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Objective To provide evidence for setting up violence intervention programs in rural middle schools, through studying the influential factors. Methods Taking variables including emotional, physical and sexual violence in the past year as the multi-dependent variables before multivariate multilevel model logistic regression model was adopted to analyze the correlations among the three kinds of violence and the influential factors. Results Among 3620 respondents, the incidence rates of emotional, physical and sexual violence weres 21.5%, 24.3% and 2.0% respectively. The correlation coefficients between emotional violence vs. physical violence, emotional violence vs. sexual violence, physical violence vs. sexual violence were 0.337,0.133, 0.131 respectively when the random effect of class difference was separated by multivariate multilevel model. There was an internal aggregation of the incidence rate on physical violence in different grades (X2=4.286, P=0.038) and an internal relevant between emotional violence vs. sexual violence (X2=4.239, P=0,039), physical violence vs. sexual violence (X2=4.482, P=0.034). The influential factors on the incidence rates of violence would include:sex, smoking status, family without harmony, tendency of bullying others and the level on self-esteem etc. Conclusion When the random effect of class difference was separated by multivariate multilevel model, the estimated results would be more precise. Other than paying more attention to both individual and family influential factors when taking measures to reduce the incidence rate of violence in high school students, the effect of environment in the class should not be ignored.
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To predict the occurrence ofposttraumatic stress disorder (PTSD),using a Support Vector Machine (SVM) on adults in flood district.Diagnostic and Statistical Manuals on Mental Disorders (IV Edition) were used to examine and diagnose the victims in flood districts.Based on the forecasting model of SVM with PTSD as dependent variables and 23 influence factors of PTSD as independent variables,prediction of PTSD was conducted among the victims.After considering 23 influence factors into the prediction model,the agreement rate of prediction of the model was 88.05 percent,with sensitivity as 75.0 percent,and specificity as 89.4percent.Conclusion: The prediction model based on SVM with 23 influence factors had good effect on predicting the occurrence of PTSD.
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Through analyzing the influencing factors of congenital heart disease (CHD), it is aimed to establish CHD risk prediction model in fetus, and simultaneously provide theoretical foundation for CHD prevention. One-factor logistic regression method was used to screen the significant factors regarding CHD, and to separately adopt multiple-factor non-conditional logistic regression method and decision tree to set up model prediction fetus CHD risk and to analyze the advantages and shortcomings. Correct classification rates turned to be 80.93% and 82.79% respectively among 215 'training samples' by the two methods and the rates were 85.45 % and 89.09% respectively among 55 'testing samples'. The alliance of logistic regression and decision tree can overcome influence by co-linearity to guarantee the accuracy and perfection, as well as promoting the predictive accuracy.
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<p><b>OBJECTIVE</b>To identify the volatile components in rat urine after oral administration of "Wu-Hu-Tang" (WHT).</p><p><b>METHOD</b>GC-MS technique was applied to analyzing urine samples.</p><p><b>RESULT</b>Eighteen components were detected in the WHT-treated rat urine other than the corresponding control. Among them, 14 components were identified, and 7 were also found in the extract of WHT.</p><p><b>CONCLUSION</b>The above detected components might be derived from WHT, and some of them are effective components of WHT.</p>