The rumors breed and spread in the complex social network. In order to control rumors effectively, we explore the influencing factors of rumor forwarding behavior based on the game theory on the graph by introducing the elaboration likelihood model (ELM) and cognitive theory of emotion. Then, from the perspective of information content differentiation, an evolution game model for rumor spreading behavior of social network users is established. The simulation results illustrate that:1) Increasing users' interest in rumor/anti-rumor information directly affects whether they forward the rumor or refute the rumor; 2) The release strength of information has a direct effect on the dissemination of the information; 3) Users are more willing to forward the information of the close nodes, when they receive the information with uncertainty; 4) Rumors are more likely to be spread when they contain positive contents and when the user's personal emotions are unstable. Accordingly, this paper puts forward relevant suggestions to control the spread of rumors in social networks, and provides some theoretical support for relevant departments to formulate effective rumor control strategies.

In order to study the nature of chaos and make use of chaos better, chaos in a three-dimensional generalized Rabinovich system is studied in this paper according to Lyapunov stability theory and bifurcation theory, including dissipation, the equilibrium point and local asymptotic stability, Lyapunov exponent, Lyapunov dimension, bifurcation diagram, global attractive domain, etc. According to the result of global exponential attractive set of this chaotic system and Dini derivative, a suitable controller is designed to realize global exponential synchronization for two identical chaotic systems. A linear feedback controller is designed to achieve global exponential synchronization for two identical chaotic systems in order to make it easier to achieve fast synchronization for chaos synchronization scheme in practical applications. The innovation of this paper is that the boundedness of the chaotic system is obtained firstly, then the precise mathematical expression of the controller of this chaotic system is obtained by using the boundedness of this chaotic system. Finally, the numerical simulation of the synchronization process is carried out and the simulations confirm the feasibility of the theoretical results.

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.

The "fundamentals" that determine expected stock returns consist of three components: The expected return component that is rationally compensated for systemic risk, the cash flow news generated by changing expectations of future cash flows, and the discount rate news generated by changing expectations of rational future discount rates. Of these, cash flow news that are not driven by fundamentals are the component most likely to reverse in the short term. In this paper, for the first time, we use analyst forecast revisions to measure cash flow news and construct a short-term reversal strategy based on residual returns to study the short-term reversal effect in the Chinese Stock Market. The empirical results find that the strategy generates higher risk-factor-adjusted returns compared to the standard reversal strategy. Further, the methodological test based on isolating cash flow news finds that liquidity shocks rather than investor sentiment can explain the short-term return reversal phenomenon in the Chinese Stock Market from both the long and short leg of the portfolio. This paper examines the short-term reversal strategy of A-shares from a new perspective of residual returns, which not only enriches the research in the area of financial anomalies in asset pricing, but also provides international evidence on understanding the theory of short-term return reversal in emerging markets, with China as an example.

With the accelerated advancement of digital government construction, online administrative inquiry plays an indispensable role in social governance of China. In order to explore the key factors affecting the effectiveness of online administrative inquiry, this study focuses on the relevant data from the Luzhou online administrative inquiry platform "Please Speak Up". This study adopts a text data mining method combining various machine learning and deep learning models to identify characteristic variables in online administrative inquiry texts, construct two public satisfaction classification models. And multiple explainable methods are used to explain the model results from both structural and semantic features. The research finds that variables such as administrative inquiry sentiment, length of administrative inquiry text, type of appeal, response sentiment, type of response agency, length of response time all have varying degrees of influence on public satisfaction. In addition, the explainable framework constructed by this study can also effectively identify key content in online administrative inquiry, such as time, location, and organization names.

To address the challenges of passenger difficulty in hailing rides and insufficient vehicle supply during peak periods, this paper is based on an actual scenario in which ride-hailing platforms implement both surge pricing and subsidy strategies during peak periods, taking into account the heterogeneity of driver supply prices. By incorporating the reference point dependence theory of the driver decision-making and aiming to maximize platform expected profits, a Stackelberg game model is established to analyze the interaction between the platform and drivers. Kuhn-Tucker theorem is applied to determine the optimal service price and subsidy level for the platform during peak periods. The results reveal that the expected profit of platforms during the peak period showed an "inverted U-shape" trajectory as subsidies increased. When the subsidy level is within a critical range, the expected profits of the platform initially increase and then decrease with increasing service prices. The maximum expected profits of the platform are achieved when the critical value (peak point) is reached. By adopting appropriate pricing and subsidy strategies, ride-hailing platforms during peak period can effectively reduce the risk of driver attrition, adjust supply-demand relationships, and enhance the benefits for both the platform and drivers.

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.

A variety of hot topics lists released by social media platforms serve as a convergence and showcase for hot topic information, which provides significant insights toward our understanding of current popular discussions. However, due to vocabulary sparsity and short text length in hot list texts, traditional LDA and neural network-based topic mining models face poor performance in topic aggregation. To address these challenges, the paper proposes a topic modeling framework enhanced by a large language model—STAB, which combines the generative capabilities of large language models for text data with the excellent performance of document embeddings in topic modeling, enabling the extraction of meaningful topics from short text datasets. Experimental results on multiple datasets show that our framework outperforms existing topic modeling methods in terms of general objective evaluation metrics and applications in downstream tasks.

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 the process of achieving high-quality development, enhancing regional economic resilience is one of the important guarantees for promoting economic development and driving overall regional development. This paper analyzes the mechanism of digital finance influencing regional economic resilience from a theoretical perspective, and measures and analyzes the level of regional economic resilience of each province by constructing a regional economic resilience evaluation index system. Based on panel data of selected 31 provinces (autonomous regions, municipalities) in China from 2011 to 2021, a benchmark regression model, a mediating effect model, and a panel threshold effect model are further constructed to empirically test the theoretical analysis. The research shows that:1) There are still a large number of regions with relatively low levels of regional economic resilience, and there is a large disparity in the development level of economic resilience between provinces, which exhibits an imbalanced pattern of "high in the East and low in the West"; 2) Digital finance has a significant positive impact on regional economic resilience, with a regional heterogeneity of "eastern region$>$ central region$>$ western region"; 3) Digital finance has a significant positive mediating effect in the relationships of "digital finance $\rightarrow$ industrial structure upgrading $\rightarrow$ regional economic resilience", "digital finance $\rightarrow$ narrowing the urban-rural income gap $\rightarrow$ regional economic resilience", and "digital finance $\rightarrow$ social security level $\rightarrow$ regional economic resilience"; 4) The promotion effect of digital finance on regional economic resilience can be more significant when crossing the threshold of internet penetration, and the acceleration threshold effect in eastern coastal regions is significantly higher than that in central and inland regions and western remote areas. Therefore, by developing digital finance in line with local conditions, giving full play to the positive effects of industrial structure upgrading, narrowing the urban-rural income gap, and improving social security, and accelerating the construction of modern information technology, it can effectively enhance the resilience of the regional economy.

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.

MOOC comments contain a wealth of user opinions and emotional information, reflecting the quality demands and satisfaction from the user's perspective. This study constructs a model for the quality characterization of MOOC based on the "Theme-Emotion" coupled analysis of user comments. Specifically, it utilizes LDA to extract thematic information from comment texts, representing dimensions for assessing MOOC quality. Furthermore, it employs BERT for emotion classification to characterize user satisfaction and attention towards MOOC quality. The study takes "Chinese University MOOC" as an example to analyze the MOOC quality characterization results. The findings reveal that the quality assessment dimensions vary in MOOCs of different subject areas, user satisfaction and attention towards each dimension differ for different MOOCs, and the quality assessment dimensions have varying significant impacts on MOOC quality. The proposed MOOC quality characterization model can be generalized to various online course platforms with user comments, offering a fine-grained representation of course quality. This model provides precise criteria for builders to design and improve courses and for learners to select courses, contributing to the optimization of MOOC development and user experience.

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.

At present, the world is facing a complex and volatile semiconductor trade environment. To identify the impact of the economic crisis on international trade networks, this paper chooses semiconductors, the foundation of the digital economy, as the research object, and uses the semiconductor trade data of 2019 in the United Nations Commodity Trade Statistics database to build a directed weighted semiconductor international network based on complex network theory. Its topological structure characteristics are analyzed. On this basis, the cascade failure model is used to explore the propagation process of the semiconductor supply crisis in the international trade network. The results show that the semiconductor international trade network presents the characteristics of a small world. The middle and high-income economies such as China and the United States are at the center of the network, and they are very active and have an important influence in the semiconductor trade market. Once the risk of semiconductor supply shortages arose in the United States, Italy, South Korea, the Netherlands, France, and Japan, the size of the avalanche of economies triggered through China is extremely large, revealing that China played a crucial intermediary role in the crisis transmission process. When China is the source of the semiconductor crisis propagation, the number of economies directly infected by China after one iteration is ranked first, emphasizing China's important position in the global semiconductor trade. The findings of the paper not only contribute to the in-depth understanding of the global international trade pattern of semiconductors, expanding the related research in the field of semiconductors, but also provide useful policy suggestions for the development of China's high-tech industries such as semiconductors, the formulation of international trade policies and effective response to the risks of the global semiconductor supply chain.

Consistency is one of the key and challenging issues in decision-making processes, which affects the validity and accuracy of preference relations and decision results. When the decision makers' preference is expressed by the hesitant fuzzy linguistic term set, in order to obtain the decision information in the linguistic decision environment effectively, this paper studies the consistency ascertaining, inconsistency repairing and weight derivation for hesitant fuzzy linguistic preference relations. First, two new definitions of additive consistency for hesitant fuzzy linguistic preference relations are proposed: Completely additive consistency and weakly additive consistency. Based on the proposed definitions of additive consistency, some linear programming models and 0-1 mixed programming models are provided to ascertain the consistency. In addition, for the incomplete hesitant fuzzy linguistic preference relations, some methods are developed to estimate the missing values and ascertain the consistency. When the hesitant fuzzy linguistic preference relations are inconsistent, a repairing method is developed, then the priority weights are calculated based on the consistency. Finally, some specific examples of decision problems and comparative analysis are provided to show the effectiveness and rationality of the models.

Considering a second-order nonlinear multi-agent systems with time-delayed, this paper investigates the leader-following consensus problem for this system under a fixed directed topology. In order to save the communication and computation resources of the system, a distributed consensus control protocol based on event triggering mechanism is presented. A corresponding event triggering condition is designed for each agent, and the agent updates the controller only when the triggering condition is satisfied, the frequency of updating information among the agents is effectively reduced. Some sufficient conditions for the multi-agent system with time-delayed to achieve leader-following consensus are given using graph theory, matrix theory and Lyapunov stability theory, and the Zeno behavior of the system is strictly excluded. Finally, the feasibility and effectiveness of the proposed control protocol are verified by simulation results.

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.

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.

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.

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.

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.

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.

Road condition detection is a core task in intelligent driving, including height limit detection tasks. Considering that the research related to height limit detection in the academic community is not yet mature, we have conducted research on height limit detection methods and proposed a height limit detection network based on key points and multi-frame image feature fusion. By adopting key points in the height limit detection task, unnecessary predictions are reduced and detection efficiency is improved. By introducing a convolutional gated recurrent unit (ConvGRU) to model multiple images and learn the contextual relationship between multiple images, improve recall rate, and reduce missed detection rate. The spatial particulars feature (SPF) module is proposed, which strengthens the multi-scale feature fusion in the decoding layer. In order to improve the accuracy of the model, the coordinate attention mechanism is introduced, and the target detection area is further paid attention to. According to the experimental results, this network can not only complete the height limit detection task well, but also balance the precision and recall rate better, with higher F1 values and fewer parameters compared with other advanced networks such as BiSeNet, PINet, PSPNet, etc; At the same time, in the task of lane line detection, it also performs excellently in terms of accuracy and missed detection rate, further proving the effectiveness of the network.

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.

In this paper, the authors present a method to express a univariate positive semi-definite polynomial into the sum of lader-like squares, i.e., squares of several polynomials which degrees are strictly decreasing. When the coefficients of the given polynomial are rational numbers, the coefficients of the degree-descending polynomials are also rational numbers. The authors have also extended the method to multi-variate polynomials. Namely, if a multi-variate polynomial has any sum of square representation, then a special representation with rational coefficients of this polynomial can be obtained using this method.

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.

In this paper,synchronization of complex networks with cyber attacks and output coupling is investigated.In the case of deception and denial-of-service attacks between controller and actuator,an output feedback proportional-integral-derivative (PID) controller is designed.By employing Lyapunov stability theory and linear matrix inequalities,a sufficient criterion is acquired to satisfy both mean square asymptotically stability for the synchronization error system and strictly $(\mathcal{Q},\mathcal{S},\mathcal{R})$-$\gamma$-dissipativity.Finally,the feasibility and effectiveness of the obtained theoretical results are verified through simulations.

Zeroing neural network (ZNN) has been widely used to solve time-varying problems since it was proposed because of its fast convergence speed and ability to resist external noise interference. However, the convergence speed and anti-interference ability of the existing zeroing neural network models are still not satisfactory. Therefore, to further improve the performance of ZNN, a new fixed-time convergent activation function (FTCAF) is designed in this paper. Then, a fixed-time convergent zeroing neural network (FTCZNN) model is established based on the proposed activation function and this model is applied to solve dynamic Sylvester equation (DSE). Theoretical analysis proves that the FTCZNN model has a fixed time convergence upper limit and strong anti-interference ability. In addition, numerical simulation results also demonstrate the superior performance of the FTCZNN model. Finally, FTCZNN model is used to realize the trajectory tracking experiment of the robot manipulator. The experimental results once again prove that the FTCZNN model has fast convergence speed and strong anti-interference ability, and its practical application ability is also verified.

This paper studies boarding probabilities of metro passengers at platforms. Under several reasonable assumptions, we prove a stochastic queueing model for passengers' boarding probabilities, which extends the deterministic queueing model (first-come-first-serve principle) in the research of Grube, et al. (2011) and Mo, et al. (2020). We present a simplified version of the stochastic queueing model that reduces to the deterministic queueing model when the parameter of the version is set to be zero. Based on the stochastic model, we construct a passenger flow simulation on a typical route. With the train schedule, passengers' tap-in times, and walking time distributions being inputs, the simulation yields each passenger's movement and tap-out time as outputs. Combining real data and simulation outputs, we provide a parameter calibration method. Real data analysis on Changping line of Beijing Metro illustrates advantages of the proposed stochastic model over the existing deterministic model.

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.

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.

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.

Automatic recognition of tongue images is the core issue of the intelligent tongue diagnosis, which is a typical application of computer vision in the field of traditional Chinese medicine. The liver depression line (LDL), which is located on both sides of the tongue surface, is an important clinical diagnostic basis of traditional Chinese medicine. Its automatic visual recognition can promote the further development of intelligent tongue diagnosis. In order to improve the accuracy of LDL recognition, we propose an attention-guided and local region reuse strategy based on the advantages of the self-attention mechanism of the visual Transformer. We then design a network, ST-LDL, for LDL recognition based on Swin Transformer. The attention guidance strategy is used to promote ST-LDL to extract fine-grained features of LDL. The local region reuse strategy utilizes the local strong response areas on both sides of the tongue surface to train the local network branch of ST-LDL on one hand and enhance the global network branch of ST-LDL on the other hand. Ablation experiments show that using attention guidance and policy region reuse strategies alone can improve recognition performance; all metrics increase significantly when these two strategies are shared. Comparison experimental results show that our algorithm outperforms other existing advanced algorithms.

The main purpose of mediation analysis is to study whether the independent variable affects the dependent variable through the mediator variable. The mediation analysis method has been widely studied and applied in the fields of social science and biomedicine, and has become the mainstream research method in these fields. Whether it is a single mediator variable or multiple mediator variables, many scholars have conducted extensive research, but it is worth noting that these studies on mediation analysis seldom use prior information. In the case of high dimensions, the prior information extracted from the existing research can improve the performance of estimation and testing. In this paper, we propose a mediator variable selection method and a hypothesis testing method based on prior information. Theoretical and numerical analysis results show that the method proposed in this paper outperforms the existing methods when the prior information quality is good, and shows robustness when the prior information quality is moderate, and the performance is not much different from the existing methods. Finally, the real data example further illustrates the satisfactory performance of the proposed method.

The overwhelming selection of movies on the market nowadays makes it difficult for users to make a decision. An efficient movie recommendation system plays a significant role in improving user experience and the market competitiveness of movie service providers. The challenge lies in how to integrate multiple data sources for personalized recommendations while balancing algorithm accuracy and diversity. Research on this issue is of great theoretical and practical significance. User portraits can depict rich user characteristics from multiple dimensions, helping us to better understand user interests and behaviors. Meanwhile, link prediction offers special benefits when modeling from a network topology standpoint. The integration of them provides a possibility to solve the above issues. Therefore, this study proposes a novel user portrait and link prediction-based personalized recommendation algorithm called UPLPR. The algorithm is designed under the background of movie recommendation. It distinguishes between the interest similarity that reflects in user behavior and genre domain. By abstracting user portraits from multiple data sources and integrating them into the link prediction process as external information of the network, the accuracy of the algorithm can be improved. Furthermore, from the perspective of scarcity, the algorithm improves the calculation of interest similarity between users in bipartite graph projection and evaluates the promoting or inhibiting effect of links in the recommendation process. Such consideration improves the novelty and personalization of the recommendation and mitigates the popularity bias problem to some extent. Finally, experiments were conducted on two MovieLens datasets to verify the proposed recommendation algorithm. Results show that compared with representative algorithms, the algorithm proposed in this paper not only achieved significant performance in accuracy but also demonstrated obvious advantages in diversity-related indicators. Additionally, the abstracted user portraits can help recommendation platforms understand their user base, thereby formulating more scientific marketing and management strategies.

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.

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.

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.

Aiming at a low-carbon supply chain composed of a manufacturer, a retailer and consumers, this paper considers the stochastic dynamic characteristics of the manufacturer's enthusiasm for emission reduction, and studies the differential game between two suppliers under the background of consumers' green trust and low-carbon preference. The stochastic evolution process of the manufacturer's enthusiasm for emission reduction is described by using Itô process. Based on the profit structure of members, the stochastic differential game model of low-carbon supply chain with and without blockchain technology is constructed. By using Bellman's continuous dynamic programming theory, the feedback strategies of members in two equilibrium situations are solved, and the stochastic dynamic evolution characteristics of the manufacturer's enthusiasm for emission reduction are revealed. Finally, a numerical example is given to analyze the influence of the manufacturer's enthusiasm for emission reduction, consumers' green trust and low-carbon preference, as well as stochastic disturbance factors on members' equilibrium strategy and optimal profit under stable and dynamic equilibrium conditions, The results show that, due to the influence of random interference factors, the manufacturer's enthusiasm for emission reduction always fluctuates around its expectation, and finally stabilizes at a steady-state value. The improvement of manufacturer's enthusiasm for emission reduction and consumers' low-carbon preference can bring economic benefits to the manufacturer. The adoption of blockchain technology is more conducive to improving the profitability of the retailer, and whether the manufacturer adopts blockchain technology needs to consider the influence of consumers' low-carbon preference and green trust.

This paper studies the stabilization problem of heterogeneous multi-agent systems composed of the first-order and second-order dynamic agents in a signed directed graph. By utilizing the knowledge of Laplacian matrix and graph theory, corresponding protocols are designed for the second-order and first-order dynamic agents, respectively. Based on the layering theory proposed in this paper, independent strongly connected components (SBiSCC) of structural equilibrium are utilized to design control parameters. The necessary and sufficient conditions for achieving stabilization of the first-order and second-order heterogeneous systems in communication topology are given. Finally, the paper provides several simulation verification theoretical results.