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This study presents a holistic model based on Fuzzy Bayesian Network-Human Factor Analysis and Classification System (FBN-HFACS) to analyze contributing factors in the pandemic, Covid 19, risk management under uncertainty. The model contains three main phases include employing a) HFACS to systematically identify influencing factors based on validation using content validity indicators, b) Fuzzy Set Theory to obtain the prior probability distribution of contributing factors in pandemic risk and address the epistemic uncertainty and subjectivity, and finally, c) Bayesian network to develop causality model of the risk, probabilistic inferences and handle parameter and model uncertainties. The Ratio of Variation (RoV), as BN-driven importance measures, is utilized to conduct sensitivity analysis and explore the most critical factors that yield effective safety countermeasures. The model is tested to investigate four large manufacturing industries in South Khorasan (Iran). It provided a deep understanding of influencing human and organizational factors and captured dependencies among those factors, while quantitative finding paves a way to efficiently make risk-based decisions to deal with the pandemic risks under uncertainty. [ABSTRACT FROM AUTHOR]

Currently, there is increasing concern about the safety and leakage of process industries. Therefore, the present study aims to prioritize control measures before and after the leakage scenario by using the Hendershot theory and MCDM techniques. In this study, two proactive and reactive layers were selected before and after leakage of tanks, respectively. Then, criteria and alternatives were selected to perform fuzzy TOPSIS (FTOPSIS) and find the best alternative based on the literature review and Hendershot approach. The linear model of the fuzzy Best-Worst method (FBWM) was constructed and resolved using Lingo 17 software. Subsequently, criteria were assigned weights based on thorough calculations of the inconsistency rate. The weight of study experts was equal to 0.25. The results of FBWM showed that the reliability index with a weight of 0.3727 was ranked first and the inconsistency rate (ξ˜*/ξ) was calculated to be equal to 0.040. Inherent Safety Design (ISD) (0.899) and passive safety (0.767) also ranked first before and after tank leaks, respectively. Using the FBWM method leads to fewer pairwise comparisons and at the same time more stability. Although ISD and passive strategies are more valid and strict, elements of all strategies are necessary for a comprehensive process safety management program. [ABSTRACT FROM AUTHOR]

A metal lathe is a high-performance tool used in the field of metalworking to remove excess material and shape metal parts. However, engaging in metal turning operations carries risks that can lead to serious accidents and physical harm. It is crucial to ensure that these systems are functioning correctly, as any malfunction or flaw can lead to dangerous situations. To maintain safety in industrial environments, it is important to assess the risks and reliability of the equipment. A study was conducted using a method called fuzzy fault tree analysis (FFTA), combined with fuzzy logic, to determine the probability of basic events. Bayesian networks (BNs) were utilized to update probabilities and overcome limitations of the fault tree (FT). A Dynamic Bayesian Network (DBN) was employed to estimate the reliability of a metal lathe in a specific scenario. The FT identified 57 root events and estimated the probability of workpiece FLY-OUTS as 0.03174329 using the FT method and 0.031505849 using the BN method. Based on the predictions of the DBN, system reliability decreased by 19.89% after 24 months. The FT diagram comprehensively captured all the factors associated with FLY-OUTS, highlighting that improper closing of the part on the tool was a significant contributing factor. The study concludes by proposing safety measures for turning operations based on the identified critical events. [ABSTRACT FROM AUTHOR]