Results 151 to 160 of about 84,208 (297)
We develop a covalent organic framework‐loaded Traditional Chinese Medicine monomer nanodrug delivery system (ISL@bCOF) via boron‐oxygen bonds, a smart response to glucose for delivering AI‐driven isoliquiritigenin (ISL) targeting ZBP1. This dynamic release mechanism, combined with transdermal delivery technology via potent microneedles, can modulate ...
Menghan Zhou +9 more
wiley +1 more source
Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong +12 more
wiley +1 more source
LISA: language independent sentiment analysis using graph neural networks
This paper presents LISA as a Language Independent Sentiment Analysis tool that exploits Graph Neural Networks, GNN, for sentiment analysis, SA, applications. To build up that analyzer two types of GNN are examined.
Mohamed Zaki +3 more
doaj +1 more source
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
A study of Graph Neural Networks and Graph Attention Networks for Node Classification
Graph representations have received considerable attention as they can capture complex data relationships represented as nodes and edges, such as molecular structures, social network analysis, healthcare, and citation networks, etc. Due to this diversity
Bhargava, Yash
core
Graph Convolutional Networks and Graph Attention Networks for Approximating Arguments Acceptability
International audienceVarious approaches have been proposed for providing efficient computational approaches for abstract argumentation. Among them, neural networks have permitted to solve various decision problems, notably related to arguments ...
Mailly, Jean-Guy, Cibier, Paul
core +1 more source
The water permeability of amorphous carbon dots (CDs) is demonstrated by investigating their plasticization. Novel polyamide‐based and amorphous nanoparticles are synthesized by controlling their inner packing density. Water plasticization is evidenced by the decrease of the CDs glass transition temperature with increasing the hydration degree.
Elisa Sturabotti +8 more
wiley +1 more source
Graph Triple Attention Networks: A Decoupled Perspective
Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, these methods face two primary challenges: (1) multi-view chaos, which results from coupling multi-view information (positional, structural, attribute), thereby impeding ...
Xiaotang Wang +4 more
openaire +2 more sources
Spin‐Split Edge States in Metal‐Supported Graphene Nanoislands Obtained by CVD
Combining STM measurements and ab‐initio calculations, we show that zig‐zag edges in graphene nanoislands grown on Ni(111) by CVD retrieve their spin‐polarized edge states after intercalation of a few monolayers of Au. ABSTRACT Spin‐split states localized on zigzag edges have been predicted for different free‐standing graphene nanostructures.
Michele Gastaldo +6 more
wiley +1 more source

