Results 151 to 160 of about 20,297 (303)

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

open access: yesAdvanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Analog Weight Update Rule in Ferroelectric Hafnia, Using picoJoule Programming Pulses

open access: yesAdvanced Electronic Materials, EarlyView.
Resistive, ferroelectric synaptic weights based on BEOL‐compatible hafnia/zirconia nanolaminates are fabricated. Lateral downscaling the devices below 10 µm2 enables 20 ns programming with electrical pulses, dissipating ≤ 3 pJ. Experimental results show that final conductance state is set by pulse amplitude, and is largely independent of the initial ...
Alexandre Baigol   +7 more
wiley   +1 more source

High‐Performance Noble‐Metal‐Free Perovskite Solar Cells Enabled by MoOX/Cr/Al Multilayer Electrodes

open access: yesAdvanced Energy Materials, EarlyView.
Cost‐effective perovskite solar cells (PSCs) are developed using a noble‐metal‐free MoOX/Cr/Al multilayer electrode. The devices achieve a power conversion efficiency (PCE) of 25.6%, competitive with that of Au‐based devices (26.3%), and a 25.5 cm2 mini‐module shows 21.3% PCE.
Wooyeon Kim   +7 more
wiley   +1 more source

Time variations of the tangential component of velocity in the Evershed effect

open access: yes, 1991
Представлены результаты наблюдений тангенциальной составляющей скорости в эффекте Эвершеда (крутильных колебаний солнечных пятен). Для шести пятен исследовался спектральный состав сигналов лучевой скорости от двух участков полутени, расположенных ...
Певцов, А.А.   +3 more
core  

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

open access: yesAdvanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour   +5 more
wiley   +1 more source

The Uber Wheel: Design and Construction of a Continuously Variable Tangential Velocity Transmission

open access: yes, 2011
The primary objective of this project was to design and produce a continuously variable transmission for a mobile robot platform. The innovative approach was to develop a system that could continuously vary the tangential velocity of a wheel without ...
Tremblay, Ty   +2 more
core  

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi   +5 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

open access: yesAdvanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia   +2 more
wiley   +1 more source

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