Magnetic Abrasive Machining of Difficult-to-Cut Materials for Ultra-High-Speed Machining of AISI 304 Bars. [PDF]
Wang R, Lim P, Heng L, Mun SD.
europepmc +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Study on the Low-Damage Mechanism of Green ZrO<sub>2</sub> Ceramics Under Ultrasonic Elliptical Vibration-Assisted Cutting. [PDF]
Chen H, Jia F, Zhu Q, Liu Y, Wang C.
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Material Removal Mechanism and Performance Evaluation of Focused Ultrasonic-Assisted Abrasive Waterjet Polishing (FUAP) of Monocrystalline Silicon. [PDF]
Ren K +6 more
europepmc +1 more source
Autonomous X‐Ray Fluorescence Mapping for Nanoscale Chemical Speciation of Fine Particulate Matter
We present X‐AutoMap, an autonomous X‐ray fluorescence mapping framework that integrates real‐time analysis with rule‐based computer vision to selectively target chemically relevant regions. By avoiding background‐dominated areas, the method reduces acquisition time by fourfold while enabling accurate particle‐level speciation.
Carlos Deleon +3 more
wiley +1 more source
Compressive Stress-Assisted Drilling for Delamination Suppression in C/SiC Composites: Mechanism and Experimental Validation. [PDF]
Zhang Q, Shi Z, Wang C, Shao G, Zhang X.
europepmc +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Characterization and machine learning based optimization of banana and paddy straw fiber reinforced epoxy hybrid composites. [PDF]
Sengottaiyan S +5 more
europepmc +1 more source

