Results 231 to 240 of about 3,278,052 (298)

An approach for unsupervised interaction clustering in human-robot co-work using spatiotemporal graph convolutional networks. [PDF]

Front Robot AI
Heuermann A, Ghrairi Z, Zitnikov A, Al Noman A, Thoben KD.   +4 more
europepmc   +1 more source

Orchestrating Green Transformation: How AI Adoption Enables Corporate Carbon Neutrality

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT As carbon neutrality has become a central goal of global climate governance, how firms achieve low‐carbon transformation has emerged as a critical research issue. However, prior studies have primarily focused on macro‐ or industry‐level analyses, offering limited and fragmented insights into how digital technologies—particularly AI—affect firm‐
Xiaonan Dong, Sungjin Son
wiley   +1 more source

Transductive zero-shot learning via knowledge graph and graph convolutional networks. [PDF]

Sci Rep
Li Q, Sun X, Dong J.
europepmc   +1 more source

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

Direct Estimation of Electric Field Distribution in Circular ECT Sensors Using Graph Convolutional Networks. [PDF]

Sensors (Basel)
Banasiak R, Stawska Z, Fabijańska A.
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, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

Predicting co-word links via heterogeneous graph convolutional networks. [PDF]

Sci Rep
Li Y, Zhang X, Bai X, Bai S, Jiang Z.
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

Adaptive course recommendation using federated learning and graph convolutional networks in IoT-enhanced e-learning. [PDF]

Sci Rep
Pu H, Hua Y.
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