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.

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.

Traffic conflicts caused by illegal riding of e-bikes are a great challenge and negative impact on the safety management and operation efficiency of signalized intersections. In this paper，three indexes including post-encroachment time (PET)，time to collision (TTC) and deceleration-to-safety time (DST) were selected from the two aspects of the number of collision objects and motion state. The k-means clustering was adopted to divide the severity of traffic collisions caused by illegal riding of e-bikes into three categories: general，serious and potential collision. Secondly，the Poisson function was used to fit the distribution characteristics of conflict frequency，random variables were introduced to describe the mixed effects of heterogeneity among traffic conflicts，and a prediction model for traffic conflicts of illegal riding e-bikes based on GLMM was built to predict the frequency of traffic conflicts of multi-grade severity. Combined with the data of 996 e-bike traffic conflicts obtained by video investigation，the empirical study shows that the proportion of e-bike traffic conflicts with different severity has nothing to do with the types of violations. The constructed GLMM model is better than generalized linear model (GLM) in fitting the traffic conflict data of illegal cycling e-bikes，and has the best prediction effect on the common conflict frequency. By strengthening the management of e-bike occupation of motor vehicle lanes and the waiting behavior of crossing the line，adding escort officers and adjusting the signal phase，the incidence rate of e-bike conflict can be reduced.

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.

In order to ensure the normal operation of the low-background experimental chamber and prevent structural collapse，a structural safety analysis was conducted using Abaqus software. Initially，a simplified model of the experimental chamber was established based on the finite element method. This was followed by static load response analysis under extreme conditions，seismic load response simulations，and buckling analyses. Finally，the stress response and buckling critical loads of the oxygen-free copper sections with varying thicknesses were calculated to determine the permissible limit wall thickness of the experimental chamber. The results indicated that，under static pressure，the weakest regions of the chamber were the top of the oxygen-free copper section and the transition area of the circular end cap，with a critical buckling load of 0.307 MPa and a permissible limit wall thickness of 5.1 mm.

To optimize the professional talent training objectives，curriculum structure，and teaching syllabus of hazardous chemicals safety supervision，the state-of-the-art knowledge system and professional capabilities of hazardous chemicals safety supervision personnel were analyzed. Questionnaires and interviews were used to investigate the recruitment needs，on-the-job training，requirements，and school-government cooperative model for hazardous chemicals safety supervision positions. The results showed shortcomings such as insufficient talents trained in hazardous chemicals safety supervision，imperfect professional knowledge system，and mismatched professional capability structure. Hazardous chemicals safety supervision personnel should master professional knowledge such as laws and regulations，safety management skills (e.g.，integrity and process safety management)，chemical processes，and chemical reactions. Furthermore，professional capabilities in risk assessment (e.g.，equipment，processes，and instrument systems)，chemical reaction analysis，and chemical process simulation analysis should be enhanced.

Tunnel construction projects in China have developed rapidly，and significant dust hazards are associated with drilling and blasting construction. To improve the effectiveness of spraying to reduce dust in tunnels，this article uses digital simulation and a tunnel model was created using ANSYS. The variations in dust mass concentration distribution under different conditions，such as surrounding rock temperature，jet velocity，and nozzle diameter，were investigated in the study. The results show that as the surrounding rock temperature increases，dust movement becomes more intense，which significantly affects dust reduction efficiency. The dust capture effect of atomized water droplets decreases with the increase in surrounding rock temperature. With the increase of water jet velocity，the water pressure in the jet pipe increases，improving the dust capture effect of the droplets. Dust reduction efficiency decreases with increasing nozzle diameter. When the nozzle diameter is too large，the ability of water mist to capture particles weakens. Conversely，a smaller nozzle diameter improves dust reduction efficiency. However，if the nozzle diameter is too small，too much splash water spray rather than affect the efficiency of the dust.