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.

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.

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.

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.

Investment-specific technology (IST) is an innovation resulting from the creation of new capital stock at the technological level, which is widely recognized as a significant driver of economic growth, and there is also a close relationship between IST shocks and asset prices. Machine learning methods, extensively utilized in finance, can uncover more influential factors that elucidate asset price volatility. Therefore, examining the impact of IST shocks on asset prices holds substantial practical importance. This paper leverages advancements in fundamental quantitative investing research to screen for the top-performing factor variables with investment performance in the machine learning approach. It constructs three IST shock proxies and nine micro-firm characteristics and market risk factors for the period from January 2004 to December 2021, based on data availability, and the TVP-SV-VAR model is employed for time-varying characteristics analysis to elucidate the effect of IST shocks on cross-sectional stock returns under varying firm characteristics. The findings indicate that the influence of IST shocks on cross-sectional stock returns varies over time across firm characteristics, with the direction and magnitude of the impact being uncertain and the lagged impact being short- to medium-term. IST shocks in the short term tend to further influence investors' future expectations of firms by affecting trading frictions such as trading volume and turnover in the short term. At the same time, in the medium term, the impact of IST shocks is more likely to affect growth factors such as changes in shareholders' equity, which in turn affect stock price volatility.

The gamma process is one of the widely used models for analyzing monotonic degradation data. Parametric estimation of the gamma process-based degradation models requires one to postulate specific forms for shape functions. However, there may sometimes be no enough information to determine appropriate functional forms, which makes the parametric estimation method inapplicable. Regarding the bivariate gamma degradation model proposed by Song and Cui (2022), this paper investigates the problem of estimating shape functions nonparametrically. An efficient estimation procedure is developed based on the expectation maximization algorithm. Numerical simulations are performed, and the results demonstrate the effectiveness of the proposed method. Finally, a real data set is analyzed for illustration.

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.

Recognizing the primary factors that influence information diffusion on social media platforms holds significant importance in the containment of harmful information spread. Previous research has primarily utilized regression analysis to identify variables that have a significant impact on retweets. However, these approaches have been limited in terms of interpretability. Using statistical modeling and causal inference, this study analyzes the variables that affect retweets from user and text features. Subsequently, the dose-response function is generated to elucidate the causal relationship of the text sentiment to retweets. Additionally, considering the potential collection bias in observed social media datasets, this study uses topical clustering for data filtration. In the experimental analysis of Twitter dataset related to the Vaccine discussion and presidential election, we have identified the variables that impact the retweets, and investigated the causal impact of text sentiment to retweets.

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 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.

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.

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.

In this paper, we adopt the spatial error model to describe the network structure relationship between individuals, and propose both estimation and imputation methods of the varying-coefficient partially linear spatial error model with missing responses. We firstly construct the estimator of the model parameter through profile maximum likelihood method and a matrix blocking technique. We prove the asymptotic normality of the parametric estimators and show the convergence rate of the nonparametric estimator. We then propose imputation estimators of missing response based on this model. Finally, we conduct Monte-Carlo simulation studies to detect the infinite sample performance of the estimator and analyze the QQ data set using the proposed method.

Information asymmetry is the main reason that prevents manufacturers from actively participating in carbon market trading and investing in emission reduction technologies. Based on the carbon trading mechanism, this paper studies the choice of manufacturers' emission reduction and retailers' information sharing strategies under the condition that retailers hide consumers' low-carbon preference information. In this paper, Stackelberg model is used to investigate the optimal decisions of each member of the supply chain, in which retailers have private information (consumers' low-carbon preferences) and decide whether to share this information with manufacturers. By using game theory and static comparative analysis, it is found that retailers' sharing of information is beneficial to the supply chain, and under the condition of asymmetric information, manufacturers and retailers can improve their own profits by formulating revenue sharing contracts. When the manufacturer's risk aversion is low, retailers are willing to share information; When consumers have high low-carbon preferences and are insensitive to product prices, the emission reduction rate of manufacturers will increase; For products with high emission reduction costs and low consumer preference for low-carbon emission reduction, increasing carbon quotas will reduce the price of products, thereby reducing manufacturers' incentive to reduce emissions. Therefore, manufacturers can appropriately reduce their risk aversion behavior to attract retailers to share information. In addition, it is beneficial for the supply chain to establish revenue sharing contracts between manufacturers and retailers; In order to strengthen the manufacturer's investment in emission reduction technology, retailers can give priority to the promotion and promotion of low-carbon products to non-price sensitive users.

With the rapid development of the platform economy, more and more manufacturers sell the products through their own channels (i.e., the direct channel) besides the retailers (the indirect one), i.e., the dual-channel supply chain. Traditional wisdoms also refer to the dual-channel as the manufacturer encroachment, endowing manufacturers with absolute control over prices. Intuitively, one finds the manufacturer can have more carbon emission, which increases the manufacturer's purchase cost of carbon emissions (i.e., carbon cost) because of the increasing sales with channel competition, especially under the carbon cap-and-trade, namely channel competition effect. On the other hand, the research and development (R&D) cost per the unit product of the carbon reduction can be alleviated due to channel competition, which results in the lower unit carbon mission and wholesale price, namely, spillover effect. Motivated by the observations, we employ a Stackelberg game between a manufacturer (she) and a retailer (he) to explore the manufacturer's channel decisions under carbon cap-and-trade. It shows that the manufacturer always has an incentive to develop the direct channel. Counterintuitively, whether the manufacturer's carbon emissions in the dual-channel supply chain are higher than that in the single channel one depends on the manufacturer's reduction cost in carbon emission. To be specific, when the manufacturer's reduction cost in carbon emission is low, her carbon emission in the dual-channel supply chain is lower than that in the single channel; Otherwise, her carbon emission in the dual-channel supply chain is higher. For the retailer, he can benefit from the manufacturer encroachment. When the carbon price is high and the manufacturer's reduction cost in carbon emission is low, the retailer benefits from the manufacturer encroachment; Otherwise, his profit in the dual-channel supply chain is lower. In addition, we identify the region in which the retailer's profit is higher and the carbon emission is lower in the dual channel supply chain than those in the single one.

The optimization of revenue management is one of the effective ways to improve retailers' economic returns. Pricing is the engine and core technology of revenue management, which plays an important role in improving retailers' revenue. Considering the complexity of forecasting and optimization problems in the practical application of revenue management, the steps of forecasting product demand first and then optimizing revenue are usually adopted. When forecasting product demand, there are usually multiple candidate models, that is, facing the uncertainty of the model. At this time, the final model is generally determined by model selection. However, traditional model selection criteria, including Akaike information criterion(AIC) and Bayesian information criterion(BIC), usually only consider the impact of model selection on prediction accuracy, without considering how the prediction model will affect the next optimization decision objectives. This paper first proposes the focused information criterion(FIC) model selection criterion in the optimization of commodity income management, uses the FIC model selection criterion to select the product demand forecasting model, considers the structure of the optimization model, and selects the prediction model with the goal of minimizing the decision-making error rather than prediction error. The numerical simulation results show that, in most cases, compared with AIC and BIC model selection criteria, FIC model selection criteria considering decision objectives performs best. Meanwhile, the empirical research results also verify the superiority of the FIC model selection criteria considering decision objectives.

To solve the dilemma of information-sensitive e-waste recycling under incomplete information, this paper constructed an evolutionary game model composed of government, consumers and recyclers based on prospect theory, and discussed the main factors affecting system game strategy. The results show that the government's increased supervision can promote the standardization of the recycling market. The negative credit evaluation of consumers is conductive to the informal recyclers. The high negative credit evaluation encourages the normalization of the informal recyclers, while the low negative credit evaluation promotes the informal recycling treatment of the recyclers. In the recycling process, if consumers suffer information leakage losses, they tend to distrust strategies, and the greater the information leakage loss, the deeper the degree of distrust. In addition, the loss factors in prospect theory have an impact on the strategies of consumers and recyclers, while the return factors have almost no effect, indicating policymakers pay more attention to loss aversion in the game.

The hydrodynamic characteristics of USV are highly nonlinear and time-varying. In order to facilitate the control of the yaw, a nonlinear parameter-varying (NPV) model based on the surge velocity is proposed. Firstly, a nonlinear mechanism model with three degrees of freedom is established by introducing Ross damping model from the hydrodynamic mechanism. Secondly, on the basis of the mechanism model, the nonlinear term is implied in the linear structure to make the model form a linear structure. Then, the sway damping term with small value is ignored, and the surge velocity is taken as the variable parameter to establish the NPV model based on the surge velocity. The NPV model has a simple structure with nonlinear and variable parameter terms, which is an extended form of the Norrbin nonlinear model and linear parameter-varying (LPV) model. Finally, simulations and experiments show that the NPV model can well describe the nonlinear and time-varying characteristics of the yaw motion of the USV.

The COVID-19 pandemic brings a crisis to China’s stock market, and investor’s choice of safe haven assets is increasingly attracting attention. This paper uses the DCC-GARCH t-copula model to test the safe haven property between gold and bitcoin in China’s stock market during the COVID-19 pandemic. The results show that gold is a better safe haven asset than bitcoin on average, because gold has a lower average hedging ratio than bitcoin, provides a cheaper hedge, and requires a lower proportion of gold than bitcoin in a portfolio with the same stock industry index. The dynamic volatility of optimal hedging ratios and portfolio weights also further points to the need for investors to actively rebalance their portfolios rather than adopt a static approach.

Addressing the price transmission issue between the supply and demand sides in virtual power plants (VPP), this paper takes into account the uncertainty of renewable energy output, grid electricity purchase prices, and operating costs of various units. The authors propose a dynamic time-of-use pricing strategy within the VPP and, considering the comprehensive demand response from the load side, introduce a two-layer economic dispatch model for VPP with EV (electric vehicle) integration to ensure low-carbon economic operation of the VPP. The upper layer focuses on the energy side costs, aiming to minimize the supply costs for VPP operators. It also introduces the carbon capture system (CCS) as a flexible resource, proposing an operational mode for the carbon capture device that maximizes the use of renewable energy and off-peak grid electricity. The lower layer considers the energy consumption costs of the demand side, including EVs, with the objective of minimizing these costs. Finally, the effectiveness of the proposed strategy is validated through numerical examples. The results show that, compared to the grid’s time-of-use pricing mechanism, the proposed dynamic time-of-use pricing mechanism can save 51.8% of energy supply costs and reduce CO₂ emissions by 81.62%, effectively enhancing the economic and low-carbon performance of the VPP.

In this paper, to address the challenges of semantic granularity and limited flexibility in effective portfolio investment caused by the inadequate perception of stock price fluctuations in portfolio return prediction models, the authors propose a comprehensive system prediction model for stock returns by integrating the sentiment situation evaluation score model (SESTM) and graphical lasso (GLASSO). Firstly, the authors introduce quantile regression to model stock price volatility, defining volatility width sequence and volatility mean sequence to identify vocabulary related to positive return fluctuations. Next, the SESTM model is employed to extract perception vocabulary related to stock price volatility from news announcements and generate news sentiment scores based on closely related themes and matching dictionaries associated with policies, valuations, and market sentiments. Finally, by combining the GLASSO method, the authors construct a network structure of interdependence among stock prices and develop individual stock portfolio strategies based on this network. Empirical experiments are conducted using stocks from the biotechnology vaccine sector during the epidemic period to compare network interdependence and sentiment situation evaluation models. The results show that firstly, investment strategies constructed based on perception vocabulary are more suitable for short-term predictions; Secondly, incorporating information the reflected partial correlations in the interdependent network, the average daily logarithmic returns of the investment portfolio reached 1.6%, which is a 14.3% improvement by 14.3% compared to not considering partial correlations, and it is twice the average daily logarithmic returns of a random combination 0.7%. Moreover, the highest return increased from 3.117 for a random combination to 3.605, showing a significant improvement of 15.6%. These results indicate that the combination model of SESTM+GLASSO provides an efficient and superior system prediction model through a comprehensive approach that can analyze the network interdependence among stock prices and accurately predict stock returns for formulating corresponding investment strategies. It has positive implications for advancing statistical research in dynamic price perception and deepening generation-based cross-modal tasks within large language models.

The digitalization of cross-border trade motivates the development of cross-border e-commerce (CBEC). To inspire the small and medium-sized companies to participate in the innovative CBEC practice, 165 CBEC comprehensive pilot zones have been established in different batches and the so-called “no-ticket exemption” policy has also been introduced. In this paper, the integrated optimization of the CBEC supply chain network design and the tax-declaring strategies are studied, and both the comprehensive experimental zones and the “no-ticket exemption” policy are taken into account. A two-stage nonlinear stochastic programming model is developed, in which, uncertain demands and exchange rates are involved. To optimally solve it, the proposed model is linearized and an optimally dedicated L-shaped algorithm is designed. The case study shows that the “no-ticket exemption” policy can significantly decrease the tax burden of the CBEC companies, and then, attract the CBEC companies to deploy their hubs in the comprehensive pilot zones. Moreover, the introduction of the “Regional Comprehensive Economic Partnership” agreement would alleviate the difference between overseas warehouses and domestic warehouses and inspire the CBEC companies to concentrate cargo logistics to a few overseas warehouses with geographical superiority. Both of the two policies can reduce the layout cost of supply chain network of the CBEC companies.

This paper is concerned with the robust feasibility for a class of support vector machine problems with uncertain data. Firstly, a robust counterpart problem of the uncertain support vector machine problem is introduced in terms of robust optimization. Then, a reformulation of the robust counterpart problem of the uncertain support vector machine problem is given. Finally, by using this reformulation and the so-called epigraphical set, an exact formula for the radius of robust feasibility of the uncertain support vector machine problem is obtained.

In the field of equipment fault diagnosis, text data such as operation instructions and maintenance records have great application value, thus fully mining and utilizing text data can significantly improve the efficiency of fault diagnosis. Semantic feature extraction and unsupervised clustering methods are commonly used to mine text data for the purpose of assisting in fault location, but such methods are not able to explain the cause of faults and give reasons for providing corresponding repair solutions. Furthermore, repair solutions generated by those methods are not easy to understand. Based on the existing mature pre-trained language model BERT (bidirectional encoder representation from transformers), this paper proposed a BERT-based short text classification model combined with knowledge graph for fault location, in order to fully explore and utilize the knowledge and laws contained in the text data of CIR equipment. Firstly, fault modules were determined by functional hierarchical relationships of CIR equipment. Then, this paper used BERT-based text classification model to obtain the preliminary fault location. Finally, causes and other information were further recognized with the assistance of knowledge graph to assist in fault diagnosis. Proving fault repair solutions based on the fault diagnosis knowledge accumulated by the knowledge graph makes solutions easy to be understood by maintenance personnel, and helps in knowledge management and engineering efficiency. In terms of text classification techniques, this paper used fault maintenance ledger records of CIR equipment to do experiments, and results proved that the performance of our BERT-based model had been greatly improved compared with traditional classification models. In terms of fault diagnosis, the proposed fault location method combining text classification and knowledge graph also provided support for rapid fault diagnosis by inexperienced equipment maintenance personnel, as well as obtaining certain practical significance.

This paper considers the problem of preview saturated control for uncertain periodic discrete-time systems with actuator saturation. The classical difference operator method in preview control theory can not be applied due to the existence of time-varying uncertain matrix and input saturation. The state auxiliary variable is introduced and the usual state difference is replaced by the the difference between the state vector and the state auxiliary variable. Therefore, the augmented error system of uncertain discrete system with input saturation is successfully constructed. By constructing the augmented model, the problem of designing the preview saturation controller of the original system is transformed into the problem of stabilization of the augmented error system. A sufficient condition for the asymptotic stability of the closed-loop system is derived by using the improved sector condition to deal with the saturation nonlinearity and the LMI technique. Finally, a simulation example is given to illustrate the validity of the results.

Hopf bifurcation is a kind of simple but important dynamic bifurcation problem, which means that when the system parameter changes past the critical value, the equilibrium point changes from stable to unstable and a limit cycle is generated. Based on Hopf bifurcation, this paper proposes a time-delay reaction-diffusion rumor propagation model with saturated control, which better reflects the characteristics of rumor propagation in real life, and studies the Turing instability and Hopf bifurcation. Meanwhile, the time delay is selected as the bifurcation parameter, and the analytic expression of the bifurcation threshold is given. Finally, the correctness of the theoretical results is verified by numerical simulation. The results show that both diffusion and time delay are the causes of the system instability. The traditional rumor propagation model only considers the time evolution, while the model depicts the traditional rumor propagation model from the two dimensions of time and space, making it more appropriate to reflect the law of rumor propagation in real life, and providing new ideas for the governance of rumor propagation.

Transient behavior, steady-state precision, regulation time, and convergence rate are the four key indicators for evaluating closed-loop control system performance. In this paper, the authors propose a tracking control design scheme that simultaneously satisfies the above four indicators for unmatched uncertain pure feedback nonlinear systems. This scheme ensures that the system output signal always stays within the envelope range formed by the performance function by setting the performance function. At the same time, under a novel error transformation mechanism control, the steady-state precision and regulation time of the closed-loop system can be pre-set. The authors use neural networks to approximate completely unknown nonlinear functions, where the weights of the neural network can be updated online by adaptive laws. In addition, the authors add the σ-correction term in the adaptive law to avoid parameter estimation drift phenomenon. Finally, simulation results validate the effectiveness of the proposed control method and its superiority in control performance.

In reflexive Banach spaces, the time optimal control problem for linear control systems is considered. By virtue of nonsmooth analysis, the properties of the minimal time function which has two variables with the initial point and the size of control set are studied such as continuity. The results that the target point should be the origin are generalized to be an arbitrary point.

Traveling salesman problem (TSP) is a typical problem in combinatorial optimization, and the relevance of solving the TSP is significant. Using deep reinforcement learning (DRL) models to automatically design learning algorithms has become a recent research hotspot as DRL is widely used in industry. In order to enhance the generalization ability of the DRL model on the large-scale TSP, this paper proposes a hybrid model of dynamic graph convolutional network to encode and spatial attention mechanism to decode for tackling the large-scale TSP. Dynamic graph convolution module can dynamically encode node information so as to efficiently update the hidden layer state of each node. Spatial attention facilitates capturing the global connections between nodes, and then calculating and extracting key features by weighting all local features. Experimental results show that our model outperforms the previous DRL model for optimization when generalizing the training strategy of TSP50 to TSP250/500/750/1000, and the test results on the standard dataset of TSPlib also show the improvement of the model for optimization performance.

An adaptive dynamic programming (ADP) algorithm is proposed for a class of model-free stochastic linear quadratic (SLQ) optimal tracking problem with time-delay. Firstly, the equivalent system of the original time-delay system is derived using the double causal coordinate transformation. A new augmented system consisting of the equivalent system and the command generator is constructed, and then the stochastic algebraic equations of the augmented system are given. Secondly, in order to solve the SLQ tracking control problem, the stochastic problem is transformed into deterministic problem. Then the ADP algorithm is proposed and its convergence analysis is given. For the purpose of realizing the ADP algorithm, three neural networks are designed, which approximate the optimal cost function, the optimal control gain matrix and the system model respectively. Finally, the effectiveness of the algorithm is verified by a numeric example.

The prescribed-time bipartite consensus control problem for linear cooperative-competitive multi-agent systems (MAS) based on the event-triggered mechanism is studied considering the possible external disturbances. Firstly, a disturbance observer is designed to estimate the external disturbances to the system, and an event-triggered condition is given for each agent to reduce the communication frequency between agents and save the limited communication network resources, while proving to avoid the Zeno phenomenon. Secondly, in order to explicitly pre-allocate the settling time, a prescribed-time event-triggered control protocol in a cooperative and competitive topology is designed in conjunction with the estimated value of the disturbance observer to ensure that all agents achieve bipartite consensus despite external disturbances. Finally, the feasibility and effectiveness of the proposed method are verified using simulation examples.

Bayesian quantile regression can well estimate the parameters in the linear mixed effect model. Gibbs sampling is commonly used in Bayesian parameter estimation. In order to obtain accurate estimation results, Gibbs sampling method requires multiple sampling. When the model parameter dimension is high, the Gibbs sampling will be very time-consuming. Therefore, we use variational inference to approximate the posterior distribution of parameters. Variational inference uses unconditional distribution to approximate the conditional distribution obtained by Gibbs method, thus making the calculation more efficient. In this paper, a priori assumption of the parameters of the model is normal distribution, and the variation inference of the parameters of the unpunished linear mixed effect model is carried out. Considering the high dimensional situation, we assume the prior distribution of the model parameters as Laplace distribution, and make variational inference for the parameters of the double penalty linear mixed effect model. From the simulation results, although the accuracy of variational inference for model parameter estimation is slightly less than that of Gibbs sampling, it runs faster. In the case of high dimension, the improvement of operation efficiency is more obvious. In the era of big data, the consumption of time and resources is the first factor we need to consider. Therefore, the method proposed in this paper can be applied to the high-dimensional linear mixed effect model.

For the purpose of comparing the development level of multiple objects to be evaluated at different times and the overall development level in a certain period of time, dynamic evaluation is required. In order to overcome the shortage of Euclidean distance calculation of closeness in traditional TOPSIS, this paper introduces the idea of set-pair analysis connection degree into TOPSIS, uses connection vector distance instead of Euclidean distance to calculate closeness, adds time dimension, and proposes a dynamic TOPSIS evaluation method based on connection degree. This method can not only obtain the evaluation value and the ranking result reflecting the difference degree of the evaluation index value, but also obtain the evaluation value and the ranking result reflecting the increase degree of the evaluation index value, and the comprehensive evaluation value and the ranking result considering the above two situations at the same time. Finally, an empirical analysis of green development level of the Yangtze River Economic Belt is used to verify the effectiveness of the proposed method.

As the core of coordinating urban traffic, intelligent transportation system (ITS) is developing rapidly, as well as, the traffic flow prediction is an important part of ITS, which is regarded as the key factor for successful deployment of ITS. Because of the complex spatial topological structure of the traffic network, the traffic flow shows higher-order nonlinearity and dynamic spatial-temporal complexity. In order to better predict the traffic grid data, this paper proposes a new spatial-temporal network model DCSTGCN, which has the following characteristics: 1) The Chebyshev polynomial (Ch) is applied to the graph convolutional neural network, and the traditional fixed traffic topology is converted with self-diffused convolution to make it more random and dynamic; 2) The spatial Transformer model is added. While considering the data heterogeneity, the multi head self attention mechanism is used to consider the multi attribute problems of nodes, local neighbors, and non local nodes, and the hidden feature information between nodes is considered from the high-dimensional subspace; 3) Combining the temporal transformer with a 1 × 1 2D convolutional neural network (Conv2d). Multiple weights are assigned to the traffic flow time series information to obtain important time features, and the Conv2d network is used to predict the output. The experimental verification shows that the method model is better than a variety of comparative baseline models.

Along with the rise of Internet public welfare, e-commerce platforms represented by Alibaba and JD have injected a strong impetus into the development of Chinese public welfare. However, the public practices of platforms and merchants often contain both public welfare and profit motives. In this paper, three differential game models are constructed for platforms and merchants to discuss their cooperation question about carrying out joint cause-related marketing (CRM), considering the government’s subsidy policy for platforms and the characteristics that engaging in social welfare is multi-cycle and dynamic. Our main results are as follows: 1) The government subsidies can incentivize platforms and merchants to provide more investments for social welfare, and motivate platforms to be willing to share more CRM costs for merchants. 2) Under certain conditions, platforms share a certain percentage of CRM costs for merchants can not only enhance its social welfare investment, but also achieve Pareto improvement of profits for both. 3) Government subsidies can improve the Pareto effect of CRM cost-sharing contracts on the profits of platforms and merchants, but platforms may retain some of the government subsidies and will not invest them all in social welfare. 4) In the joint CRM case, the benefits of platforms, merchants, and the whole system as well as the consumer surplus and social welfare are all optimal. Finally, our main results are verified via numerical simulation, hoping to provide a theoretical basis and reference for the joint CRM of platforms and merchants.

Innovation and entrepreneurship is an effective choice to shape a new engine of development. As a new development form, the digital economy can provide new opportunities for enhancing the entrepreneurial vitality of the region. First, based on the panel data of selected 30 provinces in China from 2010 to 2020, this paper constructs the fixed-effects model, mediation model, and threshold model. Second, the models are applied to analyze the impacts and mechanism of the digital economy on entrepreneurial vitality from the perspectives of the labor and data factors. Third, this paper explores the boundary effects of intellectual property protection. The study shows that: 1) The development of digital economy has a significant positive effect on the entrepreneurial vitality, among which it improves the entrepreneurial vitality in the two aspects of Internet application and digital finance, while the role of the basic dimension of informatization is not obvious. 2) Digital economy can promote entrepreneurial vitality through two paths: “Labor factor allocation” and “data factor utilization”. 3) In the southeast half of the “Hu Huanyong Line”, the digital economy has a significant impact on the entrepreneurial vitality. 4) As an important institutional guarantee, intellectual property protection plays a threshold role in regulating the relationship between digital economy and entrepreneurial vitality, and can play the maximum effect when its level is in the optimal range. Accordingly, to stimulate the entrepreneurial vitality, all regions need to further improve the information infrastructure and the application level of digital technology, and optimize the allocation of regional elements. Besides, based on location characteristics, adopt the different strategy of independent innovation, and cooperation to create an entrepreneurial environment that is compatible with the development level of the digital economy.

This paper studies the robust optimal reinsurance and investment problem under the ambiguity aversion and inflation risks are considered, simultaneously. The surplus process of an insurance company is defined by the diffusion approximation model, and the financial market consists of a risk-free asset and a risky asset. The insurance company can reduce the risk of claims by adopting proportional reinsurance and increase his wealth by investing in the financial market. The price of the risky asset is converted by inflation, and then the influence of inflation on investment is obtained. By using Radon-Nikodym derivative and Girsanov theorem, the wealth process of the insurance company under ambiguity aversion is obtained. With the goal of maximizing expected utility, the analytical solution of robust optimal reinsurance and investment strategy is obtained. Finally, the influence of model parameters on the theoretical results is discussed through numerical experiments, and the research results can effectively guide the reinsurance and investment decisions of the insurance company.

The region constrained consensus of flexible-joint robot manipulators is studied. A consensus control torque for flexible-joint robot manipulators is proposed based on the regional potential functions, which can effectively guarantee the range of the synchronization state of flexible manipulators without calculating the final synchronization state, so it is suitable for cooperative control and application of multiple flexible-joint manipulators in the task space. Further more, a sufficient condition for solving region consensus problems is presented based on the structure of network topology. Finally, the effectiveness of the obtained theoretical results is verified by numerical simulations.

In the context of carbon trading mechanism, considering the low carbon preference of consumers, this paper studies the selection of the optimal emission reduction technology authorization strategy for innovation-oriented enterprises by constructing a competitive duopoly market, analyzes the three authorization modes of fixed fee, franchise fee and two-system fee, and explores the impact of technology authorization strategy on the environment. The results show that: 1) The two-part licensing strategy is always the best for the technology owning enterprises, and the advantages and disadvantages of fixed-fee licensing and licensing fee licensing are determined by the substitution degree and emission reduction rate between products; 2) Regardless of the degree of innovation, fixed-fee authorization has the greatest impact on the environment. When the emission reduction rate is very small, the total carbon emission generated by the two-part fee authorization is the minimum; Otherwise, the total carbon emission generated by the license fee authorization is the minimum.

The randomized controlled trial,(RCT) is widely used in clinical trials, and sample allocation for the RCT is an important issue in practical applications. However, for the RCT under the causal inference framework, traditional sample allocation methods did not consider the correlation of potential outcomes. To solve this problem, we propose a sample allocation method considering the sensitivity parameter, and combine it with a response-adaptive randomization design to estimate causal effects. Simulation studies have verified that our method can obtain more accurate estimators of causal effects than other commonly used allocation methods when the independence assumption does not hold.