This PDF file contains the front matter associated with SPIE Proceedings Volume 12597 including the Title Page, Copyright information, Table of Contents, and Conference Committee Page.

In recent years, with the rapid development of human economic activities and production, the continuous improvement of science and technology and a large amount of energy consumption, at the same time, a large number of waste gas and soot substances are discharged into the atmosphere, which seriously affects the environmental quality. Based on the air quality monitoring data of 337 prefecture level and above cities in China from January 2019 to July 2021, including the concentrations of AQI (Air Quality Index), PM2.5, PM10, CO, NO2, O3 and other pollutants, this paper makes an empirical study on the influencing factors of air quality in China. Using the knowledge of econometrics and Stata software, this paper establishes a multiple linear regression model, continuously estimates, tests and improves it on the basis of the primary model, analyzes the impact of pollutants on the air quality index, and analyzes the main pollutants affecting the air quality through testing. The results show that the main air pollutants in China are NO2 and PM2.5. Based on the analysis results, this paper will put forward corresponding suggestions on improving China's air quality and protecting the ecological environment.

Mathematical relationship between porosity and thermal conductivity coefficient of foamed concrete was deduced according to the principle of calculus. Mathematical model on equivalent thermal conductivity coefficient of foamed concrete was established based on serial and parallel network models of thermal resistance. Porosities and thermal conductivity coefficients of dry foamed concrete with different dry densities were measured. Applicability of mathematical model was analyzed based on comparison between theoretical values and measured values. Thermal conductivity coefficient decreases as porosity increases. There is a linear relationship between porosity and thermal conductivity coefficient. Theoretical values of mathematical model based on serial network model of thermal resistance has smaller deflection than that of the mathematical model based on the parallel network model. Mathematical model based on serial network model of thermal resistance has a better applicability.

As the global climate continues to deteriorate and extreme weather conditions gradually, we first established FMCS, a new framework to characterize, analyze, predict and evaluate the state of forests and their forest products. We searched forestry data worldwide through EPS database and forestry professional knowledge service system to find relevant index data. Combined with grey correlation analysis, we selected 20 variables with strong carbon sequestration from the selected 202 variables. Then the grey prediction model is used to characterize the evolution trend of time series of different indexes from 1990 to 2020. Then the prediction of carbon sequestration in the last ten periods is made, which makes the future trend of carbon storage have a certain cognition. Finally, we submitted a memorandum of our work to forest managers.

This study aims to construct a moderated mediation framework to test the effects of coach’s abuse supervision on the sports performance through emotional exhaustion and the moderating role of psychological resilience. To test the model, we integrated athlete-related databases by network, collected questionnaires through an online platform, and analyzed the data using two mature statistical software. We not only used Amos software to test the reliability of the data, but also used SPSS software to perform causal analysis of variables. Finally, the results show that coach’s abuse supervision will reduce athlete performance through emotional exhaustion, and psychological resilience will buffer this mediating effect. To some extent, the findings revealed the mechanism of coaches' abuse supervision affecting athletes' performance, which has reference value for improving coaches' management mode and scientifically improving athletes' performance.

This paper sampled 2948 primary school students in Guangdong Province, according to the test results of sports projects, the linear regression model was applied to correct the sports projects such as age, weight, gender, height, vital capacity and body mass index, and from multiple levels, different school districts and grades were used to demonstrate the correlation between sports and their academic performance, including the effect between English, Mathematics and Chinese, and the effect value was evaluated by various analysis methods such as meta-analysis. Subsequently, the bias correlation coefficient was further used to correlate the one-minute rope skipping exercise ability and the cultural performance of English, Mathematics and Chinese. The results showed that the score of one-minute rope skipping was positively correlated with the test results of English, Mathematics and Chinese culture courses, which further suggested the importance of young people's active participation in sports to improve their academic performance.

The General Self-Efficacy Scale (GSES), Physical Activity Scale for Adolescents (PAQ-A), and Middle School Student Mental Health Scale (MSSMHS) were used to obtain valid test data from1528 secondary school students in Guiyang City through stratified random sampling, and the data were then processed by SPSS26.0, and the missing values and abnormal values were processed. Afterward, the AMOS24.0 was used to construct a model of self-efficacy and physical activity for mental health in adolescents with data imported for validation. The model of self-efficacy and physical activity for mental health in adolescents was validated by the Confirmatory Factor Analysis (CFA) structural validity test, proving the validation of hypotheses H1, H2, and H3. And the results concluded CMIN/DF=4.788, RMSEA=0.050, GFI=0.950, TLI=0.964, and CFI=0.968 as an ideal model adaption. Conclusion: the model of self-efficacy and physical activity for mental health in adolescents provides a more intuitive and effective method for future research of the same type, thus improving the efficiency as well as the scientific validity of the research.

With the advent of the information technology and the further development of computer technology, the popularity of campus sports and fitness apps has further increased. How to use fitness apps to solve the problem of college students’ social anxiety will become a hot research topic. This study conducts a relevant questionnaire survey of the attachment to fitness apps, physical exercise self-efficiency, the willingness to engage in exercise, and social anxiety among college students, and applies a structural equation model to analyze the influence path of fitness app use on college student’s social anxiety. The study shows that attachment to fitness apps influences the willingness to engage in exercise and self-efficacy to exercise and leads to anxiety and emotions. Secondly, the attachment to fitness apps has a strong positive impact on exercise, while the willingness to engage in exercise influences social relationships positively indirectly, forming a mediation effect, and willingness to exercise affects physical activity self-efficacy. Thus, as an intermediary, the exercise will indirectly affect social anxiety. Therefore, this study proposes countermeasures and other recommendations for using fitness apps to reduce social anxiety among college students, which provides a new research idea for improving the willingness to engage in exercise and reducing the degree of social anxiety.

In this study, we firstly combine data usability with data usability and summary the framework of scientometrics research from the perspective of data usability which includes main research fields, data sources, methods, and software. We select 1539 papers published in scientometrics to summary the research content of scientometrics based on paper analysis, and then, develop the measurement scales based on the general process of research which consist of type of application, data source and structure, data quality, users’ skill requirements, and software usage which can provide reference researchers to evaluate the difficulty of research, and also can be used to measure the value of manuscripts for editors and reviewers of scientific journals.

This paper selects the online and offline learning data of ten students to calculate six component functions of the independent variable group and the dependent variable. The three principal components with large contribution rate was selected to obtain the regression equation between the standardized variable group and the three component variables, the regression equation between the standardized independent variable and the standardized dependent variable, and the regression equation between the original independent variable and the original dependent variable, and finally to obtain the regression prediction value. The MATLAB program was used to generate regression coefficient histogram and regression prediction score chart. The conclusion that the regression equation has a good prediction effect is obtained by using the regression prediction value and the regression prediction diagram. The independent variables that play an important role in explaining the regression equation among the five independent variable factors are obtained from the regression coefficient histogram.

Complex analysis is a mathematical theory of complex functions, especially meromorphic functions and complex analytic functions, and it generalizes analytical techniques from real variables to complex variables. Complex numbers originally arose from the fact that universal solutions could exist for algebraic equations. They take the form x + iy, x and 𝑦 are real numbers. X is called the real section of the complex number, y is the imaginary section, and i is the square root of -1, the imaginary unit. Because complex numbers have two separated components, 𝑥 and 𝑦, they are especially useful when two variables must be processed simultaneously. For example, it has proved particularly valuable in fluid dynamics, where pressure and velocity vary from place to place. In the mid-19th century, mathematicians gave complex numbers a geometric interpretation to make them more acceptable. In this paper, we mainly focus on the applications of Cauchy Residue Theorem by using a specific example. And the method of calculating residues, which is called Fourier Matrix, are also included. As a result, all the three different integrals of a complex function can be correctly evaluated by using Cauchy Residue Theorem.

Based on data from the World Values Survey 2010-2014 (WVS 2010-2014), by nonparametric testing, Structural Equation Modeling (SEM), and multiple group analysis, this study does empirical research to investigate the differences between China’s and the United States’ Perceived Neighborhood Security (PNS). Nonparametric testing results show that: in the United States, residents feel much more secure in their residential neighborhoods than in China; there is social class differentiation on PNS in the United States but not in China; and in the United States, the upper social class group feels more confident about neighborhood security. Taking trust, capabilities, criminal attacked experience, and Subjective Crime Frequency (SCF) as influential factors, SEM and multiple group analysis results show that the path regression coefficients from influential factors to PNS differ between China and the United States. Based on these findings, we will not only highlight, when using PNS as an example, what factors Chinese urban governance should focus upon compared to the United States, but also determine the necessity for China to develop its own urban governance theory and governance methodology. Our findings will provide key information for both countries’ urban governance establishments.

The discussion on the association between primary and secondary school students' self-efficacy and academic achievement in mathematics has recently caught the attention of some researchers, but this research topic needs further exploration. We selected 11 relevant studies (20,029 samples) by using a meta-analytic approach to examine the link between self-efficacy and academic achievement in mathematics and to explore how various factors moderate these links. These findings offer robust evidence for the link between self-efficacy and academic achievement in mathematics. Additionally, the average age of students, the proportion of female students, measurement instruments, and soundness moderated the association between self-efficacy and academic achievement, with average age exhibiting the strongest effect. The implications of this study deserve further discussion.

In this paper, classical explicit scheme and Crank-Nicolson scheme for a class of parabolic equation with constant coefficients with initial boundary value conditions are studied, the compatibility of the two schemes is verified, and their stability is analyzed by matrix method. Finally, the numerical experiments verify the correctness of the theoretical analysis.

The spectral method is an essential way to solve the discrete differentiation and further solve the Ordinary and Partial Differential Equations (ODEs and PDEs). The solution worked out by it shows high accuracy once the grids are defined on a smooth function. The method is so practical that it is widely applied in the fields of vibrations, linear and non-linear waves, fluid mechanics, quantum mechanics, etc. Unlike the finite difference method using the neighboring grids, the article develops the application of Fourier analysis to the interpolation of the spectral method when solving discrete differentiation, which is a global method that can find a more accurate differentiation by involving all the known grids. As a result, one can solve ordinary and partial differential equations by approximating continuous variables into 𝑁 equispaced grids and then applying the spectral method to find the differentiation, where the larger 𝑁 is, the more precise the approximation is.

For the random samples of compound Rayleigh distribution based on left truncated right censored data, the estimation formulas of scale parameters are given by the maximum likelihood method and EM algorithm respectively, and the estimation results are given by the random simulation method. The results show that both the maximum likelihood method and the EM algorithm have good estimation results, and the relative error of the maximum likelihood method is smaller.

A two-dimensional visual simulation was conducted to study bubble behavior of R134a on the heating wall in subcooled flow boiling. It’s observed that bubble growth process experienced nucleation, sliding and departure on the heating wall at condition of heat flux of 300 kW/m2, velocity of 0.4 m/s, subcooling degree of 3 K and saturated temperature of 288.15 K. The results show that the bubble nucleated with large size at nucleation state, and the bubble size increased with sliding. This series of bubble behaviors was subjected to surface tension, buoyance, shear lift force and liquid inertial force. Besides, the temperature of R134a along radial direction varied linearly near the heating wall.

For two M-matrices, a sharper lower bound for the minimal eigenvalue of the fan product is proposed by applying the characteristic value containing the domain theorem. The estimating formula only involves the entries of two M-matrices. Hence, the lower bound is easy to calculate in practical examples. An example is considered to illustrate our results.

Alternating Direction Method of Multipliers (ADMM) is an effective method for solving separable convex optimization problems. In this paper, the method is extended to solve Sylvester equations with nonnegative constraint. We give a convergence result showing that the algorithm converges to a Karush-Kuhn-Tucker point whenever it converges. Numerical evidence shows that the proposed algorithm can efficiently solve nonnegative Sylvester equations.

The famous Riemann zeta function is defined as an infinite series which is convergent in the half plane where the real part is greater than one. It has an analytic continuation to the entire complex plane except at the point one. This can be achieved with a functional equation established by Riemann. The Riemann zeta function also has a huge family of variations or generalizations such as the Dirichlet eta functions and the polylogarithm functions. The Riemann hypothesis conjectures that all the non-trivial zeroes of zeta function lie on the critical line where the real part is equal to one-half. On top of this, Hilbert–Pólya conjecture states that such zeroes should have correspondence to eigenvalues of a self-adjoint operator. Although there have been such operators found that support this conjecture, many puzzles remain to be solved. It is suggested that by allowing these functions to take operator values in certain entries and acting on certain test functions, one is able to explore more on their properties like the distribution of their zeroes.

In this article, the properties of Fourier Series by discussing around the basic properties of integrable functions and kernel are discussed. With the discussion over the function f around discontinuities, it is found that f should at least be Reimann integrable functions to make Fourier series imitate the function successfully. More, about the filters, it is clear that the sharp filters will converge to f under a certain condition, with the analyzation over Dirichlet Kernel, and how the convergent becomes successful with the properties over good kernel.

Raisins are made by dehydrating grapes with the help of solar heat or artificial heating. It is popular because of its high sugar content, high nutritional value and easy transportation. Turkey is one of the world’s top grape producers. The paper investigates the classification of two kinds of raisin in Turkey based on XGBoost, SVM, MLP, and Logistic Regression. The aim is to determine which model is optional according to the results. According to the analysis, the accuracy rates are 85.9%, 91%, 87.3% and 86.7% for the above four models, respectively. Apparently, the model with the best accuracy is SVM. Besides, its AUC area is 0.91 so it also has a high authenticity. The most important feature may be raisin’s Extent. These results shed light on guiding further exploration of classifying other raisins or products.

Residue theorem is an important part of complex variable functions, and its theorems have been widely used in real life, such as mathematics, physics and computer fields, but because the residue theorem is the most difficult part of complex variable functions to understand, so some people don't know where to start when applying. However, when the original functions of some definite integral integrands are not easy to solve, using the remainder theorem to calculate these relatively difficult definite integrals is an effective method to solve the definite integral problem. This paper first gives the definition and derivation process of the residue theorem, discusses the simple calculation method of the residue theorem through examples, and then uses the residue theorem to deduce and calculate the integrals of rational fractions and rational trigonometric functions step by step, and obtain two common integrals for calculation. The method is hoped to be used in practice in the future.

In this paper, the idea of equal arc length method is proposed, and the solver is designed successfully. Through a series of numerical experiments, it was found that the equal arc length method can effectively solve the initial value problem of the first order ordinary differential equation, and it is more efficient than other algorithms.

A runway safety zone is an area at both ends and sides of a runway to protect the safety of an aircraft when it overruns/veers off the runway. A civil airport runway safety zone is a rectangular area composed of a runway end safety zone and lifts belt. The military airport has no clear concept of a runway safety zone, but there are soil runways, flat areas, terminal safety lanes, and other facilities at both ends and sides of the runway, which play the same role as the safety zone. The research method of aircraft overshoot runway risk is selected. Based on analyzing the characteristics of the influencing factors of aircraft overshooting runways, according to the dichotomous characteristics of the influencing factors and accident results, a logistic regression analysis based on the probability assessment method of aircraft overshooting/overshooting runway is proposed.

In order to understand the reasons of surface weathering of cultural relics and determine the influence degree of glass types, ornamentation and color on surface weathering, a binary Logistic model is established to eliminate the influence of confounding factors First of all, the sample data were examined and processed, and then the Logistic regression model was used to select the significant influencing factors. It was found that only the glass type had significant influence on the surface weathering. Finally, it was found that the probability of surface weathering of lead-barium glass was 7.55 times that of high potassium glass, and the other factors had no significant influence on the surface weathering.

Bubble departure size of multiple bubbles were investigated to explore bubble dynamic based on VOF visual simulation. By contrast, bubble departure size increased with the increasing of velocity from 0.2 m/s to 0.4 m/s and with the decreasing of subcooling degree from 10K to 3K. It was also found that there was little difference in bubble departure size corresponding to heat flux of 200 kw/m², 300 kW/m² and 400 kW/m², but the magnitude of heat flux increased the number of nucleation that contributed to the increasing of bubble size due to coalescence. Finally, a fitting correlation was proposed to predict bubble departure size based on important dimensionless numbers regarding velocity, heat flux and subcooling degree, and the prediction deviation is about 5%.

Because of the important role of multiphase flow in many fields and its complexity, the study of boiling heat transfer of two-phase flow has become an important topic of current research. Under the background of double carbon, energy saving and emission reduction are a hot direction in the current refrigeration industry. Therefore, studying the boiling heat transfer of two-phase flow is helpful to strengthen heat transfer and design more efficient heat exchangers. In this paper, the research progress of subcooled flow boiling based on CFD is reviewed. Firstly, the mechanism of subcooled flow boiling and the current research contents are introduced. Secondly, the development of incompressible flow and two-phase flow solving algorithms is reviewed, and the advantages and disadvantages of each algorithm are analysed and compared. Finally, a research method is selected to provide a basis for the simulation of subcooled flow boiling in the tube.

Nonlinear wave equation is a typical nonlinear evolution equation with important theoretical significance and application value. The physical meaning of damping is the attenuation of force, or the energy dissipation of objects in motion. It is to stop the object from continuing to move. When an object vibrates under the action of an external force, a reaction force will be generated to attenuate the external force, which is called damping force. This paper studies the conservation laws for one dimensional damped nonlinear wave equation with constant and non-constant damping coefficient. Multipliers and corresponding conserved quantities are derived for wave equation with simple self-interaction term and constant damping coefficient using method of multipliers. The results are generalized in presence of more complicated source term and non-constant damping coefficient. Certain type of equations conserving all energy-momentum related quantities is found. For each individual conserved quantity, the corresponding multiplier and the corresponding type of equations are found.

In this paper, elliptic optimal control problems with 𝐿²-control cost and box constraints on the control are considered. To numerically solve the problems, we apply the First discretize, then optimize approach. Specifically, the standard piecewise linear finite element is employed to discretize the elliptic optimal control problems first. Then, a multilevel Alternating Direction Method of Multipliers (mADMM) is proposed to obtain approximate solutions with medium accuracy. Finally, the Primal-Dual Active Set (PDAS) method is used as a postprocessor of the mADMM method to obtain more accurate solutions. Based on the strategies above, an efficient two-phase mADMM-PDAS method is proposed. Numerical results show that the mADMM-PDAS method is highly efficient.

Estimators of the Gini index have been proposed and widely utilized for interval estimation and hypothesis testing in economics. In this paper, Jackknife Empirical Likelihood (JEL) is developed for the Gini index with right censored observations. We prove the limiting distribution of JEL ratio statistics and construct the confidence intervals for the Gini index. Comparative simulation studies with existing methods demonstrate that our methods do better in coverage probability and average length.

In this paper, we study the Alternating Direction Implicit (ADI) format and stability of the linear 2-D second-order hyperbolic equation. The alternating direction implicit format with second-order accuracy is chosen in the time direction, and the initial conditions of the equation are discretized using second-order central difference, and the stability terms of the ADI format are obtained analytically using the separation of variables method. When the constant C and weighting coefficients r satisfy certain conditions, the alternating direction implicit form of the two-dimensional second-order hyperbolic equation is stable for an arbitrary grid ratio. Finally, numerical experiments are conducted to obtain numerical and exact solutions and their images of the equations to verify the correctness of the theoretical analysis.

In convex programming, the primal minimum covering circle case is associated with a complex nonlinear constraint. This article introduces the definition of a convex function and applies the Wolfe duality to derive the equivalent Wolfe dual problem with a single linear constraint. According to the duality theorem and the geometric characterization, the Wolfe dual problem is of equivalence basically to the primal problem with a unique feasible solution. Meanwhile, since the decision variable is optimal, the Wolfe dual problem satisfies the Karush-Kuhn-Tucker conditions in terms of Lagrange conditions. Subsequently, this article proposes a simplistic two-point covering circle case as a visualized approach to furthermore discuss the principle of convex programming in the graphic demonstration. Nevertheless, the article utilizes the Wolfe objective function and the constraint for the multi-point covering circle problem to implement the minimum covering circle and sphere across 50 randomly generated points, briefly discussing its applications.

In this paper, we study the existence of solutions for a class of fractional order q-difference equations with infinite point boundary value problems. By constructing the properties of Green's function and using Guo Krasnosel'skii fixed point theorem, we prove the existence of solutions for the boundary value problems.

In learning complex analysis, the residue theorem is the easiest thing to touch. Then in higher mathematics, these concepts are often very abstract, and all exist in graphics. Therefore, the author summarizes the practical application of the residue theorem with reference to other papers. There are many application directions. The simplest and practical application is in generalized integral. This paper introduces the research study topic of the application of generalized residue theorem in Laplace Inverse transform. The application methods and skills of generalized residue theorem in Laplace Inverse transform are discussed in detail through concrete examples. Based on the teaching process of Laplace transformation in mathematical analysis, differential equation or mathematical equation, and the problems and puzzles in the application of Laplace transformation, this paper presents a research-based learning theme. In this paper on the application of residue theorem to series, a new method of summation of infinite series is given based on residue theorem. In this method, the sum of series is transformed into the sum of residues of a complex valued function in a closed domain.

The harmonic oscillator is an important physical model in both classical mechanics and quantum mechanics which describes the dynamics of an object displaced from its equilibrium position. The damped harmonic oscillator has already been well studied on a smooth plane. However, it is hard to avoid energy dissipation caused by a plane in real cases. Hence, in this paper, the approximate solutions to a damped harmonic oscillator with higher-order nonlinearities on a rough plane is explored mainly by the Cardano-Tartaglia formula. In approximate solutions, an approximate solution is provided under specific prerequisite and a case is studied given the initial conditions. Also, the system error of the approximate solution in approximate solutions is analyzed in error analysis under the restrictions of the initial conditions. In this paper, only a short time span of the damped harmonic oscillator dynamics is analyzed because of the existence of two terms which are mentioned in error analysis.

D is a strong oriented graph, the strong distance sd(u, v) is the number of arcs of D containing u and v. For a vertex v of D, the strong eccentricity se (v) is the strong distance between v and a vertex farthest from v. The strong radius srad(D) is the minimum strong eccentricity among the vertices of D. The minimum strong radius srad(G) of a graph D is the minimum strong radius over all strong orientations of G. And G₁ G₂ denote the strong product of graphs G1 and G2 . In this paper, according to the order of paths we give the result of the minimum strong radius of the strong product of paths, and propose a new directed network design method.

In this paper, we explore an economic model of the electric power duopoly game, where firms are boundedly rational and adjust their outputs with a gradient mechanism. The equilibrium stability and dynamic behaviors like period and chaos are intensively studied. As symbolic rather than numerical methods are used in this paper, both of the procedures and the results of our computation are exact, wherein theoretical conclusions could be deduced.

With the continuous improvement of China's economic strength, the living standard of our country’s people has been improved greatly. From the website of National Bureau of statistics, we search and analyze the data of per capita disposable income of urban residents from 1978 to 2019. We hope to find out the relationship between the living standards of urban residents and China's economic development and put forward some suggestions for the economic development after the epidemic with the discussion and analysis of the results of SPSS analysis data.

In this paper, we focus on the auxiliary covariates problem base on the right censored linear model. We estimate the missing primary covariates through the kernel smoother method, the selection probability of the validation set is allowed to depend on the observed covariates. Then a Weighted Least Squares (WLS) estimator for the unknown regression parameter is proposed, and we compare the proposed method with two other methods via two simulations.

By investigating the impact of carbon emissions on green total factor productivity, this paper calculated the green total factor productivity of 30 provinces and municipalities in China from 2005 to 2017 using SBM-ML index, and empirically tested the impact of carbon emissions on green total factor productivity based on linear regression model. It is found that carbon emission intensity has a negative impact on green total factor productivity, and R&D intensity has a positive effect on green total factor productivity. In view of this, on the one hand, carbon dioxide emissions can be reduced through certain environmental regulation policies, and on the other hand, enterprises can be encouraged to research and innovation through certain tax support, so as to improve the level of green total factor productivity, in order to achieve China's dual carbon goal as soon as possible.

The internet financial model is booming in the era of big data, and gradually penetrates into all aspects of people's lives. With the continuous development of network technology, under the background of big data, people's living standards are constantly improving, and higher requirements are put forward for the quality of life. In order to promote the sustainable development of the internet finance industry, seize the opportunity and strengthen itself in the era of big data, the internet finance industry needs to uphold the development concept of pluralism and openness. Internet finance has played a positive role in improving the efficiency of financial services and formed a new payment method. With the help of big data, internet finance is constantly innovating Internet financial products, while some risks are amplified under the background of financial big data. On the basis of expounding the concepts of big data and Internet financial risks, this paper provides a method for the construction of Internet financial risk control model based on data envelopment analysis and the main forms of Internet financial risks.

Considering the birth–death process, this paper proposes a two-state stochastic model for alien species invasion of ecosystems. The state transition probability, the survival time of species, and average biomass are obtained by solving the equilibrium equation. The results of computer simulation show that the population and distribution of the two analyzed species changed significantly at the beginning of the invasion but stabilized after a certain period. The results of stochastic analysis and computer simulation can help to understand the mechanism of alien species invasion.

Carbon dioxide emission are major driver of global warming and climate change, hence one should urgently reduce carbon dioxide emission to avoid the impacts from climate change. With such a worldwide problem, the regulation and generalization have become a problem. When comparing each countries carbon dioxide emission data, mankind has missed lots of omitted variables including countries’ economics status, countries’ location etc. As the world enters the era of big data analysis, this study will mainly discuss why some developing countries cannot reduce carbon dioxide emission at the same rate as those developed countries. From the dataset, the research used 12 major carbon dioxide countries all over the world including both developing and developed countries. The research first performed explanatory data analysis to check if there is any association between carbon dioxide emission, GDP per capital, and life expectancy in all 12 countries. Then, the PCA classification is adopted to classify all the selected 12 countries, and one may see all the developing countries are classified in similar area. In the end, the study performed a SARIMA time series analysis on China’s future carbon dioxide emission, and the result showed a decline trend of China’s carbon dioxide emission in the future. This research will correct the misleading idea on simply compare with countries precisely carbon dioxide emission and help to understand the tradeoffs that many developing countries face during reduction of carbon dioxide.

In Supply Chain Management (SCM), product classification and demand forecasting are crucial pillars to ensure companies to have production in the right category and quantity for long-term profitability. Due to COVID-19 from 2019, the automobile industry has been seriously negatively affected as the demand dropped dramatically. Therefore, it is necessary to make reasonable product classification and accurate demand forecasting to facilitate automobile companies in SCM to reduce unpopular product manufacture and unnecessary storage costs. In this paper, the Canada automobile market has been chosen with the period from 1946 to 2022. To classify a number of different types of motor vehicles into several categories with general characteristics, K-means Clustering method is applied. With the seasonal patterns and random generated features for auto sales, the time series models ARIMA and SARIMA are adopted for demand forecasting. According to the analysis, the automobiles fitting in the category with high demand and low price are valuable for further production. In addition, SARIMA Model is more accurate and fits better than ARIMA Model for both the training and test datasets for long-term prediction. The classification and forecasting results shed light on guiding manufacturers to adjust production schemes and ensuring auto dealers to predict more accurate sales in order to optimize the strategic planning.

In recent years, charities around the world have experienced frequent trust crises. As the country with the most advanced philanthropy, the United States has experienced many crises of confidence in its history. This paper selects the detailed information of 8,408 different American charities as research samples, conducts data exploration through factor analysis and comparative analysis, and uses binary logistic regression analysis to construct a linear model to obtain the correlation between various financial indicators and Accountability & Transparency Score. The relationship and correlation coefficient have good predictability. At the same time, according to the model results, reminding the financial indicators that charities need to focus on to improve accountability and transparency has certain guiding significance for the development of charities.

In the financial market, optimization is one of the most important goals for stakeholders to obtain. This essay focuses on some mathematical programming methods that applied in finance marketing to meet the aims of the financial market, introduces some concepts of mathematical programming and finance, and showcases the inter-relationship between these two subjects. This paper also provides examples of models that used linear programming method for solving asset pricing and liability cash flow matching scenario, initiates analysis from previous models by other scholars, and the defects of models using these methods. The demonstration of the non-linear programming method, to solving problems which is more complicated, for instance, in long-term scenario there will be the effect of economies of scale or random variables that are not predictable. This paper manifests a method of correcting linear programming models to match real-life scenarios better by adding quadratic programming constraints and illustrates some differences between linear programming and non-linear programming.

Simplex Method is one of the most popular methods to solve Linear Programming problems. Many rules and modifications on Simplex Method are proposed to improve the efficiency of iteration. In this study, traditional rules and latest modifications are presented and introduced, focusing either on revision on pivot rules or on method combination. These rules and modifications can surely either reduce the number of pivot steps or simplify calculation. Some of the traditional rules are further modified and even combined so that latest modifications enjoy higher efficiency. For certain methods, further rules are needed to determine the extent to which the method can improve the efficiency.

Family planning is a policy implemented in China. From 2010 to 2020, China's birth rate continued to decline, and the problem of aging became more and more obvious, which will bring great obstacles to the country's long-term development. After inquiry, it is known that the economic situation, household expenditure, social situation, education and medical environment all have an impact on our country's basic population and birth rate. This paper aims to establish a regression equation through RStudio, analyze the mathematical model of the regression, and discuss the influencing factors of the national birth rate in the past 10 years combined with the basic national conditions of our country from 2010 to 2020. The main mathematical methods used are Principal Component Regression and Stepwise Regression. The experimental analysis shows that GDP, household income, household expenditure and divorce rate have a significant impact on the fertility rate, the most important of which are household income, GDP and divorce rate.

Under the multiple constraints of slowing population growth, industrial transformation and the "double carbon" goal, studying the relationship between regional low-carbon economic growth and population, energy and economic structure is of great significance to the sustainable development of regional economy. In this paper, the Malmquist index model was used to measure the efficiency of Total Factor carbon emission Reduction (TFR) in the eastern, middle and western regions. Comparing the TFR at different periods and comprehensively analyzing the industry and energy consumption at the same stage, concluded that it was highly relevant between the TFR and policy environment like the aspects of efficiency, energy and industry development. Based on the time-series data in1990-2020, the causality and regional TFR was analyzed empirically by the index of "population-energy-industry" and the Granger causality test model. Finally, the corresponding policy recommendations from were put forward for the development of regional low-carbon economy.

Forests contribute a lot to carbon sequestration. We advocate to stop felling trees for a long time, because we think it’s the best decision for our environment. However, far from our expectations, simply forbid felling trees isn’t the best decision to environment as well as human society. Actually, making some harvestings is the best choice for carbon sequestration, human society and the environment. And we try to build up our models to prove that. The first model is the Pure Carbon Sequestration Model. When you input some relevant data, it can help you figure out how much carbon a forest can sequester during a specific period. What’s more, it can help forests’ managers make decisions on forest management. But it only concentrates on the carbon sequestration itself. Furthermore, we introduce the concept “Virtual Currency” into our Complex Forest Management Decision Model. We use this currency to evaluate the economic value as well as the ecological value of forests, with which we can strike a balance between economic benefits and ecological benefits in forest management. The second model is based on the first model, which works comprehensively on balancing between economic benefits and ecological benefits after optimization. It can also help you find the accurate geographic location that fits certain forest management plan.

Accompanied by the continuous advancement of information technology and the construction of smart campuses, the application of sports-oriented APPs in physical education has become the implementation path of physical education reform in colleges and universities. With a view to investigating the influence of college students’ physical exercise attitude and physical fitness under the intervention of sports-oriented APPs, 200 college students were selected as the investigation subjects for the intervention experiment, and the research methods such as questionnaire survey method, comparative analysis method, and mathematical statistics method were employed to conduct the horizontal comparative difference analysis. The results show that college students’ physical exercise attitude and physical fitness have a positive impact under the intervention of sports-oriented APPs, which provides new ideas for further promoting teaching reform and the development of sports-oriented APPs.

Based on Functional Movement Screen (FMS) test, this study evaluates the current ankle joint condition of male students in a university and analyzes the correlation between ankle mobility and basic movements in FMS test, which may provide some theoretical and practical support for improving physical training to reduce potential sports injuries. 300 young male students were sampled by using FMS test, which includes the following: seven basic movements, three pain exclusion items, and one ankle mobility test, and the data were analyzed by Excel2010 and SPSS19.0. The average score of seven basic movements was 16.33±2.12, with 13.67% failure of the left ankle mobility test, and 13.33% that of the right. The movements of deep squat and hurdle step were highly correlated to ankle mobility in this test. There is high correlation between ankle mobility and the movements of deep squat and hurdle step. It is suggested that ankle flexibility and stability training should be strengthened to improve physical training to reduce sports injuries, especially in training programs that are related to deep squat and hurdle step.

Taking the participating teams of the National Sports College Men’s Basketball League in 2021 as the research object, a mathematical evaluation system was established through the Analytic Hierarchy Process. The degree of influence of each factor was quantified and the evaluation system for the winning factors of the National Sports College Men's Basketball League was established. The results show that the SCBA league winning factor evaluation system includes six primary indicators, and each primary indicator includes four secondary indicators. The construction of this system will help the participating institutions to improve their competitiveness and provide strong support for the construction of China’s competitive basketball reserve talent team.

Twenty students majoring in table tennis from the School of Physical Education were randomly divided into an Experimental Group (EG) and a Control Group (CG), and a functional movement training program was designed based on the students’ test data from the Functional Movement Screen (FMS), combined with the specific characteristics of table tennis. The experimental program was implemented for the experimental group in the preparation of classroom instruction. After 60 hours of experimental instruction, the FMS and special techniques test scores before and after the instruction experiment were statistically analyzed with the aid of Microsoft Excel 2007 and SPSS 20.0 using paired-samples and independent-samples T-test. The results showed that, first, the teaching program based on dynamic stretching and core exercise could significantly improve the FMS scores of undergraduate students specialized in table tennis; second, the 16-week functional teaching program could improve the hitting rate and movement quality of these students. It is recommended that the instruction of table tennis specialties in physical education introduce functional movement diagnosis and functional movement preparation training, coupled with functional strength exercises, to improve teaching effectiveness and efficiency, reduce sports injuries, and improve students’ self-diagnosis, training ability and level, thus enhancing the core competitiveness of physical education undergraduates in employment.

Football has always been one of the most popular sports in the world. However, the level of scientific research in international football is uneven. In order to grasp the development of international football scientific research and promote the competitive level of football in the world, this paper attempts to use modern computer technology and information technology, to clarify the key words, high peer cited authors, key authors and Research Situation of international football academic research. This paper mainly uses modern computer software such as HistCite to analyze the research achievements in the field of football in the core collection of Web of Science from 2010 to 2016. The results show that there is no significant relationship between the distribution of scholars' nationality and the level of soccer competition in this country. There are very few Chinese scholars, and there is still a lot of room for the development of Chinese football academia.

Multiple comparisons are general term for hypothesis testing of whether there is a significant difference between the means of each sample after analysis of variance. ANOVA can only determine whether there is a difference between the population means, and multiple comparisons can further test the differences between the sample means. This study will select the LSD (Least Significant Difference) test method in the multiple comparison method to conduct a differential test on the physical fitness test data of four different grades from 2018 to 2021 since the COVID-19 epidemic and analyze the physical fitness of Chinese students under the background of new coronavirus epidemic. Change characteristics of health level, and summarize relevant experience, urge relevant personnel to make targeted improvements to the future work of students' physical health training, and scientifically improve the physical health level of Chinese students.

The study aims to predict the possible risks of sports injuries and provide theoretical basis for conducting targeted combat fitness training and strengthening capacity of combat readiness on a continuous basis through analysis and study of fundamental athletic ability of young male cadets in a military academy and their capability to perform basic movement patterns. The result of research shows that the overall performance of basic athletic ability of young male cadets in a military academy is good. However, there is still a significant gap in the ability to complete basic movement patterns. The major deficiencies are manifested in limited mobility of thoracic spine, shoulder, hip and ankle joints, as well as unbalanced development of bilateral muscle groups and limb functions in the lower limbs. In addition, the deficiencies also include the poor level of core muscle groups anti-rotation and reflex stability functions and insufficient extensibility of posterior chain muscle groups. In later combat physical teaching and training, the strategy of functional training should be introduced. In accordance with the final list of problems found in FMS tests, targeted sports training plans should be formulated to improve training efficiency on a scientific and dramatically reduce incidence of sports injuries.

About 1.71 billion people worldwide suffer from musculoskeletal diseases, and since exercise is an effective non-drug intervention, it is necessary to figure out the status of Exercise-Related Skeletal Muscle (ERSM) research in the past two decades and future trends. In this study, research trend of articles published in Web of Science were analyzed by R software. The results of research hotspot analysis show that the research from 2002 to 2021 mainly focuses on physiological mechanisms, such as skeletal muscle protein synthesis, oxidative stress and so on. Thematic evolution analysis shows that in the future, we should focus on the research on the gene-expression related to skeletal muscle and the mechanism of skeletal muscle metabolism, such as physiological mechanisms, glucose transport and insulin resistance, which will become the focus of future scholars.

In July 2019, with the Outline of the "Healthy China 2030" Plan as the action program, the State Council issued the "Healthy China Action (2019-2030)", and a national health protection system in China with the "Healthy China Action" as the grasp for promotion was comprehensively formed. In this study, literature method, questionnaire method, interview method, and mathematical statistics method were used to investigate the participation of static sedentary people in physical activity and the relationship between the built environment and physical activity, in order to improve the awareness of self-health management of sedentary people and promote sedentary people to actively pay attention to their own physical health level. Government departments investigate the daily fitness environment needs of residents, actively improve and build a fitness environment, and improve the health index of residents.

Based on the current situation of sports events research in China, this study sorts out the developmental evolution of high-quality research in China and research hotspots, in order to contribute to the theoretical basis of sports events research and provide reference to the research of related scholars. The study shows that the research on sports events in China has received high attention from scholars, and its research content mainly includes: (1) legal protection of issues such as sports event broadcasting; (2) the role of sports events in the development of sports industry; (3) the operation and management of large sports events; (4) sports events and sports tourism development; (5) the role of sports events on urban development, etc.

The evaluation of teaching activities plays an integral part in measuring teachers’ abilities and improving teaching qualities. Among of which, Student Evaluation of Teaching (SET) serves as the key approach to assessing teaching activities. In the field of sports teaching in China, SET has been implemented nationwide, however, very few studies have touched upon the aspect of measuring sports teaching activities in higher education from the perspective of SET. Among rare studies, attention has been mainly directed to aspects of structured data (e.g., rating scales), which are easy to quantify, rather than descriptive evaluation comments/reviews that came from students (e.g., texts). Natural Language Processing (NLP) is deemed as an effective tool to deal with unstructured data and may explore more valuable diagnostic information through a multidimensional view (i.e., data mining), thus improving sports teaching and promoting learning in the long run. This research collected SET data from several universities. The NLP approach and Latent Dirichlet Allocation (LDA) topic model were used to analyze the SET corpora for feature extraction. The results were then used to identify traits that may be considered for measuring sports teaching activities. Besides, a user dictionary was constructed through thematic coding and was then further applied to investigate the emotional polarity and subjectivity of SET.

In recent years, by using of data science to explore patterns and knowledge in football games has become a hot research topic. However, most studies still use black-box machine learning methods to classify and predict the data generally, and the analysis of its specific influencing factors is not comprehensive. Even if the influence of each variable on the game results is considered, it is mainly analyzed from the perspective of the strength of their respective influences, and the discussion of the interaction between them is still insufficient. Therefore, this paper uses 126,947 football match data to explore which factors have a significant impact on football scores and how they interact by combining multivariate analysis of variance and descriptive statistics. The experimental results show that height and player position are the main factors affecting the average score of the game. The main effect and interaction effect between them are tested and displayed for better decisions.

To investigate the improvement of tennis players’ athletic ability with the auxiliary training device and training methods of Kinetic Visual Acuity (KVA). The controlled experiment method is adopted to conduct horizontal and vertical tests on two sets of players after a period of training so as to observe the effects. Finally, SPSS data processing software would be used to analyze the data. Conclusions: 1). The training of KVA had improved the scoring of baseline depth, baseline accuracy, serving capacity, volley skills, and service receiving skills. The improvement ratio of each score in the experimental group was significantly higher than that in the control group, and the t-test results reached a significant level. 2). The total score of tennis in the experimental group increased more than that in the control group after the experiment. The total score of the experimental group increased by 55.7 and the total score of the control group increased by 6.55 after the experiment. By comparison, the increase of ITN total score in the experimental group after sports vision training is more obvious than that in the control group. After the training of KVA, the improvement of service receiving ability in the experimental group and the control group was the most obvious among all. The control group increased by 4.25 points and the experimental group increased by 4.75 points, which perfectly proved that the training of KVA improved eye movement, eye tracking ability, and ball-focusing ability.

According to the testing data for physical fitness of the national excellent youth female volleyball athletes who participated in the "National Excellent Youth Female Volleyball Training" from 2012 to 2019, the effective testing data for physical fitness of the national excellent youth female volleyball players from 2012 to 2019 were divided into groups in each year and each special position by means of information technology. Establish database based on the average number, standard deviation, minimum, maximum, sample size and other statics of reach jump(cm), standing long jump(cm), 30 meters’ running(s), “Mi” shape movement(s), solid ball throwing (cm), and 800 meters’ running (s), all together six indicators. In the meanwhile, this paper makes arrangement and comparison for the average number of the six physical indicators of female volleyball athletes in the years and groups by Excel software, analyzes the advantages and disadvantages of physical quality of volleyball athletes systematically, reveals the physical characteristics of high-level reserved female volleyball talents, and provides theoretical basis for the reasonable selection of adolescent athletes as well as the improvement of the success rate of high-level reserved female volleyball talents and scientific training in the process of it.

Analyzing the hot topics and frontier evolution in the field of basketball teaching and learning is important for the development of basketball teaching and learning research in China. In this paper, the keyword network mapping in the field of basketball teaching research in CSSCI and Chinese core journals from 2000 to 2021 was mapped using the econometric software CiteSpace for visual analysis. The results showed that the research hotspots in the field of basketball teaching include teaching models, experimental research, colleges and universities, skill transfer, teaching theory and practice, and so on. The latest frontiers of basketball teaching research are focused on the integration of sports and education, teaching model, and teaching reform. It is suggested that future research should establish a multidisciplinary perspective, strengthen cross-institutional and cross-disciplinary cooperation, innovate basketball teaching models, improve the quality of basketball teaching, and build a basketball teaching theory and practice system.

This paper aims to determine relationships between 160 matches statistics and the match results in two match stages of 2020 CSL under the COVID-19 pandemic prevention and control. A team's winning probability was evaluated by a two-standard-deviation increase in the value of each variable. The smallest worthwhile change was used to evaluate non-clinical magnitude-based inferences. The results showed that for group round robin stage, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Set Piece, Cross Accuracy, Counterattack, Won Challenge, Tackle Gaining, HIR Distance in BP, Sprinting Distance in BP), two had obviously negative effects (Distance Covered in Penalty Area, Sprinting Distance Out of BP), other twenty-three statistics had either trivial or unclear effects. While for the knockout stage, the effects of nine match statistics (Pass Accuracy, Forward Pass Accuracy, Delivery into Attacking Third, Delivery into Penalty Area, Dribble into Attacking Third, Corner, Foul Committed, Yellow Card, Distance Covered in Attacking Third) turned to clearly positive, the effects of Won Challenge, Cross Accuracy turned to trivial and clearly negative, respectively. Coaches and players should take these different aspects into account when planning practices and competitions for their teams.

Taking the evolution of the best results of men's 100 m sports in China and the world, the average results of the top ten athletes in China and the world in recent 20 years and the results of Chinese athletes participating in the world championships, Olympic Games and National Games as the starting point, this paper analyzes the development process of men's 100 m sports in China through the methods of literature, mathematical statistics, and regression analysis to reveal the rule and characteristics of the development of men's 100 m sports in China. The research suggests that the development of men's 100 m sports in China generally shows an upward trend and is at the middle and lower reaches of the world. But at the same time, China's men's 100 m sports have developed significantly in recent years, and it has a large room for development.

The multiple comparison method refers to the hypothesis test of whether there is a significant difference between the means of each sample after the analysis of variance is performed on the samples of each group to be tested. In data analysis, the multiple comparison method can perform a more precise difference analysis based on the analysis of variance. Therefore, this study will select the LSD (Least Significant Difference) test method in the multiple comparison method to analyze the physical fitness test scores of males and females in the three grades from 2019 to 2021 in the investigated schools. In this way, we can understand the substantial impact of students' home-based learning on students' physical health during the new crown epidemic, so as to make targeted development plans for students' physical health in the current special period and minimize the adverse impact of the new crown epidemic on students' physical health.

With the application of modern information technology in volleyball games, the “rules” of volleyball games can now be reflected as quickly and accurately. Statistics have gradually become an important source for understanding the strengths and weaknesses of an athlete, providing key analytical data for coaches to improve the training and design game plans, for fans to vote for an all-star lineup, and for scientific researchers to carry out related research. By examining the application effect of data volley statistical software in the 2020-2021 Chinese volleyball league, this study seeks to further explore the application value of statistics analysis in volleyball and offer certain reference for sports teams in future volleyball games and in the skill training. data volley statistical software can reflect the technical and tactical features of the team on the court in an accurate and timely fashion and can assist coaches and players in conducting comprehensive analysis of the game.

In order to overall examine the Chinese research of the development dynamic, hotspots and trends of Shaolin Wushu, this paper used Citespace V software to make a scientometric and visual analysis on160 papers regarding Shaolin Wushu. The papers were selected from core journals, CSSCI and CSCD of CNKI database from 1992 to 2021. First, it was found that the number of published papers in Shaolin Wushu showed a normal distribution, reaching a peak in 2013, and then gradually declining. Based on the research institutions, the top three in terms of published papers were Henan University, Zhengzhou University, and Anyang Normal University, all of that were in Henan Province which was the main province for publishing Shaolin Wushu research. Second, the research in the field of Shaolin Wushu was relatively scattered, and a stable group of journals were not formed. Third, a stable group of authors publishing the papers of Shaolin Wushu were not made, and most of the current research were privately written. Fourth, the hotspot in the field of Shaolin Wushu were consisted of Shaolin Temple, Wushu culture, Chinese Wushu, sports tourism and sports history. The focus of Shaolin Wushu research is to be expanding, and the research topics tend is to be diversified and integrated.

Good flexibility is an important guarantee of the improvement of university students' physical health and sports performance, and scientific methods of physical monitoring and analysis are a prerequisite for problem identification. In this study, data of the sit and reach test and Functional Movement Screen (FMS) test were collected from a sample of 112 university students through videos without marks, and eight data items were analyzed and processed using the functional movement screen test, the paired t-test and bivariate Spearman's correlation analysis in SPSS software. The results showed that ten weeks of functional training significantly improved university students' flexibility test scores, and that sit and reach movements correlated significantly with active straight leg raises and deep squats. The study helps develop individualized training programs to improve flexibility using the FMS test, with reference significance for reducing the risk of sports injuries and improving physical fitness in practice.

To explore the intervention effect of two different physical teaching modes on college students' physical exercise attitude and provide new perspectives and approaches for the development of physical teaching in colleges and universities. Methods A total of 51 Grade 2020 students from a university in Guangzhou were selected as the experimental subjects and were enrolled in a single-blind experiment. They were divided into the traditional teaching class (31students) and the "online and offline" blended teaching class (30 students) for 18 weeks. Results SPSS 25.0 was used to perform an independent sample T-test and paired sample T-test for the data obtained from the questionnaire, the results show that: (1) Compared with that before the experiment, the physical attitude of students in the traditional teaching class was significantly improved after the experiment (p = 0.046 ⪅0.05); the physical attitude of students in the "online and offline" blended teaching class was considerably improved after the experiment (p = 0.007 ⪅0.01). After the experiment, the physical attitude of students in the "online and offline" blended teaching class was better than that of students in the traditional teaching class. (2) The physical behavioral intention of students in the traditional class changed significantly compared with that before the experiment, which was statistically significant (p ⪅0.05), as shown by a higher intention to participate in physical exercise. (3) The behavioral attitude, the sense of behavioral control, and subjective criteria of the students in the "online and offline" blended teaching class were statistically significant (p ⪅ 0.01 or p ⪅ 0.05), which was reflected in an increased tendency to participate in physical exercise, an increased ability to control physical exercise behavior and a more accurate subjective evaluation of participation in physical exercise. Both teaching modes can effectively correct students' attitudes towards participation in physical exercise; but the effect of "online and offline" blended teaching on improving students' physical exercise attitude was significantly better than that of traditional teaching.

In the national governance system, due to the positive significance of sports to political, economic and social development, it is decided that sports governance is one of the important foundations for the modernization of national governance system construction and governance capacity building, therefore, the research on sports governance has attracted much social attention. Based on the China Knowledge Network journal database, we combed through the literature on sports governance research in the past ten years, and used the bibliographic co-occurrence analysis software (BICOMB) to conduct keyword analysis, co-word analysis, systematic cluster analysis and multi-dimensional scale analysis to map the hot topic network and future trend of sports governance research, and found that sports governance research mainly includes research on the top-level design of sports governance, sports industry research, public These three areas are in different strategic positions. These three areas are at different strategic positions and development stages, with public sport and multi-governance research at the core of the overall research layout, sport industry research having formed a stable structure and considerable scale, while research on top-level design of sport governance is still a little weak.

Metrological testing for quantity of product in prepackages is an important national legal measurement activity primarily aiming to ensure fair market trade and consumers’ interests in the world. International Recommendation OIML R87 is a model regulation and procedural guide widely used for the compliance testing of prepackage marked with a constant nominal quantity. OIML R87-2016 presents sampling procedures without replacement to revise the unreasonable one of OIML R87-2004. Single sampling plans for inspection having desired operating characteristics by mean and the numbers of inadequate prepackages with T-type errors were established. The paper discusses four statistical mathematical models based on the random sampling without replacement. The corresponding sampling plans can be concluded to meet the requirements of OIML R87-2016. The drawback of the sampling plans that sample size decrease suddenly and sharply with the lot size increasing was carried out. New sampling plans with appropriate limited condition are proposed by VBA program.

Tai Chi (TC) has been validated to be able to postpone decline of both cognition and balance introduced by aging. With severe economic burden posed by rapid population aging in many countries, it is important to explore extensive training and promotion of TC through efficient technology-assisted approaches among older adults in order to vastly reduce relevant medical costs. This study explores effects of video technology-based TC training on improving balance and global cognitive function of older adults. A six-week intervention of video technology-assisted TC training based on tablet computers was conducted among older adults. Cognitive function and balance of all participants were assessed before and after the video-guided TC intervention. Paired t-tests were performed on change scores of the four indices for cognition and balance to assess the significance of intervention effects. Results showed that cognitive function and left leg balance were significantly improved. Given this, video-guided TC training based on modern information technologies can significantly improve balance and cognition of older adults.

With the accumulation of various kinds of text data, it is no longer possible to generalize or classify them by manual reading, so how to use statistical models to mine text data reasonably and effectively has become an important issue in academic research and practical work. This paper discusses three problems of Chinese text mining: word separation, keyword extraction and text classification. For the word separation problem, the Cascaded Hidden Markov Model and the WDM that treats the segmentation between words as missing data and solves it with the EM algorithm are introduced. For the keyword extraction problem, this paper proposes a Bayes factor and introduces CCS using sparse regression. For the text classification problem, the method of building a classifier based on the frequency of keywords and the method of building a classifier based on the probability of the topic first are introduced. We give the respective advantages of each method by comparing the above methods with two datasets using SVM and Random Forest and make suggestions of their use.

As a big country with a vast territory, China has great differences in natural resources, population and other innate factors among different provinces. After the reform and opening up, China's economic growth has made remarkable achievements. With the economic growth, it has become an indisputable fact that the income gap of China residents is widening. This paper reveals the overall trend and internal transmission path of intergenerational change of residents' income inequality and puts forward a mathematical statistical model of income inequality and income flow. ML (Machine learning) algorithm is used to classify, process and analyze the data, forecast the income of residents, and provide technical support for formulating policies to alleviate income inequality. The results show that the accuracy of this algorithm is higher than that of the comparison algorithm, reaching 95.47%. Applying this model to the research of income inequality and income flow can help the administrative department to master the development law of residents' income and help to formulate effective management policies for the problem of income inequality.

This paper empirically analyzes the volatility of SSE 50 Index ETF based on GARCH family model. GAECH(1,1), EGARCH(1,1), GJR-GARCH (1,1) and APARCH(1,1) models are used to estimate the daily return rate of SSE50 ETF, and AIC and RMSE statistical measures are used to evaluate the estimation results. The data ranges from 24th February 2005 to 29th August 2022. The results show that the EGARCH(1.1) and APARCH(1,1) models are more appropriate, and the volatility of SSE 50ETF has significant asymmetry, leverage effect and power function effect. At the same time, the volatility of SSE 50 ETF has a long and strong memory ability, and its response ability to new information is weak.

The logistics sector plays a crucial role in promoting the implementation of the "carbon peak" policy and the high-quality improvement of China's economy as it is an industry with excessive energy consumption. In this study, the logistics efficiency of 30 Chinese provinces and cities (districts) is measured using the DEA model, and the factors influencing China's low-carbon logistics efficiency are identified using the Tobit regression model. The findings show that efficiency varies greatly between regions of China, with an east to west trending steady drop. The low comprehensive efficiency of logistics industry is mainly restricted by technical conditions. The efficiency of low-carbon logistics is positively impacted by per capita GDP, region entropy, and transport network density, but negatively impacted by R&D spending and energy intensity. This study puts forward some countermeasures and suggestions on the application of logistics public information service platform to narrow regional logistics differences, improve transport structure and develop smart logistics

Under the new situation, with the deepening of China's power system reform, the market competition situation encouraged by power grid enterprises is becoming more and more fierce. In this context, it is of great significance to strengthen the sustainable operation and development of power grid enterprises to strengthen the cost control of 10 kv distribution network project and improve the efficiency and efficiency of power grid investment. This paper combines the construction characteristics of 10 kv distribution network project, classifies the factors affecting the project cost based on the explanatory structure model, and combines with the gray correlation analysis method to reasonably measure the influencing factors, so as to help the power grid enterprises scientifically improve the level of project cost control, and provide a starting point and support.

Sorting out the frontier and hot spots of exercise prescription research abroad has a positive reference role in promoting domestic research in this field. This study collected a total of 3246 related literatures on "exercise prescription" from the "Web of Science" database, and sorted and analyzed them with Citespace, a metrological analysis tool. The results showed that: 1) the number of foreign publications showed a linear growth trend year by year, indicating that foreign attention to exercise prescription increased year by year; 2) the scientific research institutions of universities in developed countries in Europe and the United States are the backbone in the field of sports prescription research, but China is not among the top ten countries with high productivity at present, suggesting that the research support in this field should be strengthened in China. Most of the highly cited authors work in the school of Public Health and sports Health of universities or ACSM organizations, and the form of cooperation is mainly in the form of team, but there is still a lack of cooperation and communication between authors and organizations. The research hotspots in this field focus on the two themes of quality of life and applied research, and the research trend will focus on the study of exercise prescription for special people and special diseases.

In order to understand the development status of China's competitive gymnastics, this paper uses CiteSpace software to visualize the Chinese core journals from 1998 to 2021 in the core collection database. The results show that: (1) the number of core articles of competitive gymnastics research is significantly lower than before, and the year with the largest number of articles is 2004; (2) The author collaboration network lacks vertical and horizontal connections, and the core authors are Xiaowen Yao, Hua Han and Xianlin Meng et al; (3) Highly cited references were all from the most authoritative sports journals in China; (4) The research focus is mainly focused on the competitive sports, competitive strength, male and female and action analysis of competitive gymnastics; (5) It is predicted that the research hotspots in the future may be war preparation strategy, strengthening weak points and developing governance.

Breaststroke is an important sub-item of swimming. Its academic research plays an irreplaceable role in China's physical education and Olympic competitive sports. In order to further understand its current research status of breaststroke in China and summarize the research hotspots and directions, this paper adopts the bibliometric method based on data mining. This paper makes a visual analysis of 1975 breaststroke documents included in CNKI from 1989 to 2021 and the development process, hot spots, and evolution trend of breaststroke research. The result shows that the breaststroke papers as a whole show a "ladder" fluctuation evolution in quantity; the research affiliations of breaststroke are mainly sports colleges and universities. Beijing Sport University and Guangzhou Institute of physical education are the backbones of academic research in this aspect. Its research hotspots include “swimming”, “individual medley” and “swimmer research”. The number and sources of overall research topics are relatively limited, and the research topics are scattered. It is suggested to strengthen basic theoretical research, strengthen interdisciplinary integration and explore research perspectives; strengthen scientific cooperation, enhance discipline integration, and pay attention to the diversity and scientificity of micro empirical research methods.

As a main body of sports, extracurricular physical exercise can not only effectively improve students' physique, improve their health and build a strong body, but also as an important means to promote personal perfection, which has more far-reaching significance for students' growth and development. This paper uses questionnaire investigation and statistical analysis method. The computer software developing circumstance of Chenzhou city rural extracurricular physical exercise were investigated, and the results showed that about 72% of rural primary schools have full-time teachers, 40.9% of which are rural middle school physical education teachers, and most schools have class physical training only one to two times a week. The main content of extracurricular physical exercise is freehand exercise, running, rope skipping and games. There are few schools with standard sports venues. The main factors affecting the extracurricular physical exercise in rural primary and secondary schools in Chenzhou city are that parents and students do not have a strong sense of physical exercise, lack of understanding of the value of extracurricular physical exercise, and school leaders do not pay attention to the teachers and extracurricular physical exercise, and extracurricular physical exercise time is often occupied. It is suggested that the school leaders should strengthen the construction of physical education teachers, pay attention to the development of students' extracurricular physical exercise, and attract more students to participate in physical exercise so that students can form a good habit of physical exercise, and overall cultivate students' lifelong physical awareness.

Based on the rapid development of science and technology such as big data, cloud computing, and the Internet of Things in recent years, as well as the application of intelligent hardware devices in all aspects of life, people have put forward related concepts such as "smart life" in this new era of scientific and technological applications. In the field of popular sports lifestyle, there are also new changes closely related to the application of computer information technology and intelligent hardware devices. In order to meet the requirements of the development of the times, based on the background of big data, this paper analyzes and studies the application of the Internet of Things to martial arts training. This paper mainly studies and analyzes the application of "Internet +" APP in martial arts training and teaching in the context of the internet. The principle and requirements of system establishment are analyzed, and the design research is carried out in a targeted manner. In the process of analysis, relevant research shows that 95% of parents believe that it is very important for children to participate in martial arts training to be able to exercise, 90% of parents believe that it is important for children to like it, 70% of parents believe that it is important to improve motor skills, 63% of parents believe that disease prevention is important, and 70% of parents think that it is important to cultivate the quality of will.

The Video Challenge System (VCS) was officially introduced at the 2016 Rio Olympics. Utilizing multiple on-site officiating tracking cameras and processing the captured images with back-end computers, this system was proven to provide fair and effective resolution for disputes. This paper adopts the following research methods: VCS review, and statistical analysis of data from the Women’s Volleyball competition at the 2020 Tokyo Olympics. The author obtained the following results. Limited simulation of flight trajectory when a volleyball is in a close range to the net and limited amount of available data to generalize research findings. According to the Fédération Internationale de Volleyball (FIVB), the most common challenges include touch, ball in/out, followed by faulty antennae. 1) VCS can produce accurate and transparent results and help minimize human judgment errors in the court owing to its extremely high prediction accuracy, and it can create an instant, objective, and direct 3D animation. 2) Mass data collection is required; the VCS analysis and application in the competition need further improvement. 3) The digitalization and intelligence process in the sports industry are facilitated through the interactions between Hawk-eye technology innovation and volleyball rules. 4) Greater granularity in volleyball sport development is stimulated by creating new challenges for athletes, coaches, and referees.

With the continuous development of information technology and communication technology, the introduction of computers into everyday work has brought a lot of unexpected convenience to the work. Many units and enterprises have started to develop software suitable for them, and there is a strong demand from sports units and institutions at all levels to combine computer technology and sports. The current manual management of school materials is inefficient and error-prone, and it is essential to develop a materials management system. With the common use of computers and the rapid progress of network communication technology, the use of computers to manage information has become a trend in social development. In order to solve the many problems that exist in the management of materials in sports schools and to improve office efficiency, it is important to develop a material management system that can manage school materials, including the lending of items, the number of items lent, who lent them, when they were lent, and the return of the items lent.

In view of the relationship between technology innovation, consumption innovation and foreign trade upgrading, selecting the relevant data from 2000 to 2020it is found that technological innovation drives consumption innovation by promoting changes in consumption patterns and consumption levels, making consumption more personalized, knowledgeable, intelligent and fast, which is the key and core of driving the upgrading of foreign trade. Through the empirical test of VAR models, it is found that there is a significant positive correlation between China’s trade with foreign countries and the consumption level and structure of foreign residents in the long run. But the innovation of the country’s high-tech, high-value-added products cannot keep up with the constant changes in foreign consumer demand, which restricts the adjustment and optimization of the country’s export product structure and hinders the country’s foreign trade transformation and upgrading. To this end, China needs to change its foreign trade marketing strategy and model, and participate in economic globalization and international division of labor at a deeper and higher level.

Electric power is an important guarantee for the overall development of the national economy. The state invests more than 100 million yuan in research and development of key scientific and technological projects in the electric power field every year to ensure the continuous innovation and development of the electric power industry. The benefit evaluation of power science and technology projects is very important for power grid enterprises, and its scientificity and accuracy have a direct impact on the correctness of science and technology investment decisions of power grid enterprises. There is still a lack of scientific evaluation methods for power science and technology projects, and the evaluation results cannot be fully utilized. This is because all factors have influence on the results. According to the characteristics of R&D project performance evaluation, this paper selects the analytic hierarchy process, which can realize that the weight setting of each level will ultimately directly or indirectly affect the results. The impact of each factor in each level on the results can be clearly quantified, and subjective factors such as expert scoring in project evaluation can be organically integrated. Effectively solve the problems of multiple objectives, multiple tasks, multiple criteria, multiple levels and multiple elements in the evaluation of power science and technology projects. Explore the whole implementation process of the project, select and design a more comprehensive and specific indicator system, and use the network analytic hierarchy process to objectively evaluate the performance of the project.

In the face of numerous suppliers on the market, manufacturers need to choose suitable manufacturers to maximize profits. This paper collects the information of 50 suppliers and the 240-week supply of a construction enterprise and builds a performance appraisal system with supply, unit price, supply rate, and expected value according to the requirements of the enterprise and the supply. The system uses the AHP-TOPSIS legal authority for the processed results, then rids the dimensions for all indicators, and finally conducts a comprehensive score through the Topics method, sorts the results of the solution, and finally selects the top five comprehensive scores. Suppliers are companies considering cooperation objects.

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward.

In the context of the revision of the new version of the Vocational Education Law, the existing comprehensive competitiveness ranking of higher vocational institutions and the setting standards of undergraduate level vocational institutions were combined. The 56 institutions with good development in the field of vocational education were used as the research objects, and the index extraction and weight calculation of the collected data were carried out by using factor analysis method. Principal component analysis was used for indicator extraction, and the Kaiser normalized maximum variance method was used for weight calculation. The classification of indicators at each level is analyzed and corrected using the kmeans method. The final evaluation system will be set with reference to the actual situation. It is expected that the evaluation results will make effective suggestions for the development of the institutions.

Personal credit risk is increasing in the background of continuous expansion of bank credit business. Previous researchers use many algorithms of machine learning to assess personal credit risk, while these models differ in real scenarios and accuracy. Based on this situation, this research first analyzes the influencing factors of individual credit risk through searching data, and then shows the relationship between them in figures. Meanwhile, this research compares three machine learning models in the predicting the accuracy. These models are Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR). Learning from the previous studies on individual credit risk assessment model, this research compares the final average value of Area Under Curve (AUC), which is obtained by calculating AUC one and AUC two. The results show that XGBoost has better performance than the other models for a high AUC value. This research provides an idea for banks to select and individual credit risk models.

This paper introduces two common challenges in designing asynchronous FIFO: semi-stable state and glitch. And from the semi-stable state and the glitch how to produce and how to affect the circuit analysis, proposed the corresponding solution. This paper introduces three codes: binary code, gray code and shift code. In this paper, we use Verilog HDL language and use these three different codes to design the FIFO empty and full state judgment module, thus solving the challenge of glitch. The semi-stable state challenge is solved by using the double D flip-flops. The advantages and disadvantages of three different code systems are analyzed and compared in the paper. The correctness of the design is verified by simulation experiments.

SoC system is a new chip design scheme, which can integrate a large number of functions in the same chip, so it has high requirements for asynchronous data transmission between different modules. Asynchronous FIFO is an efficient and reliable data transmission mode, which is often used as a data matcher in SoC system. This paper, through the modular design, layer by layer construction method, designs an asynchronous FIFO, and for the common problems to optimize, makes its performance more reliable. And according to the simulation results, it can be judged that this design can effectively and reliably carry out asynchronous data transmission.

For the increasingly saturated telecommunications market, the way to determine whether customers will churn and find the reasons that affect customer churn are the two main factors affecting the operation of telecom companies. In this study, a combinatorial model prediction composed of logistic regression and neural networks was adopted. It contains both the efficient and precise characteristics of machine learning and the good explanatory nature of logistic regression. The prediction accuracy of the final model reached about 80.5%, which is about 3% lower than that of a single neural network model and about 7% higher than that of logistic Regression. According to the results of the significance test, the main factors affecting customer churn are the following aspects: long-term costs, service duration, network services, and contract types. The same approach of this study can be applied to other industries (e.g., video site membership), allowing decision makers to predict the flow of customers and develop plans in advance to reduce losses. These results shed light on guiding further exploration of an analytical approach to contemporary customer churn and methods of decision-making.

Universal Asynchronous Receiver Transmitter (UART) is a serial communication interface, which is usually be used for meeting the requirement of low-speed serial communication between devices. Because UART has outstanding performance in both supporting close-distance data transmission and long-distance data transmission, it is used widely in computers and numerous other devices requiring data transmission. The first part of this paper illustrates the early development history, current progress, structure, and operating principle of UART. In the second part, a design of UART circuit module based on FPGA is introduced, which include the sending module, receiving module and the simulation result. The third part is used for giving examples of the applications of UART in several fields, including automatic monitoring system, SIM Card Access, and optical transmission.

This paper investigates whether the widespread use of wireless communication devices and real-time monitoring systems can guarantee the safety of trains operating in irregular terrain. Freight train transportation is characterized by its quick speeds, independence from topography, enormous carrying capacity, safety and so on. These characteristics define its prominent role in the integrated transportation system. As a result, safeguarding the safety of freight train transportation is crucial for any nation, and all nations have adequate methods and measures for railroad transportation safety. In order to increase train driving safety, transportation departments will typically require train crews to employ communication devices and video surveillance systems. This technical method allows each transportation department to guarantee railway safety directly. In this paper, researchers split the trains into two groups, had them transport the same products, and had their work in the same environment. The most cutting-edge and complete communication technology is installed in one group of trains, and this group also has a real-time tunnel monitoring system, whereas the other group has comparatively older communication equipment and no such system. Finally, the safety of the train movement was examined to determine how it would alter based on the existence of communication equipment and video monitoring systems by comparing the train movement and failure data between the two sets of trains in the same transportation environment. The researchers could demonstrate the viability and practicality of a comprehensive communication tool and monitoring system by comparing the number of failures.

Asynchronous FIFO (Frist Input Frist Output) is one of the most effective methods for solving the sub-stability caused by data transmission and storage across clock domains. Asynchronous FIFO has a wide range of applications in areas such as radar, signal processing and multimedia technology. A wide range of applications. This paper firstly introduces the Verilog HDL language and the characteristics of synchronous FIFOs and asynchronous FIFOs to solve the metastable problem as a starting point, using asynchronous FIFO design, which is one of the effective methods to solve the metastable state. This paper analyses two difficult points in FIFO design: how to generate empty-full flags and reduce the probability of metastable errors. A new design approach is adopted: using a grey code counter and a secondary D flip-flop can effectively solve the metastable problem by generating the empty/full status bits through the grey code pointer to accomplish the above difficult points. Simulation results show that the data in the asynchronous FIFO can be written and read out normally, and the null/full flag signal is correct. The design of asynchronous FIFO can greatly reduce the probability of sub-stability and make the product more stable and efficient.

Taking the data of A-share listed companies in Shanghai and Shenzhen from 2008 to 2021 as the sample, this paper uses the mediating effect test model and takes total factor productivity as the mediating variable to deeply study and analyze the impact of equity incentive on the market capitalization of listed companies in China and its internal mechanism. The study concludes the following: 1) Implementing an equity incentive plan will increase the firm's market capitalization; 2) Equity incentive has a positive effect on total factor productivity; 3) Total factor productivity is a mediating variable between equity incentive and a firm's market capitalization, and there is a transmission mechanism of equity incentive-TFP- firm's market capitalization in the firm.

This paper compares the performance of DL models (RNN, LSTM and GRU) and GARCH model in three different train sets with different time spans. The data of empirical analysis is SSE 50ETF from 4th May 2010 to 26th Aug 2022. And the performance is compared with realized volatility. The result shows that SSE 50ETF is more relying on long historical information and pay less attention to new information. And in long periods, the DL models has better performance. However, the stability of DL models is significantly worse than GARCH model. The performance of DL models are highly relying on the selection of a train set.

Normal form theory is an important tool for the study of the reduction and bifurcation analysis of nonlinear dynamical systems. This paper is to study the reduction for normal form of a class of non-autonomous nonlinear difference systems. For doing this, we compute the transformed system of a given non-autonomous difference system under a near identity formal transformation. Moreover, a recursive formula is derived for the homogeneous terms of the transformed system, which is a powerful tool for reduction of normal forms of non-autonomous nonlinear difference systems.

The utilization and allocation of water resources has always been a hot issue of global concern. In order to more reasonably solve the problem of inter-industry competition and uneven distribution of water resources, this study takes five states in the United States and Mexico as examples. We improved the goal programming model and calculated the correlation between dam level difference and total water supply. Then, based on the existing data, we simplify and improve the bankruptcy theoretical model to evaluate the importance of each industry. Based on the highest rate of return of the industry, the weighted planning of water resources utilization of each industry is carried out. Finally, the sensitivity of the model is analyzed by python program fitting curve, and the feasibility of the model is verified. We conclude that the total amount of water that the dam needs to pump is closely related to the water level difference before and after pumping. Relevant departments need to delineate the reasonable water level range of the dam to ensure that the dam can continue to use water supply in the future.

Universal Asynchronous Receiver Transmitter (UART) is widely used in the communication between main devices and peripheral devices with high reliability and simplicity. It can support serial data transmission by a single Tx line and Rx line with prescribed data length and baud rate. This paper proposes a configurable design of UART module based on Verilog HDL. This design features compatibility with multiple UART regulations. The information of configuration is stored in a register using serial transmission and read by other modules. The baud rate, data length, and validation of the verification bit are configurable to fit various UART standards. The functions and simulation results are explained, and the simulation implemented on Modelsim supports the proposal.

Many networks in the real world are directed networks, such as the world wide web, citation networks, directed friendship networks, etc. Using the algorithm for undirected networks to detect the community while ignoring the direction of edges, the directed network will be transformed into an undirected network. However, it is not a useful algorithm. Because the direction of the edges in directed networks carries significant information. According to a community detection model (RIIM) for undirected networks, we provide a new community detection algorithm for directed networks in this research. The local information and the global information of the node are considered. Finally, it is tested on the actual network and produces excellent results.

Tide is a natural phenomenon which has important impact on port engineering, earthquake forecasting, shipping and many other fields. But in some complex terrains, tide and tide strain are hard to analyze and anticipate. Harmonic analysis plays an important role when researching tide. This passage uses derivation of math and physics to elaborate the essence of tidal strain observation combination and uses it to analyze tidal strain observation combination from some main gauging station in China. Besides, this passage uses t-tide tide analysis tool to analyze and anticipate the tide along the sea area of the Guangdong Province by harmonic analysis and compares it with the observation. The conclusion shows that our analysis on tidal strain has a high accuracy, and it is precise enough for earthquake scholars to research. In addition, using harmonic analysis to forecast tide is precise and stable. The residue error follows the normal distribution, and its average is less than 0.01. The maximum of variance is 0.2294, the minimum is 0.1732. The length of confidence interval is less than 0.01. Using the information of three months and one year is almost unanimous and shows that different seasons will not influence the conclusion.

In this paper, we get some new results on Asymptotically Regular mapping which meet some new certain conditions by introducing some new functions or extending some new mappings. First, we introduce Reich and Chatterjea type nonexpansive mappings. Because of the connection and difference between the two of them and the Asymptotically Regular mapping, we extend some results in [1,2] to Asymptotically Regular self-mappings which meet certain conditions. And then, on this basis, we extend the unary mapping to the binary mapping, and also get the same conclusion. Surprisingly, we prove that the fixed points are the same. Finally, we introduce the mapping which defined by Boyd and Wong and prove the mapping more broadly.

HIV infections was caused by a sort of chimp in Central Africa. AIDS is the last stage of HIV infection. Though there are many methods to defend the infection of HIV, human still have no way to cure it absolutely. There are statistics of HIV infection, main routes of infection of HIV, the process of HIV infection and the prediction of the number of HIV infected. In this research, the deaths of HIV become less in recent years. The main routes of HIV infection are homosexual, heterosexual, and injecting drugs use. The number of HIV infected will be larger in the future by the prediction. According to the analysis, the main routes of HIV infection is sexual. The number of infected people will still increase in five years, and it is important to give more people the chance of ART treatment. The significance of this research is to analysis the main way of HIV infection and the situation of AIDS. These results shed light on the main routes of HIV transmissions are heterosexual, homosexual, bisexual, and injecting drugs abuse. The deaths averted due to ART treatment become more and more from 2010 to 2014, which explained that the decreasing deaths of HIV infections well. The number of HIV infections still grows up in these years. These results can be used to find the main routes of HIV transmissions and predict the infections of HIV, which can remind people to be care of the behavior in their life to avoid infecting HIV. By this results the death of AIDS will decrease.

In order to analyze the substation seismic risk quantitatively, Monte-Carlo method is carried out in a mathematical model building for substation seismic risk assessment. Monte-Carlo method is satisfied and applicable to simulate the earthquake parameters such as seismic center and seismic magnitude which is key factors for calculate the substation seismic risk on the basic of fragility curve. A substation is taken as an example, meanwhile 50000 earthquakes are generated under different seismic center and magnitude by Monte-Carlo method. The result indicates that the seismic risk increases with the increase of magnitude and the decrease of epicentral distance. When the seismic magnitude is less than six and the epicenter distance is more than 50 km, the substation seismic risk is relatively low. However, when the seismic magnitude raises greater than 7.5, the seismic risk rises rapidly. Finally, the seismic risk is greater than 30% regardless of the epicenter distance under the condition of seismic magnitude greater than eight. This paper provides a way for substation seismic risk assessment based on Monte-Carlo method, and gives references to the risk analysis, disaster prevention and emergency management on electric power system.

In the normal coloring of a graph, if there is at least one vertex in each color class and it has at least one adjacent point in other color classes, such normal coloring is called b - coloring, and such points are called b - points. Let G have the maximum k value of b - k -dyeing called b -chromatic number, which is recorded as φ(G). Similarly, b -edge coloring and the chromatic number of the graph is φ'(G).

With the development of teaching information technology, the teaching mode is also constantly innovating. In order to compare the teaching effect of different teaching modes, this research designs an online and offline mixed teaching mode according to the characteristics of physical skills courses. The 2019 and 2020 general table tennis students were taken as the experimental objects, and the 2019 students were the control class (100 students), and the traditional teaching mode was adopted, that is, the offline teaching mode, the teacher explained and guided, and the students practiced on the spot; the 2020 students The online and offline mixed teaching mode was adopted for the experimental class (89 students). The online and offline mixed teaching mode uses ding talk software to carry out live teaching and combined with the course teaching plan for this semester, the national-level excellent course of "Table Tennis" course of Chengdu Institute of Physical Education on the MOOC platform of Chinese University is selected. Data research uses Pearson correlation for analysis, the formula is r = Lxy/√Lxx-Lyy, Lxx is the sum of squares of the deviation of the X variable, Lyy is the sum of the squares of the deviation of the Y variable, Lxy is the sum of the product of the dispersion of the X and Y variables. The scores between classes were compared using one-way variance. Results show that (1) The online and offline combined teaching mode of "Table Tennis Teaching and Training" was constructed; (2) Under the online and offline mixed teaching mode, the students' basic skills, forehand attack, backhand push, competition results and total of table tennis lessons were established. The scores are significantly higher than the traditional teaching mode, indicating that the online and offline mixed teaching mode is more suitable for college students' table tennis skills courses, which can significantly improve the students' skill level and overall score. (3) Students are highly satisfied with the combination of online and offline teaching mode and have high interest in learning. Conclusion: The mixed teaching mode of online and offline enriches the teaching mode of skill courses and provides theoretical and practical support for the reform of skills course teaching.

In order to explore the factors that influence online sports learning platform use among college students, this study constructed an instrument for measuring students’ intentions of using online sports learning platforms based on the Technology Acceptance Model (TAM). A total number of 293 college students participated in the study through an online survey. The survey was first tested for validity and reliability, followed by hypothesis testing using Structured Equation Modeling. The results showed that perceived usefulness had a positive and significant impact on college students' using attitude, and perceived usefulness had no significant direct impact on using intention; perceived ease of use had a significant positive impact on perceived usefulness, perceived ease of use had a positive impact on using intention, and perceived ease of use had no significant impact on using attitude; using attitude had a significant positive impact on using intention.

Combinatorial optimization has found its way into a variety of domains, including artificial intelligence and cybernetics. Deep Reinforcement Learning (DRL) has recently demonstrated its promise for developing heuristics for NP-hard routing problems. The current generalization performance of models needs to be improved, especially for large-scale routing problems. In this paper, we propose a hybrid approach for the Capacitated Vehicle Routing Problem (CVRP) based on DRL and adaptive large neighborhood search. The information representation of the neural network for CVRP is also improved by the combination of multi-head attention mechanism, pointer network and graph neural networks. The experimental results demonstrate that the optimization of our model on CVRP outperforms existing DRL techniques and some traditional algorithms. In addition, our method improves the training efficiency of the model and the performance of generalization to large-scale CVRP.

Blind Source Separation (BSS) is the problem of dividing a set of source signals from a mixed one. At present, Independent Component Analysis (ICA) is the main method to solve such problems. In this paper, the ICA algorithm based on maximum likelihood estimation is introduced at first. On the basis of summarizing its principle, we implement the data processing with MATLAB code. Then we give the FastICA algorithm based on Negentropy as an upgraded ICA. Further, by optimizing the distribution function, we derive the optimizing form of FastICA. The experimental simulation of this optimization algorithm is carried out with the previous two algorithms. After we analyze the experimental data, we confirm the superiority of the FastICA algorithm and the effectiveness of the optimized FastICA algorithm, and finally solve the cocktail party problem mentioned in the introduction.

Nowadays, deep learning has been regarded as a key factor to determine education by researchers. Numerous studies have revealed that, when a student does deep learn rather than learning superficially, they tended to remember that knowledge for a longer time and had better mastery of knowledge. Deep learning has already somewhat applied to different stages of education. Thus, several experiments are used to illustrate how deep learning affected the quality of teaching. Through those experiments, it can be seen that the overall level of deep learning of students is at a medium level, but an individual typically varies widely from another one. Also, deep learning has a positive relationship with the quality of teaching. Moreover, deep learning model can be applied to the automatic classification of 4MAT questions. The purpose of this paper is to illustrate how deep learning affects online education, mainly from the perspective of data modeling and algorithm analysis.

Serving as a mainstay industry of the national economy, the logistics industry entails great accountability for the accomplishment of low carbon development. In accordance with the principal component analysis, this paper evaluates and analyzes the logistics performance levels of eleven cities in the Guangdong-Hong Kong-Macao Greater Bay Area from the low carbon perspective, of which Hong Kong has the highest score and Zhongshan has the lowest score. The logistics performance scores of Hong Kong, Guangzhou, Shenzhen and Macao are all above the average level, while Foshan and Zhongshan are slightly behind.

This paper introduces the background and significance of building a CatBoost-based spam detection model and proposes a new research approach on the classical research model. Besides, a large number of network resources are occupied which makes 85% of the system resources of the mail server are used for the identification of spam. It is not only a waste of resources, but may even lead to network congestion and paralysis, affecting the normal business email communication of enterprises. In this study, we use the Enron-Spam dataset, which is currently the most publicly available dataset used in email-related research. First, we use word bagging processing and TF-IDF processing for feature extraction, and then CatBoost integration algorithm is used for training. The final accuracy of the model is more than 98%. Compared with the conventional model, the model has better performance, which can effectively improve the accuracy of spam detection and identification.

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19.

Ancient glass artifacts are the physical evidence of cultural exchange between East and West, and their correct identification is significant to archaeological work. However, glass artifacts are highly susceptible to surface weathering caused by the burial environment, which leads to the wrong discrimination of the glass category. In this study, we modeled the weathered artifacts based on Fisher Linear Discriminant Analysis (FLDA), established the index system of the classification model, and obtained the discriminant function for each category of glass artifacts. The correct rate of the model was 98.6% on the test set and 100% on the training set. The results show that the Fisher discriminant model can effectively identify ancient glass types by using the chemical composition indices selected in this study as discriminant factors, which provides a new method for identifying ancient glass artifacts.

Geological survey is one of the important components of state financial expenditure, and the regional adjustment coefficient is an important means to ensure that the consumption of live labour and physical labour in geological survey projects is fairly compensated. The current regional adjustment coefficient was jointly issued by the Ministry of Finance and the Ministry of Land and Infrastructure in 2007 to adjust the difference in fieldwork expenses between different regions of geological survey projects, and the Ministry of Human Resources and Social Security and the Ministry of Finance also use the regional adjustment coefficient as the basis for determining the standard level of fieldwork allowance. As the level of socio-economic development increases, the technical level of geological survey progresses and the extent of geological survey work deepens, there is a mismatch in the level of regional adjustment coefficient in some regions. By dissecting the expenses of geological work in the field, five major influencing factors such as salary level, price level, transportation conditions, commuting time conditions and construction period were identified, further decomposed into natural and humanistic basic elements, and the natural and humanistic basic elements and expenses were organically combined, and a formula model for measuring the theoretical value of the regional adjustment coefficient was constructed based on the 1:50,000 standard map size with Baoding as the base point for calculation. The results are basically consistent with the field research, and provide technical support for the proposed revision of the existing regional adjustment coefficient.

Contemporarily, Asia is the world's largest carbon emitter among other continents according to the recorded data, which plays an essential role in climate change. This paper introduces Asia's carbon dioxide emission effect and mainly focuses on the emission prediction for the future five years based on the time series data from 1950 to 2021. Some statistical models are implemented in forecasting, including ARIMA, SARIMA, and SARIMAX models. Besides, two general machine learning models, linear regression and random forest regression would also be applicable in this paper. According to the analysis, based on the model performance matrix, the study found that the SARIMA model is the optimal model to explain and forecast the data. Specifically, it has the lowest MSE, MRSE, and MAE values and the highest AIC and BIC scores. These results shed light on guiding further exploration of carbon dioxide emission prediction in the next five years in Asia.

In this paper, we made a significant effort to identify actual online data to justify why analyst coverage is essential in corporate social responsibility practice. A total of 5856 datasets were drawn from three listed company information websites using the Scrapy web crawlers to investigate a resilience model in the relationship between analyst coverage, executive overseas background and corporate social responsibility. Based on big data analysis, we observe that firms followed by more analysts perform better in corporate social responsibility. The relationship is stronger for firms that have executives with overseas background. Our findings shed light on how firms choose their corporate social responsibility performance under external supervision pressure of analysts.

In order to improve the accuracy of primary network dispatching operations and ensure the normal and stable operation of the power grid, this paper designs a two-stage dispatching operation error prevention method for the main network day-ahead and real-time according to optimized association rules. The method collects error information data of day-ahead and real-time scheduling operations and determines the correlation degree of the operation error information data using the optimization correlation rules. At the same time, the method determines the error risk level of the primary network scheduling operations, designs encrypted passwords and realizes the real-time execution of error prevention signals for scheduling operations. The simulation test results show that for 100 sets of random data, this paper's method's average error prevention execution time is 2.06s, which can process the data information in a short time and has real-time and high efficiency. The error-proof performance evaluation indexes (accuracy, recall and F1 value) of the method in this paper are 92.17%, 89.34% and 90.46%, respectively. The accuracy rate of all the execution results is higher than 80%, which is feasible and accurate and effectively improves the stability and safety of grid dispatch operation.

In this paper, we present a new convolution form based on dictionary learning and study its sparse version based on Convolution Dictionary Learning (CDL). An effective algorithm for learning sparse convolution features is proposed by combining Alternating Direction Multiplier Method (ADMM) and Fast Iterative Shrinkage Threshold Algorithm (FISTA). Through numerical experiments, we show that the proposed algorithm can not only lead to faster convergence speed, but also produce better sparse features.

Air pollution refers to the release of pollutants into the atmosphere that is harmful to human beings and even the entire planet. It can be labeled as one of the most dangerous threats that humanity ever faced. As society progresses, the population surges rapidly accompanied by energy consumption increasement. Globally, especially in some developing countries, factory exhaust and tail gas emissions have also risen, causing more toxic pollution. In this case, it is possible to investigate air quality conditions and predict future trends based on machine learning methods. On this basis, scientific prevention suggestions can be provided for environmental monitoring departments to effectively control air pollution, prevent residents from being exposed to noxious pollutants, and alleviate global warming. This paper applies regression models, classification models, and time series models to predict the trend of air quality index through the content of chemical components in the atmosphere. According to the analysis, random forest regression and LSTM perform best in predicting AQI in India, whose R² is 0.906 with accuracy 97.3%. The outcomes have a certain reference value to improve the accuracy of air quality prediction.

In order to deeply understand the refining materials of ancient lead-barium glass and high-potassium glass and determine the sub classification of lead-barium glass, this paper establishes the sub classification analysis model of K-means++clustering and decision tree algorithm. Taking the lead-barium glass as an example, the polymerization coefficient cluster category broken line diagram was first made, from which the polymerization coefficient was determined to be two, that is, the lead-barium glass was divided into two subcategories. Then, the chemical components are analyzed with grey correlation degree, and the components with the highest correlation degree are determined as SiO2, BaO, PbO and SrO. Then visual processing was carried out, and it was found that the content of SiO2 and BaO in the two subclasses was significantly different. The same method is used to analyze the high-potassium glass. The high-potassium glass is also divided into two subgroups, and the contents of PbO and CaO in the two subgroups are quite different. Finally, using the clustering results to train the decision tree model to analyze whether the sub classification is accurate, and found that the division is accurate.

In order to find out the degree to which communication equipment can be put into combat use and maintained in a near real combat environment, the evaluation of the operational applicability of communication equipment in service is carried out. This article comprehensively analyzes the key elements of operational suitability assessment and builds a parameter index system for combat suitability assessment. In order to avoid the defects of the subjective and objective weighting methods, a variety of subjective and objective weighting methods are introduced to determine the subjective and objective weights respectively. On this basis, the game theory is introduced to optimize the subjective and objective weight coefficients, and the comprehensive weight of the operational applicability parameters is determined, and a reasonable operational applicability evaluation conclusion is obtained, which provides a guarantee for the equipment participating in the test to better serve in the army.

With the continuous advancement of the rural revitalization strategy, the rural IP plays an important role in the development of rural tourism and local industries. The extraction of rural IP visual elements depends on the public perceptual priority of rural unique cultural resources, but in China, the Perceptual priority research of rural IP visual elements has not attracted enough attention. This paper discusses the difference of the influencing factors of the perceptual priority of rural IP visual elements, the preconditions of one-way ANOVA were verified by independent t test and Kruskal-Wallis test, and the significant difference in the degree of gender and age were obtained by one-way ANOVA .The results indicated that (1) gender is the main factor influencing the perceptual priority of rural IP visual elements, and different genders and age match the perceptual priority of rural IP visual elements in the top three; (2) females have a higher perceptual priority of rural IP visual elements than males; (3) older (over 50 years) have a higher perceptual of rural IP visual elements than younger (20-30 years). In conclusion, the public prefers natural elements to rural IP visual elements, and it is greatly influenced by gender factors, which can be applied to the practice of realizing rural IP design.

Aiming at the problems of difficult parking, chaotic charging and low management efficiency in some small and medium sized parking lots, this paper designs a parking lot management system based on Linux online recognition. This design uses the cross-platform C++ graphical user interface library QT as the development tool. Firstly, the vehicles at the entrance and exit of the parking lot are monitored in real time through image sensors. Secondly, the license plate recognition technology is used to realize the identification of vehicles at the entrance and exit of the parking lot. In addition, this paper uses the QSqlite database to store the relevant data information of the parking lot, and finally transplants the system to the STM32 system for operation. In short, the intelligent and automatic management mode is realized, which effectively improves the operation efficiency of the parking lot.

E-commerce can provide the strong new impetus for the development of China's economy. In this sense, it is the crucial advantage to analyze the economic links and the mechanisms of action between the e-commerce and the rest of the economy, deciding e-commerce's impact on China's economic growth. However, the previous literature has not empirically analyzed to what extent and how e-commerce effects China's other economic sectors within a whole economic structure from a perspective of the economic linkages and the impact mechanisms. One of the major reasons in not yet carrying out the above research is the lack of a complete database which shows a complex economic structure including e-commerce. Another important reason is that the conventional analysis' methods can’t better describe how an impact of an initial exogenous injection runs within a complex economic structure. To overcome these issues, this paper creates a new complete economic database through innovative using social accounting matrix framework based on China's e-commerce.

The Titanic shipwreck, of the estimated 2,224 passengers and crew aboard, more than 1,500 died, making it the deadliest sinking of a single ship up to that time. It remains the fatal peacetime sinking of a super liner ship. While some elements of luck are involved in surviving, research on understanding what impacts people’s survival or death is crucial. In this study, the utilized dataset includes various features, and we attempt to determine the correlation among these features. Also, various machine learning algorithms are proposed to predict the survival of passengers.

The main purpose of this research is to observe the relationships between physicochemical properties (inputs) and sensory (output) variables in the practice of data exploring and analysis. Primarily, this paper investigates the correlation between physicochemical properties with the red wine quality. After fundamental analysis, the classification models of the red wine quality are constructed including KNN, XGB, SVC, and Random Forest models. Based on the evaluation metrics, the Random Forest model has the highest accuracy eventually. It is concluded that the concentrations of sulphates and alcohol have positive influences on red wine quality while lowering the concentration of volatile acidity can also increase the quality. According to the outputs of the models created, the Random Forest produced the best performance according to the accuracy, precision, and f1-score values. The main purpose of this paper is to perform scientific observations of red wine quality based on 11 physicochemical data. These results shed light on the physicochemical that are having a positive correlation with the increment of red wine quality, which provides instrumental suggestions for producing high-quality red wine.

The rapid development of e-commerce has prompted the emergence of massive product reviews. Quickly grasping the emotional tendencies in e-commerce product reviews has become an effective way for businesses to improve their problems. Therefore, based on Bert and GUP, the research proposes Bert BiGRU deep learning algorithm, and verifies its effectiveness in the emotional analysis of e-commerce product reviews. The experimental results show that Bert BiGRU algorithm has the highest accuracy of 95.51% among the seven algorithms. In addition, Bert BiGRU algorithm has achieved good training effect after only eight times of training. In addition, Bert BiGRU algorithm has been used in practice to analyze the emotional tendencies in four mobile phone reviews, which shows high reliability. In a word, Bert BiGRU algorithm has high performance and low cost. It has high practicability in the emotional analysis of e-commerce product reviews.

Convolution is a mathematical method of integral transformation and an important operation in analytical mathematics. The concept of convolution can also be extended to sequence, measure and generalized functions. Convolution has many applications in mathematics and engineering. For example, it can be widely used as a linear operation in image filtering, digital signal processing, electronic engineering and other application scenarios such as fluid mechanics, statistics, physics, astronomy, thermodynamics, optics. It plays a very key role in such important disciplines as it is a very common tool to help people solve problems. This paper gives a more detailed description of continuous convolution, discrete convolution and two-dimensional convolution through formula method and definition method, and points out their common application fields, and then discusses discrete convolution and continuous convolution through common scenes in life which is the simple explanation. The deep application of convolution in the signal field and Fourier transform field is sorted out. Finally, combining with the background of the era of big data, the multi-faceted applications of the future convolutional network neural field are discussed.

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic.

The neural network pruning algorithm reduces the computation and complexity of neural networks effectively. But the pruning scope is always difficult to define. The existing algorithms perform polarization operations on the scale factors, then the threshold is selected, and the pruning range is determined. However, there are still many candidate points in the selection of the threshold, and it is difficult to determine the optimal solution from them. Aiming at the above problems, this paper proposes an improved neural network pruning algorithm based on contrast loss and simulated annealing to determine the selection of threshold. Firstly, different training data from the same dataset are used to train multiple networks. The goal is to allow different data to improve the generalization ability of the network. Then, each network chooses its own pruning threshold at the same layer. When selecting the threshold, the stationary point close to zero among the candidate points is selected as the candidate set of pruning threshold. Different threshold values are used in this range to obtain the corresponding pruning structure. Next, these structures are compared and the pruned structure corresponding to the smallest difference in neural network structures is kept. Experiments on CIFAR-10 and MINIST datasets show that the proposed method outperforms the existing algorithms on accuracy, pruning rate, model storage and running time.

At the time mobile devices and online payment make people’s life more convenient, they caused an increasing number of fraud cases in recent years. In this paper, we represented trading data as graphs by graph machine learning and setting up a high-performance model which could detect fraudulent transactions automatically. The datasets that the paper used were the fraudulent transactions dataset on Kaggle’s credit cards. By random under-sampling, it was processed and shown as bipartite graphs which were substituted into our training models after being processed by graph embedding algorithm. Finally, the optimal model was found by the coming out results. The result reveals that average embedder algorithm could detect fraud more precisely than the other three algorithms.

Multi-objective optimization problem is a very common problem in mathematical modeling and daily research [1]. It belongs to the planning problem, and its ultimate goal is to find the optimal solution under certain constraints [2]. In order to solve the multi-objective programming problem, we usually use the multi-objective programming function in MATLAB, or take the traditional intelligent algorithm to solve, but the traditional algorithm is easy to fall into local optimal solution, and the error is large, which cannot meet the actual needs [3]. Therefore, this paper proposes an improved genetic algorithm, that is, non-dominated sorting genetic algorithm (NSGA-II) to solve multi-objective optimization problems, in order to better meet the research and practical needs [4]. In order to verify its superiority, this paper uses two examples, by comparing the results of the traditional algorithm and the improved genetic algorithm, using the comprehensive evaluation method to analyze, and finally draws the conclusion that the improved genetic algorithm is superior to the traditional algorithm.

Given a set of facilities, or servers S = { s₁, s₂, . . ., s_m} and a set of requests R = { r₁, r₂, . . ., r_n} with each server s_i having the serving capacity of c_i and the assignment cost d_ij occurred if the request r_j has been assigned to server s_i , the online facility problem under our consideration is to find an assignment for a new coming request r_j to a server before the next one arrived, such that in the end each s_i serves exactly c_i requests and such that the total occurring cost ∑ ⁿ _j=1 {d_ij|r_j is assigned to s_i} is minimized. This paper considers a constrained version of online facility problem (COFP, for short) in which c_i = 2 for all i = {1, 2, . . ., m} and d_ij = 2 ^|j-i| for all i , j. First, we obtain the optimal assignment for the offline version of COFP by exploiting a nice property of powers of two and the so-called the natural order assignment. Second, we design an online algorithm for COFP is based on the optimal assignment for the offline version of COFP. We show that our online algorithm runs in linear time and always achieves the optimal assignment for the COFP.

As a basic subject in cuniversities, advanced mathematics education has become more and more important, and with the rapid improvement of message technique, the application of message technique in advanced mathematics education has become a general trend. The traditional higher mathematics education methods are outdated, the education efficiency is low, and the graphics display effect is not ideal. With the improvement of science and technique and the advent of the modern electrical age, education methods need to advance with the times. It is particularly important to explore a education method that can organically combine the traditional mathematics education mode with modern message technique, unify the practicability and feasibility of education and education, which can not only facilitate teachers' education but also help students to improve their learning effects. This paper is based on the collaborative filtering algorithm. In the personalized recommendation system, more core is the recommendation algorithm. Collaborative filtering is the most familiar and widely used recommendation algorithm.

Reversi (or Othello) is a simple and popular board game played on an eight-by-eight board. In the field of reinforcement learning, searching of the game tree of Reversi is widely studied as a classic problem, since it has a small board and thus a state space not too complex to analyze. Monte Carlo tree search (MCTS) is a heuristic search algorithm for decision tree search, which is often applied to the AI methods for board games, such as the application of AlphaGo in the field of Go games. We modify and apply the Monte Carlo tree search strategy to Reversi AI. Applying some engineering optimizations (such as multithreading), we achieve significant results with high time efficiency.

With the popularization and rapid development of the internet, the negative impact caused by negative comments is exacerbated. The increasingly common negative comments on various video websites and social networking sites have exercised a malign influence on public opinion and caused adverse social consequences. This paper uses LightGBM to establish a high-performance negative comment prediction model. The data set selected in this paper is from the Internet movie database IMDB, in which positive and negative comments have been marked for our training model. After Bag of Words processing and TF-IDF processing of the dataset and substituting it into our training model, the researchers obtained the final model with an accuracy of more than 95%, which has a more satisfactory performance compared with the classical integrated model and the traditional binary classification model. Thus, it can be seen that this model has an advantage in identifying spiteful comments online.

This article is based on the principle of the mutual influence of the regional economy and logistics, and then through the construction of a system dynamic model for the development of logistics and local GDP, using the VENSIM computer software to analyze the panel in a certain area from 2011 to 2020. The data realizes simulation analysis, and then through the comparison of the simulation data, the policy experiment is studied from the aspects of the relationship between local logistics and local economic development, the natural growth coefficient of logistics development, and related industrial policies, and the conclusion is drawn from the experimental results: regional economy, logistics supply and demand show a positive relationship with the natural growth system of logistics, and the regional economic development environment and the entire local logistics system together form a dynamic coupling system. If the regional industrial structure develops in the direction of a well-developed "Three Two One" industrial structure At this time, the proportion of logistics supply and demand will be reduced to a large extent, so that the increase in logistics demand in the region will be activated to meet the rapid growth of economic needs; if the total economic volume of a region increases by 10% , then the logistics demand in the region will grow rapidly in the next few years, and the growth rate is basically the same as that of the regional economy.

This study aimed to identify the impact of bedtime in the digital age on executive function among college students. A total of 575 college students took part in the study, which used a series of self-reported questionnaires to assess sleep habits, sleep quality, executive function, and emotional state. SPSS 26 was used to perform the reliability analysis, correlation analysis, and stepwise multiple regression analysis. PROCESS 3.3 was used to examine the mediation model and moderation model between bedtime and executive function. The results revealed that bedtime had a significant positive association with executive function. The effect of bedtime on executive function was mediated by sleep quality and physical activity and was moderated by negative emotion. Taken together, this study establishes that reduced sleep quality and physical activity explain the influence of late bedtime on executive function. And with lower-level negative emotion, the executive function would be less affected by late bedtime.

For a long time, the problem of power transmission and transformation projects in China is common, which not only affects the investment benefits of power grid enterprises, but also affects the future operation and management, so the control of the project cost is of great significance. Cost evaluation is an important means of investment control of power transmission and transformation projects. The quality level of evaluation will directly affect the effect of cost control, and the efficiency and benefit of project investment. Therefore, this paper puts forward the cost evaluation and optimization technology of power transmission and transformation projects based on data mining, which provides reference and guidance for improving the cost evaluation quality of power transmission and transformation projects and improving the investment efficiency level of power grid enterprises.

Reflected in the momentum effect on the price of the derivatives market, stock returns continue the original direction in price trends, which is different from the uncertainty of risk, the premium market volatility of volatility. Based on the formula established under the mathematical framework, the empirical evidence and explanation of volatility clustering are given through the linear regression analysis of system risk premium and behavioral finance overreaction and other factors. This research discovered that factors from the system risk premium were more persuasive, while factors from behavioral finance, do not exhibit convincing evidence.

Road freight plays an increasingly important role in economic and social system. According to the statistics, it undertakes 75% of the total freight volume in 2021, which is one of the important sources of urban economic development. It is one of the important sources of urban economic development. Aiming at the research on the coordinated development of road freight transport and regional economy, this paper establishes an improved model for the analysis of road freight transport and economy correlation characteristics based on the dynamic operation data and statistical data of freight transport vehicles and uses the theories of agglomeration and connection index. Taking the Yangtze River delta as an example, this paper uses multi-source data such as dynamic track and statistical data of heavy cargo vehicles, analyzing the road freight and economic characteristics among Shanghai, Anhui, Zhejiang and Jiangsu, and exploring the relationship between kilometer freight and economic development in the Yangtze River delta region.