The “carbon peaking and neutrality” goal puts forward new requirements for the coordinated emission reductions in the supply chain. Based on game theory, a dual channel supply chain carbon reduction and financing decision-making model was constructed under different financing models when manufacturer has financial constraints and fair concerns. Analyzed the impact of manufacturer fairness concern on supply chain product pricing and carbon reduction strategies, and explored the preferences of manufacturer and retailer for different financing models. Research showns that manufacturer's fair concern behavior reduces online channel product pricing and carbon reduction levels, while wholesale price and offline channel retail price are also influenced by manufacturers' carbon reduction cost factor. The fair concern behavior of manufacturer increases the profits of manufacturer and supply chain, but reduces the retailer's profits. Capital constraints lead to a decrease in the profits of manufacturer, but the profits of retailer may not necessarily decrease. When manufacturer has relatively low sensitivity of early payment wholesale price, the retailer prefers to choose the prepayment financing model. Otherwise, the retailer prefers to choose the bank loan+prepayment combination financing model. For the manufacturer and overall supply chain, when the sensitivity of early payment wholesale price is relatively low, they prefer to choose the prepayment financing model. Otherwise, the bank loan financing model is dominant.

To explore the channel encroachment strategies of a green manufacturer when a retailer adopts different business models, a green supply chain model, consisting of a single manufacturer and a single retailer, is constructed. The optimal operational strategies of the supply chain enterprises are analyzed using Stackelberg game theory. Finally, the model is extended to a scenario where the manufacturer can also adopt different business models for channel encroachment. The research findings are as follows: 1) Unless the product’s energy consumption is high or energy prices are exceptionally high, the manufacturer can always improve its profits through channel encroachment, with the chosen business model for encroachment depending on the product’s energy consumption and energy prices. 2) Channel encroachment increases the extent to which supply chain members' decisions are influenced by energy prices. However, a retailer who prefers a sharing business model can avoid these effects. Moreover, the manufacturer’s channel encroachment behavior always contributes to improving the overall profit of the supply chain. 3) When facing the potential threat of channel encroachment by the manufacturer, the retailer prefers the traditional business model if product energy consumption or energy prices are high. Conversely, when energy consumption or prices are lower, the retailer favors the sharing business model. Furthermore, retailers who prefer the sharing business model raise the threshold for the manufacturer’s channel encroachment. 4) Under certain conditions, an increase in energy prices does not always harm the profits of supply chain members or the energy-saving performance of green products.

To enhance the scientific nature of risk prevention and control in the supply chain network of new energy vehicle (NEV), and to strengthen safety production and operational management in China's NEV industry, we integrate complex network theory with SEIR (Susceptible-Exposed-Infectious-Recovered) modeling to simulate the process of risk propagation in the NEV supply chain network, aiming to uncover the mechanisms of risk propagation. Firstly, typical NEV companies such as Tesla and XPeng were selected as case studies, with suppliers as nodes and supplier cooperation relationships as edges to construct the topological networks of their automotive supply chains. Secondly, topological parameters such as average degree, clustering coefficient, and average path length were used to explore the characteristics of the supply chain networks of these two companies. Finally, based on the characteristics of the topological networks, an SEIR epidemic model was constructed for the supply chain networks to simulate the impact of different immunization strategies on the speed and scope of risk propagation in the supply chain. The results indicate: 1) The supply chain networks of both NEV companies exhibit scale-free network structures, with comparable network densities (average degrees of 2.293 for Tesla’s and 1.845 for XPeng’s supply chain networks). 2) Comparing the simulation results of risk prevention strategies between the two companies shows that their performances are largely similar. The proposed model effectively explores the characteristics of risk propagation in the NEV supply chain. Specifically: extending the incubation period of risks can significantly slow down the spread of risks, providing nearly three months of adjustment time for the companies, with Tesla experiencing a shorter delay of about 2 weeks to the peak risk period compared to XPeng; shortening the duration of infection can notably reduce the scale of risk spread by approximately 20%, with Tesla showing a 4% greater reduction in the scope of risk impact compared to XPeng. Additionally, increasing the complexity of the supply chain network may accelerate the propagation of risks. The research findings can provide a reference for NEV companies to formulate effective risk response measures, ensuring the stability and safety of the supply chain.

In recent years, the loss control strategy of adjusting the payment structure according to the actual mortality rate so as to rationally share the longevity risk between the annuity holders and issuer has become an increasingly popular study topic in the field of longevity risk. Based on the Bayesian Markov chain Monte Carlo algorithm, this paper analyzes the three paths of longevity risk sharing of annuities under a unified computational framework, i.e., the emergency-fund fixed annuities EFA, the mortality annuities MA with the annuity payment fully linked to the actual mortality rate and the minimum guaranteed mortality annuities MGMA with the minimum guaranteed benefit, and evaluates the effect of longevity risk sharing of the various paths. The effects of longevity risk-sharing were also evaluated. The article finds that: (1) compared to FA, MA completely transfers the longevity risk borne by the annuity issuer, and its actuarial benefit spread $\Delta_{M A}^{\alpha}$ has an obvious advantage, and its attractiveness increases with the increase of the risk aversion $\alpha$ of the annuity holders; (2) the MGMA further corrects the downside risk exposure of the MA benefits, and has a benefit advantage over MA at 0.90 times the minimum guaranteed benefit, and annuity issuers' reserve deficits and solvency capital improve relative to traditional annuities. The article conducts a robustness test under the influence of risk attitude, interest rate, portfolio size and other parameters, and concludes that MGMA annuities realize the reallocation of systematic longevity risk between annuity holders and issuers, and are effective in improving the longevity risk faced by commercial annuity business.

The efficient and accurate estimation of sensitivity distribution parameters and quantiles is crucial for the design and evaluation of the reliability of pyrotechnic products. The approach developed in this paper employs a Bayesian framework to establish a semiparametric generalized linear model for sensitivity data, using the Hamiltonian Monte Carlo algorithm for posterior inference. Within this framework, the deviance information criterion and the logarithm of the pseudo-marginal likelihood are used in a data-driven manner to select the optimal model. Extensive simulation comparisons demonstrate that the proposed method can accurately estimate sensitivity distribution parameters in the case of small sample sizes. Finally, the new method was applied to two real datasets, validating its effectiveness. The new method provides an alternative and complementary modeling tool for the analysis of sensitivity data.

As global market competition intensifies and the service economy rapidly develops, businesses face the challenge of transitioning from traditional singular product or service offerings to integrated product-service supply chains. This paper, from a dynamic perspective, delves into the optimization strategies of dual-channel sustainable service supply chains under manufacturer competition and cooperation environments. The research findings reveal: (1) The goodwill growth rate and final stable level in competitive scenarios surpass those in cooperative scenarios. Competitive environments more effectively stimulate supply chain members to enhance service levels, with manufacturers and retailers' service efforts showing rapid upward trends, potentially leading to more enduring competitive advantages in the long term. (2) While manufacturer cooperation can reduce costs, it may lead to decreased service levels, impacting long-term competitiveness. The effects of cooperative strategies on supply chain members are uneven; manufacturer profits may increase, while retailer profits may be negatively affected, necessitating thorough consideration of all parties' interests in decision-making. (3) Service efficiency disparities are a crucial factor influencing manufacturers' choice between competitive or cooperative strategies, wherein larger efficiency gaps incline the weaker party towards cooperation to alleviate competitive pressure, while similar efficiency levels enable cooperation to bring more significant profit improvements for both parties.

In the era of converged media, the management of negative public opinion has become more difficult, and county-level converged media centers have gradually become an indispensable link in the process of negative public opinion management. How the county-level converged media can cooperate with the government to manage negative public opinion is of great significance for maintaining social stability and improving the social management ability of the grassroots government. This paper takes evolutionary game theory as the basic research method, chooses government departments, county-level media and the public as the subjects of evolutionary game, further analyzes the evolutionary stabilization strategies of each subject by constructing a three-party evolutionary game model, calculating the payoff matrix and replicating the dynamic equations, and carries out a numerical simulation with the media center of the twelfth division of the Xinjiang Production and Construction Corps as an example in order to verify the correctness of the model analysis. The results of the study show that the strategy choices among the tripartite subjects influence each other, and the strategies of each subject change at different stages of the development of public opinion due to the change of parameters. The government's strict management of negative public opinion and the early intervention of county-level converged media will make the public opinion tend to calm down as soon as possible and reduce the negative impact. Finally, based on the findings of the study, suggestions are made on how county-level converged media can better participate in the governance of negative public opinion.

Under the classical retrial policy, we consider a single-server queueing system with two types of customers and two orbits. The customers’ arrival rate, service rate and retrial rate depend on the type of customers. Since the joint stationary distributions of waiting time are difficult to obtain, we assume both retrial rates for two types of customers linearly converge to zero at different rates. Under this condition, we use the asymptotic analysis method to get the first-order asymptotics of the orbit queue length. Based on this, we prove the joint asymptotic distribution of the number of retrial is a two-dimensional geometric distribution. Then we obtain the joint asymptotic distribution for the waiting time and the result shows that it is a two-dimensional exponential distribution.

To realize joint inspection of power towers and wires by unmanned aerial vehicles (UAVs), and to reduce the negative impact of natural conditions during single point shooting by UAVs, the multi-UAV path planning problem for point-line inspection tasks is proposed. According to the characteristics of this problem, it is modeled as a Hard-clustered Multi-depot Family Traveling Salesman Problem Considering Endurance (HC-MDFTSP-E), and a parallel iterative local search algorithm is designed. This algorithm firstly constructs the initial solutions based on the nearest neighbor heuristic, and then obtains the local optimal solution through three kinds of local search operators. Subsequently, two perturbation and repair mechanisms are applied to escape from these local optimal solutions. After multiple iterations, an approximate optimal solution is ultimately obtained. The experimental results indicate that this algorithm has good performance in terms of solution quality and solving time. Meanwhile, the effectiveness of each key step of this algorithm is further analyzed through ablation experiments.

In response to the significant parameter uncertainties, completely unknown external disturbances, and actuator failures faced by quadrotor UAVs during flight, this paper proposes a robust adaptive controller. Firstly, a quadrotor UAV model is established that is affected by external environmental disturbances and actuator failures, while considering the uncertainties in system parameters. To address the shortcomings of traditional sliding mode reaching laws in eliminating chattering and improving convergence speed, a nonlinear robust adaptive sliding mode controller based on a novel sliding mode reaching law is proposed. Additionally, adaptive laws are designed to estimate system parameters, external disturbances, and actuator failure information that are difficult to measure directly and accurately. To ensure the stability of the closed-loop system, the controller employs the upper bound of unknown lumped disturbances as the switching gain. Experimental results demonstrate that the proposed controller exhibits significantly stronger fault tolerance and disturbance rejection performance compared to two other algorithms.

Building upon the structures of the spectral gradient method and the RMIL conjugate gradient method, we introduce an accelerated RMIL-type spectral conjugate gradient projection method with double inertial steps for solving nonlinear monotone equations. Unlike most acceleration strategies for nonlinear equations, the proposed algorithm incorporates double inertial steps. The algorithm ensures that the search direction possesses sufficient descent and trust region properties at each iteration, independent of any line search. Under weaker assumptions—specifically, without the need for Lipschitz continuity—the global convergence of the proposed accelerated algorithm is established. The numerical experimental results indicate that, with a reasonable selection of algorithm parameters, both proposed algorithms using different spectral parameters achieve excellent numerical performance.

Credit risk assessment is important for optimizing fund allocation and risk control for financial institutions,which is crucial for improving fund utilization efficiency and reducing financial risks. This article aims to optimize the credit risk assessment process through advanced data-driven methods. Firstly,the conditional adversarial variational autoencoder algorithm is proposed,providing balanced data for credit risk assessment. Subsequently,key credit risk assessment indicators are selected by combining Spearman correlation test and Relief algorithm. Furthermore,using methods such as SHAP analysis,partial dependency graph,and cumulative local effects graph,this study reveals the impact mechanism of credit indicators on credit risk assessment during loan approval and repayment stages,providing a basis for subsequent risk assessment strategies,and proposes a random forest optimization algorithm based on credit indicators. The comparative experimental results between the new credit risk assessment strategy and the existing credit rating based strategy show that the proposed algorithm can not only stably generate high-quality sample data,but also the developed credit risk assessment strategy has significant advantages in accuracy compared to existing strategies. Finally,based on the experimental results,this article provides policy recommendations for financial institutions to develop more scientific and reasonable credit risk assessment strategies in a data-driven manner.

Multi-source data are commonly encountered nowadays. The analysis of multi-source data is important for unleashing the data potential and realizing data value. However, many multi-source data still exist in the form of “data silos”. Interconnection between data remains extremely challenging. Meanwhile, the data security issue is a significant concern, making it crucial to achieve secure development of multi-source data while protecting data privacy. To address these challenges, we propose a privacy-protected paradigm for multi-source data analysis. This method is based on the federated learning framework, enabling different data sources to collaborate on data analysis tasks without exposing their raw data. Meanwhile, to further prevent malicious attacks on data, we incorporate differential privacy into federated learning by adding noise to the transmitted data to protect individual-level information. Finally, we demonstrates the practical application of the proposed paradigm using the example of predicting violation risks of enterprises. By combining data from various departments, the prediction accuracy can be well enhanced.

In supply chains where retailers, as small and microenterprises, face capital limitations and information asymmetry, manufacturers, as core enterprises, often aim to enhance overall performance by implementing financing assistance and conveying demand information. Due to several unfavorable factors, manufactures may experience guarantee capacity deficiencies. In this context, they can choose to offer trade credit financing (TCF) or together with guarantee companies to provide credit coguarantee buyback financing (CCB) as signaling mechanisms, but the effectiveness of these strategies remains unclear. To address this gap, this paper establishes an analytical model through the signaling game to compare and examine the signaling role and profit improvement of these financing strategies. Our findings indicate that manufacturers can credibly signal the demand state through both financing contracts, with enhancing signal efficiency through CCB for the manufacturer and TCF for the retailer. In addition, a Pareto improvement can be achieved by adopting CCB at low guarantee fee rates, while through implementing TCF at high ones. These insights shed light on the advantages of CCB and TCF, offering valuable guidance for managers when selecting suitable financing strategies.

In this paper, an unknown system dynamic estimator based sliding mode control scheme is proposed for anti-sway control of overhead cranes with unmatched disturbances and unmodeled dynamics. Through introducing a first-order low-pass filter, the unknown system dynamic estimator is designed to compensate for the unknown system dynamics including unmatched disturbances, such that the disturbance rejection ability can be enhanced. Then, a two-phase power reaching law based sliding mode controller is constructed to obtain the relatively accurate convergence time of the sliding mode variable and guarantee the fast convergence speed. The experimental results show that the proposed method can effectively achieve the satisfactory anti-sway performance and positioning accuracy of the overhead crane.

Aiming at partial area restrictions and the coexistence of fuel vehicles and electric vehicles in urban logistics systems, an optimizing model is constructed for open routing of mixed vehicle fleet by considering the factors such as customer demand, service time, electric vehicle capacity, power consumption, fuel vehicle capacity, fuel consumption and carbon emissions. The goal of the model is to minimize the total distribution cost. According to the delivery process of electric vehicles, an allocation strategy is designed for electric vehicle remaining capacity, which includes pre-positioning strategy, mid-positioning strategy, and post-positioning strategy. On this basis, a hybrid genetic algorithm (HGA) is developed according to the characteristics of the model. The experiments are conducted based on multiple types of instances. The experimental results show that HGA can plan the vehicle routes reasonably by comprehensively utilizing the pre-positioning strategy, mid-positioning strategy, and post-positioning strategy for electric vehicle remaining capacity. HGA can fully exploit non-restricted area customers along the electric vehicle driving routes. The proposed approaches can expand the service range of electric vehicles, make full use of electric vehicle capacity, reduce the number of fuel vehicles, shorten the vehicle travel distance, and decrease carbon emissions and fuel consumption during the delivery process. HGA provides vehicle routing solution for mixed fleet that meets the decision maker's objectives within a very short time. These results demonstrate the feasibility and rationality of HGA.

We propose a multi-bid auction mechanism for the decentralized decision-making problem of parallel machining scheduling, particularly addressing the scenario with order delay penalties. In this mechanism. we design a multiple pricing rule based on customers' private values, allowing customers to submit multiple bids. This approach enables customers to express their preferences more detailly and increases their chances of winning bids. In this mechanism, the winner determination problem is a combinatorial optimization problem with NP-hard property. We propose an exact algorithm based on a dynamic programming framework to solve it. Since the exact algorithm can only solve small-scale problem instances in a reasonable time, we also propose a heuristic algorithm based on Lagrangian relaxation technology to obtain a near-optimal solution to the problem in a reasonable time. Numerical experimental results show that, compared with single bid auction mechanism, multi-bid auctions can significantly improve the price of anarchy of large-scale problem instances, and its total system revenue reaches an average of 88.86% of the global system's total revenue.

Design of experiments with mixtures has been widely used in food industry manufacturing, mixed drug research and development, investment portfolio optimization and other fields. In order to ensure that the design is robust to the changes of the model, many scholars have proposed uniform designs for experiments with mixtures under different criteria. However, the methods of constructing mixture designs based on the acceptance-rejection algorithm or inverse transformation method become inefficient and complex when the number of mixture compositions is large and the constraints are complex. In this paper, we propose an efficient construction method with representative points method for uniform mixture design on a general restricted region. The main idea is to generate uniform training samples on the experimental region based on the Gibbs sampling algorithm, and then compress them into the optimal representative point set under the energy distance criterion by the optimization algorithm. Through numerical example analysis, the design generated by the new construction method has good uniformity and model robustness.

In this paper, we firstly studied the estimation of the population mean of the Logistic distribution under imperfect Z ranked set sampling(IZRSS). We proved that the IZRSS sample mean is an unbiased estimate of the population mean, and when ${\textstyle{1 \over m}} \le p \le 1$, the estimation of sample mean as population mean under IZRSS is more effective than the estimation of sample mean as population mean under simple random sampling (SRS) and the estimation of sample mean as population mean under imperfect ranked set sampling(IRSS). The simulation results and a real data are provided to illustrate our theoretical results for small size.

In engineering practice, exact failure times of individual components are generally not available. In contrast, only the number of component failures and the system's cumulative operating time are known, which leads to the aggregate lifetime data. Inference of lifetime distributions based on the aggregate lifetime data is of great challenge. This paper proposes a moment-based point estimation method, and uses the bias-corrected Bootstrap method to construct confidence intervals for quantities of interest. The maximum likelihood method needs the likelihood function of the aggregate lifetime data, however, it is only applicable to a few distributions that have the closure property with respect to the operation of convolution. Differently, the proposed method does not utilize the likelihood function, thus it applies to more distributions. Finally, both the simulation study and the real data analysis are performed for demonstration and illustration.

The government's implicit guarantee is an important reason for the high leverage of state-owned non-financial enterprises(NFSOE) and the distortion of resource allocation efficiency. The current behavior of withdrawing from the government's implicit guarantee such as breaking the rigid payment will lead to the change of the expectation of the implicit guarantee, which will cause the leverage adjustment of NFSOE. This paper selects Shanghai-Shenzhen A-share state-owned non-financial listed companies from 2010 to 2022 as samples, constructs A dual machine learning model based on the dynamic capital structure adjustment model, and analyzes the impact of implicit guarantee expectation change on the leverage adjustment of NFSOE and its mechanism. It is found that the expected change of implicit guarantee can promote the adjustment of the actual leverage level of NFSOE to the direction of target leverage, and can increase the speed of leverage adjustment. Mechanism analysis shows that the expected decline of implicit guarantee significantly affects the external capital constraints of NFSOE, thus promoting the reduction of leverage ratchet effect, and improving the internal governance mechanism by reducing information asymmetry and agency costs. Heterogeneity analysis shows that the expected decline of implicit guarantee has the greatest impact on the leverage adjustment of NFSOE with low policy burden and local government, and can affect the leverage adjustment of state-owned non-financial enterprises with various functional positioning. This paper provides empirical evidence for the important role of implicit guarantee expectation change in reducing leverage of state-owned non-financial enterprises.

In this paper, we study a model on joint decisions of subcontracting and detailed jobs scheduling. The jobs can be either processed by parallel machines at the manufacturer in-house or subcontracted to a subcontractor with single machine for processing at costs. The subcontracted jobs need to be transported back to the manufacturer in batches after processing. The manufacturer needs to determine which jobs should be produced in-house and which jobs should be subcontracted. Furthermore, it needs to determine a production schedule for all jobs and transportation mode for jobs subcontracted. The objective is to minimize the sum of production cost, transposition cost and subcontracting cost. For the problem under the four different production costs of total completion time, maximum lateness, number of tardy jobs and makespan, the optimality is presented based on parallel scheduling theory. A polynomial time dynamic programming algorithm is designed for the problem of processing cost as the total completion time and three pseudo-polynomial-time dynamic programming algorithms are developed for the other three processing costs, respectively.

Based on the Exponential Penalty Function (EPF), the nonlinear minimax problem is transformed into the unconstrained optimization problem. In this paper, by introducing the spectral parameter and restart condition, we develop the new conjugate parameter and restart direction, a spectral conjugate gradient method with restart procedures for solving the discussed problems is proposed. The search direction generated by the algorithm satisfies sufficient descent property which is independent of the choices of the line search. The global convergence of our proposed algorithm is analyzed with local Lipschitz continuity. Finally, some preliminary numerical experiment results are reported, which show that our proposed algorithm is promising.

To interpolate the specified Frenet frame, curvature and torsion, a method is proposed for the construction and shape optimization of spatial quintic $F^3$ interpolating curves. $F^3$ continuity of spatial curves is a special $k$-th order Frenet frame continuity and ensures the satisfaction of $G^2$ continuity and torsion interpolation. Firstly, a quintic Bézier curve interpolating the given $F^3$ data is constructed, whose control points are expressed with two parameters denoting the lengths of the curve's end tangents. Then, the optimal parameter values are determined by minimizing a quadratic energy function. Finally, by defining the objective function as the integral of a weighted sum of squared curvature and torsion, another better optimization method is proposed. Compared to the previous $G^2$ interpolation scheme, the new methods can generate curve shapes with more satisfactory curvature and torsion profiles, although using a stricter continuity constraint.

This paper considers a two-stage retailer-led supply chain consisted of one supplier and one dominant retailer. Based on option contract and portfolio contract with options, this paper builds the multi-period dynamic game models. By using stochastic dynamic program, this paper analyzes the supplier’s multi-periodic optimal production policy and the retailer’s multi-periodic optimal ordering policy under two contracts, and provides an approximate algorithm to estimate the corresponding policy parameters. On this basis, this paper discusses the effect of two option contract formats on the performances of two member. The result suggests that option contract benefits the supplier, while portfolio contract with options benefits the retailer. Finally, the above conclusions are verified by an example analysis.

In recent years, uncertainty quantification (UQ) has garnered considerable attention. Surrogate models based on polynomial chaos expansion are widely applied in addressing UQ problems. However, in practical applications, the distribution functions of random data are often unknown, posing significant challenges. Based on polynomial chaos expansion, This article constructs a surrogate model based on polynomial chaos expansion and data, and uses such model to estimate data statistics, such as moment estimation, probability density function estimation and cumulative distribution function estimation. Firstly, synthetic data was employed to validate the effectiveness and feasibility of the surrogate model, and then the data-driven polynomial chaos expansion method was applied to deal with some real-world data. Numerical results show that our method yields stable and reliable predictions for a certain class of random data.

Duadic codes are an important class of cyclic codes. It is interesting to construct duadic codes whose minimum distance has the square-root lower bound. In this paper, we propose two construction methods of odd-like duadic codes whose minimum distance has the square-root lower bound. Two classes of odd-like duadic codes with the square-root lower bound on the minimum distance are obtained.

Green consumer credit is an effective financial tool to stimulate green consumption through interest rate incentives. This paper examines the impact of two green consumer credit strategies offered by the financial institution on supply chain operations, including G strategy (credit preference without target green degree) and TG strategy (credit preference with target green degree). The results show that: (1) Increasing the preferential coefficient for green consumer credit can effectively motivate the manufacturer to invest more in green R&D, thereby securing more favorable credit rates for consumers. In addition, the financial institution can mitigate the negative effects of increased default risk by adjusting the target green degree. (2) Both green consumer credit models contribute to the promotion of green products and enhance the profits of the manufacturer and the retailer. Particularly, when the environmental awareness of the financial institution exceeds a certain value, the adoption of the TG strategy can facilitate Pareto-optimal situation among channel members. (3) Compared to manufacturer-independent R&D scenarios, green R&D cooperation between the manufacturer and the retailer can effectively expand the incentive scope of both green consumer credit models and the Pareto improvement interval among channel members.

As an important component of urban public transportation, the fare system and route network design of customized buses play a crucial role in promoting the healthy operation of the customized bus system and improving service quality. Concerning price-elastic demand, this study addresses a new optimization problem of pricing decision and route network design for morning and evening peak commuting customized buses to develop a scientific fare system and route network. The objective is to maximize the operational profit while simultaneously optimizing vehicle routes, passenger allocation, fares, and travel times. Firstly, we formulate the problem as a mixed-integer nonlinear programming model and transform it into an equivalent mixed-integer linear programming model. To effectively solve it, a tailored adaptive large neighborhood search (ALNS) algorithm is developed based on the characteristics of the problem. Two tailored operators: Minimum profit and minimum patronage destruction operators are designed to enhance its effectiveness. Extensive numerical experimental results confirm the efficiency and effectiveness of the proposed model and algorithm. For small-scale instances, the proposed ALNS algorithm can obtain optimal solutions or solutions of higher quality compared to commercial solver CPLEX within 5 seconds. For the typical large-scale Fuzhou case, the proposed ALNS algorithm can obtain satisfactory solutions that are 0.617% and 0.344% better compared to the traditional large neighborhood search algorithm and adaptive large neighborhood search algorithm, respectively. Compared with the problem without considering pricing decisions and penalty costs, considering pricing decisions and penalty costs can increase operational profit by 14.98% and the number of served passengers by 35.51% on average.

Identifying key segments within road networks is crucial for selecting repair objectives and optimizing repair sequences during the post-disaster recovery phase. Traditional methods for identifying key segments have not fully explored the interactions between multiple segments, particularly the significance of studying road network vulnerability under simultaneous disruptions of multiple links. To tackle this issue, this study introduces a machine learning model called Transportation Graph Attention Networks for Criticality Analysis (TGAT) to identify key road segments when facing multiple disruptions. This model is trained on data samples that include scenarios of multiple segment failures, utilizing the Graph Attention Network to evaluate the influence weights between segments and calculating the criticality of each segment based on these weights. The model, trained using mean squared error as the loss function, is capable of identifying segments that play a crucial role in the performance of the road network. Taking the Kunshan City road network as an example, this paper compares the effectiveness of the TGAT method with three other methods: degree centrality, weighted betweenness centrality, and eigenvector centrality, in optimizing repair sequences during the post-recovery phase. Experimental results indicate that the TGAT method is more effective in identifying key segments within the road network compared to the other three methods, and the repair sequence optimized using TGAT further enhances the repair performance of the road network.

The process of optimizing ridepool matching decisions can be seen as a strategic game of decision-making among the ridepool management platform, drivers, and passengers. Based on this fundamental principle, this study introduces a personalized biding strategy (PBS: Personalized Biding Strategy) for ridepooling, incorporating it into the overall ridepool order optimization process to enhance the success rate of ridepooling. Initially, the study identifies two crucial factors that impact ridepool service quality: detour distance and lateness duration. It presents passenger distance pricing functions and lateness penalty pricing functions based on these factors. Building upon this foundation, two models are developed: the Dominant Model of Order Optimization, which aims to maximize net profit, and the Follower Model for Ridepool Pricing (FMR), which aims to maximize actual travel utility. An optimized game model for personalized pricing and order planning considering service quality is constructed, taking into account service quality in personalized ridepool pricing and order planning. In this model, the ridepool management platform, as the dominant party, maximizes its profit by making decisions regarding order allocation and route execution. Subsequently, passengers, as followers, provide their desired ridepool prices based on the services offered by the platform's order planning. A decomposition matching algorithm is proposed to solve this game model. The effectiveness of PBS in improving the ridepool success rate is validated through 56 different scenarios with 20 different parameter combinations. The results demonstrate that the PBS proposed in this study significantly improves the profitability of the ridepool platform, the overall utility of passengers, as well as the ridepool success rates for both vehicles and passengers, when compared to the Average Biding Strategy (ABS) and the Fixed Biding Strategy without considering service quality (FBS).

The motor nervous system is a small but complex comprehensive system, and the information transmission process of the motor nervous system is often affected by noise generated by changes in the external environment, the randomness of biochemical reactions in cells, the randomness of ion channel switching, etc. To study the influence of colored noise on the motor neural information transmission process, In this paper, the fully discrete spectral Galerkin algorithm is proposed for the random FitzHugh-Nagumo neural system information conduction model under colored noise interference, and the stability of the numerical solution of the algorithm is analyzed. The information transmission process of random FitzHugh-Nagumo nervous system under the interference of a single colored noise sample and the average meaning of multiple colored noise samples was simulated, and compared with the deterministic neural information transmission process without noise interference, the effect of colored noise on the nervous system information transmission was analyzed. It is helpful to explain the dynamic behavior of information conduction in complex motor nervous system under the influence of noise.

This paper studies the formation control problem of multi-unmanned ground vehicles with uncertain nonlinear dynamics. First, a formation motion model of multi-unmanned ground vehicles is established based on leader-follower method, which describes the leader-follower relationship among individual unmanned vehicles. The uncertain nonlinear dynamics are learned online by neural networks. Then, based on the target tracking mechanism, an adaptive neural network direction controller is designed by introducing a sliding mode surface. Simultaneously, combining with backstepping control technique, a target tracking mechanism based adaptive neural network propulsion controller is presented to achieve integrated longitudinal and lateral formation driving of multiple unmanned vehicles. Lyapunov stability theory is used to analyze and prove the stability of the closed-loop multi-unmanned vehicle formation control system, and the formation tracking error can converge to the neighborhood of origin. Finally, the simulation results verify that the formation control and formation maintenance are realized under the proposed control algorithm.

Online freight platforms face challenges such as fierce price competition due to homogeneous pricing strategies. This paper addresses this issue by developing a differentiated pricing model based on two-sided market theory. The model considers the interplay between platform technology level and user quality level. The study examines three user affiliation structures: single-homing, multi-homing, and both sides of users are partially multi-homing. It investigates the impact of technology and user quality on the platform's equilibrium pricing and profits using analytical methods and simulations. When both sides of users are single-homing, a two-part pricing strategy is more advantageous, with profits increasing with better technology and user quality. Under multi-homing, the optimal pricing strategy depends on an equilibrium condition. Higher user quality favors two-part pricing, while higher technology level favors registration fee-only pricing. When both sides of users are partially multi-homing, registration fee-only pricing is more optimal. Both technology and user quality positively impact profits, with technology playing a bigger role. The research provides insights for online freight platforms to implement differentiated pricing based on their market position. Platforms should focus on technological innovation, improve user quality, and optimize pricing dynamically，which allows them to achieve differentiation and profitability in the evolving freight market.

Small and medium-sized enterprises (SMEs) in supply chains often face significant financing difficulties, which hinder their high-quality development. Blockchain technology-driven third-party financial service platforms offer a new approach to solving this issue. This paper explores the government's regulatory behavior strategy, the third-party financial service platform's blockchain information sharing behavior strategy, and the small and medium-sized enterprises' financing integrity behavior strategy by constructing a tripartite evolutionary game model of "Government-Third-party financial service platform-SMEs". It conducts an analysis on the stability of the equilibrium points in the tripartite evolutionary game and discusses the impact of blockchain technology cost, government regulatory cost, government reward and punishment intensity, and enterprise income on the equilibrium of the tripartite evolutionary game through parameter sensitivity analysis. The results show that: 1) Whether a third-party financial service platform chooses to share information through blockchain depends not only on the cost of blockchain technology but also on the government's rewards and punishments for the platform and small and medium-sized enterprises (SMEs), as well as the size of the returns from default risks. 2) Conventional wisdom holds that digital supply chain finance driven by blockchain is inevitably superior to traditional supply chain finance. However, this study finds that only when the government dynamically rewards and punishes platforms to improve the transparency of supply chain financial information and constrains enterprises to reduce financing default rates under specific circumstances, will the financing efficiency of blockchain supply chain finance surpass that of traditional supply chain finance.

The National Independent Innovation Demonstration Zone (NIIDZ), as an important engine leading innovative development, takes institutional and policy reforms as a starting point to radiate and drive the coordinated development of surrounding regions. The gradual improvement of the high-speed rail (HSR) network has opened up a new pattern for the “dual circulation” and expanded the scope of the NIIDZ's innovation spillover effects. Based on data of HSR city pairs from 2008 to 2019 in China, this paper examines the impacts and mechanisms of the improvement in innovation levels of ordinary cities after the opening of HSR connected to NIIDZs by applying a staggered DID model. The empirical results are as follows. Firstly, the opening of HSR connected to NIDDZs significantly improves the innovation levels of ordinary cities. Secondly, the innovation spillover effects are more pronounced for cities in the eastern region, cities with a better innovation environment, and large-scale cities. Thirdly, the innovation spillover effects are realized by utilizing innovation endowment, government-guided innovation and demonstration driving effects. This paper provides empirical evidence and policy insights for innovation-driven development in the context of HSR network. It optimizes the spatial allocation of innovation resources and accelerates the development of new quality productive forces, achieving high-quality economic development.

Exploring the heterogeneity characteristics of nodes and edges in emergency logistics networks has a great impact on the location-allocation optimization problem throughout the entire emergency system. First, for the disaster preparedness stage, based on the complex network theory, this paper selects the node vulnerability indicators to characterize the heterogeneity of various emergency reserve facilities. The vulnerability indicators of transportation connected edges among emergency reserve facilities, and between emergency reserve facilities and disaster-affected demand areas, are also incorporated into the emergency location-allocation optimization decision-making. And a multi-objective location-allocation model is formulated, for comprehensively balancing vulnerability, efficiency and cost-effectiveness in the emergency logistics network. Then, taking the emergency reserve network covering 13 cities in Jiangsu Province as a case example, a NSGA-II algorithm is designed to solve the constructed model and perform a numerical simulation. Not only the sensitivity analysis for the parameters is analyzed, but also the comparative analysis is discussed. The simulation results show that the location-allocation optimization problem for heterogeneous emergency reserve facilities from the perspective of vulnerability, not only performs well in cost-effectiveness and allocation efficiency, but also significantly improves the robustness and resilience of the entire emergency logistics network. This work provides some useful managerial insights for emergency decision-makers to make an effective disaster preparedness plan.

This paper uses the OP method and event study method to study the impact of blockchain technology application on enterprise total factor productivity and stock price, and analyzes whether the application of enterprise blockchain technology has effectively promoted the improvement of enterprise total factor productivity, or just created more "foam" for the company's stock price? The main conclusion of this article is that the application of blockchain technology mainly promotes the improvement of total factor productivity by reducing financing constraints, and has a greater impact on the improvement of total factor productivity for large enterprises and state-owned enterprises; In addition, after the application of blockchain technology in enterprise announcements, the company's stock price level has significantly increased, meaning that the company can obtain higher stock premiums from blockchain technology based announcements.

Aimed at the problems of insufficient optimization performance and accuracy of SMA in solving wind farm layout optimization problem (WFLOP), and the slow convergence speed and premature convergence to local extreme values in SMA, an improved slime mold algorithm based on adaptive contraction and genetic learning strategy is proposed. First, a wind farm layout model is initially established based on the specific environmental conditions. Then, for the problem of premature convergence to local extreme values, a genetic learning strategy is introduced to enhance the convergence speed and global search ability of SMA, resulting in the GLSMA. Finally,Aimed at the problems of WFLOP, the maximum rule coding solution vector is adopted, and an adaptive contraction strategy is designed to update the position of slime moulds using the power generation of wind turbines, which improving the solution accuracy. The experimental results show compared to SMA, Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), and Genetic Learning Particle Swarm Optimization (GLPSO), GLSMA has faster convergence speed and higher optimization accuracy in 19 test functions, and the A-GLSMA has higher performance than Genetic Algorithm (GA) in solving WFLOP under two wind direction distributions.

Aerocapture is one of the key technologies for low-cost transport, with only one chance to achieve it, and the reliability requirement is very high, so it puts high requirements on the autonomy, accuracy and robustness of guidance and control, and its design and theoretical analysis are difficult. At present, no research results on the control problem of aerocapture have been seen. For the control problem of aerodynamic entry, this paper adopts the PD control method adopted by engineering, but for the complex aerodynamic angular kinematics, its stability analysis is difficult. In this paper, an in-depth study is carried out for the nonlinear attitude dynamics of aerocapture, and its open-loop nature and closed-loop performance are analyzed by using the method of small-angle linearization. Finally, a joint mathematical simulation of guidance and control under the consideration of navigation deviation and multiple uncertainties is carried out to verify the effectiveness of the proposed method. The stability analysis method proposed in this paper can be extended to general aerodynamic entry control design, thus providing theoretical support.