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Mine earthquake are unnatural seismic activities during mining. In the Ordos mining area in North China, the Jurassic coal seam is commonly overlain by the extremely thick cretaceous sandstone strata. It is easy to trigger strong mine earthquakes when the fracture and slip of the extremely thick strata occur caused by the coal extraction. The movement characteristics and the dynamic response of the extremely thick strata are the basis for mine earthquake prevention and control. Based on Vlasov’s theory, this paper used data from surface boreholes, surface subsidence and seismic monitoring to investigate the structure evolution law and movement characteristics of the extremely thick strata in fully-mechanized caving mining of an Ordos coal mine, aiming to reveal the triggering mechanism of the mine earthquakes. The results showed that in the retreating period with no goaf nearby, a small goaf area behind the longwall was presented, and the low roof collapsed rapidly with fracture angles between 64° and 72°, and no obvious fractures were detected in the far-field extremely thick strata, which only caused minor surface subsidence. In the retreating period along with goafs, the roof breaking height expanded to the extremely thick strata with fracture angles increased, and the fractures emerged in the extremely thick strata, which caused a stepwise increase of the surface subsidence. Due to the large thickness and far-field strength of the extremely thick cretaceous sandstone strata, it is deduced that when the longwall chainage was about 324.3 m, the extremely thick strata met the requirement of initial fracture with a fracture step of 83.7 m, and the strong mine earthquakes started to be detected. The triggering mechanism of mine earthquake induced by breakage of extremely thick strata is that: with the increase of goaf area, the fracture height of the low roof gradually extends to the far-field extremely thick strata, and it is easy to trigger mine earthquake during the vertical “O-X” type initial fracture, fracture adjustment and periodic fracture of the extremely thick strata. The outcome of this study can provide reference on the prevention and control of mine earthquakes occurred in the far-field extremely thick strata.

The occurrence of rock bursts due to penetrating faults are frequent in China, thereby limiting the safe production of coal mines. Based on the engineering background of a 501 working face in a TB coal mine, this paper investigates stress and energy evolution during the excavation of this working face due to multiple penetrating faults. Utilizing both theoretical analysis and numerical simulations, this study reveals the rock burst mechanism within the triangular coal pillar influenced by the penetrating faults. Based on the evolution of stress within the triangular coal pillar, a stress index has been devised to categorize both the rock burst danger regions and the levels of rock burst risks associated with the triangular coal pillar. Furthermore, targeted stress relief measures are proposed for various energy accumulation areas within the triangular coal pillar. The results demonstrate that: (1) the superimposed tectonic stress resulting from the T6 and T5 penetrating faults exhibits asymmetric distribution and has an influence range of about 90 m in the triangular coal pillar, reaching a peak value of 11.21 MPa at a distance of 13 m from the fault plane; (2) affected by the barrier effect of penetrating faults, the abutment stress of the working face is concentrated in the triangular coal pillar, and the magnitude of the abutment stress is positively and negatively correlated with the fault plane barrier effect and the width of the triangular coal pillar, respectively; (3) the exponential increase in abutment stress and tectonic stress as the width of the triangular coal pillar decreases leads to a high concentration of static stress, which induces pillar burst under the disturbance of dynamic stress from fault activation; (4) the numerical simulation shows that when the working face is 150 m away from the fault, the static stress and accumulated energy in the triangle coal pillar begins to rise, reaching the peak at 50 m away from the fault, which is consistent with the theoretical analysis; (5) the constructed stress index indicates that the triangular coal pillar exhibits moderate rock burst risks when its width is between 73 to 200 m, and exhibits high rock burst risks when the width is within 0 to 73 m. The energy accumulation pattern of the triangular coal pillar reveals that separate stress relief measures should be implemented within the ranges of 50 to 150 m and 0 to 50 m, respectively, in order to enhance the effectiveness of stress relief. Blasting stress relief measures for the roof and coal are proposed, and the effectiveness of these measures is subsequently verified. [ABSTRACT FROM AUTHOR]

In order to solve the problems that microseismic monitoring can only reflect the danger of the area where the microseisms are located, and CT inversion monitoring cannot reflect the current danger level of the working face in time, taking the 3105 working face of Dongshan Gucheng Coal Mine in Jining, Shandong as the engineering research background, a microseismic-stress multi-dimensional information monitoring and early warning method is proposed. The method combines short-term microseismic monitoring and medium and long term seismic wave CT inversion monitoring technology to identify the impact dangerous area of the working face. And the method combines the large-scale monitoring results based on medium and long term CT inversion, uses microseismic data around the working face to correct them so as to realize the real-time and dynamic monitoring and early warning of the medium and long term stress evolution of the working face and short-term microseismic accumulation. According to the abnormal situation of spatial and temporal distribution of microseismic events in August 2019 in 3105 working face for nearly one month, it is judged that the coal pillar area of the narrowing section in front of the working face is the abnormal stress area, and the CT inversion technology is adopted for dangerous area division and early warning. According to the results of CT inversion, the range of dangerous areas of strong and medium impact is given, and the high wave velocity impact dangerous area of 3105 working face is determined to be 100-200 m ahead of the material roadway and transportation roadway of the 3105 working face. According to the results of CT inversion, in accordance with the principle of zoning management, the corresponding pressure relief measures combining large diameter drilling of coal seam, coal blasting and deep hole blasting pressure relief are adopted for the high wave velocity impact dangerous area. The application results show that the seismic wave CT inversion technology can predict the corresponding relationship between high wave velocity and abnormal pressure area in two roadways accurately, predict the impact dangerous area effectively and can distinguish the danger level. After implementing pressure relief measures in dangerous areas with high wave velocity, the microseismic frequency and energy in the area are reduced effectively. The microseismic frequency and energy are reduced from 515, 12×105 J to 338, 5.98×105 J, are decreased 34.4% and 50.2% respectively. The pressure relief effect is obvious.

Featured Application: Based on the geological characteristics of karst mountainous areas, this paper aims to explore a method for determining reasonable roof-cutting parameters under the varying characteristics of coal seam burial depth in karst mountainous areas. It mainly focuses on the sensitivity and threshold range of the influence of roof-cutting angle and height in order to ensure the stability of the surrounding rock in the retained roadway after roof cutting. The research results can provide a scientific basis and engineering reference for the selection of roof-cutting parameters in mines with similar geological conditions. The differential distribution of original rock stress and stress concentration caused by the variation in coal seam depth in karst topography are critical factors influencing the selection of roof-cutting parameters. Based on this, this study explores a method to determine reasonable roof-cutting parameters by incorporating the characteristics of coal seam depth variation in karst mountainous areas. A mechanical model of the cantilever beam structure for roof cutting in gob-side entry retention (GSER) is constructed, and the critical values and reasonable ranges of roof-cutting height and angle under different burial depths are derived. Furthermore, the displacement and stress evolution characteristics of surrounding rocks in gob-side entry retention under different coal seam burial depths, roof-cutting heights, and roof-cutting angles within the reasonable range of roof-cutting parameters are analyzed. The results show that there is a positive correlation between roof-cutting height and tensile stress in the uncut portion of the main roof, while roof-cutting angle and coal seam depth are negatively correlated with tensile stress. From the perspective of impact, roof-cutting height has a greater impact than roof-cutting angle, followed by coal seam depth. As for the distribution characteristics of the reasonable roof-cutting parameter range, the fan-shaped area of reasonable roof-cutting parameters gradually decreases with increasing coal seam depth. Taking the geological conditions of Anshun Coal Mine as an example, when the burial depth increases from 350 m to 550 m, adjusting the roof-cutting height to 6 m, 7 m, and 8 m, respectively, and setting the roof-cutting angle at 10° can effectively achieve the stability of the surrounding rock in the GSER. The research findings can provide a scientific basis and engineering references for selecting roof-cutting parameters in mines with similar geological conditions. [ABSTRACT FROM AUTHOR]

Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method's effectiveness in identifying cracks in complex environments. [ABSTRACT FROM AUTHOR]

Formulating hierarchical structures via systematic assembly of MOF-derived electrocatalysts emerges as a potent strategy to dictate the electrocatalytic efficacy of the oxygen evolution reaction. However, research into the microscale implications of assemblies is notably lacking and is critical for bubble modulation along with the OER process. Herein, the MOF assembly process is precisely engineered by adjusting the concentration of lyophilized slurry. This approach enables the accurate modulation of macropores in MOF assemblies at the micrometer scale. Following high-temperature selenization, the assembled composite transforms into a carbon skeleton-supported iron/nickel selenide (FeSe2/NiSe2) structure. This structure maintains the integrated architecture and the inherent concentration gradient relationship of the original composition. The OER electrocatalytic performance exhibits a significant dependence on the micrometer-scale dimensions, a relationship that becomes especially pronounced at higher current density. Observations gleaned from optical microscopy, finite element simulations, and extensive experimental evidence underscore the importance of meticulously regulating the microscale assembly of MOF derived electrocatalysts for bubble-dynamics modulation, which is essential for optimizing OER performance. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) plays an important role in maintaining the safe and stable operation of Lithium-ion battery management systems. Aiming at the problem of poor prediction stability of a single model, this paper combines the advantages of data-driven and model-based methods and proposes a RUL prediction method combining convolutional neural network (CNN), bi-directional long and short-term memory neural network (Bi-LSTM), SE attention mechanism (AM) and adaptive unscented Kalman filter (AUKF). First, three types of indirect features that are highly correlated with RUL decay are selected as inputs to the model to improve the accuracy of RUL prediction. Second, a CNN-BLSTM-AM network is used to further extract, select and fuse the indirect features to form predictive measurements of the identified degradation metrics. In addition, we introduce the AUKF model to increase the uncertainty representation of the RUL prediction. Finally, the method is validated on the NASA dataset and the CALCE dataset and compared with other methods. The experimental results show that the method is able to achieve an accurate estimation of RUL, a minimum RMSE of up to 0.0030, and a minimum MAE of up to 0.0024, which has high estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems. [ABSTRACT FROM AUTHOR]