In order to achieve $G^3$-smooth join at the joint points, a construction method is proposed for spatial quintic $G^3$ interpolating curves. Given two-point $G^3$ data, a quintic polynomial curve is the lowest-degree polynomial curve that can most possibly achieve $G^3$ interpolation. The interpolation problem reduces to solving a bivariate quartic polynomial system, then its optimal positive solution is obtained via the resultant method and the control points of the quintic Bézier curve are calculated subsequently. When such an interpolating curve does not exist in some cases, several $G^3$-joined interpolating curves are constructed by means of subdivision. Finally, an adaptive algorithm is proposed for converting parametric curves to quintic $G^3$ spline curves. Compared to previous quintic interpolation methods, numerical examples demonstrate that the new method has obvious advantages in fitting errors and the profiles of curvature and torsion.

Predicting the development of income inequality is important for understanding the gap between the rich and poor; it is particularly important for monitoring and forecasting the negative social impact caused by income inequality with more parsimonious model involving fewer variables but achieving higher prediction accuracy. For predicting the income inequality, we analyze the income inequality data set around the world with generalized linear mix-effects model. With this model, we propose a method for constructing candidate models and a novel criterion for choosing weights for achieving a parsimonious model averaging coefficient estimator. Numerical studies show that the criterion has satisfactory performance in parsimony and prediction accuracy. The real data analysis indicates that it will be of great significance to promote the openness and foreign investment, improve the level of human capital, total factor productivity and national income, and establish a flexible financial system and credit system to alleviate the problem of income inequality.

In-home services in the realm of daily living cater to the personalized, discrete, and diverse needs of consumers by directly delivering services to their residences or workplaces. This research centers on optimizing the staff scheduling and routing problem for in-home services offered by platforms, considering the order revenue heterogeneity, and the stochastic service times. This research incorporates the concept of the route operational feasibility and provides corresponding derivation formulas, which are integrated into problem constraints. Additionally, to ensure the feasibility and robustness of the service solutions, this research permits the postponement of the visits to certain customers and the heterogeneous characteristics of staffs are not currently considered. A novel approach is developed, involving an improved branch-and-price algorithm that integrates upper and lower bound predictions for precise solution finding. This algorithm leverages information from branch points, predicting bound values to expedite subproblem solving and enhance convergence speed. Experimental results using modified Solomon instances demonstrate that most instances yield precise solutions within an acceptable time. Sensitivity analysis was conducted on different revenue values and service duration distributions to explore the impact of these factors on path planning and customer access sequence. The precise solution of the in-home service scheduling and routing problem, which combines the randomness of service time and heterogeneity of benefits, can provide improvement solutions for subsequent response to order exchange and sudden orders, and provide decision support for the platform. It has practical application value and can be easily extended to other fields, thereby promoting the sustainable development of the service platform.

Aiming at the problems of multiple points and dispersed volume, high vehicle fuel consumption and carbon emission in cold chain logistics, an electric vehicle routing model and optimization algorithm for cold chain logistics considering split demand are proposed. Firstly, a mixed-integer planning model is established with the objective of minimizing the sum of fixed cost, transportation cost, refrigeration cost, time penalty cost and charging cost. Secondly, a variable neighborhood genetic algorithm is designed for model solving in response to the research problem, the greedy algorithm is used for population initialization, the crossover operation is improved on the basis of the traditional genetic algorithm, and the proportional splitting Principles is designed. Finally, the feasibility of the algorithm is verified by different scale cases and the actual distribution cases of an enterprise, and the simulation results of the cases show that the constructed model and the proposed algorithm can plan the vehicle route scientifically, reduce the total cost of logistics and distribution, and reduce energy consumption.

This study explores the enhancement of "first mile" logistics in the low-altitude economy, focusing on optimizing the harvesting and distribution of fruits and vegetables, which are characterized by seasonality, freshness, perishability, and regional specificity. We address the collaborative routing of trucks and multi-drones under time constraints by proposing a two-stage Mixed Integer Linear Programming model. The first stage minimizes the combined travel and activation costs for both drones and trucks, while the second stage reduces total transportation costs. Extensive numerical experiments validate the model's feasibility and effectiveness, and an empirical analysis using operational data from SF Express demonstrates its practical applicability. The results reveal that the model provides optimal solutions within specified time limits, significantly improving logistics efficiency while ensuring product maturity and freshness. This research offers valuable insights for modernizing agricultural supply chains and identifies new opportunities for applying low-altitude economy principles in agriculture.

In the presence of responses missing at random, this paper investigates the estimation and variable selection issues for the varying coefficient quantile regression model. By approximating the coefficient functions using B-spline basis functions, we propose a multiple imputation method to repeatedly impute missing response variables and estimate the unknown functions by minimizing the imputed quantile loss function. When there exist irrelevant variables in the model, the sparse estimators of the unknown coefficient functions are obtained by combining the basis function approximation technique with the one-step SCAD penalty method. Under certain regularity conditions, we prove that the estimators of the coefficient functions achieve the optimal global convergence rate. Additionally, by appropriately selecting penalty parameters, we establish the oracle property of the sparse estimators. Numerical results confirm the effectiveness and feasibility of the proposed methods.

This paper focuses on a class of Stochastic Multiobjective Bilevel Programs(SMBP), considering the case that the objective function of the upper-level program is multi-objective while that of the lower-level program is single-objective. Under certain conditions, we apply the lower-level Mond-Weir duality to present a new reformulation, called SMMDP, and prove the equivalence of Pareto optimal solution in local sense and global sense respectively. In addition, due to the mathematical expectation in the objective functions and constraints of the model, which is difficult to be solved, this paper uses sample average approximation method and the penalty function method to propose the penalized sample average approximation problem of SMMDP to solve the problem. Finally, we give the global weak Pareto optimal solution of SMMDP penalized sample average approximation problem and the convergence results of Pareto stability points theoretically.

In the context of big data, optimizing portfolios for complex high-dimensional assets remains a major challenge for investors. This paper employs the Orthogonal Hierarchical Interaction Testing (Ohit) algorithm for dimensionality reduction, which effectively addresses the complex structure of error terms. This paper improves the Sample Average Approximation (SAA) model by incorporating regularization techniques, and proposes the cv-PBR model along with a corresponding Short Period Regularized Sparse Investment Strategy (SPOPBR). For parameter calibration, the study refines the performance-based k-fold cross-validation algorithm by extending portfolio evaluation metrics to a multidimensional framework. The applicability and effectiveness of the SPOPBR strategy are empirically evaluated. Case studies based on real market data show that the SPOPBR strategy outperforms other short-term high-dimensional asset allocation strategies in both returns and risk control. This research provides valuable insights into high-dimensional asset allocation and portfolio optimization.

The semi-parametric accelerated failure time (AFT) model holds a classical position in the field of survival analysis. Left-truncated and right-censored data, along with high-dimensional covariates, present significant challenges for fitting such models. We propose an estimation method based on rank theory for these models and data in this paper. We construct a rank-based variable selection procedure and designs a new coordinate descent algorithm to solve the penalized rank estimation. The related asymptotic theory for the proposed method are also provided. Finally, the effectiveness of the proposed method in finite sample situations is validated through simulation studies and empirical analysis.

In view of the problem of ``difficulty in seeing a doctor'' caused by the unreasonable allocation of medical resources, this paper considers the effect of point-to-point assistance on the cure rate of community hospitals based on the service model of medical consortiums, studies the strategies of assistance and government subsidies in first-class hospitals, and analyzes the effect of government subsidies on the allocation of medical resources and on the improvement of the cure rate of community hospitals. A tripartite game model involving the government, the first-class hospitals, and patients is constructed. By comparing the equilibrium strategies and their properties under different medical service environments, the comprehensive impact of patient perceived value, assistance efficiency, and patient concerns on the operation of the medical consortium is analyzed. It is found that whether top-tier hospitals can spontaneously provide assistance depends on patient concerns. The government needs to adopt different subsidy strategies for hospitals with different levels of patient concerns. The patient scale and assistance efficiency are important factors influencing the decision on subsidy intensity and assistance level. Numerical experiment results show that when the number of patients reaches a certain scale, the assistance levels of two types of top-tier hospitals vary with the patient scale under different assistance efficiencies. The government's subsidies for economically oriented hospitals increase with the expansion of the patient scale, while the subsidies for patient-oriented hospitals decrease with the expansion of the patient scale.

Based on reaction-diffusion equations and the sex structure of mosquitoes, this paper considers a mosquito model between wild mosquitoes and carrying Wolbachia. The optimal release problem for Wolbachia-carrying mosquitoes is discussed both theoretically and numerically. Theoretically, the paper employs the theory of optimal control for partial differential equations to derive the sensitivity system and adjoint system of the state equation, leading to the existence and characterization of the optimal control. Numerically, the forward-backward sweep method is used to illustrate different release strategies for Wolbachia-carrying mosquitoes. The results show that implementing optimal release strategy for Wolbachia-carrying mosquitoes can achieve population replacement; releasing only male mosquitoes carrying Wolbachia under a single optimal control strategy can achieve population suppression; the optimal control intensity in the diffusion scenario is less than that in the non-diffusion scenario. The conclusions can provide theoretical references for the prevention and control of mosquito-borne diseases such as dengue fever.

This study considers a supply chain composed of a supplier and an e-commerce platform under cyber-attack risks, where the government may impose penalties on the platform for failed defense. Game models are constructed under two sales modes (resale and consignment) and two scenarios (with or without government penalties) to explore the platform's defense effort level and analyze the government's optimal penalty strategy. The results show that: 1) When the commission rate is low, the e-commerce platform invests more in defense under the resale mode; conversely, it allocates higher defense effort under the consignment mode. 2) The supplier's preference for the consignment mode is not limited to low commission rates; its mode choice is also influenced by defense costs. 3) If the government penalizes platforms for failed defense, the fine amount under the consignment mode is higher than that under the resale mode, and the fine decreases as the commission rate increases. 4) From the perspective of maximizing social welfare, only when the government attaches sufficient importance to consumer surplus will the government's implementation of punishment increase social welfare, and the optimal punishment should be extremely heavy fines; otherwise, the government should not implement punishment.

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

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

In non-life insurance practice, to mitigate adverse selection and control premium levels, insurance contracts often incorporate claim payment ratios and deductibles, leading to partially censored claims data. To address this challenge, this study develops a risk-adjusted model that incorporates these policy-specific features, such as claim payment ratios and deductible thresholds. Within the framework of the exponential-variance premium principle, the study systematically explores Bayesian estimation methodologies for risk premiums, particularly in cases where the prior distribution is unknown. By applying a linear Bayesian approach, two innovative credibility estimators are proposed, and their statistical properties—such as asymptotic consistency and estimation efficiency—are thoroughly analyzed to assess the model's practical applicability. Through extensive numerical simulations, the convergence rates of the proposed credibility estimators are empirically validated, confirming the robustness and operational effectiveness of the model. The results demonstrate that this methodology enables accurate risk premium estimation in censored data settings, even with complex contractual structures. This research provides a novel theoretical framework for non-life insurance actuarial science, offering substantial value for pricing specialized insurance products that include risk-sharing mechanisms, such as proportional compensation clauses and deductible structures. The proposed model holds significant potential for improving actuarial fairness and enhancing market sustainability in heterogeneous risk pools.

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

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

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

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

In practical life, there are many problems that fit the quadratic measurement model, such as phase retrieval, label-free distance geometry, and power system state estimation, estimation methods designed for linear models are not directly applicable to these cases. In this paper, we use $\ell_q(0<q<1)$ penalized least squares method to recover the unknown parameters, and establish the error bound between the true value and the estimator from the perspective of non-asymptotic statistical analysis. Under the influence of sub-Gaussian distributed noise, the $\ell_q(0<q<1)$ regular least squares method can recover the signal with a probability of at least $1-1/n$, and the error boundary between the estimator and the true value can be expressed as $O\left(\sqrt{\frac{s \ln (1+2 n)}{n}}\right)$, where $n,s$ represent the sample size and the sparsity of the unknown signal, respectively. The theoretical results in this paper have obvious advantages in the size of the error bound, the radius of the local optimal solution, and the non-asymptotic representation of the conclusion.

Urban water supply infrastructure, as one of the critical systems in the urban lifeline framework, is essential for the normal functioning of cities. In the face of increasingly severe disaster risks, measuring and enhancing the resilience of water supply infrastructure is crucial. This paper follows the approach of characterizing the water supply infrastructure network and its service performance evolution, constructing a resilience model, and conducting case simulations. Two failure modes, namely instantaneous and progressive, are considered. A resilience measurement model based on the Sigmoid function is developed, focusing on a ``node-link'' framework. Case simulations are used to demonstrate that the proposed model can reflect the dynamic changes in the resilience of water supply infrastructure under different failure modes, and the impacts of risk shock coefficients, water transmission rates, and the duration of risk shocks on the resilience of water supply infrastructure are explored. The resilience measurement model established in this research can help managers better understand the trends and sensitive factors affecting the resilience of water supply infrastructure, providing references for enhancing resilience in the face of risks. Furthermore, the theoretical framework for measuring infrastructure resilience has been expanded.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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