The complex evolution of information quality improvement in competitive market

Information is important market resource. High-quality information is beneficial to increase enterprise's reputation and reduce consumer's verification cost. This paper constructs a two-layer dynamic model, in which enterprises simultaneously conduct price and information game. The goal of profit maximization integrates two types of games into one system. The complex evolution of the two-layer system are studied by equilibrium analysis, stability analysis, bifurcation diagram, entropy and Lyapunov exponent. It is found that improving the information quality through regulations will increase involution and reduce stability of the market. Then, the block chain technology is introduced into the model for improving information quality of the market. It is found that increasing enterprises' willingness to adopt block chain can improve the information quality quickly and effectively, and that is verified by entropy value. Therefore, the application and promotion of new technologies are more effective than exogenous regulations for improving information quality in market.

Excavated Earth Area Maximization as a Basement and Outdoor Living Space on a Sloped Terrain Housing

The cut-and-fill technique frequently creates a space for housing on sloped terrain. Some developers use the contours of the land on sloped terrain for garden areas instead of developing it into space to reduce production costs when building houses. By developing structures for building reinforcement, this research seeks to use the excavated earth area in the sloped terrain as storage space and outdoor living space. A single case study in a Malang City home situated on a sloping terrain served as the research approach. Primary data were collected through field surveys and customer interviews to determine the design of the room based on space requirements. Secondary data for this study also comes from a literature review. The first step for the architect is to plan the room's layout following the client's requirements. The next stage is to choose the foundation for the project by taking the soil's structure and condition. The final stage is also decided upon the outdoor living space's finishing material and the furniture for the outdoor room. The final result indicates that the excavated earth area is beneficial as a storage place at the bottom and an outdoor living area at the top. Outdoor living spaces are beneficial because they provide extra space and address home design issues by reducing the spread of airborne viruses like Covid-19. Maximizing excavated earth as a warehouse space and functioning as an outdoor living space is one of the applications of sustainable design in architecture.

A Preliminary Investigation of Privacy Issues during the COVID-19 Pandemic

Alongside greater convenience, the rapid development of technology in the modern world has also brought about many ethical problems. This article examines privacy issues that emerged during the COVID-19 pandemic from the perspective of applied ethics. It focuses on two specific examples of privacy issues that emerged in higher education and social policy amid attempts to prevent and control the disease. Based on the moral framework of consequential evaluation, this article discusses the concepts of privacy and privacy rights and the difference between maximization and optimization in the context of an incomplete ranking of options. This article also discusses two ways that the loss of privacy has been understood: the control account and the access account. Another important discussion in the article is the place of privacy in the context of intimate relationships, and why the resolution of some issues concerning privacy requires a discussion of the concept of intimacy. Based on the above analysis, this article concludes with a discussion of how to evaluate the privacy issues in the two examples.

Effectual Seed Pick Framework Focusing on Maximizing Influence in Social Networks

Today, the emergence of social media is helpful for the healthcare system where everyone is closely connected. Large numbers of people can be reached by using seed nodes to provide medical advice, facilities, new changes in the treatment, and any health ministry guidelines. As today's world is dealing with COVID-19, the main objective is to provide healthcare services to many people irrespective of time and locality. As people suffering from corona are dealing with mental health issues, in order to deal with it, a seed pick framework using machine learning for the influence maximization technique is proposed, which will be helpful to provide pervasive healthcare. For pervasive healthcare, an effectual seed pick framework is required focusing on influence maximization using machine learning. The proposed algorithm Fuzzy-VIKOR is helpful to identify the targeted node to spread information at a high rate. Consequently, the proposed structure effectively addresses different issues related to a large number of patients, and thus, increased influence maximization using seed nodes is helpful for pervasive healthcare. The experiments show that the proposed framework has high precision, accuracy, F1-score, and recall compared to other existing algorithms employed to find influence maximization seeds.

COVID-19 Dataset Clustering based on K-Means and EM Algorithms

In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories: "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Epilogue

The evidence is overwhelming in favor of the public choice explanation of the pandemic decision-making while simultaneously refuting all the public-interest claims thereof. The theoretical presuppositions upon which public choice theory relies are more robust than the ones of public-interest explanation, and this is vindicated once again when the two theories are applied to the Covid-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach.

The basic homogeneous SEIR (susceptible-exposed-infected-removed) model is a commonly used compartmental model for analysing infectious diseases such as influenza and COVID-19. However, in the homogeneous SEIR model, it is assumed that the population of study is homogeneous and, one cannot incorporate individual-level information (e.g., location of infected people, distance between susceptible and infected individuals, vaccination status) which may be important in predicting new disease cases. Recently, a geographically-dependent individual-level model (GD-ILM) within an SEIR framework was developed for when both regional and individual-level spatial data are available. In this paper, we propose to use an SEIR GD-ILM for each health region of Manitoba (central Canadian province) population to analyse the COVID-19 data. As different health regions of the population under study may act differently, we assume that each health region has its own corresponding parameters determined by a homogeneous SEIR model (such as contact rate, latent period, infectious period). A Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters we predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas. Performance of the proposed approach is also evaluated through simulation studies.

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Scalable Adversarial Attack Algorithms on Influence Maximization

In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee 1/2 - ϵ approximation (for any small ϵ >0) and an efficient running time. © 2023 ACM.

MODELING AND CLASSIFICATION OF DEATHS DUE TO COVID-19 BASED ON MACHINE LEARNING TECHNIQUE

Statistical classification is recently considered one of the most important and most common methods in machine learning models and consists of building mod-els that define the target of research interest. There are many classification methods that can be used to predict the value of a response. In this article, we are interested in machine learning applications to classify the new deaths due to Covid-19. Under consideration BIC criterion, the experimental results have shown that the E (Equal variance) with four is the best mixture model. The con-vergence in the algorithm of expectation-maximization is satisfied after 167 itera-tions. The World Health Organization has presented the source of data over the period of March 2, 2020 to August 5, 2020. © 2023 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

Improving the Process of Early-Warning Detection and Identifying the Most Affected Markets: Evidence from Subprime Mortgage Crisis and COVID-19 Outbreak-Application to American Stock Markets.

Stock-market-crash predictability is of particular interest in the field of financial time-series analysis. Famous examples of major stock-market crashes are the real-estate bubble in 2008 and COVID-19 in 2020. Several studies have studied the prediction process without taking into consideration which markets might be falling into a crisis. To this end, a combination analysis is utilized in this manuscript. Firstly, the auto-regressive estimation (ARE) algorithm is successfully applied to electroencephalography (EEG) brain data for detecting diseases. The ARE algorithm is employed based on state-space modelling, which applies the expectation-maximization algorithm and Kalman filter. This manuscript introduces its application, for the first time, to stock-market data. For this purpose, a time-evolving interaction surface is constructed to observe the change in the surface topology. This enables tracking of the stock market's behavior over time and differentiates between different states. This provides a deep understanding of the underlying system behavior before, during, and after a crisis. Different patterns of the stock-market movements are recognized, providing novel information regarding detecting an early-warning sign. Secondly, a Granger-causality time-domain technique, called directed partial correlation, is employed to infer the underlying interconnectivity structure among markets. This information is crucial for investors and market players, enabling them to differentiate between those markets which will fall in a catastrophic loss, and those which will not. Consequently, they can make successful decisions towards selecting less risky portfolios, which guarantees lower losses. The results showed the effectiveness of the use of this methodology in the framework of the process of early-warning detection.

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly-that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition.

Social Distancing Network Creation.

During a pandemic people have to find a trade-off between meeting others and staying safely at home. While meeting others is pleasant, it also increases the risk of infection. We consider this dilemma by introducing a game-theoretic network creation model in which selfish agents can form bilateral connections. They benefit from network neighbors, but at the same time, they want to maximize their distance to all other agents. This models the inherent conflict that social distancing rules impose on the behavior of selfish agents in a social network. Besides addressing this familiar issue, our model can be seen as the inverse to the well-studied Network Creation Game by Fabrikant et al. (in: PODC 2003, pp 347-351, 2003. 10.1145/872035.872088), where agents aim at being as central as possible in the created network. We look at two variants of network creation governed by social distancing. Firstly, a variant without connection restrictions, where we characterize optimal and equilibrium networks, and derive asymptotically tight bounds on the Price of Anarchy and Price of Stability. The second variant allows connection restrictions. As our main result, we prove that Swap-Maximal Routing-Cost Spanning Trees, an efficiently computable weaker variant of Maximum Routing-Cost Spanning Trees, actually resemble equilibria for a significant range of the parameter space. Moreover, we give almost tight bounds on the Price of Anarchy and Price of Stability. These results imply that under social distancing the agents' selfishness has a strong impact on the quality of the equilibria.

Biological Sequence Analysis Using Complex Networks and Entropy Maximization: A Case Study in SARS-CoV-2

During the COVID-19 pandemic, several genetic mutations occurred in the SARS-CoV-2 virus, making more infectious or transmissible. The World Health Organization (WHO) tracks and classifies variants as variants of concern (VOCs) or variants of interest (VOIs), depending on the level of transmissibility and dominance of the variant in the regions. The classification and identification of variants usually occur through sequence alignment techniques, which are computationally complex, making them unfeasible to classify thousands of sequences simultaneously. In this work, an application of the alignment-free method BASiNETEntropy is proposed for the classification of the variants of concern of SARS-CoV-2. The method initially maps the biological sequences as a complex network. From this, the most informative edges are selected through the entropy maximization principle, getting a filtered network containing only the most informative edges. Thus, complex network topological measurements are extracted and used as features vectors in the classification process. Sequences of SARS-CoV-2 variants of concern extracted from NCBI were used to assess the method. Experimental results show that extracted features can classify the variants of concern with high assertiveness, considering few features, contributing to the reduction of the feature space. Besides classifying the variants of concern, unique patterns (motifs) were also extracted for each variant, relative to the SARS-CoV-2 reference sequence. The proposed method is implemented as an open source in R language and is freely available at https://cran.r-project.org/web/packages/BASiNETEntropy/. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Assessment of Modified BAT Algorithm for MOOC Learner Influence Maximization

Identification of a small group of individuals based on their maximal influence cascade is influence maximization. During the COVID-19 pandemic, discussion forums on the Massive Open Online Course (MOOC) platform have become a paramount interaction medium among learners, and the identification of influential learners evolved as a substantial research issue. In this research paper, an optimization function based on an independent cascade is established for the discussion forum influence maximization problem. A modified version of the BAT algorithm is proposed which memorizes the bad experience of the BAT. The proposed Modified algorithm helps the BAT to remember the worst location that has already been traversed for a reliable estimation in an optimized manner while exploring the best solution. Further, the performance of BAT and Modified BAT for influence maximization on the discussion forum network of a MOOC platform is evaluated which shows the excellent performance of modified BAT. Convergence graph for different populations on deviating probability depicts the effective performance of modified BAT over generic BAT algorithm. © 2022 ACM.

Estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples.

Wastewater surveillance has become essential for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The quantification of SARS-CoV-2 RNA in wastewater correlates with the coronavirus disease 2019 (COVID-19) caseload in a community. However, estimating the proportions of different SARS-CoV-2 haplotypes has remained technically difficult. We present a phylogenetic imputation method for improving the SARS-CoV-2 reference database and a method for estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples. The phylogenetic imputation method uses the global SARS-CoV-2 phylogeny and imputes based on the maximum of the posterior probability of each nucleotide. We show that the imputation method has error rates comparable to, or lower than, typical sequencing error rates, which substantially improves the reference database and allows for accurate inferences of haplotype composition. Our method for estimating relative proportions of haplotypes uses an initial step to remove unlikely haplotypes and an expectation maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different haplotypes in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions reflect the true proportions given sufficiently high sequencing depth.

Farmers’ Markets as Resilient Alternative Market Structures in a Sustainable Global Food System: A Small Firm Growth Perspective

A sustainable food system is a key target of the global Sustainable Development Goals (SDGs). The current global food system operates on market mechanisms that prioritise profit maximisation. This paper examines how small food businesses grow and develop within grassroot economies that operate on different market mechanisms. Focusing on artisan food producers and farmers’ markets, this research highlights the potential of resilient, small-scale, diverse markets as pathways to sustainable food systems. An applied critical realist, mixed-methods study was conducted at a macro (Irish food industry), meso (farmers’ markets in the region of Munster, Ireland) and micro (artisan food producers and their businesses) level. The resulting framework provides a post-growth perspective to sustainability, proposing that farmers’ markets represent an alternative market structure to the dominant industrial market, organised on mechanisms where producers ‘Mind what they make’ and ‘Make peace with enough’. In their resilience, these markets can provide pathways for structural change. This implies a call to action to reorientate policies targeting small food businesses to move beyond the concept of firms as profit-maximizing enterprises and to instead focus on a local food policy framework that reinforces the regional ‘interstices’ within which small food businesses operate to promote diversity, resilience and sustainability in the food system.

The impact of accountability on local officials' behavior in the pandemic prevention and control in China based on utility maximization.

BACKGROUND: The COVID-19 pandemic is a public health crisis and an inspection of national governance systems and crisis response capabilities of countries globally. China has adopted a tough accountability system for officials and has succeeded in containing the spread of the pandemic. This study aimed to assess the impact of accountability on local officials' behavior in the pandemic prevention and control based on the official promotion tournament theory and utility maximization analysis framework. METHODS: The panel data of 237 Chinese cities were extracted with local officials' characteristics, confirmed cases, Baidu migration index, Baidu search index according to city names, and data were excluded with local officials' relocation or sub-provincial cities between January 1, 2020 and May 5, 2020. Promotion gain and accountability cost were constructed by adopting promotion speed indicator, and the research hypotheses were assumed based on the utility maximization. It was the first time to apply the interaction model to empirically investigate the relationship between the promotion speed of local officials and the COVID-19 confirmed cases. RESULTS: Our study showed that the promotion speed of provincial governors and mayors significantly affected the number of confirmed cases (ß = - 11.615, P < 0.01). There was a significant interaction between the promotion speeds of provincial governors and mayors (ß = - 2594.1, P < 0.01), indicating that they had a coordinated effect on the pandemic control. Additionally, mayors with different promotion speeds made a significant difference in controlling the imported cases and those who promoted faster better controlled the imported cases (ß = - 0.841, P < 0.01). Mayors with full-time postgraduate education, titles, and majors in science and engineering had a better effect on controlling the number of confirmed cases. CONCLUSIONS: Our study provides evidence that the official accountability system has played an important role in containing the pandemic, which suggests that local officials motivated by the accountability system would respond to the pandemic actively for higher utility. Furthermore, provincial governors and mayors have played a coordinated effect in pandemic control. The above evidences reveal that implementing the official accountability system could improve the government's emergency management capability and the efficiency of pandemic control. Therefore, adopting a strict accountability system could be effective in pandemic containment globally, especially in centralized countries.

An improved voterank algorithm to identifying a set of influential spreaders in complex networks

Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node's nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.

How the hospitality industry reacts to COVID-19: structural, managerial and tactical factors

This paper investigates hoteliers’ short-term recovery strategies during the pandemic. Stemming from management crisis theory and the resource-based view of the firm, this article focuses on two environments differently hit by COVID-19, i.e. London and Munich. The findings show that hotels with a more managerial approach have more proactively applied dynamic pricing strategies. When dealing with high severity levels of the pandemic, hoteliers make use of a more streamlined booking portfolio to cope with the crisis. We provide theoretical implications and actionable managerial levers for hoteliers and the wider pricing community on how to maximize revenues during the pandemic.