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Resilience engineering (RE) is a new approach to measuring and maintaining safety in complex systems. The focus of RE is not on errors rather than on understanding and supporting normal work and what goes right. Using analytical hierarchy process (AHP), the present study aims to devise a validated method for improved assessment of RE in maintenance organizations. A standardized questionnaire containing RE and Performance Shaping Factors (PSFs) for generic maintenance operators is designed to collect data from employees in 11 regional maintenance departments of a large public gas company. Holding regular discussion sessions with experts in the field, the AHP is then built up based on the consensus emerged from the discussions. To form the middle-level criteria of the analytical hierarchy, RE items are clustered into 6 new categories using a verified kmeans clustering. Given the large number of RE items in some categories, a complete sensitivity analysis is then performed by data envelopment analysis (DEA) to identify the most important items as the final level criteria. The designed AHP is used to assess RE in the 11 regional maintenance departments. For verification and validation of the proposed AHP, the linear relationship between AHP-based RE assessment and PSFs assessment is tested for its significance. The results confirm the close relationship between RE and PSFs.

Having started since late 2019, COVID‐19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID‐19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best‐Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best‐Worst Method–local linear neuro‐fuzzy–adaptive neuro‐fuzzy inference system approach, the relationship between COVID‐19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.

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