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Appetitive traits have been associated with eating behaviors and weight in youth. However, the relationships among adults have received less attention. In this study, we measured appetitive traits among 520 female college students from a University in eastern Chinese using the Chinese translated Adults Eating Behavior Questionnaire (C-AEBQ) and found that two food approach traits (food enjoyment and emotional over-eating) were positively while two food avoidance traits (slowness in eating and satiety responsiveness) were negatively associated with BMI. Individual trait was differentially associated with eating behaviors. Specifically, a positive correlation was found between food responsiveness and higher frequency of selecting delivery food, spicy food, and sugared beverage; conversely, satiety responsiveness was associated with higher frequency of skipping meals. Using latent profile analysis, participants were classified into four appetitive patterns: food approacher, food approacher with emotional under-eating, food avoider, and food avoider with emotional over-eating. Food avoiders had significant lower BMI and higher proportion of underweight than other three patterns. Compared to food approacher, food avoiders had higher frequency of skipping breakfast, and food avoiders with emotional over-eating had higher frequency of skipping both breakfast and lunch. No significant differences were observed in food choice and dieting behavior among four appetitive patterns. The findings suggested a correlation between an individual’s appetitive pattern and eating behaviors among female college students, those with food avoidance patterns are more likely to have irregular meals, which may contribute to their low BMI. The findings provide valuable insights for female college students to cultivate a healthy eating behavior and sustain a healthy body weight.

In recent decades, the study of corporate social responsibility (CSR) has become one of the most popular research topics. In the markets of China, CSR has also attracted much attention from either local corporations or multi-national business. They have contributed many efforts to put CSR into effect, which helps CSR to be more practical and influential. By reviewing existing literature, this article mainly focuses on the environmental problems caused by lack of corporate social responsibility in China, which has led to the following research on CSR performance in China and customer view of CSR. The conditions of practicing CSR in China are researched first, and the attitudes of people towards CSR is tested empirically by a questionnaire using quantitative and qualitative research methods. Ultimately, the valid results collected were analyzed in detail along with a few suggestions. This article may help identify some surrounding problems of CSR and advance CSR practice slightly.

Diameter distribution is used to predict stand stock, timber volume and stand yield in most forest management. In the paper, opulus shelterbelts in Boai County were analyzed. A model to predict stand diameter distribution was constructed with artificial neural network(ANN) approach by using the average stand diameter, the coefficient of variation of diameter as well as relative diameter as input variables, and cumulative frequency of tree number as output variables. The structure of the optimum model was 3∶11∶1 and the total fitting accuracy was 98.18 %. With respect to the model, the max, min and average fitting accuracy of the accumulated frequency could be calculated, which is 99.93 %, 88.48 % and 98.20 %. The corresponding prediction accuracy was 99.70 %, 94.36 % and 97.56 %, which has a similar characteristic to that of fitting accuracy. As in forestry practice, a model whose average accuracy is 95% is reliable enough to meet the practice request. Consequently, it can be concluded that the model works quite well and ANN approach can be used to perform the nonlinear systems such as the complicated stand diameter distribution.