This study aimed to explore how core self-evaluations influenced job burnout and mainly focused on the confirmation of the mediator roles of organizational commitment and job satisfaction. A total of 583 female nurses accomplished the Core Self-Evaluation Scale, Organizational Commitment Scale, Minnesota Satisfaction Questionnaire, and Maslach Burnout Inventory-General Survey. The results revealed that core self-evaluations, organizational commitment, job satisfaction, and job burnout were significantly correlated with each other. Structural equation modeling indicated that core self-evaluations can significantly influence job burnout and are completely mediated by organizational commitment and job satisfaction.
Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.
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