Multi-source data are commonly encountered nowadays. The analysis of multi-source data is important for unleashing the data potential and realizing data value. However, many multi-source data still exist in the form of “data silos”. Interconnection between data remains extremely challenging. Meanwhile, the data security issue is a significant concern, making it crucial to achieve secure development of multi-source data while protecting data privacy. To address these challenges, we propose a privacy-protected paradigm for multi-source data analysis. This method is based on the federated learning framework, enabling different data sources to collaborate on data analysis tasks without exposing their raw data. Meanwhile, to further prevent malicious attacks on data, we incorporate differential privacy into federated learning by adding noise to the transmitted data to protect individual-level information. Finally, we demonstrates the practical application of the proposed paradigm using the example of predicting violation risks of enterprises. By combining data from various departments, the prediction accuracy can be well enhanced.

In supply chains where retailers, as small and microenterprises, face capital limitations and information asymmetry, manufacturers, as core enterprises, often aim to enhance overall performance by implementing financing assistance and conveying demand information. Due to several unfavorable factors, manufactures may experience guarantee capacity deficiencies. In this context, they can choose to offer trade credit financing (TCF) or together with guarantee companies to provide credit coguarantee buyback financing (CCB) as signaling mechanisms, but the effectiveness of these strategies remains unclear. To address this gap, this paper establishes an analytical model through the signaling game to compare and examine the signaling role and profit improvement of these financing strategies. Our findings indicate that manufacturers can credibly signal the demand state through both financing contracts, with enhancing signal efficiency through CCB for the manufacturer and TCF for the retailer. In addition, a Pareto improvement can be achieved by adopting CCB at low guarantee fee rates, while through implementing TCF at high ones. These insights shed light on the advantages of CCB and TCF, offering valuable guidance for managers when selecting suitable financing strategies.

In this paper, an unknown system dynamic estimator based sliding mode control scheme is proposed for anti-sway control of overhead cranes with unmatched disturbances and unmodeled dynamics. Through introducing a first-order low-pass filter, the unknown system dynamic estimator is designed to compensate for the unknown system dynamics including unmatched disturbances, such that the disturbance rejection ability can be enhanced. Then, a two-phase power reaching law based sliding mode controller is constructed to obtain the relatively accurate convergence time of the sliding mode variable and guarantee the fast convergence speed. The experimental results show that the proposed method can effectively achieve the satisfactory anti-sway performance and positioning accuracy of the overhead crane.

Aiming at partial area restrictions and the coexistence of fuel vehicles and electric vehicles in urban logistics systems, an optimizing model is constructed for open routing of mixed vehicle fleet by considering the factors such as customer demand, service time, electric vehicle capacity, power consumption, fuel vehicle capacity, fuel consumption and carbon emissions. The goal of the model is to minimize the total distribution cost. According to the delivery process of electric vehicles, an allocation strategy is designed for electric vehicle remaining capacity, which includes pre-positioning strategy, mid-positioning strategy, and post-positioning strategy. On this basis, a hybrid genetic algorithm (HGA) is developed according to the characteristics of the model. The experiments are conducted based on multiple types of instances. The experimental results show that HGA can plan the vehicle routes reasonably by comprehensively utilizing the pre-positioning strategy, mid-positioning strategy, and post-positioning strategy for electric vehicle remaining capacity. HGA can fully exploit non-restricted area customers along the electric vehicle driving routes. The proposed approaches can expand the service range of electric vehicles, make full use of electric vehicle capacity, reduce the number of fuel vehicles, shorten the vehicle travel distance, and decrease carbon emissions and fuel consumption during the delivery process. HGA provides vehicle routing solution for mixed fleet that meets the decision maker's objectives within a very short time. These results demonstrate the feasibility and rationality of HGA.

We propose a multi-bid auction mechanism for the decentralized decision-making problem of parallel machining scheduling, particularly addressing the scenario with order delay penalties. In this mechanism. we design a multiple pricing rule based on customers' private values, allowing customers to submit multiple bids. This approach enables customers to express their preferences more detailly and increases their chances of winning bids. In this mechanism, the winner determination problem is a combinatorial optimization problem with NP-hard property. We propose an exact algorithm based on a dynamic programming framework to solve it. Since the exact algorithm can only solve small-scale problem instances in a reasonable time, we also propose a heuristic algorithm based on Lagrangian relaxation technology to obtain a near-optimal solution to the problem in a reasonable time. Numerical experimental results show that, compared with single bid auction mechanism, multi-bid auctions can significantly improve the price of anarchy of large-scale problem instances, and its total system revenue reaches an average of 88.86% of the global system's total revenue.

Design of experiments with mixtures has been widely used in food industry manufacturing, mixed drug research and development, investment portfolio optimization and other fields. In order to ensure that the design is robust to the changes of the model, many scholars have proposed uniform designs for experiments with mixtures under different criteria. However, the methods of constructing mixture designs based on the acceptance-rejection algorithm or inverse transformation method become inefficient and complex when the number of mixture compositions is large and the constraints are complex. In this paper, we propose an efficient construction method with representative points method for uniform mixture design on a general restricted region. The main idea is to generate uniform training samples on the experimental region based on the Gibbs sampling algorithm, and then compress them into the optimal representative point set under the energy distance criterion by the optimization algorithm. Through numerical example analysis, the design generated by the new construction method has good uniformity and model robustness.

In this paper, we firstly studied the estimation of the population mean of the Logistic distribution under imperfect Z ranked set sampling(IZRSS). We proved that the IZRSS sample mean is an unbiased estimate of the population mean, and when ${\textstyle{1 \over m}} \le p \le 1$, the estimation of sample mean as population mean under IZRSS is more effective than the estimation of sample mean as population mean under simple random sampling (SRS) and the estimation of sample mean as population mean under imperfect ranked set sampling(IRSS). The simulation results and a real data are provided to illustrate our theoretical results for small size.

In engineering practice, exact failure times of individual components are generally not available. In contrast, only the number of component failures and the system's cumulative operating time are known, which leads to the aggregate lifetime data. Inference of lifetime distributions based on the aggregate lifetime data is of great challenge. This paper proposes a moment-based point estimation method, and uses the bias-corrected Bootstrap method to construct confidence intervals for quantities of interest. The maximum likelihood method needs the likelihood function of the aggregate lifetime data, however, it is only applicable to a few distributions that have the closure property with respect to the operation of convolution. Differently, the proposed method does not utilize the likelihood function, thus it applies to more distributions. Finally, both the simulation study and the real data analysis are performed for demonstration and illustration.

The government's implicit guarantee is an important reason for the high leverage of state-owned non-financial enterprises(NFSOE) and the distortion of resource allocation efficiency. The current behavior of withdrawing from the government's implicit guarantee such as breaking the rigid payment will lead to the change of the expectation of the implicit guarantee, which will cause the leverage adjustment of NFSOE. This paper selects Shanghai-Shenzhen A-share state-owned non-financial listed companies from 2010 to 2022 as samples, constructs A dual machine learning model based on the dynamic capital structure adjustment model, and analyzes the impact of implicit guarantee expectation change on the leverage adjustment of NFSOE and its mechanism. It is found that the expected change of implicit guarantee can promote the adjustment of the actual leverage level of NFSOE to the direction of target leverage, and can increase the speed of leverage adjustment. Mechanism analysis shows that the expected decline of implicit guarantee significantly affects the external capital constraints of NFSOE, thus promoting the reduction of leverage ratchet effect, and improving the internal governance mechanism by reducing information asymmetry and agency costs. Heterogeneity analysis shows that the expected decline of implicit guarantee has the greatest impact on the leverage adjustment of NFSOE with low policy burden and local government, and can affect the leverage adjustment of state-owned non-financial enterprises with various functional positioning. This paper provides empirical evidence for the important role of implicit guarantee expectation change in reducing leverage of state-owned non-financial enterprises.

Based on the Exponential Penalty Function (EPF), the nonlinear minimax problem is transformed into the unconstrained optimization problem. In this paper, by introducing the spectral parameter and restart condition, we develop the new conjugate parameter and restart direction, a spectral conjugate gradient method with restart procedures for solving the discussed problems is proposed. The search direction generated by the algorithm satisfies sufficient descent property which is independent of the choices of the line search. The global convergence of our proposed algorithm is analyzed with local Lipschitz continuity. Finally, some preliminary numerical experiment results are reported, which show that our proposed algorithm is promising.

To interpolate the specified Frenet frame, curvature and torsion, a method is proposed for the construction and shape optimization of spatial quintic $F^3$ interpolating curves. $F^3$ continuity of spatial curves is a special $k$-th order Frenet frame continuity and ensures the satisfaction of $G^2$ continuity and torsion interpolation. Firstly, a quintic Bézier curve interpolating the given $F^3$ data is constructed, whose control points are expressed with two parameters denoting the lengths of the curve's end tangents. Then, the optimal parameter values are determined by minimizing a quadratic energy function. Finally, by defining the objective function as the integral of a weighted sum of squared curvature and torsion, another better optimization method is proposed. Compared to the previous $G^2$ interpolation scheme, the new methods can generate curve shapes with more satisfactory curvature and torsion profiles, although using a stricter continuity constraint.

This paper considers a two-stage retailer-led supply chain consisted of one supplier and one dominant retailer. Based on option contract and portfolio contract with options, this paper builds the multi-period dynamic game models. By using stochastic dynamic program, this paper analyzes the supplier’s multi-periodic optimal production policy and the retailer’s multi-periodic optimal ordering policy under two contracts, and provides an approximate algorithm to estimate the corresponding policy parameters. On this basis, this paper discusses the effect of two option contract formats on the performances of two member. The result suggests that option contract benefits the supplier, while portfolio contract with options benefits the retailer. Finally, the above conclusions are verified by an example analysis.

In recent years, uncertainty quantification (UQ) has garnered considerable attention. Surrogate models based on polynomial chaos expansion are widely applied in addressing UQ problems. However, in practical applications, the distribution functions of random data are often unknown, posing significant challenges. Based on polynomial chaos expansion, This article constructs a surrogate model based on polynomial chaos expansion and data, and uses such model to estimate data statistics, such as moment estimation, probability density function estimation and cumulative distribution function estimation. Firstly, synthetic data was employed to validate the effectiveness and feasibility of the surrogate model, and then the data-driven polynomial chaos expansion method was applied to deal with some real-world data. Numerical results show that our method yields stable and reliable predictions for a certain class of random data.

Duadic codes are an important class of cyclic codes. It is interesting to construct duadic codes whose minimum distance has the square-root lower bound. In this paper, we propose two construction methods of odd-like duadic codes whose minimum distance has the square-root lower bound. Two classes of odd-like duadic codes with the square-root lower bound on the minimum distance are obtained.

Green consumer credit is an effective financial tool to stimulate green consumption through interest rate incentives. This paper examines the impact of two green consumer credit strategies offered by the financial institution on supply chain operations, including G strategy (credit preference without target green degree) and TG strategy (credit preference with target green degree). The results show that: (1) Increasing the preferential coefficient for green consumer credit can effectively motivate the manufacturer to invest more in green R&D, thereby securing more favorable credit rates for consumers. In addition, the financial institution can mitigate the negative effects of increased default risk by adjusting the target green degree. (2) Both green consumer credit models contribute to the promotion of green products and enhance the profits of the manufacturer and the retailer. Particularly, when the environmental awareness of the financial institution exceeds a certain value, the adoption of the TG strategy can facilitate Pareto-optimal situation among channel members. (3) Compared to manufacturer-independent R&D scenarios, green R&D cooperation between the manufacturer and the retailer can effectively expand the incentive scope of both green consumer credit models and the Pareto improvement interval among channel members.

As an important component of urban public transportation, the fare system and route network design of customized buses play a crucial role in promoting the healthy operation of the customized bus system and improving service quality. Concerning price-elastic demand, this study addresses a new optimization problem of pricing decision and route network design for morning and evening peak commuting customized buses to develop a scientific fare system and route network. The objective is to maximize the operational profit while simultaneously optimizing vehicle routes, passenger allocation, fares, and travel times. Firstly, we formulate the problem as a mixed-integer nonlinear programming model and transform it into an equivalent mixed-integer linear programming model. To effectively solve it, a tailored adaptive large neighborhood search (ALNS) algorithm is developed based on the characteristics of the problem. Two tailored operators: Minimum profit and minimum patronage destruction operators are designed to enhance its effectiveness. Extensive numerical experimental results confirm the efficiency and effectiveness of the proposed model and algorithm. For small-scale instances, the proposed ALNS algorithm can obtain optimal solutions or solutions of higher quality compared to commercial solver CPLEX within 5 seconds. For the typical large-scale Fuzhou case, the proposed ALNS algorithm can obtain satisfactory solutions that are 0.617% and 0.344% better compared to the traditional large neighborhood search algorithm and adaptive large neighborhood search algorithm, respectively. Compared with the problem without considering pricing decisions and penalty costs, considering pricing decisions and penalty costs can increase operational profit by 14.98% and the number of served passengers by 35.51% on average.

The process of optimizing ridepool matching decisions can be seen as a strategic game of decision-making among the ridepool management platform, drivers, and passengers. Based on this fundamental principle, this study introduces a personalized biding strategy (PBS: Personalized Biding Strategy) for ridepooling, incorporating it into the overall ridepool order optimization process to enhance the success rate of ridepooling. Initially, the study identifies two crucial factors that impact ridepool service quality: detour distance and lateness duration. It presents passenger distance pricing functions and lateness penalty pricing functions based on these factors. Building upon this foundation, two models are developed: the Dominant Model of Order Optimization, which aims to maximize net profit, and the Follower Model for Ridepool Pricing (FMR), which aims to maximize actual travel utility. An optimized game model for personalized pricing and order planning considering service quality is constructed, taking into account service quality in personalized ridepool pricing and order planning. In this model, the ridepool management platform, as the dominant party, maximizes its profit by making decisions regarding order allocation and route execution. Subsequently, passengers, as followers, provide their desired ridepool prices based on the services offered by the platform's order planning. A decomposition matching algorithm is proposed to solve this game model. The effectiveness of PBS in improving the ridepool success rate is validated through 56 different scenarios with 20 different parameter combinations. The results demonstrate that the PBS proposed in this study significantly improves the profitability of the ridepool platform, the overall utility of passengers, as well as the ridepool success rates for both vehicles and passengers, when compared to the Average Biding Strategy (ABS) and the Fixed Biding Strategy without considering service quality (FBS).

The motor nervous system is a small but complex comprehensive system, and the information transmission process of the motor nervous system is often affected by noise generated by changes in the external environment, the randomness of biochemical reactions in cells, the randomness of ion channel switching, etc. To study the influence of colored noise on the motor neural information transmission process, In this paper, the fully discrete spectral Galerkin algorithm is proposed for the random FitzHugh-Nagumo neural system information conduction model under colored noise interference, and the stability of the numerical solution of the algorithm is analyzed. The information transmission process of random FitzHugh-Nagumo nervous system under the interference of a single colored noise sample and the average meaning of multiple colored noise samples was simulated, and compared with the deterministic neural information transmission process without noise interference, the effect of colored noise on the nervous system information transmission was analyzed. It is helpful to explain the dynamic behavior of information conduction in complex motor nervous system under the influence of noise.

This paper studies the formation control problem of multi-unmanned ground vehicles with uncertain nonlinear dynamics. First, a formation motion model of multi-unmanned ground vehicles is established based on leader-follower method, which describes the leader-follower relationship among individual unmanned vehicles. The uncertain nonlinear dynamics are learned online by neural networks. Then, based on the target tracking mechanism, an adaptive neural network direction controller is designed by introducing a sliding mode surface. Simultaneously, combining with backstepping control technique, a target tracking mechanism based adaptive neural network propulsion controller is presented to achieve integrated longitudinal and lateral formation driving of multiple unmanned vehicles. Lyapunov stability theory is used to analyze and prove the stability of the closed-loop multi-unmanned vehicle formation control system, and the formation tracking error can converge to the neighborhood of origin. Finally, the simulation results verify that the formation control and formation maintenance are realized under the proposed control algorithm.

Online freight platforms face challenges such as fierce price competition due to homogeneous pricing strategies. This paper addresses this issue by developing a differentiated pricing model based on two-sided market theory. The model considers the interplay between platform technology level and user quality level. The study examines three user affiliation structures: single-homing, multi-homing, and both sides of users are partially multi-homing. It investigates the impact of technology and user quality on the platform's equilibrium pricing and profits using analytical methods and simulations. When both sides of users are single-homing, a two-part pricing strategy is more advantageous, with profits increasing with better technology and user quality. Under multi-homing, the optimal pricing strategy depends on an equilibrium condition. Higher user quality favors two-part pricing, while higher technology level favors registration fee-only pricing. When both sides of users are partially multi-homing, registration fee-only pricing is more optimal. Both technology and user quality positively impact profits, with technology playing a bigger role. The research provides insights for online freight platforms to implement differentiated pricing based on their market position. Platforms should focus on technological innovation, improve user quality, and optimize pricing dynamically，which allows them to achieve differentiation and profitability in the evolving freight market.

Small and medium-sized enterprises (SMEs) in supply chains often face significant financing difficulties, which hinder their high-quality development. Blockchain technology-driven third-party financial service platforms offer a new approach to solving this issue. This paper explores the government's regulatory behavior strategy, the third-party financial service platform's blockchain information sharing behavior strategy, and the small and medium-sized enterprises' financing integrity behavior strategy by constructing a tripartite evolutionary game model of "Government-Third-party financial service platform-SMEs". It conducts an analysis on the stability of the equilibrium points in the tripartite evolutionary game and discusses the impact of blockchain technology cost, government regulatory cost, government reward and punishment intensity, and enterprise income on the equilibrium of the tripartite evolutionary game through parameter sensitivity analysis. The results show that: 1) Whether a third-party financial service platform chooses to share information through blockchain depends not only on the cost of blockchain technology but also on the government's rewards and punishments for the platform and small and medium-sized enterprises (SMEs), as well as the size of the returns from default risks. 2) Conventional wisdom holds that digital supply chain finance driven by blockchain is inevitably superior to traditional supply chain finance. However, this study finds that only when the government dynamically rewards and punishes platforms to improve the transparency of supply chain financial information and constrains enterprises to reduce financing default rates under specific circumstances, will the financing efficiency of blockchain supply chain finance surpass that of traditional supply chain finance.

The National Independent Innovation Demonstration Zone (NIIDZ), as an important engine leading innovative development, takes institutional and policy reforms as a starting point to radiate and drive the coordinated development of surrounding regions. The gradual improvement of the high-speed rail (HSR) network has opened up a new pattern for the “dual circulation” and expanded the scope of the NIIDZ's innovation spillover effects. Based on data of HSR city pairs from 2008 to 2019 in China, this paper examines the impacts and mechanisms of the improvement in innovation levels of ordinary cities after the opening of HSR connected to NIIDZs by applying a staggered DID model. The empirical results are as follows. Firstly, the opening of HSR connected to NIDDZs significantly improves the innovation levels of ordinary cities. Secondly, the innovation spillover effects are more pronounced for cities in the eastern region, cities with a better innovation environment, and large-scale cities. Thirdly, the innovation spillover effects are realized by utilizing innovation endowment, government-guided innovation and demonstration driving effects. This paper provides empirical evidence and policy insights for innovation-driven development in the context of HSR network. It optimizes the spatial allocation of innovation resources and accelerates the development of new quality productive forces, achieving high-quality economic development.

Exploring the heterogeneity characteristics of nodes and edges in emergency logistics networks has a great impact on the location-allocation optimization problem throughout the entire emergency system. First, for the disaster preparedness stage, based on the complex network theory, this paper selects the node vulnerability indicators to characterize the heterogeneity of various emergency reserve facilities. The vulnerability indicators of transportation connected edges among emergency reserve facilities, and between emergency reserve facilities and disaster-affected demand areas, are also incorporated into the emergency location-allocation optimization decision-making. And a multi-objective location-allocation model is formulated, for comprehensively balancing vulnerability, efficiency and cost-effectiveness in the emergency logistics network. Then, taking the emergency reserve network covering 13 cities in Jiangsu Province as a case example, a NSGA-II algorithm is designed to solve the constructed model and perform a numerical simulation. Not only the sensitivity analysis for the parameters is analyzed, but also the comparative analysis is discussed. The simulation results show that the location-allocation optimization problem for heterogeneous emergency reserve facilities from the perspective of vulnerability, not only performs well in cost-effectiveness and allocation efficiency, but also significantly improves the robustness and resilience of the entire emergency logistics network. This work provides some useful managerial insights for emergency decision-makers to make an effective disaster preparedness plan.

This paper uses the OP method and event study method to study the impact of blockchain technology application on enterprise total factor productivity and stock price, and analyzes whether the application of enterprise blockchain technology has effectively promoted the improvement of enterprise total factor productivity, or just created more "foam" for the company's stock price? The main conclusion of this article is that the application of blockchain technology mainly promotes the improvement of total factor productivity by reducing financing constraints, and has a greater impact on the improvement of total factor productivity for large enterprises and state-owned enterprises; In addition, after the application of blockchain technology in enterprise announcements, the company's stock price level has significantly increased, meaning that the company can obtain higher stock premiums from blockchain technology based announcements.

Aimed at the problems of insufficient optimization performance and accuracy of SMA in solving wind farm layout optimization problem (WFLOP), and the slow convergence speed and premature convergence to local extreme values in SMA, an improved slime mold algorithm based on adaptive contraction and genetic learning strategy is proposed. First, a wind farm layout model is initially established based on the specific environmental conditions. Then, for the problem of premature convergence to local extreme values, a genetic learning strategy is introduced to enhance the convergence speed and global search ability of SMA, resulting in the GLSMA. Finally,Aimed at the problems of WFLOP, the maximum rule coding solution vector is adopted, and an adaptive contraction strategy is designed to update the position of slime moulds using the power generation of wind turbines, which improving the solution accuracy. The experimental results show compared to SMA, Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), and Genetic Learning Particle Swarm Optimization (GLPSO), GLSMA has faster convergence speed and higher optimization accuracy in 19 test functions, and the A-GLSMA has higher performance than Genetic Algorithm (GA) in solving WFLOP under two wind direction distributions.

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

In real life, sometimes broken-down home appliances need to be shipped back to after-sales service center, and the home appliance after-sales service has problems such as long response time and high logistics operation cost. Considering real-time order information and dynamic vehicle information, an instant cycle scheduling system is designed, and an instant pickup routing optimization with heterogeneous fleet was formulated for home appliance after-sales service platform. Then, instant cyclic scheduling genetic algorithm was proposed to solve the model, in which initialization based on minimum wait time, roulette selection combined with elite strategy, best-cost route crossover, and inversion mutation are adopted. To verify the effect of ICSGA in solving advance order, ICSGA outperformed best-known solutions (BKS) by 0.27$\%$ based on Solomon benchmark instances. To verify the performance of ICSGA in solving real-time order, based on Kilby benchmark instances, ICSGA obtained the minimum gap value of 7.91$\%$. Finally, heterogeneous fleet analysis, and sensitivity analysis for dynamic degrees and cut-off time of the platform were performed based on the Solomon instances adapted to some real-world situation. According to the results, management suggestions are provided for rational resource allocation and path planning of home appliance after-sales service enterprises.

Motif is a form of high-order network structure. This paper takes the three-node simple motifs as the research object. Based on the connection types of the edges, the missing edge matrix representing the borderless structure of the networks is introduced. Using matrix product, Hadamard product and special operation based on matrix elements, the representations of motif adjacency matrix in networks are studied. The matrix expressions of the open motif adjacency matrix are given for the directed unweighted networks. For the directed weighted networks, according to the new method of dealing with the bidirectional edge weight, based on the arithmetic mean and geometric mean of each edge weight of the motifs, the matrix expressions of the corresponding motif adjacency matrix under the two modes of overall weighting for motifs are obtained. A test example is given and the relations between expressions of motif adjacency matrix of four network types are analyzed and discussed.

In this paper, we investigate the dynamic relationship between climate policy uncertainty, global energy prices and stock prices using Granger causality test, time-varying parameter vector autoregressive (TVP-VAR) model and time-varying parameter vector autoregressive spillover index (TVP-VAR-DY) model. The results show that climate policy uncertainty is a Granger cause of global energy prices and stock prices, especially when a major climate policy event occurs, while there is a two-way Granger causality between global energy prices and stock prices. The results of the TVP-VAR model further show that the short-term impact of climate policy uncertainty on global energy prices is significantly positive while the medium- and long-term impacts turn from negative to positive, with the 2008 being the inflection point. Meanwhile, the impact of climate policy uncertainty on stock prices was generally negative until 2015, after which the impact manifests positive. Finally, based on the TVP-VAR-DY model, it is found that the total spillover effects of climate policy uncertainty, global energy prices and stock prices have obvious time-varying characteristics. Climate policy uncertainty is the spillover exporter, while global stock prices are the spillover receiver, and global energy prices have changed from the spillover receiver to the spillover exporter after 2008.

This study approaches typhoon landfalls as exogenous climate risk events, designating the moment of landfall as the critical intervention point. Utilizing the Difference-in-Differences (DID) methodology, the research examines the influence of typhoon disasters on the stock returns of publicly traded companies in China, and assesses how financial risks propagate through supply chain networks triggered by typhoon disasters. To gain a more nuanced understanding of these effects, the paper engages in a detailed mechanism analysis by examining the intensity of digital transformation. The results suggest that typhoon disasters have a significant and detrimental impact on the stock returns of firms located in affected areas, with this effect rippling through to their suppliers and customers via the intricate web of supply chain connections. Moreover, the study uncovers a distinct asymmetry in the spillover effects between suppliers and customers. Specifically, the research highlights that the level of digital transformation is instrumental in alleviating the financial risks associated with typhoons and serves as a protective barrier against the adverse effects on stock returns. Finally, a comprehensive suite of robustness checks reinforces the validity and reliability of the study's conclusions.

This paper mainly studies the insurer’s robust optimal investment under the Black-Scholes model and a loss dependent premium principle. Assume that the claims process follows the Brownian motion with drift, the insurer is allowed to invest in a risk-free bond, a stock and a European call option. The price of stock is subject to Geometric Brownian Motion, and the insurer can buy proportional reinsurance from the reinsurer to diversify the investment risks. By solving the HJB equation, the analytic expressions of robust optimal reinsurance and investment strategies under the CARA utility are obtained, reinsurance and investment strategies of insurer under hedging conditions are also given. Finally, the influences of the model parameters on the optimal strategies are analyzed by numerical simulation.

There is a problem of omission in China's household registration system, but the government departments have not yet carried out the estimate of omission in the household registration system. The single omission estimator recommended by the United Nations Statistics Division do not cover all missed population and underestimate omission rate. This article proposes using the synthetic omission estimator instead of the single omission estimator, and compares the synthetic omission estimator with the single omission estimator through case studies. The results of the empirical study show that the omission rates estimated by the four synthetic omission estimators are higher than that of the single omission estimator; The sampling standard deviation of the omission rate estimated by one of the synthetic omission estimators is slightly higher than that of the single omission estimator, while the sampling standard deviation of the estimated results of the other three synthetic omission estimators are smaller than that of the single omission estimator. The research results provide important reference for the statistics bureau and the Ministry of Public Security to formulate the estimation scheme of omission in the household registration system in the future, and improve the scientificity and operability of the scheme.

Automated forward-looking prediction based on large-scale patent data and feature indicators has gradually become the research focus of potential high-value patent discrimination. Aiming at the classification bias caused by imbalance data distribution and cost difference of misclassification in the identification of potentially high-value patents using universal machine learning methods, this study optimizes the classification strategy at both algorithmic and evaluation levels, proposes a multi-scenario optimization framework for the identification of potentially high-value patents, and conducts an empirical study on the example of the second-classification scenario of predicting whether patents in the field of microchips are potentially high-value patents. The specific improvements are as follows: (1)The algorithm level adopts the idea of imbalance problem solving, and constructs a combination of the improved dynamic integrated selection algorithm and the multi-objective dung beetle optimisation algorithm; (2)The evaluation level introduces the concept of misclassification cost, and explores the influence of the cost matrix on the prediction effect of classification based on the multi-application scenarios. The results show that the method in this paper can reduce the overall misclassification cost while controlling the distribution of different classification errors more accurately, improve the recognition accuracy of the model, and better achieve the rapid, accurate and scientific identification of potential high-value patents from a large number of patents. This paper puts forward optimisation and improvement strategies from the aspects of algorithm and evaluation, which provides a new idea to improve the identification method of potential high-value patents, and provides a new reference for relevant innovation subjects to quickly lock the potential high-value patents and carry out targeted cultivation work.

This paper focuses on the situation where the response variable in sampling surveys does not obey the normal distribution as required by traditional small area estimation models. It investigates the small area estimation method for the target parameters of the Fay-Herriot model (FH model) based on the transformed response variable, proposing an empirical best predictor (EBP) for the target parameters and its mean square error (MSE) estimator. When inversely transforming the transformed EBP, a conditional expectation bias correction term is added to correct the bias introduced by the inverse transformation. A second-order approximate MSE estimator is introduced that is not restricted by the estimation method of the model parameters. Through numerical simulation, the MSE estimation method presented in this paper is compared with existing methods, revealing that the method enhances the adaptability of small area estimation models to data with response variables that have a skewed distribution and improves the precision of target parameter estimation, with the added benefit of having a simple estimator form. Finally, the research method of this paper is used to measure the per capita financial assets of urban and rural residents in some provinces and counties of China, and the effectiveness of the method is verified through the measurement results.

This paper addresses the fault-tolerant consensus problem in heterogeneous multi-agent systems based on an adaptive distributed event-triggered mechanism, with a focus on actuator faults and bounded external disturbances. Compared to existing research, this paper introduces distributed intermediate variables to model the closed-loop error in heterogeneous multi-agent systems. Additionally, a fault observer is designed to estimate actuator fault states in real time and compensate for actuator faults by adjusting control gains online. Furthermore, an adaptive distributed event-triggered mechanism is designed, which conserves communication resources and successfully avoids the Zeno phenomenon through dynamic interactions and information sharing among agents. Moreover, a fault-tolerant controller based on a distributed adaptive event-triggered mechanism is designed to ensure that agents maintain consistent behavior even in the presence of actuator faults or external disturbances. Finally, the feasibility and effectiveness of the proposed method are validated through Matlab simulations, providing a practical solution for real-world applications.

The least eigenvalue of the Laplacian matrix of a simple undirected graph is called the algebraic connectivity of the graph. For a first-order multi-agent system with an undirected graph as its interaction topology, the larger the algebraic connectivity, the faster the consensus convergence rate of the system. In this paper, a graph operation of edge rewiring is used to optimize the undirected graph corresponding to the interaction topology of a multi-agent system, so that the algebraic connectivity increases the most, and an algorithm is proposed to increase the algebraic connectivity of the undirected graph corresponding to the interaction topology and reduce the communication volume. Simulation experiments on a system consisting of six multi-agents show that the algorithm can improve the speed of multi-agent system error approaching zero, accelerate consensus convergence rate of the system, and reduce the communication volume of the system by decreasing the communication times when the system reaches consistency.

Dissipative analysis and control for a class of average dwell time switching genetic regulatory networks is investigated. The time-varying delay parameters are also taken into account. Firstly, the sufficient conditions for the strict $\left\langle Q,S,R \right\rangle -\gamma$ dissipation of the system are derived through constructing the multiple Lyapunov functions and the dissipative performance index function and combining with the linear matrix inequality technique. By utilizing Schur complement, it is proved that the genetic regulatory network system satisfying $\left\langle Q,S,R \right\rangle -\gamma$ dissipation is exponentially stable. Furthermore, the state feedback controller is designed, and the controller parameters are obtained by solving linear matrix inequalities. Also, it is proved that the designed controller can ensure that the system is $\left\langle Q,S,R \right\rangle -\gamma$ dissipative. Finally, a numerical example is given to verify the correctness and effectiveness of the proposed method.

In this paper, the problem of output consensus control for heterogeneous multi-agent systems with denial-of-service (DoS) attacks is studied. First, aiming at the problem that the cyber attack behavior is changeable and its statistical characteristics of attack modeling method based on dual hidden Markov model is proposed, which converts the communication interruption caused by the attack behavior into the communication topologies switching of multi-agent system. Second, a distributed asynchronous dynamic observer is designed to solve the asynchronous problem when the communication topology mode (CTM) and the transition probability mode (TPM) do not match. Third, based on the stochastic Lyapunov theory and linear matrix inequality technique, sufficient conditions for the solvability of the system output consensus problem are obtained. Finally, the feasibility and effectiveness of the results are illustrated through a simulation example.

This study presents the construction of a pth-order autoregressive integer-valued time series model, predicated on a Poisson thinning operator. This model is characterized by its inherently time-varying parameters, which may conform to a particular random distribution. Building upon this foundation, we derive the theoretical attributes pertaining to its ergodicity, point estimation, interval estimation, and the associated statistical properties of hypothesis testing. Additionally, we propose a variable selection methodology, expressly tailored for this model, and substantiate its theoretical underpinnings. The validity and reliability of these properties are thoroughly corroborated through meticulous numerical simulations. Ultimately, the practical applicability and robustness of this model are vividly demonstrated through its successful deployment within an empirically derived real-world data set.

Covariance estimation poses a crucial challenge in the analysis of high dimensional data, which in turn is prone to the two phenomena of heavy-tailed distributions, small samples, and in these cases, the traditional estimation methods (e.g., sample covariance matrix) prove inadequate for such heavy-tailed data, given their lack of accuracy. In cases where heavy-tailed high dimensional data represented as tensors (multi-dimensional arrays), harnessing the tensor structure is a good choice for achieving dimensionality reduction. To this end, this paper proposes novel structured regularization methods for estimating the covariance of heavy-tailed tensor-valued data. In this paper, the heavy-tailed tensor data are first truncated, then the truncated sample covariance matrix is computed, then the CP decomposition will be applied to find an approximation in the form of kronecker product of multiple matrices of the truncated sample covariance matrix, and finally imposes a banded or tapering structure for each of the small matrices obtained by the decomposition. Simulation results show that the proposed estimators have excellent performance for different degree of heavy tailing and different sample sizes. At the same time, a set of anomalous temperature datasets with heavy-tailed distributions is analysed using the estimation method proposed in this paper.