To address the issue of insuffcient last-mile fulfillment capability for high-frequency sales services on e-commerce platforms, this study constructs a relaybased delivery crowdsourcing model that accounts for different arrival sequences of two couriers, and analyzes the sequence of“the first-arrived” and“the later-arrived” for couriers impact on the delivering effciency and profits of the e-commerce system. The findings show that: The strategy of relay-based delivering always achieves longer distances and higher service levels compared with the strategy of single-courier“pointto-point” . For two couriers with different cost structures, “the first-arrived” player wins the more tasks and profits in relay-based crowdsourcing scenario, which leads both couriers to motivate to actively compete for orders. From the perspective of the platform and the supply chain system, however, it is more beneficial if the lowcost (advantaged) courier arrives first and dominates the relay-based delivery, as this scenario yields a larger market sharing, higher sales, and greater system profits. To encourage the participation enthusiasm of disadvantaged(high-cost) couriers in relay delivery, the platform can adopt two mechanisms: An order assignment with revenue-sharing mechanism and commission rate tilt mechanism, Both mechanisms can effectively extend the system’ s delivery distance, expand the customer market coverage, and increase the profitability of the delivery system. Finally, numerical examples are used to verify the accuracy of the conclusions.

This paper focuses on designing performance-based incentive contracts for China's contractual REITs, considering the risk aversion of both sponsors and managers as well as peer effects among managers. We develop a multi-agent principal-agent model where managers care about both absolute performance and returns relative to peers, and introduce reward and penalty coefficients to characterize symmetric and asymmetric relative-performance evaluation. On this basis, we derive the optimal incentive contract under different combinations of participation and incentive-compatibility constraints, and explore how the intensity and symmetry of peer comparison shape the equilibrium wage structure, managers' effort decisions and sponsors' expected payoffs. We further embed the model into a PPO reinforcement learning framework to capture the dynamic adjustment of incentive coefficients and wage levels. The results show that symmetric reward and penalty mechanisms effectively drive high effort and balance surplus allocation when managers' reservation utilities are low and market competition is intense. As managers' outside options improve, the optimal contract tends to be more punitive and rely more on fixed pay, which weakens incentive efficiency. PPO simulations also verify that excessive punishment leads to inefficient high-wage and low-effort outcomes, while moderately symmetric peer-comparison schemes improve effort incentives and meet fairness requirements. These findings offer practical implications for selecting relative-performance mechanisms and optimizing incentive and regulatory designs for REITs.

This paper investigates the design of a probabilistic set-membership filtering algorithm for nonlinear complex network systems under hybrid attack environments. Specifically, a distributed nonlinear estimation model is proposed to address measurement channels simultaneously subjected to false data injection (FDI) and denial-of-service (DoS) attacks. The measurement modeling mechanisms are analyzed under three scenarios: no attack, single attack, and hybrid attack. To handle the nonlinearities in the network dynamics, measurement equations, and FDI processes, the Taylor expansion technique is employed. Sufficient conditions for the existence of the probabilistic set-membership filter are derived, and a convex optimization problem is formulated. A recursive algorithm for calculating the filter gain matrix is then developed, ensuring that the estimation error remains constrained within a desired ellipsoidal region at a prescribed confidence level. Finally, numerical simulations are conducted to verify the effectiveness of the proposed filtering algorithm.

In the pellet shaft furnace process, stable control of the outlet temperature in the vertical roller mill combustion chamber is crucial to ensuring product quality. However, in actual production, the outlet temperature still relies on manual valve adjustment, making it difficult to maintain within the specified range. The inability to achieve automatic regulation arises primarily from two factors: first, fluctuations in gas pressure cause strong nonlinearity between the valve opening and gas flow rate; second, the flow sensor exhibits measurement saturation, leading to partial loss of state information. To address these issues, this paper divides the gas pressure variation range into several intervals, within which the relationship between valve opening and flow rate is assumed constant. Subsequently, separate models are established for the valve opening-flow system and the flow-outlet temperature system. An empirical-measure identification algorithm based on a measurement indicator vector and a regression identification algorithm based on adaptive gradient are proposed to achieve consistent parameter estimation. Using the identified models, a composite control algorithm with an accumulator structure is designed, under the assumption of a fixed valve adjustment increment, to stabilize the outlet temperature within the desired range. The experimental verification results show that the algorithm proposed in this paper can achieve parameter consistency, and the composite control algorithm outperforms the commonly used feedforward control algorithm on multiple statistical indicators.

This study considers a Data Service Supply Chain ``DSSC'' consisting of a data provider, a data service provider, and users. Based on the unique characteristics of data such as infinite replicability and copyability and users' demand for data timeliness, we constructed two profit decision game models: Pricing Based on Data Timeliness ``PB-DT'' and Pricing Based on Usage Frequency ``PB-UF''. By constructing profit decision game models based on two data asset pricing models, this study investigates the technology investment decisions of DSSC members, data asset pricing methods, and data asset and data service pricing decisions. Firstly, the study found that under both pricing models, the decisions of DSSC members do not affect the sales volume of data services. Furthermore, the sales volume of data services under the PB-DT pricing model is greater than that under the PB-UF pricing model. However, this does not necessarily mean that the profits of DSSC members and user utility are greater under the PB-DT pricing model than under the PB-UF pricing model. Secondly, the study found that the data supplier's choice of pricing method for data assets is solely related to the data service provider's valuation of the data's timeliness. For example, when the data service provider's valuation of the data asset is low (or high), the data supplier chooses the PB-UF (or PB-DT) pricing method. Finally, the study found that the data pricing method actually chosen by the data supplier may be ``consistent'' with or ``contradictory'' to the data pricing method expected by the data service provider and users. Especially, when the DSSC members have consistent preferences regarding the data asset pricing method, the synergistic effects among DSSC members are maximized, and the DSSC performance can reach a Pareto optimal state.

Under the current context of the rapid development of agricultural product cold chain logistics and the in-depth advancement of the “dual carbon” goals, the agricultural supermarket direct supply system is confronted with multiple challenges such as the difficulty of multi-temperature layer co-distribution, the limited range of electric vehicles, and the uneven cost-sharing among multiple parties. Traditional logistics models often optimize from a single dimension, making it difficult to systematically integrate resources, achieve green collaboration, and save costs. Therefore, this paper proposes an integrated operation model of “multi-temperature co-distribution - electric vehicle routing - collaborative distribution” for agricultural supermarket direct supply. It aims to integrate multi-enterprise orders, vehicle and charging facility resources through a digital platform, build a dynamic collaborative alliance, and achieve systematic optimization of cold chain logistics. Firstly, an electric vehicle routing optimization model with the objective of minimizing total operating costs is constructed, comprehensively considering constraints such as multi-temperature layer loading, battery power, and charging strategies. Secondly, an adaptive large neighborhood search algorithm (ALNS) integrating spatio-temporal clustering is designed to efficiently solve the complex routing optimization problem, and the Shapley value method is adopted to fairly allocate the total cost within the collaborative alliance to ensure its stability and the enthusiasm of participating enterprises. Finally, the effectiveness of the model and algorithm is verified through numerical experiments and case studies. The results show that compared with the traditional collaborative distribution and multi-temperature co-distribution electric vehicle distribution models, the proposed model can achieve total cost savings of 15.29 % and 24.14 % respectively, and the average cost of each enterprise is reduced by 15.39 % and 24.19 % respectively.

This paper proposes a self-triggered distributed model predictive control algorithm to address the issue of high computational and communication resource consumption in existing distributed model predictive control algorithms for nonlinear multi-agent systems. At each triggering instant, each agent solves a local optimization problem based on its own local system state and the predicted states of its neighboring agents to determine its next triggering instant and broadcasts its predicted state trajectory to its neighbors. The algorithm significantly reduces communication load while maintaining system robustness and control performance. A decentralized nonlinear disturbance observer and a spatial decomposition technique are introduced to estimate and compensate for the matched disturbances in the system. On this basis, a robust contraction constraint is introduced in each local optimization problem to counteract the effect of residual disturbances. The recursive feasibility of the proposed algorithm and the closed-loop stability of the multi-agent systems at the triggering instants are proved. Numerical simulations verify the correctness of the theoretical results.

The snowball structured product is one of the most prominent products in China's over-the-counter (OTC) derivatives market. Its highly path-dependent structure presents significant challenges for pricing and risk management. This paper introduces the Willow Tree Method, a computational framework for pricing and calculating the Greeks for snowball products. This method is effective with various stochastic models, including geometric Brownian motion, jump-diffusion models, and stochastic volatility models. Compared to the traditional method, the willow tree method significantly improves computational efficiency and reduces costs while maintaining accuracy. Furthermore, this paper quantitatively assesses the instantaneous selling pressure of hedging positions under extreme scenarios and its impact on market liquidity, and demonstrates the framework's adaptability to other complex snowball structures. This expands the application prospects of the Willow Tree Method in the field of complex derivative pricing. It not only holds significant practical implications for financial institutions in managing risk and optimizing strategies within complex market environments but also provides quantitative references for regulatory authorities to prevent procyclical systemic risks induced by structured products.

The Panel Smooth Transition Regression (PSTR) model is widely applied in economics and finance to capture coefficient dynamics across regimes, but two limitations constrain its applicability: parameter estimates are sensitive to initial values and lack robustness, and the single-transition-variable specification cannot characterize the synergistic nonlinear influence of multiple explanatory variables on coefficient dynamics. To address these limitations, this paper proposes the Panel Multivariate Smooth Transition Regression (PMSTR) model, which replaces the polynomial expansion of a single transition variable with a multivariate logistic function. This specification simultaneously identifies the independent and synergistic moderating effects of multiple variables on regression coefficients and quantifies their relative moderating strengths. Parameter estimation employs a Bayesian inference framework combined with Hamiltonian Monte Carlo (HMC) sampling, in which prior regularization resolves the saturated solutions arising from the boundedness of the logistic function, while gradient information guides exploration of the high-dimensional non-convex parameter space. Monte Carlo simulations across four typical coefficient-dynamic scenarios---constant, discontinuous, linear, and nonlinear---demonstrate that PMSTR stably recovers coefficient trajectories and the relative influence strengths of variables; under a logistic data-generating process with one hundred independent replications, the model achieves strict parameter recovery with 89 % and 95 % posterior density interval coverage rates both meeting or exceeding nominal levels, whereas PSTR estimates exhibit substantial dependence on initial values. An empirical application following the analytical framework of Gonz\'{a}lez et al. examines investment--cash flow sensitivity for U.S. and Chinese listed firms. The results reveal that firm value is the dominant moderating variable in both samples, yet the synergistic moderation pattern between cash flow and firm value differs structurally across the two markets, underscoring the framework's explanatory capacity for complex economic and financial dynamics.

This paper constructs a duopoly supply chain model to examine the governance effect of digital technology on manufacturer’s greenwashing behavior under direct and resale channels, and analyze the impact of different sales channel choices on green innovation and corporate profits. The results show that the direct sales model provides the stronger incentive for green innovation in the absence of digital governance. After introducing digital technology, the channel advantage depends on the governance cost. The direct sales mode is more conductive to improve green levels under low governance cost, while the resale mode with cost-sharing mechanism becomes more advantageous under higher governance costs. Furthermore, when digital governance cost lies in some range, the resale mode can simultaneously enhance product green levels and green manufacturer’s profit. When the cost exceeds some critical threshold, green manufacturer’s profit falls below the no-governance scenario, while greenwashing manufacturer’s profit rises instead, leading to governance failure. Numerical simulations further demonstrate that as the proportion of prosocial consumers increases, the disciplining effect of digital governance on greenwashing behavior diminishes marginally, and excessively high governance costs may trigger an adverse selection where green manufacturer is harmed while greenwashing manufacturer benefits. The conclusions of this paper provide references for manufacturer’s sales channel selection and greenwashing governance decisions, and offer theoretical guidance for policymakers in designing cost subsidy mechanisms and differentiated regulatory strategies.

This paper employs a Stackelberg game model to investigate the effects of three government environmental policies-command-and-control, command-and-control with subsidies, and tax-and-subsidy policies-on enterprises’ capacity upgrade and emission reduction decisions. The results show that command-and-control policies directly constrain enterprises' emissions and indirectly encourage capacity upgrades; however, when the government sets a high target capacity upgrade ratio, such policies are less effective in promoting capacity upgrades. When the government imposes strict emission limits and high emission reduction requirements, only the command-and-control with subsidies policy can achieve the expected targets. Tax-and-subsidy policies can directly encourage capacity upgrades and indirectly reduce emissions; however, when the government budget is limited or when the emission reduction and capacity upgrade targets are relatively low, tax-and-subsidy policies are not cost-efficient, and command-and-control policies incur the lowest cost. These findings provide a policy selection basis for governments to promote enterprise capacity upgrading and pollution emission control.

Many practical problems in economics, management, engineering control, and artificial intelligence can be formulated directly or indirectly as unconstrained optimization problems, and the conjugate gradient method is one of the core algorithms for solving such large-scale problems. In this paper, we propose an improved Dai-Liao conjugate gradient method by modifying and truncating the Dai-Liao conjugate parameters and introducing a restart step into the search direction. Without relying on any line search conditions, the search direction generated by this method can satisfy the sufficient descent condition. Under standard assumptions, the strong convergence of the proposed method is proved. Numerical experiments on medium-large-scale problems from test collections such as CUTE show that the algorithm performs well. In addition, applying the method to image restoration and machine learning models further validate the effectiveness of the proposed method.

The deployment of medical AI agents represents a transformative paradigm in the healthcare sector. The medical AI agent ecosystem spans multiple disciplines, and its evaluation requires the collaboration and joint decision-making of experts from diverse fields. Due to limitations in domain-specific cognition and expertise, individual experts can typically provide only partial evaluations, making comprehensive assessment of all alternatives difficult. To address this, this study proposes a large-scale group decision-making model based on inclusion degree to overcome the dual challenges posed by numerous alternatives and large expert groups. First, the model classifies participants into parallel communities based on inclusion measure, thereby reducing group dimensionality and matching experts with appropriate alternatives. Next, a hierarchical consensus mechanism is constructed: type $\alpha$ consensus aimed at selecting the best alternative is used to manage conflicts within communities, and type $\gamma$ consensus aimed at ranking alternatives is used to reach a consensus across the entire group. Finally, the proposed model is validated through a study on the evaluation of medical AI agent deployment prioritization.

As competition among platform enterprises intensifies and development accelerates, introducing private labels and supporting small and medium-sized merchants have emerged as key strategic initiatives for platforms seeking new sources of profit growth. Accordingly, under a hybrid marketplace, this study employs the Spokes model to characterize an online sales system comprising a brand manufacturer, a small and medium-sized merchant, and an e-commerce platform. It further examines how factors such as product substitutability and perceived quality influence the platform's motivation to introduce private labels, as well as the subsequent effects on the decision-making and profitability of supply chain members. The results show that: (1) When product substitutability is high and consumers' perceived quality of the private label is low, introducing a private label increases both wholesale and retail prices of the brand manufacturer's and the small and medium-sized merchant's products. (2) Even when the seller's products are highly substitutable with the private label, introducing a lower-perceived-quality private label can still enhance sellers' profits. (3) When product substitutability is high and the private label's perceived quality is low, the platform still has strong incentives to introduce a private label, and doing so can achieve a win–win–win outcome for all three parties.

Aiming at the modeling problem of integer-valued time series data with limited value range, correlated counting events and hysteretic characteristics, this paper proposes a class of self-exciting hysteretic generalized binomial autoregressive models. Firstly, the strict stationarity and ergodicity of the model is proved, and the probabilistic and statistical properties of the model, such as conditional expectation, conditional variance and transition probability, are studied. Secondly, in both the cases where the threshold parameters and the initial values of the state indicators are known and unknown, the conditional least squares estimation and conditional maximum likelihood estimation methods of the model parameters are studied, and the asymptotic properties of the estimators are given. Among them, when the threshold parameters and the initial values of the state indicators are unknown, an algorithm capable of estimating the two threshold parameters is constructed. In addition, the testing method for the nonlinear structure of the model is studied. Subsequently, the effectiveness of the parameter estimation algorithms and the testing method for the nonlinear structure of the model are verified through simulation studies. Finally, the model is applied to the analysis of the number of precipitation days every two weeks in the Berleburg station of Germany and the Honolulu area of the United States. The results of the empirical analysis show that the model performs well in both fitting and prediction.

Joint mean and covariance modeling approaches with multivariate normal distributed errors are well-studied for longitudinal data. However, robust joint modeling approaches are less investigated when longitudinal data contains outliers or exhibits heavy-tails. In this paper, we establish the general form of joint mean and scale covariance models with multivariate Laplace distribution, sketch out the general maximum likelihood estimation methodology, and develop the general quasi-Newton algorithm. More specifically, we carry out three most popular decomposition methods as special cases, and establish respectively the corresponding joint model and estimation approach. Then strong consistency and asymptotic normality of the maximum likelihood estimator are proved. Moreover, a number of simulation studies are carried out to illustrate the modeling capability of these specific joint models, and real data is analyzed utilizing the specific decomposition method which performs best in simulation studies.

Air cargo is important in the global supply chain, and its demand is affected by many complex factors, which makes it difficult to forecast. In this study, an air cargo demand forecasting model is constructed based on TEI@I methodology, and the time series of cargo volume is decomposed using the complementary ensemble empirical mode decomposition method, the complexity and smoothness characteristics of each sub-series are analyzed. The appropriate seasonal autoregressive integrated moving average model or long- and short-term memory model is selected for forecasting. Besides, an expert system is introduced to deal with the irregular and important events, and finally the results of the various parts of the results are integrated to obtain the forecasting results. The results of the empirical study based on the data of Beijing Capital International Airport show that the forecasting effect of the model is better than that of other benchmark models, which provides a powerful support for the resource allocation and operation decision-making of air cargo enterprises.

The Modular Autonomous Transit System (MATS) has emerged as a pivotal direction for the intelligent and automated advancement of urban public transportation, owing to its exceptional flexibility in allocating capacity across both spatial and temporal dimensions. Existing research on modular bus scheduling has largely been confined to the decoupling and coupling of modular units between consecutive trips---essentially a redistribution of capacity within a fixed timetable framework---leaving the core advantage of MATS, namely its ability to break free from original schedules and respond dynamically to passenger demand, far from fully exploited. Meanwhile, insufficient attention has been paid to the impact of road travel time variability on bus operations, limiting the adaptability of scheduling strategies to dynamic changes in network traffic conditions. To address these shortcomings, this paper proposes a novel en-route express service that allows modular vehicles to decouple, skip stops flexibly, and overtake preceding trips, and further develops a robust scheduling strategy for this service under travel time uncertainty. To study the operation of this express service, a Mixed-Integer Nonlinear Programming (MINLP) model is formulated to characterize the dynamic processes of vehicle overtaking and passenger transfers between modules. With the objective of minimizing total system operating cost, the model jointly optimizes the decoupling scheme, stop-skipping plan, and timetable for each modular vehicle. To obtain a travel time uncertainty-robust time-table, the Sample Average Approximation (SAA) method is adopted, and a customized Simulated Annealing (SA) algorithm is developed for efficient solution of the nonlinear programming problem. Experimental results demonstrate that: compared with the Genetic Algorithm (GA) and Adaptive Large Neighborhood Search (ALNS), the proposed SA algorithm achieves significant advantages in both computational efficiency and solution quality; compared with the traditional all-stop service, fixed stop-skipping service, and non-overtaking en-route express strategy, the proposed en-route express service strategy reduces total system cost by 2.7 % to 13.2 %, substantially improving system efficiency; furthermore, the scheduling scheme incorporating travel time uncertainty reduces average passenger waiting time by an additional 0.7 % to 1.6 %, effectively enhancing the practical robustness of the scheduling strategy. The findings of this study provide solid theoretical support and valuable practical reference for the development of more flexible, efficient, and robust urban intelligent public transit systems.

In this paper, linear Bayesian estimator for parametric vector of linear model with interval constraints is considered. Firstly, linear Bayesian estimator is constructed based on the interval-constrained least-squares estimator of parametric vector. Secondly, the dominance properties for proposed estimators are analyzed by mean square error matrix. Finally, a simulation study and real data analysis are performed to illustrate the theoretical results. It is shown that linear Bayesian estimator dominates the interval-constrained least-squares estimator and approaches Bayesian estimator.

To address the challenges of high-dimensional state spaces, the infeasibility of exhaustive enumeration, and the difficulty of discrete-state modeling in power system reliability assessment, this paper proposes a two-stage reliability evaluation method that integrates a branch search algorithm with a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). In the first stage, the entire system is decomposed into two subsystems: generators and transmission lines. Potential critical states are efficiently identified through prefix expansion, upper-lower bound criteria, and logical pruning, thereby avoiding the need to evaluate all system states as required by traditional enumeration methods. To account for transmission constraints, a maximum-flow-based evaluation algorithm is developed for the line subsystem, which can rapidly determine system supply capability without relying on power flow equations, significantly improving state evaluation efficiency. In the second stage, high-value samples obtained from the state space via the branch search algorithm are used to train a WGAN-GP generative model. By combining the Wasserstein distance with gradient penalty, stable continuous relaxation training is achieved, enabling sample augmentation and diversity generation within the critical region. Experimental results demonstrate that, for the RBTS system, the proposed method achieves a 98.5% coverage rate with only 535 evaluation analysis in 6.75 s. For the RTS-79 system, 2,070,564 critical states are identified within 15.82 s. Moreover, compared with traditional Monte Carlo simulation (MCS), the proposed method yields reliability indices LOLP and Expected Demand EDNS that are highly consistent with MCS results. The relative error is approximately 1.1016% for LOLP and 1.4064% for EDNS, fully validating the proposed model's high efficiency, stability, and accuracy.

This paper investigates the time-optimal control problem for the one-dimensional heat equation. The objective is to drive the system state into a given target set in the shortest possible time. A direct deep learning-based approach is proposed, where the differential evolution algorithm is employed to optimize network parameters, and we obtain the optimal time and the time-optimal control. In order to verify the rationality of the obtained results, the Pontryagin's Maximum Principle is used as a consistency check. Numerical results show that the computed control exhibits an explicit Bang–Bang structure, which is in high agreement with the necessary conditions. The optimal time and the control profile remain stable under different perturbed initial values, demonstrating strong robustness. The results in this paper indicate that deep learning provides an effective implementation approach for the optimal control of distributed parameter systems.

In classification tasks, accuracy serves as a core metric for evaluating the predictive performance of models. However, a single predictive model often suffers from model uncertainty, and directly maximizing classification accuracy leads to an NP-hard optimization problem. To address these challenges, this paper proposes an Accuracy-maximizing Model Averaging prediction method (AccMA). Based on a classification-calibrated surrogate loss function, AccMA assigns weights to candidate models by minimizing the $K$-fold cross-validation criterion of the sample surrogate loss, and the weight selection process is completely data-driven. Theoretical analysis shows that the proposed method is asymptotically optimal in the sense of minimizing classification error risk, which is equivalent to maximizing prediction accuracy. Numerical simulation experiments verify the effectiveness of AccMA, and applications to three real-world datasets (machine predictive maintenance data, sonar detection data and AIDS clinical trials group study 175 data) demonstrate that AccMA has a distinct advantage over competitive methods in improving classification accuracy.

The Twin Support Vector Quantile Regression model (TSVQR) not only exhibits good generalization performance but also captures the heterogeneity characteristics within datasets. When addressing nonlinear problems, this model initially requires mapping input features into a high-dimensional feature space before fitting a linear quantile model within that space. Given that there may be numerous redundant features in the high-dimensional feature space, TSVQR is susceptible to biased parameter estimates influenced by these redundant features, which further affects the predictive performance of the model. In this paper, we propose a Hybrid Twin Support Vector Quantile Regression (HTSVQR) model that incorporates both $L_1$ and $L_2$ regularization terms into the objective function. This integration endows the HTSVQR model with the smoothness of the $L_2$ regularization term while also achieving sparsity through the $L_1$ regularization term. Consequently, it enables sparse estimation, enhances generalization performance, and improves robustness of the model. Finally, the effectiveness of the proposed model and method is validated through numerical simulations and analysis of air quality data from Jiangxi Province. The analysis results show that HTSVQR exhibits better robustness and generalization performance than other models for different distributions of noise.

Given the substantial global manufacturing transformation that promotes servitization in manufacturing, service-oriented manufacturing(SOM)-a new industrial paradigm that combines manufacturing and services-is crucial to enhancing industrial chain resilience and reaching carbon peaking and carbon neutrality targets. Although multi-scale synergies spanning micro-enterprises, meso-industrial networks, and macro-policies are essential to SOM's effectiveness, current research faces enduring obstacles such as fragmented innovation efficacy evaluation frameworks, underdeveloped modeling of technology-organization-institution dynamic couplings, and multi-source heterogeneous data integration. This paper creates a collaborative measurement framework that combines multi-scale optimization, causal inference, and hypernetwork modeling to close these gaps. In order to quantify cross-scale transmission dynamics, we build a tripartite "service-industry-policy" hypernetwork model in conjunction with differential equations. We introduce two metrics: dynamic viscosity for meso-resource allocation efficiency and collaborative entropy for micro-interaction stability. The framework incorporates asymmetric evolutionary game strategies to resolve SME-LE collaboration imbalances and leverages federated tensor decomposition for cross-modal data alignment. Empirical analyses substantiate the model’s validity, demonstrating significant policy multiplier effects and operational feasibility of service module assembly patterns. Collectively, this work establishes rigorous theoretical and methodological underpinnings for SOM’s digital transformation, enabling integrated stability monitoring, adaptability optimization, and resilience enhancement across manufacturing ecosystems.

Longitudinal data are widely used in critical fields such as medicine and genomics, with complexities primarily arising from within-subject correlations and potential heteroscedasticity. Most existing feature screening methods are designed for cross-sectional data and are not directly applicable to longitudinal data. This paper proposes a feature screening method for ultrahigh-dimensional longitudinal data based on generalized estimating equations and expectile regression, termed GEEES. By incorporating a working correlation matrix to account for within-subject correlations and utilizing expectile regression to capture heteroscedasticity, the proposed method offers broad applicability. Theoretical analysis shows that, as the sample size tends to infinity, GEEES can select all important variables with probability approaching one, even when the working correlation structure is misspecified. Numerical simulations under both standard longitudinal data and heteroscedastic longitudinal data settings demonstrate the superiority of GEEES. Finally, the proposed method is applied to an ophthalmology longitudinal dataset, further illustrating its practical applicability.

In this paper, strict co-radiant separation theorems in vector optimization are established on locally convex spaces using the normlike-base, that is, a cone and a co-radiant set can be separated by a Bishop-Phelps type normlike-linear separating function. Meanwhile, by using the concept of the augmented $\varepsilon$-dual co-radiant sets, a sufficient optimality condition for the $(C,\varepsilon)$-Benson proper efficient solution of vector optimization problem is proposed, and the nonlinear scalarization characterization results for $(C,\varepsilon)$-Benson proper efficient solution are obtained by applying the strict co-radiant separation theorems.Finally, considering the uncertain vector optimization problem, the results of linear and nonlinear scalarizations for $(C,\varepsilon)$-robust Benson proper efficient solution are respectively established.

The intricate coupling of massive heterogeneous information renders the evolution dynamics of public opinion increasingly unpredictable. This study investigates how coupling correlations among public opinion information influence evolution patterns, thereby providing a theoretical foundation for public opinion crisis governance. By systematically analyzing the coupling-driven evolution mechanism of public opinion information and drawing on the Lotka-Volterra modeling approach, this study constructs a coupling-driven evolution model for public opinion information, solves for the equilibrium points and stability conditions of the model, and further reveals the evolution patterns of public opinion information under coupling effects through numerical simulation and empirical analysis. The research results indicate that there exist five coupling-driven evolution modes among public opinion information, each with distinct evolution characteristics; under different modes, the coupling driving force and information attractiveness exhibit significant differences in their effects on the evolution scale and stabilization time of information supporters.

Against the background of the continuous advancement of energy transition, single-dimensional risk analysis can no longer fully depict the complex challenges faced by the international energy market. The study first constructs a comprehensive index of multiple uncertainty that integrates economic policy, climate policy, and geopolitical risks, providing quantitative tool for understanding the synergistic impact of compound risks. Based on this index, it reveals the asymmetric impact of multiple uncertainty on the energy market under different market conditions through quantile regression. Second, the study develops the TVP-VAR-DY model to systematically analyze the time-varying transmission paths and dynamic spillover effects of this comprehensive risk to the energy market. The findings show that multiple uncertainty significantly impacts the international energy market through risk resonance, with its effects exhibiting time-varying and asymmetric characteristics. Additionally, the impacts are particularly prominent during major event periods as the global financial crisis, the signing of the Paris Agreement, and the COVID-19 pandemic. Historical key event shocks have significantly amplified the transmission effects of multiple uncertainties to the energy market, revealing the structural vulnerability of the international energy market under extreme risk scenarios. In summary, this study not only provides dynamic analysis tools for emerging economies to address uncertainty risks, but also offers a theoretical basis and decision support for policymakers to optimize the climate governance framework and enhance energy resilience.

Most existing studies on multi-attribute group consensus decision-making primarily focus on experts’ rational preferences, while the quantitative integration of emotions from both the public and experts in social media environments, as well as the dynamic role of emotions in the consensus evolution process, remains insufficiently explored. To address this limitation, this paper proposes a multi-attribute group consensus decision-making model that integrates emotional computation from multiple stakeholders. First, decision attributes are extracted from public opinions using the BERTopic model, and their emotional tendencies are quantified through SnowNLP sentiment analysis, based on which an attribute weighting approach integrating opinions and emotions is developed. Second, a dynamic expert weighting mechanism jointly driven by social influence and emotional polarity is designed, where social influence is measured using the PageRank algorithm and expert weights are adaptively updated through iterative emotional propagation. Subsequently, a dynamic feedback adjustment model incorporating emotional polarity and preference deviation is established to facilitate efficient consensus formation. Finally, a case study on the selection of elderly care service schemes, together with comparative analyses against multiple existing methods, demonstrates that the proposed model effectively enhances decision-making rationality and improves the robustness of decision outcomes.

Effectively reducing the impact of estimation errors has always been a critical challenge in constructing high-dimensional portfolios using the mean-variance model. From a perspective that emphasizes both the mitigation of estimation risk and the control of suboptimality risk, this paper investigates the problem of regularized high-dimensional portfolio construction. It introduces generalized position constraints that can incorporate investors' personalized preferences and based on these, establishes a regularized model framework for high-dimensional portfolios. This framework aims to mitigate estimation risk while controlling suboptimality risk, thereby reducing the impact of estimation errors more effectively. Furthermore, the solution algorithm for the new model and recommendations for selecting tuning parameters are provided to facilitate its application. Simulation experiments using generated data and empirical analyses on real-world data from several major domestic and international financial markets demonstrate that the new model outperforms existing regularized high-dimensional portfolio models not only in reducing portfolio risk and turnover rate and improving the Sharpe ratio but also in achieving portfolios with a more flexible and diverse sparsity pattern. Simultaneously, the study provides new empirical evidence that can inform the development of portfolio management strategies

With the increasing prominence of interdependencies among insurance businesses, how to balance investing in loss prevention and purchasing reinsurance for limited capital, has become a core issue in insurers' risk management. The current research on joint dynamic optimization of loss prevention and reinsurance under dependent risk is insufficient. This paper investigates an optimal risk control decision-making problem for an insurer under dependent risks, where two risk management measures of loss prevention and reinsurance are considered and the objective is to maximize the terminal expected utility. By constructing Thinning model to characterize the dependent claim processes of insurance businesses and establishing the Hamilton-Jacobi-Bellman (HJB) equation based on stochastic control theory, closed-form expressions of the optimal strategy and value function are obtained. The study reveals that the optimal fund allocation strategy of insurers highly depends on their risk aversion level: when risk aversion is low, priority is given to investing in loss prevention; when risk aversion is high, the proportion of reinsurance is increased to transfer risks. Meanwhile, the scale of prepared funds and the claim probability also significantly influence the decision-making: abundant funds for a high risk-averse individual enhance the marginal benefit of loss prevention, while high claim probability increases the cost of reinsurance, prompting insurers to prefer proactive risk management through preventive measures. This research provides a theoretical basis and decision-making reference for insurers in managing dynamic risks under dependent businesses.

This study addressed omnichannel inventory and order fulfillment problem with the decision maker's risk aversion and customers' time preference under uncertain environment. To handle the ambiguity in the probability distribution of uncertain parameters and satisfy customer's service level requirement, a distributionally robust optimization model with joint chance constraints is developed. Based on the ambiguity sets, the proposed distributionally robust optimization model can be transformed into equivalent mixed-integer linear programs, and a conservative CVaR is adopted to transform the chance constraints. Numerical experiments are performed to illustrate the effectiveness of the proposed model and solution. The results demonstrated that distributionally robust optimization model shows superior out-of-sample performance compared with the traditional stochastic optimization model; WMQD criterion has a better performance in risk measurement; the lengthening of popular period and the increasing of short-term and long-term factors increase the objective function values; adopting both SFS and BOPS is not always better than a single fulfillment strategy; the service level have significant impacts on operational performance of retailers.

Green Total Factor Productivity (GTFP) is a core indicator of regional green transformation, and the synergy of innovation, industrial, and capital chains (the “Triple Chain”) is key to its improvement. Based on panel data from 30 Chinese provinces (2011-2022, with Tibet excluded due to data unavailability), this paper employs the composite system synergy degree model, super-efficiency SBM-DEA, and fsQCA to explore the configurational paths and regional heterogeneity of Triple Chain synergy driving GTFP growth, resolving the paradox of inverted regional rankings-the eastern region has the highest synergy but not the fastest GTFP growth, while the opposite holds for the western region. Findings reveal that GTFP improvement relies on the systemic configurational effect of Triple Chain synergy, exhibiting a configurational non-linear relationship where synergy quality outweighs quantity. Four equivalent driving paths are identified, including the Triple Chain synergy type, the innovation-capital dual-drive type, the industry-capital dual-drive type, and the innovation-industry dual-drive type. Triple Chain integration driving GTFP evolves dynamically from “scale-driven” to “innovation-led”, with significant path heterogeneity across China's four major regions. This paper reveals the configurational logic of Triple Chain synergy driving GTFP, offering differentiated policy implications for regions to optimize their Triple Chain integration models.

Leveraging large language models for disease diagnosis can effectively improve diagnostic accuracy, lower technical barriers, and reduce diagnostic costs, and their effectiveness largely depends on prompt engineering. However, due to strong reliance on clinical diagnostic experience, high similarity of symptoms across different diseases, and complex diagnostic decision-making paths, existing mainstream prompting methods are unable to meet the medical requirements of precise diagnosis. To address these issues, this paper proposes a Multiagent Knowledge-enhanced Prompting (MKP) method for disease diagnosis. MKP proposes a knowledge retrieval strategy to address reliance on clinical diagnostic experience, a multi-perspective reasoning strategy to handle similar symptom presentations, and a decision fusion strategy to manage complex diagnostic decision paths. Experiments on three real-world disease diagnosis datasets demonstrate that MKP significantly outperforms current mainstream prompting methods on disease diagnosis tasks, providing new techniques and methodological guidance for intelligent healthcare. Additionally, a robustness analysis is conducted on the key parameters of MKP to identify optimal parameter settings, providing practical support for its clinical deployment.

Under the background of green and low-carbon transition, there is a research gap concerning the dynamic characteristics of regional low-carbon innovation network resilience. Based on low-carbon patent data of enterprises and academic institutions across 31 provinces in China from 2016 to 2023, this paper constructs a network and quantifies resilience from three dimensions—resistance, recovery, and innovation—by incorporating a cascading failure model. The study employs Kernel density estimation, standard deviation ellipse, Gini decomposition, and $\sigma$ and $\beta$ convergence methods to analyze spatiotemporal evolution, and constructs a random forest model to reveal the influence mechanisms under two failure scenarios. The results show that: (1) Under both scenarios, resilience exhibits a three-stage evolution of "adjustment–stabilization–improvement", with regional heterogeneity forming a "core–periphery" four-echelon structure; (2) Spatially, both types of failures display agglomeration characteristics, yet their geographical patterns differ significantly; (3) The influencing factors show a dual-wheel differentiated driving force of "element–structure": under random failure, internal structural variables dominate, while under intentional failure, external environmental variables are key. Accordingly, policy recommendations are proposed, including scenario-specific measures, differentiated spatial governance, and the establishment of a dual-dimensional support system.

Supplier compliance is a critical component of supply chain risk management, as non-compliant behaviors can lead to supply chain disruptions and reputational crises. Auditing serves as an effective means for manufacturers to identify compliant suppliers. This paper considers a supply chain consisting of a single manufacturer and a single supplier, and investigates whether the manufacturer should choose internal auditing (relying on its own resources) or external auditing (entrusting a third-party institution). It further analyzes the impact of collusion between the third-party auditor and the supplier under external auditing on the choice of audit strategy, and explores whether the audit decision can achieve a win-win outcome for supply chain members. The results show that: (1) In the absence of collusion, the manufacturer's audit choice is determined by the internal audit cost, and an excessively high reservation profit of the third-party auditor will push the manufacturer to switch to internal auditing; (2) In the presence of collusion, when the collusion penalty is high, the supplier will not engage in collusion, and the audit choice is still dominated by the internal audit cost. Collusion occurs under low or moderate collusion penalties, where the audit choice is jointly affected by internal audit cost and the penalty intensity. Specifically, the manufacturer prefers internal auditing to deter collusion when penalties are low, whereas the internal audit cost remains the dominant factor under moderate penalties.

The application of blockchain technology has enhanced the traceability of power batteries in new energy vehicles and stimulated consumer demand; however, it has also intensified consumer concerns over the risk of privacy leakage. A supply chain model, consisting of a power battery supplier, a new energy vehicle manufacturer, and consumers, was constructed in this paper. The optimal pricing decisions of blockchain adopters were analyzed and compared under different types of consumer behavior. The findings were as follows: (1) When consumers were risk-neutral, although the adoption of blockchain technology by battery suppliers was found to increase battery recycling volume, it was not conducive to an increase in market demand. Moreover, it was found that only when the demand gain and recycling volume gain brought by blockchain technology simultaneously met the corresponding threshold conditions could the profit of the firm adopting blockchain technology be increased; (2) When consumers were risk-averse, the advantages of blockchain technology adoption by all parties were found not to be affected by consumers' risk aversion. Moreover, the conditions for their profit growth were shown to be influenced by the proportion of risk-averse consumers in the market; (3) Regardless of the proportion of consumer types, an increase in the degree of loss caused by risk aversion was shown to systematically suppress market demand and recycling volume across all models. Furthermore, corporate profits were observed to exhibit a U-shaped trend-decreasing initially before increasing-as the degree of loss caused by risk aversion intensified.

This study investigates a non-zero-sum reinsurance-investment game for an insurer and a reinsurer with common shock dependence. It is assumed that the insurer buys proportional reinsurance from the reinsurer to diversify risks, and the claim business of the insurer and the reinsurer has common shock dependence. In addition, both the insurer and the reinsurer are allowed to invest in the same risk-free asset and risky asset, where the price process of the risky asset is described by the square root factor model. The objectives of both the insurer and the reinsurer are to maximize the expected exponential utility of their relative wealth at the terminal time. Relying on dynamic programming principle, we establish the coupled Hamilton-Jacobi-Bellman equations in the framework of non-zero-sum games. Furthermore, we obtain the explicit solutions of the Nash equilibrium reinsurance and investment strategies. Finally, some numerical examples are given to illustrate the impacts of model parameters on the optimal strategies. The results show the competition coefficient exerts a significant influence on the reinsurance decisions of both parties. The common shock intensity parameter has a negative effect on the reinsurer's underwriting willingness, yet it has a positive impact on the insurer's reinsurance demand.

The high default rate in the 'company-farmer' order model severely restricts the healthy development of agricultural modernization in China, with the fundamental reason being insufficient coordination in the agricultural product supply chain. This paper establishes a two-level agricultural product supply chain Stackelberg game for the 'company-farmer' type and considers the company's service level to analyze the impact of cost-sharing contracts under the company's altruistic preference on supply chain performance and social welfare. Secondly, through theoretical analysis and numerical simulation, the specific conditions for achieving coordination in the agricultural product supply chain are determined, compensating for the shortcomings of purely altruistic preferences or purely cost-sharing contracts in coordination, and demonstrating the synergistic effect of combining the two. The following conclusions are drawn: (1) The company's altruistic preference behavior achieves dual improvements in 'supply chain efficiency' and 'social welfare' through price adjustment and service optimization; (2) Cost-sharing contracts under altruistic preference can improve the company's service level and stimulate market demand for agricultural products; (3) Cost-sharing contracts with altruistic preference can achieve effective coordination in the agricultural product supply chain. Finally, based on the research conclusions, three implications are proposed for companies, farmers, and the government, respectively, providing new theoretical perspectives and practical guidance for the coordinated development of the 'company-farmer' agricultural product supply chain.

This paper studies the reinsurance contract making problem under the leader-follower stochastic differential game between two competitive insurers and two competitive reinsurers. The claim process of the two insurers satisfies the compound Poisson process, and the dependence between the insurance businesses of the two insurers is reflected through the common Poisson process in the claim process. Based on the relative wealth, this paper quantifies the competition between two insurers and two reinsurers, respectively. Based on the mean-variance criterion, we respective establish two kinds of non-zero-sum stochastic differential games between two insurers and two reinsurers, and then establish two kinds of leader-follower stochastic differential games between two insurers and two reinsurers. These two kinds of non-zero-sum stochastic differential games are nested in the two kinds of leader-follower stochastic differential games. Based on the theory of stochastic control and the idea of game theory, the optimal time-consistent claim risk sharing strategy of insurers and the optimal time-consistent reinsurance pricing strategy of reinsurers are obtained, furthermore, the optimal time-consistent reinsurance contract is obtained. Finally, the influence of the degree of competition, risk aversion degree and the dependence of insurance business of two insurers on the theoretical results is explored through numerical experiments, and some new enlightenment is obtained.