Results 171 to 180 of about 16,533 (261)

End‐to‐End Sensing Systems for Breast Cancer: From Wearables for Early Detection to Lab‐Based Diagnosis Chips

Advanced Materials Technologies, EarlyView.
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane, Lyn I. Jones, Richard M. Martin, Helen Winter, Faezeh Arab Hassani   +4 more
wiley   +1 more source

Fast electromagnetic field simulation using a current-density- based physics-informed neural network. [PDF]

Sci Rep
Gao Z, Sun CA, Ma Z, Liu Z, Zhao L, Wang Q.   +5 more
europepmc   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

A physics-informed neural network approach for estimating population-level pharmacokinetic parameters from aggregated concentration data. [PDF]

J Pharmacokinet Pharmacodyn
Tsiros P, Minadakis V, Sarimveis H.
europepmc   +1 more source

Predicting the Early-Age Time-Dependent Behaviors of a Prestressed Concrete Beam by Using Physics-Informed Neural Network. [PDF]

Sensors (Basel), 2023
Park HW, Hwang JH.
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention. [PDF]

Commun Biol
Barducci G, Rossi I, Codicé F, Rollo C, Repetto V, Pancotti C, Iannibelli V, Sanavia T, Fariselli P.   +8 more
europepmc   +1 more source

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Sinogram-based flow estimation in computed tomography using a physics-informed neural network: Impact of gantry rotation speed, X-ray fluence and pulsed acquisition on accuracy. [PDF]

Med Phys
Guo J, Khurana GS, Grande AG, Alamo JCD, Contijoch F.   +4 more
europepmc   +1 more source

Upcycling Compact Discs Into Ultra‐Stable, Flexible, and Stretchable Nanoporous Gold Electrodes for Enhanced Biosensing Performance

Advanced Materials Technologies, EarlyView.
This article highlights the development of robust and high‐performance flexible and stretchable biosensors that maintain long‐term functionality and optimal electrical conductivity under mechanical deformation, utilizing sustainable and cost‐effective manufacturing principles.
Mousa H. Aldosari, Ahyeon Koh
wiley   +1 more source