Exploration of the Impact Mechanism of Government Credibility Based on Variable Screening Method

Government credibility is an important asset of contemporary national governance, an important criterion for evaluating government legitimacy, and a key factor in measuring the effectiveness of government governance. In recent years, researchers’ research on government credibility has mostly focused on exploring theories and mechanisms, with little empirical research on this topic. This article intends to apply variable selection models in the field of social statistics to the issue of government credibility, in order to achieve empirical research on government credibility and explore its core influencing factors from a statistical perspective. Specifically, this article intends to use four regression-analysis-based methods and three random-forest-based methods to study the influencing factors of government credibility in various provinces in China, and compare the performance of these seven variable selection methods in different dimensions. The research results show that there are certain differences in simplicity, accuracy, and variable importance ranking among different variable selection methods, which present different importance in the study of government credibility issues. This study provides a methodological reference for variable selection models in the field of social science research, and also offers a multidimensional comparative perspective for analyzing the influencing factors of government credibility.

晋东南传统民居装饰构件的营造特征——以泽州县南峪村为例

随着传统民居建筑研究的深入,当前对于传统民居建筑缺乏跨学科的交叉研究,尤其对于建筑装饰构件的研究多集中于人文社科的分析,缺乏从科学的角度进行定量分析研究。而在研究营造技术时只谈及技术层面,没有深入联系地域艺术表现。因此,将自然科学与社会科学相融合,并基于统计分析软件SPSS分析研究传统民居装饰构件的营造特征,可促进提升传统民居营造技术研究的方法及科学性。通过对南峪村传统民居装饰构件的分类调研分析以及科学计算,利用Pearso相关分析和线性回归分析传统民居装饰构件的营造特征,找出了建筑营造技术与装饰艺术之间的关系,并建构了数学模型。结果表明:中国民居简朴而注重实际,建筑的本质着眼于实际功能。因此装饰构件的产生同样着眼于“实用”或“适用”。它们在最初产生时都有着各自的功能,有的还沿用至今;装饰构件几乎都是房屋的维护构件,而主要的垂直受力构件如木柱和房梁几乎不施加装饰;建筑装饰有着一般发展规律:随着营造技术和材料技术的发展,从实用性的构件到构件装饰性增强,功能性减弱,但构件仍然保留着维护之用。

A Novel Method for Solving Ordinary Differential Equations with Artificial Neural Networks

This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In particular, we employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but bypass the standard back-propagation algorithm for updating the intrinsic weights. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial or boundary conditions and contains no adjustable parameters. The second part involves a feed-forward neural network to be trained to satisfy the differential equation. Numerous works have appeared in recent times regarding the solution of differential equations using ANN, however majority of these employed a single hidden layer perceptron model, incorporating a back-propagation algorithm for weight updation. For the homogeneous case, we assume a solution in exponential form and compute a polynomial approximation using statistical regression. From here we pick the unknown coefficients as the weights from input layer to hidden layer of the associated neural network trial solution. To get the weights from hidden layer to the output layer, we form algebraic equations incorporating the default sign of the differential equations. We then apply the Gaussian Radial Basis function (GRBF) approximation model to achieve our objective. The weights obtained in this manner need not be adjusted. We proceed to develop a Neural Network algorithm using MathCAD software, which enables us to slightly adjust the intrinsic biases. We compare the convergence and the accuracy of our results with analytic solutions, as well as well-known numerical methods and obtain satisfactory results for our example ODE problems.

The SPSS-Based Analysis of an English Mid-term Test ——Take Grade Eight Students for Example

Based on some language testing theories, an analysis of an English Mid-term Examination of grade eight students of Jingzhi Middle School is made in this paper. By means of SPSS （Statistical Package for the Social Sciences） statistical software, the paper makes a whole analysis of the test paper, which covers descriptive statistics, reliability, and validity. The study aims to find some problems of the test paper, teachers＇ teaching, and students＇ learning. Thus, based on the results of analysis, this paper aims to improve the quality of test papers and give some advice to language teaching

The Application of SPSS in Statistical Analysis Project-A Study of the Inheritance of Chinese Traditional Shadow Play in Schools

Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from people’s sight.As the earliest statistical analysis software,Statistical Package for the Social Science(SPSS)is comprehensive in analyzing and managing statistical data.This study explores the application of SPSS in minimizing the workload of researchers while raising the validity of data in supporting the analysis of the survey data which reflected the inheritance and development of Chinese traditional shadow play in schools.

SEIR-SW, Simulation Model of Influenza Spread Based on the Small World Network

This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR（Susceptible-Exposed-Infected-Removed）, through extensive simulations of the effects of social network on epidemic spread in a Small World（SW） network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.

Anomaly traffic detection of database network based on flow statistical features

The traditional intrusion detection system has the problem of high false positive rate and false negative rate.This paper deeply analyzes the differences of statistical features between single-flow and multi-flow on the database network,and presents a group of features that are easy to acquire and can be used to detect the anomaly in database network efficiently.By applying this group of features in Fisher algorithm for anomaly detection,the false positive rate and false negative rate are dramatically reduced.Simultaneously,the model made by using the group of features has the advantages of low algorithm complexity,good detection result and strong generalization ability.Experimental results show that there is higher accuracy when using the features of single-flow and multiflow to construct the anomaly detection model than only using single-flow features.