Results 181 to 190 of about 1,523,345 (294)

Influence of photosynthetic active radiation on sap flow dynamics across forest succession stages in Dinghushan subtropical forest ecosystem. [PDF]

open access: yesHeliyon
Huang J   +14 more
europepmc   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

open access: yesAdvanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao   +9 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

Species sorting and mass effect along forest succession: Evidence from taxonomic, functional, and phylogenetic diversity of amphibian communities. [PDF]

open access: yesEcol Evol, 2019
Hernández-Ordóñez O   +7 more
europepmc   +1 more source

Minnesota Forest Scene, Issue 18 (Spring 2017)

open access: yes, 2017
1 electronic resource (PDF, 6 pages; includes color photographs)University of Minnesota. Department of Forest Resources; Department of Forest Resources, University of Minnesota. (2017). Minnesota Forest Scene, Issue 18 (Spring 2017).
Department of Forest Resources, University of Minnesota   +1 more
core  

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

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