To ensure that the equipment support system of professional emergency rescue teams could meet the requirements of rescue tasks and gradually adapt to complex and variable disaster risks, a text mining method was applied to analyze the factors influencing the equipment support capacity of emergency rescue. Based on technical personnel support capability, equipment resource support capability, equipment and facility support capability, information resource support capability and management system support capability, an assessment index system for emergency rescue equipment support capacity was developed. To reduce the impact of fuzziness, randomness, and subjective-objective bias on assessment results, a combined weighting method was adopted to determine the weights of each assessment index. A comprehensive assessment method was established using the matter-element extension model and the integrated cloud model. Professional emergency rescue team A was selected as an example for application to verify the scientific validity and effectiveness of the model. The results show that the index system comprehensively and accurately reflects the overall level of emergency rescue equipment support capability of professional teams. The assessment model reasonably and effectively assesses the capability level and identifies weaknesses in the current equipment support system, providing improvement points and theoretical support for the development of the team's equipment support system.

To explore the "last-mile" problem of grassroots emergency response capacity, a MCDM based on grey theory was proposed to analyze the rural emergency response capacity under conventional and unconventional states based on the resilience theory. Firstly, from the perspective of resilience, the influencing factors obtained from literature review and field investigation were selected and optimized to construct a model of the influencing factors of rural emergency response capacity. Secondly, the MCDM model was used as a framework to analyze the causality, logical hierarchy, and characteristic state of the influencing factors. Finally, the key factors for the enhancement of the rural emergency response capacity and the resilience of rural villages were identified through the multi-criteria decision-making analysis. The results show that the centrality of leadership team structure is 2.95 and the driving force is 6, which is a tier 1 factor. The centrality of village grid management is 3.08 and the driving force is 6, which is a tier 2 factor. The centrality of normative document development is 2.7 and the driving force is 5, which is a tier 3 factor. The centrality of village network construction is 3.54 and the driving force is 9, which is a tier 3 factor. Leadership team structure, village grid management, normative document development and village network building constitute decision-making intersections, which are key catch-alls for the improvement of village emergency response capacity and resilience.

To avoid worsening the consequences of oil and gas pipeline accidents due to emergency failures，the causes of emergency failure in 27 accidents at home and abroad were analyzed using the HFACS model. Based on the results of grounded theory (GT) statistical coding analysis，a classification model of failure causes of emergency response in oil and gas pipeline accidents was proposed. SNA was used to develop the relationship network of the causes of emergency failures in oil and gas pipeline accidents. The core-periphery，centrality，and association direction index analyses were used to identify core factors and factors with high association and strong mediating roles in the classification model of the causes of emergency failures in oil and gas pipeline accidents. The results indicated that the classification model of emergency failure causes in oil and gas pipeline accidents was divided into five levels: government and emergency department factors，operator organizational factors，operator unsafe supervision，preconditions for unsafe behavior of on-site personnel，and unsafe behavior of on-site personnel. The emergency failure causes were further divided into 16 bottom-level factors，among which there were 9 core factors: inadequate safety supervision by government and emergency departments，ineffective emergency rescue，regulations defects，insufficient supervision by pipeline operators，technical environment，and skill errors. Skill errors，regulations or procedure defects，technical environment，and insufficient supervision by operators were highly associated factors. Moreover，pipeline operators' regulation defects，procedure defects，technical environment，insufficient supervision，improper resource management，and decision-making errors were strong mediating factors.

To meet the growing demand for emergency talent in regional industrial development, the "government, industry, academia, research, and application" education model was used to investigate an integrated training model for emergency talent from undergraduate to master and doctoral degrees. Based on the issues of unclear hierarchical demands, core competency assessments, and knowledge system construction in the Western emergency industry, the FP-Growth algorithm was used to mine and divide the association rules between courses and corresponding competencies to evaluate core competency. Safety engineering capabilities were compared to determine the focus of capability training and explore talent training pathways. The industry-specific undergraduate "3+1/1" programs were developed by integrating industry, education, theory, and practice. Moreover, an "order-based" talent innovation training model for master and doctoral levels in government-school-enterprise cooperation was proposed. Finally, the concept of regional emergency talent full industry chain service was proposed by integrating industry-education and science-education efforts to cultivate emergency talent. The results indicated that the core competencies based on the four-tiered talent demand framework were highly associated with the curriculum system. The issues of homogeneity in talent output and competency emphasis were addressed by clarifying the training direction of safety and emergency disciplines from professional focus and application backgrounds. The proposed concept of regional emergency talent full-industrial chain service closely connected educational content with market demands, enhancing students' comprehensive abilities and contributing to the regional closed-loop development of the modern emergency management industry.

To improve the theory and practice of nuclear power plant emergency management, a bibliometric approach was applied using CiteSpace software. A total of 355 relevant journal articles were collected from the Web of Science (WoS) database. By constructing mixed co-occurrence maps of researchers and institutions, keyword co-occurrence maps, keyword clustering maps, and visual timelines of keyword frequency, the research hotspots, focal areas, research forces, development paths, and frontier trends in nuclear power plant emergency management were analyzed in detail. The results indicate that, influenced by the Fukushima nuclear accident, research on nuclear power plant emergency management has gradually increased since 2011, particularly in the areas of risk assessment and decision support. The field of nuclear power plant emergency management research is characterized by its diversity, with a primary focus on accident management, performance monitoring, decision-making, and the application of simulation technologies. The interconnections between these topics fully demonstrate the complexity of nuclear power plant emergency management as an interdisciplinary field. Currently, the nuclear power sector is undergoing rapid development, and the emergency management system and capabilities of nuclear power plants are continuously improving to reduce the risks of potential accidents. Research from the perspective of interdisciplinary collaboration on emergency preparedness, response, and strategies for nuclear power plants has become a significant and emerging research direction.

To explore the effects of dust-generating processes and environmental fluids on the spatiotemporal distribution of dust during the regeneration of old industrial buildings, field studies and numerical simulation was used to investigate the dust suppression characteristics inside the construction area under different processes and background wind speeds. The dust source characteristics in the main processes of re-construction of old industrial buildings were clarified by analyzing a large amount of dust data. Typical old industrial buildings were selected for the field studies of sanding and shoveling processes. Numerical simulation was performed to investigate the dust mass concentration distribution under five inlet air velocities and two dust-producing processes and to identify the dust-aggregation zones in the reclaimed construction space of old industrial buildings. The results showed that the maximum dust mass concentration in the grinding process exceeded the limit by 54 and 37 times in the grinding and shoveling process, respectively. Inlet wind speed affected the dust mass distribution of the main impact zones in the construction area and changed the process of dust reaching dynamic equilibrium. Dust-generating process variations impacted on the dust mass concentration distribution. Moreover, the workload increase of a specific process resulted in a proportional increase in downstream dust mass concentrations. This study can provide fundamental knowledge for dust management in the reconstruction of old industrial buildings.

In order to evaluate the heat strain level of outdoor workers in high temperature environments, a thermal response model was developed based on the Tanabe 65 thermal physiological model and principles of heat transfer by modifying basal metabolic rate and human surface area of the original model. The experimental data in the literature and the Tanabe model were used to verify the effectiveness of the model. The results show that the predicted results of the high temperature human thermal reaction model established under different solar radiation intensities are in good agreement with the experimental results. In an extreme environment of 36 ℃ and high solar radiation intensity, the maximum difference between the simulated value of core temperature and the measured value is less than 0.24 ℃, and the maximum difference between the simulated value of average skin temperature and the measured value is less than 0.3 ℃, which is more accurate than the classic Tanabe model. Thus, the established model can be used to predict human physiological parameters such as human core temperature and skin temperature in hot environments in the presence of solar radiation, and provide reference for the safety assessment of outdoor workers.

To improve construction workers' safety behavior and enhance the effectiveness of both implicit and explicit safety attitudes on this behavior，this study investigated the interaction between these two types of attitudes and their combined influence on safety behavior. First，an experiment was designed to measure the implicit safety attitudes of construction workers，and Implicit Association Test(IAT) was used to evaluate underlying attitudes. Then，the relationship between implicit and explicit safety attitudes was analyzed based on an explicit safety attitude scale. Finally，the study examined how the three components of both implicit and explicit safety attitudes—cognitive，emotional，and behavioral tendency—affected safety behavior. The results show that construction workers generally exhibit positive implicit safety attitude. However，the correlation between implicit and explicit safety attitudes is weak. Explicit safety attitude，particularly the overall，emotional，and behavioral components，has a significant positive effect on safety behavior，while the correlation between implicit attitude and safety behavior remains weak. When implicit and explicit safety attitudes are aligned，their correlation with and explanatory power for safety behavior increases.

To address the classification and quantitative evaluation issues of unsafe behaviors of civil aviation pilots，the management mode of their unsafe behaviors was optimized. A method for managing unsafe behaviors in civil aviation was proposed focusing on the civil aviation flight field. Firstly，based on unsafe behavior classification theory，intervention and improvement methods for errors and violations were systematically analyzed to distinguish between errors and violations. Secondly，expert interviews and questionnaire surveys were used to propose a classification evaluation index system for pilots' unsafe behaviors. Furthermore，a classification method of unsafe behavior based on quick access recorder (QAR) data and a quantification method of unsafe behavior scores based on flight operations quality assurance (FOQA) were proposed to achieve a classified quantitative assessment of pilots' unsafe behaviors. Finally，the classifying and managing unsafe behavior process was analyzed and validated. The results indicated that the 68 FOQA monitoring events obtained by screening and calculation had different causal behavioral tendencies. The two proposed unsafe behavior classifications and quantitative evaluation methods can be combined with QAR data or FOQA records. Moreover，the classification management of unsafe behaviors of civil aviation pilots can be achieved by combining intervention and improvement measures for errors and violations.

In order to achieve a more comprehensive and effective investment in coal mine safety，aiming at the influence of psychological and behavioral factors of decision makers on safety input decisions，a regret theory-CBDT safety input decision model was established. By analyzing the applicability of CBDT in coal mine safety input，safety input decision-making index system was established from two aspects of safety input and safety output，based on improved DS evidence theory and the evaluation of experts，and the weights of each indicator were calculated. In the CBDT perspective，based on regret theory，the comprehensive perceived utility of each scheme was calculated，and the optimal safety investment scheme was selected according to the comprehensive perceived utility. The result shows that in the process of safety input，policymakers pay more attention to safety facilities and safety technology input，and policymakers will try to avoid safety input schemes with unsatisfactory safety output. The model is based on historical safety investment cases and combines the subjective psychology of decision-makers，it helps select the optimal safety input scheme，makes safety investment decision-making more objective and scientific.