In order to solve the safety constraint problem of uncertain space robot (USR) with time constraints and output constraints, a second-order sliding mode global fixed-time emergency control method was adopted. The dynamic model of the uncertain space robot was established by applying the momentum conservation theorem and Lagrangian method. To achieve the safety output goal, an obstacle Lyapunov function was introduced to limit the system error within the preset range. Based on this, the upper bound of the system's uncertain terms was estimated, and the system's time constraints were further considered. By using the power summation integration technique, a second-order sliding mode global fixed-time emergency controller was constructed. The results show that root-mean-square error (RMSE) of the proposed method is 55.71% and 56.45% lower than that of the terminal sliding mode fixed-time controller without output constraints in Case 1 and Case 2 respectively, and it can ensure that the outputs of the USR system are strictly limited within the preset safety boundary, indicating the superiority of the tracking performance of the designed control method.

In order to explore the theoretical connotation and practical path of the systematic causation model of oil and gas pipeline accidents, 241 domestic and international oil and gas long-distance pipeline accidents were counted first, and the types of domestic and foreign accident causes and the contents of investigation reports were compared and analyzed. Secondly, we integrated the factor system of HFACS model and the hierarchical structure of STAMP model, and put forward an accident causation model that covers 4 system levels and 17 influencing factors. Finally, the N-K model and social network analysis method were used to carry out risk coupling calculation, network model construction, centrality analysis and core-edge analysis. The results show that equipment failure and pipeline corrosion account for 35.3% and 22.8% of the causes of oil and gas long-distance pipeline accidents. The risk coupling value is directly proportional to the number of system-level couplings. Inadequate safety training, delayed emergency response, lack of safety oversight, lack of risk awareness, lack of risk communication, behavioral violations and equipment process issues are the core influences on the accidents. The full life cycle management of accidents includes the reduction phase, readiness phase, response phase, and recovery phase.

To address the reliance on a single factor selection approach in traditional dam deformation prediction and the difficulty of comprehensively capturing complex inter-factor relationships among high-dimensional influencing factors, a factor screening method based on the fusion of multiple algorithms was proposed and applied to construct an LSTM model using optimally factors. Specifically, influencing factors were selected separately using the correlation coefficient method, neighborhood component analysis (NCA), and least absolute shrinkage and selection operator (LASSO) technique. The results from these individual methods were subsequently integrated. Since correlations exist among the factors, highly correlated ones were further eliminated using symmetrical uncertainty (SU), thereby an optimized factors set was obtained. The selected factor set was then used to develop a dam deformation prediction model via an LSTM network. A concrete-faced rockfill dam in Xinjiang was used as the case study. The model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), and the coefficient of determination (R²). The results demonstrate that, compared with traditional factor selection methods, the proposed multi-algorithms are integrated and factors with significant influence on dam deformation are comprehensively and accurately identified. MSE is reduced by 20.11%-59.09%, RMSE by 10.61%-36.05%, and MAE by 9.95%-37.86%, and a superior predictive model was obtained, compared with models using conventional factor selection methods. For specific monitoring points, the maximum reductions in MSE, RMSE, and MAE reach 53.5%, 31.9%, and 34.7%, respectively, while the highest R² value attains 0.986 0.

To qucikly obtain the source strength and location information from hazardous gas leakage incidents, a multi-factor optimized Gaussian plume dispersion model was proposed, and the leakage source parameters were inverted by combining it with a CAFA. Key environmental factors such as wind speed distribution, surface resistance, and surface reflection were incorporated into the Gaussian plume model to enhance its fitting capability under complex conditions through multi-factor calibration. Furthermore, a chaotic mapping was introduced to improve the population diversity and global search ability of firefly algorithm(FA), thereby achieving an effective balance between global optimization and local refinement while reducing the risk of falling into local optima. The results indicate that, after optimization based on wind speed distribution, surface resistance, and surface reflection, the error of the Gaussian plume model is reduced by 16%. CAFA effectively can avoid falling into local optima, reducing the source strength inversion error from 63.56% to 0.22%, and the leak source coordinate inversion error from 1.5 m to 0.2 m.

To achieve safe and efficient work in job shops, this paper proposed a BP-DRFJSP model that considers workers' boredom perception. First, the impact of boredom arising from repetitive tasks on work efficiency and work safety accident risks was analyzed. A mathematical model was developed to quantify this impact, and a scheduling model was established with the optimization objectives of minimizing makespan and production safety risks. Then, a MO-FTTA was designed to solve the model. Finally, comparative experiments and a case study involving MO-FTTA and three other common algorithms were conducted to verify the performance and feasibility of the proposed approach. The results show that when solving BP-DRFJSP model, MO-FTTA achieves better hypervolume (HV) and inverted generational distance (IGD) in 95% of the test instances, with C-metric values close to 1. Moreover, it yields superior objective function values in 90% of the instances compared to other algorithms. In the case study, MO-FTTA reduces the makespan to 79.635 minutes and lowers work safety risk by 1.25%, demonstrating its effectiveness in enhancing production efficiency while mitigating safety risks, thereby supporting safe and efficient workshop operations.

Non-caking coal was often used as gasification and power generation coal, and the heat generation was low due to the coal formation stage, and the heating rate had a greater influence on the heat release of coal-oxygen composite at different stages. The heat release process of Non-caking coal at different heating rates (10, 20, 30, 40 ℃/min) up to 1 000 ℃ was tested by synchronous thermal analyzer, and the changes of heat release and ignition heat in the coal-oxygen composite process were analyzed. The apparent activation energy was solved by the Starink method, and the effect of apparent activation energy on the heat release was clarified. The coal-oxygen composite stage with the greatest influence of heating rate was analyzed by the gray correlation method. The gray correlation method was used to analyze the coal-oxygen composite stage that was most affected by the heating rate. The results show that the heat flow changes of Non-caking coal can be divided into five stages: evaporation heat absorption, oxidation exothermic, decomposition heat storage, combustion exothermic and combustion exhaustion. With the increase of heating rate, the heat flow density gradually shifts to the high temperature area and the energy redistribution phenomenon occurs, and the heat release per unit mass of Non-caking coal increases by 19.2%. The percentage of exothermic heat in the exothermic stage of combustion increases from 70.6% to 75.1%, and the heat of ignition is 360~380 J/g. The apparent activation energy decreases gradually from 0 to 0.90, and increased abruptly at 0.95, with a peak value of 143.44 kJ/mol. The exothermic stage of combustion and exhaustion stage were most affected by the warming rate, and the evaporation and heat absorption stage was least affected by the warming rate.

To address the safety risks and geological hazards encountered during urban subway shield tunneling in soft-hard uneven strata, the applicability and identification characteristics of the surface transient electromagnetic method for advanced detection in such formations were investigated. Taking a metro section in Qingdao as an example, and considering the generally shallow burial depth of urban subways (typically 30-50 m), a numerical model of upper-soft and lower-hard strata was established using COMSOL finite element software. Through simulation of the transient electromagnetic response process, electric field response results and anomalous characteristics at different measurement points were obtained. Combined with field-measured data, the numerical simulation results were compared and validated, and the criteria for identifying soft-hard uneven anomalies in the strata were summarized. The results indicate that soft-hard uneven strata induce significant variations in the electric field response. The response curve at the measurement point directly above the anomalous body exhibits the most drastic changes and the fastest decay rate, demonstrating that the surface transient electromagnetic method can be utilized to identify anomalies in soft-hard uneven strata. The rate of electric field attenuation is jointly influenced by the properties of the medium and the distance from the anomalous zone, manifesting as a "trough phenomenon", with faster attenuation near the anomaly and slower attenuation farther away. In the apparent resistivity contour maps, the tunnel-crossing areas exhibit intense small-scale contour fluctuations, with soft rock showing low-resistivity anomalies and hard rock showing high-resistivity anomalies. Resistivity often transitions from low to high or from high to low, reflecting the heterogeneous characteristics of the strata.

In order to effectively respond to sudden mountain rainstorm disaster and explore the impact of emergency subjects' psychology on disaster evolution, a method combining game theory and BN was used to simulate the disaster's progression. First, historical disaster cases were analyzed, and consultations with relevant experts were conducted to identify four key scenario elements: emergency scenarios, disaster-forming environment, emergency measures, and human psychology. The Jaccard index was then employed to determine the relationships between these scenarios, while game theory methods were applied to optimize the BN node probabilities, which were initially estimated through case analysis statistics and triangular fuzzy number methods. This led to the construction of a BN-based disaster simulation model that incorporated the psychological factors of emergency subjects in the context of sudden mountain rainstorm disaster. Finally, the model was applied to analyze the "8·21" mountain flood and debris flow disaster in Jinyang, Sichuan, and comparative experiments were carried out. The results demonstrate that the disaster reasoning model is feasible and superior, and the neglect of psychological factors leads to an underestimation of disaster risks. Therefore, emergency management agencies should consider the influence of human psychology on early warnings, evacuation, and resource allocation when designing emergency strategies, in order to improve the effectiveness and adaptability of their measures.

In order to explore the safety of interactions between pedestrian groups moving in different directions, a risk assessment method for pedestrian safety was proposed based on a Seq2Seq network integrated with an AM. By analyzing the interactions among pedestrians, local density was introduced as an additional input feature to better characterize dynamic interpersonal behaviors. The observed sequential features of pedestrians within a continuous time window were fed into an LSTM-based encoder-decoder architecture, and AM was employed to capture multidimensional critical information during motion, enabling the reconstruction of complex crowd movement patterns across various scenarios. Furthermore, pedestrian safety risks were quantified by introducing a crowd pressure metric, and the pressure value ranges corresponding to three typical movement states were calculated to enable risk stratification. The results show that the average displacement error (ADE) and final displacement error (FDE) in trajectory prediction are less than 0.3 m under both unidirectional and bidirectional flow conditions at different densities, indicating the model's high accuracy in trajectory prediction. Based on the risk variation observed in bidirectional flow scenarios, it is found that the probability of accidents increases significantly when pedestrians' velocities change abruptly under high-density conditions. Therefore, timely warnings of crowd states and proactive intervention measures are required to mitigate potential safety risks.

To reval the disparities and shortcomings in safety and emergency management information disclosure, and to promote the strengthening of corporate safety responsibility and to improve of emergency management capability, textual information from 1 593 listed companies from 2008 to 2023 was collected from multiple sources, including CSR and annual reports of high-risk industries. Based on the three dimensions of reliability, relevance, and quantification, TF-IDF weighting method was applied to construct a 12-indicator evaluation framework for the quality of safety and emergency management information disclosure. The results show that corporate social responsibility reports provide more comprehensive safety emergency information and have become the primary disclosure channel. The overall quality of disclosure is relatively low. Only a few companies score in the upper range for disclosure quality, and the gaps between compaies are significant and continuously widenning. The disclosure content gradually focuses on the construction of emergency plans, emergency response and risk prevention and control strategies, but quantitative disclosure in areas such as safety investment and regulatory compliance remains insufficient. Significant differences exist among industries in terms of the number of companies disclosing information, focal points of disclosure and disclosure quality, with a lack of unified standards.