Search

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.

Rice disease detection is of great significance to rice disease management. It is difficult to identify the rice leaves with different colors in different disease periods by RGB image and without aided eyes. Traditional equipment and methods are relatively inefficient in meeting the needs of current disease detection. The accurate and efficient detection the infected areas from hyperspectral images has become a primary concern in current research. However, current spectral target detection research pays less attention to the time and computing resources consumed by detection. A disease detection method based on random forest (RF) and adaptive coherence estimator (ACE) is proposed here. Firstly, based on the spectral differences between diseased and healthy leaves, 18 characteristic spectral wavelengths with the highest importance were selected by an RF algorithm, and the spectral images of those characteristic wavelengths were synthesized. Then, the ACE model was established for the disease recognition of full wavelength spectral images, characteristic wavelength spectral images, and RGB images. At the same time, three other familiar target detection methods were selected as the control experiments. The detection results showed a similarity between the detection performance of the four detection methods for full wavelength spectral image and characteristic wavelength spectral image. This detection performance was higher than that of the RGB image, indicating that characteristic wavelength spectral image can replace full wavelength spectral image for disease detection. The detection performance of the ACE algorithm was better than other algorithms. The detection accuracy of 18 characteristic wavelengths was 97.41%. Compared with the hyperspectral full wavelength image detection results, the accuracy decreased by 1.12%, and the detection time decreased by 2/3, which greatly reduced the detection time. Based on these results, the target detection method combining the RF algorithm and the ACE algorithm can effectively and accurately detect rice bacterial blight disease, which provides a new method for automatic detection of plant disease in the field.

It has been more than two years since the outbreak of the COVID-19 epidemic at the end of 2019. Many scholars have introduced the “resilience” concept into COVID-19 prevention and control to make up for the deficiencies in traditional community governance. This study analyzed the progress in research on social resilience, which is an important component of community resilience, focusing on the current literature on the impact of social resilience on COVID-19, and proposed a generalized dimension to integrated previous relevant literature. Then, VOSviewer was used to visualize and analyze the current progress of research on social resilience. The PRISMA method was used to collate studies on social resilience to the pandemic. The result showed that many current policies are effective in controlling COVID-19, but some key factors, such as vulnerable groups, social assistance, and socioeconomics, affect proper social functioning. Some scholars have proposed effective solutions to improve social resilience, such as establishing an assessment framework, identifying priority inoculation groups, and improving access to technology and cultural communication. Social resilience to COVID-19 can be enhanced by both external interventions and internal regulation. Social resilience requires these two aspects to be coordinated to strengthen community and urban pandemic resilience.