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Health anxiety has been correlated with life satisfaction, personality, and alexithymia in junior college nursing students.  The present study sought to provide a model of the relationship between feelings of loneliness and personality traits with the mediating role of health anxiety among nurses.  According to the data collection method, this research was part of a descriptive-correlational study performed using path analysis. The statistical population included all nurses at Ibn Sina, Omid, and Erfan hospitals in Tehran in 2022. A total of 300 nurses were selected via the convenience sampling method and completed the Loneliness Scale, The Five-Factor Personality Inventory, and Short Health Anxiety Inventory. Data analysis was performed using SPSS software and Smart PLS3.  The results demonstrated that extroversion, agreeableness, and conscientiousness had a direct impact on health anxiety and loneliness (P<0.05). The mediating role of health anxiety was confirmed in the link between extroversion and loneliness, as well as between agreeableness and loneliness (P<0.05). Nonetheless, the indirect effect of conscientiousness on loneliness through health anxiety was found to be non-significant (P<0.05). Overall, the model showed a good fit, indicating that personality traits and health anxiety play a significant role in determining nurses' feelings of loneliness (P<0.05).  A thorough understanding of health-related aspects of nurses can assist mental health researchers and managers in developing and implementing precise and efficient psychosocial interventions.

This paper presents a correlation-based framework for feature selection to estimate the lithium batteries state of health (SoH) using data-driven methods. These features can be calculated online for partial charge/discharge (C/D) cycles in electric vehicles. The main contribution is to provide a framework to determine a subset of independent features with maximum impacts on SoH estimation. Despite statistical features, the selected features are independent of C/D profiles, without requiring a full cycle information. Using the correlation criterion reduces the computational burden in a data-driven model as required in an online battery health estimation method. Extracting features, considering battery electrochemical reactions, and reducing the number of selected features, using the suggested correlation criterion, increase the robustness of the model and avoid overfitting. NASA labeled battery aging dataset is used to evaluate the performance of the method under various partial and random C/D cycles. The results show that the correlation-based features in Gaussian data-driven model can estimate the battery SoH with an average error of 10% under random patterns in a partial C/D cycle.

Reduction and management of risk of industrial plant especially energy is an important concern. Probabilistic and reliability method are used in risk and cost estimations. Oil, Gas and Petrochemical plant are the most energy producer plants that are in focus of cost, life and operation. These plants consist of several units, parts and equipment’s. In order to study the risk of plants, it is needed to study the equipment’s. The main goal of this research is probabilistic seismic assessment of fixed horizontal vessels. In this paper, finite element model of designed and constructed vessels in a real project is prepared and incremental dynamic analysis (IDA) is performed. Response of vessels components including the vessel body stress, piping stress, flange and elbow deformations, anchor bolt stress are extracted and their related limit state are defined. Finally the fragility curve of vessels components is prepared. The result shows that flange connection is the components that are potential for starting the damage of vessels.