A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster stage

Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by evaluating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Optimization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are introduced to enhance its execution efficiency. Finally, the effectiveness and performance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid metaheuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

A multivariate spatiotemporal model for tracking COVID-19 incidence and death rates in socially vulnerable populations.

Recent studies have produced inconsistent findings regarding the association between community social vulnerability and COVID-19 incidence and death rates. This inconsistency may be due, in part, to the fact that these studies modeled cases and deaths separately, ignoring their inherent association and thus yielding imprecise estimates. To improve inferences, we develop a Bayesian multivariate negative binomial model for exploring joint spatial and temporal trends in COVID-19 infections and deaths. The model introduces smooth functions that capture long-term temporal trends, while maintaining enough flexibility to detect local outbreaks in areas with vulnerable populations. Using multivariate autoregressive priors, we jointly model COVID-19 cases and deaths over time, taking advantage of convenient conditional representations to improve posterior computation. As such, the proposed model provides a general framework for multivariate spatiotemporal modeling of counts and rates. We adopt a fully Bayesian approach and develop an efficient posterior Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs steps. We use the model to examine incidence and death rates among counties with high and low social vulnerability in the state of Georgia, USA, from 15 March to 15 December 2020.

The Random Walk and Systematic Risk in Indonesia

Purpose: During the period 2022 until January 2023, several new global issues emerged besides the COVID-19 pandemic and had an impact on economic. This study aims to examine the weak form of market efficiency in Indonesia under the assumption that uncertain economic conditions tend to affect systematic risk and cause stock returns randomly move. Methodology: This study employs time series data based on the stock returns of 766 firms in Indonesia during the period January 3, 2022, to January 31, 2023. To detect random walk, the runs test is conducted with supporting of the variance ratio test. Findings: Systematic risk plays an important role in risky assets' efficiency during uncertain economic events which is consistent with the random walk theory. Otherwise, the impact of uncertain economic events on less risky assets gives the investors possibility to obtain extraordinary returns or abnormal returns. Originality/Value: This study examines market efficiency by taking into account the systematic risk of assets that are rarely analyzed at present.

A class of anomalous diffusion epidemic models based on CTRW and distributed delay

In recent years, the epidemic model with anomalous diffusion has gained popularity in the literature. However, when introducing anomalous diffusion into epidemic models, they frequently lack physical explanation, in contrast to the traditional reaction-diffusion epidemic models. The point of this paper is to guarantee that anomalous diffusion systems on infectious disease spreading remain physically reasonable. Specifically, based on the continuous-time random walk (CTRW), starting from two stochastic processes of the waiting time and the step length, time-fractional space-fractional diffusion, time-fractional reaction-diffusion and fractional-order diffusion can all be naturally introduced into the SIR (S: susceptible, I: infectious and R: recovered) epidemic models, respectively. The three models mentioned above can also be applied to create SIR epidemic models with generalized distributed time delays. Distributed time delay systems can also be reduced to existing models, such as the standard SIR model, the fractional infectivity model and others, within the proper bounds. Meanwhile, as an application of the above stochastic modeling method, the physical meaning of anomalous diffusion is also considered by taking the SEIR (E: exposed) epidemic model as an example. Similar methods can be used to build other types of epidemic models, including SIVRS (V: vaccine), SIQRS (Q: quarantined) and others. Finally, this paper describes the transmission of infectious disease in space using the real data of COVID-19. © 2023 World Scientific Publishing Company.

A Fast Initial Response Approach to Sequential Financial Surveillance

Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on Chinese Stock Market during COVID-19—Based on Random Matrix Theory

This paper focuses on the three industries that are greatly impacted by COVID-19, including the consumption industry, the pharmaceutical industry, and the financial industry. The daily returns of 98 stocks in the consumption industry, the pharmaceutical industry, and the financial industry in the 100 trading days from January 2, 2020, to June 3, 2020, are selected. Based on the random matrix theory, it first analyzes whether the stock market conforms to the efficient market hypothesis during the epidemic period, and second it further studies the linkage between the three industries. The results show that (1) the correlation coefficient is approximately a normal distribution, but the mean value is greater than 0, which is greater than that of the more mature markets such as the United States. (2) There are three eigenvalues greater than the prediction value, of which the maximum eigenvalue is about 11.18 times larger than the largest eigenvalue of the RMT. (3) There is a significant positive relationship between the maximum eigenvalue and the correlation coefficient. The specific market performance is that the stock price fluctuations show a high degree of consistency. (4) In the sample interval, the financial industry has a restraining effect on the consumption industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the consumption industry in the short term. The consumption industry has a promoting effect on the financial industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the financial industry in the short term. The consumption industry has a promoting and then restraining effect on the pharmaceutical industry in the short term, and the financial industry has a promoting and then restraining effect on the pharmaceutical industry in the short term. (5) In the sample interval, the consumption industry is mainly affected by itself, while the role of the pharmaceutical industry and the financial industry is very small. The pharmaceutical industry is mainly affected by itself and the consumption industry, while the role of the financial industry is very small. The financial industry is mainly affected by itself and the consumption industry, while the role of the pharmaceutical industry is very small. This situation has consequences for individual investors and institutional investors, since some stock returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency. The research on the correlation between asset returns will help to accurately price assets and avoid losses caused by price fluctuations during the epidemic.

Collaborative Filtering Model of Graph Neural Network Based on Random Walk

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.

Using Social Interactions to Redefine Vaccination Strategy

The pandemics are believed to change the human perception and significantly affect the socio-economical, environmental and psychological outlook of affected people. The recent Covid-19 pandemic has challenged the state of art healthcare systems and has put modern day technology driven healthcare system to a task. While the doctors, biotechnologist, epidemiologist and technologist put their heart in, to model and study the impact of Covid-19;the researchers were tirelessly working on identifying a vaccine that can efficiently put an end to the pandemic. The mass vaccination has always seemed a solution to communicable diseases, pandemics and endemics. The authors believe an efficient vaccination strategy / model is needed to reach the major population in least possible time. It will facilitate to reach the goal of mass vaccination and decrease the spread of virus. The paper presents a PageRank based vaccination model that utilizes the depth first search to traverse a social graph that proves to converge faster than most widely used Random Walk. The idea is to prioritize the vaccination of the most connected individual who is more likely to be a victim or be a super-spreader. The paper also studies the hesitation and acceptance of vaccination among various communities. © 2022 IEEE.

A class of anomalous diffusion epidemic models based on CTRW and distributed delay

In recent years, the epidemic model with anomalous diffusion has gained popularity in the literature. However, when introducing anomalous diffusion into epidemic models, they frequently lack physical explanation, in contrast to the traditional reaction-diffusion epidemic models. The point of this paper is to guarantee that anomalous diffusion systems on infectious disease spreading remain physically reasonable. Specifically, based on the continuous-time random walk (CTRW), starting from two stochastic processes of the waiting time and the step length, time-fractional space-fractional diffusion, time-fractional reaction-diffusion and fractional-order diffusion can all be naturally introduced into the SIR (S: susceptible, I: infectious and R: recovered) epidemic models, respectively. The three models mentioned above can also be applied to create SIR epidemic models with generalized distributed time delays. Distributed time delay systems can also be reduced to existing models, such as the standard SIR model, the fractional infectivity model and others, within the proper bounds. Meanwhile, as an application of the above stochastic modeling method, the physical meaning of anomalous diffusion is also considered by taking the SEIR (E: exposed) epidemic model as an example. Similar methods can be used to build other types of epidemic models, including SIVRS (V: vaccine), SIQRS (Q: quarantined) and others. Finally, this paper describes the transmission of infectious disease in space using the real data of COVID-19.

Plausible explanation for the third COVID-19 wave in India and its implications.

Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict the observed third wave for India. In this work we present a more complete set of simulations for India, that take into consideration two aspects that were not incorporated previously. These include the stochastic movement of an erstwhile protected fraction of the population, and the reinfection of some recovered individuals because of their exposure to a new variant of the SARS-CoV-2 virus. The extended simulations now show the third COVID-19 wave for India that was missing in the earlier calculations. They also suggest an additional fourth wave, which was indeed observed during approximately the same time period as the model prediction.

Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) distribution is proposed for the first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of the new distribution, such as the moment generating function, mean, and variance are presented. Furthermore, confidence intervals are constructed by using the Wald, Bayesian, and highest posterior density (HPD) methods to estimate the true confidence intervals for the parameters of the ZICG distribution. Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the Olympic Games in Tokyo 2020. The results show that the HPD interval performed better than the other methods in terms of coverage probability and average length in most cases studied.

Testing Weak Form of Efficient Market Hypothesis before and during COVID-19 Pandemic Periods: Evidence from Indonesia

This research examines the Weak Form of Efficient Market Hypothesis (WFEMH) on the Indonesian Stock Exchange. Specifically, the study empirically tests the extent to which future stock price changes are not determined by the previous period's stock price movement or the stock price changes are random. Thus, future stock price changes fully reflect new relevant information on the market. This research utilizes the daily closing price of the Composite Stock Price Index on the Indonesian Stock Exchange from 2011 to 2021. The sample of the study is divided into two groups. The first group is from January 2011 to December 2019 as the normal pre-COVID-19 period, and the second group is from January 2020 to December 2021 as the economic crisis period (during COVID-19). We apply three statistical tests: A unit root test, serial correlation test, and regression model examining the WFEMH. The study found that the WFEMH is documented in the Indonesian Stock Exchange in some periods before and during COVID-19. These research findings advocate that regulators and policy-makers should monitor the issue of the market efficiency of public firms in Indonesia. © 2022 IEEE.

Market efficiency and global issues: A case of Indonesia

The efficient market hypothesis assumes that the stock prices fully reflect all relevant information. Under the weak form, the future prices are independent of current prices or in the other words, they follow the random walk hypothesis. Global issues tend to have an impact on capital markets around the world. Therefore, the objective of this study is to assess the effect of global issues on the movements of expected returns in the Indonesian capital market from January 1, 2022, to June 30, 2022. The sample of 755 listed firms is used to test whether the expected returns have a random pattern during the observation period. The results of runs tests and variance ratio test show that the expected return movements are not random. On those results, the weak form of the efficient market hypothesis is rejected, and it can be concluded that the capital market in Indonesia for this period is inefficient. The findings of this study imply that the information about global issues does not affect the market. The success of the Indonesian government’s strategy in dealing with global issues (including the Covid-19 pandemic) in the form of a vaccination program and also followed by excellent fiscal and monetary policies has led to more predictable returns in the capital market. Moreover, investors can set their portfolios to get extraordinary returns as the market is more predictable.

Machine Learning and Deep Learning Strategies in Drug Repositioning

Drug repositioning invovles exploring novel usages for existing drugs. It plays an important role in drug discovery, especially in the pre-clinical stages. Compared with the traditional drug discovery approaches, computational approaches can save time and reduce cost significantly. Since drug repositioning relies on existing drug-, disease-, and target-centric data, many machine learning (ML) approaches have been proposed to extract useful information from multiple data resources. Deep learning (DL) is a subset of ML and appears in drug repositioning much later than basic ML. Nevertheless, DL methods have shown great performance in predicting potential drugs in many studies. In this article, we review the commonly used basic ML and DL approaches in drug repositioning. Firstly, the related databases are introduced, while all of them are publicly available for researchers. Two types of preprocessing steps, calculating similarities and constructing networks based on those data, are discussed. Secondly, the basic ML and DL strategies are illustrated separately. Thirdly, we review the latest studies focused on the applications of basic ML and DL in identifying potential drugs through three paths: drug-disease associations, drug-drug interactions, and drug-target interactions. Finally, we discuss the limitations in current studies and suggest several directions of future work to address those limitations.

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm.

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.

The application of Markov random walk model on the epidemic spread of Covid-19 via SEIR and SEIRS model

Since the outbreak of the Covid-19 pandemic in 2020, most countries are still suffering from the virus, and human society has been greatly changed. As the new virus is highly contagious, many people are still infected with the virus every day, and even face death in serious cases. However, there are still a lot of people who do not realize the harm of the virus, in order to make people more intuitive feel the spread of the virus in a certain period, this paper will use two classic epidemiological mathematical models based on the Markov chain called SEIR and SEIRS model for simulating the virus spread in New York City in 180 days. In both models, there are four states: Susceptible, Exposed, Infected, and Recovered. At first, Markov chain was used to randomly generate a populous population, and only one person in the population was infected, and then the changes in the number of people in these four states were observed over time. In addition, by incorporating certain coefficients in the models into a formula, an index for measuring infectious diseases called Reproduction number (R0) will be obtained. The R0 of Covid-19 in New York City is about 5.93, much greater than 1. Indicating that on average one person can infect about six people, which is highly contagious, so measures need to be taken to reduce this number. Finally, the SEIRS model is more suitable by comparing these two models since people do get re-infected over time. © COPYRIGHT SPIE.

Scalable Estimation of Epidemic Thresholds via Node Sampling.

Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today's interconnected global society, such contagion processes can cause global public health hazards, as exemplified by the ongoing Covid-19 pandemic. It is therefore of great practical relevance to investigate the network transmission of contagious diseases from the perspective of statistical inference. An important and widely studied boundary condition for contagion processes over networks is the so-called epidemic threshold. The epidemic threshold plays a key role in determining whether a pathogen introduced into a social contact network will cause an epidemic or die out. In this paper, we investigate epidemic thresholds from the perspective of statistical network inference. We identify two major challenges that are caused by high computational and sampling complexity of the epidemic threshold. We develop two statistically accurate and computationally efficient approximation techniques to address these issues under the Chung-Lu modeling framework. The second approximation, which is based on random walk sampling, further enjoys the advantage of requiring data on a vanishingly small fraction of nodes. We establish theoretical guarantees for both methods and demonstrate their empirical superiority.

Are African Stock Markets Efficient? A Comparative Analysis Between Six African Markets, the UK, Japan and the USA in the Period of the Pandemic

Namen te studije je preizkusiti in primerjati hipotezo učinkovitega trga v njeni šibki obliki na borznih trgih Bocvane, Egipta, Kenije, Maroka, Nigerije, Južne Afrike, Japonske, Združenega kraljestva in ZDA od 2. septembra 2019 do 2. septembra 2020. Studija temelji na naslednjem raziskovalnem vprašanju: Ali je globalna pandemija (covida-19) v svoji šibki obliki zmanjšala učinkovitost afriških finančnih trgov v primerjavi z razvitimi trgi Združenega kraljestva, Japonske in ZDA? Rezultati potrjujejo dokaze, da finanční trgi, analizirani v obdobju te globalne pandemije, ne podpirajo hipoteze naključnega sprehoda. Vrednosti variančnih razmerij so nižje od ena, kar pomeni, da se donosi sčasoma samokorelirajo. Ugotovljen je bil tudi povratek k povprečju, pri čemer razlike med razvitimi finančnimi trgi in tistimi, ki so v vzponu, niso bile prepoznane. To potrjujejo eksponenti detrendne analize fluktuacije (DFA), ki prikazujejo, da finančni trgi kažejo znake (ne)učinkovitosti v svoji šibki obliki, kar kaže na obstojnost donosa. S tem implicirajo obstoj dolgih spominov in potrjujejo rezultate Wrightovega (2000) testa variance, kar dokazuje zavrnitev hipoteze slučajnega hoda.Alternate :The aim of this study is to test and compare the efficient market hypothesis, in its weak form, on the stock markets of Botswana, Egypt, Kenya, Morocco, Nigeria, South Africa, Japan, the UK and the USA from 2 September 2019 to 2 September 2020. This study is based on the following research question: has the global pandemic (COVID-19) reduced the efficiency - in its weak form - of African financial markets compared to the mature markets of the UK, Japan and the USA? The results sustain the evidence that the random walk hypothesis is not supported by the financial markets analysed in the period of the global pandemic. The variance ratio values are lower than the unit, which implies that the returns are self-correlated over time. A reversion to the average is also observed, with no differences identified between mature and emerging financial markets. In corroboration, the Detrended Fluctuation Analysis (DFA) exponents show that the financial markets present signs of (in)efficiency in its weak form, thus showing persistence in the yields. This therefore implies the existence of long memories validating the results of the variance using the Wright's Rank and Signs Test (2000), which prove the rejection of the random walk hypothesis.

Stochastic harmonic trapping of a Lévy walk: transport and first-passage dynamics under soft resetting strategies

We introduce and study a Lévy walk (LW) model of particle spreading with a finite propagation speed combined with soft resets, stochastically occurring periods in which an harmonic external potential is switched on and forces the particle towards a specific position. Soft resets avoid instantaneous relocation of particles that in certain physical settings may be considered unphysical. Moreover, soft resets do not have a specific resetting point but lead the particle towards a resetting point by a restoring Hookean force. Depending on the exact choice for the LW waiting time density and the probability density of the periods when the harmonic potential is switched on, we demonstrate a rich emerging response behaviour including ballistic motion and superdiffusion. When the confinement periods of the soft-reset events are dominant, we observe a particle localisation with an associated non-equilibrium steady state. In this case the stationary particle probability density function turns out to acquire multimodal states. Our derivations are based on Markov chain ideas and LWs with multiple internal states, an approach that may be useful and flexible for the investigation of other generalised random walks with soft and hard resets. The spreading efficiency of soft-rest LWs is characterised by the first-passage time statistic.