Results 151 to 160 of about 997,975 (319)
Advanced Functional Materials, EarlyView.An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu +12 more
wiley +1 more source
The impact of media on a new product innovation diffusion: a mathematical model
, 2014 Joydip Dhar, Mani Tyagi, Poonam Sinha
openalex +2 more sources
Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth
Advanced Functional Materials, EarlyView.This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh +4 more
wiley +1 more source
Sistema de creencias sobre las matemáticas en los estudiantes de educación básica
Praxis, 2015 This article describes the influence of the beliefs of elementary and middle school students about mathematics and its learning process. There are collected appreciations, works and findings of several investigations of the development of mathematics ...
Jhon Darwin Erazo-Hurtado +1 more
doaj
The Model Object-product-cognitive Operation Through Mathematical Education
, 2014 Costică Lupu
openalex +1 more source
Amorphous High Entropy Alloy Nanosheets Enabling Robust Li–S Batteries
Advanced Functional Materials, EarlyView.Amorphous ultrathin FeCoNiMoW high entropy alloy nanosheets are incorporated into the polypropylene separator of lithium‐sulfur batteries, enhancing their capacity, rate performance, and cycling stability. Abstract High‐entropy alloys (HEAs) show great potential for catalyzing complex multi‐step reactions, but optimizing their parameters, i.e ...
Ren He +20 more
wiley +1 more source
Mathematical supply-chain modelling: Product analysis of cost and time
, 2014 Daniel James Easters
openalex +1 more source
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