ObjectivesRecent surveys have paid insufficient attention to the psychological status of medical residents, but medical residents, as a special group of medical workers, need to be focused on. This study aimed to investigate medical residents' levels of social support, psychological resilience, and coping style, and explore the mediating role of psychological resilience, which can ultimately provide a new theoretical basis for improving medical residents' psychological status and quality of work and life.MethodsA total of 577 medical residents from China were investigated by an online questionnaire, using convenience sampling. Associations between social support, psychological resilience, and coping styles were assessed using Pearson correlation analysis. The mediating effect of psychological resilience was tested using SPSS Process.ResultsPositive correlations between social support, psychological resilience and coping style were found (r = 0.474, P < 0.001; r = 0.473, P < 0.001; r = 0.353, P < 0.001). The mediating effect of psychological resilience in the relationship between social support and coping style was significant (95% CI: 0.168, 0.384), and accounted for 25.84%.ConclusionAttention should be paid to the psychological status of medical residents, and social support and psychological flexibility can be used to increase the enthusiasm for their coping style and promote their mental health.

No abstract

No abstract

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

The torsional low strain integrity test (TLSIT) is now regarded as the most potent alternative to the longitudinal wave test for the existing pile evaluation. However, the lack of the 3D wave theory for the TLSITs greatly hinders the application of this method. This paper establishes a coupled 3D soil-pile model based on the continuum theory. A corresponding analytical solution of the dynamic pile response is derived and subsequently verified through the comparisons against the 1D wave theory and the Finite Difference Method. The proposed model exhibits the transverse wave interferences and finds: (1) The transverse wave interference that occurred during the TLSITs can be seen gradually alleviated from the pile center to the pile edge. (2) The larger the pile dimensions are, the greater the transverse wave interference would be.