQuite a change from the menu plan before..\n\nLots of water...and no regular soda (an occaisional diet soda is ok)\n\n8 months to go...\n\nI hope to lose 50 lbs in 8 months....starting weight...well, I know what that is..I will post progress as I go.\n\nGo Ray.\n\nIf I am successful, I will receive at leas \\$50...which will go into a challenge account...I'll be thinking about a challenge acccount between now and then...\n\n## Frozen body parts?\n\nFebruary 6th, 2008 at 04:55 am\n\nThis past weekend, the hub, DD3 and I rented a house in a resort a few hours from us. The house was cheaper than a hotel room...and it included a hot tub, movies, puzzles, games, free iternet and long distance, and all the snow we could ever enjoy.\n\nHave you ever hot tubbed in a snowstorm??? wow. The hard part was getting out and running into the house before body parts froze and fell off.\n\nWe spent 4days/ 3nights...watched TV, enjoyed the scenery, tubbed, put a puzzle together, bbq'd and visited.\n\nAny idea how nice a little mini-vacation like that is after you've been through---well, some very tough times? wow. We really savored it.\n\ncost for 4 days=\\$400 + food, which we cooked at 'home' (no meals out).\n\nThe hub learned today that he will not have surgery, but rather a shot of something to help with the blown disk. (please let it work!)\n\nMy bills have been coming in for the physical therapy after my last pinched nerve...and the sweetest words known to these eyes....\"Patient has no financial obligation for this bill, paid in full\" (so dual insurance does have it's value!!)\n\nTomorrow. My job became self insured this year...so tomorrow, we have an opportunity to join (for free) an 8 month wellness program to help us get healthy. I will have to be weighed (gads) pricked,measured (wt..?) checked and fill out a personal evaluation...but when it is all said and done, I will receive a roadmap to help me get from here to healthy in 8 short months. If I succeed, there is at least a \\$50 bill waiting for me to spend however I want...not to mention a cache of wonderful prizes to be won.\n\nSo, tomorrow is day one. Pray for me...I am a non-recovering junk food junkie lately.\n\nLife seems to be slowly turning around and promising to be rich and rewarding once again.. It is amazing how your perspectives change on what is important and what is trivial.\n\nJust let me make it through the weigh in without fainting or breaking the scale...lol. Then, it should be downhill to health and....well, we shall see!!" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9668079,"math_prob":0.9988556,"size":23108,"snap":"2021-43-2021-49","text_gpt3_token_len":5573,"char_repetition_ratio":0.09097992,"word_repetition_ratio":0.9579173,"special_character_ratio":0.24922104,"punctuation_ratio":0.18603362,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9993104,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-10-19T09:05:36Z\",\"WARC-Record-ID\":\"<urn:uuid:8ceade50-deca-4c8e-af20-579d549a669d>\",\"Content-Length\":\"509957\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:bcc8979b-8f18-445f-bbd0-d2bd51a8bfd3>\",\"WARC-Concurrent-To\":\"<urn:uuid:3770fc2d-386e-45c4-b751-e98400c06b20>\",\"WARC-IP-Address\":\"173.231.200.26\",\"WARC-Target-URI\":\"https://thriftyray.savingadvice.com/2008/02/\",\"WARC-Payload-Digest\":\"sha1:GGGR2EB6BAO37O3JZPFQESOFLOQFVEGE\",\"WARC-Block-Digest\":\"sha1:DVDLYWSHZUVXQ6ILSC6M5ERQBQVITDBX\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-43/CC-MAIN-2021-43_segments_1634323585246.50_warc_CC-MAIN-20211019074128-20211019104128-00394.warc.gz\"}"}
https://www.tutorialspoint.com/how-to-sort-a-list-using-comparator-with-method-reference-in-java-8	[ "# How to sort a list using Comparator with method reference in Java 8?n\n\nJava 8 introduced changes in the Comparator interface that allows us to compare two objects. These changes help us to create comparators more easily. The first important method added is the comparing() method. This method receives as a parameter Function that determines the value to be compared and creates Comparator. Another important method is the thenComparing() method. This method can be used to compose Comparator.\n\nIn the below example, we can sort a list by using the first name with comparing() method and then the last name with the thenComparing() method of Comparator interface.\n\n## Example\n\nimport java.util.*;\n\npublic class MethodReferenceSortTest {\npublic static void main(String[] args) {\nList<Employee> emp = new ArrayList<Employee>();\n\n// using method reference\nemp.stream().sorted(Comparator.comparing(Employee::getFirstName)\n.thenComparing(Employee::getLastName))\n.forEach(System.out::println);\n}\n}\n\n// Employee class\nclass Employee {\nint age;\nString firstName;\nString lastName;\npublic Employee(int age, String firstName, String lastName) {\nsuper();\nthis.age = age;\nthis.firstName = firstName;\nthis.lastName = lastName;\n}\npublic int getAge() {\nreturn age;\n}\npublic void setAge(int age) {\nthis.age = age;\n}\npublic String getFirstName() {\nreturn firstName;\n}\npublic void setFirstName(String firstName) {\nthis.firstName = firstName;\n}\npublic String getLastName() {\nreturn lastName;\n}\npublic void setLastName(String lastName) {\nthis.lastName = lastName;\n}\n@Override\npublic String toString() {\nreturn \"Employee [age=\" + age + \", firstName=\" + firstName + \", lastName=\" + lastName + \"]\";\n}\n}\n\n## Output\n\nEmployee [age=35, firstName=Chaitanya, lastName=Krishna]\nEmployee [age=28, firstName=Jai, lastName=Dev]\nEmployee [age=25, firstName=Raja, lastName=Ramesh]\nEmployee [age=23, firstName=Ravi, lastName=Chandra]\nEmployee [age=30, firstName=Sai, lastName=Adithya]" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.67109376,"math_prob":0.4015696,"size":3758,"snap":"2023-14-2023-23","text_gpt3_token_len":855,"char_repetition_ratio":0.18327118,"word_repetition_ratio":0.09601449,"special_character_ratio":0.2373603,"punctuation_ratio":0.1503268,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9714613,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-03-25T18:00:47Z\",\"WARC-Record-ID\":\"<urn:uuid:b723a04e-6bf8-45fb-94d2-75b964529908>\",\"Content-Length\":\"42142\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0384d97f-6b07-4c0c-895e-0317490be5a4>\",\"WARC-Concurrent-To\":\"<urn:uuid:1d57fcf5-f99d-4e70-b80a-13d22c492fdb>\",\"WARC-IP-Address\":\"192.229.210.176\",\"WARC-Target-URI\":\"https://www.tutorialspoint.com/how-to-sort-a-list-using-comparator-with-method-reference-in-java-8\",\"WARC-Payload-Digest\":\"sha1:3HV7CQVKMOU4YBYMFQRKIESXOZCUIRBG\",\"WARC-Block-Digest\":\"sha1:EMGJZVEB4DXMXUWP6SY2CLBRWDGFDBBO\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-14/CC-MAIN-2023-14_segments_1679296945368.6_warc_CC-MAIN-20230325161021-20230325191021-00271.warc.gz\"}"}
http://zjzhenxing.cn/qspevdu_d001020008	[ "• 湖南\n• 长沙市\n• 常德市\n• 郴州市\n• 衡阳市\n• 怀化市\n• 娄底市\n• 邵阳市\n• 湘潭市\n• 湘西土家族苗族自治州\n• 益阳市\n• 永州市\n• 岳阳市\n• 张家界市\n• 株洲市\n• 山西\n• 长治市\n• 大同市\n• 晋城市\n• 晋中市\n• 临汾市\n• 吕梁市\n• 朔州市\n• 太原市\n• 忻州市\n• 阳泉市\n• 运城市\n• 安徽\n• 安庆市\n• 蚌埠市\n• 亳州市\n• 巢湖市\n• 池州市\n• 滁州市\n• 阜阳市\n• 合肥市\n• 淮北市\n• 淮南市\n• 黄山市\n• 六安市\n• 马鞍山市\n• 宿州市\n• 铜陵市\n• 芜湖市\n• 宣城市\n• 广西\n• 百色市\n• 北海市\n• 崇左市\n• 防城港市\n• 贵港市\n• 桂林市\n• 河池市\n• 贺州市\n• 来宾市\n• 柳州市\n• 南宁市\n• 钦州市\n• 梧州市\n• 玉林市\n• 河南\n• 安阳市\n• 鹤壁市\n• 焦作市\n• 开封市\n• 洛阳市\n• 漯河市\n• 南阳市\n• 平顶山市\n• 濮阳市\n• 三门峡市\n• 商丘市\n• 新乡市\n• 信阳市\n• 许昌市\n• 郑州市\n• 周口市\n• 驻马店市\n• 吉林\n• 白城市\n• 白山市\n• 长春市\n• 吉林市\n• 辽源市\n• 四平市\n• 松原市\n• 通化市\n• 延边朝鲜族自治州\n• 广东\n• 潮州市\n• 东莞市\n• 佛山市\n• 广州市\n• 河源市\n• 惠州市\n• 江门市\n• 揭阳市\n• 茂名市\n• 梅州市\n• 清远市\n• 汕头市\n• 汕尾市\n• 韶关市\n• 深圳市\n• 阳江市\n• 云浮市\n• 湛江市\n• 肇庆市\n• 中山市\n• 珠海市\n• 辽宁\n• 鞍山市\n• 本溪市\n• 朝阳市\n• 大连市\n• 丹东市\n• 抚顺市\n• 阜新市\n• 葫芦岛市\n• 锦州市\n• 辽阳市\n• 盘锦市\n• 沈阳市\n• 铁岭市\n• 营口市\n• 湖北\n• 鄂州市\n• 恩施土家族苗族自治州\n• 黄冈市\n• 黄石市\n• 荆门市\n• 荆州市\n• 直辖行政单位\n• 十堰市\n• 随州市\n• 武汉市\n• 咸宁市\n• 襄阳市\n• 孝感市\n• 宜昌市\n• 江西\n• 抚州市\n• 赣州市\n• 吉安市\n• 景德镇市\n• 九江市\n• 南昌市\n• 萍乡市\n• 上饶市\n• 新余市\n• 宜春市\n• 鹰潭市\n• 浙江\n• 杭州市\n• 湖州市\n• 嘉兴市\n• 金华市\n• 丽水市\n• 宁波市\n• 衢州市\n• 绍兴市\n• 台州市\n• 温州市\n• 舟山市\n• 青海\n• 果洛藏族自治州\n• 海北藏族自治州\n• 海东地区\n• 海南藏族自治州\n• 海西蒙古族藏族自治州\n• 黄南藏族自治州\n• 西宁市\n• 玉树藏族自治州\n• 甘肃\n• 白银市\n• 定西市\n• 甘南藏族自治州\n• 嘉峪关市\n• 金昌市\n• 酒泉市\n• 兰州市\n• 临夏回族自治州\n• 陇南市\n• 平凉市\n• 庆阳市\n• 天水市\n• 武威市\n• 张掖市\n• 贵州\n• 安顺市\n• 毕节市\n• 贵阳市\n• 六盘水市\n• 黔东南苗族侗族自治州\n• 黔南布依族苗族自治州\n• 黔西南布依族苗族自治州\n• 铜仁地区\n• 遵义市\n• 陕西\n• 安康市\n• 宝鸡市\n• 汉中市\n• 商洛市\n• 铜川市\n• 渭南市\n• 西安市\n• 咸阳市\n• 延安市\n• 榆林市\n• 西藏\n• 阿里地区\n• 昌都地区\n• 拉萨市\n• 林芝地区\n• 那曲地区\n• 日喀则地区\n• 山南地区\n• 宁夏\n• 固原市\n• 石嘴山市\n• 吴忠市\n• 银川市\n• 中卫市\n• 福建\n• 福州市\n• 龙岩市\n• 南平市\n• 宁德市\n• 莆田市\n• 泉州市\n• 三明市\n• 厦门市\n• 漳州市\n• 内蒙古\n• 阿拉善盟\n• 巴彦淖尔市\n• 包头市\n• 赤峰市\n• 鄂尔多斯市\n• 呼和浩特市\n• 呼伦贝尔市\n• 通辽市\n• 乌海市\n• 乌兰察布市\n• 锡林郭勒盟\n• 兴安盟\n• 云南\n• 保山市\n• 楚雄彝族自治州\n• 大理白族自治州\n• 德宏傣族景颇族自治州\n• 迪庆藏族自治州\n• 红河哈尼族彝族自治州\n• 昆明市\n• 丽江市\n• 临沧市\n• 怒江傈僳族自治州\n• 曲靖市\n• 思茅市\n• 文山壮族苗族自治州\n• 西双版纳傣族自治州\n• 玉溪市\n• 昭通市\n• 新疆\n• 阿克苏地区\n• 阿勒泰地区\n• 巴音郭楞蒙古自治州\n• 博尔塔拉蒙古自治州\n• 昌吉回族自治州\n• 哈密地区\n• 和田地区\n• 喀什地区\n• 克拉玛依市\n• 克孜勒苏柯尔克孜自治州\n• 直辖行政单位\n• 塔城地区\n• 吐鲁番地区\n• 乌鲁木齐市\n• 伊犁哈萨克自治州\n• 黑龙江\n• 大庆市\n• 大兴安岭地区\n• 哈尔滨市\n• 鹤岗市\n• 黑河市\n• 鸡西市\n• 佳木斯市\n• 牡丹江市\n• 七台河市\n• 齐齐哈尔市\n• 双鸭山市\n• 绥化市\n• 伊春市\n• 香港\n• 香港\n• 九龙\n• 新界\n• 澳门\n• 澳门\n• 其它地区\n• 台湾\n• 台中市\n• 台南市\n• 高雄市\n• 台北市\n• 基隆市\n• 嘉义市\n•", null, "大量生产紫铜大弯头厂家推荐\|好的多种用途弯头推荐\n\n品牌:鑫雷,,\n\n出厂地:忻城县(城关镇)\n\n报价：面议\n\n绍兴市上虞鑫雷制冷设备厂\n\n黄金会员：", null, "主营：铜弯头,铝弯头,不锈钢弯头,铜三通,闭塔弯头\n\n•", null, "铝弯头厂家-绍兴品牌好的铜弯管批发\n\n品牌:鑫雷,,\n\n出厂地:忻城县(城关镇)\n\n报价：面议\n\n绍兴市上虞鑫雷制冷设备厂\n\n黄金会员：", null, "主营：铜弯头,铝弯头,不锈钢弯头,铜三通,闭塔弯头\n\n•", null, "闭塔弯头厂家-实惠的铜弯头供销\n\n品牌:鑫雷,,\n\n出厂地:忻城县(城关镇)\n\n报价：面议\n\n绍兴市上虞鑫雷制冷设备厂\n\n黄金会员：", null, "主营：铜弯头,铝弯头,不锈钢弯头,铜三通,闭塔弯头\n\n•", null, "浙江环氧防静电地坪_买涂料认准郝伟地坪环氧防静电地坪\n\n品牌:郝伟地坪,,\n\n出厂地:忻城县(城关镇)\n\n报价：面议\n\n诸暨市郝伟地坪工程有限公司\n\n黄金会员：", null, "主营：环氧地坪,环氧自流平地坪,耐磨地坪,基础漏水注浆,墙面漏水注浆\n\n•", null, "暖管道零件铜U管加工定制厂家\|绍兴价格合理的铜配件批售\n\n品牌:鑫雷,,\n\n出厂地:忻城县(城关镇)\n\n报价：面议\n\n绍兴市上虞鑫雷制冷设备厂\n\n黄金会员：", null, "主营：铜弯头,铝弯头,不锈钢弯头,铜三通,闭塔弯头\n\n•", null, "浙江移动式大型铸件打磨除尘房伸缩房\n\n品牌:新迈\n\n出厂地:凤山县(凤城镇)\n\n报价：面议\n\n山东新迈节能环保科技有限公司\n\n经营模式：生产型\n\n主营：废气处理设备，粉尘处理设备，喷烤漆房\n\n•", null, "浙江绍兴可导向防撞垫 TS级防撞垫厂家\n\n品牌:格拉瑞斯\n\n出厂地:宜州市\n\n报价：面议\n•", null, "报价：面议\n\n绍兴市上虞鑫雷制冷设备厂\n\n黄金会员：", null, "主营：铜弯头,铝弯头,不锈钢弯头,铜三通,闭塔弯头\n\n•", null, "报价：面议\n\n绍兴市上虞鑫雷制冷设备厂\n\n黄金会员：", null, "主营：铜弯头,铝弯头,不锈钢弯头,铜三通,闭塔弯头\n\n•", null, "北京转印标_高性价比的转印产品信息\n\n品牌:华彩,,\n\n出厂地:忻城县(城关镇)\n\n报价：面议\n\n绍兴市华彩印花材料有限公司\n\n黄金会员：", null, "主营：植绒纸,植绒浆,隔离浆,转印植绒烫画,烫画机\n\n• 没有找到合适的绍兴市供应商？您可以发布采购信息\n\n没有找到满足要求的绍兴市供应商？您可以搜索批发公司\n\n### 最新入驻厂家\n\n相关产品:\n大量生产紫铜大弯头厂家推荐铝弯头厂家闭塔弯头厂家浙江环氧防静电地坪暖管道零件铜U管加工定制厂家浙江移动式大型铸件打磨除尘房伸缩房绍兴可导向防撞垫大量供应家装建材机械专用管件生产厂家专业生产空调制冷部件系列批发北京转印标" ]	[ null, "http://image-ali.bianjiyi.com/1/2018/0720/09/15320513534373.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://image-ali.bianjiyi.com/1/2018/0720/09/15320507900458.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://image-ali.bianjiyi.com/1/2018/0720/09/1532051968441.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://image-ali.bianjiyi.com/1/2018/1206/17/15440872493665.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://image-ali.bianjiyi.com/1/2018/0720/09/15320505651549.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://imagebooksir.258fuwu.com/images/business/20191118/9/3664948501574041297.jpeg", null, "http://imagebooksir.258fuwu.com/images/business/2019416/17/4506979151555407242.jpeg", null, "http://image-ali.bianjiyi.com/1/2018/0720/09/1532049887274.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://image-ali.bianjiyi.com/1/2018/0720/10/15320523484761.jpg", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null, "http://image-ali.bianjiyi.com/1/2017/1125/14/5a1911231430d.png", null, "http://www.qiye.net/Public/Images/ForeApps/grade2.png", null ]	{"ft_lang_label":"__label__zh","ft_lang_prob":0.5493832,"math_prob":0.4711275,"size":669,"snap":"2019-43-2019-47","text_gpt3_token_len":848,"char_repetition_ratio":0.2586466,"word_repetition_ratio":0.0,"special_character_ratio":0.23318386,"punctuation_ratio":0.2795031,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9873433,"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],"im_url_duplicate_count":[null,5,null,null,null,null,null,null,null,null,null,null,null,3,null,null,null,null,null,null,null,2,null,1,null,null,null,null,null,null,null,null,null,null,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-11-18T17:55:57Z\",\"WARC-Record-ID\":\"<urn:uuid:4f453eba-d986-43d6-8561-4d697f83caa0>\",\"Content-Length\":\"99107\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6f9325b0-0d19-431b-b957-c3c952e6e6fa>\",\"WARC-Concurrent-To\":\"<urn:uuid:d2e0edb8-65a6-4898-9cb6-a429711cf0dc>\",\"WARC-IP-Address\":\"154.213.171.152\",\"WARC-Target-URI\":\"http://zjzhenxing.cn/qspevdu_d001020008\",\"WARC-Payload-Digest\":\"sha1:RUE7MUTZJTG2IORFZR5V6654XEV23KSY\",\"WARC-Block-Digest\":\"sha1:5PWRLDVIRLFWJODZJWMIIGIKALG5EWMJ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-47/CC-MAIN-2019-47_segments_1573496669809.82_warc_CC-MAIN-20191118154801-20191118182801-00046.warc.gz\"}"}
https://programs.wiki/wiki/typescript-type-declarations.html	[ "# TypeScript type declarations\n\n## basic type\n\n• type declaration\n\n• Type declaration is a very important feature of TS\n\n• The type of variables (parameters, formal parameters) in TS can be specified by type declaration\n\n• After specifying the type, when assigning a value to the variable, the TS compiler will automatically check whether the value conforms to the type declaration, assign the value if it matches, or report an error\n\n• In short, the type declaration sets the type for the variable, so that the variable can only store a certain type of value, preventing the variable from being arbitrarily assigned to any type of value\n\n• grammar:\n\n• ```let variable: type;\n\nlet variable: type = value;\n\nfunction fn(parameter: type, parameter: type): type{\n...\n}\n```\n• automatic type judgment\n\n• TS has an automatic type judgment mechanism\n• When the declaration and assignment of variables are carried out at the same time, the TS compiler will automatically determine the type of the variable\n• So if your variable declaration and assignment are performed at the same time, you can omit the type declaration\n• type:\n\ntypeexampledescribe\nnumber 1, -33, 2.5 any number\nstring 'hi', \"hi\", hi any string\nboolean true,false boolean true or false\nLiteral itself The value of the restricted variable is the value of the literal\nany * any type\nunknown * type-safe any\nvoid Null value (undefined) no value (or undefined)\nnever no value cannot be any value\nobject {name:'Monkey King'} any JS object\narray [1,2,3] Arbitrary JS array\ntuple [4,5] Element, TS new type, fixed-length array\nenum enum{A, B} Enumeration, a new type in TS\n\nExample:\n\n```//#Simple example of region type declaration\n\n// declare a variable a，Also specify its type as number\nlet a: number;\n\n// a type is set to number，in the future use a can only be numeric\na = 10;\na = 33;\n// a = 'hello'; // This line of code will report an error, because the type of variable a is number and cannot be assigned a string\nlet b: string;\nb = 'hello';\n// b = 123;\n\n// After declaring the variable, assign it directly\n// let c: boolean = false;\n// If a variable is declared and assigned at the same time, TS Can automatically perform type detection on variables\nlet c = false;\nc = true;\n\n//#endregion\n\n//#region\n// JS The function in does not consider the type and number of parameters\nfunction sum1(a, b){\nreturn a + b;\n}\nsum1(2,\"2\");//can be called successfully\n// console.log(sum(123, 456)); // 579\n// console.log(sum(123, \"456\")); // \"123456\"\n// :number means that the returned value must be a number\nfunction sum(a: number, b: number): number{\nreturn a + b;\n}\n\nlet result = sum(123, 456);//Can only pass parameters strictly according to type\n\n//#endregion```\n\nLiteral\n\n• You can also use literals to specify the type of variables, and you can determine the value range of variables through literals\n\nExample:\n\n```// You can also directly use literals for type declarations\nlet a1: 10;// definition a1 The value is 10 and cannot be changed, similar to a constant\n\n// can use \| to connect multiple types (union types)\n// express b can be\"male\"，can also be\"female\"\nlet b: \"male\" \| \"female\";\nb = \"male\";\nb = \"female\";\n\n// express c can be boolean，can also be string\nlet c: boolean \| string;\nc = true;\nc = 'hello';```\n\nany\n\n```// any Represents any type, and a variable setting type is any After that, it is equivalent to closing the variable TS type detection for\n// use TS hour,Not recommended for use any type\n// let d: any;\n\n// If you declare a variable without specifying a type, then TS The parser will automatically determine the type of the variable as any (implicit any)\nlet d;\nd = 10;\nd = 'hello';\nd = true;```\n\nunknown\n\ntype-safe any\n\nExample:\n\n```// unknown represents a value of unknown type\nlet e: unknown;\ne = 10;\ne = \"hello\";\ne = true;\n\nlet s:string;\n\n// d is of type any，It can be assigned to any variable\ns = d;// correct\n\ne = 'hello';\ns=e;// mistake unknown Variables of type cannot be directly assigned to other variables\n// unknown is actually a type-safe any\n// unknown Variables of type cannot be directly assigned to other variables\nif(typeof e === \"string\"){\ns = e;\n}```\n\nenum\n\n```enum Color {\nRed,\nGreen,\nBlue,\n}\nlet c: Color = Color.Green;\n\nenum Color {\nRed = 1,\nGreen,\nBlue,\n}\nlet c: Color = Color.Green;\n\nenum Color {\nRed = 1,\nGreen = 2,\nBlue = 4,\n}\nlet c: Color = Color.Green;```\n\ntype assertion\n\nIn some cases, the type of the variable is very clear to us, but the TS compiler is not clear. At this time, the type assertion can be used to tell the compiler the type of the variable. The assertion has two forms:\n\n```// Type assertions, which can be used to tell the parser the actual type of a variable\n/\n grammar:\n* variable as type\n* <Type > Variable\n\n */\n// The first\ns = e as string;\n// the second\ns = <string>e;```\n\nExample:\n\n```//The first\nlet someValue: unknown = \"this is a string\";\nlet strLength: number = (someValue as string).length;\n\n//the second\nlet someValue: unknown = \"this is a string\";\nlet strLength: number = (<string>someValue).length;```\n\nTags: TypeScript\n\nPosted by hamza on Fri, 25 Nov 2022 00:52:39 +1030" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.68870234,"math_prob":0.98459905,"size":4941,"snap":"2023-40-2023-50","text_gpt3_token_len":1190,"char_repetition_ratio":0.16690297,"word_repetition_ratio":0.07735426,"special_character_ratio":0.27929568,"punctuation_ratio":0.15030675,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9770279,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2023-09-27T19:17:56Z\",\"WARC-Record-ID\":\"<urn:uuid:cbb5c76e-f36c-421f-aaa6-c260ef5167fb>\",\"Content-Length\":\"17083\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c9bfa1ea-8e90-4b91-830a-9695b04c0253>\",\"WARC-Concurrent-To\":\"<urn:uuid:b3b16e7f-411a-4ec1-ac38-02709af0c978>\",\"WARC-IP-Address\":\"178.238.237.47\",\"WARC-Target-URI\":\"https://programs.wiki/wiki/typescript-type-declarations.html\",\"WARC-Payload-Digest\":\"sha1:P73RGP2XVUVYC5XSUTQEL2VT5PWGF2WL\",\"WARC-Block-Digest\":\"sha1:MHBGX2GKHYHMDA64FI4PXZUYSRRIKUGT\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2023/CC-MAIN-2023-40/CC-MAIN-2023-40_segments_1695233510319.87_warc_CC-MAIN-20230927171156-20230927201156-00334.warc.gz\"}"}
https://answers.opencv.org/questions/59263/revisions/	[ "Ask Your Question\n\n# Revision history [back]\n\n### iterate through a matrice, int to uchar problem\n\nI'm trying to modify in a for a loop a Mat. I'm following this article. However the following code does not work :\n\ncv::Mat tmp(20, 20, CV_32F, cv::Scalar(0,0,0));\n\ncv::MatIterator_<uchar> new_mat= tmp.begin<uchar>();\nfor( int i = 0; i< (tmp.size().width) ;i++){\nfor(int j= 0;j< tmp.size().height;j++){\n\n(new_mat) = 1;\nstd::cout << (float) (new_mat) << \", \";\nnew_mat++;\n}\nstd::cout <<std::endl;\n}\nstd::cout << std::endl << std::endl;\n\nstd::cout << tmp << std::endl;\n\nstd::string win = \"Local Maxima\";\ncv::imshow(win, tmp);\n\n\nPrinting (float) (*new_mat) give a Matrix of 1 as explected but tmp if full of 1.4012985e-45 values and I can't seem to understand why. My guess is that it as to do with the way I'm assigning the value but I can't grasp the real problem here.\n\nAny help is appreciated." ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.82743293,"math_prob":0.9205367,"size":787,"snap":"2021-21-2021-25","text_gpt3_token_len":249,"char_repetition_ratio":0.12899107,"word_repetition_ratio":0.0,"special_character_ratio":0.36848792,"punctuation_ratio":0.29353234,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9958168,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-06-16T09:03:58Z\",\"WARC-Record-ID\":\"<urn:uuid:465b2593-f8a2-4edf-9783-19bcd344c76c>\",\"Content-Length\":\"15708\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:946646c9-0d0b-44e8-b180-9d85caf34d0f>\",\"WARC-Concurrent-To\":\"<urn:uuid:3ad2b231-c6c6-45ae-ae18-9083fbc732e3>\",\"WARC-IP-Address\":\"5.9.49.245\",\"WARC-Target-URI\":\"https://answers.opencv.org/questions/59263/revisions/\",\"WARC-Payload-Digest\":\"sha1:IKIHUIGPL2RVMPPSLBPBWAAAQHLGU46W\",\"WARC-Block-Digest\":\"sha1:4YLEI5G4I7BFLUSZXXVLTSP33ACHUV55\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-25/CC-MAIN-2021-25_segments_1623487622234.42_warc_CC-MAIN-20210616063154-20210616093154-00080.warc.gz\"}"}
https://voer.edu.vn/m/orbital-hybridization/cc72cae3	[ "Tài liệu\n\n# Orbital hybridization\n\nScience and Technology\n\nIn the Valence Bond model, atomic orbitals s and p can be mixed to yield a set of hybrid orbitals for forming sigma bonds with neighbor atoms for example of the sp3 and sp2 hybrid orbitals below.\n\nThis provides a means to ensemble molecular structure from individual atoms such as in MolDesign tool in Avisto. Let examine the concept of orbital hybridization from the molecular orbital theory, i.e. analyzing molecular orbitals from semi-empirical MO calculations using tools in Avisto.\n\nThe procedure below is for using tools in Avisto. Dowload them at Astonis.\n\nProcedure:\n\n1. Use MolDesign to build BeH2, HCCH, BH3, H2C=CH2, CH4, and H3C-CH3.\n\n2. Use Basic QChem Edu, Basic QChem, or MopacGUI Cloud or Pro to search for stable structures for these molecules. If use MopacGUI Cloud or Pro also select options to calculate localized MO and perform hybridization analysis under that properties tab.\n\n3. Use PsiViewer to analyze the molecular orbitals both delocalized and localized forms.\n\n4. Open the output file in the Files type to view the results of the hybridization analysis.\n\nExample: BeH2\n\nAfter using Basic QChem Edu to search for a stable structure of BeH2, view the results in PsiViewer.\n\n1. Delocalized molecular orbtials\n\nThe figure shows both occupied delocalized\n\nand MO and also the HOMO-LUMO gap (in eV).\n\n2. Select option to plot localized MO in PsiViewer. The two delocalized MO's above are mixed to produce two equivalent localized MO's showing the two Be-H sigma bonds.\n\nNote the the orbital energies of localized MO's have no physical meaning as those of delocalized MO's.\n\n3. Using option in PsiViewer to turn-off the contribution of Hydrogen atoms in the localized MO's. This yields two Be hybrid sp orbitals.\n\n4. Open the output file in the Files tab of PsiViewer and find the table 'Sigma-Pi bond-order matrix'\n\nSIGMA-PI BOND-ORDER MATRIX\n\nS-SIGMA P-SIGMA P-PI S-SIGMA S-SIGMA Be 1 Be 1 Be 1 H 2 H 3------------------------------------------------------------------ S-SIGMA Be 1 0.996282 P-SIGMA Be 1 0.000000 0.961062 P-PI Be 1 0.000000 0.000000 0.000000 S-SIGMA H 2 0.498141 0.480531 0.000000 0.983320 S-SIGMA H 3 0.498141 0.480531 0.000000 0.004648 0.983320\n\nAlong the diagonal matrix, the first two numbers indicate that Be makes two sigma bonds from an s and p orbitals and no pi bond. This also gives the degree of orbital hybridization to be a sp type.\n\nYou can repeat the lesson for other molecules to learn about sp2 and sp3 hybridization.\n\nTải về\nĐánh giá:\n0 dựa trên 0 đánh giá\nNội dung cùng tác giả\n\nNội dung tương tự" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.7591136,"math_prob":0.8525953,"size":2499,"snap":"2019-51-2020-05","text_gpt3_token_len":656,"char_repetition_ratio":0.15951903,"word_repetition_ratio":0.019900497,"special_character_ratio":0.2777111,"punctuation_ratio":0.116,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9629989,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-12-11T04:01:10Z\",\"WARC-Record-ID\":\"<urn:uuid:a98ba9b6-d420-45bc-ab5b-035be499f3b4>\",\"Content-Length\":\"24383\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:0904419e-e720-4b59-b8ca-c712677cf098>\",\"WARC-Concurrent-To\":\"<urn:uuid:bde5e723-3ccd-494f-864a-3d35e2ac2cd1>\",\"WARC-IP-Address\":\"115.146.126.85\",\"WARC-Target-URI\":\"https://voer.edu.vn/m/orbital-hybridization/cc72cae3\",\"WARC-Payload-Digest\":\"sha1:VJR2POWIB5LZVDVN62W6HEAGJQLHZ6TU\",\"WARC-Block-Digest\":\"sha1:PN7QKNJFCEBXDAZ3BZLYIFPYZIV4DVFY\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-51/CC-MAIN-2019-51_segments_1575540529745.80_warc_CC-MAIN-20191211021635-20191211045635-00415.warc.gz\"}"}
https://codeaccepted.wordpress.com/2014/04/08/depth-and-breadth-first-search/	[ "# Algorithm #9 : Depth- and Breadth- First Search\n\nThis post is about the graph traversal algorithms, Depth First Search (DFS) and Breadth First Search (BFS).\nBFS and DFS are one of the algorithms for graph exploration. Graph exploration means discovering the nodes of a graph by following the edges. We start at one node and then follow edges to discover all nodes in a graph. The choice of first node may be arbitrary or problem specific.\n\nThe difference between BFS and DFS is the order in which the nodes of a graph are explored.\nIf you are not already familiar with BFS and DFS in theory, I recommend that you read about them. Because I’m going to focus more on their implementation here.\n\nIn a nutshell, DFS continues on one path and explores it completely before going down another path.\nBut in BFS we progress equally in all possible paths.\n\nThe following gifs will give you a good general idea about the two.\nHere, the nodes are numbered according to the order in which they are explored.\n\n[Both the images were taken from commons.wikimedia.org]\n\nIMPLEMENTATION:\nWe can use any of the four graph representation methods that I introduced in my post Representation of Graphs. In this post we’ll use Adjacency list and assume that the input is the edges in form of pairs of positive integers (i.e. Type 2, if you refer to my post). The nodes are numbered from 1 to n.\n\nFor DFS:\nDFS has to be implemented using a stack data structure. As recursion uses the internal stack, we can use recursion as follows:\n\n```int adjlist={0};\nint degree={0};\nint done={0};//this array marks if a node has already been explored\nvoid dfs(int at)\n{\nif(done[at]==1)//if the node has already been explored, then return\nreturn;\nprintf(\"At node %d\\n\",at);\ndone[at]=1;\nint i=0;\nwhile(i<degree[at])//for each of the edges on this node\n{\ndfs(adjlist[at][i]);\ni++;\n}\nreturn;\n}\nint main()\n{\nint n,m,i,a,b;\nscanf(\" %d %d\",&n,&m);\ni=0;\nwhile(i<m)\n{\nscanf(\" %d %d\",&a,&b);\nadjlist[a][degree[a]]=b;\ndegree[a]++;\nadjlist[b][degree[b]]=a;\ndegree[b]++;\ni++;\n}\ndfs(1);//start with any node. node 1 is the first node here\nreturn 0;\n}\n\n```\n\nFor BFS:\nBFS needs a Queue data structure for its implementation. Here I use an array queue[] and integers front and rear to implement Queue.\n\n```\nint main()\n{\nint n,m,i,a,b;\nint adjlist={0};\nint degree={0};\nscanf(\" %d %d\",&n,&m);\ni=0;\nwhile(i<m)\n{\nscanf(\" %d %d\",&a,&b);\nadjlist[a][degree[a]]=b;\ndegree[a]++;\nadjlist[b][degree[b]]=a;\ndegree[b]++;\ni++;\n}\nint queue,front=0,rear=0;\nint done={0};//this array marks if a node has already been explored\nint at;\nqueue[rear]=1;//start with any node. node 1 is the first node heres\nrear++;\ndone=1;\nwhile(front!=rear)\n{\nat=queue[front];\nprintf(\"At node %d\\n\",at);\nfront++;\nfor(i=0;i<degree[at];i++)\n{\nif(done[adjlist[at][i]]!=1)\n{\nqueue[rear]=adjlist[at][i];\nrear++;\ndone[adjlist[at][i]]=1;\n}\n}\n}\nreturn 0;\n}\n\n```\n\nThe array done[] is used to mark the nodes that have already been visited. This has to be done to stop the code from re-discovering already visited nodes and running forever.\n\nAbove is a bare-bones implementation of the two algorithms. They do nothing more than exploring the graph. Apart from only exploring the graph, DFS and BFS can also be used to compute other information too.\nFor example, if we have a tree as input, we can modify the above DFS code to compute the depth of each node in the tree, and also the size of the sub-tree rooted at each node.\n\n```int adjlist={0};\nint degree={0};\nint depth={0};\nint sizeofsubtree={0};\nint done={0};//this array marks which node has already been explored\nint dfs(int at,int currentdepth)\n{\nif(done[at]==1)//if the node has already been explored, then return\nreturn 0;\ndepth[at]=currentdepth;\nprintf(\"Node %d at depth %d\\n\",at,depth[at]);\ndone[at]=1;\nint i=0,size=1;//initialised to 1 as current node is also part of the sub-tree rooted at current node\nwhile(i<degree[at])//for each of the edges on this node\n{\nsize+=dfs(adjlist[at][i],currentdepth+1);\ni++;\n}\nsizeofsubtree[at]=size;\nreturn sizeofsubtree[at];\n}\nint main()\n{\nint n,m,i,a,b;\nscanf(\" %d %d\",&n,&m);\ni=0;\nwhile(i<m)\n{\nscanf(\" %d %d\",&a,&b);\nadjlist[a][degree[a]]=b;\ndegree[a]++;\nadjlist[b][degree[b]]=a;\ndegree[b]++;\ni++;\n}\ndfs(1,0);//start with root node. assuming that node 1 is the root node here\ni=1;\nprintf(\"Depth of subtrees:\\n\");\nwhile(i<=n)\n{\nprintf(\"Rooted at %d: %d\\n\",i,sizeofsubtree[i]);\ni++;\n}\nreturn 0;\n}\n```\n\nA second variable currentdepth is passed to each dfs() instance that represents the depth of the current node. Notice that at line 16, currentdepth+1 has been passed to dfs(); because child of the current node has depth one more than the parent node.\n\nEach recursive instance of dfs() returns the size of the sub-tree rooted at a node.\nAt every node, we sum up the values returned by dfs() for each child node ( This is done at line 16 ). The size of a sub-tree rooted at a node is the summation of sizes of sub-trees rooted at its children + 1. This is how the size of all sub-trees is computed.\n\nCOMPARISION BETWEEN DFS AND BFS:\nIf all the nodes of a graph have to be discovered, then BFS and DFS both take equal amount of time. But, if we want to search for a specific node, both algorithms may differ in execution time.\n\nDFS is more risky compared to BFS. If a node has more than one edge leading from it, the choice of which edge to follow first is arbitrary.\nAs we don’t have any intelligently way of choosing which edge to follow first, it may be possible that the required node is present down the first edge that we choose and it is also possible that the required node is present down the last edge that we choose from that node. In the former case, DFS will find the node very quickly, but in the latter case DFS will take a lot of time. If we take a wrong path at some node, DFS will have to completely traverse the whole path before it can go down another path. That’s why DFS is more risky than BFS.\n\nIn BFS, all paths are explored equally. So, in some cases the search may be a little slower than DFS but the advantage of BFS is that it doesn’t arbitrary favor one path over the other.\n\nBFS is very useful in problems where you have the find the shortest path. This is because BFS explores closer nodes first. So, when we find the node the first time, we can be sure that this is the shortest path to it. Whereas in DFS, we’ll have to find all possible paths and then select the shortest path.\nBFS can also be used in checking if the graph is bipartite.\n\nDFS is useful in problems where we have to check connectivity of graph and in topological sorting.\n\nSuppose we have an infinite graph. If we use DFS to find a specific node, the search will never end if the node is not in the first path that the algorithm chooses. But, given sufficient time, BFS will be able to find it.\n\nCOMPLEXITY OF BFS AND DFS:\nThe complexity of DFS and BFS is O(E), where E is the number of edges.\nOf course, the choice of graph representation also matters. If an adjacency matrix is used, they will take O(N^2) time (N^2 is the maximum number of edges that can be present). If an adjacency list is used, DFS/BFS will take O(E) time.\n\nRelated Problems:\n\nAdvertisements\n\n## 10 comments\n\n1.", null, "Siddharth says:\n\nawesome sir. still refer to anyone wanting to learn coding dfs, bfs. 🙂" ]	[ null, "https://2.gravatar.com/avatar/88252bb59b29ad9d8d066f09f17978cc", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.84478384,"math_prob":0.9608051,"size":7304,"snap":"2019-13-2019-22","text_gpt3_token_len":1866,"char_repetition_ratio":0.11726028,"word_repetition_ratio":0.09810387,"special_character_ratio":0.27354875,"punctuation_ratio":0.13810112,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9980953,"pos_list":[0,1,2],"im_url_duplicate_count":[null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-05-19T22:25:09Z\",\"WARC-Record-ID\":\"<urn:uuid:726d843b-ab4e-4072-85ca-33a2f86f30f6>\",\"Content-Length\":\"69289\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:deaaeda7-f25c-4463-8994-f4c83278aa85>\",\"WARC-Concurrent-To\":\"<urn:uuid:9ef6d27b-3fa8-4fe2-9ee7-321a7dec86cd>\",\"WARC-IP-Address\":\"192.0.78.12\",\"WARC-Target-URI\":\"https://codeaccepted.wordpress.com/2014/04/08/depth-and-breadth-first-search/\",\"WARC-Payload-Digest\":\"sha1:QWD4SGD2D4O5B2ETKIIJOKQY7WWAPW6S\",\"WARC-Block-Digest\":\"sha1:EHAVYS65435CEN22OENHBG6X3SPHTCVZ\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-22/CC-MAIN-2019-22_segments_1558232255182.37_warc_CC-MAIN-20190519221616-20190520003616-00140.warc.gz\"}"}
https://electronics.stackexchange.com/questions/190569/problem-with-driving-the-mosfet-through-optocoupler	[ "# problem with driving the mosfet through optocoupler", null, "I want to run the mosfet (irf540n) through opto coupler(4n25) for driving the motor. I refered some formula from this website, but my circuit is not working.\n\nI calculated opto coupler current limting resistor by using this formula:\n\nRf = (vin-Vf)/If = (5v-1.15)/10ma = 385R\n\nthen calculated the mosfet gate resistor using\n\n5v/150ma=34R\n\nbut this circuit is not working.\n\nI tried this circuit too but not working.\n\nThanks for your answer, now I attached the image of my schematic. Please check it.", null, "• You can do all calculations you want but if your circuit is not correct it will never work. Show us your schematic first. – Bimpelrekkie Sep 15 '15 at 6:52\n• You have to charge/discharge the gate capacitance to turn off/on, using just one gate resitor wont work, because MOSFET aint BJT. – Marko Buršič Sep 15 '15 at 6:56\n• @MarkoBuršič Well, we don't know whether there is a pull-up (or pull-somewhere) resistor in that secret circuit … – CL. Sep 15 '15 at 9:18\n• R2 is way too small in your second schematic. It is only there to pull the gate low when the opto-coupler is off. Try something around 10K. – Tut Sep 16 '15 at 11:50\n• For more on determining the pull-down resistor value, see: Calculating the pulldown resistance for a given MOSFET's gate – Tut Sep 16 '15 at 12:07\n\n## 1 Answer", null, "simulate this circuit – Schematic created using CircuitLab\n\nIs a circuit that should work for you. D1 and Q1 are used together to form the opto isolator. The values are just ball park, the schematic is just meant for topology purposes.\n\n• Where did you get the load resistor value from? – Andy aka Sep 15 '15 at 8:25\n• @Andyaka It's just balk park values. I was going for topology not values. – vini_i Sep 15 '15 at 8:27\n• sir ,actually i dont know to calculate gate resistor please say me how to find it – vivek j Sep 16 '15 at 8:00\n• the gate resistor needs to be just large enough to keep the inrush current in check. The maximum rating for the 4N25 is 150ma, so at 5v i would make the resistor (5/0.15=33.3) about 33ohms. – vini_i Sep 16 '15 at 10:33" ]	[ null, "https://i.stack.imgur.com/qlRHv.jpg", null, "https://i.stack.imgur.com/m2zbL.jpg", null, "https://i.stack.imgur.com/iKiUj.png", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.9195696,"math_prob":0.8237947,"size":490,"snap":"2019-35-2019-39","text_gpt3_token_len":138,"char_repetition_ratio":0.12345679,"word_repetition_ratio":0.025641026,"special_character_ratio":0.2632653,"punctuation_ratio":0.10309278,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9760351,"pos_list":[0,1,2,3,4,5,6],"im_url_duplicate_count":[null,5,null,5,null,5,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-08-19T18:55:09Z\",\"WARC-Record-ID\":\"<urn:uuid:cfcf65c4-20c1-4c59-9d4d-ef4e6553623d>\",\"Content-Length\":\"143380\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:6f11ce1f-9d1b-4798-9a3d-f7ef8899c83b>\",\"WARC-Concurrent-To\":\"<urn:uuid:e3ba8fee-4788-4cbf-8961-2916eaf713eb>\",\"WARC-IP-Address\":\"151.101.193.69\",\"WARC-Target-URI\":\"https://electronics.stackexchange.com/questions/190569/problem-with-driving-the-mosfet-through-optocoupler\",\"WARC-Payload-Digest\":\"sha1:SPKSXVTUHSGNSMQRG4ZJNCOS3PUQE2Z6\",\"WARC-Block-Digest\":\"sha1:TAGFOYQNBMEWRENJXRCC5ATDWND7VAYG\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-35/CC-MAIN-2019-35_segments_1566027314904.26_warc_CC-MAIN-20190819180710-20190819202710-00091.warc.gz\"}"}
https://argoprep.com/blog/what-is-1-6-as-a-decimal/	[ "## Want to practice?\n\nIn the fraction 1/6 , the number 1 is\n\n• Numerator\n• Denominator\n• Decimal\n• Fraction\n\nIn the fraction 1/6 , the number 6 is\n\n• Numerator\n• Denominator\n• Decimal\n• Fraction\n\nWhat is the equivalent fraction to 1/6?\n\n• 4/24\n• 8/32\n• 8/24\n• 4/30\n\nWhat is the equivalent fraction to 1/6?\n\n• 3/12\n• 3/18\n• 9/52\n• 2/18\n\nFractions are used to denote values other than whole numbers. A fraction has two parts separated by a line. The number in the top is called the numerator and the number at the bottom is called the denominator.\n\nThe numerator is the number of parts that are represented and the denominator is the number of parts in the whole.\n\nFractions can also be denoted as decimals.\n\nThe long division method is used to convert the fraction to its decimal form.\n\nIn this method, we use the numerator as the dividend and the denominator as the divisor.\n\nWe can see that the number 1 is not divisible by 6. Hence we can add a decimal point to the quotient and add 0 to the dividend to make it divisible.\n\nWe can see that the same remainder 6 is repeated after the first step. Stop the division as the digits in the quotient are repeated.\n\nThe answer to the fraction is 0.16666…\n\nTo make the answer into a terminating decimal form we write the number with a bar over the digits that are repeated.\n\n0.16666…. is written as\n\n## Remember\n\nHere are some common terms you should be familiar with.\n\n• In the fraction , the number 1 is the dividend (our numerator)\n• The number 6 is our divisor (our denominator)\n\nENTER BELOW FOR ARGOPREP'S FREE WEEKLY GIVEAWAYS. EVERY WEEK!\nFREE 100\\$ in books to a family!\nSee Related Worksheets:\n\"Twelve\" Robots Spelling\nWorksheets\n(0)\nSome number words are challenging, and that is definitely the case with \"twelve\". It's worthy of a solid revie...\nCrawling Up the Doubles Tree\nWorksheets\n(0)\nThese creepy-crawlies love doubles plus two addition problems! Students will get some great practice matching ...\nKindergarten\nIce Cream Party - Draw It!\nWorksheets\n(0)\nThis delicious worksheet is the perfect introduction to addition. Kindergarteners will love it! They'll read t...\nOnly Tens Allowed!\nWorksheets\n(0)\nAdding double-digit numbers is a new adventure for many first grade students. Using whole tens to begin is a g...\nWorksheets\n(0)\nYour artists will have a ball with this blank canvas! This one-page, five-problem worksheet requires children ...\nDinos and Perimeters\nWorksheets\n(0)\nThe dinosaurs are roaring about this perimeter resource! Learners will love finding the perimeter for various ...\nRelevant Blogs:\n\nShare good content with friends and get 15% discount for 12-month subscription\n\nExplore our workbooks:\n\n{{#post_arr}}\n{{#h_block}}\n{{/h_block}}" ]	[ null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.872909,"math_prob":0.9572534,"size":1573,"snap":"2021-43-2021-49","text_gpt3_token_len":407,"char_repetition_ratio":0.18291906,"word_repetition_ratio":0.10135135,"special_character_ratio":0.27082008,"punctuation_ratio":0.07594936,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9829009,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-11-30T03:58:48Z\",\"WARC-Record-ID\":\"<urn:uuid:2020391b-1dcc-4e2f-8fab-422bb7356b2e>\",\"Content-Length\":\"193802\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:288cd48d-5ae6-423a-a26c-20e249ff254d>\",\"WARC-Concurrent-To\":\"<urn:uuid:645851ce-0d61-4c2a-bb4f-e05252fa2fba>\",\"WARC-IP-Address\":\"35.245.210.119\",\"WARC-Target-URI\":\"https://argoprep.com/blog/what-is-1-6-as-a-decimal/\",\"WARC-Payload-Digest\":\"sha1:SK44T5FONKXIBMCTB6MJXP27A2DXNYCF\",\"WARC-Block-Digest\":\"sha1:3H5FM4QWGKV6QA2AFVVFJCSVOIF7IXOV\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-49/CC-MAIN-2021-49_segments_1637964358903.73_warc_CC-MAIN-20211130015517-20211130045517-00049.warc.gz\"}"}
https://rkm.com.au/CALCULATORS/CALCULATOR-cone.html	[ "all calculators / Cone CALCULATOR: ...enter known values for cone radius & height ... then click GET button. No letters or units (e.g. enter 22 not 22 cm) Formula / equation: ENTER Cone Radius > = r ENTER Cone Height (h) > = h Cone Side = = s (length of slope / slant / side) Cone Circumference = = 2 π r (circumference of base of cone) Cone Base Area = = π r2 (area of circle that forms base of cone) Cone Curved Surface Area = = π r s (surface area of the sloping wall of the cone) TOTAL CONE AREA = = π r2 + π r s (area of base + area of wall) CONE VOLUME = = 1/3 (π r2)h (one third base area x height)", null, "Figure shows a cone with equations (formulae) for circumference, circle area, surface area, and conical volume. This cone also available as a 3D stereo anaglyph. This calculator will work out the surface area of a cone and the volume of a cone if you enter the cone radius and height. Only enter numbers (e.g. enter 22 not 22 cm). If you try to enter a unit of measure (e.g. 22 metres, 4 miles, 10 cm) you will get an NAN (Not A Number) error appear in each box. When you have entered the numbers, click the GET button. SURFACE AREA OF A CONE is the area of base (π r 2) + area of the sloping wall π r s. VOLUME OF A CONE is the area of the base (π r 2) times the height (h) divided by three = 1/3 π r 2 h. To convert between lengths (e.g. centimeters to inches) see our Length & Distance Converter. For weights and volumes (especially in recipes) see our Recipe Converter. Sphere: Calculate surface area and volume of a sphere.", null, "Russell Kightley Media\nPO Box 9150, Deakin, ACT 2600, Australia. Mobile phone Australia 0405 17 64 71\nemail RKM" ]	[ null, "https://rkm.com.au/CALCULATORS/calculator-images/MATHS-CONE-equations-white-500.png", null, "https://rkm.com.au/LOGOS/RKM-logo-stone.png", null ]	{"ft_lang_label":"__label__en","ft_lang_prob":0.80849797,"math_prob":0.99322075,"size":544,"snap":"2021-31-2021-39","text_gpt3_token_len":161,"char_repetition_ratio":0.14074074,"word_repetition_ratio":0.051282052,"special_character_ratio":0.30514705,"punctuation_ratio":0.096296296,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9868244,"pos_list":[0,1,2,3,4],"im_url_duplicate_count":[null,3,null,null,null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2021-09-24T17:28:49Z\",\"WARC-Record-ID\":\"<urn:uuid:36e6d4b6-7bfe-48d6-8659-bc2d3e107fd3>\",\"Content-Length\":\"13535\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:4748daed-2c4b-4290-8892-0bf78f6513bf>\",\"WARC-Concurrent-To\":\"<urn:uuid:16436719-d835-46d2-b25b-c7a8cf7c3793>\",\"WARC-IP-Address\":\"160.153.51.198\",\"WARC-Target-URI\":\"https://rkm.com.au/CALCULATORS/CALCULATOR-cone.html\",\"WARC-Payload-Digest\":\"sha1:KBN77AHHRDNZFPZILOMUQ2AO54DDSDGL\",\"WARC-Block-Digest\":\"sha1:ZCDTH3NEYPU6TKA3RPSLUTRBJIEJHILB\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2021/CC-MAIN-2021-39/CC-MAIN-2021-39_segments_1631780057564.48_warc_CC-MAIN-20210924171348-20210924201348-00573.warc.gz\"}"}