Results 161 to 170 of about 70,475,198 (271)

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source

Modeling Phase Separation in Polymer-Derived Silicon Carbonitride Ceramics through Extended Machine Learning Molecular Dynamics. [PDF]

ACS Nano
Mortier F, Cadars S, Masson O, Boero M, Ori G, Wang Y, Bernard S, Bouzid A.   +7 more
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

Predicting freeway incident duration using machine learning. [PDF]

PLoS One
Ju Y, Liu J, Ardakani MK, Li W, Li G.
europepmc   +1 more source

Leveraging Mechanical Resonances for the Selection of Promising Materials in Complex Phase Spaces

Advanced Materials Technologies, EarlyView.
A central challenge in materials science is the design of new functional materials with enhanced performance for targeted applications. This paper demonstrates that “listening” to materials through measuring their mechanical resonances is a rich, and largely untapped, platform for the rapid identification of promising novel compounds and a powerful ...
Christopher A. Mizzi, Osman El‐Atwani, Tannor T. J. Munroe, Saryu Fensin, Boris Maiorov   +4 more
wiley   +1 more source

Toward Perception‐Native Electronic Skin: Bio‐Inspired In‐/Near‐Sensor and Neuromorphic Computing for Humanoid Robots

Advanced Materials Technologies, EarlyView.
Dense tactile streams from across the humanoid body converge on collide in a central wiring and data bottleneck. By relocating computation closer to and then into the skin itself, near‐ and in‐sensor architectures, together with neuromorphic computing, chart a path toward perception‐native electronic skin, in which the conversion of stimulus into ...
Mijin Kim, Chang‐Jae Beak, Jiyoung Park, Youngdo Jung, Sunjong Oh, Sin‐Hyung Lee, Bo‐Yeon Lee   +6 more
wiley   +1 more source

A self-organizing developmental framework for the head direction ring attractor. [PDF]

J Comput Neurosci
Liu S, Si B, Herrmann JM.
europepmc   +1 more source

Machine learning-based prediction of magnet errors in storage ring light sources

Magnet errors in storage rings significantly degrade beam performance, impacting the brightness and stability of the light source. Therefore, beam-based correction is crucial for the safe operation of machines and the stability of radiated photons. Unlike traditional correction methods such as linear optics from closed orbit, this paper proposes a ...
openaire   +2 more sources

Spatial Stiffness Programming in Single‐Filament Material Extrusion Through Data‐Driven Thermal Foaming

Advanced Materials Technologies, EarlyView.
Single‐filament material extrusion enables spatial stiffness programming through locally controlled thermal foaming of TPU. A data‐driven G‐code framework tunes porosity from 15.7% to 40.7% and hardness from 90 to 63 Shore A within and across layers. Monolithic hands and soft actuators consequently achieve distinct programmed motions without material ...
Rosanna Rifino, Gianni Stano, Mahdi Bodaghi, Gianluca Percoco   +3 more
wiley   +1 more source

MEGPNM as a multiscale edge-aware GAT network with hybrid pooling predicting permeability of non-peptidic macrocycles. [PDF]

Commun Chem
He S, Wei L, Ye W, Zou Q, Zhang F.
europepmc   +1 more source