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The chapter aims to investigate the impact of the COVID-19 pandemic and crisis on the implementation of the game market strategy of clustering business structures. The chapter contributes to the literature by clarifying the concept of economic clustering from the perspectives of game theory and stakeholder theory in the COVID-19 pandemic and crisis. The scientific novelty and originality of the research results are that they revealed differences in the effectiveness of the game strategy of clustering business structures, first, between developed and developing countries and, second, between conditions of stability and conditions of crisis. The theoretical significance of the results and conclusions is that they opened a new perspective on the clustering of business structures – from the perspective of game theory (as a game strategy in its alternativity with the strategy of individual business presence in the market) and from the perspective of stakeholder theory (as a market strategy, the effectiveness of which is evaluated for all stakeholders). The practical significance of the research lies in the fact that it allows rationalising the decision-making on the implementation of the game strategy of clustering business structures in the context of the COVID-19 pandemic and crisis, considering the peculiarities of developed and developing countries. The authors provide their recommendations for each category of country.

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series dataset contains numerous observations of important predictor variables that can effectively be classified into groups based on their values. In such situations, cluster analysis is often employed to improve the MLR models predictive accuracy, usually by creating separate regressions for each cluster. We introduce a novel approach in which we use the clusters and cluster centroids as input data for the predictor variables to improve the predictive accuracy of the MLR model. We illustrate and test this approach with a real dataset on fleet maintenance.

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

This study examines associations between accrual earnings management (AEM) and real earnings management (REM), and earnings quality between countries considered under insider economics and outsider economics clusters. Countries included in the outsider economics cluster are Singapore, Malaysia, and Hong Kong. Meanwhile, countries included in the insider economics cluster are Indonesia, the Philippines, and South Korea. Earnings management practices have changed from AEM to REM since the publication of the Sarbanes Oxley Act and DFA 954 implementation of the Claws back provision policy in the United States.

Research data were obtained from the Bloomberg database, 2010–2016. Regression analysis and t-test were utilized. This study compared AEM and REM to determine which is stronger based on country clusters, as well as the association between AEM or REM and earnings quality.

The results of this study indicate that AEM and REM are associated with the quality of earnings in the insider economics cluster. However, AEM and REM are not associated with earnings quality in the outsider economics cluster. Furthermore, associations between AEM and earnings quality are stronger than associations between REM and earnings quality in insider economics cluster.

This research is to determine scenarios and perspectives for improving the cluster strategy of business integration in the post-COVID-19 era with the help of the methodology of the game theory.

The methodology of this research includes the complex method, statistical method, correlation analysis and the game theory of decision-making.

Based on the analysis of scientific approaches, we formulate the authors' treatment of the essence of the notion of clustering, which characteristics are evaluated in this work. In this treatment, we distinguish factors that influence the development of clustering of business structures of the state, which level is assessed within the analysis. The components of the competitiveness of business structures are among such factors. Cluster structures of certain countries successfully functioned during the COVID-19 pandemic, using effective strategies created independently (United States) and based on the strategies of non-market regulation (China).

The scientific novelty of this research consists in the identification of the types and characteristics of the strategies of clustering of business structures formed during the COVID-19 and post-COVID-19 eras.

To determine the influence of the development of clustering of the national business environment on the level of digital competitiveness.

The research was performed using the following methods: statistical analysis, correlation analysis and comparative analysis.

We study the influence of the development of clustering of the national business environment on the level of digital competitiveness. It is revealed that the studied developed countries (Singapore, Denmark and Switzerland) demonstrate a high level of clustering of business, which is assessed through the use of the indicator ‘State of сluster development’, and a high level of digital competitiveness. The considered developing countries (Peru, Mexico and the Philippines) have medium values of the above variables. Only Peru was able to use a highly effective mechanism of clustering, which influenced the digitalisation of sectors of the economy, which have business clusters. We also describe the competitive advantages of the development of cluster entrepreneurial structures, which ensure their economic and market success.

The scientific novelty of the results obtained is due to the elaboration on the specifics of the influence of the cluster strategy of business integration on the level of national digital competitiveness.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.

The paper aims is to see why social conflict caused by class segregation did not occur in Gading Serpong? What factors prevent conflict from occurring? This research seeks to find the causes of the nonoccurrence of social conflict due to class segregation in the Gading Serpong cluster area and explore the factors that restrain conflict there.

This research is qualitative research with data collection techniques through in-depth interviews with several parties identified as brokerages in the research object area. In this context, one of the media and analytical tools is to recognize agents or brokers who connect two groups of people. Brokerage occurs in sectors, patterns or forms of informal, personal relationships; to understand it, one must pay close attention to micro-level relationships and social psychological processes. However, brokerage can have a significant impact on macro-level social relations, as it is generally associated with social integration processes.

The lack of involvement of developers in overcoming social conflicts that occur between Gading Serpong natives and migrants in Gading Serpong housing has given rise to new actors. These new actors are what we can call brokers, where they have a role as brokers who are able to connect between migrants and natives in the Gading Serpong area. The broker phenomenon is actually familiar in academia, where in practice the broker acts as someone who is able to find solutions to problems. The broker is the reason even social segregation is created between migrant citizens and native citizens in Gading Serpong but never becomes a conflict between them.

Even if the brokerage phenomenon is the reason why there is no conflict over social segregation brokerage is not the only factor in this nonconflict segregation. Therefore, to cover the larger area of these suburban segregation problems, there must be further research on this topic.

The practical implication of this research is to encourage the housing developers that create urban housing, such as clusters or other gated communities, to evaluate the social factors, such as potential segregation and conflict management. Also to encourage the developers to get involved and create some social engineering systems, like brokerage, market and other social agents, to create some nonconflict segregation or even more inclusive communities.

This research is uncovering the main reason why social segregation between migrant and native people in Gading Serpong, which could potentially lead to conflict, is never a conflict. The main reason is social actors like brokerage.