To enhance the scientificity and effectiveness of work safety management in mining enterprises, modeling and simulation studies were conducted focusing on the miners' safety responsibility pressure. A SD causal feedback diagram was constructed based on miners' work safety responsibility pressure scale, and questionnaire data was collected. The BPNN reflecting the relationship between independent and dependent variables was built by using the Neural Network Fitting tool in Matlab 2023b. Furthermore, a system flowchart was established through the Simulink platform to conduct dynamic simulation and scenario simulation. The results show that miners' work safety responsibility pressure and the pressure dimensions tend to increase over time. When pressure sources act independently, the punishment apprehension plays a dominant role, whereas when pressure sources act synchronously, the cognitive pressure dimension demonstrates nonlinear inhibition. In the process of pressure management, comprehensive consideration and balanced adjustment of multidimensional stressors should be emphasized to enhance the effectiveness of work safety management system in coal mining enterprises.

In order to enhance the ability of civil airports to cope with adverse weather conditions, a two-layer assessment model for airport transportation service resilience was proposed, addressing both the overall level and specific elements. The first-layer model—a cloud-based assessment of airport overall resilience—was based on a clear definition and understanding of airport transportation service resilience. Starting from the four core resilience capabilities of absorption, adaptation, recovery, and optimization, a resilience indicator system comprising 28 evaluation elements was established. Based on the cloud model, evaluation standard clouds were determined, and the numerical characteristics of each layer of indicator clouds were calculated layer by layer to assess the overall resilience level of the airport. The second-layer model-identification of key resilience elements of civil airports was based on "importance + obstacle degree" of airport elements considering the contribution and obstruction degree to the overall resilience of the airport, proposed methods to characterize the importance and obstacle degree of airport elements, and comprehensively identified the key elements of airport resilience. Subsequently, a case study was conducted using Airport A as an example. The results indicate that the indicator system and evaluation model constructed in this paper can accurately assess the resilience level of airport transportation services and identify key resilience factors. Airport A's overall transportation service resilience score was 0.284 7, which is at a high resilience level and consistent with the actual situation. Among these, employee emergency rescue capabilities, employee psychological qualities, and employee years of service are key factors affecting airport resilience levels and require special attention and improvement.

In order to reduce couriers' unsafe behaviors caused by job burnout and effectively avoid safety accidents, this study aimed to explore the influencing factors and management countermeasures of couriers' unsafe behaviors based on the Stimulus-Organism-Response (SOR) theoretical framework, employing mixed research methods. Firstly, it integrated data from on-site interviews and questionnaires, and analyzed the action paths of work burnout and safety attitude on couriers' unsafe behaviors from the four-dimensional factors of man, machine, environment, and management. Secondly, it constructed a conceptual model that includes the four-dimensional factors (man, machine, environment, and management), work burnout, safety attitude, and unsafe behaviors. Finally, it used Smart PLS 4.0 software to verify the structural equation model and put forward corresponding management improvement strategies for the state, express enterprises, and couriers based on the analysis results. The results show that work burnout and safety attitude have a significant positive impact on couriers' unsafe behaviors. Among them, the four-dimensional factors of man, machine, environment, and management indirectly affect unsafe behaviors through the mediating role of work burnout; the three-dimensional factors of machine, environment, and management indirectly affect unsafe behaviors through the mediating role of safety attitude (p<0.05).

In order to control the dust pollution problem of coal mine transportation roadway, the geometric model of Qipanjing coal mine transportation north roadway was established according to the ratio of 1∶1 by using numerical simulation software. Firstly, based on the standard k-ε turbulence model and particle tracking model, the airflow-dust coupling characteristics of the transportation roadway were studied, and a multi-section spray dust reduction technology for simultaneously controlling the transfer point and the transportation roadway was proposed according to its characteristics. Then, the effectiveness of the new dust reduction technology was verified by the combination of numerical simulation and experiment. Finally, the field application was carried out in the north roadway of the transport roadway in Qipanjing Coal Mine to verify the practicability and high dust reduction characteristics of the technology. The results show that the airflow distribution in the transportation roadway is affected by the running speed of the belt and the traction force to produce the induced airflow. The overall average airflow velocity is about 0.6 m/s, and the dust particles are dispersed by the airflow of the roadway. The dust with a particle size of less than 25 μm is suspended at the sidewalk, while the dust with a particle size of more than 25 μm is deposited on the surface of the belt and the floor of the roadway. The new dust reduction technology realizes the perfect coverage of the transfer point, and a supersonic fog curtain is formed in the main roadway to intercept and capture the dust that escaped from the transfer point. The test results show that the supersonic spray has a large range of fog curtain, strong wind resistance and significant continuous dust reduction effect. Through the field application of the north roadway of the transport roadway in Qipanjing Coal Mine, the highest dust reduction efficiency of total dust and respirable dust reached 80.12% and 83.15% respectively.

To improve the accuracy of structural safety evaluation results for hazardous chemical storage tanks, a finite element model construction process for storage tanks based on 3D laser scanning was proposed. This process utilized point cloud data to build a finite element simulation model that accurately reflected the actual structural topology of the tank, such as out-of-roundness and local concave-convex deformations, thereby enhancing the accuracy of the analytical model. First, high-precision spatial structural data from the field were acquired using a 3D laser scanning system. A lightweight regularization preprocessing method was proposed, including coordinate system transformation, node correspondence, and coordinate value updates. Next, combined with the finite element model unit construction method, the point cloud nodes with updated coordinates were used to generate a numerical simulation model that accounted for initial geometric defects, followed by an analysis of model accuracy. Finally, taking an in-service dome-roof tank as an example, deformation analysis of the storage tank was conducted based on 3D laser scanning data. A high-precision finite element model construction method was employed to build a full-scale finite element model of the tank wall, and an evaluation of the wall's strength performance was carried out. The results indicate that the point cloud data processing method proposed in this paper enables the construction of a finite element model that accounts for geometric deformation of storage tanks. The spatial position accuracy of the unit nodes can reach 0.01 mm, and the volume of the reduced point cloud data can be decreased by 70%. Compared to traditional finite element simulation models that consider initial geometric deformation, the structural safety evaluation data obtained from this model are more conservative, leading to safer assessment conclusions for storage tanks.

To explore the impact of underground lighting on miners' cognition, an EEG experiment was designed under different lighting conditions (100, 60, 30, 15 lx). EEG signals features were extracted using power spectral density, and α, β and θ rhythmic waves were selected for analyzing attention feature values. The analysis results were validated by using the Schultz grid test. The results indicate that β waves always occupy a larger proportion in the energy proportion graph. In the brain map, it reaches the maximum value in working condition 2, indicating that the subject is focused. The attention eigenvalue (β/θ value) shows a downward trend in the four working conditions. The β/θ value of working condition 4 decreases by 54.86% compared with that of working condition 2 at 55 min of the test, which indicates that the miner's attention is optimal at the light intensity of 60 lx, and significantly weakens at 15 lx. The Schultz grid test has the shortest duration and lowest error rate in condition 2, and the reliability reaches 84.83%, while under condition 4, the reliability is only 73.15%. The number of errors made by miners significantly increases, and attention shows a downward trend.

To further enhance the risk management mechanism during flight operations, an early warning for pilot handling smoothness was developed by integrating flight big data. First, core parameters related to unstable approaches were filtered from QAR data. The XGBoost algorithm was then utilized for feature optimization to identify key risk early warning indicators. Subsequently, a dynamic risk identification architecture capable of effectively capturing spatio-temporal dependencies was constructed by incorporating the attention mechanism of Transformer networks. . Finally, the method's performance was validated using flight data from B737-800 aircraft operated by an airline in Shandong. The results indicate that this method can effectively predict in-flight risk events, particularly in providing high-accuracy risk warnings during critical phases before landing. Compared with traditional warning methods, the approach demonstrates significant advantages in identification accuracy, model generalization capability, and feature extraction efficiency.

In order to optimize the design of fire-fighting equipment, the influence of apertures on the performance of compressed air foam jets was investigated through experiments involving various jet apertures and gas-liquid ratios. The data, including the expansion ratio, liquid drainage time, foam jet range, and width, were collected. It was found that as the jet aperture decreased, the deviation of the actual expansion ratio from the ideal value increases, and the deviation increases as the gas-liquid ratio increases. The minimum jet aperture threshold meeting the stability requirements of the compressed air foam system is determined. The critical gas-liquid ratio for a stable foam jet decreases as the aperture diminishes, and a relationship between the jet aperture and the critical gas-liquid ratio is established. The effect of jet aperture on the diameter of foam D₃₂ and 25% drainage time is not obvious. For gas-liquid ratios below 20, in the same gas-liquid ratio, the jet aperture is inversely proportional to the range and width of the foam. Under the same jet aperture, beyond a certain gas-liquid ratio, the variation in foam range becomes negligible. Additionally, the underlying causes of the observed critical gas-liquid ratio, 25% drainage time, and variations in range and width are analyzed.

To prevent slope instability accidents, a combined model based on HEOA optimized XGBoost was proposed to predict slope stability in response to the uncertainty of slope instability and the complexity of influencing factors. First, the main controlling factors affecting slope instability were analyzed. Six key influencing factors related to slope rock mass were selected to establish a slope stability prediction index system. Second, range normalization was applied to unify the feature scales, and SMOTE was employed to balance the distribution of stability classes within the dataset. Third, the HEOA was used to optimize the maximum depth, learning rate, subsample ratio, column sample ratio, and minimum loss of the XGBoost model. Finally, the prediction results of the constructed model were comprehensively evaluated using the following metrics: accuracy, precision, recall, F₁ score, and Cohen's Kappa coefficient, and the model was applied to specific engineering cases. The results show that the XGBoost model optimized by HEOA achieves the best performance when the maximum depth, learning rate, subsample ratio, column sample ratio, and minimum loss were 6, 0.583 8, 0.461 5, 0.584 6 and 0.024 4, respectively. Compared with other intelligent algorithms-optimized XGBoost models and single XGBoost model, the HEOA-XGBoost hybrid model shows improvements in all evaluation indicators in predicting slope stability, indicating that the model has high accuracy and generalization ability in predicting slope stability.

To clarify the fire combustion characteristics of the pottery jar liquor warehouse, a full-scale experimental platform for the pottery jar liquor warehouse was built to simulate the mouth fire and flowing fire using the Maotai-flavor Baijiu jar with a volume fraction of 53% vol. The flame temperature at the mouth of the pottery altar ranges from approximately 380 ℃ to 550 ℃, and the peak heat radiation flux is approximately 2.5 kW/m². Both the flame temperature and radiant flux of the flowing fire increase significantly as the combustion area increases. Under a 10m² flowing fire, the flame temperature is approximately 400-760 ℃, and the peak value of the thermal radiant flux is 8.2 kW/m². The response conditions of different fire scenarios were simulated by arranging the thermocouple matrix on the ceiling. The results show that the two-point temperature detection and alarm system responded rapidly to 4-10 m² fire, with a response time of approximately 3 to 39 seconds, but failed to detect fires at the mouth of the jar. Besides, the test observed that the collapse of external objects can cause a fire and a brief explosion phenomenon, resulting in a sharp increase in the peak of thermal radiation. The flying combustion droplets will intensify the risk of fire spread. It is recommended to install infrared and other image monitoring detectors in the liquor warehouses. The maximum linear distance between adjacent temperature detectors during installation should not be more than 6 meters to enhance the rapid response to fires in the liquor warehouses. Additionally, the fire isolation should be implemented to reduce the impact of fire spread and thermal radiation on adjacent pottery jars.