In order to enhance the emergency response capability of the firefighting and rescue teams and the overall efficiency of the forest and grassland fire prevention and control layout, an optimization method for the site selection of forest and grassland fire stations based on hybrid fire prevention emergency roads was proposed. By combining the eight-direction tilt point algorithm with digital elevation model data, a hybrid fire emergency road network was constructed to enhance the fire brigade's early prevention and emergency response capabilities. Subsequently, the location allocation model of the improved NSGA-II was adopted to optimize the site selection of the fire station, ensuring the rational allocation of resources and expanding the coverage. The results show that the coverage rate of the hybrid fire prevention emergency road in the overall area is 96.91%, and the coverage rate in the high-risk area is 93.51%, which improves the ability of the rescue team to deal with complex terrains. The optimized layout of the fire stations has a coefficient of variation of 0.26, ensuring the inspection and response capabilities of the teams. The overall demand satisfaction rate is 0.86, ensuring that the key areas are fully protected. The optimization model proposed in this study can provide a theoretical basis for the layout of forest and grassland fire prevention and control, improve the utilization rate of rescue resources, and promote the precise development of forest fire management.

To address the frequent spontaneous combustion accidents of photovoltaic silicon sludge, a multi-experimental approach was employed to reveal its dual-stage exothermic characteristics and critical spontaneous combustion size. An isothermal microcalorimeter was used to analyze the heat release sources of silicon sludge under a low-temperature environment of 30 ℃, and to investigate the effects of pH and particle size on heat release characteristics. Simultaneous thermal analysis was applied to investigate the thermal behavior of silicon sludge during the programmed temperature rise process of 40-1 300 ℃, and the flynn-wall-ozawa method was adopted to determine the activation energy of the oxidation stage, revealing the high-temperature reaction mechanism. Based on metal basket self-heating tests and Frank-Kamenetskii theory, the critical spontaneous combustion temperature and critical size of silicon sludge under different ambient temperatures and sample states were calculated. Targeted safety control suggestions for the safe storage and transportation of photovoltaic silicon sludge were proposed. The results show that in the low-temperature stage (30 ℃), the heat release of silicon sludge is dominated by silicon-water and silicon-alkali reactions. The alkaline environment and small particle size can increase the maximum heat release power to 837.5 μW, significantly enhancing the heat accumulation risk. In the high-temperature stage (>405 ℃), the silicon-oxygen oxidation reaction becomes the main heat release source, and the oxidation activation energy decreases from 177 kJ/mol to 141 kJ/mol, with the reaction transitions from interfacial chemical control to diffusion control. The critical spontaneous combustion size of dried silicon sludge, alkali-containing silicon sludge, and silicon sludge with small particle size is significantly reduced, and for every 10 ℃ increase in ambient temperature, the critical spontaneous combustion size decreases multiplicatively. The minimum critical stacking size is 2.2 m at 60 ℃.

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 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 enhance the obstacle-crossing and terrain adaptability of mobile rescue robots in complex environments such as unstructured scenerios and mine disaster roadways, a variable-diameter wheel based on the waterbomb-origami principle was designed. Addressing the limitation of exisiting modeling approaches for waterbomb wheel structures, which rely on a single basic unit, a unified modeling theory was proposed that incorporates square, rectangular and parallelogram units as fundamental elements. By establishing multi-coordinate kinematic models for the wheel axle layer, wheel support layer, and wheel connection layer, and systematically deriving the corresponding constraint equations, a unified description and parametric analysis of the folding and deployment process of waterbomb wheels with different unit configurations was achieved. The variation trends and effective ranges of key state variables during folding and deployment were studied in detail, and deploy ability tests were conducted on prototype robot models fabricated via multi-material 3D printing. The results show that the kinematic modeling method is applicable to waterbomb wheel structures composed of different basic unit types. The maximum deployed diameter obtained from testing is 124.35 mm, with a 2.5% deviation from the design value, and the deployment ratio reaches 2.015. During testing, it was observed that the fold and deployment process of the actual structure closely matches the kinematic model, demonstrating the strong applicability and accuracy of the proposed modeling theory.

In order to effectively predict hydraulic support loads and evaluate the operational status of supports, a hydraulic support load prediction model based on MTAM-LSTM was proposed. The CEEMDAN algorithm was employed to decompose the load data of supports and extract intrinsic mode functions. Redundant components in the intrinsic mode functions were eliminated according to K-L divergence criterion, thereby forming the input sequence for load prediction. An MTAM was constructed to capture the variation characteristics of hydraulic support loads. Static attention generated attention weights for feature information of data, while dynamic attention optimized the focus on different sequence features. Residual learning was introduced to maintain the integrity of feature signals. LSTM networks were then utilized to establish deep dependencies between feature information and hydraulic support loads, enabling advanced prediction of support load data. Field data from the 402102 working face of a rockburst-prone coal mine in Shaanxi were used for empirical validation. RMSE, R², and MAE were used as evaluation metrics for comparison among different models. The results show that the RMSE and MAE of the MTAM-LSTM model are significantly lower than those of the comparison models, with RMSE reduced by 0.16-0.45 and MAE reduced by 0.16-0.45, while the coefficient of determination R² reaches 0.91 under different scenarios, thereby validating the prediction accuracy and generalization capability of MTAM-LSTM model.

In order to solve the problems of high equipment price and large quantity demand in the existing forklift driving obstacle safety early warning distance measurement, a forklift driving obstacle safety distance early warning model based on image information was proposed. Firstly, based on deep learning technology, Squeeze-and-Excitation (SE) networks channel attention mechanism is introduced, and methods such as replacing the Intersection over Union(IoU) localization loss function with the Adaptive Threshold Focal Loss (ATFL) function are employed to improve the YOLOv12 algorithm for identifying obstacle targets in forklift travel. Secondly, on the basis of the improved YOLOv12 algorithm, the Kalman filter was introduced to improve the motion prediction model. And the distance detection method considering the camera pitch angle was used to accurately obtain the actual distance between different types of targets and the driving fork workshop. Thirdly, the kinematic process of forklift braking and forklift obstacle avoidance was analyzed, and the classification criteria of safe braking distance warning level and safety obstacle avoidance distance warning level were established, respectively. Finally, experiments were carried out to verify the feasibility of the safety warning distance of forklift driving obstacles based on image information. The results show that the real-time distance warning model can accurately identify obstacle targets in real-time and precisely determine the distance to obstacles within the permissible error range, enabling risk-level warning for obstacles during forklift operation.

To accurately identify landslide deformation characteristics and triggering factors from monitoring data containing excessive noise, this study proposed a noise-reduction method for displacement monitoring data based on isotonic regression. It further established correlation rules linking deformation in different subzones to multi-level hydrometeorological conditions, incorporating time-lag effects. Using the Zhakoushi landslide in Fengjie County, Chongqing as a case study, displacement monitoring data before and after isotonic regression processing were comparatively analyzed to preliminarily investigate deformation patterns at different locations. The time delays between displacement at each monitoring station and rainfall and elevation of reservoir water level were calculated, enabling the extraction of association rules between deformation in these subzones and hydrometeorological factors, thus clarifying long-term deformation characteristics and its triggering mechanism of the landslide. The results demonstrate that the isotonic regression algorithm effectively removes non-physical noise while preserving intrinsic deformation information, considerably enhancing data quality. The landslide movements exhibit significant spatial heterogeneity, with the front part experiencing the most extensive deformation controlled jointly by reservoir drawdown and rainfall, followed by the rear part influenced by topography-enhanced rainfall recharge. The synergistic effect of rapid drawdown of reservoir water (>0.5 m/d) and intense rainfall (>30 mm/d), which generate an outward-directed seepage force and reduce matrix suction, is the primary triggering mechanism for the landslide.

In order to reduce traffic safety incidents, the mechanism by which negative emotions affect drivers' CL was investigated, and the influence of driving scenarios on emotional intensity and multi-channel CL levels was analyzed. A simulated driving experiment was designed, integrating emotion-inducing materials with a driving simulator, and young drivers were recruited to complete driving tasks. The interaction characteristics between negative emotions and CL were systematically examined through the Self-Assessment Manikin (SAM) scale, the VACP multidimensional assessment model, and retrospective interviews. The results indicate that negative emotions significantly increase CL. Specifically, anger and fear tend to trigger transient fluctuations in load, whereas anxiety is associated with the highest average load level. Furthermore, stressors embedded in driving scenarios induce negative emotions of varying intensities: aggressive cut-ins and sudden lane changes commonly elicited high levels of anger. Traffic accidents and pedestrians running red lights predominantly evoke intense fear, and unfamiliar routes primarily trigger heightened anxiety. Finally, emotional intensity is significantly positively correlated with CL level, highly arousing emotions lead to a substantial increase in the demand for visual, cognitive, and motor resources.

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.