In the decision-making process, how to obtain optimal group decision scheme under the premise of achieving the expected utility of individual decision-makers, is one of the main contents of multi-criteria group decision making(MCGDM). However, existing methods of MCGDM didn't consider decision-makers' expectations and risk preference for individual attributes. In this paper, we study the problem of MCGDM considering decision-makers' aspiration satisfaction. Firstly, we discuss how to apply the best-worst method to MCGDM to determine the weight of attributes. Additionally, we rank the score of alternatives by considering the aspiration and risk preference. Finally, we present a new methodology by a combination of best-worst method(BWM) and aspiration satisfaction function to solve the problem of MCGDM. We verify the feasibility of the proposed method through an example. Furthermore, we do a simulation analysis for two attributes with the largest weight.

As looking for high-quality online public opinions from mass reticula data is the practice of the new concept of online public opinion, both quantity and quality aspects should be considered by relevant studies. Based on the three-dimensional evaluation system of the quality of online public opinion, through the change and reconstruction of the nodes and edges of the public opinion supernetwork model, an Opinion-Noise Detection Supernetwork model was proposed, in which there are four subnetworks:Environmental subnetwork, emotional subnetwork, social subnetwork and content subnetwork. The noumenon of online public opinion “noise” refers to the public opinion data that cannot provide suggestions for the formulation of management decisions. Based on the proposed model, 18 characteristic indexes were extracted. Finally, by employing machine learning algorithms, the public opinions with high quality were successfully identified.

Complex networks have been widely used to explore the regular pattern of complex systems. This paper uses the quantile graphs method to map the daily closing price series of six stock indexes in different markets to a complex network. It analyzes the characteristics of the quantile graphs of stock indexes, and explores the changes of the network structure of stock markets in two different regions. The results show that, firstly, the network characteristics of stock index series quantile graphs in the same market are similar, but there are great differences between these two markets. Secondly, Shanghai Securities Composite Index and Shenzhen Securities Component Index have long-range correlation, and Hong Kong Hang Seng index is relatively random, but the three stock indexes in the US market, include S&P 500 Index, NASDAQ Composite Index and Dow Jones Industrial Average, are inversely long-range correlation. Finally, the quantile graphs of the two markets have different community structures. This method reveals the nature and potential dynamic behavior of stock markets in different regions from a macro perspective, and it can provide a wealth of information for stock index forecasting.

This paper is concerned with an optimal contraception control problem for a size-structured vermin population. The state system model consists of a first-order partial differential equation with a global feedback boundary condition and two ordinary differential equations, and the control function is taken to be throwing amount of contraception medicaments. Firstly, the existence of a unique non-negative bounded solution to the state system is established, and the continuous dependence of solutions on the control variable is shown. Then, the Euler-Lagrange equations describing the exact structure of the optimal strategies are derived by constructing a proper adjoint system and relative normal cone. Finally, the existence of a unique optimal policy is proved via Ekeland's variational principle and fixed-point method. This work supplies a novel modelling approach for contraception control of vermin.

Aiming at green logistics distribution problem with uncertain customer demand, a green vehicle routing model with time window and stochastic demand (GVRPTWSD) and its solution algorithm is proposed in this paper. Firstly, the GVRPTWSD model is built by considering cost impact on distance, time windows, vehicle load and fuel. The object of GVRPTWSD model is to minimize vehicle total cost including fuel consumption, carbon emission, transportation and fixed departure. Secondly, a two-stage solve algorithm is designed for GVRPTWSD model. In first stage, a mixed search algorithm is presented by integrating large neighborhood search algorithm and tabu search algorithm. The mixed search algorithm is able to yield a preliminary result of GVRPTWSD model in terms of predicted stochastic demand and random capacity constraints. In second stage, vehicle distribution procedure is conducted once again. Meanwhile, the actual customer demand is updated until vehicle reaches customer point. Then, the subsequent route is optimized by employing the failure point re-optimization strategy. The experimental results show that the proposed approach can not only reduce carbon emissions, but also achieve a lower total cost of logistics distribution, which is typically superior to some traditional methods only considering the objections of carbon emissions and fuel consumption.

Model uncertainty usually exists in the prediction for longitudinal data. To solve model uncertainty, we propose an optimal model averaging prediction method based on semi-parametric mixed effect models. The optimal weight vector is obtained by minimizing a leave-subject-out cross-validation. This paper shows that when all candidate models are misspecified, our proposed method is asymptotically optimal in the sense that it yields a squared prediction loss that is asymptotically identical to that resulting from the infeasible best-possible averaging estimator. In a different scenario, we show that our method can put the weight one to the correctly specified models. Both simulation study and empirical example show the superiority of our proposed estimator over other competitive methods.

The large-scale interaction data of the online live streaming platform provides experimental datasets for the quantitative analysis of human behavior, and offers a new opportunity for the mining of the online interaction mechanism with collective dynamics. Given the lack of empirical research on real-time collective interaction, this paper collects a one-year-long comprehensive dataset of real-time live streaming statistics, involving more than 1.9 million streamers from Douyu (the largest live streaming platform in China), and designs a generalized evolution model for exploring the interaction mechanism between streamers and viewers. First, we construct a viewer-streamer bipartite interaction network representing the dynamics of the entities in the platform, and then propose an evolution model with adjustable preference strength of viewer-streamer interaction. The preference strength can be adjusted with two parameters:The fraction of random choice and the preference coefficient of viewers. Experiments on empirical datasets show that the model can accurately and robustly predict the evolution process when all viewers have linear preference on the number of existing viewers attracted by the streamer when they select a streamer to interact with. This paper reveals the dominating mechanism of preferential attachment for the viewers selecting a streamer and reflects the human tendency and preference for valuable content, confirming the cumulative effect of reputation or word-of-mouth in social systems. Our study provides a quantitative model for exploring the interactive behavior characteristics and internal mechanism of large-scale online crowds in live streaming, and is of great significance for describing and predicting the formation and development of social relationships in more general settings.

This paper presents a noncooperative-cooperative biform game framework to analyze how different power structures (the simultaneous model, the manufacturerleader model, and the retailer-leader model) to affect the product pricing and lowcarbon technology investment decisions in a low-carbon supply chain consisting of a manufacturer and a retailer. The results show that: 1) There is the unique lowcarbon technology level and cost-sharing ratio, that make the retailer and supplier maximize their profits in the three decision-making models under some conditions; 2) The supplier’s low-carbon technology level has greater development potential in the retailer-leader model; 3) There is an inverted U-shaped relationship between the low-carbon technology investments cost-sharing ratio and consumers’ low-carbon preference; 4) From the perspective of the supply chain system, the different development periods of low-carbon supply chain need different market structures, rather than fixed in a specific market structure. This study contributes theoretical methods for lowcarbon supply chain production and operation, and also provides new solutions for achieving carbon peak, carbon neutral, and double carbon goals.

Hopf bifurcation is a kind of simple but important dynamic bifurcation problem, which means that when the system parameter changes past the critical value, the equilibrium point changes from stable to unstable and a limit cycle is generated. Based on Hopf bifurcation, this paper proposes a time-delay reaction-diffusion rumor propagation model with saturated control, which better reflects the characteristics of rumor propagation in real life, and studies the Turing instability and Hopf bifurcation. Meanwhile, the time delay is selected as the bifurcation parameter, and the analytic expression of the bifurcation threshold is given. Finally, the correctness of the theoretical results is verified by numerical simulation. The results show that both diffusion and time delay are the causes of the system instability. The traditional rumor propagation model only considers the time evolution, while the model depicts the traditional rumor propagation model from the two dimensions of time and space, making it more appropriate to reflect the law of rumor propagation in real life, and providing new ideas for the governance of rumor propagation.

The optimization of revenue management is one of the effective ways to improve retailers' economic returns. Pricing is the engine and core technology of revenue management, which plays an important role in improving retailers' revenue. Considering the complexity of forecasting and optimization problems in the practical application of revenue management, the steps of forecasting product demand first and then optimizing revenue are usually adopted. When forecasting product demand, there are usually multiple candidate models, that is, facing the uncertainty of the model. At this time, the final model is generally determined by model selection. However, traditional model selection criteria, including Akaike information criterion(AIC) and Bayesian information criterion(BIC), usually only consider the impact of model selection on prediction accuracy, without considering how the prediction model will affect the next optimization decision objectives. This paper first proposes the focused information criterion(FIC) model selection criterion in the optimization of commodity income management, uses the FIC model selection criterion to select the product demand forecasting model, considers the structure of the optimization model, and selects the prediction model with the goal of minimizing the decision-making error rather than prediction error. The numerical simulation results show that, in most cases, compared with AIC and BIC model selection criteria, FIC model selection criteria considering decision objectives performs best. Meanwhile, the empirical research results also verify the superiority of the FIC model selection criteria considering decision objectives.

To accurately assess the comprehensive quality level of manufacturing small and medium-sized enterprises(SMEs) and address the issues in their production and management, this paper proposes a comprehensive quality assessment method based on combined weighting VIKOR method. The comprehensive quality assessment index system was designed by referencing the requirements of the quality management system and considering the characteristics of manufacturing SMEs. It consists of five dimensions:Quality certification, quality supervision, innovation and development ability, financial quality, and social reputation. The improved information overlap index selection method was used to optimize the indicators. AHP and CRITIC methods were used for weight assignment, and the VIKOR method was applied to calculate the interest ratio value, to achieve comprehensive quality assessment of SMEs. Empirical research was conducted on 119 SMEs in the electronics industry using the comprehensive quality assessment method proposed in this paper. The research findings indicate a strong consistency between the results obtained from the proposed method and the objective quality development status of the sample enterprises, which verifies the effectiveness of the method proposed in this paper.

Consider that an online retailer can cooperate with a physical retailer to allow consumers to pick up online orders in the physical store. By developing competitive pricing models in the case without cooperation and with cooperation, we investigate the condition under which two retailers are motivated to cooperate and how the cooperation affects the pricing competition and the express firm's decision of delivery efficiency. Results show that two retailers are motivated to achieve a win-win cooperation although the cooperation intensifies the pricing competition when the proportion of time-sensitive consumer is high. This occurs especially when the product differentiation level is low and the convenience for consumers to pick-up online orders in store is more. Because the online retailer can benefit from the increased market penetration while the physical retailer can benefit from the increased store traffic. These positive benefits will outweigh the negative competition effect. Additionally, the express firm will improve its delivery efficiency to increase the profit when two retailers cooperate for picking up online orders in store.

Barrier option is a popular over-the-counter derivative in the Chinese market. Due to the discontinuity of its returns, financial institutions are mainly faced with the problem of delta value fluctuations in the process of dynamic hedging, resulting in higher hedging risks. We propose an efficient and stable willow tree method for barrier option pricing and greeks calculation for dynamic hedging of barrier options assuming the underlying asset price follows Merton's jump-diffusion model, which can also be extended to other stochastic processes. Compared with the existing methods, the willow tree method is more stable in calculating the delta, and the hedging cost is lower. An empirical analysis of the hedging effect of barrier options on the Shanghai Stock Exchange 50 Index is conducted from January 1, 2010 to September 30, 2021, and the model parameters are calibrated from the market data. The numerical results show that the willow tree method reduces hedging costs and hedging risks, and it can provide a new approach for domestic financial institutions to hedge barrier options and related structured products.

By virtue of the vector order structure, the concepts of Nash equilibria and cooperative equilibria for set payoff population game are introduced. Due to Ky Fan section theorem and generalized Scarf theorem, the existence theorems of Nash equilibria and cooperative equilibria for set payoff population game are obtained with the assumption of upper semi-continuity of set payoff function, respectively. Finally, an example is given to verify the feasibility of the conclusion and illustrate the difference between Nash equilibria and cooperative equilibria.

The randomized controlled trial,(RCT) is widely used in clinical trials, and sample allocation for the RCT is an important issue in practical applications. However, for the RCT under the causal inference framework, traditional sample allocation methods did not consider the correlation of potential outcomes. To solve this problem, we propose a sample allocation method considering the sensitivity parameter, and combine it with a response-adaptive randomization design to estimate causal effects. Simulation studies have verified that our method can obtain more accurate estimators of causal effects than other commonly used allocation methods when the independence assumption does not hold.

This paper presents a Euler-Maruyama,(EM) method for Riemann-Liou-ville fractional stochastic differential-integro equations, and analyzes the strong convergence of the presented EM method. Specifically, the considered fractional stochastic differential-integro equations are transformed into their integral forms, and then the left rectangle rule is used to construct the EM method. The strong convergence with order $(1-\alpha)\wedge(0.5)$ of the presented EM method is established, where $\alpha$ is the order of Riemann-Liouville fractional derivative with $0<\alpha<1$. Finally, numerical experiments are demonstrated to support the theoretical results.

This paper considers an MAP/PH/1 queueing system with impatient customers. Customers arrive at the queueing system according to a Markovian arrival process, each of the customers requiring a service that is of phase type, and the impatient time of customers is assumed to be exponentially distributed. For analyzing the queueing system, a level dependent quasi-birth-and-death process is constructed. Firstly, the stability condition of the system is obtained by using the mean drift technique. Then the stationary probability vector of the quasi-birth-and-death process is given by using the RG-factorization of Markov process. Based on the stationary probability vector, the probability distribution of the stationary queue length and the average stationary queue length are obtained. Moreover, a Markov process with an absorption state is built for analyzing the sojourn time of any arriving customer in the system, and the probability distribution of the sojourn time and the average sojourn time are given. Finally, the effect of some crucial parameters on the performance measures of the system is analyzed by means of numerical examples.

Crude oil price fluctuations have a significant impact on the development of the global economy, and accurate forecasting of crude oil prices has become a hot research topic in the field of energy economy. Traditional econometric methods and machine learning methods have many deficiencies in oil price forecasting. Aiming at the complex characteristics of crude oil price series such as highly nonlinear, non-stationary and time-varying, this paper presents an ensemble forecasting method for crude oil prices based on multi-scale decomposition and dynamic error correction. First, on the basis of multi-scale decomposition and machine learning methods, a diversified base model generation mechanism for ensemble forecasting is constructed. Further, a dynamic error correction model is established based on multiple strategies to improve the forecasting accuracy of the base models. Finally, a diversity-based selective ensemble strategy for base models is established. The empirical results for the Brent crude oil market demonstrate that the ensemble forecasting method proposed in this paper can effectively improve the forecasting accuracy of crude oil price, and has promising forecasting performance and generalization ability.

The region constrained consensus of flexible-joint robot manipulators is studied. A consensus control torque for flexible-joint robot manipulators is proposed based on the regional potential functions, which can effectively guarantee the range of the synchronization state of flexible manipulators without calculating the final synchronization state, so it is suitable for cooperative control and application of multiple flexible-joint manipulators in the task space. Further more, a sufficient condition for solving region consensus problems is presented based on the structure of network topology. Finally, the effectiveness of the obtained theoretical results is verified by numerical simulations.

The digitalization of cross-border trade motivates the development of cross-border e-commerce (CBEC). To inspire the small and medium-sized companies to participate in the innovative CBEC practice, 165 CBEC comprehensive pilot zones have been established in different batches and the so-called “no-ticket exemption” policy has also been introduced. In this paper, the integrated optimization of the CBEC supply chain network design and the tax-declaring strategies are studied, and both the comprehensive experimental zones and the “no-ticket exemption” policy are taken into account. A two-stage nonlinear stochastic programming model is developed, in which, uncertain demands and exchange rates are involved. To optimally solve it, the proposed model is linearized and an optimally dedicated L-shaped algorithm is designed. The case study shows that the “no-ticket exemption” policy can significantly decrease the tax burden of the CBEC companies, and then, attract the CBEC companies to deploy their hubs in the comprehensive pilot zones. Moreover, the introduction of the “Regional Comprehensive Economic Partnership” agreement would alleviate the difference between overseas warehouses and domestic warehouses and inspire the CBEC companies to concentrate cargo logistics to a few overseas warehouses with geographical superiority. Both of the two policies can reduce the layout cost of supply chain network of the CBEC companies.

Aiming at the introduction strategy of innovation products, this paper constructed models of sequential introduction strategies and simultaneous introduction strategy based on the innovation level, obtained the equilibriums.y by backward induction under different strategies and discussed the firm’ equilibrium decision. Study shows that the optimal introduction strategy is influenced by the unit production cost, the relative innovation degree and the degree of consumer strategy. In general, when the unit production cost of high-innovation products is moderate, the simultaneous introduction strategy is better than the sequential. The relative innovation level affects the high-innovation products’ price in period 2 from two aspects: The innovation effect (the greater the innovation level is, the higher the price is) and the innovation-willingness effect (innovation level indirectly affects the product price by influencing the consumers’ willingness to pay). In addition, narrowing the price difference of low-innovation products in two periods can effectively alleviate consumers’ strategic waiting behavior.

In this paper, we examine the impact of Blockchain adoption on the income changes of regulatory authorities and food producers. Specifically, we employ an evolutionary game-theoretic approach to analyze the adoption strategy of Blockchain by regulatory agencies and the evolutionary stability strategy of production behavior by food producers. We derive equilibrium conditions and perform parameter sensitivity analysis and numerical simulations. Our findings underscore that the acceptance rate of novel technologies is a function of various factors, including interest coordination between regulatory agencies and food producers, which is critical in determining the implementation effect and landing speed of Blockchain. Furthermore, we find that the adoption of Blockchain may not necessarily enhance the effectiveness of regulation. The regulation department's punishment for low-quality production behavior of food production enterprises significantly influences the behavior of these enterprises. Under certain conditions, the production behavior of food production enterprises is independent of the regulatory authorities' Blockchain adoption strategy. Therefore, to design effective food safety regulations, it is imperative to comprehensively consider the interests and motives of food producers and regulatory agencies. The accountability level must be reasonably designed to avoid ineffective food regulation strategies and adverse social impacts. Our study provides insights that contribute to the literature on technological adoption and regulatory design.

In this paper, a neuroadaptive tracking control algorithm is proposed for a class of uncertain strict feedback nonlinear systems with time-varying asymmetric new state constraints and unknown time-varying control gains. Unlike the commonly studied time-varying state constraints that are only related to the running time, this paper also considers the influence of the desired trajectory and the partial state of the system on the constraint boundary function. This new type of state constraints includes the commonly studied time-varying constraints and constant constraints, so the new state constraints researched here are more general and practical. The derivatives of such constraint boundary functions will involve uncertain nonlinear dynamics, which are no longer suitable for controller design. In this paper, the neural networks and virtual parameter technique are combined to deal with the uncertain nonlinear parts. This method does not need to estimate the weight vector parameters directly, which greatly reduces the computational burden. In addition, the complexity of controller design and stability analysis is reduced by constructing a non-piecewise continuous asymmetric barrier Lyapunov function. The control algorithm proposed in this paper can ensure that the system can achieve better tracking performance without violating the time-varying asymmetric new state constraints, and both theoretical analysis and simulation results verify the effectiveness and superiority of the presented control algorithm.

To solve the dilemma of information-sensitive e-waste recycling under incomplete information, this paper constructed an evolutionary game model composed of government, consumers and recyclers based on prospect theory, and discussed the main factors affecting system game strategy. The results show that the government's increased supervision can promote the standardization of the recycling market. The negative credit evaluation of consumers is conductive to the informal recyclers. The high negative credit evaluation encourages the normalization of the informal recyclers, while the low negative credit evaluation promotes the informal recycling treatment of the recyclers. In the recycling process, if consumers suffer information leakage losses, they tend to distrust strategies, and the greater the information leakage loss, the deeper the degree of distrust. In addition, the loss factors in prospect theory have an impact on the strategies of consumers and recyclers, while the return factors have almost no effect, indicating policymakers pay more attention to loss aversion in the game.

As an important open source software in the field of statistics, R language has a long history of development and a relatively mature ecosystem. This paper explores the core components and dependencies of R language packages from the perspective of complex directed networks, divides the dependency networks between packages, and conducts an in-depth study of their developmental lineage, so that R language developers and users can quickly understand R language. Our study shows that the dependency relationships among the R packages obey the power-law distribution and “small-world” phenomenon; the dependency network of R packages includes five subcommunities “Statistical Modeling”, “High-Performance Computing”, “Data Visualization”, “Statistical Modeling”, “Data Visualization”, “Web technologies”, “Bioinformatics”. In summary, this paper has found the following:The R language ecosystem meets the needs of the entire data analysis process, scales with the times, attracts developers from all over the world, and focuses on the long-term maintenance of the package's health. Finally, the paper explores the open source model of R language to explore the implications for domestic open source software and its ecosystem.

The problem of attack detection for the physical layer actuator of cyber-physical systems is studied in this paper. First, a dynamic model is established to describe the whole cyber-physicalsystems. On the premise that there is an attack signal in the actuator, a class of attack detector is designed based on the assumptions. The Lyapunov stability theory is applied to analysed the stability of the system, so that the observer satisfies the H_∞ and L₂-L_∞ performance conditions respectively, and finally it can be transformed into the form of linear matrix inequality, in this way, the gain matrix can be solved. Finally, a simulation example is given to demonstrate the accuracy of the design method.

With the rapid development of the platform economy, more and more manufacturers sell the products through their own channels (i.e., the direct channel) besides the retailers (the indirect one), i.e., the dual-channel supply chain. Traditional wisdoms also refer to the dual-channel as the manufacturer encroachment, endowing manufacturers with absolute control over prices. Intuitively, one finds the manufacturer can have more carbon emission, which increases the manufacturer's purchase cost of carbon emissions (i.e., carbon cost) because of the increasing sales with channel competition, especially under the carbon cap-and-trade, namely channel competition effect. On the other hand, the research and development (R&D) cost per the unit product of the carbon reduction can be alleviated due to channel competition, which results in the lower unit carbon mission and wholesale price, namely, spillover effect. Motivated by the observations, we employ a Stackelberg game between a manufacturer (she) and a retailer (he) to explore the manufacturer's channel decisions under carbon cap-and-trade. It shows that the manufacturer always has an incentive to develop the direct channel. Counterintuitively, whether the manufacturer's carbon emissions in the dual-channel supply chain are higher than that in the single channel one depends on the manufacturer's reduction cost in carbon emission. To be specific, when the manufacturer's reduction cost in carbon emission is low, her carbon emission in the dual-channel supply chain is lower than that in the single channel; Otherwise, her carbon emission in the dual-channel supply chain is higher. For the retailer, he can benefit from the manufacturer encroachment. When the carbon price is high and the manufacturer's reduction cost in carbon emission is low, the retailer benefits from the manufacturer encroachment; Otherwise, his profit in the dual-channel supply chain is lower. In addition, we identify the region in which the retailer's profit is higher and the carbon emission is lower in the dual channel supply chain than those in the single one.

An optimal control problem and the basic properties for solutions of a population system of hierarchical age-structured are investigated. First, by using the fixed point theorem, the existence and uniqueness and boundness of the nonnegative solution are proved. Then, a feedback optimal control law is established by means of adjoint system and normal cone concept. Last but not least, we obtain the existence of a unique optimal control via Ekeland variational principle and fixed point theorem.

Formation control of multi-agent systems has become a hot topic of interest for domestic and international scholars because of its wide application in military, aerospace, and biological fields. In practical applications, when the system is subject to external disturbances such as environmental noise, the controller cannot form the expected formation by using the relative state information of the leader-follower. To solve this problem, this paper investigates the leader-follower formation control problem for a class of nonlinear multi-intelligent systems with external disturbances. Firstly, a disturbance observer is designed to compensate the external disturbance. Secondly, a fully distributed control protocol is given using an adaptive approach. Then, the distributed control protocol is proved to be able to achieve the formation control of the system by selecting the appropriate parameters. Finally, the validity of the protocol is verified by simulation examples.

This paper analyzes the climate change of Hong Kong, China based on the climate data of Hong Kong, China observatory from 1884 to 1939, and from 1947 to 2016, using the Mann-Kendall trend test, the t-test, the wavelet transform, and the cross-wavelet transform. The results of the Mann-Kendall test and the ttest show that during the first period from 1884 to 1939, the growth rates of the annual average minimal and maximal temperatures in Hong Kong, China are equal, while in the second period from 1947 to 2016, the growth rate of the annual average minimal temperatures is significantly higher than that of the annual average maximal temperatures. For doing the temperature mutation test, the missing data of the period from 1940 to 1946 is has been imputed by China, Macao’s data in the same period with appropriate modification. The result shows that the temperature had a significant mean abrupt change in 1958 in Hong Kong, China, and 1997 was a suspected year of abrupt change. The wavelet transform shows that there exist multi-scale periodic changes in temperature and precipitation in Hong Kong, China, where the first major periods of temperature and precipitation were 52a and 42a respectively; the crosswavelet analysis shows that the ENSO characteristic values Nino3.4 and SOI, the correlation with temperature, and precipitation in the whole time is not significant, but there exists a local correlation.

This paper explores how retailers can choose the best retail mode for multi-channel retailing based on consumers' channel preferences. It compares the pricing and profit of a retailer under dual-channel, showrooming, and BOPS modes. Results show that as consumers' online channel preference increases, the optimal profit under dual-channel mode decreases then increases, while the optimal profit under BOPS and showrooming modes increases. Dual-channel is best when consumers' online channel preference is low or high enough, but BOPS and showrooming modes are more profitable for moderate online channel preference. BOPS has an advantage over showrooming as online channel preference increases. Showrooming and BOPS are expected to have more advantages as online channel preference continues to fluctuate. Our results suggest that retailers in specific industries can select the best model based on showrooming and drainage effects.

In this paper, to address the challenges of semantic granularity and limited flexibility in effective portfolio investment caused by the inadequate perception of stock price fluctuations in portfolio return prediction models, the authors propose a comprehensive system prediction model for stock returns by integrating the sentiment situation evaluation score model (SESTM) and graphical lasso (GLASSO). Firstly, the authors introduce quantile regression to model stock price volatility, defining volatility width sequence and volatility mean sequence to identify vocabulary related to positive return fluctuations. Next, the SESTM model is employed to extract perception vocabulary related to stock price volatility from news announcements and generate news sentiment scores based on closely related themes and matching dictionaries associated with policies, valuations, and market sentiments. Finally, by combining the GLASSO method, the authors construct a network structure of interdependence among stock prices and develop individual stock portfolio strategies based on this network. Empirical experiments are conducted using stocks from the biotechnology vaccine sector during the epidemic period to compare network interdependence and sentiment situation evaluation models. The results show that firstly, investment strategies constructed based on perception vocabulary are more suitable for short-term predictions; Secondly, incorporating information the reflected partial correlations in the interdependent network, the average daily logarithmic returns of the investment portfolio reached 1.6%, which is a 14.3% improvement by 14.3% compared to not considering partial correlations, and it is twice the average daily logarithmic returns of a random combination 0.7%. Moreover, the highest return increased from 3.117 for a random combination to 3.605, showing a significant improvement of 15.6%. These results indicate that the combination model of SESTM+GLASSO provides an efficient and superior system prediction model through a comprehensive approach that can analyze the network interdependence among stock prices and accurately predict stock returns for formulating corresponding investment strategies. It has positive implications for advancing statistical research in dynamic price perception and deepening generation-based cross-modal tasks within large language models.

This paper considers a censored quantile regression model with multiple change points. By the linearizatin technique, we propose an iterative segmented algorithm via bootstrap and backward elimination algorithm by BIC criterion to simultaneously estimate the number and locations of change points and regression coefficients. We establish their asymptotic properties, including the selection consistency of the number of change points, the consistency and asymptotic normality of the estimators of location of change points and regression coefficients. A weighted CUSUM test statistic is developed for the existence of a change point at a quantile level, and its limiting distribution is also established. Numerical studies show that the proposed estimating and test methods have good finite sample performance in both homoscedastic and heteroscedastic cases. An application to the Chinese household financial assets data illustrates the usefulness and validity of the proposed method.

This paper is concerned with the robust feasibility for a class of support vector machine problems with uncertain data. Firstly, a robust counterpart problem of the uncertain support vector machine problem is introduced in terms of robust optimization. Then, a reformulation of the robust counterpart problem of the uncertain support vector machine problem is given. Finally, by using this reformulation and the so-called epigraphical set, an exact formula for the radius of robust feasibility of the uncertain support vector machine problem is obtained.

In this paper, the finite-time preview control problem for a class of uncertain saturated systems is studied. In order to avoid the difference between the time-varying coefficient matrix and the saturated nonlinear term, this paper utilizes the difference between the system state variables and the corresponding auxiliary variables, instead of the usual difference between system states, and constructs a new form of "augmented error system", which converts the finite-time preview saturated control problem into the finite-time stabilization problem of the augmented error system. The augmented error system no longer contains error vector, which not only reduces the order of the system, but also allows the output matrix to contain uncertainties. For the augmented error system, the feedback control is introduced, and the sufficient conditions for the finite-time stability of the closed-loop system and the design method of the predictive saturated controller are derived by using the improved sector condition and LMI (Linear Matrix Inequality). The numerical simulation examples also illustrate the effectiveness of the results in the paper.

In order to study the long-term impact of government subsidies and manufacturers' reciprocal preference behavior on carbon emission reduction supply chain operations, this paper constructs a non-reciprocal preference for manufacturers without government subsidies on the basis of considering the dynamic impact of product carbon emission reduction levels on consumer demand. There are five differential game models, namely the non-reciprocal preference model for manufacturers with government subsidies, the reciprocal preference model for manufacturers without government subsidies, the reciprocal preference model for manufacturers with government subsidies, and the centralized mode. Carbon emission reductions are used as state variables to solve the optimal trajectory of supply chain emission reduction, comparing the carbon emission reduction levels of manufacturers and suppliers and the government's subsidy coefficient under different models, and using centralized decision-making as a benchmark to explore the impact of government subsidies and manufacturers' reciprocal preference behavior on supply chain carbon emission reduction levels and profits. Research shows that the reciprocal preference behavior of manufacturers will not only improve the profits of the manufacturers themselves, but also the profits of upstream suppliers. Government subsidies to supply chain members can improve the level of carbon emission reduction and profits in the supply chain, but government subsidies will weaken the reciprocal preference behavior of manufacturers.

E-commerce live broadcast marketing ushers in an explosive development due to multiple factors such as policies and COVID-19, and how to choose a suitable marketing strategy has become a problem that plagues most companies. Considering a dual-channel supply chain consisting of a single manufacturer, a single retailer and a single influencer, this paper establishes two game models that consider TM strategy (traditional marketing strategy) and IM strategy (influencer marketing strategy) respectively, and explores the properties of the game equilibrium and the marketing strategy preferences of the channel members. The results show that: 1) The increased ability of the influencer to attract traffic will lead to higher retail prices in both channels at the same time. However, the improvement of the CSR level of the influencer is conducive to enhancing the relative price advantage of products in live broadcast channel. 2) IM strategy will improve the manufacturer's profit when consumers are more sensitive to influencer marketing or when the ability of the influencer to attract traffic is high, otherwise, the manufacturer will choose TM strategy. However, the retailer always prefers TM strategy. This finding is still robust in the situation that the influencer dominates. 3) The spillover effect of influencer marketing can alleviate the adverse effects of influencer marketing on the retailer by ameliorating channel conflicts. Specifically, both the manufacturer and retailer will prefer IM strategy when the CSR level of the influencer and the spillover effect coefficient of influencer marketing are high.

The hydrodynamic characteristics of USV are highly nonlinear and time-varying. In order to facilitate the control of the yaw, a nonlinear parameter-varying (NPV) model based on the surge velocity is proposed. Firstly, a nonlinear mechanism model with three degrees of freedom is established by introducing Ross damping model from the hydrodynamic mechanism. Secondly, on the basis of the mechanism model, the nonlinear term is implied in the linear structure to make the model form a linear structure. Then, the sway damping term with small value is ignored, and the surge velocity is taken as the variable parameter to establish the NPV model based on the surge velocity. The NPV model has a simple structure with nonlinear and variable parameter terms, which is an extended form of the Norrbin nonlinear model and linear parameter-varying (LPV) model. Finally, simulations and experiments show that the NPV model can well describe the nonlinear and time-varying characteristics of the yaw motion of the USV.

Adaptive control charts are a hot research topic in Statistical Process Control, and widely concerned by practitioners. This paper first introduces the idea of the weighted likelihood ratio test into the design of adaptive exponentially weighted moving average (AEWMA) control charts and develops an AEWMA chart for joint monitoring the process mean and variance (denoted by the AWLRT chart). The effect of the smoothing parameter on the proposed AWLRT chart is investigated in terms of the mean and standard deviation of run length. Subsequently, the out-of-control performances of the AWLRT and other existing control charts for monitoring the process mean or (and) variance are compared. The results show that the proposed AWLRT chart has consistently optimal performances. Finally, the application of the proposed AWLRT chart is illustrated by a real example.

The evacuation of overseas Chinese is an important issue in emergency management. Based on the minimization of evacuation cost and evacuation time, this paper proposes a multi-objective planning nominal model for the evacuation of Chinese nationals. Considering the uncertainty of the cost of evacuation, we adopt the idea of robust optimization and introduce two types of uncertain sets (box and ellipsoid). We then establish robust location routing models and reformulate them into tractable LP and SOCP. Finally, a numerical example is given based on the Libyan evacuation operation in 2011. For different conservative parameters, the Pareto-effective evacuation plan is calculated, which verifies the feasibility and effectiveness of the model.