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In today's interconnected global economy, maritime trade is a pillar of prosperity, yet maritime accidents loom as a formidable challenge. The intricate nature of these accidents, coupled with rapid technological advancements, necessitates the evolution of systematic analysis methods. Conventional systemic approaches, while valuable, struggle to encapsulate the intricate web of mutual and dynamic dependencies inherent in these incidents. Furthermore, the call for more quantitative support in decision‐making and the ability to account for emergent factors has become increasingly imperative. This study aims to analyze maritime accidents by introducing a quantitative and dynamic model. The endeavour begins with establishing an extended Accident Map‐based model, a robust framework that unveils a sophisticated accident causation model. This preliminary action establishes the groundwork for integrating an innovative Spherical Fuzzy Set, navigating the complex landscape of knowledge acquisition. The subsequent phase charts a transformative course by mapping the model onto a dynamic Bayesian Network to conduct a forward and backward analysis. The essence of the model lies in its dynamic nature, allowing for real‐time updates that reflect the evolving maritime accidents risk factors. The approach is validated through a partial benchmark exercise, a reality check, an independent peer review, and a sensitivity analysis. The model can explore emerging contributing factors, reduce uncertainty, and consider relationships between factors that yield designing more effective safety measures. [ABSTRACT FROM AUTHOR]

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]

This study presents a responsive analysis of the role of artificial intelligence (AI) in risk management, contrasting traditional approaches with those augmented by AI and highlighting the challenges and opportunities that emerge. AI, intense learning methodologies such as convolutional neural networks (CNNs), have been identified as pivotal in extracting meaningful insights from image data, a form of analysis that holds significant potential in identifying and managing risks across various industries. The research methodology involves a strategic selection and processing of images for analysis and introduces three case studies that serve as benchmarks for evaluation. These case studies showcase the application of AI, in place of image processing capabilities, to identify hazards, evaluate risks, and suggest control measures. The comparative evaluation focuses on the accuracy, relevance, and practicality of the AI-generated findings alongside the system's response time and comprehensive understanding of the context. Results reveal that AI can significantly enhance risk assessment processes, offering rapid and detailed insights. However, the study also recognises the intrinsic limitations of AI in contextual interpretation, advocating for a synergy between technological and domain-specific expertise. The conclusion underscores the transformative potential of AI in risk management, supporting continued research to further integrate AI effectively into risk assessment frameworks. [ABSTRACT FROM AUTHOR]

In recent years, sustainable development has become a challenge for many societies due to natural or other disruptive events, which have disrupted economic, environmental, and energy infrastructure growth. Developing hydrogen energy infrastructure is crucial for sustainable development because of its numerous benefits over conventional energy sources. However, the complexity of hydrogen energy infrastructure, including production, utilization, and storage stages, requires accounting for potential vulnerabilities. Therefore, resilience needs to be considered along with sustainable development. This paper proposes a decision-making framework to evaluate the resilience of hydrogen energy infrastructure by integrating resilience indicators and sustainability contributing factors. A holistic taxonomy of resilience performance is first developed, followed by a qualitative resilience assessment framework using a novel Intuitionistic fuzzy Weighted Influence Nonlinear Gauge System (IFWINGS). The results highlighted that Regulation and legislation, Government preparation, and Crisis response budget are the most critical resilience indicators in the understudy hydrogen energy infrastructure. A comparative case study demonstrates the practicality, capability, and effectiveness of the proposed approach. The results suggest that the proposed model can be used for resilience assessment in other areas. • A Holistic taxonomy of resilience is proposed for hydrogen infrastructure. • A Novel IFWINGS is proposed for advanced qualitative resilience assessment. • Importance of regulation, government preparation, and crisis response budget are discussed. • A Practical and effective application on a real hybrid wind-hydrogen power plant is presented. • Framework provides a tool for promoting sustainable development. [ABSTRACT FROM AUTHOR]

Background and objective: To control and prevention of accidents, attention to root causes of accident occurrence is very important. Safety risk of process units located in metropolis, must be always under control and in accordance with risk acceptance criteria of the community. In this regard, the purpose of this study is identification of functional failures, root cause analysis and incident outcomes arising from gas release in the Town Border Stations (TBS). Materials and method: Using at the same time of both methods the Functional Hazard Analysis (FuHA) and Failure Mode & Effect Analysis (FMEA), identification of the failure locations and qualitative risk analysis were carried out. For identification and analysis of the causes accident, Bow tie analysis method was also used. Results: Occurrence probability of identified top events was 0.71 and its failure rate was 1.24 per year. Unsafe behavioral (FP=0.36, &lambda=0.446) and mechanical causes (FP= 0.133, &lambda= 0.142) had highest and lowest the contribution in the top event occurrence. Vapor cloud explosion (VCE) had the highest probability (0.261) and failure rate (0.243) among the all identified incidents outcomes. Conclusion: Prevention of the root causes and attention to the human factors have the considerable contribution in accident control in the process units. In the combined approach used in present study, if are considered to be, the barriers role against of the root causes and incident outcomes occurrence, it could be an appropriate approach for identification of root causes and control of the hazards process.

Process safety has drawn increasing attention in recent years and has been investigated from different perspectives, such as quantitative risk analysis, consequence modeling, and regulations. However, rare attempts have been made to focus on inherent safety design assessment, despite being the most cost-effective safety tactic and its vital role in sustainable development and safe operation of process infrastructure. Accordingly, the present research proposed a knowledge-driven model to assess inherent safety in process infrastructure under uncertainty. We first developed a holistic taxonomy of contributing factors into inherent safety design considering chemical, reaction, process, equipment, human factors, and organizational concerns associated with process plants. Then, we used subject matter experts, content validity ratio (CVR), and content validity index (CVI) to validate the taxonomy and data collection tools. We then employed a fuzzy inference system and the Extent Analysis (EA) method for knowledge acquisition under uncertainty. We tested the proposed model on a steam methane-reforming plant that produces hydrogen as renewable energy. The findings revealed the most contributing factors and indicators to improve the inherent safety design in the studied plant and effectively support the decision-making process to assign proper safety countermeasures. [ABSTRACT FROM AUTHOR]

Introduction: using fossil fuels, some hazards such as explosion and fire are probable. This study was aimed to consequence modeling of fire on Methane storage tanks in a gas refinery using analyzing the risk, and modeling and evaluating the related consequences. Method: Hazard analysis by PHA was used to choosing the worst-case scenario. Then, causes of the scenario were determined by FTA. After that, consequence modeling by the PHAST software was applied for the consequence analysis. Results: Based on some criteria, the fire of methane gas tank (V-100) was selected as the worst-case scenario at the refinery. The qualitative fault tree showed three factors including mechanical, process, and human failures contribute in gas leakage. The leakage size and weather conditions were effective on the distance of radiation. Using consequence modeling, thermal radiation was considered as the major outcome of the incident. Finally, for outcome evaluating, probit equations were used to quantify losses and the percentage of fatalities due to the methane gas leakage and fire occurrence. The maximum number of fatalities caused by fire was obtained 23 persons. Conclusions: In conclusion, the methane gas vessel in the refinery can be considered as the main center of hazard, therefore the implementation of the safety rules, eliminating mechanical failures, personal protection and education, and Effective measures to prevent and fighting of fire are proposed for decreasing the probable losses and fatalities.

BACKGROUND: Patient safety culture (PSC) as a main component of the organizational culture plays a key role in providing safe, effective and economic cares and services in healthcare organizations. PSC provides a way to assist hospitals in order to improve patient safety and prevent medical errors. OBJECTIVE: The present study aimed to measure PSC and healthcare professionals' attitude towards voluntary reporting of adverse events in two hospitals in Iran and to develop a hybrid intelligent approach for modeling PSC grades. METHODS: The Hospital Survey on Patient Safety Culture (HSOPSC) questionnaire and a two-part questionnaire were used for examining the PSC and healthcare professionals' attitude towards voluntary reporting of adverse events, respectively. Principal component analysis (PCA) was applied to extract of the main components in the HSOPSC questionnaire and to construct 12 dimensions of patient safety culture. The overall grade of patient safety culture was modeled using adaptive neuro-fuzzy inference systems (ANFIS) as a classification problem. RESULTS: Almost half of the participants have experienced a medical error and adverse events. The PSC grade was acceptable from the point of view of 55.5% and 50% of participants in hospital No.1 and hospital No.2, respectively. The overall accuracy of ANFIS in modeling overall grades of patient safety culture in both study hospitals was 0.84. Of those individuals gave an acceptable grade on patient safety culture in both study hospitals, more than 50% believed that all medical errors and near misses should be reported. CONCLUSIONS: The ANFIS algorithm was proposed for modeling and predicting of PSC for healthcare organizations. The results confirm the capability of the proposed model to predict patient safety grades in healthcare settings. [ABSTRACT FROM AUTHOR]