Results 201 to 210 of about 39,156 (266)

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, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +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, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

Enhancing green hydrogen forecasting with a spatio-temporal graph convolutional network optimized by the Ninja algorithm. [PDF]

Sci Rep
Yassen MA, Alhussan AA, Abdel-Fattah MG, Ismael I, El Deen Salah Mostafa H, El-Kenawy EM.   +5 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, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Multi-view fusion based on graph convolutional network with attention mechanism for predicting miRNA related to drugs. [PDF]

PLoS Comput Biol
Sheng N, Liu Y, Gao L, Wang L, Huang L, Wang Y.   +5 more
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, Fengming Zhou, Xinying Li, Ying Deng, Zexing Wu, Siyuan Feng, Yujing Zhu, Dan Zhang, Zhenyu Xiao   +8 more
wiley   +1 more source

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network

Dissanayake D, Brunner V, Adlard D, Morrone JA, Fowler PW.   +4 more
europepmc   +1 more source

Spatial domain identification method based on multi-view graph convolutional network and contrastive learning. [PDF]

PLoS Comput Biol
Liang X, Xiao S, Ba L, Feng Y, Ma Z, Adilova F, Qi J, Jin S.   +7 more
europepmc   +1 more source

Nanofiber PVA‐Based Membranes Incorporating Functionalized Spanish Broom Derivatives for Sustainable Water Purification

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study investigates Spanish broom (Spartium junceum) as a renewable source for electrospun composite membranes in sustainable water purification. MCC and biochar were functionalized with eco‐friendly precursors and nanomaterials (i.e., HNT, β‐CDs) to develop hybrid PVA nanofiber composites.
Giulia Rando, Silvia Sfameni, Jozefien Geltmeyer, Alessandra Palella, Lorenzo Spadaro, Alberto Riminucci, Karen De Clerck, Maria Rosaria Plutino   +7 more
wiley   +1 more source

PT-TDGCN: Pre-Trained Trend-Aware Dynamic Graph Convolutional Network for Traffic Flow Prediction. [PDF]

Sensors (Basel)
Yang H, Wei S, Wang Y.
europepmc   +1 more source