Results 201 to 210 of about 1,009,382 (298)

Development of Forest Tree Species Composition: Selected Results of the National Forest Inventory of Lithuania. [PDF]

Plants (Basel)
Petrokas R, Manton M, Kulbokas G, Muraškienė M.   +3 more
europepmc   +1 more source

Harnessing Digital Microstructure for Simulation‐Guided Optimization of Permanent Magnets

Advanced Intelligent Discovery, EarlyView.
An experimental‐to‐computational workflow is presented that transforms experimental 3D focused ion beam‐scanning electron microscopy data into a simulation‐ready digital microstructure for multiphase functional materials. Using heavy‐rare‐earth‐free Nd–Fe–B magnets as a model system, the approach quantifies grain connectivity across complex secondary ...
Nikita Kulesh, Anton Bolyachkin, Hajime Nakamura, Tadakatsu Ohkubo, Hossein Sepehri‐Amin   +4 more
wiley   +1 more source

Does climatic variation drive the adjustment of functional traits? An assessment of Tropical Montane Cloud Forest tree species. [PDF]

Front Plant Sci
Andrés-Hernández AR, Rodríguez-Ramírez EC.   +1 more
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Correction: Siam et al. Forest Tree and Woody Plant-Based Biosynthesis of Nanoparticles and Their Applications. <i>Nanomaterials</i> 2025, <i>15</i>, 845. [PDF]

Nanomaterials (Basel)
Siam AMJ, Abu-Zurayk R, Siam N, Abdelkheir RM, Shibli R.   +4 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Aboveground and belowground trait coordination across twelve boreal forest tree species. [PDF]

Sci Rep
Spitzer CM, Jämtgård S, Larsson MJ, Gundale MJ.   +3 more
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Restricted Dispersal in the Late Successional Forest Tree Species <i>Nothofagus Pumilio</i>: Consequences Under Global Change. [PDF]

Ecol Evol
Soliani C, Sekely J, Zamora-Ballesteros C, Heer K, Lepais O, Mondino V, Opgenoorth L, Pastorino M, Marchelli P.   +8 more
europepmc   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source