Results 111 to 120 of about 4,990,305 (257)
Recent advances in single-cell RNA sequencing (scRNA-seq) technologies provide a great opportunity to study gene expression at cellular resolution, and the scRNA-seq data has been routinely conducted to unfold cell heterogeneity and diversity. A critical
Rao, J, Zeng, Y, Lu, Y, Zhou, X, Yang, Y
core +1 more source
A novel targeted and pH‐responsive MRI contrast agent was integrated with a 3D nnU‐Net deep learning framework to enable accurate delineation of tumor boundary morphology in HER2‐positive breast cancer imaging. ABSTRACT Breast cancer continues to be a leading cause of cancer‐related mortality in women globally, where precise diagnosis and clear tumor ...
Jiaying Zheng +7 more
wiley +1 more source
Atomistic Kinetics of Dislocation‐Mediated Grain Growth in Monolayer MoS2
Atomic‐resolution in‐situ heating microscopy directly visualizes dislocation‐mediated grain boundary migration and grain growth in monolayer MoS2. Mobile grain boundaries migrate through collective motion of Mo 5|7 dislocations, whereas S 5|7 defects remain largely immobile.
Chang‐Won Choi +11 more
wiley +1 more source
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar +9 more
wiley +1 more source
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali +3 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
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
ABSTRACT Aim Artificial intelligence (AI)–based surgical video analysis can automate time‐consuming manual assessments and enable objective characterization of surgical workflows. We aimed to construct a large, multicenter, fully annotated dataset of robotic distal gastrectomy (RDG) videos and evaluate the feasibility and performance of an AI model for
Masaru Komatsu +8 more
wiley +1 more source
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing +4 more
wiley +1 more source

