Results 121 to 130 of about 263,832 (306)

Recent Progress and Opportunities in Oxide Semiconductor Devices for In‐Memory and Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
This review surveys oxide‐semiconductor devices for in‐memory and neuromorphic computing, highlighting recent progress and remaining challenges in charge‐trap, ferroelectric, and two‐transistor devices. Oxide semiconductors, featuring ultra‐low leakage, low‐temperature processing, and back‐end‐of‐line compatibility, are explored for analog in‐memory ...
Suwon Seong, Taejun Ha, Sangwook Jung, Wontae Jeong, Yoonyoung Chung   +4 more
wiley   +1 more source

Research on the spatial state and electrical distance variation of overhead transmission lines with changes in foundation

AIP Advances
This article conducts a thorough investigation into the spatial state variations of overhead lines in response to foundational changes, as well as alterations in the electrical distance between the conductor and both the ground and tower structures ...
Shuai Wang, Xiaokai Meng, Siqing Xu, Zhumao Lu, Yang Bai   +4 more
doaj   +1 more source

RRAM Variability Harvesting for CIM‐Integrated TRNG

Advanced Electronic Materials, EarlyView.
This work demonstrates a compute‐in‐memory‐compatible true random number generator that harvests intrinsic cycle‐to‐cycle variability from a 1T1R RRAM array. Parallel entropy extraction enables high‐throughput bit generation without dedicated circuits. This approach achieves NIST‐compliant randomness and low per‐bit energy, offering a scalable hardware
Ankit Bende, Gokulnath Rajendran, Regina Dittmann, Anupam Chattopadhyay, Vikas Rana   +4 more
wiley   +1 more source

Shedding Light on Common Misinterpretations in Photocatalyst Characterization

Advanced Energy Materials, EarlyView.
For heterogeneous semiconductor‐based photocatalysts, Marschall et al. highlight common misconceptions in material synthesis, characterization, and performance evaluation, together with detailed explanations on how to avoid them. The guidelines thus presented can help to improve reporting of photocatalyst performance in environmental applications, such
Roland Marschall, Lion Schumacher, Jennifer Strunk   +2 more
wiley   +1 more source

Making predictions for overhead power lines applying predictive analytics

Вестник Северо-Кавказского федерального университета
Introduction. Currently, failures are one of the main and urgent problems for the normal functioning of power supply systems, they are at first glance unpredictable, however, in addition to accidental, there are failures that occur according to some ...
Yu. N. Kondrashova, A. M. Tretyakovͬ, A. V. Shalimov   +2 more
doaj   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Adverse Peculiarity Arising with Overhead Power Lines

Power and Electrical Engineering, 2016
In the paper, the power lines with flat arrangement of phases are considered. At the end of untransposed power lines, the unbalanced voltage appears. As an alternative to transposition, unbalanced load can be applied. Considerable difference of phase loads is the shortcoming of this method.
openaire   +3 more sources

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source