In order to effectively reduce the casualties and property losses caused by cabin turbulence，a decision analysis method based on a dynamic Bayesian network is proposed for cabin turbulence emergency response. Firstly，according to the relevant laws and regulations at domestic and international，combined with the emergency duties of key personnel on the ground and in the air，the turbulence emergency disposal process is analysed from pre-flight，in-flight and post-flight，and 24 key events are selected to construct a structured BT model. Secondly，the mapping conditions and transformation rules are established to form a DBN model. Then，the objective direct node a priori probability and the supplementary node fuzzy probability obtained by the triangular fuzzy probabilities of supplementary nodes obtained by the fuzzy number expert judgement method to obtain the a priori probabilities of all nodes. Finally，the time slice intervals of 1 and 2 min are selected to focus on the simulation inference of moderate and heavy turbulence，and to study the characteristics of the influence of each dynamic element on the failure of cabin turbulence event disposal. The results show that: the emergency response nodes are significantly affected by the degree of turbulence and time changes，and the optimal time for emergency response is within 5 min. Among them，the probability of failure for the failure of the crew fixation measures in place increases with the increase of the degree of turbulence，human factors such as the failure of the crew to fasten the seat belts and the over-servicing by the cabin crew are the key reasons for the failure of the response.

The construction speed and number of vehicles passing through multi-portal underpass tunnels are increasing，and special driving environments such as long downhill slopes at the portals and underground merging areas cause frequent traffic accidents. To deeply analyze the variation characteristics of lateral offset in tunnel special sections，a test vehicle equipped with inertial navigation and Mobileye was used to perform a real-car natural driving test in a typical multi-entry underpass tunnel. Based on the tunnel's alignment and spatial variation characteristics，the test section was divided into the external tunnel section，the downhill section，the internal section，the underground merging section，and the internal tunnel section. The results indicated that the vehicle trajectories in the other four sections were more complex than the external section of the tunnel，and the lateral offset increased significantly，reaching up to 1.888 to 2.184 times the external section of the tunnel. The lane offset variation rate of the underground merging section and the inner section of the entrance was the smallest. The land discreteness of the underground merging section in the tunnel was the largest，and the discreteness of the entrance downhill section was the smallest with a standard deviation of only 0.111. The average width of the predicted interval of the entrance downhill and underground merging sections were 0.2 m and 0.24 m，respectively. Therefore，the driving safety hazards of the entrance downhill and underground merging sections were higher than other sections.

In view of the difficulties in obtaining instability data of open-pit mine dump and the small amount of sample data，a discrimination model of slope stability state of open-pit mine dump based on migration learning algorithm was proposed. According to the actual geological conditions and rainfall conditions of the dump slope of F open-pit mine in Shaanxi Province，a similar simulation test scheme of slope with different soil-rock mixing ratio was designed under the condition of rainfall. The data of water content，earth pressure and pore water pressure of the slope model were collected and processed. Considering the influence of small sample data set on the classification accuracy of GBDT model，using the idea of transfer learning，the sample weight of source domain data set and target domain data set was iteratively updated by TrAdaBoost algorithm，and the GBDT model was used as the weak learner for data sample training. Finally，according to the classification result of the weak learner，the weighted majority voting method was used to generate a TrAdaBoost-GBDT dump slope stability discrimination model based on transfer learning to improve the discrimination accuracy of small sample data label categories. The results show that the proposed dump slope stability state discrimination model has a better performance in judging the stable state than other algorithm models，and the values of accuracy，precision，recall and area under curve(AUC) are 93.3%，87.5%，100% and 93.8%，respectively. Compared with other algorithm models，this model can improve the accuracy of slope stability discrimination of small sample data sets，and make up for the low accuracy of machine learning classification results of small sample data sets.

To explore the changing characteristics of driving load under the construction environment of underground tunnels，a test platform was built，and a driving simulation test was carried out in the construction environment of underground tunnels to obtain the driver's eye movement and ECG data. Taking the time domain index of heart rate，heart rate variability(HRV) and average number of blinks frequency(BF) as parameters，a comprehensive evaluation model of driving load based on factor analysis and entropy was constructed，and a classification method of driving load based on K-means clustering algorithm was proposed. The results show that in the relatively monotonous environment of non-construction sections in underground tunnels，psychological pressure could more accurately reflect the driving load compared to visual pressure. However，in the complex and changeable environment of construction section，psychological pressure is prone to being influenced by driving operations. Moreover，when line of sight is limited，a single visual pressure indicator tends to overestimate the driving load. The comprehensive evaluation model of driving load based on eye movement and ECG exhibits high sensitivity and good stability，and can correct the evaluation results of a single indicator and effectively quantify driving load.

In order to explore the effects of organizational ethical climate (specifically，caring，rule-based and self-interested climates) on coal miners' safety performance，a chained mediation model containing positive emotions and role-width self-efficacy was constructed. Firstly，a questionnaires survey method was used to gather data from 432 front-line male coal miners across three large-scale coal mining enterprises located in Liaoning，Henan，and Shandong Provinces. Secondly，with positive emotions and role-width self-efficacy as joint mediating variables，and safety performance as the dependent variable，a structural equation model was constructed. And the mediating effect was tested using the Bootstrap method. Finally，the survey data were empirically analyzed using path analysis and mediation effects testing methods. The results show that the mediating effects influence safety performance. Specifically，the caring climate indirectly enhances safety performance through enhancing positive emotions and role-width self-efficacy. the rule-based climate mainly affects safety performance through role-width self-efficacy，whereas the self-interested climate exerts a negative effect. Notably，fostering a caring and rule-based ethical climate，along with enhancing employees' positive emotions and role-width self-efficacy，can significantly improve coal miners' safety performance.

In order to timely and effectively detect the explosives hidden in luggage，packages and individuals，multiple explosives such as trinitrotoluene(TNT)，hexahydro-1，3，5-trinitro-1，3，5-triazine(RDX)，triacetone triperoxide(TATP)，ammonium nitrate(AN)，etc. were detected by FQ and IMS instruments by wiping and aspiration sampling methods. The comparison was mainly made from two aspects: alarm time and recovery time. The experimental results show that under wiping sampling，the average alarm time of FQ is about 2 seconds less than that of IMS，and the average recovery time is about 30 seconds less than that of IMS，which has higher detection efficiency. In the case of aspirated sampling，FQ instruments can detect TNT and TATP，while it is difficult for IMS instruments to detect explosives.

In order to improve the durability of the construction machine and the working environment in deep buried tunnels，based on the theory of gas-solid two-phase flow，CO and dust were selected as the main objects of study，and a physical model of deep buried tunnels was established by Fluent software. The effects of different surrounding rock temperatures and the outlet speed of the wind pipe on the transport process of soot in deep tunnels were investigated through simulation. The results show that after blasting，CO is uniformly distributed in the throwing area. With the increase of ventilation time，the CO transport shows two modes of translation and diffusion. CO is discharged out of the tunnel in the form of a mass，and the CO transport speed at the tunnel wall is larger than that at the center of the tunnel. At the moment of blasting，a large amount of dust gathers near the working face，and with the increase of ventilation time，the dust is continuously discharged out of the tunnel. Among them，the temperature of the surrounding rock has a certain effect on the transportation of CO. The higher the temperature of the surrounding rock，the faster the transportation of CO. However，the effect of the surrounding rock temperature on the transportation of dust is relatively small. The outlet speed of the wind pipe has a greater impact on the transportation of CO and dust. The greater the outlet speed of the wind pipe，the faster the transportation of CO and dust. The field application should be combined with the actual conditions and economic budget to select the relevant equipment.

To quantitatively analyze the coupling relationship between risk factors of personal injury and death accidents in Chinese electricity production and identify key risk factors，these risk factors were categorized into four dimensions: human，machine，environment and management based on the 4M accident causation theory. Utilizing grounded theory，32 secondary risk factors were identified，and the coupling methods were categorized into three types: multi-factor，double-factor and single-factor risk coupling. Drawing from investigation reports of 196 personal injury and death accidents in Chinese electricity production from 2016 to 2022，the N-K model was used to measure the coupling level of risk factors，while DEMATEL method was used to pinpoint key risk factors. Finally，the countermeasures and suggestions for risk prevention and control were put forward. This study shows that the frequency of accidents is closely related to the coupling level of risk factors，and the coupling level of risk factors is proportional to the number of participating factors. The risk coupling value of the four types of risk factors is 0.162 8，indicating the highest coupling level. The risk value of human factors participating in the risk coupling is relatively high，followed by the risk values of management and environmental factors. Among the three-factor risk couplings，the human-machine-environment risk coupling has the highest risk value at 0.104 7. For the double-factor risk couplings，the human-environment risk coupling has the highest risk value at 0.05. Both risk couplings involve human risk factors. The failure to implement safety production investment and main responsibility，illegal operations and illegal command and decision-making are the key factors in the coupling effect of risk factors of personal injury accidents in electricity production，and priority should be given to prevention and control.

To explore the effects of vibration and time pressure on monitoring tasks in DCS and reduce human error，a monitoring experiment was designed to measure the monitoring performance and workload under vibration conditions (static，low and high) and time pressure conditions (no time pressure and time pressure). Statistical methods were used to explore the influence of vibration and time pressure on the monitoring performance and workload. The results show that the vibration (monitoring time and accuracy) has no significant effect on monitoring performance. Both the monitoring time and the workload show rising trends with the increase of the vibrating level. The time pressure has a significant impact on monitoring time and workload，but has no significant impact on accuracy. The monitoring time in the state confirmation task is significantly longer than that of data comparison，but the accuracy difference is not significant. The monitoring performance and workload of DCS operators in the vibrating condition are basically the same as that in the static condition. In the time-pressure condition，the DCS operators' workload is heavy，but the monitoring time is short.

Respirable dust in coal mine operation space seriously endangers the health of workers. The existing pneumatic spray technology is not effective in reducing and removing respirable dust. To this end，a new supersonic pneumatic atomization technology was developed. The atomization characteristics were studied by experiments and numerical simulation. The transient dust reduction performance of this technology was compared with that of supersonic siphon and internal hybrid pneumatic atomization dust reduction technologies through multi-scale experiments. The results show that the high-speed fine mist domain is formed in the spray field of the new supersonic pneumatic atomization nozzle，and the droplet size and velocity gradually increase with the increase of spray distance. Compared with supersonic siphon and internal mixing pneumatic atomization nozzles，when the pneumatic pressure is 0.3-0.4 MPa and under different water flows，the new nozzles have smaller droplet size，higher droplet movement speed，and higher dust reduction efficiency，which can reach up to 90%. With the increase of pneumatic pressure，the range of high-speed fine mist area formed by the new nozzle increases，and the concentration of micro-mist increases，so that the dust reduction efficiency of small particle size dust increases at different times. When the pneumatic pressure is 0.4 MPa and the water flow rate is 10 L/h，the dust reduction effect of 2.5-10 μm respirable dust is the best.