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 effectively apply micro-nano bubble water stemming in mining operations, this study investigated the key performance of micro-nano bubble water as the internal filling material in stemming for dust suppression and CO absorption. Experiments including surface tension measurement, contact angle analysis, spray dust suppression, and solution adsorption were conducted to examine the fundamental properties of micro-nano bubble water, such as wettability and oxidation capability, as well as its effectiveness in suppressing blasting dust and CO absorption efficiency. The results show that: compared with tap water, micro-nano bubble water exhibits lower surface tension and a smaller contact angle with coal, thereby enhancing the wettability of coal particles. With prolonged standing time, collapsed microbubbles generates abundant OH radicals, improving the catalytic oxidation performance of micro-nano bubble water. Micro-nano bubble water achieves higher dust suppression efficiency than tap water, reaching up to 62.27%, with a more pronounced effect on respirable dust. In addition, it significantly enhances CO absorption efficiency. As the circulation time of the micro-nano bubble generator increases, along with higher air intake and larger scrubbing water volume, the CO absorption efficiency gradually increases, though its growth rate first rises and then declines. Under optimal experimental conditions, the CO absorption efficiency of micro-nano bubble water reaches 64.27%.

To solve the limitations of traditional fault trees in accurately capturing the complex correlations among components of communication base station systems, this study proposes a method for seismic fragility and importance analysis based on T-S fault tree. First, a three-subsystem architecture consisting of power supply, machine room, and transmission is constructed, and a T-S fault tree model for post-earthquake functional loss of communication base station systems is established. The functional correlations among components and subsystems are quantified using gate rule tables. Second, the seismic fragility models of communication base station system and the machine room subsystem are comparatively analyzed. Then, the seismic fragility of communication base station system is calculated using traditional fault trees and Monte Carlo simulation, and compared with the results from T-S fault tree. Finally, key impact factors are identified by combining the T-S critical importance analysis, and the weak links of the system are located through the sensitivity analysis of component fragility parameters. The results show that analyzing only the seismic fragility of the machine room subsystem underestimates the risk of system functional loss, and it is necessary to comprehensively analyze the seismic fragility of the communication base station system by integrating the three subsystems. The T-S fault tree method has advantages in describing the fuzzy logic relationships among components, and its results are more reliable than those from traditional fault tree. Cables under the slight damage state and machine room buildings under the severe damage state are the key impact factors. Substations, transmission lines, and machine room buildings have the most significant impact on the system function.

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 achieve scientific prevention and control of avalanche disasters in the southeastern Tibetan Plateau, a dynamic deduction and assessment model for avalanche hazard chains was developed, focusing on a high-frequency avalanche site along National Highway G219. A foundational geospatial dataset was first constructed using high-resolution digital elevation models, multi-source remote sensing imagery, and field survey data to support numerical modeling. Building upon snowpack instability mechanisms and physical kinematic theory, a multi-factor-driven model was established to quantify key physical parameters of avalanche processes, including flow trajectories, dynamic characteristics, and deposit morphologies. The SEEP/W model was then employed to evaluate the internal seepage stability of avalanche-induced natural dams under different water-level gradients. Finally, the Hydrologic Engineering Center's River Analysis System (HEC-RAS) hydrodynamic model was used to simulate the post-breach flood propagation, calculating critical variables such as downstream water levels and flow velocities. Results reveal that the dynamic modeling method proposed in this study can quantitatively characterize the evolution of the hazard chain: avalanches in this region are characterized by dense snowpack, high flow velocities, and strong impact forces, with deposited material prone to obstructing adjacent rivers and forming temporary dammed lakes. These provisional dams exhibit poor structural stability and are highly susceptible to rapid breaching, generating destructive floods that pose severe inundation threats to downstream infrastructure and nearby settlements.

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.

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 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 reveal the influence of small perturbations on the failure and re-initiation of normal detonation and quasi-detonation waves, experimental were carried out on acetylene-oxygen mixtures. Firstly, thin metal plates of different lengths were arranged in the explosion chamber to introduce small-scale perturbations. Then, helical springs with wire diameters of 7 and 9 mm were used to construct rough wall surfaces for generating quasi-detonation. Finally, distributed photoelectric probes were employed to record the arrival times of detonation waves, and a high-speed schlieren system was combined to observe the diffraction and re-initiation processes of detonation waves. The research reveals that introducing minor perturbations significantly reduces the critical initiation pressure threshold. Below this critical initial pressure, re-initiation of the detonation wave is impossible, even with perturbations present. Conversely, above this critical pressure, planar detonation waves within the tube consistently transition to spherical detonation waves in all repeated experiments. The re-initiation site for normal detonation in a smooth tube consistently occurs near the thin plate, whereas the re-initiation location for quasi-detonation in a rough tube exhibits randomness. Quantitative analysis demonstrates distinct critical initiation criteria for the two detonation types: for the successful re-initiation of detonation, the ratio of the critical tube diameter to the detonation cell size must be greater than or equal to 13, while the critical threshold for the successful re-initiation of quasi-detonation is reduced to approximately 8 for the ratio of the critical tube diameter to the cell size.

In order to improve the efficiency of data fusion in the wireless sensor network (WSN) of a uranium tailings pond, reduce redundant data transmission, and extend network lifespan, an innovative data fusion algorithm was proposed, namely the SAPSO-BP data fusion algorithm based on improved SA and PSO optimized BP neural network. The algorithm integrated the global search capability of the SA algorithm with the efficient optimization mechanism of the PSO algorithm, incorporating dynamic inertia weights and mutation operators to enhance global search ability and avoid local optima. Furthermore, the improved algorithm was used to optimize the weight matrix and threshold parameters of the BP neural network, constructing a high-performance multi-sensor data fusion model, which was applied to radionuclide monitoring in uranium tailings ponds. The results show that the SAPSO-BP algorithm outperforms the compared algorithms in terms of data fusion accuracy, network energy consumption, and network lifespan. Compared with the traditional BP algorithm, it reduces mean relative error(MRE) and root mean square error(RMSE)by up to 40% and 45%, respectively, and improves the goodness of fit to 0.908 3. Additionally, it delays the first node death to approximately 1 180 rounds, extends the overall network lifespan to about 1 500 rounds, and achieves lower node energy consumption and a more balanced energy distribution.