Results 91 to 100 of about 6,836 (219)
Pulse‐protocol optimization in an Au/MoO3/TiO2/FTO bilayer memristor enables linear analog synaptic conductance modulation along with digital resistive switching for memory. Controlled filament evolution produces stable learning‐forgetting characteristics with low nonlinearity, resulting in significantly enhanced neural network inference accuracy for ...
Girish Chandrashekar +2 more
wiley +1 more source
This work focuses on how to improve the selectivity and activity of electrocatalytic CO2 reduction to C3+ products, by the integration of electrocatalyst and electrolyte co‐design. We summarize key C3+ formation mechanisms and provide a comprehensive reaction network through thermodynamic analysis.
Ling Chen, Damien Voiry, Yan Jiao
wiley +2 more sources
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
Due to the complexity of multi-dimensional sustainability measurements, the meaning and representation of sustainability itself can vary considerably across different organizations and industrial sectors. This is reflected in numerous ways to communicate
Annette Korin +3 more
doaj +1 more source
Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors
Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley +1 more source
Regenerative tourism has emerged as a critical evolution from traditional approaches to sustainable tourism, which have proven insufficient to address contemporary environmental, social, and economic challenges.
Blanca Miedes-Ugarte, David Flores-Ruiz
doaj +1 more source
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Modeling the Energy Transition in Türkiye’s Transportation Sector: A Machine Learning Approach
The global shift toward clean energy is reshaping supply chains, with road transportation, a carbon-emitting sector, at the center. Electric vehicles (EVs) offer a decarbonization pathway with minimal consumer disruption.
Mustafa Çağrı Peker
doaj +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source

