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 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.

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 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 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 improve the detection efficiency and automation level of hidden defects on the outer surface of elevator traction steel wire ropes, and reduce the incidence of elevator accidents, an online detection model for defects on the outer surface of elevator traction steel wire ropes based on improved YOLOv5s is constructed. Firstly, the GhostConv module is introduced into the feature extraction layer C3 module to reduce computational complexity, and the Convolutional Block Attention Module (CBAM) is integrated to enhance the feature extraction capability of small-scale defects. A feature extraction module GC-C3 (GhostConv and CBAM-C3) that integrates GhostConv and CBAM is constructed; Secondly, in the feature fusion layer, Path Aggregation Network (PANet) and Bidirectional Feature Pyramid Network (BiFPN) are used to construct a multi-scale feature fusion network PBNet (PANet BiFPN), which combines multi-scale weight allocation strategy to improve the fusion effect of multi-scale defect feature information; Then, dynamically adjusting the quality weights of prediction boxes using Weighted Intersection over Union(WIoU) loss function, reducing the interference of low-quality samples on training. Finally, the model will be deployed to the developed detection system to perform online testing on the surface defects of the traction steel wire rope on the elevator car roof, verify the improvement effect of the model, and provide grading reminders for the defects. The results showed that the average detection accuracy of the improved model was 96.2%, with a detection speed of 192 f/s, which was 4.1% and 12.3% higher than the original model, respectively. The model volume was reduced by 38.9%. According to the online visualization experiment, under the actual operating environment of the traction steel wire rope (light illumination of 200~400 lx, speed of 1.5~2 m/s), the average accuracy of the system for 8 typical external surface defects is still stable at 94.6% or above, which meets the application requirements of online detection of hidden dangers of external surface defects of the traction steel wire rope in service and reduces elevator accidents caused by external surface defects of the traction steel wire rope.

To address the issues of high water consumption and the limited dust suppression range of traditional spray-based dust control technologies, a 1∶1 geometric model of the discharge port in an open-pit mine was established using numerical simulation software. Based on the Realizable k-ε turbulence model, the velocity distribution of induced airflow at the discharge port was investigated. According to the flow field characteristics, a swirling air curtain dust control method was proposed. This method utilizes induced circulation to encapsulate dust particles and employs a dynamic pressure barrier to suppress their diffusion, thereby forming a closed airflow barrier that prevents dust escape. By combining numerical simulations with schlieren observations, the velocity distribution of the induced airflow was analyzed, and the integrity of the swirling air curtain under varying jet angles and airflow velocities was compared. The results indicate that the formation of the swirling air curtain is jointly governed by the jet angle and airflow velocity. As both parameters increase, the vortex core structure becomes more distinct, and the degree of flow field closure is significantly enhanced. When the jet angle ranges from 10° to 20° and the airflow velocity from 15 to 25 m/s, a stable closed reverse-pressure swirling forms at the discharge port, where induced entrainment and circulation effects are most pronounced. When the jet angle is 15° and the airflow velocity is 20 m/s, the negative pressure core remains most stable, and the swirling structure is most fully developed. In contrast, excessively large jet angles or overly high airflow velocities may lead to increased turbulent dissipation and vortex instability, resulting in the breakdown of the air curtain structure. Schlieren observation further confirm that the swirling structure is most complete under the condition of a 15° jet angle and 20 m/s airflow velocity, thereby verifying the reliability and effectiveness of the proposed swirling air curtain dust control method.

A dual-wavelength photoelectric smoke detection simulation system based on amorphous gallium oxide broadband photodetectors was proposed to resolve the limitations of traditional light-scattering smoke detectors. These traditional detectors were susceptible to interference from dust and moisture, exhibit high false alarm rates, and feature prolonged response times. Based on Mie scattering theory, dual wavelengths (980 and 405 nm) were employed by the system to measure smoke particle volume surface area concentration. And SMD was derived for effective differentiation between fire-related and non-fire-related smoke. Measurement bias was minimized by optimization of detection angle parameters (980 nm: 60°, 405 nm: 120°) through theoretical analysis and simulation experiments. The system's effectiveness was validated by simulation tests. Results indicate that the system maintains low false alarm rates for test smoke (fire tests ≤3.3%, non-fire smoke ≤6.7%), demonstrating high sensitivity and low false alarm rates.