In order to elucidate the combustion and explosion of volatile gas leaks caused by degradation of the floating roof seal performance and its destructive mechanism on composite floating roofs, a multiphase coupled model of liquid storage-composite floating roof incorporating the cell structure of the honeycomb core layer was established. Multiphase coupling analysis of the tank's combustible gas, composite floating roof, and stored liquid was employed in this study to compare damage patterns in the roof panel and honeycomb core under implosion loads. A methodical inquiry was initiated to explore the impact of panel layering angles, honeycomb geometric parameters (including wall thickness, height, and edge length), and cellular element configurations (i.e., regular hexagons, circular, close-packed, and sparingly packed cells) on the blast resilience performance of floating roofs. The results indicate that, in circumstances where liquid levels are at a low ebb, the upper panel is primarily subject to matrix tensile damage (6.82% area fraction), accompanied by 0.16% fibre compression and matrix compression damage. The optimal panel lay-up angle [45°/90°/45°/90°] has been demonstrated to reduce matrix tensile damage to 5.03% area fraction, thus yielding the optimum level of explosion resistance. Hexagonal honeycomb cores have been shown to demonstrate superior blast resistance in comparison to circular cores, while densely packed circular honeycomb exhibits greater load-bearing capacity than sparsely packed configurations. Increasing the thickness and height of honeycomb cells, or reducing cell edge length, has been demonstrated to enhance the floating roof's capacity for blast resistance.

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.

Data reliability is essential for accurate identification of coal mine disaster risks. To accurately evaluate the data reliability of coal mine disaster monitoring and early warning systems, relevant policies, regulations, standards and literatures including the One Regulation and Four Detailed Rules were systematically reviewed, and a reliability evaluation method for coal mine disaster monitoring systems was proposed by integrating big data and GIS spatial analysis technology. The method was verified via field practice in disaster prevention and control at a coal mine in Shanxi Province. Results indicate that extracting the characteristics of imprecision, heterogeneity and conflict from multi-source monitoring information is the core to accurately identify unreliable data, including over-limit values, equipment failures, missing information, positional errors and abnormal frequencies. A reliability evaluation index system covering 3 primary categories (legitimacy, compliance and rationality) and 437 subcategories is constructed, which can fully restore multi-source associated information of the monitoring system throughout its full life cycle. During normal production in February 2025 at the test mine, 56 753 pieces of unreliable information were identified by this method, with a 100% accuracy rate verified by manual inspection. Furthermore, this method can dynamically assess whether existing mine monitoring systems meet disaster early warning requirements during the data preprocessing stage, and timely prompt mine maintenance and system upgrading.

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 investigate the effects of solar irradiation and wetting-drying cycles on the crack evolution of compacted loess and clarify the underlying mechanisms, laboratory tests were conducted using a xenon lamp to simulate solar irradiation under varying irradiance levels, dry densities, and wetting-drying cycles. Surface crack images were periodically captured using a self-developed acquisition system. Crack morphological parameters were extracted using the Particle and Crack Analysis System (PCAS), and micro-pore structures were quantitatively analyzed based on scanning electron microscopy (SEM) images, enabling a systematic macro-micro analysis of crack evolution characteristics. Results indicate that increasing irradiance accelerates crack initiation and increases crack ratio, main crack length, and overall fractal dimension. Within the dry density range of 1.5-1.7 g/cm³, higher dry density effectively reduces crack ratio and connectivity, thereby inhibiting crack propagation. Under wetting-drying cycles, porosity generally increases, pore circularity decreases, and fractal dimension shows an initial increase followed by fluctuations, corresponding well with macroscopic crack evolution. Solar irradiation enhances surface evaporation, intensifies moisture migration and deformation heterogeneity, and promotes the transition from pore structure adjustment to macroscopic crack propagation.

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

In order to reduce accident risks caused by leakage during the refueling process, the hydrogen leakage and diffusion behavior of a 35 MPa hydrogen dispenser was numerically simulated using Ansys Fluent. The characteristics of hydrogen leakage and diffusion under the canopy structure in the refueling zone were investigated. The effects of leakage diameters, ambient wind velocity, and local ventilation on hydrogen concentration distribution and the evolution of flammable areas were analyzed. The results show that when the leakage diameter of filling hose is 2 mm, no flammable area is formed on the underside of canopy. However, when the leakage diameter increases to 5 mm and 10 mm, a flammable area can develop on canopy underside. The location of the highest hydrogen concentration on underside of canopy is concentrated near the axis parallel to the jet direction. Specifically, when leakage diameter is 10 mm, the hydrogen concentration on underside of canopy along vertical leakage direction exhibits a Gaussian distribution. When the ambient wind is perpendicular to leakage direction, wind velocities of 2 m/s and 8 m/s can effectively reduce hydrogen accumulation near the leakage hydrogen dispenser. In contrast, at the wind speed of 5 m/s, a vortex structure was formed near obstacles, leading to hydrogen accumulation and increasing the risk of fire and explosion. Under no ambient wind conditions, local ventilation is provided in the refueling zone. When the ventilation velocity reaches 5 m/s and 10 m/s, the hydrogen cloud concentration within the flow field can be successfully diluted to below the flammable limit within 2 s. Moreover, a ventilation velocity of 10 m/s shows a more pronounced effect in reducing the hydrogen concentrations in front of the leakage source.

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 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 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%.