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.

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.

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.

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.

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.

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.

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.

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.

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.

The water rights trading mechanism is a market-oriented institutional arrangement for promoting the construction of water ecological civilization and realizing national water-saving actions. Exploring how it affects the green transformation decisions of high water consuming enterprises and promotes green technology innovation is of great significance for practicing high-quality development. Our article establishes a quasi-natural experiment and explores the impact and mechanism of the water rights trading policy on green innovation based on the samples of high water-consuming enterprises. The results show that the water rights trading mechanism can drive the "incremental improvement" of green technology innovation in high water-consuming enterprises, and this conclusion is still reliable and effective after robustness testing. Heterogeneity analysis shows that water rights trading mechanisms are more conducive to stimulating green technology innovation in state-owned, large, western regions,regions with low water resource endowment and highly competitive enterprises. The mechanism indicates that the water rights trading mechanism mainly enhances green technology innovation through channels such as increasing executives' green attention and R&D investment in high water-consuming enterprises;institutional investors' shareholding has promoted enterprises' green innovation, but the equity financing cost has a hindering effect. The results provide useful insights for promoting water rights trading mechanisms and enterprises' green innovation.

Currently, most studies on predator-prey models with slow-fast effect focus on temporal dynamics, while relatively fewer investigations address the influence of slow-fast effect on spatial pattern evolution in predator-prey systems. This paper establishes a slow-fast modified Leslie-Gower predator-prey model incorporating time-delay and reaction-diffusion. We investigate the conditions for Turing instability and Hopf bifurcation, derive analytical expressions for bifurcation thresholds using time-delay as the bifurcation parameter, and explore how slow-fast effect impacts spatial patterns and stability region. The study reveals that reaction-diffusion drives Turing instability, with different diffusion coefficients inducing distinct spatial patterns. Time-delay can trigger Hopf bifurcation near bifurcation threshold. Both slow-fast effect and time-delay significantly alter pattern morphology, generating rich spatial configurations. Furthermore, slow-fast effect nonlinearly modulate stability region of system. These findings demonstrate that slow-fast effect plays a crucial role in spatial pattern evolution and stability regulation in predator-prey systems.

Based on the Chinese herbal medicine supply chain where the medicinal farmers have the adulteration behavior, this paper studies the governance strategy selection of Chinese herb enterprises against the adulteration behavior of medicine farmers under the quality sampling inspection mechanism. We construct the supply chain Stackelberg game models for four scenarios, namely, without governance strategy, return penalty governance strategy, liability cost-sharing governance strategy and combined governance strategy. The operational differences in the Chinese herbal medicine supply chain under different governance strategies are comparatively analyzed. Governance strategy choices for Chinese herb enterprises are revealed. The findings of this paper are as follows: Under the return penalty governance strategy and combined governance strategy, when the unit penalty is higher than a certain level, it can effectively eliminate the adulteration of medicinal farmers. In contrast, the liability cost-sharing governance strategy is unable to eliminate adulteration. The increase in the return penalty parameter benefits the governance of adulteration and the profits of Chinese herb enterprises, but not the profits of medicinal farmers. The increase in the liability cost-sharing parameter benefits the profits of both parties, but not the governance of adulteration. The medicinal farmers prefer the Chinese herb enterprises to choose the liability cost-sharing governance strategy, and they least prefer the Chinese herb enterprises to choose the return penalty governance strategy. From the perspective of governing adulteration, the Chinese herb enterprises should choose the return penalty governance strategy. From the perspective of gaining profits, the Chinese herb enterprises should choose the combined governance strategy and set a higher unit penalty.

We discuss the maximum information subspace of square integrable functional data in L² in order to find a suitable m dimensional projection subspace, which reserves the most information of the original functional data in all of the same dimension subspace to achieve dimensionality reduction while preserving important information. In this paper, the existence of the subspace is proved by the convex optimization method, it was further proved that the subspace with the eigenfunctions corresponding to the first $m$ largest eigenvalues of the sample covariance operator of the functional data as base functions is the maximum information subspace. Then, from the perspective of information reconstruction, it is proved that the subspace is the most powerful space for reconstructing the original functional data. Finally, the 2 dimensional maximum information subspace for functional data of 35 weather stations in Canada is studied. It is found that the cluster analysis results in this space are consistent with those based on discrete data. This shows that projecting functional data onto the maximum information subspace not only dynamically presents the overall characteristics of each category from the perspective of the function, but also ensures the reliability of the clustering results by retaining the maximum information of the original data.

In response to the fact that existing environmental performance evaluation methods are mostly based on the optimistic frontier constituted by efficient decision-making units, this study introduces the concept of double frontier evaluation into the directional SBM model, proposes a new double frontier directional SBM model considering undesirable output, and develops its super efficiency expression to improve the model's ability to distinguish decision-making units. This method evaluates environmental performance from both optimistic and pessimistic frontier perspectives, while simultaneously accounting for potential biases introduced by improvement directions and slacks, effectively strengthening the recognition of the evaluation results among all participants. On this basis, a global Malmquist index satisfying the circular test based on the double frontier directional SBM model is constructed to accurately measure the dynamic evolution of environmental performance across provinces in China from 2011 to 2021. The results indicate that China's green total factor productivity shows a fluctuating upward trend, with technological progress identified as the principal driver. Significant regional heterogeneity is observed in technological efficiency changes among different economic zones. This study is important for the continuous improvement of green total factor productivity in regions according to local conditions, as well as for the orderly advancement of the carbon peaking and carbon neutrality goals.

Under the carbon cap-and-trade mechanism, in order to solve the problem of renewable energy cooperative investment strategy, this paper constructs a two-level power supply chain composed of power generators and electricity sellers, considers the intermittently of renewable energy, and studies the problem of renewable energy investment decision-making between power generators and electricity sellers under three different cooperation modes: no cooperation, semi-cooperation and full cooperation. Through the analysis of the equilibrium solution, the following main conclusions are drawn: (1) When the carbon emission per unit of traditional energy power is small, the total renewable energy investment of the supply chain in the non-cooperation model is the largest, while when the carbon emission per unit of traditional energy power is large, the total renewable energy investment in the supply chain in the semi cooperation model is the largest; (2) The supply chain profit under the complete cooperation model is higher, the supply chain profit under the semi cooperation model is lower, and the supply chain profit under the non-cooperation model is the lowest; (3) The increase in carbon emissions per unit of power from traditional energy sources will increase the amount of renewable energy investment and electricity prices, and reduce the electricity demand and total supply chain profits when renewable energy is intermittent.

The rapid development of the e-commerce economy and the upgrading of eco-consumption demand have made e-commerce platforms one of the main driving forces for the sale of green products, which also brings about a diversification of logistics service strategies, along with some new challenges such as delivery delays.Due to uncertain market environment caused by intense competition, there is insufficient sample data to infer demand for new green products.Therefore, considering the consumers' perception of the time difference between the actual and promised delivery time, uncertainty theory is applied to study the scenario selection problem under four combination scenarios consisting of different e-commerce sales modes (resale mode or agency mode) and logistics service strategies (logistics outsourcing or logistics sharing) in the green supply chain. It is assumed that supply chain members seek to maximize profits under uncertain demand with a certain confidence level, from which four Stackelberg decentralized game models are constructed.Furthermore, an effective delivery time reward-penalty mechanism for non-self-supporting logistics strategies is designed.The results show that the green investment cost coefficient, the maximum delivery cost and the promised delivery time have a negative impact on supply chain behavioral performance.Moreover, the logistics sharing strategy outperforms the logistics outsourcing strategy, and the win-win-win equilibrium can be achieved under the logistics outsourcing strategy within a certain range of commission rate and decision-maker confidence level in different e-commerce sales modes.More importantly, the delivery time reward-penalty mechanism has a positive impact on supply chain members and society as a whole, and the relationship between the optimal profitability of the actual incentivized party and the promised delivery time depends on the incentive strength when consumers and the product owner implement equal reward-penalty incentives for delivery time.

In the context of information asymmetry, how to stimulate third-party logistics service providers to provide the optimal logistics service level is the key issue which restricts the development of e-commerce enterprises. Based on above, according to the multi-agent problem on the logistics outsourcing for e-commerce enterprises and the double moral hazard problem of hidden effort for e-commerce enterprises and third-party logistics service providers, combining the specific features of logistics outsourcing activities for e-commerce enterprises, the principal-agent theory was adopted to establish an incentive contract model considering consumer behavior (i.e. consumer preference, consumer evaluation and consumer refund). Results show: 1) Guided by consumer behavior, e-commerce enterprises and third-party logistics service providers can effectively mitigate moral hazards stemming from their own limitations in capabilities and costs. 2) With the increase in the benefit-sharing coefficient and consumers' preference for logistics services, the effort of third-party logistics service providers will be improved, leading to a reduction in the cost ratio associated with discrepancies in consumer evaluations. 3) E-commerce enterprises do not intensify their cooperation and supervision efforts solely because of revenue sharing. However, the sensitivity of consumers to logistics service levels drives e-commerce enterprises to enhance their cooperation and supervision efforts.

This paper considers the supply interruption of a supply chain composed by two suppliers and one retailer, where one supplier is an integrated supply and marketing supplier and the other supplier is a pure supplier, and the retailer makes replenishment from the latter supplier and makes and emergency order from the former when the supply is interrupted. For this system, we mainly investigate the retail price decision and the emergency replenishment from the supply and retailing integrated supplier in the case of a supply interruption with a random end time from the second supplier. Based on maximizing the benefits of each member in the supply chain, we establish an optimization model, and its solution is obtained via a theoretical analysis which gives the optimal decision for each member of the supply chain. Some numerical experiments are made which gives the impact analysis of main parameters on the optimal decision of each member of the supply chain and their benefits for the supply interruption period.

This article aims to improve estimated precision of total number of the population and net census error by means of triple system estimator on the basis of the census list, post-enumeration survey list, and administrative record list. In order to realize this goal, the triple system estimator and its related questions are studied through mathematical model. The research shows that the triple system estimator can not be constructed and used in the population directly; the triple system estimator is a biased estimator, its sample variance, bias, and squared error should be calculated. The triple system estimator will produce main role in the future of net census error estimation, which will gradually replace the widen used dual system estimator including correlation bias.

To address the stability challenges faced by Industrial Cyber-Physical Systems (ICPS) under non-periodic Denial-of-Service (DoS) attacks, this research investigates an adaptive event-triggered sliding mode control strategy based on observers. A mathematical model is developed for DoS attacks constrained by attack frequency and duration, accompanied by the construction of an observer to estimate the system state. A sliding mode controller is designed to mitigate the effects of DoS attacks, and an adaptive event-triggering mechanism is employed to conserve communication resources. The stability criteria and conditions are solved using techniques involving piecewise Lyapunov functions and H_∞ theory, which also guide the coordinated tuning of observer and controller gains. With a batch processing reactor system as the target of control, simulations carried out on the MATLAB platform confirm that the proposed control strategy effectively ensures system stability amidst non-periodic DoS attacks, simultaneously reducing communication resource consumption.

This paper studies the mean square consensus problem of linear discrete multi-agent systems under the simultaneous influence of communication delays and packet losses. The objective is to establish conditions based on delay information, packet loss rates, and communication topology, under which an effective control protocol exists that can achieve mean square consensus for the multi-agent system over channels characterized by both delays and packet losses. First, sufficient conditions for achieving mean square consensus in multi-agent systems are derived using optimization theory and the stability conditions of stochastic systems, considering two scenarios: one with only packet loss during communication and the other with both communication delays and packet losses. Second, to mitigate the effects of delays and packet losses on the mean square consensus of the multi-agent system, feasible state control protocol design algorithms are proposed by solving the positive definite solution of the modified Riccati inequality. Compared to existing results, this algorithm is easy to implement with low computational complexity. Finally, numerical simulations are conducted to verify the feasibility of the obtained results.

This paper discusses a type of online time series prediction problem where data arrives in batches in a streaming fashion. Traditional time series prediction models typically assume a static dataset, often resulting in lower prediction accuracy when dealing with dynamically changing data streams. To address this issue, this paper proposes an improved time series prediction model based on the Transformer architecture. Firstly, the paper improves upon the traditional Temporal Convolutional Network (TCN). By adjusting the connection structure and embedding the temporal convolution module at the front end of the encoder and decoder, the proposed model can not only capture the relationships between elements in the sequence data but also capture local features in the time dimension through temporal convolution, thereby expanding the model's "receptive field." This improvement enhances the model's understanding of time series data without significantly increasing computational complexity. Secondly, the paper introduces the experience replay strategy from reinforcement learning into model training. This strategy allows the model to be trained more fully on a limited dataset, thereby improving the model's generalization ability and prediction accuracy. Finally, the proposed model is validated on multiple datasets. The results show that, compared to the original Transformer model, the proposed model achieves performance improvements to varying degrees. The performance improvement is particularly significant on larger, more complex datasets. Additionally, the paper provides detailed proofs and supplementary materials in the appendix to further support the rationale behind the model design and performance improvements. In summary, this paper combines the advantages of Temporal Convolutional Networks and the Transformer architecture to propose a new online time series prediction model. This model significantly improves prediction accuracy while maintaining computational efficiency, providing an effective solution for the prediction of dynamic data streams.

Inspired by the Routh-Hurwitz stability criterion for planar linear systems, we investigate in this paper the stability of a class of planar nonlinear systems with unknown parameters. Drawing upon the framework of homogeneous system theory, we study the stability of a nonlinear system, which is inherently a homogeneous system, under various conditions of unknown parameters. The analysis employs both Lyapunov's method and the particular solution method to derive insights into the system's stability characteristics. As a result, several sufficient conditions, and necessary and sufficient conditions for stability are derived. The particular solution method adopted in this paper can be effectively applied to analyze the instability of the system, thereby enriching the methods available for the stability analysis of nonlinear systems. Furthermore, the results obtained in this paper have practical applications in the controller design of certain nonlinear systems. To demonstrate this, specific examples are provided to illustrate how the results can be utilized in practice.

The construction of battery swapping stations supports the promotion of the "vehicle-electricity separation" model for new energy vehicles, and it is crucial to design a contract mechanism to encourage supply chain enterprises to jointly participate in the investment of battery swapping stations. This article studies the joint decision-making problem of investment and product pricing in new energy vehicle supply chain enterprises under different power structures, designs two models about battery price discount contracts and battery price concession contracts based on the number of battery swapping stations invested, and analyzes the impact of correlation coefficients on the decision-making of all parties. Research has shown that both battery price concession contracts and battery price discount contracts can make the supply chain achieve Pareto improvement; Under different power structures, either battery enterprise or car enterprise investing in the construction of battery swapping stations is beneficial for promoting the battery swapping model and increasing the profits of both enterprises; The construction cost of battery swapping stations significantly affects the investment willingness and pricing decisions of battery and car enterprises. The research results provide cooperative ideas and new contractual mechanisms for enterprises to make investment decisions in replacement power plants.

Individual selection preferences and the abuse of recommendation algorithms have trapped the public in an information cocoon dilemma. It would trigger differentiated collective behavior, exacerbate the formation of opinion polarization, and even have a serious impact on social public order. In this paper, we systematically analyze the effects of differences in public behavior within the information cocoon on the interaction between heterogeneous opinion groups, including the intra-group homogeneity restriction weakening-strengthening effect and the inter-group inhibition-promotion combination interaction effect. Then, based on the Lotka-Volterra modeling approach, the opinion polarization dynamic model with the interaction of heterogeneous opinions is constructed. Besides, the equilibrium points and their stabilities are estimated, too. Moreover, we also explore the law of opinion polarization through numerical simulations and empirical analysis. The results show that under the influence of the information cocoon, the weaker the intra-group homogeneity restriction and the stronger the inter-group promotion effect, the faster and the larger the expansion of the two groups, and the more likely to generate binary polarization situation. What's more, when the inter-group inhibition effect is stronger and the intra-group homogeneous restriction of heterogeneous opinion is weaker, the expansion rate of the group would slow down and the size would decrease and even disappear after reaching the peak, and generate single polarization situation. In addition, the potential diffusion range positively affects the expansion rate and final size of the group itself. Furthermore, the potential diffusion range would also slow down the expansion of the heterogeneous group under the inter-group promotion effect, but does not affect its final size.

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.

In response to the scenario of a two-stage production structure that includes undesirable outputs and shared input factors, a two-stage data envelopment analysis (DEA) model has been developed. This model not only enables the rational allocation of shared resources between the two stages but also addresses undesirable outputs by applying the weak disposability theory, which aligns with real-world production dynamics. Furthermore, drawing on the concept of non-cooperative games, the model decomposes the efficiency of subprocesses by considering scenarios in which either the first or second stage is dominant, thereby establishing subprocess efficiency models. Ultimately, we employ the proposed model to evaluate the innovation efficiency of Specialized, Refined, Distinctive, and Innovative (SRDI) small and medium-sized enterprises in Fujian Province. By conducting a thorough analysis of both the overall efficiency and the subprocess efficiency of these enterprises, more accurate and comprehensive evaluation results can be obtained. Additionally, comparisons with various models further enhance the rationale and feasibility of the model presented in this paper.

Based on Nash non-cooperative game theory and variational inequality theory, this paper constructs a supply chain network equilibrium model under the combination of historical emission method and industry benchmark method, aiming at the production decision of different carbon emission manufacturers under different carbon quota allocation strategies. By comparing the effects of single carbon policy and complex carbon policy on supply chain network equilibrium, the implementation conditions of the optimal carbon quota allocation policy are proposed and its effect is verified. Secondly, it expands the supply chain network equilibrium model in which the government is a participant, and reveals the double leverage effect of the linkage between carbon quota allocation ratio and carbon price when the government is an endogenous player, which provides a quantitative decision-making tool for the design of “precise carbon control” policy. Finally, the model is simulated by projection shrinkage algorithm. The conclusion is as follows: Under the historical emission method, compared with expanding production scale, the two manufacturers with different carbon emissions are more inclined to achieve the optimal profit through carbon quota trading, while retailers are faced with the risk of profit compression. Under the industry benchmark method, when the unit carbon quota is greater than the unit carbon emission, the profit advantage of the emission reduction manufacturer over the ordinary manufacturer will gradually decrease with the increase of the carbon trading price; when the unit carbon quota is less than the unit carbon emission, the profit of the two types of manufacturers will decrease with the increase of the carbon trading price, and the profit of the retailer is generally stable in both cases. Under the composite carbon quota policy, when the unit carbon quota is greater than the unit carbon emission, the industry benchmark method accounts for a large proportion, and the profits and social welfare of the two types of manufacturers are the best, but the profit growth rate of the ordinary manufacturers is higher than that of the emission reduction manufacturers. When the unit carbon quota is smaller than the unit carbon emission, the larger proportion of historical emission method is more advantageous to the profits and social welfare of the two types of manufacturers. The profit of retailers gradually decreases with the increase of the proportion of historical emission method. After the government is included in the decision-making body, the increase in carbon price significantly increased the income of emission reduction manufacturers through cap-and-surplus trading, while ordinary manufacturers could only pass on the cost due to technology locking, resulting in a negative imbalance in social welfare. This study provides theoretical support and decision-making basis for the government's carbon quota allocation policy and supply chain network response strategy.

With the rapid development of the platform economy, e-commerce platforms' store brands have been presented with opportunities for swift growth. Digital traceability technology, as an effective measure to enhance consumer trust in store brands, has become a key to strengthening brand credibility. In view of this, this paper constructs four e-commerce supply chain decision models with and without the introduction of digital traceability technology, considering both the competition between national brand products and store brand products, and the two sale modes of reselling and agency selling. It deeply explores the impact of different sale modes on the decision-making of e-commerce platforms to adopt digital traceability technology. The study shows that high product substitutability and high consumer trust can both lead to higher profit levels for manufacturers and e-commerce platforms, and the introduction of digital traceability technology can create greater profit margins for both manufacturers and e-commerce platforms. By comparing the profits of both parties under different sale modes for national brand products, it is found that for manufacturers, when the investment cost of digital traceability technology is low and the commission rate is low, the profits under the reselling mode are higher than under the agency selling mode. Conversely, if the investment cost is high, regardless of the commission rate, the agency selling mode is the best choice. For e-commerce platforms, when the commission rate is high, the profits under the agency selling mode are higher than under the reselling mode; if the commission rate is low, then the profits under the reselling mode are higher. Finally, this paper further explores the corporate decision-making when sharing the cost of digital traceability technology. It is always the best strategy for manufacturers to choose the reselling mode to sell their national brand products, and when the commission rate for national brand products and the investment cost of digital traceability technology are both low, the profits of e-commerce platforms under the agency selling mode for national brand products will be higher.