Results 231 to 240 of about 1,006,138 (295)

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

Advanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning, Lu Lu, Sophie Leanza, Ruike Renee Zhao   +3 more
wiley   +1 more source

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models. [PDF]

Database (Oxford)
Asim MN, Asif T, Hassan F, Dengel A.
europepmc   +1 more source

Phase‐Resolved Dual Control of Phenol Photodissociation at the Air–Water Interface From Structure‐Resolved Statistics

Advanced Science, EarlyView.
Faster phenol photolysis at the air–water interface arises from two cooperative factors: a more favorable initial microenvironment for solvent‐side electron stabilization, which lowers CI access, and a more labile hydrogen‐bond network, which more readily reorganizes to stabilize the dark‐state intermediate.
Qiang Yin, Jialing Shi, Jinping Zhao, Chengjun Li, Yongbo Xie, Benkun Tan, Da Wang, Ziyun Wang, Yu Mao   +8 more
wiley   +1 more source

Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study. [PDF]

JMIR AI
Agmon S, Singer U, Radinsky K.
europepmc   +1 more source

Bioinspired Ionochromic Neuromorphic Transistors for Robotic Intelligent Perception

Advanced Science, EarlyView.
A bioinspired ionochromic neuromorphic transistor couples synaptic conductance modulation with reversible color change through voltage‐controlled ion doping in P3HT/ion gel, mimicking biological synaptic plasticity, and chameleon‐like color regulation.
Quanxing Yao, Xiaojian Zhu, Runsheng Gao, Qian Jiang, Cui Sun, Yi Du, Xuerong Liu, Lixun Wang, Haolong Li, Yuejun Zhang, Run‐Wei Li   +10 more
wiley   +1 more source

Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu. [PDF]

iScience
Cao D, Chan MK.
europepmc   +1 more source

Wake‐Up and Fatigue under Electrical Cycling in HfO2‐Based Ferroelectrics: Mechanisms and Strategies toward Reliable Devices

Advanced Science, EarlyView.
HfO2‐based ferroelectrics exhibit wake‐up and fatigue behaviors during electrical cycling, significantly affecting device endurance and reliability. These phenomena are governed by defect dynamics, including oxygen vacancy redistribution and charge trapping.
Hongseok Kim, Shinhyeong Lee, Hyojun Choi, Minseung Park, Hyunah Cho, Min Hyuk Park, Yunseok Kim   +6 more
wiley   +1 more source

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients. [PDF]

Sci Rep
Song Q, Ni C, Warner JL, Chen Q, Song L, Rosenbloom ST, Malin BA, Yin Z.   +7 more
europepmc   +1 more source

Symmetry‐Imposed Selection Rules for Excitations of Nontrivial Plasmonic Topologies

Advanced Science, EarlyView.
A unified group‐theory selection rule governs the excitation of vectorial nearfield topologies across three plasmonic spin states. Derived from first principles, it predicts spin–orbit vortex splitting and multidimensional nested vortices, confirmed by phase‐resolved in situ measurements.
Jie Yang, Pengyi Feng, Fei Han, Weijin Chen, Zhongwei Jin, Jincheng Ni, Yuanjie Yang, Anxue Zhang, Guy A. E. Vandenbosch, Niels Verellen, Ewald Janssens, Jiafu Wang, Benfeng Bai, Cheng‐Wei Qiu, Xuezhi Zheng   +14 more
wiley   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

Advanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source