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.

With the continued increase in international students in China, the problem of their academic adaptation has become increasingly prominent. Analysis of the factors affecting the academic adaptation of international students and corresponding management practices can suggest measures to improve their academic adaptability. Based on grounded theory, this paper first summarizes the four main factors affecting the academic adaptation of international students, then uses structural equation modeling to construct a model of academic adaptation of international students that is tested and verified by a questionnaire survey of 2540 international students in China (51% male, 49% female). The main conclusions of this paper are as follows: (1) learning communication, course learning, and self-regulation are the main factors affecting the academic adaptation of international students, of which course learning is the most important factor; (2) academic communication and course learning have significant positive effects on self-regulation, while academic communication, course learning, and self-regulation have significant positive effects on the academic adaptation of international students; and (3) there was no significant difference in academic adaptation between genders, though there were significant differences by age. Among them, the mean score for overseas students is the largest for those older than 41 years (M = 4.79; SD = 0.33), showing that these students are most adaptable to study. Accordingly, this study advances policy suggestions for strengthening international students’ academic adaptation on the part of both universities and the government to improve the academic adaptation ability of international students in China.

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.

With the gradual formation of the open pattern of higher education, the issue of the cultural integration of international students has attracted increasing attention. This paper conducts a questionnaire survey of 2000 international students in East China and used factor analysis and structural equation modeling to determine the effects of life adaptation, interpersonal communication, and the training system on cultural integration, with possible moderation by study adaptation to explore the cultural integration of international students. The study found that the cultural integration of international students is jointly affected by multiple realistic factors, among which life adaptation and interpersonal communication are the primary factors. Furthermore, the training system has an important impact on the cultural integration of international students through study adaptation. Accordingly, we argue that universities should strengthen the informal organization and guidance of international students, enhance individual adaptability, promote international exchange, cooperation, and policy regulation of international students, and improve their training system. Moreover, efforts are needed to improve the study adaptability of international students so as to comprehensively address the problem of the cultural integration of international students and improve their education level.

Based on prior research on the relationship between personality and safety behavior, we construct a moderated mediation model that tests the effects of each of the Big Five personality traits (openness, conscientiousness, agreeableness, extraversion, and neuroticism) on the safety behavior of gold miners in Ghana. The model included safety competency as a mediator between the five personality traits and safety behavior. Management commitment to safety was used as a moderator to test the strength of the mediation of safety competency. Data was collected from 344 individuals employed across six large-scale gold mining companies in Ghana using a questionnaire survey. Amos 26 was used to conduct confirmatory factor analysis. The hypotheses were tested using Hayes PROCESS macros models 4 and 7 on SPSS 26. Findings show that openness and extraversion have an insignificant direct influence on safety behavior. Neuroticism negatively affects safety behavior. In contrast, conscientiousness and agreeableness positively affect safety behavior. Mediation analysis revealed that safety competency partially mediates the relationships between (1) conscientiousness and safety behavior and (2) agreeableness and safety behavior. The relationship between extraversion and safety behavior is fully mediated by safety competency. Additionally, we discovered that management safety commitment has a significant conditional indirect effect (Index of moderated mediation = 0.168 95% CI = [0.122;0.222]) on the relationship between conscientiousness and safety behavior through safety competency. Management safety commitment also significantly moderated (Index of moderated mediation = 0.075 95% CI = [0.021;0.120]) the relationship between agreeableness and safety behavior through safety competency.

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.