With the rise of research on autonomous driving technology, the establishment of a scientific and complete test and evaluation system for autonomous driving algorithms has gradually attracted attention. The test and evaluation system of autonomous vehicles mainly includes testing methods, as well as the selection of evaluation plans and indicators. Different countries have also formulated standards and regulations for the development and application of autonomous vehicles, which are helpful to the establishment of the evaluation system. Therefore, this article has carried out related research work on the test and evaluation of autonomous vehicles, and introduces from four aspects:Background, testing methods, evaluation plans, different national policy and research. In terms of the background, the significance and application of the test and evaluation of autonomous vehicles are introduced. In terms of testing methods, it specifically introduces Monte Carlo simulations, game theory method, test matrix evaluation and worst-scenario evaluation. Accelerated evaluation of automated vehicles is also introduced. In the aspect of evaluation plans, this article specifically introduces the evaluation plans in Annual Research Report on Autonomous Vehicle Simulation in China, German PEGASUS project, Chinese Intelligent Vehicle Future Challenge and the European AdaptIVe project these four projects. It also introduces the evaluation plans mentioned in other academic literature. Finally, this article briefly introduces the various standards and policies formulated for autonomous driving in China, the United States, Europe and other countries and regions in recent years. Related companies and their simulation software are also introduced.

In this paper, we study the convergence of proximal alternating direction method of multipliers (PADMM) with over-relaxation stepsize parameter for nonconvex two-block optimization with linear constraints. The existing alternating direction method of multipliers all require the iteration step parameter of dual variable $\theta\in (0, \frac{1+\sqrt{5}}{2}] $. In this paper, we analyze the convergence of PADMM when $\theta\in(0,2)$. First, we prove that PADMM is globally convergent under suitable assumptions. Second, under the assumption that the merit function satisfied the Kurdyka-{\L}ojasiewicz property, we prove that the PADMM is strongly convergent. Finally, some preliminary numerical results are reported to support the efficiency of the proposed algorithm.

{Big data is characterized by large volume, rich types, and rapid growth, but it also has problems such as low value density and poor representativeness, which brings opportunities and challenges to sampling survey. In the context of big data, how does sampling survey adapt to new changes and what kind of development and application does it have? This paper discusses it from three perspectives. First, there are some new sampling methods with strong adaptability in the data stream environment, which can obtain representative samples efficiently and accurately, and take into account the storage space, processing time and ability. Secondly, some non-probability sampling methods without sampling frame have been developed by means of internet survey or social network data collection, which can obtain a large number of analysis samples in a short time at low cost. Third, the advantages of big data and sampling survey are integrated to integrate online and offline survey data. In the case that online sample is non-probability sample and offline sample is probability sample, this article puts forward the basic idea of data integration: On the one hand, probability samples are used to carry out the ``probability test'' for non-probability samples; on the other hand, the information of probability samples is extracted and make inferences based on model or pseudo-randomization.

Superpixel-level hyperspectral image classification is a representative spec-tral-spatial classification method. Compared with the pixel-wise classification method, it has obvious advantages in classification accuracy and efficiency. However, the main disadvantage of superpixel-level classification algorithms is that the classification results depend heavily on the segmentation scale of superpixels. Existing literature shows that the optimal segmentation scale of superpixels is usually an experimental result, and it is difficult to be specified in advance. To weaken this dependency, a superpixel-level hyperspectral image classification algorithm based on superpixel merging is proposed in this work. Local modularity function is first used to merge the sparse weighted superpixel graph constructed. By the newly defined mapping, each superpixel is represented as a sample. Then popular KNN method is adopted to classify the merged image at the superpixel level. The superpixel merging enhances the role of spatial information in classification, effectively weakens the dependence of classification results on the segmentation scale of superpixels, and improves the classification accuracy. To evaluate the effectiveness of the method, the proposed algorithm is compared with some competitive hyperspectral image classification methods on four publicly real hyperspectral datasets. The experimental and comparative results show that the proposed method not only effectively reduces the influence of superpixel segmentation scale on the classification results, but also has obvious advantages both in classification accuracy and computational efficiency.

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.

In this paper, the technical analysis indicators are fuzz{\rm if}ied by a triangular fuzz{\rm if}ier and non-subjective transaction rules are generated using the Apriori algorithm and neural network. A fuzzy inference rule library with non-subjective categories, a product reasoning machine with the meaning of Mamdani, and a central average defuzz{\rm if}ier fuzzy Decision-making system, using recursive least squares method with forgetting factors to estimate the structural parameters of the system, and proposed two non-subjective types of trading decisions (Apriori strategy, neural network strategy). Empirical results show that after deducting transaction costs, non-subjective trading rules strategies have higher annualized returns and Sharpe ratios than passive buy-hold strategies and subjective investment strategies. Such a result can give decision makers effective investment guidance and suggestions to try to overcome their psychological and market benchmarks when fund managers or stock investors make trading decisions.

In order to avoid the shortcomings of the commonly used trajectory planning methods, such as the cubersome model coupling and the difficulties in the model operation, a trajectory generation and obstacle avoidance method is proposed for manipulators based on the Learning from Demonstration (LfD). This method combines Gaussian mixture model (GMM), dynamic motion primitive (DMP) and rapid extended random tree (RRT) methods after pretreating the data recorded by the robot platform. The gaussian mixture model, aiming to optimize the set of demonstration data, is employed to generate trajectories containing as many motion features as possible. DMP is used to model and generalize the movements. And then, the trajectory can be adjusted by RRT algorithm to meet the operation requirements in cases of complex environments with obstacles in different shapes. Finally, the pick-and-place experiments based on Franka manipulator validate the effectiveness of the proposed method.

This paper deals with the Zero-Hopf bifurcation in high dimensional polynomial differential systems. First, we reduce the problem of bifurcation analysis to an algebraic problem, and we give a method for determining the bifurcation set of the Zero-Hopf bifurcation points of differential systems by using symbolic algorithm for solving semi-algebraic systems. Then, based on the second order averaging method, the algorithmic framework of the Zero-Hopf bifurcation analysis of differential systems is derived, and the limit cycle bifurcation problem is studied through specific examples by using the methods of symbolic computation, and some new results are obtained. Finally, we propose several related research problems.

The widespread spread of the epidemic has had a huge impact on economic development and daily life. Therefore, it is of great importance for formulating corresponding control strategies and economic recovery policies to collect epidemic data and analyze the spatio-temporal patterns of incidence rate or the intensity of infection. In this paper, epidemic modeling methods based on hierarchical Bayesian spatio-temporal Poisson model are discussed, including different settings of data model, process model and parameter model, discussion of parameter prior distribution, model selection and so on. Based on this idea, we can analyze the spread and development of epidemics, study the spatial differences of different regions and the influence of other covariables on epidemic trends, and study the spatio-temporal dependence of virus transmission and the heteroscedasticity structure of spatial effects.The modeling method discussed in this paper can provide theoretical reference for the study of related problems. For parameter estimation, the Gibbs sampling algorithm under the default Markov Chain Monte Carlo algorithm (MCMC) in WinBUGS and OpenBUGS can be used.

Multidimensional systems are often described by polynomial matrices, and problems on the equivalence of multidimensional systems in system theory are often transformed into problems on the equivalence of polynomial matrices. In this paper, we mainly study the equivalence of two kinds of multivariate polynomial matrices, and obtain the discriminant conditions for the equivalence of these matrices and their Smith forms, respectively. The conditions are easily verified, and an example is also used to illustrate these in the paper.

With the advent of the big data era, in order to improve computational efficiency, Wang, et al.(2018) proposed an optimal subsampling algorithm for logistic regression, which provides a better tradeoff between estimation efficiency and computational efficiency. To solve the problem of multicollinearity among variables, this paper proposes an optimal subsampling algorithm in the context of ridge regression, and proves the consistency and asymptotic normality of the estimator from optimal subsampling algorithm. Numerical experiments are carried out on both simulated and real data to evaluate the proposed methods. Results show that the optimal subsampling algorithm produces similar results compared with the full data analysis, while significantly reducing the computational costs.

This paper focuses on the preset-time bipartite consensus tracking problem of a class of heterogeneous nonlinear fractional-order multi-agent systems with signed directed graphs. By introducing a class of time-varying functions with generalized properties, a time-varying function based preset-time distributed controller is designed to achieve accurate preset-time bipartite consensus tracking in a fully distributed fashion for the heterogeneous linear and heterogeneous nonlinear fractional-order multi-agent systems, respectively. The preset time can be preset by a time-varying function, and does not depend on any initial values and parameters. Finally, one example is given to verify the effectiveness of the theoretical results.

In $x$-ray CT (computed tomography) imaging technology, there often exist the detection objects with the special sizes, shapes or materials, where the projection data can are only collected in some limited projection angles. In this case, the obtained data does not meet CT accurate reconstruction conditions. The reconstructed CT images using the conventional algorithm show some serious landslide artifacts, which are difficult to provide valuable information for many practical applications. In order to better suppress the landslide artifacts, this paper proposes the limited-angle CT image reconstruction method based on the ResNet model, in which the used residual learning method can extract the features of the input images and capture enough detail information, and then the deconvolution algorithm is used to restore the learned features. The numerical experiments prove the effectiveness of the proposed reconstruction method, which can significantly reduce the landslide artifacts and effectively retain the structural features of CT images. The reconstruction results can provide high quality CT images for clinical diagnosis.

In this paper, the existence and uniqueness of stationary distribution of a stochastic predator-prey model with Holling-II functional response is investigated. A traditional approach for asymptotic stability using H\"{o}rmander theorem, support theorem and Khasminski\u{\i} function to show stochastic models admit a unique stationary distribution is quite technical and cumbersome. In contrast to the existing approach, a distinctive feature of this paper provides more practically viable approach with a more simple and feasible rank condition for ``Foguel alternative'' through the state space exact linearization method. Then by using Khasminski\u{\i} function, the existence and uniqueness of a stationary distribution is obtained. Finally, the numerical examples are carried out to illustrate our theoretical results.

In this paper, we extend the subsampling method under the linear model to the nonparametric regression model and propose two subsampling methods for the nonparametric local polynomial regression model. First, we derive the convergence rate of subsampling based weighted least squares parameter estimation to full sample weighted least squares parameter estimation, and the asymptotic normality of the subsample parameter estimation are derived. Then, we use the criterion of minimizing the asymptotic variance, and two subsampling methods of OPT and PL under nonparametric local polynomial regression model are proposed. Finally, numerical simulation of OPT subsampling and PL subsampling, uniform subsampling and Basic Leveraging subsampling are carried out respectively, in terms of mean square error, fitting effect and computational cost. The results show that the subsampling method based on OPT criterion and PL criterion has great advantages in improving estimation accuracy and reducing calculation burden.

Locally repairable codes are a class of erasure codes which can repair multiple failed nodes. They are widely used in the distributed storage systems. A main topic in the distributed storage coding is to construct optimal locally repairable codes at present. In this paper, the following two classes of optimal ${(r,\delta)}$ locally repairable codes based on cyclic codes over $\mathbb{F}_{q}$ are constructed:1) $[3(q+1),3(q+1)-3\delta+1,\delta+2]$, where $q\equiv1(\bmod~6)$, $r+\delta-1=q+1$ and $2\leq\delta\leq q-1$ is even; 2) $[3(q-1),3(q-1)-3\delta+2,\delta+1]$, where $q\equiv7(\bmod~9)$, $r+\delta-1=q-1$, $2\leq\delta\leq\frac{2(q-1)}{3}$ is even with $\delta\not\equiv0(\bmod~6)$.

As a promising nonconvex penalty, the minimax concave penalty (MCP) has been a widely used technique in variable selection. Asymmetric least squares regression is proposed as an alternative regression to investigate the whole conditional distribution of the response variable. In this paper, we investigate the minimax concave penalty in sparse asymmetric least squares regression models (MCP-ALS). Under some regular conditions, we prove that the MCP-ALS estimator enjoys oracle property when the covariate dimension is fixed. In high dimensional model, we obtain the weaken oracle property of the estimator when the error has finite moments. As a by-product, our proposed method is able to detect heteroscedasticity by taking different asymmetric weight values. The results from simulation show that the proposed method has good performance on variable selection and can detect heteroscedasticity efficiently. Finally, the proposed method is applied to the diabetes dataset. The real analysis shows that the proposed method can mine the potential relationship between explanatory variables and response variables while realizing variable selection to provide a reference for the prediction and control of the condition of diabetic patients.

Recently, incidents of tourists being stranded due to the overloaded reception of tourists in attractions are very common. Therefore, accurate and effective prediction of the tourist volume in attractions and rational allocation of resources has become a challenge for scenic spot managers. Because of the influence of external factors, such as holidays, the time series curve of tourist volume in attractions usually presents nonlinear characteristics, which undoubtedly increases the practical difficulty of accurately predicting the tourist volume. This study proposes a method for forecasting tourist volume in attractions that considers the effects of holidays, namely, the Prophet-neural network autoregressive (NNAR) hybrid forecasting method. First, the Prophet model, which considers the effects of holidays, is used to predict the original tourist volume of attractions. Then, the NNAR model is used to predict the residual part of the predicted value of the Prophet model. Finally, the two results are combined as the final prediction result of the Prophet-NNAR hybrid model. Taking the historical tourist volume data of Jiuzhaigou scenic spot (from January 1, 2013 to July 31, 2017) as the data source, the effectiveness of the Prophet-NNAR hybrid forecasting method is verified using the R software. Results show that the Prophet-NNAR hybrid forecasting method is effective. The prediction performance of the Prophet-NNAR hybrid forecasting method is not only better than that of single-model methods (i.e., Prophet model, Prophet model that does not consider the effects of holidays, and NNAR model) but also stronger than the seasonal autoregressive integrated moving average and exponential smoothing models. Moreover, the combined results of the Diebold-Mariano test can confirm that the superiority of the Prophet-NNAR hybrid forecasting method over the other methods is statistically significant.

The observer-based secure consensus control problem under consecutive asynchronous denial of service (DoS) attacks is studied for multi-agent systems with incompletely measurable states. Based on switching theory, the multi-agent system subject to attacks is modeled as switched system with persistent dwell time (PDT). The sleep phase of attack is considered to be the $\tau$ portion, in which the leader in the communication network is globally reachable. The activation phase of the attack can be regarded as $T$ portion, where effective DoS attacks will destroy the connectivity of original communication network. By constructing model-dependent Lyapunov functions and solving linear matrix inequalities, the gains of controller and observer are obtained. Finally, simulation results verify the effectiveness of the proposed method.

In this article, Fisher information in the corresponding samples about the scale parameter $\theta$ from Inverse Rayleigh distribution under simple random sampling and ranked set sampling will be respectively studied. The numerical results show ranked set sample carry more information about $\theta$ than a simple random sample of equivalent size. Then we respectively use the simple random sample and ranked set sample to construct some optimal estimators of $\theta$. The numerical results of these estimators are compared.

In this paper, the tensor response regression model and the least square estimation of its coefficient tensor are studied. In order to improve the estimation accuracy of the model's coefficient tensor, CP decomposition and Tucker decomposition of the coefficient tensor of the model are carried out to construct two tensor response regression models. These two models can not only capture the spatial structure information of tensor data, but also greatly reduce the number of parameters to be estimated. Then, the parameter estimation algorithm corresponding to the model is given. Finally, Monte Carlo numerical experiments show that the estimation accuracy of coefficient tensor of the two improved regression models is significantly improved, and the estimation accuracy of coefficient tensor of tensor response regression model based on Tucker decomposition is the best.

Sampling survey is still an essential tool in the era of big data. However, traditional sampling survey faces the dual challenges of increasing execution cost and decreasing data quality. Split questionnaire design can has been paid more attention by researchers as an effective way to reduce the cost and improve the data quality. In this paper, we discuss the sub-questionnaire assignment process in the split questionnaire design. Based on the assumption that participants arrive in accordance with the Poisson process, the sequential randomization method considering covariates balance is designed with the goal of improving the similarity among sub-samples and the population. Both theoretical and numerical results show that the proposed method has superior performance compared with the existing methods on sub-sample balancing and estimation accuracy.

The rapid development of blockchain token incentives provides a new perspective to solve the problem of insufficient motivation for user content creation, but it still faces many challenges in the actual implementation process. This paper takes Steemit, a knowledge community based on blockchain, as the research object.By collecting block data, this paper analyzes the situations and problems existed in the token incentive mechanism of community from two aspects, including incentive equality and knowledge contribution efficiency, thus revealing the problem of token incentive allocation monopoly. Moreover, this paper identifies the influence of token incentive allocation monopoly on user knowledge contribution. The results show that the token incentive distribution in the community is monopolized by a small number of top users, and the incentive distribution mechanism in the community cannot effectively reflect the users' knowledge contribution levels. The inequality of token incentives allocation results in the decrease of users' content production and content discovery levels.

This paper proposes a distributed fixed-time adaptive formation control algorithm for multiple quadrotor unmanned aerial vehicles (QUAVs) with external disturbances and parameter uncertainties via the command filtered backstepping method. By introducing the fixed-time command filter and constructing the nonsmooth error compensation mechanism, the "complexity explosion" problem is effectively avoided as well as the influence of filtered error is eliminated in a fixed time.Besides, the issue of singularity existing in the distributed controller is skillfully addressed by designing a piecewise function. Based on the fixed-time stability theory, it strictly proves that the closed-loop system is practical fixed-time stable and all the signals of the closed-loop system are bounded, meanwhile, the formation tracking errors converge to a sufficiently small neighbourhood of the origin in a fixed time. Finally, a simulation example verifies the effectiveness of the proposed distributed fixed-time control strategy.

The promotion of new energy vehicles is of positive significance for China to maintain energy security and achieve goals of carbon peaking and carbon neutrality under the new development philosophy.However,fuel vehicles still hold a top post in China's automobile market.New energy vehicles still need to be further promoted,and the influencing factors of new energy vehicle sales remain to be determined.This study uses web crawler technology to obtain online review data about new energy vehicles.It takes 129 new energy vehicles on sale in 2021 as research samples to reveal the internal mechanism of the impact of specific content in online reviews on the promotion of new energy vehicles through text mining and empirical analysis.The study's main conclusions are as follows:1) The number of comments and the emotional polarity of comments will have a positive impact on vehicle sales.2) The contents of "noise control","acceleration performance", "fuel vehicles","battery performance and charging infrastructure"and "weather" in the online reviews of new energy vehicles will have a significant impact on energy vehicle sales.3) The influence of specific content in comments on vehicle sales is heterogeneous in vehicles of different manufacturers and energy types,and the impact of specific content in comments with different degrees of emotional polarity on vehicle sales is also heterogeneous.This study shows that the specific content in online reviews of new energy vehicles will significantly impact new energy vehicle sales.Manufacturers should improve their services according to the user feedback reflected in the related content in the online reviews while conducting online word-of-mouth management.

In this paper, we proposed a systematized design approach for asymmetric sine motion profile with smoothed jerk to reduce the vibration and impact in the deceleration stage in motion control. Considering the unsmooth part of jerk in the closed-form sine motion profile will be retained with the simple asymmetric design, we remove the uniform deceleration stage to get a smoother deceleration stage. So that we can increase the movement stability and decrease the residual vibration at the same time. The effectiveness of the proposed asymmetric motion profile is illustrated with motion profiles and the dynamic model by simulations.

This paper adopts the AdaBoost ensemble learning technique to boost the performance of mean-variance (MV) strategy. Firstly, this paper conducts an ambiguity decomposition on the quadratic cost function of expected utility, which proves that ensemble learning can boost the performance of portfolio strategies. Secondly, we parameterize the shrinkage intensity of the mean and covariance shrinkage estimator of return to be out-of-sample driven, and use iterative active set and gradient descent algorithms to maximize the value function, constructing parameterized MV strategy as the weak learner of proposed AdaBoost.PT. In terms of empirical study, we utilize the full panel stock data of A shares in near 25 years and American shares in near 40 years, and examine the performance of ensemble portfolio strategies in terms of Sharpe ratio, standard deviation, turnover and maximum drawdown, and then conduct a hypothesis test to check the significance of Sharpe ratio's difference. The empirical results show that the ensemble strategies based on return shrinkage estimator are superior to the baseline strategies under all four indices and statistical tests, and the robust tests based on industrial portfolios also show the same results.

With the development of information age, how to mine useful information from massive data quickly and effectively is a new challenge. As an effective tool for large scale data analysis, sub-sampling method has attracted extensive attention of scholars at home and abroad. However, the traditional sub-sampling method usually does not take into account the uncertainty of the model. When the assumed model is incorrect, the conclusions may be wrong. In order to solve this problem, a sub-sampling model averaging estimator (SSMA estimator) is constructed by the sampled data. Theoretically, we prove that the SSMA estimator is an asymptotically unbiased and consistent estimator of the model averaging estimator based on full data. In addition, we propose a weight choice criterion for the SSMA estimator, which is based on the Mallows' criterion proposed by Hansen (2007), and derive the asymptotic optimality of the weight estimator. It is worth mentioning that, in the proofs of these theoretical properties, we consider the double randomness brought by the model and sampling design. Finally, numerical analysis further shows the effectiveness of the proposed method.

Aiming at the credit risk prediction of small and medium-sized enterprises,this paper proposes a credit risk prediction method based on Long Short-Term Memory (LSTM)-Convolutional Neural Network (CNN) of small and medium-sized enterprises.Firstly,according to the national standard "Enterprise Credit Evaluation Index" and the characteristics of small and medium-sized enterprises,this paper proposes a credit risk prediction index system of small and medium-sized enterprises.The index system includes three kinds of financial and non-financial indicators:Credit intention,credit ability and credit performance.Then,this paper optimizes the network structure and parameters of LSTM-CNN,and applies Dropout and Batch Normalization methods to prevent over fitting.Finally,collecting the information of the listed small and medium-sized enterprises,and after missing value processing,standardization and oversampling,LSTM-CNN is applied to automatically extract features and predict credit risk.The experimental results show that the index system of this paper comprehensively reflect the credit risk situation.The credit risk prediction effect of small and medium-sized enterprises based on LSTM-CNN is better than the comparative models,which overcomes the limitations of traditional methods that cannot dynamically predict the time series data,and ignore the development potential and time continuity of small and medium-sized enterprises.

This article adopts high-dimensional variable screening method to make analysis of influence factors for employee turnover, as well as to predict the possibility of employee turnover. For high-dimensional data, MV (mean of variance, see Cui, et al. (2015)) method and LASSO method are used to select variables related to employee turnover, which can be entered the classification model. To ensure the prediction accuracy of the classification model, this paper uses four models including support vector machine, random forest, XGBoost and Logistic model to predict the possibility of employee turnover. In 100 experiments, compared to other 7 models combined with MV method, the average classification accuracy of the random forest model combined with the MV variable selection is more higher, as high as 95.43%. The above experimental results are verified by changing the ratio of training set to validation set, sampling 80% sample data, and adding random disturbances. It is found that the average classification accuracy of random forest model with MV method is still higher, this means the model has robustness.

In order to improve the green supply chain performance, the government gives retailers green product subsidies, and manufacturers give retailers green promotion subsidies. Based on the Stackelberg game analysis of the secondary supply chain composed of manufacturers and retailers, we explore the impact of green product subsidies and green promotion subsidies on the supply chain performance considering consumers’ green preference. It is found that subsidies have a positive effect on product greenness and profit. When consumer preference is not high or product greenness coefficient is low, green product subsidies are more suitable; When consumers have high preference or green promotion efficiency, green promotion subsidies are more suitable. As an external subsidy of the supply chain, the regulation ability of green product subsidy is limited; As an internal subsidy of supply chain, green promotion subsidy can produce higher performance.

In this paper, a two-stage method is proposed to solve the optimal voltage regulation problem. Reactive power disconnecting switches are used to regulate voltages while minimizing network losses. Firstly, the grid is linearized and the optimal voltage regulation problem is solved to determine the disconnecting switch operation plan. The reactive power distribution method and tree path optimization method are used to distribute the reactive power of the disconnecting switch, and the Pareto front solution is obtained to reduce the voltage loss, and the optimal scheme of the voltage operation of the grid is selected. The results show that the proposed algorithm has better accuracy and efficiency compared with other nonlinear optimization algorithms.

A model selection method for the higher-order spatial autoregressive model is studied in this paper, which can select the spatial weight matrix and covariate simultaneously. The asymptotic efficiency of model selection is proved in the sense of Kullback-Leibler loss. Further, we propose a model averaging method for the higher-order spatial autoregressive model, and its asymptotic optimality is established. The numerical simulation results demonstrate the merits of the model selection method and also show that the model averaging method can further improve the performance of model selection.

In this paper, we investigate scaled group consensus problem: The agent network is divided into two sub-networks; the agent network is divided into arbitrary finite sub-networks. For these two cases, we design distributed scaled group consensus protocols, respectively, and derive the convergence states and the convergence criteria for the agents reaching the scaled group consensus by utilizing matrix theory and Routh-Hurwitz criterion. Finally, several simulations are presented to guarantee the effectiveness of the theoretical results.

As the basic industry for economic development, electricity consumption is closely related to industrial structure and economic growth. Based on the panel data from 2004 to 2018, this paper empirically tests the causal relationship among industrial structure, power consumption and economic growth in Gansu Province by using panel vector autoregression (PVAR) model. The result shows that, the industrial structure and economic growth influence each other in Gansu Province. Economic growth has a positive effect on electricity consumption, and fixed asset investment throughout the country has a positive effect on economic growth and industrial structure optimization. The findings of this paper show that we should adhere to the economic development goal of steady growth and structural adjustment, and vigorously develop clean energy such as wind energy and solar energy through policy guidance. At the same time, we should also improve the efficiency of energy investment and utilization, and guide and support the development of the tertiary industry to realize the multiple goals of industrial structure optimization, energy conservation, emission reduction, and economic development in Gansu.

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.

When facing high-dimensional situation, to better estimate expected return, increase the stability of portfolio strategy and obtain better out-of-sample performance, this paper uses information of double-sorted portfolio on circulating market size and book-to-market to introduce a Group-LASSO (GLASSO) regularization term into the regression-type mean-variance objective function, and constructs the GLASSO-MV portfolio strategy. Comparing to $l_1$-norm regularized LASSO-MV strategy, GLASSO-MV can effectively utilitize the pricing difference among factor portfolios, and output sparse between-group weights, attaining more effective high-dimensional weight estimation and better out-of-sample performance. To obtain suitable regularization term parameter and weight sparsity, this paper adopts 5-fold cross-validation and adjusts the sparsity based on the parameter result. In terms of empirical study, this paper uses daily data on Chinese A share market from 1995 to 2019 of 3695 stocks, and compares GLASSO-MV to multiple common portfolio strategies. The result shows that, compared to strategies including LASSO-MV, MV, GMV, TZ (Tu-Zhou), BS (Bayes-Stein), GLASSO-MV has better out-of-sample Sharpe ratio, lower standard deviation risk and turnover.

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.

At present, the univariate control chart with measurement error has significantly reduced the impact of measurement error on the performance of the control chart, and plays an important role in the actual production process monitoring. However, due to the complexity of multivariate control charts, especially when the covariance matrix shifts, there is still insufficient research on multivariate control charts with measurement errors. Therefore, this paper combines the multivariate measurement error model to construct ME-KQE and KME-KQE control charts that have significant advantages over the covariance matrix drift in the monitoring process, and analyzes the effect of measurement error on detection efficiency. Finally, this article shows that it has a good monitoring effect on the data with measurement errors in practical applications.