To solve the problems of large error of layout parameters and low extraction efficiency of artificially designed high-level gas extraction boreholes, a high-gas mine in Xinjiang was taken as the research object. A design method for pressure-relief gas high-level extraction boreholes based on two-dimensional physical similarity simulation and an intelligent system was proposed. Through the two-dimensional physical similarity simulation test, the evolution characteristics of the horizontal and vertical fractures of the overlying rock were revealed. Additionally, the geometric boundary between the gas migration area (maximum height 36.7 m, maximum width 22.7 m) and the reservoir area (maximum height 26 m, maximum width 17 m) was accurately divided, and the spatial evolution characteristics of gas occurrence were clarified. Based on Python language, the intelligent system of high-level gas extraction borehole was developed, and the 3D geological model is constructed by integrating OpenGL technology. Combined with the parameters such as the horizontal distance between the borehole end point and the opening point, the azimuth angle and the final hole height, the borehole layout parameters (azimuth angle, inclination angle and length) were automatically generated by the self-developed parameter calculation system. Subsequently, the borehole trajectory was simulated by the visual demonstration system. It is shown by the application that the final hole position of the borehole designed by this system is accurately located in the upper part of the caving zone and the middle and lower part of the fracture zone. The gas extraction concentration of 2 # drilling field is recorded at 6.52%—10.94%, which is found to be 2.52%-5.19% higher than that achieved by the traditional method

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.

To address the issue of insufficient small-object detection accuracy in remote monitoring of heavy industrial workshops, an unsafe behavior detection algorithm based on improved YOLOv7 was proposed. First, the traditional upsampling was replaced with a lightweight content-aware reassembly of features (CARAFE) module, which effectively preserved the semantic information of small objects through adaptive feature reassembly. Second, an improved Bi-level routing efficient layer aggregation network(Bi-ELAN) module was proposed by integrating the BiFormer dynamic sparse attention mechanism into the head network, which strengthened the multi-scale feature fusion capabilities and established target-background contextual relationships. Third, the loss function was refined by introducing the shape intersection over union(ShapeIoU)loss function, which enhanced bounding box regression accuracy through geometric shape constraints. Finally, ablation experiments and comparative experiments were conducted on the improved YOLOv7 model based on constructed remote monitoring perspective dataset. The results show that, while maintaining model lightweight characteristics, the proposed algorithm significantly improves small-object detection accuracy in remote monitoring scenarios. The improved model achieves a precision of 84.2%, a recall of 78.6%, and a mean average precision (mAP@0.5) of 78.8%. Compared to the original YOLOv7 algorithm, the improved algorithm increases precision, recall, and mAP@0.5 by 5%, 0.3%, and 2.6%, respectively.

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.

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.

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.

To enhance hydrogen safety regulation in China, this paper systematically reviewed the development trends of the global hydrogen industry and its safety regulatory frameworks. Based on this, a comparative analysis was conducted from the perspectives of safety supervision institutions, laws and regulations, standard systems, and technological development for hydrogen industry both domestically and internationally. Drawing on international experience and considering China's specific conditions, policy recommendations were proposed in five aspects: clarifying regulatory responsibilities, improving the supply of laws and policies, accelerating the standard system, strengthening technological support, and deepening exchanges and cooperation. The results show that the global hydrogen energy sector has entered a phase of rapid industrialization. Countries with relatively mature hydrogen industries have built safety governance systems characterized by top-level coordination and planning, full-chain coverage regulation, and standard system support. In comparison, China's hydrogen industry still lags behind the demands of its rapid development in terms of the comprehensiveness of safety regulatory system, the coherence of its standard system, and the capacity for key technology support. It is therefore necessary to further clarify the regulatory responsibilities of relevant departments across the full chain of hydrogen production, storage, transportation, refueling, and end-use applications, and to establish and improve cross-departmental collaborative regulatory mechanisms. It is of great importance to accelerate the formulation of systematic hydrogen safety-specific laws and regulations and to build a standard system covering the entire industrial chain, as well as to strengthen R&D on safety technologies and the construction of professional experimental platforms. By doing this, the safety supervision efficiency shall be enhanced comprehensively in China's hydrogen industry.

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 investigate the influence of pick cone angle on coal-rock fragmentation and dust generation characteristics during underground roadheading operations, a multi-scale integrated approach combining experimental tests and discrete element method (DEM) simulations was adopted. Four picks with cone angles of 78, 92, 105 and 118° were selected as research objects. Roadheading cutting tests were conducted to analyze the particle size distribution of generated debris and dust. DEM simulations using the PBM in Particle Flow Code (PFC) software accurately reproduced the entire process of coal-rock fragmentation and dust generation during roadheading. The evolution trends of crack quantity, crack type, number of minimum-sized discrete particles, and ejection velocity were analyzed under two operational modes of the cutting head: penetration and slewing. The results indicate that as the cone angle increases, the coarseness index (CI) of coal wall debris decreases from 728.92 to 696.91, while the dust proportion increases from 0.4% to 0.67%, demonstrating that the degree of coal wall fragmentation and dust generation increases with pick cone angle. The fragmentation index rises and the uniformity index declines, indicating a broader dust particle size distribution and a higher proportion of fine dust particles. Simulation results show that the total number of cracks on the coal wall increases from 22 980 to 27 272, with tensile cracks consistently accounting for over 73% of the total. The number of minimum-sized free particles increases from 371 to 459, and their average initial ejection velocity decreases from 0.250 m/s to 0.221 m/s. In summary, increasing the pick cone angle intensifies dust generation to a certain extent, but helps suppress the dispersion range of dust.

In order to address the challenges of quantitatively evaluating public behavioral responses to rainstorm disasters and clarifying the degree of their alignment with disaster risks, this study took the Shenzhen "9·7" rainstorm as a case study. Employing the PSR model and public LBS data, public response behaviors during the disaster were comprehensively evaluated from three dimensions: pressure, state, and response. A response adaptation index was established to measure the alignment between public behavior and rainstorm disaster risks. The findings indicate that, compared to normal conditions, the public's travel patterns during rainstorms exhibit similar spatial characteristics but with reduced intensity. Across all time phases, the response effectiveness is highest during non-peak daytime hours. Among different administrative districts, residents in Futian and Longhua District demonstrate the highest level of responsiveness, while those in Yantian District exhibit a relatively weaker response. Among various functional area types, schools and recreational areas show the most significant reduction in travel intensity, indicating the most positive public response, whereas residential and office areas showed a comparatively weaker response.