Influence of coplanar double fissures on failure characteristics of sandstone and fracture mechanics analysis

The influence of fissure angles and bridge lengths on rock mechanical properties and failure was analyzed using the uniaxial compression test and the Digital Image Correlation (DIC) technique. The research findings are as follows: 1) Peak stress and elastic modulus of the samples exhibited an obvious change trend with the change in fissure angle. The fissure angle has a more significant effect on a rock’s mechanical properties than the length of the rock bridge. 2) With an increase in the fissure angle, the number of surface cracks, main failure cracks and surface spalling decreased, whereas the area of the falling blocks significantly increased. However, with an increase in bridge length, the characteristics of crack propagation and spalling are essentially the same. During crack propagation, the connection of the rock bridge is related to its fissure angle and length. 3) At a low fissure angle, the failure mode of rock samples is dominated by tensile-failure cracks; with an increase in fissure angle, the tension-damage to shear-damage crack transformation will form a mixed tensile-shear damage mode; at the same time, with an increase in bridge length, the rock bridge becomes more difficult to connect, and the local crack expansion failure changes from tensile-shear cracks to tensile cracks. 4) Stress on the coplanar double-fissured rock sample was simplified and analyzed to explain the behavior of fractures on the sample. These research results have an important guiding value for engineering optimal designs

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.Comment: 12 pages, 15 figure

Senescence-associated lncRNAs indicate distinct molecular subtypes associated with prognosis and androgen response in patients with prostate cancer

Cellular senescence has been considered as a hallmark of aging. In this study, we aimed to establish two novel prognostic subtypes for prostate cancer patients using senescence-related lncRNAs. Nonnegative matrix factorization algorithm was used to identify molecular subtypes. We completed analyses using software R 3.6.3 and its suitable packages. Using SNHG1, MIAT and SNHG3, 430 patients in TCGA database were classified into two subtypes associated with biochemical recurrence (BCR)-free survival and subtype 2 was prone to BCR (HR: 19.62, p < 0.001). The similar results were observed in the GSE46602 and GSE116918. For hallmark gene set enrichment, we found that protein secretion and androgen response were highly enriched in subtype 1 and G2M checkpoint was highly enriched in subtype 2. For tumor heterogeneity and stemness, homologous recombination deficiency and tumor mutation burden were significantly higher in subtype 2 than subtype 1. The top ten genes between subtype 2 and subtype 1 were CUBN, DNAH9, PTCHD4, NOD1, ARFGEF1, HRAS, PYHIN1, ARHGEF2, MYOM1 and ITGB6 with statistical significance. In terms of immune checkpoints, only CD47 was significantly higher in subtype 1 than that in subtype 2. For the overall assessment, no significant difference was detected between two subtypes, while B cells score was significantly higher in subtype 1 than subtype 2. Overall, we found two distinct subtypes closely associated with BCR-free survival and androgen response for prostate cancer. These subtypes might facilitate future research in the field of prostate cancer