This paper studies the reinsurance contract formulation problem under the game between two competitive reinsurers and one insurer. The insurer is engaged in two kinds of dependent insurance business, and the dependence is characterized by the number of claims and the amount of claims. The insurer signs reinsurance contracts with different reinsurers for the two kinds of insurance business. For these two kinds of insurance business, we don't restriction the reinsurance type, and the specific form of the reinsurance is determined by solving the stochastic optimization problem considered by the insurer and two reinsurers. Through relative performance, we quantify the competition between the two reinsurers, and establish a non-zero-sum stochastic differential game between them, and then establish a leader-follower stochastic differential game between the insurer and the two reinsurers. Under the mean-variance criterion, the explicit optimal reinsurance contract, i.e., the insurer's optimal claim risk sharing strategy and the two reinsurers' optimal reinsurance pricing strategy, is obtained by using stochastic analysis and stochastic control technology. Finally, the influence of model parameters on the optimal reinsurance contract is explored through numerical experiments.

This paper deals with a Tikhonov regularized primal-dual dynamical system featuring variable mass for solving convex optimization problems with linear equality constraints. Under suitable assumptions and through the use of energy functions, the convergence rates are first established for the primal-dual gap, the residual of the objective function, the feasibility measure, the velocity vector, and the gradient norm of the objective function along the trajectories. Then, the strong convergence of the primal trajectory of the dynamical system towards the minimal norm solution of the linear equality constrained convex optimization is demonstrated. Moreover, numerical experiments are conducted to illustrate the obtained results.

In practice, overhead cranes usually suffer from double pendulum effects which make the control of the crane systems more complex. However, for most existing traditional control methods, the people often not taken into account this double pendulum phenomenon. In addition, some trajectory planning methods have been proposed to improve work efficiency for crane systems, but the rope length in these methods is constant. To address these problems, this paper proposes a novel trajectory planning method with lifting/lowering operation for double-pendulum overhead crane. Specifically, to improve the efficiency and security of the transportation process, the trajectory is designed into three phases (acceleration, constant speed and deceleration). For each stage, the desired swing angle curve is directly constructed according to the requirements of the swing angle constraint and the zero residual swing angle, and the acceleration trajectory of the trolley is further obtained through the analysis of the dynamic equation of the double pendulum system. Then, the optimization mechanism is introduced, and the objective function about the transportation time and the maximum swing angle is constructed, and the trajectory planning problem is transformed into an optimization problem of the objective function. Finally, the simulation results are shown to verify the effectiveness of the proposed trajectory planning method.

In this paper, the controllability of signed multi-agent networks based on the consensus protocol is studied from the perspective of topological structure. Firstly, based on the eigenvectors of Laplacian matrix and the leader-follower structure, the necessary and sufficient algebraic condition for the controllability of undirected signed topologies is obtained. According to the condition, for the composite topologies obtained by connecting two sub-topologies, two methods are proposed to construct controllable composite topologies by connecting the controllable sub-topologies. In addition, based on uncontrollable undirected signed topologies, the same sign double controllability destructive nodes (SSDCDN) and inverse sign double controllability destructive nodes (ISDCDN) are defined for the first time. By analyzing the characteristics of these nodes, the necessary and sufficient condition for the controllability on multi-leader undirected signed topological graphs is obtained. Finally, on the basis of the existing results, the design methods of two special types of uncontrollable signed sub-topologies connected with controllable signed sub-topologies to form controllable composite signed topologies are proposed.

In recent years, the frequent occurrence of emergencies worldwide has not only severely impacted the socio-economy but also led to a large number of casualties. Emergency resource allocation is a key aspect of emergency management. When there is a shortage of supplies, transferring materials between disaster sites can improve the delivery efficiency and ensure the material needs of disaster victims. However, current research on dynamic emergency material allocation rarely considers the transfer of non-consumable materials between disaster sites, resulting in low allocation efficiency, insufficient flexibility, and lack of fairness. Therefore, this paper focuses on a scenario involving multiple supply points, multiple disaster sites, and various types of emergency materials. It establishes a dynamic allocation model that considers the transfer of materials between disaster sites, combining the allocation of materials from supply points to disaster sites and the transfer of materials between disaster sites. The model takes into account time-varying information such as supply and demand volumes, urgency of demand, transportation capacity, and road conditions, as well as the requirement that non-consumable materials must meet a certain service duration. It aims to formulate an emergency material allocation plan with efficiency and fairness as objectives, thereby improving allocation efficiency and optimizing the allocation of emergency resources. Through the solution and analysis of case scenarios, the model's effectiveness and the rationality of the allocation plan are verified in terms of emergency material scheduling efficiency, fairness, and the efficiency of considering material transfer between disaster sites.

The existing quality-related fault diagnosis methods fail to reveal the intrinsic relationship between faults and quality due to the increasing complexity of industrial systems, and they also do not consider system dynamics thoroughly, leading to false alarms. False alarms lead to unnecessary maintenance and affect production efficiency, which will increase equipment costs and waste human resources. This paper proposes a quality-related interval fault diagnosis method based on multilevel decomposition to address this problem. Firstly, the method takes the nonlinear relationship between quality data and process data into full consideration and constructs the data model using multilevel decomposition strategy. Secondly, high-order discrete statistics are utilized to detect the system state, and a quality-related fault detection scheme is designed. Further, the interval dynamic fault diagnosis results are given by analyzing separation trajectories of fault samples and normal ones. Finally, the effectiveness of the method in this paper is verified based on the Tennessee Eastman platform and the wind turbine system.

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 has a moderating effect on ESG rating to reduce corporate financial risks, and enterprises' voluntary disclosure strengthens 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.

With the continuous development of Internet technology, data streams have been attracted wide attention. However, outlier may adversely affect the statistical inference of these data streams. So it is very important to study effective outlier detection methods. Because of the real-time characteristics, traditional outlier detection methods for data streams have been encountered challenges. To address this challenge, an online outlier detection method suitable for data streams are proposed in this paper. Firstly, the sample mean function of the data streams are updated online, and the principal component scores are updated to obtain the least trimmed scores set and evaluate the robustness of its mean estimator. Secondly, threshold rules are constructed based on the asymptotic distribution of distance to detect outlier, and one-step reweighting procedure is presented to control the false positive rate of outlier detection. Finally, the rationality and validity of the proposed method are verified by simulation and example analysis.

In this paper, we introduce an approximate strong Karush-Kuhn-Tucker (ASKKT) conditions in response to smooth multiobjective fractional programming problems. In such problems, we obtained ASKKT-type necessary and sufficient optimality conditions at efficient solution. We also give the sufficient condition of ASKKT condition for Geoffrion properly efficient solution. Furthermore, we give the relation between ASKKT and classical SKKT conditions.

The growth curve model is a classic model for longitudinal data analysis, which has two important assumptions: 1) the error matrix follows multivariate normal distribution; 2) the group matrix is known. In this paper, the above two assumptions are relaxed. Firstly, we extend the distribution of the error matrix from the multivariate normal distribution to the more general multivariate power exponential distribution, and discuss the parameter estimation of the model. Then we discuss parameter estimation of growth curve model with unknown group matrix (called growth curve mixture model). As a by-product, growth curve mixture model provides an analysis method for the clustering of longitudinal data. Simulation analysis and real data analysis show that the proposed method is effective and reasonable.

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 reveals 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.

This paper investigates the problem of formation control for a large-scale agent cluster with a chain topology and time-varying input delay. In contrast to previous contributions, the multi-agent system is mathematically modeled using a nonlinear second-order wave equation, and an observer-based piecewise controller is proposed to collaborate the leader-following agents. Firstly, from the continuum perspective of chain topology, a wave equation is derived by employing the reverse application of spatial difference method as a substitution for the cumbersome ordinary differential equations; Subsequently, an error state observer is proposed to effectively address the time-varying input delay and accurately estimate the actual positions of leader agents; Then, within the framework of Linear Matrix Inequality (LMI), the sufficient conditions for a piecewise controller are derived by integrating the Lyapunov-Krasovskii function, a variant of Writinger's inequality, Jensen's inequality and Halanay's inequality; Finally, the effectiveness of proposed formation controller are demonstrated through two comparative simulations conducted in both 2D and 3D spaces.

To address the issue of manipulator control often neglecting the dynamic characteristics of actuators, it is proposed to control the manipulator body and permanent magnet synchronous motor body as a whole. In order to improve the dynamic performance and stability of manipulator system, a cooperative control strategy of recursive dynamic surface sliding mode control method and dynamic expansion error port Hamiltonian method is designed. The Cauchy function, Gaussian function, Hyperbolic tangent function and Sigmoid function were compared through research. The research results showed that the hyperbolic tangent function had the best performance and will be applied in the cooperative control strategy, this function considers the effects of position error and velocity error. Regarding the external disturbances and friction exist in the system, they are considered as lumped disturbances, and a extended state observer (ESO) is designed to observe and compensate for lumped disturbance in the controller. Finally, it was verified through simulation that the cooperative control strategy and controller can enable the manipulator system to have both fast dynamic performance and accuracy steady-state performance, and improved the anti-interference characteristics and robust control performance of the system.

Considering the high volatility and complexity of electricity spot price time series, a combined forecasting model based on wavelet transform and LGBM (Light Gradient Boosting Machine, LGBM) is proposed. By introducing rolling time window and Wavelet transform, the dynamic multi-scale decomposition of electricity spot price series can be realized, and the frequency characteristics can be extracted to reduce its modal complexity and effectively avoid data leakage. In this study, the proposed model is constructed by utilizing the complex nonlinear feature extraction ability of the LGBM algorithm. The spot market data of Shanxi electric power was used to verify the validity of the proposed model. The results show that the proposed model is superior to the mainstream forecasting methods such as long-term and short-term memory model, support vector machine, elastic network regression model and extreme gradient lifting model in many key performance indexes, such as root mean square error, average absolute error and determination coefficient, among which the R-square R² reaches 0.9792, showing high forecasting accuracy. At the same time, the proposed model shows robustness and adaptability under different market conditions, which shows the proposed model can be seen as a reliable forecasting tool for power market participants and helps to optimize trading strategies and reduce market risks.

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.

With the development of intelligent mobility and sharing economy, Mobility -as-a-Service (MaaS) platforms have become a key component of modern transportation systems. The success of MaaS platforms relies on data sharing between platform operators and transportation service providers. However, balancing the degree and quality of data sharing and coordinating the interests of all parties remain significant challenges. This paper aims to analyze the data sharing issues in the MaaS model, focusing on the impact of both the degree and quality of data sharing on system efficiency. To achieve this, a transportation system consisting of a MaaS platform operator and a transportation service provider is constructed, and differential game theory is used to analyze the optimal strategies and payoffs of participants in three different game scenarios. The results show that when the income distribution coefficient meets certain conditions, a Pareto improvement is achieved for both participants and the whole transport system from the Nash non-cooperative game to the Stackelberg leader-follower game, and further to the cooperative game. Furthermore, subsidy incentives and cooperative mechanisms can effectively improve the data sharing efficiency between MaaS platform operators and transportation service providers, thereby increasing the payoffs of both parties and the overall system.

This paper proposes a novel multi-attribute decision-making method under social networks based on Jensen-Shannon divergence, aiming to address the group decision problems where attribute evaluation information is represented by probabilistic linguistic term sets. Firstly, a trust-consensus-hesitancy-driven social network model is constructed, and based on this social network structure, an initial optimization model is built and a dynamic decision-maker influence model is designed by integrating the PageRank algorithm with a primacy effect. Simultaneously, an individual opinion evolution model is formulated to facilitate the attainment of group consensus. Secondly, a Jensen-Shannon distance measure of probabilistic linguistic term sets is defined to precisely quantify the discrepancies among the diverse evaluation information provided by different decision-makers. And thereby, a three-level consensus measures are proposed to test the consensus level of decision information. Subsequently, a nonlinear optimization model is formulated to determine the attribute weights by incorporating both the fairness principle and the idea of maximizing deviation method. In addition, a multi-attribute alternative ranking method is developed, which is accomplished by calculating and aggregating the weighted Jensen-Shannon divergence of each alternative from a calculated average solution. Finally, the effectiveness and rationality of the proposed model are rigorously demonstrated through a case study; the decision results indicate that the proposed consensus method is effective in achieving consensus and displays a high degree of robustness. This case study presents a detailed step-by-step illustration of the solution process and incorporates a comparative analysis to highlight the advantages of the proposed approach.

Based on the observation of the reality that brand manufacturers use revenue-sharing contracts to expand B2P（Business-to-Peer）sharing platforms as new sales channels, this article establishes a dynamic game model that incorporates the strategic interaction between a manufacturer, a traditional retailer, and a B2P sharing platform, and reveals the impact of revenue-sharing ratios on the channel decisions of manufacturers (between traditional retail channels and B2P sharing platform channels) and the profitability of each player, based on the corresponding equilibrium outcomes. The results show that: (1) when the product usage rate is too low or too high, the revenue-sharing contract has no effect on the channel choice of the manufacturer. However, when the product usage rate is at a medium level, the revenue-sharing contract will affect its channel choice. In this case, there exists a revenue-sharing ratio threshold, and when the revenue-sharing ratio is higher than this threshold, the optimal channel choice for the manufacturer is a single B2P sharing platform channel; otherwise, the optimal channel choice is a dual channel. (2) The revenue-sharing ratio threshold increases in product usage rate but decreases in production costs and the platform’s service level. (3) When the revenue-sharing contract affects the channel trade-offs, an increase in the revenue-sharing ratio will increase the manufacturer's profit, reduce the B2P platform and retailer's profits, and have a non-monotonic impact on the overall profit of the supply chain.

Equity financing offers enterprises distinct advantages in terms of capital, technology, and channels, serving as a crucial strategy for them to win market competition and achieve rapid growth. Hence, it is particularly important to clarify the interaction mechanism between market competition and equity financing. Present study focuses on two retailers operating in separate supply chains, both encountering good market opportunities and employing equity financing to exploit the market. By constructing a Bertrand competition model, this paper analyzes the mutual influence among market development, market competition, and equity financing, and examines how retailers' equity financing and market competition strategies evolve in response to suppliers' game behaviors. The results are summarized as follows: Firstly, when both the retailer and its competitor simultaneously utilize equity financing for market development, it dampens market competition and exhibits a certain "spillover effect", enabling the competitor to capture a portion of the benefit from the high-growth retailer by setting a relatively lower price. Secondly, the supplier's game behavior inhibits the equity financing and market development of the retailer in its supply chain, whereas its impact on the competitor depends on the retailer's growth. Thirdly, the market development exhibits a pass-through effect of market competition, where minor changes in the retailer with a market development advantage can transmit to its competitor's supply chain through market competition, resulting in significant fluctuations. Lastly, numerical analysis reveals that the supplier offers relatively lower wholesale prices to the retailer with lower product substitution coefficient, providing them with greater flexibility when formulating market development and competition strategies.

Considering the differences in the credit policies of the automobile industry in China and the United States, the Cournot game models of duopoly automobile manufacturers are constructed in three scenarios: corporate average fuel economy regulation, the separate management regulation of corporate average fuel economy and new energy vehicle credit, and dual-credit policy. The optimal decisions under different situations are solved and compared, and the following conclusions are obtained. The effects of the separate credit management regulation in promoting the increase of new energy vehicle output, restraining the production of fuel vehicles and increasing the total output value are stable and not affected by policy parameters, while the effects of the dual-credit policy can only be released when the credit trading price is higher than a certain threshold. Through the adjustment of the price signal of the credits, the dual-credit policy can achieve better effects than the separate credit management regulation. Therefore, the dual-credit policy shows higher flexibility and adaptability. The implementation of both policies contributes to the reduction in the price of new energy vehicles, thereby exerting a guiding effect on consumers’ environmentally-friendly purchasing behavior from the perspective of demand. The impact of policies on different market participants varies. Implementing the separate credit management regulation is beneficial for new energy vehicle manufacturers, while the dual-credit policy provides stronger incentives for traditional manufacturers when the credit price reaches a certain level. If certain accounting discount multiples are given to new energy vehicles, it is easier for the dual-credit policy to exceed the "valley of death" of the lowest credit trading price. The research presented in this paper is instrumental in conducting a comprehensive analysis of the micro and macro mechanisms underlying policy actions within the automobile industry, thereby offering valuable insights for optimizing and enhancing the efficacy of the dual-credit policy.

Computer experiment is widely used in scientific researches and engineering designs.It often involves a large number of factors at the early stage, thus space-filling design with good low-dimensional space-filling property is essential to identify the active factors. Researchers proposed the projective stratification pattern (PSP), based on the effect sparsity and effect hierarchy principles, to quantify the stratification when a design is projected into one dimension to full dimensions, and effectively select designs with good low-dimensional space-filling property. But for large-scale experiments, it requires massive computation. In this article, we propose a computationally efficient metric for PSP, called maximum projective stratification enumerator. The enumerator is proved to be a linear combination of PSP, and can be used to develop a rapidly calculating method for PSP. We also establish a lower bound and some relevant conclusions for the enumerator, and provide a flexible numerical algorithm for constructing designs with maximum projective stratification.

For the problem of nonparametric interval estimation of population quantile, this paper proposes a nonparametric estimator of population quantile by using quantile ranking set sampling method, and proves the strong consistency and asymptotic normality of the new estimator. The confidence interval of the population quantile is constructed. According to lengths of confidence intervals, the relative precisions of the confidence intervals under proposed sampling and standard ranked set sampling to the confidence interval under simple random sampling are calculated. For the problem that the expression of confidence interval contains unknown probability density function value, the approximate confidence interval of population quantile is constructed by using the kernel density estimation method, and the coverage and precision of the interval are simulated. The results show that the coverage and precision of interval under quantile ranked set sampling are higher than those under ranked set sampling and simple random sampling, and the proposed sampling method makes up for the deficiency of the standard ranked set sampling method in extreme quantiles. Finally, the actual analysis results of coniferous tree data verify the correctness of the theoretical research results.

In the research of dynamic network link prediction, accurately and comprehensively extracting node information and deeply understanding the temporal evolution patterns of the network are crucial. Addressing this issue, this paper considers temporal link prediction in complex higher-order networks using hypergraph neural networks, aiming to capture higher-order relationships and temporal variations between nodes through hypergraph structures. In terms of the network evolution process, the network is divided into multiple timestamps for study. For each snapshot, the process is as follows: First, construct a hypergraph and generate its adjacency tensor, which is then subjected to higher-order singular value decomposition, mapping the feature tensor into a low-dimensional latent space. Secondly, extend the hypergraph neural network to tensor learning to aggregate the latent feature interaction information between nodes. Finally, by integrating the feature information of sequential hypergraphs and subgraphs, calculate the link probability to achieve dynamic link prediction. This method not only maintains higher-order proximity and network consistency but also incorporates global information,enabling joint learning of different latent subspaces, thereby effectively improving the accuracy of dynamic link prediction. To validate the effectiveness and practicality of the proposed method, experiments were conducted on three datasets using the algorithm. The results indicate that, compared to mainstream static and dynamic link prediction algorithms,our method not only performs better but also identifies trending topics and trends. This capability is beneficial for timely adjustment and optimization of information dissemination strategies, thereby enhancing user engagement and activity levels.

In this paper, the stability and robustness of a recurrent neural network(RNN) controller with saturation function and ReLU function as activation function are analyzed for first-order linear uncertain systems. The necessary conditions for the closed-loop system to converge to the non-zero target and the sufficient conditions for exponential stability are provided. The quantitative relationships between the recurrent neural network controller's parameters and the robustness of the initial state value, the target value and the unknown parameter of the plants are analyzed. The analysis results show that the RNN controllers with ReLU function as the activation function have stronger robustness.

Privatization reform is regarded as an effective strategy to reduce waiting times in the public healthcare system. This paper focuses on two modes of privatization reform: one is the competition mode, which allows private hospitals to enter the market and compete with public hospitals; the other is the collaboration mode, where public hospitals and private hospitals cooperate to achieve common goals. This paper employs a queueing model to describe the patient consultation process, analyzes the service rates and prices of public and private hospitals under different privatization reforms, and studies the impact of these reforms on the number of patients covered by medical services, patient waiting times, patient welfare and social welfare. The study finds that the competitive mode can significantly reduce patient waiting time, thereby expanding the number of patients covered by medical services and enhancing patient utility and social welfare. In contrast, while the cooperative mode can also reduce patient waiting time, it exhibits uncertainty in increasing the number of patients, patient utility, and social welfare, and can effectively promote the expansion of the number of patients covered by medical services, patient utility, and social welfare only when the service capacity of public-private partnership hospitals is relatively large or the degree of privatization is high. Finally, when private hospitals choose between the cooperative or competitive mode, it mainly depends on the subsidy rate provided by the government to public hospitals and the level of privatization pursued by public-private partnership hospitals for their own interests. Specifically, when the subsidy rate or the level of privatization is high, private hospitals are more inclined to choose the cooperative mode; conversely, they are more inclined to choose the competitive mode.

Credit risk assessment can be seen as an imbalanced binary classification problem. This study further expands the application of the frontier of Data Envelopment Analysis (DEA) in the classification problem of credit analysis. In view of the shortcomings of the DEA frontier classification methods in the existing research, such as sensitivity to outliers, overfitting and failure to consider the preferences between categories, a double hybrid support vector frontier method was proposed to improve the above shortcomings and further improve the classification performance, so as to better identify the default samples. Through the verification on the credit risk analysis dataset of bank loans and the default dataset of credit card customers of a bank in Taiwan, the results show that: 1) the proposed double hybrid support vector frontier classification method is competitive in terms of classification performance, at least not inferior to the DEA frontier classification method.2) The double hybrid support vector frontier classification method can solve the overfitting problem of DEA frontier to a certain extent.3) The double hybrid support vector frontier classification method is less affected by the imbalanced dataset than other classical classification models, and can better identify minority classes. At the same time, this study provides a theoretical reference value for the development of the intersection of machine learning and frontier analysis.

Improving the competitiveness of regional logistics is an important way to optimize the allocation of regional logistics resources in the province and ensure the sustainable development of the regional logistics industry. In order to scientifically and accurately evaluate the regional logistics competitiveness, a hesitant fuzzy evaluation method of regional logistics competitiveness based on the multi-reference point prospect theory was proposed. Firstly, the hesitant fuzzy element (HFE) was used to describe the inconsistency of multi-time collection of evaluation data to ensure the integrity of the evaluation data. Secondly, considering the nonlinear characteristics and evaluation requirements contained in the competitiveness evaluation, the prospect theory is used to transform the evaluation data and construct a comprehensive prospect matrix. On this basis, in order to solve the singleness of the reference point setting in the evaluation process, the index data of all evaluated objects were set as the reference points of superiority and disadvantage by drawing on the idea of PROBID multi-level reference, so as to realize multi-level reference. Then, combined with the evaluation of regional logistics competitiveness, it is necessary to take into account the integrity of the evaluated object and the strength and weakness of each attribute, and the PWR performance of the evaluated object is expressed in the form of strength and weakness ratio. Finally, the method is applied to the evaluation of regional logistics competitiveness in five provinces in the southwest urban agglomeration, and the feasibility and effectiveness of the method are verified by examples.

Multi-depot Capacitated Arc Routing Problem (MDCARP) is an important extension version of the basic Capacitated Arc Routing Problem (CARP). In MDCARP, each task can be assigned to any depot, so MDCARP is more complex than CARP. Large Scale Multi-depot Capacitated Arc Routing Problem (LSMDCARP) is extremely challenging on the problem structure and the vast solution space. Though, RoCaSH2 is a state-of-the-art approach which outperformed the other algorithms on the test data with a larger scale, it will encounter the straits when the problem scale ranges up to a certain level in which the scale of the sub-CARPs appears to be larger. In RoCaSH2, the operator for the sub-CARP ignores the scalability of the problem. In view of this, a multiple divide-and-conquer strategies based approach (MDCSbA) is proposed for LSMDCARP based on RoCaSH2, where the divide-and-conquer strategies are distributed in the decomposition of MDCARP, the collaborative optimization among sub-CARPs and the decomposition of sub-CARPs. As a result, the complexity of the problem is decreased at multiply stages. In order to verify the effectiveness of MDCSbA, it was evaluated on two test sets (i.e., mdHefei and mdBeijing) in which the number of the tasks is up to 3584. The results show that MDCSbA can outperform the compared algorithms significantly within runtime budget.

Most of the existing cost compensation mechanisms are proposed based on the traditional cross-efficiency methods, which are all in the range of 0 and 1, resulting in the cost compensation mechanism not being able to realise two-way incentives, which is not conducive to the improvement of enterprises’ performance. The self-evaluation may result in an overestimation of one's own efficiency because the evaluated DMU is allowed to freely choose weights.In the process of cross-efficiency aggregation, although the decision-making unit considers the optimal weights between self-evaluation and peer-evaluation at the same time, the self-evaluation efficiency is overly diluted in the final efficiency aggregation, resulting in the value of self-evaluation can not be fully reflected. In order to address this problem, this paper firstly proposes to improve the cross-efficiency model by considering the peer-evaluation as a whole called the comprehensive peer-evaluation, and correcting the proportion of self-evaluation and the comprehensive peer-evaluation by Criteria Importance Though Intercrieria Correlation(CRITIC) weighting method, so as to solve the problem of over-dilution of self-evaluation. Secondly, it introduces the co-operative game, considers the competition and co-operation relationship between DMUs, and uses Shapley value for cross-efficiency aggregation, and proposes a method to improve the cross-efficiency of the co-operative game.Finally, taking 135 listed enterprises with specialized, refined, differentiated, innovative(SRDI) characteristics in China as an example, the proposed model in this paper compared with the traditional model, and the results show that the proposed model in this paper has a wider range of positive incentive effects, takes into account the efficiency of self-evaluation and comprehensive peer-evaluation, and solves the problem of over-dilution of the efficiency of self-evaluation, so as to make the cost-compensation incentive mechanism have a certain degree of fairness and practicability.

Operator quantum error-correcting codes (OQECCs) is an important research problem in quantum coding theorem. By isolating active errors into two subsystems, quantum information is effectively protected. This is a relatively new type of quantum error-correcting code. In this paper, we use the defining set of constacyclic codes and coset theory to construct three new classes of maximum distance separable (MDS) OQECCs. Notably, we find that MDS OQECCs constructed in this paper with new parameters by comparing with known MDS OQECCs.

This paper proposes a new method for differentiating between unit root and stationary series based on adaptive bridge where adaptive weights are imposed on different coefficients in the bridge penalty. Different from unit root tests, discrimination between unit root and stationarity becomes a variable selection problem by applying adaptive bridge in ADF regression. Meanwhile, adaptive bridge select "zero" parameter on the lagged dependent variable and the lags of the first order differenced dependent variables. In this way, we can select unit roots and determine the lag orders simultaneously. Theoretical analyses indicate that adaptive bridge can correctly identify the unit root process with probability approaching to 1 under the nonstationary case. And in the case of stationarity, the adaptive bridge estimators are consistent and asymptotically normally distributed. And for the lags of the first order differences, "zero" parameters are estimated to be zero with probability approaching to 1, and "nonzero" estimators possess asymptotic normal distribution. In addition, for the efficient use of adaptive bridge, we also propose a modified BIC criterion where the trace of the “hat” matrix is used for approximating the effective degrees of freedom. Simulation results show that the proposed method can accurately distinguish between the unit root and stationary series. Compared with the ADF unit root test and method proposed by Caner and Knight(2013), our method has better finite sample performance. Thus, it can be a good alternative for the unit root test.

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.

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.

This paper investigates the consensus control problem for a nonlinear multi-agent system with dynamic merging function. Firstly, a dynamic model that satisfies the dynamic merging function is established for the system. Secondly, by introducing the principle of node similarity measurement, a heuristic network topology generation strategy with strong interpretability is designed to provide a communication scheme for agents during the dynamic merging process. Thirdly, combining the impulse mechanism with the saturation effect, a saturation impulse control protocol that satisfies power constraints is designed for the system. Furthermore, some sufficient conditions for achieving constraint consensus of the system are obtained by using the Lyapunov stability and matrix measure theories. Finally, through a series of simulation experiments and comparative analysis with relevant literature, the validity, practicality, and superiority of the proposed theories are verified.

Aiming at the theoretical shortcomings of the existing group DEMATEL (Decision-making trial and evaluation laboratory), such as not considering the reliability of experts' opinions in the decision-making process, and not designing the mechanism of experts' information interaction in a reasonable way, an interactive group DEMATEL decision-making method is proposed. Firstly, the concept of reliability consensus is introduced as an interaction driver, addressing issues such as groupthink, opinion manipulation, and insufficient exploitation of collective wisdom that arise from relying solely on a consensus threshold. Secondly, an expert interaction mechanism based on RCGA (Reliability consensus gap analysis) is established to effectively identify experts whose opinions have not been sufficiently interacted with, and to offer tailored interaction strategies that respect their willingness to revise their opinions. Subsequently, a weight updating model is developed to reflect the individual contributions of experts to the reliability of decision information and the enhancement of consensus levels during the interaction process. Utilizing this model, an expert group decision information matrix is formulated. Then, the implementation steps of the interactive group DEMATEL decision-making method are given. Finally, the scientificity and effectiveness of the method is verified by analyzing the accessibility problem of smart healthcare services in a hospital in Yunnan Province.

In view of the huge amount of excavation earthwork, strong uncertainty and dynamic characteristics of earthwork allocation in major projects, the comprehensive utilization mode of earthwork and the dynamic siting strategy of the consumption sites are proposed. To maximize the expected total profit of the project, a two-stage stochastic programming model which considers the excavation sequence, machinery efficiency and the uncertainty of excavation amount is established. According to the structural characteristics of the proposed model, an improved Benders decomposition algorithm combined with sample average approximation method is designed, and two acceleration strategies are introduced to speed up the convergence of the developed algorithm. Finally, different scale numerical experiments are conducted for testing. The experimental results show that the proposed comprehensive utilization method and the dynamic siting strategy of consumption sites can improve the utilization value and reduce the cost of earthwork. The two-stage stochastic programming model can effectively characterize the uncertain excavation amount, and the designed accurate algorithm can quickly solve the problem. The research results can provide decision basis and algorithm support for making the dynamic location plans of the consumption sites and earthwork allocation schemes for the major projects.

Under China's “dual carbon” strategic goals, actively practicing ESG (Environmental, Social and Governance) concept has become the key to realize the sustainable development of enterprises and their stakeholders. In the context of manufacturer's implementation of green design, four game models of low-carbon supply chain were constructed when the manufacturer not considering/considering ESG based on two different subsidy modes: Production subsidy and cost subsidy, and the impact of government subsidies and manufacturer's ESG behavior on low-carbon supply chain operation, environment and social welfare were analyzed. The research shows that the increase of consumer sensitivity coefficient of green design and cost reduction coefficient of green design can stimulate manufacturer's ESG practice motivation and stimulate the government to increase subsidies. The increase of government subsidies is conducive to improving the green design level of low-carbon products, and manufacturer's ESG behavior will strengthen the positive effect of subsidies to some extent. Moreover, manufacturer's ESG behavior is always conducive to improving the performance of retailer and the whole low-carbon supply chain, and improving the total welfare of society, but it may not be conducive to reducing environmental impact. From the perspective of ESG, government implementation of cost subsidy is more effective.

Semi-supervised graph-based dimensionality reduction is a kind of meth-od that utilizes data structure graph to deal with semi-supervised dimensionality reduction problem. However, most of these algorithms only take account of data information while ignore class label information;and they don't take account of the differences among samples in the training process, which reduces the robustness of the algorithms in the case of noise or outliers. In this paper, by combining sparse representation with self-paced learning, a self-paced learner is proposed to obtain the linear dimensionality reduction mapping based on sparse discriminant graph. In detail, the proposed method firstly constructs a sparse discriminant graph by integrating the propagation of class labels with sparse representation of data. Then, by considering the distance between each low-dimensional data point and the corresponding class anchor, and the ability of low-dimensional data to maintain the discriminative sparse structure of the original high-dimensional data, this paper proposes a self-paced learning problem for dimensionality reduction. On the one hand, the proposed method constructs a sparse discriminant graph that can extract the discriminative information of data more effectively; On the other hand, the proposed method is based on self-paced learning mechanism, which makes it can automatically calculate the importance values of training data, suppress the negative impact of unreliable data or labels, and improve the robustness of the model to noise or outliers. The results of five experimental data sets demonstrate the effectiveness of the proposed algorithm.

Aiming at the multi-objective aircraft sequencing problem with interdependent runways, an improved multi-objective restart variable neighborhood search algorithm is proposed. An initialization method based on rolling and swap is proposed, which considers the impact of multiple flights on the solution when constructing the initial solution, increasing the probability that the initial solution jumps out of the local optimum. A variable neighborhood search strategy based on neighborhood delay feedback is designed. The adjusted flights and adjustment strategies are selected based on the delays near the flight, which improves the search speed and local search depth of the algorithm, and adds a restart operator to avoid premature algorithm convergence. Finally, through 30 public examples at different scales, the proposed algorithm is compared with the existing algorithms for aircraft sequencing such as Explorative Perturbative Search, Iterated Simulated Annealing, NSGA-II and Receding Horizon Control Strategy combined with Simulated Annealing and Large Neighborhood Search, the Inverted Generational Distance and Hypervolume Ratio of the solution set are better, which verifies the superiority and stability of the proposed algorithm for multi-target aircraft sequencing.

Directed, undirected and signed graphs are systematically compared and analyzed in terms of distributed controllability performance under the consensus protocol. The influence of edge direction and weight on performance realization is explored, providing a new perspective on the relationship between complex network structure and dynamic behavior. With the same number of nodes, the number of topological structures of directed graphs increases exponentially compared to undirected graphs due to the three possible edge directions and the diversity in the number and position of superimposed pilots. This difference in topological complexity between directed and undirected graphs evaluates the performance of directed graphs different from that of undirected graphs. A new equivalent partition method, $\pi^\ast$, is proposed to fully describe the performance of four-node directed graphs, addressing the limitations in performance determination of four-node undirected graphs. For digraphs with more than four nodes, a large class of uncontrollable digraphs with arbitrary node counts has been constructed. It is demonstrated that the $PBH$ criterion can determine the performance of directed graphs under certain conditions, and a new method to influence system controllability is identified. The study extends the directed path graph to more general graphs, applying the $\pi^\ast$ division to complex graphs to establish its relationship with the zero-forced set. Numerous controllable and stabilizable general graphs have been identified, and the accuracy of the results has been verified through numerical simulations.