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.

In order to study the effect of different metal doping and precipitation temperatures on the catalytic oxidation of CO by Cu-Mn type catalyst, the co-precipitation method was used to prepare Cu-Mn type CO catalyst, and the catalytic oxidation of CO by Cu-Mn type CO catalyst under different metal doping and precipitation temperatures was tested and analyzed. The pore characteristics and surface crystal structure of the catalyst were obtained by automatic physical adsorption analyzer and X-ray diffraction (XRD). The reaction process of catalytic oxidation of CO was revealed by in-situ diffuse reflection infrared spectroscopy, and the potential application of the catalyst in coal mines was introduced. The results show that during the test time (within 80 s), with the increase of reaction time, the volume fraction of CO gradually decreased, slowly increased and then tended to be flat, and the amount of reactive CO substance gradually increased. The better the catalytic oxidation of CO, the larger the specific surface area, the smaller the average pore size and the larger the total pore volume. When the doped metals are Sn, Fe and Ce, the catalytic oxidation characteristics of the three catalysts are as follows: CuMnO_x-Ce>CuMnO_x-Sn>CuMnO_x-Fe, the amount of CO involved in the reaction was 0.015 3, 0.009 3 and 0.020 3 mol, and the removal efficiency of CO was 61%, 47% and 77%, respectively. When the precipitation temperature is 70 ℃, the number of crystal nuclei of the catalyst is significantly higher than that of the precipitation temperature is 60 and 80 ℃. When the precipitation temperature is 60, 70 and 80 ℃ respectively, the catalytic oxidation characteristics of the three catalysts are as follows: CuMnO_x-Ce-70>CuMnO_x-Ce-80>CuMnO_x-Ce-60, the amount of CO involved in the reaction was 0.019 45, 0.020 3 and 0.019 8 mol, and the elimination rates of CO were 74%, 77% and 75%, respectively. Abundant surface oxygen vacancy is the key factor to improve the performance of CO oxidation reaction and catalytic oxidation. The presence of CeO₂ contributes to the formation, oxygen activation and migration of carbon-containing species.

In order to solve the problems of ambiguity and randomness in the process of fire safety resilience assessment of subway stations, and then effectively improve the level of fire safety resilience of subway stations, the fire safety resilience assessment model for subway stations based on WSR- extension cloud theory was constructed. First, based on WSR methodology, the factors affecting the fire safety resilience of subway stations were analyzed around "physical", "matter", and "human". Combined with the characterization of the resilience absorption, resistance, recovery and adaptive ability, the fire safety resilience assessment index system of subway stations was established in five aspects, namely, equipment factors, environmental factors, organization and management, material and technology, and personnel factors. Second, based on the blind number theory to construct the blind number matrix and calculate the comprehensive score of qualitative indexes, the fire safety resilience level of subway stations is derived by using the theory of extension cloud theory. Finally, a station of the Qingdao subway was used as an example to carry out the example analysis. The results show that the subway station fire safety resilience level of Ⅳ, in the higher resilience level, the credibility factor = 0.003 4 <0.01, indicating that the assessment results have a high degree of credibility. The WSR-extension cloud theory assessment model can provide a theoretical basis for the fire safety resilience assessment of subway stations.

In order to reduce the losses caused by landslide disasters, taking Changde city of Hunan province as an example, the fractal theory and information method were applied to evaluate the regional landslide susceptibility based on field investigation and historical landslide data. The sensitivity of influence factors was quantitatively studied by fractal theory. The information values of each secondary impact factor were obtained by using the information method, and the comprehensive information values were obtained by combining the fractal dimension value and the information value. Based on the values, the susceptibility zoning of the study area was carried out. The results show that the slope, engineering geological rock group, elevation and vegetation are the influencing factors that have a second-order cumulative and fractal distribution with the landslide, while other influencing factors have a first-order cumulative and fractal distribution with the landslide. The areas of very low, low, medium, high and very high susceptibility areas respectively account for 5.24%, 8.84%, 35.06%, 39.21% and 11.65% respectively. Annual rainfall greater than 1 600 mm, slope of 20-30° and elevation of 900-1100m are important factors.

In order to explore the evolution mechanism and the best joint strategy for improvement of construction safety climate, with the help of the Mobius ring structure, a three-factor structure model of construction safety climate based on cognition-behavior-environment was constructed from three dimensions of construction workers' cognition, behavior and environment. According to the classification standard of the three-dimensional spatial structure model of construction safety climate, the grading standard of safety climate was divided. The DBN was used to study the changes of the construction safety climate with time. The results show that in terms of influencing factors, safety incentives have the greatest impact on enterprise safety climate and its evolution. In terms of dimensions, the behavioral dimension has the greatest impact. The best joint strategy to improve the construction safety climate is to strengthen the control of safety incentives, safety supervision, workers' safety response, safety consciousness and workers' learning and communication in turn.

A thermal safety warning system was established based on a thermoregulation model to mitigate the thermal safety risk for elderly people under high-temperature conditions. Firstly, the improved model suitable for the elderly was established by replacing the physical model and adjusting the active and passive systems in the classical model. The improved model was validated using publicly available experimental data. Secondly, the model simulated the temperature changes in elderly people in high-temperature environments. Statistical analysis was used to assess the impact of various parameters on thermal safety. Finally, based on the analysis results and the improved model, a thermal safety risk warning system for elderly people was developed. The warning system was demonstrated through a case study. Results indicate that the improved model accurately simulates the body temperature of elderly people, with a root mean squared error less than 0.12 ℃. Physical activity intensity significantly impacts thermal safety, with a standardized regression coefficient β larger than 0.8. As heat exposure time increases, the impact of activity intensity on thermal safety is decreased (β decreases from 0.945 to 0.806), while the influence of environmental factors is increased (β of temperature and humidity increases from 0.249 and 0.137 to 0.370 and 0.348). In the case study, the safe duration of continuous activities for the resting and working elderly people in Baoding/Hong Kong is 172/175 minutes and 108/122 minutes, respectively. The highest thermal safety risk period for elderly people on that day is between 17:00 and 18:00.

In order to improve the evacuation efficiency of the teaching building, the classroom structure was optimized through control experiments and numerical simulations to enhance evacuation efficiency. Emergency evacuation tests were used to obtain the movement characteristics of students aged 6-7 years old. And Pathfinder simulation software was used to study the impact of desk layout, classroom door position, and exit position on evacuation. The results indicate that for a single classroom, although shortening the pre-action time can reduce the overall evacuation time, it cannot improve the congestion caused by the building structure. Appropriate evacuation routes and desk layouts can significantly reduce evacuation time. For buildings with classrooms on one side of the corridor, increasing the width of the corridor and exit is the most effective way to improve evacuation efficiency. For buildings with classrooms on both sides of the corridor, the structure of the evacuation corridor inside the building, including the number of corridors and the intersections inside the corridors, is the most important factor affecting evacuation time. Therefore, it is recommended to develop optimization plans for classroom evacuation structure from different aspects.

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 improve the detection efficiency and automation level of detecting road surface pits and grooves in road safety inspection work, reduce the probability of traffic accidents. A road surface pit and groove hazard intelligent detection model based on an improved YOLOv5s was proposed. This method incorporated the ASFF module into the original YOLOv5s network, replaced the backbone network with the FasterNet network, and further introduced the Efficient Channel Attention (ECA) module. Ablation experiments are conducted to analyze the effect of the improved module on performance of the detection model, to verify the target detection effect, and to develop an interactive visualized detection interface. The results show that the improved model accuracy, recall rate, and average detection accuracy have increased by 4.1%, 9.9% and 5.6% respectively. Compared to the original network, the improvement is significant. It demonstrats good detection performance that meets the application requirements for automated detection of road surface pits and grooves, thereby enhancing inspection efficiency and effectively reducing traffic accidents caused by road surface pits and grooves.

To reduce the risk of rear-end collisions in highway tunnels, a tunnel reflective strip space model was proposed based on the tunnel reflective strip characteristics with depth perception information. The effects of spacing between tunnel reflective strips on vehicle distance maintenance were examined through driving simulation tests. Braking headway, minimum headway, following headway, and minimum collision time were selected as evaluation indicators. The results indicated that when the leading vehicle traveled at speeds between 40-80 km/h and the following vehicle approached at a speed 20 km/h higher than the leading vehicle, if the following driver received 3-4 visual stimulations from the reflective strips before the distance between the leading and following vehicles was less than the minimum safe distance, the braking headway, minimum headway, and the time headway under stable car-following conditions was improved by 27.6%-56.6%, 54.2%-60.3%, and 20.1%-31.6%, respectively. Furthermore, the minimum collision time was increased by 34.7%-60.5% once the leading vehicle braked urgently, reducing the probability of a rear-end collision. Therefore, tunnel reflective strips enhance drivers' perception of speed and distance perception ability, reducing the risk of rear-end collisions.