Results 111 to 120 of about 3,264 (232)

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

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Exercise-Induced Meat Quality Improvement Is Associated with an lncRNA-miRNA-mRNA Network in Tibetan Sheep. [PDF]

Biology (Basel)
Zhao P, Jiang Z, He X, Tian T, He F, Ma X.   +5 more
europepmc   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Occupational exposure to bioaerosols in the Norwegian salmon processing industry. [PDF]

Ann Work Expo Health
Thomassen MR, Hollund BE, Özgümüs T, Madsen AM, Nordhammer ABO, Smedbold HT, Bråtveit M, Höper AC, Bang B, Grgic M, Karlsen Linchausen M, Kirkeleit J.   +11 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Porous Phenazine‐bridged Tetraoxa[8]Circulenes for Selective Gold Recovery and Heterogeneous Catalysis

Angewandte Chemie, EarlyView.
Gold recycling from electronic waste using porous materials is an emerging concept to reduce the environmental impact of gold mining. In this work, we report the synthesis of a highly porous phenazine‐derived tetraoxa[8]circulene polymer and its utilization in gold capture and heterogeneous catalysis.
Patrick W. Fritz, Eylül Attar, Timur Ashirov, Krzysztof Piech, Ali Coskun   +4 more
wiley   +2 more sources

Use of Live Biopreservatives and Bacteriophages to Enhance the Safety of Meat Products. [PDF]

Life (Basel)
Rodríguez-Marca C, Domenech-Coca C, Nakamura M, Ortega-Olivé N, Puigbò P.   +4 more
europepmc   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source