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Results 201 to 210 of about 4,990,305 (257)

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao +5 more
wiley +1 more source

Graph convolutional network with reinforced dependency graph and denoising mechanism for sarcasm detection. [PDF]

Sci Rep
Yan P, An T, Yu J, An J, Tong D, Wang J.
europepmc +1 more source

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza +4 more
wiley +1 more source

CasDacGCN: A Dynamic Attention-Calibrated Graph Convolutional Network for Information Popularity Prediction. [PDF]

Entropy (Basel)
Zhang B +6 more
europepmc +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li +4 more
wiley +1 more source

MFST-GCN: A Sleep Stage Classification Method Based on Multi-Feature Spatio-Temporal Graph Convolutional Network. [PDF]

Brain Sci
Li H, Zhang X, Guo K.
europepmc +1 more source

A Review of Grain Boundaries: Formation Mechanism, Synthesis Strategy, and Application in Electrocatalysis

EcoEnergy, EarlyView.
An overview of grain boundary engineering in the field of electrocatalysis. ABSTRACT Key electrocatalytic reactions such as HER, OER, ORR, CO2RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs)
Jingyu Gao +8 more
wiley +1 more source

Deep spatio-temporal graph convolutional network for police combat action recognition and training assessment. [PDF]

Sci Rep
Wang Y.
europepmc +1 more source

Dynamic Reconstruction of Atomically Dispersed Copper Catalysts for CO2 Reduction: From In Situ Observations to Rational Design

EcoEnergy, EarlyView.
Dynamic reconstruction transforms atomically dispersed Cu catalysts into authentic working‐state active phases during CO2 electroreduction. By integrating operando characterization, multiscale simulations, and AI‐assisted predictive modeling, this review establishes a framework for understanding, predicting, and rationally engineering catalyst ...
Jin Liu, Yue‐Wen Fang
wiley +1 more source

Graph convolutional network-derived pathomics and clinical integration for predicting chemoimmunotherapy response in advanced lung squamous cell carcinoma: a multicenter study. [PDF]

J Transl Med
Wang D +11 more
europepmc +1 more source

graph convolutional neural network
deep learning
medicine

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