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Accurate differentiation between microseismic signals induced by hydraulic fracturing and those from roof fracturing is vital for optimizing fracturing efficiency, assessing roof stability, and mitigating mining-induced hazards in coal mining operations. We propose an automatic identification method for microseismic signals generated by hydraulic fracturing in coal seam roofs. This method first transforms the microseismic signals induced by hydraulic fracturing and roof fracturing into time-frequency feature images using the Frequency Slice Wavelet Transform (FSWT) technique, and then employs a sliding window (Swin) Transformer network to automatically identify and classify these two types of time-frequency feature maps. A comparative analysis is conducted on the performance of three methods—including the signal energy distribution method, Residual Network (ResNet) model, and VGG Network (VGGNet) model—in identifying microseismic signals from hydraulic fracturing in coal seam roofs. The results demonstrate that the Swin Transformer recognition model combined with FSWT achieves an accuracy of 92.49% and an F1-score of 92.96% on the test set of field-acquired microseismic signals from hydraulic fracturing and roof fracturing. These performance metrics are significantly superior to those of the signal energy distribution method (accuracy: 64.70%, F1-score: 64.70%), ResNet model (accuracy: 88.04%, F1-score: 89.24%), and VGGNet model (accuracy: 88.47%, F1-score: 89.52%). This advancement provides a reliable technical approach for monitoring hydraulic fracturing effects and ensuring roof safety in coal mines.

Accurate topological connectivity is critical for the safe operation and management of low-voltage distribution grids (LVDGs). However, due to the complexity of the structure and the lack of measurement equipment, obtaining and maintaining these topological connections has become a challenge. This paper proposes a topology identification strategy for LVDGs based on a feature-enhanced graph attention network (F-GAT). First, the topology of the LVDG is represented as a graph structure using measurement data collected from intelligent terminals, with a feature matrix encoding the basic information of each entity. Secondly, the meta-path form of the heterogeneous graph is designed according to the connection characteristics of the LVDG, and the walking sequence is enhanced using a heterogeneous skip-gram model to obtain an embedded representation of the structural characteristics of each node. Then, the F-GAT model is used to learn potential association patterns and structural information in the graph topology, achieving a joint low-dimensional representation of electrical attributes and graph semantics. Finally, case studies on five urban LVDGs in the Wuhan region are conducted to validate the effectiveness and practicality of the proposed F-GAT model.

In recent years, Cutting-edge machine learning algorithms and systems in Industry 4.0 enhance quality control and increase production efficiency. The visual perception algorithms have become extensively utilized in surface defect detection, progressively replacing manual inspection methods. As a crucial component of the Industrial Internet of Things (IIoT), this technology is pivotal for ensuring industrial production quality and has garnered significant attention from the military and aerospace sectors. Nonetheless, most existing methods rely on Euclidean space, which constrains their effectiveness in handling non-Euclidean space data. Additionally, challenges such as addressing pre-trained feature redundancy and bias in the pre-training process persist. This paper presents HADNet, a hyperbolic space-based anomaly detection method. Specifically, we begin by mapping the extracted features to hyperbolic space, a non-Euclidean geometric space. This mapping leverages the unique geometric properties of hyperbolic space, particularly the hyperbolic distance metric, to represent the distances between features more effectively. Next, the most relevant features for anomaly detection are selected through the anomaly-aware feature subset selection module, enhancing anomaly detection performance. Finally, we introduce adaptive residuals discrimination, an adaptive analysis technique that discards residuals lacking anomaly information, thereby isolating the most effective regions for anomaly detection. Extensive experiments on four benchmark datasets NEU-Seg, MT-Defect, FSSD-12, and UCF-EL demonstrate the efficacy of HADNet, achieving mIoU scores of 87%, 81.46%, 77.04%, and 59.41% respectively, significantly surpassing the current state-of-the-art methods.

An accurate topology of low-voltage distribution grids (LVDGs) serves as the foundation for advanced applications such as line loss analysis, fault location, and power supply planning. This paper proposes a two-stage topology identification strategy for LVDGs based on Contrastive Learning. Firstly, the Dynamic Time Warping (DTW) algorithm is utilized to align the time series of measurement data and evaluate their similarity, yielding the DTW similarity coefficient of the sequences. The Prim algorithm is then employed to construct the initial topology framework. Secondly, aiming at the topology information obtained from the initial identification, an Unsupervised Graph Attention Network (Unsup-GAT) model is proposed to aggregate node features, enabling the learning of complex correlation patterns in unsupervised scenarios. Subsequently, a loss function paradigm that incorporates both InfoNCE loss and power imbalance loss is constructed for updating network parameters, thereby realizing the identification and correction of local connection errors in the topology. Finally, case studies are conducted on 7 LVDGs of different node scales in a certain region of China to verify the effectiveness of the proposed two-stage topology identification strategy.

Accurate transformer–customer relationships are crucial for the efficient operation and high-quality service of the low-voltage distribution grid (LVDG). This paper proposes a novel two-stage transformer–customer relationship identification strategy for LVDG using physics-guided graph attention network (PGAT). First, considering both transient and steady-state voltage fluctuations, a modified piecewise aggregate approximation (MPAA) algorithm is developed to preprocess raw measurement data through compression and denoising while preserving key voltage correlation features. Second, electrical similarity among customers is explored using the Modified Piecewise Aggregate Approximation K-means (MPAA-K-means) algorithm, enabling preliminary identification of transformer–customer relationships. Then, a training paradigm based on PGAT is introduced to characterize node features constrained by grid topology and electrical properties, achieving refined identification of transformer–customer relationships. Finally, testing results on real LVDG demonstrate the effectiveness and accuracy of the proposed two-stage identification strategy, providing new insights for transformer–customer relationship identification.

In the process of in situ stress fracturing drilling in coal mines, obtaining downhole vibration data not only improves drilling efficiency but also plays a key role in ensuring operational safety. Nevertheless, the energy supply techniques used in current vibration detectors reduce operational performance and escalate excavation expenses. This research proposes a self-powered vibration sensor based on the triboelectric nanogenerator, designed for the operational environment of coal mine in situ stress fracturing drilling. It can simultaneously detect axial and lateral vibration frequencies, and the inclusion of redundant sensing units provides the sensor with high reliability. Experimental outcomes demonstrate that the device functions across a frequency span of 0 to 11 Hz, maintaining error margins for frequency and amplitude under 4%. Furthermore, it functions reliably in environments where temperatures are under 150 °C and humidity is under 90%, proving its strong resilience to environmental factors. In addition, the device possesses self-generating potential, achieving a maximum voltage of 68 V alongside an output current of 51 nA. When connected to a 6 × 107 Ω load, the maximum output power can reach 3.8 × 10−7 W. Unlike traditional subsurface oscillation detectors, the proposed unit combines self-generation capabilities with highly reliable measurement characteristics, making it more suitable for practical drilling needs.

Vibration signals generated during hydraulic fracturing drilling in coal mining are critical dynamic indicators for revealing borehole conditions and evaluating drilling efficiency and safety. However, the power supply methods of existing vibration sensors limit the practical application of this technology. To address this, this study was inspired by the African drum and developed a vibration sensor based on a triboelectric nanogenerator (TENG), enabling simultaneous measurement of vibration frequency and acceleration in a self-powered mode. Tests demonstrate that the device measures vibration frequencies from 0 to 9 Hz with an error below 3%. It achieves vibration acceleration measurements at thresholds of 1 g and 2 g with an error less than 4%. The sensor operates effectively at temperatures range of 15 °C to 75 °C and relative humidity below 90%. Additionally, the device possesses energy-harvesting capabilities, delivering a maximum power output of 72 nW at a load resistance of 1000 MΩ and a vibration frequency of 9 Hz. Unlike existing underground vibration sensors, this sensor’s high redundancy design and self-power generation features make it particularly suitable for the practical working conditions of upward drilling in coal mines.

Bladder cancer is the second most prevalent urological malignancy. It's a big contributor to cancer-related deaths throughout the globe. Researchers discovered that the hedgehog signaling (HhS) pathway contributed to the onset and spread of many different kinds of cancer. Nevertheless, the present understanding of the function of HhS in the bladder cancer molecular landscape is incomplete. Raw data were gotten from the IMvigor210, the Gene Expression Omnibus, and The Cancer Genome Atlas databases. Bioinformatics was used to examine the HhS score of each sample, and the enrichment of differentially expressed genes (DEGs), differentiation characteristics, immunological infiltration, and metabolic activity. The HhS prognostic signature was developed with significant assistance from the least absolute shrinkage and selection operator regression and Cox regression. An HhS-related nomogram was developed to assist in the prediction of patients’ survival probability. We found that HhS was linked to poor prognosis in bladder cancer, and its activation was linked to the Basal subtype of bladder cancer. Bladder cancer with high HhS activity has higher glycolysis, nucleotide metabolism, amino acid metabolism, and other cancer-promoting metabolic activities. Furthermore, HhS mediates an immunosuppressive microenvironment in bladder cancer on the basis that HhS negatively correlates with the CD8 + T cells and correlates positively with immune checkpoints and T cell exhaustion scores. Finally, an HhS-related signature was developed for predicting the prognosis of patients with bladder cancer. Targeting HhS may be a potential therapy choice for bladder cancer.

Poly(lactic acid) (PLA) holds significant promise as an option in the field of packaging materials due to its biodegradability and antibacterial properties. Therefore, it is vital for developing packaging materials while improving their heat resistance, and transparency is essential for guaranteeing its application. Using a self-assembled nucleating agent with hydrogen bonding and thermodynamically compatible transparent polymethyl methacrylate (PMMA), this study fabricated PLA micro-crystals with an interface blurred grain. Furthermore, the crystalline structure-property relationship was investigated in different isothermal crystallization conditions; it was possible to achieve higher crystallinity while maintaining the transparency of PLA/10 wt% PMMA/0.3 wt% nucleating agent blends. Compared to other temperatures, the crystallization rate of PLA blends under annealing conditions at 90 °C was higher when induced by three different nucleating agents. Particularly, in the presence of the TC-328 nucleating agent, the system exhibited a crystallinity of 32%, the smallest grain size, and an increased Tg of 61.3 °C, as well as an elevated heat deformation temperature (HDT) from 54.13 °C to 63.2 °C. The smaller nucleating agents with high surface energy enhanced the interaction between the PLA and PMMA, enhancing the PLA/PMMA tensile strength and HDT. These findings may pave the way for designing novel blends for packaging or heat-resistant devices.

Clarifying the mechanical properties and failure patterns of layered coal–rock combinations in coal-measure strata is critical to guiding hydraulic fracturing design in petroleum and mining engineering. This paper investigates the mechanical properties, failure patterns, and stress distributions of sandstone–coal–sandstone (SCS) and mudstone–coal–mudstone (MCM) combinations under different confining pressures and thickness ratios based on the 3D combined finite–discrete element method (3D FDEM). The results show that the mechanical strength of the SCS combination is higher than that of the MCM combination under the same conditions. As the thickness ratio increases, the overall peak stress and elastic modulus of the combination decrease gradually and eventually approach those of the pure coal. As confining pressure increases, the peak stress of layered coal–rock combinations rises gradually, plastic behaviors become increasingly prominent, and the failure mode of the mudstone layer transitions from tensile-dominated to shear-dominated. Under different thickness ratios and confining pressures, the coal layer in the combinations primarily develops shear-dominated cracks, whereas the sandstone layer mainly generates tensile-dominated cracks. An increase in confining pressure elevates the critical thickness ratio for sandstone layer failure in the SCS combination. Essentially, the changes in stress state caused by rock types, thickness ratios, and confining pressures are important reasons for the variations in the failure patterns of each layer in layered coal–rock combinations. The key findings of this paper are expected to provide theoretical guidance for the field design of petroleum and coal mine engineering.