Results 211 to 220 of about 1,642,949 (300)

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Mechanisms and scales in modeling forest responses to changing disturbance regimes. [PDF]

New Phytol
Xu X, Scholl C, Ma Y, Gora E, Uriarte M, Anderegg WRL, Liu Y, Han T, Luo Y, Koven CD, Hansen WD, Morton DC.   +11 more
europepmc   +1 more source

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

Advanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen, Christina Ertural, Philipp Benner, Gian‐Marco Rignanese, Janine George   +6 more
wiley   +1 more source

Drivers of understory shrub and herb biomass in subtropical Chinese fir forests: a cluster analysis and SEM approach. [PDF]

Front Plant Sci
Zhang D, Huang X, Hu M, Yan W, Cao X.
europepmc   +1 more source

Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction

Advanced Intelligent Discovery, EarlyView.
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong, Lejia Wang, Tianqing Zhu, Keith Butler, Sichao Li   +4 more
wiley   +1 more source

Effect of Disturbance on Population Structure and Regeneration of <i>Abies fargesii</i> var. <i>faxoniana</i> (Rehder & E.H.Wilson): Mild Disturbance Promotes Growth, Severe Disturbance Leads to Decline. [PDF]

Ecol Evol
Zhao Y, Li B, Yu W, Qi R, Gao B, Ha X, Liu T, Li Y.   +7 more
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Insomnia and Sleep Disturbance in Stroke Survivors: A Meta-Analysis of Prevalence, Risk Factors, and Recovery Outcomes in Ischemic and Hemorrhagic Stroke Survivors. [PDF]

Cureus
Mogombaye NG, Jagdewsing D, Elijah W, Rashidi F, Rashidi M, Moharam H, Jagdewsing S, Angel, Iqbal F, Liu L.   +9 more
europepmc   +1 more source

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

Advanced Intelligent Discovery, EarlyView.
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang, Bing Sun, Zhigang Chen, Chenyu Zhao, Eugene Bakker, Yaojie Lei, Lingfei Zhao, Shijian Wang, Chao Liu, Virginia Xiaojun He, Bahram Shirzadi, Hao Liu, Xin Xi, Ruiliu Liu, Dongqing Wu, Guodong Long, Guoxiu Wang   +16 more
wiley   +1 more source

Disturbance Mosaics Shape Multiple Pollinator Taxa Across Spatial and Temporal Scales in Fire-Managed Forests. [PDF]

Glob Chang Biol
Ulyshen MD, Stober J, Horn S, Reynolds SK, Forrester EJ, Young AD.   +5 more
europepmc   +1 more source