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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

MOOC comments contain a wealth of user opinions and emotional information, reflecting the quality demands and satisfaction from the user's perspective. This study constructs a model for the quality characterization of MOOC based on the "Theme-Emotion" coupled analysis of user comments. Specifically, it utilizes LDA to extract thematic information from comment texts, representing dimensions for assessing MOOC quality. Furthermore, it employs BERT for emotion classification to characterize user satisfaction and attention towards MOOC quality. The study takes "Chinese University MOOC" as an example to analyze the MOOC quality characterization results. The findings reveal that the quality assessment dimensions vary in MOOCs of different subject areas, user satisfaction and attention towards each dimension differ for different MOOCs, and the quality assessment dimensions have varying significant impacts on MOOC quality. The proposed MOOC quality characterization model can be generalized to various online course platforms with user comments, offering a fine-grained representation of course quality. This model provides precise criteria for builders to design and improve courses and for learners to select courses, contributing to the optimization of MOOC development and user experience.

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.

The "fundamentals" that determine expected stock returns consist of three components: The expected return component that is rationally compensated for systemic risk, the cash flow news generated by changing expectations of future cash flows, and the discount rate news generated by changing expectations of rational future discount rates. Of these, cash flow news that are not driven by fundamentals are the component most likely to reverse in the short term. In this paper, for the first time, we use analyst forecast revisions to measure cash flow news and construct a short-term reversal strategy based on residual returns to study the short-term reversal effect in the Chinese Stock Market. The empirical results find that the strategy generates higher risk-factor-adjusted returns compared to the standard reversal strategy. Further, the methodological test based on isolating cash flow news finds that liquidity shocks rather than investor sentiment can explain the short-term return reversal phenomenon in the Chinese Stock Market from both the long and short leg of the portfolio. This paper examines the short-term reversal strategy of A-shares from a new perspective of residual returns, which not only enriches the research in the area of financial anomalies in asset pricing, but also provides international evidence on understanding the theory of short-term return reversal in emerging markets, with China as an example.

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.

In the process of achieving high-quality development, enhancing regional economic resilience is one of the important guarantees for promoting economic development and driving overall regional development. This paper analyzes the mechanism of digital finance influencing regional economic resilience from a theoretical perspective, and measures and analyzes the level of regional economic resilience of each province by constructing a regional economic resilience evaluation index system. Based on panel data of selected 31 provinces (autonomous regions, municipalities) in China from 2011 to 2021, a benchmark regression model, a mediating effect model, and a panel threshold effect model are further constructed to empirically test the theoretical analysis. The research shows that:1) There are still a large number of regions with relatively low levels of regional economic resilience, and there is a large disparity in the development level of economic resilience between provinces, which exhibits an imbalanced pattern of "high in the East and low in the West"; 2) Digital finance has a significant positive impact on regional economic resilience, with a regional heterogeneity of "eastern region$>$ central region$>$ western region"; 3) Digital finance has a significant positive mediating effect in the relationships of "digital finance $\rightarrow$ industrial structure upgrading $\rightarrow$ regional economic resilience", "digital finance $\rightarrow$ narrowing the urban-rural income gap $\rightarrow$ regional economic resilience", and "digital finance $\rightarrow$ social security level $\rightarrow$ regional economic resilience"; 4) The promotion effect of digital finance on regional economic resilience can be more significant when crossing the threshold of internet penetration, and the acceleration threshold effect in eastern coastal regions is significantly higher than that in central and inland regions and western remote areas. Therefore, by developing digital finance in line with local conditions, giving full play to the positive effects of industrial structure upgrading, narrowing the urban-rural income gap, and improving social security, and accelerating the construction of modern information technology, it can effectively enhance the resilience of the regional economy.

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.

In financial market, trend-following trading strategies based on technical analysis trading rules are the most popular strategies employed by investors. This paper investigates the performance of trend-following trading strategies in the emerging cryptocurrency market. By taking empirical tests on three main cryptocurrencies including Bitcoin, Ethereum, and Ripple from 2013 to 2021, the results show that the profitability of strategies based on variable length moving average (VLMA) and moving average convergence divergence (MACD) outperforms those based on fixed length moving average(FLMA) and relative strength index (RSI). Among those three cryptocurrencies, trend-following trading strategies based on Bitcoin outperform Ethereum and Ripple, evidence indicates that specific strategies tend to exhibit significant predictive power and can generate higher excess returns as well as Sharpe ratio than buy-and-hold strategy. Those findings are robust during bubble periods of cryptocurrency market, COVID-19 pandemic, and after the People's Bank of China declares that virtual currency-related business activities are illegal.

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.

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, 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.

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.

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 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.

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.

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].

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.

Information asymmetry is the main reason that prevents manufacturers from actively participating in carbon market trading and investing in emission reduction technologies. Based on the carbon trading mechanism, this paper studies the choice of manufacturers' emission reduction and retailers' information sharing strategies under the condition that retailers hide consumers' low-carbon preference information. In this paper, Stackelberg model is used to investigate the optimal decisions of each member of the supply chain, in which retailers have private information (consumers' low-carbon preferences) and decide whether to share this information with manufacturers. By using game theory and static comparative analysis, it is found that retailers' sharing of information is beneficial to the supply chain, and under the condition of asymmetric information, manufacturers and retailers can improve their own profits by formulating revenue sharing contracts. When the manufacturer's risk aversion is low, retailers are willing to share information; When consumers have high low-carbon preferences and are insensitive to product prices, the emission reduction rate of manufacturers will increase; For products with high emission reduction costs and low consumer preference for low-carbon emission reduction, increasing carbon quotas will reduce the price of products, thereby reducing manufacturers' incentive to reduce emissions. Therefore, manufacturers can appropriately reduce their risk aversion behavior to attract retailers to share information. In addition, it is beneficial for the supply chain to establish revenue sharing contracts between manufacturers and retailers; In order to strengthen the manufacturer's investment in emission reduction technology, retailers can give priority to the promotion and promotion of low-carbon products to non-price sensitive users.

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.

The optimization problem of split delivery truck multi-drone collaborative routing for emergency supplies under traffic restriction scenario was studied. A model is formulated for truck multi-drone collaborative routing by considering the factors such as the road network conditions in the disaster area, the truck launching/receiving drones en route, drones deliver multiple nodes in a single takeoff, and split delivery. The goal of the model is to minimize the completion time of emergency delivery. According to the problem and model characteristics, an improved ant colony algorithm(IACA) is proposed. The experimental results show that the proposed approaches can reasonably allocate the delivery tasks of trucks and drones, and scientifically plan the truck multi-drone collaborative routes for split emergency delivery under regional traffic restriction situation. Truck launching/receiving drone en route can effectively shorten the drone flight distance, reduce the collaboration time between trucks and drones, and shorten the distribution time of emergency delivery under traffic restriction situation. The proposed approaches are feasible, reasonable and effective.

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.

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.

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.

On the basis of the soft parameter sharing model, the authors set the adaptive regular term coefficient $\lambda^{*}$ and adaptive parameter decay ratio $\theta$ by the similarity between tasks and the relationships between parameters. In this paper, the authors propose an adaptive soft parameter sharing method based on multi-task deep learning. On the basis of $L_{2}$ norm based on the mean constraint, the effect of removing information with low similaritities between tasks can achieve by adaptively removing the number of terms in the regular term of the loss function. The approach in this paper dynamically transforms soft parameter multi-task learning into joint soft parameter multi-task and single-task learning. Compared with soft parameter multi-task learning methods, this method reduces the impact of negative migration phenomena. Compared with single-task learning method, this method can greatly reduce the risk of local minimum solution. Both simulation studies and case analyses have confirmed the effectiveness of this approach, demonstrating that its achieves superior predictive accuracy compared to traditional multi-task learning and single-task learning methods.

In this paper, we adopt the spatial error model to describe the network structure relationship between individuals, and propose both estimation and imputation methods of the varying-coefficient partially linear spatial error model with missing responses. We firstly construct the estimator of the model parameter through profile maximum likelihood method and a matrix blocking technique. We prove the asymptotic normality of the parametric estimators and show the convergence rate of the nonparametric estimator. We then propose imputation estimators of missing response based on this model. Finally, we conduct Monte-Carlo simulation studies to detect the infinite sample performance of the estimator and analyze the QQ data set using the proposed method.

This paper studies the controllability of the multi-agent systems based on equipotential nodes, and proposes a method of constructing controllable graphs. Firstly, the relationship between the eigenvalues of the Laplacian matrix and the controllability of the multi-agent system with multi-signal inputs is provided. Secondly, it is found that there are differences between the controllability of the system with single-signal input and that of the system with multi-signal input. It is easier to realize the controllability of the system with multi-signal inputs than the case with single-signal input. In addition, the relationship between the topological structures with equipotential nodes and the eigenvectors of Laplacian matrix is provided, and a leader selection method is proposed to ensure controllability. Then, we analyze how to construct controllable graphs based on equipotential nodes for the multi-signal inputs system, and present a necessary and sufficient condition for controllability. Finally, a numerical example is given to verify the effectiveness of the method of constructing controllable graphs.