To address the challenges of multi-modal vehicle coordination and dynamic scheduling in emergency supply delivery for mountain forest fires, this study proposes an optimized route planning method for emergency supply distribution. Considering factors such as mountainous road network structures, demand urgency, motorcycle and drone participation, and air-ground collaborative delivery, a dynamic collaborative truck-drone-motorcycle route optimization model for mountain forest fire emergency supply delivery is constructed, aiming to minimize delivery time. An improved adaptive large neighborhood search (IALNS) algorithm with a repair operator is designed to solve the model, and it is compared with traditional ALNS and tabu search algorithms(TSA). Research findings indicate: The proposed model and algorithm are applicable to emergency supply delivery in mountainous forest fire scenarios. The IALNS outperforms both ALNS and TSA. Dynamic coordination strategies yield superior results compared to static coordination, reducing the total delivery time by 2.49% to 25.75%. Leveraging the advantages of motorcycles and drones is essential for establishing an optimized multi-vehicle collaborative delivery model.

In order to address frequent hazardous gas leaks in large and medium-sized chemical plant areas, this study proposes a leak source localization method based on a multi-strategy improved PSO(MSPSO) algorithm, leveraging the collaborative capabilities of a small number of UAVs. First, considering the physical constraints UAVs face during actual movement, an acceleration control strategy was integrated into PSO algorithm. Simultaneously, the chemical plant area was divided into distinct zones to more accurately simulate the UAVs' flight states during the search process. Second, an upwind search strategy was introduced based on diffusion characteristics of leak sources, utilizing wind direction information to accelerate the search process. Third, to prevent UAVs from getting stuck in pseudo-leak sources, Cauchy mutation perturbations and simulated annealing mechanisms were employed to enhance the UAVs' ability to escape local optima. Finally, a three-dimensional simulation environment for large and medium-sized chemical plant areas was established to compare and analyze the performance of various swarm intelligence algorithms in simulated scenarios. The results indicate that MSPSO exhibits faster convergence and higher localization success rates, with performance better meeting the leakage source localization requirements of large-to-medium-scale chemical plant areas.

Focusing on the path planning problem for the resolution of high-risk head-on flight conflicts, a UAV resolution path model based on minimum energy consumption was proposed. Multiple factors, such as paths and turns, obstacles, and safety separation, were comprehensively considered in this model. In the model solution, an improved SSA with multi-strategy integration was proposed by improving the chaotic mapping, the golden sine and the follower position update strategy. Its effectiveness was demonstrated by comparing it against 3 other mainstream swarm-intelligence algorithms on 8 standard benchmark functions. For the issue of flight conflict resolution, multi-aircraft conflict scenarios and reduced conflict scenarios were constructed, and 4 algorithms were applied for multiple trials. The comparison was made from two aspects: convergence performance and running time. Simulation results show that, in 4-UAV head-on conflicts scenario, a fitness of 8.86 with a runtime of 4.06 s are achieved by the improved algorithm, while a fitness of 1.26 and a runtime of 3.03 s are obtained in the 3-UAV induced-conflict scenario. All results are optimal, indicating that reasonable resolution paths for head-on conflicts among multiple UAVs can be rapidly provided by the proposed approach. When dealing with complex conflict problems, both computational efficiency and accuracy are taken into account. The new algorithm can quickly plan paths for the resolution of multi-machine head on flight conflicts.

To maintain the operational capability of airports during wartime, an analysis was conducted on the vulnerability of the airport runway to deliberate attacks with conventional weapons. By analyzing the target and functional characteristics of airport runways, a vulnerability assessment framework for airport runways under deliberate attacks by multiple types of conventional weapons is constructed based on the Monte Carlo method. A airport runway vulnerability assessment model is established, and a runway at an airport on the coast of China is selected as a case study. The results show that the binary attack strategy is significantly superior to the random attack strategy. When attacking airport runway targets, the impact of the weapon's circular error probable (CEP) decreases as the weapon's lethal radius increases, while the impact of the weapon's lethal radius decreases as the weapon's CEP increases. Both the CEP and lethal radius impacts decrease with an increase in the number of strikes. Optimization of attack strategies can to some extent compensate for the shortcomings of weapon performance. Under the binary attack strategy, the strike effectiveness of different weapon systems can be ranked in descending order as follows: conventional foreign cruise missiles, large unmanned aerial vehicles, long-range rocket launchers, and howitzers. In terms of protective measures, measures such as multi-band smoke screen interference, Global Positioning System (GPS) jamming, and enhancing the strength grade of concrete can be adopted to reduce the accuracy of weapon strikes and the radius of weapon damage, thereby improving the take-off and landing efficiency of the airport runway.

To promote the engineering application of VIMD in high-rise base-isolated structures, a 20-story base-isolated steel structure model was considered. First, the influence of the VIMD's inertance on the structure's natural periods was investigated. Then, 10 real ground motion records were selected from the Pacific Earthquake Engineering Research Center database and were scaled to match the target design response spectrum. The control characteristics and seismic mitigation effects of VIMD on the structural response were studied. Finally, based on the Davenport fluctuating wind speed spectrum, the spectral representation method was used to generate 10 stochastic fluctuating wind speed time histories. The control characteristics and wind-induced vibration mitigation effects of VIMD on the structural response were studied. The results indicate that VIMD can further extend the natural periods of the high-rise base-isolated structure, mainly in the first six modes. Under seismic excitations, the control effectiveness of VIMD on the relative displacement response of the isolation layer is comparable to that of Viscous Dampers (VD). However, the vibration mitigation ratios of VIMD for the response of the superstructure is improved by 19.5%-24.5% compared with that of VD. Under wind loads, VIMD and VD have basically the same response control capabilities for the high-rise base-isolated structure.

To fundamentally enhance the safety of lithium-ion batteries, phosphorus-based flame retardants were added to the electrolyte, and the effects of different valence states of phosphorus-based flame retardants on safety performance and electrochemical performance were studied. The impact of phosphorus-based flame retardants on the thermal stability of the electrolyte was compared using self-extinguishing time and differential scanning calorimetry. The influence of phosphorus-based flame retardants on the basic properties of the electrolyte was analyzed through linear sweep voltammetry. Further, cyclic voltammetry tests, cycling performance tests, rate capability tests, and electrochemical impedance spectroscopy were conducted to explore the effects of varying volume ratios of phosphorus-based flame retardants on the electrochemical performance of LiFePO₄|Li half-cells. The results show that electrolytes containing 5% trimethyl phosphate (TMP) and trimethyl phosphite (TMPi) exhibit significantly reduced self-extinguishing times, with the former also expanding the electrochemical window of the electrolyte. The LiFePO₄|Li half-cell with 5% TMP demonstrates a smaller potential difference between the oxidation and reduction peaks, reduced battery polarization, and maintains a higher discharge capacity after 300 cycles, with a capacity retention rate of 99.6%. In contrast, the addition of 5% TMPi leads to a decline in discharge capacity. Methyl phosphate flame retardants exhibit weaker molecular activity and less adverse effects compared to methyl phosphite flame retardants. Under the premise of not compromising electrochemical performance, adding 5% trimethyl phosphate is more effective in improving the safety of lithium-ion batteries.

To address the issues of low accuracy and weak robustness in existing detection algorithms due to insufficient lighting, scale differences among personnel, and frequent obstruction by equipment in coal mine environments, as well as the challenges posed by high parameter and computational requirements of some models, which make them difficult to adapt to edge devices underground, an improved YOLOv8n model was proposed to optimize personnel detection tasks in complex mine environments. An enhanced SPDs-Conv module was introduced to enhance the extraction of small target features and improve the recognition accuracy of low-pixel personnel in distant views. Cross stage partial feature fusion + selective kernel attention (C2f_SKAttention) module was designed to strengthen the model's focus on targets of different scales and cope with the scale differences of underground personnel. A dynamic detection head was constructed to adapt to the diversity and complexity of targets, and to improve robustness to occlusion and other scenarios. The WIoU loss function was improved to increase the bounding box localization accuracy and reduce the localization deviation caused by low illumination. The results show that the proposed improved YOLOv8n model achieves an mean average precision (mAP) @0.5 of 83.5% and an mAP@0.5:0.95 of 39.0% on the mine personnel detection dataset. Compared with the original YOLOv8n, the P is improved by 8.5%, the R by 11.9%, the mAP@0.5 by 4.7%, and the mAP@0.5:0.95 by 3.3%. The number of parameters only increases from 3.1M to 3.2M, and the Giga Floating-point operations per second (GFLOPS) rises from 14.0G to 14.4G. The proposed model maintains a lightweight structure while improving detection accuracy and robustness. It effectively alleviates missed detection of small underground targets, insufficient multi-scale adaptation and weak anti-interference capability in complex environments, making it suitable for the limited computing power of underground edge equipment.

To manage risk transmission and reduce the probability of construction safety accidents, the evolution process and characteristics of construction safety risk transmission in high-altitude areas were explored. Firstly, based on construction accident reports of hydropower projects in high-altitude areas and the 4M1E theory, a construction safety risk factor system was constructed from five aspects: personnel, machinery and equipment, materials, management, technology, and environment. The Decision-Making Experiment and Assessment Technique (DEMATEL) was used to analyze risk events triggered by risk factors. Then, based on the SFEP theory, a construction safety risk transmission network was established, and the risk transmission probability of each path was calculated through association rules. The SD method was utilized to construct an SD model of the construction safety risk transmission network. Finally, a large hydropower project in Xizang was taken as an example for simulation verification. The results show that the SD model of the construction safety risk transmission network reveals 29 risk transmission paths from 17 edge events, 15 process events, to 5 final events, as well as their evolution trends and sensitivities. Environmental and management risk events are identified as key nodes, and three key transmission paths are identified. Based on this, targeted risk transmission prevention and control measures are proposed, providing a theoretical basis for the management of construction safety risks in hydropower projects in high-altitude areas.

To prevent safety accidents such as bursting and leakage caused by wear in mine filling pipelines, CFD-DEM was employed to investigate the wear characteristics of filling slurries on pipelines. An L-shaped pipeline and a solid-liquid two-phase flow model were constructed to conduct numerical simulation experiments. Particle size (0.001-0.1 mm), slurry solid volume fraction (60%-80%), and slurry flow velocity (2-6 m/s) were used as variable parameters to explore the maximum wear rate of the pipeline under different conditions. The results indicate that both particle size and solid volume fraction exhibit a nonlinear relationship with the maximum wear rate. At a particle size of 0.1 mm, the maximum wear rate is 13.4 × 10^-5 kg/m². There is a critical solid volume fraction of 70%, at which the maximum wear rate was 7.15 × 10^-5 kg/m²; beyond this value, the wear rate tended to stabilize. The slurry flow velocity shows a linear relationship with the maximum wear rate; at a flow velocity of 7 m/s, the maximum wear rate is 8.25 × 10^-6 kg/m². The influence of each parameter on pipeline wear was ranked as follows: particle size > solid volume fraction > slurry flow velocity. The interaction between particle size and solid volume fraction has the most significant impact, followed by the interaction between solid volume fraction and slurry flow velocity, while the interaction between particle size and slurry flow velocity had the least effect.

To effectively reduce the contagion risks in the "last-mile" of emergency logistics in epidemic-stricken areas, a truck-drone collaborative delivery mode was first designed. A "basic reproduction number" function was constructed based on epidemic transmission dynamics to quantify the number of infections at various demand points. Then, a routing optimization model for truck-drone collaborative emergency supply delivery was established, aiming to minimize both the total number of infections and the total delivery time. In view of the multi-objective and non-linear characteristics of the model, the IMOABCA was developed. Finally, experiments were carried out through multiple types of instances. The results show that the IMOABCA could scientifically optimize delivery routes by integrating epidemic data, demand point distribution, and population size. Compared with the basic multi-objective artificial bee colony algorithm (MOABC)and Non-dominated Sorting Genetic Algorithm-II(NSGA-II), the total number of infections is reduced by 922 and 746, respectively. Additionally, the total delivery time is saved by 3.71% and 1.41%, and the task completion time can be shortened by 14.06% and 3.6%, respectively.