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The coal-rock identification technology in the roadway excavation process is the core of the automatic adjustment of roadheader’s cutting head, and it is also one of the key problems restricting the development of intelligent mines. In view of the current mining imbalance, the excavation face lacks a mature and effective coal-rock identification scheme, and the existing image based coal-rock identification models have problems such as poor segmentation accuracy and inability to flexibly deploy, a coal-rock cutting interface perception and precise recognition method based on image segmentation is proposed in the heading face. This method combines the actual cutting situation of the excavation working face and uses the MobileNetV2 feature extraction network as the backbone network of DeepLabV3+, so that the model can better balance the segmentation accuracy and model complexity. The channel attention (SE) operation is performed on the advanced features output by the Atrous Spatial Pyramid Pooling module, and channel weights are assigned to strengthen the training of key feature information. The channel spatial attention (CA) mechanism is introduced into the shallow features output by the backbone network to weight the low-level representation information in the shallow feature map, thus designing a coal-rock cutting interface identification model that integrates the double attention mechanism in DeepLabV3+. At the same time, an experimental platform for coal-rock identification in a dusty environment is built to simulate the coal and rock cutting surface formed by the roadheader after cutting, and the coal-rock cutting interface acquisition system in the process of roadway excavation is developed. Taking the actual mine excavating face as the engineering background, the recognition accuracy and practical applicability of the coal-rock identification model are verified. The research results show that the average intersection ratio and average pixel accuracy of the SE-CA-DeepLabV3+ network model are 97.15% and 98.51%, respectively, which have better segmentation performance than other network models. The established model is used to verify the original coal and rock images from the heading face of the experimental mine in northern Shaanxi, the average error is 0.7%, and the number of transmission frames per second is 43fps, which meets the deployment conditions of downhole field applications.

Abstract Real‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining.

As an important component of intelligent mine construction, the underground intelligent separation of coal and gangue can effectively improve the green utilization of mine resources. At present, the visible light image recognition technology for the identification of coal-gangue mixture in dark and humid underground environment remains to be improved. Based on the thermal infrared imaging technology and improved YOLOv5 algorithm model, this paper proposed a thermal image identification method for the coal-gangue mixed situation under dark and wet conditions. The neck part of the YOLOv5 model was changed to Bidirectional Feature Pyramid Network (BiFPN) structure, and the identification efficiency of coal-gangue was improved through multi-level feature fusion. The CIOU function was used as the loss function to improve the accuracy of coal-gangue detection. An experimental platform for the thermal image acquisition of coal-gangue mixture was constructed to simulate the low illumination and high humidity environment in underground confined space. The contrast enhancement and edge enhancement preprocessing of the thermal image collected by the infrared camera were performed by CLAHE and LAPLACE operators. The results of thermal image identification of coal gangue mixture were systematically analyzed from different data sets, different improved modules and different algorithm models, and the influence of humidity change on the accuracy of coal-gangue identification under dark and wet conditions was explored. The results show that the average accuracy of the preprocessed image is 1.7% higher than that of the original image, and the F-Measure value is increased by 6.9%. The average accuracy mean and F-Measure of the improved YOLOv5 model reaches 80.2% and 84.6%, which are higher than 74.6% and 79.7% of the classical model, which can effectively improve the detection accuracy of coal gangue thermal image. The relative humidity of the environment is positively correlated with the recognition accuracy, and negatively correlated after the humidity reaches a certain threshold. It is proposed that the thermal image can accurately identify the coal-gangue mixture in the dark and humid closed environment, which provides a scientific basis for the accurate identification of the coal-gangue mixed situation under the dark and wet conditions.

Abstract The rapid development of ultrawideband (UWB) communication systems has resulted in increasing performance requirements for the antenna system. In addition to a wide bandwidth, fast propagation rates and compact dimensions, flexibility, wearability or portability are also desirable for UWB antennas, as are excellent notch characteristics. Although progress has been made in the development of flexible/wearable antennas desired notch properties are still rather limited. Moreover, most presently available flexible UWB antennas are fabricated using environmentally not attractive subtractive etching-based processes. The usage of facile additive sustainably inkjet printing processes also utilizing low temperature plasma-activated conductive inks is rarely reported. In addition, the currently used tri-notched flexible UWB antenna designs have a relatively large footprint, which poses difficulties when integrated into miniaturized and compact communication devices. In this work, a silver nano ink is used to fabricate the antenna via inkjet printing and an efficient plasma sintering procedure. For the targeted UWB applications miniaturized tri-notched flexible antenna is realized on a flexible polyethylene terephthalate (PET) substrate with a compact size of 17.6 mm × 16 mm × 0.12 mm. The antenna operates in the UWB frequency band (2.9–10.61 GHz), and can shield interferences from WiMAX (3.3–3.6 GHz), WLAN (5.150–5.825 GHz) and X-uplink (7.9–8.4 GHz) bands, as well as exhibits a certain of bendability. Three nested "C" slots of different sizes were adopted to achieve notch features. The simulation and test results demonstrate that the proposed antenna can generate signal radiation in the desired UWB frequency band while retaining the desired notch properties and having acceptable SAR values on-body, making it a viable candidate for usage in flexible or wearable communication transmission devices. The research provides a facile and highly efficient method for fabricating flexible/wearable UWB antennas, that is, the effective combination of inkjet printing processing, flexible substrates, low temperature-activated conductive ink and antenna structure design.

Abstract As a recognized special resource, tar-rich coal can extract the country's scarce oil and gas resources and generate semi-coke that can replace anthracite and coking coal. The tar-rich coal in northern Shaanxi is prominent, but due to the dense structure and high strength of tar-rich coal, it is easy to cause frequent dynamic disasters in coal mining. Therefore, the realization of pressure relief and disaster reduction has become the primary problem in mining tar-rich coal. There are many shortcomings in conventional pressure relief methods, so a new method of microwave-weakening coal is proposed. Through different water saturation treatments of tar-rich coal samples, the longitudinal wave velocity degradation trend and surface crack expansion law of water-bearing coal after microwave irradiation were analyzed, and the strength softening characterization and energy evolution relationship under the combined action of microwave and water were studied. Fractal dimension and its internal correlation based on the equivalent side length-mass of coal sample fragments. The experimental results show that: (1) Under the same microwave radiation condition, with the increase of water saturation, the deterioration trend of physical and mechanical parameters such as longitudinal wave velocity and peak strength is obvious. (2) After microwave radiation, the uniaxial compression results show that the coal sample is damaged by load, there is still a high residual strength, the ratio of elastic energy to dissipation energy decreases, and the possibility of rockburst of the coal sample decreases. The strength softening degree of coal specimen under the degradation of microwave and water is the highest, followed by microwave and water. (3) The fractal dimension is inversely proportional to the moisture content and microwave radiation intensity, and the fractal dimension has a significant positive correlation with the peak intensity and longitudinal wave velocity. The mechanical damage law of water-bearing tar-rich coal under microwave action is revealed, which aims to solve the problem of weakening and reducing the impact of hard coal on-site to a certain extent, ensure the safety of working face, and improve the mining efficiency of tar-rich coal. It provides basic theoretical support for microwave-assisted hydraulic fracturing technology and effective weakening measures for hard roof treatment.

The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.

The stability of section coal pillars is one of the most important factors affecting the stability of coal rock systems in the stope and roadway. This study aimed to develop an artificial intelligence methodology to predict and evaluate coal pillar stability. Data from 125 published coal pillar historical cases were collected to build a sample dataset. Meanwhile, a mean impact value-genetic algorithm-back propagation neural network (MIV-GA-BP) fusion model was established to predict the stability of section coal pillars. MIV tests indicated that the main factors influencing coal pillar stability are (in order of decreasing importance): the coal seam buried depth > coal seam thickness > working face length > coal elastic modulus > cohesion > tensile strength > internal friction angle > Poisson’s ratio > volume weight > coal seam dip angle. The relative weights of mine design parameters are generally greater than those of the physical and mechanical parameters of coal and rock mass. After the BP model was optimized by GA, the relative error, R value, and mean squared error were 5%, 0.95, and 0.13, respectively. These results confirm that the machine learning model has significant potential for improving coal pillar stability evaluations. The developed prediction model was applied to two field cases to verify its effectiveness, and the results indicated that the innovative method can be extended for use in similar geological conditions or other mining and geaological engineering fields.

Saponins have been extensively used in the food and pharmaceutical industries because of their potent bioactive and pharmacological functions including hypolipidemic, anti-inflammatory, expectorant, antiulcer and androgenic properties. A lot of saponins-containing foods are recommended as nutritional supplements for diabetic patients. As a medicine and food homologous material, Corni Fructus (CF) contains various active ingredients and has the effect of treating diabetes. However, whether and how CF saponins attenuate diabetes is still largely unknown. Here, we isolated total saponins from CF (TSCF) using ultrasonic microwave-assisted extraction combined with response surface methodology. The extract was further purified by a nonpolar copolymer styrene type macroporous resin (HPD-300), with the yield of TSCF elevated to 13.96 mg/g compared to 10.87 mg/g obtained via unassisted extraction. When used to treat high-fat diet and streptozotocin-induced diabetic mice, TSCF significantly improved the glucose and lipid metabolisms of T2DM mice. Additionally, TSCF clearly ameliorated inflammation and oxidative stress as well as pancreas and liver damages in the diabetic mice. Mechanistically, TSCF potently regulated insulin receptor (INSR)-, glucose transporter 4 (GLUT4)-, phosphatidylinositol 3-kinase (PI3K)-, and protein kinase B (PKB/AKT)-associated signaling pathways. Thus, our data collectively demonstrated that TSCF could be a promising functional food ingredient for diabetes improvement.