Background The enhancement of nursing care quality is closely related to the clinical competence of nurses, making it a crucial component within health systems. Objective The present study investigated the relationship between nurses’ clinical competence, moral identity, and moral injury during the COVID-19 outbreak. Research design This cross-sectional study was carried out among frontline nurses, using the Moral Identity Questionnaire (MIQ), the Moral Injury Symptom Scale-Healthcare Professionals version (MISS-HP), and the Competency Inventory for Registered Nurse (CIRN) as data collection tools. (...) Participants and research context: The research population for this study consisted of all frontline nurses ( n = 251) employed in a hospital in southern Iran. Sampling was conducted between May 1, 2021 and September 30, 2021, during the COVID-19 outbreak. Ethical considerations The present study received approval from the research ethics committee of Rafsanjan University of Medical Sciences, with project No. 99267 and code of ethics ID No. IR. RUMS.REC.1399.262, dated 15.02.2021. Results According to the study findings, 42.2% of the nurses demonstrated high clinical competence, while 51.4% exhibited moderate clinical competence. The results indicated a positive correlation between moral identity and clinical competence but a negative correlation between moral injury and clinical competence. Furthermore, the variables of moral identity and moral injury were found to predict 10% of the variance in clinical competence. Conclusion According to the results, moral identity and moral injury had an impact on the clinical competence of nurses. Therefore, implementing a program aimed at enhancing moral identity and providing training strategies to address moral injury during crises like the COVID-19 pandemic can lead to improvements in nurses’ clinical competence and the overall quality of care they provide. (shrink)

. This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest (...) object in the image. On the local level, fingerprint's foreground and its border image are treated using different fuzzy rules. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages: pre-processing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach. (shrink)