Results 131 to 140 of about 15,047 (284)

Forecasting solar energetic particles using multi-source data from solar flares, CMEs, and radio bursts with machine learning approaches

Scientific Reports
This study presents a consistent method to the inherently imbalanced problem of predicting solar energetic particle (SEP) events, using a variety of datasets that include solar flares, coronal mass ejections (CMEs), and radio bursts.
Mohammed AbuBakr Ali, Ali G. A. Abdelkawy, Abdelrazek M. K. Shaltout, M. M. Beheary   +3 more
doaj   +1 more source

Exploring NAS for anomaly detection in superconducting cavities of particle accelerators

Frontiers in Physics
The European X-Ray Free Electron Laser is the largest particle accelerator for X-ray laser generation worldwide. To ensure a safe and efficient operation, the plant uses various monitoring systems, especially in the linear accelerator.
Lynda Boukela, Julien Branlard, Annika Eichler, Annika Eichler   +3 more
doaj   +1 more source

Turning Unpredictable Biomolecule Adsorption to Controlled Corona Formation: Focus on Carbon Nanomaterials

Advanced Materials, EarlyView.
Controlling the protein corona formation onto carbon nanomaterials (CNMs) enhances their functionalities as platforms for cancer theranostics. Here, we reviewed the effects of the intrinsic and acquired properties of CNMs on protein corona formation, the consequent biological and toxicological outcomes, and the strategies to reshape corona formation ...
Yajuan Zou, Yalei Hu, Jie Yu, Naoki Komatsu, Yuta Nishina, Alberto Bianco   +5 more
wiley   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Foundations of Radio Frequency Transfer Learning

The introduction of Machine Learning (ML) and Deep Learning (DL) techniques into modern radio communications system, a field known as Radio Frequency Machine Learning (RFML), has the potential to provide increased performance and flexibility when ...
Wong, Lauren Joy
core  

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

ML@GT Lab presents LAB LIGHTNING TALKS 2020

, 2020
Presented online on December 4, 2020 at 2:00 p.m.Professor Ghassan AlRegib is currently a professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He is the director of the Multimedia and Sensors Lab (MSL) at
Essa, Irfan, Huo, Xiaoming, Chau, Duen Horng, Maguluri, Siva Theja, Zhang, Xiuwei, Li, Jing, Chava, Sudheer, Dovrolis, Constantine, Prakash, B. Aditya, Romberg, Justin, Pananjady, Ashwin, Xie, Yao, AlRegib, Ghassan, Cohen, Morris B., Gupta, Swati, Davenport, Mark A., Desai, Deven, Riedl, Mark O., Kira, Zsolt   +18 more
core  

High‐Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning‐Assisted Motion Recognition

Advanced Materials, EarlyView.
A transparent, deformable stevia–PVA hydrogel triboelectric nanogenerator delivers significantly enhanced mechanical strength and electrical output through biomimetic hydrogen‐bonded networks. Coupled with machine learning–assisted signal recognition, the self‐powered hydrogel enables accurate human‐motion sensing for intelligent wearable and IoT ...
Thien Trung Luu, Bui Minh Quang, Toan Minh Pham, Jinsoo Kim, Kyungwho Choi, Dukhyun Choi   +5 more
wiley   +1 more source

Machine learning for radio galaxy morphology analysis

We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.We investigated what morphology in total radio intensity ...
Mostert, R.I.J.
core