In order to effectively address the escalating challenges of natural disasters and enhance the efficiency of the enterprise-led emergency supply reserve system, an economic information disclosure mechanism was proposed to boost enterprise participation. Firstly, an enterprise-led emergency supply reserve model was constructed based on an option contract to clarify the intrinsic driving mechanism of disaster occurrence probability on enterprises' reserve decisions. Then, the Stackelberg sequential game framework was further employed to evaluate the effectiveness of government subsidy policies in increasing supply reserve volumes and improving government-enterprise objectives. Furthermore, Bayesian persuasion theory was introduced to design a government information disclosure mechanism, aimed at guiding enterprises' reserve decisions and improving the overall efficiency of relief supply reserves. Finally, a numerical analysis was conducted with flood disasters as a specific application scenario. The research findings indicate that the volume of enterprise-led emergency supply reserves is positively correlated with disaster occurrence probability, reflecting enterprises' sensitivity to disasters. Although government subsidies can increase reserve quantities and reduce government costs, they have limitations in improving enterprises' profits. The information disclosure mechanism designed based on Bayesian persuasion theory outperforms both the baseline model and the subsidy model in terms of reserve quantities, government costs, and enterprises' profits. Through effective disaster information disclosure, the government can enhance the relief efficiency of the enterprise-led emergency supply reserve system.

To effectively identify and prevent the risks of chemical fire accidents, a comprehensive research framework was proposed, integrating text mining, SIF model hierarchical analysis, and social network analysis based on rich accident investigation reports in the era of big data. Firstly, the key causes of the accidents were systematically extracted through text preprocessing, term frequency-inverse document frequency (TF-IDF) keyword extraction, and latent dirichlet allocation (LDA) topic modeling, combined with 75 representative chemical fire accident investigation reports from 2000 to 2024. Then, based on the SIF model, the extracted causes were classified into the micro, meso, and macro levels. Subsequently, the accident cause network was constructed using social network analysis methods. Core nodes and key influence paths in the accident cause network were identified through frequency statistics, centrality analysis, and key relationship mining. The research results show that the micro-causes accounted for 56.3%, representing the most crucial factors contributing to chemical fire accidents, with individual factors and environmental equipment risks being dominant. At the meso level, insufficient safety supervision has the highest degree centrality. The key cause path for chemical fire accidents is: inadequate organizational management → insufficient safety supervision → insufficient personal preparation → violation of regulations.

To investigate the stability of tunnel faces under pipe shed support in deep environments characterized by high stress and high-water pressure, a failure model for the deep tunnel face was established by the variational method. High water pressure and surrounding rock stress were incorporated into the mechanical model, and the external power and internal energy dissipation rates were calculated using the upper limit theorem. The analytical solution for the potential fracture surface of the tunnel faces was derived based on the principle of virtual work. The safety factor of the deep tunnel was solved by Matlab software. The influence of diverse parameters on the failure surface curve was analyzed, and the variation characterstics of safety factor under high stress and high-water pressure environment were discussed. Additionally, the effect of pipe shed support was evaluated. The results indicate that when the surrounding rock strength is low, the failure zone is larger and the longitudinal depth is greater. The high-water pressure and the surrounding rock stress have remarkable influence on the stability of tunnel face. Without considering them, the safety of tunnel face will be overestimated, and the relative error of safety factor can reach more than 60%. Pipe shed support can enhance the stability of deep tunnel face, increasing the safety factor by 79%. Meanwhile, high stress and high-water pressure environment will significantly reduce the effectiveness of pipe shed support. These findings provide theoretical guidance for the support design of deep tunnel, high-stress, and high-water pressure environments.

In the scenario of air-ground collaborative autonomous operations, the collision risk of aircraft was assessed to ensure operational safety. The collision risk of parallel routes under autonomous flight was studied based on human factors and the performance of CNS systems. Firstly, an in-depth analysis of the characteristics of autonomous operation scenarios was performed. The Cognitive Reliability and Error Analysis Method (CREAM) model was improved by incorporating cognitive behavior and interactions between pilots and air traffic controllers to assess human reliability in autonomous operations. Subsequently, combined with the CNS performance and human factors, a collision risk model for autonomous operation aircraft was established. The lateral, longitudinal, vertical, and overall collision risks of the aircraft were evaluated separately. The feasibility of this method was validated through a parallel route example, and the impact patterns of human reliability and CNS performance on the collision risk of parallel routes were analyzed. The results indicate that the collision risk values in all three directions and the overall risk remain below the target safety level (5×10^-9). Furthermore, it is demonstrated that improving human cognitive reliability and CNS performance contributes to reducing the spacing between parallel routes, thereby enhancing the safety of autonomous operations.

In order to improve the anomaly detection performance of dam monitoring data, a dam abnormal data detection method based on the improved Prophot-long short term memory-particle swarm optimization Prophet-LSTM-PSO was proposed. Firstly, by improving the Prophet method, the trend component features obtained from the decomposition of abnormal data points were clearly visible. Secondly, the decomposed trend, periodic, and residual components were represented in a three-dimensional space, where the original time series data was substituted with the mean distance of the nearest neighbors in this space. Finally, abnormal data points were identified precisely by combining the LSTM network and PSO algorithm to set and optimize anomaly thresholds. The results show that the method proposed in this paper significantly improves detection performance and exhibits high stability compared with traditional methods. Notably, while maintaining a stable recall rate exceeding 95%, both accuracy and precision surpass 95%, thereby validating the effectiveness and practicality of the proposed method.

In order to conduct a comprehensive analysis of the air leakage characteristics in the goaf of shallow coal seams, five factors were selected, including the average length and width of overlying rock fractures, the porosity and thickness of overlying rock fractures, and the pressure difference above and below the well, to establish an index system for the impact of air leakage in the goaf of shallow coal seams. The dominance-based rough set and response surface method were combined to construct an analysis model for the air leakage characteristics and their factor effects in the goaf. Taking a coal mine as an example, empirical analysis was conducted to generate preference class rules for air leakage in the goaf and extract preference class features. Using the 3-factor 3-level response surface method, regression function fitting was performed on the air leakage volume in the goaf of shallow buried coal seams. The results indicate that the porosity of overlying rock layers, thickness of overlying rock layers, and pressure difference above and below the well are the core influencing factors of air leakage in goaf, with a single factor F-value of 96.06-226.82. The F values for the interaction factors of "interaction between overlying rock porosity and overlying rock thickness" and "interaction between overlying rock porosity and wellbore pressure difference" are 62.34 and 24.66, respectively. Compared with a single factor, in the interaction of multiple factors, the interaction between the porosity and thickness of overlying rock layers has a significant impact on air leakage in goaf. Therefore, when conducting analysis and prevention of air leakage in shallow coal seam goaf, it is important to pay attention to the role of individual factor indicators, as well as the interaction of factors that have a significant impact on air leakage.

To enhance the intelligent level and risk-bearing capacity of three gorges locks, first, SNA was used for stakeholder identification and extraction for Three Gorges lock reservation scheduling. Stakeholder network characteristics were characterized by three centrality indicators: degree centrality, betweenness centrality, and closeness centrality. An assessment index system was construct from four dimensions: legality, rationality, feasibility and controllability. Then, based on the potential coupling relationship between the indicators, a BN was used to construct a social risk assessment model for the Three Gorges lock reservation and scheduling mode to quantify the direction and intensity of the role of each indicator. Finally, key factors affecting social stability were identified through a sensitivity analysis. The results show that the social risk level under the scheduling mode of the Three Gorges lock is low. The order of influence of the four first-level indicators on the overall social risk is legality, controllability, feasibility, and rationality. The compliance with rule revision, approval, and issuance, the vulnerability of negative public opinion, the vulnerability of mass incidents, the success rate of appointment, and the coverage of security management strategy are the key factors affecting the overall social risk.

To solve the challenges in supporting fractured roof coal seam roadways, a rock layer detection recorder was used to conduct tests on the coal seam roadways at Xindeng mine. The analysis focused on the evolution characteristics of surrounding rock fractures. The R value for the direct roof rock and the β coefficient for roof fragmentation were introduced as evaluation indicators for roadway surrounding rock stability. The surrounding rock was categorized into four types: easy to support, relatively easy to support, relatively difficult to support, and difficult to support. Specific support parameters were provided for each category. Zoned support technology was applied in the gas drainage chamber of +90 North wing of Xindeng mine, achieving excellent support results. The study shows that the state of the roof surrounding rock is zoned, with the shallow fractured zone, followed by the fractured-fracture mixed zone, the developed fracture zone, and the intact surrounding rock zone. Through fracture analysis, it is found that the width of the roof fracture zone gradually expands to the deep surrounding rock over time, and the number of fractures also increases. The existing support cannot effectively control the continuous deterioration of the surrounding rock integrity.

To address the complexity of fire environment and the difficulty in predicting the temperature field in modern commercial buildings, a fire temperature field prediction model was constructed by combining CNN with SVM. Firstly, FDS was used to construct a commercial building fire model, and the sequence data received by the temperature measurement points were obtained. The temperature, position coordinates, and fire duration were used as input parameters to build the dataset. Secondly, the Rime Optimization Algorithm (RIME) was introduced to optimize the number of hidden layer nodes, regularization coefficient, and learning rate in the CNN-SVM, and then the prediction model was established. Finally, experiments were conducted based on the established dataset and prediction model, and the anti-interference ability of the model under different sensor failure rates was discussed. The results show that the model performs optimally in the prediction of the temperature field plane, with an average absolute percentage error of 5.6% and a maximum relative temperature error not exceeding 25%. The anti-interference performance is the best under three working conditions, and the maximum error does not exceed 15% under extreme conditions.

In order to address potential safety hazards of dust explosions caused by the dispersion of combustible dust during the intelligent transfer of medium and heavy flammable materials (such as coal) in mines, triggered by the movement of robotic arms and materials, this study takes 6-axis industrial mechanical arm as the subject. Referencing the technical parameters of the arm's joint motion and the wind speed threshold for coal dust dispersion as motion constraints, the "4-5-4-4-5-4" polynomial pose interpolation method and the proposed SEKOA were employed as the motion trajectory planning model for the robotic arm. This model analyzed the nonlinear combinatorial engineering optimization problem for achieving safe and efficient operation of the robotic arm. Under the established motion constraints, the SEKOA algorithm demonstrates higher efficiency compared to other algorithms in safely transferring coal powder materials, achieving the fastest time of approximately 9.5 seconds. The material movement is stable, with no tilting or collision. The motion trajectory planned by the "4-5-4-4-5-4" polynomial interpolation method is smooth and continuous. During the material lowering phase, the peak velocities of the main drive joints 1 and 2 are approximately 0.72 rad/s and 0.47 rad/s, respectively, with peak accelerations around 0.5 rad/s². After decelerating for about 2.5 seconds to 0 rad/s, the robotic arm can gently place the material at the designated position. This approach effectively prevents secondary dispersion of coal dust on the sealing container.