In order to solve the problem of increasing difficulty of water injection in deep coal seam and poor inhibition effect of emission，based on the theoretical method of wetting modification of coal seam，wetting agent was used to act on gas-bearing coal body to inhibit gas desorption and migration. The fluorocarbon FS-3100 surfactant with strong wettability was used to test the influence of wettability on the gas-water migration process in coal through gas-containing coal desorption test and fracturing fluid displacement flow test，and to explore the change mechanism of coal wettability on gas-containing coal desorption efficiency and flow capacity. The results show that the surface tension of water is reduced to 17.9 mN /m after adding wetting agent in water，and the contact angle of coal water is only 3-3.5°. After the wetting agent was used to modify the coal body，the desorption rate of gas in the coal is significantly reduced. The gas desorption rates of Xinjing and Baode coals are reduced to 46.08% and 39.2%，respectively，which are 8.4% and 9.8% higher than that of water. After the wetting agent acts on the coal body，the displacement flow rate of the Xinjing coal sample increases from 8.59% to 14.10%，and the Baode coal sample increases from 10.65% to 16.67%，and the water injection capacity of the coal body is significantly enhanced. The wetting agent molecules are adsorbed on the surface of the coal matrix，which reduces the interfacial tension between coal and water and the surface energy of the coal body，so that the friction resistance of the water molecules flowing in the pores and fissures of the coal body is reduced，and the water molecules can infiltrate smaller-scale pores and produce stronger water lock effect. Finally，the gas desorption and migration adsorbed on the pores and coal surface are inhibited，and the gas plugging effect is formed.

In order to improve the safety condition of highways，26 320 highway traffic accident records in France from 2018 to 2022 were selected as the research object. Three representative algorithms were selected to impute missing values in the data，including the RF algorithm，the expectation-maximization (EM) algorithm，and the K-nearest neighbors (KNN) algorithm. The impact of different imputation algorithms on data stability was compared based on the changes in variable variance before and after imputation. The Apriori association rule algorithm was then applied to analyze the causes of highway accidents with different severity levels using the completed dataset. The results indicate that after missing value imputation，the RF algorithm demonstrates superior stability. Compared to the model trained on the original data，the accuracy is improved by 5.66%，the recall rate is increased by 9.22%，and the F1 score is enhanced by 9.91%. It is found that passenger vehicles are more likely to cause property damage accidents; motorcycles are prone to cause injury accidents on roads with lower speed limits and fatal accidents on roads with higher speed limits. The use of safety equipment is significantly related to the severity level of accidents.

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 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 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%.

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 prevent the impact of external risks on cluster supply chain network，the propagation mechanism of external risks in cluster supply chain network was explored based on cascade failure theory. Feasible strategies to curb the spread of risks in cluster supply chain networks under external risk impacts were also explored through numerical simulation using Python. The results indicate that implementing risk tolerance enhancement strategies in important enterprises cannot prevent the eventual collapse of the network，but can effectively slow down the speed of risk propagation and reduce the impact of each step in the risk propagation process. However，the implementation of risk mitigation strategies by important enterprises can effectively prevent the spread of risks in the network and prevent the collapse of clustered supply chain networks. The implementation of risk tolerance enhancement strategies in the global supply chain can effectively prevent the spread of risks in the network and prevent network collapse. Although establishing a supply chain risk sharing mechanism cannot prevent cluster supply chain network paralysis under deliberate attacks，it can effectively reduce the number of failed nodes under random attacks and prevent cluster supply chain network paralysis.

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.

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.

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.