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.

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.

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.

Trend filtering is a widely used method for extracting long-term trends and eliminating short-term noise from time series data. In order to accurately capture the global change pattern and local fluctuation of the potential trend, this paper proposes the generalized trend filtering model with composite $\ell_0$ constraint (L0CTF) based on the primitive function representing sparsity, and the optimality theory is analyzed. However, solving the L0CTF model is a challenging task because of the combinatorial property and indivisibility of the composite $\ell_0$ function.Therefore, based on the properties of composite $\ell_0$ function, we reformulate the L0CTF model as a mixed integer programming problem with special ordered sets of type 1 and analyze its equivalence with L0CTF in the sense of global optimal solution. Finally, experimental results on simulated and real data sets show that the proposed method is superior to some mainstream trend filtering methods in extracting potential trends.

Under the market mechanism for charging services, some charging stations have designed discriminatory service pricing schemes. Rational logistics electric vehicle(EV) drivers are likely to travel farther to lower-priced charging stations for charging, which will affect delivery efficiency and customer satisfaction. In view of this, a Multi-Depot Multi-Objective Electric Vehicle Routing Problem Considering Charging Price Difference and Customer Satisfaction(MDMOEVRPCCPDCS) for intercity logistics scenario is studied in this paper. Firstly, an EV energy consumption model is established, given the influence of some factors on the energy consumption of EVs, i.e. vehicle load, driving speed and vehicle characteristic parameters. Next, a MDMOEVRPCCPDCS optimization model is constructed with the goal of total cost minimization and average customer satisfaction maximization. Specifically, the total cost includes fixed operating cost, delivery time cost and charging cost. In view of the fact of delivery time is the main factor affecting customer satisfaction, so the goal of average customer satisfaction maximization is transformed into delivery time window constraints. And then, the multi-objective optimization problem is simplified into a single-objective problem. In order to solve MDMOEVRPCCPDCS model, a hybrid Genetic-Adaptive Large Neighborhood Search(GA-ALNS) algorithm based on 3D K-means spatio-temporal clustering is designed. The hybrid algorithm is based on 3D K-means spatio-temporal clustering to reallocate customer resources in the three-dimensional space composed of time and space,which can contributes to enhancing the breadth and depth of the solution space search process. Through several sets of arithmetic examples, the MDMOEVRPCCPDCSCS model is verified to achieve multi-objective balancing among logistics cost, and customer satisfaction, which can provide a theoretical basis for transportation and logistics enterprises to optimize the decision-making distribution scheme.

For a class of uncertain multi-sensor systems with uncertain noise variance and one-step random observation delay, the augmentation method is applied, by using the extended noise technique, the system is transformed into a multi-model multi-sensor system with only uncertain noise variances. According to the minimax robust estimation method, the steady-state local robust Kalman predictor is proposed. Based on the steady-state local robust Kalman predictor, the steady-state local robust white noise deconvolution estimator is proposed. By using the matrix-weighed fusion algorithm, the robust matrix-weighted fused steady-state white noise deconvolution filter and smoother are proposed. By applying the Lyapunov equation method, and introducing several permutation matrices, the robustness of the proposed local and fused white noise estimators is proved. The precision relationship between the steady-state local and the fused robust white noise estimator is analyzed. The effectiveness and correctness of the proposed method are verified by applying it to a simulation example of a multi-sensor IS-136 signal system.

The traditional data envelopment analysis method based on the farthest goal improves non-effective units by maximizing slack variables, which has high improvement difficulty and cost. Meanwhile, the existing data envelopment analysis methods based on the nearest goal mostly ignores the problem of indicator weight freedom. Therefore, the article improves the preference setting method in the existing model so that it can be better applied to the model of nearest target. Based on this, the RAM-DEA model considering preferences and the RAM-DEA preference model based on nearest objective are proposed to construct a preference DEA efficiency evaluation method based on nearest objective. This method can be better applied to efficiency evaluation under various preference situations, especially for small and medium-sized enterprises with certain practical significance of cost reduction and efficiency improvement, while considering the economic feasibility of improvement schemes. Finally, the effectiveness of the model improvement was verified through the application analysis of case data from specialized and innovative small and medium-sized enterprises.

This paper investigates the testing of structural change points in linear regression model with long memory stochastic volatility errors that can simultaneously capture both the long memory and heteroscedasticity. We propose a self-normalized CUSUM statistic that does not require the estimation of scale parameters, constructed based on the residuals from least squares estimation. Under the null hypothesis, the limiting distribution of the test statistic was derived, and it was found that it is not affected by the long-memory parameter. Under the alternative hypothesis, the consistency of the statistic was proved. Numerical simulation results demonstrate that the proposed method not only effectively controls the empirical size but also achieves good testing performance. Finally, we illustrate the effectiveness of the proposed method by modeling and testing structural change points in a data set of PM2.5 concentration and SO$_2$ concentration in the air in Xining city.

This paper proposes a location-scale invariant stable correlation(for short ISC) to measure the dependence between two random vectors $\mathbf{v}\in\mathbb{R}^r$ and $\mathbf{y}\in\mathbb{R}^q$, where $r\geq 1, q\geq 1$. The ISC satisfies $0\leq\mathrm{ISC}(\mathbf{v},\mathbf{y})\leq 1$ and if and only if $\mathbf{v}$ and $\mathbf{y}$ are independent, $\mathrm{ISC}(\mathbf{v},\mathbf{y})=0$. Based on the ISC, we further develop a new feature screening procedure called ISC-SIS. ISC-SIS does not require commonly used model assumptions and finite moment assumptions, and it can be directly used to screen grouped covariates and multiple responses. In theory, we establish the sure screening property and ranking consistency property of ISC-SIS. Numeric simulation studies and a real data analysis both indicate that ISC-SIS has quite strong competitiveness compared to the existing screening procedures.

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

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

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

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. Second, 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 DK-MHSMM, 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.

Considering the carbon cap-and-trade mechanism, this paper studies the emission reduction decision-making problems of coal power supply chain dominated by coal enterprises. A dynamic game model is constructed to explore the impact of environmental awareness and emission reduction difficulty on enterprise pricing and carbon emission reduction under decentralized decision-making and centralized decision-making respectively. Based on centralized decision-making, the profits and carbon emission reduction of coal and electricity companies are compared. In view of the efficiency loss of decentralized decision making, profit-sharing contracts are designed to coordinate the coal power supply chain. The results show that: 1) when the environmental protection consciousness of coal enterprises is not strong enough, the profit of coal enterprises is higher than that of electricity enterprises; When the environmental protection consciousness of coal enterprises is generally strong, the profit of coal enterprises is lower than that of electricity enterprises. 2) emission reduction difficulty can change the relationship between power unit price and carbon emission reduction under decentralized decision-making and centralized decision-making; When the emission reduction difficulty is less than a specific value, the power unit price of decentralized decision-making power enterprises is lower, and it is also more conducive to increasing the carbon emission reduction of coal enterprises and power enterprises. 3) when the difficulty of emission reduction is greater than a specific value, the profit of centralized decision-making is higher than that of decentralized decision-making; 4) with the increase of profit sharing ratio of electricity enterprises, the wholesale price of coal will increase, the carbon emission reduction of electricity enterprises will increase, and the carbon emission reduction of coal enterprises will decrease.

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

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

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

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