This paper investigates the equilibrium strategies of customer in single-server Markovian queues with multiple vacations and catastrophes. Whenever a catastrophe occurs in the system, all customers in the queue are forced to leave, which causes the server to fail. And customers are not allowed to enter the system during the repair period. The steady-state probabilities of the system are obtained under different situations:fully observable, almost observable, almost unobservable, and fully unobservable. Based on the reward-cost structure, we obtain the customer equilibrium strategies and social benefits in four situations. In addition, we provide some numerical experiments to illustrate the effects of information level and system parameters on the equilibrium behavior of customers.

The design of set-membership filtering algorithms is investigated for discrete time-varying networked systems under hybrid attack environment. Firstly, considering the effects of DoS attacks and deception attacks, the deception attack is modeled as an unknown bounded signal, and two sets of random variables following the Bernoulli distribution are utilized to describe the occurrence probability of the cyber-attacks; Secondly, the set-membership filtering algorithm is designed to ensure that the state estimation error satisfies ellipsoidal constraints, the filtering gain matrix and corresponding ellipsoidal constraint domain are derived by solving two recursive difference equations, and the boundedness problem of time-varying ellipsoidal domains is analyzed; Finally, the effectiveness and superiority of the proposed algorithm are verified by an numerical example.

It is difficult for the total monetary policy to boost output while taking into account risk prevention, while whether the targeted regulation and control monetary policy can take into account both and alleviate the dilemma of small and micro financing remains to be examined. This paper constructs a DSGE model that includes the trade-off mechanism between banks' policy dividends and bad debt risks and the characteristics of credit preference, focusing on the relief effect of targeted RRR reduction and small relending on small and micro enterprises. The results show that from the perspective of the mechanism, targeted regulation of monetary policy can promote economic growth through credit transmission channels and signal transmission channels, but the two are heterogeneous in risk prevention and control. From the perspective of the relief effect, the targeted RRR reduction has a more significant relief effect on small and micro enterprises than the policy of supporting small and micro enterprises. From the perspective of policy mix, the total loose monetary policy will weaken the relief effect of targeted regulatory monetary policy on small and micro enterprises, and the combination of directional regulatory monetary policy can further alleviate the financing difficulties of small and micro enterprises and improve the output level. In addition, this paper further comprehensively evaluates the regulatory efficacy of directional regulation and control of monetary policy from multiple dimensions such as central bank welfare loss function, policy frontier curve and social welfare analysis, and further verifies the robustness of the research conclusions.

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

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

This paper proposes a new high-dimensional data selection method (PHDT) based on the principal hessian direction matrix (PHD). The method selects significant variables by utilizing the changes in the trace of the principal hessian direction matrix when gradually adding or subtracting covariates. Compared to the kernel matrix of the sliced inverse regression method, the calculation of PHD is more straightforward. The selection consistency of the PHDT method is proven, which is a crucial property of any feature selection method. Furthermore, the large sample properties of the test statistics used in the PHDT method have been established, which provides a solid foundation for its statistical validity. Before the actual operation, the initial screening of covariates using BIC criterion can narrow the scope of finding significant variables, and make the subsequent feature selection process more efficient and precise. Data simulation and real data analysis verify the effectiveness and feasibility of the proposed method, which provides strong support for the subsequent modeling and analysis.

The article investigates the problem of parameter estimation and variable selection in high-dimensional semiparametric variable coefficient measurement error models with missing response variables. Firstly, based on the inverse probability weighting method, a random auxiliary vector for parameter partial correction and a nonparametric partial estimation function for correction are constructed. Under appropriate conditions, it is proved that the nonparametric estimation for correction follows asymptotic normality. Then, an empirical logarithmic likelihood ratio statistic for the correction parameter part is constructed and it is suggested that penalized empirical likelihood (PEL) be used to select variables. Under appropriate conditions, it is proved that the proposed penalized empirical estimate has oracle characteristics and obeys the asymptotic chi-square distribution under the null hypothesis. Monte Carlo simulation research suggests that the proposed estimation performs well in finite samples. Finally, a real data analysis is provided.

This work studies two-agent multitasking scheduling on a single machine with job rejection, where two agents can't interrupt each other. The model with the objective of the makespan are considered. An upper bound on the total permitted rejection cost is assumed, and there are two options for each job:accepted or rejected. Since the problem is NP-hard, we pay attention to providing pseudo polynomial dynamic programming algorithm, $n_A^2$-approximation algorithm and the fully polynomial-time approximation scheme.

This study focuses on the prescribed-time control problem based on dynamic event-triggered mechanism for a class of nonlinear strict feedback systems with uncertain time delays. A state feedback control strategy is proposed for the high-order nonlinear system with unknown time delays based on the prescribed-time stability theorem. It ensured the convergence of the system within the prescribed-time, and the prescribed-time can be set according to the actual needs. In order to approximate unknown continuous functions in the system online, neural network approximation technique was applied, while the Lyapunov-Krasovskii functionals(LKFs) were employed to compensate for the effect of state delay. In addition, by introducing a dynamic event-triggered mechanism, the controller reduces the communication occupancy rate. The controller we design makes the closed-loop system is bounded stable within a given time range, and the convergence time is independent of system initial conditions. Finally, the feasibility of the design scheme is verified by simulation experiments.

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

The issue of employee behavior in the process of intelligent manufacturing is not only a problem in the field of cognitive science, but also a management problem in production. This topic focuses on two dimensions, behavioral cognition and lean, which are interrelated and have their own characteristics. The core issue is to explore the factors and mechanisms that affect labor behavior in the continuous promotion of intelligent manufacturing based on the theoretical perspectives of cognitive bias and lean, and analyze their impact on Jidoka systems. The focus of this study is on the imperfect production process of assembled products, where labor may overlook inspection items due to various factors during the production process. Taking into account Type I and Type II errors, an extended EOQ model is used to optimize the order quantity in order to minimize total costs. Proved that positive lean strategies can eliminate overlooked occurrences, reduce total system costs, and measure the degree of reduction. This article provides some numerical examples, sensitivity analysis, and graphical tables to illustrate the model.

In addressing the three-dimensional trajectory tracking control problem for a six-degree-of-freedom autonomous underwater vehicle under internal uncertainties and external disturbances, a sliding mode control method based on a fixed-time extended state observer is proposed. Initially, the fixed-time extended state observer is employed to estimate the unmeasurable velocity and aggregate disturbance of the system. Simultaneously, a novel predetermined performance function is introduced to enhance the transient performance of the system. Leveraging the outputs of the observer and the predetermined performance function, along with the sliding mode surface, a fixed-time sliding mode controller is designed. The proposed control approach ensures rapid convergence of tracking errors to zero within a fixed time, exhibiting strong robustness and high steady state precision. Numerical simulation results validate the effectiveness of the proposed method.

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

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

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

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

This paper provides an in-depth analysis of the triadic coupling and coordination among strategic emerging industries, high-quality economic development, and common prosperity. Based on the data of 90 A-share listed companies and panel data of 30 provinces from 2011 to 2020, this paper firstly measures the levels of strategic emerging industries, high-quality economic, and common prosperity each year by using the entropy method. Secondly, the dynamic coupling and coordination among the three elements are analyzed by using the coupling coordination model. Furthermore, the coupling and coordination among nine strategic emerging sub-industries, high-quality economic development and common prosperity are studied in depth. Finally, the GM(1, 1) model based on the gray system theory is used to predict the coupling coordination levels among the three elements under discussion in the next ten years. The results show that the triadic coupling coordination level of strategic emerging industries, high-quality economic development, and common prosperity gradually increases. Also, the levels of the coupling and coordination among nine strategic emerging sub-industries, high-quality economic development and common prosperity are different. Specifically, the new energy industry and the new energy automobile industry are particularly outstanding in terms of coupling and coordination with high-quality economic development and common prosperity. In contrast, the coupling and coordination level of related service industries is the weakest. Based on the empirical results, this paper puts forward a series of policy recommendations on how to make strategic emerging industries more effective in promoting common prosperity in high-quality development, aiming to provide valuable references for policymakers.

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

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

This paper considers the equilibrium strategy of the M/M/1 retrial queue with two types of parallel customers and an N-policy. In this queuing system, two kinds of customers enter the system in parallel and respectively follow the negative exponential distribution of different parameters. An arriving customer will be served immediately if the server stays idle; Otherwise, the customer enters the retrial space and waits for retry. There are some necessary conditions for the server to starts service:The number of two types of customers in the retrial space reaches a given threshold N. Moreover, the service rate dynamically changes according to the number of waiting customers in retrial space. A benefit function is introduced according to revenue-cost theory, and equilibrium analysis is conducted for two types of parallel customers in a fully observable case. The average social benefits of the system is also analyzed. Finally, the numerical examples are used to visualize the changes in customer behavior strategies and the average social benefits as the different system parameters.

The financial attribute of crude oil plays an important role in the measurement of crude oil market risk and its spillover effect with financial market risk. In this paper, the VMD-LZ method is used to decompose and reconstruct the financial attribute components of oil market price and risk. Furthermore, we employ the MVMQ-CAViaR method to measure static risk spillovers in the crude oil market and financial market under different income trends and whether the financial attributes of crude oil are considered. Finally, this paper uses DY-spillover index to measure the dynamic spillover index between crude oil market and financial market or different industries, and analyzes its heterogeneity. Empirical results show that considering the financial attributes of crude oil, we can better grasp the evolution characteristics of crude oil market risk in high and low frequency. Further research on risk spillover effects between the crude oil market and the financial market shows that the financial market risk reduces the volatility of the crude oil market price, while the crude oil market risk plays a role in promoting the financial market risk. What's more, return trend, risk trend and oil financial attributes have a significant impact on the risk spillover effect between the crude oil market and financial market.

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

Recently, freight rate volatilities have significantly impacted supply chain management of multinational companies. This study investigates the effects of different shipping contract models on competitive companies A and B. Company A adopts long-term shipping contracts to stabilize freight rates, while company B relies on the spot market, where prices vary with demand. Both companies independently observe demand signals and adjust their selling prices accordingly. Our analysis yields the following findings:First, despite company A's shipping cost advantage, company B, utilizing spot purchase, gains a competitive edge during a large freight rate volatility or with strong forecasting capabilities. Second, spot price volatility affects companies' profit adjustments, prompting them to be more responsive to market change. An increase in spot price volatility may be beneficial to companies. Third, enhancing the forecasting capability of both itself and its competitor consistently benefits Company A. However, such improvements are advantageous to Company B only under specific conditions.

Fuzzy logic expresses preference in uncertain language and increases the flexibility and practicability of decision-making process by dealing with uncertainty. It has become a research hotspot in decision-making field in recent years. In the past decision-making methods, the relative value of each scheme is given by the complete ranking, or the aggregative ranking is given according to the classification information, while the complex fuzzy decision-making problem should be made from a comprehensive multi-dimensional perspective. In this paper, the rank-sum ratio method (RSR) is introduced into the field of fuzzy decision making, and a comprehensive ranking method based on Pythagoras fuzzy numbers is proposed. Based on the prospect theory, the prospect synthesis matrix for scheme evaluation is established, and then the prospect matrix is ranked, and the complete ranking of schemes is sorted by the rank sum ratio. Finally, the data are mapped to the normal distribution curve according to various cases of rank, and the aggregative ranking is carried out according to the correlation partitioning method of normal distribution. The effectiveness and superiority of the algorithm are verified by comparative analysis. The purpose of this study is to provide a new idea reference for the application of rank sum ratio method in the field of fuzzy decision making.

In this paper, the rejection problems of the two-agent single machine under generalized parameters are studied. Generalized parameters refer to the parameters that are given in a dvance according to the position of the job in sequence. Under the three parameter types of agent A jobs with generalized weight (GW), generalized rejection fee (GRC) and generalized machining time (GPT), the jobs of the first agent can be rejected, and its objective function is to minimize the sum of the total weighted completion time and the total rejection cost. The maximum value of the function related to the completion time of the second agent does not exceed the fixed value. We find a schedule to minimize the objective functions of the first agent. We provide NP-hardness proofs for the generalized weight and generalized rejection cost problems, and pseudo-polynomial time optimal algorithms. We also provide a polynomial time optimal algorithm for the generalized processing time problem. Finally, we give the case experiments.

This paper discusses a polynomial-based barrier certificate construction method for verifying the safety of neural network controlled systems. First,the neural network model is abstracted using methods such as global sector constraints,local sector constraints,and overlay sector constraints to obtain corresponding semi-algebraic constraints. Then,using Positivstellenstz in computational real algebraic geometry,the barrier certificate conditions are transformed into corresponding Sum-of-Squares constraints,which are solved by using semi-definite programming. Finally,the effects of the above different neural network abstraction methods on the ability of constructing the barrier certificates of the neural network controlled systems are analyzed and compared through examples.

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

For the mismatch between supply and demand of medical resources in the hierarchical medical system, two modes are proposed:the cooperation mode composed of a tertiary hospital and a government-run and managed community hospital, and the competition mode composed of a tertiary hospital and a market-run and managed community hospital. Through the construction of a three-stage Stackelberg queueing game model to analyze the dynamic decision-making process among delay-sensitive patients, a community hospital and a tertiary hospital, which will give the patient referral rate, the community hospital's service capacity planning, the tertiary hospital's transfer patient rate or pricing strategy. It is found that under the cooperative mode, if the tertiary hospital emphasizes the waiting time of patients and actively refers patients with minor illnesses, and its benefit will be increased. Under the competitive mode, the patient referral rate, service capacity of the community hospital and the profit of the tertiary hospital are higher, which means that the appropriate competition can promote each part to seek for a more optimal strategy. Numerical results show that when the number of patients in the tertiary hospital is large, the total patient waiting time is smaller in the competitive mode, and when the number of patients in the community hospital is large, the cooperative mode is more favorable.

Aiming at a class of second-order nonlinear multi-agent systems with external disturbances,an adaptive neural network formation control protocol with collision avoidance strategy is proposed. In the design of control protocol,due to the inherent complexity and uncertainty of nonlinear dynamic systems,this paper adopts adaptive neural network to compensate for the uncertainty of nonlinear dynamic systems,effectively solving the difficulty of control protocol design caused by unknown dynamic functions. In addition,in order to prevent collisions between multi-agents,the artificial potential field method is integrated into the formation control protocol. Through Lyapunov stability analysis,it is proved that the proposed control protocol can complete the expected formation control task. Finally,two numerical simulation examples are given to further illustrate the effectiveness of the theory.

In this paper,we consider the one-dimensional wave equation,with the Dirichlet boundary condition in the one end,and the control with the boundary disturbance in the other end. Firstly,we are devoted to design the sliding mode surface,and impose the sliding mode control further,and then we prove the stability of the closed-loop system. Secondly,we construct the high-gain estimator,and then design the active disturbance rejection control. The stability of the closed-loop system has been proven. Finally,we simulate the states of the closed-loop systems after imposing the sliding mode control and the active disturbance rejection control. The numerical simulation results show that both of the sliding mode control and the active disturbance rejection control are effective to make the original open-loop system stable.

In this paper,we introduce a weaker constraint qualification than that in reference [15] and establish a Karush-Kuhn-Tucker type optimality condition for quasiconvex programming in terms of Greenberg-Pierskalla subdifferential when the objective function and the constraint functions are extended real-valued upper semi-continuous quasiconvex functions.

Relative difference,also known as relative risk reduction,mainly measures the additional impact of risk factors or treatment factors on individuals,and has important clinical significance in epidemiological research. This paper presents nine interval estimation methods for relative differences using traditional interval estimation methods and MOVER methods under independent binomial sampling. The advantage of the MOVER method proposed in this article is that by borrowing the MOVER method,the confidence interval of the relative difference can be constructed using the confidence intervals of two independent binomial distribution ratios. Compared with traditional interval estimation methods,this method does not need to calculate the asymptotic variance of the relative difference in the process of constructing confidence intervals,and does not require the Fisher information matrix and its inverse matrix,which greatly simplifies the calculation. In addition,this article investigates the performance of nine interval estimation methods under different parameter settings through Monte Carlo data simulation. The data simulation results show that the MOVER method can provide a more accurate confidence interval compared to traditional methods. Finally,this article demonstrates the practical application of the nine proposed interval estimation methods through actual data cases.

Considering the impact of mixed information for high-dimensional portfolios, this paper introduces the idea of mixed information extraction under the Vine Copula framework,substitutes the mixed data sampling model MIDAS into high-dimensional D-Vine Copula, and proposes a CVaR portfolio selection model based on high-dimensional D-Vine Copula-MIDAS, so as to simultaneously address the challenges of "dimension disaster" and "insufficient mixed information extraction" under the framework of Copula. Firstly, estimate the multivariate conditional joint distribution of assets based on the high-dimensional D-Vine Copula-MIDAS model; Secondly, simulate the dynamic features of assets returns based on the estimated joint distribution. Finally, the optimal investment weight of the assets is obtained by minimizing CVaR, thereby establishing a minimum CVaR portfolio selection model. This paper selects 7 stocks on the Chinese new energy market for empirical studies, and the results show that the CVaR portfolio selection model based on high-dimensional D-Vine Copula-MIDAS can fully reveal and simulate the dynamic features of financial assets returns and obtain lower investment risks.

Considering the influence of consumer environmental awareness,the Stackelberg game model between enterprises and e-commerce platforms is constructed under the government's carbon cap-and-trade policy. Enterprises choose resale or agency sale to enter the platform. E-commerce platforms make efforts to promote green consumption by developing green marketing,and the green market promotes enterprises to reduce emissions. This paper studies dynamic pricing and green level decision-making of enterprises under resale and agent sale,and analyzes the economic and environmental benefits of supply chain under different sales models. The analysis results show that under certain conditions,social influence and consumers' environmental awareness play a positive role in the benefits of corporate; However,excessive carbon trading prices will inhibit the carbon reduction rate and environmental benefits of the supply chain; The level of commission rate affects the choice of marketing mode of enterprises. When the commission rate is low,the agent sales mode is the best. When the commission rate is high,the cost sharing contract can realize the win-win situation of enterprises in economy and environment.

Fresh products face the dual pressures of time-varying demand caused by freshness change and logistic service uncertainties. Efficient collaboration between the supply chain and logistic services is crucial for devising optimized operational strategies. This paper analyses the impacts of pricing,preservation strategy and logistics uncertainty of fresh-product supplier and retailer on the supply chain based on consumers' time-varying utility function by constructing a Stackelberg game model. The coordination issues in fresh supply chain under centralized and decentralized decision-making are explored. The study reveals that,from an overall supply chain perspective,considering logistic uncertainty leads to a significant decrease in supply chain efficiency. For supplier,in situations of lower logistic uncertainty,profit is greater under the baseline scenario(without logistic uncertainty); it leads to an increase in positive demand and thus to greater supplier profitability when logistics uncertainty exceeds a certain threshold. Regarding retailer,profit is higher in the presence of logistic uncertainty when the product has a shorter sales period,while in situations with a longer sales period,profit is higher under the baseline scenario. As the profit-sharing ratio increases,the efficiency of the fresh product supply chain improves and steadily approaches 100%,indicating that profit-sharing contracts can achieve supply chain coordination.

Interval-value data contains more information than point-value data, which has become a hot topic in the application of complex big data. Most of the existing interval-value regression models are built on the basis of representative element framework or traditional Interval-value subtraction, but the representative element framework model does not involve the whole interval in the operation, and the traditional interval-value subtraction has unreasonable operation results. In order to solve the above problems, this paper proposes a $p$-dimensional interval-value regression model under the framework of generalized Hukuhara(gH) difference. On the basis of ensuring reasonable interval operation results, a regression residual evaluation method is constructed based on the support function. The least squares estimation is derived, and the characteristics of the regression parameters under 1-dimensional condition are discussed. Finally, Monte Carlo simulation is used to verify the effectiveness and accuracy of the new method.

This paper studies static output feedback controller designed for the switching polynomial fuzzy model-based control system. This paper uses the switching method which based on the operation sub-domains and the switching Lyapunov function to design the polynomial static output feedback controller,so that the system can achieve asymptotic stability. At the same time,the boundary information of the membership function is considered and the slack polynomial matrices are introduced,which reduces the conservatism of the stability conditions. Then,the optimization algorithm is used to transform the non-convex control algorithm into the convex conditions,which can be solved by SOSTOOLS. Finally,a numerical simulation is used to verify the effectiveness of the controller design method in this paper.

In order to improve the shortcomings of the current core indicator estimator in China's sample rotation survey,which does not fully utilize the information of the previous data,this paper investigates the AK composite estimator,which is the international mainstream in sample rotation surveys. It explores the construction method and statistical properties of the AK composite estimator. The main contributions are twofold: first,the AK composite estimator of the 4-8-4 type sample rotation scheme commonly used in existing research is extended to the AK composite estimator of the r-m-r type sample rotation scheme,and its bias,variance and mean square error are given; second,Bootstrap method is introduced instead of the current balanced half method to construct the sampling variance estimator of the AK composite estimator,and the optimal coefficients of the AK composite estimator are determined by minimizing the mean squared error. Simulation results show that compared with the balanced half method,the Bootstrap method more comprehensively analyzes the sample overlap and sample correlation between the resampled samples in different months,and the optimal weights of the AK composite estimator based on the Bootstrap method have a smaller mean square error. The results of this paper will help optimize and improve China labor force survey system.

In the context of big data,it is easier to obtain more available auxiliary information. However,due to the high dimensional characteristics of auxiliary information,the computational complexity increases and the estimation effect of superpopulation model is reduced,thus making the traditional model-assisted estimation method no longer applicable. Therefore,in the framework of model-assisted sampling estimation,this paper introduces the Bayesian model averaging method to estimate the superpopulation model,and obtains the combined models auxiliary estimator under the complete auxiliary information. Firstly,the traditional model-assisted sampling estimation method and Bayesian model averaging method are systematically sorted out. Then the Bayesian model averaging assisted sampling estimation method is given and the asymptotic unbiasedness and consistency of the estimator are proved. The numerical simulation and empirical analysis show that the Bayesian model averaging assisted estimator is better than the generalized regression estimator using a single model in the presence of high-dimensional auxiliary information. Finally,based on the research summary,the prospect of improving the Bayesian model averaging assisted sampling estimation method is proposed.

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