Results 191 to 200 of about 277,827 (338)

Analyzing Electronic Excitations and Exciton Binding Energies in Y6 Films

Advanced Functional Materials, EarlyView.
The Y6 molecule is used for increasing the efficiency of organic solar cells. The exciton binding energy is calculated for ensembles of Y6 molecules that are representative of the typically used films. The calculations show that the excitons typically spread out over many molecules.
Sahar Javaid Akram, Sophie Meißner, Stephan Kümmel   +2 more
wiley   +1 more source

A privacy-preserving federated meta-learning framework for cross-project defect prediction in software systems. [PDF]

Sci Rep
Potharlanka JL, Shaik KY, N BK.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Deep multi-metrics learning for mobile app defect prediction using code and process metrics. [PDF]

Sci Rep
Abdu A, Abdo HA, Ullah I, Khan J, Gu YH, Algabri R.   +5 more
europepmc   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Multilabel classification for defect prediction in software engineering. [PDF]

Sci Rep
Pachouly J, Ahirrao S, Kotecha K, Kulkarni A, Alfarhood S.   +4 more
europepmc   +1 more source

Integration of Feature Selection with Data Level Approach for Software Defect Prediction

, 2019
Ade Suryadi
openalex   +2 more sources

Emergent Spin‐Glass Behavior in an Iron(II)‐Based Metal–Organic Framework Glass

Advanced Functional Materials, EarlyView.
A one‐pot, solvent‐free synthesis yields an Fe2+‐based metal‐organic framework (MOF) glass featuring a continuous random network structure. The material exhibits spin‐glass freezing at 14 K, driven by topological‐disorder and short‐range magnetic frustration, showcasing the potential of MOF glasses as a plattform for cooperative magnetic phenomena in ...
Chinmoy Das, Soma Salamon, Joachim Landers, Jan‐Benedikt Weiß, Wen‐Long Xue, Pascal Kolodzeiski, Roman Pallach, Heiko Wende, Sebastian Henke   +8 more
wiley   +1 more source

Features extraction and fusion by attention mechanism for software defect prediction. [PDF]

PLoS One
Qiu S, E B, He J.
europepmc   +1 more source

Enhancing Software Defect Prediction with Large Language Models: A Multi-Method Explainable AI Framework for Trustworthy Code Analysis

Anonymous
openalex   +1 more source