The current study discusses the social and psychological studies conducted on the concept of the social responsibility, as well as the symbolic interactive trend as a theoretical basis for interpretation; moreover, di...The current study discusses the social and psychological studies conducted on the concept of the social responsibility, as well as the symbolic interactive trend as a theoretical basis for interpretation; moreover, discussing the social responsibility in the light of family variables. The aim of this study is to investigate the family variables, including domicile, parents＇ education, their age, the type of work they do, family size, and family income. As to the social responsibility＇s dimensions for sample selected for this study; hence, it includes personal, ethical, national, environmental, health, and social responsibility. The current study adopted the descriptive approach along with a demographic data form and the social responsibility＇s scale. The sample selected consisted of 330 female students from various colleges within King Abdulaziz University. The results indicated that there was an impact on all the variables, thus such impact was in favor of those living in rural areas as to the ethical dimension and in favor of urban people as to the dimensions of accountability, parents＇ level of education, parents＇ seniority, working mothers, greater family size, and higher income.展开更多
A new customization approach based on support vector regression （SVR） is proposed to obtain individual headrelated impulse response （HRIR） without complex measurement and special equipment. Principal component ana...A new customization approach based on support vector regression （SVR） is proposed to obtain individual headrelated impulse response （HRIR） without complex measurement and special equipment. Principal component analysis （PCA） is first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the measured anthropometric parameters. Compared with a back-propagation neural network （BPNN） for nonlinear regression, better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR algorithm.展开更多
文摘The current study discusses the social and psychological studies conducted on the concept of the social responsibility, as well as the symbolic interactive trend as a theoretical basis for interpretation; moreover, discussing the social responsibility in the light of family variables. The aim of this study is to investigate the family variables, including domicile, parents＇ education, their age, the type of work they do, family size, and family income. As to the social responsibility＇s dimensions for sample selected for this study; hence, it includes personal, ethical, national, environmental, health, and social responsibility. The current study adopted the descriptive approach along with a demographic data form and the social responsibility＇s scale. The sample selected consisted of 330 female students from various colleges within King Abdulaziz University. The results indicated that there was an impact on all the variables, thus such impact was in favor of those living in rural areas as to the ethical dimension and in favor of urban people as to the dimensions of accountability, parents＇ level of education, parents＇ seniority, working mothers, greater family size, and higher income.
基金Project supported by the Shanghai Natural Science Foundation (Grant No.08ZR1408300)the Shanghai Leading Academic Discipline Project (Grant No.S30108)
文摘A new customization approach based on support vector regression （SVR） is proposed to obtain individual headrelated impulse response （HRIR） without complex measurement and special equipment. Principal component analysis （PCA） is first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the measured anthropometric parameters. Compared with a back-propagation neural network （BPNN） for nonlinear regression, better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR algorithm.
