Results 191 to 200 of about 69,680 (260)

Environmental Variability Shapes Life-History Trade-Offs Within and Between Populations of a Long-Lived Seabird. [PDF]

Ecol Lett
Mohring B, Potts JR, Wilson AJ, Réale D, Phillips RA, Weimerskirch H, Barbraud C, Bennison A, Delord K, Wood AG, Peroteau S, Rouby E, Ventura F, Patrick SC.   +13 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Earlier hatching date rather than chick phenotypic quality enhances first-year survival in the black-headed gull. [PDF]

Front Zool
Indykiewicz P, Barczyk J, Kowalski J, Minias P.   +3 more
europepmc   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Association Between Sperm Metabolites and Field Fertility in Angus Bulls. [PDF]

Metabolites
Roberts SR, Moorey SE, Lonas AB, Hessock EA, Abiodun BA, Campagna SR, Schrick FN, Menegatti Zoca S.   +7 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Contract Labor Reliance and Subsequent Patient Experience Performance: Evidence from U.S. Short-Term Acute Care Hospitals. [PDF]

Healthcare (Basel)
Beauvais B, Ramamonjiarivelo Z, Mileski M, Shapley R, Pradhan R, Shanmugam R.   +5 more
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Pattern matching with Elastic-Degenerate strings and Elastic-Founder graphs. [PDF]

Algorithms Mol Biol
Ascone R, Bernardini G, Conte A, Equi M, Gabory E, Grossi R, Pisanti N.   +6 more
europepmc   +1 more source