Study of hydraulic cutting as a method to prevent coal burst disasters.

This study explores the causes of coal bursts in the Xinzhou Kiln Mine, identifying key factors such as residual pillars, hard coal seams and/or roofs, stress concentration due to complex geological structures, and the stress distribution characteristics of the primary rock. A significant finding is that hydraulic cutting not only diminishes and redistributes the stress concentration region inside the coal seam but also mitigates the burst potential of the coal-rock mass, fundamentally reducing the likelihood of coal bursts. By taking Face No. 8937 in Xinzhou Kiln Mine as the test object, a coal burst prevention test was performed using hydraulic cutting. In combination with theoretical analysis and numerical simulation, the mechanism of hydraulic cutting for preventing coal burst was discussed, and reasonable cutting parameters were established. Onsite monitoring revealed that hydraulic cutting disrupts the integrity of the coal-rock mass, releases internal stress, and increases its water content, thereby weakening its burst tendency. Additionally, the deformation and fracturing of the cutting slots and the closure of boreholes shifted the stress concentration from the coal seam to deeper areas and to the two ribs. Post-cutting observations showed a significant reduction in both the frequency and impact energy of coal bursts; there was also a noticeable increase in the convergence of the roadway in the cutting area compared to non-cutting areas. Furthermore, displacement of the roof and floor increased by 78.9 % and that of the two ribs increased by 47.4 % after cutting, preventing the coal-rock mass from accumulating high stress. In conclusion, hydraulic cutting is a promising method for effectively preventing coal bursts and enhancing the safety of mining operations.

How Big Data and High-performance Computing Drive Brain Science.

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.

Gclust: A Parallel Clustering Tool for Microbial Genomic Data.

The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.