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 effectively address the escalating challenges of natural disasters and enhance the efficiency of the enterprise-led emergency supply reserve system, an economic information disclosure mechanism was proposed to boost enterprise participation. Firstly, an enterprise-led emergency supply reserve model was constructed based on an option contract to clarify the intrinsic driving mechanism of disaster occurrence probability on enterprises' reserve decisions. Then, the Stackelberg sequential game framework was further employed to evaluate the effectiveness of government subsidy policies in increasing supply reserve volumes and improving government-enterprise objectives. Furthermore, Bayesian persuasion theory was introduced to design a government information disclosure mechanism, aimed at guiding enterprises' reserve decisions and improving the overall efficiency of relief supply reserves. Finally, a numerical analysis was conducted with flood disasters as a specific application scenario. The research findings indicate that the volume of enterprise-led emergency supply reserves is positively correlated with disaster occurrence probability, reflecting enterprises' sensitivity to disasters. Although government subsidies can increase reserve quantities and reduce government costs, they have limitations in improving enterprises' profits. The information disclosure mechanism designed based on Bayesian persuasion theory outperforms both the baseline model and the subsidy model in terms of reserve quantities, government costs, and enterprises' profits. Through effective disaster information disclosure, the government can enhance the relief efficiency of the enterprise-led emergency supply reserve system.

In order to enhance the emergency management capabilities of metal mines on Qinghai-Tibet Plateau, a comprehensive intelligent emergency capability evaluation model was developed based on the application of intelligent technology. By analyzing the region's unique geographical conditions and complex mining operations, a five-dimensional evaluation index system was proposed, encompassing intelligent technology, behavioral safety, resource allocation, institutional mechanisms, and community relations. This study employed ANP method to determine indicator weights and applied the VIKOR multi-criteria decision-making method to systematically assess the intelligent emergency capabilities of three representative metal mines (Mines Y, V, and J) on the Qinghai-Tibet Plateau. The findings indicate that the comprehensive evaluation system of smart emergency capability based on ANP-VIKOR covers 5 dimensions, including "intelligent technology, behavior safety, resource allocation, mechanism, and community relationship", with a total of 20 evaluation indicators. It fully integrates the complex geographical environment, extreme climatic conditions, and mining operation characteristics of the region. It is helpful to make up for the lack of quantitative research on smart emergency capability in plateau areas. In the case, the mine J exhibits the strongest intelligent emergency management capabilities, particularly excelling in intelligent technology and emergency response. Mine Y demonstrates a moderate level of technological application with notable improvements in emergency management over time, while Mine V shows room for improvement in resource allocation and emergency response speed. The significant differences in emergency capabilities reflect the uneven development of intelligent emergency systems in plateau mining areas. The effectiveness and adaptability of the constructed model are verified.

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 cultivate talents with both theoretical innovation and practical abilities in safety science and engineering discipline of universities, this paper explored the educational practice of safety discipline from the integration of science and education to the integration of production and education. First, the integration concepts of science and education and integration of production and education were analyzed under the new Degree Law, establishing the connotation requirements and an educational framework for science-industry-education synergy. Utilizing macro, meso, and micro perspectives, this study constructed robust educational systems, management mechanisms and training programs. Examples from School of Emergency Management and Safety Engineering of China University of Mining and Technology-Beijing and National Academy of Safety Science and Engineering illustrated the effective cultivation of talent seeds, the creation of nurturing environments, and the achievement of substantial educational outcomes. Results indicate that this collaborative model significantly enhances the comprehensive development of safety professionals and improves the overall quality of education in the safety discipline.

To reduce the incidence of accidents in high-risk enterprises and enhance enterprise value, based on the panel data of 1 016 listed companies in high-risk sectors from 2010 to 2022, this paper empirically examined the nonlinear mechanism of safety investment on corporate performance with the safety information disclosure after text analysis as the moderating variable. The results reveal that there is an inverted U-shaped relationship between safety investment and corporate performance. When safety investment is lower than the critical threshold of 12.203 8, it has a positive effect on performance. Furthermore, safety information disclosure positively moderates this relationship. The effects of both safety investment and safety disclosure on corporate performance are more pronounced in non-state-owned enterprises. In the short term, a virtuous cycle of "safety investment-safety information disclosure-corporate performance improvement" can effectively support safety governance in high-risk enterprises and achieve a dual goal of improving safety management and economic performance.

To reduce the negative impact of psychological fatigue on construction workers' safety, the relationship between psychological fatigue and UB and their underlying mechanisms were investigated. First, based on the characteristics of construction workers and literature review, psychological fatigue was divided into three dimensions: reduced motivation (RM), cognitive weakness (CW), and emotional exhaustion (EE). Secondly, based on protection motivation theory (PMT) and Conservation of Resources Theory (COR), HP was introduced as a mediating variable and SS was introduced as a moderating variable. Finally, Structural Equation Modeling and hierarchical regression analysis were used to explore the mechanism through which psychological fatigue affects construction workers' UB. The results show that psychological fatigue is found to have a significantly positive impact on UB through the three dimensions of RM, CW, and EE; HP is identified to partially mediate the relationship between psychological fatigue and UB. Both the direct effect of psychological fatigue on UB and the mediating role of HP between them are moderated by SS.

In order to clarify the mechanism of perceived organizational support on miners' unsafe behaviors, a chain mediation model was constructed by introducing regulatory emotional self-efficacy and job burnout as mediating variables. A structured questionnaire was developed utilizing the Perceived Organizational Support Scale, the Regulatory Emotional Self-Efficacy Scale, the Job Burnout Scale, and the Unsafe Behaviors Scale, and a quantitative empirical study was conducted based on the sample data of 245 front-line miners. Semi-structured interviews were conducted to deepen understanding of miners' perspectives on these relationships. The results indicated that perceived organizational support significantly and negatively influenced miners' unsafe behaviors; regulatory emotional self-efficacy and job burnout exerted partial mediating effects between perceived organizational support and miners' unsafe behaviors, and regulatory emotional self-efficacy significantly and negatively impacted job burnout, with both variables exhibited chain mediation effects between perceived organizational support and miners' unsafe behaviors.

To reduce casualties and economic losses caused by disasters, a multi-objective ambulance dispatch optimization model that minimizes the maximum rescue time, the standard deviation of ambulance service times, and the total psychological deprivation cost of casualties was developed, while accounting for psychological deprivation factors. Given the non-deterministic polynomial (NP)-hard nature of model, the INSGA-III was proposed, incorporating a multi-chromosome hierarchical encoding strategy and dynamic crossover and mutation methods. Using the 2019 flood in Xingguo County, Ganzhou City, Jiangxi Province as a case study, the proposed INSGA-III was compared with NSGA-III and NSGA-II. Sensitivity analyses on ambulance quantity and relative deprivation cost coefficients demonstrated the effectiveness of model and algorithm. The results show that the maximum rescue time for casualties is minimized to 9.234 hours, the standard deviation of the latest ambulance service times is reduced to 13.156 minutes, and the total psychological deprivation cost of casualties is minimized to 1729.001. With the relative psychological deprivation cost coefficient set at 0.3 and 500 ambulances deployed, the timeliness and fairness of the rescue operations are significantly improved.

This study aims to improve the accuracy, stability and interpretability of the model for the prediction of the air temperature in the mine water-drenched shaft. Firstly, characteristic variables were analyzed by Pearson correlation coefficient. Secondly, BiLSTM model was optimized by KOA, and the prediction model of mine shaft air temperature based on KOA-BiLSTM was established. Then, under the same sample conditions, the algorithm was compared with back propagation (BP), random forest (RF), least squares boosting (LSBoost) and support vector machine (SVM). Finally, interpretability analysis was conducted using the shapley additive explanations (SHAP) algorithm, which was verified by an example. The results show that the absolute error range of KOA-BiLSTM model is -1.24-0.5 ℃, which is 3.98% higher than the prediction accuracy of the unoptimized model. Compared with the other four models, the average absolute error (MAE), average absolute percentage error (MAPE) and mean square error (MSE) of the proposed model are the smallest, indicating that the model has the best prediction effect and generalization ability. The SHAP analysis shows that the wellhead air flow temperature has the greatest impact on the prediction results, while the surface pressure has the least impact. The absolute error range of KOA-BiLSTM model example verification is -0.49~0.38 ℃, and the prediction accuracy can meet the work needs.