Results 181 to 190 of about 239,639 (261)

An On‐Demand Neuromorphic Vision System Enabled by a Multi‐Paradigm Neuromorphic Device and Hierarchical Reconfigurability Designed from Device to System Level

open access: yesAdvanced Science, EarlyView.
An on‐demand ultra‐reconfigurable intelligent vision system with hierarchical reconfigurability from device to system levels is demonstrated. Through co‐design of a multi‐paradigm device, reconfigurable circuits, and adaptive system architecture/algorithms, the system enables seamless switching among spiking, non‐spiking, neuromorphic imaging (NI), and
Biyi Jiang   +7 more
wiley   +1 more source

Electrolyte Additive Strategies in Aqueous Zn‐Ion Batteries: Recent Advances and Prospects

open access: yesAdvanced Science, EarlyView.
This article provides a comprehensive overview of the current status and future development directions of AZIBs electrolyte additives in three aspects: stabilizing zinc anodes (uniform deposition, inhibition of dendritic crystals), protecting cathodes (structural stability, inhibition of dissolution), and enhancing electrolyte stability (wider ...
Yuanze Yu   +7 more
wiley   +1 more source

Drivers of Variation in the Optimal Spatial Structure of Collective Information Gatherers. [PDF]

open access: yesBull Math Biol
Walker RS   +5 more
europepmc   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

open access: yesAdvanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan   +8 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

open access: yesAdvanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray   +3 more
wiley   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

open access: yesAdvanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao   +9 more
wiley   +1 more source

Home - About - Disclaimer - Privacy