In order to prevent and control the fire hazard of a road tanker coupling with diesel leaking and burning, small-scale experiments were first conducted using a 5 L tank filled with 1.65 L of 0# diesel fuel. The diesel was released from the tank bottom and burned, and the temporal evolution of the fuel temperature, tank pressure, thermal radiation, and flame morphology was investigated. Subsequently, numerical simulations of tank thermal exposure were conducted. The results show that once the temperature of leaking diesel reaches 227.8 ℃, the diesel fuel leaking to the external environment will boil and burn. This leads to a sudden escalation of fire hazard, i.e., a sudden increase in the tank pressure, the thermal radiation and flame size. The onset time of hazard escalation increases exponentially with filling level. When the filling level rises from 33% to 80%, the onset time increases by an average factor of 2.32 across different thermal boundary conditions. Flame offset relative to the tank reduces the thermal load on the tank, thereby lowering the diesel temperature-rise rate and increasing the onset time. Moreover, the rising rate of the onset time grows with the flame offset degree. The increase in the onset time provides more available safe egress time (ASET). Thus, ASET increases as both the filling level and the flame offset degree increase.

To investigate the spread characteristics of forest fires under complex topography and multi-factor coupling conditions, this study develops an optimized forest fire spread model that integrates terrain-slope correction, wind-field effects, and vegetation indices. First, Gaussian filtering was applied to correct the digital elevation model (DEM) to reduce noise, and terrain slope and aspect were derived from the refined DEM. Subsequently, the enhanced vegetation index (EVI) was introduced to improve the forest fire spread prediction model, enhancing prediction accuracy in areas with dense vegetation cover. By combining the model with CA, the predicted fire spread can be visualized. Finally, the predicted fire variable values were compared with the observed data from Muli Tibetan Autonomous County to verify the scientific validity and effectiveness of the model. The results indicate that the model is highly sensitive to vegetation changes in low EVI value ranges, with an effect size of 0.870, suggesting that the introduction of EVI improves fire prediction accuracy in areas with high vegetation cover. The improved fire spread model achieved an area prediction error rate and perimeter error rate of 29.40% and 5.79%, respectively, which are lower than the pre-improvement values of 44.27% and 16.99%. The Kappa coefficient of the improved model is 0.8238, which is closer to 1 compared to the pre-improvement model.

Aiming at the problem of traffic offenders evading law enforcement by prejudging regulatory measures, this study explored the interference mechanism of compliance camouflage strategies on the reconnaissance behavior of traffic offenders, to improve the anti-reconnaissance efficiency of traffic management. Based on the dynamic game theory of incomplete information, a sequential decision-making model between managers (government/enterprise) and offenders during the reconnaissance stage was constructed. The signal distortion mechanism was used to quantify the interference effects of four camouflage strategies (honest performance, camouflage government style, camouflage enterprise style, random interference) on the Bayesian belief updating of offenders, and the optimal strategy combination was solved using the refined Bayesian Nash equilibrium. An empirical study was conducted based on the road network of Xi'an High-tech Zone. The results show that differentiated equilibrium conditions exist in four typical scenarios. The government core area strategy increases the post-reconnaissance compliance probability by 82.3%. The enterprise park strategy induces a 40.3% rise in surface reconnaissance behavior. The suburban combination strategy reduces the violation rate by 41.7%. And the transportation hub area maintains a dynamic compliance index of 0.87±0.03. The spatio-temporal evolution demonstrats that the camouflage strategy delayed the convergence period of offenders' beliefs by 42%, drove an exponential decay of the violation rate by 73.7% within 30 days, and achieved an input-output ratio of 194%. This study indicates that, within legal frameworks such as warnings and indications, dynamic camouflage strategies can reverse information disadvantage by interfering with offenders' cognitive decision-making, thereby constructing a "cognitive-spatial-economic" collaborative governance paradigm and promoting the transformation of traffic supervision toward active intervention.

In order to explore the microscopic response characteristics of hard rock under the loading and unloading stress paths, the diorite numerical model under 20 MPa confining pressure was carried out by using the PFC^3D program. Then, the numerical simulation results were compared with the laboratory test results to verify the reliability of the numerical simulation scheme. On this basis, the change characteristics of micro particle velocity, contact force and tensile shear micro-crack along the axial and radial direction of the model during loading and unloading were studied. The results show that in the pre-peak stage, driven by synergistic effects of terminal energy input and lateral confinement, particle axial velocity exhibits higher values at the ends and lower values in the central region, while radial velocity increases linearly from interior to exterior. Contact normal forces demonstrate enhanced distribution characteristics along both axial (ends > center) and radial (periphery > core) directions, with sustained growth during loading. Tensile cracks dominate damage initiation, concentrating radially near unloading surfaces while distributing uniformly axially. In the post-peak stage, an abrupt reduction of lateral confinement triggers a dramatic particle velocity surge. Disintegration of force chains precipitates rapid decay in contact normal and shear forces. Accelerated propagation of tensile-shear micro-cracks occurs at the mesoscopic level, particularly with shear cracks concentrating and coalescing along double-shear planes, directly precipitating macroscopic bearing-capacity collapse.

To safeguard employees' psychological health and improve the accuracy and interpretability of psychological stress evaluation methods, taking multimodal physiological time-series data as the research object, a LSTM with AM(LSTMA) method was proposed to accurately evaluate employees' psychological stress states in the paper. Firstly, using the multimodal physiological time-series data (Blood Volume Pulse (BVP), Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyogram (EMG), Respiration (RESP), Body Temperature (TEMP), and three-axis Acceleration (ACC)) from the WESAD dataset were adopted as the research carrier, the gating memory mechanism of the modal-specific LSTM modules was utilized to accurately capture cross-time-step temporal dependency features, effectively retain key physiological features strongly associated with psychological states, and filter out short-term random noise, thereby ensuring that the physiological feature data could truly characterize the dynamic evolution of employees' psychological states. Secondly, after feature fusion, the attention mechanism was introduced to adaptively assign attention weight coefficients based on the feature importance of physiological data across different modalities and time steps, enhancing key features and micro-response features sensitive to psychological stress states while suppressing the interference of redundant information. Finally, the accurate evaluation of psychological stress states was accomplished through a fully connected neural network. Experimental results show that the LSTMA method achieves an evaluation accuracy of 94.56% for the four-classification task (neutral, stress, pleasure, and meditation) of psychological stress states. After Leave-One-Out Cross-Validation (LOOCV), the accuracy is improved to 98.08%. Ablation experiments verify the synergistic enhancement effect of the modal-specific LSTM and the attention mechanism, and model interpretability analysis further confirms the scientificity and rationality of LSTMA.

This study analyzes the muscular fatigue characteristics of mine emergency rescue personnel during pull-down dynamometer training, aiming to provide scientific support for enhancing the safety and efficiency of mine emergency rescue training. Forty team members from the Datong National Mine Emergency Rescue Team were selected as participants. sEMG technology was used to record changes in muscle activation, muscle contribution rates, root mean square(RMS) amplitude, and MF under standardized pull-down dynamometer training conditions. The results indicate that the triceps and latissimus dorsi are the primary force-generating muscles, while the erector spinae plays a critical role in movement restoration and trunk stabilization. As the number of repetitions increases, the triceps brachii begins to appear fatigue in the second half of training. To maintain the training, the latissimus dorsi and erector spinae show enhanced compensatory activation, and the lower segment of the erector spinae exhibits a higher activation level while showing significant fatigue characteristics. This compensation pattern driven by triceps fatigue is the key inducement for increased lumbar load and elevated injury risk in rescue personnel. It is suggested that the strength endurance of triceps brachii and the stability control and anti-fatigue ability of core muscles should be strengthened in training.

To quantitatively evaluate the particle matter barrier performance of different firefighter protective clothing, a test platform was established using a high-precision environmental chamber and a particle generation device. Aerosol mixtures of sodium chloride, dioctyl sebacate and titanium dioxide particles were released to simulate a smoke-filled fire environment. The inward leakage rate was calculated based on the particle matter concentration distribution inside and outside the garments, allowing a quantitative assessment of the particle matter barrier performance of 4 types of protective clothing. The results demonstrate that firefighting garments and emergency-rescue suits provide inadequate protection for the upper and lower extremities. The light-duty chemical protective coverall exhibits the best overall performance, with an average total inward leakage rate of 4.69%, representing a 92.4% reduction compared to the control group. It demonstrates excellent protective capability. However, all four types of protective clothing are ineffective at blocking small particles with diameters of 0.3-1.0 μm.

In order to address the issue of human-machine collaboration failure in the takeover decision-making of autonomous driving and achieve precise recognition of the driver's cognitive state, this paper simulates three typical scenarios, night high-speed curves, mobile phone distracted driving, and combined scenarios of strong light and heavy rain, collecting and analyzing the dynamic interaction data of driving behavior and eye movement features construct a dynamic weight allocation(DWA) distribution feature fusion framework for multimodal perception and cognition collaboration, constructed DWA-RF model, and explore the dynamic mechanism of the driver's cognitive state and takeover decision-making behavior in complex environments. The results show that the distracted state significantly prolongs the takeover time. In scenes of strong light and heavy rain, the superimposition of distraction and environmental pressure leads to a sharp reduction in the range of scanning. Fatigue causes an increase in lane departure distance, accompanied by a reduction in pupil diameter and abnormal scanning behavior. The cognitive state classification accuracy of DWA-RF constructed in this paper has reached 93.6%, verifying the effectiveness of this model in identifying the driver's cognitive state for autonomous driving takeover decisions.

In order to explore the influence of miners' alertness on risk perception ability, miners' alertness tests and risk perception experiments were designed and implemented. During the experiments, fNIRS, behavioral data, and peripheral physiological signals were collected. Methods such as the normality test and one-way analysis of variance (ANOVA) were applied to investigate the differences in risk perception ability among miners with different alertness levels. Thirteen significantly different indicators were selected as feature variables. Thirteen significant differential indicators were selected as feature indicators, and Sine-SSA-BP was introduced to construct a classification and recognition model for miners' risk perception ability. The results show that miners' alertness significantly affects their risk perception ability. With increasing alertness, the correct rate of risk perception improves notably. As the alertness level rises, significant differences appear in the activation index β values of the dorsolateral prefrontal cortex and frontopolar areas. The mean skin conductance (SC_mean) in electrodermal activity (EDA) increases significantly, while the mean inter-beat interval (Mean_IBI), standard deviation of normal to normal R-R intervals(SDNN), and root mean square of successive differences (RMSSD) in heart rate variability (HRV) decrease significantly, and mean heart rate (Mean_HR) increases. The constructed miners' risk perception ability classification and identification model based on the Sine-SSA-BP achieves an accuracy of 92.30%, demonstrating excellent overall performance and robustness.

In order to improve the development of safety behavior of flight cadets and enhance the level of flight training, conduct an indepth exploration of the relationship and intrinsic mechanism between authentic leadership style and safety behavior of flight cadets, a theoretical model of the safety behavior of flight cadets based on the authentic leadership style theory was constructed. The questionnaire was developed by drawing on established scales and consulting experts to align it with the current training conditions of aviation schools. AMOS26.0 software was used to test the mediating effect of authentic follow and the moderating effect of basic psychological needs satisfaction, and verify the effect on all constructs, the applicability of the theoretical model of flight cadets safety behavior in different stages of learning to fly, flight level, instructor job groups and work environment. The results show that there is a positive correlation between the authentic leadership style of flight instructors and the safety behavior of flight cadets, and the authentic follow of flight cadets plays a mediating role between the two. The authentic leadership style has a positive impact on authentic follower, and the basic psychological needs satisfaction plays a moderating role in this process. Different groups between the four dimensions of stages of learning to fly, flight level, instructor job groups and work environment have no moderating effect on the model, further verifying the structural stability of the model.