With the deep integration of information technology and manufacturing, the sharing economy has fostered an ecosystem model. Compared to traditional contract manufacturing arrangements, this model sees leading enterprises providing incubation support to start-ups, thereby influencing the profit distribution within the ecosystem.To elucidate the operational mechanisms of key elements, a supply chain comprising leading enterprises, start-ups and retailers was constructed. The study examined the impact of product brand strength, incubation capabilities and competitive intensity on supply chain profitability and competitive-cooperative strategies, analysing these factors from both the contract manufacturing model and ecosystem perspectives.Under the contract manufacturing model, collaboration between certain parties may to some extent undermine the interests of others. To a certain degree, the ecosystem model can effectively foster deep cooperation among supply chain members without compromising the interests of other stakeholders. Appropriate levels of competitive intensity can enhance product visibility and broaden the target audience. When a start-up's own brand gains consumer favour, consumer willingness to purchase products from leading brands also increases. At this juncture, leading enterprises will be more inclined towards the ecosystem model.This study provides theoretical underpinnings and practical guidance for the promotion of ecological chains, as well as for the optimisation of supply chain models and the formulation of collaborative strategies.

Under a trust mechanism, when a terminal within a dedicated P2P network initiates a resource transmission request to a cloud P2P system, the cloud peers must undergo identity verification against Certificate Revocation Lists (CRL) before being granted access to the dedicated system. This mechanism classifies resource-carrying peers in the cloud into two categories—Peer1 and Peer2—according to their historical record of resource transmission within the system. In this classification scheme, Peer2 with a transmission history are granted preemptive priority. Based on this, a Geom/Geom/$c$ queueing model is constructed in this study, and matrix geometric solution methods are employed to solve it, thereby obtaining quantitative indicators of the system's operational mechanisms and enabling an in-depth analysis of this P2P network system. Finally, regarding the preemptive priority characteristics of Peer2, a Stackelberg game equilibrium analysis is conducted on the model, yielding the unique arrival rates corresponding to the profit-maximizing conditions for both types of peers.

The application of AI-assisted detection in the medical field is becoming more extensive, effectively enhancing the diagnostic efficiency and accuracy of medical institutions. Capacity allocation can manage service rates and patient queues, thereby improving medical service quality. This study develops a two-stage capacity allocation model for medical systems using AI-assisted detection. It analyzes capacity allocation, profits, and social welfare in medical institutions with different characteristics. The findings indicate that medical institutions need to allocate base and safety capacity for detection and treatment stages by referring to the empowerment level of AI-assisted detection. This ensures base patient needs are met and provides flexibility to address demand fluctuations. While AI-assisted detection can boost the service rate in the detection stage, if medical institutions don't correspondingly increase treatment-stage capacity, it may cause system bottlenecks. Moreover, AI-assisted detection doesn't always enhance the profits and social welfare of medical institutions. This study offers theoretical support for the integration of AI technology and the medical field and provides guidance for capacity allocation decisions in medical institutions.

Promoting the construction of “waste-free cities” is an important practice to implement the twin goals of carbon peak and carbon neutrality, promote sustainable economic and social development, and build a beautiful China. This paper explores the impact of “waste-free cities” construction on corporate ESG performance using the 2019 “waste-free cities” construction pilot as a natural experiment. The study finds that the construction of “waste-free cities” has significantly improved the ESG performance of enterprises in pilot areas, and this phenomenon is more prominent in non-state-owned enterprises, manufacturing enterprises, and enterprises in regions with high environmental protection expenditures. Further results show that the construction of “waste-free cities” has increased the frequency of environmental terms in government work reports, the number of environmental penalties in the cities, and the number of green factories. At the enterprise level, it has alleviated managerial myopia and financing constraints, reduced the cost of equity capital, and increased credit access. Additionally, the enhancing effect of “waste-free cities” construction on ESG performance is more significant in enterprises that have not received environmental protection investments, environmental protection honors, or are in highly competitive industries. These results indicate that the construction of “waste-free cities” improves corporate ESG performance mainly through channels such as increasing government attention to the environment and enhancing stakeholder recognition of ESG. An analysis of its subsequent economic impacts reveals that the construction of “waste-free cities” can reduce the level of short-term debt used for long-term purposes by enterprises and drive the development of common prosperity of enterprises. This paper expands the research on “waste-free cities” construction and provides useful references for guiding enterprises to actively participate in social governance, making coordinated efforts to cut carbon emissions, reduce pollution, pursue green development, and boost economic growth and accelerating the green transition in all areas of economic and social development.

Climate change has become a key issue in global development, and actively addressing the challenges posed by global climate change has become a common consensus in society. In the context of big data, detecting and analyzing change points in meteorological data to assess the impact of policies or extreme events is particularly important. This article develops an estimation approach for generalized Logistic distribution change point model using generalized fiducial inference, with comprehensive comparisons to traditional maximum likelihood method and Bayesian method. Simulation results indicate that the point estimates and confidence intervals obtained based on generalized fiducial inference demonstrate good applicability. For change point estimation, the generalized fiducial point estimators based on mean show better effect, and the fiducial confidence intervals have more stable coverage rates within a reasonable range. Regarding scale and shape parameters, the generalized fiducial inference based on median is superior to other methods in most cases, and the fiducial confidence intervals based on highest posterior density can provide more accurate coverage, especially when estimating the shape parameter. Finally, the validity of the generalized fiducial inference in the generalized Logistic distribution change point model was verified using the monthly maximum rainfall data from the Beijing Meteorological Station.

The equivalence of multidimensional systems is closely related to the equivalence and reduction of multivariate polynomial matrices, and the Smith form of matrices plays a key role in matrix equivalence. The problem of the equivalence of multivariate polynomial matrices to Smith form is very complex and this paper studies the problem of equivalence between quasi weak linear multivariate polynomial matrices and their Smith form, characterizes the sufficient and necessary conditions for this type of matrix to be equivalent to its Smith form, and provides corresponding algorithms.

In this paper, we consider a supply chain consisting of a manufacturer and an e-commerce platform. The manufacturer sells products through the platform's reselling channel and decides whether to encroach on the end market to gain additional profit. If the manufacturer chooses to encroach, it can do so via either direct encroachment or agency encroachment. The e-commerce platform, which possesses private demand information, decides whether to share this information with the manufacturer and simultaneously invests in promotional efforts to stimulate market demand .The results indicate that first, compared with direct encroachment, agency encroachment more effectively mitigates the double marginalization caused by wholesale pricing and leads to higher sales. Second, information sharing by the platform benefits the manufacturer; however, the platform is willing to share information only when the manufacturer adopts agency encroachment and the commission rate is high. Third, when promotional costs are high and commission rates are low, the manufacturer prefers agency encroachment, and information sharing further encourages this choice. Moreover, as demand volatility increases, both the manufacturer's preference for agency encroachment and the platform's willingness to share information first decline and then rise.

In this paper, the global leader-following consensus issue of discontinuous nonlinear fractional-order multi-agent systems is addressed via hybrid control strategy. Under the framework of fractional differential inclusion, one hybrid distributed control protocol with dynamic event-triggered mechanism is proposed. By utilizing Lyapunov stability theory and algebraic graph theory, the leader-following consensus for discontinuous fractional-order multi-agent system can be achieved, and Zeno behavior is excluded. Finally, a simulation example and a simulation comparison are respectively applied to verify the correctness of the obtained results and the effectiveness of the control strategy.

The intensive, efficient, green and low-carbon development path of new-type urbanization is highly consistent with new quality productivity theory. Based on panel data of prefecture-level cities in China from 2006 to 2021, new-type urbanization pilot policy is regarded as a quasi-natural experiment, and the impact of pilot policy on new quality productivity is estimated by using a multi-period difference in difference model. The study found that new-type urbanization pilot policy significantly promoted new quality productivity, and conclusion remained valid after a series of robustness tests, including parallel trend test, placebo test, replacement of explained variables, exclusion of other policy interference, propensity matching difference in difference model, and instrumental variable. Heterogeneity analysis shows that new urbanization pilot policy significantly promotes new quality productivity across different city regions, city sizes, city resource endowments, city economic development levels, city industrial structures, and city marketization levels. Specifically, it has a stronger promoting effect on new quality productivity in eastern cities, mega-cities and above, non-resource-based cities, cities with high economic development, cities with advanced industrial structures, and cities with a high degree of marketization. Further analysis shows that new-type urbanization pilot policy promotes new quality workers and new quality labor objects, and has a positive effect on new quality productivity through industrial structure rationalization and digital technology penetration. This study enriches the understanding of the policy effects of new urbanization and provides insights for accelerating the development of new quality productivity.

Multidimensional system reduction is often converted to the equivalence between multivariate polynomial matrices and their Smith forms. This paper focuses on the unimodular equivalence of a class of multivariate polynomial matrices to their Smith forms. A criterion for the unimodular equivalence of such matrices and their Smith forms is proposed, with the result extended to non-square and rank-deficient cases. Finally, a general algorithm for reducing such matrices to their Smith forms is presented and an example is provided to illustrate the algorithm.

Multimodal data (image, speech, video) is a common form of data in the field of information science. Based on multimodal sentiment analysis techniques, the recognition algorithm can more accurately refine sentimental tendencies and attitudes, thereby greatly improving the user experience of artificial intelligence (AI) products. Therefore, it has important potential application value in the fields of economics, management, and sociology. Multimodal sentiment analysis techniques in its ascendant, how to effectively integrate multimodal information for sentiment analysis is being an important topic in the field of artificial intelligence. This paper implements facial emotional recognition based on Efficientnet-V2 and BiLSTM, speech emotional recognition based on CNN architecture, text emotional recognition based on BiLSTM architecture, and word bag model integration. By optimizing the model parameters and using four multimodal fusion strategies, such as early fusion, late fusion, semantic fusion, and stage fusion, we construct a multimodal sentimental recognition algorithm. The experimental results show that the accuracy of the fused audio-visual bimodal algorithm reaches 73.86%, compared with Reference(Zhang, et al., 2019), the accuracy of the classification has been improved by 3.1%. Multimodal recognition algorithm under different fusion strategies can significantly improve recognition accuracy compared to single modal or bimodal recognition methods. Meanwhile, testing on simulated datasets shows that the semantic fusion method has the best effect in the fusion strategy, and the late fusion strategy is better than the early fusion. We also find that when the influence factor of stage fusion is 0.84, the multimodal classification effect is the best.

In this paper, the three-axis attitude stabilization of the axisymmetric spacecraft with actuator saturation is investigated. The linearized attitude motion equation is firstly transformed into its Luenberger canonical form, and then the open-loop system is decomposed into a cascade of neutral stable linear systems. Based on the absolute stability theory, bounded linear state feedback controllers for subsystems are proposed respectively. The global stability of the closed-loop system is proven by providing explicit conditions on the parameters in the feedback gains. Optimal feedback gains such that the convergence rate of the closed-loop system is maximized are also obtained. Numerical simulations are given to show the effectiveness of the presented approaches.

With the rapid development of fresh product e-commerce, the quality of fresh product is widely concerned. For the supply chain of single fresh product supplier and single fresh product e-commerce platform, considering the fairness concern behavior of the platform, we construct the supplier-led Stackelberg game, the e-commerce platform-led Stackelberg game and the Nash equilibrium game model under the two scenarios of suppliers fulfilling and not fulfilling CSR, respectively, and study the impacts of fairness concern behavior, whether supplier should fulfill their responsibility and the differences in decision-making under different power structures of the platform through the solution of the six models, the comparison of profits and the numerical simulation. The study shows that: the platform's fairness concern behavior reduces the decision-making efficiency and negatively affects the supply chain's profit and social welfare; the supplier’s active fulfilling CSR can suppress the price and improve the quality of fresh product, but it will reduce their own profit and increase the platform's profit; under the power structure of Nash equilibrium, the supply chain system can achieve the optimal market performance, but the party that has the advantage of the power of the channel usually lacks the incentive to actively give up the power. Finally, this paper proposes a dual collaborative contract that achieves system optimality while achieving a Pareto improvement in the profits of supply chain members.

With the rapid development of e-commerce and logistics processing technologies, the cost of consumers’ searching for product information and purchasing has decreased substantially, leading to an explosive growth in the gray market. Motivated by profit-seeking incentives, some brand-authorized platforms may also be involved in gray market speculation activities. To effectively address the gray market speculation issues of authorized platforms, some manufacturers are contemplating the introduction of direct sales channels. Three scenarios are considered: the authorized platform engaging in no speculation, the platform engaging in speculation while the manufacturer does not introduce a direct sales channel, and the platform engaging in speculation while the manufacturer introduces a direct sales channel. A Stackelberg game model, with the manufacturer as the leader, is constructed to analyze these scenarios. The findings reveal: (1) When consumers in the low market (L) have a relatively low willingness to pay for the product, the platform tends to engage in speculation. As the willingness to pay increases among L market consumers, the manufacturer, without introducing a direct sales channel, will raise the wholesale price in the L market. With further increases in willingness to pay, even without any intervention from the manufacturer, the platform loses its incentive to speculate. (2) The introduction of a direct sales channel by the manufacturer can effectively boost its own profits and deter the platform's gray market speculation. (3) As consumers' willingness to pay for the manufacturer's direct sales products increases, the manufacturer's profits will rise while the platform's profits decrease. Therefore, manufacturers should strengthen their brand management for direct sales products and enhance consumer recognition.

This paper studies an M/M/1 queueing-inventory system with an emergency replenishment policy, where the inventory product is fluid type. There is one server in the system. After receiving service, the customer will take away a random amount of inventory products. When the inventory level reaches zero, the system will issue an order of emergency replenishment and the server will take a vacation. At the end of a vacation, if there is still no inventory in the system, the server will return to the system with probability $p$ or take another vacation with probability $(1-p)$ . It is assumed that the service time, the vacation time, the quantity of products required by customers, the lead times of the regular replenishment and the emergency replenishment all obey exponential distributions. Firstly, a modified system model with zero service time is considered, and the steady-state probability distribution is obtained by using Markov process theory and differential equation theory. Secondly, the steady-state probability distribution of the original system model is obtained by using the results of the modified system model, and it is found that the steady-state probability distribution has a product form. Based on this, we further obtain some performance measures of the system. Finally, the average cost function of the system is established. The optimal inventory strategy and the optimal cost of the system are analyzed by numerical examples.

This paper uses TVP-VAR model to study information spillover effects between international oil price structural shocks based on a new decomposition method and Chinese stock market sector. Furthermore, we employ GARCH-MIDAS model to examine the impact of geopolitical risk on spillover effects. The results indicate that: (1) Compared to the spillovers of volatility and tail risk, the spillover effects are mainly concentrated at the return level. (2) All industries are receivers of oil price shocks. The energy industry experiences the largest spillover effects from oil price shocks and is the largest receiver. (3) These information spillover effects are mainly driven by oil price risk shock. (4) The spillover effects exhibit time-varying characteristics. During sudden financial events, the connection between oil price shocks and Chinese stock market becomes more pronounced. (5) Geopolitical risk has a strong explanatory power for these information spillover effects. Against the backdrop of unprecedented geopolitical tensions, these findings not only help investors adjust their portfolios and reduce investment risks, but also provide valuable reference for policy-making and financial regulation.

This paper deals with a Tikhonov regularized second-order primal-dual dynamical system with slow vanishing damping for solving a linear equality constrained convex optimization problem. Under some mild conditions, we prove the trajectory of the dynamical system converges strongly to the minimal norm solution of the optimization problem. Additionally, we provide convergence rate results for the primal-dual gap, the objective residual, and the feasibility violation along the trajectory generated by the dynamical system. Furthermore, by conducting numerical experiments, we compare the obtained theoretical results with those reported in the existing literature.

In actuarial science and financial risk management, accurately characterizing the quantile function in the tail of a risk distribution is of critical importance for the measurement and control of extreme risks. Traditional risk measures, such as VaR, ES, CTE, and distorted risk measures, often exhibit large estimation biases and insufficient robustness under small-sample conditions or unknown distributions. To enhance the stability and consistency of risk estimation, this paper proposes a credibility-based estimation method for the quantile function, grounded in a Bayesian framework and the linear minimum mean squared error principle. The proposed method constructs an analytical credibility estimation model, effectively avoiding the computational complexity associated with high-dimensional posterior quantiles, and establishes a unified estimation framework applicable to multiple types of risk measures. Theoretical analysis demonstrates that the proposed estimator possesses desirable statistical properties, including conditional consistency, mean squared error convergence, and asymptotic normality. Numerical simulations further indicate that the method exhibits superior stability and accuracy compared to conventional empirical estimators in small-sample scenarios. Finally, we conducted an empirical analysis using daily data from six representative stocks in the Chinese stock market to evaluate the proposed estimation method. The results show that the method remains robust under conditions of high volatility and noise, adapts well to different market environments, and provides a reliable and practical approach for tail risk measurement.

Minimal linear codes play a crucial role in secure communications, particularly in secret sharing schemes and secure multi-party computation. Numerous studies have focused on constructing linear codes with few weights and, more importantly, minimality, using algebraic or geometric methods. In this paper, we propose two novel code constructions and employ Boolean functions and vectorial Boolean functions to construct several classes of binary linear codes with three and five weights. By applying the Ashikhmin-Barg theorem, we establish sufficient conditions for the minimality of these codes. Finally, we demonstrate the practical applications of the duals of these minimal linear codes in secret sharing schemes.

Information asymmetry between banks and enterprises and the lag in policy transmission constitute the core constraints limiting the effectiveness of technology innovation refinancing policies. This paper incorporates financing premium effects and credit rationing mechanisms into a DSGE model to investigate the impact of central bank digital currency (CBDC) on the effectiveness of technology innovation refinancing policies and analyze the primary transmission channels. The results demonstrate that implementing technology innovation lending through CBDC significantly enhances the optimization effects of technology innovation refinancing policies on both innovation input and output structures. Furthermore, mechanism analysis reveals that CBDC primarily enhances policy effectiveness by reducing firms' financing constraints and optimizing credit resource allocation. Particularly when CBDC accounts for more than 15% of technology innovation loans, the structural adjustment effects and welfare implications of technology innovation refinancing policies surpass those of direct monetary policy instruments. Extended research findings indicate that the "forward-looking conditional trigger mechanism" of CBDC contributes to further enhancing the policy's optimization effects on economic structure.

Advances in artificial intelligence have made generative rumors more challenging to distinguish from truthful information than conventional ones. Identifying the dissemination behavior of critical users accurately during the rumor propagation process has become a crucial strategy to mitigate large-scale rumor spreading. Given that the influence (weight) of rumor dissemination generally decreases as the propagation distance (number of hops) increases, this study assumes that the influence becomes negligible at three or more hops. Based on this assumption, we formulate the critical user identification problem as minimizing information interaction under a weighted two-hop constraint and examine mathematical properties of the objective function, such as non-convexity and submodularity. To solve this problem effectively, we propose a discrete gaining sharing knowledge based algorithm (DGSK), extending the original continuous framework to a discrete solution space. The algorithm incorporates novel strategies for population initialization, individual learning, and population update to enhance its performance. We also analyze the time complexity of DGSK and evaluate it on ten synthetic and real-world datasets. Comparative experiments with four mainstream heuristic algorithms across five evaluation metrics demonstrate that DGSK achieves superior performance in identification accuracy, stability, and convergence speed.

To address the issue of improving educational quality, this study deeply integrates the WSR (Physics-Affairs-Human Relations) systems methodology with system dynamics, and constructs a dynamic governance model for the continuous improvement of educational quality based on the analysis of causal relationships among various elements of the system. Simulation results indicate that the quality monitoring and evaluation system——which conducts assessments based on real data of talent cultivation quality (i.e., student development) and is linked to the reform of personnel and distribution mechanisms——serves as a value-oriented carrier functioning in feedback control. This system is not only an important means to optimize the structure and functions of the overall system but also a driving force for its operation. By driving comprehensive reforms in the system across three dimensions——namely, guarantee reform in the Physics (W) dimension, efficiency reform in the Affairs (S) dimension, and motivation reform in the Human Relations (R) dimension——it promotes cross-cycle iterative optimization and continuous improvement of the system, thereby achieving the enhancement of educational quality. The innovations of this study are reflected in the following three aspects: First, it organically integrates systems science with organizational behavior, providing a research perspective rooted in systems science and a research entry point based on organizational behavior for the field of educational quality management. Second, it breaks through the limitations of traditional static analysis and offers a logical analysis framework that combines static and dynamic approaches for this field; this framework is built on the integration of the WSR systems methodology and system dynamics, thus compensating for the lack of such logical analysis frameworks in the field. Third, it integrates cybernetics, system management theory, dissipative structure theory, and chaos theory to reveal the operational mechanism of the quality monitoring and evaluation system. This study embodies the academic value orientation of systems engineering——i.e., “practice-oriented and problem-driven”——and the perspective that “integration equals innovation”. Furthermore, this study provides decision-making suggestions and management insights for the effective advancement of policies regarding the reform of personnel and distribution mechanisms in the educational quality management system, the formation of a collaborative force for talent cultivation among local governments, schools, and society, and the improvement of educational quality centered on student development. Consequently

To investigate the impact of indirect network effects on electric vehicle (EV) manufacturers' charging station investment strategies under competitive environments, this paper develops a Hotelling game model involving two competing EV manufacturers with quality differentiation. Four strategic scenarios are examined regarding charging station investment decisions considering indirect network effects: neither firm invests, only the low-quality manufacturer (Firm A) invests, only the high-quality manufacturer (Firm B) invests, and both firms invest. The results show that: (1) Under certain conditions, indirect network effects positively influence the decisions and profits of both Firm A and Firm B. However, when the quality difference is small, stronger indirect network effects can harm both firms, which is counterintuitive. (2) When market entry costs are low, a strategy where only Firm A (or Firm B) invests is beneficial to that respective firm. Otherwise, the non-investment strategy is more favorable. (3) When indirect network effects are relatively weak, enhancing these effects lowers the investment thresholds for both firms. (4) An increase in the compatibility ratio of charging stations intensifies the free-riding effect between EV manufacturers.

This article aims to optimize the long-term management of the traceable agricultural product supply chain, enhancing its efficiency, competitiveness and quality and safety guarantee capabilities. For this aim, this article constructs a dynamic model based on differential games to study the optimal strategies of various levels of participants in the supply chain, such as Tier 1 suppliers, one Tier 2 supplier and one large retailer, in terms of effort input and pricing during long-term cooperation. The model integrates Stackelberg and differential games, considering the multi-level structure, dynamic interactions, and information asymmetry of the supply chain, and solves the equilibrium strategies of participants using backward induction. The findings indicate that the optimal effort input of the primary supplier increases with the unit planting cost, while that of the secondary supplier decreases. The retailer's effort input remains stable. Additionally, participants show low sensitivity to the total cost of traceability technology, suggesting limited incentives for additional effort from technological improvements. This research provides theoretical support and decision-making basis for optimizing the supply chain management of traceable agricultural products, which is conducive to enhancing consumer trust and promoting sustainable agricultural development.

We consider the infiltration of counterfeit products into the manufacturer's own direct channel (OD channel), which triggers consumer disappointment aversion and elation seeking behaviors. The blockchain-enabled retail channel (BR channel) can eliminate counterfeit risks but may raise consumer privacy concerns. This study develops pricing game models between the manufacturer and retailer under three scenarios: without the BR channel, with the BR channel considering disappointment aversion (or weak elation seeking), and with the BR channel considering strong elation seeking. Then, we conduct comparative analyses to explore the manufacturer's BR channel adoption strategy under the influence of disappointment aversion and elation seeking. The results reveal that (1) although the BR channel ensures product authenticity, introducing it is not always beneficial for the manufacturer; and despite the counterfeiting risk in the OD channel, maintaining it can, under certain conditions, improve the manufacturer's profit. (2) Depending on the interplay between disappointment aversion/elation seeking and privacy concerns, the manufacturer may adopt one of three BR channel strategies: equivalence, competition, or substitution. (3) Consumer disappointment aversion reduces the manufacturer's profit but does not reduce the retailer's profit; elation seeking increases the manufacturer's profit but does not increase the retailer's profit.

In enterprises competition conflict, the preference acquisition of decision-makers (DMs) is an important condition for mitigating conflicts. This paper constructs a preference-approval structures (PASs) based on the third-generation prospect theory (TGPT), which obtains DMs’ preference information in a multi-attribute environment. Specifically, due to the susceptibility of DMs to psychological behavior in a multi-attribute environment, this paper considers DMs’ risk attitudes, and establishes an uncertain reference point based on positive and negative ideal solutions and mean value to calculate gains and losses of feasible states in different decision-making situations, thereby obtaining the value function of feasible states. Subsequently, the cumulative weight function is calculated using the value function and probability. Finally, the above function is utilized to calculate the prospect value of DMs regarding the feasible state in a multi-attribute environment. The prospect value reflects DMs’ perceived value of feasible state and forms the basis for the formation of DMs’ preference information. The higher the prospect value, the more in line with the DMs’ expectations for the ranking and classification of the state. In addition, this study introduces the PAS into the graph model for conflict resolution (GMCR), constructs a GMCR based on PAS, and redefines five basic stability definitions. Finally, the effectiveness of proposed model is validated by applying it to the competition of enterprises, and the decision supports for the government are provided to promote the development of rural economy.

Emission reduction subsidies and taxes are important measures taken by the government to promote the green transformation of enterprises and implement the "double carbon" goal. However, the lack of working capital is the main obstacle restricting the implementation of low-carbon production in the upstream and downstream enterprises of the supply chain. When facing the loans provided by banks, the supplier and the manufacturer with capital constraints need to weigh whether to choose separate financing or portfolio financing. Under separate financing, the supplier and the manufacturer adopt bank financing respectively; Under portfolio financing, the supplier uses bank financing and provide trade credit for the manufacturer. According to the different combination forms of emission reduction policies and financing schemes, this paper analyzes the equilibrium strategies under the four models, and explores the financing and emission reduction policy preferences of supply chain members. The results show that the manufacturer always tends to choose the financing model with a lower wholesale cost, while the financing decisions of the supplier and the supply chain also depend on the market factors and emission reduction cost. The wholesale cost and initial carbon emission cost jointly determine the manufacturer's carbon policy preference. Furthermore, numerical results analyze the operators' optimal financing model choices and carbon policy preferences under the endogenous wholesale price.

This paper investigates the efficiency and customers' strategies in a retrial queueing system with disasters and working vacations. When a disaster occurs, all customers are forced to leave the system, and the server breaks down. During the repair period, no new customers are allowed to enter the system. Furthermore, upon arrival, customers can decide whether to join the queue based on the real-time status information of the server. The stability condition of the system and main performance measures are obtained by the method of probability-generating function and difference equation. Based on the reward-cost structure, the equilibrium joining strategy of customers, socially optimal joining strategy and optimal social benefits are studied. Also, system efficiency is further evaluated by examining efficiency measures such as throughput and the price of anarchy. Finally, through the numerical example, the effects of each parameter on the customers' joining probability and optimal social benefits are analyzed.

With the development of natural language generative AI big model technology, AI with superb semantic understanding such as DeepSeek has become the infrastructure of the digital economy and society. The synergy between AI and HI has the characteristics of optimising time-intensive processes, reducing decision-making bias and mitigating all kinds of systematic risks, which is conducive to the enhancement of timeliness of emergency plan generation in highly dynamic environments, the effectiveness of and the Operability. In emergency management practice, government departments need to weigh the costs and benefits of introducing AI. In this paper, we first consider the benefits and budgetary costs of human-computer collaboration between government departments and social organisations, and construct the Stackelberg game base model of AI-HI collaboration for emergency information sharing and emergency plan generation system, and obtain the equilibrium decision-making and optimal benefits of government departments and social organisations. Subsequently, the robustness of the model was enhanced by further extending the base model into three scenarios (customised AI, emergency plan amendment and data trust). The findings suggest that AI investment is not always beneficial and government departments need to choose whether to invest in AI technology based on their own budget. For government departments, customized AI should only be adopted when the marginal cost of AI technology investment is less than a certain critical value, and if the accuracy of an emergency plan is low, it is better to use manual preparation rather than human-computer collaboration followed by correction. For social organisations, the adoption of customised AI is always more beneficial than generic AI, and the amendment of the contingency plan has no impact on the optimal benefit of the social organisation. In addition, government departments should only adopt the data trust model if the benefits of improved digital governance capabilities outweigh the losses of human-computer collaboration.

In this paper, we study a single-machine multi-task scheduling problem with deteriorating job processing times and job rejection options. Two distinct resource allocation functions and position-dependent deterioration effect functions are considered. The objective is to determine the set of rejected jobs, the set of accepted jobs, and the optimal job sequence for the accepted jobs, such that the linear weighted sum of the maximum completion time, the total absolute deviation of completion times, the total rejection cost, and the total resource cost is minimized. Under both resource allocation functions, the problems can be transformed into an assignment problem when the number of rejected jobs is given. We design a polynomial-time algorithm with time complexity of \( O\left( {n}^{4}\right) \), respectively. Finally, numerical experiments are conducted to demonstrate the feasibility and effectiveness of the proposed algorithms.

The multi-state weighted $k/n$ system contains $n$ components where each component has its corresponding weight and a variety of possible states. The system works if and only if the sum of the utilities (the product of weight and state) of working components is at least $k$, a pre-specified value. In view of the missing or invalid sample data in the practical systems, it is necessary to rely on the belief degree of domain experts. Hence, this paper introduces uncertainty theory into the reliability analysis of such systems, and proposes the concept of joint reliability importance in uncertain multi-state weighted $k/n$ systems based on the idea of Birnbaum's joint reliability importance. Assuming that the states of the components in the system are independent uncertain variables, a formula for calculating the joint reliability importance of components in the system is derived and a binary search algorithm is presented, which enriches the basic theoretical system of reliability. Furthermore, the aforementioned results are further extended to multi-state weighted $k/n$ system with uncertain weights, which estimated by experts and characterized as uncertain variables. Similarly, a mathematical model for the joint reliability importance of components in this system is established, along with the corresponding formula and binary search algorithm for calculating the joint reliability importance of components. The results show that, driven by the data of belief degrees, the established model and proposed algorithm can obtain the joint reliability importance measure of system components, and can specifically improve and enhance the overall performance of the system.

In this paper, we study the equilibrium and socially optimal joining behavior of customers with heterogeneous information in a multi-server queueing system with the threshold policy and catastrophe. The informed customers master the queue length and the servers' status, and the uninformed customers do not have any information. A disaster may occur when servers are working. Once a disaster occurs, all customers will be forced to withdraw from the queue, but receive some compensation. We discuss the equilibrium and socially optimal joining behavior of the two types of customers. Besides, we reveal the impact of the informed customers' arrival rate, catastrophe incidence, the threshold policy and the compensation on the customers' joining strategy, the service provider's revenue and the optimal social welfare, and explore the relationship between customers' joining strategies under equilibrium and social optimization, providing a theoretical basis and decision support for the management practices of related multi-server queueing systems.

In order to reduce the transportation risks and logistics costs of power batteries replacement and recycling for new energy vehicle (NEV), a mathematical model for mixed fleet open pickup-delivery vehicle routing problem is proposed by considering factors such as battery classification, customer demands, recycling destinations of waste power batteries, vehicle types, vehicle capacity, and population density along the vehicle routes. The objectives of the model are to minimize total logistics costs and transportation risks. An improved non-dominated sorting genetic algorithm-II (INSGA-II) is designed to solve the model based on its characteristics. Numerical experiments were conducted to verify the efficacy of the proposed approaches. The experimental results indicate that the INSGA-II can scientifically plan routes for electric cargo vehicles and hazardous materials cargo vehicles based on battery classification and dedicated vehicle delivery strategies. The hazardous materials cargo vehicles can avoid densely populated areas and carry out logistics for the recycling of waste NEV power batteries according to factors such as customer coordinates and population density along the vehicle routes. The INSGA-II has been demonstrated to be effective in reducing logistics costs and the potential risks associated with the transportation process of NEV power batteries. The proposed approaches are practicable, scientific and reasonable. The proposed approaches can provide logistics decision-making references for the relevant entities of NEV power batteries.

Most of the existing research on financial restatement detection is based on single-modal information such as financial data and text data, while the effective information among multimodal data has not been fully utilized. This paper innovatively introduces textual and vocal data of earnings conference calls, and uses BERT variants, traditional text analysis and acoustic analysis methods to extract features of each modal to improve the detection performance of financial restatements. Based on 5,212 samples of listed companies in the United States from 2010 to 2023, 112 features covering speech acoustics, speech emotions, text narrative methods, text question and answer situations, and text emotions were extracted. Empirical research has found that introducing multimodal data from earnings conference calls can improve the performance of financial restatement detection. Among multimodal features, considering factors such as the fluency of the management's speech, pitch, emotion, and the way of content narration is more important for restatement detection. In data from different periods, multimodal features have shown stable performance, and in recent years, the effect improvement brought by vocal features has gradually outperformed that of textual features. This research can provide a reference for investors and regulatory authorities to detect financial restatement risks and optimize related decisions.

This paper proposes an integrated fault estimation and fuzzy adaptive fault-tolerant control scheme for a class of output-feedback nonlinear systems with concurrent sensor-actuator failures and full-state constraints. Firstly, a fault compensation mechanism is designed to reconstruct the system's healthy output states, and a fuzzy observer integrated with unknown nonlinear function approximation capabilities is constructed. Secondly, an adaptive law based on the generalized intermediate variable method is developed to enable online estimation of actuator bias faults and dynamically compensate for their impacts. Furthermore, a barrier Lyapunov function is introduced into the backstepping framework to enforce all system states within bounded compact sets, while a command-filtered error compensation mechanism resolves the inherent ``complexity explosion" issue in backstepping design. Finally, numerical simulations validate the effectiveness and superiority of the proposed method.

Important coefficients refer to the small subset of coefficients within the input-output direct input coefficient matrix that have a critical impact on the target variable, representing vital links within the production network. Identifying important coefficients is of great significance for studying the transmission effects within production networks. Addressing the limitation of existing identification methods—which mainly measure coefficient changes under the assumption of uniform variation—this paper proposes a novel important coefficient identification method based on comparative statics analysis(ESDA:Elemental Level of Structural Decomposition Anaysis). By extending Structural Decomposition Analysis(SDA) to the level of individual matrix elements, this method integrates both the actual magnitude of coefficient changes and their marginal impacts to identify important coefficients. It further provides an economically interpretable attribution framework and introduces new classification dimensions for important coefficients. To examine the differences between this new method and existing approaches, this paper, without loss of generality, selects total employment as the target variable and compiles China's employment-occupation input-output tables for 2012 and 2017 for empirical analysis. The results show that, from the perspective of comparative statics analysis, the identification outcomes and distribution patterns of important coefficients differ significantly from those obtained through existing methods.

Incremental innovation and breakthrough innovation serve as the primary avenues for third-party seller development, and which innovation mode to choose is crucial for them to maintain their competitive advantage. Nevertheless, platforms can exploit access to third-party sellers' non-public data to engage in data self-preferencing, whereby platforms imitate sellers' innovations to launch store brand products and compete with them directly, which in turn fundamentally reshapes the innovation mode choice of third-party sellers. This paper constructs a two-stage co-opetition game model involving a third-party seller and a platform, to examine the interaction between the platform's data self-preferencing and third-party seller innovation mode choice. We show that the data self-preferencing of the platform induces a price competition effect, prompting the third-party seller to lower its sales prices in the second period, thereby enhancing social welfare. When the unit production cost of breakthrough innovation is too high or too low, the data self-preferencing of the platform does not influence on the choice of third-party seller's innovation mode. Interestingly, when the unit production cost of breakthrough innovation is moderate and the platform's imitation cost is high, data self-preferencing of the platform instead pushes forward the third-party seller to pursue breakthrough innovation; otherwise, it holds back such innovative pursuits. In addition, data self-preferencing of the platform may not always be the dominant strategy for the platform, prohibition of this practice may unexpectedly diminish social welfare.

To explore how core actors coordinate multiple data sources to drive value co-creation in the manufacturing enterprise service ecosystem based on intelligent interconnected products under digital servitization, this study focuses on the ecosystem characteristics of "manufacturing enterprise leadership, multi-party data collaboration, and data-driven digital services". It then constructs a tripartite evolutionary game model involving manufacturing enterprises, third-party developers, and end consumers. Starting from the sharing and integration of data resources, and considering the non-competitive and replicable characteristics of data resources, this study analyzes the strategic interaction and evolutionary stability among subjects. Through numerical simulation, it is found that the improvement of collaborative benefits and the reduction of data resource sharing losses can significantly enhance the enthusiasm of all parties to participate in value co-creation; after the collaborative benefit coefficient breaks through the critical range, the probability of tripartite collaboration shows a step-like increase; manufacturing enterprises can optimize system stability through dynamic revenue distribution schemes matching data sharing volume (such as gradient sharing ratios) and differentiated incentive policies (such as data contribution reward mechanisms); The fairness of collaborative benefit distribution and the scientificity of incentive policies have a dual-driving effect on the sustainable development of the ecosystem. This study provides a theoretical basis for the design of data governance and collaborative mechanisms in intelligent interconnected product-led service ecosystems.

This paper primarily investigates the output regulation problem of an unstable heat equation with variable coefficients and boundary disturbances. The heat system is described by an uncertain parabolic partial differential equation (PDE) with mixed boundary conditions, where the spatial diffusion coefficient is subject to unknown variations, and the boundary control end is affected by unknown disturbances. The proposed robust control method is composed of linear feedback design and the "Twisting" second-order sliding-mode control algorithm, which are suitably combined and redesigned within an infinite-dimensional controller configuration. Based on the theory of maximal monotone operators, the well-posedness of the closed-loop system (as a class of differential inclusions) is established. Subsequently, by introducing a non-standard Lyapunov functional and assuming that some tuning parameters in the controller satisfy specific inequalities, the global asymptotic stability of the closed-loop system in the Sobolev space is proven, thereby achieving output tracking of the original system to the given signal. Finally, the numerical simulation section of the paper validates the effectiveness of the proposed controller design through simulation experiments.

In this paper, we use the definition of ideal projectors to discuss the computation of multiplication matrices in multivariate ideal interpolation problems. We first use the tool of formal power series rings to describe the closed ($D$-invariant) subspace of interpolation conditions, then we derive theorems for computing the multiplication matrices from the closed subspaces, which avoids the computation of Groebner bases. The results in this paper can be used in the discrete approximation problem of ideal interpolation.