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THE APPLICATION OF COBB-DOUGLAS FUNCTION IN FORECASTING THE DURATION OF INTERNET PUBLIC OPINIONS CAUSED BY THE FAILURE OF PUBLIC POLICIES

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Journal of Systems Science and Systems Engineering 2018年第5期27卷 632-655页

作者： Xuefan Dong Ying Lian Ding Li Yijun Liu Institutes of Science and Development Chinese Academy of Sciences Beijing 100090 China University of Chinese Academy of Sciences Beijing 100190 China Institute of Development Southwest University of Finance and Economics Chengdu 610074 China

Gushes of Internet public opinions may trigger unexpected incidents that significantly affectsocial security and stability, especially for ones caused by the failure of public policies. Therefore,forecasting this kind of Interact public opinions is of great significance. The duration could be citedas one of the most direct indicators that can reflect the severity of a specific Internet public opinioncase. Based on this background, this paper aims to find the factors that may affect the duration of Internet public opinions, and accordingly proposes a model that can accurately predict the durationbefore the release of public policies. Specifically, an index system including 8 factors by consideringfour dimensions, namely, object, environment, reality （offline）, and the network （online）, isestablished. In addition, based on the dataset containing 23 typical Internet public opinion casescaused by the failure of public policies, 9 prediction models are gained by applying the multivariatelinear regression model, multivariate nonlinear regression model, and the Cobb-Douglas function.

关键词： Public policy internet public opinion multivariate linear regression model multivariatenonlinear regression model Cobb-Douglas production function

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Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

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Forest Ecosystems 2018年第4期5卷 400-411页

作者： ilker Ercanli Alkan Gunlu Muammer Senyurt Sedat Keles Faculty of Forestry Cankin Karatekin University18200 CankiriTurkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

关键词： Leaf area index multivariate linear regression model Artificial neural network modeling Crimean pine Stand parameters

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Analysis of the effect of unemployment insurance on enterprise management

Analysis of the effect of unemployment insurance on enterpri...

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2013年教育技术与管理科学国际会议

作者： WEI Rui-qing Inner Mongolia University of Finance and Economics

Unemployment insurance can provide a certain level of economic subsidies for the temporarily unemployed workers to ensure their basic survival needs. The unemployment insurance becomes a welfare system with protection function of enterprise salary system, so majority of workers will focus on the payment of the social insurance in the enterprise, such as unemployment insurance. This paper establishes an unemployment insurance enterprise management forecast model to solve the complex problems such as the influence of unemployment insurance payment on enterprise management (current employees’ recruitment, welfare systems design etc.). It takes candidates and potential candidates in the labor market as the main objects, the survey method to obtain the sample data, and multiple linear regression model to analyze the impact of unemployment insurance on corporate recruitment, corporate consolidation competitive. The results show that the unemployment insurance can significantly improve the competitiveness of enterprises in the labor market and the attractiveness of the candidates, and enhance the corporate external image while bringing far lower economic pressure than the invisible receipts enterprises obtained.

关键词： Unemployment insurance Human Resource Management Recruitment multivariate linear regression model

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