Classical Mechanics Principle of Control Engineering (System) depends on that 3 state variables including position, velocity and acceleration can be accurately constructed and estimated for the controlled system output, and the 3 state variables are used to make negative feedback control functions for the control system based on Newton's Laws of Motion. In the reference paper (Kai P A and Shen Z L, 2022), an OUAM observer (Observer based on Uniform Acceleration Movement) is constructed by Kalman filter and an MFCNLM system (Model-Free Control based on Newton's Laws of Motion) is designed based on Newton's Laws of Motion. The desired transient process output of the closed loop system is designed with the desired transient process time T. All parameters in the control system are only calculated based on the desired transient process time T of system output without controlled plan model. From the reference paper (Kai P A and Shen Z L, 2022), the theory and application of Newton's Laws of Motion in the MFCNLM system and PIDCC system (PID Control with Compensator) are emphatically analyzed in the paper. The unbiased estimation of OUAM observer and the effectiveness of MFCNLM system are demonstrated. The unity of classical mechanics principle are explained in these control systems. A simple and effective method is designed for time-varying system with time-delay (dead-time) loop in the MFCNLM system and PIDCC system, 2 simulation examples in MFCNLM and PIDCC systems demonstrated fine control quality and robust performance of the design method in the paper.

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.

Factor analysis (FA) is a popular statistical technique that is used to identify the latent common factors among a set of variables. Nevertheless, it is only applicable to vector-valued data, where observations are vectors. To apply FA to matrix-valued data, where observations are matrices, one common solution is to first vectorize the matrix observations. However, the vectorization may cause FA to suffer from two problems: Poor interpretability and curse of dimensionality. To solve the two problems, the authors utilize the inherent matrix data structure and propose bilinear factor analysis (BFA) in this paper. The novelties are that BFA uses a bilinear transformation, which greatly reduces the model parameters and thus can overcome the curse of dimensionality; Moreover, it can simultaneously identify the interesting common row, column factors among the row, column variables, respectively. The authors develop two efficient algorithms for finding the maximum likelihood (ML) estimates. The authors give the theoretical property of the ML estimator and derive explicitly the closed-form expression of Fisher information matrix to evaluate the estimator's accuracy. The authors then discuss the model selection issue. Unlike the traditional FA, where the factor score is a vector, the factor score in BFA is a matrix. The authors further develop the approaches for calculating the matrix factor scores and visualizing them. Empirical studies are constructed to understand the proposed BFA model and compare with relevant methods. The results reveal the superiority and practicability of BFA in matrix-valued data analysis.

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 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.

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.

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.

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.

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.

Improving green total factor productivity (GTFP) is the core of establishing a green logistics system. This paper constructs a global SBM model of undesirable output, and employs the GML index to calculate the GTFP of the logistics industry in 30 provinces selected from China between 1998 and 2020. Then, the Dagum Gini coefficient is used for analyzing regional differences and the causes. Finally, a spatial econometric model is established to identify the convergence mechanism. According to the research findings: During the sample period, while the GTFP of logistics in China gyrates up, growth imbalance remains a concern, with the GTFP level in the east being far higher than in the west; The differences between regions are the primary cause of the imbalance; the difference in the rate of contribution in the same region stays at around 30%; the regions with lower GTFP levels in logistics are expected to catch up with those with higher GTFP levels and finally achieve the same level; the growth and convergence of GTFP in logistics are affected by spatial factors and show regional heterogeneity; the eastern region may optimize the industrial structure, while the middle and western regions may increase their investment on energy infrastructure and R&D to bridge the growth gap of green logistics in the region; coordinated development of different regions can be realized based on a comprehensive measurement of the positive impacts of factor inputs on the local region and negative impacts on neighboring regions. The aforementioned analysis provides a theoretical basis for the construction of the green logistics system that will help promote the healthy and sustainable development of the logistics industry in China.

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 structure description and index evaluation of consecutive-k-out-of-n type redundant systems is a key and difficult issue in the field of reliability. In this paper, we establish a model of two linear m-consecutive-k-out-of-n: F systems with sparse d that sharing components. Based on the finite Markov chain imbedding approach, an evaluation method for the joint reliability function and the joint signature is proposed. In addition, we study the reliability evaluation and the structure comparison of smart street light systems based on the model of m-consecutive-k-out-of-n: F systems with sparse d that sharing components, which not only verifies the correctness of the finite Markov chain imbedding approach by the traditional definition method, but also shows that it can be applied to the evaluation of the joint reliability and the joint signature for large smart street light systems. Therefore, the model and the method studied in this paper can provide the basis of reliability evaluation and management decision for redundant systems with shared components in engineering application.

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.

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 investigates the effect of individual investor attention on the herding effect of China's stock market. Using Baidu Index as a proxy of individual investor attention and constructing an indicator to measure the strength of individual investors' herding effect, this paper conducts an empirical study on the performance of China's A-share market from 2017 to 2021. The empirical results show that:1) Individual investor attention has a significant positive impact on the herd effect in the stock market; 2) individual investor attention will have a more significant positive impact on the herd effect in the stock market when the stock reaches the limit up or down; 3) small-cap companies individual investor attention has a more significant impact on the herd effect. The robustness test results show that the conclusions are robust.

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.

Bayesian quantile regression can well estimate the parameters in the linear mixed effect model. Gibbs sampling is commonly used in Bayesian parameter estimation. In order to obtain accurate estimation results, Gibbs sampling method requires multiple sampling. When the model parameter dimension is high, the Gibbs sampling will be very time-consuming. Therefore, we use variational inference to approximate the posterior distribution of parameters. Variational inference uses unconditional distribution to approximate the conditional distribution obtained by Gibbs method, thus making the calculation more efficient. In this paper, a priori assumption of the parameters of the model is normal distribution, and the variation inference of the parameters of the unpunished linear mixed effect model is carried out. Considering the high dimensional situation, we assume the prior distribution of the model parameters as Laplace distribution, and make variational inference for the parameters of the double penalty linear mixed effect model. From the simulation results, although the accuracy of variational inference for model parameter estimation is slightly less than that of Gibbs sampling, it runs faster. In the case of high dimension, the improvement of operation efficiency is more obvious. In the era of big data, the consumption of time and resources is the first factor we need to consider. Therefore, the method proposed in this paper can be applied to the high-dimensional linear mixed effect model.

In this paper, we propose a robust adaptive control method based on singular perturbation theory for a class of non-affine nonlinear systems with parameter uncertainties and unknown disturbance terms. Firstly, a fast dynamic subsystem is constructed for the system through the control input to introduce time-scale separation. Then, the closed-loop system can be decomposed into two order-reduced subsystems:A boundary layer subsystem and a reduced slow subsystem, on the fast and slow time scales, respectively. On the fast time scale, the structure of the boundary layer subsystem is designed to make it exponentially stable around the equilibrium point. On the slow time scale, a robust adaptive controller is designed for the reduced slow subsystem with parameter uncertainties and unknown disturbance. According to singular perturbation theory, the tracking performance of the closed-loop system can be approximated by the reduced slow subsystem. The proposed control method considers the effects of parameter uncertainties and disturbance terms, achieves the control goal without ignoring the non-affine structure, does not rely on the time-scale separation of the original system, and avoids the "complexity explosion problem" in backstepping method. Simulation results with comparisons to reference control methods verify the effectiveness of the proposed control method.

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.

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.

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.

This paper constructs a logistics service supply chain (LSSC) consisting of two logistics service providers (LSPs) and one logistics service integrator (LSI), considers the emergency decision under the background of both demand disruption and cost disruption, introduces two influencing factors, data empowerment and fairness concern, and then solves and compares the optimal decisions of the LSSC under four different scenarios. The results show that:The pricing of emergency logistics service product, the level of emergency logistics service increase with the disturbance of demand, and decrease with the disturbance of cost. The impact of disturbance of demand on the LSI's utility is related to the unit fee of emergency disposal. The decisions of supply chain members increase with the degree of data empowerment, while the impact of data empowerment on the LSI's utility depends on the investment coefficient of data empowerment. Fairness concern has a positive impact on the pricing of emergency logistics service product, the level of emergency logistics service and LSI's utility. When the data empowerment investment coefficient exceeds a certain threshold, the additive positive impact of data empowerment and fairness concern on LSI's utility is insufficient to cover total data empowerment investment, and this threshold is the "upper limit of effective investment" for the LSI to carry out data empowerment. This paper is helpful to grasp the influencing rules of demand and cost disruption, data empowerment and fairness concern on emergency decisions in LSSC, and provides reference and guidance for strengthing emergency capacity building.

In the propositional logic system $\mathscr{L}^{*}_{n}$, the concepts of $p$-randomized truth degree, $p$-randomized similarity degree and $p$-randomized pseudo-distance are proposed. $p$-randomized logical metric space ($F(S), \rho_{p}$) is established, and the concept of $p$-randomized divergence degree is proposed in the $p$-randomized logical metric space ($F(S), \rho_{p}$). It is pointed out that the $p$-randomized divergence degree of the theory and the values of randomized $n$-point distribution are closely related. It is proven that the $p$-randomized divergence degree of the set of atomic formulas with different values of $n$-point distribution sequence can be full of the real number interval (0,1].

Aiming at the inventory routing optimization problem of refined oil secondary distribution under random demand, considering the storage capacity of each petrol station, the constraints of loading and unloading with full compartment, and one-to-many services in the process of refined oil secondary distribution, by setting the replenishment quantity of each petrol station as an integer multiple of the single compartment capacity, a two-stage stochastic integer programming model is formulated to minimize the sum of distribution cost and expected loss. A multi-cut L-shaped algorithm is developed. Based on the multi-cut L-shaped algorithm, a two-phase algorithm is designed for solving large scale instances, two improved strategies are embedded in the approach to accelerate the convergence speed. Different scale instances are used for simulation, the effectiveness of the improved multi-cut L-shaped algorithm and the fast effectiveness of the two-phase algorithm are verified respectively. Compared with other methods, when solving the large scale instances, the average solving time of two-phase algorithm is reduced by 31.34% and Gap of the average cost is lower than 2.63%. The research results provide theoretical basis and algorithm support for making the secondary distribution plan of refined oil.

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.

Aiming at the two-subject online and offline supply chain system composed of mobile phone manufacturers and mobile phone retailers or leasing platforms, under different market conditions, the article discusses the profits of supply chain members under three different mobile phone sales models (RM model, DM model, LM model) under different market conditions. Supply chain members According to different market conditions, the selection of the optimal sales model of the mobile phone supply chain is clarified. The research found that: 1) The three sales models have their advantages and disadvantages under different market conditions. Supply chain members should comprehensively consider the market conditions and choose the appropriate sales model. 2) To maximize their profits, manufacturers and retailers are more inclined to choose the same sales model. 3) The subsidy given by the mobile phone manufacturer to the rental platform is not the better that the greater of the subsidy: When the service level influencing factor is large, the manufacturer's profit is positively correlated with the subsidy; when the service level influencing factor is small, the manufacturer's profit is negatively correlated with the subsidy. Affected by the demand, the profit of the leasing platform increases first and then decreases with the increase of the subsidy. These conclusions can help decision makers related to the cell phone sales and leasing supply chain to develop a more scientific sales model, which will help the cell phone leasing industry to develop with high quality and increase the market concentration.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Under the major public health emergency, the accurate prediction and rational allocation of emergency medical supplies are of great significance to curb the development of the COVID-19 outbreak. This paper focuses on the optimization of the allocation path of emergency medical supplies to ensure that emergency medical supplies reach the demand nodes quickly and accurately. Firstly, the Bayesian sequential decision-making model is used to predict the infection rate of the COVID-19. On this basis, the demand of each demand node is calculated according to the relationship between the number of infected people and the quantity of emergency medical supplies. Secondly, the emergency medical supplies allocation model is established based on to the number of emergency medical supplies required by the demand nodes. Thirdly, a particle swarm optimization-adaptive large neighborhood search,(PSOALNS) algorithm is developed to solve the proposed model, and then the validity and accuracy of the algorithm are verified through several sets of numerical examples. Finally, taking the outbreak stage of the COVID-19 in Wuhan as an example, this paper considers eight types of emergency medical supplies and predicts their demands, the designated medical institutions in Wuhan are selected as the demand nodes, and the path optimization is solved and the optimal configuration route is obtained. By predicting the demand for emergency medical materials in advance and optimizing the emergency medical supplies distribution route accordingly, this study can achieve the optimal allocation of emergency medical supplies more accurately and efficiently.

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.

Computing the greatest common divisor of polynomials is a fundamental problem in computer algebra and is one of the typical applications of subresultant theory. Euclidean algorithm is a classical method commonly used for computing the gcd of several univariate polynomials, where the phenomenon of coefficient inflation often appears. As a practical solution to this problem, the modular method can effectively control the size of coefficients and is helpful to achieve high computational efficiency. In this paper, a modular method for computing the gcd of several univariate polynomials is proposed. As the most key issue in modular arithmetic, a criteria for selecting good primes is presented by establishing the relationship between the generalized subresultants of a polynomial set and its greatest common divisor under homomorphic mapping. Besides, an upper bound for the coefficients of common factors for several polynomials is derived with the Landau-Mignotte inequality. Compared with the previous approach based on the recursive gcd computation of two polynomials, the new method can compute the gcd of several polynomials with a non-nested loop, which significantly simplifies the computing process and improves the computational efficiency.

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.

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.