## 4 Results II--Review of Research Clusters
### Cluster 2: Human Factors
There has been growing research on Human Factors, which deals with the impact of human behavior, cognitive abilities, and psychological and physical limitations on system performance and safety, particularly in system design. [PERSON] et al. (2024) [42] identified cognitive errors and working environment conditions as the main causes of accidents in small fishing vessels in Korea. The Cognitive Reliability and Error Analysis Method (CREAM) was used to predict Human Error Probability (HEP), and risk assessment was conducted by combining the Fuzzy Set Theory (FST) with a BN to reflect uncertain working conditions and human cognitive factors. This approach aims to estimate accident probabilities for each scenario and predict human errors that may occur during real-time operations. The results showed that the probability of accidents increased significantly under high fatigue and insufficient working hours, with falling accidents and being caught in equipment identified as the highest risk factors. This method provided high predictive reliability even under uncertain conditions. However, the analysis covered only a limited range of conditions, suggesting that continuous research and data accumulation would improve model accuracy. [PERSON] et al. (2024) [46] specifically analyzed Human and Organizational Factors (HOFs) in collision accidents on fishing vessels. The HFACS-BN model was applied, with HFACS (Human Factors Analysis and Classification System) classifying human factors into four hierarchical structures and BN used to quantitatively analyze the relationships between factors. This model used data from 443 collision accidents in China between 2013 and 2023 to identify human and organizational factors and employed CPT and sensitivity analysis to determine accident pathways. The most influential factor was identified as Unsafe Acts, such as improper collision avoidance maneuvers. The study suggested that fishery management agencies and maritime safety authorities should prioritize regulatory compliance, strengthen training, and improve safety management systems to prevent collisions. The HFACS-BN model enables systematic analysis of complex human and organizational factors, making it effective for developing accident prevention strategies. However, as the model is based on specific regional data, it may have limitations when applied to other waters or countries. The study suggested collecting diverse regional data to tailor the model to local characteristics and incorporating fishing methods and environmental factors into additional analyses. Collision accidents between merchant ships and fishing vessels are mainly caused by human factors (76.1%) and system/equipment failures (25.3%). [PERSON] et al. (2024) [50] applied the HFACS model to systematically categorize various human factors and used complex network theory to analyze the propagation mechanisms of accident causes. The model aimed to identify major risk factors for collision accidents between merchant and fishing vessels (CAMF) in Chinese waters and develop effective accident prevention strategies. The results indicated that specific risk factors (e.g., non-compliance with collision avoidance rules, inattentiveness during work) played a crucial role in accident occurrences. However, HFACS primarily focuses on human factors, addressing technical and environmental factors to a lesser extent, indicating the need for multidimensional analytical approaches. In addition, research evaluating fishermen's exposure to hazards exists, providing foundational data for analyzing accident causes. [PERSON] et al. (2019) [33] addressed noise exposure issues on small fishing vessels in Newfoundland and Labrador, evaluating noise sources and hazardous noise levels within vessels. Real-time noise levels were measured across various locations during seven fishing operations, with most primary areas exceeding recommended noise levels.