The study aims to develop a high-performance prediction model tailored to the blood collection and supply scenarios unique to China, considering its specific national conditions. It begins by analyzing seven factors:workdays, holidays, weekdays, months, seasons, winter and summer vacations, and the blood collection volume from the previous day. Statistical analyses confirm that all these factors significantly influence daily blood collection volumes. Subsequently, the study proposes a CNN-LSTM hybrid model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The CNN component extracts periodic and local features from the data, while the LSTM component captures long-term temporal dependencies, enhancing feature representation capabilities. Experimental results demonstrate that the CNN-LSTM model outperforms other models, including CNN, LSTM, Generalized Regression Neural Network (GRNN), Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Linear Regression (LR). The CNN-LSTM model achieves the most comprehensive extraction of time series features across multiple factors and delivers the highest prediction accuracy. Specifically, its Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Square Error (NRMSE) are reduced by up to 25.80% and 26.54%, respectively, while the coefficient of determination (R²) improves by up to 320.85%. The prediction results provide more precise decision-making references for blood collection and supply institutions, enabling better adjustment of collection plans and inventory management strategies.

As an important component of the modern economic system, digital finance has a crucial influence on the development of new quality productivity. Based on the panel data of 30 provinces in China from 2013 to 2022, this paper uses the entropy weight-TOPSIS method to measure the development level of new quality productivity at the provincial level, and analyzes the impact effect and mechanism of digital finance on new quality productivity through the two-way fixed effect model and the mediation effect model. The research finds that digital finance significantly promotes the development of new quality productivity, and this conclusion has passed the robustness test and endogeneity treatment. In the heterogeneity analysis, it is found that this promoting effect shows differences in different regions, different innovation capabilities and different degrees of enterprise agglomeration, presenting a pattern of "Central > Northeast > East > West", "High innovation capability > Low innovation capability", and "High degree of enterprise agglomeration > Low degree of enterprise agglomeration". The mechanism test finds that digital finance promotes new quality productivity through the positive effects of promoting the level of science and technology, improving the efficiency of resource allocation and optimizing the upgrading of industrial structure. The threshold effect analysis finds that when the level of innovation output crosses the threshold value in the process of digital finance influencing new quality productivity, the promoting effect of digital finance on new quality productivity weakens, and there is a marginal diminishing effect. Therefore, this paper discusses relevant policy suggestions, providing useful ideas for the formulation of policies on promoting the development of new quality productivity by digital finance.

Online travel reviews encapsulate travelers' authentic experiences and serve as a crucial reference for potential tourists' decision-making. However, due to the vast number of reviews, it becomes challenging for potential tourists to browse each one and effectively extract valuable information. Moreover, existing research on travel itinerary recommendations often lacks sufficient sentiment analysis of online reviews, making it difficult to accurately reflect user needs. Considering that selecting a travel itinerary typically involves multiple constraints, this paper proposes a MACONT multi-attribute decision-making method based on picture fuzzy sets to recommend suitable travel itineraries for potential tourists. Firstly, we utilized the Octopus web scraping tool to collect travel itinerary review data from the Ctrip travel website and preprocessed the text using Jieba for word segmentation. Subsequently, the LDA topic model was employed to identify decision attributes and their weights for travel itineraries. Next, SnowNLP sentiment analysis was applied to extract the sentiment orientation of the reviews, quantifying them into picture fuzzy numbers. Then, integrating the MACONT multi-attribute decision-making method, a picture fuzzy MACONT decision model was constructed to achieve a comprehensive ranking of travel itineraries. Finally, a case study was conducted to validate the proposed method's rationality, and further sensitivity and comparative analyses were performed to demonstrate its effectiveness.

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

This paper explores the characteristics of optimal estimation for the population variance σ² in a normal distribution N(μ,σ²), where μ is known, utilizing balanced ranked set sampling(RSS). The theoretical findings indicate that the balanced RSS estimation of population variance exhibits greater efficiency compared to the estimation derived from simple random sampling (SRS). To enhance the efficiency of statistical inference, we propose a Fisher information maximization approach and a quasi-sufficient complete statistic framework for the RSS design. Furthermore, we investigate the optimal estimation of population variance and analyze its characteristics under these two methodologies. The numerical findings indicate that the RSS estimation of population variance utilizing Fisher information maximization, as well as the RSS estimation based on quasi-sufficient complete statistics, exhibit superior efficiency compared to the balanced RSS estimation. Furthermore, the quasi-sufficient complete statistic RSS estimation for population variance demonstrates greater efficiency than that derived from Fisher information maximization. Furthermore, this paper explores the characteristics of optimal estimation for the population variance σ² in a normal distribution N(μ,σ²), where μ is unknown, utilizing balanced RSS. The numerical findings indicate that the balanced RSS estimation of population variance exhibits greater efficiency compared to the estimation derived from SRS when μ is unknown. The real data analysis is provided to illustrate the numerical findings.

This paper deals with robust optimality condition and duality theory for a class of minimax fractional semi-infinite optimization problems with uncertain data. By virtue of robust optimization, Dinkelbach technique, and robust type constraint qualification conditions, we first establish robust optimality conditions for this uncertain optimization problem. Then, we introduce a Mixed type robust dual problem for this uncertain optimization problem, and explore robust duality properties between them. As a special case, we investigate robust optimality conditions and sum of squares relaxation properties for minimax fractional semi-infinite optimization problems with sum of squares convex polynomial structures.

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

Cyclic codes, an important subset of linear codes, are widely utilized in communication systems, consumer electronics, and data storage systems owing to their efficient encoding and decoding algorithms. This study aims to investigate construction of optimal ternary cyclic codes with parameter,[3^m-1,3^m-1-2m,4], By examining the existence of the solutions to specific equations over $\mathbb{F}_{3^m}$,we have obtained two distinct classes of optimal ternary cyclic codes. Furthermore, it is proved that such codes constructed in this paper are not equivalent to all known results, indicating that our results are new and have not been studied by other scholars.

Under the "dual-carbon" goal, advanced manufacturing enterprises take green innovation as the core of development and actively improve their competitiveness. However, the green innovation of advanced manufacturing industry is a green innovation system composed of the government, the public, and advanced manufacturing enterprises (referred to as "enterprises"). The overall green innovation efficiency of the industry should be improved through the effective synergistic development within the system. Therefore, a three-party evolutionary game model consisting of enterprises, the government and the public is developed to analyze the impacts of the government's external incentives (including incubation platforms, R\&D subsidies and tax incentives) and the public's green preferences on enterprises' green innovation strategies. The study finds that:the effects of the three kinds of government external incentives to promote green innovation are not the same, among which R\&D subsidies are more effective in promoting green innovation at the early stage of green innovation. When the public's green preference is increased to a certain degree, the government can no longer give any external incentives to the enterprises, and then the enterprises can realize spontaneous green innovation. In the premise of no external incentives from the government, if we want to realize the spontaneous green innovation of the enterprise, then the enterprise's Green innovation is not the higher the better. In contrast, there exists an optimal degree of green innovation, when enterprises can realize spontaneous green innovation with the lowest public green preference requirement.

Learning from Label Proportions (LLP) is a weakly labeled learning problem, where the instance-level label information is abstracted in the form of bags, that is, only the label proportion information of each bag is available. Consequently, LLP can be grouped into learning with bags community, where bags consisted of instances are related. Similar to typical classification, our aim is not only to learn a classifier to greatly recover the instance-level labels in training data, but also to generalize this label prediction to unseen data. However, due to the ambiguous or approximate property in statistic estimation and the existence of label noises, a more realistic situation for this learning framework is prone to conceive an interval-type proportion information, instead of real-valued proportions in LLP. Thus, for these universal scenarios, the standard LLP methods are failed to offer a satisfied label predictor. In this paper, we propose a new learning framework called Bounded Label Proportions (BLP) to tackle this puzzled problem. In addition, we perform a robust algorithm for BLP based on Random Forest (RF):BLPForest, which is naturally able to deal with multi-class and high dimensional problems. For the purpose of comparison, we divided our experiments into two parts. In the first part, we degenerated BLPForest into standard LLP problem, in order to verify the evolution between these two similar learning problems. Consequently, the results demonstrated BLPForest with a natural advantage even in the case of real-valued proportion information equipped, which mainly benefited from the application of RF algorithm. For the second part, we chose large datasets with multi-class and much higher dimensions. In a meantime, appropriate noise for proportion information in each bag was deliberately added. All experiments showed that BLPForest can yield the best accuracies in the most cases. Finally, we offered the corresponding discussion and necessary analysis.

Accurately predicting and analyzing the complex trends of road transport carbon emissions among regions is crucial for setting and optimizing carbon emission reduction targets and promoting the synergy of pollution reduction and carbon reduction in the road transport industry. Aiming at the multiple time-varying characteristics of road traffic carbon emissions, such as time-varying peaks and seasonal changes, as well as the tightly coupled spatial characteristics brought about by the interconnections of different regional transportation, this paper takes into account the differences in climate regionalization and economic geographic distribution, and constructs a multi spatio-temporal fusion interregional road traffic carbon emission prediction model, MTGCN. First of all, a time-dependent relationship between short-term fluctuations and long-term trends of carbon emissions is captured by the temporal feature extractor. On this basis, the static and dynamic adaptive map structure information is integrated, and the spatial feature extractor is used to explore the potential spatial dependence of inter-regional road traffic carbon emissions. Finally, the validity of the proposed model is verified based on the daily carbon emission data of the Yangtze River Delta region.

Model averaging is a hot issue in the field of statistics and econometrics. Many model averaging methods have been proposed and the statistical properties of corresponding weights have been verified. Multivariate model is an important statistical model, which is widely used in various fields. In this paper, the convergence rate of weights in the model average estimation is obtained in the sense of MMMA and MJMA based on the linear statistical model with multiple dependent variables, and the verification results also cover the convergence rate of the model average estimated weights in the case of single dependent variables. In addition, we verify the convergence rate of weights by corresponding numerical simulation.

This paper considers a two-period dynamic system with a proprietary brand manufacturer (PCM), an original equipment manufacturer (OEM) and strategic consumers, where PCM produces end products and proprietary components (PCs), decides whether and when to provide OEM with PCs, and determines when to enter the end market. The dynamic game models are constructed to explore the effects of product quality differentiation, purchasing behavior of strategic consumers, and bargaining power on the choice of PCM's coopetition strategies. The results show that:1) The strategy choice of PCM mainly depends on product quality differences when PCM has the independent pricing right for PCs. When OEM's product quality is low, PCM monopolizes the end market. When OEM produces high-quality products, PCM will provide OEM with PCs in the second period and enter the end market in both periods if the product quality differentiation is low, while PCM will provide OEM with PCs in the first period enter the end market in the second period if the product quality differentiation is high. 2) When PCM and OEM bargain over the wholesale price of PCs, providing components in the second period and entering the end market in both periods is PCM's inferior strategy. Whether to provide OEM with PCs in the first period and enter the end market in the second period depends on bargaining power, consumer patience, and product quality differentiation. 3) Compared to PCM's autonomous pricing situation, PCM is hurt by bargaining. However, if OEM is willing to redesign the profit-sharing mechanism, bargaining can achieve Pareto improvement in both parties' profits.

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

By combining radial-based neural networks and event-triggered control strategies, the distributed formation control problem for nonlinear multi-robot systems with uncertain models is studied. And the conditions for triggering controller updates based on state information are provided. This control protocol can quickly achieve the formation tracking control objective by communicating with neighboring robots. The convergence of the system is independent of the initial states of the robots, effectively reducing the update frequency of the system's controller and the system's resource consumption. By using Lyapunov stability theory, it is proven that under the proposed protocol, the multi-robot system with uncertainties can achieve formation tracking without Zeno behavior. Finally, simulation examples are used to verify the feasibility of the theoretical results.

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

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

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

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

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

The increasing occurrence of extreme climate events poses a major threat to regional financial systems. Thus, exploring its impact mechanism is vital for preventing systemic financial risks. In this study, a comprehensive index analysis method and a DY variance decomposition matrix are used to calculate the regional financial risk volatility spillover values of 31 Chinese provinces and cities from 2014-2022. Three climate proxy indicators are constructed for a balanced panel regression to measure their impact on those spillover values. The findings show that extremely high temperatures have a significantly positive association with spillover, while extremely low temperatures and extreme precipitation have no significant effect. Analyzing regional heterogeneity reveals that regions like Inner Mongolia, Tibet, and Northwest China, with lower economic development levels and more homogeneous industrial structures, are more vulnerable to spillover from extreme climate shocks. Moreover, regional carbon emission intensity positively impacts the relationship between extreme high temperatures and spillover. Strengthening regional carbon reduction mechanisms is key to mitigating the effects of extreme high temperatures on spillover. In conclusion, enhancing regional governments' awareness to prevent extreme climate shocks and strengthening carbon reduction mechanisms are crucial for addressing risks from extreme climate events.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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