Results 211 to 220 of about 65,460 (275)

The interactions between climate and soil factors influence the taxonomic and phylogenetic diversity of woody plants in the subtropical karst region. [PDF]

PeerJ
Zhou R, Shi W, Yi Y, Wu Y, Tang X, Mao Y, Wang X.   +6 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

A Two-Stage Deep Learning Method for Non-Invasive Sow Body Temperature Prediction Fusing Thermal Imaging and Environmental Parameters. [PDF]

Animals (Basel)
Xu S, Qin Z, Huang Q, Tan C, Xu X, Li X.
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Air Pollution and Alzheimer's Disease: A Systematic Review and Meta-Analysis. [PDF]

J Clin Med
Baiamonte L, Tarantino D, Lodico M, Bellante G, Migliazzo C, Allegra P, Maniscalco L, Piccoli T, Vanacore N, Matranga D, Salemi G.   +10 more
europepmc   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

Woody Plant Diversity and Community Structure Along Elevational and Soil Gradients in <i>Betula platyphylla</i> Forests, Southeastern Tibetan Plateau. [PDF]

Ecol Evol
Norbu N, Wang RF, Dolma D, Tashi S, Jiacuo Y, Zeng ZF, Bonjor N, Tso N, Wang JW, Qiong.   +9 more
europepmc   +1 more source

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

Tracking Plasmodium knowlesi Through Fecal DNA for Monitoring Zoonotic Transmission in Wild Macaques Across Southeast and South Asia. [PDF]

J Infect Dis
Wannigama DL, Amarasiri M, Phattharapornjaroen P, Hurst C, Modchang C, Cynthia B, Miyanaga K, Cui L, Fernandez S, Melhem NM, Luk-In S, Singer AC, Ragupathi NKD, Htun TS, Sei K, Ngamwongsatit N, Hui NS, Shimotai Y, Ounjai P, Kanthawee P, Tacharoenmuang R, Kauba A, Eang C, Romone L, Dharne M, de Araujo JC, Ndatuwong LG, Werawatte WKCP, Chanthasiri T, Zhao J, Mori H, Besa JJV, Kurt Ö, Kanjanabuch T, Zahraei-Ramazani AR, Higgins PG, Aoyagi T, Kicic A, Trowsdale S, Hongsing P, Yang X, Wang Y, Khatib A, Sano D, Shibuya K, Abe S, Hamamoto H.   +46 more
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source