Results 141 to 150 of about 97,457 (192)
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source
The bone Gla protein osteocalcin is expressed in cranial neural crest cells. [PDF]
Kalev-Altman R +3 more
europepmc +1 more source
Isolation and Culture of Cranial Neural Crest Cells from the First Branchial Arch of Mice. [PDF]
Ueharu H, Yang J, Komatsu Y, Mishina Y.
europepmc +1 more source
Triboelectric nanogenerators are vital for sustainable energy in future technologies such as wearables, implants, AI, ML, sensors and medical systems. This review highlights improved TENG neuromorphic devices with higher energy output, better stability, reduced power demands, scalable designs and lower costs.
Ruthran Rameshkumar +2 more
wiley +1 more source
Magnesium ions regulate the Warburg effect to promote the differentiation of enteric neural crest cells into neurons. [PDF]
Xu Q +13 more
europepmc +1 more source
This Perspective examines practical power solutions for wearable healthcare systems, highlighting the limits of standard batteries. It categorizes wearables into four domains—point‐of‐care diagnostics, episodic monitoring, continuous long‐term monitoring, and therapeutic platforms—and analyzes their power needs.
Seokheun Choi
wiley +1 more source
Polarity and migration of cranial and cardiac neural crest cells: underlying molecular mechanisms and disease implications. [PDF]
Salinas E +5 more
europepmc +1 more source
This study introduces an affordable machine learning platform for simultaneous dengue and zika detection using fluorine‐doped tin oxide thin films modified with gold nanoparticles and DNA aptamers. Designed for low‐cost, hardware‐limited devices (< $25), the model achieves 95.3% accuracy and uses only 9.4 kB of RAM, demonstrating viability for resource‐
Marina Ribeiro Batistuti Sawazaki +3 more
wiley +1 more source
Spatiotemporal distribution of neural crest cells in the common wall lizard Podarcis muralis. [PDF]
Pranter R, Feiner N.
europepmc +1 more source
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source

