Template-Type: ReDIF-Article 1.0 Author-Name: Changfeng Yuan Author-X-Name-First: Changfeng Author-X-Name-Last: Yuan Author-Name: Xing Sun Author-X-Name-First: Xing Author-X-Name-Last: Sun Author-Name: Lulu Niu Author-X-Name-First: Lulu Author-X-Name-Last: Niu Author-Name: Yating Tong Author-X-Name-First: Yating Author-X-Name-Last: Tong Author-Name: Qing Zhang Author-X-Name-First: Qing Author-X-Name-Last: Zhang Title: Coupling of risk factors in emergency processes for oil storage system fires based on the Bayesian network and N-K model Abstract: Frequent secondary accidents caused by emergency treatment of Oil Storage System Fires (OSSF) show that risk factors and their interactions in emergency processes can lead to recurrence of accidents. To quantitatively evaluate coupling effects of risk factors on accident development, a novel Risk Coupling Effect Analysis (RCEA) method based on the Bayesian Network (BN) and N-K model is proposed. Based on a statistical analysis of 252 typical accidents, risk coupling types caused by different factors are defined. Risk coupling value is calculated using the N-K model. A RCAE model based on the BN and N-K model is constructed. The model is used to analyse the coupling effect of risk factors, including: coupling degree variation characteristics, sensitivity and joint adjustment measures of risk factors. Some risk management suggestions are proposed. This study presents a new research idea and measurement method for evaluating risk coupling effect in emergency processes for OSSF. Journal: Int. J. of Reliability and Safety Pages: 36-70 Issue: 1 Volume: 20 Year: 2026 Keywords: oil storage system fire; risk factor; Bayesian network; N-K model; coupling effect. File-URL: http://www.inderscience.com/link.php?id=150484 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrsaf:v:20:y:2026:i:1:p:36-70 Template-Type: ReDIF-Article 1.0 Author-Name: Fuhai Wu Author-X-Name-First: Fuhai Author-X-Name-Last: Wu Title: Construction fatigue prediction model based on improved random forest algorithm Abstract: Construction workers are prone to construction fatigue in high-intensity working environments, and failure to receive effective rest may result in casualties and property damage. The study uses deep learning algorithms to construct an intelligent fatigue prediction model aimed at accurately assessing the fatigue status of construction workers. The study takes smartphones to collect basic data and inputs it into an improved random forest algorithm for fatigue feature recognition. Then, an intelligent construction fatigue recognition model is established based on the improved random forest algorithm. The research model had an accuracy rate of 94.7% in recognising different human movements, and an accuracy rate of 91% in predicting construction fatigue. The designed method accurately predicts the complete exhaustion, fatigue, concentration and excitement states of workers, and its predictive ability is superior to other prediction models. The research model can effectively assist construction managers in accurately detecting workers' fatigue status and taking timely intervention measures to reduce safety accidents. Journal: Int. J. of Reliability and Safety Pages: 71-90 Issue: 1 Volume: 20 Year: 2026 Keywords: RF; PCA; PSO; fatigue; construction. File-URL: http://www.inderscience.com/link.php?id=150487 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrsaf:v:20:y:2026:i:1:p:71-90 Template-Type: ReDIF-Article 1.0 Author-Name: Qiang Wang Author-X-Name-First: Qiang Author-X-Name-Last: Wang Author-Name: Hongyan Song Author-X-Name-First: Hongyan Author-X-Name-Last: Song Title: Power grid fault diagnosis technology based on improved deep Q-network model Abstract: Owing to the increasing complexity of the power system, the difficulty of fault diagnosis in the power grid is also increasing. In response to the issue of continuously decreasing fault diagnosis accuracy, a power grid fault diagnosis model based on an improved deep Q-network model is raised. This model enhances its information integration capability by constructing a fault parameter recognition model and introducing alarm information for text processing. By identifying power grid fault parameters and processing alarm information, the efficiency and accuracy of fault diagnosis can be improved. The experimental results show that the model shows significant performance improvement in multiple state dimensions, and is significantly better than the traditional algorithm in single fault diagnosis and multi-fault diagnosis scenarios. The results show that the proposed method has significant advantages in the accuracy of fault prediction, processing efficiency and anti-noise ability, which verifies the validity and practicability of applying this model in complex power systems. It also emphasises the importance of combining reinforcement learning with unstructured data to further promote the development of smart grid technology. Journal: Int. J. of Reliability and Safety Pages: 1-18 Issue: 1 Volume: 20 Year: 2026 Keywords: DQN; alarm information; power grid; reinforcement learning; fault diagnosis. File-URL: http://www.inderscience.com/link.php?id=150488 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrsaf:v:20:y:2026:i:1:p:1-18 Template-Type: ReDIF-Article 1.0 Author-Name: Muhammad Ahmed Alshyyab Author-X-Name-First: Muhammad Ahmed Author-X-Name-Last: Alshyyab Author-Name: Yousef Sameer Alasheh Author-X-Name-First: Yousef Sameer Author-X-Name-Last: Alasheh Author-Name: Rania Ali Albsoul Author-X-Name-First: Rania Ali Author-X-Name-Last: Albsoul Title: Patient safety culture: a narrative literature review by characteristics of the safety attitude questionnaire dimensions during COVID-19 Abstract: Patient safety culture is defined as the perceptions of staff toward patient safety in their healthcare institutions. The COVID-19 pandemic has placed excessive pressure on frontline healthcare workers. The aim of this narrative review is to explore the status of patient safety culture in healthcare organisations based on the findings of the Safety Attitude Questionnaire (SAQ) during COVID-19. The review was carried out in four databases in 2022 using the search terms; safety culture, patient safety culture, safety climate, COVID-19 and safety attitude questionnaire. The search was limited to English-language articles published in peer-reviewed journals. The review identified that the job satisfaction dimension of patient safety culture was strong among all of the included studies with a range from 75 to 88%. The findings of this review may inform decision-makers to identify areas of weaknesses and strengths for patient safety culture in healthcare organisations, particularly during pandemics. Journal: Int. J. of Reliability and Safety Pages: 19-35 Issue: 1 Volume: 20 Year: 2026 Keywords: patient safety; patient safety culture; patient safety climate; COVID-19; SAQ; safety attitudes questionnaire; literature review. File-URL: http://www.inderscience.com/link.php?id=150493 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrsaf:v:20:y:2026:i:1:p:19-35 Template-Type: ReDIF-Article 1.0 Author-Name: Yuchen Cui Author-X-Name-First: Yuchen Author-X-Name-Last: Cui Title: Research on PCB defect detection in intelligent ships based on hybrid CN-YoLov5 Abstract: With the progress of intelligent ship engineering, Printed Circuit Boards (PCBs) have become indispensable in ship control systems. However, during PCB manufacturing and subsequent operation, various defects frequently occur, undermining their reliability and threatening the stable operation of ship control systems. Accurately identifying microscopic targets, balancing detection efficiency and precision and detecting small-sized and complex defects are significant challenges. To address these issues, this paper presents Hybrid CN-YoLov5, a novel PCB defect detection technique based on an improved YOLOv5s framework. It enhances the model's target feature - capturing ability by replacing the C3 module with the C3_SAC module and the Conv module with SAConv, and improves small-object recognition accuracy through the integration of the NWD loss function. The incorporation of the CBAM attention mechanism strengthens the model's feature extraction and overall recognition and classification performance. Experimental results show that compared with the original YOLOv5s, the mean Average Precision (mAP) of Hybrid CN-YoLov5 reaches 95.80% (an improvement of 2.50%) and the precision reaches 100% (an increase of 6.79%), indicating its effectiveness for PCB defect detection and great potential in the quality inspection and fault diagnosis of ship-based PCB systems. Journal: Int. J. of Reliability and Safety Pages: 91-111 Issue: 1 Volume: 20 Year: 2026 Keywords: PCB defect detection; YoLov5; SAConv; global attention mechanism; NWD loss function. File-URL: http://www.inderscience.com/link.php?id=150495 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijrsaf:v:20:y:2026:i:1:p:91-111