Results 91 to 100 of about 87,865 (217)

Is the Scholarly System Breaking Down?

Learned Publishing, Volume 39, Issue 3, July 2026.
ABSTRACT On the back of countless warnings that the scholarly system is seriously being threatened, indeed, upended by fraud, fakery and numerous bad practices, we set out to establish the extent to which this is true by asking the people who are, arguably, in the best position to know—early career researchers (ECRs).
David Nicholas, Eti Herman, John Akeroyd, Abdullah Abrizah, Marzena Swigon, Jorge Revez, Blanca Rodríguez‐Bravo, Tatyana Polezhaeva   +7 more
wiley   +1 more source

phys-MCP: A Control Plane for Heterogeneous Physical Neural Networks

Physical neural networks (PNNs) embed computation directly in material dynamics, including molecular, chemical, biological, photonic, memristive, and mechanical substrates. They are attractive for edge computing, especially at the extreme edge, where computation can be placed at the interface to sensing, actuation, or the physical process itself ...
Fischer, Stefan, Hariri, Maliheh, Otte, Sebastian   +2 more
openaire   +2 more sources

Do Early Career Researchers Consider AI as an Opportunity or a Threat? A Pathfinding Study

Learned Publishing, Volume 39, Issue 3, July 2026.
ABSTRACT The article presents the latest (2025) iteration of the Harbingers longitudinal project on early career researchers (ECRs), artificial intelligence (AI) and scholarly communications. In conversation with a purposive and diverse sample of more than 60 ECRs in six countries and numerous subjects, we present an evaluation of a pressing issue ...
David Nicholas, David Clark, Abdullah Abrizah, John Akeroyd, Eti Herman, Jorge Revez, Blanca Rodríguez‐Bravo, Marzena Swigon, Tatyana Polezhaeva, Anne Gere   +9 more
wiley   +1 more source

Physiological Interpretation of the Lactate to Pyruvate AUC Ratio for Hyperpolarized [1‐13C]‐Pyruvate Studies

Magnetic Resonance in Medicine, Volume 96, Issue 1, Page 247-258, July 2026.
ABSTRACT Purpose To explore the relationship between potentially rate‐limiting steps in lactate production and the lactate area‐under‐the‐curve (AUC) to pyruvate AUC ratio for analysis of hyperpolarized (HP) [1‐13C]‐pyruvate data. Theory and Methods Simplifying assumptions are introduced to a pharmacokinetic (PK) model with three physical compartments ...
Ryan T. Boyce, Collin J. Harlan, Qing Wang, Christopher M. Walker, Stephen Y. Lai, Matthew E. Merritt, James A. Bankson   +6 more
wiley   +1 more source

Comment on “A proposal for in vitro/GFR molecular erythema action spectrum” [J. Appl. Phys. 104, 034701 (2008)]

, 2009
The recent article by de Souza, Lorenzini and Rizzatti J. A. V. de Souza, F. Lorenzini, and M. R. Rizatti, J. Appl. Phys. 104, 034701 2008 in this journal needs corrections and clarifications on several points.
Björn, Lars Olof,, Diffey, Brian, Norval, Mary, Björn, Lars Olof, de Gruijl, Frank R.   +4 more
core   +1 more source

Retrieving Hourly Aerosol Absorption From Space Using Physical Informed Deep Learning

Geophysical Research Letters, Volume 53, Issue 12, 28 June 2026.
Abstract Retrieving aerosol absorption properties, such as single scattering albedo (SSA) and absorption aerosol optical depth (AAOD) from single‐view satellite observations remains a significant challenge. This study introduces the Physics‐Informed Neural Network for Aerosols (PINA) framework to retrieve hourly aerosol absorption properties from ...
Tao Xia, Wenfang Liang, Fangwen Bao, Xiaochuan Zhang   +3 more
wiley   +1 more source

Generating distributed entanglement from electron currents

, 2014
This work is partially supported by a Royal Society University Research FellowshipSeveral recent experiments have demonstrated the viability of a passive device that can generate spin-entangled currents in two separate leads.
Lovett, Brendon W., Kolli, Avinash, Jefferson, John H., Ping, Yuting   +3 more
core   +1 more source

Physics‐Informed Neural Networks for Modeling the Martian Induced Magnetosphere

Geophysical Research Letters, Volume 53, Issue 11, 16 June 2026.
Abstract Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape. To date, global models of Martian induced magnetosphere have been exclusively physics‐based, relying on computationally intensive simulations. For the first time, we
Jiawei Gao, Chuanfei Dong, Chi Zhang, Yilan Qin, Simin Shekarpaz, Xinmin Li, Liang Wang, Hongyang Zhou, Abigail Tadlock   +8 more
wiley   +1 more source

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, Volume 2, Issue 3, June 2026.
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, Volume 2, Issue 3, June 2026.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source