This study approaches typhoon landfalls as exogenous climate risk events, designating the moment of landfall as the critical intervention point. Utilizing the difference-in-differences (DID) methodology, the research examines the influence of typhoon disasters on the stock returns of publicly traded companies in China, and assesses how financial risks propagate through supply chain networks triggered by typhoon disasters. To gain a more nuanced understanding of these effects, the paper engages in a detailed mechanism analysis by examining the intensity of digital transformation. The results suggest that typhoon disasters have a significant and detrimental impact on the stock returns of firms located in affected areas, with this effect rippling through to their suppliers and customers via the intricate web of supply chain connections. Moreover, the study uncovers a distinct asymmetry in the spillover effects between suppliers and customers. Specifically, the research highlights that the level of digital transformation is instrumental in alleviating the financial risks associated with typhoons and serves as a protective barrier against the adverse effects on stock returns. Finally, a comprehensive suite of robustness checks reinforces the validity and reliability of the study’s conclusions.

With the increasing demand for rural delivery in mountainous areas, the routing problem of rural delivery logistics in mountainous areas (RPRDLMA) has become an academic hotspot. Based on the background of rural passenger, cargo and postal integration development, the RPRDLMA under the cooperative distribution of bus-electric vehicle-drone (RPRDLMA-CDBEVD) is studied in this paper. Firstly, the village service points are divided into type TC and type FC, meaning that they are served by EVs or by drones, according to their geographic location, distribution characteristics and volume of cargo delivered or mailed. Next, a continuous function of bus idle capacity is established based on the tidal rural passenger flow characteristics. Then, the RPRDLMA-CDBEVD model is constructed with the goal of total cost minimization. Specifically, the total cost includes commissioning cost, capacitybased cost, distance-based cost, time-based cost and electricity consumption cost. In order to solve the model, a hybrid algorithm of multi-constraint modified clustering algorithm and improved adaptive genetic algorithm (MCDCA-IAGA) is designed. The experimental results and case studies show that the collaborative delivery mode of passenger shuttle bus electric vehicle unmanned aerial vehicle effectively reduces delivery costs by 2.9% and delivery time by 8.6%, providing a feasible solution for logistics path planning in mountainous and rural areas.

The rapid expansion of shared mobility services has introduced innovative solutions for urban transportation. Ride-hailing platforms, facilitated through user-friendly smartphone applications, seamlessly connect individual preferences with immediate vehicle availability. In carpooling services, passengers can share a ride in the same vehicle, setting their respective destinations as waypoints to increase vehicle utilization. This study proposes a carpooling and dispatching model for ride-hailing services based on a self-attention reinforcement learning network. Initially, a carpooling travel topology network is constructed, considering factors like passenger pick-up and drop-off times and locations. An on-demand algorithm is designed to identify ride-hailing orders suitable for carpooling. Subsequently, a self-attention reinforcement learning network is employed for order dispatching optimization. Through the implementation of policy gradient techniques for learning and training, the integration of masking methods ensures the efficacy of order dispatching. Leveraging the strengths of “offline training & online decision-making”, the proposed strategy tackles the challenges of enhancing the real-time responsiveness of large-scale ride-hailing dispatching services. Finally, the real-world case study is conducted based on ride-hailing orders in Beijing, China. Results underscore the efficiency of the order dispatching algorithm in achieving near-optimal route selections while reaching a real-time demand response. Although carpooling slightly increases passenger waiting times, it significantly boosts ride-hailing operational efficiency, alleviates traffic congestion, and mitigates environmental pollutants.

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

This paper examines the impact of digital inclusive finance on the operational performance of regional commercial banks using a fixed-effects model based on balanced panel data from 78 urban and rural commercial banks spanning from 2011 to 2021. The results indicate a significant negative relationship between the two. This conclusion remains valid after addressing endogeneity issues and conducting robustness tests, suggesting that the current competitive crowding-out effect still exerts a substantial influence. Further analysis through moderation and threshold effects reveal that the technology spillover effect of digital inclusive finance drives business innovation and enhances risk-taking capacity among regional commercial banks, thereby mitigating their negative effects, with the moderation effect on risk-taking being more pronounced. The threshold parameter estimates show that business innovation has a more significant negative convergence moderation effect on rural commercial banks, while risk-taking exhibits a more significant negative convergence moderation effect on urban commercial banks. The findings of this study provide important practical insights for the digital transformation of regional commercial banks and the sustainable and healthy development of regional economies.

Small and medium-sized enterprises (SMEs) in supply chains often face significant financing difficulties, which hinder their high-quality development. Block-chain 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.

To solve the problem of instability and imprecision of carbon price prediction with single information source, single decomposition technology and single prediction method, a hybrid prediction model of carbon price based on multi-source data feature and multi-scale analysis is proposed, called CPS-MEMD-SVR-MLR. 1) Multi-source data analysis: This effectively integrates historical carbon trading prices related to carbon prices, macroeconomic development levels, fossil energy prices, exchange rates, and social media sentiment data based on news text information; 2) Multi-scale analysis: This uses multiple empirical mode decomposition technology (MEMD) to decompose multi-source data into prediction features under different modes; 3) Hybrid prediction analysis: This uses fuzzy entropy theory to orderly integrate econometric model and machine learning models, and then integrates the predicted values of each mode into the final result. This paper takes the carbon price of the European Union (EU) from February 11, 2015 to February 27, 2023 as a case study. Based on seven scenarios and DM tests, the results show that: 1) The prediction accuracy of the hybrid model proposed in this paper is better than other comparison models; 2) Social media sentiment can improve the prediction accuracy of carbon price, and it is better than the single factor prediction; 3) The introduction of MEMD decomposition can significantly improve the prediction accuracy of carbon price.

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 are 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 are 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 is 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 this paper, the problem of output consensus control for heterogeneous multi-agent systems with denial-of-service (DoS) attacks is studied. First, aiming at the problem that the cyber attack behavior is changeable and its statistical characteristics of attack modeling method based on dual hidden Markov model is proposed, which converts the communication interruption caused by the attack behavior into the communication topologies switching of multi-agent system. Second, a distributed asynchronous dynamic observer is designed to solve the asynchronous problem when the communication topology mode (CTM) and the transition probability mode (TPM) do not match. Third, based on the stochastic Lyapunov theory and linear matrix inequality technique, sufficient conditions for the solvability of the system output consensus problem are obtained. Finally, the feasibility and effectiveness of the results are illustrated through a simulation example.

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.

A data-model dual-driven stochastic process model is proposed for equipment health diagnosis and remaining life prediction problems. Firstly, a new signal scalarization method is proposed for non-vibration signals, so that continuous signals can be scalarized to form a data type that can be input to the hidden semi-Markov model. Secondly, a new deterioration kernel-based modified hidden semi-Markov model (DK-MHSMM) is proposed to realize the process of mapping the observation scales of mechanical equipment to the potential states, and to dynamically screen the equipment state patterns. Then, the adhesion coefficient is introduced into DKMHSMM, and the genetic algorithm and the co-evolutionary algorithm of the Sea Sheath swarm algorithm are used to estimate the model parameters instead of the conventional EM parameter estimation method, and the corresponding remaining life prediction method is proposed according to the characteristics of the whole life distribution of the equipment and the current state values of the equipment. Finally, the method is validated using the turbofan engine dataset, which verifies the effectiveness and feasibility of the method.

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 “ual 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.

This paper addresses the fault-tolerant consensus problem in heterogeneous multi-agent systems based on an adaptive distributed event-triggered mechanism, with a focus on actuator faults and bounded external disturbances. Compared to existing research, this paper introduces distributed intermediate variables to model the closed-loop error in heterogeneous multi-agent systems. Additionally, a fault observer is designed to estimate actuator fault states in real time and compensate for actuator faults by adjusting control gains online. Furthermore, an adaptive distributed event-triggered mechanism is designed, which conserves communication resources and successfully avoids the Zeno phenomenon through dynamic interactions and information sharing among agents. Moreover, a fault-tolerant controller based on a distributed adaptive event-triggered mechanism is designed to ensure that agents maintain consistent behavior even in the presence of actuator faults or external disturbances. Finally, the feasibility and effectiveness of the proposed method are validated through Matlab simulations, providing a practical solution for real-world applications.

Vehicle routing problem in takeout delivery is characterized by dynamic order arrivals and the need for continuous updates on rider status. To address this challenge, a multi-objective dynamic optimization model maximizes the interests of customers, platforms, and riders while considering rider physical condition and road familiarity. A dynamic weights multi-objective heuristic algorithm adaptively adjusts the weights of different objectives based on real-time data, optimizing delivery paths dynamically. Results demonstrate superior performance compared to the Gurobi solver in key metrics such as order fulfillment time, rider idle time, and platform profit. This highlights the effectiveness of the method in handling the complexities of real-world takeout delivery operations. Analysis of dispatch strategies for different types of riders provides valuable insights for operational decision-making. In summary, this research offers a practical solution to enhance delivery efficiency and customer satisfaction while ensuring fair treatment of riders, contributing to improved operational strategies for takeout platforms.

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.

The integration of intelligent fault diagnosis and alarm technology into automatic production lines can avoid production interruptions and economic losses caused by faults. Traditional fault diagnosis method collects physical characteristic data reflecting mechanical fatigue faults through technologies such as machine vision for prediction. But a significant cost for high-precision fault diagnosis will be induced by the large amount of noise present in complex working conditions. Therefore, a method for mining potential occurrence patterns of faults based on production line data was proposed. Firstly, five types of highly generalized derived feature variables were constructed to characterize the faults based on the direct production data of the production line. Secondly, a CNN-LSTM-Attention model was constructed for fault diagnosis and early warning with the scarce fault data balanced by near neighbor under-sampling (Near Miss). Finally, numerical experiments were conducted using a total of 75 million data from 10 production lines, and compared with traditional machine learning, CNN, and LSTM models. The experimental results illustrated that the prediction accuracy of the model reached 99.97%. It demonstrated that effective production line fault diagnosis and warning can be achieved at the level of data mining and feature engineering without advanced learning methods and mechanism features.

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.

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 “ata 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.

Using ESG rating data of Sino-Securities Index Information Service from 2009 to 2020, this paper examines the impact of listed companies' ESG rating on the level of stock price, financial and operational risk in the next year. The study finds that better ESG rating has a significant inhibitory effect on all three risk levels of enterprises in the next year. Specifically, for the risk of stock price crash, ESG rating higher than the benchmark level, as a strong market signal, has a more significant reduction in the risk level of stock price crash. The trading volume of individual stocks, which reflects the attention of investors, has an intermediary effect on ESG to reduce the risk of enterprise stock price crash. ESG of large-scale enterprises that occupy an important position in the market and attract more attention from investors has a stronger inhibitory effect on the risk of stock price crash; In addition, the negative relationship between ESG and the risk of stock price crash is more significant after the implementation of the “Environmental Protection Law”. For financial risk, ESG has a marginal diminishing effect on reducing corporate financial risk, and the improvement of ESG rating from low to medium can improve the level of corporate financial risk. At the same time, enterprises' voluntary disclosure of non-financial information could strengthen the inhibitory effect of ESG on financial risks. For operational risk, ESG rating has a marginal diminishing effect on reducing operational risk; At the same time, the nature of equity has a moderating effect on the reduction of operating risks by ESG rating. Compared with private enterprises, ESG has a stronger inhibition effect on the operation risk of state-owned enterprises. Finally, the sub-sample heterogeneity test results based on the length of enterprise life in this paper show that the inhibitory effect of ESG rating on risk is stronger for enterprises with a long establishment age, but weaker for enterprises with a short establishment age.

The registration-based system represents a pivotal reform in China’s capital market development. The inquiry system reform aims to transfer pricing authority more substantially to market participants and enhance IPO pricing effciency. Consequently, systematic research evaluating IPO pricing effciency and the effects of inquiry system reforms under the registration-based framework have attracted considerable scholarly attention. This paper employs a bilateral stochastic frontier model to measure IPO pricing effciency across 2365 listed companies in China’s A-share market from 2016 to 2023, and empirically verifies the systematic impact of inquiry system reforms on IPO pricing under the registration-based system. Research findings indicate that during 2016–2023, underpricing effects dominated overpricing effects in A-share market initial offerings, with overall pricing 7.06% below reasonable levels, exhibiting distinct characteristics across different boards, years, ownership structures, and break-even status. The inquiry system reform generally elevated initial offering prices, primarily driven by the distinctive characteristics of the STAR Market, while other boards demonstrated declining trends. During the registration-based system expansion phase, significant competitive dynamics emerged between the STAR Market and ChiNext Board, while reform effects on the main board remained limited. Furthermore, the study identifies two critical transmission pathways explaining these impacts: The number of inquiry institutions and the effectiveness of price quotations. Based on these conclusions, this paper proposes targeted recommendations for regulatory authorities to guide future inquiry system reforms.

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.

Comprehensively considering the assumptions of the option pricing model and the change characteristics of the underlying asset price of carbon options, based on the EUA DEC22 carbon futures option market data from January 4, 2021 to September 27, 2021, the genetic algorithm is used to estimate the parameters of the pricing model. The option pricing performance of the B-S model, fractal Brownian motion model and Heston stochastic volatility model are compared and analyzed according to the stabilized parameter estimates, the most suitable pricing model for the carbon option market is selected, and to provide relevant suggestions for the improvement and smooth operation of the carbon market pricing mechanism. The results show that the Heston stochastic volatility model has the best pricing performance in the carbon option market, followed by the fractal Brownian motion model, and the B-S model is relatively poor. Therefore, the pricing of carbon options based on the Heston stochastic volatility model can improve the pricing accuracy of carbon options, help complete the pricing mechanism of the carbon market, avoid the risk of carbon market transactions, ensure the smooth operation of the carbon market, and promote the realization of the “dual carbon” strategic goal.

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.

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 paper 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 is employed to validate the effectiveness and feasibility of the surrogate model, and then the data-driven polynomial chaos expansion method is 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.

The cryptocurrency mining activities has a high energy consumption, which means that there is a potential linkage mechanism between the cryptocurrency market and the energy market. The frequent occurrences of various unexpected events have brought a huge impact on the global financial market, which has further intensified the risk transmission among financial markets. This study firstly uses the TVP-VAR extended joint correlation method to measure the total spillover between cryptocurrency and energy markets. Subsequently, the time-varying causality method and quantile-to-quantile regression method are used to study the impact mechanism of unexpected events on the risk spillover between cryptocurrency and energy markets. This can help to understand the magnitude and direction of the impact of news media coverage (such as, media coverage rate of the COVID-19 and news sentiment of the Russia-Ukraine conflict) and uncertainties (such as, geopolitical risks and economic policy uncertainty) on total spillovers between the two markets. Finally, the empirical results show that: First, the total spillovers between the two markets increase significantly during the Sino-US trade friction, the COVID-19 and the Russia-Ukraine conflict, and reach the highest point during the COVID-19 epidemic. Second, the impact mechanism of diverse unexpected events on total spillovers is different. During Sino-US trade frictions, geopolitical risks have a causal effect on total spillovers between the two markets, and exhibit a positive effect at the higher quantile. During the COVID-19 pandemic, both the media coverage rate of the COVID-19 and the economic policy uncertainty index have a causal effect on the total spillovers between the two markets, and also show a positive effect at the higher quantile. During the Russian-Ukrainian conflict, the Russian-Ukrainian conflict news sentiment index has no significant causal effect on the total spillovers between the two markets, but it has a negative effect at the higher quantile. In terms of the degree of impact, the media coverage rate of the COVID-19 has the strongest impact on the total spillovers between the cryptocurrency market and the energy market.

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 paper 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 paper provides policy recommendations for financial institutions to develop more scientific and reasonable credit risk assessment strategies in a data-driven manner.

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

This paper focuses on the situation where the response variable in sampling surveys does not obey the normal distribution as required by traditional small area estimation models. It investigates the small area estimation method for the target parameters of the Fay-Herriot model (FH model) based on the transformed response variable, proposing an empirical best predictor (EBP) for the target parameters and its mean square error (MSE) estimator. When inversely transforming the transformed EBP, a conditional expectation bias correction term is added to correct the bias introduced by the inverse transformation. A second-order approximate MSE estimator is introduced that is not restricted by the estimation method of the model parameters. Through numerical simulation, the MSE estimation method presented in this paper is compared with existing methods, revealing that the method enhances the adaptability of small area estimation models to data with response variables that have a skewed distribution and improves the precision of target parameter estimation, with the added benefit of having a simple estimator form. Finally, the research method of this paper is used to measure the per capita financial assets of urban and rural residents in some provinces and counties of China, and the effectiveness of the method is verified through the measurement results.

Among traditional gradient-like methods for solving unconstrained optimization problems, conjugate gradient method has the advantages of small storage requirement, simple iterative form and fast speed of computation. Barzilai-Borwein (BB) gradient methods are a class of improved algorithms for steepest descent method. They have good theoretical convergence and can avoid the zigzag phenomenon of steepest descent method. Spectral conjugate gradient methods are a class of conjugate gradient methods with good numerical performance and they use one of stepsizes in BB gradient methods as the spectral parameter. In this paper, we choose the parameter in a family of Dai-Kou (DK) conjugate gradient methods as the negtive of the reciprocal of another stepsize in the BB gradient methods. Furthermore, by combining Fletcher-Reeves (FR) conjugate gradient method which has good theoretical convergence with a variant of Polak-Ribière-Polyak (PRP) conjugate gradient method which has good computational performance, we present a class of hybrid truncated conjugate gradient methods with a convex combination form. In order to improve the numerical performance of this class of methods, we present a class of hybrid truncated spectral conjugate gradient methods with a restart step by combining a restart strategy and the idea of spectral conjugate gradient method. The choice of the spectral parameter guarantees that the methods in this paper possess the sufficient descent property without relying on any line search. Numerical experiment results show that the algorithm given in this paper has better numerical performance than the DK, DK+, PRP and a hybrid Dai-Yuan (HDY) conjugate gradient algorithms. Finally, we verify again the effectiveness of our algorithm by applying them for solving image restoration problems.

A composite optimization algorithm based on deep reinforcement learning and multi-objective particle swarm optimization (MOPSO) is proposed to solve the problem of large-scale and irregular multi-color cut order planning (MCOP). Firstly, the MCOP multi-objective optimization model is established with the production error and production cost as optimization objectives, combined with the constraints such as the number of equipment and the number of layers. Secondly, the global optimization solution strategy based on twin delayed deep deterministic policy gradient (TD3) is designed, the Markov decision process of TD3 algorithm is constructed, and the global solution set is obtained by designing the reward function based on error and cost. Furthermore, the local optimization algorithm of MOPSO cut order planning on linear decoupling is proposed, and the decoupling strategy of linear programming is designed to realize the fast decoupling calculation of the size combination matrix and the fabric layer matrix, which effectively improves the solving accuracy and speed. At the same time, the Pareto optimal solution of MCOP problem is obtained through elite file strategy. Finally, the feasibility and superiority of the proposed method are verified through the actual case and the algorithm comparison experiment, which can provide a good reference value for garment enterprises.

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

Covariance estimation poses a crucial challenge in the analysis of high dimensional data, which in turn is prone to the two phenomena of heavy-tailed distributions, small samples, and in these cases, the traditional estimation methods (e.g., sample covariance matrix) prove inadequate for such heavy-tailed data, given their lack of accuracy. In cases where heavy-tailed high dimensional data represented as tensors (multi-dimensional arrays), harnessing the tensor structure is a good choice for achieving dimensionality reduction. To this end, this paper proposes novel structured regularization methods for estimating the covariance of heavy-tailed tensor-valued data. In this paper, the heavy-tailed tensor data are first truncated, then the truncated sample covariance matrix is computed, and the CP decomposition will be applied to find an approximation in the form of Kronecker product of multiple matrices of the truncated sample covariance matrix, and finally imposes a banded or tapering structure for each of the small matrices obtained by the decomposition. Simulation results show that the proposed estimators have excellent performance for different degree of heavy tailing and different sample sizes. Anomalous temperature datasets with heavy-tailed distributions is analysed using the estimation method proposed in this paper.

For multi-objective switching constraint optimization problems, the presence of switching constraints can render the Karush-Kuhn-Tucker (K-K-T) conditions potentially invalid at feasible points. Therefore, it is necessary to investigate weaker stability concepts and applicable optimality necessary conditions. This paper first defines the generalized Guignard constraint qualification for such problems. Under this constraint qualification, we then construct and prove the necessary conditions for optimality. Due to the possible discontinuity of the feasible region in switching constraint programming problems, traditional nonlinear programming methods are difficult to apply directly. Finally, we propose a relaxation model for this problem and prove that, under certain assumptions, The Pareto efficient solution set and the Pareto weakly efficient solution set of the relaxation model converge to the Pareto efficient solution set and the Pareto weakly efficient solution set of the original problem in the sense of Kuratowski-Painlevé respectively. Additionally, the Pareto KKT set of the relaxation model converges upwards to the set of Pareto weakly stable points of the original problem.

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 the proposed algorithm is analyzed with local Lipschitz continuity. Finally, some preliminary numerical experiment results are reported, which show that the proposed algorithm is promising.

Motif is a form of high-order network structure. This paper takes the three-node simple motifs as the research object. Based on the connection types of the edges, the missing edge matrix representing the borderless structure of the networks is introduced. Using matrix product, Hadamard product and special operation based on matrix elements, the representations of motif adjacency matrix in networks are studied. The matrix expressions of the open motif adjacency matrix are given for the directed unweighted networks. For the directed weighted networks, according to the new method of dealing with the bidirectional edge weight, based on the arithmetic mean and geometric mean of each edge weight of the motifs, the matrix expressions of the corresponding motif adjacency matrix under the two modes of overall weighting for motifs are obtained. A test example is given and the relations between expressions of motif adjacency matrix of four network types are analyzed and discussed.

Firstly, the port-Hamiltonian differential-algebraic systems are transformed into the port-Hamiltonian ordinary differential systems with parameter $\varepsilon$. Then, based on the parameteric ordinary differential systems, two structure-preserving model reduction methods are proposed. The first method is parametric moments matching: Constructing the parametric moments based on the frequency parameter $s$ and the embedding parameter $\varepsilon$ of the parametric systems, and then obtaining the reduced-order models of the parametric systems through parametric moments matching. The reduced-order systems match the parametric moments of the original systems. Finally, by taking the embedded parameter $\varepsilon = 0$, the structure preserving reduced-order models of the original port-Hamiltonian differential-algebraic systems are obtained. The second method is low-rank balanced truncation: Using Laguerre functions to construct the low-rank decomposition factors of the controllability and observability Gramians of the parametric ordinary differential systems. The approximate balanced systems are obtained through projection, and finally, the reduced-order models are constructed by truncating the states corresponding to smaller Hankel singular values. This procedure offers adaptability and enables the construction of reduced-order models meeting specified accuracy conditions while maintaining lower computational complexity. Both algorithms use Gram-Schmidt process to construct new projection matrices, thereby preserving the differential structure of the original system. Finally, the effectiveness of the algorithms is demonstrated through a numerical example.

This paper aims to introduce the demographic analysis model of the American and establish a demographic analysis model, which is suitable for application in China. Literature interpretation and probability modeling methods is used to study demographic analysis models and related issues. The results show that the research of demographic analysis model in China is still in its infancy, and the establishment of demographic analysis model in China can not copy the American demographic analysis model, but should be improved and innovated in combination with the actual situation in China; The establishment of demographic analysis models can be divided into two levels. One is to establish a demographic analysis model based on comprehensive data, without calculating sampling variance, the other is to establish a demographic analysis model based on sampling survey data, which needs to calculate its sampling variance by jack knife method approximately; The advantage of the demographic analysis model is that it fully utilizes population administrative record data such as birth, death, and international migration, saves data collection costs, and is independent of the population census, the disadvantage is that the estimation results are uncertain, especially with significant differences in the estimated results of international net migration count. The demographic analysis model is expected to be applied to estimate the net error of China’s 2030 population census, creating a precedent for China to use demographic analysis model in this field.

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.

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 issuance of green bonds is not only a key driver for enhancing enterprises' new quality productivity, but also an important means of deepening environmental responsibility practices. Based on the data from A-share listed enterprises from 2010 to 2022, this study employs a difference-in-differences model to analyze the impact of green bond issuance on enterprises' new quality productivity in China. The findings are as follows: 1) The issuance of green bonds significantly promotes the improvement of new quality productivity; 2) Mechanism analysis shows that the impact of green bond issuance on enterprises' new quality productivity lies in enhancing green innovation capability and reducing enterprise financing costs; 3) Heterogeneity analysis reveals that the promotion effect of green bond issuance on new quality productivity is more pronounced for non-state-owned enterprises, small and medium-sized enterprises, and high-pollution enterprises; 4) Further analysis indicates that public environmental attention and regional environmental regulations play a significant reverse moderating role in the process of improving enterprise new quality productivity. The results of this study provide empirical evidence for enhancing enterprises' new quality productivity, achieving green development transformation, and promoting high-quality development of enterprises.