To investigate the influence of ammonia admixture on methane explosion propagation characteristics in tunneling tunnels, the explosive behavior of methane/ammonia/air mixtures was systematically examined under varying ammonia blending ratios, initial pressures, and initial temperatures. The explosion overpressure evolution and flame propagation characteristics of the mixtures under varying ammonia blending ratios, initial pressures, and initial temperatures. Results indicate that the incorporation of ammonia exhibits a dual effect on the explosion behavior: initial suppression followed by enhancement. The explosion overpressure is observed to decrease first and then increase with rising ammonia content, while the flame front position and propagation velocity progressively increase with higher ammonia ratios. Regarding the influence of initial pressure, the peak explosion overpressure, flame propagation velocity, and flame position all show linear increasing trends with elevated initial pressure. The influence of initial temperature on explosion characteristics is more complex. Within the range of 300-700 K, the explosion overpressure increases with temperature, and the flame propagation trajectory show a significant positive correlation with temperature. However, when the temperature reaches 900 K, flame propagation is markedly suppressed and manifested by significant decreases in both the flame front position and velocity. The study reveals that the ammonia blending ratio exerts a non-monotonic influence on the methane explosion process, while initial pressure and temperature regulate explosion intensity and flame propagation behavior in linear and nonlinear ways, respectively.

To enhance the safety and on-time performance of flight rerouting under thunderstorm conditions, the airspace was first discretized into grids. Considering meteorological avoidance zones, aircraft performance, and fuel consumption, an ARRPTC model incorporating multiple safety factors was established based on the aircraft's origin, destination, and departure time. The model determined both the aircraft's airborne holding time at the departure point and its optimal rerouting path, with the objective of minimizing the total flight time. Subsequently, a multi-strategy improved DBO (MSDBO) was designed according to the problem characteristics to improve convergence speed and accuracy while avoiding premature convergence of the algorithm. The performance of the proposed MSDBO was then compared with four other swarm intelligence algorithms by means of six benchmark test functions to verify the effectiveness of the improvement strategies. Finally, a case study based on an actual aircraft rerouting scenario under thunderstorm conditions was conducted. The results indicate that, compared with traditional models, the proposed ARRPTC model reduces the detour time and distance by 52.3% and 53.0%, respectively, while the total flight time and distance decrease by 16.5% and 14.3%, and fuel consumption is reduced by 13.6%. Compared with the other four swarm intelligence algorithms, MSDBO demonstrates faster convergence, stronger global exploration, and superior local exploitation capabilities. In the ARRPTC model, MSDBO can obtain more optimal flight trajectories, significantly improving the efficiency of flight rerouting. Furthermore, as the thunderstorm safety threshold and turbulence coefficient threshold decrease, the total rerouting distance tends to increase.

In order to effectively monitor the potential performance degradation of core components in aircraft air conditioning systems, a baseline modeling method for aircraft air conditioning systems based on modified MSET was proposed in this paper. Firstly, QAR data of key components was selected as the feature parameter. Secondly, using healthy historical data as training sample, the density peak clustering method was used to screen state data and construct memory matrices for both non-low-temperature and low-temperature datasets. Then, the adaptive diagonal loading technique was applied to MSET process to reduce the abnormal fluctuations caused by the pathological state of the memory matrix. Finally, a performance baseline was established between the multivariate feature variables and the system operating state, and real flight data from Airbus A320 aircrafts was used to analysis. Results show that the proposed method can simultaneously establish performance baselines for multiple key components such as primary heat exchanger, main heat exchanger and air cycle machine. It can effectively detect flight cycles with performance degradation before component failure, and the detection results are relatively accurate, providing a reference standard for airlines' condition based on maintenance and health management.

In order to prevent and control the spontaneous combustion of residual coal in alkaline coal mines, the effect of coal particle size on coal oxidation self-ignition in an alkaline environment had been explored. Through water quality tests and Fourier-transform infrared spectroscopy (FTIR) tests, the microstructural changes of coal with different particle sizes soaked in alkaline solution were analyzed and judged. The oxidation kinetics of coals with varying particle sizes soaked in alkaline solutions had been investigated through programmed heating experiments and calculations of apparent activation energy. The results show that during the process of soaking coal particles of varying sizes in an alkaline solution, the redox potential (ORP) and total dissolved solids (TDS) exhibit significant changes as soaking time increases, the content of active functional groups in the coal increases, the coal samples with a particle size of 0-1 mm exhibit significantly higher levels of active functional groups compared to the other test groups. After soaking in an alkaline solution, coal samples with smaller particle sizes exhibit lower apparent activation energy. As particle size decreases, the amount of oxygen consumed during oxidation increases, enhancing the oxidation capacity. Consequently, the quantities of CH₄, and C₂H₆ gases released in the later stages of oxidation also progressively rise. In an alkaline environment, smaller coal particle sizes experience more severe erosion. The lower the energy barrier that must be overcome for oxidation, the more intense the coal-oxygen composite reaction becomes.

In response to the frequent and high-impact accidents in key marine areas, a risk assessment model for accidents in such areas based on TAN network was established. To address the issue of partial sample bias in accident reporting, the boxplot method was employed to eliminate outliers and improve data quality. Considering the complexity and correlation of risk factors, a random forest algorithm was utilized to identify key risk factors and establish a risk evaluation index system for accidents in key marine areas. In addition, the performance of TAN network model was compared with six machine learning models for validation and analysis. The results demonstrate that TAN network achieves the highest accuracy of 93.02%. The findings indicate that ship speed, ship length, and pirate attacks are the primary factors contributing to risk events in key marine areas. Vessels aged between 11 and 20 years should be prioritized for maintenance and inspection. In addition, ships navigating in shallow key marine areas should operate with increased caution.

To address the challenges that static prediction models face in accurately forecasting dynamic landslide trends and the high computational costs associated with dynamic models—which hinder real-time prediction—this study proposes a novel real-time model for landslide displacement prediction. The model integrated Particle Swarm Optimization (PSO), a Gated Recurrent Unit (GRU) network, Online Learning (OL), and dynamic Model Retraining (MRT). First, a static landslide prediction model was established by integrating PSO and GRU. Second, an OL strategy was incorporated into the static model, enabling dynamic updates and real-time predictions as new monitoring data were acquired. Then, small-batch MRT was performed based on prediction accuracy evaluation to predict landslide trends dynamically and in real time. Finally, a comparative analysis of several related models was conducted using the Wangeryan landslide in Sichuan Province as a case study. The results indicate that the OL and MRT methods significantly improve prediction accuracy. Specifically, the PSO-GRU-OL-MRT model achieved Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R² values of 0.795, 3.53, 1.40, and 0.954, respectively, with an average prediction time of 25.0 seconds per instance, demonstrating the highest prediction accuracy. In comparison, the GRU-OL-MRT model yielded values of 1.73, 7.82, 2.54, and 0.917 for the same four metrics, with an average prediction time of 0.496 seconds per instance, significantly reducing computational costs while maintaining relatively high prediction accuracy.

In order to mitigate biomechanical injuries sustained by middle-sized children during frontal collisions of school buses, this study investigated the optimal design of intelligent airbags. Initially, a validated school bus simulation model was constructed based on sled test data. Subsequently, methods for determining head, thorax, and femur injury values for a 10-year-old THUMS dummy were established, and the model, including the school bus restraint system, THUMS dummy and intelligent airbag, was developed. Next, based on NSGA-III, the adaptive propagation factors were proposed, the Gaussian mutation operator and the evolutionary mechanism of the particle swarm optimization method were introduced, and the non-dominated sorting level was improved, thus putting forward the improved NSGA-III. Finally, the design optimization of the intelligent airbag was conducted using the improved NSGA-III to determine the optimal configuration of the airbag. The results demonstrate that the improved NSGA-III outperforms the other three state-of-the-art optimization algorithms. Under normal, 10° forward tilt, 20° forward tilt, 5° right tilt, 10° right tilt and lying sitting postures, the head injury criterion (HIC₁₅), intracranial pressure (IP), liver pressure (LP), left and right femur forces (F_L and F_R, respectively), and weighted injury criterion (WICC) for middle-sized children are significantly reduced when the proportional coefficient of the gas mass-flow rate of the inflation valve, the opening pressure and opening coefficient of the deflation valves, and the installation height of the air bag are set at 1.23, 1.37×10⁵ Pa, 2.10, and 478 mm, respectively.

To control the simulated smoke flow field (used as a substitute for real fire smoke in the airworthiness verification experiments for aircraft cargo compartment smoke detection), this paper established a closed-loop flow control experimental platform for simulated smoke based on a full-scale aircraft cargo compartment mock-up. Control law design was conducted using MFAC theory. The closed-loop flow control effects of simulated smoke were verified with real fire smoke mass concentration as the control target. Additionally, the influence of control law parameters on control effect was explored. The experimental results demonstrate that MFAC method can effectively overcome the adverse effects of unmodeled dynamics in turbulent flow fields on closed-loop control, and achieve the approximation of the non-stationary flow fields between simulated smoke and real fire smoke. Compared with the PID control method, the control performance can be improved by more than 20%.

In order to ensure the safe operation of autonomous ships, it is necessary to deeply analyze the risk factors of contact accidents and formulate targeted safety prevention and control strategies. Based on the contact accident investigation reports and international literature on autonomous ships, this paper adopts systems theoretic process analysis, the decision-making trial and evaluation laboratory method and the fuzzy cognitive map model to analyze the risk causes of contact accidents for autonomous ships from multiple perspectives, and assess the risk level of two typical accident scenarios, namely, berthing/unberthing in port waters and navigating through bridge areas, and base on this, propose corresponding prevention and control measures. The results show that during the port waters, autonomous ships face ten types of key risks such as inaccurate perception and interrupted information transmission and during the bridge areas, there are nine types of key risks such as perceptual blindness and decision-making errors. And insufficient communication and collaboration, significant wind/current interference and other factors are the key triggers of contact accidents for autonomous ships. These factors involve the key control behavior of perception, decision-making, control and communication of autonomous ships, and there is also a complex coupling and amplification effect among different factors. Finally, prevention and control measures are proposed, including improving the ship-shore data exchange protocol and joint exercise mechanism, integrating multi-source sensors and machine learning algorithms, developing a plan for human-machine permission switching, and using VR/AR technology to conduct immersive emergency training.

In order to prevent dust cloud combustion and explosion accidents in the high sulfur content concentrate warehouse after beneficiation, lead concentrate and zinc concentrate of Panlong Lead Zinc Mine were taken as examples. Tests on explosion intensity and the lower explosion limit at different mass concentrations were conducted using a 20 L balls. Main parameters, including the maximum explosion pressure, explosion lower limit, and explosion index of the lead zinc concentrate powder dust cloud, were obtained. The results show that maximum explosion pressures of lead concentrate and zinc concentrate dust clouds were 0.335 and 0.251 MPa, respectively, both occurring when the mass concentration of the dust cloud was 1 000 g/m³. The lower explosive limits of mass concentration are 160-170 and 210-220 g/m³, respectively, indicating low explosion risk. The explosion risk and hazard of dust clouds in lead concentrate are higher than those in zinc concentrate. The explosion mechanism of concentrate dust cloud particles being ignited, exploded, and subjected to a chain reaction is obtained. These findings provide an experimental and theoretical basis for preventing dust cloud explosion of concentrate in the concentrate warehouse.