F. Kiani, M. R. Khodabakhsh,
Volume 3, Issue 3 (12-2013)
Abstract
Introduction: Researches show that workplace quality play important role in developing job involvement. The purpose of this study was to determine the predicted power of job involvement by perceived supervisor support and perceived coworker support.
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Material and Method: This research was a cross-sectional study. The participants consisted of 189 employees from Isfahan Steel Company in 2012 and they were selected according to the stratified random sampling method and responded questionnaires about demographic characteristics, perceived supervisor support, perceived coworker support and job involvement. Data was analyzed using Pearson correlation coefficient and regression analysis.
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Result: The results showed that there were significant relationships between perceived supervisor support and perceived coworker support with job involvement (p<0.05). Also, results indicated that the variables of perceived supervisor support and perceived coworker support significantly predicted almost 13% and 11% of job involvement variance (p<0.05).
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Conclusion: The research results maintain the importance of psycho-social variables in predicting job involvement in workers.
Zahra Khodabakhsh, Leila Omidi, Khadijeh Mostafaee Dolatabad, Matin Aleahmad, Hossein Joveini,
Volume 14, Issue 3 (10-2024)
Abstract
Introduction: Domino effects are a chain of low-probability and high-consequence accidents in which a primary event (fire or explosion) in one unit causes secondary events in adjacent units. Bayesian networks have been used to model the propagation patterns of domino effects and to estimate the probability of these effects at different levels. The unique modeling and flexible structure provided by Bayesian networks allow the analysis of domino effects through a probabilistic framework, taking synergistic effects into account.
Material and Methods: Firstly, collecting the basic information related to the location of the storage tanks and determining the scenario of the accidents were done. Furthermore, the values of the heat radiation as escalation vectors in case of a fire in one tank were determined using ALOHA software. The received heat flux values were compared with the heat radiation threshold of 15 kw/m2 and the escalation probability of the primary unit and the propagation of the initial scenario to nearby storage tanks were determined using Bayesian networks.
Results: The analysis of the heat flux values showed that among the 8 studied storage tanks, two storage tanks had the highest potential for spreading domino effects due to their location in a tank farm. Also, the implementation of Bayesian networks in GeNIe revealed that, compared to other storage tanks, the probability of domino effects propagating to other nodes is higher when a primary fire accident occurs in the two mentioned tanks, while considered as primary units.
Conclusion: Domino effect modeling and appropriate preventative measures can decrease the escalation probability in the process industries. Consideration of the synergistic effects of events at different levels by taking the escalation vectors into account leads to proper risk management and the determination of emergency response measures in storage tank farms.
