In order to deeply analyze the influence of pedestrian panic on the efficiency of group evacuation under emergencies，this paper analyzed and constructed a quantitative model of pedestrian panic，which was composed of two parts: self-panic and panic propagation. First，the model considered factors such as detention time，local density，the distance between pedestrians and exits，the density of people around and the spread of pedestrian panic. Then it was introduced into the social force model for improvement and optimization，and verified the validity by comparing with the classical phenomenon. Finally，the simulation analysis was carried out using Anylogic software. The results show that the improved model can better reflect the evacuation movement characteristics of pedestrians in panic situations. A moderate panic，such as when the panic degree is less than 0.3，can accelerate the evacuation speed of the crowd. A high panic，such as when the panic value is greater than 0.3，can exacerbate the bottleneck phenomenon，and the so-called "fast is slow" phenomenon occurs，thus reducing the evacuation efficiency.

To propose effective safety cognition strategies and reduce safety accident occurrence，it is urgent to investigate the safety cognition evolution process and characteristics of cavern constructors. Firstly，factors affecting safety cognition were identified based on a complex sociotechnical system and the 24Model，then a safety cognition index system of cavern constructors was proposed. Subsequently，the weight coefficient was calculated by the Super Decisions software. Then，system dynamic (SD) theory was used to determine the interrelationship between factors. SD numerical model was developed including organizational safety management and other three subsystems，and a cognitive correction mechanism based on safety investments was applied to the SD numerical model. Finally，numerical simulations and sensitive analysis were performed by the SD model based on actual engineering examples. The results indicated that the overall safety cognition of cavern constructors changed from a downward trend to an upward trend by increasing safety investments and exerting the role of cognitive correction mechanisms with project processes. Organizational safety management has the greatest influence on the safety cognition of carve constructors，which was consistent with the assumption of 24Model. Therefore，the scientific performance of the SD numerical model was validated.

In order to achieve the goal of cost reduction and efficiency enhancement in emergency logistics，the comprehensive capabilities of emergency logistics suppliers were evaluated from the perspective of suppliers by utilizing the Cloud TOPSIS. Based on the characteristics of emergency logistics，a comprehensive evaluation index system for emergency logistics suppliers was constructed from five aspects: emergency response capability，material quality，cost control，emergency response flexibility，and internal and external conditions of the enterprise. Drawing on the ideas of game theory，the objective weights obtained by the improved entropy weight method and the subjective weights obtained by Analytic Hierarchy Process (AHP) were taken as game opponents to determine the optimal combination of weights. The cloud model was used to solve for the decision-making cloud of indicators and the weighted cloud for the fuzzy qualitative evaluation semantics quantification of suppliers. Finally，using the TOPSIS method，the positive and negative ideal solution sets were constructed，and the relative closeness to these ideal solutions was determined by calculating the distance of alternative solutions，thus identifying the optimal alternative. Research indicates that the Indicator Decision Cloud can accurately quantify evaluative language，and the results of the Cloud-TOPSIS method are more reasonable. In the ranking of suppliers' relative closeness，the difference between the best and worst calculated by the Cloud-TOPSIS method is 0.331 5，while for the TOPSIS method，it is 0.088 2，with a difference of 0.243 3. This suggests that the evaluation results of the Cloud-TOPSIS method have a greater degree of differentiation，which can more intuitively assist decision-makers in making optimal choices.

In order to reduce the risk of fire and explosion caused by the anthraquinone process for preparing hydrogen peroxide，a risk assessment was carried out for the extraction and purification process，which is a more hazardous process in the preparation of hydrogen peroxide by anthraquinone process. Firstly，hazard sources of this process were analysed and a fault tree model was established through Freefta software. On the basis of this fault tree model，a dynamic Bayesian network model was drawn using GeNIe software. Secondly，expert scoring method and fuzzy analysis method were used to calculate prior probabilities of basic events in the model，and GeNIe software was used to calculate posterior probabilities of basic events in the model under the set preconditions. Eventually，by comparing change range between prior and posterior probabilities，important basic events were determined，hazard sources causing fire and explosion were revealed，and emergency response technologies were proposed. The results show that four major hazard sources，including pollution caused by degradation products and impurities，generation and increase of side reactions in process，catalyst failure and active chemical properties of hydrogen peroxide，have great impact on fire and explosion during extraction and purification process. It is more effective to develop emergency response technologies for important hazard sources from the perspective of preventing fire spread and liquid evacuation.

A comprehensive database of MWSA was established to facilitate the data management to reduce the frequency and severity of accidents in China. From 2010 to 2022，a total of 278 records，supervised by the State Council Security Committee，were collected and stored in the MWSA database. The distribution of accident characteristics was explored for the dimensions of accident time，space，industry and type，which were examined in this study. Using a comprehensive quantitative index，a quantile regression model was developed to identify factors that significantly influenced accident severity. The results show that in terms of spatial and temporal distribution，the number of accidents and deaths from June to September is at the peak，and the number of accidents and deaths on sunny days (7:00-18:00) accounts for as much as two-thirds. There are fewer accident records in Beijing，Tianjin，Jiangsu，Zhejiang，Shanghai and Fujian. Explosions and vehicle injuries notably stand out as the primary accident types. At a significance level of 0.05，the accident severity is correlated with various factors，including accident type，date，season，sunlight，weather，average temperature，company staff size and company establishment time. Weather and average temperature emerge as pivotal factors influencing low-severity accidents. Moreover，enterprises with less than 100 employees are more prone to severe accidents.

In order to reduce the casualties and property losses and improve the emergency rescue capability in coal and gas outburst accidents，an SSA optimized SVM was proposed to evaluate the emergency rescue capability of coal and gas outburst accidents. First，according to relevant literature and research reports，four first-level indicators，including emergency prevention ability，emergency preparedness ability，emergency response ability and recovery and rehabilitation ability，were constructed. These indicators were further subdivided into 18 second-level indicators，and the score data of each indicator was used as the model training dataset. Then，the network analytic Hierarchy process (ANP) and entropy weight method (EWM) were used to determine the subjective and objective weights of each evaluation indicator under the mutual influence，and the Lagrange function was used to merge the weights to obtain the optimal weights. SSA optimized the radial basis parameters g and penalty factor C of SVM. The result of optimal weight calculation was used as the input of the SSA-SVM model，and the expected value was used as the output for linear regression prediction. Finally，taking a mine in Hebei Province as an example，the prediction results of the SSA-SVM model was compared with the traditional SVM，particle swarm optimization algorithm (PSO) optimization SVM and Whale optimization algorithm (WOA) optimization SVM，and the predicted results and the expected values were analyzed. The results show that the prediction results of the SSA-SVM model are consistent with the reality，and the average absolute error decreases by 8.04%，5.15% and 4.82%，respectively，compared with other models，which proves the superiority of the proposed model. This model can be applied to the evaluation of the emergency rescue ability of coal and gas outburst accidents in actual mines.

The purpose of this research is to clarify the research progress of the theory and technology of pedestrian abnormal behavior recognition in public places. Firstly，with the help of China National Knowledge Infrastructure (CNKI) and the Web of Science (WOS)，a broad definition and universal characteristics of abnormal pedestrian behavior in public places were given. The existing research results related to abnormal behaviors were divided into three categories: harmful behaviors，dissociable behaviors and violations. Then，from the perspective of data and technological foundations，the existing abnormal behavior recognition methods were divided into four categories: artificial design，human skeleton，Red Geen Blue(RGB) images and wearable sensors. Secondly，this study sorted out the abnormal behavior datasets of mainstream populations both domestically and internationally，and analyzed the performance of relevant algorithms on the datasets. Finally，the limitations of existing research methods in available datasets and data fusion detection were summarized，and future research directions and optimization suggestions were provided. The results indicate that these four types of abnormal behavior recognition methods have their own advantages and disadvantages. It is necessary to construct a diversified，well-defined and high-quality international benchmark dataset of abnormal behaviors among the crowd. Future research should focus on robust and accurate methods，models，and algorithms for identifying abnormal behaviors，explore multi-dimensional data fusion complementary detection methods，improve the application scenario consistency and adaptability of the theoretical results of abnormal behavior recognition，and eventually enhance the level of public place crowd safety governance.

In order to explore the response mechanism of spontaneous combustion of underground coal and surface carbon flux under complex conditions，the surface CO₂ flux，soil temperature，fissure，environmental wind velocity and soil humidity in Fuxin Haizhou open-pit mine were monitored for a long time. Field tests，data analysis and other methods were applied to study the correlation between the underground coal spontaneous combustion and surface CO₂ flux. The influence of surface fissure，environmental wind velocity and soil humidity on the response mechanism of underground coal spontaneous combustion-surface carbon flux were analyzed. The results show that underground coal combustion is the fundamental factor triggering the abnormal surface carbon flux response. The surface CO₂ flux is positively correlated with the surface soil temperature distribution. Surface fissure is the main factor affecting the surface CO₂ flux distribution. The environmental wind speed is positively correlated with the surface CO₂ flux. The surface CO₂ flux response is generally not affected by soil humidity，but it is greatly affected by rainfall.

Deep mining faces high temperature and pressure environments. In order to understand the adsorption gas characteristics of deep coal bodies，an isothermal adsorption experiment was conducted to study the coal's adsorption characteristics under different temperature and pressure conditions. The experiment examined both macro and micro perspectives of the coal's adsorption behavior，molecular dynamics isothermal adsorption simulation and theoretical analysis. The research results are as follows. When the gas pressure is low，the difference between the absolute adsorption capacity and the excess adsorption capacity is not significant at different temperatures. When the gas pressure is high，the difference between the absolute adsorption capacity and the excess adsorption capacity increases with the increase in temperature. In the process of gas pressure increasing from 0 to 20 MPa，the amount of gas adsorbed by coal is divided into three stages. The growth pattern consists of a rapid increase during the initial stage，followed by short-term stability in the middle stage，and a gradual decline in the later stage. The equal adsorption heat and adsorption capacity of coal meet the exponential function relationship. The temperature-pressure adsorption model based on the equivalent adsorption heat deduction can predict the quantity of isothermal adsorption gas under any temperature and pressure conditions，and the relative error between the measured and predicted values is 10%.

To deeply explore whether ride-hailing services directly affect road traffic crashes and whether they indirectly affect road traffic crashes by changing the usage of buses and private cars，based on panel data from 114 cities in China from 2005-2017，the multi-period DID method and the causal steps method are used to estimate the impact mechanism of ride-hailing services on road traffic crashes. The results show that the entry of ride-hailing services increases road traffic crashes by 5.6%，and this conclusion still holds after performing robustness tests such as parallel trend and Propensity Score Matching-DID. Ride-hailing services significantly reduce public transit usage by about 19.3%，thus indirectly increasing road traffic crashes by 0.71%，which indicates that public transit usage plays a mediating effect. Ride-hailing services significantly increase private car usage by 5%，thus indirectly increasing road traffic crashes by 0.14%，which indicates that private car usage plays a mediating effect. The decrease in public transit usage is an important reason why ride-hailing services indirectly increase road traffic crashes. Government departments should strengthen the regulation of ride-hailing services，while encouraging the synergistic development of ride-hailing services and public transit.