Results 211 to 220 of about 258,206 (308)

Multi‐Omics Profiling Reveals Immunomodulatory and Pro‐Regenerative Effects of a Graphene Oxide–Collagen Scaffold in Massive Rotator Cuff Tears

open access: yesAdvanced Science, EarlyView.
A graphene oxide/collagen scaffold is developed for chronic massive rotator cuff tear repair. The scaffold improves compressive stability, supports reparative mesenchymal differentiation, and modulates the immune microenvironment. In chronic MRCT models, it reduces muscle degeneration, enhances tendon–bone regeneration, and improves functional recovery,
Renwen Wan   +24 more
wiley   +1 more source

Specific Random Trees for Random Forest

open access: yesIEICE Transactions on Information and Systems, 2013
LIU, Zhi, SUN, Zhaocai, WANG, Hongjun
openaire   +2 more sources

Introgressed Variation in TaMYB7‐A1 Drives Graded Dormancy and Climate‐Adaptive Pre‐Harvest Sprouting Resistance in Wheat

open access: yesAdvanced Science, EarlyView.
TaMYB7‐A1 directly activates TaABI5 to enhance ABA signaling and regulate ABA‐GA homeostasis, enforcing seed dormancy. Its superior allele, derived from wild einkorn introgression, harbors a MITE insertion that elevates expression and two amino acid substitutions that enhance transcriptional activity, collectively generating graded PHS resistance for ...
Hao Wang   +18 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

open access: yesAdvanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu   +8 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

open access: yesAdvanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin   +12 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

open access: yesAdvanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan   +8 more
wiley   +1 more source

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