To mitigate the occurrence of berthing accidents in fog, this study constructed a cloud model-based visual risk assessment framework for ship berthing operations under dense foggy conditions. Within this framework, a comprehensive risk assessment index system was first established, encompassing four dimensions: personnel involved in berthing, vessel characteristics, berth facilities, and environmental conditions. Subsequently, the combined AHP and DEMATEL method was employed to calculate the integrated influence weights of each assessment indicator. To address the uncertainty inherent in berthing risks, the cloud model was introduced to quantify the risks associated with foggy berthing operations. Finally, the proposed framework was applied to evaluate four typical berthing scenarios at the Waigaoqiao Container Terminal of Shanghai Port. The results demonstrate that the marine clearance (fore and aft distance) at the berth, the proficiency of pilots in foggy berthing operations, and the positioning effectiveness of berth leading marks or lights significantly influence the risk levels of ship berthing in fog. This risk assessment model accurately evaluates and visually represents berthing risks across different scenarios. It thus holds substantial importance for ensuring all-weather berthing operations and enhancing port cargo throughput capacity.

To enhance the scientificity and effectiveness of work safety management in mining enterprises, modeling and simulation studies were conducted focusing on the miners' safety responsibility pressure. A SD causal feedback diagram was constructed based on miners' work safety responsibility pressure scale, and questionnaire data was collected. The BPNN reflecting the relationship between independent and dependent variables was built by using the Neural Network Fitting tool in Matlab 2023b. Furthermore, a system flowchart was established through the Simulink platform to conduct dynamic simulation and scenario simulation. The results show that miners' work safety responsibility pressure and the pressure dimensions tend to increase over time. When pressure sources act independently, the punishment apprehension plays a dominant role, whereas when pressure sources act synchronously, the cognitive pressure dimension demonstrates nonlinear inhibition. In the process of pressure management, comprehensive consideration and balanced adjustment of multidimensional stressors should be emphasized to enhance the effectiveness of work safety management system in coal mining enterprises.

In order to enhance the level of flight students' safety behaviors, the match between authentic leadership of flight instructors and authentic followership of flight students was used as the independent variable, the safety behavior of flight students was used as the dependent variable, and the satisfaction of basic psychological needs was used as the mediating variable, Polynomial regression and response surface analysis methods were employed, and data from 256 valid survey questionnaires were used to investigate the influence of match between authentic leadership of flight instructors and authentic followership of flight students on safety behavior of flight students. The results show that the more the flight instructor's authentic leadership matches with the flight student's authentic followership, the higher the flight student's basic psychological needs satisfaction level is, the flight student's basic psychological needs satisfaction level is higher in the case of "high authentic leadership-high authentic followership" than in the case of "low authentic leadership-low authentic followership". The level of basic psychological needs satisfaction of flight students in the "low authentic leadership-high authentic followership" scenario is higher than that of the "high authentic leadership-low authentic followership" scenario. In the match between authentic leadership of flight instructors and authentic followership of flight students, the satisfaction of basic psychological needs mediates the effect on flight students' safe behavior.

In order to promote the development of theories on the modernization of work safety governance, a four-dimensional model of the work safety governance system—comprising governance actors, governance rules, governance tools, and governance resources—was constructed based on systems theory and governance theory. Using CAS theory, seven fundamental characteristics—aggregation, nonlinearity, flow, diversity, tagging, internal models, and building blocks—were analyzed. Furthermore, by integrating fitness landscape theory and the NK fitness landscape model, an NK landscape model for the evolution of the work safety governance system was developed, revealing 16 state combinations of the system's genetic morphology (A-R-T-S) and their fitness distributions. The results indicate that the system's evolution follows a three-stage mechanism: Initial Stage: Random walk and low-state locking → Development Stage: Adaptive walk and local coordination limitations → Maturity Stage: Adaptive leap and deep coordination. Empirical analysis indicates that the evolution of China's work safety governance system encompasses three periods with corresponding mechanisms: the System Initial Establishment Period (1978—2002), the System Improvement Period (2003—2012), and the System Modernization Advancing Period (2012—2024).

In order to reduce the occurrence of accidents during construction in urban underground spaces, a combined methodology of content analysis and social network analysis was employed to deconstruct human factors in accidents during urban underground space construction for risk reduction. Firstly, based on PAR accident causation model, a tri-dimensional analytical framework of "Subject-Factor-State" was developed for human-induced accidents in urban underground construction. Secondly, authentic accident cases were utilized to encode and extract causal factors, while causal subjects and states were systematically identified. Finally, a structural model of human-induced causation was constructed through network co-occurrence, centrality analysis, and correlation analysis to reveal inherent causal mechanisms. Key findings reveal: Collapse, poisoning, and suffocation constitute the predominant accident types, with 24 high-frequency human factors identified, including 9 core contributing factors. The structural model demonstrates that decision-making managers bear critical safety responsibilities among causal subjects. The core factor "inadequate safety technical disclosure", functions as a pivotal risk hub significantly influencing the causal network. Causal states exhibit strong interdependencies, particularly highlighting the pronounced interaction between information deficiency and other states.

In order to reduce the accidents in industrial scenarios which were caused by workers'unsafe operation behaviors, meanwhile improve the performance of visual-based action recognition methods in industrial scene with poor lighting, limited field of view and occlusions, an improved decision-making strategy based on self-adaptive ER (S-ER) was introduced in this paper. This strategy could integrate video information and inertial measurement unit (IMU) information effectively. It firstly analyzed video information and IMU information with attention mechanism-based multi-task convolutional 3D (M-C3D) model as well as one-dimensional convolutional neural network (1D-CNN) fused with attention mechanism, then ER theory was introduced to achieve decision-level fusion, where the set of evidence weights and reliability under different environmental conditions was optimized through the firefly optimization algorithm for improving the recognition accuracy and robustness of the model. The effectiveness of the proposed algorithm was verified on the public dataset Multimodal Human Action Dataset from University of Texas at Dallas(UTD-MHAD) and the self-built dataset Multimodal Human Action Dataset from Zhongyuan University of Technology(ZUT-MHAD). The results show that the identification results of S-ER for workers'unsafe behaviors in complex industrial scenarios can reach up to 98.53%, which is 17.52% higher than the maximum value of traditional multimodal fusion methods and single-modality recognition methods.

To address the dynamic risks in hazardous areas and the lack of timely warning of personnel intrusion within deep foundation pit, a digital twin system integrating BIM and UWB technology was developed. Initially, a virtual scene representing the deep foundation pit was constructed using the OurBIM 3D graphics engine. Second, a method for calibrating hazardous areas was proposed, and UWB technology was utilized to locate construction personnel and hazardous sources in real-time. Third, a real-time warning mechanism for personnel intrusion behavior was established, and a digital twin system was designed for regional intrusion warning of personnel in deep foundation pit, incorporating functionalities such as scene visualization, hazardous area definition, and warning data logging. Finally, the system was applied to a specific deep foundation pit in Wuhan. The results demonstrate that the system is capable of monitoring personnel intrusion behavior in two distinct types of hazardous areas within the deep foundation pit, achieving a comprehensive warning success rate of 97.4%. This system validates the feasibility and effectiveness of digital twin technology in the construction safety domain.

To effectively prevent WMSDs caused by cumulative fatigue, this study proposes a fatigue quantification method that integrates computer vision and biomechanical modeling. The method extracts 3D posture data of workers using the MMPose model and builds individualized musculoskeletal models in OpenSim. It reconstructs complete work cycles and simulates muscle loading. By calculating joint reaction forces and muscle activation levels, fatigue indices for joints and muscles are developed. The method is applied to analyze fatigue progression in the lumbar spine during a typical lifting task. Results show that the L5-S1 joint and associated multifidus muscles exhibit significant fatigue accumulation in a typical bending and lifting task. Some muscles show lateral asymmetry in fatigue, and both deep stabilizing and core muscle groups are involved in posture maintenance and load response. The method can dynamically track fatigue changes during work tasks and identify high-risk areas of fatigue, and can be applicable to fatigue risk warning in dynamic, high-load operation scenarios.

To enhance the emergency response capacity of public hospitals in dealing with public health emergencies, the Bayesian network approach was adopted in this study. From the perspective of public hospitals, scenario elements, tasks, and capabilities of public health emergencies were extracted and an emergency response network was constructed. The network was centred around the emergency leading group as the core node, the emergency expert group, medical departments and other core departments as well as pharmacy and other supporting departments were collaborated internally. And it linked with local disease control, public security, communities and other administrative agencies externally, thus forming an emergency response structure with internal and external collaboration. Through diagnostic analysis, the priority of response objectives was clarified. The results show that "preventing nosocomial infection incidents" is the primary objective (accounting for 40%). Three key paths are identified: establishing the emergency leading group-establishing the emergency expert group, establishing the emergency leading group - strictly implementing various prevention and control systems, and establishing the emergency leading group - virus testing.

In order to further prevent coal mine safety accidents triggered by miners'personality traits and psychological factors, this study adopts the Big Five personality model to assess personality traits. From the perspective of emotions, this study explores the predictive role of the Big Five personality and emotions in miners'unsafe behaviors, while also examining the moderating effect of emotions on the relationship between the Big Five personality traits and unsafe behaviors. First, SPSS 26.0 and AMOS 26.0 were used to analyze the reliability and validity of the questionnaire. Then, a SEM was established to verify the hypotheses. On this basis, the hierarchical regression analysis method was employed to test the moderating effect of emotions. The results show that: the three personality traits of extraversion, neuroticism, and openness, and negative emotions positively influence unsafe behaviors. The two personality traits of agreeableness and conscientiousness, and positive emotions negatively affect unsafe behaviors. Positive emotions play a moderating role in the relationship between the openness personality trait and unsafe behaviors. Negative emotions have a moderating effect in the relationships between the four personality traits of neuroticism, openness, extraversion, and agreeableness and unsafe behaviors. Therefore, coal mining enterprises can identify potential safety hazards and develop strategies to intervene in employees'unsafe behaviors based on workers'personality traits and emotional states, thereby enhancing the on-site safety level.