The Mediating Role of Perceived Stress and Academic Procrastination between Physical Activity and Depressive Symptoms among Chinese College Students during the COVID-19 Pandemic.

Depressive symptoms, a prevalent mood illness, significantly harm college students' physical and mental health. Individuals have experienced some degree of psychological harm as a result of the COVID-19 pandemic. Taking this into account, the purpose of this study was to investigate the relationship between physical activity (PA) and depressive symptoms among college students during the COVID-19 pandemic, as well as the mediating roles of perceived stress and academic procrastination. A total of 586 college students were subjected to the Physical Activity Scale (PARS-3), the Perceived Stress Scale (PSS-10), the Procrastination Assessment Scale-Students (PASS), and the Patient Health Questionnaire (PHQ-9). Findings from this research demonstrated that there was a significant positive correlation between perceived stress, academic procrastination, and depressive symptoms, while PA was significantly negatively correlated with perceived stress, academic procrastination, and depressive symptoms. The results of the chain mediation analysis showed that PA had a significant direct effect on depressive symptoms. Perceived stress, academic procrastination, and perceived stress-academic procrastination had significant mediating and chain mediating effects on the relationship between PA and depressive symptoms. In conclusion, PA among college students during the COVID-19 pandemic affects their depressive symptoms directly and indirectly through the independent mediating effect of perceived stress and academic procrastination, as well as the chain mediating effect of perceived stress and academic procrastination.

Improved 3D U-Net for COVID-19 Chest CT Image Segmentation

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. The rapid and accurate automatic segmentation of COVID-19 infected areas using chest computed tomography (CT) scans is critical for assessing disease progression. However, infected areas have irregular sizes and shapes. Furthermore, there are large differences between image features. We propose a convolutional neural network, named 3D CU-Net, to automatically identify COVID-19 infected areas from 3D chest CT images by extracting rich features and fusing multiscale global information. 3D CU-Net is based on the architecture of 3D U-Net. We propose an attention mechanism for 3D CU-Net to achieve local cross-channel information interaction in an encoder to enhance different levels of the feature representation. At the end of the encoder, we design a pyramid fusion module with expanded convolutions to fuse multiscale context information from high-level features. The Tversky loss is used to resolve the problems of the irregular size and uneven distribution of lesions. Experimental results show that 3D CU-Net achieves excellent segmentation performance, with Dice similarity coefficients of 96.3% and 77.8% in the lung and COVID-19 infected areas, respectively. 3D CU-Net has high potential to be used for diagnosing COVID-19. [ABSTRACT FROM AUTHOR] Copyright of Scientific Programming is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)