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.

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 this paper, we utilize the Fourier neural operator (FNO) for the first time to investigate the derivative nonlinear Schrödinger (DNLS) equation and fractional derivative nonlinear Schrödinger (fDNLS) equation. For the DNLS equation, we successfully establish the mappings between the initial conditions of the equation and their respective solutions. The transition process of the soliton to the $M$-type wave is studied, and the periodic solution is also obtained. Simultaneously, the FNO learning method is employed to investigate the transformation process of the periodical rogue wave. Moreover, we focus on learning the mapping between the fractional order exponential space and the soliton in the fDNLS equation. By comparing the data-driven solution with the exact solution, the powerful approximation capability of the FNO network is highlighted. Finally, we discuss the effects of the full-connected layer $P$ and the activation function on the characterization ability of the network.

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.

This paper studies centralized state estimation problems for multi-agent systems with random packet losses. The random variables satisfying Bernoulli distribution are employed to depict the phenomena of packet losses during measurement data transmission from sensors to the data processing center. First, an optimal centralized state filter in the linear minimum variance (LMV) sense is designed in the case that the fusion center knows whether data packets are lost or not at each time, where the filtering gain requires computing in real time. To reduce the online computational burden, a suboptimal filter dependent on probabilities of packet losses is also designed and its steady-state property is analyzed. Then, an optimal centralized state filter in the LMV sense is designed in the case that the fusion center does not know whether data packets are lost or not at each time, and its steady-state property is analyzed. Finally, the estimation accuracy and computational burden of the proposed three filtering algorithms are analyzed. A simulation example verifies the effectiveness of the algorithms.

This paper uses the textual vectorization method to digitize the text of earnings conference call of Chinese listed firms, and then analyzes whether managers choose the tone strategically and how investors respond to it. It is the first time that the management tone is refined into market, industry and corporate component. We find that managers strategically arrange their tones for their own interests at earnings conference call; and the net positive market tone moves the stock price up in the long window of the event. Furthermore, investors can gain from analyzing the corporate tone of firms with high investor attention during normal market and industry situations. This paper shows that the text messages disclosure on earnings conference call can provide a valuable information, and also provides a new perspective for management tone analysis. In addition, under the highly dependence of semantics on context within Chinese cultural background, this paper provides new empirical evidence for such hot issues as investors' information acquisition and comprehension.

A fuzzy adaptive control method based on command filtering technology is proposed for a stochastic system of flexible joint manipulators with dead-zone input, which achieves tracking control of the system output on the expected trajectory. Firstly, command filtering technology is used to solve the problem of "explosion of complexity" inherent in the traditional backstepping method, and error compensation mechanism is introduced to eliminate the influence of filtering errors on the system control precision. Then, a fuzzy logic system is utilized to deal with uncertainties and stochastic disturbances in the system, which overcomes the influence of stochastic disturbances and improves the control effect of the system. Finally, considering the system with dead-zone input, the control signal is constructed by backstepping control method, which conquers the adverse impact of dead-zone input on system performance. In the stability analysis, the effectiveness of the control strategy studied in the stochastic system of flexible joint manipulators with dead-zone input is proved, and it is verified by Matlab simulation.

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.

In recent years, due to the superior performance of semiparametric joint elicitable risk models in joint statistical modeling and prediction of value at risk (VaR) and expected shortfall (ES), they have attracted widespread attention in the field of financial measurement. This paper first studies the statistical properties and risk prediction performance of the model under the framework of the asymmetric Laplace distribution. Unlike the existing semiparametric joint elicitable risk models, this model jointly models VaR and ES by assuming the conditional distribution of asset returns follows the asymmetric Laplace distribution based on VaR and ES, taking into account the typical asymmetric characteristic of financial markets, and regarding VaR and ES as dynamic structures composed of conditional standard deviation process of returns containing the asymmetric feature and a parameter to be estimated. Based on the structure of the model, we discuss the quasi-maximum likelihood estimation method and establish the consistency and asymptotic normality theorems for the estimator under certain regular conditions. Subsequently, numerical simulation results considering various conditions confirm the finite sample properties of the estimator and the effectiveness on predicting one-step ahead risks. Finally, empirical results show that the proposed model performs best in predicting multi-step ahead VaR and ES.

In this paper, the edge controllability of multi-agent systems under matrix weights is studied by using the transformation of topological graphs from point graphs to line graphs, where dynamics occur on the edges. Firstly, from the perspective of graph theory, a quantitative analysis is conducted on the incidence matrix of a line graph, and the relationship between the rank of the incidence matrix of the line graph and the number of connected components of the line graph are given. Furthermore, we find that there is a certain relationship between the algebraic multiplicity of zero eigenvalues of the Laplacian matrix of the line graph and the number of connected components of the line graph. Secondly, under the leader follower structure model, two conditions that need to be satisfied when the multi-agent system is edge controllable are obtained. In addition, according to the definition of canonical transformation, the balanced symbolic line graph of matrix weight structure is transformed into an unsigned line graph without negative edges. The results show that the controllability of the line graph before and after transformation is equivalent. Finally, the relationship between the controllability of line graphs and point graphs is analyzed, and it is found that when the point graph is structurally imbalanced, the controllability of line graphs is equivalent to that of point graphs.

In complex system, resource allocation and internal interaction of the system are key factors that need to be paid attention to when studying the innovation efficiency of the system, but in previous related researche, scholars did not take the mutual feedback between divisions inside the system into account. Based on what we mentioned, this paper has expanded the previous model and constructed a three-stage network DEA model with shared resources, incorporating the interaction effect between the internal divisions of the system. We prove the necessary and sufficient conditions of effective system and define effectiveness of the system and each stage. We measure the knowledge innovation efficiency of 54 Chinese universities and divide the knowledge innovation system into the knowledge production stage and the knowledge application stage. Compared with the distinction of different methods (GP1: Focusing on the first stage; GP3: Focusing on the third stage), the results show that there is no DMU with systemic efficiency; Compared with GP3, GP1 will underestimate the efficiency of knowledge application and reduce the level of distinction between DMUs at the efficiency of knowledge production. This paper also analyzes various sample universities in accordance with different characteristics and puts forward relevant policies and measures.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.