Results 101 to 110 of about 154,891 (246)
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Human Activity-Based Machine Learning and Deep Learning Techniques
International Journal of Computational and Experimental Science and EngineeringHuman activity recognition (HAR) has been hot research issues in recent years. The studies have differences in data types, data processing, feature description, etc. HAR constitutes a fundamental component of intelligent health monitoring systems, wherein the underlying intelligence of the services is derived from and enhanced by sensor data ...
null Mohammed F Ibrahim Alsarraj +2 more
openaire +1 more source
Advanced Functional Materials, EarlyView.Molecular engineering of a nonconjugated radical polymer enables a significant enhancement of the glass transition temperature. The amorphous nature and tunability of the polymer, arising from its nonconjugated backbone, facilitates the fabrication of organic memristive devices with an exceptionally high yield (>95%), as well as substantial ...
Daeun Kim +14 more
wiley +1 more source
Chemoselective Sequential Polymerization: An Approach Toward Mixed Plastic Waste Recycling
Advanced Functional Materials, EarlyView.Inspired by biological protein metabolism, this study demonstrates the closed‐loop recycling of mixed synthetic polymers via ring‐closing depolymerization followed by a chemoselective sequential polymerizations process. The approach recovers pure polymers from mixed feedstocks, even in multilayer formats, highlighting a promising strategy to overcome a
Gadi Slor +5 more
wiley +1 more source
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Advanced Functional Materials, EarlyView.Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Machine Learning-Assisted Systematic Review: A Case Study in Learning Analytics
Education SciencesTraditional systematic reviews, despite their high-quality evidence, are labor-intensive and error-prone, especially during the abstract screening phase.
Zhihong Xu, Xiting Zhuang, Shuai Ma
doaj +1 more source
Active Learning of Mealy Machines with Timers
We present the first algorithm for query learning Mealy machines with timers in a black-box context. Our algorithm is an extension of the L# algorithm of Vaandrager et al. to a timed setting. We rely on symbolic queries which empower us to reason on untimed executions while learning. Similarly to the algorithm for learning timed automata of Waga, these
Véronique Bruyère +4 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken +6 more
wiley +1 more source
Small-cell-based fast active learning of machine learning interatomic potentials
Computational Materials ScienceMachine learning interatomic potentials (MLIPs) are often trained with on-the-fly active learning, where sampled configurations from atomistic simulations are added to the training set. However, this approach is limited by the high computational cost of ab initio calculations for large systems. Recent works have shown that MLIPs trained on small cells (
Zijian Meng +5 more
openaire +2 more sources