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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Based on the development concept of "Lucid waters and lush mountains are invaluable assets",this paper takes the establishment of five provincial (autonomous region) green finance reform and innovation pilot zones in 2017 as a quasi-natural experiment, and uses the panel data of 286 prefecture-level cities from 2011 to 2022 to construct a difference-in-differences (DID) model to empirically examine the impact of green finance innovation on green total factor productivity. The results show that:(1)The establishment of the green finance reform and innovation pilot zone has significantly improved the level of green total factor productivity and achieved a win-win situation between economic development and ecological protection.(2)The mechanism analysis shows that industrial structure upgrading and government innovation preference are important channels for green finance innovation to play a role.(3)The heterogeneity analysis found that the policy effects of the pilot areas were significantly different due to different geographical locations, resource endowments and economic development levels. This study not only provides empirical evidence for the double dividends of the economic environment of green finance reform, but also provides an important reference for the expansion of the subsequent pilot zone and the design of differentiated policies.

The Group Recommendation System considers the preferences of each group member. Traditional algorithms overlook implicit trust relationships and preference consensus among users. This paper proposes a group recommendation method based on trust perception and an adaptive group consensus model. The method consists of two stages: the recommendation stage and the consensus recommendation stage. In the recommendation stage, an implicit trust measurement method is used to build a trust network for each group member. This generates their individual recommendation list. By identifying common predicted items, the common predicted item preference sequence for each member is obtained. In the consensus recommendation stage, the adaptive group consensus model is applied. It simulates potential preference changes during group negotiation and provides preference modification directions for conflicting members. This generates a group recommendation list that satisfies most members. The method is evaluated on the MovieLens 100K and FilmTrust datasets. The results show that the method improves recommendation quality and satisfaction. The adaptive group consensus model enhances recommendation quality in most cases.

Passenger flow prediction is a core pillar for the efficient operation of urban rail transit, enabling dynamic optimization of train scheduling and effective management of large passenger flows through precise anticipation of their spatiotemporal distribution. The dynamic variations of passenger flow in both temporal and spatial dimensions interact with each other, exhibiting distinct spatial characteristics across different period lengths. However, existing methods face limitations in capturing temporal dependencies and spatial features under multi-period scenarios. To address this issue, this paper proposes a Multi-Period Adaptive Graph Convolutional Network (MPAGNet). This model leverages Fast Fourier Transform to identify latent periods within passenger flow data, and integrates an attention mechanism with an adaptive graph convolutional network to capture temporal dependencies and spatial features across different periods. Furthermore, to enhance the model's interpretability and explore the impact of external factors on passenger flow, this study incorporates external features such as subway schedules, land use around stations, and subway accessibility. Experiments were conducted using real-world passenger flow data from the Beijing Subway. The results indicate that the proposed method outperforms benchmark models in passenger flow prediction tasks, reducing prediction errors by 25.10% compared to ARIMA and 9.91% compared to SVM. Ablation experiments are performed to validate the effectiveness of external features and the contribution of each model component. The findings provide a novel technological approach for passenger flow prediction and offer practical guidance for the intelligent operation and management of urban rail transit.

The China labor force survey uses a weighted summation method to estimate target variables, which has certain gap in estimation accuracy compared with the calibration composite estimation method. The calibration composite estimation method is the mainstream estimation approach for rotation sample surveys. This study aims to construct a calibration composite estimator for China.s labor force survey by introducing the calibration composite estimation method and fully tapping auxiliary information of rotation samples improve estimation accuracy. The main research work is as follows: (1)Propose a one-step calibration composite estimator based on the 2-10-2 rotation model of China.s monthly labor force survey and discuss the approximate unbiasedness, variance, and variance estimation of the new estimator. (2)Conduct a Monte Carlo simulation to compare the one-step calibration composite estimator with the calibration composite estimator and the weighted estimator used in labor force surveys, and test the superiority of the one-step calibration composite estimator. (3)Through applied research, the specific process of the one-step calibration composite estimation is fully demonstrated, clarifying the technical details of the estimation method for practitioners in the statistical department. The research content of this paper is expected to be applied to China’s labor force survey and promote the reform of government statistical survey systems and methods.

To deeply investigate the challenges faced in handling big data, we extend the poisson subsampling algorithm into the classic Cox proportional hazards model in the field of survival analysis under the framework of big data. This algorithm eliminates the need to precompute the sampling probabilities for all sample points, signi cantly enhancing computational e ciency compared to traditional sampling with replacement. It e ectively addresses the challenge of statistical analysis of large-scale survival data when computational resources are limited. Rigorous proof demonstrates the consistency and asymptotic normality of parameter estimates based on poisson subsampling, ensuring the theoretical reliability of the method. For practical application, a two-step algorithm is proposed. Simulation and empirical studies show that the application of the poisson subsampling algorithm in the Cox proportional hazards model is not only theoretically feasible but also demonstrates remarkable practical e ects, providing a new and e cient statistical tool for the field of survival analysis.

In cloud computing environments, user-driven resource selection is increasingly prevalent, as users tend to choose servers based on their own needs (such as latency and cost) to execute multiprocessor tasks. However, this decentralized and self-interested decision-making can lead to imbalanced resource utilization, thereby prolonging the maximum completion time of all tasks (Makespan) and impairing overall system efficiency. To address this challenge, this paper investigates the scheduling game model for tasks occupying consecutive multiple servers and designs a ‘Widest First’ (WF) coordination mechanism. The mechanism guides user decisions through a simple local policy: servers prioritize processing tasks with a larger width (i.e., those requiring more servers). Theoretical analysis shows that the WF mechanism limits the Price of Anarchy (PoA) to a constant upper bound of 4-3/m, a significant improvement on the worst-case performance guarantee compared to the unbounded PoA in uncoordinated scenarios. Further numerical experiments validate the mechanism’s excellent average-case performance: with an improved tie-breaking rule, the efficiency loss of the resulting equilibrium can be controlled to around 5% (PoA ≈ 1.05), far outperforming the theoretical worst-case bound. This research offers a practical strategy for cloud platform managers to balance user autonomy with system efficiency, holding significant theoretical and practical value.

To address gaps in national standards for fire extinguisher configuration, this study uses fire dynamics simulation to model initial fire evolution through mass loss rate analysis. By incorporating combustible material distribution and personnel response, an optimized fire extinguisher configuration model is proposed, improving on traditional methods. Simulations in three representative buildings quantitatively link extinguisher configuration to fire suppression effectiveness, recommending strategies tailored to different fire characteristics. Results show optimized configurations enhance initial fire suppression efficiency, improve equipment utilization, and reduce fire losses. Sensitivity analysis highlights the influence of fire management and extinguisher design on configuration outcomes, providing practical guidance for fire protection equipment and scientific support for complex building fire safety designs.

This study proposes a multi-modal artificial intelligence model designed to quantify the impact of housing price policies on the real estate market and to predict housing prices. The model integrates transaction data from secondary housing market, unstructured text data on housing price policies, and geographic information, to capture the multifaceted factors that drive the market. Using Shenzhen's 2021 secondary housing guidance price policy as a case study, we conduct an empirical analysis of the policy's effects. While this policy aims to stabilize the market by setting price limits, our study reveals its substantial dampening effect on high-end housing transactions. The policy's restriction on disclosing actual transaction prices has posed challenges for the analysis. By incorporating a multi-modal approach into the hedonic pricing model, this study effectively addresses the data limitations. Using transaction records from 2015 to 2020, we predict the counterfactual early 2021 housing prices in the absence of the guidance price policy, which shows that the multi-modal model significantly improves prediction accuracy. The findings indicate that guidance prices are set lower than market prices in most areas, especially in high-end markets, resulting in higher down payment requirements and a reduction in buyer demand. By integrating geographic information and policy text data, the multi-modal model displays strong predictive power, offering a robust scientific framework and practical guidance for the quantitative assessment of policy impacts on housing prices and future market forecasts.

The issue of carbon emission pollution resulting from excessive consumer consumption poses a significant challenge to achieving the “peak carbon dioxide emissions” goals. To effectively address this problem and safeguard the interests of supply chain enterprises, this paper constructs a supply chain model composed of a supplier and a low-carbon manufacturer. It explores the impacts of excessive consumer consumption behavior and various guidance strategies on both supply chain enterprises and environmental protection. The research findings indicate that: (1) While the traditional guidance strategy can curb consumers' excessive consumption behavior and thus prevent its adverse effects on the low-carbon development of the supply chain, it may also harm enterprise profits. (2) Guidance strategies driven by explicit low-carbon consumption targets can discourage overconsumption without necessarily eroding supply-chain profits. Hence, closing or consolidating high-emission firms is not the only path forward: target-driven guidance can both offset the adverse effects of consumers’ low-carbon reference points and, from the demand side, support firms’ low-carbon investment, thereby advancing low-carbon development across the supply chain. (3) For products with varying levels of carbon emissions, from low to high, companies should adopt strategies: traditional guidance strategy, target-driven personal-goal guidance, and target-driven normative-goal guidance to simultaneously improve profits and enhance social welfare. (4) Whether in new markets or in markets that previously relied on conventional guidance, high-emission firms can profit by adopting low-carbon consumption target-driven guidance to cut emissions and curb overconsumption, whereas continued reliance on conventional guidance may be counterproductive.

This paper investigates fixed-time trajectory tracking for robotic manipulator systems with parametric perturbations and external disturbances. At first, a novel adaptive event-triggered mechanism is developed to optimize the network resources utilization by adjusting the triggering threshold according to system states dynamically while guaranteeing both control accuracy and robust performance. Secondly, an event-triggered-based adaptive fixed-time sliding mode control strategy is proposed, which not only compensates for the lumped uncertainty of robotic manipulator system to improve the control precision further, but also ensures the global convergence of the system within fixed time. Next, the stability of the system and the boundedness of the tracking errors are rigorously validated using Lyapunov theory, and the existence of a minimum inter-event time is established to exclude Zeno phenomenon. Finally, a numerical simulation and comparative analysis substantiate the superiority of the proposed scheme. The results confirm that the strategy optimizes the network resources utilization while achieving high-precision trajectory tracking.

This paper establishes a hybrid hierarchical age-structured model with encounters mechanism based on the reproductive laws of vermin to describe the optimal contraception control problem of vermin. The state system consists of a firstorder partial differential equation with a global feedback boundary condition and two ordinary differential equations. Firstly, the existence of a unique non-negative bounded solution to the model is established, and some continuity results are given. Then, the optimality conditions given by the feedback forms of state variable and adjoint variable are obtained by constructing a proper adjoint system and using tangent-normal cones techniques. Meanwhile, the existence of a unique optimal policy is proved via Ekeland’s variational principle and fixed-point theory. Finally, some numerical simulations have been performed to demonstrate the feasibility of the obtained results.

In the Internet and digital era, recommendation algorithms serve as essential technologies for enabling users to extract useful information from massive volumes of data. Traditional recommendation algorithms typically prioritize user ratings as the core basis, while overlooking the emotional information embedded in online reviews and the phenomenon of potential changes in user preferences which results in low levels of recommendation accuracy and personalization. To analyze the hidden emotional information in film reviews and the variation law of user interests, this study proposes a dynamic movie recommendation algorithm considering sentiment dictionary construction and user interest drift. First, considering the perceptual bias of general sentiment dictionaries toward movie reviews, a domain-specific sentiment dictionary is constructed, tailored to distinguish between different movie genres. Then, sentiment analysis is performed on movie reviews using this specialized sentiment dictionary to correct user ratings. Additionally, user interest changes are modeled based on Ebbinghaus’s forgetting curve, leading to the development of a dynamic movie recommendation algorithm that accounts for user interest drift. Finally, the algorithm’s effectiveness is verified using the Douban Movie dataset, with evaluation based on recall, precision, and F1 score. Experimental results demonstrate that the sentiment dictionary built in this paper has significant domain adaptability and emotional recognition advantages. The proposed dynamic movie recommendation algorithm is better able to capture hidden emotional information and user interest changes in reviews, providing recommendations that are more aligned with users’ current needs compared to traditional methods.

The shuttle-based storage and retrieval system with two lifts exhibits remarkable features such as compactness and high efficiency. Dual-command operation, favored for its effectiveness in minimizing equipment idle time, is commonly employed in warehouse system scheduling. However, incorporating dualcommand operations while managing conflicts between the two lifts introduces significant complexity into the scheduling decisions. This paper addresses the scheduling challenges of the shuttle-based storage and retrieval system with two lifts by focusing on minimizing the makespan through a comprehensive consideration of dual-command operations and dual-lift constraints. A mixed-integer programming model is formulated to tackle these issues. Given the NP-hard nature of this problem, a two-stage algorithm is proposed by combining the adaptive large neighborhood search algorithm and dynamic programming. To align with real-world scenarios, three types of instances, namely small, medium, and large-scale, are designed, and the two-stage algorithm is benchmarked against the Gurobi solver, the Sequential Matching algorithm, and the Particle Swarm Optimization algorithm to evaluate its efficacy. The findings indicate that: (1) Lift speed exerts a significant impact on system efficiency, yet its marginal benefits decline with increasing speed; (2) For a given input/output point, there exists an optimal speed ratio between the upper and lower lifts; (3) Dual-command operations enhance efficiency by 42.72% compared to single-command operations, while dual-lift systems achieve a 47.09% improvement over single-lift systems. The study provides a scientific foundation and algorithmic basis for managers to develop effective scheduling strategies and offers theoretical support for optimizing the configuration of shuttle-based storage and retrieval systems with two lifts.

During the critical period of transformation in the real estate industry, clarifying the optimal business layout strategy for real estate enterprises under multiple entities holds significant implications for implementing the principle that "houses are for living in, not for speculation", establishing a long-term mechanism for real estate development, and resolving deep-seated contradictions in the economic structure. Based on the framework of "policy regulation credit constraint", and in accordance with the theoretical systems of risk-return, credit constraint and social welfare, this paper constructs an evolutionary game model among real estate enterprises, banks and the government, and conducts simulation to explore the evolutionary game equilibrium state of the three participating entities and the specific impact of different parameter values on the stable equilibrium point. The research finds that, The scissors gap in investment returns between the real economy and the virtual economy is the key factor determining the strategic equilibrium of real estate enterprises. When the deep-seated contradiction of the separation of industry and finance is resolved, real estate enterprises can increase their layout in the financial market. Meanwhile, real estate enterprises are not sensitive to changes in the risk coefficient of financial investment, which will lead to the concealment and transfer of potential systemic risks. When the banking sector relaxes the entry threshold, that is, when competition among banks becomes the optimal strategy choice, it can alleviate the financing constraints faced by real estate enterprises, and the degree of friction in the credit market can determine the time it takes for the game to evolve to an equilibrium state. Regulatory costs and social welfare are key factors influencing the balanced strategies of government departments. The research of this article provides a useful reference for improving the real estate market and preventing the economy from deviating from the real economy to the virtual economy.

This paper addresses the finite-time anti-disturbance control problem for a class of chained nonholonomic systems subject to unknown parameters and mismatched disturbances. Leveraging a finite-time disturbance observer, a novel adaptive finite-time anti-disturbance control scheme is developed via the power integrator technique. To tackle the challenge that nonholonomic systems cannot be stabilized by continuous time-invariant feedback, a switching control strategy combined with the prescribed performance control concept is employed. Moreover, the restrictive assumptions on mismatched disturbances in existing literature are relaxed. The proposed approach effectively compensates for both unknown parameters and external mismatched disturbances, while ensuring practical finite-time stability of the closed-loop system. Rigorous theoretical analysis validates the convergence and robustness properties. Finally, numerical simulations are provided to demonstrate the superiority and effectiveness of the developed method, highlighting its potential for practical applications in robotic and vehicular systems.

Vision-language models (VLMs) can address the lack of interpretability caused by the black-box nature of end-to-end autonomous driving systems through natural language interaction, thereby improving the transparency and reliability of decision-making in such systems. Typical VLMs align visual and textual features using lightweight projection (‘glue’) layers, but due to their simple structure and random initialization, they often struggle to capture the complex relationships between modalities. When handling video question answering tasks, conventional VLMs rarely consider both fine-grained feature extraction and temporal modeling across video frames. Moreover, existing methods usually involve large-scale parameters, making practical deployment difficult. To tackle these issues, this paper proposes a lightweight driving VideoQA model based on fine-grained feature skip connections and spatiotemporal attention fusion, which effectively aligns video and textual features and fully exploits fine-grained visual information. The spatiotemporal attention gating module dynamically captures intra-frame fine-grained information and models inter-frame temporal dependencies by combining spatiotemporal attention with a gating mechanism, thereby adaptively fusing multi-frame features to enhance video representation. The alignment adapter and loss module guides the adapter to learn cross-modal feature mappings through an alignment loss function, achieving semantic alignment between visual and textual features. The fine-grained skip connection module introduces a residual connection variant and gating mechanism to inject detailed information into the encoder features of the large model, enabling better utilization of fine-grained features during decoding and mitigating the issue of detail loss caused by deep networks. Experimental results demonstrate the effectiveness of the proposed model, which achieves strong performance across key evaluation metrics.

Noise, outliers, and class imbalance issues in real-world classification tasks can weaken the robustness of machine learning models. To address this, this paper proposes PSO-FTSVM, a hybrid framework combining the particle swarm optimization(PSO) algorithm with a fuzzy membership-driven twin support vector machine(TWSVM). The model combines methods based on class center distance and kernel density estimation, introducing a hybrid fuzzy membership function that dynamically assigns membership based on the global distribution and local density of samples, thereby enhancing the representativeness of critical samples. Additionally, PSO is utilized to adaptively optimize the model’s key parameters, thereby improving its generalization capability. Experiments on 10 datasets show that the PSO-FTSVM achieves an average accuracy of 92.011%?1.863, representing improvements of 14.080% and 10.587% over the baseline TWSVM and FTSVM, respectively. The model demonstrates outstanding performance in handling highly imbalanced datasets and maintains stable performance in noisy environments, showcasing exceptional robustness.