In view of the current trend of rapidly promoting the electrification of ride-hailing in many parts of the country and the background of differences in the process of electrification of ride-hailing between different regions. Based on the strategy of differentiated subsidies for different types of vehicles on the platform, taking into account the competition between ride-hailing and cruising taxi, the service supply of drivers and the bounded rationality of passengers, the optimal supply of different types of drivers is solved by using Kuhn Tucker theorem. Aiming at maximizing platform revenue and minimizing ride-hailing carbon emissions, a three-level programming model of " ride-hailing platform-driver-passenger" is established to optimize the platform reward ratio, unit mileage price and subsidies, and the existence of Nash equilibrium price is demonstrated. In terms of model solving, the optimization variables are processed hierarchically, the genetic algorithm is used to optimize the reward ratio and subsidy, the sensitivity analysis method is used to describe the derivative relationship between the demand of different travel modes and the price change, and the relaxation method is used to solve the equilibrium price. The study shows that: (1) Promoting the electrification of ride-hailing vehicles can achieve a lower equilibrium transportation service price in competition;(2) When the proportion of pure electric vehicles in the market is stable, the platform will increase the reward ratio allocated to all drivers, which will help to increase the platform revenue;

This paper focuses on the core issue of how the quantification of data assets acts on corporate credit risk in the era of the digital economy. Aiming at the biases and distortions that may arise in estimation and statistical inference from traditional model selection methods, we adopt the model averaging method to construct a more robust econometric analysis framework, so as to systematically identify the impact mechanisms and heterogeneous manifestations of data assets on credit risk. For this purpose, we use the panel data of China's A-share listed companies from 2012 to 2024 and employ the model averaging method based on smooth penalty, which effectively alleviates the problem of model uncertainty and ensures the reliability of statistical inference. The empirical findings show that: First, data assets exert a robust and significant negative impact on corporate credit risk, indicating that they have a distinct risk mitigation effect. Second, the heterogeneity analysis reveals that this effect is more prominent in non-state-owned enterprises, high-tech industries, manufacturing industries, and mature enterprises. Third, the mechanism test demonstrates that data assets reduce corporate credit risk by enhancing information transparency, boosting total factor productivity, and strengthening executive incentives. The contributions of this paper are mainly reflected in the following two aspects. Empirically, it provides systematic empirical evidence for the credit enhancement effect of data assets and uncovers their applicable contexts and impact channels. Methodologically, by introducing an econometric framework of model averaging that can effectively address model uncertainty and heteroscedasticity, it improves the robustness and inferential reliability of research on relevant topics. The conclusions offer theoretical insights and practical reference value for deepening the assessment of data elements and improving corporate credit risk management.

In treatment effect estimation, practical constraints such as imbalanced study populations and the costs of data collection, annotation, and computation often result in substantially fewer treated units than controls, which undermines the stability and precision of inference. Within the conformal prediction framework, we investigate the impact of imbalanced treatment assignment on the construction of prediction intervals for treatment effects. Simulation studies reveal that increasing imbalance can substantially inflate interval width. Motivated by these findings, we introduce random under-sampling and probability-based sampling strategies for propensity score estimation, thereby alleviating the adverse effects of treatment imbalance on the construction of conformal prediction intervals. Results from simulation experiments and semi-synthetic data analyses demonstrate that both sampling strategies can significantly reduce interval width without sacrificing empirical coverage, with probability-based sampling yielding the most pronounced improvements in interval efficiency.

In view of the problem of information sharing in emergency collaboration for emergencies, this study constructs a network evolution game model of information sharing between emergency response decision-makers, and analyzes the influence of factors such as the capability heterogeneity of decision-making entities, sustainable interests and network structure on the evolution of information sharing strategy. The results show that: 1) When the sustainable benefit is too small (or too large), all decision-making entities do not share (or share) emergency information.; when the benefit is in a certain range, if the difference in the ability to obtain emergency information is larger, it is trapped in an inefficient “half-sharing” dilemma; the difference is small or even the same, the result of “full sharing” or “not sharing at all” appears. 2) The network structure effect leads to equilibrium “drift”. 3) The impact of key factors such as information sharing costs, sustainable interests, group size, external environmental uncertainty, number of neighbors, and probability of random rewiring on the choice of sharing strategies is interfered with by the heterogeneity of decision-makers' capabilities. 4) In the context of homogeneous decision-making capabilities, a higher proportion of groups choose the “information sharing” strategy.

In this paper, taking the data of some component stocks of Shanghai Stock Exchange 50 index, combined with fractal theory, return-risk ratio, and multiple realistic constraints, an optimal asset allocation model with maximizing return-fractal risk ratio as the investment goal is constructed. In order to obtain the optimal asset allocation ratio, an improved teaching and learning optimization algorithm combining multiple "learning" operators is proposed to solve the portfolio model. The empirical results show that the returns of single component stocks themselves and between two component stocks have fractal characteristics, and fractal theory is suitable for the construction of stock portfolio. Whether in a bull or a bear market environment, the return-fractal risk ratio model with different adjustment periods, different levels of volatility and time scales are generally better than traditional portfolio models such as Mean-Variance and Mean-VaR in terms of average rate of return and Information ratio.

In the highly interconnected information environment shaped by social media, rumor propagation exhibits greater complexity and suddenness. To more accurately characterize the positive reinforcement mechanism of social media in the rumor diffusion process, this study develops the ISDR-M (Ignorant—Spreader—Debunker—Removed—Media) model, incorporating both the activation of social media by spreaders (activation rate $\eta$) and the amplification of rumor diffusion by social media (transmission rate $\beta$). The basic reproduction number $R_0$ and the existence conditions for four types of equilibria are derived analytically. Furthermore, the Routh—Hurwitz criterion is employed to investigate the local stability of each equilibrium. By applying the global asymptotic stability criterion and transcritical bifurcation theory proposed by Castillo-Chavez et al., this study systematically characterizes the global dynamics and bifurcation structure of the model as key parameters vary. Comparative analysis with the traditional ISDR model reveals that the positive reinforcement mechanism of social media significantly enlarges the scale of rumor diffusion and weakens the dominant role of traditional contact-based transmission pathways. Finally, numerical simulations are conducted to validate the theoretical findings. This study provides a theoretical foundation for understanding the nonlinear mechanisms and key regulatory factors of rumor propagation in the context of social media, and offers mathematical support and policy implications for the design of effective intervention strategies, such as platform governance and anti-rumor information dissemination.

With the rapid development of China's financial sector, the number of bank branches has grown steadily, leading to a significant increase in bank density around firms. As an intuitive signal of financial service accessibility, changes in the density of nearby banks may exert profound influences on information transmission and investment behavior in securities markets. Using data from A-share listed companies over the period 2007-2023, this paper takes stock idiosyncratic volatility as a representative indicator of stock market microstructure characteristics, and examines the effect of neighboring bank density on idiosyncratic volatility and its underlying mechanisms. The empirical results demonstrate that greater neighboring bank density significantly reduces stock idiosyncratic volatility, thereby mitigating the noise embedded in firm-specific information. Mechanism analysis reveals that improved financing conditions and enhanced corporate information environments serve as important transmission channels. Furthermore, heterogeneity analysis indicates that this effect varies significantly across firms with different ownership structures, regional locations, industries, and sizes. This study extends the literature on the spatial distribution of bank branches and firm-level microstructural characteristics, and offers policy implications for deepening supply-side structural reform in the banking sector, improving resource allocation efficiency, and strengthening banks' capacity to serve the real economy.

Unlike standard least squares methods that only consider observation errors, the total least squares (TLS) methodology accounts for perturbations in both the dependent variable and the system matrix, offering enhanced practical relevance. Inspired by recent advances in subsampling-improved randomized singular value decomposition, we develop an accelerated randomized algorithm for truncated total least squares (ARTTLS) problems, specifically tailored to large-scale ill-posed matrices. The algorithm employs a dual strategy of row-space subsampling and random projection to explicitly approximate the dominant spaces of the augmented matrix. We establish an upper error bound for the proposed algorithm and perform numerical experiments. The results show that ARTTLS algorithm maintains a comparable or superior solution accuracy while significantly reducing computational time. This study presents an efficient and robust solution to TTLS problems in large-scale scenarios.

Against the backdrop of the digital economy emerging as a key engine driving high-quality regional development, this paper focuses on provincial-level digital economies. It constructs a measurement and forecasting framework tailored to provincial data characteristics, categorizing the digital economy into foundational and integrated types. By incorporating substitution and spillover effects into growth accounting theory, it quantifies the contribution of digital technologies to traditional industrial growth. To address data gaps, a combined forecasting method integrating the GM(1,1) model with node interpolation is proposed. Empirical results using Shaanxi Province's data from 2002 to 2020 demonstrate: (1) The framework exhibits strong feasibility and effectiveness at the provincial level, with a forecast error of only 0.78% for the 2022 digital economy share—outperforming traditional single models; (2) During the estimation period, Shaanxi's digital economy expanded from RMB 17.766 billion to RMB 412.601 billion, with its GDP share rising from 8.21% to 30.22%; (3) Digitalization outcomes exhibit significant sectoral heterogeneity, with secondary and tertiary industries serving as core contributors; (4) Projections indicate Shaanxi's digital economy will exceed RMB 1.12 trillion by 2027, surpassing 50% of GDP. The proposed analytical framework enables continuous measurement and trend forecasting of provincial digital economy scale, providing quantitative references for establishing provincial digital economy accounting systems, identifying key industrial digitalization sectors, and formulating differentiated digital economy policies.

Hazardous chemical safety is crucial for people’s life and property, and it is of great importance for governments and policy makers. With the rapid development of China's national economy and chemical industry, chemical accidents have also shown an escalating trend on its occurrence. Therefore, there is an urgent need to assess potential chemical accident risks to support decision making on accident prevention and disposition. To this end, this paper proposes a novel approach, called RAG-Risk, to utilize RAG (Retrieval Augmented Generation) with large language model to predict the root causes of potential risks for given chemical production enterprises. Specifically, this paper constructs the context for retrieval-augmented generation of large language model using the collected 716 chemical accident cases, and refines the context of the given chemical production enterprise by using deep learning to improve the performance of root cause prediction for chemical accidents. Experimental results on the collected 716 real cases show that, compared to the baseline methods, RAG-Risk can effectively improve the precision and recall of root cause prediction for chemical accidents, with average improvements as 30.16% and 48.81% in terms of micro-F1 and macro-F1, respectively. This research provides great management insights for chemical accident risk management and emergency decision-making.

Against the backdrop of accelerating digital and intelligent transformation in the current economy and society, cybersecurity has become a critical strategic issue with far-reaching implications. This paper constructs a combined risk management framework of “direct defense + cybersecurity insurance” to investigate optimal cybersecurity investment strategies for centralized and decentralized enterprises under uncertain losses. Breaking from existing research reliant on fixed loss distributions, this study adopts a data-driven approach. It constructs a high-dimensional loss uncertainty set based on Laplace kernel density functions and derives optimal solutions through robust optimization methods. Findings reveal: The strategies derived from this model exhibit greater robustness than traditional models; Compared to decentralized enterprises, centralized enterprises face fewer investment constraints and enjoy greater decision flexibility, with distinct investment strategies emerging between the two; When budgets are constrained, firms should select strategies aligned with their risk aversion levels. As budgets increase, firms exhibit a pattern of rising direct defense expenditures, insurance premiums, and wealth utility followed by stabilization. With ample budgets, firms should proactively opt for full insurance coverage. This research provides theoretical foundations and practical guidance for firms to develop scientifically sound, practical, and effective cybersecurity defense strategies.

Acceptance sampling is a critical strategy in quality control for determining whether to accept or reject an entire product lot through inspection of randomly selected samples. However, existing variables sampling plans have two main limitations: First, they ignore the variation of the process fraction defective and fail to effectively incorporate prior process information, resulting in excessive sample sizes and therefore low inspection efficiency; Second, the most commonly used process capability index, $C_{pk}$, lacks a precise correspondence with the actual fraction defective, which makes it impossible to accurately evaluate the real process risks, thus resulting in lot misjudgments and significant economic losses. To address these issues, this paper first develops a Bayesian statistical model based on the process yield index $S_{pk}$, aiming at minimizing the inspection sample size. Subsequently, a Bayesian economic loss model is established with the objective of minimizing expected total loss by introducing an exponential consumer loss function that captures “threshold effect” behavior. Comparison results show that the proposed statistical model can significantly reduce the required inspection sample size while maintaining the same producer's and consumer's risks as existing plans, and the proposed economic model can further lower the average expected total loss.

The rapid growth of the new energy vehicle industry has accelerated the optimization of energy structures and promoted sustainable economic development. Improving the resource utilization efficiency of retired power batteries requires careful consideration of quality differences during the remanufacturing process. At the same time， the integration of blockchain technology has provided new momentum for the green and low-carbon transition. Against this backdrop， this paper develops a closed-loop supply chain Stackelberg game model involving battery manufacturers， vehicle manufacturers， and third-party recyclers. The model examines the optimal strategies and profits of each participant under centralized and decentralized decision-making， taking into account the effects of battery quality grades， battery traceability levels， and investments in blockchain technology on recycling decisions. The results indicate that centralized decision-making is optimal for both maximizing total supply chain profits and enhancing consumer benefits. However， when the quality grade of remanufactured batteries is either too high or too low， the recycling price decreases， leading to a lower recovery volume. The adoption of blockchain technology significantly improves battery traceability. Moreover， when the reduction in recycling prices and the increase in recovery volume together offset the cost of blockchain implementation， the use of blockchain by

A green supply chain network that integrates carbon trading, carbon reduction investment, carbon pledge financing, and consumer green preferences was designed to address the operational, financing, and carbon reduction needs of capital-constrained supply chains. A mixed-integer linear programming model was established with the objective of maximizing supply chain profits, and the NP-hard of the problem was proven. A decomposition-based two-stage heuristic algorithm was developed to efficiently solve large-scale problems. In addition, a genetic algorithm with a greedy decoding strategy is proposed as a benchmark for performance comparison. Numerous test results indicated that the genetic algorithm can obtain feasible solutions with average gaps of 9.07% and 5.20% for small- and large-scale instances in 0.24 seconds and 32.28 seconds on average, respectively. The two-stage heuristic algorithm could obtain near-optimal solutions within 1 second, with an average gap of less than 1% in small-scale instances. For large-scale instances, the algorithm achieved satisfactory solutions with an average gap of less than 1% compared to the upper bound in an average of 520.38 seconds, where CPLEX failed to find a solution. Sensitivity analysis showed that increases in carbon price and consumer preferences positively impact the total profit of the supply chain. Carbon emissions increase with the improvement of consumer preferences, while first increase and then decrease with the rise in carbon prices.

This paper quantifies climate transition risk in China using 28,600 text entries from mainstream Chinese news media between 2010 and 2022. By applying the Latent Dirichlet Allocation (LDA) model, a topic classification technique in machine learning, it measures the exposure of listed companies to climate transition risks and further investigates the relationship between this exposure and stock price crash risk. The study finds that exposure to climate transition risks positively impacts the risk of stock price crashes. Mechanism analysis suggests that higher exposure to climate transition risks triggers more short-term speculative behavior in companies, thereby increasing the likelihood of stock price crashes. The positive effect of climate transition risk exposure on stock price crash risk is more pronounced in firms with lower information transparency, higher stock price bubbles, high-polluting industries, and less government focus on climate issues. This paper provides new evidence on the relationship between corporate risk exposure and stock prices in the context of climate transition.

Green electricity consumption is the primary pathway to achieving low-carbon on the residential side. Exploring the evolutionary trajectory of investment behavior in green electricity consumption and identifying guiding mechanisms constitutes a vital means of propelling society towards dynamic green development. To achieve this objective, we develop a mixed-game model grounded in prospect theory, taking into account both the residents' heterogeneity and the operational characteristics of micro-grid. This model analyzes the strategic evolution of both parties and undergoes numerical simulation testing. Subsequently, we construct a cooperative investment revenue distribution model based on inequality aversion theory, examining revenue allocation strategies under various scenarios. The results show that for distributed photovoltaic investment projects, the ultimate strategy evolution for both residents and micro-grid converges upon cooperative investment and participation under the prevailing circumstances. This approach actively promotes efficient green electricity consumption on the residential side, enabling both residents and micro-grid to maximize their respective benefits. Risk response mechanisms, investment costs, and shared energy storage exert a significant influence on the strategic evolution of both parties. Heterogeneous residents generate cooperative value through collaborative efforts rather than independent investment. Benefit distribution strategies grounded in inequality aversion theory can effectively safeguard the returns of diverse resident groups, and maximize residents' perceptions of equitable gains from Behavioral Economics.

With the continuous expansion and deepening of the knowledge system, disciplinary boundaries are gradually dissolving, and cross-disciplinary integration has become an irreversible trend. This paper proposes the Cross-Disciplinary Latent Dirichlet Allocation model (CrossLDA) to investigate the integration and mutual influence between disciplines. The CrossLDA model can automatically identify shared and discipline-specific topics from two sets of domain-related texts (e.g., research articles), and track the dynamic evolution and development patterns of these topics, thereby uncovering the patterns of interdisciplinary integration and interaction. Taking statistics and computer science as examples, we model the content of articles published over the past decade in ten representative statistics journals and the AAAI Conference on Artificial Intelligence. We extract shared topics such as machine learning and intelligent decision-making, and examine the shifts in their research focus as well as the development trends of both disciplines in these shared areas. The findings reveal a close connection and mutual influence between the two disciplines. This study not only demonstrates the effectiveness of CrossLDA in uncovering the patterns of disciplinary integration but also provides data support and methodological references for academic exchange and cross-disciplinary innovation.

The traditional TOPSIS method frequently compromises the validity of evaluation outcomes when handling correlated assessment indicators, owing to distortions in the Euclidean distance between the evaluation subject and both positive and negative ideal solutions. Concurrently, as the dynamic nature of evolving phenomena imposes new demands upon evaluation methodologies, the limitations of conventional static assessment models become increasingly apparent. To address these issues, this paper introduces elastic net regression for indicator screening, constructing an entropy-weighted TOPSIS dynamic evaluation model based on elastic net regression. This model not only effectively handles highly correlated variables but also accommodates comprehensive evaluation needs within complex, continuously changing environments. Finally, using Chinese eight major comprehensive economic zones as a case study and comparing it with relevant models, the results demonstrate that the method achieves scientific screening and dynamic integration of highly correlated indicators. Moreover, it exhibits superior identification capabilities regarding efficiency structures and levels in empirical tests, demonstrating good applicability and practical explanatory power.

This paper investigates network systems with nonlinear coupling via fixed-time control. The network system is based on discontinuous activation functions, nodes with communication constraints, and switching coupling dynamics. The node coupling of the considered systems switches according to information from all nodes of network systems and an isolated node. The fixed-time controller without sign function and it can be used to overcome the chattering effect introduced by sign function. Based on the Lyapunov stability theory, the fixed-time synchronization of network systems is established, and an estimate of the synchronization time is provided. Numerical simulations verify the effectiveness and feasibility of the theoretical results.

Allocating a total cost-reduction target among decision-making units (DMUs) is a significant managerial challenge. The existing data envelopment analysis (DEA)-based cost allocation methods primarily focus on efficiency scores and lack a systematic consideration from an incentive perspective. Moreover, research that adequately balances incentive mechanisms with fairness remains insufficient. To address these limitations, we propose an incentive-oriented cost-reduction allocation method based on a non-cooperative game DEA framework that prioritizes incentives while accounting for equity. First, DMUs' efficiencies are evaluated and ranked using a super-efficiency DEA model. On the basis of the efficiency ranking, we formulate an allocation model that minimizes the reduction-responsibility gap between adjacent ranked DMUs, thereby balancing incentive and fairness. We design an iterative solution algorithm under a non-cooperative game framework and prove its convergence and the uniqueness of the optimal solution, ensuring the stability and feasibility of the allocation scheme. Finally, an empirical analysis is applied to the data of a commercial bank in Chengdu. The results show that: 1) The proposed allocation method establishes a distinct reward-punishment structure where it assigns lower responsibilities to high-efficiency DMUs and higher responsibilities to low-efficiency DMUs. This results in a fair allocation scheme; 2) The iterative game promotes active efficiency improvement. The study extends DEA-based cost allocation literature and provides a practical tool for allocating cost-reduction responsibilities in organizations.

The spatially varying coefficient model is a widely used and effective method in spatial regression analysis, designed to address spatial heterogeneity in data. However, this model generally relies on the assumption that the response variable follows a normal distribution, making it difficult to effectively capture the spatially nonstationary characteristics of other types of response variables, such as count or binary data. Moreover, with the rapid advancement of modern observation technologies, the scale of spatial datasets has grown dramatically, posing new challenges to computational efficiency and statistical inference of such models. To address these challenges, this paper introduces a generalized spatially varying coefficient nonparametric regression model based on Distributed Heterogeneity Learning (DHL), and proposes a DHL algorithm founded on a domain decomposition (DDC) strategy to handle spatial regions with complex boundaries and irregular shapes, thereby preserving the intricate spatial structure within the data. Under certain regularity conditions, the estimators of the varying coefficient functions are shown to be consistent in the $L_2$ sense, and their convergence rates are established. Simulation studies demonstrate that the proposed model performs well across different settings. Furthermore, the model is applied to the analysis of road traffic accident data in Florida, assessing its practical applicability and predictive performance in real-world research, and further validating the effectiveness of both the model and the estimation methods.

To improve the picking efficiency of robotic mobile fulfillment systems (RMFS), it is necessary to dynamically optimize commodity slot allocation in response to changes in order demand during replenishment. Existing research on dynamic slot allocation primarily focuses on the correlation between commodities while neglecting the quantitative demand ratios among them, which results in high-demand commodities within correlated groups still requiring cross-shelf picking. This paper considers both correlation and demand quantity relationships, and establishes a mathematical model aimed at maximizing the overall correlation degree and quantity ratio degree across shelves during replenishment. A hybrid neighborhood simulated annealing algorithm is designed to solve the model. Simulation results show that, compared with methods that consider only correlation, the proposed approach—which also accounts for quantity ratio relationships—can effectively reduce the number of shelf handlings. This improvement becomes more pronounced as the system scale grows and the disparity in commodity quantity ratios widens.

Against the backdrop of global population aging and the high prevalence of chronic diseases, Formula Foods for Special Medical Purposes (FSMP) serve as critical clinical nutrition intervention vehicles. However, their development face significant challenges including information asymmetry, heightened quality-safety risks, and insufficient corporate social responsibility (CSR) fulfillment, leading to elevated regulatory costs, inefficient stakeholder coordination, and weak market trust foundations. To explore effective pathways for resolving this governance dilemma, this paper constructs a two-stage evolutionary game model consisting of a two-player game between manufacturers and consumers and a tripartite game involving industry associations, manufacturers, and consumers. The study finds that in the market-driven two-player game, the system tends to converge on the inefficient evolutionary stable equilibrium of “non-fulfillment-non-purchase.” The high cost of CSR compliance for manufacturers, insufficient consumer perception of the health utility of FSMP, and significant information asymmetry among stakeholders are the core causes of industry trust barriers and the overall lack of motivation for CSR fulfillment across the industry. In the tripartite game incorporating industry association-led science communication interventions, the strategy combination of “association science communication-manufacturer CSR fulfillment-consumer purchase” is the sole ideal equilibrium with strong stability. This strategy combination can form a positive value cycle through the mutual empowerment of multi-stakeholder behaviors. Numerical simulation results show that core cost and benefit parameters exhibit chain transmission effects on system evolution, and each parameter has a clear critical threshold. The cost of association science communication and the consumer subsidy threshold mutually constrain each other; the social reputation effect for manufacturers and the negative social effect threshold of non-fulfillment form a mutually reinforcing complementary relationship; the price threshold for FSMP on the consumer side is positively empowered by the former two factors. The initial strategic participation willingness of agents significantly moderates the effective range of parameter thresholds. High initial willingness can broaden the reasonable regulatory range of parameters and reduce the sensitivity of system evolution, while low initial willingness compresses the threshold range, causing even slight parameter deviations to trigger system degradation toward the inefficient equilibrium. In summary, the science communication effect serves as the core lever for resolving the governance dilemma in the FSMP industry. It effectively reduces market information asymmetry, enhances consumer trust in FSMP, and lays the cognitive foundation for multi-stakeholder collaboration. To achieve multi-stakeholder incentive alignment in CSR fulfillment within the FSMP industry, it is necessary to construct a systematic governance system from the synergistic perspective of government, market, and enterprises. Industry associations should take the lead in building a platform for co-construction and sharing of science communication resources, establishing dynamic adjustment mechanisms for science communication cost-sharing and consumer subsidies to improve the implementation efficiency of science communication and subsidies. Manufacturers need to deeply integrate with the association’s science communication system, transform reputational capital into actual market premiums, and internalize the negative social effects of non-fulfillment through transparent communication mechanisms. Consumers should leverage the information disclosure by associations to form rational perceptions of FSMP, reduce purchase costs through community bargaining, and generate positive market feedback through proactive purchasing decisions. The coordinated efforts of the three parties are essential to fostering a multi-stakeholder collaborative positive cycle characterized by “science communication boosting trust-fulfillment improving quality-demand expanding,” thereby achieving the synergistic optimization of social and economic benefits in the FSMP industry

The frequent movement patterns of human groups in space profoundly reflect the spatiotemporal distribution laws of social activities, and the research on such patterns has important practical value for social governance. Based on mobile phone signaling data, this study analyzes the temporal dependency rules of semantic tag sequences through association rule mining to explore semantic frequent patterns under different date dimensions; meanwhile, it uses high-order rule mining to identify geographic grid transfer paths, capturing complex transfer laws in the continuity of micro-spaces. The results show that the mining outcomes of the two methods exhibit significant differences: the frequent patterns identified by association rules present a chain structure at the semantic level, with trajectory sequences dominated by short sequences of length 1 to 2, and there is an obvious rule of movement centered around residential areas; in the results of high-order pattern mining, 26.8% of the dependency patterns have a circular characteristic, revealing the rule that users often move back and forth between 2 to 3 core locations, and can effectively extract long trajectory patterns of length 4 to 5. By comparing the individual movement pattern mining results of the two algorithms, the study reveals the structural characteristics of urban activities from the perspective of travel patterns: association rules combined with geographic semantic information can provide scientific suggestions for enriching regional functional positioning; high-order rule mining can not only predict user movement trajectories but also excavate the long-range dependency relationships of movement behaviors, which helps to further improve the accuracy of trajectory prediction and provide empirical support for urban construction, road traffic planning and optimization. This study clarifies the different roles and applicable scenarios of high-order pattern mining and association rule mining, confirming that high-order pattern mining is an important supplement to traditional pattern analysis methods. Its application value is not only reflected in providing scientific suggestions for urban functional area planning to government departments but also in guiding transportation system planning, thereby optimizing the setting of geographic transportation networks.

For the low-carbon supply chain composed of an e-commerce platform and a manufacturer, taking logistics models and blockchain technology into account, two types of low-carbon supply chain models were constructed. One is for logistics outsourcing and uses blockchain technology, and the other is for logistics self - operation and does not use blockchain technology.The logistics strategies of e-commerce platforms and manufacturers, and whether to adopt blockchain technology for supply chain were studied. Using Stackelberg game method, the relationships between platform commissions, market demand, carbon reduction rates, enterprise profits, and system profits were compared and analyzed between the two models.The results show that when outsourcing logistics and adopting blockchain technology, the commission of e-commerce platforms is always higher than that of logistics self - operation without adopting blockchain technology. When consumer green trust is low, the relationship between market demand and carbon reduction rate is only related to green trust. When consumer green trust is high, the relationship between market demand and carbon reduction rate is not only related to consumer green trust but also closely related to the commission ratio in both scenarios.By dividing consumer green trust and commission ratio into four regions, it was found that the relationship between enterprise and system profit size is related to the regional division. In two of the regions, the logistics decision - making preferences of e - commerce platforms and manufacturers are always consistent, while in the other two regions, they are inconsistent. There are three regions where the decisions of dominant enterprises are consistent with the optimal decisions of the supply chain system, and one region where they are inconsistent.Therefore, it is proposed to make collaborative decisions from the perspective of the supply chain system based on consumer green trust and commission ratio, which is consistent with the national proposal for collaborative green development.

Against the backdrop of the continuous accumulation of local government hidden debt risks and the intensifying regional credit linkages, systematically uncovering the risk transmission mechanism of China’s municipal bond market holds significant practical value for safeguarding against systemic financial risks. Based on provincial municipal bond credit spread data from 2015 to 2024, this study constructs a multiplex network of risk contagion to systematically identify the directions and paths of inter-provincial risk transmission. Furthermore, a TVP-VAR-SV model is employed to analyze the time-varying influence of macroeconomic variables on the intensity of risk contagion. The results indicate that: first, the multiplex network framework, compared with traditional single-layer network methods, more effectively identifies hidden transmission paths and risk hubs; second, risk diffusion exhibits pronounced regional heterogeneity and fiscal stratification; and third, macroeconomic fluctuations exert a strong time-varying effect on the intensity of risk contagion. This research transcends the limitations of single-layer network analysis, providing a novel identification tool for municipal bond risks and offering a scientific foundation for the construction of risk identification mechanisms and differentiated regional risk supervision.

With the rapid advancement of digital technologies, the deep integration of the digital economy and digital national defense has become a critical pathway for enhancing national strategic capabilities. Drawing on systems science and synergy theory, this study constructs a multi-level theoretical analysis framework encompassing foundational coupling mechanisms, institutional compatibility mechanisms, bidirectional transformation mechanisms, collaborative innovation mechanisms, and holistic linkage mechanisms. The Analytic Hierarchy Process (AHP) is employed to quantitatively evaluate the weights and interactions among these mechanisms. The results indicate that the holistic linkage mechanism has the highest weight at the criterion level in the synergistic development of digital economy and digital national defense, while the weights of collaborative innovation mechanism and bidirectional transformation mechanism are relatively close. Additionally, open scientific collaboration and innovation show the highest weight at the indicator level. Based on this, a double helix structure model for the synergistic development of digital economy and digital national defense is constructed, and the reliability of the double helix structure model is confirmed by taking the military civilian technology collaborative innovation system as an example, clarifying the interaction relationship between the double strands and bases of the double helix structure. Our findings provide a theoretical basis for optimizing resource allocation and enhancing national strategic capabilities in the context of digital transformation.

For the distributed optimization problem over weakly connected directed graphs, this paper investigates a distributed optimization method. Firstly, the original graph is decomposed into multiple strongly connected subgraphs using a block Laplacian matrix. The convergence of each subgraph is demonstrated using a distributed optimization algorithm based on Hessian matrix and Lyapunov stability theory. Secondly, an exponential decaying bias term is introduced to adjust the Lyapunov function of the inter-subgraph connections for a weakly connected graph with imbalanced weights, the convergence of the inter-subgraph is analyzed, and the convergence of the entire weakly connected graph is proved, ultimately achieving the global optimization objective. Finally, by introducing an event triggering mechanism based on periodic sampling, the communication frequency is reduced. In addition, theoretical bounds for convergence have been provided and the efficacy of the proposed method has been demonstrated through numerical simulations.

Index tracking is an important topic in financial economics, which aims to replicate a specific market index (e.g., the CSI 300 Index) by constructing a portfolio, so as to achieve returns that closely match those of the target index. However, due to the frequent presence of noise and outliers in financial data, traditional tracking models based on least squares loss often lack robustness, leading to high tracking errors. To address this, this paper introduces a novel sparse regression model that combines the proposed RoBoS loss function with the LASSO penalty. The RoBoS loss is both non-convex and smooth, enabling it to effectively mitigate the impact of noise and outliers and improve prediction accuracy. We derive the finite sample breakdown point and the influence function for the new model estimator, providing theoretical guarantees of robustness from a statistical perspective. Based on the concept of proximal gradient descent idea, we further develop an efficient numerical algorithm for model estimation. Extensive numerical simulations and empirical analyses are conducted to systematically evaluate the performance of the proposed model. The results demonstrate that, compared to existing models, the RoBoS loss-based sparse regression method exhibits stronger robustness, higher prediction accuracy, and achieves lower tracking errors in tracking CSI 300 Index.

Harnessing ecosystems to enhance carbon sink capacity is a key means of synergizing energy conservation and emission reduction efforts to jointly advance the achievement of carbon neutrality goals. Among this, interactive collaboration among carbon sink developers serves as the foundation for promoting coordinated coupling between terrestrial and marine ecosystems so as to increase the value of carbon sinks, and it is also the basis for these participants to enhance their economic returns. However, existing studies on terrestrial and marine carbon sinks are largely fragmented. In response, this paper incorporates stochastic disturbances and, based on differential game theory, constructs mathematical models involving terrestrial and marine carbon sink developers under four different decision-making modes, followed by numerical simulation and computational experiments. The results show that: (1) Closer cooperation between terrestrial and marine carbon sink developers increases both the expected value and the variance of carbon sink output, with actual output and returns fluctuating around their expected levels. (2) Joint carbon sequestration and revenue generation are more likely to be achieved when the developer with relatively weaker effort effectiveness among the two heterogeneous carbon sink developers takes the lead and shares part of the other party’s costs. (3) Under stochastic disturbances, the realized output of cooperative carbon sinks may deviate from expectations and even fall below that under non-cooperation; therefore, developers should leverage the respective advantages of land and sea in different stages, conduct cooperation flexibly, and seek to mitigate the impact of randomness.

To improve patrol coverage efficiency under limited police resources, this study addresses the joint routing and scheduling optimization of cooperative police vehicle-UAV patrols. A synchronized time-space network is constructed to model vehicle and UAV flows, with arc-based rewards introduced to quantify crime coverage. A 0-1 network-flow mixed-integer programming model is formulated to maximize cumulative coverage, incorporating practical constraints such as minimum vehicle patrol duration, UAV battery and recharging limits, patrol coverage requirements, and diminishing returns from repeated patrols. A path-encoding-based harmony search algorithm with a feasibility repair mechanism is proposed to efficiently solve large-scale instances. Computational experiments based on real-world cases demonstrate that the proposed method achieves near-optimal solutions for small instances and outperforms CPLEX on medium- and large-scale instances within limited computation time, showing strong stability and scalability.

Aiming at the situation that most of cloud model information aggregation operators neglect the density degree of data distribution, cloud density weighted arithmetic averaging (CDWAA) operator and cloud density weighted geometric averaging (CDWGA) operator are defined. Two novel operators utilize three numerical characters of cloud model to represent the fuzziness and randomness of linguistic information and use the density weights of density weighted averaging operator to denote the density degree of data distribution. The commutativity, idempotency, boundary, and monotonicity of the operators are investigated, based on which, a new multiple attribute decision making method is put forward. By taking an example with regard to the grain supplier selection to analyze, the results show that not only can this method effectively consider the fuzziness and randomness of linguistic information, but this method can also better consider the density degree of data distribution.

With the continuous increase of production costs in China, some electric vehicle manufacturers (EVMs) have begun to develop battery suppliers in Southeast Asia with lower costs, which involves cost uncertainty and information asymmetry. Based on this, combined with the characteristics of battery recycling in the electric vehicle industry, this paper considers a game of developing battery suppliers and studies the impact of key parameters on the decisions of EVMs to develop battery suppliers and battery procurement quantities. The study shows that: (1) Whether EVMs develop battery suppliers, that is, the main body selection of developing battery suppliers, mainly depends on market size, differences in battery recycling rates, and battery recycling benefits. (2) Reducing cost uncertainty in Southeast Asia or improving its signal accuracy may not necessarily attract EVMs to turn to Southeast Asia to develop battery suppliers. (3) The increase in production costs in China may lead to only one EVM developing the battery supplier in Southeast Asia.

Disaster relief supplies are the core foundation for ensuring the basic survival needs of affected populations and supporting post-disaster reconstruction. The efficiency of their procurement and transportation directly impacts disaster response effectiveness. To address the limitations of dynamic responses, a tripartite game model involving the government, enterprises, and disaster victims is constructed to analyze the influence of time delay and trust on strategic decisions. Three operational modes are designed: decentralized decision-making without subsidies, decentralized decision-making with subsidies, and centralized decision-making. Optimal effort levels and equilibrium states are derived under these scenarios. Results indicate that: 1) Delays in the material procurement phase necessitate greater efforts from both government and enterprises, leading to postponed accumulation of supplies, which may result in material wastage; 2) Both subsidies and centralized decision-making contribute to the enhancement of rescue effectiveness. Subsidies can incentivize enterprises to increase input, but the government must dynamically adjust subsidy ratios to balance costs and benefits. Centralized decision-making, characterized by “high effort-high efficiency” while separating input and distribution but imposes higher demands on the speed of material procurement by both government and enterprises; 3) Under any mode, supply-demand mismatches and distrust among disaster victims reduce the level of effort from all three parties, and the phenomenon that is more pronounced under centralized decision-making. These findings provide a basis for designing dynamic subsidy policies, optimizing enterprise logistics capabilities, and implementing psychological interventions, contributing to the establishment of a resilient disaster relief supply chain system.

Public health emergencies have a serious impact on China's economy, social stability, and people's life safety, placing unprecedented stress on the emergency supply chain system. Drawing on social cognition theory and incorporating the mechanism of public opinion evolution, this study employs the WSR-DEMATEL method to identify crucial elements of the emergency medical supplies supply chain's capabilities. Furthermore, it analyzes the dynamic interplay between epidemic control measures and the allocation of essential medical resources, leading to the development of a dynamic model for the emergency medical supplies supply chain. Finally, contextualized within the backdrop of the Wuhan pandemic, a sensitivity analysis is performed on parameters such as government crisis management intensity, mobilization and procurement lead time, emergency policy enactment duration, exceptional production regulation timelines for medical resources and official news transparency. This multi-faceted investigation aims to uncover key optimization pathways for strengthening supply chain's capabilities. The results indicate that during the initial phase of epidemic prevention and control, emergency provisioning relied predominantly on the mobilization and collection of medical resources from the community. It is crucial to maintain a measured extent of governmental crisis management intensity, to avert shortages in the quantity of resource collection arising from either a surge in public sentiment or inadequate attention. Moreover, improving official news transparency steadies public attention and boosts social medical resource mobilization efficiency. Taking a longer-term perspective, bolstering governmental crisis management efforts, shortening the duration of emergency policy implementation, and reducing the adjustment time for exceptional medical resource production are all conducive to enhancing the rate of demand satisfaction. Unleashing the synergistic potential of systemic interconnection mechanisms to enhance overall coordination capabilities emerges as the pivotal factor for ensuring the provision of medical resources and optimizing epidemic control strategies.

Frequent emergencies pose a serious threat to human life and health. These events often trigger a sudden surge in drug demand, leading to serious supply shortages and unstable prices. Although governments have taken action through emergency supply and price caps, ensuring drug access and market stability remains difficult during emergencies, To address this, our study constructs a market game model comparing scenarios with and without emergencies. We analyze how the government should respond under demand surges, and examine how emergency supply and price cap policies help stabilize the market, ensure supply, and affect both drug prices and corporate profits. The results show that whether or not a price cap exists, the emergency reserve supplier does not charge excessively high prices. However, the price cap shifts the drug supplier’s strategy from “profit maximization through price increases” to “profit maintenance through output expansion” . Specifically, the government’s price cap effectively controls drug prices during emergencies, while emergency supply contributes to price stabilization and supply expansion in the market. Moreover, the government further curbs price hikes by selecting emergency reserve suppliers with larger market shares. Although the drug emergency supply improves consumer surplus and social welfare, a low price cap may weaken these benefits. This study proposes a coordinated strategy for market price and supply stability through strengthened government-enterprise collaboration and the integration of emergency supply with reasonable price caps. This approach is crucial for optimizing the allocation of government emergency resources, enhancing response effectiveness, and advancing the practice of emergency management systems.

The application of blockchain technology in the natural gas sector is becoming increasingly advanced. This paper examines an upstream enterprise, a downstream enterprise, and the government as research entities, exploring the optimal strategies for joint investment in blockchain technology by upstream and downstream enterprises under government safety regulation, as well as the downstream enterprise’s natural gas safety management. By constructing a differential game model wherein the upstream enterprise shares the downstream enterprise’s costs, the study analyzes and compares equilibrium strategies under four scenarios and conducts a comparative analysis of optimal profits using a numerical example. The results indicate that: (1) Blockchain technology demonstrates a monotonic growth trend, whereas the level of gas safety may exhibit non-monotonic change; (2) An increase in corporate marginal revenue, the marginal contribution of blockchain technology and gas safety to demand, blockchain investment efficiency, and gas safety management efficiency each has a positive effect on the level of blockchain adoption, gas safety, and corporate profitability; (3) When the upstream enterprise shares blockchain cost, it not only achieves dual optimization of blockchain and gas safety levels but also leads to a Pareto improvement for the upstream and downstream enterprises as well as the government. However, whether the dual cost-sharing model can achieve an optimal Pareto improvement depends on external conditions; (4) Government intervention tends to decrease when an upstream enterprise assumes safety management cost.

With the rapid development of the new energy vehicle industry, consumer adoption behavior has become a critical factor influencing the market diffusion of battery electric vehicles (BEVs). This study systematically analyzes key variables such as sales volume, vehicle stock, life cycle cost, and overall vehicle performance from the perspectives of micro-level consumer preferences and the macro-level policy environment. Based on the collection and statistical analysis of industry data, a forecasting model is constructed to provide a scientific projection of future market development. A comparative analysis with conventional fuel vehicles is further conducted to identify the major proportional factors constraining adoption intention. Moreover, a fuzzy logic control model is introduced, with corresponding control rules and membership functions established to dynamically simulate consumer adoption intentions for BEVs. The simulation results indicate that, alongside the continuous growth of BEV sales and stock, life cycle costs exhibit a significant downward trend while overall vehicle performance steadily improves, leading to a progressive increase in consumer adoption intention—from 0.11 in 2011 to 0.85 in 2031. By validating the simulation results against existing research findings and industry development trends, this study proposes policy recommendations aimed at promoting the healthy development of the BEV market and enhancing comprehensive vehicle performance, thereby providing both theoretical support and practical reference.

This paper proposes a class of spatial varying-coefficient autoregressive models with autocorrelated errors. The proposed framework simultaneously incorporates spatial correlation in the response variable and spatial autocorrelation in the error term within the spatial varying-coefficient setting, thereby jointly capturing both heterogeneity and dependency structures in spatial data to better reflect their complex characteristics. To overcome the endogeneity issue of the model, an effective three-stage estimation method is proposed that integrates local linear estimation, generalized method of moments (GMM), and profile least squares estimation methods, and the asymptotic properties of the estimators are derived. The Monte Carlo simulation results indicate that the estimation method for the studied model demonstrates good efficacy under finite samples. Empirical analysis based on the Boston housing price data further shows that this model significantly enhances the explanatory power of spatial economic phenomena.

In real applications, high-dimensional data are often accompanied by resampling noise, outliers and class imbalance problems, which pose severe challenges to machine learning algorithms such as support vector classification models. Although fuzzy support vector machine (FSVM) has demonstrated certain potential in outlier suppression and class imbalance handling, it still has significant room for improvement: 1) FSVM fails to effectively reduce biases caused by resampling noise; 2) FSVM exhibits high sensitivity to redundant features. To address these issues, this paper proposes an affinity and transformed class probability-based fuzzy sparse geometric twin support vector machine (ATFSGTSVM). The model measures the within-class affinity of each sample via the least squares one-class support vector machine, and adjusts the weight distribution of imbalanced data to reduce the interference of outliers, noise, and class imbalance on classification performance, and incorporates the $l_0$ norm penalty to achieve feature selection and redundant feature elimination. Due to the non-convexity, non-smoothness and discontinuity of the $l_0$ norm penalty, the corresponding optimization problem is difficult to solve directly. Inspired by the recently proposed variable sorted active set algorithm, this paper designs the ATF-VSAS algorithm for efficient solution of this problem. Experimental results show that, compared with advanced models in recent years, ATFSGTSVM achieves optimal performance in high-dimensional imbalanced data scenarios with noise and outliers.