This paper considers a two identical components warm standby repairable system where operating component has c failure modes. Applying the Markov renewal process theory, the Laplace transform and the Laplace-Stieltjes transform, we derive the analytical solutions of the distribution and the mean time to the first system failure, the system availability, and the rate of occurrence of failures of the system. Further, some numerical examples are provided to discuss the influence of system parameters on reliability measures.

This paper proposes an adaptive dynamic event-triggered control strategy for networked nonlinear systems with unknown dynamics and external distur-bances. Firstly, to address the communication resource limitation problem in networked systems and decrease unnecessary data transmission, the adaptive dynamic event-triggered control strategy is established between the controller and actuator channel. The triggering threshold is adjusted by a dynamic variable to conserve network resources. This strategy updates the control signal based on the system performance, which promotes network resource utilization. This approach aims to achieve bounded control of uncertain nonlinear systems and decrease the frequency of control signal updates. Next, an extended state observer is designed to estimate the unmeasurable states and generalized disturbances, including the system nonlinear term and external disturbances. At the same time, the introduction of a tracking differentiator avoids the problem of explosion of complexity when computing the derivative of the desired signal and virtual control laws. Moreover, this paper adopts an extended state observer adjustment technique with fewer parameters, which applies to a wider range of nonlinear system models. The adaptive dynamic eventtriggered control strategy is designed by applying backstepping with the extended state observer and tracking differentiator technique. Finally, based on the Lyapunov stability theory, the established control law can guarantee that all signals of the networked nonlinear systems are uniformly bounded without Zeno behavior.

This paper investigates a class of infinite-dimensional forward-backward stochastic evolution equations (FBSEEs) driven jointly by Brownian motion and a Poisson martingale measure. Under a general framework where the system coefficients depend on the state variable, the adjoint variable, the control variable, and the jump intensity, we establish the well-posedness theory for such FBSEEs with jumps. Based on a control-monotonicity condition, we construct a duality structure between the forward and backward stochastic evolution equations (SEE and BSEE) with jumps. By combining the method of parameter extension and Yosida approximation, we prove the existence and uniqueness of global solutions to the FBSEEs and derive corresponding a priori estimates. Furthermore, the developed theory is applied to an infinite-dimensional linear-quadratic (LQ) stochastic optimal control problem with jumps. By constructing a stochastic Hamiltonian system that satisfies the control-monotonicity condition, we obtain an explicit dual representation of the optimal control.

To address the issues of follower states being unavailable, slow asymptotic convergence, and limited communication resources in achieving consensus tracking for general linear multi-agent systems, this paper proposes a dynamic event-triggered finite-time tracking control algorithm based on a finite-time observer. First, utilizing output information and the Implicit Lyapunov Function method, a finite-time state observer is designed for followers to estimate actual states accurately within a finite time. Second, based on the relative observed states of followers, a distributed dynamic event-triggered finite-time tracking control protocol is developed by incorporating a sign function with fractional power into the control law. This protocol allows followers to update control inputs and broadcast state information to neighbors only when specific triggering conditions are met. By introducing internal dynamic variables into the triggering conditions, the number of triggering events is further reduced, thereby conserving communication resources. Finally, the general linear multi-agent system is proven to achieve finite-time output consensus tracking without Zeno behavior by algebraic graph theory and Lyapunov stability theory. Simulation results validate the effectiveness of the proposed algorithm.

With the acceleration of the digital transformation of the catering industry, users’ online evaluation information has shown explosive growth. Effective analysis of catering evaluation has dual value for consumer decision optimization and business service improvement. This study proposes a comprehensive evaluation method of catering stores based on user comments, which aims to quantify the fuzziness of user preferences and improve the accuracy of evaluation.Firstly, based on user comment data from Meituan and Dianping, the hierarchical evaluation index system including environment, service, taste, price and health is constructed by TF-IDF high-frequency word extraction and LDA topic model mining; Secondly, the multi-dimensional fuzzy evaluation of users is transformed into probabilistic language terms, and the language set representation model including probability distribution is constructed to quantify the uncertainty of comments; Finally, the index weight is calculated by entropy method, and the comprehensive score of catering stores is generated by weighted linear combination. Based on the empirical analysis of 8 catering stores in Jinshui District of Zhengzhou City, the score ranking of the model output is highly consistent with the actual user experience, which verifies its practical value in reducing consumer decision-making costs and guiding merchants to accurately optimize operation strategies.

New quality productivity itself is green productivity, and its key to development lies in improving green total factor productivity. How enterprises can adapt to and mitigate the risks brought by changes in trade policies by improving new quality productivity, especially green total factor productivity, is an urgent problem that needs to be solved. This article explores the relationship between trade policy uncertainty and new quality productivity from the perspective of enterprise green total factor productivity using the DID model, based on the merged data of Chinese industrial and commercial enterprises, customs, pollution, patents, US import tariffs from China from 1998 to 2014, and the Tariff Download Facility database of the WTO. Research has found that: 1) The growth rate of green total factor productivity of enterprises during the inspection period was 4.50%, mainly driven by technological progress.The regional growth rates are: Eastern>Central>Northeast>Western, and the industry dimensions are: High tech>Resource based>Medium tech>Low tech. 2) The significant reduction in trade policy uncertainty has significantly improved the green total factor productivity of enterprises, which is conducive to cultivating and developing new quality productivity. This conclusion remains robust after a series of robustness tests. This promoting effect is more significant in mixed trade, pure general trade, high export density, eastern regions, and high-tech industry enterprises. 3) The scale effect, technology effect, structural effect, and income effect generated by the decrease in trade policy uncertainty all exist. The scale effect is mainly achieved through the ‘quality’ of enterprise exports, while the technology effect is jointly achieved through the ‘quantity’ and ‘quality’ of enterprise patents.

Climate change is one of the major challenges for global green economic development.This paper, from the perspective of climate vulnerability, uses panel data from 150 countries (2010-2020) and combines the Green Solow Model with spatial econometric models to examine the spatial differences, convergence paths, and factors influencing global green economic growth. The results show: (1) Significant regional differences exist in green economic growth, with a strong positive spatial correlation. (2) Spatial convergence tests reveal significant absolute and conditional β convergence, forming five convergence clubs. (3) The factor analysis reveals that climate vulnerability and the total amount of natural resource funds significantly promote the convergence of green economy. In contrast, per capita GDP, per capita income, and the share of goods trade have a significant inhibitory effect on the convergence of green economy. (4) The moderation effect shows that as the level of agricultural development increases, the impact of climate vulnerability on the convergence of green economy gradually weakens. Conversely, as per capita income rises, the influence of climate vulnerability on the convergence of green economy strengthens. This paper sheds light on green economic growth convergence trends under climate vulnerability and offers policy insights to address global development inequality driven by climate change.

For control challenges of the difficulty in accurately modelling the actual unknown nonlinear system, the impact of external disturbances on the operation process, and the uncertain number of simulations and tests required to verify the control performance of the system, a finite-iteration adaptive fuzzy iterative learning control method is proposed to ensure convergence of the controlled system within a finite number of iterations. Firstly, a novel fuzzy system along the iterative domain is established to characterize the original unknown nonlinear system by utilizing historical operation data information of the system. A finite-iteration convergence condition is constructed based on a composite energy function, meanwhile an adaptive fuzzy iterative learning control method is designed and the required number of simulations and tests is determined according to the expected control accuracy requirements of the system, thereby enhancing the efficiency of system development. Additionally, an adaptive iterative learning control algorithm is designed to estimate and compensate for external disturbances during system operation, improving adaptability of the controlled system to the operating environment. Finally, the effectiveness of the proposed control method is verified through two sets of simulation examples and comparative simulations.

This paper investigates the security control problem based on the predictive approach for discrete-time cyber-physical systems (CPS) under false data injection (FDI) attacks and denial of service (DoS) attacks. Firstly, considering the situation that communication networks are subjected to cooperative FDI and DoS attacks, the predictive model is introduced to address the impact of DoS attacks on system performance, and analyzes the upper bound of the predictive cumulative error that affects system stability. Secondly, a predictive scheme incorporating the termination step length is proposed based on event-triggered strategies, and the closed-loop switched system model with the mode characteristics of hybrid cyber attacks is established. Then, the design method of mode-dependent security control strategy is presented by utilizing Lyapunov stability theory and linear matrix inequality (LMI) techniques, and the theoretical feasibility of proposed termination step length prediction algorithm as well as the security performance of the switched system are demonstrated. Finally, the correctness and effectiveness of the proposed security control strategy are verified by simulation examples.

Blockchain technology effectively addresses trust issues in both product sales and waste recycling processes. However, its implementation increases the overall operational costs of the supply chain. How to balance the benefits and costs of blockchain investment has become a critical management challenge in low-carbon E-commerce Closed-Loop Supply Chains (E-CLSCs) under different sales models. To address this issue, this study investigates a low-carbon E-CLSC composed of a single manufacturer, an e-commerce platform, and an online retailer. Based on the manufacturer’s blockchain investment decision and the differences in sales models, the supply chain is categorized into four distinct types. A game-theoretic model is developed to explore the manufacturer.s profit-driven incentives for blockchain adoption and to reveal the underlying mechanisms through which key factors influence the investment decision. The research findings indicate that: (1) An increase in blockchain investment within an appropriate range can enhance the profits of all members as well as the overall system. Meanwhile, while an increase in the sales commission rate (i.e., the degree of price differentiation) within a specific range does not affect the profit growth of e-commerce platforms (or online retailers), it may reduce (or improve) the performance of other members and the overall system. From a waste recycling perspective, increased verification costs improve the effectiveness of waste product recycling when manufacturers delegate the process to e-commerce platforms. (2) When blockchain technology is implemented, manufacturers achieve higher profits under the agency selling model. However, when blockchain investment level, price differentiation, and sales commission rates are relatively low, the reselling model achieves higher profits for both low-carbon technology and e-commerce platforms, while the agency selling model benefits network retailers more, and vice versa. (3) Compared to the absence of blockchain technology, a low level of blockchain investment increases overall system profits across all sales model, but manufacturers earn higher profits in the agency selling model when blockchain investment is relatively low. The increase in low-carbon technology levels and profits for other stakeholders depends on a higher blockchain investment level. (4) Blockchain investment enhances profits for each member and the overall system only when low-carbon technology significantly impacts the demand discount factor or price differentiation, or when low-carbon technology investment costs are relatively low. If the sales commission rate or waste product verification fees are lower, the agency selling model yields higher profits for all members and the overall system.

In recent years, Transformer has achieved remarkable progress in time series forecasting, with numerous improved variants continuously proposed. However, in practical forecasting tasks, the choice of Patch configuration plays a critical role, significantly determining forecasting accuracy and stability. This paper proposes a model averaging approach based on K-fold cross-validation, which aggregates the forecasting of multiple candidate models with different Patch configurations. Empirical studies in electric power deployment and financial investment markets demonstrate that our method consistently outperforms model selection and equal weight averaging, significantly achieving superior accuracy and stability.

Stochastic variance reduction algorithm is an effective method to solve large-scale machine learning, which has been widely concerned by many scholars in recent years. However, how to choose the appropriate step size of such algorithms is still worth studying. In this paper, an adaptive accelerated stochastic variance reduction algorithm based on BB step size is proposed to solve stochastic convex optimization problems. Under the assumption of the strong convexity, it is proved that the algorithm has linear convergence rate. Finally, numerical experiments show the effectiveness and superiority of the new algorithm.

This study employs panel data from 278 Chinese prefecture-level cities (2012-2021) to construct a three-dimensional real estate health index incorporating price, structure, and speed, and applies a panel vector autoregression (PVAR) model to analyze pathways toward healthy real estate development driven by land finance transformation and economic structure optimization. Findings reveal that real estate health is a core driver of economic growth and sustainable land finance, with its enhancement significantly boosting land revenue (coefficient 2.4867, 1% significance) and indirectly stimulating economic growth (coefficient 1.8646, 1% significance). Regional heterogeneity is evident: eastern regions leverage a ”land finance + innovation industries” dual-driven mechanism, while western and northeastern areas require revitalizing idle land reserves and intelligent upgrades to activate resources. Industrial structure differences further shape transition trajectories)service-oriented cities should adopt ”land finance + services” synergy, whereas industrial cities need to strengthen ”land finance + manufacturing upgrading” integration. The study proposes regional differentiated policies, heterogeneous industrial coordination, technology-driven innovation, and institutional reforms to address the ”real estate dependence-fiscal fragility-economic imbalance” cycle, offering theoretical and practical insights for sustainable urban development.

In this paper a bilateral boundary control strategy is developed based on the infinite-dimensional backstepping method for a linear $(1+1+1)\times(1+1+1)$ one-dimensional hyperbolic partial differential equation system in which one state exhibits zero transport speed. The proposed approach ensures the global exponential stability of the closed-loop system in the $L^2$ norm. Addressing the limitation that traditional backstepping fails in the presence of zero transport speed (potentially leading to unbounded controller gains), the Volterra transformation is introduced only in the subsystem with nonzero transport speed, while the zero transport speed state remains unchanged. The state with nonzero transport speed is treated as an external input to the subsystem with zero transport speed, thereby guaranteeing its input-to-state stability. Bilateral boundary control implies that actuators are installed at both ends of the spatial domain. Since the actuators act on both boundaries, the existing Lyapunov functionals are not directly applicable. To overcome this, a modified quadratic Lyapunov functional is constructed to rigorously prove the exponential stability of the target system. Numerical simulation results further verify the effectiveness of the proposed control strategy.

In view of the phenomenon of credit alienation in ride-hailing service, this study constructs a transaction game matrix between passengers and a ridehailing platform based on the evolutionary game theory, establishes a dynamic model of the behavioral evolution of the two players to the game, and analyzes the evolutionary stabilization strategies of the two players’ trading strategies. The results show that the two sides of the game present a variety of evolutionary stabilization strategies under different parameter conditions; appropriate rewards on the platform for the passengers who trade honestly help to create an incentive-compatible effect, which encourages both parties to choose the strategy of trading honestly; however, when the rewards are too high or too low, a party participant can choose a dishonest trading strategy; worse still, when the rewards are neither effective in reducing platform costs nor significantly increasing passenger revenues, both sides turn to dishonest trading tactics. By simulation analysis, this paper not only visualizes the evolution paths of the two sides of the game, but also proposes management insights to promote the honest trading in the ride-hailing market, which provides theoretical support and practical guidance for optimizing the quality of ride-hailing services.

A personalized driver-passenger matching decision-making method considering passenger’s behavior heterogeneity and no-show incidents is proposed, to address the common issues of passenger behavioral heterogeneity and frequent noshows in ride-hailing service. Firstly, to account for the diverse characteristics of passengers in terms of age, occupation, and income, passengers are classified using the Gradient Boosting Decision Tree (GBDT) algorithm based on the historical data of trip behavior. Secondly, the psychological expectations and perceptions of both drivers and passengers are considered, and comprehensive prospect values are calculated based on the prospect theory between drivers and passengers. Furthermore, to address the frequent cancellation behavior of passengers during the vehicle reservation process on the ride-hailing platform, a cancellation probability function for passengers is constructed to calculate the no-show probability. Based on this, a multi-objective optimization model for personalized driver-passenger matching is established, and the optimal driver-passenger matching results are obtained through model solving. Finally, the effectiveness and feasibility of the proposed method are further validated through a case study of passengers booking vehicles on a ridehailing platform.

The accurate estimation of market volatility is regarded as crucial for risk management and investment decision-making. Traditional methods, such as the Exponentially Weighted Moving Average (EWMA) and the Simple Moving Average (SMA), have been widely employed in practice but are constrained by their reliance on fixed weighting mechanisms, which fail to capture the complex and dynamic nature of financial markets. To address these limitations, an Image-Enhanced Weighted Moving Average (IWMA) method based on deep learning is proposed in this study. General price patterns are extracted from market images using a residual neural network (ResNet), and these patterns are transformed into dynamic importance weights through the generation of two types of saliency maps. These dynamic weights are subsequently incorporated into the volatility calculation, allowing the weighting mechanism to be adjusted flexibly and enabling more accurate volatility estimations. An empirical analysis is conducted using data from 508 indices in China’s A-share market, where the performance of the IWMA method is compared against the traditional EWMA and SMA methods across various historical windows and forecast horizons. It is demonstrated that the IWMA method significantly outperforms its traditional counterparts, with the ResNet34-supported IWMA achieving the lowest root mean square error (RMSE) and capturing local market dynamics effectively. These results suggest that the IWMA method offers superior performance in integrating the intricate relationship between price trends and volatility, thereby providing new insights into the underlying drivers of market fluctuations and presenting a novel framework for volatility modeling.

Local stationarity is crucial for understanding and analyzing the dynamic changes in spatiotemporal data. Most existing studies focus on kernel regression estimation for locally stationary processes with a single-dimensional covariate in either time or space, which performs poorly when dealing with complex and varying spatiotemporal data. Therefore, this paper extends the concept of local stationarity to spatiotemporal processes, while also considering the influence of multidimensional covariates. It proposes a nonparametric model based on local linear estimation, allowing for a more comprehensive capture and analysis of complex nonstationary spatiotemporal data. Under relatively weak conditions, the consistency and asymptotic normality of the estimator are established. Moreover, simulations and empirical studies confirm the effectiveness and applicability of this method in finite sample conditions.

China has introduced a large-scale equipment upgrade policy that can renovate livestock manure collection and processing facilities. However, the impact of this policy on manure management has not yet been explored in existing research. Additionally, there is a gap in the analysis of refined market strategies regarding the collaboration between third-party companies (TPCs) and small to medium-sized livestock farmers (SMS-LFs).To address these issues, this paper constructs an evolutionary game-theoretic model that examines the "equipment upgrade" strategy of SMS-LFs and the "classified pricing" strategy of TPCs. The study incorporates prospect theory and mental accounting theory (PT-MA) to explore how farmers decide whether to invest in equipment upgrades, considering their risk preferences. By combining the expected utility function with the value perception function and adhering to the principle of "those who invest receive the subsidies", the paper analyzes which party would benefit more from implementing the upgrades in the context of effective policy execution. The study conducts simulation analyses of strategies and summarizes the systemic archetypes for upgrading manure collection and processing facilities. The findings are as follows: 1. Providing large-scale equipment upgrade subsidies to TPCs, allowing them to enhance the manure collection and processing facilities for SMS-LFs, is the most effective strategy for advancing the policy. 2. TPCs should actively implement a "classified pricing" strategy. 3. The large-scale renewal and upgrading of livestock manure collection and treatment systems exemplify a "Limits to Growth" archetype. The solution is removing constraints from balancing loops through a policy mechanism allowing TPCs to obtain equipment renewal subsidies. This subsidy mechanism encourages TPCs to invest in upgrading manure collection and treatment facilities for SMS-LFs. Subsequently, these companies can implement a classified charging strategy to secure higher-quality manure-based raw materials. This creates an incentive mechanism that motivates SMS-LFs to increase their investments in manure treatment. Ultimately, this virtuous cycle enhances the proportion of subsidies received by SMS-LFs through improved environmental performance.

Existing methods for resolving conflicts among experts in group decision making overlook the multiple social relationships among experts and the coupling relationships between multiple networks. This paper innovatively proposes a conflict elimination model based on trust-conflict multiplex network, aiming to effectively facilitate the elimination of conflicts among experts. Firstly, the sparse representation method is used to calculate the conflict degree among experts, and the concept of the trust-conflict multiplex networks and its construction method is defined. Secondly, the Uninorm operator is utilized to perform a nonlinear combination of weights in the trust layer and the conflict layer to calculate the comprehensive weight of experts. On this basis, this paper designs an evolution algorithm for conflict relationships and a development algorithm for trust relationships to simulate the interaction between the trust network and the conflict network. Finally, a case study demonstrates that the proposed model can effectively detect and eliminate conflict relationships among group members. Compared to traditional methods, the method proposed in this paper demonstrates significant advantages in handling the diverse social relationships among decision-makers and the coupling relationships between dual-layer networks.

In this paper, we study the exact controllability of strongly degenerate wave equations in one dimension, as the control acts on the degenerate boundary. By using the spectral analysis method, the hidden regularity and observability inequalities of the dual systems are established. The exact controllability of the controlled system is obtained by means of the equivalence between observability and controllability. Moreover, an explicit expression for the controllability time is given.

The strong stochasticity, nonlinearity, and non-stationarity inherent in financial markets pose significant challenges to stock price prediction. This paper proposes a stock price prediction model based on the Sample Convolutional Interaction Network (SCINet), which is structured with multiple SCI-Blocks arranged in a binary tree architecture. By splitting financial time series into even and odd subsequences and employing distinct convolutional kernels for feature extraction, the model effectively captures local patterns in stock price time series data, thereby enhancing prediction accuracy. Leveraging the price variation information derived from the SCINet prediction model, a hierarchical asset allocation strategy is designed and optimized using Particle Swarm Optimization (PSO) to maximize investment returns while mitigating risks through adaptive threshold adjustments. Empirical studies are conducted on datasets including the S&P 500 Index, Shanghai Stock Exchange Composite Index (SSEC), Shenzhen Stock Exchange Component Index (SZI), and eight constituent stocks of the S&P 500. The results demonstrate that the SCINet-based prediction model outperforms SVR!CNN, LSTM, and CNNLSTM models in accurately capturing price dynamics. Furthermore, the proposed asset allocation strategy informed by these predictions achieves superior returns, validating the effectiveness of the SCINet framework and its integrated approach to stock price forecasting and risk-aware asset allocation.

This paper proposes a new criterion for forecasting accuracy, a groupindividual effective measure of forecasting , by combining the continuous interval ordered weighted averaging (C-OWA) operator with the individual and group regret values. The interval values are transformed into the real numbers with parameters by using the new criterion and the C-OWA operator, and the positive ideal point sequences and the negative ideal point sequences are introduced. Furthermore, an interval combination forecasting model is put forward based on VIKOR method and the group-individual forecasting effective measure of forecasting. For the new model, some new concepts are defined, including a non-inferior combination forecasting method, a superior combination forecasting method, and a redundant combination forecasting method. Finally, through case analysis, the rationality and effectiveness of the proposed interval combination forecasting model are demonstrated, and sensitivity analysis of the parameters is conducted.

This study investigates the coexistence of recycling price competition and recycling technology cooperation in the closed-loop supply chain of retired power battery recycling. A noncooperative-cooperative biform game model is developed to examine both the recycling price competition among electric vehicle manufacturers, battery producers, and recyclers, as well as the recycling technology cooperation between battery producers and recyclers. By solving this biform game model, the optimal recycling prices and profits for all three participants are derived, along with the optimal cooperation strategies between battery producers and recyclers, including cost-sharing proportions and recycling technology levels. Furthermore, the impact of key parameters)such as the resale price of retired batteries and the ladder utilization rate)on optimal strategies and profits is analyzed. The results reveal that intense competition among recycling channels reduces the overall profit of the closed-loop supply chain. Additionally, when battery producers and recyclers engage in recycling technology cooperation, it not only significantly increases the recycling volume but also enhances the profits of both parties. Moreover, as investment difficulty rises, the recycling technology level declines, leading to a higher proportion of cost-sharing for recycling technology. The recycling price and profits of battery producers increase with the unit revenue from regenerated materials. Lastly, while recyclers.recycling prices decrease as the ladder utilization rate increases, their profits exhibit the opposite trend. This study provides valuable insights into pricing strategies and technology cooperation in power battery recycling, offering theoretical support for the sustainable development of the battery recycling supply chain.

Exchange-Traded Funds (ETFs), as a burgeoning tool for index-based investment, have yet to be fully understood regarding their feedback effects from capital markets to the real economy and their influence on corporate behavior. This study examines A-share listed companies from 2011 to 2023, employing a fixed-effects model to empirically test how ETF ownership impacts corporate misconduct. The findings indicate that ETF ownership significantly curtails corporate violations, primarily due to ETFs exerting both resource and governance effects. Further heterogeneity tests reveal that the inhibitory effect of ETF ownership on corporate misconduct is more pronounced in samples with lower information asymmetry, more severe agency conflicts, and non-state-owned enterprises. This research aims to deepen the understanding of the real economic effects of ETF ownership and provides robust reference points for regulators in formulating policies to standardize the market behavior of institutional investors.

The order batching problem (OBP) is often studied to improve the picking efficiency of the robotic mobile fulfillment system (RMFS). Most studies of OBP in RMFS focus on the robots’ picking efficiency as the optimization objective and ignore the negative physiological impact of the pickers caused by the high-intensity picking operation. Ignoring human-robot collaboration may result in the decline of the comprehensive picking efficiency of RMFS. Thus, this paper proposed the order batching strategy by considering both pickers’ energy expenditure and robots’ picking efficiency. This paper evaluated the tasks and postures and model the expressions of the pickers’ energy expenditure. After that, the dual-objective mixed-integer optimization model is formulated to minimize pickers’ energy expenditure and robots’ picking costs. The non-dominated sorting genetic algorithm (NSGA-II) is improved by providing the order batching strategy and the shelve selection strategy. To verify the effectiveness and efficiency of the proposed model and algorithm, the numerical experiments are conducted under seven different instances. The experimental results show that the improved NSGA-II algorithm can find the batching solutions with lower robots’ picking costs and pickers’ energy expenditure. The batch capacity is negatively correlated with the robots’ picking costs and pickers’ energy expenditure. The item storage strategy based on items’ similarity and energy expenditure performs better in improving comprehensive picking efficiency. The order batching strategy proposed in this paper provides valuable enlightenment for decision-makers for planning, design, and scheduling management in RMFS.

For many industrial products, the degradation processes exhibit the randomness of initial values, the dependency between multiple performance characteristics, and the heterogeneity of degradation rate and its correlation with the initial values. To this end, this article proposes a multivariate degradation model based on the Wiener process, along with a methodology of reliability analysis. Several statistical properties are derived from this model, and an expression for the lifetime distribution is provided. Additionally, an ExpectationMaximization (EM) algorithm is developed for parameter estimation, and its effectiveness is verified through Monte Carlo simulation experiments. Finally, the applicability of the theoretical framework is demonstrated through the analysis of the multi-dimensional degradation data from transceivers.

In recent years, China has actively pursued energy transition. As a key engine of economic growth, the industrial sector, which is also a major consumer of traditional energy, warrants examination regarding its susceptibility to energy transition impacts. This paper leverages China’s New Energy Demonstration City (NEDC) pilot policy as a quasi-natural experiment and employs a Differencesin-Differences (DID) model with panel data from 155 prefecture-level cities from 2011 to 2021 to empirically examine the effects of energy transition on industrialization and its transmission mechanisms. The findings reveal that energy transition has an overall insignificant impact on industrialization levels but significantly enhances industrial benefits, a conclusion that remains robust across a series of rigorous tests. The mechanism analysis shows that the heterogeneous effect of green credit mechanism, the employment hedging and benefit superposition of enterprise innovation mechanism and government financial direct expenditure mechanism explain the above empirical results. Further research demonstrates that industrial scale strengthens cities’ resilience to energy transition shocks but diminishes the extent of industrial benefits improvement. Unlike industrial scale, expanding energy demand enhances the stability of industrial employment, while economic agglomeration and shifts in employment structure exacerbate challenges to maintaining stable industrialization levels during energy transition. The study concludes that energy transition and industrial development exhibit strong compatibility. It emphasizes the need for region-specific energy transition policies and provides empirical evidence and policy insights for simultaneously advancing energy transition and high-quality industrial development in the new development stage.

In-kind donation is one of the important sources of relief supplies when facing various emergency events. However, once lack of suitable coordination and management, in-kind donations will pile up and prevent the delivery of other relief supplies, which leads to‘ material convergence’ and brings troubles to relief operations. An effcient approach to solve this problem is to screen and filter the in-kind donations. However, there lacks studies in relevant literature on how to process the screening and filtering, and how many labors are in need. Hence, in this paper, we incorporate the considerations of capacity building of relief organizations into the screening and filtering of in-kind donations, and simulate the process based on queue theory. The optimal strategy is then determined by optimizing the effectiveness of the relief operation based on deprivation cost. We consider three situations including no screening and filtering, suffcient capacity and insuffcient capacity. Through constructing mathematical programming models and conducting numerical experiment, we find that in most situations screening and filtering will increase the effectiveness of relief operations, the relief organizations will first guarantee their screening and filtering capabilities when the budget is suffcient, but they will also consider the balance of budget allocation.

Data is becoming an increasingly important factor of production in the digital economy. However, the controversy surrounding data property rights is hindering the process of data factor marketization, making it difficult to fully release the value of data factors. The article explores a scheme for allocating public data property rights in scenarios where shared property rights are involved. It does so by analyzing the mechanism of public data property rights allocation and establishing a shared property rights allocation framework based on federated blockchain and game theory. Using adverse drug reaction monitoring data as an example, this article demonstrates how federated blockchain characteristics can ensure fairness and auditability in the property right allocation process. The article achieves quantitative analysis of the property right share of multiple subjects in the property right allocation process through the evolutionary game and cooperative game. The research results indicate that the framework and scheme for allocating property rights not only maximize the rights and interests of multiple parties during the definition process, but also analyze the allocation of property rights among multiple parties during the allocation process using empirical quantitative relationships.

The rational allocation of basic education resources is key to achieving educational equity and quality improvement, while the enhancement of human capital is the core driving force for building a strong education nation. Based on provincial annual panel data, this paper employs fixed-effects models and differencein-differences (DID) models to verify the impact of the accessibility, quality, and balance of basic education resources on human capital accumulation. The results show that the accessibility of basic education resources has a long-term positive impact on the stock of human capital, with the most significant effect observed during the junior high school stage. The quality of basic education resources has stagespecific impacts on the quality of human capital. Mechanism analysis indicates that the quality of education resources attracts more high-level population inflows, thereby further enhancing the quality of regional human capital. The balance of basic education resources plays a positive role in improving the structure of human capital, especially during the junior high school stage. In addition, the DID model further confirms the positive impact of education balance development policies on human capital accumulation.

Cross border IP (intellectual property) authorization for enterprises has become one of the important means to enhance core competitiveness and realize technological monetization. Parallel imports, commonly referred to as "grey markets," involve the act of exporting genuine products from one country to another without authorization from the intellectual property owner. This article explores whether companies adopt blockchain technology to curb parallel import behavior in gray markets when authorizing cross-border IP by constructing a Stackelberg game model under different market structures. Research has found that in a gray market, when consumers have lower trust in authorized manufacturers’ products and are more sensitive to time costs, both parties are willing to adopt blockchain, which can better curb the gray market. When there is no gray market, adopting blockchain can curb the risk of false reporting by authorized manufacturers, but it can increase their sales and price, while also increasing the authorization fee for IP authorized manufacturers.

This study investigates the effect of the digital economy on the high-quality development of the tourism industry, with a focus on its regional heterogeneity and spatial spillover effects. Using panel data from Chinese provinces spanning the period from 2015 to 2021, the study constructs indicators for the digital economy and high-quality tourism development, with the entropy method employed for indicator weighting. Through the application of a two-way fixed effects model, the findings indicate that the digital economy exerts a significant positive effect on the development of the tourism sector. Regional heterogeneity analysis reveals that digital technologies are more effectively integrated into the tourism industry in the eastern regions, whereas their application may be constrained in the remote areas of central and western regions. Moreover, analyses using the Moran’s I index and the Spatial Durbin model highlight that the digital economy has a significant positive indirect effect and spatial spillover effects on the high-quality development of tourism. The empirical findings of this study provide valuable evidence for enhancing regional digitalization and promoting high-quality development in the tourism industry.

The existing literature has extensively demonstrated the significant value of technology adoption, but the interrelationship between fairness concerns, technology adoption, and their effects under asymmetric information has not been adequately studied. Under the background of asymmetric information and manufacturer's fairness concerns, we construct two game models before and after manufacturer adopts innovative technology. The impact of manufacturer's technology adoption decisions, the design of licensing contracts for innovative company, and equity concerns were investigated. The findings indicate that, regardless of the presence of fairness concerns, the manufacturer is motivated to adopt innovative technology and experience a “market expansion effect” only when their retained profits and the technological disparity with innovative company meets certain conditions. Moreover, the optimal licensing contract design for innovative company under asymmetric information includes both fixed fees and two-part tariff. Additionally, manufacturer's fairness concerns can increase their motivation for technology adoption, enhance social welfare, facilitate the redistribution of profits between manufacturer and innovative company, and mitigate the inequality in profit distribution between the two parties.

Faced with the thorny issues of difficulty in achieving true randomization, sample selection bias, and variable confusion bias in quasi natural experiments, this paper proposes a new method for identifying causal relationships based on counterfactual reasoning, data intervention, and data augmentation. This method combines data-driven and logical reasoning to create a new paradigm for multimodal high-dimensional causal inference in the big data environment. By combining the unique characteristics of nonlinear and non-stationary financial markets, we apply them to financial asset pricing and conduct in-depth research on the application of causal representation learning in adaptive stability prediction and financial management decision-making. We have developed a causal factor pricing method based on counterfactual reasoning, which achieves reliable prediction of financial asset returns through joint optimization under the causal mechanism. This provides theoretical support and technical ideas for improving out of sample generalization performance, promoting the practical implementation and sustainable development of "artificial intelligence+" in the financial field.

In the context of global climate warming, China is experiencing increasingly frequent extreme high-temperature events, which leads to a rising trend towards climate risks and severe losses of crops. In this paper, the deep learning algorithm N-BEATS model is used to iteratively forecast the future evolution trend of temperatures. Based on the intensity and duration of extreme high temperatures during a day, a novel extreme heat index and corresponding weather derivatives contracts are constructed. These contracts are used to hedge the extreme weather risks faced by crops. The results demonstrate that the proposed model significantly improves the prediction accuracy of future temperature changes, and the newly developed weather derivatives provide an effective hedging tool for extreme high-temperature risks.

In the platform ecosystem, platform enterprises and complementary enterprises work together to improve the efficiency of value co-creation. Because the level of effort of both sides can not be completely observed by each other, it will lead to bilateral moral hazard. Therefore, considering the factors such as platform governance ability, the external effect of the network, the embedding degree of the relationship, fairness preference, risk preference and so on, a multi-task principal-agent model is constructed, which is composed of platform enterprises and complementary enterprises under favorable and unfair conditions. The formal contract to maximize the value of both parties is explored. And the effects of various factors on the optimal effort level of the complementary enterprise, the optimal effort level of the platform enterprise to improve governance ability and the optimal income sharing ratio are analyzed. The conclusions of the study are as follows: the synergistic value, the degree of relationship embedding, and the enhancement of the pride intensity of complementary enterprises brought about by external network effects will increase the optimal effort level and optimal profit sharing ratio of complementary enterprises, while the enhancement of sympathy and jealousy intensity of complementary enterprises will reduce their optimal effort level and optimal profit sharing ratio. The research can help solve the bilateral moral hazard problem between platform enterprises and complementary enterprises, reduce the negative impact of information asymmetry on the platform ecosystem, and thus improve the operational efficiency of the entire platform ecosystem.

Determining the effective and efficient lower bounds of the discrepancy criterion in uniform designs is a critical aspect of experimental designs. In this work, we investigate the issue of lower bounds of the newly proposed absolute discrepancy. The lower bound of absolute discrepancy on symmetric multi-level designs is presented and some new sharp lower bounds of this criterion on two- and three-level designs are also displayed. These lower bounds can serve as evaluation metrics for design uniformity and act as benchmarks in the construction of uniform designs. The construction of uniform designs based on absolute discrepancy is also discussed.

The carbon quota allocation mechanism is one of the core institutional designs for constructing a carbon emission trading system. On the one hand, free allocation may weaken market efficiency and distributive fairness; on the other hand, a direct transition to a full-auction mechanism could lead to a sudden surge in corporate compliance costs and exacerbate transformation risks. However, existing research has been mostly confined to the binary opposition paradigm of "free allocation-full auction," and there is a lack of systematic research on institutional designs under incremental transformation paths, resulting in a theoretical gap for exploring phased transformation paths in China's carbon market. This study breaks through the traditional analytical framework by innovatively constructing a multi-market linked carbon emission trading model that integrates the primary carbon market, secondary carbon market, product market, and intertemporal corporate strategic adjustments into a unified system. It simulates the impacts of three quota allocation methods—free allocation, consignment auction, and auction—under different paid allocation ratios on carbon market effectiveness. Simulation research based on Chinese data shows that in the early stage of carbon market construction, adopting a small-scale consignment auction can activate market vitality while controlling transformation costs, but caution is needed regarding incentive distortion effects from revenue return mechanisms as policy stringency increases. As the carbon emission trading system matures, a high-proportion auction remains the optimal allocation method, as it significantly promotes long-term technological progress and carbon reduction. This finding provides a critical path for the incremental transformation of carbon emission trading systems: through dynamically adaptive institutional design, it ensures market stability during the transition period while achieving deep emission reduction goals in the maturity stage.

Optimizing the business environment has been a significant initiative continuously promoted by our country in recent years, aiming to drive the rapid development of new productive forces by enhancing resource allocation and stimulating market innovation vitality. This study takes the 2021 business environment innovation pilot city construction as a quasi-natural experiment to investigate the impact of the business environment on corporate innovation. The findings reveal that the pilot policies effectively stimulate local corporate innovation, and the promotion effect is more prominent among firms that are non-state-owned, with high institutional transaction costs, low degree of digital transformation, and high degree of development of the factor market in the host city. Mechanism analysis shows that the policies promote innovation through increasing market competition, alleviating financing constraints, and activating intellectual property trading markets in the pilot cities. Further results indicate that the promoting effect of the business environment on corporate innovation is mainly reflected in the "intensive margin" rather than the "extensive margin," meaning that enterprises with prior patent applications are better positioned to leverage policy opportunities to enhance their innovation output. These findings contribute to the understanding of how the business environment influences micro-level enterprises and provide valuable insights for future policy optimization.