To accurately identify and evaluate the fire risk in large-scale urban complexes，a fire risk assessment indicators system was constructed，consisting of 32 secondary indicators across six dimensions: building fire protection，fire protection facilities，electrical fire prevention，fire safety management，emergency response and coordination，and fire protection technical services. The order relation method was employed to determine the subjective weights of each indicator，while the entropy weight method was used to calculate the objective weights. Subsequently，a game-theory-based combined weighting approach was utilized to derive integrated weights，and a cloud model was applied to quantitatively analyze the fire risk of a large complex in a specific city. The results demonstrate that this combining model not only mitigates the subjectivity limitations in indicator weighting but also accounts for the fuzziness and randomness in the evaluation process. The primary risks of the large complex stem from fire protection technical services as well as emergency response and coordination，requiring preventive measures. Furthermore，the evaluation results are consistent with the actual situation，validating the feasibility of the proposed model in assessing fire risks for large-scale urban complexes.

In order to solve problems of land shortage and transmission line construction in the old urban area，taking the underground goaf of Xiuwu North power transmission substation as the research object，on the basis of clarifying the basic geological conditions and mining conditions of the underground goaf of the transmission substation，SBAS-InSAR monitoring was used to study the development characteristics of surface subsidence. The evolution characteristics of surface subsidence of the transmission line through the mining right area were quantitatively analyzed. The influence of goaf settlement，inclination and curvature on the transmission line of the substation was evaluated. The results show that the maximum average settlement rate of the surface of the study area is -53.6 mm/a，and the location of this area coincides with the location of Guhanshan mine，and the subsidence is consistent with coal mining，and the settlement rate of the tower position is between -16.5--0.3 mm/a. The average settlement rate of No. 11 and No. 35 towers are significantly greater than those of other positions，with the average settlement rates of -15.88 and -16.21 mm/a respectively. The historical tracing of the deformation law shows that the maximum cumulative subsidence occurs at the end of the monitoring period，which is -104.91 and -106.97 mm respectively. According to the most unfavorable principle，it is concluded that the actual monitoring and predicted settlement of the unfavorable points of the interannual deformation rate of the tower in each region during the service life is less than 400 mm，and the maximum inclination and curvature of the tower are 1.2 mm/m and the curvature is 0 mm/m²，which are all within the specified minimum allowable value. The surface of the transmission line crossing the mining rights area is in a safe and stable state.

In order to address the issue of missed detections during small target detection of aircraft rivets，an improved YOLOv8n algorithm for the detection of aircraft rivets and their anomalies was proposed. First，by adding a small object detection head，the shallow detail information in the backbone network was better fused，enhancing the model's feature fusion capability and its ability to recognize and locate small rivet targets. Second，the first two convolutions in the backbone network were replaced with SPD-Conv，which reduces information loss during down sampling through the combination of feature map reorganization and non-stride convolutions. Finally，large separable kernel attention (LSKA) was integrated into the spatial pyramid pooling fast (SPPF) module，capturing the dependencies between spatial and channel dimensions by calculating spatial and channel weights on each feature map and adjusting the feature maps to enhance the algorithm's ability to extract and recognize rivet feature information. Ablation experiments and comparative experiments were conducted based on a self-built aircraft rivet dataset. The results show that the proposed algorithm can achieve real-time identification of aircraft rivets and their anomalies，with precision，recall，and mean average precision (mAP) values improved by 6.5%，16%，and 15%，respectively，compared to the YOLOv8n algorithm. The detection performance is also significantly better than other mainstream algorithms.

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 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.

In order to study the damage characteristics of gas explosion from double explosion sources in roadway wall，the gas explosion model of roadway with double explosion sources was established by using LS-Dyna，and the pressure propagation and impact stress of shock wave in roadway under the gas explosion action of double explosion sources were analyzed. The change of axial pressure in the roadway and the damage to surrounding rock in the symmetrical section were measured when gas explosion with double explosion sources occurred. The research results show that the interaction between the shock waves generated by double explosion sources in the roadway results in a significant disparity in pressure compared to that of a single explosion source. At each axial position where the gas explosions from the double sources occur，there are two distinct pressure peaks，with the magnitude of the first peak negatively correlated with its proximity to the nearest explosion source. The value of the second peak pressure is negatively correlated with its distance from the collision surface where shock waves from two sources meet. The peak pressures before and after this collision were not a simply linear superimposition，but a sharp rise. The damage evolution process on surrounding rock wall surfaces resembles that of diffusion for shock wave pressure propagation and shock stress，although it is lagging these two phenomena. Furthermore，the duration of shock wave action plays a crucial role in determining the thickness of damage inflicted on surrounding rock formations，with roof damage being most severe in roadways subjected to double explosion source gas explosions.

To prevent and treat the early hearing loss of firefighters caused by occupational noise sources，the occupational noise exposure detection for firefighters was conducted，and the degree of hearing loss in firefighters was evaluated. The ratio of hearing loss in firefighters was quantified，and the relationship between the hearing test results of firefighters and occupational noise exposure was analyzed. The results indicate that the intensity of the noise source for firefighters exceeds the exposure limit of noise levels in the workplace of 85 dB(A). Among the hearing screening results of 50 firefighters，the number of people who falisd the distortion product otoacoustic emission hearing screening in both ears is 21，accounting for 42.0%，and the pass rate is significantly lower than that of normal individuals. The number of people with left/right ear pure tone audiometry hearing thresholds ≥ 26 dB is the greatest at 6 kHz，accounting for 30.0% and 26.0% at n =15 and n =13，respectively，suggesting high-frequency hearing loss in firefighters after occupational noise exposure.

In order to improve the accuracy of emergency consequence severity assessment，clarify the correlation between the risk causes and consequence severity in urban traffic emergencies，the improved discrimination model of emergency consequence severity (IDM-ECS) was constructed and experimentally verified. First，based on the IFSA，the risk causes of emergencies were screened to obtain the important risk causes such as train fulfillment rate，punctuality rate，and daily network passenger volume and so on. Secondly，the improved hybrid restricted Boltzmann machine(HRBM) model was used to calculate the relationship between different risk causes and the consequence severity，and the discriminative relationship between risk causes and the consequence severity was obtained by comparing the probability values. Finally，the dataset of rail transit emergencies was used as an experimental sample for validation. The performance was compared with four models，including Generating Restricted Boltzmann Machines (GRBM)，Random Forest (RF)，Deep Forest (DF)，and Light Gradient Boosting Machine (LightGBM)，in terms of recall，precision，and F₁ value. The results show that train fulfillment rate，punctuality rate，daily network passenger volume，line 5 section full load rate，line 10 section full load rate，signal failure，and vehicle failure are the seven optimal risk causes. The IDM-ECS model has an average recall of 90.55%，precision of 91.89%，and F₁ value of 91.06%，all of which are better than those of the comparison models.

To enhance the accuracy and reliability of geological earthquake disaster events predictions，a predictive model combining knowledge graph with GCN was proposed. Initially，the knowledge graph for geological earthquake disaster events was constructed，and the multi-source disaster-related information was consolidated into structured data. Then，the KGCN model was employed for deep learning of entities and relationships within the knowledge graph，uncovering potential association rules to forecast the evolution of disasters. Finally，the effectiveness of the model was validated through a set of geological earthquake disaster cases. The results show that the predictive model combing knowledge graphs with GCN exhibits excellent effectiveness in forecasting the evolution of geological earthquake disaster events，especially in dealing with complex multi-source data. The information can be efficiently integrated，and potential relationships can be accurately uncovered by the model. Excellent prediction accuracy is achieved in various aspects，including disaster levels，casualty levels，and disaster victim categories. Notably，the accuracy in predicting the disaster emergency response levels reaches 89.92%.

In order to deeply analyze the structural characteristics and collaborative mechanisms of the emergency response cooperation network for secondary and tertiary earthquakes，the "12·18" Jishishan earthquake in 2023 was taken as a typical case，and the social network method was used to analyze the emergency response collaboration network systematically based on the network structure，organizational relationships and organizational functions. The results show that the density of the Jishishan earthquake emergency cooperation network is low，and the cooperation between organizations is not close enough. There are 8 cohesive subgroups，the units of the same level are more inclined to cluster，and the units with similar functional attributes are more likely to form cohesive subgroups. The main body of the two or three level response is the provincial-level department，which is mainly the cohesion subgroup of the provincial command and coordination and emergency rescue functions. The cohesive subgroup formed by units at the national level mainly plays a coordinating and supporting role. Central enterprises and state-owned enterprises played an important role in this emergency response. However，it is necessary to break the administrative barriers and establish a cooperative emergency collaborative mechanism between the local government and the state-owned enterprises.