To enhance the flame retardancy and smoke suppression performance of EP-CE composites，a novel flame retardant was synthesized using DOPO and BPS as raw materials. The flame-retardant DOPO-BPS/OSEP/EP-CE composites were fabricated by the solution casting method，where DOPO-BPS and OSEP were jointly introduced into EP-CE matrix. Based on Fourier transform infrared spectroscopy (FTIR) analysis，nuclear magnetic resonance (NMR) analysis，thermogravimetric analysis (TGA)，limiting oxygen index (LOI) test，UL-94 vertical burning test，and cone calorimeter analysis，the effects of different ratios of DOPO-BPS and OSEP on the flame retardancy and smoke suppression properties of EP-CE composite materials were studied. The results show that the phosphaphenanthrene ring structure of DOPO-BPS could form a stable carbon layer and block the transfer of heat and oxygen. The synergistic effect with OSEP significantly improves the flame retardant effect and smoke suppression performance. When the flame retardant DOPO-BPS and OSEP are added to EP-CE at a mass fraction of 9∶1，the flame retardant performance and smoke suppression performance are the best，the flame retardant performance reaches V-0 level，and the LOI increases to 31.4%. Compared with EP-CE，the peak heat release rate，total heat release and total smoke production decrease by 29.7%，32.2% and 32.7%，respectively.

In order to effectively control low-frequency vibrations in engineering practice，the structure and the formation mechanism of the band gap were analyzed，and the finite element method was employed to investigate the influencing factor of the band gap in combined periodic cross-isolation trenches. Subsequently，this study analyzed vibration isolation performance using the model experiment. The results show that the band gap characteristics of the combined periodic cross-isolation trenches are mainly influenced by the periodic constant，the depth of the channel，the elastic modulus of soil and the filling medium. With the increase of the period constant，combined periodic cross-isolation trenches are more likely to obtain the low-frequency band gaps，but the bandwidth narrows. By increasing the depth，the low-frequency band gap with a larger bandwidth can be obtained，and the number of band gaps increases. With the increase of elastic modulus，the boundary frequency of the band gap migrates to high frequency synchronously，and a more low-frequency and wider band gap can be obtained by filling the medium. The experimental results show that the maximum attenuation amplitude of acceleration in the band gap range reaches 0.001 24 m/s²，the average amplitude attenuation ratio in the band gap is 0.206 2，and the maximum attenuation degree reaches 98.4%. The combined periodic cross-isolation trenches have remarkable vibration isolation performance.

Gas explosion disaster is the most serious coal mine accidents. In order to summarize the research progress of gas explosion risk assessment，firstly，the risk factors of gas explosion were identified. Then the shortcomings of existing risk assessment methods were analyzed，and the following conclusions were drawn by sorting out relevant literature. The analysis shows that there are subjective problems in identification method and evaluation method of coal mine gas explosion risk sources. There are also some problems with risk factors，such as the uncertainty of gas source and change，the unknown ignition source，the uncertainty of ventilation and air control. The application of objective weighting method and evaluation method based on mathematical theory can improve the accuracy of weighting and evaluation results，but the computational complexity limits its wide application. Although the application of computer models has made the assessment of coal mine gas explosion risk more accurate，it is necessary to solve the problem of expanding the integration of data collection and deep learning. Based on the current research status and existing problems，the future risk assessment of coal mine gas explosion can develop in the direction of multi-source data fusion technology，deeply mining precursory warning information，establishing intelligent models of disaster information based on information depth perception and data mining，and realizing dynamic risk assessment of coal mine gas explosion.

In order to predict coal gas permeability more accurately and ensure coal mine safety production，a prediction model of coal gas permeability based on ISSA-optimized BPNN was constructed. Firstly，the sparrow search algorithm (SSA) was improved by introducing Sine chaotic mapping and Gaussian mutation to enhance its global search capability and local optimization accuracy，thereby optimizing the weight and threshold configuration of BPNN. Secondly，the data on the factors affecting gas permeability were processed using Pearson correlation coefficient matrix and kernel principal component analysis (KPCA) to improve the computational efficiency and accuracy of the model. Three principal components with a cumulative variance of 88.59% were extracted as model inputs，and permeability was used as the output for the experiment. Finally，the model was applied to a coal mine in Shanxi for case verification. The experimental results show that ISSA-BPNN outperforms PSO-BPNN，PSO-SVM，PSO-LSSVM，and SSA-BPNN models in four indicators: mean absolute error (MAE)，mean absolute percentage error (MAPE)，root mean square error (RMSE)，root mean square error (RMSE)，and coefficient of determination (R²). Compared with other models，ISSA-BPNN has reduced MAE by 0.032 7，0.022，0.017 9，and 0.018 2 in the test samples，respectively. MAPE decreases by 5.15%，3.14%，2.76%，and 2.36% respectively. RMSE decreases by 0.031 6，0.027 9，0.018 8，and 0.022 2 respectively. R² increases by 0.077 5，0.065 8，0.040 1，and 0.049 3，respectively. Finally，the case verification shows that its reliability and stability are high in practical applications.

In order to accurately identify unsafe behaviors during the climbing process of high-altitude workers，this paper proposed an unsafe behavior recognizing method for high climbing workers based on vision，which included the human pose estimation and the one-dimensional convolutional unsafe behavior recognition models. Quantized autoencoder was used to structurally model human key points in human pose estimation，enabling the detection of human key point coordinates. Combining with safety behavior knowledge in high climbing operations，the unsafe behavior recognition model was constructed based on one-dimensional convolutional neural network model，and it was validated by industrial data experiments. Experimental results show that the accuracy of this method is 93.91% and 90.34% on unobstructed and partially obstructed datasets，respectively. Moreover，compared with support vector machines (SVM) and K-nearest neighbor (KNN)，this method has stronger generalization capability.

In order to investigate the leakage and dispersion patterns of gas-phase CO₂ transport pipelines with different hole sizes，outdoor CO₂ leakage test with apertures of 50 mm，100 mm and 233 mm were carried out based on industrial pipelines. Firstly，according to the pressure drop experiment，the variation law of the pressure in the pipe with the leakage time was analyzed. Then，based on the established theoretical model，the leakage flow and pressure drop changes were predicted and verified by experiments. Finally，the variation of CO₂ volume fraction at different positions of the leakage port was studied. The result shows that the pressure drop rate in the first stage of leakage is the largest. Among them，the pressure drop rate of the three leakage hole sizes is 93.4-1967.5 kPa/s. It takes 14.2-149.8 s for the pressure in the pipe with three leakage hole sizes to drop to the ambient pressure. With the increase of the leakage hole size，the pressure drop process tends to be a cliff type drop. According to the established theoretical model，the relative error between the calculated leakage quality and the experimental actual release is 0.25%-4.54%. The pressure drop curve obtained based on the predictive model is generally slightly lower than the experimental pressure drop curve. However，the variation trend and duration of pressure drop are very close to the experimental results. It shows that the established theoretical method for predicting leakage quality and pressure drop is reliable. In the range of 5-17 m from the leakage port，the peak volume fraction increased significantly with the increase of the leakage hole size. The peak volume fraction of different leakage hole size outside the range of 17 m approaches the same. The dispersion distance of 5% volume fraction of gaseous CO₂ leakage is 26 m.

In order to assess and monitor flight risks in real-time，clustering analysis was utilized to explore the abnormal patterns embedded in QAR data，and the influencing factors of abnormal flight patterns of civil aircraft were analyzed. The Euclidean distance was employed to characterize the similarity between samples of QAR parameters，establishing an abnormal flight pattern recognition model based on K-means to define the deviation degree of abnormal patterns. By considering the number of fatal accidents and the proportion of deaths in global commercial jet accidents，in conjunction with the deviation degree of abnormal patterns，the duration of abnormal patterns，flight phases，the likelihood of unexpected safety events，and the severity of consequences following unexpected safety events，a quantified assessment method for civil aviation flight risks based on QAR data was proposed. The feasibility of abnormal flight pattern recognition and risk quantification models for civil aircraft was validated through the practical QAR data of a certain airline. The results indicate that abnormal patterns are more prevalent during the cruising phase and critical moments at the transitions between flight phases. Significant differences are observed in the distribution of abnormal flight patterns and risks across different flights and flight phases. The average total risk value for flights is 166.94，with outliers exceeding 386.97. The abnormal flight risk during the takeoff roll phase is relatively low，with an average of 5.95，while the risk during the cruising phase is relatively high，with an average of 93.46.

To investigate the methane adsorption mechanism in porous media，a methane adsorption model was constructed using the LAMMPS(Large-scale Atomic/Molecular Massively Parallel Simulator) software. And large-scale molecular dynamics simulations (28.6 nm×14.3 nm×125 nm) were conducted over an extended duration of 1 000 ns to obtain a stable adsorption system. The study primarily focused on exploring the effects of temperature and surface properties of porous media on methane adsorption behavior，revealing the underlying microscopic adsorption mechanism. Additionally，a nano-scale methane adsorption recognition algorithm was developed to precisely identify adsorbed methane molecules within porous media. The results showed that methane adsorption decreases with increasing temperature. The effect of temperature becomes negligible when it exceeds 500 K. When the gas-solid interaction parameter (ϕ) is less than 0.8 (contact angle less than 75.6°)，the surface characteristics of silica have a minimal effect on adsorption capacity.

To investigate the impact of different injection source gases on the adsorption and diffusion behavior of methane (CH₄) in coal，three types of gases were selected: hot gas power generation exhaust (heat injection，multi-component)，carbon dioxide (CO₂) at room temperature (strong adsorption，single component)，and nitrogen (N₂) at room temperature (weak adsorption，single component). Using giant canonical Monte Carlo (GCMM) and molecular dynamics (MD) methods，these gases were mixed with CH₄ and injected into coal to analyze the adsorption conditions. Based on a fixed amount of CH₄，the changes in diffusion behavior were analyzed after injecting each of the three gases. The results show that with the increase of gas injection ratio，the reduction of CH₄ adsorption capacity under CO₂ injection condition is gradually greater than that under thermal power generation tail gas condition，showing better inhibition performance than thermal power generation tail gas. In contrast，although the adsorption capacity of CH₄ decreases after N₂ injection，it is always greater than the previous two. In terms of diffusion，with the increase of gas injection ratio，the diffusion coefficient increases first and then decreases，and the coefficient is always larger than before gas injection，and the displacement gas mainly promotes CH₄ diffusion. Under N₂ injection，the diffusion coefficient of CH₄ is the highest and the decrease is the smallest，and the promoting effect is the most obvious. Under the condition of CO₂ injection，the diffusion coefficient of CH₄ decreases the most and the promoting effect is the weakest. Therefore，the selection of hot tail gas from gas-fired power generation for CH₄ displacement is more cost-effective.

To guide healthy travel for cyclists in hot weather，a cycling measurement platform using micro-sensors was developed to collect high-resolution data on cyclists' heart rates，as well as atmospheric particulate matter (i.e.，PM_2.5，PM₁₀，and BC(Black Carbon) on the non-motorized lanes of the Western Third-Ring Expressway in Fuzhou. Statistical analyses were conducted to characterize and interpret variations in cyclists' heart rates. Results indicate that cyclists' average heart rates near residential zones are higher than that of riverside segments，showing strong and sustained associations with all measured particulates. The number of diesel vehicles，ambient temperature，and atmospheric pressure are found to significantly influence the heart rate changes across sections of the entire route，riverside，and residential sections，respectively. Cyclists' heart rates also fluctuate due to varying environmental and topographical conditions along the route. Immediate effects of PM_2.5 and BC on heart rate are observed，while PM₁₀ effects are delayed. Therefore，implementing road-segment-specific control measures for motor vehicles，particularly strict regulation of those emitting pollutants with immediate effects on cyclists，and guiding the selection of routes with better roadside ventilation and higher greenery coverage，can effectively enhance the travel quality for cyclists.