As a typical public place, a university library has a large collection of books with heavy fire load, dense population, and large flow of people. The situation of safe evacuation in case of fire is very serious. This study utilizes Revit, Pyrosim, and Pathfinder software to research evacuation of a university library. First, a Building Information Modeling (BIM) is constructed based on Revit software in 1:1 scale. Second, the evacuation passage with the highest utilization rate was determined through Pathfinder software. According to the “most unfavorable principle,” the location near it was assumed to be where the fire occurred. Pyrosim software was used to determine the smoke spread, visibility, CO concentration, temperature, and other conditions at each stairway exit in case of fire. Finally, the evacuation situation is compared with that after man-made route planning. The results indicate that evacuation exits 1#, 7#, 13#, 19#, and 23# have the highest utilization rate. The safe evacuation time was 739.275 s, which was shortened to 638.025 s after man-made route planning, a 13.67% increase in evacuation efficiency. Evacuation efficiency can be significantly improved by increasing broadcast guidelines, adding signs, widening staircases, and other optimization suggestions, which can provide reference for the study of evacuation effects in public places and the improvement of the layout of public facilities.

At present, major public health emergencies frequently occur worldwide, and it is of great significance to analyze the research status and latest developments in this field to improve the ability of public health emergency management in various countries. This paper took 5,143 related studies from 2007 to 2020 from the Web of Science as research object and used CiteSpace, VOSviewer, and other software to perform co-word analysis, social network analysis, and cluster analysis. The results and conclusions were as follows: (1) the related research identified three periods: the exploration, growth, and outbreak period; (2) chronologically: the relevant research evolved from medical and health care for major diseases to emergency management and risk assessment of public health emergencies and then researched the novel coronavirus (COVID-19) pneumonia epidemic; (3) clustering analysis of high-frequency keywords, identifying three research hotspots: “disaster prevention and emergency medical services,” “outbreak and management of infectious diseases in Africa,” and “emergency management under the COVID-19 pneumonia epidemic.” Finally, this study combined the data and literature analysis to point out possible future research directions: from the research of the COVID-19 pneumonia epidemic to the research of general major public health emergencies, thinking and remodeling of the national public health emergency management system, and exploring the establishment of an efficient international emergency management cooperation mechanism.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.

Global warming and other climate issues seriously threaten global sustainable development. As citizen environmental behavior can have a positive impact on the environment, it is of great theoretical significance and practical reference value to study the impact of psychological distance theory on citizen environmental behavior. This study obtained 2,633 related studies from 1980 to 2020 from the Web of Science as research objects, and used CiteSpace, VOSviewer, Netdraw, and other software programs to perform a bibliometric analysis, which can show the complex relationship implied in the citation, intuitively grasp the development context and hot frontier in this field, and help scholars better study citizen environmental behavior. The results and conclusions are as follows. (1) The related research covered three periods: infancy, growth, and outbreak. (2) Chronologically, the relevant research evolved from building models to analyze the impact of environmental enrichment on human environmental behavior, to studying the motivation behind citizen environmental behavior, and finally to paying attention to sustainable development. (3) A cluster analysis of high-frequency keywords identified three research hotspots: “drivers of psychological distance of environmental change perception on citizen environmental behavior,” “impact of social distance on adolescents’ behavior,” and “construal level theory and citizen green behavior.” Based on these findings, possible future research directions were identified, including changing from a single theory to a combination of multiple theories to comprehensively study citizen environmental protection behavior; analyzing the motivation of citizen environmental behavior and summarizing the general motivation of environmental behavior according to its internal relationship; and determining how to narrow the global psychological distance, strengthen the awareness of the community of human destiny, and explore the establishment of an efficient global climate cooperation mechanism.