In the numerous terrorist attacks that have occurred worldwide, various terrorist organizations have shown a trend of collaborative cooperation, posing significant challenges to international counter-terrorism efforts. Based on the global terrorism database (GTD), this study constructs a terrorist organization cooperation evolution network from 121,074 terrorist attacks that occurred globally from 2001 to 2018 and conducts a time-series topological structure analysis. Based on the characteristics of terrorist organization cooperation, the network is divided into time slices of three years each to model the flow patterns of terrorist communities at multiple scales. The analysis shows that the robustness of the terrorist organization cooperation network has been continually strengthening over time, which is necessary to develop corresponding strategies to disrupt it. Focusing on the largest connected sub-network within the terrorist cooperation network, whose influence is continuously expanding, this study proposes a community structure-based neighborhood centrality index (CSNC) to measure the importance of nodes in the largest connected component. Experimental results demonstrate that the network disruption strategy based on CSNC, in the process of disintegrating the terrorist cooperation network from 2001 to 2018, achieved a 16.45% maximum reduction in the R value compared to benchmark strategies, proving that the CSNC-based disruption strategy can more effectively dismantle terrorist cooperation networks.

A large amount of evidence suggests that companies with good ESG performance have a lower risk of collapse and stakeholder risk, thereby diluting systematic risk. However, compared to fundamental financial indicators, ESG as a non-financial indicator has not yet reached a consistent conclusion on its mechanism, dynamic variability, and heterogeneity of impact on stock systematic risk. Based on this, this paper selects data from January 2009 to November 2023 in the A-share market for empirical research. Based on the conditional CAPM model, the systematic risk $\beta$ is dynamically characterized as a linear function of ESG performance (non-financial characteristics) and company fundamental characteristics (financial characteristics). Then, the MCMC Bayesian estimation method is used to obtain time-varying estimates of $\beta$ for results analysis. The research results are as follows: First, there is a negative correlation between ESG performance and stock systematic risk, which has become increasingly strong and significant in recent years. Second, the impact of ESG performance on stock systematic risk shows heterogeneity across industries. For industries that are more affected by energy or national policies, good ESG performance helps to reduce systematic risk. Third, although ESG performance can affect stock systematic risk, investors respond less to ESG risk than to fundamental risk, leading to asymmetric investor reactions. Therefore, ESG risk can be considered a secondary risk, and its impact on systematic risk is moderated by fundamental characteristics such as market value and book-to-market ratio.

In recent years, there have been more frequent disasters occurred in China, which pose significant threats to the lives and property of the people. To cope with the increasing complexity and severity of disasters, decision-makers need to store and dispatch emergency supplies rationally based on the real situation. Current studies on regional dispatch considering multiple warehouses and demand points are insufficient, and the problems such as ‘who/how/how much to dispatch’ have not been well-answered. To solve these problems, this paper proposes three regional dispatching strategies (including strict administrative hierarchy supply dispatch, cross-administrative hierarchy supply dispatch and free and nearest supply dispatch strategies) based on a comprehensive summary of relevant case studies, then builds a multi-agent simulation model based on deprivation cost. A simulation experiment is conducted in Mengcheng County, Bozhou City, Anhui Province, and the result shows that when the regional demand is large in a short time, the free proximity strategy can minimize the total social logistics cost. On the contrary, when the regional demand is small, the differences of the total social cost among three strategies are small. In conclusion, our research suggests that, when facing severe disasters and catastrophes, governments should cooperate and coordinate on the dispatching of relief supplies. However, when facing normal disasters without the risk of life, the demand can be satisfied with the strict administrative strategy.

Currently data elements as an important strategic resource for enterprises, data security is the basis for the survival and development of enterprises, the theft, leakage, tampering, destruction, abuse of data and other issues, will bring serious threats and damage to enterprises. This paper focuses on solving the problem of security risk assessment based on enterprise data assets, constructing risk evaluation indexes from four aspects of assets, vulnerability, threat, and protection, and proposing to realize the independence analysis between different indexes based on principal component analysis. Meanwhile, considering the correlation characteristics between data assets, we design a multi-asset correlation analysis enterprise data risk assessment model based on the comprehensive gray correlation, which effectively solves the problem of repeated risk assessment of correlated assets, improves the accuracy of risk assessment of enterprise data assets, and provides decision-making suggestions for the security protection of enterprise data.

Identifying key segments within road networks is crucial for selecting repair objectives and optimizing repair sequences during the post-disaster recovery phase. Traditional methods for identifying key segments have not fully explored the interactions between multiple segments, particularly the significance of studying road network vulnerability under simultaneous disruptions of multiple links. To tackle this issue, this study introduces a machine learning model called transportation graph attention networks for criticality analysis (TGAT) to identify key road segments when facing multiple disruptions. This model is trained on data samples that include scenarios of multiple segment failures, utilizing the graph attention network to evaluate the influence weights between segments and calculating the criticality of each segment based on these weights. The model, trained using mean squared error as the loss function, is capable of identifying segments that play a crucial role in the performance of the road network. Taking the Kunshan City road network as an example, this paper compares the effectiveness of the TGAT method with three other methods:Degree centrality, weighted betweenness centrality, and eigenvector centrality, in optimizing repair sequences during the post-recovery phase. Experimental results indicate that the TGAT method is more effective in identifying key segments within the road network compared to the other three methods, and the repair sequence optimized using TGAT further enhances the repair performance of the road network.

With the rise of shale oil industry and the acceleration of low-carbon transformation process of China's energy structure, the role of clean energy in China's energy market has become increasingly significant. The timely discovery of the risk linkage characteristics and evolution rules of China's major energy markets has important practical value for the sustainable development of China's energy system. Based on this, this paper uses the co-integration test method with structural change points and the research framework of DCC-GARCH-SJC-Copula to conduct a detailed analysis of China's six major segmented energy markets from different perspectives. The research results show that the risk characteristics of structural mutations, dynamic adjustments, and asymmetric tail-dependent structures are common in China's energy system. The occurrence of major macro events such as OPEC production restriction, natural gas pricing reform, and renewable energy subsidy policy adjustments have significantly affected the structural changes in the long-term linkage relationship of China's energy system. According to the calculation results, various energy markets show different risk characteristics before and after the structural change point. Among them, the wind-solar market has the strongest risk linkage correlation, while the natural gas-coal market is the weakest. The crude oil and natural gas markets have “decoupled” significantly in recent years. Compared with the non-renewable energy market, the renewable energy market shows more significant risk characteristics. In addition, it is worth noting that under the influence of government regulation, pricing mechanism and other factors, the risk of simultaneous “slumping” of energy market prices in the context of extreme events is significantly greater than the risk of simultaneous “surging”. Based on this, this paper puts forward a series of policy suggestions with a view to providing important reference for the pricing reform of China's energy system.

In examining the evolutionary processes of cooperative behavior between enterprises and low-carbon service providers in establishing supply chain alliances, this study employs evolutionary game theory and catastrophe theory to transform the traditional game's replication dynamic equation into a cusp catastrophe model. Additionally, it establishes stochastic dynamics that incorporate Gaussian white noise. The system introduces an elasticity measurement index to quantitatively assess the degree to which the system absorbs disturbances. Simulation experiments further analyze the impact of relevant parameter changes on the nonlinear evolution and elasticity of the alliance. The results indicate that when the game parameter combination lies within the mutation set, a bimodal phenomenon and disturbing mutation occur. Conversely, when the game parameter combination crosses the boundary of the mutation set, a structural mutation arises within the alliance state. Furthermore, when excess income and punishment intensity surpasses a certain threshold, they positively influence system elasticity. However, alliance member synergy negatively affects system elasticity up to a certain threshold; Beyond this point, an increase in synergy leads to a decrease in elasticity. This change can prompt alliance members to transition from a "not participating" strategy to a `participation' strategy, ultimately stabilizing at the "participation" strategy.

To quantify the impact of the stability of power supply on the development of the digital economy, a system dynamics model is constructed, including variables such as power supply installed capacity and the scale of the digital economy in the three industries. The model explores the mechanism of how the stability of power supply affects the development of the digital economy in scenarios such as power outages and electricity rationing. The results show that the scale of China's digital economy will maintain a high growth trend during the “14th Five-Year Plan” period and is expected to exceed 70 trillion yuan by 2025. The scale of the digital economy will decrease with the reduction of power supply. If the average daily electricity generation time or the number of working days per year decreases by 1%, 5%, and 10%, the digital economy scale will decrease by an average of 9.28%, 14.24%, and 20.43% respectively. By promoting technological innovation to improve the value-added coefficients of various industries, the impact of power outages or generator failures on the development of the digital economy can be reduced. Finally, policy recommendations are proposed to enhance power supply stability in China.

With the rapid development of the Internet, people have become accustomed to sharing hobbies, obtaining information and discussing common hot topics on the Internet, studying the law of public opinion communication in multi-layer social networks is beneficial to public opinion analysis and public opinion governance. Based on the traditional SEIR epidemic model, this paper considers the influence of node importance on propagation probability, and introduces dynamic parameters, and constructs a single-layer network public opinion propagation model. At the same time, considering the impact of different time steps and degree correlations on public opinion propagation, a multi-layer network cross propagation public opinion propagation model is proposed. In this paper, theoretical verification and experimental analysis are carried out on various communication performances and laws of multi-layer network public opinion communication model. Experiments show that time step and degree correlation have a significant impact on public opinion communication. Finally, some public opinion governance mechanisms and public opinion response measures are put forward, which can help the government and relevant administrative departments to improve the efficiency of public opinion management, ensure rapid response in public opinion events, and reduce potential negative effects, and this is of great significance.

In view of the huge amount of excavation earthwork, strong uncertainty and dynamic characteristics of earthwork allocation in major projects, the comprehensive utilization mode of earthwork and the dynamic siting strategy of the consumption sites are proposed. To maximize the expected total profit of the project, a two-stage stochastic programming model which considers the excavation sequence, machinery efficiency and the uncertainty of excavation amount is established. According to the structural characteristics of the proposed model, an improved Benders decomposition algorithm combined with sample average approximation method is designed, and two acceleration strategies are introduced to speed up the convergence of the developed algorithm. Finally, different scale numerical experiments are conducted for testing. The experimental results show that the proposed comprehensive utilization method and the dynamic siting strategy of consumption sites can improve the utilization value and reduce the cost of earthwork. The two-stage stochastic programming model can effectively characterize the uncertain excavation amount, and the designed accurate algorithm can quickly solve the problem. The research results can provide decision basis and algorithm support for making the dynamic location plans of the consumption sites and earthwork allocation schemes for the major projects.

The volatility and nonlinear characteristics of time series have made modeling and prediction difficult and have attracted widespread attention from scholars. This study combines the decomposition and integration framework to achieve effective information extraction and modeling to improve prediction accuracy. Correspondingly, our proposed methodology involves four main steps: Data decomposition via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); Component grouping via sample entropy (SE); Prediction of the reorganized low-, mid-, and high-frequency component groups through persistence (PER), convolutional neural networks (CNN), gated recurrent unit (GRU), and ensemble prediction via weighted addition using ant lion optimization (ALO). Taking the hourly PM_2.5 concentration of Xi'an as the sample, experimental results showed that our proposed hybrid decomposition-group-ensemble forecasting framework (i.e., ALO-CEEMDAN-SE-(PER-CNN-GRU)) significantly outperformed the benchmarks, and the final prediction error obtained the lowest value (2.53%). This validates the superiority of the decomposition integration framework with excellent neural network models for PM_2.5 prediction.

Enterprises must choose a live streaming method that matches their own development to make profits through live streaming sales. This paper considers three live streaming methods: Manufacturer self-broadcasting, entrusted internet celebrity live streaming, and self-broadcasting + internet celebrity live streaming. Based on game theory, a live streaming e-commerce supply chain model is constructed to study the streamer's ability to sell products and fans effects, the impact of supply chain members' decisions and the best way for manufacturers to carry live sales. The research found that: 1) A streamer's improvement in product delivery ability will help increase product prices and the streamer's effort level; The stronger the fans effect of Internet celebrities, the higher the product price and product sales. 2) The price of products in the internet celebrity's live broadcast room is not always lower than the price of the manufacturer's self-streaming. The relationship is related to the internet celebrity's ability to sell products. 3) Self-broadcasting + internet celebrity live broadcasting is the most beneficial way for manufacturers to make profits and expand market share. The conclusions of this paper can help members of the live broadcast e-commerce supply chain make reasonable decisions and help enterprises cooperate better.

The anomaly detection has been a widely concerned topic of great application value and research importance for a long time. There are many machine learning algorithms dealing with the anomaly detection problem without clear statistical guarantee on the degree of false discovery. We propose a general framework for anomaly detection based on conformal inference that enables online false discovery rate control and does not rely on any model or distribution assumptions. The proposed procedure can incorporate different machine learning algorithms and online multiple hypothesis testing algorithms, thus providing a flexible and versatile approach for anomaly detection. We verify the effectiveness of the proposed procedure on simulated data and apply it to Server Machine Dataset to detect anomalies.

The development of rural revitalization industry is the basis for stimulating the vitality of rural areas and the premise for solving all problems in rural areas. Based on the development status of the “new community factory” in the Qinba Mountains of Shaanxi Province, this paper identifies the important participants such as community factories, local governments and leading enterprises, and constructs a tripartite evolutionary game model considering factors such as cost, subsidy, investment and income, and then discusses how each subject makes strategic choices in the process of rural revitalization industry development, and conducts sensitivity analysis and numerical simulation. The results show that, on the one hand, the evolutionary stabilization strategy is affected by the local government subsidy, and the appropriate subsidy is conducive to the joint participation of the three parties. The investment of leading enterprises in production enterprises should be within a relatively reasonable range, and at the same time, it is necessary to increase the investment of all parties in society to the local government; On the other hand, for community factories and leading enterprises, reducing costs and increasing profits will be more conducive to promoting the development of rural revitalization industries such as “new community factory”.

In time series forecasting, prediction error metrics cannot assist researchers in determining whether poor prediction performance is due to an inappropriate model choice or if the data inherently lacks predictive information. Intrinsic predictability characterizes "the upper limit of prediction accuracy" for the data, which can help researchers assess the compatibility of the current model and data. In this paper, we briefly review the concepts of predictability and provide a detailed introduction to the studies of time series predictability based on permutation entropy. Based on this, we propose permutation entropy with covariates to characterize the complexity of target time series when covariates are available and demonstrate its effectiveness through experiments with real glass bubble data. Additionally, we further present a strategy for model selection based on intrinsic predictability, aiming to choose simpler models and reduce the time cost of modeling and forecasting while maintaining reasonable accuracy. Numerical experiments on economic data validate the efficacy of this strategy.

To enhance supply chain efficiency, it has become a standard production mode for geographically diverse factories to collaborate in completing production tasks. Constructing scheduling models that reflecting real-world conditions and designing simple yet effective optimization algorithms are key to achieving efficient collaboration. Distributed flexible job shop scheduling problem has emerged as a promising tool for modeling and optimizing such problems. However, existing researches rarely account for sequence-dependent setup time for machines, instead simplifying the time as constant. This approximation can lead to inferior schedules, thereby impeding system efficiency. This paper establishes a mixed-integer programming model to describe the distributed flexible job shop scheduling problem with sequence-dependent setup time. A Q-learning based iterated greedy algorithm is proposed to solve it, wherein the Q-learning mechanism is utilized to dynamically select the appropriate perturbation magnitude, effectively overcoming the decline in search performance caused by unreasonable perturbation in conventional iterative greedy algorithms. By introducing the correlation between machines' setup time and operation sequences into benchmark instances for distributed flexible job shop scheduling with machine eligibility constraints, 207 instances are constructed. The proposed algorithm is compared with three iterative greedy algorithms with fixed perturbation magnitudes, simulated annealing, scatter search, backtrack search-based hyper-heuristic and random permutation descent-based hyper-heuristic. Experimental results demonstrate that the proposed Q-learning based iterative greedy algorithm achieves higher search quality and faster convergence speed.

The setting of the lowest sampling rate of the subway white list security inspection channel is a difficult problem to balance safety and efficiency of the white list channel. This paper summarizes and condenses three kinds of white list credit supervision modes of subway security inspection: Government supervision, professional institution supervision and meta-regulation supervision, and then constructs the lowest sampling rate model of white list channel considering credit preference under the two scenarios of “credit” constraint and “credit + convenience” constraint. The main findings are as follows: (i) When the influence of the convenience income of passenger transport is not considered, the credit supervision mode with the largest credit gain-loss distance is dominant, and the meta-regulation supervision mode is generally better than the professional institution supervision mode; (ii) The higher the mutual recognition of the results of passenger credit supervision among regulatory agencies, the lower the lowest sampling rate of the white list channel; (iii) The credit gain-loss distance is positively related to the degree of credit preference of white list passengers, and the two types of credit preference are superposed; (iv) Credit constraints and convenience constraints are complementary. At the same time, when the mutual recognition rate of government departments and professional institutions for credit supervision results is low, or the credit preference of white list passengers for both types of credit is low, the sensitivity of passenger convenience to the lowest sampling rate of white list channels will be significantly enhanced; (v) When the proportion of white list passengers is too high or the number of white list channels is too small, the convenience constraint effect of white list passengers will be significantly reduced, and even the credit constraint effect will be eroded. Finally, the lowest sampling rate of white list channels under the “credit + convenience” constraint will be higher than the value under the “credit” constraint.

For the sales system composed of a manufacturer and a retailer, this paper constructs three decision models that the manufacturer does not introduce the live streaming channel, the manufacturer introduces the store live streaming channel and introduces the anchor live streaming channel, and uses the game theory method to explore the introduction strategy of the manufacturer's live streaming channel and the pricing strategy of multi-channel supply chain under the introduction of the live streaming channel. The results show that after the introduction of the live streaming channel, the manufacturer's revenue increase, the price and sales in the direct sales channel decrease, and the price decreases while the sales increase in the offline retail channel. When the cost of the store live streaming channel is low (high), the introduction of the store (anchor) live streaming channel is conducive to the “low price” strategy of the live streaming channel. When the cost of the store live streaming channel is low and the professional capability of the anchor is weak, the introduction of the store live streaming channel is conducive to the market “penetration” strategy of the live streaming channel, under other conditions, the introduction of the anchor live streaming channel is conducive to the market “penetration” strategy of the live streaming channel. Finally, the price is not necessarily cheap in the live streaming channel.

Salary incentives are an important means for enterprises to stimulate employees' work passion, creative potential and improve corporate performance. On the one hand, the internal pay gap has a positive motivating effect and promotes the effort of employees. On the other hand, it can also lead to a sense of unfairness and cause some employees to feel “flat”. This paper constructs a mathematical model including core executives, non-core executives, and ordinary employees to analyze the impact of internal salary gaps on corporate performance, and conducts empirical research using data from privately-owned listed companies in Shanghai and Shenzhen from 2008 to 2020. Both theoretical and empirical results show that the relationship between pay gap within management, executive-employee pay gap, the degree of compensation incentives of non-core executives and corporate performance all show an inverted U shape. Further empirical research shows that non-core executive-employee pay gap has the strongest effect on corporate performance, while core executive-employee pay gap has the smallest effect on corporate performance. This research shows that non-core executive-employee pay gap is the most important compensation relationship within the company and core executive-employee pay gap is of least importance. In addition, in the salary incentive design of private enterprises, the “constraint” of operating profit is greater than the “constraint” of operating income, which reflects that private enterprises pay attention to seizing the key points.

Due to the non-subadditivity property of value-at-risk (VaR) when measuring tail loss risk, we propose a risk parity investment portfolio model based on conditional value-at-risk (CVaR) and provide a numerical calculation method for implementing the investment portfolio strategy. Using Sharpe ratio, maximum drawdown, and Calmar ratio as performance evaluation indicators, the risk parity investment strategy based on CVaR is compared with common investment portfolio strategies. Numerical experimental results indicate that the comprehensive performance of the risk parity strategy is more robust than the equal-weight investment portfolio strategy, the maximum Sharpe ratio investment strategy, and the global minimum variance investment strategy. Among the three risk parity strategies, the CVaR-based risk parity investment strategy has advantages in risk control, significantly improving both return and risk diversification effects. The robustness test results also suggest that the CVaR-based risk parity investment strategy can maintain stability and effectiveness in different situations.

In group decision-making under the social network environment, the weight of experts in the group and the trust relationship between experts are key factors that affect consensus reaching. However, in many studies, the trust relationship remains unchanged and the expert weight is only determined by the trust relationship. Therefore, this paper innovatively proposes a group consensus decision-making method that considers the social influence and trust evolution of experts, effectively promoting the reaching of group consensus. Firstly, the incomplete social trust matrix is transformed into a complete social trust matrix using trust propagation and aggregation methods. Then, the social influence of experts is obtained based on their additive preference relationship and social trust matrix, and the weights of each expert are obtained. Subsequently, a trust evolution model is established based on whether the optimal solution of each expert has been adopted and the difference between the ranking vectors of each expert's solution and the group's solution. Based on the trust evolution model, a consensus reaching process considering trust evolution is proposed. By using simulation methods, the weight coefficients of various indicators in social influence are calculated, and the feasibility of the proposed consensus reaching method is verified to demonstrate the rationality and effectiveness of the proposed model. Finally, a numerical example is presented to illustrate the detailed solution process of the method proposed in this paper, further demonstrating the feasibility and effectiveness of the model.

Traditional neural networks process real-valued inputs and outputs through the manipulation of neuron weights. In order to investigate the impact of introducing complex numbers into neural networks, this study employs two deep learning methods to solve the time-dependent Schrödinger equation. The physics informed neural network (PINN) focuses on incorporating physical equations and boundary conditions as constraints into the training process of neural networks, making them more in line with physical laws. On the other hand, the deep Galerkin method (DGM) utilizes the nonlinear fitting capability of neural networks to minimize residuals and approximate the true analytic solution. Numerical experimental results indicate that whether complex numbers are included or excluded from the neural networks has no substantial impact on the resulting numerical solutions. Complex operations can be re-expressed using real-valued tensors. Therefore, these two deep learning methods for solving the time-dependent Schrödinger equation are feasible, greatly simplifying the solution process while avoiding grid-related limitations. The high-precision approximation demonstrated by neural networks in numerical computation is not only simple and easy to implement, but also possesses strong parallel computing capabilities.

Since the launch of a new round of power system reform in China in 2015, the establishment of a single-track-based electricity spot market has gradually become the focus of attention in domestic electricity markets. However, up to now, a mature electricity price forecasting model has not been established in the unified spot market, and power generation and sales companies, power trading centers, and power users cannot make full use of electricity price forecasting data for auxiliary decision-making to obtain the best benefits. Therefore, this paper proposes an electricity price forecasting model based on electricity price formation mechanism and XGBoost algorithm. Firstly, according to the marginal clearing price formation mechanism and unique bidding rules adopted in the unified spot market, the unified cumulative bidding curve of the whole network is fitted by piecewise function, and the unified clearing price prediction model of the whole network is established by combining the bidding strategy of power generation enterprises. Secondly, according to the relevant data published in the unified power spot market, the XGBoost algorithm is used to select features and solve the different daily ladder bidding strategies of power generation enterprises. Finally, the hyper-parameters of the model are optimized by the highly automated Optuna algorithm. The experimental results show that the electricity price prediction model in this paper has stronger interpretability and accuracy than the XGBoost algorithm directly substituted into the data, and proves that the XGBoost algorithm has higher prediction accuracy for the bidding strategy than the gradient boosting regression tree algorithm and the random forest algorithm, thus verifying the superiority and effectiveness of the model in the electricity price prediction of the unified power spot market.

Promoting new energy vehicles is an important measure to effectively reduce the carbon footprint of the transportation system. Based on the consumer utility theory, we construct a decision model for both charging and switching modes of new energy vehicles. This model enables a comparative analysis of pricing and promotion mechanisms for the two service modes. Additionally, we examine the impacts of battery production cost, switching model technology level, driving range, and energy prices on the promotion of both modes. Our main results are as follows: 1) Compared to the charging mode, the switching mode effectively alleviates consumer charging anxiety, albeit at the expense of a premium for switching services; 2) The production cost of power battery and the level of power change technology are important dimensions that affect the adoption of the two service modes; 3) There exists a divergence in the influence of battery production costs and driving range on the promotion of the two modes; 4) Decreasing battery production costs prove more beneficial for the promotion of the switching mode, while an extended driving range is more advantageous for the promotion of the charging mode. Changes in electricity or fuel prices exert similar effects on the promotion of both modes, accelerating their application by establishing operational cost advantages for new energy vehicles.

In the context of the digital economy, the monopolistic behavior of platform enterprises and the design of antitrust mechanisms have become hot and difficult problems in current research. To solve this problem, a tripartite evolutionary game model among government, platform enterprises and the public was established under both static and dynamic reward & punishment mechanisms. In terms of model analysis, the possibility of mixed strategy Nash equilibrium (MNE) as an evolutionary stability strategy (ESS) under the two reward & punishment mechanisms and its system evolution characteristics were mainly discussed. It was proved that MNE under dynamic reward & punishment mechanisms may be the system ESS, and confirmed through system simulation. The simulation results indicate that under the static reward & punishment mechanism, all parties in the game will exhibit periodic strategy selection patterns, while under the dynamic reward & punishment mechanism, the system gradually stabilizes to MNE. This indicates that the existence of the dynamic reward & punishment mechanism is indeed a stability improvement compared to the static reward & punishment mechanism. Finally, it is suggested that government should develop dynamic reward & punishment mechanism, while increasing the intensity of punishment on platform enterprises and gradually reducing the intensity of rewards for the public. This approach can significantly increase the probability of compliance operation of platform enterprises and improve the expected utility of government and the public.

Due to “the curse of dimensionality”, both parametric and non-parametric high-dimensional tests are exposed to the issue of low power. Currently, there are two approaches to enhance the power of high-dimensional tests: 1) Add an indicative function to the test statistic, and use the marginal information to promote the power of high-dimensional tests, called as power enhancement. 2) Apply the sample splitting technique for dimensionality reduction of variables to improve the power of high-dimensional tests, named dimensionality reduction method. Based on these two ideas, this paper proposes the hypothesis tests via power enhancement and dimensionality reduction for high-dimensional means, regression coefficients and independence respectively. Numerical results demonstrate that the power enhancement method can obtain high power under both sparse and dense hypotheses. But the test level depends on the selection of the original test statistic and the threshold parameter. The dimensionality reduction method can control the significance level pretty well without considering the threshold parameter selection. Under the sparse hypothesis, the dimensionality reduction method possesses high power, but it performs lower than the power enhancement under the dense hypothesis.

Meta-analysis is a statistical method that systematically integrates, analyzes and synthesizes the results of multiple independent studies to reach more accurate and comprehensive conclusions. To solve model uncertainty in the prediction for meta-analysis, an optimal model averaging prediction method is proposed based on Mallows criterion, and the optimality of Mallows model averaging(MMA) estimator under square loss is discussed. Finally, simulation studies are conducted to evaluate and compare the performance of MMA, Jackknife model average(JMA), S-AIC and S-BIC model average estimation under information criteria, and all methods are applied to analyze the data set of BCG vaccine for illustration. The results show that the MMA estimation is superior to other model average estimations in prediction regardless of whether the variance and sample size are large or small.

To analyze the cooperation behavior from the perspective of doctors and patients in the smart medical information platform, this paper constructs a random evolutionary game model and introduces random interference factors to represent the information uncertainty in the information platform. Afterward, this paper transforms the stochastic evolutionary game model of doctors and patients into a cusp catastrophe model through the limit probability density function, proving that the sudden change mechanism is implied in the behavior evolution of doctors and patients. Finally, the simulation method is used to analyze the evolution and sudden change mechanism of the behavior selection of doctors and patients in the diagnostic information platform and the interactive information platform. This paper finds that: In the smart medical information platform, the behavior selection of doctors and patients may change drastically due to factors such as perceived value and random interference; When the initial cooperation probability between doctors and patients is high if the security and reliability of information platform are poor, doctors and patients tend not to cooperate; When the initial cooperation probability is low if the information platform security and reliability are good, doctors and patients tend to cooperate; When the initial cooperation probability is at a moderate level, the behaviors of doctors and patients in the information platform are more susceptible to the additional benefits of non-cooperation, sudden changes of behavior selection are more probable. This paper analyzes the cooperation mechanism between doctors and patients in the smart medical information platform by combining evolutionary game theory and cusp catastrophe theory. It explains the reasons for sudden discontinuous changes in the behavior state of doctors and patients. The conclusion provides enlightenment for the research on doctor-patient cooperative behavior in the information platform and the development of the smart medical platform.

Cylindrical algebraic decomposition is a basic tool in semi-algebraic system solving and real quantifier elimination. In the actual solving process, the choice of a variable ordering may have a significant impact on the efficiency of cylindrical algebraic decomposition. At present, the existing heuristic or machine learning ordering selection methods are basically based on the implicit assumption that the support set of a polynomial system is the determinant for affecting the variable orderings. In this paper, we first test this hypothesis by designing an experiment with the support set fixed but the coefficients varying. The experimentation shows that the support set is indeed an important factor, though not the only factor, determining the optimal variable ordering. Aiming at selecting the optimal ordering for computing cylindrical algebraic decompositions for systems with the same support set but different coefficients, this paper designs an ordering selection scheme via reinforcement learning. The experimentation on four variables shows that this scheme can surpass the accuracy limit of existing methods on selecting the optimal variable ordering that rely solely on the support set. In addition, experiments on systems owning up to 20 trillion of possible orderings show that the scheme is much more efficient than traditional heuristic methods. In contrast to the existing supervised learning methods for selecting the variable ordering of a few variables, this reinforcement learning scheme overcomes the difficulty of obtaining high-quality labeled data when the number of variables increases, which may lead to the combinatorial explosion of the number of variable orderings.

In the era of big data, high-dimensional survey data with mixed types of covariates brings challenges to heterogeneity analysis and its variable selection. This paper proposes a novel sparse clustering method, and discusses its application by taking the China Education Panel Survey and the social survey of "Thousands of People and Hundreds of Villages" as examples. This paper proposes an adjusted DBI criterion to measure the importance of covariates, uses different penalty parameters to control the weights of different types of covariates, and obtains the optimal clustering results and significant covariates. At the theoretical level, this paper demonstrates the variable screening consistency of the proposed method. At the numerical experiment level, a series of simulation experiments are designed in this paper to verify the good performance of the proposed method in terms of clustering and variable selection. The results of empirical data also show that the clusters divided by the proposed method have a high degree of discrimination, which is convenient for researchers to characterize each group; At the same time, the selected variables have important practical meanings. Without losing information, the dimensionality of the data is reduced, and the interpretability of the model is increased. The sparse clustering analysis proposed in this paper realizes the joint analysis of mixed types of covariates in high-dimensional survey data, which greatly improves the utilization rate of information.

During the 14th Five Year Plan period, the reform of green fiscal and tax policies was proposed to strengthen the regulatory and guiding role of incentive tax policies, such as value-added tax. This paper is based on a quasi natural experiment of China's tax system reform from business tax to value-added tax. By constructing a multi-time point double difference model, we analyze panel data of 1805 A-share listed companies and examined the impact mechanism of tax policy incentives on the green innovation of enterprises. The research finds that the replacement of business tax with value-added tax significantly enhances green innovation in enterprises, mainly reflected in substantive innovation rather than strategic innovation. Research on the mechanism of action indicates that the reform from business tax to value-added tax indirectly promotes green innovation in enterprises through the effects of division of labor and the tax burden. Heterogeneity studies have shown that the incentive effect of replacing business tax with value-added tax on green innovation is more prominent in non-state-owned enterprises and manufacturing enterprises. This paper is an important supplement to the research on existing tax policy reform and micro-enterprise behavior, providing an important basis for promoting green development through financial and tax policy reform in the future.

Digital transformation has gradually become an important driving force for enterprise development under the background of digital economy. Combining the problems of “weak digital technology foundation” and “difficult business empowerment of digital technology” from digital transformation, this paper divides digital transformation into digital technology resources and digital technology empowerment from the perspective of resources and capabilities to explore the antecedent (managerial resilience) that promote the solution of transformation problem and identify the economic consequence of transformation (firm performance). Based on the data of 557 firms listed in the Stock Exchange of Hong Kong, China from 2010 to 2020, the text analysis method is used to demonstrate that: 1) Managerial resilience has positive effects on both digital technology resources and digital technology empowerment, and has a stronger effect on digital technology empowerment; Industry competition negatively moderates the effect of managerial resilience on digital technology empowerment, but doesn't moderate the effect of managerial resilience on digital technology resources. 2) Digital technology empowerment has positive impacts on firm performance, while digital technology resources have no significant impact on firm performance. 3) Both “the promotion effect of managerial resilience on digital technology resources and digital technology empowerment” and “the positive impact of digital technology empowerment on firm performance” are stronger in large firms and non-manufacturing firms. 4) Managers with strong resilience promote the construction of digital technology and improve digital technology empowerment ability of firms by optimizing the human capital structure of management to help digital transformation; Digital technology empowerment enables firms to gain performance by improving operational efficiency and operating cost control efficiency; Managers with strong resilience can help firms acquire digital technology empowerment capabilities to improve firm performance. The conclusion of this paper provides important enlightenment for firms to implement digital transformation and obtain the economic benefits of digital transformation.

To improve the accuracy of unmanned underwater vehicle (UUV) state estimation of non-cooperative targets, an axial attention-based target state estimation method is proposed in this paper. The state estimation mechanism of the UUV non-cooperative target based on sonar observation is analyzed. The non-Markov state-space model of the problem is transformed into a first-order Markov state-space model with memory, and a recursive filtering model is constructed. Aiming at the unreliability of forward-looking sonar observation and the unpredictability of target motion, a multi-step prediction network based on transformer is proposed to describe the complex motion process of non-cooperative target relative to sonar under nonlinear observation. Aiming at the instability of observation and the unpredictability of posterior distribution, based on the Monte Carlo approximate inference principle, the multi-step prediction network is used to map the particles in the target measurement state space to the target prediction state space, and a non-cooperative target state estimation algorithm based on the axial attention is constructed. The simulation results show that the adaptability and robustness of the proposed method to uncertain inputs.

In order to investigate the impact of significant economic or political events on the dependence of financial markets, we construct the factorial hidden Markov Copula model (FHM-Copula) that allows the coefficients of dependence to follow a regime-switching process in high-dimensional state space. The FHM-Copula model is able to capture external shocks of varying magnitude, direction, duration, and short or long-term from significant events to the dependence. In the empirical study, we analyze the dynamic dependence between the stock markets of China and other BRICS countries by adopting the FHM-Copula approach. Our findings indicate that the FHM-Copula model can effectively identify the external shocks caused by significant events such as the subprime crisis, the European debt crisis, the Chinese stock market crash, China's taking over the BRICS presidency and the COVID-19 epidemic on the dependence between the stock markets of China and other BRICS countries. Our works not only provide a theoretical analysis framework based on the information shock perspective for the study of dynamic dependence among financial variables, but also provide a reference for investors and government regulators in investment decisions and risk management.

Aiming at the leader-follower intelligent networked vehicle cooperative formation coherence control problem, an observer-based distributed event-triggered control algorithm is proposed. Firstly, by analysing the vehicle dynamics, the vehicle state-space equations are modelled, based on which the system model is constructed and the trigger threshold is designed. Secondly, an observer is designed to solve the problems of system partial state unmeasurability, unknown perturbation and nonlinearity, and a distributed event-triggered control algorithm is proposed by utilizing the estimated states. The algorithm reduces the update frequency of the controller control signal by determining whether the trigger condition is satisfied, thus saving communication and computation resources. The proposed algorithm realizes the estimation of vehicle states, formation control, significantly improves the stability and reliability of the system, and enhances the cooperative efficiency of vehicle formation. Finally, the feasibility and effectiveness of the proposed method are described by simulation experiments on a four-vehicle leader-follower vehicle formation.

Currently, one of the main challenges in practical modeling lies in the fact that training and testing data come from different distributions. Stable learning addresses this issue by decorrelating all covariates through sample reweighting, thereby achieving stable predictive performance. While machine learning methods such as stable learning show good results in experiments, there are still theoretical gaps, such as the lack of metrics for model stability under testing data and explanations for why stable learning maintains stable predictions across multiple environments. This paper proposes a new metric of stability, compares stable learning methods with ordinary least squares and explores the reasons why stable learning maintains stability across multiple environments. Finally, the paper validates the theory through simulated experiments. This research contributes to refining the theory of stability in stable learning, enhancing the understanding of stability in stable learning, and guiding the selection of practical modeling methods.

The average method of statistical model is a hot issue in the field of statistics research, which can effectively improve the accuracy of statistical prediction. In statistics, multivariate linear regression model is a kind of important and practical linear statistical model. This paper mainly studies the average method of this kind of model when the random error matrix is not completely equal. We find a matrix to “unify” the different covariance matrices of each line, and then obtain the corresponding Mahalanobis CV weight selection criteria based on Mahalanobis distance by cross-validation method, and prove the asymptotic optimality of the average estimation of the corresponding model. Simulation results show that the new method is better than S-AIC, S-BIC, MMA and JMA of linear regression model with single dependent variable and MMMA of multivariate linear regression model in general.

This paper investigates the fixed-time adaptive fault-tolerant control problem for a class of nonlinear systems with actuator faults and full-state constraints. Firstly, by utilizing back-stepping control and fixed-time stability theory, a fixed-time adaptive fault-tolerant controller and parameter updated laws are designed to compensate for actuator faults and parameter uncertainties; Then, the barrier Lyapunov function is used to handle the full-state constrained problem of the system, so that the states of the system do not violate the predetermined constraint conditions; Furthermore, the proposed control scheme can ensure that all signals of the closed-loop system are bounded, the tracking error converges to the neighborhood of the origin within a fixed time and the upper bound of the convergence time is independent of the initial state of the system; Finally, the simulation example is shown to verify the efficiency of the presented strategy.

The advent of renewable energy charging stations and vehicle-to-grid (V2G) technology heralds unprecedented possibilities for the logistics sector. Within the distribution framework, electric vehicles can funnel a portion of the energy acquired from these renewable energy charging stations back into grid-connected thermal power stations via V2G technology, thereby generating significant V2G revenue. By accounting for factors such as the energy heterogeneity across charging stations, customer demand, electric vehicle energy consumption, carbon emissions, and potential V2G profits, a comprehensive mixed-integer programming (MIP) model for electric vehicle distribution, charging, and electricity transmission has been developed. This model aims to minimize the net discrepancy between the total distribution costs and V2G revenues. To address the problem's unique challenges, a hybrid ant colony algorithm (HACA) has been engineered. Numerical experiments employing multiple types of instances substantiate the efficacy of the proposed methodology. The findings reveal that the proposed approaches not only substantially curtail overall distribution expenses while augmenting V2G profits but also achieve "zero-emission" distribution for electric vehicles. Moreover, the proposed approaches offer cost-effective avenues for integrating renewable energy into the grid, fostering a synergistic, mutually beneficial relationship among logistics firms, utility companies and end-users.

The price mechanism failure caused by fake quality information disclosure restricts the development of the agricultural product market. The transparent and traceable feature of information in the blockchain provides a new solution to the problem of fake quality information disclosure in the agricultural product supply chain. However, the high cost of blockchain, consumer preferences in the market, and block traceability accuracy all affect the strategy of adopting blockchain. This paper uses the signaling game and sets price as the signal to explore blockchain adoption strategies and quality information disclosure strategies in the agricultural product supply chain. This paper finds that: 1) The agricultural supermarket will adopt blockchain technology only when the information increase is high; when farmers' planting cost is low, the agricultural supermarket will be more willing to adopt blockchain. 2) When blockchain technology is not adopted and the planting cost is low, low-quality farmers have the incentive to deliberately set high prices to confuse the market. 3) The adoption of blockchain technology by the agricultural supermarket may harm farmers' profits. Only when the consumers' preference is more information-sensitive, all participants in the agricultural product supply chain can benefit from blockchain.