Fuzzy Bayesian estimation and consequence modeling of the domino effects of methanol storage tanks.

In this study, a fuzzy Bayesian network (FBN) approach was proposed to analyze the domino effects of pool fire. Failure probabilities were calculated using triangular fuzzy numbers, the combined center of area (CoA)/sum-product method and the BN approach. Consequence modeling, probit equations and leaky-noisy-OR (L-NOR) gates were used to analyze the domino effects, and modify conditional probability tables (CPTs). Methanol storage tanks were selected to confirm the practical feasibility of the suggested method. The domino probability using bow-tie analysis (BTA) and FBN in the first and second levels was compared, and the ratio of variation was used for sensitivity analysis. Probability of the domino effect in the first and second levels (FBN) was 0.0071472631 and 0.0090630640, respectively. The results confirm this method is a suitable tool for analyzing the domino effects and that using FBN and L-NOR gate is a good way to assess the reliability of tanks.

A Bayesian network based study on determining the relationship between job stress and safety climate factors in occurrence of accidents.

BACKGROUND: Job stress and safety climate have been recognized as two crucial factors that can increase the risk of occupational accidents. This study was performed to determine the relationship between job stress and safety climate factors in the occurrence of accidents using the Bayesian network model. METHODS: This cross-sectional study was performed on 1530 male workers of Asaluyeh petrochemical company in Iran. The participants were asked to complete the questionnaires, including demographical information and accident history questionnaire, NIOSH generic job stress questionnaire, and Nordic safety climate questionnaire. Also, work experience and the accident history data were inquired from the petrochemical health unit. Finally, the relationships between the variables were investigated using the Bayesian network model. RESULTS: A high job stress condition could decrease the high safety climate from 53 to 37% and increase the accident occurrence from 72 to 94%. Moreover, a low safety climate condition could increase the accident occurrence from 72 to 93%. Also, the concurrent high job stress and low safety climate could raise the accident occurrence from 72 to 93%. Among the associations between the job stress factor and safety climate dimensions, the job stress and worker's safety priority and risk non-acceptance (0.19) had the highest mean influence value. CONCLUSION: The adverse effect of high job stress conditions on accident occurrence is twofold. It can directly increase the accident occurrence probability and in another way, it can indirectly increase the accident occurrence probability by causing the safety climate to go to a lower level.

The relations of job stress dimensions to safety climate and accidents occurrence among the workers.

Based on a literature review, likely, there is a relationship between job stress and safety climate, and in this way, the accident occurrence is affected. Therefore, the present study was aimed to investigate the relations of job stress dimensions to safety climate and accidents occurrence among the workers using regression models. This cross-sectional study was carried out on 1530 male employees in 2019. People were randomly selected from various departments. The participants filled out the questionnaires, including demographical information and accident history questionnaire, the NIOSH generic job stress questionnaire, and the Nordic safety climate questionnaire. In addition, information on occupational experience and accident history was obtained from the health unit of the petrochemical company. In the end, data were analyzed using statistical tests of bivariate correlation, multivariate correlation, and logistic regression. Based on the bivariate analysis, the variables of job satisfaction (0.998), problem at work (0.900), depression (-0.836), and physical environment (-0.796) among the job stress dimensions had the highest correlation coefficients with the total score of the safety climate, respectively. The results of the logistic regression analysis with the adjustment of the effect of the safety climate indicated that the relationships between the dimensions of the job satisfaction (Wald = 6.50, OR = 4.96, and p-value<0.05) and social supports (Wald = 5.88, OR = 3.20, and p-value<0.05) with the accident occurrence were significant. To increase the positive safety climate and decrease the accident occurrence, industries must try to reduce job stress in the workplaces through controlling the important factors, such as low job satisfaction and poor social supports.

A Secure IoT-Based Modern Healthcare System With Fault-Tolerant Decision Making Process.

The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system.

Performance evaluation and design for variable threshold alarm systems through semi-Markov process.

In large industrial systems, alarm management is one of the most important issues to improve the safety and efficiency of systems in practice. Operators of such systems often have to deal with a numerous number of simultaneous alarms. Different kinds of thresholding or filtration are applied to decrease alarm nuisance and improve performance indices, such as Averaged Alarm Delay (ADD), Missed Alarm and False Alarm Rates (MAR and FAR). Among threshold-based approaches, variable thresholding methods are well-known for reducing the alarm nuisance and improving the performance of the alarm system. However, the literature suffers from the lack of an appropriate method to assess performance parameters of Variable Threshold Alarm Systems (VTASs). This study introduces two types of variable thresholding and proposes a novel approach for performance assessment of VTASs using Priority-AND gate and semi-Markov process. Application of semi-Markov process allows the proposed approach to consider industrial measurements with non-Gaussian distributions. In addition, the paper provides a genetic algorithm based optimized design process for optimal parameter setting to improve performance indices. The effectiveness of the proposed approach is illustrated via three numerical examples and through a comparison with previous studies.