effectiveness of their teaching skills to reduce stress job psychological empowerment of nursing staff in intensive care units in the center of Shiraz Shahid Rajaee 1392 .

Background and aims: Nurses of critical care wards are faced with a number of stressors that could also threaten the ability of those affected various aspects of health and undermine the quality of their performance. The role of psychological empowerment skills training to reduce stress among working nurses in intensive care wards is unknown. This research was aimed to determine the efficacy of psychological empowerment skills training to reduce stress among working nurses in intensive care wards in medical center of Shahid Rajaee in Shiraz was conducted in 2013. Methods: In this quasi-experimental study, 120 working nurses in intensive care wards randomly divided into interventional and control groups. Then, the workshop of self-empowerment skills training was performed for experimental group and a month after their training psychological empowerment, data were collectd using Osipow questionnaire in both groups. Data was analyzed using SPSS software and t-test, ANOVA, paired t-test, Chi-square and Mann-Whitney tests. Results: The mean score of job stress before training in the control group was 256.68±14.81 in intense level and after training) without training was 265.8±5.16 in severe level. Mean stress scores before training in the intervention group was 269.26±6.18 in intense level and after training was 251.70±17.97 in moderate level. There were significant differences between stress score mean before and after intervention (P=0.002). Conclusion: According to existing stress in the nursing profession; psychological empowerment can be effective to reduce stress and increase the quality of their nurses

EXPLAINABLE MODELS FOR MULTIVARIATE TIME-SERIES DEFECT CLASSIFICATION OF ARC STUD WELDING

Arc Stud Welding (ASW) is widely used in many industries such as automotive and shipbuilding and is employed in building and jointing large-scale structures. While defective or imperfect welds rarely occur in production, even a single low-quality stud weld is the reason for scrapping the entire structure, financial loss and wasting time. Preventive machine learning-based solutions can be leveraged to minimize the loss. However, these approaches only provide predictions rather than demonstrating insights for characterizing defects and root cause analysis. In this work, an investigation on defect detection and classification to diagnose the possible leading causes of low-quality defects is proposed. Moreover, an explainable model to describe network predictions is explored. Initially, a dataset of multi-variate time-series of ASW utilizing measurement sensors in an experimental environment is generated. Next, a set of pre-possessing techniques are assessed. Finally, classification models are optimized by Bayesian black-box optimization methods to maximize their performance. Our best approach reaches an F1-score of 0.84 on the test set. Furthermore, an explainable model is employed to provide interpretations on per class feature attention of the model to extract sensor measurement contribution in detecting defects as well as its time attention