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

This paper studies the reinsurance and investment game between an insurer and a reinsurer with relative performance concerns. We consider the joint interests of the insurer and the reinsurer for the reinsurance contract design. Namely, the insurer determines the claim risk sharing strategy, the reinsurer determines the reinsurance price, and they codetermine the final reinsurance premium. To increase their respective wealth, the insurer and the reinsurer can invest in the same risk-free asset and risky asset which follows the constant elasticity of variance (CEV) model. We quantify the competition between the insurer and the reinsurer through their relative performances. Both of them aim at maximizing the expected value of their terminal relative wealth while minimizing its variance. By using the stochastic optimal control technique, we formulate and solve extended Hamilton-Jacobi-Bellman (HJB) equations under the Stackelberg game framework. And the optimal reinsurance contract as well as the optimal time-consistent investment strategy are derived analytically. Finally, numerical simulations show that with relative performance concerns, the insurer would probably spend less on reinsurance, the reinsurer tends to lower the reinsurance price, and the final reinsurance premium decreases. Besides, both insurer and reinsurer would invest more in risky asset with relative performance concerns.

This paper develops a robust non-fragile output feedback control scheme for a class of uncertain nonlinear systems with quantized inputs and outputs. An output filter is employed to augment the considered nonlinear system, and an extended non-fragile observer is constructed. It is avoided that the introduction of quantization errors and measurement noise into each state equation of the observer. Disturbance estimators are used to estimate system noise and disturbances. Based on this, a robust non-fragile controller with quantized inputs and outputs is proposed. Moreover, introducing two time-varying matrix inequalities to solve the problem of uncertain perturbations in observer and controller gains. Stability analysis illustrates that all signals in the closed-loop system are ultimately uniformly bounded. A numerical simulation case is presented to verify the accuracy and effectiveness of the proposed scheme.

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

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

Food is a strategic resource for the security of the world, the stability of the people and the well-being of the people. Ensuring the security and stability of the food supply chain is an important feature that a major economy must have. In the pursuit of economic efficiency and resource saving, this paper takes the minimum cost and loss as the main optimization goal, builds a food"supplier-processor-distributor" coordination optimization model, solves by introducing the second generation of non-dominant genetic algorithm, and combines AHP-CRITIC combination weighting method to carry out accurate policies. Case studies show that: NSGA-II algorithm can obtain more quantity and better quality Pareto solutions, and find a high-quality solution set that can minimize the cost and loss of the three-level food supply chain, so as to improve the toughness and safety level of the food supply chain, realize the two-level optimization of the three-level supply chain, and provide a scientific decision-making basis for the country to achieve "stable food supply and guarantee".

Industry exchange-traded funds (IETFs) are designed to target as specific industries, providing investors with a low-cost, high-liquidity financial innovation tool. Combining the margin financing and refinancing systems in the Chinese financial market, this article uses Huang et al.'s(2020) method to construct industry ETFs to analyze the pricing of ETFs from the perspective of hedging. The study finds that: firstly, under the hedging strategy of going long underlying stock/short industry ETF, when a listed company announces positive earnings surprise information, informed traders will significantly increase their long position in the underlying stock while simultaneously increasing their short position in the industry ETFs to which the stock belongs. This indicates that industry ETFs facilitate informed traders to use firm-specific information to hedge industry risks and realize the hedging function. Secondly, industry ETFs reduce the idiosyncratic volatility and illiquidity of their constituent stocks. This means that industry ETFs allow investors to benefit from firm-specific information. Consequently, stock prices will more fully reflect firm-level information, thus improving market efficiency. Finally, the introduction of refinancing facilitates informed traders into hedginge industry risks, indicating that Chinese refinancing system provides investors with diversified options to mitigate industry risks.

Based on the differences in green product attributes, specifically marginal cost-intensive green products (MIGPs) and development-intensive green products (DIGPs), this study establishes four competitive models related to the adoption of blockchain technology: neither MIGPs or DIGPs manufacturers adopting blockchain, only MIGPs or DIGPs manufacturer adopting, and both adopting blockchain. Hereafter, the impact of diverse blockchain adoption strategies on the greenness of product, pricing strategies, market demand, and profit improvement levels under green product price competition are investigated. The findings reveal several key insights: (1) in a highly competitive market with high consumer trust, manufacturers are inclined to offer high-quality and high-priced green products; meanwhile, blockchain-adopting manufacturers produce products with superior environmental attributes, particularly DIGPs; (2) blockchain adoption significantly enhances consumer trust in green products, allowing manufacturers to achieve a pricing advantage; (3) manufacturers adopting blockchain technology achieve higher profits than their competitors; however, the decision to adopt depends on various factors, including adoption costs, market price competition intensity, and manufacturers' cost investment levels.

Product attribute weights represent consumers' importance on attributes, serving as a basis for consumer product selection and merchant product optimization. However, current methods for determining attribute weights based on online reviews suffer from significant subjectivity and a tendency towards biased results. Therefore, this article proposes a new method for determining product attribute weights. Initially, product attributes are identified using an improved LDA topic model. Then, attribute sentiment analysis is applied to label satisfaction levels in comments. Subsequently, the satisfaction-labeled samples undergo N-fold cross-validation, and then information gain values for each attribute across different samples are calculated, allowing for parameter estimation of attribute importance value distribution. Next, the distance factor between pairs of distributions is calculated. Finally, the AHP method is employed to determine attribute weights. An empirical analysis focusing on meat and fresh produce demonstrates that this method accurately assigns weights to each attribute to express consumers' level of importance towards them.

In this paper, we study the problem of weak disturbance decoupling for Boolean networks. Firstly, we present a necessary and sufficient algebraic condition using the semi-tensor product of matrices for solving the weak disturbance decoupling problem for Boolean networks. Secondly, we use the directed graph partitioning method to propose a graph-theoretic condition for the weak disturbance decoupling problem of Boolean networks based on the vertex-colored state transition graph. These conditions reflect the relationship between the original disturbance decoupling and the weak disturbance decoupling. Moreover, the obtained graph-theoretic condition provides a new perspective for designing controllers for weak disturbance decoupling. Finally, we provide examples to illustrate the validity of the results.

This article considers the "greenwashing" risk of downstream retailers and constructs bank financing, hybrid financing, and supplier financing models based on game theory. It analyzes the financing preferences of suppliers and operational decisions of retailers under these scenarios. The findings are as follows: (1) Supplier perspective: Supplier financing is most advantageous when the retailer's greenwashing risk fluctuates within a certain range. In this model, suppliers can dynamically adjust interest rates based on the retailer's risk, reducing potential financing risks. (2) Retailer perspective: Retailers are inclined to choose supplier financing only when their greenwashing risk is high. In this model, retailers face dual competition in sales channels and financing costs, leading to reduced order quantities and profits. (3) Conflict of interest: From a self-interest maximization standpoint, supply chain participants cannot agree on a financing model. To achieve a win-win situation, suppliers can limit the order quantities of high-risk retailers based on the assessment of greenwashing risks, reducing competition among retailers.

Facing with public health emergencies such as COVID-19, production shortage of protective equipment may greatly increase the risk of epidemic spread. The management of the production and distribution of protective equipment is pivotal for rapidly and effectively responding to unexpected public health emergencies. Public health emergencies can be categorized into four phases: onset, outbreak, peak, and decline. This study investigates a multi-period protective equipment production facility location and order allocation for unexpected public health emergencies, aiming to optimize factory capacity migration, production planning, and inventory management at distribution centers. The objective is maximize the overall effectiveness of protective equipment production, Firstly, we formalize it as a mixed-integer nonlinear programming model. Then, a problem-specific hybrid adaptive large neighborhood search algorithm are developed, where we design a fixed decomposition optimization strategy and four pairs-tailored operators to enhance its performance. Finally, numerous numerical experimental results demonstrate that: 1) The proposed algorithm can obtain approximate solutions within 9 seconds with an average deviation of 3.25% from the optimal solution for small-scale instances and for large-scale instances, it can achieves approximate solutions with an average deviation of 5.44% from the optimal solution while reducing the average solution time by 82.1%; 2) During public health emergencies, capacity migration can provide more effective material support for severely affected areas, alleviating post-epidemic capacity surplus issues in the supply chain.

In practical research, the data may come from different distributions, and there are some limitations when using a single distribution model to fit the data. In order to overcome this problem, the mixed model can better adapt to complex data types. Exponential distribution and Rayleigh distribution are important life distributions in reliability analysis, and there are few related mixed models in the context of censored data. In this paper, a mixed model of two-parameter exponential distribution and two-parameter Rayleigh distribution is proposed, and the EM algorithm is used to estimate the parameters of the mixed model with right censored data. Finally, the model is applied to the actual data, and the goodness of fit test is carried out to verify that the proposed model is suitable, which further shows that the model can better adapt to the complex data characteristics and has certain practical significance.

This paper investigates the problem of adaptive consensus control for second-order delayed nonlinear multi-agent systems under intermittent communication. The main contribution is to propose a novel energy-dependent intermittent communication mechanism, where the work time and the rest time can be flexibly adjusted online according to the energy of the current error state. In this communication scheme, by dividing the non-negative real number region into three subregions, the work time and the rest time are determined by the pre-assigned subregions and the Lyapunov function describing the error state. Then by implementing the distributed adaptive control based on neighboring agent state estimation, the overall consensus is achieved under energy-dependent intermittent communication. Compared with existing time-dependent intermittent communication schemes, the proposed energy-dependent scheme can tolerate more rest time. Finally, the effectiveness of the proposed method is validated through a simulation example with two scenarios.

In view of the current increasingly severe air pollution problem in China, it is especially critical to conduct an effective atmospheric environmental efficiency assessment. This paper proposes a dynamic network slacks-based measure (NSBM) model that considers the internal structure of the system and the inter-period activities in the neighboring periods to dynamically evaluate the inter-provincial atmospheric environmental efficiency in China. The model overcomes the shortcomings of the traditional network data envelopment analysis (DEA) method that ignores the lagged effects of carry-over variables. The model was applied to analyze the efficiency of the overall atmospheric environment and its sub-stages in 30 Chinese provinces (municipalities and autonomous regions) from 2016 to 2021. The empirical results show that: 1) in general, the overall efficiency of the atmospheric environment and the efficiency scores of each sub-stage in eastern China are higher than those in central and western China; 2) low pollutant generation efficiency combined with high atmospheric pollutant control efficiency, or high pollutant generation efficiency combined with low atmospheric pollution control efficiency, are the main reasons for the overall inefficiency of the atmospheric environment in some provinces; 3) In the Beijing-Tianjin-Hebei region, Beijing and Tianjin have higher overall atmospheric environmental efficiency and sub-stage efficiency than Hebei Province.

This paper deals with a second-order conic programming dual for a robust quadratic optimization problem with norm-constrained uncertain sets. Following the robust optimization methodology, we first introduce the robust counterpart of this robust quadratic optimization problem. Then, we obtain a second-order conic programming dual problem for this robust quadratic optimization problem. Moreover, by using a characteristic cone constraint qualification, we present a zero duality gap result between them.

In statistical process control, the random-caused delay of OC-signal identification is a key problem to be solved in the design of control chart, in which the prior knowledge about the shift-direction is of great importance. Based on this prior knowledge and the latest difference information of samples, an improved EWMA mean-shift control chart with restarting mechanism is developed by integrating the performance of MEWMA and one-sided EWMA control chart. Finally, by calculating the average-run-length-of-out-control, the proposed model is verified to perform better with a faster OC-signal identification both on the simulation data and the real process data.

The new round of power system reform has introduced a transmission and distribution pricing mechanism based on the principle of "permitted costs + reasonable returns". This cost-based regulatory mechanism has become difficult to adapt to the requirements of high-quality development of the power industry in the new era. Therefore, it is urgent to incorporate effective incentives and the heterogeneous operating environments that faced by grid companies in different regions into the design of transmission and distribution pricing mechanisms. In this sense, we propose an incentive-based transmission and distribution cost regulation method that combines the meta-frontier super-efficiency DEA model with yardstick competition regulation. Firstly, we divide 25 provincial grid enterprises into six groups based on the similarity of their external environments and calculate their operating efficiencies under the common frontier and group frontier. Subsequently, we develop a cost compensation scheme for each grid enterprise based on its efficiency performance across three scenarios. The scheme aims to incentivize grid enterprises with excellent efficiency both in the group frontier and in the common frontier, while also safeguarding the production incentives of grid enterprises with mediocre performance due to external environmental factors. The study finds that 1) the permitted costs obtained by provincial grid enterprises based on our methodology have improved, and the sum of the permitted cost adjustments has decreased significantly; and 2) a few grid enterprises obtain positive incentives based on our methodology, and the majority of enterprises obtain negative incentives. Finally, we present some policy recommendations for the reform of transmission and distribution tariffs.

To effectively address the issues of fuzzy information, preference information, and noise information in sequential decision information systems, a new single-valued intelligent probability rough set model is constructed by integrating singleton fuzzy sets and probability rough set models, based on an improved scoring function. Firstly, considering the fuzziness of singleton fuzzy numbers and subjective preferences, an improved scoring function is defined to establish dominance relationships among objects. Secondly, to enhance the fault tolerance of the model, a single-valued intelligent probability rough set model is introduced by incorporating a conditional probability threshold. A reduction method and rule extraction method based on discernibility matrix are designed according to the properties of probability lower and upper approximation. The effectiveness and applicability of the proposed method are validated through application examples.

Mathematical human-like answering constitutes a vital component of automated reasoning and has long been a focal point in cognitive intelligence research, drawing extensive attention from scholars, which requires simulating human understanding, representation, and reasoning of mathematical knowledge, where knowledge representation serves as the foundation for semantic comprehension and knowledge inference. Knowledge graphs are effective tools widely cited in fields such as knowledge representation and the construction of knowledge systems. Addressing the logical association challenges in mathematical knowledge representation, this paper proposes a method for constructing a mathematical knowledge graph. By interpreting mathematical predicates and objects as relations and entities, respectively, and employing rule instantiation, the paper unifies the representation of question and rule knowledge through knowledge graph. Single-step reasoning is achieved through structural matching based on subgraph isomorphism, proving effective in the automated solving of mathematical questions devoid of complex expressions, facilitating the generation of human-like answering processes. Experimental results demonstrate that the proposed mathematical knowledge graph method can yield correct and human-like answering styles.

For a class of strict-feedback nonlinear systems with unknown control direction, a dynamic event-triggered control method based on backstepping is proposed, which can solve the problem of unknown control direction and reduce the data transmission in the network. Firstly, the Nussbaum gain function is introduced to solve unknown control direction. Secondly, a dynamic event-triggered scheme is introduced in the actual controller design, and a dynamic variable is introduced to adjust the trigger threshold, which further reduce the number of events. The stability of the closed loop system is proved based on Lyapunov function, and Zeno behavior does not appear. Finally, the simulation results show that the dynamic event-triggered control method can reduce the data transmission in the network and save network resources.

With the rapid development of deep learning technology, its application in the field of asset pricing has attracted widespread attention. This paper delves into the theoretical foundation of factor pricing models and proposes a latent factor model constructed with deep neural networks based on characteristic ranking. The proposed model overcomes the limitations of traditional factor models in dealing with nonlinearity and hypothesis testing. It uses appropriate activation functions to accurately simulate the real process of portfolio construction. In the empirical analysis of the China A-share market, the proposed deep neural network model significantly outperforms the benchmark models in performance for out-of-sample prediction and achieves the highest cumulative returns and the best Sharpe ratio in constructing mean-variance efficient frontier portfolios. Furthermore, by analyzing the importance of the characteristic gradients of the model outputs, it is found that the monthly returns of the A-share market are significantly influenced by transaction-related factors, reflecting the unique characteristic of the Chinese stock market as an emerging market. This paper provides new insights into the construction of latent factor models and the patterns of market behavior of the A-share market.

Logistics standardization is a crucial avenue for enhancing the development of the modern circulation industry. China has actively initiated pilot projects on logistics standardization to explore solutions to the “bottleneck” issues in circulation. Against the backdrop of the goal of circulation integration, attracting more participants from the circulation entities to engage in deep collaboration in logistics standardization and building a smooth and efficient circulation system are key to the development of modern circulation. This paper, based on the perspective of collaborative governance, constructs a three-party evolutionary game model, namely, “manufacturer-third-party logistics enterprise-retailer,” to depict the behavioral relationships of supply chain participants in collaborative logistics standardization. The study investigates the key factors influencing the collaborative mode selection among entities within the chain, explores the relationships of interests among entities, and further discusses different stable states of the system and the strategic choices of entities through simulation analysis.The research reveals that: (1) The ideal state of three-party collaboration can be directly achieved when the initial potential for each entity's participation reaches a certain threshold. (2) Increasing the intensity of government rewards and penalties for each entity promotes the establishment of standardized collaborative relationships. (3) The reduction of standardization costs can attract the participation of entities in collaborative standardization, and its incentive effect is more independent of the initial intentions of enterprises compared to government subsidies. (4) Third-party logistics enterprises sacrificing profits for cost savings upstream and downstream can benefit entities in converging to the ideal state.In conclusion, the study proposes corresponding policy recommendations based on the findings.

To improve the accuracy of Unmanned underwater vehicle (UUV) state estimation of non-cooperative targets, an Axial Attention-Based Target State Estimation method is proposed in this paper. The state estimation mechanism of the UUV non-cooperative target based on sonar observation is analyzed. The problem's non-Markov state-space model of the problem is transformed into a first-order Markov state-space model with memory, and a recursive filtering model is constructed. Aiming at the unreliability of forward-looking sonar observation and the unpredictability of target motion, a multi-step prediction network based on Transformer is proposed to describe the complex motion process of non-cooperative target relative to sonar under nonlinear observation. Aiming at the instability of observation and the unpredictability of posterior distribution, based on the Monte Carlo approximate inference principle, the multi-step prediction network is used to map the particles in the target measurement state space to the target prediction state space, and a non-cooperative target state estimation algorithm based on the axial attention is constructed. Simulation results show the adaptability and robustness of the proposed method to uncertain inputs.

A key issue in multi-criteria sorting problems is to consider the decision maker’s preferences, which has a direct impact on the sorting results. It can make the sorting results more in line with the real-world situation, the obtained results are usually more satisfactory by decision-maker according to his/her individual preferences. Therefore, based on the existing VIKORSort methods, this paper proposes a D-VIKORSort method considering decision makers' individual preferences. Firstly, from the decision-making process, the concept of different dominances is given, and the dominance preference of decision maker is presented. Secondly, the VIKOR method is selected to illustrate the different dominances. The dominance of group utility value and individual regret value are regarded as local dominance, while the compromise value is regarded as global dominance. Let the decision maker's preference for global dominance be $D$, and transform different dominance into harmonic dominance through this preference. Then harmonic dominance and its calculation formula are defined, which can be used to sort the alternatives. Thirdly, the specific algorithm of the sorting method proposed in this paper is given. Finally, to illustrate the superiority, novelty of the proposed method, the method is applied to two practical examples, and the results are compared with the results of existing research methods.

Most of the existing group evaluation methods with subject-object collaboration mainly focused on point value or interval number, and have not considered the impact of the trust relationship on the results. To address these issues, this paper proposes a trust relationship-based group evaluation method with subject-object collaboration under intuitionistic fuzzy contexts. First, according to the constructed the subject trust network and the object trust network, a weight determination method based on intuitionistic fuzzy in-degree centrality index is proposed to obtain the weights of the subject and the object. Second, considering that the credibility of the object's self-assessment information, a comprehensive credibility-based optimal estimation model is established to estimate the incomplete opinion of the subject. Finally, to aggregate the evaluation values given by the subject and the object, a minimum deviation subject-object aggregation model is proposed to obtain the final results. In the end, the effectiveness and feasibility of the proposed method are verified by an application example. The comparison results show that the evaluation results of each object under the collaboration of the subject and the object are between the evaluation results of the subject and the evaluation results of the object.

In this paper, a time-varying financial stability test method based on time-frequency decomposition is proposed by combining completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying quantile regression, and an empirical study is carried out using the daily data of stock markets in BRIC countries and developed countries from 1997 to 2021. The empirical results show that financial instability comes from different frequency domains. Specifically, the financial market instability of developing countries comes from the high frequency domain, while the financial market of developed countries has certain instability in the low frequency domain. Systematic shocks to countries, both at high and low frequencies, have asymmetric effects, and developing countries are more affected. Finally, the state of financial market instability in each country has a certain degree of persistence.

When a sudden public health emergency occurs, the demand for emergency medical supplies surges, and ensuring the effective supply of emergency medical materials becomes a crucial issue concerning public safety. To investigate the strategic choices among emergency medical supplies stakeholders during sudden public health emergencies, this paper constructs an evolutionary game model involving medical material suppliers, hospitals, and local governments. It analyzes the stability of strategies adopted by each stakeholder in the game and further employs the Lyapunov first law to analyze the stability of combined strategies in the game system. Subsequently, simulation analysis is conducted to discuss the influence of different parameters on the evolution of the tripartite game system. The research indicates that evolutionarily stable strategies are significantly influenced by hospital complaint costs and the strict supervision costs imposed by local governments. Excessively high hospital complaint costs can result in insufficient proactive supervision feedback from hospitals, consequently leading to ineffective strict government supervision, especially given the relatively high costs associated with such supervision. Ultimately, the strategic choices of the three parties in the game tend towards speculative sales, acceptance, loose supervision. The intensity of rewards and penalties implemented by local governments on the decision-making entities of the other two parties plays a decisive role in the stability of the system. Insufficient rewards and penalties by local governments on medical material suppliers or inadequate compensation by hospitals, coupled with excessive punishment, can lead to the failure of strict government supervision. When local governments adopt a lax supervisory stance, emergency medical material suppliers naturally lean towards speculative sales. To ensure the effective supply of emergency medical supplies, local governments must consistently enforce strict supervision.

To guide the stable diffusion of new energy vehicles, this paper investigates the evolution characteristics of NEV firms with taking the new features of new energy vehicle market into consideration. On the demand side, social interactions, which spread not only the information about green products but also public perception of environmental protection, are effective in encouraging potential consumers to buy green products. On the supply side, with the competition between battery electric vehicles and hybrid electric vehicles increasing rapidly, it is essential to consider this competition when making the supply policy. On this basis, we investigate the diffusion process of new energy vehicles and the impacts of key parameters. Moreover, two forms of social interactions are investigated and compared with each other to show the features of different development stages of the NEV industry. Finally, we show that chaos could be controlled via the delay feedback method. This study provides some valuable implications for the stable development of the new energy vehicle industry and highlights the importance for new energy vehicle firms to take the social interactions into consideration in the Internet age.

As product competition gradually develops into supply chain based competition, full chain traceability has become one of the main ways to enhance the competitive advantage of the supply chain. Compared to traditional traceability, blockchain traceability has some advantages in decentralization, enhancing consumer trust in products, and enhancing customer experience. Considering the impact of customer experience on customer purchasing behavior, this study develops the demand of each supply chain by maximizing the nonlinear consumer utility function and explores the equilibrium strategies of two competing supply chains that consider customer experience without or with blockchain. And then we compare the optimal strategies obtained in each scenario. The results show that the supply chain with a high initial share has a competitive advantage and provide customer with a higher service experience. Only when consumers' traceability awareness exceeds a certain threshold, both competing supply chains will choose to adopt blockchain and achieve a win-win situation. Finally, we analyze the impacts of investment cost coefficient, traceability awareness, and competitive intensity on supply chains through numerical examples.

The feedback control method developed by Pyragas is mainly utilized to add the time-delayed feedback into the middle equation of the three-dimensional continuous chaotic system. But in fact it is also convenient and effective for chaos control if the feedback is added into the last equation. In this paper, Shimizu-Morika chaotic system is taken as an illustration example and a single time-delayed feedback term is added into its last equation. The local stability and the occurrence of Hopf bifurcation are studied by taking the delay as the bifurcating parameter. The direction and stability of the bifurcating periodic solutions are further investigated by means of the center manifold theorem and the norm form theory. Finally, numerical experiments are presented to illustrate the correctness of the theoretical results as well as the effectiveness of chaos control.

The proliferation of online health community, as a crucial complement to the offline health system, provides patients with new avenues for seeking medical care, thus promoting the favorable and orderly development of the societal health environment. This study systematically reviews and reanalyzes empirical research on patients' selection of physicians within online health communities, aiming to reconcile past inconsistencies in research findings and to elucidate the varying degrees of impact of different influencing factors on users' physician selection behavior, offering insights for subsequent research and practical applications. Using the meta-analysis method, the study analyzes 23 influencing factors and 3 moderator variables that affect patients' online physician selection behavior, encompassing 190 independent effect sizes from 55 studies. The findings reveal that all 23 influencing factors positively impact patients' online physician selection behavior, with patient satisfaction and the total quantity of comments exhibiting significant and robust correlations. Furthermore, platform type, study data time, and data collection methods moderate the relationships between some variables and online physician selection behavior. This study provides some guidance for future research and practical implementation.

In time series forecasting, prediction error metrics cannot assist researchers in determining whether poor prediction performance is due to an inappropriate model choice or if the data inherently lacks predictive information. Intrinsic predictability characterizes "the upper limit of prediction accuracy" for the data, which can help researchers assess the compatibility of the current model and data. In this paper, we briefly review the concepts of predictability and provide a detailed introduction to the studies of time series predictability based on permutation entropy. Based on this, we propose permutation entropy with covariates to characterize the complexity of target time series when covariates are available and demonstrate its effectiveness through experiments with real glass bubble data. Additionally, we further present a strategy for model selection based on intrinsic predictability, aiming to choose simpler models and reduce the time cost of modeling and forecasting while maintaining reasonable accuracy. Numerical experiments on economic data validate the efficacy of this strategy.

A new full-state constraint event-triggered asymptotic tracking control strategy is proposed for a class of non-strict feedback nonlinear systems with unknown control direction. The strategy removes the dependence on the initial states of the system in the full-state constraint control based on a nonlinear mapping and a conversion method of system. Meanwhile, a new performance constraint function is designed so that the tracking error of the system converges into the neighborhood near the equilibrium point within a finite time. To avoid an overlarge initial value of the control input, the control mode with zero initial control input is considered in this paper. A new input adjustment function is designed, which is introduced into the event triggering mechanism to ensure that the event-triggered control input of the system has a zero initial value. The designed controller guarantees the full-state constraint performance and the asymptotic tracking performance of the system, and it can let the transform errors asymptotically converge to zero. The effectiveness and the superiority of the proposed scheme are verified by the simulation studies for a manipulator system.

We consider a market insisting of three parties: a product, a seller and a consumer. The quality of the product is denoted as $\theta$. When the seller conducts market research on the product quality, there exists an quality error denoted as $\varepsilon$. So, the seller can get a signal of the product quality, denoted by $s=\theta+\varepsilon$. For simple, we assume that the quality error has a two-point distribution. This makes the seller could conclude the true quality of the product and disclose it to the consumer based on a threshold strategy. When the seller does not disclose the quality information, the consumer forms an expectation about the product quality denoted as $E(\theta|s\le \hat{s})$. When the seller discloses the information, the consumer does not fully trust the seller and believes the disclosed quality with a trust probability. We study the impact of trust probability and disclosure cost on the seller's disclosure behavior in the case of monopoly and competition, and deduce the relationship between the seller's ex ante profits and the two factors. In the case of monopoly, the seller's ex ante profits are positively correlated with the trust probability. When the trust probability is low, the ex ante profits are a convex function of the disclosure cost. When the trust probability is high, the ex ante profits are negatively correlated with the disclosure cost. In the case of competition, the ex ante profits are still positively correlated with the trust probability and negatively correlated with the disclosure cost.

In order to improve the effect of signal noise reduction, this paper proposes a signal noise reduction method based on optimized variational mode decomposition combined with wavelet threshold. Firstly, the improved sparrow search algorithm was used to adaptively optimize the variational modal decomposition parameters to determine the optimal modal number $k$ and the quadratic penalty factor $\alpha $. Secondly, the improved wavelet threshold denoising method was used to denoise the noisy mode, and the effective mode and denoising mode were reconstructed to achieve signal denoising. Finally, compared with the traditional threshold denoising method, the results show that the signal-to-noise ratio of the proposed method is increased by 1.604, and the root mean square error is reduced by 0.015, which has a better noise reduction effect.

In this study, we employ a hybrid self-adaptive technique to introduce a new Dai-Liao type conjugate parameter for solving unconstrained optimization problems. Subsequently, we incorporate an adjustable parameter as spectral parameter to construct an effective search direction and suggest a Dai-Liao hybrid conjugate gradient method. Its search direction satisfies the sufficient descent property regardless of the line search. Under some fundamental assumptions and weak Wolfe line search, we establish the strong convergence for uniformly convex function and global convergence for general function. Moreover, under Armijo line search, we investigate the complexity analysis of the proposed method. In attaining a point with a gradient norm below $\epsilon$, it requires at most $O(\epsilon^{- 2})$ function evaluations. Finally, some numerical experiments show that our method outperforms some existing methods on solving unconstrained optimization problems and image restoration problems.

This paper measures multiple price bubbles in the global iron ore futures market, China's stock market and industry stock markets from 2013 to 2021, and examines the risk spillover effects among markets based on bubble dates and causal relationships, in order to reveal the guiding role of industry market bubbles and the financialization characteristics of the global iron ore market. The results show that: (1) there was a continuous capital rotation relationship between iron ore and Chinese stock market in the initial stage of iron ore futures market, which has evolved into the linkage effect in recent years; (2) there is a bidirectional bubble contagion relationship between iron ore and China's stock market in the post-epidemic era, which confirms the financialization of iron ore market; (3) the bubble infection relationship is complex and dynamic, and the outburst of comprehensive market bubble often evolves from the industry stock market bubble. Once there is a bubble in some sectors, market investors tend to buy stocks that have risen relatively slowly guided by loss aversion bias, and the behavior encourages bubbles to spread from sectors that rose ahead of the market to the general market. It is of great significance to evaluate and avoid the financialization of iron ore market to maintain the stability of financial market and reduce the systemic financial risk.

Currently, one of the main challenges in practical modeling lies in the fact that training and testing data come from different distributions. Stable learning addresses this issue by decorrelating all covariates through sample reweighting, thereby achieving stable predictive performance. While machine learning methods such as stable learning show good results in experiments, there are still theoretical gaps, such as the lack of metrics for model stability under testing data and explanations for why stable learning maintains stable predictions across multiple environments. This paper proposes a new metric of stability, compares stable learning methods with ordinary least squares and explores the reasons why stable learning maintains stability across multiple environments. Finally, the paper validates the theory through simulated experiments. This research contributes to refining the theory of stability in stable learning, enhancing the understanding of stability in stable learning, and guiding the selection of practical modeling methods.

This paper considers a single-machine scheduling problem with slack due date assignment, where the objective function is to minimize an objective function consisting of penalties for both early work and late work, we prove the NP-hardness of this problem and provide a pseudo-polynomial time algorithm with a time complexity of $O(n^2P(P-p_{\max}))$. Furthermore, we also consider two special cases of minimizing total late work under slack due dates: identical processing times and identical penalty coefficients. For these two problems, we propose polynomial time algorithms with a time complexity of $O(n\log n)$， respectively.