To address the issues of complex environmental interference and low recognition rates in early-stage tunnel fire detection，an improved YOLOX-based detection method，YOLOX-T，was proposed. The proposed method incorporated a NAM into the YOLOX network to suppress environmental noise and interference，thereby enhancing the model's robustness. A weighted BiFPN was integrated to improve multi-scale feature extraction and fusion. Furthermore，an α-IoU(Intersection over Union) loss function was employed to enhance the detection accuracy of early-stage tunnel smoke and flames，which often exhibit indistinct contours. Addressing the scarcity of publicly available datasets，a tunnel fire dataset encompassing both real-world and simulated scenarios was constructed through web data acquisition，simulated fire experiments，and the augmentation of existing datasets. Experimental results on the self-built dataset demonstrate that，compared to the original YOLOX model，the YOLOX-T method achieves improvements of 1.89% in mean Average Precision (mAP@0.5)，0.88% in mAP@0.5~0.95，4.57% in precision，and 5.45% in recall. The improved algorithm can achieve better detection performance.

In order to reduce the risks associated with the continuous growth of airport flight area size and flight volume，the safety resilience assessment of airport flight areas was carried out. First，risk factors were identified by analyzing the historical data of airport flight zones. Second，key risk factor weights were quantified，and a SD-based safety resilience assessment model for airport flight zones was constructed to propose safety resilience indicators. Then，the safety resilience of airport flight zones was assessed through simulation analysis，and targeted enhancement strategies were proposed. Finally，a large domestic airport flight area was taken as the research object to assess its safety resilience. The results show that among the personnel factors，the performance of the flight crew has the greatest impact on the level of operational safety resilience. By controlling the flow in the controlled airspace，enhancing safety awareness and increasing management inputs，the operational safety resilience of the flight area is improved by 9.11%. Among the environmental，equipment and management factors，the degree of improvement of the equipment updating mechanism has the greatest impact on the operational safety resilience level. By accelerating the frequency of equipment renewal，improving equipment deficiencies and increasing management inputs，the operational safety resilience of the flight area is increased by 21.49%.

To address the current challenges of lacking unmanned detection systems amid frequent forest fires and inefficient personnel evacuation during uncontrolled fire scenarios，this article proposes a forest fire safety detection method based on collaborativeMUAVs and an optimized shelter location strategy. A dynamic forest fire spread model coupled with multiple influencing factors is developed on the NetLogo platform. MUAVscollaborative search mechanism，grounded in an improved ant colony algorithm，is enhanced by introducing attractive pheromones (guiding searches toward fire clusters) and repellent pheromones (avoiding redundant paths)，thereby optimizing the transfer probability of unmanned aerial vehicle (UAV) flight directions. Additionally，a flight model incorporating obstacle avoidance and water-carrying capacity-speed constraints was established. A dynamic evacuation simulation environment was constructed using geographic information system (GIS) data from Rhodes Island，Greece. Experimental results demonstrate that the improved ant colony algorithm reduces convergence time by 15% and 14% under 50% and 60% tree density scenarios，respectively，while search coverage increases by 35.02% and 32.16%. Furthermore，optimized shelter placement combined with the A^* algorithm-based evacuation strategy reduces the overall mortality rate by 2.525%.

To address the oil-type gas emission hazard in coal-oil-gas coexisting mines，a quantitative risk assessment technology has been proposed. Firstly，the stability of the coal seam roof and floor strata was identified as a key parameter for evaluating oil-type gas gushing. The horizontal resistivity distribution uniformity was used to characterize strata stability. A direct current resistivity method was employed to investigate the resistivity distribution characteristics of the roof and floor strata，and a dynamic quantitative detection method for strata stability based on the resistivity variation coefficient was proposed. Additionally，comprehensive consideration of geological structures was integrated，supplemented by static parameters such as mining-induced damage depth，mechanical properties，permeability of the strata，and fault structures. Analytic Hierarchy Process (AHP) based on variable weight theory was employed to quantitatively calculate the weight of each factor relative to the evaluation indicators，thereby establishing a comprehensive quantitative evaluation method for oil-type gas gushing risks. Finally，the proposed method was applied to quantitatively assess the oil-type gas gushing risks in two typical areas of the Huangling mining area. The results align with field data from actual oil-type gas extraction boreholes，validating the method's reliability.

To investigate the methane DDT distance and maximum explosion pressure (P_max) in shale fractures，a multi-scale adjustable 3D planar slit detonation system was developed. Experiments with methane-oxygen premixed gas under 4 different hydraulic diameters，along with numerical simulations，were conducted to examine shale gas combustion under high pressure. Results show that methane-oxygen premixed gas can sustain self-propagating explosion within a hydraulic diameter range of 1.9 to 11.43 mm. Both P_max and peak pressure rise rate increase linearly with initial pressure. Under a hydraulic diameter of 11.43 mm，P_max closely approaches theoretical detonation pressure. As the hydraulic diameter decreases，the P_max-to-initial pressure ratio decreases. The initial pressure and the DDT distance follow a power-law relationship. Increasing the initial pressure or reducing the hydraulic diameter can shorten the DDT distance，thereby accelerating the DDT. The simulation shows that methane-oxygen premixed gas explosions can produce an overpressure of 330 MPa，capable of fully fracturing rock cracks.

In order to investigate the impact of drill pipe rotation on coal particles transport efficiency during negative pressure sampling，CFD and DEM were used to explore the effects of drill pipe rotational speed and coal particles mass flow rate on gas-solid flow characteristics. The results show that：the maximum axial airflow velocity inside the drill pipe decreases and stabilizes as the transport distance increases，while the maximum tangential velocity rapidly decays and vanishes. As rotational speed increases，the axial airflow velocity remains largely unchanged，but the tangential velocity significantly increases. With higher rotational speed，the spiral flow of coal particles becomes more pronounced，the length of the vortex region increases，and the length of the suspension region decreases. The number of coal particles entering the drill pipe decreases，and the distribution of particles above 5 m/s increases and becomes more dispersed. During rotation，the actual solid-to-gas ratio inside the drill pipe is lower than the set value，and coal particles transport efficiency increases and then decreases with the rise in rotational speed.

To improve the safety of air traffic operations，a delay level prediction method based on the combination of spatiotemporal association rule mining and deep learning was proposed. Firstly，the average flight delay time and delay rate were selected as airport delay metrics，and their spatial-temporal correlation characteristics were analyzed. Secondly，the airport delay levels were identified based on Fuzzy-C Means (FCM)clustering algorithm，and the spatiotemporal association rules of airport delay were mined based on (FP(Frequent Pattern)Growth) algorithm. Thirdly，sample data was constructed based on association rules and delay time series，which was put into LSTM model to predict the future airport delay levels. At the same time，attention mechanism was introduced into the prediction model to learn the influence of different rules on prediction. Finally，the actual US flight data were collected for example analysis. The results show that the average prediction accuracy of overall delay levels reaches 0.91 and the prediction accuracy of different periods is all larger than 80%. The connection weight of the attention layer network reflects the influence of each rule on the prediction，which can be used to explain the prediction results.

Based on the high and steep slope project of an open-pit slope in cold region，30 freeze-thaw cycle tests were conducted. The temperature range was set from -30 ℃ to 20 ℃.Subsequently uniaxial variable upper limit cyclic loading-unloading tests as well as synchronous acoustic emission monitoring tests were carried out. Slope rock masses with fracture angles of 0，25，50 and 75° were used in potential slip zone. The freezing-dynamic (freeze-thaw cycles and cyclic loading and unloading) combined damage and deterioration characteristics and mechanical properties of slope rock mass were explored in macro and mesoscopic scales. Furthermore，the crack initiation，propagation and failure modes of fractured rock mass were studied. The results show that as the fracture angle increases，the freeze-thaw damage effect on the fractured rock mass gradually decreases，while the compressive strength and elastic modulus exhibit a linear increasing trend with the maximum deformation of fatigue resistance of 0.558 3% at 75°. Compared to ordinary uniaxial loading，the compressive strength of fractured rock masses under cyclic loading and unloading condition decreases by 5.6 MPa. The Felicity ratios of different rock masses decrease with the increase of cyclic levels，and the Felicity ratios at the final failure stage were all below 0.7. As the cyclic loading level increases，the increment of cumulative dissipated energy decreases with the increase of fracture angle. The rock masses mainly exhibit tensile failure，but when the angles exceeded 25°，there is a trend of transformation from tensile and mixed failure to shear failure.

To solve the issues that the bias，drift，gain，sticking and mutation fault modes of the current sensor in a battery pack are difficult to detect，recognize and evaluate，a comprehensive diagnosis strategy based on model fusion was proposed. A normal battery model with current as input and voltage as output (CIVO) was established. Based on the one-to-many relationship between the current sensor and batteries in the pack，the cumulative sum of the log-likelihood ratios of the residuals of the voltage of each cell was used as the detection index. A bias/drift fault model and a gain fault model with voltage as input and current as output (VICO) were established. Based on the residual variance of fault current，the model matching was performed on each fault mode. The quantitative evaluation of the bias，drift and gain modes were achieved by introducing a fault parameter to the fault model. The results show that based on CIVO，the five fault modes can be reliably detected. The sticking mode takes the shortest detection time and the drift mode requires the longest detection time，attributed to the slow-change characteristics of the fault current. Based on VICO，five fault modes can be accurately recognized. The quantitative evaluations of the bias，drift and gain modes are highly accurate，with the evaluation results of 0.396 2 A (experimental value 0.4 A)，1.641 7×10^-4 (experimental value 1.5×10^-4) and 0.201 6 (experimental value 0.2)，respectively.

In order to solve the problems that machine learning exists in the hazard intelligent prediction field of tunnel water inrush，such as relatively simple models and imperfect prediction accuracy，a prediction model based on the stacking ensemble learning was proposed. Firstly，the tunnel water inrush disaster dataset was established by collecting 232 groups of water inrush disaster data from 95 tunnels，and the data was preprocessed. Then，3 base learners and 2 meta learners were selected to train 8 sets of stacking ensemble models in different combinations，and 6 sets of optimal ensemble models were selected. Finally，the optimal stacking ensemble model was selected by comparing and analyzing the prediction results of 6 groups of parameters optimized and stacking ensemble model with the grid search parameters and the 5-fold cross-validation hyperparameter optimization model. The results show that SVM(Support Vector Machine )+NB (Naive Bayes) + LR (Linear Regression) ensemble model is obtained after the optimal single model SVM is improved with the stacking ensemble learning method. Its accuracy，recall，and F₁ score are 0.94，0.91，and 0.92，respectively. The overall prediction effect is better than that of other compative models，and it can accurately predict the hazard level of tunnel water inrush.